project_presentation.ipynb

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    "# #Model-eval\n",
    "## team-members:\n",
    "Valerie Bevan, Cassia Cai, Evi Ofekeze, Steven Pestana, Justin Pflug, Engela Sthapit, Sveta Stuefer, Melissa Wrzesien\n",
    "## Motivation:\n",
    "Downloading and processing datasets for model evaluation can be time consuming due to having to manage different file formats, spatial extends, and study time periods. Here we present a tool box that allows a user to easily compare their model output to SnowEx field data.\n",
    "## Data and tools:\n",
    "### Datasets:\n",
    "* LIS-SnowModel model output\n",
    "* SnowEx datasets from the snowexsql database, including snow depths from magnaprobes, GPR, and lidar\n",
    "**Note on the model data**: Here we provide sample LIS-SnowModel output for a 100-m spatial resolution domain over Western Colorado. The code is written so that a user can swap out the sample code for their own model output, assuming similiar format to the standard LIS model output."
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    "import dataIO_funcs\n",
    "import datetime\n",
    "import numpy as np\n",
    "import dateutil.parser\n",
    "import os\n",
    "import subprocess\n",
    "from snowexsql.db import get_db\n",
    "from snowexsql.data import LayerData,PointData,ImageData# Import the function to get connect to the db\n",
    "from snowexsql.conversions import query_to_geopandas # Import a useful function to format that data into a dataframe \n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import folium\n",
    "import matplotlib.pyplot as plt\n",
    "from sqlalchemy.sql import func \n",
    "from geoalchemy2.types import Raster\n",
    "import geoalchemy2.functions as gfunc\n",
    "from  snowexsql.data import SiteData\n",
    "from snowexsql.conversions import raster_to_rasterio \n",
    "import rioxarray as rxr\n",
    "import xarray as xr\n",
    "import fiona"
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    "## This notebook is seperated into two main sections focused on data acquisition tools and initial model and SnowEx dataset comparisons. We compiled our data acquisition tools into a importable package called \"dataIO_funcs\".\n",
    "\n",
    "* dataIO_funcs:\n",
    "    * functions used by multiple routines (e.g., fuction for selecting observation dates between a user-specified starting and ending date\n",
    "    * dataIO_func.access_LIS: function that accepts lat/lon bounds, list of dates, variables (swe, depth, etc.), the path of the LIS data bucket, the LIS output spatial resolution, and the resampling method.\n",
    "    * dataIO_func.access_snowEx (class)\n",
    "        * dataIO_func.access_snowEx.access_pointData: function that accepts a date (with a date buffer if obs don't fall on the same date), variable name, and lat/lon bounds\n",
    "        * dataIO_func.access_snowEx.access_layerData: function that accepts a date (with a date buffer of obs don't fall on the same date), variable name, and lat/lon bounds\n",
    "    \n",
    "    \n",
    "* in development:\n",
    "    * dataIO_func.access_SWESARR\n",
    "    * data extraction methods for timeseries comparisons, provided a range of dates instead of a target date\n",
    "    \n",
    "## The goal was to create multiple easy-to-use data acquisition routines provided a configuration file, or Notebook cell with a list of flags/files/paths/parameters like below:"
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    "# LIS DATA ACQ. PARAMETERS\n",
    "bucket_name = 'eis-dh-hydro/SNOWEX-HACKWEEK'\n",
    "LIS_path = f's3://{bucket_name}/2022/ZARR/SURFACEMODEL/LIS_HIST_default_chunks.d01.zarr/'\n",
    "SWESARR_url = 'https://glihtdata.gsfc.nasa.gov/files/radar/SWESARR/prerelease/'\n",
    "model_variable = ['SM_SWE_inst','SM_SnowDepth_inst']\n",
    "dx = 0.0011\n",
    "dy = 0.0009\n",
    "rsmpl = 'patch'\n",
    "\n",
    "# SWESARR DATA ACQ. PARAMETERS\n",
    "source_repo = 'https://glihtdata.gsfc.nasa.gov/files/radar/SWESARR/prerelease/'\n",
    "\n",
    "# FILTERS APPLICABLE TO ALL DATASETS\n",
    "time_sel='2020-02-08'\n",
    "lat_range = [39.01629, 39.02229]\n",
    "lon_range = [-108.21, -108.175]\n",
    "time_buffer_dy = 2\n",
    "\n",
    "# SNOWEX DATA ACQ. PARAMETERS\n",
    "db_name = 'snow:hackweek@db.snowexdata.org/snowex'\n",
    "type_name = 'point'\n",
    "var_name = 'depth'\n"
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    "## For the purpose of this demonstration, we selected a small subset of the Grand Mesa domain (lat/lon ranges above). We also selected a date (2020-02-08) that had a lot of coincident data, including a few days before and after. \n",
    "\n",
    "## Below, we can see that this subdomain was on the west side of the mesa, primarily in open terrain that included lakes and a small forested area"
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    "# spatially plot the location of the domain from the lat lon bounds\n",
    "map=folium.Map(location=[np.mean(lat_range), np.mean(lon_range)],\n",
    "               zoom_start=11,name = 'Esri Satellite',)\n",
    "\n",
    "north = lat_range[1]\n",
    "south = lat_range[0]\n",
    "east = lon_range[1]\n",
    "west = lon_range[0]\n",
    "\n",
    "#adding lines connecting the markers\n",
    "loc_1 = [(north, west),(north, east)]\n",
    "loc_2 = [(south, west),(south, east)]\n",
    "loc_3 = [(north, west),(south, west)]\n",
    "loc_4 = [(south, east),(north, east)]\n",
    "folium.PolyLine(loc_1,color='blue',weight=2,opacity=0.8).add_to(map)\n",
    "folium.PolyLine(loc_2,color='blue',weight=2,opacity=0.8).add_to(map)\n",
    "folium.PolyLine(loc_3,color='blue',weight=2,opacity=0.8).add_to(map)\n",
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    "\n",
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    "<img src=\"contributors/jupflug/domainBounds.png\" width=1200 height=400 />"
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    "## Okay, so we have our domain! Let's pull our LIS model output for this domain from the s3 bucket we defined in the parameter cell. This data is in netcdf format, with integer-indexed 'north_south' and 'east-west' dimensions. The lat/lon information is instead in 2d coordinate matrices and is curvilinear. This is a commmon issue with many datasets since the earth isn't flat (despite what some folks claim).\n",
    "\n",
    "## In order to compare this model otuput with other ground-based and remotely-sensed observations, we have to regrid this data to a rectangular grid. To do this, we use xESMF, a python package built on the Earth System Modeling Framework (https://xesmf.readthedocs.io/en/latest/index.html)"
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    "## dataIO_funcs.access_LIS\n",
    "1. Accesses the data from the s3 bucket and opens the data using zarr\n",
    "2. Subsets the data using time indexing, and clips the domain using the lat and lon bounds. Here, we bump the bounds by a small buffer to ensure that we have the data on the edges for regridding\n",
    "3. Specifies an array of lat and lon values that we want xESMF to regrid to, and reproject using the user-defined sampling method"
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       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item 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",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\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",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\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",
       "  margin-bottom: 0;\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",
       "}\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-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",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (latitude: 9, longitude: 33)\n",
       "Coordinates:\n",
       "    time               datetime64[ns] 2020-02-08\n",
       "  * longitude          (longitude) float64 -108.2 -108.2 ... -108.2 -108.2\n",
       "  * latitude           (latitude) float64 39.02 39.02 39.02 ... 39.02 39.02\n",
       "    spatial_ref        int64 0\n",
       "Data variables:\n",
       "    SM_SWE_inst        (latitude, longitude) float32 dask.array&lt;chunksize=(9, 33), meta=np.ndarray&gt;\n",
       "    SM_SnowDepth_inst  (latitude, longitude) float32 dask.array&lt;chunksize=(9, 33), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    regrid_method:  patch</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-6c659cb9-cce3-4e28-a352-a8c83a23bf22' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c659cb9-cce3-4e28-a352-a8c83a23bf22' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>latitude</span>: 9</li><li><span class='xr-has-index'>longitude</span>: 33</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-82a670f4-9345-4093-9dac-a611d7b3a45d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82a670f4-9345-4093-9dac-a611d7b3a45d' class='xr-section-summary' >Coordinates: <span>(4)</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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-08</div><input id='attrs-994f130f-1593-42b5-9f57-22ac005421b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-994f130f-1593-42b5-9f57-22ac005421b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c065496f-613e-45ca-9d81-c1f28ed124dc' class='xr-var-data-in' type='checkbox'><label for='data-c065496f-613e-45ca-9d81-c1f28ed124dc' 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(&#x27;2020-02-08T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-108.2 -108.2 ... -108.2 -108.2</div><input id='attrs-a63906d0-e901-4d61-92a5-88156f6c6380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a63906d0-e901-4d61-92a5-88156f6c6380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0483ac46-984d-4f9f-8710-65e266f0ebec' class='xr-var-data-in' type='checkbox'><label for='data-0483ac46-984d-4f9f-8710-65e266f0ebec' 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>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-108.209564, -108.208466, -108.207367, -108.206268, -108.20517 ,\n",
       "       -108.204071, -108.202972, -108.201874, -108.200775, -108.199677,\n",
       "       -108.198578, -108.197479, -108.196381, -108.195282, -108.194183,\n",
       "       -108.193085, -108.191986, -108.190887, -108.189789, -108.18869 ,\n",
       "       -108.187592, -108.186493, -108.185394, -108.184296, -108.183197,\n",
       "       -108.182098, -108.181   , -108.179901, -108.178802, -108.177704,\n",
       "       -108.176605, -108.175507, -108.174408])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>39.02 39.02 39.02 ... 39.02 39.02</div><input id='attrs-f6aa9c56-86f5-4cf9-b740-62999f4b2e2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6aa9c56-86f5-4cf9-b740-62999f4b2e2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12d4f0fd-c9ed-4af0-a8df-180f23710ef2' class='xr-var-data-in' type='checkbox'><label for='data-12d4f0fd-c9ed-4af0-a8df-180f23710ef2' 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>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([39.015827, 39.016727, 39.017628, 39.018528, 39.019428, 39.020329,\n",
       "       39.021229, 39.022129, 39.023029])</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-85f6bace-a95b-405c-ab19-80e13e6aa387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85f6bace-a95b-405c-ab19-80e13e6aa387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d2d6466-3bb4-4e8f-a67c-6de9b181298e' class='xr-var-data-in' type='checkbox'><label for='data-3d2d6466-3bb4-4e8f-a67c-6de9b181298e' 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>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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</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-cde3b784-6d11-4713-836a-3aca509fec2a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cde3b784-6d11-4713-836a-3aca509fec2a' class='xr-section-summary' >Data variables: <span>(2)</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>SM_SWE_inst</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 33), meta=np.ndarray&gt;</div><input id='attrs-5a8a0434-78d8-428b-9c2c-b9f2dcc0b293' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a8a0434-78d8-428b-9c2c-b9f2dcc0b293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79649aea-f0e2-4933-8afc-ec9af5631fd4' class='xr-var-data-in' type='checkbox'><label for='data-79649aea-f0e2-4933-8afc-ec9af5631fd4' 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'><table>\n",
       "    <tr>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> 1.16 kiB </td>\n",
       "                        <td> 1.16 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9, 33) </td>\n",
       "                        <td> (9, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 5106 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SM_SnowDepth_inst</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 33), meta=np.ndarray&gt;</div><input id='attrs-3d63e406-f1d9-4e05-bff6-2b4d9d64e266' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d63e406-f1d9-4e05-bff6-2b4d9d64e266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-584188db-5122-4f94-9277-8cfc6ab18fae' class='xr-var-data-in' type='checkbox'><label for='data-584188db-5122-4f94-9277-8cfc6ab18fae' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.16 kiB </td>\n",
       "                        <td> 1.16 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9, 33) </td>\n",
       "                        <td> (9, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 5106 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <line x1=\"0\" y1=\"43\" x2=\"120\" y2=\"43\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-83187b6a-9aa9-4331-9a46-4eac65809044' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83187b6a-9aa9-4331-9a46-4eac65809044' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>patch</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (latitude: 9, longitude: 33)\n",
       "Coordinates:\n",
       "    time               datetime64[ns] 2020-02-08\n",
       "  * longitude          (longitude) float64 -108.2 -108.2 ... -108.2 -108.2\n",
       "  * latitude           (latitude) float64 39.02 39.02 39.02 ... 39.02 39.02\n",
       "    spatial_ref        int64 0\n",
       "Data variables:\n",
       "    SM_SWE_inst        (latitude, longitude) float32 dask.array<chunksize=(9, 33), meta=np.ndarray>\n",
       "    SM_SnowDepth_inst  (latitude, longitude) float32 dask.array<chunksize=(9, 33), meta=np.ndarray>\n",
       "Attributes:\n",
       "    regrid_method:  patch"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specify a buffer about our domain boundaries so have data to average\n",
    "# from the edges when we need to regrid\n",
    "buffer = 0.001\n",
    "lat_range_LIS = [lat_range[0]-buffer,lat_range[1]+buffer]\n",
    "lon_range_LIS = [lon_range[0]-buffer,lon_range[1]+buffer]\n",
    "\n",
    "# call the data access toolbox\n",
    "ds_LIS = dataIO_funcs.access_LIS(lon_range_LIS,lat_range_LIS,time_sel,\n",
    "                             model_variable,LIS_path,dx,dy,rsmpl)\n",
    "\n",
    "# let's output what we have\n",
    "ds_LIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86abfc25-6f22-470e-8d9a-8adffca5908c",
   "metadata": {},
   "source": [
    "## dataIO_funcs.access_snowEx.access_pointData\n",
    "1. Uses a global function to determine all SnowEx point data that occurs from a user-specified number of days from our target date\n",
    "2. Loops through the acceptable list of date and concatenates observations into a geopandas dataframe\n",
    "3. Constructs a polygon using the user-defined lat/lon boundaries and clips the geopandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba63f99b-5b4b-466b-8607-4b108f6025f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(datetime.date(2020, 2, 8),)\n",
      "(datetime.date(2020, 2, 9),)\n",
      "(datetime.date(2020, 2, 6),)\n",
      "(datetime.date(2020, 2, 10),)\n",
      "(datetime.date(2020, 2, 7),)\n",
      "[[-108.21, 39.01629], [-108.175, 39.01629], [-108.175, 39.02229], [-108.21, 39.02229]]\n"
     ]
    }
   ],
   "source": [
    "# pull the point data for this location and range of dates\n",
    "df_Points,polygon = dataIO_funcs.access_snowEx.access_pointData(db_name,time_sel,time_buffer_dy,var_name,lat_range,lon_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75151a9d-46d0-47cd-8d3b-fb4767df25ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>site_name</th>\n",
       "      <th>date</th>\n",
       "      <th>time_created</th>\n",
       "      <th>time_updated</th>\n",
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       "      <th>...</th>\n",
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       "      <th>site_id</th>\n",
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       "      <th>value</th>\n",
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       "      <th>68529</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4008140</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
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       "      <td>4.322646e+06</td>\n",
       "      <td>744486.456265</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>None</td>\n",
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       "      <td>None</td>\n",
       "      <td>91.569041</td>\n",
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       "      <th>68530</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4008141</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
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       "      <td>4.322646e+06</td>\n",
       "      <td>744486.696672</td>\n",
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       "      <td>None</td>\n",
       "      <td>91.569041</td>\n",
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       "      <th>68531</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4008142</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322647e+06</td>\n",
       "      <td>744486.936511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (744486.937 4322646.646)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>91.569041</td>\n",
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       "    <tr>\n",
       "      <th>68532</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4008143</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322647e+06</td>\n",
       "      <td>744487.168893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (744487.169 4322646.836)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>92.069421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68533</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4008144</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322647e+06</td>\n",
       "      <td>744487.401221</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>POINT (744487.401 4322647.025)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>92.569801</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>7482</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4072458</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322624e+06</td>\n",
       "      <td>742077.574561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (742077.575 4322624.155)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>73.908327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7481</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4072457</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322624e+06</td>\n",
       "      <td>742077.290780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (742077.291 4322624.253)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>75.767658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7480</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4072456</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322624e+06</td>\n",
       "      <td>742077.007072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (742077.007 4322624.350)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>77.162162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4072455</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322624e+06</td>\n",
       "      <td>742076.723210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (742076.723 4322624.447)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>78.091815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7478</th>\n",
       "      <td>Grand Mesa</td>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>2022-07-07 19:57:21.141883+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>4072454</td>\n",
       "      <td>https://doi.org/10.5067/S5EGFLCIAB18</td>\n",
       "      <td>2022-07-07</td>\n",
       "      <td>pulse EKKO Pro multi-polarization 1 GHz GPR</td>\n",
       "      <td>depth</td>\n",
       "      <td>cm</td>\n",
       "      <td>...</td>\n",
       "      <td>4.322625e+06</td>\n",
       "      <td>742076.439400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>POINT (742076.439 4322624.544)</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>76.697323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61367 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        site_name        date                     time_created time_updated  \\\n",
       "68529  Grand Mesa  2020-02-06 2022-07-07 19:57:21.141883+00:00         None   \n",
       "68530  Grand Mesa  2020-02-06 2022-07-07 19:57:21.141883+00:00         None   \n",
       "68531  Grand Mesa  2020-02-06 2022-07-07 19:57:21.141883+00:00         None   \n",
       "68532  Grand Mesa  2020-02-06 2022-07-07 19:57:21.141883+00:00         None   \n",
       "68533  Grand Mesa  2020-02-06 2022-07-07 19:57:21.141883+00:00         None   \n",
       "...           ...         ...                              ...          ...   \n",
       "7482   Grand Mesa  2020-02-08 2022-07-07 19:57:21.141883+00:00         None   \n",
       "7481   Grand Mesa  2020-02-08 2022-07-07 19:57:21.141883+00:00         None   \n",
       "7480   Grand Mesa  2020-02-08 2022-07-07 19:57:21.141883+00:00         None   \n",
       "7479   Grand Mesa  2020-02-08 2022-07-07 19:57:21.141883+00:00         None   \n",
       "7478   Grand Mesa  2020-02-08 2022-07-07 19:57:21.141883+00:00         None   \n",
       "\n",
       "            id                                   doi date_accessed  \\\n",
       "68529  4008140  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "68530  4008141  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "68531  4008142  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "68532  4008143  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "68533  4008144  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "...        ...                                   ...           ...   \n",
       "7482   4072458  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "7481   4072457  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "7480   4072456  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "7479   4072455  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "7478   4072454  https://doi.org/10.5067/S5EGFLCIAB18    2022-07-07   \n",
       "\n",
       "                                        instrument   type units  ...  \\\n",
       "68529  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "68530  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "68531  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "68532  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "68533  pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "...                                            ...    ...   ...  ...   \n",
       "7482   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "7481   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "7480   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "7479   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "7478   pulse EKKO Pro multi-polarization 1 GHz GPR  depth    cm  ...   \n",
       "\n",
       "           northing        easting  elevation  utm_zone  \\\n",
       "68529  4.322646e+06  744486.456265        NaN      12.0   \n",
       "68530  4.322646e+06  744486.696672        NaN      12.0   \n",
       "68531  4.322647e+06  744486.936511        NaN      12.0   \n",
       "68532  4.322647e+06  744487.168893        NaN      12.0   \n",
       "68533  4.322647e+06  744487.401221        NaN      12.0   \n",
       "...             ...            ...        ...       ...   \n",
       "7482   4.322624e+06  742077.574561        NaN      12.0   \n",
       "7481   4.322624e+06  742077.290780        NaN      12.0   \n",
       "7480   4.322624e+06  742077.007072        NaN      12.0   \n",
       "7479   4.322624e+06  742076.723210        NaN      12.0   \n",
       "7478   4.322625e+06  742076.439400        NaN      12.0   \n",
       "\n",
       "                                 geom  time  site_id version_number equipment  \\\n",
       "68529  POINT (744486.456 4322646.286)  None     None            NaN      None   \n",
       "68530  POINT (744486.697 4322646.466)  None     None            NaN      None   \n",
       "68531  POINT (744486.937 4322646.646)  None     None            NaN      None   \n",
       "68532  POINT (744487.169 4322646.836)  None     None            NaN      None   \n",
       "68533  POINT (744487.401 4322647.025)  None     None            NaN      None   \n",
       "...                               ...   ...      ...            ...       ...   \n",
       "7482   POINT (742077.575 4322624.155)  None     None            NaN      None   \n",
       "7481   POINT (742077.291 4322624.253)  None     None            NaN      None   \n",
       "7480   POINT (742077.007 4322624.350)  None     None            NaN      None   \n",
       "7479   POINT (742076.723 4322624.447)  None     None            NaN      None   \n",
       "7478   POINT (742076.439 4322624.544)  None     None            NaN      None   \n",
       "\n",
       "           value  \n",
       "68529  91.569041  \n",
       "68530  91.569041  \n",
       "68531  91.569041  \n",
       "68532  92.069421  \n",
       "68533  92.569801  \n",
       "...          ...  \n",
       "7482   73.908327  \n",
       "7481   75.767658  \n",
       "7480   77.162162  \n",
       "7479   78.091815  \n",
       "7478   76.697323  \n",
       "\n",
       "[61367 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at that dataframe\n",
    "df_Points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f9b49f1-c447-4c31-aa37-7cd8499a82f3",
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   "source": [
    "## Motivation for using Lidar data\n",
    "* Lidar has been identified as an important airborne remotely sensed dataset due to its accuracy in representing snow depth on the ground. It can be used to evaluate and validate numerical snow prediction models.\n",
    "# Introduction to obtaining Lidar data from the snowex database\n",
    "* Lidar data is an Image Layer in the snowex database that can be queried from using the keywords - instrument, type, surveyors and by date. The instrument is 'lidar', type is 'depth' and  surveyor is 'ASO Inc.'\n",
    "\n",
    "## dataIO_funcs.access_snowEx.access_layerData\n",
    "1. Uses a global function to determine the SnowEx layer data (of type \"depth\") that occurs closes to the user-specified target date\n",
    "2. Queries the database for that raster\n",
    "\n",
    "#### We couldn't find a way to subset the ASO lidar raster before pulling the data (is there a way to do this??). This takes a while to do, so for the demonstration, we are just going to load it from a local file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa6d6cb8-8903-4f84-bda0-6437f4897805",
   "metadata": {},
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       "         0.9360703 , 1.0434922 , 0.96316993],\n",
       "        [0.8369492 ,        nan, 0.39456642, 0.6084336 , 0.5237168 ,\n",
       "         0.49686134, 0.7563828 , 0.89896095, 0.77689064, 0.52225196,\n",
       "         0.8098496 , 0.8042344 , 0.39456642, 0.9521836 , 0.9360703 ,\n",
       "         0.8786973 , 0.8354844 , 0.8354844 , 0.87064064, 0.87552345,\n",
       "         0.8306016 , 0.71512306, 0.8213242 , 0.7556504 , 0.5788926 ,\n",
       "         0.6853379 , 0.95120704, 0.85281837, 1.0144395 , 0.6323594 ,\n",
       "...\n",
       "         0.8115586 , 0.69461524, 0.54129493, 0.83401954, 0.76126564,\n",
       "         0.96390235, 0.94705665, 0.7971543 , 0.85818946, 0.88455665,\n",
       "         0.80814064, 0.9526719 , 0.69339454, 0.6355332 , 0.9853867 ,\n",
       "         0.7092637 , 0.77811134, 0.8926133 , 0.73856056, 0.9726914 ,\n",
       "         0.8674668 , 0.67776954, 0.70364845, 1.0989121 , 1.0090684 ,\n",
       "         0.9834336 , 0.7598008 , 0.46341407],\n",
       "        [0.7471055 ,        nan, 0.8494004 , 0.7383164 , 0.6804551 ,\n",
       "         0.67483985, 0.704625  , 0.85818946, 0.8547715 , 0.71097267,\n",
       "                nan, 0.74441993, 0.7214707 , 0.58597267, 0.76321876,\n",
       "         0.8391465 , 0.8120469 , 0.5932969 , 0.985875  , 0.78836524,\n",
       "         0.770543  , 0.7771348 , 0.82498634, 0.9556016 , 0.91409767,\n",
       "         0.8210801 , 0.80887306, 0.6841172 , 0.96561134, 0.7930039 ,\n",
       "         0.47537696, 0.77225196, 1.003209  ],\n",
       "        [0.89407814, 0.7268418 , 0.80228126, 0.7915391 , 0.76566017,\n",
       "         0.87552345, 1.0000352 , 0.47781837, 0.39456642, 0.81302345,\n",
       "                nan, 0.973668  , 0.8933457 , 0.60818946, 0.5273789 ,\n",
       "         0.8711289 , 0.78055274, 0.9021348 , 0.6186875 , 0.6684922 ,\n",
       "         0.90262306, 1.0061387 , 0.7598008 , 0.9087266 , 0.9592637 ,\n",
       "         1.0088242 , 0.72220314, 0.73123634, 0.72025   , 0.75540626,\n",
       "         1.03275   , 1.0071152 , 1.1321152 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -108.2 -108.2 -108.2 ... -108.2 -108.2 -108.2\n",
       "  * y            (y) float64 39.02 39.02 39.02 39.02 ... 39.02 39.02 39.02 39.02\n",
       "  * band         (band) int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    scale_factor:  1.0\n",
       "    add_offset:    0.0\n",
       "    _FillValue:    -9999.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 9</li><li><span class='xr-has-index'>x</span>: 33</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ef65cc27-bb02-41e2-99d9-268dd972a136' class='xr-array-in' type='checkbox' checked><label for='section-ef65cc27-bb02-41e2-99d9-268dd972a136' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.8343 0.3062 0.8619 0.816 0.5872 ... 0.7203 0.7554 1.033 1.007 1.132</span></div><div class='xr-array-data'><pre>array([[[0.8342637 , 0.3061875 , 0.8618516 , 0.81595314, 0.58719337,\n",
       "         0.7170762 , 0.9150742 , 0.77762306, 0.6504258 , 0.848668  ,\n",
       "         0.968541  , 0.754918  , 0.67313087, 0.6684922 , 0.9883164 ,\n",
       "         0.9328965 , 0.7070664 , 0.82376564, 0.80154884, 0.8494004 ,\n",
       "         0.91995704, 0.8379258 , 0.4382676 , 0.8188828 , 1.1130723 ,\n",
       "         0.6848496 , 0.5525254 , 0.6389512 , 0.7666367 , 0.5764512 ,\n",
       "         0.65628517, 0.66214454, 0.52396095],\n",
       "        [0.48856056, 0.4341172 , 0.66580665, 0.6233262 , 0.74881446,\n",
       "         0.71927345, 0.7158555 , 0.73856056, 0.80399024, 0.6487168 ,\n",
       "         0.7732285 , 0.88162696, 0.7732285 , 0.50125587, 0.8913926 ,\n",
       "         0.70608985, 0.8284043 , 0.9724473 , 0.7832383 , 0.9299668 ,\n",
       "         0.7886094 , 0.81302345, 0.87015235, 0.7134141 , 1.0371445 ,\n",
       "         0.60941017, 1.0281113 , 1.0549668 , 1.0080918 , 0.82156837,\n",
       "         0.9360703 , 1.0434922 , 0.96316993],\n",
       "        [0.8369492 ,        nan, 0.39456642, 0.6084336 , 0.5237168 ,\n",
       "         0.49686134, 0.7563828 , 0.89896095, 0.77689064, 0.52225196,\n",
       "         0.8098496 , 0.8042344 , 0.39456642, 0.9521836 , 0.9360703 ,\n",
       "         0.8786973 , 0.8354844 , 0.8354844 , 0.87064064, 0.87552345,\n",
       "         0.8306016 , 0.71512306, 0.8213242 , 0.7556504 , 0.5788926 ,\n",
       "         0.6853379 , 0.95120704, 0.85281837, 1.0144395 , 0.6323594 ,\n",
       "...\n",
       "         0.8115586 , 0.69461524, 0.54129493, 0.83401954, 0.76126564,\n",
       "         0.96390235, 0.94705665, 0.7971543 , 0.85818946, 0.88455665,\n",
       "         0.80814064, 0.9526719 , 0.69339454, 0.6355332 , 0.9853867 ,\n",
       "         0.7092637 , 0.77811134, 0.8926133 , 0.73856056, 0.9726914 ,\n",
       "         0.8674668 , 0.67776954, 0.70364845, 1.0989121 , 1.0090684 ,\n",
       "         0.9834336 , 0.7598008 , 0.46341407],\n",
       "        [0.7471055 ,        nan, 0.8494004 , 0.7383164 , 0.6804551 ,\n",
       "         0.67483985, 0.704625  , 0.85818946, 0.8547715 , 0.71097267,\n",
       "                nan, 0.74441993, 0.7214707 , 0.58597267, 0.76321876,\n",
       "         0.8391465 , 0.8120469 , 0.5932969 , 0.985875  , 0.78836524,\n",
       "         0.770543  , 0.7771348 , 0.82498634, 0.9556016 , 0.91409767,\n",
       "         0.8210801 , 0.80887306, 0.6841172 , 0.96561134, 0.7930039 ,\n",
       "         0.47537696, 0.77225196, 1.003209  ],\n",
       "        [0.89407814, 0.7268418 , 0.80228126, 0.7915391 , 0.76566017,\n",
       "         0.87552345, 1.0000352 , 0.47781837, 0.39456642, 0.81302345,\n",
       "                nan, 0.973668  , 0.8933457 , 0.60818946, 0.5273789 ,\n",
       "         0.8711289 , 0.78055274, 0.9021348 , 0.6186875 , 0.6684922 ,\n",
       "         0.90262306, 1.0061387 , 0.7598008 , 0.9087266 , 0.9592637 ,\n",
       "         1.0088242 , 0.72220314, 0.73123634, 0.72025   , 0.75540626,\n",
       "         1.03275   , 1.0071152 , 1.1321152 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f1e7dc36-9f28-489d-b2b2-272d554245c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1e7dc36-9f28-489d-b2b2-272d554245c6' class='xr-section-summary' >Coordinates: <span>(4)</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'>-108.2 -108.2 ... -108.2 -108.2</div><input id='attrs-97f5643d-9671-48b3-9e2c-0a00830f5090' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97f5643d-9671-48b3-9e2c-0a00830f5090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-758664df-ee71-4f6f-b396-6319f1676450' class='xr-var-data-in' type='checkbox'><label for='data-758664df-ee71-4f6f-b396-6319f1676450' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-108.209564, -108.208466, -108.207367, -108.206268, -108.20517 ,\n",
       "       -108.204071, -108.202972, -108.201874, -108.200775, -108.199677,\n",
       "       -108.198578, -108.197479, -108.196381, -108.195282, -108.194183,\n",
       "       -108.193085, -108.191986, -108.190887, -108.189789, -108.18869 ,\n",
       "       -108.187592, -108.186493, -108.185394, -108.184296, -108.183197,\n",
       "       -108.182098, -108.181   , -108.179901, -108.178802, -108.177704,\n",
       "       -108.176605, -108.175507, -108.174408])</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'>39.02 39.02 39.02 ... 39.02 39.02</div><input id='attrs-f52c6a2b-319a-49a5-b42e-7e5644651d04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52c6a2b-319a-49a5-b42e-7e5644651d04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-381e49cc-3057-482c-879e-1eb352ea5676' class='xr-var-data-in' type='checkbox'><label for='data-381e49cc-3057-482c-879e-1eb352ea5676' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([39.015827, 39.016727, 39.017628, 39.018528, 39.019428, 39.020329,\n",
