Figure8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e90afe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/weitao/anaconda3/lib/python3.10/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "# from labellines import labelLine, labelLines\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from netCDF4 import Dataset as NetCDFFile \n",
    "import matplotlib\n",
    "# from mpl_toolkits.basemap import Basemap\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "%matplotlib inline\n",
    "import cartopy\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "from cartopy.feature import ShapelyFeature\n",
    "import cartopy.io.shapereader as shpreader\n",
    "from mpl_toolkits.axes_grid1 import AxesGrid, ImageGrid\n",
    "import geopandas as gpd\n",
    "# import pygeos\n",
    "# Import modules\n",
    "import xarray as xr\n",
    "import matplotlib.ticker as mticker\n",
    "import datetime\n",
    "# import matplotlib.ticker as ticker\n",
    "\n",
    "from matplotlib.dates import (AutoDateLocator, YearLocator, MonthLocator,\n",
    "                              DayLocator, WeekdayLocator, HourLocator,\n",
    "                              MinuteLocator, SecondLocator, MicrosecondLocator,\n",
    "                              RRuleLocator, rrulewrapper, MONTHLY,\n",
    "                              MO, TU, WE, TH, FR, SA, SU, DateFormatter,\n",
    "                              AutoDateFormatter, ConciseDateFormatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcd8f46",
   "metadata": {},
   "source": [
    "# Read Seasonal GEMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4991b219",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>HCHO_column</th>\n",
       "      <th>year</th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
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       "      <td>130.85</td>\n",
       "      <td>7.169437</td>\n",
       "      <td>2021</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>2021-01-01 00:45:00</td>\n",
       "      <td>9</td>\n",
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       "      <td>8</td>\n",
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       "      <td>8</td>\n",
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       "      <td>126.55</td>\n",
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       "      <td>92.15</td>\n",
       "      <td>0.565132</td>\n",
       "      <td>2022</td>\n",
       "      <td>Winter</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 06:45:00</td>\n",
       "      <td>6</td>\n",
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       "      <th>14704701</th>\n",
       "      <td>456322</td>\n",
       "      <td>47.05</td>\n",
       "      <td>92.25</td>\n",
       "      <td>0.142487</td>\n",
       "      <td>2022</td>\n",
       "      <td>Winter</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 06:45:00</td>\n",
       "      <td>6</td>\n",
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       "      <th>14704702</th>\n",
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       "      <td>47.05</td>\n",
       "      <td>92.45</td>\n",
       "      <td>1.588609</td>\n",
       "      <td>2022</td>\n",
       "      <td>Winter</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 06:45:00</td>\n",
       "      <td>6</td>\n",
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       "      <td>92.55</td>\n",
       "      <td>1.249561</td>\n",
       "      <td>2022</td>\n",
       "      <td>Winter</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 06:45:00</td>\n",
       "      <td>6</td>\n",
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       "      <th>14704704</th>\n",
       "      <td>456326</td>\n",
       "      <td>47.05</td>\n",
       "      <td>92.65</td>\n",
       "      <td>0.639289</td>\n",
       "      <td>2022</td>\n",
       "      <td>Winter</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 06:45:00</td>\n",
       "      <td>6</td>\n",
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       "<p>14704705 rows × 9 columns</p>\n",
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       "           index    lat     lon  HCHO_column  year  season hour  \\\n",
       "0          35908  -5.55  130.85     7.169437  2021  Spring    0   \n",
       "1          36662  -5.45  126.25     6.181746  2021  Spring    0   \n",
       "2          36663  -5.45  126.35     7.101030  2021  Spring    0   \n",
       "3          36664  -5.45  126.45     5.757687  2021  Spring    0   \n",
       "4          36665  -5.45  126.55     6.489203  2021  Spring    0   \n",
       "...          ...    ...     ...          ...   ...     ...  ...   \n",
       "14704700  456321  47.05   92.15     0.565132  2022  Winter    6   \n",
       "14704701  456322  47.05   92.25     0.142487  2022  Winter    6   \n",
       "14704702  456324  47.05   92.45     1.588609  2022  Winter    6   \n",
       "14704703  456325  47.05   92.55     1.249561  2022  Winter    6   \n",
       "14704704  456326  47.05   92.65     0.639289  2022  Winter    6   \n",
       "\n",
       "                        time  timezone  \n",
       "0        2021-01-01 00:45:00         9  \n",
       "1        2021-01-01 00:45:00         8  \n",
       "2        2021-01-01 00:45:00         8  \n",
       "3        2021-01-01 00:45:00         8  \n",
       "4        2021-01-01 00:45:00         8  \n",
       "...                      ...       ...  \n",
       "14704700 2022-04-01 06:45:00         6  \n",
       "14704701 2022-04-01 06:45:00         6  \n",
       "14704702 2022-04-01 06:45:00         6  \n",
       "14704703 2022-04-01 06:45:00         6  \n",
       "14704704 2022-04-01 06:45:00         6  \n",
       "\n",
       "[14704705 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gems = pd.read_pickle('/Users/weitao/Downloads/Forests/GEMS_Seasonal_Pickle')\n",
    "\n",
    "gems"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e973af",
   "metadata": {},
   "source": [
    "# Read ERA5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1670c12",
   "metadata": {},
   "outputs": [
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       "                         time   latitude   longitude         t2m        tp  \\\n",
       "136352    2021-01-02 00:00:00  44.900002   90.099998  260.434906  0.000144   \n",
       "136353    2021-01-02 00:00:00  44.900002   90.199997  260.763794  0.000146   \n",
       "136354    2021-01-02 00:00:00  44.900002   90.300003  260.908081  0.000152   \n",
       "136355    2021-01-02 00:00:00  44.900002   90.400002  260.737427  0.000164   \n",
       "136356    2021-01-02 00:00:00  44.900002   90.500000  260.466827  0.000168   \n",
       "...                       ...        ...         ...         ...       ...   \n",
       "335499706 2023-07-01 23:00:00  -9.900000  124.300003  292.851715  0.001509   \n",
       "335499707 2023-07-01 23:00:00  -9.900000  124.400002  292.597778  0.002955   \n",
       "335499708 2023-07-01 23:00:00  -9.900000  124.500000  292.453461  0.004434   \n",
       "335499709 2023-07-01 23:00:00  -9.900000  124.599998  292.736542  0.005694   \n",
       "335499710 2023-07-01 23:00:00  -9.900000  124.699997  293.799469  0.006321   \n",
       "\n",
       "                  ssr  expver  month  year    celsius  \n",
       "136352      5930435.0       1      1  2021 -12.715094  \n",
       "136353      6001040.5       1      1  2021 -12.386206  \n",
       "136354      6029885.5       1      1  2021 -12.241919  \n",
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       "136356      6059161.0       1      1  2021 -12.683173  \n",
       "...               ...     ...    ...   ...        ...  \n",
       "335499706  13544640.0       1      7  2023  19.701715  \n",
       "335499707  13397402.0       1      7  2023  19.447778  \n",
       "335499708  13245858.0       1      7  2023  19.303461  \n",
       "335499709  13065900.0       1      7  2023  19.586542  \n",
       "335499710  13200223.0       1      7  2023  20.649469  \n",
       "\n",
       "[50779393 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5 = pd.read_pickle('/Users/weitao/Downloads/Forests/20231024_ERA5_monthly')\n",
    "era5['month'] = era5['time'].dt.month\n",
    "era5['year'] = era5['time'].dt.year\n",
    "era5['celsius'] = era5['t2m'] - 273.15\n",
    "era5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae6a0722",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>t2m</th>\n",
       "      <th>tp</th>\n",
       "      <th>ssr</th>\n",
       "      <th>expver</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>celsius</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136352</th>\n",
       "      <td>2021-01-02 00:00:00</td>\n",
       "      <td>44.900002</td>\n",
       "      <td>90.099998</td>\n",
       "      <td>260.434906</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>5930435.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>-12.715094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136353</th>\n",
       "      <td>2021-01-02 00:00:00</td>\n",
       "      <td>44.900002</td>\n",
       "      <td>90.199997</td>\n",
       "      <td>260.763794</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>6001040.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>-12.386206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136354</th>\n",
       "      <td>2021-01-02 00:00:00</td>\n",
       "      <td>44.900002</td>\n",
       "      <td>90.300003</td>\n",
       "      <td>260.908081</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>6029885.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>-12.241919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136355</th>\n",
       "      <td>2021-01-02 00:00:00</td>\n",
       "      <td>44.900002</td>\n",
       "      <td>90.400002</td>\n",
       "      <td>260.737427</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>6023858.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>-12.412573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136356</th>\n",
       "      <td>2021-01-02 00:00:00</td>\n",
       "      <td>44.900002</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>260.466827</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>6059161.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>-12.683173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259721101</th>\n",
       "      <td>2022-12-01 23:00:00</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>124.300003</td>\n",
       "      <td>296.546997</td>\n",
       "      <td>0.004746</td>\n",
       "      <td>16978912.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>23.396997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259721102</th>\n",
       "      <td>2022-12-01 23:00:00</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>124.400002</td>\n",
       "      <td>296.466522</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>16986230.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>23.316522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259721103</th>\n",
       "      <td>2022-12-01 23:00:00</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>124.500000</td>\n",
       "      <td>296.374939</td>\n",
       "      <td>0.004528</td>\n",
       "      <td>16968578.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>23.224939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259721104</th>\n",
       "      <td>2022-12-01 23:00:00</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>124.599998</td>\n",
       "      <td>296.744049</td>\n",
       "      <td>0.004280</td>\n",
       "      <td>17154134.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>23.594049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259721105</th>\n",
       "      <td>2022-12-01 23:00:00</td>\n",
       "      <td>-9.900000</td>\n",
       "      <td>124.699997</td>\n",
       "      <td>297.844452</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>17794320.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "      <td>24.694452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39313078 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         time   latitude   longitude         t2m        tp  \\\n",
       "136352    2021-01-02 00:00:00  44.900002   90.099998  260.434906  0.000144   \n",
       "136353    2021-01-02 00:00:00  44.900002   90.199997  260.763794  0.000146   \n",
       "136354    2021-01-02 00:00:00  44.900002   90.300003  260.908081  0.000152   \n",
       "136355    2021-01-02 00:00:00  44.900002   90.400002  260.737427  0.000164   \n",
       "136356    2021-01-02 00:00:00  44.900002   90.500000  260.466827  0.000168   \n",
       "...                       ...        ...         ...         ...       ...   \n",
       "259721101 2022-12-01 23:00:00  -9.900000  124.300003  296.546997  0.004746   \n",
       "259721102 2022-12-01 23:00:00  -9.900000  124.400002  296.466522  0.004696   \n",
       "259721103 2022-12-01 23:00:00  -9.900000  124.500000  296.374939  0.004528   \n",
       "259721104 2022-12-01 23:00:00  -9.900000  124.599998  296.744049  0.004280   \n",
       "259721105 2022-12-01 23:00:00  -9.900000  124.699997  297.844452  0.004116   \n",
       "\n",
       "                  ssr  expver  month  year    celsius  \n",
       "136352      5930435.0       1      1  2021 -12.715094  \n",
       "136353      6001040.5       1      1  2021 -12.386206  \n",
       "136354      6029885.5       1      1  2021 -12.241919  \n",
       "136355      6023858.5       1      1  2021 -12.412573  \n",
       "136356      6059161.0       1      1  2021 -12.683173  \n",
       "...               ...     ...    ...   ...        ...  \n",
       "259721101  16978912.0       1     12  2022  23.396997  \n",
       "259721102  16986230.0       1     12  2022  23.316522  \n",
       "259721103  16968578.0       1     12  2022  23.224939  \n",
       "259721104  17154134.0       1     12  2022  23.594049  \n",
       "259721105  17794320.0       1     12  2022  24.694452  \n",
       "\n",
       "[39313078 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5 = era5[era5['year'].isin([2021,2022])]\n",
    "era5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11008a23",
   "metadata": {},
   "source": [
    "# Read CAMS Biogenic emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4c98405",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>crs</th>\n",
       "      <th>gridcell_area</th>\n",
       "      <th>emiss_bio_hourly</th>\n",
       "      <th>emiss_bio</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199167</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>-55.50</td>\n",
       "      <td>-68.25</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>1.085354e-05</td>\n",
       "      <td>1.085354e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200602</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>-55.25</td>\n",
       "      <td>-69.50</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>4.738381e-06</td>\n",
       "      <td>4.738382e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200603</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>-55.25</td>\n",
       "      <td>-69.25</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>4.320506e-08</td>\n",
       "      <td>4.320506e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200604</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>-55.25</td>\n",
       "      <td>-69.00</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>1.105272e-06</td>\n",
       "      <td>1.105272e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200606</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>-55.25</td>\n",
       "      <td>-68.50</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>1.108898e-05</td>\n",
       "      <td>1.108898e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12346055</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>73.25</td>\n",
       "      <td>53.75</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>6.067636e-14</td>\n",
       "      <td>6.067635e-11</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12346056</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>73.25</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>4.133104e-14</td>\n",
       "      <td>4.133104e-11</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12346058</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>73.25</td>\n",
       "      <td>54.50</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>7.633602e-15</td>\n",
       "      <td>7.633602e-12</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348935</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>73.75</td>\n",
       "      <td>53.75</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>1.252466e-12</td>\n",
       "      <td>1.252466e-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348936</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>73.75</td>\n",
       "      <td>54.00</td>\n",
       "      <td>-2147483647</td>\n",
       "      <td>9.969210e+36</td>\n",
       "      <td>1.016115e-12</td>\n",
       "      <td>1.016115e-09</td>\n",
       "      <td>12</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4416269 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               time    lat    lon         crs  gridcell_area  \\\n",
       "199167   2021-01-01 -55.50 -68.25 -2147483647   9.969210e+36   \n",
       "200602   2021-01-01 -55.25 -69.50 -2147483647   9.969210e+36   \n",
       "200603   2021-01-01 -55.25 -69.25 -2147483647   9.969210e+36   \n",
       "200604   2021-01-01 -55.25 -69.00 -2147483647   9.969210e+36   \n",
       "200606   2021-01-01 -55.25 -68.50 -2147483647   9.969210e+36   \n",
       "...             ...    ...    ...         ...            ...   \n",
       "12346055 2022-12-01  73.25  53.75 -2147483647   9.969210e+36   \n",
       "12346056 2022-12-01  73.25  54.00 -2147483647   9.969210e+36   \n",
       "12346058 2022-12-01  73.25  54.50 -2147483647   9.969210e+36   \n",
       "12348935 2022-12-01  73.75  53.75 -2147483647   9.969210e+36   \n",
       "12348936 2022-12-01  73.75  54.00 -2147483647   9.969210e+36   \n",
       "\n",
       "          emiss_bio_hourly     emiss_bio  month  year  \n",
       "199167        1.085354e-05  1.085354e-02      1  2021  \n",
       "200602        4.738381e-06  4.738382e-03      1  2021  \n",
       "200603        4.320506e-08  4.320506e-05      1  2021  \n",
       "200604        1.105272e-06  1.105272e-03      1  2021  \n",
       "200606        1.108898e-05  1.108898e-02      1  2021  \n",
       "...                    ...           ...    ...   ...  \n",
       "12346055      6.067636e-14  6.067635e-11     12  2022  \n",
       "12346056      4.133104e-14  4.133104e-11     12  2022  \n",
       "12346058      7.633602e-15  7.633602e-12     12  2022  \n",
       "12348935      1.252466e-12  1.252466e-09     12  2022  \n",
       "12348936      1.016115e-12  1.016115e-09     12  2022  \n",
       "\n",
       "[4416269 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yr = '2021'\n",
    "cams = xr.open_dataset('/Users/weitao/Downloads/Forests/CAMS-GLOB-BIO_Glb_0.25x0.25_bio_isoprene_v3.1_monthly_'+yr+'.nc', engine=\"netcdf4\").to_dataframe().reset_index()\n",
    "yr = '2022'\n",
    "cams2 = xr.open_dataset('/Users/weitao/Downloads/Forests/CAMS-GLOB-BIO_Glb_0.25x0.25_bio_isoprene_v3.1_monthly_'+yr+'.nc', engine=\"netcdf4\").to_dataframe().reset_index()\n",
    "cams = pd.concat([cams,cams2])\n",
    "cams = cams[cams['emiss_bio']>0]\n",
    "cams['month'] = cams['time'].dt.month\n",
    "cams['year'] = cams['time'].dt.year\n",
    "cams['emiss_bio'] = cams['emiss_bio']*1e3 # g m-2 s-1\n",
    "cams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c63b3b",
   "metadata": {},
   "source": [
    "# read monthly GEMS sampled to CAMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36189f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022030245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022100445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022030545_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022100345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021110145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021040145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023080245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021102345_025_P15.nc',\n",
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       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021070345_025_P15.nc',\n",
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       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021020345_025_P15.nc',\n",
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       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050745_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022042245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022030645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022100045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060745_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023082245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021100345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021050345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021030545_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021100445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021050445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021030245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022020045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023062345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023070145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022040645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022032245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023010045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022052345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022102345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022040145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022110145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023070645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022100145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022042345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022100645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022050645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023060145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023072345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022030045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023080745_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021020245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021040445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021110445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2020120345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023080045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021020545_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2020120445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021040345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021110345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023070745_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022110045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022040045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023010145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022020645_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022052245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022040745_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023070045_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022032345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2022020145_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023062245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021030345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021050545_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021100545_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021030445_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2023082345_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021050245_025_P15.nc',\n",
       " '/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/GEMS_HCHO_2021100245_025_P15.nc']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lisGEMSM = []\n",
    "for path in Path('/Users/weitao/Downloads/Forests/grid_monthly_CAMS_B25/').rglob('*.nc'):\n",
    "    lisGEMSM.append(str(path))\n",
    "lisGEMSM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e597fec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>HCHO_column</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5886</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>91.50</td>\n",
       "      <td>5.187640</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>91.75</td>\n",
       "      <td>4.794158</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
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       "      <th>5888</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>92.00</td>\n",
       "      <td>4.735064</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5889</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>92.25</td>\n",
       "      <td>5.069019</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5890</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>92.50</td>\n",
       "      <td>5.162107</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>72491</th>\n",
       "      <td>46.50</td>\n",
       "      <td>102.75</td>\n",
       "      <td>1.133199</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72492</th>\n",
       "      <td>46.50</td>\n",
       "      <td>103.00</td>\n",
       "      <td>0.629389</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
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       "      <th>72493</th>\n",
       "      <td>46.50</td>\n",
       "      <td>103.25</td>\n",
       "      <td>-2.118789</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>72795</th>\n",
       "      <td>46.75</td>\n",
       "      <td>98.75</td>\n",
       "      <td>0.675741</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72796</th>\n",
       "      <td>46.75</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.776671</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9549095 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat     lon  HCHO_column  month  year  hour\n",
       "5886   -5.50   91.50     5.187640      3  2022     2\n",
       "5887   -5.50   91.75     4.794158      3  2022     2\n",
       "5888   -5.50   92.00     4.735064      3  2022     2\n",
       "5889   -5.50   92.25     5.069019      3  2022     2\n",
       "5890   -5.50   92.50     5.162107      3  2022     2\n",
       "...      ...     ...          ...    ...   ...   ...\n",
       "72491  46.50  102.75     1.133199     10  2021     2\n",
       "72492  46.50  103.00     0.629389     10  2021     2\n",
       "72493  46.50  103.25    -2.118789     10  2021     2\n",
       "72795  46.75   98.75     0.675741     10  2021     2\n",
       "72796  46.75   99.00     0.776671     10  2021     2\n",
       "\n",
       "[9549095 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns = ['lat','lon','HCHO_column'])\n",
    "\n",
    "for i in lisGEMSM:\n",
    "    dfT = xr.open_dataset(i,engine='netcdf4').to_dataframe().reset_index().dropna()\n",
    "    dfT['month']=int(str(i)[-17:-15])\n",
    "    dfT['year']=int(str(i)[-21:-17])\n",
    "    dfT['hour']=int(str(i)[-15:-13])\n",
    "                    \n",
    "    df = pd.concat([df,dfT])\n",
    "df['year'] = df['year'].astype(int)\n",
    "df['hour'] = df['hour'].astype(int)\n",
    "df['month'] = df['month'].astype(int)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bac296b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>HCHO_column</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5886</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>91.50</td>\n",
       "      <td>5.187640</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>91.75</td>\n",
       "      <td>4.794158</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
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       "      <th>5888</th>\n",
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       "      <td>4.735064</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5889</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>92.25</td>\n",
       "      <td>5.069019</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>5890</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>92.50</td>\n",
       "      <td>5.162107</td>\n",
       "      <td>3</td>\n",
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       "      <td>46.50</td>\n",
       "      <td>102.75</td>\n",
       "      <td>1.133199</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.629389</td>\n",
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       "      <td>2021</td>\n",
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       "      <th>72493</th>\n",
       "      <td>46.50</td>\n",
       "      <td>103.25</td>\n",
       "      <td>-2.118789</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>72795</th>\n",
       "      <td>46.75</td>\n",
       "      <td>98.75</td>\n",
       "      <td>0.675741</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72796</th>\n",
       "      <td>46.75</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.776671</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6742532 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat     lon  HCHO_column  month  year  hour\n",
       "5886   -5.50   91.50     5.187640      3  2022     2\n",
       "5887   -5.50   91.75     4.794158      3  2022     2\n",
       "5888   -5.50   92.00     4.735064      3  2022     2\n",
       "5889   -5.50   92.25     5.069019      3  2022     2\n",
       "5890   -5.50   92.50     5.162107      3  2022     2\n",
       "...      ...     ...          ...    ...   ...   ...\n",
       "72491  46.50  102.75     1.133199     10  2021     2\n",
       "72492  46.50  103.00     0.629389     10  2021     2\n",
       "72493  46.50  103.25    -2.118789     10  2021     2\n",
       "72795  46.75   98.75     0.675741     10  2021     2\n",
       "72796  46.75   99.00     0.776671     10  2021     2\n",
       "\n",
       "[6742532 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['year'].isin([2021,2022])]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4fc39df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>HCHO_column</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>hour</th>\n",
       "      <th>emiss_bio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5937</th>\n",
       "      <td>-5.50</td>\n",
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       "      <td>8.061073</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>0.737615</td>\n",
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       "    <tr>\n",
       "      <th>5938</th>\n",
       "      <td>-5.50</td>\n",
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       "      <td>8.160925</td>\n",
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       "    <tr>\n",
       "      <th>5940</th>\n",
       "      <td>-5.50</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0.146800</td>\n",
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       "    <tr>\n",
       "      <th>5942</th>\n",
       "      <td>-5.50</td>\n",
       "      <td>105.50</td>\n",
       "      <td>8.750162</td>\n",
       "      <td>3</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>0.702586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>72490</th>\n",
       "      <td>46.50</td>\n",
       "      <td>102.50</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>0.000808</td>\n",
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       "    <tr>\n",
       "      <th>72491</th>\n",
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       "      <td>2</td>\n",
       "      <td>0.000648</td>\n",
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       "    <tr>\n",
       "      <th>72492</th>\n",
       "      <td>46.50</td>\n",
       "      <td>103.00</td>\n",
       "      <td>0.629389</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72493</th>\n",
       "      <td>46.50</td>\n",
       "      <td>103.25</td>\n",
       "      <td>-2.118789</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000390</td>\n",
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       "    <tr>\n",
       "      <th>72796</th>\n",
       "      <td>46.75</td>\n",
       "      <td>99.00</td>\n",
       "      <td>0.776671</td>\n",
       "      <td>10</td>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2818335 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat     lon  HCHO_column  month  year  hour  emiss_bio\n",
       "5937   -5.50  104.25     8.061073      3  2022     2   0.737615\n",
       "5938   -5.50  104.50     8.160925      3  2022     2   0.472810\n",
       "5939   -5.50  104.75     8.339818      3  2022     2   1.131962\n",
       "5940   -5.50  105.00     8.512846      3  2022     2   0.146800\n",
       "5942   -5.50  105.50     8.750162      3  2022     2   0.702586\n",
       "...      ...     ...          ...    ...   ...   ...        ...\n",
       "72490  46.50  102.50     1.376698     10  2021     2   0.000808\n",
       "72491  46.50  102.75     1.133199     10  2021     2   0.000648\n",
       "72492  46.50  103.00     0.629389     10  2021     2   0.000605\n",
       "72493  46.50  103.25    -2.118789     10  2021     2   0.000390\n",
       "72796  46.75   99.00     0.776671     10  2021     2   0.000094\n",
       "\n",
       "[2818335 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cams = cams[['lat','lon','month','year','emiss_bio']].set_index(['lat','lon','month','year'])\n",
    "df2 = df.join(cams,on=['lat','lon','month','year'])\n",
    "df2=df2.dropna()\n",
    "df2=df2[df2['emiss_bio']>0]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d65b17",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43edc619",
   "metadata": {},
   "outputs": [],
   "source": [
    "HCHOColor = 'black'\n",
    "def plotMonthly(ax, count):\n",
    "    sns.lineplot(data=cams_2, x=\"month\", y=\"HCHO_column\", ax=ax, color=HCHOColor,errorbar=('sd',0.5))\n",
    "\n",
    "    ax.set_xlabel(\"\", color=\"black\")\n",
    "    \n",
    "    ax.tick_params(axis='y', colors=HCHOColor, direction = 'in')\n",
    "    ax.tick_params(axis='x', direction = 'in')\n",
    "    ax.set(ylim=(0, 10))\n",
    "    ax.get_yaxis().get_offset_text().set_visible(False)\n",
    "\n",
    "    ax.xaxis.set_major_locator(mticker.FixedLocator(list(range(1,13))))\n",
    "    \n",
    "    ###########################################################################################\n",
    "    axX = ax.twinx()\n",
    "    sns.lineplot(data=cams_2, x=\"month\", y=\"emiss_bio\", ax=axX, color=\"forestgreen\",errorbar=('sd',0.5))\n",
    "\n",
    "    axX.yaxis.tick_right()\n",
    "    axX.set_ylabel('', color=\"forestgreen\")       \n",
    "    axX.xaxis.set_label_position('top') \n",
    "    axX.yaxis.set_label_position('right') \n",
    "    axX.set(ylim=(0, 2.1))\n",
    "    axX.yaxis.set_major_locator(mticker.FixedLocator([1,2]))\n",
    "    axX.tick_params(axis='y', colors=\"forestgreen\", direction = 'in',pad=-13)\n",
    "    \n",
    "    axX.get_yaxis().get_offset_text().set_visible(False)\n",
    "\n",
    "    ###########################################################################################\n",
    "    colora = 'red'\n",
    "    axY = ax.twinx()\n",
    "    \n",
    "\n",
    "    sns.lineplot(data=era5_2, x=\"month\", y=\"celsius\", ax=axY, color=colora,errorbar=('sd',0.5))\n",
    "\n",
    "    axY.yaxis.tick_right()\n",
    "    axY.set_xlabel('', color=colora) \n",
    "    axY.set_ylabel('', color=colora)       \n",
    "    axY.xaxis.set_label_position('top') \n",
    "    axY.yaxis.set_label_position('right') \n",
    "    axY.tick_params(axis='x', colors=colora, direction = 'in')\n",
    "    \n",
    "    \n",
    "    axY.set(ylim=(-10, 30))\n",
    "    axY.yaxis.set_major_locator(mticker.FixedLocator([-10, 0, 10,20,30]))\n",
    "    \n",
    "    if (count==1 or count==2):\n",
    "        axY.tick_params(axis='y', colors=colora, labelright=False, labelleft=False, direction = 'in')\n",
    "        axX.tick_params(axis='y', colors=\"forestgreen\", labelright=False, labelleft=False, direction = 'in')\n",
    "        ax.tick_params(axis='y', colors=HCHOColor, labelright=False, labelleft=False, direction = 'in')\n",
    "    elif (count==0):\n",
    "        axY.tick_params(axis='y', colors=colora, labelright=False, direction = 'in')\n",
    "        axX.tick_params(axis='y', colors=\"forestgreen\", labelright=False, direction = 'in')\n",
    "        ax.legend(labels=[\"HCHO (VCD)\"], frameon = False, loc='lower left', bbox_to_anchor=(0.02, 0.03),fontsize=12)\n",
    "        axX.legend(labels=[\"Isoprene (g m$^{-2}$ s$^{-1}$)\"], frameon = False, loc='lower left', bbox_to_anchor=(0.02, 0.10),fontsize=12)\n",
    "        axY.legend(labels=[\"Temperature (Celsius)\"], frameon = False, loc='lower left', bbox_to_anchor=(0.02, 0.19),fontsize=12)\n",
    "    else:\n",
    "        axY.tick_params(axis='y', colors=colora,  labelleft=False)\n",
    "        axX.tick_params(axis='y', colors=\"forestgreen\",  labelleft=False, direction = 'in')\n",
