diff --git a/.gitignore b/.gitignore
index b9b874860..619de48a8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -220,6 +220,13 @@ docs/source/experimental/*.pp*
 docs/source/experimental/*.png*
 docs/source/experimental/*.nc*
 docs/source/experimental/*.geojson*
+ocs/_build/
+docs/source/tutorials/*.tar
+docs/source/tutorials/*.zip
+docs/source/tutorials/_*
+docs/source/tutorials/earthkit_use_cases/*.grib
+docs/source/tutorials/*/_*
+
 
 autodocs/
 
diff --git a/:ref: b/:ref:
new file mode 100644
index 000000000..e69de29bb
diff --git a/README.md b/README.md
index 767e9602c..d2c6b5c1a 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@
     <img src="https://github.com/ecmwf/codex/raw/refs/heads/main/ESEE/foundation_badge.svg" alt="ECMWF Software EnginE">
   </a>
   <a href="https://github.com/ecmwf/codex/raw/refs/heads/main/Project Maturity">
-    <img src="https://github.com/ecmwf/codex/raw/refs/heads/main/Project Maturity/incubating_badge.svg" alt="Maturity Level">
+    <img src="https://github.com/ecmwf/codex/raw/refs/heads/main/Project Maturity/graduated_badge.svg" alt="Maturity Level">
   </a>
   <!-- <a href="https://codecov.io/gh/ecmwf/earthkit-data">
     <img src="https://codecov.io/gh/ecmwf/earthkit-data/branch/main/graph/badge.svg" alt="Code Coverage">
@@ -35,7 +35,7 @@
 </p>
 
 > \[!IMPORTANT\]
-> This software is **Incubating** and subject to ECMWF's guidelines on [Software Maturity](https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity).
+> This software is **Graduated** and subject to ECMWF's guidelines on [Software Maturity](https://github.com/ecmwf/codex/raw/refs/heads/main/Project%20Maturity).
 
 **earthkit-data** is a format-agnostic interface for geospatial data with a focus on meteorology and
 climate science. It is the data handling component of [earthkit](https://github.com/ecmwf/earthkit).
diff --git a/docs/source/concepts/caching.rst b/docs/source/concepts/caching.rst
index 07d2037f8..77d57907e 100644
--- a/docs/source/concepts/caching.rst
+++ b/docs/source/concepts/caching.rst
@@ -40,7 +40,7 @@ The cache location can be read and modified with Python (see the details below).
 
 .. tip::
 
-   See the :ref:`/how-tos/misc/cache.ipynb` notebook for examples.
+   See the :ref:`/tutorials/misc/cache.ipynb` notebook for examples.
 
 .. note::
 
diff --git a/docs/source/concepts/config.rst b/docs/source/concepts/config.rst
index b924c7b11..4326492d6 100644
--- a/docs/source/concepts/config.rst
+++ b/docs/source/concepts/config.rst
@@ -11,8 +11,8 @@ The configuration can be accessed and modified from Python. The configuration op
 
 See the following notebooks for examples:
 
-    - :ref:`/how-tos/misc/config.ipynb`
-    - :ref:`/how-tos/misc/config_env_vars.ipynb`
+    - :ref:`/tutorials/misc/config.ipynb`
+    - :ref:`/tutorials/misc/config_env_vars.ipynb`
 
 
 .. _config_get:
@@ -138,7 +138,7 @@ Finally, unset the environment variable and check the config value again, which
 
 See also the following notebook:
 
-    - :ref:`/how-tos/misc/config_env_vars.ipynb`
+    - :ref:`/tutorials/misc/config_env_vars.ipynb`
 
 
 .. _config_table:
diff --git a/docs/source/concepts/data.rst b/docs/source/concepts/data.rst
index 224420562..eb49c3523 100644
--- a/docs/source/concepts/data.rst
+++ b/docs/source/concepts/data.rst
@@ -87,7 +87,7 @@ To access the stream we need to convert the data into a stream fieldlist (GRIB)
 .. code-block:: python
 
     >>> import earthkit.data as ekd
-    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test4.grib"
+    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test4.grib"
     >>> ds = ekd.from_source("url", url, stream=True)
     >>> fl = ds.to_fieldlist()
     >>> for f in fl:
@@ -102,10 +102,10 @@ To access the stream we need to convert the data into a stream fieldlist (GRIB)
 Examples
 ////////////////
 
-    - :ref:`/how-tos/source/data_from_stream.ipynb`
-    - :ref:`/how-tos/source/file_stream.ipynb`
-    - :ref:`/how-tos/source/fdb.ipynb`
-    - :ref:`/how-tos/source/url_stream.ipynb`
+    - :ref:`/tutorials/source/data_from_stream.ipynb`
+    - :ref:`/tutorials/source/file_stream.ipynb`
+    - :ref:`/tutorials/source/fdb.ipynb`
+    - :ref:`/tutorials/source/url_stream.ipynb`
 
 
 Special cases
diff --git a/docs/source/concepts/encoders.rst b/docs/source/concepts/encoders.rst
deleted file mode 100644
index ef4c10606..000000000
--- a/docs/source/concepts/encoders.rst
+++ /dev/null
@@ -1,54 +0,0 @@
-.. _encoders:
-
-Encoders
-===============
-
-.. warning::
-   This guide is currently under construction and may be incomplete or inaccurate.
-
-
-An **encoder** is used to generate data in a suitable format that can be written/added to a :ref:`target <targets>`. Encoders are typically used implicitly via :func:`to_target` but we can also instantiate an :class:`Encoder` and work with it directly.
-
-create_encoder()
----------------------------
-
-We can create an :class:`Encoder` via :func:`create_encoder`.
-
-.. py:function:: create_encoder(name, *args, **kwargs)
-
-  Create an encoder specified by ``name`` .
-
-  :param str name: the encoder. ``name`` can refer for a built-in encoder (see below) or an encoder plugin.
-  :param tuple *args: specify encoder parameters
-  :param dict **kwargs: specify additional encoder parameters.
-
-
-Built in encoders
----------------------
-
-**earthkit-data** has the following built-in encoders:
-
-  .. list-table:: Data encoders
-    :widths: 20 60 20
-    :header-rows: 1
-
-    * - Name
-      - Description
-      - Class
-    * - grib
-      - encode data as GRIB
-      - :py:class:`GribEncoder`
-    * - netcdf
-      - encode data as NetCDF
-      - :py:class:`NetCDFEncoder`
-    * - geotiff
-      - encode data as GeoTIFF
-      - :py:class:`GeoTIFFEncoder`
-    * - csv
-      - encode data as CSV
-      - :py:class:`CSVEncoder`
-
-
-Examples
-----------
-    - :ref:`/how-tos/target/grib_encoder.ipynb`
diff --git a/docs/source/concepts/encoders/encoders.rst b/docs/source/concepts/encoders/encoders.rst
new file mode 100644
index 000000000..8a6c415bf
--- /dev/null
+++ b/docs/source/concepts/encoders/encoders.rst
@@ -0,0 +1,158 @@
+.. _encoders:
+
+Encoders
+========
+
+An **encoder** converts data into a serialisable format that can be written to a
+:ref:`target <targets>`. Encoders are most commonly used implicitly through
+:func:`~earthkit.data.to_target`, but can also be created and used directly.
+
+.. note::
+
+   The encoders are still under development. The most advanced encoder is the :ref:`grib_encoder`, which can handle a variety of data types. Currently, the other encoders only support trivial encoding of data into a serialisable format. For example, the NetCDF encoder simply calls :py:func:`xarray.to_netcdf` on an xarray Dataset/DataArray.
+
+Creating an encoder
+-------------------
+
+Use :func:`~earthkit.data.encoders.create_encoder` to instantiate an encoder by name:
+
+.. code-block:: python
+
+    >>> from earthkit.data import create_encoder
+    >>> enc = create_encoder("grib")
+    >>> enc = create_encoder("grib", template=my_field, shortName="2t")
+
+.. py:function:: create_encoder(name, *args, **kwargs)
+
+   Create an encoder identified by *name*.
+
+   :param str name: Name of an encoder (see table below).
+   :param args: Positional arguments forwarded to the encoder constructor.
+   :param kwargs: Keyword arguments forwarded to the encoder constructor.
+   :rtype: Encoder
+
+
+Common constructor parameters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+All encoders accept the following keyword arguments in their constructors:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 80
+
+   * - Parameter
+     - Description
+   * - ``template``
+     - A default template object used as the basis for encoding. The exact accepted types depend on the encoder.
+   * - ``metadata``
+     - A dictionary of default metadata applied to every encoded item. Metadata supplied
+       directly to :meth:`Encoder.encode` is merged on top, with the per-call values taking
+       precedence.
+   * - ``**kwargs``
+     - Any additional keyword arguments are interpreted as metadata keys and merged into
+       ``metadata``.
+
+
+Encoding data
+-------------
+
+All encoders expose an :meth:`encode` method:
+
+.. code-block:: python
+
+    >>> enc = create_encoder("grib", template=field)
+    >>> result = enc.encode(values=arr, shortName="2t", step=6)
+    >>> result.to_file(open("out.grib", "wb"))
+
+.. py:method:: Encoder.encode(data=None, values=None, metadata={}, template=None, check_nans=False, missing_value=9999, target=None, **kwargs)
+
+   Encode *data* and return an :class:`EncodedData` object.
+
+   :param data: Earthkit data object to encode (Field, FieldList, xarray Dataset/DataArray, …).
+   :param values: Raw values array to encode.
+   :param dict metadata: Per-call metadata merged on top of the encoder's default metadata.
+   :param template: Per-call template overriding the encoder's default template.
+   :param bool check_nans: Replace ``NaN`` values with *missing_value* before encoding.
+   :param missing_value: Replacement value used when *check_nans* is ``True``.
+   :param target: Target object; some encoders use this to optimise the output format.
+   :rtype: EncodedData
+
+
+Automatic encoder selection
+----------------------------
+
+When writing to a file target without specifying an encoder explicitly, the encoder is
+selected automatically from the file extension:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Extension(s)
+     - Encoder
+   * - ``.grib``, ``.grb``, ``.grib1``, ``.grib2``, ``.grb1``, ``.grb2``
+     - ``grib``
+   * - ``.nc``, ``.nc3``, ``.nc4``, ``.netcdf``
+     - ``netcdf``
+   * - ``.tiff``, ``.tif``
+     - ``geotiff``
+   * - ``.bufr``
+     - ``bufr``
+   * - ``.odb``
+     - ``odb``
+
+
+Available encoders
+-----------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: 16 52 32
+
+   * - Name
+     - Description
+     - Class
+   * - ``grib``
+     - Encode data as GRIB. Supports Fields, FieldLists, and NumPy arrays. Requires a
+       template or sufficient metadata to construct a valid GRIB message.
+     - :py:class:`~earthkit.data.encoders.grib.GribEncoder`
+   * - ``netcdf``
+     - Encode data as NetCDF via xarray. Accepts Fields, FieldLists, and xarray
+       Datasets/DataArrays.
+     - :py:class:`~earthkit.data.encoders.netcdf.NetCDFEncoder`
+   * - ``geotiff``
+     - Encode data as GeoTIFF. Requires geo-referenced raster data.
+     - :py:class:`~earthkit.data.encoders.geotiff.GeoTIFFEncoder`
+   * - ``csv``
+     - Encode tabular data as comma-separated values.
+     - :py:class:`~earthkit.data.encoders.csv.CSVEncoder`
+   * - ``bufr``
+     - Encode data as BUFR (Binary Universal Form for the Representation of meteorological
+       data).
+     - :py:class:`~earthkit.data.encoders.bufr.BufrEncoder`
+   * - ``odb``
+     - Encode data as ODB (Observational DataBase) format.
+     - :py:class:`~earthkit.data.encoders.odb.ODBEncoder`
+   * - ``zarr``
+     - Encode data as a Zarr array store.
+     - :py:class:`~earthkit.data.encoders.zarr.ZarrEncoder`
+   * - ``covjson``
+     - Encode data as CoverageJSON (CovJSON).
+     - :py:class:`~earthkit.data.encoders.covjson.CovJsonEncoder`
+   * - ``geojson``
+     - Encode data as GeoJSON.
+     - :py:class:`~earthkit.data.encoders.geojson.GeoJsonEncoder`
+   * - ``pp``
+     - Encode data as Met Office PP (UM binary) format.
+     - :py:class:`~earthkit.data.encoders.pp.PPEncoder`
+   * - ``text``
+     - Encode data as plain text.
+     - :py:class:`~earthkit.data.encoders.text.TextEncoder`
+
+
+
+Examples
+--------
+
+- :ref:`/tutorials/target/grib_encoder.ipynb`
diff --git a/docs/source/concepts/encoders/grib_encoder.rst b/docs/source/concepts/encoders/grib_encoder.rst
new file mode 100644
index 000000000..77c9c81e3
--- /dev/null
+++ b/docs/source/concepts/encoders/grib_encoder.rst
@@ -0,0 +1,231 @@
+.. _grib_encoder:
+
+GRIB encoder
+============
+
+:py:class:`~earthkit.data.encoders.grib.GribEncoder` encodes data into GRIB messages.
+It is selected automatically when writing to a file with a ``.grib``, ``.grb``,
+``.grib1``, ``.grib2``, ``.grb1``, or ``.grb2`` extension, or explicitly by passing
+``encoder="grib"`` to :func:`~earthkit.data.to_target`.
+
+
+Constructing a GribEncoder
+--------------------------
+
+.. code-block:: python
+
+    from earthkit.data import create_encoder
+
+    # No preset — template and metadata supplied per encode() call
+    enc = create_encoder("grib")
+
+    # Preset template and metadata applied to every encode() call
+    enc = create_encoder("grib", template=field, shortName="2t")
+
+.. list-table:: Constructor parameters
+   :header-rows: 1
+   :widths: 22 78
+
+   * - Parameter
+     - Description
+   * - ``template``
+     - Default template used as the basis for every encoded message. Accepts a
+       :py:class:`~earthkit.data.core.field.Field`, a ``GribCodesHandle``, a raw GRIB
+       message as ``bytes``, an ecCodes GRIB sample name as a ``str``, or a raw ecCodes
+       handle as an ``int``. See :ref:`grib_encoder_templates` for details.
+   * - ``metadata``
+     - Dictionary of default ecCodes GRIB keys applied to every message. Keys may
+       optionally be prefixed with ``"metadata."`` and format-independent dotted keys
+       (e.g. ``"parameter.shortName"``) are also accepted. Per-call metadata passed to
+       :meth:`encode` is merged on top, with per-call values taking precedence.
+   * - ``**kwargs``
+     - Additional keyword arguments treated as metadata keys, merged into ``metadata``.
+
+
+.. _grib_encoder_encode:
+
+The encode() method
+-------------------
+
+.. py:method:: GribEncoder.encode(data=None, values=None, metadata={}, template=None, check_nans=True, missing_value=9999, target=None, **kwargs)
+
+   Encode one or more GRIB messages.
+
+   :param data: Source data. Accepted types: :py:class:`~earthkit.data.core.field.Field`,
+       :py:class:`~earthkit.data.core.fieldlist.FieldList`, NumPy array, or xarray
+       Dataset/DataArray. Cannot be combined with both ``values`` and ``template``
+       simultaneously.
+   :param numpy.ndarray values: Raw values array. Takes precedence over any values carried
+       by ``data`` or ``template`` when provided. If the array contains ``NaN`` values
+       they are replaced with ``missing_value`` when ``check_nans=True``.
+   :param dict metadata: Per-call metadata merged on top of the encoder's preset metadata.
+       Accepts ecCodes GRIB keys, keys prefixed with ``"metadata."``, and format-independent
+       dotted keys such as ``"parameter.shortName"``.
+   :param template: Per-call template overriding the encoder's preset template. Accepted
+       types are the same as for the constructor. Takes precedence over ``data`` when
+       forming the GRIB handle, but values are still taken from ``data`` unless ``values``
+       is also provided. Cannot be combined with both ``data`` and ``values``.
+   :param bool check_nans: When ``True`` (default), ``NaN`` values in ``values`` are
+       replaced with ``missing_value`` and ``bitmapPresent`` is set in the encoded message.
+   :param float missing_value: Replacement value for ``NaN`` entries. Default is ``9999``.
+   :param target: Target object passed through from :func:`~earthkit.data.to_target`. Some
+       encoders use this to choose a more efficient output path (e.g. bypassing
+       re-encoding when writing an unchanged GRIB file to disk).
+   :param kwargs: Additional metadata keys, merged with ``metadata``.
+   :returns: A :py:class:`~earthkit.data.encoders.grib.GribEncodedData` instance when a
+       single message is produced, or a *generator* of
+       :py:class:`~earthkit.data.encoders.grib.GribEncodedData` objects when encoding a
+       FieldList or xarray Dataset.
+
+
+Combining data, values, and template
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+``data``, ``values``, and ``template`` cannot all be specified at once. When two of
+them are provided together the following rules apply:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Combination
+     - Behaviour
+   * - ``data`` + ``values``
+     - Template comes from ``data``; values are taken from ``values``.
+   * - ``data`` + ``template``
+     - The GRIB handle comes from ``template``; values are taken from ``data``.
+   * - ``values`` + ``template``
+     - The GRIB handle comes from ``template``; values are taken from ``values``.
+
+
+Encoding from values and metadata only
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When neither ``data`` nor ``template`` is supplied, a new GRIB handle is constructed
+entirely from ``values`` and ``metadata``. This is an experimental path and only
+supports:
+
+- **Regular lat/lon grids** — inferred from a 2-D ``values`` shape ``(Nj, Ni)``.
+- **Reduced Gaussian grids** — inferred from a 1-D ``values`` shape matching a known
+  Gaussian grid size.
+
+Compulsory metadata keys (such as ``shortName`` or ``param``) must be present in
+``metadata`` or the encoder's preset; a :exc:`ValueError` is raised if any are missing.
+
+
+.. _grib_encoder_templates:
+
+Templates
+---------
+
+A *template* is the GRIB message used as the structural basis for a new encoded
+message. The encoder copies the template handle and then applies new values and
+metadata on top.
+
+Accepted template types:
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Type
+     - Description
+   * - :py:class:`~earthkit.data.core.field.Field`
+     - Any earthkit-data Field object. Its GRIB handle is cloned as the starting point.
+   * - ``GribCodesHandle``
+     - An earthkit-data internal GRIB handle wrapper.
+   * - ``bytes``
+     - A raw GRIB message buffer. Parsed into a handle via ecCodes.
+   * - ``str``
+     - The name of an ecCodes GRIB sample (e.g. ``"regular_ll_sfc_grib2"``).
+   * - ``int``
+     - A raw ecCodes handle integer, e.g. obtained from a low-level ecCodes call.
+   * - ``None``
+     - No template. A handle is constructed from ``data``, ``values``, and ``metadata``.
+
+
+Metadata keys
+-------------
+
+All three sources of metadata (constructor preset, ``metadata`` argument, and
+``**kwargs``) are merged together before encoding, with later sources winning:
+
+1. Encoder preset (set at construction time).
+2. ``metadata`` dict passed to :meth:`encode`.
+3. ``**kwargs`` passed to :meth:`encode`.
+
+Keys are accepted in three formats:
+
+- **Plain ecCodes keys** — e.g. ``shortName="2t"``, ``step=6``, ``edition=2``.
+- **``"metadata."``-prefixed keys** — e.g. ``"metadata.shortName"``; the prefix is
+  stripped before the key is applied.
+- **Format-independent dotted keys** — e.g. ``"parameter.shortName"``; these are
+  applied first to produce a base handle, then any plain ecCodes keys are applied on top.
+
+
+NaN handling
+------------
+
+When ``check_nans=True`` (the default), the encoder scans ``values`` for ``NaN`` entries.
+If any are found:
+
+- ``NaN`` values are replaced with ``missing_value`` (default ``9999``).
+- ``bitmapPresent = 1`` and ``missingValue = missing_value`` are set on the encoded
+  message automatically.
+
+
+Usage examples
+--------------
+
+Encode with a preset template and override a metadata key:
+
+.. code-block:: python
+
+    import earthkit.data as ekd
+    from earthkit.data import create_encoder
+
+    template = ekd.from_source("sample", "test.grib").to_fieldlist()[0]
+    encoder = create_encoder("grib", template=template, shortName="msl")
+
+    result = encoder.encode(values=template.values + 1.0, step=6)
+    field = result.to_field()
+    field.get("parameter.shortName")  # "msl"
+    field.get("step")                 # 6
+
+Encode without a preset template (template supplied per call):
+
+.. code-block:: python
+
+    encoder = create_encoder("grib")
+    result = encoder.encode(
+        values=template.values + 1.0,
+        template=template,
+        metadata={"shortName": "msl"},
+        step=6,
+    )
+
+Encode an entire FieldList and write to a file:
+
+.. code-block:: python
+
+    import earthkit.data as ekd
+
+    fs = ekd.from_source("sample", "test.grib").to_fieldlist()
+    ekd.to_target("file", "out.grib", data=fs, encoder="grib")
+
+Encode a FieldList using the encoder directly and iterate over results:
+
+.. code-block:: python
+
+    encoder = create_encoder("grib", template=template)
+    for result in encoder.encode(data=fs):
+        # result is a GribEncodedData for one field
+        result.to_file(open("out.grib", "ab"))
+
+
+How-tos
+-------
+
+- :ref:`/tutorials/target/grib_encoder.ipynb`
+- :ref:`/tutorials/grib/grib_modify_metadata.ipynb`
+- :ref:`/tutorials/grib/grib_modify_values.ipynb`
diff --git a/docs/source/concepts/encoders/index.rst b/docs/source/concepts/encoders/index.rst
new file mode 100644
index 000000000..83fd2c6f4
--- /dev/null
+++ b/docs/source/concepts/encoders/index.rst
@@ -0,0 +1,10 @@
+.. _concepts-encoders:
+
+Encoders
+===========================
+
+.. toctree::
+   :maxdepth: 1
+
+   encoders.rst
+   grib_encoder.rst
diff --git a/docs/source/concepts/experimental/sources.rst b/docs/source/concepts/experimental/sources.rst
index c8055634c..8200eb1b4 100644
--- a/docs/source/concepts/experimental/sources.rst
+++ b/docs/source/concepts/experimental/sources.rst
@@ -42,4 +42,4 @@ iris
 
   Further examples:
 
-    - :ref:`/how-tos/iris_pp.ipynb`
+    - :ref:`/tutorials/iris_pp.ipynb`
diff --git a/docs/source/concepts/field/ensemble.rst b/docs/source/concepts/field/ensemble.rst
index fb10f35ef..b15a2575c 100644
--- a/docs/source/concepts/field/ensemble.rst
+++ b/docs/source/concepts/field/ensemble.rst
@@ -60,6 +60,6 @@ Accessing ensemble information
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_ensemble.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_ensemble.ipynb`
diff --git a/docs/source/concepts/field/field.rst b/docs/source/concepts/field/field.rst
index 04590116b..6fb859bed 100644
--- a/docs/source/concepts/field/field.rst
+++ b/docs/source/concepts/field/field.rst
@@ -115,7 +115,7 @@ retained in the result without modification:
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/grib/grib_modify_values.ipynb`
-- :ref:`/how-tos/grib/grib_modify_metadata.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/grib/grib_modify_values.ipynb`
+- :ref:`/tutorials/grib/grib_modify_metadata.ipynb`
diff --git a/docs/source/concepts/field/geography.rst b/docs/source/concepts/field/geography.rst
index 8c2169032..2512011d5 100644
--- a/docs/source/concepts/field/geography.rst
+++ b/docs/source/concepts/field/geography.rst
@@ -18,6 +18,9 @@ the :attr:`geography` attribute of a field and is represented by a subclass of
     (70, -20, 35, 40)
     >>> field.geography.grid_type()
     'regular_ll'
+    >>> lat, lon = field.geography.latlons()
+    >>> lat.shape
+    (19, 36)
 
 The same information is available through the generic :meth:`~earthkit.data.core.field.Field.get`
 interface using the ``"geography."`` prefix:
@@ -32,16 +35,38 @@ interface using the ``"geography."`` prefix:
 The geography component is **immutable**. Use the
 :meth:`~earthkit.data.field.component.geography.GeographyBase.set` method (or
 :meth:`~earthkit.data.core.field.Field.set` on the field) to derive a modified copy.
-When changing the grid it is typically necessary to supply new field values to match the
-new grid shape:
+
+You can update the grid by supplying a new grid specification:
+
+.. code-block:: python
+
+    >>> new_geography = field.geography.set(grid_spec=[10, 10])
+    >>> new_geography.area()
+    (90.0, 0, -90.0, 360.0)
+
+Alternatively, provide new latitude and longitude arrays:
 
 .. code-block:: python
 
     >>> import numpy as np
+    >>> latitudes = np.linspace(90, -90, 19)
+    >>> longitudes = np.linspace(0, 360, 36)
+    >>> new_geography = field.geography.set(latitudes=latitudes, longitudes=longitudes)
+    >>> new_geography.area()
+    (90.0, 0, -90.0, 360.0)
+
+When creating a new field, pass new values alongside the geography update to match the
+new grid shape:
+
+.. code-block:: python
+
     >>> values = np.random.rand(19, 36)
     >>> new_field = field.set({"geography.grid_spec": [10, 10], "values": values})
     >>> new_field.geography.area()
     (90.0, 0, -90.0, 360.0)
+    >>> new_field = field.set({"geography.latitudes": latitudes, "geography.longitudes": longitudes, "values": values})
+    >>> new_field.geography.area()
+    (90.0, 0, -90.0, 360.0)
 
 
 Geography types
@@ -56,6 +81,40 @@ The appropriate geography subclass is determined automatically from the data:
 - **GridsSpecBasedGeography** — grid defined by an eckit-geo grid specification string or
   dictionary (requires optional eckit-geo grid support).
 - **EmptyGeography** — placeholder used when no coordinate information is available.
+- **SpectralGeography** — placeholder used for spectral fields that carry no grid geometry.
+
+
+Getting latitudes and longitudes
+---------------------------------
+
+:meth:`~earthkit.data.field.component.geography.GeographyBase.latlons` is the primary method for
+retrieving the coordinates of every grid point. It returns a ``(latitudes, longitudes)`` tuple of
+arrays shaped to match the field's :attr:`~earthkit.data.field.component.geography.GeographyBase.shape`:
+
+.. code-block:: python
+
+    >>> lat, lon = field.geography.latlons()
+    >>> lat.shape
+    (19, 36)
+    >>> lon.shape
+    (19, 36)
+
+Pass ``flatten=True`` to get 1-D arrays instead:
+
+.. code-block:: python
+
+    >>> lat, lon = field.geography.latlons(flatten=True)
+    >>> lat.shape
+    (684,)
+
+The ``dtype`` argument controls the output array type:
+
+.. code-block:: python
+
+    >>> lat, lon = field.geography.latlons(dtype="float32")
+    >>> lat.dtype
+    dtype('float32')
+
 
 
 Accessing geography information
@@ -86,7 +145,7 @@ All geography keys are accessible through :meth:`~earthkit.data.core.field.Field
    * - ``geography.y``
      - Array of y-coordinates in the native CRS. ``dtype`` argument supported.
    * - ``geography.shape``
-     - Grid shape as a tuple of integers (e.g. ``(latitude_size, longitude_size)``).
+     - Grid shape as a tuple of integers (e.g. ``(latitude_size, longitude_size)`` for a 2-D lat/lon grid).
    * - ``geography.projection``
      - :py:class:`~earthkit.data.utils.projections.Projection` object describing the CRS, or ``None``.
    * - ``geography.bounding_box``
@@ -98,7 +157,7 @@ All geography keys are accessible through :meth:`~earthkit.data.core.field.Field
    * - ``geography.grid_spec``
      - Grid specification. Can be used to construct a new geography of the same type.
    * - ``geography.grid``
-     - Grid definition understood by eckit-geo (when grid support is available).
+     - ``eckit.geo.Grid`` object. This is an experimental object and may not be available for all geography types.
    * - ``geography.unique_grid_id``
      - A hashable identifier that is the same for two fields sharing an identical grid.
 
@@ -106,9 +165,9 @@ All geography keys are accessible through :meth:`~earthkit.data.core.field.Field
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/grib/grib_lat_lon_value_ll.ipynb`
-- :ref:`/how-tos/grib/grib_lat_lon_value_rgg.ipynb`
-- :ref:`/how-tos/grib/grib_nearest_gridpoint.ipynb`
-- :ref:`/how-tos/misc/projection.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/grib/grib_lat_lon_value_ll.ipynb`
+- :ref:`/tutorials/grib/grib_lat_lon_value_rgg.ipynb`
+- :ref:`/tutorials/grib/grib_nearest_gridpoint.ipynb`
+- :ref:`/tutorials/misc/projection.ipynb`
diff --git a/docs/source/concepts/field/index.rst b/docs/source/concepts/field/index.rst
index 554875571..5693e8323 100644
--- a/docs/source/concepts/field/index.rst
+++ b/docs/source/concepts/field/index.rst
@@ -7,9 +7,9 @@ Field
    :maxdepth: 1
 
    field.rst
-   vertical.rst
    parameter.rst
    time.rst
+   vertical.rst
    ensemble.rst
    geography.rst
    proc.rst
diff --git a/docs/source/concepts/field/parameter.rst b/docs/source/concepts/field/parameter.rst
index 19401cc0a..3a0e3b3b1 100644
--- a/docs/source/concepts/field/parameter.rst
+++ b/docs/source/concepts/field/parameter.rst
@@ -114,7 +114,7 @@ All parameter keys are accessible through :meth:`~earthkit.data.core.field.Field
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_chem.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_wave_spectra.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_chem.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb`
diff --git a/docs/source/concepts/field/proc.rst b/docs/source/concepts/field/proc.rst
index 179b34b04..14df97b19 100644
--- a/docs/source/concepts/field/proc.rst
+++ b/docs/source/concepts/field/proc.rst
@@ -91,5 +91,5 @@ All proc keys are accessible through :meth:`~earthkit.data.core.field.Field.get`
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
diff --git a/docs/source/concepts/field/time.rst b/docs/source/concepts/field/time.rst
index 316c6dd68..263274347 100644
--- a/docs/source/concepts/field/time.rst
+++ b/docs/source/concepts/field/time.rst
@@ -90,8 +90,8 @@ All time keys are accessible through :meth:`~earthkit.data.core.field.Field.get`
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/grib/grib_time_series.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_temporal.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_step_ranges.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/grib/grib_time_series.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_temporal.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_step_ranges.ipynb`
diff --git a/docs/source/concepts/field/vertical.rst b/docs/source/concepts/field/vertical.rst
index 2ca623c63..b88a67e30 100644
--- a/docs/source/concepts/field/vertical.rst
+++ b/docs/source/concepts/field/vertical.rst
@@ -54,9 +54,9 @@ following metadata:
 - **standard_name** – CF standard name (e.g. ``"air_pressure"``)
 - **long_name** – human-readable description
 - **units** – native units of the level value
-- **layer** – ``True`` when the type represents a layer (two bounding levels) rather than a single level
-- **level** – indicates which bound is the *representative* level for layer types: ``"top"``, ``"bottom"``, or empty string when undefined
-- **positive** – vertical direction in which values increase: ``"up"``, ``"down"``, or empty string when undefined
+- **layer** – ``True`` only when both the bottom and top bounding surfaces are defined, i.e. the level type describes a proper layer between two bounds; ``False`` when only one or neither bounding surface is specified (see for example ``"low_cloud_layer"`` vs ``"medium_cloud_layer"``)
+- **level** – indicates which bound is the *representative* level: ``"top"``, ``"bottom"``, or ``None``  when undefined
+- **positive** – vertical direction in which values increase: ``"up"``, ``"down"``, or ``None`` when undefined
 
 When earthkit-data encounters a level type that is not in the predefined list below, it is
 automatically registered at runtime so that arbitrary model-specific level types are handled
@@ -79,7 +79,7 @@ Predefined level types
      - Level
      - Positive
    * - ``abstract_single_level``
-     - abstract_single_level
+     -
      - abstract single level
      - ``abstractSingleLevel``
      -
@@ -87,7 +87,7 @@ Predefined level types
      -
      -
    * - ``cloud_base``
-     - cloud_base
+     -
      - cloud base
      - ``cloudBase``
      -
@@ -116,10 +116,10 @@ Predefined level types
      - ``d_bsl_layer``
      - m
      - Yes
-     - top
+     - bottom
      - down
    * - ``entire_atmosphere``
-     - entire_atmosphere
+     -
      - entire atmosphere
      - ``entire_atmosphere``
      -
@@ -127,7 +127,7 @@ Predefined level types
      -
      -
    * - ``entire_lake``
-     - entire_lake
+     -
      - entire lake
      - ``entireLake``
      -
@@ -135,7 +135,7 @@ Predefined level types
      -
      -
    * - ``entire_melt_pond``
-     - entire_melt_pond
+     -
      - entire melt pond
      - ``entireMeltPond``
      -
@@ -143,7 +143,7 @@ Predefined level types
      -
      -
    * - ``general``
-     - general
+     -
      - general
      - ``gen``
      - 1
@@ -167,55 +167,55 @@ Predefined level types
      -
      - up
    * - ``high_cloud_layer``
-     - high_cloud_layer
+     -
      - high cloud layer
      - ``highCloudLayer``
      - hPa
-     - Yes
-     - top
+     - No
      -
+     - down
    * - ``hybrid``
      - atmosphere_hybrid_sigma_pressure_coordinate
      - hybrid level
      - ``ml``
      - 1
-     - No (parametric)
+     - No
      -
      - down
    * - ``ice_layer_on_water``
-     - ice_layer_on_water
+     -
      - ice layer on water
      - ``iceLayerOnWater``
-     - 1
-     - Yes
-     - top
-     - up
+     -
+     - No
+     -
+     -
    * - ``ice_top_on_water``
-     - ice_top_on_water
+     -
      - ice top on water
      - ``iceTopOnWater``
-     - 1
+     -
      - No
      -
      -
    * - ``lake_bottom``
-     - lake_bottom
+     -
      - lake bottom
      - ``lakeBottom``
-     - 1
+     -
      - No
      -
      -
    * - ``low_cloud_layer``
-     - low_cloud_layer
+     -
      - low cloud layer
      - ``lowCloudLayer``
      - hPa
-     - Yes
-     - top
-     -
+     - No
+     - bottom
+     - down
    * - ``mean_sea``
-     - mean_sea
+     -
      - mean sea level
      - ``mean_sea``
      -
@@ -223,29 +223,29 @@ Predefined level types
      -
      -
    * - ``medium_cloud_layer``
-     - medium_cloud_layer
+     -
      - medium cloud layer
      - ``mediumCloudLayer``
      - hPa
      - Yes
-     - top
-     -
+     - bottom
+     - down
    * - ``mixed_layer_depth_by_density``
-     - mixed_layer_depth_by_density
+     -
      - mixed layer depth by density
      - ``mixedLayerDepthByDensity``
-     - m
+     - kg m-3
      - No
      -
      - down
    * - ``mixed_layer_parcel``
-     - mixed_layer_parcel
+     -
      - mixed layer parcel
      - ``mixedLayerParcel``
      - Pa
      - No
      -
-     -
+     - down
    * - ``mixing_layer``
      - mixing_layer
      - mixing layer
@@ -255,7 +255,7 @@ Predefined level types
      -
      -
    * - ``most_unstable_parcel``
-     - most_unstable_parcel
+     -
      - most unstable parcel
      - ``mostUnstableParcel``
      -
@@ -263,7 +263,7 @@ Predefined level types
      -
      -
    * - ``nominal_top_of_atmosphere``
-     - nominal_top_of_atmosphere
+     -
      - nominal top of atmosphere
      - ``nominalTopOfAtmosphere``
      -
@@ -271,7 +271,7 @@ Predefined level types
      -
      -
    * - ``ocean_model``
-     - ocean_model
+     -
      - ocean model
      - ``oceanModel``
      - 1
@@ -279,7 +279,7 @@ Predefined level types
      -
      - down
    * - ``ocean_model_layer``
-     - ocean_model_layer
+     -
      - ocean model layer
      - ``oceanModelLayer``
      - 1
@@ -287,7 +287,7 @@ Predefined level types
      - bottom
      - down
    * - ``ocean_surface``
-     - ocean_surface
+     -
      - ocean surface
      - ``ocean_surface``
      -
@@ -295,29 +295,29 @@ Predefined level types
      -
      -
    * - ``ocean_surface_to_bottom``
-     - ocean_surface_to_bottom
+     -
      - ocean surface to bottom
      - ``oceanSurfaceToBottom``
-     - 1
+     -
      - No
      -
      -
    * - ``potential_temperature``
      - air_potential_temperature
-     - air potential temperature
+     - air_potential temperature
      - ``pt``
      - K
      - No
      -
-     - up
+     -
    * - ``potential_vorticity``
      - ertel_potential_vorticity
      - potential vorticity
      - ``pv``
-     - 10E-9 K m2 kg-1 s-1
+     - nK m2 kg-1 s-1
      - No
      -
-     - down
+     -
    * - ``pressure``
      - air_pressure
      - pressure
@@ -335,7 +335,7 @@ Predefined level types
      - top
      - down
    * - ``sea_ice_layer``
-     - sea_ice_layer
+     -
      - sea ice layer
      - ``seaIceLayer``
      - 1
@@ -343,7 +343,7 @@ Predefined level types
      - bottom
      - down
    * - ``snow``
-     - unknown
+     -
      - snow layer
      - ``snow``
      - 1
@@ -351,15 +351,15 @@ Predefined level types
      - bottom
      - down
    * - ``snow_layer_over_ice_on_water``
-     - snow_layer_over_ice_on_water
+     -
      - snow layer over ice on water
      - ``snowLayerOverIceOnWater``
-     - 1
+     -
      - No
      -
      -
    * - ``soil_layer``
-     - soil_layer
+     -
      - soil layer
      - ``soilLayer``
      - 1
@@ -367,10 +367,10 @@ Predefined level types
      - bottom
      - down
    * - ``stratosphere``
-     - stratosphere
+     -
      - stratosphere
      - ``stratosphere``
-     - 1
+     -
      - No
      -
      -
@@ -383,7 +383,7 @@ Predefined level types
      -
      -
    * - ``temperature``
-     - temperature
+     - air_temperature
      - temperature
      - ``isothermal``
      - K
@@ -391,7 +391,7 @@ Predefined level types
      -
      -
    * - ``tropopause``
-     - tropopause
+     -
      - tropopause
      - ``tropopause``
      -
@@ -399,15 +399,15 @@ Predefined level types
      -
      -
    * - ``troposphere``
-     - troposphere
+     -
      - troposphere
      - ``troposphere``
-     - 1
+     -
      - No
      -
      -
    * - ``unknown``
-     - unknown
+     -
      - unknown
      - ``unknown``
      -
@@ -415,10 +415,10 @@ Predefined level types
      -
      -
    * - ``water_surface_to_isothermal_ocean_layer``
-     - water_surface_to_isothermal_ocean_layer
+     -
      - water surface to isothermal ocean layer
      - ``waterSurfaceToIsothermalOceanLayer``
-     - 1
+     -
      - No
      -
      -
@@ -492,7 +492,7 @@ Supported keys:
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_level.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_field_dims.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_level.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_field_dims.ipynb`
diff --git a/docs/source/concepts/fieldlist.rst b/docs/source/concepts/fieldlist.rst
index f212ffdbd..abb973643 100644
--- a/docs/source/concepts/fieldlist.rst
+++ b/docs/source/concepts/fieldlist.rst
@@ -190,8 +190,8 @@ pattern:
 How-tos
 -------
 
-- :ref:`/how-tos/field/field_overview.ipynb`
-- :ref:`/how-tos/grib/grib_overview.ipynb`
-- :ref:`/how-tos/grib/grib_selection.ipynb`
-- :ref:`/how-tos/grib/grib_order_by.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_overview.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/grib/grib_selection.ipynb`
+- :ref:`/tutorials/grib/grib_order_by.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_overview.ipynb`
diff --git a/docs/source/concepts/index.rst b/docs/source/concepts/index.rst
index 0cc0f1ac0..a56ff99f5 100644
--- a/docs/source/concepts/index.rst
+++ b/docs/source/concepts/index.rst
@@ -7,7 +7,7 @@ Concepts
 
    inputs/index.rst
    data
-   encoders
+   encoders/index.rst
    targets/index.rst
    streams
    config
diff --git a/docs/source/concepts/inputs/from_object.rst b/docs/source/concepts/inputs/from_object.rst
index 12b2b8e51..6cfca7c84 100644
--- a/docs/source/concepts/inputs/from_object.rst
+++ b/docs/source/concepts/inputs/from_object.rst
@@ -14,6 +14,6 @@ from_object
 
 Examples:
 
-    - :ref:`/how-tos/other/from_object.ipynb`
-    - :ref:`/how-tos/other/pandas.ipynb`
-    - :ref:`/how-tos/geojson/geojson_geopandas.ipynb`
+    - :ref:`/tutorials/other/from_object.ipynb`
+    - :ref:`/tutorials/other/pandas.ipynb`
+    - :ref:`/tutorials/geojson/geojson_geopandas.ipynb`
diff --git a/docs/source/concepts/inputs/from_source.rst b/docs/source/concepts/inputs/from_source.rst
index aec84485e..c32f77d3f 100644
--- a/docs/source/concepts/inputs/from_source.rst
+++ b/docs/source/concepts/inputs/from_source.rst
@@ -141,15 +141,15 @@ file
 
   Further examples:
 
-    - :ref:`/how-tos/source/files.ipynb`
-    - :ref:`/how-tos/source/multi_files.ipynb`
-    - :ref:`/how-tos/source/file_parts.ipynb`
-    - :ref:`/how-tos/source/file_stream.ipynb`
-    - :ref:`/how-tos/source/tar_files.ipynb`
-    - :ref:`/how-tos/grib/grib_overview.ipynb`
-    - :ref:`/how-tos/bufr/bufr_temp.ipynb`
-    - :ref:`/how-tos/netcdf/netcdf.ipynb`
-    - :ref:`/how-tos/odb/odb.ipynb`
+    - :ref:`/tutorials/source/files.ipynb`
+    - :ref:`/tutorials/source/multi_files.ipynb`
+    - :ref:`/tutorials/source/file_parts.ipynb`
+    - :ref:`/tutorials/source/file_stream.ipynb`
+    - :ref:`/tutorials/source/tar_files.ipynb`
+    - :ref:`/tutorials/grib/grib_overview.ipynb`
+    - :ref:`/tutorials/bufr/bufr_temp.ipynb`
+    - :ref:`/tutorials/netcdf/netcdf.ipynb`
+    - :ref:`/tutorials/odb/odb.ipynb`
 
 .. _data-sources-file-pattern:
 
@@ -255,7 +255,7 @@ hive_partioning=True
 
     Further examples:
 
-    - :ref:`/how-tos/source/files.ipynb`
+    - :ref:`/tutorials/source/files.ipynb`
 
 
 .. _data-sources-url:
@@ -281,7 +281,7 @@ url
       >>> import earthkit.data as ekd
       >>> fl = ekd.from_source(
       ...     "url",
-      ...     "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test4.grib",
+      ...     "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test4.grib",
       ... ).to_fieldlist()
       >>> fl.ls()
         centre shortName    typeOfLevel  level  dataDate  dataTime stepRange dataType  number    gridType
@@ -295,7 +295,7 @@ url
       >>> import earthkit.data as ekd
       >>> fl = ekd.from_source(
       ...     "url",
-      ...     "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test4.grib",
+      ...     "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test4.grib",
       ...     parts=[(0, 130428), (260856, 130428)],
       ... ).to_fieldlist()
       >>> fl.ls()
@@ -305,9 +305,9 @@ url
 
   Further examples:
 
-    - :ref:`/how-tos/source/url.ipynb`
-    - :ref:`/how-tos/source/url_parts.ipynb`
-    - :ref:`/how-tos/source/url_stream.ipynb`
+    - :ref:`/tutorials/source/url.ipynb`
+    - :ref:`/tutorials/source/url_parts.ipynb`
+    - :ref:`/tutorials/source/url_stream.ipynb`
 
 
 .. _data-sources-url-pattern:
@@ -395,7 +395,7 @@ stream
   .. code-block:: python
 
       >>> import earthkit.data as ekd
-      >>> stream = open("docs/how-tos/test4.grib", "rb")
+      >>> stream = open("docs/tutorials/test4.grib", "rb")
       >>> fl = ekd.from_source("stream", stream).to_fieldlist()
 
       # f is a GribField
@@ -412,7 +412,7 @@ stream
     .. code-block:: python
 
       >>> import earthkit.data as ekd
-      >>> stream = open("docs/how-tos/test4.grib", "rb")
+      >>> stream = open("docs/tutorials/test4.grib", "rb")
       >>> fl = ekd.from_source("stream", stream).to_fieldlist()
 
        # f is a FieldList
@@ -428,7 +428,7 @@ stream
     .. code-block:: python
 
       >>> import earthkit.data as ekd
-      >>> stream = open("docs/how-tos/test4.grib", "rb")
+      >>> stream = open("docs/tutorials/test4.grib", "rb")
       >>> fl = ekd.from_source("stream", stream).to_fieldlist()
 
       # f is a FieldList
@@ -441,9 +441,9 @@ stream
 
   See the following notebook examples for further details:
 
-    - :ref:`/how-tos/source/data_from_stream.ipynb`
-    - :ref:`/how-tos/source/fdb.ipynb`
-    - :ref:`/how-tos/source/url_stream.ipynb`
+    - :ref:`/tutorials/source/data_from_stream.ipynb`
+    - :ref:`/tutorials/source/fdb.ipynb`
+    - :ref:`/tutorials/source/url_stream.ipynb`
 
 
 .. _data-sources-memory:
@@ -516,9 +516,9 @@ list-of-dicts
 
   Further examples:
 
-      - :ref:`/how-tos/dict/fields_from_dict_in_loop.ipynb`
-      - :ref:`/how-tos/dict/list_of_dicts_overview.ipynb`
-      - :ref:`/how-tos/dict/list_of_dicts_geography.ipynb`
+      - :ref:`/tutorials/dict/fields_from_dict_in_loop.ipynb`
+      - :ref:`/tutorials/dict/list_of_dicts_overview.ipynb`
+      - :ref:`/tutorials/dict/list_of_dicts_geography.ipynb`
 
 .. _data-sources-multi:
 
@@ -596,7 +596,7 @@ ads
 
   Further examples:
 
-      - :ref:`/how-tos/source/ads.ipynb`
+      - :ref:`/tutorials/source/ads.ipynb`
 
 
 .. _data-sources-cds:
@@ -651,7 +651,7 @@ cds
 
   Further examples:
 
-      - :ref:`/how-tos/source/cds.ipynb`
+      - :ref:`/tutorials/source/cds.ipynb`
 
 
 .. _data-sources-ecfs:
@@ -707,7 +707,7 @@ ecmwf-open-data
 
   Further examples:
 
-      - :ref:`/how-tos/source/ecmwf_open_data.ipynb`
+      - :ref:`/tutorials/source/ecmwf_open_data.ipynb`
 
 
 .. _data-sources-fdb:
@@ -800,9 +800,9 @@ fdb
 
   Further examples:
 
-      - :ref:`/how-tos/source/fdb.ipynb`
-      - :ref:`/how-tos/grib/grib_fdb_write.ipynb`
-      - :ref:`/how-tos/grib/grib_fdb_lazy.ipynb`
+      - :ref:`/tutorials/source/fdb.ipynb`
+      - :ref:`/tutorials/grib/grib_fdb_write.ipynb`
+      - :ref:`/tutorials/grib/grib_fdb_lazy.ipynb`
 
 
 
@@ -884,7 +884,7 @@ gribjump
 
   Further examples:
 
-      - :ref:`/how-tos/source/gribjump.ipynb`
+      - :ref:`/tutorials/source/gribjump.ipynb`
 
 
 .. _data-sources-mars:
@@ -967,7 +967,7 @@ mars
 
   Further examples:
 
-      - :ref:`/how-tos/source/mars.ipynb`
+      - :ref:`/tutorials/source/mars.ipynb`
 
 
 .. _data-sources-opendap:
@@ -984,7 +984,7 @@ opendap
 
   Examples:
 
-      - :ref:`/how-tos/source/netcdf_opendap.ipynb`
+      - :ref:`/tutorials/source/netcdf_opendap.ipynb`
 
 
 .. _data-sources-polytope:
@@ -1042,11 +1042,11 @@ polytope
 
   Further examples:
 
-      - :ref:`/how-tos/source/polytope.ipynb`
-      - :ref:`/how-tos/source/polytope_feature.ipynb`
-      - :ref:`/how-tos/covjson/polytope_time_series.ipynb`
-      - :ref:`/how-tos/covjson/polytope_polygon_coverage.ipynb`
-      - :ref:`/how-tos/covjson/polytope_vertical_profile.ipynb`
+      - :ref:`/tutorials/source/polytope.ipynb`
+      - :ref:`/tutorials/source/polytope_feature.ipynb`
+      - :ref:`/tutorials/covjson/polytope_time_series.ipynb`
+      - :ref:`/tutorials/covjson/polytope_polygon_coverage.ipynb`
+      - :ref:`/tutorials/covjson/polytope_vertical_profile.ipynb`
 
 .. _data-sources-s3:
 
@@ -1149,7 +1149,7 @@ s3
 
   Further examples:
 
-      - :ref:`/how-tos/source/s3.ipynb`
+      - :ref:`/tutorials/source/s3.ipynb`
 
 
 .. _data-sources-wekeo:
@@ -1199,7 +1199,7 @@ wekeo
 
   Further examples:
 
-      - :ref:`/how-tos/source/wekeo.ipynb`
+      - :ref:`/tutorials/source/wekeo.ipynb`
 
 
 .. _data-sources-wekeocds:
@@ -1252,7 +1252,7 @@ wekeocds
 
   Further examples:
 
-      - :ref:`/how-tos/source/wekeo.ipynb`
+      - :ref:`/tutorials/source/wekeo.ipynb`
 
 
 
diff --git a/docs/source/concepts/misc/grib_memory.rst b/docs/source/concepts/misc/grib_memory.rst
index b68d9bb47..27c2ab8d0 100644
--- a/docs/source/concepts/misc/grib_memory.rst
+++ b/docs/source/concepts/misc/grib_memory.rst
@@ -21,7 +21,7 @@ We can read :ref:`grib` data as a :ref:`stream <streams>` iterator e.g. with the
 
     import earthkit.data as ekd
 
-    url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     ds = ekd.from_source("url", url, stream=True)
     for f in fields:
         print(f)
@@ -37,7 +37,7 @@ We can load :ref:`grib` data fully into memory when we read it as a :ref:`stream
 
     import earthkit.data as ekd
 
-    url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     ds = ekd.from_source("url", url, stream=True, read_all=True)
 
 With this, the entire ``ds`` fieldlist, including all the fields and the related GRIB handles, are stored in memory.
diff --git a/docs/source/concepts/misc/parts.rst b/docs/source/concepts/misc/parts.rst
index 1e16df168..c95a4b92f 100644
--- a/docs/source/concepts/misc/parts.rst
+++ b/docs/source/concepts/misc/parts.rst
@@ -69,7 +69,7 @@ When we use this mode the ``parts`` kwarg cannot be used!
 
 Examples:
 
-    - :ref:`/how-tos/source/file_parts.ipynb`
+    - :ref:`/tutorials/source/file_parts.ipynb`
 
 
 Specifying the parts for S3
@@ -79,4 +79,4 @@ For the :ref:`data-sources-s3` source the parts have to be specified within the
 
 Examples:
 
-    - :ref:`/how-tos/source/s3.ipynb`
+    - :ref:`/tutorials/source/s3.ipynb`
diff --git a/docs/source/concepts/plugins/encoder_plugin.rst b/docs/source/concepts/plugins/encoder_plugin.rst
index 739a07c9b..d35a84b2e 100644
--- a/docs/source/concepts/plugins/encoder_plugin.rst
+++ b/docs/source/concepts/plugins/encoder_plugin.rst
@@ -85,4 +85,4 @@ The integration is performed by ``entry_points`` defined in  ``pyproject.toml``.
     entry-points."earthkit.data.encoders".demo-encoder-png = "earthkit_data_demo_encoder_png:DemoEncoderPng"
 
 
-.. See the :ref:`/how-tos/demo_encoders_plugin.ipynb` notebook for the full example.
+.. See the :ref:`/tutorials/demo_encoders_plugin.ipynb` notebook for the full example.
diff --git a/docs/source/concepts/plugins/sources_plugin.rst b/docs/source/concepts/plugins/sources_plugin.rst
index 2f90f2def..c097d17da 100644
--- a/docs/source/concepts/plugins/sources_plugin.rst
+++ b/docs/source/concepts/plugins/sources_plugin.rst
@@ -83,4 +83,4 @@ The integration is performed by ``entry_points`` defined in  ``pyproject.toml``.
     entry-points."earthkit.data.sources".demo-source = "earthkit_data_demo_source:DemoSource"
 
 
-See the :ref:`/how-tos/misc/demo_sources_plugin.ipynb` notebook for the full example.
+See the :ref:`/tutorials/misc/demo_sources_plugin.ipynb` notebook for the full example.
diff --git a/docs/source/concepts/plugins/target_plugin.rst b/docs/source/concepts/plugins/target_plugin.rst
index 70b303040..d76e17df2 100644
--- a/docs/source/concepts/plugins/target_plugin.rst
+++ b/docs/source/concepts/plugins/target_plugin.rst
@@ -85,4 +85,4 @@ The integration is performed by ``entry_points`` defined in  ``pyproject.toml``.
     entry-points."earthkit.data.targets".demo-target = "earthkit_data_demo_target:DemoTarget"
 
 
-.. See the :ref:`/how-tos/demo_targets_plugin.ipynb` notebook for the full example.
+.. See the :ref:`/tutorials/demo_targets_plugin.ipynb` notebook for the full example.
diff --git a/docs/source/concepts/streams.rst b/docs/source/concepts/streams.rst
index 10ab039d6..9cf1f48d1 100644
--- a/docs/source/concepts/streams.rst
+++ b/docs/source/concepts/streams.rst
@@ -25,7 +25,7 @@ The example below shows how we iterate through a GRIB data stream field by field
 .. code-block:: python
 
     >>> import earthkit.data as ekd
-    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     >>> fl = ekd.from_source("url", url, stream=True).to_fieldlist()
     >>> for f in fl:
     ...     print(f)
@@ -42,7 +42,7 @@ We can also use :meth:`~earthkit.data.core.fieldlist.FieldList.batched` to itera
 .. code-block:: python
 
     >>> import earthkit.data as ekd
-    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     >>> fl = ekd.from_source("url", url, stream=True).to_fieldlist()
     >>> for f in fl.batched(2):
     ...     print(f"len={len(f)} {f.get(('parameter.variable', 'vertical.level'))}")
@@ -56,7 +56,7 @@ Another option is to use :meth:`~earthkit.data.core.fieldlist.FieldList.group_by
 .. code-block:: python
 
     >>> import earthkit.data as ekd
-    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     >>> fl = ekd.from_source("url", url, stream=True).to_fieldlist()
     >>> for f in fl.group_by("vertical.level"):
     ...     print(f"len={len(f)} {f.get(('parameter.variable', 'vertical.level'))}")
@@ -75,7 +75,7 @@ We can load the whole stream into memory by using  ``read_all=True`` in :meth:`t
 .. code-block:: python
 
     >>> import earthkit.data as ekd
-    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test6.grib"
+    >>> url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test6.grib"
     >>> fl = ekd.from_source("url", url, stream=True).to_fieldlist(read_all=True)
     >>> len(fl)
     6
@@ -83,7 +83,7 @@ We can load the whole stream into memory by using  ``read_all=True`` in :meth:`t
 Further examples
 -----------------
 
-- :ref:`/how-tos/source/data_from_stream.ipynb`
-- :ref:`/how-tos/source/file_stream.ipynb`
-- :ref:`/how-tos/source/fdb.ipynb`
-- :ref:`/how-tos/source/url_stream.ipynb`
+- :ref:`/tutorials/source/data_from_stream.ipynb`
+- :ref:`/tutorials/source/file_stream.ipynb`
+- :ref:`/tutorials/source/fdb.ipynb`
+- :ref:`/tutorials/source/url_stream.ipynb`
diff --git a/docs/source/concepts/targets/index.rst b/docs/source/concepts/targets/index.rst
index cb142ab21..abfd2c18d 100644
--- a/docs/source/concepts/targets/index.rst
+++ b/docs/source/concepts/targets/index.rst
@@ -3,7 +3,6 @@
 Targets
 ===============
 
-
 A **target** can represent a file, a database, a remote server etc. Data is written/added to a target by using a suitable :ref:`encoder <encoders>`.
 
 Details
@@ -19,12 +18,12 @@ Details
 Examples
 ------------
 
-  - :ref:`/how-tos/target/file_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_file_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_file_pattern_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_fdb_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_geotiff.ipynb`
-  - :ref:`/how-tos/target/grib_to_zarr_target.ipynb`
+  - :ref:`/tutorials/target/file_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_file_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_file_pattern_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_fdb_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_geotiff.ipynb`
+  - :ref:`/tutorials/target/grib_to_zarr_target.ipynb`
 
 
 Overview
@@ -41,7 +40,7 @@ There are different ways to write/add data to a given target:
     import earthkit.data as ekd
 
     # read GRIB data into a fieldlist
-    ds = ekd.from_source("file", "docs/how-tos/test.grib").to_fieldlist()
+    ds = ekd.from_source("file", "docs/tutorials/test.grib").to_fieldlist()
 
     # write the fieldlist to a file in different ways
 
diff --git a/docs/source/concepts/targets/target_objects.rst b/docs/source/concepts/targets/target_objects.rst
index 1ad9bf93f..e76b1362e 100644
--- a/docs/source/concepts/targets/target_objects.rst
+++ b/docs/source/concepts/targets/target_objects.rst
@@ -31,7 +31,7 @@ This example writes a FieldList to a target.
     import earthkit.data as ekd
 
     # read GRIB data into a fieldlist
-    ds = ekd.from_source("file", "docs/how-tos/test.grib").to_fieldlist()
+    ds = ekd.from_source("file", "docs/tutorials/test.grib").to_fieldlist()
 
     # writing in a loop field by field
     t = ekd.create_target("file", "_my_res.grib")
@@ -56,7 +56,7 @@ Target objects can also be used a context manager. When used as a context manage
     import earthkit.data as ekd
 
     # read GRIB data into a fieldlist
-    ds = ekd.from_source("file", "docs/how-tos/test.grib").to_fieldlist()
+    ds = ekd.from_source("file", "docs/tutorials/test.grib").to_fieldlist()
 
     with ekd.create_target("file", "_my_res.grib") as t:
         for f in ds:
diff --git a/docs/source/concepts/targets/to_target.rst b/docs/source/concepts/targets/to_target.rst
index c9d8438e8..e589a4bf2 100644
--- a/docs/source/concepts/targets/to_target.rst
+++ b/docs/source/concepts/targets/to_target.rst
@@ -86,10 +86,10 @@ file
 
   Notebook examples:
 
-    - :ref:`/how-tos/target/file_target.ipynb`
-    - :ref:`/how-tos/target/grib_to_file_target.ipynb`
-    - :ref:`/how-tos/target/grib_to_file_pattern_target.ipynb`
-    - :ref:`/how-tos/target/grib_to_geotiff.ipynb`
+    - :ref:`/tutorials/target/file_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_file_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_file_pattern_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_geotiff.ipynb`
 
 .. _targets-file-pattern:
 
@@ -128,7 +128,7 @@ file-pattern
 
   Notebook examples:
 
-    - :ref:`/how-tos/target/grib_to_file_pattern_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_file_pattern_target.ipynb`
 
 
 .. _targets-fdb:
@@ -172,7 +172,7 @@ fdb
 
   Notebook examples:
 
-    - :ref:`/how-tos/target/grib_to_fdb_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_fdb_target.ipynb`
 
 
 .. _targets-zarr:
@@ -193,7 +193,7 @@ zarr
 
   Notebook examples:
 
-    - :ref:`/how-tos/target/grib_to_zarr_target.ipynb`
+    - :ref:`/tutorials/target/grib_to_zarr_target.ipynb`
 
 
 .. .. _targets-multio:
diff --git a/docs/source/concepts/xarray/dim.rst b/docs/source/concepts/xarray/dim.rst
index ded28cb33..e3bae51e6 100644
--- a/docs/source/concepts/xarray/dim.rst
+++ b/docs/source/concepts/xarray/dim.rst
@@ -109,8 +109,8 @@ Each element of ``time_dims`` is a role name (e.g. ``"forecast_reference_time"``
 
 The following examples demonstrate the temporal dimensions modes:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_temporal.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_seasonal.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_temporal.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_seasonal.ipynb`
 
 
 .. _xr_level_dim_modes:
@@ -149,7 +149,7 @@ while ``dim_roles`` (together with ``dim_name_from_role_name``) controls their n
 
 The following example demonstrates the vertical dimensions modes:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_level.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_level.ipynb`
 
 
 .. _xr_chem_optical_dims:
@@ -165,7 +165,7 @@ The following dimensions are applicable for chemical and optical parameters (see
 
 The following notebook illustrates the use of the above dimensions in a CAMS dataset containing chemical and optical parameters:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_chem.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_chem.ipynb`
 
 
 .. _xr_wave_spectra_dims:
@@ -181,7 +181,7 @@ The following dimensions are applicable for 2D wave spectra parameters:
 
 The following notebook presents an example 2D wave spectra dataset:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_wave_spectra.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb`
 
 
 .. _xr_squeeze_and_ensure_dims:
@@ -195,7 +195,7 @@ This can be controlled with the ``squeeze`` option. Alternatively, the ``ensure_
 
 See the following notebook for examples of how this works:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_squeeze.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_squeeze.ipynb`
 
 
 .. _xr_dims_as_attrs:
@@ -221,7 +221,7 @@ exposed as a variable attribute.
 
 For a detailed discussion and examples, see the following notebook:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_dims_as_attrs.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_dims_as_attrs.ipynb`
 
 
 Extra dimensions
@@ -274,7 +274,7 @@ This rule applies to all predefined dimension roles listed in the
 
 For a detailed discussion and examples, see the following notebook:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_extra_dims.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_extra_dims.ipynb`
 
 
 Remapping
@@ -292,4 +292,4 @@ In particular, a virtual metadata key can be used:
 
 For a detailed discussion and examples, see the following notebook:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_remapping.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_remapping.ipynb`
diff --git a/docs/source/concepts/xarray/overview.rst b/docs/source/concepts/xarray/overview.rst
index 87086c27c..0bc027f6e 100644
--- a/docs/source/concepts/xarray/overview.rst
+++ b/docs/source/concepts/xarray/overview.rst
@@ -128,7 +128,7 @@ The generated GRIB fieldlist can be saved to disk using the :func:`to_target` me
 
 For further details see the following notebook:
 
-- :ref:`/how-tos/xr_engine/xarray_engine_to_grib.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_to_grib.ipynb`
 
 
 .. _xr_grib_to_netcdf:
@@ -212,4 +212,4 @@ To control the Xarray conversion we can pass options to the earthkit Xarray engi
 
 For further details see the following notebook:
 
-- :ref:`/how-tos/grib/grib_to_netcdf.ipynb`
+- :ref:`/tutorials/grib/grib_to_netcdf.ipynb`
diff --git a/docs/source/development/docs.rst b/docs/source/development/docs.rst
index 30d15af6b..f791c2292 100644
--- a/docs/source/development/docs.rst
+++ b/docs/source/development/docs.rst
@@ -25,10 +25,10 @@ To see the generated HTML documentation open the ``docs/_build/html/index.html``
 Notebook examples
 ~~~~~~~~~~~~~~~~~~~~~~
 
-The notebook examples are located in the ``docs/how-tos`` folder and are listed in ``docs/how-tos/index.rst``.
+The notebook examples are located in the ``docs/source/tutorials`` folder and are listed in the ``index.rst`` files in the subfolders. The notebooks are also part of the test suite and are run automatically. However, some notebooks are skipped mainly because they use remote data sources. This is controlled via the ``SKIP`` list in ``tests/documentation/test_notebooks.py``. You can modify this list to add or remove notebooks to be skipped.
 
 
 If you add a new notebook example please:
 
-- also add it to ``docs/how-tos/index.rst`` so that it appears in the documentation.
-- ensure the title and the subchapter headings have the same size as in the other notebook. Title with ``##``, subchapter with ``###``.
+- also add it to the relevant ``index.rst`` so that it appears in the documentation.
+- ensure the title and the subchapter headings have the correct sizes: Titles use ``#``, subchapters use ``##``.
diff --git a/docs/source/development/tests.rst b/docs/source/development/tests.rst
index 9ed0cc078..b7374ca4d 100644
--- a/docs/source/development/tests.rst
+++ b/docs/source/development/tests.rst
@@ -68,7 +68,7 @@ Tests marked with ``pytest.mark.no_cache_init`` use the ``pytest-forked`` plugin
 Notebooks
 ~~~~~~~~~~~~~
 
-The notebook examples from the ``docs/how-tos`` folder are part of the test suite and are run automatically. However, some notebooks are skipped mainly because they use remote data sources. This is controlled via the ``SKIP`` list in ``tests/documentation/test_notebooks.py``. You can modify this list to add or remove notebooks to be skipped.
+The notebook examples from the ``docs/tutorials`` folder are part of the test suite and are run automatically. However, some notebooks are skipped mainly because they use remote data sources. This is controlled via the ``SKIP`` list in ``tests/documentation/test_notebooks.py``. You can modify this list to add or remove notebooks to be skipped.
 
 To run only the notebooks tests you can use:
 
diff --git a/docs/source/faq.rst b/docs/source/faq.rst
index b51059663..0cfb34088 100644
--- a/docs/source/faq.rst
+++ b/docs/source/faq.rst
@@ -1,6 +1,6 @@
 .. _faq:
 
-Frequently asked questions
+Frequently Asked Questions
 ============================
 
 
@@ -59,3 +59,15 @@ When calling :func:`to_xarray` for NetCDF data it calls :py:func:`xarray.open_mf
     r = d.to_xarray(
         xarray_open_mfdataset_kwargs=dict(decode_cf=False, decode_times=False)
     )
+
+How to use ls() in a script?
+--------------------------------------------------------------
+
+The FieldList :py:meth:`~earthkit.data.core.fieldlist.FieldList.ls` method (also available on a Field) provides a convenient way to list the contents of a FieldList, designed to be used in an interactive session. It returns a pandas dataframe, which renders nicely in a Jupyter notebook or IPython session. If you want to use it in a script you need to print the result, for example:
+
+.. code-block:: python
+
+    import earthkit.data as ekd
+
+    ds = ekd.from_source("sample", "test.grib").to_fieldlist()
+    print(ds.ls())
diff --git a/docs/source/getting-started.rst b/docs/source/getting-started.rst
index de65049c8..52ec4d756 100644
--- a/docs/source/getting-started.rst
+++ b/docs/source/getting-started.rst
@@ -9,7 +9,7 @@ Install **earthkit-data** with python3 (>= 3.10) and ``pip`` as follows:
 
 .. code-block:: bash
 
-    python3 -m pip install earthkit-data>=1.0.0rc0
+    python3 -m pip install earthkit-data>=1.0
 
 Please note that this is a **minimal** installation supporting only GRIB and NetCDF data and cannot access remote services other than URLs. If you want to use more data types or remote services you need to install the optional Python packages. For more details see :ref:`install`.
 
diff --git a/docs/source/how-tos/bufr/synop_10.bufr b/docs/source/how-tos/bufr/synop_10.bufr
deleted file mode 120000
index 4383aebdf..000000000
--- a/docs/source/how-tos/bufr/synop_10.bufr
+++ /dev/null
@@ -1 +0,0 @@
-../synop_10.bufr
\ No newline at end of file
diff --git a/docs/source/how-tos/bufr/temp_10.bufr b/docs/source/how-tos/bufr/temp_10.bufr
deleted file mode 120000
index 664b8e30a..000000000
--- a/docs/source/how-tos/bufr/temp_10.bufr
+++ /dev/null
@@ -1 +0,0 @@
-../temp_10.bufr
\ No newline at end of file
diff --git a/docs/source/how-tos/field/field_overview.ipynb b/docs/source/how-tos/field/field_overview.ipynb
deleted file mode 100644
index b661708d2..000000000
--- a/docs/source/how-tos/field/field_overview.ipynb
+++ /dev/null
@@ -1,401 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fffb922f-f8d2-45a5-b1ff-4ca9daeb63b6",
-   "metadata": {},
-   "source": [
-    "# Field overview"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3e75c1c-2dfa-4936-b8cb-2029dd0b3fc3",
-   "metadata": {},
-   "source": [
-    "Please note that this notebook is still under development."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c7f4e186-4f1e-437e-a27b-ef8f53cbd659",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import earthkit.data as ekd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7926fb1b-d9d1-4f7e-8807-5c0420da25d8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style><div class=\"eh-description\">GRIB file</div><hr>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>path</b></td><td>../test.grib</td></tr> <tr><td><b>size</b></td><td>720</td></tr> <tr><td><b>types</b></td><td>fieldlist, pandas, xarray, numpy, array</td></tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "GribData(path=../test.grib)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "d = ekd.from_source(\"file\", \"../test.grib\")\n",
-    "d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b6c10045-d894-4ebd-9e8d-82f99af3ae19",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\"> /* Based on:\n",
-       " https://codepen.io/htmlcssfreebies/pen/WNpVeRK\n",
-       " https://github.com/pydata/xarray/blob/main/xarray/static/css/style.css\n",
-       " */\n",
-       "\n",
-       " :root {\n",
-       "    --eh-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "    --eh-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "    --eh-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "    --eh-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "    --eh-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "    --eh-background-color: var(--jp-layout-color0, white);\n",
-       "  }\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --eh-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --eh-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --eh-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --eh-border-color: #1F1F1F;\n",
-       "  --eh-disabled-color: #515151;\n",
-       "  --eh-background-color: #111111;\n",
-       "}\n",
-       "\n",
-       " /* Reset */\n",
-       " *,\n",
-       " *::before,\n",
-       " *::after {\n",
-       "     box-sizing: border-box;\n",
-       " }\n",
-       "\n",
-       " .eh-description {\n",
-       "    color: var(--eh-font-color2);\n",
-       " }\n",
-       "\n",
-       " .eh-section {\n",
-       "    padding-left: 0px;\n",
-       " }\n",
-       "\n",
-       " /* Tabs Container */\n",
-       " .eh-tabs-container {\n",
-       "     padding: 0px;\n",
-       " }\n",
-       "\n",
-       " /* Tabs Block */\n",
-       " .eh-tabs-block {\n",
-       "     display: flex;\n",
-       "     align-items: center;\n",
-       "     justify-content: center;\n",
-       "     padding-top: 4px;\n",
-       " }\n",
-       "\n",
-       " /* Tabs */\n",
-       " .eh-tabs {\n",
-       "     display: flex;\n",
-       "     flex-wrap: wrap;\n",
-       "     width: 100%;\n",
-       " }\n",
-       "\n",
-       " .eh-tabs label {\n",
-       "     width: 100%;\n",
-       "     display: flex;\n",
-       "     align-items: center;\n",
-       "     justify-content: center;\n",
-       "     padding-left: 6px;\n",
-       "     padding-right: 6px;\n",
-       "     font-weight: 400;\n",
-       "     cursor: pointer;\n",
-       "     color: var(--eh-font-color2);\n",
-       "     background-color: var(--eh-background-color);\n",
-       "     border-top: 1px solid var(--eh-background-color);\n",
-       "     border-bottom: 1px solid var(--eh-disabled-color);\n",
-       " }\n",
-       "\n",
-       " /* Tab page contents */\n",
-       " .eh-tabs .tab {\n",
-       "     flex-grow: 1;\n",
-       "     width: 100%;\n",
-       "     height: 100%;\n",
-       "     display: none;\n",
-       "     padding: 2px 2px;\n",
-       " }\n",
-       "\n",
-       " .eh-tabs [type=radio] {\n",
-       "     display: none;\n",
-       " }\n",
-       "\n",
-       " .eh-tabs [type=radio]:checked + label {\n",
-       "     font-weight: 500;\n",
-       "     color: var(--eh-font-color0);\n",
-       "     border: 1px solid var(--eh-disabled-color);\n",
-       " }\n",
-       "\n",
-       " .eh-tabs [type=radio]:checked + label + .tab {\n",
-       "     display: block;\n",
-       " }\n",
-       "\n",
-       " .eh-tabs [type=radio]:not(:checked) + label:hover {\n",
-       "    color: var(--xr-font-color0);\n",
-       " }\n",
-       "\n",
-       "\n",
-       " @media (min-width: 768px) {\n",
-       "\n",
-       "     .eh-tabs-container {\n",
-       "         padding: 1rem 1rem;\n",
-       "     }\n",
-       "\n",
-       "     .eh-tabs label {\n",
-       "         order: 1;\n",
-       "         width: auto;\n",
-       "     }\n",
-       "\n",
-       "     .eh-tabs .tab {\n",
-       "         order: 9;\n",
-       "     }\n",
-       "\n",
-       "     .eh-tabs [type=radio]:checked + label {\n",
-       "         border-bottom: none;\n",
-       "     }\n",
-       " }\n",
-       " @media (min-width: 992px) {\n",
-       "\n",
-       "     .eh-tabs {\n",
-       "         width: 800px;\n",
-       "     }\n",
-       " }\n",
-       "</style><div class=\"eh-description\">Field</div>\n",
-       "<div>\n",
-       "<section class=\"eh-section>\n",
-       "<div class=\"eh-tabs-container\">\n",
-       "<div class=\"eh-tabs-block\">\n",
-       "<div class=\"eh-tabs\">\n",
-       "\n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"298ac00c-ffd2-4504-aa9d-238389f8f7f1\"  />\n",
-       "<label for=\"298ac00c-ffd2-4504-aa9d-238389f8f7f1\" title=\"Keys in the data namespace\">data</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>number_of_values</b></td><td>104</td></tr> <tr><td><b>array_type</b></td><td>ndarray</td></tr> <tr><td><b>array_dtype</b></td><td>float64</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "     \n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"455440c8-920c-40df-8c47-42bd9d014825\" checked />\n",
-       "<label for=\"455440c8-920c-40df-8c47-42bd9d014825\" title=\"Keys in the parameter namespace\">parameter</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>variable</b></td><td>2t</td></tr> <tr><td><b>units</b></td><td>kelvin</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "     \n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"44d58d4d-0492-41d3-a19c-64ffb18be6d6\"  />\n",
-       "<label for=\"44d58d4d-0492-41d3-a19c-64ffb18be6d6\" title=\"Keys in the time namespace\">time</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>valid_datetime</b></td><td>2020-05-13 12:00:00</td></tr> <tr><td><b>base_datetime</b></td><td>2020-05-13 12:00:00</td></tr> <tr><td><b>step</b></td><td>0:00:00</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "     \n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"117221e7-efc3-4460-ab01-c18a82130cf7\"  />\n",
-       "<label for=\"117221e7-efc3-4460-ab01-c18a82130cf7\" title=\"Keys in the vertical namespace\">vertical</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>level</b></td><td>0</td></tr> <tr><td><b>layer</b></td><td>None</td></tr> <tr><td><b>level_type</b></td><td>surface</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "     \n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"f98144d4-41ba-41cf-a199-4849d3bc6e6a\"  />\n",
-       "<label for=\"f98144d4-41ba-41cf-a199-4849d3bc6e6a\" title=\"Keys in the ensemble namespace\">ensemble</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>member</b></td><td>0</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "     \n",
-       "<input type=\"radio\" name=\"eh-tabs\" id=\"cf69a57d-3ba8-49a5-bcfc-c192347d665f\"  />\n",
-       "<label for=\"cf69a57d-3ba8-49a5-bcfc-c192347d665f\" title=\"Keys in the geography namespace\">geography</label>\n",
-       "<div class=\"tab\">\n",
-       "<style type=\"text/css\">table.ek td {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "\n",
-       "table.ek th {\n",
-       "    vertical-align: top;\n",
-       "    text-align: left !important;\n",
-       "}\n",
-       "</style>\n",
-       "<table class=\"eh-table\">\n",
-       "<tr><td><b>grid_spec</b></td><td>{'area': [70, -20, 35, 40], 'grid': [5, 5]}</td></tr> <tr><td><b>grid_type</b></td><td>regular_ll</td></tr> <tr><td><b>shape</b></td><td>(8, 13)</td></tr> <tr><td><b>area</b></td><td>(70.0, -20.0, 35.0, 40.0)</td></tr>\n",
-       "</table>\n",
-       "</div>\n",
-       "    \n",
-       "</div>\n",
-       "</div>\n",
-       "</div>\n",
-       "</section>\n",
-       "</div>\n",
-       "        "
-      ],
-      "text/plain": [
-       "Field(2t, 2020-05-13 12:00:00, 2020-05-13 12:00:00, 0:00:00, 0, surface, 0, regular_ll)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f = d.to_fieldlist()[0]\n",
-    "f"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fbd72d33-9b21-467d-a36c-93e6255ff665",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74a157d8-c38b-4149-94d7-d56d77509a7d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7bae9ccc-9b74-470b-8cd4-1d8b104a1b2e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8172a231-8ee3-4d38-94dc-4148e526cf1e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a2bc49f-5633-455b-9382-136a992cfeac",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "dev",
-   "language": "python",
-   "name": "dev"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/source/how-tos/field_latlons.ipynb b/docs/source/how-tos/field_latlons.ipynb
new file mode 100644
index 000000000..820dd7004
--- /dev/null
+++ b/docs/source/how-tos/field_latlons.ipynb
@@ -0,0 +1,143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bfb7500a-b3b2-43f9-94b1-8f2536c8cecc",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# How to get the latlons from a field?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cc118d78-6b55-438f-9c96-43d796671c31",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50129a73c32548a3987c9b026eeaf99f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "test.grib:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(array([[73., 73., 73., 73., 73., 73., 73., 73., 73., 73., 73., 73., 73.,\n",
+       "         73., 73., 73., 73., 73., 73.],\n",
+       "        [69., 69., 69., 69., 69., 69., 69., 69., 69., 69., 69., 69., 69.,\n",
+       "         69., 69., 69., 69., 69., 69.],\n",
+       "        [65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+       "         65., 65., 65., 65., 65., 65.],\n",
+       "        [61., 61., 61., 61., 61., 61., 61., 61., 61., 61., 61., 61., 61.,\n",
+       "         61., 61., 61., 61., 61., 61.],\n",
+       "        [57., 57., 57., 57., 57., 57., 57., 57., 57., 57., 57., 57., 57.,\n",
+       "         57., 57., 57., 57., 57., 57.],\n",
+       "        [53., 53., 53., 53., 53., 53., 53., 53., 53., 53., 53., 53., 53.,\n",
+       "         53., 53., 53., 53., 53., 53.],\n",
+       "        [49., 49., 49., 49., 49., 49., 49., 49., 49., 49., 49., 49., 49.,\n",
+       "         49., 49., 49., 49., 49., 49.],\n",
+       "        [45., 45., 45., 45., 45., 45., 45., 45., 45., 45., 45., 45., 45.,\n",
+       "         45., 45., 45., 45., 45., 45.],\n",
+       "        [41., 41., 41., 41., 41., 41., 41., 41., 41., 41., 41., 41., 41.,\n",
+       "         41., 41., 41., 41., 41., 41.],\n",
+       "        [37., 37., 37., 37., 37., 37., 37., 37., 37., 37., 37., 37., 37.,\n",
+       "         37., 37., 37., 37., 37., 37.],\n",
+       "        [33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33., 33.,\n",
+       "         33., 33., 33., 33., 33., 33.]]),\n",
+       " array([[-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.],\n",
+       "        [-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,\n",
+       "          17.,  21.,  25.,  29.,  33.,  37.,  41.,  45.]]))"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import earthkit.data as ekd\n",
+    "\n",
+    "# get fieldlist\n",
+    "fl = ekd.from_source(\"sample\", \"test.grib\").to_fieldlist()\n",
+    "\n",
+    "# get latlons from the first field\n",
+    "fl[0].geography.latlons()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1f240a3-dd04-4711-b5ab-bafeccf608f8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/how-tos/geojson/NUTS_RG_20M_2021_3035.geojson b/docs/source/how-tos/geojson/NUTS_RG_20M_2021_3035.geojson
deleted file mode 120000
index 86a48918a..000000000
--- a/docs/source/how-tos/geojson/NUTS_RG_20M_2021_3035.geojson
+++ /dev/null
@@ -1 +0,0 @@
-../NUTS_RG_20M_2021_3035.geojson
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/missing.grib b/docs/source/how-tos/grib/missing.grib
deleted file mode 120000
index 5c682bb7c..000000000
--- a/docs/source/how-tos/grib/missing.grib
+++ /dev/null
@@ -1 +0,0 @@
-../missing.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/pl_regular_ll.grib b/docs/source/how-tos/grib/pl_regular_ll.grib
deleted file mode 120000
index b02cfc9a5..000000000
--- a/docs/source/how-tos/grib/pl_regular_ll.grib
+++ /dev/null
@@ -1 +0,0 @@
-../pl_regular_ll.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/test.grib b/docs/source/how-tos/grib/test.grib
deleted file mode 120000
index d7b3681fe..000000000
--- a/docs/source/how-tos/grib/test.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/test4.grib b/docs/source/how-tos/grib/test4.grib
deleted file mode 120000
index 930125f6c..000000000
--- a/docs/source/how-tos/grib/test4.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test4.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/test6.grib b/docs/source/how-tos/grib/test6.grib
deleted file mode 120000
index 144816218..000000000
--- a/docs/source/how-tos/grib/test6.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test6.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/time_series.grib b/docs/source/how-tos/grib/time_series.grib
deleted file mode 120000
index 528c7422f..000000000
--- a/docs/source/how-tos/grib/time_series.grib
+++ /dev/null
@@ -1 +0,0 @@
-../time_series.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/grib/tuv_pl.grib b/docs/source/how-tos/grib/tuv_pl.grib
deleted file mode 120000
index ae0fd948a..000000000
--- a/docs/source/how-tos/grib/tuv_pl.grib
+++ /dev/null
@@ -1 +0,0 @@
-../tuv_pl.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/index.rst b/docs/source/how-tos/index.rst
index ba7496a4c..2274bb67b 100644
--- a/docs/source/how-tos/index.rst
+++ b/docs/source/how-tos/index.rst
@@ -1,25 +1,10 @@
-.. _examples:
+.. _how-tos:
 
-How-to guides
+How-to Guides
 ===============
 
 
 .. toctree::
     :maxdepth: 1
 
-    source/index.rst
-    grib/index.rst
-    netcdf/index.rst
-    bufr/index.rst
-    odb/index.rst
-    covjson/index.rst
-    geotiff/index.rst
-    geojson/index.rst
-    shapefile/index.rst
-    pp/index.rst
-    dict/index.rst
-    other/index.rst
-    field/index.rst
-    xr_engine/index.rst
-    target/index.rst
-    misc/index.rst
+    field_latlons.rst
diff --git a/docs/source/how-tos/netcdf/test.nc b/docs/source/how-tos/netcdf/test.nc
deleted file mode 120000
index 2218dc4dc..000000000
--- a/docs/source/how-tos/netcdf/test.nc
+++ /dev/null
@@ -1 +0,0 @@
-../test.nc
\ No newline at end of file
diff --git a/docs/source/how-tos/netcdf/tuv_pl.nc b/docs/source/how-tos/netcdf/tuv_pl.nc
deleted file mode 120000
index 7d49a994a..000000000
--- a/docs/source/how-tos/netcdf/tuv_pl.nc
+++ /dev/null
@@ -1 +0,0 @@
-../tuv_pl.nc
\ No newline at end of file
diff --git a/docs/source/how-tos/odb/test.odb b/docs/source/how-tos/odb/test.odb
deleted file mode 120000
index c9c1e3f22..000000000
--- a/docs/source/how-tos/odb/test.odb
+++ /dev/null
@@ -1 +0,0 @@
-../test.odb
\ No newline at end of file
diff --git a/docs/source/how-tos/shapefile/major_basins.zip b/docs/source/how-tos/shapefile/major_basins.zip
deleted file mode 120000
index b81611a2c..000000000
--- a/docs/source/how-tos/shapefile/major_basins.zip
+++ /dev/null
@@ -1 +0,0 @@
-../major_basins.zip
\ No newline at end of file
diff --git a/docs/source/how-tos/source/tar_files.ipynb b/docs/source/how-tos/source/tar_files.ipynb
deleted file mode 100644
index b5980440b..000000000
--- a/docs/source/how-tos/source/tar_files.ipynb
+++ /dev/null
@@ -1,385 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "# Reading tar or zip archive"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "raw",
-            "metadata": {
-                "editable": true,
-                "raw_mimetype": "text/restructuredtext",
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "We :ref:`read <data-sources-file>` a tar file containing 2 GRIB files. First we ensure the example file is available."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "import earthkit.data as ekd\n",
-                "\n",
-                "ekd.download_example_file(\"test_gribs.tar\")"
-            ]
-        },
-        {
-            "cell_type": "raw",
-            "metadata": {
-                "editable": true,
-                "raw_mimetype": "text/restructuredtext",
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "The files are extracted and stored in the earthkit-data :ref:`cache <caching>`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "d4e31ff9247d4fbaad3f8305d84b308c",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/2 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<style type=\"text/css\">table.ek td {\n",
-                            "    vertical-align: top;\n",
-                            "    text-align: left !important;\n",
-                            "}\n",
-                            "\n",
-                            "table.ek th {\n",
-                            "    vertical-align: top;\n",
-                            "    text-align: left !important;\n",
-                            "}\n",
-                            "</style><div class=\"eh-description\">GRIB file</div><hr>\n",
-                            "<table class=\"eh-table\">\n",
-                            "<tr><td><b>path</b></td><td></td></tr> <tr><td><b>types</b></td><td>fieldlist, pandas, xarray, numpy, array</td></tr>\n",
-                            "</table>"
-                        ],
-                        "text/plain": [
-                            "GribData(path=)"
-                        ]
-                    },
-                    "execution_count": 2,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "d = ekd.from_source(\"file\", \"test_gribs.tar\")\n",
-                "d"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can convert the data into a fieldlis."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [],
-            "source": [
-                "fl = d.to_fieldlist()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>parameter.variable</th>\n",
-                            "      <th>time.valid_datetime</th>\n",
-                            "      <th>time.base_datetime</th>\n",
-                            "      <th>time.step</th>\n",
-                            "      <th>vertical.level</th>\n",
-                            "      <th>vertical.level_type</th>\n",
-                            "      <th>ensemble.member</th>\n",
-                            "      <th>geography.grid_type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>2t</td>\n",
-                            "      <td>2020-05-13 12:00:00</td>\n",
-                            "      <td>2020-05-13 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>surface</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>msl</td>\n",
-                            "      <td>2020-05-13 12:00:00</td>\n",
-                            "      <td>2020-05-13 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>surface</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>t</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>500</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>z</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>500</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>t</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>850</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>z</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>850</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
-                            "0                 2t 2020-05-13 12:00:00 2020-05-13 12:00:00    0 days   \n",
-                            "1                msl 2020-05-13 12:00:00 2020-05-13 12:00:00    0 days   \n",
-                            "2                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "3                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "4                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "5                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "\n",
-                            "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
-                            "0               0             surface               0          regular_ll  \n",
-                            "1               0             surface               0          regular_ll  \n",
-                            "2             500            pressure               0          regular_ll  \n",
-                            "3             500            pressure               0          regular_ll  \n",
-                            "4             850            pressure               0          regular_ll  \n",
-                            "5             850            pressure               0          regular_ll  "
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "fl.ls()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>parameter.variable</th>\n",
-                            "      <th>time.valid_datetime</th>\n",
-                            "      <th>time.base_datetime</th>\n",
-                            "      <th>time.step</th>\n",
-                            "      <th>vertical.level</th>\n",
-                            "      <th>vertical.level_type</th>\n",
-                            "      <th>ensemble.member</th>\n",
-                            "      <th>geography.grid_type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>t</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>500</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>z</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>2007-01-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>500</td>\n",
-                            "      <td>pressure</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
-                            "0                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "1                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
-                            "\n",
-                            "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
-                            "0             500            pressure               0          regular_ll  \n",
-                            "1             500            pressure               0          regular_ll  "
-                        ]
-                    },
-                    "execution_count": 5,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "a = fl.sel({\"vertical.level\": 500})\n",
-                "a.ls()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "dev",
-            "language": "python",
-            "name": "dev"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.13.1"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 4
-}
diff --git a/docs/source/how-tos/source/test.grib b/docs/source/how-tos/source/test.grib
deleted file mode 120000
index d7b3681fe..000000000
--- a/docs/source/how-tos/source/test.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/source/test.nc b/docs/source/how-tos/source/test.nc
deleted file mode 120000
index 2218dc4dc..000000000
--- a/docs/source/how-tos/source/test.nc
+++ /dev/null
@@ -1 +0,0 @@
-../test.nc
\ No newline at end of file
diff --git a/docs/source/how-tos/source/test4.grib b/docs/source/how-tos/source/test4.grib
deleted file mode 120000
index 930125f6c..000000000
--- a/docs/source/how-tos/source/test4.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test4.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/source/test6.grib b/docs/source/how-tos/source/test6.grib
deleted file mode 120000
index 144816218..000000000
--- a/docs/source/how-tos/source/test6.grib
+++ /dev/null
@@ -1 +0,0 @@
-../test6.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/source/test_gribs.tar b/docs/source/how-tos/source/test_gribs.tar
deleted file mode 120000
index 30376f61b..000000000
--- a/docs/source/how-tos/source/test_gribs.tar
+++ /dev/null
@@ -1 +0,0 @@
-../test_gribs.tar
\ No newline at end of file
diff --git a/docs/source/how-tos/source/tuv_pl.grib b/docs/source/how-tos/source/tuv_pl.grib
deleted file mode 120000
index ae0fd948a..000000000
--- a/docs/source/how-tos/source/tuv_pl.grib
+++ /dev/null
@@ -1 +0,0 @@
-../tuv_pl.grib
\ No newline at end of file
diff --git a/docs/source/how-tos/source/wekeo.ipynb b/docs/source/how-tos/source/wekeo.ipynb
deleted file mode 100644
index 13a0bb49a..000000000
--- a/docs/source/how-tos/source/wekeo.ipynb
+++ /dev/null
@@ -1,998 +0,0 @@
-{
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "id": "3599bcd8-a5ee-4911-96c9-976b849138fb",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "# Retieving data from WEkEO"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "id": "8ab6e404-9de8-4220-b1b6-41745ffbb59e",
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import earthkit.data as ekd"
-            ]
-        },
-        {
-            "cell_type": "raw",
-            "id": "8e2e0f15",
-            "metadata": {
-                "editable": true,
-                "raw_mimetype": "text/restructuredtext",
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "The :ref:`wekeo <data-sources-wekeo>` and :ref:`wekeocds <data-sources-wekeocds>` sources provide access to `WEkEO <https://www.wekeo.eu/>`_."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "e32b8f13-137a-436e-8523-fcee759dfb02",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "#### Using the WEkEO grammar"
-            ]
-        },
-        {
-            "cell_type": "raw",
-            "id": "e6a6d76d-6563-4e6b-8aa6-e4f5a6b7b3d5",
-            "metadata": {
-                "editable": true,
-                "raw_mimetype": "text/restructuredtext",
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": [],
-                "vscode": {
-                    "languageId": "raw"
-                }
-            },
-            "source": [
-                "This example demonstrates how to access data from the WEkEO catalogue.\n",
-                "\n",
-                "To retrieve data using the WEkEO grammar (see the `hda <https://hda.readthedocs.io/>`_ documentation), use the :ref:`wekeo <data-sources-wekeo>` source.\n",
-                "\n",
-                "The dataset used here is `Vegetation Phenology and Productivity, yearly, UTM projection <https://wekeo.copernicus.eu/data view=dataset&dataset=EO:EEA:DAT:CLMS_HRVPP_VPP>`_.\n",
-                "\n",
-                "The example retrieves the Total Productivity variable, representing the integral of vegetation productivity accumulated over the growing season as the sum of daily values. The product is derived from Copernicus Sentinel-2 observations and distributed in GeoTIFF format using 1°×1° tiles at a spatial resolution of 10 × 10 m."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "id": "15d5599f-1242-4238-8d5d-e459694bc296",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "                                                                                                                                                                                                                   "
-                    ]
-                }
-            ],
-            "source": [
-                "d = ekd.from_source(\n",
-                "    \"wekeo\",\n",
-                "    \"EO:EEA:DAT:CLMS_HRVPP_VPP\",\n",
-                "    request={\n",
-                "        \"dataset_id\": \"EO:EEA:DAT:CLMS_HRVPP_VPP\",\n",
-                "        \"productType\": \"TPROD\",\n",
-                "        \"productGroupId\": \"s1\",\n",
-                "        \"tileId\": \"32TQR\",\n",
-                "        \"resolution\": \"10\",\n",
-                "        \"startdate\": \"2023-01-01T00:00:00.000Z\",\n",
-                "        \"enddate\": \"2023-01-01T23:59:59.999Z\",\n",
-                "        \"bbox\": [12.28091069558975, 45.40759897973081, 12.384715295944709, 45.45978517718846],\n",
-                "    },\n",
-                ")"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "f3e4b991-277c-4cfa-9c0a-158fe842e40f",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "To inspect the downloaded data we convert it into Xarray."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "id": "78c943ba-9a46-4a62-8bcf-ad697d9212f1",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "['xarray', 'pandas', 'fieldlist']"
-                        ]
-                    },
-                    "execution_count": 3,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "d.available_types"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "id": "ad4040b2",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
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-                            "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-                            "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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-                            "</defs>\n",
-                            "</svg>\n",
-                            "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
-                            "\n",
-                            ":root {\n",
-                            "  --xr-font-color0: var(\n",
-                            "    --jp-content-font-color0,\n",
-                            "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
-                            "  );\n",
-                            "  --xr-font-color2: var(\n",
-                            "    --jp-content-font-color2,\n",
-                            "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
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-                            "  --xr-font-color3: var(\n",
-                            "    --jp-content-font-color3,\n",
-                            "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
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-                            "  --xr-border-color: var(\n",
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-                            "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
-                            "  );\n",
-                            "  --xr-disabled-color: var(\n",
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-                            "    var(--pst-color-on-background, white)\n",
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-                            "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
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-                            "  --xr-background-color-row-odd: var(\n",
-                            "    --jp-layout-color2,\n",
-                            "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
-                            "  );\n",
-                            "}\n",
-                            "\n",
-                            "html[theme=\"dark\"],\n",
-                            "html[data-theme=\"dark\"],\n",
-                            "body[data-theme=\"dark\"],\n",
-                            "body.vscode-dark {\n",
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-                            "    --jp-content-font-color0,\n",
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-                            "  );\n",
-                            "  --xr-font-color2: var(\n",
-                            "    --jp-content-font-color2,\n",
-                            "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
-                            "  );\n",
-                            "  --xr-font-color3: var(\n",
-                            "    --jp-content-font-color3,\n",
-                            "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
-                            "  );\n",
-                            "  --xr-border-color: var(\n",
-                            "    --jp-border-color2,\n",
-                            "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
-                            "  );\n",
-                            "  --xr-disabled-color: var(\n",
-                            "    --jp-layout-color3,\n",
-                            "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
-                            "  );\n",
-                            "  --xr-background-color: var(\n",
-                            "    --jp-layout-color0,\n",
-                            "    var(--pst-color-on-background, #111111)\n",
-                            "  );\n",
-                            "  --xr-background-color-row-even: var(\n",
-                            "    --jp-layout-color1,\n",
-                            "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
-                            "  );\n",
-                            "  --xr-background-color-row-odd: var(\n",
-                            "    --jp-layout-color2,\n",
-                            "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
-                            "  );\n",
-                            "}\n",
-                            "\n",
-                            ".xr-wrap {\n",
-                            "  display: block !important;\n",
-                            "  min-width: 300px;\n",
-                            "  max-width: 700px;\n",
-                            "  line-height: 1.6;\n",
-                            "  padding-bottom: 4px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-text-repr-fallback {\n",
-                            "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-                            "  display: none;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-header {\n",
-                            "  padding-top: 6px;\n",
-                            "  padding-bottom: 6px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-header {\n",
-                            "  border-bottom: solid 1px var(--xr-border-color);\n",
-                            "  margin-bottom: 4px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-header > div,\n",
-                            ".xr-header > ul {\n",
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-                            ".xr-obj-name {\n",
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-                            ".xr-group-box-contents > label {\n",
-                            "  color: var(--xr-font-color2);\n",
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-                            ".xr-section-item {\n",
-                            "  display: contents;\n",
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-                            "\n",
-                            ".xr-section-item > input,\n",
-                            ".xr-group-box-contents > input,\n",
-                            ".xr-array-wrap > input {\n",
-                            "  display: block;\n",
-                            "  opacity: 0;\n",
-                            "  height: 0;\n",
-                            "  margin: 0;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-item > input + label,\n",
-                            ".xr-var-item > input + label {\n",
-                            "  color: var(--xr-disabled-color);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-item > input:enabled + label,\n",
-                            ".xr-var-item > input:enabled + label,\n",
-                            ".xr-array-wrap > input:enabled + label,\n",
-                            ".xr-group-box-contents > input:enabled + label {\n",
-                            "  cursor: pointer;\n",
-                            "  color: var(--xr-font-color2);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-item > input:focus-visible + label,\n",
-                            ".xr-var-item > input:focus-visible + label,\n",
-                            ".xr-array-wrap > input:focus-visible + label,\n",
-                            ".xr-group-box-contents > input:focus-visible + label {\n",
-                            "  outline: auto;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-item > input:enabled + label:hover,\n",
-                            ".xr-var-item > input:enabled + label:hover,\n",
-                            ".xr-array-wrap > input:enabled + label:hover,\n",
-                            ".xr-group-box-contents > input:enabled + label:hover {\n",
-                            "  color: var(--xr-font-color0);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-summary {\n",
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-                            "  color: var(--xr-font-color2);\n",
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-                            ".xr-section-summary-in:disabled + label {\n",
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-                            "  color: var(--xr-disabled-color);\n",
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-                            "\n",
-                            ".xr-section-summary-in:checked + label:before {\n",
-                            "  content: \"▼\";\n",
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-                            "\n",
-                            ".xr-section-summary,\n",
-                            ".xr-section-inline-details,\n",
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-                            "  padding-top: 4px;\n",
-                            "}\n",
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-                            "  margin-top: 4px;\n",
-                            "  margin-bottom: 5px;\n",
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-                            "\n",
-                            ".xr-section-summary-in ~ .xr-section-details {\n",
-                            "  display: none;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-                            "  display: contents;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-children {\n",
-                            "  display: inline-grid;\n",
-                            "  grid-template-columns: 100%;\n",
-                            "  grid-column: 1 / -1;\n",
-                            "  padding-top: 4px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box {\n",
-                            "  display: inline-grid;\n",
-                            "  grid-template-columns: 0px 30px auto;\n",
-                            "}\n",
-                            "\n",
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-                            "  border-right: 0.2em solid;\n",
-                            "  border-color: var(--xr-border-color);\n",
-                            "  width: 0px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-hline {\n",
-                            "  grid-column-start: 2;\n",
-                            "  grid-row-start: 1;\n",
-                            "  height: 1em;\n",
-                            "  width: 26px;\n",
-                            "  border-bottom: 0.2em solid;\n",
-                            "  border-color: var(--xr-border-color);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents {\n",
-                            "  grid-column-start: 3;\n",
-                            "  padding-bottom: 4px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents > label::before {\n",
-                            "  content: \"📂\";\n",
-                            "  padding-right: 0.3em;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents > input:checked + label::before {\n",
-                            "  content: \"📁\";\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents > input:checked + label {\n",
-                            "  padding-bottom: 0px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
-                            "  display: none;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-contents > input + label > span {\n",
-                            "  display: none;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-group-box-ellipsis {\n",
-                            "  font-size: 1.4em;\n",
-                            "  font-weight: 900;\n",
-                            "  color: var(--xr-font-color2);\n",
-                            "  letter-spacing: 0.15em;\n",
-                            "  cursor: default;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-array-wrap {\n",
-                            "  grid-column: 1 / -1;\n",
-                            "  display: grid;\n",
-                            "  grid-template-columns: 20px auto;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-array-wrap > label {\n",
-                            "  grid-column: 1;\n",
-                            "  vertical-align: top;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-preview {\n",
-                            "  color: var(--xr-font-color3);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-array-preview,\n",
-                            ".xr-array-data {\n",
-                            "  padding: 0 5px !important;\n",
-                            "  grid-column: 2;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-array-data,\n",
-                            ".xr-array-in:checked ~ .xr-array-preview {\n",
-                            "  display: none;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-array-in:checked ~ .xr-array-data,\n",
-                            ".xr-array-preview {\n",
-                            "  display: inline-block;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-dim-list {\n",
-                            "  display: inline-block !important;\n",
-                            "  list-style: none;\n",
-                            "  padding: 0 !important;\n",
-                            "  margin: 0;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-dim-list li {\n",
-                            "  display: inline-block;\n",
-                            "  padding: 0;\n",
-                            "  margin: 0;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-dim-list:before {\n",
-                            "  content: \"(\";\n",
-                            "}\n",
-                            "\n",
-                            ".xr-dim-list:after {\n",
-                            "  content: \")\";\n",
-                            "}\n",
-                            "\n",
-                            ".xr-dim-list li:not(:last-child):after {\n",
-                            "  content: \",\";\n",
-                            "  padding-right: 5px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-has-index {\n",
-                            "  font-weight: bold;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-list,\n",
-                            ".xr-var-item {\n",
-                            "  display: contents;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-item > div,\n",
-                            ".xr-var-item label,\n",
-                            ".xr-var-item > .xr-var-name span {\n",
-                            "  background-color: var(--xr-background-color-row-even);\n",
-                            "  border-color: var(--xr-background-color-row-odd);\n",
-                            "  margin-bottom: 0;\n",
-                            "  padding-top: 2px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-item > .xr-var-name:hover span {\n",
-                            "  padding-right: 5px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-list > li:nth-child(odd) > div,\n",
-                            ".xr-var-list > li:nth-child(odd) > label,\n",
-                            ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-                            "  background-color: var(--xr-background-color-row-odd);\n",
-                            "  border-color: var(--xr-background-color-row-even);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-name {\n",
-                            "  grid-column: 1;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-dims {\n",
-                            "  grid-column: 2;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-dtype {\n",
-                            "  grid-column: 3;\n",
-                            "  text-align: right;\n",
-                            "  color: var(--xr-font-color2);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-preview {\n",
-                            "  grid-column: 4;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-index-preview {\n",
-                            "  grid-column: 2 / 5;\n",
-                            "  color: var(--xr-font-color2);\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-name,\n",
-                            ".xr-var-dims,\n",
-                            ".xr-var-dtype,\n",
-                            ".xr-preview,\n",
-                            ".xr-attrs dt {\n",
-                            "  white-space: nowrap;\n",
-                            "  overflow: hidden;\n",
-                            "  text-overflow: ellipsis;\n",
-                            "  padding-right: 10px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-name:hover,\n",
-                            ".xr-var-dims:hover,\n",
-                            ".xr-var-dtype:hover,\n",
-                            ".xr-attrs dt:hover {\n",
-                            "  overflow: visible;\n",
-                            "  width: auto;\n",
-                            "  z-index: 1;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-attrs,\n",
-                            ".xr-var-data,\n",
-                            ".xr-index-data {\n",
-                            "  display: none;\n",
-                            "  border-top: 2px dotted var(--xr-background-color);\n",
-                            "  padding-bottom: 20px !important;\n",
-                            "  padding-top: 10px !important;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-attrs-in + label,\n",
-                            ".xr-var-data-in + label,\n",
-                            ".xr-index-data-in + label {\n",
-                            "  padding: 0 1px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-                            ".xr-var-data-in:checked ~ .xr-var-data,\n",
-                            ".xr-index-data-in:checked ~ .xr-index-data {\n",
-                            "  display: block;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-data > table {\n",
-                            "  float: right;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-data > pre,\n",
-                            ".xr-index-data > pre,\n",
-                            ".xr-var-data > table > tbody > tr {\n",
-                            "  background-color: transparent !important;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-name span,\n",
-                            ".xr-var-data,\n",
-                            ".xr-index-name div,\n",
-                            ".xr-index-data,\n",
-                            ".xr-attrs {\n",
-                            "  padding-left: 25px !important;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-attrs,\n",
-                            ".xr-var-attrs,\n",
-                            ".xr-var-data,\n",
-                            ".xr-index-data {\n",
-                            "  grid-column: 1 / -1;\n",
-                            "}\n",
-                            "\n",
-                            "dl.xr-attrs {\n",
-                            "  padding: 0;\n",
-                            "  margin: 0;\n",
-                            "  display: grid;\n",
-                            "  grid-template-columns: 125px auto;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-attrs dt,\n",
-                            ".xr-attrs dd {\n",
-                            "  padding: 0;\n",
-                            "  margin: 0;\n",
-                            "  float: left;\n",
-                            "  padding-right: 10px;\n",
-                            "  width: auto;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-attrs dt {\n",
-                            "  font-weight: normal;\n",
-                            "  grid-column: 1;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-attrs dt:hover span {\n",
-                            "  display: inline-block;\n",
-                            "  background: var(--xr-background-color);\n",
-                            "  padding-right: 10px;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-attrs dd {\n",
-                            "  grid-column: 2;\n",
-                            "  white-space: pre-wrap;\n",
-                            "  word-break: break-all;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-icon-database,\n",
-                            ".xr-icon-file-text2,\n",
-                            ".xr-no-icon {\n",
-                            "  display: inline-block;\n",
-                            "  vertical-align: middle;\n",
-                            "  width: 1em;\n",
-                            "  height: 1.5em !important;\n",
-                            "  stroke-width: 0;\n",
-                            "  stroke: currentColor;\n",
-                            "  fill: currentColor;\n",
-                            "}\n",
-                            "\n",
-                            ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-                            ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-                            ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-                            "  color: var(--xr-font-color0);\n",
-                            "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-                            "  stroke-width: 0.8px;\n",
-                            "}\n",
-                            "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 482MB\n",
-                            "Dimensions:      (x: 10980, y: 10980)\n",
-                            "Coordinates:\n",
-                            "  * x            (x) float64 88kB 7e+05 7e+05 7e+05 ... 8.097e+05 8.098e+05\n",
-                            "  * y            (y) float64 88kB 5.1e+06 5.1e+06 5.1e+06 ... 4.99e+06 4.99e+06\n",
-                            "    spatial_ref  int64 8B 0\n",
-                            "Data variables:\n",
-                            "    band_1       (y, x) float32 482MB ...\n",
-                            "Attributes:\n",
-                            "    TIFFTAG_COPYRIGHT:  Copernicus service information 2024\n",
-                            "    file_creation:      2024:03:16 10:35:37\n",
-                            "    input_time_window:  2018-10-01 to 2022-02-29\n",
-                            "    TIIFTAG_SOFTWARE:   Timesat : TIMESAT4.1.8\n",
-                            "    AREA_OR_POINT:      Area</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ec49eca9-36a9-47de-99bd-ae7308ea2346' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-ec49eca9-36a9-47de-99bd-ae7308ea2346' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 10980</li><li><span class='xr-has-index'>y</span>: 10980</li></ul></div></li><li class='xr-section-item'><input id='section-a6fd0ba7-bfd6-4178-9bd0-0938e0b1f9e2' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a6fd0ba7-bfd6-4178-9bd0-0938e0b1f9e2' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7e+05 7e+05 ... 8.097e+05 8.098e+05</div><input id='attrs-ea31bb5e-01c1-4c9c-8b16-bc3ef2b09aa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea31bb5e-01c1-4c9c-8b16-bc3ef2b09aa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-224924b0-292c-49f2-b988-ac717bb21556' class='xr-var-data-in' type='checkbox'><label for='data-224924b0-292c-49f2-b988-ac717bb21556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([699965., 699975., 699985., ..., 809735., 809745., 809755.],\n",
-                            "      shape=(10980,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.1e+06 5.1e+06 ... 4.99e+06</div><input id='attrs-568bfdf8-ca57-4f24-841c-b8dd812a1ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-568bfdf8-ca57-4f24-841c-b8dd812a1ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c02ad0ce-d093-4846-b110-5f442fa085d9' class='xr-var-data-in' type='checkbox'><label for='data-c02ad0ce-d093-4846-b110-5f442fa085d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5099995., 5099985., 5099975., ..., 4990225., 4990215., 4990205.],\n",
-                            "      shape=(10980,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f02e9277-4889-449f-81f4-e42668717281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f02e9277-4889-449f-81f4-e42668717281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-832e5598-6bbe-4a23-82fc-755406ad655d' class='xr-var-data-in' type='checkbox'><label for='data-832e5598-6bbe-4a23-82fc-755406ad655d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>699960.0 10.0 0.0 5100000.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b95735aa-c53b-435e-a4bc-6ed27b24af45' class='xr-section-summary-in' type='checkbox' checked /><label for='section-b95735aa-c53b-435e-a4bc-6ed27b24af45' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band_1</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f998123d-7e59-45af-bde3-fd2858ae82d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f998123d-7e59-45af-bde3-fd2858ae82d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15818545-31c6-4501-8142-bdd204dc758e' class='xr-var-data-in' type='checkbox'><label for='data-15818545-31c6-4501-8142-bdd204dc758e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>PhysRange :</span></dt><dd>0 to 1095</dd><dt><span>long_name :</span></dt><dd>Total productivity (Large integral) 2023-season1</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[120560400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3c7e20b-e5aa-4768-8852-4a40bb937550' class='xr-section-summary-in' type='checkbox' checked /><label for='section-e3c7e20b-e5aa-4768-8852-4a40bb937550' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TIFFTAG_COPYRIGHT :</span></dt><dd>Copernicus service information 2024</dd><dt><span>file_creation :</span></dt><dd>2024:03:16 10:35:37</dd><dt><span>input_time_window :</span></dt><dd>2018-10-01 to 2022-02-29</dd><dt><span>TIIFTAG_SOFTWARE :</span></dt><dd>Timesat : TIMESAT4.1.8</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-                        ],
-                        "text/plain": [
-                            "<xarray.Dataset> Size: 482MB\n",
-                            "Dimensions:      (x: 10980, y: 10980)\n",
-                            "Coordinates:\n",
-                            "  * x            (x) float64 88kB 7e+05 7e+05 7e+05 ... 8.097e+05 8.098e+05\n",
-                            "  * y            (y) float64 88kB 5.1e+06 5.1e+06 5.1e+06 ... 4.99e+06 4.99e+06\n",
-                            "    spatial_ref  int64 8B 0\n",
-                            "Data variables:\n",
-                            "    band_1       (y, x) float32 482MB ...\n",
-                            "Attributes:\n",
-                            "    TIFFTAG_COPYRIGHT:  Copernicus service information 2024\n",
-                            "    file_creation:      2024:03:16 10:35:37\n",
-                            "    input_time_window:  2018-10-01 to 2022-02-29\n",
-                            "    TIIFTAG_SOFTWARE:   Timesat : TIMESAT4.1.8\n",
-                            "    AREA_OR_POINT:      Area"
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "ds = d.to_xarray()\n",
-                "ds"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "id": "ae25fd50",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<matplotlib.collections.QuadMesh at 0x1218767b0>"
-                        ]
-                    },
-                    "execution_count": 5,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "ds.band_1.plot()"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "23cd1784-1478-4ac0-8ad8-cf6d8d5358f8",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "####  Using the CDSAPI grammar"
-            ]
-        },
-        {
-            "cell_type": "raw",
-            "id": "4673ee18-c3be-49dd-b704-2f5bacb56b42",
-            "metadata": {
-                "editable": true,
-                "raw_mimetype": "text/restructuredtext",
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": [],
-                "vscode": {
-                    "languageId": "raw"
-                }
-            },
-            "source": [
-                "We can use the :ref:`wekeocds <data-sources-wekeocds>` source to access datasets from the `Copernicus Climate Data Store (CDS) <https://cds.climate.copernicus.eu>`_ available through `WEkEO <https://www.wekeo.eu/>`_, using the standard `cdsapi <https://pypi.org/project/cdsapi>`_ request grammar.\n",
-                "\n",
-                "Compared to the wekeo source, the wekeocds source accepts queries directly in cdsapi format, without requiring the dataset_id to be repeated in the request payload. This allows users to copy and paste requests directly from the dataset forms and examples provided in CDS-compatible interfaces.\n",
-                "\n",
-                "Dataset identifiers can be derived automatically by prepending ``\"EO:ECMWF:DAT:\"`` to the CDS dataset name and converting it to the WEkEO naming convention. For example, the CDS dataset name ``\"reanalysis-era5-single-levels\"`` becomes:\n",
-                "\n",
-                "``\"EO:ECMWF:DAT:REANALYSIS_ERA5_SINGLE_LEVELS\"``\n",
-                "\n",
-                "In practice, this corresponds to replacing hyphens (-) with underscores (_) and converting the name to uppercase."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "id": "777ec204-fbff-41f3-8ae2-3f60c6acb39f",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "source": [
-                "The following example retrieves ERA5 surface data in GRIB format:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "id": "eff2587c-eb03-4de5-b3c4-f894d31027be",
-            "metadata": {
-                "editable": true,
-                "slideshow": {
-                    "slide_type": ""
-                },
-                "tags": []
-            },
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "                                                                                                                                                                                                                   "
-                    ]
-                },
-                {
-                    "data": {
-                        "application/vnd.jupyter.widget-view+json": {
-                            "model_id": "e0b87b34a7964ab79d6a50151b2fb4b7",
-                            "version_major": 2,
-                            "version_minor": 0
-                        },
-                        "text/plain": [
-                            "  0%|          | 0/1 [00:00<?, ?it/s]"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>parameter.variable</th>\n",
-                            "      <th>time.valid_datetime</th>\n",
-                            "      <th>time.base_datetime</th>\n",
-                            "      <th>time.step</th>\n",
-                            "      <th>vertical.level</th>\n",
-                            "      <th>vertical.level_type</th>\n",
-                            "      <th>ensemble.member</th>\n",
-                            "      <th>geography.grid_type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>2t</td>\n",
-                            "      <td>2012-12-01 12:00:00</td>\n",
-                            "      <td>2012-12-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>surface</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>msl</td>\n",
-                            "      <td>2012-12-01 12:00:00</td>\n",
-                            "      <td>2012-12-01 12:00:00</td>\n",
-                            "      <td>0 days</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>surface</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>regular_ll</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
-                            "0                 2t 2012-12-01 12:00:00 2012-12-01 12:00:00    0 days   \n",
-                            "1                msl 2012-12-01 12:00:00 2012-12-01 12:00:00    0 days   \n",
-                            "\n",
-                            "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
-                            "0               0             surface               0          regular_ll  \n",
-                            "1               0             surface               0          regular_ll  "
-                        ]
-                    },
-                    "execution_count": 6,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "import earthkit.data as ekd\n",
-                "\n",
-                "d = ekd.from_source(\n",
-                "    \"wekeocds\",\n",
-                "    \"EO:ECMWF:DAT:REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS\",\n",
-                "    request=dict(\n",
-                "        variable=[\"2m_temperature\", \"mean_sea_level_pressure\"],\n",
-                "        product_type=[\"monthly_averaged_reanalysis_by_hour_of_day\"],\n",
-                "        year=\"2012\",\n",
-                "        month=\"12\",\n",
-                "        time=\"12:00\",\n",
-                "        data_format=\"grib\",\n",
-                "        download_format=\"zip\",\n",
-                "    ),\n",
-                ")\n",
-                "\n",
-                "d.to_fieldlist().ls()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "dev",
-            "language": "python",
-            "name": "dev"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.13.1"
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 5
-}
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 70dec6de4..257d73911 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,14 +1,13 @@
 Earthkit-data's documentation
 ======================================================
 
-.. important::
+**earthkit-data** is a format-agnostic Python interface for geospatial data with a focus on meteorology and
+climate science. It is the data handling component of :xref:`earthkit`.
 
-    This software is in **release candidate** stage. It is not yet considered stable and may still undergo significant changes before the final 1.0 release. We welcome feedback and contributions as we approach that milestone.
 
-    Please see the :ref:`migration_1.0.0` guide for details on the changes in this release candidate and how to update your code to be compatible with 1.0.0.
+.. important::
 
-**earthkit-data** is a format-agnostic Python interface for geospatial data with a focus on meteorology and
-climate science. It is the data handling component of :xref:`earthkit`.
+    The latest major version 1.0 introduces significant breaking changes. Please read the :ref:`1.0 release notes <release-notes-1.0>` and the :ref:`migration_1.0.0` before upgrading.
 
 
 .. grid:: 1
@@ -96,7 +95,6 @@ Have a feature request or found a bug? Feel free to open an
    how-tos/index
    concepts/index
    api-reference
-   faq
 
 .. toctree::
    :caption: Developer guide
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index d1a986411..f8ea7e325 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -9,11 +9,11 @@ Installing from PyPI
 Minimal installation
 +++++++++++++++++++++++++
 
-Install **earthkit-data** with python3 (>= 3.10 ) and ``pip`` as follows:
+Install the latest release with python3 (>= 3.10 ) and ``pip`` as follows:
 
 .. code-block:: bash
 
-    python3 -m pip install earthkit-data>=1.0.0rc0
+    python3 -m pip install earthkit-data
 
 The package installed like this is **minimal** supporting only GRIB and NetCDF data and cannot access remote services other than URLs. If you want to use more data types or remote services you need to install the optional Python packages.
 
@@ -24,13 +24,13 @@ You can install **earthkit-data** with all the optional packages (with the excep
 
 .. code-block:: bash
 
-    python3 -m pip install earthkit-data[all]>=1.0.0rc0
+    python3 -m pip install earthkit-data[all]
 
 Please note in **zsh** you need to use quotes around the square brackets:
 
 .. code-block:: bash
 
-    python3 -m pip install "earthkit-data[all]>=1.0.0rc0"
+    python3 -m pip install "earthkit-data[all]"
 
 
 Installing individual optional packages
@@ -58,13 +58,13 @@ E.g. to add :ref:`data-sources-mars`  support you can use:
 
 .. code-block:: bash
 
-    python3 -m pip install earthkit-data[mars]>=1.0.0rc0
+    python3 -m pip install earthkit-data[mars]
 
 List of optional dependencies can also be specified :
 
 .. code-block:: bash
 
-    python3 -m pip install earthkit-data[cds,mars]>=1.0.0rc0
+    python3 -m pip install earthkit-data[cds,mars]
 
 
 
diff --git a/docs/source/release-notes/deprecations.rst b/docs/source/release-notes/deprecations.rst
index 0e2565e8f..1e445abe1 100644
--- a/docs/source/release-notes/deprecations.rst
+++ b/docs/source/release-notes/deprecations.rst
@@ -33,7 +33,7 @@ The name of the ensemble member :ref:`dimension role <_xr_dim_roles>` changed to
 The "to_grib" method on the earthkit Xarray accessor is deprecated
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-The :func:`to_grib` method on the ``earthkit`` Xarray accessor is deprecated. Use :func:`to_target` instead. The method is still available for backward compatibility but will be removed in a future release. See :ref:`/how-tos/xr_engine/xarray_engine_to_grib.ipynb` notebook for details on how to use the new API.
+The :func:`to_grib` method on the ``earthkit`` Xarray accessor is deprecated. Use :func:`to_target` instead. The method is still available for backward compatibility but will be removed in a future release. See :ref:`/tutorials/xr_engine/xarray_engine_to_grib.ipynb` notebook for details on how to use the new API.
 
 .. list-table::
    :header-rows: 0
diff --git a/docs/source/release-notes/index.rst b/docs/source/release-notes/index.rst
index 2048b33f4..75a8dfb97 100644
--- a/docs/source/release-notes/index.rst
+++ b/docs/source/release-notes/index.rst
@@ -23,7 +23,7 @@ Version updates
 .. toctree::
     :maxdepth: 1
 
-    version_1.0.0rc_updates
+    version_1.0_updates
     version_0.20_updates
     version_0.19_updates
     version_0.18_updates
diff --git a/docs/source/release-notes/migration_1.0.0.rst b/docs/source/release-notes/migration_1.0.0.rst
index 41143fe78..5386ab0a1 100644
--- a/docs/source/release-notes/migration_1.0.0.rst
+++ b/docs/source/release-notes/migration_1.0.0.rst
@@ -1,6 +1,6 @@
 .. _migration_1.0.0:
 
-Migration guide for 1.0.0
+Migration guide for 1.0
 ============================
 
 from_source
@@ -53,7 +53,7 @@ Then we can call any of the corresponding ``to_*`` methods to convert the data t
 
 Examples:
 
- -  :ref:`/how-tos/source/data.ipynb`
+ -  :ref:`/tutorials/source/data.ipynb`
 
 
 The ``read_all`` kwarg
@@ -68,7 +68,7 @@ Old way:
 
     import earthkit.data as ekd
 
-    url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test.grib"
+    url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test.grib"
     fl = ekd.from_source("url", url, stream=True, read_all=True)
 
 New way:
@@ -77,7 +77,7 @@ New way:
 
     import earthkit.data as ekd
 
-    url = "https://sites.ecmwf.int/repository/earthkit-data/how-tos/test.grib"
+    url = "https://sites.ecmwf.int/repository/earthkit-data/tutorials/test.grib"
     fl = ekd.from_source("url", url, stream=True).to_fieldlist(read_all=True)
 
 
@@ -134,8 +134,8 @@ Field modification
 
 Fields can be modified using the :py:meth:`~earthkit.data.core.field.Field.set` method. This method allows to set new data values and/or change  metadata keys. See the notebook examples:
 
-- :ref:`/how-tos/grib/grib_modify_metadata.ipynb`
-- :ref:`/how-tos/grib/grib_modify_values.ipynb`
+- :ref:`/tutorials/grib/grib_modify_metadata.ipynb`
+- :ref:`/tutorials/grib/grib_modify_values.ipynb`
 
 Field arithmetic
 ++++++++++++++++++++++++
@@ -153,7 +153,7 @@ Added :py:class:`~earthkit.data.core.field.Field` arithmetic. The basic maths op
 Notebook examples
 ++++++++++++++++++++++++
 
-- :ref:`/how-tos/field/overview.ipynb`
+- :ref:`/tutorials/field/field_overview.ipynb`
 
 
 Changes in the Field API
@@ -221,7 +221,7 @@ The Field API has been redesigned and many methods have been removed or changed.
         When the key does not exist in the raw metadata, it raises a KeyError.
    * - MetaData object accessed by calling metadata() without args/kwargs
      - N/A
-     - This object is no longer available.
+     - This object is no longer available. The field itself provides the same functionality through :py:meth:`~earthkit.data.core.field.Field.get` and :py:meth:`~earthkit.data.core.field.Field.set`.
    * - dump()
      - N/A
      - Use :py:meth:`~earthkit.data.core.field.Field.describe` instead.
@@ -329,6 +329,23 @@ The following table gives an overview of the changes in the Fieldlist API (the `
      -
 
 
+Indexed mode removed
++++++++++++++++++++++
+
+The GRIB indexed mode, previously available via the ``indexing=True`` kwarg in
+:py:func:`~earthkit.data.from_source`, has been removed:
+
+.. code-block:: python
+
+    # No longer supported
+    fl = ekd.from_source("file", "data.grib", indexing=True)
+
+In indexed mode the GRIB file was pre-scanned and all field metadata was stored in an
+in-memory index, making repeated :meth:`sel` and :meth:`order_by` calls faster for large
+files at the cost of upfront indexing time and additional memory. This feature will be
+reimplemented in a future release, potentially with a different interface.
+
+
 Xarray engine
 ------------------
 
diff --git a/docs/source/release-notes/version_0.10_updates.rst b/docs/source/release-notes/version_0.10_updates.rst
index 18d374b89..b04afd7b9 100644
--- a/docs/source/release-notes/version_0.10_updates.rst
+++ b/docs/source/release-notes/version_0.10_updates.rst
@@ -95,7 +95,7 @@ New features
 ++++++++++++++++
 
 - Refined :ref:`GRIB` data memory management when reading from a file (:pr:`428`). See :ref:`grib-memory` for an overview.
-- Refined how GRIB the metadata object manages the GRIB handle (:pr:`430`). See the :ref:`/how-tos/legacy/grib_metadata_object.ipynb` notebook example for details.
+- Refined how GRIB the metadata object manages the GRIB handle (:pr:`430`). See the :ref:`/tutorials/legacy/grib_metadata_object.ipynb` notebook example for details.
 - Added the ``index`` keyword argument for data subsetting to the following methods (:pr:`407`):
 
   -  field:  :meth:`~earthkit.data.core.fieldlist.Field.to_numpy`, :meth:`~earthkit.data.core.fieldlist.Field.to_array`, :meth:`~earthkit.data.core.fieldlist.Field.data`, :meth:`~earthkit.data.core.fieldlist.Field.to_latlon`, :meth:`~earthkit.data.core.fieldlist.Field.to_points`
diff --git a/docs/source/release-notes/version_0.11_updates.rst b/docs/source/release-notes/version_0.11_updates.rst
index 937ae1121..da0248f3e 100644
--- a/docs/source/release-notes/version_0.11_updates.rst
+++ b/docs/source/release-notes/version_0.11_updates.rst
@@ -71,28 +71,28 @@ API changes
 - Removed :py:class:`ArrayFieldList`. Its functionality is covered by :py:class:`SimpleFieldList` (:pr:`471`).
 - :meth:`~earthkit.data.core.fieldlist.FieldList.from_array` and :meth:`~earthkit.data.core.fieldlist.FieldList.to_fieldlist` now return an :py:class:`SimpleFieldList`
 
-See :ref:`/how-tos/grib/grib_array_namespace.ipynb` for more details.
+See :ref:`/tutorials/grib/grib_array_namespace.ipynb` for more details.
 
 
 Changes
 ++++++++
 - Added the :ref:`data-sources-s3` source to access AWS S3 buckets (:pr:`484`). See the notebook examples:
 
-  - :ref:`/how-tos/source/s3.ipynb`
+  - :ref:`/tutorials/source/s3.ipynb`
 
 - Added support for geotiff files (:pr:`503`). See the notebook examples:
 
-  - :ref:`/how-tos/geotiff/geotiff.ipynb`
+  - :ref:`/tutorials/geotiff/geotiff.ipynb`
 
 - Added :ref:`stream <streams>` support for the :ref:`data-sources-file` source (:pr:`500`)
 - Allowed concatenation of :ref:`stream <streams>` sources (:pr:`500`)
 - Added :py:class:`SimpleFieldList`, which can store a list of arbitrary Fields (:pr:`471`). See the notebook examples:
 
-  - :ref:`/how-tos/grib/grib_array_namespace.ipynb`
+  - :ref:`/tutorials/grib/grib_array_namespace.ipynb`
 
 - Added :meth:`~earthkit.data.core.fieldlist.Field.clone` and :py:meth:`~earthkit.data.core.fieldlist.Field.copy` to alter field metadata and values (:pr:`493`, :pr:`496`, :pr:`522`). See the notebook examples:
 
-  - :ref:`/how-tos/legacy/grib_modification.ipynb`
+  - :ref:`/tutorials/legacy/grib_modification.ipynb`
 
 - Reimplemented and documented the :ref:`data-sources-lod` source, which is now generating a :py:class:`SimpleFieldList` and is not bound to GRIB specific metadata (:pr:`461`, :pr:`511`). See the notebook examples:
 
diff --git a/docs/source/release-notes/version_0.12_updates.rst b/docs/source/release-notes/version_0.12_updates.rst
index 2f2095d91..6fee7474c 100644
--- a/docs/source/release-notes/version_0.12_updates.rst
+++ b/docs/source/release-notes/version_0.12_updates.rst
@@ -71,7 +71,7 @@ Changes
 
 - Enabled using environment variables to control the :ref:`settings <settings>` (:pr:`565`). See the notebook example:
 
-  - :ref:`/how-tos/misc/config_env_vars.ipynb`
+  - :ref:`/tutorials/misc/config_env_vars.ipynb`
 
 - Re-enabled ``headers_only_clone=True`` when calling :meth:`GribMetadata.override() <data.readers.grib.metadata.GribMetadata.override>` (:pr:`567`)
 
diff --git a/docs/source/release-notes/version_0.13_updates.rst b/docs/source/release-notes/version_0.13_updates.rst
index f7d0a4cf8..310b08efc 100644
--- a/docs/source/release-notes/version_0.13_updates.rst
+++ b/docs/source/release-notes/version_0.13_updates.rst
@@ -115,12 +115,12 @@ New writer API
 
 See the notebook examples:
 
-  - :ref:`/how-tos/target/file_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_file_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_file_pattern_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_fdb_target.ipynb`
-  - :ref:`/how-tos/target/grib_to_geotiff.ipynb`
-  - :ref:`/how-tos/target/grib_encoder.ipynb`
+  - :ref:`/tutorials/target/file_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_file_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_file_pattern_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_fdb_target.ipynb`
+  - :ref:`/tutorials/target/grib_to_geotiff.ipynb`
+  - :ref:`/tutorials/target/grib_encoder.ipynb`
 
 New features
 +++++++++++++++++
diff --git a/docs/source/release-notes/version_0.14_updates.rst b/docs/source/release-notes/version_0.14_updates.rst
index 78d6cadec..63761fc42 100644
--- a/docs/source/release-notes/version_0.14_updates.rst
+++ b/docs/source/release-notes/version_0.14_updates.rst
@@ -64,7 +64,7 @@ Xarray engine ``split_dims``
 
 When :meth:`~earthkit.data.core.fieldlist.FieldList.to_xarray` is called with ``split_dims``, the engine will now return a tuple with two lists: the first list contains the Xarray datasets, while the second one contains the corresponding dictionaries with the spitting keys/values (one dictionary per dataset) (:pr:`688`). Previously, a list of datasets was returned so this is a **breaking change**.
 
-See the :ref:`/how-tos/xr_engine/xarray_engine_split.ipynb` notebook example.
+See the :ref:`/tutorials/xr_engine/xarray_engine_split.ipynb` notebook example.
 
 
 Patterns
diff --git a/docs/source/release-notes/version_0.15_updates.rst b/docs/source/release-notes/version_0.15_updates.rst
index 11c4e8c07..5a637f81f 100644
--- a/docs/source/release-notes/version_0.15_updates.rst
+++ b/docs/source/release-notes/version_0.15_updates.rst
@@ -76,7 +76,7 @@ Breaking xarray engine changes
   - allows handling cases when the ``step`` key contains ranges as a str in the form of e.g.  "12-24"
   - allows proper sorting of the "step" dimension values when building the dataset
 
- Please note that due to this change when ``dim_name_from_role_name=False`` is used the step dimension will be called "step_timedelta" instead of "step". You can still reproduce the old behaviour by using: ``dim_roles={"step": "step"}``. See the following notebook example: :ref:`/how-tos/xr_engine/xarray_engine_step_ranges.ipynb`.
+ Please note that due to this change when ``dim_name_from_role_name=False`` is used the step dimension will be called "step_timedelta" instead of "step". You can still reproduce the old behaviour by using: ``dim_roles={"step": "step"}``. See the following notebook example: :ref:`/tutorials/xr_engine/xarray_engine_step_ranges.ipynb`.
 
 
 Other Xarray engine changes
@@ -94,19 +94,19 @@ Other Xarray engine changes
        }
 
 
-- Improved the serialisation of GRIB fieldlists to reduce memory usage when Xarray is generated with chunks (:pr:`700`). See the :ref:`/how-tos/xr_engine/xarray_engine_chunks.ipynb` notebook example.
+- Improved the serialisation of GRIB fieldlists to reduce memory usage when Xarray is generated with chunks (:pr:`700`). See the :ref:`/tutorials/xr_engine/xarray_engine_chunks.ipynb` notebook example.
 - TensorBackendArray, which implements the lazy loading of DataArrays in the Xarray engine, now uses a ``dask.utils.SerializableLock`` when accessing the data (:pr:`700`).
-- Enabled converting :ref:`data-sources-lod` fieldlists into Xarray (:pr:`701`). See the :ref:`/how-tos/dict/list_of_dicts_to_xarray.ipynb` notebook example.
-- Enabled converting Xarray generated with the earthkit engine into GRIB using :func:`to_target` (:pr:`730`). See :ref:`xr_to_grib` and the related :ref:`/how-tos/xr_engine/xarray_engine_to_grib.ipynb` notebook example.
+- Enabled converting :ref:`data-sources-lod` fieldlists into Xarray (:pr:`701`). See the :ref:`/tutorials/dict/list_of_dicts_to_xarray.ipynb` notebook example.
+- Enabled converting Xarray generated with the earthkit engine into GRIB using :func:`to_target` (:pr:`730`). See :ref:`xr_to_grib` and the related :ref:`/tutorials/xr_engine/xarray_engine_to_grib.ipynb` notebook example.
 
 New Xarray engine notebooks
 ------------------------------
 
-- :ref:`/how-tos/xr_engine/xarray_engine_step_ranges.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_ensemble.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_squeeze.ipynb`
-- :ref:`/how-tos/xr_engine/xarray_engine_chunks.ipynb`
-- :ref:`/how-tos/dict/list_of_dicts_to_xarray.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_step_ranges.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_ensemble.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_squeeze.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_chunks.ipynb`
+- :ref:`/tutorials/dict/list_of_dicts_to_xarray.ipynb`
 
 
 
@@ -114,10 +114,10 @@ New features
 +++++++++++++++++
 
 - Added the :ref:`zarr <data-sources-zarr>` source to read Zarr data (:pr:`675`).
-- Added the :ref:`targets-zarr` target (:pr:`716`). See the :ref:`/how-tos/target/grib_to_zarr_target.ipynb` notebook example.
+- Added the :ref:`targets-zarr` target (:pr:`716`). See the :ref:`/tutorials/target/grib_to_zarr_target.ipynb` notebook example.
 - Added new config option ``grib-file-serialisation-policy`` to control how GRIB data on disk is pickled. The options are "path" and "memory". The default is "path". Previously, only "memory" was implemented (:pr:`700`).
 - Added serialisation to GRIB fields (both on disk and in-memory) (:pr:`700`)
-- Enabled specifying earthkit Xarray engine options via the ``earthkit_to_xarray_kwargs`` kwarg in :func:`to_target` when converting GRIB to NetCDF. See :ref:`xr_grib_to_netcdf` and related the :ref:`/how-tos/grib/grib_to_netcdf.ipynb` notebook example. (:pr:`729`) E.g.
+- Enabled specifying earthkit Xarray engine options via the ``earthkit_to_xarray_kwargs`` kwarg in :func:`to_target` when converting GRIB to NetCDF. See :ref:`xr_grib_to_netcdf` and related the :ref:`/tutorials/grib/grib_to_netcdf.ipynb` notebook example. (:pr:`729`) E.g.
 
     .. code-block:: python
 
diff --git a/docs/source/release-notes/version_0.16_updates.rst b/docs/source/release-notes/version_0.16_updates.rst
index 7684e5375..5ebf27965 100644
--- a/docs/source/release-notes/version_0.16_updates.rst
+++ b/docs/source/release-notes/version_0.16_updates.rst
@@ -95,15 +95,15 @@ Xarray engine
 
 - Implemented mono variable (single dataarray containing all the parameters from a GRIB fieldlist) in Xarray engine (:pr:`734`). See the notebook examples:
 
-  -  :ref:`/how-tos/xr_engine/xarray_engine_mono_variable.ipynb`
-  -  :ref:`/how-tos/xr_engine/xarray_engine_mono_variable_remapping.ipynb`
+  -  :ref:`/tutorials/xr_engine/xarray_engine_mono_variable.ipynb`
+  -  :ref:`/tutorials/xr_engine/xarray_engine_mono_variable_remapping.ipynb`
 
-- Allowed specifying metadata defaults for :py:meth:`~earthkit.data.indexing.xarray.XarrayMixIn.to_xarray` (:pr:`738`) via the ``fill_metadata`` option. See the notebook example :ref:`/how-tos/xr_engine/xarray_engine_mono_variable.ipynb`
+- Allowed specifying metadata defaults for :py:meth:`~earthkit.data.indexing.xarray.XarrayMixIn.to_xarray` (:pr:`738`) via the ``fill_metadata`` option. See the notebook example :ref:`/tutorials/xr_engine/xarray_engine_mono_variable.ipynb`
 - Improved the Xarray support in the encoders (:pr:`750`).
 