       "       39.021229, 39.022129, 39.023029])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-943f8a2f-92d6-4cda-9270-d5020cdbb973' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-943f8a2f-92d6-4cda-9270-d5020cdbb973' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c25e0240-a0e3-441b-85f1-328726eefe9b' class='xr-var-data-in' type='checkbox'><label for='data-c25e0240-a0e3-441b-85f1-328726eefe9b' 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([1])</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-6b7cd53b-16e8-4bf6-b962-f717e0e2910c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b7cd53b-16e8-4bf6-b962-f717e0e2910c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3c7b0b1-e41d-46b1-973b-26963d640025' class='xr-var-data-in' type='checkbox'><label for='data-c3c7b0b1-e41d-46b1-973b-26963d640025' 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>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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>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;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>GeoTransform :</span></dt><dd>-108.21011352539062 0.0010986328125 0.0 39.015377044677734 0.0 0.0009002685546875</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-e799ab1b-b005-4521-b22d-aba6c01a72da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e799ab1b-b005-4521-b22d-aba6c01a72da' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>scale_factor :</span></dt><dd>1.0</dd><dt><span>add_offset :</span></dt><dd>0.0</dd><dt><span>_FillValue :</span></dt><dd>-9999.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (band: 1, y: 9, x: 33)>\n",
       "array([[[0.8342637 , 0.3061875 , 0.8618516 , 0.81595314, 0.58719337,\n",
       "         0.7170762 , 0.9150742 , 0.77762306, 0.6504258 , 0.848668  ,\n",
       "         0.968541  , 0.754918  , 0.67313087, 0.6684922 , 0.9883164 ,\n",
       "         0.9328965 , 0.7070664 , 0.82376564, 0.80154884, 0.8494004 ,\n",
       "         0.91995704, 0.8379258 , 0.4382676 , 0.8188828 , 1.1130723 ,\n",
       "         0.6848496 , 0.5525254 , 0.6389512 , 0.7666367 , 0.5764512 ,\n",
       "         0.65628517, 0.66214454, 0.52396095],\n",
       "        [0.48856056, 0.4341172 , 0.66580665, 0.6233262 , 0.74881446,\n",
       "         0.71927345, 0.7158555 , 0.73856056, 0.80399024, 0.6487168 ,\n",
       "         0.7732285 , 0.88162696, 0.7732285 , 0.50125587, 0.8913926 ,\n",
       "         0.70608985, 0.8284043 , 0.9724473 , 0.7832383 , 0.9299668 ,\n",
       "         0.7886094 , 0.81302345, 0.87015235, 0.7134141 , 1.0371445 ,\n",
       "         0.60941017, 1.0281113 , 1.0549668 , 1.0080918 , 0.82156837,\n",
       "         0.9360703 , 1.0434922 , 0.96316993],\n",
       "        [0.8369492 ,        nan, 0.39456642, 0.6084336 , 0.5237168 ,\n",
       "         0.49686134, 0.7563828 , 0.89896095, 0.77689064, 0.52225196,\n",
       "         0.8098496 , 0.8042344 , 0.39456642, 0.9521836 , 0.9360703 ,\n",
       "         0.8786973 , 0.8354844 , 0.8354844 , 0.87064064, 0.87552345,\n",
       "         0.8306016 , 0.71512306, 0.8213242 , 0.7556504 , 0.5788926 ,\n",
       "         0.6853379 , 0.95120704, 0.85281837, 1.0144395 , 0.6323594 ,\n",
       "...\n",
       "         0.8115586 , 0.69461524, 0.54129493, 0.83401954, 0.76126564,\n",
       "         0.96390235, 0.94705665, 0.7971543 , 0.85818946, 0.88455665,\n",
       "         0.80814064, 0.9526719 , 0.69339454, 0.6355332 , 0.9853867 ,\n",
       "         0.7092637 , 0.77811134, 0.8926133 , 0.73856056, 0.9726914 ,\n",
       "         0.8674668 , 0.67776954, 0.70364845, 1.0989121 , 1.0090684 ,\n",
       "         0.9834336 , 0.7598008 , 0.46341407],\n",
       "        [0.7471055 ,        nan, 0.8494004 , 0.7383164 , 0.6804551 ,\n",
       "         0.67483985, 0.704625  , 0.85818946, 0.8547715 , 0.71097267,\n",
       "                nan, 0.74441993, 0.7214707 , 0.58597267, 0.76321876,\n",
       "         0.8391465 , 0.8120469 , 0.5932969 , 0.985875  , 0.78836524,\n",
       "         0.770543  , 0.7771348 , 0.82498634, 0.9556016 , 0.91409767,\n",
       "         0.8210801 , 0.80887306, 0.6841172 , 0.96561134, 0.7930039 ,\n",
       "         0.47537696, 0.77225196, 1.003209  ],\n",
       "        [0.89407814, 0.7268418 , 0.80228126, 0.7915391 , 0.76566017,\n",
       "         0.87552345, 1.0000352 , 0.47781837, 0.39456642, 0.81302345,\n",
       "                nan, 0.973668  , 0.8933457 , 0.60818946, 0.5273789 ,\n",
       "         0.8711289 , 0.78055274, 0.9021348 , 0.6186875 , 0.6684922 ,\n",
       "         0.90262306, 1.0061387 , 0.7598008 , 0.9087266 , 0.9592637 ,\n",
       "         1.0088242 , 0.72220314, 0.73123634, 0.72025   , 0.75540626,\n",
       "         1.03275   , 1.0071152 , 1.1321152 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * x            (x) float64 -108.2 -108.2 -108.2 ... -108.2 -108.2 -108.2\n",
       "  * y            (y) float64 39.02 39.02 39.02 39.02 ... 39.02 39.02 39.02 39.02\n",
       "  * band         (band) int64 1\n",
       "    spatial_ref  int64 0\n",
       "Attributes:\n",
       "    scale_factor:  1.0\n",
       "    add_offset:    0.0\n",
       "    _FillValue:    -9999.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this takes a little while because you can't filter the query by location (e.g. lat/lon??)\n",
    "# ds_Lidar = dataIO_funcs.access_snowEx.access_layerData(b_name,time_sel,time_buffer_dy,var_name,lat_range,lon_range)\n",
    "\n",
    "ds_Lidar = rxr.open_rasterio('raster_depth.tif')\n",
    "\n",
    "# reproject the lidar data to the EPSG:4326 crs the LIS output is in\n",
    "ds_Lidar_reproj = ds_Lidar.rio.reproject(4326)\n",
    "\n",
    "# clip the raster to the polygon extents (output from the previous step)\n",
    "poly_WGS = polygon.to_crs(\"EPSG:4326\")\n",
    "ds_Lidar_reproj = ds_Lidar_reproj.rio.clip(poly_WGS.geometry)\n",
    "\n",
    "# also reproject the lidar to match the domain, resolution, etc. of LIS\n",
    "ds_Lidar_regrid = ds_Lidar.rio.reproject_match(ds_LIS)\n",
    "\n",
    "ds_Lidar_regrid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9755ea51-a212-4cdf-b32d-9ae96d331545",
   "metadata": {},
   "source": [
    "## We now have LIS modeled snow depth, snow depth point observations, and lidar snow depth for the period around February 8th 2020! Let's do some comparison plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "096bbddd-abcb-4cdb-b2a9-9ca722f512b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U2B2nvONMjaRU50sISHO1eLk7TDicpKw1bVxT45ndKfuDkCwrsCydhmdxPAzRNIHvWdQcY7Fo1apA7mQUcWG1wXO3+jx2bYmjcUR/GHF5o4lWDfyebaAJwVLNxDM16raOZQjajkXNMPjXT+xyedlnlhQMw4wzLYcXD6bMogzPNojSgjDOWWm77BzN1KI3Lzm7WidIcq6sNfjUC4d8w2PrLPkGS75JLiV/6p3nv5Cen18W6N0H97T6WfLDJxBCrEsp997sfXgj8YaW/CoTw08KIZ6q2kD8L9XrjwrVyf4ZIcRPz5uovsLnv0UI8ZIQ4qYQ4nvvef3vCSFeFEI8LYT4CSFEq3r9/UKIx6vtPi6E+IbX3EfU5LHWcrl2psWZrs/59QaebWBZOrquLSZFy9QopCRMcjShBpkvBLWazV4/QNMEYZwTxjlBrCZvtWJWKy1dgzQriZOcMMmJs4K8hDiXi6Cg4+noGvR8k6ZrqOyRhFlaMpwlpJmaRIazFIBJnFN3TfYnCe++2KJTt+lPE6ZRRlFKrix7OKbONMrYHsaUpaQf5hwHOXkp6fkGWSHJCknbM/ANHdfQMarMQSEluZSEmRrE8xKCVA3IvqXR8Eye3Q+5eRyhaYKdQUTd1olzSZSW7A9DhmFGXpT06jZSSoIg5crZFqaprsHeMGQW58ySgkGQ4lSrW9vQ6DUcPNvAMQVRVhIkOd2GQ15KjqOEhqOyKmFWkuaS/XGClBIN1PmP1bnOSzWBLNctvvdnX/yCru/nwtDEIlOT5SVZXpLkJa6psdr2qDkGt/sxW4OYYZgtVtx5ySIrkuUlh0GqMkGFOr9NR+eF/dlipfngssv9Sx4bTQtQE4JjaFi64KTK1LzzYpOHlmqLDGWU5GgCXEvnbRfamIbGuY7LfcsezSq4Looq+6kJikItHoSA41nOp7amPLc/wzQ0ug0by1DPSqdmYxuCSZiiC7Wqn8UZtqmxVLfxbYMkLbCrbdV9izgruHs8q+71cnHfLNdMWo7BB1/q8ys3Rvzi04e8uD0izgrSrKDmGJSlJMkK8lJydqnG5dU6g1lCXpbEVXCwcxLwix+8wWE/YDpN0A2dv/TH38kf/roLbB/NeGJzyMEwJCvUNXIsnTwv2e2H2KamsryWTpTkhFEGeYrjOayst1hbeTkzPc/GNX0Lx9LVQsfSMQ0N09BU9qrKnLZ8i4arrpfnGCw1HMZhyvFJQJ6XdDouvm+RV/dMVkjW2x5uzcVv+KydX6HVa/HCrT5H45goLfjMnSFz0bZvaQjB4t7rVOdeSpXJ1IQgqQKmxb1ZLfTirECbZwWlGotWWm6VqdfoNR0cS2cS5Ri6wLVUthXAs3TuHM2YhGk1Lgm8alwrSxXcjmYpZpX5bngWG12f5brFUsOh6VlYpsZyw2apbmMaGjXXXFxz19LVtqTK7GR5SZIW99yvEKUFRpUFbLkGF3suR8fq2tfqDq5rkJeSJ7cnzOKcQZyRF5Il36LtmBwHOdujFE0IHlzzsAzBJCm4tuqz2nZJq+8TAvJ7MrmgnlkA1zZYabm87coSl5Y8vv6t60zClPde7ZKm6r50TNU5KohV0N3zjcWCSEo4ChKmWca3v2UZx9BICrlI6diGTl5IkqzkZBBy+9YJ24dThADLUhlitRhXi7d2yyVICl46inhiN6Bmvvm5FCHEg2U8Qmtexlj5KvTug6+re4AQ4l8IIY6EEM9+nr+/TwgxFkI8Wf37G1/ePX/9eKPPagJ8g5RyJoQwgY8IIf4zqhv7X5RS/lrVa+svAZ/ljVP1Kfs/gPcDO8CnhBA/JaV8HvhFVPPRXAjxd1FOxv8jyvjw90op94QQDwP/hdfo1WXogpZncnXJYRSpgSTrOOxNMnq+wcE0ZRpluJbBOEhVcNFwONN22BslzOL8dZ2If/wdj/Dev/drnF+pE8QZSy2HcZAu0t2ozCw3DibcPBBcWWuQFiW7/YDLqw3yssQzDZKqNLczStXqY8kjKyRJUTKJUqJUo+6a6Jrg6Vt9Wg0b29Q533XVqt1RmYWwCtZ8x6DlW2SFGqSitOCxM3UudB1+7cUTmr7Fua6Pa2pM4oILHZu2bTJMMoQo8A2dtCjISknN1EkNDccAx5CkheRWP8ExBOc6Lpv9iDMth/dfbfMjn9rj03eGBFVw+rXXetw4Ctg6mnF2qYZj6rSaDi3fYhKlhGHGLMyYThN1HfKC/jTGNnQuLtdIqpR2lKqV5vZJQLdus9awcQ2dF49U2XWW5BxN8sVKue6a7B3N0HUN19LZHiUkWUGvria77/uF6/yNb776uq7xHN/1w4+rwbaQbHQ98qIkSgvO9zy6nrEoLwRpwSzOSYuS2SyjP03I8pJPBinrHY93XGpzNM14Zi+g6Zq0PYPjIEMgeGCtxrmWxe1Bwo2TmJMgI8kKlQ3KCnZHKjDI8pKarYLJ3WnMmbaDRJUuRlHB9d0xYc+n5ppsD2Mu9Vzqts40KRjOEkAdR6/hLCYwTYBl6kyijKZvESY5dw6nnFurE2cFH7s5wDF1XMsgSnMVZJk6bc+oMmcakzinKCRnez5FIXnr+Sa3jkP+4xP7nF+q8cCqj2/qjOKcIM5558U2Kw2bj794RK/hsHk0o+lbXF32eDLJmcU5Xc8gLSQHQlBzTCZhysc+scm4P+YvfM/X8MLelN2dEW95eI1ffv4YTROYps5okrDS9VSZpFRBrO8aND2L9bbL8TTh+a0RVzealKXkA9/1Ado1uyrdF4vSmmupCbKstmFoAkPTGYcZNceg23Aoxirb26vbi3L/zt6Eu4XENHXe8eAKB6OImmOw1w+5tF7jxf0pvmNSd03e9Z5LbO9OeM8jazx7Z0Cel/THEbLh0PBMdvoB0yij4Zm0PZODcUySF0yjbJGd8myDWsNgFKZIqTKmtqmz2w+YzVI0TVDzLQxdEIQpnabL115p86vXBxiawHcMwiSnKCXvuNThbj/muc0BAFE2z/KU3DkOePx2Qp6X5HlJzbewDY3ltktZSlq+haFrjIKUo3GEoWuUUuJaBqmuUXMMLF1jGqvFRpjlNH0Ly9RZbjpVsJgyjlLKUlEUlnyT5abDeBKTFSU3TyJGQUpRlHQ6Hqap8b5H1vBMjfNdT5UmSzX+3hnEi0XKat3k6d0ZddtjEqsA6vaJyqAtdT0mMzUOGYZGkuRkWcEs0DAMDSlhqenw/N0h+/tTLl1s0/QtjkcxP/axbSxLX1yTNC+5uFxjluTMkpIl32K9rhFkBTVLJytLHF3nQttmf6Lmn3GsgrEsL/EdA9s26HR9XNfEMjRmYUael1zu2VhGm/df6nJrNGN3nPHC/pTdNOdsy/qCxrQvB7TGheeMlbcjhEBrXqQ4eRohxENSyude46M/BPwg8K9e5T0fllJ+25dtZ79IvKEBVdXsdVb9alb/JKov2Yeq138RFfh8rtngO4GbUsrbAEKI/xP4duB5KeUv3PO+TwD/dfV9n7nn9ecARwhhSymTz7ePpia4r+fQsAxsXa20pmlOWkhcU2O5plbrrqmTFSVpnPHQmlqVrjQs9sav38dL1wWrTZvbSY4uFO9jNEuxTG0x+eSFxDE1OjWTW4czbFNnpx9wfqlGmKnSzpIvOJyqQTqvav5BKvEdk42GGqSmUUYQ5Sw1XdKswNQFcVpUHBSdoAomdF0N7pO4WPBGjoMc39L4mqs9rh8GfMuVLs8cT8hKia2rLN04zum4Br5pMEkzNdCaBkFeVKvVkjhX+6trqpxgaIKDScK5tuISoEGtmigutG1m1crNNDTiVAUHybxMEGaUZYKmaYqvUGrkhcTQJDsDVa5p+RaOqXHrcEav4RDEGY6pMY5zskJxIUZByk5V0omijDvbI3zfol6z0ITgaBzRayjulGOqVf73/uyL/J3fc//rvs5elQkYZxm6ECozAHim4nA5hsZJoALxOb8orXhI86BlFuccCcE0zhhMk8XK9GCkypQXu666f3XBzihhEqos0DBIaPs2mqWusZTw7M5kUWaK0pxr603qjslTmwMc2+B81+P5vQmPnG3ScQ32spQoLbi82mC9ZbPeMJnEBWFWsjdWgbxrGRiaYBZnhHGO7xpkecnFrsvm4RRD08iKkuE4pu6YGJ56tppVGfhgHKNrgpWGw/Ywpusb7Ax1NCEYzBKe3C741SDlTNevroVAT9RZ2O2HSCm53HWo2zp7g5A0LfhEmrPa9riw5BNlJdf7IaODI77mm9/KR186IQhSVlYbdOs240AtlAAcx8A0NMWPQZXm1jvegm82mCXUqvLh/L16NcELwDQ03IoLmBcqA6SCM6g5BtMoIytUJkUTKmtVSri+P8G2DSxLZcPXej6upePauioVGtqipJNmBbgmay2PspS8sDXC99TYNJ4kFFLi6hqmLhkFCYYumEUZcTbP3pRYVUbEMXWitFhks7K8JIhVljbPS0xTW9zDih9WcuMkxrV0PNtYZOjRFMVgWJV3y1KdK8fUGQYpUZxjWzpJkquSp6ERpQWuraadmmPy3PYQUJxC3zHJi5K6o56D/kzdy3khSXIVRFi6yhi6pspUWaZOWnEHbVPjOMjo1GxaTYfjk4C1tkuWlzSqZ7rVUIFwnJeMk0LxPLOSUZRTShiEuQrASknNUQHK/ihGCDjbdumHGVleUpZycb7iOMOyDPK8xLYdkrQgTHKCQAWnm3dHNBo2xpyDmpeqGhCrMbqUkpWWS1qUjOOMQVjgmIKmbfDxrSnf+dAqt8YzyuraQcUL01XG0DR1VpZr2IamFjqzlPE45mCScaFts9F0aVgm71gT3DicUXdUFuzNhHXlD0g0Hc1XbTGFEBhr7yY/fPxZXoNLJaX8kBDiwpuwm18yXrXkJ4T4qdfx74deYxu6EOJJ4Aj4RSnlrwPPAr+vest3AGdf4aMbfHaX+R1eOdv0J4H//Aqv/1fAZ14tmALQhWDZs2k7FhcaPmfrHg3boO3ptFyduq3jmjq2rgZCTQgajo5T1brnE9vrhVkFLWmuCKVpqibWMMmZRhm2oZGXJeNITYZNz+LgaMY0ypjEObvjhEmsVoFtV634LV1xgryK9O1VA99618OuCMZHs4w0L7i67DGcJTy0Xmejo8p8QZKrUpRl0PAsTqYppia41HGwDI0gyzma5QRJwd4kwdI1TF0Ro0Fxv/Jq9asBhlAk0Lqt0/UNdE0wqyaT/jThY3cmtHyb9bbHctOhU5sHUyVnOh5rDXvBQXjmVp/ROCaKspfJwYZaCYbVRNVrOItSFsCV1RoPrtcQQjBNCoK0oOWqEoxvG9UqUjIaxQRBRpaV1ByTZ2/3Gc4UJ60oVdlEnwsUXif+8s+8SN016dYdNroehVQlUstQxOtBqEqotqHOj1OVMFQwqC/KyEVRUqIyJkWVOTwcx7RrNkIIbhyHXD+OmcQFtqGz3naxDJ3VprMgJGtV6XK1re7Rd19qkeYlNw+mhNWq+tJKneW6SVlKOp6a6GxDZQoeXPN4aNmjbin+3tWei2Pq3D2esT8KSauAaTyOVdmoKHEMVR47GoQ8+dIx23fVhGnrgkJKNocJSa7O97mOi2dpjGaJyizGGa6lV6VFQX8Y8cnnDlhtuzy9O+OJOwO6Fe8GoOHoVaCsskLjieI2lhKe3xqydXfA//o/fouaQOMM29bZWKmrDNJ8ErZ0XNugKBSROUpVKbTuqPs2L0sGo5g0V9k8KaHpWszijChRz+685JMXkjQvOJmoIaeU6hoYVRAwizPKKoqxDTUO6JpgNIppNx2avkWSlxia4j0uNR10TXEd58HQ3jDk8DhgOAxxLbWP84pTKRWRes6NjNICQ1fBkWere3BeRj4YRQtBhluR3LOsQNcFuq4xDlKSvGQWqkl/ZxDi2QbvOl/nPRebC4HJ0SynBHptD13XOBpGi6y9Nt93S91Xhq6pwBBVDj+exGRZSZaV2KZOWpGvR0FKVI2Lcy7VnPMJKhM4S3K1WBMCtyLzlxK2+xEl0K7ZFQlfcP9anceu9GjULOqOiVk933VbZ7VuUkhVIjd1wZmWxXsuNSkl3L+sgrAoVbSLo2lK2zOJqvtvHhzNYVWlYsPQOBiEi5+1qnQqKh5lWUo826A/jLi4WufqWp20ypR9YnNCx1MZ5axaLOelJMoLJd6pxoO8UM+QENCuWTQqPpltaBUnT2dvnLDq2wjgP9864j++cMBeP8SoOF5vJorDT2GsveezXtP8VRA6Qoh3fxm+4j0Vteg/CyFerdH6G4rXylA9AHzPq/xdoMpynxdVs9DHKp7TT1SluD8J/EBV6/wpIP082/4Nm/usNwjxV1FNZX/kc15/CPi7wDe/4k4L8aeBPw2wtHaGh1ebPHswZlY9xE3LUhG0ENzpJ6zUFHlzGOW0fJUqHUU5Xd9guUrzvx64rlrpNzyLIFbcpTDMcByzUltpND1VQnnmdp/9vSnnzreo1Wx2+wFx5iAEdJdVsLDaMBmGBR3PYK1h8Ym7U4K0wNJVpkIXQhF4DY2GY1A0HAaV2msQFgyDFNdSvKmHNuq0XJ0nttVg8dz+jKtdj17d4ihMqNsab1nx+OjdCc/uBzimzpmWzUZTre56vslJlGAbOhpwudng1ni24P7kheT9V9v88s0R0yhjqeEwjTPuW/KYJAW/dmNIXpQ8dq5Jy9W521eDbqNhq0HfNdnbm+J5Jrc3Rywv+5SlZBqkDKaJmliA53bGmNUkYhoaDUenZusczvKqrGsqXtU4IkkUudSu3us4Jq6tY2ganqkRV1wG39b5H376BUxN0J8lBHHOcJYghKDumgyniSKmuooL063ZOIbgQsfimb2AkabR803unIQcjiK+/oEler7KLq7WLXYrfommGdQcVXoNkpz+JMa1DR492yLMSvqzhLMtm6NZxnY/RkrJ287WuXEcA4I4KwjijFLCA2s1HENw/SjibWdrRGnBjeOoeojURJ8kBZ964ZAnb+o06jabgwRNwMWujaV7tFwDXdMYJymWoTGKcypqCi3PYhbn7O6MWVqu8baLHZ64M+CXXzhhd2/C/tYJf/a7voqDScIvfOQOf+ibr3LnJOJC12WaFNQdnSAteekg5F2X28xStchYbroMg4S3nKlze29MvW6T5SXjJKdds/mm+zv80osDdo9n/JuP7eC7Jn/2fRf48J2xCvQGIdfvDAjDjG/7+vv4oV/ZxPctum2XlmeRl5JhkGKbOrapMwkzlrseJ5NYle+KkiBIORqEPHyxS69usbFc46AfEAQZ59YblFJtYzyJObtaV+e0CpRUaa5aZBSSZ+8MqPkWpq6x2nLRdUEQ52z3Q0xD48atAQ9c7WGbGi9tDTm/quikj13s0HJ1pklJmpVs9wP2ByG2qeM4BkGQcXtrSL1uo+sak5kq3/UaDklWYBkaLd+iVgUhgyBVIhtNUOoqmzOrxqD5GNBsOMRJvlDR3d0aYRgazabD4SDEtXQ+cmvM/SsehqbRrRuMIlXu9WxDiS40USk+1aJgVgUfyx0P29RV0JgV5IXkqB9gmjplKQniHNvUFbcUtbgsq6xXw7MW3NW5eAZgf5Isyq5zLtj+IGSv/zIPzdAEXV8tCPoTi7ecqXM8y3Gq53sCLNdNRlGOU/FQk1wyiTJ+9OMnGBX/zbMNCin51af2FxwqTRPkeYEQgjwv0XVVQo5jpWpeXakxnaVEUYbnmYv3dLset/cmrHR9znccXtifEVecKk0IDqYZ93VdCin5litd/sMLh2QV/0zXBL6lcTJRJdcwyak7JpeXfbYGEbom+P1vX6N//xLP7U74Bx+8w9WNJk+8dKyC6azkuWnC0Th63fPWlwPlbPdxzWm//XNf17xlyvGt7xZC/OA9L/9TKeU//QI2/wRwvqIWfSvwn4D7vrQ9/uLwWgHVX5VS/tqrvWFONH8tSClHQogPAt8ipfzfqIIdIcRV4Pe8wkd2+OzM1RlgoQgQQvxxVLPTb5T39M8RqtP8TwB/TEp56/Psyz8F/inAmWtvkVFasOo77AdxtQ1wdR1HV8FSVkpcU1QraIP9SYaugWfqCwuA14O3X+rw7PaYBzaaFIValem6tii1TaJskelxXZMkTphMEkxTTfYn45iVtkuYlfiWRtsxGUUFca5sFjxLJ81LDscxQZxzYblGXkjGYYqli0WqXtfVYKppgqWaxeEo4m4/ZuiqdP57qkzGR+6OKSWEmSJSF1KiC0GaqaBtFOUIAZO4YBwrhZlr6GSlZC+IcA210jIrFVEJtDyDyTTERGejaTOLU67vTtAtk+Wmy1PbE+quySzOF5yU8TQhjlXZYPfuMZqusb5eZzZLsSydo3HElbUGlq5KhaarsT8IefBci65n0LANPFNNJrOkoOmqjMz27gTL0vFdVb4yTU1ZTOhKNXkwzZBSKX7yoiTL1Yq5W7cRArb2pyojUpaYpip5DWdqso4ygV9lWyZRiqnb1F0Tx9TZHMQU0qbl6jiGWJT8rCqNH8eqzNnwlDouzkvMKvPwzO4U19LpNRylTJ1k+LbOyVQFx03XZhSqTOa5tk0hJU9sz4iSnAfX6+wOQoJIBexBkHLfxQ437gyUUCItON+x0VCKo45tUbcMxsk8O6hEEFle0nBNwiSn0XRoNBxW62pRMA1SZtOEP/Wdb+OXn97n979jg1+1DG4fzbi0XFP2G7pgpa4CW13zeKDnM0oz8qKOY2oMZglFCbZtYOgacVYspOr9IGcSpvieRRCmjKcJgyilRF2bZ5/YJk9zvv1bHuTZzQG2rRR0jqmz0nTY6oc4prqeKy1XTdZFqcpEvo1j6dw5mBIEaVXaU+TswSDCdRU/J6myEs2qjGRogkmUESY5y00H1zIWWc2ltktSCTV6dSXVd02djzyzD0AcqWxMu+bRabps7k8wDI2LPY+0kuxHac7xieI3vfWhFSQO02lCEFSLMV2QZer7unWlxC1Lpe7LDJXRMDSxWAxqlWS/WQWYx+OIKFKZWsvSaTcdRZK+f4misk+RUilp07wkL5XC9oGuxxNbEzQhCOKMRs0izUtGsxTPMWjWbJKswDQ1fNuo7Fa0ah8knmctsmKWqTGcxHiuie8YDCYJemXNkRWKKzQnn8PLRPq5Us61DCZaRpKoMlxWBceTKOO5/YCNlkMhlaimU/EYg6RglpZYkRoXNQGepXHjOFYZvKwgTQvKUjLRBf1xvPi9KMoFN0zXNSxLI01VlqooJI6jk2YlnmdW5TmVpUySnNlMLcCWqmfYtxVfz9AEmDqjSGWdZSXyCVO1UJrPDWml6izKkigpFupzAMvQudB0eWTJZGcU8/yNE7a3x5w/3yKOc1xXEIYZ09kr5TDeYGivkHxQ6dVfklL+t1/sZqWUk3t+/jkhxD8UQvReqfH5G41XDaiklP/+tTbwau8RQiwBWRVMuajO839XCLEspTwSQmjAXwP+8St8/FPAfUKIi8Au8IeB76q2+y0oEvp7pZThPd/XAn4WRVj/6GvtO8AsKfg7v3qTt2zUee+5LgI4CFXNPMxz4rzEMTWKEpbqdqW2KOnZBneHCU3n9Weovu8D1/j67/8Q5rkWNdckLUql5KnKDyeDkHrdZjJJKIqS3nKDLCsIghQpY+p1pYDpNRxsQ+P2IF4ogOKspOXq9AO5KPmMg5SVpsM4VFwbIQTrTYusMHnpYMZXnW/y/EHAOEhZbqqy0EbbZRDmnAQZ4yDl8rLPfW2fj26NmMRKAfT+B7pkRckvvThgf6TRq9scThJark5clAyjnCVPPSs9z+JMXWeaZvzEp3fAMDFNk4NpwWjnhO0jdfk0Dd799Zd4YMXlcJrx1jM1Prk54dJKnc1KCjydpsRhTKPdUCWqih8RBCk39yf4tsFaW02Sc7n8zigF1IS7M06ZpcrOYW+YY9sGcVWeSHKVjUqykjtHM24UJYNJTJIUvPvBFaJUkeDTTCmk4qyg3XLISzWgakLQqcpx/WmCZxucGBqmrjHJ1fUwNAGGxsk0ZhgkfP3VDrf6apJqeyYns5RezSLLSyzDouWZHE8T9oZRpbwSJHlBf1piGhofeGiJ41lecUk0fEun5ShifsPR+dSdEXmhJmTXMnhmd8K7L3d44u6IWs/neqEIzcEs4fzZJlGaszWELRK6ngk9lRJ2DQ1H12k3LaK84Pn9GeNQSbavXuwQpQW745SdvQlZVvJnf/8D/NjHtmk0bP7Ls0f8zT/yFr7/Z67z2LkmeSl5YNnj0zszlmoG51s2uZTsjFJqtuLDXFmtceMoIAwz3nZ1iZv7k0UAlBaKQP2tDy/xk08dMpml/OgndimrbO/XvecilqmzdRJwfqWOZWgcjiNWWi7Pbo+qcqxNmORsnQQEYcqF1QYrTQddg6aj8/Caz91hyv4o5Oc/vE+r7VKrWbRbLnmVFTB0gW3oC3FDWUravkU8z14IUXEclQ9bt2ETJAVP3u7T74cc7vZptBt80++6iK4Jbu5PWG17ND2Tk2nCk1sjgigjirIFUbzVcpiEKWGcs7RUo9MpOTyckaYFpqkjRKICoppNlOTYhbJLcCxVTtN1FVjmpfLSitKCk0nMcBQxm6UkSc7KSo3lyn+tW7PZPJ6R5SVLDYcXb/VxqsnbqvhQm3sTLlQWKK6lM5wp/pFrq8DCtXU6NRtD1+hPYnzHYKOtLFwanolru9imzizKCIJUPY9ViToIUnodD0MTKnBwTaJKIZpXJe2sKh+Po3RRLg+ClCxT5bHnb56wvtZg+yQgzVSg0qt8w0Zhyt4gXPBX07SgXbcZVHYt8/JcVpUp44pqIYTAqcZ9y9KJwgzDMDFNsSjrjccxmiZot13yvKQoVPDnuvMAuGS9ZbMzSri1P2E2S3nkvh7DWUKv7vDCcUBWSJqOrlSrFU1kMktwK7HJPENmGhrXD2d87ZU2NVsnKQqajkeaFTiOwWymzmujZim+o2/Radive976skF/hXDjlYKsLxBCiFXgUEophRDvRDFP+l/yhr8IvC5SepVF+kvA+Xs/I6V8LVuCNeCHK8WeBvx7KeXPCCH+vBDiv6ve8x+Bf1l9zzrw/5NSfmul4PtzKMK6DvyLe9QAPwjYwC9W/IVPVBHunwOuAH9dCDEnuX+zlPLo8+5hVV7ZHafcHs/ouepGK0rJp3entFzFp4ozxd2ZpSVnWxYdz0CIfMFfeL0oqgejqLgfWVagacZ8Vzg5CanXLZJEPbjTaUKa5Fi2Ij1ahhocS2ks+CN5IRlEBZPKkHO56SyCtDgvcS3FBbN0DcfQOK4Gvd2x+v/SWoNrKx5LvsFxkLM/SRFC0PAs6rZOmOdMk4L3rNZJc8UDempniq4Jzvc8Wo4ioY6iYmGOd3sYc7XrKgM7Kbl+d0y3Uwep0ukA40rSD8o8MIoiut06euUV5Zg6SzWL3YGGZRmsrtaQcoW9O/uMx02m1QCVV0HVfDUbZwWFlMziDKerAsSilOiOWvUdz5RMem7KCiwmxVKqcscszkkSZT1xfX+iSL6VQWlRqkydMHRsU0P3BQcnwUIxOS9VDMOMpbpFq2YzjhQ/aFAp5+qO4m5keUmvZmHpYuGrpQm1Yh+FilRsVQHbfLtZrsjPLx5F5IXkUtdhFOdsngS8kCgeynrlxXR5tU7N1tnuK87M7jhZlE127g5I4gTbsfFso1p5q+O3DEGvkvVburp3TE0jlMprqSheNqicBimjMCMMc779vRf56M0hmiaI4xzPNdmbJDQa9sI7DFSAf6Hl8OJxxO1BwjBIaXkmcV7y9jM1jiZqFT+NMuKKq7TetCoLDqUWO79UY+imbB1OmU4TDENlAcZBuij7zm0rBlNVop0vHO5b8fEdk4NhuLDpOJ5mxJlJ09EZhenCZuDS2WZl3KkyBIameDtltWiRnjpPeWXhMOc9OaYil7d8i17d4WAcVWozl0vnr9CuWUigP1VKuMEsUYTmMOXt15bZPgkYVFycZtPBsnSmgcoseI6B7ZmkaVEJNkqlWBwpXpLrGGRFiam/zAPUNPnyxBylTIOMMFQ+bwCeZ5IkBXuDkHbNZmcQMgrUedg6meG6Js0q2NI1rfKxYmGAalTlPiHU/T0O08U9NZhFGJpaYGSFXHDd9gchUZxhmjq+b6njqQLJslT76zsWrmWQZAWzOFMiAEun5VlMqtKgvMc0d85rkhIsyyAIUwxD5+BgRpYV+Je6HI9jZnGmBDxhSrfhkFpKKdyXYJo6UZSpCoKuMRnHdHs+ui4Yj5NFqVIIsSj3zuskRVEu1H6aEDiOsSgTSinp1B12DmfKGmEcMR7H2NW95pi6enaykhf2pgvemyZUFcOsyoKgTFk1TQmaXEuZdl5q1PjRZ/b5ieCISZTR6XjEcY7jGMoUuspyzZ/dNxWfP0P1qhBC/CjwPqAnhNgB/meUwA0p5T9GidL+jBAiByLgD99btXoz8XpVfj+GyiL9M6B4jfcuIKV8GnjrK7z+94G//wqv7wHfes/vPwf83Cu878rn+b6/Bfyt17t/AHXX4Bvu73J3mPL8UUjTSTjfctiZJFxdcnANnaMwVeoRV7nw3hnEZIXNLClwzC/sxvzQX34ff+pHn6ThWTRck7GvygDjseLEqFWHSr2Px7HKUE0CRKuG56l0uG+/HEzNuTUPrCrl4c4gIkpzznZ92p7B1iBiME24dWLiWjpbQzWxrLVcdochF5d8HENZIkgpmaUlyzWTcy2bZw9CntqZsj1KabkGn96Z0XB0ntiasNZy8C2dcVygC2j7FrOkwDYEcaYUkv0o42O3x6y6OsFc/1CluGdJjqFJTFPDrngeH7oxYPkk53zXZRjldGomvZoi09Z9E9cyqPtKIXi4N2JptcnRUcBD13oc9kPiOOOhs8r497ntkVIIVeWJSZLj22plO1fKFNUAFSUFR0ezxX6MJjH9foTvm5SlIKpUbOeWa2R5uTB1TapSVMPTWOt4DGYJd49mNDyLRBRsdH2CtGS1bnI7ydloWlzuOkyTgpZrMAxV8HM4SRYS+qNxVAkF1Iq/LCU7w4BLaw2mUcZ9q3XirOTT14958uYJZSnZHSiy+IPrDT5zd0QU5zRdE6Pnk2YlUwmHowjX1tkfFOwcTFldrrG81qR/POPhh1d58Vafb/iqs2w0LW4eRyzXTMK8YBBmnG+6nEQJ0yynYZr4ls4gSGlbqvxX80xe3B7xp771KkFaMJoleJ5Z8V9MPnV3zO9+dJVJrIi9yzWTr9qo0bQskiJgo2ny1nWfF45CZknBrRNFcjcMjZ2TgDNLNaI05/aJUjh2fItfvj4kSnN2DmcstV3edqXH/kjxQpq+Ra9m0XIVx6ftW4xDxdsbzBLOdLzKRR5W2x7jKOP5O0r2f26tztQxOdNyuNsPcVwTU9foNawF+b2oJr6i8r4qKluMufprjrjKiHTrNkGSc3t7zLseWsExlBIVXnZTX+16+FUXhSh12B+G+I7B8nKN4TDC9y1MQ5G617s+J5OY8TTkzEqNwTRhOFQlyXnZrT8I8apA73gY4TgvL9qkVIuJKMqxLJ00LajVbAaDECGUwu7O7pi1pRqduo0mlEHlW670eOuZGj/5mQPCRJ3XLCsZTF8WA4yClE7dZlJZzYRJzsEoUs7ihuDG7pgwzLh0tokuBHGSEwQZhqHKa8o7S53DLCu4uzVmZaXGUstR6r4qsFJmmGrceX5HZWJNQ2M0ihdlQE1TwUxRSCaTsNpmyZWeMlB9/rkDNs62EQL2jtX1kG2X+840efrGSaVcLOn1fBzH4Nxag/W2x5O3+7iOwWgcK+pGtdgdj2PabXdBQp9ntmxLJ47zBYn9gQeXiZKCJ+4OmYVqUfnI5S6uZSBExt4gpCxdGp6JEILd3TFudf3qrqnc2aUSQPiuwUrd4mvOtfiPzx/xazdGPHerr/hjusbOzphHHlhmc3dMo+GgVz5i+1X3jjcVrxhQvfb8KaX8I6/x9x9EJVm+4ni9AVUupfxHb+iefIVgaIKWYzD1FA+o5RqchJlq02FbxIWSF9csRaIdBsqN+U5e4tsGbe8Ld55Ii3Kxkj3b89GE4EZRVnwCjSBIsW1FcJSlxHZtirzAtg3qjknL1auyU8l6w+RgFLEzVuq7eVDmWipImoSKpDz3FiqkatHwjnOKr3JfzyFISzYHCY4psI1Kmj5J2Z8klft0QWe9zv4oZq8yLrzSU75d28OIvDQ417LZHqf4lo4uSkZxwVLNYDiL8U0f7Z7TJJE81NOQskV6MFms8maRZGNJ43CSViansDVMaVRE/aNhRM0z6XY9ipaDaeqMRypLkySqhJcWJSeTmIZnYuiCfphz+2CiMjBVGXCj7VYpfjUYJklOGKrVnGVq3LqhlGmOY5DnKjviu0opdZLE5BURFlTpY84Lubjk0/IsBjPlJ+Vb2kKW3XRN5eytCVbrJoNQ2RC0fVV+AmWAmReSVCurlj9Ug6zgeBxTlCV7w4goLVhqu2RFydlejSdvHLPU9fAtRUQezxIOx3FlVFoyniZYlsE0yFipDCObngWrdcajmOO+mmyitFhkkCZxwUmgJN1t12AUF4yihCBVq//LSx4H02xReun3Q861bO4MY+qepSxGspel+Ve6Hj/6+B7LTZdl32Z/FlOiVK+yatNSt3UOJynbw4jzXW/hzv4HH1nmV26NuHs8QxOKEJ9mBYamMZ0mfNtXrfNLzx7hOyatlkXbV+onS1eE5M1jVS5abtiVgapShKV5yfmOw80jZcNw68V9zq1d46WtIe6VHncPp7zl/iVc26DpGkxjxZ8xKnWravkEafly66BZZRVA1aJkTtSO0pzlnqeerWFEwzOxDZ3jibIjmAcLUaqctoMqEzPPLlimyswNZ8mCk5VXdgezWcpkojJ0pVT7ZdsGaaq8vubl0LIs8TwLq+Im1iu+VZLki8VFt+NiG8oD7vxSjaWaCvL2TgL6k5jbfXNx7M9sDRcJBr8S1ih3ehaKvCDJFy10to5m3L55TJZm6pgsfVFaC0P1fxTlFHmJ55voukZRFIxGkaIPNB1cS8fQlbVIlFXZ6Or8z6/LXHBhGKocL2VR8c10HrrUoR/mdOsqQItj1Yomr4LhPC/pD6NFMNTr+ay2PULPUgF43aRVs+g1HKI4p+6bjCYJaVqQJVmlutQqO4N5Cy0WHTc0TfDk3RFJVrDSUkKJ0UiNKXFW8MzNEy5sNLnUc7lxHHIwVJYgjm2QF0qJnVftteYWFs/sjHlqa4SmqUy5EBCGGWllcGobGktdJQqY29Dcq0580/BFZqh+K+G1bBM6QogO8NNCiD8rhFibv1a9/lseArB11cZDlcUEJZKlyuNlEGa8cBgxjgv2x0ryXVRlJYCW+4XXgMtSETylVI66ui7wHIOabzGtFGOGoVGrWVi2geurNPtqz2elYbHkm1i6YLmufFKWGg5bxwGDIGVcOW47hrYw2au7JgeTZNG/zTK0ajAqGUUFB9OM+5cd7ut6tF2DrVHCjeOILC8XXj1HM2UcWXdNNpo2m8NEkbctg+WaiWcpH5+6rQLPuq1xPMtZaTpkUgUgsizRgDApeP6kZK2ljuve7Oy8Tcd8cPRMFSSaupo8t3fHJEleudgL8ixnGqYLMuosytjan+JaBr5j8sL2aNHH6vb+lFGQsj0I6Q/UoDmbpYxGEVmSMatKLlmaITRRDUolaeUPFsT5YjCfE6V1TVQ9z4TyxdKVn46mCZ66O+LZHeXf5ZkalqEm4GmiJoNxlFNUxy4ENDxlcTBP6fcnMYau1INJVpBkJVtHM1xL59JKnZZvc/+KS7PhEEQ5zx8EDAM1se4PwspdWk2UF5ZrvO1yl7KUXF5vqAWDZ9HuuGzdPqbX8xECxpWJYVwNuLqmeokVpaQf5IRZSV6q+z5McrwqyHccg7RULuerLZdOzcZzjAWxOC0Kbm+POZ4o4cdHb44AuLbkKuFDXihrhmUXzzbo+garTVfJ4g2duqNXxGHB/csO6xXPxnEMXjoMiSJlCzKNM2aJUpEFacnBJFv0ZRtU5bL+LGV7lDIJUzb7EZsHE86tNdi4sMTZjofjmAwr1ZxvG7z1TI04qzhw1TnJK68pYBH43tszTghV+kor7ttcgTerXLGlVN0FOnUbt1KZzku5UkrG04TN3cmC+JykKoPs24ZqC5OVpGnB0UlAUnlW7e2M2Lw7IknVwizP1XuKouRwb8iwHxBFWTW+6ASBem5MU6fbdFjueTQ8C7dqq1JUgoyGo2OaOoWUbPeD6h6vvKRqNsstlU0ZVefXqALN+bnSNcU53N2dYLs2tWaNo6OAra3xIpgJw4wgyMirjPxkFBEEGbouGA1Djo8Djkcvi4ZUWV55QpmGpmxJKl7RnCQuhFgQy4NJQKPhUEq4dTSj6Rqcu9ChVrPwPJPRMKyCL1RwlJU4jslG12e97fKuS20uL9fwLOX1dK7jUPdNHj3bYq3n0245IKic443F89VrKkPcq2dbFVFeMgkzLizXEAIGFUH9zsGUvFDnAeC+rmpLNanI41GszDr1yoZj7nYPMJop37goLajXbKbTlOEwwrZ1Oh31fC01Xdo1VdofTGI6rS/M7ufLAs34jf9eR4bqtxJeK73yOCz6WoLiUc0hgUtvxE69mQizkqcPAjxLtUd4fCfgYtdmb5oiZUJcNZKd97s713G4049ouiZLNYM7/Ve1uXpF/Mgfezt/4t8+qSTZTWV9oAnB4ShC9nxaNYtRtXJbXa1zdslno6McrK/1PLKqX15WKF+fm/sTtQpNcpJMkVBBZRwsQ+dt5xp8+u54oSQxdY29iTK93ByqFixxbi7UXJe7Dr6lMQgVCf1sx+OF3TG2qXy3oqxkFCqZsfK5khzPlHz37lCdjxcPVEp5vjqVAEJy53jGuy93FurAe6FpsNMPuLTaUJOHpRQv77uvzSjKOdNx2e6HvHhnQBAonler46NpgrdcXWJzf8J622X7OCArSuVQHmV8+zs2CDOV7dg5nDEaxyz3PKSUDAcRq2t1jvaGWFYLy9Q5f2lp4SEz55jkRcntvTFfdXWZ/VHI8ShG1wXnl2vUHYMMFYTMA9h56ee+1Trb/YiVpsPHb494y0Zj0djZrngMcxPO5YZV+QcprlchJQ3XwjZ1DkcRD200yErJZ+4MadVsSik5muV06jZB1TonrnrYzc1d33GpQy4l2/2IVlVCuNB1GUUFT98d8vCVHr5v8Z5rqlR6+zgkqRYL11Y8NocJti4431KZzO1hzOWey7P7ygJiuenw3O0Bf/r9l/jQzRGWodP0TI7GkTLydC3GUcont2d87++7xvf9+HP8w2nCO690OQ5Smo7BCwcBpqHx0KrHmaZd2Xrk7A5ChtOEzxxMOai4X92axSgqOJ4m3N4dU6vZ3D2c0m27ypcHmIQpkaHxyetjXMdc+JQNZsmin944fNnYs1GzmYQpG2t1tvoh55dr3N6fsNR2ed+VFoWEjqfMOC1DI52WNB0LS1fEbM9Wprlpplq82FVz6CDOF5ma41HE5bXGgghumRpt365MNnPl4TSOSKrgKQzVs7iy7C+MOG9vj8nzAtc12bx5hOM7OK7FeDDFb/h4NYc4Sjk4mNFs2nTaLoNhpJrkujZZqoIWs2r7Y5rKLsM2NHp1B8tU98xuP1Tu33nJ9d3xwk5iqelwZdnnk7eUE37Ds9g8mJJkqndn3TXRBNiVCfI8qDiZxBwezVhaUnyeIMj4lq8+zyhMub03YblnY5oatZpSz5pGj827I6Xg3R9imAazqeKGTacJtZrFmaUa9eq5zPKSWZBSFJIoyhbmpEaV4QuClGanzngcc10Omc1S7uyMcV2T+zaafOjTO1i2SRhmCAEPX1tC0wTnuh5d3+DuIOFsy6Ll6gyrEvJ6w+LRsy3edbaBoQt2hjFL7zq/8B2TUnlN6UIwnCU8e6tPva5EK2UpmUYZe/2Qb3h4mV9LC870fK7vjPjat23QdNU9kxWSf/Adj/K9P/s8o3FMraYyinOFbVoF4EJAkhakaU6jbrO2Vl8sUle7HtMo42zPV/0bHZO6X3BwNPfbfhNhvDGk9N9MeNXwUEp5UUp5CXig+nnxD3jwzdnFNxaaEHiWxsEkI0hL2p5BXkiGYc7hTMnPgUpOPzeyVBLzUVQwTV43peyz0KnbLNcMep7Fcs3ANlQftG5dDbJz4uVy2+VCz2O9oQjipqZVHA4Nx1Sk3bwoF2RQQ1flM9tQ0mhNKJfpUkqWaiYlkJdw52i2MN9crpncOIk5mGbsjlOOg5xBWBBlyo5hexCy0nK5tFxje5QQpAV15+WUflJI+mG2IHz3K/J2z9U/y0xsMAxYabmLFXrD0XnkUpe3XOrylosdylKVYQ6G4cL9WEqJa2gs+Sa+VU1gnold+d4IIVSjXE2VSIeBWoHvHc/wLZ2VrsftfoxnqjR8kuQqw2UoXkO74+I4Blmasbc7JghTTFOj1XI4t1an1/PpdTwcS8eqSjJlKal5qqFrKSEplGAhrYK2efZiqeEQpAV7/YDDcUySFoyifGGDUJSScTWp1xwTXahrA1RKRRVgBlX7lWmiMkBZXvLYusejZxp86lYfQ1OKsv4kpukpc8hxqFRPB5OEo0mCbWo8cXvAJEzpeAZmdZ9Yuqa4YYXkcJIsTDO3TmbsT1KStODOIGEYZ1UGyePJ7QnH45gwVq7ShqExigoGU/X5lquItVZ1H9uGavb6U88e8x3vvcjW1oi3r9cI0xJHV5nNtYbF7X7CLC24O0zZHcUcVpyep7YnymzTNckKySQu2Dqasdrz6fcDPEdlyfzKZmEaZby0NVqUGzf3J4zDlNEspZTqWg2mCf1BxGHlsu/ZBkGULwjtqx2P9z/Qw696ns0zT6YuFn5BlqEt+m9KqZSWc4PK4SzlcBBya2esCO2V0WYQq3Y9aaaMUIdBwjjM6E9V9iWq7EEMQ8P3TWZBilsZc/q+uu/LUmKYxqKk2Oo2KAvFu9R05cY992/KKwNhIQS6riwkrKp9CihCeVaUpJXLu14Ry1sNFdxEcVaVCyWTMFVdGcKU3ZNgYb4LEFfl4lIqwnNceU3Ne+45joFXWVRcvNBaZFo812Sj41Gr2YvS+3iSUK/b2LbBuYtL1Js+WZItSmd5ZSIbZwUSxQubl+zmJdZ5cDUaRirT6KuKw7w/4uHBBNcxVONj18BxDFURsFSweW21RlJIpklZGcGqDOU4Lmj7JufqHm1PBUy3j0O2T4LFcRuaoFNZVBxP4kWAVfNM8rzg6kZTlSqLkk/fGfG2S11Wmw7f+tb16r1waxBRlPD3PnSbcWUUm1cLNlnNW1GaL8azuShhzj00TR3HNjgaRgzGEceTmLZvY1UZ9q8IZVvTf+O/32Ylv9dLAPoY8LbX8dpvOZi6YKNh0XD0RVC1N0oopVw0C216FmlRcnQ0ZeBZTCNVKz8cqZT9F4Pv/70P8A8/dhtb0zhTd1n1lWnnKMk4nKZcaC+x6jlkZYml6dyZzJilBWGW4xg6vqVau2R5iaErpUcQ5/SHEZFn8eyuajZbcwye3g240PO5VfWzG4epMmyMctqugWNolfpHcjRNsS2d/iylP0041/OJqrJFVrXjmSUFSzWTg0nKct1amA+e6zgcTFPcivS9NQyokh1sNE1qSy00AfsTZWNwMghoVaorhMDQlDfOLM65tmoSpCVd32AU59iGxkbTol81/Gx4FseTmKPjgDubQ4JlnyQpuHM05UzP53AUVc2Cazy5OeT5uwOGw4jL59u8dGvANz/YY2tvsvCs8Woelm1y906ftY3WIv3/8Lk2z++MVPPkMOOpMOXymRaXlmvcOpypskI1ac0VSPpilaqc+N9+ucuz2yM2uj67w4jzPY/tYapKZdW5OhpHjEx9UU5OM2X0163bi7LA5tGUSyvKSPLXN6dsncwoCsn2cYCuC9Y73qKRN4BtCp7fHGBZBru7E67d18XUNT69NaUsJVc3mmhC8MztMbf3JovJ+v2PrHAwzdgehPTqDk9cP+Z4UqfmGMRpwdE4Jk1zWnWHg5OAC2sNjoOcRkWCHoZq9Tz3PTINjUsdm45rMIhyvudbr/JX/+3TfOc3XObnXxpw30qNvXHKtWWXn3v2eGH8qGmCmmfS8iwaroFv6XzkpWPuW29yfqVOfxrz2P3LlTpT3deWoXPYDzFNjSDIFuo/3zHZ6Pi8sDNiNI6ZThP27x6xcXGFu5sDpsMplmPR6jawTZ1ew+YXXzjhbedbiz6WQVVWaXsms3mz6uTlXp6+bWBoGrv9gJZv8fDFDk/eOFGBiKlzc3uEWYlYgiBb9AyMkgLb1JRru2MwnsT0+yHnz7epVQKIt19o8cRdVVqsuybn1hokeYGpa2ztTajVLNI0J5gElEXJ3mbE8VGPdsdbCATKUtJsOjTrNueXatwVKjicN0M+GIZs70+5eKbJ5ZUaozBjOE1469UOg1nC/knALzy5r8qVRckzN/ucX29gVko0hArG2p5q2L43jCu/JEmz4XC259OuWURpwQc/s0uz6fC7H13Ft1V5bhKmHI4i/Krly7yXZKPh0Om43L19ooLBJX+hdtVMFdSaps7JiQqO54FVHOdouobrGgsfu7lNSjgNOemH7HY8Gg2HLFPE/E7dZqmypQF1jTs1U7XuyktajoGhwUmc4JkaH98e05/G9AehaoztqWbfWcV3kqhgvNFwWGmpEt6NvTG+a9JruRSlZBCki8z/Ss3kYJrye+5bQheCDz53SFlKdF3xzIqGTcdX3oHzBWKz6VBUqmXFA1Pl4TwvabcUSf7B9TqagGfuDogi5df1puOLJKX/VsKrBlSVv8MG4Aoh3srLpb8G4L3B+/amQAA1y2RzmJBWsmi78m2ZRpmqS1clnDn5GNTKLsnLV29C9Cr4O79yg7qteAmaELRsE1vXaNsWnqHjGjq6EDQ8W5XptDq3JzPiys8kzSVRWtKuWfSnauCaK0aEgJWmw1ObA2qVfDrJJXk1CJ3p+sRZgWfp5FIusmzLdYMbhzPOdx22BsptWZly6uwOQzo1G11TpoiDUGVqVJZLY7XuMopVGTDOS7qeSRDqXFryeWF/xv4kY7llLva1XbNxvM9u0Hl2pcVuP+TSap2Go7M9TlnyDWqWjl0Z413uOQyrXlt118Q/02Rzd0yWlSRJjm/7dGoqy3c4yzieKk+vlY7H8bEacC1L4/Yg4dLZJnd2xvRaLnu+g2FoFHnBcBjTbDqcVP3mZCWj1vWcWs2m4SlhQF6UdOs2dcdgXJ37vFAk7igtSPKiaj1j0vRVIL5/EiBQbTiSXJGc267FsFrFdmo2cVbQnyaVvF2VfmpVRtAxNXzX4MbeGNvSWWo5bO5OVJ/IlsvuIKSslFy7+zNWV2r4Vdlpd3+qnOcrsuxay+Fwokq+9ZoqtY0mqg1MXpR89OOb9FYaRJEi7r7ngRXGYcbm/oRu22U0jXnrfUtc3xvTrsoRnm1wNEmI0wLfUSrAolT3uK4JrnQdPrMb8M5H1njxYMpG2+Ny1yYrJc/sKSl5mpf8ia8+y0vHEdcPZ9QcA6Oa0DxHZXM3j5Q7t23oi5Ybz++M2N3rU6vZGIZOnsdcOtdmo+uxWleWC3tDi0llU5BOx9z54AsQTaC1QuI2mA4mtFoOhi6YhBnXD4NFO5e46qmXFep8RmledVSAoiqlm4ambAss1f7FMDSu3x1ydq3BdJrwmY88h12vY7s2B7vwwMMbxHFGCOhtj8k0WVgI+K7B1bU6u8OIaVIsVHOOpY5ZCBYye/XcC0zLJJgEuL6LbugLMnS77VIUc7sWZUfg2UbVV1CSZMqws912mcUZW33ljO45Bks1kzQvmExiDEPHdY2Kp6RKoHMJ/nw8cAzBNJELL7xSquddObgrM+RWy8UyNQZRTsOxOdOyyeoWw0C58p+rOH3K7kLdz/WWTxTlC34aKN+4WaiyaEmc4fnWZ3EyhWBh3WBZ+uJv3ZU2tZrNg6s+sziv7ldDVQgMDcdQjuRzEUbdVqrOhq1TAlFesFFX3mUfz162wVH7qjJIQVU21YQyVc0LSbfp4NrGwnurXbNYadgkeUmcS7qmwThImWUZ+0G8yFbOCfOaUM9BkhWLRdv8OIUQi7Klaep02y5Nz6JT+W4Fueo/OBrFp6T0NwivlaH6APDdKJfy7+flgGoC/E9v3G69eUgLydMHaiD3LY04l2SFWAwGWVYQ1VU/JCHUhFGUkrxUSov5ZPUFf28uEY5S/Pmmcl23DA271JimBrksScsSqygRqGDC1DSOggwcCNKCrFQlm7mMv1WzmFau5C1XX6zUAHYHwYKcu9a0CVLVdy3OJaMwq2TgLJr4tjyTUZjy0t5E9fVKcmoX1AQ5d0t2TJ1ZogKvOZZrikeVFCUPn2kB8PYLbT58vb9YzV9YqtEyJU/uzXAqfkucFzR9i91+yHJDKQadqnVMLiV6KWnZJpDQ8EwOhlGlZtTpdjy1X2lBXkpW6gZbecHWSUXMTtVKvlazOOyHJEnBE7eV71utZnO267Pb9bj14h66oZOlGf1+iGnqHELlXp7heRbdhs0syqBtL4wGNU0s7gfb1BaNoPOCSuUjWG26vLQ3ZjZLCZuqhUZaeYSdBBkNT51bJecX+E5BPFVtbgCGk3jRG+24H3LtfJu0UnlpVblTq/yzilJNloP+jEbDplOz2bzTx7RMHr22xGCqVv6W/rKHTpqV5HlBlhWMk4Ltk4AoiDjYyTEqwnXLVT3U5vvxsQ/f4F3XlknTguEsWfSGO9912T6eLXrdWbrGU/shl7s200S1SrrYsfk/P3yXt55tMooLwrTkQtfl/hWfO4OYJddm/YLLzkgFtTVLZ2eUcGm5znZfZVptUyfJ1fmPsoJZ5UWmiNwFD9+3xANrNVxTU8raXGWU4jjnZL8Pk8pIWdOhuVJ5CpQcVE2L675arOSFZKPrKYVVKReO1cAiOFaKSslzN0/oNN0FP+vyRpMkK4iSnOOjKZQFtmujGzpJmCyyCstdX/GOqvYlvZ6PbehKLOOYHE6ShQ9TnBWMxjFhmLK8XKvsAUykTGm2VTNpTdOQpUSvSoC6rnFprcGnnj1A1wVHVSDa8iyGs5T+VJWkzy3X2Dqasbk3oVcRmvfGCXXHxKtKiEIILEsFa3kpF42hhVBikklcMEsKkkx5SuV5yXAYUa/b1D0L19bpNmyOhpHqQlCV1JSflMr0dn2DnYES62S5Iog3GpJ+P1z48c39taIoW5Q7hVDXfx6IzK0uHMeg1XIro86StbUa777WwzI03nquwe44peUYOKZGzzcYRQVNR0dKJWLQhcq05lL5kd0eJMTNEq/iyx3tj+h03IUtxb1o19TCMYhVz9SNrlokzwUGvqXxket9rqw1MHXVnHtvFlNKiV+pJl/2xFKdKtK0oNDV98z5mno19s+PeX6bykrlnZV55Xuozs2bjt/pGSop5Q8LIf418EeklD/yau/9rYokL9kZKv5CmOTsD0JOTgJGg4DuUl35ighBmhcLAuCccJnmBTcPJvz3P/EcP/AHvrB+jH/jm6/yd37lBpM44ly7ZJpmXGnXVbAmS8ZJxjjJWPUd6qbJIE4XrXCkhBvH0UIZ5Jh6ZWII3YbDWsPm128N8CpXYlAch25dubI/vjmkLCXnlmqESU5/mvDSjT6tloPvW3woLV5uBO1Z2JZSdLUrUvNRpdSq2Tptz2B/kla+WMos9F3napxMk89qvLjR9YlSxV0aRxnX91Qvs4NxTFlKXEMgjALXVuVGIQSjmSKD2gbkUrI5Dknzkis9B10IDsYRF3rKwHJe5jruh/zMOMYwNFa7HksNh6NBSCElyz2PC8t19gYhOwdTbFvn7EqdJC/pdVxOuk1m4xm6ri9k02ma43sWyx1vUd7dOg4qN2jFochy5Uita4Llhk3d1nhmEnPfan3hrF+3dR670OYlS/FhjsYxax1PZT2qwGpYBQSmoQjLZ9su46RgEqZcW2/wy4/v0LrUBeD+VZ84U/fj0sM9nj+IeGF3srjWvmPy9sc2+OSnt7h985g0Tlm9r0GSvcx1iTLFmzm7WufRs010DZ7fm/Gxp/exbZ33fM19PPnkDp1eHV3X+Pe/dgfft4jjnF/4hef4K9/z1fzkp3ZZ7njct1Lj7iDi7vGMC12XmqeI8YYu6Hom59oW+5OMlw5mFKXkwfU63/T2DX7kV27R7fqcX6nxe69tMIgTlmomLw1mqqVLmPLsrT62rfPQhQ6//sIhy11flTeFasT8/NZITTRpsTBIvLLRJC8kP/3JHYRgIWI4f7bJ2fUGvm+y3a4TTkMldzd04jBG0zX6+31sx8I536qyL5LdfkizamA9X5hkuWrrNG8zEyY5jqPsOual3P1BqMo6g5A8y7n82DXqdZvZLCEIVFY0ywSzSGUWltserqWz1HB48s6An//0hLNrdWqOSZTmPHS2zebRdGEUaRkaFzea2KbO/iAkdAx6PZ80VSVqzzOpu+q5bXvKmHM6TRdlu/nkbxk63bqDZxtcXmvw0rbydpqXeXf7AVmmzm+v5arFkcZn3ft5UVIaGs/tjBmOFHfpLZe7nOs4/MqzR6x2PExdWyyEjoYR//oXbpLnJUtLPu96YIXHLrT5zJ0hT21POBpGXF5vcHN3jGMbXDrTXGTu1tsuaRXc7dgG6z2fVtNhOFKeXUlSLBTTUaSI+HlesNLxuLLe4KG1yqevEtGcadoL4YFXtdnSEJVHX87tQaK4h5ogSAsats7WUGXQz/R8jpbqnF1SXoBlKZnG2aKcOpwl7O5OuP9Kl+EoIul42K7JYJag6xqPb454YKNJxzMYhjmPbNTYGaeViloniLJFlkoXqhOD8uwqFiXk5ZbLztGsMjpWmW/bmLdtgrWGpSgiWbkION906OZvfE37HRRQAUgpSyHEf8PnNCD+7YKilBxVpZ2TUcRwGDHqz0iihLTpqdJAruwGGp65cEGehGnVkFTiWl9cliorJIWEWycxK3WTlp2o1hZ5gWPoxHnBUZjQaltERYGpqT5oHcfCtw1Opqo5qGloSvmRFrRtHdfSuLiigrN3btT54J2xkmdXfep2B6FaFdcsEtdgOEspSzW4XjvTXLRccC2DmqVxNFUE52mcY5k6tqHaV+ia6gIvhJJJx7mkBLZGKSZg61BIMARcW3bZHCTcOJhQJDmaePmcSeBgrFSVF1brXO65DKKcNFfHbGiCpmUSVU1fPbPKXJUOR1OlhtSkoFVT6f7RSLXpcUydyz2X/tRntemS+LZa8bvK1uDB8x2GgSJSu5ZBs+VQliWarpHnEsdR3Iy5s/i8tYVtKluCi6t1JSevzB5NrXKiD1RmZhRmdGuKXJ4juXsSstp2GQUp01nCheXaojRoGho1p2qBUWgVCdqkbuuUpXIQtyyl9vJ9i36QU0q40LGRck5+VYPkcstFAI5pcuXqitr3rGCl6zGo2uJIqbJJmlCTaZwpwmvTV/3VNlbrnF+u8Yyhc+6M4lo99fQeD17pKeuJO8qbKo5z1aKnUByNt1/q8Mnbw4VDty4ESzWDrmOR5pJZnPP2Cy3aniL/vv2hVf7Lr7xEq3aBW6MZcS759Ttjzvc8+pVk/OQk4I//7qs8tzfF9y3Vc802aDnKqdu19arMqYKEJClouBYffWafu7cO8Rs+mq7RaDiLgKgsJd1ejWbLU6WlkxnxaEhZFmh+A8vSKKtyvGcbDKcJga769U2r4KesvINWWi5RWrDSdTkaR4s+ei9nDCRBkPHYY2dIsoL9/Sm6LnBdZe2RZYrz0vRtlpoOtcq00rZ0JlKy0nJpe4pX9OCKg6HBJMxYabuc63rsj2JsQ1uo3uI45/JGkzv7E+VjZSlBzUl1Pi1LZ6ntclwRtkspeeRci6yQ2LpglpY8dqWHb2nUKsXwJ8J0QRyPUtXIuCjLxQLTsUxmsTo/zz27z2w84+pDZzANVTaTUnI8jjm/XGMSpnzj1TZrDYv/MFLna29nBA+s0HKUs7ldKRHXmzYHQ/X8bbTdRfCma+BaSmHcaTgcj5X33Kz63LzLgeeZzGYpg5MZptlgFudcXqlxHOSMokK1y6o8rfqhalYOoFXFmCRXNgdnWjZBWuBbOndHKXePZnzDA2pxs3kwVZSAKmNWd02cXK9aPqn7bXW1zs3NIfdf7qqFmKZKgk71uev7E850fWxDo0QFdY6hsdr22DsOFjyqQqp7a94BYE7CByrfqZc7QAyniRI1tW06tsWRpWwo5qT1Nx2/0zNU9+AXhRB/Efh3wMJiVUo5eEP26k1EGKY8/vgW4SzEMA2QUG/X0U2dWs2iUbNYbrqcTOOFgigvVLmr5lvMgpTj0RfXufuvv/8qf/MXrpNVZYS7k5CmbXA4S2k5KnAZVq0VrrRrjKKMW3nIIE7ZaFqYugqw7vYj1c7B0Xl2e4zvKIL6ctMhygvedbbG3jRlf5Ky3rA499gKN09iwqxcNOf9v37gKlkp2R1GvON8g51xqgwzC8m5joNtKel+27dIcuUA3Z+llBJaVV+rYaACg5vHIRc7Fs/dneFZGuc6HsNEPeSmrnEwLjAtgSeUyqMsVanhYs+rFImqJNpwLVqOIqVbuhqYB2GOb+m890Kbj22POJplBHHGAxtN3Krc9qvPHnBhtcHZtgrMHlivU7d1VYpIlbKq3XJ4z8UGQVpwMMkIs5Kra3WGYUZRqD5fx6OIdtUI+XCgSoCrbU8p+xKlcGu4FtsnU6ZhSq/p8JGXTmj5Fistl1mkVqnDIOVMx8W19IVzdpL5PHXzhKvn2gTV8fmOuVCM1R2Ll/bGNDyTb7zW4fYg4V0PrPDLn9pmacnnw88c0Go67I/shR/N5ZU6tw6nnO95PL8zIUqV8eA8uImzgrWOoj5mecl2X5VEew2HnVGMJmBvoPx4bFO5lK9vtHjx+gndrodhGoyClLZv0VvtcP+Sw0drFgcHM341zvnat6xypmnx5GaBY1lMwlTZWIxU+fZwlvHQRoPtYcyn70S8+3KHD1zr8OjG2/lXv3KHx1uqZGLogp1BxN4gZDyO+cZ3n+fD1Xmtu8oGYRplxGlByzPxHZP+JMa29YVc/uc+cofJcIoQyqvs8oVlek2HOztjVpZ8Oi130SLFtQ02D6eUV5cZjVQft+ODEZ5nkToFeV7SqNscnYSLprhhmLG2VqdWcWPavqWk65niNZ5MEsiVnP3wcMaZMw18R7Wj0dYb3N0aEQUJLyYFnY6L66pswsEw4uJyDcfSWO94BGFKEOc0XYMrSx5P7Sny80Pn26zWTTaaFs/tjImSnKsbzYXqsuFZuK5JEKT0Kxf1SZhxYbXOo2fqHAdK0dhrOJVHlwqGmr6FBjy04tIPVRsqKZUj+dmez/ZJQJwWGPrL+ee6a6p+e2mhCPX7J9Q7qt/mM7f7+J7FRk+VNG/uqrL3xxoOK3W12AgmIWVZ8hO/9BLNlsfGeoNLK3We3hzwmbsjdE35Lm0eq76jrq3oCboG59oWWemzeTSl5Vs0PIszLYdxUuBbGrePAu7uTdjePOHocMqDl7ocTRIarsWj6x7Hs4xpUtKwdRxT0A8y7o7U+Hu2ZdEPc64s+zy2UieXqutCP8j5pdsDlpsObz9b5//y1WfohzlBUpKVkv4sJStUv8QwyYmTnMEgxHVN4kwpPXVXLYq/9kqLF49intocLAyBz/V8tvshVkUDAfi2d2zw4ZdUifrwJFj0GKzVlDHoYKDmoLmvl2WpLPvcK69pK97nypKPZeh83dUOf/H7vqhp64vHaUC1wJ+s/v/v7nntt4UPlZSSYBKQZzmaprG03mV9vbFY/Vm6YBIXi7LaJExJc0UQdUydUf6ywd8Xg3kfqpNZzhE59y+pbE+J8rSYezVJCUnxcq+8aVLgmhpLNXUJt4cxD676LDcdXrw7VLYCLeV6vDWOiPOSUZhh9Bxqlo5v6Wz2Q8ZBSpwVrNZVFuTZ7ZTDaYata8ySDEvXiKtGo41qQMgLSd3VWK6Z7I1TsopHYJkak6r/3PP7ymB0HBdcP5xxtuezeRKx2nJ4aKOBAJKs4NO3T/j2t5/haKrk9tcrQ1HbVPyRu6OEUZwziCrHcktxhUaJ2r4Qigw8CjOMukXdVn3/5qahR5ME3zErbpziZxiaRi5UE2lQK10hVKd5U7Pohxnnuh5HQ+XC7tsGj1zq8nTFu8qq1HmUFott6LqSz6dpTu4Y9HzlFRNnBW3fwjHUfg6DhJpjoomK91SVP6K0WCgE5w12QWUiVK+7jNk9kvq0Ikjv90PedbXH/ihmEKR8x1tXeXJfSdp7DUdZBMwS8kIsysPrTaXM3B2ErLY9Hlv3uH4c8/zuuDq2guNRzLnlGlHD5vg44J0PrvDrzx0wnsSqtFNKrh/HXDvTouaZjKsmzqA4RXHFW5sr5G6cxJxrW8qo01ILkxcPA3YnKW/b8Pkj773ATz++x0MXOgvu39bWiK9/51mOJ4pEa+galqHTq1s4hsZTW2OVCapW6aoMIhYE5GZH+ZnlWbFoXaJXPf4antqXhqu6HYRJzslENUVP4xRNUx5kg0FEmqQ0mi5hkGE7hhJARAlBYC/Uc6bhVf9XHDpdMJwmHB3NeNtDK1WJsMSylCnm3v6ESECRK0KzU3ExTUNjfxSx0lQGp5alCPk1S8c2VGuiopS0fIsDwNYF623V9mitYeGabR6/ebK4f+I4x/NMLFOJKGxTZfN8S1s4uF9d8blxFJLkyvrCsXQOpkrxPC9JlxIajkGU5AsitK4pKxKBok6kmRJ0mLZFs9NgdalGKRU5/XAULVR2zabDkzdO2Nsd8bZHNzBNxccaDCKiKGel5eKYqvFvw1XNzgezRPVQtHVqromugSEEpq6y6XXHZKPlkJWS5brJasPkcKoWNJ22i2Es85ZLXd57qcnPv6ie471JSstVRsRRVrLesNmqgv+0kLQdk3E4IzQ0fNPANw2GcbqwHJlEGW1XKTs1IdBFzt5EKcDzUpCXap549tkDELC2rnpC+lUfz27dYRAWJHmJWwkEunWbo0nMcKYyyXmh+F8felFd03GgbF2SpFi0DZrPY7quEUXqes8NdedWP7auhE6rbY+LXZerHf+Ln7S+SGivUN4Tv8NI6YDyo3qjd+QriSzNaHQatLoNHrivy9W1OjVLp+UqY8kgSRYS+Ll6RwiqlhF8WRQTs7TEs9QNV7N1NCCoVGIAeVkSFwW6pgwhbUOw5Ju0HGUM6lrK6+d4EhNUq9RuzSIrSyZxQZxLenVrEaDpGizVbRUwOCYtx+DWIFZu00lJx1OEzKhqkTPnPgwqp2SAolTp/ihVDuJtz6Rw1OQ0d5UGmFW967p1B4OiIvgr8mWWFhxNlXeVoSt+gGvprDZsXEtZNBQlNB0VBAqhZPkvHquV+ltWXeV/lChjy1nVMyvMSrqeQbdmk+bKjysrlGouzgoMXRBmyirC1gWeabA3yTB1oRraOg55rhSe11aVq/EzoprQCuWenqQFx5O4UnupEpBp6viOIpLq1ardMnXiXJn5NX3VwsI29UX/to5vsd0PSDKlvNtNIyZRSsMzubpS4xee2CMMUzzPwnEMlhoO+/vTRXuJOX/teByTVKXYr73a5WimXMPnmYiTSUzdMZgmqr/g3lBZhmhC+ZE1XItu3SGOc0zzZRWZYWicbVpsL9c47IeV55dGUvVPu2+tyaGnvG482+Drrnb5xeeOsCuLjXGUMQlT7uv1Fr0Vz/U8xlFO11Mq1PWGxZ9//2V++Nd36NYdnr5+zMP3K7PR42GEXjWIbvkWhhD0fINO3SZKlEeXY+mEps6ZlRo3NtWCotk0qrYr6aK/mhCw3HSp2TonsxTX1FipmdyxDY4lRFHOuD/GtE3CQDnmF1nBdBKTpzmz8QzHc3A8Z2G5MQtSiqZDkpc0XBOJuhesKsu93naRUvGu4kwyDBJaLZfJOCLP84WKreaYnOt6+JbOzaOATt3mylqDIM6YJgWzVFEP7MoHKy9K9iYZl3oq26ZV5VVRKcxsU8fzTFxX9QDNisqfrHSVr1JFpu8HOVqlTvNtYzHmLPkG/ZnqsFB3TVxT8XikVGOfVpV0o1QR0A8PZ+zc2uORd1wmz0tqjsEsVlwrdU/phGHKlbMtOnWbn9oZsd7xeM/lNp6lsT1K+flf32KpbjOOVCspIQQ938Q2FK+p23BI0qLyARMsexbbTkrPr9H11dhsaqrbha4JfMeg7tYZ1ZSScBTnPLJRZ5oUVSmSz/LoK6uSWss1+PjWlFmsDJ3jQo3F14czknnp39DISokuKk5dqRZpcwqEbegUmqS3XGc6VXPIeBKTLanxRGpi0SDdrSoAq20PgSrD+7ZBq2azcxIwi1WgFlVN24vKd2wuEgDl0RUEKZalY1kmvaZDu2ZhVVUIS9d4YMXjwaUaZ5tvvkhfvBJv