    "        ax.tick_params(axis='y', colors=HCHOColor, labelright=False, labelleft=False, direction = 'in')\n",
    "\n",
    "    plt.rcParams['figure.dpi']=600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "856639c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gems['season'] = ['Fall' if a=='Autumn' else a  for a in gems['season']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f08555d5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:84: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = local\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = gems_2['Local time'].dt.hour+1\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:84: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = local\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = gems_2['Local time'].dt.hour+1\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:84: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = local\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = gems_2['Local time'].dt.hour+1\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:84: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = local\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  gems_2['Local time'] = gems_2['Local time'].dt.hour+1\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n",
      "/var/folders/4y/knznw84s4p5d2d77tb6wyq6h0000gn/T/ipykernel_27977/1950260804.py:95: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  temp['Local time'] = temp['Local time'] + offs[count2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ex44dyy0kbdmyBa+99hpkMhnmzZuHb775BuPHj0dsbCwePnxY4nNPnDiB06dPY/DgwVi2bBkWLFgAsViMiRMnYvHixVafM3bsWJw7dw6ffPIJFixYgNTUVAwePNgi2faIESPwxRdfICwsDMuWLcPzzz+PiRMnYu/evXa/t7///huOjo5o1qyZ3c9JSEiARCIxM5zu7u5YtWoVZs6ciXr16mHcuHFYsGABmjdvbvUYixYtAsdxmDlzpt2vW1v4999/IZfL4ezsDGdnZwwbNgx37tyx6Ddp0iQ0b94c3bt3R6NGjXDy5EmbYVjZ2dmG89C0FRYWWvTt3LkzdDqdmWDDKD/jx4/H4cOHzcK3169fDy8vL0PKguJs3LgRWVlZGD9+PL799lt8+OGHuHXrFp599lm7vhd77cLYsWMhlUqxbt06i2P88ccfSEtLw5tvvgmA5o/s3bs3oqKiMHnyZHz//feYMmUK5HK51QshpiQkJGDdunXo1KkTPvvsM3z11Vdo27Ytli5dancoCz9J7dSpk80+y5cvh1wuh6OjI3x9fTF16lTk5uZa9Pvyyy8RExODsLAwdOjQAQqFwmooPEAnRN27d8fXX39dqj2uSs6fPw8AVgsTPf3008jNzUVkZGSpx6mofVWpVFZtSWZmpkXfNm3aQCqVWhVOGFUDIcQgonl4eJjts/e/v6Lk5+ejZ8+ekEgkWLx4Md58800cPHgQXbt2tXoOffvtt/jiiy8wevRoLF68GK6urpgyZUqpeZ9M6devH5KSkjB37lzMnDkTN27cwPPPP29x/o8ZMwbvvfceGjRogKVLlyI8PBzZ2dno3bu3WUqLiszLAGqvmjZtCicnJ7vfgzV69uwJoVCIWbNm4cCBA4iPj8fVq1fx2muvQaPRYNasWWb966Jts0VCQgIAwMvLq9zHYLatelmyZAlycnLw+eefl9r3f//7nyFX9MyZMzFv3jyo1WqMHDkSixYtsuifl5eH7t27QywWY+HChQgPD0e3bt2wYsUKADT0cPPmzdi8ebPFb/7777/HwoULMWrUKCxfvhy+vr6YPn06tm7dWqb3d+zYMfTq1QsymQwnT55Ex44dzfaXZ524e/duvPPOO+jfvz+++eYb9O/f3/D59OnTB3l5eZg9ezZWrFhhyNP2ySefmB2jonNGoHSb9eDBA8yZMwefffaZRZqgivAk2KznnnsOubm5GDt2LK5evYr4+Hjs378fM2fORMuWLfHyyy8b+jKbVc3UqN9gHYN3hbbWmjZtSiIiIsz6R0VFEY7jSKdOnQzhCoTQUCQfHx/i6upq5qocHBxMAJD169dbvHb37t0JAItQ0KVLlxIA5ODBg4Zty5cvJwDIgQMHzPpmZ2eTwMBAM1fitWvXEgBmoZCEEHL69GkCgPTu3dvuz8U0lOall14iTk5OZqFW1rAWfpefn2/RT6fTke7duxMnJyeiVqsN23m35QEDBhC9Xm/Yfv78eQKAzJgxw7Dt0KFDBAAZPny42bH//fdfwnGc3SE+QUFBJCwsrNR+PH/++ScBQEaPHm11f1paGjl79ixJTk622FfcTfmtt94iAMiZM2cMfSojLNeU0sJyTbEnLPf5558n8+fPJzt37iQ7duwgU6ZMIVKplDg7O5MbN25YfU5UVBS5cOGC2XnDw3/ntpq18IlTp04RAOSLL76w630xLDE9z9PT04lUKiULFy4khBBSUFBAnJ2dyUcffUQIsR6yYC0sIykpibi7u5P+/fubba+oXRg5ciSRSqUkLS3NrH+/fv2IUqk0jIVPp3D+/PkS3/uGDRsswsyKioqs2rc5c+YQAOTff/8t8ZiEEPLZZ58RAOTSpUsW+2JjY0nPnj3JypUryZ49e8iGDRvI0KFDCQDSqlUrq5+nWq0mly9fJhEREWb2kMc05OTcuXMEAJkwYYJhf2WEgZhSWhjIwIEDCQBSUFBgsW/VqlUEANm3b1+Jr1EZ9tVWCw0NtXrM0NBQ0qRJkxLHxSg7vI359NNPSWpqKklJSSFXr14lb775JgFAOnToYOhblv9+QmgI2tixY822WQvL7d69OwkODrbYBoB88MEHZtt///13AoC8+eabFu/B0dHRLIyrqKiIdOjQgQiFQhITE2PYbs3W8dvefvtts+07d+4kAMiaNWsM23777TeLbYTQMPZ27dqRkJAQw+dj77zMGlqtlggEAovQMmuUFpZLCCHbt28nvr6+Zuebv78/OXfunNX+dc22WUOr1ZKnnnqKiEQiEhkZWeFjM9tWdRRf1/Tr149IJBKzNQKKhbVmZmYShUJBQkJCzEIs8/PzScuWLYlIJCKxsbGG7bxd+eyzzyxe356wXF9fX7Mwyvz8fOLh4UGeeuopu94jAOLn50ekUilp0KCBzfVPWdaJ/LjFYrHFb/zhw4dEKpVarMEIIWTy5MlEIBCQ6Ohow7ayzBmtYY/NGjBgAGnevLlh/mgrLLc49qSeedxtVmFhIXnzzTeJWCw2sy2DBw+2mVKM2azqgXnulYM33ngDR44cwZEjR7Bv3z6sXLkShYWFeO6553Djxg1Dvz179oAQgmnTppmFK7i7u+Odd95BZmamhdrs7u6OsWPHWn1dgUCAyZMnm23r1asXAJh5Qf38889o2LAh2rdvb6Z6q9Vq9O7dG6dPnzZ4N+3atQscx2HOnDlmx+3SpQt69eqFo0ePIicnp8yfkYuLC/Lz8/Hnn3+WOTm1qSuvSqVCeno6MjIy0KdPH+Tk5Fj15Pjggw/MQls6dOgApVJp9rns2bMHADB9+nSz53bs2BHPPfec3eNLTU21cEO2RVRUFMaMGQM/Pz8sX77cah93d3d07tzZriu5c+fOhUKhsHgPtZl9+/bh008/xSuvvIJXX30VK1aswKFDh5Cbm4sPP/zQ6nMaNWqE9u3blxjm88033xjOQ9NmmlSfx93dHQBKTYLLsA83Nze8+OKLhqpbv//+O7Kzs82SoBfHNDQgLy8P6enpEAqF6NSpk81qp6aUxS5MnDgRRUVF2LJli2FbfHw8Dh8+jBEjRhjGwnt67d69GyqVyq73ziORSAyhpFqtFpmZmUhLSzPYEnveU2pqKgBYtSdBQUE4duwYPvjgAwwaNAjjxo3Dr7/+ivnz5+Pq1atWQ/zEYjHatGmDZs2alVjxGqDegkOGDMH69esRFRVV6lirAj582dp5zlesKynEubLs68CBA63akk2bNtk8JrMlVceCBQvg6ekJLy8vtGrVCuvWrUP//v2xe/dui772/PdXFsUr1r/00kto3Lix1XGNGjXKLIxLIpHgww8/hE6nsztSoPj/o635nkKhwODBg83me1lZWXjhhRcQExNj6F+ReVl6ejr0er3dc5/S8PLyQvv27REeHo7du3dj9erVkMvleP7553Hp0iWL/nXNtllj8uTJOHfuHMLDwyscjgYw21adLFmyBFqt1mKtZMqRI0eQn5+P999/H87OzobtcrkcH3/8MbRardXicOUtpDB+/Hgzb3W5XI6nnnqqTLYvMzMTRUVF8Pb2tvCK5inPOnHAgAEWv/Fff/0VRUVFGD9+vIVX1gsvvAC9Xo+jR48a+ld0zliazdqxYwf279+PNWvWWE2pVVEed5slFotRv3599O3bFz/++CN27dqFadOm4eDBgxg6dKjVCsDMZlUPj1dJw2qiQYMGFmLQiy++iCZNmuDtt982uAvzuWGsuZ3yufSK54+pX7++zdxnfn5+FiW6edEiPT3dsO3WrVsoLCw0qypTnLS0NAQGBuLevXvw9vY2HKf4GI8dO4aYmBi0bNnS5rGsMXv2bJw6dQovvfQS3N3d8cwzz6B///4YPnx4qSEdeXl5CA8Px86dOxEXF2ex35prbv369S22ubm5mX0ud+/eBcdxVismNWvWzGqOLmtwHGfXxPj+/fuGaroHDhyoUBgGj4+PDz788EN8/vnn+OOPPzBo0KAKH7Mm6N69O7p164Zjx46hsLDQrAKTvXTo0AFPPfWUXX3576u0P1iG/YwfPx47d+7E6dOnsX79enTs2LHEUPW7d+9i9uzZOHToELKyssz22fO9lMUudO/eHU2aNMG6devwwQcfAKC5T/V6vSEkFwCGDx+OrVu3YtGiRfjqq6/w1FNPoU+fPhg+fLgh501JrF69GmvWrEFERAT0er3N8diCf99lWWjPmDEDn3/+Ofbt22cRvlZWFi1ahD/++AMzZ86skZwlvGBbVFRkYQP4C1DF82/xVKZ99ff3L9MFHkIIsyVVyBtvvIHhw4eD4zjI5XI0atTI6hwFsO+/vzJwcXGBj4+PxfamTZti9+7dyM7ONlvQW8uvxNvHu3fv2vWaxd+brflefn6+1bHxJCcno1GjRhWal5XHVtni4MGDGDhwIPbt24e+ffsatr/66qto2rQpJkyYgMuXL1foNWrathVnzpw5WL16Nd58880K2+2ywGxb5dCyZUuMHDkSP//8Mz7++GO0bt3aok951nyenp5mdqMsWLN97u7uZbJ93bp1Q8eOHbFgwQL07dsXBw8ehFKpNOtTnnViw4YNLfry4bum53xxkpOTDfcrOmcsyWZlZmbigw8+wPjx49G1a9dSj1Ud1DWb9dprr+Gff/5BRESEYf42ePBgNG/eHGPHjsUPP/xg4ZBkD8xmVRwm7lUSISEhaNKkCc6cOYOCggLI5fISJ0G29tlayACAUCi063iEEDRr1syqZwcPL/yVZ4z2EBoaioiICBw/fhx//fUX/v77b0yaNAlz587F0aNHSxQBRowYgX379mHixIno1q0b3NzcIBKJsH//fqxYscJiEQ3Y/mwqYyJaHE9Pz1IX7jExMejZsydyc3Nx5MiRMoujJTFt2jSsWbMGs2bNwoABAyrtuNVNSEgITpw4gczMzHKJe2UhIyMDAEoUvBllo0+fPggICMC8efNw/PhxfP/99zb75ubm4plnnkFBQQGmTJmCsLAwKJVKCAQCLF68GMeOHSv19cpqFyZMmICPPvoI58+fR4cOHbBhwwa0bNkSHTp0MPSRSCQ4ePAgLl68iEOHDuHkyZOYN28e5s2bhw0bNmDEiBE2x7N8+XJ8/PHH6NOnDyZPngw/Pz9IJBIkJCRg3LhxVu1UcfjfY2Zmpl1iIkCvlvr5+Rm8/ipC48aN8frrr+N///sfzp07V+HjlRV/f38A1Kuy+GKAz/NiLYl1VdpXe8jIyGC2pAqxdgHVFtX1329r8WDrwpG1/mW9yGTPeyOEwM3NDTt27LB5nBYtWgCo2LzM3d0dAoHArosWpbF06VLI5XKLRb67uzueffZZbN++3UIsLSs1bdtMCQ8Px8KFC/Haa6/hhx9+qNULUWbbbPP555/jl19+wYwZM3Dw4EGL/ZW95iuNktaEZWH+/PngOA7z5883CHymYn9lvS++74YNG2wWp+AFy8qYM5Zks+bNm4e8vDy88847ZvlZVSoVCCGIiYmBWCw2zFGqg7pksx48eICtW7fi/ffft1i/vfLKKxg/fjyOHz9eLnGvrDCbZQkT9yoRjUYDQgjy8vIgl8sRGhoKAIiIiLBwZ42IiAAAQ5/KpFGjRkhMTESvXr1KrYAaGhqKAwcOID093eKqTEREBAQCgUXlOHuRSCTo27evYQJ34sQJ9OzZE0uWLMFPP/1k9TlZWVnYt28fxowZY1E96a+//irXOHhCQ0NBCEFkZKTFVbebN2/afZwWLVrg77//hl6vt/r5xsbGomfPnsjKysKRI0fQvn37Co27OEqlEnPmzMGUKVNsfo51gTt37kAsFtv0yKhM+GTs/CKHUXEEAgFee+01LFq0CA4ODhg+fLjNvseOHUNiYiLWr19vUU23pDAXnvLYhbFjx2LWrFlYt24dcnNzcf/+fXzzzTdW+7Zv3x7t27fH7NmzkZiYiHbt2mHGjBklintbtmxBSEgIDhw4YGYHrE36bcH/HqOjo9G2bVu7nlNYWIj4+Hh07tzZ7tcpifDwcGzZsgXTp0+3Wo22KunQoQN++OEHnD171kLcO3v2LBwdHS08ravavpZGUVER4uLi6qzXNKN8ZGZmIikpycJDLjIyEh4eHhaeb9bmFLznijWPm/LSqFEjREVFoUOHDnaJYeWZlwHU3jdt2tSiOnB5SEhIgE6ns+pxodFoANBUBxWlJm0bD3+xaPTo0diwYUOpc/KahNm2kgkODsbbb7+NlStXWhWXTNd8xYXrsq75qlsAnjdvHgQCAcLDw9GnTx8cOnTIYE8qa53YqFEjAFR0K+3iTUXnjEDJNismJgb5+fk25w/16tVD48aN7SroVZnUFZvFX3zl7bUpWq0Wer2+Umx4aTCbZZ3a+y9Tx7hx4wZu376NgIAAQ3jQ4MGDwXEcli1bBrVabeibkZGB1atXw9XV1Wpp84oyZswYpKam4ssvv7S639Tt+aWXXgIhxKIK7T///INjx47hueeeK1dlNGsl49u0aQOBQGDworIGfyWq+NWgxMRE/Pjjj2UehymDBw8GAIty4+fPny+TcNijRw/k5eVZnbzHxsaiR48eyMzMxOHDh828hCqTt99+G/Xq1cPcuXOt5jWoLaSmplr1YPrjjz9w5swZ9OnTp8S8epXFuXPnIBAIao37/eMC7/WxZs2aEheWts7rw4cP25U7pTx2wd3dHUOGDMG2bdvwzTffQCaTWeRjtGanfH194evrW6Kd4sdUPERfq9Xiiy++KPX98HTv3h2AsWqsKUlJSVafM2fOHGi12kqbzPj6+mLKlCk4efIk9u3bVynHtJcXX3wRcrkc33zzjdlE8OLFizh58iReffVVSCQSw/bqsq8l8d9//0GtVhu+O8aTQ/Fze9euXYiKijLMLUz5+eefzaqJq9VqrFixAkKhEC+88EKljWnMmDEghGDmzJlWvWhM53vlnZfx9OjRA5GRkeXKw2xKs2bNUFBQgJ07d5ptj4+Px5EjR1C/fv1KuehXk7YNoB5R4eHhGDVqFDZu3FirhT2A2TZ7mDNnDpycnCzybwJA7969oVAo8N1335mdIyqVCsuXL4dIJLL73Hd0dARgX3qPymLu3LmYP38+/v33X/Tp0wfZ2dkAKm+d+Morr0AqlSI8PNxqLt3s7GzDeqaic0YeWzZr5syZ2LVrl0Vr3rw5xGIxdu3ahVWrVtn9OpVFXbFZjRs3hlAoxJ49eyxCpjds2AAAFlWXqwJms6xTKz33oqOjsWzZMpw7dw43btxAkyZNzApV8Ozfvx+zZ8/GrVu3EBAQgKlTp+Kdd96p8vFduXLFkKhdrVbjzp07WLt2LXQ6nZmg1rBhQ8yYMQOLFy9Gly5dMGLECKhUKqxbtw5JSUnYtGmTWcLQyuKDDz7AkSNHMGPGDJw4cQLPPvssnJyc8ODBAxw9ehQymcxQyGPcuHHYvHkzli9fjpiYGPTq1QuxsbFYtWoVnJycsHLlynKNoU+fPnB2dka3bt0QGBiI7OxsbNq0CXq9Hq+99prN5ymVSvTp0wdbtmyBg4MDOnTogNjYWPzwww+oV69ehfLo9O7dGy+99JIh5GPAgAGIj4/HqlWr0Lp1a/z33392HWfo0KGYPn069u/fb+YJlpubi549eyImJgbvv/8+bt++jdu3b1uMwdvbu9zvgUcikWDBggUGsaK4sMJfjR87dqyh6EFJ7N27F1evXgVg9HL7/PPPDftNr5RlZ2fj22+/BQBD2faTJ08a+nfr1g3dunUDQBc4K1euxNChQ1GvXj0QQnDu3Dls374dXl5e5f59AcChQ4esXpFzcHDA0KFDDY8JIThw4AD69u1boTAfhiVBQUEIDw8vtV/Xrl3h4+ODjz76CDExMQgICMCVK1ewefNmhIWF4fr16yU+v7x2YdKkSdi2bRv++OMPjBw5Eq6urmb7P//8cxw+fBgDBw40hMUeOHAAly9fxrvvvlvimF5++WXMnDkT/fv3x5AhQ5CTk4OtW7eWKTGzp6cnevTogQMHDmDZsmVm+95++20kJSXhueeeQ2BgoMF78eTJk3j66afx3nvv2f06pTF9+nT88MMPuHjxotX9ISEhiI2NtSvUMTY2Fps3bwZg9FbYu3evQegYNGiQIYzWw8MDixYtwpQpU9CjRw+MGTMGaWlpWLFiBby9vTF//nzDcavSvt65c8es+Iopzz77LHx9fQ2P9+3bB5FIhCFDhpT5dRh1Fw8PD/z+++94+PAhevTogTt37mD16tXw9vbGvHnzLPo3atQInTp1wltvvQWlUomtW7fiwoUL+PTTTxEcHFxp43r55Zcxfvx4fP/997hy5QpeeOEFeHh4ID4+Hv/88w+io6MNeb7KOy/jeeWVV7Bq1SocPHgQr776qtm+a9euGYoFnDlzBgCwefNmnD59GgAVIfn3PWvWLBw8eBBjxozB33//jdatW+Phw4dYs2YNcnNzsW7dukr7fGrKtq1atQpz585FUFAQevfujW3btpkdy9vb25AztKzHLgvMtlUu7u7umDZtmlXvMRcXFyxfvhxvvfUWOnTogPHjx0MsFmPLli24cuUKFi5ciKCgILtfJzQ0FNu3b0eDBg0MBYb4ojpVxaeffgqBQIA5c+bgueeew5EjRyptnRgQEIDvv/8eb775Jpo2bYrXXnsNwcHBSE1NxfXr17F7927cvHkTISEhFZ4z8tiyWZ06dbLaf+XKlYiOjrZ6wea7774zCFkajQaxsbGGdU9wcDDGjBlj15hKoy7YLDc3N0yZMgXLly9HmzZtMGHCBLi7u+Ps2bPYsmULgoODS51D24LZrEqgKkrwVpTdu3eTgIAAMnToUBIWFkaaN29u0efs2bNEJBKR119/nRw7dowsWLCACAQCsnbt2iobF19+3LRxHGcoy3306FGrz1u3bh1p27YtkclkhvLZBw8etOgXHBxss7R29+7dSXBwsMV2W+XSNRoN+frrr0n79u2JXC4ncrmcNGjQgIwcOZIcOnTIrG9+fj6ZNWsWCQ0NJWKxmLi7u5NXX33Voux1aZ8LXzKeEFo6vXfv3sTHx4dIJBLi7e1N+vTpY/G+x44dS4r/DFNTU8kbb7xBfH19iVQqJS1atCD/+9//yIYNGwgAcvz4cUNfvlS4tRLu1j5PlUpFpk+fbij/HhYWRrZu3VricazRv39/0qJFC7NtpZXxLj52e+CPOWnSJIt9er2etG7dmgAgjRs3Ntv3xx9/EABk1qxZdr0O/z3YamV5n6a/xdOnT5NBgwaRoKAg4uDgQKRSKWnQoAGZPHkyefjwYZk+Cx7+u7LVvL29zfrzv8+9e/eW6/UYFGvnuS14O2fK1atXSd++fYmLiwtxdHQk3bt3JydPnrRqAypqF0xp3LgxAUCOHTtm9T29+uqrJDg4mMhkMuLi4kLat29PVq9eTbRaraGftdfQarVk0aJFJDQ0lEgkEhIUFEQ++eQTcvPmTas22RY7duwgAMjFixfNtq9bt4706NGD+Pj4ELFYTBQKBWnbti1ZsmQJUalUdh27ON27dydSqdTqvq+++spwDv3zzz9m+9zd3Ymfn59dr2Htf9K0bdiwweI5W7ZsIW3atCEymYy4ubmRYcOGkXv37pn1qUr7WlI7cOCAob9erychISFk6NChZXodhn2UxcaU9b8fABk7dmyp/azNtfhtd+/eJYMGDSJKpZI4OjqSQYMGkTt37lh9Dxs2bCBff/01adCgAZFIJKRBgwZk5cqVFmO11/6V9D4IIWTTpk2ka9euRKlUEqlUSoKDg8lLL71Etm/fbuhj77ysJJo1a0YGDhxosZ23kfaem7du3SKjR48moaGhRCqVEhcXF9K7d29y5MgRu8diSm2zbaXNqYr/7spjN0uC2baKUZItys/PJ76+vgQA6du3r8X+PXv2kC5duhC5XE4cHBxIhw4dyM8//2zRz9a6jufs2bOkU6dORCaTmf1mTG1McUqyHcWxNX5CCFm0aBEBQNq1a0cyMjLsXifaWpOacvr0aTJ48GDi6elJxGIx8fX1JT169CDLli0jhYWFhn5lmTOWhC2bZY2S7EhwcLDd53NFX6su2Cy9Xk927NhBnnnmGeLt7U3EYjEJCgoikyZNIomJiWX6LAhhNqsyqZXink6nM9wfO3asVXGvX79+pGPHjmbbJkyYQHx9fc2ez2BUBWfPniUAyj0RrWo+/PBD4uLiQtLT02t6KDXOoEGDSLt27Yher6/poTBqgObNm5PQ0NBa+/1rtVrSvHlzMmrUqJoeilWuXr1KAJD169fX9FBqnN9//50IBALy33//1fRQGNVIaYtwU0paeD8ObNu2jQiFQrsv/tZmmG0zwmwb43GF2azHk1phs1avJiQsjBClkranniJk/37jfr2ekLlzCfH1JUQmI6R7d0Ju3KjyYdXKBBCl5aUoKirCsWPHLBK4jxo1ComJiXaHVzIY5aVz584YNmwYPvvss5oeilUOHTqEOXPmwM3NraaHUqNcunQJe/fuxYoVK2p1hTpG1XDs2DFERERg0qRJtfb7FwqFWLZsGbZt22ZIuF+bOHToEFq1aoWxY8fW9FBqFEIIwsPDMX78eIuCTAzGk8Lw4cPRoUMHq6HIdQ1m2yjMtjEeZ5jNevyoNTYrIAD44gvg4kXaevUCXnwReBQCjaVLga++Ar77DrhwAfDxAXr3BnJzq3RYHCF2BG3XIOPGjcPFixfNcu7dvHkTzZs3x4EDB9CvXz/D9tTUVHh5eWHz5s0WidMZDAaD8WRw7Ngx3L17F4sXL0ZeXh6io6PLVRiIwWAwevTogZiYGMTExJTal893u2HDBowbN67Kx8ZgMBgMBqOW4OYGfPkl8PrrgJ8fMGUKMH063VdUBHh7A0uWAJMmVdkQamVBjdLgqwe5uLiYbeeTpduq+qVWq7Fv3z74+voaKvEAtDiBaUU+BoPBAAAUFYEzKfWu0+kQk5iI3gMGVInNYDaqcpg+fTouXbqEBg0aYPny5UhISEBCQkJND4vBqHyq0UY9qfYpPz8fGo3GLs/a2NhYALTYVG30xGUwqhVmnxgMRh1Bp9Mh9s4d9H7mGXObIZXSVvKTgV9+AfLzgc6dgfv3gaQkoE8f8+N07w6cPVul4l6tzLlnirWce6dPnyYAyLlz58y2azQaAoB88803Vo+1d+/eUpM1ssYaa6yV1qqqOAezUayxxlpltKqwUcw+scYaa5XRmH1ijTXWamuLAwgxbSUVyLt2jRCFghChkBBnZ0L27aPbz5yhz01IMO8/YQIhffpUuv0zpU567vEeerwHHw//mN9fnJCQEADAjh07UK9ePcN2dlWHwWBYpdhV53v372PAsGEGW1LZMBvFYDDKRDXaKGafGAxGmWD2icFg1BHu3r2LF154Adn//IOAZs2MO0ry2mvcGLhyBcjKAn77DRg7Fvj7b+P+4vm+CbHcVsnUSXEvNDQUEokEt27dMsu5d/PmTQBA06ZNrT6Pd9MOCwuz2YfBYDBsQRwdAcAs5KMyYTaKwWBUhKq0Ucw+MRiMisDsE4PBqO0InJ0Be/N0SyRAgwb0fvv2tHDG118b8+wlJQG+vsb+KSk0714VUiur5ZaGVCpFr169sHPnTrPt27Ztg6+vL9q0aVNDI2MwGAwGg8FgMBgMBoPBYDwxEEILZ9SrR6vjHjli3KdWU6++p5+u0iHUSs+9goIC7N+/HwBNTpyTk4Nff/0VANC9e3d4enris88+Q7du3TBhwgSMGjUKZ86cwdq1a/HDDz9AIKiTmiWDwWAwGAwGg8FgMBgMBqO2MmsW0L8/EBgI5OYC27cDJ04ABw/S0NspU4BFi4CGDWlbtAiQy4GRI6t0WLVS3EtJScErr7xito1/fPz4cfTo0QOdO3fGnj17MGvWLGzatAkBAQH45ptv8Oabb9bEkBkMBoPBYDAYjOqFECA5GUhPBxQKQCYDHBzobWkV/hgMBoPBYJSd5GRgzBggMRFwdgZatqTCXu/edP+0aUBhIfDOO0BmJtCpE3D4MKBUVumwaqW4FxISAkJIqf2ef/55PP/889UwIgaDwWAwGAwGoxaRlwfcvQs8eADo9VToAwCxmOYCcnCguYOcnJjox2AwGAxGZbFuXcn7OQ4ID6etGqmV4h6DwWAwGAwGg8Gwgk4HJCQAd+4AOTmAlxcV7Xg0Gpr3p6CAVvHTaun24qKfUmkU/Bwc6L4qruTHYDAYDEZl8/2/3+PQ7UO4l3EPUpEUbf3bYnq36ajvVr+mh1atMHGPwWAwGAwGg8GoC2RmAtHRQHw84OhI8/0UF+TEYtoeVSc1YEv0E4moN59MRsOLmOjHYDAYjDrEv3H/YnSb0Wjp0xI6vQ7LTy/H2F/G4tD4Q5BL5DU9vGqDiXsMBoPBYDAYDEZtRq0GYmNpGG5REeDrSwW8smBL9NNqAZWKtuxsKgJynFH0k0qp6MeH9/Kin1TKRD8Gg8Fg1DgbX95o9nhJvyXouLojbiTfQMfAjjUzqBqAiXsMBoPBYDAYDEZthBAgNRW4fRtISQHc3ABPz8p9DZHIUvADjKJfUREQF2f09BMKqTefTGbM6cd7+vFNIKjcMTIYDAaDYSe5RbkAAGeZcw2PpHph/7wVICIiAiKRCIcPHy7X8y9evAiBQIDTp09X8shqF+Hh4eA4DjExMXb15zgO48aNq9IxMRj2UKQtQm5RrqEVaApqekhlwpqN2rhxIziOw4kTJ0p9PiEELVu2xIQJE6pwlDVPTEwMOI5DuJ1Jb8eNGweOeaswagF12UZVZA61bNkyeHp6Iisrq/IHVovo+8wz6OPvD/z7L/WoCwiwWWnv4sWLaNe+Pfbu3Vt5A+BFPzc36ikYGEiblxcV8zQa4OFD4Pp14Px54PRp4NQp4ORJ4NIlmhMwPp5W8s3Pp7kCGU8MT5J96tGjB0JCQgyP2fzJOmz+xKgOCCFYdGIR2vu3R2PPxlb71GX7VBJM3KsAH330EZ566in06dOnXM9v3749Bg4ciKlTp9pVHRgwLsw5jsOyZcus9rly5YqhT3WJZFeuXEF4eLjdAh6DURdY8+8atP62taEN2TmkpodUJipqo/gJ2/r163H16tUyPY/jOISFhdns06pVK0O/6iI8PBy7d++uttdjMKqaumyjKmKf3n33XYhEInz++ed2P4dfVHIch4sXL1rts2LFCkOfjRs3lnlc5WH37t2WC2O9HoiPR9OcHNTTaABXV8DHh3rM1QZEIkChMI6LF/18fOh2nQ5ITgZu3gQuXDCKfn//TR9HRVFPwLQ0WvGXiX6PJU+qfQLY/InBqEnCj4YjMjUSKweutNmnLtunkmDiXjk5d+4cDh06hClTplToOFOnTsWFCxewf//+Mj1PJpNhw4YNVvetW7cOMtOqadXAlStXMG/ePCbuMR4r3ur0Fq68f8XQfn/195oekt1Ulo166aWXEBQUVKZFNEBt1I0bN3DhwgWLfZcuXcK1a9eq3U7NmzePTU4ZjxV11UZV1D45ODhg0qRJWLVqFdLT08v03JLmTxs2bKh2u7R7927MmzfPuCE7G/jvP+DSJYgIQYJQSL3k6gL8WF1cAG9v6mkYFAT4+Rk9DtPSgMhI6tl35gwV/k6eBC5epJ5+CQlARgYNB7bzwjejdvKk2iceNn9iMKqf8KPh+OvuX/j51Z/hq/S12a+u2qfSYOJeOfn+++/h4uKCF154oULH6d69O4KCgvD999+X6XkvvfQSbt68ifPnz5ttLyoqwtatWzFkyOOhPjMYNYlUJIVSqjQ0ubjuVFuqLBvFcRxGjx6N3bt3IzEx0e7ndenSBV5eXlYX0evXr4eHhweeeeaZCo2NwXjSqas2qjLs05gxY6BSqWwKdbZ46aWXsG3bNhQVFZltv3DhAq5fv15z8yeNhhbLOHeOhrJ6eSFHLH48ClYIBMZKvF5eVPQLDDSKfno9zScYEUHDe8+coYLfqVPA1avA/ftAUhKQk0M/J0ad4Em2TwCbPzEY1QkhBOF/hePwncPY8uoWBLoElti/rtqn0mDiXjnQarX4/fff8eyzz0IqlZrty83NxZw5c9CpUyd4eHhAKpWiQYMGmDFjBgoKLGO5OY5D//79cfDgQWRnZ9s9hueffx7e3t4Whn/Pnj3IyMjA+PHjbT53w4YNaN++PeRyOZRKJXr27Gk1p0RISAh69OiBiIgI9OvXD0qlEs7Oznj55ZeRlJRk6Ddu3DjD6/Xs2dPgKl48zESlUmHatGnw9/eHVCpFq1atSvVYVKvV8PLyQpcuXazu/+qrr8BxHI4cOVLicRiMJ4mSbJRpn/DwcAQHB0MqlSIsLAxbt2612nfAgAGGY9qLSCTC6NGjsW3bNqhUKsP2oqIibNu2DaNGjYJEIrH63Bs3bmDo0KEGG9q4cWPMnz/fYjHO5/OMjIws0bacOHHCEL7y008/GWyUaX4cnt27d6Ndu3aQyWTw9fXFJ598Ai2fRN4GH3zwATiOw+3bty32paSkQCKRYNSoUSUeg8F4UijNPqnVaixduhStW7eGXC6Hs7Mz2rdvj++++86sX2hoKBo3bowdO3aU6fXHjx+PzMxMCy+UDRs2wNPTEwMHDrT6vIKCAsyZMwcNGzaEVCqFp6cnhg0bZnHem+agKs2ehISE4KeffoIHgM4SCV5q0AA9+/XDxaQkWrDiEcnJyZg+YwZ69OiBLl274t333kNsbCzdqdcD58/D6eZNBADgHoW4RkZGol379li1apXV9/Phhx+iS5cuyM3LK9PnV6nwoh/v6RcYSD39vLzodr6Qx9WrVPTk8/n98w9w6xbw4AEtNsLy+TEqidLsU0JCAkaMGAEXFxc4OjqiV69euHz5ss3jsfkTmz8xqoe5f83F7lu7sWLACjhKHJGan4rU/FSoNKrSn/wYwcS9cnD58mXk5eWhU6dOFvsSEhKwbt06dOrUCZ999hm++uortG3bFkuXLsVLL71k9XidO3eGTqfDqVOn7B6DqeEvLCw0bF+/fj3atGmD1q1bW33erFmz8PrrrwMAFixYgBkzZiA2Nhb9+vXDzz//bPX99OrVC/Xq1cOyZcswYsQI/P7773jttdcMfSZNmoSJEycajr9582Zs3rzZ4ur32LFjce7cOXzyySdYsGABUlNTMXjw4BJDeSUSCcaOHYuzZ88iMjLSYv/69esREhKC5557zuYxGIwnjZJsFM/06dOxfft2vP3225g/fz40Gg1GjRqFdevWWfRt06YNpFIpjh8/XqZxjB8/HllZWdi1a5dh265du5CZmWmwQ9bG/tRTT+HIkSOYMGECvvrqKzRu3Bhz587F4MGDodfrLZ5Tmm1p2rQpNm/eDAB45plnDDZq5cqVZsfZv38/Jk6ciAEDBmDFihVo1aoVli1bhqVLl5b4Pnn7t379eot9mzZtgkajwZtvvlniMRiMJ4WS7JNarUbfvn0xffp0+Pj4YMGCBVi4cCHatWtndXHcuXNn/Pfff2W6OBoWFoZ27dqZna8qlQrbtm3D6NGjIRaLLZ6j1WrRv39/LFy4EGFhYfjqq68wfvx4/Pnnn+jYsSNu3bpl8Rx77Mk3S5ZgdNu26Ahg4dSpmDh3Lj6ZPx/16tUz9ClUqTBx4kSIRSK8+957ePWVV3Dp4kVM/egj6C5eBMaOBd55B42++QZ7AAxYuBB44QU0WbkSy52dId+xA/oDB4AbN4CsLIAQpKWl4ezZs3juueegtFYlt6bhc/oVL+Th5kZDf3NyaPXgy5eBs2eNop9paG9qKg0BTkujBT3S02m4b0YGkJlJW1YWbdnZxpaTQ1tuLm15ebTl59NWUEAFVcZjSUn2KSsrC8888wx27tyJYcOG4csvv0RISAieffZZxMfHWz0emz+x+ROjevj56s/ILcrFyB0j8dT3Txnan1F/1vTQqhVRTQ+gLhIREQGAXjUuTv369REXFweRyPjRvvvuu/j000/x+eef4/z58+jYsaPZc/jjRERE2LxibI3x48dj+fLl2LVrF0aOHIn4+HgcOXIEX3/9tdX+t2/fxhdffIFOnTrh77//NlyReuutt9CiRQu8//77GDx4MBQKheE50dHR2LFjB1599VXDNqFQiNWrVyMyMhJNmjRB586dERUVhf/973/o3bs3evToYfX1PT09sXfvXsMVoJ49e6Jjx4744YcfsHjxYpvvc+LEiVi2bBl+/PFHsyIi//77LyIiIjB//nxWeYnBMKEkG8WTlpaGa9euwdmZloh/66230LJlS3z00UcYPny4mR2QSqUICAgwHNdeWrRogfbt22PDhg0YMWIEADqBa9euHVq2bGn1OZMnT0ZhYSEuXLiAtm3bAqA2dOLEiVi7di22b9+OkSNHmj2nNNvi7e2N0aNHY8yYMQitVw+jhw0DtFrq6ZGdDUFWFjwApN+4gUtHjyLQzw/Q6fBWr14YMmAATnz1FWb16kUXdA4O8M7ORhAAJCYCYjGaBwaix1NPYdPGjfj888/N7P/69esRGhpq0y5WCEJoiJpaTZvpfX4RWlQEyOU0HE4up94wfBOxKQCj+inJPq1cuRInTpzA7NmzLfJUWVuYhoaGQqfTITIyssSLGcUZP348Jk+ejLi4OAQGBuL3339HVlYWXn/9dasXEjdu3IiTJ09iypQpWLFihWH7iy++iK5du+KDDz6wiICIiIhARESEwcPlrbfeQlhYGL799lvMmjEDSErCIB8fxDg54RcAvYrZNZ6srCy8NmYMxo4da9gWKBBA+dNPEL71Ft2gUKDQyQlcYiJkhFDblJiIHgB6AMCnnxoPqFBAIJPhC50OzdVqYOdOwN+fNj8/M4/BWodEQptpxWC9nto8lYqG9hYXWYrPD/nHxW/t7ctx1H56elKPQ6WSCpEC5i/xOFCSfVq6dCnu37+P77//Hm89OvfefvttNGvWDJ988gmCg4MtnvM4zZ/q16+P0aNHW33tEu3drFk232fz5s3x9NNP46effqre+RPjsePux3dregi1AvZPVA5SU1MBAG5ubhb7JBKJwTBptVpkZmYiLS3N4Fn277//WjzH3d0dAHU/LgvNmzdHx44dDaG5P/30E8RisYXh5tmzZw8IIZg2bZqZq7m7uzveeecdZGZmWlxZ8vPzMxP2AKBXr14AqPBXFnjXa54OHTpAqVTizp07JT6vYcOG6NGjh+EKDs+6desgEAhKDEFmMJ5ESrJRPG+//bZB2AMAZ2dnvPXWW8jOzrZ6hdnd3b3MNgoAXn/9dRw9ehRxcXGIi4vD0aNH8eZrrwG5uXDQaqEEDB4TadHRiDhzBsN690bbkBDqafHI82LuO+/AE8BfW7fSKoxJSXDMzYUPgGmjR4N7+JB6a8THo4OXF8IUCuRdvgxcv04Tt587h2cANE5KohUb+XxOp09DeuECngbwTvv2CExOBq5cAa5dAxcZib5NmkCYno6CpCTqxfHwIXyzs9EaAP79l+aGOnUKM7t2RePkZJxdtoyGkN25g4u//47MW7fw3ogR4PLyqNBWFo8PrZYKdFlZxkXr/fvGCpQnT5q9D/zzD32vN27QcLXsbCr4paXRELYLFwzjxd9/0zC3mzeB2Fj6mWZn00Uyg1GFlGSffv75Zzg7O2POnDkW+wRWxJPyzp9GjhwJsViMTZs2AaAhuR06dECLFi2s9t+1axc4jrMYV5cuXdCrVy8cPXoUOTk5ZvsGDx5sFrrGcRx69uyJvKQkFPzzDz0fCwuRrlDAMmmLEQHHYfjw4fRBXh7w3XcYvHUregPQcxwwZAiwaxci5s5FFwCHP/oIWLcOmD8f6tdfxwGhEHf5XHcAkJ8Pt/R09ATgdeQIsHQp8MEHwMsvA126AAMGABMnAvPmAT/+COzfTz3i4uKogFbbsBbaa9oCAszFS19fWtXX25t+Jp6exubhAbi70+bmRqsBu7jQiyNOTkYhT62mHoLnz1Pbe/o0LRKSmEi/I1YIpM5Skn3as2cP3N3dLTzJ3n//fShNBediVOb8yZbXXmpqKs6cOYMBAwYYhD2eTx+J+9a8n8u7NiuOLXuXlJSEvFJC/ydOnIikpCTs27fPsO3s2bO4desW3njjDebAwWCUAXbZvhzwRobY+PNevXo11qxZg4iICIsrzZmZmRb9+eOUx3iNHz8e77zzDmJjY7Fx40a8+OKLcHNzQ1pamkXfe/fuAaCiYHH4kut8H5769etb9OUn02WtUGftWG5ubnYdZ9KkSRgxYgT27t2LIUOGID8/H9u3b0e/fv0QEBBQpnEwGI87pdkogIZaFKdZs2YAgLt3La9+EULKZaNGjBiBjz78ENt++AESlQrthEKMCgoCTp1Cs/R0uqg9fZqGikVGoguAvo6OVIAyvjj8ATzr4ADnmzepiMVx8E9IQEcADTMz6eLT5P12cnCA08OHVLgSCAChEIZgO7EYkEppeJdQCJ1Wi3gA8kaN6GLQBM7PDykAskUiyD08AACZCgXiAdr3kfdctz598N2aNfhr9250CwsDNBr8s3o1nhYI8FpoKH0/IhF9bQcH6kUnlxs9UXjvk6IiKujl59P7Wi0V6Ezz1giFxmPxxxOL6fbSIIQeV62mYl5KilFwFIvpWBwcjAtZU08/mezxSO7PqFFKsk937txBWFiY3ZUgyzt/cnV1xeDBg7Fx40aMHj0ax44ds8jpZ8q9e/fg7e1tmP+YEhYWhmPHjiEmJsbMo6Z+/fr03CoqMrQGAgE6Aci9cQPysDBqh0oZu6enJ6QiEbB7N/D990B6OgQA/gUQN2QIXp45kx8kAKDI0RFo1Qpo1QoSAP9lZSF8zx7s37kT7o6OuHn0KNZ89hlGde+OTgEB9KII3woKqNCfnExDXq3BC4WenuYCGX/fywtwdKxdtsKal15FcHCg4h9ABc+CAiAqin7fMhl9/56e9LPiBcHa9HkwbFKSfbp79y7atGlj5l0GUO+8+vXrIysry+oxKzJ/mjp1Kn766ScQQiCRSAxefMUpaY0XGBgIZ2dnizUeULG1WWnHMV0vOpYQ/v/qq6/iww8/xLp16/Diiy8CoA4cIpEI48aNK9M4GIwnHSbulQNPT08A1oW65cuX4+OPP0afPn0wefJk+Pn5QSKRICEhAePGjbMaVpKRkWF23LLAG/4JEyYgOjq6xMlpSQt9W/uEJSwWSzpeWY5lz3GGDBkCDw8P/PjjjxgyZAh++eUX5ObmsjwMDIYVSrJRPNYmmiUtlDMyMspko8Q6HZCcDJesLEzr2hVR69ZBTAhefuopKJ2dAakUuRIJ0gDqLQFAk5yMZAA5jo7Uw4IPgXrEQ6EQAonEIMDlODkhHgAJCKD9TUgUicBJJNRL4xFZAAolErrwMuWRR5A1zyAem3aK4wCJBDI3NzwzYAB++eUXvC8UQuHkhJ1nz6JD165wa97cKNJpNFRUS0+n24ofl+OoyMaLdwqF8XFlwIeUWRNPNBqjuJiVZRQUhUIq+kmldKHq5EQXt1KpQTiFUGi8b3rLFrSMYthjn+ylIvOn8ePHY8eOHZgwYUKJi2ag5HkKp9NBDkCYnQ0kJkIUH48mAIJ4L2E+ZF6rhXdSEggAnZ8fPX/soJVGA4wZQ3PMAUBQENLHjME7CxdiYgne2TxDhgzBb7//jj///BNjx47FjvPn8a9IhPDZs40CFX2T9Lx/5AFtEPwSE6nYl5JChSw+N11Jnj0ODpaCX/Hm6vp4hLLy9tTNzXjxJD+fin2EWBf75HJmG2sppdmn8oh0ZZ0/8bi4uBguQhBCMHjwYLi6ulrtW9Y1GU9F1mb2HMeeYzk4OGD06NFYvXo1Hj58CCcnJ+zcuRPPP/88fE3mcAwGo3SYuFcO+LANa2GpW7ZsQUhICA4cOGC2UDx48KDN4/HHsRUOUhLOzs546aWXsHXrVgQGBqJ37942+5rm9mvcuLHZPntydJVEVbtM84U1VqxYgfj4eKxbtw7e3t4VLlPPYDyOlGSjeG7evIlBgwaZbeOTwhe/AltUVIS4uDiL/mbodIZE5G0BtMjMpGGfAF549lm8d/Qo1ADGffopFYcA6AQCaAGDcBUQHAw9Hl2BLrboS05ORl5eXq321B0yZAi279iBP//8E66urigoLMTgwYPpe+E99GozvCdgcfFTqzXm8ktOpgt/vd4ovgqFlrcCAW28SGnqach7GZYkDJoKu7byXtnaV1KfYoIxo/opyT41atQIUVFRUKlUdnnvRUdHQygUWvVELo3evXsjMDAQR44cwciRI+Hi4mK9IyFoXK8ejh88iIzbt+GmUFAB51GhBeGZM+gGICQhAUhPhzg5GQ0AyPjCC7yHrkiEbGdnZBU7vM1fY3w83rp7F22zs2nxB0dHYMIE4NVXUfQodNAemjRpgqZNmmDPnj0YOnQo/vrrL3R75hnLsEOOo4KbqytgbT5KCH3Pycm0WAUv+BW/n50NFBbS1AAPHtgemEhEL8rUqwfUr09bvXpASIj1iw91geIXT0zFvshIQ95WODpSgZP3kC6L2EcI/b/V6+mtPU2joeKjt3fVvffHhJLsU2hoKG7fvg2tVmvmvVdUVIR79+5ZDeW1a/5UAq+//jq2b98OAFizZo3NfqZrvOLEx8cjOzu73Gu86mDSpEn49ttv8dNPP8HLywt5eXnMgYPBKAdM3CsHbdq0gZOTE86fP2+xTygUguM4s6sUWq0WX3zxhc3jnTt3DgKBAF27di3XeKZPn46GDRuibdu2JXqeDB48GNOnT8eyZcswcOBAQxn1jIwMrF69Gq6uruVOWsq7W1fGlXhbTJw4EcuXL8fMmTNx+vRpTJs2zcI1nsFglGyjeL7//nuzvHvZ2dlYs2YNXFxc0LNnT7O+//33H9RqNbp3727cSAj18MrJodUHU1LoAkajgRcADcfRhZtAgDZ+fnjj0SKveEEhU1xdXdG6VSucOXMGUVFRZhch+EpqxcdWFuQODsgtlherMgkNDUXLsDDs2bMHLi4u8PTwQJcuXars9aoNkYg2udxyn+lCs3jjF5X5+fS+aV8ejjP3XuTFPdP9xW/tTXpvrY9YTBfVLi5GD0RGtVKSfRo1ahSmTZuGzz//3KKghrXQtnPnzqF169ZwenTBoCwIBAJ89913uHz5MoYMGWLYLiooQCAAZVISzWGZl4dJTZpAc/Ag9nzyiTHPr0iEm9HRuPTff2jVoQMU9evTMH+hEA8BFCgU9HdWCvJH51VOTg59H3l5NGfetm1oq9VCB0D4yivApEl2Hc8aQ4YMwcJFi7BkyRKoVCp60aGscBwVopRKoEED2/34whYlNd5zmRcA//7b/HX8/MwFv/r1qehnzQbVZqyJfSoVtYm3btHHDg70M/X0pJ7apqIdf2GFL5bEp2gwtbmmQp+pLTW1rRxHbXjjxvTztCeFwxNKSfZp8ODBWLRoEX788UdDQQ0A+Pbbb5Gbm2tV3LM6fyoDzz77LBYsWGC4bwtPT0906dIF+/fvx5UrV9C6dWvDvoULFwKAmZ0rK46OjlW6xmvevDk6d+6M9evXw8PDA35+fnj++eer7PUYjMcVpoyUA6FQiCFDhmDPnj0oKioyK07x8ssvY+bMmejfvz+GDBmCnJwcbN26FWKx2OqxCCE4cOAA+vbta5bcviy0bNnSZuUkUxo2bIgZM2Zg8eLF6NKlC0aMGAGVSoV169YhKSkJmzZtMquQWRbat28PgUCAxYsXIzMzE3K5HC1atCiXN6ItGjVqhB49emDLli0AgDfeeKPSjs1gPE6UZKN4PDw80KlTJ7z++usghGDDhg148OABfvzxRws7sG/fPohEIgwZOJAWZ+BzteXkUIFPKKSLEg8PQCxGCoBQkcgs3NXeK7Aff/IJJrz5JiZOnIhXXnkFXl5eOHfuHP4+eRKdn3oKffr0Kffn0qJFC/x7/jw2bdoEb29vODg4oFu3buU+njWGDB2K8PBwxMXH4/Xx40sMVXks4BeNlQW/QOUbYHlrbZtp/5L6FBZSDxqALqpdXIweNE5OrIJwNVCSffrggw+wd+9eLFy4EBcvXkSfPn0gk8kQERGBqKgo/PXXX4a+d+/eRVRUFJYsWVLusQwaNMjoUaNWAw8ewO32bbQF4PLwIfVIk0jQ54UXsPf4cXz3xx+4lZeHDh06IDExETt37oTQ0REfTJtWbsGkRYsW2LFzJ75cvBivSqVodPw4pPn5AIAIpRKrJBKsnj693O8RAPr164eVK1di/4ED8PH2RufOnSt0vBKRyYCgINpsodVSL7+4OJor8P592u7epf8vfEiwae5VgKZZsCb6lZDPq1bBcdTu8BcVeJuUm0s/D2tpGop7NPOPRSJLr+eSwpxzc2mxpbw8oEmTuusdWcWUZJ8++eQTbNu2De+++y6uXLmCVq1a4cKFC9i1axdCQ0OhNc2N+wjD/KmcwppAILBaYMga33zzDbp164bu3bvj3Xffhb+/Pw4fPow//vgDffv2xbBhw8o1BgDo1KkT/vrrL3z55ZcIDAyEQqGo9OipSZMmYdy4cYiOjsasWbMe//kTg1EFsFlsOXn77bexceNG/Pnnnxg6dKhh+yeffAJCCNatW4cPPvgAPj4+GDZsGMaPH29IVm/K33//jQcPHmDVqlXVMu5FixahQYMGWLVqFWbPng2hUIj27dtj9erV6Nu3b7mPGxwcjLVr12LJkiWYNGkStFot5s6dW6niHkC9906cOIFu3bqhUaNGlXpsBuNxwpaN4lmyZAlOnTqF7777DsnJyWjYsCF+/vln82rbej1Ibi7+2rgRk7t3h9/du3RhYBpa5O5eqWGOTZs0wcaNG7FmzRr8/vvvyC8ogJ+vLyZNnIhx48aV6J1cGtOnT8cXX3yBtWvXoqCwEL4+PvaLe3ZWue393HNYvmwZ8vLyDImhGWWguibzej0VptPSqIggElGB2tOThq85OdW+ogCPEbbsk0QiweHDh7F8+XJs3boVs2bNgkwmQ8OGDY0ec4/YvHkzpFKpzeqRdkMIFVbu3AGSk6EXChEHoMAkjFEE4KtVq7B+3TocPnIEf588CUeFAl27dsVbb71lViWyrPTt2xcFx46h3bFjqKfTAQAKfXzgMGMGvv7pJzxMTKzY+wP1Duzbty9+37ULL7zwQoXsaKUgElGhztcXMPXmJoSGIPOC3717xvsZGTT/X2Iirfptire3dS8/3uvYtPEFiUwf1xQcZyyuVNUolTQ8/N49Kig2a0ZzADIssGWfXFxccOrUKXz88cfYvn07tmzZgo4dO+Lo0aOYOnUqYmJizI5DCMGWLVvw4osvwq9YXuCqoG3btjh37hw+++wz/PDDD8jNzUVISAjCw8MxY8aMCp33q1atwjvvvIP58+cjLy8PwcHBlS7uvfrqq5gyZQqys7OZAweDUU44Ut4MnHWQW7duoVmzZrh582a58rMUp1+/fsjPz8ep4lcWy8CLL76IhIQEXLhwgZX6toNff/0Vr7zyCjZt2oQxY8bU9HAYTxiVbUOq+vhltlF8CGVeHvXKS0/HmUOHsHzhQny5ciVCW7Y0LpjqMnxIcVYWXUhmZVm24ttzcqj44+dnu8nlUKvV6N+/Pxo2bFhifpwqIT+fihR86FtaGv2ueC8af38alsqwRKulv3s+hFgmo4thb2+6AHZyqhOeLlVpo2rcPplQWFiI+vXrY+TIkVi+fHn5B5GfT8WOmBjq9eTpWb1iT0wM8PXXRg81Jydg4kTg5Zcr3c4uWbIEv/76K/7444+6maQ+K8so+JkKf2lpFTsu731sS/wr3pycqOdb8+a0PSoIVWfQ6ahA6uhIBb5q/C08KfaJZ9euXXj55Zdx6dIlszBZhnWKiorg7++PVq1a4ejRozU9HMYTRlWv8aqLOr5Cq1mWL1+OVq1a4fDhw+UKFbt06RL27t2Lv//+mwl7dvLtt9/C3d0dL7/8ck0PhcGo9ZRoo3iBixfzMjKMidC1WoDjQBwcsGbbNrR58UWEljMnaJWi0dD3UFhI8xgVFtL3YkugM21qddlfLy+PVqzkq1YWx8UF+XI5pmdno6FCAfz6KxXUeA8VO6tjWqDT0RxVfMJ6vvGP+dtHoXw2EQioCMmLfYGBQHAwve/t/WTnYRKJaIgun9NMpaLfd0SE0bvG1ZWKP3wI75P8eVUCFZlDrV69GhqNBp9++mn5Xlynox6bd+5Q4d7Ts3rzL+bkAGvXAjt30rEIhcCrrwJvvlkl3lS5eXnYt28fnn766bop7AH03GzThjZTcnKMYb284PfgAS1kodWat0eekWYQYqxkbi9nzxrve3tTkYwX+5o2rd1hwkIhEBBARdFLl6hQyfLwWVDRNR4hBOHh4Rg/fjwT9uzk559/Rnp6ulk+QwaDUTYqRdzTaDRISkpCQUEBPD09rSYUfRxp3ry51fwK9tKuXTvo7Qz1epJJSUnB0aNHcerUKZw8eRKLFi2CA0uCzmCUipmN0miMnkk5OXRiX1BARQyAhus4ONBF7iOPEQ7A5p07K3dQajUQFUXz/xQWWjZepCsu2llr1hZqZUEmMwo6rq7G+6aN3+7kRD+3hw+tt5wcICsLrllZ6APQBPGmSeIB+tn6+lLBz8/PeN/V1ViUxJpol55ud1gwFAqaQ87Tk7ZHecQQF0c/0/h42kwXpwD16AsMpC0oiIp+vPhXLPS6UKXCw4QEALSy8mN5cco0CT4fwpucTD9Hsdj4OfMhvGWpdskAULE51EcffYSPPvqofC+ckUFzu8XHUxEmMLD6vjutFvjtN+B//6MXUwCga1dgyhQaSlpe+KqsvHfZI6KjoxEVFYU///wTBQUFFQ9hro04OQGtWtFWGnxRn+KiX2nN9DmpqVT0j4iggmJyMm3Hj9PX4DhqN3mxr1kzoFGj2lct3cODzgmuX2d5+KxQ0TUex3G4evVqJY7o8WXv3r2IjY1FeHg4mjZtWqHCHwzGk065xb28vDz8/PPP2LZtG86fP4+ioiLDvoCAAPTp0wcTJ05Ehw4dKmWgjCeXmzdvYuTIkXBxccFbb72Fjz/+uKaHxGDUbnivPF7MS083Fr/QaukVej5nnodH1S9seTHpn3+ACxeMgmJlIRYbk5TzHlbOzkZhzlS4M71f1oWMhwfN52SFV59/HpL0dHT098fIHj3goVZTzyBe/CssNHrcXbtW9vcoFNLX50U7T0+jiGd6ayt3EyH0d8BXp4yNpULVgwf0+1GrjV4vxZHLgcBA6AMDUeDujmSZDJmOjtArFHAqKIBnYCD9LKXSx1PgEgjoucJ74/Dh63fvUi9OBwcqMHh60j4KBW3ME6Z2UVREw2Dv3aPfoZ9f1acY0GhoCGR8PD3XfvuNCkIAtSVTpwJPPVX+4xNCRcLsbPo75KusPtp3YdcubNmxA85ubvhs8mS0Cg2l9pcPP30cz9eSMA3BrQh89Eh+Pi3QExEB3LxJbxMT6e8sJgbYt4/2E4mowMcLfs2bUwGwpnMfOjrS/8979+j8oFmzcldkZjDKy/vvv4+HDx+iXbt2+PHHH1khDQajApTr323FihVYuHAhQkJCMGjQIMyYMQP+/v5wcHBARkYGbty4gVOnTqF379546qmn8O2336Jhw4aVPXbGE0KPHj3wBKWGZDDKjlpNFxn5+TTkND2diklFRXTxJ5FQgcbLq3ry5RUW0nAfXtCLizPf7+ZmDINzcKDCkFxu/pgX6vj7JbVakANw5/79tnfyC3BTsc+0ZWUZPxNrgp2XFxUlKzLh5TgqDnp4AG3bmu/T6ajnCS/8mbaHD+miLyoKgqgoOAIoMeCM93jjG/99ltZM+0kk5pUhbd3y1SGtNVv9HR0rvqAWi40CMSFULDGtdsl7wbq60saLfTLZkyem1AYIAZKSqBCbnk49USszbDI/3+gRy7eEBHqblGTpdevsDLz9NjB4cPltl6mo5+xMvda8vOi5zIeYajQY0bw5RsyaZfR21miMqRdMvZL4c4QXvsRi8xx07HdrHYUCaNeONp6MDKPQx7fsbLrt5k3gl1+Mz23a1Ojd17w5vUjAewla8zC0ts+e/goF8Nxz1i9oSaU0TDcxEbh4kY6lGoo/MBg8xQuRMBiM8lOuWcXZs2dx/PhxhIWFWd3fsWNHvP7661izZg3WrVuHv//+m4l7DAaDURXcuUO9sPgwVYGACgtKZfV45QF0oXnvHhXyzp4F/vvPPH+RUAi0bg107gw8/TTQsOGTtVjkOKMY1Lx5TY/GEqHQWBSkmBeROj8fqVevIuf6dYiTkuCamwtZcjLEqakQqFS0mYoEKlXle2ZWJkol/Q7CwuhtixYV81ThOKPIzFNURM/HhATqvcNxdFGtUNBzUqk0Cn6swEnVkptLPSwfPDCGnpdV3CWECjbx8fRCBS/c8S0zs+TnS6U0/D4ggIo5w4fT30B5IMSQAsAQjurnZ1++QEKo0MOLf2q1+X3T1AharfFWq6XPBehv2fR+cfG8JPH9SREJ3dxoqDWfp5YQepGEF/pu3gRu3aKi8MWLtFUHP/8MLFlC0y4UxzQP3+XLdGz16zPvYwaDwahjlEvc+4W/6lQKUqkU77zzTnlegsFgMBj2kJtLJ+I+PtXrwZabC5w/TwW9f/6hnl+m+PlRMa9zZ6BDBypkPEKr10KtLUdBCzsRCyQQC2vem68uQwhBeno64uLikKlSwaldOygUCqQU66fV6ZCWnIz6fn4I9vY25kjkRT5rj201vp9aTb2dTJtORxfJxbeb7rN2y/fhyc0Fzp2jjScwkIp8fGvUqGKim1RqXjxFr6fviw+R1+uNoeROTtSTjBf75PKaD9V7HNBqqaB39y793D097QvDz8sD/vqLXjAxFfAKC0t+nrMzFUestcq6yJKTQ4VEJyegZUtDhW674Tj6uyvtt11cBNRojOG+fNPraR9TgZB/Du89aHp+8vdNhUH+tUQi6u1q2h4nEZDjqLjr7w/whRm0WhqebSr4RUeb55HlPSntvbVW8Ze/vXyZXggcMwYIDwd69rQ+Vj4P340b9LZpU5aHj8FgMOoQbPXDYDAYdR0+hKoq0etpIQw+1Pb6dfOFiFRKQ5N4QS842OYCLSUvBQk5D6tsqBKhGHKxAo5SR8hEMshEMkhFEgg4JprYQ0FBAeLj45GUlASBQABvb28IbAhOIqEQTi4ueJCeDqW3N9xqaziXXk8Fh/v36W/3xg16yxcbiYsDDhygfSUSmlzeVPDz9S2/4CAQUBHGVIjhwyNTU6knGGAMT3Zzo2KRaTgvw37S0qiQkZREhbDAwNKfo9cDe/cCq1dTAbY4HEerotoS8KqyOirvqefoSH+LAQEWol5RURHy8/Mrp6CdvSKgLXhBz1QItHafb7m59D0WFdH3qdFQ0Y8fh1RqFP1qQQqGSkEkoh7sDRvS8GzAKIxWRSh0SgowcyZw9SrwySdU5Hv3Xeufp6Mj/axjYmhKhubNWR4+BoPBqCOU+V+ysLAQGRkZ8Pf3N9seERGB5rUx3IjBYDAY5SMzk3o4nT1Lb4uHn4WEGENt27SxS4TQ6XVIL8iAABzkEkWp/csOgUanQZYqC2kFaQCo2CcVSuEoc4RCpHgk9kkhEdWy6oU1jFarRWpqKh48eICCggK4urpCauqBZgO5XI7CwkLcv38fDnI5HGqjGCUQUJGgSRPaXnmFbs/Opp4zN24YW04OLXpiWvjE3d0YztuiBc1LpajA75cXT5yc6GO+2mlhIV1Um4bYu7rS8EsWwlsyhYU0PUBMDBWR7C2YceUKsHw5DZUEqBjYtasxlDYggB6ruqud5ubScGBHR/p7Cwiw+psrKirCtWvXkJubi06dOkFRkd9lZcCH4Zb196rRWHry5uRQD7KCAir88V65vLefqfBX1739KiKoloaXF/DDD8C339Lw3M2bqa1bvJh66xVHIqG//6QkWgSrefOKXeBgMBgMRrVQJnHv119/xYcffgg3NzcQQrB27Vp06tQJADBmzBhcvny5SgbJeMzgw6WKN6GQTtTEYjaBqIsUD7dh1B34hPPR0TSMLTqaer7cu2f8XgG6sOzY0eid5+tb5pfKV+ejQJMPZ5kzhFzVeGGIBGI4iHnPFir2FenUSM5Nhl6vh0AggEQogYPYAUqJEg5iB8jEMkiFUggFJjmGeC8TPu+UXk8XPdXhKVnNZGVlIS4uDmlpaZDL5fDx8SnT893c3JCcnIwHsbFo2LChTU+/WoezMxWnn36aPiaEevHx3n0REdRjNT0dOHmSNoDaufr1qdDHC3716pU/RxWfl08mo2IeQH9zubnUE02jYeKeLfR6mtPszh16AcLDwz7hNSmJih2HDtHHCgUwYQIwbFjNftZ5eVTUk8uNop4Nz0Be2It7VLQoPj4ejRs3rs7RVh68uFU8H6FeT4VvU+EvP58Kf4WFtr39RCL6mOOMoe4CgXEbv/1JmbOIRMCHH1J7tWABzY07ahSwcCHQvr1lf6GQCnxpabRAVuPGQGgoy8PHYDAYtZgyrU4+//xzXL58GZ6enrh48SLGjh2L2bNnY+TIkaya6eMM/90SYl2YsxZmwTdrSZtN86+Y5mMRCIwLZ0dH2mQyY/4i/iptdS6qTcevVhtbQQFter3l2KzlPTF9XFcWvdbgJ9mmja8UmZdnvKLOT9L5q+tisTGhtmnibWuPTe8zKp/sbCre8e3uXWNeKms0akSFj86daZ6nCi56c4pyoCekyoQ9SziIhRKIhRLwdV71Wg006kIU5qcjV50ATqeDSM9BCgEcRQ5wEMkhFUkgFTlALHMAx5+/HEc/J96OEUJ/p/xvnL84UdPCn6koyYd5SSRWbU9RUREePnyIhIQE6PV6eHh6QlSOc4/jOLi5ueFhYiKcnJzgWw7ht1bAcTThfFAQMGAA3aZSUYHv+nWjl19iovHc2bOH9pPJaI6qZs2M1S/9/csvHohE1HMvN7dy3tvjSFYW/Q7i4+nnHxRU+uetUgGbNgE//UT/wziOhka+/TYNia4pTEW9xo2pB2EJhTfUajVu3LiBuLg4+Pv7o7CwELGxsfD394djVYYJVze8B6u1oiGm3n6852tODj1neMGPz7nJ5/zjm+lje+FFQd7e8/O/ujSve+45Gg48bRo9d955h7axY62fO6Z5+PLzqeezPQVcGAwGg1HtlGkFotFo4OnpCQBo3749Tp48iSFDhiA6Ohrck3LlqzLgw294T5Dik4yStlnbX1xkM00gbjqRMRXpANt9ivczvc+/runr8PdNMf09mAo2plXTxGLzbbwYqNHQ5Pzx8cbXNRX+ZDKj+GcqrJV1ksUnfzYV7UwrxuXnWyaUNn1PvIehqZjJv3d+3KYCFt9Mc8jwt7YSIRdv1QH/mZgKeAUFdLJcUGAUO/kKmfxElw+L0WjoZ1c8iTZ/Vd30d8VfObdVZY8XCiUSOpnkPy9ePOT3MW9PS1Qqml+M98TjW1qa9f5CIQ2zbdDA2Jo2tR6yU060ei0yCzMhE1VB2KaO/tY4nQ7cI2GL09L7nGk1Vw4gAgEkQiEgkoLIFSBiEYokQhSKgUwhoBECEIshkckgkcnhqvSCXOEMhdQRSk5qXEgWFdHfOn9uVLXwx3sQ8qKd6WNTG8xxgEgEnYBDoV4DBScGxy90AUAggF4kQkZeHuKSkpBVUAAnNzfIy5Kc3woSiQQKuRwxMTFQKBRw4kNO6zoyGQ2LbdXKuC0tzTyU9+ZN+hv47z/aeJydzcW+Zs0q9Zx6ItFoqNiQlka9i1UqGnZYWtgsIcCRI8DXXxsLALVpA3z0ERUsaor8fCrqyWT0YkpgoDFc2wa8sMeLeSKRCEqlEg8ePMCDBw/QrFmzahp8DWPL248vClJ8vlzRW97u8oWscnKMBYD4eWpdEP2Cg4GNG2lY7v79wHff0TQE8+ZZF5T5PHz379P33by50cOYwWAwGLWGMq0yvLy8cO3aNbRs2RIA4O7ujiNHjmDs2LG4ZpqbhkHRai2rAZomDtZqLa8iApbiR/GriqbbeEGjeHgBf3WxeJ/ilNSneH/TK5YymbkQUxnCk0hkXmHQFFMvwPx8YxgGj6mXmFxuTETOL6hNBSt+Ec5/B3wzFRL54/HvlRfeygo/ueTFP62Wvn5envGxte9XJDIXvUwFUVNBix+nNSHQlpccYCneFRXRMeXm0vu80Mn/Jk1FCrm88hJb26psyX9marVxkl78szL1iuS/JwcH4/iKf06PUzJuU3Q6mpD/xg3zsNr4eEvRncfPj4bXNGhgvA0OrvJQtDx1Hgo0BXCRudju9Oi3wJmIdcUfcwahnQDgABAQk985EQhARELoFQ4gUhn0DlIQiRhEJAIRi0BEYkAsMjzmF2DiRw0A9ESPIm0R8rUqZKgeQl8QD6lICl+lL/yc/KAsvrDhK6KanlO2hD/A/IIFb/dM7RHfil8sML0Q4OhIf/O8h/Oj37pOKECGJhfxBcnI1uYhyMEHQXJviDT0nMpLSUF8ZCTSHjyAlOPg7+AAwSNvF/LIrtPPRmy4tVc8VyqVSElJQUxMDJo2bQrx4xpK6uEB9OhBG0C/19hYKvLxFTDv3KGesnxFaR5vb3Oxr1mzqi3IUNdRqej/E+/ZlplJL8Cp1VRgsEcsjYwEli2j+fUA+h1MmUK9mGrqwlBBAQ33lkqpHQ4KomJwKWg0GkREROD+/fsGYY/Hzc0NcXFxCAgIeHzE9fLAz1WrEp3OWOG7sJB+n9nZ5nOp2iz6OThQMa9VK3punDwJjB4NLF1KPUeLI5FQ4TkxEbh4kdotPz92YZXBYDBqEWVa6W7evNlsEgHQK/Xbtm3De++9V6kDqzPwXnjFc4HwV/V4kYRfnPELOV4wMhXkTHOBMMzhhSlbidq1WuNnnZVFK4OZetrx8BM+XhBSKIwehFXxuZdHTOLFLV4YNH3MTxaLe8UVp7jwaioUAkax01QwMxXBnJyqZwLKC5n865eF4iJIdjZdKBUXAfnvm//O+fAeuZw+dnYu1Uui1jJqFPDbb/R3YQ1nZ6MXHi/i1a9fY0JCrioXUKshLsoH90g85vQ6UIHOeEMEQkAoAERCEIEQRCgAcZBBLxGDSCUgEgmISAgIRSAiIQgv6j26T8QmYbTlRMAJ4CB2gIPYGH5UqClEbFYskvKS4Kv0ha/SF0rpIy8HaxVReewR/gDjb1SpNP5Oi3uqmoa9Fzs/9USP9IJ0xGfHIzU/FSKBCA5yJe4UJaJAJkCIawiSHyYjOjMThU5O8OrRA0KOQ4FGA06tBqfRQKBWQ1BQQJtGA0FhIfWCBABCqNj3qEEopGKgSSMcB3cXF6SkpCBOoUC9+vWfDM9+oZCeW/XrAwMH0m1qNRXaIyKMot/9+9RrLDkZOH7c+PygICr28YJfo0ZPZpVcQuj5wJ8faWnG3Gp8zku5HPD0tO//NSODVsDds4ceWyoFxo2j1UJr6vMtLKT/VWIx/b0EBdldjbS4sFdcPHd0dERGRgbi4uJYkbuqRig0RpGYotMZBT+Vyij65eYaRT8+koEvBiKT1Yzox3HA0KHUS3/6dHqhcPx4GrLLV/E1RSCgqQbS04HLl6mQGRr6eF44ZTAYjDpImaxxQECA2eOkpCRD0u0uXbpU3qhqI8W98EyrePGeTnzoF1+VrzpFkicdXrypYFhZrcA0JLW8FA+ZNr0F6G/S0bFuh7Pa403JewTyYcS85ycf1qjT0SvULVpUz5grG46j9kcisfTEa9CAVvis6e9XrwcKCqDJyUb+w0g4igUgLk7QuXtDL5WASKi4ToT0N08eiXR0m/G+rfeh0+mgVqtRVFQEVX4e1Go1pFIpHB0dIZfLK1VY4sW+Qk0hYjJjkJibaCnyWaM04Y8XZ8sZtqsnemQUZFBRryAVQk4IT4UnRAJ6LKlQiptxN3Ex7SIkeRL4uPnA45G3EwGgsyZyEAJOqzUT/Ti1GoLCQghVKnAqFd1HCLhiKR1Eej289HqkXrkCl7w8uNnKY8Z7JZreFm/WnlPSbWl9Oc74/1zVqQ4kEqNnHk9BAfUi4wW/mzfpgvrBA9oOHKD9hEJ6DjduTAt1tGr1ePy/FUenM3rlZWdTMa+ggM6zAKNHtotL2b4vjQbYsQNYu9YonvftC7z/PlDGYjHlhhDreXvFYpoCISioTKGNvLB37949+Pr62vSKdXd3x4MHDxAQEABnOzwBGZWMUGiMHjHFXtGvNIrbs+KROqYFREz3KxS2Q9ebNQO2bAE++ww4cwb4/HMapjttmnUR3N2dnrN8OoJWrdg6h8FgMGoBFbrU0qdPn8c7HFejMYbV8LnGeC883uOI98JzdmZXrhi1h8oQCB8H+PPU1ueQlGQ7dLUu8NlnwMsv08WEn19Nj8aIWk0n/gUF9LGDA/JcHJCi9IerdwgKnJRlFlZMRbyioiIUFhYiLy8PhYWF0Gg00Gg0ZoWdxGIx5HI53NzcoFQqoVAoILUV9l9GeJGvQF2A+5n3kZT7yJPPyReOkjJ6RfLJ4ssBL+olZCcgtSAVHDi4O7hDLKSLfkIIioqKkJCQgNSHqcjR5cDH1Qecgx2CJ8fRsNxHAoKFHzQfHv3o/OFMcmtyj9JM5KenI0IoRNtWraBwcLCdN7Z4/lg+VL94DtjybCueY1anM+Y40+uNNoL3oOHDm6sKuRxo25Y2nqwsc7Hv5k3qGRMVRRtAq1w+DvC2IS+Phtemp1OxQ6MxngtKJQ21LY8wTwgVJ776ioqlAM2n9/HHQOvWlfpWANDfkKl4x19IMp0n8r8tV1d6wdfDo8z5yrRaLW7duoV79+7Bx8cHkhLyCyoUCmRkZCA2NtaQRodRCyhN9OO9u63ZMGu5t61FcRR/bNonOZmeW7a8RJ2dgRUrgA0bgDVrgD/+oBcili6lFZuL4+hIf+MpKXTcrMgGg8Fg1DgVWvk/9hVyVSogJobed3BgXniVjUZDJ/eZmXRi4Otb/gk9g/Ek0qgRXSQnJdXsOB555yEvjy5wec/QwEDDgjY1Pw5FmQCULqUcSm8Q8ExFPJVKBbVabRDxOI6DRCKBWCyGQqGARCIx89JTq9UoLCxEzCMbLpVKoVQq4ebmBoVCAYVCAWEFPbfkEjnkEjny1fm4n3EfSXlJ8FP6wUfpA4VEUeJz9Xo9VCoVCgoKUFhYCJ1OB0KIoen1esOtaSOEQKfTIbsoG0mFSchUZQIEUAgVEECABJJgdhydToeCggK4urrCU+qJbE027uXeQ4AiAO5S9/J7Nj7yrOSxNhtwcnJCfHw8IjMy0Lp16wp/3pUC7ylp6kHD58Llc7vx+VxNvfB54a8q/p9cXIAuXWgD6KI8OZkKfteu0fyZj4qZ1TnUamMesrQ048VSnc6YJsHNrfRiGPZw/z4VJ86epY/d3IB33wVeeKFi8zbT4lv8rWkBLf73IZdT8cTR0ZgHk28V8JLXarW4efMmoqOj4ePjY9dFCnd3d8THxyMwMBCurPBB7caW6FeZ6PVU7I6KojnzvL2tnxMCAfDGGzSaYc4c4PZtmocvPNyYX7T42BkMBoNRa6iQuPdE5NEB6OK0MiaejzuFhUaxLjOTeiNkZVlu4+/zoTKmyGT0CmFgoPE2KIje9/JiwmptghDq/ZKYSMWlzExjTknTJpNZLnT49qTYkMeR4t55cjldMHh50QshSqN3nlqnRmpyqpngVVzEKyoqQm5uLgoLC8sk4tlCIpFAIpHA2dnZIKRlZWUhNTUVQqEQDg4OcHFxgYuLCxQKBRwq4HWgkCigkCiQr87H3fS7eJj70Ezk44XGwsJCFBQUIDs7G9nZ2TSUWKWCXq+3eE/8Y47jDA0A8nX5yNBkIFeXC3CAUqyEiBNBrVFb9AUAkUgEHx8fwzYXiQvyNHmIzYuFRqeBt9wbAq5q7CrHcfDx8cGDBw/g4uKC0NDQKnmdMsF7hzk4mHtPEUJ/08Xz5+bkmFfF5D2yTG1dZYf4chwNHfXxoYJfbm7lHbu6uXWLXiTV6+lnxduJyvQqz8mh4bc7d1LRTSQCRoygIkVZ84sSYhQjTQvZ8LkvnZ2pbZPLLf/XqsBTXqfT4datW4iOjoa3t7fd3sdyudzgvefi4vLkzNcZ1hEIaCi4kxP1DI6Lo+ehrbyTnTrRMN2ZM+kFho8/Bl57DXjnHRYRwmAwGLUYZqHrMnw4iEplXv1UozF30Te9NS3QYK2V9Bw+N441oY73visrQiH1WpBIqGu/SkUTkEdHW/aVSKjIx4t+pgJgZS0W+IIcGRnWhUm+Ul9WFl1QSKX0aqujo/HKa/FmbR+/TS6vvVc+NRrqPZKURFtiolHI45taXbHX4MU/a8Ifv10mo4spT0/aPDyM95VKJhBWF7a88/hk8M7ONhcKOaoc5Gvy4e3oDZ1Oh4SEBKSkpECj0UCtVhtEPLFYDIlEAoVCAbFYDEElifkCgQByuRzyRznLtFotCgsL8fDhQ8THx0MqlUKhUMDNzQ2Ojo6G1y8rcrEcQr0Q6bnpOPfwHDgdB5lGBplWBqIh0DzyCBOLxZDJZIbXLO19EkKQp81DmioN2UXZ4GQcAsQBEAvKFz7qKHaESqdCXH4civRFCFAEGPLzlQYhBKdOncLevXsxYsQItDUNL7WCWCyGi4sLbt++DWdnZ0Ouv1oHn4tPKrWsWGor5y5fOCsjg/4/8lUx+Qsa7KIg/ewkEir6VzY6HbB7N/D99/Q/GQCeeYaGMAcFlf14OTn0OE5ONNehaSXq6srTaIJOp0NkZKRB2JOVsQCIh4cHEhISEBQUZDvvJePJws0NaN+eeuTdv0/nobY8O729gR9+AL75Bti2Ddi0CbhxA1i0yL4K1QwGg8Goduq0uLd7924sXrwYt27dgoODA7p06YLFixejsbUS7jWNTkcXAHyVvNRU8+qJvDhnKtTxwp3pftPGhw7VJvi8Mi4u1m/5xj9WKo3eeBoNFY/i4miLjzfeT0ign8G9e7QVRySiOceKi36BgXRMpiKdqTBZXMSz5k1Y1Tg4WAp/crlxYcF7mVh7bGubPV5xfDgnL9iZCneJiTSEqrTQe46jIpuPD02wzFf0LV4ZlG8qlXkVY357eZFKzcU+/r6Xl/n2xzERfVXDezLl55t75/n40M+U92CxQ4BLL0iHgBOgSFWE+/fvIzk52VDwwtnZudJEPHsRiURQKpVQKpUghECtVqOgoAAZGRngOA4ODg5wcnKCi4uLYZzFx6jVaqFSqQzed3l5ecjLy4NGo0FRURHNd0eKoBVqoZQq4af0g5enF2RC+xfohBDka/ORqkpFZlEmdEQHJ4lTuUU9U2RCGYRSIZILk6HRaxCoCIRMVPLY7t69i6+++gr//vsvAODSpUvYunWrobCWLZycnJCfn4/IyEi0b9++zCJFjSMSWa+KWTzEt6CA/o/k5ND/FrXaKBoywa/yyMsDLl2iecHu3KHb6tUDpk4FOncu+/Hy82nuP7mcFhYIDKzx/wxe2Lt9+za8vLzKdc7IZDJotVrExsbC1dWVee8xKDIZDbt1caGetQkJ9H/dmnAtFgMffQS0bAksWEAr5I4aBSxebJ43lMFgMBi1ggqJeyUl9K1q/vrrLwwZMgSjR4/G559/jqysLISHh+O5555DREQEnJycqm8wOh0VQlJSjOJdSor547Q0c1GjshEKjQsHsZguuPkKk3xxBWuN40ruZ7pfKKSLGxcX22KdXF5+TyqxmF5tt3bFXauln2Nx0S8+nja12lhtsKLw3oTF35ubm/l2JycqSObnGxOE5+dbNlvbea+3wkLa0tIqPnYejjMX/fj7MhkNOUpMpOMqDamUXr319aWTP/6Wv+/lVfbk87wXjDXhz9Z2vopiaiptfO6moiI6MU1IKPk1FQrrIqBYDLRrRyeuTyJ8BWGNxti0WuM5LBYbvfP433wZF5lF2iKk5qeiKK8IEfERyMvLg6enJ0S1JLSH4zhIpVJDuJtOp4NKpUJaWhqSkpIgEokMhTlkMhlUKhVyc3MNwp72UZV0kUgEqVQKmUwGJycnMzGwQFuARHUisrKz4Cn1hJvMDVJhyeF1eRrqqZdRlAEt0cJJ7ASJsHL/c8UCMdxkbsgsyoSGaBCkCIKj2DKMMSsrCz/88AN+//136HQ6iMVieHh4IDExETNnzsTatWtL/T69vb0RFxeHO3fuoEWLFo+H0GArxFerNYri+fnmgh9/UU4iMdpkJvjZhhD6v37tmrHdu2e88KRUApMm0cJCZbUpKhX9L5FIaP7SoCB6vBpGr9cjKioKt2/fhqenp13Cnp7oUaQrgoPIPL0A770XGBhYe71mGdWPQGD8vUdG0nm0l5ftohi9ewMNG9LquffuAW+/Dbz3Hj3vGAwGg1FrqNDq6uLFi5U1jjKzfft2BAcH46effjIsEoKDg9GpUyecOXMG/fv3r5wX0umoqJSZaS7cmQp49gp3QiEVFby9jd5EpmGIfDgPn7jb2j7+fvF9tWShXGWIRIC/P23F0evp9xAfTxcBxQVAnc5SqLPWrHkTViVqtTHM0ZoAyHuD8M2ex7wXHCHG7SXh7Gwu1pne9/Wln0dlL8JtecGUlaIio+DH3/Lno6kIaPqZxsZaHqd3b+CVVyo2ltqKVmtdwAOMgr1YbMwlxXuPmtoZR8