 
 Experimental features
 ------------------------------
 
-- Added GPU support for the Xarray engine (:pr:`745`). See the notebook example :ref:`/how-tos/xr_engine/xarray_cupy.ipynb`
+- Added GPU support for the Xarray engine (:pr:`745`). See the notebook example :ref:`/tutorials/xr_engine/xarray_cupy.ipynb`
 - Added the ``grid_spec`` property to Xarray earthkit accessor (:pr:`751`).
diff --git a/docs/source/release-notes/version_0.17_updates.rst b/docs/source/release-notes/version_0.17_updates.rst
index c97640353..9e79be43e 100644
--- a/docs/source/release-notes/version_0.17_updates.rst
+++ b/docs/source/release-notes/version_0.17_updates.rst
@@ -21,11 +21,11 @@ New features
 
 - Implemented the ``allow_holes`` option in the Xarray engine to allow using GRIB input not forming a complete hypercube (:pr:`780`). The default is ``allow_holes=False``. See the notebook example:
 
-    - :ref:`/how-tos/xr_engine/xarray_engine_holes.ipynb`
+    - :ref:`/tutorials/xr_engine/xarray_engine_holes.ipynb`
 
 - Added the experimental :ref:`data-sources-gribjump` source for fast retrievals of GRIB message subsets from the FDB (Fields DataBase) using the :xref:`gribjump` library (:pr:`689`). See the notebook example:
 
-    - :ref:`/how-tos/source/gribjump.ipynb`
+    - :ref:`/tutorials/source/gribjump.ipynb`
 
 - Added the :func:`to_geojson` method to CovjsonReader for conversion to geojson from covjson (:pr:`794`). Requires ``covjsonkit>=0.2.2``.
 - Allowed reading multiple GRIB messages from a memory buffer with the :ref:`data-sources-memory` source (:pr:`740`).
diff --git a/docs/source/release-notes/version_0.19_updates.rst b/docs/source/release-notes/version_0.19_updates.rst
index d5216fb30..b940d12a4 100644
--- a/docs/source/release-notes/version_0.19_updates.rst
+++ b/docs/source/release-notes/version_0.19_updates.rst
@@ -3,6 +3,15 @@ Version 0.19 Updates
 /////////////////////////
 
 
+Version 0.19.4
+===============
+
+Changes
+++++++++++++++++++++++++++++++
+
+- Improved the speed of writing GRIB data to the ``file-pattern`` target (:pr:`974`).
+
+
 Version 0.19.3
 ===============
 
diff --git a/docs/source/release-notes/version_0.2_updates.rst b/docs/source/release-notes/version_0.2_updates.rst
index 2644f9bba..ec4283e3a 100644
--- a/docs/source/release-notes/version_0.2_updates.rst
+++ b/docs/source/release-notes/version_0.2_updates.rst
@@ -9,17 +9,17 @@ Version 0.2.0
 New features
 ++++++++++++++++
 
-- added new source :ref:`data-sources-eod` to retrieve ECMWF open data. See the :ref:`/how-tos/source/ecmwf_open_data.ipynb` notebook example.
-- added new method :func:`from_object` to use a objects like numpy arrays or xarray datasets as an input. See the :ref:`/how-tos/other/from_object.ipynb` notebook example.
+- added new source :ref:`data-sources-eod` to retrieve ECMWF open data. See the :ref:`/tutorials/source/ecmwf_open_data.ipynb` notebook example.
+- added new method :func:`from_object` to use a objects like numpy arrays or xarray datasets as an input. See the :ref:`/tutorials/other/from_object.ipynb` notebook example.
 - redesigned :ref:`bufr` handling. :ref:`bufr` data is now represented by a :class:`BUFRList <data.readers.bufr.bufr.BUFRList>` made up of :class:`BUFRMessage <data.readers.bufr.bufr.BUFRMessage>` objects. In many aspects it behaves similarly to a :obj:`FieldList <earthkit.data.core.fieldlist.FieldList>` offering iteration, slicing, selection and message dump. For details see :ref:`here <bufr>` and also check the notebook examples:
 
-     - :ref:`/how-tos/bufr/bufr_temp.ipynb`
-     - :ref:`/how-tos/bufr/bufr_synop.ipynb`
+     - :ref:`/tutorials/bufr/bufr_temp.ipynb`
+     - :ref:`/tutorials/bufr/bufr_synop.ipynb`
 
-- added the ``group_by`` option to :ref:`data-sources-fdb` stream source. With this option we can read data in groups from a stream. See the :ref:`/how-tos/source/fdb.ipynb` notebook example.
+- added the ``group_by`` option to :ref:`data-sources-fdb` stream source. With this option we can read data in groups from a stream. See the :ref:`/tutorials/source/fdb.ipynb` notebook example.
 - changed how the field geography can be accessed. On a Field object we can now call the following methods:
 
-   - :meth:`projection() <earthkit.data.core.fieldlist.Field.projection>`: returns an object describing the projection. See the :ref:`/how-tos/misc/projection.ipynb` notebook example.
+   - :meth:`projection() <earthkit.data.core.fieldlist.Field.projection>`: returns an object describing the projection. See the :ref:`/tutorials/misc/projection.ipynb` notebook example.
    - :meth:`to_latlon() <earthkit.data.core.fieldlist.Field.to_latlon>`: returns the latitudes and longitudes for all the gridpoints
    - :meth:`to_points() <earthkit.data.core.fieldlist.Field.to_points>`: returns the geographical coordinates of all the gridpoints in the data's original Coordinate Reference System (CRS)
 
diff --git a/docs/source/release-notes/version_0.3_updates.rst b/docs/source/release-notes/version_0.3_updates.rst
index cdbb2dc5f..2b45001b4 100644
--- a/docs/source/release-notes/version_0.3_updates.rst
+++ b/docs/source/release-notes/version_0.3_updates.rst
@@ -16,17 +16,17 @@ Version 0.3.0
 New features
 ++++++++++++++++
 
-- added new source :ref:`data-sources-ads` to retrieve data from the `Copernicus Atmosphere Data Store <https://ads.atmosphere.copernicus.eu/>`_ (ADS). See the :ref:`/how-tos/source/ads.ipynb` notebook example.
-- added pandas and geopandas support. See the :ref:`/how-tos/other/pandas.ipynb` notebook example.
-- added :ref:`caching` policies. See the :ref:`/how-tos/misc/cache.ipynb` and :ref:`/how-tos/misc/config.ipynb` notebook examples.
-- changed the return type of :meth:`~earthkit.data.core.fieldlist.FieldList.data`, which now returns an ndarray. Previously it returned a tuple of ndarrays. See the :ref:`/how-tos/grib/grib_lat_lon_value_ll.ipynb` notebook example.
-- added :meth:`~earthkit.data.core.fieldlist.FieldList.data` method to :class:`~earthkit.data.core.fieldlist.FieldList`. See the :ref:`/how-tos/grib/grib_lat_lon_value_ll.ipynb` notebook example.
+- added new source :ref:`data-sources-ads` to retrieve data from the `Copernicus Atmosphere Data Store <https://ads.atmosphere.copernicus.eu/>`_ (ADS). See the :ref:`/tutorials/source/ads.ipynb` notebook example.
+- added pandas and geopandas support. See the :ref:`/tutorials/other/pandas.ipynb` notebook example.
+- added :ref:`caching` policies. See the :ref:`/tutorials/misc/cache.ipynb` and :ref:`/tutorials/misc/config.ipynb` notebook examples.
+- changed the return type of :meth:`~earthkit.data.core.fieldlist.FieldList.data`, which now returns an ndarray. Previously it returned a tuple of ndarrays. See the :ref:`/tutorials/grib/grib_lat_lon_value_ll.ipynb` notebook example.
+- added :meth:`~earthkit.data.core.fieldlist.FieldList.data` method to :class:`~earthkit.data.core.fieldlist.FieldList`. See the :ref:`/tutorials/grib/grib_lat_lon_value_ll.ipynb` notebook example.
 - added the ``valid_datetime`` metadata key, which can be used in :meth:`Field.metadata() <earthkit.data.core.fieldlist.Field.metadata>` and :class:`~earthkit.data.core.fieldlist.FieldList` methods like :meth:`~earthkit.data.core.fieldlist.FieldList.metadata`, :meth:`~earthkit.data.core.fieldlist.FieldList.sel` and  :meth:`~earthkit.data.core.fieldlist.FieldList.order_by` etc. It is particularly useful for GRIB data because the this piece of information was previously only available as two separate keys (``validityDate`` and ``validityTime``).
 
   .. code-block:: python
 
       >>> import earthkit.data
-      >>> ds = earthkit.data.from_source("file", "docs/how-tos/test.grib")
+      >>> ds = earthkit.data.from_source("file", "docs/tutorials/test.grib")
       >>> ds[0].metadata("validityDate")
       20200513
       >>> ds[0].metadata("validityTime")
@@ -34,8 +34,8 @@ New features
       >>> ds[0].metadata("valid_datetime")
       datetime.datetime(2020, 5, 13, 12, 0)
 
-- implemented FieldList for NetCDF data. See the :ref:`/how-tos/netcdf/netcdf_fieldlist.ipynb` example.
-- added experimental Metadata handling. See the :ref:`/how-tos/legacy/metadata.ipynb` example.
+- implemented FieldList for NetCDF data. See the :ref:`/tutorials/netcdf/netcdf_fieldlist.ipynb` example.
+- added experimental Metadata handling. See the :ref:`/tutorials/legacy/metadata.ipynb` example.
 - added experimental input transformer tools for earthkit subpackages. It uses type-setting, or explicit mapping, to try to ensure that the inputs passed to a function are converted to the appropriate type for that method. This means that earthkit users do not have to worry about the format of their data.
 
 
diff --git a/docs/source/release-notes/version_0.4_updates.rst b/docs/source/release-notes/version_0.4_updates.rst
index 36348cedd..4dd013612 100644
--- a/docs/source/release-notes/version_0.4_updates.rst
+++ b/docs/source/release-notes/version_0.4_updates.rst
@@ -7,8 +7,8 @@ Version 0.4.0
 New features
 ++++++++++++++++
 
-- added new sources :ref:`data-sources-wekeo` and :ref:`data-sources-wekeocds` to retrieve data from `WEkEO <https://www.wekeo.eu/>`_. See the :ref:`/how-tos/source/wekeo.ipynb` notebook example.
-- added new source :ref:`data-sources-polytope` to retrieve data from the `Polytope web services <https://polytope-client.readthedocs.io/en/latest/>`_. See the :ref:`/how-tos/source/polytope.ipynb` notebook example.
+- added new sources :ref:`data-sources-wekeo` and :ref:`data-sources-wekeocds` to retrieve data from `WEkEO <https://www.wekeo.eu/>`_. See the :ref:`/tutorials/source/wekeo.ipynb` notebook example.
+- added new source :ref:`data-sources-polytope` to retrieve data from the `Polytope web services <https://polytope-client.readthedocs.io/en/latest/>`_. See the :ref:`/tutorials/source/polytope.ipynb` notebook example.
 - added the ``append`` option to :meth:`FieldList.save() <earthkit.data.core.fieldlist.FieldList.save>`.
 - added the ``dtype`` option to the ``to_data()``, ``to_latlon()`` and ``to_points()`` methods both on a :class:`~earthkit.data.core.field.Field` or :class:`~earthkit.data.core.fieldlist.FieldList`.
 - allowed access to 32-bit GRIB data values without requiring a cast in Python from 64 to 32 bits. Only works with a recent ecCodes version (ecCodes >= 2.31.0 and eccodes-python >= 1.6.0 required). In order to use this feature set ``dtype=np.float32`` in the ``to_numpy()``, ``to_data()``, ``to_latlon()`` or ``to_points()`` methods on either a :class:`~earthkit.data.core.field.Field` or :class:`~earthkit.data.core.fieldlist.FieldList`.
diff --git a/docs/source/release-notes/version_0.5_updates.rst b/docs/source/release-notes/version_0.5_updates.rst
index 137354052..3d9f0c236 100644
--- a/docs/source/release-notes/version_0.5_updates.rst
+++ b/docs/source/release-notes/version_0.5_updates.rst
@@ -39,13 +39,13 @@ Version 0.5.1
 New features
 ++++++++++++++++
 
-- changed the default :ref:`cache policy <cache_policies>` to :ref:`off <off_cache_policy>`. See the :ref:`/how-tos/misc/cache.ipynb` notebook example.
+- changed the default :ref:`cache policy <cache_policies>` to :ref:`off <off_cache_policy>`. See the :ref:`/tutorials/misc/cache.ipynb` notebook example.
 - enabled the :ref:`off <off_cache_policy>` cache policy to access remote sources like :ref:`data-sources-mars`
 - allowed creating source :ref:`plugins <plugin-overview>`
-- enabled reading :ref:`data-sources-url` sources as streams. See the :ref:`/how-tos/source/url_stream.ipynb` notebook example
+- enabled reading :ref:`data-sources-url` sources as streams. See the :ref:`/tutorials/source/url_stream.ipynb` notebook example
 - enabled reading :ref:`data-sources-polytope` sources as streams
 - added the :meth:`FieldList.to_fieldlist() <earthkit.data.core.fieldlist.FieldList.to_fieldlist>` method to convert to a new :class:`FieldList` based on a given backend
-- added the :meth:`nearest_point_haversine` and :meth:`nearest_point_kdtree` methods to find the nearest point out of a set of locations. See the :ref:`/how-tos/grib/grib_nearest_gridpoint.ipynb` and :ref:`/how-tos/grib/grib_time_series.ipynb` notebook examples.
+- added the :meth:`nearest_point_haversine` and :meth:`nearest_point_kdtree` methods to find the nearest point out of a set of locations. See the :ref:`/tutorials/grib/grib_nearest_gridpoint.ipynb` and :ref:`/tutorials/grib/grib_time_series.ipynb` notebook examples.
 - enabled using multiple keys and dictionaries in the :ref:`split_on <split_on>` request parameter for :ref:`data-sources-cds` retrievals
 - enabled using list of requests in :ref:`data-sources-cds` retrievals
 - added the experimental "constants" source type
diff --git a/docs/source/release-notes/version_0.7_updates.rst b/docs/source/release-notes/version_0.7_updates.rst
index 56cd1acbb..a89fca366 100644
--- a/docs/source/release-notes/version_0.7_updates.rst
+++ b/docs/source/release-notes/version_0.7_updates.rst
@@ -14,10 +14,10 @@ New features
 ++++++++++++++++
 
 - implemented the :ref:`parts <parts>` option for :ref:`file <data-sources-file>` sources with :ref:`grib` and :ref:`bufr` data
-- added shapefile support. See the :ref:`/how-tos/shapefile/shapefile.ipynb` notebook example.
-- added the :ref:`data-sources-opendap` source to access NetCDF data from OPEnDAP services. See the :ref:`/how-tos/source/netcdf_opendap.ipynb` notebook example.
+- added shapefile support. See the :ref:`/tutorials/shapefile/shapefile.ipynb` notebook example.
+- added the :ref:`data-sources-opendap` source to access NetCDF data from OPEnDAP services. See the :ref:`/tutorials/source/netcdf_opendap.ipynb` notebook example.
 - added the :ref:`data-sources-sample` source to access data used in tests and examples
-- added the ``array_backend`` option for :class:`~earthkit.data.core.fieldlist.FieldList`\ s to allow specifying other array backends than numpy. Also added the :meth:`FieldList.array() <earthkit.data.core.fieldlist.FieldList.array>` and :meth:`Field.array() <earthkit.data.core.fieldlist.Field.array>` methods to extract the values using the given array backend. See the :ref:`/how-tos/grib/grib_array_namespace.ipynb` notebook example.
+- added the ``array_backend`` option for :class:`~earthkit.data.core.fieldlist.FieldList`\ s to allow specifying other array backends than numpy. Also added the :meth:`FieldList.array() <earthkit.data.core.fieldlist.FieldList.array>` and :meth:`Field.array() <earthkit.data.core.fieldlist.Field.array>` methods to extract the values using the given array backend. See the :ref:`/tutorials/grib/grib_array_namespace.ipynb` notebook example.
 - added support for Lambert Conformal projection when using :meth:`Field.projection() <earthkit.data.core.fieldlist.Field.projection>`
 - changed the default of the ``bits_per_value`` option to None in :meth:`NumpyFieldList.save() <data.sources.numpy_list.NumpyFieldList.save>`. None means the original ``bits_per_value`` in the GRIB header is kept when the data is written to disk.
 - added the ``model`` option to the :ref:`data-sources-eod` source
diff --git a/docs/source/release-notes/version_1.0.0rc_updates.rst b/docs/source/release-notes/version_1.0.0rc_updates.rst
deleted file mode 100644
index e57fa06bd..000000000
--- a/docs/source/release-notes/version_1.0.0rc_updates.rst
+++ /dev/null
@@ -1,136 +0,0 @@
-Version 1.0.0 Release Candidate Updates
-///////////////////////////////////////
-
-
-Version 1.0.0rc12
-==================
-
-Xarray engine changes
--------------------------
-
-- Four new built-in dimensions were added to the Xarray engine (:pr:`1008`):
-
-    - chem_variable
-    - wavelength
-    - wave_direction
-    - wave_frequency
-
-  See :ref:`xr_dim` and the following examples for more details:
-
-    - :ref:`/how-tos/xr_engine/xarray_engine_chem.ipynb`
-    - :ref:`/how-tos/xr_engine/xarray_engine_wave_spectra.ipynb`
-
-- Represented the ``_earthkit`` attribute as a JSON string instead of a dict  (:pr:`1021`). With this Xarrays generated by earthkit-data can be serialised into NetCDF without removing the ``_earthkit`` attribute.
-
-Other changes
--------------------------
-
-- Added more keys to the ``parameter`` component of a Field and replaced the existing  ``chem_variable`` key with ``chem`` (:pr:`1008`).  The following keys were added:
-
-    .. code-block:: python
-
-        chem: Optional[str] (replaces chem_variable)
-        chem_long_name : Optional[str]
-        wavelength: Optional[Union[int, float]]
-        wavelength_bounds: Optional[Union[tuple[int, int], tuple[float, float]]]
-        wavelength_units: Optional["Units"]
-        wave_direction: Optional[float]
-        wave_direction_index: Optional[int]
-        wave_direction_bounds: Optional[tuple[float, float]]
-        wave_direction_units: Optional["Units"]
-        wave_frequency: Optional[float]
-        wave_frequency_index: Optional[int]
-        wave_frequency_bounds: Optional[tuple[float, float]]
-        wave_frequency_units: Optional["Units"]
-
-
-
-- Added support for hybrid level coefficients in the ``vertical`` component of Field (:pr:`1016`).  The following keys were added:
-
-   - number_of_levels
-   - coefficients
-   - coefficient_names
-   - parametric
-
-
-Version 1.0.0rc11
-==================
-
-Xarray engine changes
--------------------------
-
-- Added the ``reference_field`` property to the Xarray ``earthkit`` accessor to allow getting
-  the reference field of the dataarray. Also added the ``set`` method to allow setting the
-  reference field of the dataarray using a Field or any args/kwargs accepted by :func:`~earthkit.data.core.field.Field.set`. :pr:`990`
-- Allowed using null values in datetime related objects :pr:`999`
-- Fixed missing coordinate variable attributes in Xarray engine :pr:`1001`
-- Fixed grid consistency check in Xarray engine :pr:`1003`
-- Cleaned up within Xarray engine profiles and docs :pr:`1006`
-
-Other changes
--------------------------
-
-- Allowed using high level field metadata keys in the GribEncoder :pr:`997`
-- Ensured :attr:`~earthkit.data.core.field.Field.values` returns a copy :pr:`1009`
-- Ensured :func:`Field.geometry.grid_spec` returns a copy :pr:`1010`
-- Added ``sync=False`` keyword argument to :func:`~earthkit.data.core.field.Field.set` :pr:`1011`
-- Allowed using raw metadata keys in :func:`~earthkit.data.core.field.Field.set` :pr:`1012`
-
-
-Version 1.0.0rc10
-==================
-
-- Previously, when using the :ref:`data-sources-mars` source via the standalone MARS client the ``MARS_AUTO_SPLIT_BY_DATES="1"`` environment variable was set to enable splitting of MARS requests by date when needed. However, this approach has been removed and now users must set this environment variable themselves when needed. This change was made to give users more control over the behavior of MARS requests and to avoid potential issues with unintended splitting of requests. (:pr:`992`)
-
-
-Version 1.0.0rc7
-==================
-
-- Fixed issue when could not set data/metadata on fields created from other data than GRIB (:pr:`981`).
-
-
-Version 1.0.0rc6
-==================
-
-- Changed the earthkit grid spec attribute in the Xarray generated by the :ref:`Xarray engine <xr_engine>` from "ek_grid_spec" to "earthkit_grid_spec" (:pr:`979`).
-
-
-Version 1.0.0rc5
-==================
-
-- Ensured that the GRIB encoder sets single metadata keys with ``set()`` instead of ``set_multi()`` when there is only one metadata key to set (:pr:`977`).
-
-
-Version 1.0.0rc4
-==================
-
-- Replaced the ``time_dim_mode`` keyword argument with ``time dims`` in the :ref:`Xarray engine <xr_engine>` (:pr:`967`).
-- Added the ``aux_coords`` keyword argument in the :ref:`Xarray engine <xr_engine>` to specify 1- or multi-dimensional auxiliary coordinate variables whose values are derived from any metadata key (:pr:`969`).
-- Changed how GRIB data is handled in fields created from GRIB data when the field metadata (related to the field components) is modified (:pr:`968`). GRIB related metadata keys are now not available on the modified field. Added the :func:`~earthkit.data.core.field.Field.sync` method to adjust the low level GRIB data to the field metadata. Having called this method the GRIB data is updated to be consistent with the field metadata, and the GRIB related metadata keys are available again on the field.
-- Added the ``standard_name`` and ``long_name`` metadata keys to the field parameter component (:pr:`971`).
-
-Version 1.0.0rc3
-==================
-
-- Fixed issue when setting time on a Field failed
-
-
-Version 1.0.0rc2
-==================
-
-Changes
--------
-
-- Changed the default value for the Field ``ensemble.member`` metadata from "0" to None. It is used when the Field does not contain any ensemble member information. (:pr:`960`).
-
-Fixes
--------
-
-- Fixed issue when the geography for GRIB data on ORCA and ICON grids was not correctly handled (:pr:`961`).
-
-
-
-Version 1.0.0rc0
-==================
-
-This version is a release candidate for the upcoming 1.0.0 release. It includes a very large number of changes, including new and removed features. Please see the :ref:`migration_1.0.0` for more details on the changes and how to update your code to be compatible with 1.0.0.
diff --git a/docs/source/release-notes/version_1.0_updates.rst b/docs/source/release-notes/version_1.0_updates.rst
new file mode 100644
index 000000000..f320b0ed6
--- /dev/null
+++ b/docs/source/release-notes/version_1.0_updates.rst
@@ -0,0 +1,151 @@
+.. _release-notes-1.0:
+
+Version 1.0 Updates
+///////////////////////
+
+.. note::
+
+    This release contains breaking changes. Please refer to the
+    :ref:`migration guide <migration_1.0.0>` for a full description of what has changed
+    and how to update your code.
+
+
+Version 1.0.0
+==============
+
+New features
+++++++++++++
+
+Data object returned by ``from_source``
+----------------------------------------
+
+:py:func:`~earthkit.data.from_source` now returns a lightweight *data object* instead of
+a format-specific fieldlist. The data object defers actual loading until it is converted
+to a concrete representation via one of the ``to_*`` methods (e.g.
+:meth:`~earthkit.data.Data.to_fieldlist`, :meth:`~earthkit.data.Data.to_xarray`,
+:meth:`~earthkit.data.Data.to_numpy`). The available conversions for a given source can be
+inspected via :py:attr:`~earthkit.data.Data.available_types`.
+
+See :ref:`migration_1.0.0` for migration details.
+
+Notebook examples:
+
+- :ref:`/tutorials/source/data.ipynb`
+
+
+Component-based Field metadata
+-------------------------------
+
+:py:class:`~earthkit.data.core.field.Field` has been redesigned around a set of
+format-independent, polymorphic *components*, each responsible for a distinct aspect of
+the field's metadata:
+
+- :ref:`parameter_component`     — physical quantity, units, CF names; extended with chemical, optical, and wave-spectra sub-types.
+- :ref:`time_component`     — base datetime, forecast step, valid datetime.
+- :ref:`vertical_component` — level value, level type, layer bounds, parametric (hybrid) level coefficients.
+- :ref:`geography_component` — lat/lon arrays, bounding box, projection, grid type.
+- :ref:`ensemble_component` — ensemble member identifier.
+- :ref:`proc_component` — post-processing operations (e.g. accumulation type and time span).
+
+Components are accessed via field attributes (e.g. ``field.time``) or through the generic
+:meth:`~earthkit.data.core.field.Field.get` method using ``"component.key"`` strings. Raw
+source-native keys (e.g. GRIB ``shortName``) remain accessible via
+:meth:`~earthkit.data.core.field.Field.metadata` or with the ``"metadata."`` prefix in
+:meth:`~earthkit.data.core.field.Field.get`.
+
+See the :ref:`field_concept` page for full details.
+
+Notebook examples:
+
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_chem.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb`
+
+
+Field modification via ``set()``
+---------------------------------
+
+Fields can now be modified in a non-destructive way using
+:py:meth:`~earthkit.data.core.field.Field.set`. The method accepts a dictionary of
+``"component.key": value`` pairs (and/or a ``"values"`` entry) and returns a **new** field
+with the requested changes applied, leaving the original unchanged.
+
+See the how-to notebooks:
+
+- :ref:`/tutorials/grib/grib_modify_metadata.ipynb`
+- :ref:`/tutorials/grib/grib_modify_values.ipynb`
+
+
+Field and FieldList arithmetic
+--------------------------------
+
+Element-wise arithmetic operators (``+``, ``-``, ``*``, ``/``) are now supported directly
+on both :py:class:`~earthkit.data.core.field.Field` and
+:py:class:`~earthkit.data.core.fieldlist.FieldList` objects. Each operation returns a new
+object with the computed values; the metadata of the left-hand operand is preserved in the
+result.
+
+.. note::
+
+    The ``+`` operator no longer concatenates fieldlists. Use
+    :py:func:`~earthkit.data.concat` for concatenation instead.
+
+Notebook examples:
+
+- :ref:`/tutorials/field/field_overview.ipynb`
+- :ref:`/tutorials/field/field_arithmetic.ipynb`
+- :ref:`/tutorials/field/field_assign_constant_value.ipynb`
+- :ref:`/tutorials/grib/grib_overview.ipynb`
+
+
+Xarray engine
+-----------------------------------------
+
+The Xarray engine has been refactored and a new default profile ``earthkit`` has been
+added. This profile uses the format-independent component metadata keys to build dataset
+dimensions and coordinates, making it consistent across GRIB, NetCDF, and other sources.
+The legacy ``mars`` and ``grib`` profiles are retained.
+
+Other notable Xarray engine changes:
+
+- Four new built-in dimensions were added to support the extended parameter component
+  metadata (:pr:`1008`):
+
+  - ``chem_variable`` — chemical constituent or aerosol type
+  - ``wavelength`` — central wavelength for optical parameters
+  - ``wave_direction`` — wave propagation direction for 2-D wave spectra
+  - ``wave_frequency`` — wave frequency for 2-D wave spectra
+
+  CF-compliant attributes are set on the corresponding coordinate variables automatically.
+
+- The ``"number"`` dimension role has been renamed to ``"member"``.
+- The ``time_dim_mode`` kwarg has been replaced by ``time_dims``.
+- The ``_earthkit`` dataset attribute is now serialised as a JSON string, enabling
+  round-trip NetCDF serialisation without removing it  (:pr:`1021`).
+- Added the ``aux_coords`` keyword argument to specify 1- or multi-dimensional auxiliary coordinate variables whose values are derived from any metadata key (:pr:`969`).
+- Added the ``reference_field`` property to the Xarray ``earthkit`` accessor to allow getting
+  the reference field of the dataarray. Also added the ``set`` method to allow setting the
+  reference field of the dataarray using a Field or any args/kwargs accepted by :func:`~earthkit.data.core.field.Field.set` (:pr:`990`).
+- Allowed using null values in datetime related objects (:pr:`999`).
+
+See :ref:`xr_engine` for full documentation.
+
+Notebook examples:
+
+- :ref:`/tutorials/xr_engine/xarray_engine_overview.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_temporal.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_ensemble.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_chem.ipynb`
+- :ref:`/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb`
+
+
+Dependencies
++++++++++++++++
+
+Geography support for GRIB data — including access to latitudes, longitudes, grid
+specification, and other grid properties via the
+:py:class:`~earthkit.data.field.component.geography.GeographyBase` component — requires
+the `eccodes <https://github.com/ecmwf/eccodes>`_ binaries built with
+`eckit <https://github.com/ecmwf/eckit>`_ geo support. Without it, geography keys will
+return ``None`` or raise an error.
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diff --git a/docs/source/tutorials/field/field_arithmetic.ipynb b/docs/source/tutorials/field/field_arithmetic.ipynb
new file mode 100644
index 000000000..d0677ddf2
--- /dev/null
+++ b/docs/source/tutorials/field/field_arithmetic.ipynb
@@ -0,0 +1,687 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e0c215b3",
+   "metadata": {},
+   "source": [
+    "# Field arithmetic\n",
+    "\n",
+    "earthkit-data supports standard arithmetic operators directly on `Field` and `FieldList` objects.\n",
+    "Operations produce new objects and never modify their inputs.\n",
+    "\n",
+    "The table below lists the supported operators.\n",
+    "\n",
+    "| Operator | Example | Description |\n",
+    "|----------|---------|-------------|\n",
+    "| `+` | `f + g`, `f + 2` | Addition |\n",
+    "| `-` | `f - g`, `f - 2` | Subtraction |\n",
+    "| `*` | `f * g`, `f * 2` | Multiplication |\n",
+    "| `/` | `f / g`, `f / 2` | True division |\n",
+    "| `//` | `f // g`, `f // 2` | Floor division |\n",
+    "| `%` | `f % g`, `f % 2` | Modulo |\n",
+    "| `**` | `f ** 2` | Power |\n",
+    "| `-f` | `-f` | Unary negation |\n",
+    "| `>`, `<`, `>=`, `<=`, `!=` | `f > 270` | Comparison (returns boolean array field) |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e9b4222e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import earthkit.data as ekd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff4ff3a",
+   "metadata": {},
+   "source": [
+    "## Load data\n",
+    "\n",
+    "Load a multi-field GRIB file so we have several fields to work with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6cc528a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "910c1c6d52984abb8c5dbb5cf6e07096",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tuv_pl.grib:   0%|          | 0.00/4.22k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = ekd.from_source(\"sample\", \"tuv_pl.grib\").to_fieldlist()\n",
+    "len(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "19ea2a30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (7, 12)\n",
+      "Level: 1000\n",
+      "Param: t\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract individual fields\n",
+    "t_fields = ds.sel({\"parameter.variable\": \"t\"})  # temperature at all levels\n",
+    "u_fields = ds.sel({\"parameter.variable\": \"u\"})  # u-component of wind\n",
+    "v_fields = ds.sel({\"parameter.variable\": \"v\"})  # v-component of wind\n",
+    "\n",
+    "t = t_fields[0]  # single temperature field\n",
+    "u = u_fields[0]  # single u-wind field\n",
+    "v = v_fields[0]  # single v-wind field\n",
+    "\n",
+    "print(\"Shape:\", t.geography.shape())\n",
+    "print(\"Level:\", t.vertical.level())\n",
+    "print(\"Param:\", t.parameter.variable())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bce7550",
+   "metadata": {},
+   "source": [
+    "## Scalar arithmetic\n",
+    "\n",
+    "Arithmetic with a scalar operates element-wise on every grid point.\n",
+    "The result is a new `Field` whose metadata is inherited from the original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "af416200",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(K)  mean: 279.71\n",
+      "T(°C) mean: 6.56\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert temperature from Kelvin to Celsius\n",
+    "t_celsius = t - 273.15\n",
+    "print(f\"T(K)  mean: {t.values.mean():.2f}\")\n",
+    "print(f\"T(°C) mean: {t_celsius.values.mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "725a7158",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scaled mean: 239.85\n",
+      "scaled mean: 239.85\n",
+      "squared mean: 78623.10\n",
+      "negative mean: -279.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling and offset\n",
+    "t_scaled = t * 0.5 + 100\n",
+    "print(f\"scaled mean: {t_scaled.values.mean():.2f}\")\n",
+    "\n",
+    "# Field as a right hand side operand\n",
+    "t_scaled = 0.5 * t + 100\n",
+    "print(f\"scaled mean: {t_scaled.values.mean():.2f}\")\n",
+    "\n",
+    "# Power\n",
+    "t_sq = t**2\n",
+    "print(f\"squared mean: {t_sq.values.mean():.2f}\")\n",
+    "\n",
+    "# Unary negation\n",
+    "t_neg = -t\n",
+    "np.testing.assert_allclose(t_neg.values, -t.values)\n",
+    "print(f\"negative mean: {t_neg.values.mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0d649f2",
+   "metadata": {},
+   "source": [
+    "## Field–field arithmetic\n",
+    "\n",
+    "When both operands are `Field` objects with the same grid, operations are applied point-wise.\n",
+    "A common example is computing wind speed from its components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0d5bb8a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "u mean:          -0.38 m/s\n",
+      "v mean:          -0.07 m/s\n",
+      "wind speed mean: 7.45 m/s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wind speed: sqrt(u^2 + v^2)\n",
+    "wind_speed = (u**2 + v**2) ** 0.5\n",
+    "print(f\"u mean:          {u.values.mean():.2f} m/s\")\n",
+    "print(f\"v mean:          {v.values.mean():.2f} m/s\")\n",
+    "print(f\"wind speed mean: {wind_speed.values.mean():.2f} m/s\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9683f941",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T@850 mean: 272.73 K\n",
+      "T@500 mean: 252.89 K\n",
+      "diff  mean: 19.84 K\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Difference between two fields (e.g. temperature anomaly relative to first level)\n",
+    "t_850 = t_fields.sel({\"vertical.level\": 850})[0]\n",
+    "t_500 = t_fields.sel({\"vertical.level\": 500})[0]\n",
+    "thickness = t_850 - t_500\n",
+    "print(f\"T@850 mean: {t_850.values.mean():.2f} K\")\n",
+    "print(f\"T@500 mean: {t_500.values.mean():.2f} K\")\n",
+    "print(f\"diff  mean: {thickness.values.mean():.2f} K\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "588a6072",
+   "metadata": {},
+   "source": [
+    "## FieldList arithmetic\n",
+    "\n",
+    "Arithmetic between two `FieldList` objects applies the operation element-wise across\n",
+    "corresponding fields (i.e. field 0 op field 0, field 1 op field 1, …).\n",
+    "The two fieldlists must have the same length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c9ec4047",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original length: 6, result length: 6\n",
+      "  level 1000:  279.71 K  ->  6.56 °C\n",
+      "  level  850:  272.73 K  ->  -0.42 °C\n",
+      "  level  700:  265.17 K  ->  -7.98 °C\n",
+      "  level  500:  252.89 K  ->  -20.26 °C\n",
+      "  level  400:  241.53 K  ->  -31.62 °C\n",
+      "  level  300:  227.84 K  ->  -45.31 °C\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert all temperature fields from K to °C at once\n",
+    "t_celsius_all = t_fields - 273.15\n",
+    "print(f\"Original length: {len(t_fields)}, result length: {len(t_celsius_all)}\")\n",
+    "for i, (orig, conv) in enumerate(zip(t_fields, t_celsius_all)):\n",
+    "    print(f\"  level {orig.vertical.level():4d}:  {orig.values.mean():.2f} K  ->  {conv.values.mean():.2f} °C\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a85ef9d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wind speed at each level (mean m/s):\n",
+      "  level 1000: 7.45\n",
+      "  level  850: 9.14\n",
+      "  level  700: 9.22\n",
+      "  level  500: 12.39\n",
+      "  level  400: 15.83\n",
+      "  level  300: 19.13\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Element-wise wind speed across all levels\n",
+    "wind_speed_all = (u_fields**2 + v_fields**2) ** 0.5\n",
+    "print(\"Wind speed at each level (mean m/s):\")\n",
+    "for f in wind_speed_all:\n",
+    "    print(f\"  level {f.vertical.level():4d}: {f.values.mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96334c8e",
+   "metadata": {},
+   "source": [
+    "## Comparison operators\n",
+    "\n",
+    "Comparison operators return a new `Field` containing boolean (0/1) values, which is\n",
+    "useful as a mask."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9681210c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fraction of grid points above 0°C: 58.33%\n",
+      "Masked field min: 0.00 K  (should be 0 where frozen)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find grid points above freezing\n",
+    "above_freezing = t > 273.15\n",
+    "fraction = above_freezing.values.mean()\n",
+    "print(f\"Fraction of grid points above 0°C: {fraction:.2%}\")\n",
+    "\n",
+    "# Use as a mask: zero out sub-freezing temperatures\n",
+    "t_warm = t * (t > 273.15)\n",
+    "print(f\"Masked field min: {t_warm.values.min():.2f} K  (should be 0 where frozen)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fca23ac0-3b19-4947-868b-68a70b515fce",
+   "metadata": {},
+   "source": [
+    "## Using numpy ufuncs"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "065f4e13",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "`numpy` universal functions work transparently on `Field` objects via :py:meth:`earthkit.data.utils.compute.apply_ufunc`.\n",
+    "You can also extract values with ``field.values``, apply any numpy operation, and reconstruct\n",
+    "a field with ``field.set(values=...)``."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "970604f8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "log(T) mean: 5.6312\n",
+      "param: t, level: 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Using values directly with numpy, then setting back\n",
+    "log_t = t.set(values=np.log(t.values))\n",
+    "print(f\"log(T) mean: {log_t.values.mean():.4f}\")\n",
+    "\n",
+    "# Verify the metadata is preserved\n",
+    "print(f\"param: {log_t.parameter.variable()}, level: {log_t.vertical.level()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f5ac34e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## apply_ufunc\n",
+    "\n",
+    "`apply_ufunc` is earthkit-data's mechanism for applying arbitrary callables — including\n",
+    "numpy ufuncs and custom functions — directly to `Field` and `FieldList` objects without\n",
+    "manually extracting and re-inserting values.\n",
+    "\n",
+    "```python\n",
+    "from earthkit.data.utils.compute import apply_ufunc\n",
+    "\n",
+    "result = apply_ufunc(func, *args)\n",
+    "```\n",
+    "\n",
+    "- `func` receives plain numpy arrays and must return a numpy array.\n",
+    "- `args` can be `Field`, `FieldList`, scalar, or numpy array objects.\n",
+    "- earthkit-data iterates over matching fields, calls `func` on their value arrays, and\n",
+    "  wraps each result back into a `Field`, copying metadata from the first `FieldList` argument.\n",
+    "- If one `FieldList` has length 1, it is broadcast across all fields of the other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "61e1b404",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from earthkit.data.utils.compute import apply_ufunc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2af6a7ec",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Unary functions\n",
+    "\n",
+    "Pass a single `Field` or `FieldList` to apply any unary callable across every field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1f005595",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sin(T) fields: 6\n",
+      "  level 1000: sin(T) mean = 0.398520\n",
+      "  level  850: sin(T) mean = -0.029065\n",
+      "  level  700: sin(T) mean = 0.161524\n",
+      "  level  500: sin(T) mean = -0.067683\n",
+      "  level  400: sin(T) mean = -0.096225\n",
+      "  level  300: sin(T) mean = -0.189231\n"
+     ]
+    }
+   ],
+   "source": [
+    "# np.sin applied to all temperature fields in one call\n",
+    "t_sin = apply_ufunc(np.sin, t_fields)\n",
+    "print(f\"sin(T) fields: {len(t_sin)}\")\n",
+    "for orig, result in zip(t_fields, t_sin):\n",
+    "    print(f\"  level {orig.vertical.level():4d}: sin(T) mean = {result.values.mean():.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "4b617af1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  level 1000: original [240.6, 320.6] K  ->  clipped  [250.0, 310.0] K\n",
+      "  level  850: original [232.5, 304.5] K  ->  clipped  [250.0, 304.5] K\n",
+      "  level  700: original [223.3, 287.3] K  ->  clipped  [250.0, 287.3] K\n",
+      "  level  500: original [227.8, 271.8] K  ->  clipped  [250.0, 271.8] K\n",
+      "  level  400: original [216.0, 264.0] K  ->  clipped  [250.0, 264.0] K\n",
+      "  level  300: original [206.7, 250.7] K  ->  clipped  [250.0, 250.7] K\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Custom unary function — clip temperatures to [250, 310] K\n",
+    "def clip_temp(arr):\n",
+    "    return np.clip(arr, 250.0, 310.0)\n",
+    "\n",
+    "\n",
+    "t_clipped = apply_ufunc(clip_temp, t_fields)\n",
+    "for orig, clipped in zip(t_fields, t_clipped):\n",
+    "    print(\n",
+    "        f\"  level {orig.vertical.level():4d}: \"\n",
+    "        f\"original [{orig.values.min():.1f}, {orig.values.max():.1f}] K  ->  \"\n",
+    "        f\"clipped  [{clipped.values.min():.1f}, {clipped.values.max():.1f}] K\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2566847",
+   "metadata": {},
+   "source": [
+    "### Binary functions\n",
+    "\n",
+    "Pass two `Field` / `FieldList` arguments for element-wise binary operations.\n",
+    "`np.hypot(u, v)` computes wind speed more precisely than `(u**2 + v**2)**0.5`\n",
+    "because it avoids intermediate overflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0e20f178",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wind speed comparison (hypot vs manual):\n",
+      "  level 1000: hypot=7.4502  manual=7.4502\n",
+      "  level  850: hypot=9.1436  manual=9.1436\n",
+      "  level  700: hypot=9.2167  manual=9.2167\n",
+      "  level  500: hypot=12.3901  manual=12.3901\n",
+      "  level  400: hypot=15.8330  manual=15.8330\n",
+      "  level  300: hypot=19.1302  manual=19.1302\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wind speed via np.hypot — avoids overflow for large values\n",
+    "wind_hypot = apply_ufunc(np.hypot, u_fields, v_fields)\n",
+    "wind_manual = (u_fields**2 + v_fields**2) ** 0.5\n",
+    "\n",
+    "print(\"Wind speed comparison (hypot vs manual):\")\n",
+    "for h, m in zip(wind_hypot, wind_manual):\n",
+    "    print(f\"  level {h.vertical.level():4d}: hypot={h.values.mean():.4f}  manual={m.values.mean():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "752a9f06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wind direction at each level (mean degrees from N):\n",
+      "  level 1000: 162.6°\n",
+      "  level  850: 166.3°\n",
+      "  level  700: 184.6°\n",
+      "  level  500: 241.0°\n",
+      "  level  400: 189.3°\n",
+      "  level  300: 250.3°\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Custom binary function — wind direction (meteorological convention, degrees from N)\n",
+    "def wind_direction(u_arr, v_arr):\n",
+    "    # atan2 gives mathematical angle; convert to met convention (0=N, clockwise)\n",
+    "    return (270.0 - np.degrees(np.arctan2(v_arr, u_arr))) % 360.0\n",
+    "\n",
+    "\n",
+    "wdir = apply_ufunc(wind_direction, u_fields, v_fields)\n",
+    "print(\"Wind direction at each level (mean degrees from N):\")\n",
+    "for f in wdir:\n",
+    "    print(f\"  level {f.vertical.level():4d}: {f.values.mean():.1f}°\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5312366",
+   "metadata": {},
+   "source": [
+    "### Broadcasting a single field across a FieldList\n",
+    "\n",
+    "When one argument has length 1 it is broadcast against every field in the other argument.\n",
+    "Here we subtract the 1000 hPa temperature from every level to produce a temperature anomaly profile."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "4ff7cd78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reference field count: 1, all-level count: 6\n",
+      "\n",
+      "Temperature anomaly relative to 1000 hPa (mean K):\n",
+      "  level 1000: +0.00 K\n",
+      "  level  850: -6.98 K\n",
+      "  level  700: -14.54 K\n",
+      "  level  500: -26.82 K\n",
+      "  level  400: -38.18 K\n",
+      "  level  300: -51.86 K\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_1000 = t_fields.sel({\"vertical.level\": 1000})  # single-field FieldList (length 1)\n",
+    "print(f\"Reference field count: {len(t_1000)}, all-level count: {len(t_fields)}\")\n",
+    "\n",
+    "# Broadcast: subtract t_1000 from every level\n",
+    "t_anomaly = apply_ufunc(lambda a, b: a - b, t_fields, t_1000)\n",
+    "\n",
+    "print(\"\\nTemperature anomaly relative to 1000 hPa (mean K):\")\n",
+    "for orig, anom in zip(t_fields, t_anomaly):\n",
+    "    print(f\"  level {orig.vertical.level():4d}: {anom.values.mean():+.2f} K\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cffb151-11da-40c9-baa8-c5c8599a029a",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials/field/field_assign_constant_value.ipynb b/docs/source/tutorials/field/field_assign_constant_value.ipynb
new file mode 100644
index 000000000..33d206865
--- /dev/null
+++ b/docs/source/tutorials/field/field_assign_constant_value.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4b8c8eb9",
+   "metadata": {},
+   "source": [
+    "# Assigning a constant value\n",
+    "\n",
+    "A quick way to produce a field where every grid point holds the same constant is\n",
+    "the `* 0 + c` idiom:\n",
+    "\n",
+    "```python\n",
+    "const_field = f * 0 + c\n",
+    "```\n",
+    "\n",
+    "Multiplying by `0` zeros out all values while returning a new `Field` with the same\n",
+    "metadata as `f`. Adding `c` then sets every grid point to that constant. The original\n",
+    "field is never modified.\n",
+    "\n",
+    "This is useful for creating reference fields, filling missing data, or building mask\n",
+    "boundaries — all while preserving the grid geometry, parameter, and time metadata of\n",
+    "an existing field."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9784cbcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthkit.data as ekd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbed02fc",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "64984175",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6e287baeecb43a6816fe069f1775d13",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tuv_pl.grib:   0%|          | 0.00/4.22k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "param: t, level: 1000, shape: (7, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = ekd.from_source(\"sample\", \"tuv_pl.grib\").to_fieldlist()\n",
+    "t_fields = ds.sel({\"parameter.variable\": \"t\"})\n",
+    "t = t_fields[0]\n",
+    "print(f\"param: {t.parameter.variable()}, level: {t.vertical.level()}, shape: {t.geography.shape()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "496ef41f",
+   "metadata": {},
+   "source": [
+    "## Single field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7eed124e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min:  273.15 K\n",
+      "max:  273.15 K\n",
+      "mean: 273.15 K\n",
+      "\n",
+      "param: t\n",
+      "level: 1000\n",
+      "shape: (7, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Fill every grid point with 273.15 K (0 °C), keeping t's metadata\n",
+    "freezing = t * 0 + 273.15\n",
+    "\n",
+    "print(f\"min:  {freezing.values.min():.2f} K\")\n",
+    "print(f\"max:  {freezing.values.max():.2f} K\")\n",
+    "print(f\"mean: {freezing.values.mean():.2f} K\")\n",
+    "\n",
+    "# Metadata is inherited from t\n",
+    "print(f\"\\nparam: {freezing.parameter.variable()}\")\n",
+    "print(f\"level: {freezing.vertical.level()}\")\n",
+    "print(f\"shape: {freezing.geography.shape()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f9da0cb",
+   "metadata": {},
+   "source": [
+    "## FieldList"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "86a3861d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Constant zero C FieldList:\n",
+      "  level 1000: all values = 273.16\n",
+      "  level  850: all values = 273.16\n",
+      "  level  700: all values = 273.16\n",
+      "  level  500: all values = 273.16\n",
+      "  level  400: all values = 273.16\n",
+      "  level  300: all values = 273.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply to a whole FieldList — every field gets the same constant\n",
+    "const_all = t_fields * 0 + 273.16\n",
+    "print(\"Constant zero C FieldList:\")\n",
+    "for f in const_all:\n",
+    "    print(f\"  level {f.vertical.level():4d}: all values = {f.values.mean():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db639466-41fa-4b41-a863-9e5cdec0dbe2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials/field/field_overview.ipynb b/docs/source/tutorials/field/field_overview.ipynb
new file mode 100644
index 000000000..96b95918e
--- /dev/null
+++ b/docs/source/tutorials/field/field_overview.ipynb
@@ -0,0 +1,806 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fffb922f-f8d2-45a5-b1ff-4ca9daeb63b6",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Field overview\n",
+    "\n",
+    "A `Field` represents a single horizontal slice of a geophysical quantity at a particular time and vertical level. It bundles together the **data values** and a set of replaceable **metadata components**, each responsible for a distinct aspect of the metadata:\n",
+    "\n",
+    "| Attribute | What it describes |\n",
+    "|---|---|\n",
+    "| `field.parameter` | Physical quantity: variable name, units, CF names |\n",
+    "| `field.time` | Temporal coordinate: base datetime, forecast step, valid datetime |\n",
+    "| `field.vertical` | Vertical coordinate: level value, level type |\n",
+    "| `field.geography` | Horizontal grid: lat/lon arrays, bounding box, projection |\n",
+    "| `field.ensemble` | Ensemble member identifier |\n",
+    "| `field.proc` |Post-processing operations (e.g. accumulation). Experimental.|"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "c02b487b-6f85-4938-b54a-dc3e6543b84c",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "All component keys are also accessible through the generic py:meth:`~earthkit.data.core.field.Field.get` interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3e75c1c-2dfa-4936-b8cb-2029dd0b3fc3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c7f4e186-4f1e-437e-a27b-ef8f53cbd659",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthkit.data as ekd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7926fb1b-d9d1-4f7e-8807-5c0420da25d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "965cc32eb45e4b39aa99c918664216f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "test.grib:   0%|          | 0.00/1.03k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style><div class=\"eh-description\">GRIB file</div><hr>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>path</b></td><td>/var/folders/93/w0p869rx17q98wxk83gn9ys40000gn/T/tmp7di2ty1r/url-3d2bc4976d3b9f59146160e41a1ba98175e42d42e500f37b9d97e62e3bbe41fd.grib</td></tr> <tr><td><b>size</b></td><td>1 KiB</td></tr> <tr><td><b>types</b></td><td>fieldlist, pandas, xarray, numpy, array</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "GribData(path=/var/folders/93/w0p869rx17q98wxk83gn9ys40000gn/T/tmp7di2ty1r/url-3d2bc4976d3b9f59146160e41a1ba98175e42d42e500f37b9d97e62e3bbe41fd.grib)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = ekd.from_source(\"sample\", \"test.grib\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b6c10045-d894-4ebd-9e8d-82f99af3ae19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "     display: block;\n",
+       " }\n",
+       "\n",
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+       "    color: var(--xr-font-color0);\n",
+       " }\n",
+       "\n",
+       "\n",
+       " @media (min-width: 768px) {\n",
+       "\n",
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+       "\n",
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+       "\n",
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+       "\n",
+       "     .eh-tabs [type=radio]:checked + label {\n",
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+       " }\n",
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+       "\n",
+       "     .eh-tabs {\n",
+       "         width: 800px;\n",
+       "     }\n",
+       " }\n",
+       "</style><div class=\"eh-description\">Field</div>\n",
+       "<div>\n",
+       "<section class=\"eh-section\">\n",
+       "<div class=\"eh-tabs-container\">\n",
+       "<div class=\"eh-tabs-block\">\n",
+       "<div class=\"eh-tabs\">\n",
+       "\n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"eb4fd164-7393-47f1-ac8a-a042747ca9cb\"  />\n",
+       "<label for=\"eb4fd164-7393-47f1-ac8a-a042747ca9cb\" title=\"Keys in the data namespace\">data</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>number_of_values</b></td><td>209</td></tr> <tr><td><b>array_type</b></td><td>ndarray</td></tr> <tr><td><b>array_dtype</b></td><td>float64</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "     \n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"253489a4-8d17-404b-905d-bc519e801f28\" checked />\n",
+       "<label for=\"253489a4-8d17-404b-905d-bc519e801f28\" title=\"Keys in the parameter namespace\">parameter</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
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+       "\n",
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+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>variable</b></td><td>2t</td></tr> <tr><td><b>standard_name</b></td><td>unknown</td></tr> <tr><td><b>long_name</b></td><td>2 metre temperature</td></tr> <tr><td><b>units</b></td><td>kelvin</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "     \n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"84300d80-675a-46e2-b0b6-efb15fe40427\"  />\n",
+       "<label for=\"84300d80-675a-46e2-b0b6-efb15fe40427\" title=\"Keys in the time namespace\">time</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>valid_datetime</b></td><td>2020-05-13 12:00:00</td></tr> <tr><td><b>base_datetime</b></td><td>2020-05-13 12:00:00</td></tr> <tr><td><b>step</b></td><td>0:00:00</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "     \n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"c319fbb9-a82c-429c-8122-e5ddf5936268\"  />\n",
+       "<label for=\"c319fbb9-a82c-429c-8122-e5ddf5936268\" title=\"Keys in the vertical namespace\">vertical</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>level</b></td><td>0</td></tr> <tr><td><b>layer</b></td><td>None</td></tr> <tr><td><b>level_type</b></td><td>surface</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "     \n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"b322da27-9191-410b-90a6-b94910f45afa\"  />\n",
+       "<label for=\"b322da27-9191-410b-90a6-b94910f45afa\" title=\"Keys in the ensemble namespace\">ensemble</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>member</b></td><td>0</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "     \n",
+       "<input type=\"radio\" name=\"eh-tabs\" id=\"c0402ae1-2d6c-491d-84ad-aa40a7db27f5\"  />\n",
+       "<label for=\"c0402ae1-2d6c-491d-84ad-aa40a7db27f5\" title=\"Keys in the geography namespace\">geography</label>\n",
+       "<div class=\"tab\">\n",
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>grid_spec</b></td><td>{'area': [73, -27, 33, 45], 'grid': [4, 4]}</td></tr> <tr><td><b>grid_type</b></td><td>regular_ll</td></tr> <tr><td><b>shape</b></td><td>(11, 19)</td></tr> <tr><td><b>area</b></td><td>(73.0, -27.0, 33.0, 45.0)</td></tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "    \n",
+       "</div>\n",
+       "</div>\n",
+       "</div>\n",
+       "</section>\n",
+       "</div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Field(2t, 2020-05-13 12:00:00, 2020-05-13 12:00:00, 0:00:00, 0, surface, 0, regular_ll)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = ds.to_fieldlist()[0]\n",
+    "f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "178172f9-9f9b-4d0f-ba3c-882999182df7",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Parameter component"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "fbd72d33-9b21-467d-a36c-93e6255ff665",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :ref:`parameter_component` describes the physical quantity the field represents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "74a157d8-c38b-4149-94d7-d56d77509a7d",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2t\n",
+      "kelvin\n",
+      "unknown\n",
+      "2 metre temperature\n",
+      "2t\n",
+      "kelvin\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f.parameter.variable())  # short variable name\n",
+    "print(f.parameter.units())  # native units\n",
+    "print(f.parameter.standard_name())  # CF standard name\n",
+    "print(f.parameter.long_name())  # human-readable description\n",
+    "\n",
+    "# Same information via the generic get() interface\n",
+    "print(f.get(\"parameter.variable\"))\n",
+    "print(f.get(\"parameter.units\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d79a63e1-0d3b-45df-b940-e5091e006149",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Time component"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "7bae9ccc-9b74-470b-8cd4-1d8b104a1b2e",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :ref:`time_component` describes the temporal coordinate: base datetime, forecast step, and valid datetime."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8172a231-8ee3-4d38-94dc-4148e526cf1e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2020-05-13 12:00:00\n",
+      "0:00:00\n",
+      "2020-05-13 12:00:00\n",
+      "2020-05-13 12:00:00\n",
+      "0:00:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f.time.base_datetime())  # analysis/reference time\n",
+    "print(f.time.step())  # forecast step as timedelta\n",
+    "print(f.time.valid_datetime())  # base_datetime + step\n",
+    "\n",
+    "# Same information via get()\n",
+    "print(f.get(\"time.base_datetime\"))\n",
+    "print(f.get(\"time.step\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccd5f187-4269-4cf8-8ade-a0b6840b7040",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Vertical component"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "4a2bc49f-5633-455b-9382-136a992cfeac",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :ref:`vertical_component` describes where in the atmosphere, ocean, or land the field is defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "06d2ba2b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "surface\n",
+      "dimensionless\n",
+      "None\n",
+      "0\n",
+      "surface\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f.vertical.level())  # level value in native units\n",
+    "print(f.vertical.level_type())  # e.g. \"surface\", \"pressure\", \"hybrid\"\n",
+    "print(f.vertical.units())  # units of the level value\n",
+    "print(f.vertical.positive())  # \"up\" or \"down\"\n",
+    "\n",
+    "# Same information via get()\n",
+    "print(f.get(\"vertical.level\"))\n",
+    "print(f.get(\"vertical.level_type\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74cfaa8b-875f-4763-b368-051f09ae6c7e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Geography component"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8a24da3e",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :ref:`geography_component` describes the horizontal grid: shape, bounding box, projection, and coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5252c2e2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(11, 19)\n",
+      "(73.0, -27.0, 33.0, 45.0)\n",
+      "regular_ll\n",
+      "(11, 19) (11, 19)\n",
+      "(11, 19)\n",
+      "(73.0, -27.0, 33.0, 45.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f.geography.shape())  # grid dimensions\n",
+    "print(f.geography.area())  # bounding box (north, west, south, east)\n",
+    "print(f.geography.grid_type())  # e.g. \"regular_ll\", \"reduced_gg\"\n",
+    "\n",
+    "# Retrieve all lat/lon coordinates for every grid point\n",
+    "lat, lon = f.geography.latlons()\n",
+    "print(lat.shape, lon.shape)\n",
+    "\n",
+    "# Same information via get()\n",
+    "print(f.get(\"geography.shape\"))\n",
+    "print(f.get(\"geography.area\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11404179-247d-49bd-9c68-8de406076489",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Data values"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "9af15fae",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :py:attr:`~earthkit.data.core.field.Field.values` returns a **copy** of the underlying flat 1-D array. Use :py:meth:`~earthkit.data.core.field.Field.to_numpy` to get an array shaped to the grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c64ccb51",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(209,) float64\n",
+      "min=262.78  max=315.46\n",
+      "(11, 19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "v = f.values  # flat 1-D copy\n",
+    "print(v.shape, v.dtype)\n",
+    "print(f\"min={v.min():.2f}  max={v.max():.2f}\")\n",
+    "\n",
+    "v2d = f.to_numpy()  # shaped to (Nj, Ni)\n",
+    "print(v2d.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f018c83-8b91-4b31-af4f-c3adfb7061d0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Modifying a field"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "2cb0fef7",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/x-rst",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "All components are **immutable**. Use :py:meth:`~earthkit.data.core.field.Field.set` to derive a modified copy. The original field is never changed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "862cc511",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:00:00 -> 6:00:00\n",
+      "2t -> msl\n",
+      "0 -> 500\n",
+      "original mean=283.99  modified mean=293.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Change the forecast step\n",
+    "new_f = f.set({\"time.step\": 6})\n",
+    "print(f.time.step(), \"->\", new_f.time.step())\n",
+    "\n",
+    "# Change the parameter variable and units\n",
+    "new_f2 = f.set({\"parameter.variable\": \"msl\", \"parameter.units\": \"Pa\"})\n",
+    "print(f.parameter.variable(), \"->\", new_f2.parameter.variable())\n",
+    "\n",
+    "# Change the level\n",
+    "new_f3 = f.set({\"vertical.level\": 500, \"vertical.level_type\": \"pressure\"})\n",
+    "print(f.vertical.level(), \"->\", new_f3.vertical.level())\n",
+    "\n",
+    "# Replace the data values (must match the grid shape)\n",
+    "new_values = f.values + 10.0\n",
+    "new_f4 = f.set({\"values\": new_values})\n",
+    "print(f\"original mean={f.values.mean():.2f}  modified mean={new_f4.values.mean():.2f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/how-tos/field/index.rst b/docs/source/tutorials/field/index.rst
similarity index 60%
rename from docs/source/how-tos/field/index.rst
rename to docs/source/tutorials/field/index.rst
index ea931b3d7..0fd26b5d0 100644
--- a/docs/source/how-tos/field/index.rst
+++ b/docs/source/tutorials/field/index.rst
@@ -7,3 +7,5 @@ Fields
     :maxdepth: 1
 
     field_overview.ipynb
+    field_arithmetic.ipynb
+    field_assign_constant_value.ipynb
diff --git a/docs/source/tutorials/geojson/NUTS_RG_20M_2021_3035.geojson b/docs/source/tutorials/geojson/NUTS_RG_20M_2021_3035.geojson
new file mode 100644
index 000000000..15e9f2281
--- /dev/null
+++ b/docs/source/tutorials/geojson/NUTS_RG_20M_2021_3035.geojson
@@ -0,0 +1 @@
+{"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[[[[9954236.1162,-3059379.3164],[9961022.5883,-3052221.3084000004],[9962207.5849,-3043952.4389000004],[9966368.8737,-3042731.9552999996],[9981217.182999998,-3029077.4118999997],[10004105.814,-3022000.3822999997],[10011115.9914,-3029614.9675000003],[10026276.277600002,-3037765.7117999997],[10022438.8638,-3047511.6344999997],[10023263.545400001,-3054383.3331000004],[10019371.9826,-3059478.1809],[10004542.501200002,-3069371.0193999996],[9982971.773200002,-3075439.1478000004],[9968774.596500002,-3076354.2174000004],[9963428.860800002,-3074007.5577],[9962512.987,-3067589.0903000003],[9954236.1162,-3059379.3164]]],[[[8737673.169100001,-2781863.5176],[8730534.706999999,-2796912.1023000004],[8725005.334199999,-2798337.3146],[8727773.3464,-2787602.3579],[8722225.1893,-2785917.261],[8722328.1778,-2777185.2276],[8715127.6587,-2775650.1951],[8720954.6415,-2768056.2412],[8728685.4551,-2771597.0116999997],[8738366.936700001,-2769577.5140000004],[8737673.169100001,-2781863.5176]]],[[[4204641.590399999,2142132.0298999995],[4218982.039899999,2162323.2841],[4231319.703600001,2170252.6466000006],[4247173.9882,2178937.1284],[4262693.118899999,2179540.667199999],[4267956.9877,2203511.7971],[4272726.9737,2206568.5271000005],[4277160.856899999,2198929.6067999993],[4279529.9571,2169742.498400001],[4281636.9375,2144027.551999999],[4283217.6996,2124880.8871],[4275779.8531,2098088.0387999993],[4271177.4068,2084154.0124999993],[4269200.538699999,2063100.6881000008],[4266947.621200001,2057943.9748999998],[4254394.283600001,2033308.8670000006],[4222844.971999999,2050157.67],[4220326.791300001,2058514.3307000007],[4225270.7096,2063613.5944999997],[4210676.568700001,2094896.5561999995],[4204641.590399999,2142132.0298999995]]],[[[3767290.5777000003,3128769.7853999995],[3781813.0760999992,3131655.112299999],[3799789.3935000002,3135288.8575],[3800998.5786000006,3125219.9060999993],[3802065.476500001,3115282.907400001],[3803955.8708999995,3105280.812999999],[3817784.9420999996,3090853.9849999994],[3829391.7752,3097020.708799999],[3835046.9453999996,3097095.6535],[3840321.6635999996,3094043.8906999994],[3844678.012599999,3088813.8804],[3847376.6117000002,3067260.2842999995],[3863949.8621999994,3062907.0984000005],[3868536.232000001,3061788.467599999],[3871119.5032,3058361.0966],[3871760.4505000003,3051082.309699999],[3874198.5286999997,3043050.4312999994],[3896518.9996000007,3044671.3225],[3898139.7984999996,3042375.0494999997],[3906668.8485000003,3030336.4782],[3906801.6226000004,3012746.9146],[3901203.5385999996,3001917.1602],[3907618.9147999994,2999071.3099000007],[3921742.4297,2996168.7018],[3938651.0985000003,3000784.0792999994],[3949420.6549999993,3017233.0233999994],[3953226.993899999,3014546.5687000006],[3948981.2667999994,2997537.0343999993],[3952076.8312999997,2979219.8595000003],[3959123.5232999995,2977871.6417999994],[3971752.4340000004,2967666.1932999995],[3988312.6208999995,2955361.1799],[3993083.8806999996,2941704.5809000004],[4012456.508300001,2945942.9268999994],[4018497.2259,2945664.4036],[4023633.5628999993,2939874.9432999995],[4057554.6148000006,2933924.9442999996],[4071243.3954000007,2928770.9639],[4082335.1054,2905956.7978000008],[4086500.5523000006,2899317.7832999993],[4097015.840500001,2904764.181],[4109552.371200001,2897821.1248000003],[4119997.8747000005,2893175.6603999995],[4129088.6479,2897694.362400001],[4137313.7149,2896832.4646000005],[4148169.9397,2885085.8198000006],[4168238.4449000005,2883501.2454000004],[4179638.1773000006,2878090.5392000005],[4191568.4299,2874149.0747999996],[4170832.9875000007,2847105.0318],[4160390.073000001,2835147.0760999992],[4148737.4970999993,2796455.1335000005],[4140622.8365,2781605.5660999995],[4140555.6732,2780969.7829],[4141914.8444999997,2764471.1477000006],[4136889.5688000005,2739716.5875000004],[4134652.9635000005,2731311.9546000008],[4139577.4244,2722539.9049999993],[4136938.8966000006,2719812.1586000007],[4133383.940300001,2713039.7738000005],[4123319.4144,2705527.9356999993],[4111415.3851999994,2705785.8334],[4104716.988399999,2714128.7832999993],[4090009.1479,2707007.728700001],[4089614.7380999997,2700541.7173999995],[4095928.0726999994,2695439.0349000003],[4082730.7678999994,2677464.1921999995],[4051584.482999999,2653202.8627000004],[4050888.773,2644010.056500001],[4046573.5471,2633336.8390999995],[4024713.6202000007,2612631.9044000003],[4018233.3856000006,2597241.8249999993],[4024036.8116999995,2591969.0944999997],[4022416.092599999,2586011.9647000004],[4008290.6635999996,2566176.7631],[4030112.0298999995,2572259.6481],[4029619.3168,2585045.5176999997],[4052007.3910000008,2599217.4836999997],[4076472.880999999,2595665.6992000006],[4074932.2623999994,2565753.8264000006],[4091539.9584999997,2538951.4132000003],[4072064.4948999994,2523773.4279999994],[4085303.2194999997,2506413.6849000007],[4087227.483100001,2493525.136600001],[4094309.3320000004,2488413.1999999993],[40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diff --git a/docs/source/how-tos/geojson/geojson_geopandas.ipynb b/docs/source/tutorials/geojson/geojson_geopandas.ipynb
similarity index 100%
rename from docs/source/how-tos/geojson/geojson_geopandas.ipynb
rename to docs/source/tutorials/geojson/geojson_geopandas.ipynb
diff --git a/docs/source/how-tos/geojson/index.rst b/docs/source/tutorials/geojson/index.rst
similarity index 100%
rename from docs/source/how-tos/geojson/index.rst
rename to docs/source/tutorials/geojson/index.rst
diff --git a/docs/source/how-tos/geotiff/geotiff.ipynb b/docs/source/tutorials/geotiff/geotiff.ipynb
similarity index 100%
rename from docs/source/how-tos/geotiff/geotiff.ipynb
rename to docs/source/tutorials/geotiff/geotiff.ipynb
diff --git a/docs/source/how-tos/geotiff/index.rst b/docs/source/tutorials/geotiff/index.rst
similarity index 100%
rename from docs/source/how-tos/geotiff/index.rst
rename to docs/source/tutorials/geotiff/index.rst
diff --git a/docs/source/how-tos/grib/grib_array_namespace.ipynb b/docs/source/tutorials/grib/grib_array_namespace.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_array_namespace.ipynb
rename to docs/source/tutorials/grib/grib_array_namespace.ipynb
diff --git a/docs/source/how-tos/grib/grib_contents.ipynb b/docs/source/tutorials/grib/grib_contents.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_contents.ipynb
rename to docs/source/tutorials/grib/grib_contents.ipynb
diff --git a/docs/source/how-tos/grib/grib_fdb_lazy.ipynb b/docs/source/tutorials/grib/grib_fdb_lazy.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_fdb_lazy.ipynb
rename to docs/source/tutorials/grib/grib_fdb_lazy.ipynb
diff --git a/docs/source/how-tos/grib/grib_fdb_write.ipynb b/docs/source/tutorials/grib/grib_fdb_write.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_fdb_write.ipynb
rename to docs/source/tutorials/grib/grib_fdb_write.ipynb
diff --git a/docs/source/how-tos/grib/grib_lat_lon_value_ll.ipynb b/docs/source/tutorials/grib/grib_lat_lon_value_ll.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_lat_lon_value_ll.ipynb
rename to docs/source/tutorials/grib/grib_lat_lon_value_ll.ipynb
diff --git a/docs/source/how-tos/grib/grib_lat_lon_value_rgg.ipynb b/docs/source/tutorials/grib/grib_lat_lon_value_rgg.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_lat_lon_value_rgg.ipynb
rename to docs/source/tutorials/grib/grib_lat_lon_value_rgg.ipynb
diff --git a/docs/source/how-tos/grib/grib_missing.ipynb b/docs/source/tutorials/grib/grib_missing.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_missing.ipynb
rename to docs/source/tutorials/grib/grib_missing.ipynb
diff --git a/docs/source/how-tos/grib/grib_modify_metadata.ipynb b/docs/source/tutorials/grib/grib_modify_metadata.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_modify_metadata.ipynb
rename to docs/source/tutorials/grib/grib_modify_metadata.ipynb
diff --git a/docs/source/how-tos/grib/grib_modify_values.ipynb b/docs/source/tutorials/grib/grib_modify_values.ipynb
similarity index 99%
rename from docs/source/how-tos/grib/grib_modify_values.ipynb
rename to docs/source/tutorials/grib/grib_modify_values.ipynb
index ff8fca779..fc396d1c7 100644
--- a/docs/source/how-tos/grib/grib_modify_values.ipynb
+++ b/docs/source/tutorials/grib/grib_modify_values.ipynb
@@ -696,7 +696,7 @@
     "tags": []
    },
    "source": [
-    "See the :ref:`/how-tos/grib/grib_modify_metadata.ipynb` example for details."
+    "See the :ref:`/tutorials/grib/grib_modify_metadata.ipynb` example for details."
    ]
   },
   {
diff --git a/docs/source/how-tos/grib/grib_nearest_gridpoint.ipynb b/docs/source/tutorials/grib/grib_nearest_gridpoint.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_nearest_gridpoint.ipynb
rename to docs/source/tutorials/grib/grib_nearest_gridpoint.ipynb
diff --git a/docs/source/how-tos/grib/grib_order_by.ipynb b/docs/source/tutorials/grib/grib_order_by.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_order_by.ipynb
rename to docs/source/tutorials/grib/grib_order_by.ipynb
diff --git a/docs/source/how-tos/grib/grib_overview.ipynb b/docs/source/tutorials/grib/grib_overview.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_overview.ipynb
rename to docs/source/tutorials/grib/grib_overview.ipynb
diff --git a/docs/source/how-tos/grib/grib_selection.ipynb b/docs/source/tutorials/grib/grib_selection.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_selection.ipynb
rename to docs/source/tutorials/grib/grib_selection.ipynb
diff --git a/docs/source/how-tos/grib/grib_time_series.ipynb b/docs/source/tutorials/grib/grib_time_series.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_time_series.ipynb
rename to docs/source/tutorials/grib/grib_time_series.ipynb
diff --git a/docs/source/how-tos/grib/grib_to_netcdf.ipynb b/docs/source/tutorials/grib/grib_to_netcdf.ipynb
similarity index 100%
rename from docs/source/how-tos/grib/grib_to_netcdf.ipynb
rename to docs/source/tutorials/grib/grib_to_netcdf.ipynb
diff --git a/docs/source/how-tos/grib/index.rst b/docs/source/tutorials/grib/index.rst
similarity index 100%
rename from docs/source/how-tos/grib/index.rst
rename to docs/source/tutorials/grib/index.rst
diff --git a/docs/source/how-tos/missing.grib b/docs/source/tutorials/grib/missing.grib
similarity index 100%
rename from docs/source/how-tos/missing.grib
rename to docs/source/tutorials/grib/missing.grib
diff --git a/docs/source/how-tos/pl_regular_ll.grib b/docs/source/tutorials/grib/pl_regular_ll.grib
similarity index 100%
rename from docs/source/how-tos/pl_regular_ll.grib
rename to docs/source/tutorials/grib/pl_regular_ll.grib
diff --git a/docs/source/how-tos/test.grib b/docs/source/tutorials/grib/test.grib
similarity index 100%
rename from docs/source/how-tos/test.grib
rename to docs/source/tutorials/grib/test.grib
diff --git a/docs/source/how-tos/test4.grib b/docs/source/tutorials/grib/test4.grib
similarity index 100%
rename from docs/source/how-tos/test4.grib
rename to docs/source/tutorials/grib/test4.grib
diff --git a/docs/source/how-tos/test6.grib b/docs/source/tutorials/grib/test6.grib
similarity index 100%
rename from docs/source/how-tos/test6.grib
rename to docs/source/tutorials/grib/test6.grib
diff --git a/docs/source/how-tos/time_series.grib b/docs/source/tutorials/grib/time_series.grib
similarity index 100%
rename from docs/source/how-tos/time_series.grib
rename to docs/source/tutorials/grib/time_series.grib
diff --git a/docs/source/how-tos/tuv_pl.grib b/docs/source/tutorials/grib/tuv_pl.grib
similarity index 100%
rename from docs/source/how-tos/tuv_pl.grib
rename to docs/source/tutorials/grib/tuv_pl.grib
diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst
index d667db2e2..102902425 100644
--- a/docs/source/tutorials/index.rst
+++ b/docs/source/tutorials/index.rst
@@ -1,4 +1,25 @@
+.. _examples:
+
 Tutorials
 =========
 
-This section is currently under construction. Please check back later for tutorials on how to use earthkit-data for various use cases, including working with different data formats.
+
+.. toctree::
+    :maxdepth: 1
+
+    source/index.rst
+    grib/index.rst
+    netcdf/index.rst
+    bufr/index.rst
+    odb/index.rst
+    covjson/index.rst
+    geotiff/index.rst
+    geojson/index.rst
+    shapefile/index.rst
+    pp/index.rst
+    dict/index.rst
+    other/index.rst
+    field/index.rst
+    xr_engine/index.rst
+    target/index.rst
+    misc/index.rst
diff --git a/docs/source/how-tos/misc/cache.ipynb b/docs/source/tutorials/misc/cache.ipynb
similarity index 100%
rename from docs/source/how-tos/misc/cache.ipynb
rename to docs/source/tutorials/misc/cache.ipynb
diff --git a/docs/source/how-tos/misc/config.ipynb b/docs/source/tutorials/misc/config.ipynb
similarity index 100%
rename from docs/source/how-tos/misc/config.ipynb
rename to docs/source/tutorials/misc/config.ipynb
diff --git a/docs/source/how-tos/misc/config_env_vars.ipynb b/docs/source/tutorials/misc/config_env_vars.ipynb
similarity index 100%
rename from docs/source/how-tos/misc/config_env_vars.ipynb
rename to docs/source/tutorials/misc/config_env_vars.ipynb
diff --git a/docs/source/how-tos/misc/demo_sources_plugin.ipynb b/docs/source/tutorials/misc/demo_sources_plugin.ipynb
similarity index 100%
rename from docs/source/how-tos/misc/demo_sources_plugin.ipynb
rename to docs/source/tutorials/misc/demo_sources_plugin.ipynb
diff --git a/docs/source/how-tos/misc/index.rst b/docs/source/tutorials/misc/index.rst
similarity index 100%
rename from docs/source/how-tos/misc/index.rst
rename to docs/source/tutorials/misc/index.rst
diff --git a/docs/source/how-tos/misc/projection.ipynb b/docs/source/tutorials/misc/projection.ipynb
similarity index 100%
rename from docs/source/how-tos/misc/projection.ipynb
rename to docs/source/tutorials/misc/projection.ipynb
diff --git a/docs/source/tutorials/missing.grib b/docs/source/tutorials/missing.grib
new file mode 100644
index 000000000..99721ff2f
Binary files /dev/null and b/docs/source/tutorials/missing.grib differ
diff --git a/docs/source/how-tos/netcdf/index.rst b/docs/source/tutorials/netcdf/index.rst
similarity index 100%
rename from docs/source/how-tos/netcdf/index.rst
rename to docs/source/tutorials/netcdf/index.rst
diff --git a/docs/source/how-tos/netcdf/netcdf.ipynb b/docs/source/tutorials/netcdf/netcdf.ipynb
similarity index 100%
rename from docs/source/how-tos/netcdf/netcdf.ipynb
rename to docs/source/tutorials/netcdf/netcdf.ipynb
diff --git a/docs/source/how-tos/netcdf/netcdf_fieldlist.ipynb b/docs/source/tutorials/netcdf/netcdf_fieldlist.ipynb
similarity index 100%
rename from docs/source/how-tos/netcdf/netcdf_fieldlist.ipynb
rename to docs/source/tutorials/netcdf/netcdf_fieldlist.ipynb
diff --git a/docs/source/how-tos/test.nc b/docs/source/tutorials/netcdf/test.nc
similarity index 100%
rename from docs/source/how-tos/test.nc
rename to docs/source/tutorials/netcdf/test.nc
diff --git a/docs/source/how-tos/tuv_pl.nc b/docs/source/tutorials/netcdf/tuv_pl.nc
similarity index 100%
rename from docs/source/how-tos/tuv_pl.nc
rename to docs/source/tutorials/netcdf/tuv_pl.nc
diff --git a/docs/source/how-tos/odb/index.rst b/docs/source/tutorials/odb/index.rst
similarity index 100%
rename from docs/source/how-tos/odb/index.rst
rename to docs/source/tutorials/odb/index.rst
diff --git a/docs/source/how-tos/odb/odb.ipynb b/docs/source/tutorials/odb/odb.ipynb
similarity index 100%
rename from docs/source/how-tos/odb/odb.ipynb
rename to docs/source/tutorials/odb/odb.ipynb
diff --git a/docs/source/how-tos/test.odb b/docs/source/tutorials/odb/test.odb
similarity index 100%
rename from docs/source/how-tos/test.odb
rename to docs/source/tutorials/odb/test.odb
diff --git a/docs/source/how-tos/other/from_object.ipynb b/docs/source/tutorials/other/from_object.ipynb
similarity index 100%
rename from docs/source/how-tos/other/from_object.ipynb
rename to docs/source/tutorials/other/from_object.ipynb
diff --git a/docs/source/how-tos/other/index.rst b/docs/source/tutorials/other/index.rst
similarity index 100%
rename from docs/source/how-tos/other/index.rst
rename to docs/source/tutorials/other/index.rst
diff --git a/docs/source/how-tos/other/pandas.ipynb b/docs/source/tutorials/other/pandas.ipynb
similarity index 100%
rename from docs/source/how-tos/other/pandas.ipynb
rename to docs/source/tutorials/other/pandas.ipynb
diff --git a/docs/source/tutorials/pl_regular_ll.grib b/docs/source/tutorials/pl_regular_ll.grib
new file mode 100644
index 000000000..1a3fffaa9
Binary files /dev/null and b/docs/source/tutorials/pl_regular_ll.grib differ
diff --git a/docs/source/how-tos/pp/air_temp.pp b/docs/source/tutorials/pp/air_temp.pp
similarity index 100%
rename from docs/source/how-tos/pp/air_temp.pp
rename to docs/source/tutorials/pp/air_temp.pp
diff --git a/docs/source/how-tos/pp/index.rst b/docs/source/tutorials/pp/index.rst
similarity index 100%
rename from docs/source/how-tos/pp/index.rst
rename to docs/source/tutorials/pp/index.rst
diff --git a/docs/source/how-tos/pp/ukmo_pp.ipynb b/docs/source/tutorials/pp/ukmo_pp.ipynb
similarity index 100%
rename from docs/source/how-tos/pp/ukmo_pp.ipynb
rename to docs/source/tutorials/pp/ukmo_pp.ipynb
diff --git a/docs/source/how-tos/shapefile/index.rst b/docs/source/tutorials/shapefile/index.rst
similarity index 100%
rename from docs/source/how-tos/shapefile/index.rst
rename to docs/source/tutorials/shapefile/index.rst
diff --git a/docs/source/how-tos/major_basins.zip b/docs/source/tutorials/shapefile/major_basins.zip
similarity index 100%
rename from docs/source/how-tos/major_basins.zip
rename to docs/source/tutorials/shapefile/major_basins.zip
diff --git a/docs/source/how-tos/shapefile/shapefile.ipynb b/docs/source/tutorials/shapefile/shapefile.ipynb
similarity index 100%
rename from docs/source/how-tos/shapefile/shapefile.ipynb
rename to docs/source/tutorials/shapefile/shapefile.ipynb
diff --git a/docs/source/how-tos/source/ads.ipynb b/docs/source/tutorials/source/ads.ipynb
similarity index 99%
rename from docs/source/how-tos/source/ads.ipynb
rename to docs/source/tutorials/source/ads.ipynb
index f19e6420b..8b8e34b1a 100644
--- a/docs/source/how-tos/source/ads.ipynb
+++ b/docs/source/tutorials/source/ads.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving data from the ADS"
+    "# ADS: retrieving data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/cds.ipynb b/docs/source/tutorials/source/cds.ipynb
similarity index 99%
rename from docs/source/how-tos/source/cds.ipynb
rename to docs/source/tutorials/source/cds.ipynb
index 3c431646d..9bd18a55d 100644
--- a/docs/source/how-tos/source/cds.ipynb
+++ b/docs/source/tutorials/source/cds.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving data from the CDS"
+    "# CDS: retrieving data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/data.ipynb b/docs/source/tutorials/source/data.ipynb
similarity index 100%
rename from docs/source/how-tos/source/data.ipynb
rename to docs/source/tutorials/source/data.ipynb
diff --git a/docs/source/how-tos/source/data_from_stream.ipynb b/docs/source/tutorials/source/data_from_stream.ipynb
similarity index 99%
rename from docs/source/how-tos/source/data_from_stream.ipynb
rename to docs/source/tutorials/source/data_from_stream.ipynb
index dd6f3efe2..58251707c 100644
--- a/docs/source/how-tos/source/data_from_stream.ipynb
+++ b/docs/source/tutorials/source/data_from_stream.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading data from a stream"
+    "# Stream: reading data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/ecmwf_open_data.ipynb b/docs/source/tutorials/source/ecmwf_open_data.ipynb
similarity index 99%
rename from docs/source/how-tos/source/ecmwf_open_data.ipynb
rename to docs/source/tutorials/source/ecmwf_open_data.ipynb
index abe97717d..b5adbfc6b 100644
--- a/docs/source/how-tos/source/ecmwf_open_data.ipynb
+++ b/docs/source/tutorials/source/ecmwf_open_data.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving ECMWF open data"
+    "# ECMWF Open Data: retrieving data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/fdb.ipynb b/docs/source/tutorials/source/fdb.ipynb
similarity index 99%
rename from docs/source/how-tos/source/fdb.ipynb
rename to docs/source/tutorials/source/fdb.ipynb
index 94f8175db..2c77923eb 100644
--- a/docs/source/how-tos/source/fdb.ipynb
+++ b/docs/source/tutorials/source/fdb.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving data from FDB"
+    "# FDB: retrieving data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/file_parts.ipynb b/docs/source/tutorials/source/file_parts.ipynb
similarity index 99%
rename from docs/source/how-tos/source/file_parts.ipynb
rename to docs/source/tutorials/source/file_parts.ipynb
index 9942f60ab..bb1984691 100644
--- a/docs/source/how-tos/source/file_parts.ipynb
+++ b/docs/source/tutorials/source/file_parts.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading file parts"
+    "# File: reading parts"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/file_stream.ipynb b/docs/source/tutorials/source/file_stream.ipynb
similarity index 99%
rename from docs/source/how-tos/source/file_stream.ipynb
rename to docs/source/tutorials/source/file_stream.ipynb
index 0d0e7bba8..aca5af1c4 100644
--- a/docs/source/how-tos/source/file_stream.ipynb
+++ b/docs/source/tutorials/source/file_stream.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading files as a stream"
+    "# File: reading data as a stream"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/files.ipynb b/docs/source/tutorials/source/files.ipynb
similarity index 99%
rename from docs/source/how-tos/source/files.ipynb
rename to docs/source/tutorials/source/files.ipynb
index 10f38d628..c66e6bb25 100644
--- a/docs/source/how-tos/source/files.ipynb
+++ b/docs/source/tutorials/source/files.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading files"
+    "# File: reading data"
    ]
   },
   {
@@ -841,9 +841,9 @@
    "source": [
     "See the other file related notebooks for more details:\n",
     "\n",
-    "- :ref:`/how-tos/source/multi_files.ipynb`\n",
-    "- :ref:`/how-tos/source/tar_files.ipynb`\n",
-    "- :ref:`/how-tos/source/file_parts.ipynb`"
+    "- :ref:`/tutorials/source/multi_files.ipynb`\n",
+    "- :ref:`/tutorials/source/tar_files.ipynb`\n",
+    "- :ref:`/tutorials/source/file_parts.ipynb`"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/gribjump.ipynb b/docs/source/tutorials/source/gribjump.ipynb
similarity index 99%
rename from docs/source/how-tos/source/gribjump.ipynb
rename to docs/source/tutorials/source/gribjump.ipynb
index 4182fedd2..d95182c77 100644
--- a/docs/source/how-tos/source/gribjump.ipynb
+++ b/docs/source/tutorials/source/gribjump.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "## Retrieving subsets from Grib files via GribJump"
+    "# GribJump: retrieving subsets from Grib files"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/index.rst b/docs/source/tutorials/source/index.rst
similarity index 100%
rename from docs/source/how-tos/source/index.rst
rename to docs/source/tutorials/source/index.rst
index 6caee79cc..c117e493d 100644
--- a/docs/source/how-tos/source/index.rst
+++ b/docs/source/tutorials/source/index.rst
@@ -8,21 +8,21 @@ Data sources
 
