05 Spatial Autocorrelation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import itertools\n",
    "import collections\n",
    "import re\n",
    "import json\n",
    "import requests\n",
    "from shutil import copyfile\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import colour\n",
    "\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "# https://matplotlib.org/3.1.0/tutorials/colors/colormap-manipulation.html\n",
    "\n",
    "from shapely.geometry import Polygon, Point\n",
    "import geopandas as gpd\n",
    "import pyproj\n",
    "import folium\n",
    "from folium import Rectangle\n",
    "from folium import Polygon as f_Polygon\n",
    "import affine\n",
    "\n",
    "from tqdm import tqdm # Progress bar stuff cos progress bars are good\n",
    "tqdm.pandas() # now can use progress_apply instead of apply\n",
    "\n",
    "import pickle # Pickle stuff, cos pickling is good\n",
    "\n",
    "# Styles\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x) # pandas\n",
    "np.set_printoptions(precision=5, suppress=True) # numpy\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "\n",
    "# seaborn plotting style\n",
    "sns.set(style='ticks', context='poster')\n",
    "\n",
    "import rasterio\n",
    "from rasterio.crs import CRS\n",
    "from rasterio.plot import show\n",
    "from rasterio.warp import transform_bounds\n",
    "from rasterio.features import geometry_mask\n",
    "\n",
    "from shapely.ops import transform\n",
    "from functools import partial\n",
    "import pyproj\n",
    "\n",
    "# Charts\n",
    "def hide_splines(ax):\n",
    "    # Hide the right and top spines\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "\n",
    "    # Only show ticks on the left and bottom spines\n",
    "    ax.yaxis.set_ticks_position('left')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    \n",
    "def add_value_labels(ax, spacing=5):\n",
    "    \"\"\"Add labels to the end of each bar in a bar chart.\n",
    "\n",
    "    Arguments:\n",
    "        ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n",
    "            of the plot to annotate.\n",
    "        spacing (int): The distance between the labels and the bars.\n",
    "    \"\"\"\n",
    "\n",
    "    # For each bar: Place a label\n",
    "    for rect in ax.patches:\n",
    "        # Get X and Y placement of label from rect.\n",
    "        y_value = rect.get_height()\n",
    "        x_value = rect.get_x() + rect.get_width() / 2\n",
    "\n",
    "        # Number of points between bar and label. Change to your liking.\n",
    "        space = spacing\n",
    "        # Vertical alignment for positive values\n",
    "        va = 'bottom'\n",
    "\n",
    "        # If value of bar is negative: Place label below bar\n",
    "        if y_value < 0:\n",
    "            # Invert space to place label below\n",
    "            space *= -1\n",
    "            # Vertically align label at top\n",
    "            va = 'top'\n",
    "\n",
    "        # Use Y value as label and format number with one decimal place\n",
    "        label = \"{:,.0f}\".format(y_value)\n",
    "\n",
    "        # Create annotation\n",
    "        ax.annotate(\n",
    "            label,                      # Use `label` as label\n",
    "            (x_value, y_value),         # Place label at end of the bar\n",
    "            xytext=(0, space),          # Vertically shift label by `space`\n",
    "            textcoords=\"offset points\", # Interpret `xytext` as offset in points\n",
    "            ha='center',                # Horizontally center label\n",
    "            va=va,                      # Vertically align label differently for +ve and -ve values\n",
    "            fontsize=16)                # Set fontsize  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Utilities\n",
    "def bng(df):\n",
    "    # Takes dataframe or geodataframe and converts it to a geodataframe with CRS in British National Grid\n",
    "    if type(df) != gpd.GeoDataFrame:\n",
    "        df = gpd.GeoDataFrame(df, crs={'init':'epsg:27700'})\n",
    "    elif type(df) == gpd.GeoDataFrame:\n",
    "        df = df.to_crs(crs={'init':'epsg:27700'})\n",
    "    return df\n",
    "\n",
    "def wgs(df):\n",
    "    # Takes dataframe or geodataframe and converts it to a geodataframe with CRS in WGS84\n",
    "    if type(df) != gpd.GeoDataFrame:\n",
    "        df = gpd.GeoDataFrame(df, crs={'init':'epsg:4326'})\n",
    "    elif type(df) == gpd.GeoDataFrame:\n",
    "        df = df.to_crs(crs={'init':'epsg:4326'})\n",
    "    return df\n",
    "\n",
    "# Transform shapely polygons\n",
    "def poly_to_bng(poly):\n",
    "    project = partial(\n",
    "        pyproj.transform,\n",
    "        pyproj.Proj(init='epsg:4326'),\n",
    "        pyproj.Proj(init='epsg:27700'))\n",
    "    return transform(project, poly)\n",
    "\n",
    "def poly_to_wgs(poly):\n",
    "    project = partial(\n",
    "        pyproj.transform,\n",
    "        pyproj.Proj(init='epsg:27700'),\n",
    "        pyproj.Proj(init='epsg:4326'))\n",
    "    return transform(project, poly)\n",
    "\n",
    "# Mapping outlines\n",
    "gb_outline = gpd.read_file('../../Data/shapefiles/Download_UK+boundaries_1183810/gb-outlines_2856975/greatbritain.shp')\n",
    "england = gpd.read_file('../../Data/shapefiles/Download_UK+boundaries_1183810/gb-outlines_2856975/england.shp')\n",
    "wales = gpd.read_file('../../Data/shapefiles/Download_UK+boundaries_1183810/gb-outlines_2856975/wales.shp')\n",
    "towns = bng(gpd.read_file('../../Data/shapefiles/Major_Towns_and_Cities_December_2015_Boundaries_V2.geojson'))\n",
    "\n",
    "def plot_uk(df):\n",
    "    # Check a GeoDataFrame is in the UK somewhere. Expects dataframe in CRS BNG\n",
    "    check_bng(df)\n",
    "    fig, ax = plt.subplots(1, figsize=(8, 8))\n",
    "    gb_outline.plot(ax=ax, color='white', linewidth=0.8, edgecolor='0.6')\n",
    "    df.plot(ax=ax, linewidth=0.5, color='red', edgecolor='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurence/anaconda3/lib/python3.7/site-packages/pysal/model/spvcm/abstracts.py:10: UserWarning: The `dill` module is required to use the sqlite backend fully.\n",
      "  from .sqlite import head_to_sql, start_sql\n"
     ]
    }
   ],
   "source": [
    "import pysal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PV Data EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurence/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (11,12,16) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/fit_repd_pv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>postcode</th>\n",
       "      <th>cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REPD</th>\n",
       "      <td>771</td>\n",
       "      <td>8159889.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fitsDB</th>\n",
       "      <td>839694</td>\n",
       "      <td>4965576.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        postcode        cap\n",
       "source                     \n",
       "REPD         771 8159889.00\n",
       "fitsDB    839694 4965576.27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('source').agg({'postcode': 'count', 'cap': 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add in a type to REPD\n",
    "df[df['source']=='REPD'] = df[df['source']=='REPD'].fillna(value={'type': 'repd'}).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>REPD</th>\n",
       "      <th>repd</th>\n",
       "      <td>1083</td>\n",
       "      <td>8159889.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">fitsDB</th>\n",
       "      <th>Community</th>\n",
       "      <td>3038</td>\n",
       "      <td>270053.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domestic</th>\n",
       "      <td>807081</td>\n",
       "      <td>2831636.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non Domestic (Commercial)</th>\n",
       "      <td>27823</td>\n",
       "      <td>1578374.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non Domestic (Industrial)</th>\n",
       "      <td>1752</td>\n",
       "      <td>285511.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  source        cap\n",
       "source type                                        \n",
       "REPD   repd                         1083 8159889.00\n",
       "fitsDB Community                    3038  270053.93\n",
       "       Domestic                   807081 2831636.56\n",
       "       Non Domestic (Commercial)   27823 1578374.13\n",
       "       Non Domestic (Industrial)    1752  285511.65"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['source', 'type']).agg({'source': 'count', 'cap': 'sum'}) #.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "source\n",
       "REPD     8159889.00\n",
       "fitsDB   4965576.27\n",
       "Name: cap, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('source').cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13125465.269995676"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['source', 'type']).agg({'postcode': 'count', 'cap': 'sum'}).cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>source</th>\n",
       "      <th>type</th>\n",
       "      <th>cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REPD</td>\n",
       "      <td>Commercial &amp; Industrial</td>\n",
       "      <td>8.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Commercial &amp; Industrial</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Community</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source                     type  cap\n",
       "0    REPD  Commercial & Industrial 8.16\n",
       "1  fitsDB  Commercial & Industrial 1.86\n",
       "2  fitsDB                Community 0.27\n",
       "3  fitsDB                 Domestic 2.83"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "  .transform(lambda x: x/1000000)\\\n",
    "  .reset_index()\\\n",
    "  .replace({'type': 'repd'}, 'Commercial & Industrial')\\\n",
    "  .replace({'type': 'Non Domestic (Commercial)'}, 'Commercial & Industrial')\\\n",
    "  .replace({'type': 'Non Domestic (Industrial)'}, 'Commercial & Industrial')\\\n",
    "  .groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "  .reset_index()\\\n",
    "#   .sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "grouped=\\\n",
    "    df.groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "      .transform(lambda x: x/1000000)\\\n",
    "      .reset_index()\\\n",
    "      .replace({'type': 'repd'}, 'Commercial & Industrial')\\\n",
    "      .replace({'type': 'Non Domestic (Commercial)'}, 'Commercial & Industrial')\\\n",
    "      .replace({'type': 'Non Domestic (Industrial)'}, 'Commercial & Industrial')\\\n",
    "      .groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "      .reset_index()\\\n",
    "      .sort_values('cap', ascending=False)\n",
    "\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "# https://www.sessions.edu/color-calculator/\n",
    "col1 = '#ffd181' # orange\n",
    "col2 = '#81c0ff' # blue\n",
    "col3 = '#ff9281' # red\n",
    "colors = [col1, col2, col2, col2]\n",
    "\n",
    "repd = mpatches.Patch(color=col1, label='REPD Database')\n",
    "fits = mpatches.Patch(color=col2, label='FITs Database')\n",
    "plt.legend(handles=[repd, fits], fontsize=16)\n",
    "\n",
    "ax.bar(x=grouped.type, height=grouped.cap, color=colors, width=0.6, bottom=None, align='center', data=None)\n",
    "\n",
    "ax.get_yaxis().set_major_formatter(mtick.StrMethodFormatter('{x:,.0f}GW'))\n",
    "labels = ['Commercial & Industrial', 'Domestic', 'Community']\n",
    "plt.xticks(ticks=plt.xticks()[0], labels=labels, rotation=0, fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.ylabel('Installed Capacity', fontsize=16)\n",
    "\n",
    "hide_splines(ax)\n",
    "# plt.savefig('img/chart_pv_databases.png', bounding_box='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Domestic', 'Commercial & Industrial', 'Community'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped[grouped.source=='fitsDB'].type.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>type</th>\n",
       "      <th>cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REPD</td>\n",
       "      <td>Commercial &amp; Industrial</td>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Commercial &amp; Industrial</td>\n",
       "      <td>29575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Community</td>\n",
       "      <td>3038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fitsDB</td>\n",
       "      <td>Domestic</td>\n",
       "      <td>807081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source                     type     cap\n",
       "0    REPD  Commercial & Industrial    1083\n",
       "1  fitsDB  Commercial & Industrial   29575\n",
       "2  fitsDB                Community    3038\n",
       "3  fitsDB                 Domestic  807081"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['source', 'type']).agg({'cap': 'count'})\\\n",
    "  .reset_index()\\\n",
    "  .replace({'type': 'repd'}, 'Commercial & Industrial')\\\n",
    "  .replace({'type': 'Non Domestic (Commercial)'}, 'Commercial & Industrial')\\\n",
    "  .replace({'type': 'Non Domestic (Industrial)'}, 'Commercial & Industrial')\\\n",
    "  .groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "  .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "grouped=\\\n",
    "    df.groupby(['source', 'type']).agg({'cap': 'count'})\\\n",
    "      .reset_index()\\\n",
    "      .replace({'type': 'repd'}, 'Commercial & Industrial')\\\n",
    "      .replace({'type': 'Non Domestic (Commercial)'}, 'Commercial & Industrial')\\\n",
    "      .replace({'type': 'Non Domestic (Industrial)'}, 'Commercial & Industrial')\\\n",
    "      .groupby(['source', 'type']).agg({'cap': 'sum'})\\\n",
    "      .reset_index()\\\n",
    "      .sort_values('cap', ascending=False)\n",
    "\n",
    "fits = mpatches.Patch(color=col1)\n",
    "repd = mpatches.Patch(color=col2)\n",
    "plt.legend(handles=[fits, repd], labels=['REPD Database', 'FITs Database'], fontsize=16)\n",
    "\n",
    "colors = [col2, col2, col2, col1]\n",
    "ax.bar(x=grouped.type, height=grouped.cap, color=colors, width=0.6, bottom=None, align='center', data=None)\n",
    "\n",
    "ax.get_yaxis().set_major_formatter(mtick.StrMethodFormatter('{x:,.0f}'))\n",
    "labels = ['Domestic', 'Commercial & Industrial', 'Community']\n",
    "plt.xticks(ticks=plt.xticks()[0], labels=labels, rotation=0, fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.ylabel('Log of Installation Counts', fontsize=16)\n",
    "ax.set_yscale('log')\n",
    "# ax.set_ylim([1, 10000000])\n",
    "\n",
    "hide_splines(ax)\n",
    "\n",
    "# Call the function above. All the magic happens there.\n",
    "add_value_labels(ax)\n",
    "\n",
    "# plt.savefig('img/chart_pv_databases_count.png', bounding_box='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chloropleth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9352087414379794"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - len(df[df.cap>4]) / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "840777"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Domestic only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# domestic only\n",
    "dom = df[df['type'] == 'Domestic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown    80382\n",
       "Name: lsoa, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom.lsoa.value_counts().head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "80382 unknown LSOA values in FiT Domestic database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0898825059667968"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom[dom.lsoa=='Unknown'].cap.sum() / dom.cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(16,6))\n",
    "\n",
    "bins1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "dom.hist(column='cap', bins=bins1, ax=axes[0], color=col2)\n",
    "\n",
    "bins2 = range(10, 100, 10)\n",
    "dom.hist(column='cap', bins=bins2, ax=axes[1], color=col2)\n",
    "axes[1].set_xlim(left=9, right=None)\n",
    "axes[1].set_ylim(0, 5500)\n",
    "axes[2].set_ylim(0, 65)\n",
    "axes[1].set_xticks([10, 50, 100])\n",
    "axes[0].set_ylabel('Number of Installations', fontsize=16)\n",
    "axes[1].set_ylabel(None)\n",
    "axes[2].set_ylabel(None)\n",
    "\n",
    "bins3 = range(100, 1000, 100)\n",
    "dom.hist(column='cap', bins=bins3, ax=axes[2], color=col2)\n",
    "axes[2].set_xticks([100, 250, 500, 750])\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_title(None)\n",
    "    ax.grid(None)\n",
    "    ax.get_yaxis().set_major_formatter(mtick.StrMethodFormatter('{x:,.0f}'))\n",
    "    ax.tick_params(axis='x', rotation=0, labelsize=16)\n",
    "    ax.tick_params(axis='y', labelsize=16)\n",
    "    ax.set_xlabel('Array Size (kW)', fontsize=16)\n",
    "    hide_splines(ax)\n",
    "# plt.savefig('img/chart_domestic_hist.png', bounding_box='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filter to less than 10kW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8840462986535247"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom[dom.cap<10].cap.sum() / dom.cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9852208142677129"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom[dom.cap<10].cap.count() / dom.cap.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dom = dom[dom.cap<10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by country and select England"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cap</th>\n",
       "      <th>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>2159812.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotland</th>\n",
       "      <td>182079.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>756.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wales</th>\n",
       "      <td>160649.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                cap   id    x    y\n",
       "country                           \n",
       "England  2159812.23 0.00 0.00 0.00\n",
       "Scotland  182079.68 0.00 0.00 0.00\n",
       "Unknown      756.15 0.00 0.00 0.00\n",
       "Wales     160649.76 0.00 0.00 0.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom.groupby('country').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8627867658191783"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom[dom.country=='England'].cap.sum()/dom.cap.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8651416771363498"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dom[dom.country=='England'].cap.count()/dom.cap.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Autocorrelation of Domestic PV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could assign LSOA based on intersection with postcode district.  \n",
    "But I have 90% of Domestic PV in LSOAs anyway.  \n",
    "LSOAs try to be equal population so would have to consider that.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tidy up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Do Moran's I here before adding (and dropping out lots of regions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add in everything else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dom_lsoa = dom[dom['lsoa']!='Unknown'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by LSOA\n",
    "pv_lsoa = dom_lsoa.groupby('lsoa').sum().reset_index()[['lsoa', 'cap']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01000002</td>\n",
       "      <td>22.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa   cap\n",
       "0  E01000002 22.68"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_lsoa.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LSOA shapefiles and ID\n",
    "lsoa = gpd.read_file('../../../Dropbox/ESDA/Spatial/Coursework/data/Lower_Layer_Super_Output_Areas_December_2011_Generalised_Clipped__Boundaries_in_England_and_Wales/Lower_Layer_Super_Output_Areas_December_2011_Generalised_Clipped__Boundaries_in_England_and_Wales.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsoa = lsoa[['lsoa11cd', 'lsoa11nm', 'geometry']]\n",
    "lsoa.columns = ['lsoa', 'lsoa11nm', 'geometry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>lsoa11nm</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01000001</td>\n",
       "      <td>City of London 001A</td>\n",
       "      <td>POLYGON ((532105.0920000002 182011.2300000004,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa             lsoa11nm  \\\n",
       "0  E01000001  City of London 001A   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((532105.0920000002 182011.2300000004,...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsoa.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# House prices\n",
    "# https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/medianpricepaidbylowerlayersuperoutputareahpssadataset46\n",
    "house_prices = pd.read_excel('data/hpssadataset46medianpricepaidforresidentialpropertiesbylsoa.xls', sheet_name='Data', skiprows=5)\n",
    "\n",
    "# Take average median house prices from 2009 to 2018 inclusive\n",
    "# Replace : which is missing value, with NaN so mean works properly\n",
    "house_prices['price'] = house_prices.iloc[:,57:97].replace(':', np.nan).mean(axis=1)\n",
    "house_prices = house_prices[['LSOA code', 'price']]\n",
    "house_prices.columns = ['lsoa', 'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01011950</td>\n",
       "      <td>40020.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa    price\n",
       "0  E01011950 40020.62"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_prices.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1000000)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "house_prices.hist(column='price', bins=200, ax=ax)\n",
    "ax.set_xlim(0, 1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>MODE1_TYPE</th>\n",
       "      <th>MODE1_PC</th>\n",
       "      <th>MODE2_TYPE</th>\n",
       "      <th>MODE2_PC</th>\n",
       "      <th>MODE2_MODE1_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01031349</td>\n",
       "      <td>4</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa  MODE1_TYPE  MODE1_PC  MODE2_TYPE  MODE2_PC  MODE2_MODE1_RATIO\n",
       "0  E01031349           4      0.73           5      0.25               0.34"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Property age at LSOA\n",
    "property_age = pd.read_csv('../../../Dropbox/ESDA/Spatial/Coursework/data/voapropertyage.csv')\n",
    "# property_age.head(1)\n",
    "property_age = property_age[['AREA_CODE', 'MODE1_TYPE', 'MODE1_PC', 'MODE2_TYPE', 'MODE2_PC', 'MODE2_MODE1_RATIO']]\n",
    "property_age.columns = ['lsoa', 'MODE1_TYPE', 'MODE1_PC', 'MODE2_TYPE', 'MODE2_PC', 'MODE2_MODE1_RATIO']\n",
    "age_encode =  {'BP_PRE_1900': 1, 'BP_1900_1918': 2, 'BP_1919_1929': 3, 'BP_1930_1939': 4, 'BP_1945_1954': 5, 'BP_1955_1964': 6, 'BP_1965_1972': 7, 'BP_1973_1982': 8, 'BP_1983_1992': 9, 'BP_1993_1999': 10, 'BP_2000_2009': 11, 'BP_2010_2015': 12, 'BP_UNKNOWN': 13}\n",
    "property_age['MODE1_TYPE'].replace(age_encode, inplace=True)\n",
    "property_age['MODE2_TYPE'].replace(age_encode, inplace=True)\n",
    "property_age.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# epc = pd.read_csv('../../../Dropbox/ESDA/Spatial/Coursework/data/EPC.csv') # large csv - 173mb file, 1.388m rows\n",
    "# epc['postcode_outward'] = [x[0] for x in epc['postcode'].str.split(' ')]\n",
    "# epc['postcode_inward'] = [x[1] for x in epc['postcode'].str.split(' ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# epc.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deprivation indices\n",
    "# http://opendatacommunities.org/data/societal-wellbeing/imd/indices\n",
    "deprivation = pd.read_csv('data/Deprivation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# just get index of multiple deprivation and take decile rather than score or rank\n",
    "dep = deprivation[(deprivation['Indices of Deprivation'] == 'a. Index of Multiple Deprivation (IMD)') & (deprivation['Measurement']=='Decile ')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep = dep[['FeatureCode', 'Value']]\n",
    "dep.columns = ['lsoa', 'dep_decile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>dep_decile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>817998</th>\n",
       "      <td>E01008649</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             lsoa  dep_decile\n",
       "817998  E01008649        3.00"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join up LSOA geometry, Property Age, Price & Deprivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge = pd.merge(lsoa, pv_lsoa, on='lsoa', sort=False)\n",
    "merge = pd.merge(merge, property_age, on='lsoa')\n",
    "merge = pd.merge(merge, house_prices, on='lsoa')\n",
    "merge = pd.merge(merge, dep, on='lsoa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = merge.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>lsoa11nm</th>\n",
       "      <th>geometry</th>\n",
       "      <th>cap</th>\n",
       "      <th>MODE1_TYPE</th>\n",
       "      <th>MODE1_PC</th>\n",
       "      <th>MODE2_TYPE</th>\n",
       "      <th>MODE2_PC</th>\n",
       "      <th>MODE2_MODE1_RATIO</th>\n",
       "      <th>price</th>\n",
       "      <th>dep_decile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E01000002</td>\n",
       "      <td>City of London 001B</td>\n",
       "      <td>POLYGON ((532746.8130000001 181786.8910000008,...</td>\n",
       "      <td>22.68</td>\n",
       "      <td>7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.22</td>\n",
       "      <td>676346.84</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lsoa             lsoa11nm  \\\n",
       "0  E01000002  City of London 001B   \n",
       "\n",
       "                                            geometry   cap  MODE1_TYPE  \\\n",
       "0  POLYGON ((532746.8130000001 181786.8910000008,... 22.68           7   \n",
       "\n",
       "   MODE1_PC  MODE2_TYPE  MODE2_PC  MODE2_MODE1_RATIO     price  dep_decile  \n",
       "0      0.76          12      0.17               0.22 676346.84        9.00  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check what we've got\n",
    "# df.plot(lw=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Regression\n",
    "https://www.earthdatascience.org/tutorials/intro-to-spatial-regression/\n",
    "\n",
    "and \n",
    "\n",
    "http://pysal.org/notebooks/explore/esda/Spatial_Autocorrelation_for_Areal_Unit_Data.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pysal\n",
    "\n",
    "from pysal.model.spreg import ols\n",
    "from pysal.model.spreg import ml_error\n",
    "from pysal.model.spreg import ml_lag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Regression with My Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_back = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 13 here is unknown, so lets get rid of it\n",
    "df = df[df.MODE1_TYPE != 13]\n",
    "# and drop missing values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lsoa</th>\n",
       "      <th>lsoa11nm</th>\n",
       "      <th>geometry</th>\n",
       "      <th>cap</th>\n",
       "      <th>MODE1_TYPE</th>\n",
       "      <th>MODE1_PC</th>\n",
       "      <th>MODE2_TYPE</th>\n",
       "      <th>MODE2_PC</th>\n",
       "      <th>MODE2_MODE1_RATIO</th>\n",
       "      <th>price</th>\n",
       "      <th>dep_decile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4265</th>\n",
       "      <td>E01005081</td>\n",
       "      <td>Manchester 039C</td>\n",
       "      <td>POLYGON ((383233.6579999998 393096.5189999994,...</td>\n",
       "      <td>872.91</td>\n",
       "      <td>8</td>\n",
       "      <td>0.62</td>\n",
       "      <td>3</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.54</td>\n",
       "      <td>150917.50</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           lsoa         lsoa11nm  \\\n",
       "4265  E01005081  Manchester 039C   \n",
       "\n",
       "                                               geometry    cap  MODE1_TYPE  \\\n",
       "4265  POLYGON ((383233.6579999998 393096.5189999994,... 872.91           8   \n",
       "\n",
       "      MODE1_PC  MODE2_TYPE  MODE2_PC  MODE2_MODE1_RATIO     price  dep_decile  \n",
       "4265      0.62           3      0.34               0.54 150917.50        1.00  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deal with outliers\n",
    "df.sort_values('cap', ascending=False)[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deal with outliers\n",
    "# df.sort_values('cap', ascending=False)[:20].loc[15996].geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.sort_values('cap', ascending=False)[:20].loc[19176].geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to work with my dataframe `df`.  \n",
    "My y dependent variable is the solar capacity `cap`.  \n",