cKnQ/p+em4++Au8pPzIRQK4e3tXauFHaFQaCi4AQAajQaFhYWIjY01hA9LpVJDuKk9IqVcJIeD0AGFukLEFcQhtSgVnjJPuEvdLQS7fE0+0lRpSC9Kh47ooBQrK13UM0XICeEudUemOhN3c+8iUBEINykN4dNqtfj111/xv//9Dzk5OQCAnj174oMPPgDHcRg9ejSuX7+OVatW4YMPPijxdQQCAby9vXHv3j24uroiwFr1xccFkYieT6bhvVqtUewrSfAzzVH6JFJQQHOCXbsGXL9Ob7OzLfv5+9MQ3DfftF390xZqNf1v4DggOJi2WlJ0Qq/X4/bt24iKioKnp6fduUCz1Fl4WPAQAfIAuEhdDNtlMhkIIYiNjYW7ewUK6DAeT1xdqQcenw5HLqcXsq0REgL89BMNyz1wAPj6a+rJN2VKdY6YwWAwGCVQZxUhjUYDpVJpNlFxeTTBq5Qqvvfu0YljUhIVj0pDKKSCHZ9Q3tvb8r6bW+3Nr1aXEQiMolT79ub7+N9CbZzQ8hX2yrowKQmdzrwKZHEBsLCQipe8kFeXw1WlUtuCryn5+ZYiIN8SEqjHRl1HrTaGAWq15ongefGOz/Po6GgU7kxvq8A25efn45///kFcfBwC3QINOe/qEmKxGGKxuMLe4BzHGUS+Al0BHuQ9QKoqFV4yL7hJ3aAlWqQVpiFdnQ6NXgOlWFmqd19lwXEc3KRuyNXk4n7ufah1atz97y5WrlyJ+/fvAwAaNmyIjz76CO1NbOzcuXPx8ccfY/PmzWjXrh26du1a4uvIZDI4ODggMjISTk5O1ethX9OIRNTz1fQ984KfqYdfdja9Na3Y+7h69hFCbTAv4l27RgWG4hdLJRKgaVPqYd2yJRAWVr6cX1ot/Q/Qamkaj3r1aDqJWjI/0Ov1uHPnDiIjI+Hh4VGmIj856hxkq7Oh1WsRjGC4So1ipbu7u8F7z6sqch8y6jZSKQ1Hd3amYbrx8XR+aO3ClYMDMH8+0KoVsHw5cOoUPWcPHnxyIyAYjyWEEGRlZdVI6hgGoyJUirinUqlw7do1pKSkQF9MCBs0aFBlvIQFb7zxBp599ll8++23GDNmDLKysvDxxx+jadOmePbZZ0t8bl5ensELAYBZSJYBd3fg4UN6XySiIh3frAl3rq5MuKuN1JJJe7UhFBq9rxgU/vMIDrbcl5RE8yvVMuyyUTwSCRVsxWK64FUoLL18a8C7Nzk5GVcjriI6MRr+nv6QS+uesFcVcBwHhUgBuVCOAl0BYvNikaJKgY7oDKKes8S59ANVAUqxEvfj7+OH3T/g+qnr0KRp4OLigrfffhuDBw+GsNh/XI8ePTB8+HBs374dc+fOxdatW+Ht7V3ia7i7uyM+Ph6RkZFo27ZtrQnPrhFKE/wKCoyefbXocyqTfTJFpaIhgLyQd/06zXVXHG9vo4jXsiUtblGOAjcG9Hr6ORYUUNGiXj06f6tFCzZe2Lt16xbc3NzKdCFEq9ciV5sLZ4kztHotYvJiAMAg8EmlUnAch9jYWHh4eLCFKsMSjqO5qpVKYx4+W/mKOY6G4zZtSsN0CalVBTbKbZ8YDBPS09Px33//wcXFBfXr14e7u3tND4nBsIsKzxYPHjyI1157DWlW8oVxHAddFeWZ69atG3bt2oWRI0di8uTJAIBmzZrh8OHDpRrxjh07mj2eO3cuwsPDzTs5OQEnTtCwznr1ypxnisFgMMqLXTaKp2nTii9+KxGdTof79+8jKioKOdocKN2UUEiY2FwcU5GvUFcIsUBcY6IeQBdEu3ftxpG/jkDP6SH1luL5Qc/j7eFvw8PF9sJt8uTJuHr1Km7duoXZs2djzZo1pQp2Pj4+iI+Ph4uLCxo9Dp6zlUlxwa9+fbp4rkUXquy2T/HxwJEjwLlz1LsnKoqKl6aIRECTJlTIa9WK3pYiENsNIcbK9u7uQPPmtj2SahBCCO7evYvIyEi4ubkZUgLYS6GuEImpiQhwD4CTxAm5mlzE5MWAgBhC7PkcmampqaUK8IwnGGdnGqbr5ETP2YIC28Jd8+bA+vVUCKwlYe1AGedPDIYNkpOTUZiWhsLCQqSkpCA4OBghISFwrGhKIQajiqnwDOe9997DK6+8gs8++6xaJwxnz57F6NGj8frrr2PQoEHIzs7GokWL0L9/f5w5c6bEcJ/z58+bVdS1KgZyHP2Dy8+vVVd3HxsIMSbv52/5EEKptNZNvhmM6sQuG8UjFNYar2G+Iiqf36mQFEJUJGJ5nkqAD9etKbRaLU6cOIFff/0VeY+K7LRp0wbDRgyD1E2KNKTBQeMAhdi64CCRSLB48WKMGjUKV65cwQ8//IB33323xNcUiURwd3fHnTt34OLiwkIFS6OWnT9226chQ4ALF8y3ububh9c2bVo1+QWzs6mwx4sVfn61MryZF/Zu3rwJFxeXMgt7hBCs37Ievx3/DaE+oZg9ezaUYiVyNbmIzaM5Zt2kbpBIJBAKhbh//z48PDwsvHAZDAN8GLyzs7HYhi1R3Nm51sw/eMo0f2IwrFBYWIi069cR+PAhxIGByHZ1xZ07d5CYmIj69esjMDCwRouKMhglUWEFJSUlBVOnTq32K4GTJ09Gr169sHLlSsO2rl27IiAgAD/++COmTp1q87mOjo5PVq6f6kanMwp2fNNo6HbTHHgiEW1iMXX956uo5uQYr+4LBMawQr4xsbV2wud64xeiHGfeGHZTF21USkoKbt26hYyMDPj5+UEv0CMnK8eieiOj9hAREYEtW7YgLi4OABDgH4BRo0chLCwMAK3AyRfaCFIEmSXqNyUgIACffvopZsyYgQ0bNqBt27bo3Llzia/t6OiI/Px83Lp1C46OjnUyH+OTit32qVs3WiyqQQOgUycq6Pn6Vu3/QV4eDcFVKKh4GBBguwJoDUMIwb179xAREQEXF5cye4RotVp8seQLHLh0AJyMQ1RUFE6dOoXu3btbFfjc3d2RnJyMlJQU+Pr6VsVbYjwucBzNZ2wapsun/ajl1MX5E6N2kR4bC0FUFByVSnAPHkCckQFlYCBSCcHVq1fx8OFDhIaGwsfHh6U5YNQ6Kizuvfzyyzhx4gRCQ0MrYzx2c/PmTYt8fp6envDz88Pdu3erdSyPJbwIZ+22uMedRmNedEQoNBfuFAoq3jk4UKFOLKYinemtUEiPXVRkLAShUtGQgJwc6kGZk0MLBvACkkhkFPx4b7+qFpH0etp4oZIfi0BgFLEEgsdLgNTpzEVaU9HWVKwVCumt6W+FbzzlKXbDH5P/TVlrTDysMUzDcDmOQ2BgIDiOQ7oqHSq9Co5iFsJQ20hKSsL2bdtx8RKteK9QKDB06FD06tXLLKRWwAngLnVHtjob93LvIUAfAE+Zp1VPzOeeew6vvPIKfvnlF3z66afYunVrqR55Xl5eiIuLw+3bt9GyZUs2SX7c+PJLYMQIWsW8qr0zVSpaJEkqpWkKgoIqXpG9CiGE4P79+4iIiICzs3OZhT2VSoWZM2fi9LnTkNWXoXmj5rh2+Rp27tyJDh06QC6XWxX4RCIRYmJi4OXlxbz3GKXj5EQ9X52dgTt3jGG6bM7FeEzRq9XIOHsWCq0WWk9PAIAwOxvyW7fg5+UFV39/pOTn48KFC/Dz82P5+Bi1jgqLe9999x1eeeUVnDp1CmFhYRAXy/vE58OrbIKDg3Hp0iWzbUlJSUhISEBISEiVvGaVwQtGhJjfmjZr+0y3Fa8KW5GKwcX/tE09sQQCo6AildI8G3I5zUlYXLDjb+2dBHAcPY61/IZaraXwl5dHQ2+Kiuh9vrogx5mLfmIx/Tx4QU6ns/x8rX3e1uCFO4HAGIpg67sxFbqKv09+v6kwaEsgLL6Pf1zavtIgxFKw4+/r9cZx8t+5WExvlUoq2Do4GD9nviKrQGAu6tnT+LGU1PR6o9hbWGj8PZiKjPzvzFRcNhWZefGRUWkUFBQgKioKMTExcHV1hVKpNOzLUmdBzIlZSG4toqCgAH/s+QMHDx2EVquFUCjEs88+iyFDhpQoLjhLnJGvzUdsXizUejV8HXwhFFgKA1OmTMHVq1dx+/ZtzJkzB6tXry4x/x7HcfD29jb8foKtFb1h1F2q49xXq6moJxQCISG0VWYF+iqAEIKYmBhERERAqVSa2U17yMrKwpQpU3Djxg04eDhg3Jvj0LVVV8yePRsPHz7Erl27MGrUKAAwCHwxuTEAjLn3kpOT4efnV9lvjfE4IhZTwdzZGbh5k4bp+vqy9DmMx5Lsq1dRdPcuZCEhhv8wnYsLdFotRGlpUGZnQ+Lnh3xPTyQmJiIlJQVBQUGoV68ey8fHqBVU2DJv3boVhw4dgoODA06cOGG2kOM4rsrEvXfffRfvv/8+3nvvPbz44ovIysrCokWL4OjoiNGjR1fJa9rE1JuLF454gYTfZurpBZQs8BQXe0zFJF6kEInMRQyh0Fx4MhXkKvNWIDAKd9X5x86/z+IhAYTQyb1KZRT/TL398vOpYGX6ORb/PPn3wgtU/GfMN76v6X3+luOsi7C2xFnTxv8u+N8KL1SZ/n7455r+fngvSVNht/i+4r8za5iGRotEVKxzc6O3Dg7Gz8JUvOMFvJpGo7Hd+CqTKpWlCAgYzzuhkAqFjHLBh+Gmp6fD19fXLP+ISqdCriaXheTWEnQ6HU6dPIVffv0F2dnZAICwsDCMGjUKAQEBdh1DIVJAxImQUJAAjV4Df7k/JELznDNSqRRffPEFRo8ejcuXL2Pt2rV4++23SzyuVCqFUqlEZGQknJyc4FqLErMzaik6HbXdeXn0vp8fFfXc3evEBZzY2Fhcv34dSqWyzOGD8fHxmDx5Mh48eAAnJyfM/HwmXEJcIBaLMWbMGCxZsgSHDx9Gjx494O/vD4AKfHmaPMTkxiDYMRhSqRT37t2Dl5fXk12tmmE/HEcFPT5MNy6Onm/s98N4nEhJQc7Fiyh0cIBj8XQOIhG0Xl4QFBRAdu8eRBkZkAUFIUcmQ3R0NJKSklg+PkatoMJWec6cOZg/fz5mzJhRrSE17777LiQSCVavXo2NGzfC0dERHTt2xKZNmyo/l0hGBr3lBRnAXJwDzMUfkciYK04spp5ofKEIU2GuuCBnqzGPI9twHP1crSXM5b291Grzz7K4OFebMRXtrN2WtM9WH8BSsJNIal1S5BLhx10Spp6JJYmALDdLmdDpdIiJiUFUVBQIIQgKCrLwzsvT5EGtV0MpLptHSkXQarW4eOEiUtNS0bRpU9SrV4+FnQGIjIzEls1bEBMbA4BWqh01ahRat25dZq9KqVAKV84VKaoUqPVqBCoCLYqBBAUFYfbs2Zg9ezbWr1+Ptm3bolOnTiUe18XFBQkJCYiMjES7du3YxJhhhE/XUVhIm05H/78dHGh4YEgI4OlZOy462QEv7JUnL1hkZCQ++OADpKenw8fHByu/WQmVqwoE9AJeWFgY2rVrh0uXLmHLli2YNm2a4Rx3FDsiT5OH2LxY+Cv8kZaWhqSkJLvFfQYDAA11b9PGGKar1VqPtmEw6hoFBSi6ehWZGRlwsFUhGoBeLodeJoMoKwvyiAiIvb3hGBCADK0WV69eRUJCAho0aMDy8TFqjAqLe2q1GsOGDav2HzDHcZg4cSImTpxYdS8ikdBJo15vDPHkvbyKi3TFt7E8YDWPQEBDhutyovayhtoyjHCcfSIgw25Mw3BdXFysLk4JIcgqyoJIUD1VcouKinDq5Cns278Pqamphu2Ojo5o1aoVWrZsiZYtW5Y59K260Gq1SE5OhkajMTStVmt+q9HS+1rLffx9a31UKhXu3bsHAJDL5Rg8eDD69OlTIW8dkUAEd6k7MosyEa2LRoAiAK4SV7Pvum/fvrh48SJ27dplyL/nUcJkGaCiY3x8PKKjo9G0aVMWzv2kolYbhTy12piuw8EB8PamooJCQVsdExV4YU8ul8PZ2blMzz137hymTZuGgoICNGrUCF9//TUcXBwQmRUJZ4nxWCNHjsS1a9dw/fp1XLp0Ce3btzfs4wW+hMIEOAoccf/+fXh7e1uk02EwSkQkAho1oufirVvGlDgMRl1FpwOiopAbG4tMiQRepa0bBQJo3dzAqdWQJCZClJUFUUAAnH19kZadjfPnz8Pf35/l42PUCBUW98aOHYsdO3Zg1qxZlTGe2oVUCnToUNOjYDAYjBonNTUVN2/eREZGBnx8fGx6V6l0KuRqcyEXVq2onp+fj7/++guHDh1CTk4OAMDJyQkNGjTArVu3kJeXhzNnzuDMmTPgOA6hoaFo1aoVWrVqhZCQkBq7opqVlYXo6GjcuXMHd+7cwf3796GpwsWRQCBAjx498PLLL1daBUEBJ4Cb1A152jzcz72PAocC+Dj4QCQwTik++ugj3LhxA3fu3MGcOXOwatWqEj0phUIhPD09cffuXbi6urJqnk8CfHgtn0eXv5AqkwE+PjRNBC/kOTjU6YtcvLDn4OAAlzLmBPzzzz+xYMEC6HQ6dOzYEUuXLoWjoyNSClOgh94s/6W3tzeef/557NmzB1u3bkXLli3NbDUv8OWIcxCdFI2QpBAEBgZW1ttkPEl4e1NPPr6QDYNRV4mLA4mJQRIhEEskds8PiUQCjY8PhLm5cLh9G+L0dIgDA1Ho7c3y8TFqjAqLezqdDkuXLsWhQ4fQsmVLiyuAX331VUVfgsFgMBg1hE6nQ2xsLKKioqDX6xEQEFDixCdfmw+1Tm3mTVKZZGVl4eCBgzh67CgKH+VM9PDwwIABA9CtWzdIpVJotVpER0fjypUruHbtGh48eIDo6GhER0fjt99+g7OzM8LCwtC6VWu0CGtRZZMunU6H+Ph4g5AXHR2N5ORki34ODg6QyWQQi8UQiUTGWxG9FYlFhu0l9Sm+TyQSISgoCD4+PpX+3jiOg1KsRJGuCAn5CcjX5sNf7m+ojiyTybB48WKMGTMGFy9exLp160r1tJfL5cjPz0dkZCSUSiWbDD9OlBReq1TS8Fq+WJNC8Vjl8oqNjcW1a9cgl8vLJOwRQrBx40asWrUKANCvXz/MnTvXMM/OUmdBLLD0unvhhRdw6tQppKSkYP/+/Rg8eLDZfkexI/KQh3Sk4+Kti/D29mah8IzywZ+vDEZdJT0diIpCHschs6AATo+iPBTXrgEA8sPCSo3E0ymV0CkUEGVkQB4RAZGPD6QBAcgjhOXje5xZvBj4/XcgMpLOZZ5+GliyhBYg4iEEmDcP+N//gMxMoFMnYNUqoHnzKhtWhWdP169fR5s2bQAAN27cqPCAGAwGg1E7KCwsRFRUFO7fv28zDNcUQggyijIsCi2Yoid6PMh7AJVOBT30IISAgBhu9URvdstvz8rOwvUb13H33l3o9XoImwgR4BKAJs2aIDAoEOCAU+mnIBFI4CnzhFc9L7za+FUMHz4c6enpuH7tOq5cvYKIiAhkZ2fj9OnTOH36NAQCARo0aICWLVuidevWCA4OLndIaH5+vsErL/pONKLvRkOlUpn14TgO/v7+aNiwIRo1amTIzVJXw1ClQincZe7IVGdClaOCv8IfblI3CDgBQkJCMGvWLHz66adYu3Yt2rRpgw6leMN7eHggPj4et27dQps2bVjC/8eBnBwaYiuVPhbhtWUhJibGEIpbFmFPp9Nh2bJl+OWXXwAAY8aMwfvvv2+4sFKkK0KhthAyoeVnJ5PJMGLECKxatQp79+5F165dLcLiHcWOIM4EESkR8L/rj45NO5b/TTIYDEZdRKWiwoxGgzSdDlqtFhKJBI6XLyNwxQoAQG7btkgaOxZaN7eSjyUQQOvhAU6lgjQ+HqLMTAiDgiD390dWbi6uXr2K+Ph4NGzYkOXje1z4+2/g3XdplKdWC8yeDfTpQ6uK8xc9li4FvvoK2LiRpjP4/HOgd28gKope0KwCKjxrPn78eGWMg8FgMBi1iIKCAly+fBkpKSnw8fGB1I6wm0JdIfI0eRZFFkw5l3oOfzz4o3yDCgCcAswFxkhEIjIx0mp3sUAMD6kHvB284dXYC8+1fg7DxMOQHkvFvqtXriI+IR63b9/G7du38euvv8LFxQUtW7ZEq1atEBYWBrmN3CuEECQmJhq98u5EIz4h3qKfg4MDQkNDDWJeaGiozWPWVQScAO5Sd+Rr8nE/9z7yNHnwk/tBIpSgf//+uHjxIvbs2YM5c+Zg69atJeag4TgOvr6+iIuLg5OTExo1alRnhU8GaIVNV1ejkCeX1+nwWnshhBhCcRUKRZly7KlUKnz66ac4fvw4OI7D1KlTMWLECLM+hdpCFOmLDJ6yxXnqqadw9K+jiIyKxPbt2/Hee+9Z9FFKlFA6KHE26iw8PT1Rz6Ne2d4kg8Fg1FUIoUVhkpOh9vZG6rVrUMjlEKWlwe9//zN0U16+DPnNm0gZPhxZPXuW+v9FZDJovL0hzMmB/NYtaFJTIQwKglNgINLT03HhwgX4+fmhYcOGZU7RwKhlHDxo/njDBsDLC7h0CejWjf7GVq6kot+QIbTPTz/RC5xbtwKTJlXJsCos7i1evBje3t54/fXXzbavX78eqampmD59ekVfgsFgMGqGoiLaHsHl5dXgYKqX+/fvIyUlBYGBgXZfYczT5EGj15TouXc+9TwAwEPqAQeRAzhw4DgOAggMtwBQWFCIlJQU5GTRfHpET+Di7AI/Pz84OzmDAwcBJ7B4fqG2ECmqFKSp0qDRa5BYmIjEwkSzMQg5ITzCPBDWMQyd9Z2Rl5iHhJsJiLoUhaysLJw8eRInT56EUChEw4YNDUJfQUGBWYhtnpXfg7e3Nxo2bGho/v7+j13VXq1Wi+zsbHAcBzeTq9kKsQISoQQpqhQU6grhL/eHk8QJn3zyCW7cuIG7d+/i008/xbffflviZyISieDu7o7bt2/DycmJ5d8ridpuo/z8anoE1Q4v7F27dg2Ojo5lEvays7MxdepUXL16FWKxGAsWLMBzzz1n0S9Pm2ewfdbgOA6jx4zGZ599hnPnzuG5555DkyZNLPr5uPog5mEM/on6BxKxBP7O/va/UQajNGq7fWI8ucTHA/fuAd7eyMrJQX5BAbxcXeG/YgWE+fm4JRRiiUCAzx0dEZCZCd+NG+H8zz9IfOMNqEubk3AcdM7ONFQ3MxOi7GwU+fvDy88PaoEADx8+RGFhITp27AjZY+y5Xlfh8vJoxAGPVGpfXtHsbHrLz4vv3weSkqg3n+mxuncHzp6tveLeDz/8gK1bt1psb968OYYPH87EPQaDUXdZvJjmSnjEk7JMzcjIQGxsLDw8POxPLEwIMtWZkApt/wE+LHiIpMIkCDkh3mn6joWHn16vx+XLl/Hnn38iOjoaAC0I0alTJwwcOBDBwcF2vwc90SOjKAMpqhSkFKYgWZWM1MJUpKhSoNarkaxKRrLqUf47OYD2gFd7LzhyjuByOGTHZyMzNhN30+8i6rco7Nixw+I1JBIJ6terjwYNG6Bhw4Zo0KBBmatg1iV0Oh1ycnJQVFQENzc3FBQUICcnxyxcWywQw03qhhx1Du7m3oWvgy+8HLzwxRdfYMyYMTh//jw2bNiAN998s8TXcnR0hEqlws2bN+Ho6Fhrqx3XOE+ojaqtEEIMobhlFfYSExMxefJk3L9/H46Ojli+fDnatWtn0U9P9MhWZ5doawEgJCQEvXr2wl9H/8KmTZswf/58izB3gUAADycPZKRm4PrD6wDABD5G5cHsE6M2kpVFw3HlchCpFKmpqRCJRPDevRvyO3egEosxTaPBQ50OL2Vm4jWZDG9ptZBHRaHerFlIGzwY6QMGlJ4bViSC1tMTgsJCyGJiIM7IgCgwEH4+PohLSMDdu3fRrFkzFp1Qy/DrWCxNxdy5QHh4yU8iBJg6FejaFWjRgm5LSqK33t7mfb29gdj/s3fe8W1WZ/++tGUNS7Zsy3vHdvZkpRACYZcyS5lh/Rh9maFQZgthlg1hU2hf2sDLDBBmgAQSSEjIjrPjveUhWXtLz+8PYRHHI14hg+fKRx870jPOI0vnOed77vv+1o1IW3tj2OKexWLpdVU9NTWVlpaWXvYQEREROUi4665YZ/0zzTt3wp6d/iFGNBqlqqqKcDiMdhCFsn0RH56QB62i7302WDcAUGYo6ybshcNhVq5cyeeffR5PbVUoFBw741hOPe1UzHveGAeAVCIlRZ1CijqFMcYx8ee7Jsa7i35tvjba/G0xp1/BBXqQjJaQPPqXqLSIM4LQJJDjyaE0v5RRo0aRl5f3m6gJF41GcTqd+P1+jEYjxcXFJCcn09HRwY4dO1Aqld1Wn6USKUaVEW/YS727Pma2kZvFnXfeydy5c/nnP//J5MmTexUudsdkMtHU1MS2bduYMmVKD8MuEX6TfdSBiiAI1NTUsHXrVvR6/aDcqXft2sVNN91ER0cHZrOZefPmUVxc3Ou23rAXf8SPXrF3wfvcP57LylUrqa+vZ+nSpb1GAer1ejwWDy67ix2KWIkDUeATGRHE/knkQCMYjAl7Xi9kZ+N2ubB1dpJZW0vKp58C8FRCAs2hEDOOOYYdO3bwRns7i4AHVCqmBgKkffABiT/9RMtVV+EvLNzrKaMJCURVKuQOB5pt25CbzWSkplJdXY3JZNonpmciQ6d59WoSdzfFGEjU3g03QHk5LF/e87U9xVtB2KtJy3AY9qwkJyeHFStWUFDQvVbHihUryPwNpmOIiIgcQuwRii38Btw7W1paaG5uHrSg5g65CQvhXt0bISaqbbRtBGCKaQoAgUCApUuX8sUXX2C1WoFYjboTTjiBk08+eZ/UI5FKpCSpkkhSJVFq+OXmLQgCrpCLNn8brb7WuPjX5m/DE/YgS5RBIrRL2ilKK8Kcbj7ghb1wNAyAXDq0dgqCgNvtxuPxoNfryc/PJyUlJX7dZrMZj8dDXV0dqampPd4PjVyDUqrEGrDij/g5+sSj+f3a3/P5Z5/H6+8lJSX1eX6JREJ6ejpNTU3o9XpGjx4trnDvyW+wjzoQ6RL2tmzZMmhhb82aNdx22214PB6Kiop47rnn+u1/fREfESEyoO+1Xq/nvD+exxv/eYMPPviAI444okcUrEQiQafT4Wx3kmJKYUf7DgQEshKzxO+byPAQ+yeRAwlBgKoqaG6GrNgCRmdnJ1Krlbx//QuAXRMn8uGmTWg1Gi677DJkMhmLlyzh448/5hq3m1OBO2Qy9A0N5M+di+3kk2k/91yEvaXXSqWEk5KQhEIoLRaSXS68SUns3LkTg8FAQkLCPr54kYEi6HQwiHs4N94In3wC338P2dm/PN8l2lossfrDXbS19YzmG0GGPTO56qqrmDNnDqFQiOOPPx6AJUuWcPvtt3PrrbcOu4EiIiIiIr8OgUCAyspKVCrVoKKkulJgldK+a+1VOitxhVxoZBqy5dl8/NHHfPX1V7hcLgAMBgOnnHIKs2bN2i+GExKJhERlIonKRIoTu0fMuENuGjwNfNfyHfWeepa3Luen9p+YnjadGekz0MoHHuH4a9Dh7+DHth9Z17EOpUzJH/P/2E3IHAhutxuXy4VOp2PUqFGkpaX1+pnIzc3F6/VitVpJS0vrIQbIpXJMKhOukIsqVxWX3ngp27Zvo6aqhnvvvZd58+b1m/otl8tJTU2lsrISg8FAVpYYUSRyYDEcYW/RokXMnTuXcDjMlClTeOqpp/aagu4MOgcl2M88bibffvct9fX1LFiwgMsvv7zHNjqdDovFgs/hw5hmZEf7zxF8osAnIiJyqNDSEjPRSE0FuZxQKERrSwvj330XucuFPzeXJyMRAI499ti4kdwpJ5/MMUcfzaeffcZXixaxMhzmVuA0QcC0aBH6tWuxXHklnvHj99oEQaEgZDYjt9nIbm+nyuWiMjmZcePGiX3twYYgxIS9jz6CpUthj0A3CgpiAt8338DkybHngsGYy+5jj+2zZg1b3Lv99tux2Wxcd911BINBANRqNXfccQd33XXXsBsoIiIiIvLr0NDQgNVqJScnZ1D7ecNevGFvn86N8EtKrtFt5NZbbsXv9wOQlpbGaaedxjHHHDMgR979gU6hY7RxNGWGMnY5d/FN0zc0ehtZZlnGyraVHG0+mqPNR/frEryvEQSBKlcVK1pXsMMRi7wBCEQD/G/F/3Jk6pGcln1av2YnQLyOnlqtpqioiLS0tH4LPsvlcgoLC/H5fNjt9l4j8bqE00AkQEeog+vuvY6/3/h3Vq5cyX//+99exYbd0Wg0+Hw+tm/fPug6ZiIi+xJBEKiurh50Kq4gCLz11ls8++yzAJxwwgncf//9e+0Dg5Eg7rAbtWzgRdjlcjmzZ8/m4Ycf5ttvv+W4447rUb9UIpGg1+tpbm4mNTUVjULDzvadRIUoOYYccdIpIiJycONywfbtsUjSnxeQ7Q4HKQsXkrhrF1GVio0XXMC6xx9HAsyaNavb7lqtlgvOP58TTjiBDz74gHuXL+dL4B4gvaOD3Mcfx3700bRddBGRvdUIlkgIm0zIHA7yOjtpXrkSU3IymeLi5cHF9dfHXG8XLgS9/pcaewYDJCTEUm/nzIFHHoFRo2KPRx6Jff4uumifNWtgldL7QSKR8Nhjj9He3s6qVavYtGkTNpuNe++9dyTaJyIiIiLyK+B0OqmqqiIpKWnAJhpduENuQkKoz2iSQCTA5s5YsfaNCzfi9/vJy8vj+uuv5/HHH+eEE044YIW93ZFIJJQaSrl+9PVcWnwpGQkZBKNBvm35lsc3P87i5sX4w/5ftU3BSJDV7at5dtuzvL7rdbY7tiMgUGoo5YpRV/C7tN8BsKp9Fc9vf55GT2Ovx/H7/VgsFgKBAHl5eUycOJHc3NwBOblpNBoKCwuJRCJ4vd4+t1PJVCSrktGmabn4louRGWS8/PLLbNy4ca/nMJlMeDwetm3bFl9IFBHZn+wu7CUmJg5Y2ItGozz99NNxYe/CCy/kkUceGVAf6I14CUaC/UZJ98bo0aM58ogjiUajzJ8/H0EQemyj0+nw+Xy0traiV+nRKDRUdFTQ4GjodXsRERGRg4JwOFZnz+UCkwmI9d/e778n/5tvAGi54go+3bQJgImTJvVZGiHFZOLP117Lgw89hHPcOM4D3gaigHH5cgruuIPElStjUV17IWIwIE9MxNTcTP3ixXh/zmQROUh4+eWYQ+7MmbG0267H7gZ8t98eE/iuuw6mTYOmJvj665gYuI8YsYJBOp2Oww47bKQOJyIiIiLyK9E1SfX5fKSkpAxq36gQpTPY2WckiSAIvLf8PcLaMKHOEJJOCZdffjmzZs06aKNBJBIJY4xjKDOUsc2+jW+av6HV18ri5sWsaF3BjPQZTE+bvlc3y+HgCDpY2baS1e2r8UZigppSqmSqaSrTzdNJVacCUGoopcxQxvu179Pub+elHS8xK2MWMzNmIpPICAQC2O12ZDIZWVlZZGRkoBtCXSSTyUReXh6VlZUolco+6xHKJDJS1CkcNf0odlbv5KdvfuLuv9/N/83/v73WWMzIyKCxsZGKigrRYU5kv9LVZ27ZsgWDwTBgN+dAIMDcuXP55ucJ5Zw5c7jkkksGfF53yI2AgFTSfQEmGo0SDodRKvsW/S648ALWb1jPjh07+GnVTxx51JE9ttHr9bS0tJCamopOE+sHdnXsAiDXmDvgdoqIiIgcMNTUQGMjZGbGjQw8TU2kv/giEkHAPmMGbVOn8v1//gPAib0YD+1Jfl4ed95xB+Xl5bz99tt81djI34Eil4usl14i8ccfsVx+OeGfxcS+iOp0aDIzCWzbRl1CAqVnnon0IFjsFmFAAi4SScxpd29uuyPIgV0NXERERERkn9Pe3k5DQwOpqamD3tcb9uIJeUhU9oxa6ejo4PXXXsdSYkGtVaPt0HL7I7cPyf32QEQqkTIuaRxjjGPY0rmFb5q/od3fzldNX7G8dTkz0mdwVOpRe02FHSiCIFDvqWdF6wq2dG4hShSAJGUS09OmMy1lGgnynkWZRxlGMWfsHD6u+5jyznK+af6GHfYdnGQ8CaPCiNlsJjMzc1C1wnojKysLr9dLS0sLaWlp/UaAJioTufz8y6lvqKe1ppW/P/R35j3ef/09mUyG2WymsrKSxMTEQaePi4iMBIIgUFVVxdatWwcl7LlcLm699VbWr1+PXC5n7ty5nHLKKQM+b1SI4gw5eyykBAIBbDYbEomEpKSkPiMAU1JSOOMPZ/DBgg94+523mTR5Uo/IXK1Wi8ViobW1lYKCAnSqmMBX0VGBXConM1E0yhMRETmIaGuDXbsgORm6Fh2jUWQPPIDK4SCQmYll9mxWrFiB1+cj3Wxm/PjxSH0+5DYbEb2eSD9jowkTJjB27FhWrFjBje+/zxl2O/8P0G/ciPr227FeeCGdxx8P/YxtBI0GTX4+znXraNPpSD/+eBhA1oSISG8MOy1XREREROTgJRwOU1VVhVQqHVAK5p64gi6iQrRbSq4gCCxdupS77rqLbbXbUOXEJps3nXXTISPs7Y5UImVC8gRuGXsL5xecT4oqBU/Yw5eNX/L45sdZ3rqcUDQ05OOHo2E2WDfw4vYXeXnHy5R3lhMlSoGugNlFs/nr+L9yTPoxvQp7XWjkGi4svJA/5v4RpURJg7eB+Zb52Aw2SktLhy3sAUilUvLy8jAYDNhstr1un6hN5PrLrkeZqGRj80ZefftVokK0333UajVarZbt27fT2dk57DaLiAyG3YU9o9E4YGHParVy9dVXs379erRaLc8///yghD0Af8SPP+zvFhHscrmw2+3k5OSQnZ2NzWbrN4X2tN+fRlpaGlarlc8+/azXbRITE2ltbcXj8QCgU+lQK9TsbN9Ji6tlUG0WERER2W94vbE6exIJ7JaREPnvf0lYv56oQkHTDTcQVan4ZvFiIFZrTxqNIrPb8efmgiCgsFhiqb19IJPJmDFjBo88+SSO887jCqWSTYAiGCT9P/8h/d57UTY19dtUpV6PkJFB2+rVeFesALd7RN4Ckd8eorgnIiIi8humqamJlpaWQafjws8puaFO1PJfRMHOzk6eevIpXn/9dXw+H7lH5yKRSsjT5ZGSMPhzHExIJVImmyZzy7hb+GP+H0lWJuMOu/ms4TOe2PwEK9tWEo72PUDcE3fIzZLmJTy2+THerXmXRm8jcomcqaap3DTmJq4tu5axSWN7pOj1RiQSwW63kx3N5sayGylNLiUYDfK/5f/LP5b9A4ffMZxLj6NWqykoKEAmk8WdkPujIL+Ai868CCEi8O7id1m8fjGBSKDffZKSkggEAmzfvj1uzCIisq/ZU9gbaPq6xWLh6quvprKyEpPJxGuvvTakMjbesJdQNIRCqiAajdLR0UE4HKakpISioiJycnIwGAzY7fY+j6FUKrno50Len3/xOa2trT220Wg0+P3+bq8lqhKRy+TsbN9Jm7tt0G0XERER+VWJRGDnTrDZIC3tl+c3b0b68ssAtFx8MYGcHHbs2EFjYyMqpZJjjjkGRUcHIbMZf2EhnvHjCaano2hvR+Z09ntKlUrFGWecwY3PPMO7J5zAExIJHiCpro68u+5C8/bb/YqEiSYTVo2Glk2biK5dC+ICpsgQEMU9ERERkd8oXq+XyspK9Hp9nzXS+sMT9uANeUmQJSAIAitWrODOO+9k46aNyOVyLrjgAlImxwS9KaYpI938AxaZRMa0lGncOu5Wzsk7B4PSgDPkZGH9Qp7Y8gQ/tf/Ur8jX7G3m/Zr3ebT8Ub5p/gZXyIVeoefEzBO5c8KdnFdwHpmagaXHRaNR7HY77e3tJCQkMHbsWKZPms4jpzzCZZMvQy6Vs7pxNTd9dhNrG9eOyPUbjUYKCgrwer0EAv0LdQDHH388h086nKAjyAvzX2Bj80bq3fW0+lrp8HdgD9pxh9z4w37C0TCCIGA2m7FYLOzatYtotP9oPxGR4TJUYa+hoYGrr76a+vp6MjIy+Ne//kVJScmQ2uAMOVFIFYRCIVpbW9HpdIwZM4aMjAwkEgkqlYrc3FxCoVC/ovfUqVMZN24coVCIt//v7V63MRgMWCwW3LtFjxjVRiRI2Nmxkw5Px5CuQURERORXoa4OamshPT1eZw+nE+6+G0kkQtvkyTiPPx6AxT9H7U2fPh1jOExEq8Wfnw9yOVGtFl9JCb7Ro2NRfK2t/Qp0EIt+nn3ZZUx7/HEenjCB5YBcEMj74guSb7oJtm7tdT+JREJySgoN0SjW6mpYvx56WYAREemPEam598MPP/Dqq69SVVXFBx98QFZWFvPnz6egoICjjz56JE4hIiIiIjLC1NbW4nQ6h1y7zBl0EiWKy+nijTfeYO3amDhUWFjINddcgyxZxvJty5FJZIxPGj+STe+VcDiMw+FAJpOh1WpRKBT7/Jz9IZPKODz1cKaYprCmYw3ftXyHI+jgo7qPWNqylFmZs5hsmoxMIiMcCbOtcxs/tv9Irac2fozMhEyOSD6C0frRSJEi+AUcXkc89S4ajcbFrWg0Gn9+99Q8vV5Pfn4+KSkp3UTcs8eezaTMSTyz/BnqHfU8tPQhTh51MldMvaJbNOZQMJvNuN1uGhoaSEtLQyaT9bmtRCLhyiuvpKamhtaaVt747xv8v2v+H8LP/wCkSJFJZcgksYdSqkSil7Bm5xoCigAFuQXIpXIUMkXsIVWIhhsiI4IgCFRWVrJt27ZBCXvV1dVcd911dHR0kJuby0svvUR6evqQ2hCKhnCH3IT9Yaw+K5mZmeTn5/eor5eSkkJ2djZ1dXWYzeZea1hKJBJmz57N3Xffzdp1a9m8eTPjx3fvnxMSEnA4HFgsFoqLi+PPJ2uSsXqtbG/fzljJWJI1yUO6HhEREZF9htUaq7NnMECXyZAgwIMPQksLvpQUWq64AqVEgs1mi49dT5kxA0Ih/KNGEdVqfzmeTEYwI4OwXo+qoQGlxUJUqyWyl7IM6enpzP7rX6nYtYvn//lPLmltxexykfroo2yZMQPFVVf9Ijz+jFKpRJ2QQLXfj9ZuR7NhA4wdC2KNYZEBMmxxb8GCBcyePZuLL76YDRs2xFfpXS4XjzzyCF988cWwGylyYNDa2kptbS0FBQWkpqaKEycRkYMYm81GbW0tJpNpSN/lSDSCPWhn26ZtvP3G27hcLmQyGWefdTan/+F05HI5XzTE+v8yQxkauWakL6EbXUXlU1JSCIVCOBwOwuEwCoUCjUaDWq3eb32WXCrnqLSjmJYyjZ/af2Jpy1I6g518UPsBixsXU5xQzC7vLpyRWMqHBAml2lIOMx5GpjoTmUyGRJCAJFbXTi6XI5PJ4r9LpVKkUikymSz+/O6PpKSkPoXOgqQCnjztSd7c+CafbP+Eryq+YrNlM3N+N4eSlKFFGEFMQMjLy8Pn89HR0bHXWosajYYbb7yRuXPnsmn1Jr7P/Z6zzjor/npUiBIRIrFHNIIn7CEiRHBKnXy3+TvqffUYEg3IpXJkUhkKqQK1XI1ariZBmYBCGhP9NAoNWqW274aIiOzGUIW9HTt2cP311+NwOCguLubFF1/EtBfXxP7whDxYrBaMciPFxcVkZmb2aT6TnZ2Nw+Ggs7Ozz3NmZWVx4oknsmjRIubPn88jjzzSI3rbaDTS2tqK2WzuVlvQpDHR7mlnR/sOxprHYlAbhnxdIiIiIiOK3x+rsxcKwe4mce+9B999hyCXs/mii9AnJQHw7XffEYlGGVtaSr5Ohz8np0+H26hOh6+khIjRiKquDkVrKyGT6Rejjj4YVVJC8RNP8M3y5aT8978c7/cz4fvvqY9G8Vx9dQ+zDYPBgKW1lTqfjxKdDtnGjRAMQkFBv8YcIiIwAuLeQw89xCuvvMKll17KO++8E39++vTpPPDAA8M9vMgBQiAQYNeuXbS2ttLW1kZeXh6FhYUDHuiKiIgcOESjUaqqqgiFQmi1QxM6mq3NvPrfV1n7w1oQIDc3l2uuuYb8/PzYOYQoG20bgX2fkut0OvH5fOTl5ZGbm4tMJsPj8eB2u7FarbhcLhwOB1KpNC70DSUNeThEIhGC/iBlsjLy0vLY4tvCGuca7GE7a12xVWOdQsdxBcdxUuFJpOpiCygSiQSpVNrt50ijlCm5cuqVTMuaxrwf59HsaubOr+7kT+P/xHnjzkMm7Tvqrj8UCgUFBQX4fD7sdjtGo7Hf7fPz87nsssv417/+xYIFC8jNzWXKlNhnRyqRIpVIUaCA3ZqTpEqivb0db5uXnOQcZIpYFGQ4Gsbhd2CNWokIESRIEBAwqA1Mzpw8Yg7GIocu0WiUyspKtm/fPihhb+PGjdx88814PB7GjBnD888/j8EwdAEsFApR1VCFWqVmbNlYkn6elPaFUqkkLy+Pbdu24fP5SEjo3WjnnHPOYeXKlTQ3N/PNN99w6qmndntdrVbjcDhoaWnpYRySqk2lzdPG9rbtjDWPRa8amLGIiIiIyD4jGo1F7LW1dY9027EDnn0WgIZzzyXyc2mEUCjEd99+C8CZhx1GKC2NQE5OPJrO7/f3NJrbLYpPXVeHoq1tQFF8EomE8cccQ/ioo5h/++3Mbm8nd/lyrOEwbdde20MgNCUnY7FYMBqNZOj1sGULBAJQUrJXMVHkt82w5d+dO3cyY8aMHs8nJib2W9RX5OCitrY2LuolJydTVVXFypUrqampIRQaugukiIjIr4/FYqG5uZm03YsMD4Jly5ZxzY3XsHHTRmRSGWeeeSYPPPBAXNgDqHJW4Qw50cg0lBpKR6jl3YlGo7S3txONRiktLaWwsBC5XI5EIkGn05Gens7YsWOZNGkS48aNIzMzE0EQsNlsWCwW7HY7wWBwn7RNEAT8fj+dnZ1YLBY6OjqIRCKkpKQwfsx4rj72al475zVmT5rNETlHcN0R1/Gvc//F/zv8/5GTkoNarUalUqFUKuORevs68nBC+gTm/X4ex+QfQ1SI8k75O9z51Z00O5uHfEydTkdBQQHBYBCfz7fX7Y877jhOmHUCgiDw8ssv07QXhzkAk8lEZ2cnDfUNKKQKNEoNiepEkjXJpOnSyNBnkK5PJ02bhsPvGDHzEJFDl2g0SkVFxaCFvZ9++okbbrgBj8fDlClTeOmll4Yl7Lndbpqam9CkaBg3etxehb0ukpOTycrKwm6391mTUqPRcN555wHw4Ycf9jpmNxqNtLW10dra2mOsl6pJxRV0sb1tO+6g6OwoIiKyn2lqgpoaMJt/iXDzeOCuuyAUInjUUVRPmxbvz9esWYPD6aQoMZExRx6Jv6AgLpw5nU46Ojqor6/vVnu0i6hOh7esDF9JCUQiyAdQiw9ALpdTfO+9PKbREAZMq1aR/eyzSPaoT9yVdVJXV4dLIoHk5JhByJYtsSg+EZE+GLa4l5GRQWVlZY/nly9fTmFh4XAPL3IAYLPZqK6uJiUlBalUilqtJjc3F6lUysaNG1m7di1tbW3dajyJiIgcmAQCASoqKuLC0WBwuVzcd9993PrXW3FFXZiTzdx7772cd955PSLh1tvWAzAheQJy6civMnYVldfr9YwZM6bfWlZqtZqUlBSKi4uZPHkyEyZMoKioCLVajdPpjItvPp9vWP1YMBjE4XDQ2tpKa2srfr8fvV7PqFGjmDhxIlOmTKGsrIz09HR0Oh1alZZzx53LXcfexUmjTkIlV+39JPsYnUrHrUffyi2/uwWNQkOFtYJbPr+Fryq+GvJ7k5qaSm5ubjxVem9cMvsSysrK8Pl8PPPMM3g8nn63l0qlmEwmmpubaW7uW4iUSWVIJVLR7VOkX7qEvR07dpCUlDRgYe/7779nzpw5+P1+jjrqKJ577rkhZzcIgkB7ezsul4vC0kIycjNI0g1M2OsiOzubpKQkbDZbn9vMmDGDwsJCfD4f77//fo/XVSoVMpmM7du3s2HDBiorK7HZbITDYSQSCWatGYffwfa27XiD3kFfp4iIiMiIYLfH0nG1WuiKthMEeOQRaGiA9HTqLr8ciVSK/OcawN988w0a4Njp0wkWF3ers9dVUqG0tBSfz0d9fT1e7x59nExGMCsL77hxhFNTUba3I+1FCNwTo9HImNtv5w6pFD+g37SJ3McfR7rHWCcxMRG/3099XR0RlSomWlZXw6ZNsGdbRER+Ztji3rXXXsvNN9/MTz/9hEQiobm5mbfeeovbbruN6667biTaKLIfCYfDVFRUEAqFegxSjUYjmZmZ2Gw2fvrpJzZv3tzr6oaIiMiBQ0NDA1arddD1n1auXMn555/P559/jkwj4/hTjuf+v91PUVFRj22DkSBbO2NuYJNNk0ek3bvj8XiwWq1kZWUxevRoEhMTB7yvXC7HaDSSk5PDxIkTmTRpEmVlZSQnJxMIBOKlB1wu116FqHA4jNvtpr29HYvFgtPpRKFQkJeXx/jx4+MRg1lZWRiNxl89FXg4HFtwLPNOn8d483gCkQAv//QyDy99GLvPPqTj5eTkkJaWRkdHx15FQrlczo033ojJZMJisfDSSy8RiUT63UepVKLT6airq+s3a0Cv1GPz2UQhQqQHgiBgtVopLy+PC3sDLVvw9ddf89e//pVQKMRxxx3HU0891TOda4CEw2EaGxtRKpVMmzaN9Nx0wkJ40OK/QqGIR1P3mJT+jFQq5dJLLwViEdm9LdYnJSWRlpaGVCqlqamJ8vJyNmzYQE1NDQ6Hg2R1Mp2+Tna078Af6tulV0RERGSfEAzGhD2fD3aPbl64EL76CmQy/PfeS0cwGC8xUFtbS01lJWaplAnnnNOtzp7X60WlUpGTk8OYMWM46qijKCoqwuVy0djY2MONPKLX4y0txVtSgiQcRt7WttcovuKiIoqvuILrAReg2bWLvIcfRr7H+MVkMtHW1obFYgGVCrKyoLERNm6Muf+KiOzBsMW922+/nbPOOovjjjsOt9vNjBkzuOqqq7j22mu54YYbRqKNIvuRpqYmmpub+yyGLpfLSU9Pj6fqrlq1itraWjFVV0TkAMTpdFJVVYXRaOyzGPueeDweHn74YW688Uba2trIycnh4ace5vQ/nI5a1fvkdat9K8FoEJPKRK42d8Ta3zX59vl8FBcXU1xc3MMtcjDsnr47ZsyYbmKc5GcXta703UAgQDQaxev1YrVasVgs2Gw2BEGI7z958mQmT55Mfn4+JpNpyJP7A4VUbSr3n3A/V0y5ArlUztqmtdz82c2sblg96GPJZDIKCgrQ6/X9RhJ1YTAYuOWWW1AqlWzatKnXqKI90el0RKNRqqurewy+u0hQJOANebH77YO9BJFDlGg0SltbG+vXr2flypXU1dWRkpIyYGFv4cKF3HPPPUQiEU499VT+8Y9/DDoquguv10tTUxNZWVkcdthhpKenY/Pahhz93LWQ4XA4+hTIi4uLOfroowF4c/6bvabxSqVSdDodZrOZ1J+L1NfX17Np0ybKN5Xjt/qpslSxrXUbgXCgx/4iIiIi+wRBgMpKaGmB3TM4KivhiSdiv//P/2DLzu5Wg/Sbr78mHcg87DA0EyZ0c6212WxkZmbGF44NBgMTJkzgyCOPJDc3l87OTpqbm7uXdZHLCWZnx6L4kpNRdHT0iMTbk5kzZ2I6/niuBqyAuqGBvAcfRNHautth5Wi1Wurr63G5XLG04exsaG+H9etjzsAiIrsxIpYrDz/8MB0dHaxevZpVq1bR3t7Ogw8+OBKHFtmPuN1uKioq0Ov1e4046UrVlUgkbNiwQUzVFRE5wBAEgerqanw+34BrQK1du5YLLriAjz76CIALLriA+W/NJ60grV/32/XWWEruZNPkEasTFw6HaW1tRa1WM2bMGLKzswcsUA6UrvTdoqIiJk2axIQJEyguLkatVuN2u2lrayMYDGI0GiktLWXSpElMnjyZUaNGkZaWhlarPeRcxKUSKWeOOZMnT32SPGMejoCDR5Y9wourXsQX2nsNvd1JSEiIl+sYSJR3fn4+V111FQCfffYZP/744173MZlMOJ1O6urqehUpJBIJKpkKi8tCVOi9FpnIb4NwOExLS0t87Nrc3ExSUhLZ2dkDFubfeecdHnzwQQRB4JxzzuH+++8fcoRuR0cHdrs9vtCg1+sJRoLYfXY0iqG7jWdlZZGSkkJnZ2ef21xwwQWo1WoqqypZsWJFv8eTyWTo9XrMZjMmkwlBEKirrcNSaWHpmqUsXr+Y1vbWvUbbioiIiAyblhaoqoo543b1vT4f3H13zIDiqKOIXHwxra2tcWHP5XKxa+VK3MC02bO7GVQEAgGkUinZ2dk9TpWcnMykSZM44ogjSE9Pj2dt7J7lEdHr8Y4eja+4GEkohLy9HfrpC2fPng3FxVwJtMhkKNvayH/wQVR1dfFt9Ho9gUCA2rq62Lmk0lgEn9sdE/gsluG9hyKHFCM2M9JoNEybNo3DDz9cdFA9BBAEgcrKSlwu14ALOMMvqbpWq5XVq1eLqboiIgcI7e3tNDQ0xKMu+kMQBObNm8ef//xnWlpayMzM5JVXXuG2224jLA8TiARQy3qf/DqDTiqdsdSuyckjk5Lr9/tpb28nLS2NMWPGkJycPCLH7Y+u9N3s7Ox4+m6XmDd27FgyMjJITEw8qFJth0N+Uj5PnvokZ40+CwkSvqn8hhs+vYEf638c1CJOUlISeXl5uN3uAZmZTJ8+ndNPPx2A119/ndra2n63l0gkmEwmWlpa+qy/p1fpcfgduAKuAbdb5NAhGAzS0NDAqlWrWL16NVarlbS0NDIzMwcVbfvvf/+bJ598EoBLLrmEu+66a0gLDl1puDKZjClTplBaWopCoQDAHXDjDXtJUPTueDsQ5HJ5vE5yX/UrjUYjZ511FhATLPtK4+3t2Hq9nvT0dFJMKWglWtbtXMeCpQv4fvn3VFVV9WvqISIiIjJkbDbYtg2UStDstgDyxBOx2nQpKXD//ThcLpxOJ7qfU3J/XLIEwmFCRUWMOeKIPQ5pw2w29znOlEgkpKamMnXqVI444oh4+ZDW1tZfRD65nGBODt6xYwknJaFoa+szik8ul3PzzTfjNhq5PBKhISEBucNB3iOPkLBzZ3w7U0oKHe3ttHQJeRIJZGTEHILXr4f6+lgUo8hvniHNSv7yl78MeNunn356KKcQ2c9YLBbq6+uH5KYpl8vJyMjA5/NRVVVFW1sbxcXFZGdn/2YmwiIiBxLhcJiqqiokEsmAJq+LFy9m/vz5AJx99tnMmTMnnqLmDDhBEovo6o2Nto0ICORp8zCpB1fXrzfsdjuhUIiCgoL91odIJJIBp+gdyihkCi6fejlTs6bywqoXaHW38vj3jzMlcwrXHHYN6fq+TU12JysrC6/XG3ds3psgct5559HQ0MCmTZt45plneOCBB/qNPlUoFOj1empra9FoND0G6UqZkmA0iM1rw6AeupOpyMGF3+/HYrFQW1tLZ2cnCQkJpKenD7pPEQSBF198kTfeeAOAa665hquvvnpIUbs+n4+2tjYyMzMZPXp0j8+1M+BEEARkUtmgj707BoOB3NzcuJlSb9d8yimnsHTpUiwWCwsXLuTCCy8c1DnkcjnJxmQSExNp97ZTY6/BarWSoEogKSmJzMxMkpKSSExMPOQinEVERH5l3G7YvBn8/pjI1cWXX8Inn8Si2x56CJKTsVZUIAgCcpmMSCDA5m+/pQI4/8ILu/VF4XCYSCQSz0TrD6lUitlsJiUlhdbWVmpqamhpaSEhIYHk5GSkUimRxES8o0ejNBpRNzQg9XoJJyeDrHt/bjQaufmmm3j44Ye5xOfjTZOJHKuV3Mceo+nGG3FPnoxcJkOn19NQX0+iXv/LvSIlJWYmsnFjLFKxqOgXp2CR3yRD+utv2LBhQI+NGzeOcHNFfg38fj+7du1CoVAMq2ZUQkICOTk5AKxfv541a9bQ3t4upuqKiPzKNDc3Y7FYBhS15/F44osyV111Fffcc09c2ApFQ9hDdhJkfUeRbLBuAIZvpBGJRGhtbUUikVBWVkZ+fr64OHCAMD59PM+d/hznjTsPuVTO+ub13PTZTbxb/i6hyN7rrUokEvLz8zEajVgHUC9GJpNx3XXXkZ6ejtVq5fnnnt+r2UlXinRNTQ0+X8/0Ya1Ci8VtGVB7RQ5uPB4PlZWxdNP169cTDAbJzs4mNTV10H1KNBrliSeeiAt7c+bM4ZprrhmSWNXZ2YnVaqW0tJQpU6b0EPYEQcDqtaKSjYyLdnp6OqmpqX3WvJTL5VxyySUALFq0qF/n6f6QS+WkaFLwq/zIkmUYjAZcLhcbNmxgxYoV/PTTT9TV1eFyucTxoIiIyODx+2HLFujs7F5nr64O/vGP2O9XXQXTpuHz+eiwWmNGGoJA1YoVbO7spEOv5+RTTul22M7OTkwmEykpKQNuikwmIzMzkyOOOIKpU6eiVqtpaGiI12TuiuLzdEXxtbf3GsU3atQoLrvsMtzABVYrTYWFSEMhsp99FsPy5QDotFpCoRD19fWEdh8DGY1gMMDWrTFjkb2Mj0QObYY0U/ruu+9Guh0iBxC1tbVYrda4MNdFQ0MDSUlJg0q7lkgkJCUlodfraW9vx2q1kpeXR2FhoRgJIyLyK+D1eqmoqECn0w1oIvvPf/6T9vZ2srOzufzyy7u95g658Uf8JKt6T1eweC20+FqQSWRMSJ4w5DYHAgFsNhspKSkUFBSIpR4OQFRyFRdPupiZBTN5Zc0rbLZs5u3yt1lWs4xrD7+WiRkT+91fqVRSWFjItm3bcDqde3U81mq13HLLLcydO5cdO3fw5vw3ufyKy/vdJzk5mdbWVmpraykpKUG222q5TqmjzdOGw+8gRTvwgbzIwYPD4aCpqYnGxkbcbnc8em2oUWORSISHHnqITz/9FIlEwp133sm55547pOO0traiUqmYMmUK2dnZvbbJE/LgCrjQKUem/5PL5fGUeLfb3Wu/2lV6YMOGDbz55pv89a9/HdL7pZAqMCgMWHwWpFIpmaZMUlJS8Pv9dHZ20tLSglqtxmQyxWsCDsccSURE5DdCOBwTsJqbY8YSXf1TIAB33QVeL0ydCv/v/wGx7A+/z4chMRG5w8E3K1eyAzjrrLO6BbBEo1F8Ph9jx44d0kKyXC4nJycHs9lMS0sLVVVV1NfXYzAYMBgMRAyGWBRfSwvq+nqkfn8sim+3/vW4446jurqa75Yu5eLmZt6ZOpX0devIfPVVZC4XtlNPJdlkiqXnNjeTm7ubYZ1OF6sdWFkZE/uysoby7oocAohxmyLdsFqtVFdXYzKZuqVKLV26lHPOOYczzzyTzz77bNCrrV2pukajkcrKyrgj3d6iL0RERIZHbW0tDodjQLUzKysreeeddwD461//2iNy1xF0IJVI+0zJ7TLSKDOU9Wu40R8ulwu73U5OTg5lZWWisHeAk2XI4oFZD/CX3/2FJHUSza5m7ltyH08tfwqbt39X3MTERAoKCvD5fH2623Y7V1YW//M//4NEImHxksV8++23/W4vkUhISUnBYrHQ2NjY7TWZVIZUIqXd0773ixQ5aBAEAZvNRnl5OT/++CM7d+5EqVSSl5eH0WgcsrAXCoW45557+PTTT5HJZNx///1DEvb8fj+NjY2YTCYOO+wwcnJy+myTO+AmEAmgko+c6KXX68nOzsblcvU5/rr44ouRy+WUl5ezYcOGIZ9LKVOSqEyk2dtMi7cFQRBQq9WkpaWRm5sbX/RdvXo1y5cvZ+fOndjtdjGaT0REpHeiUdi1C2pqYqm4u6e3Pvts7DWjMZaOK5MRiUSw/GzEJvP7aWtq4sMdO3BLJD36b4fDgcFgGFI5qt3put9Mnz6diRMnIggC9fX1OJ3OblF8UbUaeUdHjzp5l156KaOKi3H5/fy5pYXWE04AwPx//0fq++8jl0rR6/U0NDRgt9u7n1ytjgl8DQ2x90rkN8mwc5weeOCBfl+/9957h3sKkV+JcDhMRUUFkUik24TabrfzyCOPIAgCDoeDuXPn8tlnn3HXXXeRl5c3qHN0pera7XbWr19Pc3MzxcXFpKSkiDVYRERGGJvNRm1tLSaTaa/fL0EQeOyxx4hEIhx33HH87ne/6/Z6MBLEGXKikfUu2kWFKBttG4GhpeRGo1FsNhsymYySkhLS09PFPuEgQSKRMKNgBlOzpvLWprdYtGsRP9T+wLqmdVw08SJOLTm1z5phZrMZj8dDXV3dgNIkp0yZwrnnnssHH3zAf/7zH7KysigtLe1ze7lcjsFgoKGhAa1W2y3dRqfU0eHtwBfyDcuwQGT/E41GsVqt1NfXx90Lk5KShj1Rg5ggd+edd7J8+XLkcjn/+Mc/OO644wZ9HLfbTWdnJ8XFxZSUlOy17IndZ0cmGV6tvd7IyMjA6XTS3t6O2Wzu8Xp6ejqnnnoqn376KW+++Sbjxo1DqVQO6VxKmRI9epq9zUglUtITfunXExISSEhIIBqN4nQ62bZtG5WVlaSlpcWj+YZ6XhERkUOQmhqoqIC0tJiJRhdLlsD778d+f+CBmHMu4HS5cDocJBsMyGw2FpSX0wIcc/TRPdxwXS4XEyZMGLEIYrVaTVFRERkZGTQ0NFBXV0d9fT3JycnojEZ8paWoKyqQt7URTk2N18lTKBTcdNNN/P3vf6ehuZn7MjK4/7zzML//PimffILM5cJy+eV4vV7q6+vRarVxAyYAkpOhvT2Wsmwaft1rkYOPYUfuffTRR90e7733Ho899hhPPfUUH3/88Qg0UeTXorGxkZaWlh6D4UcffRSbzUZhYSHXX389KpWKtWvXcsEFF/Daa68NyPFwd7pSdXd31d2yZcuA3dlERET2TjQapaamhlAoNKDot88//5wNGzagVqu59dZbe7zuDrvxh/191n+qclXhDDlJkCVQZigbVFvD4TBtbW1oNBpGjx5NRkaGKOwdhGiVWq457BoeP+VxRplG4Q15eX3t6/x10V+p6Kjoc7/c3FxSU1OxWq0Dito588wzOeKII4hEIjz33HN0dHT0u71Go0Emk1FTU9PtPqNRaPAGvXT6Ogd+kSIHFJFIhJaWFtasWcPKlStpbm6Ou1yPROkPr9fLnDlzWL58OSqViqeffnpIwp7dbsfhcDBu3DjGjRu3V2EvFAlh89nQKIYWAd0fMpmM3NxcVCoVLlfvjtFnnnkmSUlJtLW1sWjRomGdTyVToZVrafI20eZr6/G6VCrFaDSSm5uL0Wikra0tHs1XUVEhRvOJiIhAU1MsHddggISE7s8/+GDs98sug+nT4y9ZOzoQBIEEux1XYiL/XroUgD/96U/dDu1yudBqtaSnD8wUbDBoNBpKS0s56qijKCkpwefz0dDQgEehwFdSQsRgQNHW1i3SLikpiZtuugm5TMaadet4A2i54goEiYSk774j64UXMOn1dHR00NTU1P2ESmXsWEOsmSpy8DNscW9PE40tW7bQ0tLCrFmzuOWWW0aijSK/Ai6Xi4qKChITE7tFTnzzzTcsXrw4noZyxRVX8O6773LUUUcRCoV49dVXufDCC1m3bt2gz9mVqmswGKioqGDdunUDKq4uIiKyd7pSEQcSueJyuXjuueeAmIlGbwMcR9CBVCrtU3TrSsmdkDwBuXTgQeFer5eOjg7S09MZM2YMRqNxwPvuiSAIRAUxFWF/U2wq5tGTH+Xaw69Fo9BQbavm9kW388rqV3AH3D22l8vlFBYWotFoeqaZ9IJEIuHqq68mNzcXh8PBvHnzCAQC/e5jNBrxer1UVVXFzyGRSFDKlLS6W0Xx4CAjGo3S2NjIypUrWb16NR0dHaSmppKZmTksI7Ddcblc3HDDDaxduxaNRsNzzz3H9N0mjgPFarXi9/uZOHEiRUVFe3WHBnAH3XhD3n0i7gHodDry8vLweDy9pueq1WouuOACABYuXMjatWuHVUZFLVejlqlp8DTQ4e9bjFer1aSnp5OVlYUgCGzZsoUff/yRtWvX0tzcPOjFZBERkUOAjo6YgYZSCXr9L8+HQnD33THn3AkT4H/+J/6S3++no6ODpEiEiEbDwp07cXi95ObmcsQRR3Q7vN1uH7EFob7Q6/WMHTuWo446isLCQtrb23FLpfhKSwknJ/cQ+EpKSpg9ezYA77//PstMJppuuIGoXE7imjXkP/ssSSoVDY2NdHbusUBpNMbEPXfP8ZbIoc8+qbmXmJjIAw88wN///vd9cXiREUYQBKqqqvB4PN0m1larlUcffRSAK6+8ktGjRwOQnZ3Nc889xyOPPILJZKKuro5rr72W+++/f0ATsz3ZPVV37dq1NDQ0iBMtEZFhEAwGqaysRKlUDiit6aWXXsJms5Gfn8/FF1/c4/VAJIAz6Oyzjl4wEmRr51YAppim7PV8/ogfm99GbVstzY5mDJkG9Ol67CE7La6Wbg+Ly0KLs6XH8z22c1to9bTS5mnr9nyruxWr14rD78AddBMIBwhHxVqf+xqZVMapJafy0hkvMbNgJgICi3Yt4vpPr+e76u969PEajYbCwkIikciAorjVajVz5sxBp9NRU1PDv//9737vGxKJBJPJhN1uZ8uWLezatQu3241epcfus+MK9B7BJHLgIQgClZWVrFu3DpfLRXp6Ounp6SOawtnZ2cm1115LeXk5iYmJvPTSS0ydOnXQx2ltjQnHkyZNIi8vb8ARya6Ai6gQ7TOdfSQwm82kp6f3Gfk6ffp0SktLCQQCPPvss9x88828/fbbQ3bR1cg1qGQq6t312AL91+PcPZrPYDDQ2traLZrP4XCI40QRkd8CTids3hwz0tgzzfSFF2IOsXo9PPxwrN7cz3Ta7QQ7O9HIZPjz85m/cCEA5513XrcFFp/Ph1KpJDMz81e5HIPBwLhx4ygrK8NqteKRSPCWlhJKSYkJfJFIfNtZs2Zx3MyZCMTG6ZV5eTTcdhsRtRrt1q2UPfssCpeL2rq67gsfOh14PNDa+qtck8iBxbBr7vVFVwqCyIGPxWKhrq6uW4SPIAg88sgjOBwOSkpKuPLKK7vtI5FIOOmkkzjqqKN4/vnn+fDDD/n000/54YcfuOWWWzjttNMGlVYnlUrJzMyks7OTDRs24Ha7GTVq1JAci0REfus0NDTQ0dHRw/G6N3bs2MGCBQsAuOOOO7rX7vgZd8iNP+pHp+g9vXerfSvBaBCTykSuNrfXbXbH4XcgcUtI16YzatQozKlmJBJJ7NH1T/LLT6Db/3f/2dtr4WiYUDREOBLGH/bjC/nwh/2EIiG8ES/hSJiw8IvAJ0GCXCpHLpMjl8R+yiQy5FK5mB48TIwJRub8bg6zimbx6ppXaXQ0Mu/HeSypWsK1h19LjuGXz6jJZCIvL4/KykoUCkWvn8XdSUtL46abbuKxxx5jxYoV5OXlcdppp/W5vVwuJzU1lUAgQHNzMzabjYyMDASdgM1nI1Hdv2OvyIFBTU0N27dvx2g07hPDnba2Nq6//npqampITk7mxRdfZNSoUYM6hiAIWCwWEhISGD9+/KBq/wmCQIeno88SCCOFVColJycHh8PRq2O1RCJhzpw5fPbZZyxfvhyHw8Hnn3/O559/TklJCcceeyxHHHHEoCIltQot7pCbOncdUqQYVca97qNWq8nIyCASieB0OtmyZQsqlQqz2UxmZsyFd299xUgQCoUIBoOEw2F0Ol03920REZF9gM8Xi9hzOnu6vy5eDG+9Ffv9vvtiBhs/E41GaW9pQev3EygrY1VdHdXV1ajVak4//fRuh7HZbOTk5GAwGPb11cSRSqWUlJQQjUbZuXNn7P7w8z1G2dZGMDU1LlReeumlNDQ2UllZyTPPPsvc++4jetdd5DzxBAk1NUx+8UXWXH45TQYDBQUFv5xEr4fGRsjNhV+hfxQ5cBi2ctKVytWFIAi0tLQwf/58TjnllOEeXmQf4/f72bVrFyqVqlsR0S+//JJly5Yhl8u5//77+xw46fV67r77bn7/+9/zyCOPUFVVxX333Rc33Ohm0z0AkpKSUKlU7NixA6/XS1lZ2T4NkxYROdRwuVxUVVVhNBr3mv4VjUZ59NFHiUajnHTSSRx22GG9bmcP2lFIFHtNyZ2UPGmvYpjVYcXlcnFUwVFMGT+lx4RyXxKKhGLCXyREKBqK/z8QDuAL+/CFfIQiIfxhf1wA3D06RCVTkahOHFTasUiM8enjeea0Z1i4fSHvbX6PLa1buOXzWzhz9Jn8afyf4o6gWVlZeL1empubSUtL2+sEesyYMVx00UXMnz+fd955h5ycHMaPH9/vPiqVivT0dDweD1VVVUgTpAheAfMEMwkq0VjjQKaxsZFt27ah1+tHXNgLh8OsWrWKJ554gqamJsxmMy+99NKgjcOi0Wi89t/48eNJTk4e1P6+kA930P2rmLxotVry8vLYsWMHCQkJPcZ6er2eCy+8kPPOO4+NGzeybNkyNm3axK5du9i1axfz58/nyCOPZObMmRQVFQ1oMUSn0OEMOqlz1yGRSDAoBzaplslkJCUlkZSUhM/no6WlhYaGhniNxbS0tCHfT8LhMMFgkGAwGBfxgsEgPp8Pj8eDz+cjFAoRCoWIRCIkJydTUFBAWlragNKsRUREBkkoBNu2xaLPsrNh976lpiZmnAFw6aUwc2a3XZ1OJ/6aGhIKCgjk5PDeyy8DcNppp6HfLa23K9otOzv7V1/IlUqllJaWIggCu3btigl8JSUIcjlKi4VQSgrI5SgUCm7+2WCjsbGR1157jRtuuIG6v/+d3McfR2WxcPirr1J+7bUYDIZf7jcGQyw1t70dfqWoRJEDg2HPUJ555plu/5dKpaSmpnLZZZdx1113DffwIvuY2tparFZrtwiftrY2nnjiCQCuvvrqAa1YT5w4kbfeeos333yT1157jTVr1nDBBRdw5ZVXcumllw4qXUaj0ZCZmUl9fT1ut5tx48ZhEh1/RET2iiAIVFdX4/V6BxS1t3DhQrZs2YJWq+2zRqo/4scVcpEg732i6Qw6qXRWAv275Pr9fux2O0FFkKljp/K7cb/7VaItdkchU6CQKfqdNEeikW7iX1cEYDASxOaz0e5pRylTYlQb92nK3KGIQqbgj+P+yDH5x/D6mtdZ07SGBVsX8H3t91x92NUcnn04UqmUgoICQqFQ3M1zb4Puk046ibq6Or7//nteeOEF7r///gEVxtZqtbE6fw47m3ZuQu6WM7lsMmazWZywH4BYLBY2b96MSqUa0SiLhoYGPvnkEz777DPa29uB2GTvpZdeGnSqVjgcprm5mdTUVCZMmDAksckVdOEP+TGqjYPedyiYzWYcDgctLS19ft/kcjnTpk1j2rRpdHZ2snz5cpYuXUpraytLly5l6dKlZGdlM+PYGfzud7/b698nUZmII+igzl1Hkb4IrWJwi7hdTrtd0XybN29GrVbHnXZNJlP8/hKJROJi3e4Cnt/vx+Px4PV64xF5oVCIcDiMRCJBEASkUmk8ilgul6NWq5FIJNhsNtra2sjMzCQvL4/U1FQxyltEZKSIRmHHDqiri0Xs7X4/9njgr38FrxemTYPrruuxu6O6mpBKhWLUKCwdHSxbtgzoaaRhs9lIS0vbb3NMmUxGWVkZ0WiUioqK2LiluBgkEpTNzYRTUhAUCpKSkrjxxhv5xz/+wU+rV5P/2Wf84Q9/oLZL4GtuZuJzz1EtkaA9/fRYsI5MFovYa2iIRTWK/dNvhmGLezU1NSPRjiHzr3/9i+eee46dO3eSmJjIkUceySeffLJf23SwYLVaqa6uxmQyxScygiDw0EMP4XK5GDNmDJdddtmAjyeXy7n88ss54YQTePTRR1m1ahWvvPIKixYt4u6772bKlL3X4tr9WDk5OVgsFtauXcvo0aPJyck5oAZPbrebaDT6q0YeiYj0R3t7O/X19aSmpu51W7vdzgsvvADAtdde2+c+7pCbYDSIXqHv9fWNto0ICORqc0lRp/R4PRwOx2txZmZlojAqmFQw6VcX9gaKTCpDJpWhpmeqWW40lw5vB/X2eto8bahkKgxqgyjyDRKzzsw9x93DTw0/8fra12n3tPPI0kc4PPtwrpp2FWm6NIqKiggGg1itVlJSen6udkcikXDFFVfQ3NRMZVUlzz7zLPfNvY+EhL1HPkkkEpKMSUSUEZo6mwitDpGZmUlhYaG4qHQAYbVa2bx5MxKJZNCRcL3h9/tZvHgxn3zyCevXr48/bzAYOO2007j88ssH/fcPhUI0NzeTlZXFuHHjhpx1YPfb+zUvGmkkEgm5ubk4nU4cDsdeTY2SkpL4wx/+wOmnn87OnTtZunQpq1evprGpkf/7v//j3XffZfLkycycOZPx48f3GX1rUBqwBqw0ehop0BeglA2+ZuKe0XzNzc00NjZiNBpJTEzE4/EQCAQIh8PxhyAI8fe2S7hTKBSoVKq4iLc30tPTCQaDWCwWLBYLmZmZ5Ofnk5ycfECNU0VEDjoEAaqqYg+zuVsdPQQhFrFXWwtpafDII91fBwKdndhaWmD0aKI6HR/+979EIhGmTJlCcXFxfLtIJEIoFCI3N3e/LubJZDJGjx4dr39vNpsRiosRpFJUjY2ETSYEpZLS0lIumT2bN954g/fee4+8vDwmTJhA3d/+Rs6TT5JQXU3RU0/RJpWScdZZsX4oKSkWudfZCSNw3xQ5ODioc4vmzp3LM888wz333MMRRxyBzWZj0aJF+7tZBwXhcJiKigoikUi31JaFCxfy448/olQqmTt37pBq3mVnZ/P888/z9ddf89RTT1FbW8s111zDmWeeyU033TTgFXeJREJGRgZ2u71bHb79LQp4vV4aGxupra0lGo2Sm5tLfn4+Gs2+cbUTERkI4XCYqqoqJBLJgGogvfDCCzgcDkaNGtVjNbMLQRCwB+z91p7bYN0A9DTSEAQBl8uF1+vFZDKRnZ2NLEGGVCLFmGAc3MUdIMikMsw6MyaNiXZPO42ORtrcbajkB67IFxWiSCUHZhTaETlHMDFjIu9tfo+F2xayunE1m1o28cdxf+TkUSdTXFzMjh076OzsJCkpqd9jKRQKbp5zcyx1pamRV155hZtvvnnAg3adUkdIEcKQYKClpYW2trZ43757Go/Ir4/D4aC8vJxAIEDGbnWVBosgCGzdupWFCxfy9ddf4/F4gNhY46ijjuKMM85gxowZQzLmCAQCWCwW8vLyGDt27JAde8PRMDav7VdJyd2dhIQE8vLy2L59O4FAoFuZlr6QSCSUlZVRVlbGpZdeysqVK1m2bBnV1dWsXbuWtWvXkpyczDHHHMOMGTMwm809jpGkTMIasNLsbSZXlzusvmr3aD6Hw0Fzc3NcuNNqtSgUCmQy2YiJb11F+AOBAE1NTVgsFrKyssjPzx+W67uIyG+ahoZY1F5SEuzZj771FixZEhP0Hn20p2AVDuOqqaHDaCQpP59gMMjHH38M9Iza6xpXDGQxfF8jl8sZM2YM0WiU6urq2H2usBAkkpjAl5SEoFIx6/jjqa2pYemyZbz00kvcf//9mM1m6u+8k6x589Bt3Ur6o4/iHDUKw7hxoFLFDDpaWkRx7zfEsMW9f/zjH5jN5h6GC//+979pb2/njjvuGO4pemX79u089NBDfPHFF5x00knx588+++x9cr5DjYaGBlpaWrqlnFgslnia9Z///GcKCwuHfHyJRMLJJ58cN9z46KOPWLhwId9//z233HILp5566oAHWEajMV6Hz+PxMGbMmP1Sh69rAFdTU4PT6YzXNNu5cyfNzc0UFhaSnZ09oo59IiIDpbm5OR5BsDfKy8vjA5477rijTxHfH/HjCrvQyHoXri1eCy2+FmQSGeOTf6lz1pWCq9FoKC0tjddOa3Y1U2IqGVKExoGEXConQ59BiiYlLvK1elpJkCdgUBv2u5gWjoZxB9z4Qj4kUglSpCQlJKGQHXjRkmq5mksnX8rMgpm8uvpVtrZt5a1Nb/Hu5neZljWNw1IPQ2FT4HK59iqyJSUlMWfOHB566CHWrVvHRx99xLnnnjugdqhkKlwBFwFJIF73r7KykpaWlnjfPlTBRmTouN1uysvLcTqdZO1ZUH2AdHZ28vnnn/PJJ59QXV0dfz4rKysegTaQNO6+8Pl8tLe3U1xczOjRo4e1AOkOuPEGvSRrfv2JWGpqKna7nebm5gGlw++ORqNh1qxZzJo1i4aGBpYtXcbyFcux2WwsXLiQhQsXMmbMGI499limTZsWFw+lEilGpZE2fywSOkMzdPG2C5lMNiLRnQNFpVKRlZWF3++nrq6OlpYWsrOzycvLEzM7REQGQ2trzP1Wo4k5vu7OunXw/POx32+9FSZM6LG70NJCi0RCOD8fiVTKkiVL4qm3M3eryycIAh6Ph7Kysv0eMNKFXC5n7NixCIJATU1NbCxfWIgglaKurydsNIJazWWXXUZjYyOVVVU8++yz3Dd3LuqEBBpvvZWcxx5Du3Mn4f/7P6IPPRRb3DQYoKkJ8vNBrGH/m2DYM5BXX32VsrKyHs+PHTuWV155ZbiH75M33niDwsLCbsKeyMBwuVxUVlaSmJgYn9QLgsADDzyAx+NhwoQJXHzxxSNyrsTERO655x5ef/11CgsL6ezs5N577+X666+nvr5+wMdJSEggKyuLpqYm1q5dS0dHx4i0byCEQiHq6+tZuXIlGzduBCAnJ4fExER0Ol08XXjTpk2sWrWKpqYmIrtZmYuI7Gt8Ph8VFRVotdq9RtuGw2EeffRRAP7whz8wadKkPrf1hD0EI8E+xbguI41SQylauZZwOExHRwdut5vs7GwmTJhARkYGMpmMQDiAUqrcL5PWfYVCpiAzMZNJmZMYbx6PSqbC4rJg99uJCtFftS2RaARnwEmLqwWr14pKrqI0tZRJGZNI1abS4e2gw9NBJHpg9k25xlweOvEh5kyfQ1FyEeFomFUNq3h+/fO83vw6i5oXUddZt9fjFBcXc8UVVwDw0UcfsWbNmgGdXyKRoJQqsQVsCIKARqMhNzcXuVxOeXk5q1atoqGhgXA4vPeDiYwIPp+PzZs3Y7VayczMHJTYFA6HWb58OX/961859dRTefbZZ6murkalUnHqqafyyiuv8NFHH3HVVVcNS9hzu910dHRQWlrK2LFjhz1RdAfdRITIfjHt6UrP1el0OByOIR8nJyeHS2ZfwvPPP88NN9zA+PHjkUgkbNu2jZdffpmbbrqJN954g9raWgAUUgVauZYWbwv2gH1kLmY/oFaryc7ORqvVUllZycqVK9mxYwdut3t/N01E5MDHbo854wLsGfna3g533x2LQjv1VPjjH3vu39GBSxBoNhgw/FzK47333gPgnHPO6TY2djgcGAyGXqOJ9ycKhYKxY8dSUFBAc3MzoWiUQH4+/vx85HY7Up8PhULBTTfdhNFgoKGxkddfew1BEBAUCqxnnAGA4fvv6WxpiR1UpwO3G9ra9uOVifyaDHv0YLFYek2TSE1NpaXrg7UPWLVqFePHj+fBBx/k+eefx263c9RRRzFv3rx+J6u/daLRKJWVlXg8nm4F9xcsWMDq1atRqVTcd999vdZIcbvd2O12UlNTB5SysTuTJk2KG268/vrrrF69Om64cdlllw1oQCyXy8nOzqa1tTVehy83N3ef1TeJRCJYLBaqq6vp6OhAq9WSk5PTI81LIpFgNBrR6/XYbDbWrl1Leno6hYWFpKSkiPVXRPY5dXV1OByOAblTf/DBB+zatQu9Xs+NN97Y53aCIGAL2PoU9qJClI22jQBMTp6M0+mMp+Dm5OT0SEtyBpwkJSSRqDr0IhmUMiWZiZmkaFNod7dT76jH4rKgVWpJVCXusz5AEAS8IS/uoJuoEEWn1FGUXERSQlK3NGGTxkS6J506ex2tnlY0Cg0GleGA65skEgkzC2cys3AmtZ21fFv9LctqluHwO1gXXMc61zoyEzI5LPUwJiZPRCPvPaL02GOPpb6+nq+++opXXnmF9PT0ARnMaOQa3CE33rA3XuDfYDDE+/Z169ZhNpspLCwkLS3tgHv/DiWCwSBbtmyJpzoONL26N3MMiLkqn3HGGZx88skjlmZtt9vxeDyMGzeOwsLCEanb1OHpGHRksy/k4/W1r5OdmM0Zo88YVnkAtVpNfn4+27ZtG3B6bl8oFAqOPPJIjjzySDo6Ovj+++/5/vvv6ejoYPHixSxevJi8vDyOOeYYfve73yFVSWnwNqCUKfv8bh8MaLVatFotbrebbdu20dDQQH5+PtnZ2QOqAyoi8pvD44HNm2M/94zQDoXgjjvAaoVRo+Cee3qaQ3i9EAjQlpyMr7OTFJWKbdu2sXnzZuRyeY+sPqfTybhx4w7IaHylUsnYsWOJRqPU19fHIvjy8kAiQV1bC4JAcnIyN954I4888girfvqJ/IICTv/97/GMG0fQbEbZ2orvo4+IXndd7L6k00F9fcx1+ACJVBTZdwxb3MvJyWHFihUUFBR0e37FihWDdhkbDBaLhfXr17N161ZeeeUVlEol999/PyeeeCIVFRX91rtwu904nc74/1Uq1bAGMAcTFouF+vr6mOX2zzQ2NjJv3jwAbrjhBvLy8nrsFwqFsNlsZGRk0NrailqtxmQyDWpyo1AouOKKKzjxxBNjjj8//cQrr7zCV199xd13383kyX07bXYhkUhIT0/HbrezceNG3G43JSUlIxpWHY1GaWtro6amJn6tWVlZfRaF7kImk5GamkooFKKtrY329nays7MpKCgYUWc/kUOfwfRRnZ2d1NTUDOj72NHRwcsvvwzA9ddf32/qkj1oxxVyoZX3HsZf5arCGXKilqlJCiQhUcTqL6Wmpvb4rgiCQCgaIl2ffkgLIkqZkixDFinaFFrdrTQ6Gml2NaNX6tGr9CN27f6wH1fARSgaQqPQkJUYO6dBbehVGJBKpKTp0khKSKLN3UadvY4WVwuJ6kR0Sl0vZ9j/5Cflc+XUK7l08qWsb17PksolrGlaQ7OvmYX1C/ms4TPGGMcw1TSVUYZRyCTdP3MXXnghjY2NbN26lWeeeYYHHnigW33Z3lBIFYSEEM6Qs5t7p1QqJSUlJR6Z2t7eTk5ODvn5+XutBXgosq/HUOFwmK1bt9LQ0DCge+/ezDHOOOMMRo0aNWLtg5jBRygUYuLEiSO2yOgL+XAFXWgUgxO2vtz1JUuqlgDwY/2PzJk+hyzD0FKYAVJSUsjMzKSxsXHQ6bn9HfOcc87hrLPOYuvWrSxbuoy169ZSV1dHXV0db7/9NpMmT2LqMVORT5Azyjhqv0QvjiQ6nS4eBbl582bq6+spKCggKyvrNzPn2B/8lud4ByWBQCxir6MjJj7tybx5UF4eE6gef7xnHb5wGNrbCRYVUdfeHl+8ef/99wE44YQTupkjud1uNBrNsCK29zVKpZJx48YRjUbj90Hy8hBkMtTV1SAIlJaWMnv2bN74z3947913ycvNZfz48XTOmoX5//4P41dfYTv/fFJSU2OpuRZL7D0eRt1akYODYd85r7rqKubMmUMoFOL4448HYMmSJdx+++3ceuutw25gX0SjUdxuNwsWLGDs2LEATJ06lYKCAv75z39y++2397nv4Ycf3u3/9913H3Pnzt1nbT1Q8Pv9VFRUdLvRRaNRHnjgAXw+H1OmTOH888/vsZ8gCFgsFnJ/7jja2trYuXMnDQ0NpKWlDXrlIzs7mxdeeIGvvvqKp59+mpqaGq6++mouvPBCbrzxxgHVrNu9Dp/X62X06NF7nbjtDUEQ6OjooLa2lubmZuRyORkZGYM2FVEoFGRmZuL3+6mtraW1tZX8/Hxyc3PFVVuRATGYPqqxsZFgMNhNsO+LefPmxetW9lef1BF0UO+uRyaR9RlFsq59HQClCaXk5+T3W5PME/SgVWhJTjh0UnL7QyVXkWvMxawzY3FZaHQ00uJqQafSoVcOTeQLRoK4A278YT9qhZoUTQppujSMaiNqxcD6YIVMQZYhC5PGRLOrOd6upIQk1PIDbwUbYvUND88+nMOzD8fqtvLRuo/4yfIT7aF2NnduZnPnZvQKPVNMU5hqmkpaQux7IJfLueGGG7j33ntpa2vjhedf4La/3rbX/jxBloAtYCNNndYjAkoul5Oeno7f76e+vj5uoJCXl7df6sDuL/blGCoSibB9+3Zqamr6vf/uS3OMvdHa2opMJmPy5MkjuojtDsbqZBpUA18MjApRvqr4CoiJ+BXWCm754hYum3IZp5acOuT6nzk5OTidTjo7O0e0fp1UKmX8+PGMHz8et9vNypUr+eGHH6iurmbd2nWsW7eO97Lf43djfsd5J55HyaiSETv3/sJgMJCYmIjD4WDjxo1xkS8jI0Os0bwP+K3O8Q5KIhHYvj1WEy4rC/aMfl60CN55J/b7Aw9AbxH4FgtkZdFuMOCsriY7Oxu73c5XX8X6xT2NNOx2O4WFhQe8UZZKpWL8+Fgt67jAl50di+CrrkYSjTJr1ixqampY9v33vPjii9x///1IZ8wg9YMP0LW0YFm2jKSzz0Yml4NMBo2NkJ7eM/JR5JBi2OLe7bffjs1m47rrriMYDAKxsP477riDO++8c9gN7Ivk5GTMZnNc2APIyMigrKyMrVu39rvv6tWrKS0tjf//t7KiU11djdVq7Zae9O6777J+/XoSEhK49957e00rsVqt6PV6SktLUSqVZGdnk5SURGVlJXV1dSiVykGnn0okEk455RSmT58eN9x4++23WbduHQ899NCAzDwSEhLIzs6msbERj8fD2LFjh+x6ZLPZqKuro7GxEYlEQlpa2rAHXWq1mpycHFwuF1u3bqWxsZHi4mIyMzMPmAKuIgcmg+mjQqHQgD5P69at48svv0QikXDHHXf0GQ3jCrmoc9cRJYpB2XOSKQgCHfYOttpj/ezpE06nuLC433O7g27ykvJQyX8bfW0XKrmKvKS8mMjn/kXkS1QlolPtfTEiHA3HJ/wKqYJEdSLFpmIMCQa0Cu2QI2rUCjWFyYWkalNpcjbR4mzB6XcesKYbXZh0JmYfOZvDdx7Oztad1Ag1bLJtwhVyscyyjGWWZeRoc5hqmsqE5Ano9XpuueUW7r//frZs3cI777zDJZdc0u85EuQJ2IN23GF3r59/+KW2ltvtZseOHb85Q6V9NYYSBIGKigoqKysxm819vpdOp5M5c+ZQXl4ef26kzDH21j6LxUJCQgLjx48f0ILKYLD77EgkkkF9rze1bKLV3YpWqeXxUx7nn6v/ySbLJl5b8xqrG1Zzw1E3kKod/LhIpVKRl5fHtm3b8Pv9+yR9TafTceKJJ3LiiSfS0NDADz/8wIoVK3C0OFjkXMSnH33KqPRRnH766Zx88skHtQNtV/mWxMREOjs7Wb9+PcnJyRQVFZGenj7oRWSRvvmtzvEOOgQBKiqgpiYmOO35HaishIceiv1+5ZUwY0bPY9hsoNEglJbStGsXSqUSqVTKxx9/TDAYpKysLC6QQSzIRS6XD9mc6ddGrVbHI/iam5vJysoimJUFMhnqqirkLlfcYKOqupp58+Zx73334Zg+naSlS0lctAjbzJmkpqTE3Ifb2mK1DX+DGQe/JYZdIEQikfDYY4/R3t7OqlWr2LRpEzabjXvvvXefpl+NHj261+cFQdhr3ROdTkdiYmL88Vvo+Ds6OqipqSElJSX+/tTV1fHCCy8AcPPNN5PdSzi01+slEAj0iIzTarVMmDCBadOmkZCQQH19PT6fb9Dt6jLceOaZZzAajezatYvZs2fzwQcfIAjCXveXy+Xk5OTg8XhYu3YttbW1RKMDL2TvcDgoLy9n5cqV1NfXk5ycPOKrqXq9ntzcXKLRKOvXr+enn36ipaVlUO0U+W0x0n1UOBzmscceA2KFhXdfFNkdT8hDvbueUDTUq7Dh9/uxWCxUeisJCSHMOjPTCqb1e+5QJIREIiFFkzKsaziYUSvU5CflMzVrKiUpJYSFMM3OZtzBnoXWo0IUV8CFxWXB6rWilCopSSlhStYUJmdOJsuQhU6pG5H7q16lpzSllIkZE+OmG1av9YA13YDYRK2oqIjC5EKma6Zz14S7mF00m9HG0UiR0uBp4OP6j3lk0yO8Xf02foOfq6+5GoBFixaxaNGifvtemUQGAjgCezcU0Ol08ZTMTZs2sWbNmm513g5V9sUYShAEqqur2blzJyaTqU8xKRAIcOutt1JeXj7i5hj9EY1GaWpqQq/XM3ny5BEX9iLRCDavjQT54KL7v9z1JQDHFR5HVmIW9826j2sOuwalTMkmyyZu/uxmltUsG9B4ak9MJhNZWVl0dnbu8/FKTk4OF110EfPmzeOWm25h4uiJqDPU7KzfyRNPPMGpp57KHXfcwfLlyw9qUxupVBp/X/1+P2vXruWnn36iublZNGIbIX6Lc7yDktpa2LkTUlJgz7+R2w233w5+PxxxBFx7bc/9/f5Yrb2yMhxAe3s7RqORSCTCggULgFjU3u5jJZvNRnp6+kFVTiMhIYEJEyaQnp4eM2yMRglmZOArLkYSCpHg83HTTTdhSEykvqGB119/HdusWQCklJfTtnVrrM9Uq2P1C/ehH4LIgcHwq/8CP/zwA3/+85+5+eabSUpKQqVSMX/+fJYvXz4Sh++V008/ndbWVrZ0OesATU1N7Nixg4kTJ+6z8x6MhMNhKioqiEaj8dShSCTC/fffTyAQ4PDDD+fcc8/tdb/29nZGjRrVq2mKRCIhMzOTww8/nNLSUjo7O7FYLEMaBB5zzDG88847HHXUUQQCAR599FFuvfVW7Hb7XvftqsOnVCrZtGkT27dvj0eR9kVXoeOVK1dSXV1NYmJiv2mFw0UikZCcnExWVhZOp5PVq1ezbt06rFbrPjmfiMjuvP3221RXV2M0Grnuuut63cYX9lHnqcMb8WJUGru9trsLbl5eHrXRWgBmFszcq8jkCrgwqA0Y1GLdyQRFAgXJBUzJnEKxqZhwNEyLswVP0IM36KXV3UqruxVBEMhPymdy5mSmZk8lPykfg9ow5BS7/pBIJCRrkhmXPo6JGRPRKrW0elqx++1DEgR+DbRaLUVFRcjlctxON2OTxnJZ8WXcNfEufp/9e9IT0gkLYTbZNvHvin/zrfpbpl0+DXmSnDfffJO///3vbNq0qc/r08g12EN2gpH+7yPwS0ROVlYWdrud1atXx6OdRAZOQ0MD27Ztw2Aw9JniHIlEuPfee9mwYQNarZb//Oc/PPjgg0ybNm1EzCz6IhwO09jYiMlkYvLkySOaptqFJ+jBE/QMqt5eu6edtU1rATh51MlALDX3tNLTeOa0ZxhlGoU35OWZFc/wxA9P4PQ7+ztcr3RlarS2tuJ2u/d5nyCXy5kyZQp/ueEvPPjgg8y+aTYlY0sIhUIsWbKEOXPm8Pvf/5558+ZRXV29T9vSG+FwmFAoNOzjyGQyUlJSyMjIwOFwsGbNGtasWUNLSwter/eA7XtFREaElhbYtg30etDs0edFo3DffTEDiPR0ePjhWErpntu0tkJ+PmRl0dbWRjAYRK1Ws3z5clpaWjAYDJx00knxXcLhMNFolOzs7IOu9nNCQgITJ04kPT2d5uZmotEoofR0fKWlSKJRzFIpN950EzKplFWrVrHabsdbUoI0GkXz1VfYbLbYgQyGWAq017t/L0hknzLsOPAFCxYwe/ZsLr74YtavX08gEADA5XLxyCOP8MUXXwy7kb1x9tlnM2XKFM455xweeughlEolDzzwAKmpqVx99dX75JwHK121gXavDfPWW29RXl6OVqvtM8qyywm5qKio344wISGBMWPGkJKSwq5du6ivryclJWXQNfBSUlKYN28e77zzDs8//zzff/89F1xwAffffz9HHHHEXvc3Go2o1Wp27tyJ2+1mzJgxPWoq+Hw+Ghsbqa2txeVyYTKZhpzKOxRkMhlms5lgMEhLSwttbW3k5uaSn59/wNd/EDk4aW1t5Z///CdAbHWvF3MXf8RPnbsOT8hDsio5/n0XBAGn04nP5yMlJYXs7GyiyiibV24G4NiCY/d6fl/YR7GpeFgOjocaWqWWIlMRZr2ZFmcLLa4WJBIJGfoMUrWpGNSGXz2FWSqRYtaZSU5IjptuWFwW9Gr9AWm6YTQaKSoqivf3Op0OvULPMenHcLT5aJq9zay1rmWTdRPOkBOn0Un6Zen4Nvmo+66OJ554gtGjR3P++edTXNw9rVwtU9MR6MAVcmGSmfpoQXdkMhnp6el4PB527NhBe3s7JSUlpKcf2iYyI0FLSwtbtmxBo9H0eR8UBIFnnnmGJUuWoFAoeOqpp3r83fYFoVAong41bty4fVZb0RVwEY6GB5UWv7hyMVEhyjjzOHIM3WtRZRmyePTkR1mwdQHvlr/Lj/U/sq1tGzcceQPTsvuPtt4dpVJJSUkJ7e3ttLa2xk3GdDrdPk8lzUzKRDVDxaknnErIEuKLz77gyy+/xGq1Mn/+fObPn8/YsWP5wx/+wEknnURi4vCc2LvudxaLpcejtbUVi8VCe3s7SqWSWbNmcc455zBx4sRhfb/lcjlmszm+gNaV9q3X60lNTSUxMRGdTodGoxH7EZFDA6s1ZqAhl8fEpj35739h2bKYq+tjj0Fv6fhtbWAyQUkJwZ8XX7q+/++99x4AZ555ZreADZvNRmpqKikpB2cWiUajYcKECWzcuJGmpiaysrIIpaYiSCQkVFQwLjWVU045hc+/+IKPFy7kmBNPRLNrF1mrVrH158w0uV4fE03b2mLCqMghybDvzA899BCvvPIKl156Ke90Fb0Epk+fzgMPPDDcw/eJTCbjyy+/5JZbbuGaa64hFApx7LHH8vbbb/+mClvvDafTSWVlJYmJifGBWHV1Na+88goAt9xyS69pLJ2dnSQkJDB69OgBpahKJBLMZjNGo5GamhqqqqpwuVykpaXt1elud6RSKRdddBFTp07lb3/7GzU1NVx//fXMnj2b6667bq+1xbqcbVtaWvD5fIwZM4a0tDQCgQDNzc1UV1fjcDgwGo29ugL/WiiVSrKysvB6vVRWVtLS0kJBQQE5OTkHpDW7yMHLM888g8/nY8KECZx++uk9Xg9GgtS763GGnN2Eva7IXZ1O180Fd+G2hUSFKKUppWQm9l9M3hv0olVqSUo4eFIgfk10Sh2jUkaRmZiJVCIlQbH/DXd6M91odjZj0pgOuJqJqampBINBKisrkcvl8b5TIpGQpc0iS5vF77N/z3bHdtZ1rGOHYwcJExM4LvE4fvj8B7Zv387cuXM57LDDOO+88+ILYBKJBIVUQWegs9t3YiBotVoSEhLo6Ohg7dq15OXlUVxcjGbP6AQRIJZKVV5ejkwm67em2ptvvhkfY86dO5dp0wYuUA2VQCAQN00ZO3bsPr0323w25LKBD8nD0TBfV34NwCklp/S6jUwq40/j/8TUzKk8++OzNDgaeGjpQ5xYfCJXTr1ywP2NRqMhLy+PjIwMbDYbFosFq9WKRCIhMTFxn2Y8JCmTsAatZGVn8Ze//IWbbrqJFStW8Omnn7J8+XK2bt3K1q1befrppzn22GP5wx/+wBFHHNHruDMUCsVFuj1Fu67HQMrLBAIBvvjiC7744gsKCws555xzOO2004YlLnaZ9QiCgM/nw+Vy0d7ejiAI3cQ+vV6PXq8XxT6RgxOXKybsBQK9u7auXg0vvRT7/fbbobcSMu6fy5mMHg0JCXQ0N+NwOMjOzqa2tpaffvoJiUTCH//4x/gukUgEv9/P+PHjBzUnPdDQarVMnDiRjRs30tzcTGZmJuGUFHxSKQkVFZx5xBF8/fXXVFZW8tPZZ5NuMKB0OFCsWIE1Jwez2QxaLdTVxcw5xFqfhyTD/qvu3LmTGb0UuUxMTBxQSuVwSEtL46233tqn5ziYiUajVFVV4fV64yYa4XCYuXPnEgwGmT59OmeeeWaP/fx+Px6Ph8mTJw+6gLFKpaKsrCwexdfY2EhycvKgo9JKS0uZP38+zzzzDAsWLGD+/PmsXr2ahx9+mPy9rDbI5XKys7Npa2tj7dq1FBYW0traitVqJTExMV4jaThUVlayZs0aJk+eTFlZ2ZCPo9FoyM3Nxel0snnzZpqamigqKhqSS6+IyJ6sWrWKxYsXI5VKufPOO3ukroWiIerd9XQGOzGpTN3SPq1WK2azmcLCwm6Tt6U1S4GBRe25gi4y9ZlolKKw0R9a5YG3ILW76UajoxGLy4LD7zjgTDeysrIIhULU1tZiMpl6LADJpXLGJ41nfNJ43qp6i82dm0mYksCTxz3JggULWL58OWvWrGH9+vXMmDGDs88+m+TkZDQyDc6QE1/Eh0Y+uM+vVColLS0Nv99PVVUV7e3tlJaWkpmZuU/TRw82Ojs7KS8vJxwO91srb9GiRcybNw+AOXPmcPLJJ+/ztvl8Ptra2iguLmb06NH71ATLH/Jj99vRKgbeD6xpXEOnrxOj2sgR2f1nNhSZinjqtKd4c+ObfLr9U76p/IZySzk3Tb+JsWm911/tDaVSSXp6Omlpadjtdtra2rBardjtdnQ6HVrt0E1++kImlZGoSKTF14JapiZFncLMmTOZOXMmNpuNL7/8kk8//ZTKykq++eYbvvnmG1JTUznppJOQSqVYLBZaWlriguRA0l2Tk5NJT0/HbDZ3+9n1aGlp4cMPP+Trr7+murqaJ598kueff54TTjiBc845hwkTJgz5fZBIJGg0mvhiQF9in1arJS0tjcTERFHsEzk48Ptjwl5nZ0xY2hOLBe6+O5Zy+4c/wFln9dhECIUINDfjycvD4XbTUVuLw+GIG2m8//77ABx99NHdstUcDgdJSUkjXit1f6DT6boJfFlZWYSTk/GWlqKTSDj7yCN5b/lyFn7+OTOOO47Ujz8m58cfqZ4xg2STCYXRGHuvOzpiac8ihxzDVg8yMjKorKzsIbgsX758QI6nIvuOlpYW6uvru3Vm//nPf9i2bRt6vZ6//e1vPQYD0WiU1tZWiouLezXYGCgpKSkYDAZqa2upqqrC6XRiNpsHJVip1WruuusujjrqKB588EF27tzJJZdcwq233spZZ53V70CmK5LQ4XCwdetWdDodOTk5w5pYhcNhvv/+e959913WrVsXf/64447j2muvHVaKUFfqRWdnJ2vXriU9PZ3i4uJfNWVY5NAiGAzy+OOPA3D++edTUlLS7fVwNEy9ux5r0EqyKrmbsOfxeFAqlT0iSevsddR01iCXyjk6/+h+zx+JRogKUdJ0B/9g6reMXqWnLLUMs84cE/ncFpQyJUa18YBJtc7JySEYDNLU1ERqamqf95njMo5jc+dmNndu5oTME7j22ms57bTTeO+999iwYQPfffcdK1as4OSTT+b0008nLAvjDDoHLe510eWYbrVaWbduHe3t7RQXF4slGIiVbikvL8fr9XabhO3JmjVrmDt3LgAXXXTRXt2Oh0s4HMblcuF2uykrK6O0tHSfR3p0OWIn6gYe+bVo1yIATig+YUBiu1Km5MqpV3JY1mE8t/I5Wt2t/O3rv3HmmDO5eOLFgxLspVIpycnJJCcn43a76ejooK2tLZ5SOtIpuyqZilA0RJOnCZVMhV4R+/4kJydz8cUXc9FFF7Fz504+/fRTFi1aRHt7e58L/yqVqodYt/sjLS1tr5GIKSkpjB8/nr/85S988cUXfPjhh1RVVfH555/z+eefU1RUFI/mG+53vTexr2sBfvv27QiCEE+TTk1NxWAw7DOhVURkyITDsH17TFTKyoI9P5vBINxxR8zJtbQ09rtEQjQaxefz4fX58Ho8eCsqsKvVtCuVRNvaUKlUaDQakpKS8Hg8fPbZZ0BszNuFIAi4XC4mT558yLjZ6/V6Jk6cyIYNG+IpuhGjEV9pKTNOOYVvV6xg67ZtbDjpJE6USjFUVRHavh1rRkZsIU0qjdXeM5t7/i1EDnqGffe99tprufnmm/n3v/+NRCKhubmZlStXctttt3HvvfeORBtFhoDP56OiogK1Wh13itq1axevvfYaALfddluvKxitra2kpaVRUlIy7AgDhULBqFGjMJlMVFRU0NzcTGJi4qCjAWfOnMnYsWO577774tF7P/74I/fcc89ej2UwGHqtMTYY7HY7H330ER988AGtra1AbHA7duxYtmzZwnfffcd3333HiSeeyDXXXENBQcGQztPlomYwGGhvb8dms1FUVERBQcFB4fYVjASRSqTIpWLE4YHAf//7X+rr6zGZTFy7h9NYJBqh0dNIh7+DJFVSzCG067VIBKfT2asIsbR6KQBTMqeQqOp/IuoKujCoDBgTjCNyPSL7jy7TDWOCEbPHTL29nlZPKxqFhkRV4j4x+hgMMpmMVyfxNAABAABJREFUgoICgsEgHR0dpKWl9Xr/ytRkMsY4hm32bXzX8h3nF55PTk4Ot956Kzt37uSdd96hoqKCTz/9lG+//ZaTzzyZE2aeQFpC2pCvUSKRkJKSQjAYpK6ujo6ODkpKSsjOzj6o04OGg9frpby8HLvdTlZWVp/b7dq1i9tuu41wOMyJJ57InDlz9kl7/H4/brcbn8+HVCpFr9czYcIE8vPzf5VIS2cgZnQx0M9Ys7OZTZZNSJBwYvGJgzrX+PTxzPv9PP617l8sqVrCx9s+Zn3zeuZMn0Nh8uAX5HU6HTqdjoyMjLipmtVqjb+PI5Wyq1PosAVsNHoaKdQXopL9MiaSSCSUlZVRVlbGzTffzA8//MCPP/6ITqfrId4ZjcYRE730ej3nn38+f/rTn9i8eTMfffQRX3/9NVVVVTzxxBM899xznHTSSZxzzjmMGzduRM4rkUhISEggISGWUr272NcVmbi72NcV2SeKfSL7DUGAXbti7rgZGT3NMQCefhq2bkVITMRz3314HA7cTU3Y7Xb8fj/BUAil241SoUAYNYqM1NQeffMXX3yBx+MhNzeXww8/PP68y+VCr9fHUlIPIRITE5k0aVK3FN2IwYBu7FhmTpvGB2vW8O7SpRw5dSqJa9aQu3o1TaWlsQyH5OSY0Gq3w0HkHCwyMIY9C7/99ttxOBwcd9xx+P1+ZsyYgUql4rbbbuOGG24YiTYeUgQCARwOBzabDZ/Ph0qlQq1WI5fL4w+FQtHtd5lMNuibck1NDTabjdzcXCBWa2Tu3LmEw2GOPfZYTjvttB77OJ1OZDIZZWVlI1pDJTk5malTp5KamkpFRQWNjY2YzeZBpbmkpqbywgsv8NZbb/Hiiy/y3XffsXXrVh544IF9Vntn+/btvPvuu3z99ddx912j0cjZZ5/NueeeS3p6OtXV1fzzn/9k8eLFfPPNNyxZsoSTTz6Za665Jp4KPVjkcjkZGRl4PB62bdsWL8yelpZ2wA7OvEEv29q2oVaoKUkpQSk7NFbHDlaampr43//9XyBWV3N3c5uoEKXJ00Sbv40kVVIPMbazs5OkpKQeDtmRaITva78HYGbhzL22wRfykZuSK4q9hxC7m260ultpsDdgcVnQKPe/yKdQKCguLo4Xpu8r/WZWxiy22bex0baRWZmzSFHHimuXlpZy7733sn79et5/730amxr58J0PWbZ8GZeceAnnnnbusKKRuiJh7XY769evj6d8Jv3GBtaBQIDNmzfT1tbWr2uhxWLh5ptvxuPxMGXKFObOnTtiQpsgCHg8HtxuN8FgEJVKhdFopLi4GKPRiMFg+NXKYkSFKB2ejkHV2/yq4isApmZNxawb/IRVo9Rw41E3cnj24by06iXq7fXcvuh2LphwAWePOXtIEbkqlWqfp+walUasAStNnibydHm9trPL7GLWrFnDOtdgkEgkTJgwgQkTJvSI5vv000/59NNPKS4ujkfzDdZsbm/n7k3s83q97NixIy72abVaMjIy4gvIYnkAkV+NxkaoqICUlJhJxm4Eg0FCH3+M9oMPECQSKmbPprWtjXBTE1KpNP7ZTQYUgLesDGkvIp0gCHEjjT/96U/dPt92u53Ro0cfknVvDQZDPEW3paWFjIwMwsnJHHvKKSxas4aNGzey/corOWLNGlLXrGHXSSfR0dERG9+HQjHH4d/YGOS3wIiMXh5++GHuuecetm3bRjQaZcyYMSN68zrY8Xg8OBwO2tvb6ejowP1zMVC5XE4kEiEajXbbXiqVIpfLkclkcZFPpVLFhUCVStWrCNj1e2dnJzU1NaSkpMQHU//+97/ZtWsXBoOBu+++u8cgKxgMYrfbmThx4j5xEpLL5RQWFmIymeK1+PR6/aAmNlKplNmzZ3PYYYdx9913U19fz//8z/9w2WWXce21145ITZxQKMSSJUt47733KC8vjz/f5ap44oknxqPoBEEgvyCfRx99lF27dvHqq6+ybNkyvvzyS77++mt+//vfc9VVV/WbctQfXYXZ29raWLNmDQUFBRQVFR1whhu+kI8d7Tuw+qzgjb0vJSklB1zx/d8STz75JIFAgGnTpnWrTxUVojR7m7H4LBhUhh7Cm9/vByAvL6/H5HZL6xasXitapZbDsg7r9/z+sB+lTEmyJnmErkjkQEIhU5BtyCZVm0q7pz1ek0+j0JCo3n8in1qtpri4mO3bt2Oz2UhO7vn5y9JmUWYoY4djB9+1fMd5BefFX5NIJEydOpVJkyaxYsUKFixYgN1h59lXnuWD+R9w/fXXc+yxxw5LpDAajeh0Opqbm+PR2Xl5efu0ptuBQigUYuvWrfE0or4EBofDwY033kh7ezuFhYU89dRTw45eD4fDuN1u3G43giCg0WjiYpTBYECv1++XxTNP0IMn6CFRPbCU3EA4wLdV3wJwyqjejTQGyhE5R1CaWsrLP73MTw0/8ebGN1nbtJabp99Mhr6XYvcDYF+m7EolUpKUSbQH2lHJVGRqMg+4Bc/do/nKy8v56KOP+Oabb6isrOTxxx9n3rx5nHTSSZx77rmMHTt2xNvfm9gXCATweDxs2bIFhUJBcnIymZmZmEwmca4msm+xWmHbNtBqERIS8Pv9+LxevF4vdrsdYccOxjz9NAD1J5+MY9w4jD8HvMQRBBStrQSysgj1sWi3bt06ampqSEhI6GYc5/V6UavVPRarDyWMRmM8RddisZCRkYFh9GhmTp7MZxs28EZ5OROzslA3NZFTXk5TWhomkwllYmJMeM3Lg4T9b+YmMnKM2NKkRqP5VdzLDgai0SgulwuHw4HFYqGzsxOv14tMJkOv15ORkdFvOk4kEiESiRAOh+M//X5//P+CIMSLAkskEqRSKTKZLC4GdrWhyzV4+/bt/Pvf/wbgjjvuwGQydTufIAhYLBby8/P3uYOswWBgypQpmM1mdu3aRUNDA2lpaYMauJeVlfHWW2/x9NNP89FHH/HGG2+wevVqHnrooXik4mDp6OhgwYIFfPjhh1itViAmSJ5wwgn86U9/Yvz48d0GYf6IH4vXgjfiJUmRRHp+Oo8/8Ti7du7ilVdeYcWKFXzyySd8/vnnnHnmmVx55ZX9FgzvC6lUSnp6Ol6vl507d9LR0UFpaSlms/mAGNQGwgF2tu+k3dOOWWdGQKDZ2YwgCJSmlooC337g+++/54cffkAmk3HHHXfEPyeCIGDxWmj2NpOoTEQh7S4mCIJAZ2cnOTk5vYruXUYav8v93V7rMzn9TtJ0aeiU4sThUEYlV8VFvg5PR1zkS1AkYFAb9ovIp9PpKCoqYseOHTidzl4dLGdlzGKHYwcbrBs4PvN4TKru90SZTMaMGTM48sgj+XLJl3y56Etqt9Vy2223MWHCBG688UYmT5485DZ2mT45nU7Ky8tpb29n1KhR+2Rh7UAhEomwfft2ampqyMrK6lPgCQQC3HbbbdTU1JCWlsZzzz035Lple6bb6nQ6iouL49FLCQfAhMYVcBGMBAcc7f5j/Y+4gi5StalMzhz6Z7ALo9rInTPu5Lvq73ht7WvsaN/BnM/mcMXUKzh51MnDGmf0l7KbmJg4JMFWLpWjV+hp8baQIE8gWXVgLiBJJBImTpzIxIkTu0XzVVdXx6P5SkpKOPvsszn11FP3mcgmkUhQq9Wo1WpMJlM8e6ilpQWtVktKSgoZGRkkJycfcAvHIgc5Xm/MQCMUwq1WU715Mx6Ph0AggAAkhEJMfv11ZOEw7okT8V54IbpeFnzknZ1EEhMJ5ObGasX1wrvvvgvA73//+27fJZvNRl5e3rDLMx3oJCUlxSP4LBYL2SkpzDr+eBZt2MCatWupOessRjc1kbl8OTXTptHR0UFmRgbU10NbW0zgEzlkGJK495e//GXA2z79syJ/qBMOh3E4HNjtdlpaWnA4HPF0D51Oh8lkGvAgqUuoG2jhz92FwK5IwC4hKRgMct999xGJRDjhhBM46aSTeuzf3t5OUlISJSUlv0oNIJlMRm5uLklJSVRWVlJXV0dCQsKg3qOEhATuuecejjrqKB566CG2bdvGxRdfzG233cYZZ5wxoOMIgkB5eTnvvvsuS5YsIRKJAGAymTj33HM555xzeky2BEGgM9hJi7cFT8iDSq6i0dsYa5M8AWOukQeeeIDq7dW89uprrF69mg8//JBPP/2Uc845hyuuuGJIEziNRkNOTg4dHR2sXr2agoICiouL9+vkpEvYa3W3YtaZ4ykyZp2ZFlcLUSFKaWrpoNKNRIaH3+/nySefBOCSSy6J138UBIFWXytN3iZ0Cl2vE0mHw4Fer+/VSCcQDrCyfiWw95TcqBAlIkRI0x24aeQiI4tKriLLkEWqLibyNdgbsLgtJMgTSFQl/urGG8nJyRQVFbFr1y68Xm+PdJwcXQ4liSXscu5iactSzs0/t9fjKJVK/nDKH5hyxBTWLVrHgv8uoLy8nKuvvpqjjz6aG264YdhGShqNhra2toOuxupgiEaj7Ny5k8rKSjIyMvqMUoxEItx7771s2LABnU7Hc889N6hFMUEQ8Hq9uFwugsEgSqUSg8FAUVERSUlJJCYmHnARkjavbVBmFl1GGieNOmnEvlcSiYTji45nnHkcz698ns2tm3ll9SusblzNDUfeMOwI7K6U3dTUVBwORzxlt7OzE51Oh0ajGVSaqFqmJhQN0ehpRCVVDcpleH+QmJjIBRdcwPnnn8+mTZv46KOPWLx4Mbt27eKxxx6LR/Odc845+ySab3dUKlXcqM3r9dLS0kJDQ0O8PmFaWhrJycm/Wlq6yCFKl4GGzQbZ2bTW1NDR0RHvh6VA9jPPoOroIJiSQtOf/9yrcCf1+SAaxZ+Xh9CH+GyxWFi2bBkA5533SyR+IBBAKpUOyxzyYMJkMjFx4kTWr19PhyCQVVbGjAkT+La8nH+3tPCoWo3KYiGjoYEmgwGTyYRKq40JfFlZIH7nDxmG9JfcsGHDgLY71Cd2gUAAu92O1WqltbUVl8tFNBolISEBo9H4q62CdYmBvfHPf/6T6upqkpOTufPOO3u87na7iUQilJWVxSP9fi263H5SU1PZuXNnPIpvMO/b8ccfHzfbWLt2LQ8++CArV67k7rvv7jViA2ICyNdff817773Hjh074s9PmDCB888/n+OPP77XCUAwEqTV10qbvw2ZRIZJ/bMYqYCIEMEf9tPibaFF0oI6V81dj99F7Y5a/vv6f9mwdgPvvvsuH3/8Meeddx6XXnpprylj/SGVSklLS8Pn81FZWYnVaqWkpISMjIxf/bsWjATZ1bGLFlcLabq0bpMMmVSGWWem1d1KVIhSllqGRnno1bo4EPnf//1fmpubMZvNXHXVVfHnOwIdNHoa0cg1qGU9v1+hUIhAIEBhYWGv379VDavwh/2YdWZGp47utw3uoBudUodRbRz29YgcXChlSjITM0nRpsQj+drcbajkKgxqw68q8pnNZoLBIFVVVchksh6C2azMWexy7mKddR3HZxxPkqr3EhFSiRS9Vs8ZF5zBJWdfwuuvv87ChQtZvnw5K1as4LTTTuPPf/7zkNN+5HI5mZmZuN1utm7delDUWB0MgiBQXV3Nrl27+o3SFwSBp59+miVLlqBQKHjyyScHJJyGw2E8Hg8ulwtBEEhISMBsNndLtz1Q64sFwgHsfvuAF8BqOmvY2bETmUTGCUUnjHh70nRp3H/C/Xy24zPmb5jP+ub13PTZTfz58D/v1R19IMhksl5Tdtva2pDJZPFSJAP53OsVeqwBK42eRgr0BQdFnV+JRMKkSZOYNGkSf/nLX/jyyy/j0XyffPIJn3zyCZmZmUydOpVp06YxderUIWV8DJQuF95oNIrb7aaqqoqqqiqMRiOZmZmkpKSI9flEBo8gQGUl1NVBZib+YJD29nYSExPj48uUjz9Gv3EjUYWCxptvJtpb5GokgqyzE39hIeF+giIWLFhANBpl6tSpFBUVxZ/v7OzEbDb3yFY7lElJSaGwsJDy8nLS0tP5/cyZfFtezrI1a2iZPp3s5cvJWrmSdfn5tLe3k52eHjPWsFpjzrkihwRDEve+++67kW7HQYPH48Fut3ernyeRSNDpdJjN5gNqtWvz5s3897//BeCuu+7q4SwbDoex2WyMGTNmnw4g+qNrVSUpKYmqqipqa2uRy+Wk9uKE1Bdms5kXX3yR+fPn8/LLL7N48WI2b97Mgw8+yJQpU+LbWSwWPvjgAz766CMcDgcQi8w4+eSTOf/88ykrK+vzHI6gg2ZvM86QE4PC0GMgKZPI0Cq0aBVaokI0lrbrs6DOU3PjwzdSv6ueD978gK3rtvLmm2+yYMECzj//fC655JJBuwcnJCTEo/jWrl1Lfn4+xcXFv1qx2FAkREVHBU3OJtK0ab0aJnQJfG2eNra3b6cstQyt8sBeXT/Yqa+vj3/fb7311nhUp9VvpcHdgEquIkHe+yTSZrNhNpvjK/p7sqwmtio6s2DmXidenqCHouQiMSX7N0yXyNdVk6/J2bRfRL7s7GyCwSD19fWkpqZ2uz/n6fIo1hdT6apkqWUpZ+ed3edxNHINrrCLzORM7r77bi666CJefvlllixZwueff87XX3/Neeedx5///Och98NdEUzt7e2sXr063q8fCKmjw6GxsZFt27aRlJTU77XMnz8/nlp1//3377XMSzAYpK2tDYlEglarpbCwMC5GHCyF010BF96QlzRt73Wk9uSrXTEjjSNzjyQpYd8UQZdKpJwx+gwmZ0zm2R+fpcpWxZPLn2STZRNXH3b1iIloXSm7mZmZOJ1ObDYbNpsNh8OBQqFAp9PtdaE3SZmENWCl2dtMri53v7t2DwaDwdAtmu/DDz9k8eLFNDc309zczKeffgrE+rDdxb6+jIKGQ1eadGJiIuFwGKfTydatW1EoFCQlJZGVlUVycvKQ0+NFfmM0NcXccVNTQaGg02rF6/XG55na8nJSPvwQAMvllxPIz+/1MHKbjVBaGsF+Iu8CgQAfffQREDPS6CIcDhMOh8nJyTkkFskGQ5fZY2c0SmZJCUeOHcuqrVt5KxDgDkC/YQPGs86iubmZlJQU1BJJrPZeWhr8xt6rQ5UDR4k6QOmqn2e322ltbY3Xz5PL5ej1+n6LQu9P/H4/c+fOJRqNcuqpp3Lcccf12MZisZCVlUVhYeF+aGF3tFot48ePJy0tjYqKCurr60lJSRlwHRKZTMbll1/OYYcdxt/+9jcaGhr485//HH/uvffeY9myZXHzErPZzHnnncdZZ53Vr7gWjoZp9bXS6msFCZhUpr0OIKUSKRq5Bo1cExf60kelc91911G1s4rPFnxGZXkl/3nrP7z//vtcdNFFXHTRRYMaOEkkElJTU/H7/VRVVcVr8WVkZOzTz2M4GqbKVkWjo5E0bVq/6US7R/Bt+//snXd4W/X9tu+jvbflbcd2nDiJE0gChDBDGCkbQoCWMkqhtKUFChTKW8oeLaNQOn4to0BLoWWPFgghBBJWgBASMkic4W3J2ntL5/1DWMTY2XZiJ7qvS5ds6ZyjI1v6nnOe7+fzPK61TLBPKHqwDROiKHLvvfeSTqc57LDDCt/3QDJAR7QDmUSGVja4uBoOh1EqlVtNrvTH/axwrADg6Lqjt7kfqWwKmSDDqtl/ZkqLbB25VF4Q+Tyxryv5oi6U0j0j8gmCQG1tLel0GofDgd1u71flPrtiNhvXb2SZZxnHlB+DSWEadDsKqYJgOkgkHUEj0zBmzBjuueceVq9ezZ///GeWLVvGM888w/Lly/nDH/6wy955EomE0tJS4vE4GzZswOv10tjYOOzj+nDSV5W1rWP5/Pnz+eMf/wjk070Hsw/Zkkwmg8PhoK6ujurqaoxG44hrt90RwskwwA59D+LpeMH3dHeDNHaEalM193znHp5b9RzPr3qetze+TauvleuPuh67bugEJoVCgc1mw2azkUgkCIVCeDyegs2NQqFAq9UOWvEpESSYFCZcCRcqqYoyzd6ZpN4dtqzm+9WvfsWKFSv4/PPPWbZsGevWraOrq4uuri5effVVAGpqapg+fXpB8Btqn06ZTFaorkwmk4RCIZxOJxqNhpKSEsrKyrBarUV/viKD4/PBmjX5gAaNhmw2i9PpRKVSIQgCcrebir/+FUEU8R9zDMGjjhp0M9JwGFEuJ1lbizjI2C6KIj09PbzyyisEAgFKS0s5+uhvzk/9fj8Wi2WrE9b7MjqdjqqqKlpaWrCUlHDGrFksXbOGl5Yv58fjxmFqaaH6009ZeeSRuD0eqq3WfGpuMAg7WWxSZGQyJOJeIBDg73//O1999RWCIDBhwgQuueSSUW9gmUwm+eKLL/B6vSSTSVQqFXq9fqe84fYG8UycPz38Jzp6OrCV2rjm2oEeiV6vF61WS1NT04g5KRYEgbKyMsxmM62trWzevJlQKITdbt/hishJkybx9NNPc9999/Hf//6Xxx9/vBAmAnDQQQdx7rnncuSRR253m5F0hO5YN4FUAL1cP2g74/bYUugTRRFds47Gpka+XP0lC95YQEdLB48/9zj/efE/nHfOeZz3vfN2qj1apVJRXV2N1+tl2bJl1NbWMnbs2GExZ87msmzybqLd345Na9shnyCJIMlX8EVcrO3NC3x6ZXH2d6j58MMPWbp0KQqFguuuuw5BEAilQrRH2hEQ0MkH/zz0tbQ1NjZu9TPzftv7ef9E23gqDNtOfg4nw5jUph1OfiyyfyCXyinXl2PT2PqJfAqpApPKNKwiX19SezqdxuPx9Gt3rdfXU6erozXSymLnYk6vOX2r21FJVXiTXmwqW2GCp7m5mb/+9a989NFH3Hrrraxbt44f/OAHPPTQQ/3ag3YWtVpNTU1Nv3F9ypQpo1bg29Z+f/rpp9x6660AnHfeeXz/+9/f5rZyuRwOh4PKykomTpw4av0Jc2IOT8yz1Wrqb7O4dTGJTIIqQxXNpc3DvHd5ZBIZ5x1wHhNKJvDABw+w0beRa9+8lmuPuJYDyw8c8tfrC36w2+3EYjFCoRBut5tgMIjf70elUg1I25VL5GhlWnpiPaikKkxK05Dv155Cq9Vy+OGHc/jhhwN525wtxb7169fT0dFBR0dHoVqptra2UNU3ffr0IW1BHMyfr6OjA71eT2lpKaWlpUV/viLfEIvlhb10Gr62qQiFw4SCQSwWC0IqReUf/4gsEiFeX0/vBRcMuhkhnUYSiRBvaiL7tb2SKIq0t7ezfPlyvvjiC5YvX05vb29hnblz5/YLlIzH40yaNGm//WxWVFTQ1tZGRC5nzNixTB43jlUtLfxXo+ECwPTeexiOP56e7m5sNhvqZDIv8BXFvX2C3f7UL1u2jDlz5qBWqznkkEMQRZEHH3yQu+++mwULFvRrixxtpFIpfD4fer1+r7Wt7izBVJB3VrzD/5b9D1Wdigt/ciHdYje9gV7kEjlyiZxcOl+NOHXKVFLSFMFEEKlEilwiRyqRDtpmuSdRKpU0NTVRUlLChg0b6OnpwWAw7HD7qkaj4ZZbbuHwww/nrrvuIp1Oc/LJJ3POOefs0AVXNpfFnXDjiDvIilksSgtSYfcvPgVBQCVToZKpOHr60cw4YAZLly/lrQVv4ehy8MT/nuC515/j3DPP5bx55+1wa5EgCNhsNpLJJK2trQUvvqGsKs2JOTb7NtPub8eqsQ5ozUllU7T52xhrHTugsrFP4OuN9hYEvpEi/qTTaYARI3DvCtFolEceeQSAiy66iOrqaiLpCG2RNnLkMCq2Psni9/uxWq2UbsNr473N7wHbr9oTRZFkJkmZrWxUtUcV2XP0iXx96bqdwU5cURdyiRyT2jRsxx6FQkFDQwPpdBq3243Vai1U8B1bcSyPtTzGZ+7POKbsGAyKwccmjVRDMJWv3ttyGUEQOPzww3niiSe48sor6ejo4JJLLuH+++/fbmvptugb1yORCE6nk3Hjxo2adtMdpaWlheuuu45MJsPxxx/PL37xi+2u43Q6sVgsNDc3j1phD/L2BZFUBL1i+5NdoigWgjTmjNu9BNtdYWrFVO4/6X7uXXIvm3ybuH3R7Zx3wHnMnTR32Mb6Pj+4srIyIpEIwWAQt9tNIBAgk8kUnpfJZKhlalK5FJ2xThRSBRrZ8H1PRFEkm83uEdFAp9NxxBFHcMQReb/DcDjMihUrWLZsGZ9//jnr16+nvb2d9vZ2XnzxRQDq6ur6iX1m89C0b3/bn2/z5s1s3rwZo9FIVVUV9fX1o3byocgQ0Beg4fHAFm20Ho8HURSRyWSUPfYY6rY2MjodXVdcMWhFHqKIzOslUV7O2nCYz599tiDo+Xy+fotKpVImTpzI4YcfzkUXXVR4PBgMYjQah6WFfbRgMpkoLy+no60No8nEvGOPZVVLCw+vXs05ZjNKv5+KNWtYP3YsbpeLGqMx35pbWwvFqtxRz24fna6++mpOO+00Hn300cLBLpPJcOmll/KLX/yCJUuW7PZO7m2G8sK/b/Zh1apViKKIQqFALpejVCqRy+WD/r7lTS6XbzU8I5KOsMG7gaeefopMOMMRRx3B1ClTyYpZkrkk8WycdCaN2+umoqICJ056e3qRCJKCqCcV8vcKqQKFTIFapkYulQ8Q/7a8DdeJptVqxWg00tHRwcaNG+nq6sJut+9wivBxxx3HYYcdhiAIO+xbFMvE6In24Ev60Mg1GGXDU30qCAJquZpjZhzDUQcdxYeffMhrb76Gx+/hH2/9gxcXvcgl51zC3O/M3eETJqVSSXV1NT6fL5+W5PEwduzY3fZJyYk52vxttPpbMWvMA7zUwskwt75zK5t8m5hZM5NfHPaLAcsIgkCpNu/Bt8a1hgklEzCpTbu1X7tDLBbD6XTS0dFBaWkpEyZsOyRiJPPoo4/i9XqprKzkoosuIpqO0h5pJ51LbzUkACAejyMIAtXV1Vu9UOkIdLDZvxmZRLZdQ/VoOopGocGi3r1kxSL7PjKJjDJ9GTatDW/MS1ewC3fUPawin0ajYezYsWzatAm3241KpcJgMNCgb6BWV0t7pJ3FzsWcWnPqoOtLJVJERELp0KACYFVVFY8//jjXXnstK1eu5Oc//zk333wzJ5100m7t975aeeBwOLjyyiuJRqNMnz6d2267bbvHOpfLhUajYfLkyXs8AGyoiaQipDIplJrtC5TrPetpC7ShkCo4pm6gxcqeoFRXym/n/JZHPn2EhZsW8q8V/6LF08JVh1017H66W/rzhcPhgtDn8/kQRRGNRoNWrSWYCdIV7aJeX7/LY4goiqTT6QG3bDYL5M9lpFIp6XS6EALy7WrC4UKv13PkkUdy5JFHAhAKhfjiiy8KYt+GDRtobW2ltbWV559/HoD6+noOOuggDj74YA477LDdFsS/7c/n9/vZtGkTlZWVo94ftMguIoqwaVMhQKMv9TYej+Nxu9Hr9Zjeew/T4sWIgkDP5ZcPCMjIZLO0t7XR9cUXrN28mefb2nCGw/2WUSgUNDc3M23aNKZOncqUKVMG/cyFw2GmTJkyqid/hoKqqiq6urqIWSw01dUxtq6Oja2tvG+3c5zfj2XhQvRTp9LT00OJzYba7QaXC2pq9vauF9lNhqRyb0thD/Ino9dff/1uzVrvS0QiET799FM+/vhjPv74Y5xO525tTyqV9hP7lEolco0cWZmMJEl623qxWq1c+P0LUUr7D27uoJuG0gYmjJtQEC2zuSw5MUcmlyErZkllU8TTcbJilmwui4iIgJC/FwRkggypRIpEkCCTyJBL5ShlSpRSJUqZcoD4J5Pkl5dL5Ts9y9vXUmW1WgsCn0ql2uHW6B2tdMiJOXxJHz2xHpLZJGaleY+ZvkulUo467CgOO+QwPv74Y15+7WW8YS8PPfMQb7z7Btf/+Hqaxm497GNLBEHAarWSSqVoa2vrV8W3NVF4W4iiSLu/nY3ejZhUJlSy/jM6oWSIWxfeymb/ZgA+7vgYf9zPr4/+9YDqPEEQsGvtuKIu1rrW0lTShEWz54QgURQJBoP09PTQ1dVVSIre2dTikcSaNWt45plnALjuuusQZSLtkXZi2RgWxdbfVy6XIxAIUFdXt82K2D6Pp2kV0zAot11tGUlGqDHVoJIXZ/2K7BgyiYxSXWmhXbc72I0n6kEqkWJRW4Z8DDYYDEyePBmPx0NPTw8ulwu1Ws3sstk8sfEJPnF/wqzyWejlg0+IqGVqfEkfpepS5JKBk34mk4n/+7//45ZbbmHhwoXcfPPNOBwOfvjDH45oK489TTAY5Morr8Tj8dDQ0MD999+/3Um7QCAA5Fuhh6oaaW/ii/l2WIDqq9o7csyR6JR7z7dWIVXw85k/Z7xtPA9/9jCfdn3KdW9ex6+O/hW1ptphf31BEArCUp/Q5/f78Xg8eL1eRER65D0oJIqtBmzkcrlBxbs+P2ZBEJDJZIXza51Oh1arRavV9jvvjsVieL1e3G43LpeLTCaDUqksLLsnvu8Gg4Gjjz664DUWDAYHiH19FXbPPfccJpOJU089lbPOOouqbYQU7Ch93uORSGS3t1VkFNPTA+vXg80GWxTD+P1+4vE4pT4fpV+HvbnnzSM6eTLpdJrNmzezbt061q9fT8uGDQiJBCbgc8BJ3p5iypQpTJs2jWnTpjFp0qTtHifC4TBarXbUdNsNJ1arFbvdjsvhQKfTcfacOfz2b3/jwbY2ZstkqDdvxtrbS5tGQ6/LxRitFjo6oLISduF6scjIYbfFPYPBQEdHx4Ck0c7Ozv02WSmbzfLVV1+xdOlSPv74Y1avXl2Y9YN8JeDkyZNRq9Wk02lSqVS/22CPiaLYb/vxeJx4PA6AIBNQVCiQeqVkghkALr300gHCVjgcRi6XM2bMmH7ViFKJFCnSHfJQy4m5gWJgKkUoESIr5h/vEwIBZIIMieTrykBBhkwqQyVVoZArUMlUhcekghSVXIVWPvhJkdFoZOrUqZSWlrJ+/Xo6Ozux2+1DYuqbyCZwxBx4Eh6UUiVW1bY9S9LpNNFolEQiAVA4+dvdkzmZTMaRRx7JzJkzeeONN3jlf6+w0bWRy26+jFOOPIXLL7wcnXbHTuwVCgU1NTX4/X4+//xz3G43Y8eO3SkfTFEU6Qh0sNG3EaPSiFref4YslAhx8zs30+Zvw6gycv6B5/Pk8idZ517HDW/dwM2zb6ZM3/8AKwgCpbpS3FF3IWRjuMMXcrkcHo+H7u5uHA4HqVQKk8lETU1NP8+O0YYoilx++eVks1lmzpzJQTMPoi3cRjQdxaK0bPPz2Ne2UFGxdQ+9nJj7JiW3ftY29yWTy4AANu3QmnsX2T/oC9+xab6p5OuN9qJT6LYrKu8sMpmMsrIybDYbHo+Hrq4u9CE9FaoKehI9vO98n5OqB6+2U0lV+JI+wukwFuXg4rlSqeTuu++mvLy8kODucDi44YYb9tkqvJ0hmUxy7bXX0traSmlpKX/84x+3e64YiUSIRqMceOCB27QQGC2ksikC8QAa+fYnH0PJEB+2fwjsmSCNHeH4xuMZYx7Dve/fS0+4h+vfvJ6fHfozjqob3Bx/OJBKpZhMJkwmE9XV1YXE3V5PL2u71hLWhKnUVxbEu75zaEEQCgKdXC7HaDSi1+tRqVQF8U6pVBZ+3tpx1GQyUVFRUUiXDYVCuFwuAoEAfr8fQRDyFYVbCQMZDoxGI7NmzWLWrFlAXhBfvnw5y5YtY/HixfT29vLUU0/x1FNPMXPmTM466yyOOOKI4rhUZNfx+/M+eyoVbFFNnc1m6e3tRZ/LUfWnPyFJp+kZO5bHUynW3XUXmzZtIvW1LQ6ABBirVCKbNIlzjziCqdOm0dTUtNOfzUAgwLhx40Z9ZfdQIJFIqKmpwel0ErfZOKC+ntqaGto7OlhTXc3kzk4sCxfiPf98HE4n9okT0fh84PXmk3OLjFp2e0Q/99xzC/4yfS2QH3zwAddddx3f+973hmIfRwVut7sg5n3yyScEg8F+z9fW1jJz5kwOPfRQpk+fvlPl630eH1uKf8lkknQ6TSwZoyvWhS/lQ5VVkc1kMZvN1NXV9dtGKpUiGo3S1NSEwbDrF0sSQYJEmp8NVbLtExZRFAsiYN99IpMgmoqSiWYQRbEgAkJ+Vlir0GLVWNEr9WjlWtRydeHkSiKRUFVVhdlsZtOmTbS1tSGTySgpKdklrw9RFPGn/DhiDqLpKEalcdBqDMhfkMTjcRKJBDKZDJ1Oh9FuRI4cr9uL0+kstGfsru+ITCbjtNNOY+bMmfzzqX+y8quV/O/j/7HksyX8/IKfM2fWjnvumM1mdDodXV1dhSq+qqqqHTpgdoe62ejdiE6hQ6PofxESSAS4ZeEttAfaMavM3H787VQbq2myNXH7u7fTE+7hV/N/xY3H3Mg427gB2y7Rlnwj8JVMGBZRKJVK4Xa76ejowO12IwgCZrN5n2kdEUWR888/n/Xr13Pxjy6mI9JBKB3arrDXN3Y0NjZucxZ0de9qvDEvWoWWgysP3ua+hJNhTCoTJpVpV99OkSJIJVLsOjsWjYWeUA/tgXacYSdWjXWHJp92hj6Rz2q14nK5OLLlSJ7tfpaPXR9zZOmRg3qhSQQJUkGKP+nfqrgH+WPVVVddRXl5Offffz+vvPIKvb29/O53v9uvLzqy2Sw33XQTK1asQKfT8dBDD21XrEskEvj9fiZNmkR1dfUe2tPhJZKMEM/EsWm2f9xbtGkR6Vyaeks9Y61j98De7RiNtkZ+f+Lv+f0Hv2elcyUPfPgALd4WfjDtB3vct3nLhNeamhp6PD24vC6iQhSjxUiJtiQf1qFQfXOvzIt5MokMiSDZ5cnZLV97zJgxxONxQqEQfr+f3t5e/H4/qVSqUAWo0Wh2qYtiVzCZTMyePZvZs2dzzTXX8OGHH/Liiy8Wuog+/vhjSktLOeOMMzjjjDP2y2TRIrtBPJ4X9pLJfDvuFgSDQYJ+P9P/9S8UHg/dEgnnbdxIZOPGwjIGvZ7x48fT1NTE1NJSbE1NJJubB/fi26HdiaNQKLY5ab2/UVJSgsViweNyoVUoOPPEE/nDww/zF7ebvwGGTz7BeN55dMfj9Pp81Mnl0N1dFPd2hiVL4L774PPPweGAl1+GM8745nlRhNtug0ceyYvhM2bAX/4CkyYN2y7t9hH4/vvvRxAELrzwQjKZTMFH7qc//Sm/+93vhmIfRySpVIovvviiIOht3GLAgnzq1SGHHFIQ9HZnsOlrE5DJZP2q8XJijs5IJ6a4iXpl/VZbmHK5XMGTa0/OeguCgFSQ7nBrVTKTJJ6Os8m7CRERlUyFTqHDorGgU+jQK/UoZfm2h8mTJ2O329mwYQOdnZ1YrdadSohNZVP0xntxJVxIBSlW1cA230QiQTQaJZVKoVAo0Ov1VFdX055o54XNL7B69WokgoQSTQlWpRVdTIfWpaVUU0qNuQazyrxb1XwlJSVce821LF++nKeefgpfwsddj97Fawte4/rLr2dM9Zgd2o5cLqeqqopAIMAXX3xBZ2dnYbZaqVSiUqkK930nnT2hHlrcLajlanSK/n/XQDzATQtvojPYiVlt5s7j7qTSWAlAtamae+bcw53v3slm/2Z+8/Zv+OWRv+SQqkMGvj9tCd6Yt9Cia9cNzcFkSz89v9+PUqnEbreP6tCMwZBIJPzoRz9iQvMEVnWvwp/yY1Vat9v67vf7qaiowGbb9oVlX5DG4TWHb1dYiafj1Fu2PgYVKbIzyCQyakw1mNVm2vxtOMIO1HI1RqVxyNvd5HI5lZWVzLXN5aM3P6Iz0slbm99iTuUcdDrdgNfTyDSE0iHimfh2k07POeccSktL+fWvf83HH3/Mj370Ix566KH98iJaFEUeeOABFi1ahFwu5/e//z1jx25brMpkMvT29tLY2EhDQ8M+09ocSobIibntjpc5McdbG94C4MTGE0fc+zeoDNw8+2b+/eW/eWH1C/xv3f/Y5N3EdUdet0ctN7ZEoVAwpmIMBpOBVDZFmjR+/PmJ6bQEISsgiUnygh5C/vGvb1vayMikskJniYR894kgCAWB36we3LpFrVajVqspLS1l3LhxhMNhQqEQXq8Xr9eLw+FAFEXUajVarRaVSrVH/q8ymazQwtvV1cXLL7/Mq6++Sm9vLw8//DCPPfYYRx99NGeddRYHH3xwMRyjyLbJZGDdurxH2yCTLm63mzFvv41+9WpSEgnX5HJI9HoOa24uCHoVFRX571QkgiSdJlpfv8vCHoDP56O6unqnOpT2dWQyGbW1tXzudpMymzlk7FjKy8r4zOnEaTZT5vdjWryY8OzZOB0O7GPHonU6IRiE4t9xx4hG4YAD4OKL4ayzBj5/773wwAPw5JMwbhzceSccf3y+lX2YOlx3W9xTKBQ89NBD/Pa3v2XTpk2IosjYsWP3uVS3viCMPjHv888/L7RlQl7ImjBhAjNnzmTmzJk0NzcPa6m7KIr0xHpwxp2YlKbCSUafd5xOpkMpVSIIAj6fD6PRSG1t7Yg7OdwSpUxZCGIQRZFEJkE4GcYT8wCglqsxKAx5sU+pw1Ji4RDzIbS2trJ582ZCoRB2u327f/dgKkhPrIdQOoRRbiwkv4qiSDweJxaLFfxTjEYjNpsNjVbDKt8qnlz5JJt9mwvbyok5eqO99Ea3aO8MAD0gF+TYVDbsajs2pY0SVQk2Vf7+216I26LPa+K1117j9bdfZ3XPan5w3Q84+4SzueS8S3a4NdlkMqHT6fIzasEgmUy+hVsikRRaUDQaDWlFmp5UDxqlBrVeTZJkoT3FF/Nx88Kb6Qp1YdVYueO4O6gw9BeuLRoLd51wF/e9fx/Le5bzu8W/40cH/4gTx504YJ+sGiu+mI+1rrXkxNyANt4dZTA/Pb1ev8teg6MJb8aLP+OnXlm/XWEvFAqh0Wi2WwHTGejko46PgO235MbTcdRy9V4NSCmyb6JX6plon4hVY6XV14oj4sCqtg4I7BkKlEolF0y/gLsX382q+CqOzBxJxJkfR7acOFJKlYRSIcLp8HbFPYCjjz6aRx55hKuvvpqWlhZ+8IMf8NBDD21X2NrXeOqpp3j22WcBuP3225k+ffo2l89ms/T09FBbW0tTU9M+IzaIoog35t2hc4AvnV/iCDvQyDUcWXfkHti7nUcqkXL+gefTaG3koY8e4iv3V1z7xrVcd9R1TLRP3Gv79W1xMSfmyIm5QkfJlj9nxWyhs0QURbJittBZkhNzCIKQb+sVAQGkgpQJ9gkDzn2+jUQiwWg0YjQaqa6uzlvYhEIEAgFcLhehUAiPx4MgCIXQkD3RIltVVcUVV1zBj3/8YxYtWsQLL7zAihUrWLRoEYsWLaKmpoa5c+dy6qmnFoWSIgPpC9Boa4Py8kKARh+xWIzckiWMefttAO4WBDYCN111FePHj++/rUwGaSRCvLGR7G581tJft/hWVVWN6OvcvUFpaSlGkwlvJkOFIHD6Kafwt8ce45/xONcDpnfewXvSSYRCIZyhEA1yOfT2FsW9HeXEE/O3wRBF+MMf4MYbYe7c/GP/+AeUlsIzz8CPfzwsuzQkR5FEIsHq1atxuVzkcjna2toKz5122mlD8RJ7hWAwyIcffsiaNWv49NNPcTgc/Z63Wq2FyrxDDz10m8b0Q01vvBdHzIFBYSi0kobTYR5d/yiuhAsAuUSOTqpDI9FQYalg+arlWNQWzGpzv3utYs+Y/+4MfWmyfT5vOTFHIp3AF/fRG+lFkAhoZBqMKiOWCgsTDBPoae+hp6cHg8Ew6P8ik8vQG++lN94LAliVVhAhGo0Si8XIZrOoVCpsNhtmsxmDwYBMIeO9ze/x8icv0xPuAfIXdyc0nsBpE05DKkjpCffQHeqmO9RNTyj/c2+kl7SYxhF34Ig7BuyLXq6nRFWSF/y2EP7MSjNSYaAYpVQqOfvsszn88MP5xz/+wbqOdfxn0X9Y+OFCrr7kao46bMe8bmQyGVZrf4+7bDZbaPXu8HSwObQZQRTQSDV0ybqQyWTI5XJS0hRPbHoCV9yFRWXhxsNvpEQ9eAWKWq7m17N+zd8++RsLNy3k4U8fxh11c/6B5w8QoCwaC4F4gK/cX5ETc9s9Yd6SbfnpDclnOpnM375GGIHG0elsGrkgH/RzsyWZTIZYLEZTU9M2W5Pb/G3csvAWEpkEjdZGJpRsO0k4lAxRpi8bUOFZpMhQIJVIqTBUYFQZafe30x3uRiFRYFbvXmX0YBxcdTBjzGNo87fRrmhnVuUsenp6cDgcGAyGQkutUqrEl/RhU9l2KCRq0qRJPPHEE1x55ZW0t7dzySWXcN9993HIIQMrmneaUTBGvfvuu/zxj38E4Oqrr+b444/f5vKiKOJwOCgtLWXixIn7VNV1LB0jnAzv0HjZF6Qxq37WgDCrkcaM6hncd+J93LP4HjqCHdz09k38YPoPOGX8KSPi/LKvOm8o8Mf9dAY6sWlthcnhHUGhUGCz2bDZbDQ0NBCNRgtegX3BHNlsFrlcjlarHfYWXoVCwXe+8x2+853vsHHjRl588UXeeOMNOjo6+MMf/sD//d//cfzxxzNv3jyam5t37f84CsanIjuJwwEtLWCxwCDWLsG1axn7dYDG8oYG/rtpE2Nqaxk37lsWPaKI3OMhVVZGqrx8t3bJ6/Vit9sHXN8UyV8/1tbW8qXXi12n44jJk3nRZuMVj4crFArUHg+6FSswNDXR29uLvbYWfWcn1NbCfpo4LEQiEAp984BSuWt/i9ZWcDrhhBP6b+voo+Gjj0auuDd//nwuuOACvF7vgOcEQegXJDGaWL58OYcccsiAIIypU6dy6KGHMnPmTMaOHbtXTlq8CS9d0S40Mk1h9jeeifN4y+O4Ei4kSMiRI51L48/58eOn29ENAzUmIC8CmtVmLJq84Pdt8a/vXq/U77WTNIkgQaPQFLzfsrks8XQcV9RFT6gnn9pbJken1OHp8eAKuKgpr0GlzJ8QR9IRumPdBFIBNBINYkrEE/CQy+UKLRQWiwWdTodKpSKejvPWhrd47avX8MV9AOgUOk4efzInjz+5XxKsRWOhubS53/6ms2mcYSctjhY29m6kK9CFL+0jkAkQzUYJp8OE02E2hzf3W08qSLEoLVRrqzm+4njMyv6pgBUVFdxwww18svQT/vXsv/BmvPz6D79mxvwZ/PLyX1JetvMHSKlUilQqJSFJEM6EKbGVYFDk318mkyGTyeCNeXmq4ykCmQB6qZ7TTafTs6EHt9xdqPjTarUFk+q+NvLLpl9GibaEf3/5b15a8xLuqJsrZ145oM3TpDYRTARZ716ff5/bEfi+7acHYLFYdspPL5vN8uGHHzJjxgymTJky+EK//W3eK+FrRrOTh8/no6SkBPs2vDQ2eTdx6zu3Ek6FqbfUc9Psm7b5ne8L1ynVjX6T+yIjG61Cmw/g0Vpp8+Vbdc1q84Cgn91BEATOnXwu9yy5h/kb5zN38lxKSkro7e3F6XTidDoxGAxoVBpCqRDRTHSrybrfprKykscff5xf/vKXfPHFF1xxxRXcdNNNnHLKKbu30yN8jPr000958MEHAfj+97/P97///e2u09vbi9FopLm5eZ/xSO0jnAyTzCaxyrZ9EeqNefm061Ng5ARpbI9KQyX3nngvf/n4L7zf/j5/X/Z3Wjwt/OzQn414cXJnMKqMOMNO3BF3wZJkZ9myWq8vmGPLFl6fz4fD4SCXyxVSeNVq9bCJfWPHjuVXv/oVV1xxBfPnz+eFF16gpaWF119/nddff51x48Yxb948vvOd7+xcZ9YIH5+K7CSBQN5nT6GAQayQMpEIxrvuQh6PE2to4De+/PXTCSecMOBcUhYMktVqSdbW7lY6a1+BQk1NzT5T4T3UlJWVscloJJDJYOvu5uRTTuHJJ5/kvxIJ5wDmhQuJTJtGKBzGGY2iz2a32nK9P1Dx7YnXW26BW2/d+Q05nfn7b1uilZZCe/su7duOsNvi3s9//nPOOeccbr755n0ixayP5uZmVCoVJpOJmTNncsQRR+x0EMZwEEgF6Ih2oJAqCi1BqWyKJzc8iSPuQCfT8ZOmn6CX62l1tKIyqtDYNASTQfxxP76YD3/cn/857iOSipDOpXFFXbiirm2+tkwiw6K2YNPaKNGUYNPasGqs2LQ2bBobNq0NvWLPCIBSiRSdUodOmT+4pLNp4pk4gkFAIVXgcXpY2r4Uq8aK1WzFEXUQiUWQpWVEhAgajYbKykpMJhN6vb4QLBBKhHhp5Uu8sf4NIqn8DKNVY+X0Cadz/Njjd/hCUi6VU22qptpUzbETjiUYDOJyuXC73YQSIVKKFBEhgjvhxpPwFO4zYgZ3wo074Wa1fzUnVp3IjJIZ/WacBUHg0JmHMuWAKbz40oss+nARn7Z9ynlXnscFp13Ahd+9cKdbO0KpEB2RDkREjIpvSrFlMhmRbISnu58mkAlgUVi4dPylmBXmfiEv0Wi0X2WrRCJBKpUik8lokDZwZtWZvNr9Ku+3vU9vsJefT/s5BrWhIALK5XKMKiOhZIivXF+RzWWpMg4srx8KP71oNMprr73Gf/7zH7q7uzn99NM5/fTTB1/4//0/uOaawq8969fDUFTb7GGi0ShyuZzq6uqtnvy0eFq4bdFtRFNRxtnGcfPsm7dbXRJJRTAoDcUgjSJ7hL60baPSSEewg65AF5FUBIvaMmR+jzOqZ1BjqqEj0MHr617n3CnnMmbMGOx2O729vfT29hIKhcgoMwQ1wR0W9yCfZvnnP/+Z2267jQULFnDrrbficDi49NJLd/24OYLHqJUrV3LdddeRyWQ4/vjjueqqq7a7jtfrRSaT0dzcvFvhXyOVYCK43SprgIUbF5ITc0y0T6TGVLMH9mxoUMlUXHPENYwvGc8Tnz/B+23v0x5o54ajbtipqvyRjESQoJFr6Azmq/eGwiZAJpNhNpsxm83U1taSSqUIh8OEw2FcLhfBYJBAIEAul0OlUqHRaNBoNEMuZmg0GubOncuZZ57JmjVreOGFF3j77bdpaWnh7rvv5qGHHuKkk07irLPO2jFrgRE8PhXZSRKJvLAXj0PlIKK2KJK56y40nZ1k9HpeP+ooep94Ar1Ox6GHHtpvUSGRgFSK5MSJ5HbTxsvv92M2m/dLL9sdRavVUl1dzQaPB4tKxayDDuLVV17hqUCAswHdqlUoHA6MJhMut5vy8nJ0nZ35//MoE0w/7fyURz97lNW9q3FFXfz19L9yQuMJ219xC3o+/RTDli3ku1vB+O3zO1Ec+NgQstvinsvl4pprrtmnhD3Il6uvWrWKr776CrPZvM1UyT1FJB2hI9IBgE6ev+jO5DI8tekp2qPtqKQqLhl3CTaVDb/fT6Wxcrsz36lsqp/Y923xr+/nUDJEJpfZrgiolCr7CX4l2pKC8Nd3P5SVFn3IpXLkUjkGpYFSXSnV1mq6nF1s6thEe1s7WrkWm8GGtdJaCJPYUgBzR928uvZVFmxcQCqbAqBCX8GZk85kVt2s3U5q7PNdKS8vx+1209vbiyFhoNHQiK4ib9qeE3MEU0FcCReLHItoj7TzaserrPSt5KwxZ1Gi6n/g0mg0XHD+BRx15FE88c8naHO28cTrTzD/3flc/7PrOWjaQTu0b+F0mLZIGxkxg0lh6vecL+nj0fWP4k/l0yEvG3cZJmV+mT5hbjCy2WzhlslkGK8az1z7XF5zvUaLv4VbF9/KGbYzsKgsyGSyghCoVCrJSrI4e500mBsYYxlT+O45nU66u7sJh8O75KfndDp59tlneemll4hGowAYDAaqqqq2vtK3SrHFnQhtGSlks1lCoRANDQ1bvVj+yvUVt797O/F0nAklE7jpmJsGJCQPRiwVo9HWOORJpkWKbAuVXEWjtRGL2kKrvxVnxIlJZUKr2P0kWokg4Zzmc7j/g/t5bd1rnNp0ar5qXKOhrq6uIPK19bSxvnM9+io9Rt2Oe9MolUruvPNOysvL+cc//sHDDz9MT08PN9544675bY3QMSqdTnPWWWcRjUaZMmUKt91223aFiFAoRDqdZurUqdsN/BlNiKJIMBGkN9JLT6hnu5/TbC7Lgo0LgNFTtbclgiBwStMp1FvquW/JfXQEOvjlm7/kqsOuYkb1jL29e0OCQWXAGXbiirioNg19dYtCocBqtWK1WhkzZgzJZLJQ2dfn1xcIBBBFEZVKVajsG6rJdUEQaG5uprm5mauvvpr//e9/vPjii3R0dPD888/z/PPPc+CBB3Laaadx0EHbONccoeNTkZ0km4Wvvsp7sW2tmuvll1G9/TaiIND9s5/xyiuvADB79uz+19C5HLJAgERNDeldGOdTqRSxWKxgp6RQKKirq9un7BuGg4qKCtra2ggnkxj8fk466SSefuYZPlUomJFKYX7nHVLnn08wFMKRTNLo8YDXC6NMNI2lYzTZm5jXPI/LX7t8l7Yh6nQwFJOLZV/7yDudeX/KPlyugdV8Q8hui3vz5s3jvffeo6GhYSj2Z0Qxkk4u45k4HdEOUrkUFmXeKDgrZvlP63/YENqAQqLgh40/pFxTTjweJ5PJ0NjYuN1KQ4VUQamudLttdelsGn/cjzfmxRPz4Il68vdb/BxMBElmk/SEewr+dIOhkWu+Efu+FvxKtCXUGGuoMlYNySyoUqakoaqBSlslgUAAtVqNTqcbIAZ1Bjt5ac1LLGldQlbMt2A3WBo4a9JZzKieMeTpn31tGOXl5Xg8HhwOB06nE7VajV6vx6w0Y1aaaTQ0stS9lPld82mLtPHQmoc4ruI4jiw7csCsf21tLTffeDNLlizh2ZefxZlxctWdV3HMtGO45vJrsFi2nloXSUdoj7STzqUHtAB7k14eXf8ogVQAq9LKZeMv61fVN9i2JIIEjUxTaPXdkoNMB1FhreDJDU/iS/t43vs8F9ZdSImipFBWH4/HyWazRNNRWltbKVWUYlfakUqlZDIZjEbjTvvprV69mqeffppFixYV2uzHjBnDeeedx/Tp0/d5Y/u+Wc2tJXavcq7irvfuIpFJMLl0Mr+e9esdEuCTmSRyqRyrpuhxUmTPIwgCNq0Ng8pAV7CLjkAH0VQUq8a62+P2zJqZVBmq6Ap18XrL65zdfHbhOa1WS319PbYSG2vb19Ib6CUcCGO1Wne4sl8ikXDFFVdQXl7Ovffey3//+19cLhf33HPPTqW+j2Tkcjn/+Mc/uPLKK7nhhhu2O0Eai8UIhUJMnjx5q2PVaCMn5vDFfDjCDjxRD5lcBoPSsN2Jk8+6P8Mb82JUGplZM3MP7e3QM9E+kd+f9Hvue/8+vnJ/xW8X/5Z5zfP43pTvjfpkdYkgQavQ0hXswq6zD0vIz5YolUqUSiU2m436+nri8XhB7HO73QXvPqAg9g1VEq/RaOT73/8+5513Hp999hkvvPACixcvZsWKFaxYsQKj0ciiRYuYNm3abr9WkRHK5s1bDdAAYM0axPvuQwB6zjyTtTod69atQyqRcOyxx/ZbVObzkTGZSFVX71D1UjqdLnijZzKZghXQmDFjMJvN6PX6fbLKe6jpKzBxuN0YgNlHHcVrr73GU5EIMwDj++/jmjcPk9FIr9dLmSCg7+4edeLerPpZ2w0C3GPU1eUFvrffhqlT84+lUrB4Mdxzz7C97G6Le3/+8585++yzef/995k8efIA5fzKK6/c3ZfYJ0mlUqTT6Xwi19e3XC4H5EMCtnw8mU3SEesgko1glBpxifngkgW+BayNrUWKlFMsp6CIKHBG8v3dY8aMGVJxUi6VY9fZseu27teVyqby4l+0v+i3pRAYTUWJpWN0BDroCHQM2IaAQLm+nBpTzTc3Yw0Vhgpkkp3/uKpUKsrKBiawtnhaeHHNi3zS+Unhscllk5k3aR5TyqYMe2uxSqWiqqoKu92Oz+ejp6cHt9udb081GpHJZBxmP4wmYxMvt7/MhtAG5nfPZ5V/FWeNOYsKTf+LH4lEwqxZs5g+fTrPPvcsH3z+AYu/WszSHy/lR+f+iHlnzhsgtsUyMdoj7SSyCcyKbwl7CS+PtDxCMBXEprRx2fjLCj5830YURQKpAHKJnFQ2RTqXxiA3DPo3rNBUcPmEy3liwxP0xnt5dOOjnN9wPuOM/Y12SyghkUkQyUQQVSIWpQWlYsdPnrPZLIsXL+bpp59m5cqVhccPOeQQzjvvPA477DAkEgnOPj+EfZREIp/+V1NTM2hV0ErHSu567y5S2RQHlB/Ar4/+9Q5fpISSIaxqK3rl8ES5FymyIyikCuot9YUqvt5oLzqFDoNy10/2pRIpZ08+mwc/fJDX1r7GKeNPGSB4G/QG6sbUoUOHOqqmq6uLYDA46PFma8ybN4+ysjL+3//7f3zyySf86Ec/4g9/+MM+0wlx+OGH8/DDDxd8UbdGKpXC4/HQ1NREXV3dHtq74SOdTeOL++gOduOL+xAQMKqMOzy2vtXyFgDHjj121FdFWzQW7jj+Dp5c/iT/W/c/Xlj9Ahs8G7j2iGv7eRePRgxKAz3hHpxhJ7Xm2j362mq1GrVajd1uZ+zYscRisX5iXzgcxu12I5FIUKvVBbFvdxAEgUMOOYRDDjkEt9vNq6++yksvvUQul6OxsXGI3lmREUdPD6xfv9UADQIBuP56hHQa96RJhM44g7cfewyAgw8+uF+BgSQaBYmERF0d4lYmfHZEzBusWKPI9qmqqqKzrY14LIY2leLEE0/k+eefp0cqpSIWw/jRR4izZxMMBnEkEugcDoT6+qGpYttXiURg48Zvfm9thRUr8t+Xmhr4xS/g7ruhsTF/u/tu0GjgvPOGbZd2W9x75plneOutt1Cr1bz33nv9LugFQSiKe1+zZRlx32ClUCgQBKFwk0gkSCQSBEEoVD7lyNEV70Iuk9OobkQmlSGRSHi17VXWxtYiESRc0nwJ00rzM2Z96+7J5N4+FFIF5fpyyvVbD3WIp+ODin7OsJOOYAfhZLhQ+be0c2lhPZlERoWhglpjbT/hr1RXusMJaKIostK5khfXvMgq56rC44dWH8rcSXMZZxu3jbWHB4VCQVlZGTZbvpXa4XDg9XqRSqUYjUYsSgs/bPwhy73L+V/n/+iOdfPnr/7M0WVHM7t8diEpuQ+9Xs+ll1zKrKNn8cRTT9Dt7eZPz/6J/739P3515a9onpQP/ohn4rRF2ohlYliUln7fW0/CwyPrHyGUDlGiKuFH4360TWHPl/ShkWmo0dWQE3N0RbvwJr1bTf41KUz8dPxPeWrTU2wKb+LJDU8yd8xcDrL1b+1QyfKzzo6EA0EiUC4r3+5sfzQa5b///S///ve/6e7uBvLtwyeeeCLf+973BqZ1bYdkJllo04Z8ufdoQRRF/H4/1dXVg1ZvLutexj2L7yGdS3NQ5UFcf9T1O5z8lxNzZLIZSvWlIyIJsUgRk9rEZOVkekI9tAfacYadWDXWXRZHjqg9gme/fJaecA9vtrzJ3ElzByxjUBryFa8TJlNeXs7KlStxu9075f1zxBFH8Mgjj/CLX/yCDRs2cPHFF/PQQw/t8MXySB+jtjc+ZDIZHA4H9fX1NDY2jurxJJlJ4ol66A51E0gECj7FO/MZdIQdfOH4AgGBE8bunE/QSEUmkXHpQZcyzjqOvyz9CyudK7n2zWu5/sjrabSNXlFIEAT0Cj1dwS5KdaWo5HsvNKTPg6+0tLSf2BcMBguVfb29vRgMBoxG42579ZWUlHDppZfy3e9+l02bNm3VUmCkj09FtkMgAGvXglw+aIAG2Sz8+tfQ20vCbqf94ovJRSJ89NFHQD5Io0AmgywUItbYSHaLa9Qtxby+tGi1Wk1NTQ0Wi6Ug5u2SbUWRflitVsoqK3H5fIyJRDju2GP53//+x7/jca4lH6wROOYYTCYTvZEIZW43BpdrnxX3hmR8WrYMjjnmm9/7PEYvugiefBKuvz7vU3n55eD3w4wZsGAB6IevMGK3vym/+c1vuP3227nhhhuKKTVbMJiYp9FoqK2tLaSy9vlj9Al6ffd9ZHNZWjwthAIhGnWNhcq1Z798lve63wPgiplXcEz9MYPtwohELVcXgia+TZ8vTXugnY5gR6G6ryPYQTwd/6bab4uAGaVUSbWxuiD21Zry4p9F/Y1glc1l+aTzE15c8yKbfJuAfCrt0XVHc+akM6k27v00IJlMRklJCVarlUAggNPpxO12I5PJMJlMTLdNZ5xxHK92vMpq/2redbzLav9q5o2ZR61u4Izx2LFjuf3m21n4zkJefP1F2uJtXHbDZZx85MmcccYZJHQJsrIsVpW132fOnXDzyPpHCKfD2FV2fjT+R1s1jM+JOXxJHzq5jlptLVp53kdIKVHSGe3El/BhVBgHFYxUMhUXN17Mi+0v8oX3C15oewF/0s9xFcf125++NOjuWDexTIwyddmgQmOfn97LL79MJJIPQjEajcybN4+zzz57l6tY//bJ3/jjx38s/J4JZHZpO3uDYDCITqcb1FPwk85PuO/9+8jkMsyonsEvj/jlTl2ARlNRtAotZrV5+wsXKbKHkElk1JhqMKvNtPnzibpqmRqjyrjTopFUImVe8zz++PEfeXXtq5w0/qQBiZ9quRp/3E8gHqDSXsnkyZP54osvCq3wO8qECRN48sknufLKK2ltbeXSSy/lnnvuGWBCPhijeYzK5XI4HA4qKyuZMGHCqL14i6ViuKIuekI9hJNh1HI1dq19l1pP39qQr9qbWjGVMv2OV4GOBo6qO4pacy33LL6HnnAP/2/B/+Mnh/yE48Yet7d3bZfRK/U4wg6cESdjzGP29u4AedFRq9Wi1WopKytj3LhxRCIRXC4X7e3tdHV1oVarMZvNu/2dk8lkVA4WrvA1o3l82u9JJPLCXjQKW/Omfvhh+PRTRKWSLy+4AI3Nxuuvv046k6FuzJh+k1Ryj4dUaSlRm41YMEgsFiOdTiOXy9FoNEUxbw8gCALV1dU4W1tJR6PogBPmzOG/r7zCzwUBVWcn6pYWGD+eQCBAbyyGvr0dobp690MlRiBDMj7NmpUPyNgagpBP2t2VtN1dZLe/OalUinPPPXe/F/b6xLxoNFow+NydMuKcmKPN30ZHsIMSbUlB2Pvvuv/y7y//DcClB106qLCXzCQREZFJZEgF6aiZCRcEAZPahElt4oDyAwqPi6KIJ+bJi36Bb0S/zmAnyWySjb6NbPRt7LctjVxDramWKmMVa3rXFDwAlVIlxzcez+kTTqdEO/J8BCQSCRaLBbPZjN1up6urC7fbjVqtxmAwcH7D+azyr+LV9ldxJ9z8bd3fmGmfyZzKOQUhrA+pVMqcE+Yw45AZPPOfZ/h09ae8ufxN3vjkDaQ6KdK4tJDOZjabUdvVbK7ZTFqSxiwxc1bJWaiFwT2ksmIWf8KPUWGkRldTSG6GvHBXp69DJVXhjDtRiSq0soEG4jKJjHPGnINJYeJdx7u843iHQCrA3Nq5/S6KlFIlFomFYCpIJB3BrrZjV9lRSBWsWbOGp59+mnfeeafgp1dbW8t5553HySefvNttKD+Z8RN+eNAPC7+3rG/hoLt2LKhkb5JOp0kmk0yYMGHA3+DD9g954IMHyIpZDq89nKsPv3qnW94jqQh15rph9xkqUmRX0Cv1TLRPxKqx0uprxRFxYFVbd/rzelTdUTy76ll6I70s2LCA0yacNmAZlUyFM+Kk3FBOWVkZzc3NrFixAqlUulM+QOXl5fz973/nuuuu4/PPP+eqq67ixhtv7F/5MAijdYwCcDgcWK1WmpubUY7CC4dwMowz7MQZcRJLxdApdJTpy3a4m+DbpLNp3tn0DjA6gzR2hFpTLfedeB8PffQQn3Z9yp+X/plIKsIZE8/Y27u2SwiCgE6pK1TvDUdg3O4iCAJ6vR69Xk9VVRUul4u2tjacTicymQyLxTJsoYGjeXzar8lmYd26fBDA1oS9xYvh8ccB6P7hD0lWVaEG3nknP4adcMIJ31x/er24Ewk8MhlCIIBGoyl0lRTFvD1LSUkJ5ooKPH4/5eEwc044gflvvsnrySRzAcvbb9M9fjwmkwlnMEhpVxcGt3vrn4NRzL46Pu32N+miiy7i2Wef5de//vVQ7M+oYWtiXl1d3ZB4AnQGOtnk24RZZS5UPr2z6R3+vuzvAHxvyvc4pemUAesFEgFSmRRyqZxMNkNG/EaFFhCQSWTIpDJkQv5eKkiRSWQjWgAUBIESbQkl2hIOqvzmS5fNZXFGnAWxr6/iryfUQywd4yv3V3zl/goAnULHyeNP5uTxJ48KnxdBELDZbJhMJjweD52dnTidTgwGA5PNk2nQN/B65+t87v2cj1wf8VXgK+bWzqXROLDFxWQycflPLmfW2lk8/+rzOL1Owr1hMmIGp9OZP8GzyCiZVIJUIiXlTrHmxTXcmLgRQRAwGAyYTWbMlrwIaDKbUFgUVBmr0JXqSAkpVIb+xs0yiYwqbRUqmYquaBeBZACjYmD1jCAIzKmcg0lh4tX2V/nc+zmhdIjvN3wflfQbUUoqSLEoLSSzSTrDnbz36XssemkRXy79Er6eMDn44IP5/ve/X/DTGwqUMmU/QUAj336C7EjA5/NRWlo6oD1wcetiHvroIXJijqPrjubKmVfudHVJOptGKkixaUdO4FCRIt9GKpFSYajApDLR5m+jJ9yDXCLHpDbtsPgik8iY1zyPvyz9Cy+veZk5jXMGCIR6pZ5APEAoEcKkNlFdXU06nWbVqlVIpVK02h1P8DUYDPzpT3/i9ttvZ/78+dx+++10dnZy9tlnb3Wd0TpGuVwutFotzc3NO/U32tuIoog/7scZdtIb7SWdTaNX6qkw7H4IyEcdHxFOhrFqrEyvnD4Eezsy0Sq03HD0DTy94mleXPMiTy5/klQ2xTmTz9nbu7ZL6BVfV++FndRZRrZnpFKppLq6mvLyctxuNx0dHbhceR/vnQkF2uHXG6Xj035Pa2s+QKOsDAa7ju3shFtuASA1dy5t48ej1+n4fNkyvD4fRoOhUHkuiccJuN0oZ8zggAMOKAjNRTFv7yCVSqmtrWXF5s2UxmIYlEqOPfZYnnvjDeYC+mXLkAUCYDIRBHqDQfQdHQgVFYOHqYxi9tXxabe/WdlslnvvvZe33nqLKVOmDAjUeOCBB3b3JfY6oigOu5i3JY6wg43ejeiV+sIs4McdH/OXpX8B4LQJpw16EhRMBMlkM0wsnYhJZSKTy+QDDrJpMrkMyUySeCZOPB0nnU0TT8fJ5rKkc+nCNgQEpBIpcok8X/knyYt/I1EAlEqkVBoqqTRU9kuUS2fTdIe6aQ+00xnsxKw2M7t+9pDOqGZz2fzfMZcPRQEQ+ToE5Wu1aWs/D4aAkN/O139iERGVVIVZbaasrAyz2VxI1nU6nZjNZs6uO5sDLAfwUvtL+FN+/r7h7xxkO4iTqk5CIxs4QE2cOJFbJuYPxslkEr/fj9/vpz3QzgfSD8hIMsgjclRfqMjpcvjTfrLZLMFgkGAwSFt7G0hBppeR9qfJuDKImfx76ktxMxqNmEwmjEZjwdtFZVKRNWaRaWWU6kox6A392tIBZpTMwCg38szmZ9gQ2sDD6x7m4saL+7XgJhIJFi9ezFtvvYUn4kGQC6ir1Bw99WguOPcCxo8fv3v/1H2ESCSCUqmkqqqq33f2nU3v8OeP/4yIyLENx3L5jMt3qW0snAxjUpswqraenFykyEhBo9AwwT4Bm9ZGq6+V3kjvTrVMzqqbxXOrnsMddfP2xrcHTKrJpXIyYgZf3IdJbQKgrq6OdDrN2rVrkUqlO1VBrFAouOOOO6ioqODxxx/niSeeoLOzk5kzZ6LR7BsnnoFAAIDm5uadal/em2RzWXxxHz3BHjwxDzlyGJVG1JqhO6+Y3zIfgBPGnjDq02S3h0SQcMHUC1DKlDyz8hmeWfkM6Wya8w44b8Sda24PQRDQK/V0hfLVe9tLQx4JyGQyysvLKS0txefz0dXVRU