     data.ipynb
     files.ipynb
-    multi_files.ipynb
     file_parts.ipynb
+    multi_files.ipynb
     file_stream.ipynb
-    tar_files.ipynb
-    data_from_stream.ipynb
     url.ipynb
     url_parts.ipynb
     url_stream.ipynb
-    netcdf_opendap.ipynb
-    mars.ipynb
     ads.ipynb
     cds.ipynb
     ecmwf_open_data.ipynb
     fdb.ipynb
+    mars.ipynb
+    netcdf_opendap.ipynb
     polytope.ipynb
     polytope_feature.ipynb
     s3.ipynb
+    data_from_stream.ipynb
+    tar_files.ipynb
     wekeo.ipynb
diff --git a/docs/source/how-tos/source/mars.ipynb b/docs/source/tutorials/source/mars.ipynb
similarity index 99%
rename from docs/source/how-tos/source/mars.ipynb
rename to docs/source/tutorials/source/mars.ipynb
index caf59849e..6405f5227 100644
--- a/docs/source/how-tos/source/mars.ipynb
+++ b/docs/source/tutorials/source/mars.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving data from the ECMWF MARS archive"
+    "# MARS: retrieving data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/multi_files.ipynb b/docs/source/tutorials/source/multi_files.ipynb
similarity index 99%
rename from docs/source/how-tos/source/multi_files.ipynb
rename to docs/source/tutorials/source/multi_files.ipynb
index 87c02c0aa..e2a61549f 100644
--- a/docs/source/how-tos/source/multi_files.ipynb
+++ b/docs/source/tutorials/source/multi_files.ipynb
@@ -10,7 +10,7 @@
     "tags": []
    },
    "source": [
-    "# Reading multiple files"
+    "# File: reading multiple files"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/netcdf_opendap.ipynb b/docs/source/tutorials/source/netcdf_opendap.ipynb
similarity index 99%
rename from docs/source/how-tos/source/netcdf_opendap.ipynb
rename to docs/source/tutorials/source/netcdf_opendap.ipynb
index 213edfd70..33f4dd4f0 100644
--- a/docs/source/how-tos/source/netcdf_opendap.ipynb
+++ b/docs/source/tutorials/source/netcdf_opendap.ipynb
@@ -5,7 +5,7 @@
    "id": "e4a4f2e7-c9dc-44eb-90ba-ee22a07bf7cc",
    "metadata": {},
    "source": [
-    "# Reading NetCDF data from OPEnDAP services"
+    "# OPEnDAP: reading NetCDF data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/polytope.ipynb b/docs/source/tutorials/source/polytope.ipynb
similarity index 99%
rename from docs/source/how-tos/source/polytope.ipynb
rename to docs/source/tutorials/source/polytope.ipynb
index b4d82ad34..6a8b68806 100644
--- a/docs/source/how-tos/source/polytope.ipynb
+++ b/docs/source/tutorials/source/polytope.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving fields with polytope"
+    "# Polytope: retrieving fields"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/polytope_feature.ipynb b/docs/source/tutorials/source/polytope_feature.ipynb
similarity index 99%
rename from docs/source/how-tos/source/polytope_feature.ipynb
rename to docs/source/tutorials/source/polytope_feature.ipynb
index a8a7a60c3..14c9fca22 100644
--- a/docs/source/how-tos/source/polytope_feature.ipynb
+++ b/docs/source/tutorials/source/polytope_feature.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving features with polytope"
+    "# Polytope: retrieving features"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/s3.ipynb b/docs/source/tutorials/source/s3.ipynb
similarity index 99%
rename from docs/source/how-tos/source/s3.ipynb
rename to docs/source/tutorials/source/s3.ipynb
index 0f910377c..7a663b597 100644
--- a/docs/source/how-tos/source/s3.ipynb
+++ b/docs/source/tutorials/source/s3.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Retrieving data from S3 buckets"
+    "# S3: retrieving data"
    ]
   },
   {
diff --git a/docs/source/tutorials/source/tar_files.ipynb b/docs/source/tutorials/source/tar_files.ipynb
new file mode 100644
index 000000000..cf3d1a325
--- /dev/null
+++ b/docs/source/tutorials/source/tar_files.ipynb
@@ -0,0 +1,385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# TAR and ZIP: reading data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "raw",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "We :ref:`read <data-sources-file>` a tar file containing 2 GRIB files. First we ensure the example file is available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import earthkit.data as ekd\n",
+    "\n",
+    "ekd.download_example_file(\"test_gribs.tar\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The files are extracted and stored in the earthkit-data :ref:`cache <caching>`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4e31ff9247d4fbaad3f8305d84b308c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">table.ek td {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       "table.ek th {\n",
+       "    vertical-align: top;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "</style><div class=\"eh-description\">GRIB file</div><hr>\n",
+       "<table class=\"eh-table\">\n",
+       "<tr><td><b>path</b></td><td></td></tr> <tr><td><b>types</b></td><td>fieldlist, pandas, xarray, numpy, array</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "GribData(path=)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = ekd.from_source(\"file\", \"test_gribs.tar\")\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can convert the data into a fieldlis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fl = d.to_fieldlist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>parameter.variable</th>\n",
+       "      <th>time.valid_datetime</th>\n",
+       "      <th>time.base_datetime</th>\n",
+       "      <th>time.step</th>\n",
+       "      <th>vertical.level</th>\n",
+       "      <th>vertical.level_type</th>\n",
+       "      <th>ensemble.member</th>\n",
+       "      <th>geography.grid_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2t</td>\n",
+       "      <td>2020-05-13 12:00:00</td>\n",
+       "      <td>2020-05-13 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>msl</td>\n",
+       "      <td>2020-05-13 12:00:00</td>\n",
+       "      <td>2020-05-13 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>t</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>500</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>z</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>500</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>t</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>850</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>z</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>850</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
+       "0                 2t 2020-05-13 12:00:00 2020-05-13 12:00:00    0 days   \n",
+       "1                msl 2020-05-13 12:00:00 2020-05-13 12:00:00    0 days   \n",
+       "2                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "3                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "4                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "5                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "\n",
+       "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
+       "0               0             surface               0          regular_ll  \n",
+       "1               0             surface               0          regular_ll  \n",
+       "2             500            pressure               0          regular_ll  \n",
+       "3             500            pressure               0          regular_ll  \n",
+       "4             850            pressure               0          regular_ll  \n",
+       "5             850            pressure               0          regular_ll  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fl.ls()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>parameter.variable</th>\n",
+       "      <th>time.valid_datetime</th>\n",
+       "      <th>time.base_datetime</th>\n",
+       "      <th>time.step</th>\n",
+       "      <th>vertical.level</th>\n",
+       "      <th>vertical.level_type</th>\n",
+       "      <th>ensemble.member</th>\n",
+       "      <th>geography.grid_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>t</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>500</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>z</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>2007-01-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>500</td>\n",
+       "      <td>pressure</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
+       "0                  t 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "1                  z 2007-01-01 12:00:00 2007-01-01 12:00:00    0 days   \n",
+       "\n",
+       "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
+       "0             500            pressure               0          regular_ll  \n",
+       "1             500            pressure               0          regular_ll  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = fl.sel({\"vertical.level\": 500})\n",
+    "a.ls()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/source/tutorials/source/test.grib b/docs/source/tutorials/source/test.grib
new file mode 100644
index 000000000..c9871b644
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diff --git a/docs/source/tutorials/source/test.nc b/docs/source/tutorials/source/test.nc
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index 000000000..9b60af83c
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diff --git a/docs/source/tutorials/source/test4.grib b/docs/source/tutorials/source/test4.grib
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index 000000000..ccd17c499
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diff --git a/docs/source/tutorials/source/test6.grib b/docs/source/tutorials/source/test6.grib
new file mode 100644
index 000000000..dc60dffcd
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diff --git a/docs/source/how-tos/test_gribs.tar b/docs/source/tutorials/source/test_gribs.tar
similarity index 100%
rename from docs/source/how-tos/test_gribs.tar
rename to docs/source/tutorials/source/test_gribs.tar
diff --git a/docs/source/tutorials/source/tuv_pl.grib b/docs/source/tutorials/source/tuv_pl.grib
new file mode 100644
index 000000000..7c941e409
Binary files /dev/null and b/docs/source/tutorials/source/tuv_pl.grib differ
diff --git a/docs/source/how-tos/source/url.ipynb b/docs/source/tutorials/source/url.ipynb
similarity index 99%
rename from docs/source/how-tos/source/url.ipynb
rename to docs/source/tutorials/source/url.ipynb
index 6f10e1a90..7cd51a48a 100644
--- a/docs/source/how-tos/source/url.ipynb
+++ b/docs/source/tutorials/source/url.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading data from URLs"
+    "# URL: reading data"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/url_parts.ipynb b/docs/source/tutorials/source/url_parts.ipynb
similarity index 99%
rename from docs/source/how-tos/source/url_parts.ipynb
rename to docs/source/tutorials/source/url_parts.ipynb
index 148377de7..87b2c2d56 100644
--- a/docs/source/how-tos/source/url_parts.ipynb
+++ b/docs/source/tutorials/source/url_parts.ipynb
@@ -11,7 +11,7 @@
     "tags": []
    },
    "source": [
-    "# Reading data parts from URLs"
+    "# URL: reading data parts"
    ]
   },
   {
diff --git a/docs/source/how-tos/source/url_stream.ipynb b/docs/source/tutorials/source/url_stream.ipynb
similarity index 99%
rename from docs/source/how-tos/source/url_stream.ipynb
rename to docs/source/tutorials/source/url_stream.ipynb
index 812969b24..589a0c518 100644
--- a/docs/source/how-tos/source/url_stream.ipynb
+++ b/docs/source/tutorials/source/url_stream.ipynb
@@ -12,7 +12,7 @@
     "tags": []
    },
    "source": [
-    "# Reading data from URLs as a stream"
+    "# URL: reading data as a stream"
    ]
   },
   {
diff --git a/docs/source/tutorials/source/wekeo.ipynb b/docs/source/tutorials/source/wekeo.ipynb
new file mode 100644
index 000000000..7e3015256
--- /dev/null
+++ b/docs/source/tutorials/source/wekeo.ipynb
@@ -0,0 +1,998 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3599bcd8-a5ee-4911-96c9-976b849138fb",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# WEkEO: retieving data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8ab6e404-9de8-4220-b1b6-41745ffbb59e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthkit.data as ekd"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8e2e0f15",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The :ref:`wekeo <data-sources-wekeo>` and :ref:`wekeocds <data-sources-wekeocds>` sources provide access to `WEkEO <https://www.wekeo.eu/>`_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e32b8f13-137a-436e-8523-fcee759dfb02",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "#### Using the WEkEO grammar"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "e6a6d76d-6563-4e6b-8aa6-e4f5a6b7b3d5",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [],
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "This example demonstrates how to access data from the WEkEO catalogue.\n",
+    "\n",
+    "To retrieve data using the WEkEO grammar (see the `hda <https://hda.readthedocs.io/>`_ documentation), use the :ref:`wekeo <data-sources-wekeo>` source.\n",
+    "\n",
+    "The dataset used here is `Vegetation Phenology and Productivity, yearly, UTM projection <https://wekeo.copernicus.eu/data view=dataset&dataset=EO:EEA:DAT:CLMS_HRVPP_VPP>`_.\n",
+    "\n",
+    "The example retrieves the Total Productivity variable, representing the integral of vegetation productivity accumulated over the growing season as the sum of daily values. The product is derived from Copernicus Sentinel-2 observations and distributed in GeoTIFF format using 1°×1° tiles at a spatial resolution of 10 × 10 m."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "15d5599f-1242-4238-8d5d-e459694bc296",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                                   "
+     ]
+    }
+   ],
+   "source": [
+    "d = ekd.from_source(\n",
+    "    \"wekeo\",\n",
+    "    \"EO:EEA:DAT:CLMS_HRVPP_VPP\",\n",
+    "    request={\n",
+    "        \"dataset_id\": \"EO:EEA:DAT:CLMS_HRVPP_VPP\",\n",
+    "        \"productType\": \"TPROD\",\n",
+    "        \"productGroupId\": \"s1\",\n",
+    "        \"tileId\": \"32TQR\",\n",
+    "        \"resolution\": \"10\",\n",
+    "        \"startdate\": \"2023-01-01T00:00:00.000Z\",\n",
+    "        \"enddate\": \"2023-01-01T23:59:59.999Z\",\n",
+    "        \"bbox\": [12.28091069558975, 45.40759897973081, 12.384715295944709, 45.45978517718846],\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3e4b991-277c-4cfa-9c0a-158fe842e40f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "To inspect the downloaded data we convert it into Xarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "78c943ba-9a46-4a62-8bcf-ad697d9212f1",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['xarray', 'pandas', 'fieldlist']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d.available_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ad4040b2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 482MB\n",
+       "Dimensions:      (x: 10980, y: 10980)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 88kB 7e+05 7e+05 7e+05 ... 8.097e+05 8.098e+05\n",
+       "  * y            (y) float64 88kB 5.1e+06 5.1e+06 5.1e+06 ... 4.99e+06 4.99e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    band_1       (y, x) float32 482MB ...\n",
+       "Attributes:\n",
+       "    TIFFTAG_COPYRIGHT:  Copernicus service information 2024\n",
+       "    file_creation:      2024:03:16 10:35:37\n",
+       "    input_time_window:  2018-10-01 to 2022-02-29\n",
+       "    TIIFTAG_SOFTWARE:   Timesat : TIMESAT4.1.8\n",
+       "    AREA_OR_POINT:      Area</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ec49eca9-36a9-47de-99bd-ae7308ea2346' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-ec49eca9-36a9-47de-99bd-ae7308ea2346' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 10980</li><li><span class='xr-has-index'>y</span>: 10980</li></ul></div></li><li class='xr-section-item'><input id='section-a6fd0ba7-bfd6-4178-9bd0-0938e0b1f9e2' class='xr-section-summary-in' type='checkbox' checked /><label for='section-a6fd0ba7-bfd6-4178-9bd0-0938e0b1f9e2' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7e+05 7e+05 ... 8.097e+05 8.098e+05</div><input id='attrs-ea31bb5e-01c1-4c9c-8b16-bc3ef2b09aa0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea31bb5e-01c1-4c9c-8b16-bc3ef2b09aa0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-224924b0-292c-49f2-b988-ac717bb21556' class='xr-var-data-in' type='checkbox'><label for='data-224924b0-292c-49f2-b988-ac717bb21556' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([699965., 699975., 699985., ..., 809735., 809745., 809755.],\n",
+       "      shape=(10980,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.1e+06 5.1e+06 ... 4.99e+06</div><input id='attrs-568bfdf8-ca57-4f24-841c-b8dd812a1ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-568bfdf8-ca57-4f24-841c-b8dd812a1ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c02ad0ce-d093-4846-b110-5f442fa085d9' class='xr-var-data-in' type='checkbox'><label for='data-c02ad0ce-d093-4846-b110-5f442fa085d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5099995., 5099985., 5099975., ..., 4990225., 4990215., 4990205.],\n",
+       "      shape=(10980,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-f02e9277-4889-449f-81f4-e42668717281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f02e9277-4889-449f-81f4-e42668717281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-832e5598-6bbe-4a23-82fc-755406ad655d' class='xr-var-data-in' type='checkbox'><label for='data-832e5598-6bbe-4a23-82fc-755406ad655d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>projected_crs_name :</span></dt><dd>WGS 84 / UTM zone 32N</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>0.0</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>9.0</dd><dt><span>false_easting :</span></dt><dd>500000.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>scale_factor_at_central_meridian :</span></dt><dd>0.9996</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 32N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,9],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32632&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>699960.0 10.0 0.0 5100000.0 0.0 -10.0</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b95735aa-c53b-435e-a4bc-6ed27b24af45' class='xr-section-summary-in' type='checkbox' checked /><label for='section-b95735aa-c53b-435e-a4bc-6ed27b24af45' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band_1</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f998123d-7e59-45af-bde3-fd2858ae82d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f998123d-7e59-45af-bde3-fd2858ae82d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15818545-31c6-4501-8142-bdd204dc758e' class='xr-var-data-in' type='checkbox'><label for='data-15818545-31c6-4501-8142-bdd204dc758e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>PhysRange :</span></dt><dd>0 to 1095</dd><dt><span>long_name :</span></dt><dd>Total productivity (Large integral) 2023-season1</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[120560400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3c7e20b-e5aa-4768-8852-4a40bb937550' class='xr-section-summary-in' type='checkbox' checked /><label for='section-e3c7e20b-e5aa-4768-8852-4a40bb937550' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TIFFTAG_COPYRIGHT :</span></dt><dd>Copernicus service information 2024</dd><dt><span>file_creation :</span></dt><dd>2024:03:16 10:35:37</dd><dt><span>input_time_window :</span></dt><dd>2018-10-01 to 2022-02-29</dd><dt><span>TIIFTAG_SOFTWARE :</span></dt><dd>Timesat : TIMESAT4.1.8</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 482MB\n",
+       "Dimensions:      (x: 10980, y: 10980)\n",
+       "Coordinates:\n",
+       "  * x            (x) float64 88kB 7e+05 7e+05 7e+05 ... 8.097e+05 8.098e+05\n",
+       "  * y            (y) float64 88kB 5.1e+06 5.1e+06 5.1e+06 ... 4.99e+06 4.99e+06\n",
+       "    spatial_ref  int64 8B 0\n",
+       "Data variables:\n",
+       "    band_1       (y, x) float32 482MB ...\n",
+       "Attributes:\n",
+       "    TIFFTAG_COPYRIGHT:  Copernicus service information 2024\n",
+       "    file_creation:      2024:03:16 10:35:37\n",
+       "    input_time_window:  2018-10-01 to 2022-02-29\n",
+       "    TIIFTAG_SOFTWARE:   Timesat : TIMESAT4.1.8\n",
+       "    AREA_OR_POINT:      Area"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = d.to_xarray()\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ae25fd50",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x1218767b0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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M2urRkI7bW61ZenPcCp1e6/nIBatv7jNfpmTVVMRqYb8F1orWCPgs3xOx0ErZgrMZq3TFzVuumTdbtsJw0ryVuq6JNcxmbotZ/2NpK/RErdzis0C2ZoF8TdfeO7di8/dsssRE2ULLRasHfFZuCVqp1afz8tQauqbR+bIFFws285qUdR/N6trmh5JW6PJb+1OzVh5otUJH0PzFusXOztvybb3mqZmFVqrmrTYskClbw+e1QlfIEueXzTI5Gh6aBQNmsai+r6diZtWGlTsi5itWzZev6Dyr8YBVIz6dS3Q8bdktSYtfTVu5PWq+Us0ywxELL1UtkC5boSts0em8rkXD79WY9OWqGoOEfyFtVqtbvS1u9aDf/Ms5s9W0WarFSr0t5itUreH3mKdu5i1XzXNp3HK377T46VnL7u2x0GrFAhOLVtzabcEnzljmjTdYaK1qQa5dyG+l1qBF5vI6D286Z5YvmEVCVm9rcQ9srWGeWs0q7VFreDwWXC6YZ2mNRpFmibhVO+LmPXXZPJv6rX7xmv3V8h/YWw/+rKX3duiZ5Lj8M8tWHmy3SkvAApmq3tZXqFi5NWyRaytW6k/qPBhH3N9aT5v5Ftes0t+ma+JfLVl2JG4tT41ao6vN6uevmDcSNk9fj+YET7VunnTWKgPt5l/JW+3cZTOP1/wdbdbobNV4aHi9VosHzJ8uWS0aNP9S1uqJsHmK7nga45Pmjcd1f6tTM+ZrTZknGnH3od6wRjJua7tTlnpqUt/XEo6h8GWKukaK2XlofT2XtbWM+Xu7zGAysjmr93WYN1O0cm+LBadWrD47b54tm8yTL1qtNabnpTEzZ56uTiuMtFrgoWPWuG2/zsezmrXa/IL5Ng1afXbOvC0tZtGwlftSer7rU7NmuzfbRx99r7314L9zx8J8GQ9ZoTtqsedGrbap23wrOSsOt1nd67HI0xfQkFjmju3Wcm5ZY8xyeTe2i0Wr9bSat1Cx2rkr5muJW33rgOYx79YR8xRLZoWiVecX7P+u/aGbe4Z+yDxdHfpML/PKwpL95dh/sTff90ELLOasPj5l3u4O3dPAXFrzr3c5beWhDo0F7o9/bkXHZOmsGe2tAj6zbMEq9aI9tvrHXxUx8HcNmCvA1djzw9aS+OpZsnSmbpsOj+r9mgDrGzxF+OJIpVLyhnqx79RXEui3cFSfm5v7nJ/zfxD7lwr0XF9M0+VdzFj9tgPWCPmsEalZucWvBSkcjFotEbTQTNq8tYA1mAyzNSsNtpv/8ePmfeNha70G8IhbYqpg1daYtZ5a5IMs356wyr4Ri5yesspgj8UuzrhFMRKx1FTV1g73mrdi5l1rmD/P4paw2Nk5iz11xWz7iFWTIct3hSx5atF8RbPQWNE8i8uWPzJilfgmazm9bNX2FqsmgxaIx61lumSFvoSZz2Ox6awVdvRogf/2137Y7MxlCxzcb95s0WzbdqslImYvnLX6rfssVPWYx+czb9gvYAmoCl+a06JQjkfN3yhYy4rPar1tAls2OmulkT3WSPgsuH27eUplq24btnxPyJ768/fYHW/4gBbZ2MCQtU16zO8L273//L9ZaXfKuqdytnrHkPlLDfPW6xa9tqpFC2BVT4bNz/El2ywxXbLGSsCK3QlLrDbMl0habl9CC2RsdM0q7TELLGXt2b/8kL3mWz5k9UDYgqNLVgtHzVsqWWqmYYkVj/k2D5sHIFCvmn8uZ/k9veZpBKySClrQzMK5ilVGus1Xa1hwYtaKu/osWDerJH1WSZr5inXrP1GzYLZmxXjUEitVAajwcsF86aot3L/dgjWzRipgwRMzVjiy2QKNhvlKXi3egYWcFQeSFl4pmmetZN2XPBaazVphT5/F5/JW74yZv+G3ciJmkZJZIFc1b0e7tY03HJg6O2v13nbzrlasEQubp+A3vz9sjQ4H+lnMBeDDYatGoua/OmOeVMI8Hp81khELLOfMGwlYdCKv19YuT1jrct4KBzZZMF+1SmfA6q1+S/eZBdMNazuZtaVDnRZdrNlnPvoeu+vOXzb/2WtWuXWXhSbXrBFLWGZ7yvz5ukUvL1k9kbBab4dAd25Hwtp+72nz7d5q5a6EBReXrL59qyWfGrfazk0Wy3qtHg5Y8fBmi59dtNx9N1rr3x63ymv2WbBUskbdLMi5eIJmPjNPMmCeSFyAwvINy29vt8h42hphFuoWi8wVzW8Vq+zYZNWoz8LzBQuPzVu1WDd/umyN3bvszW/7LQtHY9Z2ctlm3zBoHcdz5unutqD5LPLUNfPEY1bvSpl3rWK1rpT56z6zit/8EwsCAd52n3mCIavsSFowXbJqKGjB1TV75v/+qt175OfNzl+z3LffZi0fPUZLBz1HvsUla7SkzJczs/m0eTwB8+1Z98Fb38B4ltNW6u6zwGTGAnW/NZaz5osnzBaXzQMI6uixwu5ei1xZMj/1Osl1gEkhuN9njUDA2i6XrT7Ub4Gr0+aLxMy7ug66cwWrD3SbN5a0RqlknlDIvLmKfXLqt+3eyHe7+W4gaN5izjwlnzV6u6wx0mfVkNeiFxfMV/CYp1Az6x2wWjJq8RcmzbNrt9lCyRrMnV1R8ydSoACzvkEdDyDEU/WbN5E0344Wq8bD9h1Hfsl88RaBfMZenfHo8Vnl8HYLLpfMF2xYsB40X65ijf27rBoLWCzTML83qDnBU+Na+aw6vWiBxbSV7txvgdaU+c6Ommc+b9WbDpp/MWPV/m7zTy+bN5S3b+v4F9aoVqxx+0F76NGfsfuS32f1QtE+Vf4ju3/HT1uwLWaNzpAFAhGrtUYtMs+GwWtW5NoGzL9U0ealsZa22r4tGtfeYt3K23otOL5sjf5Ws1zWbNV1+/j7jnjCo6+vNurWlL38kxG5f76pJ47nvb29X3VembJxjD5frKfi/+i7vtJo7NuqXWtwqahJLLRYslrQY77VnPkzZcsPp8yeO2OfevLfWq0tYcX2gGW+/Ubz1sw8a1mxS9W2mHuvWNTKfUkLLRStHvSaRSMWmE2blUFTXqt3pqzUGbXkhYzFJ/JihWCoWh48Y/WBLvO2t9nKDUk90LA8xaGUebJFa0QCVtvUa5HJjLVcWNOOimOOnpoWu8GO3l+sWTnuNe/ssq2N+G11S8AK/XEt2pqA6w1bPdBh9bDPVt9xkwVm1rST5zi9k/O2uq9VzM0D137Nat1J85Xr1gj5LTSVtuBS3u3Q21JiVUKZuhUHW+yByx/UefOz19/+S1aNesW0MbH6V4u2+upBMSTdH79qjZaohZarlji9qN342Ld0ih3zLq6ZPXPKbGxaI7WWjGgXCcjleJhwo2NZC88WrNIWtcBi1h5Y+G0tco31OaS8qd0yW2O2erBd//fU6pYbimsStkzWCjt7BNC4Vp6GWezoqHlLFav7PGISGm0t5s9VLDMYsLrfY5W418IzObGWuV2dlri4ass7g7Y24rNSa8iWXt1r7S+sWfJq0RIPXzBrS+lYih0Bi52ccUzG/LKFx9d0PDAhgdWSNVri5ivW9Nkt59LWiEcsuFK2esBj3kJV42Fxf1RMUr2z1WoRv1i/aktIIMdW1syTK4kVEHskNiSsa7Tyus3mXy2IteJYAK6A7GprVAuH54adYnpg4vLdIbEd8emq9T6+ZtWIR8fUdjZn/rxLTQTns9YoV6wS84ntWNvTKlbNV6lbaVOrNQDmMJpdEUtdLJjdvM8aoYCVk36r9LcKsFf3Dlst5hcAZwyF54pWGkhZaKlsnmjUGl6PQKiNTolVqbY4NoZjL/UlxVjCTEUmMlZpjVgNVgKGq1K3ejwsVhhw5U0XxMxUX3/IGq0Ja1was8jxMfOs5XSNU5cdQ+up180HExMOiVmutEUsv6PTIk9dFEDxwVAHArp/HAPX0p+v2uqOuHn4zO5Wu3/w3wgserxea/nYCXcvLoxqvqhs7nbj99KoPbj4u/ap+p9Z7exljYX6ucsCV1YqW2h0Wc8Az22jUrEHT/yieVJJe2DyP4upDWSr1phftEalao2A3/I7OqzanbQHzv2KWK/0zhaN7eL+TXreK30pa7TE9FzUT54X+1VbWbP68qqBXu/r+EHzwjbv3myeq1Nm4ZDYQJjr4HzGwguct99qqYg18nmrdCes8fwZseia23xe/euZmreG32f12QWrxyPaVDZSLeY5ddns6oTYtMBcxqodCbHp3lzJPPmSBc9PWiPgcczu2St6voOXZ803Omv++bSeDW+lpveDUa7PzOl+NF59wDw7NltkbNX8Y/O6Rgt3D+E7ZJXepPkujFtjNW2fKP4f+2T5j9y4ncvY7W/+oGXu2WOefds0L2mOSjMn+8yzmhZrCEhk41hLRR0Lyhwdj4ot9J2+at6pBd2L4OSKVccmrHb6gtXGJuyVilqj/rK/mvFPwAfrPe95j73pTW9SWhAzTvr24ZJ+9uxZ6+zslEN6f3+/NFIElCa/I97whjfIRZ0v+v5t3br1uk0DJqC/8zu/I+8rbBpIOaLB+nxt1peKdDptyWTSXr/5X5u3rc2W9ies/URakyfhKdds4caE9Tw0Z+l9HWJeWAxXdkas47lVy2xtMX+B3fyyPXDhV+31d/yyA1WAyL6gpS7mzDe7YpmDfWJDoi+MaUEknRe7sCT2Ibev22LHJkGIephZDFhEmHQABuVdAw5orBbFprEgKYVFeiabt+qmLqvE/Zq4wtM5q3REBIj4/8qBdvOVSas0LDpbMv/lKWt0t5uHRSSft+rWfvMvZKx66YqV33iThR583uzwbvOtFXRsDaXdgpokc9tbdQ6AkMC1ee3yHkz/gc6VyQxAEsjWrdjq03kDDEqpgJVbvAI28cmSBZbymoCLnSGLTmQFNMPTGSv1xC18dtqyRwatGvZYy+WsVVpCAreV1pAATyBTEVAtD6S0+GU2RSx5IatJHOYx3wd4K5un3tDrGiyA+ZJAZfpwr4UXy1qMy61BMWekgAEza1sjFlmqWWQ8a8W+mPkLNcv3BM1XauhzARQwd5UOB6BJt2UGAtb9mQWrhwJapOqxkOX6Iw54rlQscH5CrEM9FnapZlLOQb/SudyXwHJBP/ctZqzWGrdqImhLe0LW/WzGcoMxqwXNQum6VaJei4/lrNQWVgrSt5xTKim3KaFUIYC3kgxbAFCVCGlc1KJ+C42vaoEEUPjXSBvVzLO4akv3brGWqwWlMgF0S3uD1vlCyYKLuevsChuMOqmY9larzs2LfXnw1C/Z/dt/ysr9KV1DgRCuRVvMaiGfVWAz01UrdAQskKtrg8I9KreHrR70WGjBFZbAbMJUcI+Cj5+26s27NBbio1n9fHV7xCKLNYtdy1h+OGHB1YoVuoIWmyzo96SUGRO1KNe0JrDszZXdNWYcwHyUqwJG5WRQ1zq05NKPHCepU/+pa2YD3eYplF3qanzKKq/eY6ELs+75g5HxmlKePEfpPW2WuATjFBXo8RYrVm6PWPjKokBA5ZbdVgt7taHyLaatEQvpfdM7kpa4kjHvUtpq3SnzzbvfeVYy7BDF/JQGWy10fsoemPovuj739/+wWTxmtrpm1S392tDd1/K95hnodcBkYcWlz5IJt1lLRjVeIrNF808vWbWvXcBD8fw588LQZTLm62hXGrc+MW3e3m7L7um0yN++YI0ju5UyZX5KnFnUM8OYBiiKKZuZt/qOIcd+cs0rdaXqGMsCTytpa6QSZtcmzdPZbnN391n3w3MCjjwTjCWl/df/hrQoKXkxcR6PgCgAq7R30ELXlvSsLr2mzxITJfMWa9oI8rxx3ZnjvJXG9fQ3zy6bCQJwuhH3j/yYNZg3b9hs+d6gxaZKFpjPmgfwFPAr7VxvbdGcwbxIGthCQbNKRelW3R/Ad6HoUoPVqkvT7tyqa1vNrdlD478peUkL6dG/h9hYl2YvDL3sFGHPjvG/12Ntxj8CBmtyctLe/va3S+T+tre9TVV7Tz/9tMAVMT4+LvH7RgDCaEfDFz//0Ic+pO9/4Ad+4PprvuM7vkM/x5D0wIEDAmwPPvjg3xlcfW54rBoPWudn5mxtZ4st7YmKAWHh6/nTC0pPsNCyg/Vfm9Vuncn8iT9/j1XiPitsbtO7PPTYz7jF04fupmrFzrDV2xJWSgIyGlYd6VX6MJCu2MqNXbZya68WEXaHtf5O7aDQxvgnF8WCMdkGr84LtHmqVTEt6JU4Nu2S0abUG0pDRp8fla5KLEejoZ1729OzFsi7XUwNHU5rUsxS5mCPrb5ui3mfOytg6B8aUPoFcJUZiVm5p8VKm9rNk85ZqdvR6dHRjEUuL5r/xBWluCpHttvd3m/Xe0fHMxZcq1lkKmu1kEeTLyk7wGfqfNZiU2Wro/mq1zVRBtcqDhBU61ZNhi08sWblLd22Nuyz+FjB6s+fttD0mhV7I2KwALVMyOxYM0MhgaRn/tePCdzUWkL6HeCq1BYUmFs72GWzd7RbIxK00kiHxa9lrBZCP5PXZ+e2tWohqcQD1vHZOQHY1b1Ji5B+jPuVAovMlaSJCi7krTDYorSxL1sRWxIoNARu07taLLs1pesNmwnjCDNUODRilZ4W806SajUrt0WsRGpiNq1J3TO7KHBQ7Wwx31reQpOr1nGyoEUMIBhZrIqBgqmsJENu7MB0dpESDlt0pmDlnoQ+lwWHe1oPkVfzmK9QE9sEEPc9e06fVRhKWnHfkAVyDf0cxgIWre1cRawSf0/khxJWGEiYd/dWAXH/lhGrnXH9NFmcYfMAiTY5pzRzORmQjis2ltX9DWbrjv1rCZi3ULLIsWu6tizQ1UTAfJmyheZhhStWuW2P3kuL3OS8BU5ctY7nVy1xat7qUdKaWZ0fIIUUUHhsRbrGQm9Uej2YCEAH4EpMkN+rxZrFnHOUtq9ct3IqZIGJZbFqLMikBTn2lVt6dZ28gBe0TX6/7plYv+W0hSdWrZ4IWcuFVT17LO7e4xfNU6pYcDGva124a58FnjhlwQeft8ZzpxyAml0yT7ZgsYmCWBqef21YAn5burHdrCWu76fv69EYV1oPYDDwrzUH8Kw9MPdb5js3avce/jnztLc6QAJA5XvOLR7R2AY4Jk4v6DpWNnWK0fJNLbnUMQEDBDPjB9z7rL5/mzZN8WdGzbtzs4Ay7A9sqZ7NtqjuG+f2wNlfttquYXdNYea4f1NL2vxtrCbViWldj3o+LxDf/rtPW2Np2fIjreZdy1t+MO7uBzq1yxMOGMG2Bv3asKA5S796swVWimIRKwOt1v7krJvnuGb1hvlzVccm+j16lgrdYfPmy04ru7RmtVOuqGljLkLa0NjUKya59bMTFrzIMZat3hIRaCru6NFGlXEnzeSWbqu1r6eiuc6MhRXHOnMuMJYwgMgz9PM8+cRmNONzo9kq56UYrM7vt6Vv2SOGqvXUmmVHEgJPd7zxA5YeDljnsazNH4lbw2fW9WxOO8t7bvr3mtTZOTPZf+qJf6v3vOX/+zVLnlmzcheiaL929oCw1S0+a71YVaqr3ElqhLS/1wJrZb1P4PhVy9y1U0JgFklNaKQt57NaIKHISZkAtkhl1iN+gQH+H3/6mnao0jMxmYZ8moRgcOqtcS3chc6gtTw7afXOpISfMBzep06ZN9linkhEjBT6CPQssDikLkJjS0oBsiskxcaiH1wr63wlnAZIHr0m8W5+JGW+Ut2C83kr9saUXvNm8lbtSir1BFulxX2tbPm+iK1u8Vr38yXL9gcttFqzcsInMBheKFkAMbzP50BKqSqmZ/ytfdb3RN6KHUGBkE999n12f8+/stXXbhG4K3b4Ba5Sp1Z0H2qxkC3viVlsDkAKEKk7MCJ9x7rG6cyimEner4yOaq1q1bhfAJhj5WekXEPp9ePL1S2Q5jU+/T8yXxF7A8AgSh0RC0+klToNfeaMUs8wcbBF0XPzWlgBzgLMsDizGatH0MiUlBYJji1ZeajNglfmbPU1Q5br9lr72bIE6L7VvGV2tVtsLCOGFYZTjFLVMW353pBFZxHtpwWGpD2pfcTufvUviXWB2QEMol3S5iEWdGLuSk2LqxiBpbyt7W+z+IRbRPiMamfC7OlT5t2/U/e7fuyM+XduE/OTvX+/xSbzVkkElUYj9da4MqpFzrN92Iq9cTFIjCXAsFiLWNBqYZ+0aPwendHia3osNl0RU+EfnbPinn63sMLWAF6LVb0W8A74hVEUM7K4JgYFtqjuN6WiAVWwXBuAqzbYre9JQddvP2CVhN8i0zmBunp/53UwIiaU41xNOxH4+KxYJFKK/A4w4KnWrNoeV3pJG6BGwwLLeWtcnVDKsQHrUW9YvVQ0f3eXGa/fNiAgA2hDT6fxV6qZd35ZqTXAiieTN/Oi/6pIEyY28dxVJ10YnbVGtSp9V/HAsO4ZYnPYQ99z58VIkbbybR1RyrhRIvUaseLufm0OuO7MA7XOFvOcvOTek/sU9JnNLJh1d5oBlBJxq/e4jWL9hbO29M9vs+6/vqafeedXrZGKm03MajNYX0tL3yiGCt1Ye6s2PxL1n7lo2e+8xbzlhsWvZMwuXjPPtmFtHIMvXDFPJOxAZq6gDSIbhmJ70GKfOGm2a7PGthjrlYJltiUtcW7FKp0x8z152mqv3qcNp4T+q2tW279V96J68ap5A36lCe/f9V4rbEppQxmZLei+eVczbsKPhCX3CC0WBYhJ3xJ3vfZXlK6sd6SsceaSQKl0qQjq19LWqDes9E1HlGmolnL20MLvvSIM1vSFgZfNYPXtmGwyWP/UNFj/6CIRt66nly15Ka8HkrQGKZFqxGvdn1lSuqv34xPWcbKodM+tb/+w+aYXLb0lqgWUSRPAdfDdv26pk8vaLZLyQotUSjkdTP+nVsXorOxP2tKeoBaT6JUVC0yuiO1hcgZcedE9+TwWOTOtRViLw7omJXRtUQsZEz4pwvBszmIXV7SI5La3abEA1PiuTEsLw24VZq4a9VswU9MOjh0oupZcf9iqtx8w6+1Uam7lLfus0uKXpivfHbTQ+LKq2+674d9ZaXOXrgUASpqUZ885HUy5rjSjTc1bdHRNbFNuJKHfkaokuHaVVMSCqyULrFcr1gNmPU8XLLAI22fSbRFrm3yWHYoIlLGAkQbiXNGX9D+SEWPCAkvqlqguLFnyLGDMY63Hly06U9b5kQLlWiSvlgSsuP6lNrRmPr0naVFSmvmtbVq0YUYAV1p8xxwDSJqQCToxXhRwY/HW+cZ80lgVUx7H1kS9Sn+gH/KyK46RqmtosSBtyWu4Tw0KC9CHjC25FO9a2Tylqk3fkZDuhWtf7XGLa3lbj1KULWM1jQGuATqRYKaqBQlWqBF07JEq0EI+pVURDHNO9Ru2WeGbb7S7XvcrYilh0GAnSCuubYuKBYI95N6wixdrQwViX0IgVyze1JKtHegye+qE+bs61isSKQ0wsRuLbz9ghTavNZ45aeFz0wJD6AQF1m/YprQNYJ33JzXkXc6I7SBVB3giLVwPuTRX6+msNhuAZ4GrTEWLKJsEjX2PRxuM8HxJWh0KMdBcoWWCOUlczVpitCCWKrCQNc+VSY3Tse/bqpnPf23G/H09Ap8wmB5YlXxhHThVzJMrSqjNdQBsFqkY3TOk9/jk0z9rn3j+FwQcYUFgUkjT+jMlC16YUkqp/KrdVts+ZN4uZ57sSyaVdsrdukUpOIoyEJyjlaKS1Vssa7H3LK3o82GCrF4TwPJcmzbPxTGr3rhTwBsWmyIBa2+1Rz7xU+b57HHpuTyfecGBrs0d0lOSOoQhZx5Bx8UGjrlA1w/G69nT1tiz1da2x5X+R8fH+8KEo6usjvToPPny7d8lcEWqDBD7wPh/VEq1cmCLAJlvoM8yezqsPj4pgXytI2G+Z88qhQkbnnp+wRIXVsw7Pa9j9KTz5s9XLHvnDgnGAYeNRMxKXTHzPnlSKeH6wR26H/bkcfOvlVwKkksCuCpWrfaqfRoHVDzW+tqUNvXPrUmzxUYCUA+Lld/sqoyjnzrlmHwKdzZ3WXlTh841+InnBZphZ+/b/2/tvtYf0Ps/MPGfzM5fNR/Vh6+5wYEsP8L9hPk72y323LjGRuMlWkp9raNGBejL/GrGKxNNgPUSwWRRGGqxhYMxm39Nh1VDHrO1tCUevWhT97Rb8OKMNaIRpwlKhi3xF8/b3JtGrJT0WHrYARgWCLRasARMoLmDA/bY3/6khRcr+ow6FgflusBH3ycXleZgUp+/q98yB3ps8f87ZHOHnG4B1kYlwtgo9CUsPJXWREx5PGABhqrSGRcbUOmO67MBEkw2LFiFwyOO7ib9Uqophcii3ujrlLCdxRWWYnVryBZvarfY5RULpmvSyrCDTj0/b6WhNqWENLG2+JX+Is0GG9coly30zAXL9QSs0B2y6r4RK2xKSk9DiqfQFdC1Ko2029yRkJVTLCpVaWwWbmxVGhCh+dTrU9Z2KruudzKLz7jjmnl1zB3vMyd1jg4E+ARUWGB9C6v22nver4kV+4XwZNYKAy1a5AEbsEAEu30CnRfsWz3gtVJHWOwdbBTpgNaj89KuodMA9LJ4AQLj4wVb2xrT38Ey8gWwiZ1bELuGaDqzCZDTcLo7L6nRuuU2xQR8Cn0xLXKhlZJN3p0SoCDVDIvFQuHLFsVA9T+aNm++YtFra5bvdws4ICF+cUVjhddyDfiXVIo0SDG/VTti5ptZVgk6Cy+T/yef/Vl77K9/wkrtIYtfXtX5V2/ba6VWv44pvbvNkpcLFrvqQKRSVEqneZXK4b3D80WlD4vbunXPJdJeXDI7fl6vXfueW+2e4HdZ27m8vsr33WjVwU5XRh8NiM1AbwS7SOqJ1CDvC7hnzAbSJS2MMJ2cszYFRXd9Y9fSLqXYaMgSgrEvYXq5armtrVbsIl3qGCzSOjBNSrmWKmJFw8sVJ7I/vEXgrveJgtgiGFaE7zxv/jOj1hib1LiQvQG6mu6kLAG8s4t67kJzBfM/dVbAkbg3/A7zbhqUbQVp5dwmZ1NBRXCtt93CF+dckQY6n52b7cGV/271hSU3Vvx+8z93wVUHetHgecXeVK+NX7dt8FElGAzaA7O/aQ8u/zddw8ALl8VuwkDD1lbPX9JxePwBp63r6pQG0v/Qc5b8X89Y9fJVHUP+vhvs/qEfsUcf/CmrT05brSVitVMXzLdvh65z68lVq0/PuCpAUswXAQ4N8526Yvfc+osS8FO4I5ZJViBBu+/Az0orGJxatQfP/JIAizSAh3aZd2HNvJcmzNvXq+eisbwq5pnnSNWPm/pcqtLjscRzk9rksDmstkeVhvWlUgKCCNgVtx1QSpYUeOzhs7JgoYjC+9gx8+fKYqkEPNEKTkxp7gNY1ctlgSbkBGyI2GQojRxZ34jkHIvLddC9A8zyDB7aar7xebvb9x2OXatULHCS1HZd1g/MddhxNLI5jZEa/zajGZ8XzRThS1CxN7/pFy1oIS36q5v91vfnV11lECCnWrXsoX4tzomzy+ahlByGY3uH5btI03msEjMLZswC2Ya1XGNhdlVVTIykaKCrSYuwMy+1ByU8Jw2FJsG7knV6J/QG2bLl+6MWf27ClefPrTidFTsxaP5WB6YAYBIRd0al+ZLfEimYmQVV2aQPdCvtFWQBzeWl7UBb0QgHnZieyrOIX5Q+57myP2UtV/JO2xJ1LIV+v1bU5+A75H/ytJtQT1w0z/Cg2I5akJQkQuKypTdHLLRWs9gLUyoxl2Yq5hVIiEwXLDsSU6qsFjArtnms/1NLWuBgrJKnl23+tg6Lz1Rlb7C8K2zh1br5C2gwSC2ZBZecRknaCQTLnz1lvk0Dlt3bpWsncIjNhtILRWm8CF0D/JT8Plt+9YB0QgjpYZlajs/Z9Bv7LLTakLCW84EhrCSDVg17ld5VlaLHI3CMmDnX67f4VHU9tes8nwjAlFKP3QjSywKeAVhDgF7FabnYydeTMad58nlUjci/sEekARljsGlowWC1cgMR+W5R1Qc7hegXpsD2bTcDmCytWe5Av0XH0p8j9KU8Pfe63WLPOH5E4hwr5+XLIwZ3n4n+Kru5xcKLVHDVZePBop7eFrPEtYLGgO/RF7TY+OJxq+M5dGin2IXFQylrvVjQYlnpiFt2MGTJ81lVnvl7uix96ybL9fis63ePmueGHZbeFlfVJGMWxhKrBirNAJUtZ5as1hK2YnfEwjMI9oM6NgeuHXhVgQTsYKFihcG4xc4vWi0V0+dV7zrkijxmsqq2lLYm5bfE45eteHDE6cxkoZC2B65+yO7b9z5tWDL7uyw6XTDP8YtWfP1+82erqhgDDMJwAXaovsN3SdVw2YLV21vMMzpthZu2qvLOU8Q3r2YNmJVMxnLffoslPn7C6oWC0oSk7NJ377SWT1+wxnCf2Zkr5u3vUWVojSKCnm49kxtCd4G5jnarAoIYO/hhjQxY4/KYeTvbrQHTls+bZ+cWVd3W0hkBNE93hxUHUzpXxj4pNkkYYAE9Hlu8ucNaz+ekSdOGA9YS/dT5q6q+u3/3z7gN3/0j1vXpKSts77TI+Vkrj3RZ8PSYWH5AWSOdsVo2a95g0Br7dzhBPGxJK3qrkqon+TnXS0B2x7DSfgA4ifWRMcBqAd5IhXo8Gruk6ZgjYbrZGPrGZlQ1zf1Vqtv77QL7WIcIdNfqrpglGrIHj//762Of80Cc39jc7yo9a3V3DUanrHzjDrfRGIlY22cmnJCd86JiuVqzemvMPev5stUuXTUvHohUYy6viCHztSatPNJpjzz3y69IinDsfN/L98HaOd1MEb4C0WSwXirWy/YzA36LLDVs6fUjlt/Xpyo+NAdUhlHGrgodqoxUaeSxyHLNklcr1nGmqrRXZKEq9qPjoXFN+OziWSBCYysCVPwNGiMsIAIXp7TDwiCSdBwTO2aHLIp6yLNFqwx3OiFvCCGvV6wH758djst+ARNIaHZKzWEHmMBJM0WnXTmytDWAq8sTtrq/TZVYBH+PkFTl/vW6hRersppA4JwZiTq9CUDmhbPa9ZEyWvnOw2IP1t5yg0SjVIxhZho7v6TdJzoqdBdovJjAw5NrVk54lWZb3R23+LWc9FHBbEPi6uy2lFiH1PFFnW/nsbSq+wCZWAeQkotdwXDGnGaJSiSsH8YXHDDFb2mw1aKTOVUASotBpVGpZsXuqC3tCdvK9rCVhlIqnc/v6rHwctVqAY+1nE8LGJF6bD9bslLKY7M3ogWhGtM9KgJtMxmlDQEEsH5UD3Y+sSDguLY9KkCHLocFlmvLF2AIoA64Ukq0VLfQpTmbv6VVYwn2keo63+SifsfxEOVU0AJLOTGR7N7R4EVnXHrSny5auQ3Tz7IV7z4gM1ZABveu0OGT6SRAHg8y/rXhfhUlAM6ST47pvWGzKhEAZEWpXIT1AJj4lTVnXYExaMEZ1bY+t6B7zayBr5W0KiMDWmBIcXG/2k+QZvULdFCCH52rWKE/av5Ng1Yb6rLEpTXrei7jFqW2sEuxlioWvrpo8aMu3eIfn7fIfEkAmApJ0jB8PmawhcGE+WdWtaEIXluQmJ3nj2q+yExeaToW8ex33GS53oCFnzxvlVRY9yB0eswiH33GqksuHVuPBM17aVwMsKrMLo+LAcMuhLG+8h0HLfzQSV1/nnk2N4ArZAIevJeWczLYZBFGuO5pb7PouVmrHzurn0uPNdQrQJU8NifvKZiZKjYLI30WmypKeJ3enrDya/ZKP1SdmTNvNGrVqWmrzsza/d3/0gm1/X6l+AAwupZskBJhAbbq+KTV0ARR3BELmqe3yzEymIdOTMu8lXkieGbcXS/OmQ1TJqcCBxhPmJz8cIsAOiATcHb/8I8K9JOe7Xx6UdcJ02TSmDxPqkBsjaliub57ROOhccCBq+qVUateueYqZSMhAb/8QFQ2MtUjOzSueGY9aMUuj9qDJ/+D07XVGrZ0uE3Vrfe1vct8nz2hDZjv6rQ8+hD6w8hi4nrvjb9g/r5epfSY1yiI0MYGe4sXgSvAsCQVfd3SmbJBkd7M57PyzQ5cwdZhmoydTr2vy1lTtMWsPNzuNj7rRq2frPyJeVoS0qZpAT2wUywWbN0rFXVrWO1lfPH3zXhloslgvcRO4c6b/63lt7U6zRO+SIWGY4AuzbjSXibW1oQ0M/WpGfO2tVq9PWkLN6fMX2xYdK4qLU9sNCNhJsJfhNloRKi4044RcWulJv3I2kjQWsapjiuab2zOrCXhyoe7WyWUlhEfpfCkVagOYoLPFqQjYBEoDKcs+DfPyiMmcGHSyrsGNfGQOlk60mZtJ9ZsZV/SYjMVC4+uWHp/p3aZLGakj1h8vehOiELRmS2u5KyRTkuXgSgaJoCqOcT0Km3v75LYfqPsfaP8H1NJqHvf9LJZLCLfrlLSb8ljuHd32cp2v7Wfqei6otMCWDG5ysm9VFN6FQsGVX3lygKMkavLqqYyNM5Bn5g1z9yS2ID69kHH2J29YsXX7bPIVRzxW6S/4Z5lB0KyhIBRATjCSAEgEKMDDOKn5232nl5p66gOzHf7LXmlaLn+kFKX2C0AkLhe0q2Z2cp2zEXr+jkpQSZ3gCNWAErfFsqWPthjlZjHEuMlVdRhTst9AtSEVqty9S/t6LHgdEbVnrCCiHvlzr9YFHhAH4OtRfupvHmePWP1W/cKTK7tSFjy3JrVj5+3xqtvUDUcf8/8iecPqSlSSaRh1nbEre2hKyoxL2/uUoqFdHe9t1PVU/nesK6f7EUuLFh50KVTKVqAbQR8oT3i+NEXadxnsfUoyOT2sb/5Sbv7Vf9BgI/FCL0iIJfUucbB7Io+G2ZhozoRJra+smq1Q9vdZzNWPFTYxR3LcnHCPJTKh4JWm54z764tSv3inYVNhW9iwTEJPa16f3za7gl8p/Q95Tv2y6cr8lfPmG+XM76V5mh9YwKoQ5fnS5cEzig8CS9VVGBAZdjK3dtUbQYzhSGvd7BfrHV+b69FnrqkBZb305gD4A0kzffwC+aLRa/blBD3977b2VrsdVqiSldC7K86JNC/bltSRSaIxVnw7dKoebYMWe30RenDYLTyN2628HzeacPw19vSbb5Hntf7A2oAGLJMuHRVIMu/dbNSxIBVAQEYoUTMGripwxZJG5iXd98GE0ThDjo0qlfxEfNdm7Ha0rLZkT2WHYo6m5TRogpNEK4Hzk1Yfajbad4YJ1eu6V99Ng7w/T0SlVO8oiKBo6fNu2e7rj/pQ8Y8zwPea3Rd8D973hq7t1h2OGYtnzhjD679voClv7dH97gy2G6B8UVr0B2ir9saaNYKdFVoSEOFJu7u2/6D7ivzAxWn/ouTzqdrfsH8w5vEuGrOVsWmTyCPTQssvHegTx5tekavrBtVh0LaiIXOTVttoEPVkpURHF5NKeHoiQlZP3B9q/WSfTr7R68Ig3XtfK8lXgaDlcnUbWTnTJPBegWiyWC9RNAuBKAkr5m6WTmOtiokcIWhHhV2aKvmb+9WdRbBQ9txImvtj01aaDanlBNGiKqqagnayk3d2qku7wqolJjJjok1NJcVuJJYNuS3/JFh+d6g5WBiZefIYsvkV+9ISo+DUSTaBpW1wxgsl8xz8355xRQODmtnhngbHVLbqYwm4WC6LuYHN+bwUlnpLSYLGBKNhkZDx1ja2auS6lpHi9KLMG5Q64iR2ZWmR8JW2T+i6kTeD1E/1XVK33CMmAf6fVbc6cSrBVXzNZT25LP6Hs2ImYK1YydOSoZKNoIUJecrAFVrWG44odQb5fOlToCOq/aTZiIWNQ+tM46dVwXY9L88KLYvu6PV1rYELd/llTBdflsrBUudWpPoHMsAzh0RO7FwR4928ysH2nQ+eGBxLajAw0MqUKgrrQaYgv3AeJQUokxKvR5b2eoTC5kdjOo1lPyjNQPc4THFDpnjVpEBPrNzJbVDwmgUXzACBgFAs7TbK38rmB/Yy5YrOWu9UJS3ExVT+Gnxc0xp5Yh94x4LTK1IlA0Dyb2CudwASaRoWk+5CkzAlXbsjJueDh0T4yVxMS1PtOiZGWnVOCcq4dCKha8tmU8pJRPLk71xSCmfWmfKqovLFnzgqFJYEr5nKhaZTLvPnZjTOPCcviKgUtjSfr19Cz5QbDLk34R2aHxBFalqt8M4iwasePM2takCZPiGB1113syaJZ+acGX+bS1W2tol2xPADmBG4ulQSMUXaL18+0hdOnNOKvZkdHttRqJxDFwBtZGLC5Z8YlRawtr5yzL2BWiKMZ5etNJrb7DG0op0R2h5ike26JhkVjo5I5NPmY3ecUDsBtqkO+//gApcAJCwLPLXQvDPRm1mVawaILLlxLzE4jA4iKk9gNDLY+bbu13MSH1Tj4UfOy1WDh8yhO8UtaBJIk2I7o33psAAdkXs1+WrVs/lLffaXVabX5RQGxsET7LF8mhB55dt9cYeMUEer0c+W5w7wBXwvJE+BKzBHD35kR+31j8/LoCT2Ye/XsCBq7kV3VO6CXgP7dFnN+YWrLFtyFYOdThbjGdPu5Ty8JDlNyWcSSd6uck1sac8/59++L2aP2Gw4uM5q+8alj2FGNJyRX5hG+2lPB1tumdygYfBrzfEnmJdQRU37DqV2lzj2pY+6df0PpWKVYY6BDSVfkdPViyrQAZ2jfHDcw+44nWAWOZ1pBbV4W7Nb5kbB2WWSlVtrs8v38LGjmExl/h9vVIBA/Vyv5rxykQTYL1EIFZnUkeH1PrQZXlYYfRIeGIRpVyKrV6BMPQe1aFOs+k5gSx24ohHAVI4vOd7IxYeX7XobEVlxH2fWLTq3Uc0+cAgUOocPnrZ0oMOsGjCxsFdWo6qdoqkgLwzS6pMU/okU7bqga2aBFZu65clBJMaWhUACmJwUmukpOT8DVNzedVVxB0/r53b/I2wL3VpicodsB8NnWvo5JjO03dt2lkEXMZLqLxuBREQUOO9maxIf7FwUEUHoNOCJy8jFilTykzXCDf3TFkl+BJzd8Wt5disKtMIOY1jg9CT0ALL58DShRApkxIaXbDQJ16wxokLFv3r57TbxS4Cpq1430FVS3UfLUiQDcghpRhZcMdAmpDruHBTSkBP/leFmqq+Mv0+C6/UxUKRspo/hOYD9sxpn9BgZXv9Vmz3Sle2uC9sgXzDYtNl/Q0p0NSVmvRVLNQwdaQKl3aHVKWofoZlQJnTscXOLjiA2AIDUnPguzNm0ccvSIQ9/NElAQgWc1KqhZ6IK/OPBix4adbqQz0CRvmBmLQzVP+RUmaRwKUeVkAl+xGfUmMCwWG/Vbb0mPczJ+RcjU0AP5N/kZmqPH3pgrQ1HCuic7RNMK44c7NJCNEbzuux+Kk5s73b9HdYHNTvOCSRcf3OQ2LOLr+j3cIfe9Zqm/ucCWd/r0vHsUjW3XHjsyQAk0xY4OqcZQ8PaMyiEwIgB0lVYr+A7cTOActvbddGBM+v8tZuCd3V769Uk9YM8MSmhNYpCI4jf/O8ec9ek42DnLmzWasvr6jfI9W1OMnLCuHCqDVWVq3RkbLgallsDpueyExRmygAPPqfBqLsLEa5PoucGHc6TFrORCN2yzs+rA4L/pWilXb0afFmjJMSgxkRg5TOWHY3Pe2qSjuRkoMZY+HHQoD4ROF/uQq1/l7n5bTmig6oPJ2+p0sGoT5YbewZqKTtaDdfd6c9cOkDYntIJ34i+z8FKLyxqHyt1r7ziL323vfreU/fNKCxXBvstJa/Pmn3b/tJy73lRlkOxB85LyaJVGKpO26V7f1ifUr7N7mForPd8rt7LPHEFYtMZTQ+q0Nd0rdtVOV5e7qsesMW3evWY4sWPT2tVB72JWhTw3MFs2jUGXZWsAKpC/grBdqoy+oDxtt7Ydw51zMnbB9wRroI0693KAgp/Y3XHUx2vi90vV8j587zLTF6zD1vvD9gFYBYH5t01htLK9aIh6UHlOFuOi/JBq73sHTMwR3HVgUi/VemXW/Zq2n5A5L+p30U/UElr8DviwrQVyiaVYRfP9EEWC8R9GFjV5/eHFXFiPsZLuu92rXQIgRzSR5oUj0YgaLNUFkxfdSoDvKavLMK7T5NkohNl3dFrTTQYsEnzmr3hiUDKbjSoS0WWawrXUTKjd2zdr65wnVTQbk9Z3O2dMeg/gsTg5cMLMcdb/rgupO1xz7x3M/r9/hMPfT4+1TGrYlkJeNM/aoVpVG2/SEVY+YqxS7MWm4P+oOAyr8l9gwFxYrAaBW7Y+ZbyTrB9krFKq2u2sotyGUJPZmQ8BNCDM7vwleXpMmByUmdy1htXe8FiAK80CaHSa3YRd88n8AJolbOoxYJKKVCo1WA1gOjvy4PGs+ebfLboSHw4Xf9uoTVAKalfTGbvSmidBc6LawSAFeJyzn5VsEgJCbRzfkE2viXc0heqzh7iaqpQGH4r1aVOqJKEusArifFCa3n2WWTInT3ItcftNhEzmJTBdlE4HuVOzKkhYqUYMt4zYKrzj4DYIIGTxWefSlpkdiFp7e3OFuEoE8LKq13sE6A6QGs0C4p/sQVAUwJu7EL6IxIZ6fKvlJd/mgwMxQl4OHFmAUkBNH3Ye/QGdUx4HnGAk3jatiCybtcY1rAGgJ8xgW6LrWMuTIpA1aqRhkftCFiQ/GJ3B8q/YSnWWZLTMA11xe0+/a8T4Lz6oXLtu3XLlrhW27W/fdPr6itEqkn7Dfw19Jnra2pZY0WW8ZYoS5Nls4RQILWSwPYNe0OP3JKLu74YfGayIV5iY3VvBoxcjxmoYWCWEvctWFzCq/ZpUbNpJWUotu6SQu70pgsVPTZbEvJZFRO3U+fsPs7/4V98ql/J3NOqsc4V8TutJmpzs4rDUiVImwGpqtU/FHhShqdCOHH1GioEjTx7Ph6n9GwWSppsccvWjUasMBzFx0o2TZsDUwqSxUJ7AE84z+wQy1wYE10jFQZT85b7x+c1POLeF2NptfyTrM1NW33b/0Jx9Kkkvob2B+1/AkFLXkxa/5PPWeFNxx0E361ofQfgInrnPjkGVXW5e7cqUpK0nHhyws2dUfEqqNjVgt47TVv+aBSa2grxXquZK4buWr8rGU1ZhdfQzPyhu6Rjn1m1ryDfRaZzKqVFywQrbY4bj6H6mK0Ut7De6VTg+lDX1rbM6JzQJhOKpWUJM9qgwKA1qieb645Bs7ou576ox83z1pGFX/qLfjsRbmvqxqW4/O5Z0ttgDYPCuRT7MEz1v3ghFKKRqEPmkNYzlhYMo1iT8w1lg4EpA9kQ4J+lnkRVpgqU6wjNIZpsN6MZnxeNAHWS4S0OpenrPXkipU742IX0kNBLdqrhzslcIfBQp/ALpl+gsuv7rO1va1W3s5ONmPx6YYExpiV9v3tjHPF7nRWAYXX7dXuVw836adaQw7R9ZawvHHQFsFUMaEyUde6WpWyY/INrzi3chYV/FpInSEeJ+2AKzbpCU26jYZEztXXH3FWDoWCqHR8aYptfrVXAdSUcJfvTImFqvW3a2GnX2Lm5iEZrKJNYYePBxbaGhZigKDck9d3jwCGldcMSkxOuTYL2PKtPVZs8yn9AAtHi5Ol23os2x+y6LEx13Mt6FeVGMAsssj7OZ8rdpFyeY4GVAlJj8Hcq7fZ6LekbPKbum15V9BKKdc+hvRt68WiBdOmxVpC3XJDYIcyenRU9LoLzznXdrRE0ROTSs8hYi+1+CzX47X2Z5fkKaWKtCevKCUaOz4lwXbw2rwTmc85sX3bkzPOe2xqyaJXll2rlLmCAKF6DtYaco6Wh5rfI90JIEfO4TmadK+qE0Dw1JizIOjrFXuAGJnUhVKrPWFL37lNi4D/6TO699wHABILDcASm4or/8YndpRxSMcBNHsb/lqAfNJuaO3ob4ezPmno9jMIzKv6XqwebGo8pEWsvnVQvQABl+kdLVp4qOq6N/Y95kHUTqublaqKNNAmYqKKcNp5PYUs/sgFp5HK55UWxWhSTtmIm8+OaTwW9w+p+i2zv9uli0jZrhR1Tmi1qDKtDnbYypFOicABZhSShE+MWW5vtwCUGkX3uXZF3IuuZ9NmkzMSrUcvLVm5P2meYECmnRpr4YD0cb6j53WdFVgyDHWaDw3lULdE7Nia1FZXVQHIZoRUGl++eExCaTnL49BerqlFFL5xpKbSR/qlCQtOLLsWO/T9m5i28bd0q6KPRX/p2/ZpA6RekGycSiW1HSLdNvBfjrsqu6dOmvfgbhUikOai0g4hub+3Wyl6tazCNLb+Z1a9OupYmvVWXmuv2mT5Q5skJcBzrfCWmy1+bsmiHz1q8ePTuj/o1WADG1sHXaNkWB/OYbBNadpz//5HrfSmmyx6DauWqoTuYs3ml6VDUp/DRMD5r5XK5n38Bet4aEw6TdgnTD7t4G6XGqc4Jp2zxb2uvRbpNMY2x3TPLf/eGZN2t1th34Bz8C+6/qxKc1OpvIJfVtU82zZpI5K4nBbAJBVLmtMN9KDYRywo5t6xT1pR/p4AbEuDVarY/Ks7BJoxYeZzSAfWzl10fmFUCyZiYkd5ftX3MeQXYyphP5qvtpTrS7icFpCEgWaDKH3XKxT1r8FXM16ZaIrcX8qm4Zt+0VKjBS00pCtIG2kR4vu1qhZLgBGtU7AhKO7qvV7mXuiLq8myLjIP6xXcoztFd+d2drgGxbTreOQFTQCUGcMEsXNWv7x0TpOoBOM7B6SHQUsVnsvJoJPFiN1o+NyMRPDaHUZ916vPMCeFHbvzvveLDbPTl5WCoIcZzU4BfACy1ONjNvbPRqz72bLE8PLkCfj0fgAnJiH1QKTsHmNEUnVzWe3+SGNS4q7JpVyxzM2b5CgO6ELUnu8OWNvRBVt4VadSaviIURJOChERNylE7BIoy8fDacOyIji2eL1PI/oT0ib1HcPXJ9P9P/rrlpioqWoPcKVz9pgljy+I3aO0HeFvfiTpmganvFZq9VjPU85ZHRG63OVHF6za3y4ghzgftgCNxeqhTvldkbLEEJaqO/pK4uODzoyInZl3bUlgA70eVVsyPtR+ZahNCxbgMLM5YT4MRsX00drGJ9E7uiyYy+K2TtdiBSF1T5eKF3RdMwV5kbUfW7NqKqx0CilI+Ts1TOwVgEHjdUtUlZDRi04jQ8qW9kWNUFCpR1K0FDVgJKsqTlUj+uVAPfkdw9b3aXR8PvPNr2hBRDcXvjTnROHoXEjlLa5ZPZWQ+/X0Nw9afKou00iuMcdSagtJiwVTRFDmT1AtCPOALkdMk3oPei27KaIqOoAVBpq1vZslzqd1kD9bc/0BR+essrXXMSv93c71HAfxiTkr3rDJ9ZekGfBa3h64+H6xF7AquKpnB8OqREUL1PrUjFJxMDqI/uW5hL8SffXQ8Mmuwy+hNiCLz9qwt4AVqdx9yIKffsEqrz2o6x/EtHRuSUUo2BDwDNTbE5obBLZgNNBL0ph4+8j/6wMI+NmdtNRz8xKCN7YMOT0QwuyNRtZdMTUCB7xhWswmh/Qo1YmNRFT+VVgb4OKO6SipRjVF5/i7Whz799RJPZMIxOXThJj+3r0W/9sTYiDvjXy3GjxLfE6rl0DAibjpY5rOCNCpWABbhXTBNYymVyaO64CjrhbXE5UCHExRNw/J6Z6qRwAfqUqMhn1IGfiMYkXPytLBlCXG6SqQ0fnBZsMS85zDgOMoX71pl3y1GssrVqPfIc8RLcK29rgxe5pxGRaLJhNQqgQR+c8tqcKQkLUElhrRiHoNVq+OCRzjvI5ZKHMOWjFPX7eeXwpjvFhn8P30vDUGe1UEAJMlLRpfZy6pDyceZ6R40cCqqfiFcVWKFlo89vhn/v0rInI/c67rZYvc9+yab4rcX4FoMlgvEYknr7oJdaWoFBeaHFW28bB6PeYv1ZWOYYJA8B5YoReaV6JsFkHKyNkls5DKTmEOobFzPl/eF7fZW4JWuueQzDGpAly6ISY6XCmGza5UGFM+gAC7qkCuqp2jjCJpXZOp2Nz9m+QwTWNfHOABXXL2Dvvshh/5demMAEuAKyJ/524Js72luqWemtKucdP/mVg3Zqxq50epNmX7GF1CkwfOjmvHKZuBxYJLJdGkliqg4XYZDBb3Dlj8wooVBlz/rvClBaUnEHvHpqtq+Jy64Cqhoo+dcyalSzmlUliY0NKgOQIMwACIUcFf6t79Vtu3RboyWQ2Y2clf/1GdQ67XgSesMNJDXpt7Xbct3zWiaiIAKmlZzrPtVNraT+PJFVW1GdcEcEf1JQxOYDEn3YiaxKYzsmtAjwRApA2SolwWUAZY0RZHxqA0D06FBK6whKACqbi1U+dAFWShP+bE7LT5wZA14lLGqbNZCctXbuuzwMMvOKfs4v9RChQABzsFu9H5qXGzc5clzlcbEI+rUgTEcx4URPivTFnq7JpYHxa+xFPXXBuaiNOf0FIpNJ224nCrpTcFxTJRMYc/GItq7xMOdHqvTlp+T681kjHHInWvt2ZhXFwc0z1GAwbz0TJaVYoVcAVo9M2tWWi5pMUVsTYggAUel+3aSK8qwrAGUOVe1C+zUFK29K9UZd2hbc5E1+uRyShNpsUuoBkr16x8cLP83rTIpkKWPzSsjggE7CaNvuksADMBiFMD51JDLvepM2tW7Wt1rGx3UjpG2TxgMIoejJ6RVC5OTJl/2xarrazpvirdNPyjcuuOnJpUOhKmk+o9HfPOQedUvprTwsxzGZhwWkIFpfud7WLXaNkz9bpWl2o+Ouf81zwey25JqN0MFbcwbDxrYpkxwcTdfaBDfnXRo1cte0Ovzd/WLiYUEXj9hu16RrxLGQGJ9A1dri3Xk8et+vpD5h/ot/rqmnmQFGzqs+hfPitzTICTmi5fm3Atfui44PWa99yoNjqAKzRhCNYZl+i7/Mcvu1Y0A906HoCvvMaKRWcxQrFJMiHRPY3t8zdtdrpKrCvoaxgJiq1NXi2qzVYjSm9QGN66RT5xXNeVIGUqNmtuQR5TdFagcg+NpWxtSC1j/qr+i0kJ/GHuAHCquN6IQlFyBQ/9E9Fl1T4iI1b+pWcrFd8Axfq1cdneeFNJ+aDRwF7giubZpbLmXBg6nOdpgI0HGpkEmfomo5I1AK5oao0f3SsVsjd8mV/NeGWiCbBeKmJhpxVKBNfZA1cF5KoKG9IWsOjBPKHJwlOH9AMpKjW0ZSJAu1WvSWBb7WuT6R9/gxZo4KGcWAdcl0mBdTyfcUAHQ70sO26XruHf0FxelWK0lGBH3PLCrGs3g5npUkkAAVdrqqcAK1DqaMjYaW3cZfnF+D0CYYCX9OE++ThRiYXnFoLRYkdIVTowEvwdVZAIolm0AI8SntNzbaBVbtzsRnnd6pag/g5AQ2nz2pFex4JNLajEnJ0nC+zsq5PStgBS2MUCtLKDwfWqQK/SlTn8cjyOJQQsIizGbqHcEtDkz2JKuiuQNbGJsFiJybq0UTjP5wYjtnbzgFJXVPtgFYCLPO2KVm/pV/Nntd5JBLQ7xW3eua57NVF7xqbVR67t4VH1MwT4lYc7VdWY396pnoCwJEy+oYW8zhmWic9QKxJcwn0egRi5YtO4mUq8Ql1pNYxB5f20VBV7KYHy7p/Rv7BBNHKmgAEdUvn2/SrlZ0HCRgCQR5pz8bUDstZASM5xAG7lWI+/2ZrrSSdmZi0jsAZL1nY2pzQmju3++TUxeXzhwWUDPUodbnheEehq5l6dssrhbWJHEfSS0iF1BwjiusCgNPA5OnVZaTk2C+pxiOdTsSr3dlW6JV1PTF6PIB+/LFJenql52Z6Q0kSvpcVyas651Hs95j1zVaAeNkFavExZ2rL8vn7zP3ZSWjkE8bBb0gThkj+xYrHxrEvTzi2pGTN5EYn4AYrJmNzrF+4eEvOMfko2DKRpEZhfGDU/6cLFZastr0p7RbVY6qFLAmMENigUCyBcp0AFFhlR/MI9m5TqKxxwKTq0R8nnZ23gY9PX02naOA33q7ghd7D/eoo9enbW/J8+JtPR+tKy+ZayYqAwRaXJdTBTd55o6OTyZT0zAH/GL+k/QCGp/8AjJ+QYz+bkgYXfVvrRv32zPgPtYuWW3XKWr9M+CuCORUSpJOkAUbt8TalR4vAP/rp5erqcqB9mstdVy3nOXJZ2zTvUr/GDIWz6vr36HcasMugEeK23NJKVQdQvKwQ2hDI9HV1QUUJkriKfPUIWOF6v+TcPqxqSjatsJdBkUsF85qq7rzML5t3mvLcAhrSIuh6wefRERXqRyTpH95KrUFZT8u0jAn0Aq/ooFhktYvo9py6JUQS8wW4yVtnwAEJ5XlUpyJhczSldKm9C2MmhXgHib9R4/PHH7U1vepP19fWZx+Oxv/qrv7r+u0qlYj/1Uz9l+/bts1gsptd8z/d8j01Puw3QRiwvL9s73vEOsXupVMq+//u/37JZt7nbiJMnT9prXvMaC4fDNjg4aB/4wAfs6z2aAOslYv7uQWlhCFgqpbBWC669StCrSYIdEXokespRQlzujrtqK3xyohHn/M5OM5VUNQqABj8nOZBH/Lb0qh4r33+j9A94S1GpV+ptcYaOy6vO+ZgJnoqvzhalipho8JKiOmfggfl1/VZdtgctZ5fFoJAGQZjN4soxbiyYABY0PLELSxJUq09gT1DmnAC7xLEp6WRUXl9GY1MSQyPQhw6CCaXREF2PAJxSaUTW6J34HiCEMBYtVaHTr8WEc6h1tUi70vupRatu6dP7omnI9viln0IkDijJDnit1OKx+YM+S48EZcYZOj4q1o9Q6nFTixaXtgslfe7yDr8E6tr9szsPuC/Aoa9kEmGHlqtiCuipx/HSo86B46pSlwi01Vpla5smTG8ibvkbBpRuS2/Fmyrg2LdLi5bf2+fc1cM+AQM+h355iVGnFZFRLG72Aa8KBPhctC2cAyk6MWWjUwLD98bfablvu0VAyLdnu8aEFr2wX4s2r5fpJumlzaRVghKY06cP7y61symhWSOdy2f7pR9CHIzQlxSbc2MvyWcKNovgeFQBRgpnHVDJgLYtYUuHSAlR8Vmy7qfTujbca+4ZuiqNgSknZIaJo2DD29UpK4bgckEp4vr+bSrR12t2jKhPHMeeG4zpmsFEMY6rOwat3tmq6wYbCgvn6eqQ4a7viZOWvXuPfeKFXxDww/YDgTaLW3R01Ro37ZFrPemk4u4+mXau3tCuRsMwQ4jD5ciNfjEeEIvifey4AAFmrfSkhB2BLYGpUVEHDMq2IZmRUpGndjUN3PEzrk8glhyHO6WPxFUfsAlDJG1RJGydf3vFeaypRVJNoIrnFrYr8PwlGe5SJFPuiLm/qTuxPc2pYQft1v3SGlE5uHB7j4CpxvMLZy11elVdF2AhqXpreX5arEr07IysOuyWfVabmlFFLYAi/umzrkKPKFXMu32LVS9fuw7o5ExOdTEMVke7RPb3b36PFd54WAwWrXW6HpuTRky2FABn7DHSGVVHop/jmV88mDDfWsESVzO2uD+iDYgaRNOVYAssccWN5fE1pdAjo7RrqkgYzrXCGiE+WtC1qne1WX3bkNUTUaU4ATXyoEMjRv0DTajZOKTcpkPgvVpXqx6Clj5ILFTZzLPQ2+m0qOvAGDsQNXlmOqGYZ+9WaWWZk0l1FruiskshvYsdRezUnOY/embybOhvKhU5yqMzlJZyjTSsfcNqsHK5nN1www32G7/xG1/wu3w+b8eOHbN/9+/+nf79y7/8S7tw4YK9+c1v/pzXAa7OnDljn/rUp+zjH/+4QNsP/uAPfk7685577rFNmzbZ888/bx/84Aft53/+5+13f/d37es5mhqsl8h1v37TD2nSpryXHmzylSpVVWWFeJbFkyo0MRZ+r9gcGYeuZGzl9iH5FOH7IrE6VUNdHWI+8OGhDBrAlu1D42LWejZrjaNnrHwfzuiwUkX11tPEQDoKmr1UkXkf5e6AosRYQRM5wAeg4FnNaJGj+pAddGR0RTtJJlDKizGCZKJa3h2z5DXYFRoq+zQZYcQZStcs9vyE0oYYReLGjpuxBOdUgmVdZaQ3X5JgHLp/7XCP0nTdn5ywmTcOCvSsjYSs45ir4FHDY0TftZo0XNJ1ZYsCkaSicGGnLQrHUE763ESbCKj6cOxtvdZxumrhB14wO7xLlPyGF1UdhmilIr0GLEKuG6+qhiXGis6klKbJpNoSQSuj3RjL2OLhlCWvlCw4n7Fif4vl+gIWm6na6hbScmahNed9RIk59yu3rU06L/oTZgb91v3oolJK0uKFfK7VStDvfH6yzrEcAb20RG0Bi13NaDF3hqYBpZdhd0ghf/Yv3qPm4LHpko6XxUd+TZGgre5rtdZn55QuZgcNuBcrmSmp6pAKqsX9Ues85iqzQlNpmS36r05rQWEhwsVfppkLK9Irha8sCuxzrWDcaOcCEGTxwnuIe4ygGONVRM2AUEAiAErs3nobEjyAaEZMmo77FZlf76mJh9AjJ5TqJHDZVosddC2nLsrWQazFUs7pugBzaN6uzllxd78VO/wqRICpkEZqwfUVRAeHDYn8vo5fuP7+aAnpzacWNZfGxSA3EJ2jGRwdN88NOwUgGGcqBJjJ2fxtKet5wDFWlb3DKlwgJcax2rmrrrIum5fQXZq4REJu6BjXYr5KeowigeVv2mnJi3nLDkcE3LFkUHeH1qSeTzYfLacXtUHK3rZFbK3mhmhQeinMSqNn5qy8iUo+k/4PoEIVMuOusLffAp84quo3L8xfrmClO/c5L7zLE2quTLFIy4U1tcWh/QtC8tUDbZb8i2O6RjLp7O6S7gyhvdgXjGfbk+adWbDi/k0WgCVFG8jzmSsKdEv+QHuhs1NK0dJ2Rs3g+9tdsUYkYIXesHSJ6PrUu5D37WlzVgmw+3m3IdCctZpVOlaygsWMJAFqU4POcGLBVu7YJH0m14jNpxq508Q5EbTApWmrZ7LSmlGtSsDWo0/U+7a1WHZr0hKnFqSP1Bx8ec6uvmuTKoGlHV3MScQukAUAG/5R6dq4z7Nv2mRdT66udyso6R42GnX5nEUuuFQuDDEsL9YceLLJx4xWYegBu1zFOL0Lq7WCPTT+m6+IBuvY2W6LvwwNVjZTt0O7576qY4XB+uhHP2pvectbvuRrjh49ajfddJONjY3Z0NCQnTt3znbv3q2fHzlyRK958MEH7Q1veINNTk6K9fqt3/ote9/73mezs7MWJK1tZj/90z8ttuz8+fP29RpNBuulIhSUOL3a26YHC0BUanNNgdWgOe30MGqjwg4GMEP4fTK2lPg01aJqH4klEa9X65Y/uMk9qJmKpS7m5Sk1e2vC/EP9qnxSX6xtMTFgasAKTX7hmptAlksCF5GFihZt2KrM1phbsHpaNamQlgS8YICIwLM+5to4AK74/NWdTngP88bCAUhpe3ZeFYRUC6WHHWsHY8BkB6hSPz8WOSquOuKyKMjv6hIj1Pk8ZddBi83VLTS6LL8wuXXz2nhAxoZMsDBJTFaUVjOZYjfAccCkAUw4Jy042YrARf+jOYGZ2R+6UamBMov/WlX2C/jXAGLy29otOpW37sfnZQOBGJ9z3wjAJ+AKfQsplg3ndlgnrjsGqNH5usVnaqoGRbuT2dUmcMU9AsxU4l5rP10Qq0SajxQjeiquNWX3SkfgUF2qy4UdEAlIUSNv2g6ZqcSdMQITEXvwpL3url+16ExJoDX89EWBq4Xbu9WWCGBe7Uw4CwL6Lc7T1sN9HilGQBqthRiL9RB9azy2siuqYoiVG5LSwVGIAHMGEIJ9Wr6117Vxmspb8MGj0i6RMsPqAr8gPNBYcCNzJQEaSujFjGHuuZATM4k9B+74LM6NgMf1b6TJN75Vq6Xr4EcTy8ySAAbApXFwp1g33g/dFmCd+8GYSt8yZOGTY0oTcX1gndTwnFJ4iiVaA1ZtCasazjsy5Nr+wMpQbk9K9IVzrjADvdFKxtYOdUvM7F3OqhEy4ww3cdLXXU+vWmWg1R5c/F01WpbeaNtPukVyZEBu4TzzWI1ID8T3Q/0CxtrkwOK0t2kcAd6TZ/CUW3VdD9Yy1phb1P/pmylg6fUq5UuLn+JQ0rFgmNeenrFqb8r8VDJOrAgMl9tDStUBrmBT/Du3WePGvdKfeVOu7yjzSW33sETaySfHBYqwH2BMASJoycU9uOt1v2K+/bus0ZZShZv0RkmXglOnBuxTJtcEZMWiFcpi2bDiCDxzTjYNzGHeJxHLN9RDdHV7TNfQf2lSetTwmSlXhIOgf71PH43GxYbCNuG4DlO6pVvPPgwXAJUUIBWcYs/6OsQkx88vueIPWloVKmK8SJHXNnVb7t590qDSrJx+mfIIXFxVtR+glGcfcMWzwoaXGHjYAValUOXV5iwqNG6kK6yLles8hkY0qKrT2uS0rdy7XWMj8MnnHGgc6NKYV1oxFDTfakFjFlCHGzwbBKUISUXynl9nAWB78VcJpvJrEAA3gFgqldL/n3rqKX2/Aa6I17/+9eb1eu2ZZ565/prbb7/9Orgi7r33XrFhKysr9vUaTYD1ElHpcH22aIYLI0Jaj8kaxoFFoLB53SkbgfaVOe3O1Mx4qMMC8xlpMBZu63Td2PGgml+2Km1UFgoWOH5VqSf/3Jp2jSzyTFgABvyh0A/VEyFb/O4jmrzrh3dqN45+h9QOu1kq21iAqQQkmNy0KFON1O4csxF2bix8CEYRsHc/23BMG+XePp/ADGlIgCSLOa7hAMfMrlZb2Z90vecoowdMtIR0zGidKNVn4lNV2K52i19zrANCbCYm2JalPUEr7B+UpxJeXbBPxd6IDA9lgBoDJAaks4I1oYJOWp0ltE1VXeeTv/ajShXB5KDZKiW9Fpulpx9u6xiZ+sUMSh/nh1Fy/ekAiOiGKOVH2F5KuR6IpE1Xt4eklUteyKqqDyE6Gi6qG0lXUqJd95mARuJqTmk9WRiEnVCdHTf3DRYrem5eKUOaNwPSAJF+nOexWmiNOrPP+UXzr+TkwO3t7dZCwcLBNVp9424xhR1HV9bTrn65/oudWi4LnEjQXKjqPJjUaaCMto3CBixEAFyA/vaHxx1QpihitSAndqq5Wk8su6on3Kvvu9G8T50WAMMSg1Y2nJeYOAxtCxVdD5gGGk8zDnDZx3oDMBQ+Ne70Zes6RN4DzR7VYxuBcSeC8cqtu1SdVkrSL3Je4nY+A9BCBSA2Gmhc8I8SU8Y4TtOeKWNlKuqWKhIQY4uQH2m9XplbO3nO6ZRwPB/ut8aWATGvLedWXLqMdjiRiK7Zyk76aJalU+T5RMOHaefrb/8l1yjd79W18Z8eda1PAq6XosDFSKvTw1Vr8mciuDYUGdCWaO529Ek+sz1b1KeQTQ39Dz9x9OfEviD2BmwCmvQ6QInPJ4sNwBufFbowK2YGVmb+UEDFGuXuhGtm/dQ5W7p3i7SdCLepvqSjAYwRzds5R397u9XuOmzhE6M6vnx3UCwpgAdwg3WBgE+MqlC/FQ6NqK0OGwVAYB0n9nxZ81P9wHZdO92HUEipQY499UfPquG8dbTKTR9whCxCVgh4RVVr2oyW+pLSuNWHezX2xUau5s2Ty1v+AKC4ZgvvPOTu4XqfTj4Ds1j5bPk8sojxLq6qKIjnkfPG4w9tHcxXZVufdF0wWWj8dKz0Rm0NW+bGAQuemzD/7Kp0qtp87HSWJbBYpGG5Bkr3wphhCUM14a17reUjz6rCMv3/3arfU9GpSuaZnDpmcD2Dz122xDnupVcVo/UL1xzQb3Pi+1ciwK8v94tA5wQjtvH1K7/yKy/72IrFojRZb3/726+zY7BSXV2uxdBG+P1+a2tr0+82XtPd7bzfNmLj/xuv+XqMJsB6iVCl4MVJt7itu6mziyGdVMJ0E5NHaHGvRxoT0h/YF4jtSrArjVj7yYzVdwy5MvV1s1J26bQBaT2xop0jjXdhTvCPoacbYA6TwrmbEnITnn73IQnL8W4ipQKAUrVfuSygRYpF/eFoE0I6L+Cz8MOnPudcaCWBf8vygaQqwJhYmFxho5hoi7ftVHWX58q4Fmr0QRLMs57RDgbDylRQ7YNYICR+D3skOt/oYSeqveRSPETLxbTK+RGir+1tt/SWmETsocWS0pzl1qCVWgPSM2QGgra6jWsWlu8NKS90NGqHQUXniTnLd3N+DUteLUtfEj45Lj2ZfHMo517JuzYaNXywXDNquaijHWEyrbi+flT7JcYrFplIawJuO037i6zFJ/Kq1CRIAwDglLpALwLjd2Z0XdjqevSp5RBNc9X+xqVqU+czzrYBN3v6KOZJjzbM+roEEABsLPJUpql4YaWqZtdEYajFMsNhLQ4AWr4AmrKUODfhgOdyTjtn9aTkfdFeoeubL+t4JOJub1GfS64BaTrSZuh2EBdntqdcFSVjBe+fCP825FDPfed1FB9wDEoLl+rW8tyU07bByhCxqK1tc+lQsT+1hsYtlXUIrQkZSO7cbMH5nMYp4xu9ECyLej3S+5C/JVWULgjYBC9M2QMzvyE7Avkz5ejlidYxqHQTFiXBsxMCEzA8WA7Iqd3vXkNrKtJTgB98mBr7t2lD0/4nxy23JaWqOVJeGlPxmITqWCzwXOv8tg6qopGKSWknq1WLnJ7S+ysF+ORxB3pGF2zpzkEdS/enpwUQeB7R/9BYmEbMfA+Io+UKwvCJN7vFgu9r41MSuCMMr8Mwk2o6elopr8G/XbX2kznnpUa6bMuQQPPyoXZtylo+dlLvg1yA8YlWCiH7w5/6aWuUK/LwSlzL2epWv7u2Z7gOO1yLJjSSPQlXiLF9i3qPyoE9k3dMjN+va4S5L5tD2vZUJ6esPj5pvk2DFj85p36GNLGmeIUKQVgjKurkqdXWIj0VDJRsHeYyFnrmgs2/qtP5dvk9Vt7Rb6lLrpk8nSBgRgl6TALWuBcUqaDZQu9E+rXQ4Ta4astDCrLakP0Im6rSSIfmpdqZSyokiV9ccT1Y2+LO4qYzrg3j58fCXYMCvpovYzDRfiu//pBVbttjrf/3tPRhAEsqbumOQE9HAJXacq2bq9IGCRsSKim9mEG/QlEzz8v+IiYmJsQ2bXy9972uo8BXGwje3/a2t1mj0VDKrxlNgPWSgYg5+6otmuSpRCsOt5knk9OiTXqP3RQpIhliUlnXiceOSxdWWh09zQ5NhovQ5ewoA16b/datTnMzv2y+cef83PLENTvy/b8mjQDl3kzEfZ+at/xg3AI5zEtymgiYeFhMAAzQ/xwXwmJEvmInOqPSOmzYMuCwffN3/5oc3YuDSUtdKigFpKaoVE0tZGQ+SJse/G4QkWr398Q1a7lK+rKmdEhmJCIwE1qtKX1VavXre0rhAR9KP2aLVumIaMKTdUXAp0UAtirf7RWDhbUFIIN0K+k/WvfUNvUKuBQ6PAJ1MIecH8acVDbd+l0flkgY1goGjJY7VKDhSePLlbT7xiEcsAjLxn2hgS+BbgkGC8Fq6lLR2p5btLUtIaUGMYTFk2tld8Iyu9rFwjCBk+4CnJCOxfNIqdHlVaVrOC+0X6QIJUb3e2zp5k6ngytVrdAHY+Wz6Mkpiz0/bnb6kkA4u/nsQMBil1euMzwcr9rDpGhC7LXIREYGsqRz8ALjule647LMYPHkHGkULUYFHcvSmmX2d0k4jE+aegwC1PrXjTcRA/t8askDixSezatJMqnh6i27ZeGA0zlpIio0VfE4u2LLexOq9IOFhQXD4Dbfh/eaX8CE9En7I84MFbD4qc++TylPgDqM6UaguePcACwJii8yGQtPrMrSITZZWG8cXnT9CQd6lOLEIJcNBAwM9xaWjopFrB9goMq7B6U54/xJ70WevyZAj+cbrOXybb2W39enqjkc2cWKbhnSfVMDY/p3BrxiJCo7Bmz2vn7n5wQDmS1a8OK0hTAgxT+JdkLdrdI05m8ckeP46oEOmZLSaxRzTITPvI/vMyesOj2ryjpdg1DQcjeNrM8HDet7LCMLEIBR8f6DakG0vDcu77NCX0zVfVQ8cg6wtrCXpe6Irp9Seis1qxzYLNCGdkzeeqQ+426cs9F5cOW/W25Xh9574P/OaB4o3bxD9xcwQSqSZ5/qz2p7VPOF0X4m4NfcBeCXce5yztLbE1a6cZv5+/usXqk6L6zVNasXCjIVlY7J67Gp17fKGR9DZjZ4NOWGraXggsjfsUttsqbfutmio2k9M1N3hlzBwcySJT593orbu3XOXEPGPqymGoFPTEtQTlP0hYNRi5yfNe/TZ7QRZOwyZwSePmuxo6NmN+8TCMZqBnBKpR9FHjy/2JwAPEkRsgHgNQQ2FsgXxH7FvJIg8KxyD9XndWlZra/ij1+0pbtHBDYZ7wBjmatSABEMKhVdTziW7OspYJhe/BWCsXyZ4ArdFUL2F2u7enp6bH5+/nNeX61WVVnI7zZeMze33mR7PTb+v/Gar8doMlgvEb5qXSkadvHyHMpWrIo3jRgKZ9vAoofAmVQDTAK7ecAD/kbBZae9Qi+A2B0GCpDS86lZGfOxQ5M+ixLjRsM6H59VW5x6W8LtrManxAzFp6qWPTIodoh2NfQQdD0MaRjr0Q5LoILdKSnKnha751ZnlAgTRC9F0e+UsSO2rzpWZnV/m9KDcny+bZuzYmiJWH5Hp1qAcOzhhaKE6pHFqiwfIhNZsUUtl7NiQpi8s1tbbGFfyHL7usUORR89q+ogwA4TauJS2lKXK9L6BBCBM0kFPGKvaDcEGO18dtm6ny/KHZ/3kPZnpWivu/tXlYoEwMavpCX+J80HM4NIWAwGgMXnkSYNlgmbh1xvQEJVgCWso17jcWnfjhfSljqXFRsAC9N2YtVi1zK6RrqXVHWht6IcG5alWrfacI8a1JJyA6DQqw7hMiwTrEypAz2Uc6R/4s9+XJ9HSxXSLKQMeY+Oo6uqcgO8FPrjjl1bXNOYAcDkRlrkAwWQwOSTcw6OLrnmzTdstmo8qJ6IaLsAuTAGpKuqPSkr9bjrAIuKmzwVo6RWSHOo9xppEWwf0G1FgtLdVS9cEQsA8I48cV6MB2Cq9VxW95V0D9osFlB0N4H5rBzWYckw1YURzG5Jyt09dGlW1xKNFI2OKXvnWHhPACfpFTRFsGD4ZlGwQOq9fu6yqhsR2OOU74NVqFLpWXeLcLqgz1AbqnJN14PjrSfCLrVdLsv/6fZv/qBYZaVOp7NK1eH5RHoU5gZtFEBIYKNcMxvskaN8dL6m1B3sGd5QYq6oPKTwrj2optCACd6TMZc8s+LE6LRdSefE5PhPXlUzY89NezW28WGrz847fabXdG7a0Ph8VuprUdcA7AoQd6sFzadPudQhFWpj067X5wLO8yZrlnqHYx2DF2dUgTv1Xdts+XCHeY+dU8oL4FDucQsahSkwNwCTzL5O54BeqdrKncPOUgXx/rFzYolVbABgZnNU4963iN3Dk6vlXFoAho0bXmB4xeGIb4d3q7qPDQ9MXGK8bpEXRmVvAmCjJY8KDgI+zV/RUdJ+Zn1/cVWMFxvOtnN1gWmAXX37oOZKLUa7t2kzpGenI2XF1+6zemuL/NtqQbPqupYUJiu3rdU+/ejPWPnVe6y2uKTCIQAiIEt+gx6PruOGpQPAzbvu9o4WjGbuxTv2WGlnv+ZBInlqUZskmdtiutrf7aQdne1iEekdKx/DuVUVO2hupxH5vp2q0v16Y7C+VrEBri5dumQPPfSQtbd/buPrW2+91VZXV1UduBEPP/yw1et1u/nmm6+/hspC3msjAGo7duyw1tZW+3qNJsB6iVCpNWXUCInXK6VURo9lQYFfuE7uTu/j1Y5+A8TkvvmIJmSZRJKb3jfkRN94L0VDbhLEbXjLgDRbs2/d5ihwwA89CBdWVApN2gb7hUCmZsWBuKVHAlY/tMtmbw5e16wg4qZfG5oWOVuXa9q9w4j5Z5YFAtXWo96wxRtilt+csvxwSvYIlZjfMiNxaY4AgCz84fmCXs/igDgVoTo6G9lN5Euq+pPwHb+s3oSc7LteKKoSEF8o+RvVzVV/oalIhizX4xcbsrYlLB8uSs9hq0otXgtP4FZO4+OGzElZXLhW07cnxQyxeydFRMNhyvvF4K26nSnaIZpGozmDmaCVDmCz45lFW37NgFXiPqVFQotFZ/qK9047lZ8Va+zeoiojuW7DRJICg8GaWNbEDNhEC6N0LCBopNXZM9DKCNCCMaEfIXNBujp5Sj13Sgte7uCAjnX1TXvVRocJm1SMqu4aDaUnPXma6/oEDOlNKANT+aA5wSwNcikkkPkqjN/0mkUWXWse0mqEWotQgl+qOWAY9FuxO+KEx+W6NGJox3T/eW257nzFBlLWePUNjgllnLe3qdKTr3Jr2DmJP/K8zHNhDQGVsGlaVJg40Mp5TcAUjSFpLnUiYEFHm3Vm2jwzixqjsVMzGmcwm5wz/SfpIoBfEqkngAcaLIxWATO1Uxcs9KljcuTHoR2PLzypVFVLq5yrS2o1A0DDSBNNVejjz1no+Ssu5bfiWkVVbz9gnllXmScvpUrdLbgXxwQ6Z9++Sxqv7IE+6XokJvd6LL/NbaLQsmkD5PVadnPL/3MlT8YFILBxmX3LZjE9GIvKFJOqxqOnJZpHk6cKxquLSikBcOm8kPrL4wKeqScnnZi8JSE9E4CZz/CNzgocsJkBPDO/UNhiCKujfuv7vVMqouHcMfYsv/Em+/Qj73UtsS5dEVDLHOq32ChNmct6dpJn1vTMM3cg4Aesao7DrXx51TGwOVehB6jBBFUp2fm8VecX1CQZUISukfNQ8coueqeWrUG7GICRx6PPpGI5szmudNvSkTZtQGgoTSqdDRKmwIxXmCtafHkO7NL1Reawke5nntE4K5at7feetP5H1syXSumzYC1pmE5gO4KXHI3eZXqay7tiIyq3sW0J+CTNYCPj7esVg4UUI35syiKfPa/WRbKA4HkbITsQkrSAjYc0quGQrGH4N3xtWZ0f0KqSPhWIG+hWsQGf/UpFveF52V9fSeBXdfz4cX0R165d0/fj4+MCRN/2bd9mzz33nP2f//N/rFarSTPFV5kKUzPbtWuX3Xffffaud73Lnn32WXviiSfsh37oh+w7v/M7VUFIfNd3fZcE7vhjYefwkY98xP7Tf/pP9mM/9mP29RxNm4aXKIe95Y2/aH4/QMMjzRA7OpgLKn4QI/MgYs7IQ4wAlEkwPpqTEJ3FhAVHAvYry45xQJcws+Yq/nCBRnTeaNjcm0YskG1YYryodM/kW/qtHjKLzDcsvFoXg+OpmcrpVdlXrlluKCoXeS3GYqXqYmvmbgzY0ANpW9mTEMOAPxQLYfL0suVHUnov0n6kagrDrQIg4aWy5ehIX2tYdK4sFic/GNUOmgUoPeLAVuu5vLQztH6BUscoMzNISs6s/clZKw23OcsKtGuZkmtsnICdM4tMrFmF6kM8qdIsdA2l+gBY9DBTGThMEaxXDZ+tgFJ18pQK+FwJd6Fqq7viAlzs6LGY0DEulsW+sV0otmGHULPIVEYVeXIHPz4p0SpFCJlNIYtNV3QvEa+ir5FgNls0K5a1oAGe0CtRFYj3E6J5pYERc7cimK9boTOg8yctR1ECZfeU/1PC33o67dKKVWcTgU4FdstPw2Qa9nYkLDsUk/aOBSF9zy6ZUdLGBbCLFgy9miwcYriuA1S9WpRpMisvpA3bDBZepZ+jFlgracEiFbjROil6YVGu2zTp5vxpDg4rm9kUttYXljQmAPC0g+IaoxnDnZ6xQ5Vm/KozeiTtpErYaMgqqbAFTl6z0o1bnY9af0TjBKaKvpPs9kmRqZ9cd0KGqKTqMMsFwNnknJUPb1HqmD6KulalmvyuCFreYIIJUMJhm/QO3lb0BvTcvF8LJ0AAdlZghArCnVuscWlU2jIAGxou2C+YIYGUo6fNNzhg9daYNjA6Plgjj8emvn3Eeh9ftWIfrGLdQrNZ16id53owapHZosxKvZsGBNbxBYNVSxyfdaX91ao0lYxXb6akvoxKM1fr0mHSBsZHCq1cttqezWKQ8J2qzc0rNYj3WvTqyvXWWCzigAz/9JL8sdCVAZIA87IeODNqjZF+JyCfWLRGIqZChPhoVial3vZWZ7EwvujYcNJeco/3Wn1+wRr7trq2OOuggPfFWJfiC8ZIYLXoimCuTqlqcaN5M0UEPANUG0rX1xKxxskLtvLdN1nHZ+ecncNAh9P9rWRs8e5h63h6QfIGNbz2eiXQV1Vqn3Ne53mlMEFFGZmK+U5ftfS9e6zl4Quyp/B2tCn1SsA+sXnxhsJqIk1hjo0MSKIghhR9Vq0hsb1Sdz0ppROlLyu7VknyO1v3y1LRAWCMDfHhQTG/GOZK3D/QLSsX+rri0k8boPqdhyw4vuxYzrX190Um0NVutezqK2bT8NnTfS/bpuHVe6f/zsf66KOP2mtf+9ov+Pk73/lOeVWNjLjij8+PRx55xO688059TzoQUPXXf/3Xqh5861vfav/5P/9ni8cdW7xhNPrud79bdg4dHR32wz/8wxLMfz1Hk8F6iaDcGnFudKagxQDWiokdrQx0O8wFugNVXS2W1RZFrUBWnDCZybuc8kn3IQPRtqCVBlut2pUUkGAyqg51WXy6am0PXxNIOPeTPSJwATTtJ9KWODYtQIRmRaX6EZ9lh13bGkAIYKbQG1UKkt1h/2MufcauUa1qgs50szCYtKU9fk3CLISWzlop5TMfLMfEyjpYcA7yMCKx8ZxFp4sCcOGVusUnK2oxEsys73wxW8UuYrFmrafXJHLFbwqtE+L11d1J+dgItJ2akjUDwXGhHQGYIbbH0oHrAGAot0U0sdtTJyz02bNaQFgk0EUADDjv8GpN7AfCewHak3Oa5LIDQVlEtJxettBy0Qr9CVX30YxYUa2Zf7VobS+saNLk/tCcl8/jfFk4SavgcM7CDKiRON3jwKIWXRmTNsSEJK45zRNi+PBkVmaU6bt2qOEyf79h4ojzeH4g5sAQ1Y49gJyQGyvc/30jur8wILA7/BwAEV6sqD1J9PyCnLsl1Edn1uYMKllMSClyfFge0LibccCxSEeTw2W9KkGyQA0tPtZgNwFpZfklFYaSuv94NxGcC6HejLmyxR846UrRYQLCAS3ypOFYIEtHtopJgjVLHqMKLq/nAS8xSvNZyNYOdDpH/pWy2veQ2oTxJOVClSU6NdJnsgtYWhO7cF/bu3QtaNOCBgawBYulxWy9rydpRHyMCj0RpfM8OzbrelCVV9+zWcafXEvAHSX0jJ2l771ZzBkMBY2Q6UGojc7cvPV/dFzu99EXJnR9bHzGVvcmrfH8GYmmMW319vequi14blKpS8xfASsYpSKARksHM8pCH/jUMQE/ABqmwwL/oaDc0WlthKYJc1VSphwj9w69EWnFWmdKeib/2JzkAoDGDXAFy5MeDjuWNl3QPAMgQ/Atu4140IGm2LrRLH0CSXHnC2KLGKOq6qS3X2+LNhTMA7Q3QqfGPQ7OZaxx/oruOek73PWr9FRMxFx3iHMTjt1GZ4WZ6/YtdvT3f8wB783dAnNqYu3xWPvzyw5cJWJW7WxRao55C2NPxONsbHhekTqoXyog7lU7tcGgZQ0pyfz+fjc2N2+SCSqVgFRU8yytvXGvDG/xsFLmAFkHqbpY1Ao7usx3fkwMHYVEleFOVThvFGHQx5DemtPfOiygFX3ikja8aNz4XNzx0Xxpk7Clx1a+91Z5bAmoYQKNs30wKN8sfOEs/4VC+m+UFCEgCeH653/9j//xP2x4ePiL/o6vO9fBFUHF4B/90R9ZJpMRsPv93//9zwFXxP79++0zn/mMKhHxx/p6B1dEE2C9REgHtFZWyTxpLcASLAytS5hs0RSx2PF/JnFShMHJFaWa2LVSWp385AWBAXae8bOLEiyTdkPTwCQG0xV7btym3rbZ5g8Grf15nwUyZoOfWLNid1Q70OiZGaWJvM+fU9otOlsUcIJxUNl2wmv5I8NKbwUuz6iCUam5WMBS59JKS9HvKz7VkHg0di2t9hCwW/j05HZ1avco9+MAKTen2yGdCWOllNaVJWmeYI1I/9HjjfQcoGzhxpSOD6odZml1i09ACONFmJBab7tMOHmul/YGBRTbT2UFXGglIkNKTC1ph0E7njsPme10aUbSRjS3jTx+zlrOLEvjFJsqSXsm4NvRooo2NGK40cMe8P6Io9OHnTFg7tCAXMLV7iJXVPqFaqeN1IDE+MsF7bKZkLX4kA6iOaxSgq7yDzYJxg/mUgsYui9EyNQgHOxTJR/HLKaoPeq8pNIFefUA+KRL8Xuc2WrJ6eDYbdOSw9pSOnZYKsadUptUBKZi/y+9lyu6ZsJl549Fyxs0K1xDGiczvuZelZKY2XtxQscJSCPVgaca6RaYWKpYYXzCz1yy0DnXXQA/raWDzqOK0nj19Du0XYCZpsOM+1ospJQhXlgAKjRQbDSKmzv0mcWuiJhNgOx9+94ncTILnhoSr4NCzCmpOsNZvbytx8Lnpi0wvaoUo2/PNjFQ9LW89+DPCZDA2N575Oe16MFe4SXF3wFOdV238tlRpbZp7DzzqrgYNopRqGLjmvMcskHAKRwGAmAnY8lwyHxdna7MHp8oij6ePWeecMjaHrxovn071MKqfuaiFnSMRQFK6duGtbmp3LxL1ac0x+a5ou0VVYpqS8PG6/yY0qeu0tgrwfjiG7aZv6vDvCtpifhhZ6N4gO3arLQi14VYvWNYLXJIH8LuyDh2rariAsYk7BFi6+qmbj2njA9MafN371OrKtku0PaJCsFa3Va2BzVugs9cEBCCpbJ8XsUW9yW/z2qnL8oPC+CqhWHF2R9QpVd64xGNBwAd+i8aHNMPEYG9p1S2+w78rGwtmHvo28frll63Se+HdpS/5dpVh7s1JingoVAkPAob5DHvsQu2ujOhim2eWwot8pvi5mlLaZPkhf0iNb3u00RHjNrZyxadLashNB5WgVPXxBSrmwOauqWiFW7d5sYCHSkmllRlLKA29CPuHCfnrGWs6gT7OMSHQypcsGjUoheXHFPFvdgesZZxZ8qqqFYdI1cuW/HGrbq+pC5fqaiZ92V/NeOVieaVfomQGedixkJHL6+L3Rt6kAkM98jfV7riYiAKbT5Vs7AgsmNVCwreIx5TCxxaqRDs1tlRojkilRi9uCBBOX31ep8uqF9cbK6qCpvoc9csO5KQ/iRw/Ipl3nxI1VUsHLRlwc0dJ2nsHvDEYuLNHxqSKBlfGSaGzJaEWs4gdG974KJ2uIA9JjZ8jfA5Il3nZZe+rilTS5xcUemA+DiVhFUrjrQrVSgvqigMkqsczPX6rZxwbVzkCzW2ZAN/cs0i42mBUHyr0CIVW70y4ex+OmcrO8I2c1tczM7YN7fZ2Fs6BEIBqQAL0nDaeT9zyu4Nv8MeTP+BbCRwUcfGQU2n0/iRBTSCcVrP9tOyp6qFBL0Tk2p0qiAGCSZSjuSUqqPlagmqAlM97pi4NyZOfI8EhLzOmgEBZyIgVo/rAQgF5EWvrGqxVuNYKiQ3xa+3s4F5lNYu4rPE2SXX7+3yhIUfPqE0HiyS2i2RLumISQjP/UR8DxiF4bDTl5U+lfiY61Cm1UtIIKC0rcfKQ22WHQzrWEkXI/S/9M6A2eqa9f7FZYHFyv4RV6DQldK1BbxR8QlzhJ0C1aaYVMKghsZXBdhaz+bN96wD8ZTK5/rD0q5hfok7OalTgfjVkmtMXF2/bjB7sHmFmvo9Sqc3kpK2DtaFxRMtW74n5BoyB7xqGwMbJGE3erGltHSHLFykOu38VfMe2qPPo1K2gUM994nWS2X80IIS7Ic+c8b8T5+18GxBoL/n6bz0iGqWzqHRyiRXUppeKdtex9bBJOp+reuelDZEW3TTLn3O2uvWF+f2NvN1dykl5is6UXrL8zMSdocmV5XeC4+5Vj2rR7pdu520SwV7Ui0S1vNMFfY4rQkAnHNefO2g3jN0clQMMxID+iNKiN5oqKNB8DOnnVXBsWt6tvAfW3nNoFjo+rGz158ZQAji//iJGYt9+owFPnPKMV5HBtWWhyKa1KWy836iDdH4ooof0JcJPAIObtln1auj6n3oiUSsOjGtjSBzHMwkQnmAfv9fTem8SBmrgtjncxsQAn+vwyMCOe0PXlI7L/zTPOmspV5YVHFA9NmrjmFNl9Z7CPrlIYaIff4tO1RQU9jRbZG/fUHdM+rtCbFrsF+1/k6X2gsErH7HAY072Z4ABA9v0YZURR31unoDknbF2FQdCgbbrY72FWuaqRkxWdXlFYs9fnH9vnAxK6p8BVTCnlKRyrPX+dis5hGMYJ2xaMi16+FzUz4xYEpNv0LReJn6K/6+Ga9MNAHWSwQLHxMmi5X/hUvS9pAOaDmzJHEji2TgxFU5H7f++XExNms7nLYgeGFaVUYI2GErgqtV1yaHsvfOlPM4wnm4VNZD3PXItCY+0gOxYxNu4qeU9tisVW/aqaqV+EePirmiTQ2sD2k9yuwl0o45loVFT2aYAZ+1nc1r19v94IQDfG0p7dZgUVQ5g7h4paRFlKq52RtDVuwIaCdJugBARHUbwA2/GgBm5OSErBcEEANeS53HO8qs47lVy/X6rIFQnAbKAwmbuCdmmQEE2l7reHxGaTkcx4/99o+qOXOhw2f9j+at55myBPA4ppPq4P0BDYVvOmLVW/boOjz6wE9KE4W2ZaM/JIwIgJX0LBYMANvex5YFTNj1i/VbXNN1ZnKnBxmLLcJjUrvqbVZ1qVwAU2moTcAGEMJELTsDvMAKJQFnri3mr6RkEMdjccFCS1Ujn41VAiJ/UofSNQ2lxHDRW63yqn3SopGWJIWC6SgaKVJZ0qXAaKJJuWFYbVFINcJI1MN+MYo0jKaHHwteoTNoobWaRNRqbrxUtB2/UxRwop2H2I6pVS1kSkVTco7PU9AnTZhSXnidURWFJmN3h8UurRdDHNqhYorwckUu/bw/dhWqGF3JCtigVyO1yKJEKoXzolJTwKcjprRk6OPPSlNElWPrqVV9TvLZKbP+Lmf3sKnPqvOLrlNBtSrhMM7ZLF5y825vNe/YrHo2Ajrw1ZLdSQVvsKwANmwaz4aH5snr1XpiSBJhPZfS6PC7QkEaSC/s8YVxNRDO94XFDmNMi05Pthmbe50e5+YR9SqUg3epJJ85UuAwpgjN0YUBVvgcXcfOhMTW8Y88reeWyjRc4qnwg51jXMj3bAOA12pij7kv0+/YIYDEpiHxseMCovXBbrGYWK2Q2kKkzSYPAIjzuaxf9m6Xro5KWfr2weSisaoc2S7xe+30BQEuQDspVzV3P9gjkJ7f3SPwVr1hi5XvOmiNvVvENJbedJOsHspHtoplk4VDhr/1ux6RxaoVt673AsQ2BHsP+qZSLRmPyL4CYbzMTYd6xLBSbAPbSLqTtFptS5/AX2ZzwnIDEeedV66qqq/rIVclGJ5Iq39l4NETbiOXK4qp4xmYffOw7kmQqtWr83peafrMeIBB1DUGtKG5XDdkVfudJ06aF/PYbE5aLh/Vk426xqOKIKbnHEvWaFjw6AUBprYnZ6xx4arOl8pBbWZ29SlDgSaLqsXU01MOcL242XQzmrEeTZH7S4gJX/3an7dInhY5bhIOzGXNMw917BXrhI+V6GUATmuLFk+8VFqeHre1V22SCF2C79WiVVvDAlUs6GhlQjg3YwQ4vagSagwnYWE6n10xuzphnhQLV0MTNmCBnRICdLy56NtGWoGdPgJspZzSRbOAT8aXOCfDzNCMFmPQ2HTZlcwvrYnhQviMFkyMBKwaQKRUk9EnPQgx8sRigvQF1UKhuayOAcaK84MxAvDc9h0fFuCLT5V0XLAN6Fg0Ufl9rqltwmuJa67FBG732UP9AiQSTKvFUMMxSrSEWXW6JbXjifltbZNfvQ1xusYeA8ZPgvJT0zKjBFRSMYfGB/0QVYb44yDYBuRsVFnK9ytTdFWbrQmnA1JpesOCU2tKt7gWH3U3OVMdFHKmi4jNw8s1V30X9uoYZfaZirrUW9Bv2aGoJY/OqA2LLCjCPqfZws9pMKaFGTCqYgmcqGlxFPSKZSSVCLhiUfKevCQdEUwGY0hu5jfvkEA81+dMPlvOLTvtVLHu/KHwkAIU9EcETCNPXVRVW/DJs2pYvQEYEPAyVrgHlZEupXEQ8WICy0JIk3KMPVd3RNW+iaa4MsAk5cK9QojO9aEFklqulHT9uXcAdrnZJ2Ou0KAnpuIP0kbSD12bdMJqPN9YiGiHgdh/bMKy33Kj9IWFnrCqBfl73LF9w4O6L9wHdI3o69Rqiio+HNmxM9g94ipc81UBPIBv4IXLlnvdbuc3Njat5wgvK8CldHAX6DnYoWeYzY//xBUr3bLj+ngnAOPyv1IvUI+AEkL79P5OgXra1ChFBOPVndSmCC2YKj79HvvMR98j5hUhuyr/khFn+rq0ZuWt3eZ99JisDrgWS7cPWGymYuUWnyXOrQiwolnM7+tXEQZgF3Ya41QaT1N1LN+mNsZN1rW56UgIlGISi9eXWEEYGZ9Pnych+TEnyMfbS/eRlB86r2pVAA7wALtIf0dACKkyVT4yPywsqZm3ROF+V5ULuCmPdOkY9P9nLzqx/+v2SQdI9wHSfVpk8G/rT2gzx1hiLsCrCtaOtlH8DSBeRRwArql5bQZJJfuWM9qEwh6p4TeO6vtGnJv8tTHLfPuNlnp6RgBTbX/oH9idUppRtg3r90gFRlMrGnecT12Nvesu7bzeixHNWm3nJs2LbGAJTFdJy9MuiEpr2u8EcjULX1owq9dcepmuBZcv26ezf/SKiNw/eWqTxV6GyD2Xqds9+8b+Xo+1GS6aDNZLRAifo9kVmT4iBBdLRCm3Jldz4IoH1e+Xvic0kzFf2VHILLj4RsE2sHMiJYKIEgBBign2Q1GpWOqFJTV97np42qUlOtotf0O/ZQ8PaJHGlDR1YklmitfNPqM+iZ9JHTKB0U0e5iK9JWoPnP1lGQmyqCfPp12locdjucNDAkBQ88V2J0Znd0drDXahtIppO5OXXgu2hDQR+pm5V7XZ0p6AROU4u69sc7s1FsbwclW6FiZP2n3ABHBdqIQi9UHKiBYp8nIa7tRirlY2pL0iPrEKLPJqAYNdwLq7OEauncfzYiSoMCRdgf8XbFuN3pBBv1WSIYuMZx0rGAvpOGCH6EUI4MLUVX5CtA7a1CqwCiCB+QAEyaKAdjqJoGuDw4JOuTk/91GgEJK2i8o4CgPUPgcWqDUmg1LXOscv/VNhe6drhExaOVNROpJei1SgYd+Q3ozJY16eT/htqUACBnTZpYt9+bJl79unHm5UaaoB8qt2KR23tCeqnyWuZeVdFFooujRgW1RsDiCn0Om1yGTasq/baQs3BK2Oi/lIr3yT/FmnqcI5uzrY4Xb0y2k1uY4+dNqWD7VZ6OSYgC0eWJwjQnVAKSkbNg4sfALFiOppus0mAJ+4mYyMQ2GJ6Ou2steBP8CVmgfTp20nhps+K+3ul7CY9AusS+V1B1U04Z9YkNWIZ3G9nc/OzY6NrdTkcSXbjnzBaYRqdZ03LE3j2dNOs4Zzf6XqBNRdHRZ/+porUEDT1NMmbZgeNfyoAFeBgMYQaePG9k0S62NMyflzPXGV1zXY3GWlrS7VKLuIa+75rs3OiQnK7emSHmj1dVsFZrjvgKv7d/+M2lNVp2f0LMoGo9GwzJEBC7xwxXy7tgt4wSS1/sUJC1+a06aFzVsURtrnteiJCfPOLqoiFBNcf2+3eZIJifFX97qGyQDk4pYOs2PnnWdYoaL7DTjM3jikeYlngl6fmIQybwGqVNl6aVLpQH6Gyz2gNf+a7bq+9ADEOwo7CnUKaGt14IoNUaGoFCzvzXllt7cKmK+9cY9lvukGpw1dXJVtCWNOtiMrGQs/fkbjlIbkGKXCCMUfOmuezYPO3mMOP7ia2C16AjaCPitSxIBL/tS0NhVKmScTsochfAN9ljy94lhzqmkByPhSUSFZrSl9S7GF2N825y2nHpdcy5v2SjAPwyYwWqtb4VXbzXdu1OkFOXY0pSuuKTSpXQA6MgqlxgsFucFz/dg817dvslcqag3vy/5qxisTzSv9EkE+ngdWFPBq2lUvQUHHI5ow1VIDsMVEtV7yvbTLLxNBBKQIRjHZJPVU27tZixaMEloi3td7YdTyR0bMZuat9WLB1g71SPiNxoaGsKT32EXnt6Q0YTBxlzpCMu+jTx40/dT377FiV9TufvUvCYABeI78wK/J7bvQHVLahF06AWPSSCakK1P1WJtPDBAieAS3iSs4LbtKOXlSnVqy9OaoJaZI25lSUoiY+/74gr3q2z4kJodm0VQQMbmLOfO5FCYTZuKzVwRES7vw83ItfAhSb2qvs0Z/uIB6/7nfYzDqrj1ghGo3GsECUGhma7u2qukzFVowAkRme4vNv6rdlvfGZHSqhqxMzr34ebnjkfcRk+K6XxkTKBV2XCN6pLEI8Vnu5jpPLFJ+GxYIMIAAQFKmcjvPl6X5AcTBHuJJBWhC54SxJb+n2pRFRjoWzFEXqkqfFXb2SJzM+dOYGY3S3E1RAWtYTATUOF4DcimwAIh2f+yyPLK4fuiR9NSevyoNCikltbpZawh4YCOSHMUeImDF7rCAPMyb+iCiQYoFzHv2mtrWdL6Qk1Abhm6jQRk2A44RC6lzAYsT7I582Wg2DMPIPX3Nnuv+UmqNRMsQGryey6q5MZWy6PEAN1Qiii1cyEtoT0NddFqweTQdFuPp8ehZ4Ho1royLrQKkyqKBXoE9bS7dV8a1v271bM78fT36u08+/bPqp+cj5U5Lm9aka/kTCZj3/DXdawAJ9gqA7Gpfq2sRA+NxzblzkwLG2BWdjica0XULTq5a6PyMjuH+gX8t2wiaZVM5B9CRF15r0oJrMBk1AQcc1TEB5Xn0b9sis1P/2LxYmcSTV8UuKmU7uyAvNJzc127s13Vo9LTbwnpbGZhmWHK1AFouyyKBeYh0ZOvReQtcnHJeY9y3Azstc/Mms3VGGmuIMJpAr8cy+7vduLt1mzYqnL+0g8xPXM9aXZW4jL/Ip0/rb+Sjhl5rcVWeUbpml6+654P5bjXttIwJn6uypPp1tmzJF+Yt8eyE+rdyfQBJhLpPHNkhMC6QXqnI3Ljw6p1uPshUBMZzm9AUVnV+gDKZlLYnrfr6Ixb5zDkncgc49QY1PkgrY66s9kijs7Z0MGXlXQMOCJbKmptoXYQtSHQy5xjqng79Lff6/q0/oc8nnQr7qr6qWwelHbXpOafZajSs7WxRm7bG5TE9m8HnLsqgtfOpRWvMLTg5COn6ZjTj86IJsF4icJbGoVj6gZjbBTdWVrU7Z/KnEq3W02q1oR6JJ4mBT6/pYaMfViMZE8VPxRW6JmjxtqdmLTqWkeiVNhbs/j2UcOOdlfCo9Jk0FztEdCSwQbELDthhFPj4x35CC2niU2dt5ZZeaxlzFWBTt0fleo5IHCaq1BOTASgMFKmUmTto81MQoJh9fY8tHPBL90B6LUDn+oRz+JZWqWbSXHGOwSzgpyEridDleWt9ds7Sd2yV9oHFlao70jYyTl1elSgf00jparb0W522Euz+8lULnZ2wlRvapEvKb0qoT5z0NuumhzT1hfGLXlrSpIihJ47c6Ff8O7ZaNRWWdod+dexWA4+csORTE9Z+IqeqRJiT4lBy3XfJATDvwtq6qLZohQFXpVcacJongeRt3QIDvAaQBVhQSmomrfdD2+FMPjOqfGK3jUBcRqWAtUJF6TWYHRqB0y4IPVdxMKWyfVfVSGrQVSSqvQkgdmbJ2p+at/BiyVovOnBO2rDl6KSOhSIGMXrpnNX7Oy10cUZMD5M7gLHy6j1i94hSym+Jq3lXTYaGJF3VokWFKMADxssZzTpzUBvqVQk+AuH0cNAiM/RwdIUZDdJMpZosPghMIyXsRdOyqcOxPJmKtHAAJ5hOfo/Zq4DsOgtBdazlchZ88HkJsUmd0QqGVNc9we+SPgomhGowhPs06mVBZHFbfMdBvTa9LeaYq9H1ZsUAsXBIfmLl2/fq2QieHhOIYWzILJbG6ONT7t6cGzVPT5drl8Riz9+rwS+98nxaRKlC41hgsAiNiZ39um716Vnph1hE8XSqDLZJPM01olvCI5/8KaX/MEUlTQfYwlE9dXZNFgFsvLyJhBojyx2ee8NcMLtqDTZuXFefz1o+dc7mbu+wxZvarfPjl628o8+iT1/RBg+QB/PIAg/7glUKqTw6CzRa4i49upK1xNEJ6QMB+HiTUWWLPUH80QsSaJNmVJB+83jUxxE9qdKJVN2dn7Tybbu1uYudmpMBLnMa5qVcS1g32TPwzHe1qVE4pqIUM8CcwzojO+C9AJRotrjnWHOgA1TrJ6470opwWD/jGGHhlDLMVSx+ZU0sMgBo+bXDkiVktyZt7qagWvSQUm0UCmI9BaoyBVfN2t9qpb2D1no+p01Pdkebm6+rVVv41l1KqyrlR2VsLCj2VUDc57O7Xvcr0lvhwSaPrcl515O1o02bAM4zOJtW9SqsqVjUgV638V5a0dxKM3uZkr5CUTeP1c37Mr6aIvdXKpoA66WCFGC5LC2K6PJ41OpUCDExes3yu3st3xtxzFappLQLpnR68E+PSUtDua8Wov3DLq3I7g3WJlfRDlDpnRuGRLeXkx6be+Owcx9mB7910BnhLa/qM0nDUb5OQ+Ds63fb0//7x/Rz9Ca9TxUdCFil+W9DFYCxSyta6JZ3x6zrWF4LBDvW1OWyDX4q53QqaIZC6LqC8rzJHO4XW4KeR8aPjYZSeAALtCjTb+ix9BDC8vWUEbqv9rg7t9akKpzQtlT72sSwyOYg7rOFGxO2ctcWpVhCY0uup9/Yiuu9lneTUy3mTDyLI206d6oJmfRI2TCx41a9cGuHuzdj0+br73FamRK9Af0WXCpIlE7VoNJhiZDzH6Jq0uu1YLrihMuIzbvjKu2nHQ8/YzHjXKSXi7jFhTRqoTts2X7XOgf9EiBiw2le/lB+rxVbA/o9YItUIalhStAR69J0GRaCfpG4YgOi1GNuuFtMn+tBmJVGDvCF1gyGa3VHTM2FSbXiWo4wG+0UAWMJUKQ1Dfcaw1L0ab58TW1ouA4sEgAoJn4+H1YDEB2eccAJi5HoWFbAGZ0T1Yyq7Eo40OspukUduwU7d9Xqk9Ou1yHGijCNlZqaJXOOsFPysspXzLtMv72a9EOqKDu8R2CBTQPA2Htgp3n2bbPyEdJAflkhAIZpQ1Qaaddxd31mXp0NUseXnVfYnmHd78yuNqW6YIC4lqSASNlTDLFhfdG4YZu0Z41nT1nutbskRBfLFvDpfmNwqXY5eJ8d2erG8JYh13YnQMVr2EITKyqI8NLrcjUvtqdlrGT+45fV1F0mr7my3OLRPMFoNY6eseqFy2onhDWHx+PVPUbTAwDKHOwRyMI+AaCU/pYDbmMC6B3ut47fetI6ji5bdXOfUuHMNd7Hj1tjecWBAr9PPRbVb49CFTSCMIiwrOio2pPmm09Ly6eJfb1PauaunXotVXhU/tF4nt6R9/e+W62UqNLL7um05bu36n5TMbt6S6+c5dnkRU9Pu6biMDroE+t1W9uTkv6PjSasOILzCKx60Ouee57pTFHPi59jCvsc25/JClDlbxhwjA+4/cKc6x1YLAu8OCf3uiXGCvIWSzw/ZZv+97jSgaSXuZ6hhZKsQdiE0crI98wZgUpMQmHOkCFg0AoTSKGPp1BRat2zmpH+karEOmlg5AInr+m9CV88btW5edm9kFqEgaNSFO85/NTwb2OM2OSME+6THowGLPCJo9YYdQL9f4qtcprxpaMJsF4qqJYplvSAakHF8ZtUSDSiXVv06FVLPDfpSuFhueijFAy6livxqLWcxefF6/RHLX65Qlc2dQrQSK+DWSbu8BfmLHJ50fr/bNTaT+WU/6faLTsS006QoKkvYA4QgCgWoEKD3ehHj9ravjaBERYPJprYWFY7wlJviya+zocnzXd2VBVNeFKFT4ya/+qMjAZhQVjcYaRIsVHtmOsPSnd0+Ad/XZV+7FQfffCn7N4bf8E6j+WtZbzmKg8BgqT9ag1pvjQRUfJeqkokjfAagFdM+axlvCo9WPL4gpi34GJeqUXADwsQHl+kO9A5hS/MCjDgME+apXDHbqX87njTB63j2JoWtrX7d0kfAxCQ71O5Lv1UbDyrVjeUjmsX34FmwzEXCORJfQFuQ9eWxJTJOZ6UU8Drvi9Xlf7juJLH58UGJcYrSrXCKF15W1y7Z6Um0G7gEbZQVo/IwmBCKU6uO75J+j1jJld1HlixoIAfQIrUG6k77A/QLjlxPXYHVbmxp/7oOfP4A9IEEfPv2G/FDuw1fBZ66LgzYZ11XkWZm4cE/KgAlbEmaURK/Z+bcIACUAi44xxjIefoH/JaYShukas0u62J0WHcAS4plIBJkG3FUNIKd+0z27tNvk707IMRYdGHucWmhHSw+g22hiUwRnOopt9Jn3lOXbJGX6f6HmrCoahjX1KgCoaUzQYtb3Lb2yx0ZdGqV8fMShXr+ZtxCZZ1nmfHzYb7XZ/P7UNKI6qg4tiYwL16U5695sZesWr5g5usfvtBi6pvpmnhJQTqT09ok0GBSqEDCw5Saf9vNsREVaCLDRXPOj0ED+5WgUv1wFbzXp7U+dKE3Tc2o2o5vKD8g31aoG1i2pmykso8cUljkMWaSlfAae3KmIBX6uic68U3Patenv6+XhVf2DOnrEHaLxBwuqtw2AJjCxoH2R6/riVgWMaa2CxgYgoLNTmruQatHClril4wi1VfzKdO6vTEsj13Rt8LSCQDlt0U0ziJzlfUiJprgK4pP+zaBtHgGHE+zDGfpb6OCxWngexv1aZSaUO/V4AIQMWmhxZF2MuItZvPawzSaof7LjuSS5PSaJaHO50gHWamJeIAKPMflYkBn+X2O/DD/eA54bnhmmJZAcuEV9bCPztiPhzbtw1LvuG0lH7ZULBpxBONccZ9Z8PDs4KhKClvecpFI5pLa/u2mH9owIKnxvSZD576JTG46CZtZc110aDqO9kifSYieoAmdiLemGuw3oxmvDiaAOslQi0rAgG5M2PYp0WQySyTs9DZKTexlEry8FHrDUAY1DMpk5FOLWRqvpwMyAQycXpRFDasA2aNpHDkwL2j26UvQkHR696ZBVW4JC6nVckG+4WpIkCBtIl6ruVrWuj89HJrmEWn81ZKOfE5x8NEC31e2JzSzs96u9wEdmncrL1VND9i4/BjZ6zSGbfUc/OuwS6M2EzZkhcyclmn0TQA5q7X/op5R2fE6LScWnQGgfQuWyuIsSPNhz4Ml3ICM0g60VMt2Xo2oyohmVcmoxafKIpdotqMoMVO+Y79zqaCxsxFV31Jamn2rm65qpP6lCv7+Wv6vNBKzXyffl6idsTsSvtATtHiRM2M1yw47/oMVqN+LfhoQDhPsU6bO2QnIE2SBNyk2gKqRINJozoNvQsh0Typ1o6kbf/tGateGbXAIy8ItGHnsKEd4xzRK3HdSaeoFVFrxKX0EkGBWVJh6okH21ltWOzktDyt6i+ck4WAjDQTXqu9ep8YOnR89/f8K+t+YFw2F56WuHlb4hZ65oLVp2acVoyizdWC2D9E1SxmGHDKAoTWIdOr1gDUZ0mBrvdOnMvJ4JZrhbllsTdi+YGoUtbYMJCKKvTFlF6klyBtmGBaEM6rmS7AbktM6RP0M2JNsxX1MQT0oK9D/E9/vwdP/KJrskvKceY3LHm5oMIHrjP6OnyeYCV4lngdVgBU5fL8wTiU9wxJUxW7tOrSa7AR2GuEglqcWZTrO4ZtdVdC95uNDykvFmotrNuGzQ8bBNAOBMSQkmpMPXJFrAngGNZR71sqy7uMMa/WL10tSmXz88B8RgwKIFDgDbF8NKDzlwZzsE9+bXRNwCTVm2zRRuqON35ALE5ub7db2E9cEuuke9XZbi2PXpIgnpR3+h03q4IS5rpRLFp9ZdVGv3tIaa+O42m5zNNLkTE3/eZBgVIVBHS2a+7heGB2MrvbpKXC78nf0abiE9/Msnn27hB7hcA7/H+fsSf+/D3SZjEm2SjJeqETZ3pnTeLfjNO5X+MVTR33N/jCVYET2SklYH792pDA3ovNBiwNdFn15l0CkYtHUmrBRKUy/TQZk5nbt1o17LHg6IKASmZ7SpsiedXREijiV1EQkgiYOyoaZWacLbj0PzYLAOFI0LqedA2uNW/S0JziHaoqMSHtalEFIueOwSp6NM3T0ag2yp7uTsei40CP+e1Ilyx4SB0S90a+W02mK9v7zSMw5lhxNkl8trR8a3mr51yK+ZWIpsj96yeaAOslgsUXwabEvC0svEGnycCjioctGbPq1n7XWDQcsvxNmwW6WIBY4GOn51yDXNI6w62W29nuRNfPnNKCjZYEi4PImEsBMqHPfdOI02vkC840T2koj1yI6ffmLzTkHowjPAaemOI99cc/rh1VbCwnkfdGM1o1Ip7NW63FeTfRW4z0woYgHxNUz6YB1zNtIKVJhuo0dvaklGQrwGLdGdSETuk0n4H4k4VNLVbyBQEJqulILeIhs7w3rvepd7UplUJ6BtEwx7uR1qBRs9oJsasPeK3Q4XdpBDypFpbs04/+jFoSxWZpQOyaQ29UD8EQslvG94eFUde3K6oJmB6NnlJVaSTXK4xea+Qq12+q35Xrq4EuKYDZnMAeCwxpUUAdjB46I0Cjeo+ts5e8jgnWv2XY7PBuKwwnLXJpQSlDQBhWA6TY5B4/EHVNwUmr+Tyy1uB8ZXkAq4m30bOXrJHOWjBTt/Ibjlj+tm22dqTXEpdz8n7CpwkWgPL36vikjE4xgKyRNtq3Rc2quT/xKxkBEMDsLe/4sLvmNChPtah6VT0icXZvCclbS/YYaL76OhwzR9ulqNdCq64PIWkmhOC8HyCIajdC+quxOVcqH/GpOAK7CvR62DSoOi1XcWnGIsfg08J+b/ydn/NcsWkQszG2pOIArAMQSldHeuzO+96v/o+kXGE5cKlnESYVQ5Wh3PkZB2iyQiHXXBnWZjVnqVNrciTnvNGtIWCHRcSxG50N7VmoogyPr7pUFkxEb4vYW8Yw+kcYENKcbIQov8d7CwYSbZIq7ACqvd1ybq8NdmpTwPgiRcczJvuNExPm37pZzapr5y9b8IGjepbWhv1WuMW51ZNuv+vOXxaQquweMm80atXX7Le2R8edGeviktg6j99vm/7rGXnwkXpET4R2k16ivX94RiyT+i22x628G9d0j9X2jKh5OAy5CgHaUra6J6kxhFYNv6/7R35MxwhzQzUybWCoRFbXCXz08JgjdYzDfShgmRsHZM6q8dzX5SoU57NKZ9MHkZ6IArwrec2T3nUHf8Zl12OzTvTOHJLJWvCFKxpzrc8taF6lQTWWNlzzxukLAvMUJQCMcJlXn0+agGN10hIz7/isuiXAKtG1whUpJKy0Z8g1jMajbXFVLYEQs2NPA1NGYQCFDmv7Oly2ARAbccUQpDQBuDz7aK9gLGkijhcZYNT71GkxkaTZ+WzGJuBcrH2jYR+d/2/2ymqwXt5XM16ZaAKslwhVjpA2uDCpiULl7aWae8j2bXJ+S7RM6WLydfoU6HGE75g3Zm/oseC1BXV+z/UGrBz32uLhlHn273AiYHWLr2lyZOfHghdZqut7nKsjp6e0KKIrqnY4oMbxhJ++qPJiFtHgiauq6KMqEBYCATeTCw+9OsgvrGr3iNAXSl2sGzqOmQX12KKikRJmfKxYLNiJJq7krJT0W/wyDt8eVQeFz0xKC8ZnIPKWJmw2p4mLCigYHLQUS2/dZ52fmdf7SPyaDGs3iQCWY2dnqobQsIGNhlr95Lv9Ys2Cxy5rp4wu57a3fVggMXHOldAnz64KdLA4Adg4rqUfuEWpJZVoM/H4vRYbzzvH886IUi646APOqGyjOAC2TjoqtFExnywIqFjk3uHITXoQ9gmRstyy1zKu7Ui1ZtW4X0J5UkCkCtXomx0toOHUZQstOcbICdY9WozQ2LEzxhQWTQ2ictKnLHS1dEal4VSJwdJhwRFeqQjIwG5IP3XzLlXd4Zvke/asa7MSjZr/woT5FlbFztFCR2nlekO2EgAQCinSN3RZ+abtYvpgMAHlMKFo8rwT9HB0XlrcN5qJk0IFMLrWK86pn2vDOaHdqp26YJWtvdr1kzIOLmR1P1n8ywOtup68FgsRm5i1hz/109a4PC7x9D23/qKqtgAVaIHK2/vkXcQCTXEBFVrYdqhhMswopqJojaJBK+zqFfhvdLRZywkn0iYlJQuByXmNSwmsL42ab/dW91xRmn94i57X+lC3ebYMW3S6oAIGxgALPCk1mC4/PTh5DienlFoizU9vTzSI1dl5NYpWlWUmK32WetAVi7IDACiFptIuvefxiO3TM1Zw+rXGq/Zb7bWHpePr+9i4Pfa3P2kPrv2+tIUad7WaM35ta7XAM+ckN6jtGlY6krQgzBwbodClWXe+FMHQ1qlQlGUBGz3AHwwum41Su2NKJ+5rVXspGaKOTznW+PVHdG9gJwUuKDTAOb5QlLv5fS3fq3QaDbjpIyrz2ExRGykKPBYOxxyLF0fXFnD/er0WmyrYA+P/0R64/EFbOeLsSojlfQmlHWk2zrXmb6fevlWNlLE74HyYS0vfdJPGgDY6e7Y7U9ty3aXhuzsFNnGD5/mAES7tXbdEwJOrNegKVNZyMiuGUQufmZLwHEE8PRAR1sOcSWC/lrVyi0e/4+/RXqLNg+307t7qUuV+nzXGJtXr87X3vF+g37N/u9hAbEPoxmCdbZIBoDsUCG1GM75INAHWS4TSR8WaY6UuT5nnyqQWM1J1ftJGtJRZy0rXAqgSyOmKiL1AF4KeCME3gCm8UrPWR0ctulATM8VExwPqO3PVskNhK7WFbeFIiwTaboH2a0co07uKM+Nkd0xbHtyWqe5BB5W9fbvSMyvbwurjFz49qUayLFKI8GGzYBRIg6DBoLIo/erNcotG1CzBaJvPaYe2dGthhbWAzSFVADsxfX+P5Q8MapIkdcB5I4bHp4eeZghX0Q5lRmLWeiarXSGMFQu6qva6o1ogmThljcBnkAIoVi15ctk6nnQTPT3gqP6hgkyWEcWyzdzVpV6JaEIAjw3SSE/+W/U2bBl3/RNJHXINAHwyquS6Ft0kzeKFhox7h34ocHXOgbvJGVlAhK8sOg+t+axShmqTky1abXVVbtn14V63ICPJ63Cu9FyX/kdrEkSzyMAQAoRg5WB20EClnl/QNVEborKrRiT9hYaMqrng5VmldykNp00RDKAsECp1K/RFBUhU7eTx2O3f/EF5SKmlCZ5rNComVdC5bgSKTQaMznNnxLxhbopODPsLsTavP6KqS4AeoBtLEMY0ABgAg58Y9zt6Ykrjg2NWCg7PH3bzaadBy731Jmn9crs6XPn72LQFjl9VdSjaNtzbYTLwJsvdudPu3/aTYgAABGwIEKirQrZaVYWsY7qqruryjj1q2aOqzC5XeUdqBhDOhoIqXBnQoiNqNGQMCQuha7KuhaF3Yu2Ma31y9Z19StdWkw4AU2FHX00830gxscFpTM7IN6oaduC0dtdhPYcyQR3qsXtu+fdmt+433/CQBY5dstU7N0ssjjCc3nWIpG/9rg9bfWxSpr+kS8UOE9gc0F7n2EULXVvUcz/5rUN25/0f0K/VtDgaVMqUa82YQJwPM+afWXYtYAA+iaDzoEKCUKmo+pgKVDSf9c6UlXb0CBCTzgUkJM4ty75h6A/WW+Cw0RroM+9Tpyx09JL6MBK5gwMysfWspi17oE9jvZbN6h55Lo3LZR9dpaUzrjXVcxet8zeeVGEB+khsUxrPndKzKN8pM7WxSZ5Nu+bL4YB1fGZWPxfjSFFBd8J6f/M5pemil12aWXox9JOhgIALYwGW1U/xANpAtIwDvfIeg+WEzUR4juyCjS26OgA27BpVx7wPDbxhuD2tKVve454FbSqrdc3T7aRaB3p0XBil0lJKXliwnYurSgdjjEsbHZhtMgyyduloU7q+fuKs7jMAEH0a9+ytN/6CvVJRf5l9CPn7Zrwy0bzSLxFMuogqYWlYcD2xqOV3dGgh5UFEfEqKgsUNvQCNc9WUF88lfH/mMCz0Op+ipbIt3DciHx0YFXbTcsdWM9iGSvxbzxUscnZGjImavvocq8R7ABgQwsNasfhGpl1qDFE3tgodL2SsEvWYJRPmqVatNJCU8aRnGl1Cw7kqKz3nleC20O8mnvH7Y9b2keOaRFgoBS4QmC8UrNQWcA2c2ZA2zPI3jgiwYOWghb2rTaaHVJLl+6IWm3b9+AAegBVpj5KU/TOB+pyzOi1NUiHznx51otFKVelThOEGcMAtfueAe5/OuFKiahI8z3F5zbN9ROASQ0cxZ6ThcmWl6WCFYCxgm2AVYGYAW41Tl+RQzWLGRI2GRY1caamzpUMO6jBiSg3OrYnBgnWAhdlgxgBAHZ8el9s42jcq+FgISEFJNIuvGL0B0WLhtxQJKlXMrpo2S0zQr/7WD0kjwwJPfz8WBRoZow/L9zqmUm1bpD9zBp2qEj026XomHt6txba+llbaUJM+1VNzS7JL8G0eltGsWhl10cy7oXsNawaTwLWiMID7O3/PkNzR2QzAKEYvLVp+b5+zqQjTyiTutFkj7QJ+VLwmTy+JAUOrl3/VNpl6Sks08xtq4QJoqM3MmufqhNoaYW+AhgXdGRq+2sqa5be0umervdU12CZVOJtxbCCtc0h1nh5TZS7NkQFg9L5DF0NKSKndF87JqJQFEU0U/7Kw6l7deUg98Po+W9Z9hpUjXQUjRvub1sdgyvzaJKx9017dV1zzabpNJWMgV3X9ROOuHyZeb/g+YfeQeti1q5HOMOBTGj312KhYVSr4sOZAc4R1iarNmGD7e/VvcJVKV7PI+VkB5vv7f1jj4/7uf6l7s3AwZPV0Ru1mNDapdN3c5SxPAMO9SfU7RK9GkFalIwRjU3PDJ4+7gpOlFVWbzn7bdue+XixJU+bdvU3pRWlKe1LysyPlRn+/+PHp6w792CE0KlVtBiSgj0adD1pHu/n27bxu94BtA/dbf9MSsfv2vM9JJyAWMZilw8HeLqtHQs7brCUq93fOTT0o2SB0JwW2uBfoN7H4WNkR0bWCYWOeLfW1qBqbsS17nPYWafU4fkJN7Tf36Tnk9cTyO29ytgzrPUZn7u1RyljHfWX6eksc2DM0nBQEKC2eilh1etZKm9o1f6Phos8l1aF1ihfox9jfqfMGQCqVTcPs9qRrAP0KRVOD9fUTTYD1EoEAVwaCM2lr3LRXkxW7eRgPUoFUBGqnj6h9a7f+hhRHdDQjJoS0AfoNFnkW7kChoQVibYT0GEAkIr8sDEVJXfA3SjFSWu7xWGkopd05/dxUAdYaF+OBySaTMsJNKProsXGJ19HysECpuW+ro/DRd2CDoIUdN/R1B3m8rQCLw+8/brZ9xFlBVGuW2dmmdBA98miWnDi1YJ3PLgvQRUZXnbarJWbxc0tqVowDNexA4syi9CkIpPG8QVDN5EeajAUdtmzuUMgyW11VJGJtqpjEGtEnL4BrZsVqbTRrxQbDq/eLLtasFvA4B/yxjEwBVTqunbFfjJHuAdo20lqIXTHLxBF9YkGeWDiDV/pcc99PVv5ErVRIr0jgHfbKNJNUqbzAktHrJfBMsLCYTKQyQI2E5XtU6nSl8Jgswliqyghx9blZFS2I1Tx5Tq030ttb9HmkIvC82mhOC5jKbI6qpQjMI2lZWE8YJ/VcXMy7dNBM2urLK/JckhUAHk8wEzQTTmfljVXZ1if2DsCavJTV/UMcvlGV2PXYnNzn+RyN0aWCdT04pvL8xCdO616RTlQfOcD1bE6Vkbquus4epYjQm+BOHzp2RS2TVJW2Hjhek2Li+iLwVios/QfmCQYsdHzUMXbbN6vNUXFHj3PUpnCEXm9UecHg7R5WOx0tZAuLMuZVq5O+NvOMTas6kKjcfUi6GRY22RSEQ2o5xTnDOPP8hUdXlF6KnplxLZJgdcs1MUAAB6oF42NuQebepW8FqNEW6P9n7z+gLM3O+l74OTmfUznHrq7OOU3QRKWZEQgJZJL9GRbm2gvbgE0OMjgQBQYZY3+wfD9jggFjMEGARtJoZjSjydM5h+qunPPJ+Xzr999VjWDB3Atz3bYuvdeq1TPdVafOed/97v3s//MPpsMHLXMFfsMXQ03IgNS+sqpiE4QZ9LM82m3Z9++XelPUFny0OuM6ODCKg850mPbY4G85BSOxRcX9feK24YcFj432OQ7jIDr4Z3EoCZKFSiEQD5vvzA3XemVEIzrURacyFlqiNVay4geOOC+mYNDyXWGpMIXwteAzFbDsSEpq6PrSsnnP3VAWYmQ2o0JWXL+zV8Rp5P55e7vsAw//uFrLFEtObYdaNyokyv/mNcVwgd6BUnKPcqNNau+zNtGipK3PuoVCGN8wBXmjrIQTpgia+N3vhSsWfe2Wnr9msk3vLKrQ4x5zH5Ov3BHvszDa7u5JvSEVJs8ZHEQsNziU5gcSoi+0Pz/juFnFknU8P2vdfzghlMmW16TC5DDCwVPO8hskBJTMd3PKAtemNUcJ4Aado+1ffWifPPgQIwgNRHVqZoWhJqd47OkQx8vd/Hsz3p0H1n0E616O+wXWOwyRfNuTao1xkm30dmihk2puNe3I0moRws/CPdJrm7viMvlksacthJs2hQJEYxAmEIKeP51TwVDoDAgF4ZQK54mTPBs2myJtBZRYud2t+n82WJzfIV6CnoHu0ALMDca02dI2TFxacmaP6awI8eTK4T5ebuF0CKrmYHBOkhCmySQECqeNIh6Z32fJl8fESRBxdWZFiisWboxBkfNX42wkHrVHCh0BLfJE33CK3XYWhwTNAAEQOZ6CIhGytssVtThlugk5mQD7pDMcXN0fVE5hoYtcQJRAYRVdtKvgJQkh6olb+ZmTOqGzAbCw8rngZcAV8V8a1+ev37xt+cP94gvFXnf2ABR4cGJw5I5fWZajOu0IWhWFdkfQp71Vbou6SIxizXzEekws341ayY+0WLk1pFM8yAFKUGwsKACUc5fJWtPrM1IbEqRcenCPjFpR94kIzTzKObWlDEafv23+rGud0nahsMMWYjvDbfz/GBI/T0gWbaXa75jn5H6rveeQihCiTlBrfv7lj6t1jSUD/lZcJ+ToFGvcG23yFFugqqfHhLxQyEcWi7IToKVJuDP3CEl9ZiSpViAFCZs3RQf3iViYWsQrJCp5O2ee8zfNQ94b6sCrP6lNkfuCRQIDVEOmjD6vVfvbpdIr7uoSIpI/2u8y747sVtGTHYipuMCx3NPVbt6mlK496l3mw+ZTe4TIgjaA9oKM6r4SidSSsPLOLjnmS0QBWkpGI2aQZMWBMJBdGHQcL5A7nhFUmxi4ci2S5xdt41i7+U/fsPCz55QHiI0JLSpaolLHlcoqQGip89xRjNPWQhzAASV8YVKihEx/QEak+V1t8kNDUUemHXQBRWt5PBYeX7WVAwFrjDrbiZ6XM3o9CgOKwcgr18WdAj1XAZFMyI2c+67swfWsy8/z+TQ/aXPSqsceJj6dv6tKxlyYgxutNIQfiqbp7rTE1TUdFCnWUPOC2NImhPwO0T/fGzH/iJt3Evj0tVmuJ6j2qLe1RYcZ0Lv1U50q4EDB+N0cQKLnp8XRgjgOmmdXb0sQUCeuJ1+wRqms1+Je6R4ubIjMrxSI1pBQPOgCrKUcuKQYvDamPFY4e3yu9JFOHXjguLIGgMbD0WK+UnBz0JJTvQpflxTA+gbaG3r5sllbsztIbmTl+VXf2ecsMPh+vPPed1zqU3Ic4XnWZuY0L7XO6ZBSciapHGhjUZePeX/cH39h3C+w3mH4Z1fMe2taMDILI9wpZNacxggbhteUOdqjDUjtL5/XWt5e1mkUxINigaBeXMHxXUrdcYGmDLVcrm5Kkk9URmzGSfspPgT9p2JqASHjJxSVCI700/sVI8JCQdgpSE7yi7e10OANRSQPhRD8Coq21tOrlri+ZpHbq87GgODc1rh4GyxqLHZyF1/NaQMSzyMUsshrNy35kmuHsBlywoZXw6YB0RtJNYomkuTxnoKsK6NJFEZsxsdGhXSsHUopY3Ftb0C/JzMQsOBq0cpNfhUyFAF8Xhar1AR5eR7x1ihG8l38jEc+XhRZEH65psSLcJKE+8FiDtoiU9H2hNWzWaEhdmK/Mt0obKu7+hzcf+WmVdriThWay7trUqjKggP1GVwLWmQM2htYN5QODSqzjs2JjTy0VhSKpzaez6P8vrY/vSUyMI7r5aM7ZC3guTFh/s+fFccMxBG0kmIZE0qK0ti5GWfquaNb84ZFXEjZHUwXneybtqZ+bnJRqqrtWA9iYfI9IYVC48TN+3i6+f+4a/ZKe7LU7FzLFbWENB6xBsrNlYKsFii8KOZKbWHz7N4hNIRWmojkGKqmqxZaq6gVRuE39UxAjv8gZaB2tSeOqvisl4pWXVsX+Rf0yhuPK8uN+wFHq35rXNd34WtGzd64IISDti6vi0JUvkhzaxZadpFSaol/7rQ2Uf6t2tNq9SVnCZK6vOaKrZU1IQfMHcn0t5SlFK+YftLy499B/8jz1JzmeQT1KdfEN4OPAyqISi/94IDVz10V1yj16SvWKFfsc+Xf0jNE3hwtKrWsaIn3uDZT0/W8JV+fvOttxtzOnRqWi7xnuM/aX3RUAQpxijPuZ2B6Te3+0pEhvcfV93TZwO/O6n3qsNTrVKcyceUwBM9sZMC8L5+TzQW+VdggNAZ7pI5TwZAvSOiAH1jy4rISD2hnsu4Ud4H+QZ7yW2BsXkW81Jter8xwPavrd33cwl+4LM8sHYhaEkJf5dtVq1u2L6S/5z6R4iBhyrFeJx6QmtQjzpgK2XjYCTRmf9Fdc8KiMVIl6HspLSsWDiGegN+1/m9NiP+knMRo1AIz6w71yhStcnBYaQLMUVp2tVP7LNPn1cGLNI3kaxN3bXNkINrbLJUgiFlxoMlFaWH2SoEWCdvS04M2+w07pahsHBp1XmjZglA1Pe98/1aYOQW3rChWN2WcjLO958heF4YNvYCN88JNodCyxdgKz75Xo9bwvOuv++PejPsF1juNYtmKJ0bUslp9vF/SaCIq8seGLL2nWZs4KACOxWySnDY5pZN9JqUc3kotfqkN83s7nBko8Sk72lzbL+jUc8mbm5ITs/izGUJ0Ryo8+1SbrT3ar/YMiwBZdZWH9trGg706naPcgRdFgcbr0o6jjVZK+Sx3sFvcBX43CxKIG/48LJSENsNhwfBQZF98boJB1w7Bl+rkqE6jGvjKpIJObXdzVmGuEXLOcIDvcm0X5Mq0OCHb8rkXHghb9OayhTbrMkVtuk0Qb8OSExUr9BAgDfnWIVwEWaMEYkEH6aGYEi/mTsHi19etEQlYcHZDcnpQN6k5izVlvUGuxdzzs2//SxHFGw8cVHyIbBDiYQu+dlUqH4oGz+E94mTx2nCYyNmDZ4OCkjZdORmQ0zuxM9s2BrkudypFLSd1GftROChUiWIk+/CI5R7coRYD6ASu8GpvDvdZ+anjtrErIgUSHl6+tYwKQjYWTugi86aCmiPJM3MWuTYvBRl2BHLCbm+14V+ZtJUPDkuxx2ZJIcPACNLYlGIRFUvbIcwUUXyRnQiBnfaROhfwU5azriWHUe0fnXGqs3RFAcgUdihcGTJaRWEZ9WkugjLu/O2s1Ipw3ShyXnj+B+3ZxV8SF0X+Vg8cVJsMZRZKVbWbUH+BIphZ1x9PqM2CC7rCj30eKRIpHrBNADkDcaAI9re2utxC7DGwCRnoFb8R412QFO7dtkEp1xDrEd/sslpF1pRylhx4oW2mZR0BnwpEFlsMDiy0nNhkvYN9VuwASSybPXxEqt066BJk7b7vtNj5WSEhPCuB69O2sdcZzJIHCponA1nMPBc39bxzEPKMDjoEdW5eLVMI4fIaCwesNj1jjc2MEBYONK0vz0lFSaA7KDMZfuRSFjpCVh+fFueLtrIvlZLiECNSmR5Pzwvxm3+q2+qra87apCmpww+j0dJk8TMzFppJi/cE/4rWO8W9OI+IAzjg7e5zgdY9KSu8/6CQTopV3oN/aNACl8Z1YGy6tK51sN6ecr5Pt6Ys1+l3wo4H91nTraLjqHa36mCo8GTmEWpSLG1uT8mPq9LXrENMY/eQ0PL4nbRVT+11872305mupmKO31mq6lkF2YveXlc8FM9k7384a+HZtNVH+4Vq6bOkM0KMPV88Z88u/H9VYIbH1/TMYy+B0SjtxY5Xlq33007gQutPDv+Ykq5tSGnNMwJCrx0RoH9nn/zBEMfUm2N6vkjx8PQ6KxGihji8ru/ayqK9h+PdENy3v+6PezPuX+l3GvGIuBD0/SkUSj0kumM14LX4RE6BraibICzDWYG/guM4A/4ELRnUZJA41QrK12VJwP9v7IpqwVeMBKrCjqRztw46Q0hOp33PLil3iw1w4dEWC1+fVyFXbPHas9O/YA9//c8JEYtP5YQC+CcWzZPGL6vqSMMo6vweq+4bdG2rmM9J0zfLamPCB6Jgwnm70XAtTga/B7WaZNCtUSepxljyoWGZA7J5sGhhnknLEF8YCP2oGmlnDvzRkmT7sc9d0kYEwZ9iBXO++I01yfEJM6adKEfu8Q1xIcQdShJ5k1fBJNNHHKO7UCnWbPlo1FYOJ1TMUXDw9/xOZdGl2KTr4oawUOo0fnhUCAMn/21PLEm5dw9Z+Nqc+V+/qrgg7jFB1tgnJC+vqdgCUULaziIvEjtxG3DkQAFxOw/7LX5hQQUh3lXu3scURo3lALFG7a+t6LWLzV61SuBLIW7gnlHwcB+kEKV1VK2ZZ3LeQjfmtCFiD0BBQl7k+x/7Ccvv6ZBb+DO7f1Au5CqqZhcs9ukLrqVGGwmVFJ8XJLFhKkCUgyj3a694aDJVbG/V3IMXpGKnr002GEJO6iaVJMUsbWwKK7XWSB4A4YJcThGy94eEnJEriBL2ld//XiEGeAX5Wpql+PTFokJaCWrm94O+vf/xn9T8ajxyWOpGClIKrcbNcfON7hBKBCpQb4mbf2xWBWFlqF25hvVrt53pZYW8Q5+K+9zju6WwdKrHmiP9zy9bfe+Qk95vBXMjJCj2p4RCYN4q49LxtGX7QwpFp2Arv/ewVR8/ZPXuVhV3FA5+VMTJhKVuubxGBio9qc7gXvY1qz0Mcql2UaFolfcc1EYeXnSByiAl3j0j4v1xLUHEG/Gw7hliidhkRgcDWo3xZy9a/fgeK4y0qsgBlcXsk2v47PIv2+Q/2WuF9oB1f3pWRqZ4SvG7/Cs5K+7vVduaQn3jSIstn2rW3C8fGBRaBTJEAQJKjDs+c3h9T0RKYoo+ckJpHfI5Nz+w22Y+0i3id3moVUVqoT+pg1jTmPOsIlbKf2VCzyycJTItOfjQJtY1DnhViERmcyrOMaLFYoU2KOajDBIFJFKYndczTAs5c6DNxfoQ2I35ayJu/reumR3Y6TIINwtaf3imOOxmdiSUeqDRlJTAQAjWVoC53Rx36s5cXj+P9x/B3bQL4WfiD8bvwgSZYp6YJA9msNubZLogbqk3EraFJ9r1bGBEyvrfdjZjuZND5l2Dh3V/3B9/ftwvsN5hNBIxtb8omkCHID+CelBksEEhRc8/OCJ+0PxDERGm4V0hIaZvX2j1qWePPJxcO3ytWl+Z1wadnCjJqDK0WBBSwGYBMiPOEnL0/nab/soOKQRBPLo/O6/3w2Ydn6vZIx/7t5b83DW1Bdb3xiXdzp4c0CkwenVeG1PyJotpRQURr4mlAa7xxEqgPMJ/KXl6VicwBb9ygo+EdULGPwbCLCgIRH2I0bHpnCXv5EW6ZcGWWWq27MKaOdFv5FVwbRxpkx9P/cgux48o15QtlhmOWGZfq0NtbjmyPRwSFjc2CSJaWIx574X+uFA2CgJQFAqBztc3LTFTsc2RsDLrQJ/YsIS+ef5MfedFJu7xiDenSI4uOCMNx3MixgTFFC2jLTfo4FJGRGwUQeWuhDZVTtuQofEAksIRBRI8K3zRVgoWvrUoFVT06oLVycv9/Fn9W/nkbvOMDulkzmZH4aXWMJ5W/RRCVaE1bLK8P5SbEH0x5CwdG3FFrt9vtfceFVk/9uYdbYQQ6PP7u50fWjRq2Yd2OLPRg6MqxhXdkq+oIJI31XrBtQUhDKeiNvf+NhWNmL1CzAaFY45SIHINCNXFFiM/lFD7mjnDl8jX5Zo2OowoKe5oB9bHp4RY0U6D1C4PpYDfnm75h0IkQAJxNUf9JkPc7qQ9feRH5SknZ+23rolXyHUBCfts8TeFbuGOz7+JtHzMFUkMbEV8ne1WPbVHhQnIEagGRRKqTq5rfle7Nd66rNYuCj3axhRVtHvF5Rtfs+rBYanz2DT53E1X0xaZp4XqVeFB2xeDUgom4qYIBYcsDm8OBFLWCSj98KCC0wgaBAewXBNSgwM9CB/FHkXlsxOfdF5UkZArTmn70lKG+0Ng+XLWNvemxAmTUnR0yLznb1rk/KSl37dbzusyMr7sNvxr/+a7rPnsqjz2Gp2t1hjusfrVW2bzSxY+O27hq7MuJsrrsfY3Vl1GIeT2cFD3GFSOglImtBsl6/i9a44LVWtY86cuK+qH+B64knUiOXm2G6aDCIcBUiVqUZ/EKqiVKVwin78khC07FFcsD+379Uf7pcDTJpPfygXd8sHzr+cUPwW6Gr04I3d6FI/BmXVZeiTemJTSEY8z79ySCt5tjhZEd7m9g0oXK8q8LLR7rfbwAeW0ygz02k1ntTC/aA14Zsyfppg10hmHAm6krToxKe6Uon4eOuxMSQn89ngsv7dLPDUQZk93h9UmZ1QwrnztAUWU8VxgJO3pbNNchMqwedIpn+/FqDe87/rr/rg34/6VfqdBb53+fLXuDPaGe2xzOCQzx+zuZotfWrTI5Kb4TpHlhgjfDNRa0TsbigRBTYMajzbQygcGbe2hbvGxgJYVmdMZsemnkpbvc7ExkGYJ9+VEHl1u2MaI31YPxWzjRKc2/7O//F3ybyJ2p3rIOcfDtaJgAB1iQVp/T58QF5ALcTUAp2aXHadpbEWSYtR3bL5yw0bCzGbR0mTl3pRFzk04sjCSc79XPCEKkxxE5MVNnSBRJVGAwB2h8Cj0JdwpMOyz6DwuzjXB/SyYqO4gr5aTXmUdRmfyQimEtCCzpt0QD+ozw2WAtIvflmwXKjUhSixqMvysEeVTtbkPdatg3NgZ1imZ/EFl0fEaYdf2y422iGAs1WWu6E7dmCxyjzAjhQ+TjFh2tFmydwqclcMhFSoUNTitIwenuFbkyviMYmdol4JuCDXJ5hVK7Ns/qvdMO5HTvnIQsdcoVM2fLtrqAy6Sg+spQv7sqtuw4zFxvNhcwzcXRFwn3gNPKKI5aoPdQo9QD770p99vnrcuy3gx/Km3XX4jWX/ZnIplCO6gbCLUUzhiIplwCQStV0FYvZp7cvGv1XX9ZEArN/i0rgWcQbglS+9pU+uXjRr+DGab8KqqK2vKYsMug6IIbhh+XpDlJfHf0asijJgnUFXuD4aj8oxbXHUmnCBLvV2yG2GOknH5TPu32TMD/1yFgrezTZYM0bMT4juSucg1UywLrZo7k/L+4lqWOmPiA+kAUaiZf6DX/C9dtOKoU+SFby2r2BFHMl9QgScD2O6UzHC3rUS4HgzmQWOoR/cHi5bcjpTeM882LUGKJA5BKp6bYluHC7PG+WsWmSFuxydlHe97WxwB0sFG/JmLP+64aOWqaz/dmdVcTd7MCsHKDII+Vqx6co/4ebJ8wZqgq0XXmlahTC/LFYu/PaXXBv2WwpnAZ1qyiZj4fS1nVnQ/8WpSOz8ZEtUAflHsMxcdUR5vqsM7rNgRFuKYf3yv7i3FBShNA6eYJQrYkkuFuLWooHZQb5zXxUWtVnWgkMHncskKHzlpz+z4Xms+t6pIJxBXHXbeuOJe64sXRfjHVoXnH7+z2n78xXJCnCh8OQBwMMJYllgvLEMyH9in6x9+e8w598NDw7OrXLXO/3pJyQ/FvrhsT3Cor3amZIFDIZv+yGEV7oR6WzCgtjut6KX39tjmewZt9UBMfzInEERE3rilAg/LHIo57+5h/X3b71625usFzU0hYpioXrpu0em8Uxnfo3G/RfjlMzyNxpaU4/64O9LptKVSKXv/ju+02o5utR0kyZ2YFREXZ2U5tyfjkr+DjKjVEvYJkSKFHZQKnx+Z28GD8HhUKBCZwukw1+23tvMutxCC5fqhJms+71pZbFCbT+y0jVGvtZ+riOOBQSOox+KpmLVeKcvnCdflRnNCJ2EQLKJGMoNhKbxYjGTAN9LjkIz5TYcmyHqi4Vzg18tCOpBfs4h617KuDeLzytMHcj4nb9A6W1wWL4EoDIowjFalPvThEeW16M0V2Uj4VtLilnjvzJintUVQPu1IonBa3lyUKjGwXpJRKIgBxV9obtOprOJ+KcFApZQ7RtsuW1YMDi032TkECSX26NqyaQghq9bMN7koP6anYt8kUi3XncKNtgSxLQxaHJDm+Sy4o9PmkmrIaxIsRMdWRRYnb5HNiPsZfOWK1VGjsfm+/5j+lIHpq5es/tABoVCcpGlXsNmB6kF618MFpw0+WARn9IYj/Wbz1uhotXoyrE2iOjvnYlzef0yohVqzIAMJEDz3mngnMe+qdybueg/BEwKdoLClFUUBIlPGNAVas1Cy9G482kxtH6w0VIDBv8N2Ya0ozl/44pTmiLzW8hVdxwZKrx39CteFHxf53EVt3qBVXF8hf6GQoqIa4zNqxVKQkBmHYIGCgzBxXgdek3fviIjatAcRHlAwahDEfHtKRpJwBXnO/Bt5q926I7Uebv7RxZLjfsFtCvpt7VDcmq9RoDvVra790oq4MaBLFL4oc/FJAzHDk83/9nUVDWyuEqzwLKYcV1IGo6AqtBZRr7ZikWLiVz577afsA+/5cVs5HLPmWyUXFE1rEFQtFBQSBrrz/Is/pMKHglJKzMkVHXJo78cuziuXFNUpAcVkC3KPMczFtRxCuS+RENqH+39gs2IrhyPW/RtXzRMKKvtRcT+Zkq0fb7Wm379gma84ZIlPnVdeaeGDR+7yxaSahD9IXio2M71Nav1RJCJOoFjg0ML94dpBAAddJoQ+cXXVubsjKhjqdesQXlK8HkUi/KtV1+797Jl/7Xhhg/0K95ZJbtin4PClJ7p0TdpOb1ixJ2aR12/JQ5AQZRBx+UmBunqdYANnf2cwGlK3AGsbhDWNCzc0z0AcORghwqEdTKarBEd7upXGwLUBiUWIwLNYuzpm/t0jeuYo1tYe6rLmCy5MWq1lDhfVmi0/0mGt551JKG1ceK1NL0/Y3Md2WMuNsjoRGPNy4MBlXoe0zYxlT/TLTgX+J0U+cxLEfOlQzM791sdtc3PTkkmHeP/P2pf+z7PHLZrYsuz4G4x8pmb/8NiZ/6nv9f5w4z6C9U4D36vXr6nAQO68+rEDVt/ZbxuP9EtODifLf2dOGy6hqxWpSvyW6w3LxE/8gS2rAsirLNqx2bK4WBRXbOQil0JMni3rhAtqkHtghzV8ZgOfWnVGpXAJ1jZ0Iu98M2ehxazuHCRSeXKFA470Wa1Z82uzThHD5tbfKZI3RPb8aJtakKi3WMg5hbIokt21HasjSTMwOl49k+sOPofvDWrR3GTeaxNagMnzYqFmQQS9Qy0lQ8qo37KHOrXoNfpdG4pTJcoxiNfkHW6T2ymu+P3wN/I7mrXhkTuHwky5gBtbWWbhgFpWZB/CIYF8jRIPbhXkYyJjPJdvO3NGTncn9og/ARk3cO62eS7ecC3IgE/tPEblwJBUl1IMRQOOL7PsojMopHFwpv1IGxUll685pfxBCkO+4Nf5BnodPyniime7M60AZqT/LMREbWCgmtnb4loaENA30ipUaFGUmkNqGdYfP2a+3m7ZByBfhz8jX6ZD7dqscaQmjgW7EDYpUB7cpEEs9b4XcnLxrnQmbOXkVugwtJZwwBJ3chYfzzjPoVJNaFog44pN5h3XGV4d1zV4ZcrlyK2sWvXwiDY87B9ir92WG3ujWtHPgRaK2xIOSaZeO75bNiAUdfiV4cBNcQUvZe2jB6xxar8+/zO7fsC8r110Lce1jCO2NxpWOb7LZcGValKj8b2+ftduCW1UHcq6lWzgvT5uqbGiXp/njeKKYofiBD6aTCxvLwpteur4v3TeS16PlR47IE8ykC+JHAJehVjLRHc1b6G3x/RsoUBjY4UbR+GNMSzoTnK6KkQx++CwCzFG/bmwpPYhVhIgVvAUNa8vT0jSH1l214t7RzsJF/UazyOu6l0d+oy5oYT4VYX37BHyGv70GSUjtJ/L6xBX3t2rKCkOMeQctjx32xoHRqTc88QiKhrJO1XRR8h8xBG3hTyHnc0G7XyQJeaF5rvUvCGrNkWF0BFkHX/hugofeKCeZlrFFXlSzXxNr5TJ8LZkUpzNqZCHeydhQDImNFvPQ6kul/3WixQ9Ddvcl7TorVVb+eq9EjiozYkfmWgDNaFZIJfi3wV88nvjXoZvLsqDb/MbTlj6/XssML4kGwlMUmWIGgnqYMEBy3nXRdQt4ACJi78v6Vq3ucGELT/WZTX0CPiDbeEI28rJtrfXzK6MOXSzKWFNby9IFUmcEa1kDj9C2ZhjHATSWSVkkArBIY/CE66f1seFVUuOb3mq3YMB1vpuFITbsaz3x//8cb/AeoeRPtEnbkB2p3Noji5WlcWHoSeDlhuqQjYNSMipSyta0CG844WDkSRtoI3RiFPeYajIJpStaION3d5Q4C0yfIoGNs/N4/j5lKTqWjvWYpE/Oa0TdvHwoFRFKJhEPCVOJOKXLxOowObhdrXXUPZRGOFF5Z1bUcI8CqfouUlxUVDD8XvJF2TAh3EeXj53gu1qU3GmBbU15U6nO3ocQbqrQy0gFsQ8Cjt+X2eTZfe2aoNgk2Qxx8gPjkR6Z1QoGYugMu+yFQuPLdvyqSbZFhDrQrvThSI7J3TQMIocFjG4O7QfkxBvifcYaVLUR9Or0/bcKx9X6DFj86OHXeuJhQeDyP4e5b2R9eft6VZxwYaOBJ2B8ao+OwVwre6iPuC9NUcF+RcfGJUvFqpPijNPJCI0gFxDqUD7U5KOsxn7xuec/UUu737n2rpQReZNaDFnkT98WwRZNon66IAczMu7e0Sq535QYBLgrBNxqSSiO58l+daM/MrKLWHNC/ggtblFp+ZqbbbG2rok47T/aE8U20LWcimrwotNz6EKzrJA12hfUnOH3wN6haqM2BiSAModMVv74KgQs8qTR1V4sUnS/quurspQchs5A3HYfO+oXNpRN/revCLUilM8uZE8DyBi+XZMT/PiH1JIFXa0mDeRcOT5rmYhsrSdUGMqJgWuUzikuUWAOQNuIv5WcMNoJdcOjGiOUXhwGKjemVRQcOT0HWUu0hbCI62OJ5HXK5Us6Idcy/e7liYb89KxsDXOXNGmzHv7zNr/KdVpHVFLZosPubopX7nYzXUdSlAE4p+GObD870IUKRHNfzhaoK82t+Rc3bu79Iyj6MW13TbTVjk6ooKGDV2mt16PXPHL/S0WfvmKSx5oc2RyOecPtSsnMfXcDSF4IM21wU4p4FpfmJLFBApCxABs/IrxQc0GukYgOJ8n4NUzqHSCFDYQNX3e1K+/LmsOz2bOQjcW1N5d+btHFD7PobKxumYLD8Ws7/dnnfBmqFvPSn3fsFXH7pj1dJp/fMGZvfL71jc1n+CpUTgxQNE3j3ZYYrriiv6mpMQUkMgRzjTSaSu1hs3f16tnSDxQWqddzWoTJ2/nLXVhWbzT4FrJyi0RK412utibSs0Z+Hb6nd8VxspDLULUFLeVy2supsZL1v6nY2rRbhxs1twCmV99uFP3jkKX/FdZbaSiVn70gBBQ/pt5hDgBc1aKMtm7YGa8vC66A4eb6rKzy+D6iO5wj8Z9o9Evn3G/RfhOLcLh79DDA4kW/xWKg8LuDhd8GwuafyltGyc7LbxSVeEk1RCncjLeFK/iCOtqaeWcbYN4QqS91xviQcHhyuxrsejvv2X1x48I4WExTe9O2fIRr438tzXxnVAYbYyErY0cLRAnSMDDTeLbECMDyqYFP+xUijIJ3dUl3oTeQ9NWhttmVmaI1RO7FYDKoHWo9wgxemzSxXuwKKIsZMGl9dDX6UKQZx3fi9eqx8KWHUlYdK6olh7vRZ8Ns9Na3ZaPJywxvXVtVsra4FH9AdPTqkRyjWINlI5rBfS+ORqTRcN2K4nrEB/P6aQtntLkuk7nyL89OwedtL3NZ+2/f01qSVBBlHpslLTLKF4gjMMDUntroFfcrNiNVRk5eof71SqR+3SlIQIvyBWWCxhwugLAtYBpq8CDA12DW6e2Samq18HxGaSCzakx2K3WQwPVEtcqGJBSUFw2XmejZL6JBZv7ulHr+pXzjtiMJ0+tbtXuJhmeyqssU5Z9h1o/bByFopBT8VBon/S0OlsCIVJ+ZweB1QWhz5Mbtn6sTYpSUDDmC7Yd+K3Bm4M3Av+PzQuUEz4OhSM/ixEnZGEGbbbPnP839szQdynXkvnDNfX3dss3TKaoVxzKhQmuNsB8QWHObDpwATO7kpa6tOraM0ureqbI2OMZAVFjzioWpcPxELnm8oSSO3/DFcG45WObkIqqfWTH99ry0bh1fnHFWWckHIcPIj+tKlIQuIfKpbx0W8UZLR84gWzWPCO0Sp0JpQtdx0AYFAxRC0pBpSqQD1lrSC2suB4/HDevCt7IzWVrRELiRKEmxK0eXy0KVFDq4BcvW/qrj1jyWlqIoKwnKvRsY3p+aEHCGVILvlpzG3tzWAcA+HV2YFTtcexDaMGq9ZfLiwpAsUYBQB4l7XgKLg5bxSafpW5k1fajLcihLHhtWi1nDonVm3fM39JsmUd3WuzZCyr0bN+I2mkUwYha1g+mrOXtVbdBoPCL8Rmrurcgd2oJYzo73C3jVxBSikGQPf4e1SWHDsXQlGuaw1wfVJEeWlK5nGwxCKVffd+QpcbyUoxSsBQ++oAlzs3ps1LwSw3I2LruKsDbI7axg3WOiDDX5oPDRnwRSCeu/nKx725XMUZRmd7fKmsI8f9A6z1k+lSEWjdAgykWAz5L74jKAJjPpnm5RIZhTXYWogxslOQzphb04R+RwEEtzhs37IXC79yTFuEvnT1pEQr6v+EoZKv2j4+9fb9FeA/GfQTr/2JIoTS1oEUWgz58mzC24xQLdM5CiuxchNTVtFMGrRS0gCPBZpPIdbkcPk7vmb6AlZr9UvZEpjflvp24tSmSNBvS0lEChENSKva/ULLMaEpeW7x+66WMUJ3l4ymZE2LFQOQNrQkWUhYgfi+tKxYoOFTrD/fa5qk+s5kFRXgA8yv/7faig9vJ6oMzQ7zMlwypb9jYMfLDEXrFBZ7CPVl9sMNK/QRK07Lz6jSM3LoS87m2GQafsaB1vLmpViDEdkwWUXKB3LHpEu2iLLCYz7VlNipauDAXhL9GUDTXgfw4wqdLTQHbHAnKlVz8iq4OW3qw2eJjG9Z2ZkMLt/eNKzLIVGDyoCMnew/tEen5me5/qvYWGwyxHBgM2r4dUq5RzOH9xGmda0F7F9K9COoTi7Jz4DXwYoKHQ7GtBZy2ZC5vtVN75SjdWFgyTyBg2eGE0BNMPbFbkHIzX7HAK1cs/NaYeE4UU13/+Zx9NvtrjiOGgrNaFdpA+4FNFiUcyJnQOTaDWk18rMZwr7N2YHOkICEbcbVg8bFNoVNkQ8INRPHJhsApnMLQhyVEypnZwhMEXdzetKRqhJNUbai4QhXIoLhipE/2ie9FPBR2EQpu/uwZXScMR5+Kf7NDMzweFQAQ+7nPnolZi/3eW0JvaZ0WTuyQOgz1HG0zAqpBWnimKLbkE3Vz1nJ9EV0zij+NeFStLwUdH9sjO4yOXzsnErOiqF67LHUexaTcv5nDXkw7667I9bjCTeKKrQMAyJ+K+5BPhq7rR1q3FIF1eYxRtDEHOPTU83nxxfBc81y8ZeHTt0WE1gparcqrDT4dLVl8qECkmV88n9xflMUcWEq7uh1a2hKzo//kkxKfgIZQXIGywvlRBuDenSoc8JNjDlLcssljSWGvnRcqDNojUcNgt94/SDrWIiidE2/PSMix7XnVwHCVlujwgJDSxNlZ8+weVguclhzUAPnMnbsq5JFCmLak3lvIL/qCVL07B/V+EMSocOpK6KCGt5X+nviq9oRQJdBDuGMgi9AJDK8xPKU6WjSv4HO1vjxj/sklKxwbtsajRy28WnZI85YnWYU1C8VgpqAij4NC5JUb1vWfz1vHn4y5+dbfbh54g2OTZueuysw0/dhOyw0nHZp364473LLhzSy5da1QkNCA55DiSlYMG3lr+uwNBX1TOEJHYH2GFuF//bI4dsST0WGgMOawwedC9FJ5YI/dq3E/i/DLZ9wvsN5hzD3dbev7EiJn4p+khPZ01hlyglTtdYTH1fcO2MZo0Fae7FN4LlJ3nc7xhlqvmL/YEPmdthfeSMkrq5bZEXMLIP39tbSDrPMV634tow2Hn0GxKKVhW0ivp6iVzbJ8aCgiIHGrsJtetErCyZgZbCQMFj+MTP3FuhVP7tTpHZTH2+ICd51hY0hFEY7S4rnQBqToYmHtarPFDw06uTztGxbPjpQ1X83Ir0kbXrWhjYAWH/wEoWRb6AMonywI8OIiTgaX8mpD10dGgB6PvHOIFRISt1xS8QLyIDfuYkVcqIVTDpVK3UYp57Ncf8xy+zqs8+VlFVJaNL0ekbCRyH/urR+1yKs3nSKxL27e0WHxVOAB+fbslORbxddaRnwmbBooUpGXU3wR+Ct/prPXFFvCRuTbOWTP3vm3an/5nz+rQsM7s6jr8vmXfli8G1qEoFYUzBgcfvbcv7aNb3pA6KDQG/x55CzuVVumsX+n1HNSJcERIcR2KzNRiEU4JGSAgquxDsG4TWpCNnnmQrHNFQB4PCFQwDSUduj6MeKVnI8Vg4gUfj9tZxA01I6gr9wbOC7rB+Liz0HmhRD+wQf/jYj9EJkZcG4SNzekYqQgKeztVs6fL5V0CNBDh83b1iJeHiIGhoLMaUl1tJn36F7nqbazy6FLB/rVcpLi8MhetdNEzCebrimk8F5QS6k0t40fx6dlE/HszL93z0xTSmahyiNE/n9qn+YsP6/gbuY6QeNYlPj9up5wpmRiuhU2zOtT3Ivr19GighT0ifmAPQeFFepAruXCtx5VtqIWzb4eq+/oc+pB3MRBHtsS5sNSAbuQ1pQOXuKTUcz6vbrmeNERZC2j2nTBWi/nrXhwwEXWcOi5PKnWm4QZy+tym6c4KVOUTc4J5YMTihEoakTf2Kx5z97Q/KeoFfpNYPl60QVhR3wq/FAVgkKpaCE+ZrhP7WbPwopzxy+4gxOFDoR7oXX5vJAZLCAQbTz+FT/jLFMQaiTjlt3VLDuHzR0hmQBLZYt/21CnU2RiWYK/X2tUaDqFqxTZ+Mnx/HH9b20pIff2CDX2vn7ZCu1BfR6MS/lTLcpIUF5cMvq9PaV5421usurSiiKDfLmSVKPw0+C2pZ/ab8kLS1pPmH/przup30NWobiaoMF+v6MGxKNCo8U9JQWCeT29KJsHDiBY1BA27t0xqO8rjXTqQAMXLXO8V9momNeGJh2N4V6Munne9df9cW/G/QLrHUbPp+d0mkOxwmIJV4r2WGWkS/Jn4G/aYMnxolph0YWqhTZBbCryveL0g1S/7YVpeaxoow36rdSXstSVDS2AFAS4Tq8cisq9XYv+as4C2Zo2A1qLDJR2kNVR6cFDWd8blWUA3I2Vrxi1yHRWaJu8YjpC8rEK3Vq0QltQGxA/Q8Ehb6Nk3J3eeFgjfrV5CKtmI9GQytApjppvFhXJU9rZodniy7vfASeB02V0rqTTfnAhrdYoLScKNgohikC+V1J5flfQJ4SChavchES+Jn4N7Rj8h2jr0aLATye9u8mpAIt163qzICk1hU2u0yene4jM5Y64eFxLH9ll5YEWe/xDP+Ok/izeFDObGQUnN+5MOQVTLKxTuWwpME491mOLHx21+vXbVhhqdvL6QECZhv7LE+JewVmTY/T6pj35wU/onjEgX2P8+OzYz+r/G3CWer9DpG1akxRLoGbN//Uti759x7Ww2lot+8CQs0kgoHhhTVEz+FppgwwELDC9Kg8mcVGkOGxxsR6ppFR9lQ8elxqPTYy26+ZuN8fIiFP8TipkqbGcNqXgzIa753j8FKsWnynpvpCPCaEZyw5I9S0X0xa6Pu9MZYnfefuK1R45aPbgYRWl5FxCfCZbEM6OIlk4+Y/0qYVCW4hsSYoTNhvf1QkVyCjCaPOAgFBAyKU8HpL6i7YicUAgYNsmrswv5jNkd8LL5QsF3fHcVbUgMZR98qlPuBbdjg6huLoX85u6bvy9fL1A9rjGlbqEBlL80vLmOhJVlUwoVoeDDO+T60bxQGsWZBryfuTMuFBmO75PgoGu19PyWXITuS5UCrQRlFW+WviccUhis+bfiXda2lT7nmtHIDgoCR5d2YGwFQZSDnl89Yrz6eJQ1N4iVIdnkvnqGep33mVxv61+dJ/Z7KJtfuSQsvP0Noa65R4v0904ggcX7cMcwDuKuBfI/Wolgix3NovMnh9MSqQhtSiJDt1NKjjmvmbIKkd3qvgHqWLAJyNWh2cWAU72SI+KZw6W9UOjVqEzt1F0qCyFiw5ufrP5ZQkDuLaiFECNyBT1uVlXUMF6CFBmbbu9IkTdc3i3Jd+clkJV3lz1hnnOXVfhP/9wWEpY3dc7s0KtOCypYNqyOJHCeajVxS6lMw4NhsuXqVvyuWsuN7Wvza0FXOepGQVAg8yxJhCMvfxwmwRD6aNdLliaz9Pmwr45/BD8XuqM6hAaf/GG5T/2oHiA+Gzdq3EfwfryGfc5WO/Q637y+A+ZNxYXUkA+m07SQa9tjASs9WpRp7dgGi6Vx2ohj22OmHW/XrXo5y/LZJPiQuaULAJ0T0o1WRY4l2yfeEFHvuOTFshRSDUsPpU33/SyrT8+KGJux3PTTgYdCVhgJSuzRlpy+BQFN2tSLnHyE1oU8qk4AeIvffiUhRcLatPxfoG2xdFqjTvJ8fic1fYNOZfvwpbxZSrs2qHIllEjYYcAwoVLNDygUkk8LhZZTsKZE30WG8+IGwERmxYVBRHtFVzY5W+FUozFf4t7xcCAM7BZlPtybAauVEE+VPComm4W1LbJDITFHWoEPApjhuNBCxKUDAUU39PyxqJtnOhQgcfmzGIupMfjsWJ/UhEyK3DYPnlD5ojyslLorVMUqX0Elw0p+syieGbIwimaiEPJjTZb7OqyU1bVG4oAee8HftpCY0sSEOAUTrtJRY1aOxW1Q4RwolTCiBLEgE0nm9d7A6VhM737+6fn76JpbIQE/3qnFnWqRtmEpxkbsuwBmlNCIZQh2BoUIgIK0tgu/BugpHkVYc46A7d5r2W7fdZ0uyznfIkzVLRXJWkvDDVZeCGvTdz36kVtpuLWtTkUjbmqnM2Q12JfvCn1pRA1LCRSUbXE4bIQgyIX+NcvmL+3x0XmhMPawBm1PYOKUaIYhtPjwnnLMtpFBbkd2M0cx7sJtAmEENSWAXqMNQCFJYRv8WwyBWfeueXThkkl13vbRmDbxkJK3ku3zNu6VWjFoo4MDjm5t10FHgWCkNf1jMvKg5Dd3iQEDMQPzqXa615T+5XRwLWdQooDRyhg5faoa++h0ht0Lf7wet1Sb8+LlydHd3hF6xtWfN9hBRODGLHZg4zifs5zhhqUFqE8ojj83FwU8RorCPhwIpb7vDJWVcQLaHoiJgNfeHXyWbtw07zRqMLpxZmbnBaZXArVLbuC9J4miXHIGkxdd6pkChQKYG+2ZOmDbfIy0zXjZ1g/upqkEkXIoHkS8jsj27YmuZ9TfBWbvNb2yoIEAyKtIwrAZmMjo0IL9SLPAOg2c4tnN/jmDVl1wN2DzpB6a0avhRhHKCaqVYjmHUlXFFfqtvhAzHp/e0yZlbr3mih+MwxbmxOaR3jGodAtD3eolSf0jWcttOX6Pjkn8QNosygD245F0YjjjJ256Q42kPMX1tXqV4seLiLrYL5gmQcGpMKkSH3+2s/dEw7WJ08//K45WN914rX7HKx7MO4jWO8wIDnCA9GFKkNcdf/dfjanDTw+W1VyfWS5asFs3fq+ULFcl18k0PRwRJvY9FNNLix1JaPWGJs1RReIAW7srZcKQhDSg14Fm+KzkvjtN6z1V9/WyYlWImgFsDotPkmu4UwVahbYLFlg1oUaU2hRJNGmwEuHxQ/TSxYED+aFzTF5Gmlj2dWvnwFRqDS7NqNakMSpoMJicUJBU67KhkI8o+4WxfDQDkDZk3h7WrMHiB5kjk0eJIXQYDgJMghtcqfvyB23UIJaBbZIxiB93nM3VDREFopWx/4p5hf/BiNXVHbZHk7CSyqu2OTZgEEU8RFLH+6w5M2MuGPBpZzbxCgK+pIqXlmkBz+dF1ckNLMh1WHg/G3LdzvZe67fbQDkM9b3DAoJ4OS+jVK8/EffJ44avBKc79ncQjObuie0IDixK99vYU2tG4orKd16W4VwspGg8oJrg1Izv6/bcgNRFQdanEFbKLwhq0/NupP5+JyV9w+Ig6KWC+99bElBv9Ubt1XQcd+iL9+wpeMxIW+NizfFKZJNAG3rlIvKgYsXmc1b16fGbfF4UMHiBFVHJ2gFe3XdQQPVmrqzZN5IRNmWxb098hejhUsgNkkA8dPTMt9M7wjb0odHxdHCIBQLAvhHoEe0HDFvhOOHxxitUv4NhR6u+yK843qNxB3Tx0hAwd3iD7ZENFe4LjwzFPLK+9sKRVfBNPRdTtyBSevtWds80KK5C3GdYhJPIjZP7in3CrNKitjsUExcqPzhPks/skNFwurTO2WZsG0PwM+IP+bzyneJ16FNFDw95pBY/buJz4Y1gNrbKs5LcrQXipX0Oz+2QkWFbHS5ZqnXpy19vFspCRRX3MfKowctesVlFdJOQzQD6krhmd/VqmcphEUKzvN3VoUsgWhjOwHCyAEFE061QpsSVp1fVBi7zC4v3jDfzSnxqhpd2KYEFUbvPbbfMif7NH+0FjRFLHE7I5FN86evSxmr57XXFaQUM4kb6yoaURrCc6v2NCvqSe1tUhvwf6s1ZJ5MxiXvk3Z/2xvLtnkUe5iSEG55wlGkBck89TpU6PIttaaxBiml/AqH5zWxnUl86qyjDiwW3YEC6xgOgO1Jy/dEbHNn2PFfGfGYii35xoF2rq4LWWetzPVGLPN3Tmo+0vbGwwsUU6rghRU9X6iMFXrPANXaTIsSwO9CQYzil6IUQQr/DndRxswU13K/rykvlSJNv/8ejftGo18+4z6C9Q4nhfcd+H7zhSJCWwKos/BnyoL44Di+Fa7LKbve0AlpWzVDIQF3gMWg5WJWiyHqt/UT7dZ0ZVMEz6cP/Qv17z0nDlp2OKYFPNvttb7fnRCCxekOo0KKjM7X1t0DXa3LQRoyMDmAqMYSr9621WdGzVdqWHSxrEUTcj3FFIt0OeGz1Pklyxxst8TFJSv3N6sQ0slx1bUkKQyQ9uuzsNHQzulLaCOQfQMnw3JZaJoQtieH5fOjWKBEUO8H8jrICIMFkE2TE71O5R0Jcami01nn1Ex2Ic7r1apUSeXHDlmh3S+CLt/H++N0zeKZ3hGR/w++SiBY4RsLut7YFVBQcgJv/qPLKnoS4y5uBPQNA0VaLQ6B8SlGBJ6JuGScrmn7ZXOWPzEkH6/snlaLvzJm1tZs9YkZeQxRtMHhcE+KxxkLYn1AhmQiYt6Lt7SZSXU2tiKzUYj7LNoEeoM2RicdOtC4eMNWv+UB63hxXidxChKKRFR9IAeo3shzLHfGtcFC1PaOzWiDKD1xSJs7USy4hQsBml8WMVjtukbDav3t+neQy22VoPywIIBTtISDlh6NW3R+K9po+zNtHbGQnst4djl3FzHkd277ZWE10vTrb5ofd20QheaUpPHyCiJEGv+l67fFVaGdKgVlMiHOFDw3UA/mGHOBlp4sQdY33CbX3noX7cIKgN/JM4YwQr8Lg1myNONBGc/yJ0Wy+Dlhn74HUQBzmmeUYoE5LaUnmZmtMYdyLTn/NrhOKAJBbuH0VB7e51zvfT4Ro4nfUREMLyeZUAsIm4rHP/yzQvw4VNSb41boiVrsCs9UiwvH3hK1VGJ+i95akTdVjTl0ZK9znW8LW/TCrNSXDBBaIWSrZbXVEVqALGFLgh0AnluoCnmOsXNB6EBMD4o7kE0Vra9fUpA59xkOJ75upBVg37CtToTUDQrDdaGl3Pz2khNPQPxHSIBSdjvMHKHMSsbmP9hlXS+vSuXLXCA+iutNK1O/YzljpUHsNnJOOYoPF+3lYEAmuipKA767hscy0C2VhRArv7Kvx60pBEJjbgwBf2nTrTWVqmUPdsoWQy3m89clAir2JJz6E1ECeartYXnnCZkKBO4azorUfvKAvg/lYuGBnU6lPF+0fE/YPXNwHBGqdLXpWZI9zdZaCJK5+r5Bazm/4VD+pQ0rj3SqkwEXERSYg58nFpVoITS+YsWJO/aFxh/cEwTrZ95+9F0jWN9/8ov3Eax7MO4jWO8w5Bru96pA8eVrWkS3YWTsAPQAXxzfknp7ZHwJhM2mxYZEhl56NCZjULgg0aWKYOn3vu+nxdOBrFroiYjQS8ZVz4sbtvHIgMVmivKBgcvVcr2o9gkEZk6E6aGgre9xBRk8D07bLASQ4rEJoPWgE95WQHPyypo2PEi3uJQHp9ctMrWpdo1/aklyb5QxLMRS9REQjOs0lhNsbgG/TrZsPirCWlOWGM8752siK7ZaGZGZtBZrNjJaomx4IGT4gNGGxM8L7gTeOPB24D0sv69fPKfwhQkVVwsPROVwn3p1QpwuwqjD6zVlriGp14m+VldWogpD0PzFingjqbMLrtAA0Yj4ZH4JRw3kUTEpKPE4RROxAtLUlrDCkUGnGFvfdMhgOCQ0ADNEkA5eF7SGhRv+FpLv7ZaQf27VvD1dah2xMfJ6LPSZoYjIvng44XPESV4txI526/jclApcxfUE/HeLKwabKIHH5YTfnr35CSFiG0/ttupDB2S5APkfQ0W7MaHiFQk6rWJsG/DygTcVHXeck3ybz6nLtlpJeDxReHLf4LpIFbeVeanMQUjQeCcS5kwBWaopLgmnbVpT5Mu1nN+U4ary77rbFBMDXwolaX1+UYcGPMc43WNeCmoDDwhD1+rSsl4LBSftIryUyIKjuHqu9jtq86iQHe4TV01B6cWaWjvb6EexM2r+0zdUcIjsTezRes6CN+acIvb0JSfygPAO3w2Sf3tSBQJmoBTmEMdBu6q7+yVAAB1pHBrVZi1X87W0iqtCT1xKOGwHyH70nDqoEONAGoVqzQo72xx3rNzQvabIx/eOooMDDmgixr4YkxJjQ6GHf1X0xoosO6JnxoUkhucLCkTnsAYKSaJC6qIzUJW4olpXRqY4yRTID/TrfnIIAnGGJ0gLzOWAFhQPJY+m9Q29T+xIKKQUy8PcnVyw6EJFhQ3XmLUL5S7zWiazsYAVxd9MWHS5LuSKOUF7HyNg5Wt2RrXu5fa2CY1Wrme1Kp8+5f2F/bJaAXXkd3Jduf+0ZnF01zi+V4psRA8Ud4hZNnZHnRFpKKBnaTt2jOJm+R8/qPUyMr6uz5ztDcpiZOHBgOWHki7rEQ7UcJ8TV4RQytYscPaWxAisZRwuKRxReTLHhFbudJ5+tARRKW4cbZXXHter9a0VIfbbg/WM582mF9QeXv7YPq2FFFdw4igA74/74y+O+wXWOw3iGsZmBJmnh4POuwrLgLaw+Qt1C12aMuvpEKGdjSV/Ytg2d0bEsWp/m9adRw7m8o/xewSHFx7aZYHXrggOh7sS3KxqIcJZnbshFCrmd/lokDwx8GQRwiwz7LOWq3lre2lOvJDYW+N6HaB8NlUWRng5ntklEasjF6fNQ6smFLTIjSUtLNkDHTpRygMrEbNKl1ugOI1KabWc1SlVXjF44MRDQpRAZDBblCs6SBebdMi1AFnsidWg0FJYLtyUUtWic6AbJfFlfDuHrXx8xFKvTVlutEmoR/N/eV3cp2cXf0loRWyxYZ1fWFLw8eITHTL7pLClNYIdQ23CqY64LksPE1Rcl5mnEI5E1Nb3J1VIcK3YtECIlo/H9BqFfd3O8wazwc6YeW9MObHAaNDKeyETc3KumPfquFqCiAhAbiBWE2aMYSshsiywZEVqVCoWfPmilFhqERIefNlxweRIP73oeGE+r3yhaLNi50DWoNosixtbm4pfMnBvU8riN9ZleYCnT/PpZamqONEzp8gHXPqmI7KsoNWISagiPfCUevzIlhK1bB1vrrusQbyFKDYujYmUnR6JycASHo1ED3i1JR2RP3lxWRxB7r2yDEGqtiJ5vLRHiEmBXwbqdXNccxZVH+1k3jeu3BSNucG4PNto36DAQ9FZfvqkU3Ae3K3WovfoPnG3fLGofcD39SqAFAXUFHFxSJGAfg6rB9poELPDczmrHdulTV0tRoxEb4zZxuNDIidDyKdQFPpVbYgArxZ/sSoFK0HZfB82K3DYIOzzO0BkaV1KCdrbKqk/bVHmAwcskcxTzsyX72ncGJd/G++Za4hfGerDzj8aUysd81WI/uGXLuuAhEIWcjSczOVHO1WcySdqdkFxUSjfhCQVuB8e8SRBnCiKifmp7exVG5OWM4g2tg7r792hAp6wYYpQuFg8c5G3xpQBii0FqCoDQ1fuK/dFhQiHDVzVQbZ6OrZQIowznZUM5HWuW2SprGKQYorCiwNINe4EHhTnsUuL+m+hiYmocjspamnhNl/ckOqw0OfCn/0d7Wq3V584op/h+YK6INSRtaTWsJbLGbXfxIW7cMu1X5MRFTFdv3nF5j4y4ILO+zqt5dy6itUdv70sBJAkCBSHzOPNfU3OV45rgiIw4/JghYyBshHNNbWhWCcKO96/Dl6xkJ5djGWVhLG8JisHu3pHikbf8oZCthF28Oy2XM3p9/FzQvDu4cBo9N20Cfn5++PejPtX+h1G/dQ+nYZoARDy6a2ZVCREggRXi1Y46hRhsdmCEC169M3XcvJ/wbm75UpesDKRDflOv+U7nBnn5PcfM8+bl5yyplSz5QeaxCuC+EmrotSER1Hxrqy81BIS/A9KRAsN+J8WFyd/sgdZNBLTBPNuycbj0btKKplTHugVCRMFH7ycRqGowopiSTlf6eyfORFXsCjYClOoVrXQ+cp1C09v6ATPBgYKgKKRz4LKj2uSO9BpnlfOW/ahHZYbiMsLSu2B+SXL7G0VWsDmiJIJkjrqO393l9RhDLLbSkmP1Hwgdh6cMPwe2xh1iqL1wy228fdOCZInyiJ1xwU/UzBhUcFMbrmwIXPSesAnPyiR4a8SVOvRZ8C7a/1AysIvXLAGvka1hnX91lXxf2Q6CSF9oFsnaCI/2GB9w4PmHxlWSwwko7a5aU9F/r49O/FJkYfrqNPgRVy7KaI6iAvtMq4JbQvaxpxy4dGAALKBUFhxjbF2qM7Oqx3kHezXidqztmGlR/fb5r6UrT3QIRUp1hiMplsla7uYVwGjjQnrBe6Dwpsbav3lh1KuTerzqoXIPVc24+KqJcfwTatZ4fCAWrv4RinUGFJ5d8q8Kxsi6QduzcnagUGLr3F41BG6k3EhqfJoypdUHMEBg3OGPxUeZXBSghOr9uyNn7bao4fFaZMZKdw/eGUsOus5K37VSVlBeI7vd1yiVtDZsHLoSCAQLwlX+9cvmGfngEjUKu7IAiW6CUTm+AF7/be+x2qXb4oriMhCmZpbatjg1SkVmdzH6PiGng2+QqtlzUuKLBUdV2+JZE5xbXuGnRryxoKMQ3W42TIGliloZ5s84VBNUtBxfTlsgHZxMCCTkJYiyBht+mJbQHwyWuXtL8w4b6U+Z5MSe/6KeFEYkK7ti+q1mIfPTv+Cy81DlIErPwjJ9UkdJvg9oFA8h0R1wUukWOK5wYIldHVa6BcIJMpicY4mZoV217paZEkhon+xZOk9zS5Ca2xG8yc8taFiEmNgEC5a6FhGwCvzXRm32LlZtcJDM2lXWIzPOWpEU8Qqh3e4aC2/T4T26J11i52eMt+BXTrAkLmIiEbPyuS0rby333xrGaufvXJ3vdk84dA4LD+wsFE26syCebrarfuldaF0a0dbhJzn9rQ5/mCmaqG1ki189U5dq6YLq5Ybdn5tFHXwuChwQdK5PxsHUgqJjp6eELLs3yg6SsHSmjVuTZiHSCEOHjt7nTnrnh16nxwIZEaKOGXrAOlsRLyiBsi77B6NesP7rr/uj78FHKx/9a/+lf3rf/2v/9zf7d69265fv/5X/szv/u7v2o/8yI/YxMSEjY6O2ic+8Qn70Ic+dPffs9ms/eAP/qD94R/+oa2urtrw8LB953d+p33bt33bX9/JffDbrbqrz22WtOenF620r18LEMo8iN3llN/CKxVB0siu4XVkdjWZv+QUfulBkCiThQMbPjBzbDIjVEabH5mFQZ9zPEZtky3I+4Y2EsRS2n2clJaPJbS5uvZfTQsQnjk6maHUG2q3wMy61VMxcSR4TRRfLCrA7ixWjXjYZQ9itNeU1KbKJit/mrWscxFH2lwmzqRlS/Ze1wJEYRcmv414F0jqUxsivMuBG97QYtY5zq9XLNcbstBaVYXMdgHiDfjdfzfqKlholYHmFHd1OkPP1bwWwbX9uKqbdZwt6JrQliBOJrRWVqtRaidcveNRy+5uccHFGxVnzMm1igSknkSQABpTbQ6LxI3yB9NWXOcL3WER6/EQkgP5wY8LQYEUDGka93vlBlIAIIPfUl7RPgSlY9NRRMcWsZU2xnZBy8/R+vKc3C/kADI7vDO1ulAVJuMiRmPrUHvPQRVe8H1ANiiqiQ/htfzZqqWHQtZ0q3BXOcUmm3tkVJls3POlpwYtOVm2pWNB6392TfJ70JDws+fUhgHNAskgWw+RhYqoSv2uczqIQ2NxxcpHR/Q5QhcnpLikoMXigc9DoSV+z5balLkip36sBVY3rUZ7knYVStNCUW2sxPU1EY3lg0VLq1DUfGKjom2HchM7gfCFSbUct809tdGyZxXLinrh83raWlzSAFwYYnJIAFjMqfCn1QU3K98Xk2ov+akLVnjfQRlBsgGqXcQcaOLwU9dhh/kJWqoAZvzmnjstErhie1az4hBhrwIKBvdICsKuuMstRGUL/w6lI4UNm3Ei4ub/Us5sat4qh4bFE6Nw4tBFG1itsji2Gevu8/O5ujtc8HY8LARn5WjCOj89IfsB/NgYoIIgkOJVrpclFlDGKDFY3Z1CQpXcgHoV3hnzlOud3zLRBC0immakU/MMJ38Uz62vzKv4IGCbg18l5rGO1xCiVEVhQM2q18JRn2eW9YJtAq6Ux+vUhCsZtYn5vcyF4kBK1wAVLEam1SM7VWDGbq07nzfubSAgrqBvasmenf1Fe+KZnxHPz85cs8qThyUU4HrB52QeR89Oap3Sc7UtDvGiEm5yYfe8N1p9tJ4P7Np6zxUVuHiziXs4u2CNXYPOBDrpHPTVgo0ExTNlQJmAs1kYblbYvD4rIoa1jOUPdFs15BEvLj/SbNFrS1abmjXfUL+QVZIhqmPjVnlgl730+o/fEw7WT771pIXfBQermK3aD5968T4H6x6M/+Wl7P79+21+fv7u1yuvvPJXfu9rr71m3/iN32jf+q3faufOnbOPfvSj+rp8+fLd7/nu7/5u+8xnPmP/9b/+V7t27Zr983/+z+3bv/3b7VOf+tRf+73VkzGRKpHosulAtGahVVEUCco0kUw/FmJO/WwQEGrZxAObZZf1Nl+1pht5Kzc5pZ8/XxO6hdMzG1AlGVQrBJ8auBCKcihWlG3IAH5mUW8aK9va3qiQDeT1HEJKe3q1aG0+1O8cjfGzgZtVrrmA6KakMxjccqGnjaeQ4I5WEa1pf3D6VBhqwK/3wp8QyAm+lcs1BpS5spAqh4z4tOCTfai8wOaIZfsCyvqi3YmxYSBXt2xvwHIfOmq+eEzRHNmvPCoOj2//LgXI0oJhwXPqq4aKK6T8uNXXeMtwZbB02FJOSnWVCspktLi32yptMcm4WZDxVRI3hQkNgkFkSjSg/L1sb0i+UuKVrZRt9SD3ByTJhEbgncXJm3vC/cOXCKm+Dfao+OL6CfnANHJsVoifM+WsuNMsyjjyAUFhJqdVSPkhyL91yRoh2i4+tVY55eLqzmbgbW0x794R8758XggDrRAKbwoXTv7h+ZxO1uENJ1EHTaCQrO4f0n0APWHTTE2UVPz3vOIimtiw4WGxWdH6grDNAQDRBbYKyibMlTTf5I3UFLX0B/ZY8NxtpQvkT+6wcpNf9wTXcL4gzgeuTulwIbLx7KrQKpljymvKqQNlmhsJW+LqmlA7IYukC5ALaKZ2HIUWdhdsZmoFcp1B9IpV8QEh58t+4dqY1WjZ9naoDUvbWpYnmaLUpWpNdyasirJ1fNbCf/Smebn9+0ZkX7FN4qeQ4xnj8/K8BDJbcVUY5pIWgKFnJOIMLcfnXTzQ9JwFvnjJfLmKI/nHQxa+OCUFKW1ffJwIm5bVA4cbOcab4391dyi8mIIPDpkySrsjKsD5+42TXUIOiw+6YqAxPqPrwLzreH1ValN9blqzO75XRRmtSV4bp3asDyhUNr/miBV3tgtxQyFHIaDnEtSxUFSRJl7WijP+Dd6Y1cEqeX3DEhNFxyFriVn4tevW+qc3ret/0JIrSAhRObFLhZVsFFDLUVQpaiuuVqsKmnjA8rsdaScl90YAAQAASURBVJ73jqExKLGKvFTCagdHLDCxrKKQokS2Hj0dKq54bjYeG9JnpNXKoFgJX51zEVcL6yKaIzyp7OpVEUu7F24omY4gdaDUDIQgGMIiklAR2wSfbEtoc2NK89uGetWWljIXy45C2Rbfh49hXLw5iit5rW2mpZqFGgESyiGSQjU8lxVvi4Nb9OyU0EffyKBlDrTb5A8es9qdCfP3dInvea9GzTzv+uv++FtSYPn9fuvq6rr71da2pdr6S8Yv/MIv2NNPP23f933fZ3v37rUf+7Efs2PHjtl/+A//4c8VYd/8zd9sTzzxhA0NDdk/+kf/yA4fPmxvvfXWX/u9NYJeITe5Qz0yvwRyZ+GleGKxl+8OSFLBtV14yClKWAwpXiCrF9r8tvBATL5O2uxSLO5eS91wi2J4ZlNIg3gBhEoDP6cLbpOdWJT6j1OpgpQ36hafLAjdoODAhgFyZ+JO1nzpkgozFkfUTaiSFt7boVM5J3hsDoD161Fn8AncruIxU7ZCX1KnN4aIv0G/I2Nny5btxxQxrhYhBQAkcgio9aALrQYaT40VLD2IF5fXZcV5TchK4uKiZT643wrHhy1+O2O5R3fptSEoM1g0MQ1VXt0Ox3nivwf+YNF5JnHarFZFwFXgNqT22YITG9Aea4+7kGQ2AgpMshibo0KyFFUiTtSmnPDJufN+8YLF56vaeHhtvHZCnz+vlmmuJ+BiVJDUN6WEZp365p833505h9gUylY8NuycwGfW3Ya/mXfo4NyKWnD+gT7ztOAA3pAFA1wSe+OSHNAbNyfMv9shRXCBQNNyHzulgpEA4cSVFbVW555yyjGKs/BiUW2Pxcfadf+x5MA2gEHbBgQUlEyeVv1JZwYJcuP1WGVHp8XfnLDXfud7NF/h7lA81q7cUuuNeUOxFF0oWfbJ3dpEUIEm3phUKxxEkPvvfplbJvxfoqiUJcSOTllyMF+Fcg406foTPO2bWnAZeDML+rzP7P5BbbC4xJMlyeYKL4nin00Vf6jEmVlbPdUu1R2GppCsXRyMTwpLPV/bqKrPtWqQ1vv3jKrQ5tnh37gfEjwk4JcFVSxQpKnd98ULagGHl1yyQvk9+yV2gAfIKDy61zy7hp1fWd0kllBB1hKTYpINuby37277nQJsOzORQgNVMCgZhwF87WhhqUB55Ii9/tvf43Isy3WhR5kPHdLvhHzOZ8QyhIEPm6KIJpb1jMauLeuzZUaTVhntseYvTFiNgry7U35XyiPMFiQyqc0vmOfty64dXSiqxVtbcfmhiFhAgEA04U5i9EmYNwo6rAlUfIPCZ3LOnubqbSvjy7WypoOGIntiYSG8oeWC8/bLu5/h8JjZ3ezI5vAzOXQQaD22pjVBOw0RQamwNb05b0e+/ZP6zNxHRetEwk784fHK1oVDB2pTChqtqYSBtzTJBkdr3GBKFAS6Azj/rx1v03NgoYAVv+qUm7ZTS0KrpIym4NzMW25fuyWmKirooH3o3xi4z6czljvYLcQb89PaYPcWiudMn5nHcFfJkEy8dsd6XilZ6YPHZG2i2KR7NO63CL98xv/yAuvWrVvW09NjO3bssL/39/6eTU05IvNfNl5//XV7//vf/+f+7qmnntLfb4+HH35YaNXs7KzR/XzxxRft5s2b9sEPfvCvfN1SqST49Uu/GL6lDXE3Yhfd6UoRL36v5bvDzlfF70iwnJA59XOClNLN51MbEUQG36buV/BrqgiFgGsFwiM36paQfebST2gjgLOCv5Eyw7Ye+sWvcBs6BHTIzrwHtQ4jPv3MtnVDsT0i9Q4nNHn0cDqcz1pkuW6r7+lyZHlOaVuGp0IbyEzk1IV79npJyifes9AsNoukXxs+wbGgJukDbWrxoCaCMxSbQx0XEMkUIm8wbZYZDNjy8aTjc4wty12d2Bhl34V8Mk+lmKCQ0fUl5iXu08bRdG5VaA3tVf0bSAabaSYvawoI3VrIMw7RUrbfZtGhQ/B7aGNhzeD3WL4/bvGpgjVdWnctQuwHNsvmHxlSq3H9VKetHozLa6fyxGHxsWT2upKz3APD4lARnAunq7xvQMRmVKBSTqIsKmGcmNUJV2KA7UGRRwBza8q8e0bUXqRViIcRrQTCuSmyaAuFVytWC3sUQJsZDOqe0Frr+c0b8l+i+OH60mIkUkWxNx6PM5PkPuIZGg3ItZtTvDhmrUFtVvBk4Ac9O/8f9btBXOX7tH+neY/tk2IPRScJAr43rwjZgZCMBxmtG07tWFzw+hi9YrHAJp1+z/Ddz7mxO2b5zpDNPxi0wkirDhwUD8wFrmvu1LACcwundroihGBsyMtbmZco5rCqgNdSIuYEztKODmv5g0tSTNJSxDTUtfgiThWbK+hAolal1+Naxcf3CbXwzaw4Dyf28awz3KVtj41D4PaCU7PFA7b07Q+64qxYFtqqgnnNZcu9/7GfkA0LQwgOrc9s1anwQK5DtBwr4kzJEZzoqrk1FWt8P/FRtDdl1RD3u8ByqAR7unUgIhyYgRKYwin19qx+HlVmNRl2Ie2bZYe0pBJqf4rnQ+A6xUWPV+3N+samxc5MOxFKPi8fK1qbIDW+pibxBrU2eDz2wYd+zHxtrVqrxBuioN3YUscViuY7tNehrlNL+rysJXwuii2iiHToSSV08GFuqNWXzYsIzrOMKteXLTq/u8WijIqF7vqcwpq5zjPC3/G1uSNs+f2dFsw0LL07KUoC6ksVxDMkAiQcj/TaHVcwN0wINQg3PldtL06riI+8ck3tv/DEmmxRmp+9LgSOdmT87Kx821jvVh/udCpoXi+bU8uStRDiOhw7tZFX1hxSV29orVeodqUi1JWMQ4QtIFr1G+NWG5+09733pyTiANUUAraFYt4f98dfHP9Lp8UDDzxgv/qrv6qW3i/90i/Z+Pi4Pfroo5bJOInuXxwLCwvW2dn55/6O/+fvt8cv/uIv2r59+6yvr8+CwaAQr//4H/+jPfbYY3/l+/ipn/op9ba3v/r7+/X3nAx1Cm5NauNgcyb3KvXWrPOk6W3VYstDCom00BW2jROdLsYj7vyxiClhAeTkqHYAJyEChSmIgl57Zvi7LTMSc2q1dMYatyeklkEZ2HyrKNl4iEWk1FBxRRQEHjt4xrBhC06f2FBLjA2FxQkSvgJ8ox4VDiKyU2StpbUAFrsiW+od50TNaRfEIHZpXugWsyIyV9DpW3ycpbwiLPJ7O0SSrw106JRHMUNRlR4KWMv1krWfTlvnH9yU/xMtDLIYlYkW8opbhAeT+++QCPO0XOA2JK+vC4VSrmG14VpmkMMXswo1ZpFX+DJI1q5m50afL6sYhODKCROyNjmQIjTPOKfulRPNKkYpiFncyYvDtTn5u2csslIzz/mbFr46a55g0EIbVSFiHgKRm8L20h9/n/go/jeuWunxA7pXIIRsAFqA2WhAJjYz8itiAxTxeT3nTrwYq3bGxaHRhgNqs+iKxdUDcX2m5J2CuCEIIyhocOrHh4v2ZOZkv0jNCv5W9JBfrVWUZ8rrSwXd30f9UhKSmaYsx2TAcZY8Hnv463/OFRolQo79f5ZPWKtbffeAFR7Zo00IZMtXqOtzYOgpif0Xz7lCHgJzMmT5rzohbzJxzQpFa31zWb5jw78yoQJa5ovwuL54RQgGBxOKovDLVzRP4BFWH9rniMLwnYpVy+9qE/dPIeAtzjsO00l81XxzK64Q53uzJcW1YHALt4s5S3FNESbvJhRqgy6uxje7rAISVMsuj7lDBOR8nt83bljPc8uu9Z2KOusNR0TVn+JAvnJB7WeuGQUZvC9ECSCZFB3MTVlyVGqWPdwtviJms8qOJN5mcVXzPn59VXMZVSLWG7QDbXzGMjtj1v7ygviT8AJxB+fawTOkiKMgxPpCkTS0BtdyckpHfEDxrecYyxFa/eGAAszhg4IwwYlCMUwEjO6Hh8NP0KrD3Uom8E7MO17SMrFCrk2ktr/fL3SR+YyFBJ+DopUoKtSokNQZ+Jk1bk26tTEatNAVZ7LKiE5kxAGV5UG97lrKO/vNu5qRfx5CGviADVwRri9b6jdet9TldddmRUl862eUJSpuX60mdBM0DNSW+8O6izUJ6j+QqNqhnQ4trlRs8YO94iZ6xqYlFAE9o3VPG50YMxWpu4ZF/md95AAiN/n+dpHsrbVZ14jIr0YsatXb4zIYfabzH7v3jvAFd/29I+Y5tFtCpKUnnZcZRReHyQCigHs0aIC/uxbh/fG3osB65pln7Gu/9mvt0KFDQqI+/elP28bGhv33//7f/8avSYH1xhtvCMU6c+aM/dzP/Zz903/6T+3zn//8X/kzP/RDPyTC3/bX9PS0/p7FhQ2Jh5JIDxVI6Zzl93WZpZJW6AzpAS+1hBXqGpvMWurKuv6+6a0Fi15dkKkgIdEsXpGlkiMrl1Ch1GzxhN82T/Va6jPXzHNgt7xU1v7ucUVoBF4858jrLVHB96AwpZE2IUicGgk7LrRCzA5sBSzX1W7a3BkT1wgUoPlKRsgGBQMn8/VH++VoDVmdEzfvnSH+CHywgTadDlmQ2MixA6AwKvbEZHDI7wNBgldS3N2lTbT1Ys5aL+VtdX/IFt6TtHo25+Jr4KXALwoHLXpz2fw5gp7rWsDxxAI5ATFjI6AQ4ffwPigoOUWDRq0ecxEsIiNnyzIkjE1hy+CVGabyIDFT9Hls82SvECBaaLTYuHbJCRfYGl6r2PKDTVJzrT/UrZw9hAbepqRTyVWqktbzfihW746+TvP2dQvhwCcK3h0ba2m4Td5TbNxy6Ua1Rngu6B8bU2dCCi8QOOT3SOwLvQlxOygIYotVfSbaz9hsUFyAHsEtAZGTPD/ksczOpJPJR5y3Gq7mkLlBRQIrBedBFva61ojXI3dsWsH5rrDQ1tQLt2SQiQcVHlKeuWVXcKxtWOPNixaZzSrvzrOj38Lnxq1x4YbVl1ft6ZZ/KNsDoZ4AHR0BFWCYeIKq1Ae7pFyD46SYFLXGfUIRQVQ8p686FJbOzMiAWl2ey7edBUi1brmhhJAs2UFEQjLR5L3wfBEwLLdv8jJBjCDiB3x38yNVDGXzVmoDaSGKqqg5RAGqcPGRLkeKRo3W2yVDUCtV1JKsHB/Vswz66b8xLU8nriFolsLFCS0/uNtlE5arrn3PPaUAGe5Wm1jFMwafSxsWu7Op1hacJ6lKJyZlwYBfHckJskQYHbLE2Vmrw406stPiE9hMVHVI4ZmjIM0OuLYaKBfzXcHRxa2LD8Gcw1C1KgECRabH47XsvjYV5LRcKWQp5BtnrqjAqZ/YJ7I5LdzQQkYHKH+2pqQCCjGc9kkqAJHCfkOGoMvOXJUiGxSUViBzRkgW1yidU/FSO7XXSkeGXIEWDqkQ5aCHyhNRjlA1irtQ0HKDMYluxHEKhyz00mVr/8yE1bgnDx5W5iPzCKsLkFaFi2OmTKu1XJPlA/ePe6n2Jm1qjJ43c+7gOtgm1AsPwNhLNySIyPWEpW6kM+B79ZIQP8U4Xb8jvmh6V0qFGxxEiS0+fErzCQRP+ZqRgPkO7tH1o0UIMsqziypTyNqVMa23zTfyOmgQr5U5wvOwleN6D8b9FuGXz/jfCthsamqyXbt22djY2F/673C0Fhed/8r24P/5e0ahULAf/uEftp//+Z+3D3/4wyrcILh//dd/vf3bf/tv/8rfGwqFpPz40i8NYivmNrUZovrjwUL1hsM5p+bE5RWdklhs1AIM+Z2KhxSFtoT8YYLpihW7oiqAOKHOPRLWRjn3eMw6zlYtkK8rqoVFFC4UERuzXzts2a85Kf8bpy6E6F62XHdQCBJFFwgZXBmRaeFvdIQsfmvdQumaWh/h8VUhHZDq4XywuSVv5YT4aNGECIyNQGtcbY/y7h4XhCveSUQFWWitomKQn5HiyWdS8CTenBSHAZQJVA0FYM9nl6znt29avVCQdFvqqzbagf67HAi5qm+69ivu7UKFJhds+bDjscAjk4kmtgoTC9b2qrvX3u3FNunaDZkdcS36KInExWqJCE2hDQQ/jOKD15AtRNinE2fnC4vinoTSrrjl9Wh7PZ38Fit84JDafeJ2bRXVxK2A7sBF8xZKQsUUTp0vyXYB1EKIUzCgz1c6NWrZ0SZbfaRbqi48gzJ9AbXO2KRRvrGxl1oC2jyxhlg54FebTHP38IAVeuPitjBPkjfTtrkDw9aKRebyFpnNCVVsen3GIaZb3BdcwIsDTbZ8JKqWGveNaxE/Nys1FxvXY1/1szJFldlqLq84nsajRy0/QLZkSUgdwcG+jja1heDmqBV7aUq+arRvKYyJqBGBe9NtLvxe0AbvG6BWDYfigASNDDq/NFpxm1kJAGrHd1u5I6HPGL+5rpWnHPfKp40ii8+kQGKsHFBcYv6KmhOFa6EiJA40jd+DQeV2K09qr1zprmccLXvI+bTKUeOy4aOeZZ5wvXiOpQgd6RF3EnQTNAtUrTo375SzhYrzB9N7IRwzq+Ie9Rhu4t6FFcse73P2BL3NulYMjz+gNhTPKS7/PHPEDi18xYAKN10bkJhU3HyLm87r6sF9snkJ/clbKiokRChUhEqB2PK9oEA44xMMT8uq0ahLSQovkjWHYpoiF3GFUgJ47YFOx5/sT1q1LeZSJ8gTfevSXRL9+574SfGKPBQNvq24oStjUoRSbOWeOqg/mb/EIkmYAqqWKYtnp7zIoF8HLdB8OFriW0lx6LfYdM6paAnuDgXNO9RnG48OKvgeW43I+UnxnkBfhZr6ES/E1faTL1c05FrwzPcz1509BkUrA7SPiLDBfmu6nlUgOWty6vUp8wQDCm4HcaKwY25wmII7SRQP13P58W4XSL1cUjyYno1iySqp8F27CZ77RrGoAyLFZbEjItsemScXayqqKeqi8P/u4bgf9vzlM/63KrCwWLh9+7Z1d3f/pf/+0EMP2fPPP//n/u65557T3zMqlYq+vNvExa3hgxO17e301xjyfEpFLHBjxkI3550/zs1FR7wN+pUdpoiLZeT1rkUCbE8+4caeuFOg+TzKgHOxJJCDMco0C62ZLR33WynlSMurhxLiQlXDXmu55sjF2wos1GBwbQLZukz/1veELXVhTe7saltEg5a86Rbs6ISTcrO51AIEQ/u0qNPK4LSPdJxNQkTovmZ3Euxr1vuE8yMH9HTJCm2Oz6I2EUhCpW6tF9IW+MIFq3e0aAF67tV/Iak4RoUofSq7+6z++DERUFkM2SQgOkuxGPVZvickvydvPC6+CWPha3db358uKfICwrSue8i5jOM87z1zzTZ2RcSBAulhYydHDQm4cuFQq8V9zguKApGfp6XVHRa5n7aTPJgSFLZBR87lRNwaN19LszXgn+RqUklF//i0ChDMKOHY1a+OWfAWWY5VLbx5xAMdSbU/CZQlhFgeVZB3I16Rm1vOrolQT5ENUjX5kRZHCmeTweh0a2piotp22UngRQQOeiWIYHMNLKTFw+k4AxLo2qtyJsdpe3eXa/FGguadWlDRSnHfeqW4FVxckrKuhr9WS5M4TOEFVwCsPjlgjYqLUaJwCz97RghD8T177bNv/0t3XZKunc39IzuPP6Wi7GwVOkKbXKG7XS324md/QHPcu2eHI32T4bd/l/u7bNE55ne3mN2ZtsD0mgUv3LHgs28rpLl2dcxSf3hBxRcbKO0d8ZXa2MCiVh3pcSrBsuNgYVbK/ZVcH4sLWr89cLPqej1EIiB5KP9oPwq12FI5YvmgTLutbEOht2edFQyosu/imHg2ub/zoK0fSAolAymmCId3xUGEQrP+4H5xFfGiAiVWth68pkJZ6EvmY8fFdVMR39XhHP83Ctb16Wm5w6Osk0FqKmz15pgKeQ5evMbm339IPEx/rmyF/ritHklKKclmz/cSy4I32MoTfZZ/eFRrEAcK2prxtyZVmOUP9el9MrcpnlkfKOZF/Ee5eWi3EE24ZsRA0eaXCTLtOMQv6ZzZwV0OfU1GLH47bfXpWWdDAnr03Gl5zsF9w2gZRFjJDfLzq6rwpeXm49CQda1yCQCiEbmkU5DDQeVZ1GhOWfrREa1Lz/T/M2uc2m/eL5x1wctsTvmSaBrMH/yoaCeGpzdV3HKwCb50UXxJEDoSAyTwSMbFjxKqv5nV+q22eHPKtdg/eERrV+vvXLBGf7fa5kJmaa9yoL6z5Cwu8MACKT8wpGeUuaXsTJTQeMetpOXxBj+Wg0ED78H74/7436nA+t7v/V576aWX5GmF+u+rv/qrVQxhxcD4pm/6JrXvtsc/+2f/THwt2n54ZeGjdfr0aaFUDJCnxx9/XCrDL3zhC+J0wfH69V//db32X3eweLC51Xb0CKnCtJAhL5xoQJt1ZDaj9gILLsVLYYCwYb81XUVa7gocUCHFyhSr1nG6LE5KaqJssRmTmjC9Awm9RzwqVHKQkqPzRcHZvOYX//D7FBsDQkWhEluoyXcmkK2pmEHezmmb38fGLNfwEAR8n8XennDE8VJZPji4S9PuhFiqU3vPll8OTtJb6l18pAivloqvYVpI8Y7C96v4zFFFkaD2gbTLYqO2BhyfskPPkDNzIoZfg/GoeB/4Va1tKaoqFct3ueKz64/uiOfGYiiTyWLNkucXLffgDsfHCIWs4wsYPtYsemPZ8ieHrdBDyGtD6FyhN6ZNklBfTtpsimwoSNIV/cI9QNnWEXXcnullbWrwaygYSo8dsODbN6wxNmVeigP8vyisu1utcWKf41v5vCqmon9y1m20uPJ3E8gctJWjMb13eGe0ljBE5b9lC5CuWNMtNqKQlR7cI0Qi9eaMFIsgDhSS8x8ZRGWhwjo+U7b6tduSwGMgikqUYoi238bhVocM4hK+UHAWDPsGdCrXnBTSWbH0zrgVT4yYN5HQ51t6tE3tXvhGra/Oa3OOfu6iWjFL3/aAfjbwuTP2VPjvyeBSJPRsXgR5NmP+zI4kVJSX8VpDuQq6EA4oPoZrTfEC+ihy9gZoj1tW+JPrj1QeBSseUYSRw8/x79ph3o425RfSOsVzDLQJNAQeGfOEYotNU6kAl2YVZqwicmxemyIteYoJlK0kDtAKVKF9aUyvCWdOsU20EWn1k8xQ2+Ja7R+1wFLGzd2jo/Klik/kLDFJrh6FWlnFNghRI52xz1z8cefXlnWtIEViUTzgdVau6FqhEoQYTcGc298hkYgKz3BIKEitp8XqeLSt5GStQMSOoppqDWu6kVWRRqsvNrZuzb/yuopgWvOoNFFZ5g/3Wup2wXHW2iMqqoTg7urRoeiuoWvILwRzOwAcJSvPMcpOyPxq1XZ1CLnJ72y96x9H7ijtUqktl1wOH4rK6sysVKDFjzyg1iLFDa1VCnv8tdQKplCBcwr3DbEBxf34nNaHWm+7pa6nLTSfsUqMdSFjjY4WvX7yDLzPug44rEmacxOfdAcR0FFatIM9MsElFL0xv2TxN8bd+tLTbeuHmtRWly/d2oZ5SmVbeazb0ke7rN7edDeKiiKbFjwkdp6F6ondrvVK4YiHGzwtuGcQ/wl3xjA1U5AxK/ffEC8Q49Ph2qBKY0Ddupy357/ww5Yf+J/jffWXjYZ5rP4uvvj5++NvQYE1MzOjYgpz0a/7uq+z1tZW8afa2x3fAkUh3lhfqhD8rd/6LftP/+k/yXrh937v92QoeuDAgbvf89/+23+zkydPSpEI2f2nf/qn7Sd+4if+Wkaj2wMUh8USGTleMvlTO6zS3ypomUUa7hUmgyoa4MDkXOSNlDqbebV/Qot5QfTwTsj9Ar0qtPls+bAzIKXdhaoQxR+uz8rkUrujYam3ZvSakC1f/d3vUYuFwoJTqfghlYZTAFYatngyfjfgmGw2Nm/4V+lHdsibCDf3+GROyh4hCxBI1wpagGlpyWOoWHN2Dj6P8sfIeYNAjUqKoFR4LLGba04OPj0jQi/O6gTIyuU97BfBmoFD+fTf6bXYC1cts6dFPxe56u4lp/v4RFZ8IhRRksLDZYGQy2aZjDi3bcZAtyJGiCkhrodFnU0YU0OCZuGWsAFTZGkzK8OTqTs3+CI2FEWR/Nl8KGAhtlIA87MM7tVn0v/FPLuGVLTIsyhTlDBBHj+DrS7YGYXWyKBaLpmBgJWa/FJOBdMNyxPDgzeh12upsZzF7mTUUoRPhJoRDhMFUKU9JvJyfBalmE9FecebGas3Jy2yUnZo25E9ChSOTG4664XNiu43ikhtpj0pIQNEnAhdbEkIecTDzDs2bU1vzFrolavyZALhaL5Ji7Jujcs3HKF9oyw1IS20zv9y3hGS4zHz9vWorVK/NW715RVtqAzQjtgfnRYHhY3XNtJyDwfhUX4dhQvcH3h8LP5NcR0oaJ8KOQHhgaMVCUmlKTSpp01Sd3kWHXSeUEIotkZ0fF2oAIahmQNtKvhzx/rMDoxaNe5Xq4vrx32STYfPo/gjEg4w6Sw+cUBos1DjsF/tPhC0OrmOdzZUsFA0EjzNZ+AAAQmbdjJFav5In3iQ5dFuzTv4UwwKFxR4pCpgk0EoN5mGFFAULjj8Y7FA2522bnS+IDI4US60yES2puDhWhVL1vLmkvvAIGG5khA0lGmKUTqwWwII5piQ7L07LfL6LZH1aTfjRK5W9XrWuZHrcOXMOFmLKBYI/uYZX/sHD+p1sDOgpWcr60KYUCa/9OnvF3K1PdfFl2KEtkjyrTHz796pz8TBAqQIfhZGtEKntxz/ZWWCgIdWJOae/OyhYRVxviU3d0GEUheW5UnFnICDVV9c0jyQmvrOxF2DVQjzPO/1KzetAYJFizEcsvWv3CerisUnOuX7l7qedVmJrVFxw7BLaPvsuCVfn9QzS0cBpE7tPnIHcwXFdAUuT7iiCkpET0pot4LgiRFiPaf4CvjlkSbj1VxOrUUVfNuH7HhAnxMki/X6Xo37LcIvn/G/1Mn9f9ex7Zj75PEfkpIL2T0nYgqI8M0F5X/JgTtfUoyOSKBaYGrOIuDtKZmAhpBzw3Gq1lWEhDZoR1UtsJyz7J5miyw6WTtDm6DfeUlxgpWkv1BzCkBO5XdmnZplSz7OYibDRr/Xcn0UbhRbVTmYc7qmzYR8PnJ2XLlyqNfkHI/0fDntlFAgRHhbJcLiF7FIs1GjsmksLgt5YJNHisxCGsDXCJUbnjGZrG0+usMSn75o1ZN79Dspelhw1g+ldNJmcwUBlMFqjvflc5vunRnB87QEWbS5JtogAi7SRiagXFP4NfmyWkRYAiRupV14Mi2Q3ohk4VKk5SuSjbO5lPqaxJvToJXUEjNvumjF/qSzMmgOiCiPCACOj7yMOPnuH3WIXbnqWmIej8j9eFixqcAbUVB1lgwfEzdNzt6lijytKJIZuLETN8KCy/3P72m34Kpz7Mc81f/5s2YPHFTWJCHRRB5lDnVavt1nnS/M656ROwjKBueDIoN5B5oljt71Jau1JdUu5CDK9eRew5+h9URxicUEqAioBdcUvhKtTvyMICmDHpI+oOgSr4kEzwBB8e3d5aTp5YqI1RhEKqaG/w+FbOPBbms6t+KKI/hntHIxdORQsZKWylLO9ZtptYxpkYFsUfgrNYBokcs37HOV/6Z8QtAE78yirAqw5IDro0gavLVCWwgNuYIhr9Bifr5+8br521pcBAs2DPAcO/hMZbUOxbGrOLd5WWeAel24Y7X9w85G4vKs5Q/1al7jKI/VCNFKoH/Nz99We1Jh2B6TIpC5Q8YgiC0Gq/wuCmhlTdYb4rT537wmng9k/HJrxIIreWcx0RoVR5GiypHxg5bvCljqJuhkWDwqNn4QUdrM8QvzVl9aMW97q9q8hEVDfs/tJPyZLFTsTjzW+qoLN+e+OyqCOxghrACRLT68R/M9sJp3flNbRZyiXWirY9yprMKMlHDl9x6WiznPK21WnjvUlPqe+bRSGzhwQQ+gOKX9iCAEagBIoFztUSfu7LfFh5qs5Rq/y4TwyIw3HHTPFgVvLKgDKkiWMhGrVan48O9C0Uu0E3OSIp/iZ9uPjUxBCiZQTYQAoMDME1rKHNKgHVB4h67NOU4VfMrOlEvKeP2yWpAM1s3Vwwlrf21F9AbuA6bRIGYEl2NYC8eSQbC8rgfmq0SLzawIVeQ1yKQlt/LzL3/c3nvy4/bi6Z+8J07u3/faV1gIBP5vOErZiv3sw39638n9bxsH63+34aNdwkmOQ+aF2xZ6+5bInCLUZvJmmxk9/JIRA3sH/Ra/sWbF/X36O3hQnJBppyQmChZaLqq4QqmXePWOoHl4SqgMQTlo0UHSxooAl2F5YHWgRvJbY7BHpzmR5RV2a3Jbz/VHtJD6yg1twnLIDpF31iSiOa02FmBeH9I7bU/xs2ghJsMqrvg7ETjfumyRya1QZU6geBVR/JAjiPosEhaPAoSJyCAWerLbOAmHL0y4RXw9beGNmq3vjuh0zcKFiqzQHTXvuRuSqTOWTzUJBYq8dlPFB5s9yAvti+D4kr6XIgeHeJCa1NkFqb5wkJfj+uZW+y8CP6wmCTg8rdD1WXfajoW0OMNponhkE6a4wvNpc1dcxVXmWI9O9cjDURlxnZ+9+pO2erRJ949WKnl7WCJg+IpxJAPUUEabTSErd8QsNpUXwkNxxQaq4rDeUM5bZDpj/i3fIewuvEf3ulDsJReRg7s3fBaMWcu9TfIfAyGhqFK7rCUk7hKbIhsIKANIC7J+/2uXtVEpV/LqmIorIkzWRv3O1yzosejtDV1Tgr3ly1VrWOJPL1poPi0iMRvHM+3fZs/0fofaM3gCoc7CdR5FGb5OKqIoHAZaLD7lLD8aSyt3ncNVqG9lK7IJ5va0SnXHvKK4ojhVW25hRTFO3n2jzniU4ndhRTw4zelUXEVe48J1XQuhKVvnP5A30GJ5UWF5UqmqaKfQpVgNpvFUQyGIQhPlpSvOKMj4vvKJnc6It96QL1X01qpV2uI62ETHVuWK3nx2RRwqPfu4y28WFcdCccX7AhXldTFwpQDhM9IKo10mBI68SgQNF6ccWjW/pHUDcj4ojG9yUW2q5reXVOyEr8zYxtE2ITkUTxRtQneG+tUm8/X1SEQih/5XeE48EjVg7IuymLnDHAKZRZHK52cQDQM6LtsLxDmK0HEoYrktprDy4NkxC12edBFgqaSKfc1LomI4XPQmdXDhNeAxcS0gxHvxwJvKyypGh66FtJBAzYldg1pTun7jkpAzRzsALY8KpeOZlN/XllqSxASQdfh9II8U3YhkeK9H/8knlW0pPt34rJ41te2Y17PzCiwHXVJRFPE7ZD9XvtvCVbAzPNOEa197Du9W/itked/CmnW8uixEmc8qsn2lqsB4dQ/GZ1X0QQ9gDdX1K1flt1VvTanIwg6EeSNDVhD7LW+/ezHqDc+7/ro/7s24j2C9w0nhvYd/wAKNgNnsogwTaduwkZT6UpbtCVrz1YwevJWTzRabqwqqZiEAKSp2R9TaYQFXhlpT0PzFmssV2yi74NITrRZdqrqFMBnQCZa2mb/QUGuQn0eGD7IF3E9hgHeS560rVn30kBCS7bYRmwiFBv5ZnBqlekTl3Raw1OvTgupFdEVNhYfW3g73fhNBhyLRHhQp3qvfTVxI8b2H1UJgAxbCsLxupQP95nv+jBU++oDeX2R6U7wb+Du0SPCMUsGUrUopF7+w4CJkHjrklI5jG+LzgNLAK/OWG/qMKI48NybM09dtjYVlW/uqvUKKmi87p3ud/lMQ1f26FkK2mqPKyOP36bNtcX4oalHHkRNHkG/94UMO5VrekM2AC4gOmv/zp8UlI3gY13QKU0WPdLRbfW7e6sf26hrme8LyIYMnBe8rMrFpq6darfXchjggFB1wpSDLK0wXBI6irwOeDFYNdaEvSw+1WNvZjAq2WtBr0Yuz4vZlB8IK8OW+FTH8lNkkhHiPgq1RWjVam91GjRlka1DXPnp1Xi3H6GRW3kmc6rVJXp4UqjL7eNgGf+ac1U7s0XWS2o12VyCgAiZ4ZcqKR4ddsbeyIcsKtTtA8hZXRXL39/U6VR/mpn6/IpxwfOdwoODlLT8w0BrI6i7uyYRsMK9JEpC7v8djpfaw2s20grf94EBHQCUo5hWbU3L5kyg/eT0pK9e2rBsYd6aFzlAIgDyI93d9xkoHB4REUUjzvIBgERFUavZJHEJigMxCac81zBZOha3rbcf588+tCp1TKw6bgXBQhaUKCA4W+YIz2QTle+QnZPGQfmJUxr9SqBUqlt2RtPBKyXxvXdW88TFnw6junH8a2YlKGSAWa2zCfMMDIntL+ZjCp62qQoGhcOhs1QodQbX1tz87SCnGvVwr+HgaE7Pm6Wx3h4lyXT5soE1C8eCvLW3IF8qu3rHyYwf0rNNqVNG8f1jvDYSP96n1ZEsgtHmkXYe41NUNlxWJP13QK0RMZHi/36FnWwW0FIi0gpNhV3CDVuFZlsmrcMEOQnyy01fN294mkYjNLJoH367edoeugWah2GxtdobIV26qVYrgpHCoX4c/IWXRkA5UHIC4JqwdRDoh8MGKxNpblFPK/EpcWnKin8FOiX3gVVmhIMSMQxyHDgLSo5Nph45GAs63DuUia0g4qIJdBzXm+cKKU1y2N+vASxHMtfidM/9Ce8a9QLD++atf9a4RrH/3nk/dR7DuwbiPYL3TxUnn1QZc+cgei16YFpwsTx5aCpvOP4cTYvNVuCjuUoZOj0nlpZBTLZacpE2FiJRJmDIGvDo9JSa3ImMSENRpjzWkQMoMeBUJgcM3pPZsj98WH0jod7IZM3wvnrHc3nbnukwRt5Hbgt6rDvWiNZQuWfLauqVP9YmELp+huVWpBkOLBW1AeFPBu6K1EL427zZr2nQerwU/e8Y+8J4f1ykddI6WYK4raNUPnFB7DvSATYjijbZO48guB7Wz6RDtkq5a5miXHNRp3fAzFGO0OUBbwksl2VjEv3DDNvYmFFOyfqzNlr5+n9R4rS9MagHHqRwOG9dNXAeIvBiTsoFv5LRhyhephmS/7PxqyKtrjVrjgYOudcLmD9pCAdBoyB/I39Ntz9V+R++TNqs25ffsE4+jTlgy0veVjNqCcOsw8kSxiHoxOVlSEZu8nXdu0xtuE+RzEfsjXlq2KiUWBUl+KClHdoqrbQdo3MD5nbFZ/LqKajdGb6zodYiGka1FImzr7xvRxiWvJJRym1W1JnAsp3WGwpTPxr0FIQERoLjpfy5v9cO7nLN4E0aUUWek2JkSkkpMCveCHEZCwWmBUTApZWBPn/l7e+zZqX/n2kotSRWszGNQi82fJHzb8d6Ippn+Khejo1ZzOKiWJHylZ29+QgpFeDjR0+PymgpOu6ghisjM/jbnm8TmRbsNpILuXLkiryL5z7112T5z/t/IUR+xAHYbtH1AK+DXlPf1W/Ctm3JIp4hknud6XYB0dKHs4pggZV8adwhI0Gvdb2xFy0T8QqVBAeHg8VnzfVHn+xQh/of8PZ/zaALJXtxUADzRQmpz3ZpWAYK6Um1uFHAe098hqJCPWSzoiimUjZWqeUeHhQILYdtSItLylWns9JqKRCJZUpe4P0Fdn8VHWlwbenpB/wby/JkLP2blE7vEp+TvNHIFiS040CFKyR3pdTyjxw5Y4IVzKlZrewZlLyAuJsiLl4KU4shFTWF0DH0BygGFNyg9hZiKq7W0U1GjOlTAfUGfrXFrwhqTM045CBVA6uaAK65pUSZi5r81Y95+l58KYbwx0ieOKFw6hAUK2t7X71SlRScc4PUoboOrRaveuK1kisaNO2rzUrRDkoeUHruy5Ij6TUlxwOKv3Xbt/ID/z4orCkNGU1JebfAueX0oGRwoINJLVVirOV8xYsMu3FDRyhB/DYVixHn2IYpRwRcO2MdGv8f+3zpefvll2R6RuOLxeMR7/tIBy+hHf/RHpf6PRCJKWyGh5UvH2tqaeNEUn9gxkSeMa8CXjosXL8poPBwOy+z7Z37mZ+zLfdwvsN5h/I/zPy6H4fZXkO5CjnYLA6dnJPmQn0EicHBXJlYqaIX37BLSIXlw2GvROxtOJZiICOHAW4iCCo+afHdI8TE4r4OMQGgGSen79LKVk/Ap3KaemKxY55sZh1oslMUlqDx10mJnpiyzr9XSI1FlbAmBWaVNaNqE4BEsP9iqVhE8KAjfKGvIY1N7CI4GG7nXLHx93ipD7bb0cJOKBIKQUXrxmiAYtG3YAFO3suKl+S7fUa4iCACfPTucsMxwVIsNBqtqRcYJijW1KykYvGdvyH07cXZO3jgE29KqzD6xW98XnstZ0+UNa77mMsIg0NMCpcgsJ9iIanJDd0RjEAKfOG5sJrRZZVlBi25uSUo43xcvaPMSuTrit/pAlxSVtINA41BWMihEaTFx7fhs/IlAQaT72xMKs46PbaooIbsvPF/QZrmxMySVJzYekK9xnmfBRi0JKZ1FeGN/ym3ktMsC8MWqOl3TBi41hyRqoD0KikBxCe9oc2dE/Bc+E61U2svwyEDqJM1/8Yw+c+XgsCUvr9nGaESt7NJIpxBM8aGYB2wCOI73ghTWZGuA75S4O2G/cvNATCn6axevOc7SFhEb4jRKShIG5CWFomwrkw1EIPXRGRGTcdRf/ooRG/jdOeduT8GfLcpZnWcA4jxDRpyjfdrwCS1WnNK1eUu8PS0zRwQBvjM3VBhSIMER3PYYIwORAr52co+eqW17A+5/5kSf5rAnHrPOPxlXHqCMTeks4s/JHAl5Lb0zauXjI05tCWq1knPcHIroySWHFi4VVRgRy1SdnrPw6dv6WYpMNntsEmTOS1GZL1t+MGHW26m5rtxA7tEjRxQGTcGD55sic7bUflxb1hOuPYUVRa/8vyIUV679lTvQKXRw7YEOp8oU6ua1zt+5JpEJ64vsG8o1e+KZn9H1gtMIl03FDmiVB05ZSfMHtJzCgfa0r7dbc0Mt61pdBH7/5Qm3DiykxR8l9Jp1Am4b1AOKE3FAaeHeuCMUSia5iHuyea0/dmPC+acN9zlifiok01GeIYpvcUZ51lqaRD+gzc3fw2FLvjZh+ZEWq3c222eu/IT5aPE1GlafmdfcQwzxzM7vs8bpS2plpn7jTbWU8SwDHUPNR5QThwYUsJlDHTL/taaUvABliAv6ROGXxOk+YLldLdaoVlxBjekuhHa/X21B8Qc5gM1saG4XPnRUAggyUSG4i15BbmO2JGd+DiaehVUhWv9vbRHmcjmJykhE+csGhdC///f/3n75l3/Z3nzzTYvFYjIOLxYdgMCguLpy5Ypslf7kT/5ERRs5wV+KzhFnNzg4KIPwn/3Zn5VLAIK2L+dxv0X4DlDs+we/3Txd7Y4Lwok76DyhQBtwTY/OgVzBlXIGh5yyeAiXTzarpRReqapFgUSZSAky0ThRESgqwmqUdhxRMh5twMlrazotg0CF76zoQd4m6mJHoCJsLqOFiEWHTYj3UWz2Wuv5jBAFFl42OaIxQFXEtYp69d8YBeJsLhUdxNs709bYPSSuVGN13Taf3ivVGwUWJ+rAesGWHmxWARafdygO7zV1K6eWEMUMi9Hmzqg1X0lLtYMxJ4ukCOrTi7JpiF9atPzudgs9d15kWpzRQTSIvQjPuDBq1Fsbe+NqmfKa6eGgdf7+TccDWs8JPYFvQrGmCB7afAqx9om3BDeFDUqE79U1s852WVls7AxY9/PLrv2wrQbraDK7Pq58QJGuUT+2RGRcKmQsV1KxAxEZPkbp+Igk5qvHW6zl/KZO5v6VrFAPImVwgw6tOPNW5QNG/TIZTcxUrJzwWXQBblbACu1+iyyzUNfVAkNdCi8sOuuIwLR+QLSwJ5CfGQRtiOsUNRQ9a4gjWnUvEVmgyOPEvXo0JYdsxBilrriFFrLaDPE9okjEww0OFgWPfKUSIRWLau/lClZfW5cRqAQIZ66ZJxKRWaNCjuddDJU3FNbfoeDDOkOLRyfPRt3WHuq2ls/ctPWndlnqyqY7tm2p0diY9N8Bn4q/0MVJSfQJNiZo29fa7Not18bMu2vE8X0mFy1/bMAVA3nXPoebqLbWpetWf+KY5rXaiRGf2s0QyV2sT1DXPP6HZ8y3c8j5ZmULaokz71QcFSu6TxQuwVvzOkBkHhq2+IvXrT46oMKN4T8/Zp7eLrWsMbJkw/d1dmhz54DD7+deKly8P2GRqbREC9g7YGLrObzH2R1kc1Y5vEPO4vD9Hv66n3OGl+1tcsKHn4TVipIMJjJaK6RSbouoSMKbq+X8hnnml6060utMdPFKA3GKBqS4yx3sVCtatgKYgW4UVKSDFuJbRqHGwLGddQUnf3ILSyd3quD0bKG/cCjz+7st8uaY1XYN3BXvUOgq75OWYiKk646nGPQD1Hc8C/h8cUgL3Jpz92ptw3wDvRJINFIxken5/B94+MfN3rggdSIWC6C2hR0tOkBEXnGO7G4hzijGxpbXHGk9Erba+JSVnjqqNjrt2+qW8pDPwlpNa1rGxLemNY+zJ/p1cH3+hR+yZ7r/qVkkYo1kVCHkONpjxIvJKfQG27dDHLHgYsaZKK9sRX21xt3vmFiQAGJ7jqVem1RhyPqU743Zy6/8m3vSIvz2V776XbcI/8Mjf/A3eq8gWH/wB39gH/3oR++iVyBb3/M93yPbJQavS4Tdr/7qr9o3fMM32LVr16Tof/vtt+3EiRP6HuyWPvShD8lJgJ8nKu/jH/+4Yu+IuGP84A/+oNAyLJm+XMd9BOsdBjJhwf46BYP4wA9xgajicsBnAj7ngS7j1I6nTc06XloQqsPpEfQBCTkn2Y3HBgWvgxhwwsbBmcMEkRucFlHDwC2QGo9TPnl2bVGpkiiuJL/2mj32kZ+1Z8d+Vqqn5LkFa3t7Q79bJFIWg2xeDsU6lV+flzIJk08WGqE0M3AZao6/1RwWYTn/2G7zUJttFETYz3cFbf1gkzXdKlnnF1eEpOHNVU54FJS8/ECT1GgUDqWUa3UoQxAVV7Fs6/sTUpHRfhNnosVvtYcPCP3Te21FnZgWadn78nkhai2nV7Vwghx0/M5VnULh+WCGKc8usgQhkrfFZNzINaSQgQsDlwckCZuG1Q/v0WYbuTJnycmqWozK52tJivPESZ0Tt4i5Wxl6FDzp/a3a1PhdIGniDR0bsfDZcbVGUEZSaBc7wooqgVOW/foHda1lfgmBGwTG77H4LK2Ghu6rCr90WQUbxYDaeEGnqqPdh3KLuCAilkA7WLz5d4o++UG1OMk8A38jUBVaKKiuaK+0ntuULH+7uEIYUHn0oFWaQ1KJscHTghEfCoNS+DQJ5xvFBlF6dL+KN9pftIYpJp5d/CW5+3uP7Zd56GcLv6EYnM8Wf1OHBKwLCL5l7rS8NCV1aWymaLkdCW3GcG4ormizwIejxQfHDEUrBQ+FE87xbM54TBW+8oRDMEAhu1stenN1i0RcFW8LxJF2vS+RcC1s+FoFZygKabrck9DGx0i9MW35Dx9zSB22AfCD+L7ZVXktITqpxAMqUjHnzJ8YUtQV0UhYTuh1X72oaJna7Uln8jn83UJO8ETyHt2nVrhaZtWanhNf3rWel97bY/WNtPn6+6Q8BmFjPP/iDzkxBfdwOm/Vhw9Y+oF+K3bHNM+IiALNZrNX/l61bpHxdSEqzZc2Nd9xE8fGBNUaXmlSkM5tuNbtXN6CL18UEszhjYOCUKcY6LoLeOZQtP5gt1Cq/Nc8oIKZNmtg0uUzoprFyBavKtqIDN/kvHkm57cyRX1qkwlJXspLiarDSt55c/F8Ueg28gUXLXRol2xElA1YrZuvvdWePvQvJEogZLp6845sM+TaP5226OV5oU8Q+FHzVZdXLD/UZPW+Ttm+QG6H8xR+8bJ5Xjmveaj5EI1abe+gvKr0nkHoejudie9zl3R9EXFQ9CLU0JwY7t1CZ82ebv4/XJuUdIbmgA5jFLgSWKCkBenE3R9RA2t+rmKpc4uuWEftGgs5C5Mvs0HB9qVfpZILYv/rDLwmKYpoC24PikByhl9//XX9P3/SFtwurhh8P4bgIF7b30Ne8HZxxQAFu3Hjhq2vO7rNl+O4X2C9wwjNOZ4FX5yudMEqDbXfKDZ8U0vOegB+ByopTnsbWcH1tIhAs2SfsFYU5yYxnrdiu1vwIreW9fMog9J7m8VHku0CESKtUSEXROzQElF+YQHSas1l2b05bh8M/l1xDFgwQJJY+PDlYqHA0FAoFbyMSNgRoRfKzvBznvgRn6T4ZLOVkz5LP31Ar8nrYbJZ2NlmkaWKtZwhx6uu7K+1vWQUNiwxU7WOV5at808nLDaTlxqv54+ntdHB5aE96Flas9Y/valiA8n4/BOt1nR1U8Rc3hfRNpCX4USs7Y9b7mOnJJ8nJgMCviwAejvUbgRJyw/GdbIUjwJkrFC16LUFFZBNt7AB8Flmb6sUgfxsbKHq7C/KFL8QnJ1bM20CjDnjr95W+0WO5eWqrR9IOY8olIlej9pMa0ebnVoRpI9Q2UhYga4UYOlByN2OpJ28lbEi7vjtYamVGDj3IxpA7AAioZDokE9EXBz/KRZA7eTovTWv4PXFpgsqiGQ+Oxp3dhuLGaE38FdAmxprG1I91afnrHHygDhOQvhAsyp1CQhAkUJjS9o8cfdXFFOl4tpI63k5l0PAp41DAHCxxad2H15NtOGebvtHilJRAQEnpVRVpFD9oYPalOq0TAJ+Kz5zXB5i2vym58z36kWL/vFZca5q8wtya7ert6Uu1M+hdExndcgQN6kPBCelPD3MI/m9zB0JGNpQgkJirwpRrA53aT5Q5NHSowin5Skn8fEZ81OYz2f1XJVGuyz++atyJOeAIkf1fNX5laUzTt0Y9KiohVsJskVhb7NL5t8xqM/s3z2igoqiiLgUTFbhpOWf2OcQnHZifxLmuz1nzRfWLHj6pp5/HXZ2DVlhN6KSuvNnG+jW9Xym7zvl8cQcWx8NW/LCkgoV740J8ane/PXvdnOBuJmZBedojmXG9XHn9dQUlBr4vR/4aReW3hIRX1BxQZt5pSQod5CoG0KqWbdo7cqKJSKLk+RY1rIPDltosyoPOvHAUnELnLklp3Lec/LGhopR+YOlklbfyWHEnDJwE+7alm0Cr4u3XE+bu6ag+ahrj+50cVybqAWdlQL3gAEKHVpwIebwH2vXbjrrl5l5xZDRFkal99nsr+n7USvazXHzxaLmJQpoclooqr/V+dPlH9+rdiftSBzvyfTkMCHkH1Xnjn6pcOFxgXJRLOm9Ty2oMKtfuOr+v7VFB4PYeMZK/c3uGe1I6vojXFh4rEWfKTMUUeYniD/t0uqdSatfv22+Lbf7ezFqDc+7/mLAc6IY2v76qZ/6qb/2e6G4YoBYfeng/xe2/o0/Ozo6/ty/+/1+a2lp+XPf85e9xpf+ji/Hcb/AeqdBWzDst0Jf3J2+OeVwUvPDA/HJMBGFGSc/wmw5mUPgxDcILgcLMXwUuAEsBhiWQnwGkgeBgtcVu7ai6BcKOBy3Qb3g5KhYqzaUQQfPCS4Rp/nGxLTV+zst/bXHxdESwZ3TFovRbUcwBwWhfUH8BagYhE++b/VUm07lyNNJiQdVY6GNLJflkM5iRtZcejBogdeuKLQYbgytvPhcZSsjraQFDX6UvGG6mhUSTdQJtgJL7+8XEbR8YFBtwMbIgPX8zk0tqCBspdaQha/OWuz8rGWO9ljbmQ2ZD8pkE8I/+XFEVOB9lHAqscgCnCfalsi8Q+abX3WcCWJiNotCteK3N8Vtw0gUBAl0q1EoOk8vn8eygxG11GgTrn7FLnFfaE+AGLa+veLk9dfnXRRKxEXaQMiWOeFm2jIHO2z1wQ5t7D2fW7boOBmVZmsHk+LNYR4rw8gt9RRE47WDuIyH9JXvdMq/yEzW5r96h1O6UYSlSyoAxQuaX7PsKMWH36LzZct3BoQQMQdBLyAKV47vstrqmnl2DGgjembvD6lQFG+FFsvFaUUFVXtaHLKaLppvJSOlFpl1zGlPa7NTRN6asXomI2Tt4a//Ob1/CrrGUI/UgiiuaOvIK+zAiNpSbFK+3i7zdnaoZczvL3/ohDY9UBvPwVF7puufyIqB8HIMTBsn97kwcwpVAoKvT1rw1avmOX9DPCSQtfX37jDrbhf6g2cbRqOeyTk9U8xhkCeKQGKWUKDR3km8Nu5CsB/aK9RUQgi+Fy+43UPa7APrJafsXS9ItAFXDUGHBB9kE+7GfNNngelVK57a6YKIJ1xiA2ie/ojHZN4r65IYE8PxIyNv3LLMYzvVOq4dHFHMkBC8LZTDK9NPE78No0qC1kGCQKE63ljbureu7co1e+/7flpcMwYRLQRvo+zEE4siDBQ7dW3Tsr1Bq9DuH1t0ByBiavqaXKYiRGysC0BTMc1ddeHp5A3C3aLIJxnC/9JFq53ap2QCiOz4T4GKksSgw0cwqHBm1KYUvBw2KNBWH2i9i35zHeXTh3pzNScuHeh5cEvdLOEChU7Ab57ZJeelhn0CsVwxvwQ0oKOFoWbzYDQc9UnJifcUOYmIUKq3x80Ld4zCYmLKau89qogr5hGJADize4nHyWTNO7vsSOjcJ6wjSBGYWbTauhMAPLv8y/bi535AhzUOCRS+/qFBRfmQpqC5uZmz0NVps1RCyuBGd7tajZjyynH/09esUSi4awynct9OvZ/sAZeH++XEwZqenlY7b/vrS1NT7o//Z8b9Ausdr47jHYT++C0XwaHcMHcyLDUF1F6T/9R62eqHRhX8yqmzONrpSK/ZsuV3NDmzwfk1FR9SC+YrOslSgND2gl/FZsgCSpsjMxJT642RHk1oM9YiSYsCF26Rl82if/C2iiQPG8Kbl7TAwt9hM2HhVIuJ0dos1KL1uXFrHN2jwoSiLzKbFX8pMxCyUtKrzQUkgXT6tW886vy+aMVsmaDiQ4WqjoVLyjSUUjMrtnw0Yisf2+fIoPWGjDNp+xHDQwsE9AfLBrUJCjVbf5xWaYslzi/oNcq9KWfCmSmb/+aMO1Ur/NkVVvCkJKefXJTZKeRr2pGNLeIw7wspd/jCpJy9wy9cUuxOLYNLuuNPJCbyIgEj9245t275/qhFry+rlQvHhrYA/BFabBRnTTcLipqR4eKuXot99qK1vrZgnqgLBqZYjC5VFEjN52r9A3cSJjYnN5yw9K6EtVzKqhBXuwdjz1xVG2r72ZzLpcQoMbdlNuv1WH5vlyXemlIxzebUfHHDrKfTvDennS0CRocbRfO1tTpfodaUih/mHa04/JfYxPC9kjptIyfEiDYHiCGu+vJn8vstPpWz0oFBaxzZLZ5Z8uqabe5zmzQbc/hl3Nm9QnFoI/quT1r6cIdD81BOpdOWeOGGDg7RS3PWqDeELii/r6vNcduwdoCzRUHeGRUZGRQKTyobHVIMi++Fc7qXoHYy/SyXLXR7RfOBNpV4iLW6cvrgydQ2N9VS5L/hw9AuBb1SFEq66iwNmLNL63rOsD4gkxNFKYHLHAaw56AgF39pMWvByVUdOEC5cOdWEXvrjnl8PiF3EPTVIltYsiDq4VpDyQ7VA0OWeOW2tVxK62DDvKVd7Z7PunmnFjW/vJGIFQ8PWfTCjBA11Hy8Hhu7nO4jEannhIIrOzEk6wmP16vCixYvyB3PVGYkaambeZl8gqCoHQZRm+dlLSNklDkPWk3sE9EukZm02d4d5suULXZ9VR5kxEAF5jedWfLuHqvu7lerTlYVHCwTYSs/ekBrmExCF1c1H1r/x2XNQxAgJUHkiprLiC/wF9OcSbsAc5HjM3l3WNrZa/mTO3Q4oEXPs+qfW9P8CXz2bT1jILWoEfGUC5y9peeP6B0MTDkUMBf5nZqD1ao8y/h8iFc0ws4rizSBwNSKo0t0d5i/p0sqUAo6bDa4jhR0HIQwxoW7yKFVwopM1hY/slNoXPT2ug50tFI9kPgrVfNQzHW2q4CHlkBQ+szf32UxOJP3aDQaXqu/iy9+ngH/6ku/Qlvcyr/O6Opy135xcfHP/T3/37X1b/y5tLSVWrA1qtWqlIVf+j1/2Wt86e/4chz3C6x3GJ580UmxD+/TiRAfKnrtbJ7OFdxvsWvLzoeJzRIUJhqRRUL6ZJ/QK1AL5bH5/ZacIg6l5k51DRexwikp9tbEXaM60IzYbNF5rtTxw6pbrg8zUJzF4zKeJIcMkmz2607J7R33ZH9XhzbryLxzbhYxnogWFEbj01YvlhT9wVg8HlaGIYs9asjWN5YsmHHB0SxeIDptn5+ywJ1Ftf1WDoSEeMi8cnpOiw9GlQwW8baLRWu6VZRxIJ8RyXrlg8fNd3NKppwUWBGUWXEXD0QBp42sv0UbpE7CbG4Rv+UfHLHaviEt2nwO4oUoTuHBsRHkHhqRY3jj4g0Zq+ZHmixyacb56kSj2mwgbCs2xB9Qm4IiNNvvTve4Miu6BYn6xJSUf6UTO51ZJyaWmYJItypysjm1kbxvXDHPyIBr3UTctfOcv6kCL7u/XRl5jaFetd0w6UTxGFmuCDkBOaSAwj6DTZgCmM9CAUjx6RArr070kbms5Y71q+AhyoO2L63cxg6k9k5IUWmLWG2w03knrbp2KsVI4/w1p+IDzUI5VarKRR7EhvcU/eINbdKQyNUuDPp0D3HM9rS3qs2hOQ+hmZP8FtG48chha1y8qeDo5MVld3LvbnVKrf4uZe8JTXzwoHmO73eE6sVVFyFza8IhupmS41yhsgv6VUDp90CsPrTHqdA2chbYLAmhqC8ti4PFZ5Gv2eKm0B1vb5fQAjZK73C/PffKx6XqdFYHjshc7ogLsWts0rpFyetTi55rj1eV2rxfeUqCAllFJMNqs4H6+q9OSnGKF5pvsF/FAsaXT7f8Q4ckxaJ6LYVOt4AAB7TpM8f8U0sikEsosLwpvlLx2LAQLJIOEMaUdvco6slzaLc8nsTxqdetcmRESBBcPFqFirVZWrb6nmFFtzQG3XMLWkKou7IVYyEXMt4al0edCg+Px2VTMg9AbWgdboXBy5uKa14oWmmgSW1E5nNhFKFCQ7+zsrdfXEPWLbtyW1xQGbYyWpqsMJBSugOHPVz0QbQo6KXo5Bn+wnmrNyVEjudzyKG9r03WExx8KAoRCcA3g6eFaz8GoxSyPvznNp23V+HIoFWP7JSCF+Pfpa/bb/WFZfPF4+KDMYcxOvaNzzkaRqnirGSSMVmRKPQ5GtFzh2oWtB1rGYWZo7Qe6FWBJUsGvPe2vMBAe0F04Z1iYirBS7GmzE89CwtLW9mkHpmVstZknjpgvc+tqbX9t3EMDw+rAHr++efv/h18LrhVDz30kP6fPzc2NqQO3B4vvPCC1et1cbW2vwdlYQWV7tZAcUiMXnPzvVNo/j897qsI30lFuOu7rbC/23GT8OZpmM0/HLTEZEMIB20LBnwM+FNs3IWBuKwZ1Fo5tkcqP1pJ4VXH72GDZ3OD34DLN8UKUPN2W0/eNCikClVtuhQkLJRwLiji2NQpsuA84N8CnC9bAtCc5pRTTa2l7dqP99jwb2FBUFZLRrD2wV0u54yIChRhW1ltoCpyHu+JWCXqFZG97UJOqEl6d5P5izinV8x3+rotf9NRa7pZksovPoNa0KzY6rem12assb5phffskT9WrjdosfmyyOPwLuB1wV+jpKfoWDwZsb5PLQgVgcslXyNO3XFnvogpaO5wj64lC+DyY13WfKOgjUD8k1TQItcXXEArJowH2izf4bPOP5lQMC6nf09TyiwWsc2DbWqDCiXEWX9yxj6b+3URbuGBSTU1kxHKoWgRCObJkDzMcOZXfh5RL/zstTG1jiDs0wrBkNQ7PCAiPUTdUl/ShVavZqwRCbmw3XTWqkOdaivpc9I+wvuINg5IR75s2Z0poaG0CoNnbqugqeOMjtKsq0VKT9kWcI0PDzgfLZz4twpzrpkcsrMFS5/oVWC2LAU4pTfH1cKVOgsO0mbaqg8dEPGa4qF6cFhohr6fzZIWpM8nftV2HiEtT9AK0CbeL6gCLT0cysn+i56ZMKPo4jUw7fT7xK/xj464QoLNbXxKeY6gFswDBfHCM8K0Mgai4DW7fEtGnXgPwaHiQIOoAJSYecOGT1EOd4nPrgPQwppVRroc4XtjU4gPfkt4WMnEF1J1oWrFtuDdOKlSS0gpAhQ3qMYI0266sOocvadnbf3rj1vr64tqkaWHQ9ZyKeMQr7F5y54atMifnLby+4661m6mpPYUilfuA+pW2tVw8YQIisPpuD/4thGHxBzI9YYtPo3ZacUFXoP4cLBCFHFnUcgKiKjeU2ezQ7/SZcsORC3xR2et9sB+tW0psvx4bnUSp+MEAMzh+pnLasHhSQUPDnNizekLC5q3oNniZ3EdcSQHVaPFHQqo4FDW4Gif3h/B1bFJZ6yMLQHFJ6gTqBiFCtmT3NPASsEdIGnDYtBJO5973+cyJeGUBp9922rvOy6OoG9+TQghiGT+qUNqX5Ke4KVlSfIAsThj42aNulOy7hhQu5L2Pkg1yByIK8+5Irw4zOYKVt7ZKY+04q4urauaG1txUnxWUTDml1VcUkR7m5tcjiYKt0s/IVK8FLQPH3GHLa6N4rhKMiqVdQmq5GDAqvsGNW+fX/+1e6Ii/NaXvs6C70JFWM5W7D8//t//b79X/KrGxsb030ePHrWf//mftyeffFIcqoGBAfvEJz6hzN9f+7VfU8H1Iz/yI/K0unr1qjytGM8884wQKawcKKK+5Vu+RaR3soUZvBeKKawafuAHfsAuX75s/+Af/AP75Cc/+efsHL7cxn0E6x0GcRG6SIqm8diLn/0Bi083LLJS06ID6pE+2Gb+qWVbfCghMjZtPgWu1mqKhwmtVeRWLnNSHLNxbpbppzkDwqDPuT0T1Bz0yq6Bdhynd1zV1Y7AWDGMgsenO4Z0W47YzVGHFFBcoaS5PWmrx5vVgmt7GaWUz9IjMRVXXooNHtJdcZv7UI8g/cUn2sQTgxeFMSPvFWJ701hZ/k4s0hQmGDXSdqo+sNdSt8tCPlJjzvIAjgttE8waSw/sVnHFIppv96j9qMW9zSF+Qqvga1ydstYrFUWlRGcLZmeuqXhTcXVpzG2YA20W/vQZEaXxDkKVh/M7BShZexCaIedq1GpC4jq/sOQk4w/sNQ8KtXrdNo60Wer8kovUeeuy1e9MitOBSIBiiby36LUlZ5pYqMonS474Kb/Fzzu0TkgU6GSjoTgPiM/Pf+GHrTTSZp7RIRUGoGigCjivI3wgiBbBACTZ8q5ukdtRcobHls17/qZaaZBlkX6vH25WdJIEDkm/NYZ73LwbHtAmywaYPdAhdAaOSfi16+LHMTfweQI9QFlGCDnoVfL0rH4v14esTK4ZRVRjfcMsmVDYNkgHxGvaXBTSIE8oV0HoKK7YdO6aa2LQCReK/DdMF7N5V1yFAnKJhxyttmAkKCNTmWiCKh0/4DbHctmZUZ7a7zhCFBo4mrfEhHjIFwvkJFNUu4ysPzZQ/L5oE8evrwrRkzs6Dvi31yQ2UExLS0QFFehT9mCneZqbxC2ioKflrADnUk3Fgvyhbi8KqVCx7/FIeAFSEZsryQ6An6N12XJ+XQKC4NiCRRFN0Cqq1m3jsSG10eGUgVxTnPGZ8seHzHN7xiUlZMtqB0tlRgG4uikOlQrIclkmmfArm84s6doj+pALe74isvnmSNgsHnWFSjhky08NCd3JdwR0CFKH5+AueXmxBqkY3dOtvFLcxeW8Tjj5ob2Kx6HQx7ICpAmEG+8oz+4dsjJQG1nmqz7LDyTEz/TgG0XLMxSyMgkTyZBMguEuQf5G1adrN71qNQw5l5w5Lui87kfQr/xQ5R3GYyr8ZImxkrPo2JryLoXo8nsXV+zYP/6kQ5RqLg8z93dOqS1Xu3LL5p7qNO+RPVZ78rhVH9ynArQxtyjFIcigZ2LOqv1tWjtUXGHH4vVovYIfCt9T6uJS2ZZPtZjv7E2H6m3xGmmNktygGB4+WySkg5f811pbxVPU4YRsz420LB6wGVEx3det+QpSVjo6bPdqcGveHQfrr/f7Tp8+rcKKL8Z3f/d3678xF2V8//d/v33Hd3yHCqGTJ0+qIMOGIbxVXDF+8zd/0/bs2WPve9/7ZM/wyCOP/DmPKwrHz33uc1IlHj9+XLYPvP6Xc3HFuI9gvcNJ4dEn/qUFPCwyfsXHvPDcD9pDf/fnZCSp1pzXIxTgyQ9+wsITawoDxn1ZJ6lK1bKHOhVAC+oCcsWJHSNJApTxhPLu2ymSNZxDH95TLNhp513F5qDWYwmZsF8nYjaXlf1+a71Wlf0BRQNRNvBEVj920FoublpuMKFiI99NzIbjg+C/xUbduD3lwlR39LpQ6ivLLu4iEpA6aPk97Wr1Ba5PK+R3/XBKG1Eg33BmqEVnbsRnh/RvU/MyCV3b47OeV/JO7bilVKzvG1bxgFWDODFnb5oHjgTXhkVi94DjIK2mzbw+W32sx1pfmrHq1KzVHz9ivlcuOVJ0MCiVGoojjB75b18yYcUHd1n41Wsi0WM4KNNQWjrNW0UxfxfwOyuGhSX5GdFueCr+za5V9uRxC12Z0vfCC/EmEkKN4PxAQg6sF525KlEdnF7TOSsPt2sDLD60W0UvXkIiQjfqMqKkaPPuHtbmU+pKaE5QbBQ74G05XhlIDPwdDAqLfSm1hDVo98QDsoSoDfc4sjwO2BduWO4jx6U42w64tUs3zQ6MOoUfxT+oJREhcRc3I2+roW5tnqA4gQ3XPsKXDKl5diBmwUzNItfmpeJzdgY+tZ5ERMbWgXlCfuFGWgrL/IEep3gj/605JV5OtTlsQSw/aHP1NMl2AsRBGx2vg4t2S0yFLaHFILW0YWV6my2plaaPjkcSRQAtIvhdq2suT69cVSsPE1eUfuVUUK74oL9eUgRiPos9f8XyT+6zl//o++z9j/+kuH7Rl2+4Q8cAeYh+K/REpNKlMOd3o4gjEJx2ku71WsEqTVt8RQ4wFycdD7CjRdy+mW/cYe0XSs4Qlmgpn8fCb96SfYM+49Ss5uejH/1Z/R6eWxl3jrSoNQonUottwYlR8FWi4MDiQf5T6YKLt+EQEvOr+IDrSPEEYkexxpqA75Q80LbyTNUCLG6hMfhvcZ92tljklevWwM8L3yq5xAd1gCFEeX1v3Nq+OO94TaCkC8tS5xEgrlQERrFk64/2WxOeZlvvG56kOEc4ruPdhVqQeCPOgtAlWjH7Lbm0hS1zVN3vgMeib92R+70I+ZfGlCO5HfUFmldoD1jL5287ZPbGojWYcwPdQq6ZiyhI7bXz5h/oE0qe+eB+Z1B6pN/FiYVc0U28T+rlOyrqxKei6FtYt8Wn+iVO8pXMElNF2WCQI1s4PqwMRgWjb3EVdc+LFfd5WR+27FGEVoEu4v0GwT/pbHR4BlVQlgr2/I2fuycI1rd8AQTrz+wM/rqjnC3bf3ni/z6CdX/8zcd9BOsdBpssPXgWTRZheCDBDaegkeHo7Iad+Naf1yLDyVTF1aYjgYNuyCsLm4aVgtyNWTAFm6NEPLXfNvemtPEi6YcrAgcHZRgn2dxA1PLdcK9In3enfoqcjrMoDBs292hYxZXGcJ/aS3ZlzGVvkcH1u2+bvX5B0m7aU3IwR9G1s18bDMo8vKFQ5vF58juarf3lRRVtxSNDaju2vrWiFkZkNm/h9YryujgBbw65LLCNZ/bo9/W8WlC7QxYH3UnztLWKJ7b+/p1azIDnvSn3IFPc1bNZcX/Y1DllImlvusoCVzMfiq3ZTSu/74g2eSwn/P0O0ZFXzZE9QinCb9y0wuP7hJRwjfNH+pwxJvl0Kxtqi4mMHg9pUSQ+A6NBL20sj1foCMXf6tM7RdDG5bu+sCSSNGomTrm+7i7xiLb5FZyKPT2dQulAKiiq2Fhpw/DaOE2zGXnmV6RWguNCi4SigHYuxQDiB7UX7kxb8PNnXfF5ZVzu33CQiPGAn9a4ftvNnyN7LHF1TUaMysxDzdXeZp5bUy5smWgaFJGndkg5CPLHZkaLlbBpITZrtGkr+rwoP+HSYYor7hSb9GracUq2LQK4V2woi8tuM2lPWfTijFARcuKU+aicPY/7/oDfSddvTWqTE8/L63Uq1OWM5PDPvfovVFwFV/JqZaqggVc20K7fKw85CjP+Oxq5G+AdXEhbeC4rlEDKy3JN5rQKQJ7KWPXoqIpWTY/XLjrH/NEBGXvSDiL2JnZ7wynCPO6ZFndvuFnPcOjGgr6PZ06WKpenrTbYbZmHd7j28ZF+S0zXFW+kaCnQsM2ySNe0buF6qfgf+i6LX1yUkAKrAAKvue+ocFVYcR1jYaF2ROhQXDHXZEHRFpfXGQUuLXU5rYMQ3ZpznK1yTfmS+NnRlkY0A/8M5FyZo7OLQh/Z9BEcVI6N6h4R/0SRyvOXe/qQrmnrW6vuGnPY8XjN29osxah85kAxQSGb4hafKal4qEWDmnvMfYLeG6mEa4E2gbrVZGTK+5PQRxmkLkczsJIVtxDEitaxf2zW8j1R2/zqI5b5O8cteO62lYZaVFAlxwtWPDhgkTPjmr+yFeGgAPIJ3w9xUTCowxOdgVd/93s0dwjsBnUWz9Pntdd+53tUICHW4RpzwAOR7fzstDX/yuvWfHZV3nUSLw11i0zPf3syOTf3/H4pKhFiBFESoi7kWaDIIqNxsEdzneJUfNLFVXdNeB5Aq+/ReDcE9+2v++PejPsI1jucFE595Metebwov5ulEwHrOF2RIkeKGlQoTQlJw4G22eSltttq7cHdAbbm4UOGXhps1uLM/8PzIaYB36XtDDa4VrmesMVmCvJ7YtEs4/6s8FvHz3L8CpddyCmahZ5YC/LJGGwqOFOzKH329L+yJ57+hOP0XJ8RX0IIw+KqrX9g1FYOeqz35YreM5tKZbTHucsnQAdqaoE90/mPrba2LnSHVpJQkAKKtKizHMASYGnZ8h8+Ib+fuUei1vsCBaZHHBcGXCHahIVdRPl4Ldvlt84X5tX2++BDP+ZaKlNLWsAgxIJOKPYGNKNUVrQKGymtD1AQ2oTxt6csd3xAJ2aQN06Q3oUVEY8hvUdfuakQWYqVuw7uybjjq6Fq6mnSZ4W3kjvWJ2WVEKSxO84RmnZqvabN00MRAp9oZVXtQfhIkN69nJL3DZm9ftG8Ab/jcBCXgSx835A2zNBqybWAOmJWaAvY67/9PUI7A1+4IAdzTtrydroxbvmvPKbCB8PI9X0JOdqrDco1X09bZbBdfCMKbpBM2o2pS2uaE7S42GiFFpyd1OZTOjToDgi5kjaE+mi/2r6glsVhNi1sHTw6wXuyRav0NTuTSNCw6Xndd1o5NrcoA055/ewYcshHKiEkEmNJ2kpYjzDgtnA/QLeUJ7iV+0ghCKLhf/2y+FXEH6FalMM731f8M7SLliCtVsK42cADF+4olFgZh16v1IzloyNS3CGAgDCN6s4Lh+jamFpfMlydz8jJHO5YBbUvPnIUOihSlzdU2MMJUlizjIJr5l3ZtFp7k94THDKKAop8cZuwKthIW72jxZG7t0KuVUTDSeM6DfW6wqPkWoq0g6WsPLJH38PhI3hjzio7SXJAkexQSo3TV8TpItBYqr1pHObrKhYpuGVkLEWtM/WlPYh3lDZ9ioT5ZanpQMOwZ9l+XXEHee7LVUfaJuIFnzkc9isVrQtSPW6/Ps/jsksRwPqBuKnaI4et0B50Bqm8l1JNvLbwvEO8yDwEiZXfG3zOWl1mxKRL+JY3hPD5dw6rlYgKlHnlWdkihVO44K6eTovML2T9zIySB+ThlS3Y/Ff2W9vFglA42zdiuYGYxe5kXLuQA+vKprtW7Sn3+RjZnEOdejucRQ6FbCpsSyei1vlmzvwTi06wQdA0cT4tTS5GKldQWDRIna5fV4tDiosEQPtdMRaLuOego83FGeFaPztrn1/5lXuCYP39F7/xXSNYv/Hkb99HsO7BuF/KvsNInpuzjQNNguaT4859XeTRgS7bPNWnB48WoD9bdfmEY0tCP+JX8bcJykeJtgYtARYwCiVOzaBGnEZ9oCUxv5RLcB1kSrilniNnTkHM5BGeG7e1vSwSbBCEOJdVXPnnN2zh8VZb//A+tczgqRQO9gmtQI7MohfYKDpV2OuXrHFzXFyZ5hfHbfQXxy0ysan3S4gzhYt/YcPC05tqSzx9xPXXfR3tVjmywyXKk+M2vWjRmZzac0Ip3k9sice8Z67bwH+5Kf+iz73+I/bSn36/ZXsDav2wkLMJE//S+byL0aC4IhNQsvpwSJYDpNuzwTjYPaAFM3Z9xYVXP3RQQgBatSz+2W6/NgKk06BW9a42LXQo+FhUURmpPQjvyOu1zYOt8koSMjC/6cwSAwEVNLKJwF/s8WNWfGBUJ+fa3KKKNNqJ1eFuFVcQXylCfMi04f2QtXZ0r0wK+R7I8CjyGIsntzLoQKiePyOjV1q58NcmfvSke39bg2KLe0/bd/HBpBVbCKd2m4uQorUN8ay4r7S54P1ku70ytYVAzyIfevuWRa/M29r7R6y6smaha3NqEc6/v8NquwfEsaHoZ2Mlo42CneKqjIR+esYhPBRXWIHkCkLncFanaEc6DwLJdSo8NCqFFpYFFHsUuaAqcsAGZU1EpLhVQHTQry/awyB3BGhTWCQ/d02F6Prx1rtqOooqNmjaNRQR8BUVgr6zXzwmOFuQqRuDPWoNUlyJFwTPhyK0VLHMR46ZZ2HFgqsFqyUjKq5UlBVqElAIRQoRBI6AIaQiTSKDM9ec8KS/VQWJikGMXVNJW3t8QIa/UhsOuaxHUCjcxkGNGpdumW2k3X2fXTT/lQmhhIFM2bXd8ALLFIXmKWcvElZxRGEHCoLNCeRp754RPSPiaV2/Y8UTI1bbOyRkkaKM+4NyVdYHqBijQSntPOsUyAVZbqDuQ8SAD593bNrKrWEVV1wjKS0Rt2whQ7I0CAVVFIPSqz1LoRDzO4NN0hYCXoWlg4iTOrE5GnP2GWG/hZYLml8Ur6xToFisd/7bc7a5M2atFzPmQ5jh96vAhIvHwQNvKyGjWALUG2oLYhBaOTQsfh9rp6wWCJc/0KJWc+pOxdJDYfMM9asY4hnnucB6BPHH+nv6XDu7XFURC99PBTBtxo2skN+Vk822ORKxYLqhbFgOoQqdhm/Y06ZDoSxP9vZLPCKHeq4RKMR6xoV1075Oxe7SHBCcSMQyOWdGu/j+uD/+wrhfYL3DKA+0KeomslS2+CwbYFmhxizSsbmi1GksTPCwWLDxMSLKhAWKzby8o8PCCzlZMFBcUaAh16YFQDuHUzLqtc0DrWoT8u9SrMAJABYnyJXCbLTPWi/mnAImXXBKqqhfrbX4bM2afv+CfeHZ79cipVwxNt2gV+HK64earDo1I3l0/ukj+lzyfdnY1AYa+dxFi/3eG3LgplWAGsi5lHtUjAlloRUU8NjKYUcqzwzHnTqts9lCX7hkifGcwqHx+iFIGq7U0wc/bu2vr4jXwWJVTgW2DAwD4jmB6uFTJVPMjpSTTMOZyhI/Uhdywmm0sYifWEW+Nr4vnHPeVwG/tV4BbQloQwlem9FMhkuW+qOL+nnMAsVTITKjUrH477+tjRBUjNZoZicuzcSHlFXgYobJ5oEnD6iFb6jf6psZ82Jq+OYlkW8xhMTotba4bN49O/S6iAQwjKW1C/eGCB1Qq77niYOpWHqPkxjTOkk/c0CFxMgv3BDhd/XhLvHY4LTQ7iGeqOvlNev5/IruO+0arpOMK18+r2zF6IVZIRGRVbdR1gY61JYSqT8UUmsHThivWRputc5XNqQ61YCkm4pbvSnm/Jc2suJnkS0YOzuj4kGcrlrNnk79A3um/ducBcSh3TLbVMQIJq5rG1Z58rDmPnO53NesMGzMdrFtUOG5simxAuq64pMHJIJg7rLpepqSarE1n1u19P/nIafkOnvVBXjfvK33mx1JStHK7yz2xRWODf+LaCUMbREgUHCA/BIxBEGddIVGZ6s4bwzQFIoM0C+K30JPbCvoOmChBbI7K3pP5ccPuVDwBQp2Iq8aMpjM7m2V1xku6hR9vmuTen4VHqyiwsX9UCRAjidWCfI/yj+eeVp++Ewx3/ynb2iOYwnB88lrgCxT8BIppOw7iq2NrDhR8BxFDaCNOtgp24p6qejayRt5PQcoDfk8CBngh0WmN+3Z+f/orBIoZnIVt2ZgXIwJMoaxTTEr7+xy2X9YOdTrsmwgvYDCMIg1Q9SvqBpQcl1HWu0Br+5X9MqilJ1KjaA1vFYQUsoo7u2RN1V00WU9ErwMx9ODncKxfU6kgjM9CBpq00TcIjeXnV8aCHxfs2gUMi7ezDpVZzBgkS9es5Y/vibUa+19w+YvNDRX1H4M+K35jGu5MuCnUfQJFQv4tEYxCDUPr9cU69Xy+rx517IqdvXsUNRzcE5nVZRTQMJbU8vU51GkkUK5m2IqtkCycLenMKMTwXzebm9/OTm53x//88f9AuudhsdsY5cjcsLtyA5EhDSIG4Ov3SpGhiGRaznFKeOtHcQrIE6T4lHIFpzbFJoEWT47GHOn2XTB2TBsZvWaerD5fgxF63UpdAhvzuxw5p4qOljsMJvcLFpocl0oRGzKLcgQrIstAW2InAAD525rQW7+w0uK+MCjKn521hFTufEd7XKN3vzYUVv6zoet9P4j4nTxXsn3UxtkYtaqt24rYJr2HkUeUHpkpayQWNp4c//kmLgOk393QMo7+FKc8qS62XQqJwolNsVid0QtO6khCcR98rg2NgxE4UixmGaP9wl+Lx/baUuPd5qntUXIWiMRlWFgeGZTCAqbOPlzFIGgQbRGkGt7dg66WA42cdqqtKkgcHd1upMqm0zELxsD+BqYW9Ji5fXSAwHlzKl9Akemv8eq/e3m72gzXypl1tPhXLVB3MgwrFQtcP62c5YPh+zzL/2wxU9Pq/WAdH9tf8yS19et8ehRxxlZ4H7GrNHTLl5XrsdjHqw1xqbk45W4nXMLfSTk7DxA/4olEb7hy4TfHlPLlM0FQYD4IbSYM3lZIGDqCWcJ9RioH4gqaBIEcYKL65msiihJ5zl5b0UF2e4hfU6KB5nFbmcN7uyVFYZvakF8FDbS2MV5XTesKMo9KRclUnStLuYhbWnavyK5cx0DAbWSZHOy7mxNKM4JX6Yl2HxmVZukf2TItVsO7ZaqM/KHb1t80qluCQCGBwly7LlwS0o3Wqa0aeF4xV+6adXVVbWtuBbiMmKh0mhYeNZ5U8kWAkJ0lAQAj5A7YoX4E3NZIWcUtLQCsSLIlywyVxCCzIYr+5Q9g5q7cKpQBeZ2NqtY4VCjlIdKXV5RtJYpeCjqUH/qoLTFz2Mu0/5SFE4irta8kD5sDwYdH40II5zaOVgQQ4OfHEaroIqoQeG4YbVS3Ncj5ETGwggaupP21NF/6cyBr0+6LEzEMldu3eXbgYgpwofCZnTABYlnuc4Np5htjerf60d3Sw2sFm+MwHi/e1YCfufrBg9xu71JcPr1cct3BWSSLNX1VnA17VqJAap1Fz6/d1AcMYQSDAQNoIYSEi1nxc+TZQTrRDQoA1iivECl4RC2PHfb4n9wRnMFexxZqnQkXKwWvl5DKUVU0eKnwF87lBTi1P0/xpzIZMsPtEZiBEURxHs57nvls0Zot2wdbs3Z2j7Hl2Odz/VFHcq3RXpnXaEwpOVem11QQXqvxn0O1pfPuF9gvcOAK5WYKevBl0vzYlmLpRZsuBtbRY8uJOokjwnJYsHldEX7DTKx8uACXotN5rQw+XIVbQQQfrNHe3UCZzGgLbJymLZawFae7BO6QaAqRYEUhVgFsFfMLut0FZxYFaI296FuKz5z1BLPXbUQ6j4ch3FvRxmHbH5tXQvjs1P/zgJLWatdvmH1xSXLDsWVjxibr1t0fF15cdk9rRZdpijxCmHyHditjMDYrXUrtUMQNZHQUbvRBup9fl2IT+vVqjhl+M7olMdCFItosST7MDfabOHFghWHnIEl7yd0Z0nFgqwBkgmhMPGb6+YJBCx0Y846vrAgWTR5i+IMoV5CRLA1a+E1Ie/XQPkmHycKOL9Da2gjDDYpPofPQyuGEZ7acPEd5KLNpa3wyG6dtDteW9G1pYihAMjtbdPpno1QYdiLq5KVy7eJDXFHp1qzjWRcbSPGs9O/4IokHNtfXdb7E5qYLwshSV1YE3kaZLD/UytCF/H2ociTgarfK2I2CIG4HUTerGdlcukJhy12Y1UbD6aKkKYRC+AjRAguzvlsehQH4Sszui8cEJZOxqy2f4cKRoJ8QS8pRuEh5YebVJwyd9U+Qy7f3encq4tV5VjK5ToeluxeEUutmDlWLNvvBBLMeSFD5LNFffbMzu9zOXe1uq2832X78dkpIuEssRmChMj+oVxRYQPfR5makaBV70yY59QBa7x1Sf5CcLD8t2elnvS2NElVqA3/0JAKy+zju1RUhhfyVu1usfknmkUcB0nUhohSDw8jGXuaU9amS+K4gcAsPNriIoQKZVECQC/U1i+ULHpj2XGTaN15PTKKRalH+6nQ6tNzCfIM0RqBSHgup0KMQxL8o+g0Adt+oZy8JlYSFEhw4kBgKSiYGxSpfCbCyHl/kVlXIFI4EQu0PdSqL1TkG8Y6o3gc0OYASsuCZUY5WFSE3EJPQKwC2lrub9a9ZW7rwJKMyuKAYhKEjLYzIdC0MeUKv8VlIvwd42AI++Fr8ypq+IwExjOPQXCxRUE13HKa2K+cWsZ4uWFSLCQ2GTcvKkfWybM3tAbxHMsnrr3VFk9E3LWEfB4LybBXYg5Q0ELFUmddvimHUDhT5fce1jWFf8rzhZGpWtHZkm3sDNjK0aQ7mOTy1vbcpP6bgi09gAEzrQmySWtujcDPrVJ1xROh7+tlHRihHDT8prYwVh7JSyvypZPRKOsPvLeLNxxPraXJvLQ+74/74y+M+wXWO4z8YFL5f8DntEYouHgQWbhAp1hQedA9XzynPj/5attFF7lf4WUXTsyC4U8XdbWLzT5b3x+3zLFeEZNR6MXHczb5TcPiFPS8lLP0zri1nl6zwiN7RHye/+iQInQqzSHJxMt7+tRSKo62WyXqsa7/3zkLP3tOhFeRXFdWhRA5lMIrawYWFSwKaL35Ma88ultSfUJdIdaD+sC3QO1G3trqyTaHfN0cV+YZr4Ube6XVFRKgMNgx4LlDYYNBYLHNrzw/CQBQP46Ni7SKcV/s0qKtHkpYeHJdijMiLti8Qckgnat4wl+pUJRRKMaEvC4iAkKrG/NL5sWLqb9dPB/Qk6Y3ZrXpoUiSemhtw5GHIXbjA/XWZSEwcqPG7oEQ7V19Vp+YsfrUrLs+tCfIb4yH3GdLxJWTSIsm3+7TfaQo4P1j0Inqisw6340pceLIOoQcy6L8zL4ftg88/OOuLcMGtZFW/hxxMFgCUPzhzA6RmmJEkSRPHFOR4elql7M9LuTFFq+VUz4V3XCPVJSM9N29rxQA4t40GvbU8X9pT+//uK29x1k7yOGbdgmZbOmMDggdv/iG+a7cEaqqAhaUD5Tx/A1x3LjmbKT+xU2hTxSbtPUoUBVCXiyLm7VdEPvfvKYirunqloVD55bZaUuTJd6eVvEK0lfb2LC2T9+yFfLrKHR8Pud1hYijXnNiAlpkuaLmmH9u3aEjB50B5ubff0gKTxHcO1ut+uB+tUS1aXtQHjac3H+1bNe/KyZjTXh8Xa+m9Yxikuv4XTw7EQvNph23DdQOfuOWOIKBrxbXOfFbr0t5i52ASPhyKm+o8OCeRiczjtQ/OaeWEygTQ4rE9ZJamKwNekZScfGS8GHy3prS72UD91y8JXd6fY/fITB4czFXQcGwVdnc3+RUcPW6uH8cfmjbhiZXtU5Q8Cl6aiMnEUQ94Ypc1gtQSD2v05tW3j/geIeQ4gMBi715Rz5YavuP9qvYUyblnUm5oStl4Oq0/l2KZEQ6cOBAlfDrg8geDTgLia5mqSRR8sGJIxKHolLWCohiSBUgklA8wnUp+Da/5ogz69wa0AXqxCeGPLb4eNtW3mnRKTRBvmYXHRoK92x5XX9Gxtd1jRCPiCfFYalcdZY3XnMHRBBDbF5AqWjhJeLW+3sT1vHpcdmZqGCGf7VnUIWevm+rVSluFlzZIHSIqA4t6yfarX54l5AqijqeCYxa/W9dd3N7895F5dTtXWYRqsq8P+7FuF9gvcOgpZAeiWqBhcguCS95ZLiql53rOqq37Dc8qNZg7dgu8RXw4mHRgbi+uTth5SQnXUcObfvMbfNWzRF0o5yKnUS9+WZNv4+5H1liEQvKgwoPrdTtil4PU09O0yIkZ7IWOnPbmm7mpKyBd0NGm8JUeQjTGfN0d6oowGjU09Vh6Q8fskaxJK4BLYTwrWWRufkspc6YFtpCf8pSF9es9cUpW33voNUrVWX0rZ1st2rKcXnw8mKDonWlaA6v155u+0fmgQZBFlksbKWumBW+8oTFvnhTfCwQofa31iUlh9Be6ozIDRsfK9qhnjvTrj0IQrV3p2sjhkMiZisfrafT6jPzIgVLzj6xbKUdHU5ajQIoUzDv6LCKC4oYZOj4aYFoiKxL24soHJDH4X4zYkhiYW28kP0hQXNdKfawDuC0jJkqrR8VaLQdeV/tzWr3epJJnYLTRzrlJVWlrdmVuBtArfaJ1yuyf+M9h4R4pY92uZba5LzMPOHdEAINh4nYDVqMbIzJybLQUlkY/P/Zew8oS7OrPHvfnCvnXNU5h+nuyUGTRxGQwCSL3whskg1IApNsTBQiCAwYsMGYJEAIWUKBmdFopMl5pnOurpxz1c35X897brUGEIMCHoFWn7VqdXd1hXu/73zn7PPuN6yuO2UWYoB921VgQrTn/xSXQyHn91p4zUW2KCYmEbH5O9otc2RACtHKbQd1/1F0isi+5Ij/GGqinGSDSG9r0OvgXkEQj4wjgABhSavwzLXHFG2SHqrXRkOxSJxKvr9ZhfrM7Q01B8SKLBBoKfoG+/U5xSihlsQvqCUurh8xUbTCRBjOZCVoKAw0W+TEuGsVZgrWeC5pdt0uR04/PyxOkdIQljKOC1auuPakz2s7fjsnFCt7z34daOBNkbfI4N6qSPd7pVCVkzxTdWZJbeP259adrxmb+PUUbHnzDPU60cSWdrUV4Y7JRBNCPabAOwdFyEaoAUKp5wCkDKQShEY+URHL7O+RUi9703bdL1S+FLi8V1rcGHXK/gMEkDY4hZfHY+GlkrMpaa1T8HDlpv320PofKiSedWFTrQcinN/W7lpcmYIlzq8IkQKl5llmLlbOXZYvmxAb0JqxObW35o/G1foiBLlw234rXbisa1ka6lIkjYpf2qXZopAzkCDZlyRzNbNkfif/zkrsI1UkZrW4sFPolauO9tAeE1IFhaLuCoWeExrAReN+9j68am0vblj7M6vOG69Q0lz24G2HxUpjQq8JYrp4gETW1MWdF18ypfUBlV94Yt26PzohOxQNOJqbrpqVskO4vV6R2OG/Sh1Kqy/KgTnglIcYGQ+1qTXY+diK+fJVfU09h4nnTzt+YytWJX6XI3r9Lpn3Yjz6eo0qBdZX8MH3Xxuvz7hWYL3GoJXU9MSkZVuC2qQ54RSObbeNoYil+iMyuGMhJwZHp8eiMz8EUodLBfcEh+i6y0mRlVFFZQ73W+PJFbVuYo9fVGC059K4IHLaFriqb7YyMClEsQfSwc+mKAE90OdAQ/BzGpnRyR1iMkRstXOu36PXD39KiyTozZUxbdrVUlEFRuHQkOPIICFfS1v4xJht3LpFvzO5q1GFCzmIFAK4hEfniuJ60BKBtyJrhNllec6woFO01b8yr2sEIgH3Bc8sTskgWNFLSzr1s1BD6iXuRhsw3T2QLDPZMdB6kfP1c2dt4ZY2yaX5eRBccXJeu63fjGigSNjlr60nRdqWc/r4lNAXik8CriG/w+8KX1nSKRp5OUgBcnGFEg+PW3ByxUotMfGB5Bg+viiUknYZVhUMnd7JOMQ6gFiQZhRYtNCKck3XprNnmyvEMVo9fdmhJJGw3XnPL7kYmmdOWPxvT1ppS7fz1wENyJas4bERKfDInFM8x+iE+zmrOW1ki2/aInRJmYRSkJVFtAfVIbZF3mo1LzCKlcmv61Tx23w6bdFXJpxze6HskC/MEeGbNCZUWIrP192lDT3+7IiKVk7neCzxdRT13OfAclpoCK0/ccyeP61iK/mWg2oVgdQ2DtcMRusTmkMLt7SqqIIbtRnDwnXl2pHnF728ZP5Rx7vT9Tg34owd/X7N83JDxLWqhqdt4bq4rX3bMc1z5d4dP6foJplwLiYtuOzaOxQ0BJgH15whKEPt2lhIggdQP1pa2sAJKaZwCbnCT+246UUV8GQ+llGi6ZBVEvEddIuim4NDvhEbBK9TlrW3WmhsxfH8kjwfPlvf3+RyAc9PWXg+bYGxRb1eWvpYW1DwYNAqS4T1nLMR4VBAbE1TwoqDbWrxEvdCgaL7tJqR6IDwZ3iIHBZYO1gXVEQHvXpWCKn2nR9TfirXgXnOQUdu/tt6nDK2u1W8wbpJ5/qPYljxTTjvR32yL6kOdNV4dI48jpUKKDFIN0hS5OyMs+SIowY0ZxwLMlnzTWNOUrSCyjEfFPgc8uj65nsaxBMFSVKIOfOXNY/2PtxGinfWJvlPOb4Tz502rFzpaqYmSC6+bKCpojTQem2s08FS15Q2ZCbj0GLahPw87vOGe7aEBOddLifoOPYZylRMFazaQDZpwGKTaffaLo0qCxJByMKNjeZdT6utr+vOs1zzkXs9xlfm4u4+ro3XZ1wrsF5jrF7XKmQKawAWsnJ3szgsjSdWLJCqWHgZ009MCB08jBEhZpxskOFF5z0DN4tCBGTElynLlRlH9Zbnlyx1504rJHxWuH6HxefIXIuo8BCngsw6NiAy5lIlRWToe/dtUyiswpv39kuS729vE1eKYgjo2vfyRfk3QYrFFwjjRgoAPG3kPL6yZqELs+Jk2fFzzjyvrcn82Yr5nzurhfDBkV+1urPL4nYd+a4P2MquoOwlaLNAZkcKnryh/2rQLqo7LYrA7HBWrkw4Ptr0sgj4oEWgSqBkUkytpC3f26jWE8pLcYRWklY5N2zl2TnztTZb46WsigGQFQpIuGtwmPC3YaFHqUmrUj5HWFhsG3CLYWerVGWgeLSUOPU//PLPmOepE1JOoojjNFu4ZY8Wc9RYtJRKjTEt0OK1+DxqvwUx6swUZOTpe+Gc2i+RGSf5Bz2AcC/e02rSFVKQ43cMalMQuvPCJXFtdC8GehVcLTuAnjbH+2ist/BSXoUl6k5MKzVn1pIqdkLrVWX6UdCkBmLKhPO2tVj1xbPutQ/PiuvCZkoOX/djOOOb7Bx00o4E5fkD8ibbiGLJkZXldp8XSTu/o8PJ5tmUKHZo3bRgErqhTYQCGSI+mxSBxcwzRuJvT9nK9W3aJGk1ynR0cUUE/wailMamHQ/w0riKO9BLIp5w+9/0JkPpuH643Yo37XEtqvZGOX/7L01JpECbqeNzS9Z4NimhAW3Nym2HtBGG5tPivsnWpBasjdSf10PRxT2Q5UetncZ7pVUvrs/0rENIaqNMJihF9Ny8ig0R26fn5AROgam2s0yCKxZIFZ21QyToLBva68VNAhnj9yT++kX9iaJPFgn1MbXiq0vLrhWPq31dRMWbiNx4b0XCrkBAtVlzwpepKJ53Ib8QPMwxK52tshxhjYBvCVpGcQ46BOJbPbpXKQmZgYTzl/P7LDZSE9k0BC1546Da4fCiYo9dkGKP1hqiG3n0pUoSPDhvqbLm7saeJh1suG74nuX39rrUgFoGpiwj6sPilUpEc25Epq9C+HudbQl2EQ0vzuk1ovAVhSGbk8hHaQ61DEY9vxQRzXUunzIc1DWiMM70xPRM+La6lAj5wiVCzueNInR2pcaPAkaGFvB588/0sUFnMNrWoOss+45M0c3LgFfrs4QKNYrBJoeOuSpe2B171doGLW/70DmzdFrqwdCTZ8VjhI95bVwbf39cK7D+iUGeIKc6JPS0X1DCJHc0akOjfcciku+uc9lyGwXL9iR0qmGxyvbXu40unxdPi5gTFCqpNx6wXG+9zPJQ5MHlgkfCwsDmxImPBZkT1cxdjkcCt0AcsLkVe+yh/yxSs9ok1+2x8vKqWgLiq7AIsAH2d9k9N/+8Vc5f0ftg8cOzyjfYJzVOaXpGhQ/+TixOqwebFX5LSG79xaTde8PPChUJz2at6Y9fsK5PL1iuPWyV0UnlLfKaEifnpXAio4xiAwJ0ZZzFM60wZRA5FlTPvm1CoWhPkv0lWwS/39a3hK7GhyimBb+uHYPm6++19ZsHVLDISmAlY55EXHw3oXn9PWbkBXbWCWkAyaIwwz+I94nHkrU0KidQm3lt4MGDP1huR4c2FopmfifttuiFBYdAgBigHON3XRyz0syc2fKqss8Ult3S4AKWM1lbHwoKScJcsrCtw0Wg4LtFsY1S9HMvi7gPskaYbeXyqDYOyN3a5OOhGnncFa8gfDe/41eFYFaaErZy54DVPTdhxXuP6PvjH31RG2nyUJf5tw+pLYnTOK0/BT23NqvApoBFZOHZcOHPzCPv7KKFLkwrYWAzJ2/sG901CI268F7aMIgnQNLkpD7ULLXofUd/RjYctGiW94akotTi0dkuFSBKMQj3uXbHcaPI4dnI37jTKrtc0Qu5eeKBOvELJdpIZ5QTCfqTGN5wEUA1tGT+67fr/nkXVlT4I7LgeSoc2Sp0VqRyom5w6aeIyhWk2pMdQ5ubE6BZhCPTDpW4gQDlE1ecWCLsU7FL4S67BT6wRent0vPgefGcBZ4759zO4S/yPNXQD2wM9Pd92yVOoQhEDSzH+pEp9173bBc6QttcKOv0gmwqGKBlyvkDvVrOqJDj9egZxT9rdl0ICsUm9iX4TfH1vqZGZ2WRL1pkHsuYvOwohHZ3timGS1y9gFdFodaMpjqbvbNVSKv4Y2NrFn/0nLy6KC6r+NMtJCWu0YGhNSwEl0KMYjp7aEDXvv6lWQsmuUZliSzwFZOIhddMgbSWFieKa4VNgmf7gHl3bdXBhWc9uJC6aiZb7MYBv8UZy3Y36wCLrQSxSRQyClROZsw7MS+krIDYA+7a5VkLbpREL0hva1L7W0HYiBYGunUtWH80CMduwo4kIfNSjHIjky4KR3MG/l0kYLnOmLwEae8iwgiOL7uDGu9N4oiKngfmGIUnbV0KRk97izNGRoDSUK/10Hf8kr1e45qK8J9vEFj9pXw0Nzfb+Pj4F/3zrzm5v4Zj7h3HftJCea9lu+LKCkT+S+sh1RMSwVcu7TMrLrl9LikOzuqOsLU+B9m6ZNP3tFjDaNkqPhPfqvFvL6g1h+0BC9f6UMj8ObPGF+bV2qJtw6kWF2sGqFHXp2a00GLVwKlbHIrBPknyUY89sP0/i4cgzhW8BgjmzfXiY1FAyRRzZdVyd+6z6EtjVppfMP/WIYcgAJtDBIV7IC8XjwqayvKKTX//Iev6jRcV6QPSgtkkpE6d7nJFS+1ptfjJOZ2QF+7osLZHJi23rV0mlpmdrRaaz5rn7LDj6zx/2nz7dlj59EX9uXyowRr++Fm1JEDo8u1RCz1+xjxb+1Rw0SbDnDDbE3cu64WC+CnwRkRKZlOJRpVqD/mZhVa/e3hRhFViK1j0OQFDhAV1Y7HE+wcj0+TOJotfXlVbcHlvxNqfXlUBSEGBqjC9xS3UmMGWp2bkYE6LyTe/JiSJIo0Cgo1e4cMXl3SKltKQEGHancfHrbSlSygkBc6moq1y/Lz+n3aDjFrbYuKSYJDJhgbSAL8lPLmmwteFzCbdBg0n/8K44oYgT6vNgqqJ6J1QUG01mXVS/MDZwnMonbXCvn45cYfWSlc9ztT6grydLriNCtQR9+xIQCkAt7/xly16blbFqDhGtSxAJRksrgqdwRJAHKIrc+KGUWzM3d5g7c8knXUFDvhTM+bxB8z2btVrTh3uNn/GPUuo9CDAN1zKqv3GPZAz+vyqiz6Ss31K6k14TBQDpYvDan9RQAXWc0I2QDbLsYD+pFiRIz3IUDSg2BpEIbSHJYDIFXRdia6Cw1gZ6lHLkEBjrCxyN+2Udx1tJ3h75QvDUjQKjV7esMJAqw5BbNRYomBqylxj84a7o2ubLcmrik2c+KqHcx+U+AELCA5koKmaQwksLFKyBiAOSPMbpGxsUpEydcPOL6t85pKtvfN6a3lsytL7Oy06SnEZViuP+09RCWcIxRtrErxQInNUsPKs1IoZGZS+dNqhiqC4zL9tHbo22JSIy7SRlrGtYbcCqid1Xs7ZWNBmR8FI8UE8DOg1eZKoauuiOvRoDUHZW6797lpEVm6AaCKvDqYUXeLZQXPYLKz5mRigImhoqlO6AAUNKJ68tCDXMzd4LZWqrR1utcRYRiH1IO1az0CtCkVnEgoiRmGt9nnlatwNIc8UtYR/F3f2OiR0nPxYl/hARFJ0KqNClfgqbGmcCS7ZsHQIaBmX5ImFfQmGw1AwWB8+M/Kbr4uT+9s+/Z0WiH35Tu7FdMH+5t7/t67z/1qG1+u13/iN39B1/adGtVq17/u+77MzZ87Y0NDQF/XzrxVYrzGRb7r7Z8wbiwi1om3AaSp8aV6nGNo4WtQxHWSRYBHpiMnvafVwi8icqd6o1T8zLtSDB3t9e9wazieFiqFmQtFVaHOhpJvhswq0feQVtYrIPuSkqjT3jlbZPeg1nJsRTwOPG1pJLIj4RZXGxl3xhKKFrLSJafOwiF6/w4IvDSvDb/1IpyWubAh18j192jz7t7uFsIILd83ZedkZUpZPnTe78YCt7opaIF21hifHLXWkV35aID/E/+C1lDrQKYsGTt60FipwnFqbrUIcBif6/p6rizL8E1okIvNyCmx1eXVSe83MmWdLn7PBWFxxkRVuZtvKkVZrOLduua64lJhNz827DTwelicR6E15YtoFREfCQoog52KKyUkdKX/ihUm1RhTsGgqqAGPxxa9rkxCLkk8FQ0+LQzaKZW2UcD/Ee8OwkaIGFKYh7hA4JOFsCjirU+g0x4UoUASXO5ucceypi1a8dZ+FLs9bNZVWFI3dcMChBcjyx9bkai6n67q4Td/XqgKu7YV0DVlzRSOkZ3F32FxpJUUjlhmgjV2SGSskYxknLizJt4tEAeUMcn3bGlSQQPjGuR7Crnf7FiFspaM7HYF8LimzTxSlMiMdnZbtQK4lKMIvnBpCrx/97I8rSolQcHyWqkd2OzsKIohobWNMurhs6du2K6MPzprCqyG614XkTg76Fn7sjFAAmbjivXVkpzhjmIhiyQCniwEqB9k8dGXJKguLzqfrzkN6vbRownNZ8y04lRkIX/zKuorXgGKX0vI9EkeoFt3jefqU5d90xGIvT+i1wlekjfuG+96vQlYEagqIpXUJJWgZyeF9KSWivgpVWkqEsYf85p9cEhKKipH3QOuWFq2QVaYwB5XVjFrU9/j+ja69xo0HhFaLOySVaV4HKsQ1ZPRhLOppcZwuOZTTzqyhMIp7oXjqapYYARI7hSgeWpFnLymWpvDAUa1JeHbRQmWQ/4d306cLf+7Czw9sczw/8aAqVmL+1ooqUHgVuYSPy+TXp0IKF34OjKXd/bWECVfIyHaFuBmKnWJRz5GKolqBA6oqYnpHsyOKryWF1orTyf3ZLOQW1mzxvn5r+dgFS9+63XH/ikWb+aZt1vXgrFqXHCi0duhn13hFmKfisVULpVZRlC9arrtOvLGrxHaQcOKL2hMOIV9N2vqNfZbs8Vrb8ZzWdf6f8HJ5zB3c6Vr6Na4XKGF+a5vmYzUetvLSkn1m9n9eK7D+FRZYc3Nz1tb2xYkUEomEnTx58osusK61CF9jRObSOm0X5ULu12k1u6NdRYhg4+W0i/DIFmVHQK7f2sFmIVIbW2KC1otbOvQwLx1KWP3ltLgisXMoEUvKoKPnT2QHiwSnUJLlC288ovZe6NKscungUNnSqnyAwhfntJlT4MGzACFAXcQGQuhwZWJKfCVaeQ+n/ljBywTawvFhcUh84oQ7FYe85uvvEVrhGZl2BG68vC6MOjf1dM68h/coj63lxVVreH7WCts6Ldfks/y2Dp0mQ/hJFUsW+dvjuhbJobhLru/qEOKEmSlcMFQ7m8HBvoE+F1sCaoZLO3+nUCiV3Pf54KB4xHmqsPE3RnWCbHp6RoVf9MKiNXz4uGTfIgafHbb8DTtcFmR/j06UFBm0bEEfFPmxrdcSx+fcQjw5o+Jw6bZOxxtZXBURFr4UrU2CcTdu6FMRrMITUuz8mnmW113hkiA2JWJlFE2hgOW2tIjfhE8P5F4I9/gJ0SpFEYlVBLElxOmAXGb2dakYxmTVv7AuWT1CCcwzQWb4WTj7dz26bF0fm9DGSQxJcijhiqealxfctLWbelRcoGrNNQcdbyUUsEJbXMU0rx1zVwpBeDVqjaDOpB30wCHXUl7bsPJ1O1R05Rv9uk98XkR32m8N9eLnwENUNuLyquYJ87Owu0/FoW/rgAoQ+GpYLWwWRRiWYtap+8shAHQ06Lfg7IZsNIiXWn3HQRWUxPnYriFxdRAi2J4tVpqd0wap4uDl8yrmQUqqe7dY8d7rFEEEPwwxBcgyWZbwmRLHZ7Rh086rnDyn7/FNLEicoesM6R9LknML4uhxPxmIRBiY6q7tq5cgg8MD7Uvxgwh5Zz4Gfe4D4Uvt8zyTzOXKwe16D2zubOw43ldu3Kv5iVDhgcF3m7+lScWc7ChoU+cLVjl1QSgN84nfx2ECIjbtL/6/0N0gzhe/a31L2BmmUtRkc0LgOJzJu4mYoc+cUMxO4f6jshsBWYf7hrkodAcKxvLN++y+8LfJrFUFEve91vbznbni/M3a4xZ56ryLI+JQAhcdhKpQVGu42tvpENDLE3rOQOaUQ8lhhQKSSKu+qK0ecv5sIF7EB21G0IgonsoIsYSriVqVAlSoWDxqrQ+Pu5SB4VW1lPl57c8lhZ7TDoanB5dL9y+X1zUoD3Q449bGhFBhWYzkSxa5uKADBjmUm8ibOhD5shBrrHBSPV6LLlXFa1OqQJj4KJ/Z4Z3mOX1ZysNSQ9TZZ6QyrrVOZunlL75l9M8xvhIF4ebHteFGpVL5oosrRjKZ/KKLK8a1Aus1xtqeevFxfNmK4zgE/fL8Gf36ep1uC+3wggJSq4BGwU3AuJOTJvA+BplyVjezxgs5m70xLl8tSLGcJsmgg3PhvTghiXR5fkExExQR88diNvMNA5ZtwbAzatWaGpC2IOHTtGAIRfVGozqF8jOEcnR1SgG0+M7DDmEgOyvkSKiZbS32cPZPdWorR3xatB/Y9qNWSWdcxh4xJnBOImHxUzhRglBAXM0PtmhxbvnMhHgl3ok5yw422sw7BkVIZxGrf3rc2j89LfSERY4TZ+7GHSpCOelOPuBk+SxskHUxJ8z31FtqR5OzU5ialZpHXjeTG+JQwd1Ru4+Ntc6FIwvZgGfR6JLuaevJRgHp9QH4Xnn5ZJXvOCSbCoJ0OeHSyqVYhVfR8tlJxwVrbhRPg4zAlXu36efXPTXiJPgQf3kftB+EHIQt21OnyBb4NiJQc3jeVDjNLKnlyaAFo7iSrZBrPVI2FtviztWcqJbxZZHvA587qY0aBd/Dx39GYdT1z044M1IWAE7H9VHLNXrVKmOjUrxHMqO5hus5IzaetOSWhOMCLqZt8bYOoZKlsJun3lfOy1sJ3ya4a3id5d5yRG0lBXgPNVl0JqcMTH9To9RqSiyQ4MJv/sdOaA6V92+VFQXhyrxu/KMo6MRLw6E8m5WjuNqTzY1qrRAVY3he+V27SeHUR7bKJLXp+IojKC9sCF3DboMQY+45BSftMfIOCYkmNFjEa59XvCHPEVckiMReqcpmg3tBkUVxpA10j7OUoO3NoUKB69gLkFvX1yTEeHNQ9ECc9+Yreg5p6alAPHfFxeLgMk7xVyN3i8PHXPZ7nB8cPm7PnnSke8je6az5k0XLdNR4P00J1wIrlsRr0+tbXHOBy7cckCIU5bKMNil2QFerVYkQ4AcJfUuErOGPnpXzOs8Xhym4RIhgaJuqeDy8U8as0TMz5n3sFb1eEedLqJIz8kQLvHLZZWguLttDJ35WBS7FDUheeS+2L1VnEoyQYmZNhT8tdX6HrA7qI3pW4dvZYI+Us3D3tKNQYLGOcEhdLFhwoyxbCeYILVxQJA6XoG+VvnY9t6XeFt0b0iX0/cm0RAmgixRAoHjQAbi/0UdOKxczfdMW8eMefvGnVWChahQ6ReE5MSv6gzzXaHki5MEqp401yAkCZNpMXFKuYo0XCwCXlhjPKXeSOak2t262Q8homRIaDZe0zLqCuTE2Mdv6ncDndRrXVIT/esa1Aus1RnwyJ8UQfBWu1MremPmKVRv68KpOe8TnFBsilr6uTzJkEdMTQaEwRHqw6LOhEVviX8tZ96MrOuWKkPzkccsc6pd9wkOrf2DVWw7aynccs+DTZ+W63P1XI9b+QsrisxV78NL7rbKtz4rXbbe7b/sFCz9x1hlzsnHsGDBvR5tCinEhFx/rlbPW/ti8FIZyx56Z0+8kCodBdmL8qWHxG1ikce3ObG2whTs6zXpRlAUdryURUfsnOLqgHEDxOrI5hSuDMIFodD62KmSLDRUeDnlinu4OZ3ja0mCR01PO68jjsd5PLYtXoUIwmRHSk2sMuBBiDlUyJvTY2r5Gq45Mugna1+04GOspkZdlNojibaBbfCzeO0ojED61wwi3HRu3wMkRZ4w51O+Kxlze2QBEnYJRGwDhrbT/UDFtpKzpqSnX5uP1rm/IdFNqKjyrpmes+vJZqwQ9VtjdK4SI9mDg0y8JaZOR6cKSrjfxPbQkkdJzAuZrK3uGHJEavk61KgQNFIg2DYgQbWcyHNN72pybNy3aDiToBRW6jXhJscHkCrZwc4vLS5xfl6cUKCNtI7L4KMZAk1qeW9bm/MyH3uNO2ru2WsNLCypwfE+fkhoTE1HvyctXswrZbJgbWCuotct1waPqlbOWv+ewrhvIgcwmT12wz336P1vk5IRtDIYtOLlqpYtXpDDj96YP9wh5lJ8bvlGgDDVri5UD9TpoYPsAX4eiUN5I1BTNCWcQSds14DP/UL+7jn6PIosgjCuPE4k9dhg7t2hjVWRRpixzYH4/PCS+hrgh4msqR3bLakLcNPZM/J3wacIs+JVz5u/r0f2wkUlFxIRPTejrcNdHQQaRn2Kq2BJTHBGtQa6bbEdoKU6vOWT3tkM1i4ZJl4PH+WgK5WRSRYha1B2ttnSQlmBVKlRELhTqWDbwzMEBkw/UworzOitU9FxlOsOW6Qyaf8dWyzcFZMHBwYURncxY4JmzamEqJ7AuIVTNbjro7C64xtgJXBjVIcPT1a5Cyd/XLeSuFA8o8ssP6kw7FDI+RSRIKMaxPK+ZomudbiRdwgGcJg5w8J0oinBsp41GaHJ7QiplxfLMZDUHsKsIzCeFmGfefFhzS3YpDQiDnCu+0gR8PrUWxXVrjMoGRGpRitlQwMbfe0jGrxTCDFBBcdEo+jjkUHCD+i+uONEJxWUqLcELRRM2MKWWhN4n7U5iqVinW84U5ArP13mHp9TylTKUuRIJqzCjcOZA6vF6lFXK2iRRDYeLa+OrPp588kn79m//drvxxhttenpan/vTP/1Te+qpp76knwM3bWRk5Ct+PdcKrNcYkFC1CPq9alXVjRWsHPSoYIJ/BewuD57a4sgmyYMqJGj7gPx0OOVje1DEKRri7abJ4Z7tgt9pB4IiEYTa+onLlr5/vyt8sCWYWrLYdF4bEb8P+J/f421pslxXzCpk6bWEbfX6jquu5JgG+pvhN/hq3kwVnZhBPPC7OfKuD9hdd/yie4PJlK3c2qtomshMxlr/6rQQGCT6hPZyyodQX8UQ8Ob9ek3EkxQTfkt2+bVZik8z4uTgeH35T15RsakNenVDHCHyxigoGfj/cAqHjErBAB9MhRyciL5ufU39pZSQKJFT4THVRYReEVOi4gcXcIi0ibjzuClVbOP2rfI2wtIAbycpfUBRarLvB6d+U4RbkXHzRRWt5fEpx5/zeGQIqeiYgz1W7WyxB+d+Rxsun8vu69EGzDUI4H6Pe3Op5LyRcMVHHTk9a76dW+WwTzsRw0daYQpzDga0aIMEyBk8V7TmF5dVKAjJIAapUNaGFPrkSyo0U1vrdT0VJUOrlNzK+WVtatGFsryEQBUJCS8POv8e+XPRXh1s1Pc9eM7dZ7hpG7tquYW4f7e1qm0EkqFFoFxVWHj1zEWpR72RiC29aZuudW5Pj4oGihNl8FHwJXOWfvsxEbfLS8vW/MKyEE/m22cf+TGRvDl8qPWD7xGByyALoIxmQg9dqDk2DF5t2KBeIKNwvjBuxbWe5AKI1yh4tbl2tyqLLzPU4MxpYxHnYbWyZitv263gakUJEfIbwkDJI+UpbTLuC+1B+D2gECg2MQSOXVq17NuOCol45OmfEnrDc41AwfmLlVXwBBdci0rIUdoVeKgxKdj8uKTzTGdLarXa+RFH7q5l8rmL7BU/kKKTz7V9ZlJzY+7OFsVnURDS0kr3RWVXIvPX64cscGFScwN0LrResrrLKaE7anlnslLqBT/1gkOGDu+wfJ/j44GSFxtQ4OEhVXb+Z/GIECnas2uHWtQ2Bu3DXDU8sqxnlHlPwbqpmtRBBKVsWy07kPfTUOfMf0F1sLPgwNTYYAU82TAa9ft1oLv9Tb+swk5IEKrGRhIxfEJ3E2cWXVoDVg3E6GD0ny6Im0kHgOcFVWqmO2r+KzM6jHnH5xw1I23KPIycm5UFhvhexHJhS4FQgN/n9eo5Lh7c8vn1vN21riHTs7ZJ5FO7P4gSwudcMgSvaeltO/VaaONzeCkcGNLBEsWhr7FevEFvfUJJF566uFUwP32dxjUE6wuPj3zkI3bfffdZJBKx48ePWz7vEgMg8//iL9b2vC9yQGj/5xjXCqzXGMHVvM1fH7OFQ2QJ+izX7LfwSll/4gXFyZe4GwofNlWKCwxIOfGBtsioETVKtiKyrcjbx89ZeXhMBVt1x4Ay2ThdrQ/6LL+/3xV0LFIsxiurFpheFYkdZCG4kHQtqHLFQivuhM9ofGVJ1g3pw72Ka5EthJRlAW0AS//uqDYFWlqN591GUdzVa5Vs1pJ9Hlk6TL+hTgoq4HHvhXFtnpn+hKV3NFvyrp16LxQ82sROzVrrnx135pG0ESCtJqJS1vC7801BtV+uBqNGIuaLx13Y7+q6Q0mmZ618+oLjrq1nhOLQgiD/jwVSpFsI5F5UYSkVpwoa7mh2CBYLDWgNRFnaZY9ecA7zPBx1cUe0ZYMdmzLbPmj3xd7pfLcKJaEBOJbDHSJXDzk3yBSoJOacKmRYdC9Nicya6g44WT0h2/CHdm9RwSduCsRdSPtD/eKs6LTM7192iFp575CKHT5PSxBXfKkiL4+5zQ81ILVxrfWkh7JQscRz49okRabfu0URKyCeKFoRFKzuRMXmteClWSFcIfhPGaeUw+Bx40CbbAJ0/QN+e/bP36N5xtx7cPq3nHiiLmHFG3epjX1/43c5TtbJy5Z88wELrVes1N9moRcvq32Z2k5rpax5zam9/sSCy7Q7utdsdsER5xNhFYxTd8Wt7rHLEmKs3dqvQiS1pa6mdvTLIoONEpk7bVvI4KW4z7XA9gw4i4w9Wx3fMBSwuo+6Npe4PhTu+Yr4ZnDrIIbT5k2M5YQQiQjuMQlT0l2umJHCd2u7eIK5Nly8vVbet8ViZxfE04t87Hn5IN2//6eE8GHg6Ts3Zt6VlAQJrjjE3835ckkggcO/TEADuu/8XlA2ngFSAmRsSpuNogGuEKjO9m6XS4idAq7q2Zy1/c8Xr9pH0EZNnHfZnrRVIadnjgzq/kY/d84CT511PCmKUgYtNtptdxzW80nBjr+d1qOaMk/eViPzmkscsrBPSd292+rPrQu1AlmjhQ7K7WtosHuP/axDgtJ5WSUoRga+E4jVJqoJ8kuLDyf9paRQK+41bUcVpdAZShVlFuqQFApqnvM8b8bkLN7aXgti92o+yQSYn02BtLAmbhUcPQlTMDjtbJXFC4eo7geXLDGadveBAjuZknUDhscoqIUmSZCSE/ouP7h8XuIVInhoO3quuHuiVujsiosOWlq2hTd0qZ1cfyXnsgcJbicJ4PgVHWLgjVos5tY21JMHd2iNEtL3Oo1rBdYXHj//8z9vv/d7v2e///u/bwGESLVx88032yuvuDXk9R7XCqzXGIW6oLWczlvXExuW6glacL0sI9CGJ8YEe1duPSAEix5+bleXVbf26v+kbHvpnGtJsb68OKyNgJOtZ/8OKVKAyr3LSbXo2Dh7/viipTuCUuiBkEm6vHNQiwDtvwd6/pO8qtgQqusb5jl+wYXJHh+3B8+/T6+X10E75KGN/+Mc0FHXrWxY+99ctmpbs5WvjMtziQKHE2bq/n1WN1YxG52ynoeWLfz8ZS3OLIoLx2JWDngs2eO36HTW8ULCIVvbXa/NTWgTJPN8UfyI8tnLcsLGyBT+EqgWhcumlYCnp1PEcNpPD3R+v/m7OsR5IUZIJ0q8jJZSynrDtA+ZvVoQ8wvOUwp3aRzaZxdrNg0Rtc/gcFQo7jaSaiuC3qmgCQbE8yof26XN0dvT5YqjgHOmZ8jYEfQok7f4mQW3ecaiQg4eGPhhqySTIqq3vLymDVEoAByoyXkVfCBUGIaKeC50oyL0BS4OSAEInlAlCu76iD0w9F4XXQIZ/dBOR/SHHH15yhZubhXCOP+fbnCoY0uDebMFW7293xaOJvR3UCScwde2hazjry/p9a7eMWCprQ2yDxCJ5JwjKNc96SwIbnvbr+i9o45Dcu7b0i+LAKnj+lprlg1lBSrL5LSv22Iffs6in3hF6EP2xu1WXV23xPOuZcYGqk1eqs9Z8ZRW37zrap4h96Tz6ZwVd/eJeN/w1MTnC8aGiHyp/OfGVVz7zo8L0QTxBEHldVOcqzAG7QqFVGyTQaj3Ntgj3lwx7lAKMv30rKAyjPn1XNF+sudOyxaj7tKGUDE2T5zxec35Op/MMiluCZWGcO7v79WcAFmkPY2lSvHAkGV3tJknGtVGDYepcuGK7hXFDxYrtJaQ/AfmncUCVhN6LXGMMYOuIEFVR/usVpRBgJeCtr/LSjfudiHeHa0WPTUt6wFc4GmH8XUUXdHLzkSTwgOTYQpAyPwkF6DmlNM+71GcLVPBBR1BSCXu7KhgWxslPqgsLguhjA+vK+MTgjfIC55VHLaqg11SnIIycU8oaOANUjAJsaIIrOUH2u4hRzCfJSuwIEUzRauufzgkVFpmuXAq8wXL7ulyByDua7lsrZ90xRg+WHCm4OBtkt8ZWJsgFFq7pU/KSlAuKBjwJ5WPyIEEYQwFItc87AyPZTmSrHmVZbNOXIAqFaVqQ51I7uvXdSjbU4UWnLVyxVLbGi135wFrfX7Zcrfv1bXBpJn1jFYtqFt4ZEkcRK49g5xR7D4oEvXcXxtf1XHx4kW77bbb/sHncQRYW3P+hF/soM34z2Fhca3Aeo0RXspaxeexhWN1FshUhRjkm4NW7m4VtIxfEcUJp0f4KBA5V+4clG3A+tsPC6IOQqTG3wXJ8517RZBmg4ZMSmYchRmu77iyx6fytnhPn8izyze1K34BJVVkbFWnsky7x/zYH+wctI1vOGyl8UlLHx1wERpD75W3UPiFYZHbywtLVk6l5O6NbBnpvd7TMxfUcmRg8Nh4fFmGipDUeZ06bZtZ5x+ftbrHh5URxsmcvD420aZHhtUe0QJGyOrSmk6/qIY4+Xl7OqWgK01MuTiSmXltxHCcVET191puP+ozrxZ7AnPVurgyoXYgi26uJaRYFEi/WDxA2i71tcmhHQK/3LOb4y5GY3xWnAr/lgHzdnfKoFJSc7y0Zhd1evddmdGpW0XBucuOwA3hempJBPnS8Kjem7hMDTGLXlkTLwT/IgxOr3xzozZBWlkin4OaIT2n6KLIO3VBRpuS7aNSwr6htVliAXLtpIJTaHPMZSZOzDu/owb8frIy52x9cVV/735oQQgOxYhrBxat88EZd9qfXRQi2f7wpK3fsVVmoo1PTQqB8b94wUKvXJEqTG0cWlLjs9p4sBWAiA3fjOIP6X9mV4esBUComOO+U8PyVHvw4i/p/sMNo4UVfPhlFQPLdw/Y+Lf2ihAva5BY2F2fbMGaHrmi9g+SfgYcJXy3Es+PS/1FUQCyIJUqhUJ/p+P6yBqj4rL42BzzRUt1kdnp+DjMB/GZ5pO2eDCsdjQeZonPXlTbFvVj5Mqy0NroySkrNIddKPatB6Rok5I0GhRHj9dMG7Lx9JpI0rTxk996o0QQFAn31/07HWoaX1yQqAVBB/Megjn/L4Tjxn2KosIotvlUUggQn3eGwmGrnL2kZ98FIJcdqRplG624poQ4RRQLKE1lWjq6aJ7mRucs3hBXeDk5pPmmkLg+uo+5vOXaIzb7HXvMTlyw9LZG84/MqCDxP3PGFfcUctm80GdSDQptUSsiSOD1UazGglIbV/dtdW7+Y9M6IMI3QrkJ8ognHRxO7iNFiagIuaIFTo/q58sagkMiZPK1jLNtSKZkNqssTtqR5ICiIGWOEaOFQz18qs5m5y1GW7iGwJX7O92hiSIrGnCWKSgVOVDu7NTvbzi9qiJZCQE8uy31KvoVbwS/r6XeiUe2d1v9E1dcXA3IdSRspaFOdxCLR2UFIwQPc9WGhMUms661DOWhp0Fk+sRLUzrAWE3Vq+cN9A1zUsxKa2HQ2EJwkGAgMFGeK2tRDa1/PcY1BOsLj46ODhsedrYorx7wr74U5R/jd3/3d62lpcW+0nGtwHqNAfESAmt0oWKlsEctmExrzQzQ57V8o8+yN+8Ul4PWCKgQxPji4W0Onr887swit/QpKZ68QQiSV4NBp+ct+MQpncbZIPGlis2V9Gfz84sqWJByY65Iu6nr8Q1xJOBHwcXIve168TfW7tmmYge4H7QLNZyUfThQD/U68ndDg4iZ8htaW9dmrhwuTsexmEjInKZVDELSDwUtd2hQi5Zcmok6u3nILTIUjdNzLt6jt12nX4i3mfsPCkInOBaPpyoke7LAlJPnPH7k9ryetwI+XlcoHCq2vq/Jqnu2upiW8yOuYNi5zXwvXbDq3KKTZE8uirBMm0i8l6klq45O6T2yObGYZrY2O/RjYVHk3ezBfmdYibpwYVFFlmfvDhVgXgKPQcFqOYKo6UDafGtsBAUVjhSuSLuHPrTm7AKODVpmd4feNydeeDp8Le1BlFygdnDaUBpyKmfj9M+sWOzMvOV63cmbE/zEd+0Qr4w2EugGRPONHfVukynVnMFXM7a8P2GprqDQAzbc5bfsdMhmNmeJv3nFNvY0uwDeQ7tVXGZu2maVvg59b2mwUxtZ/YlFZ2g5k9Y8o9ChoI6+PCokhAISIQIkfHhp9+/5Sc05BkRhCmLa2c1Pz1n/Hw5bvjUsZJIiG1SVzYvB68OYlHZZZmuTxa8knf8b9gvwb0IBx6lqaZCXmbzE2Ljx9RqeEAIEUlk3UbLw+VkdXtaP9bgIkmzOWo9nhZIU6rxy5uY+B0YXnKcSisGmOoucmXbqQZ/Xbnn7r6oQz3aG3e/paBRBu9Aatdwb9upaN5xctsp1u4Q2euBMkXHXEJMYhJDu8FRK7UXQGtAphaxDau9odQ7jpaqQDp5BtZoG+iQ6UbFRKEs1p3BolGqjMypSmHtaV9ZTIrjTmqToIZiZ60rrNjyfUcuV4hXncvhsiYmybEgomFkDSldGRfCnDbmZhgBahqM9Bz4ENsFpvObM/BcnzVsX13URL7C/S2sICB1FBMUaBwMKFagOKvDrw07ViaEv82B0TgUSaxdzm3Y+qDRK5M31C684iio9j7QFsY9orXP+UcWyDkworkFOEQ8QtE2RBWLrmV5wpPxQ0MKX5mrmt1WJZUDFZL3C39dTQtloS/Lv4t4B3ZtyX4f4jXBQ1c6fxujW7xDaZE4cvEpj3B0OQWJr2bLcX2WsNqAIDzmLB+4PBT/oVtGZ1ypiDPFEe8JlWw50iAbBoYk2qcLYX6cBjviVhT1/bY7v/u7vth/8wR+0559/3jwej83MzNgHP/hBe+9732vf+73f+wW/5zd/8zctl/s8PeOfGrQgsWr4Ysc1o9HXMBq98f6ftVjSq3Zf7p6DUvrgRKyFNVOQZJlTKBYDtN5od1WIwEpVLHZyxjJ7Ol3wbDwgHgqcFR7KwEuXpAjkpB54+EUrvOmYRV8clZM37Rr4XVGiHTAJbQpb4LPHrUw7ci4p4vJdd77PcTZ4rYNha/jzlxTinH/zMUnaQQyyvXXmqZgt7Q1Yx/MZbQ5P/M2PiFuFt1Lp0ohk+plWv7U9Om0FcvMuz1ppdl4u6o4bVBVax+aQPzigxTvbEXGqIKwJ4Mw0RNTurMCjII6nt8mC5yZE8mXzlc3CQI8rfCampfrjNMkpsNCVsNDpCWWpYQaKbxYbJoUdYbcUmHBxQP2Q/Cd3NVn8M+esOtRrnjHyCwe1eEIOhqMxe0ejdTy5Jg6HeFgBn83e5j6Hsz7cF7UiaDXiZE37UnYPcdlOZK4btOiwc47X4oyzNV49kGhpHxaKQjQ2neHFpaqLOQdyrhuqrvkF8+3fpQ1KWWnBGqka7hyIWzKjU7RaJ+WyfJa415yYsVOQZxkFSUNcvkBqtVIcwk3pbLfs1hZxU2gvY4oKUlR3alGclrqJguZFpj1oDZ+5rDYdESCZLU2S7KcPdWte6vdvGkcyh/Y2WXSOeZNX8YPpaXagQarRMuT9/l6npGyOi4uDK3d4bFVGohS4CB9wVQeNSPXHrP7FWV0bilZxd3L5z7dQUKYtroovSEs7uJqTGEQJBvUuT1BRMRj3Epi+mFWhAikewnPko89LGUerjoNBdWPDPOGwM3ql0CgW1VpkIyR7EnUurwtjVFCqif+033ofWre5m+qt849OWXVLn6UHE+K1wS8UP7E+bMGTI2Y9HQ6hbSX83GtrOxMWWSzKbZ/WsjZmkBDoBIS2YygLL4mMQbJEVzMqLGdvrbeOp5PiX8mYFG+lWrsVDiMiADiVJZzn17Lyu6KlR6wRzzzFV67FL7RZZrStLSryUZrKmLSVZ91ZyVDE0Jrl2mIJgtIWNEempKGQImQoBgg453DzyDM/JeNTX13CPJ1tej+saxQtIIYoHGUF4vUo5obDFS142qQypiULEdUe6l6ek1DQFm/vtNZHJ50Bck+nM73FliUe1lpIOLUK7GBA4ggUmDxPKliCPhfdU6k4ZA2eIgUaaFIqbenrB7XGcW05HKpljyqY+dbVJLuK6Eees9Q332B1FzdkI4GwBTsdFbvFsmwdCGHHx0/FFF547c1OOYn1CkU0rVV8sCjEai1xitZNd3ippDmUTs47g9zmuH1m9LdeF6PROz/1PeYnWuvLHKV03j77pt/7mnNyr1arIrO/733vs0zGBcCHQiEVWD/3cz/3Bb/H5/PJaLS1tfWL+h1crxMnTlwzGv3nGCyYi4eiaoUQdyO1D8TQ82NO+gzxG0fz1pBbFCCcv7hg4fms3JUjz162VF9EBqW0ZTYO1gJB+7u0EGdb/OYfGlDUCnyh0BNnnJS/WDXP6IwtHqlTa4d8tMCpUcngGXjbAKGzOPszVase3S3lV+z0rCMeg1xVTchE7ycXtEhFh9fsgR0/puxAFjpaavFnR6z1o+etGgupJQK65KXtNz4rhY94QMOTVunBXLWkk2v8xQmZV3LSFfoFJ6MxqnYgcDqEaBYcihhaUJ6hPhFQsTJAGViZnlP+F9yOZG9Qp2ZMUFFGYvQJpD95d1CIHNedU7RIrrMLlnhlRnJpiiWCVtlQRPRvapCLPq7nivKA6Ioh4nrKuj48LL8ekCo2LfyepILCZZ4iEfRidFoLLHmELNT5Le1qk0jd2BC3zB7QLb+IvBSZLNS0ePE1guOmIOshAnBjLnA7nVOsi8j6FFdj09oAKdZUkK2nxBsj3kdB4jhGr6Us0xYQ+Z/ruFlc0SbDbFFKxdEJOamv7kmYhV2MDvNQgc45FIJVtds2CcC087g30RdHdF1j55cseV23fI9076ZmVfTFP/qyiiuuZfTktC0fdXNXbdUjexTDg3eWf3zemZc2+WVj4QHx7G6zYsInQjiByXUX1107inYqysjz77OZt/aqSML0Ub5xwaCy4VBtSfEX9KlACa7knCVKqSKDXRSScJl4H75xQpjLQkbV+sFXi4GjOlxASPOELve0aOMLXJwSsgfqRbFwxwO/rLnT82hKm2PXR0Zk9YH3WGitKCRK6tZy1YKLKcsf3iLUhVY7HD0OHA3nN1Rc8Txxb+TZpddQEfKkiJx00aHXyPiJWFlYs+6PTQr1JucPxEZmqoXyVc6WDm2FkvkzRWd+C3KC19f0hhzIo6OriubCVNUbDDozXSwCQFsiQan+uD4UMlyr0GpRaKJipUCcZ1ZEAIeTRPA2US++F86rBcqAe4cvmfzL2HSeOq0iLNsSFNFe1iqY3NZ8v/TMUyzy7xqCxlohnlkoaK2fnVIxR3EllWATbdCoCiHMYpXtSFg05sCZkjhaug7jSxbmwEVIdFNUXmsExgv5oj3ZWG+RqZRToK4nHXEeNSB+VNjHxAIWm0yr3dnw7LTaxOXJKdcOrFZVTDLXUAdTOOb6CZuvuvWjM36VWyjbEArtxpC+HjHDZgSVEMOLY+59MT9AzjBxhvd5bXxVh8fjsZ/8yZ+0lZUVxdk899xztri4+I8WV5tF2V133WWHDx/+oj6y8Pq+hHGtRfgaA7WgP1tVKwRVUjHms5VdARF/ycnDh2jlaKsFkiUtzoFU2VK7W+RKnO6JaKNseHrS0v1xEcFRE0K2ZaFkM2x88ILN3t9lyb2t4gsxkPIHH3rZfe+lvNzhNXkScfMe2iXPGmTz2lh9HkuMplTE2e4tDsb2u7w+pPcYl5YaY8qIkyHi+KSVV9cdtwN/IwjaTY3azGmJ4LIs8nWxpFO1FGP9XZolbrELyBpC+YCcoL1etZ+wExCa1dXsYkWaG7UYZg50K+0+OpE0b3ub1DjewV69H4j2TSfXnY/QRlrfSyuCNkn340WdhsUrOz5s5XPDCk2mIAEZc9E2QcddQVFVrlh4ekPEXlzj4d04NIPTpt/JsXkwOJF6Pba+r0Vckbn7u90GkXCRN6n9eAP5LPjKsNADNiA2MdBFiLPEn/Bel25oVqQNhRFKO6nMcKwfnzJvPO68cTjV4p6+kVZLLrm72ZJHehyHi2uHt5PPI1sDFQmJmDU+Pm6luw+roIR3BjGYlh9qP7IL2Qj53oYLKaF5LPogTxRsidGskENaNxCAxXnK5p1NwsEBtXSJx4n97UkFR4MKVLf3q0BETSm/pXOjQipanph17WU2mUTQuWsXKpY62qcWZWi1JLRV7vCQjzMVIaBLB8J6LXCrZt61X2astM47P7tkiwcizrB0LeNia5bWxKtL90ZEHg7PJFVMwT8q14Ws+bkF5T3yQTsLXyf4VpLzb2Rt6eY28f6w40Amjw+aJP5+j0OIYjEdUkBjlDOYLjqEmIih1XXLHO4TukReJ7YobKK00HAXB9GA6K4CKRwSL600Ne0icWg1hfyy1Hhw5FeFYOTaY/KewvZCz+Xzp8z75En3Oii0CB0O+5XrqPgWn0+Fg9pVAJuZoqUGE04Bem7S+UHFIzpQZdsCem+a56wDWwbMs7VfBRFtPZ5RImMUkXNwi665MhnnFtzBJJdTKxK7AUQOQpQWlxXeTUEI7+7ewDerfQpflGuFyz++ePUnXVudQ0Wldu8ofuFEEdelUawJPCgKOZTwWnqbXaRM3LVndQAAOYbHxBoFR6ohKjWvECI8puBwgnLCb6TVue6U0KRcUJCqtRoNWuX4ObOzVxy6DcoVD4mPR9sUHqzn9LAzCMa+BS5h8S91TWS0yoZX8yckvDl8wXFDSUPg8KwQcAqudEY/VzmTRQ68jk/HzxC1Ap+2SsXSgw45lBiH/MPXaVzjYL32CAaDtnv3bjt27JjF4/HX/Nqf/umftre//e32tre97Yv6+Kmf+imFPn+x41qL8DWg2Ntu/i+W3Nto7Z+ds9yAu6iE8KZ2NstFfeNYjzIFEx952ZJvv85i0znzPnvayd2P7VWhRUsp1xSwunMrrj0Ui2rDwlRy+c4+ZfxFFvJydJcEGARE3KZWFTmRiXWpl1iYKqipzCzbHrb4iCsoIHxCHrfJObOudi1w/H8wWTL/46fMu23QnTo9HiEXFGhsnr7ODm0aRLZw+mZjAGUo1PntyY/9iF3/zg9Y08OXrdrSpCKJhYcNMDg872IosDLAVyrkdzyzmp9UtitmscssflWhL0t39sp5Gi6P0JulNZfFh/FfY71k+aAunB6XjzVby4PDV7MEOaFiVYEVg6e91amQ8CyFh1R23jy0Q8Xtwu/kcIfsA+R/FY2IT5PtiljkUy+7FuvwvL4OQ0ic1MV5mp6XWSOFrbhpHq/UU6BjHtRItcxCxWuwcIPwbaQk99Y1C/ttaX/Euj4+4Vo2hZJaCygIM7s7XY7b3JLI0WoJTS8rmqeytce1NlCR7u1UC1n5frt+XJsp3C6uIYULqI6ily5sWIG29HLGqdMoHNnAElHdn4m3tFrnc1mdzheOBK3//y7a+r5mFWFIzJmr2HnILfvsFeVFpq7rUdQMdhS0bdT2IKqEjWOzRQz5megXDEZp20w56fvSsWZrvLiJ4lTFueH9EzSOIhK0Kt8YsuhpZ/gnKf+VOfEA07dsUxsrhHHl2WHXAm1tFjKDMWt1aVmBw8xd0AXQWIkCcEL3+yzfXeeQpLFFW7uxx7wls7rnJjQ3hf4Nz+l3UgDLWLZQtMyuNouMr+tZoegFcaxMTkul6H/+vFUO7VCYdnlXvw5IGLcGplbVoqV9jYFt6e4j5s84V3eeY4rVSqFgqW86pjgpjFIzPTG1ffFtoqgoDXWppc6z6p9YcM9PtiDiO0WwEK+Az7XlY1F3jUFRGHA2O5pEXvfhu0VsT6agwwUHHtBuDgRKDSD/koJnI+msBFD/0Q7v73Jmx+mii8UBTQXxa4yZf7HmSB6JuPYqpPDmuArO0NSapXc26wCpQOjVzNXcS9lfjC07g1i+v1Kx4jaQ+arUwJV4yMXRJFNqPXL98ddTnuMMAc4R9xzzerEzuTxi3j3brwabq5UadiICKSoJdK8FjytcnfY9CB2HgB09jrDP+6VVjJAgERaChfUGXlWg4QxQfPl31drbalWSmdjfaes748pXxBdQsUNnLuo1UWyB8CqTVGHTZp71pLN8uDCqiK+Sp2SfufSB16VFeNsnvu8rbhE+8Zbf+ZprEX7913+9UKy/P/hcOBy2rVu32rd+67fajh07XrfXdA3Beo1Rivit+XRaGxktQE4ztI1KEY/Ip7i6113YMM++bfLqoS1B4bLynTfIWwU1DCd/ImQortIHuiy1s9Huu+6nLber0+pGc7JAgEcjJKW7Q5sv3AQ2X1SLIlTevF+qLhY4An3Dy/DA3CKZ7Y7a6kEChcuycMCwlP8PIh0/tsc8y6u2ur9RUR5EWRCtA+Fdcu6uTitHffKEUQtjOWOxy07O2vTsnJW2dOv3b+byrewMa8HM4/vDokqQ8pUJ8y1suJT5fFGqOfFS+L9EVIaamHSiLMp1JZTNyIKf/bqjkj7T6gTur45MWPPxNauwIQQCtrarzmUutjYraxECtpCr2YWrBpWKxsgWbPotXVJmxSfSknEXtneqUCBSJn5yVvfE9+RJt5B2NKlAgU/CKG/vE1dHkD/+WhgWQqZl8a9UbOIbXGYhtgYU2fj4JI/hx5MXmpUcCFvHM+sqCsUFA5Ebn1ExGb28ZJ7ZRUvdMGje1ZTz22mqE1ldMUSxsLIb8ULDmfr2t/yKs0GIBGzsW7rkQQTixvtsfG5WBR6ndHmvzS85uwc2ZArw9aTF5l2LEB4fPCO8uOITGQtcmlZrA9d1FJTZ7pjUnhSPWGpUXzyrCCZaTBCKKbwd6d+hR6XBDrWE2bhxok/dOKRTf/PJDaFo3nRBhS55iWxuHB4gk7OZwqGSI3Zns61viVh5YdHly42sW3Alr0ICsjYKN1pFIJWrN3a6VnAyrWsP50wFXjrjNlaKpY6ABS9MOUTviTGrw3iTDZYDB60rrAJ6WlQwYapKS5YiEM4gOX0UA8wVb3+vskYhsi/vj1lle68rJNq84q8hSijcvNshoDcdtCAxUfmS7BRAfpifzGXFwcBvyhUs+tBJd1jZ228btwwJCZVtAopT+Fxs7BxyVlOagyBmFBO8Zt6fWloUHnABKbazRedvhc8WBHS824Jeixwf01yGi6jiikFhDiJEdAt+WLStEXTU4o00v2gtYo0wteRMikNBmdZSMBBdZc+ecocu1JMUI5DGsZaoVsVRRNTDfFXeIM9iXVzXk5ZnsS4o1SrPgRBsEihaYuJaOeuIggQHFIjEWdF+BB1GoUlxhW0Dcwoxi+f502pLemYWNT+JEUI5mxuseexFwpY5OuiMXvEEa2tw75WDx8UxoZMU+5vFFaNCC14CjqjWMBkQ1yVEhQikK7peoGjMX2gXFFVaDzA2Zr04NywzY6ktz4+4pAkOfv9MxpTXxpc/KD4/+9nPyvOKoooPDEf5XKlUsg996EN24MABe/rpp+31GtcQrNc4Kdxy53+z+OVVy+ztsvBCRv42RGDkDjoy+sZA2BrOp8w3s6TFCSUZHJb0wW6LXVgSIkVMS+bokGXb/PbCH71byjQ8qdgsFu/qlceS1EJTM7b+dQes7q9elNLNs3nKvHjF/Du22PKxFkt1e6zzmZz5Xzhv3kTcZZj5vSLaw3vJvfE6i52eV5uQhZpFjnw6TpV1J+bFj0DKDYrCosrijUxZ3BFaCJBS2bBXVhUPggcPbSVQMKE2LGDjs7Zx5w6LTWcVwIvRHuGnmesGtBFq4Xv5vForbJacFPEHU9FVrkqlByGUk6BiaYhVua5XC7lCXYcnzNvRautHuxUx4j11WQG6LPrew7tFdIa3pPgX2iYYFcpx2xSmq5YgMUa0ymhLeD0ihkfOzsg/LHPHbrUsN01aKXQ2+RMYgR77jg9Y82dGndoOZGw96cjinJBpYzTXuZbTak5trtSRXoufnhc/iuxD2ixwc1AXJbfVyalehFik+eeuWPq+/ZZ4ZVoIm5RYpc9zsFw+WsXyg80iAlcoaOrgW4WkzkIlmjzQLi4SyE9yS53Qz0cf+4mrc/f+A/+lRphPWLIvZM0vLpmBcMJva2qQIhX/Mdz8Kf4RP7Bp8XtRhKFeIyuR+5Vvjdra1oC1P7VmdnlM7tUqEFg4JmftoaX/5ewNEAQMdTvC8VJSUU+yXqBNS+Hf3qA5iJcaHCa8weTx9NQlzTeGOFvtrYqOUn5jKm0bN/ZbMIl5qiMYg6KkBiJWf3bt81l9/A7meyjgJP/hgIoPeETlm/bK4oHrnByIWGzW8bF4jkFz+JNgXwpavq8AIlSqymcr9vApW3v7QYtP5KQkBhUMzWeEqIlPVqooKid6Ykr3ElI9aCbFXWDFtZg2eTuVExdcqgJcpVpgs1SEiahet8LW19aUrch72DT15Gv5ObTXIJUjJnEXqxY8XR91qCEEcwodhBeIMXBfhzfX16T3Cw8M9NC3kbfU1jqre2ZMz4580EhQwD4DkBuSPggZAoig38qnzuvX+Xu6rTw75/iFO4ZUdGAdgfGvXg7vvzVmgbWcI5PTSoPsz/uAjwmajCkwh8hau43fMXNXk3V/eEyxSvBWmR8S6aSdnQXvH0VuaDXviry1jCPUb6RVpNGqxekeJB0FceTpC2qnPzj5368+DxgvbyYa6N9D75VtjHewT/zA+5u+W8ppoVbb+kR6RzGZ391twRcuWengVhcRlcrag6Mf0PcL6avN2U3FNRSRz1z+9dcFwbrl49//FSNYT731f3zNIVg/9mM/pmv027/92+attdQJc0ZZmEgk7Bd+4Rfse77ne+zs2bNfcnTOlzuuIVivMcJXlqww1OZIqHn8aYJSmrHQs8g1Pz2rRYMsPtomhbhXD18p4rXq3IJ5UMwtL+vUV/8nz7owVHgQxZIV+1vV1pi9vVFfixkkFg+yNPCaZfvqnIv3zftF8G744AvW/+E5bR4qOOrrlNGW7gyKb1C+5YBIzApFrjmaw7NBpp+4uGq5oRa1D2kF0aLLDTVrAeOk6l9Kql2hU288KkI1mwztMAKp5Z8Uj6qFQRiwXLwXk1qkvMm82l+hxZxUYJg/IieH13Q1K3FhTdwIfJl4bSqu2BTCIbknyz+KTRT3aMjupbLVvzhtheaQVIe8Z39Hm9Q/tC9FICaagoUulZF8nIIUbycp1nDRX06J4wFqATFcX9vfZbHjU1os4a9wz1aua3HFT6WqtlbjmXVn37C4KpSrvKVL7VtO+Cymq3twJK8qqohNjKJw7ViH+Xu7xFXRxlkXtfU99Vb/3OTV4goEC5QmcXxGp3TaqqCNIJXyBwqHLLvFkYnZTAo7e8S1WXyTi/qguMpsb7XE6UULT26oKKt/ckTFFb5nVwfxLNNzQhnqL2dUXJS6GjXv5Mjd0y23bgopxBnRl8dcHM22FvO/dNFSe1rFnQE1oSht/8PjtnKoXi2yan1Chwh4Ssyh29/4yyqK4NwVmiNCV7AdwAxUppc4lpNKUDHLdkXVBl3dEVU8TXDdGdTCM2OjLxzbrrnBfJcbe8AvrhcBzXKsJ6IkmRV5WYalhZL8p9aOOkECDve0p7WxZ4qWfuthF6xOG2x62RpOrOjzirh58YJI9agVVfhXq/oaECLI7rGJlHnbWiy0VpZKjsIDzg5tPrhTMuMlL3guo+cCAYCsAdJF/UxaqbyOTW8k//YhzSc9cxhtVipWmV9y6BRqte295tu1XX9HpMHhQKkDc0suJxK0CUFH0Ck5QWeTe+AIeoUspo71q6jCdLewu8/y2ztreYhTZmcuOyuGlbT8sVBCi5C+uQHkykLSlg4kRBLf2F6v4Hk8vUC48285Jr5Z9fp9DgmClhD0WezSylXrAtplXGvPhkPRdA9oPZbKOpQhtOC18wwgHADlo3BsvFiUvx/FFbxB5oREOqBsYVfEEnnENdmkQ8gfrlTWOiayO8V9yK8iOHPHLtl63HH/+9UGx1xXoopXjUqjQ6go8liPH1r5fXs49cc6BNKuFVKHMIBnORIW75IECtqW7geUHXpFEdlY7zIKaaezbr9Oo1r1fMUfX4vjf//v/20/9EM/dLW4YvD3//gf/6P9r//1v4Ro/cAP/IAI8K/XuFZgvcbQ5sBJMeJXqwyFCmZ0pQS8p7Jlt7VKSs4peOGt2yw+kbW5b9jqNoZ9W7So0I5bP9hm/vY2p+TBh6oGKVOgdX1kVBs/RFrcpFEJZbvjFj0z6xClp05YJZUybyLhfFhiPkmR1UrwexWTA+myFPer5aH8MU7OnIDhGGElEA5KDs/3iRy+viGDPAZ8Eb1XFutE3MrDo+JL1Z9achfB71ecjRR3iZg4L6vH2sUN0fdgLphn4wpYaGpD7vMQdDUIJMZwsKdFPIXyyqpViNgAOdvXp3YGCBjE5Mikc7WvLK1YbneXy0grVcWLUjsiEtbp8b7wt7kMuI42W7inV+26TZ8lVIDzbxwQiZiiBpsKITdsdhR80Irwu6GgYvOLRa3phQXzjM24kz8KIkJ5o0656H32jEO42BCDXnG+mh8dk0cTnmhwvngv8b98zsWHQNjleiyuWuNjTmmkTXR4UoWfopI48RZKtnS4XsIJcfvWN2z23g6LnJ9VRh73CrNOXk9stuRiYUArSpvIRUStnNT1AzqBQ4SHC8QQ8llfZ7Era+LXSDnZFrKpN7UJBaGQo1BB5Rl7acKq2FfESCkoKJJHNgkL65be3ug21mLJGs+kXAhzOiNFIga5C+86or+XG7HD8Fv4lVEXJeL1yDcLkrSQuXRebWw2UcjkLS+tXbUIAN2k+EUoETox5oqU06PyXsMnjU0uOr6h953a2+ZQvoJT8XFtlKrw0oKyLiPTSXvi4z/iAoqTeYtNEL9UkH8awgXaihDRQaJUPOFvxfUamXT8wu5mzTcKGXhuPPso6K66xLO3RoNKAxCCVGudCY0i6ghuE1YkQZ+tHmnVa1BxDSkbjlVLg6J/UKChDMRWZROJ8s2vuZUYgjxk+00eScwh4hzANF/JzOwK678SL0w4o9NU2uKITszEOYJ75vvcy3qWmMO2d5tMYhFh+JNFi09m1B6k8FFkUCyge9j+qVG1XOuPz+v59/V0qVghzUGFdKmsgoU/9V7zRSt1NDh0CiNVfk5bnRBMih8UnQSR63CF+ATV3pYWFWOEI3MPMR9lLvNcQnrn65hbmjc1h3Wh2iiCy2UViLoftK83rxv2IY1R5b0SfQTSDM/w4Zf+m0jqXJO73vA+cU/dDfc4xe/MgriArx6baChonwQKrBvlsq4DnDv4XLQqxTelsPK6Vi/P+Op1X5zM/9r4fzdoA164cOEffJ7PlUF4AU3C4S/I0/p/Na4VWK8xMr0JW94TFsqzvj3u4mLwvIl7LdcRscBGwbKtfssONZivUFUboul8Tj5W6e6I1ILZG7eZH3uH7d3mn12x6a/v02LHYj7z5i4tUJNvarabvunXXDxLPmexF0YdqT2TMf82h2AUrttic/d0aiEJLzm5e7I3IFPHzL4uFSjely+olcLmKRItPjEY9KVyUuvRJpQXVF+35MsUDvGLtL6qNndDTC708GPwFmIDr14ZEwz+5EcdLF6ZnHFu158ZVnEiLgenx3xJCjbPytpV6TRcG07atE75/UJnyI07ultcLMm1i0Vd07qzy9pUMvu6rXDrXiGEeAaJAA3vIRZQMUsblNenbMKGmLU9tWiJl6cVpoyajQW+5f+8qJYt/AiKC/hCGiBYkzOOozNVi/GBR4Zi8rotlt6NKq3qYn1AIDA0hBN3eUyFICRrWhe0DvGUoqhOdXtd9MvWIW2gMge9a0AEcOWhxSI6icsYUwt40a78f126Pm2fGBZigvs+X99yOitjTSTfxYaQZftdMDAIjgwXF9Yk21cLYzUtD5/QSsGst0OtErggbCJCSJDAI3oAkSyVFKzb8xcjcrVX0ZfLW2FHtxW2d0lVChKqNitZjC+NigPIPQcZ8+4c0uvBjy2PijNI6LjX2p9eFTpbJs1gI2WpW7YK/WHjhLsEmlpsS6j4CmyQY2mW29aqwFwMQnkPXBfUo2y+WIEw1E5uabB8Ezmazj+MA3d4wdmAUFAQ6itkquZbhWAArhYFuIQIeI9hZEkbhVDuxpgI1pEXhsX7UsGKQnA5qcNJBXdzzhLZsi1f1yTEbH1vk4wnIdMv70Z9auKL5XrqhYCBCDFHeQ4RSNA25H3gy9bwt+ddK7EhJtNWKQtpYZJr2NPpDFFv3GeZwXo9o7S7IObD8/v0c/9VTu2hiRW9bjZ7JS0so/7F0sKjAwE8w/DUuhSbhCxj70GeJKR+hCtl1LzL65o3DRfTKjbh8ql9h83J8KSuK/cLx3eUeCoWOPTUo4SNqNjFNZ/iGxNR3Z9I0BWOIEGcSWq2DT7MVOfX9MzT+izUB3U9mYvMFwj6odmkUwrv7Nc6AYrF60E8I5R6ds3ZJYAqcuDh0CLBSFrvnfUWFFIoPoh7Li9OJT8DwYRzifdI9Q0PkCJLKtGxRR1wH+j9QT0r8LLwTttEnTikgI5DEeB3YnUhThtJAwiOyMasPR8Y8ZLgIAV1b5OETsmbBt26+zqNr8RkdPPja3H823/7b+1d73qX/fqv/7pagHzwdz73zne+U1/z+OOP2549Tiz2eoxrBdZrDFRX7U+v2eytzrQTxQ5FEwRYNj7aZA0XU0Jgmj55wRpOrch1+ekPv8fiY2m798afs8jkhr6XBXnxzh7r/F/HtcDhKdT1N5NakHo/tSyuicaNByy/p08nKd9gv5AtX3OTBV8ato4HJ/UlOqVOLMj1PXph0cn8QZN2DGpzW799i1o+LERqxeWLQiW0Qb102nLb23VCnbu9WRl4bLo9HxoRmgNcziLDBq4R8Aty37i+1zyDPQ4BSqZs7UibW/hYDAsFK+7svapEAi3g52tj4cSYzivux1fvvIsonLBzQDYeJYInlbGNnXVWjnhVnOp0msrLR8rJxN33l2/Zp7arHNlR67FpodyD2Ispa0PIVr/9iF4zHLXc1laRuFkkCRhmLB/EU6ei+BDarED9IAtqWWULDlFSoG/I+YIN9rnrwPuAk4XqcHpWBOzuj0447gutXL9XXI+ml5a1cYAYQUCHHEtRi2UGBPktfzxrG3tbVFTR3oWTpss8umD1z07oNRAMzume1ggcKzzGyiAstIITEZu5r03FIUW62j+LScebuuWgre8itiTqipeJafEHN00/aW1ITu7xii8TPDNu/rYWqcH4/SpcllbUPuGel7b1KGMyfGrcggsZFXgUZUQTybepp1uo1NK9A0IaaNmopbWyJr8xtbVAXxiozp46qw1SfK9OFIEVtadltEpRHsdiIyCCcWilaPmuOqFmIFUUAhUiqgJ+FQNYIoh4DxGc63dlTjYjEKcrJ89JICBLhljUReaE/VKjkt9J8Y3SNnmow5KHu4ROkPUI+tZ0al08oPpPnRG/CdPWpgtF8zx9Soo67AyWvvWQFdriMgrmHjmjT1OxLPFJR6vmgwoHCl4MLtN5eWut721WNAv3LjpCO8wjDiNFAPPygcF3y1uJVi0t/tXtEatSSGCJUalY3fkNqV5l54CPWc3yxLt/p4u6ujIjHy+eO+43BYiQIV4HNQK5fKCygz0qCkAPEbfIv4s5X6moWEUYUNo3aMWDQ4qQEtdzKaP5DmFdIpO1rO4Z5sPQA3gW5VbP/UiXXJu0VJZvHmgqyF2hp8F5VxUKLsZqNSNUUDxCv092HfjGETHEPQRJAgmjkBeVYAUfLExAA0KlQZPg2UUuOUReg0QIuJi1yCe4UxgAw81iTkIF0FpYQ7BoEzJQ8JbHJ3VAE6mda1gXE99W/wYdph3Y6Hz2PvP4T1h4uVgL17bXbVyzafjCg2KKFuEv//IvK5OQD/7+wz/8w/aBD3xAX3PvvffaX/7lX9qXMiYnJ+07v/M77csZ1wqs1xi0Dkbf3mDBjaqFVnB6zssBGF+fxIUVp6pK53W6JrJBaqCQy9hjIRfXaDWpkyImfsWYR5EmRVQ2mF3WxdQqYpEAjUJlaM+edAGtM3NSzjFEZh/qVnun/tyag+67W60Y86rto814jRN1wHkwEe8zvCyezfzXbXNtMvrSFAzX7bXPPfyfdVrt/PS8tf7VabXwtCgVSy6nD6NLzP0IOm1qlP8QChsMSgk+JRqn4ZkpGXCyeVUWlpzNAb43FC0sTLQOwn6hH3ChROjuxDOKmJp1nabVKiIDsSGu4hXyNpFEvG616jAMpcV2ZcoyQ41OpdTBIt0o8mu2Ly7kit+HuziLbMvTC7pOKPfC52dU/NBe08ZuZq1PLuj1Q0BGSq7r5fdbYNo514MKTLzVoVnYAUy+uc1m3zZgFWJf6iLapAmqHn9nv1Ua666qh0AKOCErOufKjPV8aEy2E9gg+M6N6WtA4CgQEpfX1e5qOL0ulCO9hcw2r4Jtcb/3XJnQJidi7/C03oNalcGgpPldHx6x3J4eqdLk27OypsIPlLLh5QXzX5nWQYAIH9pX4l7BMRqZrLlSu2gSNor87t6rgb25vT3y2nro5M9Z5MS4iyjascW1SzEJHRm3dFdQClpeEy1EOCmN59NyG8fsEhsENi6sDHRtCGh+/rzlW8L2cPpPrPrCad1T/K1AIGRNIuTQZ+WYXy08woudiaU5UQEt+TXn0cT1jwwvWfG6bSqeKCIZFKzYSYAsUvjJ941w8bZ6cbqULYgIAoSiq8OlEZSqFlwruWJvdEYtSW8qL6S5us1F/GCNIU7lLQfUFmbFbDrnFMW0UzeJ5RQKoHzwl5jrtNVDl+fEnfPMunY7KNgzf/UeIS659rDWC9/LF2V8yyhsaVeRwvuIn1+2zI5WKxINmMmoCOPZExJFQgA2AbsHXeub9uDxn1FIMXMfEjjvYeFmx9MSAiMaAFylqsKKOSzwzEZGVzXXVAzz/PK80WalDSZkqqQUBRWJK0m1+LNtKDlLsm/h6yiQOXBi9staBxonrhdcyXhEbUaeHe6bxCEczJobnWiG58zj0ZoptHfc+Wt5L05Y5fKoTd9ONuiKIn7kpUXrk8MkLWaoFsqCbHBrTHu9WsmKk6pW1BG4+/Zf1HPp3zLosjbP/aJLUQgGXSzYqwf8zRv3ub9XKlIYSgyzGS4/t+w4cKGAULDrvvvXZaGSePiMZVuvbaVf7fbgBz/4Qfuu7/oum52dVbgzH/z9J37iJ+TYzujr67Oenp4v6WdjXPrHf/zHX9brujYrXmPQ9ut/KGN14wXB5KANLNTEVbCIsEFxKuNhXzwc0SIfO0eAKRyghFoMlcUli70yaf5U0RovFWz+DR1anGkrYhKIj9DKkVbLNZpFptOW/JYbtNihPsRxmFNVYGzRmRKms3I4xhGaO1d/akWLNYtAHr8kCNOZvIJPxdfY1mzRhbKVFpdUDOJczGIByRlJPu2m4qGttrGV4GSvM+JsbVTAcq4lKMI5yjIWR1qhmEBWutqscvthFYeRiQ1bvqGWq4jEnsIwEdYHCyeFEr5hFEaC0DdS5sHzSCdMR5CVlDxXtLrTSzJVDW2UhdrJqRq1ESozWlfn5twGS3bhlrAsECIzGbeg1gQJQp4qFdeSyFV0gudDhPbJeecsXShY8o37nY+U1ykQIY+XRsasPIGZZMD6/u+sK6bWk3LCb39uXRwQhckm4rZ8V7/1vP95h7r1dzpPLIivfp9rNaL8K5Ws6a9OWPzMggvA9XjN19RgK0da9D4KW9tlLutbWLXYE5e0seKjhEcQWZbwmbiG4nqAntXy5jhNY/UAT4vPoRaUV1d/p4XOjMsD7MH531Wg94OXf/lqwkBhgHDl4Of9lZoarLqyaqEr8+bFbyoSdoV3bZSXV6xESDBFNYouVGc37LHEeE5RNxRY4n8119mlb4/K62rtjiEVzHPfsd9ZX1AjwZ8JBCz8mVNqYaJgBAWhOKGVBP9w5g4nry8k/FLqYdwJ74m5QyvJ/5lXXKTK5TlbvXurLBf4O8UoBSBFppATDCxbYiLOKxz45TNqY1HsCElG1VqfUFHNs0xsEAVE8OyE7pFQEkLS250ZKD+Te4Xqk99BUQinZ3lvTC0woqVUsB2/qLkCB5FMTt5z7PGL8s9iTqEaxhst9ORZXdvPPvpjFj8xo8QD4o+w3FChVlMXVghoT2dsYyBgHb/1vGXfdJ0KEnI/ZUFAEbStX/eHlh12ERi6YpY5f3+va31Vq9b6ouM/ga5xEAF5lIISInexKLUq/DXWEGVjJsIyw1UxRYHLBpEtWrE1rrbgxnVd5t0xaHXnV519wVJSzxvFP2scZr8MkCtihPg5+Y6Y1T01YnZuxFlI8NjTukW9mCk45Wcd7TpzprzdjtfoAYXGw89rNv2m9qscSe6ta8n6r0bbUMxLeAC6v1Fy4hZsLipVx53sa3XrW7UqHyzlPyJCIpe0Nh7Y9qNCurTWzi8opPzu235B9x+ULztIXJLvauIB/8e+8Oln/4vmcMffjNjrNa6R3P/h8Pv9Ughu5gqijvxiFZIf//jHX/Pjc5/7nH2541qB9Roj/NxFnTo3zfMoWLz5irLmcNrGv4WTPg7AXZ9ZFsIz9dZO15qD5wBpeqhfC02hISjfmkCmqtgcCKdr2138RL7BY42Xy7JUSHd4naFjKq3fyQLEZgwfYvXWXnGdqhspLYrZgXpJwjk9M1AfgYTgvZPZ3myRsXXxb1j0UfqFx1ZcULXfaw9t/B+z2UXlgEm9iK1DjUirMOGT80JtIFuzoDSeWrPgKoR5v05tIEg2OmUNf/6CeeMxF/AKcoVhJFmHtVgfkVgJwh2ZURECioEvDkalmDjKAycWFppEIYb0mwIQ40S1AViw6+JWGp+0wMuX1bYJr1Vs7t5OGS7Gn77iSOsMzAzHpwTjo4CDhyQ3aQ6kXW2W3uo8iOrOOt6ZoltOXFC4L8HZdnin8hQ5SUsxOdRu6e1Nnw+IbarTiZwg7tQ3HFXeX2og5vgstEoDAYtMI/sPql3Ke93M4SvfvE+b5PN/8m6JGZS4CvpAu7M+oY1i5i3dKrTG3hy20OiSjX0dyIFXmwGvG480FSZBv3lnlhTXg20IG7LLSPNb5lCvuFNYcrDxkmunCKPZDbWuSsOjuk4qIPn9BFfvadWpn0w6xgOd36+2tP/JUyosdQ9KZQterKFFhPLSnsEz6Oyw7fr5Ed3/yFJRBUrnR0flfebZLJLjMcvcf8B8tQ0NhIs2sfIyswXr+ei0eFdYotDew5yVYg9LifWb+lVEwfFhg4vN5KXmy+3q0rOZ63UIMPwn3ickZxWhXKPr9go9JEGhcn5YhZ025dWkWlUUeHDBUN9RPCBSoC2GjQn+X488/VNuXlHUPHPCYo9dsFu//let5XhSBT6ecPAVvY0N8nfDqqA6M+9Qn552pSJAZqdlDsKk+VAb+a1t8ncT74kMwMaEWuE6dOwaEtLZ8elZ83V3WmQuK8Uk6JWsHXhGifRZIGS4ILdy1I3kJ7Y9vVwrKNxziHkmflcqbkJBy3QEnScYyslkwRbesctxmhBo0L5fdQpI31rGfK9ckicX7dd8X4NDXkFMEdvQEoTfOb8q42WZpy6sWGAZvhQu7iEVt6x5Khx3DLp5hOCDfyv9wCsTUJB+2nLyYvN5bOyHHEdm6k0tVgaQp7bCj6y3yYkiuhq0BrHWKPAdjiKI1qbVRKkqLhqxST54YFiwXPwlh5hGgnr+4K6xRmu+D/ywlccmZHEiVBbkvqVZB10Qt8hTF4S+kenIukXYtP/MmA6dPGuKrKpZN7we41qL8AsPnNvxvfpSx9d93dfJpJQ/v9DHu9/9bvtyxzUfrNfwG7m7/wesvKVbnAaUKvTc2XzgDsm6QJt0SX/HZBECajHisYbLWXe6DfhFYMWPKbu91cIvXVGLw0ioDwcd0RYCOuTxnYNCF/ClkpcS7t/wbLb0KQSYQgypOgtlaXrG/M3NIlETxlu5ab/4L1ghsIhD7IRQ7ud0XJ+wyqUr4nOJi8Oik8rL1Z2vW3rXUWt/dM4VVmvris/ZlJd7pxdt5Z4tVn/JSdVp7UEuJdYD12x5OQ2PCNEKnBwxT0O9TsRsHBCOcb4mD45TP5JoTuwsbCTRO6PNkPlmVq6eTCMX552Hj8/r3OHx+EF5t7iiTVBO2OPzjoOTr1hgKeVIxzhrp535qoizXnPFWyigopdNYOnuPms65ewN4K7otVIUclqeX3YxItm888gh3BiVJOaW2DVs7dQmQ7QLbTkVDUjNzw3LPkIhuvUxKcC8w1MyL+XkD8JFy7Pl06OOd7SjW3wR0IxiS8RCYysqbnz5qoUeO22l63cJTVm/c5vlGrxWP1YQsZcIG3HP8nmpLLFv4P5tDgopu263s8koVVWc0QaivUhhjYUC/CfZZhRLagPCsQo/f9mFbPd26V6AboAU0KKTYSMD88u1dcsd2SKj0rU3DIkHREAy0VDBZ87Z0rcctLZPDlvmUL8ChjlY8PpAuNK371ALXaHcKxuW291t4dFlFxaM59Pz582zbcDxchZXRCym/YmhKm0jSfRXkw6Rwjkbx/CGiEKQQat8L12QeW5pZdXs+n1qj8IHlNP4uUlLHxu02IlpK3c1udigHV2KXxH6Ag+nPm7ZnoQOD5ilcm/5+aEL064QTSYt+bbDFv+/L1rmrUcsNp6q3X93gBIvJ19yaNrEomV3d0rFxsAqQKahcIfyJRGpN33DtHbQGgRp8XrFleK4e/WwVB+19R1xa3pqShs+6rrNdrgUlZWqxV6ZciISeGWgNbTc8LB61Xugza5icyWlOaqQdgq8zjqlGUD+51rIVZ5nMhHSPApfmnfea0sb4kKJ5wjpm9dWO7TkW8PyUFs51maNJ1fcs0obNojZaELPClFF2HZQfIGO00ouxXwKZGbdCF6akaP+6tE25b3iQQf9Af4ahd7szRF5/1FQLR2MW3ilomc/Op1xqt9aqoDoCxTasaCzbwA9ZX7Vgu2Z8/fXf6fWJrixQp5q4/59P+nUvVxfCiWvz6qraxJcSDE4MesKZgQy8ELzBSsPuoI83xKyJ/7mR67uGa+HD9Z1H/nhr9gH6+W3/7/17PpqjL/6q7+yH//xHxfn6rrrrrMYaQavGvv37/+C39fd3W2/8zu/oyicLzQId+bnbSoRv5RxDcF6jUErgUWIzR5lCaZ1la4WB3N7vTZ3S4NaXVgdQGqHp9X612ddwjxKskDgqtok1ROQMWNuf7/NPdBtY+9otZV9CWUZIo9nYZh5oN02BoNOmRMKqVhK7qgZO7KANsRs/aY+8xzbZ9WOZi0Evq2D4uXg+wS3SFE4Xo/CnMkhw3xTxRUqQKwPyqiyPCLT4lfV+qcnriJflXVUan5ttNVTlxSATHHFAuydIPbDnYrjYylbvG/A+U01NVpwcsU8rc1Oyt/cpLYmxaW/rVUbAq1ONgL8ksgM843OSp0nZVIwYAv3D4iDJd8cOFHEpWSLtr6n0bUQUmm157AXAP3AB0q/M5WVIkvhuZDFOZUvJp1aDpfpUllxQ3y+6XTNXBTri2hEJ1V4FAvXN4oDgzcZKBmxJ5i+wr9gc+R144QOMVk5eqfOa6Pkd0MQhwek3wVCIi484ocNqeWw62j442e1UVPAgTZyii7VCiFZbQS8Ut9REJO7WO1qtfljHp3cCwmfiOcoCym8aREjNGCuUMCiltJDvGe7zd2UUBt6eW9IkTDwVWgVoqSSeqtSdaHltx+UEopiSa06OHMbKSdrH54TBwp+VmV5VdcAtIBilGxL5nTir1+2tX1OrOCFq9PXbfkGs9y+PoUzr+2t13UjraC6pUcqVbl3x8MqboJPnxXvChSBwGTMS0FhOMBIcdbaqMBfWl5sbumhehUO8JPknI2r+ShRKagMS8rgrAx0CuViMy0PdkpQErw0q1ZteOHz4aygTKGZdWfgOzrhihLsPS4vi0uExxJzhJ+vormn3Yq37rPIfN48B3aKeF+sD7uUBdrVi6tqn8q3qimoIg6kQwUv12duRa+XOckaQdtW+Y8EVbNh0ypn0eagFAvI2oHCSs/V7LI1PXSpthBVHJdzcVlWLHAVw7NZFYiom0VOx6SWaCPa84WSBZ4+LdUixbwOTOQsIgog/mlzfYs502G4S5q7Gym9BmxQGBRXZHJy8OBraAsSlUP8FGsg3mAUlhRXa/ub5L+m4mrYtcsoqINTa45gjzFyc0zB1VAB5n7oJls8GFZKwMLt7RZZKFpiIm+rdwxYcqvzM6Mw7XkkqeJKpqW0fs6tKHBeAeHNDh1n/uj/haC7yC+up3IgRydchidkdhBD3tfcqg7KKA11HQhO38ioqFLBm8monewdm3XpDINd7n5SgLY3OnVw0OeK3OdGdHh8+9Gf+ZI332vjn3d88zd/s42Ojtp/+k//yW6++WY7ePCgHTp06Oqf/9igeHr55Zf/0f/H1oFQ6C9nXCuwXmMktzVog0n1x3S6pXWWGqqz1UPNkjZDfud0g+cQnICFIwHL3LrD0ttcNI2iOAolOb6/9AfvVguGzaMUMmt/oSCPo9h0wVb2xG1lZ8T8+BZOl/Q7QJp4cBPnVizVjRrJJ5QscSXp1FEsDHVR+a+Ep1KyZoDfQmsme99B/W4jv6/OFU+cgpGlQy5XsDRweTQk1SBkdIU/FwraPMTrOrLbtfaWU5be0+baWfBS2h3y0PrUgiO6bu1xfmFs1pzuiBSRqtGn9pOCW2dWHGl9YUlwPN5KMi2kTYDZabYqJVGV0N6a7LvYFre6T5yyyoURlxvHRE/nFTOzfH2zik3QtNLMnKI0uDeQsymORIRdXpU1BO0P/dzzI24RxjNswV0/7kfzHzynEy6igvQ7jjlksFyW5w0E5blbG3Qdglfmbfloi8iynIAV4MupnsKV3MT+XknAs9cNyjkeZZbalDcdNGtpNM/CslMhRsJqy8JPg08XXszb6g6HCOg9rqes79NFZRvWnVxwjvKprIXOTlilv0Oblfgna+v6vzfc+36hRQ3DmK1WreVkVq1hWqUcCPQzQeFu2qMTvP+FC4r6eHD2f6jQXHxg0Eqzc/IXKy8sqdigeCeXb3NUpucseHxEikJfV7vVffB5i3/mrDMULVes92OzKu5ojTW+sqzYETmBRwNWnVu0tT315nnpnFpYcI6wRuC1CK1k8aIVXGtNy3MsmRLqOP7WBgk5mFe8N09/j35fdkebs6ZIO1d2hBSS9MOpe+G0lJYqfJHvL6ccOiSPKq8sDRi47qvowAoil9ezgUhA3ko4w8dCrsWfLwudBQHiNfAeKYLkBQW3jjahzyuUTHMeRHT/DmeZwf8znfEPe/C4Q0VBqyhW+PkUO7GoXi/oNC1VfietcbmRx5yppvhZmGNu7zffxQkhtPn2iMQHGKhu7HctXp4x7ysXHdLS02XlPUNSdQqFqfEdtRbkCorYAbHh2XAu+UVHsifUGTI5CNVgi/nXC86olusXCijRwltwmw0xV3wtB5XQelnCADibagU/d1JzlFa07g++ZYtpFYkgPv4cBZoJbWo+kbTgE6esWOe3YKpiDceXdD0wSgUlokhlNJ7POu5rLCrRRvjstJtDNSUvbXO4ebT1KNAU49TbrWJqc2jeccistUz1fFwccwR/uGukOoAw0uZcJ6DahZ7ro1B015L7wX2ircr8RUhDEfk6crC+kjbh16rR6Ojo6D/4GBkZufrnPzZ+5Ed+xG666aZ/9P/JMPxyeVjXWoSv1SJsfZf5QzERHhffsdvaPjVi6ev6LHZmXj4wZMKxsbAA6HQYClp6lzOcIyoHmBwzQP+jr5jnuj1XYyiWbmi2lhdXHA8hFLC1vQ3WcHzZVg83a4PK9daJ7ItjNiotInXIfUNNl7xrp9U9ParFtXL+inyK2PgYgsEP/lfLt8dksgevp9BGVE7FvE+eNN++HTrtKR4nRzzOhq0dabeGZ6fUKpHtAYHEF8a1aIFGbVzXafFPnjDP1j6R+72H92izgXtS2darRQbTVXhoxKNokcKEcXrOynu3KDS3/iUnJ6dlxwazORSWe/RnhAhBPoXnQYbi/K3N1vGpCbnoEzobXM6Zb2JO6Bfjzrt+SURhNrHSPUdkmko7UgTqvzdQRYp0jvCAjYe8s6aE2pb3xd5ppWO7xBVhUd7YVWcNLy+602nBke0ZfB08m/k3Dyr4G84YMTjEBflHZm327Vut8+PjtnBfv7W8suZaFYmwWnTkOaKOo0ChPUxxB+pHQVBOJlXQ0d4i+Nq622U9AHKEpxEF0NX51hQXykTUEkglbUKQomJj6Co5/ba3/YpFx5O678ndzRa/QICzz9KDdTKjRAHlfemcy1ykyEgmrXrrIfH72EwwuQWRkn/Zy2dUTFKo+VqbrdznxAQU45utatSEzGe4R1gWEJXkGZ1WWHNusNkhXHhjvXxeESvl0xfMT8j47JylvvkGS4xmhGZqk2pv1utGgUerTaG9q0m13PClij/n5vxmJI7MKHNFy2xp0qa/aRJLoUELXaHMqaJQrc3sPewfJA7gOcwWZCWBLQDZmhDqKXRAbVwh4nMKVNpu+aLj+vh8lrtpp5WiXkuc4zCRs42jPVb30rQUoijQiAGKPXrWyumMeQ/tcq25JsdPpMXOgUd8PdrZiB0aAhZ94qI4h8rdOztslQPbnWcVET58LYM2Y7rWnmaTRwRBu49rx/vjuZ1dUbFPe8w7NS9vMmKclN8I+tLs2oDYoqi4wK5kZtY82wd1LeWenvl8dBSFBtYXuiY1RFiCDq7/hXG1ODloaJ3KZKy0uGw+eHnYuYA8h/zmH19Q0oWMc0GHapE+kOmf+r/vtaP/7gPW8uiEC9Tu7bblmzosvFp2bV1Qb0QxXLu2Vtk1kKrhO3PFtcwpPsluhYvVXC+U1BVkznU/tbXe3ae1DWchA+cKEUwup27Cq1uEtBiFFoM2I0qAVsFzGoup+EIE4L9Mu9YjlHTza0HgK21N9uHH3/O6tQgP/fW7zQfS/2WOciZvx9/x/zaY+tpw4xqC9Rpj8lu32PybB0TSxdQSI858g09cJlplSPr9M8tWTgQtfahbiBB+QOuDfhHiFw/HBFnbjfu1adM+yWxpcPmDCngNyuSw7lLK1g80KUSaDVFRKLOLljzYoTZj6zOLgsMx6EORg1pLSjkiaXJF8+H1E/CLU1U5NyxFHWaEyP8xLAQtofja2FGv0zefw1cIldmzf/EeLaRsZt6LYzI5hAwtGTwZXeWqzE/5f+TvoAmXfqDLrKdDRFhQDEnHX7ygVqpO8cTXQBKdXLT6V+ace/2GW2DFXYPcGg2qqIJDgYyagNjAs+fFN2t7ft1ZIBDOG/U6bkyppNYWsD4cIVqcHn/A8g1+m31jt4oBUCdIp5uuzfx81HCyAmCy54uylEClRmFHdE1wfNkCx6/IawpXcNoztAyuFleRf6uiQ6qsl4jRAFFZlD8Vxogs8p0POSf4tqcXpbYSp8vnUYtOpqDnpvXeKRApCHG0B5XzhsJCKfyDfZpjXD8UTCBrD63+gThuqCFBPEDgeC35/QMKGCbSpPryWQXgUnTdF/8Oi9UcvYUgesxm726R7J1oGvmJEcrNRjPYYwvfvE/FHa0ttVlo/9bFFfCr4mrHViE7+AhB5vYcv6DiKtkfUgEmxBI37DCxI24ZYW4QpQQaCiIJoZp2ne3aqoLC39piFSwlbjxg0bmCgoGxIFm9c8jJ81MZfT3B0LSjKq31lu4KqLgi65I2IsUFm5tI1ARYk6wQd2aUoIPw6jB+RT2peUMhggoy5LNAsqCvVXxKzZKDsbLTFRvyOPKSY5lw75F7vZKSGETE875Oi4ysyOiXAwSkbAbqM9/QgA4aENLZ8HNvOaKCWAgnaFst2LnY7AjmiAcIbqe4QilLoY5fFAV/ciiqopW2NxwfSOrO68yvWBsyMD2Xxq8q4/QB+TxfcLmDLRG1vfGAU1zRJrrC1/EMrm96PEXl9K72Ixw2BkUIyBntxskp98zAL4xGHJqaK+hAOffte1Qselc3HOLX0SL7EsVJ7WvQdQNtY56A/qoY2nTQDockvgEJbnlkTNeIAwzzLbJYkg8Z9963nrHq/m3mQcHa1SjulX90VmuLbGTMbOL/23aVK4kRMApruconwha/sqE1lgMyBzBEIrKxiEZFaGdwIOUwBdL54PRvqWBClEBkGc+vWqTMI9bxQMDZO4DwY1UCraKtSXSAa+Nfzjh37pw99NBD/0AN+NUY1xCs1zgp3Hnwx8wbicmHJzy5XjPNzNjSG3qt4WLGff78rNyWWSxBRthgF2/rsNZnl3SiQsXDaHppSaae8FV6Pr3ieCetCZu+I2Z9n1yx5PYGyzV6LTZfsghZaCxcGAHys89NWGm78+7gRAjCceRdH7DWz82oCEs8M+Jy/QhWpvjbucWhTeSTHd4luB50h/acRi3CpbSwZL66hMJK1+7cYg1PTsjQkjahyJ7FksJ9iagIzWyYJ5lxCyXtgx1d4q0oXgRuEifAdWdECLejenFEJp3lK+Pm29LviPwgabTraohSakeTxU/OKqZkkwQNYrAp35YhIqNSFrnb29Sok67Ua0zeeEyRKrQ4/LSrZheumgYio/a9cM48O7e4xZHNiA20NkA72Bg3x83f+GsWG3f8FM+pi66FSZHW+4NW2NZxNbwYt2e4NNosNpJyQi/ecUBfixEni/PSLR324v95tyI6xB+hpRvwW6Hf5f0xCjfvFjdKPmC44aNerI+7TXB63ua/aZe1/sGLasmV2xpd5FB3s3nPjym3z/foy3L5pyUKV08oW4/jiqgVhanopoigWFRLUNYNw78ifpTn8oSVUymzGw44q4znT5s3HrfyRlJIJ21H2obcQ2wEPO0tcvfnPWr+8Dpx/Y8EhQyh1lIxRRQKA0J0uaKDBy0h/IhoKeXbsGDwWfzsotvUu9p0SFD2XrmizXjiWwes+4mUZTojFp3KCB3lQDB1d8L6Pr58NSxYaGx/swpuDi+xuYIle1zbp+W5RR1CVDSVK+IFka2HUEGI5TxFvDOqnL2j0bo+vShzTdSWKF9F2EbYQXt1qF0HCdSvPoo0OHQAtdmS7gteZ+FPvCQeGM9M6cqoTHUxCKU4V+B5f5dDnXLkmjqjTT1fnS7jLtcetejJKcvv6lKuH++XwwftMN6jWuhquwZ1cCFvEKK/VJFXJqwKMgMfrYYkV8amzAuJvi5hSze1WcuTs45z1hRXuLRQZu4jiMZ1O2SkSqGvxy0RtQqB1jXrCJ/y+bwK8UbFCmqN39jG3iarf96h34WWmGvH+53CER8+uGmY/0bG19w60NVea78RQ1N2c2R5Vdcpc/suRR1xcNI6urhumQPdEk3QMoX8X74wLFRQdjDRoOUbQxYdW5OxLogfXDFZzWDy2hjVfON6KTwao1BoEfDZWpvVutYhprZWBFDjUoCVy1effT3/tBc5lFBMTU7r9Rdv3KV1j+sQHllSysTHP/Q9rxuCdeCv3/MVI1gn3/FrX3MI1sjIiNSAp0+f/ju8qc1onH+KpJ5Op+2XfumX7NFHH7WFhQUFRf/9n/+ljmsI1msMeaysZSz03EXH/Rio1ym55el5fT48tmpVIlE4mfbhbxTWybf1k65lB/8qkKpY05NTMrgr0AX6nePi3pA9hwy47xPLshPA/b3+St6W9vjNLoxIHShXZbhdX79DbZNNw0oe+pb/e85m3tptwfWSO42CYpH3d+sB+W9RUCXfetgeeeantLHcc9PPyy6gNDGlk1mlo0UFmBYZj8ee/fP3iGTMSU4GfpzWQFlWN6Rkk5IrnXWF3PKq2j8sSuJHUDgNTwqFgo9Fm2vtG69zHkE7h8yWVoUWVfdtk6qNYoNFMIoiKxqxR576SS2AFAXpgYSI6JgiAv8/OPKruhZ4OOGg7m1pttKeAasOdmuDD5weNf+pEYeMEZMx+G67/U2/rIBmiisp505cUHuGokomirSN4KaxuN7+i3bkuz5g8eENV9SVy1a6ca/+78Zv+TXHvxpZsMxQg4oTFuOryEGlKoSHogMLDMK04ZlpM4NM+9RJtWQoiCnmJH5gEzywTcgThQXXTCfkaEQZlGrT5vIWXayY58AO3VfI/ags4Qetv3GPBZ4h384rNHLlmw46xI5FRATdrFoyGI2imlO7KxwSusfrxmxR8nYK8UO7NT+wINDoahfSCZFfxRUFycVhFa1EMrHxX/i+ZnlqMZdAJj1rSfM/fkoF8vg3tFrmQK/72NspTg8xObw+uDYUbeGTYxaed2ot0C5aL9rEcyUdWGhddbyYk60HlgGoyTCJ5b71f3BSX8/rV7FPpmWjM88khWCjL2iNF9IWTDorC4QLtH7m7u4Q+sphAQd4mczWR4Wqcc1L0G/YiMm8pKjg2QWJBM2pLdJqXWIm2hqVWEE+ZeWypQ/3yBTUt3e7levCOlz4hwbUDsZHjrYsJGlQbkXzzC3LXFYt+c5my4M2VasWPTur+xa6sqQDAXwi1xINyrBUfmQIK5ZTElyERhbMs7bhAqjxTTu4XUgySQ60xODQ4ZslvhgdP9DgDqcyxIqCOUYrEs87kDcoDCp82Igg8MN5K1dVXNEmhIvKILWCQgofv8hiwSF/mby4abkmnxUSXhHZN3MWEaEQmUTsFAHX5dY6eUrxp9zmsVbZM2SR2YxQZeaW1oB83gLrNe+3ZE7FL3Ozcvy8PgeKRHHFdWGddYrOGj8KJ/nxRRHUMVyVCjCXtwcXf08HIq1jNXSageCC/0Nw8eriSu9XxPei4rhydx7QIY/YH1+6KERU+ZmY6r6O45oP1hceP/iDP2iDg4MqjqLRqJ09e9aeeOIJO3LkiD322GP2Tw1MSgmMvvXWWxUKzc979ceXM64hWK9xUrjpvp81XzBs0StresAVj5Mp6ZSN6zYn4XzCa+1PLEids3Fjv1AQEUs54RYqluwLWd1YThYPtH6wTqD1Qztx/uZGC6Sr1nAhJRdsWiUNZ9Zs5WCjvr/pqRkRTTmhL94A2dqsHDRrPp+3uWMh6/uds2pbzN3apEgfIU1zKS1uKPDqLmxo4aFlRcGEyolTPSo3tS5a6tXGYPNlkydahg1QSqDeVm3sy0ebreWRcYdo1YGKhK165qK8d+5v+fci0oPaqLXgqZmVsrlhmAj5GxQKDgTO9RQmkzPiZmlRrHFbdMrHOgHHaH4WHKGA3yo1VR+tJYrCzSGiPF4+IIo4y3e2agPD3HKThyWFkAJZPba+r8VCayVb3hO0zifWdDrWSZr3FAq66A8iRx77CZc1Nr9ohQND8glStAqLdjzqLCHSxA5t6PVBFt60gsCTiyxG3icIyNKBhL3yP39YhUrlyG7zj80LPWGDBLnEtyn4/EWFgnNfKXRo8yE6EDr5zKIK08LeftsYDFnDJXLkUBf6LPC541Y9ulft3MzRQSEG5L5hDUKkCqaLylKj1bG4LL4KGz1FCvl5shvZ9qNWGnFROWy28KYoZhmbhqAgXigSg3/7klSpCCcohmmvCIG9MqbWIsgEcwmOIGgo94OhZAG8paJ+WUKQm6j2KTL3PYPiFBk+SI0NV9s8qaN9cgaXtcBGTrYBQlSrVRv/xk7rfWTDebkpZJnczbT4WtXhcUs9sN82+n3W9T9PyPZEReeZYZv+gcPW83/OW7W304VhE8tTCxfWHKQ9up5WAYLreQBVaiovbzZabrIQGV6ScKKyo0/fJ8PO9by4eEQc0daEq8Q1hPdHa1rmsgO9QnI8Z664MO1cydZ311v9GbL4wuL1bNy+1eqeHXetWLIRUfuCDq+n9CzqdcyvOssVrkdNUQdSRjuUolrXDzNaTt1EN9UntGYpnBoyfc38uHr2slVu3CsHeUQDoD6FgWYVSEIFaTVOzem+yhCY/EOeNb9XwfYEqsNJwpeLAsgpRJ0BMHObCKfM4T6LXlk1o5hprJcZLvYk5csj4jUVd/Q4J3zuI7zIGjeNgydcLeYq8+ymf/Nrsjkhjkz3imvC/IFsDhLO60vVUHNU2yAUFVN+ZOj5i+YBKRfXzJH8hYLT7scOhLZmJisUnNxUWrzQAkRXmJm7qjxk6LDR32vrB1otMZJ0XMhtPyoRDeaqUAqy8ao9+djPvC4I1v4Pv/crRrBOfeOvfs0hWC0tLfbZz35WdgxcpxdeeMF27Nihz73nPe/5Jz2yGhoa7FOf+pQUiP9c4xqC9VoXRyagnJbTguMxF2SDxclbEvwTy9ZyKmUZXH6jEas7s6QNGfUYrUPMEbOtHrUSDn/Pr4srtXZbv342pzSI3M0PXtbiUdjVoyBncriaXlm2pkdHxRtYHwppUWx9bs3aPzFioY2q8tF6H14XMRZOR+en56zUEJbBItyD6pUJ/Sw4G1LqgRYMtEqSTdF09f1NL4pMne9IyOsGAjCFGAajZMQxWp5bcgsZG+bElMizcJ/YhPF7gkvF6Q6EBk6HUKBswTK7ndeVm7l1Wki1ubU0yx9HjuLTC27BK5WtumPAsjvabf2WAefEzOK3fVDoGzYN8MuQv1OkVnBhTmakMCzs7tXmqLzHLYNX+VelqWlnqlksWeyvnxPhufvDo85rbEeHO+2GgjZ/d5dO6yicUG1iqImMHtUevDnaerQHcLMGqeI0jdkoG7wCb5Npy3ZF1Z5U5EmNa9L+SceZgtxNYUTxzTXFhoLCAjI/RGg2KFoYFHbKgSRKbckZYlJcseHkGjwypWWEx1fNDu22+etjKngjT19SO4bWzPrN/ZZrC8tlnNfLZlc6uFXXwT+3puuf6g2r+FRx1dRo6aMDVqFVaGa3v+VXnNkohTJtzJ5Ge/wTP+LsKHJ5baC8ThGhMd+8YZ/IzyATFCLMR8xUERKkhxL6fbRu8fgCxRE5m/m1rU/tRNpvcI6QvuOXhGQfB3Tal674cXwfxCIcSHo+l5I8H5QP929x1uaw5Kiat7lJ3KjOZ1KuhZMvquh9OPun1vvXk5a+dbt4WBRibPgUb4qXYv5i3xEOSpACMls5fk5tVnlzhUPiRGLrAAcSib/4j5mSuDeRpYI2ZO/IlHiGbLzwxVAS+urrVIRuem6Jzxj2W3Qmr4NBtiNs1c4WkeQzB3td0HpDwuZurlNxpbm3ti4nep7BQhdeez616piDvj3bbeaNHS7GJZN34gRMd/HfawRpyzpTUopeiqszF82zf7vuCUUtdAeeNdY23itFpYqR9lar9Dk+FW7vIHkUnuFFiPcueDp6whG+idlJd4dqgeE+RYBFLy4J8VNREwpY8MKU/s2hAJ89/3LGvGRJcv8TQVEEuAcIIjjM0L6l5Q063/zCouYAbUJC4lUUNdQrzFph3hySagUU6CbPr9ILaMkSLE1xyDOJyjIRd99DwPSWLvPU17mfhyUHHQPQoWz27xRXDKFahYJijnQgbfn3Vl1asfALw0Ik7fQl8z1+yl6vcc1o9AsPWoCJROJqsTUz42KX+vv77eJFR814rdHY2GhNTU32zzm+qgXWf/tv/0390Vd/7Ny58zW/58Mf/rC+JhwO2759++xv//Zv/8HXnD9/3t761reqisVs7OjRozYxMfElvz6Iwo0nVtQGdO2GiBRvpRAtjVo/F7LsIsRWby0Dq2qZfd3u30DV7F0nLli+3ixzx24rBz1qEWyGEG/cttXS/XG5mGdaPZa9bZeyCkEivAd2W/PppNpQ3kzelu8etBf+6N3mz1Ss8spZtTrEw1DLzePaYiwk/T3WcM6d+mhrwjmgAJEiCnI9i7CXTcWdgkDlsBGgLcKilOuJu8ibi6NCIWgzrN61xS009Qm1PuD/+M6PSdrOwBKgfPqieCoUNXjkSElIaCsuzK1RfR0ckGJD2LWXiC1BJQWZOJUTClN/YlF8F06bNuP4Zt62Fl0DCjsWP9qFvE4+J7+jdNq8Jy6p0ONET9Ho7+81//YhlzV400F3usY0c2nFgrX4GLgtsfmyZTrCkshzkhXJm9cNGkV78OyEzGJBfbCOGP3GZm3U+IuJWJ0vWOyFMSEb8gGCwwTiwqn6wDbFf+QbA5b49FnX8m0Im5csNJBE+E1EyRCenMnK/PO+Qz9tkamUNqaNgZDlu+qt++Ela35x2QILyavWAt0fGdd9L+0dUMAxraX1Ia+VQkTsuPBjNg//8cvm7+4UV4+io+Hjp0UEV7slmbTYi2NWuOuQVeYXVEzxeVrHFKoUnVzLMsjN8oo9eP59zkYD9Vlbg/nOj5uvs8OWvv2w5jL/ro5PWXl6TnxCFUn4jm11qI82N9qBM0vuWQm4VAAG99H78gUVacvv2CdUQyq+SMBlAyKMeN61IpWSQIAxc+/IHodqNMTVuqN1JBSG9tPIhNsMkymLPzsikr7MXKdRepbUzqNgobUGGhmaTykOh9apnMlp4ba6Ez4tQLnl0xLDvmRuRV/L/YPcTctaBH8K5EtLrpgMh6zwxiOaZ7SfMARlblBwYHtCuw0nc+gFfA8WH8yRrg8N63tpKWMrEn9l2nKDTXrWOfTJ9HdqXnOo48k1lxzA+84VrNAYFpooJWbIJ9GMi9cx8xzc5VqfzO24ex4D80lxmDAmpfhHSUnhpcOR12vBmWStcKna6p6ExDhy8qeoLZQt2xK0TC2Hj+dDWYeYrsLbaqxXka0sy2zBoemgUHxuW59amGpPEuQd8lumi15tRQcDb11C3oFzdzr1arY9ZJl2v0Oe83kdFqsnzruCvafNGRNjv4DIhoMcB8twQHw5Xivosb4u6NPrSneHlZABRYKDDR+YwL5aXfh39oKkO4QoKJr5n0qZJxKWB1vlul1WvcG5z78eY1PX8JV8fC2OvXv32smTJ/X366+/XkHPTz/9tP3sz/6sDQ0N/ZPf/3M/93P2X//rf7UMXLyvFQRrz549CmTc/Hjqqaf+0a995pln7Fu+5VvsXe96l+C+TSv7M2fOXP2aK1eu2C233KIijL7rqVOn7L/8l/+iguxLHTK/ZFHIZAUHk6v1hvveb+2Pzqj1kdmCEabPSrGA5foatGBzslrZGbDVm3tEbm7/m2G1UPyoo7v8tTiHshbE5WOt8rl66iPvFbG768m0Rc/MaqHwnLxsK4fq5aosxUs2Z41n1sWlik2krHD/URc7sbyqRS80uqyfKWNN8RayamNER53SDZIoxocgDJJ3V8ri/RQPDJnv1LAV9vU78jKePc9dMe/yhs1/71FtKiy2+O1gbgkPSaaPEKM7267yMSA3s5EHTow4/6FMUQiNeBbD4855++Kw2dkrasXJLRkk6PKYpbc5+b332TNaXNlUve0tVtrd7/yEVtbEHULNyCDmpzq7IIQos7/HsocH1X6Rog2peSKuUzG8E4oXVFjVuQURwjfu2ekcm2m9BDwWf2rYYjPu50qWPdhja994yHxtLVqMUe0xB2gT+NdyNvhXS+a7MuNQt7V1Ky0vW6W7VciGDfXa6O92utP0QIcCetkoKaZBkmjZwd3AtFGKzhqZl8281Nmk4F2ietjY8ODyArJdmXfFAKan0aA82OCnbRxz7R02vmxbyNL72q3vr2fNx5xlyCm83mU5+rwW+ZsXLbWtUe1C0L8bv/XX5OoOShF89LhO8bS2ZFQ71Ke2rNy7KxVlKEKkJyYGFEEO/eNzlju2VXOh6X8/o/aKMhd3Dlr2jYes9VNXZMqp/Dw4UzOLLsuxxs3RASSb0zUC/ZKp6S17ZHPQeCZluW3tVupv13UF2YQ/5x/o1/2kJbP5HhlYYSxf5xANObjPJeXNxM+j8BHfqLvVghMr9uhnf1zoqNp22YJThKGyC/hdi2q1llHJ7+A+rGet2O5I3BRiiE7wTWKgGA5NrFlmf7dDu3g9m8RxfJV8XgstZpUEoXxBWt8bSaEn+YEmq3vojNmerToY0bICKct3Jix1/YAZlhUXr6ioBXHFiZ/7TovYt5FXsoFa5BSTEPJ5zkCLljPuENjXqudWbUbZE5hTMpbKtnHzoNpwFHYyNeVwA5/owog4XaWuZlcMZ3NWao6KP8dz3nRizYpRj0xFKVgochOjKet6ZNG8j78iJFgB5QsbcuqnWAPh0nUGocKVn9cKuljjTfFsYsGCIjc+Cs8zc5X/GZ8qWsvJjC3c1CSzVw6X1t2meaF52d5WO/yCfFVVvPK6ZdeAICARlnkuPwuUeP6GOvOtZYU6R2dzmgsocDeH4sP+kYGB8F13vk/vB1oGBs8oCMk49W1krx4Uro2v3vipn/qpq8R0iir8r+BTAcL89//+3//J7/+1X/s1e/jhh629vV3gzeHDh//Ox7/KAouQxo6OjqsfQHv/2OAi3X///TIG27VrlypO3vhv//ZvX/2an/zJn7Q3vvGNql5xb92yZYvQrLa2WijxlzBCowsKc16+rcfCz12yUsQrXhUbd+j0hFAstXtGFtSCYhGZekPcej4xaw2nVpzrOzwAr89Cq1WLLpYtMe7iI8rDo9bwFy9pE6N4yAw2aBGbfEevrW+F+FqxurG8hUeW5R+E8SgGpMlBRwpeHwpYZceAku7hgaGIIhMsMpUUoZ5FLDi64Ph2IsESAAEAAElEQVRDpZJOl+JpZLKyb5DZ3pUxVyhFwrIq2DSvxAeGArHzM0si0irxPuw8ceAJsWiB1qDE8b94QdcK12idbOWMHNBJGuJssT6oKBblOe7cppaNBtYN8GzaWiz84MvOA+nW/VfNSdmMyPtC0o+lAWiNTDs5RTZ9t/6kCAut5ORlBMcFREnIBrluGEziDu7zWXp/l1V2D0qeXf/4FUdw9vss9PgZvVfaZ8TvwA3BOydf5xEhGqWUP1M0H+0kLAYg0oMcUBwUi+KMiQNzYdRZXWQLNviTrhj3nLgoXyDaSbQfvM+c0vUh445Nsv7FWW1sqOwg4cJJc476PudK7/FYw9l1kX6RpINo5Vujyo2kQI+Pp9XeQclJm4qMTAo3CgR8w1j8KcJK8wtW4T51tFlsMm2ZrY6knhhOm3/roAu2PbpXpHdt2P6Ac+7e2u2QPLyyOloUW4PdA1wkEB4XXeS1bGtQrSpGqatJJGQCrlEtplD0yVm7VhCh4tpw7TJ4a0IJQbKiERWwwRcuyT9OrcVUUX8y70E280NtljrQ4TY1ciLrQtqw2dB9lyZE5K6+cMYpGZed5UDo/IzzYzqwTZ51cIdQUGIVkW+NaJ6C/ILqcO3J0VObkH9HnWhCLWPmbrogsrZyJAe6rTw7J1I7RUj0hREhlYG1nArFtaPtlmsPy8aBmCwhlNsHXcuaefLCGQVbUwAjwOBQAZcouWsztcG5tzPgwWHzQSuV1qsyJfGoo0CZXRTx3YMSD2J8DXVCiMF1UOEYDMoeBGsHVHtcg7pTi05YUnL2DiB1FI/Yf8BHY14Kmea5R5GXCDnkp1i25pNJiQg2ttQyEU9fssrIuA58cO10GKANXiir8AlMuoOfiqu1pJBC/LhAxXnNUBdo84Nsy6wThArupN9vkYl1W9sWUTwOiBjXgrWLOCTWMhWOm4pSxCz1Uc2ZTHekJlrI6znXQSuds84PX5ZbPdeIUOcvNGRE+vf+rigdIqQWU85GhEIeygNFNUUvQg3CrF+ncY3k/oXHfffdZ9/wDd9w1Rz0woULtrS0JNL7XXfdZf/UAKyBq/Xe977X3vGOdyg659Uf/yoLrMuXL1tXV5cgvG/7tm97zVbes88+a3ffffc/uKh8nkH1Cklt+/bt+jxFFVDhxz72sS/vxVWqChcloFmk3HJVhooY3rEIhE6MidcgtR1miqPT1vFi3ubv7hRpHA4IPB58jBrOp0TYTHejiiqJR4OPFTyXfH+jrW4P2MZQ2NpeyVsgVbWVb7lOCxgtClqFZPCF1ormz8Fr8FvHkytuURkZV/gsGw5mkhR5tOuI54HPkdzTYuvHuhUtw8IAFyMynVFYbvHmfRa8OOMyD9tadILzzC6JpN3w8EUZgcobhhYKCA8ePJChp35TSE95bU1ZieGZlKXu3q0CQ6hJOm+hl4Z1MiXUVy2KckXoCygYKKBaAZDeKQBpyQR8WnDFmUBOHg0pt5Ak+9yBAS2cbArku2EZoIIA36MXz2oDy9y9V+7b8k86d1nxLzrZBgMWe2XSfIsbauNpswHJo9W1c0h5dvLfGV4S6ubLVqzj8SWrxIIqNMSfIWSXTRBDZ4j4waB4NrQbUVxKGRiPWHkU3pZr18IJGvnl63UN8VDCOgNyM2gkIb0rtzhLheiTl6ySzal4QrFFu2fjQJvbLNigltO1ANuKRS4tqs1FW2l1d0I2FLhk83/k33F9QAdpIaIEwwiVwGMKXO6j0J31ggVWs1ZoDisQnGsPwd7b3SnkwBuLWnpvuzYn4nuYezYyKSSVYolNDYUbXLZywGP1T1zRZgOvi+8BxYRYz2j62BlL9UXFueMZyR4ZklFn9mC/no3iGw5IGg+akn7TIRX6tM+5P1gRUNhS/AYuTlloclWu9/J4glMUqOVlssmVKzrQIOGnvU6wN6PcRViwm1fEERE7Ja5RfUJxMIqUwWB2X4vsBJD2gwBReKESpAik1YsFi9SnRPPwc89cNO/ubUKohWJu63GoS7UqAUb96RX5YVGUcigBvVLuKPOVFvK2IYsPr6lIU14oz0epZHVnl3XIijxyyok9qhUhxhT/tAbdQ+h1bvG0mclao8iIRl2rnVZaZ9QCHKywg2D+p9LihclgkxisxqitHmtXnBXXFh4c609moN6q9THzTS9+HvVtqrNcc1CZn+HzMy5KZyMr49um5+fNNz5ruXsO6vCBnQJEeRAxFWxJ5z3Gs0Wbm7WBtr/CsYm9OjfmkGCPxwIvXRLHSocjPLqwkIgEhURF50uW+ORJWSRwgHjyYz/izD8DPj3Leo4xIkXNTJRSMmd1x+dc4ZjKOiQbFWA0ZPl9fTKhJSZLoc54mC3+3t9Z8iHQb6JaHOzEy0ThXDNfRaENGqbik7WG6ByPxz664OxhXo9xrcD64gecKvhX1AT/1Pjpn/7p1/z4V1dgUfz80R/9kUzBfvd3f/cqpJdM1rx0/t6Ym5sTfPfqwb/5PINKNZVKycsCpOvTn/60fDGoah9//PF/9HXk83kpNF79wfjIufdbCdVbyGO5lqBCa0FLIjMp58jMZlNfpwUKv6mRd++yfEPA2h9bUNgu6BQPOZsMwagNp93JemVPzOZvSmhTXjgSkcy9+6EFa35+2bJtASskPJZr8tjiwZAt749Zvj2qU7eI9jNZQdIsWJzKs/cedGaAAY9bxFdcMDUhuCyq0U++Ys986D1qx7FoyhR0dlkSYwxHhRg1N2khZvMn6w8nd09dnXycsD2Q4ej4rFvMq1URzg1n6yP7rqp74pdWLdffqMUR5SPtItoy8twBufD7rPHpKUWLhF4clgcX+Wzy3Zqc18kZBSOGn/CgIK6vvWGL1GeYjVJ0sSlIiQiqRnQIZOfbD9ri/QMWSDtOnDxzGpykXBsKmXMJgpiDep0iycMLyhbENUP9VFlaVsQHbdHQUs5lm21k5XJN25f3tPiOPZbpDLuFv6vJwvMurwzEiXZs5dQFKcaw3IheWBQq0/9QQZtssWZEiCiCU/r8rS0WnS8640zMRPt7au+xbKktdRb/1Em11zZ2Nmqzwvss3xK2Ql+TJfsCsk+gXTz3H45Y6OKc+FbKfyT4+YaftcqFK26zYuNZXLMHL71f/4dreqEhKCNIxBrR8wvmvTBq1//bD+geV2oGqLGXJyS2INOSa5R5w27ZSvB6addt3DJkK9e3W90LU3Kmh/gLJwvUgI2LFq1EEHuHLPbXL0iVRssK8QdCikCyKM4f3mpkCbLRJ16ZcTwu0Ds2vsn/rt+X2dGqjZHrTnud6Baem8gVDH7Djps10K3rxP1Frcbmy3VwSJizLlBY+oZrlSmuaHJVKk38p+CsgU57njzueIt9Dol1SQtB96wHfIrHAs1ATEEsU2FHl8jUmF8yUNPltnc4818iqc6NCrFE2p/qjaqY9FBQ+eCHTbr5CvpUKAjxg7sIquQh6y6fl9hgM2/SP7PqzD5BGRdXnc0I6AyfwzCU52IjpeKH90dhx/VBOADnUoHIkYByNeuupPUM8X25PQSQpyzy2TPO+oKDD98PGhkOWnzEqeRAhfkAeRb6DO+rLmGZFp/zZ2NdecZRNWTzwtkJkU2lohapuJDbBiSMye7tVgg4tAShTNiG1Hai9J27nfDg8rh+Zng+LfsZjD+Va4rdTHNC5HjmPEUyiD32KFxfWtXcM0Wb8WzSEoSfRpvT55E6lrmJYGbz2m4O7G+8Az06lGmNY40BKYuEtZ551lI6NGJZg/v/8sE6dzA6NWxv3+3SFL4WSe6Qx6HZYIEQiUTUFaJ79Op8Pv4Of6mzs1NfAxBy+bKzK9ocKysrAlFQLqLYg+rDfv3/erC/Qx36Yge5hH/2Z3+mj39KefgvusB64IEH7Bu/8RslqwRxole6tramVOwvZ2z2X4HzSNQm5PHHfuzH7M1vfrP93u/93ZPKq8f73vc+EeI3P3p7e/X5r2v6dyL9Nj56RSfSXGvYcp1x8Yaql8YcisINbAioJbXlj+YUK4OPE1lZpZExQf/4xaBGXDraJMPR1r8+Z40XCiI7d39m1aolbA7KNn1/i63u9FrrQ6PWMFK2YNLxwCChq7UGJ2RqSdE4kF4Da3nxQFjY8ZuBGExgsbLF2tsUlEsEC47mcDFYCMUdaq63bHvYys1x8YBAF4RUQdB/w26rDnVLVUWLEb8e2kie2gldESk37rWN+/fqtMsGB2+EQdwI/0+MTbG3uRYJUtLizukXP6u5b9qhxap092FFiujkCRmWn91Q74ipKN/GJ63+U2e0WcdfmnRIUdAvcvHyfVss/YZd8lUqhX3WfHxd94CNx5spWubokKWP9NnGUNS8h3cr8HVjT5NiR+RYPtgrZBApOQgIZpoQn1du7RH3CWsCfhenczYIsgWbzqYtcXFVvJKlQ7XIEVABNvi5RVv4gRvk3u9pabKpt3VK0g/aSatJmyabV9K53qMERWUqKwVUULm8xca42T6LThL7UlFoczBZ1uvkflHUgzy1fmZShR+Kr44/OO5aKrTSLo2II8L8wEKD+SePq3TW7rrjF233T/26Nij8luovplSUKVutudFCG2Wn+uT65/Ii++NNRHRJBK8h0KJSWaHaIJLwBhsuJGVsezWeqFy1S/9fxOUf4pbtD5j/yoz5h/qtvKNPVhRqj+XLIndzzzc5U/LEYr7QMqTtFAnb0e/8gFzMaRNRVGkuY5uRyjnnbOJW+sOO8L20prkMh4rW9fKBhIpJWlvwEOfv7JDHVrY1IE4R9iEUaag0eb7il9csMp9TqxM1XvT0jBAvKXCZM8RgQcQfWRBCRjGOGCF4blJqTThVFBzcTxAwihda/rQSQVxBglCyao0an3S+TLToQaDxI0vEJSbgMBB5ZdQ84bDsAyTSqE+Im4WQAzsUDm3ic41OiVcIUpXtTTh+Fa01DkHiXKa0JuEALzUfLvDr5IQ6A1UJPjweZ3a7vK7XgPWJIrhAEVH61lAwteDwauN5RlEci+pa83NbHp9xP5fNJB7TgYqv1b8JNG9vvmry6xmf0fNOoU1gM/eOgxXqTHisOLDHX55S0UL7n3u+saNOhxzPnm2W6QhZtjMs+gFZnxIc5IpyhKdwIwORAkwoMmg4bv9es8yeThVeQtOJFKt82Hwtza5dXfPDArVCFCIhR6Ho/PtACsk5xe6kLqZUj9yBfifwyJWs9ckFIccg/xzGv1bH+9//fgEgUHEQkPFvKDi/9Vu/dfVr+Pdv/uZvap99/vnnJS677777LJdzc4FBcYU31SOPPGKf/OQn5VH17//9v7d/KQOA5s4775QojsBoPgiCpr24uPiFW8r/4luErx5UtUB5w8PDX/D/4WjNz8//nc/xbz7PgL8Fp2v37t1/52vga71W6/HHf/zH5Qmy+TE56U6XhTccEILD6ZJWFH5YwbW82fZBK5NhV6lq8wgvFxy5tVCw3N37He8qEhY/Z+kt223uWNjqP3bSUj0e29jdJOXX6o6g5YaaVaz5u7ts+s2d1noib4N/PG2V9Q0Z77U9MinTUkiwN7/jV3Vqwp+K4U8WBdGzwEIQTR3pdV5EnNogXFfKFnvikhlSZBZiiJ5rLrONET/hFjs5XVcq4sBweo+dchuT1ID5glUvjooAzSaSIg6oq1XGnv58xcq0gsZnLPQsaJnj2Ej9x+s7M6afJ6L7lQm5YbOhNJ/O2NoN3ZZr8osUTnEoJR0FYFPMlo80WWBkXgUYG5TUTsDxkFbjISEhKCQpSGh9RJ67LLsJjEJRZtE2BYXCPfy5D77HcVN6u63+mXEhc6BFFESS/19wFhlCxapVi0+4Uy88OzaVyPi6onuI6NC1wmzVY9b+sWGnJEJdxebT1mJdHx1XMbByQ7t1P7Jiheu2yD8KlRR2HamDXc6L7Oxlbep4K6W3Ntr8N2x3J2Qk/PUxKU7h63R+dskiFxfkdbWxs0HmnyAreCRRqNBWg3gum4TuVivfeUgk7syhXm3++S3tztOL9vUTxxUkzryEF0Qxq2zBGt8ldnZBaBYWIhwaIPvDYym3JGzlcLMt7/EJLWFTrUSCanvCE0ORh6pKSr0zF23nj11QwUlbC4WnTGV5Lp47LW5TmQDyCyPOr4tcRFzuFzbUtqF4emj9D62KfxTqx1zVoiNrkvezyUc/fUptT9AK35Mn5VnV+Pi4igQ8qCCrU4CSnwlPKHdwwEoRXM3T1vbkkriMja8sySsNxA0+Eya1wZWsTby52SEdnMRByuoId887pWi+6FILUjmpSf0bObX41f7iOWeuY4GRLeh+ch9pO4UeOSGivSdbFNrFNeXZBIkBnaKAYH3gALR831Zdd9qrPPsUVVUc8ZdWda3hNaEmFOqEkS+tRop7PpZXRKTnXoESqWXFwQ9O5My8FTrrxFmTd1dD1Mr9nUIJZRRLe43wadaUUNApCDN5vWYQJP1fIqznTnFNF0blV3dVilapykNLXC+4anVxy96yQ4IBfh9KRtpr5Vgtjgm0PO3yHeE4ZtpDKgQ9pxxKHXjlspX62yywUbSFe/t0qK0/WRNGnB+2xGcvWuKJYXeQq1TUTsaXj9Y68wu7GfHyaspmUHeEAZHJDSkEeU20spmvrOmbpHaoCNwz5h/IVnlrtyuq5QPmFQ8QW432h6ct8Nnjmj8yJ63Fab3e4/VWESIuA7R405veZAMDA+In3XvvvfKZcq+nar/xG78hkjlfB2DyJ3/yJ7JJ2KTnUJjRqfqDP/gDda4QolGg/eVf/uVVO4Wv9viP//E/qntGEQjaxgcCOjpaFFv/6gss4EKgPGDGLzRuvPFG2di/elAN83lGMBhU9fn3PS8uXbokL4x/bIRCIcGWr/7Qz8MXilNhLUiZfDBGcnudWnCe7QPaPODukJ3FA8mJH9fjXK9rU4VXKxZZqtrGWw/Y4J9MWvxKUptB159fdEXG9kFbva3P/FmzpQMhLZir7zhgntPDUpZB9iZ6JXEeRMRrqYGI5d561PKtmHEGFMa8cFe3RR86odO6r6dLizDxKmQKpne22Npb9or0vunRJN6C1ym7JJ9vjDsLhtV1EZWD5ybke6Ooj5YmeVXJEmKNn1GxjXt2Wez0vJR2soBgIZqZV0vD29MpeJ3w4Mor58zP9/f3iCDqjUREek185GWLTeds9rZGxU2weIGgcLJueXZRbQvaRyzctIlEvsZVHwJzBbl4XIRZ2gKpO3Zoo1vdX69sxcxgvWb1LW//VbU8UamBJjG4HiKg0/bhRA9isbwuB3b8fvLNQVu5vs3Cl+Zdy2ktpU2aLEdJy5vr3UbMaT8cEN9DyBQtEwj+Pp+F1svaVFZ2hpRBqXndH3MtQIqhWw9YXe1eBpIlGc2CsMhbigdy91ZXmJ4b1r0lWJav9/f1iOeCQoy2tNAppPG9zSKWf/aRH9O8izx7Wfyx4EuXHKGXTe26vdb9F8MqduAIwaFiU5PZIqhEuaz2Y2w6r02QwqvuxLzZy+et8VzSBv9iTkisAsLn17VZJfe32ewtTgWKwsobjWou0FqEZ6fBpt7XpuKPTRHiv6KSuBeFkoqj5Vu7zN/epo2XHMm1+3Agb1VsEygQHCCUlck3H9BBg3Bg5f4Rl4K557qL0hGReSOj1jzICxmVFMiINOCOoWpDlacWke/fyLctd3Srirf+jy7IV4tWmCwJQK4g57c26PpAcBepWTysogtWhqO0uCyrBp4fvLrCDx2XMzytJxAXSNm0Qu+5+eed/P+63c5uBM7UelKtWz5f/6fP6h6IF+TxyuoC5LY82OVe66X3u9xShAVhv9AtWrl8LL99rwpVnhFaa1xzWt6baQNwtziUbXKcQGh1/eFnUfSkXfi1TIGLZZnJCrXbMehCnWstat5vZf82+dUx9zFZpcWo55d7SewNlglYT9A+5LC14CKiyFlU7BWvye8Tjwv0PjaV1b9p98c/e8FKB7bIeJWUDKLGEK5ULo+KU0mGpgpUEHGQpXzJch0Rcciqq2tC82U3U4tDEX+us1V2FiClHgyR11M6oHFtafVSaGGNQmHFQAAhekG5aqWBdkvvbJbq2p4/Lb4XJrKgw1/t4Yokz1fw4X7O36fF0Er7QuOmm27Svss+ysAKAbU/HSgG1B5oOq/mR9MJuv7666/yo/kTAAVX9c3B13u9XiFe/xIGBeDv/M7vCJDZHIA1/+N//A978MEH//UVWLD14UaNjY2pSoYv5fP5ZMXAeOc73yl0aXNgV89FQE6JQgAfrZdeekm29psDheGHPvQh+/3f/30hYcCan/jEJ+z7vu/7vuTX5ylUtMELCaDgmlm3ciSg4kDhr2PT8jJi0VV+3sKSOFcoq4jwkHuyz6zhck48LlpQuU5nwLd+5zYFLYMmwM1COdT+G8/Y6tv2WvMjoy53L+yzyX/Tr9P8+v4mCy9k5aMVu7ImhAYSL87xbY/NulgYxaUUhZIp6NbnEScjMZpR7IxO55Gg8xiilYAB6IUr5l3PqI3IqQ6fIRbK4OOnlEtH0bh8rEWIBUR5Fqv6p8ec6zc+MJDSQWA620Qyx8DSGw7J+RsjygqbPI7OGymd0ingVr/tiDbLk7/5wxZEzZOImz9TtupLp0WwD01tqMDQBkFWHyGzFDuNMfHIGs+mZIfBoollBRtQYiLvLCxOzqh9J04PknDaUu0trpiASB/wKvxVXBZO17RNKbKHFyy4VrKG80nJ13VC7m622IlpFywL5wx7BxZvCOgbjivEJkzh4plekPVC7PyS7kPHn50VsoGaLJAqi/ic296ujSnbUye38kxn0Oovpaz1xTWdlEFFVvc32OSbW614z2GRnld3xlxGYw0ZVJ4klgqt3yMCrpSWN+zT/YNrRNGwviXiHOIrFWX8cX/xAIL7ktnXJfTKM9QrMj1fs3pTt4xMld1GPiCoJq3Cm/ddbeHBidKmnMsrmoVr1ftrr5iXZAKMWK/bpra1XOPHxvVaHz7+Mwq7JqeQzd/b1yMDS0w1GSsH663xr46rjcTGnt/bb43PYlNS1T0CMabNROss8alTsjhoeX5JfD75OVEYQKqmBQsq116vggA0iaIUt3G1p7MF146tVlwKwC0uO5KwZ4VG54vuGca1n0EbHRTk+Dkr1TmvONkLsDPRNqPYbqqzjXt3WXR8Q+gWAzQqtJJX5I2+Bt6R3yckhzUk1x7RJp65dbv8vjZNcTl4gO7y78rhHRIaCHEen7XyHYecdQbKwVqBp8MR/L+uNmv53JSNva3RZm+M25V3xJ3PWluruEO02sXXpJCibbiwKqK4ngX4eYgPvF7NDxchlZKoBDsaqBBwBz2jjhuHHQf3kq9hjWOuooYOLmSsMjppqZ2Nmk8oSOW111IvXmq5vUGiBtYf3pMMR2n3U6StptViLR7aap6GOikXsVzRMwaPsCa6kPFtzdqDYizXWyeUM7heFDJOxBPXh7VCNIq1tEPrKegQBdASha+GienMqrIOdbBob7XQvGsb8izJ/Bj+1sySDhKssSCIf9949GtlQIV5NTUGqswXGtBsvvmbv1nWR4FAQOr8H/qhH1LLj7HJgX4tfvTc3Nw/UPLTbYKEvvk1X+7YNAj9xz7gdX+xFCPe398ffO7v5xJ+scNJT75KY2pqSsXU8vKytba2CjZ87rnn9HcGbT0q3FdX0n/+538uKPInfuInbNu2bYIgMRjbHBRp9IGZLMB6WOV/5CMf0c/+UofsCeAY9dTJI4iIj9U7OyQbTnzighYFoHi4EfMP9FvLcXf6BaWav7nBcre3WvsnxxxJvG+bPf+n79YJvOFiwcJLRbXlhPoM9tr6FrP4A0et/nJaRQ3cm5mbwzbwFzMulHZ02irbCJlOu7BYMxFoWXxXvu1Ga3pqyirtyPy9ammpNQOy5IeEnRMSgnuyNkfQknRG5pH8/txAk3MIrymkCKqFl2RjU3p/jWc2lB2miJFYwHylkuJ1qjOodcpWZYNYWHIcDK/HKtmsVYdHzDPqs4eLf6kT/MT9MRv89ax5hqetZTZmqzd2qsXHQoyXTCnaaqFj+5xibmnFPMf2WikCv6gqPoxneNV8DS4MGbd8FkkKATaJW77hV+2pz/247C5Q+Om0DBKA+eBSUS1BomAokCr+evN0d+hUWrp+nxyoMagE0bjznl+ytV0Jazy1ps0R5aHFIlJllXcOunYGYb6RqHlp2xRLlttJkWu2cv92q7+c0vXGt6gY81nsqcuWbu+y9idWJOkHGaN4jTw9qc1PmXlI2DMFIRSo0pqmlqypLm6V8Slx0lqecR5H5cPb1TqtdDSp2EXByfAmEi66x+Ox1aNtKtar/nohKzPvHLK6sYqFZgjh9Vrk8qJDOuIRqaEiC6g7qxZIVxzxmeuJapCg8UDAfE+fdvzAoQFd21JXvfk9TSqKq6vr9lD2T0U0xoSWiCdSBVRsHtknFAui9afPv8/ueOCXLVh2LZ1sV8TCC3khPfB3Hsz+qeYwxPHilmYhHgo5bm5UcRf61EsqjER0pgUW9ut6lcloXM9YoafBkahrcTcofP11YUv1hiy8xHwNmg/+DwUxNh9ssH5HHget89T8qhgy6Jyj/VR19+TQbhVv+CvR0sQTTu79InIHLD6REZry0Pn3yRpBRpYBr9qI/D7lDXa0OGPafNlCywWX75erKE7r8RqahokpRsJ6Zi9NiUMYvbJimSOD9vinftS1t1aTLo5nPSPUr/mz4+4Z3tIjhJFW2dg3NOj3L93dp8IrQGHe0iAlJRy06PSyPPYUaj25auXxKfPuwNzUp8MIhy9ZnoDKagH0OgQfqgDXqFIV0ksWHyiT0DzWZ/IuQUV49mnbtdfrdxIthoBgYyBgqe6t4iwJ8QvEJT7IN4UtuJKTKlq8J9qKyYIldzVZLBq0+WP1Vj9SsFLUp0MKKDAFXLI3aJGTZaHLQq0QExEXFI+oqKLoVgGuiCC/CiYyBVE8T9/bbD2fmFeRjr+ZfNEYmDUvrch8918CSvVaYxOJ+kq+nwEV5tVROXRyvtCAE/3BD35Qey++lSdOnFCBhfr/O77jO+yrPX7jN5wi9Csd8K8Acf7iL/5C740xPT0tPvcXY/Pwhca1LMLXyHy64U0/Z7ENj5Q3lfWk+FX5ejg6Vat/blILWdPxdcHdlalZ8SpkMdBab2Nf12BtL5cU/QEStPGt11t0oWiZ9oA1nF4XtJ0+3KsE+d0/8evWcrYkB+/VvfUWTFUscWpBm3VmyPE61rcEreX3npMUPdsd0+mKoRNxX8LiZxcttadVf8K5IgaCwq7a0iSXck7cakPhJ1Uquxyz/l7JmDmtlccmzBuPq+0AkgXxE2I9nIVNSTND6prDO519gXhnRbUyhJiBClUqVpmYNs+2AZ3+U30Re+7P3i1eELlzfJ5CB+Iqju6ovmg/sRHQ2uKEySYJjyo1GJMhIPJvSbBbmvQ70zcMCSkCSfJOzFlhV694PbSMZB+AinJ6WaG4gRNXLHPrDhmu0vaDN2Vnh832bhOXghZB5JzjleS3dypYV2hSvRMz4ChNqxSlGm70tMkodObfPGgNlwmkdS0WeGayOkBiPr9k1f4u884sWGlpRU7fm61mCNKBly+JP4N9AiT0tZt7rf78uvyi5FpeH78aRyMHaZ/Xsjdus+ilZcepeeK4+RIJoUUiMEvpSIVgQh/wZ4O0z7VKXYfE34UJg15hT1CoDwgxhJdEsaXNfW39at4jhW+5Me7u6dyiwp5pd+FITsgwJqCoUOG+CKXlPswsWRnVaLGsnwdCodd2YURtMdBF5oa4bPPLsgbR+0cdBn2vv9HlYOINNTWjcHA2XtR/FMPE9RR2dMu2gjYezv3MJRSKla094nKpBQtyEvCZf2Hdlm/ptOZHx2UOSosaAjPKWLg+8pBjAHJsfm8D4oWcsxWJR2UkzO/HHkOZk9iAgLxMLDqX+Z52HcKYx3hggYZJ3NLVpO8DMVKuZ1udXOpRqBU66hQJs5k96aFA2/g/ImqrOFvNWL6zTugaZrqai7TqG2h9e52xJqfptQ057FMAUxQLMRNC63yhUBirPRjxuQOX11x0Dqav3JegTzFNQisZ5apSH5gfpRgO++57QqfG5YMmRJfgaNIeJufMetqdj91K0l03lMwcaOAkZvJqOVN0BceXHXqFHUsLqDwGrpiepoV4w/tSgcatwN4BM9pSyZYe2GqliMc6PjEmI9fwTFptfdBCDpi0ZEGz+V7oEKBWiiXau91set5lpGJaSmIF6FWxaPmtbSrgQBLDf/vyP2shtblnvB5ZhFv+9MfNF/3SjbM3RzmTsyv/9n1f9GsF6QLF+v7v//6rn/v5n/95qezoJI2MjEhZiOIOYdnmuP322/Vv/Cv/8A//UB5Tq6vuIM8olUoyACedBWDkqz0oOPHMhIO1KXTjcwA4H//4x62np+dfNwfrX9pYH/BpwVu7c4tcxDnNYvQId6Aw1GZNp5OK7WDTVnGF83W5rOywoT9fsNjIupXX1616835LTOSc07HX7NK76kRGjp2asVu/7lcsvEKIs1c2AYnJvFOScTIsldUWJFC0FDJZEhSaIxYdT7pcPhQzpy6qjSKS9okZneiRgqcP9aj1gsEorUS1PAhi3dqkNh6nU8xE8U6Cu6QcNDZ0NhdOOQe2qSiiuAKtuv6dH9Bpm82y+sJpoQkK2aV1QcGSgU/hde2Wrg61CYrxgIjFt73tV9Qa9fZ0CVGAkEt7IXBmTC1BRfagxmM2FoviziCJr39+Sh5IhMxqM0ZBlUpb7PKqODUsxngQgXDREoGLVB4Zc+7bQ+1S8eF9RSuBYmmTfGu7tlq+JaI2Gyognbw7mqR+lDR7Peky1u7bb+X+divvGlDrhYwzuGkUcmrr8XbxZZqY1em41BLX+88f3ar2GCRv2qVa4NkowwEVCNXt/Tb73Qdt5WirlUGkilWZRlJcgcxh1cD30KJce+Mu8zQ1ishMERS8OG2VOw6LNwLJFosQChrUW1zjyMiKWrlcH34G9gMUVxtHukUepljPtPqkHoO74l1YUUHOfQR1omCnjYStCITm4v5BFVfMndC5Sb0+BQjTxouFavYWSYc84Ul1YUTIHsRuNtO1bzkihZznxTPmPT/m2qyRsPICtemub6hohENDq5jnh7aj99KkVIts3Dw/amXRCkznVRhQcFTODYvUznsiqBwEA3SP4oqvb352QRYd/pNX9Cxh3QD6JrI1tgJLGy44mU26o1H5ibLh6G5WsQCfUgq+UEBIDcU7QgkFCBMZNbMgpaEievxe136anRcvU+3L+SVt+vbMCbPdQyrchLpMLbniirkRDAjhBSVT1FU6b+ELsyqy4DaW9gw49G4jKS82BavznDY1CH2CR+idW1IBJIuSOfL9CJunvV62QLpkgadPW3AxI3WeWo2ZgriEmx5SqP5op0bG1nWACC3ndHAAqSoPdYm7RJwN/CghgYR254pWaHFcMhXoxZKQRykIK1ULTq9b8Ao5kRXHm8I4tUwbcsNyXTUeHgVVsazrAXKo10PB3tkqbmLHI7NqTVNceSGd0+ok0Jq2K8UVodaxqKxGWIM3W3msZYoDYj2mHYqhbH3c/I+d0ByMPHrGxW1dG1/UIDrm1Z0kBlSezbYZ9g0IzV7Nj6YYfP7556/yo/kThwAsEDYHIcz8DLhaX+54tVXEVzooql555RV5aYLQ8YGzAZ/7coorxrUC6zVG5x+eVCBxw+Nj5u/ssGyrX8GmMmZksadtFwlp4V2+o9emvnWrFbZ3Wfyi48tw4i3dc0S5V7M3Riw0tqKoic4nzUnVoxGLPnLa2v76vEWns1YYbLvqyFxsT9jKDR02c1udXkPPX41KPYXUnE3Ky8KaLVrxDYf0s4Dm4Q6tbgur2MFck99PWLAKoHMjWrhQH7IQwZFKjGRURFGsafHDuyqVsjfc+34RTKfvaVABKL8hKBCN9TIJJYeQFslDp39BJ1mpKjub3Kba5EKGU8f6LbyQcbEVvNWXR137MpU338iMC3bF9wfZPggIOW3pglNBcQ0eP+H8h1qa9L2KigHN2TmozY9cPkX2MFh455cdH27PdkXyIOEPbBREcg9Mrlj10E7nlr29QTJ/uBcohyDCUyyT48b7Rn1Z3tmv/MDobFavj80KRAhSfvjEmOOsjM+6TavJryJOCFiLg9hBDmhbom6E/A0KJeIwCBoneI/Huv7wtDV+8rz8fKJE9dQKahXZ55YdGpgrWf0FyP5+l70Ggpf4fFsDNDF4fET/XziyVddbilFQ1KBXhSuh5PP39+lPChbctokgQSWKHYCQTVplvM6lZbXG/GPzQjBopdLiEeoZCVvmcL9l8Q6DhB9ziA4bMoo+FKa5jph5An4r9DfLrgEEp/mZOUvetVM8IznyR4JXER7uO47xwfElG/6WuHvdOHwvLImvJ9Xk8XMKqFauIkhT1nnAZQ70qigkh5GCAV8vxBIq/EH0KPjXN6QUpS0uw95cydmOEGRMcVTzdUMEke6OqNAlEocihWdacv8wnJ8GqyRCLsalp0OIFH5uKj5qhrnMCQj2IKOF+6+zQlv086kFHq8KdnysMHrNb+tw7XR4VIWipXsiTiixsOTWjeZ6Gatuqks1vyk+QUdX19znCSleTZpnPe2iiDAerVQdzy9VUDYlhSboOz53qFApFl3gts/FLNHyh4NGEeL3qHjzDk/pNWAvwvpCqw/nf9ArFIhYOcAhZL0Ij3IydJl/OkQ01jvkisMERPdYRM8cc4vXphZvY51FT04LXeeglWuLWG5Hh+WGWlyOIPYVYb8OsQhSVNzD10/EnDUK9wzjYw5FFJzJtKxGmLe0sYX8xmPOV0xKx4o8x3ivIFYcTJQ9iAL3X+l4vY1G3/KWt9gv/MIvqPCAL/3Rj37UPvCBD1xFncgQphgB1QLpOX36tPjTXV1dckdnQBzHm/K7v/u7pT4kIxDuNNyuzXbclzNoWaJELNQsk/6xgSfX937v98oj87UG7+Wee+6RopCPV5Pyv5xxrcB6jVHBwbtQsJW7BlUMNX7stFW9HnF1II0q58zvOEyhtbI1DpdEhNcolqxYF7R8vd+yzV6LLFa1oCiIN1m26ETSsn31lnrjAS2emI+metwGTWQG5OXGkyvW/eiqrT2w0y79WruKqdBKyarzi9pkODlu9ActtFKwTHfUNgbC1vbMknOVZoM+cUkEcyIwUG8ppPaZM+J0iADdEtIGwcLj7XC8N99gv4UvzYlc2vvhSYuOp+RS33ApLYKpCqN1R+wFmcCmAQPDQn3Q7jv6MzpFg4JFp9JCE7zDk8riy+/vF/cDRRDIDsokT1e7VaIBoUDYCmixh4O1sqGWEK7yus4tTSryPIM9ToG05sixbLycrHGERu1TmVvQRkYkT2Isa6u7oo5g6/UIIVi9tdfqXoSbktW12/eeX7dsd8L8z58XwoWyD5QA8m3kUy874vPyuss4q49a10dGtKmqXQJxX2HeFW1GvL7oExdVoAWH5y3xwoSVUXOh9mIzXU1KdQbJnrxISOGFQ1ss+MqwrBgqIBTZrOaNPvxey/ZiO1FVMUubC6Vbcp+7T7pX7a1OPbeaVMwM8vrkvXtkxIiVAEHb8FUaL+YsNLpkJQJ9Ax6Lnp8z/zNnhMQJ2aPwwSJisMfyPfVW7mlxJrTligXGF23tECIB1zaKzGTU1qS1S9EHAsT1Aq2l1Spu2fCciuBif6sQLmXM9Xc5fkw8bP6ZFc1D2op6zhoStuP3Flz0D3wZ8gzv3GvRE26z55kTEkyhB+l5bd0iz14yux7OHnYZYfm6yTZhck5FUm53twopDka0j4nTAT0VQRukpLPVsrudWpn2L151CBoK9X61koRG8nqZzzjBr2akblQhPzLrAozTedcWIx8R8UiuqN9BXBGtNowreY2+WNSCY8uW3N0knzZ+BwWjCPOxqCX+5hW1smRTkYhYORHUe0VAgomlkBjmEJwoWqq0Bjl8EGvVENf9Qk2KVYHa6Sosyp8vIFsarHjvEUfs3kQiQEjPXNS9ADWOXV5xhqS0brNFC8ysuXgdnNZPjrgWpN+r9mz0woKt3uQ2RXl5RRzCB0pOQHWh15ma4vtGEaTiER4qflvw+7Q+Fq2YCFpwrWChuZSLRiqVrTw37wK4s0ULPXbKyuOTaifS9hd6xdpGjirIFQq/UFAxSnK+z+YUUwQHy71Hp5D1drQJjdW697Uwqv8MH1/CwE4BawaEYhRKiNP+w3/4DzIb3Rw/+qM/qoIEXyuU/DgC/P/svQeU5OdZpf9Wjl2dc56ck3LOluRMsL2YXTL7hyUsBhvwsmBswNiENbssy5JZWJO94IQkS7KiFWc0OYfOOVd15fQ/z/26x2MhjSzJOIj+zukzo1F3dYVf1fd+973vc++///6vyADGx4VRHj8TcXb4ov/wD186XPvV3Lff+q3fkoL2nve8x37zN39TvwffNUiIn/7pn7arr75arUraoRRZL7fgezEgt7re/e53W2Njo3V2dl4MkX61a82D9Qr99Hd1/IST8jtadPpj08KMXFzfJmVk8dp2K8S8FlqqWO1RpHqM8T4rdNSIkIznYXFHrfhJDV8clO9KtOWwC+7Fl4WJM3Zm3uavbNbEYHCpZPNbglY7WLLY0QmdApHYx79nq8UmK5ZPeK3+VNYWNkes9Qsj8sQAOKQ1oM2trcER1pMZRVPoVFgs6uT2pqs/olaOCpHFrE58BM9CBFeo7HhKY+K05Gjf+YambOI7NljbXx6Vb4WCCcwBa/G2dVb3/JQzAB8+qzaqf/MGKUj4u1AR4BEF57KW7olb7L7Daj2iJKDCSVXglFwTtLmdUWv77JD7UGQjpeCA/cNpG3N6nZu+xNcVPDNhxrh/riDVQDBFgml7mu3BL/1XtYKAERJztHhdp0UnXV4khQGnapRC/9PHrLpns9AB8vLo3eBR1mB56wphulKxmWubNKWp03c0qE1s1ZfimZy1KipcMCB1kGIss7tTTCmZZrnvxRU0BkZ1kBMYqPk3jNflqi1ur7X6g3MytdP2YgINtYxiXD43fhaFALI65vxSWW0oTO+8fsS/EA3CEAJFBhOmFFzJa3vEDUJh5DUG4snjQgVCscnesMmCc3nzz6YESI2emXWev/564SDwCBY2tZt/gdZ4SUUvz7mUuEJJ3kBaSd7TwwLH4u+KDCf1u2g5077mmvfPLkthE0Msl1dcDspiOepXMoHUF1QHSPIMPKTzCv+ODi65dhTZdk11VmiJKfFAmIeuNlcYburXxp/cXKcYq5pjzpPD9Ve9cY+URGjq+h3NjZbvqbPgs6fFX9PLze+l0AkGpQzlr9qoAhSeWaHFMa5or3nW9zkfI1RvBbW7+B1eX9/4vDyXtGgB9GpIgzaoikmnbq/Ch3hNmUIG4Jvd2m7FuM98hYqQJzDQ5F/KF8QRI2+TQQbvvu1q/XJNCbi5tDJBe+SsWoe8d2nlFeqDFhlb1vAA/rpijV+KXOTIiPIbKdYjI8vyQcoryGdUa40UbiFc2HdrIgIoCz8DDJUDEModnSCKTIY+uNZjIZvdk9BnVWzSGfcDU8vO8wQFvjYi/xxYlcrpAateuc38/Kz8hD6rMhxErRgh2sqvIh6+G4c2CnwOB5oGHRh3zDA8jKuMve3rFTQu3xYk+Y1NFj07a6mdLRY/v6Q4MAz48NJQ5n293e5gQyGYzf2rTQR+PT1Y6/78F8z7OjxYlUzOLnzfr/2r3tev93ryySdVHD3xxBM2NDRk2WxWXEwmHgGeMvHItOHlFq1OijOG6cA/UWBxm5j8GbgjGebVrjUF6zLrHXXfp5FnJG592KfSOk1TwGRbQvJ9hBZcCyk+ktHmxCbK9BobKrC+8dvqLDJTtJkrXEQHi9Pf1M3Nlq/z2sKmkPxXmF/ZkPAhkfvV+vvPylA/8dZuW75xg829fYt1/v2girnWfx4SVPGFP3if2k9M32iRXdjX6j7Qh8bFqZGnis08GHRASE7vKFkUEMMTlt7W7EJfRQRnR/EpcmLq1hZN+RQ3dljb43P6eTZFBdRS/LQ0WN2BGZnlfVOLAq96Q2G1wPx9vc4onKjR6RTfGBtX/pYdruVWKInZJM8O+WktIWt9fFbZbBQmhc5aW7p5nRlQw5XHpfHwwXF9kANiXC2utDhdpzNS0eDbyM+SLOhUW3tiSbDP7JY2y+/qU7sDj5Lt3KTioLprszYBh4GIWubGTWqRcR8nb2mypv2LjrZOqPBM0jKtQUEfVWzNOAXGMYhcUC0+p+UdLQKB6sSNX2Z0wqmHyawULFGrpdaVxZ6iaMrftF3qDD49yPF4vWQST0TE9qpu7NGIu55XJhozLhqGSUCKK28yZ/GnL1ixMaaCIT64LK+Mgr29XivuWaeTq4rhSFhFFEHcoBVWPXxsnP6cQxSUJqaUU4nPiGJDpH824fPDlu0jZLqq4rTCphoISG1yWZcx2/LhTyiQl+vl4vNQLFr6uvX26H0/K18j7yOebwr97LoGtZ4KDRF5BHkfTN7SKByFEB3pnKXbg+arTQgjIXJ5NCofVa6jRs+BHgO+m4UlTTESNsyoPsR/ngeKncBs1sq7NjgfIIDbqRndPrmdpZ39UlGiZ4ngiahFB//Ktm9Q0YOXS4+Doc4VE7kCof1+xR9VN3SrpauijeGPFUP/qveO1hkFM604MkIZRKg5u6SDldrq2KGYhJtfFEoidftm9wGdzruWmoZTyOpL6fomJB3VjPgXoTAKFcv01qhok7k97LV8vU+JBrTcAssl52Paus5F2eQKFjw54nxwK4MUyhLd2OsKvWhE/k34V0RB0YLjMegaXPHeVPxm85tD8lsxkVfqaNRzh7JJUgBFtXfreg1LcN1JYYzHxJ2j0FJxNbagtq0dOycwqoC7oFSOnHaYlnhMlgPlGGLop7gCirqwpPdZ9MSEjb2t3Z78f+/X+0F8uGJJgxo+IsDyBQWWc7iUH3JtvSHXjSvgUiYcMdJDkIdSAKKJVuQrFVcscBGr5nZI8xRYAFVR555//vnXdL/WCqzLPTl7Ntvwezrl9WFSBfowCgaRK7GRtC1uDCok2EvebMQvD0wW+F0ya4FHDsqs2/nFBSlUbU9VNQacaXEjxwRIs+E1HM9a4qlBy7QFlUFYiHtkUK9cv8vqD81Z2xMLttjvs6YHBnTKb/qrg1Yac1NvFBOF3et0Wp28sU4TO5jWiVHBHE2UjCbvutpFj2fayrP/hFVOnncn7aYGTS76F3NWJcSU4oeW2NZN1vzcovxFeDhQLcgNwzQOCLUCI+noKWUG+uJxh0Z44qAVbt4hc3dqT5s+SCkO2EDDLwxY7QtTKi48IxOufRr3m+f8qBSIxOFp3Z/8Xfts/O5Wm9sWtvhQ2rXWmDhkI4yF5Y3KbHAZjxisKVgoFMXi6m6z0IMvyJOCD4lWDPehWB9RvAbj57S9GDfXxkQhubFHpt/y9Tv0GvsOnHaeo5Cbbqo/UzDvxIwKY/GQ0hkLz5V08qdAwGDM85rf1m25lrBG+GFRKd7o9IiUJqY0Ne0H7LRalerEffAl8cCULXhqVMWH2pPErcxlLDK4YMHRRSlNZEvGDo65gNzlvOW3tAsoyyao+JqFjHLuQIkkb95gwdEFqaMoCLTK4k+dV3sTJYqCiggZFoZnUgn4vfjKUE1o1fqfPmHVA8etcvMel4fXWC8MglY6q3ZL+EsnLTSZssqV24Tl4LVhWpGNGE9Z/amKeEahUUfYplUFqDH61Eo2WTYnWr7a2E8e1qABm7sOFxNpiw4mrfWpRYdAmXDTog1fOGel+QUV70TZoETSJmbajuuWkG5G7BX1QnHq8cg7RYtbLV1SAGoJxi5LVQN3QNGqFp82dZ+YZVLbUEqbndpEMVbsrHc4AvxG4DOKJQsk8661TEFB7uBi2oX/JiJqJUp1nF+0ysi4e51HFnQ7wQPnxZ6Tr2pl8k7mcF5PlLTeDstsa7fEiXldXy4ondDmjDxSiueiEIP07vFYZGBBih9qOS1c8C94MhNnlixxOmmBZEkFi6JlOBySiJCIqI2MCsvjRJXioCSMRm+t7hMToRSpsNP0OhVKanM6jIVZ3bmc1Z9yAejZ/nrXqi6tTE5yIIH1d0OvKz4DvpXwZ7+Vzp6Xygw3jkQIZYRSwBFftZtirGi+Lx1x3kUeO0ogiIVYxB3ylpKK7tKi/bmcNt+X01hUWPE5yyAP08WY5FfjnL6VfVffSJL7v5VVX19/McWF9uYqOBUjPXmMr2WtFViXWWyiDafKGuvOtTtqcHCWiSE+qHzW9g9nJFE3PD+ntpUYWE9fcPK1x+tk//mkijFAorlrN5m3aFZIMDpdNV+hqhDp3I4uq3t+0mITZfMVzML3HbB8Q9BmrmuSsTqQNSkIxGjQRuIkJkVtpVXBhtSyP638PplCkymr+ZvnZLROXtWlHD5UE8bRMSGLVXXinO47k0CaQsvmNe1Unppx8Ryc0ms8CvYFOIiCEDk6asm7t5t31xa1YPiwR3rnQ5NCC84TRR9QVUWhZIsqKChQaLGpzdHfqQ2X+Aq8L1JyAn5b2tlo4ZmstT6/bLGpijZqOF20QEBO8CHNFBOIDDYdnbbnky7p/vhZFXx8AC/e2G3lHev1/RigU70hiz92xik7ZK+dndTfKQZ8tCB8Phm/mS7jlAsKAQL14ta4hQ8OWBVvB61FVjxmuUa/YKHcFsqO95nj+l/RwZSmo4CSMvWU29cv9ZPJMtQBEAgMBSzuqJNvyDM4Zpn1DVJAUNeUzcZjyualEGlTY+PrqFf7Dc8OxS9FC54YzPq0tRTIO5LRVCCvl65biouWsKV6gvK7MYwBfoDDAGwlVBuYQqgj/uWyWSwmX2FoNisFgzYKrWbaamyQRL6sfNKoEKPFhuKH0gdMkolQcCSMz4NDSPZ5rRL0SJWFryZwKx9as38oIKobl8/re4Gk6qZD+HHyKmjwxfFccj95Hqeur7Pi5i6N4E/e2+k8SYGAfl4F9XPHpPoypSqlEXWvVFESA9gKvI7y9VIUL2SkZqHs4a/iYFRIBCywmFU0C1mDvO+V51l0bVxXDBddYb57o65f1ChgwBpcqAnp+qbIomhSfh9F2+KSeTb0WnJnk5sgPTdh1t5i3vo6eZXUboW83t4s9hT3iwNH9OCQEA0oihSjGPplIl/JzlMUDsMlLGK1vnDI+fDmARp7hfHQIMxyXgW3FEM8fVvarNAYcsMkTBUPT7h0BHh51+2WokxQOQMcZH7CiKNdqlioWMgpfS1kSAalAqMQNh3OKLybiUEmf+URWzGX1x6ZdeHdK+1xDi2kCjBYU21rdNc404/1MRXLgG61KdXU6HMFxUooCoq3liZ99vH5xULNjH3xhO5b/em8hioYqKFQxNfG9CJRSG/E9fU2uf9bWd/+7d9u733ve2Vyh825SqoHP7Fhw4bXdJtrBdZlVvWFUxb+zPOaQqKNhweDaSEMoCocEjUWGU3a0u4GtQ1Xg2ox8pZu2mXFpoilruqyhW01onJnWvxWfzxp0cmiJR4+rbZZ4rFzFj49qdBllK1Ut0eTcLk6r+NhjbnKmSBfTePwO5jaG5uwfGtMXC2m9zzPHVM0S259k0zLi997jU60BPM+9Xc/ow9BVB/8RkBE+QCLvjBo4ZElx8uKhWzpe66zwu271UosHz9jpYjJoMy0HIUck4GJ4/PyhOQbQ+bt7dYmg+8Kf5AYRLGQ1R125mfo7RrbLhZVsKW3tciMzzQVhYFI3/NpbSaQt9mcML4mDlCsVtS+C52dkseF511RRKcmrVpfo/ZAhZaQzyt0BCfeyp5NiljRyPeB4xqf92erCoLFlJ3a0SzDrQpgwNydLTpxR547p/t+5y0fFYNp+d5d1nBwXh4YJpRyW9r1mtPWTJxbdu2+hYyYUN4I04p5/RttMpS5gXfR/qwIpkqRjQcE/AGw2tpPH7HAkQEVTzz/qKIUPrR6F652mybB3Zz2oZ7jmQLVwf2QQRlg57Y+8cHYzAB3CkvRELWmfz4nNQ+/EIV+01PTltxYo+EE0ejLFak/TIHFHzyuQoApQDAERO3YsbMq8iiCyIuD54XReZU4zr9BJKcIoggVGoMps9qA5a7ZKGSEqOlZCs6kkASoLSJsoy70/JSQDKFjI7bcFxd8lesZzxz+HRUlfq9M+OHJjMz9DIk0Hs+pCE73J5RlqQBhInAqVWFECEVWTEt93ClCsJIyBZcneXRU1z7FEu/hPBiTEkqPmzoivxGop0Kj03m1zQp1QfPBI4MaMDbnWmvCGWRlCqedqfdhNKL8TA4uFPS0Ryn4mQRVzExvt4rixNFZPRZUIedpqrgJxdMX5OMjZ0/q4NiMU35uXqcAZFpt6Wv6XWjy3IKN/7tNzj/VUOeinDCh19fq9/AeUzvxwICihyjo0psbV5SykuVbYmojcoDQgAyFYCuTexwEo4IRy981NKpCmPcYzCh51PAZglNYzJovnZcKqDZoNicmnNIW6tx0KcMSc/dslOdTBSITikTbQJInQHp2SVgJCnQQIKQzUMRTFKs4Jl+1u815ryIR12Ls6VSItw44t+5wh1fYWvhb37LNSnGfWsOAl/W53Vgr1UrxRGtrbX2V6xOf+ITaicTj4MGK050xs4mJideUBMNaM7lfxkx458b3iRRcmVswz4YefVAxoULgLXBOMVsoWkoVm9tXZ43/eMKsp10TZRiv+fBlKm5hR9wSF3KuBVco2cQtddb++JI2P3xYrfePaCpo+toGi02XdTLz1NfqxJluD4j4zYaKaoYiU394yfGGaHetfBChhBR3r3PAzVaAfm6SsAx359FD5tm9RT6KpR2NFp4rWvDgefmpxPjhC28WFTeUbMzti4z8z8joaiPjmjDL9CUs0+Sz+lMZtQ8gsNNmIFYDVhcnSBQBwTr9fkWXcNIHZqlxdsa/CY6N+i00vOg2p3NjVm1vkl8N9QS/UezEjFSqpet61OpQ+yS1ctJmwg7sQUezLeyut8S5zMVcPhUGgETxbmQKltrSYDUHx6Xs0fasnDl/ES7IBKS4X7s32tyuGms4kVHbSJT8bM5yWzs0TQmxHD/K8rX9irchOHfxph6rOzSn2yFAl02HzZKT99JtG6328IzN3tBijc/PyTSPUkeensLBmxLKg6OQI96ITZI2DG0yiOd4c1B3qodPma+1RSoIJl+m8WjHgI+oOTwl39UXnv5FZ+QlVqlrZQiDa28h4zIcq+bAntWqIKmBJ46qbU3wNEBa2kNEo4T3n3cfBi1Nij3RgAOvzflxccZgWJGjJ2VmYNzm377VGv7xmH4GhS/5bXss/jfPmOeaXe71RJ1AGUyEtHHyZ2A2bdnuhMjkDFoINnly0Crb+lUY4F1aYGCkxqPCpvWvjltx73q1zvEwYohHXaKdjd+Nwie/sVURRzJAdzaraEs8O+LUnotTZBU3CEEhTygxj3NmwZav6rHwZ55zZvhC2cXvwGWaX9Z7QhNwrfWaYFv9b01bRlxLEZWQ6UXl8DHMALoiGlBLXYpPD/EtKxDSGRTsygpU08T4qmzqdnR07I9AN5nwq1Qs15nQ8ADYEIo1sC+B6WX3/kln5VP0tTRL0UYtplBZPdip6GFijwGM6Xnd36WrOgVF5rHw+nF/KIrD52fV2hUslPDnupisDYqxOT9stm29WtF8nuA/U+FKjiFqNI93ZQBBi8EDIrGKRZv6tk16/dq+MO5wGahZQo6sGOnJRFyBudJKVTD9smtfikGHDYPr5ey4AuZpb6Iul4bHFBsUIq6Mz6eFjOX66i18bFRKNwdGDlmruYJf7/X1NLn3/ckvvm6T++AP/sobyuT+zbrWFKzLrE/t/2V9aEz86D6dXuVXOOVk57kb2mz0zS1SVsburLf6Y8tSFvggkuyez0tZIE+w4UhSgMj5bTFNjRVqzGaurJUq1HQwpZZJamOttT4wbPFnBmzkR3dqbDs8mbaGvz9sX/qH9zvfQrliiYGcIkGY2GPjLd+wy1J377C5d2zV6Xvuzj7dx6UNjrqMMsFGjeoAZydxcl7tD8zc+lDjdDq76Nob50dlDM8BmZyadQZmlsej1haA06YDi7rfhA6f+6mNlu6rscjQolpWFDrZ1oBUham7Os32H9fzt7whYfm2uDsFzy5L3alEQ8JYEKvDJoFXjOImOuAgkZWZWYuNubaYuDcrwMAyYbQdzdpAGp6e1O9k/FosLzahVQNuKGDZRp9CbME2YN6nuLo79j36/4z9Z+/aJYN+y32D5p9bdq2kcMBKvS1O2aEViaG9s9nC0zkVbaXxSat7alS0egy/8KZ4bPiRuF/RqbwUq/oTy87n1tJkZfxbtE7wXg1P6v9L3VhalgoUOTQkyGf4mTOW7Avp9/jaWqWKFq7fpvuLAid/FOIbCkq56nhlGMFXNjraTLy2+c6EvE+0svVchP0yQ5eu3a7NP3ZmQX4y5fPxc2xsjNNTTCrs2aepUMjbXMPlG3fL7PzAgQ/rmomPFDSRVd6+zrydbRYfypq/pVnMscy1613razF1sVXHkIEew/0HhC4guoQNUpBQ4mcIKQaAWala62cuWMuzS5pUVTF5Lm2BhZzasbRG7eygIJm0HRfXhQRQrXS4FmTNuaQKCbUkfT5n7u+qd4BfCphq1ZKbamX8Di0Uzb+uT1gDDi6TV0fd4efNnc73FAw62jiTbhDH+R3cXnNc3jcZ3y81s/BaP3nI8tt7bPBHNzuF6tR5Cx4f1vfmO2qtGiSyJihDvXhjs87bxiCAcjNRL0FdxFby0EIhYVHw11GcVTMZYVRgl2GE5/2Cx0gIj2hIrWTavXo+9R6as9oXJqUuySAPcmEhq6Jk4l6HG+Hzi0KIgwOfWSqkUMlOXtCEIWqQ/F8UkG318h9S+OEtZOJVOAzihiiesjlrOrxs8Qnis4IqqsSv4wDHe3dg2L2f0xlNYUvl5jmUpcJ9MfkcGF3Q0AqeQ+W/ttaZd982wU/VZswUNBiBd5DHQFA9BdY3qrj6eq81D9a/7jpx4oQ8WDC9Lv16LWtNwXqF08h3Nv2ILb/zCvNjIJ2GoeNzSkc6b9nNLWrnsHHygQNXCkMyptHV07gvV7WmLwzo35au7rSlfq/FJqFpe6zzkSWb3VdrLY9MSKHAjxX9f8+av7HRZt+2yXINHms6WrBHHvg5tWlIescXw+/TiPXKyc7f1KBTKyfLkXtqrfcvhqzc0WBfeMZJ5rRgirUhFQPR41MCQ6b6I1Z33ympM5xQOd3rw69Ssfnb+zUh1PTPZ110SU1YmIWa/aMr2WRu8m3oe/rlUas5NKnb5CS+vLlBfiJI10Aes++8RuPTq6d82oC0iTBxC7twYUhRMpmdnRZMFh1yYaXwE6IA+GJvx4pRNmcTd7dZaKkq5AUcJ21MjHyHAhejM2Dk0HLCLAyzBwWCU6+Myvg3Tg7a9Lu3W+vj01Y+NyBFgM2B4kKso3zZvIspy2xts8j4sj70Me7XnJxzXpTasIXOT2kjZzOQQsJmiUoB64yiYiljaXIKT806+CWTkIJCAhQtWYXsRlq+8aiLKxpgI0xIbcr01VnkS6dcsVNf68KHCQ4emdTmDGRVzydRNpGQeWiXsUGVy5bb1WPhwQWXLjCXsqWr2i2QqVjkS2c0NAA53jcx515HQq+Z0iu5UHP/+XFFQuF98TBdyXM6Pmfl8SmrXLtdk2EyXKPkgH3YtcFxyBbTCtxWpmBry5fvA74kAJjsCHiYMivQyXhUY/S0DEVFX0FRONWtauWw38KD8zZ9a5s1Pb8o/xih1TznUjHh0yWiUmP0emZKlm8KW2QsJWM/rVueU/GrMkVLd8csmCrpNfA/tN/Kt11hISJc4ETRcp2cMh9FRTCodhkqbGAqZaVzA87Ef9UOjf4XWlzLAH8aE598FjAxnOqP6zBA+xAelS9VEHoEpUjIAVRR1ORoQC3k6IWFixRy5QLOZVSQMl1YPnpaxQSYkdD+c/IcoSIKssp1EA8LEiszOC3FlcB12u2Ko4JVxf0/O+6YUcGAI8j3tKhFhzpECzQ8mdUUJtdUdX2Xiks4af5k/mJR7kvmpGwr5zFF7mO9i2BaKYZ4b6Iywn3juuYwo3YghxxUvjBRR2UZ63n+oNPrNURpY5F9h+LGIQ9vFVgWVGXa3Ci+mNgTDs8ioCoHgQQT3Rmr4m9DJZ2b05/f6PzAr6eC1fvHr1/BGvqhN56Cdcstt9gP/uAP2rve9S6L0NV5lYvIH+CpgFIBjq5S4vk767UY3dcUrMus79j5QX3ABpMliz17QZI+XhBaDHwwwJiRZF4b19QaKgcUZjbZyHTRYhMltfckoYMMODpn3b9/1Dxls64H5hSpg3pCywtfTuKJ8+blQ345bZG5skWnXNwFC0/B2Pdvs9nrW5yvpVDWhzmGZAzUMnkXStb5xbTuE4oDUTD3dv/ni4VW5MnTGgnHf1H/7KSbEmOMuTYif4vyADuarOGh89b0MEVSQqdrPiBrvnRexHN5O3Z36AO759OzanGSNUbxpniRyazaGmwcFIQy+GcLlu2IaXKI3xXYf8Z5O5iq6+6Q6gBwEGN/ZWxCrDE2DZ7H7A1bRN32zizJ0Nv65KI17J/VY0RFY2ORsoL3d12LlYZHZRSnuEK5YFqKzSDfHDH/8LRCb2njtnzqpJXPD1nlup1Wnp5xmy3squeOqriitRJ66JCy7hhPr3nqgi3uaXJtuLCjrqtVxOld7SJo3MSvpFxsyci4+dNlW9rTItVELUJaVzxuolp6u9RGQr0UMiMY0GkfNQDPTXHvBhnUUZWIvcFjRcGF32nq5hZ52DC5iz3EZg+6olS25Y6AnkPuJ0T1xOklix6b0Gs9f0uPzPuFjY6nBdJD0UG8DqhZgGQb6/W6oWjgZYMZ5uvqsIce/wWHaqiv1aa39JYdUv20mTLRuueXVFxB0OaaZdp0YVet2sOKOIIVFQ5baXTMSqfPu2GMfF4sNKZE4Xn5njupggjP3/w1rdbyEF40n81d32rFhIswohWFKZprHWI5AwcMLFBIz++tt2RfUIUvrU/eq/qQQ10qVRXH49u11fG32NBBFOCn62xXccD1hflbCImz5/XeR70rx/wqrvBx8Tu59iGbc51RvNU+N6qWJYUnxRVFDspYcDrtiPXhgG4XRY6J4srgqAuCrgnp+7JExyikPKfDRronpilNDPFAdvFHqSCjuMDjlswrHia/zU34UWzj7wOpUXnhhBRDIp6E3oAfRxGaIpWg6KZUx1CRYW6VzNPeIu8dBwd8hhTQKq7mlm3qpmZNHPIaSNXjd4FLmJlz92dDtwUOnlfBzHOuon+luFIEE5+XcdQ0j143WQfYrCikVjYt/dndJoAvxVypvcG9l/uJkXJtTd4Xyau73FCHYKkey129QcUVn4vf6OLqjQ4a/VZZe/fuFQQV6CjE+GeeeeZV/TxBz7CwpqenLRqNKpPw8ccfF8390UcffU33aa3AutzKFWz5rXtlkp369k06kV70dtCCKBTVNlSm1lJasRLVY6e1meNDwD+QODBu6U0NzqDZVWsz79lpwVTZpm9oFP1aCIi+Nlu6e4tUlIV/d4VyDdkQ/NmKze6KyC9EXlnXJ8/a3B6z2du7Lbkuasn+iF373b9t+/6/T6hFgy+imAjY6NtapUqVNnfb4o09dvX3/TcLn51x9PbmqGJzlB3X1WHJWzeqKIg/eU5mXszBrMyODpt4U5sLcQ27dgutPT7sIsPLYtuwQdSdyVo170CebHp4KvzPnBAtHWN29fBpFRvkAdI+UhQHfrH1bc60nS9ok4ebFDu3oIw6oQQg5DeHLfzYMasS0VFf4553rzklwe9TNAah1LSdUM9ALPg3rJPPCWQDBmfPuREVLTDF8DMxFIAyA0cHWj35b0wppe/Y7mjlOzaruKr5/GHz7NwoqCXtQrxGIBoIkiUySUVbY8gxuuAjsYIBm7m5zXlVqhVt5Ikvnpa6QcGoTLTmeoUIa3M/ccEKCb9aLhTLTGehSuD9wrejzYni59A5tUoofoHUJoYKij/SyL7fr7w7T3LZyhs6rf74sqYX4TRhMue0j1pGG5DCmcEJteyYhKOwI3exMe6yKqF70wqrr3WUd0zKrFDAtWA17eeo9TUD6ZVIHtd6o91JK5WivlgbsGo6aw3/dEyxRKLZ02KampbagCmdabTM1eu0IYOxYMCD5wwTdL41ag1fGndsplLFIjMlCy4WpbTo+abgWGmLMiWov4eD1vjszMUWNrmRTEWiGoVmKA4cLJPBkNXpvPHv6FvJwfS7Aj5RIz8dwc3EQfnW9UmxgY+lP1fM7LotWnttdRa/sALypW3LByoqcKlihQanMHjPDiuYms8OJmBjoxmFPlNM8L5DbUJ5qwyPuunIUNBqnh2WOkmeHtOLgrzyPC0tu7Y1k4db+jUAwuNgGhYkgVqK+7aZb2FZMF9uC7VVB7zhCSu2kLUY0IQm+BQVPnMLVkFBolW7+hjwFMbCQrV40+79ibIrTh9E9uZGKeRqK8IYiwRUoAM/9ZwdlnKl4G+KeIpcVEmuKyClTbVqrYPT4DbK/e2urU8RnC04E30yrWtU/izio2aWLP75wy4N4NApW7yqTZ7FVfzCv7W1NkX40ut3fud3bHx83P7sz/5MRdLNN98swzqk96mpKXul9fTTT9tHPvIRAUrJXuQLvtav//qv20/+5E/aa1lrBdblVsCvzXHympjVnctrCgk5X1ykriYZmhUfAVw0EVWRUb1mp6VbfGJIod6MfVu3RR85IYBiqicgMzUwwJbHZy25pU6bUSkWtPB8yZbv3G4Nnzmp0yBmYLxXjcfzNvxj22309pgN/cBGa32Gwgtuj7uLiuGZK1vqhnUOHVDrTu2oSWQg1h6bt/p/OGy5jc1W2dqn8XkyB7XmF/WBz+j90u0bJbsD9yz1t8u/E16sKguO7Ds2T9QfBS9PzAjwiI+GzUxto3hU1HNaOMqcYyNKprShDn5wnz302H9xCIR1XfpgzzavhK2Ggi5WAwjhUkrgQmJvMNzGDgxb+cotKlgYQ0cZQw3gec711LnJqb4uqywm5XGBYzX87W1uOq6pUarN0j1bbfTOGkXnMOaN+pXcGDPf9o3aHNgcfYka8+YrUqqG3tFotY+clQ9l1bfDYmNZ7gwoeLnhIFElcYUmz11R71qCFJyT09b8yLild7Zb7vbdAkcu37JJ3jkvZvczA1Lxoufm1Z4rXbfNap48LyUH5ACtJD3G2XnzTM+J+C1lg1bKkttk77vwWy6gmjdvQ72uUVSPEobhwUm1fRguCJ0et9B8To+JqCRek+Vr+iy4WFLBXF1KuoiXhoR595+wbBc8pKB5jpx2eYGxgM1f0SjFBg9O7s1XyJzO1Bt4hsUtcdci83ss39soBUgG8Zq42GrKtdzaJ7wHyA6uBwpZ34Z+x++aTbl2NZ6kcwMW+MIBN92aiIkjpuecEf3Dp/R37od3elG0frIAUcZS62ss/Pw5p4KwmTfGxT3jmmSYAtUIJAFFXHAi6Qz/ZIii0tQnrP5UwZJ7VnAHqDDttTZxS4P8iiA+tPGjcE3MOzVopX2IAuAbmTL/0JRae5ogxkKFWZvXJV9UTp9a0qg+OzdoojVAW2901qmPK7E1fnxqU3N6blbbtuKiNSU0hMHkqsKnWxpcMUgOIay68Vl9Bim7MxYxX7qogkVsLdQ1/H3RkAswpyXX12n+44Nu0vCFYU3/KXWivVnIA1AtXDu0aTW4s0J3Z2KYIhrIrg5YOzc4jlmuZMW+ZhXcMsfTRgTIvFJMi3mH+uT3qOWrnYZhBHArtNPJWGRq8NyYoMgociyUWgpm2HXTd/W4+14o6Bom65KJ4af/+mde02a3tt74y+/3C7fw6U9/WqBRsAu/+Iu/KIAouYgETL/cogVYU+P2RoosijVWb2+vnT59+jXdn7UC6zKLBHsI2V1/P6h4lNBjxzSCLTZNZ0SGcbFtXjilk7E+GCM+a3xhyWoupPVh1vlXZ626uc+ih0YstFix8++KiIEF2oH8s1ydz0KnxuSzKtR41QZCVcGMfcetH9XUTOvzBev7wzPW+6dnrRT2CO9QxyRfqWqRuYoF0hWLn120xWs79eHf9Q/DYtlEpvJWOXXeyldsliemmAhZbP+wuDtQ5/mwJl4FhgzMmpn37DIPhY7HlE1Wd9BNyrFQp0LjSY20c/okdJoNjZZO8YpNVj43aNGZkssl6+t001V8ELe3Wt8/LmriCe5Tpitq1tpsNWccIb2aXNaHbK6/0Yob2q3xWFZqzOK+ZnnBYOPg9wGVQMEhhk9LnTIYq/GI3Xfy12X45zkkq7Dn09PyvxQ3dVrmho2WOJW07gcWVRRQTLBRouKwkeBloTAub+8XJJZTc9/fOlK0NtbhaRWq+GFQhWibVsYnXfEY8FnDXzxnTZ8/6/IVh8fE0aLdi1cv8sxZFW/xMwQH563c32He7k61Bbl+UNMChy5IeQDHwGZM4DX+ntKVm933USizedfELbe7V8XuXb73KPNRJO9kSn42igO8UhSp+Gg8B0+5WBu/T0HKTKOyWYen8yq8FGtC0DBeMDbTYFAEcwoM4oOKjVFNvNYfXRKwFNM/1w1qDMVveWZO7Ldcd8ICA9OOTo6puj5m2U3Natmi+FHQxMZdlAoxTr4tG5wBm8GHbU1SyGjbobrQ6qZIRr0IjS6qkGTAYOm7rlZLlsJG6ICpJbXMuM5qDzHl2mJz17fp9vDg4Usk9kmTpMWyIJ4oytw2RRepB6g3qIULW4IW+8wLzt+2nLHA8Ky1/+VxtZZ9WdTphINmKv+u6ijiK5OZKJEoijy3DIhwPfB+ouigHaYsQQ4OPe0aFFCmH+R00hbqw4rbQjFUniH4BgYQltNCUAhDceiUQMAUQrTa8OapIILCHgpaZneX+U4MugKujozTqougAhOBwofCbmZ33vxrKlTlr2xtvhg2jh2gfGFQni/gnfgsmRqmxSnz+cr9jB4ecd5GNi+o7smcK/RGJy1wetQBVqMhp1SiiNXVOnVuOa/3CXDZ0JFBRW5xLQvr0VkrSjxIFqKTpEzz+9rqLDC7bAu39Vv8i6esZqRg49/R79Q5v//iwWJtrbUHX2kRKP2hD33Ifvu3f9taWlrsgx/8oIqmt771rWojvtTasWPHxczBa665xn7jN35DodSoWuvWrbPXstZM7pcxE95R9z3m94XcpF04ZHP3bpTCpJZIU4MM2ighqCuM2gcfesFmfvgaa/qDZ6Tc4OHwbOq3QmtckTgNJ3OW6g5Jgco2e1dgo2YNT4y6jLmZWSkfbFihubzN7eD0aNZ+37iVWmvlK8nX+y2YLKu4Ck5nBHlc7gpaaKlskamcJvc8/oBTqRI1NvaWNuu8f9r5IzJ5m7ux3WITRRV05J3BwtGHLtye0WlbvHWdWpO0ZGAEgThg+Tev1+a4sCNhjU9Py3NEO5EJJzak4P5zCiuGs1X1A1AsufiZ7maFGbNpaDwbgGA84to0Ho+lKVQPjDvDOLloXo82w+D+MwoOzjUGrRTxyMsG1wsfDKd1mWePnLLxD1xrPZ+ipehTBh04BoP83t0uhhMt2MBc2mxsWm3Y6ETeCnUBix+bFnuMdgob6ezemLU9NOX4U+GgAyTiO6HImV8w29ynE3TwvuetdOeVFvzScZGh7+3/adfui0Vc5lk2Z8Xrt5kvW3Yj5mcnHCSUgmfFfM+GyCYPwBK/F1ywUlNMG7yUkslp5d8pPgXWUTjkcvrmUlYeHTdfX7eGIkAW0JqGJq720coIvVANQ9MqSGjHUKSinEkhiUXc/cVsT4svGtKUI8Z5m12wqe/cbK1fnHIwS/xibJhMhOWLltzRpJYY1xzmbYod6OS6fe4nBdtyWoUmLDWBRbv/s41/W581HSPTsOLwHicHRe3GS8TzoFibwUln6CdTEtP/TEq/V8UsPrGzgyo8adcS0UPhrueirdVNug2MCkjL9UPqgm9l46ftRCHB90HBV+ua4j8clPIyd22LNX7hvF6j1F3bFDG0OowBmoE8xuDAjJ5/rndM7fxJ1ii3pQDpUtXFPlHk0B5e8dlduoiREQWdNixIhNODyvaE+5S6Yb2iXvCliWNGO7CA0hi0Ul1Y1zyWA64PcgiJytGgCMXz5laXCbmwLBO6kBCptKWu7LL42QVHyMcHR4Gmx1XQIWA15onPCs/uzeYdnLAK8NKVjEW9VrNL7ppiArU+6jxa7O3c3uikC5/2elQAoRBS0PM5oilS1CuURa5h3qvhoOWbwy6yJ1+yXEvE/NmyhU6O67CRvnunxQZTVqyLWKHOLzJ/9MioIpv+tfIDv1VN7t1/8CHx917rYgJ45P/78BvO5D49PW1/+Zd/qRbh2bNn7W1ve5v90A/9kLIIV43qZBbec889CqN+8XrggQcsnU5LATt37pyKsTNnzijwmUzC22+//VXfpzUF6zKLTVqsJCag2pqs8UEHiCTcltYCrRl9gNXF1apg2qjtc0O2/O6rzYdCwAfYxKy8OHXnCqK9R+bLVjOwbP5M1YKpqjU87z68dZrt7nTQw1JVJG5/vmptnzxhyb1t5js5ZMGlggN+dgSU8cerR4uh8alJy9f5zHPojHlhK63rkZF0/tpWi8y6ky/FFb4XIKerJkd5WijsYkEZxjEos+IHRs3//CkhEWZ+9FoVV5zKOW03PjerzXf0zoQtXNGoIo+YDibU8HKoZbCY0nQZmzjFVXZvn/M9YVrf3KqQVzZUNpH4fUdkbNWEGl6aYslCJ0akpBG/Q9FYezolf1ZoIiliNO0Z2hnFN11hPX9+VoUuWAliVJiom3r3Vj0mNl4KnPKJc25wYLoohSOQKluJkXFam7Rphqet+XmCh93mwKaX3spYPupA3Krb1ks9AtJJG4R4lovXCLBTiirG97n/29br9V6lw8NQA8KJP2bq1ibn3WqNuddxQ8IqqA/ZvFNCUR0CAbXKpCzNLjo1A69fLKBJxuKtu+X7E0aCwGOf1/KtcfG61CJriJr38FnL7O1W24qsN6kXqHJMYXm9KgSXtzS69gtZdiuMqPtm/re1PjzplJRETL8bHpayI1tqVEiD5QAUKop7bdRlyZ0dtMKGNlu4c4N5a+LOwF4s2S1v+00dPNofmdXUKEqWVm+HiioUHrhR8LxQcVAUNRU7k1JLfXVKkEKZgkp/zxYV18Nz4t22UV5Divb8tZtViKZ3dWhKEgWJ9p3M6nu2Oor/+Vlb7q9RsQBnipUYyFp2d49gwtHJvDxLKkbhOqEAHh9WfiWtcYWgz3FtF3VborynCvI90h6VUtfRIOq8oLtEY21v1++Rwuj3KfuR+ycfU9SvCbr4o6ft3tYfdT5H+FcwpvJF+bmIv/KdGjL/sUG9V30Xxq0IKmGFmA79npXd2HyRt0VhzfAJRnzhL5aWHYcK1t3KVGLl1n0rF3BFxY7zonmdkrqcc20/JjbnF3Vt+F44o9+hqUQOSX0dVulrVwFOoUdRqcKQ+45HLOyX6Z5DG4+LlqE/XXLokmLZYgdHLTS04HAYne0Wf+iE2elB8z9+SG1zfGml8YlvieLq677WTO4vubq6uuyP//iP7Xu/93vVHvyHf/gHFVOrxRVr165ddtVVV73kz1OIUVyxILefOnXKZmdnVbi9luKKtVZgXWZVjjr/B5sYQbbpK3qkUMCoqjm9YNntHc64HPTb4j1bZNSGcF3zqQPu5zxe4Rb4gEEx4gMx+vyAPtBqz2ctcf9x52dYytjCDV0KXmZzW+4KWPzUnNWey1p1XaeF5wqaKKKtUHMhY3XnczZ5c4M2PjYnDKkNnztlC+/eKz5U5eyApTfWW8VnVncyqU2S4gYDKcbf1QJBcRnFoloNN3znb2lDjE4V5KOq7tqo74mPl23i9ma7b/R/KIxVp9Jg0Hr+flytSRUpvOcTcZt837X6YIZbs7S7WUoCqgT0dU74kKkj5+dcuyNRo5ZN9s6d5p+YV8uDWJiZW9vdVFxjrQomZQ0CTUUVgMd1Q7dM9fKxwGlsazK7ZqcjnWcyNn9Hv4WSVfM8e9QVFkwHXrPTqldtU9srt6lNQb60peDpcOqmGME34/hRQflSyHBDLdDmMjaj+0L7x9vTqck+8hjdu7pd5n1al9ogF1wESr7Orykovh9YLZtS24MULxkLHx8TJTuxf8wKLc5YXGLqi0XblBbLzKIKXvIk2RTZxICmhs/NiAMlXwsrk7Xw0WHLXc/1N275xqDU1uj5BYFVNRlZX+OQFtWKI6bHohaaL8hzh68JBZaFGkebUJcuGyVt0sWMY7uhUkFaR+FoqrOlG/pcmDEfIl0dgqOG5kuKNmFyDdJ2ZDgpVVeTipWKuGy0S7lGlU3n8egx3n3lLztV+EtHdHvpLY2KaIF7JYK/zPw+FZUU6qyZe/ut0BLTc41KF74wZwVR2ldGq7nGgi54nNYrhUF6R6va8rr2I06ZRjUlp5LWVCnmnlOGQzBpozRSlFKkR4aWLHcLgxDuPUPOJAUc03KQ+PG96XVLk6no0TXr8XotPJWx0si4CmBQFqEVzxi+R1RoDmzyMHq9Vh4YEkwUBW/pynYrdTeZVcrmiUQcToXJRx5TMu/U3lBIRnAGOSInJ5yvSw/eIy4fLT1NK9bVKIaH4tuBkctusIQDye4tmiqkUKXdBw0ddQ/Vz8OBqbneHtj/y1bZtVHxSvJcMmiAQshnAYeKUlk+sdUgealgK/4zCjA8VrDGUD3ndkRls0DZ5tqdu6lDRRyPj2BnJgJpmZNO8W/VxL62Xtt6+OGH7eTJk/aBD3zAmpubX/J7UOweeeSRy94O6hVqVjabtYYGN9X6WtdagXWZRZuPDxZNPk0uKueLDwK8AhhTI6enNMbMKY14GEaq87ftNh9m3dSyla/aIgP66kiycAB61j3mPzEkMN75H+5xH4iPD1u2wWdLN/aphbi0s8myLSEZeQnqXdrVYKlremQ0n98StjIDXj6f/DhsXqnbNksdI6+QaInA0opJtSVqpWhAbQUWnhqUg5vf/pvmn0nqxE/RGBvOWD7h1SYqivfEvDwd0dGMtT02q0lGPDHVwRE3Xt9SazXHZrUR4/vJ9tVa12enLLmrWdNRiYdO2eK2uMCAmL75neABlNk2tWAzN7eq5aEJr0BAH/IoF03Pc6oNuHBlWpc8TE7TtE7mk3qeac96F5I6EQNu1akdY/aGTn1//QNnNF4PcT1zRb9r36AiTCctkCroPkKhp1XGZhOayVr13JCo4MrgyxO+7JM3jNM8BYVal40hqwyNStXkZA3glNzGyOMn1doQCZ2g6WxRGzkFJa2cZH9YKqRAl7dssHJXkzao+wb+myu8Uzz2pDZtfp4WKxspz/m9Wz/oNvpgUK+3Qnqhb9Nqg3/WWGvJG/rt0ft+1iptTfbkp96v+0dRBN8LZU0EdtpitIa6moR04PF5mhrdJk5bMhJQm5Y4FACSYifVRy21tdERwOsj5mWWYTjtWmgoKFzTTTVWagJTUlCWJMotSqft3eZaZlvWa7PNbGoWO45FS4lJM9TNu278NV0PFOa01GG6xY5MOOik3y/PmzxqY7wuZeEGMKg37V+UUkg7K9MWEupBMTtHx1wRxEGIIpi/e93EL+wnsZXAAdQG1WJc2tviHkdNxIKPH3MZhPmSU5Iyeb32ev3Jm2Q4gYm48Slxm1Coctu71L7X7dJayxbs4Uf/i0LX70/+mYoTrpVVCOb9838kEO+9nT/hvGRdLWqFW13CqlfvEGqEa5Liy3mlatXaJFIJAj+FCe8NFcLgCja1WWVzj5uEpCXellARpBYd1w2qEtgQlOWCK6wctd4pzGpF4hWDLRYOOMo+H1Gb1juzPEo0753JRas5MKZpRNhZtDIx2ls2K28j73ksAcBNPYPjLt2grUlWAmJzKMBR7OrO5i2/vtW1S2ti1vjQgHuNfD5NU7NQ09fWZXemr8HXG2996EMfssVF18Z+cWv1q1GgyB+84447bNOmTfbmN79ZETks2Fo/8zOvbbBircC6zPI1wqDJqn2AFwlOFcWATobtza69A+emo0WbHuynbBMIB6/aipwyI+MZMaZ2/fQndDrj34NzeSvu7NfmEkiZzV/doqKC8OfIdMHqji1pShD2Ev4jWgW1JxYt1emMq43Hs9bxWFIRNUub4lJzyC6MjKRs8j9eabM3tVuqL2zFuEcTi5h9xUZaSGmChw/Zxz/zActuaJKZnYgaVJr6p8ZksEe1QH3hgxdlh1YTLUBaeYJfVuAm0VZyJ2YMyGJDFQqWeGFS/wYQM1/nUWgsiARtuph0zw1Zem+nhRcqrvXSGhaVWSfffF5E+IV9TTLQ8vspFESEpuhYjcopFDS1Bt28fv+MTV1dI59bpj1shbjHrLlBFGoo4JEnTzoyehrfTK15TwxYut0nxpSMyEMjru2EMhUOqbhAtQo/e9b5ZWqdf6V0YVARRpjL71/4Yz1GNlBgmWyepZjPUjuazLb0W2prg7AKymqbz1jdiWWLjmel7PD8oD7l1jXqNlbJ8ihthAB71/WqtaLWjqCaeccmQu3b0CRFB9VQAb60J80sPF+02+7+uPw6eJ6yt+/QBquJM9py5YquQc+K30LFTcoV4P7hGcvs6ZbCQKsQg7taPvW1uhYi03ldZxwuggsFR3Y/N2CJp4ccG+rcqDZ08jdR73iuKOpo48qLMz2vti3tbRUEPbVq82qjb653qQc8z4PD5uvttuKWbhWeRAyxUMIIqwZLwPCCiPj8+4lz2vxp9dU+O2rZm7Za+MKslFQt/EGrpHUKdRhV5IjOLFvw7KT5uT9XbFVbkGvOnj1q+Vt2qBBQGxD1c2W4gyk6njNHHfeab32vebradc0wcELKAqw51OT7Tn/sZT9P7o5/71f8txIUxmactykHzyvnVK1y1eIPHVehl2+KWGhwXj65+P4Rtdr4XCnGXLsXhISUxfq43h+aPKVl2B13eAeKNN7HKMgonzH3HEptonUXCui1z69vUloFxRgFDkqXCh2fVzgUKascclqa1N7UoQbvWyDg2o+EooeCUqGYFJYqStGJ4rW5z4Vvn5t0bf7ZtBQ/rsvUtb36ORTyVV7f2nqFtdYifMn12GOPWQG1+0Url8vZE088Ya+03ve+91kgELDh4WFxsFbXe97zHpHdX8taK7Ausz519rd1ildyPIDF1kZburbbtTNmFnRilBwOH6tilmsMWChZturohC1+515hCha3OARA51+etuve+9uK02GjAcYIxwqPVMOzUzILexFaPCZ4pmCHcI3+/pBOoeAAakbLNrvDtWuAEeYa/RYfzdvS5rj5c1WN0Lc/vmCJobzVns1Y2+PzYkNReDDSr7HqyWnJ7xQGgWRBbT9GpoFOsmETCgwCgTYmhZZOwKm0ImJUbO7sd7fDKZ7CrT5hoYmUxR857RSSjnrdDtNPbX98SODVhidHxaIKji5Y+u5demw1zwyZP5lTKw4fiT6Q01m1zcKzRZlwUTbE/CHTjIUxG5MuOAHaTh6PwKvxiYp8QYFUyRIjJeEeUBM4Yc99x061/jDYq3Doahes1HNmSBiFws273GbDeHss5NhRYCiu3mDl7lanBDBa3tMlXhMbN1BHiuN7dv1X194B4vrgEas5OqPbxU8CeqCwo/di+21xU1SPh40leGpUY/xvCr5X6qgUOwCLKyBSKXErMUWoSlCsyb8MTbkR/lUVRsXu+LTArUyJUhwwYRkdSqqwwYwsing2p5ZgChUNhSaVM9+ZYbWf5ONZKspLhYqg/LyeJktub3S+wENnLPjFw+J1YdrH50UYOVw3xvYxsgOvlCF65T6jjuCpEux0W4+V+tukDBW6G8SUm3xrr2tBTsyI6K52247NapuB89j9U59wwxfLaeUsempWYpYIWJfaFLT02/e5QQsU15Z6xyZbiQnSKpVs9s5e+c3UVoSCTiFHHiDtqFxBE8KQ1Sl6vXu3WuTCvDO38xyvoB9WDduKmooEhWShJck1xn0Hp/LVLk2ZrixBW2nftjZaameLM45TvFFkDU4IzYGZnpY6FgCFIft9Kq5KTTUWPTauIgelSErh7JIL9FZiQEYJAlD7uf4Io5aCGQm4yC0icrjea8LySzHgEDoxJt8dxSQTv6A8YO9xwOD3ObWrrPcfKjSWCaEg6mucuT1G8LUbrBHuoatJyraUc96qy1nHVgOhEXOtTcz+RBbdN/iJV7drra21dck6cuSIviCvE3Oz+t98HTx40P7kT/7EOjtdd+Ny6wtf+IJ9/OMfl5fr0rVx40YbGhqy17JWmvZr66XWd+z5r7Z4e5/Vl6vm54RWLFuu0Wu1fPhyUi5yKsxosyMrjU0k9Ln95tnQbzVDWVu6Zb3Vnkur3UagceJ0UmZpu263CilUqubHJm361nZrfmrWEgfP2uLbdtit93zcKh0Bm9sZtdaZDsv31FnjC4s2cXO9CqbZK+pUjDU+PKj7kd8ZEbNq8js2WtsXp82/tOIBmU9atqnBWh8Ys/lbe63hwKzdt/SngnNiGmbD9He2aVrKUxPSZBfRLwHGrfF7oBScHrClf3el1f7NtE6qBM/yQa0x/3TWbGDYKns2m7ezRSqT9+BpK16zVR4jz3JabRY2UD8+jfZGndCZBpx6+zqdxFofGtdmVdjZa/5nT5p3JZfM39zkIKTnR52SEw7pA9zD9Fe+6CI5qiWr/+R+eYsKN+22yIEBqVezb+q3xs+ekkel/hQ07bKKXRSd8qlz5rlqu1o8uU3O90QBBWKAAuix/b8sFELk3IoSgpeEDZdpqskZq2zsseTGduEH/PmS2r6CcF6xzWx8Xm1UNoyb3/Gbluz2W9vRrGU7ItZ4cFGFtefoOStD0O9pc5NAXe1WuTBsRmtw52ZH0aaQUIC3zwJDMyqa8Ffp30plFSYUHWxmqd0tFj+fUkEBLd+Xr0hN5PylxIHGWo3H49uJ5sruuZuctsr2dSp0mTZk46yMTVpl72b9Gys0X3TZhBt6xUgKLZZsZk/YOh6ec+oODKO5ebtn+y84mjyTieAM8CeFfRY4M2mldR2a4Cw2xvRcwbISp8nvToeEjd+z+xcdvsJrKlpR1Op2uzxAz6Y+d/3EI1JwGHrwd7RbYLxsnicHrXzjHsu1RYU3iY8VxPxCxeK1Qk1uemzc8vs2uKlc2sTjU+aDXJ7Pi82FqgNSg+KBFqUmUeuj5o2EVCTouoisIDuyJRWtX1gZBHm1C6VSU6fd/1n5gbruMNLXxi3yT89a6aa95nv2uHtfdrXqOXcIDb97zVCCoKfXxqUicsihxe4pB61YX2ueWJOD5uK5wgPYGhF3Dm8U3y9qe6ki4r4XdZR24uZOy3SErO7pMYF/iQfyUlBCix8a1zVGe1QtxEBA6BS4emKhkfLAZCRh2Hi1mKDlmmLidGbB/JmoDloV8CR49Xg9C2H5sfzkc+JPTOfl+Vpbr3K9Xhr7G4zkvmfPHhnZ+XqpViCxOb/7u7/7irfDBOGlytXqmp+ftxAswtew1hSsy6xqMGjNT0zLu4C5upwIW+ujToJnTd3RcdFfFXzhnCWOzopSzYkOs23iwVNqSdEKY5OnMMK4SVsRPEFktqwP9eZ/PGkTdzbJfFz/0DlbWhe0QLoqEzeKxuzOkMKWQTp0/v2g1V7IW3SqaBPv7LfJW5ut7ZFZq39yxFqfmFN7jUmvdFfEpt7Sp5F7jKSxyYLabaAZHsj+pRW7GzT9pClC4n2iAZu+IioFKrO5WW0gbqt69XarPZs2L5gBTvB1EadWlCsKmfXW1OiELGNsTdC8iRq1YWBHKYajXLbiTTsteW2P+eZTmh4KDy1YyyePWOMfPaPTcOqmDfLPFK/dphO9P+0ULKaUaEesUt8pZMm2U+tQvpG85e/aY772NkUNEU7M9Ffd2ax5AgENJaBS8NjkYaK1uXm9MAG5K9db6MB5C48siRwPeoCAWXxVxBWVh0asCgmbmJruRj0feIJ8s0mr+9xxXRMqsLu7zBuJCCBJcYG/BIUremHRWp5L6f7HT8870OYKKZsoGkj4yj48dc5s+3rzNzfa8rqErhepBm3O96P7je9nDqp1SUwqXZtLKSvXxy1xeNrlF/bXWWQ8LVUydnhcEFs4TbSAhDngNoh5SqYtd8NWbbrEOyWv69W1ycaOqlWs8asooo2zdPN697xEQzLXd35+UptnYXOH7oMAqPoU8clXttpCY+Mvree9YVY9O6Tbw8umMHFaVljrgPVipAajUB+XwkISAoVD9NCoy6CD8YR6yfDb2MLFybdyc8Iqt+yTSurLlC06WRAyAZP68s5WhXYHB+esOjunFl74/KyYXQrePntBt4HnTQppsSxwKUotRYyQEQvL7jmZTwnCas8c/Yop25daF1u9L7MorijEKa7u3ehULwYzUJP9mzdYYGTePZ8c5LzmgpFRpihmycmkQGQToWiemNZBiGuDNr0gprQZCwUrNEdVOIamsro+uf5LdVEFTYtBhvpJAHNns2KGEmdSbop3cOZifBXFNpOetI7FRAOh4feJ3o/PSlmUkaCzCfi9mi6sbuqVFQGlmtvWYSgRd0wuim/UQwaFbt+ma5t26v1HfvWV9qe19ZIbk+f1f72B1sDAgJ0/f14KFvwr/nv1a2xsTB6sH/iBH3jF27npppvsL/7iLy7+NwVbpVIRD+u22257TfdtjYP1CkyTa3/jT6z3/wza8Hv7rOOJtAKd021+5aQl97VZaKGkcFk+7Bgth+2CKZ3suaUr2tykYE+7Tr95WkaVqqV6Qtb42IgtXU30h/udQBOZvCNUOjKybPN7a81bNAsvuhZU9OCIipSas0uW7ayx6IkJnYDLW3pFlJ58xzpre2jSpm9pM1+xKt8PLT21dLIFncSVl8YHLOZX2jfQ2DMlwUZT3T7r+OyYgwViOL0waJm3X2mRiZyN3Raz3k8OX9z05Lkol7XRuTZi1XJ99RYeJsus4lhS8bA2BaJ5Akk8VB61c9jAsu1uis1/esSqLY1mI+NWXl427+5tGhUnTw1j8E3f9lsWP+yCpCE701ZUW2VhyapwtoiJ2b1eLalsZ9SxrYCfsqnUR6UEMVKvRYEWCep0zwZE4UCOWiWdMV+vMyoruHhmUSHPhTqf1X7umGVv2abnPr+5wwJfOqpNkHxEec6Ysjy/5FhOTx+10s17LDQ8L68Uk2lMSfqOnrfKtn5R7xV+PDqlDLXUe68Tzyx8/0GRtIE9QqkGccDPM3kpz0uu6NQWTMMXRpzCMTru/oSNRYuoWNL9FxCSgowFhdwHad2n5wCQK4UtbSRNukFI93gvtr7ZGKV0LCbljUJpGr+7xTo+eVrqK+R+WmirbUDawah14j0R/Dy/4BAhhbIzvR8+K5wCrS8GEeqOwcliGCGgSU3axdzW8rX9Fh1OmXc5LwAlhRGvN8HELJAWhRqfJQ5NWb6/0cqwxRYLet8wYUobirYugN/wVM6FCncm5LGiiGWaNnZ0yjJbWizVHbDGP3rKvHu36br0FSpSWnV/huacL4hL5fgF+etYd4e/2x7IfdK+FouBBd4r+LQ0ILH6O/Bmbewzz/lhFSCxgaSKIN4ntL2ZYhSOAlI8rDHCvWnZ9XZZdl2dvJdcaxrE4T3J+5yWXe1Ki25hSaBbR1cHtBpU3qNwC+eGzbats3xLTK16VO3AbFpBzhxSAPbO7I5Yx9+eMWusV1v0xY9J7WriolYOmxTFRDTlNjRb+OyUVRpqzLuQtmJXvaCpDz/yQXujra8rB+v3Pvz6OVg/9qE3HAfr9a5jx47J5L5v3z4R39/+9rcrjxAFC+Do+vXrX/VtrilYl1nvbPh+CzLp391snY8v60OfD/eWfx4Q3BFzMa0DFhsZk3DEuHCKTG9rsarPrLxjnTZ7sgxhVmF+jk6XLLOzw4oxj8J4y0GPwwW0enXipj2ACbzhiRGNhz/22Q845YLiZnzGUr0Bq8IqymRs4J0xfehSXJH513AyYzXDefMNO65UuiOsfLBcc8jmbunSJBJqSra3Viyf6StiFj+NpO+YOEyFCczY3mbRsYxOpb1/Oy6vFZs9xcTYW1xrrRQPWLavXu0BfFiY7eXrwEsytaRxbEbYUUaYmuTDnYnL2MkZxcegOOGDYooKZQ/vGSP1FBy33/Uxi37hiIjehArDxkle36e2hNamfvPU1yqnjzYDmxJKBAXH8qZ68x27oAk/TuWF7nqd4GlPYHRmvByauZ5PInMwuwe8Von4NQUVPTtrdQ+fs/KuDfLPkaMWWMyat7lJKghtzthQSkMIKEIQuvEKEWbL78I3wyo0uBM7Ch/FRHpdjVltjflqaqz2Hw9Z7PlBKYTyGjWvPK6llIUnlhUTglrARocpvjowaqWrt0r1AUbLBJwdO6fNk6IWrxUFNygAKUm0eNMZFdG02NQ6K5eldMztTihnr3LdDk0cyg9FZxZ/TT4v/ADt0I7PjGmjLjTHdZ8oULM7u4TRQH1TQHJlhbPm86kVyPfw3LM58+9MvCXOpjV1C72baw8oJUUUKleZ630+pcdJsYMhnZZSttPl5cFZY3oRFS00lnQHmJmk1Djl6aFGZcsWPzjmWFPVqkjunhPnlbMXOwm8tWKR0aQ1f/KQVW7eq+Il/Nw5Ma4wkaOwcZ0xrKEDg8+nTEXW16q4YsESo7hCyVotrvQ7lv+PJbc4ha0YWzHnHz0jRRJfnZQqEAwYzYnMwYtYrghOy8AMxTTQVyKLyBQkTghPobIOG+Mi/fMe8eaK8iWGjg67NmJHwrxtzZqA5UCAyug/fF4Thdw+gdG0FTv+34At3bpBVgJajHi0mEJmEZieXl+nSB8mOVVc4WPL560S8lp5bFKeMD4v8dC9EYurr/fi8ni9X2+U9ZnPfMaKWDhW/n65r1dakNwBi5I/+I53vOMidBQf12sprlhrCtZlTgo33PHLVjOe14g4WV6LO+ut9uSSi8oYnVBLrzQ1LeVF5PJ8SQoBKg3xHNHnLiglfnFTzMohjwUyVUv83X4pJuWhUcvdu9cWNvgtNllRccWfBMLmGnzW8tiU2SInz3ZF8Uz9p6vEter80+OWvnWLZRu8mhJsIE+tN2jNT8/a6L1N1ro/Z8GjQzb35k0qtIInRyx9db/502VNuuHRCR8ecipFoSzliUkkEdVPzgvK6duxSYwhaOTzW6PWdHDJlvviFv3H580P8JEPfPwhKFmcppvqtXmk8XMcmFY7YHFPo9Uenbd8e0Kn2NTeDgvPQqePWvMLzni7eE27Jc6m1EbgfgQfO2Jz332FNT3vWmp8MAN0TTw3qnZkqjdsz/7FTwvKeN/U74sWXu3v1MbIh7d+JltUgRU/MetgkylXiDIFWR6bsOJtey00tWypjbUWncipTcaggkzvMHvwR4VCGj3P9DAV6rOmh4a1SanVwf8PBGRMjg0vKwJGagyb33LaZS3SBqTArU1IzQM2y8+jAFJoaNEehETe5dptUv4yGZnGxSM7PuVagj3NUiAVRlytWn5Tu+JemL5bNfqzIVZqY864PDymaURlDba1qOC6d/PPq7ii6Gd6lBamlKdSWYBQ7+OHzN9Qb9UqvKqqlXb0Oar8KoF8Yt7KzXXuMUVd7iatKlqQYkxxOxDPJ5JWGRh2Zvm+bvPM4j1stuqp847wD7OKomkiqYgjVBymA5Ob4mKqEX6+vL7Wak7NKxCb650VPzzhvGdsLg0JtYdR28RaglzPpCktPlhZ4B5SGTdFyMKAvbqr+HxS12gFUhSX5hd0rSveSW39smJiqrPzFydFv54LU3lyfdQanp4UPNe/oV8RQLHHTjsVnNzD+oTUcQ1XBAIyikOt52BTSoTlkdT1jKIEqgLFq7tVLU7eL9mrN6iYjp6cVlF5l/ddUvSI1iJUHaVJKjHIkO3rrJgI2tjNQR3AiOxC+eL3M5Ag9WpmzhXZivIhkNEuTn9Wy2UdnMQ5O35e1oQ36vp6Klhdv/v6FazRn3hjKFher9cmJycVh8PfX27R7iNr8Ou91hSsyywmnuRfWNetdldsFMioV14sCMzVhjrz93ZfJIJzeg+MzNn0lTViNNE2sQMnxXqJTZWsGPWoJcMHHyoN+W7hhaomAGuGy+bLVS0+lJZChooE/sF7elj+p+aDWQstVtUWg2reeGjJmg9mzJcrW8sXx+U7iU5XZWbO7+pT/AetEzZSUAzjN4Us+ugJtfFor+GZggcFENP7wmlF98jc2tkurwaeqFRfRPcbzAFGav/6PitPzzqzNuP/IATqEprMYhqLTVKICo9HxtnUZkz5VRlvveWqFeqC1vLckuND9TVaeM7Fi2BADyzktSk2nEg7nwm2oWhIU4U6oc+nreHZaedjmfp9FQmVDV1WaAhb4Oy4K65mlizTV6ewYCj2tEhoZTH5COzR19ejUzUtDVpO3CZFCQofKI7stnbL7+oVvZ3fiYJQdzojj5qUIp/XCv34yipW88KYjdxTa4FD56WciY3Eh5XPY76BcStNz0g1Y7OvdjS7PLhU1rXwMKgvZxTwS/uk0NvkTPxbetWSQjVTYQBaAPM1XoDWeuXHhY4PmxfDda5o+a5aPe5Se51anrTDyGPESyYq/MKSPHcgOtg0VVz1/JRiYXhNFm/scZOBd+7TdUJxB6dJxRV+IK/PlnsjMivjPSvPzSs03BmkO1SUysjcXK/XH7+XJxp11HW4VMQCnRsSo8khFWh3Taow0gRmX6cV60NW//B5RUMVmmIWnchKdQTI688QjeSAoXo+KKIYbgBLECayJqvpUFq+BKbzWHQ9QSXXpxt+E4ep4P2Z76qT8gVgl6EKiit9W66ka90OnLDS+QFFPsEg+3ovJvwaHjzvpjf3bVNLuRD3WmWTm1yWeR8wbYqwZPdewy/GNCUWAE3AUohSdAuI7JU6WjlwzO5t/zHR17EohCfTGghAjfKv73ch2ImEm5SMu0xBtX8hXJwYt/5f2W+xiaqFR4j6cZ9zFGalMxfcbcLHI3+UoGdeq/4ueTMZ5rl774fEAnsjF1dr6xu3KpWKiqvVv7/c11dTXIFiIEpndf3e7/2eDPQERi8sLLym+7dWYF1m+WdTilERAyeVllKCj0Qehy3rnEEZ1lAqo8BfMuDYLNsen5MPy9faolOcXnzGk4NmmSv7tQEu3rnRlq/qsWyjR/9vdpdPzKvZvQmbv63PyjfttuTWhGWv36QT4tjNEWt+ctqSt2609IZ6td/YCP1HB3RKzbT4ldfHvxOumt/RrcBhWF20ChtOVnQipXVAO8c7OS8/V+wfnpMHg+lDTqMa7T5yVgTuxLllix0Zdxyd2UUVErPff5V4TnNXN4lkr80AKGO5qsKPCBZ8IUwJwRjSiPbSshWjXoueX5TiowiS0+NuMorCtKlOrbvCzTvUosFMDNOpcvKcm0xjipCCIZM138Z1KrIw0vrOj1toKm3ZPb0KGKadFVguKjao4ZEhtTeJqQmOLVr2nVepkAMKqsnAXF44CrxH3DZsJsCxtJeq54ddwQYAE09RfdhK/e2uvTXlsAuoUW1P5zXhCFusfP0OtaJoy4BnYAqSgpXol/LRUy5Eu1i00uiYM24T0NzdIZUMYrk8Nc8eVUvqkS/8nFSU1M5mte9oe9Ja9QyNW2FLl4odbiOwkNNEn39iUe3J2Kk5XYsocSgw8reRL7exT9evlCtagA118sigxoYH5izwyGEbfm+vzNd8lYZHHc8pFLC6Ry/oxKtNd/cWqZ34gSiiqxt79Lu8MwsWnslpapXpTAYECBbG/J98+y7zxqLu8RVLltnV6Ypu1CTy7ZacIgh+gCBqFBPatHizYLjRMlehFAw6ACrvo2hAz4UM4WUgmn4LzmVU7AIz1UDBwgpwkPYBkNjueveBly87ECewVACqDVEVbiQk6D3f2mKVQuEbomCxuPZR9+zkBb2v6k4s6X7m2+I6MPBaUTRxcMh117lC6uQFFZcElsNLo5Bn6ICAdNqP/pZmFZ1MSiq3MFNw+JbeGg0Y8Hxw4BFb7eyQ83qRMJEp2NRb+6x89TZremJSv18QXtp9zU3ySRItxOugz0gODl6P0gco1D3PHVPbf219jdeayf0l18iIy819rQsCPCoh6+jRo/bTP/3TAo5ilufvr2WtFViXWcXWhPwmLIoiImYE3oSjM7dk3j1b1PYgGqZwx16FMcM4wutDzhzTbyhclflFKyS81n7/uEVfGLJqoWDBpbK8JeH5qoUXitZ4vGLLHV61ButOpVSABBfLFprPmycUtMbjjuOz3O612PmVzaNSteXbNmtDbNw/pyIGJAKqEuZ1BVKT57actsSxeeEiyk0JwU6Xr+hy0R6gAQ6d0vRdeed6BxpM/4WgqID/FBdDpA4QwVzRWh+d0on0uT//afk25GOamLVK0Gu5rlq1eTC1CwExk9KJefa2LqsZzFipMapIEooTomH4f2Ts4QXhtF7xe6zQWiOwYWAhK/AkxZzR7gL42NagIgsOU7Yr7pS4c0MW2X/Brn/Pb2tjAFAKMFWF3+ySRY6Pu5N+tiJqNo9RU3XRiKYIFerc067HsXB9p4Kds3fsvPiaeyfmpLrgvxNbCjWpNqa/B5bI8vPp/uGt0xAAcTmPH5K3Cb4V1wj+LPG74lHz7doqSCZ0e74/MpFx023nh1Q8ElcjPlZbs8XPLsrILlYZo/h1taLC8/u5xrRQdmhblssaaNDgQ1PC7t32X5yCRzGdyVtmV5cKGWUbon7Q0p5ZsPTWZqteuc2Of+x9IndzrYj1tW4lcT4UlOqqaJfJefmrUI/wSymvDvM1LdEL466NyqRlQ72VT56Rcvf0X/2MVbhPZy+ogKc1VTp11hVZFE4HTiiyh4IXThIxQuQZwkeKn1uUD5Dwb0dKd+qj8hWDARXmPFaZ9imamFrlNsm7AyOCuX9Tm0KQ/emCijV+PjSZUkuZ7EPid/JtNV9R4Fwa0UIbk6/Xu1DEUBO/2kVhRKG1sLNWhxIidnxTi0qJQCEs7lnvBi1oTe/aKB9ZoTEiszvXO4oeh4O7r/iQJW8EiVIVTw62llho+ZKFZvOOWUUgeDKvArl03TYpeOQVoii2/sNpcfcIF883hoWOoGVM+1uXx/kpYUSAlfL6oi6vLj5D1tbXflEcv96vN+Lq6+uzW265xf7oj/7oNSlOFFLbtm3T3z/1qU8pLPqjH/2olKz77rvvNd2ntQLrFRYnee+pIcnfmsoK+m1pT7PlN7e5AONoUCbk4HxecSU6xfV3idzNBxMtFW93hwWTFU1BwZ7RYuAGbECuqnZcKeyxzt8/5OjoeG22ueBnqWaoH/ifdzVY29NJy/TXOqL5Cq9o9kevt9lrGq3+vlPmbWywxStbBSxEcVpaF7IK4M3BEUV7MFkl7k+xasX6sCjd+KqI/KBI4oS642cd+I9CB+MrC1I0379Kb9caHJMKQUgwhm1aCFM3NmjDYxKJ5265K2RNz8wKLIjRXrywVFbGddoPbIioUKhk5LYRWMu4eba7RkWDd3hS4bmYoFFx5GOaWbDYc4NW3L1OeW/cBtE0wFCJK1KLCE9TNKLWGhsVrUEwCChlFBxSNGjzRoIuOmV0THFFy/fusujgkrwoKD0UyQs397hQ7xUzJYqWQJtzSavWxqxCy4a8yqOn9Jj9PZ2OU8WYPCiAlibLXtHvYKZhv9Su2LEp0crJl+T+y+QO+qKnSZsoP4tpHJO4wsYJiV7fZLHJkn4/7TcM2kxU6vVZSpp/eklB5LTdNBWI8oVh3e+z0GxO0UHVMwOKXkld2WXpK3s0cPDgU/9Vpu50b1yFIa3ghetdRhzKUWl2XrwqbeZkO3rNRdbUhDUVqCKvJi6yO6osRRy+HgFt173fsnftssKd+8yDNyyZdJ7FbMGCJ4ZdK4mWVj7v/FyEEa/r0DWEeXqVQZXvrdeQRL4hrPcRz/9q9h4xPsnNCbXJKQThchH7otYkUGAV8kELP3XKHv7iB/VzPLe0Cnl/BZJ5S/VHvqIgWC2qMKNfaki/3JLX6OXWJf6kV7PqjxEJVRTuQ0VSTdhGvq3DfE8etsgXDsnj5j07IpUrfHpSqrao+I8cUNi4cgqniQxyz5WYZTynU4tOsWKqlpYzQN+2Bg1/4CMEMKvszr39ChMPDs9bYLmk6xVsibx93f/ZKV0TcwLEkmhABNDa+ldeayT3l1z79++3q6++2j7ykY9Ye3u7vfOd71Tgc54D6FexgpAAMk7Jfuihh+xNb3qT/k4e4aqy9WrXmsn9MmbCW+0dFt7qMtXkwYnHbPGqNvFgMLGnuvzW+uC4zMOxEy7epDI5Y966hPtAA853bbvFxvJiQ7FpLu1tlZk821dn4cmMZTuigpN6d21RRMrMWzfqhNH0pWlXRBWLtnRdj9W+MKnfw4dcvj4gbwaoh/zGVgudnfpy7lgybaPf0WNd//eMTX3bJvG0Ehcy5js9bMVd/S60d2DcMjdtstjZBcfEIfk+V3I/n8nbwjVtVndk3u4/+muaFlrY6Leuz0zIcM5mhWE+cmhIbZzlDr81HclIscq0Ba3uyWHBDYfeUmPtTxU1RUihpzDklYgT7+Z+5w+h9cjvHZ201K2bLDaakceGwkCUaDAFCykZsVFugIdW01lF+8DQwbxNgaRYmWUXMk2IsDGAkFjZuGMRGfDxpyx3BsxTMas7k5EXh3YgqgsneAKvpfidH1SBIXr82UG1uVAcPR2tarMpiDrkt/Lxsw6vsLhonmt2aWqyOjfv2o+oeoRPowZRLO/sseB4SpObkbGU0AKBcxNSYJR3NzVjHrALYxNWunqLsBNMXnowntMqvH2z1ZxZVO4l8NnEIFmJBAnnxDKLDC06bxKbZ7GkoGLvqQFR7AGxMqkIU21+e9RaH5uxkz/ZYH3/hOev7JS3aEjFZ/zxM24jL5YstatVZnMZwDGtoyAdPq0BDXnvohF5mlCAdMCYhdhd0TWDCsfEKionS2DbpgarQOfv7tSkqrhOfADRqltIqVCl6EF5pXhDNaV9x+2DGhC1XUT1kA4CsJ+41lH48A5xOyisyl0ES8I1sLlfVH7ua3Zzq+Nqec2iR1fUP6ldlYvgz0uBoF/LRZFZGZ941ROJd13/q1JOQVVgIwikS2J/cX8p5ioHT9rC911jTc/MyRtVPnZGbepLFTi8UlgCfBv6nSG9tVntPO/wlEz+ytRs+GGR8/mM4z2dq/dZw98c1MEQdVaBzRxGaiLOR0gCAJ9vXOs+r6aXUR0vxwl7o6+vK6bhdz7y+jENP/VLbwiT+0steFiPPvqo/dVf/ZWUKDxYTAP+6Z+6PNCXW2AZiNq54YYb7Fd+5VekaEGAh/D+4z/+45owfLVrrcC6XIF11S9YeDy50oYpWCWZcllbPW2W7o5ZzaFJjZWLNzO3bOmtTRaezkltIMiWtg6tN9oxFFuoIbB7UDWWr+yWihR+9JiM66KVx6KKEynU+q0U8Vrt/Se1UcsgDdxvxXg6e12jJQYKlu4MCgJKKPHM2zdZ8xdHBaDM3rDJso0+a3zQ+bNQIUbf3Wcdv7ffPJv6Fa+iF5/WlUjmfo18Qw1fuLFbfKboE6dlXMVfgwqEiZq2EGoIQba3vPk3LHpy0vlgwBzAZFpM29K+NgvPFS3wzAmpQcsdPjF0Ctt7rBT22cLmgHV+bkIAT4od35FzVrxik7wxjN/TzkHZoJ2HfyvfFhP2gLgOPFWK/Dh9QWZalAVI4sBPBYl85rgrAHi9KE6DAakqeLDY1LVRj45b+cotOr2jhDFN6dQMVD2Xl7a64d5x26+b//lT5qXo8HgVtIyihmqA0leqd+0x6PWeDT1uqnIlYJjXgRBtfGUMPIClAPpYqQnJp0X4M0WTGFoEN58ZcbE5oaAKAqZRKyGfCg6gsbVPj2iCjunHxLMjFwsSJhgH/kOH9X5mUZsmAw60bTCx0/Kh3SPlKEr0St6Wd7Q48ClFYDbvvEodrVYdGlVmnFShctXyzWGLHp9wE5WRoDb58NkZbaiZjU1SV3mOKRyW37pXkF38P/CUNOX42BGrloryNOEb4zEyVMBzxuuI6sHzBJg1NICpPiDTNMbzTF9C7eLwTN7l7rUlBO6lsEJxEo8rn1dQOb4/JgxLzS5JAVgpYcsUNeWR0a9QpZSpt2p6T5OB6Nd7TmiLb8LFASK9xeE7Hv/0B175++Fska6AgkthtMKtYjqRFh7Xd74hqIECiPkoVnwWcV2hAgaPDbmBjDODblAhFHAB1tMrIdccMvdtUej2qrcK8/y/5cLqG1JgfeJXXn+B9b5ffMMWWJeuF154QWHNxOa8ktGdDML/9J/+k7xcP/mTP6mfW80o5Gf/x//4H/Zq11qL8DLLP7VgpfEJy21uU0yJFwNsd6tO6oAy9SG9nLEiIbRMBuLzwXuERyEQkAmVDy+xa6pV88+m3cnd77PYk2ctMpqy3K07rHjFRhVvyvA7dN4inz/gsgVr4vIgyVsSi4pGzemz+f5BCw3NWeKTz1q+YcU0vuwm0vDpRE/NqHihwMAQm9vWYc1H8HKFLLm1XkXR/Fu3aLOcuLvNPEspKWypfZ3KAQxPZi1/1UaBDx0B3Szw1AlBAikQgWFGDwxYfgOeF682Koor2oiJM0sO3njNVmEQWp9Lya8hT1myaJ3/OCwljN+nybJQyPyLOYFEM5ua1NagzYcqgc8t8IUDltnYaN4XTjru08ColABPa7NeIwYN8HrBd/L1dcv8DeRURV8spNYGxRwFsBhdm/q1aYPUkMEejxIFDmG056ekvt3wrt+2W+/9DQXnVnZtlDIGmoDHqBbw6KQ29aWNjppNIWHDE1bFF8TrDbGd8OaGOqEn1G5hWOICE4BF+eQSzwyrkOB+S8WprdFr5S48n1qsodEl8w9OqRCvzM0LdxD91DMqrlCKKP4oGPt++4hVj5y29HXrVdjCp2ISlGnI5O2bpeaoRVUsWuzRU8I6cB0JtdHUIOWL6UPPhTHXPpqYU4QMiAYmISmu8UbJ67WctuipaRfQ7fXY+I9faYkvDUjNYtAg9Mhh++KDPy9oKkpK+uo+sY8wr6Oy0Y4E7ClwZih0sbhScPGGfmfornWZiPiC4Dhx3RVrg1aoCzjGGMVzvmCxQ2N6LOWBIUFl5Y8L+hyWIhw0H+bulcXEnIpf2rzQ7TmsgCNg0vebcGlKNh6yyFj6qyquWDLHF4j48SsJgecG9ZBFcZVpCymj0H9hQs+FILUok+eGnHcUVAVDFRwoKi6DU1maXPN467o6NFBA9uqqn2ytuPoGrLUW4WXX6Oio6OtMANIyjMfj8lG90urp6bHPfe5zdvjw4YvFFesTn/jEayquWGsF1uWWx6v2jyanMH62O0+IJta8Huf3iYQttP+cGEjh46OS8DM72vVhNb/NFSeeyTkXWxJ0o+zKtcPUGwlYodYnn5K3q8MW790ipYoolchYxgrrWsw7NKnW1NRdHbbcFbBca0wp9igRxPLMb3bGZRAQ+IZmbu8Qm6r+gNuIYCKFnjmtx0CcBYUX7bPGhwZVgMTHHWKCQjE2khYna+z2GgufmlARQ6ROJRqw3O07tYlxn0sttTodo65UE3H337TpTrvoE4CdI3eGZYjm8dNuK2zqUHFGYUK8C4ZsJsIYl6cVF0yWxA0TVqElrjaZzOk7NllkcEmTTETKEPPCwpB9b9/7hJUQOb42KqUl1xYTP0vGaGCTTK89echlpOWLlm+Nii8mP0tbg2V7yKQra0Nn6orYnMTTQw5DEQnL1+QbmnJQS5QP2ky97SoGQWGw4YB/yNyyVYUqxHPFvyyTM1ejAlJTeAxG9HSqqOK1Lk9OWezIhFpx3mm8RwHXRltckuoGCgT1M3VNr5AQhRu2u+vRH5DBnWk/WpegHkCG+DrbhZVYfvNuXSOoZJVIyOLDaadGUHCjlNbWuI2UiJj6qOV7GnSAIEdSl/yx8yoOMTT7Byb0uzD5r7Y+S+va5aOKHB1VRErNcEWt89CE8yhQ/N5298cv+piig0mX7fjCgFTS2970cStFfZbe2OBYTmFXeE3d4qJ2eNwcHCJTORXZePdQ/2iDxs4uWuncBYcSYPgEpQb/WleHsgdRt3jdyO7Ej1WamFSLjMVkG8WHXsOFJSudPqfMSNAU34yrsK3bfXastFK/6lVLsHlGwxSlwSEV+WBYyLKMDy5brm/Fo8hAQnbFj0kBhXJaR74goeN+q0xOa0IVNAgQ0dyOLimCHIwYfmFKcG2trW+m9Qd/8AcyuWN2J/LmPe95jyJ0nnjiCfuRH3Hg4K92veUtb7GJiYnXfZ/WCqzLLJQI/CW0NigMRB/3erVJcIpgQ5VvKRG30R/eLu9LZW7BwhOu/VB/qqD2oVAO+aIMx45yXFSIriZvvB6rGcqp9RYfyallxQQPuX3k65HHx8bQ+vkha/r7Y6JWo05EHzjibsfMpq+pt6bPuf4w2YmV3jZLbWtwHqREXBv8wpaYeXJML5bELUJpWdqSsECqrA9XCkQKE1+6aK0H8sI1VMYnZV5lQZOHjJ5vckUHAFHgpKhq4Cx4vCApyFBjdT5aUIEGUgBT+cLmsGU7GOP22vL2ZvllStGAHiNtVzxSZBJaV5trp/E8URDMJ61cH1VLDyAp3qOb3vmbrkDoaBQqYnF3oxs9T6bVPlq8okVj+xQ0bNrTP3G92cikm5KK+fT8yvcE2+z4tMbcaY9yyi9s7bZqbY2FB1daYKhRlYpjQ2GADPhVyELtZ6NCPQP/QGEqlTJTEkuJRXsmd91mFcLJ3S2alCS2heeI1lVmW5sKCVGvY2GZ9L0dbfIX0c6kkI5M5jQJJ8BjU4OYaJpchMAeCpl363oxhuB8oTjELyQtMrKkApx3N61HDgcyowcDVupqkpJHca2JuqE5tTBTGxNW3tZ3sRUdHkvqupj+rl3uOvL7Lb+1wyZuiKmVSSEcOTtjNRdSjhqfzQmfcfdVH7bwmSkVVek7tqvYLN61TwUkBTatv+i5ebV0p2/rcO+rSsV8haq8erwPCndfoXYq38OG/9Djv2DZXtce9+3YrOcZM7uK3VDQSuRG+j0W3H9Gnq3g1LIM7dzPS/1IaglGI1buafvKf/8mW3jYlDTg977qvD7hNcguzBfN396mYRnbscGWblqn1q8KYdr6tJg4ZDTViqhfmZq1cl3U/KQOFIuyEmR2d5t3IWnep49Z+Ii7Zl8clbO2vgFrTcF6yfWrv/qrds0119iBAwcUe/PBD37Qent77bWsxx9/3LLZrL3etVZgXWaVj512ZGTgfXBkTg7Ks0Dwba4paJNv71cRwoeUj8Pg0ISiJ7wjU2pLsfAVrX6YaVOEsFwpi3/Ehg7SxPfcSQECAWZyamRhSIWoTduD0eel67rdhFXcL9zC1A/tk6m687MTFkqiIkRd+2N+UR+u0bGsldsbNYq+8N1XquVIG4sCAP/EqR9xahyTf7TVqus6XVhsY1iGbCI1yldsllKCXwN0AJBKCi2KDlhbtU8PKzOPAoM4loV37ZHxm6IsfGRI7R2QAtCoaWFSHFK4PfGP77dsJ3R7r2Wv2WC5Xb0y70N690zOym+jcXO8TNms+ccXFBXD5sH3RUeWZYLnuUWxSLeuBDr6fDJD155YFIBSMM/FJWt7KmnpmzdpQ87Ve9Wyy1+72f33+ibzrOvW5ut/+pj5lwvmYXpuKSnfEgpXYVuP+Gb4wgDP0mISDBUGU03Ycrv7ZBhHMfEfuXBx84acjW8IbxEQ1mJTRK89E4M8rzCJGD6A9j95c515926VyR4TPCoLRRA+LIpCtfRoI/d3OkZToaBWDt4ZyPaYmwmK5vqcvrHJgktFy7ZFrLK5R+qp2nKQypcyLiNxMaOAZIK9iVNKnFqQyoFvjGuRmB6u6+YXkhrDT+9otXLQa+1PZaReCi1Bxlya4jAkzxktOaCWUN/BU4Q//ay8c7xmlYVFwTDVXuyvt7mtPmt6YVGFJYcQAszJUqS41vQsgwAUtU8f1qAF6QYeAq/1qeV1Lb90Rn5Ef1en+R/cr/geHgPFNP48CgKuW5YYYLyvQn7FxnwzLtAarPsG/pvL5Dxw7FXfxpuu/YhrD5I/OD1joeFFfR7VPjPiDniEpa8ouNVYVJO5+CAZCEh3RfV5JtButWrRgQWpfLR711qB30RrrcB6WQ8VrcHdu3fbN8taM7lfxkx4x7b3m98XEkAUyjIndNp3mNfxOdCWwEMCi8dLeww2DFL7CrKfWA6Mtz6KLEb5VzxVjN4TZ4MhGTYPxHhuH+9I6dyA+ft7ZGzHByVjan1AXCDUJdpqs7vj1vrYtKZ3itdsVaFCODH5ecUt3ZZtdoG/gERpSyY3xq32+KJVTp7XRBnBuEHamrRMNvWbF74TTCE2yoDzWdGegVEF7DH2T89b+YZdUpvKW3ulJqlgTKUtdXWPVBawC7PXt1hiIKc8xtRVXfbkp94v/g/KyMLWqDU/Numo0wdPmL+nS/wubyhs1Z0bRaRmacrytAsGZhqwsrlPat5qyDBtDE7oZCrGj8+oCFr9XuvtkIpVbqkXXZ/R/drD8266Dg4VP7uc0WZPVt7y7VtkpKbQCgxMuxN9KxFCbmxdINShcef3yhds6t5ea/mbo1J5KKpWV4AMPT73aFctZqX04eEavzFgnY8VVGzO3rvBmh4acoHdpxfcROGQg0jCwGITYzH6ThwOvxu1gcJSKIruDpnS5ftDuWlrEiw039+kooRcPQ4BmT1d4pBh9paxnBH/M0NqYeKtw1MFLiN+dMrlxs0nxf5C7UMBKzCSPwnawW9eJjMJz55NWXp7i4Vm8lIWaWnKII2HZwVKyXtCKhwICb8bGMAj5lnfY+l1tYLo1j0zodBlFF6BXGGbMdFG9BTmep5z7neu5FpjKC2KAsorD7RQH7TI8JJem+kbWyy0VLFK0GPP/N+vhABS2L5U64+WMq0vb23iK3hN34h16bQifqaXa7m92qlGFWkLSyqIaNF6Otsu5lXq+hcRv+omYfEkPnPUFefRqOJtKrfsM+9jL3xTK3z2b93k/ltfA5P7+98YJvcjR44oQ5CYHP5+ubVr166v+na5TdhX3d3dr+v+rSlYl1n4buAGUWhwMuRErKKEzTTodxtgDB+I6URP263UnLjo8eGkIeI1RQAROw01olCjHMBFmt8aMl+hIln+qu//b5oYSn/H1drM2MTz9S6EuBT1ahKOQgOzdt3ZvC3sazJPb9fFsOm65yb1J8VP4onzFlguy6DqGRhTbM3cvnq1l0pEhfg8lrpjiyXfsdu8k7PyR4lNNblonicPy2O23BOROkWwcfXqHeb70hGrbOg2/9C0y76DUD49a7EHjpj38BkrN8SEP/Av5fU8UFzRJqLoYZNsOLYsSCjQRH9nh07GvnjcStdu00YNu4siJvHAcW36ye0N4j/xmHk+YIExCICRl3ZY/MyCnmsI1/nOhNQ92inkDRJVQxGQOJm0DCyllQKAld3RaaWrtjh694Ex83zpiApYBeSWvowOoAWKSiVg5UpsTeOxtHANxOmALcDXFnrmlGNU1YdkSLeJaV0rtM96/zktxhLKmkNMZBT9w+MLP3hIv6t8bkAgTxmzua7qa1zBNJeRkV3m86ZGp6qFApa+Zp3amCh0FFehC9OamlNhz1zDXN58zx63wCMHLXh2QlOPs9+5Q0Z5ithSzG+Rz78g5IcI+/0tzqO1nNHj9qVLVjk9YN4l2uIl843OOmP7uUUV0bSVabOp1Z3JOqjlxi6pmND+OTiEXjgvJVDP5XxSahrQXFQ3DhxqCS5lFDiMgV1eRNhiZBhHAg4qi1cRRbI2qqBz3/ELwitUL4zY/JXN1vLktEUnc5avXVEvL1kUV6thzV+xaD2D1lhhh30jF0UTxTQohsv5mS4trlAqmdrT3/ve99I/ML+oFi/TtWRR6toPh6zS1SpqOxR2rn3gqv4RlzepYmpjn1qqDCSsFVff5GuN5H5xYWSfnZ29+Pe9e/fqz9Wv1f/mz1ezaDG+3uKKtVZgXWb5jw241tFD+2XsZAHTY8MVH2hyxj2JJwa0wZPRpqkyzLtsys8eNf/4nDYF1CvPifOCGzIJR0sJsjvFFhtV48FFizxwyGqfGrLwfQcVBePPViz41Amrf25Km1OxkwnAgn6mfv+MVRJh850clHqlU2lLgxSR5evXuww3isFCUYRyX9Fs9p1bLDSdtkfv/zkLpspW99igFTd2SAEj+JUvXzymk2/dZ49pGi06mDL/1JIVb9/rrpaamFUa4jb/7j1SXbx1tVbZs0kkb9ozKEepDQm768ZfE08K35X/3Jgl18ccyHAxZ7mtHSJM27puC54ZlxJY+8ApNziAj6uryeIjGavs22yGz01PskcQVb0u8KFQNp45bI888HPyJ8HHuvPmX9MmAfhUj91ryt8DyolvDcUNjxYFTPKe7fJYAQWlAOS+Fzd1WgVA5dyCnmNeA5REOFqoAKhTfG/w4AULDS2o4Cjt3ehAp186ZcUN7VL4+B7iYmTqx+TP6+L3W3HPOgerra2R0Z0Cjg3fYjFthLfe83H9GwHdtD5vv+NjNn1Dky1d1S7VjduNDqeEtFDxky1ZblOr2mUokPq9Mb+ViO3Zu03+O/6t7lzOIoMLUqwKCb8Vb91tvlzFMtvaLdMeVgFLOLRClFNZTWNq0i6bs9y2ThcMjnpWrVrNXz/jmFTJtFREeatSWZmyeX1RZZVHOLMobx1DEL5M0cKPHFXxiLdLIc0rZHaI4r6FjNruLAzwLFqzC1e1qFAmQmjye3bKFF/dvsHCFGsETNcFre6cK+RevKr5vN3b9ZNfmSm4Avv0wSj7JljgQJiq/GoWBROq22qrjonjF687bv91Tf2S0YhvUQv8S76g4peiFhsD1wSpFPcN/478gQwCYBtYI69/a6w1kvuXF6yq5ubmi3+/cOGC/lz9Wv1v/nwldhbfV1o5ZMPD+tu//VuZ5VcLuNey1lqEl5Fib/N9u/k9AcEmK7n8xTYOH0jp77xWSAIS7jGIE9QLdsDlxIVcUcVk3aZ+UbUrYZ8FT4zIYIv64aUlxtg4BmEWOAQ2kFDQis0x8WoAPVIglVprVQAs3LnBohMFC3zpqNn2DTJbJ/e1qU1Se2JJVPmZfTHBRXkTNvzjMVt4+w5r2D/jCjA8TUOjNvee3db8mTMyPasNE4/KtCz/TTBo5WTK/B1t2hyZ6JvfFrb6U3kLnRoTywtvDB/wZNHZiQsXg1xhY4XmcyoOvHMpq0xNW+ZNu3TgghlWjYTkrRFz6vGDMiwrDDkeUb4i3iro5Ew60toqxQOagGT6MdUft/g/HTAfBREb8cCwZb7tKjG7wmenXETQjo3iK/F6iCrNaR/zfV1cWAZaWBjuNcq+mBT+AvM00FRtvrUJm7qjTfEgFMSAFSkGlrYlrO74kqCV+KVE/wa5ASOsrclSmxKWOL2kCUbig0A/6M11YcS1OKcXFGGDRwjcArBNSOO0/3I3b9f3RsaXna8rEbeZG5otOl226JCLCBLkEU/e7vUKJa7/9DE993hrRPlfaaNpOgzlFPzE9Kzidmg/jn7gKuv9yyH5+EILJaUIhE9OSGnE8wfZHa+OwJsoUxRXyWVbuK1fgdyhWeCSZP4FzA95fgXOqsdI7AotPo9HHjMVf7RNabnWxBS+DZ6jkADyWrXws2etur7L5QWu8uKiDmuh30/rA9wCBfGpAbXAMa7LfA3GgoEMIok47Bz5VRWlHBj+BeJgceki2JMWHMozQwKQ73kdKAwvBYx+PRf+u/vO/sbL///u/3zxvl3KmeLQgj9OLdSV6zizs8NCDxw0Xyfv15gUQ/kxc3krj45r8lRsOFqEhYKVp6bN19So20RhBjS6tr61WoQ9v/Grr7tFOPyz//UN0SJ8sTH9+uuvN//KRPTqomh66qmn7Oabb7aXWqdPn7a7775b/Kt169YJLPqud73LTp06pcIrGo3q5zdu3Givdq0pWJdZvuYm87W3WfKeHWrj8IFEccUI9FN/9zNWPT1glfqEeSIRqRkYw/EzAOajUFn6ziv0QQjDKbD/jJQABffCU0oty+8jEnVznbwxqB6cPIFH0hZkE6KQ8J8ZtUp7s9UdnLNS3OcUMkbb39SpPEPy3lCNADhCVa8ZLihig1N8w6NDltwBIsJnnlTaFr5ztzXfP2CDP7JZm9DC3ZuElQA6CpMLLhLoA3mRqlXxcRpO5NQOJT/Qf2LIZm/rtuKVm5RhiErxpqs/oucr9OALUmykLqHy7d0sj1PNUxesUh+zajRo/vF5tRH54BfQcgOtqrRFR9NqURG4DO4C8nu2yW/p2x1JH/o9ykOpzfGLfNs2yL+GARylpbJvq1OfQiF54Sj2lD3Y2aiWFwWCF6p3oeCe8+39Vj43aKFHj+q+g80odNVZ6wNjCuImKoYWGj/jQyQplgUYpbiiiBBUlNZLJGC1R2ZVHGFa9yzn5F3ypnOCx6Y2oOLERNFHvULxg5dF+4/nLnj/8/JiFRqjVq1LiOHV/KUZi52dV/GztKvJxb801AkBUvvXz6ltuTq+z0Yt71N3k/6bzDimEjW0QHv6ym3W/TsHVZjERrMOJcLzT7tubNqN6Rcqjoi/nFEcj6YnWxqsZjB70cNFDqH/7KgKIRAbxNZ4M0XFSDGwUU6ErBgnEaDsVFpalrmCRY+Ny+hPkSVAKOHTZpZrDluuJeSidyCD+xxPrTo26Vp5i2nztrWoBSolMJly3kYOAQRaL6TktQLK++J13+T/ulhc0SqkBadsP7AMS3/qoodWPX3fgHW54kr//5LCj0KIyUyKMgC/vvF5WRc0QNJcZ5HBRfNu7FeoOB5LKbcU5LRxeS9Q7HMdEPURDOqzozTlQMNrxdW36Fozub/kuu2222x+fv5f/DuFJP/v5dbP/dzPyRh/6NAhe+tb3ypEQ1dXl/IMub3rrrtO8TuvZb3qAut7v/d7VSn+W1ipq1wPVqPoiRorvukK86/rU9H0put+RRtkjtDhhoSNv8lBN4FSyqNSW6NsO9QnFUR4adJ5lzpfKlnqhvXycc3trlFhxoa7+N6rbXETHomq1T94Vhu8XbtTGz5qyPxVjWInwULCA9P6+IzFn74gmGB4vmi5tqilu8IieRPE61nfp5id5U6vVSHOT05b7dmMTb11nfV+es5KV2+1+gMz5s1XZJinbTR3Q5taB+TsLe1otFJ/u/mTObXImPKyjhZ7/s9+WpluhXuusDJBzEfPugGAbhcgTZtU2XEoegQCM3VWLJsdPaPWDkpH4c1XWr4rofYewdMqOqtVZfFl9rnsv9h4QWR8IRhSGcvt7NEmrqm1UMBq/uF5i5+aU1Yhmz/KGIucSFHmT16QmqacP49HRHF+x+yehCanOAVCYIfjhLdKRnG/z+qeGJJxGkwHI+yJJy+YB7hluSz4pbAdS0ltauABUHOWbuhT8VXoaRBugC98X4kzKReO/PwpB+oMBGRwx7yMYsYoPQb90NlJFcAof1KRSmW1EmsPus2w3FZv943+D/ccQ2WHQt/upHFUOSE/5PVzKhBIi/TGevnj5BtcGbxAqSzUhS6ykKChK7aHadWR/27B6bQLrM65UODA+UkpZ6iduX39tnjrOvm4fLQAmXjtahWUlPBkFEg8c6HRRRVC2U3NltvabtmmoHnhoyUirsjMFBSxFBnPmodC2+sTjgGK/+y7d6oYLl0Y1IQnCAYyDV30lN8pvTyOuri8Vk/97c9c9j2sdvGLFgXX15vjxCHk0qzCe3Z9da1BVjEREtuOzxw+awITS7rW8XOKuQZst6PN8nfu0QGDQ5lNzci0jrpM8YzvDAsA/qo1j9XaeiOuKrmjHO5ftObm5iyGDeNlFurUhz/8Ydu5c6dQDyhX73//+y0QCFgoFLKf//mff801z6tuERKg+M///M/iS3z/93+/Ci7yet5Ia1WKvT30bgtEE+aJRW3+tl5t1vVfcnE0mLc5SQZOjzrSMe0LTosr0i0gSrxNNQfHXT5hd4f4TxQEi3uarO7wnBAKmNJH3txktQMVq33krBW3dsuITttq7L0brf2JpIqPQne9NqrYWNb8s8uKBgEVgdLlLRAR4rHoZFEUdTwxTFkFlytWe3LJzv6HOuv7bE6TXZjX40NZtWbwHC1ujFjjkZQ+rEEMlHatU74d4dW+o+fN+jpVzDDmjzeo3N8u9STdV2M1x2ZdILDfZ+XZOVv+tqssPphWMZXtjlsx5hUygaIGZYfg69B40uXVbfiAnq9Ce8Llys0tWbmnRbdN65NCBSUPmOXi+oC1f27Yir3NNnp7xPr/asq1wtIZW7661yJMx9Fumlt2AwZswis5hBpX72sz31LWqlOzgrnWPzUm6r13xpmdCXr2ERtUqdrCW7dafDRv/mdP2uJ37LGGpycdHoOWGBOhxy9oqhACeCW5rCItf81mCyzmLd8asejzAyrKpBr4POY/OqCcN4pCnkPfU8c0bIBnjM0P9IVateRYNtWaJ1OQisk1ED44oEII7xI/TySSWmRkGC4tW3ZLm7hg8f0jQjiwystplz2HzygQsEpzreU64hZ5/KSKSHyD6Vs262dy2zstkCyY58hZmalRaMFseEJBKSeKCkrm9HqI+p4rCjpJMVVocnBRVMDVbEnlB9aHZf6HXF/qc6HjKJWC7NIWJOmAKVQI7E0JKatCR9RGVYzPXtVgzU9OO54ThUjtD1g5lTL/+n61VXXtEDLMfYSF9U2+XtwO5DC02lL/qm+DiJ+lpI2/Z5NVfWZNx/I2fn3Img+XLPLPB2Vd4IDDwauSz2mIBKbamkL1xmwR9n78V80bfh0twlzOhn7ujdMi/PZv/3b9+elPf9ruueceFUWri4gbpgs3b95s999//0v+PC1AiipI7qyamhqpWevXr9d/0zqkPZjL5f71Fax/+qd/srGxMfvRH/1RmcCgpt57771KrS7CYXoDrYXv2K1QZNoJ+F7qHz4vw2h5aUkFgeeJgyquzn9fu7w45Y4mm7mlTSfG6MlpTdihcmhKC4+Txyy1tdGW1nlVXOGzWtrZYD3/b0IFEsUZbSAM8BQJnf80ptbM5F3t5n/ulIUXiuafWNTEnj19RIVFKeTRZBrtnODhC87vMzNnTQeXrG7/lBSl8LRHMTC05xJnU2rNoK745zLW/NiE1C7iY0Z+ZLvaMdFjE+Y9cNKq652StNwbM1+2aNNv26gWpydX0hRfZWjEsapAIOzdJo9Nrj1iuY6YRSYyVnt8YQWhMCVi98R1QSlRZKahVoCxgPNEa1AbtMdj47fVyQNEJiAFWOTYmLV/ZkhUe8zSIRTgatWSu5pV+AQXi6Kv43EjvoWcSIphihKpQG1NLgA6nVVocv3BOf18KRFW+xC1K/+2q628qUfenNpTyxacSMpzx2vOFJsAomdHLd0dkfcJdQ4eVeW6HTb9XTuFYfAtpi16elZqGFEkTBQSjZS9bqNaaihKFIzVq7ZZeXOP2ZZ1UiQo7lBrSp2NLp+P1vTR8xZcyFlxW49jEg2OSR1TIZYv6DVBicLvpcgmismudilNKq4wzpfKUiz5veEvHrXivo2W2tao6VRUT66v4HxOLT6M40yx8rOCiObygl0yhYjHDnI6gwgoZL5k3pKb6zTlyDBA9i37zCZm1BbXdTSftamr4lJJufagxQM5Bb8gJEXYL08ir7H33IiKK64JPIbp9XXW+LeHVVxhUGdxf0WvL7tQ6Hs7f8IdYsCefI0WapK4Wv8Ka7W4WrUXfDXFFR4yCjPu0x23flRIBT5nOj4zbJ1/T7vbrO93T1i+zme+9b0q0lE2Ka5QpxhY8Le3uinetbW23uCrtrZWX2hFFEer/81XW1ub/cf/+B/t//7f//uyP9/R0SGG1uqCpdXS0nLxv2dmZqy+vv4bY3InTPHP/uzP7I//+I+V+fPv//2/V2DiazGEfdNxsOLvNX9fn2MijU0qG/C+sd/9F9+vNPq2ZrVFVkN+y1dtsfmtUas7lzd/umD2winzbtvgjO+pjFoeqDRET2T666wYd0pU4rlRq9bErNBRY6GzU7p96NvZlpCo30yyMeauPDbCbWFwgU1A4cAwnKhRe4pJuYVtcas/wWh+0Bnrc2Wb3ROz1qcW1GJMbm9UbA3ZgMler3X+4REhAfDEwKjiw/ruvR/S70N9mLmy1lrvp43mdy2ndMbK3c3usbERwovK5UVXj5yb1f+nxTh6R8z6/nxQBY3n0GnzNjVKPSHQGpaVWj/k48XDUklSO5rF1vJRzKVzYhflb9wmSCqLNp04SrQVF5M2845N1rR/URBF8bLiMXlUvKeHrVosqpWLkRoVZnljvUXHM65QzJdUrOJryW5rV/yLIn3wHR0/p99V3bXRZQUWSorBefS+n1UUTHA248zsPo+e93Jvu4o8YLH8TmttNs9iUqZ1RuPVcitX1QZkLe/rFFUfnAFmdSYWZSIfndXrD1Rz+bp1yg6Un6piYkx5Hztknqu2u0zDaFC8KyKLKPCAerJoC/KYmTq7O/69AsZKaV3x44DXYFEg+pdyMu+vcqFQjGhnAyOlxctj4roRp2xxxUDNR0Yw6BAKAEw9JnYW8TTeclWTimBCwIFED4+4uJ+D42q56jpdyuqx0mKkwISJxnWDd4pFcVdqbzBfpqCIm1UWFGiC1evy0vce65s5uoVCSVE9r/A9ZAKidLvAcL8tXN3qDgR4xhhagELf3y50BpO4FOFqg+OtwlNHoPze9Wrfr603sIL1sV97/QrWz//CG0bBWl20+T7wgQ9IkXo1ixidK6+80n7ohy6ZOL5kfexjH1Pczuc//3n7uprcyep58MEH9eXz+ezNb36zHT161LZt26aAxG/1lbt2szbXiTtbzNPeaqXJaUnxq4sRcLwVROVwwhaBmSe1tsb8h85Z49G0/FDTV9VoMwPP4IqArJvC83ks211r0fOLVjOQUWguNPZia1zhyovXd6mIYoIq/vAJy3REnIerWLLU7lZbvLZdVGqI855DZzThxoJvw/Rb4kLOSjVBCz5/2oIjC2oLNv/RszZ2e73zMJ1esPS2ZoFEO//0hArIzPZWcab8mzdoaml+T61NX+vwDy3PLMjUL57XwJAI4eT0YbIFuUBRwwd/aCqjEGKUPzbJ3v83q40dEr5ny3r5SBbXB6U+0fYpnT5vZTxLbNwFF+fjpy3l9Yo07o1FVVxhfPcfGxQ/CWUPRSV9/XprPLDkyO0+n+V3dDuPEkVRKChTOS3CyviE4Jk1zw654mop7aYKs0X5lpiUU6wIYcfjs+YFBbF9g/hWxbqQsAThiWW7Z/cvWqHGhRH7lnNSz0qz8w40S34h1P+uNsfmokCbnNFzV2yIWnZdnYplaPaoj4GFrLxTFMW00Ciu0ns7NUWa3dMrXlVh3wa1+Lx4ruCXfdc1+hnM9TxGWomZ7qhUy8UtUVeUQqavqxXFnA1bwFQ4YEjnZF12dSigOjCdcm07MjVXlJPyjnWaRgXNUWgImY+W88C4VWfnrTQ3J6QIBa8yHjMFhZP7yLkcnVBbG/M7DLTljoBFxlJW2NxhsYGUpXe1u/dGMmfLm+otCU+rXFaxhPl8tbhijb+t2/yLmYtFySoLiu9bLa5WyewqrFbOiPK1fRMuHgfok8ut8tHTus65DirTs254IeBRC5b3EqBZVCyC5QHDPvKFnzPv7JLM+/gg+RPC/1px9W9grZncX3J9z/d8j7prL15nz561wcHBl/4hM/vf//t/v2xxxSLTEAHp66Jg0Qb8zGc+I9WKcUboqNy59773vRer4X/8x3+0H/iBH5AL/1txrZ4U7uz/CVu6qc/i4wXzLxWkQvAB6Dk/qhMjU3JM9iDngwmAuO2pq7ViX7MFRhekBC1d06UpMxhM5BSiLMy+dZMm/YLTKSk2+b4GC40uySNUhUaOCX5zvdWcWVR7CA8YkE3buk5tJE3zUYDMzMnoqw9voIwbe2z6mhqrPV+04AMHzLtri8zxqbu2iamFX4epPuCOmfag1e2ftszmJpG/pXCEQzZ1b4+1fMm10QgLpjhAvWHTx5sldABw0m09Gp9XIXNmSN9PPAngRNHg0wWZmWkHUYhIsSCmozbqAJN+jwVG5pzZGuWvrkZKihYeKr/PsptbLTyZtvKRk1a9cY8zXE9MClDK74INxOvBoEHkwqLUIZQofsYzNSePWTnsUzYewweMt6PM4NXiuS7t7HftwlhAbVl/Ki/KvnLgYDGtq7PoM+eVBym6O0MH+JrqYgqSJoCZoiqwlHNtulJFwdhSGYl1gdxfG1UxCN5Cfiy4YQ1x53mby6gQw0dHqw/zMqsyMqZCVPd1OmnZ9Y1iXgGbFc7A49VUJi05ClQUMl4b2Gfy4A1PWn5njwUWXCYdzxE+KiYw/efHrLQeartjUel1R42anb/YYqINB04CxAhtuczOTmE/hLOAbbWwqOsOTpfvkQPmlefN6wYGhifkmZr/oeut+dl5ebVQUwUepciG/l4suSm4oE9DHfv/+Mskdq7l6uY+e+D5D31V71UOORQWKHUUGV8N/ZzfwfVz2dvlflQqF2/ztSyUuMrMnMbivxo/FEVjaUOnni/o+YKEwrGqVKR2Q8gPDy2ohSrPFcyrjX2vqI6trTeYgvXrXwMF64NvPAXrlltuUd2BL/zSRXuQAunRRx/9ut+nV11gNTU1WaVSse/6ru+yH/7hHxYl9cVrcXFR5FTAXd+Ka/VCvtXzbRYMxSx35y5l8MHcQQ3QRpYp6ZTPZuPhIg34Lb25UXEkFBRk1vmOndcHOa0NDMnBpYLZ/uNWvWanvCdao1OuWNu9ybwHT5u3v1sqCOHAuTqvtdw34DZv2k71tcq1W/2wxmPhb2605Wv7NQHIQj3Kt8XUoiN0mGLEk6hRK4+g5vDoshUbIubPFG3y2rg1nCpaqjtgzX9zxDyNDhuR3BCXOV6bcyggL41nJc6kVBsSZqFcE3Q8rOSyGGB4qZhs4nmh6Lzuvb9tdc9PuXYHlxin8+lZS75pq0Wm8gKOql260qajBYYnBxwBxaoXgnqhaJPvWGctf/aCNu/quWHzgjLoaBJtHrAisTyMq1OwFGpRTZadKbsmIiYQLC/P7LxVWxulQlFIFK/bqmk5sYSyORU/tNqEoqiPWrI/YnV/vf/L8TV97xNUM/rQMRW5FJQeFKFoRArQ0rY6ZT0GFwpWqglYZGQl1JvXDZP93IL5OlrV9pm/rl25hKArMlf0WSHhs7rHBy2/pVOKBQyw8KkJFTi6JvJFy3bFpfaR8QiaQK2gjR0qyLIbmuRTw/RN4UfRxe9MX7feok+dtdL2PvmmNPHHYqIwHnPtvUTY/ORgbuh1AxsXXEu6mslaNZMxb02NWW2N2q6VxaSee0XiXBhSmxcvFNeYmHAPnhRXTG3nAxNWHhm10s17FCZdbqpRWzC9qcHCE1nzjc3IQ6Z25Y51ioli3bPzF1SwQ+t/vUuHDo9Hqiwt0dXX8tWuu8PffRH58PVcDBjgQyTwW4U9rwlK4/jK5wUZnR3tmixdW//GCqyPfg0KrP/yxiuwEomELEsbNmz4in8/d+6cWoDUJS+3CHYmJLqhoUEduEsX5va/+7u/k0L2r94ipPU3Pj5uv/d7v/eSxRWrrq7uW7a4unR9evHPNfHFRB3FVfCLhy36wpCFppbNd2LQZblBF/d5LbmzyUKfe04bIoUEpuhyOmO3venjVh6fssAX9pvn6DmzK7bJxKzQ1VPnnXciGNQGRBGB0pPra7DwbMGa/uqg88tQXOEjKpbMuwImJTbD39Zic3dvsNh5d+EQrQP0EjO9wIJU0E2N7kO6WNbGD69oaV3IFrbGrOPhOQvO5Sw8XxY7avrObn1f4tyyzV5RZ8PvaHGtqPOjAoQSFst0HVfN7C4HM7TuNrVzUCjCA3NW8XukKnjKpo1VhPC2Bk33Za/fZPGBtFpjCr1m0g6mVTJn+eawPCYUVRithUBorLWm//2UeRvghPmdPwWVcGhCrdng6KJVpmbNc27EKVXTWRVeGigg247bI8+wu9VhIlBQahMOqjk8Ybn+Rg0meJ8+ZuELs7ptipbYRFEbMrluRNgwvUjhk79pu1NWutrlJ7KZeW1+tQ+eFseLDTF6aNQpdKGAZdbXS3n08JrhW8vmzFeoWs3RaSut67Docxc06Ykqh5pJCxJyv+JnrtlqnuePmXdsxgLJkvmPO4mb6y2zr9cVQ16viNyzd/ZqsAIOGOHTld42KY3Fnf2a2IPijQpSrY2Jp8ZhwLuct+BkUips9fQF8zx5SL649BXdaplW8PgBRp2YskrKQVIpUjUNuHeLK44Gh9VKjn3qOXl/8NGhulZn58y7dYMUTqZr8bhp2GIiqzQDQLClPRs0oQpJnqVrZnH5dRVXmMhXFwcbrqfK7NxrLq5YlxZXqGQv9/u+VgvDOu9t+F5gM2gdytQfi5mHw8zSktQ5tVbXiqt/k2uN5P7SC0RDKrWS/HHJopBkmvDl1pkzZ2zr1q0CkYJqQAnD/nTpz0NMeC3rVRdY/+E//AcLv47q+Vtpve2tv22ZK/oF+sy0Bcyze7MrdKhflpKW3FSrNh1+CfxTUotoqUUi+h6AnXglfA11Gp3GY+GbWnQBrPCANq3XhyUm5urgiNQsjMzAIJXZRmTJFVsvKg78zPJb96qVoNiMsd+1hsOLUoGCi3nzlswyzV4LT6U1tefdvU2AwdSmOlvYkRB1no05Mle2mqGCleoi8hvVnEtaYUObNR5bVoBwJeS3pufmrPv+BRV+sI5gP2E8pghEJWn/4ozlyOTjdE2A8gsn1L5CmfLU11ntgUnzTS1JFaNViPE/OrBopdqgg3TCz1pIiRSPKkiIL/cBtEJ5x3or375X989z9U4BOGXqhqMEj+nK9U4B9Pssf+tOhTYTmWMHTjjWUDBgXqCvS0kxyDDDU/BU6mqcYZhiZHOPhU+MKTqE1yff36jXjNeS1wx17L4TH1U7htYMU5G0NTG4c5+LzXEXnksh29qszL9Kc70yDX2TBEznNEmKOiSC+8SUnoPYSNb5vFJZqyynrQL0/drdVmipkcLm7WiTgsk0nqCQM3Oa5kOJQeEBZ0BhRvFYgXs1Nm1NnzsjxhqxM3rcmOmL5YsQTkE1SyVX0AOvrY+5YQCPx2UCFkvm27rJAl88aLHj08JPAMwtT0w6FcjrdTE/Q+NuuODUkFq7hTv3SVH1bdkg8z3kfgry8q4NGgCgTay4odqYni+M8vKHUVRniirsvvQP79d9pM1XmZl9+Yy9F60XFzusS83vV/zHT+h3X1ogvdJU3cv9f4o/ih6mgWmf3rvu/TLXoyAJGUFhBHbkq1h33fCrL/3vq8UaHLOauJv05DOkp1PXYWlwyMpTM2sMq7W1tl5mUSD9+q//+lcUU/ydf7vxxhsvCxol3Hl6elpUdyYRb7jhhq+YLHyt619ikNfWxeV94rCFtm23cn+HNT43qympEETw6UXl4RHdknrHFZLz89f1W82RKasMjDgDMCPvKwG8ENzZCDlRM46/asoFuaD/T+vt2m3CLIAqwGOV7gxZIl2w5Z6o1U4uaNx+aW+LChdy3xjx903MKzqDzdNTG7X4haQljhcttaXBYkMpFSUUgoAyadHJa5OIqJ1Ii5CfVyxJ2K9WkTb3YlHTY5ogSwSVr4gHDEiml/gSptnOD5h/43rLNfotOOPQArQtuU8ze+PW+jcTZj3tbjOH/ZQumpeNdT5p/qWwo3IHg1ZY3yrlC8YUeIjg+JJlNjZa+LHjZpmM+bds1G0wAQnWIjCVEvk9cmZGY/5M0UEaD4/nNO3o29BvFYzmtObyBSk5MnEncyocPfm8lTZ1SdXxZQuWvK5XcUS0pURnR1Vj8977IXv44Ie1acJa8vL/x2ctOumVQb/YGLXg2UlLXd1tofmiBadSyoFMbk1oWAEK/syVCWt+btHyzVGLTM9LTYJojg/OW4jZcn+NJdJ5baRgETwZwo+DynX0VOLmm0m64vK5o1bN5cxT8iuXke+zni4rtiVsfnvUWvGtMSFJS3RgTEW4Z3BMmIk4xR/PNYeCUNDSN66zxLMOrYEy6iUqZ3re/ETIUDjF464QXDGf046Ux6/hh8376AtWvHmvYKxcozDdMOr7NjkcA7mTaoe+cMp8sajwCvjVqi118vIFz7gTIcWaNTcq3PmLuU86xhP30eOxzL175RG8NCrm5dblvFGQz1sGJwQZ1ZQhH7jV6sXWOub/l1K1Xs4nJY8XrcKVScuXWi/H5Lr5Hb9pj3/6AxcLtQcXvmyWxUOo2z/uMjRVpPF+JJSZ3NGVzEF+zhsKv2p+1tp6g5vcX8/PvwHXxz/+cRVZMK9uuukm/RvTf7RWv/jFL14WNPrQQw/J/sTXZz/7WZEQuI1HHnnkspDSV1prUTmXWdXrdwlRoBH4uqj8TcApmeIpb+gUITyQKolvVPPcsJWbE04x6WhTBh0SPowd4IgUOiyCbVHEiBkpt9ZKjfJNLqhFlL1mg+Ju8HDVfuG02kOJ+49boa9ZH7w1nzuswOdS2KefKXU0OkAlcE0wDNOLUhDipxfUPpq5vsmyb7lC0EgpYNmc1KfIkycFrYQtVeptkfl76Z27XeHn98kjU+islfJz5qf6nOF9atZm99WJaeTdt90yGxqs7oUZtZjKV25RePP8tpi1fWHcyouLKto0fYdhl8y8SsWm3tIrThf+IVpmwZF5ead8B06rNQq8kxM8WIHqTXtdLEquaKVTZ4UTyGxqlKkcune+q1bPJ0WX2lbHTluhLSF/2fKV3WbNDeYdnXZFKDBUr0eKH/cHLAbKY81DJ52ahlpIbE9vvUz6+JV43UottVKNvE8dsek3r7MSSIpKxQIHz1txXauKq+l9YVve3KD7UPfwOZmTaak2HklboSlqoSdPWG5Xjzhk4cEFiwwvy+gd/+wLImvjqwFvQXFDtInv2AWN4TOpCb/L39ttpW29ahOxCXv7e6w8OGKBwRlr/eyAlaZnrNDbaIWWmFV72y2/y4VNL37nXj3G1HX9ukZIFIiO51RAk8NI9FPl1AWpaLrWzw0pg5LCDCo4i+IKMCavp7+rU9e5Im262yz82f26bjLr6qU0ViGH97ZJcSXYGl+gTNp4PiJ+m3xHv5AVtNJLp8/ZFx/8efd7KdBRZq/tV5vVf3rEtXZfw4KdhQqF/43BBIorUduX/vQrTO0MmnxFCPQl6+Wm/VDCUM24TQrPy96PvveptcxaLa5WC7WL39P+Y/ps8Syl9L2+3m5FZfFcpLtjFjjjpqGqG7r1c2vF1dr6Rk4RMp0HgqmxsdEikYhaafv37//yXapW7Zd+6Zesvb1d///OO+/U9N6li9iZ7/7u75ZXChvRD/7gD9ry8teOZ4d3Cqjou9/9bqlRtAvxTQERRaG6nP/q0vxCWo2///u/b29729vULqSF+FrXWtjzKxgW37P+Z63S0+oYRdNL8hV5z42aJxoVyNFHGw9/SyToTNj4czC+oyrQBmshBzAgozXRKBQrq/+NN4giIHnjOovMFCzTGrLax8+rRSAGzlNjVuxutGI8YP5c2YJjSzK74n3K7e238JlJmcxVZG3tE9Zg5oqYNR7NWmAmbbPXNlqM0OBz8wrvFcwznRE8FUYRvXx/umyZ1oDVH55Xq1F8rVVgrDIF/ba8q1U+L+J2qodPW+Zt+6zmwJgjj9OKaolZ8OAFs85Wy/Yk9PvEfiIvb2BUkEvv44fMt90pUqhBGNUV5nvtNuUryvy8nLMyAFAUtaW8pilpa9mV2803nxZINLL/gllTvRVaa/QYKSKb/vqQcuuWrmgTfwmFLt8QUAGVu3aTC2A+P2zelmYVkCokV0O2S2VN6UVOT1tuQ7MFmMIbnJTfiPDmyMFBbfjzN3VZYLli8XOLll5Xa/EXxr5MYG+oMc+FMZv899ut/Z8G1T4rNSdcdh8kdjxw+aIKC03prUyXiXBP4cQ1MjapaBOAkZZOWyWdMW9zo4qh7HWblFmon+36SRXuIDRo21aGx6QKlZeXzd/XK9wF3DPM5VyTtGEVQcQU40JGihUZjbwGSzets/hIxrxnRxxglIKWIYOZOUd2975LBHXaoEwt6vrxezWFak8dsomfud7ansuaD/Wzv1vBzuFHjpqXQG6uI9pd6Yxld/coY1JZg7Gwch1RJWl9UbDkrt6g4YiHHneKzutZFMT59oTavCweg9S4lWnD1aU2+yVoiNV16TTii/+d14fgd2+i5mIAM61D3tf3nfn4y9+nPb+kAwdIj8nv2GRtD07Ip4nKS2uYEHVNhzI0MDLumHJ+vwrDtfWtsb6eJve+X3n9JvfBX/zqTe7QABhaI88PwHhzc7OKJ0jnq7Rz1CNacf/n//wf6+/vt1/8xV8UsunEiRMXLUUAyfE2/cEf/IFoBPiarrrqKvurv3rtHsmvxbr66qvtJ37iJ2R/evH68R//cfvkJz+p5/5yPq6XW2sF1uWicnb+nAVG52REH/2xPdbzdyMuYiWVc+G4AD/p89ZErNAYsnLQY/Gzi+aZmLVqZ4vMzvhxULzkPRmdstlv22rNDw4LuskHrjAB6zvFqNJG1tMqerZtXy/vkojcgyNWvXq7C809OmTVtkZ5biybVXspf9VGS3cErO6Tz1n1qh0ync9d3WS+fNXC8yWdWPwP7ZfXR9R04JgALaGdYxqfcrgEJh0ZxdfEWGeb5dY1OfM3hUS1KtUKyjueK1pO5a4m8+TL5l1Kq6hkQpDcw87PTcpovroJMTavxz80rv8uXLPZQqcn3SRZT7tN3tRgVa/Z4f/xPp3+4R2hiHmnFhQBk7t+i9qR0OqZOJP60NvhNvsjZ9TuYQghcn5OSooM5e0tepy6yKfmLLenTyiGUsSnUOZMZ9SpMPu2mXc55yCaoC6gk79pl8UOjqmFGVShErLMhjpN8gVG5i8WYjCdPNm8fsfy7jaLDqbMOz0vXlrpjn0WGkta+eQZtXdgo4FOQCkDygmewffwAVHK8YChRJFLqLggWpK8vjCn6hP2wMEPy7vjP+cQC4rrqYlr2o8Cb3UjplUdOHDGlu/arsLZV6i4KcumWsFMBS4dSAr/oGnYZ46Lh4U/sNJSp+uO1h5KFsMUeLNAffD7KUgqSyn5kOCvxf/mGfO3NFvmqn4rhb1KE1i4vtPqji0q5YBioVwfl4pJjBAqsN4vfEgFApctSL4ei8KoPL9gXyj+zSsWV6sLCCvP9c1v/009v2Qurkb6XLouvZ1Vbh6h8YUNrVI/9doxOEDrlgMX0UcTM0odAIGyCn1dW9866+tZYPV/5PUXWAO/9NUXWGTxfelLX1K77aUW6hU09J/5mZ9Rhh+L225tbbU///M/t3/37/6dnTx5UgrT888/r4k+FtE1sDNHR0f18693vVJeIO3Dl1oUhjw2IgBfatEuhJUFPeHVrrUW4WVWakONLd+2WafLnr8eEutHYMqFJW3aUnx8PvMPTJh/uSTAIqRqeX/8XrVGAEtSXCgmZGuv1f/Z066wWPEK0SJSccWkUG2NxuWZOsO7BDKAAsuzc6NI7oEjA1atVlxx5fUo866yvKziJLRYttybr3CU91zBGr84bIsbvRY+P6uxf6a9/LMp854ddr97hbUFVHLuTeutOreguB7YUWAp8DBhtuekjUqytKvJMs0+p2jduEFtOIq09Loay/c3SSnCYB+bqDgUQCDw5YsMdlI4aKP/3y7F74SODjt1gxbJ7KLVjJWt/bF52/wrn7Dc5jYZ1Wl3UlwxbUbhVHsqab7Tww5TEI1IXcLfxOABxQeFIdBUJjcp2ihsUGtYhCL7H3rBijG/gKL8XrhgFFe8ToBftSLO5xJ54JBrT1aqVuhrtFx3wmJHp4Q7qEzP6PmGi1VJhPXYq4tLam/RnqUY9fe7TCv8cb4dmy1z9y75oHL1AfMWKpbc1qBJRgoUsgxp0ykOhunJuCtmyXhEzci1x+zGb/8t8x4+q0kyqWIU9SiAKGDRiFSae5t/xAIvnHXeq0dOC92gYGwQFrGgWtdMpmKkR/nyPXdCRnbUHczTTF96ggFdTz7Cr6PRr/AkofZ41vVYdW7eQvMl93Nz8xYZXLTop56x+Ru7LJiqCI3BNCLXD0WiJm7HZpzyWio5GO/CCjLiX2Gtkt1faaHWXVpcsS5ytOLfexFkivdKdHs2hJVC9vHPfMC++PDPv2Rx9RW3E/5u/Yk6yGIQgMXwgIqrVXVzbklFFaHWa8XV2nrli9fz+r9WCrZLv/KE0b/EgntJUfSud71LETKoWX/0R19OToAYMDk5qbbg6qIQvOaaa+zpp5/Wf/MnbcHV4orF93u9Xnv22We/Ji/6rbfe+i++UN1Wv15uffCDH3zZ4or1v/7X/3pNxRVrrcC6zKp9etgyLT7zbF6nk7faLw0xZcTx4YjRmEILgzOBxRh2011RKRAP7P9l8z17XCwsnVIxAp8bM+8VO+RjoX0mb9TZAY3Oq2WUzljw9Lg2zcCxQfOdHFK7yDuXVBzJyA9vteVbNjnaeludZVtDYhVFDg9b/MCoxY5MWLE2ZNVEVBEk3R973grdDTKoq92Hx2Zjj3hTM29Zb4knzlumM2I1w3n5gGKPnxEZHPAoGXjgA1YxEk/97c9Y/cm0Ns5Mk0+xPoKBNvq02dC6o1ASNRyjfk+z3XnLRzV2ThFJK7T7H0bMTp7T2H+lMWGeZje5p1WqmD9tlm3y63u5jVXfjGJChibEf2KzKm5oV9EVuTCv6UUFCns8Fnth1Oy6XXq+hz5yvXvMQ+POvF6t2GP//LNq1fH6MDHH5J9gqG0NapuhuFCseHu6XLsumZepm9/DNGamN27emrjaXRQtUN4DBB/vXa/b0sJYXqlK4SLIONNXKzI9odWx8Zx5D5yyxIFxm7yzVUUX2AValyhJs3f0CDjL4+f+FN90hYWnMxb754NqgWLsr7Y0SgEBB6J4Jr/f/I2NZg11Nv3enebZvlEFOkV85OGjUucCp0Y02el57rgOCeRh4veiwNDrc+U257VKpfRvBEZTAONnQr1iYTyXGbu1WcgRBhrG33eNVFnaiBSjsafPWzWbtTDqJBOSibBjjXm9amuiTLIwn/9rrctF5uB7uvh98Ok6f0KG9xevaqGogpIii0Lz5Vp1qFN8zypBXjmJFFZXfMipthv73DeiqPq85qcVvKFbxa7wJYmIK6pewdC/ttbWv4YHq7u7+yty+1ByXmpduHBBniTi7x544AG1CX/yJ39S7UAWxRULxerS1draevH/8eel+X7ubeEXd2r1e17vopV56Rc+LFQy2pBA0b/aRaH5csXmq11rBdblVrls9adzmhrL7OlWLIyo1AsZbaIUI7TKxFxipTOWeHrI7MKIPrg96/usvHO9lCiZqVErQn6BSTFj8yGM38KbybuYDLhStFBQfHJ5W3jrVv2JmT12/xHr+ePTVnNizqJHx62QCIg55WltsszeXpt6c58V+pstNLls6b4aK9xzlSXffaVVUYrwfi1nrNzTZt4Lo1Llmv7+mBSTxKNnV9pgQVHGw0+dUj6ilLlOZ0b3NDW4aa9y1codDdb83Lw8Mws3dFnzM3P2pms/og05uFSUBwpqOtBR2mFT9/YKa8DIvIz4tJi+fY/arLQgS/1tjuPl91rr83mLThfFiFpd0M+r+byeM7w8nPAp4hSiSwsLv8ristosFL2afowGbd1/O6EWGsWh2rB3XSlljGIr21ljvqZGq3S06LUrxxxMFXUPaKYmKTf2Wb41qucO6jgG8viJWZf12N4gUn/0xISgpqETIyoipESGgip+q88eEesMNldgdtkiJydc8Yl/yeez1kdnnakZZlcyaf4N66xx/5wUUKZEi/VhW1wfsEJDxLyw1pbTakUvb653U6NcTxRzeMq4robH7IXff595aTW1NIhrBqxViiqqXmpZWZgY62u+dF7PixSWSFjtbS9KamOj/g3livZZ9cbdX8YXRCMugJmT3HW7FT7d/mRahRv3o2YoZ6X5BUvesVmvC4kEX3j6F6UEfqMLiNUCaLVlvboYenjxNCHFka+1WUop18/qcApLeIbE98v/xpIC5vd/mRqPeX77L5h3PiXECgpq+abdem5JJSj3tpodO6f3FQWemG1ra219g9bIyIhaeatfKDkvtVBv9u3bZx/96EelXhGeDGScttk306q9pFjki4nAu+66S/6wn/3Zn73szxL3R7uSUGeyDPni7/wbE4bfkgXWL//yL8uxf+nXli1bLvszf//3f6/vwTjHJMPlpD1CHLnN3/md33ltd7BalaIEIRzQJC05FAGYSrmt7RaayVihv0Xqhaa4muvl4als6TdfY715MlnlAqogQ03BLL01qg0YDpA3GtVGKCZRyHlfmAhUll5/lyXOZ2SCllpBjhzYh9kFK7fVW3gibYFMRf8dGU1a64Njzl9VqVj8zIJCdqNTBUWslJpr5CcSBDWbM3+mYvlrNqsoKW3udspIwGdTd3Xa3LfvsFxXrRQacAOoK5WxCd0nooJ8c8uWb4troqzu0LyLwhmasuLmLhVURL4wKenLFpWt1/zJQ1L30r1x+YmYiIxOuUDn8OFB850acigFFL5UwUIDc7bcF5ECwkQfxmQm53jOCGO+/t2/rQIQpYlwaG1uRO2gEvAc87J5XeFZ3LtBChwtqeBcVp4uaOe5Jr8rxgbH9NoBPi0mgvpdoDdQf5bXJTQ1Ksp8uepURIqZTNaKtUH53ygslKEIKgNfTVuTLV7Z6iZBd25xuX9ANkMBFR2BgWnz1tdZoaveTae2tlhuX7+KR/AUhbYaKWzkyfkfO2J154uiy9M6pmjyZvMKwa421lupMW6pnc22vBvoaUyUdfAEo+/daPmOhB4D1wmToJjlBRgdnVSxpDaU300kosLiq6MdW0mlVMxpEs8fMM8TB/U8e67YbqVzA/LcCVuRKWqqM9sWtsyWFuU8FuqD5l/Xa7VP0f71unBriphvAhgmzzkHhFve/Bsvm1coqOzWD5odP6frngNU/totYtndcetH7fa7PiYvHsMEJBXoZ1awCrQQ8Q7SPhVpfTFp5dqIClva+qi1XEsgN2hBf6MLzrX1rb2+VqBR/FeXfoXYY15iMRn4Yro5YM5VTlRbm5s6nppySRCra2pq6uL/408UpUtXqVTSZOHq9/xrLZQ0+FYvt1DiKKQoygCpf+5zn9MXf6etyf/7y7/8y29NDtb27du/okK8dFzypXgVRPQgZb71rW/V9ME73/lO4fFfPIZJHuIzzzzz+sxzlaoKkshYxjzlsiU3Jax2Oe9goqWqPEDk3jEaH3jyuHwVld0bzXPsvECLtG0KHTUWOjFmpb5WFVstjztkgThN2aw2RojYnt1brLi9QxNvgjsmMzZ7U5M1eTrNP74gb8/sm/osPla04GxGrRttFvGYLW+os3LYY3VPjtjStd2aSPSeOmuhUkn+I7UxG+osta3Bstc2WHixYvmE1xqTAfPPuMwz7+yCNZ8aMNu23nJtUfMulJ3Jnnbe7k3ymbl4llnzpxLKRgtOJM0/uWilde1uQk2eNK/leuudL+zYvJsoyxXlT8MnVG3oM2+xYqHFis2/aaM1HJi1cgSSfcaqEZ84THUPL4sbRTQMbZ1LlYdb3vIbVmyMme/qnWYnB5UDiemf6B5vOqfbyXfVmXdg2ILFDittX6f7numOWzzlsvkaHjhrlZ428yRiVg4FZMQOH3dj8SgaTKJFJ7LmnZyV2gReAD+U5+od5okERE/XRFwiLnCsCi+QBDNzVpfJC4kBaJT2bPBUUj6q6PyylLHktT1SDUsbu8QVK8Z9tnRXp7U+MCxGWvXCiF39vf/NGikqo16rUMAuZaVEoRoR2q2kgEqrhYJe8z9z3Dy0DykkljLW9bmMa0l3t1m2NWzlnqhFJ3Lme+GMVXasN+/h7MUsPh4nRR/FLFR1z8ikiiMULIqsS8GdKLL5xrDM4aWt3eZ76phFg1sdt+26HSr4pSp+My6mYgs+i56ft/teIqMQBdZXLFlmS7NVtjRa9MGjCi9nEpHWKKw1chM5MPGcSHlubLD7hn/HFVakD8AyI2eSTNINvS4rdEOvFGnQKN+0z83a+tZbX2cOFtDNFxcooAt6e3v1d6YGKZIefvjhi+kueLqeffZZtRNZ1113naJqiKO54oor9G+wqVDH8Gp9LRaIhheb75la/NjHPvayqTOsX/u1X5MI82M/9mULwer6vu/7PkFKP/KRj7zklOE3fYuQgooXZ/ULWe/l1n//7//d7rnnHvvABz6gCvpXfuVXJF3+z//5P/8Fs4OxS8YrA5eYrV/tKq9rt2q/m1bLdcSlGGEKr54esEAyb4FU0Sonzgl5UL56q4ogtcW+f4+M1pn1dRY6N22V9kbzzy5rclBm32pVf8f8XeiuN8+OzVY5eFI/60llzTOzIPN00x88Y4sbIxqRRx2JzpQtfHZGRnj8TfiBSjOzFvn08xZcKstLlDizZMGTI5pIw+uhliNFQsBn0fGstTw9Z/GhtHlLVZu4scbdH4qDUNAW3rXHvLNLVox4ZVBObW2QeqINvqNJzKnpb9us5yZ4ctQy/fVW6qi35Z6IU5AwZ6OQPbTfIqcmrXTlZrcBN8UF74QdRjAyBZY/mRcaQrDQuWU9dt/EnGXu2ukmG584qN9DcYXpmJ+HF0R7NDg0K5UM/IMglfCwBqYvcpVCZyakzlDoEG6MUheezllmveNVUXTyOHkeQUOEMd0DU13lEzHFl8zp5xQdUyq7dtn+45rAow0JB6sKeJXry++XulPe3i+EBtE9FKbBwRmhEIjPQSXMUkDPFZwfZ2BCj7nm8JQ1HU5ZeleHeQ+dEQMMhY/7Gh1xxdLinkahIwKHLsi3k759m2X76gXCrezbasWeJufz8XgUs6R279ScshNRlIj/qVJcnR6WgoIKQ7GrqU6K/Z52Ga0rG7qlrsjUXf1KUyeFZ/T4hGX39tnDj/4X8/V0uvghnvv5jK6XSz1O30yL9041EbO5677SA8KCd8ZzW2yvtfBkxqJfOCKTOm1nFs8HAetMr2LQR80SkDef13AFxamikJhArYu5lvTYlFm+aFM31CuSaa24Wlvfyut973ufxApahOT6IWz84R/+4cWChC7RT/3UT9mv/uqvyhAPngH+VEdHhwQQFvs1ezetxeeee05TiSAQmDD8WkwQsiiiaGHy5+rfUZ8KhYLCnl9uocRdatB/8brjjjs06fha1je8wIKnwRO8bt06Qcguh6dnEuHFT8Tdd999cVKBRUVMpUkRhjr21SwMbS+eqGBRWNC+8qXzijTxZ8qWbQ9b/pYdlm2PauKrdMsumcIDYwuO7VStWvvnxwSzjIynLbOjXYZ3Ik58IzPamFm5lrCV+9vVFsP4zUSbfxnoZdmWbuiz0MCs+Tevl2KFp4PNMPj552zkOztFHQ+cn7RqyU1zea5yjxMvkTbNaNQWb+6TQVdtswA+o6BTmeaWtLE3/M1B6/yT41ZpqtPtkBfY+Pkz2qijU3kpJrQSNU2XL8hkzube8oVhRcaUu1ttaZ0rXmNjhFuHFVBN7qIXInl9wlG/IwGZ++3MgPkWMypeULc0yTjuiqJiS41DLtTXWDBZssEf2uDYRSvTW9DNK0dO2eC7W8Ueg25dnZxWll5hW49iWPKb2tXGxK+FQZ/JQECfEOP13C9mLTSbUwZf5fgZ4Tb8c0TIlN3rVihIyaCgq07NyhPGtGbsxIzy/c6/K2S+jetcBuD8othP+aaIVUN+IQ5gSWnRxqVoofWLx+34vJQuWoQwtYL4tWgnkusX8KkITPXHlB3o7Ww3/6FzmgBEbcm1RaxQF9LU3uJtbtCiesSdJH2Q56G2o1aRXdjbokIW6jxFq8Ktg0EVdWzwi5vjMlgL97Blo4zm4BxWeWAV2G0nzuvvq6Zu2oMycm/8WRdNk8srRByD+/KOFiEyWJ7xGXnAXuxx+mZYFFDFhqgKzuf+/Kf/xf9HMSSOiOWdS6mdqNiaS5R02pw8B6hVREEVO+r0/Pqnkw5SywQwqjMTxjDx0lmFXR/8vfetsazW1td+vd724KtUsDCJ0xH667/+a3WKEDZQfNivVxceJ0QN/Fl8PwDR+++//yti9RA8sPdQsFD4oAxRqH2tFtOMGPL5k6+hoSHLZDLqfF3OekSd8Cd/8icv+///9E//9F+0SL8lOFj33XefXgjQ9kh5H/7wh6U+HTt2THlAL17BYFD9UtqEl45Q8nOr/V/ah+DtmXagsu7r61N1zdflvGDcxovXHTXfbYHaRm382V53wmXRQmPSFQPz7O4aazyWNnv6sPl7upya0ZwwL6yiliYXiMwmFgjY5F1t1vIn+822bxARHsM4rQfUHPwxGIaTe1ot8dSgPrApxiB2g3p48Kn/Kk/S/I6YNf3FfvNsWS+IJ5NfvtpaZf6xAec2tip0GezC8q2brebknAoYzOKK7mE0fG7BrLvDBShPz6soqWzokioEwX1hc1gxL5VI0LxZpuH8whIoX25sxpZu7FOmnu/koHlq3eu0eF2X1R6ZVyAto+cY/WmBUpTgLVLUypuuVOYgBRFMLxaeK4jp/mE3OZjb2qGMQFHwoWoTHbKtX9Exsze3a5IRAzkm/uBSQROEgmlSJMzMugnAmrDaZZmtLRacy8sT5ckUzJPOWBXERkOdU9vSWXHHXNGH+dxj3qFJ+aJCo85HQ+Cyr79XqhOFV3V9l8zq+M54TfMb2/TfwfmslWpCis0BdEnskbU3WzkRMf/glKWu6ZXS6NuxSdBaWplSqQYX3fNLpiPcKwYRBif0+2beusFqhosWPjig141onpovXbDMFX0WnsoI5YEayDVEHBE5j8Hnzuh6w5TPa0bbthoO2NidjVYzWrHEgydt7p3brOmhIakzt977GxZ8+JCGBf5/9t4DWrK8rhbedU7lfHPOoXOanJgBZmBmRPAZABHDZ3o+fQoPkCcCohIUfYD6fD4V/T4jShIQ0ZlhmMjk1Dn3zTnfupXTqW/t/e/b9Aw9d3p6eqL1W+uuvrHq1Km6ffbdvx3WXZsyNGRzKM/OnYkyUFVM7gsCXGRGKYhf/MFB1N81LnB6rtDOC53Lfv5zWmNTS8gWhPMZrvLoYGT6/NM+T50UhfZTf/p9P6PcsGWCbAeuQlHMqTM5rRUff7fo5s1v64R3MW0CWmkUaGsRuHdFI8awwOsVDQjLK7Dj8Q1djNV57c5LmYPV+9Hfg/0CcrDKuRyGP/nhF/VYX01z7733SnJEkocEzrobkpiCa0+Ctn//939/1hytV6wGi8mu67Nz507tYrnX/fKXv6wY/ec73O9yjUhNFsHV+Q7dE+9///uf9kKmhTX9uk2ID2dQaArrQk0Ninc5r8wrDe/DBRQjHgT6e+FMzwqsWIE+s5bjRaqtAYnNYYQn8mh6YBnOzk0ohz3wlBwxZNbsArLXDMIT6xY7Qhce++4oNE83u1G/EBD4ILtSaAig4eFFATHn1BhKV2wWcMlcvwnBe49KA6YLPVPEPW74VorIdsVNijbdjvVxZDtiCD6ZRb45At/4smFF0lkBBq0B9w6hIdEmxivV4YdvzQvfnGHyKm5bFypetMrjU3B2b1bcROXkKOL3FnSBUsAk2bLGetgU7y+twEP3XCQC675DQHuL2DcBnHAI+a46BTYSFGS2NiN4fEFsijRA5S9p7cT6GVbH1D+8INDDCxkjFyhM1u2w945Cd1bCnBoW0K1EWG2UlTCcF8fS4jLczY1ar1FjU7x6CzwrOTh+j9ygEuhPJ/T8+R4+Bld9Hdy87e5OLF3ThOhIDp5kGq5ERms4iwyUywXvUkZ6Oep83JW4wBbPa8U53Tc5vwZnaRnhu9JIv+1yhZFS4F6uCYqlSm2pQ+DfnxTbRNMDQZO3oVbMWsP9c+r8EytWqQhcsVImfHQJyW31CH/nsLRCFbdf4NBVdMSYcI3FjsfVnbVyZTJzq/mPHxIoKg90Iv73j+q5I1PjcRlXIx/v+nDFePszgkAJrrhWJODiz9ExWPOFJ1DZ1IPbD16gieRZpm5fQi7Tcvy5O8CkhwqHzpkfxQiFwpYOeI+cmxUnA1qZnlMsBQX6Z+daqZSZzy/B1VICpZ4WLO0Ioen+eenVwJXqwpIxqrjd1RLm6lTnZZj//b/P30TDaIlzDfOySOowioKr0PXYCEqWiFFoliNR86oUuZ89VOwPDg5qz3uu4QPeyKnANFY6FTo7TdAjh/H2TJglpTk6OnrO26V74lwOiuA9h+Fs2yKNlW25lARe8ViKILBzJeRrfajfnxIDoroVip3pKEukjaanbHoFax9KIr2tES4nAM+8YVtAQTFBWGsTvCsFuKeXpMvy3bMfqbfsQeiOg8i/badJGCeWO3gKwa52E9JIvQeF6QwuvGoXkm1uBNtbJJwnE0PGhknhvmOGCWHMgzQ/2QIC0ymUe1uVW7ReulzZPYgiWZBMEfPv2IbGhxZRagii5okFucwqTxyEtWur6moUAppIwm5vRSHohr2wisqWXlSGp0x6OtdXqbxYMl54KPSnUDt/7Wb47nxKNUHzN7WIIWNYKFPVldNFNsnjUqEx1y7u9YwsrtMWl1Fui8OeTsNm5U9tXOwWVzNMaF/t9yI8VULoZASuZBp5lkinCwJm6roLh2Dv2IS1/qj6+AhmPItZlGI+VfJ48yWxRsUtHbAiPVqcc53Lwunpdwyg9bYZJHc2wsrXw17LwbuQUfI2NWXuOT4fFsqNdaqo4c9Sd1fa3i23Y767FoWdjQifSqjCh/U/xVhIzIkT9iE4lYHV3SktnufJk/AeGkPm8l4ETy6KeaIrkwxqpjeO0NFFsTp8TiIPDaPEepxMHu5h02H3bZYSh34a2R/chehCGjX3j2tlVRhshbfSq9cOs7mYzl45eFKg4Kp3fxbx/zj6tNd9+eAxpeWXE4mnAQeVJ8d+DoW3XIH7/u2DZpU29XRn0MUYxhdwJVk5ldE6UnEPp4e5XRSXc958xcf1OpQLd/3rW35TILwc8MDTZIrVzwW+GEjqDHbATkfh2tqDcqWiLDBliBVLKNy0E4EHjqO8tRt2JSq2tumkWx2XXGfzeTwTz1Cd6ryUUy17PjN0+p09CwsLWgsSS3AorGfkAjO4ng1gcQigGOdwsedl12CdPVwXDg0NyRZ6rqETgZTdM/Mr+HkOtVd0Euzbt+/MG/Vd1GNxZfi8Z7Bb/5kSWBFUcchW0VVE1sO7WsDK5hBKdLCVHPXjCbSwU6yrUQnpLImmZoMgyvXAfsPAUMjOJGeCh+FxLO4Jqc6EjBZDTXkxruzoR/wJEzhZoQuKq7KQ74w2xF1bo8RwupWa712Q1kniZqZ7F0ooRN1AJKT4B/fCmhFjc1Up4XsFVqFsso7YeZctSgjNkuKmb56SS08dfHQE1ntRfsOlctTxwk52CuGgNCbc56uGZWkNzqZOgRsBnKawCoHp7nPGJnSBpF6LoajsR2y6a1a3479rvzRGPF+VlnpV0XA9R2clYwXEUCnLyoNcg88k35OpCge0omGAqf/oDJq+dNRkVLEmp1CA7/A4XPuOy9FVGexCqasJrtUkIkdXUIp4FN2wcEVc6zUxP3RLEkBniibZnevZvmZV2zQ9ktTzF7n/lFkjEthSO2ZZsA8OmW69UklxDGSufIdMnRIZMbJ+3ukkgt98As6Rk+o7dM0u6fXD1Z3ykhhn4HFLr0bRfmlpCcHHh5EZqEeuzdD3XJMxSZ55XKXjQ3AvpvXa4WqYIKy8vQ/FXb0CHASnsQdG4Zpf0rlYeUOPNHtK16+Nm3T3gOdM/lP8tmOmo88pC3QQZCi4tLNF4Eog6qwhQ0ZwtZ5SfrFXYrx/3qd6/3z+p4Erzjq44nz7sY8JpPJ37dof+4xx1ToVdYWyEolaNArynznr9TUsVUYooD9e5PwtFY1RxeNG8KlxAcxSyIPyoePmd7AmhvnrGgQAL+ZKtDrVeTliGl4LM3Jab8U3ugEpbmctD+Mf+Mb3aYSjbuzlmJcVYLG36L777hOzRCHaD//wD8O27TMaKzoRzg4/e+973yvh3Gc/+1k1ZFM7xUZvuhE4bPqmCO/sN7oIyXBR5/V8hyGPxYhXF2VepCgs9j1wRDlAjGewnjyK2n0J/UVBtqjss2TZ5kWYomp3toJyyC33HQXmqiApmiBMpyGmi55VG4edq5icKL9xf/ECb08tAYk1OZiYgUWht7WS0s/wgsIL0PQPtKrEmZoe6n9YfcOLRqovhsjeWbFBWntkskhd0qbHRAcc12arg0HpoyTQJQNyOgeJQIzxCyyiRqGo2hXvfFIrEoqpyz5bOT9WLAr34VGttPK9jbBnV7QmK3Y3CEDcfuCTcCXSWi1x9v2f96kqqO6AiYVgUKfV1aHziWQamZ6Y8qhYPM37clGsn0iikstJtB8eSaIw0Ay0NcrJSYDDAFNdIGtrdPxMei/1tyG3s0srOsZLWLPLMikUuxpQPnYK7jufQP66rWj61rAiKjLtIaXpkwkjwHTPLAv0MYaDTBQ1Z0x0J6tEYExAWe4yfwAwHqEc8qkmJrOrTcdQOW0IEPgankA5HhDLYtfVmgDSiqNeQBoGKomkmgFUsROLamXFSiMCWDJYzF4j48lgS3Y92mwC2LNF1T1Ky9/RI6NAvt6nc85RzRF1cbVxrUwDC0U9frKYBGPUwLnH5hSkyeFKVDqldBbZqwZMTtPCXyhhnHM2g3X26Hl1WWKULubw/t2tBtzx/fWRyP6skT7vNNvlnl5G9MER5G7YLjMBV+iBqeT3gbP122EvIF9HkZMJ5NtjKFy7VY5cKxw27QK9HTJ9FG++XCt31xU75Cxkf+KTn3/fRX281alOdS7OsGD6T//0T592ref7ZLk++tGPbviz1HJTf/WOd7zj+0icxcVF6bNedQCL1keCKZ4EPjACJO5A2dbNoaOQ4vf1ueaaa85YRHft2oWvfvWr+MY3vvF9GVgXa+jq4zooeHReomxXriRmiYGd+caQ0r65Zkp1GAEyRde5KweMdTtfQGQkBe98WmuqysikevXI3LAKRRlT1A5ZFuq+uE+ieWtyzriRZhZUNcOsJNr/yaYQ2HAVwvWFe2xeOTyNT5nViLW4Cs/+YUROrCJyaAGRw4um6y8aljCemq7QyVWUTgwroJKaqfqH5sWwpC/tlP6GVna7rcX8HFmt5ZQR/DbUKTaCF2XqepjgzqGIWkGbPguZZg8qwYBcZJ6JJcUNMBGbbMM6W8Cx17J67KwJSe5uNmxQPKbzwJofMm/xI2uwT4wbQb7XY5LBs1m4hqdUkuxKZoy7k7qZZF4XVDrxqE2ixixf61U4Jx12dDvSUTjyo7WKcXB3dUi/5Z9Yw/hP9iq/KvzIiNgc73wGxd19CkINjK7IVODMLqDc0QArmRGrxPR0iucZosn7KrTG4J5PoPaxea2T+FzmL+uXe5MCaTrS7HRBQITsB4+brKHE0omMzjV1d4o7yGSR64jLAJAbbDL6n/sPILGjHtbe42II09f0CTAm+rzS+4l1tG0E7j4k7RddgIHhVVQmjeOzuL1bmWmsq1HUBoHp6aEYnIwP11wMD2UfYvDApNxydN6RoWKo67mG+jgCNGrkzmaULtacK5z0mSXM64J8DgE/g1jtPF2vJnOKAPGZ7BvZsdQtO/THBjsT+XtGIO1/asSs61kgPj2HfFPIvE4tyAH87Uc+dtEfY3WqU52LO8QKDC995lAm9Exp0TN1XNxy0WlIqRAdjmfXBvHn6Uh81QGsL37xi5ienlZMAsEWP+7r63uaup9t3GcPCycZesafoTCNJ2OjITu2kYNwo5HTzW0hs6lBJb0EVtQhUYDOrjmu7JgnFT+aVJEugx1ZicLAQf6HnWkLGr1GUz1yb9guEbLFdSODHYsluQ25Xjr+Z9sQufMIUF9j7jgYgPv4hAJGmS/E22Wcg+pgKLwul1HobUQ5YGtlxqgFrjXKEa6ryihyVclVmmUpbZtMEmflZ680a0ivV5Z+fj305DgSl7XoYqOuOOb4UD+WTOn78p1xgS8CDbrVuGKik46Ah2spRgok2815euONnxYrwzqc9aEoeh1k6TioUWNv4HhKafil6Rk4U7MojU0I8FlTCzp/ZTodGQHx1ce0DpM2xnaZzCeCCZ9XKeWMZli4NKoeRcYx8KJYaIygdPVWFOJepAZr0PpgQcnm7GhU5pPjoPMLI3BGJlCaX9CFl8NKG7JIZAW5LrN6OlCM+iSwz+3p0YXbmZgWUEn314jpo+6N98t1Eo+TxdpMAtdz7PehFPWpCqcyNWteL7u2onDtNmn2nN525De3GBek1wvfUyYmwT++qqR7S8noYyas8sQEQnunUJ5fRPNXTyD/um0m4DQaEZCj5sjZOaAVbfGyQQE4rh21qvR74FpeRYEJ9NSwcT3NJOUrtkgIzhBRskV8LqR3O3QczmVbEZrKGnbrGZN65xWqcHomgHk+Q+aLIO3GN35//5kqec7nNnqMMYUrTgrRy0H7aVERXKGePZUdAygFLK1Isjs74CTW4Nk3JPDL8nIZPga74Llnv3Rb9/37xvUa1anOq7mL8LU2N954I37pl35JJrezjW8MO90o5+ov//IvVV7NPE0mtjOFgKzXxz72wv+welljGl6ps26Hfd3rfxtex/zVb2VLWq0V68Mohd36y5esExkdgi+yEawLYaK1dFuJrII4yXwUtneh7Lf1vR5ewMtlZPvr4UmVzMounVdoJVdHrJxh1QtF9BJaUzNFwEMBe11MwaY2YwZOB6jSSm9TJE0R+EOHYJ9O9eaaias3sWTUjxBUnRrG/HuuQeuXTqE42CYtz9r2WkROrGF5t7kYxf/uYaTffhWitx827rKAH8tv7NFFKf5vh1T2y+wnggJV6UwtqGeQsROsbCHQ4srLnl/D5H9pQ+v/feLMmpAXZDr8nMVluPw+5C7rg++7h82FjYaA9laxRtRc8UJHQJWvNQ45O1OG7+Ss2DiyDwKakZCAC/OdFE7KKIZsXvU1ZAh50WWfHNqbVEpMNi7fVQPf2AqckP9MkTG1S47f1nNKRoo5WwRIrDtiAr6LoHB5Vfooe8ug6lAUwUFdEwEp4xWaapSDlG+PItPoQfxr+0xS/56twPFRlC7frDWzKnKml03Y7NyCipZzTX6E7j2G3FWDKpYuj4zDGugxOqpIWHET/um0ssS4BgsemxcwV+TG3ILRoxHQ0dXK89LZgsqpMbj6OuEcG8bMr16O2FgZkSenBLzJjLrHF7Re5VqaCfh8Tslschheyry3wNcfhXPDJdIz5WvdeOQfDaAhYF6Pb7iQoRDdGZ2Ek8/pY9by8PknI3a+c8Nb/5dy4ZSsHv4ZMVwExc+mCVNa+9SSAC1ZZZVQ5wuqkKKxQDrA1cT3sWPVqc4rLaah/0MvPKbh1KdfezENCwsL+Jmf+RnJiNYDxsloMSuTRM0zy6bXhyL4I0eOPM0pSPKGoOxnf/ZnRdBQy00m61XtInylDW34dIKVoybxO7GjTqGaBFEcdtsxyJEsFvvJ+Fd5ek8bApMpZPprERhfEyvlXsvBM1+SoB2dbVppsZuMQIQVPNTUBEZWzH/ylkvMmXcqhczOdoVj2ispsVTF+gDuLvyTAg+dBsNm5Xe06Xu8jAbY2g9Qw8V1R6VicrROuxbJTLgH+tDyNwdR6Wk3nxseR9Rtifmqv33IlE1HIogdWFTFDN2I1KDV/PtRVHrbUN7WK6t6w98/hcQP74adryAyvwpn3zEdu4vgJ5NHsTkMe7qE1s/vQ/bNu0+XLBdhM9usVJbehdEDXNVRL6MgRwraWRJcLuOOxD/IxcYMsTP/jWzvF6BxmN1VEzHuuOYIPGOTKlyG36sKmUrZgRXymRLnnmbYh0e0Vly9ZQsiQ2ksbvehzlWrrC3mPXHVZodYbZMRA8RMLpu3c9lWgGDL70bFIgjJmH6+bB4Oy6CZfn8aoLgWluAazqgY2H0MiFYcMAedFn5n7xEdPteZXDepQ3H7JhkNCtvbpOsLnVhBpbsNgQMTZr28cxAolLQSpHOQ7kq61jDQbdZgHIaZUvPV2yENHf+1Ull9KbEpjhhzm8gIhoICudjSD1inGa8njuA2xi40/4pqb5yGIDyrea2fqRO09p9A8PEC7O4ulO7fB7/XC28+hzcNf1Kr0+TbL3/eIItFySyalpaKz1FnG+44/mlzO6cF9+vRHM81XG3SUeswSPes9eFGgnutYcNB+Kb4B45jQHFrvVoPlHFWE8HtL8K6szrVqc5LM5QWsZuYNT4Ut693GzOZYKNhewyLr88GWJQdscrnjW98o7ZsrwkX4SttyKDQGcj1C0FWZDgFPLRPrA1XP2RQAkdm1MdHEMH/pANTaXWPBfdPqfam0ByVtohnmgnwZAoYT0DdFNkMOvgomOcahzom2vc9ibwunmQqCK4INLg+y9Z7jHuLFwimbh8bkRiaXXoc/VVu2wIstx39fXjGFgQ0VMcTDWuVxQyfbFtYmiRXf6ccf+6pJSzf1KdyWjJBi1c3SBidbQ3Ce3BMDBDZtaWdITR9ewKF67cj+rWnELr9gNgo5ksxi0iMUdAH/yFTK+DiCnLfFHJNQQnS5969wzAsjiOASIDEc0ZgSndlaWhE7Bwvwly5cW1lNdXD1dtpHiMZGtuFXGvE1NkUHKy9bZcpXi6VBdZcLY2KhVANzrExOKmUQEv0ZFJgtuXeVfhHl8/ofArXbEVxc4e0dXo+imVUNnUrjqPUFEOhLghrYRWlzkYMf/JyFLrrBHSKPY2K0ODz4iJAi4Z1HujsJOtjBQK68NOBSd0XOxAJjN3dXdJycWVHrRfLxHOdsTOCc7pL+drKt0QQempSjyt8aN5ohtYy8O41a8TCYIueaybou7hCrVTEYjIGg2tF9kMS5VFXxtogOR9VOl3U6lc5T2UH9gP71bmX7grB8rjlEqQ5oPjGPSiNjulznOVfuEb3wQnOmiJnlSOf55wBV2wNIHO1mjgjVCfIIvC0oxGVKp89XN+erePjsE6Jf+zkbji/pgZOsTaA8tAYZm5sMLEa8wt6TXNFzPOwLuqvTnVeFVNdDz7rEFC97W1vw1vf+tbnBFccJsp/7Wtf+77PM72dgncGol/oVAHWBuNdyWk9opXdHHObgsj8yJVIXtMjZkvuvQ6T1s6V1PLuGtWncPVANx3/I/cemzSaoJogAgcnFVoamDKgiRfA+WsbdOEki0THGf+VW6+zWTqoQnuNWfftPYL4/aOKcVA1S0Od2AnFNzA/isNkaVKil/0OrvvRz5iam7W0tEKp3a2wV9PKOFIK+OSU+gN9syyXthD9wsNiElJv3Axv0kQ9hB4dMX2GBDaxqFxtlVgYdraE5A9dot48iwny1PRwXUVGxXad0YpRCzT9w10InmBBdB5NDy5LsJ94fb9hBx89KhddZkervt/d3qaUcgINd1OjLrrlqRmUj56Q2SC30+SbcdVK9xfrauKPTImV4WNnWS+BmtLxHz0qkTdLtCkEVzTF1IIBGumMAXUjY/A+eFhCc3t4WkCa610CSzJ/zPNi5yRDS6m/6//0ERMDkMrCfWDYCNdnl00cBkFvNIyKzwvv1KrE40wXv/uuD8HV3CDdXqGnEZWlZYFsrW5Z09NSJ+aKQnpp90oluB8+ItF9ob/Z1BzVR5C5pBOFjKko/gABAABJREFUrjpkXjdoXlejS6g01MA7nTDnXmvUktjQ5BWdqnEioFREAzPGWhsFBslilXf2q1uPjBuf3+t+5DMI33bgzCoXj+yH7zGTk2W1tgg81/71Q+pi5DCvS6+L1Pfyp8417I888/7pNR7vg7dLtpRxGRzdFlcXiYS0hte/7X9970Zs62lMmfLPHOChL33geWmk7r7zQyi+fpde23JuXr1Da3ZGQdA1WZ3qvGqmqsF61vn7v/977NixA4FAQG8MMKeuaqP50Ic+pO97thodMlkXqseqAqwNhgJnd64M70IKmS3NWNzmRmA+r7BH/idtrazB8dGJ16GPa/cuw50to9DfpLRy5hbldnTKaec5TjaiZMqfacuPR3XRrjuYNJqhdEG6mHQr15F5c/9bOuB+7KgukGRBlm/sMU4wy0K5uUZC3EJ7XLfPct7Eznpd9OmAYiI8v86CWq1kChWBGHvHZqR6Ilp38f4nb6mDUxuWNurWtl9D2euCN1FCxefB2nW9poKnUkGpPoKGLx5QeS0fW2gyK80S7ezZnhrDILU1C8RwqJW66+7fRGy0ZBLuqZHKFvCd+z+i26fmSQzKk8dV4MwVDoNW/feZizi7DpW/NdgH5/o9cm0SLKx3/lHTRtFyoatebr617fVKbtexMpndtjF+c0DMkfK+KNxuawBCIQE4ut8Y5UAgxOR4xGNItftkYmDnJN2WRbJXR5kHZmsdTCaPt03QQ52YhjEXhaJqWiiAJxh0xg2DJ3au+316bQROLggMuWpiKB85hfTOFuWW8XxRJ0ahP4HU4lsGpE+z6Nx85IgAGV1s/nsOSSskVosNAY1mHUuHanJXE+auZhDropyskcfGVfXDrK1Mq1/HaGXyqu1R3teBU3jdD39GgJpg54Gv/Tqs2hq87r/8L4FanjsyfyzXLk9OC4DYXGueHkaE8OdYtbPRrGvceJsEV/yXoJNhpUxPd7iSHPie/VnA6/HDMl5QeE8wxfvi6pDD46HT7/5vfk94/4Y3/4GqdS7/uc895392vidOwTptMuIfMQSmZ0dBVKc61Xn1zuc+9zkJ2ml8YxsM31gwzST2ZwaSnj0EV9RaPdtwXfjbv/29Pxafz1QB1gZDpoQBowQbq/0etN2dFHvDuplsg1dOvlydF4G7DhnRMP+6n0mgEPMoPTvZG1Kgp38+Y9KkI2GUm+JKGCfLxVUZmS8yLiwzpsaI5cDlsA9OwC0go7iBVBqV1gbU3juuuAbH55aInnZ6z1IGoW8fRHlqFlaxgtU99Sh3N8O7lMUtOz5inF7pjKpl6IJLbIsrU4oiagKDtrtW4Tx1WGLt3LZ2xB8Y1+N2Tc0j+uS0ksgxOqULNTsB6Rx0GmtN9+LQlLKjlGXFHrcRA34K9SG4Zhd134HpDPLXb1dxMY/jpus/hXSLB64H9imziQ64Siyktaq9ZJyLZBeYP8UKnemb6wViixFboZHJ1w/CM76oIEgGTXJNyLVqPuYSQ6f6IXYL9nWiYZ9j1qIuC3Z/D3D4FHL9DSivJKS/Yp0PV67M9KLzsvaJBQWj2nc9iVLIrfWoq64WLmqbSiWThD+5Zhygva0K/Vy5rAGlkXG4GMWwpRfWpdtR2b1J8RhjP1QHZ25RXYT8eTFXZQfuzjaEHhtFYnuNcUg21isPjeCi/u4JifaZdWZFw3K6MQeLwJGvgdKU0QNQsK0anUoFvuUiGvZlZBZwHTgpR5wywuh1GEqqiFluUAsKTS3v6EN43zSuftdnv7d+8/tQiNgCOdm3XKrXHTPUbIr5CUg89hkmiYwTNVXnO+tZWu6WZoEy9fhVHLioNxwe0/Oz/j2Kfpj6U4G/9fqexE9eqX95PGd3E9JFyLWlJ1NB7cGkHInncj2uDzVakdGs+ivJLPP3oTrVebVNNWj03MMMLNbdMJGdK0K+/eEf/qEyrp5Ppc7FnCrA2mAKTG1PFgWAmh5LqmaEoIh1M1xT2KkiwqMplC8ZFFtBdokXvZLfhaVtfsSOJOA9NStGiUyGVnblCpx4yGTxpPMKFrUePqguNK5NXAyfDHkUbkmdCMMslWTeakqVizGPLqrUdlFgzZWfLv47BhD8tycQf3zOgIzFhG6fwnZa0rUy5Noz6QhQqfiYdTYnx+Hu65HlP9XmESO1vDWoRHVFBxyd1uoq+l2j/WGpMPVjcsd5vdL/cF2Z2d5q4hmCQTkgmbLOiyaTyj3JIkLHFlHubIQ7kUPN0ZTWgQRJua2tAi662JXKSN+wSeCLx0zAWX8wrws/K2U40SPLYuVKPc24ZddvoRwwjsaGfx9SEGTp+ClFSeRaQwh/7XG4HjsM165NcnQySd4/vKjaHOZEcbVp0XXJ81cy5c+FG3aict1uATeGizLage4ysUxMkM8WdK5YPr36M1eqTsjd2izmkMPgVY775CS6/3HCMCRMgGcAJjVqXMvFwvr++MNTui0ycLx916XbFAjK2p58nc+EhV6+XTlYjFSonBgVgCt1N+k1EBpZO8Pmubii5HMRjcA/n4edLarvkOJ4q7sd7nv3wX1kDJ4TU4r9KLXWIn7vsFi8wU/9ESrLK3j0H94v1ojZYGTtCJDXtWqlqF+sEgXmYq+Gzevp+cx64TJBFm+DgE2NBpXTwv3TpdJnvn/4M3BdtgO13zhkfn+eMaXxKbgTBcRuP2qy5e59SpleGw3NKNRbnR3nUJ3qvKqmuiJ81hwsZmU+c/i5s/M0X8qpAqwNJtvoRro9oN4+AiMGZfIveYrUg9OsmMkDh06JUVGY41pOjjBvoozmexZ1Qc1taxOjpPynWESRDAyLDB9eMJqp03ZS1dh0tWqd433oCKz5ZbnQJAjvaEKuzq33g6NrsPIleOeSsEemJZqmToZibLurQ9Ut5UgATl3U5CQVS9L4RA4ahi14bMGkhaezyg9ilABXU8xoig1lFXVQ/1QC99zxG7rwq7yZa8BLelT2TI0SV3lWKo/k5R3IbW7RfQSfGkW+p14rJfvIKJYvq1MulkAes8N6ahXUSqBoD01LR5a6ogvFsI2pt7aq1Jnskn8uZxinbEmPgZb6yLcPw5Uva0WkOiImltd71edIjZPcha310p5x1bnWYet7CB7snk5U9h2F/75DmHnnJoEpisjp6AOZwUhQ6fsKJY2F4B9dwepAQJlYgYmEvjd79YBWpdRJkYGiiD470IDQTEkrPOrpuA4mqPUspcXEOYyGOD0MjSXISl7ZJbBarAkg01ejc5vaVKsyYq0FcwUJ85n2HphO67HStZe9cQe8E8uwOloFKJmRRr0XXw8EKIGhJVipHPIdNeZzJecMy8WAWzKw5Wt3InX9oJLu6XpNDAQFosim9f3VuGHKyHayjqhQFIOV/rGrBKoYPppt9AK7N7/g/j2u+6jHUi1P+GcUeLrOXlHgfkYHdnrIUlJ4X4k+vfj51qZfhr253zQjpDMCTM+WOl+d6lTntT/9/f1aCz5zvvSlL2FgYOBlOaZqDtYGeSM3DbzvTLkrR111LhcKdX7lN5GxSW9twP3/err49qpdcC8mUWqIwD2zalLRKWZnDlUoJK2TErxX18QQLW73ovW+hFikzA1bEBxb08U32+iT1otUMIXc7OYjY6H6Gq4Vl9YM8IkHdWHlxZsaJ6bK0/ZPIENHHGtcmIrOVSDrP1zpHKZ/oAX1B3JwP3xIKerrnWq8YDH7iQxIdncXfItZ3PH4b5v8oGmWLdch2xyAfz4H96kpCdVjD40rbiHfVYdS2IY7VYZnOYvV7THUfvskCls74V7NSevFTkb35OIZcbdcZaGfRvGqrTpmAguWQ1fIJnHd5vOKTWNVEPO/EttqxLgIcNIAcOiUHIYUnaufkSyIy4JD1o/RBWPTujATILi2DUiDlN5cB89aCYWYG+EHh3QfySs7EfjG43KwMSqBLkWdX5Zgu20xitkd7aqice8fgousUjKF4o4e89jo3KT7koYDAmbmbvE10mPCafHwAbj7uk3u1ei4QF/6x66QI3XsLVH0fHnBpNFPziB96y5E9s0C6TRW39gPd66C73791+Wks+prUeHKkM9pMqvHzeMjS8VAWmVyURDPXLDLthrHX6Ui/RYLkyX8PzyqcFM+H3K2sh5nawv8U0avpePPm8qdzOZGBJ8ckX4t9cOX46Evf+Ci/sfD3xcCIgJrZo3R7bqu2TqfYVMAuxwJvKoOwOr8Z8rBGvz134PtewE5WPkcTnzmtZeD9S//8i945zvfqfyqa6+9Vp978MEH5QQk8GIV30s9VYC1EcDq/BXYsRr9lcwLQK4lrHBQrn/kXCNDlM4KSDmz82bdxIs9WQBmKZEJCAWAlgYTjulxK56Ajq9y0KN/6ZqSwDvkFWuT6goh+vAYSjOzyo1SsGfQp9UUhdfUPakkenlV0QBkeehmZLwAL6Ic6sHygy0S51dOjsG1qUeaKh5v5rJuVf+QvSDgCHznoFxiJYZ/Wi6Ur9uFYtQN30Je4KywqxdlvyXhN/VojtsFT8qUIntPzmLk57rR8/cTBsQFA4qEkH5pKS8hu2cuJZAiYEWWjs656VmBKTJ25bAf1oGT5lzt2iTASLE4dVFk/JR+Pj6rKhSWUJO5ih5e0uOszC2i0tOqHC7L7zPxAlGPyRRjTtTp1Huyaus9c7xvrTLJRMVCWqGtba1F6CuPyFFW2T2o7C//gTGUl5Yx+T+vRMO+ovRt/rEVkz1FQOB3w5pfNdllAa/ALB8Hz2Xyml4JzTlMR1dMAx8787+cioJD6YC76YbfQzHkFlNG1inXyRqfhM5JKeaVFsx15U4Uo174T8wZFqfkKIuLyfeuy7fBSublnCSgHntLBD1fWdJrlREEYkfJdPE/1VgAmdYAIo9PYumNnYiM57VqYP+jzlEkIHaMr8G1viAi4zmtqSs+G2W/+5ylyS/ncJUpN+QDH3m5D6U61XlpAdYHLgLA+uxrD2BxmOJOsTtzsDhbtmzBBz7wAezZswcvx1RXhBtMnlELfEFGAia7qALk69iR51XVCZkH/dXvdmu1UWqrMxoQCprzebEOFDCzy48rLH4/GSwCKu/IAnxDiyhFvLDG53QRW94RQeTeEwIqzFEq7eoTAybXoW0p8ZtFw6ntjbL+V+IRieTJ/jBCgh2FtOeTzfBNrOh+eAyr2+M6HrrovMt5OJHg6aocl/oUCRLcjH2IRGAVHfhv22sSzVNpCc0Z68AVGoEWV232Y0cEDHk/PX83bhiYljqxMIyvYOq66/FDqmrB3IJJoWe34OKK8r4IQtDeIs2afXRU6yhqyMiYsQvOamuBwziD2QWdLxYxL1xVD8+hUUSfmEJyS506+5zBDhkC3LU1WPuhXabrz2+hdHJIx0KQKyDCmIbWJsMknmZ2GEyKY8MCbdTMKbuqu136JIJIFkYzIb/jjoQeP+MsyBjxeaR+jbP6uk5ke2sl6udzwONmnEVwMqOQV92fy1JOFk0OPAcEdd7Vgpg1AhjmmM28oV6vMXe6JHBlL64h2e7F2k9ebUwQjL5IJJHpigpcKcqhvlZJ/0yZdzNo9aljaH6spJLtTAdLsJmqb+u1k2+LwnXwFAJzecWHRIezMksk+nxYuLwGqztrZSSQG9VyofY7Q3AfHBGITAyG9ZpQP+Glv22cfC/SkGk7H+brhrf8oVa2VXBVnepUh1MsFvFzP/dzqKmpwT/+4z+qIodvfP98wBUBGZ2CDBZlXV9LS4vchUyGZ/8xq/kuZKoAa4PxjczrAkxtTGpLHfxDiwidWJamxipXkOutR763UYCFqw4Obeh0CjqFAtau7ABWEgJk7M0Tg+NymVqZYADO3LxE5GRqfI+fRPx4RoCMjFVl54Cp1TkNCJijFbxtL1wHTyIwbRK7Nck0nP4Oaasq6Qz8B8aNoy/kR2pzncT3wZkC1m7ZLtaM+iiuYuyjYwgfnFMHYWZri0TX7Bhc2mbSscmQETiWa0PSKzFjybeYl6CdTj+N261sp8JAi5yNdL2RifENzysGorSwaCplbAu3H/4UnOTpVPlyGU7EZwqvqTvjHB/VxXPxx7bLLbb8rksl7i82R+EfXkLj7aOGJcvlER5eg/34Ma0sXQ/sV4WNf9Ek5Qe/cwjuujpYzQ2qPJH1f/cmvdKdeESVNXT9YWlFLkbPQhr+Fa42MwJ84aemEBxZMU7ElVVTY8QV8cQsKgycDfvkHqWoOv7YrNLoWUJNsLd6aQOciF8rZB5fpalOrjgK2+m6c6J+BV6yk5KVRgq/rAuj9d/Yw2jJ3WZzhVwqId3iQs2TSyjWBk3RdDSM0NGF0/U8HmmmxPCNT4kVW/nJyxB48ISiFqhjS+1s0vNKkO0bWULmpu26fdY5eajfy5YQG8qj/olVRIcz8Dx1Uk5TCuqpZ1JG1qMHEBnLKTMr32zCXfncvFjz7Yd/a8Ovc6VMJ6J3pSBWsjrV+U85VZH79w2rcbgivBDGiytFgrAHHngAV155papxPvGJT+Anf/InwSbBj3zkI6rKoTvx+QKt6opwAyr2+mt/C8HZrITjhcaQwi65aistr8B1xXYDECgqZlkvq0xiUThhn8mCYgDoWtKwGqxVoWaqNoRCjV+ic34/WQ0BM7I7Ho9SuBeuqReYKHU0wL2wZhLe3ew+LIm1YYAma0a4BtIqKJFBYk+jcqnccwl1HFI4HprMSOS8el2nKmJ04eZ6i8XQdMIlkqi0NUqcT1CQawopTqIY88P7xAl1ARJEOEwrLxW1spNGqqEG+ZYo/AfH9T7ZOa1LGRbJSAlGEfCxk3Xjv1722HkR+u4JuLiy4mOlfqg2ivKBo7DDYZRTKTF27D0kcOA6b714l2n1zu5B2AeHzIovEoSz/wgWf/ka1P/FI3B3dehiK53V5ByWfmAQsX98FM71uxVuSmCb3tqIYsjCw/9sdES08it36ootAg0EEHSplRtrTI8kdW1kLqMBU4G06UNyHarrcHXttMC8IpE8oy3oTCQ7xR5CgkGuST17h0yqvM+j5658+IQyyKR94nkZHoPVSBYyLPCmX8b6WgOoC0VVMAXGVpHYXofod44hc90g7JwD/6kFAb+1N22Bf6UozRuHOWGB4/OGmVtawcw7BtH0108B2/qMpoy6NoIzrrUtG8X2GmWSnT2qJyqXkbx5O4L/8ojcpcWWmIJYK7GIAOzSu/fgib82nYQv9dDEwDwy1kY9H71WdarzWloRbnrfC18RHv+j196K8Gd+5mewe/duvO993+84frbp6enBBz/4QfzET/wE4vH4s37fww8/jD/5kz8Rq/XhD5+/XKLaRbjBeGcSSFzWidjDE3AfHUL2pt0ILq7KeVXwMeHdJ2fhus5FWiiCLq8X5dZa2HM+Obu4riMrxTT4wGwFZQZeWlAXXHZPNzxrBembPKs5NDyyqOR296phMlwlG8UGrq0ssSp0cV3+s59D/X3Tcr2VGqOIHk9Iz8X15C3bPgIf9UHZon6e4GptMIyah05b+mNBuOeX1ZFIwX4lFtRFnzogzCwg9YObUTfZCMflEpPBi64LpoqnsKUDpaAN35374FBPNr8ocXlxV6e6FVkaTMAlENYQQPDwHOxiEZ4xCw6Tu3vaYLH6h6GjSwnT7Vcbx51rf2NMAtms1nnlrd1wTyxieXsU0eAg3KtZZUMt7o6h/mtHgKt3ITJRQvHNl8I9umLAVTqHcl8rav5lP5yKo6gIGQpSaQTuPoRQbY0yuAgqSsOjAmauVSNIJ+BavbodmSYLzX+1V848Jp+7Hj0opxvZn9DQKlyLyxJ9U+nmkOFjfNQJN0rjk2LKuPar+H3wMjKBhdUUzCfSCgXVTMzC6WqVMJ75X/qe4Qm5A5UET/BDR2MshMATwyhu7VRgLM8RYy4EvugI3NqtOhw6VBeuiKPuYEZRGHwueJ8Ess3/eBiujlY4ZerQWEZekVnANb/8rBEFTFZ/NpegMqd6Ol42cKU5Ogy0NlfBVXX+c886g/VCfv41OAMDA/j4xz8uYfull16KEGUSZ8173vOe7/sZ9hauF0NvNFdffbXeuIp8PlNlsDb4S+GGa35LNSjULXFVtLqnDpGRjAkVZaca9Tgnx+EKBpT/xGobZhdxrUVA5P7uAdgUa5O1YRYShd2WS4XC7uklRQSwj8+dKsJay0mITBci10bUUnH1xKJi/hwZF6utWQwOc6goGmcu09rmKMJfegTutlbdTyXMqhi31pp6ginQn1uUZoVgzE4XMH9VDZrumFAWEtdG1JdRU0a7PwXm2NyLlV0x1D0wg/L4lMTvZ9vnWTNTmpoxIaiXb4W1/6TYGGQyypaiq5AaLR6D2D3HQW57O7z3HZBzkaCGF/n1wmBa93kfLp8Prv4uYya443GJwxkOSf0PXYHM3GIu0873/REan8zAs5jGbUd+T8fEYE+bDNPsIir5ApxNnbBOTSJ3WR8Ch6fVHeheykgMLi1Yc53AJkEUj4H6osrBk1pLqhNw+yalqZdm5rD27iu1riN7VYlHUWDUxN17gSt3mPukS/LEMOyaGFyBgBH0M6ussxbe4XkUO+u1muNqi+s5iuJdi6vKvipv7tJzXpmlCcGtGIfcQAN8jxyX8N/q6TDAis6+/lr4p1MGxJfKyAzUa62n54QBmwyv5X0zxqK3Tsn37kRerkGukjnr6eu3Nvy3c1bEEFCyQ5LrVX0c+mk9Z5ndHc+rmuZiDhsGZFo43bNZner8p2aw/sdFYLD++LXHYPX09Dzr11j8PDw8/JIej+63wiVjdc75Qr7qBz6OQM6jGAGK2xku6rtjrzrNfDPG2l46NQy7tsa44ugSy+VR6GuCd8ikd9O+zz5BanwUp0AHYcgr4XAp6IHv8LipcDl8ClZ7KxZf14KaY2lVtqztbEB076yCSbmu4+0zkJMrK67i1suduZZkNAMt+xzmZMmWf2oKFYabtjUrgJOONF6cyR6RLSm31MIemVEsg5134Nk3BFc8agJI6+tQCfiknWJMwNmVImSb1gEd158UpLP2hE4+rpjopkxe0obw/SeUhJ7pq4XvzqeQ/cHLFDvAuXXrh1E6dlLvnyu/SIGTLkvrydLrTfCnZ3oVzsQUrM52TN/ajMY/fchY/fs/KNDiHB+B3dSgSAmKvlkcTbCFjmat+1jKTW1ZaXu3ssuKXSy1LqBY45djjyPnJsHrWkpuQ5Y72w8chLWlD6V4AJ5jE0ZAXnHg1EZQOXAcrt1bTFzGWsqAM4KsSMgwmrkCnNFJFRyTNWN8A92XTGR3t7boNUIW0/G6kK2zUbs/gcSWGOJ7l0zoLMNZuSr2ehS9QEApMEU9X8r0FXrHlgx7FTRuQoEpxkK0NaiPkAylgC61eUvLJp7jdOjnuYYZVa5YxNQsnRWpcDFn24f+CIc/ff5UPrWNpelZadqqU53/1ADrvRcBYP3Jaw9gXcg8n4T3czFgzzVVgLVRTEPve+F2+4F8XmCkEPOaCABmUjEJnIJ1iqBZokv7vOOohNjz0BFUdvSfqcShZkQXOV6Aa6MK2/RPJrB0eR2CsyWJqslerfWH4c468C8W4F5Mm4ulniWXvp6v9yO4f0qrJb6Vo3ScJaX/4dc57uFp048HILWrBaHhhFgs5XEF/GLMyIxplZQvCZhIRB8PKOeLmiRptDJZLN3YhZov7ZVgX06/bQNwjU0b4OhyobinX0n1fEyumUUDuAJ+MSve2x+XrkpaqnJZLBGzthgH4e7pNIGfE9NnLphkSs5eT1ETpSiKYEAF0P7ptKlreexjWoMS+HHWWTA6ywIPHFPXH8XdrNaJHVgUWyVmj0n7x4ZgbRsU47S2rQ6RoytwEYAlklovVh47aGpbyl8SqCCDNv+OrWi6exaZ/jr4F7Imf4t6pOZ6AdX0nnZFd7AAmZEaOs/lskqtudqURo3PY23clGavJBQ8y3JpRTxEQyaCo5FxG7YYL+rxCMrZXegEfbDIyjXVIdsRQWB0VaxmbrAZ/mMzyG9q1nnwHZ/VapGBuHw80sSpgDtl+iQZqprJKhvslvr/qoDPs4dhogTvBFdkDc8UK5P5HJsQwCKTyOYB1T69hMPn2K6vq6avV+cVOy8lwNr8nhcOsI7979c2wKqc5o3IXF0o63UxGLCqi3CDkWCbUQzZnICUugmnZsT+rJcaU8ztSmbhKpZUvMvkcTrtyKAomsBto1Tjh4usgsejUEQW/7LfjmxF8MSCwNHs1RHEDywjcnRZ4IdrKIraDXgry5IffMw8wYlLzUWVzj3V72SycE8swL2UUlAnq1i4fgzMZk+v6ComQ2ktBe/kqv7lRVq1PvEAlvfUwsqVJfDm/ZERow6sdu8KXP2dRtA/0AMrZVZaHF6o3Y8fU1GyksMZqkqwsromcOVuqFfgJ1k5u6NdP8MLsx0Kau0oZ6XHjVu3/KYKe2d/fo9WhWe68fIFramGfm1AqymKmgmuuJ7EzLxWWXoBUxDPhPqDp11lZQfBo7MIzuYFouhgJLjC1Dxmf/VKnVseY+SuYygfO3WmWkWMX18PKtftMsGee7Yqu6rxS4dRaIsj8PAJCazzN2wXMKL7ksDSf9uTJuurrVkmAp43DmtpuHoVq8n1MIEXgTnjOtgrSEE7QWp9SHEO7qFpeMeXjX6vLqz2AFYzWUsJFV87fjcCw6u6zZVr2+G5dz8yu9rgWTldv9RUo8R25oZRa1XY2gFw5cr74era45H7k0DWVVujx3j2CFxRb2fbAsJ6jrkmzGTPsFcC3lyHv8RDg0W12qY61anOc83/+//+vypn9vv9euP7f/3Xf/2s3z8yMnJebxe6XqwCrA3GVSgrVZsMTIVZSvxcXyewYITCmfaQPl8J+83KjsW988twaqKwNvcCtPeXHTFDvPAyA4lMwXrcgJXMwakJKz6h7cvD+lmyD96JFfgmKVy3ZI9fZ8t0Uc7mED26on+ZVUR2hICK5cByuvGCPjKpCzHXgQtv7gLIBNG9FglJbC/Q11aHqTe45BCru2fcVOAQLOYKsJirROBWKKHQGJEAn68UVuooRdzlErthRcLIvGmHWJl1VqOwrRNu6rE4k3Nab9LZRs3Pm679JFwtTSZ0cy0pXddtR38f7u88gcb/85B0WNJkWW9X5ARBQO9nj5x5PgjAyMyQYbkj9Xf6mHU/EsgXi8hftUk/xzgGz9QKQv/6hAEzwxMo7OlD27/PCiSRQSpv64G9tV9GAz437CIsj01qZVi6cgvuePJ3VcFCgMLVKR8z4zm8iYLOd25XN1yJFHDpVlSCXiXVV8j21Ib0nJQ3dZpzvS6K9HhQqgnJ+ek6NCQAXSEYnVmTe3HxB/qlzePrgu5PRnSs7KkT+6Y1X76s11NiTxMiI1lp3wLja9LbETTzeROjupYVM+kdXdLzLnDFWA2ynrwtOkGDPq18yWQ9U+QuXRaf39pfNECLJoCe9xsdlL7ppVUU8Pclc+3LU3NRneq8Iqca03DO+djHPob3vve9eOtb34qvfOUreuP7dBXyay/HVFeEzyFyDyzTjVcWIGDIJ3Uw7JzT0H1QLEpYLraCcQwcrmZCfjhHh5R3pDBSBoaS4djSKzCTGAij9tE5FNrjYpi8J2fkvmPOEod6obmbWhEbLigGgCJ3XvjdKZP8Lh0VgQovmHMLYlBYl1M+egL2zi0CImQzyKRRP5TYFpdAX+GjY7MAtVYUW5Ph4u/saSek1pKnHXKlvjZYXG1SJD05h8z1mxB6aAhLbxnEY3/7dDcZAZRYrGBA4M09OgdncQmuwR4xK66hSZRX6cBsR3l6TqCVbMm3C/9kfn7gf+pxpN+4Fd/9xgf1OQIA9gB6//0xVF63R1UvrmBQ3Ygca3hSrj4yLGTBCNSoc8pubkY+biP+yJR0SgQNTE1nsGeuPQxPoqDSY6bDEzCQmXFm5uR0tHZvVs9ivj0O37EprTKLm9rhGV2QfonPAQ0M/pNzYgld1F4Vi0YLdXRC4I0AiuCzONAKq1BWXY0eY9uvqajZPj4uILx+nvJbWuE7OG5MENGwXh98XpJXdCAwmzO1S5mM1oQMNWU0Axk9Pma+Hr1zKfPcEaBzPZzJosJy6VBQgJMuRK0NyVSdQ9h+rqHOi0wjTRpkRUs7egQ+mV6/LpR/KWZ9dVmd6ryS56VcEW751Re+Ijz6f157K8KGhgZpqt71rnc97fP//M//jF/7tV/D4uLic97G5OQkvvnNb2J8fBwFxtqcNUyIf75TjWnYYCgW5zrNWU2ocDf0KLvwKuonlPWd60Ne0BygXBOG5fVgbVst/EtF1dRYzHVqiAk0eYZmTUBnno7BEoLzPjEd1NzkB5oErgiKmGUlxsrjRtN/jJ3Jl2LdDYXxFMh7RubFWLmHZ1BpDKHS14mKBbhOTWi1hRwT5l0mKqE2jlRvDPH7RlHY1ArvEmMSXDreypOHkXrnFYjvXZQ+TEAwnRX44f2SreL6T6ungU4VTbPEuPbJJYmOyYhRqE6AUxpo15qSPX3lQCNcrfXI72xXUjnrevhHF4XxKlcuFSXwpiNMWitqoBKJM6uoq37yc4j/2yGUt/Xivn/7oPoQ+fOMH6hkMljZ3gn/ioPvPmZ0RLcO/gZ8yyvAlkGtFr0rOQSempF4nA5Cft1jW0pLD4wlDCgi2zizCIfOx+4OlV5bl2yDs/eoipF90wkTv5HLwzOTMHVIuwfFFjHPjBVFNB6sdzly9Vbp71J1jau1WcwPhfSsRmLIaHawAf66GNwLSVRaG1GZnod71tb32ekSSn2tWhNyXcyIjXJLHSKPTZh6o+0tWoFyfes7Oat1IJk3X12NXm/eeZfYNGqsyI7SCSmHZzZrXJhkqvi6pXbuWeZs7ZV58Vu6HbkS1wrINPsRPZzRfb9Uw3XwbeN//JLdX3WqU51X7xSLRVx22WXf93lGNpQ2+L9vfdhZ+La3vQ29vb04duyY1oujo6PSc11yySUXdEzVFeEGw9UdU8rJOoX2ThrGg4BqndGamNT7pbqgNFrZXrJEKXifOiU2obyjTxdhsiV0g3EtldhRq4um5579Wg8SpPmm1kxgKVmwUknBkNRzMTG8MNiq1RSKrETJCPQRxLjn10xoKBmp6Xmjg+psMUXP2TwKDWFptuhQDB9f1oXcnaDTLakSZgKwpV+8CuGx06nwAT9Se9qQubzXMB9kwLj65OowRAel6fZjhlPF70WFoaUz8yi/4VK86bpPwabYP51B4k2bJKJnmjmrhVRZUypKKE8XWPnIKbj7e9XRp1VgLIzyWlKZWHLHAQiPZ5G7epOAhlaqdPelC+oAZFlxzaEUwnunzjxPt534AzgDHQAjJhxTcMyV5b23/4bYlorHrdWdPb2IQlNYq1qtzljmzBJsatg626WrczfWq5NPYvShMZUnE4yWWutQCnnk7CxGfXCeOizguz5ykY5OSYvH201trRcIV4J/qaScKq2TGVJLgOdUxJot3tQF98gM3IspE9zK5y9fhD2/AqcxLtchoxnU4ej3COCsh5USFEdOruk1wLW0Vtn5vOIdyFStC9n5r0DXBvoprgfPHp5TsnBMoee4c44em+77JRqHoLk61anO06e6Ijzn/NRP/RT+/M+/34Dz+c9/Hu9+97vxXPObv/mb+PVf/3UcPHhQ+i0mw09MTOCGG27A29/+dlzIVAHWBuNepUPQMuCFf8nT1MeofAIsAo7Ltsld5plYEpPAYE0CKl3IamJwH58w+pdwSLlXZKdiB5cFTOyudnOx4u2kMmKJpNfhmpEXMr9fNS7eY5PK3WIv3OJV9ag8fhhOKoXS8Bhc6by668jGcHXkWk0ZcNVdB3e6gOU3DwgcSbxeGzVuxKVluB8+pBiIxgcXtaKiNoigIPzkpByN0oeFgiaslMBsYlErPunLpmbgPHkIzpZugYpCzA17JaPH4qTSiH5jn4qtvd95CoG9owrR5IXf3detgFYCGD5OKxjUG0X//Lxr56AKs8kE2U8eh+fbT6By6LiAEI+Vt19sDKsqyB6hk7FsNFln1ay4ohHk+hu+J5Q/PcuX1WnNltnZrufQmllAmXEN84sCfARRt43+EfJbO1ChBipbQmlhCTbdjuwc3BLWqrQUtBC584iE8jbZslJJOiXGZpRTaa03ZYJYWEZwNCkDAEEWgbLA8eyiyrMZf8HUerKEdU8wYqGinkPWIQnIkm1KZwW2mAhPXZUKmRluS2CVzMJpbzRar4gXzsgESjGfGFI6McVa1f7i084B4yrItvHc3BL7ue97rZMdVN4YmaPmXxHrxZU216Y0NPgXcshsqjemi5dgbtnxERPAWp3qVOfpUwVYzyly/4Vf+AW97dixA3/1V38Fy7Lw/ve//8zbs/UR/vRP/7Ted7vdyGazCIfDCi9lTc6FTBVgbXRyUhnTM+d360LJqhDa8wWEYlGJyP0zaWlnIkNrJok7ENAqbv51jQaU0Yo/t6BIAgIXghQxRGQUqH+iFioehrWURCUURHGwzYjgvV5T7FsTE4PlGp5Cqs0Fd0sT7K4O2Jv7sXJNq5gOsl2e1Szym1vg1IQUMEkgEZ7IyR1IELZwVZ1ch7S7Wz2dJmQzW0D60k6VBfPiXuqol/6qwkylxlrkmkMmWoAxByoudqmmx/J6YQ9NY/VNA4g8OIRyPIDK1CzsxnoxVcxYYjYUtTus4WFFjBHXu1HqaZHtX+xOY4NYLAZxWrPLEq5bdbUCCcwWIzAjKFjb1Yjy4ZPwzKfUMUh2hutFOhKvfftnJbjn8HPUYeVuvVTrJX6NuidPmqCljOChaaXqJ27o0/qWJdH2tgFULtuq7+f6jdooMo7u5kbkumpgtzWj7u4xAefww8Oq7wkcnESxISSdE0X9zB8r3XiJqmzI+qhkmaxfb4dWi96pVcVjUEPleeSIfpYOP2zrl06OzzlLm7lS5Gui1BBB9tIeYyzY0i0Axu8hmC3GfSbQlanmsYieX2Zs8T4YqbAOOgWyWH1zemgG4NBEQMD0zGFnJIuidR5n/69hvdxuAW3lYu09hmLAkvj/xR7q+WiuYHZbdapTneqczxw6dEirPGqxhoaG9FZfX6/P8Wt79+7V2759+87580x+X9ddseyZP78+56PfOtdUNVgbTb6AUgdDJStwOmrhDvgUHmmvh0iyxy9mEtwZOaApFOA9Po3GIyaHCOkMCpdvQqrDi7rvjMLpbFZdzNr2WkSGPHKBuYemTE4SmZFMEZWWehUPc+xEFu7lNCqdLej81qp0T8raeuAwgnQYNsThigRRivnhOzKltZWVzek27Jklia/ZyVd3IIlSyAv0tGidpxVUXVSggGCHfXxMM5cNn+uzmQUETtfelBqicKfSihgg0FDNSziE6Nf3IXvDdjnr0NuBysikgAnXeq6lFZU7V3ydyLf5VPXCDj03K3YY0dBUj9LomByHzmNH4diWyZ6KxfS4yeBlf/hKAaRooYj0Wy5DYCqFfFvTmaennM4g9I3HcXvxi997Qfd2w5rJ6Hl48CsfkP4qun9eOWbFiBfuu55ChG7L1iZlZHG9VqjzoRBvhXetCNdjhyW8PwNObBuF7gZ4RxcEBMlm5na2a/3pDQXhPTUnEK08NL8XmUu6EXxyRCaFXF893N85KaG4XR8xrsOBbmnpZI44cFwhqoxaKLREVbhc3tkPK1dEYHQOJ3+tB31fof7Lguf4JOxUGvnXbTsTXUDhusvnQeWa3QBDZZvrJfKn5uu24c+cAVXnMyxxXtdh8bFTv8XKoPU1I7/20JdNl+OLPfmd3ShG3bj7IoebVqc6r4VhspPrBf78a3HuueeeF/TzV111lQqft2zZgh/4gR/ABz7wAa0Lv/a1r+lrFzJVBmujqQDfue/DShAnyBEAmphTmjY75ljEzGLgcs/pQmei39NuPF7seFEny+AbX0bdHUNaGSmHqVCEN1FSZICyq/izjEVYTUkHxBUbIxpO/UQIa9vrzdfmlpSJRODF0mkCFN+BUVT2HUWxPgzPUkYOtfLMrNgPuQzpBAzYKEV8WNwd0dqwHHJrZZjvaxIb4b3/AMojY2I+qPGRk61YQnmwE+XOZjgBH9wnJ7Vi4qpq9Y39cJJmRUmrP3VOXGVp1dnVKkYs311rwJVTgXtmGaGDM0hvrkfizZuMMYDVP3OLpoDY6zUarUwGqXdepdsRuCkWET62Aqe5VqXU/tms6RY8PVzNKfzU/fQeKeValRytK7kO48qu1BTTutM/ZjQ962su1uXw3Hr/4wl1MbpHZuX6vOEH/lCie++Dh3V7ZATpFKXYnc8fmTBmT2V2twOhAMrDo2IMHY8Ni5lUrY3I9dbDN5dWaOny27bAOjkhoMaCazy8Xxo2skmOzUJst3ooS7v7lb6vKAT2SI5xnbmkHC4aLSRSZ5r77P+VoJ1MYa7V/AEgTRaf80wGTl3kgv4z4PnkipbxFEzmd/H5AeTARFujAl5pSnixxzeyCP+/Pvqi3091qvOqnOqK8EUZugSvvPJKvf+7v/u7uPHGG/GlL30J3d3dWj1eyFQB1nlAfYrRXYnT9SNLK2IIxPLw47DfaKkIHIIBVGaNk65YG1CekQCVxy2AwpURtTYEGL4HjhgBtteWzsWpjyF5mQnkJEtFvVbXv5cQObFqVoY1Md1frilk0sFzeRS3dMCuq5VOi1omslC8aAfGE6e72yytMO1sEY1fPQrr+Dh8R6fhBLzINnlw8+W/C4uVOHQM7tgEN4835MfCO3eonsU6PqoohNLSktgMrjWpsSIYYiAn+//IbCS2xZTvtLalBsHxJOxMCXZ/D9wdrapbKTfXoOyzEL97yLgvSyUUrt8Od1uLETK7LLibGnXbXF9xHVlaWFS5MmMB1qMrmOzOBHwORexkmgjyFFB61cdVtsz1kjV/et24dcCkwz+0z9QKZegU3Gpua2rWnGsez5U7lJVV7myU6zPw2CmtKK0mw8axy69C4JlMGZCVycD3+EkETy2f6fhjT2Ex6hEYox6q7LewtikmUKJIi5YGRWy4N/XLLUkN1q0t/11MWDFmugnZlZjb1WWCSQE0f+2kOhw9B0eMA3X7gFLjeZtcM3MCowkxXmQXc1tbTWyH28LNe377e9lV5zF0hXL1ytcizysZWX5MPRaz0gggaebQqvtFHFYoLV3fetGreapTndfK8P+MF/pWnadPuVxWRENnZ+eZdeFf/MVf4MCBAxK7d3WZgO3nO1WAtcE4jTXmApvIKAOJ6esMw9TEo3D8XjnTeEHMb2oF2OHH9PauVrgfOyZ2Q2XNyTRczQ1ig5JdjHUoqw+OTM3c6+pRvmY7XNkiQncckIZLeVb1Efhmkqbmhnb5soNibxMCk2vI7mhHaX4B9lMn5BhzVtcktGYwJ9dsEr7zAmzZyHRHMPqDERUpO5u7UO5oEJAKzBWxuiWij12X7UByIIZSO/VlZdQ/vmr0Ym3Nypk6k+01PC43GsGQ/ehhFVLnL+tHzUPTKNQFFIBKETYeOagU+vTOVlz2C59TUGn00KKYF66uCNa8Dx6RPolAiBf08uISrNq4xM2lyzcrMkGuTcdBIeoRc0imj/VA62Ls9Zn60FVyDkqzRX3W6Z4958hJ6bMUI3GKERsOnKeO6JwypTy5uVagmOs8lkyzcBtHhpV0XtrWjcpqAovXNev5V11NOmMqZ7pbZTQoNEeRuKZLTB3BGYX57nRRgDf05DhiD0/AtbxqGDnq11I5rFxSj+vf9r/M+jgehTtdUkNAJRZRkKh//5j0eDJHeDzwDy+JWdJrgCvQpRScE0NnSo9ZGUR3IkNr/UemBc7tyUUVVZ9v6jqBHtlVslPMAWO6PvPGFO/gcpmqp2Qa+Vrvi94FSOBb9+Bp8Fud6lSnOi/B2LaNN7/5zVhZubjO5SrAeo6ZublF0QZWV7sSwinMJjAotsaxNhiGw/yqSgU212QEF/Eopm+qBzZ1S3yu1WEwoAurvZJCZDSrHrnlN3QLaDR//RS8h8claOd9TL+tDenBGuUpFetCAhTlpjgyA3VKJ2dpcWDfGHJvu0Kgi45FV2+nCZfcMQDr0u1yvNGRx5Vl+PACOr6TRfqyThSjXthcSbI2xwJqDqwKEPH7g9NZ2GtZJHbVCkyo8mVpReXHFKlTuM3bp3uQ1S2r77hUeVNclaV2NsG7mBELxNVp4qeuFBsTOrmM+n85ovcrc4til1THEg5JLE8NGFkWMlB2b7fWXtnOGNKtXhOGOj4jYOG//zAKW9rF+jDRfG1zjUTp6264I596nxF3X7nD3MdpACZBN+te1gulmcZ+4yXA4SGBisB8Xgwii5LTvTG40hlUdg0g11Orx0XReP0DsyaLjLlZDFHtaYG1nBJg5XMe2zsnoTxBTemmyxTtcaZ3kIAhkxW4xti0AFn8cAKhx5jAXxGQTnX44T4wrIgJroFL/W0otcRlfFC3I9eCvO+GOlgTc6Zy5xnDCiEaKRjhoCR5BuDSYBAPfR8YPdewlYBAl85MlYqnMtJi8TFSa8fVNJ8HO//i/+mbvX5LNVi0OtXZaKorwhdl6D680EqcZ5uqyH2DyTUF0fKvo7Lsuwlm2hpNWnm5jGyjD7HDCViLq6absFRGfkenErmbHw6ICSleuUXuLrIevuWSnHTU2LhDIfjH3OZCGA6idGIYWF7B6k9fifjJIryrBbEZ+RoPSm0+xPcvIbicRpli87mEyo/DT0wAdTXS8rBKhbool9ZtLuSu24rAUlwZRi4KpJfSsAoBRUUwd2p5dw1qn1pSzIObkQW9rcjX+xBM5RG/65RuVyvPUEigpsKwUduFctgj11q2zY/axxeMlsnyIfzIiDRnpUs3AY8dRc1BA+DyrTH4yg7KJ0dg19WIpVKIKQErheHxmFip9I9dYdieUFArL9/JERSu2wHr3qeAZFLhqdkGL9x0zx0bRizfAacuptu4ZffHUA564e7vQeXwMG477ZBjVyEfOwNEOVyxWS1N8J9aQIURCR4Pyn4bq1fWoeZvHkWYLFE2J3ekHfCjNDkFdLZrdUt2iGYFru/sXBml5jjcdFVOLKDcWg+b2rBKBb6ZNenhGNOg0NZgUKCs0BRBqb9WuWSzPzqI5ntLytliJEPs8Aoyr9skE0CxOQrP5ArKTUbor+F62Gcpk8zKhxTxwPUrV3frIaccrla5unZa4sr5clHv57ZgLZgV5nkNuyRDAQFfDplAMqNcm5KlzdW+uBENN77x9+FnVlt1qlOdjae65rvo88lPflI5WJ/4xCcUTso14dlzIan3VYC1wQQm1lQh42rfjjJpxIU14yibWkX0wMIZnZNCIBcWYU/PAi1NKEW8uDv7D7j5st8xzFJDDYKjrDvJIn1VL0KH51GqC+POhz4qFoaRC7Tr1983jXxPPcpBN+xkToJpq1AxVv7WKKyCA9vlQnBkVQXGmZ44gt89DofREazh2donh6J/OqnjrjTGkW0OwfFaAkFiXw6cRF2hJK2Y09sGiw7IVA7Bw8NYfet21CwlpKfC/KKykZi3xOBLRjp4FtNi0ILffAL563cjX+tB5PiK0aIFvQKEIbJpZSO69h2aQG5nJ/y2JWblzpk/EzDIs69wYkVhlgRCkW/uQ+rmnQjeeRDO9IzOPTv60NYqjRbF+OzDKxUKYtTE3PHc542Lk0aDbE8NKoN1YsjonlNX4bWfVCArXZD2akbnX8xSuaxICv/4KlxODNbOzahMzKC4s0fMkmc5A3egz7Bbh4eAng44LXXwLudUk0Q3YaGvSVoz6bP8HhQ6W1GIuBEaTym6gU5BCdZr41obMtOMTGbTFw6a/xvpOl1YQqWnDY7HhcTuRsQOLYl1tBeT5vmMRVHqaYZ7bF5dgytXtyD+H0cBRlWcBa7Wh+tXDs9pOZPBnQ//lkJgn2vW3YMMKVUgaa5kdIYEkbYFl9vG6p461Nw3hhdzWAF0+76Pv6j3UZ3qVOe1M9/85jfP+3uZ0r7R0Dm4/n2u0xsIDpPc+TF1Ws93ql2EG3Q+vTHwTjjX7ZFgmXEG93z7N3Cz/91wbeo1MQ25gpLBfeOrqMzMSXtFAGIfHEJ5ex9KEQ98Dx8DetpNL2FdDQqb2+EdomjYwdpVnQagMOHbbSHZG0H02IpWRdQfJbbXir1yWMI8Mq1OuWJbDTzzSa2bvCemkbi2G6GvPiI3HfOc3JOLuihSaC+A1FovwT2ZtfQVPQjMZOA6MaY8J2t0Rv2A1mCfGDMCK+l/mNTudWt96AQ9clCq15CAL3UapHD1yRUWBdnZHJzFZWBrrwllpauNIIv1PXRbMuR0fEbicGp7XPGYWBZn/xGJyZWCHoti9dIGxL87ru/XY+b9zSxJT8Y8La2vuOabm1caPAu2md9F4CcQGvXpIl2q8Yv9cvf1aE3GCAXmUDGoMzC8LEaRx60gUB4r+yE9tgA0V6tWOKSaHe9qHvbwtJLvKVCnS3A9eJagh5O+pEOvDTJ8gdEVZHpq4J/P6D7J4Kkr8PA4Vm/sR+z2Y3BFwygzjPR01ALPx9IbOlH33Wk4tWEDZqmfovieTBtBDoNtfV5k9nSc6WXM1XvxwL/8Oi7GcE27rlujho/rVMZbkLVzyIIy7b5YRnJr3UW7z3MeR/8HjQu0OtV5lc1L2UW4/b/+HmzvC+giLORw6POvjS5C67Qh6LnmfADSfffdt+HXmej+fKfKYG0wzvZ+MRF2viydjPKdCBDSOTjxgFZzZDFUrLy1R4nmLPelOJw9fhW3Cws/vhO1h9ImwdzlgndsyVw4LRdCkxmt6bj2YaZW7P55gSBfxI9C3IvAYlHrJd7mxHu3outba6qMIfvl4gXYsgTIQFanswXZOoKMFgSOzKg6hkwIXYdaOdbEELj7kFlbMtup1g//rA92W4sBGeyy42qIVSsrq3C1NKLQFoVnNa+MJu/EioT3ZX9EQKTU0WCS3i3AoiDe44ZrwTjsXHW1Ct+0WecTD4uVkPj+DZfCd2hMmi3W91Qu24FsUwDBo/MCS97VslZ3zIuyp7NwupoNgEul4aKerbkBpeMm/E0F16z32VWHiANFSXiLJbEw3mgEZdtGuS4Mi9q4lQQ8M/PwUihOXVGphMUfGEB4qlXsksqUCWQ8HpQu2yRgzG5HVuaUdnYpX4xaKttxkL9yE9wP7AdU4m3DP5+V7qrkc4sVDLIYmw5Tvw+ucgTe8WWVhNNUQIaMhdACtDPzZg1ruVD37SGTX7aaxsytrWi+z4XVnbWouWvIxGY4jhHTuwHXzZfLSRlkT+bpxPPbD36PpSIjuh4kypgK6tCea9bBFYfgSuL21ka4cjZcXe0KeaW4fT3Q9UUrda6Cq+pU5/w1WBc6r6H1osNNxkWaCwFQzzVVgLXB8OLspW4oEtDa6Q1v/gP4KR6enYeHDE65LOfb2p5mRJ+YQmV1DVaCuU4lFPb0ioFpvHPCACpGNYQDYifkjiPtSJanUFAaRKGzFnZtCMF7j6CyqRu+pRxyTQG4U2WxI91fW1bKNwFLvjUklxr1S9mdbeZiWygjuG8SJ3+tG/0TIYVqOp4YvHc8qfBJOuIcZj49cQLZazcj+NSoWKMK62Ja6+BOZUwVEEt+yUwVS2KDmH3lOT6KCiMmbMvkgTETK1tEtjMMx+1CZGoBlYFOasVh8a8Ex4E9vSxhNIueyYbwwn3T9Z/SCi784BCcziatQlm+TOBJBikwlZTgnOeLLI9dbwwEc2/tVa0PVlNwXrcL9oMH9Fi+nfj/5Jx0Ltmk3CurtRn5njpkG9wIzbRrzcjbXbuuF/7FglZ/itQoFBAdycEqVVRhQ8ZO2joWRTN1fVsDgnR0Wi4Fe5ZmyZj1oFQfhu+pITiXbkPRb0u8bx0ZgR2PwUuAxnqFwQb4FkwQLcEU9W00F/gPjCN5TQ+sgXatf51NnQKqlYAXMz/UiZbvkHl0oeWrp+Qkjd9+zHQd5vLI7elB7MFRPd+FS/uUck6BPfO6gvPLWp8yDoPD87I+6+CK2VaKq3iWoeC/sKkNd937YX28Hi76fXNW9+LFHAI3hsZWpzrVqc7LNYxkeDb2i92EjHDw8Q/05zFVgLXBqNg4EBSwUcDiYLPWNZUd/cDMskJE0/01iN5/CpWWBpQ66+E+OCI9CzOi3BMLKuyVpunkMBLvugLxo14FjIqZmJo3rEw8KLaEF/fi7n6tvNa21YlF4XiSFSS2xuFdi8C3kDvzebrTKArPt8dV81Jur0f/7x9Wce/izXvQfnsCzu7NQCqPSk8r8m2DCI6tweVUUOptRbbFj/BJtzRMhf4mlUEzS8l7/yH1BErEXiyald7JUbiZ4J4vCBwxEDW0sqZVlkNX4ZYIYidSCsTM7erQfRA4BSiczmSVy+TJF5G+pg/B+4+jFPXDun8fXH3dWjGWW+PKmFIlEdPy21p03ly2haZ7zEqVZdFeuugIGH1eoxPii5gMVCufmxLcqSJqRpeN246uw+U1RB9NIT/QbNaeQ+PIX7cV/pmUOYlrKTlB+UtUqQkbPdpkGi5GbLgs+GfTcK7YDkwuwj1XRmFHj7KuBMy4Vt3UDWf/ca0VCUwZucCxj40hdf0gQsMJ+Mfnle2l4NSIT5lXVl1Mx1usr0X9/qxWiZhbxqn3DaL/r6dR3tQJ99A08jtP56+USrp936l5AfV11modXBGkuJgYTwfjM2YjcKVhbMa0yRfbaJ5PMvzzGVYOvdjxD9WpzmtlXmiW1Ws5ByudTmvVNz4+fqb2Zn3e8573bPizu3fvfpr26pnj8Xjwzne+E3/5l38pwHU+U41p2GAoXq6wsoaMDkNEbRfygy1Y6wuZCzj1NasFlAba4Rw+YfRHnS148xUf1wWWNTXW+BwqlkvdfLVPLJ4OJS2isrSM8uYuJbMzh2l5s0fRD1wBci3oTpcFpFwOlNIeGUnrfV6Iw4fmUepqUoSD0uWzdPwFYE8soNLdpoDM9q9PYHV3HeavjEsfxt5EiuOznVH4Hz0pLVHk6DJcuaIAjXcqAWt6UenjTInnBZ2OukoiKaCSedMOpaqzxoTsDJ2VTGPPbmuV3qj23nGTJt7bAU+qqDWlf2oN5bkFMX2usWnppEJHFpC+YRM8K1ldVBmJQG1RIeYxTFZiDZXjIyYFv+Sg3FgjbVZ5bFLhpk57k+IvBEj4n8VAl4TxzvQsnNkFnUsKxOkyZKef4gqaagRAaT7gipRBrARbPPfO4hLSPRFUAh4ZBAj2FMMQDpkeyJj5RWIGGZ9z78g87BPj6pRkuCszsphjpigHnxeek9MKR6XWKjiRMitOZlw5xqyQ6gwZTdmxYZ0/AhuBG658PR70feaoKbhO5c1Kef+wNIDKxYqEkRtsetpKcJ25UhxFsXhO8TsZrI3mtpN/KMMB58VcA55rZAQha1md6lTn/KYa03DOYc9gf38/3vWud+FXf/VX5Qr8H//jf+DDH/4w/viP/xjPNV//+tcxMDCAz3/+8+or5Bvf37RpE/7pn/5Jae533303PvrR546+WZ8qwNpgWI5MZklVJQEPPIkCrJKDmgcnkd3dJS1UiY6/RAZ2eyvso2O60DLvivZ8CaNTadhHRo1Ym2tBAiyWPQ92iTlSkKhtofnBpGptkn1RieDpOqS7rBCxsLKzRutEsi4sRTbHZgvYMF/JPjQE5IuGialUUAn5sHJtO6LH19D48CoqfZ2GjbJtreSKe/qwuCMg9kZdibV+gMCGbNPkrNaDZHyU4E7d0moC/oU80l1hlIO2nHNcO7r8Pvgn1gQICcIS/SEjarctueyo3aE2q0RHIkMvjwzBCQcQfnxcx83UbjrzAqOrCO2bMpVAkTAslhhT3+W21JkYGcnA7u+GlS0ppV0OvEpFjstifQjZm3fDamtB6cotAGMZvF4J+93HJ0yyOcX68wtyYXJFurqrTm5AAmL2KnqTJemopn+wVee22FErMEXQRqaKfZAEybmtbVi6oQPZK/oVZcFEdScakI6rvK0XpfFJOTUzO9t0DBTl032pANqAX92QsX1GqE9QYcWjKLTFFPNRaI0pcsPpbTfMG5Pn41EDwj0uMXjleAj+0WcPwtPzd455LgaLLsL14XG+VENdXrIvojaB6lSnOtV5IfO+970Pb33rWxUWGggE8Mgjj2BsbEyRC5/5jHFYbzSf+tSn8Cd/8if4+Z//eezYsUNvfP+P/uiP8NnPfhbvfve78ad/+qcCYuc7VYC1wVBIXva74X70KJZ3xgS2VBvi98EqVmCdFhoT/DC8k448MkIcxigQPFkNdVrt5Le0GjcYV2cAkj1hJcHrApxKoxwySd25Wgsrr+vA4pX1yEddCM4UED+SkACaqyWlczNKYCWr7sJKfwfWbt0OZ2bOsDrFsmpdAgtFHTM/Ryegay0toOHKl/RvzYmCmB0yLEoApx6LNn2uBpkHde9eU7zMVSbBwaFhMV6BB47DNTyFStRkPLkyWXhPzQog1H5nSCtRm2nmkwYIEPQ4N+wWA0QQVDl8UowO++5Kx07CGp9VsbR0aqR0mUBfG0fl5CjKdC4WSyjEvEqnt/m9jmNch6kM7r7rQ/AenRQrRvBBjVW5q0kZVvY9T6Lc14ry/KIMB67BHrgXkihcNojY0YQBcyNTclmyPoj5U61fGUZ+oMmwR9EIEj+0E/lmEwvhmU8JSNV+4xCCR2fF4rkIUOnKCXrEatHZyPMdfGJELFqpr00GAbFPTNxvjOv4l69oRPYHL1PAp2ffsBykrBJa+tEdsBdWTT4aK5W4ajxwCqH/2G9eZ3xu502r+7lqcC5khSdR/PJfmdtkttbcn+PW9o2p9IsxrOZxrt8D/2JRjtfqVKc65zfVqpxzDxknFjTTWchk9nw+j46ODvzhH/6hWKznGhY7n6sSh5/j19bXiDMzxgV+PlMFWBudHBYE7x9F/obtqDmWhnchhXKLKQhmcTJXWGRwxExl8oocYC4T36SvqlRQ6mxAubsZvmMz+jxFzc7ULEKTWa3pct21KPW26P7c2TLiQ3kEFkoIzpfQ9OCqgFS+MYRiS0x1PPnOuIIoubpinEG2LYww14f9nbq/YkMIK7tMUGX9Q/PIKQfLhlMTRTnilR7Ks5qF/8SsYdMYJJrNamXp6u4QCOAF193SJGBYmpk1xcycUlkMEwEOa4OSV3YhN9CE0tQ0vLNrSnh3njpsMqqYOXU6z4rhpGTcnPFJ5G/areO0omHY8bhcfS6/H0WyYF4vykwiX0nAaqxXHyIBSeD4HOzZZWm9BMDKDtau7FD3IEEKV1yMzGC8Bdek6R8zzeesz2ENz21Hf1/AMrm9Ht75pFnnUavU0iCWicwRWTE+Vt/YCpy9R5DvqkH8kOk+pLidK0Um3buiEWNmyBUEdrkidNFV2NIgUMS8rUp9LSp1Naq1cT9xXI5L6dbmV7XerH1wGsHb94vB4SqRbJ7nzqdQ/60TZlWYSsMZGRfrmL1hq9agPL5MV1hRIM90/j1zng9AWncc6jZPrxfXa3herLn27Z/V+nytx6+MNr6mqlOd6pznVFeEz6qRWo9taGxslA6Lw2iLiYkJPNds3rwZn/70p5+m3SoWi/ocv8aZmppCU1MTzneqIvcNhtqlijeIwMFJU1lCZqGjweiDyhVpd0JjSTi8+CaySN0wiMj9p6SHkm6LFSMhRjAU1BFHbc/apS2wu3YiOJIQeyQWjIAjU1TNjFMXQSnkgXelKEDEImAWNpO9ENtFxqQurJUVS5BZR6PE9MU1rF3SjLVOG94k4DB5vSaIwIkFwMfYAQuehYI0U6zacQQILFT8Xqxd1oj4PcMmcZ3xA3rwFa03Mz96ldaD3lHDErmKfljzZYnMA//6ONz1tcDmAaPLinjhvmIHrMlFid3dfd0m4mFkAaWBJpSv2Cr3IutgQCD18H5YDAvdPQj3/iGxRnxc/JdryRKzta7cYVap+ZLRSJEB9HkR+fZhVdm88U2fhm9qTZESqT21iB9NIfbdYZTdHl3IH/zKB8zjSazBztUi3xqFZ60gFi/T6kfi1np0/Ouc2DPn9ZegsndImWK+0WXFUrACx3VqVG5RarLIxLGyiNU/Lj52l0vnoFAfhns1Z1a0p8bEXOZ7G2E1RVCMeBA8vGTYt8Z6gWyLafnM4SqW4GzqlvuywqwznneGv/J5WF5DMJXD7C1taHjCi+Dt+3B77gvnfK2yIog9jGcDpLM/90oZVhpF+3swf30Tao+aBHxq+6pTneqc51RjGs45e/bsweOPPy4dFSMXPvaxj2FxcRH/8A//oBqc55o/+7M/U8hoe3s7du7cqc+RuWJ+1re+9S19zCqdX/mVX8H5TjVodINAt5tafknW/6VdUTTcOW4Kd2mDJ4vCcM36OKx0TsxFriMGz0reMCmpHMBaF7JDkaAYCuqVtAZTrlEF6e1NCEwb4ESxNYuGuW6rBANYvqwOdQ/Pic1RSGZjBB5WiFQqYqQo0ua6cPZH+hGeLkv0nhmsR3B4BcktdfCkymJeuFZL9YQQHs/AnlmWYF3rNQ4jEJwKbjv+6e8lnjfF4Hr0IKxAQEwNHYOl5RXYoSBc7S0K9XQxF4w6MIJIZj6VSkpFd48viAlTyjoZnKUVOMkk7JZmpbHn3rAdvtufVDGyu6FOejCyMNL/kAGkEJwrVK7TOAw7dblQbAybuqHTgZu8Dwaoqnw6Ss2XAV2Z7a0K+OS6zjO7ZlZslYrYK17U3XQZ5vLIXtqjsFHqq3I3bEPw5JIAjy70QS/s2RVUVhIoJ5Nwd3cpo4yMHL9WivhQqPHCyjsInlxEJeQXk8joCpV6ezyoLC6jcEm/xOnJN2xCdO+sCTtlnQ6NEcXimUiJiseNXGcMgZMLphInn0eZ7B1fX1znch1cqSB5VRciR5b0WF7IvBIBF4d1PHwdVqc6r9Z5KYNGd/4/Lzxo9MDfvjaCRs+eJ554AslkEm94wxswPz+Pn/7pn8ZDDz0kwEWBOtd7zzX8+S984Qs4ceKEPqbA/Sd+4icQYdbkBUx1RbjBOM01Wus0fOukVn25vnq597jWM/oliqbyWuVw1UEwREeewjrralDsqFMxsW6rtdGIm/MF5Wox/IoruuRgXGsl2vcJLlj6TBaGDjh7OS09lTtTRL4+gNVtMQQeOCagw2ynuoMZhA/OqQomeGJRLFXksXET41B2xPzEHpsy1SsMteSEArrI8jgJ4Ogac08sik1jWKfd0S4QWJqeQaWl3tjn+THBFZ1wfhvZrS0mTiEaRKG3EYn+oFgllgMnbt0i9yU6mmFt6VeWFMFE8NAM7K4O2Kx/6W9DeX5B4Cp/WT/Q3gSnIa7V3fJ1raZ+aGFZDk6m1VNIzqJtriJZeiwwxFBUujZ9PpTa6+FbzCHbFlLERWprvRyfBMFcw7k7WhXxQI1cYP+4KTXubNNfglzbMoaDGjXlkjFsdFcf3A31WptW/G7TAzi1IBYvdO8xBB45qWPi82c/dtRoyEpl5Hrr4GzuUn5YYVcvwtROUWsW8aHUaP4j47pYRdchv55z/+On9D3Fnkb1NNo9ncrIYtQGwWulPo7IA0NizFiBQ2E9A0Sfj0tQ3xP4qVckuOJUwVV1qvPq0WBxZcY4Azr01ieXy+G///f/jrq6OoTDYfzoj/4o5ubmnvZzXNm95S1vQTAY1Arvgx/8IErcDF2kueyyywSuOLz922+/XaD0ySefPC9wxSGQ+m//7b/hc5/7nN5+6Zd+6YLBFacKsDY6OUtrxu4/0I7ZK4MK1XTPJ+A/OWdYl2IZ5dZaYGQSwUeG4CJDlCug2N+i9Zx7LW+qb7j6oribDFVdDIXGEPxzrHQJIjiTU0+fPbWknyH4sifmpPchw0HHHi/kgZEVREazKFwxCDtZQPjUmoTZyZ0UZedMXhWzorqbEBhbVbXM4lX1KPQ0yAVJIFLc1avyYgqk2R2o9RfjJ5IpVGbnDaM1/BnMv3Mr7B2bUbFtpXozsoDJ8g5rYh48YEDKaUG9dy6J+D8/YbRl7OtbLcM5NmTWYXNLKDfXCHDl+xsFLou7e4GH9mkNR/DluWcvyodPGHdkqYz4kTWde8ZBuDIFVOJRsT/MJHO3tmgdyhUiv9+VZOhpXsdGPVvgrkNKevd/8zGtU8keuuvqxBxaDHp1KsZpx1iAlQSCp5ZkXGAIq0TvmQKcyRnYT50QIOY6TzELB08oK4paNMZgoK1RgAuJpFaHrtoa6bEKUTdcB08h0x2BO1mQQ5EBnu5HjqiXsDDQDOvEhBypVjKjIFue2/LSihySBKhYXhWwZq6WAPfcEhCPSaulrK1SWQGiN77+9wS4mAVmdbXrnN3a+T/OCMjPHgKyO7L/gFfaXPEzJmKiOtWpzqtDg8UVHHOg1ldoZzv4/u3f/g1f+cpXlEM1PT2NH/mRHznzda7ZCK6obyKr9Hd/93f427/9W63xLta88Y1vxOrq6vd9niCLXzuf4TrxuuuuQ2trqxyIHLoI//Vf//WCjqkKsDaYfHcDivUBiZXbvzWHxR0eTL21VUGPpibGkpB69md2otJcpwsi2Q7PQkoAKdsaRrEuhNXXdcJuakClydjRvXMpCc+thw8ZDRaZLTr15lYFDCgW5+fy7VEFjpIBKTZH4ZlLwndgDGubo1jdHlNMwXroKBmlciwA9/SKyXKanEft/oSqWpj6XWqJqxNvaXdUmheGoHLNRl2RRMbtLRJ+Uzhe95cPiVmjHoiMUfLydlXCSG/E1VZTnUT3Ajh0urU2wVUow9l7FPf9x/+EFYnACfhOr0OhuAaGc5IdYuUOrtplLvgtDWZl2NstwT4T8OmWJHhb6/Hq8bgIiLhaY79iLqeqHYIjAR/LhUJPI3wreeCRgyhfMgh7az8Kt1yulRvZP64KU5vrdD8EVmtv2mxWtT6vtGyZZj9cLU3qG3QVSwIxBEbsXKQGy1pYhcMKHrr3WJ2UzinmQanmAT9y29rN8QDSe7m44mT5dcijjC2ee6dQUJgm+xHLiTU9N6XhUWQ6I2LiqHtjhEOxr1nOR7Jj6St74DTEjO6PAJBMKUNep2Zwy+6Pmb9E5U4s4LYjv6f7v23cZL3cNvEnT8u0Orsuh67BV8o89nfvP6cbsjrVqc4rb1KplKIK/uqv/go1NWYzw+GqkSs4Mj4EMoxF+Ju/+RsBKUYlcL797W/jyJEj+Md//EexSbfeeis+8YlPSPf0zEDQC5177733nLdFdu273/3uc/78n//5n+P973+/jo1RD+vdhXys55Ojda6pAqyNTk6xDN9kQnlRXKd1/sMw2v59VhduWutZzPum6z6F5rsXBawqjBJYTCLTExewop2f2VaMWmDKuoTqs4twLSXkrOM6qFDjRynk1kowtdusx8haqSy5AkSfnEGqwy8g5oR9SF/Zi9jhFcQPJeA7MYPQVFahl6lL25XrRJE80lmtoshASUifzJvC5u4W+JcdieOz29uUScULd35TM5auqBMb5Nq1Ce6mRkQPLJhMrFwe4XuOqwSa4m4eHx+3+9CoAIAiBppr5LQjgLn58t9V9hXzqwQaUzms/OyVsDNFlJrjipMoh9wSp9Np6bp8m5gtF7sb6WYkkMnmUP+VQyiFGfTqEztUPjkMtDWpZohho9LDud2KZlCFz2Cv1oN0NwYm1/R1J+RFcaAV4X3TOjau/WIPjCLP6ASXC9bhYcT2zolh4/qvdGoYldYGs6KkBoorSMtWPY+ruRGVfUf1uogfXDEF1uUyfE+ckkOQQO6WXb+F8tZuBOZyiq4g0LTH5uDubJMgf+XnrkbxTZcYN+RVuxA6MA2H61T+IrtteI5OoBTyohT1IUjzBINueT9ut0wJzCSzWppQjPvlXKTb81yOP65Fz4SPbuAafEUMdWbVqU51znv0h+8LfFtnds5+Y6zBRsMVIFmom2666Wmf5wqObruzP0/XXWdnJx5++GF9zH+ZK3W2A+/mm2/W/R4+fPgFV9ys19wQxK1/zDeGjxL8tbW1PeftMOOK4PEjH/kI3Ota5dOrx/WYhuc7VRfhRifn6DgqHR1AXUwuQbIWx3+lCZs+N25cgmR0ZldRro2IkXIdnMDtqb9T9x51NRTDByZOsy2MLqAwnmzEjkFUvLaYCfepEYV2soswMJMRE1WsCcA7PA/vskeuw9BsAa58Ga5S6YxrkL8k5aZW43AMBhEcS8HZf0RZTCxJZgq7hOxkx9wWSpGgdEl0PeLwKXhaW0zulNuG7/gs7r7zj00C/cySgkH5wqiwxNntRnlLl1yOvH8nFMHEmyLo/jKjKQoCWFxpaf25fRNcC6uqDVoXjZd9NsJTJaQ7wwgfX4GrsQGFiBvhe48rBoKAtBINm1/8YFBde9RC0XnIUNfyoeMCfBTeKywVtgAezzk1b2T83Azl5PEGvIpBQG0ECPq00nSv5RQl4Sdrlc1JF8e4Dac2gjtGPoebbvg9eE5M6bGSSXM8Nio1UVjT87CyOQHX4FQG6c31CPF8JVNwFbyoxCNI72hBYCIilyg1UnrNENTGo2KRbtn50e8ZHMoOGu43lT8Ux1eCHrFX1pNHTXG0x4PMVX1aNfNf73ASpaZmuIdn4LQ1wErmlFVGEGgNW3Bt0AvI3seNhkCQKfQv93Cluc66Vac61XlpXYTMiDp7fvu3fxu/8zu/c84f+eIXv4innnpKK8JnzuzsLLxeL+KM3Tlrmpqa9LX173lmvMH6x+vfc6GzXnHDt3OtAhk6SvD0XDMyMiIn4jOH/YOs4LmQqTJYGw0rXjJ55FsiSuOm3orgqpLJiFmp+NxGu8Ny41Re1nqCK7nSGKYZYZWNrfBR6q2UIzXQbfKnuLKKx4CrdpjCZ658yFqRcu33m1JnrifTZXhnk8g3BbF8SR2WL29QjhXrc7yJAtKXdoo549ddl+3QMVHsXu5vgzM+JaE2NV/stfPMJ1GoCyp0k8nm0lERQLXWmsJdhp1WKnDX1qCSyeKO1N9pLUX2zJqcE5hx7T+Jhv0lAUTeD1eAFIqTSSEYSl7SJoDIr1nHxuBO5OGfTiJydElVMXRXRh4axtD/3KrbTm1vNB2BpzugfCNL6kRUDdHokpie9BXd6tkr1vgNc5hIKz1fUyioYkhdfXNLJth1bAaleEDuQ1euBN/MmsAe14ZkDpn+bq2kcWvTL8MzNIu5/9KP4u4+FNprYE3MwVK8RkisY/jIonR1LNemjs1prpcgny5O31IeFsNfF1fUC0mdFMNa+ZpgRyBdnEzsZ72NEuYZqJnOIN8Uhmd8ERZfM53tqMTCWjMyjgPhIPx3HxSz45tc0/NAcFU6NaLXkx0Oy3jAfK9nG7omN5qNwNX5COYv1iy8ufMlu6/qVKc6Tx9mQ3G9t/72m7/5m8/6fe9973vlrjvfDr6XckZGRjA0NIRKpYLHHntMH6+/MbeKLNnP/dxzSyN6enoUVvrMoVh+y5YtF3RsVQbruYZroJkkCls6kG3yIeB3w7N/WF9yLayg0lBjypspFg8bZxlF72ish2tyBuUdfXAXSsi0+BE7XtQKi6DL5jpvchru5aBWdxQ5M//K8kYQG8qj0BRWlx8jGpYuq1NoqG/OJw0Qxe+pTTWwixUERxMoR0xgI0FPoq8W4S8/BpuC6GBQkQDex07gtsT/J8G6j9lXJ0eNLiiflz6HIuiKz6ccJ4aZcgjiqNdx1dWicngIFda3eNx6Y6ehVqK2BfvgEKyAH7e2/He4B/oQnMzAHptBpbnexAyw4iXsg3s+j3y9H/4iwz3ncPJD78Ob/u2TYt6Y97W4K46Wrye12qNGSeBTwLOipHaJ+IsOyhSIp9jL6MAVCaE8NQNl4NPpuLgsDValNgLP6IJZE7JcmavZ0WmUBjsNGM6W4MRCqNRHVWvU+IUDQG8Hkv1RYGsnrKKj7KrA0JKANHsm4bJgWxYqNCQwTLQ2qmgK5puRBSSAdi+tKAg02x6D547HgZZaifH9Sx4xnoXuBhTiHpVJl1rrZJhggGixNgg72IZixAv/xBycXYOwWCbOiI9KBauXNCBORpS6v/o6idupE2PP47mA1oUWJ/P18ZzF0Bdxnvjr979k91Wd6rxW5mKVPTOi4XxiGrgCZOzBJZdccuZz1Cfdf//9+D//5//gjjvukPaJAvOzWay5uTk0Nzfrff5L8HP2rLsM17/nQmc9fd3hlugFDPVXXINSs7UO1v75n/8Zv//7v4+//usLK7qvMlgbjNZZ3Q24/cAn4R1bROirj6nPTqDh4DEUB9vgWl6TaFy2/dWkHGJ0gzmz83CxY60CZLviphKkuR7lyWlpkVzpDCy6C6l7OjEGV7ms7kL31BI8S2lZ/XNtUbFX8ZNZwxhxfcU1WMCD0HhK0QTUWbnn2BWYkxjfv1xC6cZLULx0AKXBdqWA0w13a/f79JioHyteu0Op87kfuBS39n9QcQrMliLwsfNlpZ3zdik6p6uwUioi87pBlIfG4GzrhWv2dK8iM69q4yp+ZqI6V57Uj3FFSOavMNCi+AMCR7I6dO2J8fF6VPLL4+bjZol17bG8yRejYJwhnMwbIxBdTktsXuprhefAiFyLrnjMxGQwN2vPVuVhOTVhxTEwMsFay6LY3WDKo6kZYyjsdQOwGKswuah1r7Wa1DmlDs3VRr2aC+HRlMAznXq+2bTRf03OYuEXr4Q10IPk5R2oPHpAjJtqiuJhOMNjOk7W+eR2devcBI7OwN3GbspRrQoL3XU6Fs9qDqH7jqsH0T02p59jnyWrhezRWfgnVrVOZuq91pl1IaCnHfHvjgmI6b4yGTGLvN11cPVM1+CFDiuSXqpZ14lVpzrVeWW7CG+88UZpkNYLkPlGXRIF7+vvM0X9rrvuOvMzx48fVyzD1VdfrY/5L2+DQG197rzzTgG8rVu3XrSXAJmsX/u1X5MejG/vec979LnzmV/4hV/AH/zBH6jMOZPJKP+Kwnf2E/74jz89Gud8pwqwNppCHt6xJa172H1HrZR7clGOO15Ak10BMVfSIjE5nYzF+BwW3jYoR1tlbQ3u8XmBMlbrjP5wrTroeAHnWoi9eeXtfRJQk/kigKJgXOAlVxKDleuIyilIhoQAAeMzqOw/Bhwb0VqNwabKcmJkRDyow/YuZ7UKY24VmTCupm4b/SMU9vQqLsI3NKd4gNDheemB2InoDHRK+0PN1Mg76k16em+HrPTMr/Ikirqg87gUPjq/Jv0U86ncjfV481UfF1sm8JFIodBRIyaIzFKiz2t0aKqk8QhYMNWejF+2PSqg5U4UJPR32k8nydM1GA7IXJBtDsC9mDLZUMzzsq3T4nAH9nJS6z+aBZKXtkmbxFwwz8SSxPMErGQNPcmy4h7YEYlcAeWGONxj87hj7+/quSXTxOJhgjeaAqyZBT0njGBovmdez1lgPq+VJUEj+yK5quRauDQyjuDjw7AKRqzOWiIWPvMcX/cjn1F8g1yeQS9ccYrjHYFPTjlgwx6Z1nmito6ifGakkTGlUJ6rSEZ7EHDdkfsCKgwkfcYQbD3XWnCjWXcbvpTs1cKvXPOS3Vd1qlOdCx/mQDEJ/ey3UCikzCu+z/BTliKTAbrnnnvEeP3sz/6sQNVVV5nasje/+c0CUj/1Uz+F/fv3i/UikCFjRI3TxRjeJu+DzBNjJPj26KOPYtu2bQJz5zMEjSdPnpRjktqwyclJPbYLnSrA2mCcedablAUIGLpJNofOOLJPBAq1jxk07mI33fFxZVlRWM7Ud+qIcpf3a4Xkvn+fWJSu/7VXKeCufcdRGRpXxAMZH4IIJYFTuH462JKdgmRJ2E/ICzddeOWQFytv3QprsA/Y3KNg0GJjBPmGoBxv+RoffHNpsSVcGZL9IROW32a0Lnff+SG4dg6iND4pdxzrbqiVKtQZbVNgdFXOye5/TUi0Xo4FEZopSrtE0T2TwCtkV6gbY9L64jJyLSF1KSYGT3cUskB5dkFRFRT+J3c2oun2Ca1a6Z4rM3AznzfnslKBn8fLKAYLsI+MSrgubZhyqfJyU4YfHxf4IstG8MTQThY3V6jpcirqH/QfGBOI4scEtYqI0CvcNmveIRPIWo4GkNnWhHyDH+W2Otyy4yOGDbNtxA4sym1YaI2ol2/6TQ1yUSpBnWvJUlksIeM4ah6fR36gWStKhqeSlfMen9IqcOnSGIKHZ8R0Rh4egXeVkR4ulU5Lb1UoiBFlwKZnMWvKrblyTBb0/BdiHniPTug1JhYwV5RejQ5NOgcpUj973njjp2F5vU+LYFjPxDqfOZfb8MWelm+ZnrDqVKc6r66g0XMNs6J+8Ad/UAGj119/vdZ+X/va1858neXLrJvhvwReP/mTP6mk9Y9//OMX7Rg+9KEPKY+LoGo9KJTvMxD1N37jN55XjtZ6GOrzzdF65lSrcjaoJLj2pt9F5Oii1jMq4a1UxEywkqbQFJGAWysvicUN+0Rb/tSP9yM2Wkb4sTGz8mGlDldS4YDRPxVLYlrshjpkdnfAkyxKF5RrDiIwvoZsWwSwXQg+NY4yuw8DHml3Mg026p9cNWJ4WvtpTOyOwc4ZhqwY9QloMNuKUQ2VWEhrJq7Wypu7BDyYzUSRO4FLqT6EZKcf/tUy3OmS2Ct3pmRE7WSqoj54Zk5nMKUzqr4pvnEPfNNrAoQEamT1VG/DOpmJKVitLXCmZxQnkN3UhMChKTgNNYYNyubllnSTIRqfEyhJXd4pYJRt8iB+50ljDmC1zFpWuiuu/8ohn8AkBeDOwhKsthbj0gz4zfeyo5BdfpGgqbuZW1XGF1ea5et2wHt82oDk2ihcMwsod7VIJ0bAJwcg3YlTc2KvCDrhsXH7vo9rjWVHIlqDkqmzTrNJiS0xxB+dEWiSa5GOPors25qR3FqHyN5ZAbXZH+hA072LqkBiBAcrjbJ9dbCKFZPnRWCWzqDc1gB7fBarb+xD/LvjJqeLon/b0nrZN29AMw6dFIt1vnqq2xc/j1fi3LrlN19w7U91qvOftSrnkh//1Auuynnqix95zVXl+P1+rSFZjXP2sPaGbBa1VRsNi6LJWq0Dq/XhWpMxD4yieL5TZbA2mMDhaemCkntaUUmZi1xgKoVSTUihnVwjsb6lPDcvPRB1NJzWby9Ka8MLr7O4jHxzCKWxCZQjgdNBlA7s/m7kN7fBt5Azzra4D97lPIp1QWQb3QieWFKK+VpfSFlPwfEk6veuCVwxwHT50jp1FgoY+anbcWO134vJdw+Yzr6aiMl7Yin01m7k6+mAq5U4Winncu4B3pQDd7KItS6fgB6BHIFaoSEoFo6gSQL+QkFaLd/DxwTail0NJtaAlTUEmGTxmhtRbK9B4kcv0edZKq3VoO2S2Jxfo1HAWkigtLQkh1zwO4cUxxCczov948qSLFZmU715HG7LrGX5tWjIOBuZlcXzmC8gX+c39UPsJcyXBEa5/pOLMRpW0CoDPEtTM3rcTGi3snkxXFoZui0U6kNGf0QRP+MlvG4xQIy8YG5UuSmuIu7c1lakuyOI3z8qBo+uQ6W9tzQiddM2o7NKEfiWtcpsvsOUNxMwBaYymHprCwLHZhU+Wu5tRXJHg86BPb2o4yHAZGQFb4fsIVk8FlNT9C/H5gbRDLcO/M8XpKe6peYX8FLN4tUNL9l9Vac6r7V5JTJYr4RpaGg4pwuQn3smaHoxcrTONVUX4Ubj9aDQFkPkGMuNg7oILlxBbVEF9QtRMSzuTf2qNyGDoQJift/MPNDZgsrkLFbesQc1h9dgDfQBFDY3N8Kdi0k/xY5Bro0YX8BVnuPzCBDULvlRbIqoxiV2ZA1rm+OIDCWVcs708cBUEr5leecEMLgOZBhpzck8yl4L6a0NCJ5aNdqtkBu+bE4ZW6WwF14WEgd9yLWHEdw7AQy0SHxd91gaa9tqERlOauVIXEB3YpmCeGZwxYKwklks/9AO1D6xgEIzV3UxuIemUNjWCTsekiPPc3IavnCXOhzpsCQY4+Pw3XsQnvYWxBbTAhzMarq1+VeQvWYQwafGkN/aodv0rGSR64iru5CsILVRrB5iVhVXhIwRKF+6SaGipZq4HjvXcrmWMAJ7R+HNZFHoazKF1IGAxO/2Y0ew8pNXovabRwQwmWrvXlgxq8bROXgnZg3DRebqwCeNAHvzgPoQCTyyN2xCeHoZ/pFllOojel1QE8e1KeMunEu3qAOSk+htgFVsVMREakcTgrfvh5PPwd3YgAOf+xhu/dZvaBWauX4TIk9OCZwlN9eKhQwOLZvk+nwBtuMov4uMInVp1N+5mp79P4nbTv7h0z5+PnoquhLF0L5EU/fkykt2X9WpTnVe2/Pxj38cv/7rv45f/MVfxH/9r/8Vw8PDuOYao/F88MEHJVynPuzFztE611QB1gZTSazBe4I0j1fsz9KuCBrvo/vLkrBctS7UO6XSuui7HtgHd2c7yps6tYKzmhoQWCgZXRFDRlvq5CArddRLgE6HG4Xp0m4xjJJrII8b2c4ogkMrsH0elGI+5GothEcsdQraGWZoOcg3BLCw0432u5JmZVhyjEi+3g/vSgGF5ogYMa6ecru64F3MwLP3FBAJa10X3D9lugNPTCOzqwO+5RyiJxK67cDJBRTbamAfG4NrLYXSDTvhm0sgM1CPmqeWdMx08xUawyYpnoL6chkrVzQhMpJFYDoFpzGOXFNQDFvg1CJAZoaVN5aF0knj6ijNLyB4PITClnb4jkyok69cKsJ146XqLqxYLvifGhGTpvogad9dsI5PqjCawzT09K5WBEfXTCq4y9JKlOex3NNiWMVIBHXfGUFpS7cchBS383xLlG5ZWHvTFvgSJdxzx2/IbSnwxzyz5jqJzLna0+sh4FVFUrGzXsn4fB3k37ALvu8eRuqm7QgfXULTt0bNarO5HuEnJ1G4cgu8Q3PSXd34ht+HxRLvhSWETq4YMA4gPLymfLJ8e1yu0Uo8pOeSzB7jImy3yQjzpnMvyiqQTlYGub5UQ0NGdapTnZc3aPS1Mr/7u7+rgubf+q3fkiD/s5/97JlML3YKMjyVbsJnG+ZlMZaht7dXAnkyYevDAFWyX9SOXchUNVgb7LrfcOlvwje5inJns0TRBAsUJ5PtyGxqgH86LQDknU5oTaaAUQZy8skgm0VXXX/H99LO15Ko1Ma1MmN4qWO7YOcdxSIokDRbQK67xrjOKABfLkq0nW7zITKWQ7bRh8jjE0he0YHI0WWtofLdtSiGbOmYfMPzSiqne8+eXRHYYCQEQQXXibXfOITSrj4DCt2WqnYYNUCXXamlFqWIx6wNl/MoB93wzqcUlik2iA4+PiYWWq/Xm6iKx2UynWZXUYkGTc4X16llxwAUivepKeK6b2VV4NM9vSy92tTrw2i/05w7a3pRIIpMDdko30JGYFRaspZ6rTTLQQ++c/9HxDBR+8XianUF8l9qtgoFrSYrB08qWkLfMzMHq71VbBsNBdZyUis/e4Wp+WUTe+HzaF1IALieLcW+v8qxIVj1dUhf0mGyvySqhxyWztgkLGZ1cQ3cyBViBuktDTIl2OkS7MPDyhDjVAI+RWvc8cTviIFjOXM56keqM4To0WWtcv0jJvqCGjAXGVGWQNeEkekIIjCTQ7o9gIe+/AFcrCFrxXUruyf5eGVgKBReeVU61anOq2BeSg3Wpe/4FNyeC9dglYo5PPnl144GyzqHdiqZTOpfAq6Xc15WDRaR5To1t/7GDqONhm3d/B4K2tht9B//8R9nvkYRGt0C/DxtpESvdCqw2ftCJtsclJPLXlwTK0TAQSAiV9pSDuneiPQ0ym6ifmd5TRepSmOdHGqsgqGrjsGUqsrh2qczJnaLKd0sKfas5XWBp+aJmUgEOMGZHKJHlrHa54d3ckXgigAmNJKUYF7givEM8ZBAmjdRguNxCWjYyYI6/DJbm3VhJwC6/fCn4F8po3jJgL7OUFPfyOJpR2RetTEVj6V1G9PhGQiqiIfGsII56ZYr9bZi6YYOJG7ZAmd5VYweHyPTzbnmpHidURUaRjHEIuoaLPSapHZVAO3oQbotYL5eqaDzG/MCUaneqLoTSzEjlqd2Sz2AXFs11MpNyTBXOuwICPJvvUJgSADM59P3cnVaWUnAWlqDtakHuHqX0WX5uLY1nYhkn6jPskdmUKEpocF0EkrfRGats0HaOOqZKIbH9gEs3dgN32Je90HXp4vgbX5RERZaGzNSI1tUnlbo+JKKvPMNPjibulFZTaDcFEO2N65YCoG2bf0S4i/tiCA0mdX55yQuaUKhoxaru2uR2dNl1oRTCwjM5fXc+Jeev8Dy7HlmyTPBFYfgiqDvtoW/qIKr6lSnOq/KcZ1uAlkfAqsLAVeMaPj85z+PT37yk1o9nv12QcdVITf2MgKsr371q/jOd75z5nMsWayvrz/n97OdmxZQJqvSEvpP//RP2q+yI4l5HETkP/ZjP6Zd7K5du9SIzYh/ps4+8cQTz/svhZu6fhVutx9OJKi8KToHVwcDaHhkyaw58kWVK4v9SGdQ6mk2zMjMPFyxCEqttaqo4VpOrA5jBqZm4G5tRrGnUToiOdhcLqWIuzMOgicWkOurR6bJg5r9K6p84aqMnXrUaBEoCBQQtDgOlvbEEB1jnU0Z6Xa/xOJksBju6f7OE7DjcaWvV8jWdLWY4yaY5bqJyeQEhmspfa7Q3wTvyVmTFUXgwLT5sN84AKm/urYVNQdXdb+uGQrNPdKeUSPENSVjFVQfNLMgMMXgVGtmSc67dE9UCeZimjgEovGIOQ72GJLt6+0wsRVhv2p7yAyy4For0GLZPObTfxKwZkagtezAqQmZx0JG8PS6teL1GIE5c7M4maz5froL6fYko5hYE1NEcJzprUHw5JI5/ql5FHf1Sg9G9lEic7oE4zGMvqsVXd9YNKzf2ITWiTdd/yl4hueQ39IKz0pOYLDU0aCgUeVeBfwoxYMS669e24H4k/MotMcVy8ASbYrimZ2ldPrlNQNA6VCcX0T2ui3K2GIJ9bNV3DCn7baRz+HVMOuMWXWq81qal5TBevsnXziD9ZWPvqYYrFgs9n0g65mzvLy84ddZ9PzLv/zLwh+MmTj79vg+ccarToNFQHW+UflMVL3lllvwwQ9+UB9/4hOfUIAY4/r/4i/+Qif5mYFi/NoVV1yhVFm2ez+fmX9jK1rvIEAKotgQFpPQ+PXj0lzpwlkTk4DbmkjKcm8fHtGF2Q51SHfFot7ipnZdfHM7OuF96AiyP3S5BNGMZSCQEOCxXVoH2ilTYsyv+ZdcYjo806sCC+VYQBlMhdaYjo3go1QfRu2BlBLJF3cF0fjIKla3xVD7+AKsgh+ubYMo1oex1u1D/V3jqBwflZ6MXYqu3i7pwsrUBHlsueNCI2sSiFO8TVDAdSWZtnI8AIR8qPnmIbgYAVEqq+uQAaDUJ+UbQ6qGoeC/MjIJV18nPPuGdC68blvgLLwvZdLZmU/lOw1yxubU7Vdqi5vVa7mMbGdMmi076Ta9fT01xo3o9aJEUDq1IuaO54HsoVLZFxI6J4ylUKZVuYL5q+NofCwBa3IVlaY6OPUR5WWtp8MTSBX4nDACgS5G5lZRaxfwwdXTiorbhcrxEbj6O1WmzaJruga7/uyIHHrlvNFDsSDb9cRBlN0eFC/rgDtJt59xPjrdbQZA5gpwjyRQmptH5KuzSN96KUKnVlCZmkV5a8+Z/DOrVIF7ftk8B/MprWK5cmQ8BcEV9VZk5G6beobg8iVMYH+hY/P1U53qVOdlr8p5remwYrEX9n8LWatPfepT55WZ9aoBWKTkuMrjyo8BZGSnng0IPfzww9/nBrj55pvxjW9841lvnyid6POZTd9nTz6f19vZfylwGr8zAdheCbgZ9OmZS6K4pUPZUKWmmC6inumi0TRNzcDqIIORNexPMi13G0t9KZb3D3vlaoscpiXfRrI3jOixVRX/ulNFsVkEBmQxrKwF9yprXmytvmyGWyIAJ+gTU8UwUDoX7dl5VeKkW32ITJT0/dGhjIAZs6FWrmqBYwPh6aKqY8pbWvTzdAU6vA2fB+6JRX0tMJdTXEG+lu7ERumxvEt5gT/3/JqOI3fNZoGf3KZmHXN437RYHP/iMiqXbANW00jdsgOetANPxK9zQU3U6tYY4kcSAIuTB7sFFuTUowOTf4EUHYnafftG4RueQOGKzRK2FwmiCo7yqlh87b1rHyrhEAq99VqtUmdW6GmEHfVLn8XoDIIS3mft0Zw+V9jVpTUqHXql7ia4R2ZNXMXUAnxzSwr5JEvnnBiSZs3FLsSWJqxeHkfjdKfWt4pNCPsl7NdaNRhAqb9ZtTq3P/YxPYZbdn4U3/3GB/Gm6z4loJbvqoFvbMXounh/WR/c1KyVywjceUB1QK6mBtjsHCyWUB5o0bGxfkmvJ8ZNDLbBzhZRCQZws//dsOpqBa6Y2n523yBDUS9Ug/VSz21zf/6S32d1qlOd1/b8+I//+IZRDOcz3Hi9/e1vf+1osK688kr87d/+rdqq2flDNf/rXve6MwK1Zw6FbE1Nxk22PvyYnz/XMFiMaPRd73rXhlQoQR3R7/pbR0eHPu/MLYrtcY4Nn2aaLGmYJMrOl4xrLZVGqacFlR39WLi2Aa6lhOmQ83pM8GU8AmdrD5avaVGcAXVWvKD6VksoxfzwTyZMBY7E0B7pgZjnxBVctiOMYsyHYmscyZ6ABPUEPtJyXdJjNFXZEmLHk9Jg8RiZd7W2KYr01kZ4k2Vprzh0OPr3jcKdLmr1N39FTLdPEOheNaDQu5hG9NCimDqGnJLF4eot218v3RKrewQelZa+oAs/rtmNSm+bbpOC+ujBRQQOTprsKn4u6kftwzNas1nsGByZ1EqLbks+1lRfBFauCN/xWRR2dMmNKSYv7IN3KQP3o0e1Zgwcn4fd2gRXfa0AGe+P55c9ftbRUXgnVrSKlcg/kTHl13QhHp+F7zv7UPF75B6kY86hsL2hxgR9Usu1kkL5dbuk3WKWF8Fk48PLAruMtmD6O1eXrLLhGthZTeivwNtX/tpEHHCm55WddecDH8FtJ/4Anu8exMrljahEQsi0B1Gq5WvCa/K3OttQmp4xP+fxwGk0BgOmtjOHrBS0tZbNNPu18iwfPSFBvZNY0zqSyfEEdJxbm34ZFzIvB7har+SpTnWq8+rpInylj+s5VoPnOwRX3/72t3Ex52VlsG699dYz7zNplYCLzdhf/vKXX1D/z7rg/R3veIfslwRvGw0tnWczY2SwCLKK12xFtiGC0HQOruMTWg2WrtwCK+DRaoqaHwaHFmNeud4a7hhDubVe6zHUxJHpjSMwnkIp4hXQYYmvd2oVue3t8B+ZFhuytqsR0b2zJgLgtODZifjhmlsw4moFUlUQO1EUYOGFnvlM/uWgWWmFfdIjBadcSA5E4XggUbwAIMEQy5f9XpRuuAQLfT7UfWGvWJrmxbjcdFx7UUhvUZeVzEqU7Tswps47AhmGmjLCgatOsniVEyPwJVNAiMGeBZYKSFiuVPfGBq3PnGwOdl8XXIvLcPvdxhlXDpioim29Rv+0vKqfj9w9j0pPq0mmJ+JnLdBqGvZ8AfmBJviWjamArB/YbZjL6xyaj0twaoJwsTCbafvMwIqTNSzCf2jSiNAZANvWDCRSWrVx7UimiCGu0mWxKqi7QaCO69RcvdeATAJLlnRPmBUpBfK+REbMZWVrn84Fx9XTrsJsp7ddz9+bPT+OCgFofw/iB1cUsBp51JQ10yRQ98g8KrPzOleloVG4ezphTc/Du/8IrF1b4T05Y9L7Y0HEvjuM3M5OWP3NqOwfNvlfxyYEim878EncHPgp3JH9B7wYQOjFqM9JvGX7Rb/N6lTnP9u4HPP2Qn7+tTSViyQj7+/vV9TDI488IqMcC6zPno2iHl6xK8Kzh2u8wcFBnDp16pxfp1Zrbm7uaZ/jx8/UcK2Dq7GxMdx9993PKeRj2eS5Cifte/fh/so3lI2knKquZlPzkikg01+rXkGvz6MqEwmw+TPjs0BjPTJ9tQhMZwC3hdV+H8oewJusAJU6MUAcJxpA5MiyiTtwucRuuQplMR4hp0OrQAIk5WPJwu8BKIxvrtMFmN9L1oqhpJ7ZNYQJUDIFBYQyjZ3xDwRXLEL2rObhX/GgdNVWLG/xo/nrp/SzXuqS2BNYE4bL6zZ9hr2tsE5OwHXwFHwsoy45KDQFEFzKoHjNdlj37YObq7h4VCJ+h/ET1+0GJldQ6WhCoT4A/6MndczMd/KfmDWPIeAXE0ZHYqm7Bv4D40BHszRUTFsnC5WnG9EVEWhlHhfPOzPH1gbCiN8zrCgHFjxXvD6dD2aJVfZsRsHvhneiCN+/P4HC63YZoFIbgs1k9skZYHOvDAV2MW4yzKjbsixkdrSaCAaeW3gQfXIGTiwEa34ZAQJIrwf28XFU2pvhonPTY8H95FE4hYLAlGv7JpSbAiqVZtl0eDIoN97Nl/62wlbtlEdsGXskmYlGkFkZ7IJrKYmV/6cf9V85KDDD1WIh5IZ/ZQ32Wk6ieL4uvA8chtXUiEpLI75z34f1uiFrtus9f4T9LwK4oqPwxSp9jn7j+1OWq1Od6lTnhYxD89JFGLoHw+Ew7rvvPr09kyW7EID1iqrKYYP10NAQWlpazvl1arTuuuuup32OonZ+/pngitouuhPZ+H2hozwrui7a6+X6YzdfpjMi5xpjG/xHp2GNzRqXGQFEOKiASWdsAoHJNSzujiLbEkT9vhSaH15DzbG0wJhvlLqbotZP1DjRMVZoCKvkl6AncnQJTtADx2OrFJplv8X6ECw6CCMhLF1Wh7XeABYuiyDfFBTT5DCKgKya1y32yruQUkI4j8s3m4QTOF2Nc2pWCe+Lt5o8rNKsKUaeuzoips23mIOL0RNtTXBxXXnwFFwnx+EqVlCqC0orJv1P2dEqjoDH2TUIO826mqDWl1wlUuBeopMwXTSZWEsrqCRTqNTVwD7N1LHAmB2AjEcoBVlyHYDv8DjsTEErwcn/0qqSZwHElIPiQCvshVVlWtHFRxDknTDOED4uJFNijnif7B2059eUPebs2WQ6Iy1o3UngQ1DJ+2U9EJ2C1DxRYyVwxZ7FnmbdHgFRYU+vgKKes4gHVkebMrbsjnYdAx7er3NJkTrB1a2bPiThP1fKrEVSav/0MoLH5gXoCNRYel1zLIPy9j684eY/0EqT60xlhiWSYjsL/c0oXbFFTs1K0Pu09d7+//2+Mx9z5fpCZ70qh+DqltpfVC7WxR6r4cJ/F6tTneqcnuqK8EUZSpSe7Y3p8BcyLyvAYrw9keLo6KgiGH74h39YianUTHGYYbWeyMph5AL1WkxqPXbsmGIeGL/wq7/6q2fAFWMa+LkvfOELimegPotvBQZlPs+x+ntwa++vy73HChiuBOm0qywuwz44JCt+meutWNToqFIZY7Mnqg75EB0vAOzsXUqJUVrPw2IuEzvoKAJXFMLpqhw6xpiH5UxMm5iFxaRykwotUXiWMvqXayt3voLQVAHxUwUker0oxH0CZIwa4DqRVTcUuvuXCsqWUir4QwfgXywoFLPl68Oov2NE2ibL70OpsxE1J4pY3OlH5cnDwNFTilUgaOAFt5xMwj+XxlpPQLd/a8d7gRBT0wkOAffYnPRkCuZcyyLTHdMajwJ/V9lBub0e5Z39qHS0KOeJIHB5kxe5K/pR3Nmjkmn/TArWyhoqjbWKrGCGV9NjGTFDTDT3z2a1PsxsaYZ7ZMaUPa9nXPFjrgwJTpg9FTCZZAQm7PzTmpV071PHtPLjOWCKvatU1vOq2ArmbiXTAkyp7Y1wT6+gUl9r9HIn54wxoKnGVPisJU0qvVM2QE4sVlxp8hSgEzAHDkyY4ykUtbpdH2+iqJqdxEBYmjDrwEnp6LxHJxXVoDDTcEj6N7o5CzG3WLxnSz8n43QxXITUk33vhe9SLtZFnwv4HaxOdarz9Kl2Eb565mVdEU5OTgpMLS0tKZ7+uuuu0/5zPaqe0QrMuFgf9gsx++qjH/0oPvzhD6s1mw5CZmBxpqam8M1vfvNMv9DZc8899+D1r3/98zo+18IynO4Ord2ctgblFpE5cfratf5SJhWHF9uIXxfCYksMlf4mufX8oyvqMqTWx+XEDZMzMi7Bcmlnv9ZrWnktrEoHlG7xGt3O9j74D45LPH/2sKdQyey8II8t6qLe/B+zEnuT5Up1NcE/50JgdAWpzXUITqTMsYd8qFyx3bAv390Pq63F5HFt6oNFrdXEAlxOHZofyKB87U64ljIIH19G4dIBuf2Y9URdTmxfBtg+qPR33iZvm8nmBASlgA175yZUGEmQNllbzsISXA1xFGp8uOfbv4Fbtn0EoZkC0p0htH59FM7CIspXbNUalSXX3uU1uFI5RB8ZN8XJTH/nY2OQKHOxRuYRSAd1zhhNQaE9TQWMy6ATMNfM28jDfWTMxEGEQ7DnEgZ8cZV4Wuelep/RGZQG2pHf3oEygzznM2KoEI0g/OAQMlf1qU6IzkM7kVFnoTRdxRIKWzsNc1Yua03Lf/kqpT7NuX43SvkyKg1heEYXBPJiT82KvWOOlpVmefMcaoc8pmNxyqf1picaURREqqcGkYcX4J027CnrdvRafBadAVmh20b/6Mzq0AoFL9ipR00XexP5fL8oQ5F/dapTnRc2/L/gheiOXr7oy1fcUHvNuCcGk2/UV8j53Oc+9+oCWF/84hc3/Pq99957TqX/s1kpu7u7L5rgTROPwtl7BOn/ciUiDw6JXSEj4fjcuuhqHbe0JjbHWkkrzdzLi6rPJ30VNVXuFfboNcjRpou8U5Fg3EXBNlPiZ5ZQZifg0BzsdC3SvTExTXImeiyJqQs1Xth5L1xuS6BCvyDhoGIKuL60kzk536InU4omSHWHlQaf7qJAvSgWJDScQJB6qZ2bxabN/2gXWu9agossi9eDfK0fVrkipyFBBkXZLCx2AgG88cZPw9dYD3s1Ia1SuiuE0FhaTBVDQH3M8ZpOa7XGImkfHX1cp12+GZ6lNHxPTeFN134S9sw8vOkMvNQhMZtqalo/72aJdTRg4hNGZ7By8yAiIxk9LgItAkqlxOcLYsTsQtm4MW1L4ahcW1rjswgUG8TWuYJBw7BR68bATq5Bu1phbxtAoSlszv32LngPjcEdCiDL+IqJOblCnfFJ3JH7Al5/yx/IAcjboKvTaaiBE6LjMqHQV6a0k5Fb2xxFYL4ovRt/7sbX/54YNj2+hpgAKNePViSI8oFjcDc3mtUhw19dUHq7/9QCbhv+DN50zScRPrlinl/qxoIBVStVWMvT+L1+rKfNepDqBTgDz6S7s6YpGIDVVA8XA3FfpKEjtzrVqU51Ximzd+9ebb7W37/YTsVXlMj9lTZkn4K2D6lWG+GmOrErt43/MS77+c+h9oijVVS5MYTAoSlUCBzSaa2FVi9pQPyJOWBiSgwNBegoh7TCCywswWZZcW+r6bCbW1EcADVeZGqYR2Wt5cRYaB24mIY77dFqLtcaRjCVl8Cd9TSLb+zQqo/36cQD6hckAAmPJGElc4gMTRtQiJAYLt4nV5mesQW0DZWR3d6GwGpKF3vmRBGMEFBQBE5RNnVB6KtX/QtBGJ1yrA2KTi1Io0SmSmGf7D3M5U3PInVdDREBQeZSseqGF3Cu+jJXDyB4YAoTPz2ItnvX4LpiB7KNfoRPTMLyNJnEdreNmoem4dSG4VpYQWFzuwGtXGX2tOi88P54zlK7WxE+smhAhtcrQ4BrLWPcgsn06Z7FguqLuF71HxiDl/cxt6DHwvNWbqmFlyB4JYFKfxtczTVau/n7u+AqFFEOReAeWoNVKsNaTZpoDmrT/H5kW0KI719C6dhJuNpalVKOa7ajwrJnj42JN0XR9TenVKRc6KozhaF8K5hfaOv+vXA31Os4aKRw83hY/r3qEespBtMFuDN10oadc0JB6aeetuI7z5FerPlXlKO1HmL6QoujNxoXWcXqVKc6L+z3qBo0etGGm61zvX+x5hUlcn+lDQELtT4t/zqKfHNExcHUZDV86yTsyUVYBQe+7x5WzQtXQes5R/GHJ3HbyT9E9mazpkxvrpe70HfnPrgCATFE7oU1uIancNvEn4ixYHipohUIpDpjKgMOjDHF3Q07U1JVT/D4omIZbBYjRwKIH8/ogs+oBWZZMTdK9TJ+c4FWzhMZopTJ7qII3hxQVhd1/3RSx6uMpnQGuU4Txkq9D1keZlN5FrPK5HKGx/S17GCDHqvNqhrqr05NqQA6c0mXIglcK0m4Z0xXIYdFxuXlFaxd1obAZFIi/Y6/PyFwyuMJPzYGp7tFGi6HTkwfz6FtDACRsJg9AZJ8Ee75hMAVV6NcT4buO24qiMiOZLN6HGSWCK5KU9PGZRgLw+psl5Ox1N8moX5pa5fOIQNg7fF5kwYfDeux8HyReROLxIwsBp22Nhr9UCaL7KYm6cy4OqQujQn71iXbsHpdJ+z2VoHK8mCnDABtn35IDkuz1i2q55C3W1pe0XE51+9BfmeX0WoRuKWzZ7RW5R196jQUi2fbqPjMylD6t9Nz7ds/q8wtgiu+Li/oNU4hP8HW4ue/PyH+Is+FBKJWpzrVecZURe6vmqkCrA3Gf2wGhcFWsVN03rkGulAJ+7F2fb9Kl+n64+rGFQnDRfaGdlE65SJBMRKeZEnslX8hp1yp+V+6XBd19tDx4s2+QmYoEURRu6Wi4lwBPoIa6nhsl0TXc1dFkNxUg1JjVCGiFFuTwXFTX+RySZtFhkpuuGhA4Id9gnzftbSitSHrZYqNYQEXApp8d61+BpkM3AtJZHe0w50uqXOPeigFdSbWYLFzb3lFF3lmcDFEtELGqFhWIObKmwbg9HcgeJAmAEt1NRSo01lJ4OCbTqBw0yWIPjmt9aJWfR6PzgurYvQiXEmZVRz7EtmL6LYNKDwd7SDWJ5NBsY0ifkfuQFcyo/Umew7zffUCNSy5dhhSm8vD3d8rIORi/hUpYLfbnBfLZbRRkRBcrU0yGxC8VXrakN/epQwuCvRpEiDD6DkxBYvFz9RxhUICvQRbZOjoRiQg4mP1rpW1Il590wDc00so1UfE0NEkwHMphycfD4+to9W4Fo9NwvPAYTjjUyifHDbnm72WhYLCV0NPTUpjxttVxyVBCgH56XnwKx+Q2+/WLb+JSsgn9+L5Dlk6BqNaXKdWpzrVqU51LvpUAdZGw1iDY5MoXNoH/xNDyPTEUBmeQHT/PKx0DsETi8gNNGglxXoWsRCsQCmUsfy6DoEJMgy88NMx51utaF3FQmU6EFWRUh+Ri87KllDoqle5s/PUEViJtIIvndZ61B4vIDRpgFA+amFpd0xMC/U7yR0NKpxObakzbsFMQUCr4nabfxtq4F5OI9salDONwIWAy3dwXKtAAaqWGPyPnDDBpBRyc1PHeAWP2wC2HZtgRSMm66tUQua6QZTqQwiOJY1WanbZJMKfmjLl0JWKuhcFOAnK6KjLZGENT+rrLEXmfaKnXQJx9i9StO8amTaasBQBrSVHoBgqx0G5p8X0MvJzhYK6DMttDXo+7GwZdz70Ufj3j8FuaxHzxeR23b/Hoxoe3g7Ph2c5g9zmFoBAjmGnLIZm/+CpCfhm1qQhU/m0bYlhZFciGcdKNITKkqm1qYQDerx0EIrpYkaaZYBueCwLpz5m2LaVDN581ceVyUVnaLkmKF0fb0POR5aAVxxV4Lh2bYaL+iyec5Znj5ggWgJf6vDSm+tMHttpMbv+3fPbApSVhSUVbN92/NPn/YtP4Md1N4/7YsQ8VKc61XlppuoifPVMFWBtMOXWOnXDeRcycLksBEcSsGriKgoWo0ERW+p0ztPpzKzJWxskYmYmkp0uSePC7wmMJxD/+n5YPZ0ozy0ocZxaHnt6WXZ8ZlDxYufePwTX5dtQaq1RjANXab65tJgSgoaGR1dR/7jJfmJgafhUQhd5/3xeoIsgiFomRh7wY94HP8fcLv1MsSz2qNTXqnwpVzSq1SRFzvYa2ZyC+gi1JgyH4Ow7BtfsojKrKI5n7AGDOT0Ty6icGoN9hAXSHlTmFwUYGEXBzC7/yTmTSN5o4h4EGhrqlLFl370Xru/uRb45rNwwmwwWNVxdrUbYHQnBOvW9mAO59egkZBBrPAZEw7AX1pScThcdIxVubfnvRtdGQTuT3RmO2lkrgMtkeq4HaU7ItkeVRcZgU7oIqWEiy+cMdJqkeYJI3v5aFhWuCBn0yrDSkE9djIp04Co3XxALVYqYgNr7/s0UkNusHaKxtK9ez5dYvXweuTds120KWBLE0kjAX8D2VuVxWatpILGGysy8ehJ57rO9tXrd8D4ZAUFm9M1XfwJWLCpQRLazfOU2uBrrz1s7RdaKQ90Wb4OOw8qqiRapTnWq8ypyEb6Qt+q8JFMFWBtMrt6PYtgt4XR5dVXrGq1ouDLKFcSKSIvEMuiRaZNvRZy1sCxnmXtoSsxL5dEDuniz6oTMTPH1uxAYS6hkObvNiN1LIRve2SQq/R2wmBbPizh/l6gDK7NjMABMzGplJ5F1uSzdlRgvuhmzRfNsUsMzNm0cigQu7E1cyyLb6BPg48qMYCjTGjCBnJGAdE3suUsN1KgOiOXIrnQO2c3NcLc0IXNJN5zBDvjGlsTGUWdV7KhV3IOrJi5tmtZ4TkU6KM/wHNYubUXZ74aVK2ldyWNIbTK6LAJId1MjPIm8jp01Qakru7X2JKPnnBxB6g2bAMuG09mE9OXdqgnSmkzaqILONStsDAsYVSQDV3MEH9LDlR3k6ry4ZcdHpKFyPXoQhZgX7lwZ5aAX7rW8GEECuMyl5r6xHixbE8LypXXA1l5M39SgAFSe38BTI7pfuQAtFxyvLY3WupOPWitXOiMW0X9yQatWPo+FXb3wzZnMMwJmvha47i01RMUirq//eO6pp+LastDXpHUmARfZLt4WJufw7Yd/C0s39ZieS+anTa3AGZuUS5Pz5is+/jSd1tlz8+W/K+E/U+a51qZm8Ia3/i/1OxJwnXEVVqc61alOdV7wVAHWBhM6sSi2BtksnGt2CoSoHoVuKHbf8WI/MYvUjiatCaffUIOmJ3JaH5X9vAhbYoLc3V1aaQmMMdLg5Jxhmh48oIt2ti2k+ymH/Yp7YAAmdUlcMTJ7C+Mz8D98/MxxUcvDny/zAju7pmJlAi7r1KQE8uhs0X2zdsaVyQvA+JeLBkQEA9J+OTR08Q+Z0SnFSrBTL3z/CVi1cfiXqM3KIjC0hPQlHfCQpeMkkvCNrYiBsdfyWh8yXsBqrMfaTZuNOD3k0X1HHxhWbpUYIQJSAIFpgkOY9RgzrtxG10Xg6lsuCPyRAbO7OxA5siQmKt0VQfChk+bY0xmlm2vGZ2AfGla+lD0xJ6BCxiu3u9sIx/N55Wkpp4rM0kAvgqeWBGIJlsg+yTUZ8GN5swfO3qM6T/NXRHQ+w1MFnePWO+YUz8DyZcVrkNEioOI5K1VMPlXAr1WgQO561pPb1ipYeV7L5nGT2cvH3QpeJXCmE5QxIC6K8tlxmDcJ98XLBsVe8ngzr9uE7O4ueI9Pq1T71vb3IDpiQDaDYvlz1FERzFLPZ08tmNXl6SHYokbr1rZfEyBkyGnl4EmUW2vFmPln0notp2/YJFchU9yr8/+z9yZglp7ldeD7L3dfq+6tfa/uqt537UggtCC1TWzwEjtxTOJ4xjOJPbEhBoxhsI0N2NiGOBPHTDITYzvx4OAVEreEEJJAa0ut3tfq2vft7vv9l3nO+W41AksNSCDc8v8+Tz1Sd9+6de9/b9V36pzzniPeePP3djyJ8MYZb2/6OuMi/btaI7tiB3SJbx8SfT2r/jGTozSmBTSJnl7hVljff70smaPj0naqKo5P47o+ZTqAAGxrIT4AnYIRFCTnRe/tVuXFOZXurTbrTJWfNbkoWsAvTjwosn1ASV6dEal0+iQ+VeFhuXpzSPq/UCKYKI73i97K0EKKud2XYn4UGCFkMQFYVIaT4jd0MZuWhFcaYi5nRLo6CKacqVXGCUgyIb58jWXIADWRs8uUkMCsEZCYBs3p7u4RscI+ufvoJyRkO5J4dp5m8uCUQ/YF2Vu+si3+F66KvWdEikNBic7XxCjayiMV8LdM57oIfGJgcUyT5nIavcH4DXZKZK4kGl8HZXpHFyQqeTTHJkhrjqocLN6P7agcr2SCZnkwMzSMu67qJdwoSjMdJ7jxL+Sk0ZeURtInHWfqYt9zSCywUqgkjITo6wI4hkznKyg5kF/H1KU+kJbgbJaAiksA3UkVRIogUzBDGLz2kBjnV9WCw4/vlu6/uCJhbUCMmRXR4O3C80HPIBYN8PzHRslA2amg+HG/XQmJnFtV9+VTmWt8vks5Gv59paDYYVPMYEB8z16Qyt17JfziLF9/sFVYaPDj9VpapQRszm+oLsQdIyKoQ8J11TRpdkQlvFBWQaXtba/Hzx1vvPHmtW4RvpbP9+Z1GY/Bus6ASXLaY8xaiizWmMsEdsBBthSCLHGoB/zS7G8TbXWTn9P2hQsi/V0SPbdGYIAtQAcBi4gfAPOB1Hds0tm2VA72S/XICJkq3/ym+DM1gqu1myJSOTIi+f3t0oz7RZ9bJfBoRgwxGkKpK7sjJKlzSFJ3pHp4hPU5AGoYHMKQncDE4DBHUnz2UIryGOpm8LjNXE1cmPLhL9rISP1th8XZOcLIBzAjsrwmja6YZO/oU16sFy+IYFOy0RQt1S7G4qZUu3yUyGq7esiI1HcPqOqaalMiEzkJTG6IO9xHjxSKjn2XFxQT1JsUd2ZebdUxAl15nAAi7GiA4KE+3iNGpkxDvtoirHJjsNEeEvvMRQKD2u4+bgTifhi7EPIrBsm2uVRQ3ZYSqy8l7uo6vW7oMERoKxkl1xU/CrLPrrIKh2XYkxvS85WcCivFax8w2VWIbDEd7GM2zwwypNLja5FVg0wM7xreCzStu6qoGfU1ftXGDgDV/T9mGT7quzAn9lAXK5FKY0mxRnsk/7Yd6vaWTQYqdGqWzwH9hZQ/8d6BH81xmVLvZnLMR4MPDBlljbFu0Yf6xazY9NbxPWY57KjM3NkvzUPbVSE3srTwGA1DSbOlsjSTATGOX+RGJBoG8HdgvSCtficHjQDeeOONN/+QxgNY1xvXUaW9jiO5sRCrTdygX2US+XxMYLeXVpVPyrLF3tYrDjryABxQZIxNPsg+4yPK2AxvEIDbWkaKtw1J+MU5dZjCUN+VJOMA83vnC2VWt8DHBRmqeNd2qbcFGEIaP7ki/nxDOo5n+bm10RRjHVDSbOaVr4hJ5U1LnFRCnIEu2bi7n8AM22jwQuH5IDTTDSqZU0+1cSOS8l3F4uPHtuOjj/+yJM+oDUFsEspGlqXNZLFyeYks1MR47AT9RmDUwJIhzwqGfQ2Fxe1REdTHLCsAA/muNhAX89RVbguiGgYsULU/JvVUUHmK1kvMBAtAXqvXxTe5oqRYn0kJEQXbiD+A5GpWLDJ1iw+meW0ZrdCFOIsErxGLnNG3DAM73uwNS8JTOYao8j4LJamPpKQ23q2iIiCzInfKdsUo1BiOSlkX8QiXf1Os1TXRT11hpQwAdWU4TkCDyAimtmPxAdfG1BiQig1Qe7CbRnRrIK1qfYIBRjYEnrsssePzBMvYDLV626UyllKesMNDsn5Pv4imKx8dmDGAJlyzjpQ8lPnPooFBtSy+l3wvXCFAQyBrbjzEfkwsOZgTCxJZajB2Qo9FVd4Z3n+VujhBQ5wdg+LL1pgBRoM/ehzzBfoMt6TVrWHH4msYs6/3NX2+N954o8aTCG+c8QDW9abZlND5JXp/UidzPPgp8cHk7Lrqt38wB4U6DzWwJNqBHWKjlxAgyzTI9mAavQnFHmjq8AVN66aTMvcvxlin00wEyC7B7A6pEEbpaqePFSxIkg8tIJOqJrXRtGgnL6lDF34pgBufQRanmQ4xsRwRAWDVGp1h0SoNiS41Jf7cPL1PCCplX2I8Ig+f+DW1pQd2BODAEdHOTqi4hu1DfNxaXjElACX1g8Ny6WM7WSejxWNiFBtiRKOUyOAFg8EbfweTPHxG9CvtHJHaW/eRmfF/5ZyELq0QXCG3qjqQIMgJn5jh1iXADbOyAqbYK2viJmPqsTE6wmU8QulIP/1G2EZEVtTm3qj0/9m0ejNfmJZap0q557bi+WkaxSGvolKIwAq1MKjQ8ZlS29vPzkj4qOyOuDQHUgSfzY4I5VRW82zkWNYNVsfs7FB1NDtHxckVJLSkyqjL+3u4uUlvXMAnmgMw54hWa6gew1yV4atgP2H4BxjH18f2IFjFxFRDHBReP3qazzd8al5i802V3u66Utnbo1i+aICfi+wrbDgypR8REmBGARgtW9LHJulJgwwNnxhAL78WUuoBvF2A7yZlRrCcYK6Q78VCacMQIxKWo13/6u9EPjxi/9mr+gED/xdkZOSOeeONN9+Bwc+v1/rhzesyHsC63mAjD4Z20xQNHiAccjCnNx1u4iGIFMCEfYSGJvM/NiS1TlTWaPRb1ca66FvCOj3CJmE6R1wBDt/wUoUyzsBDWQKjwHKRwMGft2Rzb0AqI0lmKmG6ns1LeVuSMhSzmg7tJGsCRgJAytwsiXF+inKRf61E2cp3cV4ee/j9TFeHtEiGBRKfiArabK3q1ne0TOMbWUqJ7sFxFYtQb7KPDx4mgAJ4hFCKvOsj06L5/WQ7IH8KJMD5dakNt0t9rIvestCFZW5Cgl3RM0UJn5iW2p5+sW7dJc3BNJksmNvB0tGQnYhLcDoj9UEktNdFK9dl81/cTOnUmppRhvhWoXX0hXn2G+LrQD7sfHRRxUAMdDE4NHxhmRlg9a4wzfwAbpAl8TpgI1S9frY0O2O8LmDMEKMB0AyZFuNfLRL4ELwUisqrta2Lrx2WHPBdw+t0YZIANfi3JxTgRVURQkux3wDzPoJc8fMw4FNLEbNLfN3cbI5MFDYM8ZiC05vi+HSRfeMEgtbKmvgeO8luR7BUoakc/WUAQ878ojLz3zaumNNaXWwkz+N92mwqKbCjTbS1TWmMdiqWEl61mI8diQTUyBkL+Hm9mYVVqYi9skqT+0OFPyR4wyD64rVOeazdWw33xpvv5HhJ7jfMeADrOmOvbxBgQHbCaj7W9cE0gPFoHhlnzIIT9KugSR6kIqGZPAEUQz8tRyRXkPJ4O7OssBFI+cxxpDgSEW1D5Vnh77GxBm8QzPHBrCvhi2ti5iqsxCkPRiW0UpXCziT/bMCcHgjI4lvCZEfKO1Li7BpWElSjqXw4SIkfeQ8rfaKXs9fMzOg+xOYd5DRsjPm+epaeqPLt25SUuLBBlgdRBdxcBKi8eFU2b05L+PQ82Tek1iOgk6BhYZVgMjiTEfOJM8qQ7TiiVetkRuAXQtZV8Owc/U9g6cAagaEjU5YtSnFvmqwakuGdWJCgMf1inhuVeiBIQILnDZBhLS1LozumktlRhD2kWCdIfPn9qtdP38hJcL5ABgosF7cxry5SBjVml2X9SEJ8cxsSyDYpjYGhwgDEsYQZ2WGGxnDPwoN7Gc7ajPv4mlp3H5Rme4hxCVxSCAXFevNBSmp4Lpu3dIh5bkY9P9tRER5F5IvVxd6/nX/nbO/nfWF7NHJpgyn7/o2KaJMLBEGNB4+IMTTAAFZI0BpyqgCI8R48souSrT9XV74w01QBrOEA4yb0ni7RCmU5tv5phuTi6+C1x0YoZG1reYVhp3h94JEDiEJdjh6Nfl1HIebY8u+/5h8wX/n8e+Xxh94vxy5+/DXflzfeeOPNjTQewLrexQmHxOloY6AlIgqYQYVS7QuT4jtxRYxyQ/mxcgV6kIY+tyou1v81EffylNh+XZy+Dok8PUkQAN8LQBQM4onzWeYeVQdiqm5lNStWIsC+vvYvg7WxyFDAk2U0VThcaL0h+bGwkh8NXZJXHWmmIhLYqEt5IELJkUGYjkvQBA9PY7yHCeKMi8CmGqQaTSPDVLx3pzhNi9Jn5Pi0pJ/buFZmbM6tKXkzFpXa9x2RxAQqXJqycE+QG5PS28mcLORjARxCNnRv3q2SygEGsDUJea4zTZkNjy08U1Tephcus9SYkp3PJ2uHDFk7HJLI+TWyNL7VIoM36yNp0ft7pH7zGP1EAG+1H7hFAheXKHVVBlpdhTDB54sSWq1LozvObDE8Z3igCIrgPYpGuJUHQJQ+U+J/GSPRFqdhnV4t3A/YG59BmTNyfEYSz8yJNrMkkRNzfN0CV9fEePSE3H/Hb5BNqqCbEREMXTH6rFKPznD7k8GpISS866Tk69u6xLg0y8wz3A8rcGKGuD6TUjOiE7SWKR5mdXw+jPuQoFUivSnW/JKYawVmaAEI83FHg2SjkGgPAInniOe05ZnC7ax0lNU9WMzQD+xW79e2CEEfQBRiR15NWbQ33njz+o/2Wn1Y3ov2uo0HsK4zSPfW5pd52DV29EptJEV6FlKXi4qXaEAlrLekNwz8PqWhkNTv3i+hc4sqByoSEi1XEt/0murGgzm51pTmbbsluFTiliCkMjA8SDcHCGqOdJIdo9l9Nk8Ah/GVHWWe1zSJX8iIFVVJG+HlGu+T4ZV+QyrdQSaZky2qNQlywDah9w71KtiSi/7taTH2jqtuRBQKA1gsLtM4vpVUDzAGoGFOLXErrusFS7RilV6jredrn70ksms7ZSgwXwSAAAuNppR3dai+PwwkLZjkdwxLZneEmVkwwvd9tSFtl1tZW4Yujb4E/VaIxmDFDaTCHQOy8gPDzMtyelJi9bRzu5PSJ+puegCmdGnGTZr/0RPJouyNopj5GgEnrgNey2bMzzJtu6ddyZwICQ2bKrXdsrktCu8dtv1QaeQO9DD5HWxXZXe3GPt2ig752DAktFgU8/FTZDbNqWU+tsKbRsRJxhTYzuZFCkWppX1MW9czJYIs3F/yJDoLI8wWw/sJYBOAB9t/BKnwn+HaIa4jFRezr4dMH9LisfnJ67+Zl3p/nP4pJMfrlaY4py8oz1Q0onodZ9dEf/EiwaeGEFoArXyFW5nwlm0xVt+twQbh3vd+ioypt03ojTevcbwk9xtmPIB1nTHmVsniWHOLKrG7Nb7NijgHx7lhxsLfXIGSE4zs7sSMJE9uSGCzRqCEqARKRdEwowcADDjVmvhyVYKaym3bpNYbI3OBwmWAFAR5gsECgAGYgfEdPp3Y41fInEBeRGSAf7NOyRK+L0Q8gDWCsdtftMW/XJBHnvqQ+nqMV2gTd/+YGNtHxF1eZR8dy6KXC8yccq/MMFrCivilvL9X9SzaDoMuKRV2tosGNq1alVpHiM8RTBNkPICTwHqFjxNsFTfS+lISObMkdj4vjc6Y6Bt5Kd82SnN9+/mSPH7sfdzYCywVuBFY2dGhtuwqTV5H+qmaTVm+3ceoh+4vr9E/Bp+TUWlI/mCHNJHmDlBUqbOCJ7vNpB8r9PQVGt0R1QD/HEJd8fXBKDajBv1bkAbR9cc8qopFpgvGc1TlwHcGpqnSFxKrLah8azCgT2wqxnE9J/ZNO0VbWhejv1cCMxmxRnroq4ssVlWEAyIbNjfFWtuQ6GyZNTfMs5pbpd8LMh1iG1DnY44OEWBB1kU3JaW8XIF1Q8dmPsX3CSqEfAtZgmNItATvliXB84tcOsDAc2W/9QiN8BzTVOBw13aGl6JXc+1tg4zTYOVPo8EE962ew625/86P8r83/8tPvuYfMo84n5PklM02BPy/N954480/hPEA1nXGHlYGcO3IHm6L+bM18V9dUSnc56bIAuTuGpTK4UHJ3NVP87W7b7vyOuXKZKCYPeQ49A1hfd+OmPRIIauqMB6X7O6oBFcqrK3h9l3I4Bp/aVuMHiwcogAt8OhwMw3ZWgN9TCsn69KwuF1o1G2CDnQDgvlBkjyS1tGFSDM0CqnboszFApBw9ozyueH+4V1iSTTM7el2+fKjvySRi+uMiKju6GTaPGXFtYwYdcWgMd0dv0khUby/hywTZK7AXIaABkCOOVf4vEO7xb9eoscJMQlIHEfqPB6bXLwqzqVJUte4j3p/gtuY7h7lVwIzM/rHS6qaZmaBIMeOh9iPiMeCUFSauxdXxLr3sPT/1bxi+9AHmSsq7xE6HkcGeX/wT8VOLolvtcTXxVdoUqr0XVkUc25d5JnTZHogvQIgxq7kGDBaH1PvhcKBTuaD4XlZIUNthtqO2G1R9Vrky+xJBGPIbKnDe8QcQvG3TjYNshz8cWSnsNHX0c7roQJhbVUmjVLunWMEm4ixeHDPB0Uv1FR+Gl6zuUWx9o2IHfXxfbDygyOKaZteYJURBzIlirIbTXHw+uPadkVo6E9O1KTZFlK5YYh+aEsQxL10Hnnyg9wmfP6/fGeKoKOPnPfAlTfefAfGi2m4ccYDWNcZA5LP7Ly4fkMclBnjcMvmRC5Ni6CY2HEl+diUWEFd4pMVsk3wGOEwhVRX744QbDXGe6Xe5pPghSVu4oH5wiZi4mJedEukOBKVZsIv4eksE8ThAYpN5KU6lJRmW4CHOf1K+Pr9nTyM/ScmJXh1nRtyTjRI4/va7W1i7xwSvenS7wQAgS5EfD78UqXhqEgoxK8PEAC5BpuDieOLiokZGWSu1tGx93FzrtLtk/WDPsndu12kLdHyhSn/F+RM98wVqQ+0KSCE5z4ySH9QdaSNoJDsHTYwGxYlUpZRYykARvLhXrHWN+Xh2n+T2tEjjHmAIRrPC6yKsZYVa6iT5dIwgdM/tXe78iM5rtQODIlRd8VCAXV/Wupv2UuABgkP6ebS06GCVOsNMoKIYajs6xPfRlWc9U1GMUDiM0o1JR2GgsoftW+nGGXUCrlcSICXDuyigU7FRETiL4DNtJlbBtYLEpzd3S7GRkFljMHkXyqTeSrcNkj/HpLnuflTLEljpJOGdDxWhKJSll1eJajeisvYSo6HH67WEyVjhQFzhtdAH+glo4YqnsCFRWk/XyWwQhF5YLEgfoSQwvvl86mU+EpT5Pyk+L58UuTERTGePK0S8SdmpLK7h9cIZveXyneQDlEE/e0OvF/4wLblLf9CsV94PyGaw5MHvfHmOzDeFuENMx7Aus4U7hwVvbODNSjOgXHVD7h7G1kqFdvgSPGOEYmfWFYMDtKxwdg0HQIQMDwwtddSPnnyL36RAY6Qp8AecGXedaX92VWxg8hqclWQJ4qWGxa9WMFnr/D2YKpqaQAt4SGODTYYoq3ZeeV3cl0yEPGZJjfWwF7BMM+1fgC66WUJzuUlfmpVrJ4kq1TgkXLuPqxSvQEeYZReXpXgxWUmn/tOXpXEpYIM/sWKJI+vkFkCWELaOaIEgstVskuBhRxZmfy+NM3UyHsCawTZkREMTGtX5cgAjvrCmtQGktzUAzMIdsaoYdsyL/fe/TEGXVqjvcxxgrEdrA/ysczVPJk6eoqyZQksFChJMn/K0CW4VJTgcxNcEgCI4lZfvkhZDcsFxmpewlfWpTIcEz3dLv7Li6zPActY7wjT48THlC3ytUdhdeDcvNTuPyiBJ87RXM7XPBTkcwTLxhDUbF7FcKytU+qDxIjlBTBPsQsZgt9mMsjHjOeFAdhU0q+KjJBdo8zdItMIhnJintcJQBaZWmC1IANDKuY1bZVNM7g2EWM4LBhQetEWVxgLUdnXS4DnjA3wPdm4a6+4N+8Vo69bij96s5SHY6JtG5RSP0BYlb84bMl37CNsNF7VDxQyha5DNvT4ZxT7ZU1Os74ISxfeeOONN/9QxvuJd52Jfuk8N8MAYIojITIpRq4sxlqB8ltjuEPsgEZpBwc9Te+FEsEAOuFCV9YpZcUevSRv+b5PyIPxnyK9C8DldKdpSsch2/7kkvJ46ZpUOwOU8IJn5uhdCv3VcxJYKUvs4qb4ryDHqEF/DA5rc3xUxUFcnpOjo78ooZmsOKEAHwskQzceZQgpmYyWX4cSYl9Kjl34GCWuY1d/m0GVbiLGjT8wHvXbdopdLIq+qEqaYQwPIPMrEJDqWIeYk0vSSAVFi4QYfgljN7oIzUyZ22kr9/eIlKv0RYHRQfAqQkMBHpd+bDtZFmNuhR9avSHB8wvXevHM7k4axptdUXb4QXpjSCtCPE2drB5rdRCeHwuwMgYMVL07xmwpmV5gfpQTDiijvqHx2jqra3z9IldVZEVt/6CS7fIVJuEDxGFrjz2EqAoCEwX5D6F8kPKQzA+wyFBP5ceDSVzSbXx+AOL33PubZL4APOn7yhUoIyOLC48ZgBGbi6zFyeYJsvD3zovnWboM4IsoEGvPMEEaXjNmrcFXt7AsMjUv1d4ogaDd2UY2cqs0eqs9YONH9kphLMKNwtiXLhLoQTpGTIa5tElAnfgf5yT2las086dPFsQdHxJt5zZ6sTBIin817BUHsmTLdwXpk6PpYk1MvuqwUm+88eZrg1+2XuuHN6/PeADrejM+zMPbGuqS5LFLymi8kRE3EhBtYo5SUvLJeZXMjYBcyGeOQ2+LvZkVZ2mZGUMoSoZnCSGOsctZxSQl0S2nDli7KyGBK8s8VMN/8ayShFBPs38XDyd9ZYOxBpDM4KlBplN+PEzgg+RyLRImM0Ww1bCYkYTDG2AvMLtJVqU6GJVj05/kOr57/CwPU/9DJ+Ro/78RfWyEG36Q0bAxF7yyImZ/n5RuG6GcBSYEgZvV8Q5xNSE4QQ0NpMGZH0Q4aEN5vOaXaCzv/ts5ZoRh7I6kGMUa4yickF9S5+vihv2skKkeGpbKrk51rf0qI4t1NV85Rd+Ts3NINGxrYpsQPqdEWIHWpi31nrj4ryxxgw4yIUAbc7VSbWp7MlsgkGl0RMkAwtMFEAXwSfN5SBf9xCWyQc22INP63VqNz0FJdSq4E0DFObxLBa6GA0qqRAYYpM/lDQI+BIbyNby8xA1FrauD7Jg10i3lXWkGweZ2x6W5e1BEN9SGJe6/WmM8h7lthN4tbP1BVtarFkuXuXEKoIcFhUCAlT/hk7MEpcZKRmrbO/j+YQzF06d4n6nTBWk7vkrZ0a1W5aFTH1GJ+q0WAZjbGzfvEGe4R+yBLtk8EKOcy+yyvm5Kg0f7/g/+99XMsZX/+LX/v/rb6oeMz+T7yRtvvPkOjPMd+PDmdRkPYF3v4hSq0kiHyahgjPl1Kd29g9tXpfv3kIEB4wEGxzg3Kb4XJ7iNh4PM6OoQd/8Opp7rm0X50lc+KEcHf4F+HgxBA3oA4e05fo5J2joYGE2XLz77YXELJdEKFTI6PIxtlzJVY6xHavuHJHGlItk39UsABdHDXdwWrPXFyYIYK1luycH4DNYKzFL4qQkenPe95WMitx8Q68A20Q/vZg1NHZ8HNg0HMEBkJMz4A6Sm4zFgIxGHe+jsAit7AE4acZ9oPZ0y+ofzfD5gp0oP7hM7GZLJnxlUqeiUuBypDsbJ0ABoBebQoeeQdYHEF1ivqgLt9qgYl+ekcvMI61pwjeETQzYVjfXoIrRsqW9Lk7XxoZ5osJOSJcAYt/byBamPdpLpQ1YXypUD0xsEjNhyRFZVeayNZnuUcTuHdjAxH6CL0Q97hslEOYkwrzOKvmnkh7c8FOS1ga8McmllTxdBCs35pq6iH/rT4t+sKAkPbNzipugNVU0Rm61JLeVX0l4kzNvCL4bMLjJqYx0MWcVCAXxxKHCGhwzPAZ6x2uERAkRsKqKaCLIsnhcKsXHt8z95O6t16APD0uibdkvjLfvphWLIJ9g8SI0Ni7Iuiqzx3d/xPydp8GcV0cIyQRk2GCFnv+ofKijcbs2bfvR3KRc6meyrvj9vvPHGmxtxPIB1nUGCONLF4anSujvIIMVOrZB5iL24RKAD75EOoDXQK2s/sZ8bbA+d/nUekGBaMj+wm4zXgX/zKcqNOAydWFhCp+ekti0t9pmLYsBAftMeAhvt5j00A0OmkaBfircOKcA2uyyN/jZKf/6vnmPmU9tTC6LVbdbwPHDoV5QsiK3FcIjp3mCxyK7ho7eTQAbeonoqQG/Pw8//ilTGU7Jym1+a3XFmO+nVOlf9AWzs1TXKn/7NKpkWxDRYyZBETy1J+NySMmI3muKurLOzMPrQWYKN0c9uUj5du6uT7BJKrfX5VYI4Z2FJav1RWX1zp2hTizR3wy+FCIX64W0Smi+S4bE6YpS8lPwllCIBhJA+DtnNDvlYlAwWD8nltcGkqsvBpuC2bj6O4o42VTitG3wt4HOLXNyg9Ja5o4cGdwAe/9VV5Zebz5CJ4tYmwk7XcmSskCMFsFNPhxhxgAmfX+U1CTx7WXncLixSOoafy13fVKZ11xX/Zo3XGEsNfO/AdD85Qw8XQDZAJt5PgWcucWkAfjUY6PXHX6TECCO6NjooxQFc6waBpDm9wnohACZ7aobRDbE5VW+D4FIWTqMK80svirZ/hzwQeZeSiMG8gc1yXW5M6hdnWKuDQVI+mDdkdV3zYqV/5tv+gQLmC4sLW1MY0tUvGcjk8sYbb17zeBLhjTOa63qC7DdOoVCQRCIh9xz6JdFPT9E7gtJa9NeZZ6dVVx+YpbySpVxsrE3MiD7UT9CBg9nNF8QulUQPhURHp11XSnUbLq8xCgGSDHKfwmcWpba7l/lX6KXDZpoBiWhhjaGTOLAq+/vo48EWIu4XUuOdP/Q7EruUEastLNXuoERmlBmcA9ZorEOCyyX6bACC7O19XPUHwwQPFQesTDyiQk+7VHkzzPHwJqGyB9uIACz+WVQGNZlt1bxjN0En4hZw36jBAcsF75WVCIp/LiPNvjZV34Kkcb/JyArKdeO99J8F8pYEpjeVF0nX2fOHzwPbBhBqFKqilapSOtAj0csZdufhNmYe1TA6M7jAHPnyKKE2JXBqRpx+1YPoJqMqxgH9hUikz6qvrUpOFTdevGVQQqvKOE/vEvr8+tOqMqfZlOLhXol++RKlV3jTBHU13R0s0kY6Pzxbcm5C9MF+SoHwObGKKBkU/1Ke74vVHxqX1NkK09vR7Ud2EnloADN9KQKwZjrCZYjIhXUCr9ybBiT53DJBHjK4GLUA7xcKqEe7WMHDtPcWuAKLVbxtiAsU6I3k643exUKJILU23iX+jJIh8ee1e3oldapIXxrYQV4XALKmxdcGqfjWwiJzzZx67VuKVcD2ob2xKa7jysrP3ya+ikjqPz39dYZ5SN9Gqo15YPCveVlY3rxRz4x8Pi/xePy7+jXefOeHxTSDr/p+LKsmX3nyI9/Vx+qNGo/Bus5s7o+LuW1YhTDaDhPBa7dspwGcZl4wAogCgNSHWplimeXGOKy1/h4xh4dEGx2ggVtbRqRCU5oHR8W6OsXDLbhWpXcps9PPbb9GwpTM7rDI2Su8b4A6a2lFnvif76Pp3VpcIrh6IPSTEvvqVbGjQQIgsFHloRgP+807uthhFzo5I1q5xt5Be1sv4wKQGg7mAmDPWlsXe3GFbJtkcoxJaHSGuY2GzUcc1sZjJ8SK+aR4qFd17t28kwxS5OQiJTywH/D4gCWCwR8BrKhtQfUOgyxrTfYoosbGXt8U/2JOpc+HDCVzJWIERPWUXxrDqlOwng5IMx2VxlBaos+o60SA2XTECfvIAGIbkEA0X5XA5RVeK3i14PtyJ2YZSgqfAZgoeNec1Q2xe9v5HABUIl88y7yrLY8QAmAJruDDgrSI+C6Aq1SCjBH+n0DMca95wPT+XrGx9ACJDlllYb8q2gZoiUSk4zgYRFu0tqTYh8eVzOu6UtnbSwkXrCIALbYxwZQCLCVfWCXAw7YkfHNgzyAhWoOdapuwT8nP2NbkRMISmS1xEzN0SeWzAYyiIgjXBAGk7unL6ralMsGVkUHlEzK1DIIz1OrYfR0MIcXn2PcekYerf0KQ9a2UPeOXgC82P0tZ8Own3y2pM8rkviUPgokFQ8sQVg9ceePNax8vyf2GGQ9gXWciS02CFnbvVao83IMrZTG3j4qLaIJb+5WxeiPHDCWyVt1tykR9aYJdetjqA2Phdrar1PTpdam//RYyN5m9MeZkpc/UJLM7KIGsJcnJusi+cVatQPaCHwmSIQ4yfN4DwZ/gAei6DuMBACQqaUOiEwpUJP/bcan0h1XhM2IHAgEFNuIhSmAAdABVYBGMjpTcc/9vSn3/MEEfEuDhzYHhGqBBu3U/pbDY+Q1ZeEev+NbLIgGflA73EQiAPbvrnb9DkztN9fmSKiYWkbnva2NsAaptUDz8xcafssqlsCcl4Zm8lPuUpwkTWq6RwaocGmBkA5YHzOcucpMNdTbBpbLqZUz4KYmy7iZqykPnP0pAUz7UT+8ZpE/3wBjZNGwH+leKBK56PKpCPmt11hVt/tgB0Uyf3PvWj/MaYREAsh4AH9i4+FkwLS6lR8pw/WnJ3JQmQKRM17BYvaOdneD7g7fFcgOyrQjEkPSeF9c0pLivk0Dq8r/uFiedpBeNmVtLK2LHA1xKYADqzgFKwDTYQ54NGWKu5Bhui8BZnVuJJWns6BPzygKvG+5Dn10RJx5UxdbwDI51q+5MvA5gpw7vvGbKx9eC+R2+M2SVxZ6cFL1Sp9yM7UtU+gBAP3Dzr6n3GAz23+Lg9vvf8yl63bhBOP5+iX91kmGlFkrTWaCmX9tU9MYbb7x5o48HsK4zMHSD2agMxWkkhxTknL8i1dF20UIhicxXyU6BAcAhi3gDHOQIqDRiMR7cOOgX3xwVrViRpQe7pTHSweJgHKTBrK38VU+dla6vbIgvVxc7CP+MptikYlO09qSYvT30WEUubYgGRm3XBxjUiVyk5Ttj0vkkvFIiG4djYvT1SPTxFmvhupQGcfhDdnNSMYKaa4ecpsmXH/klCSwXxOzsYH6VuV5UhvJaQ2qdQbHTqgy5/7PT3NjbvLVDvvrX72XlD/xciD0A+AHzBHAJxgxM0ND/e0UeiP5zAqWtgEmko6MzET4uX8URbS0jK29Jqy02lFQ3HZXD9cJZHtgYZH49fPLXxHd1WfwPPc//52vz9BXKpAs/uUPCkznF5jx6nowSPFr8Lc/QWHDd2IMUd1uBl4BfOp7ZECMRF//FeXrCwLC5o32SO5gSuzOuAlrxmmp6q8PQkuSFAvPBcD+QHunLGx9mrRBCYJkejyWBthg9X2DmcC0h40K6HPmbmtgILkUNTjIs2lC/NOM+vtZIZQfrB3kRXYMA7Mi24sYigFTAxw96umbWaa6v7OwUbfuQNHcNKLkPLBvlRJf3j0Da+vZOJvfDk+V0t5Nt1MNhCS1XyHgxiiGT4zXCEgNYTYSiwpvHaUma3+q0XVJS6YNt/wvBNUJuAa7Mrk7RT1y8Fm/hjTfevPrxktxvnPEA1nXGRTULDM1zRbEiJsGJMTRAUznTxq/MqYt4eYaHenHQJ+7ahgQuLonW20WZrLQrJYN/OsMg0J7HN8XM1pgnhZX/6IkFdhgi/FEW15gGbtSU0TywijRuTZyOJA9tGM/BGqFAmAf8Rl4W3xoXf1HEhUfIdaXrIQXcrL3DsnEwIpu3d4kxt0aWAxuG8OIAlKHc9/43/Yasvn1EHjjyK+JMz/NAxsCLBYM5GK/I6SWxIz4lPaEHr9CQ1Jfn5P47fkMMsCStbT8wPZANkcdVvXMXJarmjn7RB/tE2z7I2hdkIvnOTrNnb+XWMOuBwLL1PLwsgclVptPjuTtdbbLxv9/Bx4LcMMwWQEO+1Ntu+4jywCVUd2PfIxtMZccGHmU9bMfheaDYGDlTrVwwsDhImQdLVOtPiNvbIXUALwCcsX6C5+SXJwlU8bo1kypHy9ysMOYAJc0ac7Ec0SpV0S/NkslCVQ9eQ7B/ZLLWMtIYSnFJAI+ltKOd7CaCTc1CXZnfW6GrVkCTZjrEDVMwcbXhNnZQIrIChc4oj670hQnQGFSKZHvkX+E9eXaRjBnBqa5z8xH+NfjlzOXMtW1Cgpw2vE5NvgZgPiFxIlgVABjsIt6zD7/wqwTy+AUCLCkGkR64zbcyqD0KbNS4CIDP4/fPc2fE7Egz7sO6fa+4dx6UzDv3eonu3njzWsaTCG+Y8QDW9S4OzOiuK+u3tElguUjmBWbhyIvzUnzTNqndtI3SIfKDGIdQdZl5VdvTx1R258IEQRQkQkQ00KsDFgMbW4UKwdDCOwdUv97OIZFyWQJX1wgIIHnBw2NFfKrb8MK0OOmECpm8dJVMRd+jOYnPNaRx07jaEoM8BW/P85fEqLnSfjJDMIfHHDq3qDKm1nMMKkVkwouffrc4py6J7B6VWleYoA9sBuIFYACHAR5ZUzSpl6us46nu7mFwJa4FwBhASH2si8wQNiabMUOa8QCjLcC6aCub7O6DNKW1t0ngzIz0fTlHcALZiynvABTLGdbRYJKTKkUcfrOjO36JkpO9ti7a3h2U3Uq3DvP5I74B14kdjZZDwAbwBCYHoAfAAp4jsF/ZwyowFl8jOJtlNZD/xat8jQvbwrwtzOMAlGCQghNqsxDgDT4uMDv5O4fF2tbHmIfsP9ot62/qIHsFCdk/taaS1usNSonO6QsiC6sSPT7LyAz0WmqbeVUrdPw8gZpZd8X/wlV66rDogMgJmtQhQ0Lic10pDBmSv31AirtSfF0YhortUNPgMgU+pz7UxgUD5KMhpsJZWefWZWA2S78VfHcaALGuizvSx/ceQdbBccqkGvxhmFCQfirKyq1BqfS3mn/1xeMfpo8QIBjLGehhhPRpBTWxoqY0Yz6x/ZoU/+ntr+FHljfeeOPNjTEewLrelGvciAtmbFm+G6yERkmndNOARKcKNJJjrMuTjCVI/PVpem9gLobXyUi1M6DTyFZ4SIJFIBuEbsGW/JM+3yCAMufXWdzL7bBsnlUnkBpRIA1zvRYOcbMQfhntlr2U4vRsmeGm2PiDyRwHPSQmPdUm6c+dJeN2bOp3KA81tneTcUKiOnxJABQP7v+QyK37RC5MkQ2CER/BlZT5WG+j02uGWAmYsMF66LZLqcm3mBWj3OB2YWAhT/ks9bdXJH4xS5O8WWxIvSsstUMjqiAb5c2Q0LpTjGdA+rkT9F1jnFg6fG5SSkMRCaJcWoQZTkgAh4+HRmokw6PGpmwT6JkoQAawXdpkcjoOcJj04ZmCeXvLmI4Az7ZnlilBks3CNe5oJwsG4MgeyaZDYAOWzU0nuYEHTxLAEtgtALnE41fFnFzk/bZ/ZZ4ZUrzPck2sxWX2/WldaansSEvzbTcpWVLXxbeqvGDORkacSFDq9x1kJY8/UxctGhHnyE7FiuaL3JJ0EJ2A6x+LSt//XCVACa3V+XXru3pp6N+qnQEoBijHNUdvI6I6UIuDwFeyeuhCRM5YW4QRDjoAWrUqsrTGeIxHH/sAQR9YQnczy2sNhnNrENvwrQxkQUxkoUpZErlupZGYmGemJPnYJOXu8Kl5bqa2Pbdy7fbeeOPNtzdYeHmtH968PuMBrOtM6fZRMiP+gi0dpyo0YtcGExJcr7MCx9reJ/XdfeLcdYC6ePH7D6jgSARERkPKrLxnlMGTsSsFxUzZLutOjJwKpET+EWQhru4/ekIZk6MRduoBuOQOKLmJVTLo51vK0BuFxHL4hQKZuoQur6quvJKShXg4D/SKvlmQQ//6U6pSpqSqXgBEfJkKwRF8YYgRgAncP7vJz9OvzHPzDSAP42zvF21mkWGRMLP718oMLM0f7uYBWt7dqbw6w30KJIGVMjTJ7o4y68uPnCdsUZ65rLr/IgFpHsB1tfkYwLw1+pN8bCgrjh9fELfRIANSfftNlBfhFQIAQP1NuTfI6xKcL7B0GxuRxSN9ZIQgMSLgFLlUeGcjjoJFy/U6WUN0EtZTQQZy2vEgASMAIGQ7hsYiimJsQBbe1ia+bF29jkGfyOwSPVWVm0akcmSE7B2AKjbjEDALeQ9RHpUH9tNIHz63TI8TJUWCPIfmekiDkASf+Nv3Kamx6RAMgqkKH59iDyOjJAxNpedHgpQN2y4URce1yhcZSUGmLBZSQP3cZbF629lviSBcMI/BBdTw2NxoZY1TvkTJEH/X3N7D2AgwWcbiutz1jt9mxIU5Oqy6DZMJedstH+Hm7IMHP/wt/RCBpw+G+Pve/FFZuDeinjMaCAA+/S1Wby5D4J5+bF7qw+30nXnlz9548yrGkwhvmPEA1nUm+tw0jcoACb71ktRHUqyhgWwHbwv+DQce5CG86RPPzEnozDyDJCEzuck4DeZIStfXs9zewkGL2hys01tgFXYM0z8F8GL29UptrEtqIylKgahAiU1XKAshZgDRBkj1DnzhOMEAwEK9PUCgALbC6kyImavKsbl/Jxu3p/gcXLWop4qqwRa1sqAgbTHeAFlLpinVHZ3XfE6QPSH5MRU+WxFnbFCc0X4VKAqZKxGTxPOLEj+/SbkUEhUOfaTaI3IAXq/0V9BdiBT7PEFj7Z4DlKUQxunL1chW4b7wPP0zm2RvuInZHmc3HmIKIlN5qQ4myLRZ998k2uySmHWHjwPSHTK54BWLThborYKc6WZyBKi4nigxbrarzkitWGW+WOjqhsx/f1KKoxEC0CD8co2mArDhEJmf1Lkm+wgptYUCIoM9ZJwYETGxIb5ZdR2aCT/N4bXdfQQqvqItzQGVxk/QCaDrONIYaGOqvjY8QEkQnYUAy8Zalo+zOBIWZ7Bb9F3bFNCsNCS8VCWjGJzJ8r2FcFaAEnukV1UsBZDUn5bKD9/GHDRkWIFZhTEexdEoz+bjQDWPafI9BbCG9zHuJz8e5WOInl4WfS3DeA2AcrJuJy8oo/7EjNx99BMEXPfe8/FX/D6B3xBhosXhkISwB7CxKUZ7khuhtYPDfP9A0oSUDjkZLDBCX/le88Ybb/5ez8c//nG5+eabJRaLSWdnp7zjHe+Qy5dbi1StqdVq8rM/+7OSSqUkGo3KD//wD8vq6urX3WZubk6+//u/X8LhMO/nve99r1j4pe4NPB7Aut7EIjQP4yoBQATOznGrDeXEhdGw6hxEHEKzKb6ZdQIWpJ0be8fFGu0hCMBhiXoYyD7hy+tiL6+QogWQwBo+mYzVTdWvN97DtXxs9ekL6s0Jxmr1+0cYPInOO3ffdlbdrL9tUAJffJH3BQM2QA0TfmtN5hdBvsJh3fPZy7K5N0SWhbED8Ixt5AgSKFciUmJljeGZLEDuSUttb7/aSMPWXbUmzbaA6BOqfJrdeJUqgR7ktfqOXgnMZHhoopdP+rulcOuAFA90SXMwLW4sQt8UglIZy4CDdnGN/h+wNzSTWxa39mpdESa/0yx+/JxU+2LiBHR+XjNiqK7G6bw4OwbJGCKsFLIgK2yKdeZy0Q8HibPepH/If3mJ/1be20XJtTHQLqlzlhT7dNl8ywCjE2qjKTKP1e1pdhiaVVt9c8Sj4voNBa6qVcpb/O3R7yejhXLth0/8mphfekFtg9Ys8U2ucFsOt0dsByp4YG431/JipcJSu22cNT+s00FO1mZe2p5eIgDe2hRk5trcOs33MOvDwxU+vch8LpRsVwZjSibEW3QiL2bFYYJ/INvkMkZoqSSVW0ZVSTQAoogEVopqccJ26E9re3GDciXzuaIRFTOBZH7IwrfsVbU7HSmGueJ9kh0PMm8Lni30V750UMWDvKvn/vg98uIfvJtyrpMvkmXE1ywPhlmOjmUMs9TkB5hT3A4xITDIe+ONN9/iuN+Bj29jnnjiCYKnZ599Vh555BFpNpvytre9Tcrlr2Xevfvd75YvfOEL8rnPfY63X1pakh/6oR+69u+2bRNcNRoNefrpp+WP/uiP5DOf+Yx8+MPfGkt+o46X5H69JPcD7xfDHxb3xHnR9++UpXuT0v+FVQIGLV9kXx0OVWzqseQZB3ypxkOtOpSQ0LMTDIeE8frxY++TB/d9kJIYJD8nZPIwhqfGv1Tk4QZWCSAKJvHgfI5y0+oDAxKbb0p4KssgUwKccEiW3z4g/qJLf1jk1CKN7Jt39kjq8XkFjOA9wrZaoaSYNJim41Fxgn5KfjQvg82KRcn6uCO9fGwAcmDA4MUBkADzYq4XJHdTl8T+/HkazQE4IQ9GJ4v8f4AMbKiBYcMBjwPUDsLUbEj02WkRVAHlClLbNyhBJLgXiqy1YT3OWBelUf3AbinsjEvyKzNkvOylVXFv2UOfEK4VCpuDz18V6UF8gupM1Et1mf+BLul7LC+VgajYPk2i8xUVW4Dnj9E1gqpm3BSt6Ur49Dxl3fxIQDq/uiZO2C/64rq6bXuSEQfckITBPR0R36kplZ7eluD1YcTE5QUFhGAGx3VsNKS2o1t8+boYs6tk7lwE0cLQjtofRBUMDbR6DTXW7kBqDp1fUvEe4z2M9MC/cRlgFH49Sxq9CSWzNWzFYg50iBX2SQCMqaZL6UC3aI4r4as50coVbhgCKAFwq+7BJtnNam9EApt1biZiAxKvt5aIs2+SFULooQwH6ONDLZC9uq4KwFsJ9szUCoEJTKlaIbyv0DcIkFirEyg2RzrFPHVVmjeNy6Nf/sAr/sC53/gx1Yww9j6xOuLSSAXEMYTRH954c6PO65nk/tabfvk1J7k/9sLHXvVjXV9fJwMFIPXmN7+Z99PR0SF/+qd/Kj/yIz/C21y6dEl27dolzzzzjNx2221y7Ngxefvb307g1dXVxdt8+tOflve///28P/9L+kvfSOMxWNcZyEWbB2Ni7BkjY9D3mYtiJ8JiY+PM7xf/9Jqqn1lbFwGIWUW9iU+qo0lKSQBXbtAvoYvLBCxgQmDwBmujN2yyWIGJVR622sQcDyijUOO/QUKcfteA+Cqu+EqWAlfwZ8Vj9Hb1/M2spB66KpFzatsNqeTHP/MeKdzUJ870HIMk6YWJhKWwp53gD2Z6HMD27mGpHBqS9bePqQN/W79os8vc5Nt86yC3DBu7BggSYOp2oiFWuDTfeohxEeXhmMSfm2eoJOaOf/y7rOapt/ul1m7SW4Xohej5dTIkTjRI3w8M6Fs+H7AykO7w/LHKj5T2+OdPq9LluQWGoILhAytnlOpSTxrcdoNvDIGb2tV5Pu/+v92gTy4ymROj4TCMFNlVBBi4XtWa5LYHJXJiXkLzeYIiZEx1fn6C/waZsLF7QBzkXqHQGwDZb0qzPUzfkDM+oIBmW0S0Sp3FyNWDQ6quBgCjoZi+4FxOqt0hsQe6FLBC917L5C53HFQSJGRDZGt1RlXdTjBAti+wlCdTCr+XjgqmlrQIXxyWGOywKQ2kzS9nxP/8ZcU6BXwSvbAhkRfmCPbxd9iARCk4k+HxuPBDy3YZ7AqPnjMxLWv3D4i9bxurhxwwsLEggRy3GmfXCJic2/a0Wge2KXCFr1evM7iV9T0ArwD6iLiYW1QeO0cY0eFbLRFE7X3vp+jjOjr6iwwuRZUPvFpmZ1o1I8Bwf/Iyoyqiz8+9qt5Db7zx5vUfACpMe3s7/3vixAmyWvfdd9+12+zcuVMGBwcJsDD47759+66BK8wDDzxA0Hj+/Hl5o44HsK4zTtQv6RfzzDbCoQ4DNLb4ql0qI4lMEQ7ldEoVKiN0NBmWwGqVuU5gITg4XENBqe7rZ5QBi4HBsmzVr5xfYqBo89B2kfUMTejIg+p6oSm+siONpI/sU+72PtV9CLahLyXVQ8PKxG0aNAzj4IosVkX2bJfgUpHbcji0i/1KCgRww4EKk/zmHp90/M+rcmziE+KcOCf2OMI4HUk9fJVVM2CMaHbHYW9oTKX3P32Bwasouq4c7G8BBsUaoVrHCulSj+sEIhv3DEh5dwc3GB2fQVmvAb9Ye0x5weA1wuZeICD2UI/6t4PjLEs2d2wn+ISfSJteJChNPrNAiRBRFZRVATQBIhaWGVEA8Bp9alJCszlmUvFrYFswEZPkZE3ybxqS7EH0QTbFzJS5oMCEfvQEInsMPY4wuYdRW2Rcy5eCV4sblNjyq9WlePuISmzH2A7N7mRxbIeA0kr4+Zzxutr7tzMTjFLwljEVABMecKTw4xsQSh+A6qVpAsXcLd30XqktTo0RFFtj9aUU2AE7hq8JJikaUeGmti3+SwuiA4BGI1Ld2U2mECCYSxLYZu3tkhP/6d0EbWauRrYUrJZ/ucDaJWy+4hcF1CZB/kaiPmt3UAcVCMjSfQqE8jFAJkWQa283S8kR+xGazfOXBbB1/Y9k1fuvUKD8GXzmMlkzXgM8dtRKJWISu5il8R7X/ls11XvjzT/o+Q6Z3AFuXvpRr6st7euN4zjyC7/wC/KmN71J9u7dy79bWVkhA5VMJr/utl1dXfy3rdu8FFxt/fvWv71RxwNY1xmu9MOj1PJZqUoUl+W63OBCIncmJw4YAwAugJiJeW5nAUAh2HLtljjZidp4N/vz2s7kxe5NEyRxcKht9W0DcEXDlPGQcRQ+u8QS5/BskQd48tQmDyJu/2XKjIngYY+NvF3j4qysEQABkGjLGwyQBMjr//NZ3j1KnAGe4Ifq+w8v8nBGqCRqY+ARYunxZkbshSVV6YLnBAkMCeIvXJGHS3+kvDTxiIRfmGEKuANZcabVg0fA4Mri27vFV3LFqDoqVR2AAlIU+gSRjbWZJ9jDxhvM+XgX+jfrvLaOX1PBqXWLmVzW7iEl0eFA37tNykNRmsrhnUIUAzb1zPMzUtvVy2sHpgnPDwZ95IpRAmw4kji+KOHVVvVLvak6Ga/MEGgBqKBXEB2OqJlBLMPSXT6pDbdL4lyWABAmckzs+Jz4jz3fSorXRcsVlZkcYK/eYDwFnit6EQFgAP6wccmqpUqVXi/fC1ek3hvne4DsZMDHhHW8nzC1A8MERHg/IDQUjw9sGvOsIJ9uZsVZ2xB7cpbgDxIf2SoAH4CpaFjJj6EQ3wN87btSBKdoAfCvFlmHhOcKyRBMHnLLCIg2s3xcAGW8Fnj9AOQMXTr/w9OKHQQzaOhSHUgo3yG8W8jmMlRkiJvLS2UoxrgLLRxmsn/xbXv4+BDuCqCMnkf8WdvISH40KNahMbHPXPwu/Zjzxps30OC4cF7DR+u4GRgYoOS49QEz+zcbeLHOnTsnn/3sZ7/7z/MNMB7A+iYDGQW/neM3eVSmIAjy9n/yuwxyhPyCjsDmXft4aPNAaW/jb/vY6EMRc/eX1whC7KBO7w4ONgAwgAwcxIhhAHizu5LiB3MQDYmxkiGDRUAHczk2/sDeIMW9WGLQJTf9cMhDmtnbp17MkUHKPViZb+wdEnvvKA83Z21dGukIwdjcP+6TwHqVKevwJrn7d4hrNWXlgV6mmxvtbaId2CHTP5KU+kAbtxrrnRGp3L1LHrjpV0WfX5VGZ4QsUnP3IM3K/Nrzq5I4tS7pv7wowYwrVlij3wqADGAKYBVfH+ClfGRQsrsiiqGyHG4SAoSxN7DhitaWkOwtXTzw6Xdq5T3pRfUbln+zQmYG8iEkRYAsLAaghsicW2MtEFkXxE4srJGNA2iEYR7AzOpJcisTeWHm9ApBp5EtKTN2KCCNjrBE54T3SaatWqekVrx5QKXXmz4FuMFaEtTgfhMEIohKaKTCTIDXlzcJuGrtulQOD0rx3p0MfAWTEzw1QxDoe+aiWg5oSzAuwp+zJbvDrzLLAMJth0Z9giCwgf2domNLsi0ptaOHpLqrR5n6AcgQv4DKHgykPPjtOtOil2sE3EhrpwcQ1TuQtfH4AchCQVbwcEt1+wBfWzBhAEy4hg0Y7duiYiQSNMhDfi6PtUkgW1fVPghHRV1ST0wiExmCwMizU6qqqS8lTnda4k/PMFrC9+wF0Xw+ye8CwDL4OjXimpLHYzFGgHjjjTevPFxmeo0fmPn5ecp9Wx8f+MAreycxP/dzPyf/43/8D3nsscekv7//2t93d3fTvJ7L5b7u9qurq/y3rdt841bh1p+3bvNGHA9gXWdw+JuX5wmEAGSwfQcjcHhFsRQwv6M2xvf0BXb2cUsPBb8Hu+ldAmOEwwmVOpELa0pSc2yalbk5Fw3S2wRfFRPaEyHFvMB8jvX+sW5KYQAMkMxgxtaSSmbZvGeIPim9VGX0AJgAHLBrh0OiR8I8MJH8Tblq37gYj50Q/dnzMvCfLoh26rJYV6fFfOYcn4O5bUQ6jxekOK4kQchXXS/YrIEB61buNiVyaZOVO7wukPb8fq78b/zQLiVnQR5NhClPpZ5YkOTlr22Y6HMrZHrA1ui5okTOLkv6yVXKU2C+WB/jCkEJpEaYtROXCmKnoqzcQYRBM6mYLjB6CFjlViQCPmN+tdGIbcrHX+SBDQYQ0QhgbcDSwftlvfmghC+uiTY5p9jHoCnOxqbY+NygnyxjMKOCYAOLBUm/qBikWo/qkSTQAkg0DXFv2s2vYy0ukTXEa6lVlScJ7A6AJKI5+DnLm5KYapBFA8jgAJxBGu1tJysH/xLYsvzeFBm81LmaAp+IuGgtNRAsIiQUEQd+H4NjA5sNJQVjUM3TaIg7vUAQhXBY9hyilxAsYTTMuIV6d5RAHV2Iki8otgpAR9NppofXkO/jckUKO9vIwiLsln/fkZJj05+UwONn+H7A64YQVfgM+To3HJV6X60pSXxAGWhZ8wRZt1wT58A4AWloo0kmFIZ/vqfgPQMz1vqzN954890dGNxf+hF4SYPDS8d1XYKrv/qrv5Ivf/nLMjIy8nX/fuTIEfH5fPLoo49e+zvEOMzNzcntt6vWBvz37Nmzsra2du022EjE1929e7e8UccDWNeZv3zqg/SkUHZaXqfXZeHukKwfDPK3dXN8lIyJ3tfDKAOyAZAJcRB3p8WF9HVxntUjzGfqaCcrA5Zj7QfGxVjP0UPFxG0EZa5kGfNAWe7yDA9IAKutgNKvvWq6pB6dUbIVDtGuBFf6AXJ6nikxe4hgznZoJoePSQ+FRNs/LtXbxgi4sMll37pHdHytQpGgyqw54uRUIGr0hXmGUxpnrkr6byeUsd80CHh862UFAi1bGjHlR8LjNOZXpXhTPw9XY2FDbQvCSD3Uo4zRrivVHV3Ku9Zs0khNoOo3xVzYECcWEDvsZw4TUt4hg9ZTAdb7IA2eSwJkS77mfwPIk7FhqQ3ExezpJigDu4X0e252BvyUIsFGEQyZJkEztj/1ZEKc2/cy/BSSbK3dJ+bsKjcuUbHDOqCoks7gF0OuE14nynb4uwO7Rds2qF53fCfhWlo2IxGwQQnWC38XPDvHmAlcI3qw4KNLRAh0jdWcBKc2pDDsl8hSTfx5S8xnL5A1RQsA77taEzce5dYqaotcvEdghVrNU4KkhA0AFAqKi2taKKvUe4Stzq6yXQDPG8Xj3HTcKIpWrRPk8Zoy3BRAt8LXHP2XeNzxixl+DdwfftnAtUXlk46lBDQHIHYDLK/fkLU72ul9y+yOqPd7viihmTxlRvjZsGQB+dGK+Shzm0+cIQNc3N0uRkMkf6CDAI6mem+88eaVh1ELr8WD9e1dXMiC//W//lduCSILC54pfFTRCCFCefGnf/qn5T3veQ/ZLZjef+qnfoqgChuEGMQ6AEj95E/+pJw+fVoefvhh+dCHPsT7fiVg90YYD2BdZ95598eZkQRKFYAJtSMjfzQnHadr3BqzUiqsEXIdOuFYKYPWl8Wy2JGAqpLBoQe/SU8n2QQyHJWqdP71ZW7RIYEbMha8UzjwICHiQG28aTdBALxI9Z4WE9DRJo2hNFPk6UmC8f1IJyUwslWxoJQGwuLP1FryoSPNqI89h8jPwiFZ6TL59VBDoz15msZyJ1+QzOGUhJ64QOMxU9xhzAfrAKkq3c7PhafHt1EmECJjEvJLdNFRzAPCMPcPitF0CQzpLUvE2TcIVqg+1q0yomCB6olLfXsnP5/PK2CIW1G+MpQau7fuI2tnT88ybBTeJl5DlF2jcxHyaKMp1W0pSnp6rSGhC8vXgB2YH8iq2Oq0L16R1ft75diFj9FDpqXbmfWl1e1r/jIAOAAUbEZy88+ylD9qKUc/V+XggDKGly1ud9rdbeLCFwZlE/4nnymN7hi3JbGMgL8LrdTEf36Oaez4OpEvnmUoKgAYrufWa4rnAeYxeRWA0BXjqbMq/XxiVjFiuk4JGACXhnfkVMGzBRaxWGJyPbLS+HoZBrcv+f9gp1odg4jdwHuK161VZo2sLsqbuAaQH5dX6dfi88J7x+dTSwGUbzW+5/AeRf4X/G4AugTHAZNbnl2Pr3EbMjFZE0m3McAWrGqjKyr+iRXlwUvGJXBiUmR0QErvOCKyvCaxh89JaN2WZkTj95MNT5w33njz9ybJ/Q/+4A8oId59993S09Nz7ePP/uzPrt3mU5/6FGMYEDCK6AbIfn/5l3957d8Nw6C8iP8CeP2zf/bP5F3vepd85CMfeUO/0l4O1nXyRu7d/YuiJRNcX3dyeWZHoTrGmFwSa8cADxbIQThskHoNPxO8PCqM0ifO3KLoPV3M+wGTYA13iXFxVm0M9rWJY+piRQwyI5Cl4HmBudhKBsW/kKU8I8+cFu2mffRlcbbyq+DFApOxuEbQAC8Mgij9sxsq5wgHrW1T0jn8rz4lpX6R4S8UpZkISGClRB8V1u4hSQK4kVW5MiOydzsBGLxl9MNEwqy4AatFNsxyGO4Jgz2KfI1Kk7d3sLHW3qaAkONQHkKwKgbA04n4WV0DOZQ+MVOlecOfpVealKAAnsDQ2HOLou0bI0OCXClIfcxoujzPw5tm7HCA0p3V0y46JVyHt6+3ByV8aY2mc4S7FsfiEp0pswqnfMsIpVpuYcZDYocMZpDBPwQmh6wYzOp4zDC8g0XayBDwVA4Pi7+gzPF4bQnE8FpNz4k5OkSJUZtZEg25MmDYEOFQLJP1xNcCE7R6b6+4pkjXn5xR99HXJQ+d/nV5y/d9QgKPvCjNew6RYWLKPaRayGYAu4mYNAbbxb+Qo3QHEAeQxbaAthilPSwcENS2cqn49TVNvY67f5nXFl2Y9ArGQuJenVV5W8j8wuPFz+1MVhU9I2ICoAt/Hh1QAAvSNa6PZUt1vENCk5vqfd5oko3D2BeuqrwvbHFG1GYnvWBgU3FNV9cYXsrtSzzubFEq+/oYjAr5EI/92PzvvS4/+Lzx5kbNwUI+o2m8etbHsuvy5dO/9V19rN6o8Ris6wx8Q8wGioQpsYEJcgImDeSF0ZDUuyKUX1bvShNIGblWVlWxxJBKHiatVXlrqEvMmVXGFYAVMDfKLOgNT3/NGIiDGFIkmCsnGZHCtrDqdMNKP9ibSkWsjhi3tGB2rg4nyRQ0+pJS7fATlC2+c4DVLkxiL1Vk1//5Ken8yoZs+4NpyjXBK6tkIwKXlkVb2ZRji/8XD0/6bvZsE7k4JU6xJEdH3qPYttV1qXeEuE2GeANzOce6oK3noCM3qrNddMQmxCIiYCpWN/m8uHFmGASgxoUZtf0WD1E2Rfo7Nwk3SszAQu4UUtxpZh8bUZuQYJkg0cYDZMKszU1VL4SeR11XIKjalML2KP8M6Sv0zBVxUjGmxCNQNX4pKzoysxJxArri/i6p9cXJ+AXOzVNaZH+ibbPrkIMev0qDSefcYERX4GRGtOfOMlDTmpqRyi5lzDR3bCNg0FcyogWDCtiAKcJrvrQszuVpbkcCsHR9/qp0/r8v0OQOrxpAI9LR8ZhRDI5aHLB4erqdAOzYld+SY+ufJmBGzQ/AFkz9SNBnlEO9IeXtbVyAaOwZZHI+QU97Un34fGQqubmJbUfEVmDzMVukSZ4LBpGAAmXZPBc0wNa58KUZOr18zB1b3WQqPSI/YI5Hz6KK7zDUFmO5TvYO1wIZXDDLy+Iqq48IrvA9gU5EbDqiuicWoeke27fVDpOPDSn6HrjyxptvYZzvwIc3r8t4AOt6g8MyHqU8A+kCG3OQjRAw2nYmp6pHIiHp/psp3pxhkQip3DmgJBwcQI6rzPJI+EaCdyc6A2GIL6lNrdklCc5kRFvbVNtvkHlwyBaqkrhUEj0UpBm+dNuIiiSY3xANoCtfYalwY0cftw/jJ9SmWd+fz9JThEOsfPs2Gf4vU0riTCXY64dkb5Qns2Q43SZv+pHf4WNHhx62HbWRfpFdo4oFaVrc6ENdClmOuWWCR5ib8XhoXsZtNvNSvGNEHdI4xFFyvJ5hVx83IYtlSo1GVwflQ4Z6toIqwRgBeCKGAKnp8D3Bm2XtHyWzZMdCUu4PSrU/Ku6dB+nhASBk6n2hyq7DxMOXCHCYDwVggNogyyHjCON+8+Aor40/1xCzYpPpIxAxTRZlAwS79brokF7x+Pliuty+g+eMie31ulh3HxTz7LQY+3YySBZABNe0cLhb+atgsMd7IJ1UeVs7x8QYHhAr6lP32aLmuXiAah0WbpcV6I5FpdGnZFAAJy0Ukrfd9hF6ngCi6c8b7pFGKiS1nT0iqTa+98IzRYIxXD/jxGWxh7sVywVGS4R1NPRKgX3C9aHB3q+8e+2KeeJjT8TEWVyW8NWMkoURXhqP8f3CINFYVMVrJBNf91z4OHwmN0BRoI3PBRBHZRJeg8ZQisCsMdrJkm93Y1PcxRX1OqXbxay6XGogaIdX45Y3tmTgjTd/X7YIvfnujwewrjdgIWDuxjYYMoOCQbIy9uKyaOtZcYKmNOClAVDgdp2tcqEKdeVZwtp8Z0plRLEiJcBEcQzW15lp1dOlGA/4ViyHTBeLem1bjGKVBm58Q0SfnmQ8Q21Xj1iZLM3bYHLM5y4S1FV2d6vPBaMAySxfVOxYPEomCwMpEqAE/W+o90FGFOpuwDTQywPzM8JRHUfKh/vZD4jD1bec5/2BdakdUkCKJniAGp/JPsLY2XUFKCGxWZY0dw1IcKEk1dF2xWTgmzoYoHkc25DHZj7FJQD6gMBw4PP6OsU3saQea6l+rZg6OlOVyJllypSqT89mujmAiIU+w/0jDD7lYsFIH6Ms8Dxh/o5dASBeFxug5tnTEjo1K6u3+vh6ZN8yxHgFZDMhVLWxE4sCyre0ZeL3rcForl4///Er0ji0TepdUeUfG+plb2H89BqBIYAEZDi83i5ADsCa60pgQSXII3MLSe3cBgRoqaPKJqGYulqdKej0ShGYO3zPEQy1SpG1K7MSWC7SDwaQgqT9h0/+Ghkuq7dNnANjYkwvK/kSm5J9KQIWmtghNYJlbPm6ALgod+LPuD2uWVenesyQGvF39QbN/6ofUTUPAGgDdFlpFRjLbUm8jGenxX3+PF/7RmeUzw0f/nOzBMTmMxcYhKql2kRLtbfS4RsSyIFV09jxiRJsHUsM3njjjTdvgPEA1vUGm32X5xgaSg+K6zL80ujrUcAFh+7lJWZWwfgMKQW9eWB2WOUCKQTszfQSD1xtaV1FNKD+ZSlDyQ8AjZlXrTRwlu52p8XuiDNugC/SxLyql4li29AV5y0HWY/CDbDxYVWrkm2IUWmIPaoMyOzzi6vIAgyM17W0jwckpDGyKLjv9SyBEmU3sFYoIIZ0hHBQx5HSXrU+X75pUKr7B1SXIAZdfdu6ef+Ic2AUAPxhOKA1jUyfZln0mEFWhR8KAGTp7b0iYRUZAHM+C7O722ja10qqPqbRr8AmNilZlQMJanlVBW1mCkz+Zn8jri88YXEfQ1kZa4DP6+8S16fM15C47I4kN+TgD2qO98nIZ+YJOgN5W2odITEAcBCmemVJ6qOdyuQe9F3zY9E0jtdnuE8C0xviQ5gopDPLEd9iVj3flYIycqOQG0b0bf3cmiNjhM9tXbOtVH+yTLj+xQqZMjJ9+HeknbcAEj8FjyMSZiUNpcViRcJnFsi4gck8OvDz9Nth6xAeN1QS4b7AErIgvFxXESBgtugNa/nD8D7Dhup6Vvmw+H63GXoK7yD9XwxPrbfiE8LqNcEiRqbITUp65raS5rFd2dvNr8Pt1lJFmf7BZuJ5otMwHuNrjdcS16A51strD3B17z0fVyGtuBbeeOPN3xuTuzevflq/rnvzshOPSunAgMSemVZMgt9Pr5DTHZPg1XUCGWukm0GV9EVVa2KUOxSDMdrHbCRkHRmKlFHGXsQDNCwpH+pjjxx68cgS4MBDyGi+ys5AykdT85RmeCiXylK4pZNp5Br2bLG9qCPnKCS+2XUx82Wmh/sWstwQQw2MeWVBFS0jibtel8RXploZWy47+ODl4b+RTbHox+GG2mpWZCROBgJbfJD18FhRkgy/l96M80DHRiHKpFFYHTg7p9LE/V/r3MNmXXQiJ+7kHINNceh2Hg+xDBtMloE6nmJFXICzYkmcwS4ay81CjR4eBGpKtkQ/mj40QP8Wamx8V5e5dKAP9Yt+aVoiS8pzBWkVfi4zozYXcVhvvLVfgllHosubKuk94hMjFRe9VpfwyXmyb3j9cB0A3OB/s0Z6xAka4sdGZwuwoGPQjvrE7YyIL1vjc0VumeOLqCwoFIDD9+YzZfHemAx+5irls/pwO712eI1p8kdpNKp4pufEAMDBcw0FmcmlpZMMKTVqUdFrTfrAyIKCqezupP8LQI4diql2FWwL/1ilKlore+qhlf9IUzuZNLzfMgUxwJAibZ5MYVNFZiC7CzIgljFW1xjxgGBceNxMp5eZZka+VZrNkmroyJZi2HA/AOINS0xsEmIDccew2CfO8XPc0QG+Z8AiHptSEvQDR36FNUiFIb+8+MyffN23GYqfjVRUDLxGiLHwxhtvXnleK0jyANbrNh7Aut7kihLcbKjf+rFeH/eJFTElfGKGeUNOb5sYqHPpSYoz2K7yoaYWRff76N2BAd5tC4s1kOaBbwcMGruNiiHhqRwPE2N6iaZ5dMPJxAy3Fn1NS22lARzxQPOx7BfsVWCtzEMTshukNp9lszJm+e6k9H5R1aLw8D93hdttWNcnYwIf0UuSx5mRBV/Z1UVxE+hNbJBBAivGxHBs+QF0YZOsvU2slTXRt3XTDwXJjtlR5bo0t3e3NvFa9wuQBb8SJCuAnGpDtL4eVVlTrtLwT09bICaBK8uUuQRp34NdBBfBbJkGd3iwsuMhSZ9SzBA/J2gyQBXMmW8jpvxf4RDzoox5tbkJYOqurCsg2ZFiTlfyj54Xd98Obt0F1hGFUVHAczNLRgosU328R3RkPQVNsaKGhGcK4nQkVBL8tj4xzk2Kbhji7BwiAIW8WOzXpO+Tz0v9LfsJouxkiJ8/+GeL6lpDHrywyK08bMuFp7N8eQAqkT+G7T9scFJOw+sDFqjcUHI0gBPABgztADaRMIE0ZGIL762qRZDKgeSHaIdoSI5ufy+3NlnEjWuL4M5cXqyNjHo/AFAjogG3zxdE0zUFrtqS4iwsi4Fsm2RCjKUNcQDAdo+qYNdWmr4GZg2F0GQkTS45gNlDkj+4LCTHCyIcEA9x+TfZemAFNbFuTUog74gVFnmw7X8h64aFDTB1xVu66MUKhHxiPn/pu/wjzxtvvPHm9RlPIrzOuM2G6pMb7lIbdT4AIyUJQYYC8wGWphlH8a0ummVL/oGdlEm0r54Ua7BDzKuLZC6QAQU5BD1wiHIAU4SDzLVtMUsN0bMFbiKClUCFDZgumH/JXvlM+oiSD19W8hEYjIU1Kdw5KpWxNNPcmzGR+R9IS3NAhZma24bVk4hFWUgMhmX2n48oGQ+bXAiWXM1z+8xFuCdlKZ09iTCQh9YbClyBQWs2Rd+/U4wXr4jeFNFPX6HXB/ISQOXKPWmxBju51Vfck2ZCN6Mt/DjMXd4PfEdgYLA5B6DpB/uFgx25VKjQAaNVU+wFZK5GwifRpaY8/PyvqDgCbAlWLOVVmt+kpwjRFLx/gKlGU4qH0BlpS+22candsZPRBqlzdTHhc5tekMr+fnX71XWVSQbwYegEGoEzM2I8d56AC3KrVqxyQxGAkWBlqFe0nk6Vjj7YIdG5qgz+12luPOa2BxQzWbfEj+BTgBRe5xZrVK2p6AiEaILdg4SHSILLv0nPHrx+BNNLa8rHBKTSep2R2XXNFwYZFR2NmbIYG4WvSYlRVaujFSoEZfpGjteHlUpNi7IxwdVWvY/rysabexQYArBrMVPa2JBoLb8XX5uxIcWiOTbfV9w0bT1+yJqUziExrmUobZp9vUoWxeNudVMi3wpg3Vd2JbpQl0DOlYey/w9ZN2xygglNnstJINskc+lUlHTqjTfevNLB5EmEN8p4AOt6g4Okiowmh94RMBf13oRYoz30QzXhFXJcacTga5qlrJL86iyzpty7Dl0zcVduHlHVMks5ydzUwUNISXQV9rbRzAyZaH6RwaPYCmSyOwIwESiK2pNoSEpvGeeBZo/08u9Dqyjp1XlwD//hjJhVETNXlcquTqmNtEtlZ4fKI1rP0kg89LkVVeYLEz2kLwCnhkPZDZLiFqMGXxHYp8ahUXEAzmJhPh4cvrHHLlO6Y3YWpMW1TXERZN6w+Jwix07L5pF2sXtSUukw+Ljd4V56gnLbg6I/c058T59XDA2Syosl9tBZve3im1oVdzMj7r4xiZxZktB8gfIRBv6f4vYI2SB4qrh1l1TbcDCO48+xUyuibWaZ0WXUbNEBaAHkWinnoYWC6uLbMSyZPRHF4s0uiLttUKzxfqnde0DFGHCV2WZHYGV/H2MOSmNtDOKkCRsG8pACPfalq9L18CJLk/H8kWNGOXkrCwsABoAKWVGOw+gFMoq37aPvCPlcYPzsnnaCWSTsg7FCfpkL4DO/okBNq2twy5vlIK4CHYRgkwDWAeZQ4xMJS+mIApLOJfQBOpTykMIOuQ9sJRjP5KUKGSv4ALG8QMkQ7xMMANJW4Ci+DWJhRnJQLsS0crP4Z2RadbQxJBT1PWQOa3XRwGzBvlZGB6UwsPVLT/yyPP+H7+HfH1v491x0QA4YZG68Vnhujzif+278nPPGmzfOeDENN8x4AOs6A3aJBcuaJvW2gFT39dOIDFYKrAayicA+Rf/6BG+LEmcwQEhS912aF/PcDA++8KkFJfeYhrQfXxMXAMnnE7dWY88evEnF3SnRB/vJcAEEIJ4ATBC3tiIBeejsRyX2/ILUDgzxsdGPM70m0Qsb6iD3+aT3b+Zp8kbnHpLlAxs10VCZgsNzeYOp75BmNBj0W1t9JlgIHMZgUhABtWNAige6lOEaweFlHLam6q9LxJkFVr51lI8J5ny3r1M6TlbFivrJCOldaYksA7hZ0vaHz8lDpz4ixW0xAqT0iRzreuDHspMRmp7xfJwdwwwbBRBqHtrOyh2rL8WDl4AC22z1piS/otLN4dUBK4bYBchYYKpwneD3ObbyH8Va35RyX0A2Dkb4uJFAT/anadOnhcDUzodnCTARHaEvrJJxC2RaZu+nT0llX688fux9Ygd0KQ+EJDJbVAZy25VG3Cf+jQpfa7l9P8HOVrxDvQuVQiq+4tjFj6tScIDAWoPxGDClI0Lhkac+xGgD3BasJaIccrf3yV3v/B2yTA9E/7lYe4eleXAbARskZ2yQwocGcIoqIS2M3j9Ee6iwW/qiCiWJnljgooQc2cUScbCD7BvE5iley4if/Zh6Xzfvg74svLYbm8qUjvwryIOohwITtbDKPwOMXQNWLZM+wCLeq9AEIXPjtSTrqmtytP/fyDP/378lYAWL9UqDTU0TKfFnrnxHfqh5480bebyYhhtnvCT36yXmhn5MzB07eEiDLeH2FqpkfIZo+ZJifVqVNNy88xn0GrG8eCOrDN/4jR5hmakopR1uAgJItIckcGqGt6EECeBwflqK9+6U4AY2AlseGxinITcVS1I9OCSBDXhybLFjas0eh15z77D4F3OKxcDmVygo2cNpCS83JHB5SRni4cdpNKWxb0jMbI35UQAY2KpjGr3tstcPzwGgDMGP8CSByULhM5gx/H15VwevUWBTgREat8GA4EBGETXiKDJZHsJOscgATcQT4LkhABWgiWzI0ioXAeBTA1hkCv3UGsEi+gpDV9ZpvkY5sLm0qfKYAFQQ5ooplcXp6VCp93juuM6Ib+huo3RF+Qw1RWsb0tw3omTYhTX1GLeqgCCZQZrTNOZZIcLBRYxAvSGFQ93iK9kS+Mo5hnJi484+P0GGaWuODr+b+U7+xTwfD2ItYI6H6R3bob5MhbIawCIT6+dXZePodkm9sMnk/4dr/43380DkXaIBaAEIDw+IhgyrSFhqo2kJnl9Q234bObFW16Tx/bdIaCZHiZm5ZWA6N5QXy+pE68Cq2nDFAgMGnqsWi1YfamPAKyVHMJldKbWAgPgGbPhNzYjR3/u1/kOa4zXFcLXeV2DNkNHFaxePSWl3WoJfeIEbmliSYHUQQDE3Jk1e67V/tF2Mhiupp1bk2MQn/s732/13flSyO0OS/osLSj70xpsbbF7PJPf7xt/zmpPcv3Tlk16S++swHoN1nUHyNIAIwBM8NNhUc6fmWrlWLQM3/C6LawRUYImYaVQsX8svYqgmzN3lBn0/xlqWIMbMN6Rw95jU9vSzRoWAZGxQ4mc3pDAaVNU4MLhLqztO01hPwliCNoRx2sw5chGgeXGevXwAGbWxLm72JS4XJXBpkd4oym7IMIpFWWiMImAYuMHsMFF+s0LGhyAJBAXYJRi052AKrzDXC94pbBBGXpznB8CifnpCrNGW7wYHOVg6hI3ieZfKou3dwZR3RCRA/mqmwmKFDAaIFu7fRV9WdXcP63bMsgrAZGQDJLh0XB4u/7GYKzka4RkJgO1G25b6UIrVNWCFHn3sA+o6gYVBACu28gAQYPRHTEBfl/jWSlIejKgAUNsRZ6CLSwK1/UNiXZli1tnjD71fxWSwZsYQB4kW5aY03rRHSjcPqu3RNx+Ue9/6cQaAPpj4l3zNigMByR3uYMK6HfGJ9tQZaXbFCBa5IQk2yHHpJUPUQfrxBYIrbEViIBMi2BV+KHf/GMEdthjt2XluqjLOIBUhGDK3jzJbDLEK3EoEmDpxQYFnSK3ZMrdaKdsVS8z+ArirbFfBpgFEbCCyIdFi9HIlFRwKAAU5fOe2VhWTyxgIGehWbGcrmJT+teFeaYx1K4m4WuOWqXbzHpGgXzFZGLweAFio2+lNi950xfZr0hhoe/kfQicvS9ulqmj45cUbb7y5/ngerBtmPIB1nUEqNaIPtDOXCSCMtYLU7tsv+gbyliy1oQemBCGM2Jwb66f8wm8A1MZEw18Lk5xdohzHQt+1ggrctFzJjvu5MQiJBcwWDqXUizkp3tTP3CmU6+I+YGi30zHW3ODwZtAmH6QmlSMjEpwvKO+L64o11HktBBQZUprPpwJTy2UJZpqqvmdulb6g6nCCIDH56FXFViyts/zYChviruDr+FQSOJgpSJF43qw+cUXv6xFzYoEHsURaFTaJGOUsSG+QrSAl4uBFICiYnnKvT8ojcYlNlwgkjLpD4MQ+w2REsjsjElwqyhePf1hu/6e/y9oVmrFtV5z1DXqTfM9dFH++IRu3dTDpnI8JwADZUFXFrCE6AEGt5dEENxCf/Itf5PZlZSwl+R0xyp+PffH9oh3Zw0iEBw79ilRvGqWU1uyOk/2DZy44n5NmRCeIZb4TQlBnV6V5ZIzvjfYXNiWQtaTWGeY14zIASr7xePDyOA5N/Ud7fpZAFzKe02iQWUOcgv/igmwcTojVleAGIoJsHb9OcMptP8dlSjt7BOHzwn/BJgHQYmtydFgBpEiEZnlWEqWiDPSETIsiaHRCIm8M4bRkprhJqJLst8AVPwBMwZ5hszCZUNlvkLKxcZhWAbt47wMQV0aSLBsHY6udukzQBr8ivV4AY3hNKlWpDMZEw103XGnGTHngpl/9O99nANIEb6Wyej298cabVx5I9K/1w5vXZTyAdZ1pDnfSZ7XFNuDgQT0Nt/swkJkASlAjgk01gLB8hf1+MMMjz2qr9wl1J1sbXzx8wDxlG9L5fz/HuAVzvahui9vNLUvs+BwjGZpplcQeP78pjWRAeZa62sQJGGqTz++X8Fn09jXIaAUmVkU7fo5fq7ajm1UxGHQn4vH6ryxTFizduV20yTluRYIF40ZjrcbnwOqSoiX23lFl+MZTrzak2uWjhEaGotXd5yDUM+BjJQrYEZRYA4gh6gGHLh6jOb+uZL54SNJfmpPouTUVSZCriH8+QwBqD3WJVmnQBO1enmIQaWShxtscu/AxAi5t2zDZI237kJJUm/I1YADDN8zjpsFyand8iNc/ciUr+qkr6mBHcXVAl+Tnz0qtP8oya4AwMGnYxKREOLugtvSwQQh51rKl7bkVtXEYCioGDqGkqPZBmCpuIyLh0/MSvrLB7ygGcmI7ERESS6viX8qri4jHGvCLObaNfi6koLv1hvgq6gceIgpwvVGzhK1SfD7fhwPt0uiOk3XC58AgbvWmFNMWD3GDkIGgjQaBVbUryNeCoZ2IzcgXlYeO5cwNMaeWlVF9C2gB2OO/C62OQfirUH8DlgrxHeUKi5zlpj1kY/ELQuTiujgT06JfnqNkSA8YtkGxsIH7heTZnZboqSWJzdYl9eSyRCay7JV82R9EtSavyVZxtDfeeOPNjT4ewLrOmBfnlOenUqW/BQxRY7hDrciDRcCB0J5kLhNkIXiiSuNt8ta3/Ra31yC/oUzYSgSksatfbYEBnDgOc6XMfF3suw6Q8UFiN+RIslQ4WE2DK/K+s9OKDSuUxJ+t0VxNLxHOZL9fqrePiZNO0jMFJoHAY2yUDApqZeIvLFKmaT+VI4BaffuIrN4UkuB6XTR0A6JyBSXSE5PcUnTTCEbdEKOstuVYshzz06MVm67wsOXhmctLeSTG/j48Hhe5Wa1EcgAKfXlTtBcuqAsJBq0v1TqEdVW7M7tM4ARDPcY9fpa5SvAjrf3Lm6SRCoqZr12r+WF20pqSSN1Lk5RV4ZHavBfZTq23MSSpRFgVNaOyqNIko4V+RYS+IncJwBFm/fD5VcXKwcuEDT1MwC96Ii6uaSqQBEAM4Fap0iwOFgiLA2R+IMsB1LbHyMIBUALo1LpajBCAN+TGkX5p9CZUPAI8edzQM+Th6p9QJoaRPbTeZMQDuvmQPo+2AGZ8FUrixsPcrkT+F4ALZL773vxRslO5W7qZkg8psjHeS+CF5x8938oBaz12sIqcUkUB5Fa6OoH+1nsSku4WmxUMSH5/WrGxfp9U9vSIOT5K7x9iI/CY+P7cvV2Bs6Fe9Roi9yxbpOfr2NXfVr6v7Z2UcfFnvN4A+C83eD+o16mVnu+NN968/HgS4Q0z31OA9au/+qsMLnzpx86dO6/7OZ/73Od4m2AwKPv27ZO//du//bp/d11XPvzhD0tPT4+EQiG57777ZGJi4lU9PmekV1bu7yEzpKNnsCcl/tlNKR7qVltaAT8lIDAZyLnCwQQpCqZwgqhUWEKXVujl8U+sqIMZRcZDncypwgGGqh3ffEYd9F1tzKYCcAJDgcoSrujD0wWwc3WBpnDct69Qp3wI1gSVNPBK0YwNmW49w8cPORExCwjWxCakdHVI11fWpf/zyyxYhhwFAIBNMu2WfRL7ylW1PeZTAIM+q0qDMRVkQM5e5XYdtxJRSt1UMhPkI//JKbXtV7NZggy2yOjrZuI6Ig9o8I8FKfEdW/80txGP7vglMSvqvuEvIqDRNOn+/LSEzi+pSIW2GLfRsP0IYFDtjYg+0EeTdX7UlPBKUxmuAbLAUIV9NFrDnwSQxVwwxjzYlMsCV9dUD6SuSeH2IdYb6e1tKpbANFWg6tIaASLKlOExIzBqDZLj+T4DKIEEDO9SUy04YPEhfHFFdUECGCLIM1OgpOv2digA4/cRED144P8U16eTIQ1eXhE5d5XskB32K+YTrNxW4jJAKd47eI7YVi03mIoeyFjceIRvDdEeuJ54zvDs1UdS9MEdm/899Z6wXQJp+q9qdbHmFpRUgOe8TW218r2G51YsSXilzm1Z+OvIzMFHiPdCrUk/HgHj7JLUb9+pWgjw/G0lT+OXjXvv/pg8dOY3xHz81Nd9TwEEvtw8tPGf+Hp7440332xea02OJxH+g2Gw9uzZI8vLy9c+nnzyyVe87dNPPy3/5J/8E/npn/5pOXnypLzjHe/gx7lz567d5hOf+IT8+3//7+XTn/60PPfccxKJROSBBx6QGuSvb3MALLofWhQr4hfHD+MzVt19EntxSQEWSFKZHDfrACS4Pdc6zB3UwRQbzBCiHwo1Lb1tZBPAUKArDwZuBpjGw/S2MMSyO8nD31zYoAF544d2f02K3CrJNU36Xeo9MbW2v5EjOwRDOWVGbMbhMfR0iJ4r0ntD7w4GG3QtwzIqZ8AYbLxzlxgzK/y83JuH1Lo/QAJCKmMhFY6aKSs58mqGQMfp7VQsEDr0MgWp3rqdwAVRFS4YDRy68YjonR2yenSQ/iCwTsi1OrrrAyqnKR7iViQ2NOnduTglOjbUYhF6tvLjyhgPg77VGScjg0OcjEsoKH1/Pst+xaV/vF19PbCAVYslxG4qQS8TniOZKkhiAAW1uhjYfCyWWASdvWeU1xc+LQBOc2RQGcGxERgNKt+cpouxsMHnqfV1q9c4GCAQwnsCCwdb4JCSXIulpBEcTJLfR1ACFg9jzS9JIx0Rczkn2uUZqe7qUbVAHe3sVUQdDkz4TO/Pl5iRRYN4s2XkX89LaCojuu2KfmQvI0T43IDFzk2ykzEwm2Vw7dGuf6X+HgXTeD+cvsL6JsiUkGbxngT45WPHdd/eR6DluzDXqjzSuNygGNuGYkiLNSUdxqISWCmpzdi6LQ6qci7O8n3/6OO/zK/7xeZn+d+3+X78m36/MUKircWueeONN97c4PM9B1imaUp3d/e1j3Q6/Yq3/b3f+z158MEH5b3vfa/s2rVLfv3Xf10OHz4s/+E//Idr7NW/+3f/Tj70oQ/JD/7gD8r+/fvlj//4j2VpaUn++q//+tt+bMgasrsSjCtgh93FGbIzYIXAGjH8ET19lza46o66kTf96O+KjlRveFEQf7BVpJvN02dCNioDQNTqhMPq++VplvliKw4RD5AJ6WMRYXYUcp6QgYQgU1XIGyDr48vWySqAgQCzwBV5XZShGTIO/j8cUrJepUoJDiCMnX49KUqHkJQ6vrrK/CiAj/BSXUlly+sM4Wzi8K7VpbAvTRaJG5SFomgzi+ox4mD2mRI+vcByYQAKSJ0Oy6OVF+nFP3i3uIXCtSwwApfZNdGmFwkqQ0+pehSmiCOVPOCj5Nj21AKDUKtDCTGnV8gCIXyUgKs9IptvHZT4lZJ0nKxIY0cfPw9BqUgWx6EPZokm7vkVxdwAEEfCvB/4fZDW3vb0IkEGtjw1y+UBz2JkfHOAxQKoQf4TAl/bYir+wGdysaA4rIzdW99FDEAFEGm9rgzuJLCzaGDfArboFQxMrKjwUcOg1y7/1jEp7ElJ6CqqeyzVZ2kowGYsZ8TEJmdHUtxsTpzNDNlN/4lJmt9DT14kYMR7Ex4sgEsAeMqYeF+WqgpwRgLKUA4QXq+zaPqRJz+org2WI9ojBP0Aj3xtk3Em7YOBBWPL9xQWCRDxgR7MxWUCR2wBPnzy18RoV95E/P9LByBvC2hdb1D6jABXmP+98cabVxhPIrxh5nsOsCDf9fb2yujoqPzET/yEzM3NveJtn3nmGUp+Lx2wU/h7zPT0tKysrHzdbZAbcuutt167zbczYKJoJG864svVWZUCmQdsEA4tdSPtWgUJNsOiX3iRMg+kw0p/RB1U8FBhGwsSD7cLIwpEYUus3hR9uF/M7SP0ryARHPcJtgPlwzAFm8W62EGd3h14wDBg1ADIwDDBv7NlWgarUu+Kqg0wzNKqkvHyRVbggJniB54DOuMAcoI+efMP/jZBgS9bpZ9L2pMMTw1ObfIbOnY5qwqNLUdy92ynaR+bbwAU5QPK/4MEcYC9Y3P/jofsQ+c/SsblgeBPiLVHVfeUx9p5iOLApznatqVxyziznNyxQYILZITB7O0WSmTywmeXlGy5usm4BJlZpEzWfjIj1d6QNNr8YgcN2dwfE2eom9d3K3GdYDYWVZEGhjLhX8togvQGwLO4SZO7cWlWARuyNFVlbFPT+LQAAFa/SURBVDdNRm3Ac8aoBsifMLWfXZTk+TyBJsA0QJCcvKBYH10jyFUGcocMVAAyIb7bwiFGKdR29ohcmSYoxwQ3m+zsQ00S8r/YINB0CKqRAYaUeL21IQm2CyAIXj1EOJBVA6BD5ALiQ1Bfg/cRwCUYUoBdZLXVbUqTRq5MFg0s1NHuf638ZgCfW9eF79Moi6rpaVtRW4zcJtUNcbLo0bS5MYnAVLQRHB1/PyMxivfs+Prvz8i7GLD6rQ5qkOx4UB5M/8y3/f3qjTf/IMbbIrxh5nsKsAB8PvOZz8hDDz0kf/AHf0CAdNddd0mx2DIdf8MAPHV1KVP01uDP+Putf9/6u1e6zctNvV5niNtLPzBrN8Wkvm+QEQr61AIlEtSM4BBuDKW5Go/aHByAqFpBnEDp7YfUYV5vSvSFeWUiLlWUCdrUlb8Ghxg8MTAhW5b6O8hKuC0OTjBgDZvmZoIEB8GeDXpiGkmfuKEAQ0GRu8WKmlxZdRtuZJkOjvgBFEwj/oAHOA7Z4T4GYIIVAesAloemZMh9Qb8El6vsxkN1ie+LL5BpMweV14ngMODjBlt5vF2SL65L7q3bxFwrSOGubRKeLdJzg0wkMEFYtX9w3wdVxhNW/qMRGv6d5VUJLVfEPD2pzNAwSmexsWczouHhE7+mKoU2K5LZFVZS40uLqsNh8Z2eEq09KZuHk7J5UzuBWPixC5Ld6ZfUmaIUxmNS25YWbWKOgAZJ8wQOAA3FsviuLHKzkmwZ2TffNVkPj5PgE0Z63r6kMsGwkRePUirVYRKHh2mwg7er3jGurj1kuJ5uZQrf3cOv2dg1QCBJQIm6oafO8PoDNCL+ARIevoYVx9afSHy6ytDU5m27Vcaa44qV8NNDh55HAvCWH4x+rmBAmd8hgQIM4nEAaOPftupuMKgKgrSKDUcAT2RmjY8qf1aqjb6tRn9SJcvjOiP7qlanCR8AHEXYuHb08oUUC4YUe/jPjo7+omgIKkX+WaUu8RNL176vHmz/XxVj9g0D0PVKAyaXcjgS5r3xxhtvbuD5ngKso0ePyo/+6I9SygMTBcN6LpeT//7f//vr+jg+/vGPk+na+hgYULEMXccLEljI0ysjvSgzjoobMGjeRUwC5UNsf2VK9J7gQAsv1aS+s0/dcYsNwGHcTAZULtHJidaqe5ZbexjkEjW2dZE1ICNVrjE6gFU0ACgW8oxUyXT0ckaaHRGyCZC6wJbhMAU7BfYGshiSsRn70OrDgzQE4zbknXpvXMwrC+LCjwPDPJg3mK03CoxGQJULpR7X5eYcmDQ8L8qlQV1Cj57jGn9ssiylPR00Q+Mx+CdU+TPiBQDKwPBxYwy+KQAkFCzftoeHNL1kYHnQsRgJSK0jQJB45w//Dq8HpM7OL0yIjZV+gMZYkJualBgBisIhZoXZAU1KvSbzyjRLGOb5zJ/+WwleWSW7AwM5B7IjQAOudUebxE8h7TwmTiTIzT+wTejRQzBnoz3Eqh5cM/qoTJ3GeUq5ADJghuKq4LsyHKcnzYoFpI7XD3lRAb9Uuk2GeBL44HWCVAz/1i17+dwKN/UpAIf3RzRMr1jg4pJYYVNtHCKaAbEMCAc922J0dV3qO3rpfYPM2xztopnd6mlXCwiQnOGzgxxcbwF1LCwgPBXScb6o+i9xG5rlHZriMQa2VTNV+r+4dcgL5YpRqomdzcuXH/0lsZaW6W1DLAVZMMinriubb+4l2HKqVXUt8UtDax7K/OeX/X57OdC1NUjKR24Y5VhvvPHm7w6Uj9f64c0/DInwpZNMJmV8fFyuXr36sv8Oj9bqqgp73Br8GX+/9e9bf/dKt3m5+cAHPsDagK2P+fl5/v1fPf4B9sm5sRA9PQ+/8Ktkk3DAwNyL/rj1mxPiwuSLFPJEiL/Vs2DYdWTpBweY64M6GVSUUGoB0wB2IRGVyGyJEhRZiNbGmHZhUkmEI92UB/3Ta+yE4zcFmALLFt+62sjD7czlDA9bbvoFTPEvF8S8PC/u6rpiI6qoxSmoQ1U3mAOF7CqYp5Hmzg3JWpPgke8Gy6I/iQfq8+dVZtLSKrvk/Pmm6L3dPLwBQr761++lNwpgs3J4kPIgU9e7Uuprh4JSfNM2VdXT301QRQDRaIiTTiiWptogaAQoi51eZSI4QQLYKjx3bOo1LDJPrGkBKFnPiLa8Lp1/doHsYu6to2IHhVlXkLxwbe2BLvYjQm7cinoAi4hePUhmxf2dsnhfUhbvbZflu9vEeOoMn79/HRlQPspo6OeDdAkmEdVDeG1R7wMPG71SeMkMnUnxgQvzBBgom64lNVn4x8PcONULVUZewDcGozxAEcJeC3vaxWqLSOFIL2VYAIrAcoESbWAhJ+ZmheZzJuUjyd9yxHjshGKhskVunoKFRMYYmMpmT0JdOwAt3VASYCxCWdndUFulXJCAh6/1HmSWGeptEI3gOBKezvFmyHCjRw8VUQ8eofxnblMSNiuKwJThdSlWJfXINH9ZQLwFGbZg8Nr3Fditb2UA6rfM+BgY5AFsmZbvjTfefP14HqwbZv5eAaxSqSSTk5OMWHi5uf322+XRRx/9ur975JFH+PeYkZERAqmX3gZyH7YJt27zchMIBNgf9dIPzDu7f4Z9czhItuQQsD0IAoXM5l8rE3BBqju2/Ps0qGunrvAboHi4V7qeK9HEzOoTbEcFAywnJhuClXTINo2GFI/0ST3lp5m9+H37lVG8bol/PsvDnt4aABb4uVCDMjOvNtDARmAbcHqNBnNKPDBhw1uEAYOFD3xOOskyZDA+OuTBeISyEbfrgKviAQIASJ64vTnYTzYBz6t8904lzeH2maw4C0v8e/hkAHr8lxYkuFQiUNPiceX/Cofo34mdVJIRGC3z+CUmorMUutLKO0Igan+C14ByXK1BMAJDNet8kEm1jEJrg4nx8IWR/cLGpG1L8mpd4hcLEptHF6HLPj+Zmqekizwu/9lZKdw2KBu3pbilyPoXmM/BUJ6oSSjjkv2qvv0mtZwAGTiVUAxespVODzISYaBggTIl0UtqIzU8gw7CimgAMIjsAFAxDOn+vWcldb4h9ZE0w1ghEeJ5MnwV0RwrRQlmm/TpReYq9MDh2mIhguXc8EsVVT0QwKssrpLFNPaMUz5mHyYAY64gTibLLksAMgJJbJA6tpJACyXR8mWR/i6VVwUWbKxLSYcAWfCarWSksq2dXxd+LkaRbJTJsCHTK7RQvLahCMYN9UL4qeHMLMjSP+pTgDybF3tN/ZLw0hgGvN++cR7c/yFKhy8dSJUv9WmBWaMHrV3507zxxpuXjOfBumHmewqwfvEXf1GeeOIJmZmZYQTDO9/5TjEMg1EMmHe9611kl7bm53/+5+nX+t3f/V25dOkSc7ReeOEF+bmf+zn+O3K0fuEXfkF+4zd+Qz7/+c/L2bNneR8w0SPO4dsdFvDmi2SojBV1KMMbBbO7jhDLTEG6js3yULvln39StKZFwzpyjmLnNgh4Hjr1ETHX8vTSwCito2w44FeyESSothiLjiNzZfGdvCqJk6s8yNi7h8MLKd0ADaEgvVLw2TgHx8XqSbY22lSEQ2V/n1R6AioJG/IQvEv4RmxtrtnxEH1SnX92XjbvG2EJMdgdbPPx0EfRMmShi7OiJ2KMSUC4J9LOo8/PkdUCgGocGBXrjr3yYPynlGcJoZWRsOgopr44xZgJ5kxh0xAbdgCYkzMET8ibwvNA0jrM7PR3lcr0I0WvZK/lVgEA4frQ87O2IS7Yuybyl4LibO9X4ArT36OWDxqWxK4WxGoDkxgQrbuTrx0lvkZDYhcy0vHEsthHdpB9ASDZ3GNIqS8goTVLfGVXQosI90SJNoztWCjwUSJzsP3XaNCAThYQCe14DRCPARkQoBfXGowQng/yv/aMMf8M26fYJAVgAUjHVqJRqPK5NWKmFEcjsnEkRiN7fl9KFn6oT8UxYDsVIHtuVfwLWXFGVQAnAFATCwxg+JpNVduUalebfwBbMOCzbDsnxvZh9XgB/AAo0UM50i6+LLoaNSUj4v2FLLXJjFq6wHseyw/o18RTraveS26A4rfmWl0CL1wV/cI0mcSez5wVayOjOjv3jBMofbOBvP5K0iHeaxh4AeH5cnOtRQ1vvPHGmxtwvqfJfgsLCwRTm5ub0tHRIXfeeac8++yz/H8MNgr1rZRuEbnjjjvkT//0TxnD8Mu//MsyNjbG+IW9e/deu8373vc+KZfL8jM/8zP0c+E+AcoQTPrtjr1zWHxzGXGx5j82KE7IVOnikN1gXO9MknlodISl7QLypkymgOPfxOdSWjra8b8zWBPG3sYduxk7AJCGzTOtVJPKrk6yGNrVeVbTYHAA+3AfYJ6QswSzczom5rRKwdbDfoIiVXLcoHQYevqK+PaPsiNOtKQYNYceJOQ0IRJgc29Euj5/VZzhPmk7lycIayR8Yna1qYDI3pQYV+YISFC6i38LY10f3iUYnONROfbCr8oDR35FnJMXxEVeVSLKjb4t3w5YEztiSjXtk3gtrQzVMEsP9ol18iK7C5lDhSUADEBQKMQaGF+mws09mrEBbMHANJrKr+U6ZJIAVDm1uthrG9ym09EXOdpF078frBg+D48HwA+Ap5WvhJwr3LeRLTFTq/2CLVZIp1/KrLmydktMup+oq5JtZFf5TKmPdrIwG5EMVkhjYj5yxQhCAG6DAb4uYJl8GyWymQiXBaOIQmSC1lpdnP5O0QASEYSaCLcAli7+ImRfocxaPjIqZlUo8ao3n61ei2pNrMF20ZphbpMSMGNT0eeT8KV1AmG8buib1HaMULW0925TwO/ypJgw0uM6F4oSvFBXgDzoV6b7UlXs8xNi9vWIkcuLAaDYMrnD7A+mDeycC3kXnZoobgaQhsSN1waZbq06H4BjyLOM+3iVg4qpa6Np8lD2/3nV9+WNN2/YuRYY+ho+35vXZTQX4VHefN1AVoTZ/Z597xd/U6PUwk08rPx3pqU2mmZaOzrunP1jZKLQz8acILAgbWEyQzCuQz6zB+E/Eql3hCR8eUNKu9MSmS6QKcCWGXKaGoe3S+DcLGtzvvSVD8o99/+mOuBgUIbvqJVTRFN9pSGF3e2sigmdmKYsBqM3QId95z7xnZoSZ3yA/qHKjg4JrNfYkUjWCIAMUREwt0Mqml8SZ/eIGAUFHHmbeFSt5M8sinVgm5hnp/m8Uf5MDxmAFw5i3JfPp9La4U/aP6akHeQkgR1JJ5n23Tw8xiwlhqkmovR/NQdSDKRE1Y4KT20ll7c2+pojnerrxqIEEbzGCENtAS+wWi7SxSEjxaNS3J2SyExRbdwlI/RfmaUGvTzGZomRAyhjRh8emLDc7qiYVVdiUyWa1xG9gXgLMGI0cBPI2Mo/Vq4QXGi1xtfiOQCA021ipaI0qfun19UmH5jJtYw0dvbzPvxXV1hSrXek1fPD/YFRbI9IrTOkfG11Sxpg32yXCf2QKhngCiYJnirIfpBxO2Jqw65hMzjUGuxUoBTeqHpDGnsG6eHCawfA4wQNBdggJQNstn4wQ4LN7W+XtufXGPAKaZuSNTYocwWGrJLbBgGHDVVEZFTrlPwePPhhMmL0s12YUqDO1GX5roSENlwWmCceuuiBI2/+QZ4Z8PBuWUy+W1/jvp7/TUzd/6rvx3Ia8qXl//u7+li9+Xvowfr7Nla8xRJB1toqeMZv7jikIGG1JVlijEFoJHKaYBpGnxw2AP2zG8qzAknmhOrlw0Zc9MVF0ZbWxUYwpOUQtASuLNNfBFBx/x2/IUbFkofOflQa/ShPRhK5zswhGO3R6RZerknowrLYqGPp7lBxD3cfYP8ewBXutz6UkvD5ZXYJWuko85VY4otV+GZTHMQDDPQSFJV2tKvnClYmGebjg8SEvCkyCWS24jSPw79FkAYwhDcRQMP4iKpSgSEfcRMAQpihXvFtlkUuTJKFovwHD1bNFh3SldOS/xDSia+fV1uISLtnFAGkrOfOMkmeQBUsC4AhtiDxvAGE6k0FrhbWmHOFTUDkYsGTZazmmNdlFhuMosDXAChJXipJ4olJabQHRb88I5mDbRK8us5gVbx2ZICw/Yii4/VNFXmRhVysGBt6vbANWqyR1aRpnwZzaGsGvXoohrYWl2gAxyYpri1qZPAeAEPkKzSV98p2xfFpYtRtep+4rajrBHVud4qPAUsL+ukJfj2wnwBXSJlnKCv+3bJ4nVGP1GwLijm3RqCd35duBZrq1yIc4MtKHrtEsAU/GZYzVHTHJp8zpFGCq/UcJWpurcbD9Nxpi+o9hPeMPthHCZz9kwVXKh2aVDr0Vw2umKXVmgdu/jXl10r/jDy454Ov6fvYG2+88eZ7MR7Aus745zbU//hMxhigHBkTmNxgJQsOLMYY6Lo0OsOU6hAGuX5Lm5LHsCEI87Htiv2m/RKezHKlvzHayfsx2hIsW0YyN31TSGjfOSTG+SmpdgXlgdBPiv7EiwRa+MBh87bbPiI3/9Qn6e9CoGf5Bw6L5Ao8YIMzGR7WADrNRJDen2MznyITBsBSvXefSqEPBNhfhwgIZFsBOIRnikzrdmJhenrsbb3i7BhkNhVYC27mLassMYA9bJjJFpuFtHiwaPmSAl14XuWKWG1hxYzoOg9jyn9gtoKmGHPqvhADAEDCEE2Eeg52cwux0RlTQCcUZPI5k8rNVlAoJKxqg8+BgKvRoF9JUm3SjJoSWCywgkebnBMLtTBz6/TQAVhBGlMl1k2Vco709l3DEl3AJp0tet1WBnwwfKEAYxmMkUEa3yFFMrOsUpXabePKjA5vFIJRUS3kM1VPn65L4NSMhE/Pi/PmQ+wEBFAFUPLhPXVpWmZ/ICmb+0LMxELWVOTkgvguL3ArlDLkPAJu1ZafNdRFvxklVlz/kI+sE4Ajg1GrVZHeLj6W+t4hvq7Zu4eZvB9ZUIwbX+9kTOxpVODY3Iaktw9gEKwYZMf2pPLQxYN8vwM0woMGVgxLEGAxcRt6vWoNKezvkKPb38vXNf1CVnRbxDGFZdTfzvB9ffuvU2K/9oMpW1Imfxj1q3XmqnnjjTfeFuGNNF676nWmMdQhYvt4CILRWHp7n6TP1uiFYfCopot5VSWvo/Jn7ftGpOPPL0j6hCmNjihZEyNTZP2K78UJli2DZSiNtYkfm3A46ABMUMOSzbFA2Gh1HMbOb4i7Y0Qk7Jf79R/l43HuPiy+ExPSbg0SQB0deY/EqzX6XtDxB+YHMhxAhVGzpDyaYLbVF5Gojj64Wz5CKaxy+5g88T/f93XP9f43/cY1Q/zDJ3+HURQ44PG1jb07pNYbk6A7JtriOuUhbLNhQ05fWBVnuEe0SkNc5FGRkcJz8tGLhPwpAAIrEWRKN8AG/GM6DvZETAxIV0ghR6UQYgN8hpgbefFrKhDV6U2RjcN1ZIArpFr4h6qWVLtDEkRv49Wi8ly1/Gvl8TYJrtfFGOmnqXwLYEAqFL9Jho7MIwAiCKe1ghjVKut2sHlIOXa4SwywaFhKgOcO8pqh8brbIz0SenGaQAoZWojgYGJ7l+o/pBEe4Z5GUG0B7hwRDV2ACHWNIHE9JgG1M0HGyO5NEdS6WAzA84D3bqSP7JJRqvL9A9bUAcuEKiAwqNv6mY1md8ZFH+wn4IU0DSYUgNNfKkv9tt18D8K0T8/fyqYYwwNKJsRjx3XMFcXcUBuG3M70xUUvgYnTmE1GVtHvFy0SUEAMixoAWq4j8a9OirW2LvrhPaKv5cQKQ7YWye4M833ziPO5b/oDCO9hPF/U5Lx0jl39bTna87Pi7BkTF40DdUvueufvyFf/6luLfvDGmzfs4Gcs+65ey+d783qMx2B9k0EnHnvXelVRr3+yVXAM+Qr+oO19qtRXRDqeyzAGAAyFL18TO2yqipfLM+JuG+RtModTEsg1mYCOIbhC9xy68RDfAIM2WCF0vq1nyWbpfj8PK+Orp8WpVES7Miv5bQH6mHD4wa/lbmYpXyFVHowNNv6QLwWmCAwBQyVhTl9cEX+2wb43yDD4ACtmXJgR4/w0Pw+sBHO2UPoMIDQ5J/5MXZyLkyrVvGbxkEdNDMJNAa4YaolaHnQtAgTBv9RoSGVbkuyHD1lfACrxmPgWsmIPdKh4Axz02KoMBXjQwrCN57RxU5LeJAAhGM8hVVnLK5RquemnGxJ54jIlM7Je8TA3DiNnlrj1CA+c6kJUoZU01gPAbCW103COmiGbm4vwRiGpHX4jfC1zZpUdiGBxCJg2MgQpyKoCw4WUdkZorG4qdg31MZDKJlb4+O1dQ3z+gYWCAorwNUFmhf8q7Jfezy/Kqf/r3fRVAfQicNUdHVCdkQibhdkezCTCRKNKimU6Pe6jM81/x+PGY0K2GLLOkLdG8NS0xLp1l+oyxDf5ZlGcy8qrB4CH8mb4s+DvQ40P/Vm6Jk5HUoHisxMKcAHk9qf5uMm0AWSCMYRnDgxfoynm9lEVN4JfFqDKYi9ipiFGIsEt1JcOIk++cY5Nf/KapIjN1K//6aSTycNPKURmOIbIWx/4rVf1g84bb7zx5vUeD2BdZ/xXlhhwSd/OpVnpfjrP3KF6Z0SaQx2MSgBDgc1BHDBgacAkoAJla/sPG3LwLkFCQxik0XRpiA7/5XNiZ7Iy96/2qOiGXaMM6aRsYzvSHEwr6XCkX/TeHnmb78dFj8VED4cJXjq/vKSqdIIBCcxmmZXV6I4x2JIvLP6LTcdcnuGPzuKyOLfvI0DjwH+znuPhqxfrZJwgKUHaLO/uFHnmDKMIjM4O0bYPEQQY3Z0qEX2riLi3S7EcGLBQqJEBCwZZByxHIi6Ri+tkUTbuHVTyIbYXh1Nkk5yBLmW8hjEc5nqUC7clGY6ZOl2USh/KrV2Vco/Mpn07FbDDb2CVipTu3sFtw2pvWBqpsDT3DlN+hZQH+RZdkjB7U64FMxQISDMdoXEb4A5/b56boW8NTBTT21tJ8s5mVvz5VlYXGDmAX4AOGOx9BhPzm61eSMhm9p4RaSJ5Hj4neNDgo0q1yfodaYaC8vUoV1XaPUznABzd/5oBrgCsfM2Qgg8JFenxADY+n9S7o9w6Jau1Y1i0oX5V+k2vVit0dB2g1qeAPkBJIqY2Jus2N1+b/W2iHdghzUPbeO0Q/QDJ2JlbkOyuiGQeGCPQ0meWCbaMjpRiriCPwvu2tsGNWQetAXgcAPNIWsd7FawpjP+BgAz+1Yb0PFWSwEqR1Ux4PyEs9OiuD8g99/4mQRmYUjQNvNw8VPjDv7tViCR61gY5EshblD9RqO6NN/9gxwsavWHGA1jXvTq62pCDVyfVRmnL3bddAitlghsAKDNbEysRIDjAoQVvDAHZeoHmZ3hbmLRet+i/ST6zKKWDvWJ2dTLMc+i/L6kNNXQKplNSvneP2CurLDDWOzvogYFJGfU11t5hFvtComE+Vr3xtfylSoW1Pgj3pFG9K0EAUPpHh1WwZzJBMIG8IhYhF0riYAUfSd4bGQY9lnemCQCC6zUxYX4v1qS6r58MlnNhQmxsEeKbG96jfJleKm61ARwViipxHLEEyKkqlKSOTKdKlSZ729/Ki4KlbV11/AH8IP2dQaIYMEEAPjUc2jWJPjPFTC1cQ/wdk8IbOGR1cXo6JLxQkepAVCLPTbPOBRtsvo0ypUjkl6Fuhs+1VlfMVCyimLRCUUmVkNziUQILB34rgGJkTIEFOjAmxuU5hoSyRqevQ5nYIenOLjPhHu8BPA4ky5ubJQmenKZsDECIqiJ4h0IZmzIerhkS9NX1arFm0QgZUYSKIq0frBoyvnDdjfn1awwYMtec2XnR4VuDAgmZNpVQywN4rVNhtdGJMFNspZomHxt8glia2KoM8s9l6JMD04esNvwi0H62KO2Pz1Gia4KVC/gVeAPYB7sa8ou+c1TFglyapmnfrVSYZ8VybrCtlEoVOHUNneynjk1S/Hn7gNQGVGBobXefNJAHZ2hy31s+9rLfcvhF4qWDhgSwkI1UiFJs6My8mDXnW06J98abN9x4AOuGGQ9gXWcQoYBQzcLBLgKZcl+ABzwCLcEwAETJxAyrbmDIhQGa8QaQYtCzd2VGVZ5gyyyuts+Kh3pVIjriBcCo9LWpg71YJWsCWU/ftV3Mis0kc0hAMD2D7UJJskqAd6TeHRNBojcYE0hSw93qILswKfXBdko6kHvip9dULhSYHzAblboyRrfDu9NHdgDeLxxs4ckcAQMP90BAqiNtElwpk/X6YvOzKh0csiESy0NBZSyHpBXyS+WmERZFQ8LSkUaOBYCIwV5GZH91PpdVt0VMg+VwK09v2qrnMRLifTD/als/c6rwHCH9UY5sJc4jFJOsE4zgGzka41nvEouSGcH2JrLFKAtu6+fXgTwJTxrAEXr0GL0AwDY6wNvj8fAboWERHIM5JHMDoNyDQmeVYsIE/2qNMpqGDLBQiP2I9S6AopRi7hCnAP/TpQX1Q7DRkPB8RbTTE2R9rgXL4j2CfweYAxtnmnx9CbQAHuMhJqozxR7sGYqj37yfSwju1DzT87GByZT2ekN8y3lKw3yNcb94TzQaKmJhqJfAzPEbyhSfjBEYEUCBxUR8Bzx4d36UsRmU/7Bx2Nsl7maGYBaBqijr1tPt6j1gmpSRmYkF8NpqA0BNELccwbgB8EGSrVlSHPBJYHqDERihhYL41orypSd++WW/5/A+A7O3NfAXItIicGJSaqMp/rKA/setlHhI32/z/9PvzE9Db7zxxpvv4Hg5WNfLG9n2C2I6hvL3mLpUeyNiVm1lXi/UmEtFAzw9KZbaUFtYZaYVoGtgTvlycEAQtKA+ZnlVtJF+la6tacpYfX6O6/gwIZNB2EoGrzek8JbtEn9uXv09vka+yCR53m8ixMdR3t4mkcubNEFDikL1ihOFd8fHEl8dsRHoyYOkgxcduUn4Gj6TkhlYHx748NWgsBcAALcD+4Oy6kSQJdXWwe2UxpDkjVBU49wUfT2Iq2CeF5itXImBnCw5PnFeDLB/YGZqNaawo9IHQAnxApTRTBhsXPbZMVuLkqGh4hlSbUq2A3BolRTjMSHRvjgel8TxRYJLMFW+WcRKqNfq2jYjAAy+RrWqPHOOS2BBX1lvh7iXp8gSElBA1guFxGmLkEEi8EEnI2poMmUV7pkviANwg07I1m+RtYPDEjwzJ5XDQwqMQmpEbyPypxA5AdBVLEtjvEdlZSWQm2WykodgEHle2MJ0HPV6LmXIQjEfDN60ZEz5m/Aew6WK+MU3uaLkOYSGtoJ4yfSh6qdYoaeNf5fEdqNDvxWYuCDej7iOKABvAX6+tscvEWwTiAPk4uviMQLYLyyJMTyonj+2FbH8sbOfzBsYUrCRlE7xfgFLF/aLni2LnY7zNc28eUDaTm6qOIkzV3jtH67+CX2BD238p2/5BxWiSxjXgUgQw/iab2vfB1lg/tjDX4t48MabN3QOVvtPvfYcrMwfejlYr8N4DNb1BhEC+E28aRMwoDoE+VT4jb3eExfj7KS4hZJiX6rIEwqL21Q5Wb4nz0t9MCnW1Sn54rMfvra5AQ8NugyZeB0JMwxz5Ud3qBTwHQOMIGD2VVHVsMCrZS2t8JCt7uohC4VOPBi9wW7hUARDRIbH+Jr/Ry/UCPAW3pYk8GJJMGp+ciWCBicaolSEUFPWkgBU0cDepGGaW2eognFdyY8GxS5XRHvylBhTS2S09Bcvi7NjmGnq6J+zkqrbDoc+GCz4jJAQzq04MBz93cpojsMfj2+zILWOEA9+FEcDfLJjD5VAnUmxh3p4/bEVScku6FfGe3T/2bYknppVYCDkJxPFjUD4ruAH8vtpHsfzpCQHf9pwB8NLIbPy8aCKBuGf2JzD5yJaIhYSJ+hTr7uuk6GC0R7Ah+wb5MHRAWaBbS04mMUm/+zPKb8Wtgh9uRZjGY3wdYKHCRHreAwAigAekAkJ3OMRSq0ILIXfCdEbWB5gYTbYq0pd5n4gzfvGa0TfHXLAUIOEwfvT76M3iduFkI63qijn1vl3kAiDM1kVIsvnGyCAAjOIvku8J5m9BfC3sETJGH4w5pUl4gp4lZXXDo+L9T3rm7J2RHnk6IkDUM7A06d8ZHh+m3cPSPtXF1Rg6qVpfj7AFQbg6oHoP5e7H3xl0zr+fWseefpD4rzlINk3fg+0BvElAFfYevXGm38Iw5Dl1/jhzeszHsC6zvCNGIvQl6NfmpXgxWUyLubVRW6TwYBOj8jcMg97GOEhoyBKwYF/BoxBaxUddSlIzbYvXpGH8v+FsQp2e4z9dN1fmBGtUFJbYJDgwBYM9aotrR3blSwD2apmS2OkkwnpADAsVfb5JHRuUR2YOOgMXWUzdUbJoAz+zboKhVzLkcVCthQOXuf0BZHeTko9TGNHz2FTRS/AV1MZaVOdgiKSfnSOxc9GMinW+oYY3V3KyH/iPLcfUQekffWkAnjYuAv6pNofU4d5e1yBH8hjne0KjGI7rSsp4YkN8W1W6MOCedyJBQgQAe4gb9V3D0h5f49Y8aDUulU6Pkz2zXYUQbepg3xlQ31d21HSIoBHpSrmap7RB3wMaxuUcbGw4EPZNIAuPGR4bLqhpEOAE6S+F2tMRMfrAOAGCQ1gldEUrbolJr1DYoPxvtIQA56s9SIfB2RJxlAsbUhlvIOGfTm8k7EZeF2ttggjIyDnIvG+kY5wIxBbi9iUOzr4Cyo4tmkrIBwJSv8jqGEyVOI6zOWQDPcNKRAOnxRqhPD58JRlCtJMR5U/DiCyVCNIBctHZhDAMRVWxeTcXgyqGiGAuc28ODft5hKFNbcgtYE4ATKWLfJ3b6eszfccfokYG6LJnr9YIJgWGWOtjsj8bQP0YEXn4QvTKV8/XPoj+WLjT7/++6tel8cfej8jHV5O5sPnvHQgtSOX7eUG8vTRgZ//dn72eePNjTn0wb6GD6+85XUbD2BdZ2qHR8hmFG7up6cH4ME8dZXbWqhbweYa2YyhXuWH6u1knEPpvt1i7NvBg734T29nnALLmx1hZhDX0fEmx9UHG7GVst2W4EEIxgHsEdmfbJ5bXdn7tovx5Gkll1UbKugTMhHAQb2hgj6nkNAO6cgU/2KODAoOwLU7VeI5Dl0AAEhQBG6FsooB6E5KM6m2+3AwQpaDVwaMChLdrYVFud/4MbFzCjC6+QL9OToS7ktl5mCZfb0qmR3PY3FVwlNZ+pXwOIu72vm1IB/hvmlWR1QCfEn5EsEJHgMLtSHtwYtULDHTKTyVE/P4RQmfW5by4f5rZm1u3BkGgSum0ZcQc62gUsthvIapHab71gYgJVeAqO4Ulwqu/ZBBDRHuB9LW0gavLYI1WTqNgNhkhK8T0/TbEEzqkIVrjvdx0xIeLyxBQI5EnAQjKMImJVNf2VLlzqbB56YB/5br9EMFnr0k9fEeVY+0vUdm3zUiq3eokm168nIFMTdKYsf8NLUDEAEEU1p1XFWHtHuEDBY3OhHIuponaEQ9D95rTibH9w/ffzC6Q3YtlsSPrKxqTeq9Can2RilpkqXy+1VxNN43kCSG/Yrd27NdEs/Mi5bJKVCKQu1KXc5+8t1k3GCQh7SK9yyCbVFYjtJx9DjC4P+NGVcv9VthEEHyjeCL+VjfwGbxewi+rJlPvez9oWz6pf4tb7zxxpvv5XgA6zqDAxHMVPzZOXGQMg3TcSyq8qlWMjxAuNGF9XXkQEH6QhehX2NljLV/VGJTFdGfPc/NMkpDABaOI4VD3aq/EL4t+Gsga8HTVaiLXJwi+4DIAbI7fp+0Pb3INHgW/mJLcWFD3DQ20cIEDTQ/I50bfhg8lkJR9dhVa4x0AChgT2CuSPYLX4tRE8mQlAdCEpzakMzOoDI+F6rcMgObZY31ixGNinvngWt5XE61SpZDb28T/fBuAhOwXWCVnPklVdCMdINkgH2IsYsoUHZVmjpkQkOnlISKIHjKAE7MiQUazCmbocMw4FdS4fkr/HoATeGnJpQRvFqV8oFexQDi0DU0gi5uvKHfcXVdnIBPSYpbuU1YKoBcCIADFm0LYAGQtvxmpdtGlMxYazK6AUwUDeadMamngvRB1QaTlGvBppjjozR0g5mE3LsV24AkeUhkkBcBJhk+Cobz/DS/jm+lIDI6wN5BXF94vAJQQrEhCGap5c+D5MyYELyOpQo9UWA82c2IkNbpZbJD6hoYih0EiISHK1cU58hO5m+ZF2bZXcjke/jcTIPeKvjzwpfWlLSr61LZ2UmwDmBs7N8lXV9ekYfOf1TKwzHKq8eWf18tOLRkSQzYJ/ip9PU8vXXIVKuMpSmhF28dIphEQfi3O6xO+gY2i++LbzKQ3r8xf8sbb95Q420R3jDjAazrTPDysthgO1rsAKIFcDgxsDGVUJtYSC9Hl1xnu1pPz5VFt4RbddiWQj2LsW1INvcGpb6tS9xLk5TXrJCm5LxKXeyOOD1DlHnApHSkuEXGGIipZXUowtAMIICvBRM52BSwDjBLr2wyugFfG4ecPdiptvC62si0lPd0KllnM6+25CA9Xlng48NhjzoVbJilP/00D1vEK1R2dTPfiJUy6BMEKMCBavyYaKaP8iXYC3T5USpb3KRxu3n7LiWfQepsIq+qem2LEWXZkBQZkEpTvaWYwVhEHNTIrBQUU4Sy4lRcSW2H94g5tk1R242mFG/qZyRGcLXKOheACWRq1bYp5odhnmmV58WwUWwNolOQnZIhgggAnS1/FupxuB14aVKiFzak2RlT9zG5IOb8huR2xpgnhW1FbWqeMi23EFsxBEyBn15m2CfAEJgvVARB6tROXWZuGnxOmdu6yQyVx9v5uhfGE+q5smfSlI7ni9L9ZFF5mVpRCQiJZf9iR7vK9sJ1NAwxLs6ouhp8fRRgI6YDQBG9hqWqWgRIxsgMEshi6xFp7KcnFLtnmlL8kZv5+oI59J+bFbu7XQIbKvIDfix0DB678lsqc8oVLhGwrgaAcygtleGk3PWO3xbnLYfFHh/k9wjYNci7oScvSuDFSYnMFhkN8lJZ71uV8b54/MOUnl867D38FgYGeKTAe+PNG3JgBXmtH968LuMBrOvMFuvEbKnWxhtZnz3D9MRo3Z2ycWcX2S0wBqgugf8oOl1kzAIObuRf4dDueFGZhLVtw+L6fRKbrRGsIaYBAIEADv6o5QzTw3HIcbsvHiNogmka7BWTw2FYB8OEDTPIhGBnEKrZhiqWAA98ZBohRBSm6MiZZT5ufD0CJYAOTKFEMASmBX4jc2hAVaRYDv1R/DeYxllgrNMUDukTtzEScbIXNE9vRQOUq2I+cUaskW6mkPvXSjTt42uzL2+whxuQzGXy+6V6cEi0alPclXXmdLmZrArxzJbI9CGWAt4xdNQVUFq8e0QiXzyrsrbgbQIQAkCdmafR2XnxvGL1mEyuwk+tnnZx1tb52CCRhZ65QmaRfYtRyKIdzHUyerpVtAE6A+GlQtJ7b7tEF+sMLKXxHSXHdUt1JOL9sLLOEE9rIyP+qTWyJ9qTp5VEiDBOx1UblhsZaTt2idJq5OQir00jDnAnsrlXbRAiEBR5YaVtCS5O4GtDitYvTbMb0LeEOAslf8L3h01UyKM0w7PWKUVzOgA0qoBsSJcYBJCirigeFr0rTcbTnluU+NWSApYBg8sV+Pr0i4GFrTW4tYeyZf4igMF7OYNyaVOacQX2/bmm1NtaVVJgPpHoj+UI/NLRliSrBVkZUQr33v0xRmmgo/JbnWPrn35VP9T4ucu/73myvPHGm+/peADrOsMeNz/WztWmINgCo1AXMwf2pMh09NSLOTGnV9RK/+w8zdP4Ld6XqVD+AhsEUAODM4ALPUiNpjrISxUenNVtitFBrhDAFUzJdsRHdoMGZGz/WY4yUA91UcKDjANQx4HXBh4sZDnhcQ10qfsH0PCZ4rTHCKBgLMfXIYsVCdH7wzwr1M3A04WtvERUyVJg6QpFPjYyQ01L7NV1HrIOnuumYlBYHgy5CNuLhi5GbxeXAHCQ42vBN+S7MEf2xp2YuQag4G0KzhcILnAgF24b5NcAI4SBxKf1dok1kKbfLTaRVx62RJxRFyxNBvMVC4p7yx4eppAvsUQABojF0o2GrN4aUewZFhYgo20fYLI6GDEsFcD8Tq8bHq9pig7JNxkRa6iTHjT/xQVGJ3D6uwnsuNkIc/fuYTGev8QFAIBxsHtmb7dYIUMlv1tNbhBqAH1dHUz0twY7VHCtLZLbjZR2gOUqH5Pj0yR6NSdaV5rxG8w842YiYitUGTXfZ9v7VGo/tkhxjXs7xT+9JpJMkGni5iAyviBrWxbfQ/CK4b0Fc33twUMq/yoc4hYrpEnWEmH5oTfNEFFW46xnJZCzxVdusYD08fmZl2ZWLAJof9FSSfumwdcM7zfI2vglAu8xcxS/UBhi5utiLm6K1p5ktdO993z8m/5weiDyrr/zdwB9L523fP8nWIr+cgOm92j/v/mmX8cbb26o8STCG2Y8gHW9i4MgTQAHxBcgfXu8T3lbQj7Kbs6BMRrdG+O94uQKYt9ziNIdAkdh6taCAfGfnFRRCtWm+C7Ni7GeU14hdM0hjLMrIT5UsuDwj4VZdoxvIHqtgAkWN7nCz205ZFLF/DRvQw4EcAKosbf1cuOu3hWV/OFu3gYDDxMBImIXIBUhsNPvU7EGfr8CGuWKSjy/PEf2jRlSZETSYh/ZIcHFgurCwxZjXzfBlAk5MhAUs7/vWq0Ka1rgFYLsFg6rv0PJcK2hZCkYo8eGRaYXCGDgXYPEiW5EGKdjl7d8RAF6q5DdhJohyFMEl9kifW94LcCkVXd2q6ymfEVJgZrGdO/Sj91GMIL7Rf1L3/83IZO/ckgm/+0ulS+1sMaKIwAPa6TnWho+WCLcd2NXv+jzq/LIkx9U6e/4d/jBkLa/mVcMIGSyvUOUEPWBXnlwzwfFfP6SmNuG+VxDj5wh2MHWJczbCKol8Gs2xUa10tqGpB+bl8TVKquTioe6ec39ubrYiHfQNLJQ+C/qY+DLA2vmrG7I6lHUFllST5jMtMLmImIx4MvCa4ktUwBDsEm4PjSpA3AVGwTg/myNAAmvNfv9/IZiFPE1+9OUZW/7id9VfrJQiNJo8PSsMsijFgevgaExigLydGBiVYE7SMLYFF3fJHBFwTcN+WBL8Z4wND42XOPA5RU2IOC6PRD8CXng0Mt7tLg9+w0D8IfZSnL3H3te9L7uv2OKxzx88tcYIYK6Hm+8eaMMPLyv9cOb12e8oNHrBbp1/4zo/b2iweeUKVD6QiUKDi6ArMyeqHQ8Msc1doAR1LDAy2KUamorbSOvGKlCVax0jPIf/VQARZ1tzHOq9kUlPFuQRmeEnYKL398tPf/5lDgHxxkZgPVz1IrgYMbhyO4/BJTC85NteZuYrq2LHfZJYSQobX9+mpIb2Cn2H1YtmqPhV4KkRlABhkHTpDaSkuB87lqwKbOb2qJiTC+pME48VzAQVxboBXKm55llxK0u3HYzI9qBneq3qsszUr9ztyqGRr5Voa4CMyExQspE0Gd3irEWNLhvZUuNDfN6QhaFZw3ZUTB75/e3S/KpeWVUxwGPx2cYTNHX6jCRN1XtC+ITEGaK5PGRPsUGrqmNR7ArCMUEiNMW1/icaH6npNVUvYAAhnzuYSWb4ocYbl9tEBwBBH3lb96rugP7OpQvDa+jiKzf1SWJqbpYYYNBo2AhYdAGgIQ8CpYS1UcYA+Z/GOX39Enw3AKjHzYPJbgU0f34umSOpCU+VeH13ioVh18CnXxkYmIRFRQKNg7AH689oi/AdF2ckvV3HZLOpzZVxpbflPJInL4yAEVsJeLruWcuizE6LM2uGENjacavNAiWJOCTYxc/TunX2LtD3IlZ0UYHpdkRIeDnNUWkRyom2tV5yr1gF/Gakd0CkMIPFcjViIxo2lLY1SaJs5sqqBQl1DOzLIhWJdfqfcnrHwmp26DuZ21d9MF+MpC18W7xPXaS187JFxl/gnw1bLaCsdwaMGJYdDh29bdf9gcdrh/Aljfe3OhBo/eEf1xM7TUEjboN+XLls17Q6OswHoN1vQHax+bZWoYMDaQ6rv03LLIq6ecz9MoAIKCHsLG9i4wNNvQQWAlGigcI5MapJbGQK4VDCBtlcyuU5iJXs1IdjIvx6Al6ifo+e1Uq9+0luIJHCLlIVtgU/2ZFpZ63/E7wziBrCoCJ9SgXp5iqffwz76GJvnTzID1U+DtsmdEUj4OwWFJsCAz79YYE1sqqOiceUT6uRJigwxnqVvdda4iO8mNsDiJra3yYtTru3m3cuiv+6M3MU6LktHOEsiauAWtrAKqQZG7bUt8/xOwmslGoYelsV72KbUlKUTSlr+VVLcsmJMSQJM5lpLIX24IRdfjC/O1DMOm6NNtDZJ2csQGmxKvaGZevF3vzMC6CNDMt9sVWafnYECxUFfuULcixCx/j5wG0Ic8JywoAImSS4JX74nluLzJaA1ukU4v0XgEw4/XqeGJZamm/BNeqvAaQYQGcAdD4eoG9A7gaGRJrfZOHfPAyzOudNH+j1BrLBWCs2p9akkee+hBlu/qObpUplkqoEM1ggBuaAEFceAiFuGgATx6AtRYIyIt/8G5lvgeQSQQVuAqY0uiFcR/9jnVxb9lL8IMtyXo6yPcG0/Iti+AKXqnmAzeLc/GqirfQRfyrRb4X+d4N+gmc4LECU9tsC1AGLo23Kbm39dsx0/BFJH45L+u3pyV/QG1Y6od2qwodx1WbuVt+QAKwpsz9kyFZ/V9v4l/BHxc8PcPcNUaO4PtoxzYuV7wUXGEe/fIH+PheMXC0VefjjTc3/HgS4Q0zHsC63qDeA/6X9iRX1XGwAKDgQIaMhD/Dn4PDvjKSpNEZIARmdzuEtHBVGI3kbBtyFPwyqQST2hu7B9Rv/YYm4bNLLH5mbUpnu0TPrKp8qBkVaQCZjl9ro0h5ESACh3T4zCIBEQ5VSFWNzqjKAdJ1dhriEGZXXKWiDlHkbSEssgUQKX2BOQJwqjXJUIFts89dET2DJHkU94ak3hHmmrx99jLrZVA8jciF6Lk1SZ7aVJ+LAzxTkvCpeQIzeJlcyIGIsADAKFtS6VYho/g7rVy71ttorazREF0fSkl1bx+LjQFYKSdlaqyAYW0MJNSVdVW8jNiHTJl9kBokScQrRCOKOauoOpit+AFrW5+4eByQAHvi6jHBAN+XUp4eeMfm1hQzdWmKryd8ZJAZAVwW/uUu9VohAwqAQNcYvaDv3s7rHz+3wUBPsCdgn2pdEYLsWl9cjk18gowRQlDBvBzt+lesvwFDBy/Sw8//ihiJhJjotTR0skcAPf5VxaShc5KPF1uJyOuKBFRKPAz+XWn6sXyrRf4/BtlX1tQMPXh8b+TLyo8GUI5FiQ3FwAL84X1HdtBypHS4T952+6+zqSA0m5P62w6TZcRriyR73yIyynwstrbPXCQTBRDt36wSUEYvbvI+ka1VG2q7dghM/3CbJKbrElpvsFicjGirBxG3B+gECJ//8WGZ+tkx6f9STrqeRYp8gCwetkvRZIDnBHDvzC2+4rcrrjUy2L7Rp3W97CxvvLnh5rWEjG59ePO6jAewrjOVHd3SjJpkLMAGIISToZI4ZCMR+k2Q/4Rtv9B8SbFbAC7whpycYjAjpbG5ZRrkEVuAnCbILf6ZTSntThPAMAvL5xNjbJQABmGZ9NTAWGyaqoYGBxZW6MEgWCrRHcGbZAoCfnUIrpeVhBMPsWOQh2oyJs5ov5T7wzxwAajgD2u2hfhnACUwG1zdhVSDX/Z7uhSIQOxEtSmh6SxlSszDtf/GNHcMEsQRCsoSXl1T7ApADZ5zyz+jz66o51ZpSOJ0RmQzqx4zTPRhPzfvzI4UIy/8z1+WwFfPX6tnAQizEY6K5w4mEQXPyH+aXxX/Ul7clTUVKIrOPwAFv09dY/jcAMY2M8r/BsZlcqFVd2SraAM8z6uLjFRwszlpjPWoGIC928X3zEUla/b3kPXqexSJ8Dp7Jvk1AgEyiASq6GvcyBIsIBoAxcssNF4pSOjSChP7uXV5dlJ1AOYLfB7MPCs1mRFVv3mMW4d8jrfuV6woWMNUXIJXVrhlSnC+e0QefuFX+QGWjdeaIFJjQjzB2bDKLSNrCTDbmeR7jR45vDfrdT5mvViXtfsHmeEFxjL64qKKdwiZlEnDpxdYPg5mCZlrqNBZeEcvi60Rm2G/9YhistAoUGuqqibTlMCZGQmsFGX9zd1kfEf/eFn8EyuSGwvyFw5KklF48DbJrmJBYOlotwieCuxatsvoD3oBm00Ce9wGsiKZ5NZ79JUGFTzsePTGG2+8+R6PB7CuM4FsTQLrFcohlAtbNSXMeXIdMiaIRGAwJFSOwU4W+bJwGKwKwFgyfq07zY76uNkEozAOdaPmsIuPHhp4gNYzUn3gIBPRkdWEcZZWVLJ7pkDJBIZzACj4r6ygTpCEHC14klA1k7trkLIkEsVppq/U6ZGJn1kn+COYAnsAQGS7kr+lj2wIJEM3l1dSG0ALGCNs/MGbo2uMezBHBvmYaj94KxkBgDUc4MEplFS3JCT4jpDGjiDSgV5KW/QzYTkgHhR7q98RG4swpo+9jyDrocx/Fhnuo8yIkmeAVTBa5rPnxUHti+2w55FMUrMp1ZE20TrTipkb7pPC3rQ0BttZcM0E+UiAeWKMHcCBi/gJXPctE76uSXNHP5kebBEaT5+To33/B58fu/kQfbG8RsmymQxJ88CoaPAy4XGnEhI4PqFS9VHQDTAaCvBxkTUq1egNc1FMjMeMiIjBHjk2/UnRxkeuFTQTdM8uS+D5Cd43/HINhLNuluhRAuDBVidZS0jUliNHx99P2RhmfQw2/2AmpzR3x0Ep7kqxN9LZ2FQydAE5ZBWVLRb2qT7Lps3r0/nwLJk7XFv8ogAWEdloBMhsGtDEnl8Qa2lZ9IE+Ca+5UtzdLtbVaQnMbio27fI0pcu5d6RV0XSPYtLSL2S5PJE/3CWrbx+W9vNlhpbiOYB143s7EqA3KnWuLv68SP9jVUqRdlBnECr7OluDTC5cA2xAfrM5NvU7jIT4xnlprhbAKP7MEmlvvLmRhuyw8xo+PAbr9RoPYF1nXMhMIKRwSNq2YisgEaJTrrddeX5SbTRr1/qjKmwTxmtTpx+rMp6ir4eG5GiEgZMELJBekAdVs8WXr5ElchE5MNwjkYmsNLZ3i71rmIAHHiWUIuNQ9l+cFwNp7NOLUhmMSfxCRkUzbJbUJlnEL9GZKjvqIMdg8664J60kpmxeZWg1GmTHAlNrPHATJ9dU7tPEnEpgh3TW267ytNB9B8C4ss4spS1WCmweVuP5dQtVGuXBjJR2tNPHRc8UwGWpwvwrbkDGI+L4dDFwvfJFho6yJ3BDRSAgxJJdfhNzMvXjHVLb089DHH4yXFcUMbsogG5LiL1jkMXbDiS8mSUyG+G/PC6+M9P0lDGXrFQjICWYg3G9v1vFEiyp+3Jgzj5+Qcla2ztFBxCs10WfW2H3pLa2SUkOuU3YBMW1dwa7lTEfVTYAWvCuwW9VrYk+tcAQVe3KrLiFguqonF8SZyPDsFEAMLJZ8yuMakCtT2Nnv+Tv36Huy7YZsWAHNMnd3CX57WGym0yCB+u4uMYNU2z/0deF6ws2B76wTIGF4pAtn/zLXxRzdEgab95PgAJgUjkyrEJbK015/Nj7mM3G+p9ERCoHBsg+4XpBvoafysF73LJE7+oUPRoV+94jBOBtJzYl/tw8GSUASPxygGsEIDv0uRW+HgxZBRMHOR3vlb86IZ3PZOl5u+/NH+UvIKjmQQyGVmnQ+4aexvC6zTofxEOgHumVNggjZ5ZeNr7hGwdeNngFXzpb0SIYyNzI2UKJ9NZGojfe3AiDfL3X+uHN6zMewLrOwGPCfCgEQabi6qCGPDSocqZoZIZ/qSMlwZWKYjRWN3nIGqUm+/OssE9q+wavATOyPPj/bJ6HNpgI3kcmJ422IIENpD4wEta+EXFyeQnN5lWSNypfEMRbKEnkxBxlJoIm/EaSL0hgIceuOxzWkCWx0Rh7cVHlaw2qz7cXV6TcGyTAoccLoZ1gXcDYYJPQsuh/0i5MqugCVPts66dMh8P96O5fFmtiUpy6kmGsqVkJzmZZmhz8m+f4OPSaRfkU4ETz+aR866h6Xog4gO9rx6DUb94u+olL0jy0TSWE0/yvs0B79E+WJXh2jgAWW5hgyQDSyNrgGjcd8V+Y43o+UsoBeuBvYo1RucbQVi4kYPx+5dnazKteQSTcB3z0PoGVAYgMnJoRd8cwAUt975B6LOk2ceFzAmvl9/Gac4MTIBBGd7A8YLtSbSpYE2xapcLHLz2IOAgyiFYPhUQbHWCuGYuWD25T/YQBU8xCTeKXCmR98B5itlnTFb3hio5GG2SadST4muPrmbOrChyBrUJIbW9SnPYoATNeF0jVd/7w79DvhQofFHmDoQnN5cXcrIieV2G37LvMVpheHz45qxoBAGgmZkSbWZTK/ftUYCtY2lSbfPmRXxLt8oyKmmg0yP7YpRJ/YdB2jKgUd4Sw9nRRUsfmK4A2+w8P75R6J/ocNfFNrkhxe0QqPQHeDowm3ivF4ZBUUwYZXvjTHjr7UdE7lSkes1UEjbgFSL8Pl//4W/rhttV1uDVbxnjklUFKfCnj5Y03N8y8Jvaq9fEq5vd///dleHhYgsGg3HrrrXL8+PHv+FN7o40HsK4z1d4Yf6Ou7uuXWmf4Wv8aDnqwBow8MHWpjnWoLTqM3y/OzhHFVFm2ikhoOlzv5xu7FQPAAy0eYxUKNr4APIInp8kkaIUSzfMAJDhotHxRzPMzyv9j25L/sZsYGUHA0p4kmwCWgixboSx6b7c8lP8vol+dVx2HSAWHQTqdFGOwTxKn1kSLx1jHg6GJ3N6qUXAJtMgcoQIGgZaVBg30HISkRqOi3bRPHaIHd6qwz0JBTKShw9TPChpHmvtHmCsFX021K0hp015cllrX/9/emcBGVXZ9/HTaTle6UUttacu+CMiOloioIMtLIigJqIQgKGDEFQPiqwgJUXzRDzCCxBiBuLKoIMGKH4soCFZkreyrskjL1lK60eV58z/DnXfaThd0KHbm/0sGeuc+c+eZM/fOc+5ZQ+Ria7vGU9mPn9M+ilAcYKFBqxe1ajUIl9LYCEdhz1NZ8m3mAsdj/0yxHTyhcWBwY6Jhsma7IT4NNZ9QwqJxtFplAvKK1WqF2mPazgcFY6PCRXYd0EBouOQQu5aX2lwutm8gkpSg1hRVNK3vyO5o7OzXIlnHwy2sLuOUBI050ppbcTH6GsQmaf2znDxHViZcdbDg5ebrcVGUFRmCmhSAoqmoT4YSIJfztOYXLG+wQsGdG7k/x1mDTb8jvJfNJkWNoxzFY8OCtOAtYtwCjv+pyjaUz9AzBVoXDMo7zhfEvunrr7UmQlV13Bx8e/BNsbVq7rCEIXbwdJZaS/G5Qr/bowo+lHFUfe85/P/EtEzWBuGFHVO04r9+17BUXSt2qz0iyxwthWCpu9Q9TooahTisiYj9CgnWzNbAK2XiX2S05pftcqEqxOizGVBkNBPQaoeDTEELZyPolAQtnYFEAQsoe9eLdiwghNSapUuXysSJE2XatGmyY8cO6dixo/Tv31+yshyhCsQ9rINVTb2Re7u8LIG2IEd9JgROIx4GxTNR4whuGixaSJlHCjgUlPwC7XHnv+eIozp4SYlaRFARHrWxdOGGRSUmTIPLtUbWqSzJ79ZEQo9eUpea1mLCcRIbOtxIiVFS0iBQQvdmOoKZ4yKlID5Ygi5eldO9QyTihJGGm85oFXmksOMYsPhg8dY5YWGGWzAlToO6EXcUePKCY97XAqS1EjzasEABRH0nBFRn52gtLi16eiJTM/3gGupz70xdvJH95d+upSOguKBA8junaHXvwAt56tbSIqqNY+XQmEBJWm2TsD+uaCkJvyZJjgKjoSFSHBfucL01jlOXUk67KAlf8rP2twvILlR3a1FihNjTD4pfVKQUtrlVgg9nqpJX3DxeSsICNZAc24h/g6v1SrMIrS6OmmJaQLW4TGVt1R9zyv98riowOT1TJGLvRbncPkYid2SqKwzWGmTQQXkt+/2U2BCzhRvHxDjxu3wtRg1Zi1kXHIHrsJAhU+9a/SkowVCqYG3B54LrNPhckVrWkGmosV7InmyRqM8hY06L2eLYpkxdm7BA4fuHAqe11U6f0+bJgVdKVc5wt2p9r+gGamWFGzX/9kQpDXK4Cauj979miX9xmZ6XKDCLc/V832SJ3p+nbnH/A787m2CX5ubq57c1CNe4tNIDR7TQLAqvanwXLHmQLc5bnG8AijqUQ7td4+KQkIBzPyc1WRp8s1tKu7VxtP4JDNDs29B9ZzU+zRXICPW/yj3X6iVVpK2xSCpAS5zrRSvupyS5tVzhPeBWJeSfWgfrHr8HJcDPcbP/VygxxbLRrLiuucJi1b17d5k3b55ul5WVSVJSkjzzzDMyZcqUvzwXb4cWrGpY+f0UKduxT6tQo3ikBm5fc1NBoYE1BkU5tWK1urgQFF6s9Z3U+hEUJEG/IZDYkbUGpakoOUqzslT4Z86rAhR65KJainB8WCIQo6SLa3Gx2E9nS/CpK1IaFyGC589mS+ipfCkN9pfk/78iDdcd1+y9gCS0JwlwtHuBxUkrcSNzq9Q5L+yDq8hKoYfLDwojWq2gpx5ikXQf+t01bSx+6Rn6uaDM+EdHqqsGbWCwPyA5Ufdhkcq5u5kEf58h6zf+Wy0MiKuBAof2QG3n5Ej4j4dUbraUxuJXgJpUJapk2c9elrJbb9G4HMRRIcsQzZ21yKZN1Lqk7ruWyTovFETVjEu02Dt5QeOtCls5mjajZAOsIWg1Y0dtr/MXJQDxbYdP6ndVdilb3U+anYj4oLx8KWqbIBGHciSvZZTGdKHKPep+wcWa36WJ5LWI1oKtsHAV35bsiLlCqQEUV82+7Gi/A/nEoEgm5GzTQG9YlzTmbP8RtVhCGdLg/BC7ukOhOBi4EXFOxEc5vrfjp7SKPdoYQbn0zy7QcwsuQihsRW0SteBp8N5Tem6gQbPWBUMF9jC7FLRLkB++mazK1T0D/qNV0u/t9x91GUIRQZwRalyBH9Imq9tPsxMbOzJGty2cqIkPmjWbV6AyKO7cQht722KiHSU7DhwRuaOD01WNjgLHRidpiQVkUqoCix9fJEzEoi0TrFXXFNsgu0T+ckqkbTNHmyObTeUbeKVELWWuDOj0WmXlKvEZMZnnyiliajmsJagYPyD6Cf0bNwpQ7rS2WQWgXOF5JBIQ8o+kjl2EV69ele3bt0vfvn2dz9lsNt3eunXrDfiA3gMtWG6AZh8VFSUnT568YXcjhBBCvANYl2DRyc7OVivTjXoPHPsu+ZcEyN+wYEmxbJa0SutbEAwCyAavwJkzZyQxMVG2bNkiqampzucnT54sP/zwg6Snp//luXg7jm6ypBwXrmUb4YIhhBBCakNubu4NU7DsdrvEx8fL5rNpf/tY4eHhldY3xFdNnz79bx+b/A8qWG6IiXG4Hv74448bdrF4w90aLXyUEc8hXmP8HUIEg1HlKiEh4YaJA9l7x48fV5edJ+brd613qIU76xWIjY0Vf39/ycx09FS1wDYUPlI1VLDcAP8ygHJFF2HVQDaUT/VQRpTP34HnT/2RUV3cjEPJwqMugeWsa9eusn79ehkyZIgzyB3bTz/9dJ3Opb5BBYsQQgghVYISDaNGjZJu3bpJjx49ZO7cuZKXlyejR1dOFCH/gwoWIYQQQqpk+PDhcu7cOXnttdfk7Nmz0qlTJ1mzZo00atSIUqsGKlhugC8aAX9V+aR9HcqHMuI5xGvsZsPfoboF7kC6BK8PlmkghBBCCPEwLDRKCCGEEOJhqGARQgghhHgYKliEEEIIIR6GChYhhBBCiIeplwpWkyZNtAptxceECRN0f2Fhof7dsGFDbQkwdOjQSlVoUaV90KBBEhoaKnFxcTJp0iQpKSkpN2bjxo3SpUsXzVZp0aKFLF68uNJc5s+fr/NB8Td0HP/ll1/K7a/NXOpSPhcvXtQO6K1bt5aQkBBJTk6WZ599Vvsv+op8anMOuVY8HjhwoO5buXKlz8ioNvJBo9f77rtPwsLCtNDj3XffLQUFBc79ONdGjBih+9Db8/HHH5crV66Ue589e/ZIr1699LOjO8CsWbMqzWX58uXSpk0bHdOhQwdJS0ur9B0hffzWW2/VcxpNaA8fPiw3Uz5IZR85cqRWuoZ8cA58+eWX5Y7hzfIpLS2VqVOnStOmTfU9mzdvLjNmzNC5XM+8vFlGxAcw9ZCsrCzz559/Oh9r167FVWu+//573f/kk0+apKQks379evPrr7+aO++80/Ts2dP5+pKSEtO+fXvTt29fs3PnTpOWlmZiY2PNyy+/7Bxz7NgxExoaaiZOnGj27dtn3n33XePv72/WrFnjHLNkyRJjt9vNwoULzd69e83YsWNNVFSUyczMdI6paS51LZ+MjAzz0EMPmVWrVpkjR47ovFq2bGmGDh3qM/KpSUauzJ492wwcOFD3rVixwmdkVJN8tmzZYiIiIszMmTPNb7/9Zg4cOGCWLl1qCgsLnccYMGCA6dixo/n555/Npk2bTIsWLcwjjzzi3J+Tk2MaNWpkRowYocf4/PPPTUhIiHn//fedY3766SeV2axZs1SGr776qgkMDNTz2OLNN980kZGRZuXKlWb37t3mgQceME2bNjUFBQU3TT7333+/6d69u0lPTzdHjx41M2bMMDabzezYscMn5PP666+bhg0bmtWrV5vjx4+b5cuXm/DwcPPOO+9c17y8WUbE+6mXClZFnnvuOdO8eXNTVlZmsrOz9eLBBW2xf/9+/fHbunWrbmMxxI/d2bNnnWMWLFigC0ZRUZFuT5482bRr167c+wwfPtz079/fud2jRw8zYcIE53ZpaalJSEjQRQfUZi51LR93LFu2TBf54uJin5RPVTKC4pSYmKgLaEUFy9dkVFE+d9xxhy5UVYGFDHPctm2b87lvv/3W+Pn5mdOnT+v2e++9Z6Kjo53yAi+99JJp3bq1c3vYsGFm0KBB5Y6N9x4/frz+jfnEx8ebt956y7kfMgsKCtLF9mbJJywszHz00UflxsTExJgPPvjAJ+SDOY0ZM6bcc7ixgyJU23l5u4yI91MvXYSuoPHlJ598ImPGjFET/fbt26W4uFhNvBYwDcMVBpcGwP8wE7tWoe3fv782Md67d69zjOsxrDHWMfC+eC/XMehhiG1rTG3mUtfycQfcgzDBBwQE+Jx8qpJRfn6+PProo+q+c9fQ1JdkVFE+WVlZkp6erm7Rnj17qgx69+4tmzdvdr4Gc4NLB601LPAZ8PnwWmsM3IrodeYqn4MHD8qlS5dqJUM0v4U7znUMesLB1XozrzHIZenSperiQt+2JUuWqKv3nnvu8Qn54POjV92hQ4d0e/fu3Xp+wN1e23l5u4yI91PvFSzExWRnZ8tjjz2m27hQcLHhwnQFiwD2WWMqlvi3tmsagwUUcSbnz5/XOAN3Y1yPUdNc6lo+FcHnQGzEuHHjnM/5knyqktELL7ygi8TgwYPdvsaXZFRRPseOHdP/p0+fLmPHjtWWGYgx6tOnjzNuBXODAuYKFPiYmBiPXIeu+11f527MzTh/li1bpoox4uYQfzd+/HhZsWKFxuH5gnymTJkiDz/8sN4MBAYGSufOneX555/XeKrazsvbZUS8n3rfKufDDz/Uu6KEhISbPZV6Jx8s9AjSvu2223Sx9FUqymjVqlWyYcMG2blz582e2j9SPrDIACgNVrNXLKCwWCxcuFBmzpwpvn6NIcAbSte6deskNjZWlbBhw4bJpk2b1PLp7UDB/PTTT+Wzzz6Tdu3aya5du1TBgozQNJgQX6BeW7B+//13/QF74oknnM/BnQOTPX7cXEHWleXqwf8Vs7Cs7ZrGwJWGLBP8aPr7+7sd43qMmuZS1/KxyM3NlQEDBkiDBg30zhp3mRa+Ip+qZATl6ujRo2o1wh2z5TpF9p7l4vEVGbmTDzKtABRzV9q2bauZlda84Up0BRmWcJl54jp03e/6Ondj6lo+OHfmzZunyiaseh07dtTepnB1weXsC/JBRq1lxYJCiYxKWIUt5bs28/J2GRHvp14rWIsWLVITMqwwFl27dlVlAXfTFvDH44c/NTVVt/F/RkZGuYt37dq1uvBZiwbGuB7DGmMdA24bvJfrGNzZY9saU5u51LV8LMtVv3799DPAWoPUZVd8RT5VyQgLA1K/cddtPcCcOXN0vC/JyJ18UKIAlgjMwxXE26SkpOjfmBuUQsSQuSqu+HyIbbHG/Pjjj+pKc5UPSohER0fXSoYoA4BF0HUMzm/E6Nws+SB+DyBWyBUo05b1z9vlAxlU9/lrMy9vlxHxAUw9BdlWycnJmjFSEaS1Y9+GDRs0rT01NVUfFVPs+/XrZ3bt2qVp87fccovbFPtJkyZp1tb8+fPdptgj02Tx4sWa8TJu3DhNsXfNLKtpLnUtH6Q1I4OmQ4cOWqbBNdUccvEV+VQnI3dUVabBm2VUnXzmzJmjGZPIbjx8+LBmFAYHB+s55Zpi37lzZy1VsHnzZi0H4ppij0wtpNiPHDlSU+whC8irYop9QECAefvtt1WG06ZNc5tiD5l9/fXXZs+ePWbw4MF1kmJflXyuXr2q5QR69eqlnx0ywfyR/fbNN9/4hHxGjRqlGbhWmYavvvpKy5ggs/Z65uXNMiLeT71VsL777jtd9A4ePFhpHy6Kp556StN3cbE9+OCDqkC4cuLECa1vhJopuPBffPFFZ5kCC9S06dSpk5YwaNasmVm0aFGl90JtI/zIYgxS7lGv5XrnUpfywWfC8+4e+CH0FfnUdA7VpGD5goxqkg9KSTRu3FjnBIUPdYpcuXDhgi6GqH8EZWz06NEmNze33BjUHLrrrrtUycSCjIXOXRmRVq1aqXxQ9sJVSbHS7KdOnaoLLY7Tp0+fWn2nN1I+hw4d0rIEcXFxKp/bb7+9UtkGb5bP5cuXtXQFzmso3jj3X3nllXLlFGozL2+WEfF+/PDPzbaiEUIIIYR4E/U6BosQQggh5J8IFSxCCCGEEA9DBYsQQgghxMNQwSKEEEII8TBUsAghhBBCPAwVLEIIIYQQD0MFixBCCCHEw1DBIoQQQgjxMFSwCCGEEEI8DBUsQgghhBAPQwWLEFIt586dk/j4eHnjjTecz23ZskXsdrusX7+e0iOEEDewFyEhpEbS0tJkyJAhqli1bt1aOnXqJIMHD5bZs2dTeoQQ4gYqWISQWjFhwgRZt26ddOvWTTIyMmTbtm0SFBRE6RFCiBuoYBFCakVBQYG0b99eTp48Kdu3b5cOHTpQcoQQUgWMwSKE1IqjR4/KmTNnpKysTE6cOEGpEUJINdCCRQipkatXr0qPHj009goxWHPnzlU3YVxcHKVHCCFuoIJFCKmRSZMmyRdffCG7d++W8PBw6d27t0RGRsrq1aspPUIIcQNdhISQatm4caNarD7++GOJiIgQm82mf2/atEkWLFhA6RFCiBtowSKEEEII8TC0YBFCCCGEeBgqWIQQQgghHoYKFiGEEEKIh6GCRQghhBDiYahgEUIIIYR4GCpYhBBCCCEehgoWIYQQQoiHoYJFCCGEEOJhqGARQgghhHgYKliEEEIIIR6GChYhhBBCiIehgkUIIYQQIp7lv12PLvmyQZL5AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds.band_1.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23cd1784-1478-4ac0-8ad8-cf6d8d5358f8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "####  Using the CDSAPI grammar"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "4673ee18-c3be-49dd-b704-2f5bacb56b42",
+   "metadata": {
+    "editable": true,
+    "raw_mimetype": "text/restructuredtext",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [],
+    "vscode": {
+     "languageId": "raw"
+    }
+   },
+   "source": [
+    "We can use the :ref:`wekeocds <data-sources-wekeocds>` source to access datasets from the `Copernicus Climate Data Store (CDS) <https://cds.climate.copernicus.eu>`_ available through `WEkEO <https://www.wekeo.eu/>`_, using the standard `cdsapi <https://pypi.org/project/cdsapi>`_ request grammar.\n",
+    "\n",
+    "Compared to the wekeo source, the wekeocds source accepts queries directly in cdsapi format, without requiring the dataset_id to be repeated in the request payload. This allows users to copy and paste requests directly from the dataset forms and examples provided in CDS-compatible interfaces.\n",
+    "\n",
+    "Dataset identifiers can be derived automatically by prepending ``\"EO:ECMWF:DAT:\"`` to the CDS dataset name and converting it to the WEkEO naming convention. For example, the CDS dataset name ``\"reanalysis-era5-single-levels\"`` becomes:\n",
+    "\n",
+    "``\"EO:ECMWF:DAT:REANALYSIS_ERA5_SINGLE_LEVELS\"``\n",
+    "\n",
+    "In practice, this corresponds to replacing hyphens (-) with underscores (_) and converting the name to uppercase."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "777ec204-fbff-41f3-8ae2-3f60c6acb39f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "The following example retrieves ERA5 surface data in GRIB format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eff2587c-eb03-4de5-b3c4-f894d31027be",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                                                                                                                                                   "
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e0b87b34a7964ab79d6a50151b2fb4b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>parameter.variable</th>\n",
+       "      <th>time.valid_datetime</th>\n",
+       "      <th>time.base_datetime</th>\n",
+       "      <th>time.step</th>\n",
+       "      <th>vertical.level</th>\n",
+       "      <th>vertical.level_type</th>\n",
+       "      <th>ensemble.member</th>\n",
+       "      <th>geography.grid_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2t</td>\n",
+       "      <td>2012-12-01 12:00:00</td>\n",
+       "      <td>2012-12-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>msl</td>\n",
+       "      <td>2012-12-01 12:00:00</td>\n",
+       "      <td>2012-12-01 12:00:00</td>\n",
+       "      <td>0 days</td>\n",
+       "      <td>0</td>\n",
+       "      <td>surface</td>\n",
+       "      <td>0</td>\n",
+       "      <td>regular_ll</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  parameter.variable time.valid_datetime  time.base_datetime time.step  \\\n",
+       "0                 2t 2012-12-01 12:00:00 2012-12-01 12:00:00    0 days   \n",
+       "1                msl 2012-12-01 12:00:00 2012-12-01 12:00:00    0 days   \n",
+       "\n",
+       "   vertical.level vertical.level_type ensemble.member geography.grid_type  \n",
+       "0               0             surface               0          regular_ll  \n",
+       "1               0             surface               0          regular_ll  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import earthkit.data as ekd\n",
+    "\n",
+    "d = ekd.from_source(\n",
+    "    \"wekeocds\",\n",
+    "    \"EO:ECMWF:DAT:REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS\",\n",
+    "    request=dict(\n",
+    "        variable=[\"2m_temperature\", \"mean_sea_level_pressure\"],\n",
+    "        product_type=[\"monthly_averaged_reanalysis_by_hour_of_day\"],\n",
+    "        year=\"2012\",\n",
+    "        month=\"12\",\n",
+    "        time=\"12:00\",\n",
+    "        data_format=\"grib\",\n",
+    "        download_format=\"zip\",\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "d.to_fieldlist().ls()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "dev"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials/synop_10.bufr b/docs/source/tutorials/synop_10.bufr
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similarity index 100%
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rename to docs/source/tutorials/target/file_target.ipynb
diff --git a/docs/source/how-tos/target/grib_encoder.ipynb b/docs/source/tutorials/target/grib_encoder.ipynb
similarity index 100%
rename from docs/source/how-tos/target/grib_encoder.ipynb
rename to docs/source/tutorials/target/grib_encoder.ipynb
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rename from docs/source/how-tos/target/grib_to_fdb_target.ipynb
rename to docs/source/tutorials/target/grib_to_fdb_target.ipynb
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rename from docs/source/how-tos/target/grib_to_file_pattern_target.ipynb
rename to docs/source/tutorials/target/grib_to_file_pattern_target.ipynb
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similarity index 100%
rename from docs/source/how-tos/target/grib_to_geotiff.ipynb
rename to docs/source/tutorials/target/grib_to_geotiff.ipynb
diff --git a/docs/source/how-tos/target/grib_to_zarr_target.ipynb b/docs/source/tutorials/target/grib_to_zarr_target.ipynb
similarity index 100%
rename from docs/source/how-tos/target/grib_to_zarr_target.ipynb
rename to docs/source/tutorials/target/grib_to_zarr_target.ipynb
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similarity index 100%
rename from docs/source/how-tos/target/index.rst
rename to docs/source/tutorials/target/index.rst
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rename from docs/source/how-tos/tuv.tiff
rename to docs/source/tutorials/tuv.tiff
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diff --git a/docs/source/how-tos/xr_engine/xarray_engine_chunks_on_dask_cluster.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_chunks_on_dask_cluster.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_chunks_on_dask_cluster.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_chunks_on_dask_cluster.ipynb
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diff --git a/docs/source/how-tos/xr_engine/xarray_engine_mono_variable.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_mono_variable.ipynb
similarity index 100%
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rename to docs/source/tutorials/xr_engine/xarray_engine_mono_variable.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_mono_variable_remapping.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_mono_variable_remapping.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_mono_variable_remapping.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_mono_variable_remapping.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_overview.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_overview.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_overview.ipynb
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diff --git a/docs/source/how-tos/xr_engine/xarray_engine_remapping.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_remapping.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_remapping.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_remapping.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_seasonal.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_seasonal.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_seasonal.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_seasonal.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_split.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_split.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_split.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_split.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_squeeze.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_squeeze.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_squeeze.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_squeeze.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_step_ranges.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_step_ranges.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_step_ranges.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_step_ranges.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_temporal.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_temporal.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_temporal.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_temporal.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_to_grib.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_to_grib.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_to_grib.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_to_grib.ipynb
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_variable_key.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_variable_key.ipynb
similarity index 99%
rename from docs/source/how-tos/xr_engine/xarray_engine_variable_key.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_variable_key.ipynb
index 606514c9c..4a984e60a 100644
--- a/docs/source/how-tos/xr_engine/xarray_engine_variable_key.ipynb
+++ b/docs/source/tutorials/xr_engine/xarray_engine_variable_key.ipynb
@@ -28,7 +28,7 @@
    "source": [
     "The ``variable_key`` option in :py:meth:`~earthkit.data.indexing.xarray.XarrayMixIn.to_xarray` controls which metadata key is used to name the Xarray Dataset variables. In the default profile :ref:`earthkit <xr_profile_earthkit>`, it is set to ``\"parameter.variable\"``.\n",
     "\n",
-    "Please note it is also possible to generate an Xarray with a single DataArray containing all the parameters from a fieldlist. See e.g. the :ref:`/how-tos/xr_engine/xarray_engine_mono_variable.ipynb` notebook for details."
+    "Please note it is also possible to generate an Xarray with a single DataArray containing all the parameters from a fieldlist. See e.g. the :ref:`/tutorials/xr_engine/xarray_engine_mono_variable.ipynb` notebook for details."
    ]
   },
   {
diff --git a/docs/source/how-tos/xr_engine/xarray_engine_wave_spectra.ipynb b/docs/source/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb
similarity index 100%
rename from docs/source/how-tos/xr_engine/xarray_engine_wave_spectra.ipynb
rename to docs/source/tutorials/xr_engine/xarray_engine_wave_spectra.ipynb
diff --git a/docs/tutorials b/docs/tutorials
new file mode 100644
index 000000000..e69de29bb
diff --git a/pyproject.toml b/pyproject.toml
index 765f8c003..6cb960798 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ authors = [
   {name = "European Centre for Medium-Range Weather Forecasts (ECMWF)", email = "software.support@ecmwf.int"}
 ]
 classifiers = [
-  "Development Status :: 4 - Beta",
+  "Development Status :: 5 - Production/Stable",
   "Intended Audience :: Developers",
   "Operating System :: OS Independent",
   "Programming Language :: Python :: 3 :: Only",
@@ -22,9 +22,10 @@ dependencies = [
   "cfgrib>=0.9.10.1",
   "dask",
   "deprecation",
-  "earthkit-utils>=1.0.0rc2",
-  "eccodeslib==2.46.2.17",
-  "eckit==2.0.6.17",
+  "earthkit-utils~=1.0",
+  "eccodes>=2.47.0",
+  "eccodeslib==2.47.3.21",
+  "eckit==2.1.0.21",
   "entrypoints",
   "filelock",
   "jinja2",
diff --git a/src/earthkit/data/core/fieldlist.py b/src/earthkit/data/core/fieldlist.py
index fd35a21d1..193d2d0eb 100644
--- a/src/earthkit/data/core/fieldlist.py
+++ b/src/earthkit/data/core/fieldlist.py
@@ -176,7 +176,7 @@ def to_numpy(self, flatten=False, dtype=None, copy=True, index=None):
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_array_namespace.ipynb`
+        - :ref:`/tutorials/grib/grib_array_namespace.ipynb`
 