    "My x independent variables are `MODE1_TYPE` which are decades ish of mean house age, smaller number is older house, 1 is pre-1900.  \n",
    "`price` is median house price, `dep_decile` is the decile of an aggregated deprivation index."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Electricity consumption by LSOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "elec = pd.read_csv('data/LSOA_domestic_electricity_2017.csv', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "elec = elec[['Local Authority Name', 'Local Authority Code',\n",
    "           'Middle Layer Super Output Area (MSOA) Name',\n",
    "           'Middle Layer Super Output Area (MSOA) Code',\n",
    "           'Lower Layer Super Output Area (LSOA) Name',\n",
    "           'Lower Layer Super Output Area (LSOA) Code',\n",
    "           'Median domestic electricity consumption \\n(kWh per meter)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lsoa'] = df['lsoa'].str.strip()\n",
    "elec['Lower Layer Super Output Area (LSOA) Code'] = elec['Lower Layer Super Output Area (LSOA) Code'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge in (includes handy MSOA codes!)\n",
    "df = pd.merge(df, elec, left_on='lsoa', right_on='Lower Layer Super Output Area (LSOA) Code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "employment = pd.read_csv('data/economic activity by LSOA 2011.csv', skiprows=8)\n",
    "\n",
    "employment['employed_ratio'] = pd.to_numeric(employment['Economically active: Total'], errors='coerce') / pd.to_numeric(employment['All categories: Economic activity'], errors='coerce')\n",
    "\n",
    "employment = employment.dropna()\n",
    "\n",
    "employment['lsoa'] = [x[0].strip() for x in employment['2011 super output area - lower layer'].str.split(':')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, employment, left_on='lsoa', right_on='lsoa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add rural / urban at LSOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "urb = pd.read_csv('data/Rural_Urban_Classification_2011_of_Lower_Layer_Super_Output_Areas_in_England_and_Wales.csv')\n",
    "\n",
    "urb.RUC11CD = pd.Categorical(urb.RUC11CD)\n",
    "urb['rural'] = urb.RUC11CD.cat.codes\n",
    "urb = urb[['LSOA11CD', 'rural']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, urb, left_on='lsoa', right_on='LSOA11CD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add dwelling type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "dwelling= pd.read_csv('data/lsoa_percent_detached_homes_2011.csv', skiprows=8)\n",
    "\n",
    "dwelling = dwelling.dropna().copy()\n",
    "\n",
    "lsoas = [x[0].strip() for x in dwelling['2011 super output area - lower layer'].str.split(':')]\n",
    "\n",
    "dwelling['lsoa'] = lsoas\n",
    "dwelling['detach'] = dwelling['2011']\n",
    "dwelling['detach'] = pd.to_numeric(dwelling['detach'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, dwelling[['lsoa', 'detach']], left_on='lsoa', right_on='lsoa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "owned = pd.read_csv('data/lsoa_rented_owned_housing_2011.csv', skiprows=8)\n",
    "owned = owned.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "lsoas = [x[0].strip() for x in owned['2011 super output area - lower layer'].str.split(':')]\n",
    "owned['lsoa'] = lsoas\n",
    "owned['own'] = owned['%.1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(df, owned[['lsoa', 'own']], left_on='lsoa', right_on='lsoa')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['elec'] = df['Median domestic electricity consumption \\n(kWh per meter)'].str.strip(',')\n",
    "df['elec'] = df['elec'].str.replace(',', '')\n",
    "df['elec'] = df['elec'].str.strip()\n",
    "df['elec'] = pd.to_numeric(df['elec'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge to MSOA if desired..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "msoa = df.dissolve('Middle Layer Super Output Area (MSOA) Code', \n",
    "                   aggfunc={'cap': 'sum', 'MODE1_TYPE': 'mean', 'price': 'mean', \n",
    "                            'dep_decile': 'mean', 'elec': 'mean', 'employed_ratio': 'mean',\n",
    "                            'rural': 'mean', 'detach': 'mean', 'own': 'mean'\n",
    "                            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geometry', 'cap', 'MODE1_TYPE', 'price', 'dep_decile', 'elec',\n",
       "       'employed_ratio', 'rural', 'detach', 'own'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msoa.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add in x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['x'] = df.geometry.centroid.x\n",
    "df['y'] = df.geometry.centroid.y\n",
    "msoa['x'] = msoa.geometry.centroid.x\n",
    "msoa['y'] = msoa.geometry.centroid.y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalise all data first \n",
    "# https://chrisalbon.com/python/data_wrangling/pandas_normalize_column/\n",
    "# https://stackoverflow.com/questions/26414913/normalize-columns-of-pandas-data-frame\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def normalise_df(x):\n",
    "    arr = x.values #returns a numpy array\n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    scaled = min_max_scaler.fit_transform(arr)\n",
    "    df_normalised = pd.DataFrame(scaled)\n",
    "    return df_normalised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['cap', 'MODE1_TYPE', 'price', 'dep_decile', 'elec', 'employed_ratio', 'rural', 'detach', 'own', 'x', 'y']\n",
    "\n",
    "df_norm = df[cols]\n",
    "df_norm = normalise_df(df_norm)\n",
    "msoa_norm = msoa[cols]\n",
    "msoa_norm = normalise_df(msoa_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_norm = gpd.GeoDataFrame(df_norm, geometry=df.reset_index().geometry)\n",
    "msoa_norm = gpd.GeoDataFrame(msoa_norm, geometry=msoa.reset_index().geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['cap', 'hage', 'hprice', 'dep', 'elec', 'empl', 'rural', 'detach', 'own', 'x', 'y', 'geometry']\n",
    "df_norm.columns = cols\n",
    "msoa_norm.columns = cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save and run Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurence/anaconda3/lib/python3.7/site-packages/geopandas/io/file.py:108: FionaDeprecationWarning: Use fiona.Env() instead.\n",
      "  with fiona.drivers():\n"
     ]
    }
   ],
   "source": [
    "# Let's create a format that pysal is happy with. Exporting from geopandas as a shapefile will create a .dbf file too. \n",
    "# df.to_file('data/spatial_analysis_dataframe_20190818.shp')\n",
    "df_norm.to_file('data/spatial_analysis_dataframe_norm_20190821.shp')\n",
    "msoa_norm.to_file('data/spatial_analysis_MSOA_norm_20190821.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression with LSOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f = pysal.lib.io.open(\"data/spatial_analysis_dataframe_20190818.dbf\",'r')\n",
    "f = pysal.lib.io.open(\"data/spatial_analysis_dataframe_norm_20190821.dbf\",'r')\n",
    "\n",
    "y = np.array(f.by_col['cap'])\n",
    "y.shape = (len(y),1)\n",
    "X= []\n",
    "X.append(f.by_col['hage'])\n",
    "X.append(f.by_col['hprice'])\n",
    "X.append(f.by_col['dep'])\n",
    "X.append(f.by_col['elec'])\n",
    "X.append(f.by_col['empl'])\n",
    "X.append(f.by_col['rural'])\n",
    "X.append(f.by_col['detach'])\n",
    "X.append(f.by_col['own'])\n",
    "# X.append(f.by_col['x'])\n",
    "# X.append(f.by_col['y'])\n",
    "X = np.array(X).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :Domestic PV Distribution\n",
      "Weights matrix      :        None\n",
      "Dependent Variable  :       solar                Number of Observations:       30742\n",
      "Mean dependent var  :      0.0761                Number of Variables   :           9\n",
      "S.D. dependent var  :      0.0756                Degrees of Freedom    :       30733\n",
      "R-squared           :      0.3740\n",
      "Adjusted R-squared  :      0.3738\n",
      "Sum squared residual:     109.874                F-statistic           :   2294.6957\n",
      "Sigma-square        :       0.004                Prob(F-statistic)     :           0\n",
      "S.E. of regression  :       0.060                Log likelihood        :   42979.949\n",
      "Sigma-square ML     :       0.004                Akaike info criterion :  -85941.898\n",
      "S.E of regression ML:      0.0598                Schwarz criterion     :  -85866.898\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.0576550       0.0033367      17.2789228       0.0000000\n",
      "           House Age       0.0189229       0.0013305      14.2227169       0.0000000\n",
      "         House Price      -0.1589964       0.0116298     -13.6714642       0.0000000\n",
      "         Deprivation      -0.0486817       0.0021582     -22.5561250       0.0000000\n",
      "                Elec       0.0032842       0.0063798       0.5147804       0.6067102\n",
      "            Employed      -0.0501267       0.0045213     -11.0867787       0.0000000\n",
      "               rural       0.1030859       0.0017238      59.8024581       0.0000000\n",
      "              detach       0.1257778       0.0026347      47.7381877       0.0000000\n",
      "                 own       0.0344524       0.0029870      11.5339602       0.0000000\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           36.338\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2      511295.619           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                8        7176.108           0.0000\n",
      "Koenker-Bassett test              8         681.289           0.0000\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "ls = ols.OLS(y, X, name_y = 'solar', \n",
    "             name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'], \n",
    "             name_ds = 'Domestic PV Distribution')\n",
    "print(ls.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression with MSOA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :Domestic PV Distribution\n",
      "Weights matrix      :        None\n",
      "Dependent Variable  :       solar                Number of Observations:        6764\n",
      "Mean dependent var  :      0.0892                Number of Variables   :           9\n",
      "S.D. dependent var  :      0.0768                Degrees of Freedom    :        6755\n",
      "R-squared           :      0.4609\n",
      "Adjusted R-squared  :      0.4602\n",
      "Sum squared residual:      21.511                F-statistic           :    721.8484\n",
      "Sigma-square        :       0.003                Prob(F-statistic)     :           0\n",
      "S.E. of regression  :       0.056                Log likelihood        :    9851.488\n",
      "Sigma-square ML     :       0.003                Akaike info criterion :  -19684.976\n",
      "S.E of regression ML:      0.0564                Schwarz criterion     :  -19623.601\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.0495988       0.0073364       6.7606718       0.0000000\n",
      "           House Age       0.0276949       0.0038429       7.2067394       0.0000000\n",
      "         House Price      -0.0766848       0.0187796      -4.0834041       0.0000449\n",
      "         Deprivation      -0.0796939       0.0053815     -14.8087374       0.0000000\n",
      "                Elec      -0.0543241       0.0091371      -5.9454622       0.0000000\n",
      "            Employed      -0.0276801       0.0102080      -2.7115945       0.0067130\n",
      "               rural       0.1214229       0.0040012      30.3463303       0.0000000\n",
      "              detach       0.1460853       0.0061306      23.8288008       0.0000000\n",
      "                 own       0.0652282       0.0068168       9.5687870       0.0000000\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           41.283\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2      173527.669           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                8        1418.800           0.0000\n",
      "Koenker-Bassett test              8         108.844           0.0000\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "f = pysal.lib.io.open(\"data/spatial_analysis_MSOA_norm_20190821.dbf\",'r')\n",
    "y = np.array(f.by_col['cap'])\n",
    "y.shape = (len(y),1)\n",
    "X= []\n",
    "X.append(f.by_col['hage'])\n",
    "X.append(f.by_col['hprice'])\n",
    "X.append(f.by_col['dep'])\n",
    "X.append(f.by_col['elec'])\n",
    "X.append(f.by_col['empl'])\n",
    "X.append(f.by_col['rural'])\n",
    "X.append(f.by_col['detach'])\n",
    "X.append(f.by_col['own'])\n",
    "X = np.array(X).T\n",
    "ls_msoa = ols.OLS(y, X, name_y = 'solar', \n",
    "             name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'], \n",
    "             name_ds = 'Domestic PV Distribution')\n",
    "print(ls_msoa.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatial Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/laurence/anaconda3/lib/python3.7/site-packages/pysal/lib/weights/weights.py:168: UserWarning: There are 14 disconnected observations \n",
      "  Island ids: 1621, 3354, 3413, 3573, 3574, 3576, 3589, 4802, 9701, 14785, 17655, 18492, 19103, 19122\n",
      "  \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n",
      "/Users/laurence/anaconda3/lib/python3.7/site-packages/pysal/lib/weights/weights.py:168: UserWarning: There are 8 disconnected observations \n",
      "  Island ids: 777, 829, 832, 835, 1137, 4446, 6090, 6627\n",
      "  \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n"
     ]
    }
   ],
   "source": [
    "w = pysal.lib.weights.Queen.from_shapefile('data/spatial_analysis_dataframe_norm_20190821.shp')\n",
    "w2 = pysal.lib.weights.Queen.from_shapefile('data/spatial_analysis_MSOA_norm_20190821.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OLS regression assumes that each observation is independent from all others. However, in practice, locations that are near eachother are not likely to be independent.\n",
    "\n",
    "We can evaluate spatial autocorrelation in the residuals with Moran’s I test. Our first step in that process is to create a spatial weights matrix. PySAL’s example data has a GAL file that we can read in directly to create this matrix.\n",
    "\n",
    "We can pass this weights matrix to the pysal.Moran function, along with our model residuals (ls.u).\n",
    "\n",
    "The observed value for I is much higher than the value we would expect if there was no spatial dependence. The p-value is the probability that we would observe the value of I that we did (or one greater) if there were no spatial dependence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Morans at LSOA level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://pysal.org/notebooks/viz/splot/esda_morans_viz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('WARNING: ', 1621, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3354, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3413, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3573, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3574, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3576, ' is an island (no neighbors)')\n",
      "('WARNING: ', 3589, ' is an island (no neighbors)')\n",
      "('WARNING: ', 4802, ' is an island (no neighbors)')\n",
      "('WARNING: ', 9701, ' is an island (no neighbors)')\n",
      "('WARNING: ', 14785, ' is an island (no neighbors)')\n",
      "('WARNING: ', 17655, ' is an island (no neighbors)')\n",
      "('WARNING: ', 18492, ' is an island (no neighbors)')\n",
      "('WARNING: ', 19103, ' is an island (no neighbors)')\n",
      "('WARNING: ', 19122, ' is an island (no neighbors)')\n",
      "Observed I: 0.4013957253873726 \n",
      "Expected I: -3.2529846133827784e-05 \n",
      "   p-value: 0.0\n"
     ]
    }
   ],
   "source": [
    "mi = pysal.explore.esda.Moran(ls.u, w, two_tailed=False, )\n",
    "print('Observed I:', mi.I, '\\nExpected I:', mi.EI, '\\n   p-value:', mi.p_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15 islands with no neighbours\n",
    "\n",
    "Moran's I was observed as 0.40, and expected from a reference distribution as -3.25.\n",
    "Spatial autoregression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a spatial regression model to account for spatial non-independence.   \n",
    "The spreg module has several different functions for creating a spatial regression model.  \n",
    "In this example, we will use a spatial error model, but the implementation of a spatial lag model is similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysal.viz.splot.esda import moran_scatterplot\n",
    "fig, ax = moran_scatterplot(mi, zstandard=True)\n",
    "ax.set_xlim([0, 12])\n",
    "ax.set_ylim([0, 12])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysal.viz.splot.esda import plot_moran\n",
    "\n",
    "plot_moran(mi, zstandard=True, figsize=(12,6))\n",
    "# plt.savefig('img/morans_plot_and_ref.png', bounding_box='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.p_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# local moran\n",
    "# http://pysal.org/notebooks/viz/splot/esda_morans_viz\n",
    "from pysal.viz.splot.esda import moran_scatterplot\n",
    "from pysal.explore.esda.moran import Moran_Local\n",
    "\n",
    "# calculate Moran_Local and plot\n",
    "moran_loc = Moran_Local(y, w)\n",
    "fig, ax = moran_scatterplot(moran_loc)\n",
    "ax.set_xlabel('Solar PV (standard deviations from mean)')\n",
    "ax.set_ylabel('Spatial Lag of Solar')\n",
    "plt.savefig('img/moran_local_scatter.png', bounding_box='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = moran_scatterplot(moran_loc, p=0.01)\n",
    "ax.set_xlabel('Solar PV (standard deviations from mean)')\n",
    "ax.set_ylabel('Spatial Lag of Solar PV')\n",
    "ax.set_xlim([-2, 12])\n",
    "\n",
    "# We can distinguish the specific type of local spatial autocorrelation \n",
    "# in High-High, Low-Low, High-Low, Low-High. \n",
    "# Where the upper right quadrant displays HH, \n",
    "# the lower left, LL, the upper left LH and the lower left HL.\n",
    "\n",
    "plt.savefig('img/moran_local_scatter_high_low.png', bbox_inches = 'tight', pad_inches=1, dpi=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vxA5c8+SDYQkhdMpwduwoJrLMuG1PGYD8zs7DnvNhb+ms1hYv7iTBQAt3XcHp3NIg1dbuEK67Jms3NVZYmla1uy7nXFVVdfxwr4VOPxmbC4WqOW2dAsWErM0nXyvjOCssLHRWrVrVOl2d2traJTPVn6MJJ3gjhE4ZpT+6/2m1cdE9x6p9nkgq/uAA2BtbGp3N7Q1Kw8JRef48br3tnEJP0/YNYYplWdPvxYLQBGGFsBFLm7bOqK1BSCH+tJXQUYdhCSF0Solec80PaH7+4LFoWywr284cL3/3Z7t5Y4H++BPVavPG/Rau9H0/NDY21jIwMHDlsegLOvUsXVgaf35k1tSvZQLA8yOz+dKFpfGZ6hPaBcMSQuiUkvORD+nkAJvrHjRKQW1sbA1cdEGrsqhhvW+axe62baUTq5BIhLm189TJTvc8r9TzvNyj0xl0qnvfxbXDz43U8I705P/LdKRz4bmR2fx9F9eOzHDXTns4ZwkhdKrxo9dcfbnZ3Hy+H4/fYbdtnn+oDQjRaFIIBFxaWJC0Nm6cdr8r+dbPtBuqWrvv8Wg0OpBKpUoBYMq96xCaqLI4Yt967Vnb7/4T1FxW2EUuLewiBYoJo7YGz4/M5s+NzOa3XnvWdlw2YOZhWEIInXLC73339szTT//Z6equVZedvcne2LKAm9ZBj6TTgvx+t3+g3tf1Kd/soXl549Ls2Wn+7HI3WL+gzSgunsc41wAAFEVJW5bFNE3r9jxv0lEnhCZzXlNZurI4vPmJl8oKv7ypJi9rcxpSiL90YWn8e9fXjmBQOj4wLCGETjmJX/9mmdPdUwucg/X6moXasqUbzdffaDzY8/3hkTk0HLL9kVFlqjq0qHDQevPNegAA+OhNID/047XW3DlnEkJc3/dNz/PKCSEsHA4f8sgWOr1VFkfst9ZROuGWBzhWVqxYEVu6dGl0qvJvfetb3VdccUV6Jvs0EYYlhNApJfvSCtna2PIZYGzPMat1U4M8Z84OZhgREEUbXEfyxuK54LqTjjaxTEYJXXzRoP7Kq1E+Ph6YrA6h1N3rs6aK4Wy2zS8s9A3DWAQAwDkXfN/ffhRvD6FTkuM4xHGcKTOJPc1yIDOB8Gn2Q0IIoZMAGfzCl240NzSfJZWWdDnbtn3c3dm3YL9amgZg2wCKAuB5oJ11ZitLpz1vdKzcHx7ea6sTmp+XFPLzdYHShN22eeG+Tcm187pAoNRpb9+zrhKdM8f0t23T6B8fbbFjsUV7jlM6kJube21xcfGrR/m+0QxYt27dWlVV6+rr69uPd1/QoWlra6uzLKt9yZIlZx5pWziyhBA6aaX+8ngs/uOH/uB0dV0OngdOZ+fUlU1zr/+ar61uAABQzzqrldu2xpLJEIgiKHPm7BTLytL6Cy/Ui7OqPZAkAHevQSSgBQVZdvGFRNlxVpe7/NnZ6tUf7HHWvJnHBQHEf7ygeNdfl+Kci4zzIOe8KBgMTtMxhNCJDkeWEEInnZFv311PCwuiqT/+6WvOli1XHWl7tKJiXMqL2SBKzNqwoQz8f675py5evBEICVjr1s2deI74+9/2W4WFZZqqjmmBwCuc83lOe4fv3XdfKfE8BajkiNdd3W+dfXZpUUnJ0tzcXHwcdxLCkaWTF44sIYROZ4Lx5pvftNZvuGaqPeAOlb9zZ8zfOflcWmv9+kZlUcNeI0NEln0ejYYIgOfbts0ff8Lhl15C4G9/yxNUlbuvrwlJlZXc/cF9udF/+dLXc2/8WNdR6ShC6LjAsIQQOtkw7jjjRysoHQxC6V6vayu3fGJTVlEatb6+Zu+LX16USSSuhf/6ESgNDWNua2s+AEDg/PPGWDI5Imf1nwEADuEjdBLDsIQQOumIhYXdM7nYDNG04MTPLC9GCIAvLn++FAoKLFpVNQ6EpLljyUBIPnAO5uuvq+Grrvpr3m2fTc5gVxFCxwCGJYTQSSX7jxc0Ztk3zdT1xOqqbmvLlordn4miuATAlyiNW88/HxNkJe5u2VK+u1y78IIWlkgWgEhTVltbxeStIoROJhiWEEInJKulVUw/8cQ5nHFLmT9/W8711ybiD/00J/N///ctZ3tXmXb2Wc3mxpYmsI/dGJO6ZMlGbtuy17ND3n1MrKzo9Fe9Uiyt3+AJc+e2WRuaG4OXX9amv/jSArGsNCvIMjVbWkoAoIRVVGjpvz8lRN59FZvmMgihExyGJYTQCWXnR2+83u3uuZm7bpHb19cIlII8e/arLJu5Lfnoow+7PTuaAADMNW82qWc0bbI2NO+3DtJRIQggBAKysW5d3cTDck2Nqz+7vMQPhz31jKZR7ZxlW/UXX6oPnHduu59Mevrz/2gAABBycjiRpNLMU0+/P/Luq/5yTPqIEJoRx3VFTIQQmijx8G/zrfUbfuR0dV3m7tzZCJwDeB44W7acO/LNbzXvDkq7WS2tC5X6BUe8JQTRND985Ts373WQMTBeeaVOXlDXKs2q3rPLuz82RuR583aIRUWbuW4Ixssr54LrgvHSijrC+Z4FmQRVBXlOja2+4+23uK67e6sVcqR9RQjNPBxZQgidMNKP//VbfiJROGnhZG+/+T4w3cjK9fXriSBodmtr3f6VpkZUlQm5OSNydfW4sWFjuVhW1u/195dNvKazub2BxmI6LSqy/eFhxVq7rkk755x2c/XqRS4AkFiuK5eVjRBFVYgk7tkaxR8fZ87A4ChlrDoejz8qSdKHBUHwc3Nz2w6ljwih4w9HlhBCx5W+clVg4PY7Luu+7J0/t1paP3uo57s9PXVOW9tiQgiTF9ZnQdMO+jV97cwlnSydKWKKXCr+4D93hD/6EV+sqBjdtx6zLEmuqti2+7PT11cm5OaOAQDwVFoiimJJ5aVxc/XrezbNFWIx03/oAc88+yw3kUjMicfjP8WghNDJCUeWEELH1fivfv0FY+Wq/+COQ4+kHaulpR4AQGlc1AI+E0Ckot3eMX+6CeBOT48mz561zX7ltbnKVVf2jF/yjjC58IKAmkj2Sy+8aNmrXhH9eDxPmj0rRVSNk2AQuK6Dv3NnJPTOd3Zln38+HzwPrLXratzBoTGxsnLI6+0tBgBg6XSIO06NzbkAAEApxUdwCJ2kMCwhhI6boS9/dYG5dt3tRxqUJrI3tuzZxFZpbOi0N7bWTlXX6+uvZumMK8+bm7S/9JUm9aILx0h1dR8sPsMgBAJ+PF4CANzu6i7h8XjZxHP9ZMIH8lb+URRQF9Sl7O1dOQAAQAgEzj9vs3P7nYWi4zhCefmY+qUv/OBo3SNCaGZhWEIIHRd9N3/inVbrpu+yZLLoWF1DkBQTNI2BaU455YBbliQEgh4AgL3i5XyAl/Ph4d8CnHHGUODiC7uYaQaNlav2Wy9JrKrkoUh4szs47HDbkvTn/1Ev19e1qeedF2fDw0Rf/twC7fzz+ry+fpX37nRLzzrrr8fqPhFCxxbOWUIIzbidH/noHfrKVU94AwNNB659+My1a5uUuXN3TFeHOw74iXFKy8vdicetDRuKM3/923x7fXNErp41NrFMaWrsMV9bHdRXvjLPbmlpcjq31AMAOG3t9Sw+Zjlbt84FADBXvVLudnfnE0q7Abc8QaeoRx55JK+2tnbJ1VdfPeUo7kS1tbVLamtrl3R3d8vT1bv66qtra2trlzzyyCN5R6enhw9HlhBCM2roX//tTGPNm9/lpjntD8qj5iBmChEgw35fX+5+Bb4P3LJkGsvNBC66sNNu75hFNI14g0Mxf3g4MllbLKsXAABwQQDtozd0ecvOTgj5+dIR3gVC6DjCsIQQmjFD//qNZebrb/wL1/XQTFyPFhcPOV3dB9xyxE+nc2lFue7v7AvuV5ZIaMbKVeVCNDoefveVPdlnlpf6Y2OTBiVaVDQq5udx4VO3NHtnLglnKa0BAKCUzk0kEtHc3NzUkd8VQmimYVhCCM0YZU5Nm7VuXUoIhyyWyarH+nosnY7Ks2a1MNMsdru6SqeqJ1dVDZlr1zZOVQ6cA0smY6k//DEmVVcN+2Njk4Y9lkzGhG9/c5M+e3YTn7AulO/7kWQyeUNubu5DR3I/6PTQO5RWHl+xvej1TYN5huUKAVViyxaWxD9wUc1wZXFkJveQRm/BOUsIoRmTe/NNGSEYsoGKM7JXGjcMzW5rWwwERBLeO9+QQMCRa2o61SWLm80NG6YOSm+hxcUZeU7NMMvq3pTXs23qiBLjnIOiKD2iKBoAALIsdxJCrh8dHS0+4ptCp7RXmvsjX/rxqgWjnpd/2XsXCjfcsgwue+9CYdTz8r/041ULXmnun3RUEx1bOLKEEJpR7uDgeSyZDBy45tEjyLILiuJCJvvPuUOeJwl5Mcta8+a0k8zVJYuTQig4wDl4hAoK03Vtuvo0kRSgohx83y8MhUI7HMdhpmkucByn1nGcJ2VZvjgajepH6dbQKaR3KK088Ofmmosuny8UFof3HI9EVTjj7CpSVhkjD/y5uaayOLwZR5hmFoYlhNCMGLjzrqv9RDLCstljtlTAZJS6uh1eOh3kY/G9J1kTwsB1Dzi6zh0nYa7etIA7DgAAyAvqkvLcwDZvbCyPJRK7JoWLIohlZcOcsQivLBcIAPc8L+A4jmMYRmMoFEqoqpoZGxs7yzCM86LR6PJjcKvoJPf4iu1Fc+sKycSgNFFhcRjmzi8kj7+8vfCu68444j0Rj7dvf/vbFZqmTTnK3Nvbe8wf1R8sDEsIoWNu8K4vXJh+4m9/Bm/KJ1iHTQiFdCAgsEx2rxEfITd3RCwoMGlOVLPb2/MnlknV1duIqsjWhuaGydqkBQWuVF3dzh3H55ZJiSw73HFkAABnc3sOAOTQivI0laRRospMvv9+SIdDRaquD7C8vIDMeTMARCiloGlat23bBZ7nBTVNa2eMLQMADEtoP69vGsy77L0Lp31/s2Z+EXnuyU15cAqEpddeey3nePfhYGFYQggdc1ZLy03HIigBAMi183rc/v5Z6py5zcwyPRAEIESgzLLyOWeG8ebaqn3P8VOpXEpynKna9EdHJaJpMa+3t3zKOv0DEe13D2/R8/PnWIQIAAB+To7MPY97nlcDAOB5ni+FopszAmTyZS45uk5M07w6Ho//NC8vb+Ro3D86dRiWK4TCyrR1QiEZDNM9aiveH0/PPvts66xZs6b8e3j11VfXtra2zsibsweCYQkhdEyZLS1BZlpvO1btc9+X/KHhgD80vN/cI6WxsVmqrh6kkcgwN00AVc2xN2yoZolEnjS7qkWJNMYJpdxav37/ESbGpv+FxBjY+fkqeysoAQCoqhrPZDK1hBCuqup2znnY0dNz+sU8d8xifiWhA4LnxhzH+TUAvPso3D46hQRUiWUzthCJTv30KZt1IKBJ/gx2CwGGJYTQMTby7bvvIAHVPXDNQydWVHSxVCo6VTmRJBE4z1rr1zeBJEHg/HM7SCTi09ycuNc/lO8PDZXSosIxqX7BuNffnytomuOPxRWiacCy2fyp2t1N6O+PQ3l5JaVUlySp3/M8JRaLdTHGIslkcg4AQDgc7j7DSQPzuMg1CTQlaCuK0nP0vgV0qli2sCS+vXM4/4yzq6Z8FLe9Y5gvW1gSn8l+IVw6ACF0jCR//2hB73Ufut3p6LiNRqbMM4dNnjOnwx8fr3K7e6acMG6tXbvQ3b59LgAAuC4YL66YT8PhEQCS9YeGSgEA/OGRfDES6ROI4IJlU7GwcFwqLu7glnXAn4909RshAABVVbcDgEQpTRuGITPG4rIsZwEACCFEDGigeC5RRfEPAMCCweAXj8JXgE4xH7ioZnhr+wgfGcpMWj4ylIGtHSP8AxfW4CPcGYYjSwiho27k3u81pf/02GPe8HANAAAzzaP+w12IRDy+bdukj8rE6qqMNzwSAtPc71/oXn9/yb7HmGH4fiKxa/uVRCIGALEDXV9atmxceO8/n6RpmtZKCFlCCElYllVBCHHz8vJ6CCHE933fCijjEmMLgLH/lmV5ynka6PRVWRyx77ymafsDf26umTu/kNTMLyKhkAzZrAPbO4b51o4Rfuc1Tdtx2YCZh2EJIXTUGKtfV8bu/9HX7PbNn/Pj4/9800UU80AU4WhO8vazmSkDjVRc3CuVlnrma6sPuNgkAIC7vatWrMmi+10AACAASURBVKpMejt6D/rtHHLZJX0ZWV4EAMAYEzzPq5ckKZvJZBYCAASDwR7OObUsK+k4TtR13UUAsEhV1fsP9hro9HNeU1m6sji8+fGXtxc+9+SmPMN0aUCT/GULS+KfvuP8EQxKxweGJYTQEUn//SmByHKevWlTYWb5c9+3N7W9a9869voNVdo552w0Vx9ceDkYYk7uiAuw3xYmct38tL1p02xmmJpcX9/h9vfX8GxWmi6osWw2AJLkK/X13XZb26yp6pFIBMR5c9NkVvU4RCJ79jMxTXMhpTSlKIoBAKAoSqfruhIhJCkIAqiqmgoGg/22bQcopRckEolQbm5u9si+AXSqqiyO2Hddd8bOk215gPb29uDSpUun/Dv+6U9/uv/jH//42Ez26WjBsIQQOiLZZ5dfn33hxd9w25a4bU85z8fp6qqSa2u3uDt2zOOWdVjXIpEIA0FwtKVndtrrN1YBANDK8ozf27dnFT8iSd0sqzcCALhbt85XFjV0+KOjpWJZWfd0I00skQhDRXnvVOXytVd3OZ+8RbMASjjnkUg43Ke67mZBEDzGGHMcZ55hGCXhcHizbdsK5zwoy7JFKc0ROGfmtm19KhF0cU6Nwjm/GwDuOqwvAaETlOd5JJlMTpkrDMM4aedJY1hCCB0Jwn3/bJZOT784DAD4w8M5/vBwjlQ7LyPm5w/ZG1vmsGx22gX49qAU1KamNre/vwCAC9Yba6vkWbN6xPLylLezN1dbtnSj+fobu4IQY3t+INPZs7aw3Jjjrl0XYaZVIlZXZbyeHZMvjyyKIASDwam64L76Whn/1Cd9znYtOJzZtRJ5Iedcnlgvk8ksUFW1jTEme57nEc6FxHs/UEgZi2nXXTMgVleFPEKqXdd9WpKk5w/q/hE6gX3sYx+Lf+xjHzvoN/Q6OzvXHUy9xx57rPPwe3V0nbQpDyF0fGVfWiEPffmrt2eXP3fnoZzndm4Jm6++NlcsK9t6sOcojYs67I72ef7QUKE/NJzPkskca8OGJrerq9Afi0fNtesalTOX9AIASBUVe7Y1Ea+9xnHuuLVamj9/QD3rzHFO6eQ/8yQJ1KbGTnP169VT9YH3Dyja+g1dez5zLu0blHbzfb9IURSTcx7UDYMFLjivWyws7PVtm7lDQ6aTyXicc3yjCaGTBIYlhNBhGft/P3gq+ftHH+C2fXCjQxMQVXXlmhpOZJlPV0+aWzOsNC5a727bPpvrhrRvOUuldm3I63ng9fXTwAXnt+kvrZi9u9z50Y/L1BUre8wH7i9Nfe6O+cIdt2+Z7DryrOp2a0Nz7XR9oXNrdL+6etr+7iaKYtxxnFLP8zICpSn5kktEZtslckkJsJdW2NaNN+dkfv7LyUe4EEInHAxLCKFDRYa+8tWv2pvb33E4J4sVFdvkefN6sitX1obefWUXCQaNPQ1r2q6ViSWJBy59R7fX25dvb2xZzNLpSUdwJvKHhsqMlavquWXJRNN8WlwUVy+4YAwMnQMAEABO6uqi0u8ebtY+/7mtpKTEls5aMi5//astQlERgD/9osgsmZZBVUSBEFMQhDFRFPtEURzQNG2LLMvJiXVd162ybTskCEKpLMuKv2QxYePxYOqb35rHJBmAinb673//5OF8fwihmYdzlhBCB218fLyAvvRyKPPM8m9yxznkf2wJoVBa0FTZbmmpBADI/OWvNbm3fKLHeOONrFhQAO7QcJV66TsG9QsvzPN/9vMst+1D3gNLqqvt4f/5vRzwPEiHQnN3H+cAxCAwG4qKAN55BYhXXZm2fD8XCImFsvp2ubS0iy9enCRFhZT87W/53obmqN+zY8++VHx0VPKuub4u7w+/u0mcN+8Z0zQvyWQyvzFNszQSiWxzHGfPsgOe56mqqg5yzkcIIQHKuSYUFvWzVLqKLagLips3Z6x/vPDRdG+vFqmsvG5X9xBCJyoMSwihg9Lb2/teYXj4M+Y3/+Mif3x86s2rpkIpKA31cXP1G3u9mp/41a+rAQBsABAbFmaSV11ZzjgP0HdfNS52tA97vX1TrtA9Ga97R6X6wkttmXddsf9+bxPr+X4EAECWpG7ng++XbNuu3FP42c9AcHP7RuFPfy5xX1tdKITDXvCC82+S587pKDjnnHUAADt37kz6vq8AALiuqyuK0gkABiEkRAgxOeeC53m5sizrhBAPfvLjtPLKa5vY8udVft01Hlm5imV7d9abinJxUVHRi4dyjwihmYVhCSF0MIht23e7ilIXXLa02x8Y9Jzm5vkHOkmsqNgpBAJZoioWkWTVXP1G3XT1aXm5QQAoAASM2nlzAzfd1Ep/9nMZPF/1x8e1g+kotyzB/MlPG0Jnndlnl5UGXdfNnbJ/ophwXbeUc77f23z6grpG8h//7qmjYxtpd7coXPKOzoLKyj1v8VRUVDxjmuYGx3HO8H0/KAhCuWVZqqZp2wkheYIgjBBCkgAg2radkVVVsc8/NxR81xWW77qS8NCPNxrRiACZzC0YlhA6sWFYQgjtMTIycn4qlfopY8wWBKFVEIQOURR7PM+7zrbthSAIkLnrzjmBNWvboLl5ynZIMKhrTU3ddnd3pdPZWXGw1/dGhokgCNR/a/4Qa2osdR/+TUCglCoP/qTVWf7cAu44B34057pg3XhzefDhX291qqttx3Fivu/vN+/J87xcURT7ZFkeNwxj0b7lHEA0C/IboSAfPMf5DADc8laRALv2eOtxHOcMQojtuq4DACpjLOI4TjQajRqMMYkQ4hBCfNu2DVEUaTadzjLG8pTZs1RVFAOKotQDAAF8FIfQCQvDEkJoj8LCwldM03wmk8l8CQAWT1XPq68rlRcv7nI3b66ebMPZwPnn9erPLl94qNf3120oVFe/vsk9+6xcAAArFMoDAGAA4N1xW0M4EmkzH/1D/VTnC9GIzlLpIAkGTeWmG7ekCgoawTRBluVB3/dLFEVJU0q7Oeeq53llruuGPM8r9zyvXNO0PtM0y6dq23Xd67dt29bIGOvinJ8RCoU+wxhLK4qyw3Gc2kAgsEnX9SbP82LRaLRflmUtmUwqkiS5lFKQJEnWdb2EMRYIh8NtkiRJACBwzvMSicSjubm5Hz7U7wshNDMwLCGEJuKqqj6s6/qnGGORqSo5wWCu8717ckP9A73ePfd63rZtsyeW++Pj7FAvrHzo+h561ZWmmRcLTVYu23bG3bBeBQCQFy7sd9rby8D3QSwtHaAFeQ6hUoLm5WluX19WCIUUyM0RBMYdJoDsOE5JIBAYsCyryLbtRgAAQogXCAQ6Pc8rcV03KAiCTQixJ3skBwDg+35Q0zQtm81eCwBcEITPMMbm+r4fDQaDW3VdXyRJUlIUxeFkMlkbCoV+V1hY+A3TNL9DCLlAEAQmCEKXJElPUko/YJqmJMsyJ4QIjuOcbxhGKBAI4BYoCJ2AMCwhhPbiOM510wWlPRgDJhAgsrTfOkvO5s01wUsv3a4//3zNgZqh8+fpwg9/0G9IUjUHmHKJAEdRwupXvpzU/vZUh/6Jm6qD//3b9WzLFkLz8lVr/foSlkpV73XCm2+CdtWVbfqdt9cDABiGsdc+cpxzEQBsQsgQ53yerus1mqZtM01zzlR98DzPAgAQRTGbyWSuCAQC/ZRSls1m6wAAXNfN0TStj1L6ciAQkE3TbCSE7GSMeZRSFQB827bfJzkOuCMjaVpdrRJCwPM8bprmlwOBwL8f6PtCCM08DEsIoT3GxsaKDMP4yMRjoVCo3zCMQsbYnkUhg1u2buP//VvmrFs3b79GKAVaWJB2tm/fb5Pbfalf+2qb8faL5jPO929nElZJSYX1mU+C5HkGyWZjdktrNS0qcoSAlmGp1H717aeerg/V1LRkr3znnvlIBMCNynKXI4rcdd0CSZJG95QRsnsj3F7Oueo4TuFb38Em3/d9QgjXNG0rAAQopSnLsjTHcWK7zxEEQRRFUSWEzGGMzeKcl1JKFUqp4nkeY4ylCSEB/uqrqrJmbVD42lc4IUSRJMkTBOFS13UflSTphNniASG0C4YlhNAe4XDYGR0d3Svk2LYdnRiUBELAv+feAm9wMLrv+fK8eZ2c0krw/ZC7ZcsB314T8mOUcX7Iaym5ohhQPvXJACx/DoRYTFbmzI47W7Zlnc7Oqt11aG6u7icSQfvnv6iPLFm82SgrrVT6+rbz//ljnvXBD+ps7pygKIpjnHMJACAYDO6glCqqqo4AAPd93yKEeKIopjjnsmmaewU6VVUVSZIcWZYHKaW6qqphfxeBUko5556qqrW2bScJIbrnebYkSUWEEEaXLqXeokUOkSSFUkr7k2ZbRUwyAWDKvekQQscPhiWE0B4DAwNf9X1/rzWUXNfdM4dISiRGpId/N+CMjjZNrKMtW9rDXTdFCwoE/dnlB/WKPwCAl0rbU5UFAoF2Qoim63r1ZOWWpkaEYMB229sVt719HsnJsZTGxk6WTkVpLBZ3+/pr5QULOp2Ojlr/vvsDfFNbyPa8RlpZYfLPldmO49Soqrredd0iSZIGdF2vUhSlw7btwt3XkGW5hzEWsG27JBgMdum6PhsAQBAEX1VVXRAE13EcW9O0qOd5jiAIEgBw13VdURTBdFxOAJSRjKeEZJIOyZQwxpifk8OF3FziOE6KEJKzqjubc6ko1Dmp1C3B7p67Qhdf5Bzsd4gQOvZwuxOEEAAAZJ55pjQ3namVZZkBABDyz6lIVBAM2bIz9CtfJ84zzzSB5+06XlGeBUqBO45hrVvfaKxcNVdeUNd9MNcjpaUma2rca1RJ07SNsixvlSSpDwBUx3GmnMPkCYIqFBYmdn/myaRqb9xYC0TQrHXr6/3hYZFoqqSeuWSjkBNNEs8DUlpi8V/9YsShtCYYDLZalnWGKIqy67qlAACc7/32vuM41Z7nFfq+HyCEGMFgcCOlNCvLcr9lWQFKqUgIiXDOCQBQzrkvCEKYUmpblmVvG9X1zrgfaB40YMxgUl/K9RmHQd/3ez3P65Nlud92vZeWlgfLbV+Qef/Au63WTbjmEkInGBxZQug01/fxT15JYzmmn0i+naXTl2t3f3uNkMkI5McPVikLFgB3XYBo1BQzup3q69u12awkgXbmmS325s3zpOqqQWtD8wIAAG4YqqCo+08e2gcpLHTE+36w1QyF9lrbiDGm+r4fk2VZcV3XlySpf3eQ2Vdo3fp2q6t7v0Uu3a6uKACANHt20h8cjHgDg7Pls8/KRr79Hz12bW1KB2gE2BWMcnJyBmRZJiMjIznBYLBV1/W9Vv1WFCXLGNNlWR7hnPuWZZUrijIiiqKnaZpnGAZIkpRgjOUIggAA4BBCApwIibgjaWGZjPZmWB8Vxbl9aR7SZGZX5irFjAkSABDXdZOqLNeoikdCEqR4NELNCy+q6OsfuDsaCX87HA7jCBM64T3yyCN599xzT3VDQ0P2sccem3bOXXd3t3zFFbtW1+/s7Fw3Xd0TCYYlhE5jI9/93ixuGg+mH19RzT0PwPNA+trX5/s9vQE+NCS7K1buVV85720biW6KXjyeY65evQgAgKVSJRPrCMGgIi2s30YiYYfHE2HgjLhbtu69fhEBgcVyw3sdEgSW0nJ94vs7gqqYq7u+M86IGw6KXYxQD3xPoK5VyD03AgDA3lhjTXdvYlHRqLlmza694Sj1lKVnayQ/X+Km2W/bdqFhGA2maXqBQKAjHA4nM5nMftujeJ4nBwKB7rcWr0wGAoERAIhpmkY8zzMlSaK6ruerqjrMOfclSfo/Qsg1tuPFSiPS+O/WJr5/0YLiN7tGMk+7nOWB6w97npTHGLPemgwepJSqhUEwXdf1pOJi29F1LZMY/1fgrDUcDv/vwf5ZIoSOHQxLCJ3iBgcH51uW9R3XdUskSXpy1qxZ3wcAGPvhfUQIBj6s1NeH9ZdXQvAdb09JJSXp1J8fq+DO5AMaYlkZ6P/75ykXhQRBAOk9V4mZM5pmK4qStSwrooyO9is//2WnvXJV7e5qfHhE9C975yzxO9/Z3Hfxu5hACBgM8i2DLQAg0G37u2sCwK4pVKIgZecKrg6wKyyR0dFJ12Pacw0CEry1EjhJpeKpvz7hOu977yzqeWkSCBgAEOWci7quL5RleTshhAOAzzkXKaXpYDC4wzCMWZIkRWRZdjzPU23bjjHGkpRSzhjzHcfJiUQiBiGkNJFIEMbYJwRBSCqynBYI8RrKwj/fOpTK1OYrgb6kMXZmeTgfgDPP8xxN0wKCIIDjOLrrur4sywFd16nrul5OTk4nIWTq7xkhNKMwLCF0Curr61tiWdbXRFEsNwyjbve6Sa7rvq2rqys3EAj8LPK2tw3Ff/zgZ81162NK3fwe5bprf5FdUPc++cILellRUS65+x7LTyQW+Fu3qbS62uEP/qg7LcuLtPe/v5U99NMCb9Wq4n2vK1aUDycaF82JxWJdjDHVsqyIXVBQplx6aTtZu87nhrHXHCUjmfWSHiw6mJ0+SkS2jWetJgAAIgjAi4qMfevIC+qS4Hn9zLYDLD4uyvPnNxNVAbGwkNFYTPY/drMXuOvOZOLMJXMnnuc4To0oikOEEFHTtD7GmCiKYonv+4FkMskEQZBkWd7BGItLkuTYts0opa5t27MDgUAGALKqqlqqqhb6vh8khIgA4NfkCOasHDXgcoEurYwWcub7lFLQdb08k8ko4Vj+liyRSVjwXUppSBTFFABYsGt/vL8c/J84OpX0DqWVP724tWh162Ce4/iCLFN2TkNJ/Nq3zx2uLI5M+VIEOnYwLCF0ihkdHY3ouv5j13XP2beMc04Mw/iq53nn0zk1d0Ru+cRP5MsuU1LnLJ0z5vtfYYYRhapKAACI3P/DZsswHCoIaeK4aTES9gXf327mxRq0gDa0b9vywgUj3t3fkYkoOp7nCalUqgwAQO0f6LXuu69s36AEABB45DcLIld9oCdNxOrp7kkSiKlSopiB3GaTiDzFhOrYjZ8W8/76xK5rL5jfKldWAlCJZp9+euKITAUAgA0A8NzzAADAbNtXE8khKydaHHx5VatYXKSk5tfOkyRp1HXdnHQ63SRJ0rjv+0zTtGEAGPM8LyoIgiwIQi7nfNxxnJimab2KonQmEon54XB4i6ZphYIggK7rIU3TTM65T5nj5oTDGgBkXdcVKKVqamxMpZR2M8bm67phxRNmSSBER6wwzxJCmKqqEQCIFxUVbTzwnzY61bzS3B/54R/X1yQJIUkiEE8VQOQgjLcO5r/aMpD3xesXbz+vqSx9vPt5usGwhNApJpVK3TtZUJrIcZxzR0ZG1gdmVbcYRYULuevu9WasqqpDhm1XASEqJ4RLOdGYbdsQCAQSlmWB8LWvpIM3fDitf/SmPWsPkfr6AZKbS0UA37KsGACAnMnG+Rf/JY8lkpOvHzQ6KlasfM7tvPBdrgcgTVoHAFzGta0mqftnFQ5eTm6KVlUNSXmxEat10yJncweQvDwmhMMOy2SmfIvOvP9HNcwwJLW8fNTu6Wnw//5EezAYHAKAPM65DADgum7M9/2sKIqy4zglgiCYqqp2S5I0ZBhGgSRJKVEUw5ZlFUaj0e5MJjNL13UzGo3GPc8TDcPwGGOgqqrPOQfP88B1Xc8wDCIBjHk33FgkBrU+oFJ0wfXXcdbYkEtcm/lFRWnGWFBRlHLP8+aKorh1uj9HdGrpHUorP/zj+pqdRBBs+s+/kh4BGANKMj4jP/zj+prK4vBmHGGaWRiWEDrFSJLU4nlewvO8XG1gYLk6NKxkFp9xJuEcwj09Q87AoJ099231nHNgjDHYteP9XizLKgYACIVC3b7ve7Isy6qq+qZpZiVJGhVU1csUF88N/+yhNq+5hYuaRuy6+RHbtmcDAIiiaERXrmqFdNrzqioJEFLhx+N5+16HXHjhSMdF7yrx+NRBaTJ56fFNJX/8bcz3vBx//YZi9RMf73ZffTXf7+gM0+qqfpbJzJrqXJZOSwAAXk9PAQAAFwSq63oxAICqqpsAgPm+XwgAPqV0GAAiiqJ0Z7PZ2nA43EUI0SzLyuecbw2FQoOO45hvrU2l6rrOKKWGaZrVAACO43jRaLTb8zxRkiRPDEV90XOC7sdvTme/c3cVAICtKl1SVRVYjjMk+H5SkqR8Qoin6/o50WgUw9Jp5E8vbi1KEkImBqWJbCpA0ufkzy9uK/zihxfvnOHundYwLCF0CkgmkwHbtq9XVfXhqqqqn2cymafHx8cviUlS0+B37vmcXDtvHYvH89miBlv44Af3vIVGCAmqqtoPAKOO48zxfX+vN9Q8z9MppQJjTJFlOUgI0QOBwHgmk5kby8sbdiMRkc2aJTqOY9mKUj7hvIAXy/Xtu797BgAAyc83Qldc3pF9dvn8ie2bZ5877HHY7y206cgE9LJbb65lfX0SAIBy841b3Q9dF6TDw2m/ozNMI5GEBzBlWJpIrKvLuopSCmzXvr+WZS2UJEmXZbnX87zI7lW7TdNcIMtyv+/7TiAQGPU8b6coisQwjEhOTk5IFMU2QojEGDOz2eyee+Sci47jMEopt23bDg0NR7OVs0ztkreL8l8ebxUCAQMIDXqGYaihUIXj+ZW+z5KcOwpj7OxoNPrIoXw36OS2unUwL0mE/f7xMlGSCGR160AeAIalmYSLUiJ0khscHKzyPK8jFov9lFL6kq7r1/B/vLC4sqLiNzyr38h0HZz1G5Z4O3qrmG6o7vf+UxMY8wEAnGSy1NT1ctM0z1AUpWfftqPRaI6qqtqoAbx1ULclSQpTSnc/qtJTqVQt2dmXcd/9vnqlu2fT7vMIgMt39u2Zo8THxgLmxpYS9cwzm8WKihQQAryszBy87KrAod4vIQDM8/75CyUUcl2AAnL9deMAAEw3qoiisINpy2tvD0k9O/YavXFdN2iaZqXrujm7jwmCYCqKkjJNsyqdTlebplnLOQ8GAoGs67q667qQyWQqOOcgy/IQIYQBAGiatlVRFKYoiqqqaoRRwVcdgyqyrPiDgzVScdE3rIZFa9Tmltl+KiUIApVlRYlyzjml9MlD/W7Qyc1xfMGbNirteiRnO/4hbxGEjgyOLCF0ciPZbPantm1XjI+PM9/3L1AV5ezYksX/t/P6D1vmmjdz9qrMwSOBQJIRUhB8/Ilm75lno+rll2a8Ky4v8xWlWBAEd/c+cKqqdvm+zymloaKI4CeGHct2IR1QlQJVVUdkWQ4AAAQCWsABAH7/j2ZLDz0wwhiT1MGhPvv+H+01YuQPDkb9wcEmAADh07eu337tx2I6lWoO9YYZB5W4Lt3z/tzG1mJtx44RZ/UbdQAAbm9vnlhUNOT19e33th4AgPye9+wAAAKRsAWzZ1k8GDjgz0FN07ozmcyCQCDQ4XleKefc9TzPFgTBEUVRk2WZAkBnNpttCgQCrYqitABAWFEUzjkPOo5jUEoJ8XyXv7zStOfOMUOXXvKBkv+677UtPQM32OXljjwyKnjhsOe5DgGAgGEYJQfoFjrFyDJlIodpA5PIARSZ+lPXQMcChiWETmJDQ0O/lGW53rZtcBxHAADgqVSvG44scXf0mtxx9tqnzXzzzQqxvCylfe7zvXZHZxMAgP+LXwH86jegfuubm5xlS4Pwz1nU2Xg83hCJRDrAtMiC/FBUkESFc84EQdDHx8dLFUXZYjquDQDgd3Tk0M/clgzcdeegftsd0z5a85/6Wx277sbRw7lnBfgoj8f3BCF75crYxHL1jKY2a82bk65RJH/5Sy3mJe9YyAEEgRCDcX5QI1uiKBJN0zpc1y31fT8QCAS2McYKBUEYyWazVFVVoqpqxPf9LkopoZRqpmnGOOdxQkiGEMJd1yVCYUEQohHV17Ss8q9f++TAwAAERPY/pKLsIsdxBGDM8n0fBEEgkiSdBwC/PZzvCJ2czmkoiY+3DuaPAZ0yLuVwxs9pKI3PZL8QPoZD6KSVTqdj6XT6/YZh7PWmmauqMXftWsNPjO8XBFg2G2CZjE84V/YuYABr1xVQzsd3H6KUigDAU6lUna3rgtXdlRgbG4uMjIxUZLPZ+dFodNxxnNl6aUmD+u/f2AwA4G/dmqPfdsd+W5Dsi/T3a+V/eVQ/nPumwKf9VzU3zBwg+/+uIbm53L7s0jL+1s+93UFJ07RWVVU3UEoHJ70epRlJkqKiKMqBQGBE07RhQRA8xliv53l5mqaJvu+HCCEsHA6D4ziOrutzNE1zAEDknBPOuSxJkpi17YRNqe/quswc53JFUSLhcHgDY8xjlDqU0hcIIUQQBIkx9jaYZPI9OnVd+/a5wzmcc8Wf/Cmy4jPI4Zxf8/Y5IzPctdMehiWETlKZTOYCQsig7/t73jJTstlW+vkvydbDv5W0pcu6JjvP6x8o9wYGokpTY8/E4358XBC3bR+WTXNzQFE26rq+gP9/9t48sJOizP9/qrr6/Jy570yuSTKTSSZzcQmKKOKx3ovAriKKiqvCqqDC6iosKiheCB7gKl6LB6y6C+r6dRc5BmZgrmQySSb3ncmdfK7uru46fn9MEiaTZGY8Vob99esf6O7q6qqemfQ79Tz1fqTEmuN0wBfussjRo1EhxHICuG3bGSklAQBIX3jBZu2qK05ZQFet2JDQm7cOGdu2HVPr69P6N76+Kcpo3+nmSRCkMEgGABD23Z6yG95jnqo9PXKkhJSWrkp+VRsbZrgQK3bkhUKhNsdxGl3X3aZpmksIGTv5Pk3TBnBvHxiqCoQQFAqFSDKZ3GIYhqXrekpVVcY5d+H4ipxqGIahquqQEGI2k8nkaJoWURRFFUIghBCYpqnytjabfvo2h/3il5+cv+rvv+j80yctcF3Xtu1L5ufnqzjnswDw2dO9m4D/W5QXRumNV27vK5NC5HIuiZAAUgIREnI5l2VSiBuv3N4X2Ab89QnCcAEBL1JET8+FNDe3AQCACDGkP/Jr2/3BD7egdBrx3Bzd3LZtdN17U2mDz8+Dsa159UnObwAAIABJREFUkqfSzO/tLfF3786D3bvzSFNjSnn/dX2haLQNcrIN0DSNPvlkqfjIDd1SPu+07bpug6qqk77vFwAA0He/K0x2P0P5yIh+8vOUkpIp4fkpv6W1GgAAaZrU//ZvW22ibzy57ckUdrT0CEWB2dqG0g0fvi4P2o/ET3ePEovOsZHjhpRS0yD8rXuPZsrL4yAlIISkqqqDiqIkM5nM1hPmU2iaZr9hGMTzvAXf9zcoipLCUhqzV19THL79tiP++ecVSCmThBCSTqdLVFXtURRFsW270fO8kXA4vOC6bh7nPEoptUKh0ABCSJVSciEEU1UV2badNJuaouTYBEMbyot4Or0ZITzjSBkBxpKGYbT4vu+HQiFk2/YGy7IGTzffgP87XNhckiwvjHQ89Fhv/p628RxKuaJrCj+/sXj28ktqpgKh9MIQiKWAgBchc/f/a45799ffHrr67Z0QDnHvgR9W0/Fxcylmo1dU9CZ+/vNNSNOEXMxlAgAAw4Dwl+46yqVgzvX/uAWEmFGycxb0LQ0toChSyclB7NixmPOBDzVLy5JYVSXOz3cBANybPrFBue+bLhfCIFIy49HftOPmJsUvKSkAjIFLmUfu+Vq7PjjE6Yc/2gQAgEIhYWxtOsImJojfP7B5aRjS85D7299uVa+/eYZJtCpciABARTJRvO/pgcjtn94qLnvtSPSJPyRQ+5HTiisAABzPWg5f6fV1PX5BoamZ5hRmbApjrLquuxwqRAhx0zTbfd8vtW27AQAgJyfHtm173PO8DRpCSbjggh4xN8c0TZvjnDsAQB3H2cwYa0YI+dFotM9xnPjCwkKZaZqdlNJCXddHOefUcZwcRVFSUsqIZVlKKpXymaY5UFQMuLFRC33ly2lvYcHzEQLXdeullEpWVlYLQuiuVCrlIoQeMk3z5j/m70fAi5vywig97qP04rIH6OzsDJ177rlb17t+3XXXjb385S9fdh8/Vdtzzjkncc899wz+hYf4JxOIpYCAFyGp3/zm3Xx+voDffU/BydfUmpoxtbgY/IlJqoTD00p2dpq2t1eKRMJUqipS+LxzoobPpJedPaVkZ49Kn0a8jqPNS/dbL7mg3QMAZNtIAiCeSFgAALyvTzdS6U6OUAF54skx9557twIARC7/23b/ZS/V3Y01Ndw0azXOu41dO1vdffu3SiGwPzSUzcbGS08eJ8pkIPfg3pGRbefnnnwtf3q8Nf+TN9XI7q5mCQDo5z+pCO/Y3ulFI7ZIpk6blO3s3t1k7NrZ4h5qaYbt21O2pm4He1UpOcAYu6qqjtm23XTi+dnZ2WWfpnnHqYabPwYAACalRyil9UIIAwCgqKhoamFhIeP7Pvi+nwMAQAjxI5HIkBAiZdt2LaVUAYCNkUikK5VKhRRFSVHPy3O2bA6rwyMJ9Sc/TSsfuzEifD9lGMY0IQQ8z8tZ3G3nT0xMxCsrz8g2KiDgBYUxhhYWFtbVFbZtr0j9OVXbVCp1VtkjBGIpIODFB2ITk287+aRSXp5R83KHva7uklRvbwkAAAcoN87ZNSwyGRMAAGm6lIfbks6vH0VqVSVz9x/YfnI/UkoLEAKQq4vb8ls+WSJmZ013enp5t5vz0MMN6D8fYUZRwTExM5cr6+qYe/hwo1ZTcxSZhscTyfB6E4l+6Y76+AM/7V7QrOWyKXE305X3D+/eJGemV5QscQ8c3GScs6vVPXBwK/DT75x29+1vDl133aHUW97YvF4bwzD6llaT1mPRpHKQMUYBQFkSSgghEEKwUCikzM3NZamq6vi+b3qeF0EIGVlZWRbGuEdK6XueV5pMJhsURVlYTOBGlmWNUkXh6ubNpkSIUUoLlOM8bVnWDzKZzC2e56kIoe1TU1MoPz//9NWGAwJeAK6++urZq6+++ox36HV1dR343xzP/waBWAoIeJEx+cl/vtAfGdl54jnjnF1H6ZH2Knd4eNVONCUWdYExAABgbUei7Fv3DTiHD28FunbqA0IQIqUlk3x6Jlu67nIZEqWkZFbOz2tqUdEgnZ5eEQ6TlBI2OFyklJTM+UNDeeD72OvtrV/d+0nPGhszy9762ur4V+45bFthHj20XzW+e189zM+v+bPJfW7fVvPcXa3+6FgxGxvPW69fUl01glRVuK9/bY1AaN2NLEKI2KnGZ5pmL+fczWQyWwCOC6TlOUsJk5OTxUvH4XC4kxCCFUURQgjTdd1EJpOptyxrcHEFa4YxFrcsawJjLG3bjggh8vxXv6obC6GqqnrY9/0NnPOFdDrd7LruBs55rq7rRzVNqwGAoPRJQMALRCCWAgJeZKgVG4aRrgvpulhvbu4FjJiYnVOlbWs4Eklrb35TP29vJ/6Bg5sBAKTHtMX7Bo2mJtvt6CjBKnEEpWvuKrOf3pOvbd6cIgWFx7ze3nyxsGAAAJCc7DF6uK2J5Oet+mhLTQPjfe/thFe/CtG/eeNpRdKJKNFoKnzdu5vC4oxMt8F5dt9WvXHLBJuYBESIp25pGPQ7Oqul4ygAADgctvmX74pwy4qfqkeEkMQYzyuKksc5P56ULiWY8wsTwrIIycmeoJRWM8ZM0zSPAkBMCGEYhjFl23YUAJjv+2WGYRxFCHHHceoMw5jCGKcopVHbtgsBAAgh1DAMzDlPu647k8lk6mOxWLuqqohzPuu6rqXr+pxhGN+PxWKHHMd5P2PsdQAQzcrK6s9kMsh13XwIxFJAwAtGYB0QEPAiI/u97xlSsrOOAQAgISg91FLv9fdXKzk5SW1r0yx67atz8aZ6T9u5s0spKEh7U5NW+A2vPyJcmpX6z0c2+719uXpT0xjg9f/5ex0dEdrZWarE43OgqmBd+JIjXndPPTIMiXR9VdFb43O3t2Te8DebMppWj37/X6PqK18xfqbzwZY1CWcolJagbUcKrY/f2KL87MGMfcfnapXvfHtCu/TSw8g0KXnTG4/6pxFKAABSSmTbdiMhpHfpnHW4rdO/8u8K+Vsvz5Uf+wQWrksikUi74zj1juMUUUqzPM9DhBDKGCs1DGMWIYQcx2nwfZ+kUqlihBCxLGtS07RxAABCiOW6LuacC9M09dzc3FEAII7jbJZS1iOEmOM4pXNzcz+an5//nud5O13XbSoqKsoAAFdVVZVSlv9RLyggIOAvSrCyFBDw4gMDgGWee86APzpm4nA4Q177mi5x5dtKUEGB6rhuDK66Ms5U1VIAOHFcZr/96nqRSi3/e3ee2VNjbNt22D10qGm9h0jbxmpZ2SwoWLF3P70FAIAUFc0DxqsTL0+IT/lSlio3fKgTtbQWyJmZ0yZpCl3PIV/76kFZUa4rv/oP4n3/h3Vn8hJYKAyuZWUBALj5+SXwsY+W6P9w3ZwIh7INw2gTQmie5522L0ppg2mag/70dIzf9eXjbuC+D94zezabd9zZlf6nW+pAeX4aS8V1AQBc181RVVWEQqF2SmmFqqq9qVSqOhwOT0UiEce27RbOeUTX9X5CyCTn/ELf921VVc14PN4phOCZTCafc56/2N85uq73SCmV8fHxaCwWm/Z9PwIAjQDwkzN5LwEBAX95ArEUEPAiY/K228/HobDnPLevEqQEpbR0DC44P0zD4VwN40FVVR3FMDzGGCiAMPr2/Z6bSq0qPyJsO4rD4ZRIpyNrPUetrHRoV1cln5paTtBmx45l8YWFiPWFOw7TDeWCYKwCURH7xC0bI+9/X3umqbFCSBlyDWMTefBHI9ptnw15Tz+9ohyJdtmlx2DHjmlRu5GjkTEptmzWaSi0HQAg8opXHFZ++zufT06uWr1aRXXVqqUxGg5lA0A2uC4AAJimeZRzDp7nnTI0SCnNMr/3QD+dmtq24vxTT9dp1753Bt1y87xbV7vKtiAcDvdSSnXLsqIIoQXGGBZCGAghkU6nMUIoW1VVJKU8zBjbb9v2OYqi+L7vxxVFyaRSqS2aps2qqtoJABrGeJ4QYmCMe3VdJ4qimLm5uTKRSLxxfn7+C1lZWYnTvpeAgIC/OEEYLiDgRcTU1FSOc2z8n72enoKl3Wp8dLQE/eejhmmaPa7rYsdxqtPp9CbXdTdlXKdOvvmNQOrrV+2b97q6KtSKDWvmwai1G6eRoY+eKJSWkI5DvG9+O851vTITjzdkwqHN/jfvUe2tTRUKIemldgygzPuXz1goftxDUmlqmsG/fbTXvvGjRfbFL2tyi4u3Oefu2k5DoQaMUDL85FMdzjXvbiJ5eanTvQdcWCDd3NzNp2vnOE6953n1mqYNnqqdECImq9eu6cvHj+Wq3d22aZrDJ55HCHEpZVpVVXtmZqbMdd0QQkhRFGXK8zzdNE2OEMqkjq/o/QoA5hzHqU2n0w2e5xGMMQOAJZuAPCFEHgBooVAolzFWDAAZKaXneZ6jaZqWTqfvzmQyZ9V26oCA/78QiKWAgBcJs7OzsUQicb/7rmvO1V5+8RGckz2nn3uubd700W76iZuIbdv1nuetMuTJFBQ0ynvvTqDI6gUkb/zYJlJcvKImGqmocMXcfMTr6FzXAJINDZUrH7zBMUbHhgEAhJQqFyJECFmRq8Q4N+BnDw4ZP/7BIfHFO9KeotQgAIERohghSghJ6YlEm/rhG7n72c9vBs5BJBIzalXVuu7jAADKli3HxGKplTMBITS/1vlIJDIQjUZHAQDwS16yenfdYnSRDw1L13VL8/Pzx0KhUL9hGC2KokxnMplmKaVjGEaLaZoLCCEmpTQIIdQ0zYjv+1WmaR5zHOdSzvnLFscidV2fWnTnHrAsq0XTtGOmaU4RQrKSyeQkY0znnBuMMZHJZKTneZaiKBcuLCx87kznHBAQ8JcjCMMFBLwImJ6evjSdTv8zpfQiCIVAufXTnAgxbjO2RUpZe6p7FYwdMjExQ1OpohPPqzU1I1hVZ72hoRV5PWxw0LAuvPCI19+fw8bHi2Ad+Ph4IfnUP8+SB77LGEIEAMDzvI2KokxyzpfNMn2ENuCysrBPaQ5RlDnt6/dm5MBAXHDBeHt7Fj+ej7OMPzJSi7OyZo0dOw67Bw6smVOFmprmAKB4rWvrkKVp2jHP81bMx/f9tJSygBAyzAoLkH7F5S30Zw8t+zJF3v++3sy//aTc//1/N+hvfcuYHQpBJpOpCofDh13XLVxslus4TqlhGBMYY1tV1alMJlOZTCZ10zRHGGMmY+wy27YbIpFIFyGEJJPJYiFEWNO0GSkltm27MTs7ezCdTquGYZDs7OwBx3FyMpmMKYQwMMaMc97BOX/36Ojoz0tLSw/+EXMPCAj4MwlWlgICznJs2z4HAO5hjG0EACCEDHHOQRJCVFUdNAyjHWOc0TStLxKJdKqqOg4AoCiKqwEMk/e8T9C3v3NVzpISjczRnp5madurLATs3bu3oOxs1XzJBW0nJjefDBs/lqPf9ZWjS8ec83AoFJpBCC1vRtN1fZBSmgMAgBAy6K9/U+Yfbovw9vas9foV8/M5bHw8B8y1a+ai8jKOADwEcEZGjZTSCozX3P4nKaX5nPNSSmmRc807t5jvfU8bqKoEABBj40r4S188Jh1Hxb/7f9PpdLpscZ6aYRidhJB+TdPmwuFwt+u6hbZtb/Y8r0JRlKU/gwwAUNd1zxVChNPp9AbXdR1CiGcYxgBCyKKU1uq6vsAYSyuKkpZSgu/7BCFkLxlgCiGI53l1mqZNU0q/mUwmS85k3gEBAX8ZArEUEHCWk0wmPy+lNMPhcMowjKF4PK6Gw+GZxXycCtd1G0zT7BdClKRSqU2MsULLstpVVaXq4OAcHxwKrdWve/DQVlJcPLHec8XUlOGPjBaZO3ccXk8wIV3nYqC/RD1eLw0sy+pIpVINuq53LrXBGLsn3GLjrKwzKgQqhYgr0Shb9cxY1JMP/MA0br09qX/+zglzaqpFQWgaAAAjtKpvwzAmCSELUspVeVsIIQMAQEqJpZREKArhG8ox+D4CAMj85yOV6Rs/dnw16tgxlfi+jY6HDzlCSDNN06aUliKEfNM0hxb70gEAhUKhYcdxylOpVEMoFDqyeM1gjBWHQqEFQojq+76/GG4zGWMeAKQopfnJZLKCMbac/6VpWr+u672ZTGazZVnZiUTip+Pj46sMSAMCAv53CMRSQMBZjOM4xPO8It/3hed5AmNsplIpK5FILIfedF0fcBxnA2Ns+cNv23aD67oxv7w8pl3+1v71+lcL8ifW81sixUXTbHg413n2uSZj27YjWl1tOwAAskyuVlaOAQAgXafqzZ9ISECKoigpSmm2PJ54viyQ8AlWAz5jueplrzptcVBj584WwZjFJyeJsWvniFpbuyx0jE2bxr3W1lr/mWdy2eNPFPlvf2ez8eivZ0K/+o9u/VOfSWi+33XiihNCaJwxFqeUrlVgbaWAEgLEdx9YkdwlUikNFAXE8HCOesNHMsb1H56lExMFiqK4uq5HVVVNSilDmqYp2dnZA/F4vE9RlKTv+5FIJDIUi8X6AQBFIpFBwzA6dF2nvu8LQohLKS1TFMWLRqN9vu9nU0qbpZRL5VSWx4Yx9hzHaQAA8DxvXlXVKd/3fzIzM/O9ubm56OneZ0BAwJ9HkLMUEHAWMzU19XepVGozAIBpmmOe5xmLH+YkpTQGAMAYy0MIpQFg1UfTw7gSXXVlGzz072v27+zb36zV1x1BmkakbWtSSF+JxZj0fYxUbXlVx92/fwuKx13jnF2tfGa2liWT2eYFFxx2Dx1sdPPz4kzBWsgwupfKgjDG4pqmdQGAKqW0AAAQAI8e7eq1H3l03cTxJbBlcjU/t0PGYhH3wMFyUBQwtm9vR6apguD+ye3p3fcsr7Kgq96ea91795FMYWEjAADnfN2SJkKsdMM0+vp72cBAzfMDwaC9+c2HxJWXl7qxWLExMDjAPnh9pXb757v9Gz+cZRcUhA3DGF7sCrmum8sYMznncV3XOxhjlq7rCgC4vu+nXdfdYhhGj+u6taqqLkQikTbf96Xv+9w0TRVj3EkpLWCMSSllfHlOCMUAAFRVnVeGh0MqF0fTldWWbduXRKPRPfPz81dmZWW1ne69BgQE/GkEYikg4CxlZGTk1ZzzmzRNSxFC+hVFiTiOU2IYxhGEkKEoiuCcZ3HOw7FYbDyRSBSu1Q9SVVUpLvb5+Pja3kVCcmxZ3J+b19noaN0qJbKIXFgw3Of2bV06djOZeuvKK47IqSnkbdgQ55wviyvGWLWu64cppXWe5x2/H0DJ1G40yNamY+zJp9bNuTHP2dVi79m7fSkUdnyMAtyDBxsAALT6uoH13xqATCax98EbGq2tTbMQjzuS0oj57mtaBUKIPPRwTJoGY+94B/YQqlxaxXn+5kU/BoRA27mjEy7/W7Cbty57L7mVFZXmpvpe7+DBWnzdPzjRG64fWLj4ZVsAADDGjmVZ/VJKDSGEGWNV0Wh03HGclO/73LKsiK7rR2zbHjRNUzDGchljmuu6dQghHg6Hj6ZSqTJN0+Ytyzrm+37YNM0OhJBh23bV4jNUpbjYoKZ5eUTV4oL5M6FQ6C1SysFTvZOAgIA/j0AsBQSchUxOTtZSSr9NKS02DGOCUloihMiORCItuq5nz87OllmWNZzJZLIMwxiWUmLTNFsdx9l6cl+ktY256wklAMDRqOY8+9wm68ILu9n4OJxp6RH9wpcMJq66stEwjLZFE0hECLF1XZ/JZDLlvu/XKIqywDlfXiHBlGYAYNVKj1pRMY0jYV2JRofsPXubTzUGZFoM6TqXlK6beS5TKfB2P52zfOL3/50HAMCXjh/8GZgNDa522aWqWlbWghknqcaGLSgW1bXt27rQyy92M5e9atW7BADwb78tX3n/B+ZkOm3Rn/6sWHnZSxlHiAghTMaY53leA8Y4EYvFhtPptPA8r5YQMphKpaLhcPihWCz22PT09KNCCM33/ZxwONypKIouhDAxxp4QAjmOs1y4lxCyHNL0PM+wDYOaABgEp1JK7DjOWDQaPaM8sICAgD+NQCwFBJyFJJPJryKEUHZ29jil1DUMY8H3fYExDlFKeSQS6U+lUpWGYYxblqUQQnTGWGjRKHGFiHDOO6eS1G7M8O6etRO99+/fFHrda9vsx/5Q/8fUaBM11RkAANd1G03TbBFCVGmalg6Hwzoh5Cil1EEIZVPX1RHGQh0e6SYP/jTinrCqhLKzuLGpvpse6Sjyh4aiIOWqXXsnQw8d2mied+4ATyQS7NhEnVhYWHvL3CkHL8BvazP4wEC+Uljo0N7ewtDj/9MOVVUic+fnTzkGZppR7dp3t0FZKWZlZRvCptnnY+xijEMY4zAA9LiuuzGVSslFd26HMRbCGCPbti9TVfV+Xdd/xxg7T1XVpK7ruu/7HmPMVRTFcV23YelZhmG0L+UqAQBIKQnGmKiqGuac+4wx4bruF6LR6Af/6HcQEBBwxgRiKSDgLOPYzPxXTNM8R0o5xzkH13XjGGPd87w4QkjjnM+EQiGq63qXqqpR3/cTjuMQzjmTUq7yReJChPRr3tnC/+lTzWs9T9+2bSTzm982LjmCr4c0DNCat87iokIPPB+T/IJlUeY4TrNlWcOUUs0wjAzGGATnhcbeZ2e1igrqvPe6WpZOb4Oy0kFj185Wd9/+rQAAWnFxv/QZF4lEfP0nr8bZ+2wlAIBxzq5O97l9m3BW1rRWUTEhfc9nE5MFfGbmjLbWi3Q6pkYiC0AIUM4rmedZp7uH6PqA98pXZhGiUOA87UoZorZdF4vF2gBABYCsWCzWq6qq6rquKqVMGYZBVVXVACDHcZyHGWMKAKQdx6mWUnZYlmUmk8lVJVkQQtqJx6Zp9iuKwsRxXEKILqW8bHp6OjcvL2/mTOYcEBDwxxOIpYCAs4yH949HN5XE+IYI0hFzCSHkGACEASCOMT5GKd2USBwvEea6LhiG4ei67iUSiXXLf6j9/YofCgmZyaze+iZ4+lRCSfv4TW2irs4XpSVZFONiCaAjAJcQYoP/fIaTbdvluq6PMMZcGB9H8J73hey5+RXijY2MVkjbmTbO2XVEOo6gR7saSXFRUq2o6PEHB0+b+H0ySNexVlc3iC0juVQU2LrwJUfYRDyDTCMtPd/0urs3rTc/fcuWDnro0GYAAJzJDINhnLKGHDbDrR3M3CoQgloNjmYr3PVcJ2WaZgRjbOm6HsUYO7ZtF3HOBwAAnbgypKqqzTnPFUJYJ5wjiqKsCJNqmjaAMV4QQmQpiuJyzg1N02zXdStc10UAMMw5Z0II7jjO5lAo9L1QKPQ+KaUVCoXW3f0YEBDwpxGIpYCAs4w3bo4scABHUwlyGKQZY9WqqvYhhARjLPfEtoZhtFBKm1zXXdcGhHhe2vn3X2zQN29qW1rRWcHibrWTQfE4aOfsnIMd25GblbV9se3x/wAYcILxJMZ43jCMQSFENJlMNmDTZMY7rz4ifvf7EOvoWCGC+OxsHp+dXS4twoaGY/q2bQswNASnW90CywKwj++o15ua+mhLa5FIpVbsArR3P73lxGNjx442r6trg0inV7TDWVkLiCjP51N9/s5Y9J9u7k4ZRo1cx1aFSxBisQTKIEUVikjblkqQ4zgxIQRVFMXxfR+bppm2bdtACBHLslpUVSWMMVBVVVtYWFjhuJ5MJmuTyeTysaZpB7Ozs29IJBJv8n3/1ZFI5ArP896CEDpfCBFjjBUkk8kN4XC4zXGcpZBhJcZYm5+fvz6dTn+hoKBgXf+sgLOf4Ymk/svHewv2HjmWk3EZDhlEnLelaPbNF9dMlhcG+WkvBIFYCgg4y1AUZdjUNJibmwv5vr8BAAAhlGMYRq/jOCeXNlEBAK3u5XmM7p5xd36h1jvaVWns2jnl7tufv6IBwqvuV3dsS/K7vsgzjGUDQPZa/aqqai/tdDMMYwohVEgpLcQYMwFA7Ne8uhkuexWEfvHLVu8/H9ksFhKqftGFE7CwEGajo644NrEs/LBlUsAYgPO1HgV6c3O7WFgoFKlUhFRWDpLyMurs3l0jUunT5iu5Bw40Gtu3twFC2G1tbVBycqb55GSeWlba57a07lhqx/Y+W8RCIUNyvq7wVDy7JsswJ+cZKmBS6poVHgHf1fPy8iYymcwUpVRJp9MNxcXFUxhj5LrugpQyquu6oes6m5ubC6mqOun7fsF6zzBN87e5ublP5+bmPj01NfU1QsiclPJSAABFUTzGWMowjClKaR5CiGdlZV0Vi8XyU6nUq1Kp1FYp5c855zcWFxfvO927CTj72N0yFr33oZbqXbUR9P7XFqN4mMBCmuH9Pancj9/zVM6HLm/uu7C5JHn6nv563HHHHcXf//73i7785S/3CiHQ9773vcKBgQFTURS5devW1Ec+8pHxpqYmZ6l9R0eH8bWvfa346NGjodnZWTUcDvMtW7akr7322okLLrgg80LOZT0CsRQQcJahqmp2JpOxCSEhfzHMpet6Ip1Or1gxQQgxhJAvpTylWBItrRkAAJFKRb2eXmHs2N7pHji47Eukbq5HfHYmhSLRhMykCRseKfQPHIoq6XQnGMa6JUl83zcWxzts23YdwHEvKIyxrSgKUEql7/sV6nuuDeGr33HM8zzdZqxQF6Iv/Ow+Tn/5yzmtotLlc7NRf3TsFEJp61Gvu6tW2o4KAMBnZ2tpeztoG2s6vVTvGblYuwcPNqJYzLUuOL8HR6PcGxiYJHl5oRN/RUeRCJB0xlPTqeOGm729c7Bpcx7+w+MT3isvKeWxmG5+/weJrF0XTqWaz42VmMow042Qn07keZ4Hvu8Xmqp6ECPkJaanKSCkEy5cIydbZ4y5mqaZjLEcXdeH1xunZVn/Ew6H71w6zs/PH0skElhV1UsxxhGEEKOUTs3NzVWrqppSVbUvlUr9k23bvq7rj3ue93IAgPn5+WellAc0TTMVRXk4Ozv71jN5TwEvLMMTSf3eh1qq3/GKArwh/3lXi5yoCpftyEb1ZRa696GW6vLCSMfZuML08MMP5+7c+JVBAAAgAElEQVTZsydeXl7u7ty5M9nd3W0988wz8ZaWlsijjz7aXlJS4vf29urXXHNNXSKRIHV1dZna2trM5OSkvnv37viePXtid999d++ll156VolBgEAsBQScdTiOkyWlDKmqmuCcz2uaRhljK3J/CCG9qqr6juOsmbS9AuYvx7bEwkIcqmuOmV+8c4D0D/hMIW6yrGQTvPNqLDCOqK6bJB/4UIaNjoVA0/RTdet53sZQKNTOGFNPOJfFOV9OrjYMI5XJZAzf90uXznGMTfuRRwhrac0XicQU0nSdTUwYJ/cPqgrmrp197sFDVdJ1V1kfINM6o7pwS8hEwrCffOr5kKDndSrFxUk+Ph4FOG43gD5x85x3tGtJgC3No1B5+OGF6OtfP5F66N83Wj9+sLTk7m+1zO04r3jYdvM3huKdIjGzCQBADgyC+pnbsDs5WYaEAKW0tMh/4F+PGYcPU7e1zQhdcflBgfGquUQikXbOedxxnPNVVf2yqqofCYfDNgCApmnI8zxdCOELIXAqldoAAOD7fgQAIgAAUspBQkgIAAAhJE3TfEhRlHwhRAnn/F3z8/N5WVlZwY65s5xfPt5bsKs2gk4USieyId+AnbVh9KsnevNvuGL7aZ3w/9rs2bMnftNNNw2/973vnQYAoJSid7zjHRtbW1sjP/7xj3M/8YlPHLvnnnsKE4kEufnmm4fe9a53LW9KuP/++/O+/OUvl993331FgVgKCAg4JTMzM6WZTOZqhJBCCFnwPK/M8zzQdf0gAOQCAEQikQ6EkEgmk1tO0x0Q37fZf/2u6sRz7s9/vgm/79pxtXmrzCQS9UIIdankiW8YUf7d74jwvgOH0opSf7ocIiGEoJQuhwY55yvyn3zft6SUK0JlDKBYe+97WmD/gXx/YDDfPO/cLjY+XiIBdAAA86UXDUvXTbLRsWJ3/4FquRjqW+Ph+WtfODO8nt5NSm7umPWyi4bs3c80AueAEAqR0tIFRBRXMm7wmZmYdF3Ep2fiqe89EFcKCmxkGInsr94ZK9i+fdqZmJxSJiZysWUNgGW6yqWXgjMyQhAAmFdeOYY31dvittvDqSee3KSec86ci1ATcl2EAKSC8YwyMzuOwyGdc87I7/5fQt+xAycBrvV9PxYOh69Mp9NZ8/PzD2GMuWEYZGFhYZYQ4nPOl0u3IIS4YRh/UFX1bwEADMP4ZlVV1YfGxsbeaRjGV6WUMplMvoExVo8Qui03N/fJP+e9BfzvsffIsZz3v7b4lCvFuzZG0X2/Hc+54Qo468TS5s2b00tCCQBA13X51re+dbq1tTXS3t4eAgCYnZ1VAQCKi4tX/MN+17veNe37PqqsrDzrVswAArEUEHBWIYSojEajE4wxN5PJNAIAqKo6QgiRAHBQURTNtu0yIURE07QhKaXp+/6aooEI4asfv/mYPzdfveraD3+ccXr7so03vaHDbt66YnVKNYyjmfPPLRGcnzIfSNf1/hMSjFehKApVVbXD9/3NcNLPGlZVuWwY6ezbXxd+7Ws63I5OE1sWAsuy/c7OYj49s2au1HIf83PIOGdnCz3SUS9te+1fxU8Dn5kpsZ94qsTYtm3KPXQo35uaLpHJpLK0yy/08ouPZP7w+LIo5ZOTFgBYAAA4Hh+SLa0blm3LFQW0ZPLI0qHf16vqLzlf8ERSVXJyZnE0OmM+/sQwf/CndaASLCan8ngymSeiUWl8+IYu+84v1pt/d9Vh9++vKqKUvn5sbOygoihhTdMMAFCklJqUMts0zd26rj+aTqevFUJY4XD4Add138o5b4lGowdN07wVAKCkpOSHqVRqanR09CeEEF1V1QxC6Gejo6MdiqI8jhDKtm07qqrqN8vKyg78Ke8v4C9LxmU4Hj71ZzkeJpBx2LqGrC8kDQ0Nq/KNCgoKGADA0iaUbdu2pfft2xe7+eabqx5//PHZSy65JHHRRRelDMOQH/zgB6f+2mM+UwKxFBBwdiFc1/Vd1202DKMVALDruo2+75cBAJimObpU68zzvA26rg+t1xHDWNVNS1vrmvfDH20EAND+/qoVv50uOkYTSum6qzaEkClCyLiUMnyqiRBCMpTSbXKt1SkpJYrFpEwkkL6teTjz3/9Tr3/oA0fgzW8imUymyrz98wN8+slTiiVsmpPuc/ubtdqNw2xiMi6SyT+toCzGIDmbB0Ly5ezs8kdIKSl2nYOHyte7zWhqZLSldfnYuvAlR2hff93SMdu3Px+mpjnW9QW9rs5FzAf3uw808MnJFWE4mUwi518+Ww8A4O/cYQIAGIYxhhAaB4AcjHGlEMKzbXvaNM24lPKQqqoP5+fn3+Z5XrOiKFsVRXlSSik0TfttXl7ezNTUVIHruu81DOP3lmV1hMPhDQsLC3EJYBu6vsH3/Ws555pt20WmaZ4/Pz9/Z1ZW1g8BgFBKsa7r6yznBfxvEjKIWEgznBNd13AfFtIMQiZZO8HvBSYSiawal6IoEgBACIEAAK6//vrJrq4u64knnsj6xS9+kf+LX/wiX9d1sW3btuSb3vSm2Te/+c0Lf+1xnwmBWAoIOIvgnF9LCNEJIQOu6241TXPypCYrYvm+7xcripLmnK8pXDKf+5ey0Ofu6KEn5uqcyA9/VAW337Z8KKUEhNCav7Xqut6OEDJc161mjJ02BCal5GsKJQDAE5NzLJE4LkQ8n0vHweLJpzT66ssqLZ91sZHh3DVvXASFQhlEiAkA4HX3lOtNje1eV3edpPS0P9NQKGSrJSUjOBZxpM8MRNRc6fuxk93LjcbG/sx//W6VdxUpLExqtRvnU789IbxJCPBksoSPjq74yilZ8RRtaa31urtPN6zj7RGAparDGGOKENoqhMhIKTMIITWTyVRGIpEuRNQr0pRfEsEiDwBitscBIaXf0pU4xvjyoaGh1wJADaWUUErfoRqW2L/AsykPq1Wm7LEUaaZSqbJ4PD5imuagEAJnMpmPTU1N3bno/q5qmnZfQUHBraFQaEk0/VH5YQF/GudtKZrd35PKvWxH9rqhuH09SXnelqLZv+a4zhSEThlBBAAATdPk/fff39/W1mb8+te/znr22Wej3d3dob1798b37t0b/81vfjP/ne9856zzCgvEUkDAWUIikbjENM1XUEpNwzCmAKA9nU43WJbVKoTQMcbZvu+v+BgLIVTTNNvXS/S29u1vp08+1bDWNQAA71BLlCiKyzhfDmNRSgsRQvLkXXaKonDbtleF9Nbt2/Pywl3dR5BtE7e6KtePRnMBAMzpmVb26VuXhQZta6skZaWzfuvhjcYnPz3EZqbjbGCweL1+cSw2T0qKZryOo8sCkB5ua9AaNnfzmdkQn5xc4d6t5OUdU8vLp0Q6FcbhSEZk0lne0a661T0/D4rHORseXq5hp29pOAwADBCKgqoi++lnqk/cvYc0lSNCkgCwYvcg7ereaF10Yatz4GAdyc3tRYZhet3dq94hjkVTWnX1APv6N4r46GhU/cqXKOza6WGMdUVRkp7nJbOzs6XrulhV9SIqZI7GRdJQFN1UEHI9vwIhEpZSbvA8z6GUVgAAIN2cOOrpYcqFsVFjhyEx32QbxnQ8Hh+RUrKFhYViy7KGQ5a111xInD+P0QZXiDBj7Obx4eF3hg63jfo//dlC5HWvvSL7PdfOn+qdBfz5vPnimsmP3/NUTn2ZtWaS99CUC/u70/KL1287a8NVZ0pjY6Pb2Nh4DACOJZNJ/Ktf/SrrS1/6UvmTTz6ZtWfPntD5559/VlkIBGIpIOAsIZ1Ov15KiSzL8tPpdNT3/exQKNTu+/6GeDyenJmZMcQaSc1SyjXX7DHnTPzwx2uG4ZZhDBBABgCWfzIjhNjaTdnpf21cRJ+ZOabceZfjHj68xdi5Y0I1jAn4509mjN7epPPBG1YaY0oJIIFJ11Xo3r1V63R5HEJAraleoAcOrlop89o7alE87mn1dW3e0a5GAAAcjSaV/LyMe+DAmkVx1wQhiH7pi4NiasrHv/2vduep3Q2SMaxEozmCetn04KEIAIDe1DQACHm0tbVO2o5CcnIyoKpwoqs5OA4CQv4Qe9vlN/HtzeepO3ZsdD77eYxzc73Mj368LNjUsrIe9+Ch7UvHmQ9ev9F80xu76T9cVwCEGOFwmDDGACFEVAwix5ACABRFIZJzjsKmHmGMeYteT8vCWShqJoQhwSWqMHw72wcAR9EWhOuluGvXcs5NduAAyjz4063Oc/satIaGSbjrzrC1/0AH/8pXi1PpzE7puggQ/rY/OnZTwa2fPuuSiv8vUV4YpR+6vLnv3odaqnfWhtGujdElnyXY15OU+7vT8kOXN/edjbYBZ4IQAq666qra4eFh47HHHmszTVMCAESjUXH11VfPPvnkk7Gnnnoqa3R0VIPjP5fOGgKxFBBwFpBIJDBjrE5VVSKEcDHGTNO0Ps459jwvPjc3x3VdTzqOsyovh1JaZRjGQSllLqX0+RwbjDHc8CGk/89jrd7v/7taptOrQ3VCgDxubHnibQNSylUrVUKIdVd7lu+VUuh9/f3oX78H3uHDNQAA7v4DhQBQaHzq0y3Ovv1rWx1gfEZhHlJYMEMPHqpc77pcWNBwZYUEANCqq7sEZ1lee0fN6frVGxr6AaMFpboa+MteGuJNjdjzPA0daU8AAHhHu7YstusCgDq9ubmHtrZuVLKyUsb27e3o1Zdh/6mn/BVC6fi8wPzgBzb6FRv+zvTmy3zTqg/d+XnF/dZ9K3M7Tp4958A6j5bp33tgKP2ua2qllJOEEAEAJl+EEGIkEgkghAwRQmKcc4VSuuw7pWlaKh421DwFR/cvCI71cNo24m1JjouL6WyuWAzdota2tPPMnl0AAHx0NKQnElPsi3eVioXE8t81Z+/et+FYdBIAPjz9pa9ckHfTR59eY9QBfwEubC5JlhdGOn71RG/+fb8dz8k4TAmZhJ+3pWj2i9dvm3qxCiWA4z+SwuEwm5ubU++4446Sz3zmM6OKcjzqPzIyora1tUUwxrBt2zb7BR7qKgKxFBDwAjMyMnIuAHw0lUq9xrKsHk3TsOM4eaFQaIgQYmKMOwBA2ra9ZjhNSmm6rrsdISRVVR3XNG3Gtu0tAgC7tRtroHYj6G950xjc+HGbT02tWplCUroAED2hvzBCiEopV/gsMcZyDMMYcV23bNUghIDQ737Xyn78kyp/enpNceJ1dVfqjY2dtK1ttZGklAwAQG9qOuoP9G9Yy5mblJUOq0VF82x07JT5TO6R9ibz4pd2Ssapd+Dg8uqNUl6eEbOzIXP79k5h21TYtgkIAQhGvd7eBklplXX55Z2suqpA/vsvhy3T0FK//+/j96uqBCmRdBzD2LVzSEQiivYvt7WgtsNZOCsrkrr4paXhaKRDm5g8KjxqIVWL+L29WSAE+L5fRwjZQUmhHJu39+UZog6/9CKJfvgjLm1bUTfWDJLajao3OMhlJqMAxqC/5c2t8KEPmIZlcUNV38IY+wQA5Ni23R0KhSKS0gjl3A+HwyoAlGCM78lkMn+PMU5gjEMAYGuadoQQcqfH+Fer+MIUS7NKIxQfnBEqEr63HC5EsehyjhqfnQ1bP36wz15IrFiJsy66sM3dt//yvvNfciGbmGz2urr+Mevd13zTOv/8szLR+MVOeWGU3nDF9pGz0R7gz+WTn/zk6Nve9rbIz372s4InnngiXl1d7TiOg9vb28OUUnzVVVdN1NTUnHWCMBBLAQEvMK7rvh8hVAMAgBDKpNPpXNM005xzXdM0xfO8bF3XT1tRXkqJfN8v9n2/WNO0owBgep63AQCAFhSUmJWVR9YSS9rjT0z5L71o+TyltIYQkmGMrTKlFEL4hmHMu667Ijcn9Kv/aKHfvv+UBpkimYwBxmu6V6slxfOkoGCG9vdt0Sqrpvn83CQbGa1Yuq7k5MwoOTnSeW7f6cNpvg/O409uCl36yj6SnzcJEgxSVDBKj7TXKpGobT/11Lqu3/anP7MJx2LUSySaPABQq6snSFHhtPSZDlIoyLTA3bd/g7ppk0PPP7dSnLtrOTTp19dhZdcunT34YLm2cWNv6BWXjIsNG8KodqPOOXcwlsmyuJYjpTTshYU5bBiO+bKXjqKsuJZ68KeNxrbmDvdQy2akqlytqDCNrKyI5/u2piijGOM8SqmblZX1gK7rP3d/8atf8ddchhBCW13XTWqalss5Z1JKi3MeAoBQJpN5mRAiy/O8IkPTxmyPWg4HZDOR7URyW83UzFYAALa1KReFwymZTkcAAHhf3woBZJ6zq8Xes7cZGAM+N1cIAJD6f7//uj829rrCO+/4G6Opcc2wbUDAWlRVVXn/9m//dvTrX/96UUtLS3Tv3r0xXddFXV1d5m1ve9v05ZdfflbmxgViKSDgBSYUCt1BKX1Q07QFznkFYywuhDio63rO/Pz8BowxdxwnNxwOHzm55Ml6eJ5XjzGetyzrMKV0M0qnbdHfn7dmY8dZtfsNYzwDAKE1+q0CADANo5U6TqNACIOUwB95tPTktmui4DV37Tl7n132a6Jz80Va7cbByOte10m7uzFSSUY4bjFtad2AcnOkEosL1td3Wp+ZzGN/qCb5eXPs2ERsKVmbZez192QvIhKJZZHo9/UVLv3/ktUBAIBYWNDh+Fa/58VSKJQjn3k6BADg9fTUgJQ96O+u8m3bdomimIauZ8tkMoSkBP25faZXVnpMKchHfGZOAgBIzgUAgKRUSX/py7VyQ/lhtn1bjBDykBBCy2Qy4WQy+R1VVd9nXPyyCX7LP4XEq1+zYFRXHTanpr5gvurST01PT3+Rc36jlkrPCgUrXNPydV0fZYxFAQAMQZ0QCY3oNLNsy0ALCkqsq6446H3nu8dzpvjzuwKV8rKU23akAdhJesj3wR8avtDt6NhiNDW2nO6dBvzf5pZbbhm/5ZZbxte6dtFFF6W7urpW+HjV1dXRb3zjG4N/lcH9hQjEUkDAC0xxcXF3f3//bzjnV3uet4EQMrRYHmQ6HA63uK5bhTFmlNINuq53n+iYvRaqqiYIIQMAQBzHqVIUherP7euh09M71mrPzjt3OdHGNM2jQghb07QIxriTUlp1cjgOAMD3/RLj3m8dgV07MJYSu5NTq7bYr4nvzQPAujlHS3jdPRUgYdjr6Vnhc2T+4w09mfPPK9emp9shNzcKs3MpAJDy3m/kst1Pr8yp4hyQac3AOoWA/xj0e7/eyupqs+AD18d5T0+UfPzGI1TKphVje3bftDs4tFwgF8diEbu0JF86jogODffOf/D6avA8wLGYLxIJFQBAyclpk+k0R/G4J5zlOqOgfezGQ2TXzoiiKJT29QuRl2v5vh8DAEDHJjbS224vYp2dNbD7GfB1vVa+4fV17lfvdlBpiRF51aWHvbu/Xo49X1fqamfkKy6RaGuTat15V69IJMpr8/Nm0CWXePYlF5fB4lZvZevWqLFtayvtG2hEurYsRNXi4jF3eKR+rXfC5+dDU7f+y5PJxx57zPjs7YcsyzqsKMojpmkGK00B/+cIxFJAwFlAVVXVp0dHR/89mUw+pShKUlGUOV3XTUpp3tKqAEJIYIzXXMFRVXUKYzwGAIrneVt8328GALAsq1VKSYShr7miQpqa5llWdrGpay2MsULHceoBACg9njJgGMZh13WbTr5PURTmPftsAzzyyB/lJIwUsrq9rgPQNVIUNHVVfSi/oYFxIQwnJ6cJpATIPh4N1D79qWF0zbWOHB9fkeskT8jN+XPwazdmeVKW63d+rk255VPlfm3tKgGJFYUAAOiXXNwhTYtiIbIEQtgwjFm2scZUamoS4NjEvOXm0fT73l8HAMBTaa6EQ0QtLupSi4tD2DA7kaFjNRJB9Ll9af+rd9fwgYGwfsEFfaF/vN6VJcVJ5Zk9wuns3AoAYL761b3yfe+J4gMHiv2HHs7Pu+Ozk/OfvzPOZ+d0AABv/4Ea2H8A1OrqOTY3GxbzC1EYGsqFffvBam09DLNzRExPZTnDIzUgBGgbNx4VigJGc/MhiQCJ6elTGn2KZDIi8wuqpZTbXddFjuN8Sgjxg9zc3PtCodBZl3cSEPCnEoilgICzhNLS0tb+/v6vAcDrKaUbEUKDnPMTd4UIwzAGPM8rEELonPMIAICiKEkppUMp3XZyn7ZtH/+oVlS0orw8KqenV3zk8bveOUg0VTqOs/3kewEAGGP5qqpOSilNxljUsqzDnufVIIwXyP3fmlO+868GDI8gJTeX2n94fM0ViCUif/O6bp7J6HpT0zAwf1r4vg6AOMnLQ/7YaCHJzp7wp6YL+dhYPgAAUtUVYUBUWur4sWg+QSi5JCCX8ADKjbu/2oa+cFehv3//crgRETUJAKvCj/o1V/eK4ZGQ/9gfik6+djLa1W/voRjngRBAI5FG/I2vJzRN0w2EOnzfL+ScZ4c6jx7OfObWJgQA4uUvF86552wDOF4SxvO8EoRQVN79FR7W9EmGgFn3fasDRaOY3fGFmOA87XV0NqolxZ1Kbo5w9x+oFonkkNfd/bw9QiadrVF3et62G2OlpYOkpmaA9fZWinQKu9FINqqoOIqzshT7Bz+aY0e7Ck6eg9/Xl23s2N7FpmeSJC9v3j1woNH7r9+tEsFeT8/ynyGKhMHYvHkwXFPdkf79/2w+2bQTAACHQsx7w98UO/PzoVgsNg4AViqVupsxVl5dXX3T6d5tQMCLBeXWW299occQEBCwCGNsOJPJXBsOh9MAEHZdt1hKiRcvYyFEjDEWVRQFEEK+lFJd/CCvuz0eISRIbu6kVlqW9B/7Qx6SEowH/rVNdneb6O1/71Mh6gBgzRUijHGSMVakqmqPaZoJ27YrhBAWxniSYtygCjHqP/JoDZ+YzME5OVSm06tWsJCuC3PHjtbMM3s2+719eSKRiLBjEyVibi5fzM0V8snJfOk6OhsZLdaqqkZlOp1LSkrmvb7+ohM/0PoVbxvE27fhcDicchxn1YoRM/QCeekrffWVr+gkr3nNJBxpz1UK8sf46NjzeUfxGNe+/c02+4ILNuMd20fI5voRxfMUMTK6Kj8LAEC7+eNt9hvfsElKaQAA6LrepqrqHOc83/f9MkLItD47O0Lfe10TWszrIS+9aNIvLysAAFBV9Zjv+0VSSiIBVCDKsKKqqhuP5XuqKsh559rKkTbwBwbz/f6BPH9oKF9Sqvx/7L13YCVXef7/nnOm365eVm1X0u5qV9I2r+11wYUWDIYQCKFDSCMklAAJEGryDSX0FEiMQyD00BOaA8YY17W9RXXVtepdt085c8rvD0nX0upqd50CXv/m85fulDNn5to7z33f9zwvIMSNQ52TWkvzCkmUzPkT5xOyt69UPPtZK148tkvt6Znh4+crtJaWbPzEiZwsKwNlejrHl5Z9SampVFSskLKyjMhmw7DeZoLNzZWJdDrKM5lyUlo6t1HQvSOUglJdPW3fd/8B8/jxYWQalERj80pl5RSpqEjx5eUyZc+eaaW9fUm5+24HlZfZXNcJZaxMCLGPUnoyGo3u2I7nSmFubu4PFEUpr6iouOQii4AnF0tLS+WMseWampo7/qdjBZGlgIAnEeXl5aP5fP5DuVzu06qqjgghtqTdOOc6ISSjKMoYIcTgnHPP84paCmiatogxXhBCIMdxDsGRQxD6+N92QzwBudqaDvJ3n1qRANU7mVoCACCEMACA53kHvU2pMoyxDwBgX3N1u3X18T76i3sPKJHIqnXDdav2fQ9saa5rXnVVv33//YWVcnK9oWbhM6UI6JrXk1KScFk4BMJzrQs9i2RFRd513RZK6bb0XOH5CBHnVVWHAQDCX/z8LHvjm8Paa189qOzezbzmZs7Ky5psIToBAKhptsE11wC65hpuDQx20Y99ollOToYAAJSjRxbhjX8641RXFWqxdF0fZYzt4ZxbG9t839+lZrLD2r69Y37/ud3aC184qlxztTQM4wxCqAIu8CLyPK+z8BwxLuWxmIy1tQ2g+x+UklIEUgKp25VRy8vnvL7+ekSIxldXQyiRkOiWW84hgBgXAkhTkwx9/DlDWj6P7W9+i7HR0XJ6zy9KAQBQaSnn4+NRAAD94MFZhNACrq5CfG5OIEAICMZscakULgYhoDY1ZrGh14KigHPy5BYTUHXP7gX9UGef9qd/otO+PsX7pzv2wD/dAeqd/3wG6utBCFGSTqfvAoAX1NbW/uSi1woIuAIIxFJAwJOMxsbGfxkfH9+nKMpvOetFv4QQpmnaOSmloJQ2u6570WX6hmF0U0p3CyG2CJf8gQMduq6PgecBl/LiL0wA4JxXEUIE57wgcDRNy2GM1yJRGAO85tUR+MW9wGZna9nsbK154kQXHR/fy+fmDJRI+N7Q0MVduTfBllckX1gEANiSLkSVlZ5/3bVhKSVwzrfV0aiqmlQUJUUpbQIAqWnaikNpCf7oR5IUoZCqqjnP82qKpZIkAMnv29tpfOoTfWh83MPNexAuK4v4nCs6wLQQAhNCMo7jbLMtkFKSfH3dPvjkxyE8O9fv1O2qs4WIgOsCAIBlWduiERihlOr7i76m7Qml00Oiq5tJzpHS2JDDpjXJpqeb3anpLdfSP/zBnlxT41qdFkLg33RjAt7+F+X55RXdPHqkj558pPBdbm4G7PX21ujtBz334ZOlT6TRsHHs2ADt623CmpbbthIOAEgisSIymZD/s7uxzGYK3xXu7tWgfq0mXwihp9Pp70Qikd3RaHT+cq8dEPBkJBBLAQFPQioqKt6RTCY7YX3lmKqq847jtF/iNAAAME2zq9iLfQOM8WV39VZVddSyLJRMJguCByE0zjkvRExYSSKOw2Fb5HIWAIDz4IOdSm3tlH6okyPdcEFK4IuLF61nenzs4q7QygtuH3UUZduKO0IIU1V1kTFmOo7ThDEGKSVyXbdMSgkcoFrXtFHP8y7Z084Nhw5A+7ozQ36t0wLGOK+qasv7ooUAACAASURBVFoIYWqa1oMxlsUK3gEhyNXWtFmG0WvbdsHegVJasr5KLJLNZpv0bLYH3vP+ejYw0EoiEXAcZz/QtV61bGIyrNTUJEQ2u6VFjf6WN5/LNzUW7t1w3RT+4EdCdH5BBwDYKObeCaSoaZHJXHIF4gakrm6RDg2Vi1xe9/rPbavpwtEok67n0qHhNhga3rrTpzoAgGma85qmIdM0HwuEUsCvg52aeP93wZc+JCAg4FdNKBTiGOM3EUKWVFVdoJQWjQLpuj5uWda4YRhJy7KWLiWUdF0fRAipiqJMK4qSLnqMphXEFMY47zhOfOMzQsgTQlRgjAupO2oYUVxdtbR5DDYzU+f19DZKO68AZ0W9lYqiakV/wCHXVQhC2166mqb1uq5bwxhLAKz1npJSbvmHklLapGnaOU3TBg3DeEKeQEKIEEIoTyndTSltd123wzCMgZ2OZ4zFMcbOps91tm13CCFyAACkq1vw/v4YEgJkOg0bQgkAAKQE43DnvHH06Fm1sXG0sHlgICo3/bDFI6Pz9Ny5Qs0WnZjYo7a2pgAAUCgEpLx806AAkl++yTaKRBgJWfMimSz635t5/KpBraX5PBC8zWFdmiZ4L/qtMkVRFjHG+Ww2W5lMJlcu++JPXigACMZY8L68ghBCYFhLg9NLHXs5BF9+QMCTlJqamnOJROI9hJAZwzDGNE0r1vpAt227yXXdhG3b5RcTSgAACKFK27bbOeclqqqyEMJjei7fawyPnDYz2V5jcfGs8rGP09D3//Oc9dWvDWpLS4rrugWfIsMwBjjnBmNsS8sR3NKy3XWXc/B6epv98fMJ48jh7