9PDx6PB5PJhH4Y/aCKjHAcjnyAhtk8eCtmIpH3EYtEYMoUHOecQ7qrC6VSyYIFCwA4ZvbsvB6QySB6PETKy5kyYwYl9q2HMhbZc5SWlqKvrsYfDGIJhTjxxBNZsGABKzIZDsxmMS1ahGfu3Hz1ntdLyaZNmMaNgyEM6hwuoqko7YFv/Lu6gl2sda3FpDINySTcaGC3xb1Vq1Yx9et439WrV/d7brQdoLeG0+lEqVSi0Wior68vHPiGI63HE/XQ4mlBKVOiU+TNS1c4VvD7D35PTsxxXMNxXDzt4gF/22AiSDqbpsnetM1Aiz4yuQyZbIZ0Lk0qmyKTy5DO5n+OpWMFATCZTRJNRfsJgBJBkhf/pDLkEjkKqWJEnYjKpXLGmMcMqf9Jn5gXz8TJZDP5BDKZGq1Ci4SvPRMFSb4iRMj/jSRIECQCEiSF5/qWE/g6SOXre6Df79lctmAKb9FYUCqVhQTknp4eXC4XoVCIenM9V0+6mvnd81nqWsoyzzLWB9dzes3pNJubt/p+lEolZWVl5Aw5Pk1+SiaToVJTySUHXoJmVv7ENJfLEYlE8Pl8BAIB3D43veFekq4kofYQHq0Hl8tFMBgkmUzS3d1dCLH4NoJcQF4qR2aUkY1mEdMiUqkUnU6HTqdDq9Wi0+koKSmht7EXR9zBAyse4AiOoExTxupVq1n07iJisbzZqU6nY9asWRxy1CGUWEoKYvbOtJeKokgms2/5v2QyGcLhMI2Njei2MD9+a8Nb/PWTvwL5dq/LDrlsl1rHRFEkkU3QqG/c5dazIkX2NIIgYNfZMSgNrHGtwRvzbjP5fUvkUjnzJs3jr5/+lZfWvMQJjScM8BHVKrT0hnupMlahkOaDssaOHUsqlWLDhg2Ul5fvVNubIAhcfvnllJeX87vf/Y733nuP1atXc8QRR+zU+x6JRCIRYrEYBxxwwKhIBU5lU3hjXrqCXfjjfqSCFJPatMPhQztKm7+Nr9xfIREkHD/2+CHd9kjmnMnnIJfI+ccX/+D51c+Tzqa5aNpFo+76oc97zxF20GBt2Nu7s8NIJBJsNhs2m40xY8bQ09NDV1cXHR0dGAwGDAbDfm+7tF8RDOZ99mSywQM0RDGf9rlhA1gspO+4g97ubrQaDW1tbaxvaUEqkXDs7Nkgisg9HpwqFfoDD8Q6CoSh/QWFQkFNbS3rNm3C4vdjslg4etYsnl24kAMB07vv4jntNBQKBaJMhru3F0NXF5JR8D9c5VzF95/7fuH3u967C4C5k+Zy34n37a3d2qPstrj37rvvDsV+jEjUajWNjY1oNJqCCDGc0duBeIB17nWIoohRna+KWedex2/f+y2ZXIbDag7jpzN+OuCkJ5QIkc6mGV8yfoeEPaBQjadi6xUF2VyWdDZNOpfudx9Px4mmosQzcZLZJOFkmKyY9zsTEJBL5cglcuTSfPXfrqYV7k0yuQyJdKIg5kklUlQyFXatHbPajFahRavQDut7M6vNtPpa6Q53o5AoMKvN6HQ6xo0bV/Dj83q9KBQKTq06lQPMB/Bi+4u4E27+telfTDZP5rSa07YaiNEd7ebvLX8nlo1Rra3mh40/7OedJ5FICid3yWySqnQV5epyKjQV/cTcZDKJ2+3G4/EQCoUKlX4DbuEg4VyYmDFGKpoiG/+mKnBLpB9LsZ1hAwssSC7A+y8vya4kAGVlZXznO9/hyCOPLHg0RTNR2qJtBNIBytXlW0327SObzeL3+4lGoxgMhr2SLD1cBAIBbDYbZWXfGLG/vv51Hv3sUQBOGX8Klxx0yS5fOMXSMTRyDSaVaSh2t0iRPYpKrqLB0sCXzi8JJ8PolTtWnTK7YTbPrX4Ob8zLO5ve4cRxJ/Z7XivX4oq68MV8hRAEqVTKhAkTyGQytLa2UllZudNtSGeeeWbBmmHatGk7te5IJJFI4PP5aG5uprp67wdZbYtEOoEn5qEr2EUoEUIhU/TzPx5q+oI0ZlTPwKIZmG6+L3PmpDORS+U8tuwxXvnqFVK5FJcedOmom0DSK/X0hHso1ZcWJudHEyaTCZPJRG1tLU6nk7a2Njo7O9FqtZhMpmIb5b5OJpMX9iKRrfurvfgivPFGvlX37rsJKBREYzHsJSX8+995P/hDDjkkH9bi95NSqwlarUyrry+KxCOMsrIyNldUEAkG0UYinHLKKfzqnXfwiCK2QAD9558TnjEjn5zrcGBbvx5zQ8OwprwOBYfWHMqmX27a27uxVymO1NtAJpP1S/oZTiKpCOvd60lmk9i1+YqCVn8rd7x7B8lskmkV07j68KsHVMiFEiFS2RTjS8YPebmpVCLNi1pbEQBzYo5UJkUq+82tT/iLpWP9q/7EfDVCn9i3pfg3EmZotyrm6eyYVXtGzPs2armaCfYJWDSWvCl82IFVkzeFN5lMGAwGvF4vnZ2d9Pb2YtVZuWLCFSxyLGKJcwmr/KvYFNrEKTWnMNUytd/fuSvaxWMtj5HIJqjR1vDDxh8OSILsI56JE8vEqNBUUKGpGHDC3dcCOlgq67cRRRFv0stm/2bC0TCShIRoNEokEincotEogc4APdIe0sY0trk2tOu0nNJ8CgceeOCAEwStTItKqiKYChJNR/sFbmxJKpXC5/ORTqcxm800NTVht9uH3GNmbxGPxwGorq4unIS/svYVnlz+JABnTDyDi6buXkVEJBWh0lA5KtqOihQZDJPaRJ25jq/cX6GSqXZoTJdL5Zw16Swe+ewRXlz9Isc1HNdvPalEilKqZJN3E2q5utCyLpPJmDhxIplMhq6uLiorK3d6gnDmzJmEw+Gde5MjkHQ6TW9vL42NjTQ0NIyI4/7WcEVctHhaiKQiaBVa7LpdS77dUeLpOO+1vgfsu0Ea2+OUplOQS+X87ZO/8cb6N0hn0/x0xk9HlcCnU+hwhBw4Qg4abXvm2mE40Gq1NDQ0UFVVRW9vL+3t7XskfKPIXsbphN5eKN9KkciqVXD//fmff/5zxOnT6V2zBplMRiQS4eOPPwZgzpw5SGIxyOXotdkwVFYO8H8usvfRaDRU1dfT0daGPhbDVlbGoUceyctLlvAjwPz224RnzEAulyNqNLhaWzE6nUhGuLhXpCjujQgS6QQt7hZCyRClunybSk+oh1vfuZVoKsqEkgn86qhfDbgICSVDJDNJmuxNe6WPXCJIUMlVqOQDRSFRFAuCXzKTLNxH01FiqbzwF0vH+rX7SoW8mCgVpEgkkn6/SyXSIT3JG0zMU8vV2HV2LGoLGrlmj4t5gyEIAmX6MgxKA23+NrpCXahkKkwqExKJJJ96ZDbT29tLd3c3XpeXoyxHMdk8mRfaXsARd/Bc63Os9K7kzNozMSlNdEY6+fuGv5PIJqjV1nLxuIv7hVdsSTQTJZVNUaWtokxdttsXZIIgYFPZUNqUdKm7CKfCVCorB62GSOfSPN/6PF/6vyQ2McYy7TLKEmVUaAZ+1rcM3OiOdRNMBSnXlGNSmEjE89UiEokEu91OdXU1JSUlA/xBRzOiKBIMBqmtrS1UIj6/+nmeXvE0AGc3n73bXkbZXBYRcUSmTBcpsjNUGCoIJoN0h7op15Xv0PfiuLHH8fzq5/HEPLy7+V1OaOyfYmvRWHBFXKx3r2di6cRC5Y5SqaS5uZlMJkNPT88AL8z9gVwuR09PD7W1tTQ1NY3oCg5RFOkKdpHMJKnQV+yR/9WStiXE03Eq9BVMLps87K83UpnTOAe5RM6fl/6Ztze+TSaX4eeH/nxE2b5sD4PKgCPsoExftsOVwSMVpVJJTU0NFRUVuN1u2tvbcblcAFgsln1mYrQIkE7nQzRUqnxL7rfx+eCGG/LVfbNnw/nnEw6H8QcC6HU65s+fTzqTob6ujobaWqReL7G6OgISCdNra4tVnyOUiooKOktLiW/ahDyR4NRTT+W+JUu4GNCuX4+ys5NkdXW+g6C9HduKFVirq/OfkyIjluK3bS+TyqZo8bTgiXqw6+wIgoA76uaWd24hmAhSZ67jxmNuHJDQF0qGSKb3nrC3PQRBKKTQfPsERxRF0rl0QfTruyUyCdKZvM9fnw9gKpsim8uSFbPkxFy/7UgEyaCCoEwiyz+3xQlhJpchno6TyCRGtJi3NTQKDU32JsyafKuuI+LAprGhkCqQyWRUVlZitVrp6enJz7CmZVw29jKW+pbyTs87rA+t54E1D3BE6RF86PqQZDbJGN0YLm68GKV0EMNcIJKOkMllqNHVYFPahvQiRy/XU6+vpyvahSfpwSA3DNgPuUTOd+u/S6mjlHcd77I5vJk/rf0TB9sO5oTKE9DJB7a9KKVKbBIboXSILx1fokgrqNRVUl9bT1VVFRaLZURfWO4q0WiU0vJSKisrEUWR/3z5H55d9SyQT9Y+d8q5u/0a4WQYo9JYCA0oUmS0IpVIqTfXE0lG8MV9WDXW7a6jkCo4c+KZPP7547yw+gVmN8weMClRoi2hN9JLi7uFifaJhYkvtVpNc3MzqVSKnp4eKisrh+V9jVSCwSB1dXVMnDhxxE+qhJNhgokgJrVpjwh7oijyVku+JXdO45xRVak2HMxumI1cKufBDx/k3c3vkslmuOrwq4atHXqo0Sq0BMNBHGHHqBf3+tgyfMPr9dLV1YXD4cDj8RQFvn0FpxM8Hhjs2JTJwI035qv6amvh5ptBEPD6fGQyGaQyGe+88w4AJ5xwAgqvl1RZGW6dDqNcPiq8VfdXDAYD9rFj8bS3Ux0KUVZWRv2hh/Le0qUcB5gXLsR58cXIZDJyRiO9LS1IV6/GdNBBe3vXi2yD0XG03EfJ5rJs9GzMe3ToSpFKpAQSAW5951bcUTcV+gpuOfaWAd4d4WSYRDpBU8nIFPa2hyAIKKSKbRpS9wmA2VyWTC7zzb2YJZPNkBEzpDKpvBD4dWtwNpclQ4ZoOko2940YKCIiE2So5WpKdaUFzzyNXDNixbzBkAgSyvXlGJXGQhWfWpZvARMEAZVKRX19PSUlJYXQjcnyyTSNb+KVzldoj7azyLEIgHp9PT8Y+4Ot/g9CqRAiImP0Y7Aoh8f/RylVMkY3BqVUSW+8l3QuPUCwkwgSjq04lmnWabzZ9SZf+r/kU8+nrPSv5NjyYznMfli/k/5sNks4HCaRSKBT6VBalWhKNJgrzBgNxn1S2ANQqVTU1NQgl8t5asVTvLTmJQAunHohcyfNHZLXiKVj1JhqRs1FVpEi20Kj0NBgbWCVYxXxdHyH0tTnNM7hxTUv4oq6WNy6mGMbju33vCAIlGhLCm2dTfamwhir1+s54IAD+Pzzz3E6nf18Mfd1KioqmDRp0qgQAnxxH+lcesgDM7bGBu8GNvs3I5fIOabhmD3ymiOdI8cciVwi5/4P7uf99vdJ59Jce8S1o+Z8zaQ04Qg7KNeX7zMCH1DoFrHZbNTV1dHd3U1XV1exKmu0k0rB5s2g0eS99L7N3/4Gn30GajXcdx/odHmvbZcLnVbLsmXL8Pn9mIxGjmhsJKvXk6itJeh2c8ABBxT8sYuMTKqqqugtKSHV3Y2QzXLaaafx7NfinuH993Gdcw45rRaz2UwgEiGyYAElKhU1EyYMaw5BkV2nOCLvJXJijlZ/K52hzoJJczQV5fZ3bqc71E2JtoTbjrttgHF9OBkmno7TVNJEpXHfnf3vEwDZwXFDFMVvREDxGzEwk8uQE3No5JpRJ+ZtjS2r+DZ7N/er4oP8ReT48eMpKSnJt+p6vXy37LusTa7lbcfb1Opq+X799we9eBFFkWAqiEwio0Zbg0lpGtb3IpVIqdRUopaq6Yp14Uv6MClMA6oXzEoz5zWcx2Hhw/hv53/pjnXzRtcbfOL+hJOrT2asZiyhUIhMJoPBYKC2thaLJZ8yHE6GWedehzfmpdZcu0NVOqMJQSJgL7VjsVh4/PPH+e+6/wLww+k/5LQJpw3JayQyCZQy5X5n9F5k36ZEW0KtuZaN3o07lPqulCk5c+KZPLn8SZ5f9Tyz6mYNWEcqkVKiK6En3INcKmecbVxhGZPJxJQpU1i+fDkejwfbKEie210qKipQqVQYDNsOOhoJZHIZesO9OyT0DhVvtrwJwBFjjsCgHPl/oz3FoTWHcsNRN3DPkntY2rmUe5bcw/VHXb/HRNfdQaPQEAwH6Qn1ML5k/N7enSFHEIR+4RuhUAhVsU1v9OJwgNcLg4UcvfcePPlk/uebboL6eiAf3haNRrHb7Sx4K195fOLhhyOTyYjV1RHKZtHpdPvVJNZoxWKxYB47Fn93NyXhMNXV1UimTWPj8uWMTacxfvAB/jlzkEqlmGtrSW/ezMZ338WXTDJu3Dj0RQ++EcdulbFEIhGWL19OIBAYot3ZP0hmkmz2bWazbzNmlRmFVEEyk+TOd+9ks38zRpWRW4+9dYC3VTgZJpaOMb5k/D4t7O0KgpBP6VXJVegUOowqI1aNlVJdab7aTWXcJ4S9Pvqq+A6sOJBKQyXemJdAPIAoioVlLBYLEydOZMKECSgUCurFeq4Zew0XN168VWHPl/Ihl8oZoxsz7MJeH4IgYFVZadA3oJPp8Ca9ZHKZQZcdox/Dzyb8jHlj5qGX6/Emvfxz4z/z4SDKBM3NzUyZMoXy8vLCbKFeqadUV4o/7udLx5e0eFpIpBN75L3tCSorKqmsrOTRZY8WhL0fH/LjIRP2IB/cY9VY96kqhCJFAGpMNdh1dtwx9w4tP6dxDgalAWfEyZK2JYMuI5PIKNGW0BHsoNXf2s9SoqSkhClTppDNZveLc6fy8nLMZvPe3o0dIhAPEEqF0Cv2zDgXTob5sP1DYP8N0tgWB1UdxK9n/RqFVMGy7mXc9d5dJDPJvb1bO4RJla/eCyVCe3tXhhWdTkdFxZ7xpiwyDCSTea89nQ6+3dnS0QG33JL/+XvfgxPyPrO5XA6Xy4VcLqe9vZ2WDRvQSCTMnj6dRG0tGYsFv99PdXU1Wq12D7+hIjuLIAjU1NcTtVjyScmiyGmnn87zXz9veOstyOX6FkZZVkZVLodj/Xo++eQTurq6yOVyW91+kT3PTol7l112WeHnJUuW0NzczG9+8xumTp3Kf//73yHfuX0NURRxRVysdKxkk3cTJpUJtVxNOpvmniX38JX7KzRyDbceeyuVhv7iXSQZIZ6OM942fsBzRfZftAotE+0TaS5tRiJIcEacpLNbhJRIpZSWljJ58mQaGxvJZXM4nU56e3vp7e3F4/Hg9Xrx+/20e9rJxXPYBTuyjIxEIkE6nSabze6R96KT66jX12NX2fGn/CSygwtwAgJNqiYuKLmAQ02HIhNkdCQ6+L+W/+OFzS8QzUQHrCOVSLHr7GgVWlp9raxwrMAZdg7wcRyNaHQa/rP6P7zZ8iYCAj879GecOO7EIdt+n+dlX9hPkSL7EnKpnLHWsahkKoKJ4HaXV8vVnD7hdABeWP0C2dzg46NCqsCsMrPZt5nOQGe/58rLy2lubiYaje4TSbj7Ct6YF2CPBTi8u/ldUtkUY8xjGGcbt0dec7QxtWIqNx1zEyqZipWOldy+6Hbi6fje3q3t0ndu3x3q7jfpWqTIiKK7Ox+W8e0JmHgcrrsOolE48EC46qrCU+FwmEAggMFgYMGCBQjACQccgHLCBFIVFcRiMVQqFRUVo882an/FZrNhaGggkM0iicepr6+nfeJEIoDG7Ua7Zk1h2ZxWizydpk4iIZfL8fnnn7Nq1Sri8ZE/Lu8v7FRb7rJlywo//+Y3v+H1119n0qRJdHV1cdppp3HqqacO+Q7uKJFIhKamJrq7u/nss884aISZPUZTUdr97fSEe5BJZJTpy5AIErK5LA9++CDLe5ajlCq5efbN1Jnr+q0bSUaIpqOMs42jyrj/Je0V2TYSQUKFoQKDKp+o2xPqQS1X92vpVigUhUCJUChENpslm82SSqVIpVO4oi4sCgvlynKUKInH42SzWXK5XOG+D0EQEEURQRCQSCT5IBOptHDf9/iWz2/5c9/vg6GQKqjV1aKUKOmJ9ZDOpdHL81UUfX568XgcrVbL+IbxHGY9jChR/rH8H3zU8RHzW+azpHUJ353yXU4cd+KAak21XI1KpiKQCPCl80vK9eXUW+rRKkbn7GI2l+WPH/2R99vfRyJIuGLmFRxTP7S+TdFUFL1CXwzSKLLPolfqabA0sLp3NSqZakCA1bc5afxJvLL2FbpD3XzU8RFHjjly0OXUcjVZMctG70bkUnk/j9zq6mrS6TSrV69GKpWi0WiG9D0V2TkS6QSuqGuAx/FwIYoi81vmA3Bi44nF87ptMLlsMrceeyu3L7qdNa413PrOrdw8++YRf9w2qU04w07K9eXF42eRkUcika/aMxj6V+2JItx1F2zaBFYr/O53/RJ0vV4v2WyWWCzGx0uXUgYcdOKJJGtqQCrF5/NRW1uL0Wjc8++pyC4hlUqpGj+edatWYQqFQKPhO3Pn8r+1a/kuoH3jDaKTv0lyzxqNKFwu7KWlRLVaNm3aRCAQYPz48ZSWlhaPZ3uZXfbci8fjTJo0CcibMe7tmak77riDTGbwVr69STaXxRl20upvJZqOYlVbCxcOoijy10/+ykcdHyGTyLjh6BtoKmnqt34k9Y2wV22sLn5himwVnULHRPtELGoLm32bcYTzXnxbClwajabfRWQml8EVcTFON47xtvGoZKqC8PftWyaTGfB7KpUinU7nRcJUikwm008QzOVyiKLY7/7b5dtbGzu0opbeVC9u0Y1eqiebzQ7w0wPQoeP6o65nde9q/r7s77T6W3n888eZ3zKfH07/IdMrp/f73giCgFltJp1N0xXsQiFTMN42+nxxMrkM175xLe9ufheJIOGaw6/hiDFHDPnrRNIRGq2No8LrqEiRXaVMX0YwEaQj0FGYfNsaarmaUyecyjMrn+G5Vc9xeO3hW11ep9CRzWVp8bSgkCqwafM+e4IgUF9fTzqd5quvvsJutxd9q/Yi/oSfWDpGua58j7zeqt5V9IR7UMlUHFV31B55zdFMU0kTtx17G7cuupX1nvXcsvAWbjn2lhFtFaGSqQjEA/SEegrBZ0WKjBi6uyEYhJqa/o8/9xzMn58P1/jd72ALb9hEIoHL5UKr07Hw7bfRZjJU1tZSPns2WZWKZDKJRCKhqqpqD7+ZIruL3W6nra6OyIoVaNJpxo8fz9/r6vhuayvm1avxu92kS/J2YTm1GmkohNzpRNXYSE1NDS6Xi2XLltHQ0EBDQwMKRfGaYW+xU+Lel19+id1uRxRFwuFwIfEtlUrtsda9wVi3bh1/+ctf+P3vf89PfvKTvbYf3yYQD9Dmb8MVcaFRaKjQfzNrL4oiTy5/koWbFuYvzI+4hqkVU/utH0lFiKQijLMWhb0iO8ZgVXwauQajauAMWjqbxh11U64vZ3zJ+ILoLJFIkMt33Z+wT8D79m1LwW97t0wmQzqdJpQI0RZsI5gOMrZsLGW2sq0mszWXNnP/ifezaPMi/rXiX/SEe7jzvTs5sPxALpl+CdWm/mbBcqkcpUyJmBudLTNSQYpJZUImkfGjg340LMJeKptCIVHscyEkRYp8G4kgoc5cRyQVwRvzDvC8/TYnjz+ZV9a+Qmewk6UdSzms9rCtLmtUGfHFfKxzr6NZ0lyo4hEEgcbGRtLpNBs2bKCiomK3xt4iu4YoirjCLuQS+R47z+qr2ptVP2uPBniMZhptjdxx3B3c8s4tbPRt5KaFN3HbsbcNen4zUjCrzfRGeik3lGNWjw7vySL7AbEYtLWB0QhbjnkrV8IDD+R/vvJKmNr/utQfCBCPx7HZbCxZuBA90DxvHlmTCQCfz0dZWRlWa/GccbShUCiomjyZ1jVr0IVCZK1WDjn7bJbeey+HAuo33yR94YWF5TMmEwqXi7TdTtZkorS0lGg0yldffYXf72f8+PHFz8FeYqc89zKZDC6XC7fbTSKRKKTgxGIxHn744WHZwR3hyiuv5Cc/+Qnjx4+M6pu+wIwVjhV4Yh5KdCUDTj5eWP0Cr371KgA/O/RnHFbT/8IgmooWhL0aU01R2CuyU+gUOiaUTKC5tBkREUfY0c+LL5lJ4o66qTJW0WRv2m4b2s4gkUiQyWQoFApUKhUajQadTofRaMRsNmO1WikpKaG0tJTy8nIqKyuprq6mtraWuro6GhoaGD9+PM3NzRx20GGcdfRZHHvQsagNajxxD4nM1sMwpBIpx489nr+e9lfOnHgmMomMFY4VXPX6VTzy2SOEkvuOubUgCNxy7C3cd+J9TK+cPiyvEUqEMKlNxRTHIvsFKrmKeks9giAQSUW2uaxWoeXUprwVyXOrntuuf6dFYyGRSbDevb7ftqVSKU1NTdTV1dHT0zMiOxD2dSKpCP6Ef4+Nc76Yj086PwGKQRo7S72lnjuPvxOTykSbv43fvP0b/HH/3t6traKUKcmImaL3XpGRRV/V3pats14v3HADZLNw/PFw3nn9Vsnlcrh6e1EqlSz75BNUwSA+o5GD584FKHT3VFcXi1FGK6Xl5cjq6kgEApDL0dzczPuleb9t83vvIaRShWVFpRJyORQORyFwQ6vVUlVVhc/n47PPPmPTpk3Fc5q9wG6l5fZhMpn2WvnlCy+8wMqVK7n55pv3yutviSiKuKNuvnTmEzlVMhWlulJkki28CmJe/vjRH3l65dMAXHLQJRzbcGy/7URTUcKpMI3WxqKwV2SXkUqkVBgqOLD8QMr15XhiHoKJIPF0HF/MxxjzGMaXjB/x7ZZKmZJxtnFMq5hGlbGKSDKCM+LcZmqeRqHhomkX8adT/8Sh1YeSE3O8sf4NLn/1cv637n9bTeMdbUgECfWW+mHZtiiKpHIpyvRlxTGoyH6DVWOlzlxHKBna7jhxatOpqOVq2gJtfNr16Xa3bdfaCSVDrHev75fYLZfLmThxIpWVlfT09BST5/YwgXiAZCY5pJNc22LhpoVkxSxNJU2MMY/ZI6+5L1FrquWu4+/CqrHSGezkxgU34ol69vZubRWzyowr7BrRImSR/YhoNF+1ZzJ9U7UninDrreB2Q10d/OY3/Sv6gGAoRCAQQK/Xs/zNN3ECB5x9NvKvLXL8fn9h8r7I6EStVlM6aRJBQBqNIggCFeeeiwPQpNMoFi/ut3zWaETuciHzfzO2SaVSysvLUSqVrFy5ki+++IJQaN8prBgNDIm4B3DmmWcO1aZ2mFgsxjXXXMNvf/tbDIYdn3GNRCKEQqHCLZncukiww/uSirHes56VjpVEkhHK9eX9zH7j6TjPrHyGn776UxZtXgTA96Z8rzDz30c0FSWUDNFobaTWVFu8qC6y2+iVeibaJzLJPgkRkUAiQL21nrHWsf2E55GOQWWgqaSJqRVTKdeXE0wEcUVc/SoSv025vpwbjr6BO467gzGmMURSER5b9hi/eP0XLO9Zvgf3fucYjjFqZ8iJOVxRF0alEYvaskdfu0iRvU2VsYpyXTnuqHub1TY6pY5Txp8CwHNfPrfdyhxBELDr7HijXlo8LaSy38yCK5VKJk+ejN1up6enZ0RX+ezt8WkoyeayOCPOPdYam81lWbBhAVCs2tsdKo2V3HX8XZRoS+gJ93Dj2zfSG+nd27s1KEqZkhy5YvXeHmJfGp+Gha4uiETyQRp9LFsGH38Mcjncey9oB4bVeDweRFHEsWYN69rb2SiVctrZZwP5qr5EIkFtbS1S6Z5JGy8yPJSNGYNYUUHKk58wOXD6dBZ+XeGpfvllxC1s2ESFAiQSFD09+YrPLTAYDIUJy08++YTOzs7ixOUeYqfEvXPOOWfQ29lnn43P5xuufdwqd955J6WlpfzgBz/YqfUOOeQQjEZj4fbb3/52l/chm8vSE+rhi54v6PB3YFKZsGltBXPtbC7L2xve5vJXL+e5Vc+RyqZoKmni3u/cy7lTzu23rVgqRigZYpxtXFHYKzKkSCVSKo2VHFh+IFPKplBvqUcqGX0HYEEQMKlNTLJP4sCKAynRluCL+7Yr8k0um8zvT/o9P53xU4xKI13BLm5fdDsPffQQncHOPfgOdoyhHKN2lmwuS2+kF6PKyET7xD1WzVKkyEhBJpFRb61HK9cSSAS2ueypE05FJVOx2b+Zdze/u91tSwQJJboSHGEHGz0b+1UHajQapkyZgslkwuFw7O7bGDb25vg01AQTQYKJ4B4LZvi853M8MQ96pX6bPo1Ftk+Zvoy7jr+LMl0ZvZFefvP2b3CER+b3xqw244q48Ma8e3tX9nn2pfFpyAmH81V7ZnP/qr2//S3/89y5+cq9bxFPJPB4PBgVCj5etIh1wCFz5hQ81QKBAKavfdeKjG4MBgPWiRMJp1IIXwekyObNIwKUh8O8e9NNuFyuwvIZkwm514t8EB1IJpMVwlWWL1/Ol19+SSwW21NvZb9lp8p2Fi5cyFNPPYVOp+v3uCiKLFmyZEh3bHu0t7fz+9//npdffrlQ7hmJRAr3kUhkwH728emnn/bz5+tL3dxZgokg7f72wqzvt9vXvuj5gieWP0FHoAOAMl0ZF067kJnVMwcId7FUjGAyWKzYKzKs6JX6EZ0ut6MIgoBVY8WsNuOL+egMduKOupFL5JjV5kGFS6lEypzGORxRewTPrXqO19e/zure1dz/wf0cN/a4EfWdG6oxamfpC1kp1ZUyzjYOjUKz/ZWKFNkH0Sl0NFgbWN27mkQmgUo2eJKtQWng9Amn8+yqZ/nL0r9gUBk4qPKgbW5bJpFRoi2hM9iJXCqnwdpQmBDU6/VMmTKF5cuX09vbi9E48sIC9tb4NBx4Y15EUdxjVex9QRrH1h87pJYY2VyWWDqGRq4ZlRN3u4pdZ+euE+7i5oU30x3q5sYFN3L7cbdTZRxZaZ0KqQIRkZ5QDxaNZZtp3EV2j31pfBpyOjvzYRpbJOCydGk+SEOphK0UywT8fhLhMMpolBdXrKALuOvcfIFKX8jmgQceWExI3Ucob2rC99lnZL1eJBUVHHD00az85BMOX72aUzo7Of+GGzjz7LM54YQTkMpkiHI5iu5u0mYzDBJ6aLFY0Gq1tLa2EggEGD9+PGVlRcuf4WKnzmZmzZqFTqfj6KOPHvDc1G8l6gw3ra2tpFIpTj755AHPHXPMMcyYMYOlS5cOuq5Op9upNt5vk8qm6A520xHsIJVNYdPYkEu/Sbhr87fxj+X/4AvHF/nXU+g4Z/I5nDjuxH7L9RFPxwvC3hjzmOKHvUiRHUQiSLBpbZjVZrwxL53BTlxRF0qpEqPKOOhFjlah5eLpFzOncQ4Pf/ow5045d8R953Z3jNoVkpkk3piXSmMljdbGYsVekf2eUl0poWSIVl8rpbrSrYom50w+B0fYwZK2Jdy75F5umX0Lk0onbXPbcqkci8ZCq78VuVTeb1LPbDYzefJkvvjiC7xe74i7YNob49NwkMwkcUVc/SxUhpPeSC9f9OTPC+eMmzNk2xVFEVfUhUauwRV1IREkGJSG/SaF16qxctfxd3HzOzfTEejgxrfzAl+tqXbIXyubyyIIwi6Jcxa1BVfUhS/mw6a1bX+FIrvEvjI+DTmhUF7cs2xhtbJl1d5ZZ8EgfnnZbBan04kxkWDBqlWsy+WYMmUKkyblj3HhcBi9Xl8I2Swy+jFbLBgmTCC6ZAn6sjIEiQTblVeSuvpqaqNR5qTTPP3MM3zyySdccsklVFdUIHe7kXs8pLfyOVAqlVRXV+N2u1m2bBkNDQ00NDQUxfdhYKfEvZdeemmrz82fP3+3d2ZnOPDAA3n33f4tMCtWrODqq6/mb3/7GwcffPCQv6YoinhjXlr9rfhiPgwqQz8/Kn/czzMrn+GdTe+QE3PIJDJOGncSZ08+e6vVUvF0nEAiwFjrWGrNxYq9IkV2BalEil1nx6Kx4I666Qp20RvtRS1TY1QZBz0RrzBU8POZP6dCX7EX9nhk0ef1WWepo95SP6q8GIsUGS4EQaDWVEs4GcYb92LX2gddTiqRcuVhVxJLx1jWvYw737uTO467g7HWsdvcvkqmwqA0sNG7EYVUQYXhm7HIbrcXBL4iw0MwESSajlKqG/5Wsmwuy6tfvYqIyAHlB1CuLx+ybfviPgxKAxPtE0lkEvn2z7iXQDyASq5Cr9Tv82O6SW3izuPu5JZ3bqHV38pv3v4N0yumk81lyYpZMrlM4ect7zO5zDePbbFsLpcjI2b6PZ7NZRERMavN3H3C3Tv9P5RL5QgIdAW7itV7RfY8g1XtffghrFmTr9q76KJBVwuFQsS7utCbTPxtyRLSwLnnfmMrFQgEmDBhAhpNsdNjX0EQBMomTWLDZ58hBgIIFgs5tRrfGWdQ9vTT/EKjYVE2y8ZNm7jppps49dRTmXvUUSi7u8lYrYjygYVMfdu12+3EYjHWrVuH3+9n/Pjx2GzFyY6hZNQe7U0mE7NmzRr0uenTpzNt2rQhfb14Ok5HoIOuYBeCIPSbxU9mkryy9hVeXvsyiUw+AW9mzUwunHrhNg/+Wwp7Y8xjigf6IkV2E5lERrm+HJvGhjvqpjPQiTPsRKvQYlAaiuL5IIQSIeKZOONs46gx1RTHoSJFtkApU9JgaWClcyXhZHirE3UyiYzrjryOO969g9W9q7lt0W3cffzdVJuqt7l9rUJLNpdlvWc9cqmcEu03lRMVFRWk02m6urqKY9cw0FflNpxjXpu/jfc2v8fitsWFtNShDNKIpWLkcjkarA0YVAYMGLDr7ERSEfwxP86IM996jIhOoUMr1+6znyWDysAdx93BbYtuY4N3A++1vjcsr+OP+/nDh3/g7hPu3ukWaLPajDvmxhvz9vuuFykyrASD0NEBX3vkAf2r9s45p/9zW+Dp6ECSybDI4aDd76ekpIRjjz0WgGg0ilqtpqKiOEm+r2ErL6d7/HjiK1ei+braMzB7Ntb589F5vfz99NP5bWcnny9fzsuvvMJnn3zClWeeib28nNR2Pg8ajYaqqipcLhefffYZjY2NjBkzBtkgLb1Fdp7d+it2dXXx17/+lY8++gin05kXvUpLOeyww/jJT35CdfW2T2pHAzkxR2+klzZfG6FkCIvGUvDeyYk53t38Lk+veBpfPG8kOc42jounXcwE+4RtbrevUqYo7BUpMvTIpXIqDBVYNVbcUTcdgQ56wj15z0GFfp+9uNlZ+sy9J9onUq4vL/5dihQZBJPaRJ25jnXudahkqkHtNSAvBN4460ZuXngzG7wbuOWdW/jtnN9utzLMoDLgi/lY516HTCLDrDYXnqupqcFsNqNW7x8tlnuKaCqa78BQDn37nj/uZ0nrEt5tfZc2f1vhcb1Cz3fGfYcZ1TOG5HUyuQyBRIBGW+MAoUin0KFT6KgwVBBIBPBEPbiiLhxhB0qZEr1SP6SefyMFnVLH7cfdzpK2JcTTcaQSKVJBilQiRSaRIREkyCSywmN9z8sksrxxvCArPC4Tvn5si/XCyTA3vHUD6z3reWH1CwOC8baHXCpHKkjz1Xtqy37lj7g/kUgk8Pl8GI1GtIMkz+5x2tshmQT7FtXnixfDunWg0Wy1ai8WChFua0MybhyP3XsvAGeddVZBhPH7/dTV1RXboPdBpFIpZVOm0LpqFepIBEGnQ1QocM+dS8Wjj1L9zjtcc//9LF29mn/+8590ORz87v/+j6PWrmX2jTeiMpu3u/3y8nLC4TCrV69GrVZTWVm5h97dvs0ui3sffPABJ554ItXV1ZxwwgmccMIJed8Pl4tXXnmFP/3pT7z55pscfvjhQ7m/22TWrFlDGjOfyWX4yvUVjrADlUzV7+L3S+eXPPH5E7T6WwEo0ZZw4dQLOaL2iG1eIEdTUYLJICqZqijsFSkyzChlSqqMVdg0NpwRJ13BLhxhBwalAZ1y8MCd/QFRFHFH3ShlSppKmor+P0WKbIdKQyWhZIjuUDfluq0L4Wq5mptm38SNC26kM9jJLQtv4e4T7saisQy6fB8WjQVXxMV693omlU4qVAgKglC8cBoGAvEAsXSsn5C6OyQzSZZ2LmVx62JWOFaQE3NAvqLzoMqDmFU/i+kV07cqDO8sfWN4ub6cGlPNVpeTSqRYNVasGis1phr8cT/OsJNgIkg6l0ar0KJT6Pap81C1XM2cxqHzNNwSs9rMjw/5MQ9++CDPrnqWqRVTGWcbt9PbcEfz1Xt23eCt/kVGJ6Io4nQ6aWlpwePxoNPpqKiooLy8HIvFgkSyF75nfj90dfWvzMvl4JFH8j+fey6YTAPXE0XC69cT0GrpSqdZvWYNcrmcuXPnAnkBUyqVFgWZfZiS+np6amtJdHWh/jqkNHj44Vhffx1lTw/W+fOZcdZZTJo0iWeeeYYl77/P8vfe443Vq/n+Lbcwc+bM7b6GXq8nEAiQy+WG++3sN+yyuHf11Vdz6aWX8uCDD271+V/84hd89tlnu7xze5tEJoEn5sGsNheq9TqDnfxj+T9Y1r0MAI1cw9nNZ3Ny08nbnAWNpCKEE2FUchV15jrK9eX7RGppkSKjAZVcxRjzGOxaO86Ik+5QN46Qg6yY3du7tsfJ5rK4oi5MKhPjbOMwqU17e5eKFBnxSCVS6s31RJIRfHEfVs3gLUyQT9C97djb+H8L/h/OiJNbF93KXcfftd1jfom2hN5Ib0Hg218CEfY0OTGHM+JELdu9v29OzLGmdw3vtb7HRx0fEU/HC8+Nt41nVv0sDq89fNiqA3UKHfXWHfdIVcvVqOVqyvRleR/JmBdn2ElvpBepIMWgMmw1FbrINxxddzTLupfxftv7PPjhgzxw0gM79V2VSWTIJLKC996+7oe4vxCNRtm0aRNtbW3I5XJqamqIxWJs3ryZ1tZWbDYb1dXVlJSUoFLtoe+ZKOar9tLpfIVeH+++Cy0toNXC+ecPumrG4cCRSCA2NfGfhx8GYM6cOVi+btH0+XwF0bLIvolcLsd+wAF0bdiAKp1GkMtBKsU9bx5Vf/wj1jffxH/cceiMRi677DJmzpzJ8489htbj4VdXXMGsk07immuuwTSYeFxk2NjlI8rq1av517/+tdXnf/zjH/O3vl7+UY5UkBJIBPjPl/9hwYYF5MQcUkHKnHFz+O7k72JQDX7iJooi0VSUcCqMWq6mwdpAqb4UnWL/rRgqUmRvolFoqLfUY9fZcYQc9IR7ECT7TytqOpvGHXVToi1hXMm44lhUpMhOoFFoaLA2sMqxing6vs0LeovGUhD4OgId3L7odm4/7vZtriMIAnZdfgKixd1Ck72pmFo9DIQSIYLx4C5PbHQFu3iv9T0Wty7GHXUXHrdr7cyqn8Wsuln9wlGGmng6TiaXocnetEtjuESQYFQZMaqMVBmrCMQD9EZ68ca8+GI+NHINeqW+2DK6DX588I8LnT2Pf/44Pzv0Zzu1fl/1nifqoUxfTBkdzeRyOXp6eli/fj2hUAi73V4Q73Q6HTqdjnQ6jd/vx+l0YjAYqKiooKysDLPZPLx2KD4fdHf3D9HI5eBrsY7zzgOjceB6kQjhYBCnyURWJuPtt98GvgnSyGTyoTPV1dVFO5d9nJKmJlxlZSR7e1FVVQEQPugg4nV1qFtbsb32Gr0XXADA5MmTafzd71j0z3/S9cEHvPHGG3z88cf88pe/5IQTTih+VvYQuyzulZeX89FHHzF+/PhBn//4448pLx+6NLC9RSqb4uU1L/PyVy8XZmUPqTqEi6ZeRKVx8FJkURQJp8JEkhE0Cg1jrWMp1ZWiVYwA34UiRYqgU+hotDVSpi/bp9qRtkUyk8Qb81JhqGCcbVxRNChSZBco0ZZQa64tJNxuSwAp05dx27G3ceOCG9ng3cDdi+/mpmNu2maVv0SQUKotxRlxIpfKGVcyrljZM8T44j4yYmanWmSDiSDvt73P4tbFbPBuKDyukWs4vPZwZtXPYkLJhGE/nmRzWfxxP2OtY7ea3rwzKKQK7Do7dp2dcDKML+ajN9KLO5YXLfUKPRq5pnhR9i10Sh1XHXYVNy+8mbc3vs30yukcWn3oDq8vk8hQSBV0BjuxaW3F7/goJRwOs3HjRjo6OlCpVFsVu+RyOXa7PX99GA7T0tJSqOarqqqipKQEhWKIPTBFMR+ikcnAlpWCCxfC5s2g0+XFvW+TToPXS49eT0oQ+N+rr5LJZDjggAOYMCHvJ+/z+SgpKSmmnO4HqHU6rFOn0vvGG6gqK0EQQBBwnXMOtffcg2nRIrwnnkjm68+CSq3mlPPPZ8qMGfzyxRf5sq2NG2+8kfnz53PDDTdQWjr86fT7O7t8NPnlL3/JT37yEz7//HOOP/54SktLEQQBp9PJ22+/zWOPPcYf/vCHIdzVPUtOzPH6ute5//37C2EZ9ZZ6Lp52MZPLJg+6jiiKhJNhIqkIWoWWcbZxlOpK0SiK8eBFioxE9pfW+L5k7jHmMdRb6ofM96lIkf2RGlMNoWQId8xNmW7bVTc1phpunn0zNy28iVXOVdz//v1cf9T127yYl0qklGhL6Ax2IpPKGGsdu99MQgw36Wya3kgvWvn2J1tT2RTLupbxXut7fN79ecHGQSJImFYxjVn1szi48uA9OlHijrop1ZVSY6oZcsFNr9SjV+qpNFYSiAfwxDy4o+6C77RRZSxW823B5LLJnDHxDF5e+zJ/WfoXxlnHbddbc0tMKhOuqKvgnVhk9JDNZunq6qKlpYVIJEJpaSlK5fbHgT4PVYPBQDKZxOPx4HA4MBgMVFdXY7fbMRgMQ/Pd9noHVu1ls9947Z1/Pui/dQ4sitDTQ7SkhM5QCK1KxUsvvQTAd7/73cJ7TyaT1NTUIJUWx4P9AduECXg++oik14vy689TrLmZ6MSJaNeupeSll3Bcdllh+ZxWS11ZGf+44w7+9sEH/P3vf+f9999n+fLlXHHFFcydO3fv+E/uJ+yyuHf55ZdjtVp58MEHefjhh8lm8yc9UqmU6dOn889//pNzzjlnyHZ0T/NJ5yf8esGvAbBqrFxw4AUcVXfUoCfYOTFXEPUMSgNNJU3YdfaiX06RIkX2OqFkiFg6xljrWGpNtcWLsyJFdhO5VE6DpYFIKkIwEcSoGqStaQsabY3cOOtG7nj3Dj7t+pQ/ffQnrjr8qm0KdnKpHKvGSpu/DblETp2lbqjfxn5JMBEknAxvs+ptnXsd725+lw/aPyCaihYeb7A0MKt+FkeOORKTyrQH9rY/gXigYPEynBM0MokMm9aGTWuj1lSLP+6nK9hFb7QXtUyNUWUsis1fc94B57HCsYJWfyt/Xvpnbjrmph0WZqQSKUqpkq5gFzaNrTjpNkoIBAJs2LCB7u5udDodNTVbD7TZFkqlkrKyMnK5HKFQiNWrV6NUKiktLaWyshKbzVZIpd1p+rz2RLF/1d6CBdDWBgYDfC3W9aO3F6xWes1mYm43a5Yvx+v1YrfbOeaYY4D8+zebzcUKrP0Ivd2OYcIEfEuXIt8iGMZ19tnU3XYbxg8+wHvyyaS2CFfJGo1ovV5+cu65zJ49mzvvvJNVq1bxu9/9jgULFnDjjTdSW1u7t97SPs1u1YGfe+65nHvuuaTTaTweDwA2mw25fPQfoA6tPpRj6o/BrDYzd+LcQZM1c2KOUCJ/4WxQGphon0iprrTY7lakSJERgS/mIyfmmFgykQpDRbG1qkiRIcKgMtBgaWB172pUMtV2j/uTyyZz/ZHX89vFv2Vx22I0Cg2XHXzZNr+TSpkSnUJHV6iLcn05Knkx7GB3cUfdSATJVic5Fm5cyJ+X/rnwu1VjZVZd3kev2lS9Z3ZyEBKZBMlssl+S8p6gL4TDprXhiXroDHTiDDvRyDUYVIb9XuSTS+Vcc/g1XPvmtSzvWc4bLW9w8viTd3h9o8qIK5Kv3htOn8Yiu086naajo4ONGzeSSCQoKysbkutdiUSCyWTCZDIRj8fp6emhq6sLk8lUqObT6XbSW9PtHli1l8nAo4/mf77ggnxb7pbE89ZT6bFj6diwAZ1Ox3/+8x8A5s2bh0wmy3vJR6OMGzdun7jWL7LjlE+dSmr9etydnWhtNnRaLYmxYwkddBCGZcsoeeEFuq+6qrB8Tq1GGgohdzppaGzkscce4/nnn+cvf/kLy5cv53vf+x4/+tGPuOBrv74iQ8eQHJXlcjnl5eWUl5fvM192QRB48OQHOWn8SQNO2vs8T/o8cZpLm5lWOY0aU01R2CtSpMheRxRFXBEXEomESaWTqDRWFoW9IkWGmDJ9GdXGarwxLzkxt93lD6o6iKsOuwoBgTdb3uTplU9vdx2FVEFOzCEiDsUu79fE03E8Mc9WQyjS2TT//vLfQH6C97Zjb+ORMx7hgqkX7FVhL5vL4o15qTHVbLcNfLhQSBVUGCqYWjmV5tJm5FI5zrCTQCKwQ5/9fZlqUzUXTbsIgH8s/wedgc4dXlcqkaKU5av3UtnUkOxPTszlrTi+DkrpCHSw1rUWT9QzJNvfH/F6vSxbtoyVK1eiUCioqqoalutdtVpdCNtIJpOsWLGCDz/8kJUrV+J2uwtdctskl8tX7QkCbNkqPH9+3oPPZILBOuu8XqisxCuREAwG6ezsZO3atSgUCs4880wAQqEQer2+WLW3H2KorWX8sccyzmwmk07j7O0lmUzinjcPURAwLFuGatOmfutkTCYUvb1Ig0GkUinf/e53efbZZ5k5cyapVIq//OUvXHjhhWz61npFdo9hm3Lr7Ozkhz/84XBtfo/w7YvhbC6LL+bDFXGhkqmYXDqZaZXTqDRWFkW9IkWKjAhyYi7vKaXQ0lzajF23+6brRYoUGYhEkFBnrsOiseCNeXdonaPqjuInh/wEgBdWv8BLa14azl0ssgX+uJ9YKoZGPrgP8rub38Ub82JRW7j2iGs5oPyAEWFj4I65sWvtjDGP2euTNAqpgkpjJVMrpjKpdBIyQYYz7CSYCCKK+68AfdK4k5hWMY1UNsUDHz5AOpve4XWNKiP+hH+nxbd0Nk0kFcEb89IT6mGzbzNfOr7kk85P+KzrM5Z1L2NFzwrWudfR7m+nI9BBNrcD4lCRAslkkvXr/z979x0eVbU1cPg3vaZMek9IaAm9dxGRItLEjgUbio17L9feKKLYr35iL6BeuygCIoiChS7CBQSk1/SeTJLp5/vjmCFDEgjJpMF+feYhOefMnj0xs5KsWXuvvWzatIn8/HxiY2MJDAxs9MdVq9WEhoaSmJiIXq/nyJEjbNiwgQ0bNnD06FGKi4upqKjA6XRWf93l5kJmZu1VezfeCKZT9hytqAC1Gik+nvSMDFQqFV9++SUAo0aNwmKxAPKS3ISEBAwGse3U+Ujbrh0xycl0SU4mOiqK4uJiMvR6igYOBCDi7++ZSpJOB5KENjNTTjoDMTEx/N///R+zZ88mKCiIffv28cADD/B25V6QQoM1WnumgoICPvjgA95///3Geogm4/K4KLGX4HA7sBgstAtrR7gpXOyPIQhCi+LyuMi15hJqCqV9WPvzpmGIIDQXvUZPckgyO7J2YHVYa60Kq2pU+1GUOcv4cNuHfLjtQ0xaE6PajWqC2Z6/JEki25qNVqWtMUHm9ri9idaJaRNbzO93xbZi9Go9bUPbnrbLclPTqXXEBcURbgon25rNieITZJRmEKCVm3I0dxKyqSkUCu7pfw///O6fHC48zCfbP/FW852JSqnCqDZyvOg4ocZQn2IBSZJwuB3YXXbsbjt2l50yZxlWuxWb04bD45ATiRKgkJOvGpUGg8ZAoC7Qm5x2eVzkluWSX54v3vCrA0mSyM3NZd++feTk5BAaGnr2S2P9xGw2YzabcblcFBUV8ccff6DT6VCr1d6bXq9Hr9dj1Okw7dmDtqQEpcHgPa/9/nvU6ekQEgJXXln9QfLzITGREpWK7OxsXC4XP/74IyBvwQVgtVoxmUxER4vmL+etoCCIj8e8bx8dOnQgLCyM4ydOsGfIEPpv2oRp1y6Mf/5JeefO3ru4g4PR5OTgjIjAFRoKyPHy0ksvZcCAATz//POsWrWKDh06NNezOufUO7m3ZMmS054/dOhQfYducYoqirAYLXQI6iA2vRUEoUVyuB3yvj0BMbQLayca+ghCEwk1hpJsSWZv3l6UKDFqa64Mq2pSp0mUOcpYtGsRb256E6PGyJCkIU0w2/OT1WGlqKKo1jc81h5dS5Y1iwBdACPbjWzi2dXM7rJT7iync2RnAvWNXy1UHzq1joTgBCLNkWSVZnGi+ASZpZmYdWYCtOdXki/EGMJd/e/imV+eYfHuxfSM6UmXqC51um+gPpCs0iwySjMwqA3YXDasDivljnLsbjtOlxOX5ALkiuHKJF6gJhCNUnPGr7NaqUatVJNekk6oMbRFVKS2VBUVFRw6dIjDhw+jUCiIi4trEV1h1Wo1YWFhhIaG4nK5fG5FRUW4XC5UOTkE79uHLTgYKS8PlVKJyuOh11tvoQZOXHwxpUeOoNPp0Gq1qNVqVC4X2vJyFMHBZOfkYLfbWb58OW63mx49etCxY0dArtpLTk4m4NQOu8L5JSEBTpxAUVpKWFgYQcHBZIeFkT14MNE//0zY559zrFMneVk4IGk0oFSizcjAFRwMVV5LISEhzJs3j1GjRjF06NBmekLnnnon9yZOnIhCoThtGX5r/6GuVWmJNkdjMVoINYaiVjZaoaMgCEK92d128srySAxOJCU0pUVVeAjC+SAhOAGP5GF//n6AOiX4ru9+PWXOMlbsW8HL617GoDbQO653Y0/1vFRQXoDD7ahxCxWP5GHRrkUAjOs4Dr26+RuXVO6zl2RJIiqgefbZOxs6tY5ES6Kc5LOeTPIF6AIwa82t/u+Buuof358RbUew6sAqXln/Ci+PfblO1bxKhRKT1sS+3H3er5VaqUar0qJTyY11Gvo3SLA+mLzyPAoqCgg3hTdorHORJElkZWWxb98+8vPzCQ8Px2g8cxxvagqFAo1GU33PP7cbU04Oqqgob4WU2+0m6Kef0BcU4AgMJL1/f5z5+Xg8HjweDxJgyM+nIiKC8j//xCNJ6PV6Fi2S42Fl1Z7NZkOtVhNbpRuqcJ4KCICkJNi1CwIC0KjVxMXFUfbPf+LZsAHTkSNIa9bgHjoU9d+JPFdwMJr8fDQFBTjDq8eelJSUJn4S57Z677kXHR3NokWLvAHi1NvWrVv9Oc9moVVp6RjRkUhzpEjsCYLQYikVStqGtqV9WHuR2BOEZqBQKEiyJNEutB3F9mIqnBV1us/tfW7ngqQLcEtunvvtOXZm7WyC2Z5fXB4X2dbsWvfa+/3E7xwrOoZBY2BMhzFNPLua5ZfnE2oMJcmS1Ko60uo1epIsSfSK7UWH8A64JTeZpZmU2kube2pN5pZetxAdEE1eeR5vbX6rzvcL0gcRHRDtvYWbwgnSB2HQGPzyN4hGpUGJkvTi9PO+CcqpysrK2LFjB7///jsVFRXEx8e3yMTe6WgKClDn5+MKCvIeU3s8RH33HQAFEyYQGB5OaGgo4eHhREZGEh0URGhkJMFduhAWHk5ISAibN2+msLCQyMhILrzwQvm+BQVERUV5994TznPx8RAYCMXF3kOmuDgU110HQPKKFeRnZVFSUiIXganVSBoN2hMn5P0fhUZV798YevXqddoE3pmq+gRBEISGM2lNpIankhySLJbaCEIzUigUJFoSaRvaliJbUZ0SfEqFkukDp9Mntg8Ot4Onfn6K/Xn7m2C2549iWzEl9hLMuuoVVJIk8dWfXwFyU4S6VFk1thJbCRqVhrahbVttszaDxkCSJYnesb1pH9bem+Sz2q3NPbVGZ9AY+Negf6FUKPntyG/8cviXOt+3sSscLQaLXL1XXtCoj9NaeDwejh8/zsaNGzl06JA38aVUNiyhnpeXx4EDB3A4/NMB+YxcLrQnTiBptaA+mQgO/vlnuVrKYqHo70RdVariYhwREXiCglCr1eh0Oj7//HMArrzyStRqtbdpR3x8/HlTgSucgdEIbdrIyb0quR7FjTdCUBD6zEy6Z2aiUCjIysrCZrPhCgpCXVSEJk907W5s9Y5e999/PwP/7o5Sk7Zt27JmzZr6Di8IgiDUQRtLG+KDxS9dgtASKBVKkixJZ5XgUyvV3H/B/XSJ7ILNZWPO6jkcKzrWBLM9P+SX5yNJUo3VTzuydrA/fz9alZZxqeOaYXa+HG4HZc4yUkJSCDYEN/d0GsygMdAmpA29YnrRNrQtLslFRkkGVse5neRrH9aeq7vISxrf2vwWOdacZp6RrHLP8IySDFG9B+zbt4+tW7fidruJj49Hr2/YknyPx8N///tfLr30Uq655hqGDBnCFVdcwQMPPMCbb77JDz/8wIEDB3A6695NuS40+fmoi4p8qvYUDgehS5cCkDd+vJz4q0Jhs4FajTMy0rs/2o4dO9izZw86nY6JEycCctVeeHg4YVW77wpCbCwEB0Nh4cljZjPcdBMAwZ9/TpcOHUhMTMRaVkZuQQFOjQZdejoKP3//C77qXec9ZMjpN342mUxic0RBEARBEM4rlQk+gAP5BwDO2OBGq9LyyIWP8MSPT7A/fz8zf5rJvJHzsBjEMqiGsLvs5Fhzaq3I+/LPLwEY2XYkwfrgJpxZdR7JQ355PnGBcUQHnlsdKY1aI8khyd7GGxmlGWSWZBKoD8SkNTX39BrFFZ2vYGvGVvbm7eWV9a8w5+I5LaK6PsQQQk5ZDgXlBYSZzu+EjdVqRavVEvr3HnUNUVRUxKxZs1i7di0Aer0em83GkSNHOHLkCKtXr/Zeq1KpSEhIIDk52eeWkJBQfS+9M3G50GZkIOl0Ps0KgtesQVNYiDM0lOIa/h5XFRfjiInBHXiyWc9nn30GwOjRowkODsbtduNwOEhMTGxwNaNwjtHrISUF/vhD7qJb+b135ZXw6aeQlYVh+XJSrr2WkNBQTpw4QVZ2NsEFBaijonCK/RsbjXilnscUCkWdbj///HNzT7XZLF++nFmzZjX3NE5rzpw5pKWl4fH4vgtbUlLCU089Re/evQkMDESn05GUlMQtt9xSrz0xjxw5gkKhYOHChWd1v5tuuomkpKSzfry6uuGGG7zvMAqCILQElQm+lJCUOlfwGTQGnrjoCRKCEyisKGTmTzMpqBDL5xqiyFZEuaO8xgYnf+X+xZ/Zf6JWqpmQNqEZZucrrywPi8FCckhyq9pn72yYtCZSQlPoEdODNiFtsLvtZFuzcXvczT01v1MpVfxz0D/Rq/XsytnFt3u+be4pAXL1ngIFGSUZYvskP9m2bRuTJ09m7dq1aLVaHnroIX777Te+++47Xn31Vf71r38xYcIEunTpgslkwu12c/jwYX766SfeeecdHn74Ya6++moGDx7MVVddxUMPPcTbb7/Njz/+yKFDh3CdZp8yTV5e9ao9u52wyqq9CRPkjqVVeKv2oqK8VXvZ2dneBGRlI43CwkIsFgvhNTRBEASioyE01Ld6T6+H226TP37/fSgrwxIcTFpqKqmdOkFAAMXbtlGen988cz4P1Kty79ixYyQkJNT5+vT0dNFhpwXasGGDz+dPPvkka9as8Xl3CSAtLa0pp9WiLF++nNdee63FJvgyMjJ47rnnWLhwoc+7agcPHmTkyJHk5OQwbdo0Zs+ejdls5siRI3zxxRf06tWLoqIigqr8MtBYHn/8cf7xj3802vizZs2iY8eOrF69mosuuqjRHkcQBOFsKBVK2oS0AeBgwUHgzBV8AboAZl00i4d/eJhsazZP/fwUDwx5oLGnes7KseagUqpqTJZV7rV3YZsLm717aKm9FJVSRUpICnpN83frbWxmrZl2Ye2IMEdwqOAQ2dZsAnWBNe6L2JpFB0Qztc9UXt3wKp9s/4RuUd1ICW3+zpDBhmByy3IpqCgg1NjwqrXzldvtZsGCBbz99tt4PB4SExOZN28e7du3ByAyMpLIyEgGDBjgvY8kSWRnZ3Po0KFqt/Lycu/HVanVahISEkhJSSEpKQmdTt6LU+V20yYrC7XLRXllAxCFgj5//UXH4mKKTCY+tVqRli/3GS+otJTCoCAyjxyBvws5tm3bhtvtpmfPnrRv3x5JkigrK6Njx45nX00onB+0WkhOlqv3XK6T+z2OHw///S8cOwaffAJTp6JSqYiKiiI4KIi8bds4ePw4JyoqiIyMbN7ncA6qV3KvT58+jB8/nqlTp9K3b98arykuLuaLL77glVde4Y477uDee+9t0EQF/+vfv7/P55WbyJ56/FxSXl7eIjpg+Wser7zyCsHBwUyaNMl7zO12c9lll5GXl8eGDRvo3Lmz99zQoUOZMmUK33//fZP9sG7sFucpKSmMHj2aZ555RiT3BEFoUU5N8CkUCvTq0ydvQowhzLl4Dg+vfJgTxSf4z7r/0C++3xkTg4Ivq8NKQUUBAbqAaucOFRxiS/oWlAolkzpNquHeTcfpdmJ1WOkY3pEQY0izzqWpBemD6BzZmXRDOkeLjpJjzSHUGNoilq/6y0XJF/H7id/ZeHwj/1n3H14c82KzN0rRqrRISGSUZBBiCBF79tZDXl4eTzzxBJs3bwbg0ksv5cEHHzzj7/YKhYKoqCiioqJ89q6vTPodPHjQJ+F3+PDhWpN+CUBP4DhQWYNpAP6um+LlsjKWfvGFz330QBCwASiqYX7XXHMNIP8dHxQUJJIvwulFR0N4OOTnQ+X3iloN06bBI4/ISb4rr5T35wP0BgNxnToRVFTEPouFE9nZTdd45jxRr7r/PXv2EBQUxOjRo4mMjOTSSy9l6tSp3HvvvVx//fX07NmTiIgIFi5cyPPPPy8Se62Yw+Fg7ty5dOzYEZ1OR3h4ODfffDO5ubk+1yUlJTF27FiWLVtGjx49MBgMpKamsmzZMgAWLlxIamoqJpOJvn37smXLFp/733TTTZjNZnbt2sXw4cMxmUyEh4dzzz33UF5e7nOtJEm8/vrrdO/eHYPBgMVi4Yorrqj2Q+/CCy+kc+fO/PrrrwwcOBCj0cgtt9wCwOeff87IkSOJjo72zvWhhx6irKzMZ06vvfYa4LuEuXL/jNqWqCoUCp9Kv1mzZqFQKNi6dStXXHEFFovFm/Cq63Op7f/Ne++9x+TJk32q9hYvXszOnTt5+OGHfRJ7VV1yySU+v4Ds37+fyZMnExERgU6nIzU11fvcTyc3N5fbb7+d+Ph47/fHoEGD+PHHH73XnLos92y+dnUZH+SluT/++CMHDx4845wFQRCaUmWCLzkkmcKKQmwu2xnvE2mOZPbw2QRoA8gpy+FE8YkmmOm5paiiCJvTVmNSdNGuRQAMShhETGBMU0/NS5Ik8sryiAmMITbw/FzholFpSLIk0S2qGyHGELKt2edUww2FQsFd/e/CYrBwouQEH2z9oLmnBECwPphcay6FFYVnvljwsXHjRiZPnszmzZvR6/XMmjWL2bNnN+hN+8qk36BBg7jhhhuYOXMmH3zwAT///DNLly7l5ZdfZvr06Vx22WWMHz+ey8aMYfKAAXTu148hQ4ZwwZAhDBk8mAcTE7EAuXo9xQMHMnjQIAYPGsSggQMZNHAgI3v0oM2gQfQfPZrRo0czatQo7+22227jwr+76hYXF5OYmNjgJiPCOU6tljvnOp1y9V6liy+G9u2hrAxO/XsvMJAAoEdYGL179yYmJgaV6tx5Q6e51atyLyQkhBdeeIG5c+eyfPlyfvvtN44cOUJFRQVhYWFcd911jBo1qtbEwjlBkuCUpFOzMhq9+yb4i8fjYcKECfz222888MADDBw4kKNHjzJz5kwuvPBCtmzZgsFw8pfm7du38/DDD/Poo48SFBTE7NmzmTRpEg8//DA//fQTTz/9NAqFggcffJCxY8dy+PBhn/s7nU7GjBnDHXfcwUMPPcT69euZO3cuR48eZenfe0cA3HHHHSxcuJDp06fz7LPPUlBQwJw5cxg4cCDbt2/3eZcpMzOT66+/ngceeICnn37amwTbv38/Y8aM4Z///Ccmk4m//vqLZ599ls2bN3uXJT/++OOUlZXx1Vdf+Sxhjo6OJjMz86y/npMmTeKaa65h2rRp3iTi2TyXU23atIn8/HyGDRvmc/yHH34AqPM+dLt372bgwIEkJCTw4osvEhUVxcqVK5k+fTp5eXnMnDmz1vvecMMNbN26laeeeor27dtTVFTE1q1byffTXgp1Hf/CCy9EkiSWL18u3kwQBKHFUSqUJIckI0kShwsOYzFazljBFx8czyMXPoLT46RDeIcmmum5wSN5yCzNrPFrnF6czvqj6wG4vPPlTT01H3nleQQbgkkJSTmnqtXqI9gQTGdtZ07oT3C06CjljvJzpoovUBfI9AHTmb16Nsv3LadnbE96x/Zu1jnp1DrcuMkozcBisIjqvTpwuVy89dZbLFy4EEmSaNu2LfPmzaNNmzaN9phKpZLo6Giio6MZPHiw97j2xAkM+/bJ3W7//ttGWVFByowZADimTOH2KteDvNeeqqyMsq5dfRppnMpqtWIymYiKimqEZySccyIj5VteHlR+zyiVcM89MH06fPEFXHvtyco+hQJCQ1EeP05sXByR/fuLhi1+VO9uuSB3Apo0aZLPksDzRnm53PK5pbBaweTfjmNffPEFK1asYNGiRT7/j7t160afPn1YuHAhd955p/d4fn4+Gzdu9O6vGBMTQ/fu3XnnnXc4cOCA9x0thULBxIkT+fHHHxk3bpz3/g6Hg3//+99Mnz4dgBEjRqDRaHj00UdZt24dgwYNYuPGjbzzzju8+OKLzPj7BxjI3Zvbt2/PSy+9xLPPPus9XlBQwJdfflltueZjjz3m/ViSJAYNGkRqaipDhw5lx44ddO3alZSUFG9yzR9LladMmcLs2bO9n5/tczlVZcKxZ8+ePsePHTsGUOdfNmbMmEFAQABr164l8O8f9iNGjMBut/PMM88wffp0LJaaOzauW7eO2267jalTp3qPTZjgv43J6zp+REQEsbGxrFu3TiT3BEFokSoTfACHCw4TYgw54/K85JBkypxlp71GqK7YVkyJraTGbsNf7/4aCYk+sX28XY2bg9VhRYGC5JBkseT6bxqVhjYhbQg2BHO44DDZ1myC9EHnREfdHjE9GNtxLMv+Wsb8DfN5eezLzd6h2aK3kGvNpTiwmGBD886lpcvKyuKxxx7jf//7HyC/YT9jxoxmqWxT2O1oMzLwmEzexB6AZeVK1FYr9uhoSqrs81dJXVyM/ZQOuTUpKiqibdu2mFvS37lCy6VSydV7OTngcMh78QEMGAA9e8LWrfDOO1Dlb2/MZrkRx9GjqLt0aZ55n6NEmlSo1bJlywgODmbcuHG4XC7vrXv37kRFRVXrotu9e3efximpqamAXFVVtVS98vjRo0erPeZ1113n8/nkyZMBWLNmjXdOCoWC66+/3mdOUVFRdOvWrdqcLBZLjfuwHTp0iMmTJxMVFYVKpUKj0TD071bxe/bsqcuX56xdfrlvhcDZPpdTZWRkoFAoCAsLq/ecbDYbP/30E5dddhlGo9FnHmPGjMFms7Fx48Za79+3b18WLlzI3Llz2bhxI06ns95zaej4ERERpKen+/XxBUEQ/EmlVJEckkxSSBIF5QXYXfbmntI5qaC8ALfkRqPy3Vs2x5rDz4d+BuCKzlc0w8xkTreTUlspbULaEGaq/8/wc5XFYKFLVBfah7WnwlVxznTUvbHHjSQEJVBkK+L1ja83e7danVqHy+MSnXPP4Ndff+W6667jf//7HyaTiaeffppHHnmk2ZasanJyUFmtuANO7ieqLCsj9PvvAci77DI54VKFwmZDquyQexoVFRVoNBrRCFM4O+Hh8v57eXknjykUcPfd8sdLl8Kpf/eHhsLx477ddoUGE8m9+jIa5Wq5lnJrhCYR2dnZFBUVodVq0Wg0PresrCzyqr6AkZdrV6X9O3Nf23GbzXffIbVaTWiob9euypLwymWY2dnZSJJEZGRktTlt3Lix2pyio6OrPS+r1cqQIUPYtGkTc+fO5eeff+b333/n66+/BuQfbI3h1Lmc7XM5VeUP4FP3KajsZH348OEzzik/Px+Xy8Wrr75abQ5jxowBOO08Pv/8c6ZMmcK7777LgAEDCAkJ4cYbbyQrK+uMj10XZzO+Xq9vtP93giAI/lLZFTUpJIn88nyR4PMzh9tBjjWnxmqvxbsX45bcdInq0mxLnSVJIrc8l+jAaOKD4ptlDq1BZRVf9+juWAwWsq3ZlDladxWrVqXlX4P/hVqpZvOJzaw6sKq5p0SwIZhsazYl9pLmnkqL43Q6eemll5gxYwbFxcWkpqby8ccfM3LkyGabk8JmQ5uZicds9tmOKWTlSlTl5dhjYynp16/a/dTFxTgiI89YtVdQUEB0dDRBQUF+n7twDlMq5eo9hQKq/n3frRsMGQJuN7z5pu99jEZ5r74jR+TtzgS/aNCy3POaQuH3ZbAtTVhYGKGhoaxYsaLG8wEB1TvQNYTL5SI/P98nwVeZxKk8FhYWhkKh4LfffvO2gq/q1GM17SGyevVqMjIy+Pnnn73VeiCXoddV5bt1drvvH2Wn22vu1Lmc7XM5VVhYGA6Hg7KyMkxVvhdHjRrF22+/zeLFi3nooYdOO4bFYkGlUnHDDTdwd+W7K6c43fLesLAwXn75ZV5++WWOHTvGkiVLeOihh8jJyan1++ZsvnZnM35BQYFP4w5BEISWqjLBB3C48DChhtBm76B5rii2FVPqKCXCFOFzvLCi0JtMubLzlc0xNQAKKgoI0gWJffbqqLKK70TxCY4WHqXMUdaq9+JrY2nD9d2vZ+HWhby35T06RXZq1mYqerWeoooiMkoyCNKLhE6lEydO8Mgjj7B7924Arr32Wu69915vgUJz0eTmoiork/fa+5vSaiXk79+JcydN8lmqC1Wq9s7Q+baya2lcXJzYg1E4e6GhEBsLx45BXNzJ43fdBWvXwqpVMGUKdOx48lxYGKSnQ3y8/LHQYCK5J9Rq7NixfPbZZ7jdbvrV8C5QY/j444+9e+4BfPLJJwDe7k1jx47lmWeeIT09nauuuqpej1H5A+vU5Nlbb71V7drKayoqKnyaf0RGRqLX69mxY4fP9d9++22d59HQ59Lx7+B48OBBunbt6j0+YcIEunTpwrx58xg7dmyNjW1WrlzJkCFDMBqNDBs2jG3bttG1a9cG/dKSkJDAPffcw08//cS6detqva6+X7vTje9yuTh+/Li32lAQBKGlq0zwSZLEkaIjIsHnJznWHJQKZbXkz5I9S+TmJGEd6BLZPHv8lDvK5Y34Q9ti1Pp/xcW5SqvSkhySTLA+mMOFh8myZmHRW1rt13B86nj+yPiDnVk7eXndy8wbNQ+10v9/kkmSRIm9BJvLRqS59sROkD6ILGsWsYGxBOpPX9l1Pvjxxx958sknKSsrIzAwkJkzZ/oUAzQXhdOJNjsbzylNFEO//x5VRQW2+HhKe1dv1KIuLsYeG1unqr3IyMhqq6gEoU4UCkhMhMxMuTdB5arCdu1g9Gj4/nt47TV49dWT99Hr5aq+nByR3PMTv/0kycvLQ6FQiIBwDrnmmmv4+OOPGTNmDP/4xz/o27cvGo2GEydOsGbNGiZMmMBll13mt8fTarW8+OKLWK1W+vTp4+2We8kll3g7RA0aNIjbb7+dm2++mS1btnDBBRdgMpnIzMxk7dq1dOnSxafJR00GDhyIxWJh2rRpzJw5E41Gw8cff8z27durXdvl700+n332WS655BJUKpU3CXb99dfz/vvvk5KSQrdu3di8ebM3GVkXDX0ulQnPjRs3+iT3VCoV33zzDSNHjmTAgAHceeedDBs2DJPJxNGjR/nqq69YunQphX/vcfDKK68wePBghgwZwp133klSUhKlpaUcOHCApUuXersHn6q4uJhhw4YxefJkOnbsSEBAAL///jsrVqw4bZOdyn0Gz/S1O5vxd+zYQXl5ebXOwYIgCC2ZSqmibWhbAJHg84NyRzkFFQUEaH1XFljtVr7fJ+9HdXnny5u0KsXlcVHuLKfCWeFN7Il99uonxBiCWWfmeNFxjhUdo8xZRoghpNVV8SkVSv4x4B/847t/sD9/P1/s/ILJ3SbXe7xSeymZpZlklGTI/5ZmkFki/1vuLAfg3gH3MjxleI33N2gMFNmKyCzNPK+Tezabjf/85z8sWrQIgK5du/L000+3mK6x6qIiVKWlOCNOViWrSkux/PADUHvVnkupxBocjL2sDJfLhdvt9u6v7fF4vPstajQaEhISROdSof5CQuQqvIMHfbcMu+MO+OEH2LAB/vgDevU6ec5shqwsOQmo0VQfUzgrDUruFRUV8eijj/L55597EwUWi4VrrrmGuXPnEhwc7I85Cs1EpVKxZMkSXnnlFT766CPmzZuHWq0mLi6OoUOHehNf/qLRaFi2bBnTp09n7ty5GAwGpk6dyvPPP+9z3VtvvUX//v156623eP311/F4PMTExDBo0CD69u17xscJDQ3lu+++49///jfXX389JpOJCRMm8Pnnn1frPDt58mTWrVvH66+/zpw5c5AkicOHD5OUlMSLL74IwHPPPYfVauWiiy5i2bJlZ7U0tCHPJT4+niFDhvDtt99y++23+5xLSUlh69atvPrqq3zzzTe88cYb2O12oqOjueCCC1i7dq13P420tDS2bt3Kk08+yWOPPUZOTg7BwcG0a9futJVwer2efv368dFHH3HkyBGcTicJCQk8+OCDPPDAA6ede12+dmcz/uLFiwkLC2vWfVAEQRDqQ6VUkRIqL9E9UniEUKNI8NVXka2Ickc5QQG+ywu/2/sdNpeNpOAk+sT2adQ5eCQPNqeNcmc5To8TlUKFUWMkPiieIH0Q4abwRn38c51WpSUlNIVgQzCHCg6RZc0ixBDS6joOh5nCuLPfnbzw2wt89edX9IjuQWpEaq3XlznKqifwSjPJLMmk1FF6xsd7/4/36RnTs8YO0gDB+mAySzOJCYwhQOffbXdagyNHjvDwww+zf/9+AG6++WbuuOMO1OoWsshNktDk5CCp1aBU4na7cbvdRC5ZgspmoywujmNt2uDJy8PtdiMBCkCfl4ctOpoKSUJls6FWqzEYDOj1evR6PQaDAY1Gg1qtRqvViiIdoeESEuSltlarnLgDeZnuZZfBV1/J1XvvvXey+rQyuVdc3Dqr915/HZ5/Xq5Y7NQJXn5Z3mewmSikerZHKigoYMCAAaSnp3PdddeRmpqKJEns2bOHTz75hPj4eNavX4/FUvMPkeawZ88e0tLS2L17t7djq9Ay3HTTTXz11VdYrdbmnkqrsmjRIq6++mqOHj163na2crvdtG3blsmTJ/PUU0816mM1dgwRMUoQzl8uj4sD+Qc4WniUMFMYWpUWu8tOmbOMvnF965S8aMwY0tLjkyRJbMvYRom9hFDjyT9QK5wV3P7N7ZQ6Svn34H8zJMn/v3TbXXYqnBXYXDYkJAwaA4HaQEJNoZi1Zsw6c6Msuzzf2V12ThSf4FjRMSQkQo2hKBWtq+rolfWvsObQGiLNkcwbOY8iW5FP5V1lAq/YXnzacUIMIUQHRBMTGCP/GxBDdGA0EaYIHl31KIcKDjEkcQj/HvLvWsfILM0kyZJE+7D2/n6aQMuLT1u2bCE3N5fff/+dZ555hoqKCkJCQpgzZw79+/f36/xOpzJRV9vN5XKhLi0lZO9eHCYTHo0GlVKJrqyM3k8+icrh4OA992Dr0wedTodOr0etUqF2OtHYbNC/P+rwcG8S79RGfILgd7t3w969cqKvUl4eTJgAdju89BJccMHJc8ePQ2oqdGieRldQz/j0+edwww1ygm/QIHjrLXj3Xfn5V33uTajev2nMmTMHrVbLwYMHiTxlg845c+YwcuRI5syZw3/+858GT1IQhJpNmjSJPn36MG/ePObPn9/c02kW//3vf7Fardx///3NPRVBEIR6UyvV3iW6lQk+oe5K7aUU24qrLStcuX8lpY5SYgJiGJgw0C+PVXWprUfyoFVpMWlNxAXFEaALIEAXIKovm4BOrSMlNIUgfZC8F19pFhaDpV5VfG6PG7fk9v7r8XhweVzyx9LJpYsSEkaN0W/NJ6b2nsqu7F1kW7O55etbTnttsD64xgRedEA0erW+1vvd3e9u7l9xP78d/Y1hKcPoGdOzxuuCdEFklmYSHRB9XlTvlZeX89JLL/Hjjz8C0KdPH5588knC/FA9ZLPZvEtfT03WnVpXo1QqUalU3sSbSqXCYDCg0+nQarXodDpMx4+jq6hAER/vvdbw1luoHA6ktDRSpkzx2YcPgBMn5Eqi0zTGE4RGkZAgf/+VlEDlXo9hYXDttbBwoVy9N2gQVCaaTSa5eq9t25PHWoOXXoJbb4XbbpM/f/llWLkS3ngD5s1rlinVO7m3ePFi3nrrrWqJPYCoqCiee+45pk2bJpJ7gtCIFAoF77zzDkuWLMHj8ZyX+2R4PB4+/vjjxtkGwG6Xb39TiMpSQRAa0akJvjP+gS1ilFdBRQFOtxOt6mRjKIfbwbe75WZNkzpNqvfebGdaamvWmTFpTKLDZDMJM4URoAvgWNExjhcdx+qwYjFY8EgeOVl3auJO8uCRPChQICEnWlQKFUqlErVSjRIlapUao8aIRq1Br9ajVWlRK9W4PC4OFRwivzzfp0K0voxaI/8c9E9m/jgTp8dJoC6wegIvQE7g1beBSEpoCmM7jGXJX0t4c9ObvDru1RqTz0atkaLSIrJKs/yT3GvB8WnHjh1MmTKFI0eOoFQqvXtgN7SqzePxkJWVhUqlQqfToVKp0Ov13kSdXq/3VtCdeqtM2qnVat/f5202eZljSgr8vaUOeXmweDEAimnTqif2ysvl/cvi4xv0fAShXsxmSEqCXbsgIODk9+eNN8KiRfKefCtXQuX2TwEBkJsrL80NCWm2acPfcaqk5OQBnU6+ncrhkPcPfOgh3+MjR8L69Y07ydOod3IvMzOTTp061Xq+c+fOZGVl1Xd44TyzcOFCFi5c2NzTaJU6d+5cY0fc88XNN9/ceIPPmwezZ3s/jWm8RxIEQQBOJvgkSeJw4WFMWlPtF4sYBciVdNml2Ri0vhVbqw+uptBWSJgxjKFtzq7bZU1LbUMMIWKpbQulU+toG9oWi8HC4cLDFJQXeJN1KoUKjUqDSWNCq9aiV+vRqDSolWr5vFLl/Vej1KBSqlApVLUma40aI3/l/kVOWQ7hxvAGJ3XTItJ47/L3UCqUmLXmBo1Vm2u7Xcv6Y+vJKcvhsx2fMaXnlBqv81bvBUY3fC4tND45nU7Gjx/P0aNHCQ0NZd68edX23K4Pl8tFRkYGYWFhpKWlERgYiFqtbnjSvzLpUXWZ3wcfyInTrl1hwIDq9ykokCv2WtD2WMJ5Ji5OXm5bXAyVBRiBgTBlCsyfLy9hHTFCTkJrNHLX3MLCZk/uxZy65/3MmTBrVvUL8/LkOZ9a6BYZKVchNpN6l/mEhYVx5MiRWs8fPnxYbMopCC2U3W7n5ptvJj4+nsDAQPr378/6JnyX4Y033qBnz55oNBpm1RQwW4qHH5Z/KP19y9i8ublnJAjCeUCtVNMurB1JwUloldraLxQxCoCiiiJKHCU+XXJdHhdf7/oagIlpE9Go6taFr9hWTGZpJlaHFb1GT9vQtvSM6UnfuL50i+lGXFAcwYZgkdhrgRQKBWGmMLpGdaV3XG/6xvWVb/F96Rffjx6xPegU2YmU0BQSghOICYwhwhxBqDFUrsDUmtGpdaiVp0/IhJnC6BzZGbPGTLY1G7fH3eC5B+oCGy2xB3JH3Nv7ys3Xvt3zLYcLD9d4nUlrwua0kV2a3fAHbaHxSaPR8M477zB48GDmz5/vl8SezWYjPT2duLg4evXqRWhoKBqNpuGJPY9HTpAYDCern3Jy5OonkLuQiqo9oSUyGuXqveJi+fu40jXXQGioXI36zTcnjxsMclOKqtc2g4zNm33iFg8/fPo7nPr6k6Tqx5pQvZN7o0eP5tFHH8XhcFQ7Z7fbefzxxxk9enSDJicIQuNwuVy0adOGdevWUVRUxJ133sn48eMpLy9vksePjo5m9uzZTJw4sUker950Ovldpr9vkrnxfvEWBEGoypvgC0mqff82EaMAyC/PB/BZdvvbkd/IKcshSB/EiLYj6jSO3WXH5rKRGp5K77je9I7tTZuQNoSZwsQeeq2ITq0jSB+ESWvyJuv8LdgQTKeoToQaQ8mx5uDyuPz+GP7WN64vAxIG4JE8vLHpjVqTkgH6ADJKMyhzlDXsAVtwfBoxYgT/+c9/CApq+N6JpaWl5Obm0q5dO7p3747RWL/l0zUqKJArhKpW4C1YIC8J7NEDTq0yqrxPXJyo2hOaX1ycXIlXWHjymF5/co+6996Digr544AAOZlWdUlsM5DMZp+4VeOSXJD3EFSpqlfp5eRUr+ZrQvVO7s2ePZu9e/fSrl07nnvuOZYsWcKSJUt45plnaNeuHXv27GnZFTmCcB4zmUw88cQTJCQkoFQqmTJlCh6Ph/379zfJ40+cOJFx48b55ZcqQRCEc5VGpSE6ILrVdQBtSjanjZyyHJ+qJ4/kYdGfcmXL+I7j65SY80geCioKiA+KJy4oDrPWLPbQE07LrDWTFpFGVEAU2dZsHO7qBQ8tzdTeUzFoDOzL28fK/StrvMasNVPuKCfb6ofqvXNcXl4eVquVLl260KlTJzSaulUI11lmplwJVDluVpZ3rz1RtSe0eDqdvDy8rExewlpp4kSIjYX8fPjss5PX2u1QVNQcMz17Wi306gWrVvkeX7UKBvqneVd91Pu3xbi4ONavX09aWhoPP/wwEydOZOLEiTz66KOkpaWxbt064kVgaRUWLlyIQqFgy5YtzT2VVmfOnDmkpaXhaeYS4ob666+/qKioICUlpbmnAsB7771HbGwsZWUNfNdYEARBOKcV2gopd5Zj0pzcm3Dj8Y2cKDmBUWPkkvaX1GmcgvICgvXBJAYniqSeUGd6jZ6OER1JDE4krzyPCmdFc0/ptEKMIdzQ/QYAPvrfR96q11MF6gJJL0mn3NE0KzpaG0mSyMzMRKlU0rNnT1JSUvzf1K6sDDIyTu5XBvD+++B0Qu/e8u1UompPaGmio+XqvYKCk8c0Gpg2Tf74ww9PVusZjScT2q3BjBnw7rvy63LPHvjXv+DYsZPPrRk0KAolJyfz/fffk5eXx8aNG9m4cSO5ubmsWLGCtm3b+muOgtAiZWRk8NxzzzFnzpxW3aW2vLycG264gcceewxzC1kyMWXKFEwmE88991xzT0UQBEFooSRJIteai0Z5cm8rSZL46s+vALi0w6V16jBa4azAI3loE9IGvUbfqHMWzj1alZb2Ye1pG9KWIlsRVkfL6Qpbk1HtRtE+rD0Vzgre3fJujdeYtCZRvVcLl8vFiRMnCAgIoFevXsTENFKrkNxcuRKv8nfzjAz4Vu7+zR13VL++smqvauMNQWhuWq3c6bmiAlxVti8YNQratoXSUliyRD4WECBX7rWgztqndfXV8PLLMGcOdO8Ov/4Ky5dDYmKzTaneGYmSkhLvTaVS0bFjRzp27IharfY511i+/PJLJk6cSHx8PCaTia5du/LGG2+0+goqwVdT7QFXH6+88grBwcFMmjSpuafiY/jw4ej1+hpvjz/+uM+1TqeTq666irS0NB555JEmf/zaqNVq7rjjDl555ZUW/T0gCIIgNB+rw0pBRQEBupONNLZlbuNQwSF0Kh3jOo474xgeyUNhRSHxwfGEGcMac7rCOUylVJEckkyHsA6UO8opthU395RqpVKquKvfXagUKjYc28DmE9UbXSgUCgJ0AaSXpLf4asSmVNk4IzY2lt69exPSWJ09XS44elSuZKr0ySfy0sa+feX99k6Vny9X7VWt9BOEliAqSt6HLr9KpbBSKS/PBVi7Vv5Xr5eTgFX36Gvp7roLjhyRlxT/8QdccEGzTqfeyb3g4GAsFkutt8rzjeXFF19Ep9Px/PPPs2zZMiZOnMj06dN58MEHG+0xzxe5ubncfvvtxMfHo9PpCA8PZ9CgQfz4448+161du5bhw4cTEBCA0Whk4MCBfPfddz7XzJo1C4VCwbZt25g0aRKBgYEEBQVx/fXXk5ubW+O1W7du5YorrsBisfgsE92/fz+TJ08mIiICnU5Hamoqr732Wo1j7