         """
         pass
@@ -223,7 +223,7 @@ def to_array(self, **kwargs):
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_array_namespace.ipynb`
+        - :ref:`/tutorials/grib/grib_array_namespace.ipynb`
 
         """
         pass
@@ -282,8 +282,8 @@ def data(
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_lat_lon_value_ll.ipynb`
-        - :ref:`/how-tos/grib/grib_lat_lon_value_rgg.ipynb`
+        - :ref:`/tutorials/grib/grib_lat_lon_value_ll.ipynb`
+        - :ref:`/tutorials/grib/grib_lat_lon_value_rgg.ipynb`
 
         More examples:
 
@@ -707,7 +707,7 @@ def sel(self, *args, remapping=None, **kwargs) -> "FieldList":
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_selection.ipynb`
+        - :ref:`/tutorials/grib/grib_selection.ipynb`
 
         More examples:
 
@@ -800,7 +800,7 @@ def order_by(self, *args, remapping=None, patch=None, **kwargs):
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_order_by.ipynb`
+        - :ref:`/tutorials/grib/grib_order_by.ipynb`
 
 
         Ordering by a single metadata key ("parameter.variable"). The default ordering direction
@@ -915,7 +915,7 @@ def to_fieldlist(self, array_namespace=None, device=None, flatten=False, dtype=N
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_array_namespace.ipynb`
+        - :ref:`/tutorials/grib/grib_array_namespace.ipynb`
 
         The following example will convert a fieldlist read from a GRIB file into a
         :class:`SimpleFieldList` storing data values as single precision arrays in each field.
diff --git a/src/earthkit/data/data/bufr.py b/src/earthkit/data/data/bufr.py
index ac79c6431..29139d670 100644
--- a/src/earthkit/data/data/bufr.py
+++ b/src/earthkit/data/data/bufr.py
@@ -87,8 +87,8 @@ def to_pandas(self, **kwargs) -> "pandas.DataFrame":
 
         Examples
         --------
-        - :ref:`/how-tos/bufr/bufr_temp.ipynb`
-        - :ref:`/how-tos/bufr/bufr_synop.ipynb`
+        - :ref:`/tutorials/bufr/bufr_temp.ipynb`
+        - :ref:`/tutorials/bufr/bufr_synop.ipynb`
 
         """
         return self._reader.to_pandas(**kwargs)
diff --git a/src/earthkit/data/encoders/grib.py b/src/earthkit/data/encoders/grib.py
index ddf9d1c24..1ddf812cb 100644
--- a/src/earthkit/data/encoders/grib.py
+++ b/src/earthkit/data/encoders/grib.py
@@ -418,9 +418,9 @@ class GribEncoder(Encoder):
     --------
     See the howto examples for more details and examples of encoding GRIB data with :class:`GribEncoder`.
 
-    - :ref:`/how-tos/target/grib_encoder.ipynb`
-    - :ref:`/how-tos/grib/grib_modify_metadata.ipynb`
-    - :ref:`/how-tos/grib/grib_modify_values.ipynb`
+    - :ref:`/tutorials/target/grib_encoder.ipynb`
+    - :ref:`/tutorials/grib/grib_modify_metadata.ipynb`
+    - :ref:`/tutorials/grib/grib_modify_values.ipynb`
 
     Using with a preset template and metadata:
 
@@ -571,7 +571,7 @@ def encode(
         --------
         See the howto examples for more details and examples of encoding GRIB data with :class:`GribEncoder`.
 
-        - :ref:`/how-tos/target/grib_encoder.ipynb`
+        - :ref:`/tutorials/target/grib_encoder.ipynb`
         """
         template_field = None
         if template is None:
diff --git a/src/earthkit/data/featurelist/featurelist.py b/src/earthkit/data/featurelist/featurelist.py
index 5030076ae..b72515b2c 100644
--- a/src/earthkit/data/featurelist/featurelist.py
+++ b/src/earthkit/data/featurelist/featurelist.py
@@ -119,7 +119,7 @@ def get(
         Examples
         --------
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/test.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/test.grib")
         >>> ds.get("parameter.variable")
         ['2t', 'msl']
         >>> ds.get(["parameter.variable", "parameter.units"])
@@ -151,7 +151,7 @@ def metadata(self, *args, **kwargs):
         Examples
         --------
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/test.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/test.grib")
         >>> ds.metadata("param")
         ['2t', 'msl']
         >>> ds.metadata("param", "units")
@@ -371,7 +371,7 @@ def sel(self, *args, remapping=None, **kwargs):
         Examples
         --------
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/tuv_pl.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/tuv_pl.grib")
         >>> len(ds)
         18
 
@@ -452,7 +452,7 @@ def order_by(self, *args, remapping=None, patch=None, **kwargs):
         is ``ascending``:
 
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/test6.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/test6.grib")
         >>> for f in ds.order_by("parameter.variable"):
         ...     print(f)
         ...
diff --git a/src/earthkit/data/field/component/geography.py b/src/earthkit/data/field/component/geography.py
index 7d0e48aea..302b2af03 100644
--- a/src/earthkit/data/field/component/geography.py
+++ b/src/earthkit/data/field/component/geography.py
@@ -199,23 +199,46 @@ class GeographyBase(SimpleFieldComponent):
         >>> field.get("geography.area")
         (70, -20, 35, 40)
 
-    The geography component is immutable. The :meth:`set` method to create a new
-    instance with updated values. For example, the following code creates a new geography
-    component with an updated step:
+    The geography component is immutable. Use the :meth:`set` method to create a new
+    instance with updated values. A new geography component can be created by providing
+    a new grid specification or new latitude and longitude arrays.
+
+    For example, the following code creates a new geography component with a new grid specification:
 
         >>> new_geography = field.geography.set(grid_spec= [10,10])
         >>> new_geography.area()
         (90.0, 0, -90.0, 360.0)
 
-    We can also call the Field's :meth:`set` method to create a new field with an updated time component.
-    This is typically done by also passing a new data array to match the new geography. For example,
-    the following code creates a new field with an updated geography component and data array:
+    The next example creates a new geography component by providing new latitude and longitude arrays:
+
+        >>> latitudes = np.linspace(90, -90, 19)  # new latitudes
+        >>> longitudes = np.linspace(0, 360, 36)  # new longitudes
+        >>> new_geography = field.geography.set(latitudes=latitudes, longitudes=longitudes)
+        >>> new_geography.area()
+        (90.0, 0, -90.0, 360.0)
+
+
+    We can also call the Field's :meth:`set` method to create a new field with an updated
+    geography component.This is typically done by also passing a new data array to match
+    the new geography. For example, the following code creates a new field with an updated
+    geography component and data array:
 
         >>> values = np.random.rand(19, 36)  # new values matching the new geography shape
         >>> new_field = field.set({"geography.grid_spec": [10,10], "values": values})
         >>> new_field.geography.area()
         (90.0, 0, -90.0, 360.0)
 
+    The new geography component can also be created by providing new latitude and longitude
+    arrays. For example, the following code creates a new field with an updated geography
+    component and data array:
+
+        >>> latitudes = np.linspace(90, -90, 19)  # new latitudes
+        >>> longitudes = np.linspace(0, 360, 36)  # new longitudes
+        >>> new_field = field.set({"geography.latitudes": latitudes,
+        ...                        "geography.longitudes": longitudes, "values": values})
+        >>> new_field.geography.area()
+        (90.0, 0, -90.0, 360.0)
+
 
     """
 
diff --git a/src/earthkit/data/field/grib/geography.py b/src/earthkit/data/field/grib/geography.py
index 450d7e435..16dba98c3 100644
--- a/src/earthkit/data/field/grib/geography.py
+++ b/src/earthkit/data/field/grib/geography.py
@@ -111,7 +111,7 @@ def projection(self):
         Examplesa
         --------
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/test.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/test.grib")
         >>> ds.projection()
         <Projected CRS: +proj=eqc +ellps=WGS84 +a=6378137.0 +lon_0=0.0 +to ...>
         Name: unknown
diff --git a/src/earthkit/data/indexing/indexed.py b/src/earthkit/data/indexing/indexed.py
index 27ff09343..7b6d268fa 100644
--- a/src/earthkit/data/indexing/indexed.py
+++ b/src/earthkit/data/indexing/indexed.py
@@ -151,7 +151,7 @@ def sel(self, *args, remapping=None, **kwargs) -> "FieldList":
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_selection.ipynb`
+        - :ref:`/tutorials/grib/grib_selection.ipynb`
 
         More examples:
 
@@ -243,7 +243,7 @@ def order_by(self, *args, remapping=None, patch=None, **kwargs):
         --------
         How-to examples:
 
-        - :ref:`/how-tos/grib/grib_order_by.ipynb`
+        - :ref:`/tutorials/grib/grib_order_by.ipynb`
 
 
         Ordering by a single metadata key ("parameter.variable"). The default ordering direction
diff --git a/src/earthkit/data/readers/bufr/file.py b/src/earthkit/data/readers/bufr/file.py
index 8468d4c40..1c287dbe4 100644
--- a/src/earthkit/data/readers/bufr/file.py
+++ b/src/earthkit/data/readers/bufr/file.py
@@ -126,7 +126,7 @@ def get(
         Examples
         --------
         >>> import earthkit.data
-        >>> ds = earthkit.data.from_source("file", "docs/how-tos/test.grib")
+        >>> ds = earthkit.data.from_source("file", "docs/tutorials/test.grib")
         >>> ds.get("parameter.variable")
         ['2t', 'msl']
         >>> ds.get(["parameter.variable", "parameter.units"])
@@ -238,7 +238,7 @@ def ls(self, n=None, keys="default", extra_keys=None):
 
         Examples
         --------
-        :ref:`/how-tos/bufr/bufr_temp.ipynb`
+        :ref:`/tutorials/bufr/bufr_temp.ipynb`
 
         """
         from earthkit.data.utils.summary import ls as summary_ls
@@ -419,8 +419,8 @@ def to_pandas(self, columns=None, filters=None, **kwargs):
 
         Examples
         --------
-        - :ref:`/how-tos/bufr/bufr_temp.ipynb`
-        - :ref:`/how-tos/bufr/bufr_synop.ipynb`
+        - :ref:`/tutorials/bufr/bufr_temp.ipynb`
+        - :ref:`/tutorials/bufr/bufr_synop.ipynb`
 
         """
         import pdbufr
diff --git a/src/earthkit/data/readers/bufr/message.py b/src/earthkit/data/readers/bufr/message.py
index 270907640..874233d5f 100644
--- a/src/earthkit/data/readers/bufr/message.py
+++ b/src/earthkit/data/readers/bufr/message.py
@@ -291,7 +291,7 @@ def describe(self, subset=1):
 
         Examples
         --------
-        :ref:`/how-tos/bufr/bufr_temp.ipynb`
+        :ref:`/tutorials/bufr/bufr_temp.ipynb`
 
         """
         from earthkit.data.core.temporary import temp_file
diff --git a/src/earthkit/data/readers/grib/handle.py b/src/earthkit/data/readers/grib/handle.py
index 37deab60e..0d872f95f 100644
--- a/src/earthkit/data/readers/grib/handle.py
+++ b/src/earthkit/data/readers/grib/handle.py
@@ -86,7 +86,12 @@ def get(self, name, ktype=None, **kwargs):
                     e_str = str(type(e))
                     if any(
                         err in e_str
-                        for err in ["FunctionNotImplementedError", "GeocalculusError", "BufferTooSmallError"]
+                        for err in [
+                            "FunctionNotImplementedError",
+                            "GeocalculusError",
+                            "BufferTooSmallError",
+                            "MissingKeyError",
+                        ]
                     ):
                         return kwargs.get("default", None)
 
diff --git a/src/earthkit/data/targets/__init__.py b/src/earthkit/data/targets/__init__.py
index f1a12be3c..32131a826 100644
--- a/src/earthkit/data/targets/__init__.py
+++ b/src/earthkit/data/targets/__init__.py
@@ -251,7 +251,7 @@ def target_kwargs(target_type):
     return v
 
 
-def to_target(target, *args, **kwargs):
+def to_target(target: str, *args, **kwargs):
     """Write data to a target.
 
     This is a top level function that writes data to a target.
diff --git a/src/earthkit/data/targets/file_pattern.py b/src/earthkit/data/targets/file_pattern.py
index c3a7d83c6..7253f7e30 100644
--- a/src/earthkit/data/targets/file_pattern.py
+++ b/src/earthkit/data/targets/file_pattern.py
@@ -35,7 +35,7 @@ class FilePatternTarget(SimpleTarget):
 
     _name = "file-pattern"
 
-    def __init__(self, path, *, append=False, **kwargs):
+    def __init__(self, path: str, *, append: bool = False, **kwargs):
         super().__init__(**kwargs)
 
         self._files = {}
diff --git a/src/earthkit/data/utils/testing.py b/src/earthkit/data/utils/testing.py
index 6c2062849..b350e7ddd 100644
--- a/src/earthkit/data/utils/testing.py
+++ b/src/earthkit/data/utils/testing.py
@@ -80,7 +80,7 @@ def earthkit_file(*args):
 
 
 def earthkit_examples_file(*args):
-    return os.path.join(_ROOT_DIR, "docs", "source", "how-tos", *args)
+    return os.path.join(_ROOT_DIR, "docs", "source", "tutorials", *args)
 
 
 def earthkit_test_data_file(*args):
diff --git a/tests/documentation/test_notebooks.py b/tests/documentation/test_notebooks.py
index 747722cc0..2693e40e0 100644
--- a/tests/documentation/test_notebooks.py
+++ b/tests/documentation/test_notebooks.py
@@ -21,7 +21,7 @@
 # See https://www.blog.pythonlibrary.org/2018/10/16/testing-jupyter-notebooks/
 
 
-EXAMPLES = earthkit_file("docs", "source", "how-tos")
+EXAMPLES = earthkit_file("docs", "source", "tutorials")
 
 SKIP = [
     "ads.ipynb",
@@ -54,11 +54,14 @@
 
 def notebooks_list():
     notebooks = []
-    for path in os.listdir(EXAMPLES):
-        if re.match(r".+\.ipynb$", path):
-            # if re.match(r"^\d\d-.*\.ipynb$", path):
-            if "Copy" not in path:
-                notebooks.append(path)
+    for root, _, files in os.walk(EXAMPLES):
+        if root.endswith(".ipynb_checkpoints"):
+            continue
+        for path in files:
+            if re.match(r".+\.ipynb$", path):
+                # if re.match(r"^\d\d-.*\.ipynb$", path):
+                if "Copy" not in path:
+                    notebooks.append(os.path.join(root, path))
 
     return sorted(notebooks)
 
@@ -75,14 +78,14 @@ def test_notebook(path):
     import nbformat
     from nbconvert.preprocessors import ExecutePreprocessor
 
-    if path in SKIP:
+    if os.path.basename(path) in SKIP:
         pytest.skip("Notebook marked as 'skip'")
 
-    with open(os.path.join(EXAMPLES, path)) as f:
+    with open(path) as f:
         nb = nbformat.read(f, as_version=4)
 
     proc = ExecutePreprocessor(timeout=60 * 60 * 5, kernel_name="python3")
-    proc.preprocess(nb, {"metadata": {"path": EXAMPLES}})
+    proc.preprocess(nb, {"metadata": {"path": os.path.dirname(path)}})
 
 
 if __name__ == "__main__":
diff --git a/tests/grib/test_grib_geography.py b/tests/grib/test_grib_geography.py
index b64ec5867..ae9d5c091 100644
--- a/tests/grib/test_grib_geography.py
+++ b/tests/grib/test_grib_geography.py
@@ -390,7 +390,7 @@ def test_grib_latlon_various_grids_1(
             "icon_ch1.grib2",
             (1147980,),
             [50.24130630493164, 50.23910903930664, 50.23666763305664, 50.22787857055664],
-            [17.710683465003967, 17.689199090003967, 17.702382683753967, 17.704824090003967],
+            [17.71062114, 17.6892742, 17.70257141, 17.70474524],
             [50.6, -0.9, 41.9, 17.8],
         ),
         (