67dXPPX6AiohhAn8GeDrqpc+CPwTKWX2Bu3Xm4ZWw+GPfvfv4kzLombpqr+aa5Dkqrw0TYqFOm0+IPUaDrYuSAplmHf3YPJFf//3feAaf/uXbqBrEscQbA4THljyOAoV0TsrFBHRNnQalsmjyw0Eqh3HNM0XzVW7Vauch840GY5j5Y9USJ7ZVxXaC22Lq+06cVGgIQjSKe8502Ka5bxwHPDUQUBRwvsfWmZzmJAVqt1LlOYMg1Q5VIf5QsIfJqpkkFSDQZjkHI0j6pUz9d4gwLVUP7q0KHnhIGA4ClnueLgWgCpzPnRpiXFS8O0PLPHjzxzy3e/Y4ChKaFkmTdtkkubcHoYsVaUIXdNUKTKXzJKSfpjj2wbjSPFDvuGBLnmhJPj9Saz4DKbO8TRh6zggjjNs2+DR810+fntEzTHp1SzarkHL1Xl2V7WAeD5I6TRd8lKVRe+O0sUKe6XjMa68jFxbpbHn5eCikARxxvXDGS3PwtDmihvFrbANXZFRNdX37GgccXmlpngd05SGo9M70+BjNwcEccYjZxq888EVPvn8IW+9usSNvTHP3jih0bBZ6frkZcnBKFIreF3j43cmnG0rp/v+LOX2wRTXVhmwTt1myTdYrpvYmka2Uee+rhItjKychmfy8Jq3cHAHsJsuxeUOH7reZ7XlUkjJ+671+LFxTMfTuXM041zPZ79qTn1g6qw1bZqexSTM2B+pnoKdus1Hbo9peRaTKOVsW5X3HEPjVj9ZnP9Hz7bIpaT12AYv7Iw4GgwwTZ1WzUZKSc83OZqlLNdVma+QspLra3zgrWukuZLIX1yuMalKrk9vDnj+xgmdjsd7H1nD1ARtzyDJFUfqJMhpuCbLTYdLK3U+IuB4f4DQFM8pmBTEoxihCQzToLtUw7aNxaQWhspd3DQ0lpsOw1lK3TUZBynn1xt4pkbd1hjHBbOy4OuutDiZxJy/0GE8jglDpVI716txdcmhH+ZqrAlSrq74zFKDuychV1dq/L5HlnhyL6Q/S5hUpr7HE6XC/PSdoXKFNzWOxxEfeHiZIC349VuqN6Wpa8zinF99/hjT0OjVbaK04JmtIbaps972ePSMz51+Qj/MuXTG5/kDFYT2ahY7QzWuGIZYBFNpXnJS+ZKN+jPOXF6n03KQEp67ecLvfvc5ihI2LZ3tgylLSzUVfI0i2l2fnq9UwWsNi51BxNWLHW4eTOnUbT7w8DIvHoY8tT3CrcQcZ9sOQynRhTIGrlsmZmXdArBat8hKSZyVnGla9HyDMC0ppWRnlND2dDxTY8k3eP4o4mCqRAVmxXfr+gZ1W6fnm/zkU0ccVS1pvu92H9c1ObtU48Of2ibPcp6+fszZrscLuxP0KtM8d4kvS0lalBiaMgm+cr7Fjc0hUVRy53DK1Y0mOycBuhBc7Kn2NHXX5HAU0fJtVe6NMz5z45iylPTaHocnAaNRvOBTmaa+6A+oiPj5gkeoVwvgMMnZHWds1QK6jsXZsx62qUyj32zo+m/8zlfKWv1Wxust+f0jwAT+YfX7H61e+543YqfeTGhCNaudxKqUlZWSTmV2OU2U/Ne1VapbNa8tsXWBa2oYumSiC2q+9dpf9CpIipKapXpm6RpkpVKYaNWqU6LEEK6uE+Q5m9OAF4+U5Dh0C/bGKasNi/vaPqNoQrNuk2QlYZUOfmzdU61X8hzPMDA1jXNNG9808AyDsJVzexBTSlVObNr6ovGrYyqSe14oAm6aF3RrNnerJpxCsBhM8qLEsXRqto47lyxLScMxeHjd5yf3xhzPRtiG4OHzbYIgZK3XopSSmmnQ9i3yUmJpGidRSlIqB+dSwnGg+FqWrvZLIjmepfRDxQ2qOSZRkldlQLXf/aki+edFSbtm0/ItjrOCCyt1ri25XN+fogul2rl/2SWoVr7LTWW4t30y42Sc8fSuzt4gJMsKDEMNCqapV/3j1Gq14ZlMQgiylJ2DKesrdQJdZU50KbGrkmCSKxL6XMhwtutzPE0521Zk5K1BTG9OjNUER9OMF3dGFSG92l7Vpy2sfG4OjwO+6W0bTJOCJ64fMwxqPHq2wcEoIs8LNNcgL0uiIOfuUBFij4OMSx3VvsbWddYbNqWEtZpDvKzUmlFW0HQNhPB4/MUjvupim3GYogkYjxMajs44TCmlj2FoeJ5FGOfcPg4r81hV+rJMlb1MKiNMzzZ4YX+q7rGuwyRW0nFDMxgnBUmqevKNJzH1uo1j6YrcKyU3j1Q5c2uYLLIwpq7x6LkWDUfn5olSTy75BtM4ZxxlXN1o4rsmYawyYgC3+zG9yn0/rsqGoEpuS0s+k2FAvW7zwKUONxyTnbsneHUP11Vu5fMJTLlUKz5Lr+ksrDOOxjHtul15oqltR1mJLgSbA+UxpmmKHFwUqjffLM74xOaEolRE9KNxxPEso6h8o3xLZUSSyi5jTmpO8pLjUUS34fDC3SE138IydEaR6r1oVe1tsnuOM4pz9JZLp2aw1HS4fTBlpWHhmTpnWhbP74c8va/EK21fqVvTXPUxtS0TS1fHOHdSn81SeisNPM9kuelyt2ptMo2LhefVxY0meycB4zBle3vMN77rHI4pOJnGi3ZHnZqtSmnVAtGuyNNZXrI7CxaZvI2maqNlaIKlmrkwEDU0lSlyzIqqUGWKL3Yc0kLiGNqiiXycqbHp5uGMtqfuBZVVzjCEKofOZinjcUwSJSRhwma3QaejxEr7O31KeY7hNCGKMpZ7Hm3fXlBALF3jbNdj61D1Fm21XI6qXpyTMGUWZ6w1LM61bLaGCYPKe25/EDJ1lQAqDDOWl3xWW8r3TQgoixJdV7Y4c/+puYoVoCjU/RjE6pqbusYLJwEX2w5naz5FWtBwX0Fx9wbjNEP1Mt4hpXz0nt9/RQjx1BuxQ282bEPQcg1GUYKpS4KkZBCpAf5sy+LJrRH3rTbwbINplKm6dqk4S0rWnRHFX5rr7Pd94Br/9BN3WPItVULLCxxTo8yKSs2i41nKLPLuMORgkjEMMkxNkFkaPV8NKE8eTjmeqf5tJ0FGmpV846U2bcfi9njG3XHExZZHkOUkRYlj6JyECbau8XXnOuSyZHMc4pgaHddgZ5xiaIokfOM4Zm8QognBjf0JzYogvHk0W/DJznYtgiTn1uEMy1TcCh1BnJX8l6cPFvLyJJc8cbvPwxd61G2NXs3k2ZMJgyDll28NeWDFpe2ahFVT3qtdTynZogTf1ImLkjBVE8Wjax7jWL3vqZ0pt05idgchx6MI09R537Uu26OUCx0b16zzsduqFcint2cLDzFdKDLqNFHu3klW8I33tfmIJnjq1glZUXJhucZzdwbEcUIQpMRxhmHozKqei5pQgYNlGYxGMceDkKljLJrb+r7FctPBKnV6deXEfDyJmUQZu/2AumOS5CVH45j9YciV1QaPrHv0gxwh2gyDhG7N5qG1Gjc7PmlRsnkwZTyOObveYGcQ4TsGj1zuslyr2hTpGn/o3Wd4qFdndxYtmnI2LJPzrZKeoyZKIWBVdzA1DVfX2Z+kOKbGhY7N5iDhk88d4jgGd05C9o4DXqzZHO/1+eUX+swq5dj9Z5rc2JvQazhEac7W1lj1omx7WLrG1SWHD98c8bZzDc63XP7TM0fKIDZSViX7g5D0TJMLbZsPX+8zmqXqvFVtV7JCcagOhiG+Y3J7b7Io4/2+t65i6YJn9kNcU2MYZnQ8j5vHyqftA9e6/MTTOVfXGixXi6elmolnKqGDYyrVmGXqnExjPNdkZb3Fe96yqjyXLndZ7nl0687CjDXOClq+xXCmeFJBkLJ8ubt4puc+Q6MwZUsT1GyDSZRjWzq7o5ir602Gs4SJZzILM+JYmQhP4wxT13j/tQ57TYuP3xrynsttsrzk03fHGJrgbNfj4fu7fPzOmEfP1OmHOed7vmoLk+S850qHQVjwmbtjPNvgykqNOCt5+u6QNCtp1azFZHupp1Rgu/2Q64cz9saKuyME7A2jxQLp1uGM3SpI6jYd9ocRs5kqY8Vxztpana+5f4nDScrtwyn3rTcYj2N+7fEdikLy1//QQyRFyU8/XfDMC8f8rrdvYGiCz2xNcCuj0m7N4v5lF13AJ7dn3BkkHI5UT8h3XmoxigpOZilRqto3dVyDozCpFioqI7TeEMql3NSZJgWepVUBrWDvWJmEzgUqD644PL49wzF1dgYR9/VUZm0cFyzXSt5xqcNH85Klnk+3bnP97pCdzRNWVmoMhxFLqy3+j3/5MZbPLis7kbQgthRFYk7S3x1GvO/hFT70/BFXNpqkqco+Xl3xla1IlaA511Zq3e2TGSmqb+KdgymOY/Do+TaHk4Tf9/Z14lxyOMv46FN7CxXpbJZWyj2NoihV78KmUrQ2fUX8v3EU8rGbA777XWcwNY2T6AvvQful4ncCh+r15tsKIcTl+S9CiEvAF9fE7jcZDE2QFCVJ5XBt6GpwjfNykbUydYGtq4ekUZVx5gOmrMzovlRYFbnS1DWatknXsTA1lQnzLB1NUxP/qudwseNg6IrsG2eSrm9wtmUtAhZDE9SqUtQwSdmaBriGyjQ5uk5cKMJwkhcL7sVJnPz/2fvzKNvS9KwP/H17ns4Yc8Sdh5wrq0o1S6g0IoQGwAzGNm4MmAYtmwa3u23Darfdf5heGLtZTQMNxoZ2220hWICGhSRGlUqqKlVlDVk5Z975xjydec9j//HuOFkShZQaKkvTt1bkzbg3TsSOc/bZ+/3e93l+j9B/A5crXYeuJTuYrqNzNC/4zMtHhKHoetxW1Fu1O6SstX1fdMQ6v2D3o2ks9Ssgf37/N+zQcURbtO7aeKbG0Le4vmIzSUpsTcMzdQludWw2fYeBY+HoOr6h03N0bF2xP8uJ8ooHo5SmaXjl8YSD05CPP7MuY43A5MrA5nBecDQvsAxtOXIo64Ybqy4rvsHP3BnzxUcTFkkhN+9W6Nw0MI2kaOh2bBzHWApATVPjyasDdF3xeHdKlEi3Issq4jgnDPO2+CpboWpDmBREWck8KagbKGuJ0Xl0uljm6FmGhCUXbbba0NN5YsNnp2eha4qBbxI4Bh99chWlBByoFLht8G/dNBwvClY6Ng9GGXEhGI5VxyYta05juZhudR08QycpRcw9ywqO45T1wGTFM1j3JXLj5pU+va7N+Tyl37V5c29K0zTsHgqh/Wgac9YKum9v+FxZDQgCC983OZ5IHt/BTLRro6ikaO32SV4xjUWEvNJ1CPOaohLt0e//0DbXdnoMO3bL0lF0XRPPMagbIT47LUH9Ws8Vu3kj0Ui31ly+sBeSlRWWqdExRb8XZaXkUNo6hqYYxXJj1pXC1CQ0umngia0upqkxCfOlAP/KWoBjXnQF1FKYLQRroVjP44JpmL2dL9mSrAPbYLMjr5nemigmoXQjoqSgLCscx2St6yyjZcqm4d5ZwvbQI21p82VVt3gO0bz5jkHeBqUnRcXj85gwylnxBXcC0p0xNcV6YGK3MS2aUnzr06vkZcU4rphn1VI0P49zwZK4pjDoTJ3dUcxLr58yGsXouhQsTSPC/6qq8TyTzaFHUsjxGbriiTUX37fY2AjwfZP/6Wd329+hIQgsPnqtS94GG5ttvlzflWuWZxrkRc2rj8aUVc3mwCPKaixdLfWXadGwyCvOY3Hoako6QkUl3y/KpZiydEXXNpinFYaGdN0NucZPkwuXqEFaVLx1ljCOSzY6JnnVsNkx+eYnV/norRWe2e6wtRHQGXTI2zzRsmwI+gFN3eD7VlsgScZj3iZazOOCz94bYbVdqc0Vn82BdFC3e6ITLeua9cDENMR0Acj50YZmz1PZdAA8teYxdN+GlBotDd80NWxbCPbdrk3Xl9fugoe2SApxjacFu/OE187e/XBkpal//eM3WUH1TjtU/xnwCaXUA2STexX441+zo3oXV9Pya4aezv1RRpiVBJZGYOv88AsCLXxq3eWL+6HcgIc2J2EhRYWjc3ury1/8Ex/6VR/HH/vQNf7Hzz1k3bOwNLk5DF2LnUBfhn3WDezO46WwvGoaHpwn3Fh1MXUprC5a+j1HZ+gauIbOmutwnmY8M5Ccsu2W5HwSp3Qtk4FjcRQlGEojr6VzdRynBLbG8aLg9YMZliWC39FcOjQPmGGaGutDj0mYMQkzNvriTDINjSgtuL3VoWmgKCuSrgtNw/3jOX1LnHVmKvEOu/MEz9SYxQUdW+Ju7oxiLEOK2LvNAkvXWHFsdroC9LsUeDycR9wbyWOVgg9d6/HPo5zLaz5PrLkczjI+82DOx2/2mCYlliE35YuA3Isip6gaFnFO4Jk8uenz8v6cH3nlDENXbK/5KKV4Yt0jcExeejgib1lQRRtwWlUNnmey0rV58/4I09QIFzl1nRJ0bLKsohi4HLeE77qRG3dZvx1IqxTcP5ixsx7w7E6XuycRhqbwWpfcB7Y6HMdpG+ECP3NnwbM7Xb7rQ5e4dxxSNw1RWi5Bkx3XZKtns9OzCMuSrmnS0PD6ScK3XO+zN09omimPpyl91+CHXztFKUXPNXj/to+jC6G+6+j8vveu8xOvnbPXdijStOT7fvdz/OwLe3zgvVv0XIsvvHHCc7dWqeoGx1RsbwbsHy4IoxzH1NmbKD5wtU9a1jyepHz0Wg/H1Dhe5FwfOIySgoNZQVLUfNsTQ1Y9ucHYps6oBbXahobR7nIPds8Jgm3W+i6vn0VEecWVgcSGzNKKW6sOj6c5SVbyv37pkNEiZa3nMk0qVlyxkNdNw3pgLqnYT676nEcFt9ccHHOLk3nGwDO51Ld57UAQDDtDn5NZQsc12WljWmxbp9u1RTPYwPE0kRifImO95+KZGqdhwU7P4t55suSGXYwKO4FNnBRcXXHY6Fo8HiV84s6EpzcDTF3xhccznmgjhB6fhZwucqZpSZgUvJGW3N2bYtsG6wOXPK/43352l6pquHW5T1U3PDiL2tgoDdtU3NwIuNSzeRTYzJOcR2MJ5z6ZJkLiX2Rs9kTX07N1HhwvcBydSzs9AkeE4p4tRY9Sig/dXsUxNJ5ad/n8noBb3ziO+can1ljzDV54NOPgLOSVoxjL0Phz33ebf/76iL3TkPfdWmWna3EaygYjLWp+6q1THrTFutE6Y1d9k0kiGxFDUxzMc+xIdKKqFeM7bVFdVBLqbrTaxYvv3XV04rwmLQqmScnTGy5dR+f+SArPvVFCXjY8v+2RlzVPDgNuDRq6pslnjyaUVYPrGtx9bY/B+oCNzS5N01kWNGlWMm+F8BfSg7I1oJyPYwJHIKs3Vl2+tB/R9wzeOJHz4ekNF99qiftlzYPdKb2ew8bQ43iSYLSa3RXH4ng+JmsTC56+PlzmcG4PPU6mCd/0xIpsEu9OyApxaU7aju8PvXDIfP71iZ75rdCheqcuv3+llLoNPIkUVG82TfPu9wy/BqtqWtq0b6JQhK0A3dAUk0lCvytjkXmbkXQWlRhKuknTtMLWf+1OiCSvmeklvinH1DFN1lybOKswDbUktKdlQ2DrmJoiMxsmSckkkc5Pz9EFGApUCl48DPnuWw5bnkNaCSfFNORCZSiJjlCAoeRiVNY1UVFyPC94atXjrbOpaC90xTTKCcNsadVNU9khDTpyMynKmlGeMfAtrq5I+9xSFeMoo9E0TNPgya0uJYoXHs3byJmGWVws88B2xyk7fQdTh7XAZG+asdFqq5o2522aFiRlRd82eXpNxm1703zpBkqLCqe1x+tKcRIW4hKqmzaCQTpo++cR+4bs4vQ2YHrNN3AtQ7hXWclq1xFgX9MiFwyN65d6ABydR4Sp6GhMU2cSCrNnbc0nz2U0dHg4gQZ2dyc0TcP2dk9AiKaOZUqshmloZLncWE0N9s4XGLou402t5jTM+fLJQrRLcU7ZaHzbk0NMXTFZ5Kz1nGUEjdZ2Tg1dcbrIGceitfnOJwZ0LRlzubpO3zF4OElJyxrHlA6I3Yp1z+OCRZZy9zRmrWPjGHpL24Zex+beecxmzyEOZde8ex5SljW7pyGPTxa85/qQgW/zqJwS+D5nE9H3aEqAhaYuHZaBbbE3zTiNCjYCiyh72+l6sBAmzzSSkerV9YC6gWmYtd0f6Y5EWclm1+TLBwWbXZOjmXQsvulyn6Ke8XgsqIBxmGHpmjh305LzqJR8s3bkdzArSMuQJK84C0tGrbB8s2OyyMTh1dQNPUfH0FzOQ7HXG7q2jHe50OttDz3mcS4QxkpCzOOi5st7cyxTckI1JU60po0JyVsHn6YUA99ikYp2KmjfFx1b4z2bPfZGEeeLdKndywop7heLjJOmwbalqCuKkqurHu/Z8PiRV06ZRLnEImmKZzdcepYpjr5aiuZJLD9npWMTZSWv7E0J44KiqHBdk53tLmtdp93ciTapUDWBZ7LiGxKa3nb/AsfktUdjvuU9m5i6IisqOr7FVld+r/vnKZMox/dMHhzPuTFcY7Nj8qOfPxDcS3se67q2dNkVtbhk67phnpUtxFQI85dXPKq6wTO1FmEgeqoLZ2CTVqSFnFueKdecJK949Tim7xhMopz1rs14kRGmJVc7HgqJ+opLyQ6s2umBZensXN9k7/4hlm3iukab8ynPX1mJcxDF0i1Z1dLRnkV5i9WAjmOQ5DVnc9kkPTI1eU7LuhWXi9mibMPSs7ziRz+7x5XvtrFa5+2TV/ps9hwORjFX1wOeXPeYRjn7k4zTsGAeC1rGbM/PLCspS9VG2bz74cia/lUKqq+mq/oNvH45EvsPAM8B7wX+sFLqj35tDundXQrFwBH8wLWBzYpnUjfwRz94lS//N79LAieLaklJn7VCT7uNffm/f89Tv2bHkpZi90+rmtOoICxlVFQ3DYuk5O50sfwcRNtk6vJmn0QF07ikqmGR1Zi6Wo6MPrk75q3pgkmWk1YVUSrC6ItA5ouu1jjLKeqakzAnKWqOwow7BzOStGw7MnVrydWXO4vJRNxNcVYunWuHk5h5XLDpazw4jXl4HvPoJKSua7IkZR4LRDAvReAv4x+58Z/PU6ZJyXpg4BpaS2/W8U2DtKo4jlJOkpSzJBXxqSHHcmVgk7eW5XlcULYXWMdUTGLJWctL6eLdXhVGVVWLI++VxxIi7dmys1zr2Dyz5aFrip2ezapvsjvJJE7C0tnsuaz3XK5tdjkZReT5BRNGGDCaqonjnDjOxFRQQxpnhLOQ09MFk1nK3Udj3rg3YvdgRlNkGHpNz7Oom4a3dufcfXzO3iwjK+UCPU0q8qKkbDQ6ltbyZRSDjhxf1zNxLZ2+Z8lYpn1cWdVYusb/+tkD/st/8AqffusMECPEKBag6N2zlKKq6bkGq4HJvfOMw3nBetfm0XnE3ixl4Jt8+Ik1bqx3mI0X9F2dqqg4OA05OJRA4dEoYj7P0JUia9MFvvnJ1SXS4TSUYqfr6IR5Rdk07I5TiqrBM3RWfOkyWIbilcOIridQUqfNPBstUsbjWIpQ20TXNYaBzXkr3D6eF+xNEmZJQVwK1drQZfxttjdnDRn15ZUURxcrKSpmqWTu7U2St/liGssbNQi/6WSWEqWC+jBbgfOg5xBnJYEjQeqBY+Ja4gRUSsb1mibxL99wpUvPt6hr6WzWjbi2xrFoG9c7JoYm3eFxLE7ex9OcuCgxDY2tgcfZKObqWoBt6tzc7GDbOje3u/iexdaqL4aGucQ/9VyJnOp6oqf5Z2+M2VskAuV15Zy/YJ/5toFlvI1HuOB/XVvv4LQjJMvUl/w8yxCcycU5OvAMrq8HuK7J5+6e86/eGNE0cGnFJ8wqkrzi/mnEes/hu9+zzul5TFbK9cxxDDxHnrO1gct7r6/woRtDqrrh0XnE4Tim64letGjHhap1Gp5FJY8mGUeLnONFQVrWLXJFBOp565a2DI2rA5sbqy6G1qJBLIEUl3XDVs9mkhV0bIOirpnlBV88mbDIKhkpuyaXL/XoDrpUZb1EO5SlaDp1TbL2DE2c2XY7Jq7rBt8VjIdv6ez0JAUBRCN7HuYcT5MlOPZifHcR6XORXpFXNVf7FpqmeH4nYLNjMggsnt7w2OqKBvd4mvDoNJSirGkh1K0MI4oK+n1nGVnzbi5N0/61j9+SHao2z+8m8GXe1k41wP/ytTmsd2/p7Vx9nOTsBC7PbHj8wfdeWv77P/6TH17+/5/5R6+2J6jG6TxjJfjVuft+4frPv+0Wf/kT9/Atbflm01qn1CQusXWNutU9XJB4e20be9E6EPueQZiV3BjaHGQ5YS76gptDh7Ku6Tum5M9Vom0qyoakqDiOU7Ky5qlhhxf2Qo6mCT/9mhB5A9/iwzeGPBrFjNr091trFq7tomi4c7TAMjV0DbHdXwq4cxpRN4rnr/SFLF41qKbEtTxeeDjl+nrArGVPDXyTjcDkcF6wO6oZhxkvHQobxrd0fvLOiFXfxLM0droWr58mPLXussgLGliO7aZJRVnXPH+lz+f2QjQFw9bV1XV0xnHJrYHHYZjiWxo9zxQr9lrALJKA2B/5csrO0GMciQvnreMQs+1uxVnJwLc5X2QYuuJwHPP0tSFvPp5QVTXXtrqkRcXByQLD0JnPC6r2ZuEFLiCjvvE4YnOzy3OXugSejabg9YM5VV3z5r3zpT7n7/30fb73o9fIy4pnNjrYTc2XTlKq+ue366dphWuKHfzKQGJpOrbOWVRQVFJElnXNe670+YnPPOav/swDVrsO612HjqMR2A6B7fPqUcyjccYkynjPdsAsrfjeZ1f59MP5shg4nSd8x8dv8Y8+vcvzH7gqgdRlw2qr6ciyipcfjXn8aEJ/4HK5Z9EJbO7cH3Flzadu4MUDsYu/dBjxbz27LkylpsY3daKi4krH42E3YxQXdF2dgW/x8DTk8DjkudtrRGnB9RsrGIZgPV49ijgax4wnCU9fH3I8ifnR106le9VzmUQFf+pjV/hHr54su7jTpJLw2KbtUhka14YWmx2TT90dMwgsVtqRTJyVHI4intjp8fpxxMOjOf2uw8k0YRbldDo2J2cR20+s8eh0wWrXIXAEgpsWFeOwYL1jMYngUt+i5xgY7Q1SU7IJsQyNlx9PePpynyRvNWZZSWyJnT7LK37kpVNub3ZY8w2+3JoaRpOEZy/12Bh6PDxesD5w+fjtAf94LriKH3/tnFmcY7bMqQt7/qfuTen7FgPPwtZlRD/s2Nw5ehsB47sGHV8Kw8NxTM+TyCbbkILq4GTBh5/Z4HCeiylDV1wbuOga7I9tFknB1bWAnqPz/q2ATzyYstFzOZrGvGfTZ8UzMU2Nn/zyEd3A4tufXZcR5XnCRtfm5ordsuJqNvou0zDnG6/3+DmlOJkmXFrxqZuGlx5POD0NMQydb3v/9lI/GuUVpm5QN/DUuruMEmoa0cX2XZ3DacYsyXl6q0NZN2RVzWmUYWqKn3hrxIt3zpjNMkxTE8epY/BEq+374ivHxHHBpZ0uw8Bm92RBr2Pj2qLJMlvo6UVk1SIqmDc5cXtNPpyl9D2Lm6surx2FHE8S0lQ6YnUtETKebbB3vGBl4PLktSGWrjHwDHxXRtU/d3eE7xhkVcN5VHBl1Wca55xOEqIob3VxBVkuOteqjSay7V9ZXNqvZn21btRvtoLqnZapHwS+qWma/6hpmv9D+/Fnv5YH9m4tTUlrN7CEhPuVxdQvXH/9DzyHoUvbWLQsv/Ynw3/+bbdEROramJrGOMmhYQmWAzA06TwUldiE03aEMPBt7BawOU5KivptsbyhiV3YumiD0+C0VvRpVnC8yMmrhuM44WQmpN44FmZT4BjomqAIftftAR++PsCxXLTWsvvEdhelFFlZM01KaqDnCsLzAnKnK1DK4LP3zjENjc2O0ILfOFowjUuOFwW2rtgZeGz2XR6fhZwsxKIfpSWHs2z5e1xErRzOc05D0UNEecVZVLDec7m5YpOVFVEmAu1n1n02Aou+q/PWKOI8KtnuWktLtqWLWHe95y5ZPYuk4OZGR6Ii2jGwOPGqJYC0rhuSTC5OwzbP7WzydgTEYOCysubTCWR3aLXjLMs0SNOSN48XgAQVX2u7Zl/pbygrWA0MsqLm849m/MxuSJSVWFpN07RFVS3FgKZkczBPKxmPZFXr9FPc6Hts9xy+5XqfG9cGzBYZsyin5+isuCZZ2fCTr53z5uEcQ1PMopzPP56x28ZZOKaOoWvMkpK3Hk/43mdXCQKb29u9ZWr9NMx59tqQb3xukziWsOEPPr3Bwbzlttk6lq6RlTWBLYgQTSmO47S1scuIW1Mywg1s2dk75kV+WcXqijB0TEPiZWxT5+Ak5Euvn3B4NOf8TCzpeUvavzz0WPFlhGTpYkbwTOl6+paGY2qM245zUtRLVElR1WhKsTtOOJkmTKOcjmfx4GTBvf0pvmct2WsXtvU4LjhqyfoXBgzT0JadsbioGS0yPnN/ysmi4LQNBTd0seebunRLFq1poWnAbW96aSGsobyU9zwIxHGRFIRhxvEs5eH+jCQpeHqrw92zjEePJoStpm48Tej5wgXzbIPtFjkAYOsS3l01jaQTWJJ6MG8zH0UrVbGIc44mUkDOkpxpKBT5qm4Is2pZJLxxmnAwE3mEbxs8s+HQdXTmuZgSLp5b3xLcw856wLc9t0HgmBzN87YbKCPhvGr45Ftjwrjg0WlIkhbcPU+ZxznrPYcolfQAocFbErdSifC9biQ4etqOti468bYu3cahZ+CZGtdXHQxN4+F5zHiRcaknm+R/dX/Kz37pgPE4wXUNBgOXa1tdrm105OuGHratY9vGsmtXVQ1JVmGbOlbbOTfaDmZdNzyx0+PSWsBpWDCKS7JWg/apu2NOZylFUS27WReQ4INRTFnWZEWNbWpMEpkE3NrsEraIkayo0JXi8STn5qqLpWvM59lSdmDq8p5RStIEkqT4ummofrtDJetVYBM4+hoey9dlaUrhGQZKVXzjrZVf8uv/H9//NH/uR17jr/+B575mxzRNKzY9jaKWi9R5mBMVFcdhzt2zlDC9wDfUvHEccnPNQ+vbGJowlS5AdpOooOsaPLfl8alHc9FX1Q2B0YZzFiWTrCArK55f75DVNfst+ftDN4YMnzM4XhRcGVgEDex0TA4WBYb6+Td+AJoGpb3dWUty6DgsnYd1I4WgYxl0bY3x2QRTd/jYjT5vniRcGVjcOU15ftvj87shRVmz4pn4ttxoirJGoZikBZO4IMktxklJ35FTuOfovHESc3vN48E441LfZugaDFxxhSWlFJ2LXC4+F+P8CzApSMGU5hVbfYcwq7g6sNgfy437yQ2PqoYvPZ7Sc+WGVFUSP3Np1WccZvR8C0NXHJwuljlcZ1mBblnogGHAznaXupYQYk1TvL435fkrfWZRQVFWWJZG0QYVG4bi4SjDNDTq9sLTACsdpy0qG0ZxyTds+0zTksfTjFFU0nV0VnoWXdvgUlc4Zk+tu5wnIhC/sd1bMr++sB9yMBFr+mbf5e5pRNeTm8q33h7w+mlCklesdiwWWcXf+Hffz93Jgo8+ucqab7Y6sQXHxxL4u7Pi0+nYS/jg4VzGu1XVMAozrgxdPEuKmqsDm9eOE145SjB1xe+42kMpePl0jmdp3F5zScuG3fOYQWBza93nxcdTFlGOrmtc2wmEfeYYHB4uCKch/78f/AwAj25e5r3PrPPxJ4bM04r/4bO7KAU/+fqIrb6LY2pL/tjLhzGLpOAnXz2jboQBlZc1i6SgaZqWfC9jpsCXbM+TaUJeVNze7vHwVOzt6z2HF149ZnPgtsWWarV/DUfThCgpSLKSVw6EI7bSc3hqp8vBOFkWWEdjGeVdBN4etxiDg1FMVlR0HZ2saoiinN/5/CbZ9QFfeDihqhqCwOTHX9gnSQo0TVtuTFYHHu+7FHB73eP1w5C7JxGuZWDoGnuznP1xQpJVFK4UohduswvHme1J8XMRLB1nkhLgtO89MfYIomJvkfHkustOT/AUX9gNl5TvD13rsdOxyaqasq6ZphXfdGsgetWuwyTKuVvWPL8T8I8/f4imKUxD44O3Vnh+y+Pvff6Iz98bUVU1z+50ees45Hwm7tJr210OzkI+98YJ3/6+bVY9kTPUTcO9cYJvaewELgdhQpSLM3LNN+g5Jtf6Do+mKQrFT705YrLIODiQMXZVNSSJSB6e3gpYD2QkmhY1f+K7bnESCrPq1X0x6ax07dY8Ia+fphRXNzqMFim6ruh6JiezlBtrHu/Z6fDGcYhlaIStPsy2BdY5GLiYhsZoHJNlJefnEbNZysPjBYOOzYeu94mymqev9CnKmtcOF6RFxY2VIVlZs77mEzjGknc3j3PsoXDUynYc/26v3wocqnfaoVoFXldK/TOl1I9dfHwtD+zdWk3T0LVMnK8imPs3rb/6+579Gh4R/MDHrlO1FyjV7poVUFYiUpQYiLYIcE3GseT65VWNYwrbZNWTCAnL0EhyiRqZpRUnYcYsz5nlBQdhwiTNSdtdY1xIp8g0ZAeXlUIcpqoJqcnrGkvJTR0l0TN1A3cO5xR5zZZbYuuwP0q5ezylrKWIKcsKU4O8kBiSrmeT2V5rcVY4pk5eNtTA/VG2dBuN4oL759lX7Gzh5YOIMClaKzn0XR3P0nAtjcA2aBCIpm9pbAU2fdtikZdMExEh6xfar7jCaEnwWVFRVQ2Hk5imaXhqXejhrxxGrVZErj4XXZW8qoV144izZr1rL1ERA9/m9k7A9mrAat/j6Rury9e1LOU501pXH4BlGZzOU07nGfMo4+p6wHd++Ao3L/WI45KqaZgtkp93AbzIMARIy4pZVvLmaYKpKa6v2Dyx5nK54zKwLYaOzYpjMXQssrJmGNhYhsbtNQfbULy2P+PwPOIbb/bRW5t3nJW4lk7PNgksnTAtOJym/P6n1ziNU1Ycm+c2PNYDk2e2PFZa3MDJSchokVK1mo+uI9mXkkkntHYQrc00qbh/LoV0nJWMwoyzOKOsBCradYR/5BhSEG70XDY6Iow/OQnZHLi4rWbFsiTi5dbT22xc3YYyJ5pHZGXFo3HGS48Fh+G0ENBrQ5vzRU7HlgLBMkSHmBUVRVlzPBGtzq3NDu+90udD17otB0u1YmjJrQOw2jgW2zaWx9I00kkd+tLJ8tv0gI5n0fMtntz0cR0ZRZVVw+UVD0MXOCPArC18O7YI2H1Lxzalw7LqG9i6artu0iUvyprh0OX7v2GLq9tdDENrRd1SkASOZMJNE9GCiptOuhcn84x5LDiUi/O8bjdBF583TYNr6cKkcgwUijjOWV8POJulmLqGb+vMM+mWxLm4V6tafg7IOR/n0pF5OEn59MM5j8aiMTxZFJzOUxxT4lbivKYsKz50a4WP3V6RTlMq8Te/7wNbXN/qMopLkqxk1hailqERxwXTaUqcS8fNNXQcQ8cxBEUzzfKlQ9q3NNY9G0OJk/hq3+HawJYsxaMFeSYGlF7PFoTGJGHadv2f7He41vNY8+X9YejCW1vtu8tC1Wwdh1WrYRoG9tJ0APDEmpzPtiEbL88xiGPpHJmmzs6KR5qVbbxUTZZVSwRLUYkOse/qrAeCa/AdkS90HUHN+LbBh671MXTVbr7lObJMDdcxl8Xwu7l0Xf/XPrTfouHI/7ev5UF8PVcDeJZOUv36emG/46l1/unrx3Rso0UmNIS5uLEoZcwjNmGwDNVGxQhz5ok1h7rdNY6jgqoWpkqSl23oqVykq0YKjKKueetswu55TJiWrLbU7EfjlCfXbYoGPE1R1bDVtRhFObqt8Z7NLvO84LlNn5ePQqKi4a3znDf2pgB8eXfOZt+mW1fopsZZUnNlxQUFphKgqmOIcLxuZNwxXmR0PZO1ji18qZOFOAA1uHMa8+buhCyrOF9krHZs3rPpoikpCLe60nVZCwxWXBNT01jkAkB1DI1N32aaFyR5TZRXJLmMViT0WgTGmoJH42yZCt9xZQxVVA0PRnJscrOQC+X1oTxXdUuZN6k5LQzashMFfOjpVT7/hoSb2oaSeJR23FOWJbvnGdNFStNojMqCm7ri5qU+o9Y16DsGXsv82erLz9Pa76/phjjFkC7dTkdcqRdU/MCUi6lnGKx5FpcGNousJisbzsKSRZihaYp75xnjUEaBf+RjQl1Py4pHo4S3Hk34P37vbeLi4mZkUMeSKlBWDe+/vcbuecjLLx8x7TnYtoFpiqPu0XlFz7VwXZOirDld5NxeczmcFyiluHcuO3fbMtmbSlhtmFUtVkJMGnXTLBlFs4WMfq+t+Yyjgp2hT5gWFEXNja0OjmNwsntMEiXcvT8mTku67Yhus+fwxsGcaVJyNk+J16RwPl/kDAKLvm9xOkuYxwLFdQwZh9m6FMuBY7LedTicCDXfNHX0trgVraOG2waIX4inJVi4Ya1js7PicX3o4Ntay82S9/rJXLpQk1bQXLXv5UlcME8KntoKcC3JxXvzVMawpimhvkq1BY9tsMiESj46W6AbOlmr49k/C1nvCeKh65pkpojDp61u0DQ0PNPg8ookKjw6XWDq2tJJ2PMtklwE3n3PEtZUWwQsEinAHdNkloixRFOS9jBq3bumoVFWNedRQcdJl3Fea4FFWtTcP43IikowFFnFw1GJ4wjaou8Y/Pgrp+z5FuezlK6jc3ng8E+/cIBl6QwGLtOpjFoNQ2NlxaNjS4bkmivFS9fRlxvnztDkKErZ8gWum1c1i6Ikr2tWHIudgcfLSuF6NnFccHmni72hczyK+NxbZ0TXh3zH9XUaxNhh6RK/td2zyEt5fwgPTVzPRVWjkLF5WlRs9x0WScFbpwk7PTkvF6l0Qm37giklhX/a5vaBSAgcRwj9a12HncDhzfNYNolt58ds0xjed7nLwUwkHDJ2lKKxbpp2IyZJHO/2+q3QoXqn2IRP/mL/rpT6uaZpPvZrc0jv7jKU4tlL3a/3YXzV9d3PbALwT18/5ijMCLOKza7Nf/VdT/Cf/tjr/JXf8wwAf+mn7rZEcw1bl3iKFdfk9rrHwDX48kHE7ihmpW2375+kVO2c/kJf4tkG2wMJTV0LTGxD0XN0DmYlXQs2fY+eLTfr7Y5HXtUczOZYto0OPLvh8c/fGPHEps/mM2tkmcSk3D9PWSiNKpVsMk3TiLOCni9djU+8OWKaiA364oL95IbH0DP43OMF19Y7fPCSz+f2Qk5nCR+4vcYkykkLKap+7vGCqmqwLZ2bKzbztCbMaq50DQLLaDU5JYEpp/rAtlhzFHfGEQPfZmcgOWuns5TVjk3H1tmfpjy35bM/KyhryeOaJqJhW+/aPLnm8tqxdHqU3NGwDMX7tgJeeDyj6ygmUUOD8LZcx0TXEet0KB2b03GM72hYtkXYanTG05Rr272WUVTT70jkzZVVEXMrTWMRF6gGPEsxjTJOwoa6LPimK120uuR6L2CS5kyznIczCTK+0MiYmtzEF2nJveMF6z2HYc/l+55f45XjhN/59AqfuDPh/ijjZ149ZmvV56mtDvrT69i6xt4iwTY0NnwH19D51J0Jh5OYp3d6PLnV5fHeDF3X2m6KxSv7Cwa+zeFYOn/jFv6ZtWHVnm2Q5hVPrMuN/CyUcOrtnsVLhzFhWrASyIjt6XWPh+OU6TTlm75hh82OuOPCrGLNcjibp5zOhK+zdX2byekETVPs7s5YXfVQCnZPFoxGCcdnIZqm+Dt3zvF9CbLe3AjIC4mZ8mxDMAumxuOjkDtHgkdwLZ1ZUrB3LOfc5Y0OUVay3nNIi4qjSYxtG0RpgTHwmCdSpE+ijPsnC/keps4bb84IoxzPNfn83fMWFCpdrjwXDc7BWICoq12H+2cxnm3w7KUer+5J/uR/8PErvH6S8uruBNc28B2Dn7s74nwU43gOaZzy2oMxi0VGlpZCf+9LpNLQt3gUR6RFxTCQPL8GEXFXdSNE+EIcj8OOiMsDV+JmLmCcF6LmRZhxbEpoeVGJU3V3KviBvmfxnm2HshLe1+40p2sbVHVO17PoOjpvnkQUVc0HrvV59WDBs9sdXFPjrf0ZL+7OCRwBnbqWwdZQ+FCbXZPNdZ8kq1jrOdzc7JCVNX/oG68AYOqKWVq1GBhxkOqaRmCaHMUJh/OcNc/mH790zHiRcXwesfdoxLPv2eb5qwO+4yOXeXQacni8YKvvoWlvI2NefTjmhwc2z60HnEQpL+7Nef/lLlf6NmeRgH3dr3DQrXcd+q7O/jSj55q8vj9ja/h2MPGKb/Cxq2v82Ctn6JrGZJ5iWyL6L4qKbtdeOvJc18SxdJ7dCnAMnUs9i7Jp+BdvjNEUPLMdMG9TI3RN8fL+nL5nMYvEwei2Wsuq4euS5fdVOVS/ybAJv1Z9P+fX6Pu86+sifuTX87oorP78j7/Jf/VdTwAsiymAP//ttwH4uy88IsqFe5PkNVFWU9UleVmR5hVn8xR3xWfoW+RlTVk3TMKMoW+x3ZVduHQvag4nwjVSwJOrLl1bGE+zMOF+WIgGqdGwbLF96krxnp0OeQ2LtKCooSlrdgZOm/+lcWrqxK3V2VAwDCx0DcKs4v2XuzyeZHQciT6R4koQBzd7AbOsZKcrxzmKbV46CEWcGljsT1N0JR20Fd+gqqGoa+KiYsW1sHKNN8cLerYhO++W59Vz9GXO2kW7vO8a1Dhc7jukZUPY8oH8lmHjtsLyKK84W2REmdDLLUMxSQp80yDKa7Z68nyO44zxNKbblbeIpokGxdAVmiHss8sbHZTSCJOKZ7cd7pxJbuGN9S4PjyKStMB1TFRV4XccKmC2SDkUDBSnccPTQKUZPJwuGLg2mlIMXRlzREXFmmvTtWTXb+o2D08WfPONHm+dWfzkq+d88HqfniNFyj99YY8wzPnY0xvcP5UbnmcY2IHeEpZnvHWWLLPkAAaeQb/v0A8s6gYurwpkMHAaCdP2LNK0EF6WUtimjqGJJV8wJAqvFSpXDRyMRculFNwY2mIHXxRsbATcWBVYpmdqvL4/5/p6wGrXQQEHR4vlOKOuG8qi5Pxcnqh+3yHPS/YfL+j0A4q8pHYNsqxiPElwHINMk/SAnu+w4pm8mVXETcnOii8U/fb8NU213FybhoZl6syinEurPgejiP1RRN+38NsgcdPQBMkwikjSkn7XZq0rDKG8qBh2nTZxAOZJvsymDJTRMpBqlBKLvhDSG1Z9YznSKcqasqq5vNVhD8l7i6KcaJFiWALhnIRSyCxSEdNf3+iQtzmYVSNsJsfUWQ0sdtuwZ0COvxV113XDZJHJWLcdXyV5yTTO6bRasySvl3l5AOOkYsUX0fnBTPhiF1lyUVbiWtJdK0opqld8gzyXOB3f0rANxbW+wxcPQn74xRM+fGPI7c0uSVHx8GTBRs8lLTI0BW+dJgS26Mx2ejmeIWaIuCiZtgakRVbzs4+mPDpZUJY143HC5PiMvYHPR2+tMI5y4jZO7HSWcGnVJ0kKTFMny0p+5LP77D65hqVrjBcZD0YpV/oClQ2zUuCymRSpRdUwT0X60LF1NgeeZJtueuhKwMauYbDSdTibT9kYelJMlTWGIR3Pla4jJgdd+tJpWbM7j+lY0oGeJznDQDrP41iuS4FjLDtwWdtpnMfF0un4FX6ld21pX6V4+q0qSv+l1tfh5fmtt/7S9/7izKs/8eFrAPytn3vIIqs4mmccT2L6vs3VtYBFWhC2gborbVxJYHtkZcM4qbhglG55sLsQfpKlNTycZnhGgaUpbEvjat8RMXKY8cmX9znLQVc6J+OEsiwpCul83bjUxTLlhnBp6GLbBgejKfOsFtzD0Mc2Lmzjimc2XW70fF47W7AamGx3LdKy5jzNeGIQcBQlxIWMPa+tyLjmzkmIqWs8uebQcwTIaWoavqnTMU2KqsHR9fYiJxfJ++eS/7XTs/BtjZcPIi71HYaeQVyIYPXeKGkjKgRLcWvFJS4q7p6nPDyL2Oq7VIbOw9OQrYGLb1ncPc+4FOhs9X3eOJPRlFI6jq24tOJR1jWDjhz30TiR0acmYn3Pll3+adhgGjpJlPPJLx0RpyVvPhxz/WqfW5vB8rVOGmnd/8Klaxr/5K0R210TTcFGR9hDx1HKPC+wdQ3b1fiu59b5qbviBFvp2Nw/i/mHP/uYpmnodGzCMCfOKz5wtccXH884ilI2fLlpHMxypnHJ7Q2fwNLoOjpf2A15+sqASZQzXsjIdHPgLplgYZixueovL+6GLlq06SzjcBzjOwZRWtLQLJlPQ99ikVacL3JWOxYvPxrzzJU+k7ik70oe23rP4fc/s84ozblzLjRq29L5lKmTpiUrqz5FUTEcevQDm2957xZF1RBlJdMob0eHOV947YRbl/ts9R3uHS+oqprP3R/hWDqXV33ysmYcVoSxaGtWek6LLmlY7zqczITldUFL13WF0/7/Ws8hTCRWaPdoQVFUvPf6kBXfwDJ0Hp4s2hgaKXRsQ0cBOysehiYuwVmcMw7lnFoJbM6jkv1JSpwUBI5JlJasdB06rkmYluR5yfp6wMqKx3SakLQC+3GL/HBMvdWKWXi2wQuvn7A6dBkGNuP2bjsJc07GMZ5rskgK9k4XdANbYoACewkV1TXFNMzJ8gq7dT9eBCq/fhRzOks4nKZsdG1miXSuzxcZX7o/YtCxmUU5p7OEm5tddE3x+YcTPvzUBnlZ87ErXf7J62eseKI5+8Mf2uLlwxjPFAPMOLR482CKrin+/qcnbK8HRLbknp4ObF7aHzPwLY5nCXXd8ORmh52eyetHMYeHC+IoxbItCEeMTwfsT1PuHcwoiookKdk9nDMNc9YG8r49OAnxfZMX75xhGBpRVHD/wZjD8RpbAxdDF8egpkQCkOYVi6Tg8GRBr+dwc7PLimfQty3yqmael+zNE9HFKdG8fcvtAWuexeiW6BrdNh7qeFFwOMt5aW/eBixLvBTA6SxF1xSns7Tt6IlmMStzFlHBWt9ZFonoLMeT7+b6agXVb7IG1a9ZQfXb69fR+oGPXecvf+IeulLc3uqx0TFaCrUtocKtAN9QIlw9D1M0ZS61AI/GGa5joFMzTmGel1RVRaNkV/7spstPvTUGFNOooayg5zXsrAfcezyhaWqyrOHVOyN6ruLJW+vcPV6QF7L7d22drG54a38OCno23DuXbMCebfD0asBZmjF0LBZFScc06TkmozRjkQuAcbNrUlYOX96dstp1uN730FCcJCnrHUcCj9OMgW2JbVhTzNtg0boB39YZegZvngpnKHVEhN80YGqKKK/p90xU0WA6ok07iwrmaUnXNXli3WGeig5rJZCbSpxXBK5DUlQ4umIa51iGhqkb+A5k+dt04q2h2/KBpMOT5jWrfZejmWhaTFND08H3LapKxL62bXEWFlCWPLlhM25v0oL4FLp/XtfcPZoxWtj0fEviXVrg5SIRcvK3PTUkKcTF1nGFmH06T5nPUn7nN13jX3zmEYahs9W1uNSzueOa/JOXT3lqp8tT6y5RnnNt6HB7xSWrBAQbteOhohR790XnN85KEbm7Zvu8G9im6Jn2x8ly5OzbBklWkhY1eVOz2nXI25y3g1HE/aP5Eop4Ms+4tSp2/LIWl5mhFBsdk4N5zkZg8uFnN3jlwYhbl/q89XjC1Y0OmhK34/4sp0Y0WmFWMY8L0rRkHGZcGroyHrGMpWA9KyTT02s7nPM4R7XaKUOTrsRFN+aia9NzJSzXtw3GoYyoLVNfjm++dO8cwxC3n4ByIYpyHEd+7kWp3Hd1TmcJWVETOAbPX+7hGBp3TiP2ziNMU+eprQ6ni5zAMThpx55NA9c2OsyivOUa1fiuyeEoIvCERXfRpdAQqKamKWZxge8ICmCtJ12/pj23XFsnTApWum+HaodxgeNJpzXKJPxZV4rRV9DcQcTphqaYJwWLpGA2T6la3VP/omN+AVNVild3J3i2IQaCecanH8xQSvHifsPeKJLj6zpLvVvgyuj2fJoQbHa5vh7wYJSyexoS9xxOxjG6rvGeS10udR1e3FvQ7zusr/t0AwtN+waiebxMTQg8k8UiI0lKPE/QKfN5RpaVdLs2ZdupvMgynMxThh27TXOolqgRpSRrMytk7GyZGofzgm+9YklYuYKDRcqtVYcv3JP3uW/pAmR13jYK3B9lHE5TzmYJeVlzWkgHsqxrtvoeb+5NYOCy3nPwbIPdMzHUxG2CQpiW+K6YE75Sv/duLsP4KgXVb7KK6teqoPrN9az8JljnkbyRAkuj7xgssrwNVdWWc/asbJYOwgvWUlrUjJMSvaipSnFreSach6XoeGj4TJzLDadpWMwW1LVJ4AbkWYFtKerGwLIaHMfEMAz2zhOgoesarPY8TicR632Xwi2xHBOFBPvmZc1JWOAYOqNIjsdq4x9WdAtTE2H56aJEV4oV3yArKlYDCU2OS3HFpWVFzzbxDIOwKHEbnaoVjYdZxapvMvRkXBJmFYFrstWVHWPdNFwZWAxdA1PTeJSm9B2DpJRIkqpqWO1YXO64nOpZ6xyEJ1Zd1gKDKC8BxUagy8/PFQ0N0zDlwWnMlfUAxzLIi3Lp3GuAWjVAjaa9rW1wPQEJzmYZpoLHpwt8W2OaFNw9kzHW9zzdR+kG8/zi+YLpPCMrJNai6xicL3Lef7nDmycJ947mvHIomqairHEtcaclWYluaAx9E8syhO6tydhsnhQYukZaSMF5Ecdi6RpJWQnhu6jYCEyK0mXvPFzCDPOyYjpLWR26uLaOoWkYuhL2U162kTaKvmcyDgURIawx0Z8lec1b98fkecnamk9WCt3+gnzfd3WO44Sibo0Cxts6je21QEbHPYfAEVH34VyE7yeLfAnXvHAhpi1XarXrMAxMPHvAo9PF8ubT96w2qFk+D9qg5rKQ37Uoa84XKY6lU7bw1SSvMI2342luXepzd28qYcptnl+/77I99BhPEgGl1jVVLZwp15ROwwXz7aIzl+YVRVGz2nMwdMVGV4r6UaiwLX3JB9saigZo92hOVlQ4baFb1vL6LxJBsGiaYjxNsW2DXpujOAxk1DptI1M82yBwTDEBlAKdjNOSvJDrhG1K16Rpxc9vu9bkd3/QPk9VVROG+TJcWdPkPXIBDZbAcJOsqPiXr59hmzr3D2asDz2mYUaSS4E7WsxYhBmWJcccd2zG45g3H43JLvUwNI08L8nKalm4vrI/p6iapePUNHW2Bx4nvYjFNOR8kWKZGk9u99gceNzZmxKGObZtELeFdJIUWJa+dORJ3E/D+Vwcj7KRaQnpjdDXT6YSdH3hMk6r1lik63xke0hZNbx5I+VkljBLSxxfY3ERg6YrNCRmSMwsWovGkNfsO55eRdfFobtIChxLOr0X70FNEzeoQmHoYJn6slB7N9dXH/m964fxNV3vlJTuA0nTNLVS6gngKeAnm6Yp2i/5332tDvC3169sjRYpV1c8Fpl0dKw2psbWBYzpmJLYnlU1A98S3o6lc+8kBNVAWZEXogM4KRsaFDo1XV+0QLqCe/szNNNFVw33HpyztdHB9x1BKlRNC7Sr6AY2cVoRZRWDuma1JyHKk6JmvMhRwK0+PErkZtE0AnQMTClkxmnOOM1ZcWzWfRvX0Pnc/pyBp9P3LT56pcujRUTTwJWOR2AanCYpA9tinJUsipKzNvbk6Q0Rw/cdo3Wp1QSOyRMrHvcnMXVT07H1peXa1BRpWTNLKz5+rcdpnPG5xwvqRlx6hq7Rc8QxWVY1RX3Bn9E4DmvRlGQVlqYwDI15lDOLcixdo0IuuFXd4Ns6i6QCKukKVDm+LzyowcAhTUvyWco54NsKx7Vp6oZ/dXfGdz+3vnzdixo+9vQ6z256fO7xgk+9fsLl9Q5Xei552XCpb1M3zZIULvocgcP+3m+5wat7M779w5e5dzRnd5LxeCy2+lubXT58OeDFw4jLfYuTsOBLu8dtN0es3zs90cTdP56TFhUD32Kt5xK4JnlR03OFvVVUdUtuhuNJQpwU7NsGjiVUdKvlHT0+j3nzwZjRyYRn33uZraHHl+6d8ey1IUrBeVTwnm0P3zS4M4oFG9I6XpsWDZDkJWVVL8G2+5OshWRWGJo40FxLoIxpWnLWwmRniWi93nO5h64pThbymk1jKS46jsnBOKbvW0RpiW1qrHTspU1+EuasdR26rZP1dJGzMbC4cxKilBSqVy/1GU0TuoHFOMyWepKTs4grW13pnJY1w46NY+o8OFnwaCzOvPv7ElhuGhoni5zntjw8U0dT8NFrPT5xZ8zD4wX9wKKsGlaHknfX9UzirGw7pxKfFWclPd/C6oszLW+LnLtHs6Vb2NA1ei2fLC8EIVGUNV4b2QQwmmeUZc2gYxM4AgS9cEP2fEsQL6E4GnuBzfbQI0oLeo5C6eJMNlRBkdTsLSqSJCNOCt53Y4ChB+wdL9hZD+i4GllZ8c1PrjJNSj792gm2qTMILE5PQ1zX5M7DCeenc1bXu5w3CaNRTBQJ0uCffHaP4dCl17WJ4oLHZyGdjs3qRo8XXz3h9s3hErHyez+4zZsnkhAxuLHCo4OZaJsc0YClWclKz1miJsbzFEOXQt21dI7bGKOqbnh0EvL7P7jFF3YXzPKcuhGhv+BjNP7tZzc4jBL+/peOiLOSvm/T9y0+drWDoSvOZvK986JC18UQE0U5/+AzeziOyZOXejw+Fop/s7w+yUbVMd/+M82rrwmU+pdavx2O/Pb6GeCblVID4F8BXwD+MPBHAJqmefVrc3i/vX6lK3BFgGzoCs8SOnpVNxiWuAB3PJODmdTDugamJhlqx5OkHTfUFO2O0mjdIb5jEqdCQT48jwQ+qSm21jqsDTzSvCCK2zd73ZBkOUppPLEV4AxdXrhzzsuTBN+38F0D15aLUgO8fFyBJm/4l44itnomXdOkrCXbcGgLTHORSS5fVQsl3rUEcKohO6CoKInLkmlW4Bo6hlKUTUNeNQSWhm/p7E4yxnHJIqv5jtsDHk+zNvdOOh6L1rZfc5GvWHFjaAutvoazWcoPfuloyb9ZZDVdp2p3n2/v/M6mMdO4QJUlj/ZDlAZP3hxQVg1h2bDat/Ftg6ysCecZdV2jaSI8DVybMG/RC0pRFCU3NwOJeFEaulL0PQNFg64J3woEpxC2VuuiqvFckygt+Ik3zjmaxPyu59YZxyVxu0PPy4qqFq3PjaHNlx9JAaKUBFFv92zO5yKGvsiQjPMaz9RI8opnd7oMPJ03jmMOZjkPR7ITbxqxq+dVjd92QOw2XxKE9J/kJWEknYquJ+fCNBZB8GKR4XkW83nK6uaA1VazZJo6K55JXgo9/cL+nRQ151GJbSj6rsYT64IJUErReA1PrTu8eZpS1jW+pTONG7JSwpUDx+DqlR6nZxH75xGrXXGmKQWrrQuwKGvhn+UVTuvC6rgmw8Cm51mcz1MRh1c1SVMtQ20HvvxeZV0zSUrmcYFlGTRNSVW3OX9tR04pcYIGvkVWVnQcE1tXJIgVPy8qTmcJSik6HZthx15mxZmajKWHntES8rWWxK6R5AVhlNMNpHNiaBI1kxYymjqdCeesSAvysqZpg9aTFjY57LsyTi4rORermqqWrodj6ku6/bBjE7fjT6UUg0ACh7OsZAa4loFt6CRZxfX1gDsHU4oajgHP1Si/Iq/39maHqvJ5bXfKK7uiO4vjjAOkGB30xT13NM/J84rRIiNMC3o9h9W+y8TMmE0TfN8kTUt2drrEcYFr6Xie2UYmSdfqmUt99kYRRVFhWQb7h3MxVqz4hHnddkOr1oyg4zrm8nw1jZby75pS0Lf6LcsQTMLFGPOpS30WScHnH88Zhxk//OopH7jSYd2zsHTRzN0aBkSFGD2mUc4ikTigB+OULzycsN7mRTaNOLrDlltW1w2Ba/L+HZ8krziexMtx48U1yjQ0/BZzcfH5u71+paJ0pdTfBb4POG2a5l8jaiv5Jn8V+B4gBv5Y0zRf+tUe769kvdOCSjVNEyul/kPgrzVN85eVUi9+LQ/st9evbq36Eubcd3WiNgVe1yAvG64NLAaOSV7JiOQ8khaxY2jMFxma9vbFva4bplOxuxubgVxc8hLbES3MlVWfqiqJMmjqmoaaNCswDIMsa0jTlFceTHn/7VVMQ8e1DXbWOwCkebkcp5gGPLHZYW+S4load05T9qY5a765ZBDFpXRwABxT47XjGNfS+dmHc96/47No3Uh1LUC9/TAhyWsOWs3MU2sBSSm06aN5wfu3fQLLYKNT88ppyIe2ehxECeNCeD87PYurA5uXj2KeWRHm1qvHMY6ls0gK1nsOH7ve49P3p0wTm1VPo+ualHXDNIxBU4SLjNE4Fpp0BXceTBgMPJRS3NzpUdYS+VH7FrPTCF2Hjz+/vXRUnc4zvvzWKZ5n8egsYWfoMEkLGpQ43Wyd13dHXFntEjgm//CLJyzinPvHcwxdY63rYJv6UtD8Q5/eRdMU7725yrc/vcKLewuKquEDN4b8izfO6bgmdw5my9FQWsoF2dDgc3shdw5mbREgF8KiFpBqUdV8eXcmmX++hLRGuQQuv3W4YBBYNBedjEW6BBF2XBPfMRn6Fl+4e0a/I3Tq+28eYrs2vWGHZ26vcj5LOT4OGQwcZlnFLKtYCWy+uLfgTTshr2q++XqPhxMhXruWxrU1n7SQsNhP35+y0XO41BOOV5hVPL/j8/JBxFlbDDmOwXSW8qEbQ3bHErPyeiZjyUmYLzsNWgGJJpExIPy0qpYxUlHVy9imo7OIsuqx2XcJUwHL9n0LLVAcjiIABj1nGSo+91oekSkQTUPXOA9ziYJJCjzHwGpdeHkpndw4KwmThk/dl0ikeVzw7E6XaSQ6yIEvwchn5zJ+u9CxZaWEJR+ehYxbLVtRSEd1OHTp9xx6HZuyahj4om8cLbKWoi7vMQnuFZ2ZoStsU5xlgWsK+buRonRr1f+KEXDNIs7ZPQ+pUZg6y7ibi81IXdccj0IcV8fSFYbSsLoO3nYPmpr9sxjL0Pnnr52ziHMMQ+ONu+fYtk6v51DVDR1fRsmzWUqaVhiGRhznVI1Hnldc2+lyOkmw2q5exzW5fHuNrqPzkmuyOXC5czCjrAPCpGCj77JICtaGHg3NMqwYxOUYpsVy46AURKmM5LK8Yvc05MlLfY7GMUlaEEUFhqbxqXtTvvfZVUatFnIncNnuunzPM6v82KtnTMJMzABnYrwp2+uapikm04R+z0HXFWla8mhvyl/fm9LvO3QDmzSvsE2tjcCRXMiyktSHqm6+LhqdX4XL738G/jr/5uzg3w3cbj8+AvzN9s93fb3jgkop9TGkI/Uf/jIf+9vrXV7/6Y+9vtTn+JbOIqtZ8dpU+ULavbomYNAwr1gLhNl0tMiwLdF7KA0C1yLNSyxL4hDyoqFpBDZXVTXBqsf5LCXOClxbx/Jc+h2Noix568GIxTQBXZG6Ji/ePUfTNTY3AnRNNFNVLdZrQ2tIS9jumsRFzYd3OnziwZQkr0ntGks3l1C6vKoITJNV3+BonlO3Y7OTsFjG3GiaIkorbF26blnVMPR0XjoKWQtMjhcFWdlwEuZYPY1xLGHCh3HC40nG0+se87RixTPQlcIxpPOVtZ2bG+s+k6jAMnW2ArmAP73h8ql7U2wzpSiE+m4aOtcv9zE0SIu3u00d3yJwdOI0x7Jkp2toGhurPkenC3RNEWYleathu3m5x9EooSwrXj1KUEpGDQ0NkyO5cD+YTnl+y+f3vH+T40XBCw/G+LZB4Jr02vidRVIw1zWKoqLnvh3QezCO+T3Pr7E3irmx5vPK/oy6FkcYvE27Hi9ay3ursUnyinFUcDrPeGrD5ywqOVtkWKbOJMrbuCG52Zq6aILyUjo3PU9GQHUtiQB3j+d4rklR1RwezkmnE4L+ZZ68OeR4FHF0KLmHtm3ICE/X2B1FRGnJrc0uz2/56JrY0MOs5iwsuTawiIuaaSwhsSu+QWDrGJriwShd6q3yssb/CtTCW8eLpbtNteLtnm9J0GwihZVR1svkgqLFcFx08JKkoCikA3I4EiddXgjMsudZmC3J3NTlzwuswsUydIVrCQXed0yOxrE4ch1TnrO2Q9HzpON073jOMLCxLZ3zecbLe1LYbvbEmTYIbE5sQ+CWnslmz2Uay+g5igom53M6/QDHEe2c45hYhrw/u20gcl7Vy/Fo0wAay7G3rgm6wdAF4KlrqtVBCQOpad6+cVqGxpM7XVzLIMlLikrQLefncxapjBefud7Ftm1mszlxVPKhZ9YxdIO6Fgjo1qq4g3VdF/huR0Tiuq4Iw5yiqFuHp4dp6sTxAk2Trt5LLx0yXA0oq4aObxElBYGtszcWfIOmpOCwDf1tfV3bbbq66nM6zxgtUopW+K8UWIbOIs4pDBndXUgBmqah5wsI9f7xnDgW7dVFDmNVN3xuL+TJFsT88vmM96712Qk8tvouV1c90kI0g6auSWJEWfN4d8bamoxwhWEGKyseniOd5KpuMNusy6KqW3K/jGgtQ0dpXx9bvq5/lYLqHTTKmqb5GaXUtV/kS34v8L80UpV/VinVV0ptNU3zrkflvdOi6D8B/gLww03TvKaUugF84mt2VL+9fsXrj//glzENjWsrLk+sigvLbC96D0cZrqVhavKyZ1VNXjb0usKhWSxinru+IvqURSoFTFFITpir0w1M4qQgjitc1yTKKzwbbNPEsQxsQ3Fl1WUe5Ryc2NBAkpQEro7jWlxZD+j7JnVVscga1gIbvY1/oaj5iVfPeO5Sj915KnEMpsbTaz4aMpLUlCKtKs6SlKSo6Tg654uKj13vkpc1eSVF0iKrhF+jizsGxLln6oqDmTCu3rfjcTwv+NSj+RLeqSl4ftvj1eOI86jAMRUrnsGKb3BnEtKzDb7l2kA0Jkrxo2+ccZ5krHUddqc5TVWSYdAAz6xb3DvNyJqGbmCy+8YZCvjwB69g6oqirLizNwfgmesDplGOY+lc2elz73jBMLAoakiyAsc2ub5jcTqOlmG8pqFzNEkZ+hZdz+ZsnvDi/pzv6rtYhmSGPbvd4frQ4TQsJPJGKX7XM6t85uGM+6chLz4qWIQZv/sbtvmxl8/o+RajWHbLkkFXcziO2Rq4y45nUdV8/PaAB6OMUZgtGWcXI+VLfYeskvGUY+pcWpFcvDgrSYsKz5Z8sTgTm/+KZ/CFhxOm85TxOME0NZ68vUK/73ByNCdsXUp+YLG66jObpZzNUi6t+Mzjgo/cHPLZeyPuHWvc3Oiw2TUJbBHQW4ZG1zb4YlnjO+JEezTJeHweo4Af/fIp8yhn0I6qkqRE1xXHIymmPnh9yHkkyAKFFDEKRVnVzFpjxsk0aR1/kOYVgWMQRQWdjsVikbO/PwPAdyXk+oktQSyEYU5VSbf3QkDeDywWSbEMWAZxSV5Z9cWC346Qjicptzc7OIYYM0YLixurLudRgda6yvqexfE0YRLlGLpiZz1gGLS/Zy6xS2eThKZpuHF7gzDM2jxAC11vNWa6xsC3KWtxBseKZSGkIXR45yL7r+VUjRcFjiUjx9EiRdPk66SDVzKfpZxNZVPgu+Im1DXF2nqP8jQkikue2uzguzbFpg/AT710RNUo5lHC7UtD0CRg/JOf20UpxfpGgOeZbK54HJ1HbZfIwHHM5RivqhqubHXIsorNdZ/xPOXSWsB0nvJTrxyjlOJ8lvJyXmHbBrfXbLKyz+5ZROBIsfjoLMJqO3A9V3ATVdOgAZMoJ77YBCnZqFRtR6koZey9NfCIs5KgHRfqmuJoHAvdfpFxZcVjYJucJznPbLhMk5LPP57RcU3eeDShqmriuOCpWyuUtWA2PNdk0LGJ2p+ta4Je0JUUdXZLbFdKzt+L1/Jik/Rurl+kQ/WdSqk//xV//bebpvnbv4xvvQPsfcXn++3f/fosqFpS+idbcTpN0zwA/uwv9TillIPor+z2Z/3Dpmn+a6XUe4G/BQTAI+CPNE0z/yqP/25kNqoD/1PTNH+p/fv/Dvh+IAfuA3+8aZpp+29/AemiVcCfbZrmn72T3/E3y3IsfSkSTcqq3W2JsNq1NP7J5w/odW3ec2XAt17rsztP8Ayd0yTjMIaBJ1ThJCtolOI8qzg9jbEsRV5BlpUURYXvmuhAlldcX7NY6ThMkoq0aNA1nSeu9tF1nf3juQDlkgIFnM5ywqzAt/SljkchWgRTlwzB++cpi7RgY1OKqappUE2Db2hkSUXVwEkoTKWVwGISlww8g3lavG2j1xTTpOR4lkqHwZEk+iivGLgGB7OcopLnxqhFUB0XNZueQ9qTboamFHfORAh6qWcRmAZ1W5ikpXT6dieSO5cVJf2Ow/5INGhvnlS8/0qX++cJP/f5EQC3b6/SAA/3J8wWIjrXdZ03H0/odmxU260qy4pJlJMkBd2Os9zdrw8cRoscUO1YVjGNizYrzsXTEhZZxWsHC+rW8eboAkYsKtHTGC3v5vAswjA0bu6IDX/a0ud9x2A8z7i22cG1dI7GMWleMUkEaaAUwi1rOw91GxFz/yyRLMCkwbH0Jb37IqanbiRk2DYk0qTnW/RsnVFcEiUSHaPr0kXouhaHVcjqeoeq1U3dvDogyUuSpGASZm2XSnFjaPOqI3Evpq7oOjqLVMjwn7o/I85KFqmQvs+jYpl9ZxoaDQ1JIoyek1HSCqhN1gZiXJgkJedtcd/zrfYmKV0019CpG6G+m7roU85mKbMWfbCzFvAgm+G3Gqo0r9A0xYsPRsxmKd2uQ1GILscwdJSSTY+ha1S1IASaRm6OFz9D00SYLHl8DUfzgvut2/OVg4VsDNpct7pBeFRFxSIsGWz32O5ZPDiX99w8FufaoOdwPo7RNI26ltHYILDRW2F11XaGdV0jL6Qwtk3pDEWxdNzsWjIHbVO0ZbMob8dKallcmIbG6VKfKedunFYcHy0Io4JL2x3+8DdeolE6Lz4Yc+folDQtSJKSbtekKho+/NwWddPw5t6Upmn4pg9e4u6eiPMXi5y0a2MYOhtDlygtORtJsHBV1XRaYT/AZJbStFFXeV4RBDZN0/D05T6jRcbu8YLPPRRjxekoghWftZ6DWWms91yOpjG6rlo3ZrMUluu6wtGkA3oxmjPR0FW1NGPoumJg20tQp6bkNd1tsSAAga2z6hs8nubYhs54keF5Jru7My5d6mIaGnlaoetC2H/uUo/XDmRjeFFIXcBBlSldQ9c2UAjjrGqLvXd7/SIcqn/ZNM0P/Cq+9VebG35d2Jjv1OX3MeDvIAXQlbYg+tNN0/xHv8RDM+Dbm6YJlVIm8Cml1E8Cfw34PzdN80ml1J8A/jPg//oLfqYO/A3gdyIV5+eVUj/WNM3rwL8A/kLTNKVS6r9Fumf/hVLqGeDfAZ4FtoF/qZR6ommad78cfxfX9/y/f25pSRdYp5CUo7zG1NQy+bxphHXjuiZvHc75yOUOa76FZxh0zIrA0mgajbKW8YKuKY5tk7U1jyQpiOMcTQPPM0EpSTXvmHzk2pC9aYpqSlZdjS88XjAOc/qeyaWNLpNFyvEo4o3HE7z2Jlu4Br5nyw621XIbusZpG5QKos/J6hpTE+lxUUl8yvEso2PrLTxPsTfNlwnypqZYZMK8eeM4XLJwHEOxP5X2fl41LLKSjq1h6Yqrqw4bvs3uPEFrtUmakrHkGycxH7naIS1qzpMcpXL6tilRQGnJtYHNZsfii3shK57Ft90coCvFWZKhlMalXsWV6xKQrHTF7t6ErGwwTYPFPMd2wPdd+r7F3ccTLm/36QVO+/MN4lw6NE07Xwhci7quSXIBsbq2wek8QdU10zDhu4eDJYrAszTSSi6ytqHISrA10WLs7025dXsV1zL44uMZeS7i7A9c6fGPD+f0XJOuozONZBd+OEkks7BueP04Yvc0xLV1bmx2WQ0sDidJi1YQlo5r6QS2wXrHZHes0Jq3Uwk67bl1AZ6NYxlbbm52JCqnjdzwXbHOO5aOa8n4wrYN5vOMoqjI87eDd0GE6Yu0pqFhu2vx8FyO6dKKT9PA/TZUebXr0HMNTuYZs0XGPC6wbQPbFkNFWTWESbEUnxdVzTjMlu+Ji8LHMjSOxjF26/RbtARxy9Ipq5pB38G1Atba/L/RRIo237dwHYOiqAjaUeLFGLNpGpSmtTR9ea2UkiKwbhp0XcaAnqlxOBEDwQXOIMlLRouM0Txl/yzEsgwmk4QwzHnvzVUR8rfOxySv6AcWtqmjVjw82yBpw40vNFyzKF9qgwytJbSX9TJyqShqDs9CdF1kAlsrHmmLfajrhr5v4zvCF6OCj9wc8Nl7Y5qmoizBMHQMU6coM0Dx6fszrq963NwMuHcS8Y1Pr5CVDaiGj95cISlq7pxn2LoizWvSvMLQG1zHJI4LokQ4WBdC+ZO6abtV4oZ7kBSEYU4UQRBYPN6fyubL0LBtg257bbJtg9EixbUMVgau6LR0RZJXXF31qGvJaJzHOb5jotkKyxDHZKNYau1WO06bF9pgGWK8qGsxQxRlTeDoTMKM8SKjbjt4t1YlY3CalILsqBt0TWO973JyErLSdTDacbFS0iHcn6ZLLMgFOb9pIEyl21xUNS6CSzA0texmvdvra0hK3wcuf8Xnl4DDX4tv/Mtd73Tk9/8EfhfwYwBN07yklPr4L/WgdqYZtp+a7UcDPIl0rkCKo3/GLyiogA8D99puGEqpH0Jmpa83TfPPv+LrPgv8wfb/fy/wQ03TZMBDpdS99vv83Dv8PX/DrD/5Qy8Bb5+kjvn2LjfKSvquwdA1KNtRjDB6FDeu9NE0xTCw+czjOd96vY9ScDnweEHNqJqKpoZBq5O5MrR4dTcHGiaTnF7PxnEktdwyddIKPrs7p+cY3Fj1cEyYhilxKhfvDQWXhi6NkjFCFEuXgLYLttZ3aSqBQcZ5zZ2TkO95bpV/9vqIk0WJb4mN3TE0Akvn/jhlHJeYuiIrG57bdLk3SjkJC87nqVy8geOZFJjrXQfX0sgqEdG+b9vn1ZOYjY7Y2UdRyTgWPpHwaeYsMglyzVrdS9PAla7L62ch53HJjWHDzb7PqmfyxmkCiOjdMBR3RglVXbMRmGga/LNXJBRZARsDj0HHZjyLmS1yPNfl7GyBUjJSUgp0JbiJqoGsKKnrmrwU+KJuKCbznLqRHMa1gUuWlyStPqsXeDiGxsA3paPW8nGKr9iR/qMvnzCap7iepNS/8NoxnmdydbOLbxv4tnRCvnDnDNc1ieOczVUZr7mWziCwedwWU6YutOq+ozNvx2UXP8d3TDY7Jq8dRfjtyGQ0T6mahsAx2T2LREQd5tR1w7e9b4sHpxGnk4Rr6x10XQS9aSM7+Yt4lG7XJvBMup7FG/dH/M+f3ud73rvBGycxq77JaViQVzVPrHiMFhkfuznAszR+6k3pEsZZyfc/u0ZclGRlw/tvrjIOMzb7Lp948QDXNTk5j7AsnUeHc65sddCVEKijtCTJStb7Lr5tEGUlN7a6JFnJLM45Ow3F3TZ0ub87xXEMnnm6T9xGuvS6NklastZ35TX0LXZWPNG2xcJ2Es5Q2/lrdUtlVYOucWPV5Wq/z/4sl4BzRxhi87jgxorNz96NyMuaUTvOur8/oyxrbt8c8uh0wYklBVGSlxRFza3tLkVV8423hszSinFYMA5lhD0Os6UWp+taAme1DPKyIkzLZXHnecK/SrOS43FM4Jk4pnTJPdtYspOapsHUddI4hVZy8OROB++qdPDy1v15OE8xm4bf9+FLJHnFveMF949CjmYlvqURZxWeIxmQJ+OY21dWePPRGF3XGI0ifN9aPmc7GwHDjs0bjyacn8dsbQVsb3c4PFxwZVuwFCenEXZbgLz0eCK/Y14u8/SGgU1e1m2WI3zxwZiOa3I4lpxMpRTHEyH0X1yPm0ZGoyCRVnI8beeufe+WVcPJNME2teVYdDJLpctqGTyeZBiaRtFI4fP+y92lDnGeCHy1ziSv8Ggcc2nVx2uEVWUb+rKoLdpxd5qL/krXFXlRL6cY7+b6GmITfgz4M22N8BFg9vXQT8EvQ1jeNM3eL/jl31HXp+00fRG4BfyNpmk+p5R6Ffg9wI8Cf4ifX11erK82F/1qyv0/Afz9r3jMZ3/BY3beyXH+Rlnf+t9/EscxKUvRBnzw5pAwKdo0+gJdk3GLqcuYzzd1MmomccnxoiDJRcfy8GTBzc0uddNgaaJ/uEASWLomgb+6Rb/r8dQ1kzQvSfIaaARopys6no1jariGhm9pHM5zwqxC1w3WBiaKhvWew2bXZhwXaBps9V3qWgKMHVPDMQ0wxWI/DBSPz2NsTWOn1e10HZ03TxPWfJO0lFyqo2nCExsBUPPSUcyab5AWDatdsRVfHojt3THluJSCh6MM29BIWzFplL0dgSLHIZqtL+yFNE3Dt97s85ndBVFWtmG+aulse3LYkedMKXQtYW8q9uUN36BjmzQ0ZO249c9821W++HDMcdJQNtJREpijBKR2OjZ93+B4FKHrOl57YQaYRjm2JZqsshFO0oVzahgIAb6pGy7eilnVkJY1adEQZjWWrnAMSaD3LZ2kqNsxnMWN923z8t1zbNvg8kZHMr40xd02MidJhB6e5yU76wGrHZu9swhDF7L15kCcayfzjFmiM40l9FmE2QpdKZKiZp5IPEaUiojfNjQcU2duaktC+eqKh9F2Ydbb1/14EmNbOht9l45j4Fu6hNSGOSsdhxtrHm/cH1GWFa6l0XNNskpcbOuBycvHEv47iktOw2bJ+lkkBcdhthzLgIzt5kmB4xhc3+xwZ2+K3o7Ysrxic+Axi3NmbSfhYkRdRDWVKQG8SVYxXBFchO9b5C1na9aOuqu64fpGh1cejJhGUkQWhbDALsTGURsoDqKvudAimbpGVlQssgrH0AlsjaSo+aZrXcK84pN3J9w5S0jyitWug2MJ9ylNC4ZDD9cyyEq5+WaF0Nu7A4vrq+6yiz10JQNzGudkZcU0yul5JpttoHLYwlQNXeN8luK75lJIfxG0ftHdi+tyWYCkubgRoyhl4PYwbWsZrPzleyOapuHWlQG6ppgsEmYLKeBfeDjl1qrNe650ef5qj9f3Z5i2zaBpCGyD91zVqNpxs36lw5sHEcOhR1VJ5wrAcOR4+j2HMMzYaJ+L2SwlcExxKFo6l9cCDscxYasf1HWNrmcur6uaEpNAWrzdZb2gjSslXK5UF+Br0XafOq50DIN2HA0iuNc0RdaOTuu2+1jXDR9+ZoMv3ztnnlYtiw9m7XliGBpf3puLHqs1xkSta7RB9HuTMKPjmsSZvFYXo1op7iQOqKzBbLRlIsW7vb6qKP0d1FNKqb8HfCuwqpTaB/5rpDlD0zR/C/gJBJlwD8Em/PFfo0P+Za93+qzuKaW+EWiUUhain3rjnTywHbe9TynVB35YKfUcUgT9v5RS/xVSXeZf5aG/5FxUKfV/AUrgf3unj2kf96eAPwVw+cqVd/Jr/LpYl/7kD+EGLovJgv5qnzROuX/vnG7PEXheTy6m9/emdDo2//7HLjHLSonrCMtlsn3XM0XD4pn8tZ9+yN23Trn1xDrPXuqw2Ze4iRoIHB1LB0PXqesaS4N5WhJlrY3baHh+x+fxOGUSlwwDA0ermTkmaApT14nymoNZhqnrmLpOYJnM04KmKZnGstsNHIOuLVl3i7TgJ98YLTUyfVen38ZxTBMRnvc8i71JgqFp2JaOZ2rgwbWBzSvHCTeGNq+dJLgK3jyJsUzJ8us5Oq+fJMyTggZY61is+SbztOIjOz2OooROG9/Rty3et+3xhdYl83N7MwaeTs/R+dLxDKvVJX14p8dPP5rQNRp82yAuCgylpFBERmTPXx2wHRU0NBzMcvbP3n7blVXDzkqHjUHALM55fCxuto5nYBj6V5y8Cts00bWGRZRxcDrHtgxubPfIq5qialjrStZenJXknk7d6ExT0RYNPKGDf+BKl5+5M+bq0GG82SEvK5KspB/YXB7Y3DmJ+GPfcpVP3p0yjTLuP5yQ5BUd11w60nzH4GAUS5yIrljvucJnMmWEsdEXavzpQrpas5a0vd13WA8MfubOWMCbWckPfNcNTsOCKKvZ6LsYutwgNE3x9FaHpKg5W2RMYhmrrPccfsfNHntTGV3XdcOLeyG6ppglAtXsuzafvj/B1DUenknX6w99ZBuAf/n6iKxsOG8zLS9YTGfzlBvbPS71HRaJz/ksZXMgAc++I+OkLCvZ2ehQ1g3jMGOlYzNpQa1hmLHSUsmvrgXkZcXeecTJNOGjN4c0wKNTGY81jWi3xmMZxxmGtoR0Oo5wjEazhKpqcByDb3xilbxqOFvkfOkwZBwVrfNLoWvinDubpzy13WXFN9if5nzxrVMu7/RY7Tr4tsGb+1NBdWx2UEqx0bHou3I+G5rieCHFxDCweXQaYrd098NJQpKXdD2LnqWzP4rQdY24DVi2TB1oCHyL0ThmUtQ4jsE8zMkGLtMwJ89LLq8FfOr+BMt6OwnAaHWTF2O5fuCwiCRKxrE1HowyVro2i7Tm8mqHrKxljF3K5iTLSyzToBd43NwxcC2dx2chKx0BW55MEw5OQ7KsJM9r3rg3YnXVJwgs9s6km2hZoinKspJB16Gh4dJawDgUR+vFCPRgJE5Luw1IXus67J+FgENZVpyNcvo9h7TVbEVtMeR5FkaLmXBtnayQruNFzNBsIYXeyw9GHB+H/MvXz7i04qOUoiwrptMUzxN3p2VeBE7Le0HCuqULNghs6SD7NmfzlCwr8R1TqP1tkenaOrapMw0Tpov0a3ej+jesX+nIr2maf/eX+PcG+I9/xQf2a7jeaUH1A4g4fAfp+vxzfpm/QNM0U6XUTwPf3TTNfw98F0BLXv/er/KQX3QuqpT6DxDY13c0b3P039EstXUQ/G2AD3zgg18X8dovZ93+T36U8zffAMulLFYoi5LpuVwgNU0jzy1MU9AGg8DmuZsrnM5S/s5PP+L7PrDDui8X/rW+u4yZ0DXpYE2nKeEs5JUXE37Hk++n7byjKYVliBD94uds9U381OA8qrBMjXlSMEsFQKgVNZ6mqA2T6xsGbx3OCFOJJAGWobVpKQGqYaao6gpdM9hub0QaiiSruLRiMFpkTKKCnqOj2jo5bYORHVMnyUv6noFvaUyTimlasjtOlz/v/smCrYGHoWvcXLFF/BkWXO5bHGqKSZRzbWDzaJJR1TBKMw7mOfM20uH+NOI8KonSktdOYtl51hZDV2fNN/mxV87ouCZbHZsbQ5siq5jGBbapkdcNiyin5+jUmoD/HFOjRnF9RedSz+JHvnwqjqyeDXVFWTaYusZ3PrfOIsk5n2e87/qQqml45cGYsJHn4PHuiLIGy4Ju1+UszOnYOk9fDShqsMlRChZZja6Vy6gLQ4PNrrUUhKdlwyCwxF1o6txe94hyGW3kleyEv/+9G7yy3uGl++esdOx2VCHdOrFjN3i2ialrOK12xbXExXc4y5fJ9qahEWUlV/oSdh04JpNI9EZ5KRFIs6zixopDx9Yp2lFTUtT4lsa5ko6X1iIMirrh/qmM5VxHzpXAkdHK4Thmqyt2/e2hh++YzKKcvGzY6lg4ltD4V3yDpKi5vSZogS/tzoXg3mqS8lzE7HXdMFqkJEnBxppP15POhmsJGgKkQ+N5Fkarb7oAW+qa3PhARo1w4dDUePLqgIeWzuHhAl1X2LaO04IX86KmasGiTcPy+daV4t5JiGtJR/CVgzm2oeM7xlLftTvJOBjFWK055UKTVlUN770+ICvrpfhZV4oGll1bXbsAVQryoGoRCBt9l6Lt0tqGYBOapmnxDYqmETTGUn7gGEuG3YUr9XwRs9b1OJ9VLdi0Ic8rKk06xU3bfdENkROA7I7Pw6LdnIheqKrr5SbjYoRUN6I/irNyOcq66KZlX+HeFJxCIXExlr50LJ7PU0xTUhfirKTrmS3wVjAT0glqiJKS9VWHNx9PSFMpwMZt4Wu24m9g2Xmqqpqy5YV5roltCtT0outUtxqv81mKZRlsbAREiSA0JmHOsOtw760Trt1cW94LzAuyuiFj8DgrKaqajmMu4476bfKF3rory0qMIus9l/3ziEmr5Xu311ejs3+Vv/oNvd6py++clor+y1lKqTWgaIspF/hO4L9VSq03TXOqlNKA/xJx/P3C9XngtlLqOnCAiM3/vfb7fjfwXwDf0jRN/BWP+THgB5VSfwURpd8GXvjlHvevt1VkBVgulDnZfI7muD+vsrdtyZW6sCcDfPjGgHunMT/+pUPy926y3hWq8u01GYedRSXnYU5ZVlRlRfbwdSazZxj2f/73Tr6Cn1RUDeNY3oiupfPEhoetK0axOH/CCoqm5pW9iQBGlMK2REeRlTKOuBgTrPc97h/PKcvy572pXFsEpXlRsXsecTiJed+VvhQnyMXfaUWxN1ZsFlnF7kR2k+dzQT0cThJ2VgQmeGvVwdY1pmkp4yZDo2NrpKXBKC7Jy4YbKzaf3Q0p65pvvtHjwTjl5YOIvKolt6yqsdvohlkKdZNjtqLkz+0u+Pi1Hl+YLLjcN1s3nUT7xGWNrbduHk1Db8nFjmvyRz92iaKq+cyjOWEh1uuhZzII5MOx4HCyQOk280pCVZOsRDN0zKZhZ7N1wGkaH7k5BER3ozXFUtB8uW8ti5pVz+QkzPnM/Ql/4H0b/MTrIzZ64iDUlSItZPSz1bV5PBELuKm/zUoatUwqy9CX7iSQQrmoamxDo+dJzltZ1dS1YhiYwjrLZMycVxJzc3HjMQydcVKyN0kJbAPLELRF1tLVi6pZjlvyomboGQw9g+N5wVmrE+q4JqOFODkvRj3/8GcfoWmKD14fEuZSQL51mtB19LYwFtfhwBXUxyKrWlaWLlmPgcW0L90VoyVhN01Dv9WdSWyT6OuqFnC5teYzDjOaRvAGF7ob29Q5j0R7ZeoapSnn7qW+CJalmyHdoQvNS9WOdaxWhzNOSunCwtKpluZC5o+zkvWujaErJlHBKw9GhGHO5Z2udJmUYhJlOLZoLC+iXx6cRXTsDmFeM08r4rzCs8StJ05D4ZYN2uDv6Cu6NVbLIBNtZr2Ee1rtWFZrrfvn04TAt4jjAkydsA0YhhRL17i0EQANp+NIsujCDM+RAl2gpYKJqI2mzRutGHgGef12TmRZ10yicjky7XkWmhL92bBjM5sLoqLXc1rHX4NtKyxDcA2uJZFHkzBbdkfzsubaWsDj82j5dTKak4BxXZdOYt28jbfQdU06irYUkyCZhVlWoWmSWBBnJZapLTEGmiZi+Swr2drsYNsGri2vr6Zk5KfpGmabvXfhTr0Q3vc9SzaArelkvijZ7DkUlSJsz1tDU2imdAInYYZr6exsdTg5i34Fd6Jf3fpqBdVvyeiZtov0N4GNpmmeU0o9D/yepmn+m1/ioVvA/7fVUWnAP2ia5p8opf6cUuqiw/WPgf9P+3O2ETzC97QOvj+DCNZ14O82TfNa+5i/jqAY/kX7gny2aZofaBlZ/wB4HRkF/se/UR1+3/5XfoZ7bx1T5iVKU3S2tojm0bJbtLI5FNt6e0HudGRX4lryRiuqhic3fS4NXb70aMq3PrXCIquYxJLtF6YF3/3kkCQrufepz0Iy5+/+zR/l//Tn/x2gWQ5PA0tjnMhM39YlXHmW1uRlTXuN5ysNIwp+Pq3t4oKjyW744gLUXPxHwfE0ZavvUlQVz17qcTBO6AfSEZlEGV/enUoGVVGhUGz2XVxL542ThNNZgmWIgLTnWwSOuYRYrgYWYV5zFpUMXIO0qBgnJVEunQ/RFomTaujppKWGZxpcHzrkZcMiq/gdN3rYhsYLeyHfcr1P3cCPv3HOv/X8Oq+eRPQcneMoY3+aYuuw4putwFyhGvkVL6JcdAWmbNnluVKKZzYc3jiVUNgaeOMoIi7e3rXPojm2IWOdspDH3b42xDIkoiZcxMvvpdHwT++ERC1r6HueWOHuJOIsLJinFa8dLvhTH73Cv3o4RtMU++OYS0OPeVKQVw1rvs40qRhHOes9h91JzsE4wjR1Nvsul64PuH8qLCzfMcgW1dIu7zsms7ig08I5bVPneJbx3JbPzRWHl49iXjoImceCW3jflT73T+X5u74iIu0v74dLjZV0IjW2uhLEHNg6Oz2Le+cpbx7O+V3PrfP0akBSVXx+f7HMXPzI5Q4/5hjc2Z3yxUcTxpMExzE4PAt56d453/DEGp6p8eZJInymniWh4a7Jqm9yFkpczdW1gGk45vmrAx6dhdhrAWlRSXSHqS87Tk3TMOzYjMOMKCm5shbw1v4M3zXY7LtoSvHSwxHrA3f5loizkkkij7++1SWvag7PIoHlevayK6UpmIY5p7OEsA0x9h0T19LpuBKJM52lvLA4w7LENdjt2Gys+G3UiInR6laurAaMFim2obWQR/j0vckSVwLI75AWS06Y3d6IZxeQVl1RlTWWqROmbRi3oS873te3urz1eMJ4LJypTsdmZ8UXEbRrohTcujqgaRo6lsYwMHl1b877Nn0GPYvPPZqjlIamGvq+5Guu9gLeOpozz2GaFESJou87zFIpRssGfFsnKzTy7O2Q90VSYBoa/Z5DHBfcvtLndJZycrJgc90nL6tlBEvPs7AMoYr7jrymj9rRoa4pzhcpuhIjT9JyqmxTRmn9ro1l6EtK/fKy1zSYpmx0DUNiYi7o6p4rcThFUeJ5Fp5nsQhz/vS3X+PRJOMnvnDAszdWCMOcj3/sOmEqXauLQrfnWkRZSUpFv8V57I+T1l1r4JrytceTmKAdi+ua4mQc89Gn1jmeJszaDdK7ub7qyO/dT8D5mq53OvL7HxG0wf8A0DTNy0qpHwR+0YKqaZqXgfd/lb//q8gI8Rf+/SEiLrv4/CcQwdkv/Lpbv8jP/IvAX/zFjus3wnJdk9tPbXF0tCAJEwzLoK5qDFOIwZ5nLufsF9ZfTRPnXafd5Xzu/oTJPOXGdpcwq8mLitQU4J5paLx0FHE0jmF2Ru+D30p/pcO90wW+ZTBLSj50xcc2dCZxSaCDptn0DGhsxVGcczzX2ehaeKYiLtow5OX0VVacZChNw3AMFklO1zVpkDw8XYO+b1NUDUeThBq43Fq4O47RhtzK+GfRCkSjVFxcttJolERGiChWiraLljjAJC5wTRGPrvpyqjc0uKbGk6sub50nFLUIuC9E+Y+nCWdRSdfWWQ0MxnFJ35UdYlxW2JrG8zs+Pcvko5d6nCcZ667Dfj+nbBR1U1E3BlpLcG9Q3PQtuh2Psi3tq7rm3mTRdkksrg0gKUrGcYGlQV7kfOdT6zTAK/sz5m2mX+BbXB5YeLbESZRVQ621nR8NorzirftjbFsnCQr+wcvHlJUUl+NZwqDncBynpIU4ji6t+viWRtMYTCIJj44zYReVdcPPvHFKlpVYrTvsK/EbrqWz3nPIyrrdkcsoRjWQFTWuBdMwwzE7RHkl3c32Il5UNYezjHkixPqylpzF92wHvLS/EBxH1+HiWqsUdGydsm5amKzB8aLAtxKySsKsbwwdkrIireql0DdrO1YbQzEAxG2XaByXJHnJwLd5ZV80a++73AXAs3TMdozVNGJxH88zOr5oyMbzlDgWOnfPt7AM0e5MI3kP6pqi145cFmlBllcMOjaXhx67o5iqFkfr4TgmLyu6nkXWMqrqWjoepq61ehkR8s7jgry9WU7DjNqzaBr5fYpCHluW+RK06bRh1XXd0Ghio9d1hdV2OvIWNBmlBUlekiJIhKVIuh0XDQMbkPGZAspK/n/ds0QknxT4loFv25R1w0bXpvPUOp/84n7bfcl48/GYrm/KuaMaZmFKWcPOTsCN9YCOa3PvLOb+foyma9RVQ5LX5FXBNCmpd2PCpERXDb5nUzUQzxIWScmTmw6HU5EVKBTDroNlahxNU/K8YhblgrgwNCZRThTlS6G2rilG85Qndnr0Pbk2yPNRsdKxUUphGxrjMCNrz7m6YVk8Xox1XctYQjMv3LcXmjDhmkmH12gLzzyXrmbgCz3/4trdNA1rrsOds1Sck0qRtcVhXlaCUbEMwlTE54Do2mydUVwIwb+s6RcXcNga15Kkh2HX5rzVVX350Xj5PLzby/gqLj/9t2KHCvCapnnhF7wI5b/pi397/erWX/7EPXZWfB4czlhZ8Ug8E9s26NxaI00LOoHNatchSgtWug6TMKOo2kTxouLxOGG775C1F9skr3jrWASxZ4uMp7YCiqrhv/vr/wL2X4erz/OX//RH+NxeyNEkJtB0ep7Ga6cJV4Ye212Lrq1h64qVjsN12+LW0EVpGvfPQ3QlOXUVit2zWC4kukJpirW+g2sZHE1T4kx2jkY7mhRAYY2upECgFj3Kx653+cyDOVlZiaC87YDUtcQwZC1L54JufnFRc0ydwNJ4PBJCc2CbHExTDE1jf6Zhtq63rKz40mHEiidi94NZxlbX4r1bHtuBA0ScRSW3VwK+eBguM8teOoo4mWcEtkHfMbh7nnIwTXEsge/9wfdt8hOvn/HBqx1x1Cj5WOkHpPnbbTxd0+jZJkWdM88rHFMjsG3unkoyfZpX/P3PH/Bvf2iH00W5HDu5quLf+x1XefEooa4rzqKSrb5DmNe8vj/hM598jf/9v//NFFXD/jjGtQy+41YfkA7Wo0nCJ+5O2O453NrsCEsnrcjbArSsar7xepcXHi9oGuju9Ng9j5at+pf355xNRX/RCWzSdoQTBBYbQ2+ZY9j3LVxT49Zmh0fjjHlSkpUVx+cRe49HbG73qeqGa+sBD0YpPddg0boutwcOTQPv2fT47O6CFx7nrHcsXj1Y8PhozrWtbvtz4ME445kNl7QoOJxnWIbGy+cxTSOuqWmUo614rHQcDE0KbFNX3D9ZMGip4VYLqTycy78VVcMsluDgQcfmMy8fYdsCi0za0OztjQ6WIcyt1UBApFFWst6TvLftoScRT0qhB4qqaui7Bm9mJcPAxjQ0Xn84pixrqhXRuFw8j4amsExNRp5lLXEiSLeq58vNMkwl82+957A5cHl4NMf3LG5sdIiyEt82WvyCWOR1TRHYBraneHAWLZEG6z2Xs7lsrgxNYbvmkjE1CCx2zyKiVMCxfd9iEonLUVOi0zmfpwx8qx3/NpwtMkxd4xue2eDNRyM8TwoyGjCMBtsUsGtVN7y8v+Dz9yeAQqmGjmPx5CVhdgWWxpf2Qj56pcvVoUdWVHzq0QTfNrF0iMqGwC6Zp1Wr/5Kxo2FqmJrO9VWPRVZxNBHY7HDocXS8aDegNZOZZJOaps6DkwU3NjoiTG9ZX0lbiD88DdEUS2e0pmSEJjgLg6aul6ynpnlbOwXSYTRNHad19Va1GBECX8bwZVW3Y8JaMhbrhh988VC65q7Bg+MpAONFSp6XhGHGZJ7S79okmbgHdaU4jwp6riE6xjZq5nSW0PWspavOt3QSx+Q9t1Y5mSYYXYfz0VcqZd6d9dsjv7fXuVLqJu2kRin1B/k6YN1/q6xbKw5hXnE2s+j4kJcy/lrtOVSVTeDKhSlwDHzbYKNrM0tk1x1lIs5ctIwSw9CWDiwQqCLAj7+wL8UU8G3f/T5ePYnp2Dqpa0DTYBqKutC50rOZJYXk6pU5NOBbOYamcbnj8A1bXX7kjXPBCijoBjZ1K3YFCUBuipqeb7IS2MzigrxssAzY6AmDp0Za5E0jjq71QATsIJR0oxX3Jm0G3KIdS5R1jW3oy9181TRMEtFzgVjWHVMyuQ6nGXlVs9ax34Z9mop5JhfDqoZHY+mgSLafxs/tzvm+J9aICnk+L/ctZomIlV85jqkbWAmspW5tf56SVw0uCs2AvIYaRV5I8ddO8iiqCl27GKWKe3J/EpK3JPIbaz6N0vi5RzPSoqRj2kBDUilePEpQmkLXDDa6+jI4+qmtHua3Ps3NFZuHo4ysqIjSgqJuiIuKl45ikqwkzUXA33UMdkcJIPTkW+sej0ZCWk/yiic2fMK8IsmdpeOySGoCzyRKFEk7cjFNcYMZmsK1DTxTY56WZFWD2RbOsyQnSks8z8TxnKUgd6srdPpFJnqk1cBizTc4jyU3MW/p6e/dCiQ26Czk47cHvHma8n1PrjDLCjqmia7gYJ5TF4LVEKipztFE9nyeXS4zLB+dhZiGxlrH4nCSLvEWYVpgtKOvtKgYBjaH4xjXNVhv7fYHp4LUE+2Uhq5py3w/TSk0xDa/qItlpp2mKfKyRilHuop1g952OoqiahlFFXVdL7VHF10qOUdlQ1HWwnUrKykehh0bS9fYG0V0///s/WmwbWma3wf91jzteZ/53HnKm3NmZY1dPVWrWlLbkiVjKfCABA4cgE0E8AEHDsKAEAFBmOADDsCEjTEIC1BIarVa3epWd1mq7qquecjxZt753jOfs+e95pkPzzr7ZivSrqLVWYW66v2Sec6d1ll77/U+7/P8/79/Syjg50BVeU1llJ015gGASSR2+vMuclZWq25WkpeolXCXxD1WrVAaopmS93BBxTTM5FkTZow1lTDyWes5YmjIS25ud7ivqfhNR3KUxCRJk2PZ0tnd7tHxDKyeS1lVEl+Ultw5CvmltsUfPJgD8HsPZrjmki/c6nOhZ7NIKpZpgWtqxKrCLJKZepKLYD/Ia6IiFdSB+ixjUFPFeei65irpoa6loCpLcWyev/Z5WeGYz8aY5+/rcynBshn5SrdWbQCb0i0+F+Cn2TMYrR/njMchpqmzvdFadRU7LROAIKqwTI2ilLxEz9LxwxTbMNjeEE6b55lQSPbfZJ4wIcGxNAZtG8/RqWoxZLRtg7t7M6qqZqNjE+flKtbIs3R2uqY8A/JS4Mw/4vXRLr8f+WV8rOuHLaj++4gr7raiKIfAY/4IIvWfrh+8/t47h7i6zmbLIN7qsNUxePcwWLnXtgcumiIU7GVSYOqia1omsN622OnaLNKSw0lInlertnHL1jFVrYloKXly/wSAS3/6z/PJq332Z4k8uMuaXkvDNTQKq+ZomdOypLOjNE6gk3lGUkGU1xRFDii0TR3PUVnEFcOOTRClXBtaRCUESY1rgK7UUBuUlegeNFWhbUtkTA0keY6qqIzCHEtTiBpNGIChKbiGQZzJhq+pSjOGkA00a+5PkOQrXUicieA9TAssXU5vaqNVyouK42XO8Txho2NhGwony4K3jiM+sethayq2njFNM6axkNiHtsnPX9P52pPlSnvTs4UTlZc1G57BK7stvnEUcmPN4cks5a+8vM39ecj1rouuqpwGKZoCx8uYp9OYi32TMIe7Z1LZ7fQEglrVNTstg09eFEZUUUgxWFVw7GdsewaepXPoJyzTEstQsGwXVVFYpFIQOJbOvXHM47F0q/JSMAFZXmG4ymoEZulyj8O04N2jUFyAecXRLMExNXTNYB5mFFXFoGWt2vRxVqK3TImXacYdVQ1lWWNq0m0MGyFwXde8emVAGGYYhmxYEtkhhcK1NYfH47gpwiAr5O/sWgZd0+BiT3IfH05SlrHosDRFIa3kdTc1YYyda4aCtGhGM6wI0uJc1bAMwSkAq2gUP8lXgbddz6RlqiyXCf2eg2VqzIOUopAIk3M2k6IojbtMIp/Ox2V5Xq1GLlUToPz1B1N0VaHTjKgvbrV5erQky6VDASJsPiewn2uU6lrMBm4TbmyUKp4lIcUHk5ClnzLsOZj6M6OAriorHMT5QapsBO/nX8dZsdK/nQueVUUhSMTUMA3SVVGnqgpJJgc0U1eZB+mqi7M7cPneWUDbM3njSo+7JyEPT33y/FlHtixVFOU8wkZbie5/5lpXwJh+TlKUFCW8exxS1vDKlssbl/r8jW/u8+vvjtFVuL7uYuoq0zDj6ThmvWPRdWz8NGMZyf2qahmpTwLpHJ3rjs5/DlVVsM67RmWFrmtMlimaptBq8h5BiuZh2xIwpqaKYL+h2QtE+ZlOq6pquY+JxO1kihTpcVqskinaDYsty8oVu+q8cDPP42FMnYNxKHBfRUXTdQZdiYxJlGdjxrquSXMBtJbUHE8iOq7Bg/0pS1+cp2/fH3P1Yo+6FvfxWtvktc02905D7EaD99U/wv70z7I+2uX3J6ui+oEFVSMo/7fruv5ik+Wn1nXtf/yX9pO5+qaJqgoZ/FMXW+RVzY2X15klOfNYCokLXQFlDrY9Hk1jrvRs1j2DeSwbR1rWXNtocWW9xSLOWUYZfpJDYwN/6+4Z0YN3+Nf+J/9d/o3Xtvnu8UJO6KrCOMywTYVllBFnFS2r4tNX1jA1cYKdhAW1UnOySDhexKjArU1P4hDUCmwpynY8h7ajQ1TS7oBCxZ2TGJku1XQdjZN5IvExtYzyHK1qLOEFYZKz2bXJy5p5lFFUIhrWFIXdocvZImHQssjLiuNpxHrXZm8UUNUwnkS02xbPXehy0gTAhok4gI5mInjXVGFcLWKdlqWRFjUV8PnLbd4/izF1haNFxneeLvHjnBcudJgkGR+cJXzuchuArmny6x+MiNKCz13t0rNMQrfked3ljc0ejybHHIQxd8cxSVlxqeVgNaf9UZTzwoZLDex6Kt8EakUhygoavTI7LavpdIDSFAeqqnCpa62KoAsNwf3pNMXQVf7x3SmmIbEvSV7yaJStwIuXBg4Dt83ds5gwK+k4Jp+83GEaFbx76LNoAl5fvNDlaCHdm/MN9vZuD9vUGC0SgjjnxnaHeZQxaFlMfMmwW2vLqTgrK4xKZRlljJsx0KX1Frtdk0/e3uDtRxNUBdZbOnVQc2lgc6ln8d6hz9VhB0fX2Junov9wNX7r/oRHpz6vXxuSFRUbHYuTSKjry6TkzM8JsgpLV7jStzjRhYy/1pHx4WevtPnmUx/P1PAsneN5jGNqPL/V4izIibKSnqszDYSbFaQFb+7N8TyTOMlZ+uLeWxu4bPUcamDSxCPNfInBKRuUgmvpHE7EHZYV5SrBQFHg9m6HnY7Jw7GMytq2wcRPyXLJk1vv2RhNfE+WV+RlRccxsBpnmGsJo+p4FhMeLLBtnduX++jNewNo4mLkOdBvil9VFfdkVQtKwI/zVZCz1RRvcQPgdC2drmeiNqP5LJJOJ8DpKKTV6KpAtD9ZIVmLUVJwvBQd16Ipms+jgRrYN3Vdc/VCbwWvbVuCLhi4Br/z/ljwF464Ct85jdjuGJSqgqkpfPJyl6KoiIqaqpSxozjcoGWZq4JKni6w9FM2hxKufHQWkKYleR5TlhWDgYtpqESxPA8tU9AT5w7Gq0OHK0ObaVRie1KI9xwxbJRVTWKJG/TcKBOlouPquTL6vbHdIc5KOcC4JpOF8MY6bQu7JW5hq/mzi0VCkkgX9O6TSRMvZdBuafiRdOI/DMQ87zaeOw+zrCCOM+Z+Sp5/WIVTYyg1WVlzOI2Is5L/aH9BnApP7ccxavuo4ulPVjn1QxRUdV2XiqK80fz/j95r+RO2WqZOXJSYunyQbU1Ba8R8qgIDV8fRNNIyXZ28DE1Fryp6js48bjAEisKGq/PCpsObRyEfHCyEwRNlRFEBaxd5eLzkbwK3N4XwfDqPSbISPy44nCa4ts40qHhlp+AgqsjrmrNlSlVBxxJuSw0Nu8dERWPbUfiDB3P0hpOiNuM/R4PdnsPxMuXWuouiwNEipfkR6No6nmcLmTwvpcOUV/ixPMyLsuLUz+g44mI5W9A4XxROZoVQxQ1tRReO45yJL+45TVW5suERJBJPMwszLq+3CFOxIS8TIVbXdc1JkPF0GrPetrg6sHiqKnzx9oCjpRRTQVqw7th893jBwBWmzHrb4mCeYWiSKxjnFaNghKLAu6cBWVFz6mc812/z//reU/xC7snQ1Xj76ZwoLVnrmPTaTkP+loft9w8iNrslF/oOVQ33Tma8caUrnZmswtFUDE3jcBaz23dI8grXUBkFIjo9pzqDjDLmiYQcT4OUvDRoNdElZ37GtQ2P47nGLMiwzjsTDf8ozUrWWzptS8TUlqlxOA2f3bNJJNEZzehEbdhHYz9lOotZH7q0LJ1RKMVZkhRc2RKtVscRbtNuVxhZ87ik23sW4wHwyQstgqTg1roNSAGeFhVBVlI0dPg4K1jz7NWBYhFmXF1zOVmmvH8a8/jUp2z0XecZaK4pYdxGs2ENWgaWpjL2U8I4J4pyNtZcLF0jLcoVNiFKC6oalmFGWVYr6jlI16woawy9ZhnlXLzoMQ0zydwzVOlqOjrTuFi9f891NKoi0N0kU5kWUsRJniCr7qLeCJtNUxMNTlWvurF+nJMVZYMdgLjBZxi6CLXPEQ+dxnFX1jV745CiGf1V1bMOr2vpnM5lJDyZRhRFxWKRsFik7O520DTpNr37QGj7uq5yOBH92o3tDn4YczJLpHubpCyCQuQCjTsRpKtX11IcXezZTIKMnq0wi2tqwC8U8qLkZ28MUBWFuJaR2lrXoUDSDqKswjahripUVQKvx8sUy9LZ6jukzXhPOF8Gs1lEWVZgqA2Pq6Ion3X2+p7FOMzpN0J1Q5PX66sPZmz3HDr2swL5XNxeNh2qRZRzfBZQNDFXpqE2ZHMRxqdZSUoTMWU3IcbNA3Ayj2m3LNJcXucdx6TtmJyOfXRToptqoKoq6kYfd44fef36gA+OAsJYxS/l+VdVNWeziHbHwQ9SFsuE5TKl17OZzxMc50c/8tO1j+hQfUTX6p/n9cOO/L6vKMqvA38bWBVVdV3/6sdyVT+h6+29Bboic/vXNnr4aUFWVkRFwaWOy5M6kjl+JkXGWWNnHoUZpi5wvY6tcbXvMIkz/LRikcipyjY1lkHK3tMZs7MZg90tCdrUVJ7OM25stnj/cMlWz2HqSwtZ11RMx+Cf3Jtyc7tLVde4ps4kSEkLSKuMtq2x29GpipxaEwDfWttgp+dg6yrjRUpSQ1IIHM/T4XAeEyYFcj4RQatnyeYw8EzmUYapCzMIZNxxDtqbhalwllSVx6fSKO26Uti9crGLpStEWcUf3B2zf+Jz61KPk5ngFbqeyaBtMQsy9sch3YtdwSaUNZMg5dLA4Z2jEFWBT+yIw3G3I8nwu12Th6M5AH/zzSN0VcYuv3C9x47n8Bv3RoyCgp2OwfcPw5UrbqstXJ2yqvn1O/ssC2X1U0+jgisbbd7bm1M2m4XRENYv9Gz2JyGmClueIeLfTQ8UBUOBWSr6KFuDl3Y6RLloZT690ycpSv5gf46iwKcvtngyS9mfp5zOY4mAaVhSfpzz5bsTNFXh0sCm7trEWcm3Hk3puAY916Trmoz1BD8tGfuSq7jWEZbZIsrQGw1JnpccjkN0XaXrmewOPV6/0mfr5Q3unsU8HYvAOSsk4Pj5bY/7ZxGbLR3HVHk8kQ5bWdXcHUd4psrAM5hGJbeGKttdi+Ol/MyeqZLkOfuzhOc2XV7ccrlzGrPVNvj2ni80b1XlrUjGH1Ej4i2aoqTvWZRVze/fE3zEhYErXdBGT3PUCJhff26d00XcwDz1lUYxzkqCMMM0NdqevdK57I9D6bi0RdN0toiZRxm6KuOkr9wdc3WjzSLO/lD0SJ5XeJ6J24jJa2iijtQVW6vjGMzCjEnDWsvzkpYrDC5NkaDyYsXvkk088ouVOL3tGASJQCujVA4gjiU6n/E8w7N0akU6bufdi+k04pVb61RVjWmoXLvY497jKWVZ8Ynr69iGypffFimtY+ps9gQN8eTMp+0YdNs21DVRmKGZBn/2lT7jpCJr/JtpXmIbOmle8XgSQ11j6ypZkWPrCt9+PKdlP+uIfXh5lkFapBSl4DJuX+hx79inqiTeSVHk739yGpDnJbvb7abwPQfU1o2GSsZ4a20b19LRVfidb+2j6yqffnGLvUnK2SymKCoe7c8JAhnhmWYDYvVTDENjZ6PFex+csbHR4p33TrBsg5vXBygKXNlqEzaxUVnD8xq2LX7xRo93Trp86/0TNFM0cFUlLKu6KvGTAq8ln7Wup+DaJosoazhWCkUlz5G7JxIJNexaDLo2D5/OBWRaSSSS1cCcBz2HZZCysdHCc34M0TM/Hfmt1gCYAL/0oe/VCEPqp+uPadW1dF3KxrasKtKqV0o5BemquJAMteYsEKH0TkcyzGxDx1JVNEMhr6qm0MoJ0pLjaUTRRKg8//wm3xgvUBSFYduSTCpg7Es8SK9l4ao5p6E8JBdBzGSZ0bE1NvseLUtl5IuWKkkLbqw7VCiEWQVKia7AizttTCDKK3aGNlFWcBKURHFKv2XxeBSy1pYA1gYAzjSpuTDQGbiCfRg3sMNzO/kqOLYRAqPLAzPLSvqeidZoaHY6FoskZdC2eLxIVqfJlm1g6dpK+1HVNUku90lX5cMe5xV912AZF8yTgrYl9/z9s7iBQcq13txw6doap37OhmsRFyWOofJLV4Yss5yygu/s++RlxXbLJsgKRmnO954GbA7cD7Xba05mMjrSNZV5kNBcHmql03c0TNPkvVFCe5Hw+k4H6pqg6YaowGmcNUBMjUVSchBEpKXAK1XgJMgoqppZkDJeJFRVTaKVnE5Cfv7lLR6PQtKsJMwq5lFOyxZ3WZqV1A60bQ06Nl//YETLNfil59d5c1/GoOchuKKJUhtjgdzrnY6Bn1ac+DI6Ov+Rg0C6OmFaYeoaRQUbrskkPOcaKWRpjWuobLR0jpc5v/XBhL5n4jc8HrPZLKqqllDfKGYSpHw3LZj4KZYh+ry1js3ET5t4FHBMi0WcrdAB5x2fc63X1E8Jw4z1NY+tvkOclasg2amfEiRF4/iUolDXlFWGXZoWKw3UK5td5knB8Szi/sGCbttajWrmkXQQNEWh1zI5GMv5tONKp65sNFdFKV03IZGLUD7NBbOwjDJsS1AWdQ3bfQeaTu554XQOHW01UTamrjFoSZGnqQqXN1okmbhlN/qucI0+NA4a9GzmqsLDwwVVVXNtt8ugLYXodJmQlRVxM+pUFPCjjLxrYxtCUo8zubd+XKCbBoYC68M+blaRpylhCVGSN/T3JUVVcHVocziNWUYlS+CNSx4HfknYENAtTaGoauqqJq9BQcGPUjRVYX8SrUwq0HS05zF+UyDahsbJJFp1byyLBm8gDCnLlBief/CNvQZDUfHm/RGTSUyRFziuiW3r4swsK/JckBaGoWFZ0vnvdGw2hy66rjIahdx7MGVjw2Oj6+DZEsA88RPm84S//Mlt/sG7I75wa8CbjhSNmqbJ+LgSiKlt6CuxfBDl0lUsaz48rqvqClsXhI6hqWz0bAYtGz/OOJ3FkjiQPIuhGU2ilYnhR71+OvJrVl3X/+bHfSE/XXAYRAxs6w+xOYqqJq8q/KKka+m8exoxifKVELuqDXqOziQsWPckyHNoW4R5yc9f7vF4ETF0Db73dMbJScDrL23yb/3rn+Yr7xzT9wy22gZhVqEqBgoOVwc248jmN757hKUpHI0C8rzmu3dTPnWzxmk5dFwDQxdEw8Espe9pqM2DqWVLG3+ZiwDc1VRUS+d5x0BXXSZNOz0rhS8lD0LRfGVFxdNpQV3XDFsmkyBbgR41TVnpBtY7NmVdczgJCRUR4K45BtMg5+lY3GwAt68OOBiHOJbG0SzC0FTajsFG1+be4QJVgajZVM4fMD1Hp+/qfHAWs9E2mEUFYSb06M2O1TCsXFqGTlEFfHVvDsho4J3xglFQ8N2nc/7S61vUdc3ff2/EWttsXsuKmZ+w1rHJipKhq/MkyfiZa10ezxPi4tnrfrzM+eTVLosmcsvSVR41erArPZt3jiI+dbHNJC2ESZRVPJokfPX+dOXc+tzVLmFW8ZW7Y+aLhF7X5vKHAJWWpvLiTptlUjJrUAFJXnJjq83pImERZ4z9RDqCusqgZWHpAlYNkoLtgbg0X7jUp2tpHC9TDsYhbcfg4ShejZcWcU7PFXv9dJ5w9UKXcZivxk5f3/O52DPpOAbvHoU4ls6lnomfNkGwTUdle+AyXiaEicbtTY+ThcLjUcg8yFaurLWObOqGrjJ0ZUNKMxFfZ43wVwwM4miL0oKHpz5d1ySOc1zXRFHg8fGSQcdmo2uzCKUTd22zLZ1OV5ABZVWzM3AxdSlUzruS7x4smCwSXrwy4HQe83h/jmXpXNpuN45JFUfXmUcZQZBhGDKWm/opk0WCaWqCC2kMGXfujbl0qUu/ZbFsCrJSkbG276dMJhGdjkW/ba0wCIO2tdqstgeujMGzkutbHXquhImf2++DJMcPUnY3WhL023TxkmHJdBqzu9Vm/8znvQdjwjCnKisePxzzyis77G62GM9jDg+XFEVFyzOZzWNuXxmsRq7TIGX/xOfXvnPEX/nZi2iKxPd8d28OakqS5gw9izTJ6fc8Xrqks9tzSLKCZbLknf0luq6x1ZD9k6JeZSVe7uuMo4wwLvAcfcWsu7HVYRo8g1cenAYYhsqNC13uPp2xXCYrerrpifvtP/+9R6Rxim7q9AcemqZy68YARRGK+3Qas7HhrRAJnbYgMIIop+OaFEXFdJmg6yq3bwxZRjmTSci9/TkXNloSUpxIDM7f+/4peVHxa987+UN7gK6r2LrAm1VVXYnQHVtrzAkaYfrsQFVUNWmWrc8gOQABAABJREFUY5nSgU3zEl0Tcf56z4G6QkGh6xo4ps7INYibHMsf9fopNqFZiqL8hx/x7QXwnbqu//4f7yX9ZC8/y2mZOqCtYkNMVcWxNPKqIsxKTE3FM1VGTdfGbHhM0cpZk4jo2jLJy5BXt1t8/mKP/8w2cC3pAr1wddAUUiI290xJsb/guSySnE/f2uDtvSlZVj0bAcQVF9siEo2aSBbPNtjp2rSaTawsK9KqxjU1oqJiEmSUTYvfUCs8y8CqFfy0omWqXBo6jMOCaZiRNmOrjmti60rTjVClaxFXq6wuXVMoCtGoJElO1OTvLcJsNXrouQZpITlsQZTTa8S0IrZX8RyD00WyGod+GH9QViJQVxV5CGiqIpElqsLrOw7fOfR5YcMhzismkeTldW2N/XnGOBRa+CjM2Go27MQ2uPN0wsWhyc89twnICX4cFvzVn+lyMI950dGZpzW6LgL5OCuYhcIUGvkZF7re6nUYhwkVYntPcrHGj4JipYW5vOaiKwqXOg7zVITWsaXRa5k4puiBNFVpCkWFjZZBXunEmcH9s4CjWcy1dZfjRUrLNnjz0UTcUYZGXtbEuWQ5as3DfhFmgIltSPhqnAm64nLf5vEkJstLNjsWh9OIVksKq6yQIids+FxPZxlXhjZXejb/xf0Ze/OMvAmWdEyxqQ8cnTiVzswkEmxDlldEcY7rSIDswDMlvqaomceldGQUGbm1HZ3jaYSmScciKyq2+g4TP+VgFNBuWStsgOYpq9zL86iQspJYGc82WDb6PGenS9iM785z+zT1HNIpIm/b1onjnCCRwl06rOrKaq81mielEZBXVb2ixVuGxtUrPYZt+w9luM0bUGUcFwwGEhd1HjRu6BJ5E8Q5mqZwMAnZ7DrEmQRb64rCaBmL/rEQ16xp6o3BIGU8l8I9jnNMU6PrGiyClOk4oNNzWc6l69NuuFVZUXGmhnSbDlZZ1pwtYjY6Fustk7atczIJ+TOvrKMp8OhkzJ0z+dkNraLjWpRlxYXtLlVZ83t3jgGN7aGNbZm4jk7bqHh4FhIlhYxEVQVVBbfVxqkS/DBHofmcFhW6Kk5GEKRBqyUROGFarFyVdQ2uI+T5oqopioLesMXmZmulXdsdeAJ0tXQpJisxJ5zHwBhaEwVjaiteXJYVzJtCWVVVzs5C8gZhUBQVF5uA6iwvVyPd86Uo0HZN4rykqEBVtMYBLQYFDR0q6Q4mWYmqaWQlqEWJa8tYOEkLjs9CLu10ABXLMJkFKVNSdnoGj07iPyR0/1Gtn5LSny0buI1oqAD+FeA94L+tKMoX6rr+H30M1/YTtyogb3KqzgM5a2ocXYqp85apqkgnZZ6UEi7b0KJHYU7YUL+/+XCCH2S89/0nbF/e5Oa1Af/25y/zB3tzykrGVlXT2QBhMtm6iDrLCnqOxhee3+D+k/lqXHN+YpZVYxrCQRI7upymFE3Fz3KOFhl5BV0TtGajsRsAp6YoTWxNjZ9WXOzb+GmF0RI3zSQsSAoZ7w1cXaJy4nylsaoaKzxI3tlkEuK5InYdtC3mUUbPNdBVoQmfxOEz0GFR0WnExYsob/LUarG410JRr2sZGYh2VbLu0MQQ8HCacDRLeDqOMHWVT13u4KcCcXwwiqnqmmHL4mCR8e6hz1ZXXGGmoXI2y/m73z7g+rpLv+VQqypnQcb7pzEtx2Sna/LwNISmQBa7s8WVNYcgrWhZch8fTnO2PZW3TiJsXcatkyBd/Vw7HemI3RkFRE26/dXtjoxwG2eT0Tjheo4QoO1zeKips962WCbSlYuzgl7LJC8lbqisapJMBNoDV+N4Xsp4sK7Z6Tsisq5qTE3eS8s457kt4UileYmqCIE7bE7JmiKn6dEy4TMX16iRsffxMmPNM2hbGqFtsIwyeo7G8UK0gieLZAUk1XUVz9ZXouG6hnEgrb22bdBxDCaBWOOrukapIEwKVFXhbJEw9VNuX+wxCzP8OGfQtghi6TpYuojUVUVI2lleMfUl2Nk0NebNnznPhatqgTjazaitqCq6HZu4ue/nWkDbtAjCjP4KwSDvkUHTvSyregWSzHJhNRVlveLIHR0tCYMETZPubZoW1LWG4gjRXVcV5mFGlskocti2V5/cRw0yIm0yB8+RI2ozHtd1letbHd57OqOuhZV1ehpw5dqQ7TWPR/sLzk6XDWAUoqTAMFQ2uqL3WfopN7Y73D1e8gu3hnz7/hlFUfMPviPdmN11F0tXqSvoGTXvNeykOLRI0dhd79DS4SzMSfOUrqdzFsrBx7E0xgvpPF1YaxMkOWEDOj1PSAijjPcPF8wW0i0adh0UBZKkYDyPVwWV0hyY/CTn6f0x7Y7Dn/7MJdKiYh5mLKKMvmcQNXBUo3k2nK8kK3FaOh3XJGm62KbxLPS4qmo8z1iNe9stizgpGM0TPEdHaUwIWS4mhzQrqRVIiwpdUaEGP0pQNQnrHqUFui6au7ICy9LRVEEunB8+ztEg//Q6z3k9mufYts6Po445H1/+oev6SexQATeAX6rrugBQFOU/An4H+GXgnY/p2n7i1mmQcqXrEuYFT5chOy0HBWUV+WFpCtsdE9dQWSSinzp3AN0fp3znwZiTk0Cyne7cg6N7AKSbf5a8FFDfZy52eDyP6dkaSVFxoe2w6dmUZU1SViRlSd/R6VkmsyTjr37xBn/jSw8AhYd7cwZd+5m+Q9cIMxnx1bAK6jv1M5Lm4DVNYb15l334YVRVNSd+wa0NF9tQCMeyaeWlOLjyRmdyME+Z+Al5UfGLt4d80OT3qYrCz99e4yzI+XaYrbKyTF3lyprHu/tzdgce1zY8wiRnvSui07NFvIL3nWtjorRYOZpOZrF0rNoW06gQV6Wpcu80JMsFvriMMvJSNEAfnMX0bJ2eo9FxdKZhxsks4MXdDlGU88FhyLqrM1kkOI5FlVe8ue9za6vCVGruxhVfeH6dR4djvv9UrkFRStqOjFXVBhaoa0IffTyPqdDwS52qrrh/GqFpMt4D+FvfPOTxqb/KKGvZOtc2WlS1FMlJXjGLxRG20zFwmlw7P87RNYX1tsXNNYvvHwoh/XAS8cUX1tifZ+xPIr6/77PWtmhbGg9HEbqqEpS5uJzigulSNru2Y3Awz/jZGz3qGv7+90+wTY3dgUfb1rB0layseP/Ix2u6KnfHsWiCOianfs44zLF0lc2WgaEqfOX+lMkiod+22B64tB2DqZ9i9h00RRHmVV03GIBiBXV9fxpxNgqZz2KiIKLICl549SLXdzp4tsErF6QoPu9GLaKMJC+Z+AltR7QnbdvgdBGvOiCWpfPS1QF3ns6ELN4WFIdn6Dy36fKNRzNxRDbRI6KvElBjXdeEkYTsnm/q4tQTh2fHFSPDwSTE0OX3nOdYmobGySxiY6OFc6nXCNshasZ/SVLgOAanxA3zSyUIMuaeSVHVzMMM19Ipyoplg0a4uN4St2ItB4s0LQRoaeu8dm3I6SKm3ba4eaHHzXWXja7Dd3SV57Y7fO3O6YqzdLZIJKevZbLbNYEW39tbcjj/w12YlzZ03jwp6Fgqr17tYViCgalqUMqKC55KrmnccjWCqCD90HTq/BmiqeIwvbc3x7b11XvuwsDlG7OIz1wf8LX7k1UHOslK1vsOX//GY3RDp92VOKKtvktZ1dzPply90sfUFU4XGWGSc2HoMQmksGo7BrZtUFUVYXOv222LWZDiNbE056aCXkuI9q6tU5Y1mxutJptSWwXB+2FGt20RJSlxXmNoYFkmmlKRpCWmoVCXsAhz8jyl05ZYnbKuMU2dKiuIkxzXkfzSMMmZ+SmOpTGZiVZy6Sd02jbzZYxlG6gKvH61x9EsZh796INOPipm5ie1oNoFPGTMR/P/Ow1S4UefsvgndIVZSV4J9XcaF+hqSlyUbLk2bdMgyHM0VfQ6qgJZKQXI3izjZB7j2jqtlonrGiymW/iaAWdP0HTJXTNUlbgs6dnnieYKa7YljjVDoYUEkF5oaZiqdJpcU+XzLw548/6cftukLiuJTvHspr2d87V7AZ+/0ce1TPKybFhTz5ajIxlcWYlnSfTLaSRakjArmUQi+F4mBYoiWARNlY6ZoSnYhseTM3/14Ru0LDqO6L+SosbzTNZ7NqN5wqW+EKTTvKLrGrSawulsEeNaOqN5QssR3chO3xWbenNCN5vsrvWOLUC/ZhSQlTVRWrCMpOjY7DkYusreKORgHDIyNU6nEa4tdnRTV/ny+2OKZoR46Ocr9MW5TfqDoxBDh88/J0Xik5ncqw/bsQG+82jCbt8lrUQz0dUBS+fdg6U4PqOcS+seZV2zP8/I8xKvGcWcB6rausrxIiVIC5wGLTGPMhaJTccWAfeNDY8wKzmeJxIU2wAmz2NJkrxahQJLJ1U232UkTlNNVYR6X1UrF1XX0VFR+NvfOaJoRnxaA+6cRxlbXZu65/Do1EdV4FOX2xwvci71LUaNSD3KSi71TapaqNMt15DuWi4dm5YtHZlzQXzcaOLCpCCIcwYtiwcPp9R1jdcyWc6W5FnOk0eTFdzx1Rtr5EXFdtfiyTiSTmjLIkjyVffOOAeDNnqnWFWwmny2lvsssLgoGyG+pmIZKps9Ebf7rsHRieh4XNckywoMQ8PURe+2iLJVF8locuE0VVm5AUeTCNMUKGkY5WwOXDxbZxqkzJcpcZyv0A0gWaC+n9JtwoHPx5JdV0azs/OiyRIW1SjKWO/YbLZNvvveCW89nvKZW2t0bI2zBbxwbUDfNTjxcybLBMOQjvTm0GPYlqy4vNGoPT5Y8HgsHLOjSYRlSVftz728wVbX4iv3J5RlxSKpOZrGPL/h8HBacecg5GLfZtCX8OJ3DnyeXzfZ88ExKuJcOpoXOnAcSpdxd6PFsG3x/p58gMK0II4LzgLBP+i1dPvXOzaLOEM3dLy2g+MYK+SBriq0WnL4ADmgFk2hutuzV507y9RwLYuWrfOwEe+XZd0cVkHXNGpqqlppuoQyDjwfdQVJLjFELZPSEYhokFaoikqSVWRpimVrjVng/DmgYrV06jIjKdSm+2lSVVBVkCYpg5ZFkhXMlwlxopKkBapaY5vS7V0fCI8rLwruHQfc3u0yj+b/LFvVH2npH9EW+xNGTfihC6r/AHhTUZQvI8L8nwf+Nw3o80sf07X9xK1pXPLmic9u1yTOK85CsVyfxSnHYUJVS5BvkFZNtIz8uVmY8cJOi8u9NUxNRVcUfvfKAEtXub5m8ep6F6PZ0IeqyYWWw4NFwGd2eyLabYjJSV5iaVLMRHnJumNzHGR87tYmb1xbF25UWTKOz50mNbZlMglzzmLIlzGKqtIyhAVUAz0T7o5lRFJWUBYFjiMn2GmQ4xoSrNu2dMJUJcpFH7DmSXGy2dLxTI3jecSZL+39n7nWE6ZRXnG2TLm00eJgHLLWtfnVr+2vuD7xhS5GM4LKi4pMrdjsO6x1bHq2xvtHPpfWPKLGCp8VEutxtoixTCkeWo7BaJGw1Xe4szfn2labiZ/KiKkv2pVvvHPIxnprNRrNigpLRWzNjc4oCDMIxQl0vqoaqlrD0apVIVXXYMs8VFhVHZc4Fys1wH6QsKbqrHVsbqzZfHAa8cHejCenAVHUUJkdgzARcf/lvsVW+xykqPKlOyNatsFrl3o8OAvJiroJ1VXIK0FBfO/xFID/wS9cZbLt8hvvjlAbnMegZXEyixrnmMoyyrFNjbQQZpiqqiLQLSuOZjFv780ZdixhARkyIhsthdh9sWsydHUu9y32F0I/35vGHC8FBjrwDHzgN986xWq6TS3HIM1kJGY2XS61ko1UVRROZ+Lsms8T4iAmDqXr11vvceVij9dub3Dn0YTRmU8YZrRaJk/OAgxN5bBxwz6320VvXIsgxaI46kTfBzCZxRxMQvpti45r0nNNikpGokezmI4jXab9cUi/ZVGWNZalYVk6jq0z6FgUpYwA07zEs3WOZxG7Q484E+3XVs/BswWNULbF7n88Dul3bUbzmMOsZH3gkqYFk3FIWZQYlkGe5k2BKZ/TwcBh2RRdZhNRA7LZPjpY4DiGOC8TGe/+5V+4yqmf8/A0oONKh+4TF9t8Z8/nwcEcxxFX2d2jJXFWcHm9xTRIubHeYejpBEnOPMqke2hIDNAiyvn6wzNOl82YVhNi+bf3Ql7cUVhrOeTFkvcOltw/9mm3LXqexdNl3dBVFGxDZRpkeLbNdl+0ejt9l2kgGYOjecx6x+bW1T5PRwEKCsOOhWNqHE4inuzPURSFbtfG88zVSDfJS65e6PLG5R5PZwnPbbU4nCfsjQLK0hMul6mxO3SFe5aXvHJ9jXEDd63quol6Klb0cyHny89paCphWhAEGa5r8PkbA771ZI6pqURpvtLPRUnWjG9LHFvHMFTCKCMPpSNa12L0IS8xdAW9VMhz4W5lWcZoFNLtmriuPGMWUYXnPdtfDF0jKwrS/McTw/vR2IQfw4V8jOuHdfn9p4qi/EPg08jb+39a1/VR88v/7sd1cT9pK0xL8lLFT6W9f+LntBuKt6ogNO+6Ji+lo2NpShM9Y3OxK6LrRZpzqe3yc1c7rDkWa64lkRKN801DBLkvrXdpOzpldR7fIg8GTZOTlV5UFIoIgfNKxPFZpfDWns+ltRYg13LehQHRXqiKQlaDayq0THGsTJKKWtVIk5wUhZal8e5hQMsWgbldC1xzkYrzbeiJa7GqYR6XXBnINXmWyi/c7HPi51zsCR9qo2Nh60rDplLw/RTT1Hj+xpBFmPHkLMAxNYZtWzoLWSG6nEzGd7c3bKoa3t2bYRmyifqxON4Wy5TNoUtaVNi6GAM2OhYPjpZMlrDRd/jU5S5fLeBDtwEF6SJSyKZGLXZnAFWVkyWcP0wUKmq6loqiqszjgqIo4UNQziTPVwWV2nQtXEvHNlQ6jkG3beGY+soFJzwd0fR0mm5kxxZBuW1oXN1o0bXF9JA2epKDeYaiSDahbWo8HQWcRinzuFjFZBSVdOqE4yTjsEWYcWu7I+TqKENvRlRG03U5Xz3vmQ6r4xjsj0P+0XsZP3NjgGuqqMD+PMPU1dWI5myZUjQ2d1NXKSuNrmOytmEy9jOCRLRLoyBlNInwPHFa+X4TJdJ12dju4nkm3bbF2STEs3XabQvT1CiKijyvSNICwzVZa/R35+L/qoayycdLC9lIda3RrOQVZ7OItZ4jnY6mQ1YjG0ffMzANjfv7c1RFbPjdrrwHz0XrAjoUQG6UFrQdg6enPm5D7T7vvLWbuJppkGKaEpbccg2mmVC6Pc/EbWJ9ptOIsijJs5y1rb7kJ9rS1UuSnKyB5i7DjO2huxJr13VNd83jyeGSn7vRXzksg6Sgfz4ubFxzlqGx1XN443KHX/3WIR8cSqHiJ7nof5pRZsvWeW67Q5wVvLs/58yXonSzZ+O5JnVV8+jY55v3J1wcWLx6dYihVLx7FKA1XaGybLSjqtDbDKVis+dwMA7Jy4o4E6yCosjo0jFUuo65ysWMUhn9mrpKGGbYrtWIxeXvP89+LMqaJ9OEoqwYuDpPxtJtC9MCp5bPkKIopIV0+tq2TlVZZGXFxE+oalYdYbPRIy5CQdE4prpKOSjLmm89mZPlFWezGJBOZFmKaD1tyPNxUqCqglJwbB1NUWh7UlQfjQLKsmJjzWNZygj54DCgKivshtt1fpA7L6oB4iRn3dN4+/GUP/XKNr/7R9yn/qjrpy6/ZinyU/8p4Fpd139dUZRLiqJ8uq7rb328l/eTtRRFuilnQcH+JCIrK7Z7LroqieFJUeEYKs3ezNDTCTP53jwpGDgGezPZxK51PTZbNpomH2LX1FYzbEOXcYVsAPUqq6soayxdJcpK+UBSs+ZYHAax8KpqifMYLRN6NhiGyTyIOY9CrmtWYJHz071BzU5LYZEJ+dzRYBrI6Oz2prvqtp1vMrau4poqZ36+EvUGqYh9z8cve9OEvBTA46WeuLqyokVSVCyu9pnOE0xNQntfu9RDUWAU5Nxcd3g0kU7f/ZMlg5aFratc6Bo8dQwurXmcLRP2T31s0yIMM/azEk1T+E4ijJ531Dkt1yAvKuZBRlLUvHG7z1sPZqwPPFxHx9ZVLFNnd8PizYdnTGfPCouNtsEsllO3q8tDT9M05nFBhcJr2y3uTuXkqypSfGVVxf3DOTtrHkFUUNqi14gyObFeWm+tctiqumazYzH2M77z/ilr3gWGns7bhz5XmuIwL+vVQ/9sEbPVdwV1EeXYusr1oc3ZIuHNw5CeozdiYBnvmbqMbNMmQNizDQxNYd3TCVKHomwKIE0cUOeutjSXrpJrSaC3Y2pMFomMkAJxE+alxNAMPZ2vPRSyf5iISDzNKy6teSv6e1qUopeKCs7GEceHMxzP4dKlLjs7HRQFlsuUTzy3vgoWzouKOC2wTI3rTTzIOSjRMgQLoSiikbMNreFMScdx0oy0RrOYk5OAVkuKN8vQBE173s1SFGpNxpo9W8NxjEZ4XK70VCDFmtG850HGxLMwY61r4zTmjfMcuvPcwKjhcMn9KJkvUyxDBUPFsw1CR8TLiqJwcW3Ii9eG7I0k0Fkci+IyizN5L1d13YQx17iuyXNbLTZ7Dr/59hl+kHL7Ul/0U44hRpFc3IB1XdNrPjvCHxMm3QdPZ9jNCDZuQoBPXYOjSUTS4AJMU6esKq4OHRZxzoUX1jlepMyClJkfo5ti788L0Rj2uw4H0wiVmpZjMui6FEXJPEhxbZ3jeURdQ79jc2ngEDQHJZD7d84NS4uSqgGeJklBqyliHVPH1CQmqe0YeLbeHFxZoVqquuZoGkkeqiayibKSA2Vd1xITVFRcWPOekdG186JZCqx2Q6fPc+kEth0D29Lx/QTNFtxNGOWomoLnGliWLkyqoiQuxNgTxKF02DyT8Twhywr6ben+7m63ODkLiKMU17NWz2BFUaAq0XSdeZCjaRqbwxae+aPnUBkfUVD9GMyGH+v6YUd+/2fEhPZLwF8HfODvAp/6mK7rJ3KpjeMpalAFdQ1744B+yxKgp6YwDnNalsZmS19V/PfOIq6vOXQsKa7eO4lxdK0BPIrA+0LLEVu7LmG4WgMJlc1RRl2Gpqx4PlEmXbKyqunbJioQZhWfu9rl3ZOIo2nErqOx23dQapWqrKCsiWAVXOzHJaOqpm3pqIqMN+ahbFYv7EgeXpJXbHcMzoICS1fZn6fkRcVG26QCXEXidBTg6SThYtdsEuTlfs3iAgWFx+OIk1nEq1cG9K4POJynjJYJrU2PNU9nFOTM40Z83nP45RfW+cajOR+MEnGMFSUdW8PQHGaBROI4jtG4pxQ8R2d76PL4cMHPvrSFpiq8d7DgvSMfDZ2Xrq2RZTF3nkwZ9ltoRtMJvLrGnr1kvIwZtjW6LQfTqojTnA8eTXj58oCDswmqqrLWdpkVFbc2W7z1dIJhyKguSSqKSlhUlwcmGTq31h2WSYmtq2y1DfKq5s5RRlFUnCwS9kYhi0XK//M33wcFtra7PDnx+cJLm3z9/oR392o2uja7A4+9SdgU2DV3j5e8fyiFT9gk01e1OPvqWuKJWrYhyIOmq9K2xGJu6RK1cb6hOZbOxJeuxsX1FgAfHMzZ6DqYTQfmnb0ZG12HIMn5zK0+e/OM3353xAu7XR6eBgRJzgsXekLPb1ACALMg42QUUJY1n35xk87rO8yjnCtDm76j89vvjri62WbYMjlZJKJjClK8NY+WbVDVNftnPp5romsKsyau5xyomyAQzXkgWXtxVjKdSRbcy8+vo6kK40VC2hRqhKLVOe9ALaKcpBn7zAIZZ271XaKmU5wXFcO2vRobKYqC1Wy8VS2b4bnTr+ea+EnOZk8QD2VZkWQlrmtwY0sKwyARF+yFnQ49T8ZcjqXTdU3SouRkHHK8P+GFX7xFnBUcHi75fhMZ1O3azGYxp8uMvbEUYIYhdv2OYxIkOU+Liteu9OnaGn9wf8rpPF51tj99fcD+LGF/FPAXPrHNNCr4zuMZL+20uDB0ubHh8t6hz/UNj6ISGGmYZBzMUvI853gcU1WQdA0u9B3aRs3WRps4L3l0FhCmGZfXO6vnXVpUeI7Bixe6zKOcs4WEeJ/5mQQYqwqX1jwMTSjqbz+asFgkZEnGree3qKqatAlYj9KCcVpweUMOJYsw44Om83ceHePZMua9PHSpgNNlymkjwAdWneG0KInTkotrEnPVdk1MXcWxdOEJ5hVFUbLwK/6lVzf4T7/0iCjKGFwZAOfSANHfjacR3a4NKHzu1jpJlvHoLARVR1NgfdAAaRu3s2OLMcAwVCyjpqxV2o4pJoswZ72n0WmZxFnJZJHwf38w/mPevX7w+giTH3/CGlQ/dEH1mbquP6EoyvcB6rqeKYpifozX9RO5bEMlb7LJRFNz3vmRk0ZV08zwa4Kswq5gHBbMwoypZ5AUlXRmVIVjP6NrGszTnEVSkFcV266DZ0j4a5JX7Pkhuy2XvKowVJWWqaOpwhkq65owL4iKgryqWSQFQ1fHNSTL7FNXe5z6BRcGFmFace8s4srQYaelM41KDE1ZuRHDrOTJOBI9R8tiEWVEeUWUV4Sp5BaCFEhpQ4ROCnlr6k1Q9O1tOVU9morrT1dtXt1qM4pS3jmOGqq0WNxtwyFvyPDfeDil15JW+V4mozJDk/tcAx8cLVmEGc9f7DEOc5ZRznM7or16s6iwbV20No7B73/tIbpu8KunC7Z3+pRFzizLePvOCRtbHXYuDlFVDcMA19R4ehYwmQXkhcKNS11mQcLpNKDX9QiiDGr4/TsntB2d9b5DVlZkUc2lvrOKoAF5WIpwXiHMFXQdTE3BM1XGYcGlvsnRUgTYh5OIe/tzptOYcBmi6RqGaZA0AbfHi5Sln+I4Bjc3W3xw4rPesT8UkCtUZlVV6CDusryoUA0ZkRSlsJF0TYCqrqWTlzL6nUdZkzFWNwG/5erPGKq48LJGNB5nJZomHZNFJGML19BpWQVt22BvEhEkOcO2dKyiTK7D+hDraTwKaXckKLrv6LQtjSir6Nngx8IeK6uamxsuX3swpd9E6zimxqNjv8nPE1REWUlgsG1qmKZY36O0oNcSwbWhq2iaQl0rXN9sMw1FI5QVVWOZF7u6q6lC6I5zOo7RCN/LFXfo3FnasnWqqmazJ8VkXoroP87K5v0p3a+yquXz2nTT3GZzXoRiQDicRo2AvmA0jvjUC5vomhQKi1A6iqqq8NzlPkeHc/ZOffTmHuq6imlqPHe5z3sPJ9w9mFMUFZ+8tQ7AveMlmz2Hk1HE9a0OrqHimRo7fVfC1pGu9HrLICtr9kcB39v3KcuKKCm4NHT4xsMpQVqy0zWYRRnjZUZZy7hTVxUsx+LChjDsjsZL7p3ChaHLB0c+B6dLHEvns88NUFD43bfOWOs59NoGcZJhNjwxTVWeIQvqegW2DRo0x3QaMz6ZY3s2G32HZTPivLrZxtRU7h0vJQ+1ue91Xa8K0r4rTtj9ccgoyFbB43Fa4Fg6btMxPXcLW7rG6TwWEXsTam3qko3Ycg2CqCZLC/6Pv3kfXVfZ2e3JgbaoZJLg6BRVRacJ9nYtnbsnAVkunxlVlcJEVVXUqqLkmdZga70tCIkK6rpEaTqcRSH6LteSmJ92E51z5+PZxv5L10+jZ56tXFEUDZEIoCjKOtKx+un6Y1x/7U/f4q/9zj2SQizAli4nXM/SV8ydc7jfKMh5ftPhpm3xxgWPx9OEMKvYWTNxTZX3TyNe2WiTFhX3RwlvHgTcWHe5veaSlCV785Q3trvM0oykFI6Sn2nYusYiy1cohLyqWaaF8Iw0hcNlxqWBZHbpKtw7SzhZxHiWzv2zkAcjBbOB3a17OlFeMfIzyqrm+oaLpirs9myeTmMu9oV4/mgswuEwEfaPY+qcLVPysmKz65CWNWlZM/R0vnJvzi89v8Y3Hy94fxRypW9x0owlbu90+PrdER88mWIYGr1G+Fs1XZJfen5I19b5O989ZhrY/KVXN7k3ifjukwXPbzr8wYM5szAjK8RCf3YWEAYZiqrwG9/6CkTN+GR7h0uXh7iOw3RacfW5XSxLCNxnR3NmkwDbMdF0lYu7HRzbJMkrbMvEcQz2DmZ8cH/E5obL5kDs20EQ0mpJVt/dkwAV5Q99wDZ7DqpS88JOi9Omm+caAj3dm2VUCHX80kaLMBFbd7AIGG4NUTWV0ckC3dA53JvitBwMQ+WbhspimXJwGtDyTHGs2Tr/8usX+c13RuRFtXKZlY2rb7gt2IyTeSyW+4HLNx9OCKOcazsdbEPjyfGSLCuxbSkUNVX4YnvjELPJLNs5/7mTnONxiGXp/I1vHq66YcOOxe7QY+CZ+KkQ3C1dZW8ccthkOP6VX3kORYGv3h0z6jl4ls7eOGCz55CmBVle8lvf2OP29SHbPZdFnDGai/br0YMROxd6PL/bJUiFtVQ1B5jzDXfbeUafHrQsdgcuT84C3j9c4Nk6jqXTcQyipKAAbIOVbsVojuPnsTSXtzvsnQV0WqLv6XpCZM+Liq2uTV7WPDr1m/e/hl1rzbi7xDZEyyS4AyGcDzsW+yc+Z7OI43GIpknB++Vv7+O6xko3pipSXH3iUpfxS9u8+f0DDMtgY7ONaWpc2GjRdgyeu9Jn4qforsKDkyVrHRs/SAmawm3sJ8yjjGHboqgqBi1LkCK2zj9484QgyLh5sceFns13n8wwdJV//P6EvBAq/d604uaWaLmypGARFfQ9DdeqeTyJ0A2NyxseCgbLKGWnazGe6zx/oYNtGBwsM165MeTy0EFXFSZdG01VOFnEDFoWG22TvHGXBknBmw/GFEXF6Cwk9EOclsP2TgdVUUQPWNaczmPWOjamrvLGxRZ5VfNwLALyaZCy03cYBxlxVvDJq32O5in3Dhfc3OnQsg38JOfq0OHEl5H+lY02aVZy/2iBa+vsDjwen/m8+3jK1e0Onq1zeLykLOuGaSXvN10TRMJsLu5JzzPIspL1nk2robgvggy7YZCd66OURqcJzzScuqYQJjI6VBr9haIohHHOxTWP/XHI0SggDJ/JEH5U6yOxCT/yq/h41w9bUP2HwN8DNhRF+V8Dfwn49z+2q/oJXirPOlNd1yBrTtG2ruIYKn1HxMhPJwkdS6NnmYS5OJzMBlxo6wphUvB4EaIqCtfXbMKsZH+e0rJUnkxThp7Ok2XILBKIpmOo3By4BHmx+vfjQqjsfccgzlPqWpyIl/sm90cJYVqwiDLWOjZXBzYfnIZiz9Y1WrZOUtS8u7/AMUWvNXClwPJT6ULFuZCuzzUrUSMu7Tjy4C2a06KhqUyCjJOlImT2toWqwNNZwoNRhKVrvLLb5nJP3C2/+fWnaJrKp64N+MaDCbNlQqdlNlmIQne/PHSIipKttknLMbhzErOIMhxT7PnTWYxhaBjNtRPHYLmASjQaE/oXODkOyNOE3lqL0cmMLJXxY5Zl9AceXstis+eyDFMms4TZPKHXsfjgvX1ankXLtamqCtPQqRSTZZQSpSUbLR2aB3+aiZvTMHTWWjZpWdO1NdqmTlnXWLrG8SLHbMwEqoLwblQZw87OZhimgaqplEWJ7drEQUzlWDx5Ol+FvJ5vnJFjkBX16rRuGRpBklM0KAFF4VlAqyH5iUVZY1kCKpyHWaPbk9Hfy03MzIOzaJWpOFnEvHSxR16KNqWu4RNXB/ze28ekaUm/73Brs8UsLojzEs/UGvI4TBbCqup0LPKqJkxLep65eu10TZyHJycB13a6RFFOXlSrSJY8LzkaBVRVhW0bPBmFKA3ry9BUiqpa5eBFaUFR1quMTZCxnloJDdy1dH7uRp/DhcfX749F89R0k7OipOsJHiHPRbulKKKrOddUmZrKIs7wkxwFViJ00VJWTUCysgJW1jXMgxTPMZjPE167uc7pPGY0k3t4aafD23fOxFFbNtmXuoJlqBwsUja6Nrdf3KbtGGz2HD7Yn3NwFnA2jyXfrkGMBEnB/kjI50VZc/FiD1VRePvhmMlS3HFLNWcZaXiWzmSZNCT4nHliMF8k3LrU4627p7RaNrquUlUV3380Z3vocn3D5nCe8drFNmfLlLoKuL3p8sLFLl+/PxWJwsBmnlY8mWfsLzOur4mjtjHA0nKMRmNYr1hd40DCow9PA6bTCFVVSZMU27W5cnUgGX2lOEwTypWz1zY0Hk5StjuSQXl/JOHrRdV0zJtcwqISKv+y6eAuo4z3jnzSXMaH14cWs6jk+/cyWq6BZcr9yTI5oAV+QtrExlRVhWHoGLqkPqiays5Wm6f7C8qypuOZXNmR+/7oaP5ful9oCs0YvNGKZRKaHSYFk7k8wxRFzDp3ns5YLlOWi4Q8y/+Yd64fvD4Sm/AnrKL6YV1+f1NRlO8iwnQF+It1Xb//sV7ZT+j6n//pW/y7/+B9DE3iTIIMkrym52hYupw6LF2h4+gYqvCCogJaloTJDh2TWZJLmGxa0XM0/FSI6pau8vVHUuBUtYSLHixSbF3F1AyWWU5e1Qxsk7yqUJAPa1ZVK15R39WwtMau3rhjLvWlwOk4QgzuN/Efh7N4JUhe79i8cxSw07VZxBKTMg6y1YZxHgWSLBOJKmlYT2M/xU9UzEZEf86KMXSVRZgRZ5I9N48LLF06Ua4rLK7b6w7zuMc37pxQlDX3Rwm3Nmx6rslOx2QS5XRtEdnOgpSirHl+tyUjioHL3iik3bZYLhPwutidDqqmksQJx0c+s+9/B+oE33Shv0Nva8Du5XUO9qYEy5hux+J0HlGX4PspnqXy2eeGfP7FdUZ+znffP2NX1ajyEt3UWUTykDv1C25si8bMM3X85vtRXpEU8lokhWibDFWlZYkDz9AUlk2IahBkeB2PLMmwXIv1zS5pWtDt2oxGIf2+w3QqYbGdjsV63+W9uyPiOOcfvTciTAu2etKJrGvoNhbzc93UMsp57VKPo0UqjCfbWAmsb1/sc/9ogWloTKKcNx+M/zAtOhbdR1ZWGI154PrQ4nezkqqqJJNNU3CaqJu9ScTZPGaj5xDHhYwRi4r7J9KpWnGoEHG3ZWi4rsHpPOa1FzfZHbjsT8IVkHO5FMJ4r2UyaoCudV03NHn52QD2RwLWVFWFWSChy+fjoKqWyKcwE3eqa0tMzWSZ4vspes8WWGb1rOvVagKQ245Bksnrp6sq8zD9QwcJwTTUmLq2Gv+di6MVRSGIcuI4p+voGLrH9c0Wbz2d8dlrfUazGM+VTkZV1Q2aA46mET3PlGBmVZy2cRNN4/sFlqU3mh+NG9su7z2dcXWzvXoudWydYd/h0nqLe4cLNnoOHddktFzSb1u4ts7Bid/EEEHXUfnMCxv4Scm6Z3BpzYWq5PGiJKthuy+GmrNFxqBrE9fwvb0lQV5iaTq/d29GVdf0PZNBx6bSNO4fzJkncj83WxpuyxENWDPuzQsp4s9OlqSxPAu6ww43rw+a0Oi8SUyAySLh8mabOC0kPDzMMZr70rZ1Jr64FoVCXrE3TVaE/HPTYRhLxJJn6czCbAUdzhsnpaFKfFZVybP3aCwsMRD3oudqzVi86dwoYDbYBc/RVx2m889OkojbV8zBKpNpyNaaQ5BI2DfAsKWTlQq6rjEeh9i2ztrQZT6vmEwiXNfA9QyiH8OkTf8IBfpP1MhPUZTBh748A/7fH/61uq6nH9eF/SSv/92ff57/4J88YOgK5DLOG3dfVeOaKkVV4zX5ZmrzoVtzDY6WGVWdUVQ1Wx0DtwFkXuvbfPcwwNBk46nqmv1JyN4Yrqx7bLUN7p3FPJok6JrC67seqnIeISIMnqQQHUzX1gmyAs/U6Dk6likIBM+S/287BleHFgfzjPEyYaNr03FNtjsyuhkFmYhKbWPVMSrKmr1TH99PsW2DohCw3lrHxtBFmzILU3a6Justk997vGC3Z/PSlseDScxW2+Cd45hpFDNaJvzXP3+Rbz5e8OZxiJ/k/Ld+8Qp/+9tHPB0FZGXFo1Of/UnI7sDl0sCmqmo+e60nwbyGSsfWMDWVna5NhbhTHt45oNX1cFs2nmfQcjWOjdc42Btz/bldLlzdQFdgGRVcv7lOkRZsr7UwGlbTzatDTFMhRSNLRPnwxu0N0qpGNzT8MMVQFPZPlgzaOtBust1grSuFjWuonPkZYVZyc+hQ1TWnzQa25ul4psqbTzKyrOSzr23Ta11hGWWczETTkTWiWMfRcRydTsemaMabn77WYx6kjMfhivKsKM8QDds9GY1oqsKjU59ey+R0mbHdkTDvp6OAJC8pior9SUja6I++d29Eu2UxnkSMTpfsXOjx6Rc2ZRS8TFgEKVlW8htvj8iykquXe7Rsg6GnU1YFkzzHjyUM/GQasTZ02Rm4PDpeMgsybFOKjqyoeHziUxQl/+bPX+bqusfX744YpQVdz8TQVZ4cLrl1qYdr6cznCQ+ezBg04+s8r1Yd0qKs2Og6vPdgjKbJhpgkBZ94cZObO12WUSaas7rk3eNQUABdh42ORTJw+Y2vPH4mKlcVhgOHlm2s8tOqWsazfpJLQbVM6bQtkka7WJR1k0Mn3bW8rNgdeEyDlI4rBV+el7x/uGQ0jVgbONQ1vHPoszlw+Vde2+TIz/hbX5VO7ede2OQ790ccnPgYhsZpXjL1UwZ9h3/xlQ2+s+dzsW/z9fsTRtMI9UKXQdvCMzUu9ky+fHfKSFMYtu1GqyR5kQNH5zsfJPQvdLm20UJTVX725kCeQ1VNWVeUNczTimwU4dkmtiYJCAs/YxbUJHnFrU2PFzccvvJoiW3odGyVMqjZ6Vl0HYNpUlGUFettg6dnMnbPC5tlLvKCdU/iqQ4nId//1iNS32f98i7DtRZbGx6OqXE8ifAcHTQYLRJ21zwpzCsZMc8ROn674YcJkLXgz7ywxv2xYBE22ibfbLAhe+OgQUDISPtoKkadQdvi2qUeM18I6lM/5eJGi/eeTFesOZDCybQ+LEOWz7rRhLSPFyk7Sd5AiJ8BW/OixA8kLktVYRnmbHZtalVD00Cv4a03TxiuufT7DqapsVgKSsbzTOlGBxme96OXQJ8nfnx4/aRxqL6L6KYU4BIwa/6/B+wBVz/Oi/tJXi1LXVHRPVOn1Zxs5o2rQ1FExJaUJZ6h42cFPUc2F7WBaH714ZxWc3I2NZVlLK4tTRUxZ1HVxHnFwUJO5Frz+2ZRwW7HIlWqBrQpWYGmJmemeSxjGM+UWJCqkkiLWxuS5RbnFQ9PA9QGBHnOQKoaFovVnPp1TeX0zCdJ5YS8WCS0G4DhbJGgKgpbPYe6FlfOOT27KCsMTWcW56gKvHcSc3vDZsOz+J17UzY9k92+w3ceTum3TJZJ2YxaTEytyRyran75uQGLJCcpXDZbJstBiZ+WDD2dJK8ZBaL9yooK0zFwWzadjgUoJBlcf/4iZVny2qtXAHmtNvoKpkoTxlwTJwVdW6fv6eSFkOhlHFNRKSqaUrGMSrqexdt3x1R1zdks53peYugaYZpTVcL3aTsG2x2LMz9baXRURSHOS3qOvD9atoHfiFm7loahWizCDF1Tcbs6Dw7maJqKYWh0OhajUchikfBkkhBFOZ2OLZmMzQihKCssU6OoJAtN+EDyFJxHGZapMY8yHj+Z8Sufv0JRVjw9XPKpFzbx41wKlThvXtc2jmOw2ZZ8NLP5GXY3Wkz9FF1XcUyd6+se+/OMspKMP9fSWfgpUZRz7bkOCkKtVhSJirENjfk8oSgqXNfgP/v9p9y+2Ofh/RHdvstDRaEoylXH5vgsoG7YYFUlgdS9rsl4kchYqeGVJU1GXVGI5iUvK9oNnbxusiyzXBxhHddkmcioCKRAy/MKvclVq+oaU5GRIMi/nTSi/axxnGmq4EwMXcWPG92apUEmOX+WIcyq7YHLN+cx41nEYpFINpsq4ci2qZE2gNGyrBn0LWxDpe2Z1DU8d7HHvYM5aVpI7mUs2rQHZ6Hcn0o0SLe22/z+OyesD0T3GKYFtiHvwbW2xSIucA0Zte2f+gRJjm1ouKbKLEzRFIUrfZO3DjKCqqaqdaIsQVOaosHQMYqMrBbx9lO/wM9LUBRmcQVKzaNRSBTKuLnj6XRbFq9d7nHveMnpLMGzBXPxZJpy92jBnXcOST/4Nt7Ln8NxLfp9B01VOZ1G6Lqw6A4nIW6Ts3j+HDWbkeG5A/Nc31aUNW8dR0x90VMdL9OGxi8C8bzJQiwreS+IwUHj4WGMZekCUU3FLJOmJZ2WKSHODWOrKArKUkHTNKirD8nL5Tn5wZMp6z2rAf42CIS65spulwdPp1SVQo3KySKjZyu8fm3I/+O37lOV1YrGHwQlUZSJfMHQWCwSXNdA+yjL3ce8PkpD9RPFoarr+iqAoij/F+DX67r+h83XvwJ88eO/vJ/c9e/8zDUA/tab+3RNg45pUNY1A7vkNEopK3gwidntmpiqSpAV4rSZJyyTkrGfMl4m6JrDrU2Puychp/OY0+aEt7kh3ZM4LVjr2DiWdDiSXAKLH04TrvQtOpaOrpZ4hkaYl0R5gakreJaOn1RoqowFW01H482DgImfYDSBrM9vOjyZpYC4lSxdY+InFJUANE9HIbcu9yW2o6gYdiyJeVkm7B0umMxj2i2TzWb8tIwypkHKbtfi23s+hqZKiz4qWHdlpPG1PZ+erTFoi3A3yUuCICPtlyyaINuWbbBIcs6CgsejkI6l8cKGx4Zj8dZowUvrbV7ZbMnJ/yyg/xdf5x997cnq9ambUcrWhTUOxyG6pvALt4cr3EBVw+kyh7qm3zKpq4pb6za6YvOdo4BxIJb4o3HM4dGSLC24tNXi86/s8NajGfMgpapr4qzEtU3mYcbpNAK6FGXFVx4vVgDUoafzoDlFv3JRHIr3z0L2ZzHDlsXN7Q4DV0NBioUgyVEVBat5yC6XCe89mXLjYo8v3h7w1UcLpo2Q3zQ0xouEnmviWQJ5fX63w9FcbOMPjpcslglXr/Q5mkUYuohqB57BB/tzLu906Hli+//Z2+t0bI1pXHA8T0jyktsXe1wdOozDnC9NI9qOgWOqJLlsVLc2Wzw8C2l5Ji3P5GQWsX+45Pb1IY8PFliWxCqtDV3ef/8Uy+pwbafHnadTDNPgEy9sSthzXJCVFYuGjn52uqSqdExT58pmi/sHC+bzmDguSKKEdtdbwRh3d9vkecksSAUk2sQ8qYp0WEV3lPPoaMFkEmNZGlUlOibPkU7AOZ3cMXXirFht5DM/ZWPNRVUE3RDohcBuDU1s+w3vzDo/UIUlLUdE53Gc89/5F26iqwpfezhfBTR/5eGCoqz4lU9d4KTJVnz5Uo+3ns65MnS4se7y9sGSZZzzvacCHn3pQodPX+lS1fCVexPysiYMM167ucblvs3vvntKUVa0LI0PDoQrddhz+LmXt/n2/bHoK/2U7+37uLrKi7ttNAW++ILN20dhIxbPqYAizomzClWpWYY53whSBl1xmtZIJ2N36OKaOg+PlhxPIhZhwYuXhyiKwgsX+3z/yQRNU3iyv+TLjyZMjsewOOX2v/QX2N5sUdc186UQ1F3HoOuZHE0j3EYy4JiChbg09MiKirQQLecyEhG6oaks45xpkKIoMG0E+pqm8ujUb8bKJgfjENOQhIG8rLh/tJSEh54jEU/zhENLxzQ1vvjKFr/2jYMVViRNywYy+qy4aXsqUQJlWVIUcHAWNZFCCo6jEYU5fiCF3XkhDTCPa75xf4SqqQzXPObzmDDMcV0dTVOxLB3L0lcOT9cx/tj2qh92fVQN95PWoTpfn6rr+r93/kVd17+lKMr/6mO6pp+uDy1TE52UoaoUZUHLMDgoYyxdISnA1lTyqqZt6twdC0vm7YdjWi0LQ1fZH4XomsrxLML3U/xlSpFLAv36mstGVx7mmqLgJyWbbYOnDVhyq22w7ko2XtaEp+qq8LHURst1tCzIy0o6J0FOEOdYusYbV7rkZb0S1Md5Rd81MDWFtDB4++EExzG4sNVuxMQSIJtmwnKJohzXNcgbjYGmSjdivJTC4XCR8rkrHd49icmKir1pTlbUdBydMCu5dxqT5SWfeX5Tst1i6R4cjGVEs9FzmEYlhwspPN85VLjQNcGRE6GuqCiagqtrfOHykCd+SN++yq99/QDL0rFtDU1Tef/OEa9+6qaMxAwVEO7WLMqbB4iKY4KlWby80UdRFNZdm996MGUehgw8gydpiaaqfOGNi2iawude2OCdgyWqqqJT8GTvlFefv8jZQtg/izCj55qMwoKBIzwyPynoe8YqA67jmmy2DC73LfbmKQoK86TgQs9mHApD6rxoqprR1I0ND02BOJVOR5KXqI2QOi0qhi1xaNm6COYFjKjR3+6ga+oqmuV4HPLwLKDftvBsgbJ6lhDw/UaUq6lKM9IUHd97T2foDWRSU4QFdWVgMAqkazBo3s+LUOJ1LF3lxqUecSqFkmfp2I44FYdtG9PQhPvTUHDTomQWZCyCFE1T+cSruzx4Ihl/qqIQBBlhkGGYcjDIUjFn2LaEFZ+MJXz76rrH40PRh51rds5HhYahCQyy0YWVZUUQ5asxzjnewNBlfL+McixTI80ryrKgLCs01aDTsljGGR3HJCwKNvoyMozSgkkoWq6tocs8zJiEBZtto8FXyGdUUxRmcc7MNlZ0+qKSzevxJMY8z3g0NBEylyV705hXdltYmrx37h0vUVUxt2SlQCvDpBAuVJit9IyqIgDMtY7NyUxyELd69irUu65rkjTHT0qWUUpV1nRaJhoV06XQ+T95a41+y+TMz5hGOdTQtsSQ84krXVrPr63gpmVdM41KNFXjux+ccXqyZLJ/BIrC2vMvsbHurfAXhqERRRne0F1hA6K0oCgqdFW6kGlecnEoAep+klNVNU5zL4smm9A0VIkO0iW/0I9zokRer0JRGE8iHEen3bJwLDFQnMcQnRc8uxst3j4M8BydrBCndpqVlGVFg5sTHaCiYds1s1nWZD1Cy7MoSoWqqnA9c9XVyfMKu0myKquK00lOuAwxDI122+LWjTaLJobJ92W03mqZ8l/3R19QGR9RUX1U1+qf5/XDFlRjRVH+feA/R0aA/w1g8rFd1U/Xav3LL+8C8I/ePyHMC/bmKR+chlxbc4nyiq88XhIkOafzmAcPpwTzgN3La43LRTbDf/T+CbqpU+QFG1tdHtzZZ3o2Jb25u0q9f/XqAMdQSYqai32boqx5OktXjsCtlsmJn+OZGpstg1GYMwoLbm/YjAKdgavxzaf+CsR46hdEacHxUjQ+fdfAUKUIm4cZdpOFtztw2BtHnC0Srm216bkGQUNOl4gP4Q+dAx27rriMdE0ieq4NLe6NJDvtUl+6Aa6u8eWHc06zWLpTScGN7Q6aJo6n3Z5NnFdYusLQNfi5n7nIu6cRX7o349XdnItdmwfzgIFjNsJphWudFtuuw6cu9Mmrig/GEbO44OZf/gwHYx/bNnk6yXhxy0EBbA00RcXVhccVpxWjOMPVFd4fhex2LMYnPkENt55b5/hkwd/5vYeYpnTqkma0+9zVDsfHEXW1z86FNaqqZrpMVlqbL76wTlJU3Fx3uDFwefs0YB6XrHsSP7M3T8nLmlsbDsdBxic2u9yd+ezPMy72bfamsWhAjpd87+mMJxOr4VGJ66ssa/ptayU2btnSFXMa/k/PNUnykidngisY+wlfeGmLiz2TlqlxuBTYop9WfHAacTyLWe/a7HRt6MI3H0547cqAoqi4eqGLY+okRcVuVx74s0hGJgK8rNnstNkZuFiNnuv33z7mL372Ak8mCVtbbT55Y8i7+3PCKOel60IK95qN9OKaR1lV9BqS9HIes5zHHB4uURSFV1/aIi8rTkchvp9SFBVrDQjUtg1ajmzylxv7/eksXukGDU2l40rczHQWMxw4BGG26gTkhQTono+WTkchjqOT1qwKFOk4lEz8Z6NH2xCtpG2IKLsoKiaawlbfRVEU3j/ymQ+kuPKa7ssibl4rS2NvHHC2SLANjZ+7tcbjSYyla3zyZovffzBfXdveKORoGrH0U8qy5sbFHp//hSt8/cGML7295L/5c5f4vftzNrs2rqVzNI04nkYs45woyvngyZTLOx1+7nqPr9yfstY2qak5XUTM45I4yUmTkpev9gnjlO3tDseLiEVUcu80oBdaErMVJTw6idjo2exutPkn70+5tm7z0iWR82ootIyS+SLh/nsHoMDgwjbbuz2qSjI7y7JiPJXX743bGwB8671TyrKi1TLZWW9x1jgju57JIs45mgonT+j8hYz1YglYdlWd0Txi2BGdm21qBFG+KlDWhi5FWTGdxbiuIA/Gi2QVN+Ta+gp2XCsKuq4ynsTNuFnFMIRiryig6xpxnDSO0Io4hSBMJAfSNoFqpcNSFIiilDwvmIxjen2Xje0+SVKg6yp33j/DMHVc1yBNS0anSyZjlSIvODn+MXSoPqJ2+hNWT/3QBdW/BvwvEHRCDfx+872frh/R+jPPb/F/+9YT/FSCjO+dhlwYCJNlEWYcn/h0OhbttsX1S/Jw2T/xKYoCTddIwoQiLxidKmi6RrZc8Ph77zK/fIW1zQ6Pznw2ug6upXN9aElorgKLpJTIG13jUt9CVxTujmL2pnGTMWbSslTKCskJ01WuDS2eTFPSooQCLg0csrJeEauTxkasKArH84RpkNJvWVwZOpwG4qzZ7DlEacGsyQ8771Lpmsq1oc3jaYKpK8RZxVbbbLphEuq8N8uI0oLtgcvbDycslwnP31hbBdZqqhCUj5Y5dgMV3WobJHnFJCy41IVRmHOz32IUp9R5zcC2aJkSKwNwHKToKgRZRdZzBbpa1TycJHQslfWWTpQLZFCpwLMUHswD/ETcOEVRcvlyn2GScf8kxHFkk5f2/jMC1XDQ5k/9fJuqqvngyYyZrWPbBv2mKJhEBUfzhFd3WyvXZ67VK4heWdW0LY11R5hfhqoytE3UvsIHZzFbXRHlz+YxYVywDDKubLVXdO+ilGy2OJdNxtJVlpkgATRFkWIpzrEMjb4ndPuNttwnWxOHqaUrJHlFzzW4ue5QVNC1tVXk0Lqn020LhPMc3DmPSyaROLBubXpMo4JlLNmWhqowiaQbWlWC/Oh7YlOPc9FUtQYGsyDFbPRQax2bT15siV7oxBe4oW0ShzFlUWHZOoPmGvKi4tblPk9PpEvTcQ16LSniR76MW/xEhOEtp+kOFdXKaaVpygqeqqlKk6uoYTfuwKyoiOOcJBFt2XmeXlFUHO6PuHp9jTdurDENM2ZBykYD/yyKCq8R2JdlxXZPUAIPT5ermJ84K1cuxdsbDo9HIVM/5YsvrNO1NR5PpFv3znG8+lx1XJMaOBqHFM3PEGcFpqZSVgKd/NZTn8fHS4qyouOa/NmXN3nrwGfvLGBrzWPmpyxCMcRkFXz/IJAsxzIDpBj+3Iub7LQMtlp93j3xeX6rjaWr/PY7ZxxOYvotA9u2ubilcTaOuL5e8F/71I5ICfaXnC1TXtx20XWdk6MFWeCDYWFtiZZK0xQxHPgJ2+utlTzAj3NeuD7Ej3P2j5Z841tPSZOUX/nlF5iFKT3X5Mpmm8kyYRnlK72aY+qN41Lip+QzURPH8lrUtXSJFIVVIHvRdC0VRaHbsoizYtVxnYcygsyyYuX+LIqK5TKl5RnohnTAgqBYdbbkRYL5PGVtTUdtjEXnr9360OP4NOD1V3akc3/iozVZio5rsrHRIgwzrlzo8t7dMTs77dU17v2x7Ew//PooR9+fsHrqh8YmTIH/4cd8LT9dP2C5hspu1yQra/quwaARIquqwmdf3saPc958/0w0Ly2T5TLFNFW8ts1ismh0HSW6rkO7SxbHhH7I7PiUk4Mha1s9PvHCJmHbWImcPVM2vqQsaRs68zTnrAHZ9WydEz9n4Iq2Ji0ExFlUQnt3TJ2irNjtmgRpySzMgZIXdzs4pojf7x37K86LY6iEjfNp0LJWTKiscSWlTQzK4UI2+5NlTlLUvL7jcRZmJHnF40mK33R3skJs63FccO/xFMcxcBwDRRGnVlZI4TaOcpK84srA4miZEeYFp74wpTxdp6grzqKEgWWushFtQ6WiZpGUmJpC0hSgQSoba17V7M0S6hI816Cvq2yvWWy1DTQV7p7GHMxl/GVoiOg5lwedbZu4Rs2nXl4nSMUToqoiXi6KistbHbqexAFVdc3Ak4LyyTIizMQwoCqQlfWqYFlmOaam4eein5qEOWkhqAVNU3n5xtqK4aNrzwKKz0c6pq4RpyWWLjlmKlL2lWXNaJlweb0lwnlbp9NorcZxhq6KwN3UFa70ZZx6tMxW/KqzUchvNC6+y5vtVVyIEMLl9W5ZKn4qHRsJ31UYLRJOp1EDSITHDU9q7IuI/Y3LXb50Z4ShSc6dZ2o8maZ87+GEIEhZX/NotUxxvZYV7baF3YzmdocuA8/keBoRx7m4CQ2NshY2lalrnI1DyrIWwn1ertxiuqbQ69pEcY5j6ZhNHI9Sy8g6yYSWrmkS+SKvubqiliuKwnKZ8uDEJ0xyyrJe5SACXNts41k6B9OIKwMLU1c4nAp/C8CzdV7aWeMfvz/i6UyQFnkuxekozBvatkLajNLPQ5o/d63Hl5ICwxCGXJqX7M1SkqzEMDTeeTQhy0oOxyHFoIZ1OXyt92xe3O0Q5xVf+u4hj6cpZSXavzgrSXMYePCF17eaz0fJMi/YHTh87/GM40VK27NwTY2yqvDDmBcvDXjhQpesqPjHH8gg5IVtj7NlylsHAbtDjyiIIA2hrhgMBJ+wNnB4frfDtx/k7AxcykqE/7/84gZ/8GBK1zX44hevceLnfPP+eMW4y8uK8TJlZ+DiWOJOLis5uFZlzSKUXEJVlRzAPC9XbCnLkpDt8wKpqmpsWxdERV1TFBWpVjEfhxjN6FBVVUxTPu/nId2TaYymwXDooTUZkuf1R34uoFcVoGIyjlnOlrz2yStcGHoEkeRd+nGO4+j4fsrzVwYoyGj97sGCm9sdqhoub7R4cLwky585B39U6yPBnj9ki0pRlD8L/B8ADfi/1nX9v/2nfv0Xgb8PPG6+9at1Xf/1P/rV/tHWD8Im/LW6rv/aP+vv+f/nNY9z9iYRl4buj/tSfuD6V1+/xH/yzcerTfP+SEZadQ2PTn1Go5A8r/D9lNksxjBUZpMQgMHmQIjZhyM0W6PdbzMbgeM5FFmB//QB/pHN4ZNT3r6xTb8vp1/P0dFVFfv2kIfjhLEvBPNXLnTYaht8Zz9gbyqQz195cY23jiK+/WRJzzN5bsNhGhUcLjJ2OiaWqTFoGYzDHDvTaFsqL1zosObqzOOSx9OEV3ZbLBOJrslLOSU+OFkSZwWfuNLD1lXuHAcoCpwua6ZB2mAfpIX/6cttgqzke3s+bdtgc+iuWvO+nzabFito5ZMzn4cnS65vddBV6bj89p0JcVbyZFeyu3RFGEBd0yBMCzqWwbZn82QR8fOX+zw9nXFvUbA3DYnSEtNQeVKKNb6tK/z8c33qpsg0NTm9Xhu2mSfy2lDXqFXGyaxxZnUMPv/iNgfTkLpWUBRxX7363HrTCZN8stevr3FtYHG0zDlcZHiWhqbAMi0ZOOLKBOFXWXrJaZBJgWJqHC1zAVjWNWVZ8anLXT4401fBtlFaEMY56z2HQUuI2+fFSl1LzIyla/hJzu7Qo6prFo2g931NYRpKAPZrOx6LpMRQFXbbNrMkw9JV7hz5xFnJxrrHN75yj86gw2vXhgDMo4ILPQtLV7m5ZnO0zBi6Ehh8tEhXcR6KAmt9lw9OI6K04PZuj2Us8SyuodLzTCxDuEKGpnA4F7v8zRc32J8ltGydi0OPRZxzMA7ZGwfS8TJ1CT1WJM9x4ids9eX5kOYykvvU85sESc633j7B84zV+EZRFGxbxO5649wLkwrNlPDqs2YU3+nYOLZBp6WyDFKm05hbN4b0hx7TJk8vinLW1lzevz/hlefX+cStdW5vOKRFzeOzgEVS8vKmR5KX6E3gta6pVDW8cKHH73z/CNPUubHb5Urf5O9+94Rey6RtG9imxhsXWvzWe2PiecHQNQhiAVa+drnHnSOfo1nEL724wZffH/Evvr7Ng1HMg+Mljqnxq988IAhSbl4Z8N6hdPKuX+rx5pMZfpDycy9t8fJum//izhmfu9ZdaapcU+Pvv3nCL9wcsNH32Oi7HJ5F3OzrhGXFtxfwzYdTeo7GcztdtGb0+fAsAqSA/93ff0jkR1jr21y5uc360MUPM+68P+Lp/oJeT2j+QZyz1XfYn8sB7HAScX3DY93T+czNNQ6mMdt9KbzWu6IBOzjxuXW5L7R/Q1s5H29e7PH01KfXtZvRnhTyf/q1bd478tnqOrz5aEJZViv94XyZkOcVaeMeDAJxmrZaJoYhHDhxgioUBWRZzWgUEvopqlbjtWyKokJr2HfjcUiRF6RRyqc/e41rWx3SrOTSRovTeUxZ1VzcbJM2KQTbXYt5UvLy1QGuofL65R5nQcZmz+Er39r/kexZH15/qOvWrB/G5dektPyfgF8GDoBvK4ry63Vd/9PpOV+p6/rP/TFc6h95/aAO1b+lKMryv+LXFeBfBf7aH9sV/YhXWlbsz/75KKhAIJ9DV5LLE0PjoCmY5nMRkq+tuYRhttJPRX6Epmt0+y5RlNMddon8CEVVqMoKVVOpkhCcDgDZcsHT++BvDej3HcJQTs5/91uHtD2TtY5Nyxbui6LAkzN/Nf6J8hLXlO5Gy5INSVMV3jtc8uBM5caGx27X5GEFT8chrqXz3KbYsvdnMVeGLoam0HP0phOV0LI0Lq+3eDoSq/vxMmOjI0n357DDOydyD/w45zSQbtNomTAPM5aN9qf9ITHnk7MArREvH08jfubWGn4qXYbzk/vl9RaHywzHUCnKmo2WgZ9JtmHfMWkZsmGqisLt3TWO01OmQc6gZWJUJX5WcjoKqWu483TBrU2Fz750jTQqaDVatatDk8eTDMfSmCw16jrn1VsDzpYysvEaanVZgq6BbRksooxFmBGGGfePF6RFidMEFZu6yk7fZBwWjKOCiz0TFYV5XJI1BoGjZY6uFAyaXMZFKrFDcVYJm6hlCSQ0K1cQyHOHWl1DVpQ4pk4e57hNplnHMVYC9qtrLmEmnaqttoFraCSFgClPw5TnBx2m8UwgnIbKVr/NHxQlqqbimDI6hnM7u0JFLcHWSYlnqgw8k1mYkRYl22veyuYPMAvTxkVX8u5JtBrvqop08lRFYbtnC/+sqMgLwYFkDdXd0s+ddBnzMFuNAM/Hdlkhom9T17jYszhYyIg2CGouX+qx0bXZOwsE39BAOlVVMvzOcSHnm+vF7Q5l834zG52LqigCdtRUBgOHMMyYNQyxk0m0igC6te7IZ28S83gcYRui4/JjyT6cxYJQcRwD19bpOgZmE61zPsbSVZWvPlqQ5TIi/GY8IY5zbFvn0SjiaBSwMXB5OklI04Iwqziei7FF32ozm4kGaBakhGGGaWp87vYG1ZrHH7x3wpW+RZRX7AxcRmHJZkdDRbrYVQXfezLl+o6EAr9+pcVxWDOOEkxDgzLFsR32pjF2425E1SAtWSxTpnfewb1yk95aj17PoarhylaHvaczhkMXTVP5/r0RW+stauDtJ1NevtzHbxAq+7OE1y+0OJonBIkcLIZtcTpfvdCVzETX5B/+O5/jC//736csK/bOAqH/NwcKULjYwHc/d63HmwdSjJumTpQUgleIi8akoK2MH1VVE4Z540SUcV+eV6tkAc6DsQsI/GfxMMuZz+Ub22iazWKuc3GtRd6I2+dRhmVoq26PZ+loipiH0iY7c6stZPmDSUjHMdneafPkj2lP+mGX/lEdqh/uj34aeFDX9SMARVH+P8BfgB95HOEPXD+ooPpPgPYP8Xv+uV2eofHUj3CfzHn9Su/HfTk/cJ2TsucNbdoxxTWS5yVJUjAYuOw/nWBaJpZtMNjsEy4jFjMponoDD8uxmJ3NVtoq3WtTlRVV7INmoBs6J9/6GtNLtzFtE8uxcDwb81J3NX57chaQZiWfuD7EM1XCrOLd44ggFtDcPMqpapgEKaoqkFB50Isz6NZmiyivOPUL/CTnld02tqHgGhqjIGe7YxJ2Kk58ibSYLRK+8WCCY2rc2GqTVzUbbQPbEJxC39H5+uMFX703YXfgEkYZf+a1bfKy5svvnXE6CiV2ZG/MF37xFmlWcnPdkRDbhgL/3uGS9a7NG5fadC2dv/XdY3b6Lq/sehwtcx5NU7q2hqVpPJyHfHAW82CScGNoM/JTXEvn5mabtbbOyM+YbHVYLGLefjTlLDQJswJNgYFZU6oqmSpY1iDIOD6LUBQVra74c69u8uDEp+dZJHmNZSj4aUa3oYDfvTvis29c4KXdNo/GMVcHduOmVHj7MORsIfqYk4UgKHquScuWyKJHZyG2ofEz17o8maUYqoJfynvq5270+NL7E+F8NY67vBCaeVnVFGXFMmrGRs24SFUVhi2Tt57OuLLRpudIAXVtYFFW8LWnSzq2zitbLklZ8a3jGVH+jJ4vOqgK25GxsKZIpEfoiWg2yWUkZagKbUvYZwNXo2210FWFB+OEt57O8CyduoZPXWqRFM3oN5YxQ1oIENe1dI7nCbNIW4URPx5HOKbwpPqeyaBt8fxOm+89maFrKhtda0ViL6p6Nbb4/v6CW5stdnc7QsI2hHDtOobo0aKMli3d3a4n+quDU59u12bYsdnuOQRpwcE4xDJUtjZbjKYRZVmj6Rp7ewv6fZuyrPE8g2HX5v0HUvQsX98lKyquDB3uHPn8+ZfXmcYFZ0HBW09n7AOWobEzcHEtnUenPo9OfdTGdefHOaNlgh/nmLrK1a02p/OYF68O0DUVS1d56eqQO0+nxFnJJ26s83t3ztgeugwHDnujgDde3ORgHPKvf3aXv/f9U8I459FZsCoans5T1j2DK0OXq0OL33jrmK2ex/E8Ek1QUnM4T0jTlP6lLt95MCYva9K04NqOHO7OD0DnbkHXUrnzq38Xtq7z6htX2OjKgejdd464fnODX/zcFb719jEv314nb0LWw0S0au/tz5k2MUfDocvBOCSKc7aag3SSl3QcA8fUOV3EnM5i/me/fZdex+Lp/oI4znnxxhrv3BvR7ztc3mxjGhpv7i1Wn4PzcaCiKBRNsPr5yFVRFNpti6iJrYnTAt/PAIXPv7LN194+bmZ8TcxQVeEvQ7yOR5qkXLq+zcXdLq6lM+kLsiRpDjpxVtCyDbwmOHura3Gha/K1h3N0TeVwEuHZgjLRFIW9U3+llftRrv+Kkd8XFUX59z707f+4ruv/+ENf7wIfbqkdAJ/5iH/ic4qivAUcAf/juq7f+2e+6P8f1w/iUP0vf1QX8uNaqqKgKwq/tzf556KgUpvYg7yqOZpFxFlBVkjmXZrkFE1em+saqwDMi1eGzOfiFFlb8xiPQ5KWg+VYUlCZOnVVU9gmeZbT6rW48MVfYny2RDd0JicTVG3AZBKJQLkBHm4Ppbs0DnOcJoX9YBLyqStd7o9rnowCrqy36Dgms1BEq2HLYB7lvH6hhaEp3BsllJbOjYGLo2l843DORjMW7NoaSV5xMJaojfW+w2bPYW/yrPvgpyUvbzmoikLbMVhvW9xYszmZx+TlufCzZDqNcByD7rDLds/l7tGC7z5d8MJOm7vNOKHrmVJAqAqjMKfnmlwa2Awdg2UifC9VgW8eLmhbGmVVM/Yzkrxm0mzea22dvKwZh0JD73QcsZSP/BUp+Diqudo3SPwEBei0bcoSFKXi1m6feVyiGiY31x3RcVkqi8TEUBUWcY7tmLiWCL+zXLpPiqKwTOQ0ajSjn8NJyHye0O3a3NiWTWqn74orb5ayP4mpaumStE2Bw1a1wC/tJgNsq++Q5RXxuZBWlbHXuXPuHAEQRHmje6oY+xmqIh2dIBGkgKlpxEXFk2lKWcsY5XjWdCCSAMNQ8eNcIIZIR8k1JNrlSgOnlDy1miSvaVsyUjUbEfsnr/Y5XmbMY4Gces1o93zMsGy6Brqm8Pymy7eeLAAR7c/CjI5rYjehudfXbB6NRPRflBWUz4C0VSX/5iLM+PajKZqm8tq1fjOWLkVPVYg2xo9zNroSohynBY5j0HFNBi2Ls6aDCgJhFUJ7jePI6PDs1Kffc4jinEFH0gauXe7zznsnvHNvxGdf3ubWmsP+LMHPSuZxyTTMmc4F3LjdlxSApJDnRF5UbPUd8uY1yQopilVV4ZMX2/zaOGQeSvhxhUQ7Fc1z5XLf5Gnb4rmtFk8mGo6pcX1osz+SLNBz+Ot5Fy3LSr7zcErbkaidvjskLRWenAUrLeDFDTmnW5ZFEOf81Z+5yCzK+f7jCV9795hXn1tns+syystVrNCT42ZErhkMOxaLKOPkJGCw1sIw5P1u2zqjeSIh3A2WwPNM0VSVMn5Lkpx2y8KydJ4cLnnx+rCBdcLhNCLJSsIw48tvHWNZGv2+g23r7PQdJtsd/o3P7PDbdybceTwVcKeu0u3aGI2oPM/L1Qg4jqXbrKo0HSyNJCl4/mKPB7rKbJ7wuRs9bmy1qMqSu6cBb94dMxn5zd+Vo+s66w0OwjE1Lq638OMcXVOZhwL5dUwxJFSN+7doNF2OKRFLH+zP6bct1jq2MPLG4R//5vQD1keK0uVbX/owlukj1kfNBet/6uvvAZfrug4URfkXgF8Dbv6RLvSfYf2wLr8/sUtVFG722vyd793n73xtj6/+e1/4cV/Sf+XaapskecUiLlYnON8X94hlG7iOwfq6R5oWtFomtm1w61If9YrCaBFj6hoLI2HnQp9Wy2Q8DskyEVWGS7EOp3HK4PpQLME9h+nlPllWsrXmCQw0KdgZuFzsOyRFxTzM6K65tEwN29A4WmYrN9xmS+fpPKPvWeRlxTQqycuKni1taU1V2OkY9CyD750sKCu41vW4M/HpWBqaonDUtjhu8gd3exZni4QoLXg8qhkvBUCqqWL//pWbQ9KqYnfgcufIp+MYdNsWvY4AW07OAspaxl+zMGPN06lqm3XPwDNV/vH9OV++N2PYsSVTy1RxdfmYnEsAWqbK9b7DJCoY+xlHs4hfefH/y95/B9man/l92OfN4eTQufve2zdPHswM8mITN2JTkdql6aUtlVklmTYpqWSXVVYVJfsPuWzLlEqiKJmiTFFLkVRxueSKS2wgsIuwwCIMZjB55ubQfTuefM6bo/943j6DxYKL4QaAC/JX1YU7jdv3vH3C+3t+z/P9fr59Xj3wJeRWUSgpiZKMsizY2Wiwd7Tg868f8rGnN5ev5WbD5uZpTMeVUOOeI+62KC14bM3B0TVqllpR6uX5/Oq9gEbD5LVbA+ZBwtHQ53zPJayI3WcFRNMxOJ2GzOfSIbTPiVB9tW5w6sHdEw9T1+jWJUbjxiBkGmZV94klI6pt6+yHIX41qgIZKRQl1ajEIs1FhzXxY2F+RcL3SfKCZ3ea3Dj2efNExqxRmi91PnLzLyCW9+Ci2nyarklewGbTZH+aUJQCezz7mkU5pi7sNFOT0UbdUpkHCbcqmnmvpmNXaIesEAF8VmXinRVbYZxT6CWaJi48yaYseP0wQK/+3awol3fzs06VZWjEWU6Syil/7MUE1djRqHRUAFEs7/UzIboXSQ7i6UzYaVGc0WvaXFipczwN6TUtLF1j6hiMxyG9hkWvYaGp70ZGnb/QRddV9oYe460aAC8+mNN2TfaHUpjqmspu36FmquSB5MqddQcG8wjPE0mA6xpsdWsUlFWhJUWxa+lYhkqrKZ3PEy8lzQvePlzw6GRBs2FxuS+d4dceLQijjLprkOcFQSBjw/lcuii7aw0+9dZAxO26ynbTpPiG/fHqRotBBatcbbs0GjavvnPCT37souAyqs7g2bjUarUBGI5lHHrxXIcozenWLdK0oN+yhTEVZ9i2zo89tcYn3zzFNDQsU8MPJGFhre2wqMwuB6feMiLpmSt9hrbOPEjZXW9wOA64sFrH1lV2+jV+/e0RaeXUPCuShsOAdtsmigS3YFUwzziWQr4oSuYVaNQ0hWHWqByirxxIzuSHLjQJMtjvRximzsHeCAWFq4+t41hiFMiq0b2mKgzmEVlecHGtQcPWOZyERElOnJf4ibgxzw5CZcWna1ej5a2ey0v/wrvPH259U1L6e/P5PQJ2vu6/t5Eu1HKVZTn/uj//mqIo/62iKP2yLId/sKv9g61/5QsqXVPo1EwurDb44hfvcvEv/WPu/Td/5jt9Wf/cVZSldBeGPjVbJ0pyJpOIKIhZWWvgWjqxndNsWKy2bHoNmyDOuPloCsC5VYtm0+YjV/vsT0LiOCMMMxxHZ2enhe8n3Lt1zJ3bAz76/nNLYONWr8bByMcxNZ692KNuqsR5Sb8m+oz9UYBWWbeNagNY77i8cxJwOgt5/kIH1zDYnyX06hZ3RhEdV8M1VFbrBnemHneGET92tVttICx1Mz/31BpXVlxeezTn9olPkuX87PMb+EnO/jTh9iAUS/TpgpW6iWOqXFlx8FoWkyDjh59YQVcVBl5GvNPCT8RFFkYZqiJFjGuo2IZc/8cutXB1jUVFTL4x9DFUheurDm3bwNUltqcoS1qVnu21QylGX9rzeOFcnUejgCwrlhvt2qpLnIgG54u3Ruy8fxND19homXzxzSM+eFHjyu4F9mcJSZqz4pq4us52S7afM6DqTz27RlnCP/rqIV995YALuz28RGCfEz9Zhkx7UUYQSPjtyfGcw80mDcfgxqGIis0q4mTqJwzHIV9Mcs6t1DE0lfEsZFbZ/o/mMUGcsdZ2mPrJ8pRp6CqLacrVjQaHkxBFUdhou4yrEe/pLKLftGnaGustm7uDgCurNXY6NtttkwenC3bXGrxxf0z93CWiMOZ0LJ3Cs3GWpauYusLxIhVtnqFyZxAxDRKeXHd4+ZHHa3eGXNpqMQ1z7h7MaDVtfujxFQlXNnVOZ3JtK02bZy80+JXXTvnq3hxTU1FtGb3EWUmnZrHVtmk7Gl99MCOprOVhknFprc7L98aCLXAMWq5J2zUxDY2Hp15Fy5aRX8s1sQ0ZKTZdk+E8YqPjMphHy03wLOfv+k6by30RTSeViDpQMxZ+ytpajYZjCPh05DOrwIwvXF9FURS++PoRn3jthK1ejf2Bj9/IeOZ8h5fvjUmzgs+8PaBZwRuf2GqSFiUv3xvz/MUuKzWDz9wY8vS5NvMo4xOvnRLHGRfW2nhRRt3WefX2sBpRJdw4mGMbGvf2p/zIC9s8GPj80pf2+cn3b/HJ147ZXqlzccXln35pj7XVOpc3mrx881RMLZZOnBb8rz+4xRfuzTiYhtiGRtMBTdNQgE/fGjINZXz1zs0Bs3mEZsCXbwwAaNgaLVPl7pv3cZ/+KM++cIGpl3B5py0OvVnET71vXbh0Ky4rdYPXDzzSaqz166+fiA6souSHlfheU1VW+y6PKt2bHyR0Ow6dmskPXunwX/76bRQa/MTTq9wbxby6N2W97fDkVoO9ccSgZRMEKdcvdPEjcaoOphKO3GzaDIcC/DyLmtE0FcfWOT7xeDjwsA2NOCu48WiGrskB5lzH4oefWpWD59ObjBYRcVYwmkestp0qGcLgaBou8wZrlk7H1ckKh7opoOejecLEj7m6VmdfEaL/0ztN2o5OY93l3ij6Nu1Y767fp0P1rdZXgSuKouwCB4hu++d/97+jrAMnZVmWiqJ8AJFnfdtZmd/+QJ9/CVdJyXbLYPvCCvXWv9zi9CQruXsiLqCWI0yaKIxJEzkZ+pGczAxdZavr8ObehLvHAi58/5U+miaajpqlMpxHS0hco26JVsVQsRyLJEp4cCRFf15KbMQZ4yYrCqKsYLCIK12JbLCaKhb8sDq5r9UNYdPkJUlesojzJal8o2mQF7DVklHWq4cBYy/mK/tzToKIWZQTJPI4fpoxCTPCrxsxTcOMRZwzjzJevT2Q+JdKVAzwfef67LRN1psGXlwQZ7KJnf2+P/zEKo2ayefvTImzgofThLeOpfg713AlU01TsTWVnZZF29FZcS2ahsGv3x7z6XtTDFWh62pcWXGWGW2OqfFrrx4z9yLmXkxZCqqiVrPo9ut89p0Rpq5i6hoDP8WLCy6e6/PU1YuoCtRMlY2WJb9zLNyb9Zq9jB5adSVnLI4zcQ5WotozXdOZu2keSuxFmhY0mjY37404HAfV3ynxo5S6rbPbd1nv11hvO3zsUmsJh/T9lHbNxDHFaWlqalXUyXgxSWUsEqQFs0BO5CDGgN2VGusdCW/244J7p7K5Df2MB6OQVw+CakwN9+8O8QYD4iDmlc+/yd1bJ3hBytBPmYQZUVqIbsox6FrmUu9m66rARTWVp7YauFVHpV0TGGuSSdixdMQUDieClEjzgjgtuNB3WW3axFXRKxus5DGahoy1DE0lSsWhmGUFScVQSysBsVaNmSWDUKJ98rJcFldngvZFmOJUDKp+w+Kpcx3RvFSC67ajoasqmqpWmYUSLjyYRwxmkYwCbYN63eLzrxxURaKMK/t1k37L5upGgwcDXz6HFasoiDN5rZOCaSCmhb6rMwkyvCBFU2Bv4DMcBTTqJk1HCsVpkNBu2XQqqvbOSp1u3RIcgK3hWOIGXcQ5jq1j6CrjQMjhfpBwMPJpNCySJOPNvcly7NV2zWVEy1/84AV+4FKL4+Gckf913c8qtDdL3mUtLaKc1+7PIc9otBt4XkK7blYdm4KVts0syskLliHuSVYI6iPK8DzJgYwioaQ7jkEQpCyChFnl/H3iYo+1rouqKszDlM/fn7FYJIwWMbcH4m7O8oK7R3PKEnZ7Dte22/S6DkVRstpyJCO0KsRl7Ccdu7iiszfrJoNhQFmWfPBSjzgreHKnvWRaHU9EpqApgg7xopRpICO99Y7LdnV9dhV6r1WmGEMTfeFm02BW3RN1VRWt6zCg7Zp8z9Uel3sOrqFiqAqbze9MOPI3fr0XbEJZlhnwl4F/BrwD/GJZlm8pivIXFUU5GxX+LPBmpaH6a8CfK8+AXd/G9Z46VIqiXAX+v8BaWZZPKoryNPDTZVn+p9/i52wEAmpVj/VLZVn+3xRFeQb4G0AdeAD8+a9v2X3dz39T9oSiKD+HOAsfAz5QluVL1fcN4P8HPFc93t8py/L/+ftdY1mybN3/3MfOk+Yll/7dXyYKIg7+1r987NJffeMURYGFn3AnymjVTC5fWWF3rYFfgTC/96l1Npsm75wEXNlsstE0udp32JvGDL2EVs3keJ4KldrOefpSrxIu6iLEXW8yGvqcHHuMRiHttk1ZQhCmgiuYhjimXt0cS6ZRzoWewzTMOZqG2LrKWwdzDF1l4sV0GxZJVnBasV68Kn5k4KV0HJ1JmPG9F5r89gMYBSkNU6fr6pXwXuVX3xnyA1c6AOyNAtbaDp+5MWI8C0kSYcL84LObfHSnxd48RFMhzHI2aw47dZe9RcCrh9JWn4epiJ4j0ZNEac5HN1yOFymXezZ+kvMPXj/moxdb+ElOo66z26rxaCFC77jIWYQpl1dctlomdUPjxE+omSq6ovDcVpMbe2PiWN5TeS4tftdUWbEKzvVbDBYJR56E/57pwV4/8Li66nKxK4iCvamgAjbqFq6uE6QxD6cCOX11b8b18x22VuvLEV/N1qEqDMpSuk6Oo9Nq2WiayngcMJ6E/PQHt7k/DHk48BjMI9YbwrR6druOqapSRFT/TqdmkWYFl1ddRn6GZWiMvZimodG0TB670KEsS5Is5/xqnZW6TpSK62q1aWNpCmlRLt1nF7omR3OFvVFAzRIMwuhLvwmmg2e5MD1m7o3gcRmLqgp0XSGn359EXOzYpNX44t44QlVgd6PJ8SKV9/40FKRDKCkCs1DcT+8/3+SzN8f8/S/ss7lSYx4mPBwptGrCoarZBm3X5M2DBVGS833XejQslf1pwkv3YxaxbJL1Cqp5llsphZvoyboNi1v7UxlbdV0UlGXX4ox4bugqhq6S5hJN8sbelFeLEqMajzccg92NJk9s1Lk3irh1MMOxRAtkGkKLv/bsFr/9tUd84KkNdFXh7okgRA4mIRdWaoz9lJajY2oKD0fyfBxNpYOrKAqfeOWY+TyS2KTDBS/stnnL0jgZh9w9mXN04tGqYK9HVdzOK7cHy0Dtv/eZe1y72CPPC45nMfNFzHQW0WraWJbOYBBgWToff2adT78z4MOXe9wZBPzGWxL8/dz5Ni/em/B3Xz8CIEHDLkvKUiHPC04Hou3RquekLEuKomB0MmHlwjYffG4LP8pQFYU4z3l0vOCDT6yT5CWmpvA7bx3zhVK4boOBjMc3V+s4lo5rCRfP0jUeDrwqm08K6HtHcyxTo1UzuXeyIE0LyQp8NJOx4mqdvVOPta7Da/szarbBlVWXTs3g1tGCt++NBObZslnpOIznccWcEtPJ1laToxOPxSLmfU+ukRalIDuClH7Llsc9mvNbb5zQa8n99vxKnawoeXiyoFdJFnY6NkYFMJXPlYTP3xvFeFFa5RKKhqvbsHAtnYs9GcsfzmO+tjfn+692mEbvFrHfrvWHAXtWOcK/9g3f+xtf9+e/Dvz1P8z1/VGs9zry+++B/wvw3wGUZfm6oih/H/h9CyogBn6wEooZwBcURfl14L9GVPifUxTlL1T/9n/89T/4LdgTbwJ/5ux6vm79HGCVZfmUoigu8LaiKP9zWZYP/nkXWFZW3igred96g0EUoygKwf699/C0fGfWGWlc0+T0u7NSR1Mliy3tu6w3DA5mCXle4pgaV3pOFWxcYOkquz3RP2RFyQ891iMvJVdsFiS0XJO9JCeJEpqdOmklCrUNjUlR4gcpdVtm/wAHUwk6DuOs0taUDLwEoxLw7q41CJOc9YZEynRdnYYlROtJmOHFObauUDdEK+GFKW+d+oyCjEs9i7qp0bClO3Ghk/PMhjCP/tpv3MF1TR4/3+X1u0N2WiaWrnIwS9hoGZyGEW1LXC1FWfKhcw0MTeVTN8c0HaNyeUn47DsnIbahLrOmatXv1rZ1nlptSfizoXMSREtYXtsR5lNelkvOkqEpBFFMmv3ug1FZlhyNIv70C5eYhhmuqWNpKotMROQbq43qJKowCjLZwBFK/VodBmFMlOcMvAwvSonTgg9drHNnGGJqKhM/oVu3uLpW49E0ZqPt4Jgqv/W1Q4KKmp2mObqu8vmbQ6I4JwxF6D8PUtKsWOb65UWxpE47poYXZ2iqdF6SCsaZpAJibTomuirPh2vpWJp0yCZ+gqYqvP9cg45t8IVbCsN5hKG18GKBVDrVhgDA+mXq7TqBdpGyKAVyWkW1jIOMtCixNBU/zQkTwR18+s1T2nWT5y60+dqDKVlREgRCHu84On4impyarS9hq+fXGzQcgzgr2B9Il1dRFPJCkAB+JM7U40WKn6jMI+mA3DtZEEWix5EIHCjygrTK6qu7Bl6UkaYFhqEu0RKqKu7BtmsSZzm6KvqqSUXMPntvdGomjqXzaOijKCZpVWR1Gxbtmsmoem0urdXJCuninM7CZdByyzXRVIVZmLGowr+7rs5G2ybOSuJMOmtnxG9NE5zDRlcwJZ2axft2Wgx9MQVsdlyKsmQRpYRxzr0KR3AGyb23P8WydN5/voEXpowXMRdW6yiKCM4f22mLCy0RtMp6y+KtRYSC6N9KSh6cLpa6qvEiYaVloilQlgWWo2Pb0h1TFAiCBMM0OH+hSxgLsb/bsMhy6ewM5hFeJIYCy9IxDckG7VYCbNfSmfgJfpRxUtHgHUdne60ueYq6SpZLmPvpTGQQrmOgaTKqi9Ocjqvz/U+scrIQ8OfJNKTlCJdu71B6AYYhyJizaKy9hxPsCibcb9lLDdh6y+HOiUeS5nQbFt2KqK6qylLfZplyeNE1hSSR183SFPISerZ0Tzs1kyDOmIYy2jaqVIDBNMKxdS6tNzjXtohS0Q4Og4zVlsON04h7J4s/6m3pW64/DNjzT8p6rwWVW5bli98A4fqWJW7VcvOq/zSqrxK4hnSuAD6FtPL+42/48X8ue6Isy3eq7/2ehwRqiqLogAMkwO/H0QIE8ti0NVqWgZdldPt1Rt7kW/3Yd2TpqgKGtnQ3TWYRfmSiuSZRVrLZNJZ6hMfWXWxdJcqlaMgKuLLisNtxGAQJUWZI3tu4cgP2a2y1xD0TRZmMFarTzpkouSzlBm3p6nLMtNY0KUo4nIQSx5AXnO/XeDj0WW1azCMpEspSRn+nXkqvptO0dR5MYna7Fn6WLUWxx4tUbvTVmKdmhcySlONFFT2iqTx+sUfLMXjfdo2Dsc/NQcSFlsvxXLIHs6IgSHMMVTQFJRK5kRUlV1fFnTht2xzOYhxT5XgWM+zIGOGHLnfISolp0VQFP85pmgY3xguJVDE0zjUdwjzn7ZOAhqWy07IZ+Bk3DsbUbIWZd/Z6Qb9pUXMtglSiVtKiZOCnpLkIgLNKCLyIc0a+hNHqmkqQFnjxAkWBXk02z7EXU7N1LF2h5ehkecloEWHoKi1bsvdytSTJxGU0GgWkSUatbqFpKoNhwFNX+vhRysGpR5YXdBuCVwDI8pJm0yKOM954OGGj63I4S6r/r7J0g+TbGeKQbVVBzBe7LnVLdHFvnYR4Sc7DSYxjasz8BLcahQVxRs3Wl+NXYp8ib6EbOs1us+p+6hIDU5TynFdjJU1VmAYJNUeE8Yam0G1IbNFgFLDacpiEMupquybdusEkzCRCpShZb1ooCtw7nLE/8CTkuChxzJJew+ZkGjL2E4JEw9IF3hjEGWaVaxhnOVqiEMY5XhVBIkHH0u2wbdE91aouVphkS1H8aBHRqVtVd07BtTSmXkzdFo5XXggrax7lVdC0QdMRCOfES5gEwv6yLJ3RJKRRtzArEXqWl5xMZVP1ohTX1JiHKee6NotYIU5NBnPRzexsNIQCXsh4bLtt0XU1DE3B1tt0XJ29Scyz23VuDyIe7E8xTU2uM8qYz2P06v5j6iqOpVGzdNbaDvOFRL18dR4txeReLGLqfsPkZCaF1dyLl/oZVVV49e0htZqJqulc3O3x4hdvv+vjUsCtS0FXlDJVmFdj0d3tFkEswmvX0uVQVgVod+tCD19EKZOpdJj7XYcwzomilIeHc/K8ZGO1TsMx5LXKCnRdI4rzKuxa5Wef3+DBOOZC18LQ5HP3hbdOeOnWgIubTfTq8KjrKn6YcmG1zqs3TtENjZNHQ559/y6OqaPrGh96ss/LtwdYVVdzp+PQtDW+cn9aBXpLxz2KMs716+Sl0Phbjsg04kyipAxdRUWMNQfjAKfiYw2mEVEkXa8P7NTl+VUU3j4NyXKRthwvUuLvADbhX0fPvLuGiqJconqLK4rys8DRe/nBqtP0MnAZ+G/KsvyKoihvAj+NoOJ/jt+t4D9b75U98fXrl5Ci6whwgf+gis35xmv6d4B/B2B9c4e0KOk4Om8M5hzOE9ZW64xf+B467//Ly5+ZfPU73k2k8/6/zPYP/STr63Xiyob9/LVVPnKuwf4s5mLXqWzKOcfzFD8peO3AY61p4Rgqz2zUURWFW8OAhq0SZQV3RhHDhTBzikLa5k9st3BMjcE0QtdVgiDh0cGMdlv+/dkiZuwKKNMxNRqWxtDPuLxa46X7Y873axxOZMzwYOAzD1PeDFLOrdbp1Aw2mia3TgN+/pkN3hjMefM4oGknPLFRZ+BnPL3hcuqlvHLokWQlYZrz2TtTxl7MKw+nvO98m5NpSJzm/MOX5jy+3cY1VP7+K4dsdhzSvOTYS7jaNTgOIiaBdHfKUpyAX30wBeTPj2+3uNSzeOvRjM/dkkR6V5dOW0HJLJSoljATOn3PNjkcD3h7YOFXkRB1SyOvOhtHQUlWqNRrsuGMJyEfuNzjpQczRn5GlJW89WjG7lpjqb1wDZWRJ3qkvAJQtiyN7bZZYQJkQwb4yKUO75wEfPWh5LepikK3bjGqNsuyLDk8DZZaHE1TOX+lLw67WCz9Dceg5Uge4A9eaXPqp/z6G6fYhkaWF/jVhhTHGQdDHz9K+chlCc++d+qJZVsVMOpKXeP9F1qs1U0mUYJtqDi6xg/sdjgOIrYaFj98sc/ewucr+x4NS/RJj0685fva7KzQXW1TVqOaoixZbxgEaUHdFOZalBUkmRT21zdbbDYNPnNjxKfeOOVHnlpjGmV02jYfu9zmt29PuLpe52SR0LJFp5QXJY9OFpzrSQfXNHV+7KlVTryMm4dzPrDbxk+k6xTEGTNfqO+6pnCuX6fp6Lx8d0QY5ygI7FQcXLKRz/wEXVeXurNFKNq2ui3AzSwvqdsGfpTRrplcWmuw1TJ5q6L+T/2ERcUy2x8FldNOrv0srHrmJxyOxfzx5IUu+0Ofuq0vH0/TRJPiRxkPE4niWYQpl1ZreJE8b5977XA5Pnrr4YRX7wha4Mee36qE/CqnXsrxLBTQalny8Q+fw6oK/F7DYrfv8Nl3Bvzilw9wqsL4eBqyd7KohPzCqus1bR7NYu6fLLi80eSZDZfDeYKp1fnkGye4thSGZVkShwnb2y3yvODe/cnvMcVfvraGbemie5pFDIclF8+18aKUtbYj/KhpyKXNJoN5xNxLeDT0abgmw3HI+U2hihu6Sss1cawG947m1Kq8xZNRQLNhSRGoKTi2jKSLouSfvn7Khy52OK5Am0fTiB96ZoPX9mbUHYMPPb7G6TzCMjTyitX2Pc9u8sb9MdvbLboNi/2BR901uHssz9Gl9SYPBx5Hs5hZJK+hooCx1WKz63Jjf0rT0TmZyeda06S7B3BrEBEnOWNPdF33TzyunGuzf+oxnYZomkgtTE3l1E9xDZVbRwuKsuStRzkbHQf9myUV/zGvf92henf9JeBvAtcVRTlA8nL+N+/lB8uyzIFnFUVpA7+sKMqTwF8A/pqiKP8J8CtIJ+kb13thT3zj+gCQA5tAB/i8oii/edbl+rpr+pvV78O1J58t06LgzeOAkZfw2HqNhScib+3KC+T3XoM8pfP+v8x/9tf/z/zbH9x9L7/2H8/q7xBHyXK08fi5NltNk7qps9EsCbMcV9domwaNns6RF1O3da6vOgy8FFfXqBk6eSkEb1tXudp3mHcsjuYpQVow8DM2mwbzyGY4k9OOYWiYpk6zbpJksuE+PFnQbdqkuc6guvHbhkK/aXMyi3h4POfauc4yKyvLhV5+bb22pJLPExFllyXMwow0LzmZRbg7DdpOwamXEqY5aVbws0+t8WAmYL7fvjMhjDJ+9MkVTr2MVx5MqhO9yUpNZ7Uu7qZhGFd5ZiL8ffHBbDnaqtk6vabNLMwIUxkNupbc2IZRjK0JLiFKC9KiYJGm6IoIkRdhyr1hSL2y58+jnCTPCJKc9U6dg6FPp+EwnPgURckXb59gGjY3j8WhuNqySbNi6QZTqiic/VFI0zVwquczK4Qnc9aJnYcpmuqKUNww2Wwa2LrKa4c+z51r8mAcM5hHmBWh27YNNE06jFGUEVW6ibf3Jri2joLClx5qDOYRuiado/lCyM2d9pk4XTovh3Oxmgt/SpxHaTVyMTSFhqkzDFJMTaFu6DRNg1mS0rUtdFWhZRk8seYQpgX7hoZpaty7OwTTQdVU4ijFskXgrSoKB3PRpc1CwTAY+rusnac2a+xPE+GxpTmv7M/wo4wgTLkzjARJkBWMvZi6JbEzuqbS77h87d6YlbYtBUrVCdJUhYNZwiJKl6yrsxBjXVOJs5w019Ar0nmvadGpmyhdl7sHsyXPqTxDQZzR1SsgqvxZqdhe0mHJS8lR9CP5/ZIsp9m0mPsJeVFUWivpIkSJfF40TcwAeV6w1jAYzLXla6CpCklS0LAFj+FFcjhIspyTeSJmhVhQK/vHi2qcJSiCJMn5zJsnOLbOU+c6nMxkw75zLEWvpinsrtTw4ozjaciFnkPdNhiEKZOpuBaDMCVNC5pNi82Oy/7xgtWWTcfRObWFR3U4TxgHOS9s2xwfL9B1lSTJsW0dyzGJIvk3kiiRu//X3e0bNeF3DeYRiWNwdDhH11SStFjmEYKAjpuOCK4btoFpSEagqsjrOp/HbPVcTqYhK22H3ZUaUVpItmdltHBdA7NiStmWxmgW8eL9KRtdFxV4NPSZ+Qk128DUxH2qKgp1W8a2a20HL8q4tNmkWzPZGwUkSc7VrRYHo4Cd1QZFBc2dheLwbLsmx7OQlZbNSt2gdbVPkBZ06zISNHWVsScdStdUl53IvBCm21t3hrRaNlGU024brLYd2pYcyOK8WGI+iuoA4FVSgG/nOuPwff36Jt/6E73eazjyPYRmWgPUsiz/hQewZVlOFUX5LPBjZVn+VeBHYCl4/4lv8iPfkj3xTdbPA79RlmUKnCqK8jvAC8DvK4hqmHIqXEQpn/7SQ5otm0tX1zg+9jjKnoTJIUxP+A//8n/Ofwjc/vR/Qb/x7XNJ/Mzf/ApxknP1Iy/QbFo0aiZPn2tzoWPy7EobTVNoGAbvjBa0LYNpnGKoCk/0G1xsu8sb/f1ZQMPUWKnpbDccdluwSFNWaxZNS+dokTANM/anUt+ud2Xz3h/61Oum3Nizgl7HIcvFzWTqqkR3KArjIKfpmnIir1xWz2w3efX2gI+/sMU4yPGTgrGfEiU5v/DiAQA/+741bg1FzH5tpc2rRx5hWjANRGzcb9p85v6E41nEua7DxIupuwaGpjAJUolaqAoCU1cxK+6OV41RDmcxQSrX/+NPr3J7IJToJzdqNCyNL96f0XRNfvRal6ZpsDcPubRa5/XBjAfzAENVyAuq8ZNGpy7k8ac2XF7a97jYs6lpCmsNg/WGQZT2+cobR5UeCbZ6bRQFOjWjgmmyDNsFlmMlXVMZe7GgDxxDXFSmuHJuHnuiOdEUoiTnyXWX690Gx0FIv1Zp5sqShmPw9HaTR7OYd/anzGYRe3szLl3s0GnanI58zq/KKKDtmuyPfMIk5+pGU/QgqrIUd/db9pLyHKX5u9EuVYGzCCUYu2VbeElGw9JYdSwu9eooQJznTOKUhiFdv+vdBkd+iGvprHddFGWF4f0d1rb7aJpCt+uy2a9haCpZXtCwhJnTtGW8N6+6PrcHEcezkI8/tcJG3ea//fxDkjTn3/yec7xy4HMyDqqxtMbNwzmDccC18x0eX69xbxThmhrDWcQ7hwvyoqTXsIjSnLjacM54Vde327y9N+F0Ju+XlmuSWTqWrnFpxWUe5TzUVU7GArR0HAPL0AirjMSFn/Dkha6MkioOVtM1mQfyWh2MhNIepaK5emKrSZyX3DleYGgyUgfY6TncOZZOVq8pxXheyHvodCYYgm7d4mgaoioK7brFTs/l4TCg4Rh4USoIBkVB11XOrQte5f7xgucu97nUt/m110+Ik5zbR3N6TZu1tkOcSSTKw5MFj200mBQlNVvnnSNhiv3AU+v86lf2+fBTG8Rpzlv3x+R5yZt7E7ZW6xi6ysWezSIu6Nd0cdMOPNEG6Sq9novnJYyGPv0ViRFaXa2xcrXP/t6Ae/fHXL62Tq0KaveilCBMaTctLKvLO3eGbG+1mFVQ2ayQAtQxNSzDpumYHIx9ilLqs1l1mHr5rRNqNZPhwONgq0WjbtJwDJxqFJvnoiOsV1rLRs1kMA0ZL2L29qbouoplaZzfbvOxy23ujwUtcjoLMStJQF6U9Oom0yDl6e0mG22HjabJ/ZMFpzPpXF7danE8CaQItDWatkwQaqZ0A9Nciu5pUKU51A3GXspgLlq5hmNI0Z6XWJVjtCzFtPHEZoNP3Bwy8xPGXsx4GtFsWIRhymt3R3wHDHDvFZHwJ3p9q3Dk/9M/5/sAlGX5X3yLn18B0qqYcoAfAv7fiqKslmV5qiiKCvwVxPH3jetbsie+ydoDflBRlL+LjPw+BPyXv98PFCUc+xE/eK3LZ25NePyx1aXQVMS5Ko9uJhDMIZE5/C++8Yj/40cufotL+cOvp/6j36DbdWi35ES9vlanUzM513f5wd0OgyDG1FXm1Q18t+2ySKSToigKXpoRZTkF4Oga1ztNhlFMkEb0bIt5kqLnsmFHSIeplmtygi5KLq/WmEc5ddvgYBZhVDP+ta5LEGfLlvhgJu3uluMCKrOi5OlLfT5wvsHf+swD7Ookd7Gn8+qjBX6ccWW1zvE85tHI584oYuBnXF+1cQ2Nrqtz4ySg7Rp8z6UWpqry6dsTojTn3iAgL0rO9V0ejBPeuDfi2rkOKw2T+wOf33x7yPnVOusNg6dW6xTdkpfuTyoKt9C3p37C45t17gwjdtrCH9pqCY6gZujUTBnXDf1MgKF5iVnhIAxV4eJqna2WSd8x8WNhWd0bx5xvm5i6Kp24Sz10VeXO/pQnN2u8cxKwqEjmaa7ScqQrZpsCuTR1jbWWzcGkok2nBS3XoF7Z7XdXaiKQzUrmYfq7yOEg1xgmOXVLZ7NpEGUF6x3ZsGxbI0kLHFPn0nabKMnJy5KP7LbwoozNjuhtmtX4I61ErOPFWc6YjL/WW45s2tWxMs0LkrwkLwV+GaQ5mS3X17AN1hsONUOy45qKgWtpWJrKSj1Y8qrMZodeFWDdqL17SNE1lSQrmVURSy1HrPrbPQkDrlk6q65FyxTuk2NqtCydfs3guct9NpomLz2YMqjYVgB+Ijq+Xk1nd63BLBDzhGVoMsJLc2q2zu5KjUfjkLYttPgozqpNWr6COOPeMGQeJFim/J3TUw9dFzG9Y+l4kRwq/EpEbRua6OKqf+tkGtJr2JzvOSR5yet7Uzqu6OgMXf5ekhXUTFtifkpByBu6gEqP5zHX1mvkhUSQKIqCocnhpojlfS4brkJRSicgq3APay2H57drDGYR602DhiWf+bPX9bmdOkcz0TmeeKmECvdssrxE11QcQxU9l6ni+xINtV7do7IsJ45znj7f4c7xglcPxLXXcTV2GnVuHC24fzSXTlRlrrEdg7KEZ6+sEMQZnZpJEjforrYA2W/8MGFto0Way/u46VZQ4ip+Jc0lIklRwLF0jicBUz/BMXXK8l3t2WwWsV5lEY6ORywmC1a3ekRhSqvtYBhCiFfaDrojmY/oKp6XLA0eEjWjLjMeHUMet1OXQG9Dk5GwY6hEhkbbkc5kXpYynl/ERGlOv2bQr7WYRhlrDYNXH3msNyUVoChhP4y42HeYhRm9mk5eyLhxOI/Y7tfYaFrcOvHY6rkUJbxTOQ3LEq71pSAeLWL8UGjuw1FAs2kxHPp0Os4fy571+61vzqH67qqyvlWH6izH7xrwfmQ8B/BTvCsq//3WBvALlY5KRfgRn1AU5d9XFOUvVX/nHwN/G0BRlE0Ej/DxsiwzRVHO2BMa8D+cZfMoivKnEafgCvCriqK8WpbljyKuwL+NuAAV4G+XZfn673eBiyTjf37liH7TZqUpLo+tfo04zWmu1iWu4PwWA92ExZCV64/xP33mAet1kz/z9PZ7eAq+9fr0zVPSouCvfvIOevXh3Vyt89T1FSZeTKdmcvFCB9tQOZjFdB29GrWofOHRiEvtGm3LwNQMDFV917mY56w4NsdBWGW9qWSFUMofLHzyQjbDJC+41m1w4kfsKSFdV2NQsWFcU+V0rnFhq0XDMZbXs911uXeyICZfaqlAughelPLkZl30EqbGWtfhnWOP1aaNqcsGZBsq2x2bF841eOWRjBYejBMWbsH9UchPP7bCIIr54r05H95t0nblNKZrCv2GxbmOxZfuTmg3bZ7ZqrPZsHh2o8bBPME2FE4XGb96Y0helDxzvs2NgzlelPELv73HlZ02PVfn7iDg87cWrHdcoqxEU2NWauK2+8rhhPWGFDRJXmBqKqMw5fUjnyDOeGLNIc5lVHBvHGPrKl99OMPSNX7+hU1uDgPW6iIo/p27E2Z+wpM7bdo1k7gKb3Ysnd2ewyySSAnXUOk3bC72bOZRjm0o3B9GdGoG33Ne3IafujORDScruD+XzartiLB6py0Mtd+5P2fiJ1i6yrXzHRbVa+JFwhCbVvT0z9xS2ew4fGinwatHHklest2rMZlFy1FClhVLVEavbvHYRoPDgUeaySn8sGLnrDcMVhsGX3k0p237/Mxj69iGxthPyAp5j/3yzRMmQcob90YkSc5bX34LsoRXX8zorfdot0TbM1pEpHlBzZTfJy9K7g8EKvvkhssbh8LWef3E42guEScn05D/5Y1BpXtSMXWV45FPt21zeOxJd+HU44nznaVxAqjMFRlVXYqhyZhx6ifSxTzXwYtS9oc+avU+N3WVo7HQ1HsNm6kfk2UFs1mEben0GhaGplKviftOqw44RVEQxhkKUHcMmq6wqOyypF0zefHBlDDOcSwZbU69mMOBx4WNJoYuQEe/0mWZhrYsvuZhwos3Tnjmcl8cm550TC6t1tgbh4wWMZfXm8vxyr2TBYeTAFWBz74zZDqLlgTvLCuwNJWaJYeKzaZJu25y6qXcG/istyUdQQEeTYWTdvPhBH+tQZoWfOjxdb789jEfPtegZqp8+c6Iza5Lz2lwsIgqvV+MYai89uIdWr0WhmXw7/30NW6cRlhVx1vilEp0TZPJX1HyaOiz3nE4nUXs9Gp85JlNDsdBFZ9UYFaF6GAeEUQZ77/cYxpkPBp4DOcRvp/wsac32BuKvuzqx59mMI3IMinKTk58xicTzl9eE6df5eo8Ozw2GqIbjSLJnxzNQmZRzuE0xqg6401XhPdC+y+xDcFkCPU/5iOX2tw4DUmzgqN5TLdm0rZFWtCrm1xesTmep4wC0fFNayYXunI/ePNgwXgRo6kKOx0bSxdX7ZlZ5KPPbFIzVQ4nEb/4yjE121gW0UGQYVlCbj+33SL5DojSv1nt9N1VTr3HLD9FUT4JPHc26lMU5f8O/MNv9Y9Xxcz7vsn3/yuEL/WN3z8EPv51//172BPV938Z+OVv8n0PEbm/55XlkpjesA0eDjyJK6iKg6T6oK2tNxkdj6ifv8gPfXQXx9K5OxbYW79h/Ys83O9Zrz2cseJYeGnGsxd77I98dE2CXIszXVDTXm6aaw2xVTdNg9dO5jJndxLpFpUlWw2XOM3BhCQv0BSFnXqNcRzzyAvx04yObTKLUzRFQmc1RWERZygo1E15nJqpSgxKVmAZGo+v1zB1hTAV0Xeal2x0XVbrJveHclM7nEak1ahvHGYMFgkX1ht8eLfF3VHMyItJq1O7rgrJ2q70PmMvJkxywtQgK0pOw5iiLDkY+3xZgWmQkKQFuqbwoYsdbg8kvuP/8NHzzJOUEz+m75rYhiIdJV3EsWc28kWY0qqZjEYBx5OAz1SBoh+72ufLd8ckac5HL7W5M4x4YbvO0eLd+Jy7o5hzHZOyFAv1ua7DrUHEw1FA0zV5bquGramc75h4cUGQZgy8jEtdm/dt1/nHLx0twad5Ie6tsoSVusE4yJYgyjiXXL2jebKEPs7CBNMQLU7DNLB0lZaj4xqy0bi6xmrdIMpKHFNlt+VWkMMS29Ro2zqv7sVcWpcQ3/unC0xTwzQ0Lq/WeDgOuT3SyUuW2YebKzXmQcpg4C9HWVGcczAJRLieFfiBCK1XW8KGUhXZYFXgt94ecLHrcKXToGnrBEnOSRhxOI0YLSLaTZvxNITpCZQFhWET+RE7/RptVzaBMzJ8x9HFcalLtExZwixIsA3hntUtnR+90mUYJvzyK8ccn/rUagbDWUS/7fDUjnQ5zq/WeWd/ih+l7KcCmF1tO0KvNvVl0KyuSYfxZOijaQrnV+psd2zuf53NvOmadOpWRY5nWYwVFe4gzYtK4C8MrjDJuNC0MVRlefg4G/lOwoyRl5BW/CDX0llvOSyiFNvU8KOMNC+4tt6gaWt86q1T2jXzXa1OmLLZcZnMIuZVpMpWr8YiTDmcSSfk+maTKCuomaJd07V3Aa2qKl+bq3VOx0L7//zdKfMg5fxKnct9C9vQOF6kZEXJwUSK2bEnkS26rtJu2WS5jAcBTFPjl147Ic/lPagoCpMwYxrJqCoMU+I4p4w84tDm6mPrTMIMQxOI54uvH5JXz/X2egMUBdc1eX67hmGZ7HYtUDUudC1+zYs5GkqGn21axGlB3TZIUjExLMKUZt1kq1db8r4UReK6fvzpNT7lJTRdARBvbDTI84LR0Gd1TXoJpqHiRSVGpWssCvmz4xg0auaySE2zgo22ixelGLpKx9GJMjloTMOc822HmS2F+/VVh5uDiLjChsS5OJs3mnKPGfop81A6lGt1nVf2F4SJFFh5XtLrOpwsEuqWjqoorLSki/n4qsM0yriXCoD5DIlybrXO69OQXlecki3XZB5++zVUfwhS+p+Y9V5J6ef43cLxBLjwR34134EVpzm3bg25sT/BDxLWezXOd21ss6Id5yUrPRe37rK22WG9adG2NaZhvuQV/WGWVdmeNUVht2ctYz6EiVOy3a+x2jBYb5hc7jpc6tkczRNOAhmTnWW8DcO40jKJiDTMxI00S0Roa2sabw+8ZTEG8mZuGAabNZdRFBPnwhdydJWNhkXbkQ35ma266H4q+/oiLjhdyKafF5K0fudgRrdmcr7vkmYFL90d88adIT92vYelq9RMletrNcwqGuV0FhGlOZauMA9SHtsQXUcQZ/ypKx3ujSI+d3ta8XwkwT7NRaS51ZSw5fW2g2uI0P7GacT9ccTbxyFvH8mY5+KKy2bX5XQWLdPnPS9hPAk5nYjeIS9Lek2bSdVJmQQpLdOk7Qgr6tRP2Z+E3DiNGAdCJH981aVXk2y2lYaFowsjqW3rPL5S4/WjgLsnc+ZxhqYoZJmAGSe+3ODKEvo1g0VciGW/yoZLq4LqdB5zUhV0Z86Y0yDmNIi4umLzIxd7tCyDJC+I84K2ZVZcLIWaobPVMunVTXH5LJIlg2i3b5OmBbtrDTo1k0s9Cz9KmUaSSVgzVdquzqW1OmttR2zsqrB4xuOAo6HPg2MpLLKsCppNc8I4YxzK9fdqOtNZxG/envClgxG/9PYxf+XX3+GfvH6KH6Wsthx6DUvS7jUDghmKaWNYBm1XGE9xWjANEqFGqwotW3R/jqkTZyLmbdkaYZqTZAXTJGUR5/ihxIuUJbTrJpapsdUy+ciVnkQLmRqns4gHp95SU2Tp6lIsmxclsyBh4sWMKiHx6Sxc/v9FVYRYusq1VUe6jZmIol1XBPW2IZoo2xSi+5kIfbyI8aJs+XkxdZW2rbM3CjidRbiWTqdm0bDFBVuzDS6t1Li22WS8iFnEOUUpz7sXyRjx5qOpYAtMHdMUrldWdRbjNGc0jwjjnDCVQ87re1O5fkOih8yKveY4Bk9XhSfAg+MF47lE/DycJJxMQ670Lb7ncqfq1kiUyVkQ8/WtNr2mTZLkLKKUjX6NOMmZBQkXVhvYhsYsyokzCaIOgoQojNFqTbZ3V/m+x1eZhsKjc02NUpUwYQC1DPnAbovvu9phs+Oy1bTY7TmsmCzF+FlWLDEvZyy9NCsYLqRT167+29RFsP/4ZhPL0jnxMrb7NeKs4ANX+nzk+gp//keuYNkGtq0znUtmqFpx2cqyXJoPkiSnZhsMfAkUVxUFVRFHZpSIzKLn6iR5gWOojMME15D7YFRJB1YaFi1bQ6tYbvIal8QV+PXcSo0oE01UwzFI05xGzWC15fDq7SGHkxDL0HAMjZW6gaYKO69u6ywqB2CW5Tw4XmDbOrN5xGgs4/Yoyfl2L3mOfvfXe8zy+xOz3mtF8D8BLyqK8suI9+JPA3/nj+2qvo0rSXJCP+Sxcxf4nostwjRfAv0e22iw0rS5e7zg8vUNPvzYKldXbD53d7bk9vz5v/Myf+/ffP4P/PgKEKVys4zSku+/Ljlk98cRj605XO82mMYJji6jp9+stES/8vaAjZaFXlHBa4bGIs147XRKyzLw04xHs5iOq6OrCi8ezthpySZ76IU4uvx9Uyu4MZljVYWZrorm4nKzJp2rRLpJYZozDsRttFLTudi1OF6kfObNY1RVXWoK1hsGF9YaXOw73B+GpEXB8SJh6AsV/Sce6xNlOZ+/P+PpjRpfuj/jhfOSMQXw5qM5e9OYk3nC1E/o1E2e367z0j5sty2ur7iEWc7VtTpX+jZRVvDVgznPbAiM8NapbFppXmLponE4HQeYpsbd+xOeuNbnykaTr90b0XJ0juaJbHZBysNJwk7H5r/74kOyomQ8Dfnep9a5sury8oOZxJioCpYum2XDMXjnYEZeVCPWKqA4SsSZeOpl7I0j1roul9bq7A0DbFPjfNfixrF0Ihu2QcNSabsmlq5wfaPOwMs4GPusX2xxcUUCcJO8JCtLLndqtB2D+3OfvmMR5TmHXoSmKPQcgzeHc7bqNjVT4yt7C+4ezwmClH0F7h8v6Lds7h7N2ezV+MqeR5Tm7A0l3NnRNW4Og2VA7oWtJjNfzlFBmLLeqxEmGXvTEKNiSjmmzixI8OOMp7cb2BWT58W3jrlz5NJtWJxbqdNyDClYwpR37gvJpPPYk0xuvcPzH7nK8bHHySxiVkWCXNxocjAJxAnlJzIeXWvw2qMFWV5wMI2W4MnX7o/JMhk9f/S5LVYaEmf0zqFoeJ7bruEn0vWyTAmafv58k8/dHFOWJV6UVe68AgWNrV6NPJdYn7NCu9e0l+OWmi0U/7jiijVdA32tzmAUMF7EuLbOxKuet8oosQhTLEM+Y/NAuqZhKk60pmPgmDrTQMjuSV5wPAl4kErHaj6PuXk44+ahiNL9OFtmMV5cqTGNMgF2qgrTCmBZs3VWWw5ZIWDSC6t1zvVry0Bd+6zjUpb82Q9scjBLWOk4mIbG8cinWbcYLSI52BUlX34wZ6stOqqVusHDQclzV1cYezFpLrDVqxe7HIx8WjWTn3l2ja8d+NRMlUQV12bX0bl3f8Kjt25y6bnH+bHvvcRbD8a8/GDChdUGV/ou/+NvP6DZdJb35u95bIvtlomCwtE8YVbdd3VFJazMLd22jaIoHJx67Kw1COKMi+uN5YF37MWcziIajsEbe1OOjheig1Lk/uv5CW/uT6UQywsuX+xKoLVjcHzq0+04ZFmBaWqoqmiswjBl/3jBdtfFqkwBZ2yn01nEStPmjEygKoqM+fKSjabAcIMkZ61hLsO95cCgcmsQYWkK6ysuX747Js1cmq7JK7cGNJsWp8NADA5ZweksZLXl0HV16pbK8SLl2orLn316nb9VQXt1TZUumyJ6Jds2WHgJyXekoPq93/tu61C9V5ff/6MinH+s+tb/rizLV/74Luvbt7I0w6kJx0S0HiUnnoTiWrok09uGxqUrfbaqgkRTJFQ1y4Uw/od6/ApeaGkqrimtYk1RWKnpPN5rYRsaQSai81GUEMQZP/pYj+NFwuE8Zb1pcHcUs9My6brStZjF0pVaa0iu2ShK0BSh7OqqKngFVcBwJ16Mob2bqVRUlm5xCqpoCsRZgZ8UXO+7PJiFHMxSTF3h8TWX37ml8+NPr5HkBZ98/YRRlc7+5EaN62suSV7ycByz2TKZRzlRljMMUlbrJnkpMMLf8mI2Oy5uFUfyaBaL280UIe80zBgtxNpv6zI2adka0yhno6bgmipZpVdQFQXX0TmeJ7QcnZ22yXS7hVsRlPvV+HS94/LG3hRNVdldrTOeR6zVdfp1gy/dlpNiv+PSdXSSvKTuGDy22aBhafzaO0M2WhaPbdT4ndsxDwYeH7nU4Sv3p3iRCJBXmjYbTYOiLCU2xNLY7EgsS8OSzUyvIJdp5SparRuolXg1zYRJtd02efM4EBG8bTGKEmq6XgnShaieFSU9x+DRPGZ/mrBRt5lF0h1r1YTa3KqZjOZRRdhXOd93WalgoWGSc+qlbDUV/KRgpabTrRlc6Nq8sj8jzQqStMCPUixDo9mUUFhVlfDbM8fQg5HgFL7/2U1+48sPmU5D/tQTK4z8jN0q/uJTb80ZVvEi53f76MZTvPSpF9l84jE2Ow5PbTX43M0hWS6P2alL3MtgGjH14uVoLqugkWlesLNSkw115LPbcwjSgocjgVwqivDB5pF8hvK8pNU0xW06jzBNDdvUsHQRZjddoU/vrNREPJxU49OKMVSWEif0zpFHXpTLTL28Ajs6FXhWrlHGT6oiIuWzz5hVkeBVVaz2NVOo7H4kHUxFkY5WWcViua5BmglK4dJanTf3p5JnaOqYukKSFpIV5xp445TVls16S0aMX74zom7rdFyDeRXZklY4iCQV/c3QzxgHoj2zdJW/+AO7TKKUr9yfs9t3WfRqvHR7wMk0JKwE1Ftdlz91ucPrxx5vHy7o1Ew+/vgGwyDl3ijGNTTOt02CtOB822AUZNwZBMRRAprBR5/dJEwE6TFbxLwT57z1YPh1ozVhkl3rOfhZQQF8fQmQlWDp8vqbusbpOCCOM1argnNRxbCMFhGmrtGpmcyq9+zFc21ORgGDRcwrNwZomnTANVW0d5OZfE7CsKTRsIiTfAkyzfMCXVfRNIPJJCRIBHJsWDons5BW9f5pWFpVNBeYmgBQoywTGK6qyLg/zIhzOYg+mMT0awYrNYEd+4lQ+Pdyn/ki5omLXQ5GAXle4PkS4nzWBVzEkoLRtDRsTWORiOvRMbVlxzLPCzRNMBWGoX2HXH7/GuwJgKIo54AhX6dbUhTlXFmWe39cF/btWs2mw4ef3yZKc/xEOkV7I5+GbUgKvSMjkPWGwW7bwVAlyyvOTA4nIYOh/4d6/Ce2m9w8WjD0Y65365iaSscxCRIBUaoKGKpKVOm5Lq3WePMk4HQe8yPXurz0yFsyWM7IxIYqI78kLwnSgkkQkZfC1pnFMh459VI0FSxNpW5qOLpOWvFayrLkNIzIyhJTlcJyEefcGQfYhkrb0bgzjHjnOKBd6Qi+en/KvbtDptOIbtfhH35pnys7bc51HVbqBh/ZafPZ+xPmccY/e/OUumNw5Jg8da7NwSQkyQs6ulLRfC02miZbLZNbg4jfemfIVtflY7stbg4CznUs2qbBJJZi9nxbAIKrNYv37dT58GaP37w/YH+W0KsZ1KsximNovLE3Iclko97sONw8nMvmZOnMolwy/Taa7LQtbEPhlX2xsF9fq9F2tGVbPy3kOk1d5f27bb62N8cxpSswqfRgv31nKiHLVaCzFOcKbx0H7Pal2Lw/8PngbotpKBv3wSLlaBwQpTlf3lvw5LrLE2suJ17KKJhTt6Qg3qo5TGOx3z9buaFeO/bZaZs8mkcs4orKXolSFUWy4q6u1dE2Gtw+8bhxmNOpW5UNv+RTtyYYmspWy6Bmirmh5ZjcfDRlWpGmL53v0G5aeJ6QqhWFJW9pHqa8cL5F19WZP7NJzdJ5erXBVw8lUPYf/M4++w9GNNo1DENlNosY3LqF3l7h4m6HhqVx6qXLbL1ntxtsNi0+eXNM2RLG0KXKedp1dbwkF5aYn/CD1zrsTVw0FSmeipKGbRAm8jys1g3STPhDh+OAu8cLVtoO270a904WtGom/jjgZBoy9aTLlBclQZBwOBPg5krbwaqy/JJcCswzuvpsLsL0MM7p1i28SA5cX375EZ2uy8ZavaKK65i6iqVrMu6OMlxDcBnPn2/zyTdO2D9d8MKVFWahdGBUBSxDxpVPrrsoCtw59tjuCrrhztGcsIKHgkStHE0jHp4sOL/WwNRVHo1DTqah4Cgco8os7PLi/RlfuTdeOiWf2W4Q5wWTIMeLJLvzqQ2XV+6pXF5v8sJOnTePA851bfKiYG8cCiupZjCNMs63HHmfaxr9usH9UcwoyBj6GV965ZA0Trn2/LUKQJriuiZxJfpWVDkQaRWXzVuEUsRW98oSGQFXhkdOPWFt+VEqyApL55W7Q3w/ZaXvEic5K20H29C4vOoyDiQC694wZOYn7A18mk2L2Syi2dSZzCPBvQTJ0uFtmhqeF9PrikHCD0SIbxgalqXxq5++xaUrq6x1JRu0qAqZ/UnIExt13nw0p1UzqVvCx3vx/oxe08Y1VIklsjX2xiE7HYfbJz4XV1zWGyafuzEgTXOCIMUwVO4dLWjUDBoNi6OjBbWaiaYpnFuVTr2iwCSQQPXTRYofZYSRhJZrqoJjGxJ107Yr/M23v5T5V6FD9V41VL8KfKL6+i2E6/Trf1wX9e1cWV4wD8URpSgwqnRJu31Hwl4zsZCfb4vwbxwnzMKUiZew0hSybuff+MZIwX+xlRclrq5j6xotW/hKpiYnl6yQCJSaoWOoCv2azuk8xtRFM7PVMnl+s84zqy06tow61MpVFOcFizgnqrLlzk7SSVaiqSLIN3WFrmWx6lh0LJOuJdb1dztW5XLjHIe5UMEL6aCcziJOJwHvHAeM5zFr600Wi1h0X12XSysukzDDT6Rr1q/rtKqIih97vE9Rljy54dJrWDy306DjiN38YC56A5A3aJzkrFfPta2rTIIMS5PreOV4TpDm9Go6Lz6aczBL8ZIM11R5fNVBUyRg93gh/2ZRSARIzRTtQlGW7I/E8fjO4YIbh3MeX3dYaxh87uaYiZdwoefwYBxhGyoDP+PiigQ8z+OM9++2Od+2abom57oOO22L5841l0LXMw1OmstzGKXSJfETGQ+4ls7xImXgpdw6DXhjb8KfeXaNTrUp912T8y0HSz9DlcDQSxnHMlLSVZXjIOL+3GezaXK+7TAOMkZ+xsiLMar30dHQ52QUEKQFmipF0GgWcWt/WmlA5L3YsHVUFJJMnut+w8QwNOp1C8cR0ndYhVvHcYYXyUjLj1LiKvcxSgtMQ8OPMx7MA9zK6aRpCpqu8dT1Fa7udlldrVPb2Gbj/JqQ48uSrKD6LCo0LI1ZlGLqKk9vN+W5mieYuoKX5Iy9FFtXaboGfiKMs8N5uhRnT4OEx9dlZLpSM3h+p06U5oxmEZ6fsN2r0a+J5uTMKaUoYBoaUZIThCl5XnL3cM7EjyvjB6KFqcJ2NVWAnY5jkOcFaZoTJjlZURAnOR94bktAkbpWdYVyMZ1UuIw0K9ivUgVunPhMJiHbK3KAO9MkbnUdXjjXwLE0JhVGwjI0tloGWcFSYH57b8LzF7uoisLdgxmmqdF0DTo1E10T/MP17TarLQfb1FirmVxdr2Mb0p07g8jGWUFc3ffunvrsT2MJ0N6qse7apHlJ29F5OI14YqPOTz/Wp2GJieXIi+hYhtyHkLDzIBUOVZblXL62xgefXMeqHHkrLZtLWy02Oi62gWjsmg7rnRqXzq3wCy8dc2YR7zsq/brBSkOea02B4SwSzpQCjiUjOQF9SjfGMTW8KGXgZzwa+QKWHUme39nortm0lh2bPC9otWx0XaXZFMORWSEzzhANmqZWRHUV0zYpCsnCPIu52elJ8XU4l47Yo6FPVshzlubyvjiLsIqyEkNT2RuHwrJaJLy2PxPivmOw0nNxHINOw8IPsyXUWVWFKRbEGaYmh+iLXZcwKcRAkEtxV1bMO0D+fiTxOt3Wtx+boFT70td/fbdVVO+poCrL8qmyLJ+uvq4gRPIv/PFe2rdvNWyDAhFdH8/jJXG7YWqc71hstWQ8dRrEzKrTbxBny2IDVftDPX6SFXhphlVtfqqqyOhAl/a8pYlwNq20Qb26xcSPef3YJ8oko0xXhVA9TzKOvBg/kcDeNJfOlKUpkqWmSLBv3zWoWxrrrs04jpnGKT3XpGkJM8pQFUxVRKslUmD5cU6UFuyNo6VLRNNUXnrnhI8/u86Hn1hnZaXGwSig17RZbxhstUxx76SZjPzygjiVG3aYSHbdnaM5pwuxCoPEZQz8DEtVmUZCph76KQ+nMSt1A1NXyMqCWZTz9rFPzZD2+sRPmQQpXzuZkuYlqzVzKdIeL2K22yatmsnF9SY7bZPjWSxZadU454xefKYnC+KMhmNQt8S59Y+/dsLAy7jQtWjaOkezlPWGKfRhU6Nf1/GTHF0TbdGF1QZbvRq2ITfMaShdUFVViLKCeTWWO53HDBfRkkDdtU1+5LEeUy/mM3envHQ4Z6vq2BmagpcUTMKUumHQMg1O/JhTT2CuUZZTlrDeEKbUWdciy4TBdDQNuTcMadVMmnUpntO84P5YmGaaKiOyoixZqRms1qVwOL/eYHO1ThilJElGlgnk9WQaSqxNmuNHGb/xxmmVGyav7/FcOqJxVpKmBY2Wg64q+HHGzbePCP2Q8+faNGw5wa/Udbp1SzoUSc4bxyHXVh16rr4MYp6FGfujkDjLlyPgsw5flstoLKm4RNsti17Fd/ITMTU8f7nP9mpdojsK0VC1XZPNrkvNEhNEFEk+XL/roGkKJ+OQo6HPPEzYGwuLa6vjsAjTZZi0acoo5SxEOo4zunWLJ3d7KIrkz8VZUQU/C7TRsfRlEPTByKfRMDENDddUKUsxSdi6SrMSrb915HM8E0ClqsjzfpYBd/a9gwoX8dS5Draustk0qlGiwvdfbPH4houhqZz4CdMwx9TlcFKUJS8/nPHlB3MmYUbNNqrvyyh0p+GQFgWuobLiGpi6ykZDZBBPrzY4mCd87vaUrKLFA5WEAU68lOtX+zy122WrZdG0NVxLp2bpdGsC1rR1lbuPptx9NOXO/oR5kDCdhfz3n3/I6STCj3PRiMUZmy2Tk3nCTgUETVPpDuq6SqMhmZyLRbzsGo3mETfujvinX95j4cWsdBwUBTZX63zv42vUHIMkyem0nSoZQtyswNIQ065bwtSrHLiWpXP9ah/L0hkMfOFQVcXLGejTMTWePt8B4O4wwjF1unUDx1BZbRh4Vah8lhfYVSDyWcf4/EodvTogB1XMkVqJ8EFGz5omY3o/KYgyAQE/GHjLgi+vQryLslyK6aXo/5ZRvH/kS1V/79d3WT31nkXpv2uVZfk1RVHe/0d9Md+JZZsaz+40lyOSwTzi3/rANkbVHQqyjKYpT9MsShj4KW3X5EDxCZOCR/dPaHSbfOg//S2+/Ff+1B/oGkahBMcaFWdJreBsmgJemjGJEkZRwhfvz6lbOutNgzR3ZWM3dU6DhPU0w9akYHp6pUXT1snzkiudlJMgombo3J0EojEyRAeW5CVxIU6zIMt4czDD1jUUoGtbJHlBSck4SgSm2bG41K7xiddP2e7VaNfgx5/s8yuvnfKF22O8MKXbtkU8fHeEbcjpsChKTirmSq+Ws9ayefHhHMfU+OxNYUU9HAVYhsZHrvQxNIVffemALxgqj5/v8tHLHT7x6gkAm02DkZ8RJj7Pb7RYqxu8euTTdjR+5vEV/umNIadeRtfROPVlJHY8l5PjS3sL6fDMRcQdJhlbHZemrVGzBUlgGyqfeP2UflN+j9EiWp74kizn4cAT142lceNowd2BT9MxGMwjuq7k0b1zHKBrKhM/puEYzELRp6gKRFlB25X30+k85rmdBp+/MyFJJaB7vePwi68fMw9S/sz71vjEm0NuHc54s25xcbWGrUtxVpZQN2PUasS7yAucmkBeDU3B0KSYKsqSuSeAzscvdgmTnHuHM6Io4/GLPTY7Lqqq8LVbAz5wfZWGpXHrxMfS5fT8cOgvg379KMOxDYmACTPGldj/wNTYXWtw73iBpipsNg3eOfJFc5Nky05OHGdYlkT+aKqCbuoYpsFWz6VlC5hTVajemwVDTzaRv//bD9leb3BlvUGU5my3bXquZP1Zuso8COm5Om1HZ7dnoaLwybeHPHaugZdIIf/aowVTP+bKRov1hsHJLOLR0F9q2OKsIElzbu9P6XUc6jWT+SJGU1WKImOj53I49AVP4CXUXYPtnuiszkJ5NU0EyxLjUyzTAgQuKSNXVVU4ngQEsUmWFzy93eRi1+Kd44B7Jwu+5/E11hsGNwcRKtBxDe6e+tw69kgyydK72Hf4zFun/DM/RlEUnj7f4StVRNTvvHNKr2WjlILoSIFXHy0YezHdpsUkSrk9ED3iq4886rZOu2by/gtNLE3lC3en/Oi1Lid+wjOrTU6CiE/dmvA9l1p8+v6ElZouPLx5TJAW2LrC4TzGSwqsihH3N377IWGYcmGjyXAecXS8IAhSHrvWZ7drMw0z3jqQzHrH0rl3smDqifMPFBYLGS/Xaha6qbHbd7k9CIjKko6joKsW19atpUYtLwKiCqTqugZhmLFYJDxxpU9YjYVffOMIxzE4v9mkW7foN0zunnqYmspbB3Pu3p9gV7mIw2myLFrKSncno/4My1CxXIPJIsa2dKbzmHrNZGO9IVpDNefle+MlmqHfdSUY3NKxK6Dr4TjAMkSrpWkqtq3Tck1qtk4YZ0tx+9RPODjxuLgjWtpzXYehnzLzEx4NPOI453QSMg6zZYf5zeNAOrRVB6soSo5PfRxHXz5WUUgEz7d7/etw5Gp9AzFdBZ4DBn8sV/QdWGftdz/JWWnavD3w6Dg6qzWLeZzhGDJecw0VXVGI0qzKhiowLVOQClWcxx9knZ0egKWGKa1atramLQNzbUNjp22yP02IUrGp1w2DISmTOEUlZd216ddMvChjGMXM4pTDRUKUSRGRFXJaGQfCijqYJZzrmLQtgyDLOfFjOra4BBVF4lbSouTES7m24uCnGastm8t9mxNPuCpNx+CNWwMcx+DCWo/drs3ByOHNB2OubLW4tO7gVCM8TVG4f+oJYNQ18YKUzV6NR0Nfok4qEnJRlLQaFmMv5raq4HkxdUsjqcZlV/si8NYUhXcO5jxzrrW04D6+6mDrGp+8OZYNwxHR8eUVl5NZSFEKa2ilaRNUurkkE2fnIiq5sNogrfLSyrJkGgg/p1O3qk6ddKm2ezVuHEyXrqqhn0nXxNA4noW0HLMqRITI7ccZDVtn4qc4pmh0pmHOpdU6Nw7nfG1/QVmK0WE0C7nYquOYE/zK2v+lGwMeP99hs2XjJzn3xhF2hX4oSlixLbBhGnqMfOmk1mz5iAdBytfePqnGd4JDsCvq927fxbJ0oXpXYtqiLPEqx6L5dblhICOxnY3GcqRweLxgsyMxRWnFPjuaBLz+tT0uX9+g07JpOAaGoXFhq0kYZxyOA/I0530fuIhj6oyDjPVGzsEsJcoKdtomsyhn4BWMxyGmKZlqj53rYBsKi7hcWtI1VcGLRR8jQdhCxn5tb8bLhXSERuOQK+fanMxktHI0DtA0ZSnePRj7wmGrW7RrFkkm0MyToc/F7Ra9hsVgFhHHGaqqkGQFR5OQvJCRjWuLG891DbJcbO5x5UgzdHXJEkvyohqtyWFOVyGoqPjnV+sEScHbxwF+lLLStLF1ZdnVsk2NS31HcCZ1i0fHCxxH58HAk83S1HDqFk9sNTmex0vX6XAR45hy2Hrx4WLZPWo5BnFVAK7XTYpSYlvOulBRVvC5ezPKslziAWaRjKscU2WtYeInOXeGEuDbdvUly6wsS+ZhsmQ/aZoI8W+eBGRFwSKUg9Zm18Uyq5zEpCDwIi5fXiHPC/xq5GpaKnmpQVEyCAtsIrxEIU4LVCVfxiPleVmJruUxn9xq8ObBgpt7E7pdF9PUuLxe586xx+ksJEpzVlsOYZJhGNLZ8iLBQui6SqdlCxLm3oS1KqkgiKRzp1SYBFWVfMUzUv1oES8DljVN5ULFP5NCOlyOZy1Do9MS12TTNWi5JneP5sznMZalcWmrxX7FgWvYhnDYInEPr7cdBvNoCd795GvHtKquLki+pa6LwD6McxoNi6KK5cmqTuXZc/TtXN+sG/Wvaoeq8XV/zhBN1T/6o7+cb/9yDQ1TU2jYKvdHMW1XmE+zKOPWMOBS1+bWKOStgwXdhsX1VZk9n1aiVbfhculyj07tD57tFxcFcaWJyfKCuqMTVxDLtCjYbdUZhTGjbkq9coid69rCTaqEyZaq0rVN/Czj1mjBSwcLPrDdpG7qRFnE+bZFlBW8euizrVrsT2M+fL7Bg0nM0Sxlb5LQdjQalkZSFNydBOy2HbwkxdZUTudxFWK8wNQ1Lnddjhcz/v4X97m42eLf/cmr4pAJMu4MhXT95z+yzd4koefqvPhwQc2SPK/JIuZD1/r8ztunbPRrHE8CnrzQ5Wu3BhX7SxhHvYZ0MnbaFq8bGq8+mLDdNrl9vKBpa9xXIt4+9JYBsP/g9WPss7GmZdB0dD52oQ3Ag1HIw0nEcBxWWYQFK02bKEk58mOurtW5eewxmIb8xLPrFCV89cGUJ7aa1E2V24cz3rfbJSvgxsGU/aFPv2nzwm6XvXHIua6DY6rUTJNX9hdcWa1j6goni5Rnd5qceikn85imomDoKv2aztiL0VTh1Vxaa3DneMH/6vl1krzgF774iP/xawccjgP+tx/aogAOZg26jk7b1nn5wOPhOGKlYfJTV1a5P/e4NfZ5ot/ANhQmYUm/YZEVJaaW8Ki6kQZByupKDcvSufloSlKNKPsteT+9dTDnRx7v82ASsz8SGvgiTFmrxL2LKGW760qQ9SjAsnS+97EVkryk6ejcOBTY7Hwe4899jo8XXNpqye9ahRQ7lo6uqbz/hXN06iYrdQNLV3jlwCdMMjZbgn14MI5568GYn/jYLit1g0dT4Qq9/ihZFgi/8cYJm12XoZdw82jOycBnpeey3nG5+2i63BwVBXZ6Lq89nPDFF/fYOd9hd6NJu25haQpDT3SUhq5WESvyeZ57MSeTkEHFMiuKkj/7oW1eP/A5nASstmyKomSlaTMch5LzVvGd2k1LDAxZRJIWmIZKtyrK7x7M6LRsvlqxyj6w26IsSx4M/GUOoVnp5rK8oG7rrDRtXrw/5SeeWhHOlabImKomQMuWa3Ch7xJUMUNlWXI6i3hht8P+RKCqiyil37CXoc2WrrKodIsAo0XM79yfE2cFx5PjpTPL1DXWGiauoXKubRFVm/feNBHgKuBHwsFK01z0dqHgCy6tNfgHn3iDQV0Cjr/21gmbm018P+H+yYIwzkmSjE7HZWejgWnItYtDDfZPZ6z1ZGymqQphnPPyo5hZkHDjvrcMWS7LklpNXKGGrvLlu2OKomS9V2Or63LneM6NgzlRmrPwEk5OPOarNUajkB/4wA6GrvL23mSZebnWdlAUhZOGT1mWdGomqa2LcaPn4kXScbVNTQ5jQSKvv6lxfqUusVxV4SOHD3HbNWomdVvMMveP5tU4OF06CDstSe1wTNGUBnHG470GQz9jvWlRtzQ+eKnH3YG/DHs/GfoYhkrNNatRn9DjNU1QIbN5VF2bjh+ktJv2H3i/+oOub96h+u6qqN5rQfV2WZa/i4yuKMrP8R5o6f+yL73it+xNEjRVway0Rmle0Hc16oZBkgUcjgJcSyICNFVglElW0OvXSNOCiZ986wf7Jus/+8wdrvSlSFukgjugcvelWcE8EXzBOE7wkoLbg5BpkHCuY1EzNd4+CcjKkqdWmpTAIy+s8AQFsyTFi3OmYU7Lzrg3iqsQ0ZKGrTEMUkxNwTF0Of3kJUFSYGqivbozDthp21xs1vmMOmWrJVEaNVNlnqQsoox2w6blGGzUbYIs55YfYWgiuPz1N4dcXm9wexhzMhM3UFoFrr71aIZlSW5amhXUTJU0zRmPQz76/BbrLZs396c0XdnYzk7geSEfzIejiHNdm/W2wyxI+cjF5lKQ+aW9BWv1iMEi5s1TsbffP16w3a8CWFs2g3mEoUm3KU6kq7HWlkLrwSTG1NQK2ZAzCTJMQ+Ph0KcsKxdVZf1/cquOF6UczVUu9h0J83VNTF0hymTkluYlcSZdiyDJWakb7I2l6PzsO0N+/OlVGpbKY1tNskI6cEki48X3X+rx1Uc+11ZsRr5svrNIujFhWlA3xXmYVj93GkYCctVFCH5YZZo9cbnP6SykbhvLzSqsRrRXV2t84faILBeUgJ/kywJk4qesteV0rWsCpDyZRZjVOCGKROPzYOTTr5s4psbtE5+f/uA2Kz2Xn3x6ld++PWHhJ6JN0UWfd3WrxSJM0VWVlbqMxLwopSxhEmZ86f5cCpauy3pTXJqKAo+m4rJs2KI/ieOM46pTlGUFB3tjdF1lo+OSpgVBkHLtUpfrW02OZzGDYUAcxlzdaQsoMy8wVI3z3XdFuqMgpVnFgSiKQprmdJo2A19GQUFSECYyjtRUhWGFo7CrbqBtCB1cUxROJyGOrdN0ZQMFsdCf5eYVhcQ/CYkc+k2b4TxCUd7dgGxDY73tYOsKw1nEb7w9pO2afN/TG/zmywcM5hElYjhZxMUyKuVsdPTYqsvNY48gynhiu8VO2+TNo4AkFT2fa2p8+d5kmYWXFSV+JMkCLcfk3skCS1fpuBKPJB3dovpspEvYp2molKWIuH0/4bmrK2R5yS/+6ptcurrOYhFzNA7wZgHaTgtVVZjOY5oNi49e6wOQxSmzNGcaJqybjqBVJh66SiXAh82Wxv6iIIgy8ryg03HQVIWTU4/798a4NRPT1Fjp13BMjcOBh6oqeEHK6amPaapsrDfQ9SarHSmaLvZsPn97xNWtFifTUA4aqiRWPH25z9RPROsapdRteQ2zoqi6jAppVmX8KQq6pvAX3rfFZ/dGvH1c4No67brFPEhp1KRrvQiFhG+aGq2ayck4JKv0Xtc2hYeoaQrrbYckL6hbKideSb+uszdJuL5q8/bhXAj7sXDI6jVzieMA6UZpmoKqi95KUUQjqmmqpGl8m9c37UZ9d9VT79nl9x+9x+/9iVtZXop4uyhxDJWuq+PqGnVTpwTmacrDcUgcZ+JamsRMQ7lhhUlOrWay0XNZbdn8/C+8/C/8+F+4NeQrewuGgcA7iyqYWNOEOhdmOWGWMwmlOLINjUurddqOxizKiPOSnbaJn2b4acbeJOZ0kUqAaZhxvJDC79Esoevq1Co2SduWTcxPpJg5E8mOgmx5I4+ykoGfcOSHWKbGawdSnBzNE14/ChjMI/pNi15NR1dUvCRDU6Buqkuy8O2jOXeOF6y2HH74Wper63WSJOfgaCGMoYrFczSVcUoayyigZqq8sNulW7e4Mwj4/idW+cDFDqeeiOGbjs56w2CzaYiYWBeqe1qUYkd2NJ7arPPq3ozX9+ds92scT0O21urUbBGiz8OUk0mIH2ccTwIalsbjm00enHq8/nCCa+nElW6qW7c4GQv0cqPjYtsiXrZ1laSCT/qJRMiUpRQ3s1DQF/vTGF1lGbp8lqnYrMJrv3hXRPQqLMewZzfnp9dd7hzNeWlvwd7Q59FMBOi2obLWMBj6GUdBRJCKQHsYpBWTS8HUFWr2uxmISUVMN3SVuRej6xpBlOHFAiw9E2O/fewzrp5np2I0Afhxhh9lPBr6aKrChc0mYZgxCjKmnkAv19qOBBC7Ov/2R85JcWpoaFXHpumKsD6sxmFZUVRjOtEaNZYA0IR+0+b9u20GXsYX7s6W+reGI4G+SZrTbUrsie8n+H6C5VjsrDfYats8e20F01S5vtWU/MrjBc2mRX+9zVObdcqyXGIVzgJuFQWcSljsGiq+n8iYT5ExVprm3B3Ke6aanBElOdMKm1AU8m96UUpRQliZN8oSZn6CV426Wg2LshTnU1GW3Dn26Nd0LvdtzMrlOAszThZplWuoLoXq40VMp2ZwoWPRbttMKyFzVL3vB/OIo0nIaCGF2TBImXhitjkYh0yCfBmGLKMrkTMcjQM09d0i7upanaurDnVbp2FJR/BglnK0SNibJNw6DYSvpSrL7EeAum1g22J6uXM058KlVbKsYDGPuHdnyOZOZ4ndcF2DD19s4xoqrqGSqwWTecg8yJn4CaN5TMsxicOUKErR04yd1SZZIQ4909ToNix6DQtNU7FsgzwvmYwD0RFpKrqucvvBZKmxchxxXV7earHVrfFDz2zwhdtjTgY+944XBHGG50kX9NqKzUcvtqrXRPaLOJNw8TPe2Fnh2mlYBGGKgsJ/8s9uMI1yxouYOC04qq7H0KR7mRXCAHNtiY+xTI3VrtxXivJdCnzNVNnt2uQFgsNJ5bHmkXRLkyTjqYs9nru6wkrTXjqyZbQo47+kcjMWRVk97wXnV/7gEpU/6NKqQ8bXf6nfZRXV79uhUhTlx5FsvS1FUf7a1/1fTWT09yd+hVnB3lRiLmomLOKcv/vyIaamcjwNl66Njz6xBkjukl3Zpve8gH7LpmaJHXbqJzz5f/113vx//fh7f/wo42ASkOQF6zWLrEwoEW2KOPsKXh/M2Z8mZAVcXbWJ0pIv3p9jGxo/eKlNWhS8dDinZmo8v9GkoOQT7ww5msVM/YQP7LaZRjlvHS7oNawlt2oSigbltUOfqysSetqwNL66t+Bc1+bEE8vzOMj44Usdvrg/43QeU5QwTnOubjT4/gsdPvtgwo2Rx3rd5Ik1l71pzM88tcosynj9yMcxNGZhyo3TkNf2p0RRyl/6+BVunEZ88daQNM2ZTEL+3A9cJM4KBl7KqZey27W5deItR4k7bZPfuTXiiZ026w2DL96fLbthszgTZ19dZ7Np8ES/wZcPplzfaPDxyyv81198gK6pnIwCpl7CaOSzsf7uJDvOCvZHAY6p89hWq9JOCdF6teWw2RYL9dSP2WlbLMI6eVFyayBYhDDJmATiaOtU1PczzVGWF4wTcZKtNcTiHyY5V7dr1CydWZiiKgq3Tz3OdTqC6tAUVls2/+SNAU/utLnYtXhpfyEk8hKiVJw9qgJ+mrFRt7FUlc/vTbnQsWhYGgNPIKP9hoVTGSu+9vIeSZQQBRGarmFaJq9/7SF/8c89z51TnyTJ2WzZPByJxijJCjZbFqahstWRMcfpJODG/hRNU9m/d8KLTYvtlTpNWyNIBUj6xlHAp+djOpWDq1Y9J5qi0HF0Ho1DjsYBK22b26c+jqmz1jDRVMkUXIQpHzzfZKNuYWohd44XUhB3pcA8G/l5UUa3YclX3SK93MPUNUZByo2HExzHYLhI+MyrR9RqJh+52uczSc7/8Mk7nNtu0XRNJr4EYqeV+NiLJRy56Rhc2GrRrpn4cUav45DnJXeO5qy1HSaLmPFCCqnFIpacPN3keORjVs93vS7i8zQT/hOwDBI/moRLZMEiSplFOeNqFPfsTpN7o4i3Hoxp1k2+cHPIh6/0eGq3y1sPJxXYNpPswExGfJ26JdqnNGe+iPn579nBqLrv17Za6KrKa/dHTKrfrVs3UZHN+1zbwjBkFPsT13t0LYu//qUHXF6t4Vo6XlJQTwpe25suUwSeu7oCSFF4Mg2X3RHX0mnVTBZRzrgqJCxLZzqY0uq3mM9jPvK+TfYGPr6fcOck4AyPPPcCLMvCUCUgWVNBKcFyTPaP5uyuNTiYpTLaVxUeu9DjQxeafOqdEYah4lTvM9NUOTya47omrmvQ6ThkWc4PPrfFPEyJ0pzdnsPQz7hz6nN/b0KSFHTaDlle8sS1FaI059M3RzQcg6RyZp6tshT0yFmnKarcdKap4YXC77pz4uFVBfXZGPIMR6Gr0m3sNKRr33QNrq03mPRq3Dmec2G1zuW+XR2+SpqWRtvROJqnEqJeN5bj2J+63ucTN4fL8WIQyOMbhoZhaITV9YRhSrfrkiQZd4/m73mP+qNa3wzs+V1WT33LDtUh8BIQAS9/3devAD/6x3tp354Vxhmfev2YIMlZxMWSD9Rr2LRcE726QXhxjmOIBVlRhJfycBJVMSdCUD44mDMbvfc36vq/9XdxbJ0kLeTDUJaEWc48SXmw8BlFCTcGIQezhJ22yYWuiaVJxEBZikV8fx7RtkySrGQeyc/mRUnN0rnQc1hpSTzL6VzCTKMkx4tljFVUo0VLrzbCQgrKhq1zdyD8oJ2WyWpdiN/rDQNdVXl2u84Hdltst8wKyaCyUjNomgZmJaLXVIW6pfH0Ro2dtsla0+Lz75xy++4Y2zZomDq9mk6vabG70WRlpcZWy8Q2VJJMQo1fe7TgcOjz1GadAnjj0OPCap3H12Q806tbdGqCvDA16chYmoqpKbw9WhBnUvweBRHdhoWlS+csDFNUVSWMMtp1kzQtuLbZ4ngacvd4jqUpNG1N2vxpsTyBx2nObCEdosE8YurHJFnBhdUGdUdwE2EsXakoK5fjZFWVTlGcFZx6KfMoZ3elhm2Im22nY7NS15fjvqat0W87fOhii5YrRYahKRLgPfSXwdV5UbLZNHB1DUtVaVQZjYYmIFY/lsy3sRfzcODh+wmGaaBqKoRz8sBD1VSC8YRxIFEqP/qBHZq2wEi9MGUeJHzp9ghdVZcUfcvSK1t5jqIqHB0tmAUCFFxUG4iuKvQa1pKkbmgqvYaNpUsMyVrLZjYTG7kfyfisZqqYFaPJNjVe3l9w4EXcOhWR9mQW0XQM6rbORsel6Rq4FQ9qp1cTunWS8+B0IbEbumhJXrk54Mee32IyCbk38JnPI5KkWPLUkqzgcBpxPA2ZBuKiynJBQliGPBeqojBbxJSlBP62aiZJkjOYiGC+03HodV3OrdYJw4z5PGJYjYjP8AZpLo+5qDb0pmsQJpKHmBeljOnDFK3CPZy5QXsNmzTNGfry/JYlfOHWiNunwbJody2dJM3Zn8m1Xz/fYREVBKnkbuZFyeksxDS0ZTZhlAo3S1NK9uYpbdfgqU2XIy/iOAiJkpxJmJEVJV+7M+CV/RlxVlQZcdKVDeKMwSxiNAkZTELG85iNts2z23XSrGB1pYamSadP1VTKQkTZEy9ZatK6dYPHN+pcXnFA1YlTgRJrivxuumVyOAkoS0F8nLlm202Lw3HA7WHMasvBsnTe//gaP/Whc1iWuJw9T5yQG12XPC+XUUNnOJFX7g45ngTYtsHF3Q5q1WV9eqfFYB4x8RJuP5qxCNMq0aACKKuKhHhrork7Y/+5tk4UyWfgXM9lreNgVOPxs+7UWWcUYOrJKNG1hHpvaKKzywtxEk9COShGFc7lSt9hu23y8iMf05BO5m8/nHI8CaU7VmkGz1x+q21xrEo3K+e4it05C7L+di5V+b1f32X11O/foSrL8jXgNUVR/l5Zlt8VHalvXFleYBhiZz0uS2qVzuFC16Rf0xl46fL0ale4AUuTU+CdQch2v7YkEB/vn5KcHtD54L/P5Cv/1e/7uJf/vf+F1a0+W70aP3y1w7Vuk88+HKKpCjdOZxV0MyJICjabBl1H58RLSfOSsZ/wv3//OV46mbCICn7r3oTvv9DmxYM5v3V7Qr3Kh9ufRNw5mrM30Liy0WK377CI82WLf28cYesmXpSiq9B23o3BeG6nwSuPvIoxVPDqgY9ralxddWg7Mi48mCV8aTHjYs/m8V6LWZzQVk0OrYhxkHLiiWPLNVTmUcZ0GvJ9L2yz0bLYn8UMvIxnz7Vo2RqPbdT45DsjTF3lQs/lsVWHX/yaiGIdU8XSxJX20d0mF5p1pm7CTltuyA+mEWlR0ncs2pZB3dT56tGEtqNRNzUezSM+tttiv2Nx6zTkdCYjzLKE1ZbN6VjGl5qqcHy84F7boduwSKoTt64pvHUwF4ieqXPraE63bnG+7xKlsgk+sdngzmmAosDpIsGrgoCBqqBQOJhHdOsWRXUXmQQ52y1Lon7CnOfOtSSGYhSy1nZQUbjQtXn54ZSDccjxNOTjT69xZxjRrxlcX3XYbdXYWwSEas6Nkc8iyivXW8GHd5vL8eHxNOTSuTYXd9q8eXNA0muxulqj2bCYLzb4pU+8QWelxTxIefp8Bz/OsHQNx9TZWyx4YqfNohLI7q426NYNDicRWyvX+ezn73BwZNJv2pUpQMYVeVIyTiRcd76IsQwxPVxfdfjVNwbsbrcwNZVaNT49g+jWTMk4zMuST7015PxKnZ95boOvPpgRZcKSurbqEKUF+9Xn9WASSEyLpdN0TRZhyo8/u8G9YcjnXnrEr3xpj7XVGqeziI9/+Dz/5LfvC9rCky6uokCnbrHS1DmZigNMuELi3Jr5Qs+Oooyma9JxDT7y+Bov3hrw+HabMMnZH/kswpRL59r4UcpwHDKdhrzw2BoPBx4KEjsTVSwyU9eYLGIarsm5fk1GilmBqaskRUHX0YmijIcnCz722Cp7Y+loXVhvECYZvUrg/ua9ESNd5dkLHfZHIc9f7OIaKi89mDKYhnz4+gpJVvL68YI//YEtTryM03mEoas8ueHSsQ1+8eUjPvulBzz22BpulYG32rJ5OPC4dWdMr+fghelSkL271uDthxOevNDFsTIub7cIk5y1tsPYTxhXqICHezNMUyUIMlY2exhVEZBkMq4N4owHI5+BF2MYOi1HJy8LvKggyyEv4IO7bR5MYo4nwTJb8tKKS5YX3D+a48UZz2/XGcxFlzgOMz78xDqn80hCkmcRD47mxHHGqw8m8t4sSjY7DtsrdW4+GJOmQqT3w4yHXszD4zkb/RqOpREECUmaEyf50hkaxhJl5JiaFFS6jMTLErqVgePO8UKo6JbO3sBbphUMq+tSVYX5PMbzYuLUYaNtc7FncaFroioC1zUrXZtjaARpzq5rcziX96NtaFzcbPLyvTFxnFXRSAW1mollqHhBuhyRr6012Og4tGomb+9NvyPuum8aPfNdZvP7ViO/XyzL8s8CryiK8nvCf8qyfPqP7cq+TUuvYJrzIME0NNQkk411kdJ3JSbi2oq4zcZBRpIXTEOxTg8XYr3f7rqMvBjTMknsOjT7dD70H4CmM/md/883fVy3brO52eSnH1/h6fUWs6ot6xqqAEaDhCtNkyARsfwszjj1UtqOIByCTECgF9dqvHG6AESYfDSRFvuHLnVpOgZu1QGrn+WFJXmVFi8nwyR/V0g5DTMcQ+y2cSY393mUsVo38CLhG3VcnbvDiLKEfl3nozttZnHKMBAhd8sxMDWFrmPy5QczEeeqCjceTuh0XD5wvkGal7zyyKtOnTJ2OyOjtytBd9M02OnVSLKCl/cWEtbbs1kkOYskpWka1EqduqFL5EXTJc4KjvyQc2qNsoQX1ttkRcmdqQfAesPk/liozx/ebfHJt4cAmKYuJ9BMOo2OpeMYGrqmMPFjdFVlq1ejZkqn4nAasd6ymIXSARIEAhVtWbpOZ7DLtYaIqQ+nMe2aSdfVWcTyvNpn+WBFyaNpxGbbRlfF8TPxPKZVp+SHH+vxyoFPciq/x1k8TFGWeKnobM61LWqmWPNf2veIkpzVuk7X0Vlr2UyDBF0VFlatZlKvg20bBGGKaepkacZkIK/XmZi1rAjUmiZFbVZopJUw16hIzw3HoNVtMJsKjsDS5RR+xu15NBT6d61ywQ79jMs90T3Ng5RewxJIblEyNzUudC0WcV6R4RW2ejU6rhxsmq7JPEiYeAmrDZO0ED3RtfUaQWwwTmMe36xzdxDgRXL+O5wEZFnO1d1VOjWL4TzieBaxe6HDE9st7p16DGcR17bbTPxYzArVeEZVhOUVxBnzRczTl3o8dA3uH8xYbdqc61ii4aq6RGEkBoedymhhWRq2rXM0DXBMbSne1jUVW1OIkhzb0pcsrIatE1RdzVce+cSZjJCubLWW943zPZtXHs5YaQny43gaomkiBh8uEpquwWCR0K/LQWl3oyn5g5X7DsDW5b1Ztw3mUc5W3UavukeTacQHn99ifxLSdg1uHaRMBlPW1+tsdFxURaFfM5hGOY6t83DgMffEaRYEKZqmECd59XnR6HYdwjAl9ELavTq+l9Bs2YRxzla3xsFYDmqduk2S5liOgWHoWHpEs2aTpAWWLnobQ1OZBymOJYL4oih53yURsx8v0qVZaOJJqLoUsIJkODpa0O+7DAYezz++xsNTj4dDf9lRjeOcuZcsWWlFIS65ouqomRUp3Ta1pTYrrLqFIuSXTmRBuRT3FyXcPZyz0rbp1C1mfkLN0okqSOeZ8+/M6FJWE4NpmFMzNaah7EUNS1vmzI6imDQX96ClqzQcY4lJ0HWVOM4IgoRZWixNEnGac327xU9c7fN3v3ZImuZY1h8IQfmHWuo3qZ2+u8qpb+3y+/er//3JP+4L+U6ts1NhHGc8ttNmu2Xx+qM5Ez+lbmo0LBXX0Lg5CNFUiTFZWAVPb7oy+vFEsKprKk88s8PrL+d0rl0iTVJabYfv/6ufYzgMaLdtVnouH7nS4yevrPH5v/KnOBiHtGsm/+itQ9449NjpCJdEr8Zl0zDjct/ijUOfpzZrPLHm8MUHC57ZrvHP7o5YRDn7tYSjWcybhx5jTyCDjqnz9qFA+5473+HUS0iLkqEnN9ooLQhTcbFkhdit27a+HEfVLI17o4jzPYdrK04leFZp2hqrroWlqbz0yEcLxBkYZjmLNON6p7n80Hzi7SGXVlw0VeHXXj4kjjM+/NgqizgnLyTi5ONPrnI4T7g/DHEsnX/jmTUU4Mv7Cx7OQhxT+EuKovDDl7sskpRZnDGKYuKKp/X5vQn7k4gHk5imrfHR7Q6TKCHKCk4CGYdqqsJX9xfSYUhz2q7J0TwVQbKicG27hWNorNR1PqerPDz1GFg6Wz2XOM05nUXULIn+yQu5yWZ5uQwWjpJciOGawk7XpePqPByJnuFkISHNqqrQcQ0WccE0SDjfFZ5QnJUczBM22zYjL+HOSXUCX29WBWfJqSdi8K1+jcN5yuks5KkNh7tVThrIzapty+asVpquvUlSZe0pXFpr0rI1/CRHu9TjyqrLV+9NuLfns7nR4Mn3nWcyCX+XHui5qysMFxGnRcnNQynas4pnk5clSSoC/E7HYf/hCD9KxclYndQtQ2Ol7dBpWLz8ygG9tsNRGfD5vKDpmlxYbTAPpZMx8eT3Pp3HpHnB1bU6cS7gzpNFwg9f6bI3C/mtdzyub7Y4XcgBaLPj8LFzHZ5cS/nCgxkrNR1Lq/H6gcckzLh1Z8Sf/oFLtB0ZqR6OA/yZcJPunXqUJfSaNhe6Nq/fHbK1WueHH+txMEu5dSLwxJqtM9PV5Wg/ijK++OYxX0SiSh4ezOj1XOquwd6jGa6tL8nka113ySUaTCO2+zU0ReFwIuO6laYtI8AkJ7VLtto2g/8/e38eZ8l91/fC71/tdfbTe0/Pvmi0S7Zl2cbYJmaJTQBnYwshYcmLSyCXPPc+yQVudgJ5EpLnJhAIxEkIJJew3UBiEhsMxgvYlm1JtvbZNTO99+k+e+3L7/7xqz4aySNp3NJoNKN6v14tzak6dbrq9DlV3/oun884oTtWBuF3HZ4C4BNPbqDrGjujkP3TVfwopTeOGRXZtbqrhg+qlk7DMXi249Hrh8WFWrI9DKlWLR660AeUG4Ft6JwKE754ecj2IOTBtxygN45UQGvqfOzRVapVi5/9ka/mtx/bwrF0VYq/PEDXBAtt9f2+a38TUxOc73iTwPF9t03xJ5cGDIchWSb5s3/6drqjiJXOmCOLjcn51w+V/UyW55O+MyGgUbXpjWPqrskfn+th6RpLUxUkqk3j3MaI2aZDyzWYrRl84dKQ6brNU5d65Llku68+y426Tb8fMDNT4ei+BmGS0RmGeF5Mp+Nx+ECLw0tNljdGzLZd3nv7NL//VIc4yekNQ1oNZfOSFrZCfrHfG2MllqlVrIltjWUqa5fn/AYlpqn6rCq2QRAmPL3jc/uRKaJE2eKs95SJd28UMXu8TWec4hXWNLNV1V7Sdk3WRxHb45RTWwGXOmPiJCOMUrpF31QQqGC52XSIonTijJAkOVmmTOg/dKrDMIj5qjvm+OyprVf1OnotaFeJqG6xBNXLlvzWi3/+kJTyR69cJ4T4Z8CPfvlWNxe7aTddFxiaxh2zVWXCKQQdL0XX4NyOCqbeuq9ZjKMPGUWqXANq+qJqKf8v+x3H6Bdu5aapMz+lavvHFht81/2LHJupYegaT68P+bXHNyZ2Aq1CHsBPcoah0gaKMyWkF6VKymBUOJvrQjBbVQKCl7oBtqERxMom5e6lOgeaFhe6kZJCKMQXTU3gWjpOkX1K05yWoxFlSlU7KOxg0iznyLQ6rmMzDqauMSqa001dsOlFPL0Z8Gdvn+UPL+xweeSzOU64bVqN96ZBjltkLpaaFk9tqB6Td9wxxyhMeWZDWS3EWU6Y5kxXDeJMCVMaQjBOlFnsIMwYBCnvOTlNw9EJs4woywkTiS5yRnGIhmruDKKUuYUqb1loXjGppZFKyeWRunBZhsbFjjcR8uujShrKAkf1gVhGDc9P2D9X4/LGiJP7Gli6xuqOMiseBLESWDR1wlS53AexmqKxigvuctcnytS4d60Qy9wYqHKiqVmkeU7VNlgfRFiGM7Exun3OYXukNH2OLTSoWDrjUDXDPrE6ZODFHCo83k6tqUB+rqamr05thYwiHw3VY1I1VGDVC1IMTbDYsAgN1b/Tcg1cU+PYlMOFbYdoX4POtsfhpSY7O35h3WEX4+RKusOxDQ7NKh+8tW5hiK0JIGVcTFoBjLyYew62WEtzvChV/nFxxr6pCvWmy8iPSTIDQ9P46mMtdvxUZX98leEQQl24slwSZXkxbar6TZ7e8nAMwXRdWRrtnpvrto5ABboNR32+u7kKghYbJrat03B0BmFGUGRopmpKuNMppg/744gzW89JYmyMErY9lblYaLs0HINxkLDeC4rMkzptTrVdwihlZ8cnSXIGg5AwTOns+LiuiV549+1m82aaqsS1K9IbZ2oyT9cEAz/GtY3nfABTZZ3T92OCOJ0IMjYr1sTmZ+TH9HoBrZbDXUsNLnWVDdCRKZtBYNOpmKpJuVBrdy0DIZi8xpGZCrmUrO+aP9dt+l7M5iDENlUfzsG5Gr93qqsEP9Oc3jhmse0yDlPOLvd504kZ/CSn5egstV2OTNt84nTC6W2Pp1cHTE1VmG46rHV97j3QpF2zqTuqFN8pJkmzTKLZV7TzFoK9u6Xj7eJ8+rYjTQ5PWTy9EdAbRxia8iudrVqkmVTnX0v1vG13lfTB5eUBui6498QMvXHMwIsn9i1WIUSrCbjr6BRhnPHEmsfIS1icVu/Nxpby/bNtJXFgF+/DRBk/y7FNfeIFaZk6caoa1HczWmM/wTKUlMbBfQ2644h9UxXiJKPmGPSLfrIzWwHzDYvFhqUkUWx1AxRnOSv9mK1CrNUrpsx3LXd0XbkQmKau9KeibCIUvSskujuRGEQZyzs+mvbaC3teNUP1RgqoruDr+fLg6f1XWXbzIdWHznVMhkHMKFa6OlleyBHEGQJB09GpmgaOobFQs+j4CbOuTZQqLaDVoTrxGbqgUbdxLRVUWKbO0tEKf+meRRZbDmu9kE9e3uZjz2yT55J3Ffor68OYYSgnKd6oaIDsBxmWrlGzDFYGEe2Ksn6QUgUNlq4u5CcX6tw1r5zo+6FS8n5kxUPPpJouyWRhuKz0foI4Y3/LxjJUI7QXZTRdg+2RmngKkxynMAHd8ZUBZ5RKcqkuSkmeM1Mz+NS5Pu+/Y5owy4nzjF6UYBUnGlMTdD1l03HHvMuzO0rtXKJ6hvpBxlLTZLZqYOiClVFIzdZxTBXkJWlOyzV4eHnMVHHcLVfnaLOqvPuimH11h8tdlXHYDFTv1sGGS9M2mLItpIxo2RZhmtPzLd68VOELKx6OqXHHQo3VYUyr4nB52+fClodtG8w0HDa6PkHRxF93TfpezL6pChe3RpNSkKGrkfdm1cIp+vA0oZSTDU1ZF40K651GxcJP1LTXdM3imdUhzYo5mWg724kYhQlH5utF86oSAB0GCQ76xFwXVJBzsRvy7qNNMinVuHkui+yf8pK770CLrVFMvaakJfphRrfIZlUsDUtXvnNV22BbqAu6bRvU6zYHZqtqmm8Q0ukFqjdjsfG84w2ilJpjstB0GUcphqExHseMQqWsHiVZUZ5R74tZTLMNhiHtqvp7RKmaCLRNfTLqbZk6MslICzPpXKoy8OogYqlpFy4CSuJEE9Cu6AzihNVRRJiqoZJtryj/hOrCEqdK9d3QNY7M1ZTcyRUlj1zC2ZUBQih9pUcv9rGKfarZuvLslEx0m0xTwzB03nS4zWovoNcLCMOkCEh12i1VqkqSjChV5Zpj8zU2Bsqs2i8kI3ZNfHVNEMTKUzSIlcK9EEwa7g1NYMzWGAVKPb07jlgrhgw8L+bQgSbdIMULk4lJb8+LOLm/pfrpQvV+RInSTWpVVBZR14BcNfJrAjqjiHbNIk4zLm+O2DdXo2IbSjG+H0w+80GsvOA8L1bfZwkXOj4n5lWpPc0knznfwwtSji81OTLt8tufvsTt+xoTb7pxnCmZkoNt+l7M0EtoFmU6BESp6met2WpKzS0mJPtBxqG2zZ0LLg3b4HI/Kpq8Uwa++o4CdHZ8xkXW03EM1nsB28WywI9otSsYhhpMyS2dhmsBMaMgYTAImWrY+H4x/JNmzM1WaVRMokI0VQkQq3YJU9cmshFRnBEL9TnZlZFo1mxGvurBO39JWdwcnKlSqynhVyHAdU3CRH1Hp6vqszmOMnIkUZrT9dS+ZUVDuWEomyPV2K+RJCp7OhiEk1JznsuJNEaS5RMV92fXhpOs2mvJVXuobrGi38v1UP114IeAo0KIx69YVQc+fT137LWiXjH537/uGKujkA8/0eFDT3T46uNtqpbGlGsRpBmuodO0TBxdm/Tj7G845FJStw0cXadup7z5UIuun9JwdJrFCPk797c51R3xe+c7hIX6+cYw5p0npnn42R4Al7vh5I5xfRApY9tUaY28ZanGU5s+/+3xLR483FQCiIVukGOqqbWZuqOUtIdqomVjlHBp2+O2+RpVW+PsVkS96VC3NS7uBFRsg+OzFdaHMeNiSuc9J6fRNTgx4/DIypg0U7Y3Hzm9g2louKY+0ax69+Emz+yocslXH2vS8RPqts4TnRGmpvoI3rRU5ZEVJX4pJTyxrvy5js86DEMVpN45W6UfxRycqxCmGRtexKmtgMNtm4cuDtkZhfzR6YyFlsu9C1UGUcpyP2a+mvDomvo7HJ2y0XXBo5eHPGPpSOB8JeSuhQpneuOiJJlPFOaPNmsIIfjtL24yXbdpuCYHWhYHWmpS8vMXByzUTc7ZBufWhyy0K5MMlBBq4urITIUwVdkoWVxo00w9p11T2b1cKqHFmmPSrpj0/YSuF6MLwWovYLpuEyTZRPPo9NqAD9w/z0zFohuoEm2cqpPg++6a4VIv4rNnd9jsB3zViWlWehEPr4xZalq0K0rwFCAtPpdzdXUBWmyazFUc9tUE53oeWhGkrAxD7tlX49Pnetx5dAo/SonjDN+PyXKlqxYmGfvnajxxusN60SMVJRnbQyXsCWqiUBdKab3T8bi4NWKqeA8aRYbEMXUOLTV4/KlN7r5znu44YnUQ064YzNYtxpEKoLwwIU4ycilZ66nG8HedmGK2anK6E/Dwsz2+7S2LnO4EJLl6f5eaGk9sjrm4ExSq+qrE+/iFHTRN3RBsjWJGYUKravHAgRpb44Rnt0OmaspnseYYuHaVi6tDNrq+6kkKEu482ObZQsB17MW0GrbSz9FUYLk9igulazU5evBgi6OLjSLbqdNLMnp9laH97KkOSzNVDE2QS5gpbJV2G5lzCTujkDSXLLVdarbOOMrYGkbcs7/OM+ueCoZcJbNx8XyHd73zqDqHOSajQPm33bHU4PTakPsOtBjHORe3RhyarU20knbfb8dSAwK7E8Fbg4CLKwMWF+pcvKR6ph440mbbU1OI5y/3edvdC/Q9NVzwv7/7KLmET13ucmbTY6pu87sPLTMcRqydOsvs0cMcOz7De441mXZs/rDh4EUZ7YpBkKibRdfSANWIPghS6qZGmOQca5mkus50xeRSL0LXNZpVi0s91ex+50IVL8qpWpKWq/NfvrCOFyYERelU1wTtlkuz4eAHCb1ewDPPbKLrOq22y9HDLVpVm2fXBvT7AdPTFc5vDtnpBURRxpEDTfrjGNc1uf1Qm6cu7LA4VeHuxSpntoKJbdA4TJhvumz01YSdaarJVymh4qrPfmfHp1azcWyDrY5XlNRjWhWTMMmVTVChSTYIYlZXPCrWNE1Hx9QFUSa50I243PEIC+Fb09TpdgPqdYssk3zbVx/gqfUxZy73C60ti3bNYnljxHAYomnaxAYpy5Ro8G5W+bWkzFDBfwE+Avz/gB+7YvlIStm9bnv1GmLpgrppMgw99k1VmKkq0UBH1wjSDEMoG5OlRoVhoLIvFUMpqPcidYHMpOT2qQZ3TsNWEHFux2e+Zikz3EyJwLmmxkJdNWynmeRgy+KR4k5NCKg6qhTTrJiEqSqvjcKUtmMxU4tZ6RucnKlyvucTpZKFls1MxcIxtKJZGp7aCKjbOpuDkDDO2BhGuEXZaRRl6ELHNlVZRBPQGSoriqBoAu76KTMVJaxYs5W4qRCCBw/UWBsmyuuwZhCk6o7WKhrJO+OEsBCcyw2le1Mxtclo8F98ywJpJjm7HbLjKbHRpaaJa+jEuUEvVNYVVUtnFGaTBtO/8OYFNOBzl8fKpFZKtsYxHzsXEyUZDdfi6c2A7jjijsU6B1oWz+6o8tFi1eGhlQGnN8Y0KxZJphSkT3VHhaZXzm1zFYahEpU0dcHBls1cw2FrlHB0vs7p1QG2oamJvyKQSDN1Eh9F4rkm78JYGdSkD8Bi02a9yEikOcXItaBZNentRByaqU6EIQ1NaU71g4wDdeWlZ+oaXpzhhyl+knOh4zMaRTiWCtR1XbBZlBLPrg85vlDn3sUKZzohfpITJjmdccxi0yTJc0axCsLbjknHj7F0wWzd5nzbJU7Ue+N5caGoLOh5MScXaiS55MKKysAFsVImD+OMzV7AfNtlVDR/tyqW0r3R1RSXEEocchQkWKay4ngkXsHQBVEC22Olq1azVLDuy2wiAeBaBrahVKoHofoM9YIU1zZI85y6rXzk1ocxy/2YlZ7KUBq6RpYr4dT5qQrPrgx491v2c2jK5omVhKqlegDjVKmTG0WDeM1RpsUXgYWpCs2KxcPPbHJpa0zVMeh7MXox8u6YOlGaE4WZkiGwDGw7o9moYhoa3VHE2uaImenK5GJRcZWQbFqU30xDZSCFUFmMZtWikqkerzTLJxnyYaA8JFXZX2XVRlHGM892casuNcfk6IzL1iihauucWVc6ZZap41pK2NeLUsaRyno1XZMgzomTnLqjzjP9QJ2fbEunUjGJ4oxm0+bAbI0wUSXJMMnw/YRvu3uex7Zcen7GT/3hWWYbDqah0aiYDP2YrY0BoR/iTs3guBYzTYenNgN63nBiKA7qhrBi6WyNEmZrJoa266mo4acSyzERuXxOsb/IlJ3bGFFzjMnUdZar1oxxkKhsYJQBMdWqpdoBdEGzbmPbBk9tDWhNN0jTDNcyGPhq+tVxlPZWbxAyHis1/2rRIN4dhgwLO5koyeh4qtQ2W7M40HYIk5x+qL4Pu1kjIQSjwg4milLcIrCquyYXxjGOo2MX/Zg+yj/Wj1K+6miLpzZ9vDBl4Cd0RhG5dJmrmTzb8YoMrhLqVNN8quSoaYLf+dwKtZqFbRvoelY0y8vi+Wryr1631M1frJr5dwOs15I3vDmylHIADIDvBBBCzAEOUBNC1KSUl6//Ll5fTF0jyVWp4M6Fimo8dywyKUniBIQyJz27M8LSNU5O1xmHqVJW13X2VV1yKfHTlG6YMOfazO23ibIcP0053/eYqVhc6oUs92MW6iaplDy66tOuWZycdbmgCbw4RyKxi5NFyzWwTJ1TO2PCRNKqqJNEP8iomBob44iZisnqICZKc9quwdYwZENKbpuvsa/tcG5jzMBXPQO6EPQCNU2VZJLlgWr4/Ma7ZuiHKc9sPqf+fN++Kpf7Mb93dgfb0DjarJEz5hNnPdWoG2fcf6hF3dYZRRn9UPV2zdWUhY1japzdGpPnEtcySDNVjrpr3sXSlRr9jGtxrucxU1EXs0dXxxybtnn7oTqbo7gYXa9TKZo6P3t5SNPRsXSNtx6o8cSGT9XSuNQN0YTgthmXmmkQNHI+e2HApR2fb7xzhnvmq0RZzraf8OnzPTbHCVvjmHcen554kj2+5rOvYbIyiNgeRexruyxVDda6JmfXhtxzqE1WNBpf3BrT8VI6RXDTriihzhxVslps2oSpJEwlEggTdeHVNdX/s1Po4kSZpD+OJjYefpTyydPbPHS+S5qrfpA7DrRIkown1zzuWaoz31Q9V16cMw4Spuo2moD7DrbYGEY8suKR5pIHDtSomcoLTkOoSchIXZhbjsmOnxY9Kkpn69ktpWTvuib752rEqSrXVSyNy72Ig4t1tgYBzaoys56q25NhjmHwnAp3mmZs9wNuX2owjjLiJMc0NE7MVjjX8Tlxcp4gzqjYBkM/Zr5hK40kKZmpWZi6i6Gpi4yuCc4t99kcBLz1SBvHUJrKj6/7pLnqNbzUDTk+6/L2ww0udCO2hhGPr45xbRUELczVmKmahIkqJVqGRj9K6AYpiw2bKFMBzP62zdpA9bGcu9zHtnWOHlDlsqfPbbMwX+ftJ2foFTpQmoB61SQo/ka2ri7eD53e5tLFLrfdNotl6KSWpFV32OiMObK/yVYvoFYxOThTY7WnrJjmmw6aUA3y03WVFXxqdUjNMRj4iWqID9TfKy0kUy5d2OJr33MbQZTy8ac7LE1X6AzVDY1ragz952ywHEtXDdFRWvT5KTukHBiEGX4xWNCqWDy8OcYbevzlD9yLZWgsNFTQNV+3+PN/+X5++ZFVgjij0w9458lZ+qFqjH/i3DZPPXKek/cdIQwrNBoqiPm9jzzBwsF52m2Ht9+5wGJTTRVWLJ3ZqgqMlFacYE5X5e+DbRsvVkGlbaiJ50bFYmug7IW8KCVOc45NOayOIuaqFuNxhO+rXr4gSNna2s2MKyV2yzJYWJrCdQ1s2+DRx9fRDTWtlyYZ43GMN4647+4FGhWLlW2PimOQZTnnLvUxDI2La0MurQ+ZarmTwPf9d0zzh2d6xZSdavEIk2yiSm7bBvcfnVYOA1HKt733KBe3fUZBwnJP2UA9ttKnXbPZHCfoQnBoVul3TVUtdjzVb+eFKWFhs5Nlyq9wNyuq1M8jvMIWyTQ1wjArxFRV6c/3E7qFz2SlYhYG0jciQ3XryyZcU2eaEOKbhRBngWeBTwIXUZmrmx4BnO97zNYM9jccFcVnGX6SUrOUplGYZeyEqr/q82tdVr0AP00JMtUvYurqRGXrOqM4xSp6XVaGIUkmeWbL59luyHTVYMdPWeuFtFxVt1d38lqhcKxOdKCayNVUGUqHqqJzeRhM7uL9OOfZ4s7ci1Iud5Vid90xmSoEAcdhwrDw2srkc+O/oO7ihRBsjpVVyfEZh9vnqxydsifBkaGrYHJl7LPSVxfAwzNKObliasxWDc5uKoPOKFUiglEmOb87PVVXukQdL+HcdkjVNAiznDnXxhAajqkx5SiXdFNTUgv7ay5Hpyo4ps5jax6PrHhUbGMiqCoKHbC2qxqyD005aELw+eUx53s+ulBWNtN1h41xzOdXRqwMIjQBDx5ucXrDY+DF1GyNWdfB0jTevFTl7rk6bdegXbMIkgxTF5MJKS9KSYqgZzeDECU5MzU1SGDo6sRvXNHomWTq7jnPJTN1h74XT9zgnWIEe9f/0TK0SS/NOFQnUdfWOdSyqFbMoqlZsNS0WGpaHG7bNKsWC0VZ72DbYqllEyYZJ+dcpZOTZizULGq2TsuxWKw63DdfJ8lygjinF6SsDVWZqFW1JgKkdyzWJqrqz26HdAZKIsO1VKP7riBgtcjqGJpgqmYX9i+qyXi+ZuKaOjN1i/ffMa18A+MMy9QYjSMsU/X9jSNV3kuznGRXdDFSTf6bfVUibriWEoCtqkxCkqnsUpiq9zfL1Xt9advnUmfMM5d6PLupylyqDKvkQCxdaaGtDCK8WA1DhEUZbBTlrOx4hRhkThiqTMQz53cwi16qvp/S82KGxZi9QHnXPbPcZ60XMAjUHb9lm4zG0cTiJ8uV3tDOMKJdt5ltOHhFxmi3UXgYpliGPulr0Yv+u6pjKJX2UHlJ3re/zqmLXWzH5sC0urDXHGXXk2Tq9wRJThRnPLutzhVSQn+sBGinKjrzdfU+asDGIMLWVU9Tu2qChMhXAX/dVuKxm4OQ5V6ABJaLTIljG8r6yot54kyHtZUeCMEdR6dZWKgTxypISccDajWLet1Wme1Y/Z3nayqYsgyBY6rvsyZU+bhZDBa0HENNu5liUja2TY1W1WLbT4uSL/iJKrFVqxZ/5b1HOHKgieuq722e5fhegl8YDwshGI9jkjhhZqbC9LRLnqvm9Pe9+ygb254aEig89GxbNfGPxzHjcUS/H7K17XHqUo/zqwP+78+voWuCe49O49jKNqxZtahWlTp7tWIqW6OiD24U5YzDlGbV4omz26zuKIN1U9c4v+Wx2LC4c8FlsWmzr2Fy25zDTNWYeD9KqUp2o1FUGMgrQ+goUtm5NM3x/fR5Rsxpmk8skUbDiDjOqNXsGyab8MKfa42nhBDvE0KcFkKcE0L82FXWCyHEzxbrHxdCvPnV3v9r4Vpb/X8SeDtwRkp5BPhabpEeKoE6CTVsnYZlsO2lbHoxoyijZhhK/0TTuLAT8uSmTz/IJqPjSZ4zjlKGkdKQcg3VcK0JUdjGqBPa5kiVqLZGyaQnoWJq1G2NlX5MmORUbdUw7hiCTEKOLJRzBW3Hom7rzFYt9jUs4kwJRZ7vBOyMVaOhUl62sItep76fMttwWJquFNktaDmFjlDRkGrogrOdgLmqxb6GzaGWTcMyJ+W7o1M2+1s2H356h9Wit+u+xSpzTRdNQFxIBxiFZpEX55zdUn1Fpq5x/1KVhmswWzU5OefSCxO2RuqCtGvjUCl84lIpeXLToxclDOOEP3PPLEemHM5vjooSmMriXeqM6YUJmVSGw5auNIO644gvLqvR/uki0BlFGTtj5bG3NkzY37RpVEwkcLEb8czOiLVhxOY4YRCrwPLexQrDwrrBKfoMkkIRuVdMCA1CdbGsWuoC3x1FbPSDQpYgZxSqv7Fr6TimTrNiTk6ccZoTpRmzNYuGq4Kl2xdrLE5VJhOKmoA4yRlFOXNNl8quAWuR6RuGSkssiJXeVT/I8OK8CMpU9sZPclq2RdNSk4K7zZ8X+yFR0fCtrD3U+LtpaLiFZ1ua5eyfqQJq1HngxxNrlyyT9MbRZAIrl0w8xCxLTRmpmwvVZ1QxVCl79zNiWUoXreFadMdFSTpMGQRqUCItlOPTTHLnwba6m+/HrA5idfFIc+ZrJsMwZaZu0XB0dnyl45QUqvaHZmvcu1SjXbMxCw0tVXbXSTJl49FwVABTsw38OKM/DHFdkwfumGdhrsbZiz2klBiGGgDZ6PlFxlWf+O/tvqedYchGL2CqYXPnbTPEcaa89aKUKFFmtL2ekkjRhCq1GprSOBuFSsneKabT9GIaNy8GBpaaKlC2dI0wzRmPY2bn64wj1Ri+NF1VwpKaoF21WOmH6LrG1qDQ09K1yWi+pasJ4ThVGcB79tXo+clEfdwf+9gVm4GfEKWSfpCyXnhdfuT0DlVXTWimWU67YrDW9el3PaIgojXTUnZAmpiUpCrTsxxaavDmI1NkuWRcBLKjKFd9QT11QxFlOYNQtR2kubKbifKc5X7MxW7MnYsV7tlfV1mXYrjh3E5Ax1NZ1zhWQxH1okHeMFRZ7fbb53jLfYscPNDE82K6XTVAoGnaRFIgiRKCIOXM6oA0zTl3qUcQJCxve5iGRqvlMj3tkiQq+2PbRiGiqYKWUZDQH0fqJqmYHK1VTNo1m7mmS5TmSqLCVMM2rqXTqFi4rhKuBSV5EWc5jqkqCVVLI8mVx2zDNlhou5OerN0JvvE4JituYqRkEoyrUp8kjlJ8P8Ebx6SJKtWblq5EPy39hiilq1aA5/9c43Y68POoQbg7ge8UQtz5gqe9HzhR/PwA8Auv3p5fO9capiZSyh0hhCaE0KSUHy9kE256ctQF4EOPdwp3dZOmozNTNxmnKbmUdIOY+xZrPLo2xiv6glQZJSbMMsI0w09T5ioOcZbTC2M2/RBdg+W+6gdS2kcRQaQCnTNbStHEj5TysqELbpt1idOcUZTiRTlxWkw0GepC0LJNukHM0SmHy/2IY7MuDUeNzd/RUKWNjaESfjMNjaUpl7WeaniP0pz1Qcpcw0YXEBRTId1xxO+f2pnoB+0auzqmzl9+YIm6lbIxipmumMUUWUTfjydZr4MzNUxdcKGjMknJrtq1bXCmE6pyRjfiYsfj3v0Njkyp3qapikHD0XmsM2B1EHP7nMv57ZDf+OI6jqlzZKaiemFaLvcsVvj0+T5xpkQl/+sjG3ztnbP87iNr2LbKKrmWwb62w9NbAfsaJlVL480LDd55oE0uJZ9e7tO2LI5MObRdJRT58OWRatLMcj5XBIHzTUf5XiU5X320yShI2BlFHGi7VJaaXOyMWdlRejv9QGUod5uvd4nTbBIcVB2TnhczVbOVzUhRzgrTnHbNZqaq7mL14uTieTFxnHFkscHvPnSZB+6Yx48zPn9xOPENM3WNQ9OuMmMuLoYHWsquou2YXOqHzE+5TNkWnSCiG8ZsBiE1U33dj07ZbIwSZRtkaHS9GEvXcC2Dhy8NGQYJJ+aVV+Fq12N72+fO/S22RxGVislc02V5Ww0FWIbOWqpsbqamXGbbFdaGCQ8eqPGZS0OGcULV0ibq1HkuidOMO5YabI9izq0PqbsmApipW4yijKajylRVS2Ou6dAdq0xas2px+5xL1dJZ6Su9pK1hhB+lHCxuHGqOQdeLeXLNwwtTpisGXT/jTftrWLpguR8zV9N5didSvS/FTY+mCQaDkGd15d3YMXXuPznLZj+g2w8ZCTAMjWrxXQ2LSUFdE5O77DSTEx9O308mGY5Ww2ZsKFFKdQOihhfWez7TNZutgcp+GpoyFG9ULC5vj3n3iTZJEcC2KhYXtgOyTHLXsWnOb4xoVEx6YzVKrwnlpzfTcDg0V+P82lDdtBVlqJptTGRUWhVLTU+6OjPFxOmjT2/SaNf5lncfIUxzNoYx3XHEzo5PpzPm3W/eX1gHaTxxZsy/+b3zrK90OXH7Ao5tMPZinr7YxbYNKhWLWs2i0TjIiYU66/2QNx2oYxnq/d8dQLB1i62x8sA0isnW6YrB1lj93RYbJqYuWKiqIYe6rfPEmkfbNbiwrWyiul3VSL5/n82ZTsiFyz2Gg5B6w6HXD9jpKgmLhYU6ui44vNBgFCScv9Rj0PeZW2zyzvv28YmHl5mZqbI4VWFtxyeKM6IoZWbKpVFREhj1qoVXZCh3S2yb0qNSUYbMlqWzuuOTJBlOW5/4LO5Ode6MIjUxPI5wXZOpmo2hazRdg7SQRtjfsqjaqhdQ1wRxnvN1x9osNiw+cxb6/bDITqUkiUae59RqFsNBSJqkGKaBaRmACho1XaPVdmk2HSqOwe37GvR8dX7/zHW9sn45r6Ap/UHgnJTygtpG/DrwAeDpK57zAeA/SRVZPiSEaAkhFq+QfnpNuNaAqi+EqAGfAn5VCLHFLWKOnGSSjVFCnOZs9HyOz05zfEpNnQ0iddFxTTXld+ecyyBK+eKaR/OwhalpOLrKSiW5VvRSZVRNg1xKNJRsgJSSQAhGgQqupioG256kMwhxLZ3jMw7jIh2+q5czipQ9TJjmfGa5j6mru7ckV1mZlqtPdFhsXUwERtNcUrc1XNPizJZH3TVpVFRpSAiNRtEYGqaysBdRd+BBcXHRijr+pc6Y3/jiOvfubxSTTWpkeWucFL9H3eEEcYpdlKXMonnzvSfVHelDF4e8+3gLR9fYGISEaU7NMGi5Bot1i2nH5uH1AbfNuuhC0A/VnbiUsDGMMXR1R2cb6mJ/oG4xDhJO7muyv2ljmjpvOTrFQt3k/E7IQt2k52e0HNXM/MeX+xydsrF0NUKfIwkSJcVwx1yFbqCyUqc2AwaF3k9nGBIVGR2/kD8wNMG2l7AzCmlULA5NOVzcCcglTLs6QZIp8bxRjFmU/qq2MhbePYnMVE3OjiNmG476PaMIKWFf02IYqsZfgcBxTKYbNifnK6x3q2yPlKhoq2YTpRl3LKopNa/ITolc0HbV33S1F3DHvItrasy7DhIwNQ0/VX0nrqPT9dXvsQylnbY+8NR4tal2dF/bZeDHrPRDRkHC1rbSptI1JeMgJRPvud2MihCC2YbDhVGErglsQ3BuJ1ADHMV3YVeR2rF1RkWpM5OqlKMXo/RelNEfRzwWqMDDi9XFyLF0slQyW7fJCz+31a6HY+rcvlgnSEyGQcoDB2s8sRFwcWOEX8hAnN8pskC+xlxNBa9xJvETldEbeDHrXZ+F6SqXoiHb2/7ExsMydA7O1tjpPadGHsUZbs1Cmkx839a6SoA1l6qMNxpF1GpWkeGy8Ypy4M5QXQzjlhLVdQornpm6w3rfxzbVZ13TBLahEyaSs50AQ9eYqpl87sw2d902Q6uisns1R90E2aY+sTKZa9hsDSM18Zbkk+/+bE3dZDy5rm4GBl7MTsNUfWF+TJpKVj79KZ480ubYQp2ma/Cff+0hmtNNmlN1dSyaUJ6Q4xi3YpImSvtoXNwEOI6BXkitaELw9pMz7G9aXNga89S6x5sO1AjinBwl/QGwOYywiuzSjpfScnXCwmC7ZqlG/lGS4uhK5HepZdMLUkZhMlF+n5mpsDRVZb2vPBrzLKfRsNnc9AjGATPzDY4farPQdrl/X5X/+uiG0uFrV9A0wacfW6PRcDAMDdc2ODRfwzF1Lm6NWdtQljG13endXNJquWxtjen3Q6amlGE2MCmvzU9VlIJ6cX5Uvqu7VQTVe7c7RdsuyuutillIgahMNDDRogLojFMV0No6nqc8+/IsBwGapmFaBoap5Bzm56vEcUYYppw8OsVsw+H4jEPFLFxBQpXZfY0RV81IXZt6wxKwfMXjFeBt1/CcJeB1GVB9AGWQ/L8B3wU0gZ+4Xjv1WiKAe/dVmK0ZrA0THl8Z4UXqg1yzNaZck5ppMIwT2rZF27ZYHyQ8sj7kqw+06QSqKfpAvYKXZARpxiBOmKs4XEx8xqGyNDk2X8fQxORuMsskX3fHNB8/06VdMajZUjmJF6PEoyhnf9MsxN1yZqsGq+OA+apNmKqgYWUQMSjsZGxDKX33/WRy91l3zMnobcvRGYYZ5zo+7apFy9VZ6+W8+WBD9XX1QxZbDm/aVyNKM+5eqHC5HzOOMmZrphJVrBp85vyAkwvViedav1DobVUsqrbOobZTTO8pa5HPXBhSsQ3eebTJoyseq+MAxxSEWU63mJJsWiantj0uFdYqbzvWpm7rXNhR2kyfuzRipm5xbNrmYsfi2IxdlKpcVrrBpOfi8dUxB6dczm6HbI8ilTmSFjlKJDDLJWkuebYb8syGx8Eph6mKQc+LuP9Ag36Q0XKVBMEfn9lGCMFXn5hmGGU8uTxgc9vjtoMtpioGawOdzjBkoV4njDPaVYt7Fytc7keEiT5p6M4y1eey0g+RwKgwTr17scbjK0OSTHJha8z6tsf3vucQ68OEh86rAdqFtosQgstbY1a2PQxDY6VnYZmqsb/rp2wNAqpWlcdWhlRtY5LNXB77HG3U8NOUKM850wk50wk5Nu0wUzF5puPzm59ZZmGmyj37m1zqBhi6YGccsTSlJlqnajbejJKNeGZ1gFc0O+9azkgJnX5As2ZTc5TvXHcUcWSupvTR8pT1cUjbMSeZkiRVpblL2z71QltoN1N0eTvm+GJDaThlOV6oTIRvX6zx5OqIS9seDUdnFCk9r7pjcmzG4WI34vKOz0MXh1zueHzzmxeJs5xPn+0SJirwfGptxNTxNuuDiM1RzFzNojMM6Y5VP8ryxggh4J4TMwgB51YGPH2py/GlJqapk+cSz4uZmnKZa7pc3BopE980JwwT4jjFdUz8MGX//uZkCiuKUnRdY2mmOjFH7hc+bI6l85mz20pao2qrzAPqc1p1DJ5YU16aB6ardEZq2my+5fLEpR7NqqV6N9Oc4ws1zm2Mmakrtf3Ty316vYDlHRUs25YaHtE1cC2duZrJdMXkk6e3uXCxT7Npc+hgk8c+9U/4iY+eUZptXsJf+UtvxwtTvuP+BX7l4VUurQ1ZXx2QZRlT0xXuuGc/UZRydF8DL1Llx6cv9+h2A1pNh/V+yLfcNselXsw9C64qQ7tGkRVUWbK6Y1KxlFxC14vVhLWpMY6UYGfHi6mZBuMkJSjEhQ+2LaqWxkzD4fELO/ypu+fZ1zD54qrHt7/3KL/3yCpPffESM/tmOHR0BtPUubg6oDeOeOz8DqCCsG95yz42xymXtj3OX+7jOAa9cYRrGSRaThAmnDzc5smz29iFQGccZ+i6Rq1m4Y2VDpgQFNlIQbPpsLwxYnFWlcx3bYmSQvV/6CvbpoWWO2nX2B3uiTNJZ5wUPYuqpcDSNB5ZGzFbMwiTjLfdu8hjZzqT/XAcg7ecnC16L1X5+Z3Hp9kaJ1zsjLlrX53ZqqouBElKP8g4Nm1jXi1ddB351m//jjd/6pMf5z1f86cmy/I85yMf/h8AtwkhHr7i6R+UUn7wisdX29kXhmLX8pzrzjUFVFJK74qHv3Kd9uWGoCaxCo2WqsFyRzWkDqOM22YqxFnGmR0P19KYL1LPDUdNUFm6Ro7K1nSCSJVQdA1XV8KcBxsu3VFE1VYTO1kxytotlG7twoV8daBOJEkumbaMySizVWQE7KKvKslkoewr2AkStn3VwDz0E47PWWq8OsnY8ZXv32LDomJpPLnm0acw6S1GsgFMQ2NrnNBw1AXyrUt14ixnGGVKTsJWdjNepBo6d7xCPK/RJspyRqFqLNY1pc1zoKma7D9zaYguBF6kvO5mmy66VmF7FPLH52JqrsndixWe2QyYrhj4qZJSODRXIypsFyqm6nsxdY1WRQmOVi0Nx9L5g6e2CQsF6optEGU59y9WGUVK1mGlF05sDvbXXZJcAj6fuNjHMVRGxYtSTm94ypZjEHKxYjEMEvyGwz0LLrapjIHTXOIYgtsW62z3/ElPj2PqhEnGcl/1Ae0Sp7Lw6suYqaqppkGhj7OrQRSnGU+vqSDq7UdaREXvT2eccq7js7w64CPFyfYb7l9ksx+wuTWm1XInnyFdY5LZ2dew6Pu7I/Yp/TDFMTWCTHnHaUCUKUNXc04ZNpu6II4zjs3XaTj6JOAcBcmkBHdi1iVO1VRed6T6RJIsL+QjlH2MrmtKAyhWWZ1OZ0xvoU7L1TE1wZmtkCPTaiDCD5VkgBLsVHfslqmRFdN2SZqz2FD9b51hSKtiMQ7V5Omu4nPHUwMCu0bAO17KxlB5HkaFzpsShtWVETXKAF0TgotdNc6+W14CJhm2SsXEtQ3uWKxydsufeKMBvPn4DGGScWpZ6fzsbuNYymokSVRjs2kovaTtQcioyEDu+qYNK6aSxvCUN6dEeTa2aza9sfKXlFJlbixbNZr3xjGaqbImUeEzmRUNxwBrXV9lizNJxVaegXcuNbnzUJtWw6ZRsfDChKZrTQZcdrMESS7RNXVBXl8dICX8k4+dZRznHJ+xubADXU9N2/7+2S7bg5BeL8SwDA4tTeE6Jv2BsrN588EGp7cCLmwMVTmz7RBGKU+d32F8/z50DaqmQTdQgzlJ0bsXpkrXzovzSU8eqCBkt8ctKcQzH1/zlcaYEDiGMnY/Plthe0aJiXY8ZeYtJbRbLqZtThrOZ1vuRBJktu2ia2rg5I6ZGobuUTHVTZFXCKBu9lXPmW0bnJivYZkqo7cr8NvrKZspvTh/K8XyGMcx6PcD6nVleJ5LqbTuHINnVofMNZVnYpKqUj2o78GuRljbNSbf72e3A5VtHKcMwxQC9R3QhaBatdD1VHlsusokPc0lcaqsi+6ZqzFsJSTFRLdlCGJfEibKccMyNIZR9mpdQq+J3/qNXz92+dKl83/0yT+ZqLT/5m/8Og+89UEe+uxn/gnwT15i8xXgwBWP9wNre3jOdeclm7bLXvkAAM8ASURBVNKFECMhxPAqPyMhxPC12snrzTjKaDhKNG+xSNd6UcbaKGTbV7Yrq4OEjhez6UVYhiBMZJG1Ug2FgzghzHJ0IaiYOkmu0tZTdaWmrmlCGQcb2uTi0A+UqN/6MKbjpdRtjVGkMk5xkvHo8oiLvYhz28rfLJOSjp+oablEMgqziViorjHxT5uuGKrPZBDx5JrHOFTj3mlxIhsECXEq0YRqYPbinP44YieI6fgJjy6PeGR5PJmi2u3VWR/Gk4shKEX2XR2iuq2TSWV6fHFrzCBIih4TdcF9ZjOY2HcM/YTFmoOlC55cG/Obj26wvONxbNrm0LSSBgiL3oM8l0Ujfs7lbkgulb2DHyQIBN909yw9L8HUNLxIjZdXbYOao94DP824NAjo+wlhmrM1Uvoz+6cq3Le/zuq2x3TDYa6u1L/jIoNoGhpHZyusDiLVx1E3mWkXKsyjqLDz0CeK8I4h8OKMbpBOyrOmrvScdi0m7KK8leWSnVFIHGeq+XcUMtNyOdfxubQ5wjB0RqOYJMnJJVeUbJkEw2vDhGHhETblGkxVTaJE/X5L19jxUvpRQpCqiagp15j0cqS5UqqvFpOCUspJb1cQZyw0HaYbDi1XZ7pms9S0JnpDAL1xRCZVI/SuEjPAcBDQ2xlzerk/kc8Ik4yznZA7F6pYpj4pG9qm8nzbHSpoVCzuPNjm6bUxlaKJ3dQFddecWJQIAV6YTAKKRsXk9Kav7v5NnbuXaiqDs+bjF+XC7ihiHKbsn6qQ5s9NWD62PGBrEOIFKfNTFe4+2ObATJUkk4zDFNdRCuHbw4gc1evoFIHz1iCgXbUmhra7wZdR9EralrK02W3OTtOcoNDvahWB00ZX9VC6ls54HE8CI61QSA8LNfJdLTddU9IMSZZjWyrY8EKVjXYLaZGDMzXmaibtqurPmW/YuJbByfkKliGUR18hy3J2Y8Ta5giz0JJzXbP4vEZc6sUcbKvyepZLPvHoCr6fEAYR9brN8aUmdddkZ8dnZ8fjYjdiaxAwHMf4QcLdx6YnxsG9OMYoGpBtXWVdHFPdrC02TBxDY6UbUC2EMlf6anLZ1JTJuKkrW6GWqzKvUxWDmqUsevphymLLnbgtHJ222RhGLLZdjt62wLA7xPeVunyz6bA4XeHEQp25pott6DyzPSZOJe89MsVCy6XiGESJKtsZuuAtR6eUObimJodrrsls06Hddjm42OCb3nUEqxhSSIugKEkyqq6phhIKI/pGIaA6DlN64wi96MELYmVQ3Rmr9znOckZRxuogUnIkfkLF1IiLG/5RkKim9lrRo1a3marbhEnOfPFdXmiYBFlGlOeF3qC6ERaF6n8/zGhZ5kTf7bVCSnnhrQ++jd/49V8DIIoi/uW/+Gl+/l//zMw1bP4F4IQQ4ogQwgK+A/jQC57zIeCvFNN+bwcGr3X/FLy8DlX9tdqRG0WS5VzsRZO+pNtmlYdZDlzYUZNtuWQi4jZdSBJUTNUzlWSSI40qvShmvuJwYTAmk5JNL6JpGxyYqvDZzS0GVYu6U1EaOeOI+abDZ873WGi7nN8YcfDkNIdaDp+9PJqUsC7t+EVwlbPjp9w1W+NLGyMgoOXq3L1QoeMl6JozqbvbpppgijOlE5RLydffMc3Dy2O2RyFNV10IlntqrHq2qppVNU3wmWeHaAKOzVaK482YEmAZgtOb6m748FydT5zpIYrApV0YeJ7dHPP5C8pv7q+94wCr45An133ef9s0//nhVQZByve+4wBnd3w+f6FPkuckuWSmbvPg4QaPr3qsD9WJ5WLHI8sl9x5oMlsz2BwlNF2Tw1M2p7cC3nqozqfPCb79TYtkUrLW8/nQUyogONqs0rANBmFK1dL4yKkduqNINQdLyW1zVdaHMbNVg8WGxVtPzDBdNchydWyjMOFjp7vESc5yL2R1x+O22XkGYcZU3WboKz8/L1PBcLuqsih122a9aMb+/PJ48rfZP+WyPYoJk4zb5qtKQ6wQLl3e8fnoE5uTv1vfi7EtndkZFbgNR0odve6aVIsL8eFpF0MTPL02Yqpu86aFCn94pjcJ1u+arTJKUp7aVH09aQ5hmvPU6pAzz3bpjiM+cP8Cli7w/YRPnN5mvuly20KNnp/yxKUeS02TrZHgqQ31Gdnx1F341iDkHSemWetHPPTEOuPphGbh66drgpnZOqNRxNrakPNTFWYaDq2KyVxNicUena8zCpSQ7ELTpudphUq4KsUO/ZjLmyNyKVloV+j7qlyepDnzLZfeOKYzDCeZQ2Xvoy42AnhiZcTlTWWC7YV1KrbBzihk5Kl+vMWWwzhSU1fjUJlIt2oW9+5vqNJ5lHJxJ5g0yhq6xsiPubAxLPrhBEbRaKwV2UHb0ie+bkGc0fdjDs+pwO7xMx3yXDWaDwYhVUdlwcIkK4IuJeA6P13h8bPbvO2uBYZ+zOZAuRksTVfZGgRsDVVbwYl9zYnGVKtmM9NQ3oBnNsdUbIO6o3O2E5DnktOXetRvnyu8A+HolEM3SPnCxQHH5qo8eXqbpaUGq6tD9u1voWmCx1eGWLrGwE/4wtltHFvnjgMtPvxD7+DtP/kx3vqm/dy11GC5F9D3YioVk/E45mNfWObPveswjyYZ33zfPNuemg7UNUHdNEil5H883WG2brNQV4FbtVBqH0UZJ+arrPUjltoVNguZDiFQF39HJ0gz9jWUtIdd2Ly0qhbnN8ecXKgXsjXqXPeeYy22vJjZus1020XTBE9f7jHdcLhrX52PPraubMZGEQ8eOoxtaERpzuXtMXGS8zV3zvKHj29Qd0z++MlNksJu6G13zJNJ9RldbLuqVJxLZmaqrK4OmZ6uqCb2mSrNijVpSA+ilDNbCXNNB6fwUg3TnGEhsLk5CBj6amgll0qTzI9S2lVLff4iNSRkZTqWqfO1x9rcMe9OWi1mqgYVUyeVkkyihpmcnFnHpusNSByTZ7d97t5Xox+qQZnf+NIGbzvSfG0usFfwcz/7r2buvfe+7T/35/8Cv/gLP8+3fsd38thjX9p5ue2klKkQ4m8Avw/owC9JKZ8SQvxgsf4XgQ8D3wicA3zge6/fkbw4r70YxeuM3TvS/S2lZ7SrMj3l6oiKwdY4wdJVg3ijsJPpBxlv3ddQ9XEtx08ztoOI+YqDrmmM45Qkk/iJKl9pmmpIb7k6z27Hqo/CTzB11Sh7NlfN6M/2VGN11dIIkpyKbTBdMdkYRmQ5SgsrygnThLfvb7A8CghT5eU2VzdYHahpqGGYUbWUzpQmoOuntF2DcagXmRt1Rztds+n6GYYG7apNxdLw45zpisFKP8YvrGZ6vnJQ3y1St2s2SZrjWjo74xinMBndTX+f2vbphyl9L2Z5FBDEGTujkIqh03bVnf9/f3yLim1w//4aU67JvpbNci/k0JRD1THojVWJbxzlE5mIHS9lexjiTSuF5m4YY2qCsZ/wvrtnlc1MljGMUsI0f65npOnw9sMNVRKsmEpBOlYZt8s7PmleYWXHo1W1GHgxvXHEHQdak8zLMxu+KgEUTfcHpqsTPzOARmE4vDVOiLOcnWHIx/sB8y2XpYZFnBokmbpL9RM1fHB5R6ki33mwxcq2R28csdCuMF23ma1ZrF1xYak6BrWqhR8kGJrAKjIVcZITJKo3o1s0vJ/r+rQrhhouaDl86qIyVr68Ppx4l+2Wmw1DwzZ0NgcB9y7VOL/lMdtyeGYzmJj4Vm3Vl5flKkDfbUyuVEwWpyo4luolixJJu+1iGBqry13OXeohDreZqtaV/1iR0arYBtMNm85IeZOZusrY6prSSGvWbeqOcivYjJT8RMM1ixKbTi5Nwjhj6Mc0KhaWocbVk0yZVu+brQFqyELXVdO4ZSjF9u44orPjM91WZU9NgGsbrA9VkBwVZWQp1fSia6tpw4EXq7+FRuHxp3ogdUMFS6apMVsEN7vTnnGishOmqRcK2jAYq8zmVN2m0w8IjIxWxaLumpw8PEUulQCpKeXEjLjuKI2pXfHXXSX6JM2Ruhp8aFRMOsOQ5R24sDqgUjExTZ2za0NMQ2Urq5ZG10+Js5wvXeqTJNlEz2h+WkmFHJ6pcmZ9RJZLDi/UGQcJX3esDUClYvLA4RbDKCNKcmqOwfu/7hgXexEPn9riPYfbjCJVlmwU8ixRkXWZqSgNKlGci5aKQYyqpQKGiqmR5nnRA6oRZflEPsGLi4BZQM3SGUYpNWtXikOjams0LIOdIEYTKrAPE+W92Koq4dQLmyO+4c5pHl3xGI/VsEIQJFzsRiw0TKpmShipz4smBJqmWgkadRs/SGjXbS51xqoXLVAyE9vdQAn0RimtlsP++RqrW2Nmmo4yQNdV2TaIUqaLhvaqpbKYl7tK0iJOlVeoW2TcGo4aNDo8ZXNuO6LnRXjFdOvAi6nYasBjrmpj6rGSJzE0pboeZYzjjLm6ORGV1oVgZxQy8BNMXdBydM5tjp83kfxaIqXc+al/+tP81D/+R3z09z7C448/Vvm7P35tdsBSyg+jgqYrl/3iFf+WwA+/qju8B97wAZVeTHG1XJ2VQYKhQZqrHhNNqIvXKFJf9iSTnNsOcU2dtmOR5Dk102DNC2jaFheHHroQ7Ku6Ki2dZpMgxw9TgjhnFCbcf6DBXM3k4eUx57Z8DF1wanVIq6amuzQhWOnHWKbO5iimWgjDPbXhU7N1jk87nO36/O6j6xiGxtH5Oju+EvE0dMGTRdOrpgkePFQnytRkzYm5KvtbFhtDNakXJJky3M0F+xomfpKTGYInN3zSTAVHI1PQGUUstV3uma+wWVxc1oYqHZ0Db16qslwz0YTgvYen+emPn2PoJ8y2HDaGCdN1mzyX/P7ZLlGa87YjLf7nlzaIImXAe9tiXV0wTZ2KqS7yMw012h8kGe2KycYg5OCUy2zT4aELPQ7P1vj1z6+xf6bKTNPhC5eUBtWnz/UIE3Wh+vrbp1hqWlQtJc54uaeUtDUhiNKM+/fXecfRFgCfP7VJnFZ4z8lp/uRsl8WGxcGWzfYw5Nz6kDsPtnAtg0bFYrsYfxZCCXFWbYNLvRBD11jux7i2wUzNYrpisDqImaubeHFOx1MlRdcyaFaUEezBlk3V0rmwNWa2bnNixua2dp3/dqqDMasRpVnRC5OyuTXmY09u0qrZ3L2/gZTwhYsDvvb2KfpBxh8902GxMcOOl9KwdfZVK0xXfFZ3PO48Os3x2Qq//vEL/MbnVul2fd50xzwH2g6XdgIevqQ86d56qMnTG2pA4I4DLY5M23xpJUECaZrx2XM7aJrg9sNTSqU5VCfuWsXE1DXWgJN3LGCZKoBMMmWuHSaqyXwcKu88y9CoOUpFfze7WndMwkrG5e0xnaHBdN2mXahC60JwaMqh66sskKVrjMMEH1UGbRf9jZ3hmH1TFdJMstwZo+vq4hjESm36vffv44sXexxfbBQBe0ZvrKyIdqUjslwFeErzSGIbSg5ltuHQG8dsDYLJ319JIJi85VCTZ7shAy9m6CeMwoSD+xp0hyHdrk+j4TAYhMzOVgliFXh5YULPU/2LaZZPPO2SNJ+UeVS5yWZnFLLWVY38rqWzuu3hOs9Z9LSrNncsVvGjlLv3NzjQsvjEmR772i5Hpm0+d3HEKEyIijLUyRPTDMcx3/SuI3iREpt8tuNx6twOD9w9z3rX5y1Hp/jlz63wt37li/zgN58ElKo/qP7Lg02l1O8fmWLDC5FS8qHHNifCkhfOb/PkXXO0Kzp3zLtICbdP1/i1xzZUhk4TzNZMdjwVsFctnSDOeLbjUSu01yqWRj9IOdi2WRlEqmxraAyDlJMLNWarJlteTNMxCNOEx9ZV6wDA0akWNVvnYmfMQxdVh4rvJyTJCMsyeGp1yLPbBm89LDm2r0EYZ8zVDNp1JWcwHEWqlFcvPlt9FQidu9CbeOJpmmBqyqVZsVg4OUdQ3IjM1S0GkWof0IQqYdZtHV2D+/ZXeXRZKePHaYZl6EpGwlDl2NmqxRNrHrah8813zrA6Cvndx8akueQ/PLTMwZkabz9UZxQl2IZGSE7HS2i7BnGq2gQ644RxmNDzYpamKrimRsvVOLlQo2JpbI9vzJD+3/3xH3UPHDwY/MN/9JN83/d8d3BDduI6Im6E6/TrCSHECDh9o/fjBjIDbN/onbiBlMf/xj3+N/KxQ3n8b/TjP3kj2nqEEAaQyVsw+HjDZ6iA01LKB270TtwohBAPl8dfHv+N3o8bwRv52KE8/vL4nydV8JohpbwlNCyvxrVaz5SUlJSUlJSUlLwIZUBVUlJSUlJSUvIKKQMq+ODLP+WWpjz+NzZv5ON/Ix87lMdfHn/Jq8obvim9pKSkpKSkpOSVUmaoSkpKSkpKSkpeIWVAVVJSUlJSUlLyCikDqpKSkpKSkpKSV0gZUJWUlJSUlJSUvELKgKqkpKSkpKSk5BVSBlQlJW8AhBDvEkK8qMWSEOKXhRA/+Sr+PimE8IQQP/VqveZrjRDi+4UQ4+JYjt/o/SkpKXl9UwZUJSW3EEKIi0KIr3vhcinlH0spT77Gu3OflPLvfKUbCSE+KIQ4LYTIhRDfc5X1/5sQYkMIMRBC/JIQwr5i3ZQQ4neKYO6SEOIv7XXnpZT/QUpZ2+v2JSUlbyzKgKqkpGTPCMWrfR55DPgh4NGr/L4/DfwY8LXAYeAo8I+ueMrPAzEwD3wX8AtCiLte5f0rKSkp+TLKgKqk5A2AEOJrhBArVzx+kxDiUSHESAjxG4Bzxbq2EOJ/CCE6Qohe8e/9V6z/hBDip4QQnwZ8VFDzqiGl/Hkp5ceA8Cqr/yrwH6SUT0kpe8A/Br6n2K8q8BeAvyelHEsp/wT4EPDdV74HQoj/QwixJYRYF0L8WSHENwohzgghukKI//PVPJaSkpI3DmVAVVLyBkMIYQH/DfjPwBTwW6hAZBcN+I/AIeAgEAA/94KX+W7gB4A6cOlV2CdxjU+9C5XB2uUxYF4IMQ3cBmRSyjMvWH9lhmoBFTwuAX8f+HfAXwbeArwL+PtCiFc1QCwpKXljUAZUJSVvPN4OmMC/klImUsr/B/jC7kop5Y6U8r9KKX0p5Qj4KeA9L3iNXy6yRKmUMrmWXyqE+F4hxGNCiE0hxH8WQrxHCDEnhPhe4G9e477XgMEVj3f/Xb/Kut319SseJ8BPFfv868AM8DNSypGU8ingKeDea9yXkpKSkgllQFVS8sZjH7Aqn2/kOckyCSEqQoh/WzR1D4FPAS0hhH7F85f38Hv/DPA+4ATwaeBfojJIXwP86jW+xhhoXPF499+jq6zbXT+64vGOlDIr/h0U/9+8Yn2ACsxKSkpKviLKgKqk5I3HOrD0gjLbwSv+/f8FTgJvk1I2gHcXy698/l5c1b9NSrkupRxKKX9RSvlmKeWilPKvSik71/gaTwH3XfH4PmBTSrkDnAEMIcSJF6x/ag/7WlJSUvIVUQZUJSW3HqYQwrnix3jB+s8CKfAjQghDCPHngQevWF9HZWr6Qogp4B+8Gjslpcyv5XlCCEsI4aACuN1j2T1X/Sfg+4UQdwoh2sDfBX65eH0P+G3gJ4QQVSHEO4EPoHrFSkpKSq4rZUBVUnLr8WFUQLT78w+vXCmljIE/j5qO6wHfjgpEdvlXgAtsAw8Bv3ed9/eFfBS1318FfLD497sBpJS/B/w08HFUmfISzw/4fgi171vArwF/veiNKikpKbmuiOe3UZSUlJS8coQQIRABPyul/Hs3en/2QtEs/y9RU4F3Sikv3OBdKikpeR1TBlQlJSUlJSUlr0uEEL8EfBOwJaW8+yrrvwb478CzxaLfllL+RLHufcDPADrw76WU//S67msZUJWUlJSUlJS8HhFCvBs1wfufXiKg+ltSym96wXIdNajy9cAKShrmO6WUT1+vfS17qEpKSkpKSkpel0gpPwV097Dpg8A5KeWFom/011FDKteNF07/vOFwG21Zn1v6irY52HJe/klX4cKOv6ftTH1vca+uXav49KvDXrOdYXJNw19fxjVra78Ay9jb+7m/ube/++X+1RxUXp69/vnsPR5fEO/t75Dme/377e0Aa5b+8k+6CjvjeE/bufbeft9rTb7HYkOcZC//pKug7fEDauzxfLZX9vp9GAXXpFf75b/P3NvnpeXu7XI8CNM9bbd94eltKeXsnjbeA3rjkJRp8GXLZdB5iufbTH1QSvnBr/Dl3yGEeAxYQ2WrnkK5IVypl7cCvO0rfN2viDd8QFWfW+Lb//lvfUXb/Os/d8eefte3/8oX97TdfNPd03Ytd29f7L1eyMN0b2f0c5vjPW2314DqyGx1T9v99J85uaft/tffeWZP2+018DsxY+9pu8fW9hbw98bRnraz9njhefvhF2p3Xhv/959c3tN2dx6a2tN2ur63D+heuzD8aG8X1uXO3r5/jr23y8dsY283JnsN4A5P7+38+Ymntva03fF9e/t8fuCuvcU2Hz69s6ftPvitd79iy6ivBJmF2Hd855ctDx/92VBK+cAreOlHgUNSyrEQ4htRtloneL5u3mQ3XsHveVnKkl9JSUlJSUnJ9UfTv/znFVIIBY+Lf38YpV03g8pIHbjiqftRGazrxnUNqApBvs8X/l1PCSH+UbH8PiHEZ4UQTwghflcIcdWQXgjxPiHEaSHEOSHEj12x/J8LIU4JIR4XQvyOEKJVLP96IcQjxes+IoR47/U8vpKSkpKSkpJrQVyXgEoIsbDr+iCEeBAV1+ygmtBPCCGOFIbw3wF86BX/wpfgemeoIuC9Usr7gPuB9wkh3g78e+DHpJT3AL8D/O0Xblh06P888H7gTuA7hRB3Fqv/ALhbSnkvqov/x4vl28A3F6/7VykVkktKSkpKSm48Ym8BlRDi11DuDieFECtCiO8XQvygEOIHi6f8ReDJoofqZ4HvkIoU+BvA7wPPAL95vUV+X7IILoS4lmiuK6X8nqutKMxXdwv0ZvEjUT5hnyqW/wHqgF8o/jfp0C/2ZbdD/2kp5UeveN5DqDcUKeWVTUpPAY4QwpZS7q3Jo6SkpKSkpOTVYQ8ZKSnllzdePX/9zwE/9yLrPoxyjnhNeLmuwjuAv/YS6wUqi/TiT1CZpkeA48DPSyk/J4R4EvgWlBjXt/L8Oucu19qh/33Ab1xl+V8AvlgGUyUlJSUlJTeY3QzVLczLBVR/R0r5yZd6wm5f1IshpcyA+4s+p98RQtyNCoJ+Vgjx91E1zavNM79sh74Q4u+gTF5/9QXL7wL+GfANL7LPPwD8AEBtdvGldr+kpKSkpKTkFSNAu7WFBV7y6KSUv/lyL3Atzyme1xdCfAJ4n5TyX1AEO0KI24A/c5VNXrJDXwjxV1Fy9F8rrxBAEkLsR/Vl/RUp5fkX2ZcPokxXmTt+dykV/xry9EqPqmODlCw1DP7wS5vkEmZbgna7faN3r6SkpKTkeiAA/Y2doQImQc/fBg5duY2U8iWn6IQQs0BSBFMu8HXAPxNCzEkpt4QQGvB3gV+8yuaTDn1gFdWh/5eK130f8KPAe6SUE/GcIgv2P4Efl1J++lqOreS145kimNIEIASfP9ebiBF2+pIyniopKSm5VSlLfrv8Firo+XfAVyKruwj8StFHpaG67P+HEOJvCiF+uHjObwP/EUAIsQ9lYPiNUspUCLHboa8Dv3RFh/7PATbwB8W05ENSyh9EdfQfB/6eEGK3yf0bpJR7U2gredUYBin7pusA+FFMmkGWKYVtxzHIspwgiHDdvYlSlpSUlJS8zikDKgBSKeUvfKUvLqV8HHjTVZb/DMoB+oXL14BvvOLxVTv0pZTHX+T3/STwk1/pfpZcX0bjGHmF3YRrGXQGIUsNyYpmAWAYGv1xUgZUJSUlJbcib4Cm9JfUoRJCTAkhpoDfFUL8kBBicXdZsbyk5CV5ZqVPlEtk9lxiUwAnFhscPfx8D8Wyma2kpKTkVuX6CHu+nni5DNUjqOvc7sTdlQKcEjh6PXbqtcQxBLfN7c1b6ivl+NzePOS+6kBrT9ud73t72u7hy6M9bXeg/eXv43TDJQfiHESeYQtBu2G96Gv82Xvnrvn3/c+ntveym4yjvZnBvvv/2ltb3l493Q7P1/e03R9u7+3vnqR7Mzlu11787/lSXNyjh+PVPmfXwp97cP+etvudz6/sabuvvmt+T9v1/b158vW9vZk/33dkek/bPXqus6ftqnv0ALz3wN488vbqMbqxsbfzYBjuzVT59HJ/T9vdd2xvf7/XHMEtF0C9kJeb8jsCykJGSnmlGzRCiNcmCim5afFe4IJuiJx24/lGpTVX58Bsg7WdMQNvbyeikpKSkpLXO2/wkt8VfOYal5WUTIjTlL4XMfYjhICm+/xMRhCnHFlsYRgaB+cb6KIs+pWUlJTcsryRS35CiAWUYrkrhHgTz5X+GkDlOu9byU3OSk+J1KeAFYSYjebz1vf8GMs0J4+/4c6Z13L3SkpKSkpeK94ATekvV8z+08D3oEQ1//88F1ANgf/z+u1Wya3Au26bAgRZlvGZ8/0vW5+jYWiQ5qBrcGSh+WXPKSkpKSm5+RGApl1rUezm5OV6qH5FCPGfge+UUv7qSz23pORKVPpSxd+6rrPoPr8x+5nVLpnU6Y/BNQSOs7fG5pKSkpKSmwAhENpXPqAjhPgllCvKlpTy7qus/y6U0DfAGPjrUsrHinUXgRFKPzOVUj6wt52/Nl42XJRS5sD/cj13ouTWw3pBfDQz8/wJnTh77qPnxTnvObK3ibaSkpKSkpsDTdO+7Oca+GXgfS+x/lmUa8q9wD+msJW7gj8lpbz/egdTcO1N6X8ghPhbQogDpQ5VybXgZwJDgCVA4/nj+A+d2iS9QpcqzXLMK3qpSkpKSkpuMQQITXzZz8shpfwU0H2J9Z+RUvaKhw+hWpRuCNcqCPJ9xf9/+Iplt4QOVcn14czGiF1ZoyPTNrrxXMB0YqlJs2JxanXIOM74zjfP3qC9LCkpKSl5LRCI16KH6vuBj1zxWAIfFUJI4N9KKV+YvXpVuaaAalePqqTkWkly0IRASsm5TsDJRRVQbQ98Zpuqw+qegy0+8eQmjlNKmpWUlJTc6rxIQDUjhHj4iscf3EvgI4T4U6iA6quvWPxOKeWaEGIOVWk7VWS8rgvXFFAJIUzgrwPvLhZ9AhXtlUqMJVclzXLMwr8vSp4r+TnOc5kqCdx/oAymSkpKSm55ipLfVdh+pf1NQoh7gX8PvF9KubO7vPAHRkq5JYT4HeBB4MYGVMAvACbwb4rH310s+2vXY6dKbn7iNCcuan5OEVh97tw2eS65Y6lOw7UZhTHNZimVUFJSUnKrc71KfkKIg8BvA98tpTxzxfIqoEkpR8W/vwH4iVd9B67gWgOqt0op77vi8R8JIR67HjtUcmvgGBqiCKQM4PTKNmHhoffYxQGmIfiqk9fu21dSUlJSchPz4hmql95MiF8DvgZVGlwB/gEqwYOU8heBvw9MA/9GCAHPySPMA79TLDOA/yKl/L1XfiAvzrUGVJkQ4piU8jyAEOIoStehpOSq7AZTAEme0w+fv77mllN9JSUlJW8k9pKhklJ+58us/2tcpVompbwA3PflW1w/rjWg+tvAx4UQF1BqjYeA771ue1Vy8yMli02bqmOyvDXi8Fydte6YcZBiGDr3Hmzf6D0sKSkpKXmNEOxN2PNm4lqn/D4mhDgBnEQFVKeklNF13bOSm5qqJbAtkzSH2XYVP8k5MNtgZ+BxZPaNJeK5tdXD9zP2729hGNd6D/PimJpESoFjCMZJaShdUlJyEyDe4NYzL+AtwOFim/uEGon/T9dlr0puemZrBpYGeZZxam1ElGYcWWyC0LDMW9sgE2B9fZNRP8cwDUCCEKyve1iWZH6+he/7BIHEMKDZrAJwYMZF13TSLOexU+scnv/ywHOqYqIJODK9Ox0p+cLl8Wt3YCUlJSV7pMxQAYWf3zHgSzzXOyWBMqB6gyPjmHEkkRpsaZJ79zfYHnqMM40DLYNuAG863CKTko1BxMnFxsu/6E2O54U0Gm0MI2Y0CAABEmSeEQYZWZYSBJIHTkyzMF0jDCMu92IGfsbaZo/BMEYIQW8woH3FFOTXHKnz2GZIy70yIL21T1AlJSW3BkK8JsKeN5RrzVA9ANwppSzrCyUcm7EZx2BqADZhkrHcCxlGGf0gYbpRJUgiggzsIhsVZZKD0xXOrHc5PHdr908JoZHnEsvSEAKkBKdiq3XobG15tFoVlnshc9M1LMfGNkKSWDAYxoDa5uxlnwfvUQFVluV4UUqeZfhhSrtSNvWXlJTcXJQBleJJYAFYv477UvI6571HGqyOE3JAL/z5hBA4V5TwslzF3LalUphXIgEhTIIowrXt12anbwCGIVA2mTozcxpZlhfLNVp1C80wqNg6lqHzxIVt7jk6w1zdZbwTommQFzqoV557HFPnS5tqVLJdUWXEPJf0w/Q1PbaSkpKSvVKW/BQzwNNCiM8Dk2Z0KeW3XJe9eg2Zq9n8r+98bZx1fur9t70mv2eXn//0xT1t9/bDzy/LhXGGpoGX5SRSFZkyVMggpcQLE7JMMl03abkGOjCKcqo2CCRJpjIupgY7XkSzYrI2GLHrzX33QnVP+/l1t+/Nn9s19naXVOiZvCyPnN7iyGIDCaysD+mPVdYpjnPQDYQQmIYOQuBWHaomnFr1GXgJlqWDlEhA1wUr2x4AR+Zrk9efq6tgVNcEOnLynF0q9t4a32vO3rY7OlPZ03anL/f3tF22xzz5Z8/3Xv5JV2FprvbyT7oKj1x4UT/XlyTb4wHuBu5fKQ8Pgj1td2hhb+X7UbA3g43/+scX97Rdu723z+db7l7Y03Y7o/Dln3QVrvX88kIudW6OHkohQCsDKgD+4fXciZLXL0GcUC/sYsI0Z96FnQCqpiBOJHMmzBxq4doeozjn7E7MytaQYZhhHmlSd010AadWRwz9mLmGiWub7LdN/DihO761MiwmcGJ/i6S4uB1YbBBf6uLHuxk9yK+onEsp+fyFPl4YEwQZh/Y1OLLQRMqcP35sGQAdicxBaLBQe35D//oLBb5KSkpKXpeIMqACkFJ+8qXWCyE+K6V8x6uzSyWvF3YGIZatMYoyDAR3ztZJsox99QxNVx+djeEYL4gwspSRF7PRC0hyMDToeBkdLyPNcqTMcR2TYQTtKKRarWAb1i0XUElydB2S3dENAW+9Z5FPPrKKpoFjCCSCJE3RhEaSS0ZejO8nSJmTpjnLnRHNijkxjT65v0VRScXUNZIsR9c0slzyzMbesgslJSUlryllhuqaKR1ubzE6vRAfkF6OIXPec6ytAiOhkcYBq2GIpQssy0QHTuyziBlyfL5OkqSkEiwN/FSyM06oVG22xzFCwMowZRqV9p+uvlofwdcHloCmbSBkSioltqYm/KanVdmhN4onz61VDEzTYN9MleE4JoxT2nUHKcCLMh44PsPFrcEkmALV3D8Ic4SQrPQijszXmXI07tlXRxPwq59Zea0PuaSkpORlEag2hluZV6vlvpz+u8WIEFi6hmMJFqdszvZ9NCEZxzGpaameqlSSps9lmGbqKq4exJJRLFkdBGz2Q2ZdyULD4vh8lZpr4VgG++omh9o2i81bKxavVy0sIdE01T9m2Qar20M0TaBrgrsOt6m5BrWqhdB0DE3gaJLppkMYZ0zVTIyiG/3y5pBnzvf5+MPLbHZHNGywTQOEII5TdjtmuqH6Vy7he7764A068pKSkpKXoMhQvfDnVuLWSg+UvCqs9dTcQZTkICXraYgpJBvDiLar0bZNwmDMR095RKlEF9BuOAy9iI0tDyklSZKRpjnvfPMSm4EgHvhsj2LyPAcBW32fdtVCInBkyNLs3hrMX2/MVHTabgW/E+EAUZyyNNNgn5RsDzyEEBxZbCKlZODHmLpOiqTlGsy3XTYHIWkukRKeOKeamj0/Ya0bsjBlkyQp9aqLcRVtVAHIfG+NySUlJSXXE8Gtr0P1ah3drRVmvsHpBglb44S+n5IJQZDAM52QM5s+p7dCPvz0Do9s5NSrNtMNm0xCGEaITOK6ejGGJVhaarLS8QijmDSXLLZdDsxWyeKMwSjGC1PqrslHT41u9CG/agRRRpzB0SmbKAoxdQ1NCDQhSJPn/MRzKRGTr41gECR4UUqag65rGIZGo2HTaDg0Gg5TNYsMCy832OwHPLUyJAxDdA0O1MBPUvw0ZW/zUyUlJSXXn71kqIQQvySE2BJCPPki64UQ4meFEOeEEI8LId58xbr3CSFOF+t+7FU8lKtyrUrpVSCQUuZCiNuA24GPSCl3z9/ffb12sOS1JQxDwjhDaBCmGaNuTJbn5BI0IQjinEbFIpWSXs8nQZAmKcs7IQeWWiSDgPZMhTTNSJKU6ekqSSbpjmM0IpamK+yfq1OrWCRpysBPaDesG33Yrwp1IycVGjKTZFkGwsCPEva1XB45v02KYLw5pFExCMKMUZgx3bDRBbi2Qd3RSZMYXbfJsoypKdV3laYZG12P7aL/qlYx0HUd4pSKETG3b0rFsBKGUfwSe1hSUlJyY3gFsgm/DPwcL+7M8n7gRPHzNuAXgLcJIXTg54GvB1aALwghPiSlfHovO3EtXGvJ71PAu4QQbeBjwMPAtwPfBSClvGrkWHLz8ZkLfcaxyjBFcabq3kJppNiGhq7rpHnO+taAIJFoQmCZGnNzNXRN0q5b1GoOUZQyCmJ64xjXNtA1QRilPH62Qy7hrsNN/FRMmu8++tQm33DX/I089FfMQtMlTNURGbqOEALLNLi8E3DngSYf/9JFDi3NEyWS2VYVN3oun5QkOV2v6EeLI1wrA9RrGIZOUkxV6gKOzDdxTY26rfHEcv95+6C9aknnkpLrT5antFyLLMnwMzB0QZLlVCydfW2XlZ2Alc4YmWcv/2Ilr3v2ElBJKT8lhDj8Ek/5APCfCieXh4QQLSHEIsp7+JyU8gKAEOLXi+fe8IBKSCl9IcT3A/9aSvnTQogvXq+dKrlxbPRj8hyEkGSZLPyXJJqmYRkW40D1QemWRd2S2AZouo6hayRpjhflRFlImmYMhxG6DkuzFW7b1+KzT29hWgYzbZdhJJ838bFHrb/XFUoQQU1oGBqcXu4hEays9JFFa9MKXXTLZmPb503Hp0lyJeLZ6Q7p9SLqdZPFdoWl6SpBlLLWD9jc8clzyVTV4u23zXKxFyKlxDYE9x1qowNZ8VZe2rx1yqclty5Jqm7WkhTGmip1A2TFDcn+6SrdUcRG11d9NxpEUYRtl5ZLNy3XTzZhCVi+4vFKsexqy992PXZgl2sOqIQQ70BlpL7/K9y25CYiDDOEUE3RjmOQZTlRJIEUw9CBjNEoQko4tFin7hrYhmClF6HJnChKiOMMKSVSSkbDiJUcpto19s3XCOKcmmuia4CEIM4RArJbYFD0sdURdy3WyHPJI5c9ppougR8xN1tl7CXouoYXptQMSaOik8rnxmNbzRo7w5ReL+Rtt82RpjmGJmm4FmuZh6YJtkcJf/TUFkLTMERO43CLM9shX7qsArlvumeazy97e1ZKLyl5rTA0QZxJdE01KruGxshXIrUyk4RhRGcYkucSQ0jecec8fqh6O9e2BgzGZcbqZkO8uLDnjBDi4Ssef1BK+cGv6KW/HPkSy68b13rm/f8APw78jpTyKSHEUeDj122vSm4Ydxxu4VomnaFPnErSPCPPIU5yKraBlDmZa5KkOX6UEkQplqlTs3V03eTr3tzi9PKAle0x44FPlkucWYMLK32mWi5H51wEGrquUbN04jRjx09IM8mznQ5HZmdv9FuwZ4I459FlZQMhANPQMBsujZrDMxd2kDLn8IEmrbqDAPpehGsKTNMkTjJmWhUWZmqs7Hi0ajZ+JJFIju1vsLLpIcmximApzeDspodEIIQqnX7smb1ZnZSUvFYEQUjLtXFM0HWDNJdEac6T57ZJi+xU0xEcnVtk31SFMMn57JkOm70BQnewTJ2Th2ZIs4xHT23f4KMp+Up5kYBqW0r5wCt42RXgwBWP9wNrgPUiy68bX4lS+ieL5nSKmuSPXM8dK3nteXJ1zFLbwdR1wigmtmCrE7LW8WnULUxdMPYiRuOERs1E1zXlOacJpmo2cQ4jP+X4Qo1hmFA9MkVvEGMZGjPtCgfn6gRhwjiK8WNJy9VxHQtT08il5IurKUdu0ngqSRLmWy4bvYAsk7z5UI1camyME377o6eQUpkmHzvYJkoyVjdGk1slmecszNURmiDNJV4kMfQUoQniVKJrGgcW6mRSMvBU07muCYIkp2IZzDRc8jxjddtXnZ8lJa9TdsYpO4U7QsXWMQwd29Q4eqCFlBI9y3j7iQb/7sOnSTJJpWJgWQa+HzDdyKnWHOIkQxOC4wfrnLtclrhvFq6jl9+HgL9R9Ei9DRhIKdeFEB3ghBDiCLAKfAfwl67HDuxyrVN+7wD+A1ADDgoh7gP+FynlD73Mdg6qod0uftf/I6X8B8X2v1i83kXgu6SUw6ts/z7gZwAd+PdSyn9aLP/nwDcDMXAe+F4pZb9Y9+OosmQG/IiU8vev5RhLoGIBCKI0x7ENzBwsy6BSsfD8DMNM0XSdqSkT29LpD0Mk8O0P7qNVs7nUDTA0wfIg4cT+Jg3HoOfFnFkecGF5QBCmVF2L7YHPbFs1cEd+DBIsQ1OGwTcpVdMkyyRfc2KKMM7ws4x21UTT4OiRaYI4Zd9cnSBKuLyqLgKjoc/CYgvd0LF0WN0YMDfXxLU0NE0w9BOGXkSrbmFpGoamUXcMBqMYYWgkUUKia6x0RuyfreOFEa57awmlltw6zFc1VjqqP1PTNKI05/zFPmmSoeuCv/z1x6jaOn98eofWVAUpYXvb4y0nZ7hzqYEm4Ox2xMaOx84oouYo0/XyHuLmYS8BlRDi14CvQZUGV4B/gLJNRUr5i8CHgW8EzgE+8L3FulQI8TeA30fFEL8kpXzqlR/Fi3OtJb9/BfxpVCSIlPIxIcS7r2G7CHivlHIshDCBPxFCfAT418DfklJ+UgjxfcDfBv7elRu+zMjjHwA/Xrxh/wxVjvxRIcSdqCj0LmAf8IdCiNuklGXB/VoQSrlbE9CqWBi6II5jBn6CEDmdjoeuCyoVC9dyqVQsdA0Gcc6zl4bEUjJVNdns+/hRSpLmTNUdDi7WMU0NL0oZhymGIQjilIotMDQds2hO127iM2MK7KuadLyEGRtc18LQNP7kdAd0HdfV8cOEMEgmJ5Vq1aXf92i1quq9zgSj4ZDG/BRpJpFZRqtuAxDnkjzLCKIY04I0l9RrDoKcBLi4NUITN38fWsmti2moTJOmGYDg2L46dx1ss7k9ZnaqysY4IelHvONEG4kgQ9D3U8I05yOPrvFVdy7geRHrxZBG3XV5y50tHn1m80YfWsm1sMcMlZTyO19mvQR++EXWfRgVcL0mXHP3qpRyWTz/gveyQUpxoOPioVn8SOAkKnMFKjj6fV4QUAEP8iIjj1LKj17xvIeAv1j8+wPAr0spI+BZIcS54nU+ey3H+EZHkzldPyOT0LQ1bN3EsUzmp6uMvBDHsUnTXKXmDYFrGQgEq4OYtmsyY0GYSvrDkCSTWJZBmqbcva9FnuWEaY4uNHLUBGHDNXFsE11AzdaJkps77k2LYLRetUhziJOUUaDGl1o1k2bVRkqwHR9ZfJX6fR9Bihcm6Lpg31yreDWJEBI/yhACpJQMRwFCM1QTf5bTrloEcT6ZkLKdMjtV8vokTVPabgXQqFQssizn2fURFzeGHJiv0xkGXFwd4to6o6DGXN1gX8tBIyNPMyzb4PT6kFGYcfvhNnXbYBxE6JpKPdzcZ443Bi/RlH7LcK0B1bIQ4qsAKYSwUP1Tz1zLhkWm6RHgOPDzUsrPFYqn3wL8d+BbeX7j2C7XOvL4fcBvXLHNQy/YZukq+/QDwA8AHDxYep/t0o9y5uoWhgZpljNb1alYVdy+RTblEkTKNiZOM8IoxTI19rdcwhziPKcXCYI4Y7rpABqzdZ1m1cU2wDQNEDk1W6X6R2FKz4uZFhLLssgRONbNO52mA4fbtuqLEkpPx9Qt/o9vOE6cS3pBxB+d7SMEJFk+ObE0Gy4nlprEaU4YZHTHEWmaM9Ow8VPUNCQgc0m95pKmKV6QKp0rCr0rMiQCQxek5ZWl5HWIlJJNL6ZedwopFp0oStGkUCK1wJH9LS6tDwnilPUhbA5HvOu2NtteQrvm0A8S9muCumMQJBlVpwbAO+/bx6ceu669xiWvEvotHlBdqwrgD6JSakuoIOV+XiTF9kKklJmU8n5Uh/2DQoi7UUHQDwshHgHqqF6oF/KyI49CiL+Dqrb86rVuU+zTB6WUD0gpH5i9iafKXm2mqwaagByBrmnkAkxd4Joarmlw+7zDXYsVFpo2jYqJqWsstSwaJoSJ5FDT4k376zxwsE0uM1b6EU+v9PnS8hiZ51QsjZprUrEtslySZTmdUUqe5ehC2bHcrBxqmvhxghdn9MOUKE6QQDdKGMUJcQ4PHm7yrqNNaqaGEKL4gYohqTsmSwsNarZJGOes7QTcta/KiTkXAMPQMXUN0zSwLY3Aj+h6MTt9H8vUqFkaDxyq39g3oaTkRciznEs7IVd+xfMcxBUXWCHAtvQrHgs2hzGgvi/tikXTNYlz0HWduDBmt8xSzPZmQAgVUL3w51biWqf8tilU0feKlLIvhPgE8D4p5b8AvgGgsLL5M1fZ5MVGISm2+6vANwFfW5QWX3abkpdGV5LoCCGKTk9BP8wKzyVwTIP1UYJEo1mxsTSJYejUKxa6KdkOMsJBzFNrxeRNobPkjWMcU2Om4RLEEk0TzDRclrc9bCNnqmapu9YbefCvkOVBwnHbQRNKEyrMIQgSGo6h3k8kVUsjzyUfeGARgA893mG24TCMQBMp+5s20/tr5HmbnUHIXMtm20txjADTVJpgly8NkVIShinTMwLHtXj70SlsU2NnHCFv4qC05NZluTPmwFyDbtej1aqQZRl5nhPFOUmaoesa650RQtOwzOKyJCDOctwigyWv+K9aLZTWXVDaLd0slCU/JkHPLwDzUsq7hRD3At8ipfzJl9luFkiKYMoFvg74Z0KIOSnllhBCA/4uauLvhXyBFxl5LKb/fhR4j5TSv2KbDwH/RQjxf6Ga0k8An7+WY3w98yP/7dSetvt3v/ypl3/SFZw43OID77tf9UiJ3Ubx59bv9vLs/vvAlIsmBH6yu0xgGtrzNtjdPMulanjXVMDmmLrKhuU5Az+hVbWQwO+f2tnTsX4l1J29qS2/7UDtJdf3gwQNgWmoo86kuiAYmsDUi6wUObahI4FvuXeOL60MCTPIJQSJJI0ydsKYDS/j8iAmilMQ6oISRqqed2ipQcUxSdMcx5Q4RcZrpmbzI+8+zL/65MU9HV+n57/8k67C8/7mXwHf/MCXVeOvic9d6O1pu73eDQfR3uqorerePCpHe7QN2L+wtwzlp7+4t3tO6yso0S/MNkikZGm+wk4/oloxuPP2Wba2R5xf7pNlGe12lXbNwDEE26OIRsVETzNGuaRiGeyMExqugWWotgEvygiSBAO441CdXnj13x1G6Z6O7+zlvX3OTh5q72k7Xd/b96g7iva03WuNgMl15VblWr8R/w41ifdvAaSUjwsh/gvwkgEVsAj8StFHpQG/KaX8H0KIvymE2C0Z/jbwHwGEEPtQ8gjf+DIjjz+HkmL4g6JR/iEp5Q8WoqO/ifLqSYEfLif8rp2zF/v4UYJtGAzjmNmaQdOCUZiT5jm6ZjJbM1kfJvSDhCi1cE0NU4Mkv/pr5oUHl2MZBHGKJpSUwMCPySVYGnSHAboG4zB7XgngpiOJwbKft0gADSk5MNsqlkgGfshqEKMJeNNSncfWfRwtRxiCjSBjdUddTBqWwUAXRTOnkpa4tDbEtU10TUMzBT1fnUyllOSSST9KScmNJssykDmGaZBmObYpCKVGtWrzZ9+yiKZpnDE03KrLyE+QSBzL4sCUS83WSXLJ6U6AF2Ycn3HoRTm9Uci+tstG3yNIJIttlzCRaIZF1c6QMsMvE1avT0TZlL5LRUr5+RdM+b1s2C+lfBx401WW/wxKX+qFy9dQehK7j6868iilPP4Sv/OngJ96uX0ruTqXuiGmpmHqgkcudWlUKtiG4GDLomYZHGtaHGtJTm2PWBvE1ByNLMvRNY00h94oQOY5QlPeMjVbECeAlIRxhqELpMwZ+0rws11TAYgfZUghEVdtg7s5aDWU/16aF8WJHKQuOLbQIsko+kcEzYrLMM3ZzmIkcGTaZhymPL46Jogzslxiioy33THPWnfAM50UmcCfOtHmW+6eI04y/uBslyyDqqmz2g+puybPLPe4d75xI9+CkhIAoiuMv5t1dQPgGoJaUydI4UOPbmAYOpYuCVNouAZZLlnd9gjjmHatQpZL5tsuDVNwatNnqmox23TwwghDN3jbwRqn1jwSCaauUXdNLENjFCasv1i6quSGIbj1m9KvNaDaFkIcoyhgCyH+IrB+3faq5IZRtVTPjyYEWQZzNYOFukUQZ3S8mFGU4Bo60xWL22dstsc+X1jzEZogz0HXTabrkGQJSSaIM8jJ8MOUZsUkySRBnKPpGgemqozCBF0T5ECe5mg3eYOpaxuMohQLMB0dA3USSbIre5skpq4hk5xRBlkOrqmT5xLXMhiHCUEq6Hs+pzoZRmEO+7GzXQ5NVdB0jbm6TS4l7UqVLJOcW+5x/rGz8FVHb9CRl5Q8h5Q5eQ4g6A0CsigjiBIWFxvouk6S5dimTpBIhAY7o5CGa/KtD+zjsdUhG32P73vHQTWoIiVvPTTFIytDqraOrTu4VspKNyTOJcMgIUolVdvANjUq5s2dpQ2ihK1egOcnOI6GQGMwDKnX1BRx1TWYbTpomqaMo1+AlBLxOiytXUel9NcN1xpQ/TDwQeB2IcQq8CyvsEm95PWJkBLXMbB0QZLp1Ex4tqfKU7mEARJNZKrkJEA3dATKTHm+ZhJnEil1Br7ENmCuYSJR9ipN12BzGBMJwWLbxbV0HFOn66myVS5vjS9b3TaoabDhqQzU+c6AY7PNIgsn6PsRlhB4aY4O2Kbg888OSXMwyDk+XyeVcK6bY2k5pmGQS3j/HXNEmWRjFNEP80kTv64LDu5r8h1v/9M38KhLSp4jSyVZDiAZj2KiWPUEnDnX5fbbZmhWLSq2gWtqdP2YLFOTwn/4zDZ3LVXQhMOfnO+xMox481IFYejMNW22xzHDMKM7TtA1qNgmUZKza7IQJflNPdwy9iK8JMV1DFzHIIkz4iSjXncm/atekBLGyu9T1wR5nqNpGkmasr7pIaWkWjWZaVdv8NF8OW/4Hqqi/+mvSym/rvDy06SUpYHSLcpWd8hdh2fY9lTK/mJPsH/KpmZq+FGCn0I/yDG1jIs7IZ1AacpMVzQyBF4qaRgadx1tkuaSfpgTRgknF2qc2QyIMuXhN4oyRmHKdM3CKnzpxkHElFW5we/Aq8M4h5qrmpK/2AmpWDZC0xCAZRp8/tIOuYCKqWMg6AcpUsL+lk1nHKFpggcP1bHN4v3IcyRKxqJmGdwxq0Q8pZQ8s+2h39SXkZJbjfyKhGx6RXb2xPFpZppKCuTeeYvZZoWul/DxUzvohoYmBKe3IpIs53senOc/PHSZR9d8hBAsNm2qtkGUJCRZhm2a2KbOQrvCave5gYqbdc5VSklnGGCbGpqmq+Edx6BWtdjuhy94rnp+zTYwawaPn9shiiRqQFvgeQmtRobxOuqpLEt+KB0pIcRbin9713+XSm4kH/+jU8x+29tY7oxVKcoS3LO4j/VxSpbnrPRicinxooy5uoWjZRydrRHnqsH8QEvw+KrHue2QhqMz8CPm6ibnOyEbowRTFxxqu9i2wVOrI7ZGMXkuGYcxs81bI5i6Gp9eHUz+fbHjUXd1HEPns2d3mLIFQqhM39Y4IweyTPLJ0zuYps6RmRpZLnnTfoskzWk4GqMopm4ruYkgTvmqg9M37uBKSl5Au27QG6k22zzPyDN1oV9ou5OAZ6ZRQSBouSZHZis8sz6mbguEMDB0jXM7Hrah86YDNWzDIEpz/EQy36zQ9YaESc44DJltPOcQIAT0vb1NSd5ozi73AIFBzr6FOhJJdxiRpSla0VKhkFRsNSm848U8cLjF04aBYUCSpCSJKvmNRhHt1uvonFo2pU/4ohDiQ8BvAZOgSkr529dlr0puKP1xiK5rNKsmUsJHn1zj0OI0oIQooxRsQyPPc/wUtsYRNUsHw8ACNCQ7w4gDzTrtSpXLRRA245qkpIRSsr09IJVK60rT4MiUy/gNMI95oeOhaxrjSBKnKbWqzZnNMXMtm6mGunMXQJbnDFKNIzM1arbObTMVXF3DsDW2gwgJBEmKaxrcP+Pe0GMqKXkhKQbH9lfRBJzOMjw/nfTQZEVgsBtYSamsqACmajbb45Q4ydjxYgxDYOnqMhWmz+WeDk27bAwTDF0jSBKC+OY9ecRxzHo3RNcFpmmia+q9iaIUQ9eQaLRqOqNABaiGriRZZJpjCMFD53tMt1yEgI2Oh5QpaZqTydeX80SZoXqOKWAHeO8VyyRK8qDkFiONMyq2mpjRNQ0wGHkRjmNhGxq9ojcoTFLuWKjhmAYyzej7KSMf3nqwQcs2WR9HXOrH2EWj+XpnwDjVECKiMmk+VyfJLT+jYr++TgCvNs9c3satPtfXEKUSq9BwStKcLMsYhyl1SzDfruJHKcMgRBMOcZJiaCZJmlHVNcaZKgHaug6V19FdaElJwdYgIs9zanWHWl1Jejy7MebIQg0kBFFM1TbZHMVc2PYhl0RRzNDPqJmCL1zoMduqkOYSUxfYhiAoNO+iXGCbGuMwZXXn5tBhejF6XorrWiAlfpggTIPuKMKxdFxbR0p1rl1su+yM1M3UbitSJiWWoaNrgppjsHR7lcsbQ3aGEbr++gtetDd6DxWAlPJ7r/eOlLx+ePvtc3zmzDYVy+Dsco80TckzyV989xHSTGAgCBEgJVv9mH3TGn4Gmgb7mhaJlGyHMYYuONK20HTB2e2Iuw5OEaWSUZiRSUma5XSGEXGa0ajaL79jNznDCAwrwyqmkGxN8vTyCCEElqnT9RKkVIHW1rDH245O0YvBT+GL6wH7mil1UzBdc9jY8jg29/prOi0puZIXTqG9/54Zmq5FlOUkEh66NGYUxszULfKiT/CuRQtdE9x/sM1vPbLGk6tDjs5WGAUpuqETxjlSwFZ/zNpmwOzMzfs96A7DQmIG/l/2/jvMrvSq88U/a8eTT+WSSlmtzsHtdgcbGycwuD0YA4N5MDDkIfoOlzvDD5ghX5iBCz8GGMyYhmHAM9hchsFgTDthHNux7c5JUitLVapcJ++47h/7VKmkLkml0ypV6PfzPPups8O79/ueOmeftdda73chQs53SZKUnGfjdXPKAHLdHEnLYsnDB9lsStvKZma3goS+ks/ubRWKxSDLpepE3Yfi9Wex9ExvbeXNZFJLNplW5W9esP9nODdRzgFuBoZVdVZEjgF1shrasare3dsILs9qldL/YIXNC8DDqvr3V7dLho3A19wwxFeOzNFqRUtPQ488P8fYcKYWPjPXoFTKkyZKoxOxqz9PwbUJ4hjHsmlHCakILlDvxOyuOPTlXdpRQr7rnTpbC9k1UODEbG8K3ZuNRifmW17eh+tYTDVCwiTl9h1FHMvi48/OECZw81iJdpRydr5NlKYsltvcXs1mS9YiJdfpMBvD3i3+tGfYWty7r8pQOXtw+uhT2aSMnC34vsuBAY/ZVsJ0O2E+gvmFNqdqMdViDkeUh48u4FoQxCm2bWUeXZVNbUxB9gt/Psr2fp8gyWZOL8ryaapgQ8HNKlMk3RnBnRRaQYRrW+R9h8mFNu12xEIjZLA/h9ed/hhGMUE7oFBaX292LzlU3Ylx7wLeRFZe7ssi8gFVfXrxGFX9beC3u8e/FfhpVZ1ddpo3dEvorSmrNV1zZAWRD3WXO8jCgD8kIr+3Jj0zrDtF7/z5MqmypG9SLuUyz1LBYWd/nmrexXUsSjmPBPBcm3Y7JkxTolRoxjDficES+vI2AlTzNq04Jb+FQ32Pnq5zcqrOJ5+e5B337cC2hWaUMpB3iFIL17I4PNPh1l393Lu/SieGIEyo5l1OzAVUcxaiyVKOCcDpWohFlkNlMGwGwiDiU09P89TJeT78xBRYmdZdrFlZphO1zLvSaHRotUIioBMlzNY7zDUjqkUX33OwrEwEONFMimWz455nYCi+Z5FqVp4riBPiJCWOE8IUHFuoFnNoHJNzhSBKcB0L1SynSrr1V13PodGKWP7zbtsWfiGHpCH1Vki9de3DpCIvLIy8So/VvcBhVT2iqiHwV8DbLnH8O4D3XYUuXzGr/SU7ALxRVWMAEfmvwEfJLMYn1qhvhnXmpp0DPHF4jrT7mJTzbFQVBeqdkKKf5VQturBEsmKlc40OZ7tOJ7udMlDMPma2LUvHeDacno2p5h2aPdba2sg8OT5PXz6H71iUCjlcu8P7vnga27bwHOHlewfxXYuHjsyzv6tu/tx4g1ak5BxwXRfPtXFtYbDkEapSIAsHRpFwU7/LcMEkoxs2PmkcLYWpHjo4z3VjZdrtkLe+bBRV+OKReWabIXONDmGasLvP5/pimZNzbRYaIeWCQ6ebO1XMebTDAMgKs292bMui5AqpKmmS4thCJ05BFUuEhXZAGKeIQKsT4tg2nu+RpAmKZOE8TUnTLIXCsS3a3eT15QKfSaocGC0RxinNiTq6TrboRXSohkTk4WXrD6jqA8vWdwAnl62fAu5b6UQiUgDeDLxz2WYFPioiCvzxBee+qqzWoNoBFMnCfHRfj3UlFTZ3RqDhkrz99dfxpx94BrHg84+e4a7btzFUyVEt5LAtZaoRsbPPI4xTXCcrQzO7TDIlYclrTdItFKwKZxsJhfzW8Uw9+PljnI2EYt7Bsi12lCwePprJtYkIrVaE77skSUIrhoeenaSvINx9YBSAJ08uLNVDbEfgupCqMjk5w+joEJqmhBE8P9Pm22/ftl7DNBiuHBF8XzgwlKda8Dg6G2BZNv/4xDS7B3MUijlKxRxTtQ6DlSILQcpcu8mNI0X23jjIsZkWT44vV+xJsK3eCpxvNGrtEMsS4jil4NlUSnmm5jtkj62CoJQKHkl3EkrOsUiAIMq8UnEYUsr7YAkTM20cW1iodYhj5eR4jet2Z4WaXUc4Mtmkmne4bVcfz56pXfOxXmKW3/Rl8ppWanQxubG3Ag9dEO57taqeEZERsvq/z6rqp1fV6Stktb9o/w/wqIh8kmxwrwX+Y1fo85/WomOGjcMPf/PNjM/UKRYKpGlCs6tzkiqUfIsPPzXFzSN5SjmPVgTVvJxnVFV9eORkC9eFqu8QJIrnbh1jCmBSbXJ5mwRIE+XYfPaUuOiR63QiVBMc59wPQb2jTNUjfAviZU+MFuduPA2rQO1sg4KfSVgMO5t3erjhpYfvQigOQiZ0O9MMmG+GeC4UfY/pWsAr9vqIWlhpwmhfjlonoRWEPHV6gYNTLWzbIu/ZdIKQ2YUA19saxhRkP6aaKijU2xGVok+jFVDIuwiw0AipEeK6NqC0bRis5FHXphMmtDoprpsiljAzvUA+51Mo+t3wmp53pZxnM9eK2Na/PnlULyIp/RSwa9n6TuDMRY79Ti4I93VrBKOqkyLyfrIQ4voZVKr630TkwW5HBPj3i50EfmYtOmbYWGwfLDNf79BJFLCWQn8glHyHUjFPmirFnBArVJwUS2C44vL4RAfbs1FNUcvG2xiTTq4an3vsKJac+yopkHctgjhdKhcRhilhmNLfv+zHQKCaz2r/7RjIc2q2DUAxZy8/hChJu6575XTbJKIbNg+jlTzD1RzPnF7gkRO1bpkUGK6WsMTi6PgCz5+pk8QpjqWoZZPzLYb7iti2TVblMzMmfM9D3C32QCGZtp8toBGcnc9U4TudmCRVLAvKRZ9UU4q+Q5IoJycbFPM2Od+lmShhopw5OU8QKAODPpYlSw+8y8N+qkrRs3j21DzhOuVf2r3d+78MXC8i+4DTZEbTd114kIhUgdcB37Ns21J1l+7rbwB+raderILVzvIT4OuA/ar6ayKyW0TuVdUvrVXHDBuPvnKmSHx6romIjSvQCWPaQXaTWwyPiwiJZZH3LOIE8rbgWSC2t15dX1MqeYdwIcWxbUQy0dOCbxFGCe0EJibOVWpyHMmeSIG+vIOqUvZtnju9wP6RMrZAq9mhzTmfdpoqSZIgYtFohdd+gAZDj1SKHscnG8zXQ1SVfM5lbMDHsW3COPtMe54FHrzyxiHmGiGNTkSKsnOwRK0dMtcMKPiZrELZt2iGm7W4zAuJ45gkyR6cavWAUsnHcbISVY4rxHFCmqaEUUoQhuQ9i2LOZb4WMjzo4NrgOhZhdH5S1OKD3FKRZE2ZmW9TLuWwbJtS/trnnwng9DAzWVVjEXkn8BEy6/rPVPUpEfmx7v53dw/9VuCjF1R0GQXe330fHOC9qvrh3kdxaVYbd/kjICUT9vw1Mk2H/w3cs0b9MmxgdvQXUVWePT1PlAo37sikFKIkE+CzyAyAKBHEyqrKb2VOxnk6jRmmp7Ow5tBAnnKuyJ5tVWbrTaamhCRRBiouOwaLmXGEcOxsg1t2ZaropaJPvROzrexRGixxeLIOCnGq1JohZ+c67BgqkNs60Q7DFqfopjQ7MaKZp9YSob/kMlLOUfItZhoR+28Z5PCZOuPzHc4udBgpOzRDuGVbhTOzLSYWMiFLz0kp5z1KeY9cq8NMY/NPZEmSlFYjZHCgCJLlTLbbEQN9ORqtEM+zsW2LIEzwbRuxLZqdGFugvy9HFCZ4fvZQdv11g0ycrZPECeJmYqAA7SA+b2b2erI4y68XVPVB4MELtr37gvU/B/78gm1HgJf1dNEeWK1BdZ+q3iUijwCo6pyIbE13g2FViAg37+y/cCvR0vR+i2aY0txCDpUgCPjZP/4CcZigccC7fu5+wjDk4//8DJXBEkNDFTpBRCdIOTsf8PyZOSxsck7Kvv3DhFHM+HSdOFUEGB+vwy3DPH26hu85FHIup+Y77B0qkEYRQbd0RCHv0Q475D2HZIOI9BkMl+Pe6wapFnyUMsdn2uRd4dhswEQtuynYttCKIrx8jkIn4dnj85wqut36lCnXj+SYbNh4tlDKeUv5iIW8v6kNqiRJmZ5rMT7dolrxu/lR4Lo2SZKSpmDb2T3WIaXSV6DVCXAce8lr7TlCO1WiWAHFdy22jZZpNEOSVLFtoVryz3moNgg9hvw2Das1qKKuuJYCiMgwmcfKcA3o69E9W6iUe2p301BvSYvjjd4mfB6abPTUrq/Qm03fy02m2Wzy737n86hC365dbN9e5d3/fITHvnwE2xLiiXm2DZdptyJsEQo5B8cS5hba9FVzzDY6oEKUCrPTTRZqAduGfQ5OtWiHCf2V7D1PgWaYEl1Qh8vv3ohcz+Yjh2e5FD/1hn1XPD6A9311vKd2d+7s7XN2aLpz+YNWoJTrbULDo89O9tTO7dHD2uuP2di2Uk/t5nt8erntxqGe2nmXkS2YrAV8+XgW7o6ihFt39zFYdHl+solrCZZl4dgwvdAhTpRKyaPgCaVCjrxv8/ln5wijhG98+TYaUcqxqSb7hoqIwLSjzK7SqOpVXqHV6q3I8tHxi8+gC8O4qyknuK5NsegTxglzc206QUKh4BBECbbtoKlSLOeJk5RGO8H3M50px87yVD3PXgr1pYniuha+b9NsRpQKLmMDG6sklanld44/AN4PjIjIbwDfDvzCmvXKYNhgvPfPPgqWDarMHDrEDTd8DadOzDI6NkCpmqc23+KZZyYI2gGWbdE3UGF4tEw+n8O3lZOnaqSpkibQqNUIwwRre4EoShiq5DhyZo592/sAmKm3z3tasQR2jpZBlZxrYn6GzcFTp2ss1CLyeRfVlIeemmT3UI67dpV5crzB3gEfVeXk2QaqkPMtghjuP9BHkCiiyun5gK+eqHP99jIj1TwjFY8wUe7ZP8hHHj+73kO8IuI4oV4Ps7HmHGxbsAQc22Z0uMSZiQUWFgKKRYc4Um4/MESl5HH41Hw3hHfOGFn01olkW6VrqMRJQrnksX1gAyrIy0V1qLYMq53l95ci8hWyxHQBvkVVn1nTnhkMGwnbgdJAlutQn+eZp8dZODNO0moi+RJqexQqRbbtGmZ+ukalP0dfySUIUyrlIkzOYVlQrVgUCn20OwmDfXkc26LVCdi3rcp0rUPSTVjfOVjEcyxanZhWGFPMmQi7YXORs12Gd+bYPVjk+GSdPSMlWqnFVDulUvAp5l3qnZhqycci5RX7+3no4CwfenySWCGIYhzbYv+2Eo1OxMnpJkEYs3Mgf24GzCai04nxvMxbFkUJaZpp8gVRgggMDRY5enyBTidl52iJSslHBDzXohkkJEmCbWftVRVUcSQT+43jiFTBsS1yjoXjbLzYmgAbsFtXlUsaVCIysGx1kmX6DiIycIF4lsGwdSkNUOmv4Bc80nSAOEpIJicgCfDyPpUdO/F9ix3bitTmm0xN1KiWcgz25am3YgoFmyRO2DNSYmy4SqsdcvRMjULBopDPMbPQJiF76sy5gu9aiGRCod7Wzuk3bDGGijaO7VLO2dQ7IVP1gNfd0E8thFysBLEyUs5Ra0VML7S4Z3eRQjHHZC2gv+iwc6CIpsqRmWyu61wropJ32T9SYq4VcXS6RW5TTnSRJTvQ82yKRY8kTc/VtxOhUMhyyOZqmbinINS6yudRlBLHKXOtbN22hXLJo5K3KPoecapgWfQX1zf5/GKYkB98BVgs0bgbmOu+7gNOAL0laxgMmwzP9yj3FyiXcziOje9ZlO/8F3zqwYd53f13Uy7nsQRsscjnPW7b00+SKClC2bcou4McnG7h2zaNVkgzUgaHypkCskJ/Jc+RU/Ms1LM8mJ1DZVSVJM2ScA2GzUI7Sgk6HYIgohNnPxj/fKhOteRQdByqPhRci+koIiLLFTo126EeJKRic3S6SZjAaMXjZbsqTCwEtBLh1HSDhWaW11TKbz5Xh23LUk1OxxHSNEU109la3L6YdxfGypnJOkP9BdL0nEyEY1tUyh5xogRBRLsTE4TC4JiL7XiUXaG5UaW6ehf23DRc0qBS1X0AIvJu4APdqYuIyP3A16999wyGjcFbvv56vnpwfsmVHoQphTzc+aqbcD2Hs/NtbttRor/gM1ByKbgWTZQkVhpBSpAIe/sLpAgn5joQx6iCYwnPn5pnfqaJ5Tq4joMCk/Nt8p5DrRWwd7S6voM3GK6AIIG7d5exrUwA+LHTTZIkoRGkNDsBc23Ba6SEsdJotvliDVQTxHbQNMV3XTwbao2Qv/rsCbIEbovX3DxCQqZp94XDM+s9zCvGdS1EdMkjlSRKq53g+3B+fhQ4jsWx8QbHJxrEcYrjCIWCh6IEQUyjEVEoePjdwvKHJ9qM9StWeePW98w8VOvdi7VltUnp96jqjy2uqOqHROT/XqM+GQwbjuHBPtApIPMWWVaWUHr2zDReschQ2WOsv4BtQTnvcHy6RSsBz7FQQGNl31CR/qJPwUn5xKE25bzL4VPznB3PZjm+6c5t7N5eBpQnT9XpxAnjUw1jUBk2FTv7fGwrE6cUEUq+Tb0Drg1RnBDFKQvNJo12RBRnsyjHBkqoZrXt4jTl5rF+BNg+WKTsWTx7eh4RsFQJU1mxuNtGx3Fs4jgL17muhaZpNwSYKZvHcUK57BKGKZ3OYoHjzMBSPTfr0HVt8nn3PM+VAKQb1TWVIYhJSu8yLSK/APxPshDg9wCb7xHBYHgRfO833sz0fI3PPDlDruwSBRE/+G338LEnJhko+0zVQ/YN5GgnKWMDeZ4Zb5K34IbhPI1I2dFX5MT0AmFsMVSwOFML6KsWqFZyNBohhbzNhz53jGashGFWef7Om4Z57989ynd9y53rPXyDYVV0ggjVLKE6imOOTrWI44SZ+TapwvaBHDePVfncc9MMVXxG+3xGSh4icHTWYqicPbQcO1vn7HyHb7hjmNfcPLoUDpupB0Tx5lTtcRzBc21qtYA4SfE8G7crxGnbmUcvn7dpdiUwHAcG+vO0OzGWZRHHy40mJYoSLEsY8KFa7U1u45ohmUd+K7Nag+odwC+TSScoWWHBd6xVpwyGjcpQX4VvfU3lvG037aiSArUgpR6mDBQcbIXBXUVaKTiuxYAnPDo+Tz1SEhH2jlS5c7fwj09MI5KVjnjomWmSVAiCmDhOiSKotUK+4Y038qUnTnHv7TvXZ9AGwxUw0UgYqYRU8h5Pj2dVQDphwn03DeN7Dk43l+aVNwwx04ppBhGPzC3gO0LZz/HpxzI9tNfcNsLd+/uwLeGhQzOICK+6bmBTV17IvFQpnW65rjiO8bwI33cBRcRidjZ7z0QgSWB8vEaawuBgEcexzsu3ajQC7rtt+3oN54owSeldurP5fmqN+2IwbEq6dYtJk5QTcx2masJg0eH0fItOYlN2YM9IEcu1KNuKpkorUr50vMZAyWO2ES7lVagqpaLH/EKHom8v1U88sK838UWDYT14/EwbaNMJstDVXfuqDJd9ypYSWTbNIAbfoeRZHJqMqOR9BvI2J+Y6vPLmIfpKPq12yOcOzzNacTkwWubpM3UeOjRLu92bgPBGQdGlMJ5lCa1WTK0W0N/v43keSXIuzwrAdmyidlYsWVRJu865ajXHYgrCZkDIikBvZS6ZIiYiv3K5E6zmGINhKzOYy/KpEKi1A1oJPHKizkJoEaXKYDUTMFSFas6hr+CScy36ij7VosdAycXuZmuqKq4jfNvr93HfbaPnXacT9KbcbDCsFznf4Wv2ltk1kCfnWnTEptOsMVjKJAMePTmP6zrcs7fCaNWnE6UcmWySpimFvIeIMlELabQyI6oVxrQ3b9UZAGzbplh0u7X6ZOneMDXVodHIpCKW50eFQYJlgUhKuZyjWs11jalNhoAl8oJlK3E5D9UPi8jFdfQzo/M7gV+5aj0yGDYZ+bzHfj/iEwcXUBUarZg0VZ4/PUcYwYmTLt949w5AcSwh5wgl3+k+zQh536W/6DBTj3Bdmxt3lPBdB9tLiVsBlmWRqJJb5+KmBkMvpOh565840uGufSFiWdy+e4CdJYfxRkwjgtFqjvlmwPGZNq5j4TsWlmUxHyhRci5UttnJ5VzSNCSOM2NqsZhxoxHTrGclmSzbypTQRSiWPMobeAbfasg8VL0ZUCLyZuD3ARv4U1X9zQv2vx74e+Bod9Pfquqvrabt1eRykxj/BChfYil1jzEYXtKcqMVECdyzvw9NlUQzWT5VZaoe8pefPEYnSKh3EtJUOT0fsnswM5CSNOXMdIsoSnjVzcNsG66iKGUfSnmPou9Q8k3JGcPm5PHxDvOthE6knJpvowpfPDjF85NZrlAzipmqd3j82CwT820qBQ/HtrohMWspGb2S31rfgULBo1TyuqrpWZHj5fZGHMUgmZr64ODGqsvXG5LNjr5guWyrrI7wu4D7gVuAd4jILSsc+hlVvbO7/NoVtr0qXE6H6lfX6sIGw1ai6jtUCi45z+a1Nw1xaLrDtoEStgWfeeQU33DXGJZrEasy3Yq5cSTP2UaIbUHOdUkTSNOUnJ+VmIlTcC1BNKEVpgxWNvfTqeGlzReen0GVrDyKBduGyyBwer7DZyfmKDkO5YLLqYkmM9MNbrthhIrvMhMHWZkVoNNqr/Morj6WZTE0VCQIYlCwbCFNEibH54iCCC/nccPN25dKzmxmXoSH6l7gsKoeARCRvwLeBjy9xm2vmN7KthsMhvMo5F22FVNKvkM7Som7M3GSFG7ZN8j7P32Ct33NTkaGSiTdH4iyZ3fFPbOps0t+/y6txCafs8mbSJ9hk1Mt5qjVGjQCxRJhuO/cvn/92v1YIjTDlPHdAX/7ueMcPz3Py64fwbOEMFVEhNrmzkW/JL7vLIl0lko+O3b2r3OP1oBuDlUP7ABOLls/Bdy3wnGvEpHHgDPAv1PVp66g7VXBGFQGw1Vix8ggjSCk6LnUWwFzrWhplpMIPPTMFN/ymiKNTkScZqG8vCc4mikIv/mVe9d3AAbDGlKplKgAIyWbhaA79R9otCM+dnCONE155LFxkjSh5lt4nk2YKLft7iPvO5yaaqxr/w0vjkt4qIZE5OFl6w+o6gMXNL0QvWD9q8AeVW2IyFuAvwOuX2Xbq4YxqAyGq4k4HJ1uM9+OabUjojjFsiz27ulne0n556enAbhjrEIriMk7NrEKb37lnnXuuMFwbRgs58nbbY7MBiw0Qz7aCrv5NBbDw0XGJ+pEQUorzPQBHj06xxtvHVnnXhuuBhdJmZpW1bsv0ewUsGvZ+k4yL9QSqlpb9vpBEfkjERlaTduryaoq64jIDSLycRF5srt+R1c5/XLtciLyJRF5TESeEpFf7W5/mYh8XkSeEJF/EJHKRdq/WUSeE5HDIvJzy7a/vXu+VETuXrbdFZG/6J73GRH5+dWMz2C4mtRqc0zNtGi1s9l+aZriuRYT7SwP4o0HSjxypslzMwGxbq1pwwbDaigU8ty2s49X3zhCuizU3e5qIiyXDQC6wpeGzcyih+rCZRV8GbheRPaJiEemLPCB884tsk26sxdE5F4y22ZmNW2vJqstVfgnwM8DEYCqPk7WscsRAG9U1ZcBdwJvFpFXAn8K/Jyq3k6mvv4zFza8THb+k8C3kSm2L+ftgN897yuAHxWRvasco8FwVXhm4tzU7iTJckYOjBZ5xf4BXrF/gIXUW8feGQwbiz0VmyhKabdDZueyQsqlirukzxRsEakEwws1qFaTU6WqMfBO4CPAM8Bfq+pTIvJjIrJYY/jbgSe7OVR/AHynZqzYdg0GB6w+5FdQ1S/J+YO/rLyaZtMzFgPfbndR4EbOGUMfIxvsL17Q/KLZ+ar6THfbCy4JFEXEAfJACFxKR4sjMy3e8Z5HLzeU83jf9955Rccv8pmDcz21Gy71Fpn9w59+bU/tetUKeXayt1k4raA3pb7+Ym8qwa/aU+6p3YeeuXz5yvmFOp57rggqQJykNGNo1q8sq/aHXrHr8getwC8++ExP7eweZYw/+VxvZT3z3rXNOLhx/2BP7VLtLeWi6Pc2vrH+3mZ0js93empXLfZm4N851ttU/pPz4fnXr5bZywJPjkfs2tPP11xf5W8+dYzJ06ewbQs35xGnu3Cc3ma6Dff1NqtjsZ7elfIv7+utRNRnDs321O70bKundtcakd5/W1T1QeDBC7a9e9nrPwT+cLVt14rVeqimReQ6uslcIvLtwPhqGoqILSKPApPAx1T1i2Qepm/uHvJ2zo9xLrJSdv6Oy1zub4Bmt28ngN/pls0xGK4NYmFZQrHgkMvZdDohb7p9eL17ZTBsaKrVKq++aTvfeNdOyuUytekmaaJEYUKrvvXkEl6qbHWl9NUaVD8J/DFwk4icBv5P4MdX01BVE1W9kywZ7F4RuQ34QeAnReQrZAKhKz0K9JKdfy+QAGPAPuDfisj+F5xY5EdE5GEReTioz69mGAbDquirFMm7gutYFHwby7JYCNdsUonBYDBsCl5EDtWmYbXFkY8AXy8iRcBS1fqVXkhV50Xkk8CbVfV3gG+ALOEd+BcrNOklO/+7gA+ragRMishDwN3AkQv68gDwAMDAvlvMr53hqjI52yFJsyKmUZRyeqpGpVJa724ZDJsHi6XHZ8db7XO/YaOz1TxSF3JJg0pE/q+LbAdAVX/3Mu2HgahrTOWBrwd+S0RGVHVSRCzgF4B3r9B8KTsfOE2WBP9dlxnPCeCNIvI/gQLwSuD3LtPGYLiq3Hrg/BDfKqorGAyGZfzAt97Goyca5HIuCtRql0yFNWwChK3nkbqQy5n+izX77iYL8e3oLj9GNvPucmwHPiEij5MZSB9T1Q+Szdg7CDxL5nX67wAiMiYiD8LFM/u7x32riJwCXgX8o4h8pHu9d5HVF3yye73/3p2RaDAYDIZNwqGzTfLd2n1xnPLcRG8J94YNhGQPlxcuW4lV1fITkY8Cdy2G+kTkV4D/dbmTd42Zl6+w/ffJqj9fuP0M8JZl6ytm56vq+8nkFi7c3iBLcjcYDAbDJuXsXEim0qNsGypSq3ewXKNFtZl5EbX8Ng2rnde7m/MTx0Ng71XvjcFgMBhe0vzDF46xqDqyc3uZatFjoGhz3/4BXCv7Yf7LL06sax8NvbGC1NGWYrUG1f8AviQi7ydLFfxW4D1r1iuDwWAwvOR48HNHKFWKAMzPtxnw4d5dFQbyLrU4YbadIsA77h7lfQ+fXd/OGq6YrRbiu5DVzvL7DRH5EPC13U0/oKqPrF23DAaDwfBS4jNPniJXPicWWq3meNMtY7iukAL1ZoKqoMDslWnkGjYAgvFQASAiu4FpluUtichuVT2xVh0zGAwGw0uHBAfPy9TQs3IzSiXv0Iiz0jNhAq6txAk4W93VsRXZgknoF7LakN8/ck5UM08mmvkccOtadMpgMBgMLy1sx2akmpXdabRa3LV/hGfPNtk9lCNVyDnCUMEmTVPONNJ17q3hyhHjoQLoFhteQkTuAn50TXpkMBgMhpccI1WPM2drdCJl22BWX7ADHJzucF2/R8GBdqyICmMlI/a52RCMh2pFVPWrInLP1e6MwWAwGF6azM21aUeZ52l8JqAZTIJAOe/y+PGU3SN9AFw36JO3eyuUbFhfevVQicibyaSWbOBPVfU3L9j/3cDPdlcbwI+r6mPdfceAOllZulhV7+6pE6tgtTlUyxXTLeAuYGpNemQwGAyGlxxTtZBarUOaKo5jkc+7RFHKbbv6CZOURieiGUQAHBj017m3hl7oxUMlIjaZaPebyErSfVlEPqCqTy877CjwOlWdE5H7yUrL3bds/xtUdbrnjq+S1Xqoystex2Q5Vf/76nfHYDAYDC9F7tnrcapVxHNs4jihGSSktjA1O0+uWEaBvOdQW2jxt8dm17u7hitFevZQ3Qsc7tYURkT+CngbsGRQqernlh3/BbLav9ec1RpUT6vqecroIvJ2VqGWbjAYDIaNh2NBwXdI0xTLWt+cpE6YMjw0RClW+nzlkVNtvBRc1+bMbMj+4rljZxumDM1m5EXkUO0ATi5bP8X53qcL+SHgQ8vWFfioiCjwx6r6QE+9WAWr/Rb9/Cq3GQwGg2ETUMy5WJIVrE3T9Zs112pHVHIWOc8mSpTTdajkHWxLKTrgFxxcWwClEybUonXrquFFIiIvWIAhEXl42fIjFzZb4VS6wjZE5A1kBtXPLtv8alW9C7gf+EkRee1VGMqKXNJD1Y1FvgXYISJ/sGxXhSz0ZzAYDIZNyEaZcFXO2xw+26Dowdx8nZZVIIgSwKIWpgwUHEYrPp9+xqTtbnYu4qGavkyi+Clg17L1ncCZCw8SkTuAPwXuV9WZxe3dGsGo6mS32su9wKevuPOr4HIhvzPAw8A3A19Ztr0O/PRadOhaM1Bw+Y47t11Rm4/2+MX+4umFntoVvd7c8Z87VuupXc7t7XpFr7eZN9eNVnpqV2uHlz9oBY7P9RYyyPc4vom5dk/t/ufjL7hnrIp3vmFfT+3+/Aunemp301hv/7+TM62e2p2YbPTU7qZdfT21O9bj9YbKuZ7ana31JgPeCq7sGbfgCY5tA0o5d+WFh//ow89fcRuAH/i68z+fDz0/T94VpttKiEcQJfiuTRAluLYw4jsMFR3q9d7el9nZ3j5nuVxPk+B55GS9p3b1dm+ut5kev0fXmhehlP5l4HoR2QecBr4T+K7zzp2Jj/8t8K9U9eCy7UXAUtV69/U3AL/W2wguzyU/Md1ph4+JyF+qqvFIGQwGwxZhuh4BEQe2lda1H3v7bJ4ab6MKYlnYlhKEMY4FnVi5cW//uvbPcJXoUSldVWMReSfwETLZhD9T1adE5Me6+98N/BIwCPxR12hblEcYBd7f3eYA71XVD1+F0azI5UJ+f62q3wE80k3oOg9VvWOtOmYwGAyGrc9T420GKnmKnkWcpkSRMt0ISeI2b335rsufwLBJ6F0pXVUfBB68YNu7l73+YeCHV2h3BHhZTxftgcv5NH+q+/eb1rojBoPBYHjpMVDx8WxBLAvPsvAc8B24dcfQenfNcBV5KSilXzJZRlXHuy9/QlWPL1+An1j77hkMBoNhK5OEAa6T/RQpgIDnXXlOl2Hjc5FZfluG1WYfv2mFbfdfzY4YDAaD4aXHfQdGUD0/o0RWnhVv2ORY8sJlK3FJg0pEflxEngBuFJHHly1HgcevTRcNBoPBsJW5ZVuRMIxBFY0TDgwX1rtLhquNbH0P1eVyqN5Lpjj6n4CfW7a9rqpG+99gMBgMV4U7dpYvf5Bh0/JSyKG6nGzCArAAvANAREaAHFASkZKqnlj7LhoMBoPBYNjsWFvMI3Uhq8qhEpG3isghsorOnwKOcX6tHIPBYDAYDIaLIvLCZSux2qT0XwdeCRxU1X3A1wEPrVmvDAaDwWAwbBlEMg/VhctWYrUGVdStjWOJiKWqnwDuXLtuGQwGg8Fg2EpsdQ/VaosVzYtIiayg4F+KyCSmOLLBYDAYDIZVsfU8UheyWg/V24A2WUHkDwPPA29dq04ZDAaDwWDYOmTFkY2HClVtLlv9izXqi8FgMBgMhi3KVvdQXa44ch1WlKwVQFW1sia9MhgMBoPBsGWQLaiMfiGXq+VXVtXKCkvZGFMGg8FgMBhWS69K6SLyZhF5TkQOi8jPrbBfROQPuvsfF5G7Vtv2arLaHCqDwWAwGAyGnumllp+I2MC7yOoH3wK8Q0RuueCw+4Hru8uPAP/1CtpeNYxBZTAYDAaDYc3p0UN1L3BYVY+oagj8FdlEueW8DXiPZnwB6BOR7atse9VYrWzCluX5px9vfNvLtj+33v1YR4aA6fXuxDqy5cb/wJUd/qLH/48vpvE15LMv3LTl/vdXyLqM/6d+5Vpf8aKsyfi/fLVPuHbceC0v9shXv/KRgidDK+zKicjDy9YfUNXlt7EdwMll66eA+y44x0rH7Fhl26vGS96gAp5T1bvXuxPrhYg8bMZvxr/e/VgPXspjBzN+M/7zjJg1R1Xf3GPTldxYF06Wu9gxq2l71TAGlcFgMBgMho3KKWDXsvWdwJlVHuOtou1Vw+RQGQwGg8Fg2Kh8GbheRPaJiAd8J/CBC475APC93dl+rwQWVHV8lW2vGsZDdcUpJ1sOM/6XNi/l8b+Uxw5m/Gb8mwBVjUXkncBHABv4M1V9SkR+rLv/3cCDwFuAw0AL+IFLtV2rvorqmoUTDQaDwWAwGF4SmJCfwWAwGAwGw4vEGFQGg8FgMBgMLxJjUBkMBoPBYDC8SIxBZTAYDAaDwfAiMQaVwWAwGAwGw4vEGFQGg2HdEZFjIvL1qzhur4ioiKwo+dLd1xSR37j6vbw2iMgPiUijO5YD690fg8GwOoxBZTCsARczEETk9SJyatn6rSLyURGZE5F5EfmKiLzlEud9jYh8TkQWRGRWRB4SkXvWahwrXP/Puz/033zB9t/rbv/+a9WXS/AyVf0PV9pIRB4QkedEJF1pHCLy0yIy0X3v/0xE/GX7BkTk/V1j7riIfFevnVfV/6aqpV7bGwyG9cEYVAbD+vIPwMeAUWAE+DdAbaUDRaQCfBD4L8AAWeHPXwWCa9LTcxwEvm9Zvxzg7cDz17gfV5vHgJ8AvnrhDhH5RuDngK8D9gL7yd77Rd4FhGT/x+8G/quI3LrG/TUYDBsIY1AZDOuEiAwB+4A/UdWwuzykqp+9SJMbAFT1faqaqGpbVT+qqo93z/f9IvJZEfmdrsfrqIjcv+x6YyLyga5n67CI/Ovu9pyItLv9QUR+QUTirgGHiPy6iPzesn78A/BqEenvrr8ZeByYWHYtq3ue4yIyKSLvEZHqsv3/qrtvRkTO8yZ12/6ciDzf3f/XIjLQw1t8Rajqu1T140Bnhd3fB/w3VX1KVeeA/xv4/m5/i8C/BH5RVRvd/98HgH/V3f96ETklIv+/7nsxLiLfIiJvEZGD3f/Hv1/r8RkMhrXFGFQGw/oxQ1Yq4X92f2BHL3P8QSARkb8QkfuXGTTLuQ94DhgC/h/gv4nIYsX195EVER0Dvh34jyLydaraIat59bruca8FjgOvXrb+qWXX6JAZDN/ZXf9e4D0X9OP7u8sbyLw5JeAPAUTkFuC/khkcY8AgWdHSRf4N8C3d/owBc2QeoKvOsvfmctxK5sFa5DFgVEQGyQzdRFUPXrB/uYdqG5Aj8yr+EvAnwPcArwC+FvglEdnf0yAMBsOGwBhUBsM6oVndpzcAx4D/PzAuIp8WkesvcnwNeA2gZD/IU12P03JD7Liq/omqJsBfANvJfvh3ddv+rKp2VPVR4E/pelHIDKbXdcN3dwB/0F3PAfcAn7mgO+8hK0ZaJTN8/u6C/d8N/K6qHlHVBvDzwHd2z//twAdV9dOqGgC/CKTL2v4o8B9U9VR3/68A336xRPTLISI/ICKPichZEfkfIvI6ERkRkR8AfmqVpykBC8vWF1+XV9i3uL+8bD0CfkNVI+CvyAze31fVere22FNk77vBYNikGIPKYFhHukbDO1X1OmAP0KTr7RGRD0k226shIt/dPf4ZVf1+Vd0J3Ebmwfm9ZaecWHbuVvdlqXvcrKrWlx17nMxjAplB9XrgLuAJsryu1wGvBA6r6vQF/f4sMAz8Aplx1L5gaGPd8y+/lkOWYzQGnFx2riaZt26RPcD7u0n688AzQNJt2wv/giwseT3wEPCfyTxIrwf+cpXnaACVZeuLr+sr7Fvcv/y9nukauQCL79XZZfvbZP8ng8GwSenpic9gMFx9VPWkiLyLLDSHqt5/meOfFZE/J/PoXI4zwICIlJcZVbuB093XnwNuBL4V+JSqPi0iu8mMkU+94GwZ/5MsfPWGi1xvz7L13UBMZkSMAzcv7hCRAlnYb5GTwA+q6kMXnlRE9l5ijBfjO1R10QP27u5ypTwFvAz46+76y4CzqjojIh3AEZHrVfXQsv1rVtXeYDBsPIyHymBYO9xuwvfict4DjIj0i8ivisiBbiL2EPCDwBdWOpmI3CQi/1ZEdnbXdwHvuNjxy1HVk2RG03/q9uUO4Ifoemi63qyvAD/JOQPqc2TG2sUMqj8A3gR8eoV97wN+WkT2iUgJ+I/A/6uqMfA3wDdJJgHhAb/G+feidwO/ISJ7uuMcFpG3XW6MF2OZMXVJRMTrhjiFc/+7xX69B/ghEbmlm7v2C8Cfd8/fBP4W+DURKYrIq4G3Af+j1z4bDIbNhzGoDIa140GyUM7i8isX7A/JpuD/E5lUwpNkEgjff5Hz1cmSzr8oIk0yQ+pJ4N+usj/v6F7vDPB+4JdV9WPL9n8KcIEvLVsvs7LBhKrOqurHu7lgF/JnZAbFp4GjZIns/0e33VNkhtt7ybxVc2TJ8ov8PlnS+0dFpN4d532rHOOL4aNk/6evAR7ovn5tt88fJkvy/wRZ+PI48MvL2v4EkAcmyYzJH++O02AwvESQle+FBoPBsPnoht8C4A9U9RfXuz+90E2W/89kswJvUdUj69wlg8GwCoxBZTAYDAaDYUMiIn8GfBMwqaq3rbD/9cDfk3nCAf5WVX+tu+/NZB5vG/hTVf3NNe2rMagMBoPBYDBsRETktWQzad9zCYPq36nqN12w3SbT7nsTWUrBl4F3qOrTa9VXk0NlMBgMBoNhQ6KqnwZme2h6L5nkyxFVDcn033qe3LIajEFlMBgMBoNhM/Oqrnjvh+RcDc0dLNO7I/NS7Xhh06vHS16HqtQ3oAPb1vQ9XjdMMPciXOM3xvwfri7m/bzKbJLvg2OttkrQ+fh2b+0cqzd/Q9JjGk2Urkrd44XXS3u73snnnpxW1eGeGveAXdmjGr+wTKa2J5/i/PqZD6jqA1dw6q8Ce1S1ISJvIavacD2Z/MkLLncF571iXvIG1cC2HfzMf/vANbnWtU5X6/F79iKutzl+6rb6/+Fa50Ve+8/Ztb1er+gmMf2u9fvZqwEwXHR7arenmu+p3WDO76ldPYp6ajfeXKkm9+VZ6MQ9tfupr91//PJHXT006eDf8l0v2N75yu91VPXuns+bleRafP2giPxRV9PvFLBr2aE7ySRj1ow1Dfl1hfG+1HXFPSUiv9rd/jIR+byIPCEi/yDdqvYrtH+ziDwnIodF5OeWbf9tEXlWRB4XkfeLSF93+5tE5Cvd835FRN64luMzGAwGg8GwGgQs+4XLiz2ryLbFIucici+ZXTNDloR+fVdc2CMr5r6m3pO1zqEKgDeq6suAO4E3i8gryYqy/pyq3k4mMPgzFzbsZui/C7gfuAV4R7dKPWR1xm5T1TvIsvh/vrt9Gnhr97zfh1EqNhgMBoNh/RF6MqhE5H3A54EbReSUiPyQiPyYiPxY95BvB54UkcfIqjd8p2bEwDuBj5DVA/3rtRbbXdOQX1dBudFddbuLktUMW1Rf/hjZgC8U4VvK0AcQkcUM/adV9aPLjvsC2RuKqj6ybPtTQE5E/G7FeoPBYDAYDOuC9OSRUtV3XGb/HwJ/eJF9D5JVrLgmrPksPxGxReRRspIMH1PVL5KVy/jm7iFv5/w45yKrzdD/QeBDK2z/l8AjxpgyGAwGg2G9WZuQ30ZizQ0qVU1U9U6yhLB7ReQ2MiPoJ0XkK2S1wsIVml42Q19E/gNZBfu/vGD7rcBvkRV2feGJRX5ERB4WkYcb873IWxgMBoPBYFg1PYb8NhPXTIdKVeeBTwJvVtVnVfUbVPUVZIVEn1+hySUz9EXk+8jk6L97eXFWEdlJlpf1vaq60nlR1QdU9W5VvbvUN/DiBma4Yh47Ps/JmQazzZjJhQ5feX5yvbtkMBgMhjVFwLZfuGwh1jSHSkSGgUhV50UkD3w98FsiMqKqkyJiAb8AvHuF5ksZ+sBpsgz97+qe983AzwKvU9XWsuv1Af8I/LyqPrSGQzP0wOnTU5zsuLiOxWQjZe7UFKem2gA8dbzB975x/zr30GAwGAxrwqKHaguz1h6q7cAnRORxMgPpY6r6QbIZeweBZ8m8Tv8dQETGRORBgMtk6P8hWajwYyLyqIgsGmTvBA4Av9jd/qiIjKzxGA2r4OhUi5ZXZrByTtul1jqnn5Km0G6316NrBoPBYFhztn4O1VrP8nscePkK23+frAL0hdvPAG9Ztr5ihr6qHrjI9X4d+PUX0WXDGjA+1SJest2FUs6h3olxbfA8C8/LPoaffm6Wb7xza6rWGwwGw0sa6W2W32bC1PIzrCn1epPaBUK+aarcvafCW+/dhec5dDXZqHc2h7K0wWAwGHrAeKgMht45XtMsdh4l+LYglsXN24vnHXOtS6UYDAaD4Vqz9T1UxqAyrBntdpy5eRcRODCcO+8YkXO19aS3GqaGlxg91si95jXrZEXll8uzWWoAXmtybm8BlaLb28/ctb4deXZv44s3S3FLEbC2tsmxtUdnWFemWxHCOfGwJOwAhfOOuX3fYLZflSeOGk0wg8Fg2LJscQ+VyaEyrBnHZ1pM1TpM1zp4xNy083zNr8MTDZYJiFH1jYvKYDAYtiSyOWb5daWaLrttJYxBZVgz+vIu9+zto+hZTNTjF+xvJuevv+nlZoafwWAwbFXEsl6wbED+9wrb/mY1DU3Iz7AmJEnKzTsqANy1t58vPT9z3v6FRoeFRoBX9vFdm06UrHQag8FgMGwBREB6SIAUkT8jq4oyqaq3rbD/u8mEvgEawI+r6mPdfceAOpAAsarefYnr3ATcClRF5NuW7aoAuZVbnY8xqAxrgqVKuuy7c+/+vvP2P3y8juNYTNYDklTZV13V59VgMBgMmxLB6s0j9edkYt7vucj+o2RVU+ZE5H7gAeC+ZfvfoKrTq7jOjWSGWx/w1mXb68C/Xk1HjUFlWBOCBHI2JAqOgC6LlS8sLCxpTy2yb6x0rbtoMBgMhmtILwaVqn5aRPZeYv/nlq1+gazuby/X+Xvg70XkVar6+V7OsSEDmIbNTzuOefj4As+N12gG54fzPnGwQTtKSFMlTRUPE+4zGAyGrYyIINYLl6vMDwEfWrauwEdF5Csi8iOrPMe3ikhFRFwR+biITIvI96ymoTGoDGvCcxNtklSpBynPTTSWts/VAr72llFedWCQm8Yq1FsBr7l5dB17ajAYDIZrgWVZL1iAIRF5eNmyWsPnPETkDWQG1c8u2/xqVb0LuB/4SRF57SpO9Q2qWiML/50CbgB+ZjV9MCE/w5rTCM95oJpxSsG2ERF8x0I0XceeGQwGg+GacPGk9OlLJYuv6tQidwB/CtyvqkszoLr1gVHVSRF5P3Av8OnLnM7t/n0L8D5Vnb0wReViGA+VYU1ohTGpKkmqJElmNH3huSkaQYJqVm4mTpW33r1rnXtqMBgMhrVGuknpK3ioXtx5RXYDfwv8K1U9uGx7UUTKi6+BbwCeXMUp/0FEngXuBj4uIsNAZzV9MR4qw5qQdy1UIVWllMs+ZhP1kDMLIZ5T49bd/dQaLUbKg+vcU4PBYDBcC3qUTXgf8Hqy0OAp4JfpepFU9d3ALwGDwB91PUmL8gijwPu72xzgvar64ctdT1V/TkR+C6ipaiIiTeBtq+mrMagMa4NlIYCN0IlSPvz4BGk3uhfGymNHZ/iWe3qajGEwGAyGzYb0PMvvHZfZ/8PAD6+w/Qjwsiu+YMbNwF4RWW4jXUy2YQljUBnWHMsC9FwhZMuCb7nbGFMGg8HwUkF616G6pojI/wCuAx6FpSnoijGoDOtFHKdcv62MAlGc0K7kQZXnJ2r0Gw3PF00Yhniet97dMBgMhtXRo1L6OnA3cIuq6mWPvABjUBnWhD2DORTIWSlT9Q5qW+Q8lxu3V7luJL/e3btm/O2nDnJiMsD1HED58W++5aLH1motTtQjXEu4cXtlxWOm6x0itdjb7+HYFo12gNjuiscaDAbDRmIzeKjIEte3AeNX2tAYVIY1YbBgU/AsvnS0zlQ9RFU5MFZhwI+ArW1QdTod/uYL48RxQm0+QFOl0woIw5AH/vFZ3vSKAT7zVB1VKOccXntziWMLQqyCZSknTtd44sgM3/7q8wuc+0CkFhXPwrGzG1Mp79MMjfSEwWDY+GwSD9UQ8LSIfAkIFjeq6jdfrqExqAw9Mb4QZsUuBQ6O1xFLyLs2d+4s4zsWx5oJu1yXO/f0oUAnViwSKvniend9zfm7h89iWRadTkiSZF7jYjlPQfOowj8/OsfISJnbdlWoFHNYAsFcDVXl2YPTLDqa/9enDvP21x1YOu9A3uJYDaL0fAMqihNcx8ZgMBg2KiKbI4cK+JVeGxqDynBFWFHMXNx93X3YuGF7mcOTTcIkZb4TM1zyqOYdEgQRQBVbQNXmM89Oc/uOKn3lrR2mchyhvz9PPu+gcUw7BM+zCcOEfM4lChMOn21w1/4cqcK+4RxHJjssj9o3w3OvT0y1ONR93ecKqimCBZoaY8pgMGwKNoOHSlU/1WtbY1BdQ1YptvoCrjw1LqPXz266wvX6bQhFON3uFju+8BhVLMui7Gc/7q0QyjlIUkXJiiS7FniexUS9Q7PeYNeOge71ehzgBsW2hbzn4Do2E50ItRxyObBt2LG9j1ShWvSwLOGZUwvcvqvKRCMkimJ83yIMU0Qyo2yRYNn5Byt5rMxSZbYZ43nr+zW+mp8zg+FiRElvoe1mFPfUrhX1VmO0EUU9tQt7HJ/V6w/LtaZH2YRrhYh8VlVfIyJ1sll9S7sAVdWVE1uXYQwqw2VxSKml2RfXl2weqQgkafb30EQdjZVt5RRXlBMzbQ6O1xgsDmBZNnEU41kW7TCh3o7ZPVgixWdmvslg39YLAV6/o0yUZjeOwWqOJw5NA5AkmWFpW7JkXLuuzcR8m6laSK0Vsnd7mT3bqhyfqPHs0TkADp2ZQx0/C7GS/R9EBFWlEyfrblAZDAbD5chkEzau8aeqr+n+LV/qOBHpV9W5lfaZO7HhoiSpgqakIhRcIU1hW8VjthXiWBa3jlSohRG3bSvy+HiDZqQcmo145uQ8AI+fqjNY9igkMYWcw1wnZaw/jwKuQDOx2Wo66adn2yA2yx9w7r15mC89MwWA71qEUUqSKo4txHHMqdmAuVoHsJhcCNizTdk3VmWqlvmlWrEwWLBJ05RtVZ80BcvKzl8p+Nd6iAaDwdATG9mgugI+Dty10g5jUBkuyngtJFWouDA2UqLsZXlPY+UCYZJyptbA9Vxs4NbRAh99doYbRotsu2WYMI7JuzaHJzu0xCZppMw3AmzLoh1E9JUyw+rUdJOxwcK6jvNqkgKVnDDbzAyegmeRy7nYNjiOxUI9IOfZTM62KOYsPN+j2YqoFHxmF9rs21FlthESpym7B3za7Tb95XwWHhOLeisChYIHc42As03YXrK5caREGCVMdnoLUxgMBsNaIpKlQ2wBLjoIY1AZVmRiPsCxhVThhqE8RcfCdyxmay2OtWLsrvp5v5eFAG0Rbh8rE6ZQa0dECqmmjPXnSNMU37WY9CwaYYogeDYMFD1mGuHlurKpKLoOZ8OUsb5MdHOm2WFmrkmlkqmZ2pbSCRNcR7AcF0HYOVJCLItGJ+bm7T6HpiJUYaivwGefm2fncIliwUOShGI5RwrU6wETreyaZxoJ14+A49rscoQT9d5yOAwGg2Et2SIeqotmfxqDyvACWq2IVIQ0Ac9Wjs53KLo2rtXBd4U9fTnSVDnTCPiHL5yibYHnOkxMt4iimCRWxIL9O6t4rkOSKjsHC3iuw9zUPPVOyrRvMTZYwnO2xBdsif6Cxfa+Is9MZeE6EZt8TtgzZBMkCf3lTIPr7GybVMB3wLGgmLMRgcmG4jo2nWbIp746TjuIOXR8nn17+rhutLR0nZZmSekX4jgOYAwqg8GwsRDZMgbVRTEGleEFzLUjPNfGsWG2o9SDhDgJQQQBrh8I+OzRBiDUFaJQ8ZyUHSMlDh+fQzUl6ihPHZql4Cm33jDK4fEaUaxYAvmcTZymHDxdA4Hx6RqvuXnbeg/7qlDKuXSihIIjzDZCPMfCtR3IQRCem200OpBnoRXi2t1ZkUHKUF+eiYUA17FwXAvbgZLrEccpYZTg5zwm6xEkMTeN+sy2M8PJ6kpTOJYQpkbk02AwbEQ2dlL6FWBCfobVM9OOscOUOE4Qy6LgKDONhFRBUGptm7znkKgyP7dAktgUcxWiOCLnC0nq4LpKLufiug4np9uAUs47DPcVmZlvMdRfJIwTHNvuWU5iI9IMExQYLAie2LQSC0WZrXU4Pt1m51CBvO8QhDGaAnbXzySKoOdNKy6WfDzPYWEhwFI4Mj5PMefSCEIOT2Xxvvtv7sO2HerdKd7exp2VbDAYXsr06KESkT8DvgmYVNXbVtgvwO8DbwFawPer6le7+97c3WcDf6qqv7nKa9rAKMtsJFU90X35dRdrZwwqw3k8fXyBZpwgQBSnKDAdK4ogpFSLWY0+R+DwqQUcv4CjypFj84wOFygUciCQJIqqomlCueTT6sQ0g4SBOKFa8sl5Nu0wpt4JEWBhYYFqtbq+g3+R2KIs5oRblsNkK0I1ph0k5GwL2xbqrYhaK8J3LeJUSVGSVCl4FrV25sESQJOQfD7Lw+rvz9PpRMw3sqWUsyjmHWKFfz64wJtvH1nqQ2QcVIZNxkw9IBWLVMGXmFYr4HQjpdUJ6LRjXnV9lUrlshJAhg2O0HPI78+BPwTec5H99wPXd5f7gP8K3Nc1it4FvAk4BXxZRD6gqk9fsp8i/wfwy8BZsnlGkD333gGgqrMXa2sMKsN5PDfV6mbmpETdX2fbFiyBUsGn2YmwBM7MNLEELAvGhiuMDBYIwoh6K8GyBFGl1Q6xLIsbxsrkhwp8/pkpnpjvUCy6FHIOeT+bNajAM9PKKze3PbUktrnIxGyTejsmCUNOjjexLOHG6/qJY2WhnjI6kFsKEdbrAWmaYlkWClTyOWrdGn0iEEUxN4yVGe4rECg4IvQVHATFtjJNMAALY1EZNgefOzhNnGY/tIWcRbxsguoN28pESZGnjs/z+eebxPEC33RrkdHR0XXrr+HF04tBpaqfFpG9lzjkbcB7VFWBL4hIn4hsB/YCh1X1CICI/FX32EsaVMBPATeq6syV9tUYVIbzqNWDpRp9jmORpinz8xEimQhlFAtRlOD7LnnPYtdQiSSKaQaQJAmqKUGQ4LoOYZjS6YQ8enCGu28ewXNsnLzFjuESArTDeEkt23M2v2z2TCOir+iSpDBda2A7FvVGh7m5NiikifLc83MMDBSwRagWc8Sp4rs2WvQ4MdnEtuG1d4xhW0KqyuRCwGMHJ8nnPZ4/22Ko7NIOs7pYBc+i4Nk8enSGfcNlqkWf+c56vwsGw+V55Ng8iuDakPeyPMK4+zCQJAlT801s18J3BEuEfKXA47M21uw0X3fz0Hp23dAja5iUvgM4uWz9VHfbStvvW8X5TgILvXTEGFSG88j5Fk63Nlwp79IOYzzPJQxjwkhRTQiClDSJKA3lmZpv0wxCijkXr1hgsGIRxQnPHZ1mfq6NbQth3uXh56ZwHIuxoRKeJfielYUEBRxROrEseWg2K4em2nhzbcI481O5js3+Xf2cdixawaK3Seir+BQ8i2Y7wPezsJ5tWYwOFzk72cASaAQxYZRiWcKBXX2cnm4RxwnPTmYGbyeISVNlvhVhWcKp+gK3bS/QV8qv4ztgMFye8fkOe0dKdKKYMFbmmgFTkwvU2tlD1U17KjiehxUFxHHCLbtK5PNF0iThyFSLjzw1zV0jFsPDA+s8EsOVcdGk9CEReXjZ+gOq+sAVnfiF6CW2r3wSkf+r+/II8EkR+UeWVf1S1d+9XEeMQWVY4uxMg1v3DmJZwnStjSVCEMYkcUIxZ1EuubTaIc1mTKHg0QxT8r5F3vPwXRvfsdg1kKMZJJyazJGk0OkkFAsWpUKO3cMlSnkHTWLqgTJQ9LBti06YQJTwxcNzvOqGTaydrilBnBmEt2/PcWSyTTOBQt7h2LFMKf2+e3bjWBDFCYdO1QG4aW8f880I37XZOVbl+bMN+gsuMUI7iPA8h307qkzONbveQ8nqBC506C95VAs+0wttHjtV53U3GYPKsHH51OMTBJp5K4p5B9+xsSwYGekjnWpQb0bcvrNCzveI0iLX74KPP3aGlBa1epvrdw2ABYdqNoVCm2LRfN43DRf3UE2r6t0v4syngF3L1ncCZwDvItsvxmLJmRPdxesucAlDbDlralCJSA74NOB3r/U3qvrLIvIy4N1ACTgGfLeq1lZov2KGvoj8NvBWIASeB35AVee7+34e+CEyvcl/o6ofWcsxbiWmA6G/4OBYQrtto5ZFHMVMTbdwHCFKhU4nJkkUFCxVgiBi92iJwbLPTDsmjLOb5fV7+nAsizOTDeabIbVmSDyQMlULaAYxBd+mHSVLjxGuY2F1PWObkYlaQLXocWqmjQJPnk64a0+Zo9MBn39+FgRuvH4YBY6cmKPeCHE8oVDIc/DEPJWiTy6X5ZTFccJCG5rtiGo5h3SnQY725ZmpByQIlpUZVgvNiKLnMFjJU3ZMvM+wcWm0IoLuz5KI0OoknJmt0WjF7Npe5Du+ZicpFo8emeW58bMEQUyrFVGpuNgIr7h5G5oqz55aQFWJtcxrDxiDarOQJaWvSQTiA8A7uzlS9wELqjouIlPA9SKyDzgNfCfwXRc7iar+KoCIvF1V/9d5fRd5+2o6stYeqgB4o6o2RMQFPisiHwL+C/DvVPVTIvKDwM8Av7i84WUy9D8G/LyqxiLyW8DPAz8rIreQvWm3AmPAP4nIDapq6nGsgqJvoQhxCsW8i2MLE57N0FCRdjuk2Qyy3J2Cg2UJYaKMVT3u2dPH8bkOmqQMFi0ePlpjphHTX7TZPlSikA84M93k2RPz5HMOSZoSFjyKeS9LYAdQcOzNG+4LYhgq+rzxugEsgal29l6NllJ27c28bmILx0/MESaK67sEnRiR7Cm7r+xx6Pgcu8b6qJZyWEDOdWiFMZaVFUIGoZj30FRphTG2QN53mKp10DThcCtgbNCEQQwbk1Qza0o1IYoU13Uo5T1q9RDbcvjs8/PsHcxzYKzMobNNvu5lw7RDpRGEvOq6QYJEOTgd4AoEsRKEMX/5meN899fuWeeRGVZLj7IJ7wNeTxYaPEU2A88FUNV3Aw+SSSYcJpNN+IHuvlhE3gl8hMwp82eq+tQqLvnzwP9axbYXsGqDSkRuIJuOOKqqt4nIHcA3q+qvX6xNN+u+0V11u4sCN5J5riAzjj7CBQYVcC8XydBX1Y8uO+4LwLd3X78N+CtVDYCjInK4e57Pr3acL2VsEZI0IVHoyzt4rsWewQJPnKyhmjI/H9PXl8OyLHzPwnUd6pHyueMLVHMO1w0Xybsw1wxoB0qUxGwTi11DeRIgjBKa7QjbskAsWkHEtr4CaZqQ82wam7gOnQK2Dc/NtEhSZSgveK7LPz09DWRPZ6P9efrLPtPzLerNiFLJ4+xEDcvKyu+ICI5kchMJEEQJaZoSxGABji3MNULSVElTZbg/TxjEtOKusWXCH4YNjGvbdNodkOxn56ZdZfKew8tvGiHsyqxM1EKm6x2+5d6dtIKY8YU6R8abTDVS8q5FO0zoK2ch7onpNtft6l/nURlWi0hvwp6q+o7L7FfgJy+y70Eyg2s1/bufzDDbISJ/sGxXBYhXbnU+V+Kh+hMyT9Ifdzv6uIi8F7ioQdXtpA18BTgAvEtVvygiTwLfDPw98HbOj3MustoM/R8E/t9lbb5wQZsdlx6WYZF6JyZGydkWgSo516ZaynHTbptOGNOJUlJV4kRxbaFS8Mi5FjnHJu9anJrv0IxSPNelkHcQUUaqOUYrPtP1kJbAtv48qNIIEjxb8FwbsKnkbPoK3mX7uJHZVnQp+TYKhElCkir/5o17eW68xpH5kE4KeTtLxq9Ws9p++68boq9gc3amiW1b5LthP4D5VoDvZl/RRJUgSEi70yL7Sx6ubaGpsjjfPEw2/0xJw9bkyJkFbtlRxfM9PC/7TD96aAZV5frd/ViWsFDvMFsP2D5U5EtH59k/5PPyvX28fE+V587UwXVBM4HgW3ZWiRWSVHl6vMkt24vrPELDarA3tlL6GeBhMtvkK8u214GfXs0JrsSgKqjql+R8WevLWm3dcNudItIHvF9EbiMzgv5ARH6JLP65UoXcy2boi8h/6PbhL1fbptvuR4AfAegfHbvcEF4y2DZsK2U/9KlC0bfxbMF2bFQV31bmmjGtUKl3IgqOcOeOEkdn29Q6CUNll3IQ0Wg5pCrYtk0tiInmwLFtqgWbsu9S64QkacpCqDSCJpW8Q9kvEG/isinbixZ5z6IZRtk0b6+bD5Uoe0aKlIoeWMLEfMDJKYfFj2ocKzsGK2zrL7PQCjk2kSWql/MOjm0v+/AKvutgWzb1ZsDJiQUKvsvesQphkhInymjXSDMYNhrNRPjs83O47rk8ScexcG0LsQQRoVLyWWiGhFFKzrc4NhMwVPZoBLB9sEQQpxQ9i3asRJrNdPU9B2sTpwq8lNjotfxU9THgsa6jSICbyOyH51R1JRvlBVyJQTUtItexWClD5NuB8Svo7LyIfBJ4s6r+DvAN3fPcAPyLFZpcLHOfbrvvI5Oj/7quy++ybZb15QHgAYDdN91uHuu79BdcFp0clgieI4SxnkuKLnvkXYfpVoLvWSy0IuY6CRXfxbZTbAWxXXYNOhwaXyDsJARRDJLVuMu5Np04yQyDTkySJtiWw1h/ofvksnG/bJdjqOiy0InxHYtElZlmSDVnk1oWqkLOtUkRdg3k+bZX+PzdI2exBAb6fDRJiBLFsS3edMco7SBiqhZw264+klR56vgctSRzmT9zeIo0FTwHKtU8M82IvrzNTWNlgjCl1WqRz5vQn2FjMVNrMVwtMDWf4PsOqkoYJsRW9hClqoRxgu1YeG5mIAkw04zJudkDiO9k1QUWb9h215BKUnML3yxscA/VIm8ii8Q9T/Yx3CciP6qqH7pcwysxqH6SzAi5SUROA0eB77lUAxEZBqKuMZUHvh74LREZUdVJEbGAXyCb8XchX+YiGfrd2X8/C7xOVVvL2nwAeK+I/C5ZUvr1wJeuYIwvaTTJ0huWeyHbcXazEhGiVJltZ+GlnGtzw94Cni1MN2NcxyZQSDTlqdPzgICA7zkEUUIQJQyVc7hOdhMc7itQm6gRx6sKTW946jEUXOkaRkLOFVpximeDZ1tYtoUNpKmScx2+91U7iZKUzx2r0YoVAQYKLn0Fh2reBhKOT9Xxcz7zseI5Nu1OjOM4oMqusTJRongi3L0vS0QveBB02uv5Nmxoer2Xb/Xf62vxvlgIjSCm0Qio1Tp4rsXe7WUSTZmYbWCL0GiFFPNZKDtVpRkkJElK6mTfj2aQ0F/MBIMhy8mMkpT5VsyZ2Q6lgrvitY+nvX0n0tXNlH8Bnai3duf8AldGtEk+oMJiNYkNz+8Cb1DVwwBdR9I/AlfPoOomh3+9iBQBS1Xrq2i2HfiLbh6VBfy1qn5QRH5KRBaTyP4W+O/djo+RySO85TIZ+n9IJsXwse6P/xdU9cdU9SkR+WsyafkY+Ekzw2/1lFxoLn45u5/7kmcz245RBd8WKr7FfCcljFO6thEvTN1Z9qXp3iRsS9DFVcluHouvJxfajPblieKE7uSNTclUPWSg6CFkBqh035c4SRHAF8EWIereVESEW7flefpsB0tABZ6ZaNEKs4+sKizM1Mi5gudYpE7W7vq9/bhOFg5s1FtL57JQisVizzdmg2GtyOU8RGD/rj4A+vI2w2WXR47XuGdXhb6iw+eOLmBbDraljJQ89vW5jFRLPD+xwHRHWejEtDoW5YJHrZMZUwClnM0zk00cS7l1WxHP29y5mFsWkc3ioZpcNKa6HAEmV9PwsgbVMvXQC7cDl1YPVdXHgZevsP33yfSlLtx+hizLfnF9xQx9VT1wiWv+BvAbF9tvuDjFos9jh2bJ+Tb1TsIrdhXIuw5zrZiKC5bl0+cJqsJEJ2RiwWK04lNwhVaUzdJJL3haarZDLNvGsYV6O6SSc0Hg7EIb14b+Yo4wgfG5NiDsHti84apiziXRBFsdLJRUs6LS15U8KqXCUq2yRFOen63TUaWac9jX79MOYyYbIb4DQRDypltGUOCJ0zVqXZX1QsHj5u1FPN8ByfKzEsshSBTXgmaYUM2bfBLDxuCDXz6NCFTLLkXfQ1EWGiFJqoyUiuwcKJLzPA5Ntjg418K2bZIkpRUonThguhGhJ1rUWiG2lVIqFGhoSjvuUGvH3DBa4MxCSCdMyMRXLA7PhNh0uHG7Kaa80RA2TcjvKRF5EPhrshSnt5PJNn0bgKr+7cUarsZDtageeiNwD1lYDTJhzU+v2MKwaQkVSrZDX8Hm0FTAzgGLsYpHxbfwbaG/6LN3wKU2WEBEODHfRFSQNJvqf3K2TRQluE6WaDpczZH3bM4uBLSDhEY7wraFJFEqeY8kSbBtGxF5gTG2GYlTC9fK3gvXAkdgsFqiE55LuLfFouK7dNKQRpjiOxZFz+fgVJs4SWmHCX/98BnefvcYZ2sRrmOhCo4mfNMrtvPYRJtUlelGxK6BPM0w5ekTc3z2k0/w73/869Zv8AZDl48/cop8vlv8PFEcBzzHxeuG85450+Tx4wukKaAplYLHgbESo9U8ZU/46qkmr9nbx1hfjjhO+NSROQq+Q861aEYpJS+mEWQzj5M0Cw0WXRvH97Fl8xcAma4FTC60GarkmK51mJjrUCrY7BkqMVLNnZeWsZnYyEnpy8gBZ4HXddengAEym0fJomorctlP3jL10I8Cdy2G+kTkV1iF0JVhc9GfdwDFdYQwsthV9ZhtxTSDlFYUIgoFL8SzLHZVctw6VOIDz85idY2HcsFbCgEq0AljiJW+okd/0WWhndXvyuVguFqg0YlINAsBJlskVFW2hVQgSEARgijCsWyirk0VJQm2ZTHfTkg1y7M6MVsjjLLQ4HUjRVIsvnBsgSCK8VwfUFIsHp/oIFaWjzVSsejLZ7OmbtpZ5fbvvGu9hmwwnEcngbCdlUFrtdocPpZFNYpFJxOvLTp4br6rs5bQCVOem2ixreTz2ecbuBb808Fp8q7NG28YYFe/Tz1MqXdicp5NU6DWSUEzuRBbLJoxtGsBeX/zGlSqyieensKyrax255kGxaJHJ0yoBQln5mYByPs2Q9UcewfzVJbJzRw6tcBsI+Rl+wfIeRur8oTI5vBQqeoP9Nr2Sj55uzlf3iAE9vZ6YcPGJEmUasGh4FrEiXKmFlPyLTw3c6o7ljA+FxCk0EqUMIoAKHsOxZzNQidhsJKj0Q7YP5SnE6XUAqXkCrakKIIq5F0LWzJphnonRoEgWtXM1A3PZLBM/iFK+efj87x+dz+2JUw3QmxbODXf4uhMi139Hs1EODQVgSpjA3lShFSVnRWPV+wqowpxnDJU9kgVxushO0suOddhvB4wH8T4jlAtG5FDw8Ygis59B9LUQiSbLey5WaUES+DV+6uoCFP1iGYYk6jwzFSLVGHPQI5X7x/kPV84yQefmsa2YP9QAc+xmG9GHJtuMVrJUal41IOQWiub3JICySaWX/nok5PYtpAmKZZtM1i1QaDTESB7D1WVIEppdmIOTbVotBZ41YF+vvDMFGdms/JTx882+Y7X7V3XsazEZkhK70XEfJErMaj+B/AlEXk/mfPhW4H39NJhw8ZlZ9liqhMy31TaYULJUV51YAjXEjpxytlGDJYyPtfhzEIbS+GWsSKWCI6VoqlNnCrbCnkqOYd5Esq5bHbbsxMBcaqAMlDIc2YuQMlm09Q7IXeObt4ny8vxyRNz3D1axrUtxust5oKY20eLKFD1bb4IqAjNTsyiYPxoUQiiJEv+lyxJ3RJhZ8Un3617OFbJMT/VwBOTO2XYOHiuTdCdXLE4WU5V2bezSt7PQoEl3ybvOQwWXD72zDTT9YBS3sO2hEPTHfb1N0gsIW8Ld+2pEiUp7W6upi3CQjsElJLvLRlUyy636fjsUydpNKFUcCkXPVqtCMdzsG1But9/XTbJR0S69Q5DPvzIBFF0bv6VopydbTE6UFiv4bwA2TxJ6T2JmMOVzfL7jW4dvq/tbvoBVX2kh84aNjAjg2WePzzDmbmAYs5hIUlpBTHTzZQY5exCgGpmBFSLHkqWDN1f9LGwGasKDz0/j2NblHIutiWkCjkbxvpzjC8E3DhSyJKq584V8x3Ke/T3V9dv4NeAh8/WeebUHGpnPyj9eYsnTtRoBTFDVY++Up44SSh4Np0o4fHTLbb1pezoy6EKz5yd49691WymYJiSty1c22Zyoc2uwdI6j85gOMdrbijz8PEWiBIGAbV6ZvAUcu7ixF9yXiYYbAns7Msx3Qjpy1nMtRNUoRYLUZTwmgMDWCK0IkCEgXKOOIV6kNCOlLxPNgkEwRW4Z2/fuo27Vz72+GlSPDwvJQgTxoY8ynmPybk6iotjWaQWpGlKkqQ4lpCkKapw574+Dp5t0e5E1OpZaaokUZ49XdtQBhVsmhyqnkTM4cpq+e0GpoH3L9+mqidWew7D5mC+GVIpuDi2hV9w+dThOa4bqaCkFHybmUZIJ4ZOGlLJOYxVHSSOSOxMgG+w6LKzP4/vWEwtBAQIQZSSJkrOVk7MNGmFKdm8j8yVnXM3xRftRdFqtUhtd0lUYq6dsHekzFMn5kjS7AnOdbKv5I7+HKdnmliibC959BVcbhstggiewGwQEaWKb2OMKcOGo1Kp8LKxlKMLCfWch+O6vOmOAeY7KQGZN7UTJ+Qdh06UcGSmlVVjcIWwnpB3LL58ZI5S3l/R45T3HdpxQpxAvw37rh+8tgO8inzwSyexfYcoTkmTlFzOQdOEeiemkPOJU6gUHQo5j4VmQLyUi5lpER2eauM6FvlKjsG+PIeOzuE4QhhvrNDnJprl17OI+ZXEWP6Rc97UPLAPeA649QrOYdgEvHp/mS+d7JD3HOYWWsw2QvK2sGu4RF/OYboe4tpCEMWMDOdJVaiFKSJZeOq2HWVcoBWl7BjM0Y4SxusxYRAyUslxdKrJYDnHbCNg8St//djWn+b8+SN1RgcKy2boKGdmmwC4lsVcvbOUuE5iMVBwyHseT062KbptXrGjktVB7OovWMChOSPkadiYDA72MTgId+8/Z+xUKhCFIa0YOu0IJy8cOltDUmX/UJ7Tsy1qzZQa8Mp9ZY7OhzQ7McWcg2dBnIKmWekZEBaaAX27N/e9wyv4QFb6K4oSkjQTMc25Lo6d3SuarQgRSC6wkZI0peC52cOYLYz05ekv+tRbIXP1zoWXWldEwO4xh6or5v37ZJqUf6qqv3nB/p8Bvru76gA3A8OqOisix8jq8SVArKp3X+ZyK4mYf/elm5y78KpQ1dsvGMBdwI+utr1h81AqlXClRhQJ4zNN4lh57PAsJEq1r0i14OI4NmGUcHIuoL9gY1uZ1lTJt+mECR3JFJh9S7A8m5uHHRyrwGwropp3iLulVk7OtLa+FHWXOE2ZrbUZ7cvRjlKGSjYnwoTXXV/l4EyHID53szlbT7h7X4mF7j2x4FocmWujCnv7fJ492+KeXWUgWp/BGAxXyNx8nZt2DWCLS5woXzo2h+0EhJ2IasklCCIGqiVu3WGza6BAvRUw14x44mQNx7XY3ldAgHacEkYJp6abvGbfxgppXSn/9NT5epGOY1HyLSzLwurmSakquZxNqlDwHZpBFn2ygEiVIIzwXIcYiyBKsATiVKmUcjx8ZI4wTviaG4bWYXQvpJeQX1cY/F1kJWFOkWlCfUBVn148RlV/G/jt7vFvBX5aVWeXneYNqjq9muv1KGIOXJmH6sKLflVE7um1vWFj87U3jfGJZ84SRemSR6URKf0Clm3R6kRESUox77KjL0fZc3BtIUmzmSiLN765ZkTUdfG7dkrRc/EcoR6klDyL3YN5dvZv7aK+aZrysSfPct1QjvuuHwagHSVMNWK+65VVJusd7ir6TLcSxLIIYqUTxsw1Y4q+w1Q9ZGe12P0/KFONDikQxqYIgGFz8OTxGW7dUcUWOHh6ioMzmcfCtZVKwSdKEnaNVklS5VNPjoPY7BjM4fsexQKU7JTnzzZodWcEW5ZgWUqlsrm9UxcqxTi2kM+5tMOEOAURG1TpRCmVvJv9YKcJ7TCmE6dYlk2YgGWleK7NXCMkCGPOTDbZPVYBBMdxeOjgDAoM5oSbdw9c+4ECQs9J6fcCh7uGDiLyV8DbyCqirMQ7gPf11EeRG4EfISuMDPCMiDygqgdX0/5KcqiWK6ZbwF1kgleGLcobbh7l0LEFFr20ef+cromieK5Nf87G6rqbs1kcEHQSztQiogT6fLC7NWpyrrPk9nWtbMbK7oE86RbRn7qQw2cb+F6WuO/aFkcmOxydPcWNI3n6ygXEsphsRiiC57lUiTk80UBsG0tgoRUzVPbZO5SnGaQU/ex9PDIXsb1ocapuvFOGjc/MzAzHJjscm8zcrTuGsxqgLsJgUfjK8Tk0VRqNNmq57Bypkncy4dpOHFAtOEy1lZxnU/BtphYyfas33759PYd1VSgXXKIoK+3ViRJGK3nqnQRHLFCoN9vYTua9m+xEOE4mipwg5DwHy7KIk5SoK/5nWYLjvHDGr2Vl4cKT8xFHp89QXA+NKuk5KX0HcHLZ+ingvhUvIVIA3gy8c9lmBT4qIgr8sao+cJG2ryIT7fxjspCfkFV6+aSIfJuqfuFyHb0SD1V52euYLKfqf19Be8Mm5Efuv4EHPnQQEJ59fppX3rmTOE2J4gSxlEaYzdRRgO7fyUZE0HWezAYw3P2ULY/sJakyVPav7WCuEV8+MsfLdldI4oinJpuMFhxmax3yeZ80THnsZIMD2xRPUmZbCfccGGZiapbD81l7kZRy3sVxLCzpTje2FZKU52fbHBgxSeiGzcOXj5+fy3P9kMUzkym2J1y/rUxiucy3IuIUoiRlRx7a4nDrNodOmFJfrmnVvYfct3Pz1vxcTrsT0A4Vxwbfcxnrz/H48RqOAxpDrRUTxxGVkt81lBTPE5Igph3EFHIupZxLqxMxV+uQ8x1m5zukqbJQa1Ot5KnX2rg5FxF4+b4+JuY7zDav/cOYcNEcqiEReXjZ+gMXGD0rNbrYU/hbgYcuCPe9WlXPiMgIWf3fZ1V1pSovvwS8Q1U/uWzb34nIPwO/DNx/kWsucSUG1dOqep4yuoi8HaOWvuX5vjfu4YNfPEO17OOKgmVls28UoijmU89N8drrqhRzPnGaviBxsuBmBZRbQYKVs2hHMY6zNW6Ii/z9V85Q6N60PMfiE89OE3efGs80Imwre2pcLK9zcLyJa8MrbxhkYrbD8/PZeRb1ZhZd4185Osu2ap5IQVDKKzx9GgwbleeOTeP5Lh7wL24fZns1x+eOzBAnMfOtmNlGyC0jBY7OtXj8WJ09Qz4DgyXiRHn0xAIHBjzC0CbnpnSiFFvg1XtzVKtbQ2KlGaRYYhFEKWEQUMkN0OhEWFZ2HxCFYt4hSUKiKKugnutKTySJ0mkHDJR8gjBmrhXSbEcEQYzjQN4XXMdiYKAIQBzHHBxvcNOOKrPN+XUZ70VuX9OXSRQ/Bexatr4TOHORY7+TC8J93RrBqOpkV0fzXlYum3fdBcbUYvtPiciKXq0LuRKD6ud5ofG00jbDFsP3ff7la/cx2wzRJGGqrUv5PDnXRQmZDizONDpYlkXBFcJESYEBH56dCkiSlDQFJea2sa1xM1zk7NmzeK61FBoN4xTfgjhhKam00QyhGVKpnMsXSwFRC8/VZcJ9mYo8ZI9gA+U8nViXymmMNzrsGCpe4xEaDL0x0U7pL7rM1gM+88xZukLeOLbgew4PHa1zy/aUkbJHFCc8cbLOc+NNKiWP/pLPeCv7bogIOdfiZTs3d87Uhdi2tSTY2wojTkzWCKOEfM7BcSxa7ZBWLcbznUyzy1KIEhxbcGwhipWpWkAQBExNNejry1HozhpcaKUUl90qHMcmiOOsHNg68CJKz3wZuF5E9gGnyYym73rh+aVKVn/ve5ZtW0os777+BuDXLnKdSyWfN1fT0csaVCJyP/AWYIeI/MGyXRVWKXZl2BoMFD0eeX6SXPdbqqrEiS75Xl3HwhIhVsh7QsmzyDmC105Ry6ZiK4N9W8uYAjg21SbVc4aSAK6VGZyLysbJUo4DLFbGsCTboGlCxc+SR+eaEWGSkFv21QyieMmg2qxFUQ0vTV6+b5BWkHmi5oLs+zA2mCfnuagqR8brfOlwyI5+l5ddN4gjKc+MN7C7E1syEUuyLw7CQwenePUNw+s8qqtHkqTYtkWcJji2xROnmziOTasdY9tgWTblsoMlUCnmSFUZn2wSJwkjQ0Vqjaxc16nTTdJE8f2srt/ig5yqLt0z2u2IkZKQS5u87qYhPnaNx9qrDpWqxiLyTuAjZLIJf6aqT4nIj3X3v7t76LcCH1XV5cbPKPD+7nvgAO9V1Q9f5FK7LrBxlnd9x2r6uhoP1RngYeCbga8s214Hfno1FzFsHV5+3QgAT52ZR7CZawdYkhkLqixFuxcNCRdlW8kj523dUJVfrjJ1aIbhwQLFvEvOsfA8hx0jPo89P8XMXLB07EjZZa6jCJl6fPbUaVHvJMSacu+OEn7R56nxBpZkOSNhknDw9Dxjg0UabfMMY9gc1OpNJprKmZkmnU6MbQue59AOYm4Zq1DrxOwaGGZ8IWC+GTJXa+P6Hr7rEMcpFimjfTlOzbYQTagUfNxKgXq9Q7m8NWYGh0FCoZA9iGahPqFY8PB9hzBMQBNacfawOt5uIiJUih7T823CMKa/nKVe7NxZ4exEjWazQ6mUvTeLxpQAd24QrS67x+dBVX0QePCCbe++YP3PgT+/YNsR4GWrvMzPXGLfw5fYt8RlDSpVfQx4TET+UlXN3dwAwK1jfcRJplQ824xJkgRJMr+obQkOEIYJgeOS8y53ts1NosJNu/qINeTZYzMMDZaxHQvftbl13yBnijWmay36ig7Vch7XS2kFIc89P8sdewc5fmYGFYvhcp56mOIXYb7ewe7W6wtCJUkh51rsGdyaifyGrYeKw+1jPjeMFnj6VJ39o0WCOCFJlEY75MRcQBSGjM90SFNoVly29Sl5W9k+VCBK4Mhkk1YQsWe4jGUJNnCqmXBz+bKX3/B88rEz1ObbDPYXwHdIEqXZinBsYXq2RbWaA4TX3DhMsxVwbKYNlo0tMDyYJ0mVZidGJCvjU63mspl0EmPZHq+6YYhqYePcfDd6LT9V/QsAEdm/KNFwpawm5PfXqvodwCPdaYcXduKOXi5s2Pw4to1jw9h6TMFdB37nvV/FcVwcRxjdXkU0pc+K+PSXTzGyrcrOPUPYtoNjQSknnDpbZ2a+SarCnrEq840241N1BvpLzC+0QeDTT09QzNmMDhSJUmW8HTPUD65jL6nI53yHdhgDQjO8vMREr2FBvcbyFb3eW7e6DqysOKnp8uhFJz6tDyNll88dmqEVpYyUHKYbIXPNkChWHDuTWPHyPmNDDr5ncWa6xtEp2DVc4PDZFicmauQ8m1feMIBrCx99dJLh/jyVosNTp5tcN7q60F8r6m1GW68//rlLTBxRVf75sQlOzzSxbZuxnX0EUUIUJaSpsmMwz96REo/GWZ2+Qs7h2fEGYZTN6rPsrPahbQsWkKCoZj/kw4MlWp2IJM2Sv8t5b0N9VwRwNrBBtYw/F5EdZLlbnwY+o6pPrKbhakJ+P9X9+009ds5g2PT853f/E/NnM9m13Oh2du8dwLFdzs4n7L95J57n0Akjzp6ZY2a6Rr6Qw/Msdm6rkMt7BElKLudTynscOzHDween2bOrb6l4ab3eoFwuAcLUVOMF1x/ty2MJ3LpjCzyaG14S/PNzM8RhQgqcmU85kHNxbYsgipmpRwyWXcpuyrG5Fo5jsWekBDi0OiE7+3LMLvjcuKNEznM5U4u4/cAgewfzOJbFbGNjlVVZLX/96WO0wwSvWxgasgR9VDg72aHZCpluBARByuhQjkrBo9GOWGiE5HLZzGjLkmwG4GJOAOceMmxbCIKI2w+sj3jnJRGwN0Hmh6q+VkQ84B7g9cA/ikhJVS/7pq4m5LdYFPAnVPVnl+8Tkd8CfvaFrQyGrcXM5Cz4mcHTmZmhvtBiYrxB2G5RHShythYQd4uRRmHE0FCFQtFjdKDAQjNgcrpDrdZhaCDHk0+colTM4ToOaZriuQ5q5ai1AlpBQly0EbEoeA6dMCZVuGtP37qO32C4Uk6P1ymXcziORZqmPHZ0gZ3DBfYN5pioh9y5s8xkLUTTJnfsKHHd9jJfODxHK0gYKbtsG8xxbLbDmVrI3oEciSq2ZkXVi7nNJ7vy8MEpmp1MxDMIYlzXwXWEOFZELHZsK3Ps5AJRpPSVffZu68MSodYMLnrOzKbKklejJCUIE8pFj23V/LUb2Cq5hA7VhkJEXgN8bXfpAz4IfGY1ba9ENuFNvNB4un+FbQbD1qNYJVetYFs2naDDxHiDua9+GTSk6eVgcAdD2wfZvnuIE8dnqS00KZc9JubapKoEQUC56HDPgQFedfMw0wshX3pmkjHLJo0THNeh1VVDnWokHNieea4Kvk3aMYrohs3FdD3gVbeNUuskDBYd9g7kEVGOzMVEwPZ+l2aYMlELGOrzqSfwyMk6tTAh5zt8+tA8ilItuAxWcqjjcOTUAs0o+0EeKdp4zuZKNXj2dB3XzfqcJCk37Swx00mWQvQq4Ps2liUUcjaLM3wWK0l0OhG+72RbxWJmtsnIgE8rzKRaBBgqu9y8c+POpN7IOVTL+BRZEvp/Ah5U1XC1DVeTQ/XjwE8A+0Xk8WW7ysBDV9hRg2FT4uQLlCpFCqUcpZJLMW8zWbyb44fPcsOtuxjbPYyNUmvFHLhxBIlTdoyUEMum1Qkp7xok51sEalNvpyQC99w0QjtVHMem2QpxgFMTdaolGygjkuVJHNjAN0iD4UIm6yGqQqJCmipzrYRYOxR9l5yV5QAtNELmUaI45YZtJa7v9/jCsQYFz6HsWyQKuwZ8+osek82EOFEGSjbHJ7NweDSQp1LcXDP9FnWjIMuLvPv6YT7yxER3r2BZmVYUwPRCwPZWQKng0eycq9kZxwmNRkyaKqWcTb2VMFLxwXFwJJNryXsb03snmyTkBwwCrwZeC/wbEUmBz6vqL16u4Wo8VO8FPkRmrf3csu31C+TdDYYtS1/Vo1DKUan4gNAJYe91Y8RhxO237gbAtYWRfsGzoVrwEIF2JybI2wwUHMIYakFEFKXEqZKS1UCsNUOqJZ/HnptCFWYWYoIowbUtWmEMbDz3vcFwMeZbIZ4tXDeU55FjAe1uwfRWkGALuK4DroMVhnSSrLLAeDtlIYqxLIv5ToplwaGJBo1mRKpQzjsM9eV5xb4SB8/UODvX5uaxjSEFsFqu31HhuVO1pXp29XaUpQmIher55SVsW3ju5Dwv313qCv5m7+HuwRy7hookUczHn5iiWPSZakSU/Zi79g8SRht3Iv5mSUpX1XkROUKmzr4T+BpgVVbqanKoFoAFsgrOdOvh5IBSN1HrRK8dNxg2C//nd9/HH3/wuaV11SwRdGTbIKenm7i28NqbBil0Z+TFCUzUAiwLBoseSZJw80gByPHwyRrjCzG+73LibJ3x8RphmLBve5lX3bGdp47OUmuFJElKO0xgx+b64TC8tCm4FreOlbEFXn/zEE+caRIlSqMTkQJRu00QpoBSb0V8/lDIYNWnGSQomZDnzsEiheESz5+ucWamRZ2Y2/cVQIQbd/bxyLGZ9R7mFXPn3n4Onq4vhfg+/eRZXnV9ic8dbS8dUynbtNpKHCdEEXz+4AJxnABCLmfz5OGAp48vkKaQyzlLxlmtrXzl+DyvOjC0HkNbJbJZcqieB54DPgu8G/iB1Yb9Vp1DJSJvBX4XGAMmgT3AM8CtV9phg2Ez8qPfdCN/8fHn8X2XvG9j28Kzz45zx90H2NaXw3dtQCn6DgutKCtmqoLvQi6f45aRPgRhtJTnQwdnmKs3GSq7nDie4lgWr3vFTmxLeMWNwzx+cgHbtvEk4atPn+auW1Yl1GswrDujZX+pqLeq0goi6p2YWiPIvE1FF9GUuUaIiPDKGwcp5z2m6gEzzQgLKPkOnTjmjt0VvvbmoWwWm2S1MGdaCXeMbsyw1qWwbYsdg3kmawG2LTSDhAgPyAwqVSVVi1wOZmdDXNemkAfb9okTQTXNhDpFsG2IopRcN+qZpOkGN6Y2Vcjver3QZbhKriQp/deBVwL/pKovF5E30PVaGQwvFb7v6647b/3rbxnh84dnyeU8js50uHVbARsILMFSqOYtyr5NI0iZbIXkLeWZsw22V3xm5us0YuGGG4cZn6jxvz6ZGWtRlNLpZK77G3ZXeOTgJKfmWnzzq69fhxEbDFdGq9UhLXsIysR8k4V2QqsTk8Qpt+8foNkOGekrcHauzVw74ZkzdUoFj2rOphMEHDnTZKjis2dbhc88N8O+oTy37u4DMqOk5KY4+Q0oC7AKXn3zCP/wldPMzLRJU+XBL51haKgACCJZDlWr1cGyhDRNaTQhjjvkfAc/52Lb6VLpKhFoNju4tsVb7tl1yetuBHotPbMOjInIfyHLo1IyT9VPqeqpyzW8EoMqUtUZEbFExFLVT3RlEwyGlzRD1TypQpLC89Mdqp7FSMWlk6Q4bqaAXPSFI/MN6p0UBTRO2Dnaz0An5OBEk3w+UzSOogQ493A0NFzmdcPlDSXQZzBcimq1yEI3kfrwVKYXparce/Mou8oeI6V+npyoceP2MgXH4oNPnKUx1yEoefiez57tNjNzbXKS8K33jCEiPHayxtlawE2jeXzXXUre3mzYlrB7IM/UVAuA/6+9O4+SszrvPP791dq7uq1u7QipsVgEZhUYbFBswDZwMgbbMAgb2yTjMDjxnMks8XKyjMfn5CRxkpPEARsTxww5SVhMTIwdDMHGYMuAjQAhEEJECIxaEtpQ7921PvPH+7YoFdXdpeqlqqufzzl1VO9y3/de3e6up+57l2w2z/BgingiRjwRQRJDQ9kjj/IgCJx6e0dZ2Bm0ihNO4CqJj69fxVC6osaUqpgjAdXtBH3Hrwm3rw/3fWCyhMcSUPVKaiGYOfSfJO3HF0d2jmRUjGSD0UvD6TypXJ5U3ujpG8Wy0NwUpyMWYWlnksWtcaIR2P7GKHv6shgQj4l4PEImkycSEQ0NCVobjHPWdjGQCp51RGTk83kikbnRZu4cwNoV7Wzd1cvFZy1BgsFUlmRaLO9oZPPOXl7vHWFBc5LGRJRcPs/g0AinrnwHWt5GJpvnkZeCvlKnLA3menthzxAXnTS3F0c++4SF/EdPPwPDGZLJKMOjOXJDGVobIiRbksRiIpcLAimAdCr4mI1ERERiw/qVVcx95aS5MQ8V0GVmtxds/z9Jv1tOwmP563wlwcPe/wE8CLwC/KdjSO9cXVraGqctLg72DdNzsJ/dBwfZsquP/b2jjKTSrDuuhdVLGpFBPAKxSITuzhaaG+K0NMRpb0rQ2Rz0v8rljLbmKJeeu5LhVLD8BBj5vJFOlz0dinM149xVC1DYp6oxEeV7z75B33Cahe9o4qzud9AaF6d0JulemGA4C7945U229fQSjQQfwA3xKDv3By06s7080ky56oKVxKJjX6KCQS69QzkOHRrh8KFh+vsGyeXypFJZorEoXe1JrrtoJf/5vbX/aG88YxN7Fr/KSitdJmm7pB2Svlji+Psk9UnaHL7+qNy0JRyUdL2kaPi6HihrFETZLVRmNlSweUe56Zyrd/F4lNaGLG8OZVjUmiRqWd4YyrFvf/Ars/31PlYuFO87s5vRkRzNcWM0m6d7YYKdh9I0JqMczBv5fI6zTuqku6OJNNCUjDOUypDNB1MyNDTMrXl3nAPoTxsdMYhgjGby5A2efvUwa5Z3AOKMFS3sHTYO9KdJxKMon6apsZFdh0dpGFsnNBKFVI71Jy+qalmmSzIR5RMXr+ZHm/ey+8AwuVzQOo0FwRUWYWjwrS9Ql52ztHqZnUaVPPGTFAVuIXjk1gM8Jel+M3ux6NSfmdmvV5i20G8CNwN/RfB89fFw36TKmdhzAEquuinAzMzHdLt5r284S3NDjO5FLXS1xtk3kOLNJW0MDKZ4bschhtMJhlJZohFojxsWjZDJRxAwNJRm38FRDBHJ52huTpAeztLeHCOVyROPQSJSH9/M3fwTi0Z47KUDLF7QxBu9waLgQxljT+8oI6NpOo5vY9OOA2RykBrN0h1OExKVyEDwrMiMpmR9Pe6Ox6Jcvm4F/UNpvrvxV+QBC3uc5/N5BnsHaV7QTFd7glVL5/7HrCqfNuE8YIeZ7QSQdBfBE7OJgqKK04ZTQX24koyWMw+Vr8bq3CSWL2xm58EROltjpHN5Dg3lMKClOcnC1iS73hggdvYyAPaNGKs74oxmRxHQ2tJANgeScerKhQB0NAW/mu9oSlSpRM5Nn7NXL+Tx7QeIRkU6leO4xa0Y0NCQIJXJ8akLVnJ4NMMLr/fy6Oa9nHFyF0sWNDGayYEZBqzpqs8W2rbmBDd8aA35fI5MNsem7YfYf2iQ5LJmTlvTxerl7dXO4rSJVBZQLQd2FWz3AO8ucd4Fkp4D9gD/28y2HkPaIyR1Ab8FrKIgRjKzSVupjqVTunNuAhed1IkwIhKGMZzKQj7HiiUtvL53gMc27+bXznxrPqmlrQ1s2zdKR1OEiGDZ3O5r69yE3nNSF786OEA+XziOFVZ3tbK3dyT4AtIYZ8GCBp7b9gbHXdhNW2OcvEFMoqmpPgOqMfFYlHgsyvoz6uPxXrEJOqV3StpUsH2bmd1WmLREmuIm+2eA481sUNIVwL8Ca8pMW+x7BIsh/wjITXLuUTygcm4aDY5kaWmM0XNoOBgSHc4ntWRxC9l8sNDpxu2HWPnu5cSiEZa1J3nsuV9xySkNrFixosq5d25mHd/ZSn//EHuHc8SiUQT8ePvBI9MsbNt+gL7+UWJxeHL7QQBaklHO7u6oYq7ddBmnheqgma2bIFkPwTIwY1YQtEIdYWb9Be8fkPR1SZ3lpC2hycy+MMk5JXlA5dw0amyI88iL++kbGCWX48gInqamOG3NcR7ddogFDRHi0Qj7+tO0JmP8+nknTH5h5+pEa2szrWFHkmdePYCicaIRYUBzc4K+/lEy6bfmWhpM5WhuTFYvw25ajI3yq8BTwBpJq4HdwAbg40ddW1oC7DMzk3QewQwGh4DeydKW8ANJV5jZA8ea0Rnt5SepQdIvJT0naauk/xvuP0PSE5Kel/R9SSV73I033FHSNeH18pLWFeyPS7ojvO42SV+ayfI5V8rhwTTpdDAFQiZjRCIROprjnLIwwuWnL+Y9J3bR058OOtw6N4+dvbqLmAwzyGby7D8wCEAsHnw0mQXzr7l6EMyjVfyajJllgc8BDxEsd3ePmW2VdJOkm8LTrgZeCPtQfQ3YYIGSaSe55X8nCKpGJA2Er/5J0gAz30KVAi4On2vGgY2Sfgj8LUGnscck/Sbwe8AfFiacZLjjC8BHgW8W3e8aIGlm75LUBLwo6U4ze20Gy+jcEaOjo2SyRz+iNzPOX1Pb62w5Vy3vOq6DLT2HiUUBGU0tceLxOOl08BhweNjnX6sHU2ihImwteqBo360F728mmOqgrLST3KvigXgzGlBZMAvbYLgZD18GnEQw4zrAwwTR4x8WJR93uKOZbQv3ve2WQLOkGNAIpIFJIksRKdlvbXz5Sfu0uflqz+EhmhtEX/hTHxEsak9WNP/KbC83U+L3qSz1MtnifFXpz1m8wmVEGuJvfzByYbiw70UnBv/+n29sfKvrsKCt4SRyFWZ0NFtZC1djiXyWY3lrZY8nGypcTmffUKqidLNOQReIWiXpeKDXzPrC7fcDVwGvAbeY2aSR/YwXL5xpdDOwH3jYzH5B0MI0Ns/DNRzdaWxMqeGOy0ucV+heYAjYC7wO/IWZvVkiTzdK2iRp02BvWROgOleWrrYGchahpSVOc1Oc0VSWdd1zcyFX56rGY/S6M5WZ0mfJPUAzgKQzge8QxBFnAl8v5wIzHlCZWc7MziToXX+epNMIZh39HUlPA60ELUnFKhnueB7BMMdlwGrgf0nqLpGn28xsnZmta2lfWH5hnJtEa3Mz6VSWpmSM4ZE0+bzxi5cPVjtbzs0tNfU566ZLJX2oZlGjmY2NALwe+LaZ/SXwGwSxxaRmrQHOzHqBR4HLzOwlM/ugmZ0D3EmwLmCxSoY7fhx40MwyZrYf+Dkw0XBM56bdxy5YSc/eQYaHMyxfEOH9py2pdpacm1PetXYx8WSU0848jnPPO77a2XHTYA60UBVm5mLgxwBmVvYz4xntQxXOOJoxs15JjcClwJ9JWmRm+yVFgD8Abi2RfNKhkiW8Dlws6R+BJuB84K+npzTOle/Tl7ytYdQ5V6Z3Hr+Qpvagb3AkIjbteIOzuhdXOVduSmqvRarYI5LuIegy1AE8AiBpKaWfor3NTLdQLQV+ImkLQYD0sJn9ALhO0svASwStTrcDSFom6QEYf6hkeN5HJPUAFwD/Jumh8H63AC0EfbSeAm43sy0zXEbnnHPTqOfwMPl8jng0QjQiet7MVjtLbormQAvV7wLfJeiEfqGZjc1sswT4/XIuMNOj/LYAZ5XY/zfA35TYvwe4omC75HBHM7sPuK/E/kGCTu7OOefmoI0bd7BvOHi/YmkMDBobE/QPZUnEIZOF1iafk3ouqq346WjhrAR3ldj/bOG2pCfM7IJS1/CfSuecczXjlTREoyKXM5Qb4d0nLqMxJlqSMWKxKPFIMDfVqCqbZsBVT40/8ivXuItKekDlnHOuJtzzxOu0tDQCkMnkWH/Kcla0JwCxtz9NNhU8+osqQtTjqTmnPuKp8WcbqOFptpxzzs0X/7qph0QidiRQisUinNjZiCHyBPPhjMn5PFVzjlTz0yZMmbdQOeecqzpFIkgiHhUp8gwOjpLL5bAjo9ktWETZfN7PuapO4qdxS+EBlXPOuaprTEBbU+OR7fPPWc7u4RxdjTEM6GyMEIkFH1n7elP4M7+5pm5apD453gEPqJxzzlVdIiJe6ekFwgXFu9u576ndRCNwySmLaUgYkCMRj7JkQYI3R3ITXs/VFlHbLVSSBji68VPhtggGAbYRvHlhvGt4QOWcc67q9hwcBURf3wgSfPepPUiie0kze3qHGcoa7UmIxhpYu7iyBYhddVXaQiXpMoKplqLAt8zsT4uOfwL4Qrg5CHzWzJ4Lj70GDBB0w8ua2Xirp/yYYM6p7wJ3mdnrx5pPD6icc85VXS5rDAwOc0J3F7lcnqGRDLmcEYlAhiiKGm+m8iRTowxmEtXOrjtWqqyFSlKUYNLuDxAsSfeUpPvN7MWC014Ffs3MDku6HLgNeHfB8feb2YSLqprZVZIWAB8F/k5SA3A3QXD1Zjl59VF+zjnnqu6TF3dz4gldNCZiNCajJJMxGhri9OzvJ5MPnsREImIkkyfrvdLnHFHxKL/zgB1mttPM0gSTb15ZeIKZPW5mh8PNJwnW/j1mZtZnZrcDlxMsifcV4IZy03sLlXPOzUO7e1NEJPJmLF1Q3UdoBwaDVT5OXtzCQDZL76DR2JYEiX2HB4lFIJuHvBknLopXNa+ucuPET52SNhVs32ZmtxVsLwd2FWz3cHTrU7H/AvywYNuAf5dkwDeLrl2UP70HuA64CNgIfMTMfjbBvY7iAZVzzs1DEQlJRID+4TRtTdV5jNY3nKYpHozYS2WMvsOjDKSCT8FMNk9bY4LUcIY00Ag0N7dWJZ9u6sZpkTo4Qb8mKD1NQck2SknvJwioLizY/V4z2yNpEfCwpJfM7Kcl0v4KOEzQAnYjkA33nw1gZs9MkEfAAyrnnJuX8mZEALPqfhC8/MYwY0sfDwyNkEgkiEfzpHNGNALxPJx9Qge5vD/nm+sq7JPeAxxXsL0C2PP2a+t04FvA5WZ2aGx/uEYwZrZf0n0EjxDfFlAR9MMy4EPhq/gH7uLJMuoBlZt3Kh26a/73fF6KVPjzUuuf/8vbk/QPp4kYNFXQOtXWUNk8UMtajl4K7bXWITpakqSzWZ4ZTpPKBNMhRGXkgevfuwqAdD5f0f32DKQqSldpvbcmKnsk2ZGsrIWw1n/OxozNlF6Bp4A1klYDu4ENwMePvrZWEozO+6SZvVywvxmImNlA+P6DBP2iSvk8sMvM9oZpPw18DHgN+HI5GfWAyjnn5qm2pkTVP5B3HhiiZTBNNBqlvSlOLp9nYDRHNgfndlQ3b256VRJPmVlW0ueAhwimTfi2mW2VdFN4/Fbgj4CFwNcV3GRseoTFwH3hvhjwz2b24Di3uhW4NMin1gN/Avw34EyCUYNXT5ZXD6icc85VTVMySntzkkw2h+IxYrEYidgoV5+xrNpZc9Oq8pnSzewB4IGifbcWvP8M8JkS6XYCZ5R5m2jB9AjXEnSO/xfgXyRtLucCPm2Cc865qkkk4uTMiIfBlARtzQ2TJ3RzythM6cWvGhKVNNbIdAnwSMGxshqfvIXKOedc1axfEuXpw0d/skZr64PWTZMaX8vvTuAxSQeBEeBnAJLeCfSVcwEPqJxzzlVNZ2cnI/v205SMHRkff3xNf+66iqjyjv6zwcz+WNKPgaXAv5sdGYYUIehLNSkPqJxzzlXVVacuAmD7nl5WdiRpbGysco7cdAse+dVwRAWY2ZMl9r1c6txSPKByzjlXE05a1l7tLLgZVMstVNPBAyrnnHPOzbhab6GaKg+onHPOOTejVON9qKaDB1TOOeecm3HeQuWcc845N0XeQuWcc845NwWawkzpc4UHVM4555ybcXUeT3lA5ZxzzrkZppqfKX3KfC0/55xzzs2oqazlJ+kySdsl7ZD0xRLHJelr4fEtks4uN+108oDKOeecczMuIr3tNRlJUeAW4HJgLXCdpLVFp10OrAlfNwLfOIa008YDKuecc87NuApbqM4DdpjZTjNLA3cBVxadcyXwDxZ4EmiXtLTMtNPGAyrnnHPOzShRWQsVsBzYVbDdE+4r55xy0k6bed8pfdf25wc/d+Hq7dXORxV1AgernYkq8vLP3/LP57KDl3++l/+k2bzZM888/VBjXJ0lDjVI2lSwfZuZ3VawXSrqsqLt8c4pJ+20mfcBFbDdzNZVOxPVImmTl9/LX+18VMN8Ljt4+b38RwUxM87MLqswaQ9wXMH2CmBPmeckykg7bfyRn3POOedq1VPAGkmrJSWADcD9RefcD3wqHO13PtBnZnvLTDttvIXKOeecczXJzLKSPgc8BESBb5vZVkk3hcdvBR4ArgB2AMPAb0yUdqby6gEV3Db5KXXNyz+/zefyz+eyg5ffyz9HmNkDBEFT4b5bC94b8Dvlpp0pCvLhnHPOOecq5X2onHPOOeemaE4HVJKukbRVUl7SuqJjXwqnmt8u6UMF+6+T9Hw4Pf2D0tuHcUr6gKSnw/OelnRxwbE/lrRL0mBRmqSku8N7/kLSqhkocnE+q1H+c8L9O8Kp/hXuv0HSAUmbw9dnZrLs4T1rqfz1VP8LJf1E0qCkm4uOXRum3SrpqwX7Z7X+a6zs86XuS6avs9/9Sspf9/UvqbWgfjdLOijpr8Njs17/NcvM5uwLOIVgLo1HgXUF+9cCzwFJYDXwCkGHtBiwH+gMz/sq8OUS1z0LWBa+Pw3YXXDsfGApMFiU5reBW8P3G4C767T8vwQuIJjf44fA5eH+G4Cb50H9j1f+eqr/ZuBC4KbCOgUWAq8DXeH2HcAl1aj/Giv7fKj7cdPPdt3XYPnrvv5LnPc0sL5a9V+rrzndQmVm28ys1KScVwJ3mVnKzF4l6Pl/HuH6jECzJAFtlJiTwsyeNbOx/VsJJh5LhseetGA4Zql73hG+vxe4JLzHjJnt8iuYyr/NzJ6w4DfpH4Crpr1gZaqx8tdT/Q+Z2UZgtOhQN/CymR0It38EfGx6SnNsaqzs86Huy0o/W2qs/POh/o+QtAZYBPxs6iWpL3M6oJpAyenmzSwDfBZ4nuCHaS3w95Nc62PAs2aWKveeZpYF+gi+1VbDTJV/eXito65beG7YnHyvpMLJ1GZbNcpfr/VfaAdwsqRVkmIEwWRhPddC/Vej7HVf92Wkr4W6h+qUv+7rv8h1BK1whSPaaqX+q6rmAypJP5L0QonXRAsclpxuXlKc4IfqLGAZsAX40gT3PhX4M+C/lpPVUvcsI93EF62t8k9Uxu8Dq8zsdIJv73eUOPeYzaHy1139v+0CZofD9HcTfDt9DciGh6e9/udQ2eu+7idJX3e/+yXyMlH6uq//IhuAOwu2Z6T+56Kan4fKzC6tINl409CfGV7zFQBJ9wBfLHUBSSuA+4BPjZ1f5j17wm+wC4A3K8j7UWqs/D3htYqvi5kdKtj/dwSByJTNlfJTZ/U/QX6+T/AHFEk3Arlw/7TX/1wpO/Oj7sdNX2+/++OYKP18qH/CdGcAMTN7uiCfM1L/c1HNt1BV6H5gQ9jvZTWwhqAz8W5graSu8LwPANuKE0tqB/4N+JKZ/fwY7vnp8P3VwCNFTaKzaUbKH/YdG5B0fvgc/lPA98I0Swsu8eFS151Fs15+6qj+JyJpUfhvB0Fn3G+F27VS/7NeduZH3Y+bvobqHqpQfuZH/Y+5jqNbp2qt/qvLaqBnfKUv4CMEEXkK2Ac8VHDs9wlGOGwnHIkV7r+JoMK3EHzbXBju/zDwlfD9HwBDwOaC16Lw2FfDe+bDf78c7m8AvkPQ1+KXQHedln8d8EJ47Zt5a3LYPyHowP0c8BPg5HlW/rqp/3D7NYJv2YPhPdaG++8EXgxfGwrOn9X6r7Gyz5e6Hy993fzuV1j+eVH/4bGdxfVbjfqv1ZfPlO6cc845N0X1+sjPOeecc27WeEDlnHPOOTdFHlA555xzzk2RB1TOOeecc1PkAZVzzjnn3BR5QOWcm5CkwRm45oclfTF8f5WktRVc41FJ66Y7b845VwkPqJxzs87M7jezPw03ryJYW8w55+YsD6icc2VR4M/D9cSel3RtuP99YWvRvZJekvRP4UzySLoi3LdR0tck/SDcf4OkmyW9h2BiwT+XtFnSCYUtT5I6Jb0Wvm+UdJeCRVjvBhoL8vZBSU9IekbSdyS1zO7/jnNuvqv5tfycczXjowRrgp0BdAJPSfppeOws4FSCdcN+DrxX0ibgm8B6M3tV0p3FFzSzxyXdD/zAzO4FCGOxUj4LDJvZ6ZJOB54Jz+8kmN3+UjMbkvQF4H8CX5mGMjvnXFk8oHLOletC4E4zywH7JD0GnAv0A780sx4ASZuBVQRLV+w0s1fD9HcCN07h/uuBrwGY2RZJW8L95xM8Mvx5GIwlgCemcB/nnDtmHlA558o1btMRwZpiY3IEf1smOn8iWd7qjtBQdKzUWlkCHjaz6yq8n3POTZn3oXLOleunwLWSouGq9esJFoMdz0tAt6RV4fa145w3ALQWbL8GnBO+v7ro/p8AkHQacHq4/0mCR4zvDI81STqxnAI559x08YDKOVeu+whWqn8OeAT4vJm9Md7JZjYC/DbwoKSNwD6gr8SpdwG/J+lZSScAfwF8VtLjBH21xnwDaAkf9X2eMJgzswPADcCd4bEngZOnUlDnnDtWMivVgu6cc1MnqcXMBsNRf7cA/2Fmf1XtfDnn3HTzFirn3Ez6rbCT+lZgAcGoP+ecqzveQuWcc845N0XeQuWcc845N0UeUDnnnHPOTZEHVM4555xzU+QBlXPOOefcFHlA5Zxzzjk3RR5QOeecc85N0f8Hn73NylbiL0IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "varbl = 'SM_SnowDepth_inst'\n",
    "minval = 0\n",
    "maxval = 1.5\n",
    "mpr = 'Blues'\n",
    "title_list = ['Lidar [native resolution]','Lidar [~100m]','LIS-SnowModel [~100m]']\n",
    "\n",
    "fg,ax = plt.subplots(3,1,figsize=(10,8))\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "ds_Lidar_reproj.plot(ax=ax[0],vmin=minval,vmax=maxval,cmap=mpr)\n",
    "ds_Lidar_regrid.plot(ax=ax[1],vmin=minval,vmax=maxval,cmap=mpr)\n",
    "ds_LIS[varbl].plot(ax=ax[2],vmin=minval,vmax=maxval,cmap=mpr)\n",
    "\n",
    "df_Points_reproject = df_Points.to_crs(4326)\n",
    "\n",
    "for axcount,axx in enumerate(ax):\n",
    "    axx.scatter(df_Points_reproject.geometry.x,df_Points_reproject.geometry.y,4,df_Points_reproject.value/100,\n",
    "              vmin=minval,vmax=maxval,cmap='Blues')\n",
    "    axx.set_xlim(lon_range)\n",
    "    axx.set_ylim(lat_range)\n",
    "    axx.set_title(title_list[axcount])\n",
    "    if axcount < 2:\n",
    "        axx.set_xlabel('')\n",
    "        axx.set_xticklabels('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a3b457e-b628-418d-b6c9-ebccc74aa6a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg,ax = plt.subplots(2,1,figsize=(9,5))\n",
    "ax = np.ravel(ax)\n",
    "ext = [ds_Lidar_regrid.x.min().values-dx,ds_Lidar_regrid.x.max()+dx,\n",
    "      ds_Lidar_regrid.y.max()+dy,ds_Lidar_regrid.y.min()-dy]\n",
    "diff_spatial = ds_LIS[varbl].values-ds_Lidar_regrid.values\n",
    "\n",
    "dat = ax[1].imshow(diff_spatial[0,:,:],extent=ext,cmap='RdBu',vmin=-1,vmax=1)\n",
    "ax[1].set_xlim(lon_range)\n",
    "ax[1].set_ylim(lat_range)\n",
    "ax[1].set_facecolor([0.6,0.6,0.6])\n",
    "ax[1].set_xticks([])\n",
    "ax[1].set_yticks([])\n",
    "\n",
    "\n",
    "ax[0].set_ylabel('no. of gridcells')\n",
    "ax[0].grid(axis='y')\n",
    "ax[0].hist(np.ravel(diff_spatial))\n",
    "ax[0].plot([0,0],[0,100],'-k')\n",
    "\n",
    "\n",
    "cax = fg.colorbar(dat,orientation=\"horizontal\",label='Modeled - lidar snow depth [m]')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a375db62-9b8a-4e9b-96f4-31d3b80c879f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab the data from each spatial set that overlaps each ground point measurement\n",
    "x_indexer = xr.DataArray(df_Points_reproject.geometry.x,dims=[\"point\"])\n",
    "y_indexer = xr.DataArray(df_Points_reproject.geometry.y,dims=[\"point\"])\n",
    "\n",
    "ds_Lidar_reproj_vals = ds_Lidar_reproj.sel(x=x_indexer, y=y_indexer, method=\"nearest\")\n",
    "ds_Lidar_regrid_vals = ds_Lidar_regrid.sel(x=x_indexer, y=y_indexer, method=\"nearest\")\n",
    "ds_LIS_vals = ds_LIS[varbl].sel(longitude=x_indexer, latitude=y_indexer, method=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1fcadd68-567f-46f4-8dba-0e43427c144f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the differences data at points to the geodataframe\n",
    "df_Points_reproject['diff_Lidar'] = np.ravel(ds_Lidar_reproj_vals.values)-(df_Points_reproject.value/100)\n",
    "df_Points_reproject['diff_Lidar_regrid'] = np.ravel(ds_Lidar_regrid_vals.values)-(df_Points_reproject.value/100)\n",
    "df_Points_reproject['diff_LIS'] = np.ravel(ds_LIS_vals.values)-(df_Points_reproject.value/100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "469d5b48-88bd-4243-b56d-9ad36e4e1bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1, 0, 'native lidar - point'),\n",
       " Text(2, 0, 'regridded lidar - point'),\n",
       " Text(3, 0, 'LIS - point')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg,ax = plt.subplots(figsize=(8,4))\n",
    "ax.plot([0.5,3.5],[0,0],'-k',linewidth=0.5)\n",
    "df_Points_reproject.boxplot(column=['diff_Lidar','diff_Lidar_regrid','diff_LIS'],ax=ax)\n",
    "ax.set_ylabel('Snow depth difference [m; gridcell-point')\n",
    "\n",
    "ax.set_xticklabels(['native lidar - point','regridded lidar - point','LIS - point'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f2c45d6-6f96-4d0d-8cf5-efaaa5de9a06",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "32\n",
      "44\n",
      "47\n",
      "48\n",
      "59\n",
      "60\n",
      "61\n",
      "73\n",
      "74\n",
      "75\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "86\n",
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      "100\n",
      "101\n",
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      "105\n",
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      "109\n",
      "110\n",
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      "127\n",
      "128\n",
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      "135\n",
      "136\n",
      "137\n",
      "140\n",
      "141\n",
      "142\n",
      "163\n",
      "164\n"
     ]
    }
   ],
   "source": [
    "LIS_val_grid = []\n",
    "ASO_val_grid = []\n",
    "point_val_avg = []\n",
    "\n",
    "df = ds_LIS[varbl].rio.clip(df_Points_reproject.geometry).to_dataframe().reset_index()\n",
    "df2 = ds_Lidar_regrid.rio.clip(df_Points_reproject.geometry).to_dataframe(name='test').reset_index()\n",
    "\n",
    "# xrval = df\n",
    "for index, row in df.iterrows():\n",
    "    if ~np.isnan(row['SM_SnowDepth_inst']):\n",
    "        diff_distance = np.sqrt(np.abs(df_Points_reproject.geom.x-row['longitude'])**2+\n",
    "                         np.abs(df_Points_reproject.geom.y-row['latitude'])**2)\n",
    "        idx = np.where(diff_distance.values <= np.sqrt(2*(dy**2)))\n",
    "        \n",
    "        if len(idx) > 0:\n",
    "            print(index)\n",
    "            LIS_val_grid.append(row['SM_SnowDepth_inst'])\n",
    "            ASO_val_grid.append(df2.iloc[index]['test'])\n",
    "            point_val_avg.append(np.mean(df_Points_reproject.value.iloc[idx].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "70db83ab-ec25-49f4-8e13-49c7d964c876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Points averaged per 100m gridcell')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg,ax = plt.subplots(1,2,figsize=(10,4))\n",
    "ax = np.ravel(ax)\n",
    "\n",
    "yy = df_Points['value'].values/100\n",
    "\n",
    "ax[1].scatter(ASO_val_grid,np.array(point_val_avg)/100,facecolors='none',edgecolors='b',marker='v',label='lidar [100m]')\n",
    "ax[1].scatter(LIS_val_grid,np.array(point_val_avg)/100,10,facecolors='none', edgecolors='k',marker='X',label='LIS [100m]')\n",
    "ax[1].plot([0.25,1.5],[0.25,1.5],'-k')\n",
    "ax[1].set_xlim([0.25,1.5])\n",
    "ax[1].set_ylim([0.25,1.5])\n",
    "\n",
    "ax[0].scatter(ds_Lidar_reproj_vals,yy,10,facecolors='none', edgecolors='r',label='lidar [native]')\n",
    "ax[0].scatter(ds_Lidar_regrid_vals,yy,10,facecolors='none', edgecolors='b',marker='v',label='lidar [100m]')\n",
    "ax[0].scatter(ds_LIS_vals,yy,10,facecolors='none', edgecolors='k',marker='X',label='LIS [100m]')\n",
    "ax[0].plot([0.25,1.5],[0.25,1.5],'-k')\n",
    "ax[0].set_xlim([0.25,1.5])\n",
    "ax[0].set_ylim([0.25,1.5])\n",
    "ax[0].legend()\n",
    "\n",
    "ax[0].set_ylabel('point snow depth [m]')\n",
    "ax[0].set_xlabel('Snow depth [m]')\n",
    "ax[1].set_xlabel('Snow depth [m]')\n",
    "ax[0].set_title('All points')\n",
    "ax[1].set_title('Points averaged per 100m gridcell')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73897659-610e-4812-854f-a2c9cc14992a",
   "metadata": {},
   "source": [
    "## Where should we go from here?\n",
    "* further development of the toolbox: more functionality!! \n",
    "* CLI development??\n",
    "* integrating more sources of data!\n",
    "* Automation in a web interactive environment?\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a020c5b9-37ce-4b0e-88d5-586cd234b13d",
   "metadata": {},
   "source": [
    "# A big thanks to the hackweek organizers and all our SnowEx friends (both old and new!!!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "708424f6-f301-4a54-ac1d-fb8922080f47",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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