Nq1i2uvvZagoCAiIyO55ZZbKC6u/otWXcasicPh4L333mPy5Mk1Vu19++23dO3aFZ1OR3JyMq+88op3bqdTec2OHTu48sorCQoKIiQkhBkzZuByudi7dy+jR48mICCApKSkGivbfvrpJ2w2W423J5980nudx+PhxhtvRKVS8d5779VpGVJdvjfq+vhnct1111FSUsJnlXsxCIIgCEIVRRVF2F129OqT1XZf7vwSgFHtRxGoDzzjGPnl+YQYQ8RyXKHBFAoFiZZE0iLTcEvuWpe8tgRJliQmpE0A4O3Nb9eYwDNrzZQ5ykT13t+sVis5OTm0bduWbt26YTKZznyn+srPlyuYKhN1Dgd8/7388XXXVb++vFyukBJVe0JLpFbLe+85nfKt0uDB8r/btp2s1tPpqjepEOqs3u2j1qxZ4/1YkiTGjBnDu+++S2xsrF8mdiZLly4lPDzc+/mwYcOwWq3Mnz+fuXPnoquts4lwRjfccANbt27lqaeeon379hQVFbF161byq2Tbf/nlF0aMGEHXrl1577330Ol0vP7664wbN45PP/2Uq6++2mfMyy67jKuuuopp06axa9cuHn/8cXbv3s2mTZuqbT47adIkrrnmGqZNm+bdc2337t0MHDiQhIQEXnzxRaKioli5ciXTp08nLy+PmTNn+oxx+eWXc/XVV3Prrbeyc+dOHv67jfX777/vveZsx6xq06ZN5OfnM2zYsGrnVqxYwaRJk7jgggv4/PPPcblcvPDCC2Rn1/2Xo6uuuorrr7+eO+64g1WrVvHcc8/hdDr58ccfueuuu7jvvvv45JNPePDBB2nbtm29qgfvuOMOMjMzWbFiBWp13UJBXb436sLlcuFyuXC73bhcLmw2GxqNBpXqZKfDqKgoOnbsyHfffcctt9xyVuMLgiAI5za3x02WNQuDxuA9titnF3ty96BWqpmYOvGMY5Q7ylGgoI2ljeiGK/hNdEA0GqWGvbl7ybHmEG4Kb5GJ46u7XM26o+vItmbz6fZPuaW37+9aCoUCs85MenE6Ueao83rJen5+Pna7nS5dupCcnNz42/FkZMjNMip/P//5Z7mTaGQk9O9f0wQhOVlU7QktV0SEXMGXmyvvwwdypWliolylunEjXHyx3KG2sFDec7IxE+jnqHon94YOHerzuUqlon///iQnJzd4UnVRNbFXqUePHthsNgoKCoiu/KZpJJIktagydYPG4LdfHNatW8dtt93G1KlTvccmTJjgc81DDz2ExWLh559/9u7TNnbsWLp37859993HVVdd5TOfSZMmeavMRo4cSWRkJNdddx1ffPEF153yDtSUKVOYPXu2z7EZM2YQEBDA2rVrCQyU34kfMWIEdrudZ555hunTp/tUit56663cf//9AFx88cUcOHCA999/36dC7WzHrGrDhg0A9OzZs9q5J554gtjYWFauXIlWqwVg9OjRJCUl1ThWTW6//XZmzJjhnf8PP/zA/Pnz+frrr7nssssAuPDCC1m2bBkff/zxWSf3jh49yrvvvoterycs7OQypO+//54hQ4bUer+6fG/Uxdy5c33+Hz/11FMsWLCAm266yee6nj17VqsYFQRBEIRiWzHFtmJCDCeXxX21U95rb3jKcEKMp18u5/a4KbQV0i60HWEmsRxX8K8wUxgalZzgy7ZmE24KR6VUnfmOTUin1jGt7zRmr57Nsr3LuKDNBbQN9d0zPUAbQEZpBtnWbBItzbePVHOpbJyh0+no2bNn0xSxlJTIlUtVl/xW7rU3diyoTvk+ElV7QmugUsnVezk58hLWykKsIUPk5N7atXJyz2CAvDw5wSeSe2et3sm9lui3334jJCSEiIiIRn+sCmcFXf6vS6M/Tl3tnL6zTptG10Xfvn1ZuHAhoaGhXHzxxfTq1cunuq6srIxNmzZx5513+jRgUKlU3HDDDTz44IPs3buXjh07es+dmsC76qqrmDJlCmvWrKl27vLLL/f53Gaz8dNPP3HnnXdiNBpxVdmMc8yYMcyfP5+NGzdyySUnO+KNHz/eZ4yuXbtis9nIyckhMjKyXmNWlZGRgUKh8EmMVX5ttmzZwj333ONN7AGYzWbGjRvHwoULaxzvVGPHjvX5PDU1le3bt/vMR61W07ZtW44ePVqnMatKTExEqkcnojN9b9TVrFmzmDVr1hmvi4iIICcnB5fLVefqQkEQBOHcl1+ejyRJaFTyz6AD+QfYlrkNpULJZWmXnfH+eeV5hJvCiQ+Ob+ypCuepIH0QaZFp7MvdR7Y1mwhzBGply/pdpkdMD4YkDeG3I7/x+qbXeX708z5JSIVCQYBW3nsvKiDqvKpwdblcZGRkEBISQufOnQkNDW2aB87Jkfcdq/x7NjMTNv+9L+K4GvYRFVV7QmsRFgYxMZCeDpWJ8sGD4b//hXXrwOOR9+LTaOQKv7i45p1vK9R6W3yeYsuWLSxYsIB//etfPkv7amK1Wn2aftjt9iaaZevw+eefM2XKFN59910GDBhASEgIN954I1l/r38vLCxEkqQaqyMrO0adukwzKirK53O1Wk1oaGiNyzlPHTc/Px+Xy8Wrr76KRqPxuY0ZMwaAvLw8n/uc+gO4cpl2RUVFvcesqqKiotoyUjj5tYmMjKx2n5qO1ebUDXq1Wi1GoxG9Xl/tuM1mq/O4DXWm7w1/0+v1SJLUpM+xJRAxShCElqq545PT7SSvLI8caw4m7cl39Ss75A5JGkJUQFRtdwfkRhyVDRC0Ku1prxWEhjBrzaRFpBETGEO2NRuH29HcU6rm1l63YtKaOFRwiO/2flftfIAugBJ7SavYe89f8clut5Oenk5MTAy9evVqusSewwHHjkFQ0MljS5eCJEHv3tWTHeXlcgWUqNoTWgOlUl6Gq1RC5d923bvLFXqFhbB7t3wsIEBO7lW0nFWSrYVf3z5qrv0ksrKyuPzyy+nbt2+dGmr07dvX5/OZM2fWqYqoKoPGwM7pO8/qPo2p6p4zDRUWFsbLL7/Myy+/zLFjx1iyZAkPPfQQOTk5rFixAovFglKpJDMzs9p9MzIyvGNUlZWV5VPK7nK5yM/Pr/GH5anfRxaLxVsVePfdd9c45zZt2pzVc2zomGFhYTgcDsrKynw21LVYLCgUihr312usBFhTOtP3hr8VFBSg0+l8KkTPB/6IUYIgCI2hOeKT2+Om2FZMYUUhOdYcSh2lqBQq73La40XH2Xh8IwBXdLrijGOV2EpoH9Yei6HxGr8JQiW9Rk+H8A5oVBqOFh7FYrD49ff2hgo2BDOlxxRe3/Q6n2z/hAEJAwg3ndz+SKFQYNbKe+9FmiNbdPWeP+KT1WqlsLCQtm3b0rFjR5+VOI0uL09ellv5N5PHIyf3AGraBic/H1JSRNWe0HqEhspJ6qNH5X/VahgwAH78UV6a27nzyWRfUZG8TFeos3on907d48tmszFt2rRqnYO+/vrr+j5EnRQXF3PJJZdgNBpZsmRJnZYIbt68mQ4dOng/r0/zDYVC4bdlsC1ZQkIC99xzDz/99BPr1q0DwGQy0a9fP77++mteeOEFDH+/6DweD//973+Ji4ujffv2PuN8/PHH9OrVy/v5F198gcvl4sILLzzjHIxGI8OGDWPbtm107drVLz9kGzpm5ZLjgwcP0rVrV+9xk8lE7969Wbx4MS+88IJ3XKvVyrJlyxo875akpu8Nfzt06BBpaWmNMnZL5o8YJQiC0BiaKj55JA+l9lJvQq/EXoJH8mDSmIgwRfgsHVy0axEA/eP7n3GZbV55HhHmCOKCxHIfoeloVVrahbZDo9RwsOAgbo8bs67lvHF5cduLWXNoDXty9/D25rd55MJHfN5sD9AFkFWaRW5Zbot+7TQ0PhUUFGCz2ejUqRPJyclnXA3mV5IEx4/LSxIrG3Zs3iwvyzWb4dQmfpVVe/FiawGhFVEo5Oq9jAz5e9holJfm/vgj/PYbTJsmX6NUykvUG7mPwrmm3sm9oKrlwsD111/f4MmcLZvNxvjx48nOzmbDhg11Lpk2m83eBgqCr+LiYoYNG8bkyZPp2LEjAQEB/P77794OsJXmzZvHiBEjGDZsGPfddx9arZbXX3+dP//8k08//bRa9d3XX3+NWq1mxIgR3m653bp146qrrqrTvF555RUGDx7MkCFDuPPOO0lKSqK0tJQDBw6wdOlSVq9efdbPtSFjViYlN27c6JPcA5gzZw6XXnopo0aN4h//+Adut5vnn38es9lMQUHBWc+zpajr94a/eDweNm/ezK233ur3sVs6EaMEQWipGjM+SZKE1WGl2FZMtjWbElsJTo8Tg8ZAqDG0xv3Kskqz+PXIrwBc0fn0VXul9lK0Ki3JIcnevfoEoalULgXXqDQcyDuAS3IRrA9u7mkBoFQouavfXfxr+b/4Pf13NhzfwMCEgT7njVojJ4pPEG4Kb7HVe/WNT5IkkZWVhUajoUePHsTGxjb9irTCQrlyr2ozv8pGGpdcAqdszSOq9oRWy2KRk9IHD8rJvYED5YTe3r1yQi8i4uTS3KrNN4Qzqndyb8GCBf6cx1lzuVxcddVVbN++nV9//ZXExPOvg1Nj0Ov19OvXj48++ogjR47gdDpJSEjgwQcf5IEHHvBeN3ToUFavXs3MmTO56aab8Hg8dOvWjSVLllRrBgFycm/WrFm88cYbKBQKxo0bx8svv1znirm0tDS2bt3Kk08+yWOPPUZOTg7BwcG0a9fOu0fe2WrImPHx8QwZMoRvv/2W22+/3efc6NGjWbRoEU888QRXX301UVFR3HXXXWRkZPDRRx/Va64tQV2/N/zl559/pri4uFrDFUEQBOHcUu4op8hWRLY1myJbEU6XE51aR5A+6IxJuMW7F+ORPHSP7l6t02dVLo+LUnspHcM7EqQPqvU6QWhMCoWChOAENCoN+/L2kVeW59duzU63E5fHhdPjxOWW//VIHowa4xm/7+OD47ks7TK+/PNL3vn9HbpFdfPZ1zJQF0hWaRZ5ZXnEBjVB19gm4nK5yMzMxGKx0KlTp2pbCzWZrCxwOk8mMoqK4Oef5Y9PXZJbViaq9oTWrbJ6z2qVO0N36gR//ik31rjsMrlaNT1dfh2cxb715zuFVJ+WmS3AHXfcwdtvv81zzz3HkCFDfM6lpaXV+K7Nnj17SEtLY/fu3aSmpjbVVM9rs2bNYvbs2eTm5jbfD8tGsmjRIq6++mqOHj3qs59gTZxOJ927dyc2NpYffvihiWbYut1www0cOnSo0Zb81kdjxxARowRBaIjGjCH+HtvmtFFkKyK3LJfCikIqnBVoVVoCdAF1rgoqKC/g9sW34/K4eGrEU3SK7FTrtVmlWUSaI0mLTGtxHUuF81NeWR57c/dic9kIN4XXqVLM7XFXS965PC6QAAVolBrUKjVqpRqD2oBJa0KtVHOk8Ag6tY4AXcBpx3e4Hfxz2T/JKM1gTPsx3N7X9w3swopCdCodPWJ7nHUzmpYWn7Zs2UJ6ejput5uYmBg6d+7cfHs8V1TISQ2VCir/hv3sM3jhBWjfHj75xPf648flqr0uXZp+roLgL3/9JTfRSEyEd9+FN9+EoUPhxRfl8ydOQLt20ARbNJ0rf4PV+7eb1atXc88997Bx48ZqibTi4mIGDhzIm2++WS3x5i8rV64EqLFiaM2aNXXay00QGmLSpEn06dOHefPmMX/+fJ9zt956KyNGjCA6OpqsrCzefPNN9uzZwyuvvNJMs21dDh48yOeff16v5daCIAhCy+RwOyi2FZNXlkdeeR4VjgpUShUBuoB6NbdYvGcxLo+L1PDU0yb2Smwl6DV62oS0EYk9ocUIM4WhUWnYm7uXLGsWEaYIlAqlnLyrUoHndDuRkGsxlAolGpUGjVKDXqMnVBuKSWtCq9KiUWrQqXXyxyoNSoXS+1galYY9uXvkpN9pmnloVVqm9ZvGEz8+wff7vmdom6F0CD+5h12QPogsq1y9FxMY03hfnCbicrlISUkhNTW1efc3zs2F0tKTlXiSdHJJrqjaE85V8fFyorq4WN5378035X0mK5fims1yRWu7dvJelMIZ1fs3nJdffpmpU6fWWCEXFBTEHXfcwUsvvdRoyb0jR440yriCUFcKhYJ33nmHJUuW4PF4UCpP/hJVWlrKfffdR25uLhqNhp49e7J8+XIuvvjiZpxx63Hs2DHmz5/P4MGDm3sqgiAIQgO4PC6KbcUUlBeQY82hzFmGSqHCpDURFBBU732tSuwlrNwnv9F7ur32nG4nZc4y0iLSzli1JAhNLUgfRKfITuzN3UuONcen+q6yktWkNaFX69Gq5QSeVqVFq9aeVaI6NjAWh9vBgbwDKBXK01bHdo3qyrDkYaw5tIY3Nr3BC2Ne8D6WUqFEr9J7995rzXtXGgwGunTpQkpKStM2zjiV2y1XKBkM8r5jIFc07d8PWi2MHu17fUGB2GtPODeYTHKCb+9e6NABwsPlRPfWrXIH3crkXlGRfE44o3on97Zv386zzz5b6/mRI0fywgsv1Hd44Rwxa9ass25B35p07tyZzp07Vzv+xRdfNMNszh3Dhg1j2KldwQRBEIRWJb04nePFxyl1lIIEJq2JSHOkT0VRfS37axl2t51kSzI9Y3rWel1eeR7RAdFEB4iOe0LLZNKa6BTZiSJbERqlBo3q7wSeSuu3pg4KhYLE4EQcbgdHCo8QaY48bXLw5p43syV9C0eKjrBkzxImdTrZOC1IH0SONYfcstxWXb2XlpbW9E0zalJQIDfSiIg4eayyau/CC6FqE0tRtSeca4KD5UpVkKv3vvkG1q6Vk3tqtXyuoEAk9+qo3r9dZWdnozlNeaRarSY3N7e+wwuCIAiCIAitWGFFISX2EsKN4UQFRBGgC/BLYq/cUc53e78D5Kq92v5AL7IVYdQYaRPSBpWyGStzBOEMdGodkeZIQowh3n0n/Z14quzWGxMQQ641F4/kqfXaQH0gN/e8GYDPdnxGtjXbZxydWkd6Sbq8318r1SISeyA3FZCkk8sObTZYsUL++NQluYWFEBcnqvaawOleH4IfBQbKVavl5TBokHxs7dqTCb/K6j23u/nm2IrU+zes2NhYdu7cWev5HTt2EB0t3iUVBEEQBEE4X2lVWr8n1lbsX0GZo4y4wDj6J/Sv8RqH20GFs4LkkGTM2mbaJF8QWhitSkv7sPaEmkLJseZwur6Kw5KH0SWyCw63gzc3velzbZA+iMKKQvLK8ppi2ucuq1VOXFRN1q1eLR+PiYE+fU4ed7nkZbtRUU0+zfNNYUUh2zK2kVmaKZJ8jc1olKtTy8qgb195KXp6OlRuwRYQACUl8r58whnVO7k3ZswYnnjiCWw2W7VzFRUVzJw5k7FjxzZocoIgCIIgCILgdDvZmrGVNze9yZd/fgnA5Z0vr7ESUJIk8srlDf+jAsQfwoJQlV6jp31YewJ0AeSW1b7KSqFQMK3fNDRKDdsyt7H26FrvOZVShVal5XjxcdweUVFTb7m5clKjapfeyiW548ZBlf28KSmRkyCWs28+JNRduaOc/Xn7ybXmsjNrJ3/l/EWFs6K5p3Vui4yUK1aNRujVSz629u94o1bLie2CguabXytS7z33HnvsMb7++mvat2/PPffcQ4cOHVAoFOzZs4fXXnsNt9vNo48+6s+5CoIgCIIgCOcJq93KlvQtbD6xmW2Z23z+wGob0pYhSTU3bSuyFRGgDaCNpY1flgELwrkmQBdA+7D27MrZRWFFYa3dqmMDY7mi8xV8uuNT3t3yLj2ie2DWyYkok9ZEhbMCp9splr3Xh8sFx475JvZOnIA//pAr9MaN873eaoUuXeRkh9AonG4nB/IPUGQrIiYwBofbwYniE5TYS0gOSSbcFN5ylnOfSwIDQaWSXxODBsGGDfDbb3DDDfJ5oxEyMyE52TfhLVRT7+gQGRnJ+vXrufPOO3n44Ye9pdoKhYJRo0bx+uuvExkZ6beJCoIgCIIgCOe2bGs2m09sZvPxzezK2eWzJMpisNA3ri994/rSNaprjQ0B7C47dred9mHtMWlNTTl1QWhVQowhtA9rz+7s3ZTaS2vtJj2p0yR+O/obJ4pP8MG2D7i7/90AKBBJjgbJy5O7gFZdZltZtde/v+9xu11erhgW1qRTPJ94JA+HCw+TUZpBpDkShUKBTq0jKiCKwopCdmTtICE4gcTgxNN2mxbqITBQ7pxbViY31XjhBdi+HUpL5WW5gYFy5V5Jidhv8gwalPpMTExk+fLl5OXlsWnTJjZu3EheXh7Lly8nKSnJT1MUGtPChQtRKBRs2bKlxvNjx46t8f+l3W5n/vz5DB48GIvFglarJTY2lquuuopffvnFe93PP/+MQqHgq6++qnH8e+65p8Z3QJxOJ2+88QYDBgwgKCgIg8FAamoqDz30EPn5+Wf1HG+55RZG/91G/pVXXkGhULCicqPaGrzzzjsoFAq+/vpr7zGPx8NHH33ExRdfTFhYGBqNhoiICMaOHcvSpUvxeOQ/Po4cOYJCofDeNBoNoaGh9OnTh3/961/s2rWr2uP99NNPmM1m0tPTz+p5CYIgCEJr55E87M/bz8f/+5h/LPsHdyy+g/e2vMfO7J14JA8JwQlc2flKnh/9PO9Neo87+91Jr9heaFTVm7pJkkRBeQFxgXFEmsUbzIJwJpHmSNqGtqXMWVbr0kONSsOd/e4EYNWBVezO2d2UUzw3SZK8r5hSebISz+WCZcvkj09tpFFcDCEhvp1zBb/KKMngSOERwoxhPm8cKRQKQowhBOmDOFRwiB1ZO8Rek/6m0ciJ67IyuWFMmzZyA40NG+TzWi04HHIyXDiteif3SkpKvDeVSkWHDh3o2LEjKpXK55xw7snLy2PQoEHMmDGDzp07s3DhQn766SdefPFFVCoVw4cPZ/v27fUev7y8nBEjRnDvvffSo0cPPv30U5YvX84NN9zA22+/TY8ePdi7d2+dxtq2bRsffPABc+fOBeD6669Hp9Px/vvv13qfBQsWEB4ezri/y+FtNhtjxoxhypQpRERE8MYbb7B69WrefPNNYmJiuPLKK1m6dKnPGPfeey8bNmzgl19+4aOPPmLixIksWbKEbt268fzzz/tcO3z4cPr27csjjzxyNl8mQRAEQWiVqu6fd9vXt3H/ivv58s8vOVp0FKVCSefIztzS6xbenPAm/zf2/7iu+3W0C2t3xiW2hRWFBOoDSbIkiaVTglBHcUFxJFuSKaoowuF21HhNp4hOjGg7AoA3Nr2B0+1syimee0pKIDvbd/+8jRvlPfiCguCCC3yvt9kgNlZeriv4XV5ZHvvz92PWmmutytOr9UQHRFNqL2Vn1k4O5h+s9fUi1ENoqJzgBrl6D07uuwdyR93MzJNddIUa1XtZbnBw8Gl/cZIkCYVCgVu0LT7n3HjjjWzfvp2VK1dy0UUX+Zy75pprmDFjBpYGbPb6r3/9i19++YXPPvuMq6++2nt82LBhXHHFFfTt25fLL7+c7du3o1Kdfo+PZ555hr59+9K7d28AQkNDmTBhAosXLyY/P5/Q0FCf6//66y82bNjAv//9bzR/t6SfMWMGK1eu5IMPPuDGG2/0uX7SpEncf//9VFT4vtuZkJBA//4nO/iNGTOGGTNmMGnSJB544AE6d+7MJZdc4j1/9913c/XVVzN37lzi4+PP4qslCIIgCC1fqb2UP9L/YPOJzWzN2IrNdbIhm16tp2dMT/rG9aVXbK9alweejs1lw+VxkRqSikFj8OfUBeGcplAoSLIk4fQ4OVJ4hEhzZI1L3qf0mMLmE5s5Xnycb3Z/w4TUCTWMJtRJdra81NZQJVZVLskdM0auVKpUVibvORYS0rRzPE9YHVb25+8HiTP+7FEqlISbwuWmG/n7KbYV0yakTa17VgpnITAQdDo5kT14MHz0EaxfL1fwqVTy8tzCQnmpbmBgc8+2xap3cm/NmjXejyVJYsyYMbz77rvExsb6ZWJCy/THH3/w/fffc8cdd1RL7FXqU7Vt+1nKysri/fffZ9SoUT6JvUrt27fnwQcf5JFHHmHx4sVcfvnltY6VnZ3NN998w8svv+xz/NZbb+WLL77gk08+4d577/U5t2DBAkBeyls5n3fffZdRo0ZVS+xVateuXZ2em8Fg4L333iMpKYnnn3/eJ7k3btw4zGYz77zzDnPmzKnTeIIgCILQkmVbs1l9aDW7sndV2z8vxBBC37i+9InrQ9eorjUus60rj+ShoLyAJEsS4aZwf0xdEM4rKqWK5JBkHC4HWaVZRAZEVquUNevM3NrrVl5a9xJf7vySvnF9CdKLZaJnzW6H48d9ExQFBfDrr/LHpy7JLSmBmBjfxhuCX9hddvbn7cdqt57VVg5GrRGdWkd+RT4lmSUkWZKIC4qrMSku1JHZLCfwysqgWzf586Ii2LULunYFvV5OihcVieTeadT7O3Do0KE+n6tUKvr3709ycnKDJ9UaSJJEeXl5c0/Dy2g0NmgJitvtxlVZCluFdErp6w8//ADAxIkTz2p8j8dTp/HXrFmDy+U67fgTJ07kkUceYdWqVadN7v3www84nU6GDRvmc/ziiy8mMTGR999/3ye553a7+eijj+jfvz9paWne+TidzrN+vrWJiYmhV69erF+/HpfLhfrvfTa0Wi0DBw7ku+++E8k9QRAEodW76aub+O3Ibz7HEoMTvQ0xUkJT/NbJtqC8AIvBIpbjCkIDaFVa2oW1w+F2kGPN8TYVqGpI0hDWHFrDtsxtvPv7u8wYPKOZZtuK5eXJCbu4uJPHli+XK5TS0qBt25PHPR55qWLV5hqCX1Q20Kjpe31rxlYkSaJnTM9af6aolCoiTBFY7Vb25u2lqKKI5JBkAvUi8VQvSiVERMBff8lLdAcMgFWr5KW5XbvK1+j1kJUFCQnNO9cWTKSX66m8vBxzC3oHxWq1YjLVvytc1SWkp0pMTPR+fOzYMQDatGlzVuPXVIVXk7qMX3mu8trabNiwAYPBQMeOHX2OK5VKbrrpJmbPns22bdvo0aMHAN9//z2ZmZk+ybX6Pt/TSUxMZOPGjRQUFBAREeE93rNnT+bNm0dZWVmD/l8KgiAIQnOLCYxBqVDSPqw9AxMG0jeuL1EB/v8DtcJZgSRJJIckiw6GgtBABo2BDuEd2JW9i7zyvGqVsAqFgjv63sH0ZdP5M+dP1h9bT7/4fs0021bI45Gr9rRaOZkB8h5ilUtyT63as1rlCqZTthESGu540XGOFR0jzBSGSilv8yRJEl/s/IJPd3wKQLIlmWu6XUOf2D61JvnMOjN6jZ7cslxKHaW0sbQhOiDaO6ZwFio74Xo88tLcyuTeXXfJxyuX5la+LoRq/POWqdDqffjhh/z+++/VboMrN7RsoGeffbbG8a+66qp6j3mmd+czMjIIDw+v8bqbb74ZpVLp01hjwYIFmEymOici6+vUasVKEREReDwesrKyGvXxBUEQBKGxTR8wnQ+v+JD7h9zP+NTxjZLY80geCisKiQ+OJ9Qo/vgVBH8I0AXQPqw9GpWGworCauejAqK4pus1AHy247MarxFqUVQkV+5V3Zt85044fFjeb2zUKN/rS0ogOlo+J/hNjjWHQwWHCNAFoFXJ+xt6JA/v//G+N7GnVWk5VHiIp39+mvu/v58t6Vtq/RtOrVQTFRCFEiW7c3azO2c3Voe1yZ7POSMwUN6HsrwcBg2SG8js2ycvx4WT50TX3Fr5tXLvfFoKYTQasVpbzovWaDQ26P6pqanephNVBQUFcfz4ce/nCX+XwR4+fJgOHTrUefzk5OQaxw8P931HsOr4tak8d6bGExUVFej1+hrPJSYmMnz4cD755BNeeOEFSktLWbZsGZMnTyYg4ORmqnWZz9k6evQoOp2OkFM2xq2c66nNOQRBEAShtYkKiCKvLA97ub3RHiO/PJ8QYwgJwQnn1e+ggtDYQowhtA9rz+7s3ZTaS6s1GhifOp6fD/2MTq2jzFHWTLNshbKy5OW3VZN1lVV7F1/sW41UuZ1RlVU+QsOV2ErYl7cPpUKJWSt/vd0eN69tfI3Vh1YDcGvvWxmaNJRv93zLd3u/40DBAeaumUv7sPZc2/Vaukd3r/FnTqA+EIPGQEZJBiW2EpJDk4k0V9+/UqiFwSBX7+XlQWQkdOkCO3bAunUwaZKc7NNqISfHd1m74FXv5N6kSZN8PrfZbEybNq3acsKvv/66vg/RoikUivNy6eSoUaO8zSxGjx7t9/GHDRuGWq1m8eLFTJs2rcZrFi9eDMCIESNOO1ZYWBhbt26t9fytt97KqlWr+Pbbb8nIyMDhcHDrrbdWm49GozntfM5Geno6f/zxB0OHDvXut1epoKDAO29BEARBEGpX7ihHgUIsxxWERhJpjsThcrAndw9qpdqnC7VaqebRYY+iVqqJCxJ/ZNdJRQWkp0NQlSYk5eXy0kOA8eN9ry8pkRMdFtGJ1V9sThv78vZhc9m8DTScbicvrn2Rjcc3olQouaf/PVyUIjeNvKHHDYxLHcfiXYtZvm85+/L2MXv1bFLDU7mm6zV0jepaLcmnUWmICYyhqKKInVk7KQwspE1IG9HFva4iIuTXCcjVezt2wG+/yck9kBPgeXny68kgvqanqncaOSgoyOd2/fXXExMTU+24cG7p2bMnl1xyCe+99x6rV6+u8ZotW7accT+82kRFRXHLLbewcuVKPv/882rn9+3bx7PPPkunTp3O2OSiY8eO5OfnU1xcXOP5iRMnEhoayvvvv8+CBQto3759tWXIUVFR3HbbbaxcuZIPP/ywxnEOHjzIjh07zvjcKioquO2223C5XDzwwAPVzh86dIjQ0FAiI+verUkQBEEQzidujxurw0qRrYhES6JYjisIjSguKI6UkBQKKwpxuB0+54L1waIi6Wzk5kJpqW913o8/ygm++Hjo2dP3+rIyiI0Ftdgi3x9cHhcHCg6QX57v3UuywlnB3J/nsvH4RtRKNQ9c8IA3sVcpWB/MTb1u4q2JbzG+43i0Ki17cvcw86eZPLbqMf7M/rPGxws2BBNiCOFE8Qm2Z24nx5pT67JeoYrAQPl73uWCIUPkY7//Djab/LHJJL82CsV2ADWpd7RYsGCBP+chtCIffvgho0eP5pJLLuGWW27hkksuwWKxkJmZydKlS/n000/5448/vEtaz9ZLL73E3r17uf766/n1118ZN24cOp2OjRs38sILLxAQEMCiRYtQqU6/UemFF16IJEls2rSJkSNHVjuv0+m47rrrePXVV5EkiWeeeabW+Rw6dIibbrqJlStXctlllxEZGUleXh6rVq1iwYIFfPbZZ3St7OSD3Ihj48aNeDweiouL2bZtG++//z5Hjx7lxRdfrHE+GzduZOjQoWJpkSAIgiD8zSN5sDltVLgqcLgcKJQKjGojCcEJomJIEBqZQqEgyZKEw+3gaNFRIs2RqJUi2XTW3G44dkxOTFT9Pb9ySe748b7H7XbQaEQjDT+RJIljRcdIL0kn3BSOUqHEarfy5Jon2Zu3F71az8NDH6ZbdLdax7AYLNzS+xYmpk1k0a5FrNy/kl05u3hs1WN0ierCtV2vJS0izec+OrWOqIAoCisK2Zm1k7igOJIsSaLa/HQCA+XXidUK7drJy3Ozs+GPP07uw6dSycnymJjmnm2LI6KzcNbCwsJYu3Yt77zzDp9++imffPIJ5eXlRERE0L9/f5YsWUK3brUHxzMxmUysWrWKd955hw8//JAPP/wQp9NJUlISt912Gw888AChdfhhN2jQIJKSkvj2229rTKaBvDT3//7v/1CpVNx44401XqPX6/nuu+/4+OOP+eCDD7jjjjsoKSnBYrHQu3dv3n//fcaNG+dzn1dffZVXX30VlUpFYGAgycnJjBs3jqlTp5KWllbtMQ4ePMjOnTuZNWvWmb9AgiAIgnCOkiQJu9tOhbMCm0t+p96gMRCkDyLMGIZJa8KsM3s3QRcEoXGplCpSQlNwup1klWYRGSD2EDtr+flQUCAnKiodOQLbt8tdc8eO9b2+pARCQnyX8Ar1lmXN4nDBYSx6i7dRzKyfZnG06ChmrZknLnqC9mHt6zRWiDGEqX2mclnaZSzatYhVB1axM2snO7N20j26O9d2vZYO4Sf3pVcoFIQYQ7C5bBwpPEKJvYQ2ljaEGkNFQUdN1GoID5ebzAQHywm9r7+Wu+YOGiRfExgoJ/fsdtFs5hQiuXeeu+mmm7jppptqPb9s2bIaj+v1eu69917uvffe045fWT1Xm/nz5zN//vxqxzUaDXfddRd3Vba+rgelUsk999zDU089xQsvvIChhnX5Xbt2rVOJdGXyr7YEYKWkpKR6lVwvXLiQhIQExp+634YgCIIgnOMcbgcVzgoqXBVIHgmdRodRYyQ+KJ4AXQBmrRm9puYGWYIgND6tSku7sHY43A5yrDne/cqEOsrIkP+tusS2smpv0CA5mVFVeTmkpsqJP6FBiiqKOJB/AK1ai0FjINuazcwfZ5JlzcJisDBr+CwSgxPPetwwUxh39L2Dy9Iu46s/v+Kngz/xv8z/8b/M/9EzpifXdr2WdmHtvNfr1XqiAqLIL8/nf5n/IyogioSgBAL1gf58uueGkBA4eFD+ePBgObm3bh1Ikly5ZzLJr6miIt+EuVD/PfcEoTW4++67CQoK4rXXXmvuqdSqqKiI1157jaeffrpakw1BEARBONe4PC5K7CVkW7O9XQW1Ki1JwUl0j+lO79je9I7tTZuQNoSZwkRiTxBaAIPGQIfwDgToAsgrz0NC7B9WJ6Wl8rLC4OCTx1wu+O47+eMJE3yvLy+XkxdiSW6DVTgr2Je3D7vLTrA+mONFx3l45cNkWbOINEfy9Min65XYqyrCHMFd/e/i9QmvMzxlOEqFkq0ZW7l/xf08teYpDhUc8l6rVCgJN4UTrA8msySTbZnbOJh/EJvT1tCnem4JDJQr8mw26NNH7pCbkQGH/v5aVia98/Kab44tlEjuCec0vV7PRx99hK4Fl+wePnyYhx9+mMmTJzf3VARBEATB7yqbYOSW5ZJZkklBeQFKlMQGxtI1uiu943rTO6437cLaEWGOwKQ1ieVKgtACBegCaB/WHo1KQ1FFUXNPp3XIyZEbAJhMJ4+tXSsv0w0JkSuTqiouhrAw38Ybwllzup0cyD9Aoa2QcFM4B/IP8MiqRyioKCA+KJ55I+cRHRBd6/09kuesHi/SHMm9A+7ltfGvMSx5GEqFkt/Tf2fG8hk888szHCk84r22ci8+nUrHgfwDbM3YSnpxOi6Pq75P99xiNkNAgPy6MRigd2/5+Nq1vtdkZYHT2TxzbKFEmZBwzhs8eHC1LrgtSY8ePejRo0dzT0MQBEEQ/M7qsOL0ODGoDYSbwgkxhGDWmTFpTWJjfkFohUKMIbQPa8/u7N3NPZWWz+mEEyfkREVVlUtyL73Ud6muxyPfJ7r2pJNwZpIkcaTwCBklGUSYI9iVvYunf3maCmcF7ULb8fhFjxOoq3k5rNvjJr88Hw8eJElCp9Kd1T6v0QHR/GPgP7ii8xV8seMLfj3yKxuPb2Tj8Y0MTBjI1V2v9lYLmrQmjBojxfZidmXvIsuaRWJwotiPT6GQl9vu2iVXsA4eDOvXy8m9KVPkayqTe0VF1Ze1n8fEb1WCIAiCIAiC34UaQwnUB2LWysk80SFQEM4NkeZIHC4HWdas5p5Ky5aXJycfqibr8vLkRAVUX5JrtcqJwJCQJpviuSijJIPDhYcJMYawNWMrz//6PE6Pky6RXXjkwkcwaKrvww5QYi/B6rASYYogLigOm9NGtjWbElsJTrcTnVpX559lsYGx/Gvwv7iii5zkW3t0LeuPrWfDsQ30je/L+NTxpIWnoVAoCNYHE6ANoKCiQOzHVykoSE7yeTxycu+552DHDrnZTGDgyaR4QYFI7lUhknuCIAiCIAiC30UHiuoTQThXxQXFEagPFHti1kaSID0dVCr5VmnZMnC7oWtXSEryvU9JCaSkgF58Tesrvzyf/fn7MWlNbDq+iVfWv4JH8tA3ri/3Dbmvxgo8p9tJXnkeBo2B1PBUYgJjvJXlsUGxWO1WSuwl5FhzKLYXk1+ej06tw6w1nzHRFx8Uz7+H/JsrulzB5zs+Z/2x9Ww6volNxzfRNqQt41PHMzBxIGqlmnBTOA63g8ySTPLK8ogPiicmMKbWZOQ5LTAQjEZ5D8qYGEhOlvfc27ABRo2SrzGZ5Oq9lBTfCtjzmPgqCIIgCIIgCIIgCHWmUCgI0gc19zRaruJiuZGGxXLymCTBkiXyx6dW7bn+3m8tIqJp5ncOKnOUsT9vPx7Jw9oja3nn93eQkLiwzYXcO+BeVEqVz/WSJFFkK8LmshEdEE2SJYkAne8SaqVCSaA+kEB9ILGBsVgdVkpsJeSU5VBsKyavPM+7dFevrj0pmxicyAMXPMDx4uMs3bOUnw//zIGCA7y07iU+2PYBYzuMZUS7EZi1ZqICoih3lHMg/wBZpVkkWZKIMEegUWka48vWMun18msnJ0degjt4sJzcW7v2ZHIvIEB+jRUXiwY0fxPJPUEQBEEQBEEQBEHwl+xscDh8q/C2bYNjx+SKpBEjfK8vLZU76oolufXicDvYn7efYlsx646u4+PtHwMwpsMYbut9G0qFbx9Ru8tOfnk+gbpA2kW1I9IcWe2aUykUCgJ0AQToAogJjMHqsFJqL/VW9BVUFKBRajBr5URfTfvmxQfFc1f/u7iu+3Ws2LeC5fuWk1+ezwfbPuDznZ9zccrFjO04lqiAKAwaAyX2Ev7M+ZPQ0lASLfJ+fGea5zkjPFzesxJgyBD48EN5SbvbLVfDqtXyx4WFIrn3N5HcEwRBEARBEARBEAR/sNvlpETgKXumVTbSGDFCTvBVVVoKnTuL5YX14JE8HC44TFZpFisPrGTJHrk68qouV3Ft12t9kmweyUNBeQEeyUOSJYmE4IR6LXutmuiLDoimzFlGia2EXGsuxfZiCisK0ahqT/QF6YO4uuvVXNbpMn478hvf7vmWY0XHWLZ3Gcv3LadvXF8mpE6gY3hHzFozhRWF8n585igSghPOj6rZwEA5iedyQZcu8ufFxbBzJ3TvLl9jMkFmprxsV3meJD1PQ0QPQRAEQRAEQRAEQfCH3Fx5/7y4uJPHrFb48Uf541OX5NrtoNVCWFjTzfEccrzoOIcLD7No1yJWH1oNwC29bmF86nif68ocZRTZigg1htLG0sZvXWkVCgVmrRmz1kxMYAxljjJK7CXkleVRZCui0FaIWqHGrDVj0Bh8HlOr0jI8ZTgXJV/E9qztLNmzhK0ZW70ddtuHtWd86ngGxA/ALbnJsmaRX55PbGAssYGxGLXG08yslQsIkJN3Vqtc1TpgAKxcCevWnUzuBQTITTWKi32XwJ+nRHJPEARBEARBEARBEBrK44Hjx0Gn860k+uEHOYnXpo1chVRVSYmcmAg6D6qx/Cy3LJd9efv4YOsHbD6xGaVCyV397uLithd7r3F73OSX56NUKmkf1p7YwNhG7d5u0powaU1EB0RT7ij3JvoKbYUUlxajVCq9ib7KJbYKhYLu0d3pHt2dY0XHWPrXUn4+9DP78vbxwm8vEG4KZ2yHsVzc9mKUCiWHCg6RY80h0ZJIpDny3NyPT62W96A8dEhO7g0eLCf31q6Fu++Wr9Fq5eXvRUUiuYdI7gmCIAiCIAiCIAhCw0iSnIjIzobISN9zlUtyx4+HU6vFKiogNVUsKzxLJbYSdmbuZP6G+fyZ8ydqpZoZg2cwMGHgyWvsJVjtViLMESRZkrAYmjYBZNQaMWqNRAVEUeGs8Cb6CioKKLGWoECu+jNqjd5EX0JwAnf3v5vrul/H93u/Z8W+FeSW5bJg6wI+2/kZI1JGcGmHSwHYlb2LzNJMEoMTCTOFnXv78YWEwP798scDBsivkf375S65UVHycYNBXpqblFT9tXWeEck9QRAEQRAEQRAEQagvSYIDB2D3brmCSKs9ee7AAdi1S94/7NJLfe9XXi4nJ0QjjTPySB7KHGWU2kvJL88nvSSdl9a+xMGCg+hUOh4a+hA9YnoA4HQ7ySvPw6AxkBqRSkxgDGpl86Y+DBoDBo2BSHMkdpedYpu8N19eWR7Z1mwUCgVGjRGTxoRKqSJYH8y13a5lUqdJ/Hr4V77961tOFJ9gyV9LWLZ3Gf3j+zOu4zh0ah3bs7YTZY4iJjAGi8Fy7iT5AgPlpjQ2m1y916ULbN8uV+9dcYV8TUCAXLlXWlp9n8vzjEjuCYIgCIIgCIIgCEJ9eDxyNdFff8kJCLPZ93xl1d4FF1RP4pWUyBVIAQFNMtXWxul2UmovpcReQm5ZLla7lQpXBblluXyw9QOOFh3FpDXx+LDH6RjeEUmSKLIVYXPZiAmMITE4kQBdy/va6tQ6IswR3orCUnsphRWF5JblklueCxIYNXLVn06tY0S7EVzc9mK2ZW7j2z3fsj1zO+uPrWf9sfV0COvApR0uxePxkGPNIdwUTkxgDCHGkNaf5DOZ5NeG1Son+QYPrp7c0+shJ0fumiuSe4IgCIIgCIIgCIIgnJXKxN6ePXLizmTyPe9wwPLl8senNtKQJPl85fJCAYAKZ4Wc7LIVkl+WT7G9mKOFRzlSeISDBQfZl7cPq8MKQLA+mFnDZ5FkScLmslFQXkCgLpB2Ue2INEe2iuSWTq1Dp9YRZgojyZJEib2EoooicstyyS/PxyN5MGgMmDQmesb0pGdMT44UHmHpX0v55fAv7M3by968vYSbwhnaZig9onuQXZZNmDGMmMAYQo2hzV61WG8KhbzEfdcu+fMhQ+C11+D33+VqPr1ePq7TyUtzExLO66W5rfT/siAIgiAIgiAIgiA0E48H9u2TK/ZqSuwB/Pqr3MkzPBz69/c9Z7XKVUmhoU0z3xaqcrltiU3ejy6nPIfd2bs5kH+Aw4WHOVhwEIfb4XMfnUpHp8hO3Nb7NqICosgty0WSJJIsSSQEJ2DQGJrp2TSMRqUh1BhKqDGUREsiJbYSim3F5JblUlhRiNvjRq/RExMYw70D7uX67tfz/b7v+X7f9+SW5fLVn1/x1Z9fkRqeSr/4fnSL6kZ0YDRxQXGEGcNaZ+ONoCA5YefxQEqKnOzLzoYtW+RKPpAr9oqKoKyseuXseUQk9wRBEARBEARBEAShrtxu2LtXTu6FhoLRWPN1lUtyx46Vu39WVVwMycknq4/OI063kxJ7CSW2Eg4VHmJ7xnb+yv2Lg4UHOVZ0DI/k8bk+QBdAangqaRFppEWkkRySjFqppsxRRmZpJqHGUNpY2hBqDEVxjlRuqZVqQowhhBhDSAhOoNReSrGtmJyyHEpsJTg9TnQqHZd3upzLO13OpuObWH1oNdszt7Mndw97cvegU+noFduLvnF96RPXh4TgBEKNoY3aLdjvAgPl11d5uZy4GzwYFi2Sl+ZWJvcMBsjNlRN8IrknCIIgCIIgCIIgCMJpud1ytd6+fXJFnqGWKrGsLNi4Uf54/Hjfcy6X/O+pXXXPYeWOckodpezN3cum45vYlbOL/fn7ySrNqnZtuCmctPA0bzIvNigWBX2pQPkAAQAASURBVAqcHicOl4MSWwl2tx2tSkv7sPbEBsa2roTVWVIpVQQbggk2BBMfHI/Vbj2Z6LOX4HA5SItIo09cH6wOK78c/oXVB1eTUZrh3Zsv1BhKv7h+XJxyMT1iexBhikCvaQWJZb1eblKTnS0n7oYMOZnck6STy3C1WvmauLjmnW8zEsk9QRAEQRAEQRAEQTgTl0veX+/AAYiIOH3V3bJlcvKhZ0+Ij/c9V1oqLzc8R7vkuj1u7C47Fc4K/sr9i3VH17E9azt78/ZSWFFY7fr4oHhvIi8tIg2LwYLD5cDhdmB328m2ZgPyslWdSkeQPoggfRAWg4VgQ3ATP7vmpVQoCdQHEqgPJC4oDqvDSomthJyyHKwOKy6Pi8GJgxmeMpz0knR+Pfwra4+uJb88n+X7lrN833LahrblwjYXMiF1AkmWJIzaWipPW4rwcDh+XP64d295j72sLDh4ENq2lY+bzZCXJ1f41VZJe45r1cm9ffv2MX36dH777TdMJhPXXnstzzzzDIba3j0RBEEQBEEQBEEQhLPlcsHu3XJiLzLy9Ik9jweWLJE/PrWRBsjJvc6dqy/VbWU8koeiiiIOFx7mSOERjhUd43jxcTJKMsguyybXmkuFq8LnPkqFkpSQFNIi0kgNTyUlNAWdWofD5cDpceL0OCkoL0Cr1qLX6IkIiMCsMaNT69Br9OjV+tbbIMLPFAoFAboAAnQBxAbFUuGs8Cb78svziTJHMbHTRManjmd3zm7WHV3HjqwdHMg/wIH8A3y47UP6xPVhQuoERrQbQaCuhXabDQwEjQacTvl116ePXLm3du3J5J7JBAUF8tLc8zS51/Lbx9SiqKiIiy66iNLSUhYtWsQLL7zAxx9/zNSpU/32GHa7nVmzZmG32/02ZmOPLebc+scWcz43xm4KrfVrI74PG3/c1jq2mHPTjd3YWuvXRXyfNP64rXVsMeemG7ux1WvuTqfcsfPgwdMm9hwOB2+99RbOjRshI0NOOAwffupFcqLiLBtpNNf3idPt5HDhYVYfXM37W95n1o+zuO3r2xizcAy9X+tNn9f7cNWnV/HAigeYv3E+3+75lt/Tf+dY0TEqXBUoJSWdIjpxRecreGjoQ7w27jX+PfjfjG43mqSQJFRKFUqFknBTOB3CO9A9uju943rTN64vvWN70yGsA7FBsYSZwjBrzaiV6lb7vd3Y/w+VHvnrmBKaQu+43vSO602XyC4kBCfQP74/d/a/k7kj53J5p8uJDojG4Xaw7ug6HljxABe/dzGPr3qc3dm7m3TOdRo3MFB+LZWVyZ9X7rW3du3JaxQKOVmem9uq41NDKCRJkpp7EvXx7LPPMmfOHI4ePUpYWBgAn3zyCddddx27d+8mNTW12n327NlDWlparedPVVJSQlBQEMXFxQQG+jeL3Vhjizm3/rHFnFvu2GcbQ87W+RCjGnNsMefWP7aYc8PGbswYJeJTyxy3McdujXNuzLHFnBs2dquOT5WJvUOHICpKXhJYC6vVytALL2Tj8OFofvoJLr8cHn7Y96LcXDlZ0b8/KOtea9NY/z/zi/KJTIrkx00/UuAskCvwio9xovgE6aXp5JXlVWtwcSqDxkCEKYJwUzihxlDCjGFYDBaMSiMzps3gk48+IdAciFalxagxEqgPxKAxoFfLlXh6jR6lovm/Fq117LqM63A7sNqtlNpLyS/Pp9Reyp7cPWw8vpEtJ7b4VFh2juzM5Z0uZ1KnSXjsnpYRnyqT67Gx8pLcsWPl188PP0BwsHxNaSm4XJR07UpQZGSLiE8APPUUfPcd/O9/8t6ARUXVrzl2DO6+G1avlvfxnDwZXnhBvr6OWm096/Lly7n44ou9iT2Ayy+/nFtuuYXly5c3zv8UQRAEQRAEQRAE4fzgcMhJhcOHITq6Tn9oBwDqX3+VP6lpSW55OXTseFaJvdNxup2U2kvl7rP2EvljW4n388qutMW2YoptxSevsZdgtVupcFWQ+Fgity69tdbH0Cg1hBhDCDOGyck7U5g3iRdmCiNQG4hapUalUKFSqtCpdGjVWlw2F44TDrpEdiHcEo5erUej0vjleQtnR6vS+nTfrXBWkBqRyvCU4WSVZLHu2DrWHl3LX7l/8Wf2n/yZ/SfP/PIMFyReQOCgQBb/tZiQwBBMGhMGjQGjxohBY8CkPfm5VqVtvG7FFovczEaS5CR727byEvmNG2H0aPkakwnS01EUFzfOHOrL4YArr4QBA+C996qfd7vh0kvlvQXXroX8fJgyRX6ur75a54dptcm9PXv2cMstt/gc0+l0pKSksGfPnmaalSAIgiAIgiAIgtBS7c7djaG9gXXH12E0GqlcyCbx94K2v/9ROJwY9x9GfyILe4QFKSe72rUn18DJH9hsNsaHqVHkObEmRrNJk4HnQAZujwuPxwM2G8qycrJC0rFla3B5XHgkDy6PC7fHjVty4/a4fY5X/lthryDy5khu/vZmylxl3sRdhdN3T7v6kDySnKwzhxFmDCPcFE6EOYJIcyRR5ihCDCEYNAa0Ki06tQ6NSoNaqfbeVEoVGqUGlVKFWqn2JnhKSkpwFboIM4YRoAto8DwF/1AoFBi1RoxaIxHmCJJDkukW040be97Ivtx9fL//e3478hvZ1mxWHVpF2MQwZv86+4zjKhVKb9LPoDZ4k39GjfxYRo3Re17lURF0YRB78vbQL7DfmScdGChXtNls8r+DB8vJvd9+O5ncUypBoUCRn9/Ar5Cfzf77a7dwYc3nf/hB3s/z+HGIiZGPvfgi3HSTXPVXx4rJVpvcKywsJLiy/LIKi8VCQUFBjfdxu90A7Ny5E6vV6j2u1WrR1vAuTOU1e/fuxWw2+2HWjT+2mHPrH1vMuQWNbbejcDq9nx4+fBg4GUv87XyIUY05tphz6x9bzPksx27CGCXiU8sctzHHbo1zbsyxxZzPcuwWHJ/mrZpH+JXhTPt0Wq1jatwSbXMcRBe7yDWrcKnqXo30jEeiBFgQZeX7Vf/xORdmdZNnUrIn5zTNOE5DF6Njy94tNc9ZpcGgNqBT6zCqjd5/9Ro9Jo0JvVqPQS0nW8xaMwHaAMxaMyqXihuuuYH5i+cTGBiIipOJOpVChdKlRFWmAuSkpu3v/+rinPzeboFj+3PcSCK5Lu46JkROYGf2TlYfXM2ajWvo3K0zbsmNwyN3MXa4HTg8DpxuJ27Pyde1AwdFFNXpsYIGBbF041IC7XVIXkkS2oICFMeP47FYUKekEAJ41q4l78ABUMnfo4rSUirS01FT96/HwYMHAfAUF0NJyckTOt1pl+H7zYYNcoOdysQewKhRYLfDH3/AsGF1G0dqpdRqtfTMM89UOz5w4EBp0qRJNd5n6dKlEvLbKuImbuImbvW+LV26tFHimohR4iZu4uaPW2PEKBGfxE3cxM0fNxGfxE3cxK2l3o6DJFW9zZzp32C1YIEkBQVVPz51qiSNGFH9uFYrSZ98UufhW23lnsViobCwsNrxoqKiWvfbGzlyJF9//TXR0dGo/s7sQu3v6giCcJ475V1nt9vNkcxMRowc2SgPJ2KUIAhnpQljlIhPgiCcFRGfBEFoJdxuN0f37ydiyBDffTVPV7U3a9bJ5ba1+f136N27bpOoaa9CSar5eC1abXIvNTW12t56drudgwcPVtuLr5JWq+Wyyy5riukJgnCO6tSIY4sYJQhCQzVWjBLxSRCEhhLxSRCElqpz585nd4d77oFrrjn9NUlJdRsrKgo2bfI9Vlgod+qOjKzzlFptcm/MmDE8+eST5OfnExoaCsA333yD3W5nzJgxzTw7QRAEQRAEQRAEQRAE4ZwTFibf/GHAALlxRmam3JUb5CYbOh306lXnYRSSdLLHT2tSVFRE586dSUpK4vHHHycnJ4cZM2YwatQo/vvf/zb39ARBEARBEARBEARBEITz2bFjUFAAS5bA88/LHX4B2rYFsxncbujeXa7Se/55+dqbboKJE+HVV+v8MMrGmHtTCA4OZvXq1ZhMJiZNmsSMGTO49tpreeeddxo89oEDB5g2bRrdu3dHrVaffYlmLb788ksmTpxIfHw8JpOJrl278sYbb8ht0Rtg5cqVDB06lPDwcHQ6HcnJycyYMYPi4mK/zLsqq9VKXFwcCoWCLVtq7tRUVwsXLkShUFS7PfTQQ36Z63vvvUe3bt3Q6/VEREQwfvz4Bo954YUX1jhnhULBZ5991qCxFy9eTL9+/QgMDCQyMpJJkyaxd+/eBs8ZYNmyZfTs2ROdTkd8fDwzZ848625ldX1dLF++nB49eqDX62nbti2vv/66X8ZetWoVkydPJiUlBYVCwT333OOXebvdbp577jnva8hisXDBBRfw008/1Wn85tDaYhQ0XZxqTTEK/B+nGjNGQePFKX/EKGi8OCViVN2J+FQ7EZ9EfBLxqXmJ+FQ7EZ9EfBLxqRE98QT06AEzZ4LVKn/cowdUvtZUKvjuO9DrYdAguOoqObH3wgtn9TCtdlkuQPv27Vm5cqXfx921axffffcd/fr1w+Px+CUwA7z44oskJiby/PPPExkZyZo1a5g+fTqHDh3i+eefr/e4BQUFDBw4kH/+859YLBb+/PNPZs2axZ9//skPP/zgl7lXevLJJ3G5XH4dc8WKFQQFBXk/j42NbfCYs2bN4j//+Q+PPvoo/fr1o6CggBUrVjR43Ndff52Squ2xgZdffplFixZx8cUX13vcH3/8kUmTJnH99dczd+5cioqKmDVrFhdffDG7du0iMLAO7cFrsXHjRiZMmMA111zDvHnz2L17N48++ihlZWW8cBYBoy6viw0bNjBhwgRuvPFGXnrpJdatW8e9996LVqvltttua9DY33//Pf/73/8YOnQoBQUFfpt3RUUFTz/9NFOmTOH+++9Ho9GwcOFCRowYwZIlSxg7dmydH6uptLYYBU0Xp1pLjILGiVONFaOg8eKUv2IUNF6cEjGq7kR8qp2ITyI+ifjUvER8qp2ITyI+ifjUiBYulG+nk5AAy5Y17HHq2cT3nOZ2u70fT5kyRerUqZNfxs3Jyal27F//+pek1+slm83ml8eo9Pbbb0uAlJ6e7rcx9+zZI5lMJunNN9+UAOn3339v0HgLFiyQACk3N9dPM5Tt3r1bUqlU0sqVK/06bm3atGkjjRkzpkFj3HrrrVJSUpLk8Xi8xzZt2iQB0vLlyxs09qhRo6SePXv6HHv++ecljUYjZWVl1XmcurwuRo8eLfXt29fn2NSpU6Xo6Gif+9dn7KrXJCYmSnfffbdf5u1yuaSCggKfYx6PR+rZs6d04YUX1ukxmtq5EKMkyf9xqrXEKElq2jjljxglSY0Xp/wVoySp8eKUiFF1J+JTzUR8qpmIT75EfGpcIj7VTMSnmon45EvEp5av1S7LbUxKZeN8WcLDw6sd69GjBzab7ayy1HVR2WTEWaUFfUNNnz6dadOm0aFDB7+N2RgWLlxIcnIyI0eObPTHWr9+PYcPH+a6665r0DhOp5OAgAAUVVpdBwcHAyA1cFvMbdu2MWrUKJ9jo0ePxul0nlXl65leF3a7ndWrV3PNKV2DrrvuOjIzM9m2bVu9x67rNfW5n0qlwmKx+BxTKBR0796djIyMej1mYzsXYhT4P061lhgFTRen/BWjoPHilL9iFDRenBIxqu5EfKqZiE/VifjkS8SnxifiU81EfKpOxCdfIj61DiK518x+++03QkJCiIiIaPBYbrcbm83G1q1bmTNnDuPGjSMxMdEPs4SvvvqK7du388QTT/hlvKo6deqESqUiOTmZefPm1WufgKo2btxIly5dePLJJ4mIiECr1TJ06FD+97//+WfCVXzyyScYjUYmTJjQoHFuvfVW9uzZw6uvvkpRURFHjhzhvvvuIzU1leHDhzdobJvNhlar9Tmm0+kA2LNnT4PGrurgwYM4HA5SU1N9jqelpfn9sRqbx+Nh/fr11Z7L+cifMQoaL061phgFTRen/BWjoPHiVFPFKDh34pSIUTIRn0R8qiTiU8sh4pNMxCcRnyqJ+NRynOvxqVXvudfabdmyhQULFjBz5kxUKlWDx0tMTCQ9PR2Qs/affvppg8cEKC8vZ8aMGcybN69Be7+dKjo6mtmzZ9OvXz8UCgVLlizhscceIz09nfnz59d73KysLLZu3cquXbt488030Wq1zJ49mxEjRrB//37vOyUN5XK5+PLLL5kwYQImk6lBY11wwQV88803TJ48menTpwNysPzhhx+8Qbq+2rdvz+bNm32Obdy4EcCv7yYWFhYCVPv6Vr5j0hjvXDaWV199lb179/LWW28191Salb9jFDROnGptMQqaJk75M0ZB48WppopRcO7EKRGjRHwS8cmXiE8th4hPIj6J+ORLxKeW41yPT6Jyr5lkZWVx+eWX07dvXx588EG/jLl8+XLWrVvH22+/za5duxg3bpxf3iGZO3cukZGR3HTTTQ2fZBWjRo3iiSeeYNSoUYwcOZL58+czY8YM3nzzTTIzM+s9rsfjwWq1smjRIiZNmsTYsWNZsmQJpaWlvP32236b/6pVq8jJyWHy5MkNHmv9+vVcf/313HLLLfz00098/fXXGI1GLrnkkmqbu56tu+++m++//55XXnmFgoIC1q5dy6OPPopKpWqU5QlVS87rcryl+eWXX3jggQe47777uOCCC5p7Os2mMWIUNE6cam0xCpomTvkzRkHjxammjlHQuuOUiFEiPoGIT6cS8allEPFJxCcQ8elUIj61DOdDfBLJvWZQXFzMJZdcgtFoZMmSJWg0Gr+M27VrVwYOHMjUqVP55ptvWLNmDd98802Dxjx69Cgvvvgis2fPpqSkhKKiIqxWKyC3TK/82F+uuuoq3G53g0qrQ0JCiIyMpFOnTt5j0dHRdOzYkV27dvlhlrJPPvmE0NDQansd1Mf06dO56KKLePnll7nooou47LLLWL58Ofv27ePdd99t0NhTpkxhxowZ3HfffYSGhjJ8+HCmTZtGSEgIUVFRDZ57pcp3birf2alU+fmpex60RDt27GDChAlMnDiRZ599trmn02waK0aB/+NUa4xR0DRxyp8xChovTjVVjILWH6dEjBLx6XREfBLxqTmJ+CTi0+mI+CTiU3M6X+KTSO41MZvNxvjx48nOzmbFihXeDVH9rXv37qhUKg4cONCgcQ4fPozD4eDSSy/FYrFgsVgYN24cAMOGDWtwa/BTNbR5BFDrGnpJkvz2LkZFRQXffvstV155pV9+cO/evZvu3bv7HAsPDycmJoaDBw82aGyFQsGLL75IXl4e27dvJzs7m6lTp5Kbm0v//v0bNHZVKSkpaLXaansu7N69G6j9/0tLcfDgQUaNGkXPnj356KOPWsU7UI2hqWIU+CdOtcYYBY0fp/wdo6Dx4lRTxSho3XFKxCgRn85ExKfuPsdEfGo6Ij6J+HQmIj519zkm4lPTOZ/ik0juNSGXy8VVV13F9u3bWbFihd+aXdRkw4YNuN1ukpOTGzRO9+7dWbNmjc/tP//5DwBvvvkmr7/+uj+m6/X555+jUqno0aNHvccYO3Ys2dnZ/Pnnn95j6enp/PXXX3Tr1s0f0/SWf/urXDsxMZE//vjD51hWVhbp6ekkJSX55TGCgoLo2rUrwcHBvPrqqyQlJfn1B7dOp+Oiiy7iiy++8Dn+6aefEh0d3aD/p40tKyuLkSNHEhUVxeLFi6ttTnu+aMoYBf6JU60xRkHjxyl/xyho/DjV2DEKWm+cEjFKxKe6EPFJxKfmIOKTiE91IeKTiE/N4XyLT6KhRg3Ky8tZvnw5IJcsl5SU8NVXXwEwdOjQGtud18Xdd9/N0qVLee655ygvL/dueAnyppr13ch00qRJ9O7dm65du2IwGNi+fTvPPfccXbt2ZeLEifUas1JwcDAXXnhhjed69epFz5496z32qFGjGD58OJ07dwbkYPr222/zj3/8o0GlxJdddhk9e/Zk0qRJzJ07F61Wy5w5cwgPD2fq1Kn1HreqTz75hISEBAYPHuyX8e6++27uvfde7rnnHiZMmEBRURFPP/00ZrOZ66+/vkFjb968mV9++YXu3btTUVHBkiVL+Oijj/j+++/PapPfurwunnjiCS644AKmTp3Kddddx7p163jnnXd46623TvtuWl3GPnr0KL///rv3+oMHD3qvueKKK+o9ttlsZvTo0eTk5PDSSy9534Gq5O93vvyhtcUoaLw41RpjFDR+nPJ3jILGi1P+ilHQeHFKxKi6E/HpJBGfaibik4hPzUXEp5NEfKqZiE8iPrVqklDN4cOHJaDG25o1a+o9bmJiYqOMO2/ePKl79+5SQECAZDKZpE6dOkmPP/64VFxcXO8xT2fNmjUSIP3+++8NGmf69OlSu3btJIPBIOl0OqlLly7SK6+8Ink8ngbPMTs7W5o8ebIUFBQkGY1G6ZJLLpH++uuvBo8rSZJUUFAgabVa6YEHHvDLeJIkSR6PR3rrrbekbt26SSaTSYqMjJTGjRsn7dixo8Fjb9u2TerXr59kNpsls9ksDR8+XFq/fv1Zj1PX18V3330ndevWTdJqtVJycrI0f/58v4y9YMGCWq9pyNinO99SQ2Rri1GS1LRxqjXEKElqvDjVGDFKkhovTvkrRklS48UpEaPqTsSn0xPxScQnEZ+aj4hPpyfik4hPIj61bgpJ8tMCeEEQBEEQBEEQBEEQBEEQmpTYc08QBEEQBEEQBEEQBEEQWimR3BMEQRAEQRAEQRAEQRCEVkok9wRBEARBEARBEARBEAShlRLJPUEQBEEQBEEQBEEQBEFopURyTxAEQRAEQRAEQRAEQRBaKZHcEwRBEARBEARBEARBEIRWSiT3BEEQBEEQBEEQBEEQBKGVEsk9QRAEQRAEQRAEQRAEQWilRHJPEARBEARBEARBEARBEFopkdwTBEEQBEEQBEEQBEEQhFaqRSb3Dhw4wLRp0+jevTtqtZrOnTv7nHe73Tz33HMMHTqU8PBwLBYLF1xwAT/99FMzzVgQBEEQBEEQBEEQBEEQml6LTO7t2rWL7777jrZt25KWllbtfEVFBU8//TTdu3dnwYIFfPbZZ8TGxjJixAiWLVvWDDMWBEEQBEEQBEEQBEEQhKankCRJau5JnMrj8aBUynnHm266iS1btvDnn396z7vdbkpKSrBYLN5jkiTRu3dvAgMDWbNmTZPPWRAEQRAEQRAEQRAEQRCaWous3KtM7NVGpVL5JPYAFAoF3bt3JyMjozGnJgiCIAiCIAiCIAiCIAgtRotM7tWHx+Nh/fr1pKamNvdUBEEQBEEQBEEQBEEQBKFJqJt7Av7y6quvsnfvXt56661ar/F4PBw5cgSNRoNCofAe1+l06HS6ppimIAitmCRJlJaWEhMTc8YK4/oQMUoQhIZozBgl4pMgCA0h4pMgCC1VY/+N11TOieTeL7/8wgMPPMB9993HBRdcUOt1GRkZpKSkNOHMBEE4Fx0/fpy4uDi/jytilCAI/tAYMUrEJ0EQ/EHEJ0EQWqrG+huvqbT65N6OHTuYMGECEydO5Nlnnz3ttQEBAQDs3r3b+zGId3UEQaibkpIS4uPjfeKHP4kYJQhCQzRmjBLxSRCEhhDxSRCElqqx/8ZrKq06uXfw4EFGjRpFz549+eijj3zKsGtSeT42NpbAwMCmmKIgCOegM8Waho4rYpQgCA3RGDFKxCdBEPxBxCdBEFqqxvobr6m02gXFWVlZjBw5kqioKBYvXoxWq23uKQmCIAiCIAiCIAiCIAhCk2qRlXvl5eUsX74cgKNHj1JSUsJXX30FwNChQzGbzYwePZqcnBxeeukldu/e7XP//v37N/mcBUEQBEEQBEEQBEEQBKGptcjkXk5ODldeeaXPscrP16xZQ1JSEtu3bwdg4sSJ1e4vSVKjz1EQBEEQBEEQBEEQBEEQmluLTO4lJSWdMUEnEniCIAiCIAiCIAiCIAjC+a7V7rkn/D979x0fRZ0+cPwz2zc9hCSE3nsHERABURC7KNZT8Czo2Svq787e7/Q8u1iwd1DsWAEp0lF6kw4BUkjfOjO/P2azScgmpOymPm9fa3ZnpyUh3/3OM8/3+QohhBBCCCGEEEKI5k6Ce0IIIYQQQgghhBBCNFIS3BNCCCGEEEIIIYQQopGS4J4QQgghhBBCCCGEEI2UBPdqYcOGDVgsFn788ccabb9y5UpMJhOLFi0K85mV9/bbb6MoCvPnz4/4scJp/vz5KIrC22+/Xd+nIkSjU1EbtXfvXs4//3xSU1NRFIVzzz2XoqIiWrVqxWOPPRbx89q1axeKovDggw9G/FjhpigKV1xxRX2fhhCNXm37ULqu079/f6655pown1lojfVvv2PHjowdO7a+T0OIRiVU+1SdaynpU1VNY21XhWioJLhXC3fccQfDhw9nwoQJNdp+6NChnHnmmdx+++1Vnv23+IOl+GGxWGjRogX9+/fnyiuv5KeffqrRuQghmp6K2qipU6fy66+/cuedd/Lee+9x2223ERUVxfTp03nyySc5ePBglfZf3KEsfphMJuLi4ujatSvnn38+7777Lm63OxLfmhCikattH6r4YnbmzJn8+eef1dqu9CM2NpaOHTty1lln8fLLL5Obm1uj8xFCNB21bZ+kTyWEqA+W+j6Bxmrp0qX88MMPfPbZZ7Xaz+23385JJ53Ed999xxlnnFHl7W644QaGDx+Oruvk5eWxadMm5syZw1tvvcWpp57KJ598Qnx8fHD9yy+/nIsvvhibzVar8xVCNA4VtVEej4f58+dz0003cdddd5V5b9q0adx///0888wz/Oc//6nyscaNG8ff//53AAoLC9m1axc//PADU6dO5dFHH2X27Nn069cvuH6HDh1wuVxYLPIRJERzFK4+1KRJk2jfvj2PPvpotfbVv3//YPvncrnYt28fv/76KzfccAOPPPIIH330UblsN5fLhdlsrtX5CiEavnC1T9KnEkLUNWkFauiVV14hISGBs846q1b7GTNmDO3bt+eVV16pVnBv1KhRXHzxxWWWPfvss9x+++28+OKLXHrppXz77bfB98xmc0Q6pQUFBcTExIR9v0KI2qmojTp06BC6rpOYmFhum5iYGCZNmsRbb73FI488gsPhqNKxunXrxmWXXVZm2RNPPMH777/PFVdcwcSJE9mwYQMJCQmAkTlT1X1Xh7RHQjQO4epDKYrCZZddxpNPPkl6ejppaWlV2i4tLa1cm/XQQw/x008/cd5553H22Wfzxx9/0Llz5+D7kWizPB4PZrNZLsqFaEDC1T5Jn0oIUddkWG4N+P1+Pv/8c04++WTsdnu59z0eD48//jh9+vTB4XAEPyDWrFlTbl1FUTjttNOYO3durYeCWK1Wnn/+eUaMGMF3333H4sWLg++FqhPx4IMPoigKu3btKrevUDVaiusi/PLLL4waNYqYmBjOPPPMMutv2LCBiRMnEhsbS3x8PJMnTy6Xjn7gwAHuuOMOBg4cSGJiIg6Hg969e/PUU0+hqmqtfgZCiIrbqCuuuIIOHToAxoVs8dCP0u3CGWecQVZWFr/88kutz+Oyyy7jrrvu4sCBA7z00kvB5aHqw1RWX/OKK65AUZQyy8aOHUvHjh3ZsWMHkydPpkWLFsTGxpZZ/8iRI1xzzTWkpKTgcDg44YQTWLZsWZn9aJrGY489xujRo2nVqhU2m4327dvzj3/8g6ysrFr/DIQQZR2rD7V3714uuugiEhISiImJYdy4caxevTr4N3+0M844I7jP2ho/fjxPP/00+fn5PPHEE2XeO7o2VGV1rkL1r4rbpYyMDK688kpSU1NxOp3s27cvuP7mzZuZPn06bdq0wW63M2DAAL777rty+3/55ZeZMGECbdq0wWazBYOVofpzQoiqO1b7BEa/4amnnqJz587Y7Xa6d+/OO++8E3Jd6VNJn0qIuiTBvRpYvXo1BQUFHH/88eXe8/l8TJw4kYceeogRI0bw7LPPcs8997Bp0yZOOOEEVq5cWW6bESNGoKoqCxcurPW5KYoSLC79zTff1Hp/R1u5ciWTJk1i2LBhPPvss/ztb38Lvrd//37GjRtHp06dePrpp7nkkkv4/PPPmTJlSpl9rF27ljlz5jB+/Hgee+wxnnzySdq1a8c999zD9ddfH/ZzFqK5qaiNuvbaa3n22WcBYzjbe++9x3vvvUevXr2C64wYMQKAefPmheVcpk2bBkSmPSooKGDMmDFYrVYee+yxchfZEydO5ODBgzzwwAPce++9rF+/ntNPP538/PzgOl6vl6effpqePXty991388ILLzB+/HjefPNNxo4di9frDft5C9GcVdaHOnLkCKNGjWL27Nlceuml/Oc//6FLly6cfPLJ7N+/P+T+Bg0ahN1uD1ubNXXqVGw2W5nRD+E0fvx4Dh48yH333ccTTzxRJjNm6tSpLF26lLvuuotHHnmEjIwMzj333HJBu2eeeYbU1FRuvfVWXnrpJS688EK++OILRo4cKRfQQtRCZe1TsXvvvZcPP/yQ6667jqeeegqTycQVV1xRJqmimPSppE8lRF2ScQA1sGHDBgC6dOlS7r0XXniB+fPn8/333zNx4sTg8uuvv56+ffty5513lptlqXg/GzZsCGbC1caAAQMA2Lp1a633dbQNGzbwyy+/MG7cuHLvbd++nU8++YQLL7wwuMxsNvPyyy+zefNmevbsCRhDkbdv317mrtGtt97K5ZdfzhtvvMGDDz5Y5aE1QojyKmqjRowYQVpaGrfddhv9+/cvN+wDjNotFosluI/a6tSpE7GxsRFpj7Kysrj//vt56KGHQr4/ZMgQXn755eDr3r17c+GFF/Lhhx9y7bXXAmC32zlw4ABOpzO43rXXXsvIkSO5+uqrmTNnTpk2TQhRO5X1oZ566in27NnDm2++yZVXXhlc3q9fP2655ZZg5nFpdrudtm3bhq3Ncjgc9OjRg3Xr1pGfnx/MXgmXAQMGVJjlk5yczNdffx3sH5100kkMGzaMGTNmlMkkXLt2LdHR0WW2PfvssznllFN48803mT59eljPWYjmorL2qZjX62XFihXBOuYXXHABnTt35sUXX+SEE04os670qaRPJURdksy9GsjIyACgRYsW5d774IMP6NatG0OHDiUzMzP48Hq9jB8/nkWLFuFyucpsk5SUBMDhw4fDcn5xcXEA5OXlhWV/pQ0cODBkYA+gdevW5Rrs4nW3b98eXOZ0OoMdV6/XS3Z2NpmZmZx66qlomhYyu1EIUXWVtVFV0aJFi7C1R2C0SZFoj8CYlKgit912W5nXxe3Rtm3bgssURQl2QlVVJScnh8zMzOC6Rw85EULUTmXt05dffklycjJTp04ts/y6664L9m1CSUpKCnubBZHpR1XWZt1yyy1lbnwed9xxxMbGlmmzgGBgT9M0cnNzyczMZMCAAcTHx0ubJUQtVKX/dP3115eZoLBNmzZ079693N9pMelTSZ9KiLoimXs1UNzx0nW93HubNm3C5XKRnJxc4faZmZm0a9cu+Lp4P0fXP6ip4ga/so5wTXXr1q3C90oXni5WHLgsPUzE7/fz5JNP8u6777J9+/ZyP8cjR46E6WyFaJ4qa6OqQtf1sLVHYLRJkWiPkpOTy8wKfrSj26RQ7RHAp59+yjPPPMOaNWvw+Xxl3pP2SIjwqqx92rlzJ4MHDy43AZjNZqNz584V/j1Gos2ChtGPatGiRbk269dff+Xhhx9m2bJluN3uMu9JmyVEzVWl/1TR9c7u3btDri99qhLSPgkRWRLcq4HiwF2oBkrXdXr37s1zzz13zO2LZWdnh1xeU3/88QdAcBhsRSr7oPH7/SGXR0VFVbhNZbPxlv6QvO2223jxxRe56KKL+Oc//0lKSgpWq5XVq1dz9913o2lapecthKhcZW1UVRw5coQhQ4aE5Vx27NhBfn4+I0eOrHS9cLdHUHGbVLo9mj17NhdddBHDhg3jueeeo127djgcDlRVZeLEidIeCRFmNW2fKrvYzs7ODlsfyu12s2XLFlq3bl3pkNyatFlQs35U6e99+fLlTJgwga5du/Lkk0/SqVOn4IiIiy++WNosIWqhKu1TVf5OS5M+lfSphKgrEtyrgb59+wJlh5oW6969O+np6YwbNw6TqWqjnov3U7zf2tB1nTfeeAPgmPX7ilPOs7Ozy8xA53a7SU9Pp2vXrrU+n1Def/99Ro8ezccff1xmeaifpxCi+ipro45l165d+P3+sLRHAK+99hpQvfboaDt27AjLuYTy/vvv43A4mDdvXpmO7ebNmyN2TCGas8rap06dOrF9+3ZUVS1zIen1etm5cyeJiYnltvF4POzdu5ezzz47LOf39ttv4/V6G2yb9dFHH6GqKt9//z2dOnUKLi8sLJSsGCFqqTb9p1CkT2WQPpUQdUNq7tXAoEGDiIuLY/ny5eXeu/zyy8nIyOA///lPyG0PHTpUbtnSpUsxmUyMGjWqVufl8/m4+eab+f333znrrLOCMzRVpHv37gD8/PPPZZY/++yzEb2zYjaby93dKiwsDM7iKYSoncraqGNZunQpYEx8U1vvv/8+Tz/9NG3btj3mTNidOnXCYrGUa4+WLFkSPKdIMJvNKIpSps3TdZ1HH300YscUojmrrH06++yzycjIKDfhxKuvvlphjak1a9bg9XrD0mb9/PPP3HXXXcTFxXHPPfdUum5sbCytWrXi119/LdOn2bFjB3PmzKn1uVSkOOh5dD/q8ccfl6wYIWqpNv2nUKRPJX0qIeqSZO7VgNls5rzzzuPLL7/E4/Fgt9uD791yyy389NNP3HPPPcyfP5+TTz6ZuLg49uzZwy+//BK8m1FM13W+//57Tj311ErrHBxt0aJF+P1+dF0nPz+fTZs2MWfOHPbt28dpp53GBx98cMx9nHLKKfTs2ZP777+frKwsOnXqxKJFi1i6dCktW7as3g+lGiZPnsyMGTO46KKLOOWUUzh06BAzZ84M1m4QQtROZW3UsXz77be0aNGCU045pcrbbNu2jffffx+AoqIidu3axdy5c1mzZg3du3fn888/P2b7FhMTwxVXXMEbb7zBJZdcwtixY9m2bRtvvfUW/fv3588//6zy+VTH5MmTmT17NuPGjWPKlCn4fD7mzJlDUVFRRI4nRHNXWfs0ffp0PvroI6ZNm8bKlSvp378/q1at4vPPP6dr164hh5N9++23WCwWzjvvvCqfQ3p6erDNcrvd7Nu3j19//ZWFCxeSlpbGxx9/XCYrriI33ngj//rXvzjttNM499xzOXDgAK+++ip9+/ZlxYoVVT6f6pg0aRLPPvssp59+OtOmTcNms/HTTz+xdu3aiPbdhGgOatN/CkX6VNKnEqIuSXCvhv7xj3/w9ttv880333D++ecHl1utVr799ltefvll3nvvPR544AHAmEl22LBh5WaAW7BgAXv27OGll16q1vFfeuklXnrpJcxmMzExMbRr147x48dzySWXMH78+Crtw2w28+WXX3LzzTfzwgsvYLPZmDBhAgsWLCg3lXs4/fe//yU2NpZPP/2UL7/8knbt2jFt2jSOO+64an34CSEqVlEbVZnCwkK++OILrrvuOhwOR5WP9euvv/Lrr7+iKArR0dGkpKQwcOBAbr31Vi688MIq76s4e/fzzz/nyy+/ZPDgwXz99de89tprEeuIXnzxxeTn5/Pss89y5513kpiYyFlnncWTTz4pNxyEiJCK2qekpCQWLlzIXXfdxQcffMC7777LiBEj+PXXX7nyyitxuVxl9qPrOu+//z7nnHMOrVu3rvLx165dy+WXXw4YdaZatmxJv379eOmll7jsssuqXKz+7rvvJjc3l/fee4/58+fTu3dv3nzzTVatWhWx4N4JJ5zA7NmzeeSRR7jvvvtwOp2ccsopLFiwgNGjR0fkmEI0JzXpP4UifSrpUwlR1xS9ptMpNkJ5eXnEx8eTm5sbllmGJk6cSGFhIQsXLqzxPs455xz279/PihUrwjqTkhAi/MLdhkR6/9Vto5599lnuu+8+tm3bRlpaWq2PL4SoW5Fso+qzffL7/SQnJ3P88cczd+7c4PIvvviCyZMns2rVKgYOHFjrcxJCRE5TbZ8qIn0qIRqPSF/j1RWpuVcLzzzzDL///js//vhjjbZftWoVX3/9Nc8++6wE9oQQYVedNqqoqIinnnqKe+65RzqhQoiIq6h9Ojo7D+Dll18mJyeHCRMmBJfpus6DDz7I3//+dwnsCSHCqrbXeNKnEkLUh4gNy/X5fBw8eJCioiKSk5ODs/Y0JX369KlwOvGqGDJkiBQ/FkJETHXaqKioKA4ePBjhMxJCCENF7dPpp59Ohw4dGDx4MIqisHjxYj755BO6d+/OtGnTguspihKxoWVCiOatttd40qcSQtSHsAb3CgoK+OCDD/joo49Yvnw5Ho8n+F7btm2ZMGFCsLaaEEIIIYQQpZ155pm89957wSLsaWlpXH/99Tz44IPExMTU9+kJIYQQQjRIYQvuPfvsszz22GN07NiRs88+m3vuuYc2bdrgdDrJzs5m/fr1LFy4kPHjxzN8+HBeeOEFunXrFq7DCyGEEEKIRu6OO+7gjjvuqO/TEEIIIYRoVMIW3FuyZAnz5s2jX79+Id8fNmwYV155Ja+++ipvvvkmCxYskOCeEEIIIYQQQgghhBC1ELbg3meffVal9ex2O9dff324DiuEEEIIIYQQQgghRLMls+UKIYQQQgghhBBCCNFIhSW453K52L9/f7nlGzZsCMfuhRBCCCGEEEIIIYQQIdQ6uDdr1iy6d+/O6aefTv/+/Vm2bFnwvcsvv7y2uxdCCCGEEEIIIYQQQlSg1sG9Rx99lNWrV/Pnn38yc+ZMrrzySj788EMAdF2v9QkKIYQQQgghhBBCCCFCq3Vwz+fzkZycDMDQoUP57bffmDFjBg8//DCKotRon9u3b+e6665j4MCBWCwW+vbtG3K97777jkGDBuFwOOjatSsvv/xyjb8PIYQQQjQOuq7j0b14dK/cSBRCCCGEEM1erWfLTUlJYe3atfTv3x+ApKQkfvrpJ6ZOncratWtrtM8NGzbw7bffcvzxx6NpGpqmlVvn999/55xzzmHKlCn897//ZfHixdx0003YbDauvvrqWn1PQgghhKh/uVo+eVpBueVe3cfzue8CcHP8FGyKNeT2caYY4k2xET1HIYQQQggh6pui1/KW9759+7BYLLRq1arce4sXL+aEE06o9j41TcNkMpIKr7jiClauXMn69evLrHPaaaeRnZ1dpsbftGnT+Oabb9i3b19w+9Ly8vKIj48nNzeXuLi4ap+XEKJ5i3QbIm2UEGV9X7iAua6FVVtZ17F6VAB8djMoChOdJ3Ja9JgInmHDEsk2RNonIURtSPskhGiomkobUuvMvbZt25Z5ffDgwWCgryaBPSBkYK40j8fDr7/+ypNPPllm+d/+9jdef/111qxZw5AhQ2p0bCGEEEI0DCOdg+lr715mmZKVjZqVwUcF3wBwScyZWBULeL3E3n4vAPn/fQJsNqIVJ6q+2tjQ5ARFQUlqiSmpZZ1+H0IIIYQQQkRSrYN7R5swYUKNh+NW1V9//YXX66VXr15llvfu3RuATZs2VRrcy8vLK/Pabrdjt9vDf6JCCFED0kYJYYg3xZYbVuuZ+w3ed9/ghuCShShOL0qUD5IC2z12s/HErGE+axMAhV/3AtWE9YxzsZ11XtkDOdOMhzgmaZ+EEA2VtE9CiOYs7MG9uihsfeTIEQASEhLKLE9MTAQgOzu70u3btWtX5vUDDzzAgw8+GLbzE0KI2pA2SoiKWc+chDZiOM/lvAPALQlTse+fgSn9pUq3iw4E+WADzH2s7Jt9H4D+D4b/ZJsgaZ+EEA2VtE9CiOYs7MG9ms6QG85jHesc9u7dW2YstdzREUI0JNJGCVExU1JLTC3iSM9KCLzugandP9FzLqXolmkARD33GordAaoLfhplbDh+EZidoXcqWXtVJu2TEKKhkvZJCNGchT24VxeKM/SKM/iKFb8ufr8icXFxjbpQohCiaZM2SohqcqYBCWhZ0cbrhEHgdIK/sGSdxIFgia6Ps2tSpH0SQjRU0j4JIZqzymeuaKC6dOmCzWZj06ZNZZZv3LgRoFwtPiGEEEIIIYRoCnRdx6N78ejeOimJJIQQouELe+aezWYL9y7LsdvtjBs3jk8//ZTbbrstuPyjjz4iLS2NQYMGRfwchBBCCCGEECJScrV88rSCcsu9uo/nc98F4Ob4KdgUa8jt40wx5SYlEkII0TSFPbi3cuXKWu+jqKiI7777DoDdu3eTl5fHrFmzABgzZgzJycncf//9jB49mmuuuYa//e1vLF68mNdff50ZM2ZgMjXKhEQhhBCi0dN1HS8+AGxY67QWrxBCNCVLXKuZ61pY6TrFQb5QJjpP5LToMeE+LSGEEA1Qg6y5d/jwYS644IIyy4pfz5s3j7FjxzJixAi+/PJL/u///o93332Xtm3b8vzzz3P11VfXxykLIYQQzYpklAghRGSNdA6mr717ueXVaWeFEEI0DxEL7rndbtauXcvhw4fRNK3Me2effXal23bs2LFK9SNOP/10Tj/99FqdpxBCCCGqTzJKhBAisuJNsSFvgnh0b/B5W0sr7ErkyyIJIYRo2CIS3Js7dy5TpkwhMzOz3HuKoqCqaiQOK4QQQog6IhklQgghhBBCNAwRCe7deOONXHDBBdx///2kpqZG4hAiDHRdx+UzAq1Oq1nqIgkhhKgyySgRQgghhBCiYYhIcO/w4cPcfvvtEthrIDIKXGQWuMstd/tUrnh/PgBvXzYWh9UccvuWMQ6SY5yRPEUhhBBCCCGEEEIIUQMRCe5NnjyZ+fPn06VLl0jsXlTTrDU7eHXRpkrXKQ7yhXLdqF7848Q+YT4rIYQQoizJKBdCCCGEEKL6IhLce/HFF7ngggtYuHAh/fr1w2otW2/n5ptvjsRhRQUmD+rM2G6tyy2vTuaeEEIIES61zSiPiw69XAghhBBCiOYoIsG9Dz/8kB9++AGn08n8+fPL3HlXFEWCe3UsOcYZclhtkdcffN4jNYEoW8QmTxZCCNGM6boO7kAwz+GodUb51Sd0h95hPEEhhBBCCCEasYhEc/71r3/x8MMPc88992AymSJxCCGEEEI0MFpWJlpWOmk5Ocbr7C2oWNE9Hly3TgPA+b/XmBRn4sRx5Ut3eKPj+PvXa4BjZ+792xOZ70EIIYQQQojGJiLBPa/Xy0UXXSSBPSGEEKIZ8X3zBf533+CGwGs/C/EftY7r1mlEA9EhtlcvuxpIBCrPKPfoXpDgnhBCCCGEEECEgntTp07lk08+4f/+7/8isXshhBBCNEDWMyehjRjOcznvAHBLwlRsITL3FLs95PbuuER4a2Gdna8QQgghhBBNQUSCe6qq8u9//5sffviB/v37l5tQ47///W8kDiuEEEKIemRKaompRRzpWQmB1z0wKzZ0lyu4jrlrdxRn+TqwAIr36Dw/IYQQQgghxLFEJLi3bt06Bg0aBMD69esjcQghhBBCVJOu63jxAWDDWmbCKyGEEEIIIUTjFJHg3rx58yKxWyGEEEJUQa6WT55WUG65V/fxfO67ANwcPwWbYgVdB0+ggJ3dDopCnCmGeFNsXZ6yEEIIIYQQooYiEtx74oknSE1N5corryyzfObMmWRkZHD33XdH4rBCCCGEAJa4VjPXVbZ2XcwRN7FH3KQFXn/G8wBYvCrXPrAEgBkPjcRvMzPSMYgTHEPKbK8ktcSU1DLi5y6EEEIIIYSonogE92bMmMGHH35YbnmfPn24+OKLJbgnhBBCRNBI52D62ruXWeb4+iMc739S6XbFQT5YSNFR79mmXI196jXhO0khhBBCCCFEWEQkuHfw4EHS0tLKLU9OTiY9PT0ShxRCCBEmuq6D2228cDikLlsjFG+KLTesVjt7Cp4Txtd4JltFsvaEEEIIIYRokCIS3GvXrh2LFy+mU6dOZZYvXryY1q1bR+KQQgghqknLykTPyiy3vDrBHhmm2XjUdiZbIYQQjYyugxrIwzZHgdysE0KIJisiwb2rr76aW2+9FZ/Px7hx4wD45ZdfmD59OnfccUckDimEEKKafN98gffdNypdpzjIF4oM0xRCCCEaAFe68Tia6oKfRhnPxy8CcwU3b5xpxkMIIUSjFZHg3vTp08nOzub666/H6/UC4HA4uPvuu7n33nsjcUghhBDVZD1zEpYRJ5ZbLsM0RUNXoB1dEVAIIZqxbTNg/UOVr1Mc5Aul7wPQ/8GwnpIQQoi6FZHgnqIoPPXUU9x3331s2rQJp9NJt27dsFdwgSiEEKLumZJaQogAnQzTFA3VIX8m81zLWO75M7jMp/uwKzZ0n6cez0wIIepRt2uh7dnll1cnc08IIUSjFpHgXrGYmBiOO+64SB5CCCGEEE2Yruv85d/DPNdS1nu3lXtf0RR8v3yP+82XS7ZxuyQoLYRoPioaVusvLHmeOBAs0XV2SkIIIepWRIN7QgghGjZd1/HiA8CGVWbGFQ2Gpmus9mxgnmsZe/wHAFCAvrbujHIM4ZW8j+i8LgP942m4tx8V9LPa6v6EhRAC+VwVQghRPyS4J4QQzUCulk+eVlBuuVf38XzuuwDcHD8Fm2IF1U1C4P196kHwO4gzxRBvig3b+ei6Dm638cLhkIsfUc5TOa+RZ84BwIqF4xz9Ges4nlRLEq6/NjHl5aV0/yMDHSAqCuvkS/EFJohRzOZ6O28hRPNQrc/VEML9uSqEEKJ5k+CeEEI0A0tcq5nrWljpOsUXI1a3nwcCy57LeQefw8JE54mcFj2m2sfVsjLRszLLLa/OpB0mmbijWchV8/m5aHnw9REtlzhLFCc6h3KiYwgxpmi0jMO4334U/4/f0l3TUM0K1rPOw3n5NSh2RzC4J4QQkVadz9VQavq5KoQQQoQiwT0hhGgGRjoH09fevdzykBkGbjfwPQC3JEwFh5G5VxO+b77Ae4yAS3GQLxTblKuxT72mRsduCppDhuMB/2HmuZayyrMer08HegBwXvQERsUOxKZY0QsL8Hz8Ct7ZH4HHmDhj/fFp/HhpL+7pcysmxVZmIhghhIi0an2uhlDTz1VRM83h81QI0bxFLLi3cOFCZsyYwV9//cWsWbNo06YN7733Hp06dWLUqEqmYhdCCBF28abYkMN/PLo3+LytpZUx66jZRfFAo7bmViiWmk9MYD1zEpYRJ5ZbXp3MvZpobJ14LSsTLSsDH37AGIaqoDTZDEdd19nq28mvrqVs9u0ILu9oaUdxHsxwxyCsKni/+Qzvu2+g5+YAYO7bH2Xa9Xyc+m3dn7gQpei6jsunAuC0mht8OyPCqzqfq+Hm19Ww77OpkBEDQojmKiLBvdmzZ3P55Zfzt7/9jTVr1uAJ3GXPz8/n8ccf57vvvovEYYUQQjQwpqSWEKKTXDrLyty1e41nNm2MnfhQdZocX32E4/1Pgq/9IbZrKhmOqzwb+NW9hv3qIQAUFAbYenKSczgpeirvMQcA/+IFFL71Cvr+vcZ6bdtjv+ZGLCeMNorVZ0lwT9SNjAIXmQXucsvdPpUr3p8PwNuXjcVhDV3rsWWMg+QYmb1ZhEeuls+buZ8FX6u6asw2JAAZMSCEaL4iEtx79NFHefXVV5kyZQoff/xxcPnIkSN5+OGHI3FIIYQQzVBj7MSHqtMUM8pNbJ/yGY4Wr8q1DywBYMZDI/HbzIx0DOIEx5Ay69U0w7GuuLSSwMjHBd/gc1iwYWW4YyBjnMNoaU4EoMhbEtZ0P/4ATs2HkpCIbeo1WE8/B8US6LboVTywr1QQVa/qRkKUNWvNDl5dtKnSdYqDfKFcN6oX/zixT5jPStREY5/JdpdvHzPzZ5Or5QeXmRWZQKi0+hoxIIQQ9S0iwb0tW7YwevTocsvj4uLIycmJxCGFEEI0Q/XZia/p0N+QdZoSwNuxovqHRnDvooE3B+sfmhvJDItH1Dx+d69hZe4K7gksi1NiGBE1gpGOwUSbSrKZtL17cL/5CtgDgUuHHdt5l2O76DKUqOiancDq20ue+wvAKjWuRPVNHtSZsd1al1tencw9Ubea4ky2S9yrmVUwFxWNFFMSh7Ws+j6lBinSIwaEEKKhikhwLy0tje3bt9OxY8cyyxctWkTnzp3Ddpw5c+bwxBNPsGnTJpxOJyeccAJPPPEEPXr0CNsxhBBCNFx10YkP99Df+qp/WB+ezHkVHbDqJRl59yZeizWq5PvXjmTjfe9NfN98gV83wTgjuBf92gfY01rV/OA7P4DdH5a8tjasC3XReCTHOEMOqy2dadojNYEom8xT11A0pZls/bqf2YU/sMS9BoABtp5Mjj6V+448V89nJoQQoiGJSC/k2muv5ZZbbmHmzJkoisKBAwf4/fffufPOO7n//vvDcoyff/6Z8847j8suu4xHH32UnJwcHnzwQU455RQ2bNhAXFxcWI4jhBCieWuMQ39rTddBLTKem6OgClmJmq6z0bedX4qWlOwG6GbtyMm2gRTPwGxRjK6H7nbjnf0R3o/fhSLjWObjRwa3rdXQqIIdsOK6mm8vhGjUmspMtrlqPjPzZ7HLvx8FOCPqJE5xjgwOLRYNS2ObzEsI0bREJLg3ffp0cnNzOemkk3C73YwePRq73c6dd97JjTfeGJZjfPzxx3To0IF33nkn2HB26NCB448/nsWLF3PaaaeF5ThCCCGatyZdv8eVDjk7MSUVGq9z1oDLAaoLfgrMbD9+EZgryBh0puF1tGSlZx3zXcs4pJYdJnZL/FQ6W9uhu0oyEHVVxf/9V3jeeg09KwMAU/ee2KfdhLnPQHhmTu2+J5MGK6YYw3BbjoTMJcfeRgjRpNTnTLbhssO3l7fyZpOnF+BUHEyNnUQvWxfjTSkhWq8a42ReQoimL2LjBx577DH++c9/snHjRjRNo3fv3sTEhO8umM/nIzY2tswdkYSEBCBw10QIIYQIgyZdv2fbDJT1DxF9buD1vBPKr1Mc5Au1eY+pvN2xPwW6kXnnUOwcb+/PAvcKANpYUsttU3Tz1ei7dwI6SpsU7FOuwXLSmShmM15vqHmCq8c+ZB9KzkGwJcLxM+HbnrXepxBC1BVd11nsXs3nhT+gopFmTubquAtoaW5R36cmApplRn8YSYajEJER0eIgUVFRDB06NCL7vuqqqzj55JN54YUXuPzyy8nJyeHOO++kV69enHzyyRE5phBCCNGkdLsWPXkCRbcYFyFRz72GYq88cy9LPcJK93o2+LZxxB5NgV5EoimOMc7jGWEfiKIoLHCvQNF09D278W1egbp9eUl2YMF6lHbR2E47Dav6MBz+FrIDx/BpJed25A+wmsCZZjyqwNwmB1v/g8aL42dCdNva/oSEEKLO+HQ/swrmstTzBwCDbL25JPbMBp1h2Bw16Yz+MJIMRyHqVtiCe7fffvuxVwr473//W+vjjR49mi+++IJLL72Um2++GYDevXvz448/Yq+ggSiWl5dX5rXdbj/mNkIIUVekjRJ1xpkGJKBlBWajTRgETif4C0vWSRyIbo5iu283v7qWslHbDk7A2Yr2ltZMch5Pf0sPTAcOoG6dj3frBq7asJi0XXn4XN+gDNqHY/ABOPeoY6vLS54XBxI1O/B+yTKTB/o+AP0fPPb34j6IY8wOAPRO01DanVv2+xBhIe2TiBRd14O15GxYm102T46ax8z8Wez2H0BB4ayokxjnHNHsfg61UVftU5PO6A8jyXAUom6FLbi3Zs2aKq0Xrg+oJUuWcNlll3HllVdy9tlnk5uby+OPP85pp53G4sWLK51Qo127dmVeP/DAAzz44INhOS8hhKgtaaNEQ7LavZ5ffOvYpxoZcSZN5/jsFEbtiaXljgy0rS/i2r4FCksCaZ2Kn9jtqNoY3FlJqAvnAcfIDvRpMHNXybLizL1j0TVYdQ0mpx8124np7CfC882LcqR9ErWVq+WTpxWUW16dyS5C1dNrzP7y7WFm3mwK9EKiAvX1ehbX1xNVJu1TwyIZjkLUrbAF9+bNmxeuXVXJzTffzLhx4/jf//4XXDZq1Cjatm3LG2+8UWkm4d69e8sE/+SOsxCiIZE2StQ3l+amON9g7o5PSd5VRP8defTY5afFX4dRArPblpmv0WbH1KUbdO/OZ613cKBzPLf3fxCHJcqYUGPOWGO9CrIDsUSD1w/sKllmq2I3ZdPTKId/QfebcM/rStRVzTtbIpKkfRK1tcS1mrmuhZWuUxzkC2Wi80ROix4T7tOqF7qus8i9is8Lf0RDo7U5haviLqClObG+T61RkvapYZEMRyHqVkRr7kXSxo0bOfvss8ssS05OpnXr1vz111+VbhsXF1dpZp8QQtQnaaManuYyXOzI/s1s2j4P15YVnJJsLLvprgXgN5dd0WrD1LUb5m49MffohalbT0wdO6GYLXh0L2uy/g2AYo58N0PJWgF//hMAz9L2aDlykRBJ0j6J2hrpHExfe/dyy6uTudcU+HQ/nxZ8x3LPWgAG2/twccwZUl+vFqR9EkI0ZxHpdT/88MOVvn///ffX+hgdOnRg1apVZZYdPHiQ/fv307Fjx1rvXwghRPNTreFiqpuEwPv71IPgdzS64WK6quJftSz42nrtdfQHsKgwtXihDVPXnpi7Gw9T956YOnRGsdT//UG73411+eWg+9HbnIdvy976PiUhxDHEm2JDtpMe3Rt83tbSqkkHuY6oubyZP4u9/nQUFM6JPpmxjuOb7I0j0XjITLYNi/w+RHVEpGf+xRdflHnt8/nYuXMnFouFLl26hCW4d8MNN3DTTTdx4403cs4555CTk8Pjjz9OTEwMl112Wa33L4QQovmpznAxq9vPA4Flz+W8g89haTTDxWKz3fi/f4fC7+ZgPVwyk53frJDTKQVnj+7EYtxAi5n1PYojvr5OtWK6zkUbv0Ip2AnRHWDgi5SftUMIEQ7NJXu5Lmz37eatvNkU6EVEK06mxp5HD1unY28oRBjJTLYNS33+PiSA2HREJLgXanKNvLw8rrjiCiZNmhSWY9xwww3YbDZefvll3n77bWJiYhg2bBjvvvsuaWlVKL4thBCiSQjnRWe1hou53cD3ANySMBUcjgY9XEzXNLQVy7jkixX0XHUITdOxAq4oC84iPwBFn7xPp8SuRj28T58BaBAZeqEMO7CGIQfXoStmlJEfgS2hvk9JiHoRzjawoU12URcXnXV1YavrOr+5VzCn8Cc0dNqYU7kq7gKSzAkROZ4QlWnqM9k2trajLn4fEtBt+uqsxx4XF8fDDz/MmWeeyeWXX17r/SmKwrRp05g2reJ/5EIIIcJE10E1JlHAHAXV6MCE68KzLobMVme4mG52UXw2bc2tUCwNs9ablpWJb+7XeL79AuXQIfoElu/ukcgf47uRdPwERl3+KACtHW3q70SrQcnbwuRN3wCg9nsAS/IIKFWgW4imqC4Cb/U12YWWlYmWlU5aTo7xOnsLKtawXnTWxTEq4tV9fFrwHSs86wAYYu/LxTFnVPi7EiLSmspMtnURsKqLY9TF76OpB3RFHU+okZOTQ25ubl0eUgghRHW40iFnJ6akwEymOWvA5QDVBT+NMpaNXwTm8oGsfK2QPHssmjO1zPJwBd+ay5DZcNA1DXXVcrzffI5/yUIUTUMBXNFW1oxuy8qTOzCix7lc4ByCza1SwKP1fcpVp3qwLL4ck+Zja4vOdOh1Z+OdHUyIaqiLwFt9TXbh++YL/O++wQ2B134W4j9qndpedNbFMULJVnN4M/czDhXtx47C6S0mMsY5LOyZRDK0TlRHU5nJti4CVnVxjLr4fTSVgK6oWET6w88//3yZ17quk56eznvvvcfEiRMjcUghhBDhsG0GyvqHiD438HreCeXXKQ7yHSUWWNT5JOZ2HVfh7msTfGvKQ2bDxcjS+wbfd3PQD6YDoGBk6a04uQOuUcezxXwAgBOcQ4wMRBpZxtsfd2PK+ZMCaxTv9T2ff5nMx95GiCagLgJv9TXZhfXMSWgjhvNczjuA0W7bqplV1xCOcXR24J5DC/iuaAGKp5AHHlgSOMa5aPYtFR4jrBmIahHFLaS6bQuYo2RonWhS6iJg1VSCYk0loCsqFpHg3rPPPlvmtclkIjk5malTp3LvvfdG4pBCCCHCodu16MkTKLrF6KxEPfcair3qmXv97bH0rUrmXg2Cb01xyGw4FGfp+b79At+ShSiqCpRk6f0xrjMdu5/AqY7jSDInMD3r3/V8xrWw/xvY8hwAH/Q9jzxHXD2fkBB1p6HNMhvOTDFTUktMLeJIz0oIvO6BWbGF9aKzLo5xdHYgLOTKo9ap0wxEi0psYObzolungd8sQ+tEk1IXASsJitWeZBbXjYgE93bu3BmJ3QohhIg0ZxqQgJYVbbxOGAROpzHBQrHEgWCJLrdpbOBxtFAXnpr/CMV7bH1Yw9S2BYol9B1PEZqWnYXv+68rzNLbP7IHI+JHcKNjINEmo8NZ+ndRXbqu4/IZgUOn1Vz3HbOi/bD0CgD8PW5iY3JK3R5fiGaqPmvVNTbWMydx+LgefFDwVXBZH1s3TjENxX/b9UAdZyCqRbBmCABR/3stmLknhBCRIJN21C8pUyOEEM3YYX928HmWeoTWltRK1q4lXUdbvxb3Dz/gm/9TcHHRNZcaT2LjMLVMQWnZElNSMkrLligtUzAltURpmYySlIySkIhibr7DMHVNQ1293MjcWLIQjsrSWzmuPXGd+zLaOYwptu6YFVO1j5FR4CIzNxc8nYwFh3LB7MXtU7ni/fkAvH3ZWBzW0L+HuOgI/H40FX6/HDxZkDgIdcBjkPNc+I8jmp1wzjTbVNVXrbrGpEArYpVnPctNa9mXchBSEgA4J+pkTnIOB7c7mE1epxmI/kJYE1jWrUfIG3NCCBEuMmlH/YpIcO+JJ54gNTWVK68sm4g+c+ZMMjIyuPvuuyNx2GZJOqWiMZJ/tw3HPv+B4PNncmYyIWoU4xgY1mPohw4x5qutDFqwD//Bb8qvYLWBzwv5eWj5ebBzO2pFOzOZUVq0KBP005JaMMixl7xEB3rnnegt26Cbqh/Uaug8n76P/6fv0NNLfme7uyey4pQObBnelv5xA7nCOYw2tQzQzlqzg1cXbQICw3ffXVJuneIgXyhXn9Adetfs2AUeX/C5168SZQt0UzY+CYfmGRemJ3wMZsnyFNVTFzPNNlV1UauuMVJ1lY3e7Sz3rGWDdxsqGgAmFDR0wKhtqihK4JUQQjRtTaU+YWMVkeDejBkz+PDDD8st79OnDxdffLEE92ogEp3SfHfJ8Kw/92cxolMEM3ZEsyQXUw3fYEdfPi78DgA/Kt8VLWCNd3WpekE1o7vd+BfNx/fDt6hrVjBeD1zaOJxYxp6MZex43PfcAkD05z+gqH60zAz0rAz0zAwjrT+z+HkGemYm+pEs0NSS5aWOd37gq4+lRti4VKfB9cyjmNPaoqSkYkpJRUk2vhId02ACy7quo+fmkLYjh4RMF2rRp7gPZ6Kl7wuu4wvcCXVFWfhjdDtWnNweT8c2nOgYygWOQcSYosJyLpMHdWZs50T4MVBjcYJRY7E6mXv/9lT/uKqm88C3K4KvC7w+FIcX9+F5pKx7AAXIGvwYLpcPdd8S0gpyADh4eAlWxQJeb3BYePrW38FmI1pXiC/evxSTb9bqYqbZpqouatU1Jvv8B1nuXssqz3oK9KLg8naWNIbZ+9PP1p0Hj7xQo33Xe/kDIYSoBalPWL8iEtw7ePAgaWlp5ZYnJyeTnp4eiUM2eZHolJpNJR2GW2Yt5vnJJzBcAnwijORiqnG5NOYsvimcR6Z2JLgsT80nnqp9AOu6jrphLf4fvjWG3RaVXPTs6JPE6jHtuOS0p3BEJZT5kFcUBSU2DnNsHHTqUvH+VT96djZ6VmaZQKA/6xBb01cQm+0hNUeHggLwlESX1Hk/h84EjIrClJx6VNCvlfE6OQUlJRXFFp4MMV31GwHJQwfRDx1EO1z+K253MKiqsrLcOe/pmsCKCR1ZPzyNttEdOd05jP62HpiV8A6DTY5xkuzQwB6on5saD5ZoirwlA/F6pCaUZNUdxaN7oQbBvZcXbmDJzsPB1y2iHCzOm8uQJVNQdJWVrfrzXnwe42Y9ybhZW8sUrD9a7O2BybssKkgxeUHdzDQbimSqNw35WqEx7Na9lv3qoeDyWCWa4xz9OM7en9YWow5oVWqbZhS4yCxwl1ter+UPhBCikZIJOwwRCe61a9eOxYsX06lTpzLLFy9eTOvWrSNxyCYvEp3SKFvJuh6/xo2fLebpScMZ201+RyI86utiStTMQHsv+tt68IP+E8Uz2f479w3Gm0/hREsvKrqU0A4dxPfz9/h++AZ9f0mmmZLWGuuEM9DHj2em9T0ALnVWkl2m60bxbwBzFBz1wayYLSjJKZCcUvZcdC9vB2aA/XfSdGweDe3AXoquuQwA69RpkJONnnEI7fAh9MOH0PNyoagIbfdO2L2zwmHASkIiSnIKekoKZ8TuI6elE7XDL6ipbSAuPrie7nahHUpHP3yo1NeD6IfSjWNmZhh1444hP95OTrKT1ml9OZhkZktsLuPe+wOAd+8bRf/4AdziPI52lvI30BqzHzan88aSzWUX6jpj172J1Z2LL6YjrUZ8xp3WWJTzsskbk40/8FuzEMhu0XXwBYb1Wq2gKETrCvxhZCBKMfnmLdIzzeZp+WT5ywdrJFO98fLrKhu821juWctG73a0QL64GTP9bN0Z5hhAT2vnGtU2LSl/ULFIlT8Qor7p7pKbv1pWJua27erxbERjIhN2VC4iwb2rr76aW2+9FZ/Px7hx4wD45ZdfmD59OnfccUckDtnkRbpTOrprK37bfpA7Pv+dx84axsTe0siK2ov0v1sRfk6Tg3OiT6GARwHjd/VF4Y+s0ZdzW6n1jh52y1HDbq2nnom57wAUk8n4fWeV2tiVDjk7MSUF5svNWQMuB6gu+CkwFHS8MRQ09EmmBWb1DU1xODCltQ2+tl9wabn0f93tLhXsO4iWcdgIyB0+iJ5xOJhJp+ccQc85Atu2MCKwrcqDFFFW4eTTKjyfIIvFyA5MbYWS2gpTSivjeeCrp2UCTxU8D4AJExoaVndUMLj3z8TriItONn7WxbMXhwiCNjZbvB24f+46AC4d2pUPV2433tj5Fta9c0CxYD3hU9o6AzcKUtOgqknm/kL4w3gqxeRFJC1z/8mv/kWVriOZ6g2fruvs86cHh90W6iVZ5h0srRlmH8Age+/gDOQ1NXlQ55A30yNd/qA50/0lNV219AOYO1c8UqAha2wZSrrqR9u5A3XzBrTNG42vu3YE3y+aOhlTpy6Yhx6PZehwzP0GoNgd9XjGoiGTCTsqF5Hg3vTp08nOzub666/H6zUu4h0OB3fffTf33HNPJA4paumxM4fxxI9r+G7jXu79ahluv8q5/TvW92kJIerZ+TGn8pW6mAP+kuGS+c89AfMXlxl2ax44BOupZ2I5cSxKZdl5ANtmoKx/iOhzA6/nnVB+neIgXyh9H4D+D1b5ewhFcThQ2nXA1K5DyPd1XTcm+Mg4hH7oEN7DB/h1z1ckZLoYmNcSAsFAtFKV/6JjygTrir8WP1daJKEcNdGHruscUA+zyrOB1YXrg8s1NHr7oxmtpWBKmgVAVN428OwNWxC0ITiixnLr4em4VZURnVK54cQ+JcG9NdONrwMeh6Tj6u8khaiC4x0DGGzpUW65ZKo3Lv/Nnclea07wdZwphuPs/Rhm708rS3LYjpMc4yQ5pnzbHenyB82Z+seq4POiay7F1K0HljEnYx1zMqbWbSvZsn40xgwlXdfRD6ajbt6Aunkj2ub1Rr1bT+X/WLWdf6Ht/AvfZx+CzY55wCAsQ47HfNxwTB06NfgApqg7MmFH5SIS3FMUhaeeeor77ruPTZs24XQ66datG/YKfsii/lnMJh49axhOm4XZf+zkgW9X4vb5uXhI1/o+NSFEPRpuH0jf3FZs+e45KL6R+uNc8JtRWrXGeuoZWCecjqlVNYbzd7sWPXkCRbcYH8JRz71m3KWtTtAqwhRFgbh4zHHx0KU7qu7lp6xtAByXNB27YkMrKKDwnJMBiP7kG0wtq37hl6Fms9qzgVWeDRxSy3feb4y7jG6b34b1t8O5gYX1EASNJJ+qcVfG7RxQU2ifEMW/zzkeU6lasKhuaHMq9JKMf9HwxZliibIkllsumeoNl1/3s8n7FyvzVnJRYNlBNROL1U4/Ww+Odwygu7VTjYbdiobH1L1XyQtFQdu2Be+2LXjfeBlT1+5YRp+Mdcw4TG3b199JltIYMpT03FwjkLdlYzAzT8/NKb9idAzmHr0x9+yNqWdvTB27UDTFmAot+sMvUTeuQ125DP/KpeiZGagrlqKuWAqvPoeSnGIE+oYej2XwMJT4+PL7F82GTNhRuYgE9wAWLlzIjBkz2LFjB5999hl2u5333nuPTp06MWpUJRcjot6YTQr3TRyMw2Lmg5XbeeLHPyjy+rlyRM/6PjUhRD0p+tcd8Odqepj9wYkJ/hjVlpVjOmLqO5AL4k4PFhGvMmcakICWFRgimTAInM6SoaYAiQMb/BBKxVwyXEqJPnbWTY6axxrvRlZ5NrDXXzK5lAUzvW1d6WfrwQcFXwHQ3tq6UQRBa+OZ+ZtZ4elLlOLi2UmjiHPaymSt4EiG4e+AXFgLUW2NbeheXfHrJbVPH8l+CRcerL6Sdue86AkMjB9EVC2H3YZLrpZPnlZQbrlXLxlius9/0MgIVd0kFC9TD4LfQZzXjYRCDKZSdXKj3v8CddUy/At+QV2zCm37Vrzbt+Kd+Qqmzl1LMvoqyO6vCw0tQ0n3uNG2bw1m5ambN6If2Fd+RYsFU5fumHv2xtyzD+ZefVDatCszcqHMpGpx8VhPGo/1pPHouo62eyfqiqX4Vy1D/XMNesZhfHO/xjf3a1AUTD16Yxk6DPPQ4Zh79UWxRCycISLtGLW2RfVF5K9h9uzZXH755fztb39j9erVeAKpuPn5+Tz++ON89913kTisCANFUbjrlAFE2Sy8vmQzz81fT5HXzw2j+0jHUIhmwjfvp+BzLTCMxdxvIGA8999yMwe8K/Bq+/hPzuuMdR7PxKjRkpESQqFWxB+eTaz2bOAv/x4ClQkxodDd2okh9j70s/XAaXLg0b3B4B7QZIKgocxZu4uPVu8G4PGWz9O15XnGG/tLff/D3gCnzOAuRGW0rEy0rHTScnKM19lbULE26KF7dcmtedjp38cO3x7+8u1lt39/8D0XHuJNsRzv7EHxJFIjHINQGkhgD2CJazVzXeVnBC+teNi31e3ngcCy53LeQbGo3PNnqTZV18pv3EyZ4hMwn3EutjPORcvNwb/4N/y//YK6egXaju14d2zH+9YMTJ26YBk9DsuYkzF36HTsHYfzHBtAhpLvp+/Qdmw3svJ2bAe1/MRgStv2ZQJ5ps7dUGw16w8qioK5Y2fMHTtju+BSdI8bdd0f+FcuQ125zBi+u3kD3s0b4P23IDoay8ChmI8bjmXocExpMilkgxThWtuiRESCe48++iivvvoqU6ZM4eOPPw4uHzlyJA8//HAkDinCSFEUbhzTF6fNwvPz1/P6ks24fCp3ntxfAnxCNAOm1m2Cz62XXoHt9HMwJcfDp28CcFLU8QyIHsIXhT+y1ruFX11LWe3ZyHnRE+hv69Hs2wm35mGddyurPRvY7NsRnGERoLOlHYPtfRho70WsqfEF5cJhbfoRHp27GoB/xH/CSVErjTcK98CK64EXjdetTq6fExSiEfF98wX+d9/ghsBrPwvxH7VOfQ/dq0t5WgF/+faww7eXHb697FcPoQdvq5R1dewF9LF1Q3F7KJ8b1zCMdA6mr717ueW6ruML/KatWIzPXbeb4iDlrVHnkrziauxZa0s2kizokEzxCdhOPxvb6Wej5+XiX/IbvgW/oK5ajrbzL7w7/8L7zuuYOnTCMuZkI9DXsXN9n3bEqLtLJrvwPPfvMu8pCYmYevXF3KuPMcy2Ry+U2LiInYtid2AZagTuALSMw0bG5cplqKuWG7+vxQvwL16AB1DatMMSmJjD1LNX5TsXdacB1NpuLiIS3NuyZQujR48utzwuLo6cwJ1F0fBdNaInTquFp376g/dXbMPl8/OviYMxNfMLdyGautJ1aeyXXmHcFS6dLQa0MMdzVdwFbPBuY3bBD2RpOczMn0Vva1fOjzmVlubytaeaMp/uZ5NnM6vd69nm2ogPP16zFRSFtuZWDLb3YZC9Ny3MkRkgpes6Lp9xR91pNTfYAGumLZo7vlmFT9UY1y2VaZ7ZxhuaH5b8Dbw59Xp+QjQ21jMnoY0YznM57wBwS8JUbNXM3GusdF0nQ8tmh29vMKCXqR0pt16SKYHO1nZ0trajnTmNp3ONG1XdbZ0wKaYKQn8NQ7wplnhTbJXW1c0uCgDF4aPN71NQcteBNQF8OZE8xSZFiYvHOvEsrBPPQs/Pw79kYSDQtwxt9068776B9903MLXvGMzoM3Xq0mA/c6tDL8jH887r+L74LLjM1Lc/5t79jKy8nn1QUlLr9Xs1JadgKv79qCrati3G8N0VS1E3rkPfvxff/r34vpwFpUqneN6ZgWKxUu6PXddLPS33Zsj1Sr+l+0tupbhff9EYIly8rh5i+1DvFb8u957xVS9VNsD92guYnFFgtYLNjmK1gs0GVquRLWkNPLfawGYzvgbWUQLrYbWVrGux1M3vs4mXmWlIIhLcS0tLY/v27XTs2LHM8kWLFtG5c9O909EUXTq0K06rmYe+W8XsP3bi9qk8fOZQLCa5+ydEU1WdD/o+tm50S+zIT0WL+cW1hI2+7Ww7sovxUSdwsnMEFqUB1UIJZ20PVzrqkR3BIQav77mXIpuGVfXx+AqjAPbvo16gu7MXSXoiuAH3XyXb13CIQUaBi8zcXPAEhgcdygWzF7dP5Yr35wPw9mVjcVjNIbePiw69vC54TGb+b8C5ZBR66NIyjkdP749pTqATu/EpyFgElqR6Oz8hGiNTUktMLeJIz0oIvO6BWbE1yeLiqq6R7k/nr8AQ2x2+vRToZW88KUBrc2owmNfZ0o4Ec0lmUekJTupdBOpNKdEeoiZuQcl1gyMVRn8JPw6v9X6bIyU2zpg07NQz0Avy8f++EN+CX1FXLkXbswvv+zPxvj8TU7sOgUDfOJS0hjfr7rHomob/x2/xvP4Sek7Z4HjUk8832LZDMZsDw4F7w9/+jl5YgH/NKiOzb8VS9PSSIfi+zz6K+Pn4v5wV+WN8NTv8Oy0OAAa4ZzyPpU8/TN17YWrbvkx96RprwmVmGpqIXHVde+213HLLLcycORNFUThw4AC///47d955J/fff38kDikiaNKATjisFv751XK+3bAHt1/lybOHYbPU30Vic6brOl6MQso2rGG/4xLp/Yumx6ZYOSN6LEPt/fis8Hu2+XbxXdECVnrWMTn6NDpa2xx7J+EU4doeu3z7UDfcT++tH2A511h2w+oN5dYbseimis+xhkMMZq3ZwauLNgGBoTLvLim3TnGQL5SrT+gOvat92CqrqP6X5nbzTM/xbIxvTZzVxH+HpOLYXRLs1Dc+iQLoPR6DnZE7PyGaGl3X2eMruYj9quAXutk60klLorHfhvXqPvb4dlNcefOBI89RYC9bN86MmQ6W1nS2tqOLtT0dLW2JMjnK76w+VeczyVfq+zvyB1hNVb8ZVLCDqDM2YYr1ojvbopz8K0RJDbJwUGJisY4/Hev409ELCvAvXYR/wS/4VyxF27sb7wdv4f3gLWhkNd/UzRtxv/g02iajD2Nq3xHbNTfgvu+uej6z6lOiY7COGoN11BgA1B3bKLrmMgAs51xQduKNo69tjr7UKXftoxz1tvFa9/vwzTZKkFknX2Jk0pVeX1FK9qUctfzo90sfu9T7us+H7z0j29h6waVGQp/PCz4futcDPh/4vOje4mXGV7we9MB7Jcu94D+qcIMvsDzA//Xn+L/+3HjhjMLcrQem7r0w9zAeSuu2cm3YgEUkuDd9+nRyc3M56aSTcLvdjB49Grvdzp133smNN94YiUOKCDutdzscFjN3zVnKL1v2c+vsJfz3vJEVZoeI2qtshrTi4sk3x08xZkgLIc4UU+lQjkjvXzQ/qZYkboj7G6u9G5hT8DOH1WxezvuAAbY6rntSy9oeW3tMYUOPy/Dhw6v78Ol+3Jon+P7LeR8Sl9aGFtHTmPbAYgCyn3mENlHtMWnuiA4xmDyoM2M7J8KPgWNMMI5Rncy9f3tCvhUWFdX/mtVuEN/1OAWTrvHQsk9J+n43RRaV2MAMzAo6vq0t8XkaezhCiLpxWM1ipXs9qzzrywxFXeRZxSLPqjKTKyzz/Ekna1dSzEkN9qJM0zUy1Ozg6xdz3mO/egiT2xv8Pjy6F6cSTSdLu2BmXntLa6wNKUM8lOp8Jml24P2SZSZP1W4G5WyAhadgivWi5dpRTv0F4rqVK6khak+JicF6ykSsp0w0ssWWLca/4Ff8y3+H9APB9YoevBv7hZdhHnxcg/u703KO4H3zFXzff2VkkDqjsE+5Cuuki8oHfxopU6ksSsc1N0QkA1F3uYLBPfsV10buGIHgnn3qtFofQ9c08PvAWyogmJ9H0bWXA2A5ZzL6X9tQt28BVxHq2jWoa9cQnJ87JhZz956Yu/fCVBzwS2nV4P6NN1cR+zR87LHH+Oc//8nGjRvRNI3evXsTExMTqcOJOnBS99a8cMEJ3DprCYt3HOKGTxfx/OSRRNtDB39E7VRnhrRQJjpP5LToMXW6/8ZS90tEjqIoDLH3pbe1K98VLWCheyV/ejcF338+511Migmz28tVgWX/y3kbv9uK1e/h1sCyZ4+8hc9iRy9TCr3s8+L/66VmAHws+xWcLSF20PX8/YEFALz20An47Wasqo9bA0Nm/3fc1fjModuuPHssee5lFX6PNqz0iBvJ0NiuaFl/ANC25cTytQkjMMQgOcZJskMDeyC9LTUeLNEUeUs64z1SE4iyhf549+heCFNwL9QNAmXiSNTjuvFRwTcAXBJzJhsyVZ5fcgR0uKpXND3PvYl8RSFa9cPakwDQbe0xXfQ51sS28Fbl7ZIQzVWeVsBqz0ZWedazx18SRLBiCU6uMNI+iN3+A2RQks03q2AuPr+FaCWKzta2dLa0o7O1PW0trbAodX+TtkArIt1/mP3qYQ74D5GuZpDuPxz8HgD2qOkAJJa6iXhbwt9Ji26HqbFNDFGdelM+DWbuKllWnLl3FF3XobAAPS8X/eASTFunoWi5qNlOXHN7EH1Fuzr65po3JToG67hTsY47Fb2oEN/C+Xj+bUweqa1chmvlMkydumKbfDGWcRNQbKFrX9YVXfXj+/oLPG/NgIJ8ACynTMR+zY2YWiYb6zSR4J4ITTGZwGY3avYFlulxJfWgHdfciOJ0GjUN9+5G27IJdctG1K2b0LZvg4J81NUrUFevKNlnfEIgu68n5h69jSG9gX9Pom5F9FZXVFQUQ4cOjeQhRB0oPUxzeMcUXrl4FDd+upiVezK47uOFvHThKOKcNZvyXFSsohnSqpNZF6n9Zxd48eRa2JRftjZH2Ot+RaAejagbTpOD82NOZZijP5/kf8te9SAA+wJfraU6j/vVQ/hUCza1ZFjAAfUwXqX67Uqunk+uzUJWbHKwtsf+2DRUh5UotSQ06Evoi2KNwYYFq2INPCzYCHxVrFgJfFWsKMCswh8AeKDFjcSYotFdrgY7w2JdCHmDwAykACkJADyWt4xVKzug6RZSu+ayddRmnlIgpTCDG9d9F9xMGf0R5pQhKF65qBCiNI/uZa1nMys969nq24kWuLFhQqGHtTND7X3pYevEv7L/B8C5MeOxKzaKbEdQAzOndrG04y8yKNSLWOfdyjrvVsAICnawtgkE+9rRydIWhyl8wQe/7ueQmsUB/yEOqBmBr4dDjhoAsGDGj3GD8OLo0+lu60Six04hnwLQ2pyC0tgCe1ClelOa3g49z4+aXdKv8v68EVNBLuTlGkG8Mo880FTMqfk4J2xBsWmoh6Mp+qEHeBt4JmMTpURFYx09Lhjcs5w5Cf/Pc9F2bsf9n0dRXn8J6zmTsZ51HqbEFnV+fv61a/C88DTaju0AmLp0w37TnVj6DazzcxENn2I2Y+7YGXPHzlhPPQMwAr/arh2oWzahFQf8dmxHz81BXfE76orfS7ZPamkE/LobAT+lfcd6+k6al7C1/rfffnuV1/3vf/8brsOKUjRdK18zoBqqOkwzOc3KIxf25v5ZG1l7IJupH/7CIxf0oV1MYrMfphnOenUVzZBWuhh0W0sr7DUIgNR2/9+s2xCo+1WxatX9cqUbj6OFqUaaqD/tLGncEH8Z92Q/DcAVsedjUywoNjcELjyvipsMDieKvwh4BICr4y5At0QBSqlmTSn1/5J3fLqfGfnGsIib4i4nyuTE5laD+3+yxZ1YomICF1L3AXBX4tXVyqrz6N5gcM9aQUC9uanoBoGu6/jw4/aq/N+cTfg9RXRrFcOTZw7HbjER/dd7JPz5GibVXbJRiwG1O5nK2pBiR/6QNkQ0CLqugzvw79/hKNdXUHWVzb6drPSsY71na7BfAdDB0oah9r4Msvcm1mS0YaEmiXCaHMGbD9fFX4rqsLHPn84O3152+I3JKAp1F9t9u9nu2w0uo01tbU6pcDKKyuSouRzw7uOA/zAH1MMc8B/msJqFhhZy/SRTAq0tqbQ2p9DaYjxilejgZ8VgR1/sig1dcYXcPuLCcHPRXuRDy9mO71AG2r69weVF9083su4Ks4g51VhWePl54DfjMllh3K0AeN9+DbPmC7HnwGm1ycV5yjYUi4aak4p735ngXVft8xSR4bjuFrj6erzffolvzqfoGYfxvvM63g/fwXryqVgnX4K5U5eIn4eWmYHntRfw/2L0YYiNw/73a7GeOSk8kyWIZkOxWDB37Y65a3c44xwAdK8HbcdfqFuNDD9ty2a03TvQszJRf1+I+nv50Rjqjm1Y+vSv69NvFsIW3FuzZk2V1pNheuGllhqO9kzOTCZGjWawvXeNhixUa5imE3qebufP79ux43Ah13ywkFvOS+Pi1HHVPm5j1Nzr1U0e1Jmx3coXDa5x3a9tM2D9Q5UftJIaaTWdnEDUjdLtUW9bF+OCTS3Jeutp7YJic4KpJIuhh61zlYNvpS9s21nTjP2bS/ZvrodhZw1RRZNd6J6SP0Z1+1YUux3UIop/auq2LWCOQo9LLLO/im4QgBG8mP7NMnZlFJEUbeel80eTqhTA4qvggDFkl5ST4PC88Hxz0oaIGtrpKwm6/OnZTGtLCsnmFjW+cVZaZX9zrluNIZrO/72GYjdKEKT7M9jo285m7w5cupv8RAfeRActTYkMdfRjiL0vKeaaZfxYFDMdrW3paG3LOEag6zqH1axgoG+Hby+Z2hH2q4fYrx5ioXslAC1M8UZtO3NJ8Hu3bz+Z2hEOFe7jlMCyx3Jexecof1nhVBxlAnitzSm0MieHzBCsl5lsazgBk67rUFSIlqegHVHRD6WjHUxHP5iOeugA/0zfhbPIj5+5HJ2PrK1ebjyxqCULFQVi41DiS9pZy7gJWOPjUOLjUeKOergXo6y7FkXTIO00zBfOIsqnUHDm2PD+fEStKLFx2C++HNvkS/D/9ive2R+jbd6Ab+7X+OZ+jXnwMGyTL8F83HBjuGQY6T4f3tkf431/JriKQFGwnn4Otqv+gSk+IazHEs2XYrOXzFrM+QDobjfaX1tRA0N6ta2b0fbsCm7juvkaTF17YJ04Hhn7F15hC+7NmxemDrqoFr3UHdEMLZv3Cubwo2sRE6NOZKCtN6ZqBFOrPUwzAfZf6uKfn64nMwfe+dzFiZcU0iahYU5jHc6sukjXw2vokmOcJMeUz4CpSt0vLSsTT/q+shc7pnHoXXviefpRAOx3/suYcUpzY95izHSl9ngfTA6UhARM8WWDDJJxI8SxVTTZRWnFAQdKTXZRdOs08JtRL7saOOpvrwJv/r6ZHzfvw2JSeOa8EaTmz4dlfwf3YTDZYOBT0OUq+KxqWUHH1O1aaHt2+eW6DlogQ8rkqDj7RtqQZmuPryTj84OCr4LP402xJJtbkGJuQbIpyfhqbkGSObHKdeqq9TcHJAAjAw+Avy4eS+IV/6C9pXXYb44rikKqpSWplpaMcAwCjBuXO337gtl9+/wHydZyyfbkspL1wW1fyvsAAKvbHwzumTCRZk6mtSWFNHMKrS2ptDGnEG+Kbdg39ms4AVPxd+Rf3Rrvmrbl3g/2kOLjMbVqjdIyBXWxUQfWfst0TC2TUWJssH4EADFf/oJij8Ps9cMzcwBw3PF/OELVT935Pqy9GnQV2k2GkR+A2Qa+espybIDC2ecPxzEUiwXruAlYThqPtnEd3lkf4V80H3X1clyrl2Nq3xHreRdhHX86iqP2sz37VyzF/eIz6Pv2AGDq1RfHzXdi7l7HE5yJZklxODD36Y+5VHaelpVB4YVnGi/MZrTtW/C8uhFboK/pX7MS85ATwx7kbm6kKEMjZyk1Q9ipzhNZ6F7BITWTd/K/4AfzIk6LGk1/W88qBflqMkyzXQq8e1kq0z76jX05hfz9/fm8dsloOibVX1ZaXWTVRboeXlNW+cWOERh23fus8bJ0gOH/ngW/GduUq7FPvaYOz1iI2mkoE81Yz5yENmI4z+W8A8AtCVOxYTWmKfEE2nm7zRjurBbBmiEARP3vNTBH4Y5LrNJkF79tT+fFBRsAuPeUPgxKfwC2vWy8Gd/XuBBN7B/eWRxlWK2ooS629hCIiXQwtyZTy6FQLyJXyydXyzeGrJaioJBkSjCy+4pKgt1H1Fwceosy/a2K/uYK3UdQbrsVgBkPjcRvM4KFVix0tXWkj60rHSxt6J+UjMnaMoLffVnxplgG2nsx0G4EANyah93+/ezw7TWG7/qNQEGcEkMbSyptlASKyx881uJ2rFENq2+j6zp4PehuN7iKjGySolw6H87E6vGjWn7GW9QBlIfwfWHU9TP17gc52Wg5GURPMEYlFX7dC9QKLjityZh6dsLUKg0lpRWmVmmoqck84/iOnJZOHmvzf0Y2ucsVzKqzjj+9ZAKmQMy0ysMjt70CK643nne+Aoa9Dia5nCu2z3+QWH9h6P646iaheD31IPgdVerzV+u64hjHUBQFc5/+OPv0Rzt4AO8Xn+H7/ku0Pbvw/O8pPG++gu2s87CeM7lGExJo6QfwvPI//IFAspLYAvs1N2IZf5oETUS9Ukp9PkS99znq4gX4fvgcWAWA6193oCS1xTrxTKynnoEpVfp0NRGxT4OcnBzefPNNNm3ahKIo9OrVi6uuuor4+Phjbyxq5OSoEZzkPJ4F7uXMdy3joJrBW/mzaW1OYWLUaPrbekTkorJNQjQzLxvLtR/9xs6sfK78YD4zLh5Nt5Sq/64bW1ZdpOvhNTTHqg9UVW7Nw5YJvdnebwrb/MZsn+dHn0pna7uQw5RCBRiUpLq70BGiOjIKXGTm5oKnk7HgUC6YveGfaKaGTEktMbWIIz0rIfC6B+aK2ih/IQSqbZi79QBLdJUmu9iZlce9Xy1DBy7sHcfk9EmQt9l4s8ctMPBJMNc+K0GIcGlraRV8fkPCZcZkFJqLDDWbw2o2GWpW4Gs2h9UsvPjI1I6QqR1h7S9tAKMv8Mjh14iJVmhpTiTZ3IJkcxIp0S1IiI0lPS8BgD+iffzhXceO/G3cHzjm4Y4JdI7tzlB7P/rZuzeofoPDZKeHrTM9bJ3x6F6mZ/0bgH+1uB5boRf/hlUUV870PfcfVJvdyI41m42vJlPJQzGhmI2vJctKr2tGVTRGuv9CVxTU2Nl4TVZ0taTd8c7+CFQV3e0Cl6vMV93lAnfZZbjdoJWv+Xdl4KvKCkoGxho3F7WFO4yXllKBkOShmFLao6Sk4UlthSmlFc7WrTGnpoXMsvLoXjKyIjDr98b/wB/Tjefdb4Qhzxk/TxH0fO672DVHuWVgZJo+EFj2XM47+ByWKvX5q3NdUZ1jmFq1xvGPW7BPuQrf3G/wfvEJevoBvB++jffT97GMPQXb+Zdg7t7zmN+37nHj/fg9vB+/B14PmMxYJ12IfcrVKDENK+guGokITmpoiovHfN5FWM86Az4zbqwQFYN+KN2oS/nuG5gHH4d14llYRo2p91mmG5OIBPdWrlzJqaeeitPpZNiwYei6zrPPPsvjjz/Ojz/+yODBgyNxWIFRQHli1GhGO4Yx37WMBe7lHFAPMzN/Fm3MqZwWNYa+tm5hD/Klxjp5829j+Mcni9hyKIcrP5jPKxedSN/WZWvDSFZdZIQr+Fad+kAhz+OomlwAhVoR673bWOvdzGbvDvxWFdoDgXubOTGtMDt6Gh3zAHPX7iV3tY8KMIjGpbZ13pSklpgiGNANZ1bdrDU7AhPNGBfAvLuk3DrVmmimkclze7ll1hIKPH4GJ7mYXnQ56G4jo27425A2ob5PUYgqiTI56WBqQwdrmzLLdV0nTy8IBvp6jzrMyx8ZfZpNv7ah/+l7SSeDdDUj5H4/LjRmiLZSMnP3fYk3EBvdsG9a6bqOvncPg5bvpf3WbHx/TcW7e6dx8Reg/jy3VKCs5k4v3h8byu3P+9aMmu/YZkdxOtEdDg5ZC/E6zLSP6YzJGQ1WK+pvvxqr3Xg72SltyXZGYfrTqB/F3U+C2Vlyo2ZjOm9f1gNHjotg2mcpYb9Ro+uw9n7YYJQuofe9MOCxsF5sNxU3x08hNi42dJ/fXTKR1y0JU8HhqFKfv1rXFTU4hhIdg+38i7GeewH+Jb/hm/UR6vo/8f88F//PczH3H4T1/EuwjBhVLsNT13X8i+bjefl/6IeMEgPmQUOx33gH5o6dj/m9RZqWlYmelVluecg+YAiR7gMKalx3NKgWIydK97lj3p+Nf+lKfN9/hbpmJeqq5airlkNsnDEBzWlnGxN5iEpFJLh32223cfbZZ/P6669jsRiH8Pv9XH311dx666389ttvkTisKCXK5OD06DGMcQ5jvmspC9wr2K8e4o38T2lnSWNi1Gg66x3DesykaAdvXDKa6z9dxLoD2Uz76DdevHAUg9uVNMqSVVc7tQ2+HetDsrr1gY5WuibX7+41bHZtZbtvF1qpC5mWpkT62roz370MgP72HhV/w6LRq22dt3ANw66LrLrJgzoztnMi/BjoDE1YVPaCsArHKDPRTCOiajr/99VydmcX0MqWz9POW7Hqbmg7CYa9Bg7pnIvGT1EU4hWjj9HV2oGBrf28zBwAcg5GoSw+kWmntyZTL8n0O+zP4oieB0BLJZGhzn4McXSlOAAQY2p4N610VxHq5o2oG9ejbliLtmk9el5uoFQ6JZ/oqWkQCChYp1yNYrEYmXKlH7qGrhpf0XTQ1EAtzLLr6bqGpvpZ7V6PSdMZaO2JSdPR/f7gbIvmcRMwxcSCw47itBh9HWeicTPQ4Sz5Wvq50wl2RzAo4tG9vBDIQPx30vSSIbPFwb2JZzNn5Q5e/aHmN2ouHNEa+hrP9/kPVjxcU1M45hgXXYPVt8OW54zXAx6HPvcea6tmq62lFXGWuJB9/tITbbU1t0KxVBCsOEp1ritqegwwhmZbTzwJ64knoW7ZhHf2R/jn/4y6dg3q2jUordtiO+8iLGNPDm7juu9OtD+MYY1KSir2627BMnpcg6lz6fvmC7zvvlHpOpVdV0gpnjpQw7qjQWGakEyxO4wg3smnoqUfMCad+eEb9IzD+OZ8hm/OZ8YkHKedhfXkU1Fiw1SzuYmJWOZe6cAegMViYfr06QwdOjSsx3rzzTd5/vnn2bJlC3FxcQwfPpyvvvrq2Bs2E9EmJ2dEn8QY5/HMcy3lN9cK9vrTeT3vE9L01kB4/zDinDZmXHwiN89azMo9mfzj44X8b/JIRnRKBZpnVl041Tb4dqwPyQprclUheJir5bPGkQ0f5QPwReGPmK3GJUBrcwr97T0ZYOtJmjkZL75gcE/UjcqyZovV+iIkhNrWeTvWMOyqZgZ++lcOr23KIJJZdckxTpIdGtiNIeekxoMlukoTzUDgQiFMwb1q/b5DqO7v+6Xf1rPwr4PYFS/PtniUJLsKQ96AzldKdolodGqSDW9WYMGWw7SKjuXeCccFtyk9nPWuxKtxmOxGMCliZ189uq6jpx9A3bgOdcNa1I3r0XZsN4Jwpdls7Oocw57uiYwbciX23oNQnFHBOnL2Cy8zAmm14NG9zAr8rIYmTcfuzkLP2UnR1rkAOK88DcVeKqNEBUYcnVGiAgXGw5kGzurfWKjqjZpV/j9Y4llTbvu9Udso7iVVNlzzDOtQKs1n1lRYdi3smGm8HvoidL+hsi1EE2Hu0Qvn/z2Mds0N+L6chfebOegH9uF58Rk8M18Nrqf9sQqsVmwXXobtkqm1/hsMN+uZk7CMOLHc8pB9wBCkFE8d6HYtevIEim4xrvOinnutbDsLx87cCzNTWmvsf78W25SrUVevwDf3a/yLFxiTcLywBc+rz2MZNcbI5hs0VOpJlhKR4F5cXBx79uyhZ8+yNQL27t1LbGz4Jlp48MEHefbZZ/nnP//J8ccfT3Z2NnPnzg3b/puSGFMUZ0WP4yTn8fzqWspC10r2+dMpDu5t9e1ggDU8w3Wj7VZevHAUd3z+O4t3HOKmzxbz9KThjO3Wukln1VUkXENmoXbBNzj2h2RFNblCDpkFDvkz+dO7mT89m9mnHkT1KYCRidfO0pqhUT3oZ+9Jirns8OxSiXyijtS2ZswxL0IqUNs6b8dS1YD36bZojo9yEn3mJgAKvzEKpGunT+KaLKMdrM+sutoOXz56SHxts6Sr8/v+Yd0W3vx9CwAPJr1C79YtYcRciOtWxT0IUT/CWYriwdOHcv+3K/lk9V+0jHEw7YTys1I2hGwa3eNG3boZbeM61A3rUDeuQz+SXW49JSUVc+9+xqNPP3ydO/JGnjHZ1SlJozEd1TeIiGpklOg6uHTj9+RUPMY9hRpmlFT1Rk1ny/GcqvUpt72u6/gCn0RWLMbvPdRwTe0Y/x6W/R32f2TU1Tt+JnSeWu3vRTRupuRU7FffgO1vV+L76Tu8sz8OzoALYDpuBM6b7sDUpl09nmXFTEktQQJ0DZszDUhAywr0uRMGQXFppGKJA+ulNJJiNmM5bjiW44aj5+bi+2Uuvu+/QtuxHf+8n/DP+wklNQ3rqWdgGXvKsXfYDEQkuHfRRRdx1VVX8fTTTzNy5EgURWHRokXcddddXHLJJWE5xqZNm3j00Uf57rvvmDCh5BJk0qRJYdl/UxVjiubs6JM5yTmc7/OWsJBcAN7I+4yuvjacFjWG7taOx+yAHitg5bRa+N/5I7n7y2X8uvUAt8/+ncfOHsZpvRvmh084RHrILFQ/+BYJ+9VD/Fm4iz+9mzmkltTRUFDobGlHcTjhpvjLK8xQEnWvoqzZsF2EVIfmN2p8FO0D1z7ja9E+KNhVss6OdyFlNMT1BFPFQ2KrmhnYHoX2ahHmQGag+uiscjPA1lVWXSi1Hb5cekg8VPP3HUJVf9+bN83j/m/TARtXxH3F6cefBH3vA1PojEAhGpJwlqI4pWdbCrx+nvrpD176bQNJ0XbOH1j/Na8AfAvnoW3fgrphHdr2LeA/6ru0WDB16xEI5PXH3LsvpuTUMqv4S92ErTMhMkoy/RYyC4pgaWBajOEzwWTH7de44itjiPDbZ6fhsJiMcgAHj5TZZTjr4VV0wzqUkMM1jzVj+N7PwWKFkR9C+8m1O1nRqClOJ7azz8d65iT8i+bhfuj/AIh64IkGl60nRCQo8fHYzrsI66QL0bZtxvf91/h++cGYhOPdN8oM/9Z9nmb7dxGRK++nn34aRVGYMmUKfr8fXdex2Wz84x//4MknnwzLMd5++206d+5cJrAnqi7WFM2ZUSfxWKBWjAUzO/37eDnvA7pY2nN69Bi6WjvUKmBlBp4a3ZUHrRa+3bCHe79chtvnZ9KATnX0XdatSA+ZrS+arrPbv4/isOP/ct7G5zCaDjMmuls7McDek7627pj9dt4J/JsSDUvEL0KKqR5wHSgJ2BXtw1y0mytz5hHvzsPmewXcB406QpVZeb3x1RIDLYZC0rCSR1Tb4FDPupgBNtxZdaHUdvhy6SAlVO/3HdKxft+aj+zlj3Db/FTcegojo7dw8+RrITVEZo0QDVRts+GP/ru7dGhXsgrdvLFkM4/OXU1ilJ0TuiVH/hspRXe58K9bgW9JyXl5nnqozDpKYotgRp6pdz/M3Xsaw7AamhAZJbNW7uDVRXsIlliYU75Yf3GQD/aXe6/BT1zkyy95bnbA6I+g9Wn1dz6iQVFMJizHjQzLvmSyC9EYKYqCuXsvzN17Yb/uZvyLFuCb+zXq6hXBddz/eYyoR/5Tj2dZfyIS3LPZbDz33HM88cQT/PXXX+i6TteuXYmKigrbMZYuXUq/fv145JFHeOGFF8jJyWHEiBE899xzDBw4MGzHaYxqMgz0noRrWawuZ7F7NX/59/BC7nt0s3Zg8peHiP7gs1oFrB6dcjUOq5nZf+zkwe9W4fapXDK0a82+uQYs0kNmw0HXdTR0NDRUXUUL/KeioenGc1epu/NfFPzIBt92XEU5wWGaViz0svVggK0nvW3diDKVXBAUlfvXIZqkwwvAk1UmgBfMwnMfLre6BRhw9ELFAlFtjECds63x1Z4Mf95jvJ98IhxZA/4CODzfeBRzppUN9iX2j8z3GRDurLpQjhmk9OZCwXbI3w65m4KLzf5vIX4YSlTdFhb2/zyBu7aezAE1hfbOAp76+3WYY5Pq9ByEqK3aZsOHujlw4+g+ZBW6+eLPXdw9ZxnPXzQCIjiaSdd1tF07OWH+X3T78zC+zT/g85XNslM6d8PSt38gK68fSlrryA0R1nXjBgSAOSrsNTeb9MRF3iOw4GzgFuP1iXOg9dh6PCHRlDWVyS7qIkgpgdCGqfQkHOrOHRRdbYwQtYweV89nVn8iNmbO7Xazfv16Dh8+jKZp7Nq1K/je2WefXev9Hzx4kNWrV7NhwwZeffVVbDYbDz30EOPHj2fbtm0kJCRUuG1eXl6Z13a7HXsFf4wNWThrxcSZYznfeSonO0fwk2sxv7vXsM23mxdOdNN74IXs9Rt3QWsSsDIpCvdNHIzTaub9Fdt58qc/cPn8XDmiZ8htgucXxlp1dXGMcA+Z9el+crV8crQ846uaT66WT5aaE1zn2SNvg6Jjcnv4R2DZY0dexlNkRjVCdqi6Rsl/1St297vnDwBilZLf7wMtbsIRVZOpFURVNcg2asd7Jc/nn1H5uia7EawLPPzONObom8hxxDEl5SZs0Z3AkWLUESrNX1gS3DvpezA5IG8TZC0veeSsNYb07vvSeAB24N7oluyJa4updSy0HAkJA8Acntqd4c6qC0nXwZ1Bx5w9tCzKxnzgESjYaQTzCv4CT/lOJQDr7je+anbgfeP52gchZQAkDITYLuV/zjWll7Qfz+/syUpPX6IsOv+77DziZNayZqNBtk8NiKIo/GviYI4UeZi/LZ07Zy+j55l2YlqEL6KkF+TjX70CdcVS/Ct+R884TOncLiWlFebBx+Gf+zUA0c+/Hv4hSq50yNmJKSmQ5ZuzBlzVLMJeg0LsDWniorByHYKFkyB7S8mylEpmqBQhFbdPpSePEqE1lcku6iJI2VQCoU2ZqVXJ50mof9fNRUSCe3PnzuXyyy8nKyur3HuKoqCqaoitqkfTNAoKCpg9ezZ9+hjFbIcMGUKnTp147bXXmD59eoXbtmtXtu7bAw88wIMPPljrc6pr4awVUyzBHMcFMadxsnMkP7kWszTxD5YnuiAwd+b3jgN0tLalrS+Z4u5aVQJWiqJw58kDiLJZeW3xJp6bv54ir58bRvdBz86KeK26uqiHVx1uzUOOlkdO6eCdlk+OWvK8UC865n7SNSNLyqqW/OZztHx8evX+tBUUTJgwY8KEghvjQ/04Wz8GOfrQ3Z+KOzDctilNdtJQNcg2ypFS8jy2O0S3L5t1V/xwtgF7UplsDVX3sjAwA6KeNASq+m/IZIaEvsajS6C+kr/IyOgrHfAr2EGrwkxaFWZC+q2BbW2QOKhshl8NZ/QK69Bf1wHI2WUE7fK3B7Lx/oKC7dh9edxW2Yk4UiCmK0R3gN0fGcvang+56yC3pMA2m5+GrYGrV0sMJAYCfS0GGUWR4/uCuZrBGHcG/H4dYNwV/azgVAAeP+cEurSUwF5z0iDbpwbGYjLx1DnDue7j31izL4u137dl0Nl7oIbJrbqmoW3fgn/FUtTlv6NuXF92Nlurja2949k2IIVzx96LvX1XcLspCAT3IqKeJrtosuadanweOJpubeq6UNw+WaKsXL/7/no+m4AIZ7PWVFOZ7KIugpR1cYwGl4EYosxMY8lAVMzhq63a2EQkuHfjjTdy4YUXcv/995OamnrsDWqgRYsWpKamBgN7AGlpafTs2ZMNGzZUuu3evXuJiyu5GGmsd5zDXSumtBbmeC6KOZ1TnCOZW7iA5d51ACxwr2CBe0WZ2TS/KZpHG1MH2lrSaGlKxFTBh5aiKNwwug9Oq5nn5q/n9SWbcfn83LRvBf73Ilurrq7q4Wml6oht9GynUHeRX5RJ8b3X/+S8TmaRu8zswJWxYiHeFEuCKZZ4cxwJplhilCi+LPoFgKtiL8Cu2DDbvZSeAMHkiDICdYqJ4v/MmDCXem1SjGUKpjK/M4/uZXogEHNB7GnYFRu6GuEZ8UQZDbKNSh1T8vy01fUyaxYAlihIPsF4BHhc+3l71720z93HBJcNc/ZKY9hw1jLjEdy2VMbphieMjBLVDATKBGx9CawmUMxGtptiDj5M6AwuXIuuKJgKPzeyE0u9H1xfK/W3vf0NKNoLubuAi41lX3cHU8VpI0fscWRGJZGacDxaTGf8MR2DD90aAxiTXcQXB/dGvmP8Lgpz4PmfjWWd/w55KyFnnTGsOWOx8SimWCC+V9mAX+JAsFUwdDj9J1jxDyg6QnFwD+AfJ/bmpO6tK/xeRNPUINunBshhNfP85BOY+v48dmTms/b7thyZ6qFVdNVubmg5R1BXLsO/4nfUlcvQc8pODKG2bYN/6CB8QwdT1Lc773o+AeC4eDs29SCo7sBtWdinHgS/gzglmng9cNFT2yBDA5/sotEp2A5R7eDEH2Hb+vo+m0aruH3y6j4e8b1Stwevp2zW5q4ugpR1cYzqZQfqYAlcc/pNgBL+DMQQZWYkA7Hhi0hw7/Dhw9x+++0RC+wB9OrVi927d5dbrus6JlPlQ5Di4uLKdEwbq0jUijlakjmBybGnsTzLCO4Ntw8kXT3M4VJFihe4luPTVwPgUOy0tbSinSWNdpZWtLO0Lhfwu3JET5xWC0/+9Afvr9hOUc/e3PHy67yY+y4QmVp1tQmEevFRkOAk3beffK2QAr3Q+KoVka8VkK8XUaAVkq8VUaCXFKF/u+Bz49hufzC4d1jNDmbVORU78SYjYGcE8OJIMMcGlyWYYolSnOWGCnt0bzC418PWKRh8KzcBgmi0GmQbZW6AxdaLOZLZmNydjcndGZc0HTNWKNhRNrvvyGrw55Zss+Ex42vp4ax/3F1h4M0KTA2++rRq57X61lLHCAT3FDNFUa3Z7YwiM6oFmVFJZDoTyYhKItuZiM989Cyzh4xHYUmQ8gzrUMpNJRUI/AEw9AWwWYxZifO2GJmOR/4IPNaAN9sI/OWsg12lhltHdwgE+gYZsxQHFC64hN9cQ/jec2pw2ZiurZh2Qq+q/RxEk9Ig26cGKs5p4/mLhnP+O9/hyrVz22fLePPSsYT6hNZVP+qmDagrfse/Yina1s3ouo47MPO0wxlFdv9OLOpnYvuAFI6kFNexXgOeNcH9PJ/7LnGefFrk5TAtEGSYtfdJ/HYzoyy9GbHoJmPFQJBB13VcfmPIvdOiGH2OqgQZmuNkF7Vl1jDFu2DvJ+DaBkfWlrwX3RlOmUd6UTwgwb2aiouLI8bnxVPVSbBCqHF2UjWyWUOSbNZmLWR2oPcwui8DfIFh5larkRuouTFvuQwAtcf7YHKgJCRA9uqy2x/VllcrAzFEmZmwD8Uu3Bve/YnIBPcmT57M/Pnz6dKlSyR2D8CZZ57JO++8w/r16+nbty8A+/fvZ/Pmzfz973+P2HHrS66WT55WUG556ZoS+/wHsSnW0HdrTTG1mzkx4LyYCdgVG35bAS6+AWCEYxC7LVns9x/CrXvY7tvNdl9J4LV8wC+Ni4Z0wWmz8NB3K/l8czpFZhNFwxMwmWpfqy6UowOhRYntOKJ7KSzKolVgnR9bHyTP5iNfDwTqNCOI5yXwM84NuesKtTGnkmiOJwk7xVl118ZdRFxUCgnmWBneKkSkKIpRay62C3QMZJppPshcDj8HOtadrzSy7fxAcd+i/QVg8oKuHvXQ0HQf2707UXSNLpY2mHQt+F6ZdTUf5G819tdqIsT3BEfXkmOcn4nP5idGKyAG6FjqtHVdxxfIKbZiqbAGaJxWxWwbkwUS+hiPTpcVH8SY/KQ40Fcc9CvcCYW7jce+LynUHCxwjeLHwhEsdg3ES9n26v7ThlSYpS2EKJES66T/aXtZ81V7NqbncPvnv/P8mYOD73t//JbDf/zJoc1bwVWqHEdMMt52nfhH69EAvH3xKHx2lTZ6EW2gTN24xCgrsTHGjW0rFuI3PEP8xtfgXOP929ZsIMOfQKaayCY6GQu/uRwAt27jikOPGsdI/RcOxQtdp0G3a8t8H1XJqmvSk11Uh+qGvM2QswHyNkLuBjiynpgpfxklUFeWv07ZNehr3px3iG/XLy/ZjXaMWeVFSHU1WqecENmsir2amXui2QqZHbj2Y9j0UOgNAoqDfCEdFTCuVgZiiDIztadjauGCzY/DwW+NfmhN96TrwWt0G0ffGG++IhLce/HFF7ngggtYuHAh/fr1w2ot+wO/+eaba32MSZMmMXjwYM477zweffRRbDYbDz/8MMnJyVxzTdNLF13iWs1cV+UF2Z8PZL6VHjL7XM47+BwWJjpP5LToMRVvXE1mpaRjdl70BBSnE1VXOahmstefHnxUGvBrn8r5p7bk8x+LmLthPy0L29B73IHgOnqp4u3Zag4efz5e3YdH9+LVfXjxljwvtbzMV0re82glPcaHj7xY7mf1q+v3CmvVWbEQa4omxhRNjBJFrCk6+DpWiTK+mqKxKzYeOfISYGQH2gNBygIeB6CrtWOFWXW6ruPyGTV0nFZz5GayE6I5MlmhxcCS10OfNzoqXj/8NsdYNvwtI+MtBJ/u5aXAcPV/J02vODjvL4RPA1l0o2eVHCNQsxKTlXiTs3Y3W/yFx16nIooC0e2MR9uzSpZ7cyg8vIYFm7by4y4fi7Na4i3VHnZoEcNJ3Vrz9jIjcBllk46UEFUVneCl/4S9bPyuM7/vPMQ/n/+QfwEmwPv8f5jdeSRv9b+g0n1c8fGiCt+7blQv/nFiSZkaetyJnnp+mSDDrHVFvLoqp/JjBIJ8HAQW/VLmvapk1TXZyS4q4ncZQbzcQAAvb6MR0CvcYdz4KUUBMIHuMUPr41ES+0JMV7Ysf4k3cs/jpw82lpvyzCz9wBqp1iRYIdQ4OylENitOZ9nP7MSB9VfWRDQ+3a6FtiEmIm1MAWPNDxmLYNdsoi/8E1OsFzYVZycrcIzJHitLcCqOfdwcPwWbqpZLbgLCluDUWEQkuPfhhx/yww8/4HQ6mT9/fpkghaIoYQnumc1mvv/+e2677TamTZuGz+djzJgxfPTRR0RHN71Gc6RzMH3t3cstD5np4XZTuv4aDiNzL9LMipk2llTaWFIZzkCAEAG/g+z3HzQCfv490G4PvU6JYcPPrcncFcv6H9vwwPiXUC1ecLsoLoX7RM4MfI7w/nONVqJILDUEbpRjCM6oBCNop0QHAnhRxChG0K4qwbaq1NLLKHCRWeAut7w6d7ZroqEED/WiQvTMdDrvyCT2iBvV8wHurCNohw8F11G3bMQycEi9nJ+ouWplGIcQpynIPMx1q9DjY8H2dH7ctI/FO47gVUsq/newHGBC9BImnPss3dJa4fKpweCeEKJyuteDumUT6vo1/G3NcjpsyWatvQ13DziPH02JJHQ/iZu2zsPUozcXHNeXU3p2xNS+E4q5pLRMVfsFLWOOKpsQIsgweSSM7XOk2WTV1XrES+nPI78L8rZC1gYg8LP+rj+4tpQL4gXZEiG+T+DRG93RlcIb/onushLzzc+szS7i9cXr+S29U3CTsd3SuOy47lz94QJjQQX9tOZSFL+mqjUJlhDhFs4JVCoqkdDQA8b+Qkj/EfZ9Cfu/Bm82CqDEgu5XoN0ZKO3Pg9ST4KtOle6qqglOoZKbgLAnODV0EQnu/etf/+Lhhx/mnnvuOWb9u9pISUnhgw8+iNj+G5L4QF22qtDNDaf+WpUCfl0OYrUc4M8fW5O9L4Ylc030m7CvTE0aKxZsShR2xYpNsZX5atUtmFQbNsVGtNWKw2THplixKVbsirHcjlGj4IU8o7bWEy3uJMrkCGTVGcvOiT6lwqG/uq4H7zzXNjA2a80OXl20qdJ1ijvaoRzr7nlGoZus3HoIHno96AcPB1/7vvwMd7bRAdUzD5d0RgPDjgLltlFZw9HzZ2uZGTU6B1G/qpNhHErIWnKlSPAwPAo8PhZsS+enzftYvOMgXrXk4rRDixgm9GzLhG5JdJvX2uiTprzZYGb3E6KmIl3eRM/Lxb91A+r6tfjX/4m2dVOwTlJxdcrhvnT+WbCZh+P68Gn7oSR5Crn2mX8T43QaQ22PUtWMt6pobll1NRrxouh8kP5fUjwZjCkylXxefJ5sfC1do7VgB5i0o4J4RiCP+D7gSC3bbrpcaC4baxLb8cHspSzbmwWACY0JUUu4+sJ76JbWqszvoyJSFF+IBsCVbjyO1lwnUHFnwP6v0Xd8BpnzUPSSDwzdkoAeOxrPZ5vw74/D+fSjKKod9mYf8+ZDRQlO5TL3HCpHJzcBdZLg1JBEJLjn9Xq56KKLIhrYE41XqIBfUZyLf5hfZt3ctuSmR5H141CeOasvxX+kjyfdQabKMTLePBUGrfyUDVqZldD/Nusiq27yoM6M7VZ+hslw3T2ftXYPM5Ztq/QcjhU8VHrqWD0qemYmmtuHdqRkRjvvt19AXh5a1mH07HT0rCy0w9mQn4/LZIVxtwLgee0lTJov9EGiojmcoJDfwkG3VoOxtGwFcfF4X3sBAMvAwaG3Ew1atTKMQzhWLblIBw+h6QYQiwN6P27ey5Idh8oE9Dq2iGV8zzZM6NWWbsnxxu/HX0gFI5aEaJTCWd5E13X0/Xvx//lncNvCS85BO+ozT0lsAX368W2nDHb3bMHNQx7hfGsUBYs28N+Fm3il2xhabdjLuUO7hzfbQxzz88jkzSUqdxu23E1Ys7fgPGsDpkQX96xYEXqHthYQM6CkfuqYb6Fl+SCelpWJvjcTyClzzIV7j/DG0EtZn9AG9mZhUeCMdrFc5b+CDtaDqDlTUPNz0OMqmLW8lAZXFF+I5mjbDFhfeT28Jj+BSv5fsG+OkaGXuRh0Ldh11PLs+Pck4N+diHooFvSDgNG+VefmQ0UJTqVHy7W1tMLmVxtMclN9ikhwb+rUqXzyySf83//9XyR2L5ogs2ImoZWLAWfsZfvcbmxKz+PWz9fzjDWKRJ/R2Y10xltdHSM5xklyTPkGpzZ3z0vXJzwvxcLocV3QPW5jiLbHhe524y5yc80BY1jCa5a92LyuwPtudLexDh4PLZZn0jLPuKPs43v8Ti9KlA9TYLSe/1OjfiBmjeizjJ9V4de90G0mVieWzJ65bdxwhrbQURI6obTsFrgTk4zSsiVeh4Xnj6pfprtcweCeEt18aiM0JdXJMA7pGLXkIh08hLoJINaVAo+P+dsO8NPmfRUG9E7t1Y6uyXFS41M0ebUqb2I2E7fjIN6NH6Ku/xN1/Z/oOUeMmWwDN7QATO07Yu47IPDoj9K6LV58LAl83ikWC7jSmdIll0OzFvNB6gk8/PNaWpgPM7qtUj7bw1dqyOeRP8BqanrZHhES/DzSVCjYDkf+hJy1xuPIn1C0p+wGKcYX3eRASegLcb1KZhM/6y+I6QQ+FdbMMZaljglZo7V0Vp0GLEjpznsdh7M1LhUS2mBT/Zx5YC2X7l5BK/8RYqceBEoubNXLrqb4Argi9V8UXzQ4cnOg7jWFenjVpeuQvaokoJd71MzeiYPRkiag24aDsztmRQlm5snNh7oRkeCeqqr8+9//5ocffqB///7lJtT473//G4nDiso0kkY/LtnNq5eewE2f/M7WzDxuHHox/1v9KTFEPuONOjpGhUrNiqb9tQ2/uxA9Pw89Px8K89Hz89EL8lHzc5iSvRZnoQ+v+w98BQXo+XnBbaNvuZJQXbfSWXWdfpyNs6KsutJMJqz9c7D33VXhKkWag2/HtuOj/NPY4WsbXL4pfgcnxH5Bbo/byet7fqktcvH6a1DzRjR7kQ4eQt0EECNt7sY9LNiezuIdh/AdFdCb0KstE3q2lYCeaLR0XQ8E3wCHw/h3XIU+TnXaD40CiluLFv98Am3bZvB4yt5Xs1ox9+wbfBnz0VdEtWwR4oSPer1tBsr6h7jzOIW8LB9fF47lrh92MyPlYQYWl84rvjAsPQz0p1Fg8jSNbI9I2/YKFPxpBPFy1xuz14YS1R4S+qPH9ML96udo2VFEvbcYomONz4vi4J4ztcr9ZuuZk9CPH8UPe3OYuSWTXfnGvxqn2cQFHeL5W+cEWsYPQGFKyAtbd1wivFX5DSbRjLnSIWcnpqRAC5WzBlzVnJG3MQaVwkXq4dXMwV8g/QfY/xUU7StZrpghZSy0PdcIdEa3p1rjNuXmQ9hFJLi3bt06Bg0aBMD69WUjunIxEWHVafQruCOs21PR83LRMzPQMw+jZhxk3L4txGW78RXchT8rCy2jpLZawTWXYu7YBXP7jpjad8TUsROmdh1Qoms2xr1rShwz/zaWaR8uYDdJXD/kEt7ILaJtq6SwZ7wdLZxZdbquo+Vko24vKUDveetVcLuCgTq9oCRo53K5YewtxvFuvga9kuBbSeghp/wcQ1FRKFHRKFHR4HSiOKNQoqKwOEt+H7bL/o49OgoC7ylRxc+j8TmtPOyaiddp5tG0e7F7stFzdpaZdU+xO9h3JJ+Pv5/JnIJx5OtGYxxlgaLAj+rS8/+Pxf7TmMt28nLerPB7qWwYVEPKghLNQ10EEMPJr2lsPpDN4h0Hg8se+n518HmnpFjG95SAnmh8Qk4a4D2MXnQAz9PGrK72O/+FYrWC5sa85TIA1B7vg8mBkpCAKf6oDKhjXNjquo66fAnuGS+UnMd6Y+itEhePqU//YFaeuXtPXJjhmTkA5MUoZPnL1186eki/o9O5mFsMIfb2e7mL3zh4xjhWHLBz45HHeCfpNrrY9pXtp83cZWw8flFJ5l5zV7qdXfsA5G2C7M3AU8ayNXcZgdBiZick9IOE/pAwABL7G69tgX8fLhf+nYFJLEw1vzTy+lW+3JPHzN93ciDXCCDEOqxcOqQrlw7tSkKUvfz3cdSFrVKFmnuiGQvcHIg+N/B63gnl12nqQ0GrQurh1ZwvD7LXQMaSkmW/nVPy3BIDaRONgF6b00vaUdEgRCS4N2/evEjsVlRFdRr9EHeEvVt74Pk9CfxlOxfjAl919pQPJqUfQE0/gPp72TuNSstkTO07YeoQCPp16ISpfUeUhMRjXmB2TIpl5gUjuOalLzgQlcCVn/3Oa5eOoWNSwxuuqat+9IPpaLt3oe75i0nb/6Dl/gJ86QvwlcqoA/DN/rjiHZlKMlyVpCRMUU6IiUWJiUWJjUWJjUOJiUWNieJTZSGuKCtXtJ6CLbYFWK0UXXEhADGffhdyYhDd6w9ehNgumYqtggClX/dSmGV0QBVFKTPrng4sy2vHR+v2sGBbBjpnAdAhMZpLhnZjfM82nPzCt8aOEgfS19KD9iHql1V5lucGkAUlREOi6TpbD+WxIu8Mlrv7svqFXyg46mKwQ4sYTu3Vjgm92tK1pQT0ROMUatIA26B92AcfwHJuYMH2S8ptVxzkC6mSC1t180Y8r72A+ufqMsttN92JZfBxxk3Lo/+WSv3tLXP/ya/+RRUfm1I3s/DzQFY0JiB29Drifu1M3mEn/zj8T95t9S9aFWd7eP3ALmPjxIEhh4E2RlWpbXqgaDNRhfuxZG8lZuB+TLEevAtGYynai9l9qGSjzc8YX7VSgbPWZ0BSb0gcYAT0YrqAqWaThVVFkdfP7D928M6yrWQE6jYnRtm5fFg3LhrchRh76BqtInL8esnfZraaS6o5CVMF9bYblW7XoidPKHfDvUkMBQ1nVp3Uw6saby4cWWMMtS1+5G8tv549BdqeYwT0Wo0Ds6P8OqJBaBq9BFHiqEbfNGwE5OdBbibOYb8CRn00VJMxTHOAsVnRN73QNR96kTUY2FMSElFaJqMnJbE8Zj95iQ4mtD8XW8s0iI3BdZNR7NLx2DOB4NZOtD270PbsQs/OQs/MQM3MQF29vOw5xsZh7tDRCPwFgn56+zYoJh3dVNKQt4mP4uWVH3Lr4AvZRUv+/v58ZlxyIt1TEiL7MzwG/7yf8OzfbXyve3ej7d8bnBEPYEjplRUFUlLhkJFVYzlnMqaExGCgTomNDQbwTI7o4FCM6HdnV5oduCZrFwCmpIGYA/XqIsnl8zOnzQBmtRvMrs+XBZePcqzm0rjvGTFlISZrTLlZ3mo9y3MdZ0EJ0dDous6OrHxW7D7M8t0ZrNydQa7bC1wRWMNPrN3KoLZJ/PaX0c58fMXJRMnFpGjkQk4a4D2Mv7aZe0fR9u/F8+Yr+Bf8EjiwDctZ5+H/3LgZZzv1TOOGma6XfCaFuPA83jGAwZYe5fZ/rJtZtydPIe9CM/d8uI492S35x+F/8bbLS3xs0x2etMS1mrlFvxHnyaelK5vkomySirJJdmVxR9ERWhZlEeW/r2SD4o5Vdmb5nXX+O7QYCtF94d3AqJJRn9RJIDTf7eOT1dt5f/k2jriMWlKpsU6mHt+d8wZ2wmmVy6z6kqXmBp8/mTMDM2ZamhNJNrcwHqaS5/GmOEzVDCRpWZloWemk5eQYr7O3oGJF95RkjKrbt6LY7aAWHXNG0CordcMdgIRB4HQ2rqGgdZFV1xzr4R2LNxeOrD4qkFfBBIxR7Y0s5wNfG6/P3g7WhpdgI8qTT52m5qhGX/t+rbHcosIw46lW2AIloRVKy9TgZtbz78afHEthYhRayxboLRIhUCvRq/v4MnC3uV/8cUZ9NHdJfbSDfdvA0C7EKacSrxsfX3qRH23vbtTdu9D2lAr6HUyH/DzU9WtR168tc+r32c1ktI7B39kNHbqgpLWhpbeQF1Z9zJ3n38OWzAKu+mABr1x0In1bh6hrEwa6rqNnZRpBu+Lg3e5dFO7fB/3+BoD76cdQjh4ya7Njatce2rfnp6S9ZLSJ4bLeN+Fo2xl0nYIzxwLguObGkFl1QIMcinEgp4gv1mzm8z92kN/LGCAbZTVzTv9OXDwwjY4/X2CsKJlBQoSFruvsPVLI8t2HWbE7gxV7DpNVWLaeQJTVzGDzCoY51nPcWa/So3UaHr/KiEBmboP7ewzRkc8o9JNZUASeTsaCbSvAZMftLykXsWXbMhwWEzhagr3sRVDLGEfIEgqi6Qg9aUBPdJcLLetZAMz9zjE+U/2FsMVYwzzg3Cpd2GpHsvG+9ya+b74AVQVFwTL+dOyXnINiLaRoQaCcRBXLm8Tl/kVUFSe8KHczy+nktQuimPLGbHb42nLT56uYcenYY34PDZ7mh4IdkL29ZNnii5hQtJlTC3ZgqqgWXoDqSMUX1R7z0p1o+Xbyp01HTeyG09mRuK8Ds5cNfaFUluOciH0rR5uxaCOz/9hJvsfoD7ZNiOaqET05s297bJbIZQmKqioZZ2RCQUXlkJrJIbV8gNiKpWzgr9QjTokJmf3u++YL/O++wQ2B134WcnQvvjozgjYrdZFV15zq4YXiPWIMrS0dyCvYHnrd6A7QYojxSBwCLQaDI9n4WX0aKOnUFLJemwkJ7jVx1suvxpzWGiUxGrafBEDM7Lko1hhMpYZpWs+ZzALf4pJZIitImHo+913iPPm0yMthWqCu36y9T+K3mxll6c2IRTcBoIxfhDnNiTmtLQxvCxh333WPBy3bj3awyAj67TaCftr+vdg8ftrszEXb+RNefgoeM9Hn4n+zHufOgZPZkNCaa978nv9s+pYBRRkoVisuqxP6GUNzim6+Gt1sMmaks1iMAKXFimKxoFnMnKdtRrWY8Mc8i/7/7d13fFRV/v/x152SSS8kIQRCRykiQgCVDhaagHXtuu7aV9eGq+tXVmw/v4oF17a6X3ftrgUVXcUOqAiKNCuigPSa3qfe3x+TTBKSQEImU8L7+XjMY2bObedmJmdmPvdzzrHF1Du38huuxNy+BcobnrxZp8usdeAg7N26YenWHUvXHli6dcfo2AnDYsFpulhUPSueJb0PRiuy6prTbWW/k1FYEvebNdfU/p0+F4U74tn+QxqnbfkEX/X3oy4VhZy+dRWjH5pJfHICyb4GHbTbluk78DoiUWhHcbk/kLd5L8s372F3af02w2GzMCQng+HdMxnerSMDMmOwv3Gaf2Gn/4AlwoJ5+2rki/y8ot/xZPGZgL+9ZH7DH1wXvVMTENzeYNkVo/tz5ZgjglxRORSYlRW4Xn8Z12svQaW/C5j16BE4LrkKa+/D4LvboRXDmxxsd67s5Dj+0fFuLtp9F9/ugL+89RX3TD+6xftprca+Gxj5BXjz95JdVoTV52HvtreJ8ZZircojvlceRqyHsiWXYTFLiXEXU/ONyZyXjoG3/t9p+3tYa8bDMyyQ0MPfbTaxNyT1qXPfC6stHktlJWX3jwcgvdultcHcNuT2+igsq6LI1Y1CbzKFP++ksAr21Gmbn/3a33WtV0Yyl4zox6QBOdgs+gEcKbJstRcG7ukwkwqzkr3eAvZ6C6vv/bd8XxFuPOz07mWnd2+D/TiMGDIsjQT+pk7Cduwx/L06+eHa1N8Tgz2oM4I2Ou4otH12YGOC2WVWWXXB5SqEvGX7BPI2Nr5uQo/aQF6HoZCW67+AKu2GgnvtnOOs82u/CFUH7Jsaf6m5s0Sm/PggKT/9E07xL79+9Y8Nd9bEFRcDsA6cjfX42+uVV7kruP+nO8jcXsaFhUMwtmzFu2kjZvVkFEkeJ3NXv8bNR53G6g7duGHAdO799k2GF2ypF3jzbViPbz8TUeTWrMcW9g0Vmb+s9T+wWDCyu2Dp1qN6kpDuGNndYIE/dTnuvkeIDUF3j6WVq2qDrU3Y32QUk+PGMCVhXLP37/UY7F6fzPYf0igv7BYoT+tSRvfD83jk1texAHdUvIjb18aTXfg8sPVNWFc7qDm7FzX+ZUAkyuwtqwwE8r7ZvJdtRfV/qNosBkd1SWd490yO7t6RIzt3qJ8JEm3d1Rv5In9GuYfxFR7wVf8IssQABiYmTo//woHDZvh/GDWRuSfSEqbHg/v9d3A993+YhQUAWPoOwHHpVdiGDKtdsSVjWgV5wos+Mdt4NPNeLs+/hy827OJ/P159wG1aFQCwxGLG1QlIbf8vWyqWsql8DYnuChJc5SS6K8jelU9KZRk3OzwYMT7g77Xb+K8bE7v3xQZ1MPBiegxMo1NtYe6DkFodwEvoXm+84bZgmiblLg+FFc7aW6WLojqPC0sryB92HkUxcRQ/8WGdcUyrx/P775oG++3bMYXLRg/guMM7t7hLp4SWxbCQbkkj3ZpGv32WeU0vBb7iegG/mluBrxin6WK7dzfbvbvrb2iAIz0GZ3qq/xjpfbEaMTTqIGcEbWzc0X0FPTswFF1mD/WsupYyTXDuhfLNUL7Ff1+2oXb5/K6Nb5fQs34gr0MuONJDU2cJGwX3DgF7yyrJKy6u7f60uxisLqrc3sA663YXEWu3Ag1/MGUkxtKtbvenvjdiZp0e1MFcDZuNvC5J5HVJwpp+IY6q/HoztMbNeYREq5UnKkr5y4KlLKnK5aZhZzIn18rRKT6oHionbtalxJkeTFLAlwweD6bHAx43HncV7xV/jNXjY1LMCGweE19VJZ63XgPA8T93Yu19GJbOORgx9T+g3S5PILgHQciqMxICXZibugLW3GBrk5NRWPY/W3HN/vcUV/Heml18+N1uyqr8+421Wxh/RAYzhnSmW0YCVFVh4fX6+2+DyS6MOBf2vnvhw35QtaP+QrtmY5K2delLn2GzWbAaBhaL4b83DKwW/32grJFl1poyvFgLLsKCD+vin7HYYvDVyXI9+5lP2FxQv+2wGgZHZKdxdI+ODO+WyVE56e1rrKZGvshndoDMMFVHDi2maeJZshjn009gbtsCgNE5B8fFV2Ibd3zDC54tGdOqDSa8GBK7jjnTB3PD/NUs+HHrAdfffwDAxIh347rvbCwpVVhSK7BWJ7waS4/GcHgxqJNV9+U5HGlxcuS+u7EBSXX3asVnS8FnS8VnTcZnTcUXk4rPlobNmkLc9ocB8A5cAI7ukJIeGFOYPpcHbTy8Ul88O/aUsKOsmE0FtROY/fn1LympclFY4aSo0oXb24zM/9TO/vvqwJ4FSLUUkWYtITX5cNJiY0hKiuetn/1jmz57/niNbdoOWA1rIBtvXx7TS35Npp+vfuCvyFeCE1dg3UpfFQ5rE8G9g9TouKMQ1OzABjQRReh5XVCxFSq21Abw9n18gCEMSOzVMCPP0TZDWElkC/qvh7KyMn755Rd69epFampqsHcvB2He6o08uWQtge5Pzy9tsM5FLy5ucvsG3Z9CMZjrvrP+Lj0egHhgbkcbf827lk8rjuXGFW5mpz8B+LsDW7dejq2JbjGm6WJJ/m8AnJR+MY7qLrNl1cE9+8ixzRo0G5qfVdecLsyNjd1D4RpS7BZSmjkVe4Pxe6yx/g96X3nj52Ca/LqtipdXbGLRL9tru96mJnDO0N6cPKgHybExTe8/mJNdmCbs+Qx+fpSEs7/FsJhQBTgyodfvYe0D/vXSc/e7G4ki+7syXKNwTeuuDB+EH3YVBmlPJ/nvvvmtwZLNBWUYQP9OadXdbDPJ7ZpBgn4kigSd57vVOP/5GL61PwD+icJiLrgY+0mn+CfiiFDj+2Rx29ShzH5vxQHXtU87FdvRuVC1CaNqEzg3QdVvGFW/+ct8FY1uZ4mrvsBbN+7VYTjEp1Zny2b6M2YddR7H+u8NewpWw8Da2I495fDawwBYB4wFW8JBjSlsmialVW627ylmY2YfdsWmkLf4R3aWu9hRVMqO/Gco9SXCc1822HbFlobdK2PtVtJ8LpIL95LqqiDVXUmqq5JUdwUp1fepde4TKSfl9ysBKH1uKHiseM+/hLeovtAYadl6Efq5Gs1shpUsW0a9Lr413KaHHZ49PFT8bwC+qFzB9MTjgnr8xscdbcJBZgc2oC6zwWWa/m6z5XWDdTUZeNWPK3dRd4zIxhn+v21Cd/9kF3HZsO5h/6JTtkJ8ThufiESLVgf3LrvsMv75z38C8Pnnn3PhhRcyYMAA1q5dyyOPPML06dNbXUlpnTOG9GJ8rzT4qLpRnuhvlE3TxFk9eLnDZmmyu25Yuj/tp1tMzMejmZMxl9ti3uG99ZXclv/n2u1a0i2mcicU/YalOvDW3EGzKVzDaKuFQY5p+OKy6u2yVV2Y645J89FoPBY3vgG34R14Kz6fidc0A/den4nPNPFW7MZXuQev00l55zi8hoWYjcsxLSbery7BhwXvsKfwGjFU1DmHC55ZwPrC2izDY3t05JxhfRjTOxtrqMbvcpfAby/Ar09A8U8YABbw7ErEOu1RjN7ngOmpDe5J0IVtPJcIvTJ838nHYLNa6v2/+cz6/3sN7vct8zrx/Xg/Xix4D78en2HF6fbx+pqNgWOM7JlFclxwr/CLSC3vpo04n34C77Lqi3CxscSccS4xZ56HkbD/rPZIccqgHuwqqeAfX/wEwLiH3yYl1kaK3UOqrYpko5gU8kjx7iTFt5MUS5n/Zi2tfWzxEGu1+LtnJff13//6uP8AJy6DhG5gpMLDC/xlJywKySyz4P++VFLlZkdxOduLK9i+Yw879xaxo8LFjgo3O8tdlNVMsHPUqf77NZvq7MH/OqbGWOic4CArOYFFm/2fZ7On5JKVHE9anIO0eAep8THE2W1Nf+Y1MxOqKjmtNgMx0kTo52p7ZTdsdKoT9FtStYLj40cSb4nyISPUZbblfG4o2+TvKlu6HkrW1i57Mxu8DXt6NWCNrQ3cBe6rHyd0g7gcqJsZ6imvDe7FqGeT1Gr1J/iKFbVXFWfNmsV7773HEUccwbZt25gxY4aCexEgMzGOzFgfOKqzSLJSwtYoN7s7q90OSdmkVmcHbkvKDnQFTQFsho+7TxlH3Ke/MG917aChd33tBYsbD7+CsR4D/8VVAwMfPn5xZ2EAtzu+Jr5gDbEFK2HsMCyGD+PNJzEwq2/nYmBiee0xMEy8pgXwD2D/2OuPYmDg6TACT4fheLw+vD4Tj8/nv3n9j91eHx7PJDyucbh+24jHsOLr3BUPBh6vD0/FbjxY8dg64DHB7a29ajNiW3WQbwvwwfzm/XEHXO2/f6fmivW9/rt38xusur7QTaxRxfSu5Zw98Wz6ZKY07xjBUPQ9/PIEbHqh9suCLQEz52wq5n6NryCexCvPAqsDPJE3g3B7EpbxXKDpK8OmCb7qrgeW2KYzI9royvDYPtnEt/aHraccdvzH/3jCU2BLoMLlCQT3gnIMEWlS1d/n4P7gI/D5wGLFPnUGMRdeEryB5UMh7yso38KFjl/5R/VowS6vyd5yN/5PeAfQsfo2YL+7ctgsJMfGkBrnICXWSnJeHKnWUpK/s5MSX0K8vTaz76tNu7FbLP6LFibV9/4LimadCx0+CFz88NW54OgzTXweJ77iGfiw4Fu2Hq9hx1lnGJgb31rGntIqdhSXU96MjL40ZzmdqkroVFVMdmX1vauQXpOW0dmWh/eFIwJZdYuqs+omD+jWaDvb2kyog8lADJkI/Vw9VFTh4rOq5UyJHxvuqkhb8FT6J6moCeCVra++3+DPyDO9jW9XE9hzZPqDdPF1AnZ1g3mOjMjLBo5QNRdpfLjJLirylxWsw+usTWIJJCY0IqgTzUSgoP7CqKys5Igj/N03c3JyMM0Qz6YpEa+1k0TUncTBYhjMmjQEm2Hwyir/wKIHHp/G/8VvO7uBbGBai8/hpdLqgHUpsLmJ2Yj2lVCdLl1U94thdcPibfn/icXwn79/3C8TqwGWynIspoktOQmLAVbnbiyGD2t8ZywWC2Cyqfr4fx6explHJJOcmgNxIQjseV2w9Q1/lt7eJbXlyf3hsD9BzwvAG4OvYHzb10UCwjKeC7R995+WdE/aJys3kPmrHzoiUcMs83d3B3B/9B74rNhGj8dxyZ+wdO0etnodODvaxPvjx1ic30Hp1wSmtlh4QvUOajP638y+DqeRSLGjL8UxvSixdqXI0oliOlDiS6TIZVBS6aK4ykVRpYuSShfe6h4ae8uq2FtWM2bTMf675Q2HDbj+jWVBOvML/HdLfm2w5MuN9ScmSE9w0DklgexYK51tJtnxdjonxJAdbyc7PoZYm9Ho55G1+vPIGw1ZdaGgz62w+6zya8bFHh392XuHKncJlG5oPIBXsW3/21rjamf6TugG6x7xl09e5c+atsW3ff0PEXUTE66qLvPwBXV/YQcSExpx0IkJUaLVwb3vvvuOjh07+sfGKC1l165ddOrUCZfLhdfbRBRbDlmtniRin0kcDMPg2vEDA8G9q8YegcdwUelzAf4rz/4Yc3WXOXyYJhjVX6FNt4eYN97GBKpOnYFptWI3IeaXf/pz+A77Ez7Dhtvj441v/V+Ezx7amzi7DZvFwGa1YLdYsFoMbBYLNmvNvcW/3GLB5nXjvnsWNtNL0l0PYI+LxWY6sS0ch83wYpv8FbaYBDxeH6c9/TEAC/40hcQYe4MB+y2GgcVoOOOxWVlJ2bTxACS+uxjD7oPXqrsenVkWyB4a8eB8AM4dOy402UPlW2D9U7Dhaaja46+rYYXUE/BlngNJR/uvVG3a2fZdQaWBsIznEgot6Z5Utzv8x6OhiTE7GwhFAFFBSpH9Mt0u3Avm4/rP0yT6k+uxDhiI45IbsB4xKLyVo5HsaMPE0qECa6dSYo8rxZpVhuWXkxts5yuz4yuOw9fx6EBZ9smfEZ/SAyyNjnTXQM0ssTWBvuIqF8XlpRR/eRXF3kSK+9xCsdOkoKIqEHA7rGMKtsB3jdoJhGouKFqMfb+P1E4wZBgEJhWybH7ZP6lQ799jWO2YPjPwHeqmE46iR3oSXVIS6JQcXz2ZWwtFW1adtHsdLens8eXzedVyJit7L/L4PODM9/8uqfHj//qfl1UH8Kp/pzTJngyJffwBvKQ+1Y97++/jsmsz7zzltcG95MMV2AuymsQEF27+XvQc4I8T2LE1vBDUiINOTIgSrf5172mi21xFRQVPPfVUa3cvYRbsMblSLEmkWJJojmZP4lAn0HX+8MNaFLQyKyspu28mAInj7vRPqOEphzx/Y8HxjwUCYzVfTK8df2TLj5HnDz4mdsuoPYajOusvIylwjBppcY7o77q35CzYPR/M6qBDXGfocxmuVfG4HpgHPFF9a6hNuoLKoaMl3ZPcPvj3Jn9ZS8bsDEUAMRTHEIli5ZdfAHt2g632YnLcnMcw7JExrp59yhRsA+MwylZilK6EslUYvvrfY0zDDgmDMOOPwrLHPzi/OeYrDGs8Rt1stITuzQ7sgf8iYKLDTqLDDqnVF2M8ifDdR/7Hxz3f4MLf8xdMCM7QBK+N8j+edH+D71CnHtUz+r/ftJQu1LR7J8aP4qWyd1hcuZyx+2bvldXJki3fAsn91AWzNUwfuIrAmde8W9VecBc13M+P/69hmSOzNgMvqU/t48Q+4EjX6xYBahITLKaLnfmp1WV9sRkayxraYLbcGqmpqcTE6I8c7cI2JpeERauCuaYLs/QToJ+/cMd7YPFB1nH+rrc5M8Bix945D9uIhmNxtnlXUDl0tOSHkMsDbPI/Thvc/MHkQxFADMUxRKKM54dva5/s2Y2Rnonj3MFQ4Z/Z1Pjqj5DYwz97YHyOfyDy+ByIzWpRcOyguIohbyns+QL2foElfzn4XPXXsSdDxkjoOAYyx2CkDwdrLIanHF7zB/eUjdbO6EJNu3dkTF86WTPY5c1rmL23t86Mzu8NgNiO0OFoSK+5DQdHh9BXOtL4asdfZ92j4CltImCX3/QYdwdiTwN3of9xjwsgpX+dAF5viAnhGOQibaBNL52deuqpbNmy5cArSsQK25hcEhYHG8z1vTYRW88CDEsMgS+lh10B/S73f3DW0W67gsqhJRQBxFAcQyTKmIUFgcf2Cy7BcfYFGJv/BSurC7e+3viGhtWfQb5v0K/uLS4bLPbmV6aqzvhxH42E0pW12eo1YrMgc0wgmEfqoLYPMkrzhSKrThdq2j2LYTApfgzPlb7F4srljIs9mria7L2kw+qsafV3/9zxrv9WI7EPZBxTG/BLG+yfQbW9Mk2o2OqfPCj/6+pb7SSdfHvLgfdhT/ZPRNHcW0wa+Jy1wxYd/Q/9rmgjpmniwh+sjcHeYDgpaTut/vZ/5plnNlpumiYFBQWNLpPooUBM87W2C7OZfOCpzJt9DJu3wf59Hl+D7fbVrGBujA2j/HuMgg9gj//XlP2w6hl54+sE8oY8oACDiIgElW3UOGo+8RxnnY8RG+sPnKysnjF+0D3g3AuV2/yDoFdsg8od/kyPiq3+WxNMDLBngK0jscfvwCyPwbfsFoy4HExbauBz1ffJuRjla6ByN4ELWkXf+bPVE3vXBvIyx/gzQvTDJnKFIqtOF2oOCYNj+vOB9Qt2e/P4vOobJsVXf5/OOKZ2pdN3Q+mvkL+89lb6a/W4b+th00v+9QwbpB1VJ7vvaEjqG5UXBkzTxOUuwChYiT1/JUb+15D3NVTtanqjrr+DuE77D9ZZD6KHoM954HWkSaZpUlk9+3mc3UqJWUaJr6zBei7THZgg85qUC4kxGr9oluwzUK5kcLX6E+OTTz7hhRdeIDGx/vgmpmny+eeft3b3sh/BHg+vVbxVUFmnkS7dCLEZ/gCfNSEqP4xaqrVdmL3nX0LNbL6tPkYj+6+02OG46/a7bZPBXG8V7FoI29+GX99p+IHc7Szoew0kD4e1b+/3GCIiIgfLsFgaFtbt0tbvmoYXF31ef5ZdxbZ6QT/vj59h7lmLJd6FkeDCsJrg3gvuvdh7VG+76+8NDmcpeMf/wKyTWTPiOcgeA/GdW3V+EmLKqpMgsRgWJgey975mbOzw2uy9GtZYyDjWf6vhLICCFXUCfl/7s/sKVvpvv/7Dv54tCdKH1Q/4xXWJmIsHxb5Sf6DH58Ve+isx+SuJyV+NvWAV9uJ1WDDrrW8aNtyp/XF2yMWVnktM6lCSPqoOpI94pkVJIsoUC669ZZXkFReDs6e/YHcxWF1Uub1c9OJiAJ49fzwrPWtY6lzdYPuYeA+O6nlEaoJ8jTnJPoyJwa78Ia7Vwb3x48eTmJjIuHHjGiwbMmRIa3cv+xH08fB8HnAVVt8Kah87Cxop36fMW1X/wO/vM0udxeFvpGtu1vj6zxtbZtqxHbYX3FbY9QHEd4AmZr6JBK3twlxVd+Ds1h7DW9mwi7THB2//1PwTchXC9vdg29uw8wPw1LkyY0uCThNh2xv+58f+y/+aaXwgERGJNBarP+gW3xmonYHW6JYH+XmYgGn6wFMIrl3g3gUV2zHcu8GXh+HaDc4dGC5/1p+v4+8xk4/FzD4OXvzOv7OupyvLKthC0WVWWXUSRE1m7+2PowNkT/TfoLbLat3svoIV/jHodi/y32rEZfuDfKmDa8tKN0Jclv+7ujW27YN/lbsg/2vydr6ON28p3Uq2E+t1NVitIDaFzSk5bE7pyqaUHLYld8ZtrcnoKuEke+kBAz2BAOI+lCkWXPNWb+TJJWuBOf6C55c2WKcmyAc9Gyw7c0Rn9gxcCPhfj9iqAqx1h7GollBV571Z05Y3RpMKNVurP5XefPPNJpd98MEHrd297MfBBpMSbuiK4S0Hy/Pw/hO1wTtPaStrZEDNVRlrgv+YNc99TnA5/YHAFuwtrmY82mWnNVhuvpEDsT0gsT9QvbzwW+jQF8IwU15ruzA3Z+DsZh+jkf1bXB7gAMG98i3+YN62+bDns/oD1sZ1hpyTocvJkDUeTA+89saB6yIiIhKBWvy5XT1Wk2X843U+t79ruwoe6jQRhUQZi2FhUvxoni+dz+LKrxkXezQtHjnPMCChm//W7Qx/mc8LJWvrZ/cVfe8Pfm9723+rUTfBwrD6g3z26pttn3t7ciNljaxn1MmYzvvKPwxBzXh55ZsB6F3nFHy2eFxpg3Gl51LeYRBP2NdT4kjimpQLGWrYGdrIaSf7DhyEXFq5ig8q958IoUyx1jtjSC/G90qDmkzKiUvAGtcgcy/W3njPvOQEK3Oc/uBejq0Tjt/+3fy2vDFqy5tNl5yi2MEGkyz58/e/rj0ZYjr4Bx6NSav/2NGhYVnNY8MCryf793H6bn8GnrfKf2xvhf++7s3rv3d/+RG+lUvA5sWw+8Dmw7B5q+99YK/73Isl0Z92bXiLoHwNlK4lENz7eJT/C11cNiQd7h/ENukwfHTCdKeBo5s/i7BaMMfDi0qmCUXf1gb0CtfUX55yBOSc4g/qdRha/8Pdoyw9ERERaSPqMitRaEjMAD60fsFubz6fV33DxJgg9GSzWCF1oP/W+4/+Mk8FFK72B/v2LoWt8/zl1gT/byzwX6R3F/lvwbLwhH0KDEgZ4O9qnH4MZByLJXkAsRYrsYDDdFGS788Ay7F1wmE0MVaep/yAhx4Zl8tAx+ENyluSuRdVWpK93JjmZLw1coxMIDOmEhy/+QtifgNrHBVGbYZ035hNxDeRIe00XeCsU9CStryp85BmCXpwb9u2bfzjH/9g6dKl7Nq1C8MwyMrKYuTIkVxxxRV07do12IeUlhr4N4jt1DA45+gA9hSwHOTbYt9G2TDAFue/7Yc1ZRqWYxoZO7BOBqJZnYFo1slA9PZ7CcO1G9PMh5rxsR3p4N5R21Dt+QyAmpCUaYJZFoOvJBZfcSy+klisXf2PK6+/BEzLQY+Hd0A+L/hc1VmMhXVO1Gx6m7ay53PY9bZ/DL3qK26AP3iXMcofzMs52T8QeCP2NxZDjXW7i5q8opORGEtm4v7fFyIiIhKh1GVWpFEWw8LEuDG8UDafRZVfMdY2oOXZe81hi4fMUf5bn8tqg3un7/b/33nKwV0K7hJ/7yx3aYN7012C112M4SnD4i7H8NRZp+52vjrdbB2ZkDGienbfY/3jANqT2+IMG0ixJJFiSWpQ7jRr69faAGKzhSLw1pLs5cY0J+Mt0iYVklYJ6iffkiVLmDJlCl27dmXixIlMnDgR0zTZs2cP8+fP59FHH+X9999n1KhRwTystNSAmyNqJtuD7s466OTabjGL5/sLT94MlELJr/7Zp0p/gdJfMQvXQvl6DG8ZRpILS5ILupTU27WJFRxdwNEVqhcl/KUvmFBlWwq7TvIXLj0XqKwO1FUH6wKPXeBtotxsYrba11MhNh1sWcBt/rLll0Ncij/g6kivDsB2qM6cTPff25KaP46Guwy21ekmv3iqv0EG/3gcnSb6M/S6TIPYzAPurmVjMTR0xej+XDnmiObVXUREJIyi7oJWKAJv6jIr0qRcxwA+rPyCPd58vqxayfGhroBhqe1iS+dmjFWXtf+MN7eblLe6+5/M2BiW4Y8iTigCb6HIeFOGdLsS1ODe9ddfzyWXXMLcuXObXH7dddfxzTffBPOwcoho2ZfrwyDuMIibCh39yzISHGTay6Dkl+rAX23wj9JfMbyV7C0vIa9kB4HBQbcuA6DKjAH8wb11G78j1mg4UCxAhrWQTFtRC8/M658Vy1dcW7TppdrgW1MMW3Wwr87Nkc5eM4s8b2rt3+n9y6BgJVVegLv952AOIjZzBGSOh8xjaq8qFUFGYuUBf4Q0NRaDaZo4Pf4fCg6bpcnZqjIS2+QapgRDKK5EiohEkaBe0GovgTf9IGyVvWWV5JVVNSiP2ICxtIjFsDCpOnvvs8pvQh/c20dQx6rTTLR+oQi8teA7tWmaVFa3H3F2a/NnDFaGdLsS1Ffkhx9+4MUXX2xy+eWXX86TTz4ZzEPKISRoX65jO0LHfa6kmD6o3MG8z77lyTWVje+g5hi77276GEMSuHJYB7DE1N6sjnrP91b4yCsthY+rJ0MZ+xZ4nVRVFsNWf4BvXfd7ifUWg6cYXMX+1Hh3MbiLyfBuJtOy2z+hRdUe/63u36nodzxZfGbt32kXwKn1z2HbTbANwAvU/zs2J6suMzGOzFhf7VgMWSkRlQ0qrRCKK5EiIlEkqBe0Gmlj93pSyfOmEbiw+O4FQM2FxeqLcu+d77+w2Ocy/4/KffZfL9ATisCbfhC2Su132qapB0R0q8neK/I0EswPsbYYq+6gg0ntRZguZu/vwkBzJrtoDxcGqnxOPqtcHu5qRKSgfrpmZ2ezdOlS+vbt2+jyZcuWkZ19aF+pk4PXptlihgXiczhjTDrjBxW27hgHaDDnffdj/SDla782WOeiJTlATqPbXzG6P1eO6AWu/OqZjqtvznxwFXBGcSnjSz+H7e/6N+hzKXQ6ETOhJ06v2bxzkEOXBr095EVdF0SRNhbUC1qNtLHzVhTw5Mqi/W4WuLC4C1jyab1lDQI9CrxFvDOG9GL8YZ0blKsHROts8+wiyVOOy3TXK2syaGVJbHQMt2DwZ++N5lXn622y/5ZozVh1e8sq2VFcyI5GvhM0d+bUkIuwXijNDYSapokL/3s3Bnuj6x3KFwZKfGV8VrmcJVUrqTIP0MPtEBXUT/Abb7yRK664gpUrV3LiiSeSlZWFYRjs2rWLjz/+mKeffpqHH344mIeUQ0gossVCcYygBCltcWDLgfiGAcBMINNTDq896i8YcY2y6qT51K32kKcxNUXaUCNt7BkjKxk/qGEmhgI97VdmYpwugrSBR4qfx+GLbVDWlMlxY5iSMK7N6pPrOIKF1k/abP+h0NrvBJeMOhwGtE3dmtSCXiimCZWmA4A4w+m/dt1IL5TmBN6am1XntjopNysarJcYb/Ci9xWg6UzKkUcmMrT3CJIs8fs9xv4uwEabPd4CFlUuY3nVd3jwB0k7Wjqwx1cQ5ppFnqAG9/70pz+Rnp7O3Llzeeqpp/B6/X98q9XK0KFDef755znzzDODeciAsrIy+vXrx/bt2/nmm28YNmxYmxxHpLXUpVVEIpnG1BQJrVAEejTGW/PpbxW9rkm5kKTkJEzTxI0HADu2Jj+vki1tOzGExbBwfNzIwPMqn5NYous7f1PfCVqSuTenbpJVKLLqGsmQ3lvuIa/CUzvzryUGMKjy+LjoHX99np2RjdfqpNwej297/Z5VzQm8vbRyC/9ZunW/VdtfILR7bh49h/of7zconTKGo/cJSle4PIHHfbNSiW8HWdhb3Dv4tHIZ37rWYlaX9bDlcELcCPrYe/DXgvvDWr9IFPRX/ayzzuKss87C7XaTl5cHQEZGBnZ74+nQwXLXXXfh8XgOvKKIiIg0SRcgJBL48vMw8/PqF7r2YFbswJJeDoD3+7cx7HbwVVHzs9L77XywxGKkpmJJSau//SGcmdxeunKFIvDWXv5Wh6IcWyeSbcnhrkY9gx21aWtfVq3i+JgTwliblmvqO0Fzg0lO0wV1g3uhGNu5kbZ+3o8/Hvj/+p2aoGMB1YOTBzQn8ObsaeXP3YYzKm5ovfJgZu5B64LSzclADCfTNFnn/o1PK5fyi3tToHyAvQ8nxI+kl60rhmHU61IutdospGu320M2vt7PP//M448/zoMPPsgVV1wRkmOKiIiISNtwv/sWruefrlcWM2Qbjtwd2E6pLlh/ToPtrOvOb3qnh/CEP6EY4629BN40Hp4Ek9WwBB5/Xrmc0UljGx3b7pARprGdm/q/DmrgLbXxcRxbFAjN9z9uagzE5ir2lVLiK2tQ3uxJVNpwPMrG+Ewfa1xr+bRiGdu8uwCwYJDrOILj40bS2dYxZHWJZiHN19y6dSuzZ8/m3//+d1D3e80113DFFVc0OZGHBFmEDVIqInKoUHcxOVTYp52KbcSY+oWuPXjce8FdPVi+3U7g51/dH4XQdObegbTT7zih6PrbXgJvGg9P2kqFWcUXlSs4IX7kgVeOch7TyzbPTn5xbQqUPVH0EknWBBLs8SRY4kgwGt4nWuKIM2Kx1AmKBkNT/9fhCLyFwtLKVXxQ+cV+1wnneJQ1XKab5VXfsajyK/J8hYA/o/DY2MFMiDuGDtbUNq9DexLS4F5BQQHPPfdcUIN78+bN49tvv2XevHmsWrWqWduUlJTUe+5wOHA4HEGrU7sXinTqdvrlWqQ51EYFX3sJiqm7mIRbqNonS3oGpGfsU9ov6MdpIBTfcdopBd4k3NqifdpfBlSN/c7I6zNIqfN8YeUyxsQNi/jgUEtV+Cr5zbOdje4t/ObexhbPjsC4hzU2ebeDt4kd1GEA8UZckwHAfe9jjOgfXy7YRsblMtBxeIPylmTutaUKXxVLqlbwWeU3lJn+oTbijTjGxg1nTOwwEveZMESaJ6j/Ce+8885+l2/cuDGYh6OiooIbbriB//3f/yU5ufnjK3Tt2rXe89mzZ3P77bcHtW7tWijSqfXlWg5haqOCr70ExdRdTMKt3bdPYeoy1h4o8Cbh1hbtU2szoE6yD2Ni9eN0Syo7zQqWVK7g+GZk70Xq+GimaVLgLQ48f6joX+y17G2wXoIRRw9bF350rwfg/MSTcZouKsxKynwVjd5Xmk5MoNyspNxbiX/8u+Z7tXQBRzoOp6+9F/GWQ/c7T4olqdFutXXHqgtHBmKRt4TFVctZWrUqUJc0SzIT4o7l2NjB7S7oHWpBDe6dcsopGIaBaZpNrhPMRunuu+8mKyuLiy66qEXbbd26tV4wUBkxLRSKrDh9uY4cociiVKZmPWqjgq+9jDelH88Sbu2+fWpnnycih5K2aJ+ayoBq9oy8vtryE+JH8kLVJ3xa+RWj62TvtXp8tH2yA4PNa/oCkxa9WPI2v7CXAmcZ4B8Sa5c3D6sFMi0d6GXvSk97Dr1s3eho7YALNzflzwFgkKPvAYM3XtPrD+z5Kik3K/a5r6Tct29ZBRVm7Xevla4fWOn6AQsGPW059I/pw4CYPnS2doyY4OihaLcnj08rl7HC+T1e/N+7s62ZHB83klzHAKxG49+NpWWCGtzLzs7m8ccf55RTTml0+Zo1axg6dGijy1pq8+bNPPjgg7z11luBFOyysrLAfVlZGYmJjaeTJicntyjTT8JAX64jRyiyKJWpWY/aqOBrL+NNiYSb2icRiVRt0T41lQHVbJ7ywMMhjiN437WSPF9hvey9YGYHtkr1xXaX6WaHZzfbvXvY5tlFnnMHV1Wvkpe/iCSrnXiPlZrg3mWevvSydyIxrnfD329N5/w0ympYSTYSW9QttMJXxS0FDwAwNnY469y/sdubxwbPVjZ4tvJuxSJSLEn0t/emf0xv+tp7EncIZ/WF0hb3dj6rWsEPrnWBt0JvW1eOjx/FAHtvBVyDLKjBvaFDh7Jq1aomg3sHyuprid9++w2Xy8VJJ53UYNmECRM45phj+Oqrr4JyLJFDWiiyKJWpKe2AusyKiIgcoprRC8Va9B2netJ5v/JH1pbOY0yylRjDzmifhUGOafjisupt2pLMvdb61vkziT/OYcAvLxED9Ki+7eu6b/yzmFf4HPwH/4QLfT47n3iLM2wX2+vOSDwtYQKnG5PI9xax1rWBte71/OLaRLGvlK+ca/jKuQYLFnrZchgQ04ee9Ax5fds7d53xKB8reSnw+MiYwzk+biQ97TnhqNYhIajBvb/85S+Ul5c3ubxPnz4sWrQoKMcaPHhwg32tWbOG66+/nieffJLhw4cH5TgirdIeupuGog6RcJ4iraQusyIiIoeoZvZCGQgMDBTMBSAJSGokMNbs8dE8Tf/+3h+vWTtsyEtl75Cc3YXkDlcCkGwk0sWWRRdbJ3KsHUknDoth1F5sd/vg35v8G5+4BOyWiPoun25NZXTcUEbHDcVtetjg3sJPrvWsdW9gjzef9Z4trPdswes2qMlA/MH1C4NsvYm1tLPhJtpYma+C39zb2OjZykb3VrZ4tgeWWTAY7hjEcXEj6GTbd5IsCbagBvfGjBmz3+UJCQmMGxecKZVTU1MZP358o8uGDh1Kbm5uUI4j7VgoAm/qbioiIiIi0r61oBfK985f+KDyc+KNWC5NPsufkdecwFgQf7uU+cr5V/HrgUWdLR3pkzK8ery8rqRaD9C92eUBNvkfpw2GmODPWGuaJpXV4xbH2a0H3YXTbtjoF9OLfjG9AMjzFrLWtYGfXOv52b05sN7zpW8RU2XQy96VAfY+9I/pQydrhrqONmKl83u2O7ez0bOV3d78Jte7JfUKOtrSQ1izQ5vmjW5v2kOmWKiEIvDW1t1N9XqLiIiIiIRXC75TDzAHM78wn22+QpbE+TguvplJKUH67bLNs4unS16j0FcSWHRd2kVhm6l0fxOS1YxV/Oz545uckCw5oWWTMWRY0xgTN4wxccMojq1kIe/5yy1pFFLAr+7N/OrezNsVn5JmSaZ/TB8Os/Vo0THaC6/pY4d3NxvcW/mlYkug/NWyBVjttcOtdbJm0NPWlV72ruTYOnFf0T8BSLG2YrxKabGgBfe2bNlCt27dmr3+9u3b6dKlS7AOD8D48eODNqZf1FKmWPOFYpy3tg6e6fUWEREREYkaVsPCifGj+E/Zu3xauYxRcUObF1gLwm+X1c6feKn0Hdx4yDDSyDMLD/5EgqS1E5JdMupwGHBwx7bXGcfwprTLKLeWBLL61rs3U+grYWnVKpayKrDe40Uv4rA4iDFs2LFjN2z+2z6PYwz/c5+ndkzA39xbSbbEYqd6PcOOHVuT4ymGmtN0scm9jY3urWz0bGWTezsu/GPo1e3C3N3Whb5xXell88+OnGCJr7cPCY+gBfeGDx/OjBkzuPTSSzn66KMbXae4uJjXXnuNv//971x++eX8+c9/DtbhpYYmJmi+9pC1ptdbRERERCSqDHccyUcVS8j3FfFl5SqOiz/2wBu14reLzzRZULGYj8u/BKCfvRfnJE5jduEjB7W/YGpqQrKWZO7NcQanLpnWDmTGdWBs3HBcpptf3ZtZ61rPj85fKTCLAdjs3QHeA+yojrpBsX+UvFwv462uur/WHih8mjhLHHGGgzgjljiLg1gjNvA81nAQZ6l9bnoPLqxT7Cv1j5dXHczb7tmFb58pjuOMWHracuhi78IX7AbgqpTziW+DrtjSOkF7RdauXcs999zD5MmTsdvtDBs2jM6dOxMbG0thYSE//fQTP/74I8OGDeP+++9nypQpwTq01NUeAlbSfHq9RURERESiitWwMjF+dCB7b3Tc0DbL3qr0VfFC6dv86P4VgOPiRjA9fgJuPG1yvJZqakKyCldt/fpmpTYZTHKaLghScK+uGMPOETF9OCKmD9PiJ3Bzwf0AXJB4CmDixoPb9OA23bjqPN73eQUevqjeZ4YlDZ/FXbtundegbkhtj68AfM2va90A4m0FfycpJsYfAKwODNYEBO3UvsfuK/gn+WZRg311sKTQ0+7Pyutl70onayYWw6h+PeY3v1ISckEL7nXo0IEHHniAu+++mwULFvDFF1+wadMmKisrycjI4LzzzmPSpEkMHDjwwDsTERGRtqdxO0VERMKiXvZe1UomxDUje6+F9njzebrkNXZ787Fj4+zEkxgWe6R/4SE+mlVL1J1U40jH4S0an7DC5eG56qDYTWmX1QtSmqaJBy9u0025r5K7i54A4PKks/HipdKsotJ0+u99/vuqmuemk6rqsrI60c0qswq3r5IDyTeLMIDO1ix62f2BvJ62HNKsKc0+N4ksQc+ljI2N5bTTTuO0004L9q5FREQkmDRup4iISFjUzd77pGIZo2KDm7231rWB50rfpNJ0kmpJ4uKk39HN3rD7q4SPYRiB8fesRm23494x3VoWQHS6GcHbANyYcgnYPfUDgT4nVWYVZb4KljpXA3Bx0u84zN6dOEtscE9KwkYdpUVERA5VGrdTREQkbNoie880TRZWfsV/KxZiYtLTlsMfk88g2ZIYhBo3rthXSomvrEG5y3QHHm/z7GoyeJlsSSTFoplVD1qd72kdbenE25vuwlwT3Osb0zNsMyRL21BwT0RE5FClbrUiIiJhYzWsnBg/ilfK3uPTIGTvuUw3r5S9y0rnjwCMcAzhjMTJ2IzGJ6MIlqWVq/ig8ov9rvNI8fNNLpscN4YpCeOCXS2RQ4qCeyIiIiIiIiJhcLRjEB9VLKHAV8yXVauYEHfMQe2n0FvM0yWvs827CwsWTk+YxKjY3HrjxbWVkXG5DHQc3qDcNM3ApBF2bE3WpTlZhcoOFNk/BfdEREREREREwqBm7L1Xyt5jYcUyRsXmtjh7b4N7C/8ueYMys5wEI54/Jp9OH3v3NqpxQymWpDYPnB3q2YGmaeL0+ChzuuvfXB4KyqsC6z3z1TosBnh8Jh6fD4/Xf+/1mTi9HtZWZmP64H/sK/D5/Ot5fb7ae6+Ju3r9mu08Xh8ubwum75WwaNPgXl5eHoZhkJ6e3paHEREREREREYlKw+tk7y2tWsX4FmTvLa1axbyyD/Dio4s1i0uSf0cHa2rbVTZM2iI70FMnYPXiN78Sa7dis1iwWYzAvdViwbR42VueiGGBZcV7iLXYsVlrl9ssRuC5fzsL1uoyt8cbOMbO4nI8PpNyl5vSKjdlTo//sdNNidPJL8VZeF0WZvI1FU4vZS4PZU435dWBPI/vwFMc//PLtQdYwz8b7ifsOOC+JLoEPbhXVFTErbfeyquvvkphYSEAaWlpnH322dx9992kpqYG+5AiIiIiIiIiUclWPfbeq2UL+LRiGSNjcw+4jdf08mb5RyypWgnAkJgBnJM0rd1OkhDM7MBdJRW8seY33lyzMVD2+Oc/HmCrHACu5auDPu5pT398gDXSANjN7ibXMIBEh51Eh50Eh41Eh504m5Vlm/YAcPKR3XHYrNis1QHGOsFKLD4+rPocw2JyatIJOCw2f1DSqAlO1mxTvZ3VCJR5vCYXv/zZQZ+7tL2gBvcKCgoYMWIE27dv57zzzqN///6YpsnatWt59tln+fTTT1m6dClpaWnBPKyIiIiIiIhI1DracRQfV3xZnb23mhGxg5tct8xXzr9L3mCDZwsGcFL8BE6IG9lo1prGqvPz+kyW/baL11dv5PP1O9k3CW7qEV0Bo15X1JquqS6vl/WurZg+gy6WTnh91K7n8+H27tONtbp7q9esfxCH1UJSbEwgKBcI0sXYiHdYWeZdji3Gx1kdTiTVEetfHmMnMdZOYkx1IC/GhmWf17nC5WHEg/MB+OvEIcTHND1b7tp8/3pnprdsttwKl6fZ60p4BDW4d+eddxITE8OGDRvIyspqsGzixInceeedzJ07N5iHFREREREREYla9bP3ljLMcUSj623z7OLpktco9JXgMGK4MOlUBsYc1uR+23qsut0lFfTMSN7v/sMpv7yK+d9uYt6ajeworgiUD++eyclHdmfWuysA+NvkobhtlU0GQh8p/hKAa1LGNTsQapompVVuxjz8DgCLr5ux38DbTfkLADglvXu7zcCUthPU4N78+fN56qmnGgT2ADp16sScOXO44oorFNwTERERERERqeNox1F8VLGEQl8JX1d922D5KuePvFz6X9x4yLR04JLkM+lky9jvPtt6JttT/u8jemUkM6pXFqN6dSK3awYOm3W/27Q10zRZsWUvr6/eyKfrtgfGqkuOtTPjyB6cMaQnPdOTqXB5AsE9CH4g1Kju7ioSCkEN7u3cuZMjjmj8CgPAwIED2bVrVzAPKSIiIiIiIhL1bIaVE+NG8Vr5+yyq/DpQ7jN9/LdiIZ9ULgWgn70Xv086lXhL3AH32dYz2RrAxrwSNuaV8MJy/6QUw7tlMqpXJ0b37kTXtP0HB4OppNLFO99v5vXVG9lUUBooH9S5A78b0ouJ/bsSa2868NjWgVBpGXUpb5mgBvcyMjLYtGkTOTk5jS7/7bffNHOuiIiIiIiISCOOiR3MR5VfUuQrCZQ9W/ImP3v8kz8cFzeC6fETsBiRkRH24VVT+XZ7AV9u3MWXG3ext6yKLzbs4osNu+Bj6JaWyMheWYzu1Ylh3TOJswd3Tk/TNPl+RwGvr97Ih2u34vT4Z8CNs1s5aWB3zhjck/6dmjfmf1sHQkEBq5Zo6y7l7U1Q/7MmT57Mrbfeyscff0xMTP0+4k6nk7/97W9Mnjw5mIcUERERERERaRdshpWJ1dl7NX72bMSOjbMTT2JY7JFhrF1DSbExTOyfw8T+OZimya97i/ly426WbNjFmm15bCksY8vKMl5ZuYEYq4XcrhmM7t2JUb060TM9qcksuAMpd7pZ8NNWXl+9kXW7iwLlh3dM4XdDejH1iG4kOhoPkIVTKAJWoQgghuIYyqRsmaAG9+644w6GDRvGYYcdxlVXXUW/fv0A+Omnn3jiiSdwOp288MILwTykiIiIiIiISLtxTOxgPqxYQrHp71qaYiRySfKZdLN3DnPN9s8wDA7vmMrhHVP5w7F9KXe6Wb55L0s27uLLDbvYWVLBV5v28NWmPTzw6Xd0TolnZK9OjOqVxdHdOzYrGPfLniJeX7WR937cQnn1DK4xVguT+nfld0N6MahLh4MOGIZCKAJWoQgghuIYocikbE+CGtzLyclh6dKlXHXVVdxyyy2Y1VM/G4bBiSeeyGOPPUbXrl2DeUgRERERERGRdsNmWJkSP4ZXyv2zp16T+nsyrM3rWhpJEhx2JhzemQmHd8Y0TTYVlLJkwy6+3LiblVv2sqO4gnmrNzJv9UZsFoPBOemM6uXP6uuamhDYj9Pt5dN123l99Qa+3V4QKO/eIZHfDenFjCN7kBIXHbPLhiJgFYoAorLqIk9wO7wDvXr14v3336ewsJBff/0VgD59+tChQ4dgH0pERERERESk3cmNHRgI7iVZEg6wduQzDIOe6cn0TE/mgqMPp9LtYeWWvSzZsJsvN+5iS2EZK7bksWJLHn9f/AMZCY7AtjOe+oASp7+7p81icNzhXThjSC+O7p4Z0Vl64RKKAKKy6iJPUIN7JSW1g35ardZAt9x9lyUnJwfzsCIiIiIiIiISJeLsNkb3zmZ072wAthaWVU/KsZvlm/eQV+4MrFvidJOdHM/pg3ty6lE9yUiMDVe1RSJWUIN7qamp+42cm6aJYRh4vd5gHlZEREREREREolTXtETOHtqHs4f2weXxsuy33VwzbykAD5x6LMcd3gWrRVl6Ik0JanBv0aJFgcemaTJ16lSefvppunTpEszDiIiIiIiIiEg7FGOzMrx7x8DzUb06KbAncgCWYO5s3Lhxgdv48eOxWq0ce+yx9crHjdv/jCjSdr7++mtOPfVUunXrhsPhICsrixEjRjBz5sygHmfx4sUYhsHixYuDul+JXM8++yyGYTR6u/HGG5u1j02bNmEYBs8++2yD/W7atKltKi4iIiIiIiIS5YI+oYZEpvfee48ZM2Ywfvx45syZQ3Z2Njt37mTFihW88sorPPjgg0E7Vm5uLsuWLWPAgAFB26dEh2eeeabeWJsAnTt3DlNtRERERERERNo/BfcOEXPmzKFnz558+OGH2Gy1L/vZZ5/NnDlzgnIMt9uNYRgkJydz7LHHBmWfEl0GDhzIsGHDwl0NERERERERkUNGULvlNkZTU0eG/Px8MjIy6gX2algstW+DHj16MG3aNN566y0GDRpEbGwsvXr14pFHHqm3TU3X2xdeeIGZM2fSpUsXHA4H69evb7Rb7kUXXURiYiLr169n6tSpJCYm0rVrV2bOnInT6ay3723btnHGGWeQlJREamoq5513Ht98802DLpsSHdavX88f/vAHDjvsMOLj4+nSpQvTp0/n+++/D3fVRERERERERKJeUDP3TjvttHrPq6qquOKKK0hISKhX/uabbwbzsG3ONE1cuMNdjYAY7C0Omo4YMYKnn36aa665hvPOO4/c3Fzsdnuj665Zs4brrruO22+/nU6dOvHSSy9x7bXX4nK5GoyfdssttzBixAiefPJJLBYLHTt2ZNeuXY3u1+12M2PGDC6++GJmzpzJ559/zl133UVKSgq33XYbAOXl5UyYMIGCggLuu+8++vTpwwcffMBZZ53VovONCqYJ3opw16I+azy0IiDv9XrxeDz1ynbs2EF6ejr33nsvmZmZFBQU8Nxzz3HMMcewevVq+vbt29pai4iIiIiIiByyghrcS0lJqff8/PPPD+buw8aFm5vyg9N1NRjmpN+Eg5gWbXPvvffy888/8+ijj/Loo49it9sZPnw406dP5+qrryYxMTGw7o4dO1i9ejVHHXUUAFOmTGHPnj3cdddd/OlPfyI+Pj6wbu/evXn99debVQeXy8Udd9zB7373OwCOP/54VqxYwcsvvxwI7j333HOsX7+e999/n8mTJwMwceJEKioqeOqpp1p0zhHPWwGvJR54vVA6swxsCQderwmNdcd2u92MHTs28Nzr9XLSSSdxxBFH8NRTT/HQQw8d9PFEREREREREDnVBDe4988wzwdzdfr3++uu89NJLrFy5koKCAnr37s2VV17J5ZdfXq+bqfilp6fzxRdfsGLFCj799FNWrFjB4sWLueWWW3jqqaf45ptvyMjIAOCII44IBPZqnHvuuXz88cesWrWK0aNHB8pPP/30ZtfBMAymT59er2zQoEEsXLgw8Pyzzz4jKSkpENircc4557S/4F479Pzzz9O/f/8G5ffccw8vvvgi69evx+2uzYJdu3ZtKKsnIiIiIiIi0u5E7YQaDz74IN27d+f+++8nKyuLRYsWcc0117Bx40buv//+oB4rBjtz0m8K6j5bI4bGu9M2x7BhwwITHrjdbm6++Wbmzp3LnDlzAhNrdOrUqcF2NWX5+fn1yrOzs5t97Pj4eGJjY+uVORwOqqqqAs/z8/PJyspqsG1jZVHPGu/PlIsk1vgDr7Mf/fv3bzChxjXXXMPjjz/OzTffzLhx40hLS8NisXDJJZdQWVnZquOJiIiIiIiIHOqCGtxbuHAhV199NV999RXJycn1lhUXFzNy5EiefPJJxowZ0+pj/fe//yUzMzPwfMKECZSVlfHYY49x991343A4Wn2MGoZhtLgbbDSw2+3Mnj2buXPn8sMPPwTKGxszr6YsPT29XnmwJ0xJT09n+fLlTR6/XTGMVnWBjRYvvvgiF154Iffcc0+98ry8PFJTU8NTKREREREROSTsLaskr6yqQXmV2xt4vG53EbF2a6PbZyTGkpkY12b1EwmGoAb3Hn74YS699NIGgT3wj8d3+eWX89BDDwUluFc3sFdjyJAhVFVVUVBQ0KKMskPBzp07G/2b1HSL7Ny5c6Dsxx9/5Ntvv63XNffll18mKSmJ3NzcNq3nuHHjeO2113j//feZMmVKoPyVV15p0+NK2zEMo0Gw/b333mP79u306dMnTLUSEREREZFDwbzVG3lyyf6HA7roxcVNLrtidH+uHHNEkGvVcgpSyv4ENbj37bffct999zW5fOLEiTzwwAPBPGQ9X3zxBR06dKBjx45tdoxoNWnSJHJycpg+fTr9+vXD5/OxZs0aHnzwQRITE7n22msD63bu3JkZM2Zw++23k52dzYsvvsjHH3/MfffdV28yjbbw+9//nrlz53L++edz991306dPH95//30+/PBDAI2nGIWmTZvGs88+S79+/Rg0aBArV67k/vvvJycnJ9xVExERERGRdu6MIb0Yf1jnBuWmaeL0+ABw2CxN9krLSIxttLyuUATe2kuQUtpGUIN7u3fvxm5vejw4m83G3r17g3nIgBUrVvDMM88we/ZsrNbG/2FqlJSU1HvucDiC2o03Es2aNYu3336buXPnsnPnTpxOJ9nZ2Zxwwgnccsst9SZBGDx4MH/4wx+YPXs2v/76K507d+ahhx7i+uuvb/N6JiQksHDhQq677jpuuukmDMNg4sSJPPHEE0ydOlXdOKPQ3//+d+x2O//7v/9LWVkZubm5vPnmm8yaNSvcVYtYh2IbJSLRQe2TiEQqtU/SlMzEuDbPWAtF4C0UQUqJXoZpmmawdta7d28eeOABTj311EaXv/nmm9x4441s3LgxWIcE/OOxHXPMMeTk5LB48eImA4wlJSWkpKQ0KJ89eza33357UOsUrXr06MHAgQN59913w12Veu655x5mzZrFli1blPElYVPThhQXFzc6/ECw9r8vtVEi0hxt2UapfRKR1lD71HJO08VN+f4JD+ek34TDiJwx4CtcHkY8OB+AZTNPIT4m+PN0huIYwdRU5l5LAm+R3GU22l6Plmjr33ihEtRXZOrUqdx2221MmTKlwayolZWVzJ49m2nTpgXzkBQXFzNlyhTi4+N555139ps5WGPr1q31XjRd0Yksjz32GAD9+vXD7XazcOFCHnnkEc4//3wF9uSQoDZKRCKV2icRiVRqnyScQpEdKLI/QQ3uzZo1izfffJPDDz+cq6++mr59+2IYBmvXruXxxx/H6/Vy6623Bu14VVVVzJgxg927d7Ns2bIGM7k2JTk5Oaojsu1dfHw8c+fOZdOmTTidTrp168bNN9+sbpxyyFAbJSKRSu2TiEQqtU8icigLanAvKyuLpUuXcuWVV3LLLbdQ0+PXMAwmTZrEE088QVZWVlCO5fF4OPPMM/n222/5/PPP6d69e1D2e6jbtGlTuKvAH//4R/74xz+GuxoiIiIiIiIihwzNyBu9gt5Runv37ixYsIDCwkLWr1+PaZocdthhpKWlBfU4V111Ff/973+ZM2cOFRUVfPXVV4FlAwYM0FUbERERERERkYOkQM+hRzPyRq+gBvfqzlBktVrp27dvo8uCEXj78MMPAbjpppsaLFu0aBHjx49v9TFERERERERE2kqxr5QSX1mDcpfpDjze5tlFjNH42PLJlkRSLEltUrdQBHoUQIwsmpE3egU1uJeamtrkiwz+N4RhGHi93ibXaa5I6D4qIiIiIiIicrCWVq7ig8ov9rvOI8XPN7lsctwYpiSMC3a1gNAEepQpFlk0MUj0Cmpwb9GiRYHHpmkydepUnn76abp06RLMw4iIiIiIiIhEvZFxuQx0HN6g3DRN3HgAsGNrMoCWbElss7qFItCjTDGR4AhqcG/cuPpXDKxWK8ceeyy9evUK5mFEREREREREol6KJanNutVGA2WKiQSHJdwVEBERERERERERkYOj4J6IiIiIiIiIiEiUavPg3v4m2JDQ+vrrrzn11FPp1q0bDoeDrKwsRowYwcyZM8NdNYlS8+bNwzAMXn311QbLjjrqKAzDCMxsXVfv3r3Jzc1l8eLFGIbB4sWLW3zsiooKbr/99oPaVkRERERERKS9COqYe6eddlq951VVVVxxxRUkJCTUK3/zzTeDeVhphvfee48ZM2Ywfvx45syZQ3Z2Njt37mTFihW88sorPPjgg+GuokSh8ePHYxgGixYt4qyzzgqUFxQU8P3335OQkMCiRYuYNGlSYNm2bdvYuHEjN9xwA7m5uSxbtowBAwa0+NgVFRXccccdgXqIiIiIiIiIHIqCGtxLSUmp9/z8888P5u6lFebMmUPPnj358MMPsdlqX/azzz6bOXPmhLFm4VFRUUF8fHy4qxH1MjIyGDhwYIPsuc8++wybzcbFF19cbxZtqJ1Ve8KECSQnJ3PssceGqrrN4na7MQyj3v+JiIiIiIiISKQKarfcZ555plk3Cb38/HwyMjIaDVhYLLVvA8MwuP322xus06NHDy666KLA82effRbDMFi4cCGXXnop6enpJCcnc+GFF1JeXs6uXbs488wzSU1NJTs7mxtvvBG32x3YftOmTRiGwf333899991Hjx49iIuLY/z48fzyyy+43W7++te/0rlzZ1JSUjj11FPZs2dPg3q9+uqrjBgxgoSEBBITE5k0aRKrV6+ut85FF11EYmIi33//PRMnTiQpKYnjjz/+IP6K0pgJEyawbt06du7cGShbvHgxw4cPZ+rUqaxcuZLS0tJ6y6xWK2PGjGm0W27N67V+/XqmTp1KYmIiXbt2ZebMmTidTsD//snMzATgjjvuwDAMDMOo9x799ddfOffcc+nYsSMOh4P+/fvz+OOP16t7zfFfeOEFZs6cSZcuXXA4HKxfv74N/lIiIiIiIiIiwafUlGYwTROqqsJdjVqxsS0ey3DEiBE8/fTTXHPNNZx33nnk5uZit9tbXZVLLrmE0047jVdeeYXVq1fzP//zP3g8HtatW8dpp53GZZddxieffMJ9991H586dueGGG+pt//jjjzNo0CAef/xxioqKmDlzJtOnT+eYY47Bbrfz73//m82bN3PjjTdyySWX8M477wS2veeee5g1axZ/+MMfmDVrFi6Xi/vvv58xY8awfPnyel09XS4XM2bM4PLLL+evf/0rHo+n1efeWqZpUun2hrsa9cTZrS1+b02YMIFHHnmExYsXc8455wD+7Lxp06YxatQoDMPgiy++YOrUqYFlubm5DTJ963K73cyYMYOLL76YmTNn8vnnn3PXXXeRkpLCbbfdRnZ2Nh988AGTJ0/m4osv5pJLLgEIBPx++uknRo4cSbdu3XjwwQfp1KkTH374Iddccw15eXnMnj273vFuueUWRowYwZNPPonFYqFjx44t+huIiIiIiIiIhIuCe81RVUXZtPHhrkVA4ruLIS6uRdvce++9/Pzzzzz66KM8+uij2O12hg8fzvTp07n66qtJTEw8qLpMmzaNBx54AIATTzyRZcuW8Z///IeHHnqI66+/HoATTjiBDz/8kJdeeqlBcC81NZX58+cHsgfz8vK47rrr6NevH2+//XZgvZ9//pmHH36YkpISkpOT2bp1K7Nnz+bqq6/mkUceCax34okncthhh3HHHXfUm+TB7XZz22238Yc//OGgzrMtVLq9jHhwfrirUc+ymacQH9OyZmHcuHFYLJZAcC8/P58ffviB+++/n8TERHJzc1m0aBFTp05l69at/Pbbb/zud7/b7z5dLhd33HFHYL3jjz+eFStW8PLLL3PbbbfhcDgYOnQoADk5OQ269t5www0kJSWxZMkSkpOTAf97w+l0cu+993LNNdeQlpYWWL937968/vrrLTpvERERERERkUjQ5rPlSmRIT0/niy++4JtvvuHee+/l5JNP5pdffuGWW27hyCOPJC8v76D2O23atHrP+/fvD8BJJ53UoHzz5s0Ntp86dWq9bsH72x5gy5YtAHz44Yd4PB4uvPBCPB5P4BYbG8u4ceManUH19NNPb+HZSXOkpaVx1FFHBf7mn332GVarlVGjRgH+4F/NOHt1x9vbH8MwmD59er2yQYMGNfoe2ldVVRWffvopp556KvHx8fXeH1OnTqWqqoqvvvqq3jZ6b4iIiIiIiEi0UuZec8TG+rPlIkVs7EFvOmzYMIYNGwb4s9luvvlm5s6dy5w5cw5qYo0OHTrUex4TE9NkeVUjXZtbsj0Q2Mfu3bsBGD58eKP1qhswBIiPjw9kcEWKOLuVZTNPCXc16omzWw9quwkTJvDQQw+xY8cOFi1axNChQwPZoOPGjePBBx+kuLiYRYsWYbPZGD169H73Fx8fT+w+73OHw9Hoe2hf+fn5eDyeQJZqY/YNZmdnZx9wvyIiIiIiIiKRSMG9ZjAMo8XdYKOB3W5n9uzZzJ07lx9++AHwB1BqJi2oKz8/P9TV26+MjAwA5s2bR/fu3Q+4fkvHkQsFwzBa3AU2UtUE9xYvXszixYsD4+sBgUDe559/Hpho42C7gTdHWloaVquVCy64gKuuuqrRdXr27FnveSS+P0RERERERESao31EFuSAdu7c2Wh20tq1awHo3Lkz4J8V97vvvqu3zsKFCykrK2v7SrbApEmTsNlsbNiwQV0qI8DYsWOxWq3MmzePH3/8sV4WaEpKCoMHD+a5555j06ZNnHvuuUE5psPhAKCysrJeeXx8PBMmTGD16tUMGjQokPUpIiIiIiIi0h4puHeImDRpEjk5OUyfPp1+/frh8/lYs2YNDz74IImJiVx77bUAXHDBBfztb3/jtttuY9y4cfz000889thj+53ZNBx69OjBnXfeya233srGjRuZPHkyaWlp7N69m+XLl5OQkMAdd9wR7moeMpKTk8nNzQ1MjlIz3l6NcePG8fDDDwMHHm+vuZKSkujevTtvv/02xx9/PB06dCAjI4MePXrw97//ndGjRzNmzBiuvPJKevToQWlpKevXr+e///0vCxcuDEodRERERERERMJNE2ocImbNmkVaWhpz585lxowZTJkyhUceeYQTTjiB5cuXc+SRRwLwl7/8hb/85S88++yzTJ8+nTfeeIPXXnuN1NTU8J5AI2655RbmzZvHL7/8wu9//3smTZrETTfdxObNmxk7dmy4q3fImTBhAqZpMmTIkAbjG44bNw7TNImJiWHkyJFBO+a//vUv4uPjmTFjBsOHD+f2228HYMCAAaxatYqBAwcya9YsJk6cyMUXX8y8efM4/vjjg3Z8ERERERERkXAzTNM0w12JUCkpKSElJYXi4uKIm1xBRCJfW7chaqNEpDXasg1R+yQiraH2SUQiVXtpQ5S5JyIiIiIiIiIiEqUU3BMREREREREREYlSCu6JiIiIiIiIiIhEKQX3REREREREREREopSCeyIiIiIiIiIiIlFKwT0REREREREREZEopeCeiIiIiIiIiIhIlFJwT0REREREREREJEopuCciIiIiIiIiIhKlFNwTERERERERERGJUgruiYiIiIiIiIiIRKmoDu798ssvTJ48mYSEBDp27Mi1115LZWVluKslIiIiIiIiIiISElEb3CsqKuK4446jtLSUN954gwceeICXXnqJSy+9NNxVaxGn08ntt9+O0+kMd1VapT2cR3s4B9B5SHC1l9dB5xE52sM5QPs5j2jWXl4DnUfkaA/nAO3nPKJZe3kNdB6Roz2cA7Sf84hEhmmaZrgrcTDuu+8+7rzzTjZv3kxGRgYAL7/8Mueddx4//fQT/fv3b7BNSUkJKSkpFBcXk5ycHOoqNyoS63Qw2sN5tIdzAJ1HW2rrOh2K5xwqOo/I0R7OASLzPNqyTofa+YaSziNytIdzgMg8D7VP0UnnETnawzlAZJ5HJNbpYERt5t6CBQs44YQTAoE9gNNPPx2Hw8GCBQvCWDMREREREREREZHQiNrg3tq1axtk5zkcDnr37s3atWvDVCsREREREREREZHQsYW7AgersLCQ1NTUBuVpaWkUFBQ0uk1ND+Tt27dTUlISKHc4HDgcjjap54HU1KNufaJReziP9nAOoPNoSzV1aavRDNRGtR2dR+RoD+cAkXkebdlGqX1qOzqPyNEezgEi8zzUPkUnnUfkaA/nAJF5Hm39Gy9UonbMPbvdzt13383NN99cr3zUqFF06tSJN954o8E227Zto2vXrqGqooi0U1u3biUnJyfo+1UbJSLB0BZtlNonEQkGtU8iEqna6jdeqERt5l5aWhqFhYUNyouKihqdTAOgc+fObNiwAbvdjmEYgfJwXtURkehhmialpaV07ty5TfavNkpEWqMt2yi1TyLSGmqfRCRStfVvvFCJ2sy9cePGkZqayttvvx0oczqdpKSk8P/+3/9j5syZYaydiIiIiIiIiIhI24vaCTWmTp3Kp59+Sn5+fqDsrbfewul0MnXq1DDWTEREREREREREJDSiNnOvqKiIgQMH0qNHD/72t7+xZ88ebrjhBiZNmsSLL74Y7uqJiIiIiIiIiIi0uajN3EtNTWXhwoUkJCRw2mmnccMNN3DOOefwf//3f+GuWrPMnz+fY445huTkZLKysjjttNNYt25duKvVpPXr13PFFVcwePBgbDYbAwcObHS9BQsWMGTIEGJjY+nTpw9PPPFEiGu6fwc6D6/Xy5w5cxg3bhyZmZmkpaUxduxYPv300zDVuHHNfT1qrFy5EqvVSmJiYohq2DzNPY/Kykr+53/+h+7du+NwOOjRowd33HFHiGt7aIm2NgrUTkVSO6U2Sm1UW1L7FD5qnyKH2qfIpPYpfNQ+RQ61T+ERtcE9gMMPP5wPP/yQ8vJy9u7dyyOPPEJcXFy4q3VAn3zyCaeddhp9+/bljTfe4LHHHmPdunWccMIJETUldF0//vgj7733Hn369GHAgAGNrrNs2TJOPvlkcnNzef/997nooov485//zNNPPx3i2jbtQOdRWVnJPffcw+DBg3nmmWd45ZVX6NKlCyeeeCLvvvtuGGrcuOa8HjVM0+Tqq68mMzMzRLVrvuach9frZdq0abzxxhvceeedfPTRR9x1113YbFE7H1DEi8Y2CtRORVI7pTZKbVRbUfsUXmqfIofap8ij9im81D5FDrVPYWJKyF188cVmjx49TJ/PFyj7+uuvTcBcsGBBGGvWNK/XG3j8+9//3jziiCMarDN58mTz6KOPrld26aWXmtnZ2fW2D6cDnYfH4zELCgrqlfl8PjM3N9ccP358SOrYHM15PWr861//Mvv06WPecsstZkJCQiiq12zNOY+nnnrKTE1NNXft2hXKqh3SorGNMk21U5HUTqmNkrai9im81D5FDrVPkUftU3ipfYocap/CI6oz96KV2+0mKSmp3lTtqampgD8CH4kslv2/VZxOJwsXLuTss8+uV37eeeexc+dOVq9e3ZbVa7YDnYfVaiUtLa1emWEYDB48mB07drRl1VrkQOdRo6ioiL/+9a/MnTuXmJiYNq5VyzXnPP71r39x5plnkpWVFYIaCURnGwVqpyKpnVIbJW1F7VN4qX2KHGqfIo/ap/BS+xQ51D6Fh4J7YXDxxRezdu1aHn30UYqKiti0aRM33ngj/fv35/jjjw939Q7Khg0bcLlc9O/fv155TRru2rVrw1GtoPD5fCxdurTBuUWDWbNmMXToUKZNmxbuqhwUl8vFqlWr6Nq1KxdccAHx8fEkJydz7rnn1pspW4KrPbZRoHYqEqmNkpZS+xR91D6Fh9qn0FP7FH3UPoWH2qe2oeBeGIwdO5a33nqLW2+9lbS0NHr27MmGDRv46KOPcDgc4a7eQSksLARqr07VqLk6UlBQEOoqBc2jjz7KunXruOGGG8JdlRZZs2YN//rXv5g7d264q3LQ8vPz8Xg83HfffRQVFTF//nweffRRPvnkkwZXDyV42mMbBWqnIo3aKDkYap+ij9qn8FD7FHpqn6KP2qfwUPvUNjRaYRgsXbqU888/nz/+8Y/MmDGD4uJi7rnnHqZMmcKXX35JcnJyuKt40OqmoTenPNJ99tln3HTTTdx4442MHTs23NVpNrN6gNU//elP9OvXL9zVOWg+nw/wf5l44403AmnnSUlJnH766Sxfvpyjjz46nFVsl9pzGwVqpyKB2ig5WGqfoovap/BR+xR6ap+ii9qn8FH71DYU3AuDa665huOOO46HH344UDZ69GhycnJ4+umno+rKQY2aKzc1V3Zq1Dzfd3yDaPDdd99x8sknc8opp3DfffeFuzot8uqrr/LTTz/x0ksvUVRUBEBVVRXgH6MhNjaW2NjYMNaweWreN6NGjao3nsRxxx0H+GdiUsMffO2xjQK1U5FEbZQcLLVP0UPtU3ipfQo9tU/RQ+1TeKl9ahvqlhsGP/30E4MHD65XlpmZSefOndmwYUN4KtVKvXv3JiYmpsGYCz/99BNA1I1jsGHDBiZNmkRubi4vvPBC1F2R+vnnnyksLKRHjx6kpaWRlpbGfffdR3l5OWlpadx+++3hrmKzxMfH07179yaXN3fQWWmZ9thGgdqpSKI2Sg6W2qfooPYp/NQ+hZ7ap+ig9in81D61DWXuhUH37t1ZuXJlvbJdu3axfft2evToEZ5KtZLD4eC4447jtdde4/rrrw+U/+c//yE7O5shQ4aEsXYts2vXLiZOnEinTp2YP39+RM5AdCAXXXQR48ePr1f27LPP8uqrr/L+++/TrVu38FTsIEybNo358+fjdDoD45V88sknABx11FHhrFq71R7bKFA7FUnURsnBUvsU+dQ+RQ61T6Gl9inyqX2KHGqfgk/BvTC46qqr+POf/8zVV1/NySefTFFREffccw+JiYmcf/754a5eoyoqKliwYAEAmzdvpqSkhHnz5gEwbtw4MjMzue222xg7diyXXnop5513Hl9++SX/93//x1NPPRUx0fcDnUdiYiKTJ09mz549PPTQQ4ErUjWOPfbYkNe5MQc6jx7lp258AAACbklEQVQ9ejT4ErF48WKsVmuDD4Rwas776i9/+Qsvvvgip556Kn/+85/ZsWMHf/3rXznllFMaXB2V4IjGNgrUTtWIhHZKbZTaqLai9im81D6ND3Ftm6b2KfKofQovtU/jQ1zbpql9ChNTQs7n85lPPfWUedRRR5kJCQlmVlaWOX36dPO7774Ld9Wa9Ntvv5lAo7dFixYF1nvvvffMo446yoyJiTF79eplPvbYY+GrdCMOdB77Wx5J/y7NfT3qmj17tpmQkBDaih5Ac89jxYoV5tixY83Y2FgzPT3dvOyyy8ySkpLwVbydi8Y2yjTVTkVSO6U2Sm1UW1H7FF5qnyKH2qfIo/YpvNQ+RQ61T+FhmKZpIiIiIiIiIiIiIlEnMnJoRUREREREREREpMUU3BMREREREREREYlSCu6JiIiIiIiIiIhEKQX3REREREREREREopSCeyIiIiIiIiIiIlFKwT0REREREREREZEopeCeiIiIiIiIiIhIlFJwT0REREREREREJEopuCciIiIiIiIiIhKlFNwTERERERERERGJUgruiYiIiIiIiIiIRCkF90RERERERERERKKUgnsiIiIiIiIiIiJRSsE9ERERERERERGRKKXgnoiIiIiIiIiISJRScE9ERERERERERCRKKbgnIiIiIiIiIiISpRTcExERERERERERiVIK7omIiIiIiIiIiEQpBfdERERERERERESilIJ7IiIiIiIiIiIiUUrBPRERERERERERkSil4J6IiIiIiIiIiEiUUnBPREREREREREQkSim4JyIiIiIiIiIiEqUU3BMREREREREREYlS/x9y1PDzHPRdaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1259.84x590.551 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = 1/2.54  # centimeters in inches\n",
    "fsize = 13\n",
    "plt.rcParams['figure.dpi']=600\n",
    "plt.style.use('default')\n",
    "lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
    "lat_formatter = LatitudeFormatter()\n",
    "\n",
    "fig = plt.figure(layout=\"constrained\", figsize=(32*cm,15*cm)) # Width, height\n",
    "offs = [-0.3,-0.1,0.1,0.3]\n",
    "gs = GridSpec(2, 4, figure=fig)\n",
    "\n",
    "# cams21 = cams.sel(time='2022-03-01')\n",
    "colorsDic = {'Spring':'#6EEB83','Summer':'#F24236','Fall':'#FFA500','Winter':'#2e86ab'}\n",
    "\n",
    "\n",
    "\n",
    "# Monthly\n",
    "ax1 = fig.add_subplot(gs[0, 0])\n",
    "ax2 = fig.add_subplot(gs[0, 1])\n",
    "ax3 = fig.add_subplot(gs[0, 2])\n",
    "ax4 = fig.add_subplot(gs[0, 3])\n",
    "# Diurnal\n",
    "ax5 = fig.add_subplot(gs[1, 0])\n",
    "ax6 = fig.add_subplot(gs[1, 1])\n",
    "ax7 = fig.add_subplot(gs[1, 2])\n",
    "ax8 = fig.add_subplot(gs[1, 3])\n",
    "\n",
    "\n",
    "axs = [ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8]\n",
    "colors = ['#5296c5', '#ff8d2a', '#60b760', '#e05c5d', '#ccb6df', '#b9f2f0']\n",
    "formatter = matplotlib.dates.DateFormatter('%H:%M')\n",
    "\n",
    "extentLis = [[114, 116, 1, 3],[101, 103,4, 6],[120, 122, 17, 19],[127, 129, 40, 42],]\n",
    "\n",
    "titles = ['Borneo Island (2\\u00B0N, 115\\u00B0E)', 'Malaysia (5\\u00B0N, 102\\u00B0E)','Philippines (18\\u00B0N, 121\\u00B0E)',\n",
    "          'North Korea (41\\u00B0N, 128\\u00B0E)',]\n",
    "alp = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n",
    "\n",
    "\n",
    "def adjustFig(f):\n",
    "    f.set_xlabel('', labelpad = 0.1,color='white')\n",
    "    f.set_ylabel('', labelpad = 0.1,color='white')\n",
    "    f.add_feature(bio, linewidth=1, alpha=1)\n",
    "    f.add_feature(cfeature.COASTLINE, linewidth=1, alpha=1,color='black')\n",
    "\n",
    "    for pos in ['right', 'top', 'bottom', 'left']:\n",
    "        f.spines[pos].set_visible(False) \n",
    "\n",
    "\n",
    "for count in range(8):\n",
    "    count2 = count\n",
    "    axs[count].set_xlabel(' ', labelpad = 0.1)\n",
    "    if (count<4):           \n",
    "        try:\n",
    "#             df3 = df2\n",
    "            gems_2 = gems[(gems['lat']>extentLis[count][2])&(gems['lat']<extentLis[count][3])&(gems['lon']>extentLis[count][0])&(gems['lon']<extentLis[count][1])]\n",
    "            cams_2 = df2[(df2['lat']>extentLis[count][2])&(df2['lat']<extentLis[count][3])&(df2['lon']>extentLis[count][0])&(df2['lon']<extentLis[count][1])]\n",
    "#             cams_2['emiss_bio'] = cams_2['emiss_bio']*64\n",
    "            era5_2 = era5[(era5['latitude']>extentLis[count][2])&(era5['latitude']<extentLis[count][3])&(era5['longitude']>extentLis[count][0])&(era5['longitude']<extentLis[count][1])]\n",
    "\n",
    "            plotMonthly(axs[count],count)\n",
    "            if (count==0):\n",
    "                axs[count].set_ylabel(\"HCHO (10$^{15}$ molecule cm$^{-2}$)\", color=HCHOColor)\n",
    "            else:\n",
    "                axs[count].set_ylabel(\"\")\n",
    "\n",
    "            axs[count].set_title(titles[count], pad = 0, fontsize=fsize)\n",
    "            axs[count].text(0.06,0.9,'('+alp[count]+') Monthly',transform=axs[count].transAxes,size=fsize)\n",
    "\n",
    "        except:\n",
    "            print(yr+months[count]+'?')\n",
    "\n",
    "    else:\n",
    "        axs[count].tick_params(axis='x', direction = 'in',labelsize=10)\n",
    "        axs[count].set_xlim(6.5,17.5)\n",
    "        axs[count].text(0.06,0.9,'('+alp[count]+') Diurnal',transform=axs[count].transAxes,size=fsize)\n",
    "        \n",
    "        loca = extentLis[count-4]\n",
    "        gems_2 = gems[(gems['lat']>loca[2])&(gems['lat']<loca[3])&(gems['lon']>loca[0])&(gems['lon']<loca[1])]\n",
    "        \n",
    "        local = []\n",
    "        for i in range(len(gems_2['lon'])):\n",
    "            local.append(gems_2.iloc[i]['time']+datetime.timedelta(hours=int(gems_2.iloc[i]['timezone'])))\n",
    "        gems_2['Local time'] = local\n",
    "\n",
    "\n",
    "        gems_2['Local time'] = gems_2['Local time'].dt.hour+1\n",
    "\n",
    "        count2 = 0\n",
    "        \n",
    "\n",
    "        for i in ['Spring','Summer','Fall','Winter',]:\n",
    "\n",
    "            temp = gems_2[gems_2['season']==i]\n",
    "            temp['Local time'] = temp['Local time'] + offs[count2]\n",
    "            lg = True if count ==4 else False\n",
    "            g = sns.lineplot(x='Local time', y='HCHO_column', hue='season', data=temp,legend = lg,ax=axs[count],err_style='bars',errorbar='sd',err_kws={'capsize':6},palette=[colorsDic[i]])\n",
    "\n",
    "            count2 = count2 + 1\n",
    "\n",
    "        axs[count].set_ylim(0,12)\n",
    "        \n",
    "        axs[count].xaxis.set_major_locator(mticker.FixedLocator(list(range(8,17,2))))\n",
    "        if (count==4):\n",
    "            axs[count].set_ylabel(\"HCHO (10$^{15}$ molecule cm$^{-2}$)\", labelpad = 0.1)\n",
    "            #get handles and labels\n",
    "            handles, labels = axs[count].get_legend_handles_labels()\n",
    "\n",
    "            #specify order of items in legend\n",
    "            order = [0, 1, 2, 3]\n",
    "\n",
    "            #add legend to plot\n",
    "            axs[count].legend([handles[idx] for idx in order],[labels[idx] for idx in order],fontsize=fsize-1, loc='lower left',frameon=False,ncols=2)\n",
    "            axs[count].tick_params(axis='y', direction = 'in',labelsize=10)\n",
    "        else:\n",
    "            axs[count].set_ylabel(' ', labelpad = 0.1)\n",
    "            axs[count].tick_params(axis='y', labelright=False, labelleft=False, direction = 'in',labelsize=10)        \n",
    "\n",
    "    axs[count].tick_params(labelsize=11)     \n",
    "                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df8afad9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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