kveMCEgfdpYbBf94pda9V/cu6XTuq7rs67r7i12/GaklJhSup9SutfzvA5d159QCwnXdVuklI+LFYyZYRhnTNPs2dimKMqyaZpdGCFP5XwOAIAgtGL29Z+2/uEzU2h+ARmeN0BM86LRfHp+ErunTh3C0UhByJJUajXU0zu41nBYgjI7y5BpPK4GfR9hTZ3SWltHsGnmQMoV49jRwtxELlcCCAHSLt5TGQBAra8foQOD2yNn60gA4Z463ex1dW9zQdd/+0VDXIgQIWQxn8/vEUIApfR3JiYmXgubzDuvQAaEEPlUKhX7dU8k4PLJZrMhIYQDAOP/G+MFabiAgCcxVVVVd0xPT1+fSqVeoet6IdqgquoSxjhzOemlTedMeZ5XaxjGgJTSJmfOmO6b/mw/XBCulnv22HBuMMzm5y3j5S8fA7lW54MQ4r7v7xFChDVNO+37/uNL8uvrthcDrcNXV0PC85rNq493u719HVpry4p35uz2yAXngFR1CQCKrtJi116z5W2PMc5IKS/Z3HfLvUmJGWOaYRizrus+oXM3EEJw13UPAwBEIpFehJBUpNTYd74bFi0teTQ6avlPvzVjjp+fcd/y1iMAAOSBB6sAY+IsLV30ByoiaM1pPJlqDj/nNwYAI+o8drqE/8U7dxmNDTNKebm0Hz558MLvzOvt25yiDUvGDP1Q5xhbWa7ApSU5zTBWEYI8W1zCfGmpFgCAVFdNStcLbYkiCX7xX+EY72g4KQ8cyOm63uO6bsGGIhKJTDDG3jLxile/jmjqR3d9/s7vX3T8JyffYowdX1hYqGOMkWg0mjUMgyKEJEJXsgZ86iGlBCEEzmazoYWFhUrO+QIA/Ox/Y+xALAUEPLmRoVDo9YSQo/l8XjcMo4sQUuE4TmSLWLkM1ougmeu6zVJKhfzygTMXvnRJXR3jd3yWgpQ+EWIpLYWpaVqaUhozDGPccZxmAAB5QUGA+7zntoVdr8/+0pcPQJG0j8znLef0mXalqmrO6+quNq87Meg88OC2qJDkfMd6KmzbFDZFRzaW8z9ROOeVhJAhVVVXfN+/ZJH7ZizL6maMhXVdH1EUBeu6HhVC+GJoyLM/+vG9AADm6143Z33+Cxn3a18vRGj4yooaevqt3fYjj7YDxszsaB9ChKj+zCyQinKPzS8kgBCLJdOVyDQEn5qK5paXoyQeTykV5dN8fn4XGxmtZSOjO09uEyKVSnhd6YR57TV9zoMPbayWLCGVlSlt374BIMjnSysNSFWx3t7eJT2X+BOTbTyTrcYliXmxmqzaNihCgLWdu8XIWAwAQCGEzHPOqzRNm3IcJ8oobVFPn/alT7858/t/+OLaz/3zlSaY7hJC7PM879WLi4uly8vLuxBCKlzZ0bKnMlII4XDOFzjn9wLA1/83Bg3EUkDAk5xEIpGbnJz8FOf8/b7v78YYe0KIQlGvruvDCCEPY8w3TCgvxDCMLsdx2g3D6CmklKq3eRcCadu/4nG+ZYeuKN0IoQSltJCGQBf8pFY0bdn7xS8a9La2cQCZYXPzFXx5eWtxsO8jNjVVDQBAh4ZLzGuuHhK2HQWf2YLSmD86Wgoe3THygj7xqUrt/e8dpRjvAQDwPK9K07Q+SmlR64SLQSltRQh5uq6fRQhJIUQDpfSibVEIIa7ruvuEENr69YExNskYqzYQHjM/8uFzEI9SaG5WlExWUWZnZ9m9v6xZf2DAM5lypbR0RmSzlv3L+w7geDwnUqkwDA0BMk3PvOH6ObG4OMUEr2HTM+XgOMAdJ65WV00CAKh79kyJ1dVynk4bxVb1bUNKELnclkgRX1iI84WF+JZti4udAABqy55FHC+ZBp/GvNXtWVW9rW3I/uX9RRcZaM959ozT1NgpXZeoqrqgadpZ13X3hpLJWf2rX19wU6kOAACnq/sD9kMP/cC69trLL6T6NXP06FEOAJ84derUSd/3bwWAq2DN6DR4fz45obCWevsZAHz96NGjziWOvyyCLzsg4ArAsqyf5XK5TyuKIhRFUd31pemhUKgvn88fAAAwTXMBIbRtFYhlWT0bIooxthYxEAJgZHSbMEGKsm2bbdsdmqbNcM7LDcNY9jyvDCG0JapDpdwVOnp4wvv295oAAPQDB85sE0ub4EtL5bysdJEODVdh03D1jo4hf3S0FPDOP9bRoUOrDONCxENKaRDDYFuKpJ8AUkrd87yNtjALiqJ0SynxungirutuWX2n6/qQ4zgH1v/up5TuMwzjY5qm1Sxx/o5IY8MoxlhmMpm9ZnnZpPqOv8j6Jx8B5LpAEgnHGxislpnHLaJEKhUGAJChEFgve2mf/bk7jwAAqI2NQ7DJddwbG99nXnP1IE+lTdA1MI8dO+s88sghUl/v8clJnZSXc62padwdHKiX6cyWVCUiymX/G+8Pj1YAjFaAaYLe2X4eYZJ2z5x9XHwjyG8+npSVppFp5tjsXC163nOXJEAtAIDv+5W+71eqipJhb/3zOJua2oNMQ0rHRWx6unP1jjtfal177Zcvd15PFo4ePfoAADzw655HwK+HQCwFBFwBlJWVjaXT6c8QQp7vOM4ey7KWCSGr2Wz2gKqq0vd9RCk1VVU9RyktFN9qmjZk23Y7AIBpmv2O47QBAFi9vQP0xz/ZHoUKR+xt29ZAkUiklxBiIIQEvyD6BABADLMgoKTnXk5PNh84B5HLG15vX1Po1pvn2fIq1lpbRklFuSYdL8VXVqpYJhPVdu3KsVtuVgRAAmCtjcmcXjqXo6IhETYWEpLOqcxTBHVbijX+vRS2bVcCwJZ7MgyjC2OsCiF8KWXYtu0OgDVH9Egk8lXHcUKWZT2UyWReYVnWgOu6FaqqziOEBGMsZpSXSfMTH+vx/ur/tRgHDozl7767aATMeuXLx/yDj+9CurblO5CplOY8fLKQsnQWFg+Fnv3MHvveXx5UGhvGxcpKlfPYY836wYN9Xnf3lmvwXK4VFAWKeSXtiOOA19XTSOrqlnAk7IhszgQAIPGE3BhLqa2ZQJGIL12vSj9wYASfOoOtn959Gjo7VKX/nM4GBkDm7ag/NVUFAGBce+2sONS5LM92YYHxiwDgihNLAf//JhBLAQFXCCUlJe9LpVJXx2KxUSHEUiqVerlhGJ6qqorv+4RzHsUYe6FQ6Bzn3JVSGusmjQAAIKV8PGxz9z1Fm5yKirJcse2GYaSy2eyBcDjcTwhJOY7Tunm/LsSo/ZWv7QEAUPfsWWaLi5esp8LhcGE+IpOJ5O+9LwIbka+RUQAh6nAkksGalvG6u8u1r3x1Et7wegDDmpzCEZHzxV4AgCUKsARaJYAGcSu6WoPcIZFLX5Yn1QaEEJ9zvqUgx3XdoilNhJDrum4ZAIwmk8kvUkp3I4RkJBIZ45wrQgiVEDLvum6Vt7e1KfT7vzckV1eZduRQl/zBD2P+uYHGzePxW27Ow+jY43MpK0cwOLTzZKUEb2AwIh0XsfMTj3+/vr8t3UBKS0dAiGp/dPSiKcZi8Kmp8tCtt3Z7I8NVWn39gnP6zBHz2NFenkpxyUWDPzAYBwBgAM06Rqv0bFcJfOe7QDGGC9OEzs/vqVVe/SrhPvtZdRih+snJyWfX19cHzt4BVwyBWAoIuEJIJBK5fD7/Ltd1P2RZVkrX9RQhRMlms4VICiGEua5bwTkvFC4jhLimaaOu6+4HAMAIAT9ztn7z2KT94DIOR6S49tqtPUYeHzcGAIhzLm3b3iKUCMae9i+fj228qdWG+gXJWLnIZIv2WytAfQy6DrDR/mNz9GP9ZSuy2YLrtP/jn3TgV/1u94BecVAwWbS2KeXLEqRoTq1hDioAAkC6UkokpQxRSlvWn8eWVKWu6+copXt1XV9CCM1yzuNSyijGeIpzXrv5WQIAaJo2nMvl3rw+ljBN86yqqobv+yKfz+8FANiI4JmGMZ694bp2uV4MrN1wfYa85nU2X10ttEwRXAjR0R4K33nHALv/fh2uuRrI4ECKL69sqS3awHrajT32fUVqh5gfUhoaltjERDkAAIpGhT8yXKPU7poAgCcslnA4nOfZDGETkxVsYrICAMB5+ORBQAiMo0d7fIDC/Lye3hK1sWHcPz/RtFM9FeZrlf9Cypjv+68AgLsAipuQBgQ82Qga6QYEXEFEo9GJdDotfN9/biKREKurq1tSR+tNdz0pZUGoWJY15bpuwWJAAoC+sHiOnxso1BRpb/uzYfbbL5J+JNwEAFsiLIZhTEspU4SQFACojLHCi9fifES+/FWltKs7jDgH9dZblsz3vJvRq68i+OFHpNgk5C5E2PkYjsWZzOUu70cb5xh+9IMK/dix7mxp5fbVWuu4AkUXpV62CHop1vRFxc11gJQRVVWXDMM4TwhxCSGzuq6nVVVd9TyvRUqJOechxliVECIuhDA555Waps2oqjpHCFnVNC2pquo0Qujx2q81ARlGCGEhhMkYK4i7yJmz58RHP15GDhwYYJFINQCAxFiJxeKz3oMPFdKU2mtfPYMTiSiurmLsqmO+MEJYfu/7hszni4pNHLLm+eLStvvnq6vlwBmxbrxx0h8bT+j79vX64+MNJBzO8B1MJi+G3tY26J46ffDC7caRI2PuqVNbHdmlBByLURyProh0pqjIQ2fOZuG220yBEWGMdVBKe6PRaP8TnVdAwK+DwJQyIOAKQ0qp5PP5zrm5uWI91xBCqNAbTNO0Kc/ztpvpPfe2LS9P9x3v6sS5vC2l3ObMTAjhAEA55xUIoTwhZNEwjClT00bFe94XlcvLBK0XWZOn37osImFmtbba5je+Oouj0aLpPgAAkc0ZSnn5EgCAsn9/0fTftpvL5VDsD15zqNJOnbvUsUICXqDQ6scrhznnOqW0Op/PtzuO08AYq6WUYtu2m9cFZlE8z2vRdf0+SmmL4zgtjuN0Oo7TqSjK5MYxUsoQYyzhum7t5nP5gw9b/tmzCf7e97cqCC0CAAiMSfrENQnj8OGFwj0tr2DDMJiysGDkGdaHtUQLCBEmJSVFnx1bXmlSdu0aKXrPubzh9fVHjKNHh3kyaQIA0MnJFm3f3tlLPa/NGEcOn/VXliu2bCQErOtO9Hm9vY1F5zUxWcmXV+v0g209xfYDSD3af65gCCqEeMLRroCAXxeBWAoIuMKIRCK/QAhdaHUEAGsF3YyxghBSFCXDOd/2S5/oehoZRiFfgoQAKXjR/IkQIssYq6CUxvGaKaHmum4ddxxgj53a8kJVqqskIcQQQjCb8wSOx4q2VAEAQCHLE65LJMZgfewjS/oN12d2OvZCyp5z6/6y7GrfpY6r0mBEWZ3b5ufEOY+tR8ouCsZYAEBPOBx+vqZpd2ma9r1QKPT3iqIU6oMURZljjIGu6/OGYZw1TXPcNM0eHI8iAAA5ORkyvvo1d+N4Fg7H8fNvX934LLu7Tdd1sXfvL3P685/bsO/jfz0OT7tpnL32dcOhp996nlSUbxFNfHExiiwru9Oc2dxcuXvqVAubnGwGAADfRzyd5uaxY8NoBwG2GaWxYdI9c/YQn5reaiHR3j5sP/DgAUl3tneQ+TwWObvKuuH6bWKOTU5V2J/4ZAkSQpim2SWlbLjUXAICniwEYikg4AqjrKxshBCyWmzfhQLK9/1GhNAWEWSaZleuonyv2ta2pYoY/+sXEqoQ5y8cU9M00/O8XQAAnPPSDfHFFKVRv/WW5cK4J649J77wbxE0NEzQalJawyNpvrC4Y/pH2727zx8aqkFCQPZlr6x3H3k0utOx2+6zttbxdPOSq95yAkeRGZ4AjN0L90kpazHGO63+A4C1/lK5XO5vVVVNtba2Pru1tfU3m5qa3iiEOL1xjO/7tRjjLKW0xHXdQ47jNDmO0y5HRgv3I20nqapqIRrF77+/UNzu3HHnXv66P6hQV1PIPNA2wk4/Vse//a0D+Iuf3+sNDsW0lpY1n6XdTR6KhAUAAJLiCRly8rn5Ouexx1q0ul2XTHthw8hcaFYKACAymTJSWnrRXm8oFOJKTc2c88ijzcX2s5mZRMUjjy74vt+CEPqNXC4XtBAJuCIIxFJAwBXGzMzM0WLRIoC14uXNKIoytTnNpOv6suu6B6SUOvCtjUH9n/xXvfovn982nhCiUHmNMS6ky4SUxH3HnyPz6U+fMk9c2+X19jXYv7i3bvUlL63zPvkpKh56mONQaMdoEdYNDOpaeZRMpwnyLhn0eJxnPGs+q5lFX8ibSfmiUai6gzDZtlKMUlppGMYw3iSkNE2b1XX9tGEYPRsNcznnYdu2351Op0MAAIuLi3VCiOs2j8U5r5BSFjyODIAx2tP7uFhq3oMQQhlN0x6Mx+Ov0a869iAAgN7RMWPs2X1Gr6me9fv7iXv6zG6+mrQAAIzmlnNsairBpqalduDAAJuZ1bEVWtTbD57G5eUuTiSW4QlCBwYPGlcdm7nYMdgwthhGqrubltXm5il/bCyhNjZctK+e3tLc5Tz4YAcuK5vVr71mmFRVbYnekYpyV7a3o9LS0pTAxHxsIn11Op0OBFPAk55gNVxAwBWGlHKPlJJs3kYIYZxz5UKPISml2HQM55wTKaVi9fUN0u6e7U1obUe94HzAGBdenrZt7zdNc9pxnLVIk5SlQrAFPjbeLDKZwgov9sADrdSjhnGoc5qvrhb1XPKXliJI06T0/SfeNsKxL/uHHva9kGR+0TnYtt1pWVaPEAIRQnzHcfYIIQq9zSzLmrJtu47a9jNSb337XTlV7UbPvz3MqqvqgJBiQwIC4Mojj9jMcXD4Na8ahpJSP/O0G/dqGA9WV1ffGAqFeHb37u96R4908pmZ3Wx+4UgxmchXVzAAgD85WVh9yBcWqvjCwpp30dEjg+6pZFmRU3dEep7K5uaRdd2JZT+fN9Wbb1qUe/bknT95Y6GQWwqx5caQqk3zlZWa0M039xqHOkvpwCAT+XzRdwdPZyLIMKT+1x/wnfq6WpLNucaXv9zjfvu77cgwuPb+951zCdZVxgxDUcK1MfRFIcSXAeDtT+Q+AgJ+1QRiKSDgCqO0tPTnjLHhXC7XQgjJKIoyAgBViqLYiqJsSbl5nrfXsqwZ27ZrOedEVdU14fONb/kgJaBoVCiNDXm/u2etee2LfmsZ1p2YN1GILK2LtCQA7Cpse+ELKZr+B6TH41P+zHS5yOYMkcuvGRmWlO4YWVLKSrNsYuK/1V+LnD8fJSAFB1RUNCEAKNdRb1R4WOZzbcWO2WDdnyrlOE6LECK8eZ9t23WxZGpQfOMbzPnRj68DgOvg+/8Bkbe8qSf7G8/esnzfTCZ7oK+fodGxiPuVrx4k5WUp+3nPTRi1tW4EoSkpZT4UCnEAgMhtz8nwVOql2R/+6K/Z/MJLtt1fRbnLV5MXrelxT5/Zq+yqXWTTMxUXO27LuLt3O/ozbkXippuWeFVltWIY0s5my9WOjiTr7k4AAJBN/lcAAKAQzpeXK/L33FORv+ceME+c6HEefHCbdQEKhUC9+jgjDfW9mV217bqqjrshqwl+//dKworaJSvK5br5pufbth+JRsuxz0cVRXnv5c4/IODXRSCWAgKuMEzTXDYM4+uc899ijFV7nncEAABj7Hied+FqNsI5nwGAWlVVc5zztVRR235K8rmk/NDfLIlMxoGXvbITRaOC1taUrI8FQgiIxWLjGw17McZMSkmklFu8mHhVJeFDQxYAWMbRo/Nud3cV+D6Ebr11iGczRNu796yw7SalonyRLa8YWFezyLA8ycXFfZgugrQsDwD5AFC0bqlGh65wdqGzmBKzLKufMYallCEpZbnneQ0AUBAmCMDTMpkhPDzC0MOPlOW///1tETjnU3/Xbt710wn48N9EwXGpSMRj2n/9TNhf/0anzOcxAABfWo4rb3nrgmxry/FUCqG3vDnhKoph1NS4AADxl79smK8m/8s523XbRuuTDQQXilpaOs2XlyM7PwQJOBSeBYDLFktsctLEx49PyUwGaGlJPBQOZ6KJhJr/1Mel+p73LqJkyhPUI6S2dpXPzKytVkObetBoGkjbLipwheeB/Xu/W8WEWHNZR2itHgxjyL3utZ2maXYJzqt0XV/CGCvU87LcsfcnJb2TMfYniURie0O6gIAnCYFYCgi4AtE07ROpVOrVjLFCekkIsW3Zv67rXjgcnqWUerquT+Zya1EW8YynV7Ovfi2hIbTolZe36s9+9qR0HZUBkHA4PE4pjSCEMul0umBPYJrmCEIINoq9C8RihdocpCg5kBK0PU3n83ffXUgfoUhYsqmp2IYJpbp7t80WFh4fgxBAhk5l3t7S22xHus6WEwwOF9vF0i5dng1nlw4VO40QknZdt0YIUbTmC2BNFCLX47K0VIqus8XbtkgJfn9/A3rRSyQyTR8AsO04HfrhQ33uyUcKKS02OVXJJqcqAQDQa14rky94wdOqP/7Ruzb2q7ub/j38zKfvz3z3+28Df5MGtfNYlpSUKVWVSTa/sGPrGKRrT6juFDEGzHU8YpgaAEAymaxXuKDa9PQMcj3uT01XiGy2Tj944HRBLDEfkarKJfVlL5tT6+tCuXe8q2jbFsQYkFR6mkUjG2JpS48VSmkHADgIoWQ6nd4fj8d7w+HwSjabvZEQ8ueJROKdT+ReAgJ+lQQF3gEBVyAlJSWpUCj0OoTQtlVem1EURRFCPBAOh89tCCUAAIzxsvqJj532NK1CSGnyt71F8R98qNo0TJbP52sppWWe5xWEkmEY47Ztt+ZyuX2+72+JZCCMpXHdCRsAAEcjEhACHItv+SEms7m1aMR6Ebdat2tW5vOFGieto2ME3/m5Vf0Ft3chw7hkZ1zlaU+bVRFa1ggqFJwjAIiqeC7ipFp2Ok9V1cmLCSUAAC5EyK0oP+Q2Nhwx3v2XFy1olpQiUlo6pu9t7bNuuGEEqUqE7G3NAi7yTyvGPghRSEtOvewVf7f4nvd15e974E/01tbxwhzr6wfVxqZFpKkcJxI7WgQAAOBI9KIrCLVnPn0p/KY3zmnXXJ0lR4+mAADcv3zv/nxNdeH7CZ85M05//w+b+PKyglRVkLIyF6kqAQAgdXVp/fm3V6ofeF9S1Nfp0vfzcBHXbby0VFB8nue1GIYxvfG5tLR0nhBCXdetNQxjTtO0mBAiHotGKfz07ptn/uCPXrL8mc/ecLH7CQj4dRE4eAcEXKHEYrGxbDZrMsZu3OkYQoir6/o/+r6fxxhfzznHAAA4HpvxYtEjEsAEAJAAOL5/76jZfnBeSlnt+/4WscM5j2qaluacb4teISEWNM67/QcebGbZbNw4emSarayWiJWVHaNE5tNuTHHHSSuNjXPk8KFp/w1/VEZLS6r8q66qMtrahhXLmuWDg1t8frTn3TZFPvzBafbDH5dptz932qwrb4xpZCqPVFqv8ZEacFDITlabuj6GMV7inG/pT2cYxpDruhetX9pyPc5H6UteurUHXkf7sFJaNoEjkTRIaWLLSiFNU7y+/hZ/fLxUrKyGIu999wxMTHrYsjIilYqikgQn4QggTJKkrPQn0effPpL/5X1W6mvfuIPNzdXJXE7B0QhFCFHpOKa2u2meLSzUsvMTEetpNyyQF794gSjqAh8f35ZuIyWJMRwKCZHNRkCIbekx9aN/O0mPHq5wn3Zj2CwpGYWhYS90/XVL7PhVBhciBgBA/v2bi3xkpFxrbp4Sjl3Ns1kdqZopCZHEMLKkpMQyKqs4PPRQxN+1yzcrKnLizW/K4oHBJbG8vMVYUs1mTf/GGwwAACmlqqrq+IbbuRBiFCGkUkojpmnOM8ZMGB5Z1sLhWPbtf7HHPXX6JW53z2/k7/mFFfvtF//ycr+ngIBfBUEaLiDgCiYej3/I9/0XMsb2X7gvEomI2tpaqijKjxcXF2dyudwfA0AZwHaLASFliF533Uw+mTzueZ6BEPJVVZ2jlNYDAKy3BSnaHNdHaK919fEfGh/9SIXf2VGCV1Yc9c7PU+S6pf7UVGLDs0diDNZdPxrn/ecydkmJdF/xsqKpMr+xodR66OGpzSvE1Lt+NOoRUsmFqNP+/WsjeUXZC0KoEeByt78S10FdcRynCgGA4zj7CCEpRVGmMMacc25xziswLhbu2RmfkEbj6uNJ/+QjCQAAHIn4SNdtt/uxIwAAwsBrGgAAIABJREFUKBqVyNDBP3++UHslhVBydfWVaiSSJIqik7LSEX/8fEKpqhQkGluUgt+88N7350QudxNfXS2IHxyNOWx2LgEAQEdG92j79qb4ymql0tJqqtEwybz6ldwsK+n273ugDlnmLFa1MJufr1Lq6nQ6MrpgtLenWHLVEsvLNSKXL6Qm/T97ax353deMhhnTcN6WbHKyLjs9DcbNNw0rsdiUOjnpuH19pUrdrgUpeYjPzWsAAP7YWMy8+upu5+TJDhwODee+9OUW7bbnzNrtB/fm29eyjGbznlMwMLD1obW1zcOmnnFSSr7pbw0AsJTSyGaz+wEArJ/+LJe6/4EYm59XEQCIVKrKfuTRD0x893sn6FVX3xNJlHy2KqRfNLoWEPCrIBBLAQFXMKWlpfl8Pv+eTCbzrY1tqqrO6bqeD4VCOc/zjimKAp7nXWPbdhkAgKIoi77vN144Fpdytry8/PXJZPIdUsoK27brI5HIfC6Xq+Kcc9M0z7qu2ymlLCgtQkhSIeQuGBqJY9c1FNd15OAQcgcGayCTtsLPvW0+95O7qsD3wbjttsWslE1o/z6uqmp+S43OOsbwyIj8mw+a7I//WCjPesZpPH5egX37pINQh1yPnFBFaTYMY5Bzbtp2fq+UEqiUzYSQPOc8BADAOY+rqkp934/puj6kquqCbdvthmH0X250iSA0T7t7akksZqu7aofYarLBffSxzo39MpNBPJN5vEgdIQh94H0jKU1tUet2jdGf3LVXbWzQ+cpKnK+sAACUG0cOo9S9971dcr7WOBghACkBFAJ6W1u/v7hQRkyL08HBcvPo4SH7wYcsVFtrK2OjmOXzYSlFiC8s7pWUKigS9vM/+OEB41Bnl3vmzAEAAFJd7Rj79p91z57tBMYQPz8Rhjv/tY37FMzDR9ZMSIUA761v36Pt2zeV6+oqRM6k7Sxo+/cvY8tMgZQIhUNYbWoaxqGQDQCAbrxhCQBqCrfbtl+BH/xwyzMTLc1bTD43/7ei63rI97yc5roDWFEYhMOqfPlLpRIKTSnd3bW0qzukve3Petj118W0cHg3aFodAPRnPHZXVFeKNngOCPhVEYilgIArnPr6+u8MDQ39nFJ6i67rY7que4ZhhDjnOue8xnXdjKIov4sxzgAANgxjeaN+yTTNOSGEoJRWOY7zKtd1Dc75iOu67QAA2Wy2KhQK9efz+Tbf94VlWd2e58UZYw0AALW1tX/lT062Lv/VX/+RtG0kNQ2wZfnm4UMT/uSUL/J5G3x/renrTTfOA0CFBCDU97fU2hiLi9Poox+32eBQk3LLzXP28WOHJIACTU3b7pcQkmaMGYyxegAAXdfHOOeSMbZn83EbtVUb97K+rdkwjJ7N23ZCAJSGbrx+zH30sQavr79oFGwLUoJ757+oVkvLGXLDDSHxzW8ZQMgcbIq0uKfPFIQa2bWL67ubBryBwSa9qSmf+fdvHjKOX3XWfeTRQ+Z11604AwMtWkV5xnv00Rqzs6PP7+3bMmfpeSoAAE7EC75IfG7O5HNzh/RDh/qQqiZEJpP1Jyf2IkXl9r33Nm86F3tdXVusCfjKSqWklIlstgxZJrduvHEJGQZF4ZAtMQZ513/VoCOHXSmlAQAgTpyoB0VZE30AoLYfzPHDhxo3Pm9cCgBAlXKSLy7a4t3vrRZd3XHlD39/wP6tF+4FhAB+8/kAv/l8IBh7eSHaQQhQOB8mCC1WhfQfXPK5BwT8CghqlgICngLYtj1HCOlwXXc/51xxXVfatl1umuY3KaXW0tLSBxVFyRNCskIIyjkvJ4RwVVUnMMaKruvzALAQDof/mnN+mFJ6UFXVtBDCQAhBKBSa03WdZrPZtnA4PBgOhx+yLOvfNE17XtK2n4t++lNbpNMG4hz0luYl9/SZehwO64Cxy+fmygEASHlZlrcfjEqMcTweH3NdNwEAYD3wYC9711/W8anpSmCM4Le/bZTF4xd6PRWIRqMTjuPshrWabtB1fcnzvB2LujcjpSSMsUpN00Y45zs2csUYZzRdH2adHVFy9OgoGhgIyWTqku1VxMpqnA8PV/s/u7uUlCRSbDVZhsJhrv/e64bFwGApeN5apEXXwTp6ZMh5+GQzUtWU+9ip3XpnxyybnjbV2l1J2t9frZaUpPyJibh17NiqZCwiHQek66rbLopJWGtqHJFCejKfjwIA8Pn5CjY7GzGPH8/QwaGE9Dwsfb+4i+bm57Pe9w2blk/Ky/LeY6fqlLKyDP7nzyzz66/zOUKlsL4wSIvFRklN7YJaXb2g3nDDEiSTnj484pNMdkb74Y+4dvjQnDo+LrVTZ9LiQx+u8j/7zzViYcEAAGC9fWXkFS9bEFIWLBOklAoAgKIo06qm2z71vZJ47AuXmnNAwK8CVKwZZ0BAwJXH+Pj4m33fvyoSiXzNdd29iqJ019XV/TSdTn9mdXX19nw+XxuJRPodx4kzxmoikUiv53lxXddXc7lce2Vl5btN07SWlpbewTmfFEJEFUXJ5fP5hlgs1q/ruun7/lvj8XjP6upqveM4HzEMo8LzPEF8n8Gn/16j//mDeuP4sX7puDGeTKrINLFYTUq+slKud7T38Xw+hI8eXcEYa8J1mRgcjLHhkd2F6MTtzxt3//QN1RvRiwtRVXUGISQ453HOeQQAgBCSVxTlvOd5RZe0F8M0zbOO42yJFiGEmKZpvQihqO/75RvjAwAoQpzXf/ADx/uHz26rDdvxGseOdTmPPdap3XLToveud1qqbU+Tz32+BOdzc3xqKkqHR7aEzfTOzkGvq2ubp1PoGc/o4+kUASkkHRzeJTKZCDINKR13S+EZKimx9T27h7zungMbUScAgPDtzxvwenriJJGgIp9nfHW1VNpOiJSWZqSUlE1NVQEhYB6/ql8yzvTWZpOenyBsYbHKHxmxpKKA9oPvjzoY7wEA0HV9WFVVNZfLNV44VywE117/hkU2fr5aKgqgrVGmLZAvfeGMV1l5eOOzZVldjDEtFotFcrkcdRxndzQafVl9ff3XLveZBwT8XxGIpYCApzizs7NvzeVyf+77fhnGOKtp2rjjOIei0Wif67qGpmmuoigiHA7fGI/HU6urq9+0bfvGdDpdEY1Gh9PpdEskEjkbDofnSktLnwMAMDMz86FkMvkOhBDTdb3fdd0OBMBVzscRAIo88BDJf+nLVf7EhGHdcP05b3CoHpvmjD8x0brTPMn+fSn26U+6HKCq6H5CUgDgcM6rL9xnGMaE67qX3cVeVdWUEMLgnBsAAIqinMcYa5TSmoudZ374b+f8n9+z7frF0NvbJ/Db35qxa2t2b9RS6Y6Twu94V8o/N9C4bU4N9edIaRkSQpTSs2e3F9NjDNr+fQtaU9MiW1pCm/2cNmM97YYunkoLaTsROjy8pX8eqanJ6LubFrzRsV08lTLNzo4uYTsCMDK9s137SFXVrGCsRC4vF8Sq2tlpw6c+nnIZq4nFYuOpVGp7bnQDKcF8y9um/f7+XTses472J6/v5S9+MUMIqZ7ntem6PoIQAkVRqOd5lZTSMoxxMhwOv7a+vv77lxovIOD/ksBnKSDgKcrY2Ni7h4eHv51Op99GKa2wLKuPcx6TUjboun7a932kKIrn+z6VUnJVVW0AAF3X/6ikpERIKcFxnHJCCEMIKZTSt2wafo+u62cJIVOu63YAAEgAQglp9gjZ4/T3qf7EhAEAwH2/GhCyLy6U9mf4pz6R2UkoAQCoqnq+mFACAPA8r17X9dHLfTa+78dVVU0ZhtGt6/qYlLL0UkIJAED+zkvmNn9GpgFK854p7YW/eVp7w+tPay94/hmlujoZeedfDEnPi8PgIN4QSgAAnmnG0StfkS86p4nJ/SAlRz71zONXbb8XIYD29VeKbM7iK6sxUl6+VHSOPle9ru7DkvkRpbHh/MZ2talpmUQj0/YDD7bwuTkTHAech092et3dh72zXfsAAJSy0sXNQkliDHhf6zK6+55kSNfPCSHSGONtTYkffyAI0LGjizvuLzw3k0LTbsXzvEOu6x6wLGvMdd0WSmlDNps9QCktW7tlkchms98aHR396Orq6mWJ1ICA/wuCAu+AgKcujZzzmy3LmhZCzFiWVYkx7gcAls1mj2w+ECH001AoRAEAQqHQyvnz55cBoEpKuWhZFpVS/ry6unpw43jP8wY8z3txsYsahrGI3vRGT7nl5tM4mcRqMk1ELh8SuWxU5u2idT/qe/5y3EOos9i+9THPSilrFUXJMMa2GTFKKREhpKgI2QnXdasAoErX9eXNKbeL4TU2HNS+/G9nyOQkAs4Ues01+10p6wCgbuMY/Po/lC5AItzWNgNFHLbdY0cPWFdfPeWePFl34T7pOr53bmAXDofTxuFDA/7KSi2fnNoyN76yLP2RkTqjvf0MX1raFoFCeK2Xnz87V6nW1hRMIYXjaGo85pOSkmW+slK0Aa8EtKW3oPHed/fkT1y7VljuuqDm86PW6NhIrrVlbZuUEPmP/+zhVZXIPXq0RSiKDnuattdVbcwtkRB6U8MAaDq32w8WCtallHmMcZJzvs2tXEqpOI7zNs7YK5K33b6IdP2M3tL8H7Wf++fv7HSdgID/bQKxFBDwFGR+fv5V0Wj0a67rNjmOEwcAnXPuYYxlJpNpsyyrT0rpUUrbMMYjpaWlz9t8vmEY847jNGuaNq8oytctyzq7eT8h5D7DMH4Iaz3VhBDCXI9MYc/zGjlCIb+1dTcAgA0A8OxnQuhzd3aLrq4SvrhsiVSqBABAOX7VAn7lK+ZodVW1jvGY7/tlQogtYghjbEspLc/zyjVNW9Z1fcT3/QYhROGljBDiO/lAXQrP88osy+oWQkjXdXcUbAAAEkDzKsoPo8oKrijKMitSNC3WTaxwS7OdzWb3g5Sg63paURQ3n89XCgDQjh6h7qlTsHnlmHXdiR63q3s3AIDI5WLumbOxyAt/c1wePrLK52ZTfGVVp2Nj+wBQFABAqkq4YD2wgaqCdGlo/cZAeNTQDx8663X3HOLz81E+P99pHO7sulAsIdME6TiAMI5t3s6OHy9EmRSEpslf/U3IT65WkL//9DKXsgwQAjE4hOg/fvagEo/ntJueNsjPj2vG8avmwHXnJKAm79y5hL5v77BkDItcLuL19u+TlGItlZ5347EqAADbtjtUVc1qmjZFCHFc163gnMcRQgIApJSS4JmZOX9i4jAAdNCxsVeNP/2Z/xm5458+WrZ79/2X8TUHBPyPCMRSQMBTD6woyv+LRCLPXllZ6YzH4x5jLJvP5zlCSJVSKrZtHwBYW1mmquqPEolEwQNyfn6+xrbtg7quz0cikXeWlZU9eOEFGhoafrq8vNydyWTut227GQBACJGORCIrnue5qqqmfN/fskIt/3uv6wAAiGZzvfyxx6b9f7pjj/KMZ6zk9rYeAt8HhJBUVXWEUvp4rzmEmKqqi57ntQIArKdnygzD6NosbAzDGPE8r9Y0zV7P85p1XR+QUjYAQCFNeDFs2+4AANB1vcfzvEvaCkgpCSFk0ff9ymL7VVVdyefz1Rs1oYwxHAqF8pqmjTDGVP7qV/ra4c5xeOhkEvkUo5YWFXp6PfHAg1uEYvY7320K3XpLr/PIo50AANaNN0w4ff21AADe6TMt6u7d0yAFIdHYCo5GXOG4IZZMFubEZ2bK+MxMmXH0yAhPpsAfG2sWrldwVtf27Z0BhLOAUK1SXjbt9PYVCsxxa6utxKKuhvGQEMLDs7PYeeihAwAA+s/vOe/cfFNcAihSSgYAIFKpsPu973eY1xzvx7G4k7/rv44AACi1tTN0aHi39LyCsCRlZcvS97dEsXzfj/i+HwEAME3ztBCiUQhhSSmFqqoT5O67EwVpyRhyx8Zv933/+vTo6M/C4XBPOBz+ZCgUekLRxYCAyyUQSwEBTz3E6uqqKgG90yZhW2egI8YsQohDCPFd9/F2ckKICYzxpzef7Hnes4QQ/x4KhT5VVlY2vm30dcrKyhbS6fRXAOB9iqKkdF2fEEIYqqpmHMcpFFsTQoRhGFNCCBwKhcgKxgf1lpYJpBDGWpoLIs00zWVK6RbxYRhGn+M426I9QohyhJAvpVQBAFzXbQ2FQmdyudwRVVWzGyvdTNPMXXjuJbhs80NKaTMhhHLOt7V1kVJKhFAWAMKEkDTnPLa6uhopLy8f1TTNkFIS1tZG8w0NhXQoPn4V1x55dJJNTNSDqoJ+7bXn0OteG+L/+oVCasr+5X2F56rt2TOq1NdJ8cynu+B5wL/zPeJ19xRdreeeOt2MQiFh3XzTaefhk483+p1fqETRaBmfnNSVE9dyubKyljYkBMxnPXPO9f2wFQoxRVGi9o9/UhA39EMfabfa23vtksR+cW5gSzrRefiRNhSyCs+RzcxssYFQDrYNsw/+TSUzjKKpwPXnp3qeV7B2YIztt37nd3rVffvPwHe/t8d/+GQUqypwznXq+7/tOM5v27Z9dUVFxYtCoZC307gBAf9dAp+lgICnINlstiKVsSvm8rLS54KUhHQAkIQxZm/06gIACIVCH6iurr5v87nxePxsSUnJTyKRSOpS1/F9f0BKeUM8Hl/GGEcIIWoul6vZWPpvmuYkYyxMKS0RQlj5fD6uErKgfekrq7i2dhVdezX2Na0qGo0OSSmRpmlC1/UpjPGiruvznuftl1JuS3VxziOmafZxzmOw9qMPAUCGc14mhCjURWGMl1RVnV7vE7etd9qF6Lq+LKXEQojQpY6VUqqapo0X82sSQliEEGddQMYMwxjwfb9KCJHRNA0QQgwAfM/zSjfOQYRQo7VlAjO+iN7x557zvNuaaSQcU5eW5vljp0o3j49CoazW1JRyu7ubjec9N41aWzD/8V3qTrVIAADg+4hUVy+SWJTypeUESAlIUQTSdZCZDOG5POhtbcMkHmOktHTR6z9XhW+9OSU1TUFSgtvSTOSXvlJI0/Fvf6ci7NIp75FHthdeX8TTST1+fNa77kT9xZ6toigpTdNm/A1DUwBgGCdIY2PeeN5zHXzjjVPo8OFZXF9HGOdracm1ZnQ/CIVCyYuNHRDw3yGILAUEPAXRNO1hwwq1D0/aM8SHZpCC+76vwab/56PR6Btqa2u/8D+5TmVl5Twh5DeWl5cfNgyDAgAXQhSa7SqKknIcpx4AQAihKooypmqan/vjP2oHWKs1Mg2jl1Japuu64bqu7nlelaqqeUqpKYTYccUu57yUEJJnjG3U1WzzZqKUNgMA6Lre7XneRdNxhmF0O47TuvanMadp2mI2mz1YTKxtIKW0dV0/jRDCvu8f5JwXnq/v+xUbLuKEEI4QAoxxNp1ON/m+b2KMuaqqKUppfH0slXa0N3l7W7cUOcuKim2RErW2dsx5+OFOAADvE58sUfbtXSEVFQiGhna+QYRA2jnwunqazBMnupyTJzshFFrVGht9Z3q6ViSTFW4yWehXp995R5et6/ux6wr40N/myfKSLi4YUpwfT8JauvOy0Nv2j4OuceNNbxkF2w4DpRH8rnfO2Htbt6RsXdfdq+v6qQtOJ6qqurqul5K2/Thbt2tG1/XPYUWxMcbJRCLx9VgsFqThAv5PCMRSQMBTkK4FGq2I68dO1JtvWU1lPsQYfJFS+ruc8wOWZXUBwM/r6+s/8z+9zuzsbJ2U8jO6rlMpJcrn820AAISQjKqqE9lstiMcDvdvtFdRVTVj23bBDFJKSYQQ9ZxzX1GUGUrpfgAARVGyjLGLRncIIUuU0sJYCCGkado0pXSbxw9CKGoYRrfv+9XrUaYtmKbZ7ThOQUy5rluNMV7WNG2EUtq6ucfZZiilhXMURckYhjHruu6+C+Y5r2magjFeoJTWMsbCAACccxKNRmc3xBLGeFkIoa3/7QshVIQQqAcO6PSC6wrmR4yrjs14I6PV/th4mT8yWqYf7jyz07Myrjq2wBYXgQ4OtwMAOA891EkqKjJqQ/2cc//9RYva5T/8I9Y4z6vt7Tnn5z+vQ2KrVDJPXDvqdnU3FzsXAGBz8TnSdWZed22vfe99h6D/3JbDtH/9grB27z7NXnB7Fa2oeLz3HEKFAn7TNAd0XceqqlYzxhzG2LdbW1vfvuO1AwL+P/beO06yvKz3f775hApdncNM90xPzjO7sMAliSBJWZIIZq/y0vvCixEuiooIYsD4uz/5YUB8mRURBVFUFnCBhQ1umBw7T+fuquqqE7/x98dUN9Ozs6S76x/c8/5nt6tOnVN1TvWcp5/v5/l8nmSKYqmg4BuQqYb6s0euL731WXt65Kmhnu+t1Wr3TkxMHEnT9DWe531ieHj47U/Gcay1PwAARwghmXPOBkHwqLWWK6XGsiw7hhBSWmsWhuGFJEkOGGOCW/eR53nF9/0rUkrPOYcBAKSU3Z3A3/5bt7/p2BYAgDHWxhhfVUqNI4RIqVS6GkXRrfEncZZlx4UQj9xaLAVBcDpJkm0FA2NsKU3TYwDgqtXqlTzP0yzLjjvnMMZYIYQMAGhrbekmEXfFGBMKIeoIodQY06WUCoUQa+12e6uowhi3giCYBwBnjCkJISbzPB8PwxBJKaXneWcBYAAA1lSrJeSv/foQAAAKQ3BxDGz//pj29sTpF+8fJ921Jj1yZErOTB/B1S7Kjx59BFFMESCXPXb6BBACYAwgSpb0zOyXPqNzYJaXK2zHCAUA8O+667Sz1pnWxrC6cvVGN0wIIh94sGbOnqvdWimiSkXLySnPxXHplqcAd3cvsqHBBRQEQwCuAdo6J2Vg1tYtGPO46ygfefQAPPIo0NNnrtL3/mpTh2EXAACKYxz8278/5j79mdD9xI/7cv8+wRjLgiDAAPCkfH8LCr5aCs1SQcE3IE/f3QN//59zjXsvr/7mqfGB9/RXvBgh9MUgCPTg4ODPPBnHSNOUAcA7jDH9WZb1WWsbQggviqJDAEArlcp5SmkrTdP9Sqk+z/NOSykP+L5fv7VrpLXusdZ2BUFwyVqrGWPTUso9tz8yAOd8WWs90tENXcrz/JRzzmOMzaVp+ricOIxxG2NsAMBnjDW11lUAgHK5PBXH8cFbt2eMTWqtBwAAGWN8KeUo53yVMdbSWledc5QQslEul5fyPO+Cjh6Kcz5rrSVSyiEAUJzzy5RSK6UcvGnfV9M0PcIYs0opcM5JY0xPp1jk/rVrjQ3P2y2l7OOMJeSL94N/6tR1vbLis7HRq3Z1NZRXro4BALg09czy8pA4sP+0WV+n6tLlE2ZpecABCsTBg1N0ZDhle/bOqqnpfttubytscLWa8vHxNiBQ+cVLe/X8/CAbHp4EAN/FsSB9fdfN0tJtjSC9o0ev6pmZUTAG37LPOunpdvLylX16YaGsFxb79dJSv1ldrQEmPhZiw6Xp4wosAABbr/eIlZUpps0Cff/7m/r3/2Cf+dznR+zySg9dWl5wL34RwxjnjLHjXV1dT2yMWVDwFFAUSwUF36C87Pjw+c9cXHnGnv7SJ8Z6w1hrXQmCIDXGVCili195D7dndmn9pc6oj6VJ8lPW2u719fWdGONFQgi31saEkJVSqdQAAI9z7qdpuinALQkhZjc1TLcBKaX6GGPrAECfKOhWCDFtjKHW2hpCyHHOFed8nhAyjxCyjLHFm0XsAADGmG5K6aRSahdCKN70csIYLzrn2K1ZdM45QSltWGvL1loeBMHpLMv2aK23DCKttSFCqOWcU865gHN+jVKa5Xm+u7MPbowZkFIOBkFwVik1gDGOPM+LpJS9SqmSMaYSBMGClHKAYLwhfuf/2VAf+ruyvvsVoZekdfyeX2vIBx4ck9PTVVLtaqur13a5NH2cNovt2LFKe3oG1fX5EJwDF0VCLyz06vmFqp6ZGQhf8E11NTVdvbmz4/Kc0b6+Vn7u/OjmcplZWRng+/aeN0vLQzZJhip3v2JCTkxUbi2KUBjGLsuok1IAAJDBgTU+NnYFhyWlrl69rYbJRZEnDh2Y04tLt/XD8u56+jl9baJb/+u/7bVLy703i8TNwkIfffSxOqpW/2bg6U/7xO1eX1DwVFIUSwUF38BMr8Vf+PSF5X/6tpMjf9xut8vGmBmtde55Xuvr2d/FubXR5Vb+VyWsfIyxZ61VjIkN3/c1gFNxHB/VWg/ked5njEFRFG1ZATjnhNb6iae1OhhjujDGG9bax7k5c84XjTHCGDMAABAEwbLv+3ZjY2Ov1npQa93POV+7dfkOIQS+71spZdX3/aZSqto5VjUMwxkp5bYbOGNsWmu9EzqRUFrrAcbYnLV2u2mj1l2dQqmOEKJZlo3f7jMppQZqtdpFrbWfZdnWqD0hJFFKcc55jLQm8Cd/mui5uZEwzc7Ld717zExP94IxwEZHV8XhQ4uq0QhpteKAEIs4t65TUATPf+5GfvGSs/V6163HJqM722pyktl25N/6HOLconLJuVaLAwDQHSMzNk76XbsdQJ5DfuFCtzhyeA2Xy9O2fkP8TXbuaNlmk9hWqwYAQIeH1nG5rOWly3vN2lrPrce4GRPF3XzPnlkyPj5PXv+6BfyaVzdos6nN+noZMZbrubmRJ3zt0lKXwPh3K6+8++ITbVNQ8FRRFEsFBd/A3DXe0/6XM4uDn728cuDqmvzBzJJrx3f1fd03G5dHP1dm9iil9CN5ns9SSqcTg6qX12Slxk2AEGp2xvnB87wZpdRXLI5uBSGk4EbI97abOyGkZa31EUIVIcQCIaSBMW60Wq2tZTff90/fLNS+Gc75jJSyXylVDYLgMULIMiFEpmm6NwiC84SQBWutcM75nPPmze/duzGx90TZdsgYExBC1owx24oVQkjsed55IcRKFEUDGGNsrfU553OMsWkA4FrrAWNMaAB8b8fIkv7kPb36woV+sBb85z57hpTLdb24VM0nJka9w4dm9cJCufTSFy/bLFP+0+6cZ2Njy45Qm33x/sctPwIA8LGxJg7DWTI2tkp6e1Pc11d47vm7AAAgAElEQVS3Kys3ClGMFCmVkW00OPL9jO0YUeratW2FplleCcXRo0tsZGTJpFkoxkan1MTEVlFoozhgY6PXzeLSV7zW/PnPXcve/Ut98ptfMKT27h3UgwN9TMoJ2mg2gBBtVle/7D68U6c+WH7ZS7/qDMCCgieLIki3oOAbnFfdMfIej5GZkkd/lxH00Ne7n0ajcQAAXgPO0YGBgbf29/cvtlqtl3f5tO/wQHCGUtrQWm8tnTnnvi5zwDAMJ262H0AIac75XKlUmmKMrTnnXJZlO6SUY0mSHAMAwBhnnPOznaSR2xJF0VHf988CACilhhFCPsZ4QwjRSpLkaJZlJ4MgqDPGFgBgK0PE87zLUspdX+l9a60HCSHbTC0ZY9fSND3V8W7qc87hrq6uywihOMuyEzc7gJeuz8/IX/6Vra4T6qpa29iI8/MXxtmecct37rgAWiekt8+1PvThMXnx0kD0iX87EH/q00cgy4h35x2Poe7uLS2POHVqmv7pn8yRH3tzml25ekfwjp+vJr/567vcW36KiaNHL4vjx86Qvj6sJiZuaIicw9j3bhuCa1ZXSPafDx9B1tj0gQe3hOK4u7vhv+iF1+QtIclkz56W9653nrt1P+iOO1ctwDYTT33XXYNqbm5EXrhwAACAP/OZ58mOkaXbvQ/E+VdtGlpQ8GRSdJYKCr7B2dNfdi84NDDxtN3di3v6y+4rv+L2xHG8GwCGjTGPMsbucc41Pc/7wTRNo9AXu6y1xjm3TghRxphKx3gROeduG557O4IgOO2cMwghRik1QogFACgppboRQksAgLTWW50PQkjL87wZa21Za70DIRQ/kdYJAABjHBtjatbaUGvd01m629IASSm7CSEsz/OtIoYxdl0p9RW9hLzl5SXRajVxnqec82skCNY2o1YopSta6wFrrZfneYUxlm+KzAkhefjgQ1fyt75t3KWpAABgJ080+c/+zDV75uwus7DA+fj4RP7Y6SNOaUT7+6ZJX98y2zGyYpQuQ5IwNTHRqxcWB2lf3ywdHhbk4IHL8Naf9rK+3h2oFBr2A9/XoKWSSpWqua4uJIxpZh/7p2N2bf1L10ZrwkZ2JHp+ftvypzh5YkpevLQftMZICIX8IHJZFgBCwN7/vrX4pS/ezb7922fth/++C2kN/NWvnFS/9Is03zGyS8zOrtqZ2S0xv7nvvn7yPd89axGqcs4nAcC6ICjz4eFpc+9n+9n+fXP5e39tj0/ItH7wocdFyXiHDv51+aUvKTpLBf/lFNYBBQUFXxW9vb3/CQDfd9NDD6+urn40iqLvcc7NSylNlmX7MMYRAIBSaocQYqZjAUCMMf3OOcIYu+KcC6WU4wAACCErhFjMsmzEGOPleX6gUzSFm7lzQogZKeVezvm2G6UQQuZ5PrRpSSCl3OP7/lUp5Q5jzOM0OsYYRiltaa0rtz63iVJqm4A6y7ITvu8/thmhcjuCq9eu6Z99+5BqRyHp6W5mf/onh2yWeRjj9Y4jeRAEwfkkSY445zgAyM77X0UIOffc55TKH/nwtfTd7+mza6uUfPd3zbV37zrm//ibT8P3fv8JAKvp4GBDLy0NmNXVrSLCO3nybLa2dmxToO1kXpF/+H4hfJ/GeT5WLpWuaK0ZJiRr5fmA53mPIYRw66UvPh7c86nr8uzZL3lSYQzO2i4AADoysoRL4SogrGh/P4ajRx4BhAI5O7uH9Q9clo1GDyAEsLoaw/AQ5ODG/D/6g9Omq+qnnI87527cW1772jm493Pb9GCiXm+KsbHUGFMVcTxHk7QcPefZI6Svd921oxI4Z7MXvXAU//lfxqA149/2rRfV5z7fY+bnd+j6+hNqmgoKnkqKYqmgoOD/hMzzvAWEUEspNRSG4dUoio5sPumcS5Mk2eucowghjTG2eZ4fQwjpIAge60STqDzPj1JK65t2AUmSnMAYNzpLezWEUMNaO+acq1FKr2mt93a26w3D8Gwcx1vht1mW7eGcTxpjHmeYaK3d6XleyznXMp2YDISQxRi3N7VWt+KcQ865bkJIZIx53Nh7eP8D5+V7fvWgy3OCe3uX8I//2Lpl7AgAAKU0llIeNsZw3/cnhBDTYRjadrvd09PTM5skSZ1S2g0AIuuq9vrv+sVF026nG5XKMQAAV61wAID0gYdOBM/+b1M2y4RtNre8qpw1yL/rrsn0wQfHAQBwT2+WY0ytUrsYY+uc8xLnXDUajbFOBMtSmqaHw4cfvZifvSVHzlrA5dKCOHlCO2OsPHvuGACAvHRp+wceHOzyTp26oGZmRiFJtsbr0v6+bV5VPMsu4WZz+z0GY8Drdc87cbImoyiW9/4zyv7oA+O4uztlIzsmLMGexbhmfb8a/OZ7r6mB/r5IiBP09a9r0HpjhozuvAsAPni761RQ8FRSFEsFBQVfL0hrfSRN052MsVQIUU/TdMj3/cucc5TneaaU2uoyOOeo6Yyub/5/nudbN+yb9U6EkBVCyJpzrkIpxc65QYRQCgCMEGK13sqfhzRNR4UQM3mej3X2jTHG8U372iqMnHMgpeQY47a1Nvc871pnus5njGU3a4huJsuyUc/zVjaLJSFECyGUEozrZvduxb7z9dfMmXN9/I0/2Gjv3XNzseiMMbwj9FYYYzxR1yVtxDrN1aYxtyKEaABYriN0HHd1GX6jcOyG1bUUAIAfOnglv3CxcnOhBACQnzl7FBCC0itecTH65Cf3I9+PAQCstb4Q4nKe52VKqfQ876q74Z5pAQDI8FCIS2Fmo3iri0ZHRzfktWt79Nx1X5w6ee2JLrppNHbzN73put0zPpX09hy73Tal8xfPpj/9lmP2Ftfv4IffOG/GdxOnlDVxYnE5ZHRo6Lq6cGEnjI7uJuXyliZJju8OCCEG8hx0GNZ0GNY2i9CCgv9qimKpoKDga2JjYwNLKT/abDbHqtXqDmPMunMOa62ZMaY7TdPuNP2qPANvO2BCKZ0khKg8zw+Xy+WLSZL05Xle9X1/hhBioiga34wDAQCw1lYxxgtCiPk8z0cAbuSsbe5PCDGjtUYYYx8AeJZlO40xXrlcPtdut4/edOgFIcREnue3NcPMsqw/DMNHlVJep8irAMAADPQDfNd3Av4ebLAQbR/gcpZluzHGmRCiYa3tC4JgAQDoVF25qabMnj8a8DAQtTRNl4wxmFLKbrJVaCKEYgDoVmOje+jH/uGiKpX6ULu9goVIy1Lq7AMfTPJP/OsxUAqDc4AOH0L8+NEL+v/7/V2lOJ7VPT0eY0wghGKEkNBaD0op+zjn1wAAdL2eOAcOCAE6NLTOn/ucpj12LLc7RsrwfT+wE5T6krWE7wPkOUCn8DGLSyT6hXeMsf/5pvP4lXfH1rltBqMYIat++3cO3hqPwu9+xUz+ohe2XBD0tBv1BH78J7v0mTNbBbK+fr3EP/yhawBQY4xNaa13aa23KfaVUs9cWVnZ09/fX+iWCv5LKabhCgoKviastQOrq6svz/P82MrKSi0IgqYxhhBCvmohNwAAxvhx25dKpQvOuZ48zw8AALTb7UPGGAEAkKbpGMY48H1/lhCScM631ofSND0kpRwOguAsAIDWekuQ3ekGhVmW7XHOrW8+LqXctqRmjBmGTufly3x2vJlfdxsUAKA0TQ90umIsiqKTCKE6xhi01gYwYqf6uRGcEADQxhiapml3vV7viaJomBCSc85VnuejAADG2mrueYeU1r24VqvQUkmp/v6SfsPr+xHGiO7bN+m9+UdPq0Y9T57/vGPhn/7JIkZoIwiCnDHmK6WAc+4HQWDL5fKMMWYo0Pos/sePUvb677hE//D90/rP/sTxt/405y9+EWJJ3AaEQF65epKNj0/w/fsmSRjG4tChRbZ/31YBhSoVp155d/9moUQJaXLnpgEAEEIJe9ELZ+nznze/7eScPVcjp884cE5JpYbcTeeajO9ulf/hw1ejUrgH4EaEjXMOeZ63cfM+nHM0iqLv+HLXqKDgqaDoLBUUFHxNbGxsfL+1dusPrVarNexFcT0J/C3X7PLffOisOXiAJidPPFFhAVrrXZ7nzWZZNgpwQzuklApupx3inE9TSuNWq3W4XC5P53keOOd2+b6/pJRyGOMVSikjhJjOvtYAIAQA2HTU7unpmc6yLMjzG44GeZ7vqlQqF9rt9n7O+QZCaJVSijafvx0Y4yf8A9PzvNnNpUCt9VYgrNZ6R7vdbpXL5dn9fT7pxKHEaZpmWZYNaa2rQohZjHEDIbQzSZJt7uMIIQcAwBhzSqnROI4BcZ7wN//oGfjmFwQRpVtaIbe8jLKf/bkR/pG/Wwur1VK5XO53zikAaGVZ1scYW7aMEfnOd4xorX0AAEHImbW1teMAAH6lskCHh9b0/EKvmpzc6rDhcjnVKytboniXJIjf8+llXi1fh4//y5i8/4Fu+p1vWJPf/71grC1F3/n6kv/gQ4/AvZ/dEmS7qakKWln1pFJjjJA1e9P+yK/+ykzT97eW9DpxMMue51mlFOKcTxhjfEII1Vp/GwD86hNepIKCp4DCOqCgoOBr5RHf9w/v2LHjwPr6OhJpGruf/GnreWICU5bjsMTMu355APf2zqtjR2+bLQYAwBjbtuTled75PM9va/wYBMESQohLKXuklDXP8y4YY8pa65q1tmKMGVRK9WGMU4zxhud5dYzxOmPMIoS053nXtNZpnud9QRBMaq0HOgVKmXO+ZK1dxRiXjDGBMaZ86/EJIS3O+ZVOuO5toZTOSCl33u4555yQUvYjhFY6IneapukOjDEihKiOrivP83wYOh1/jLEMw3CSMdYkhGwkSTKstRa+75/PMd6t9uwZZFpP0MtXlmmp1OBnzs6jpaXcf9lLENl3QKzFBjxiJcaYYIy5UkoxxrRzDoIgsHmelwAAYYxbCCHqnPN0EJTZiRNL9uP/fMM+ACEIXvBNF+XU9A7Xbn+pULQWzH339ZtPfWbIzF33wTnAz3zGsj5yeGvyjTq3aD/6T0MAAI5zEL/4C6ezl3zLDgfgWecC35hr5uFHegEA8NwcFs+4a1Zxvvl6zBhbi+N4pxBiPsuyw8aYXgCIAKCmtf73Uql0W0+ogoKngqKzVFBQ8BVptVolay3q6upqI4QOZ1l2x9zcHAIAwO12LCenRvR7f2uQHz28TE+cnMxarWNw/sI2MbLv+6edcwwh5Dri7m1i3Y5PEji33QqKEBJLKRPn3FYnglIaaK0VIeRMlmVbI/3GmH6tNUcINfI8PwoAgDFOnHM7tNZdAABxHPeHYXjWOZcHQdBNCKltbGx41tqm7/u5lLILY2wopUuEkBQhNCilRHmeP2GhRAjJnXO7v9w5RAgp5xzJ8zyx1vZwzpeVUoOMscUsy8YxxsA5r+d53gcAYK3lzjkZx/ERhJApl8uzUsokTdOt86YJ6TVv+V/jqCN4x6961SX63Od4FgOy1uSeF541xgTOuaO+7zestXmWZYeUUtI5hzrnbAdCSPX09Mw1Go2RdHTn7uDOOy7a1TVBe3tl8pn/OAQYAx0Zqev5+Sf2sFpd3bas6bpqW+aT4uMfm0rAnYCbri3Nsq7NHp564ME++vafJ6X/+aNXo3179wHc6PwFQXAhz/MDhJDUGONrrUc71/D1APA408uCgqcKdOs/TAUFBQWbrKys3NFut38AY/z0np6eq9bae7TWP7O0tLS1vFY5f/5q+ra370NBkLk0FS7PbxRRlYoR3/ryS+biRQIjIyl6xtN5/MxnPuE0UxiGl+I4Pnjr457nPaqUOoQxbiul+m557uEsy+68+TFCyFoQBAtxHB/aFIHfCkLICSEe45x7eZ73A0CTENIGgB1a65QQ0iaEdHmel7VaLSulfJwNwa0QQlY55800TW8bO9I5LnTE3ht5ng9tFnA3fZ4zWZYd3ywaCSHLjLF8c6nydoQf+OOZ/EMfvjEJKAQM3PuZddNsaNnT0+ScdyGEYHV1tYoxlgihWSnlzaJ2oJS2GWOTzrkRxliulFrnccz0P34sg1qN4KkpbV/7mhHT0w3wujdUTL3+OP8qAAD2m+99RJ46eRhj3MAYL5E0dfB770MwsgPQq+52rlzu1lp3W2uZ1tpHAJJQmmApGyiOI+/yFRL/3C8c5s94xnmbxBTiOGBPu9OlP/LDA1rrbfo2zvnZkZGRU2EYmtu9l4KCJ5uiWCooKHgc9Xr95UmS/FCapnf5vp/FcTxorRWcc8sYm93MY2OMLQkhGnhyUkKpPJL/0BuFWa+XAQDo8HCkFxa+1G1ACPg733E+edbjCyYhxBUp5b7NbsfNeJ73SJZld1BKG8aYrpu3KZfLp6MoOuSc45TSOc/zTJqm3UKIttYaSymfcBkQACAMwwtKqREpZVUIsSilHHDO4VKpdDFN05FKpbIgpexSSgWU0ilrLSCEeJqmT6jFCoLgapIkty2YKKULzrlSx+HccM4v3NyxopQulkqljHPuO+fyKIraGGOqtVZSyj3OuUAIsQgACUKoK8uyHm9q6hH9I2+6AwAA9/VFlX/48Kr1PJSmaeTd+G8FANY3O3C+75+21o4ghFY78TBVKWW/MYZ2zvelLMtuFK3OAaF0tb+/3+YPPtRO3vXLoZ6Zue05Fc9/3mTy82/ffbtreCu+719xzkkAOJxlGQYAYJQ23cu+rQtu0oyxo0fj/Hd+07POkVvO42ytVvuNgYGB3/tKxyooeDIopuEKCgoAAGB+fv77Pn9x/vPLa40LURS9DSF0tFKpuCzLWkqpkjGGpWkq0jT1EELG9/3HtNa1KIoOydFREgteorvHE3rwwBX+lp96jL76lZPbDuAcwCc+QcE+fuAMYxzf7ibr+/5jCCEKAKC1rjHG1srl8tUgCM6Xy+UJhJAHnakqxlgSRdEu55xijK1LKQcrlcolz/Omb/d5KaVTWutejLHs/Bw753BHw2QYY1ONRuNglmVVz/OWKpVKTxiG5VKpFFYqlcknyqFL03QkDMOzmz8jhFy5XL4ohJjoLCdtej4Ra+0gpXQKAIAQskgpXUAIaWOMjaIIW2tFHMcH8zw/VqvV6p1zJfM83yOl7A6C4KwYHxeoUnEAAHZ1tRS/9W0VND1tGWNUSglSykGl1D5CyGLn/Z0AgKUsyw6laXooz/MaY6y+dV7OnYv9f/6XR4M//8tH/B9984L43OeXkqWljfR97xsVB/Yve0+78wLq6sqBfalpR7q7E/M/ftj/coVSEARnfN8/hxCyaZruz7LsqHMu7+vrmyKEOPbF+yfgFnG9TRLbic3ZRrlcdgBQTMUV/JdRaJYKCgoAAMDzvF/ZS3TUzvQbPYx/stFoPA8AgHOe3bydUmonY2w5z/MTAIAQQsAYA+R5K/qX3tFllBI6CLpomrbIxz4+Z+bmtkTP6rEz+4TSrVzwW+NGHrdc5vv+pTRNT3Y6MFNSyt1Syj6lVLdzjnR1dV1rNpsHNre31sYAABjjXAhR6+rquhbH8WC5XG5TSs/GcXxECHEWY0w9z/OjKOrK87zb87wZAIA8z1kYhld93w/X1taOds7JGYwxM8b0WWtTrXUZADYwxtT3/VlKqUuSpGatBc/zVpVSJaXUgDHm5iDeK1EUjd+akYcxjrXWVd/3Pa01UEo1APRZa6Nms9nt+/5CnudbHSqt9ebnyzqfF20GCXsf/MA5+uCD1Lz0Jb4IQ5NI2WIAHN+gxTlfRAhJY0wLY+xprQVj7LJS6oAQYjZN0y+dx4/8I1b3fOrU1ht957uGyfOee54G4eX4nk+dBGuBDAxcZ/v2OrWw0GPmFwJTrwfev99zFd7wHUOAkPN9/7QxhiOEUoQQcs7xJEk2c/ImO4J5P89zHUWRqlarF3JMOH/jDz0CFy9wed8Xj7J9exto1y4lrd3m4ySEWHbOJYSQ8UajMVKr1bZZFBQUPBUU03AFBQUAAJAkyZu01o+MjQz+dp7nb9Baa0rpPCHE+L6/RAhZwRg3jDG91tpSGIYXEUIVQoiy1jo/DOPcuaompAIAYBkTpWp5Wn3xgcGtbpLWiLz8pau6VNpWLFFK2zc7eAMA+L6/KKUcAABMCEmdcxgh5BBCiFJ6XWsdGmO2bqQYY2lvZJsZxphUSllKaZ0xxjc2NvaEYXg2SZKTSqn+LMtqvu9PIoRmOefAOY87+XJNzjnPsqzinENa6wFCSG6tJa1WayjP81BKWUYIXXfOKd/3fUJI6nlegzEmgiBAALDIObfWWgkAnDEWMcYWOefLGGPNGLtGKW0453zG2LrneWsAsLD5GuecrVQqqTEGVavVplIKE0ISIURLax0CwKIxZttSmBainxw8GAGlOsuyLsaYyrJszBhDtNZ9Sql+pdSQtTaQUvYZY3rCMKwTQuYwxkwp1eutrS2yq9euw31fCO3KylaYrv/MZ5wDTAblhQuhS5Ibvkr9/csIkFMTk1uhxmZmptd/5d0XWbl8PU3TU8aYPmPMsNZ6yBiztZ21ttaZXuwVQvQghGiSJJAPDhxQRw4PuWfc5YlnPH0ah6U6WVqK6dp6pA8fqgHApiC9hDE2cRz3SSnjWq1279f/rS8o+OooOksFBQUAACClXJFS7p+bm3sHpfQxpdSrN+MqOmP94wAAnPNLUsqDcRwfZowlnU4B79x0tzRK1c/fNxW997dOBHfccUZH7S6XpCFw1pa9vY8LQ0UIUYRQyjm/2Fl2G1BKbfkNeZ6X5nmuCCGtPM8PEkIAABY9z1uUUo5Za7uUUmNBEFzJsqw3y7I0TdP9vb29M81ms0cIcU4pxQEAgiA4m+f5iLuRi9KDEEIdi4GG53kDzrnIWosAAAghTedck1KKrbWp53lrHQfwUSlljTE2zRizrVZruFwu5wCQOOfKWZbNK6V2lMvl2TRN+wkh7TzPR8IwvBzH8R03fa56u90eMsbUgiC4BgBhu90estZeg063rVQqrWVZttFsNu+oVqtXpZSkU8AuRlG0FYSbSrkrDMNznufNRVF00vf9R9M0/VKHCACstVsTigihQClVY4yx0oMPncve+a6joPXj9Eg2y3rlmbPdLkm2ZBu0tzdOH3poawqRv+61k+oH/zvEhBzxrD3red5jACAIIcgYkyGEGEIIO+fKzrlKpxiFJElIGIYbhJDIWhsjhKj2/Yo+cqQSTE0/kn3u83fA5z4P7LWvngWEgP3u/2aupzvCd92Vyf37BrXWP7aysvKB/v7+ortU8JRSFEsFBQUAAMA5r2KMy9baH8rz/DOU0o8jhPYTQliWZXs3l5EQQgNCiEcJISRN0wOe55lWq7W/Uqlc8X1/ErQGmF9oJx/44H62d8+UettbBrNSqT8Iw8ksScZvVSx5njeTZdloEARzSZKccDeJeRljU0qp3QihhDEWpGm6UwhxqbP8NApwo/hJ07TKGLvunNNhGC5EUbQ/DMPzaZryjnfSgVKpdAkA5jeXruI47uacr6Rp2u953gZjbDpN08xaW+vq6rqYZRnRWtfyPD9KCDkHACJJkmHf9xeTJDkchuFZznmVUsqr1epqHMceY+xRhNDnPc/b7Xneb0gpv9059xqEUL1UKl2+9ZxHUbS3XC5fNMYsRFHU5ZyrVavVWSklklIOSCmX4jju4Zy3AQDa7fZ4GIaLlUplbWVlZUcQBOeTJNkSzMdxfBRj3OCc17XWu8IwnI7jeBcAgO/7k9baHGOcp2l60kxNrYl7Pwt4eFjA/HwA+nHSIPBOnTqn6/WRmwslAACgBNOx0WVSq83TF74Qb7zkW7YKJynlfkKIVEpt86tijK0HQbAex3ElCIJGkiQ1QggghGye56cIIdoYQznnLTYxOYHr9S3rAfrbv0uBMZ3/878MAQCQx07X6W/9xoK2drjVar2jv7//R77MV7ug4P+YYhmuoKAAAACW11tv8gWtAIDFGO+y1iIhhAUAAgBoc8nLGOMbY4aUUgOEkDZCKHDOMSVlhd7/wIJ+05sPmb/520Fotxm7487r6QuePw4Igda6KjzvnNZ6K6yWENKklK5xzm0cxzvglqETzvliZylNWGtzhJDNsmyn7/vntdaDAAAY4zQIgnlrbSnLsnFKaZsQspym6VGlVE+5XD5vrS2naTomhJi/2X6AUnrZGDOktfYYYw1jjOKcR1pre2OFjcRhGMZSyoRSmkop9yil+jsj/oe01u12uz3geV5CKcUIocfCMPzDoaGhv6pWq+eNMdf7+/t/IYoiliTJa6WUvUEQXOhonzb/WK1nWXbIWltijK2laTpsjOnqjNiXjDEB57zRMd1Murq6bBzHS9baAGOMtdbbljSdc75zLvJ9fyaO40NhGJ7DGK9kWXago70KSn/+F1eyd7/nuHnwoSGO0HL+2c8Ng3OIVCobLk19AAA2Pn7JrK+PmoWFbY7qSAgHnl/SExPdEJaq8vyFXnT3K+oOoNQ5PuWcX9y8Ppt4nregtQ4JIShN026EkPU8bxIAGkqpEecc7ny/BPv9P1jKP/qxLWdyc22ibK5c2XofbnnFJ1/4oiMvfMGExPhb8jy/t1qtznxNX/iCgq+BYhquoKAAAACVfFaVUqZKqQ0AIIyxL+Z53iOl9PI876OUXmOMNTnnMwghI4SwhJAuznnDGMP9i5euyp/9uROu3QYAAHZw/3T+kz+25cjtnMPOuS1fnDAMZwghzTRNj1prt2WA+b5/1vf984wxUiqVQiEEBwAvy7Ix55yfpunJUqn0aCc8d1cURQecc4tCiFkp5Q4hRFAuly94njftnHObXQ6EUD/GuLF5HEKICcPwMSHEBQBweZ4fQAj5PAgHL69mKSFkFQBQGIa9CKGtJUZjjB8EwZRzrhQEwTSllBBCFGPsW51z/wAAaHZ29ruiKPqtubm5v+OcjwIAMMY2GGPO87y5zs91QggKw/B8tVqdyrJsKAiCmTAMr3SOQwEAOlosMMaUWq3WchRFJxhjq1mW3XaM3/O82SiK7ui8Bm0adAIAYIRY9iyqNJIAACAASURBVI8fOwZKIVytNuTEZA8Jw2Vx5MhlPDhgvafdeUYcPz5hs2zYrKyUbt23y3OEGbsxtvZHv98a/KePpl3V6rYxNmvt1qrF5tQgY0xjjIEQsiCEeLRSqcxRSjVCiHe2c57nLQRBcBFh/KXXC+EAAFAYSP5bv3GJfvhvV+nBA1NmYqLCLl5S4JyL4/h96+vrj4vJKSh4sig6SwUFBdBsNk9opb7HGCMppVwp1UYIjVtr677vx4SQCWPMCGq1tVcuy2Bl5Tp30IoBylmeD1SS9Hz6/f99m9mh+I7XXc8OHxre9pgQa0qpft/3F9I0HTHG1AghUcdl2tvczvO8tSRJjhBCjHNuPc/zzFprtdZ9neevpGl6ZLOr4pwjzrlyEAQRxljleV4qlUrI8zyhlEqEEA2lVK+UMhBCTDjnHELIOue6lFJDzjmT5/kez/POU0ptYmjJI474FIVZllmEUEoIIb7vr1lrV6WUB4UQGaVUYoxVs9kc9TxvHWPs5XkeJUlyRQjxUsbYHZzzijFm1Dlnuru7I2stopSyUqnU0lqnnPN2mqYHEEItpVS3UqoKADYMw4gxNoEQWjHGVKy1oed5M5xzl+d5r9a6m1K6GPj+PGq3Ea3X100YVgEAKKVLuqM/IoSk1toqACACsOT/7YfW9Rfv7wYAQL5v+MEDzfzipb16dnaAj43Vs/98eB8AlF2SGJfnAgAAl8sZ6elukt7eecQZYuO75/BP/cRq3tsz5tqtmWaW3Wra6Qkhrncy+2JKKY3jeFhr7RFClgkhfb7vc8YY7nhI9Xf8ncaVUn3sk/esmLnrA97LXnoB3v1Lzv+uN8zSw4fr0ckTh7QQYTA1taIuXe419352sHTgwFlnjHL33HOy+qxnfezJ/L0oKNik0CwVFBRAmqbP1Fq3KaU+QshyzktxHINSape8oSheZYzF6Pp1Q+67j8R//MFRh1CF/u1fr7BWa52urlE6NlbXMzPdwQ+/8bzet48kJ44f9n0/StN0qzthra35vn+541ytPc+7wDkvt1qtPTe/nzzPd1FK22maDjDGMqXU2OZznPPzlNJKlmXbPH3CMJz1PK/LOZdJKTMAEHmeNxhj5VarNUQIaQshGnEcH2WMrVJKV9M03QMA4HlexRhjMcZV3/cZNyaJtQYpIcqy7EiaprS/v38BY+w551YJIReq1eqgUkpqrRUApEopJ6VMOgXTHyGEQkLIZefcTiEEcc5tIIRKmAkORmkhRJhl2bqUMjTGiDRNdwkhphljzSiKTnYCfQd937+W53m/EKKulNq5aeIIcCOwN9J6uPrhj8zmDz6Uht21S/hn3xZIIbZctq21jjF23aWpxG/+id7s2rWt5TEchjq997Nb5940GjW6a2xGT8+Msb17rtpW67B3/NgkP3xYtv7+IwdBqV4yOJAln/r0ydLBgxf1r/8Gst/73QAv+KYb+8M4Z4xNwo0u4DhjbENKueVS7pxjhJByJ9yXGWOYMcb3PO8cQmhrO/q85xJ49asumgP7a6hWW2vn+UH3tDtVKQznolZrJ+ntteL48Qk60K/id//ycUhTpBG6Y+7hRx/e+Rd/9r6v+RegoOArUHSWCgoKIEmS1yAqnrPQzPJQECJl3maM4SRJBpxznjGmx1pr3MAAynbuHObnL0yaK1cH/Gc9awb+7C8G5N9/pOa95tXX5UMP9aNabT55zauPCc+7nmXZtngSrXVFCLFGKU2NMQQhxLXWVWPMtggNay1njJ1njCUdXU4XAEAYhmeUUvs456sIodQYUy2VShcBIAYAyzkHSmnNOZfGcdwGANNqtQ4456i1VmitK57nXUMIrQFAdbMzZYxhQoiZMAxtlmURpRTneS4xxuD7vmKMXddaZx07BSKEaF9ZjjNOEfU5FWEYKqVUGwC0tRZ5nicIIQwAStZayxj7pHMOpHG9zjqEMaJaq3YURYPd3d0mCIKmlBKMMSVrLbfWlhFCklK6TCmlQRAsZFnWZ4zZ8qNCANpfr5+1YVAm//5JpB54cNS1Iy4Cf8McOZIRSmcwxopSyhBCDaeNjz7xr56r17c6eE5KwcbH12y9HgIAkL6+NT462lLT0/20f2AOKDWIUi4OHmThs5/dTu+/v+KimAIA0KNH5s2bfmQlP3J4jwNg5XL5gtY6VErtMMbUAABxzi9prQcRQtLzvLOc8zyO491hGM4DANTr9ZHOOc7yPBfW2goAQL57d58aHOhzQpBORAoHAEoImdPG9Nv9+xvojpOx+Y/P7rCzs1vfHVNvPFOvrHyk9IJv2jLZLCh4MiiKpYKCAjh7vf092Jn9kQJR5S5SSgVKqdTzvCWEUMI5X1JKjXHOJ5mDlt25Q/D/9qzl7C3/6zDxRAT/7/9m7s5Tlk5PL9qVFa5f/C29CKHEOVcul8sXKaVrm8Lqcrm8TggBQsiaUqpkrS1jjI1zjgIABEFwXgjR8n0fa60rhBDknMsppbNKqd3GmMBaaymlq1rrwY7rteScY0KIb621zWaTIIQ2EEI3PJ+s3byhIq11T8cosWytFTc9XiuVShHGuBLHscqybFwIETPGTKvV2uN53jpjTGCMBcY4nG2karjLqxKEHcLEU0oZSimTUuLOds4592kAGHHO7WCMDVOMuAEHghIMAM7zvJwQwpxzGiEUUUobnPPI9/02QogaY6qEECOlTG4VTPuPPXZG/Y83nRRzcxvm+jx36+se37fnorx4ea99+cuY1PoAY6zpnMuccyVl7S72zd80CZ/41x5ACEBrBMYgvmfPrF5Y6AMAYMND17IHHjwOAEC6a5rvGtvIHn50T/bww5X0/vu3hOTid377dPLCF+zWvr/LATBCyHqe5/3W2m0aJ2ttlVK6AgCe1nqkq6urlWUZo5TWjTFZnudDxpggy7JeSukSAGDn3Fbx45xjnPMlhNCGc65qrS1RSqUCGNKl0qCf5wtmfkFAq8UAAFye+7bVPiwOHvgrtnNnkeVV8KRRFEsFBQXwb+dWnh9Le+LsQrt9uJ9bz/OQ1roWx/G4MaZmjOlhjNURQhuYc5eWy4fkyHBf6eUvPxu96u5xTbAghCj7/OdpeOEL+7jvX2KMtaWUQ4yxxTRN94dheB1j3DTG+M65REo5rrXus9Yyz/OmGWOznaWmMUJIFsfxkO/7bSGES9O02tEErZXL5Q0pZTcA+MaYkDG2kqbp8SAIUgDQWZa1GGPWWosZYwQAIuecdc6FGOM1IcSVjpt15SY7BNmZcOvL8zyx1gqMMaWUbmitSalUaugb5IwxZozRtVLQ7YzemNswYqqR4Ro1qbVGBkHANoNwMcbDhJDQGJssR3pOaysChlkswUogwliDOCUANwT1XCnVds5ZKeVAlmXdzjmqtQ6stT2lUumylLJ385r5n7tvzi3M9+hz56tufd0DQsA7erSdP/xwP7nvC0BfeXddGVMRQixijDUhZAEFATOv+/Y+Pjp6AU6fqboso6ZR7wme/exJNTPTbbWuBc98xmU1Pd1v1utVNTPbf7vvC73zjlW5a2wr3JcbMys+c++qGhsdgI6gu+PSPdExpyxzzuvOucVOcTSOMSa3mIoqz/M+7fu+FkK0pJRVAMDGmKrv+1Oc841OUPGWDk7u21fDr3lVIo4fvWL+/Z4BAABTr4+r+Xlb/Y7XFWaVBU8aRbFUUFAAITU/PNeUI4Ih2NvjkSRJNrIsG79pE8Q5bznnFEKopzP6DjIIejjnk8aYbs/zpjzPCzQ4mef5AQAAxtg0xhgwxgQApHMOSSkHpZT9AJCUSqUpY4xfKpXidrt9WGvd7fv+esd/ZwUhRLXWGiEklVJVSmlNa12jlC4JIVqU0iYhBFFKVyllDACo7/ulZrM5oJQayvO8W2vdwzlfIIQ0EUJUSrnXGNNHCFnhnK8xxtYBYMMYsy8MwyVrbZlzvmiMyZ1zDGMsjTGMUqo5574xBhFC0utNOdtXYpMO7IgD7HpDmjDGSr7vVwghDCFEnXMkTVPp+V5XS0K5ryz+nGA0vhyb1YA6/PBcOxupiAghHEqZp1EU7aKUtgAg0lr3AwAEQbDeCTzPwzBcRggtcM4z9/7f971Dh4y8eq0EAED37ZnJz5wbB2MAymXlORuh48dRkqb7EEKWMeacc7GUcljt3NEvXvfa3PzlX3NcKjXM+nqfSxICaUp1a6OH+EHq0pTDE6Dv+8IA/f7vW8UIRWy9PkH//C/L5l8+0S9OnVjAAwNtxtiUEEImSXJECDFJCKlLKXdKKYcIIdL3/avGGMkYo77vr1FKlxFC9cHBwZc4514npawyxhY2jUkxxutpmh5SSpXCMJzxPC92zmXW2pJ1ziNZPm//6eNbk4HWuafbb/vW+8NyeerJ/D0p+L+XQuBdUFAAZ+ZjDODMXaMlAeDSIAhqCKHT1loGABIhVGWMZXmel7Ms6yGENIwxNeccNcYMUUonoig6yhhLhRALQoiLvu+XkiShSimXZdkAxlhZa5nv+5Md7dCc1rpijAkbjcYeAADOedsYs8EYo0opJqVUCCG/Y7JIEEI4z/NaR4idJ0kS+L7f9jwPYkOIT51ZX1/vZowtW2uDzaU/jHGSpukxuMkuRWu9s5PJNocxpsYYbq3FlFJrjEFCiNA5t26txWma7iqVStO+71eVUtF6hs7vHwg9Y8w/cMKeYU16iRAxYq11cRy3PM8LnXOOUrpULpd/Pcn1K3bgtWdaUwt7B4aP3js1+3szCF4fKZQsb2R0uNsHSikulUorWmvf930XhuG19fX1PZ1Q4F1SymFCyLzWutJFaZaWSi7+j//o4/v2XUW+h3GpJPWVa0COH1uHX33Peoux/QHGE77vr2qteyilOMuyrU6Rdo7SoaF1pyS2Wb6lhfIOHjyfPvSf2yYbAaEbQcgdxPj4ZXjnu3rkfV8YMNYOwPjuFurrU+ptb+/Xf/OXoUVoLAiCM0KItSzLDgMACCGuO+eaWuv9WZbt55yvCyGanaKJYYx1s9l8ECHU4/s+7UwNXpNSkjzP94VheMEY40spS0qpwTAMH8MY59ZaxowBOH50FT37Oauwb29mjh3tr7dav405f35PT882W4qCgq+HolgqKCgACq7UX+GiN2BKa2XX19d3Q8chexOM8SO+77tyuSyjKBLGmC7omFUyxiq+708CgBJCcIxx4JwDIUTAGDOU0ssAYI0xrpMtZ/M8r2itBxljdSllbxiGc2EYkiRJTJqmXZRSrZTyu7u7dZqmPmMMKaV6wjBcgU4USLValfVMs1BbE1IwlFDgtVqz2WyWtdZlzvkVuJEpVoIn8JXTWu9kjM0DALRarXEhRJMxhpVSq9baHvf/s/fe0ZJfV53vPvn8QoVbN3ffDrdzUluykSzLkjNmnI2xeRiMh2H5kd/MwJvHmjfY8JhhCEMyBgYTTDDYBmOcwQnLHlsycpDUakmd8811q+pW+oWT3x9d3asR8B6w8AtWff6rVbV+dap+Z6/fXmfv/f2GoCYnJ68AABkMBj1CiJvgdB8A+4P3PbLJ6wm39+5M9uV5UQCEoZQyds6VIQTjvZ8jhLy9k5uwaFtrrr7wy91ud+GOeXn7+eagv29e4goHyLOswzlnzrkqIaQ1mhgjaZou93q9A3EcP6m13pNl2WIURU8o50RoNPKQFyQ4V/FZpti+vTlKEuA/9IPrA8aOAgAQQipRFIksy5a11sxauw8AQAhxyVq7Pfzhu+Lk05+5MPzFX77pBReUDjiKtDdGAsbAdu1cp9u2DVCSDv1WmwfjOK6keSgKC95PsWfe0SFv/YkNhAAV3/r6Q/zM2XPl4UMH8jw/zhjbuLnHKC2yLDsGAMA5v1CpVCohBJBSsn6hVUzwlPeeAIAhhFDGWA1jrDnnEEXRsrU2cs5ljLEe53xtZFlTd85xs2dxPv3vv/mkMqZmjDkCIQAYs7C1tfUHlNLX12q1pwrHjxnzT2JchhszZgxUUf5C6YvthBCPEPLGGHfDRwxjPOScP+m9n0QIlVLKBgBknHOjlEoBrj/8tNa70zQtAACUA97P1WBQ2pAIKqSUaZZlCWNsEELoM8ZKAJjFGHcxxj1KaQEA7V6vt19K2QohFCGEnHOuMcaCc0611jbLsgXG2BoAhF6vtw8htCw5Z5SzlBEMeZ7nWmuNMe4bY+acc5MIocIY83fUwW/Fe1+llPbiOF6x1laNMQhj3GWMFZRS4pxTWmsbxzHFGHMgLO6V/vmTMflXeyZjSpHXklNSFMWUc25z1BNlRk3aRcQIbtrYeEx+gOatt1y+dAa//7StH5rmnQhZaYyZIoQMh8PhLudczBjrOedKuO411xppUBXe+7q1doZw3oRDBz2pTyyTH/t3lO/YUQzf+TvH6MTEhvz215dYyk61VmvJ68a+yHsvnHM9jHHhnJscNVpPIkIKt2OH8h/5aANpjQAAbLs9F99375NYilUyPY3MufNzdmlpkkzU8/JrDx906+uT9tq1eQiBeGNitrhbIaVKGA4s8b7lXvmKGSRExjlvJ0nSK8tyOkmSS2VZ7rxhZSOEaCql+iGE4JxjnGL82HoJkhBMwA4QQtI55wghJIRArLV5WZZaKXWUMbY5Uvzmt/Y8GWunhRBd7z250YtmrT3snNMTExNf/JePmjFPJ8bJ0pgxY6DVah323r+EMVYopfIoijKtdeO61yzOhRAaIXRjjN8opWye54tRFC2NxvdLIYSPokiEELQDik5tFDS3iHuMacLAW2Owc25QluXtxpgZa60MIZAkSYqiKLZJKTMpZRshFACA53l+mDHWxxjbsiwlAPgoijrW2tQYM+Ock8aYOYLRJoaAsyxLKaUd770PIUwkSbKklJoJISSUUjMqKf6DcM6beZ4vYozXhBBdjDEuiuI4Y2wlSZIZhBAQQjYwxoQQXN3MDW1ExMfYdgUjjFIqer3etLV2FmMMZVkKznk+HA4XGCW9ADhSHk9WJfWT07P0vul8kCZSMC7zsix3UUqbExMTJoTQCSGUSZLMhBDK4XB42DnXIIR4jPGm975mrZ3Gacriu58Nfa33sscf79gHHpzyw2EahrlLOx3ZW1jYJuIos9baoihKIQSz1k4IIU5rrT0hJNJaVz3GE+LEycyvrQkAAPH6bzvhnAf94IN3uM3Nm9NtwQeevvhFl91Wh4Qsj3CttoVrVWROPl6D/mDLv+61NXjtaxlLkjbGuDtKugmldAUhxCmlPUrpMI7jK/h69zzXWiPGWEAIkfkKg1gyaTyy7dxFCScYIPS994oxFhljAmNsKYqiyaIophljp51ztyqYI2NMnTF20Tk3DQAQRdGqc+5onucP12q1pa9D6Ix5mjBOlsaMGQMIoQ6l9NsJIYxSKhFCgBBa9t4nQogLWZbdZoyZjaIIY4yDUqrBOR865/rOuSlCyDWEkMQYZ957u5kZmxvf31aXS9yZBAfTUUqlxpi/pfRcq9VWyrIccs430zSd0og6TjGB61Nt04SQHAAKxlhgQnJGCYQQ+hjjQms9CQBAKd0cDod7vPeCEJJgjIfGmAlrrWWMLY2m+DBCyDDGTmOMB977yVt+uxNCPMk551rruve+jjFuGmMWG43GpnPOE0IEXBeepJRSCSEwTiBw5FVZlhQhNNRaUyFEhxByTWvNarVa0Fr7kTwAohicJGETU/EXPqBnGixLY/2k9x4qlcpFhJDM8zwFgNw5hwGgyLJMO+dmAQCEECpttXoyhFZIEjDGTBZF0QAAINu2bdATjy2Ter2HsmE3/+z9e2Wjccbcf78yFy7ken5+sXRugTHW1Fof4pyvF0WxiyDclB//+BK5eq3lOp3Z6A2vfzL/3u85xre2Nv2pU/ENBW8AACRl1//7fwvxd73R4avXhojz2Jw5UwMAkN/z5vXstmN7rLU1rfUMQshjjCXGGBVFcdAYMzGyjpkqy7JaqVQC57zCGEMYE+6cQ9JnxAI1ktPHEo53AYRiOBy6siwbWuvCOVdRSu3jnC8jhNqjU6m5G/cQADBCyCCE/EinCkIIyhizAwC+yRjz8TRN+1+/KBrzjcw4WRozZgxIKTezLMPe+9sxxhgAsFLKp2lamOsnQtMAAJTSq5RSyhjrD4fDnVLKM0KIi9baiHNedDqdw9VqNU8ZwtMxCixoJjm2zjk8moJqa62nEEI+iqKTIQRdFMVxSqnmnKNIq8iF4IEywbhYZ5TkcRxXvPed0gGTjMherzfLGLOMsa6U8mpRFAeTJDnNOd+01gbn3JQxZsI5N2GtnQ0h4FH5KnXOzSOEIiHEKUJIt16vZ3EcZxjjZDAYLCRJcsoYM1OtVltSSoUQwqPeI51l2aSU0mVZloQQegSBRQhZjHHunMNFUVRrtRoSQsg4jqlSylFKbRzH9bIsH7PWRlLKqyGEXgjhgDHGxnEcKKUUAJohhBgAuqNer0hrXRNC5EqpmdGD3zspvf/5/9ZAH/9LCi95cQdhjASlPfLVh/vozBlsLl8+aNfWtgVjkP3S38zYx07OuIe+PB/fd985vG3bqjFmPwAMnHMVydgl9G9/tGo/9vGddn19Ln3lK0+b73oj1CYbnhw8wO1HPib9cHgzWRI//7Orat/e7TRJNl0UFeGlL9milUqXz8510fbtVi3uvjG994RSapcQYiXP8/2c8w3vfSSE2Bz5w20CgM/zvGCMbWGMSkIIb9uYxAwte+8fGVm8vBtj/AkAWPPeHwcAzhhzcRwbSqn03leMMZV6vX7Be9+nlGqE0Ka19la19ysjixwipbwLY/w+zvlYf2nMPxkUwnjfjBkz5jrNZvP5ZVl+t5TyoLW2TghJEUIghNi+sbFBOecnAGBtZBq7QwixuLKyMoEQMnEcLxFCckppzDkX3vuglNKcc+KcM8PhsCKl7BNCXFEUaVmWC1EUNYuimEnT9EQcx5NaKUUZi0Kr7W0ldcqYPEmShFIqrbXDEALy3mullA0hGGvtohBiOc/zI4yxywghqZTafutvqlQqZ7TWJULIK6WecaNvZmJi4gohxFpr7Uil3GdZtiuEQKSUjyCEtqVpqjDGuNlsLtTr9SuUUuj3+704jgWlNO33+6lzjjPGLlJKpxhjhXPOMsakc84opXC9Xk+11r9eFMXroyiqY4xPhBC+prXegzG+bzgcSillP8uy3ZzzJsaYaa0bUsorWZbtppRaSmlPCFG6skROa6pe+rJd/MiRzLc2I28MCu0Oknfd+Zjv9WdxkmwizsC12sysrCwiKQfw+79LdJJMEELahJAWYywNISj26KPr6lfefghCANvZiqHfl2TbNpPc/exz/Q9+6Oit/2PyH3/8zOAlL97hvb/ZJ8SNOetf8eqDgBCIX/pv59h999astatlWTYajYYvy/Jkv99/jZTy8bIsb4vj+EkAqDDGhoSQCiHEhhAUAIjhcJhxzt8bx/E7p6amtm797izLqt1u95e63e5boii6YK2dNMZUhRBXEUKVIs9naidPni6OH5/TCN1sVuecX9ZaLwIA1Ov1VSHEB6anp//dv3jgjPmGZ3yyNGbMmJskSXK1Vqt9NEmSP6hUKr+FMf4d731Ta30ljuNLhBBaluVxrfWkEMLmea455500TXOtdeK9T7z3PYwx1Vo7KWVkrdWEkIhznnvvda/XO3zDZoRzfgFjLJIkoQghijAmjDHa1YpTxrbyPD/qvQ/D4ZAXRTHLOR9Ya7lSajJJElWpVGi3291DKV01xuxGCGFK6TXG2JUQQqVarZ6nlAqEkCzLcmcURRve+yiEQEMIYTAYbI/juCWllFrrpFqtNoUQHc45o5QahJAYqUdf4pzH3ntnrUUYYzzqw8mklJpSCt57NxLzNEKImlJqwBhD1lqDMT6SJMlkr9cThBDe7/dfNJImAO898d4TjLEjhHQBIDDG2oQQJKXsIoQQ5zwTQsR5lsUOISImJpb1Rz+2LQwzJPbvO0uqtSx69l3IdTpanT51yPV6M3Z1dZouLGj5guev5nfduQMQgiiKMoxxT2s9p5SaVVNTO8JrX+P9a17NkuPHHiTN5gUyP3sK/cgPt/0Xvwi+379pV4Mxpvq+e6t/a8MwJvkTT1xmjcn3JQvbfy++667vm5ubeyeldLXb7b7ce59TSgtCSJiYmCiGw+GkEKLvnKtKKRFCqJdl2Uc455+p1Wo/ODs7+/k4jsun7kvOuRoMBkVRFG8OIaA4jtfTNC37/f4iAJTpyurZ4n/78cP8W17axPX6VcbYipRyPYQQrLUzQognyrLcNRwObyvL8iu1Wm2svzTmn8T4ZGnMmDH/IFmWkWazeaJSqexBCHU2NjYWvL8+hU0IaVUqlc5wOJxO03TTWhuUUtOc8y1KqXHO+SiKouFwuEUImUQIgTHGlmV507gVIQT1ev1sWZaxEKK01iIpJbHWlkqpmbIsG5zznlLqhjfcw2VZbnfOzSVJcopS6kMIvt/vHwcAGKlwHx999kSWZbcDAERRtJIkSXDOlYPBQEgpk+Fw2Iii6GxRFAcrlcoqQijDGHvnnLbWRvV6PVntK+U8hmnpQpZlFc55dySNgMuypADQBQAkpaw450xZllxKqRFCCACwtbZ0zmXe+6RarVaVUltlWSaMsS0A4IQQ75zjg8FgsdFobGqtO9575b2vSSmNEKKKEGLGmAw6HRi8+Xsi8UM/2ObVapL/2Z9H+mtfm+I7d541y8sH+b69pXriSTnxff/zWnb/5xrVN7y+kz/wQMi/8MVt/M47z9q3/u9zdHKygzEeaK2rWuvdFONN7FyPPvBgBzWbEi5eFPInf5IYziL3n956Wt3/uZfcuFdkfj6YP/5D8N6jW+5fOTk5edfc3NzjrV/+1cXsgQdfjeN4c8d73v3elZWV+6y1PxNCmFNKraZp+nClUjkthPjicDi8yznXmZmZ+QQA3HwIbf7iLx/Iv/jFN7hu986g9CGgpEzuu++Ncz//s6d7vR5utVp/XhTF6wghfUJIxxizc47qMQAAIABJREFUPYqis7aztSteWWl19+5ZHO3NTbiu8J7GcfzEqJdqBwCAEOJqo9F44eTk5DhhGvOPZpwsjRkz5h+k3W7f45z74Obm5uxIE2epLMsdaZouZVm2Q0r5tbIs7xBCLBNCBt77Aq4/pKpa651RFJ0KIWjOuYjjeKLZbM567xFjLDPGJAAAcRw/aq1llFJMCGlgjLcGg8Gi917GcXwSro++u5GaNrLW7qxWq62iKAac8+3GmKuU0rmyLFPG2EWM8SRjDIwxajAY7AkhYACAiYmJJUopzvPceO+LEIJWSh26MWaeJMkTeZ4fEkKcqtfr0957vFXYMmFIEATGe28RQsE5p4UQE2VZDgAg55xXCSEyhGAAgGKMubVWEUKw914554osyyYIIWVRFPNCiMshhEa1Wi3guk6Vz/Occs4DY4zkeV6MxBarQoihc06GEAYYYxTOnzfR9u2Ttlr1BGPpv/q1fPBj/2HB5znjhw93+M6dQa+ubrLJhsi+8MVFUqsp+cxnnrX9Lgs/8AOADx2cdc4VGOMtSmkIH/04Mh/+cM08eWpH9M0vuWze9hOoWq1yc/r0tfxH/8MkrlQGdHrqqr5y9Q7AaBf+w99/lPzVJ8+Ddx6edx8Os7OfSf74PWf06up35vd/7l/74TDBlUq/+trX3Df3Cz93crSNENySEA0+9ekGqddjl+WExHL34FOfSe3KygvdcHCbeuzxF7qtLQYAEL/g+afyL3zxiDh08FOLf/3pl924xtWrV982GAz+cxRFTzjn6lrrBeq9sQixG1YrURQ9WhTFHbfuZUKI5ZyfM8ZEGOPz8/Pzr6pUKvrrFjxjvqEYl+HGjBnz97K8vPyjhJDv6Ha7d4yms4BSeoUQsk4IqVWr1W5RFIlzrj6aRupSSkEpdaherxdJkgwopSSKomoIoUAI8TzPE0JIf+TxtokQ6hFCbFEUtxljpqSUq3Ec1+I4zouiCMaYnUKIpvdeIoQ0xtgxxpQQIgIALaW8ZK0dUErrURQNB4PBrmq1qrz3ejgcVjnnmbU2RQgVlNKmcy631paMsVAUxf6RftENcudcY2JiQocQ3GAwsJIiJzgXIQSFELJaa5vn+TbOeUtKmQ4Ggxnn3Hq3293HOW9573OEELqusGC3CCGJ996MSnlJkiTLzrm4Xq8TrbX13lPvfYExHo4a55kQAhVFkYUQCKXUDwaDXYyxtpSygqempM6yAYljhjA2Lc4Xkxe9YM188ENVefszztLZGWyuXWsEY/tubW0qFAWl83PdsH8/9s+714XNVouVqkD9vjEIzcrZ2bi89x6G7v98GfoDTl/+coel7BZv+b4aiuIyDIcVffrMPXRm6gLbvv1B93vv2qX/+rPPsw99+Ta+uj6L/vITZvBXn3i7a7eOg3MQlGZBa+EG/VeVDz9yqvjaw0JfuSKj22/vAwBs/Jef+eb223/t09kDD3w/UPzm7vve/6222fym/HOffz3bsSN3WT4V+n2Sfsu3nPbZMAZMcjpRx8VXvnq18q++5QIAQL1e/2Ke50op9eo4jpeVUnMeIXKLJ92Wc24+hECjKFrhnC8xxvxoYnHOOTfjnNurtZ5oNBqf+H82qsb8/5VxsjRmzJi/Q7PZ3K61/lWE0JzWuss51957AQAaYxwYY1opVeGcdwCgTQhpAQAlhOAkSXJCiEAIhZEf2c8ppfYQQiqU0jWMcassyyOMsR4hxIUQPKU0o5RuWWspxlgxxhKE0JJSai6O41/VWh8yxhzUWu+IoshSSp21dkgImY6i6KVKqTdTSkWSJF4p5ZVS1etKABqklJcppTkACKVUVWu9F2PcGY3kI8ZYIaW8NCrDDYQQNMuyCAA2i6LYyRgbIITgunxTUPV6nTHGYqWU4px3QwjgnEuSJMkBAOV5bgkhnFK6HkKoI4QEY4zneR4xxjxjjIYQtBCChRAUY4wWRREbYyallKUxJgsh1BhjPYRQXJblZBzHfcYYRwhZjZDSWhP/jt/I8Tt/B5kPfHA6KIVxJI0vyxJz1vZZhl2rPU0ajdwN+g3f2Yrg5S9DIs/A/JefXYCLF/P87mfvVYSkcXNzM9q1qxU2mj374Y9M+M99Xovbb2+TODZmeXkGALBrbu6yS8tHfZbdlFzAlbSHK+mD5vKVe0OWUzI3f833eg0AAN/tVvXVa9+BK5Xgi+JNZmVluvvuP5kqvvq1X7Wd9i5+4MCmvP0OSWJp3WYrc1m2TfzSL2b2Ax+o1970navq9BldPvLIfrZzx6Xy0RPP8Hm2k87M/JE4eCAAANTr9QeUUn4wGLyKc77snGvcWFcURRcQQgljbHXU37aHUnqtLMs9IYSbWlvW2ruKorhYr9dvnICNGfMPMk6WxowZ81TQxsbG+zHGtRACCyGwOI4Lxlg3hFAaY3YJITSltKCUhuFweNhaO08pbRdFcdA5R5RSfe/9lvfejk5zZhhjjcFgMD0at7dCCG2MKZxz2DkHSql9URStG2OElBK63e60EOKcEOL/2L179891Op17AWAbxnjLOZeHELzWWud5fo9S6rBzbkspNTDGMIRQGwAmR6W9sizLulJqYSRQucE5zwghayGEBiFkEyGEOecpACjGGAEAba2NhRCGUqpCCIgxxo0xNISQK6Uy7z1ljHGMMccYt+I4ngghWHY9QwQAqDnnypHeUyVNU6+1zjjnlFKaaq2LUQO5K8sy0VrXMcbro4b2RSHEBmMsppRujNZEy7IkxpiGUmo7+dCHN+zjj+8JSmEAALe5WYvuuKOXffb+I67dmQZKQdx27Iw5c3YbjiOgc3OrtrlZwnCIca8H/PjxteSjH2u6T36KD9//54fs2tqcb7cTt7w8pR9/fMH1enPy+PG+WVqqgrXkxuYgc3PD6M5nnfOdLUQmJrS5cuUIAABd2L4GIRB29Mg5PNnoIUIK124f8lvdCdtqPWf40Y/9sNvcnBJHDp8tv/yV/XptVZkLl0Twrosr1YZ+zauAbdt2ufzjP5mWt98+lM+6o1c+9OVFvGuXp898pjD33TtRnZ+//8Y6qtXqg8PhcKYsy5emaXo6hGBHcgKzzjmJMVYIoZJzvlwUxd/2uhvhnHuu9/69aZoOvm7RNOYbgnGyNGbMmL/F1atXv7csyx+u1+sBY8yyLKtkWbY9iqKNLMsOhxC4MaYaQlix1lJr7QTnvCWECMaY1DkXcc67WuudIYSSc743z/ODjLEWY2zovdfOuZ1FUUxhjNcwxlVCSLDWNrz3Qms9m+d5Wq/X35YkyYdmZ2e/BACwtbX1CkppoyiKRcbYhtZ6X1mWC9bao5zzS4SQaJR8hBCCF0KUIQRTFMUOzvkSIWSVc97SWu8VQqyOJASw1nq7cy5FCAHGuIUxZiEEpbWujZI6hxDyZVkOhRDQ6/V2Jkmi0zSthRDCcDj0WZbt45yvGmNglEgRjDHWWqcIoaG11hBCKABgQggNIXhjjB41eLuyLBu1Wu2SUirGGBvO+TpjLJZSJtbagbXWOueYc84ppeZ5lm2Fd/3+RLiubH4TJHjhu70ErMVkaqqJhSzdxsY0cj5HFy9w8xcf2u/Pna+TauWceufvPAsAcj8YDPxwOAWjUusNQp5jHMcX7erqdgAAunvXQBw5smzX12N9+uwO19maoPNzp+j8XESmp1fBWEKqaYt862sFHD3qzcf+cr/vduu+36+As95vbSUAAHSisexarTnfasd+aysik41Nc+7cgrz9GWfx6dOAsrx07bbLP/8/DoZSMfbeP76SfdOzdmvG7i2K4mStVjtzY41Sys8WRXHMWjvrvZ8IIUQ33nPOpc65CcZYBwCs977y1L0eQkhDCKjRaHz6Xy6CxnwjMk6WxowZc5P19fU7yrL8/TRN2yEElud5FwCMc25ipGo9CwBoYmLiwnA4PIoxziilHYxxffSQXyWEbGGMC0LIJAAgQojinPcJIRFjTGKMb3jPVRBCklKqyrKcDyEwxlhnpLq9FMfxr09NTX3uxtqMMYn3/g6t9RyldK1arSqttffex977JIRglFI7Qgg97/18mqZBa73lnIu899wYcwAAYinlleFweIxSuqaU2gcAgDG2GONutVpNi6IYcs65MWY4SqSwEKJkjCUAwDjnPc55hfSv5kPHNEIoq9VqbjAYpIwxlef5jDGGSylRHMdOaz2BMR5673EURalSqqeUyr33FgA4QsgihPplWc5XKhWT53nCGNPD4XDOGLM+SsiOUUqXiqI4AADAynLo//T9E+AcuvX+uY1mNbrzm560yyuzIcsSPxxG8rZjp8SRoxmZmixRgDaZnb1Cd+4MiPF1sK5AlAa6sM3jWnXZtdozNy8WRQDeE0BIikMHn8AYM33pct13e1UAACSEjV71qh5CUPXtVh1J6QFhx5yLfLtdQq8b4yg2fN/elrcu8u12BAAQtKqQubnc93qSP+P4OZwmBBEyUH/6/iPmxGNzbn1jyjWbM+A9Cmka2ItftGauW78g59yznHN/nqbpEACAMeYJIR+z1t5bluWz/r497ZybkFI+Hsfx24UQv4cQWsIYY+fcAgBACGFfHMfv4Jy7f6EwGvMNCP1/ewFjxoz5/wZra2tHnXN/miRJC2NcYYxFWustpdQOKeWjIYRACGlTSlc7nc5tN3pDEEKQZVkFYyyFEDiKIgsA8XA4VKOyF0MIFYwxYq31WZZVhRCb1tp5a23dWlu/MfKvlJoHgHkp5TumpqYeuXV9CKGHsiw7HkXRehRFlW63Ww0hRBjjjDHWUkrtBgDgnGtjTJllmfPeb8cYtyilUK/Xr2RZ5uD6dBaEW0aBvfdcKbV9Y2MDEELzzrnLhBCDELoGAKIoCh1FEaeUSkyoNKoo6lc/yctD30tCCLQoiowxRo0xmZSym2XZM/M8f8IY4wBg0lpbM8ZMWmuvSCm5MQYxxgxCCHvvgTEGzrklYwySUsrBYHCQc36tKIojaZqeJIQUeZ4fk1KeBAAiL17EGUJTf999dL3eDJ2b28LVygqSkQVKwW42ffm1h2+WohBnX9WnTn3TzddTU4Xcv79Nd+64Zq8t7QQAkIcPnvJKT/LF3U8Uf/PQM576PdGzn/0RtdF8Fn3d60r1pjfvBgCIXvTCk+rChRSMmQlKpW6jiQKCNpaRuuVOOr5r92U6v0367pYsHzu5CAAAlALbt6dv19Zl6PU5vfvuNf9Tb80zQm6u21q7v59lf9BuZ28+MplsAgDUarUSAP4n59wvKKW+zzlXvX45ukQpfV+apu+am5s7d8vSPwAAsLS09Io8z3/aGPOsTqfz8iRJPvT3BsaYMTA+WRozZsyIdrv9e1LKHYyxCgAwrXXfWqsQQqYsy0OjvqScMVYYY6attQ1jTCWKossAsCaE6OV5vtc5ZwghfQDIlFLbrbWIc94mhHBrrdNa4xACI4QoSuk5SukmIcQZY26aohJCQr/ffzHn/MM37CmyLPvxPM/vAYCQpimhlJZaa0wIaRlj9kgpTzPGVjHGHiFUt9Yaznnbez+dJMkN41af5/leAABK6Ya1du6p/0Oapo9prbcBgI2iyDjnKmmacoQQQQgphElkPAk6mt3Cslb13tuiKCIhRH84HB4khAQpZTPP80NRFPWttYwQ0ouiqBitKcUYOwCAkWZV2ev1jlBKN5RSu0fWMaejKAqU0mYcxw0AWA0hKO/9lNZ6F+n1uu4Tn6rhWjUTx4+fx2lS0v371/ju3R27tMTZoUNt9cijR9zGxhyZmmqqR0/cdutvZPNzwa5vVNltx3L5a79yMZy/APqhh/b7YVaT995zls7ObrCFBe87W8Z1e7v91hZ/6v/E9+1d9v0e9Q99BSOEyuBsjGXU1Y89tuiamyJkGQIA4Iu7Lumz5/YDAJDJyT6qVKx+/PE9ZHZ2HVMKeGLiCq7XtjClzF5brov9+x93zeYc3r9/oJ933w4YJbcAAJzzi6ukumujcG/ulObsQkVeBACQUoaJiYnPpGn6awihv2aMfWpmZuYH5+fnP5Wmafvv2++1Wu08Qug9zrntxpjnTE5O/uk/OljGPO0YJ0tjxoyBlZWV5yGE3uScIwDgnXPGGONGzdv1G5+jlPastVgIceVGokEI2SjL8g5CSJMxtsYYywaDwVFjzGSapleFEN0oiqTWWiulmNZ62+iaxBiz0zk3izFu3TCMHX0PBoADSqkzeZ7bZrP5H621e6SUMooiBQB+OBxyxtiSMWZ/CIGMDFQnpZQ5Y6xMkkQCQIjjGMqyZBjjcjAY7ITRwxchFGOMjfeej15rKeVpjDEpy3KHc66BEGKU0j4hhABAgTGOvXOusECZTLH3ziGEXBRF3nvvpZRta20/hCCstQ0A4NZaqZSaBYC+c84Nh8PdjLF151zQWocsy47EcXyWEAIhhIqUspXn+ZE4jrvD4XAuhNB0zmGt9T7vfcQDXOYrq5o0m2tB6xj27cPup3+Kile8jMFXv4qQD1afPr0IWmMAALa4uxu00SHLbvbsuKJMKi9/WYsfPdyEY8cClGXbfvkr2yAEII2JFfXIo7fp8+dnXbM5lbz4RZf0qdN/5xQL1+sX1Je//Fy7ujoder2aOHwoIzOzA3Pp0vStn/P9wZQ4eGDFbW7W2I4dzWBMI/T7xK2tzZK5uS23tjZtV1b2+GEWQQhAZ2f66Ute3Cu/9OACfPsbSkppi3N+njHmCCFooSLhauF25da/lpaDhYiSTzPGPAAAY8xWKpUrtVrtCSHE/21ZLY5j02g0PpLn+Z0A8BUp5d9RDx8zBmCcLI0ZMwYA+v3+rwyHw2fFcRyKolBZljW8933n3BQAQBRFywihAiGUGWP2jCbhCinlRQBIoihqee+d934WAHLGWItzvh5CoEIIFEIIeZ4TSmlfCDHgnG9gjG+e7AghOhjjDiGkjRDKhBCllLKUUt6ntX5FURTfGkLYrpSqUko7IQTHGMOcc1aWZRRF0QYABM75kpRyCmPMEUKUEIIwxpsja5PZKIo2kiRZDSGsWWvrlNI2pXSLUnoZIVRorQ9jjNdurKtSqaxFUUQwxkhrbXq9Xh1jvJEIivM872qtg7VWdLvdnUKIdp7nNUppiTEupJSdEIJmjA0opZtFURxwzk1JKZ8YTfzRsixnrudYsqW1npVSakJIwTlfc84JxtimUmoHv3x5iz/4pQ082VDR+XMFW1qa0EvLiTi4v+uKIqYXL2k/N0vR5UuB33Yc6xMnaqAUAgCwyytTEElJp2eu3Bjt54cPbyBrnHjjG4n5sz8HdO7clLl2LQWEAElZ+m6vHj3n7pOkWsv9oC9Qre58qxXdumdQFFFEiI7f8G1D94u/0EF3P7sZHn+i5s6f/9uWKNYStrD9qs+ymExPl+b8+Ztj/m59fZJu39ZBhBYhz1O6Y2GT79oVhp/7/CKbnv5q+l1v/O08z7/ZWruLMXYthACMM75SQLKbqSuuv/WyoigOhBA+HcfxP1tgkhDymHNubxRFK//ca4z5xmacLI0Z8zRneXn5HgB4M6W0BwBopHotnXNTURSd8N5PCCHWEUJaa72HMdailG455zRCCBdFcSiEgJVSuxljW957r5Q6DAAUITRQSs0qpareeyGldJxzZoxBo+klEkXRk2VZ7qKUdpVSB6SUK3meH4yiqAcAVYxx4pxzlNIBIaRSqVSItbbAGAdASJ5u2XYF6yljzHQIQUoplb9e3ypCCARjPAkAIUkSo7UeZlmWGGMOjHqSNKW0W5blHd77SQCAEAKpVCqXMcZtjLFVSrk8z/ta68lGo5FhjLlzrhglgIeMMfV6vX4uiqJ6WZZACOkOh8M7GGMthJDKsuwwpXTVOzcVrlugzDrnJsuybDDGlp1zDWstEVI2tVIT1lqd5/kBKWU7juOEWVv4D33Elu/87dvYyqqGfr/vllcYyobIdba29N88dMCePFl3f/WJJH7bWzt6fi5jXGh74kQNAAAlSQj9PuGHD3fwwnYb/8gPnVef+WyNzc8V4lteGvmZaTP8tV+/bgVy/PiaOXtuBwBA8q7fLdDqSq145MQMWNMS+/dewfV6i25fWGYH958Ui7uH6slTh/Bb/9MZOjlZxdWqK595R4W32y1//kL91j1Gd+1cC87H5ty5eXgKQekK279vGXO+Za9d2xmsVb7XTwEgnXrmHW9w83Pr3vu7MMb9siyPAsC1GjIBFcNFCuD4w48K83vvugsZ81fyyGHzz4kBpdQrKKV/faPkO2bMUxknS2PGPI3pdDpRt9v9M0rpfBRFpN1uL976vnNuNkmS80VRLGitt1cqlbNFUewmhGRxHJM8z7d57wXnfI1SagghW8aYyRBC4r2P4zheBQAnpRw656jWmuZ5XpNS6qIoZqamprY45zTLstkkSZpCiKa1lgkp+4SL1Dungvc6SRJurdXe+xYAEM55ghCiAxXUZExpJRYaIaQ45324XkZU3gdOKYEQglVKGQDwIYRqWZbbRyW3C2VZ7rfWTlNKr3rvJwAAarXaKmMs7ff7ezjna3meH6pWqw4h5Ky1GUJIcs6T4XBYF0JcMsbMEEI6hBAwxqiyLBcppb2yLHdhjGW1Wr1qBwMu3vEbfXbvvcOAUY4QQoSQgRACEEJrtVrNijipWkCryDvNrlxZo198AKu9e4JyroFuO5bV3vRdPfrylxl1553T9L57nX73H8+FLJOBUgRlyej09FZ49LGu+8QnJ9T99y8AAAAhUHvjd1wlRw5dC52Oj3/5F8G22phubfVcqz3r9yy2zQc/3LDnL8QAAHR2ZsVtNKcAAPjdd2+WH/14wy8t8TAc1uz6xpxrtWdqP/W2zuCP3n0nSSsq1GtYvvKVjDQmpTe670Mo8HPvoRFlhXnk0Ztlv6CUozPTmdvYaMBTEEeOnFKPPHoESUlxErfd6tq8fMbx0+LAgQtsx473zj7nOQ855/4shPAGxlgvz/OjwZoKAED8m7/1ZPnrv3HEnj9/LDz3uYfV9m0PhhDMel/JWiz+USdN7Xb7tVrrUxMTE81/RgiNeZowTpbGjHkas7m5+bYQwhGMcYkQokVRPPVhhkYK1VUAwFLKTcbYOucce+9Baz0VQiDW2olqtbpcFMUs53xLCNECgEJKiYUQwjmHCCE9znkBACaO4xDHsS2KYsgYQ7VazWqti7Isd6dp6rasqAfv3UPXctUqIV6oUqy1Ho60lAKl1D54qa92T0Uoosg653StVqtwzpn3PmCM2SfP9dGORizAmWIkYsk45wYAloUQ6zdMdgEACyE2rLVThJCcMVbkeY6iKLpcFMU2732l0WhoSikuyzK31ibW2kwIURpjdAhhQggRrLUFpdQ556I0TVtxHDejKFLee4lPncqdNnm+f+8hIcRSpVLRaZpiAMi11rMSYXtJoTLFzgpKE3r6dHDbt0duspGVZbmrMT1dakoxjaIkDAYb7sMfseaz98/4LItrv/DzV5ODB/qm2+u5zWZkL1/ZeePmVd70nafzBx+so8EwN9eWFuCuO9vuwP6EPPKoUidPTvvHHqeozB2dnj6H06Tki3sKs7Q0Cc5h98ijlgi+FIxOg9I3G7wdZSs8TVu23dFs9y5CV1Ytuv24ZYzFdnW1xKfPlP6Tn0Juff3mXgp5XnXtdkPe/ezHyORkn0xMXEVpYsTRY2t2bS31/X4tDIeSHzjQcevrdbfVbcR33fV9U//Lj5wDAEjTtE8I+dN+v//Nzrn94D0kv/uux9SHPnwcGAvpW773vLvzzgqq1b59UOjvetuHT/+EduHisYX62f+r/d9sNp9BCLFPnbwcM+apjJOlMWOepjSbzV15nr9Na30QANBwONyVJMlJQsgGpXQlhEBCCAnGWFNK20mSLAEAK8uSI4SK4XB4ZCTsCAAACCEfQuAIIWSMqSRJUiKErDGmFEKQ0TSazvN8vzGm55wzANAvy9IihErnHDDGhlEUVbu50ZLhMwSFj++cSp4nsTfGGMAYW+89CSGgWsREIkhaFkWJMQZrbT7qm/Ja62x7hfKTK9nmTIw0Y4yMRB5rg8FgGgC6t07CUUq7UsprWusdSqkJ51wNY9w2xixGUfSkUkoPBoPJEELMOR8AAJVSUuecGSmXb4uiSHvvs1EyJ40xPsuyaVUUWP+bt+yGM2d45fDBy2zPHhlCcMYYYq0NMRdD+4d/qOqCejc3xxMpuJqf7+HZWR5CkGJ55ZolxHtCjF5b65l//7/uNJ/45MKNtbM7n7WuX/uaKgegQSmDd+7UiGDhtSaE843omXeE8vy5vWzPnsviu99USS9cSosvfYn4bi/wXTub5dce2eM2mvN+qzsBcTSgU5MZnZ3bNMvLO91GcxbSChYH9q/TmenLrtmcpYSsk1rNlg8//Czf3KzKe55TwvSUobWJ1PzFX5TqZ372GNu+bc0ZE4u7775Ye/Urh8WXvzIB3gOWUVefOnXQtVrzARPGd+9aUyceO3RzD0mRhqLEbMeOT0bPvP2/tt/52/+m89/f+XM+zx6oP/eeZlEUfYRQkTzwpa+Wf/TuV8jjt7XiN37Hunj+8ytkYfsFFkWHBqVNDszWJELopV+62Nq6fWfjxD8UA0mSbERRtPQvHFpjvgEZ6yyNGfM0RWv9/QCwp9FobHrvHcb4CaXUpLV2OwAAQshQSlc555tKqYMhhH6aphWMcVmW5YyU8hGt9dFRL9JZjLHXWh+OoqgXRVGnKIqFer1eaK07I7VqEULwU1NTl0IILoTgi6LYjjHudTqdfUKIC1LKwhgzoMER52G2M9TfvadON60NJEkS5pzjGGOfZRmaSFPw3mfGmAZCqFmWZQM9/sSQYtw3R4+kmPD+0Rkm4jgWWmuPEBJZlj0WRVEXAPYBwBbnvBNCUIPB4AgAQBRFl5RSc977uCzLY2mannDO8VEP0WXOuQIAjRCacM6hKIrqW1tbIk3TdaXUllJqb6VS2cQYewCIjDG1qNd7wkbRpG+1Jst3/KZPIO+mAAAgAElEQVSNhNxEd90pnXOFlPILNMsu4O3bv1Md3B8nFBNrrSOExM457ZwT8IUvWP277zpOj9+W25OPx0+9j/7sOaCvfpUavuD5kr7kxdQDMGMMwyGsxA8/2rCXLha1t7513dz/OY8wZuW+PYru3BmwEF3XbktEaQhaIwAAzBg2G805zJgFdV0aKbTbVLXb29ieRc6P37YCIeTlk0/ul3fdeVqdeOwwmZ0h+OpSnAdc8qNHKbrrzhN0elrS+fkVmJ2bgv37B6hed6HbJWz3roLu3LFePPTlWbm4eD5/4MEj8tixRwNCEgDqLhum0d13rSAur/Y//lcf0GfPvg4AYMDo2yZ/4PvfsrCw8DEA+Njq29/xBhzHbyofeXTK9LqWfNvrhiSO92iti5pkKSdmICgmA8P+c7fbPVOv1x/6+kfUmG9kxidLY8Y8Ten1ej8WRdEkY4xRSnGWZcQYs/OWjxDO+VW4bgmybWJiwpRlmWGM0WAw2DFS73ZSykuEEMIY45zzDefcIMuy43Ecn3fOaUpp5IDGRaBUUiDOOWWMqeZ5LhFCPWttkiTJRUrpb2CMee5gV0Ak5YRU9zRE0FoPAIAwxhLnnAohoCRJEqVUjxAiCSG9fr9/KP3sZ5ftb72zkbzohUzPLaBgyinBaAshVBFCRAghgxB6j/d+xjk3671XGOM6Y8x578E5F1lrJ4QQ56y1M6Pm77ZzLg4hVI0xDWutoJQOQwiOcy6UUtoYM4EBtrD3ThmzC2PcKbIskd1uF29srPqf/OlFv7IiAABCnse8WunhKBZsxwKhlDYtQs+ghC7o/tD26tPIAA6+zGqc8zLLsr1u/4Gp6Nz5VX3isb87vp+mGh87tsH3749CHBt8vdSJpZRNF4KGLCvDE09S8qIXBXXp0sAvLBjzP76Ql3/ynkXz5KkZXxR1Mjm14nu9GgAAP3Dgst/qON/pTNJdu0rf6TB5992lXV6mfqsbk8nJc3RmJgeMPG1MFiiWG+bqkuLb5yN66VJQH/rwo3Z17Uj58MO73VbX4jQdDN/7vt1eKUIXF/P43uc6vm0b+F5vpXz88dtAKWqbzXm3sTHtNjYqoSyBTk9v5A88+Eq3uXn4xu/0w2yfV+rDyT33tLrveW/U+4sP/gmEgMnMTO77/ankX79ZI0orCCFvreliCBgAXCIoM8a8wBjzmSiKtr6+ETXmG5lxsjRmzNOUVqv1PSGEOqWUeO9DURTYe39z7FsIccUYsyeKohaldI0xlo480xRCqBiNwQ/SNG0AQMkYi8uyrDvnSAgBGGP9EEINAMJAA1nNPJtLqffeqxBCEUIQxpgpIURLCCGFEEe11hMcI4asSgLA5sUtH83XBCaExNbaLWMMzfM8iqJYn9nUaK4qBCGEhRCu+Qe+ZKsveD5Vi3vJZUiTnZNJiTFOMMbhatcg7fxAUnQbZ2x/nueJlDJQShml1Hrv162105zzSwghSJJk1VobKaV2O/d/svee0ZadZ53n8+YdTr45Vs5RyY0kW5blIFuWcQBnExbGbcCmAzAzvYiN6RmaBTQYhhmaadrNBIKZpp0TNtiSLVm2pJIqqXLdfO+5596Td3jzfKhb5SpJ0Eyv6dVrpPP7Vjuddz9773Wfet7/+39s1XuPAQC894IQsmytLXe73SnGWB8htEa++12jf/FXtvPt206RVsug3/o31fx3f2+bYHxDnz07Te+4bYN89CMXop/4cFs98Z0R9+Y3tRGlHiG0HRCqyGKxB+WSQRgTnyW5yrPtnPMNxti60LqFDx8yhQ+8L3XnzmcoCjs+TUt0fLxFd+9asefOD7uFxSbce48KwzCklGKlVAgAHI2P5+zI4RCeeMJhznH2ux+fcY9+E4FUxEspggMH62ZlZcgrRQEAvDXcLC1PemsxGx+bix98sAlSMj03F9K9e3q0Wu37NCvJZ549DAhliNLR4MABp86c7WWPPY7lyZP3uG63AADg07RAt81uwEd+cgF++iNMHDm8jE6etNl3n8Q+TQWbmZ6j4+N1s7b2PWNQY6hZXh4P7rzjtJUqhjTlAABeKUGHh79aesvD5+u/+rH/zibpK8sf+qC1S8ved7q8+9Abq977Ze+9RwiBUop47/uEkEBKaY0x9yGEPvkP8V4aMODFGCRLAwa8TEnTNJJSvrrf7+Msy2qc85Xrndu3qjBua3l9ghCqIYRSpZRP03S3MabmvRcIoU6appn3PqeUEsaYDYIAh2GYcM4jzjkHhCDmGC9vtjrDhZBzRmNCiBFCMO99jzGGhRDVJEl8v98/EoZho9frzRTr9c1i1tF0qBZ57zKMcSilxJzzplIyGCtxt9WyBEsp+/z22wQ+dIj4UsGGyGvBsIdr9gXQy23zu0upKAfMxwxEuVwOvfdpu92eiKLoW1mW7WGM1aWUe40xw1LKMUrpirW2ttX894bAmVK6nOf5fgBASqky7fU29C//6k43N1dwX/7KJFy8JHEhbqNf/x918qpX7kLvfqdib3ywrsfHd+NKmbPvPpkHlTJC09PgvUdb1bGYMcZ8r6vpN78p7exMEEWRttbS3PuZuLGeZTt3JujNDyX56167PXzPuxbTd7xtXD/wmiH/1u8ntFRqxMPDJcsYcs5hY0xCCIkopRlEUZOVyj37ne8OQbuz5rq9YTI22vJSlcz8fInv2X3aNhrjAABs587zdq0+Ad6DXW8MkXK5mT3xxERw++1XMGPr6szZg3Rk5KpZWZl2rVbNrjdiVV+PyXvfswmLS5N2cVHc/I6haoXitzycBydPyfSn/+kBJIT1WRqr8+cnzOrauAeIgkMHz5nl5Vuc1O1msyr27F4xq6vV69twIY6a//aP3spuO363/tl/PunX6uvmkUdrKAwS8/CbS8aYQrlcdnmed6MoEr1eb28QBO08z2v9fv8IQsgAwEqapofCMFz4r/ltDXjpMUiWBgx4mVIqlZ5KkkRjjP8RpXQ1TdMjCCHPOZ83xoxyzs8XCgWEELIAwNvt9nbvfWqtvfEHzFpb8d5XgyBYS9O0tKUNSgAAY4xFkiR1xnhorVPjReFkfs2eCAAwISTAGCsAIM65jDEWxXG8uZWwgWi2cloqD+eMZZgQ46wFjHEPY+yllLFzLrnUlIYTGsu0t0sUCpn3XhNCWBxykue59t4rzqkNOYZOZvIDY0GJUnrZGBNrrQtBEDQwxhO9Xm/GGHNjmmvLULO4VV2rcM7nrbVDAABieXmNnTu3RheX1viXv5LpX/zlvdDtshvn/uA75v3P/WyVDg1lAKCdtYyu1ddDjDYyhIbx8nLHlSvajI3maZoWoihyCCEjpcyolBY3myTct89QSnWapn2l1EzQbq+iiQmBMHYOgGhCynEcN5RSJcbYBp+dzVEYhgBgAUAKIQpCCCCERKrV6rs//MNSsH1HaFtNBUptuHZrkm+bvWhW18bF/v0riPMOCgIidu826tKloev3UnjD61tASMuur8cIob5ZXp7CQ0PKtlpVuNaqBdjsrAx+4sPSHz/act/+dtF3uje0sFgE1n3py1Y98uh2yHNkms2CXV27YW7p+31Bhoa4rddvMbwMjh09nT/19IGbt5nVtX222Tzsrc3kmx+aVDPTY+77Hw75kSObulKpFovFua1myo4Q4gBgDa61AGxhjOt5nr+Bc16o1Wq/s7S0dAAhNCuEGGiZBvyDGCRLAwa8jMEYP+OcG0+S5JWFQmEhz/NpjLEKguAqQiiglAIhRBNCPuu9DymloLW+RTsjhJjXWk8ghDpKqR0A0OOcU+dcnqbpdKlY9ARjCgDMWtuJoqjovedKqSbGOCKEMGOMEUII55yx1mrGWAIEM18pJyyKKUYe5Xmuhbg27RaGIZbW5+UoLKUWqAXRqcbc9RBnGACT1ZWTLIpGTjSsqgpUTLUPA86qBQZKa4211mqrspUnSTIKANJ7f6MqQim9oJTaaYwZ8d5z7z2OomiVa72gfvSDx+wXvjTp/vbrE/bMmerNsaAPvGYxeN97vS0UZBiGQRzHmD75VEP95m/tyN/6/cPOe0KPH+/3SqU93vuS994ghNr9fh8RQox0rhTs28cxxo8Wi8WHlJQPknbb0zCkqFolaZqGhUKhm6bppJTyesPYi3meHyoUCm0AcIQQBgDo2qJABZjzMfjOky2zsNBXV65OqvPnd+FyOSW1ak8cPLiil5ZifenSftfrhS5NEN+9p0smxjDftu05jxDCnOU+z3I2u42ber1ExkbrJIpW6dTUGpueapAoRPrSJYLDwPvGBnbz8xEAQHj39z3rms0CLRV70SvuaqMgSOnE+JxZXh5DQ0MehYER+/Zd9FK2bbM1Blt9jfmhg6etVDPPdwy/jms0avz739IwgYg9QkhXKlUAAELIMiGE9Pv9fXEc95VSxSRJtmutx6y1Y977UGt9h9YadzqdYwihz5RKpRONX/+NB+JXvfLq/1ff1ICXJoPVcAMGvIwpFosqy7K1IAiWe73e8SiKnmKMxWmaljnnPcZY2VpbTpLk3caYCCHUufl8IcQZSikKggDned7FGC9LKaejKFpMkqQYx/Fyu92uEkLqGGOulNrGOV91znGtdUVrHYRhmFBKcb/fd5TSFAAiQghxtZo3xugQeWKdV0IICwgVpEUgwFnkbbnetXJTgjs2WSh4b7NIMOxza8yJZ4/YV78KcgvR1ZaywwH0hgUO89ySPM93RVG0QhkTfWWNUqqw5VQe5Hl+EACAEHLLH2rnXDVJkip1bk68+aEF+Wd/vv3F4klec3+rH0VF6Pd9jpAUGJvuz/zcvuDhNy8q5yY8APXeJ1u/sYIQkt77kS27gVxKKbXWGSHkzna7/UUQUbY6viMBj0o1k3cIQDXLsiqlNNdaB0EQnOOcs2KxuNLv93EQBAghZJ1zCmPsKEKIh2E/ESJikxMQ3veqTcxYw+7Z7bO3vuMoAIA4dGiejI837NraiFlZrZmVVUBh4H2WX9t/5Mgz8tTpI/LUaQAAQM435YUL99245127Uv1rv4qc96HYvuMkfOORYQAAu9kEs7JSNSsrVXnmLAAhwHft7ET3vep0fuKZA4Cxu9k2gG3fJr3zS2x6StOhoWZ3fT3yzaYAAEClkvXd7jWbCowBojC4cR5jG0KIepIkB7IsQ0EQnJBSTmKMNxFCo0KIupSy5r0PlFJ4c3Orr+7lyz+QNVuxTZMD9V/65deSoaHPDP+zfzqoNA14UfB/6wEMGDDgvy2EkO9aa7fHcXyOMRYwxmKEUCFN00PGmMw510EIbQJAGkcRwhi3r5+75a00xjkXxpiZPM+ngiA422q1doZhuG6vTZ0ZGsclAHCU0lUpZbbltt1DCLUwxioIwwpjrIsQ4pxzzDkPKaU+juOqUop57wkmpJgbZANGzDNr0lEmbFfatBqQC977vhDC46z/JH36qZz0e5iHIT84GpqD4zErcGQCIXAQBK5YLG4ihFy/12u4IKJRFC2naXo7APggCE4KIU7meT7zYrEyGG/XP/JDjhzY337+Pv6ud87BX391hv72b8f+ne/ZoT/8k6Pyd38vRcZA/tWvTTGA1UKhcO66vxOlNPPeBwihJiFEUUppEATcfeWvqfqdjxts7W4Odt/emEyHGDS+tiIwC8Mwi6JotVgsPiul3Jtl2bDOsiyKIkwpjTDGwhjjrJSANzaYf/JJiO67j7ulZQ/WedFqV9xPfnTf9XG7PAvo9NTm9X/TV967xt7+tjlPr/1fGjF2w0ur9L73fgc4q4qjRy+hUskCAIi77kg5YxkAgA8Ex4WCZDt3XqYT44bt2nmJ7dp5mu/b9wzfvfspAJTZTte4Xo+4TucWfZNpbDi+e3fLrKx4efFSUnrjg4ts/76GOHL4PCmXlsO7v+8UAAAeHe1qSuPr52mth6212ntPAcBhjANr7SrGWBcKhSuU0hQh5G687xh3489+/mzvvR94bb629svV97//A3pltZB845GPrXz0p9/wD/poBrzsQN4PWuEMGPAyB11dWntmsaNGRwucFqntCSEC55zrZSp1OhfO2nKpWMzo49/WctfOzDYapjcxcTAMw3NZlu0vl8sXe73erHNOxHF8EWPMOedWa62FENwBBBgACCHQbrclQqiLENIYY0FFWOxmhlQiirw1uTGGIoSYtbbjnGN5nu8OgmCN8NA8u5apOycD08igcHIlw/PtfHM0ppdfvbv89WqAG9bae/Uv/cq95KGHS/hV93SQNZFzDrz3wEUYIXA0s5BRb4AS4lutFtdaTwIABEFwIs/z224JDEKWc34BAHIA8AghBs4h9PO/MKIf+/YYAIDHGIKf+NDF7O1vn/QAsfceKMASO3lqncuceqWE/tuvV/Cb3lTnU1NBa3xsLwBAGIbPZll2DACgUqlcIv0+ztO0aD/y09rML0zyf/zBC+LHfzxAmDhjNGGMoesNhJMk6QohQqVUZlZWPPv0Zxn56Y9g/MyzTHc7yjz+RKq/+KX9ZHh4Izh8UGdPPj0uDu6/kH3r8aPRA695BlEaea0ryZe/MsYPHjhlFpdmyfBwpq9eHaef/8x50U8839jk3Y/92pjLM164//4remGB6PmFKZem1Oc5I6Oja3RifM202rvZa+7v+Cw1nLJu+vVvbLcrK0UAAH7o4JPqzNk7b44p3TZbN/MLYy/2IgZ33HE6f+qpwzeOnZlZtPX6pFeKAMYQ3veqk2ZxKXKd9jD9lV9uJPv27tk61IdheNE5N66Uiq+bpTLGupTSlS1DVIfn5nruN//NDnv6dOWm3zyZP/XUUQCA8nve/e+rP/ojHw6OHjH/5Z/TgJciA83SgAEDoNPtudN1eV/EsRsuiFhr3cEYw3fm+7B9rBJGAe8DgIY0Z+grfy3ctx4PSK93BaanGI0iiRDCzjnPGFsTQljGGMqyzCZJskNKiSkhHe+90lpnURQVrLURAITGmFLAWRZyjPM878s8H1JK1fI8r2KMF/M8P8QYSzHGKSHUMspij2n8txfa/WIsgrtni0uXNrLff8udO//3QqFw0hjzEbP/wH9/vrrtVYzAZEFQRgghzjlJMOLGmB446zljkbXWpWk67r3HQogFY0zRe19CCAFjbJlzPkcI6Ukp91trJ6y1E8aYMYqxxlm+Qf/RXQ0ahojedcdm8r737fQAN1bMOYASmZ3N/I4dOjx6tGhffV/IJiYUANicsREAAO99LYqic4QQkyfJqPnAjwTuP/4nalfXrk1jPXViSBw71rBjoymhFGPvcb66mmvGUu89FULEjLGYXLnqg4feVNK9Hu7/yI9O6c9/ccSefW4ctKZ8z+4rePv2MNi9axOkorhQaJJKOUweeXQXGRm5SirlVXX2uWNeygDH0YY4cmSJVSo2/YVf2qe+8UgpOH60g8Nw0WeZJ+VKT83NTfssu2YzkCQFOja6ioNQ+SuXR/zyahcQMq7XZYUHX58C5+dtvT7uk+SGHQUqlVx0++3zTqng5soS3Ta7zrZvu2CWlsd9mhZuxLHbLYO1GDAGOjm5ghnT6szZfT7NApIka2z/PqWjqAQIIeccY4ytaK1Hb5zvnDDGDCulqsh7a9/z/l1+dfV7U3g7d7ZsvV7zWSYAAOTpM7fRsVET3XP3I/8VPrMB/z9mkCwNGDAANjI48cWTa+8uCiSamXXDMc2+s5BQC16ORIQxDOCc03RiPFDDQ6n6zGcy/Z8+dZg9cP+yqVQEQsiGYdhCCIktA0jo9XojQRB0oiiqM8ZKvV5vSClV5pz3oiiKGGMeIdTFGKM8z/MsTfdwzleUUmMAAJzzFUrpmhCinef5KOGB/PrFNnz8b+b8d+Zaw1PV6NmDM9UfFhS9fyTwj3Z6/R/+5NN1MTVR/tDTq/l0X3k/U2ZXrDGCcx4ZY2QQBDVCCNFaZ957BgD1MAzr3vtUKbWHUroghGhKKWeNMWPXV8DdDOU89/v3SbN371B+z91j/q67wHkfPP84QoiVUm4DgKUgCBwNwyDBSAghmlsr7BqFQgE751CEUCP/w3875ZPkRgLBX/fAOv2RHzaUUgEAHlZWjY0j1JfygDPGks99oZ3/5m8x+/TTPn/D64XDOPCf/L+5lxIDANAdOxIyMozxT/xjy9JUdD/92W2YkDR77PGdpFzeQAiIPHV6/w1h9e5d80hwbE6d4hhBaJaWQn35SsGsro6apeURAN+yjcbzKkIIg/dSX7ky7lqtGpuaWDZLyzV1+UrZzM1N+yQpkbExyXftvESqtSWQeSBPntomDh28YhaXRoAxCG47fgaXy2351NO33ZwoAQAgISzft/cUnZhI9MWL28zq6sj1fXZ+YdT+1adK0cMPraowLG5ZWRiEUN97X4Dn4b0Po9mZ58w3Hr1xDToxkdlGIwBjvreCr1zGQMifi717B9WlATcYCLwHDBgAu8eK3gNcWe3b0XIc+ccXZQweielSsIZ0VvA0JN77UErZpjMzDH7j12NmzKUsDPdjhFYYY4gxPiKEcNbatnOuMDQ01DTGGEJIUSllK5VK11qbJEkiWq1WpVwuX7TWyna7fbRcLl/w3lOMseGcn97yeYqNMcPGGF4oFOY8gnC5k+FIMPmqvcNtYvPJjc3WJxhie4wx92/2dW12JJKbqfPdVLX2j8UTf3O5M+G1Su7cXhOVANMsy5pCiIJzziGEDGNMSymDMAx5GIaXrLUqz/MXaDkxxoYxdgYhZKWUx733o4yxS0EQPK213vliMVVKDYdh+Eye57uzLAu899R7D0KIpwFgexzHm5TS0WKxSPI8t/Hv/e45ccftUfrY4135sX+1nxw6lHitM8dYzDkvZWOj61mS7KhWq5fl4mKW/Pq/vjFdFXz600+bBx/cjqsV6xFgVCzkvtNBvlc15o8/keiN9SHIc4KDoAcA4Iwp6tXVG5UwCAKIXn1f2P/CF3NAGOn5hWJ436sW7Pp6GzNu0cgIlSdO3OzuDiiOgY6MMHn69PWpMPBphtnU5Jz34NHEBHZKciwCLE+eOnhLcKxNxPGjT5u1+rb8qacPoXJJBceOncXlssueeOKwv95qxRiChCjKE8+8aIwBANyXvrIG73vPBACAlXIkXlo62ZudHX/+cSzPL7hzF27RSamzZ2vRK+89lX7zW0duDK3XL8irV98AAJ/5u35zwMuPQWVpwIABAAAQcvKZLzy7ei+jhEeMVJUDBeCvjlUKAiEohIIjQkiY57lDUeS7xux03lPG2EoQBKzeN6gYUGqtbWOMOUKIeu+1tTZnjDEAYABA0zStBkFwXikF1loSx/FmHMc1jPEKAMgsy45aaycwxkvFYpFHUZQCgOhIzzljZN94EY/EGN54cIhkSpcLAQtD4rKSIMU4ELlAqFKOWc4JNOIgGKmELJgqUUdEEFqtHQBg7z02xljGWBSGITjn8iAIylmWGUJItmWPcCNpiuN4LcuyPcaYCQBAAADOuZoxZkII0TLGFF8spltibg4AmFLai+P4nPe+aIypCSFaUsrMX8P58XErrQ1QraYKP/gDfXrP3c4jhACASCnbGOMC5xzleR6yKEbyk39ZQVoDAIB95mQVPvUp8FmOxd69S2xmpo8QWpXPPrvfnH1uNDp8ZMmsrsVmbXUSjEHBgQPP6StXZ28aKLh+f149d+643dwcAucg/OhHGgBg5Lef2BscO9o2S8sVUi4nuBADLhYlIkTr+flycOcdV81avQreA5mazuWzJw/aRmPSbmxMuFZrFFcqy67ZvCV5YZOT6/nTJ27z/eRarzspianXR1yel9n2bWdtvT4GAEAqlZzOzkq7uloCe6v5Np2YWCJjYx2Yn6+FM9NLpFAA8s9/riH/zz89wN7z7isW4yrBOOFpdjn4i7/smn/x83vtmbO15z8jOjVVN0tLY6hc0uLA/mflmTPHSw+9aUHNz/+t2Ld3IOodAACDZGnAgJc9zWbzJ5Mk+Z2JEtdzTfm3CPy940Ua7BoOVhe7doRhzDqZ6ZW4R8YY6b1P+v3+LMbYhGF4lnPOlFLVaiGQxhidJEkMAF3vvQYA0u/3R5RSgBDqee8l57yHMYY8z3dVKhXV7Xansiwz3vsNpdQkY8wghDY55wVKaW6tJQghsB6jQDD2xFynd3C8wCZKIo8DVh2KOdN5bkieUMrwcinEleGY8WqAWURBxJB1AUBxymJCMCKEYK212XInx71ezwohitbaNAiCAGOMnXN9Y8z33KMxnjPGvKgomTG2crOh5d+Fc05wzjcJIYIQsui95wih1HtvEUKBUsoBQIdFEQuHhkbSNE37/X7MOU9amWPFkFcBYJkx1tPWxLxUqpvHHr82Tag19VkufJ5Tvn/fWvrVr+0Vhw6CXlwsgPfg8tzb9UZF7Nuvo3vvadmNRtuur0/cch8Tk2seI4YF1z7LgvDY0Xb6g+8oFDhb8QBYPvbYtOv1QtfvM9fvc7/VigQx1qWTk45OTqzZXo+6Zqt883XpyMiy3dycvHkbCkPtWrceBwDgk4TiMERgrfdKcZ/nlFQqBgm+6trtKgAALhR6wW3Hz+mNzSm7vDzsAUL7rccr7pN/Gbp6fQg5B2RpKeWbzWX/Lz825j7xH0YpuEVbX39htWnvnro6f2EbGIPDO267ZNsd5LOs2v/a1+6n1Vq38MBrHv/PPdcBLw8GydKAAS9TWq2W2NjY+H0A+LFWDhPa+W2vPzzxMa/zX2pnuq6M5c4T28p1uVbgtVpIDCGEb7U1WRZCGGOMDsMw1lrnCKEkDEPb6/W2FwoF7b2nSqk0CAIVxzHOsowbY8IkSbYrpUpCiAu9Xm9fqVSaB4A2xtgzxhIA6AohMkopWGuV1hpTSulyO+/3c2MqAvv1RJPZElbOGK0sQuzP/qxLJ6bKpFatWp0bRDgBuDZ9Fp6/wLpD4zFBrmWUUgghm6bpSL/fn6CULjnnyv1+fzKKoo6UkuZ5Himl6HXdCyFkwxgzCX+HbIEQYqy1L9DIvBha66GhoV62JacAACAASURBVKF+HMcfVkrtFkLsoIyVNhNH25qQIvfae28xxtRamwkhlEKcrmdQqXC/CQBfi6Io8M4VmTEuvO+VzfDtb+2GP/7jbfze9zbweiNF/X4fTU3z+Bf+hSI7ty+ZC5dKKAxY9MBrrpJCoU/KZZU9/u1tXil289hMuz2Bi8WM79y1QGdnnbp8Jba/8ZvjdGho0XzwxwQtFTftk099z+V8797zdGpqlVQrWj7zzDZAqOQ3myWvNbolPqOjK3Zj45ZkidSqqVcqhucdCwDg2u04et1rz4ExCZ+dXZHnz29DYaT5zMx51+/X2OTkkjx16tD1vnGQ58hnGQGtb1QC3fx8yT755AhkGQYAIOWyZlPTy6Zev6FXwsNDCSkVl+3qtd50uFRqI0DIrK2NFR96yI792q/es/E7H//38b33JP+QZzvgpc0gWRow4GVGp9PBGxsb77TW/gHG+Nh64tnZekak9ZW+hv+lyNyHDk6U0gjL8b6m3AMiqbJYStOuRlQghDJKaY4QKgGAJYSEjDEmhMiNMZ9ACO3nnDOtdTdJksNBEKz2er1ZpVStVCqtEIB1TOlaEARcCNGglCLOeQAAkOc5k1IepJR2wjAsG2NIv9/fnmVZOUB6ZNdkLS0FvBAKSgTFlKncsCceN3j3bnC7dzkPCCHCA+ec8d5jihHCecYVIS6MY4IRkC1d0kixWDzrvUdZlu0CAMiyrAoA2lorrgu76bVxriulJv+ueG61RBn9u/ZfJwiCpjEmBoBla+2ac+71T62aKBZkcaFrcTnEohrx2HvfdM49yhibQggFgqBgJCZGa50CwEGE0AxptXj68f+5gH7o/QoDcFqrBd23/8A2324nZGQY1Jkzk9kn/qTMwnAtPHzIxvfco22vh+3CgjXrDW03N2vXm+fewFrw3W7k+/2Cy3NhnnuuAgDglUrR61+HzfFjkVhZdq7VTqP7Xz2Xnzy1zy4vj5nl5VEAgODgwSsuSSIyVGviYqHter0SAAAZG12xjcYt8XOtVlEcPPCMrddvqW5dB3G+pC9cPGTq9dGtccW20Zjge/ac1HNzB+MH33CZxIXErK29oDpFRkcycfDgRZumNdjSn9nNZpnt2rVuFhdHAQDI+PgSm5hI2PiE0wsLw/zQwedcqz2qr16dBu9BnTuPm3/07wSOolrpbW8daJcGDJKlAQNeLjQajdHNzc3/IUmSP0AI3UMIGUIIaYfpTxHkH3CAhXFo18HJ8nvSPJt4ctVuG46YP73aU5WQbrRyNzZTi53zzjnnawi8o5QKxpjI87zNGBvSWh8ghJAkSahSatRay6Mo2rDWrsdxnPGFBY5abVsYGRkiQRBTSkOtdWaMQcYYKaU8CHBtaisMw8gYIwkhK5TS5TAMNwgh7OKmxLmyZqTAMBNcmKkpSyYmBL7WUgWccwoAGCFEO+c2TBybIAoJJVjANa8kZ4zJrbUUAIYYYxc552sY4561tmStLRFCOkEQXNJa71RK/b2JkDGmyjm/KoRY0lq/6FRdsVic01rHcRw3AcBZa6c1FiPzbSUrESssd1TEGcE1AQlj7OOMsQc2NjbuopQq2PJ4cs5xjDForXO3sNiHd7zNdpXaJaOIkscea6u//mrVp2kRcb7Opqa6aLhmzHqj4Du9TdvtdOzGBnK9nqBDwwgwBkBI+DTFKIqM2LvnNC4WW67VGvZ5zsXs7BmzNU3nut0afuwxVnzXO1swNJREO3bI9NFvzriNjVsqUzZNK67b5a7VLrCdO5btVhWHjAyv2cbGC5IiOjayZuvrL5os4VIxca3WC1ciTk+lbNu2tfTxb++1GxuVwutfN4cYu0Inxqhdb8Ti8KEl1+kiffnyLJ+evsx2bF+0WVaDPCdkfCyHPDcAgNnMdCZPnpoBhLDYvn1ZLS1PR3fctq4uXvqepskYYDOzTVKr/QXfsd0+fywDXl4MkqUBA14GbG5u/msA+Hie5wfiOA6FECXnnLLWgvbotZupeeI7c53Kel+6+/ZP/Lu/udCugvf39LT3QzGPKwEqlyICccDjPO3nmxnClZB6pVQnSRKMMVbGmJ73nltr+0qpMa11EQDAe9+K45hTSrHrJ9pPTApbKDqnlUUIeQDAnU5nFmOsrLUVQsimtXa42+3WpJRVrfWoMWaiVCr16j2NDSB0aCzAFAN1zkmEkMMYg5QyBQBkrU0JIcwCSjEAY5yPZlnW9t4T731mrZVbq9usUsoopfZ776nWegZjnAsh1hBCxa2+bdo5F/+9wQUg1tqa915zzjNrrQCAG67XQojLSqkRY0wJIdQFgH4QBOOFkEfjMUDEoBtRHI/EVBGwjxpj9qVpenu1Wu1wzolzrielpFLKspSygBCqU61QwtgM5zxBUm6gbzyCwre9tY66vb46+9we02xWg717l+TTJ7aZtbUxffnKhFlYHLaNRhkI6bLJidy1WjS48/bV4MCBlezkqcNICELHRi96pWNcLW/enMi4blcQypbZ298WG2NS+RefHAFryS1RyHN0vbmuy7JacOjgSVwuB3R8oqvn5l6gF6KTU9KurVWevx0oBTY2pvnOHVfMysotyadrtWuAUeCuLfcHdelSBawtm14vhiwnfP+Bjl5YGAatsW02h8zq6nj0kZ88KQ4f2pCPfquGK2US3HFnI3/ssW3XrtcKTb1eEzt2LMrTZ4a8lLe0udFzczsRY98svvHBy/+Zd2DAS5xBu5MBA16idDodDACwurr6T7Ise1ev1+smSbInz/M2AIC11gAAK3GojhX5GyYqot9J9AQAwGt2l87vGgp6nVSKckBQwBkPwWrmVVIrFeLpMvXee9nr9Q5zzjXnPOx2u0e992CtjaMougoAgDF2cRxzhFBkjPFucsKRKPgP1NnThBDI8zxRSkGxWLyEECLlcvl0FEUrcRwvEkJuaEUopVdXe5phjKnyEHpCBb0G896LPM9xHMejGGNACMWUUuyMjhBCVa11nyNUAACrlPKc81oYhkXnXFUptVMI8ZwxZmQrJmVjDPXe50qp7dbakTAMr9zc4gUAgHO+SgjpbiV7AABgjJnO83yMMXY5juMrAACMsZ4QIrbWFrfiUQzDMGaMcQCAOAwCQckjO0YL60PF4B1b4u9jANCTUm5KKdfTNI2MMdZaW6OUbrDPfwEHS8sVZ22MMd5UhOzUb3wwME+fQPL06e3BHXdcLr/rXWdcP4lBCCCzszkAAKqUXXD82LN0dKQtz5yNzPp6Of36Izt6n/nsEbF3z0kzP19TFy4eRZUyFvsPcFStyhs3jDGYTtdsAvf1o3dRdOddF8jOnT3+wP3rz3/v2J49TbFjx0nb7hTU5SsV22jw5x8DAECqla44fuyEOHTwBD9wYOHGsx4ebgX33i3JyMgoKpVu8TryUgIuFOYAfU/qZLvdiI1PSAAAu143Pk1vmV5Ub35oyn/wx6bi3//dpm21S/rypVuTX+cAMdZxnc4tTZFvuv6bXmz7gJcXg8rSgAEvUYwxx7z3uNls/kwQBFNaaxGG4bz3XjDGOMaYYYzBOWeVw0FP+or1HghGTwc+T5a65t1jMW0udkxjtZuLomBkLXV0rMCoNSbFGBOEUDNJkl0Y4+VyuZwjhBznnGhtaBzH69baNuccE0Kwv9ZbqRXH8Ue1Vv/MOccppYFzTm9VfSpCCMM5ryRJwrz3uFAoXDHGhISQTiC47msUXawnrdkiyhB4BQBAKSWccyalbCOEEKU0tNYamecZ5zzw3lvGOQeEnJSyBwAcY0yVUtIYU4njeE1Ked0I8xIARFrrG0JgxtiKc45470sAAEKIhFK6whjrMcY2bj4WAMBaOxwEwZLWenRLz9S31laCILjIGOsAAJZSYky5MMa3PSZ/JBj9dJIkf2iMmSCEcISQVEqFAGCCIOAY4y5CqCul3B088Z2N7E//bCL4gXckqZRTI0NDC+onfipW331yGqwFs7pa4+/8wbXsc5/bEx47ehU5p/iuXV0cxZvy2ZMHzOLSON+5o02KxXUyMX6Vjo+vk3LJ6Pn5SVQs+Oj4bUu9z31uHykUWnRyYp4OD6/iUqlTfPhh0piczmOVWPTKeyLzljeX0G23LZPXPrCAPvD+RvCKu9bQpcuJXliYMisrE67VqoFzQEdHu3Z9/QVL9snIyKZ86umjttGYYNPTV8za2gQAAN+/bz599NFZLIKV4MC+zG5sMp/nNxIuu7E5Fr7irqtmaflacuM9sKmp57xzMRZBx25u3jJ9x4eG59mxo0/z8fFRdfassSdPjUavvPe0np+/UbVi27dbs7R07fkeOXyB79ndN4uLFRSFhtRqU3a98afRK+4aCL1fxgySpQEDXqJwzteUUqG1NgSA76OUGmttDgA1hFCCEOLOOUMppREnEDDvOBel9Z7c9op9079XZu4LlQB/iFO0/Uoj1Q4RvrMESUuSsMi81lpbpdSwtTYSQnQYY6FzTrYzSzxhlIKFNE33GGOkc+6SMSZnjGHv/Vucc6MtCVgaZzgGlKbptkKhsMoYC733tt/vD3HOO2mabmeMLWCMw1IhCqohJbuGOGAEPssyQik1lNIIIWQxxtgYYzDGBAAMY4w55xTGmLBrDt6pc66EMc57vV41juMepbTuvReU0iZjbIkQ4vM833E9hmEYtvM8n3bO3RASW2u51npUaz0ihGgAQG6tLSOEbBAEpyil/a2ecg1rrZFSHomiaN57rzDGnDEWcM6R8w4ZjwqYkp1aysut1L2uFIkAIU+8B0kppcaY2HsfEkLCIAg8ACzY48cKIorXyexshqNogyGE2choBpubPbu6WuYHDqwEt9/mzHPnOvLkqV2AsNKNxhCt1pyp12MAADY1vSDPnt1vG41Ju74+jsKwZzc2hkEphISYt43GuE/T2DVbI3azOea6vSHzUx/uA9h93poxB1D1AMxyNmoqlUkbBuO4WKTmf/vjaTC3Gl/T8TFp6+sv8KHCcaFjNzdrAABeKR7ceftZUql4efLUTshyYjudIbBuzSwt3SIOFwcPnMxPPLP/5m1mbW2clEodEHzIbTZvqWThX/j5VmLtcYTxBh4b7ei/+tSkWV+vRve96jSpVDf4vn2pazWZB6B8757ToE1BXrk6DnlOESYOR1Gi5uZx5X3v/cZ/2Zc44KXAIFkaMOAlDGMsKRaL30nTdEe3271NCOHMNSX1MGOs02w2dxFC5gC8y5QjtQg7rWyZqG59fHz8271e7zMRcbcfnynvnK0KSpyio6XAAACmWUZMllGDcRzH8fpW81uEAYxReYdi1DLGDAshHs3z/G6McWKMCfI83x1FkePI6YgT0u12J4QQi3meF7XWTa11H2PcwRj3tdaT1tphAMAIoQ0ppfLeK0IIF0KQdrs9RiltGmOYMaYvhCgqpfqEEA4ACGNMsyxrAQDemnZMGGMlIURnq1oEjDEKAKrf7x9BCHWdcxWMsfHeE2stEkJcMMa8QOQdx/F8mqa7EUKArvV5qed5vhdjvEEptUmSHNgysQStddUYM1ooFNqMscA55xmleDPRxgKZjRncTgnKupkuVGiGPA0pAJgwDJkxpieEiLaSpypCqJnOzuxDYVAnhBRFGAb55ESMMNk0jzwyKvbvb7j19SGnFCe12oKZm5sNjx9/DhfipjOW86mpc2Z1dezm1iLi0IGmXlwaAu+BjI02baNxS7UMEAJ2+fKyqFYX1cTEJAAA817yZnOdLiyu8TNnl/HTz3RgrV7zvd4tWiY6Nm5svR4CAKBiwYkD+6/QsbF5u75e9Wl6TdeWZREpFOvy9Jm94D2AEBDdftup/MQzh2++Ftu+fcmmac33etHzn4dLkphNTPTt+votuiN45NEKn558DgoFq/+n35jx9XoExhBSra7nJ04c0QsLFdtuF9nk5EV15uxRl2U8vOuuNTo6ukrHxury/Plt4W3HCq7T+8vg6BH5/N8d8PJgkCwNGPAywBjzZa313VmWTVJKQyFEzzmnCSE9a63r9XoHEGG9YshtWfg18O4+KeWXx8fHFymlf9Fut/cHgm9TUipz7vwGOfEMbew6SMKTJxowNdXMsmwGADwANJXMTRQGvzE7O/uzSqk2Y+xurXWNEMKyLNvJOb8gpaxFUUR7vR6mlC4456oIoW6e5/u11uNBEDS2fIguGmNGnHNCSjmstR6SUo5471NrbSqlnNBaU2stOOcAY6ydcwmltEgpDYwx/SAIwiRJpNa6mGXZGGNsU2udUkqDPM8ZIaQPAIExhhJCGoSQyHuvhBCLCCHOOe8YY6rXO9lfB2Oceu+NtbYahuGFa2E2w4SQjSzLDhJCGkEQrBhjbkwLaa15lmUBpbSNEPLFiItuP88Fdk5w5pT1wtPAIfBrCCFtraUIIesXF/um0WAIoJcZs2dsYqKXtduW/R//F+N33UnV8krD1CoQx3FdX7xkfJZ6VqkEPs+JzzKvL1+ZsdZVWbXSlWfO7L+eKJGhoVZw7MgF09jccX11Gy6Xtc9zDlp/T/vjPdiVlVHcaHTNg28YFsYk4V996pL8+V/c7b74pSH76DfH7IkT48GxY2dJHC/jWi1wm5vXEqRqFdjkxClcLDbBmEBfnZuw9fqET9MiIASAMZBKRdLZmQ44Wwzvufui7ydKnjm75+Z4iyOHT+v62qxrbLyoWzoAAB0fm7f19VsMQn23i+1X/2bc/OmfT/h6PQK4Zi+AwrAfHD60xmZn6nR0ZE0+e/LI1kNi+urVKh0f25Rnz+4Shw9fQIQin/bHC69/3d/8v//6BrwUGCRLAwa8DAiCwAsh/mO/378TAIY558A5D3u93nQQBG1CSAOsmirGUUYJjgAg9t6PF4vFzzPGPCHkS9mly8f9pz894775rVo8M0OrSTe+fOgVJOVRElhZFZz/zNeuyvjOXaNTAHCx1+vNaa0/ppSaLJVKqTGmHQRBGyHECCE97713zqVZlh2OougyQohpra/7G9UZY40syw7BTQtRqtXqirWWGGMKURQllNK6tbaota567yEMQ9PpdHYBwGqWZdg5FxBCJKWUlEol3u/3KwCwnmXZkTzPqwghwBh34FqyVGGMbeR5vs17H1hrS0KIy0mSHI2i6PyWo/eNsURR1MjzfHJrvOve+zFjTEgpXbTWTnjvC957FobhGue8a4wpFAqFKxjjNe89RQgRa4wqRCI0WmFKKQkYxuCdJoQsYYwLABATQmKQ0vbe+o4d5nOfH6rs2r1kt29T2pgiOX9B229+M08/9q+22T/78wm7shoFd92Z6MtXxuQzz5bt+nqRbt/WEXv3nkfa5Orsc9/rsYYQxD/0gYsgpWAjI0114eIoAIBrNot8755zttF4oWWCs0ScOr2mf/t3Rs3JU2P8wP6OXW/caCJsFhbGgFJme70hNjOj2f59PbxvT0s/dWK3XVkd92l2S8PhwsMPXQRCA9vrBh7hyG5uxIiQRX3p8j4UhopUKikZGV51nU6FTU81bLM1AluNgl8MOj6+ZOv1F6y8e8H3cPy2hjpzdkZdvDiu5+fHzNLyC84x6+vD0WvuP+s6HZM//fRRU1+fjF993x/Q4aFBC5SXIYNkacCAlwmMMee9/3qWZW8Jw3DEGOMrlYre2NjYprUe8d5T55ymlOaEEOK9H3LO/UkQBEYIYYtTk3/V/+NPrNMoOpSePau7szua69t2b2xq/woWly+UsflKrSA+aLQOAk5f7Zx7D0KoEAQBbTabw1LKbZTS5SRJDjnnUJqm0xhjwTm/kiTJYULI0lYvNdhKVrC19pYVSgihK9baUrlcbnU6nSlKaSKlHInjeCWKog6lVAghNqy1Ksuy3QihepZlQ8YYkqZpXigUVvr9/l7YSnqccwGllMRx7AGgYYyZjeN4FSHUpJSuZFl2FACAENJ4frsTIcTalgfUKmOslGXZcBiGZ7z3E+VyeRMAdBRF62ma1qSU4xhjaa0dyvN8hnNe995rQggB74AQIvM811JKnWVZ5JzDWyvoDD13ntpPf1ajgwdS/va3rer77xtKkqQgHv/2qvvSVxKMsXJJErhOJxJveXjFnDtfjh94TcMzltqlpbL3QGi12vB5bvm+vetmcenafRAC8c/+rBJTUwFUK8S32sSurwtx+PAqsja3GxvXDByHhyVYi8BazHfuuKzOnt0LUiGwFvj2HStmeUtojTEU/uUvnzM//AFif/iHhH7oTVy9+r5A33ZbmX3hiz3X692SKAEA2GZLeZkHbmOTu81NDkoBMBaLfXvPIUp4cPttK3puYSw4duS8vHBpL4njhO3aOWfX6iPPvxYAgJeS0unphms2X2hJcBNmcbEoDh68jMOw7drtF4jPr13MgzdG6/MX9oG1WBw+tGrStFO4555Tf9+1B7w0GSRLAwa8jIiiKMMYLyZJci9jrCSl7OV5fmOaKAzDbhiGDGOM8zwXeZ6PVSqVv76+v/T2t57GUfgn0O48E6XJ/1q7/77fV84pD/BI1cs2Nvn7y4VIee+Nc45ijLH33hFCMillFZyrhFF0FmNsGWOLWutAKbUdYywRQsZaW0EI+UKhMJfn+TZ4nr2JEGLDe19wzmGlVFFrXa1UKhe2Vvg5rXXPWmuccx4hxIIg6GZZNmOtjZ1zoXNuk3O+fj3xIYS04jhuhWFYUEpp51ySJMnOoaGhRGuNtNY1gGti+ZsNJwkhq957nOf5PmPMBELoKsY4c87tUEpVgiDQaZpWAGDVWjvlvSdCiPNKqcmRkZGV62aeGGO2JU5nrVZrh9Z6aCtJ6lprA855G7xDsGcPoa9/rfU7d7petzs91NhY7X30n+w2Kytj6uLFMVQuSvovf+0KetcPEvbwm6yYmiwED72JwerqhltZxerSpZ12c3McMOm7TudacuA9wOjoAnrrWxCMDGN0+WqOK5UcgV/HcczJUG2TjowQvbpWCI4cOYsrlQVEqAvuvH1Bnb8wjeI4A0Ii12rdEFT7D31Q5bXalPf+luX79AtfauMwzNjs7AW2bSaJ7rl302xsMDI0tIDDkNqN702t0bFR5fsJ/n/Ye88wy7KzvvddeYcTK5xKXaG7uqurq7s6TNQIjdLMKCCEBAJ5dG0/4AAPxg/BhssFrgwYjDEXYyGBArLASAiULcnKAwjNDKMZTexUXd2Vc65TdcKOK90PfapUHWZ0fb8xOr8v9Txn7732Wjv0fvsN/1fOznUh112VMzP9anGpE+KYmlrNxY5btQC5PXXug9g49kBrQ3t6Nm/Xe+4g2HO13t3N2Ti+xYjbxxgrRkdnaH/fOuKcy6npjsK7Hv7kS43b5OVJ01hq0uT7j/UwDH8qDENPax16nrdmrdVa66wxRjVyfijG2GitD4dhuJLNZq/uHcwPD+jM61+34N//qlpOUNufcx/vz7nPaq23lVIPJEli9cyMpYUCM5//oracqwqCY4AQ5L/wxcV0ZETEcTyolOrc679mraWc83Ij1IUwxpt7xgmldI1zXqaULjPGQAihASCklHIppSCErDmOA8YYU6lUho0xbpqmHY1+bbRRDQiO46ymaToopez0fX9ZSpkTQkxhjFEURSgIgl5CSKKUynLOKwih1FobOI6zGIbh6N76hRDjnHMVRdF+SMt13dUoik4aYygAQBzHvuu6Y8YYZq0VnudNM8YEIaQCACnnnCulkiiKZENYEwHA4gHvlSOE2LDWPkF2d+Pkv71nkGWzoWwpapIkJv2VXxVme3tfL6j+X987MX7y7lGLSVxHPMkVM4mmFOGFRYgfe7wTAMA5e/YSO9Qdydm5fbFJ9iNvX6LHjmZsuRxH/+t/tdvNrSAdHz+ulpbb9PpGm97YdEFrUCsrJb252a3X17twJjOnVlf7eH//tJyevqFSjb7udeuytfUWT403O7ucjF3p02trXdkffmsIGMVqY9OVExODtKtjhbQUV9y7766B68ybre3snhglyWUlCO7aen0/X8xUKi3u2TPramn5trlLNklcREiddnWFZmfntj37kONY1te7KGfnDt1u+x7Y96mp1TKsu3MrevTxk4hzP52cWsk8+MDYSx3X5OXHbRtDNmnS5OWL4ziB4zjbCKGi1rqolAq11tlMJrMCAHGaphIAdgDAs9bG1Wr1I2EY/lhbW9tPFQqFEABgcXHxl8Mw/BWM8WPZbPYXtNbttVrtE1rrIYzxdnZjc1N/7n+i4ItfOmylxJm77tzU9SCtTUwc9js7ppJ77zUWAGOEYmOtAwBgra3szVFr3UYI2dJat2mt23K53Iy1lhJCRJIkKkmSku/76wAwZ4whtVqtPZfL7XqedxljjGq12kmMcSildF3XnYqi6GjDCDqfJMkdYRgWhRCXkiQ5Rgi5GkXRAADUMcYxAECapnV73YJ0tdb7HiWM8ZYxpj0MQ9913eUoinoa8yWNcFwQx/ERay3WWpu99i1JktSllL2O48ylaVrf84w5jlNjjLlSSksIQYyxRUppFEXREEJIWWtH6447rP/jb3L94Y9Q9R9/5xh2Xc26u6/gX/i5snrzmxhaXNr1j/aVTnh2DFW3j+fy+UWECLFaY1osKHHm9AUbhG584cIoP3bsuYPPAirvuLZWV/Jv/y7GSTqLCvncSz07pK11GXHOAQBMFPnuffedN2GAwBiOhBMjhIizs3vRZDOe4TyrjWm3ADidXwBEiGXHj0/G166R6NtPnRDHj8/zU6fmgVBq4/RQ/RuPtOwpgAMAkJaWUJZ3Dtnt7Rua7Tr33D0WPfWdky81T7211YOLhTEAuG3+khgZuair1XYxeupycunyqdvtg1wHcHsbtqkkyHGZOHvmajJ2Zbj+yCO/vXvu7FcK/+Sd9ZeaQ5OXF+i6TlyTJk2+n6hUKj8fhuHvG2NqjDG9u7sbU0rrUsqOhljlehzHxzOZzFWEkLgeTbOLjLHPa62TOI7/dRRFdwIACCFWlFLCGFOw1pJCobAghLCVcjnAv/U7Nn300Rs+bMh1EFOhUAAAIABJREFUjfvTPz2lcn6E5xYK+PhQVL3n7mHXdcejKDoBAEAIqWOMAyllByGklslklhFCRGsttdYySZJBIcR8kiRDjLHJJEmGW1paZsIwrCul2gkhO4QQEEIU4jjmjXJ+LKWUQRCMHpwP5/wSpdSTUhYppdtRFB0DAHAc53ljTAdjbDtN08OEkIAQshYEwVkAAIQQuK47EYbhEEIIHMcZBwAbx/EJay3KZrMrWustay0ghNBB7xQAAGNsN5/PbyuljOM4mUqlUo6i6CRCCCilG57n7SZJIqy1eSUlE3/xF2tIW4KPDsbm6Wdy0S//+4Kx1gMAwBhLz/OmgiAYRgjVPM9boWGo9Z/9eUf0iU+1IddVrPfQFX7mNEkvX2lNx8c7AQD4ubNT6Qvnj+7P6fDhFTk7e2PTYNc1zujoNVPZ1Xx4uKq3tltMvWZNGGpTqfbpSiUHye0r6kl/vyQjI1ssm9mIv/3kcPYND06r1TWcTEy2y+npVgAA0t5eNUni22r1xvYpCIE4M7qTnL94Q94a7eneZoOD62a3AgCQgjEdyfh4F+gb27e5971iLHr+hZMH58aHj18x9aDA+nqD6Lnnj/H+vkUkRNlqw02lMgCEVGxQ9/R2OceHj19Jr14bud26vFff/0t9n/zr/3bbRTd5WdL0LDVp8n1IPp9/n9ZaBkHws1JK7vs+lVIaIcRSHMf9SZIcz+VyVx3HyQIATtNUc86P7O7u/k4jXAcAAIyxLa11wXXdyTiODed8cXd39w7G2DznXKbv/rV2t6tzDOp1FH35KyMAAPzo4FT4gQ8MYdetAqXIfu3rXuZP3jcWtKLjnufNOI6TpmkaKaX6CCEznHNcrVaHKaXLGOMtKWWfMSYThuFJIcSiUuoIxri2vb191HGcJWMMY4wRAGglhKSu69aMMb5SKpFSmttcDhuG4SAAACFkEQDAcZyrGGMEANtBEJwmhFSllJ2Msf0vcsMIUkKI2SRJDvu+729tbfXtbU/TtCKE4LVa7bi1FjWMu/697VLKwtbWViGXy43FcVxTSpUYYxuU0lWMMa1WqyOu6y5xzpcUYxi0zZvZWZ+95Qe31cXL9ezGRiXd2iZkbi7m/8e7/CRN93J4sLVWmWefNdEnP91GSqUN2lEqJ5cun04nJgEwBnbkyDQQUqfFIkkZA5ASAABIa2tFb235FiNPHD5yFQixyHFQ9MQTo7SrK04nJpfT8fFBAAA2eGTD1Gque+7shXhi8iREEbVRdMOF1fPzTM/Pd6FTJ9f00pLY/fO/GCF9vbE4MTyLX/PqXfWNR/rMzg5zzp2djZ749r7RBhgDohRwJrcEADcYSzZJtY0im1y8eBIAgBw6VBUjJ6ZAmxpw5oA27dj3ttLJqS4xdPSirtUPI0cYzMUSyeVZtLLagXd2tiFJIJ2Y7AWAXiAE/IceHI/OXzjBh48vcEzq6czsjV4phECMji5Zazet0Q8CQNNY+j6imbPUpMn3KbVa7d9SSgfjOM5Xq9VDjea3Rdd1pzjndQBIEUIGADAhBABAKKUShNCO1jovhJjPZrMyiiKHc17hnEspZaq17hJCLEZRdEpbm0vPnS3Rxx5f0jMzXQAAuNS+RjtKWrzqlZv+r/2q1tNTcfK5z/c47/iRVURIwhgrIIRsEASHlFItSZIUAACMMTmtdae1dl90kDG26nledS8cRghZlVL2SynbXdedj+M4oZS6nHNIkgTSND3cyI9aJoQgrbWglK5qrfc+jDnXdWeiKBrWWvuU0rpSqt1aK/bm4Pv+pb18KkLIapqmQ43wn2WMRRjjZSFEHWOcU0qFlNJVSilO07QbAG4IKQEAJElSYoytJUnSJYQIfd/nxphMkiRZAGAY44AxxmD0VIROnazrIIjEQw9m0camUu/7E4+6jqkPDR2CufnEPX9hxQwMcKVU3mRzgVOrpnJmtl0tLn5XBsBaQIwitbg4YIKQe/fdNyOnp9tJZ4dJF5dKYqB/2xkdXY/Pnz8s5+YPqcXFDgAAU69TdqhnSq2t9wAAmJ0dXwwdu5zOzh3PP/xP1p0HH9yIH3us9eb1AQCQ9vYVvbnZ7f7iL1zL/PZvYdnSYuUDr7e5O+8s4zBcD7/59/seHNLauu6cHl0nbW0Ku45w771HJmNXPAAAfmJ4zErZIq9N9O7tL44c3kouXhrQm5tdem29XW9teWpxqc2Goas3NjtsnHC9ueXotbUSyuXm9eJij97auqE/HPZ9pcs7be65s5Puu9+dgSSuxF//Ri8AACoUEnHs6EX33Ll1ubTca3Z32tKpmWG1tfmVzAOvX/neb1qTlwNNz1KTJt+nYIwHq9Xq4TAM8YHfduM4Ps4YuxbH8TnP88q+71ettdhaG/u+b7e2tvqFEEGapv2VSqVKKd211hLHcXiapodd1z2PMabWWtwYs0weeL1Bjz5mbJpiZ/BIUW5tI/4jb/fqba0o+Y3/UPQ5n4q8PN+UyPQkwa6WKXZddwwh5BhjAiFE5+7u7g2l+47jXFJKdUdRtK/mTCmVSZIA53wHIdTmOE6VUmqMMdgYI40xTmNO62madnqed8EcyJPRWpMkSVqz2ey0lLIOAHnO+Waapvul6mEYjjLGQmvtjpTyqLUWh2E4GkWRbcy5EMdxzhiTA4AOay34vj+WzWZltVrtObgGIUQZY7xujPFyudwiY4xJKeuEENGSy01jzhFCyK1WqzmL0JYYGKilScI0AI4PDxymH3o/yre2bptHH5uDb34zVecvdsL7P9huowjpOG650c8DQPv7y6yzY1Ftbx9hvb2TcnW1J3jkkREAAPShD14lCJD5v3+jr/7Vr/WIM6cvJxcu3pDPg1qK+88KGzq2QUodiA4MXNv94IfOACHAh4+Xxejocv2Rvxm0lcp370upxM3ubgjb22n8la+W5SvvE+IrX4XKhz7cZarVrPuKe8ejp75zAgCA9fZuRE9957sJ9WdOX6NDR2ssX9iNnn/h7M3hNrm2niWtrXW9vX09mdvc6Dy0cfxdAzVNbwz17e1jjHFOjlwKvvHInenC4gI/fJiC6xqIIswH+ueT8xfuSC6PAc7lYjbQP643Ns9Fzz73EwDw7O3Ga/Lyo5mz1KTJ9y9oa2vrV3Z3dzuttS0AcAcAuEmSDGKMq4yx3SRJ+jKZzBW4rs4N9Xr9euiDkF3G2BxjjNZqtVOO45yP4/js/sAIxZ7nXbPWImutk4ThUfFv/m3NcrZLhIvJD78lwm99K6GUWmMMJYRQhjHXK2uylvXLe/k9GOPUGMMZYxuEEB3HcRelNFZKOUKIS47joCRJACGkrbWlOI73K73a2trm6HVLCUkpY6UUNFSxwyiKTu9VrnmedzEMw9N7xzUq15gQgifX2Y7j+I7GfKTjOFcJIQohRMMwbNtraXIQIcQkIUQbYzwASLXWQkrZ67ruhetanIYbY7qUUi2e5804lYpknZ1FjVBijNFaa4BPfToR7/hRUa/XNY/icNdzRxvXPuScX4ui6BxCyLYJsVB569taTaWa8R96aD4+f77LJAmz1ep3jQSEwLnjjou6Wu1Ua6tttlbfN3rIwEAkfunfzQbDx7u0tcX21bWF6FOfSc3srErHx/d7sCHXsd4DD8wGX/7KEeeuO8eSi5dO2jQFXCzuoExGQZp6IATWCwuuOHP6qjWGwT33VNhDDzhsaMiz1jK4dFnWfumXW9x7753FhXy72dyqyvl5nc7MngSlADAG546zU/Gzzx89eD352bPb6fnzt/VaAQBk3vTGq/Wvf2P4xbbvgbPZKj10aCcdH++/eRvp7IhIvlBLr10rAQDwwSOzOJMJ5NJym97e7gQAyP7o29dwNquD58/7zjt+ZPzQT//UK7/XOZu8PGh6lpo0+f7FtrW1/X5b2/XuEBsbG6VKpfJnADBojMlJKUEIMVev1697HhACz/OuRlHUjzHeaBhH5wEACCE3lHFba50gCM4wxupaa44ojcWDD67xn/2ZfPD4P1TkH7/fJ4cPb/E77mhveHCM1lrKrB9qrff/XTLGcAAAKWXJGLOVzWYv1Ov1U57nXQEAW6/Xj+5JAxyEUrpRrVadXC4XK6XAcRy3Xq/7AFA2xhT3DCWA65V3CCGz5wmTUnanabpLKQ2stYQQwjzPu0QIoXEc9x9M1CaE1BzHGYvj+IYkdmttRxRFudv8ZxTFcXzu4A+cc0Tb210pZUCEgDAMHc75DnvjG2icyth+8E/ddHn5EPyn3wZMiHJdd84Yk2eMLQBAqM5fMKZS7QcAiJ97zkWus8paWwzq79sx9aBbbW+3iWPHxuLnnjsDAOC95tWXwkcf218Df/vblqvHh0awlNKRcieMIgnbW0k6Pn7Dmmhnp0wvXe6gvYc25MJim03T6/doZ6courous8EjOvzWoyfE6Kl1MLbVVGsxvudukfb0IKw1IITqybGjGdLRuaOWl3N2ZoabarVbrazmGhcTEGNgU3WL9ADGaBkAXtRYqv/dN4fFubPzyQvnbzGCDmJqtRyi5LahM7227vLjw7swMwMgJaTTM4cPbvdf99pl2tkJOx/6cI/77l8fo3fe0QbXdcBulwfX5GVGM2epSZMmAADg+37gOM6n0zT1pJSvRAhRjHHloIq2tVa5rrsbx/FRx3GuEEJQw5ChQohpQsii67oVIcSatTZSShU9zxtXSpXM4CBEWju548d1ct8r8sx1feO6Fa21ZYyhNE2TVCkHIRQc9NZgjEMAoMYYnxCyLqXsklK2I4Qqe4rfByGE7AohluM4HpRSGkrpupTSRFHUY4wp7mk77aG1zgoh5qy1bkPFnDHGNgGAa63rYRiewhivhGE4aoxhB4+11opsNrtNKd2hlC4hhJKGsGaCMYaDRpkQopYkSRcA3BAKYoxVCOfAhPCVUp81xrRwzn0thKxF0bC59x4HXv3qNS+bWeYNUc5Gj7sFjDGO29r62VNPb5nt7ayNIt/UagW9tV3kAwNGnBzZVvPzrpyeGdhfb3k74732tTNWpgHt6lqEleV21j+wQn739xL2/PN1vrOT0SsrknSUFkhHxxppb1uh7e2r/MTwZnLhQq8p72RsENxwDUlb61r0xLfPOPfdew0xERspAd9xbpsOHwf30KGc1joBAA5BUMXPPadJsVCztWCXlEopKRYCWioFuJBHYmR4AheKFb2x4dk0FQAAuFCoAsLwYiKTSAjpnDl9ydSDonPu7Lqcmb29Ivfe9e7vC9XKym3HEof759TScsfNoT5ACEi+sCAXF5leXSuIV9y7Ev/5R/t0pXLev/uuyZc6X5OXB01jqUmTJvtwzm2xWPzbOI4jz/OGEEKWELKrlCpms9lrjLE6ACSN0niQUqZSypK1Viil2imlLE1Tbq2FhmJ2HmO8IqXs1YRkOOc71tqaYWwrYawUhmE3xrjMGHOiKGpLkqQ7k8lsx3G8n5Dc6Bu3wxjb9X3fc103iqIoyzlfup2x5DjO0p4EQWMOHCEkpZTFm/fdA2OcUkp3AEBYa5nWusgYWyKEFDjn01LKAWPM7TxY81LKXBzHfVLKzoZ4ZiSEWInj+Ib8JK21YIxtGGOyYC24yyvzKpst+Agtk+tVhwkhhHHOB5IkiarV6gnO+TWlVKfGuJVQuub7fhvnnCCENsIwHFVKlXxtVtMPf7gbpML0zjsq+Bd/fty74+wWbG0pwNgk5y/c4G2xacppT88shBFWq6tHWFf3avqXH+/F2aygXZ2bemOjLhcXD+tyuYN4vpOOjx/Vm5vdJpUapGQ2SfYNRlwsbrKBgQnkup5eW2vV5XIbP3p00/u93y3S172GRr6PMMZICJHXH/3Lmv7CF038zb8/yd/xo5Hzy/++Nfnbv/PT557r1uVyjnWWxuXs/GB68WIfO3JkRm9ttQOlwI8fX0jHD/S0OwDp6Fii3d0VE4SdsrzdQt/zR3X45je1qdW82+0PAIALhRXgLGNrNXbzNrm61pH74bdOJFfGb/FioUxGysWlLvGWH1yqvfPHj+sHX8/00LGwpbX1yy92riYvH5phuCZNmtyM7evr+/35+fm5KIre6/t+lTE2jxDyHcfhQRCsSylLtVptjDEmDx6IEKobYwYAYBJjnAIAKKX6GwKLlBBSrtVqJ/ZCXoSQKqUUlFKxMaa1sf++USKEWEyS5JBSKtMYfzKKon4AgCiKzriueyGKojM3zeGGNhhSyhZKaUApDZRSN1RB7cE5X1FKCc75TBRFpzDGhhDiaK3rURSdopROCSHqSZL0HjhmAiGED8oBKKWKDWPvtv+2EkIqOAw1+8xnt5LPfHak+Na3rtU//4WTdGCg7P6LnwzRQw/0aa2lMYZls9npIAiO7d8Ua9HGxkZ3JpMZ28sdcxznCgYg5OMfu6pzuXbLGGYYe/ivP+HXv/mtQ+4r7r2Gc7nYpqnDDh++AsZEanfnGHDmkZMnSiZJ0nR6OuecOzcTPf30STkxMQoA4L32NZfUyqrAGY8BQBsAgJqfP8T6+xdNpeLhbHaXHupZBkycdGxsP6xoa3UkFxeB1GsV6zpOPp/HSimdxHE5/suPD+qdHYY4B/vCBT+tVJH89rfbaV/fFO0o4fiZZ/dz3rDn5JAQxkqJQcoXbZxLWoopyWaqcmGx5Jw7dyVYXifuq35gW33q0y8aspNzs0PunXfOhMsrx27ZmCQg19dvKxpFC4Ut/OPv2FV332UBACwAVsa8Fq5XODaTf1/mNI2lJk2a3Jb+/v5PraysvJpS+vYoinaFEDmtdSqEaMEYj1Wr1ZOMscuMsQrGuEwIsY2GsOA4jg6CwAUA0Fq3ep53WWtdsNaSPUPJ87wNY4yu1+tDvu9fIoTMYowz5kB5WpqmvdlsdkFrvYQxZpxzSwgZj6LoiNZaxHF8yvf9i0mSdCul2vYOu3ktURT1Oo5zQSl15uZtjTniJElGEEJpw6OkpZTMWtteKBQWqtVqi9a6zff9chAELa7rXrTWGinlEYRQCteLZRjGeIMx1rKzs9Pued5YGIbDcCDsFsfxMEE4oYXCFhJOUPvkp657xjIZhh56wAGA2BijlVJ+kiSdvu9f0lpbAOihlKrGNdm/Pq7rEoQQ0M5OA0ptYoxtJk7atz74px0ol9Pp8nKrJYTjUkmbJOHi8EDGf+D1u6a/T5jWli0ol6VNUkguX+4Fpa4PyhiYaq0znZhoR5wb5847xtXWpufecUcUPfNsGwAAP3p0Ln7hhX3j5gakuoQ6Or4A1t6htX4nQghHcdzjvukNC2p6Fty3/RDSqUzQ1avEuevOlWT86mm1sLBvECHfN2BhS5w6uRE/9/w5K2X1xZ7RdPzqvsdJra2dajk8MB2eP3+Lx2gPUiqt0lLJia6MHwYAQMWiEe/8sTl19ZqjHn2sGwDAVCq3zUEivb0CLl4mTrUmk7e9FQAArLVd0DSWvi9ohuGaNGnyohhjngzD8JX1WPWsRajWnuGrGOMCQsg4jrMjpSxyzteMMU5ra2u+XC537fWRQwhFnPOylLJVSlmilC7EcTzCGFt3HGfadV2glKJsNlsDAEIpJZTSRGtdLBaLu4yxtTiOW5MkyXPOVznnVAjh12q1OqXUd113XSlFEUK7Usp+z/MmrbUVKWUX57yitb7Bi6S1bnMc55pSqr0xPwsAiFIaaK39hn4Taeg5FRhjK5TScpIkBa11wXGca1LKdsdxpqIoOqWU6iSEVIQQESFkiRASWmuzWuu6lLIopSwVi8WrGOMVhBABAC2EmKBC1NNjR9voK15Rxw89uEX7+ubM+fMYu24CRwd9QChwXdcihJYAAEVRNKqU8jDGq47jbCKEuAtQ5RcurplCgQVpehghtBHH8ahSqgVcZ1s9+riXfdUPXJNz81SHYb7w3vfMhx/92CDr7l6tf/WrhxHns9FH/mxAXps8pJZXSjZJxIGbDmp318/9yNsn0qmpFvZzP7dlfuInDL3vFUn6pS938YGBueTaxAgodYtmFAAAaWtNM3fd+ccqn3+nlLIFIUSEEHV8+gxVX/saDT/3+W6zuurqjc1qcuHiKEh5fRxCwLnj3BgoJdOJySHkugZnMgH2fao3Nl40hLp/3vZ2JWfnuJydvaU6EeC6ejc/MrgbP/30EYgiDADgvOvh+ehdD7d7LcVl9fgTWZCSsb7eNbWy2gkIgf/6148jRyzhfK5stssIgQ3RT/zzDGQykRBinVI6XywWP/I9X6Qm/+hpepaaNGnyouTz+Z00Td+NMfmYdz0/aRBjrJRSWillrbVVKWVHHMdttVrtarFYLJfL5WFrLSaESM75FQAAQojSWg8CAEgpO6y1ghCyIoRgxhiMEFLGGFOtVoeEEBWEkEzTlLquez6KorONFiXz5XK55DiOjuO4kCRJ3nXdeSllZ0PraMh13bEoijIIoQ3f99ettUhK2SGlbLPWUqVUCQDAdd2pNE37CSELWusOrfUNScHWWgjD8ITrupeUUh7nfHavCg4hVEAIxdZaRylV4pxfMsZIay0VQmgpZSsAgO/71xoeqU7O+QwAeHEcjzLGlpVSHaq78U0/dhToQw/sqG9+a97W65AvlfJBEOxaa10hBHYc51oQBKG1tjVN0xBjLPUHPmSTz3z2ui7R1758jXkeTdM01lo78cZGkVq7K2dmCqZW6/LOnLmYfPzj3SSbDYGSQJwYvqRnZx2zs3vbRrQAABBFkExNIxtF2OyUjXadDrqxuW4qFQd3d4fOmdFxGwRtyaWx0s2H0vbSY+nAQIqsfcFaO6q1Dur1ent2Z3dNzi/02DjmZmfHIkrd66rYp9YQIRumWhPxc8/vV+DJmZkeMXpqGhix7n33jUdPPnlib5s4fXo2nZ3ttbXa/jcMF4uxXl29rqOVze7S7u45wFhdn1MbDp/49h1ycUnzwcGrOJNRsl737A+/NSlksxvV//IHw87p0WmrtEpXVg47d9110VQrGLcWsfMzP52r/Mt/fQTV68hyDvl6MGna23fl9caDLxoibPLyoulZatKkyUuSpumvA8BZQogfhuFWFEUOxjhWSlFCCErTtIVSmoRh2BNFUQs0VKo9z7u6Z2C4rnslSZL9hGdjjOM4znaapkIIgcMwrGCMCSFkM4qifiHEeiaTycRxnOecr2KMK8YY0mj8u1fJhCilc0mSXG+/wVjVGCOEENNCCEiSpCVJkgHHcTYxxkRrLVzXnTbGZBBCVSllSWvdYq3lt1s3QihmjNUdx6nHcdzi+/58mqbtWuus53lXlFIdANeNP611l9a6VWudkVJ6vu9fC8PweONcVgixnKbpIYxx7DjOvJTyBu+HEcIlZ8+saYRKjX59HCFEgiBoq9fr3UqpLq11QWvdrpTqoLncIrl4qeL+8Xs/gXt6hi2A7/t+QC+PzdPPf2EnffLJo87Zs1tyba1AWgqQvHDe56dOrUaPPX5Kra514mwu5X29UzZJczaOb7t+kyZ52tJaV08+1QMf/UuXVitb6dh4SS0tdZK29iVdqQra1rblP/TQtlpddWwcc3Fy5NOZ17/210oPPbiSzWYfK5fLo1LKEiGExlJ20cf/AeN8PuIDA0tgTBU5LpITk51qba1D7+zshVGBDx9fJK0tC+nE1DDJ5VZByizt7NrW6+tF0tNTRgMDMcnnE2f01DLJ5qQFyKj1de6cPTOGfX8LFwtpOn51RG9tdeutrW7S0pKqldUCSImNtUU1P99JuzpjKJexZSyQn/p0l3PuTDn8+2+N2FrNUSsrHXq7XDI7uykePVXjP/WvavITn2pBWoOp7Gr0uteGnHOPc64zmcyf/v9+uZr8o6HpWWrSpMktbG9vZ+v1+sNpmr4DIXQCIWS11jmM8TZCqN0YEwCADMPwJAAA53xVSnmzpyIrhDifzWazW1tbt3R2D4LgsNZaKKU2lFKHtda1vVYmtVqtq1KpRMaYdmttO0LIWmtvCfvEcTzked5FhFBeCCEJIUxrnQuCwAGAHWttZ5IkPgBoz/MuWmszCKFakiRDL7V+hFDMOZ8Jw3AEY7yLEEpqtdqI53lzYRgOhGF4OpPJzAZBcEP7Fa01a/w1expLQogrCKEM53yTUrppjOGc86k0TW8QXjTGcEJIwBhD1lpVqVSOua57RUo5AgDged6ctTbSWmN86iTj587+h+y993y9VquVCCH3oiThcOECxadGEjEzc80mKdhKlSRXrvr86GAt/ocnjgIAoGxWg7Xt1hrmvOLe7fip72C9s+MCpeCcO3sBlEImjgdoqTS/p8fEDg9spPOLBe/Vr7qi1jYyamVliORzM+nk1Gg6PQ3uK+97BjvOR3s//rEPwIH8HfLfP3IYpZIi39s1r3k1i97/vh7v8198Ifrgh27Qmtrfv6MjIa0tE+nk1CmcyXjOmdNje94m0ta2zEZHN/ib3rBtf/DNPnv00d3orz5J08nJLufuu2ZNUO/X5R03vXrt1nuL8X7ek93dvZ5DFkaQfvJT/fDXnwAAgHRqpnbzYWp1tQdnMpsmn0fO//izS/qP/6TPTM9mVRxHIp8HpVTwUs9Rk5cPTQXvJk2a3MDKysrP1uv1f3fzxxwAwHGcC0mSjBJCYqWU53neGAAAQogfrNwCAMhms9fCMDxSKBS2K5UK3UvA5pxPMsZkHMdHEUJbWutSJpOZiuO4hVK6oZQqOo4TK6VqcRyfpZRue563prVWxhhBKWVJkjiEkF2tdcIYE3Ec9+ZyuWVrLUMIgbWW7uzsDHieN2aMoXEcHwcA2AvrvdT6hRCzxphESnmLIjQhZBtjLKSUmcb1uJokyVFrLUUIGSHEpLU2YIyRer1+BgAgk8lcauRaZdM0bXddd1wp1WatNXveKc55hBCaZ4ylnHNXa62NMWCtVVLKEud8q16vnzhoMBYKhZ+jlJ6x1r7OGEMwxoABuL1wIVFf+zrYIIjk3EIK1mb3mt+iXM449917Pv7WY3fYJAGczSb8+NBU/OxzJ0EIEMeHppO+gdK5AAAgAElEQVQr44OA0H5z3cbCwb37runomWcHW//PX96Kn39uUy4uR+n4+B0AAPl3Pfz2rj/8gy8evFbVL3/FX//VX1/V5XIWAID2HloBhDlpa11Knr8xOVycHq0hSqflymqXXlvr4MeOXdW1WrdeW8vdsN+ZMxfT2dnR7D/7p5PRI48QmyRZks8ncnGpzbv/Vctydg4nY2M3yAzwY0evpTOzx2/WTuLDxy+nV6/tG/H8+NCV9NrEiHjH2+fV5HRGzcy2QBgi3FGS/I/eMxEUCycJxrvM2HWWzSCEkBRCqI6Ojpd8npq8PGh6lpo0aXIDvu//eZqmpTRNf/PmbVLKPs/zxuX1D+nJPc8SQsh6nnctiqIhay3yff9SrVYbZYylURSVGWPYdd0VrXWOEKLiOO7VWnPGGKeURrVabbgxfqnxFzDGCaW0nslk1q21zHEcL0kSZYyJrLVRGIanAACS5Hqld7lcziGEpOd515RSvblc7nKtVhvmnF/cm3+SJMNCiKkkSW4xBAGuC0QihKI9b87NWGuLAFBFCCW+709prbnneVeCIDjNOd9IkmTQWkullDuO44xjjGUQBCfq9TrL5/MT2Wy22sh1WpNSij1jSWuthBDtQRDkCSEL9Xq9rxG+WxRClBljeUrptpSyrVAozFJKbRiGP0MIiQDAQQillFKh0lRjP0NMd1ct+cSni7S7q5qcvzC4N3/6/vdd0h/8U2Yb1wzncpFcX29rXBxILl4aFKOjW6ZW21Hr6/1s8Mikmp8/ApTV6aFDAbp6NbXVShA/90Ivzmc3US4nM699zYf9177mSwAAi4uLv50kSbfjOJ8R22Vvz1ACAFCLS90AALS1dfGWZ+41r163QcitNlu01I7S2dnjtla/xZMoZ2eHcDZbq3/8rzr40SMr8fPTJdbVNenceeeV+pe/cudt75lUOdAagDFw77rzUjx+9Zjd3XUAY4Q6O+qiq7sev/BCp0nSvPer/9fV5DX3M0WpQgjtAEIIjIlDa48DAGhjChqgkNTrieM4Yxjjy7c7Z5OXH82cpSZNmtyA4zjKWvtsHMdvM8bckMBrrXWllCWMseWcL+xVlmUymW2tNQIA7bruVJIkeWNM3hhDpJTtjDEdBMGglLLQ8K6sUkpXkyTpN8aI283DWksdx5lTSklCSB5jLBFCaRiGvQ2ZgqrWOs85Lx9oeUIaeUuzaZpqpVSP67o7Wmuw1vrWWmqtxZ7nbRzIfdpHCHE1juPRm38/sH0hTdNO13XHpJQdSqlMkiQDnuddJAjJtGHsAYCrlGpHCKXW2gIAACFknTHmEUJQFEU8iqJBIcS41rrd9/2ZMAz7isXiPEJISykLnPPZJEk6KKUxpdQghDYxxpuccwAA7vt+QSllAAAbY0KM8TMWoB3yeaJbWkz6Pz56mLa1LuiNzf38KF6rOjSTtYjgHdrZGZigntUrq4WDa9QbG557z91rQAlNx64M0L6+bTF8vBp/5+kW9+67V6qf/dxRG4ZcDA8vyenpLjk7d0/07HP/YuejH/0hOzLSG3nuO+I4/mf2uWeH9NPP3FKZhttaV2wUFdmRw1fooUNl2tFRr//N3w3G58+3Y84gnZgsQZLettLOJgllh3qmaHdnLX7uhREAAB3Fvq3VmKlWb0lY50NHZ5zTZ2pyfj4vTp2ajp9+ZoR2lpZJsbhr05SJ/v5NOT3d477qB64Gv/efjuKREzXgPMaE7CCMA0zIDqY0tNYWGgb7CmOsLqUsKKW6GGNfKhQKf/9iz0uTlw9Nz1KTJk1uoVgs1rXW91cqlU/EcfzGvXD9Xu6QlLKDkO/22JJSQi6Xc8rlMmtUru3juu41YwwGgI6934IgGPA879Ltzo0QUoyxBa11izEmFkKIJEmMlBKiKDpqreVKqRzGeNvzvIk4jo8wxnY8z1urVqt7FVOuEAIlSQJJkuQwxlY3wjDGmILWevF2KQha69t2pSeEVIUQ89ZaJ5vNXlVKMfldwwjCMDztf+GLE5n29stmYsJFxaKBN78pDQEO4+uGXQQAJk3TerVaPeG67rLruhcIIbk0TU1D0wnCMAwdx3F835+z1oKUEqy1YRzHGCFk8/l8viFDRYwxUimlpZQ1SqkRQgwhhERlezvHOjosKRZDEwT7hhAbGgrtxlYF9fdV04nJ04hSzYeGdvXa+i3K5OET3x6h3V0bAAByaqqTZLNremvrWPj44+382LGFdHy8DzEGdKB/FlNKLEZMr6y9Glx3GwAAp2kEz9+od0RKpYC84p5r7ORJir7xN3UbBtwmaYAoicXRwcSkaY+cmCzRnp4VtXTdC3U7TL1eSqdn2q3vAy21B/zwkWW1vnZLkjouFrdpV3dQf/zxEXF8aM1aEwEAqLmF74qIzi8AAED85FPH8s88d7Fyz12nbx4nk8nUXNddCMMwTNP0pDFG+L5/MQiC0xjjzM37N3l50vQsNWnS5LZ4nhe3tLR8XGv9mDHmuOd57zPGnLDW5hzHWUMIBY0qM9/zvIWdnZ1+a60AAOCcL1BKVxhjy4wxFIbhMOd8EwCMtVYghLS1lhljbmlLkclkFgkhCcY44Zyn1Wr1pO/7CwAg0zTtJoTsAgBuiFoGUspuxthGmqa9nPMFjPEOAHQ7jgNCiLUwDPsBgFtryYFzlAGg6vv+Vjab3VFKgdbaRQix27U1yWQyy0EQDCulWq21MULIKKVuUImmUzOr6f/zB6f05bEW/cyzrfoTnyx5nV1TyeGBkjGGSyk7EEIRpXTRWltKkqQvTdNiJpOZN8YorXWrMaZgjCHGmFBKmXUcZ4Mx5kopLcZYSSkVpTRDCDFSylBKicIwHJFSthiltuyH/7uRv/lbbXx5JUC+v5t9+GGqV1an2ED/hpyY7FUrK4V0YqLDf+jBhXR6psh6eibU6monAABqb0+83/yNK6Re3UAIKzk72wUNg5Ie6q7p3Yprw0iw/v45tbraaaVianGpW+/s5nl//5ycX+hC33o0YSdOLOm2thb8V3/NTL3u7Y3h/sLPTdXf+eOnoaWI5J+8v2STtCjf9qPbylqrT44CHr+SNZWKw/r71m0cE5skzs33gR05PEFaWjTJZBJ67Fic+eM/ClhPj4ne/KY8+c7TW6ZS2fcW8qFjV+OnnzkLYYjV2noO+/62Ke/cInUAAGDTlJKhY5vYcWuUM0tX19bo2tqWbWvPeb6/FoahSJJk2FpLXdc12Ww273nelFLqvfl8fvZ7vUtN/vHTNJaaNGnykmSz2bnW1tY/C8PwbmPMWWNMjnO+HIbhaWMMEULMWmsVwHVDJ03TVs75RhzHw0qpTillm+M4k1LKVs75LOd8gxCyyTn30zTdF470PG/Kdd2FRkm2xznnSqmYEEKstYG11nNddxUAFOd8RUrZ6bouSpKkIIRY4pxXEEISIaQE5wiefCoxU9Mp/uSnV+Dee0AD7CcLE0KqnucBQshB16lhjKtKqc6bq+4QQgAASGvtAAAYY3JCiE0pZdvB/djm5rJ59LEbwk7q299u9974xgukrW3N87ydhjL3IGNsXinVJoSYjqLoiOu6CxjjGCFUI4RUAAAYY1Gapl1RFLW0tLTgNE1DhFAmDMNICCG01qnrur6Uck1rXZJKdYqZmRV0cmQ7+Il/fkzMz2+Gd9/lqY//Vb+an+9FuZzJfOwvJvmJE4G5fDlUS0ttqlLp8O5/1SVVqbQ6D7xuq/aWNx9F99/vo4GBTXZ8aBmHYV38y59cUecvZEkuF5rtcl6Xyx3O2bMXaFdXRc7PdwIA4Gw2Ie3tC3pz65D9m79tddvbrti5haxz/Pis1Yrg1pZN/w1v4JbgiDzx7XWYmGxJ3/nw2PjD/+osGRw0qFYL2dNP5mwYunpjo4UfO3plL3yIODf82NFLbGBgzYZRRzp+tYce6pnGD7+zbh573Ak+8KFW/OY3SXLuTKi/9o2WPeOMtLYt683N6x4qIYAW8oEul19U3JIaq+RHP3bI/PUnfPOlLxfN177e6p87O1YvFjqllB2NZ9R0dHRsc87fRSn93VKpNPa/9TI1+UdL01hq0qTJ96Rer3tBELwnCILT1lqCEFrXWncAAHVddzVJkmFK6YrWmhljCg0pgb2wG2p4ZLhSqoMQshrH8RkppU8prRtjOACA7/ubCCHXArKLVcMcSGuO47iZTIYqpdIgCAaTJCk5jlP2fT9LKd1FCBFCSEgIwYQQTSnNWGtzEEXV9CN/XrCTU1k8ckKRkZE01boNAEAIsSWECDHGwlqbSClJGIaH0jRtY4xN6MZ+BxFCTB5YDxhjchhju9e6BQCA1Wor5huP3BI+Qve/aiUqFs8hhLYRQo7W2s9kMjsY48gYEzfOp5Ik6W/oSLX7vi+FENhaW85ms5IQQpRSNgzDVqVUD6V0DWMMCCGjtY6Ytdvid//zbvL3j46wgQELo6e2nAdeLxJHSAfssnr+hS6IY8Q7OyrxZz7rp2NX+gEA3J/8yWm9tlbihcIUymQCdc/dXCKUVx0d7XJoqFO98Q0tdmgo4m9/207m7Fkv/spXfZskWK2udpJicV1tbnaCtaC3y3m9td1Be7prfODwpF1c4jSXDZOrV/v1+nre7FYK6fMvWPO5z3uC80S2tG1O/8wvDirKmLuzvVN496/02UrluzlHwhGkkN8ETBQ/erSWjo0NIsehamurAGmKbZpitrNbDT//hePs6OAiGh3V9r++xzXb21nkOIoPHplFjHTojU0fKAXv/lfNx889P3Dbh5tS4EPHLpkgKOBicc6Uy/vep/TvvtkhDg9c0329GQvAC4VChVL6cDabfUQIEf7vvENN/nHTNJaaNGnyPdnd3VVKqQWl1OustTnG2JpSqhMAIE3TEsZYNowJhxBSjeP48IuNRSm1hJANpVSL67qTSql213W3G9IBWggufIagFqsUW+1tb293xHHcBg2xS0LIeq1W6xJCVOM4LnDOq67rOgghghBClNK6DUNQr3l1EL/yvl48OroWJsnJTCZTlVJypZRPCFmOoogopWrGmFhKeagxdmitNQe1kxpz3t5LZgcAcBxnvLHefS8UwXjDfu5/dt68XlwuZ8QPv3XG8zyfMWYa3rQtSqkyxpRyudy6MaayVxlHCAkwxhtKKUMpRcaYKI5jgzFGhJAKIWQjSRKHcw5BEFBrLTUIafStR+PsL/58ioaHpfnAn7rynrtNIuUAXZhf1099p5MdO5Zw39uNnvpOL1gLqFDQK7//3nrbnaN19dSTFO67D5JDh/pvnn8ul9uwCLWYtlZCwmhWXbjYCQCg1tY6/de+9rKcm9s3Lkyl4ugkKWJON+Lnnj9pw3A/l8gGgStGTy1HT31nEGezleUf+rEOQAjCfLHQuTp/jTAams3NAgCArVZdk8qcOHVqRS0vM5skWTF0bBJns2t6bb3LxrGPHLEBhGTdkRO16A/+8IhpVN6JUycvpGNXRvTGpg8AIM6dm7W1WhVxRhChmHSUNkmpNEPb25dpR8eaGDmxGT/73Gmzs+uxzs4lvfndhHhkDOhvPdrJ5ua2xYMPbFiM31kqlR59sWe7ycuXplR7kyZNvielUsn29fV9nXP+DMB1z8rB7VprAQBAKV2+OTx1M42KMAIAEATBSdd1xxFCke/7i3C9LD+lGGRW4BgAKjeHxdI07RVCzEZRhCml80opR2sNxhibpqmy1oq4peTueC2usZa4rltwXXcpTdN5x3EWM5nMJWOM9X0fCCFtnPMMQihpjN3HOV+9ec7GGIcxti9AaK2FW0QyXee2BTNmeQXs7i7RWidSyjCfz89EUXSiXq8PCSGWjDHKWrvvVUEIacdxsOd5juM4rdVqdURrnSCEwHVdHyEEnPOEMcYcx5Fpmg6maTpCOjtI/T//l6x9+hmpJ67lECFlIcTzaHoWAQDIyUmRzs2nFmMwb3nL4txnvzpXx7RVMQf01HQ7ujZhM/Vg3PO8y4yxTcbY877vP48QokKIhFKakh98s4fzuX3xxvCJJ06Is2fWDq6X93TPpFeu3lpRSCnIpeUSaA360qWjQ2PPTB56/slxL4mCyV/49aO1N/7QDgAA6e7WpKsrFkcHF7ErQpLLlemhnvn4hfMj6cTkCBscXBNnz87glmKXf//9C7UvfmlfBoL2922q1bWTB0+bzs310u4ublNpjNaOWlntAmuOpNeu3ZlcuXKH3tj8rqAUox7yvPjmqXudnT7/w/f8Wm9v7xO3u8dNXv40PUtNmjT5/4wxpqy1fkWjH9stFUhCiK2XMpY8z5tnjCmEUJIkSSsAQKFQ+JwQ4rjWOqGU0iRJHGNMkKap3NNSOojjOFeiKDrdaC+S45yXrbUOQihkjCEpZcIp9SoGQwvHqwggAYD/l733DLDkqO6+T1V1VacbJueZnd3ZMBtmg3aVAwhJgASPjYgCLCTA2NhEAyI9ZIxBxukBg8EGSyRbAmwMWEJCEiCEEpI2zuzs7uzkfCfc2Kni+2FnRju7Ihpe22h+n2Zud1dVV3ffPvecU/8j4zjeYlmWFELYlNIYAKxKpbI+SZJax3HGpZTVAADGGHtJdRtTSocopZPGmPQZ54XPTE6nnifVv96eRqeJH1rrOmYgSYT5/r0N5vnXSKFUnCRJtdY6BQBoSR08xhhLhJBQSmWMMcxxnKRSqSDGmMAYz7quy6SUkWVZtjEGR1GUrVQqLQih4vK4+e7djQ6lJ1FjQ0J37iyXW5p3KqWa6be/W1JTU7UAAHJuviH+3n2Dw5c+Oxthq0VoYxdsD6q/+y0fX/HMxXjL5g7OeTsA4FQqdchxnO8rpXYRQiytNZfpdIq0t+fkvffV4+qqUuq5zx1Hnmth31WGWI6JY4RdD3Q+f1bivtXRLvX8vLMsDknuvKPWufvOeq+tuX92S09bTf+RGWdqIgVJjEAqUPPzvpHK0gmvV6OjDSAlASEsRGlIamrm40d/2qWCisKp1Jwul6sBAHSx6LNNGwfVzOyTyfdRhMXgYB2pr8vp2Vw1aA20vX1YLelLkUymtFxqRc3O1tENG4okmx3XS5/Z3d2H3D27XtJ088fv+Vn39Rq/+6wZS2usscYvTRRFzBjzsiiKVkIVlmVNMsaGLMtaRAg1AUB5yXhRALBqKX46nc4Xi8VmAChijLnW2qWUpgFgwPO8lwghBpZynhoZYwQAxhFCSGudYozFvu8PRVHUboyxHcfJp1KpBWNMbFmWLpVKTa7rKowxAa1kFdaW0VpSSp1KpZLWWqcZYyNJknQppRRjTEkpbWMMXUoYbwA4ZSwppZps2x6J47hnqS7bKk0mY4zr+/6YEGJlaT4JwnHztX+pP30/uq7jhBge3oTAJHZPz0xUXb359LaWVM3zS3pWNmPshO/7ec65Vkp5UkpHa80wxlIphcQpqjHGM0qpRsuyQqVUCgAQIAR808bGpKWlWXd1LSil6gEAnNaWaXnX3Q0AAPZ73tnrbu/WaSTzZWPVWgjiDUxOOP/n6lLQ2rodAXBnfuG49Lx6jDFyXffDrut+LY5jorXebtv2tPG9WTo9KxDGRM5Mh2JoiInBoUZ37zmHWUdHRZVLJdrYNMk2dgmczSrS2DBgNTRMWd3di+LFLyury6+cwz/+UR0YA3TzptkU6LDhG19NspT4rKMdxT99LOOef96YMQAyN1ejc7lVq+JMEPi4pmZK5XLNulhMGdtmJJuZdvfsmbCamsrIdiI5Pt4IZ2A1NSVqfiEFAGDV142q3Knkb1Us1jnnnHNCTk/XAgDohQXPaJVlG7uOpq+5ety76MIX1b31LUd/uSdkjd9V1nSW1lhjjV8axlixUCisenlRSkunlxBxHGfctu0TQoislBIppZoppXkpZSYMw0hrTTnntZRSZYwhhUJha3V19V3V1dWT1dXV35icnDxeKpV+pJSq8jzvuNZ6IZ1O5yqVyk6tdavjOFPGmJzv+1Rr7RpjyoyxlOu6lSRJyowxFyHkGWMSSinFGNuu6/JyuQxaawUAoJRqKZVKwBjrdxyHJkmyyhNi2/ZBdUZ5jNMxxiAj5aq8JvbAAyQ6fV66uhbkxGQ7AABuacmVN6zfYoxZrT1ESOx53rKnraS1biGEBK7rpqIomuOcewBQ5JxjQgjCGDMAmI2iqIsQMkcIiTzPO+55XiqO4yCKIo8QsqC13kgI4VprRi4430q99jWH4+ddTeJUqlUHlSrHcQY26uIYGO2pstweExIwxo5BFFH+qht3Zj/z6Vu8iy/6cE1NzSgAQBzHf58kScwYuzn51N+/OPzBD766dAorRrMqFLPJwYOdKyeHMSCbaRPFNQAAJIzGj77tg20IwGzPTY/rz36m3cpkK8G3v9PjXnxxn5qeMVFfX7t74QV9xoDCrmsA4yq6qSuHHbeUHOldCbchRleul5mf9/H6zjJOpSqVBx5YUSs/E6u5eYEfO94EAECqq6yVcidagxgbS9m7d53QpXKjGBrKQhQTq7Xl200f/9gH4LRad2s8fVnLWVpjjTV+adLp9DQh5M7l/wkhZaVUm+u6J5aMpMk4jtsrlcrWJElaKKXguu4hpZTj+/5wkiTdAKeUsJMkWQlrFQqFN05MTHxqeHj4PZ7nzbmu+3nbtg8yxlzO+falXCSwLGsiDMNNURRtnJ+fX1cqlSghxDbGSNu2E9d10xhjgjHWCCFDCKFJkiRa69JSHbtVauGc861KKS6lXElIxxjHAFAPP+cl6eULR+R1r3S8nz7WBwDgGjNoHnvcQra9YmHJfN4ndXWLAAB6ZraaLi7OrWrD8/oZYwuEEE8pFdu2bZIkqRVChEmSzGutfaVUaimB3hBCvDAM/SAIdqRSqVHf9+cIIZHv+2ljDGit0xhjN0mSPY7jTCilGCFkjBBCVaHQSjMZ0AApYwySUq6zMDJKymYAAKWUb4yp9/NFiaSE4M1vvb786td+bPod7+wBAGhsbJzq6Oj4SHV1dWK4OCs0CgBgeBLYO3c+WcpEazBR/OQ7xnV568zE0PYf3jFkvnRrO92yZRFsFgBCEPf1damg4tsb1h+wGhsln59vSw4f7iKp1Iwcm6iVubkMSqf0clOIUnF632J4pInPTBEgT6kpeup6TExgAAB2yUW9sG074cdP7HD27u1lWzb3IZvZRkiNM5mZ1HOf0+9fecVHOm695f0/7x5Y4+nFmmdpjTXW+JUQQngAAJZljdu2XQiCoMdxnCBJkiZCyLRt2zNJkiyvCptd9jpVKpUVzwBBaNVbzRhDOecvCMOwHWO82XXdL1QqlTdFUeQv9UWSJAEpZT0hpIIxLgAAllLWxXEcaq1thFCEMbbCMExc12WUUqSUQoQQJIQgQohW3/fnEULHoijqBgDwPG/GGKMcxzkkhGghhCwihFAcx5sty3I8zztkWRY2DzyI1eKCNNVVRuzcmcWEED09nUEHDkr7gvMnxZXP6RIAYPfsGDYIMVMqp1j3lnz08CPNAADsmqvHKvX1uwghalklHCGE4zhuTKVSOcaYBADbcZzDQoi2KIq067rTtm3LMAyrMcaZOI5jQkjW9/0TpVJpm+d5/ZRSXwgRUUpdjLHknLcCAGCMjW3boxjjqFQqVenX/1FKKbV9qfDvZiGEDQAbPc87HIbhTsbYAEIoDn2XAJwqKxI99vgrxeTk5dNvfdvfqmKxGb/sJTOVDRuepd/7rnPZxRceAiEB0imjW1sM+uItLfyBn2xHjBl7187DOgyaxMBgAwAAqa/XVnNzntTVBtnrXrDTOA5gzxPi5MkaHQUefd0f9uOmxkR7nqaUQvntN+1avi/kxEQ7AICanW1w9uzujQ8c3AEAIIZHGlFdXdnMz68kxvMDh7qdvXsPxYcP71pVBBgAaFfXvJic2mSfc85B8WdvbSVDQ7PIcVT8xBNPGn4Tk7VgWeBs3/aB5k/e/NFf6+FY43eWtZylNdZY41ciCIKQEJIihHSGYbjBsqyCMcbxPK9ICFnknNcvJ39blhVIKWtOP94LgqPyda/vouecMykzmZWcn9TC4mxs27Wc891hGN6wrAYOsFICpUgpXUAI5THGYIxBtm0vpFIpDAAJYyw1kY9N2iGBZVm2UmpMaz1DgqAJOU7ieV4ghIiVUj5jbH5JH8pLkqRLStmUTqfzxhg3juNOAACttWchNIceeRTFN71zh/rJg036nvua6LETkp5/Xiz+7d/rrCueOUuIlXjt7RU5OkZIdZWSUzO+LpVc0HpcTk52AgDIo0ebslKNo317wbLtiaU5yaRSqWmEUCylZHEcW0v9N2mtfcZYnlJKPc8TSilJKRWe51EhhDbGUKWUpbWWURStsyxrGiEkMCGRY9t3a62bPc9DGGNpjLGYbU9jjCOllEMImWeMLaRSqWIQBPWMMUkIKWKMTQyw3e3pOSzv+0Ejbm7iemq6Kuk7+mxVKXfDhg3ro7a2i7UxnljX0STWdzaJ1pYmk80i98ChQ2JoaCNIidTsbKNJOLJ7evqw64Q6CLKAzDzx0yUxNtYKUoKJIgLGAPnG1ydEz451zo7tFdHcRKyJCSLuufcpVbZJff0izqRzVm3tDE77gbNly7wYHFotAprLNdGOdm61toZWU3PF2btngG3buoBdt8w6O2fjAwd30dbWAdXersj+A4EuFFapsLv79v572z9/4Y3/lefjv0KxWLwqjuPnz87OrldKCc/zFn/NplAul7skiiLieV7hNzrIpylrnqU11ljjV6Kjo+P7o6Oj3eVy+VoAAK112rbtI4yxVJIkZinhGFzXPR5F0ZbTj2WEzOIv3tLAx8Yc9rWvlbwNXfuhtsYi5TIKvvSVzenPf7YvaGho18askibAGHPOeRMANPm+P6+1DuM4Xmfb9kgYhtL3fQcArLYaV2ul0sfmE7S5hiaU0ruSfH6TxXk6dN386avrXNcdV0qlUqnUcBRFNVLKBSHEptP6LBuEUvJfbltV/0s8/ni9fNvbqwAAHECenpiQ1qzFBHEAACAASURBVPOuoW5t7Xj8pS83q1yuCgAAuc6T0gJKgQyjEk+SesZYQCkNhBC+EKIQRVGL67oLQgi7pqYG27Z9tFKptMVxnAaAUcuymizLOsE5ryaEZHzf9zDGxUqlUud53pxj2ycxQjYAIDdfNPbMVLXct6+EEOqglLJCobCsIVW0bXscAHQURdsxxkeNMa0Y42NRFO1CCIHjOJPB7l077Tu/O6gsK4uVCuiP7q+Iz/5DB9+9+yzBTULIYDqdvq7tnz73+OTr/vhj5TvufC8AgAkCL9m/fzcAgPeMS4+G9z+wDTve/JnH0wMHAnbhBSMEY2QDpMOb3t1+1g1HCNjbtvbJxcVWNTm5YlyLwWHJtm+f4X19T2pbKQViaJgBAAMAwJ4zbf78o2mrt7fEb/lyinV1HTKPPIr4lVd0+Vu2TKmF+YoulVMAAGzLlkPZl193A/w3hd4WFxc7AGBLFEW1tm0/v1gs1gkhCKX0vjiOM0qpGYTQgUwms2lhYWGfbdsjSqlHpJTP2bBhw+sBYCUEHIbhy13XvQ4AvhNFUa0xpsYY8z3f9+//7zq//+2seZbWWGONXxkhREEIcbnWum4p0ZiVy+U2IUS953kDQoi6JeHKU6uwbHvekXJIvfwP1smDh1IAAGp4pFHt39+sn9hfjxYXqXXZZUMGIURb2xYUIdnlZGjHccqMsbk4jtsYYzGltIwxjjnn9ZzzKiFEjdZ6yrIso7XmGGPcmLGpAdRgtN5jDw87uFwRvK420VobY0wKAEBKmbVt+6TWOrFtOxBC1FNK5dI+zPO8E1EUbXHy+Wm5/8Aq7xhUKoQ0Nhb14iKE3/i3DeLhhwM9OkbEwMCTCc/5fJN/+TN7xdh4A7v8mYfVc65ivKGhSQjRRCkdd113AWOsOectGGNBCCnEcdxICKlQSpXWurm+vv5ypdS3Ghsb/zaO4x2c8w5jzAAhpMp13VgIoVUc2zZCFFuWRY4eDfGePRsEAFVKcYSQkVLOEkICSumU4zjpKIqatNaebdsLAMA45x2wlL9qjCG2bR9PlOrWxngKoSrU0rygb/lSKx0ZGRfPfEYdnCr/ArZtH/Q8703L2kMmjh/UQWBkLncJSHkqV8myAPspCwhB3vnnlZDrTgGlWi+pdVv5QtHcc6+PwyhJPvYXLaS6uqwLBRcAwNm164h17r4Z9xXXFWWxlJUHDtQDIWDv2jmqpXJIKlUUk5ONcEYivnPuuYet9vYpXFU1w7ZupWTTJkJ6epg1O8vV5FSz7D/WBC96IbP37imZBx+27O4tQ1Zj00LmBb/3npobb/itlS/J5XJICPHJ8fHxv1NKXYUxvkFr/fl8Pv/C2dnZtzmO82atdafv+13lcvm8JVmJZtu2txUKhd1a67YgCN5QqVSu4Jxvk1Juq62t3WWM6ahUKm+OoqiqXC6/Ko7jq4QQ1ZVKRdm2vYlzfl6lUtnOOX/J1NTUH+fz+R1BELgAMO04zlmaUms8NeipKm+vscYaa/wyLC4u7lNKQX19/RPDw8MflVJeaIzZzRibSpIks6SIDYSQaiiV5vD9P8bxzZ/cdnobdPv2Ueemt6GotbUpXgrfOY4zDgAUY7yYJEk7Y6wYRVHb0rYhpZTvuu6C1hobY2KMseU4TlopxQkhbhRFtkVZAZTC6Ic/suzuLa7ZsF4EQRCdnjvFGJvXWtPq6uoC5zyIokjZp5K0bSFENo7jFk+IE/z3rt1MamqKam4uC4RA6qorh4JHHuk0heJTLpJx9uzpR7Yt5FxuHW1rHxfXv8KLOjo2nL6P7/snAEBTSrEQIpZSqiRJ9riu229ZluKcQ01NzTNqa2sX4zi2y+XygDGmFATBNxBC12KMU1JKWpXJ2EkQlLHjeEvFjbNaa2VZFo2iqBQEQRoAypzzzrq6uomlMGoEADhJEksIsUrE0bbtHOe8brmUC0Yoom96i1LHjqec9723t3LZpTsIIQuNjY0ba2pqzgrxjF9/w0Px4SOdam6uGQgBumlTYGUyszoKqRgdq6cbNkxizwt0GCjsOFjHCUKMal0qIbZhPeODwwmybTBJ0uI859llsm2rHX/tX0NQKlKFPBYjI1ux4xacc/aMBPfet29l3Lt6DhkhhUmSdux5JjnS2+RddUUfvOw6d6Fnb7zu6ME68dhPQzE4FPAbX1UV1dY2WoTkcRguZAdO3ttw3cve9Cvd/L8iw8PD7w+C4CMAAJ7nHbRte3u5XKZSyrP2dV13LkkSn1KqkyRJAZzSF4vjeBsAAGNsilK6aNu2BoBsqVRqsG17UinVKYTAtm2fCMOw27KsACGEpZTUdd3+MAxXxEIxxqFlWQcQQv0IoQXn298JoamxxC+8UPu+jwHgKGOsv7q6evysAT4NWQvDrbHGGr82NTU1jy//vX79+vcBAAwODt5TqVSuxBgrACgSQiY45x5Np1l45RVd6TgZD/7fp1bCLeLEiXWounohPk3kMo7jdtu2j4dhuG3p/3QqleoVQrhLq+KmgyBYv6xZhBASxpjvUkonOOfPJIQwBCZrHT8OsLELdFurMForxhhBCIHv+30AQAkhBiGUIoTQMAy7EEJzWuvZIAi2Lo8lYmxT1TdvPyn6jgpy29fnSFU2ksWSNuXKk4YSIXC6h8MYbeJHHtkJACAGh7LW5MSY9+pX94fn7l1pV2uNPc+jnHMipfQty1qwbfsg5xwZY5jrugNBEOwLguBiAGhnjGlCSF0Yhh9IpVLHyuXyuurq6jE5P+8hSlNKqUpZEr8uhTFBCLTWiWVZtLq6GrTWLIqi8UqlUkiSZIsxxgEAcBzniDgtGZoxdowxliRJspI3ZC8unpQjo93uRRf2mtpaAQBgWdb9T2kovfL6d4WPProPZ7KRvavnkJyaWS+nplImDKvtXbtmkyO97cnBgz9zeT9tbe3FKZ+J6Zk2NTWV4YODDciywMSrHSAq4XVicmpVbTaSqbKi/v7NZn7eRTU1kXvRRUf50f4NNpiZ+iDvlGtrZsVdd22VQyOW+/Lrxg2AJZSqd7LZOfbc57zrZ43pN8HQ0NAHoyj64Mr4ldL5fJ6euR+ldJExNhlFUZvW2kuS5PTNK1YVxhgFQbAjCAJwHGdBSuk6joPjOLYAAJIkaXZdtx8AEsuyaLlc3m6MWVVEWGvtcc4vBoCLAQBQEvfigSEnddVVPgDIcrmM4jhuyeVyJ13XfSSVSuUcx3m/9xQK508H1sJwa6yxxpngiYmJP8tkMg//qgdGUWQlSXIDpbTgeV7ouq4plUqbXNedDsOwywBgEobD6p57m4AQAGPA3rmzYpWKk5ZFi6K2thZOlfU4IaX0l8uqeJ7XFwTBDqVUjVKqRkrZtPzCBwCglC5orTNBEFzjum7Ztm0XikWNeGKVm5q4RihwHCcNAJgxNm2McaSUcRRF6wFgPI7jMue8nVI6KoSowRgjhFDiOE6/67pzFYw7ZXNTA0NoLr7969vVwnyjs2v3MHIcTNtaJ62a2rLV0jykhchCFFHa3DwhZ2ZWQnJ6MZ9FJwdc84LfZ8vefClljTEmIISoVCqFpZSyUqnskFI2cc7rlVKZqqqqF0gpt0ZRtIMxxjDGihByGCE0AgCLVqXikh/8UBmlEG5rJeVYEIyMYkFAJSHLoqAKY4zDMEwjhDwp5emimI2pVOqg1poyxoaSJNmqlPIYYyeWCiWDMzScI+Njufjx/TvR7//etKypaXZd91NVVVWPAwCMjo7eUC6XW7PZ7Mn5v/7bz6q5+WZTqdhqNtdktbZwNT3t0I72/nj/ge32pk1Tam5uVT7a6Yjh4QY5PV1Pm5tOsvUbynJiogaewvPiXXLx4fiJ/ZsBAOimTSHtaB9DRiPa0jwrRseaIIqoHB+vt/eec4xu2w7lV7+mHR84ENL29gExNNzOwMwn+/bVAEKQyWT+rr6+/ge/6r3+yzI3N3cBIeTPwzA83VjRGGO9fA8TQpQxBnueNxkEwdYzaxMCnKqpKKVMLf1dRnNzUYaLEYFQg0JAAaHIGEONMdbS/V0vpWyyLGtWCNFojKG2bZ9gjOWWBVhPh46OzST/+E9bxY9/bMh55wXKdYpCiCYCIDEhGQDYKoR4Yy6X8zjng6lUqvLbmrP/iawZS2usscYK431HP5crFt7FOT8PIfQdz/PKp21GP/PAJSilen5+/pwoiq7BGOcrlYrred5wEARblhrQHsILjpAR9twyyqRDnE6j6Lt3rFPfu6vWv/ii/aaxwSRJ0qm1znhRvMgef3zIrO8Uy4V7zySbzfbGcdwipWwkhBTNKWvEEoQEseOQ+JQRVLQsS1UqFcQ5p57nCc55jRCiCiGkPc8TxphJjDHjnG/0fX8QAGgcxxsxxtOc8/ZMVdUEzuWkuPe+OpAS6TAEq652ITnS26Xm5qrU7GwzbW6epu1ti6pQrNPFog8AgHw/tt/5jn70wmsVT6erTh+7bduTUkqcJEk5juNNxhgL4FQYknPeihAaFEJgxljFcRzCOQ8ppX/Z2tr6AULId8jk1NvkxZfaqK3VYARl32OTR6ajQkNl/nHuZ9ZPFpK42qcoCAKFEFoAAKm19i3LmrVte9h1XUUIkUKIJsuy8kKIBmOMbVkWMsYQY4xlETKr7/zeBl0o2mho2Gfn7u2v6ez8C8dxCnBq1dVnwzB8VyGfv5QqVSV/+tiKfpZeXLTBdQ2tbyiDMQohFHgXnj/LJyZrQcqfeT+phcUGUlU1rObmzk4qr6lZFKVSI2trHVDzC/V6cZG6e/aUk6NHa8XYeIPds6PC1q+foR0dC9HjT3Sb+tp579U3zuvGRpXcdvtud9/eQ7y3r96Nk5N6z26cyWRe7Xle9FTj+E1QqVQ+KIRoXy7vAwCwVIx6eFllnTE2QAihhJCYc179VO0YYzyMcWSModbQ8Dx617tZ8s+3rje33U7ZQw+P09bWXNJQ3w5wavWo4ziHMcYxxtgSQtQYY6iUssGyLL4UwpuWUtamxsZ7rVu+pGkuR+Sx49Uml7PR3LwKz9u3HgDA/c53J9THPlFFtmyOglSqMY7j/8M5vzYMw9FsNnv8tzVv/9NYM5bWWGMNAACYfN0f/6n44Y+ujS68YJ/WupFzvidJkgNaa7SwsHBZLpe7NQxDHoZhOkmSgud5/KnaKZfLL8QYdyRJUmfbdiEMw+7lbd74xOH4xtfs5CdOVMmZmSxwYWh724QYHT31S7dQ0OrSS7ABSDu53CTJ5croW/+RwSNj2Er5I7K6ugFOE9OllMZLoo3Ytu1jWmtsWdYikjK2v38PUdlsStjMt2171BiDlpTDC5xzT0ppa61dx3FGhRCNlNJ0GIadAABSyiqM8bxSqpoQgl3XFRjjgrnvPl/sP5AFADBx7KpcbiXxG1dXF9LPfnZFzc9TfuxYs7Vnzzzds7tMGhsVv+5lDUlV1apSKJZlBVVVVRghJABAWJYV+r5fsm07AoB513XLYRi2c85bGWMVKWV5Kfm8PYqiI3EcfwpqqhsMAlmWOrQxSCXEw40+GaF1tXmh9R5mOCgly2EYrnddN+/7ftr3/bIQgmqtaymlhjGmGWMBxthOkqQKAIAxNimEaAQA8JsaS84zLuO0rjaJ77izluw/MN3yhj+9GQBgfHz8Y0KIK7XWvtJ6vd7areBfb0+xC8/vBy5sw7ltb+0+nhw81K1LpZRaXKzlAyfrnXP39smJyaeUCFi5th0dedJQP2m1tAg1M7PiDWMbu/rl4FCHTniN07PjGLLtRcSo4cdPtIAQlLV3DEWPPLpZjo/XpK9+7lF18HB99KUvr6eZdIlmMpNybr7OOWf3iB4atrKbNx2u6+m59Rc/Hb8+xWLxzxBCtRhjaVnWgJSyyXXdY4SQmFIqMMZFznmHUsoTQmR83+9DCNlKqdVlXoxBlmXNsEpFqhtf26SLxdTSBtCL+SxRalFccnEDAABCCGGMbaVUDaVUCSFWVnRalsUJIQtxHG9gjI1a9z/A4q98tUMv5g2urdamVGZaKcWuuPykcZy0/uif1+upqSozOFSOr7yiBQBAa12TJMl1xWJxJ+f8QDqd/nUlDv7XsJaztMYaa8DiF29pig4cvAl3tGcxQqCNAc75swqFwoPFYlFprbPGGAQAVXEcb/M8769ra2vf8VRtCSGSOI6bAADCMFyV1EzuuHOVd0iXy5nw/h+vLOeXDz7Uar3lz0r0b/5q1rztJi/O5VoBAODhRwC+/GVwduwIzIfePxRnMj1LfTmO45wwxqAoirYBAEgpW71Csbf02c+1pt733mnYt1fFcdxgWVZaKaU555sppfFyOEwp5SZJUp9Op3sRQtXGGOo4Tv/Sy6mMMZ6J43iDHUXAiqWf6Q1BjhOVf/jDRjU2durF9JEPzge23c0Ym6FxEiOtEQdwAZ+y9RBCsdZaW5YFCCHCGKsmhPBSqcSjKNqmtaa+7x/TWk8yxtL5fH49AEA6nbYwxndjjDmzbco5TzwMTEo5KIR4luu62SRJIkaIRIziSqWyzhhjySiKKuUysdJpbYwpJ0myDSF0IooiSQhRlmWR5TkxxoQAAATjOXnHnYJks275M//QCACAff/Hy+cchuFrhRAN6XT6aBiGbR6ls/q5z4krb3rDVjAGGEIJfPwTZ9WNQYB+YfWI+IknVhLP2dbuXlUqt+okTpHaOmbv7JlKxsaa4if2b0N1tYKg5mHW3T2KHadMamukvbX7MB8a3kbXd6aT48crqWuuzgcPPbTeLOap1dVVoV1dJVUqR+nmlj/5ReP4L4LDMOzyPE+EYVjn+/40pXSRc77OsqyBpdWNKzsbY0gYhtts2z4KAFVnNubc94NZ8dWvthrOz5o/I+TKPC/du3PGGI9zvpIbhRBKEEITnPONjLEFcv8DcfCpT/cgAFCLizVO555DamJqlxkdTctrX7zd/ebX+1HPTpvrQ744fqLLmZ4Zl02NzRghZX/nuyfMM57RnRw4dNsUTx5yrrn6nTU1Nb81D91/N2vG0hprPA0ZHh7+iJTyHIzxEI6iHfqrX61B69Zl7L/8eGIpNVLivBPglIYSAABCCBBCgDHWAABJklw/OTl5a2tra+9ym4Xbvl4NRjeTrVtrwTol0O2F4VHh+1lAyDIAVvz9e84qcHomqv9YJvXNfx+t5HIrK3e8d900qEplCRMTafSZf0ilb3r7IZlOG2NMC0II4jhe0UdijA2aqiwmX75lqkDpJhOeygOWUoLneb1CCGNZ1mgURbs9zzukta52XXc0DMNOxliFEDIRRVEPxnjadd2pKIo2uZSOije9dQOqqqrgdCrS5YqLHEeb+MlyHmp6utk5d+9BNTa2GwBAO04KThmdTaT3yAHzvg+2Om2tk/D2t0W4Zwd4nk+WVgq6SikDAHJpbHEqlRpWSvGl0iaHoyiCdDo9AAC2ZVnaGMOVUqZSqQSO47jUGB7GnAoh6hFCRdd1HWMMIYRISumwZwzEL3vFdpPLMdHSYuz3vacXNm6EpdyYdBRFVRjj2HGcw5RSVKlUdgIAOJRO6uMnssWvf6Nt+TxpW3tm/I9e/zH5nneNaa0dAIBKpbI1k8kcdRynWn3oA+ARMj4/P1+rk8SIx59YZTAjxgC09n/ePUA3bsxjz1sEMIJPTHTx/mM7cFVV3jvvvL7g+9/fbV100RT5xu0T7n/eUTF9/RAeOLDVLCycMmQPApD6epl91R+MyZnZWMdxW+U/78iw7i1T1q5deTk2bomTg0H9m954qbOzJ/l54/ivksvlthNCHGOMWCoF1KiUqgEA0FrvtG17NI7jdacfY4whlmUhMAaccnkO9R/PEYKR+urXUtp26mhLSwkx24gTA6tClPyhh3Y6+cJMXF21/IMkVkoxx3EmhBDLIUAlhKhVSln20HA+ft/7e5atf5zNFlS+sPJ8epdcMhu++KVbAQDsrVv3q7m5RvOOm9IoCLFRyoqSpMf+yUMHkoMHz0FvemMmLhQuiKLoD1pbW38nQ3NrxtIaazzNmJ+f74yi6N2MsbLv++cEACG9+CJNN20q8tu+btTDD9e4n/iLUUlpk5TSNsZAOp0eFkL4SZJsAgBQSjVUKpUv5nK55zY0NORnP/ChdeU77vhPOT2zA3mu8DZtPqkLhVrc0c78F10bBh/58/Wpd77jpFi3rpT09a3/eeOze3bEwdf+ZcVQSn/m04P0ogszxZMnDY3imL/1bVXWD35UiZ59ZQ9CyDiOU3YcZ9AYI5Ik6aaUhgHnPWBZAGdIoxhjslrrBAAsx3HGAKA6juN2WMrHWiqeW40Qii3LSiqVyhYAAI2Qa3wviffvr7H37D4IQrrIZg26VF7gAwMbAQDYa1/dry+6SLvXjB3jt3xpgwBYefGY2jowcQzy5GCrv7h4zFBmOLIsG6QlpZSEkP1BEDQQQloBADzPqwnDsEApHVNK1UdR1BxFkampqTlBCEkFQRAuhdCY1joyxiBKaTNjrMQYy2itYwBAhBAcx3GPwLjgXnDBgjh0qFGNjmLp+RIAgHPeyBibBIAqrbUTx/FOjPGAMQY7jnMwiOPd+HWvjazX3DhE/+1bkn/jm13RY4+9BBBCIgh8/eTUCtd165MkKWutKTEmRAhVsx8/MMjLlV1wGoZzAIACAKwyElah9WJy+HAXAACuqS7SzZtGkUWFDoJTFxRj7DBmqTj2ovvu67S3bBlLCoUOUApS11x9QidxTeHWL28ArcF/1rMGQUrJjx1v4ceOt3iXXlL2zz/vrd6FF/xWDSUAAM75jVrrWYSQ7ThOVC6XV8JhxhgQQjgIIW5OWwkKABCG4eZ0bu5odP0N2wCgfmW9outqkslIREjxzL4QY1qn/JX2lVJaa43jOF5PKa0IIVLGGM8Yw32EBuLXvq7n9ONJTXXBaH1KUPbCC46ofD7NtmwZAAQJyqTrSEPDtMrlVimmg9YIACD+5r+1Z664XMrv3f2F6XL59c1/9Ze/Nb2q/y7WjKU11niaEcfxhYyxfillZ6VSGddad8Crb5wqRdGmNMK9sGc3TjCuBaWQ4zgDWusAAGxCiFFKNTiOswAARim1LYqil86+/wP3RE8cuEVOz+wAADBhRPmhQxuBUnBe8PsT4ktfqXVf/8fHky/c0kqy2THnnD0HrbY2v/Kd7654gwBj8N/xtuMa4cSanCDJkd6VEAwfG4/N/gOh/tq/bEkwxiYMLey5UwCnwg1RFHUBADDGZjDGPEmSTQgh43nejO/7nHMeAwAkSeJxzj2lVG0URU2WZS1QSmfgKRLXbds+Fsfx7rq6uhGtNeecg3/Dq+bK73hnGgEwHUWQ9PZWI9/Purf+8yGVJEo2Nbrcdc+B9jZI9x/LSYxToBQFAMBKnerDssB9xmXVitFYqUQjywJCiM8YU+l0+sq5ubkHKpXKBUqpIUqpyWQyOgzDed/3Z4wxnlIKWZaFXde1MMZICCHjOHYYYxXLGM/S2uMABaWUKJVK29Pp9DCculhZ8fa3jmdcV+oHfgKLLc07AE5pVgkhVqlmx3HcSimdwhgjAABtjMsx3kCvufoJ57LLhuVHPuqLgYFm75ZbD+PWVizuuDNNr31BWbzohWnLsiyEEM7n821KqRTcdfeqvJtlov0Hdjq7dx8xSgo+OrbblEoY4FRtPZxOh/GhwyveKL2Yz/LF/M5T23t6AQBUX1+DeM97S6L/eLO9Z3eZNjVHJp0q8ccez5goBkAoB0LUAQAE99zTherqAnvP7oO0uTnCrvvn1a959V2/5OPyazM3N9dQLpdfpZSqE0LES7pV+vR9lFKNnuf1h2G49fTPtdZMCnGW983etHFe5fMgxyfOyvcynON0/7HZyr694wihSEq5DgAgu5g/yeOYM2MgaG7qUUpVqcNHhs88XgyPdLrnnntQjo7WmYS7SV9f5+nbnQvOP2Y1NJT40NA6tnnzDGasyEdGugAAzPS0XXzBi7qt1pa5OIrunoqiV7R85tM/PrOP/82sJXivscbThNnZ2Ya5ubkblFLPiaLoCq21LaVsUkq5tbW1HGM8kjQ3NcqqqrRSKmOMIVLKWqVUEwAgIUTasqxSkiR1xhgNAIoI2co/9ffP02HQhSiNTBiu/LJFNjPqPe9yxcUXEdXenjFf+SoRIyMdcnqmyQBCJgwxSGkBQpD6k9cPOK+63oe5OR3/+7cyOl9YWV6uHnywASk5KSen2kAITNpbE/yc5+ZEbc2q/Kel0iX9SqmMbduDSqk0Y4wrpSyEkAqCYIPW2lveX2vtaa3TjLEJpdTKCiTLsuaUUvWU0jJCSDLGGCGElKuqUtaBAwJZVFv1dYkYGa0HIRBZ17EYnnfuTkXpSgK32be3KLWuSY2M9jonBgrJxz+xGYKAgNZA/NQc39oduK6bllJqjDEFgPYgCM7xPG+rZVl5pVRJSkkxxkmlUtnh+/6i4zgppZTGGCOttQQAWiwW6zzPywshpFsqVaEgkMLzrCRJSPqJ/ZPy5k8C7VxXoH/zdwGqrTUoThL+zX8LrL6jibd9+0QFzHat9SqvhjGGuq47GIbh7pVriZAmmUwhsFk3QzCoHnm0Sfb2NYqHHm7Q8/NV4qGHG8jOneNFz22lhORUqZS1oqgsPvu5dtCr7IPlyUdyZqZR5XLN7iUX95FMZhL7fpkPDGyUk1MNCIDAsoGJMYAxgBgzbMvmGeT7U1Ymu4iJFWAwFT5wspEfP16XvvKKEeOnsFpcVPFPf7ppVX9hyMjv/960/fLr3tv8h6+9+5d9Zv4rzM7O/jHn/FqEEFRVVY0YYwrLSfOnY1lWQUpZe+bnbGp6Tn3vrlWfY9/XplxJmShaVYiabO0uWbd9bbFcX9+hlKo3WrtWFM2lDx8pRJ+4uVXccmsbmpmhzo4dQyqdcm2LltT37kqBEKuvvVLgP+PSwfDBh7ad6ZWVE5N1Ooqq0y978Uj4vbu75ORkkwmClfqNbPOmMTW/kNHFUh0fH39p9MgjheyLXvg4/I6w5llaKVfVbAAAIABJREFUY42nCVLKC5Mk+YvTdXaWiePYJElCOecNlNKBTCYzzDlPx3G8AQAgSZK60/dfrv/Gjh2v5UNDmwEAnH37+hGjtpyargIAwOlMZIIgklVVjQAAmbe95Wj0yb/pMmFoy7HR6tQ11xwXw8OUVFd75vjxmtL1N0air2+Lc845BwUMtwIAkLq6Iulon6U7d+Lk8BEAAMDbt8/Auvan9FhYlpVaNuQYY0VCiBNFEbdtm5mnqFagtXaFENlMJnMgSZI053wdY0yEYVjvOM6xMAwzxpiSlLJOGVPjrF9fjr7z3dU5Jg893AHPu2ZVu0LrFkrpw2Ro8ED4L7f/IX3m5RNs5w5d+b/v7wr/8z/rvD94xXySJBWMsaWUKlUqldjzvAsBwGeMxVEUtSVJUh/HMa+pqRkxxmTm5ubaGxoahpVSwBhL5XK5Jtd1R4rF4hYAgMSyIsbYjIMQYdMzoT45qPX8PIFP/pVvyhUf/u5TkPzNX5fR1BTn9963PvmPb4P/qb89FGzcuBImY4xNMMYWKpXK7tPPx7KsgHO+EQAA1TcwdtGFR/lDDz+pxK4UhH/6xh73wx/sDb7xzQ59/EQKua7r9Ow4IkZGG2lb21zc27v9KQ2nSkXxkwPbrMamEbu7ez9g3GS01Jg5YLRCRioXWVZC6mrnNOcNMjfXpOfmEAAAWb9+0b/iWYNyejoo33FnMxjj0Y6OKdLZGaiRER8AwGprLZKPfuR42Nqyz66u/tmhv98wWutGAADG2HAQBLWEkLN0iRzHORxF0c6zDjYG0Fe+wgBjsK+5ZtZEITFbNudoOhWGN//Vimo5aW8ft+rrNH7taxbKUp6z0u4Xb8nz227fcnqH/rp1peIrr99utTTLaGq6h7S3leimjYfig4dWrr/V2JAkR4+lziwhs4y775wTovcoAkKAbNpUQJ5D1eS0ZWZnbSCkrCuVDrtnR6+RihttXj905XOembr0kpsaPvj+0V9rEv8HsWYsrbHG0wSM8VZK6SAhJJskySoV5SiKsO/7LJVKjS4uLm6yLOtYHMcbfN/vDYJgBwAAISTPGBtdWhnHjDEKujbULKtXx48/vtV71jP75NR0FWlsyNvbti3qo8cSftEFjQhAq6oqaZ937phl246YnvH44FCLmpmxkt4+bymPBQAAVFDpolu7x9HVVy/yq67oTgjZDPfed2h5u7jr++vSL33pibCtVRutcerEwECle8smy7JKSqlEKZUKw7DHcZyDhULBxhhHi4uLq0p6nI7WuoZzPgJBIL33fWAGPf95JdbdjUOENtu2PS6E4AihOd/3Z+Sb3oDs6146ZhwH48W8Vp/9h1Zk22eF8TDGge/7/0Rf9rKKeP7zfy+TyXhBLpczlgXk3e8aLJZK65xP3BynXvsaJR96WFh1tQX8ghd4UsooDMOSMUa4rhvEcdxeKpV83/fLlmVFxhgSRVGFUgqO48xxzlfEC4llQSJlp5ydDezPf76S/PD+Hc4F54+Rd91UQIyZ6IbXZNGHPoxIKiUAAJCUwN/9f3vcL3z+sKira7Msa1Ip1WhZVq3jOL1xHO9Y0usZiON4y3L5k+DcvdvwuXuBvurGOTUzu0oOIfngh1dWNhohCFAKKJNx4sOHt7ONG0/wkyc3nzlXMp9vcvedOxvef/+WM7edDtvavUDXrYswpXnU3JRW0zPUP+/cidLtX98JAEA3b5rGrnsUbLvB3bJ5Wq/vDMi+fSa+5CIauO55jLHHjDH7f14fv0kQQvmsgWHNmIsxnkmSJHIcp2hOQQGAYYzPUtxM3f6Ng/KnP/XE6Gib/Ud/eDx44bXL81LHEdLWP34xUfm8TZqacvjmv1BhfX2n7/tlqJwyjbyJyUP8ttt3ndlu6Xt3tftXXTEa/uShVrZ5U68YHdtiqqpWWUU6jj3s+xO0vf2EGB8/+1rN5jQ/MbDdf/ZVs/CK62Z4Z2eTD1BWP3kwiN7/wZ3WunUTycnBHRBFwPv7AQB6irOz58tc7hUtn/n0g7+Baf1v4xcu31xjjTV+N2hubv4EpfQ/lFKpKs874bru4XQ6fbS6uvqo53mHgiBQcRwr13WPRlHUDQCglFoJAzDGpqMo2p0kyXrOeasQogPfc9/U6b9CxdhEi33B+aO0c910+MMfdSWf/KvOzLe/2+v9622DyZ+9Y2f8o/s3Ve7+fnty+HAttu28nJlZZSgBAIjjJ9LWJRcvhM999i5JyCk3/9zcqi91XlujLa3HvOmZMfrQIwwBAKU0iKKoc/mlTuLYs/uPiSiKtlOtYxrFpeXjEUIym82ecBynD2OcV0ptFJS2oSuvqMDUlAtvfDNxv3jLIV4s1hljmpVSKAiCjsiY7UFdXU+YSm2vdLT3JDd/3LHf++755XYdx8lXV1cPMMa+XV1d/RAAvJ0QUp6ZmWkUYWh577xpwN6zu9VHaFI8+HAjtLXJ5FmXF0VPD5dS/ikhhHDO26WUGxBC1HXdo4QQL0mStOu6o/Pz842c83pjDCilXMbYSpKyEML1PK/P+eznxpIf3r8NAABJWbB831Gf+vuimp/P8t7edh1GK0vJTamE+Wte10M5z2OM0xjj+UqlUm+MqUMISd/3R6Io2ro8p8toACBXXPELa4aZILTkyEgGAAAtGWlngoi1EB89Wv9U21ZhWTy46+4NcmqqxoSRtFpbF6NlAUzLAqumxqj5hc7kyOGmcP/+WlJXC0IIJ3Ld7YSQwPf9P2lrazvwC/v5DRD39r2mSqkd+uM3o6RUygBAVTqdbonjeEeSJD1kcbGO/uMXSuQ/vo2XvW3O/Px06vDhY/Kee2vlkd7NulT25KM/rSEIrWgYKa0xu+yyIUAIUHV1yd28WWSz2WNaawEAwIwZSd781rM9VQBgSiXCJ6Ya3PPPm+InBnZ4V15x3HC+krBNGhvnsO3MJX19e+yeHdq79NJBdKpO4gqIUQFSQvC9uxr5sePAGAvcbDZjXf7MmvTr/nDUSOlDdEo9gG7cOOrs2XMIZzK0cs+9d45ff8Mf/cYn+v9H1jxLa6zxNKBYLOJ8Pv/RIAjegwHm5Je+QlAYavcVL3dxexuVP3kwQ7/1rVC89S0ep3QbwKkSI0op47ruTBzHO6WUnQghWBXOymZW505c/8ppevVzmfzLTxJ49DEAo231o/u9pK9vtd7Sli1F9KzLAzuJJ9VsLiWnp7MAAKS9fZhcdeWiev7z7NP3N48+tioMKN/4lvVmfNx2XvLigbj/WFVWqePqwYdMsveclS9/+uijuvKhj25wu7om5MR4E85kQv/T/28kyGY7CSGVUqm02bKsBUIIEUJkAAAqV16x1QYY0V/453p+2+31zvz8YeulL3Yq69e3aGNOvfQR4qlUahwhJJRSNYGU2TRjx40xNkIoUUop27YrxphRIcSdAHADAIBKp6vklc+yGOeLKp/XgJAJDx4UQXf3bgAAXizuWFIajwkhw8aYeiHEBsdxpoMg6IrjuB7glJIzAOilsNgIQkgvGzMoioA/9PDKXKNshkSf/RyhAE9eNIRW8rYAAFClgtitX8bF19zY6TjOuJTSllI2ZbPZQ8Vi8SwPxTLx9a/cxR5/4qQ8fnyjvWN7ARCaxrbDAIHRXEQAxpOF4koumA7DFqDUgBBneuJiPTd3VnmPMzm96rsuFl1dLLqAMXiXXZZDFiklx46l5NR0Da6tXbC3bh2v/OShrejWLy4vBHCMMWfpFv02CIvFVwY/+MGbC//4hY1qcdF3eDJsPvxBK7rnnkXv3h+UTBja4viJdWBMHQCA19DQH+7etRU++rEo7j/WfXpb6sDBeuff/+NEcO3vnxI/RQjKb3j9VufVr1ogQQhKKQ2FotbUOlXr7+57pOrs6OXTsxvMwsJZCeKiv98V/f0dAAC6qkqyXbtycjZn2Zs3leX8gi0LhUZn584+MT5elxzpbXT27Z0gtbWga2ry0b/e1oM9PwuWBYixGLW0eHEct+iFhXHxhjenQYgqbDOmAICu71zQ5RIWJ0/u+v/Ye88wu67ybPhZdZdTpzfNaEZlJI2kGVnuvcgFU4IxBkLHIfQUSEIIAUICGEgwvfdqU2xMscEYbHDHWLbqqGt6b2fmlN1W/X5MQbIdvvx43zdcl+f+N2efs87ee605695PuW9nx469RogWUyr+7fANLzsv/Zyr/7H6r18//397Hv5PY5UsrWIVzwJIKbujKHqDtRZpgPr4husj9IpXdUfnnrNPpFOOS3Bt8pv7NvPWtiP0gvP20ZFRUrrwgu0AixpLlNIYAIoAsLLR+v0DffLr3zitANUeOFBFL7k4jg8doYAxuN3bjxipVn60yaZNRfTRm0ZVNrumpHWXd/GFJWd2rkh/+KNRay2gq69i9uyz6qSUKViKOHEhymqg/7SOLT0y4gAAJCdOEtHbWwfv+EdPTU4pt611DF94wSwnzBf793HQGuTx42sAAPCGDbNRLtfped5JADDW2swp+jMroBij5cdpce993eLe+8B57rWD7D3vHi0FQZfneYellIgx1hyGYZ21FjDGUgiRy+fzU3Ecuwih6wuFwuXW2qBSqbQDABBCplzXTQEAstXVSF999WF024/z+N/eC67nHTTGaCFEhzEmBwCQSqUOuK47izFeIY6U0gLnfFQIsdz6357JZPqllBEhJB9GUad35a4jyY/v6GEbNkxEv39sM8lXlYLx8TZYnEywWq0QGOT7sXP2WaPRa1/dArBoYOy67mySJLVxHK9oKz0TDMYE3v9en77n30KczQ1Hjz76tIiGf/HFR8OhoUYAAHn8eJWzfdv+5PCRHrAWkOtGvKPjKO/cwJdSNn8aCE263ds/YMKoA6xdC5ROWKXWq+mpNbSmlgBCWfe9/3pQr1+fFu981xrcUJ9IjBvAWjDGEIzx/y8hOxUTExNblVLrHcfpq6+v/x+1wgdBcLdG6HJRUzOiC4UUAED86O870PNfCDZJWp/xQ7fcit1Mpo/29FTUkaNPO5x88UudqeddeyzgfCVNGft+Dfh+DRSL4E1OjsCHP9qQaqxfCB94aAMAAKmtnaQ93cPJ/gMrXXa4qmqWXn7ZMFx+Wdq0NFcHqVQ3nStMk337uQVQOOUrebC3RY+MrPxPyKkpH7/lzZOqUJDe4OB+3tbK4337LW1tLRjGKB0amtB3/LRCqqsTzOicGBzsAADAmeyIHBjcAQDAW9d4plicj3Y/uQ0Aton+votEX/+LGz9y08H/yT39c8EqWVrFKp4FqK2t3T83Nxct6QgBIsRL/fXrx1FjU1ofPxGKL36pGQBAfOObW8hdv5i273rnSujfWgtusTgSpNONnPNCkiSLono1NWkzP1/NzzvnKDQ1hyhJWPLjn2xPfvwTwNlMxb/0krHo8d3b8q/4y0E1NR3r0VEXEGCdydQprXMAABGl2aixIcv+6R8mlVJ11lqCoyjinK84m+Oh4SnNeMb97GcO2N/en7FX7yrZqiqGntwj8dwc8JbmAXjONfN0TYsn/+oNm+lDj+Qrw8MUMA5ZR8esHBioJU1N4/D+92nX9w8nSbLJGIOXtInmEUJWCFENAEAJCdSNf326lgwAyF/e3U4uvaTXOe/c/uUuMYTQPmttHQBAFEUtjLHRQqHQ4TjOkDEmSKVSjtZa53K54aX2fiaEEEqpBiBEibe9uZFrPc4dZ58QohUAgDE2u2SJgay1ThiGnblcbqCurm5senp6DSFkgnPuaq0hjmPwff9wuVzuAgDIZDJ9nueNuRdfyJMf3wHYcSZtGDWpMFohR6SmJhJ9/SsRFnbF5UfVP/8TMUrN+pybKIpatdau53n7oyjqAQDAWiv/K187LF7zqjUilao+9b7EtbXN6bbWE9EDDz5j6sckiY9SKbBBAAAAanZuLe/snAGEXOQ6k8mevWeoiYkE5fPCLizwZxpjZaxiKbf+sUfef+prxR/f4c/e/PHfiLGxHezmj81Xaqq3ZX542xPh7GwHzM4CC4JDie9vxRgnS3pSfxKDg4PvTpLkOoxxvVIqr7XOV1dXf39hYeHv8/n8zNLbcOHr39js9fRQNTM9m7n22nEAgFKpdFMURefMzMw4vKe7lba0zKuxsSoAAJv895JO4tDhTfxznx+Njxxd/0zH2TVXjSSe1/LUoms3CA7B2His29emWHV+KHngoY04l4tMsegBowmuqhb+hRccTgYG6uiaNRX7nn/NhZ6789QxVE11A9F6On58dydtazvp7ugZS0ZH6+zsHAcA8C67rKK3bG5EX//mdPT7xzabcnkBAIw8dqwZ3vxWIFu79omDvWcsj+edf96haK6wFTDKA6UASkG0Z2+jGhtbWXNqcmpj+c67fq1nZ29s+eqX/69LOPyfwipZWsUqngUYHh5+GWOsKOVi2Yi2FscXXlBGruvKl75spfMJZzJF+PCHgqitdTMAAJ+bm+R9/QU6MsLg+helEOcHAaAaAAD39RW0tbXyve9pkZxneGF+ind07EVP7M6S2tpy+Iu7dwAAJEePRXp0dLF7jTJNkmTeGRufrLSvXdlgCSHjUsplKQBJCBmnlDL+8COD5hvfyvMLzp+ptLV2w+te/ceLuuhCINZOcK1nI0p3AgCQ234QS4S0+/Ajg9DQ4KkPfyTrXHbpAHnJDQtBPt+kw7CREBISQuYZY7PGGCal7HAc5zgAhO78PAqGh1dST5ZzoC0tsR4YcKN3v2eb86XP74P29sVjS2khz/OOW2ujJEm2W2txHMcd2Wz2uDEGfN/PBUEwlE6nvx5F0bviOK73fX+kUqlsBQAQhDQTKXNa6xTnfBghxIwxrhBiG2NsznGcmYWFhU2c817XdcvW2pzWuryckYrjeL1P6QGwlmKMU9babPLt77L0c689ZrVBulSaUyMjNcj3tQ1DghwngtnZlQgLcl3QWjdrrfPW2knP83r5gw8h2dhY5dRUz8PE5Dy9++5K/LM7u93u7YfEhRecRpYAAMyVV0p45FH7jMa4WlWWiRIAgJ6YyOuJieU/MwAAulBwsi+54UT5d/ev411dRejZPmUaGiJbVWWBMQApLQoCTJoan1YMXTz7rL9wPv6xw+hnd9YH1qa54zxqGfeW5w4cB2OMQ9/3v0gpPTkxMfH2dDr9U4zxfCqVOk3YsVgs7oii6D/0kjaW7/snEUITURQ1F4vF4bm5uYfcw4ef9Kam/2L25k9soQ0NFe/MnTFtav6It6Pnk5zzVwwPD1cDAAgAJ/XB/5jkd945lzzwUJuJIo4YUzZJ6NOIk1KAAM2AMc8YyUNvemNZaX1aVAoDzON7fk3El75yJtm6dZ6/9IYJu1A8ZOO4zt26dVpOjGej++9fCwDAv/yFfUFHx2ndja7rjhApfXLHTyfDvr6tAABqeHiDGh4GnM3G7nnnHYgee6w7uueeVG7btmD+ttsXOy57D+VZ+9ppOThU71984Yl434HT0uvWWgNSQrJvf3vmL15wVPT1hficc4i96xdYz8xkkeMYnM/N66npxsrv7v/J6GtvfOOab3/zu8903X9uWCVLq1jFswBJkrxIKdXhed4+KeUGrXWVTKUir69vAZ137hHx2B+2AMbgvuud46W21i2pxx8/ZO74GdLDw7Xx7GyXyGQi7yU3DMZStgMAcM6Potpa6tx4Y2+F8x4AAFFd1SBe+IIGcv11UW5yahT95j5thSBW65UKbt3bm4fn/UVecg7uHbcfizk7rQMKIWQcxxkwxljP8wKxeXMNqatdUId66yjAmAJoOfX9GqEmlM3Ow5KliV6033Ar55+3FQCg+lOfOIJaWz3AOOfG8WwQBI1aax8AfKVUi+d5fRhjYYypohiHyQdvajt1/NS/ve9EtHMH4l/8Mkt+8cu1OAhWarQIIcAYi6y1URzHKwQrl8sNGmOcVCrlJkkyxzl/X6FQ+KkxJuV53km5JJYIAOC6bq/Wuo1zfhgABOe8TmuNM5nM4SRJWq21ruM4A5xzUalUejjn+8vl8k5K6SghpKyVytA/PM6TL30lhZ//vAJ+6Q1hMjbeHO/dtwk3NU05a9eO0oaGWdbRnk32HxCkuhrTV79yTG3YkMbT02HStaVdSZlnjA1Ya2MhRK3dt29c/uj2tiWiUrXMUMyTTwq48ALgxeIM/dSnJ83waCtdt64gDx+ud7Zs3p8c7D1tQwYAsEkSP/W1Z0K4/0Azvf2HsxKgsCSo2L5sC7IMSmkZAAgA6NNeXrcOm7/+q7QUosEj5HeqtaUHACD/za8PBqlUkmLsm/l8/p/jOL5UCPGGKIreoZS6AyH0bt/3V86vWCxemU6nR+M4zhhjIIqi9dZa5HnevNbajcLwKnLHT9aU7/nNFgAAUSplxIkTGaer66MAcOVkLnuaUGSwpmWt8/a/H5NvfQv3Z2fH4eZPZOIn92Se6frFyZM9fP36o8B5BTGWBQBlCKpGb3vrZJhOP+2+uuXyuPjSV7YCAOhDh6rMoy0VNTnVaObnGVA2o2Zn80vzB6a2jjz181prx1gr9F2/2PzUY6ZUctXYWDa1a9cBVSg4hfe+b5PVGvyb/+uQ3NRJyLe+I0wiBL744oR2bjoJvb0camsljI6mSColne7uE8j3w+T4iZw4enRz/uKLR/BZZwnYvGmf3r6tKs7nss7P79qXfO+WHcEDD35z5FWvSbV+7ztf+m8Xx58JVsnSKlbxLIDjOP1KqSSKoh0IIeCcD1prcWXt2u3uzMwIAAA/c+dxu2F9FQCAyeW53Lt347IwHa6rLQRKrVs2gSWEJHFHeyta15EH9ccHfoSQyGaz0wWtN6bOOvNk8ujvN8R79273L730ZPjAAxtW3mcMwEf+M4v//X0lY23WGMMRQuGSHUN+xS8rnwP40Aeasn39+8JT7ENORZIkaxhjA1LKp9mo2NZWkwiRd1237HleNSGkv1QqrTwNR1G0Pp/PH1lYWNgiAeq8f3j7fvuGN1ehJfIl7r/fw+edA2ZkxAcAUD09DadcL1dKeQDQ5rruYWttiDEmcRy3O44zo7VGGOOvYozfks/nxwuFwsYoija4rjtDCJnRWtdZa+1ScXkdQqhQLBZbCCFZABBa6wwAAMa4dkm0EAFAFmBROJJSGvjf/+Fg+LWvbwcA0F/6ciu57fYEZTLzpLqa8Y6OqejRR3sAAOI9ewCMAf7iF4+EV1y+aHPR1ro8Z0ZK2bEcdURVVZMAACCf0rwWRRQAAHu+K5/Ys90mCaiRkTwAAG1omHymuQGMMyiVsjYI/lsDYgAAixGRStVqYxrS6fQ0xphH0emerEqpzPz8/LaqqqoVGYm2trbvzc/Pj0iM11Yp9Q9KqZvCnu4G2tY2G3JW8X0/p5TaXiwW9xhjjhBC3kUplYSQh59ClFq01ucaYxBCyEopT+3OywAA8NnZcfGH3U9L0c7+539hvn3bzuznPzsihPAQQtZxnDLnnJdKpSpjLeh8fgF1dk7y+noi7vl1z6l6UziblaZUYqKv7zTi4lxw/kHYs9d3pdpj2tfmEOd+onUTl+qE/eBNp0WawkcfbSGZTMnMQ16eOFFHW1qOpV985fvEyb7GdDp9cAKh3wGAxRgHnPO+OI67rbXI+9qXB3mxGOCjx3D83n9bqW+yACQ+eqRTj427AAD8vPMKlZ7uddZaj9742tB90QuPLdTXL6bfXvJiAABI3fr9k8G3vrMTAABls9rt6T6SWnPl4fiJ3Tx+Yk+r29JcqaxpWQsAoF7xl1XpfP5A/KlPd4cPP/K5kVe+Omy95bvf+VNr5H8bq2RpFat4FoBS+qi19u0AK55UdZlMZtpPkuFwZraZNDdPwbve6YVVVbVpzzukd/RQ961v2R9//gs9AAC2WMr4QTATZbMZx3GOGWPaAIBQSq06hSxhjCEIgizXOkx2P7EBAMBGMQ4feWSDf8nFvbpYJFBbi+w7/r4Rf/UbsxagIjHmWut12Wx2MI5jHxYLyf8oHogQxF1bWoxSuWcSNdRaZznn00jrcQ2QQwgRsDZSxlQlSSIZY2WMsTXGRISQmHPeJ4RYtkiZN8akKKUTSqkms2ZNGt3505JzsHfQfPeWNVAspuRzX1CFhACcToOydkWhPEkSjxBSkFJWSymrAADS6fQeY4yklDpSyjnXdX8jpfwbSqlDKTVKKSyEwL7vzyCEpsMwXAsAIKWswxhnUqnUQWutiaJoxbeLMdanlNrMOe9PkmQDAIDjOHNJknSSyy5R8O3vACgFpK5uinzwP4Kwo30dttYgrTvQi18CNoph+b7Zc86eAYDl+qghjDFQSktBEKx8H+7uzgEhQHK5ii4U0oAQuDvPGEOUYf/LX91jfv/Y2qemkhCjf5wYQgC5LtggADk4VOuedebR6IEHT7PzeNocHj3melPTe1TrGg8hFBljPNd148Xlag0AIIwxqVQql55KlsbGxi631n7EcZw6jPFrAKCsOO+gAJGdnxeVuroUADQKIToZYzWbNm16+TN8PSoUCj9gSdKgXDdECCkppcs5n0iSpE1KuWZpoRlclS+ahYU8AIDFGJAx4L36VSfL11+3mWrdTwhxhRBtWusmx3GmEUIIY6yt60J042t3cs5jtyp/0DAubUM9ti3NPs3lHfH2f6gyC39UrUeelxDXlcntd7TrL3wpYzkHJASkbv7PA3ahhMS+/dlTL4BddfUAbW6M1Oe+kF+cD5Zt/PCHbgMAmJqaOsMplx+x1rYrpdJCiLXW2kUrG86zdH6+qG67vc2/4vIjcmRkjTxxMkPXtAT2Xe+sWIwROXIkBMaNS6lDXbfJGDOpc7k619o+KUQNm5oapEPDNLr1B9tYa+sIaWudN5NTzdFDD2/zd+3agxCmzgXnF/SllyycNudbNmcAAKwQJHpyz2dnbv5Eqe6f/uGnf2qd/G9i1e5kFat4FiCTyRxXSv0aACY9z5traGh48dzc3Otja1vdV75iQV96McS5XJO1Fgsh6pVStWpTZ0OmY20/IKQTzPPkAAAgAElEQVRtGPm4b2DMfc41hTAMt2utPWut43neEEJoknMeuK47SQiZl1LW8BMnx/Xdv6oGY8B/77+ecF903ULw5a9s1lNTdeTSi4fDHTs6xDlnNaSy2SnG2IKUsj6KogZCyDjGGCulTktppNPp8SRJauAp2nCZTOawEqLW/8pX59nYREV94EON6Hu3cretrd+0rtEWIZZKpZi1Vi95nVFKqSWETBhjUgih0BgD6XQ6jOO4GmNsrbV+UlvTYq7aZfWuK4C/6Lp+tG8/Yx/6j+NQWzu3RLwyCCHOGCueqoiulHIIIYHneQghlDHGXGutpUopaa2dcF235DhOIUmSjiiKWqy1DsBidMfzvONRFG0hhMSU0glrbYYQMgoAVVpr7nnenBCiDgCAMYYAYE74frN/0YX9qbe+pWjPP7cUtLV2AgBYhJA/NDwmf/KzqlPvF37VyydwTU3IGJsQQmzUWuellA2pVGovpXTW9/2JcjrVlb3wguOopWVWPfaHBu/8cw8mBw+1q6Hhen3gYLMpl0+THgAA0HFUT6uqK6ZScVKXXnLQxBGw9rUnsecXTWF+TfYlN4wCZ1OkKj/C13VMI9fjplA4rTsNz0zPRBdesC2VSpUrlcpm3/djKWWzEGKd1rpZKdWIEKovlUrZfD7/+4mJiY2lUuk+AEgopUxr3aiUIm4Uj8uvfb3d/s3bCF2sS6syxmQIIWFtbe0n4VQZBQCIDxykcnLyhfLOXzSi9rUIOGfaWpdSKn3fn4uiqBoAmE6lsm5z84T5w+NZYwzN3vmzAXnPr6vQja8NRDabJ4RMKaXqa2pqjOM4FWOMRAgFSqlaKWU9AIDruqNx9/ZqsW3rBrNhQwa3tEzZ6qqIUTarHn+8efmcaFPjfHKwd4N35hkn5NBwPVoq7ta/vrdBP/jQHw2al6xg6H99dDrc2tXF7vvdnCmX/dTFFz2YfcHzbwUAmJ2dvToMw79bsjByGWPjy/Y+LIqGxatft9VOTHhyYLAOrEnYmjVDycGDm8xf/5WRhLSrpqYmVV/fDBhHjLFprXUjpbQghGhIHT7Sr9793g55511txPfLtLEhQsbW2jiSZn4hTetqS9ETT27yXvOqWdiz16OeN4gymaKlNIOqqgD/5l7N1rQcJzW1Mnzgwev0wsLtqUsufppJ8J8DViNLq1jFswRNTU27AWD30p9oZmbmYJIkjchxxkUut+2p72euO1++7LIGe8klnBBSBmNyMo4dz/PGoihqIYTEUkqeJMlK1MB13UGttR9uWL/Bv+aq/eIXd/eYk30+BIHAzc0SokjQfJUDAEApLZXL5U6MMWCMRU1NzWwQBMvqxoAQgmw2e6JSqbQopULG2IAQYgNCSLiue9Ja21Qul7tqa2qG4ZWvoEkUS/vpzxAAgPh9799K1q0T5JtfmwrDsOQ4DpNS4lQq5QghipRSizEOPc9bKJfLGzDGJwEAtNYpQohYqunwAQBiz83mPvupA2WANpMkec/z9kspmxzH6ccYo3Q6PVypVBa1a4ypppSWhRCxlFKk0+lqpVSwsLCwub6+flhrTRhjVGs9QSkFrbVOkqQTY6wxxgwhJAGgHMdxN6V0klKaUEojY0yt1lpkMpkjS917yHEckU6nx8iZNfVKKcNqqhGrVBaklHkAAKW1Zbt27UVbNgEUS0C2bWV4wwZWDMPTuq4opWNCiDVSyjpK6b7q6upR3tiYLT/6+0mnZ/tg9MSerSDlnxQwtgtFxK+9doJls7O6oQ7po8dq1IGDK5v6wle/VuWeccbe5OChnQAAbF3HcVhqFFgG8lO2plQeJtmst+TxZ4QQp6VWEUK0Uql8rL+/v5UQMm+tjQgholKpqDiOuwEA0keP9pp0yiIAFsfxJsbY7NJn5+ApRGnqPe9rr9z/wPflwMB5AAD0d/dL9ZUvgjGGCiEqnPMIADildI5iPCm/9Z1Wcv75s6l/eadFh49qd0fPQLm5uWNp7SDP80aW5ChIGIYOpVRyzvcbYyzGGJRS1vf9QCk1bozJJUmyAwAAP/+5hv/y7gHV19cBAEDyVQU1MlqXHDzUzjra59RcoYq1NC9YrYU8fqIRAADV1Cj+g1tG4dvf1vDAg9JcfSWgqvwCjI/XsNbWw6dc5mk1Swgh6XC+H4dhSr3+je3olMiwmV/IiiVfRlwszUAmvTyHVmvdLoQ46jhOVCqVtnqeNxF0belCt3wXuR/4wJBdKMbIca2amVFqaGgdAIAYHmlOXX310eBzX9iEGQvx5ZdZHMd+KgwnrOtS8t73FPAjj/Dy93+wHgAguO+3n4L3vPtFf2qt/W9hNbK0ilU8SxFF0e5sNns/QmiAUsqttWRZ48fzvH6lVJXWOmUBiDHG14u1Rb7ruv2U0gWtNUgpTyuIppQOKqUas/f+theOHE1brYl87A91au++vPO8a3uTf/+3+nhT53LdB6KUxkopz/O8E+VyuT5JkpZl7SNCiLbWlj3PK7ium6aUGiEE4pzPx3G8YaleCBIhUiHGefqxj7t6eHhFk8gIQZz160eSxsZGQsg8xtjOzc2t45yPLXWjMQCYU0o1KqVql86/4Hne7FIUawXIdeekUq2e5x2J43hDNpsd0Fp7AFBFCCklSVLr+/5+xpgihBS11igMw61BEOQwxpMIIQ0Ak5RSRyn1sOu6OI7jekpppJSqcxxnNIqi9ZTSEucchBB5hBCVUjZijOeklPUY4xQhJOScO8aYFCHEuq5bb4wxnPN0kiQMITSNMR6nlI6buloXLruE261dUvV0N8cN9eso59Onmrm6rnuIcw4IIYdSGiqlMpzz0FoLtr2doJHRkjrY+7Q6nWeCffMbh8KLL+wyHR2u29zcZ6emuJ2ZWfHw08VilXf22SfU6Gids3HjiBobb+SdG0ecrVsXcDanqJ9aqHzyU52c8x/Ss848WCwWrzt1fM/z9kVRtAMAwFrbnCTJlZTSCgBEhBDCOa9QSodscxNzzjlnRmQyLRhjZYxJW2spY2xvdXX1LcvjTX/kozui3U9+ErSqZW1rp9TkZINZWCCOhUG2e3eZT0xGxTUt6xzHGTXGcKlUu7d+3SFWLov45k80kIsuCMX553E6PTNDR8emiecy8H3OOc8spWKTcrm8ecmsukkp1aS1blqK3DZqrVfa6S1CyLWmD5cr43zjhhkxNd1hSyVmo4iz1tY+1to6kxw40E6ymRHSsobQv7qxz7z978oJxutJe8cYWtdBJaN1nLFRgtA0yeedzBWX3woAMD8/f42U8sqVedC6VivVSN/8NmPGxrKso30KEWJsFP3RFPeiC+bwc65RXiYzhxAadV13mjGmoihahzEeJoRMYozT1lqTyWVHVE93Rt/6gyqcyQRmYd6xlWDxd2TnGaN6btZRY+N5duv3gqi6qlE5Tkp5ns9cd059+9uEJImVM7NpiGOqC4XNanLqt+mrrhz+n6y5/5dYjSytYhXPUrS0tBwHgONLf35qZGTkxcVi8XYAgCiK1jmOc9IYU2OMqQIA8H2/FyHElvR/NiOEJOe8XwixUjAdx/EOj7GR8GM3bwMAYOvXn3De9MZ+e/GFtQKg1lC68oO8VO/TCABgjMGc82kAUFEUbQQASwgZZoxFQRCsd113akkZu0QI0cmpNTNaF/wP3eQkDz18mkEwCgLQvu8u1RStRDGklHWe5/UmSdJkrTWc86MIoRBjjFOLOkJuOp3eH4bhOmPMcvdSNQCAUioPAA4AGK21jzFmQghobGwcS5KkrlgsVjuOI5MkaWCMlaWUmSRJ1ltrgXP+H4yxN1YqlR4pJVZKVSGEFpZ82DZYa4EQMhUEwXrf9w8ghJDWOgsAOdd1BwCgZIxpstZqY8xokiQOY4wt3T+BMQ6iKGoihMTpdNqWy2VijHG01i3GGG/p2lsIIQvLG7XjOLVSymmlVMr3/XmllNVae4wxFHDWkmlvP555wfOPlu+862ldU6eCX3jhUTflp8F19wEAU5dcVE/37x8j+fyoHh7J6pGRNhtFrp6bIwAAulym7o6efXJyql4cP9EKAJD09taye37ZX2bsUlsqacdx9iCEMADUI4RmlonS0jy0AgAIIXzHcSxCqBBF0bplSQV/bdvJjO/3K6XyURTVAgAwxr4LS5Gl0Rtf//rgd/d/zQoBfPOmw4Dx4l6IMai7frHGam3NZz+93/M8miRJvTEmDQCgXQfF3/7OdgCAyt+9/bS6IVtbU/R+cGu/dRwTxzEnhJQdx9kDAI1JkjTDU2EtUGOFIpgDAJg/7PbFkSNdAAA4lwucrV19ama2Ljl4cKWeTPYPbIFsFtBVu0AudQuKfG69y1gIQkC464pu2HUFKM7L3vd/2Bo+9vuX0fMvONNNpwZMU2Ot+/juUdHVlY9rqpucv3nrnPvgQ6EcHq4DTEI9N7c4l69+1bH4Na9qMcZsSGl9KI7jHkppP2NMLJ62zSKECmEYNgIAaK1nBYCPldLJ3r2bSH1d7PT09CX7969XU9NGh0Em85IbxiUhHACAW9vnfv0bpfCHt53hX3TBYdzUlPLPPutkfOJEDhMa6yi8AQAe+lPr7X8Dq2RpFatYBQAAUEpbKaUjSqlWlak5zIxAWGtjloqDkyRZ4/t+WSnFHccZXNrI867r7hdCtDHGBiilaVIoxDqXzZtiKaMnJzvslbsK8VPaqgEAjDH5pciSu+xFRwgJKKXzvu+PxnGcCoJgI8a4ND09vRLB8jzvJEIosdY6CEB6Dz40Ez/48NONcjEG2tFOCSEBADhaa7p0HS0A0OK67j6tdcupnU8Y4xOe57kAkPZ9f1IIMSuE6MAYz7iuG8ZxvI5zPmaMkYwxpJQySZJsXlhYGCSEIMdxTkZRtA0AwHGcYSllhhBSRAgVkyR5uzGmr1wuP48QEmaz2XGttaGUZpb0lFoBQFtr+VLNVpTNZlMY45JSysRxjBFCM4VCYavrulMAgEqlkqyurnbjOE6WCthRJpNJCCEUYzyPMcaO4+AkSbLGmFohRLXjOEeWyVKxWGyApS7DYrHY6DjOQUKIiqKIpgcGBisfu7nH2b5tRcwROY4mNTUlNT5eBQBA29tH6HOuKVSu+4ueEGNIEXIkCILFtOwbXl8PAOBMTIzwH962V973226rFm1lxJGjT0v7grWglVICYIWYuYND/ezHd5Sjv33bBuDPrFmZJEkbQmiN4zj74jjeAQAQSrnBieN9AJA3xiAAAM55PwDA1PvevyN46OHPrngSGg3JoUMb6Nq2KVJTHYiT/esQIRbWttUlSVK7TJQAAOjYOJWUAmtpOQmE+ABWkgsvRDaTDlnb2oWitTudIJhLkqSBc96bJMlOALCZ3sN79G/uBZia9M3MbAO/4vIJ+cSTKTNfSLFcLsENDQvJ7t0ra9gUi6mkWFzvnnnmIT09ffraLpWA/fJX4/iKy2ZIGCp2733GUOqkzj9/wg4MGHvmTsmy2U8Xf/SjT5tS6RzzxJNUT03n+OZNk9HRY5vg9h/GAADF7ds2pe76xbFk/wGPb9k8BgA1qLoK1OteU220Ti9OiaWc8xkhxDqM8eFUKnUYY5yRUroAAIyxuTiOXeW6WfdTnxhB73yX4J0bJ5cL/nWlUs9a1wwF99zT4QJMeGtaRuxDj9RHR4+28A0bjpOmpprgscdd/5yzhSnMt1R946sTC3V1u4IgIKlU6nQVzv9lrJKlVaxiFQAA0NTU9CnO+eHJycm7Zy3LlBWp3cJMsNwqr7XOh2FoEUJlrfUa13UPu66bCsOwjlJaTJJkp+d5E6KqytDnPOeE+OGPdpogoEwvDsAYm2aMzYVhuMX3/aNaa04ISZRSWwAACCELS3o/zaVSafvSaxWtdS6dTvdGUbRRa+1EUbQhnU4fUEphJoRJPvnpp228OJOR/KUvORQitJEQMqq1rsrlcnPFYnGlvkprvZZSOk8pnY+iqBMAYKnIfAghlE+SBKSUDbW1tUPGmGyhUFgLACCEaEEIVYwxGiEUOI4DGOO6IAhSvu8fWB4fIRQwxmKlVEMmk4kAwARBcFEqlRpa2mxQpVLprK2tncYYs3w+f3zZGgVjPBnH8Q5jzAAAMGNMEIbhdkJIzBgLMMbIWksJIU6SJEEYhnlCiPB9fwEhhIIgKBljmpRSlDE2zBhbsNbORFG0g1KaFkKs+MmdCpIkGh06nBjOZfC2v+tCAACERgAAzhWXHzFvflO1IZike3uPJDW1THa0t8SMtZ5yzfapYyZNTa3w9r9rzW7evD/50W1/siuOzC9EUFcLWCnpfe0bh5I77+pRUiJHqiPqX975337WWoullOswxrExxnVd94CUci2ltIIxloSQkwihEAAgfPTRf7Zh6AHG4J155n4LxjhnNfTFj/5+oxpazP7Qnp4ZhHGC0OmKB4pRwdauTUyl0qYLBQ5Kgf7cZ06QTKa8EEXn+p7XBwCBtbYJIbScykW676Qr7r67a2Wcb39npfBej08AHDl6mn4YAADyvJjU1breeeeORY/94bTj4uOf2AYf/wTQ7duOBAd7t7o7z5hQd/y0FQBKzmc+eVfbjh0/mtrR82Tll7/6uRwZ6QIAUDMzdUs3ywAApH/y0/3xAw8u6oNpjQAAbGEe2Gc/n5B3/P1+7rrU9/1csVhMi0ViSYMg6EylUscIIRJjrDDGc1qItDM/v59NTlrT2Jg1lcAVw0Pt6etfdFCNjjq0dY0Ro2PYDA8zc/8DPXpiAgMAaMfpqNzzG2KTBIPDR53Ojfuj+35HyCtfDuKJJ96RuvTSm//UWvl/jVWytIpVrAIAAIIgSFFK721oaNg1H9PrF4T42xOQjrx0ZjhNUJguTmzWWlc5jhNprVEURd1RFIHnefuVUnWcc0EpVQghbLZ2uctKlPhndy5kXnpDKc7ns0IIn3N+MgzDzQAAGON5QkhJa52llPZJKdem0+mS4zhHkyTxMMZRFEWblVKCMXaMMYYJIcxaa+M43hYDQOZd/3Q4/ujH2m0Y+qxrSz/ZsaOCurpY+ZyzFtM2QmzCGEdxHHPXdfcmSdJjrcXW2nnHcczCwkIn5/woxjiSUnZJKSkAzCOEGGMsiOPYwimFwZTSPqVUw3J0Jp1OHxZCNKfT6RN66YkcACAMwy0Y48pS/Zde6mibFkJIKeVaY0wZY1yJoihWSuk4jjekUqkD1tqctZZks9leKaUmhFQBADmlkLwSRVG37/vHpZQZpRSjlBa11g1BEASO4yCMsZdKpeYYY0xK6ReLxWaEUJJOpw9hjC1CqJYkieb3338Saut4uPOMxfTPT3+Ogy98cRvAoqgTAAC67oUlb0fP3uAlN2w1jHIAAHXRRZ7xsqMU9AKKI2OVTAEAVCqVLtd1D8Vx/LRIn7EWIc6mkeM02SR5mlAiAAC+7fY6eOubIf27B06Gd/xksfg5k6nYq3ZZz/MOniqp8DRoXfaieF6lU7VqUXjVxxgPtrW19aTT6XkAgPG3/e2u5GTfXwIAON3b90W7dy9/x0L6mqsPJ8eONdtEAHr5S/vDMDzPcZyDlNJGhNCktVbhqmqb9PWtpJItpWA5z1lrJwEAEUJ0HMfrs9nsEaUUXjY5toT8ySgJzuUC0FrjbDYhDfUjZmFhI2tfOxT86p5t7hk79sJTxFgBAEhDvSZVi5wLOXxaT0w0Oc+5eiCorr5h/MSJk3p+foflPO2cccZenElTeeJkI21pHuRf+LJN9u1r0dl0HWmoH9FT060kl7Puzp39uLFR6Te9oeKmUtXGGDkzM6OUUt7ynKZSqV7GmOu6bhVC6DgdGHSSX/xi0vzmvrZoZqaWb9l0SBw5thUAoHLHT7YDY+Cn0/28fe0IAlQhuWzKFAp17tln9iJKkVIaoYUFFP763vW6UHCrLrlk0h46FJVv+/HVVatkaRWrWMWfG+bm5hoXFhZu11pXCCFttdnafzeaXGPAqNnEdBUBwPMbCwaAtzM1jcRcAwAAY2yBUmoZYyXP86hSChBCmHRuXBk7+v4PutCP7wD35S/r1eec48brOtqBEOCcH89kMtRaq6IoGmCMcWNMqJTCcRx31NTUzIdhGDmOc9haS5MkWbFH8X1/xdg0OP/8LpZKRez5z91vXvmKfMXzugkhCwzj+WX9I8bYXDabNTMzM2f4vj9HKZ0MgqDNWjudyWQGyuXyZgCATCbTa63NVSqVlWiJEAIIIZPLNUiU0rKUsjWTyQwZY0rGGNfzvOFisdhNCClxzicopTPGGAkAKEmSdq21v9S2fQIhpGpqakZKpZLjOM4IQshNkqTdWouVUtwYE2KMq6y1C6lUqkFKeQwA8hjjJzjnJ4UQV3DOD4dhuNFayzzPO5kkSTXGeMhaWyWEULlcLlWpVGQURbNLOkGR53lTUsp0kiRrnTiew+/4Jy36+nYgzo23Y0cv2dFj4lu//zQyIs47J+9cc1VkyuWVPJhxUyeOCLYDAYNGh+/JqdkVzzFCyDPuK5Vrr+mGa6+B7KOP7ZPfv7XGCkVtpZLVc3OpZR2o+Od3Nqdrqofko79PAADcK644pm58TSpsaOhKYbz3qWNSjOdwFE3T8fFEfuCmzmRsrAUAgJ1z9gI7c+cgbm8vFh/8xNnpr37516U778LR3n0fACkXeaCUGgCANjXN41x2PjnZ1+idsbMUHzlc1hs3MgCAJEm2e57Xv0zSsOse8L//vXH8vVuJuu6FATnYGxSN2c4JSQghU3JxzFKpVNqSTqeP+db2qrk5w1xXK8exNklOC1WR6uoFuqalX03PbKTNTePY90X08CM7wXFADg5tI42Ns+A6bPn9KJeVfP2GQ3pmutkU5mvCBx9ajLYtUXnxuwc2eZddfkJdeP5r9FyhVvX11SmE2mhr65iamKgDgDrM2AE1NlYFY1DlXXTR8WhqGuK+/ibsOIKdfVbBZjKNjDGQUtp0Oi2CIBimlErP8/ZhjB0pZaZUKqU9zwuC5qaUfN1r1/vHThzXs7O1iLI/1nEhBN6ZOw/Fe/dtMvPzFADAO/ecPbalOYoefnQ769xY0J/6pEOnJmdkUxNLPfzIoWj3k0Tfey/BmfS6+aNHu6s2bz4AfyZYJUurWMUqYKlo+ixjzKLuTzz6gzrXn5oi6QLA4m9xqMyigS4xDraWEEJK2Wx2HiFUjRACKWURY8wxxiz8zndPL34VAqJvf3cbufe34/iLnwsNIVnHcWCpZsd3HAdc101prTHn3HqeN2OMSSqVymbP805EUdR16njGmJV+Z2MtOB/5UH+8YYObSqVUtLCgjTFZ13WPAgBFCAWUUhtFUdFxnLEkSfLGmIZcLjdGKa1CCCnXdYcYYyQIAjdJEuv7/oEoirqXC4YJIUZKmQYAwBgjhFASRVE151xFUdSBEDoAsCiQqbXOGmNSS7o2K+fsuu4UxtjGcdwRx7GXyWROVCqVDsbYsDGGAQDEcdy19N7jWuuaOI4TKaXruu77mpub7yqVSlVBENxSKBR2LUssFIvFDblc7mSxWFwxNI2i6NjSxlZvrWWZTGYmjmPkOE4FY3yU/fzOOOzr27E0N1g+/vg28txrevGPfzTEfnt/IL71rU12apqyyy+bTAhpkJUKz2azR+M45kqpGmqtWU+TvSPa6UaYcOXnjlgAqxFWIWFYZNITDpiKa1XZU1EWIQRRFG0AANBX7eLRBee1uq67T0ZRo1MqT6OP/GdB7NmzGRkD8de/udbZ0TPnXblrJHjnP240CGHP8/YFQbAjlUodDoKgiyA04dzzm9nkM5/dboSoWfHTWYJ8fHceHt+dR77X6VxzjQYAKN3+49fJwcELTl30vKvriJ6d6RBHj1Wxs8+e1je8qExOdGPt+5QxVvE8b9paqwkhodbaNx3tOJmdpezcc2Yra9u6nM6NA5AkEARBq+/7j2GMKYvjyNuzt5h86SudplzCdq6AdE/3JM7lynp6OgsAgDg37vbtB+Ohoc7kwMGdAAB6cvKPRfRLDQx6ZqaWNjaMolxW2mKJ4Vxey2PHuk0QnJZCTQ4f6eCdncdJNoNdzj2QEvTrb5w3/QNEDgxU05rqWTU83AIAgNKplUVpigsVAABbKKTo858bmOddqxDAp8MwnMQHDv6zHRw06etfJAEgE0VR1nXdAGMsKaVzSZIway0BAHB27kio7x806ztc58XX95rv3bKW1tT2q8JcvV3sBKUAACYRaVLfUJL9A6CGR/KpgwdPijBM/HKlXy8UJWzfKu07/r7RrasVgdYvqAJYJUurWMUq/nxQKBT+ZZkoAQBYa5FOoprGqnQYGjwbKlPLMEqksU4ZOdDg+SfiKNwYRdEQpZQuChVjAAAjpUxM/+DKEzTK54zb3T2B6uqm9UtvyJLq6jkOgD3Pq5qfn89ls9kpQohjrbWe5ykhRJlSmo+W/C601s6p50oI0UKI08iT6OyUMkm6kiTpXTp/HARBF8Y4TqVSM+VyeTssKldb3/ePhmG4JZ1Oh8YYDADGWouEEFEURTVCiGrXddWpRIdSOmuMcTjnC4yxuwFAUEqvY4wFADAeRdEWz/OGoyhqAwBQSmXz+fykUkpqrT3OeVSpVKrjOG5ACNlUKnUCIYRramoqYRjSbDZ7XAhRIYQwhBAHAIkQEgAQh2F4dhRFt0sp76pUKtdgjIuU0lhKuRJxKJVK67PZ7GAURZlUKjVRqVTaHMeZwhgHvu9bhBBKp9M0jmMZRdFme90LBfvVPePyZN9Kl5Y491xXINQudl0O5MorChhjExuTtUt6U0EQ1BNCJCEEi7C8kwHAZj997GDib7NwyhRJgEV6jYAiqjYDnHQIVo7jHDTG1C6nYK211iCEoly23sN4BgAAZ7MJa2m5D733PYEVSYOv9bDOZGIpZQvnvBdjTLPZ7CGxe3cibv74zuVFtqymfSpoU9P+9FVXvqnxox/+w/w3v5WJ9h94z6nHTSXIy+Hh9hX7mpe/bDRubgba1uZRAB6GoVcqldb5vn9kyU8QEI61w84AACAASURBVCFW19aGlfPO7VpaF2q5M9MYo8MwPM+Znd2n/+VfV+qrUCoV6iQJnB09iepo7wu6z0Kz6zc31H/uEzny5JOnCXOilpaQNDVW1BNPLjZEaA3Jvv07SFPjEN/evYB9H9sopKK/v9aUKzlcXT1jZmaaTKmUFaVSFgCAXHjBkNvW6szncrV+T08/ra8dSQaGOqs+9+mx+bf/YwtOZ1c6Q61UFgAANTUl8q1vKRrOa3OO8xCKImv27z8R/vo3Z8K559BUa6vM5XIBY4wrpbTruo4QouA4TuwwNq2f91wn/OrXO/mO7r3xhRdsTUs1HN704R4AgNwrXzFYvOXWdlJXF+DGBiSHRxaVu+MYV9717s7T5qx97Zi67NJMHMclSukLAeAm+DPBKllaxSqe5RgdHX1VHMdvfNoBY5ijpWgBPYupXSAqqcaEhLoSb7GMDfq+f0IpVcUYKzHGHGOMiaII48NHwJRLLgAAaWqKECFz0YMPrQGAFhfjA8krX7FW+V4uWdxlDAAYYwwWQkjOueO6bhoWSU21tfaIEKLp9NMyKJPJnCyVSltOec3lnD+ptQbP8/Zrrc0px1bSR5TSYSFEE6V0FCFEhRBzS7pFRkoJQojqfD4/qLVWALDXGOMJITYLITZks9lZQohvjFnX2tr6lxMTE79ramp6YHR09PvGmJo4jjcSQrTWmgAALCwsNC4VPCMhRMA5r0RRlLLWokql0un7/mQQBHYpDdfBGJuWUrpa62x9ff1IuVxOx3EsPM87aq2tKZVK1y9dw5zneTPFYnHFlNVai5RSxHGcMEkSmslkRubn57dms9njSqmi4zi+MUaGYdgFABAbw+Hzn212p6ZG8ac+U4F165yQkBXvPm1tNUKoCKfsEVrrakrpHiHETl6uFNi99w2Xr7ia2ro/OtOs3GdkdY7i+WYbToHUbWGY8GW18mUghOoQQkAIGaCZ1Jj/ly/7Wu5lL/muf+65c0NDQ+9WSm1njGUdxpSUclpKWVsul5sAAFLd3Yd1NmttqYQAADI3ffCkuOuX1eKRR6oBAHAuN5d5/vNeShvqJ2c+dnNj+PCjb9LT0ysSF4hzQ5uaAlpfPyRHR5kaHW3GiUBeKsWllDHn3Emn0+NBECwAgMQYL8CiMGOjlLLOdd0yAMxKKcXStQDvH+CipXle1tWtd175imPylls34draKfSRm6J4bdv6GAAQxq2Iu+MVlEmT6182Xn/Xz9qXz4m//GV9yY2v8zXGHo3jo3h6JkQzs2C++MVOPTi0VlLG1chI09JCBr5584Scnmr0zz3nmBwbU2p6uh0wEWLnzhD39SWpBx+eDu+7r8vGMXXO3Nlb6uzM8HvvmUIf/HCJb940Ik6cbKVNDVwcPQq8e3spoHQjMsbin995BioWuV0ors+8/31aTk4VVFMTTXtObSikRABYSllJpVJpIUQSCVFNCS3iVErzdBrjb357ijY1unzzpnGrNaipKZ3627f12l1X1BCMHfSre2aJ58rkyT0bASGAUx5KSG2tjDlPISEUpdSfmZnprKurOw5/BlgVpVzFKp7lKJfL26IoeibPrP+PvfcMsC0t63yfN6+wY+1dOZxzqk6dnDon6KaBpruRbAAbRLwIXB1lrgIy4oCIo8gwBmQcvAOIiiB4iQ2SOud8ck5VdSqHnfeKb5oPZ1dxTndzr/eD8GHq96lqh7XevdaqvZ56wv8P2JrAxMFmJJMuq7VrlHThombQDD912tiDh1qIc8vKJTeKopjc/0Ac/e77d9hWywcAsFoT7LrGtFouAIA6fryXPvX0At29qybzubK1lkVR1BXHcUFKiTo+YEmapiEAYGutVEq5xphLLTYQxniRcz6PEGoSQlYQQiRJkt1a6wFKaT2O472+7/MoigYopU1jjCuEOGmt7VZKFTnnVcdxKELIRFGUdAQ5leu6sxhjyhjjUkqPEIIppaHv+w2t9QTGWCGEdrTb7Z7+/v7PNZvNfK1W+6skSTa4rnuWECKVUpc6yyOAi2U1hFDLWlswxlAAACllxhjjK6W6AAAQQlgIcU4IMdcJqtpa6zLnvAEAEed80Vrb7ThOhTFGMcYTlNJFpVSf53lHEEKh4zgsSZI+Y4wtFAoNYwzyfb8YRRFutVqbLs2WAUIgenun9MtvLfNrrqlJBDmMccgYiymlM8aYvBCiaozB1loOAMCiaNn58lcW9H/7ix6o1cH9pTeZiAoSG7gsQ7KFpYf9oDJqZNqDEFIIIUwIaQAAs9YSxlidMeZrrZvZbPa1I7/2jk9mb3/Vk2xoKAIAqNVqN4Zh+Mue5y2hmVnkcOEaSiBJ04tGwgil+Ze+ZEHec69LN4+C97u/S6Ivf0nYlQrHxeKS2Dx2Ti0ulYKHH/lM9Myzv0X7+3rV3NwA7e1t4lJplm8YmYmffma3mpkpkGJxCSgF9ta7mg2APZ1g+usIoQ1hGObjON7COV/seMWNAazZ2lxI03Stx8uburBiPvpH3XzyQg01m1yfO5fjN7/0fHTn7VtXX1PP9p1BTEQrqR1yMFSyy4tNRsm0+L33z4evvnO7Achaax0nl2uLUpePksSDK/bN6Hvu7ceuq0y7ffE4GwN0YGBOnTvfLScmu3Wl0ss2blxkL7tlKbjpxl1sYnIp+tKXtrnXXnNCtdsl22gCO3FS0YOH2tEjj27G2dyKGN8c2STFwGiDEBryyakZ9sADyzKVt6ZPPHWbuGKfsVdfbcn45gxCyICK2VQb05xDY0aJo5SynYGLhsFY0OuuuRBcecWov7QcmHPnE0jSuikW82jXTqXe/EtxgNAm6TiUXnVVG932SkCPPhZnf+kXZ/ldv7wiurpWsOMs00IhdIeHF5JCvqdQKNggCA4VCoVL1ch/ZqxnltZZZ52fOKlDCFGc8xq0WjQVYi0IiKJoF2B8UH7sv1xB3vyLR83vfYCEYbg98/Qzxy7bQBRhtnFDU83Oro1Kq4mJjei33msyv/7OQ+03vG7v6uOu684IIXzOuRuGYZKmqWuMaQshFoUQK8YYHobhaCdbI8IwHAcAQAjFhUJhKk3T1FrLrbW9hJC0o6+UJ4TYbDY7q5QiADBNCMEAkI3jeAZjzKIoGvN9/5zjOF4YhiyO41KxWAwYY3Gapm1KKRhjXGOMVEpZQkhkrb2v0Wh8qNVqvQIAKoSQjNa6hxCyAACXCRAKISbSNB3UWhcdxzlurd2otfY45ycdx0FKqdQY48VxPAYAKAiCHa7rHgUATynlJEniSyk3AVw0KkYIpWmaRhhjJwiCbb7vH8EYI0KIY41B3rHjK/bqq6xSKuloS1lKaQgACcCP62Wu6x4Lw3AcY2wTrXoJIQHnvB3H8aC1tgAAEMexL4Q4GcfxNgAAmssRuOlGR7zm5xZRV9HGCAmOoQ0Al3nQMZV0yc7PlNKQcx6GYTiQyWRmpZSWc64ppYhzfv/g4ODzG7cxsfYO3/ePRktLDN17ryKVilBz8xT/5w/VAQDxBx+q1P/sv+5xt28/oKrVXPyRj0o9MztOh4amEaU4eva56wHg+tUNxufOF8Xu3a3k8OEcGJNTU1MAAICGBmP2y29uyuuvL+tyCUMYAuf8MUrpk1rrWymlK4SQBWttl5Ry+HnrzCCE0tVAUoVRos+e8/XZc/7qC9KHHxn39u49HL7i1j0AAF02ZguKG46RIkFQtCMbGvK+e/apP/k4eP/pA4fDa67ZYQFoHMe9WWPqld/4Dz1861aECvnE+YWfn0vGNp2HKMaQz1t85EgWDh788WpecWs7eMPrR8FaSK6/dgS0puHDj+x2b7zxcPTYY3vQ7Fw7eeZZ30YRSk+f3pJelE44rCan9qjJKYCnnwbS3b1spOyy9TrBjD5KRza45KUv2UkIySCWoRupXsQYf1muVN6BZmZIUi6lSioe+95GOjbW6MpkU9NooujRxwbcW29dpO9+Zyz/9Xs6iaJhz/NOGWPiVqt1JQAA+ptPQ2QtAEIAu3aC99z+Eyib5Xb7tn8suu41QWpurSY4eWHe8mfDemZpnXX+N4cxdioMwzu01oOEkKoQ4gec8/uMMVsppbU4jkecj3/iAvnHfwK+eXRa9vSUubUT+BOf7Ddzc45311vqbc7z3le+etbMLzp8eHhWTU/3AlwcreZ79yTy1OnMZTvVGsHCIoHXvUaYiyrNQCmtMsYwxlhYa7UQQkdRxKMo2pLP57W1ViOEao7jTIVhuDaa7nnemXa7vZUQUgcAhRACpZQfx3EBAIjv+2mapjFCyAAAjqJoRCnlMcZWfN/vFkLU6vX6eOcmXkmSpJ8QMt9sNrcqpfowxrU4jjcRQtIkSbIIIR4EwWsB4A+ttdkwDH8eY1y11vI0TYd83z/aEbpEmUzmeJIkI6tK4EqpbsdxTjDGTJIkm5IkKSOEWoQQyxhbSZJkkzHG7egs7bHW0s4UWxkAwFoLWmsfABBjTDPG5jjnPsYYSykTYwz4Q0M+4VxYAOo4jgMANk1Tmc1m/y6KopuEEM8wxpCUkhtjSquN4h0xzMsa8ztQzvkZZAywr39Dyc99fpBvHqsHAwPZ1M+1phI89vw39DA7bWTSc/FUa59SOkEISYMg2JTP56uUUkEpZVEUbY3juMdau18IEc9/8Z/eX/3Lv/oftq/XTzKZAU1ILt2yZZjnsmfR409IT/A6fP4LjvzSl0eR1qCjqBtRStKDB4f51q1LgDGX5871veATtNsEEVIxzWYWAAAVixblc5p+6YvLwYYNWwxjDsa4jRBSmUzmnVLK38QYdxljMpzzglIqJYQE1lpY7eHSWncJIQ5rrfvBWus+9fQ8dUSgq9USyIuhItu0scKVCsm99y3Rg4dm2KMPJ/nnnkr6Dj5ddf/sT0Z5u8n0yooLaQr6/gd63X37Dsvenj7HceYp56l6xcslvfWWKnvjG2pw4w1eO5fboQb6+1VXVz8eG1sxX/7nMtk81qR//3cTye5dA8baHE/TFj18ZBoOHKA2CFza3xfrKGbEdRNdq3mrk4dgLTi7djXTc+fWrH1oX+8KEOrbZpOopaVt4mW3xHJwADDGFmPMMMYORFEU/vZ7c8k3v71JfvkrveTkyQX58lv7Xdc91mi1hnnYnuGAFHa4MY88CukDDw6ZN7weUil7lFKD8GNViouBEgBwzlvutq2H6cjwVwGgD2P8slpk9cHF9Iv7RorTL3JN/tRZD5bWWed/cxhjRin1XUrpU7lc7veHh4c/nyRJmCTJz3POW5TSQACY+O7vDNOlpSTX3z8X/eqvjZvZWQcAgFK6aL74pWz66GMb9PR0FxsZrqrp6W5ULmvxa796Mv6Vtwpx5syCnpm9zG/NNJu+g9ExtXVrxlLKhRCLSZJYzjnBGLta6zBJEs8Ykw3DMJMkSaFjEbKgte4DWPML280YazmOM6mU6lJKrdmeYIxTIYSK45gxxsIgCHYLIc51shoxAOg4jhOMsUzTtJtzvpLNZiXGmHZG+BtKqaIxJquUKgsh5oIgGLfW5qWUr9FaXyWEmACAlDFmGWONJEn6fd8/JYSoRlG0lTE2zzmfM8YIa61ACNkkSS7tw4o6ViXbVwMXSum81rrfdd2GlFI4jjNpjPGstVwIMcE5T5MkiTp2J7ozVeh4lQpVQZAa3zeMMQQArjEGtNYZxthWIcTnS6XS26IoGkrT9FX/luvDGOPkcrkIfvjDevzf/mK7bTRZet/9JXLwkNG331HBjCoA7EhjyW5XH88Tu+zoFCl1URndcZzjURTt4ZxXlFJ5SmkzSRKttZbtdntDFEU3tdvt365Wq++Vrrs5/fgn9qi7v9ML//hFj195xTHb29sD7/u9PlIoLIb//JU9ZmbG99//vgly3bVL6qGHe2ynNEXyuRV5/vzgpT0wl0J6uqfYQH9MSqUJBGCdn3/TuXjLeN4CXJxyTGU1nyQfN3/+lxvl8PAbTSYjlVIqTVNfSjmolCpls9mzaZqueet5YbhEjp+YY5//O2JPn16Jn31uH+0uB6RUbtDu7raane1Kj58Y0FMXes3pMwP69Jl+c+z4gD58pJf29S3L8+e7L1vkieNFdvurzthaLUinp0M6PBS1GduTMNaTpmnR87zDUspehJBxGFsmzaZE4+OV6MorthEprfOpTx/Tf/6X3fr7PxiwQegCAPBNo9OmVi3hjN8S45svyOnpNTV9k6aA+3rBVKoXhwUKRTf3htfPIM6X+LYts6qrC2Bs1Lqel0mSpAlKsehDf1BOnnhqN+3pXtRLS0XbaBSyN9xwJMj4m621GdXfH+GjxxSsVGTwwINb6YbhkGk9iwcH6zSTSay1FiHEGWPLQogpzvkKIaSRpulz1tqXSylvZoxFtTBtdufcfx7syiz/W67Tf2/Wy3DrrLMO9Pb2LgDA11Z/T9PUJ4QErVZrtyBkUT71dBUAAGFSb3zw93ciYy72vOzedSZ84ontttW++C8iQiBnZnz3Pe+eTH7hTVSmkiqAAfKud03hk6fbpl6/LMMU/+M/7WYPPjxF/vyTMynnRc/z2saYJEmSdhAEfZ7nTTPGVrTWLEmS1SmqPABANpudTNO0CwBASpkTQljO+RQAdEVRNAgAkMvlplqtVoYxthgEwd5yuTzVbrcpY2zeGKPCMIwAgHSyWpxSmrXWWsYY45wvpmnqCiGWjTF1znlZa13MZDLH2u32Tq31CCFkPkmSoXw+v4wxxowxHATBfGdSzSGEVJPkYiWh40c3hDFuAMBa9oMQ0rpUxBEhZCilOEkSEEJUOOdps9nc7HneWYSQbbfbu9I0TYUQFxhj2BhjrLXacZxKXCqVBCG+RiharLe/1pTkzZvLjnBdN8YYc8ZYJk3Trk5vGAAAYIw1xngZY7xIKaWXZu1WqVarG7Oud1mJlezeuYKTYGspCUQvpRF42dMoakvH2j7hec0kRhYA0k7WbI4x5iGETjabzZ1CiAml1Nq1oLX2tNae77qNS2vC5DvfG+dDQydNmm7i27dhxNmR6NHHdgd/9alNzlvefJnZKh0aTiwmEclmzptWC8npmU02DNd6qehAv4wef3IEtB4BAGDFQpMvLS1Bks6D51r1nt/c3bL2L0yz6fBX33EWb9yQpmm6ixDSYozNGGNyxphc9jN/e1K12pGdn89Gx47vXQ3O+I4dFwAA1Ny8DwA+/H9AussNNTPTe+ljenpWwOveuDa4QG6/fdZznGn8mlfXgg0j2wEA5/P5C0EQFPTSkkrv/s4Av/baRTE3N4Vd15H3P7Bvzcalg4lCxwSho5eWB2HXzsxqUzUdHDwPGGWonzmDd+wYTk+e7HKvvXZev/LlIvnC328wrRYtbN16jjWbmeBLX67ibNbEExPV+L4HdgMAWG0wKRYDEwRu9PFPbBV/89fzEcZ5hdAm/drXPK0+/JEeSBKQBw7l4MChHPqfn4P8g/fNtYOgZozRWut8mqY9juMsCCEOBEHw247jTHLODQDgXg8Rx0GXlXd/lqxnltZZZ50XkKZpudls/g4AoPzd351M7n+gF2X8gA4MSNZdbpJyeZb29SXp7OyQrTfW1JhRsWjYpz81175i37gGyGmCSwAAFKy0X/2X/KqtwqXYRqNA5uaW9ctvzfm+rwCAdqiHYbg5TdNBzvmSUqpbCHHGdV1ECKkppbKe5wVSSoEQShhjgVJqJJvNGsbYNEJIJknS47ruguM4eYzxvJSyjRCy1tp8GIYblFLdruvWjDHS930XY5zGcWwIISZJEhLH8VbOed0Yk81kMqrRaHClVPdqn4oxJosxNgihqtaaAUBCKRWO4/iMMW2tBYxxTWudlVL2+b5/No7jLQCwppOjtS7ncrkjhJAFrXWWUppqrbOu6060Wq0tjuNUhBAaACJrrWGMLadp2s85b15UbUCAEAJjTB4TQkJLCCO48YOzrWPljPOSkocVACgpZYQQ2rO8vPwBznkWY7zEGKt3So8btdZ9GOMZrXX/888RAAC/5/5IHTlSWPv9A++fp/mcRJTiVMqMSaIerXW/EGK23W5vI4QsY4zbANDIZDK9aZrKKIpGAQBprYv4op0IEUJMGq0990f3nkp+533bkO6ES5wD+as/n4RaXXlaNUmxy2G7dmVNsxnp2bmMOnxkLYOIPE/hbIakR4/m1Nx8j3PNNZqUumYQJtzU6y4AAM7llvXi4lpwoh5+pGy+8c1ePjc3n/6Pv70SoojYJKEAAPj0GUxvv70tjSlRxqpCCCqEqLTb7VF65sxs+vVvXmGWltdG8AEAsv/xvSv4lptPC9d7jvb2+HJ6em19pL+/zkdGjrGNG1Z0GJYgjjGiNFktC/4k9LlzOX3qVF7+6/d66a++fSVO01FrLRBj2ugPP9Ythodn1dSUY751d5ZH0QVbr4em1VoLMPjWLdNs06ZUzs+XIYoACAUEVtk0pc6VV0yYIMykJ09uBs6x9+E/OKWfeiohCwtZiOMa27Rx1jQaqPk3n9mgDh0qU2sXou/9YA8AAC4UVsSeXRVdb+Qyd96xED3xZDe84+2WSrXAw3DO/t9/Wya9vcvsk59YUF/9l34AAOS6Bu3bNxt63rjW2rXWUoyx5pyfttbm0zTt831/xnXdjLVWeJ7nWWsP+r5/8Ccdn58m65mlddZZ5wV0d3c/1Wg0jlhrA3jbXb9L7r3314HzK6OHHrryJ73HYgzipS9ZTorFEjxP8waU1rirq6UXFvKXPixuuP6M/fk3GVQo8EwmEydJEgIAAYBsGIa9WmsHACCKoh2ZTOZCkiR9jUYj63neRJIk5SRJyoyxsJOp6FZK+VLKFc55ttVqdRUKhXlKqdfpg4rr9frezheyllK28/l8bK3NcM4xxjhFCDFCyOpNXCqlJtvt9nbHcY4uLy9vMsb4lNJId27o1lpwXXc+SZJhzvm0MSaPENIIoUhKybTWDUopMMZOhmE4Yq2V1tq1712EkHRd90Sr1dplrcWO4xyUUu5xHGcqCIIdnPMzxhiI49gxxnQ5jlMFgNXJwGaapjkhhAcAkRDCTZKkTRE4oTRZ0PZtwwWWMkpJo9Focc6hVqtZIcREGIZjxpgsIaQthDi/dg6tvSwAuAwEl9W3ore/YwcAABnbpPDffkaaTgkRAGIAAK11DwAAY2y5Wq3mPc+btNauNb9TSpeSJPEQQjQrxNnwK1/dhFazIq5rnZtfsoKMBfbkU27wyGN5sWtng1OWutdfZ8TY2Ml0YgKjYjFhe3bz6Nt390FHMwgAIPjBD/oAoI/v3HkAOs3nl3i1AQAALuRbYsvWc9Fzz10J+vIZB3nmbEH93GvzYsuWFfqJP11x6vVy/KN7EvSan1Py9tv78de+0TKt1lqgQ4aHG3b79pBuGL5bLyzukD+6pws5jrRxzAAA2NDgVPzMs1cBAPAtW06lUbQFC9F+scOMi8VF1t8/Y2WaSc+c3QoAQIeGUhSGNRCiV0mJcv/wxbbtLjeDH92zloWKvvmtfe511x7ArnsyPXt2G+nrjZHjquDe+7aIXTuWTLk0BxY8un1bG2d8L352/ybv5S9bFjt31NRb3mzigf4et9laSr/xjZXk1OldICWQUqmNrNWIMq0rVUW6u5fZpo1zNoryeqXK3TtvT5Nn99eQlBvRe37DRwgbefr0KAAA/sVfWA4zmSv9T37iGbNjO5dvuWuXCdpraS9CyErHSLvgum7Y29s70Wq1VLvdLpfL5VljjO3u7v6Hn3Q5/rRZzyyts846L0ocxxVK6Q8Gh4cfbt973w7s+9fI8xPl1efd666d0vV6DnmudK67biX74Q/VGre9csBYm3n+tlA2o8XxYwtqcqrMt22tsPf97hTp7p5Nfuc/bjXDQ4wN9Mfkon8Wajab4wihOWstvtTxPU3TvDFGEEIUY2xOStkLAEAISY0xQZIkmwgh8wBglFLtTCYDjDGhDdhDCwksB8Yb7nKXrbWKMUaz2Swzxuh2avTpiiQ9GfoZY8zVYRjmKKVBJ1vDtdae53kVrbU1xuQxxmpVcRsAQAix5LouIYRQhJDsmNwixhgLw7CYzWah0WiMOY5zKoqiXY7jzK02UhNCIq31YEccEyilbSlliVLagItimZox1pRSEq31sDGG+77f9H2/qbVWlNI0DEM/CIJeY8yS7/tZa1RwdiVUt4wVM4IiK6WUGGPd0VnapJQqZDKZM4yxOaVUf5qmawEMY2xKKfWCBmmn2TySfOSjW9GL9AOR7dtr5PZXNQilZymlkZRyE2NsRmtdoJTWMcaqI4+gjTFrDeSMsQUp5SAhpJ1oPUhue+WUufu73e6+PcdAKqovTGfTv/tCb/LccyUbhn7mTW+oyx27GDLGojAEXCjY+LHHh9TMjMKEyHRiom9NYLID7e+PbBy7pLt7SS0vD4jt28/TYvEcHRysm6DdLc+cHfpJPU6gNUL5HEnf8PosfOSjKv72d8bYY0/U6JbxZV4uNeWhw92dD2LZ7l2T6fe/N64arY3ynnuvU2fP5sSO7Yf04lI/Gxtd0nPzvTaKOACArlTK/u2vOqyXlnpMvf6Cch0dGTmTnjx5pW61ynxs9LyuVou20SAsikIRhAvwx39cTh59rBffcXsFv/tds2J0NFBPP90FAMAGB6P0/PlNzu7dx2h3z7KcmOjp+dRfhsnJU2Hy7HPbdKVSIoXCUvjQw7sAIYRzeclvuRnjDSPEPvFk3fzoHpvsP7BbbNt6Ti8vd4m3vfUCf/vbVuzhIzHr7dWkXOa60Rg2tZplQ4MSjW9uE0yYmrrAnPHN03Sgry7PnO0DACB33r4ox8b60r7eQcOY6w0Pn4Jduynx/RrnvIExXkqSZCtjbDJJkjFrbTVN0xLnfNbWainnIg/Hjn2aDQ/9v/rq/bRYD5bWWWedFyWbzR5PP/u5ifDZZ3eqlZW34WymJGfnys7u3RVgtJ2ePDVIy6ULmLIE/9ePt+rF4uiLbcdFaAK95zc8Kpy6mpnpAYDUvv51OnPT17TGIgAAIABJREFUjb7yPGuMycdxXIrjOAcAs4yxBkIoxRi3XuzGLYSoxXHcwzmfIoTMG2O6GWMNpVTJWkswxkoIcbEERhl5dKLeDlLElDHaY1gXfOF1xKOplLLpcsqVJShNkscYMtc4jhNZa8EYk9FatzulJOU4jhRCLFtrK1rrEnRKaWmadkdRlLXWRhjjGGM8RQhpJEnCCCGYEIKUUpRz3kqSpI9SGq726+RyuQlCSJimaQHgorhkLpebTJKkVylVEELMx3E8orXu7TzvxHFcRgjNBEGw21o7lyTJZgAgrusuWGslQsjvyzkUwMYA0EiSxAghBOfcM8ZMEkKWoyjagTFuSikvO77W2ixjbEFrfVkGkIXhklMuRXh0U0MfO17gd94+Q15125y45ZY6vubqWlgubdZa93POgzRNu1zXrVJK55IkGeGcS4RQjTEmdRxnqbUJYiySUvYDAOnYwzBNaXd204aD0Ve/tsc0Gq6Noh9n4Biz4oorqxyBY/buMbBvn7BSW0Jpgq01iPO6mpx64RRcsUjZW395At721gZ+212KVmsNfddbsHrtawS86Y0t9Na7KuTmm6dIrYrN9MwLgnxbrVLv1Xeeh5OnjDp3vottGLmAz57l0Q9+uHU1yPJuvPFQevr0Rj0xmTFPP91jKxUHXNeSbDbQKyslPrppQp4/P7S6TdLV1U7n5rv13Fz++fsj5fI8Epyaaq0IxoB7w/WLJk20aTYz+uzZvBCiBkZb98or5/hNNyF69VWO2b4tKF5/7fviBx58Hc5lptT0zKCan+/FriN1pdLV/trXs2pisgAAQHp6QtpTrqqZ2T62ccPR+Jlnt6CtWy+Eo5sGze+8f4Bt2lgh2axIzpztc6/Ydwbv3Z2YkQ0WPbc/iR5/fJd380uWsOtVdK0WqgvT/XZisoUobdHe3nb4yKO75Mxsn3v1VctqZtbn27ct4d5erXw/a611RDZTj3K57jRNR4wxEmOMOefn0zQd01rnOefThJCALi+j9IO/PxR89vNlzNmPMi+75cLzj9PPgvVgaZ111vmJrHzqr9/X/OY3v5GeOLkDMV4hXcVl0KouT50ZA2vBtFoF025nebHQTLZtfUEzJgKQ7DOfaarHn+wDSlzEeIr7epflG984lJEqsV/8p6rdtBEpIXwAwFrrkpSyV2vdZ4wpXlqychxnoRNECULIrNY6r5TaZK0lWusuzvmk67qtQqHAlFKKMeYEqdGlrEceOl1pvWQ0Z4QJM4yxGF30ZyHWWoIQ0jmBWpzia601NEkS5rou01o3CSGac07ERXzGmJskiUspnSSEUIyxchznnJSyu9NDVbXWRmmaxtba/iAIupRSMxjj+upUned5M8YY43nedKvV2matlcaYiyKe1rpSytWApQtjPC2l3IQQihljEUKIOI5zsuMhhwAgn8lkVhWOWwBQYIxpACDW2hgAJgghJWMMtdZGCCHcUSvvF0KEUsrCpeer04tFPM87JaXsxRg3GWNnabHI22NjY+bqq6lP6BLceceSueUW7V51pS/7+uLVLB9jbIlSuiSlzBNCYozxHEJIWWtlHMeb/ZnZC+Zd7yl6nnMqGR8f7OwUnGp13gq3EQ2PhuQfvvCCoAfnclX32qsVZDPWjo5iPjuHku9/X0NlpaEWFjN6fj5voyhjMQb3Da+bQe/41dPo/3pvXb72Nd0wMpInP7qnYv704z3wrnc2o8HBfZqQssa4S2NcUsVCv37ZLQ573euOie5yQx08VGKjm5ZNteYDAMivfb2b9vZOq+mZPj42Ohc98eT21UCJbdhwlnR1pemx4yOXrpcUixaMwSjjWz2/0GXT9OKUY3//IunuXuKbNtVpT8+K1cq37WAtS4kdJ9FJmocw5AAAslbNIaWzpKtYMfW6Jy9Ml0w7yJBCvmodJ5Bau7iv777+a675RPjkU2F84NBdYtu2Kb28XNSVal7s3DEJ1iIbBBcn48bGzsXP7d8FAMBGhhbVwmIfRFEd//CeQFer3USI1CZx29mzpxIdPDQq772vzx0equB2C8nllaJpt1vp6dPdpNhF6YaRxWT/ge1i5w6rqzWi5ueLoBSgrtK8f+MNi8lTT/U4xa6myGTa/MyZWRJFOCyVNnSus4y1Vvi+3zDG+EKIc3EcDxtjWETIOL71ZZN0ejrElDVzr7rtwX/DV9W/O+vB0jrrrPOizL77/3xzvP/Ah02rVQAA0IuLZTE6thgfOLjn+aWL9PCRovMLbzqlCClTQDNUqUXx8CPT5Cv/guT3fzgCAGC1oe71104j1/Xo0nKl+YEPbpAnT5b5zMxyesvNqzdtIoRoWGtjjLFECCWc82MIIdzJ9vhKKVcpVXYcp0opnVJK9QIAopRORlG0g1K6TCnlCCFLMJJfeW5p7poN2fzGAkkcIQjGGDdS5HkM2yRJFKUUn10OJmaaUOoSpk0ICZVSDkJIU0pdrXW0XG+XE4sjjxPaCUZskiR5rXWGUrrQse1oIYQSpRQLguAmhNCC4zjzURRdlclkmnEcdzmOcw5jXOCct9vt9igAEGOM73neIWOMRwgJOeeRUirj+34tjuMhxti5TCbTtNZWhBABALDVKUBrLXNdtyaEMB0PvTallJqLWADop5RyKaVK07TVbDZ3ep5XSZKkWymVdxznCFw0G1aMsYrWOmut5VLKXsbYEmOsmiTJNnPRI45Ya7net5fg3l4XYxwmSRIQQmiapj0AAEqpLg+hReeH9yTtwYHtSus+pVRZa10Wc/MXzO/9p4yp1TLy6Wf6vEr1OJ+cmMZ//d9Bff4L/QbRsyevueWK3umJFZg4v6bYLvbsnnRvurFil5apes3PWVQsWHPipEWV5cRGEaI7d4T0l35R8l/71Tn5nncZe9NNi3jjhj5NSIZRZuCd7/LV/fcP2jBy7f/xjqpm7PJxfQAolUrnUSaD5OgoY2fOLqBU1vTKSh8AAN24sWmDQJhaPUO6u2OQKrZhmMG5XNN/1SsX29/9130v+OPhzCJtNBsempKTUz8uc46PnU2PHN2lJie71exsNykUaqSrFNow9MWOHaeA4Ni98sqmVco1tRqHMCI2DJHYvauh5uezoBSAUoAISeLv/3CL/s53i2698aCuVp+K9x+4S87NXePs2hnJiYksAADZvUuqT37Co3fescTqtRmUSqLm50u0v39ZLixugDTFZmmpbFZWSu4N151RyxVh6o3edGKiK/OKl59DjF+wlaoGQvppsTCZHDi41Yaho5eWsljwJV2p9vg3v7QGWktcKERqfj6HMPbUzKzFxYJxbryehZ/67w46ftwxxWIox8cvPfbcWptzXXc2DMPNHePpvO/7p2NjtpGX3TKXXnnFaKlc/uz/ry+ufyfWG7zXWWedF4X29R0WO7afDR99bE25OHz88V3OlVecjvcfuMwAEzAGtLQUO6XSMf3e3xkxU1NDz3eCZ/39p+MDhwb1wkIO7rv/x/sZHALXdU8aY3oAYCVN0y2U0llrrfV9v95sNq96sfVFUTQMAMNCiBPGmD4p5bjneUejKCo4jhM4jlMIU+O9YluBj2diSAxYiR3iC5Gtr9RrBAR1CUkA0y6L6N5tPRwYMhhjjCqVykg2mz3MGMtaa83nn6vPvnwL4jfnXC9N00ApxYUQC3Ecl4wxaLWc5ThOEyGkGWPtJEm2Y4xPCCH2R1HUBQA4DMOd+Xz+FOecuK67GIbhUOezbHVd92ySJEOMsWocx1uUUjXO+ZTrujnGGOGcI8aYrVarKp/Pn0qSJOWcZzsWJiljTBJCRJIkqdaaCSHSJElQEATSGJNJ03Rvp7+qq9FogLUWxXG8p5M9WtBaE0JIXWtdAABwXbfaarW2AgB0Jv0AAEBp7fuMzTYajd2Xng8qZSg++7kzySOPbdaViu9XVg7aVAKqVinZuYNGf/OZftNqXSw9WQt2brYvve++ok0SBADgnjjaS8HA8oc+2hiqV2astZAcObrPhlFsG00SnzqJHClnGMa+CoNCurhUiB9/otf/kz8+GO3e5RlCRovF4hTGuBhHUcDDCKNTx6XauSOOLlzoBwAQSvv4+InTaOpCABcuaDS/wMXrfo6zV786n9TqFfvB3+9K9h8Y5VvG19zuEaWBabUSAIBk/4ENzpVXHNL1ep93441z0f4DfS/W84S0ScTuHcsmjpHYt+ewlYrQ7m6VTk5dpgKuZmZ7+NYtx90bb5wNH3xwHwCAPD/xwmv9scf73euvPQQWcR20h7AQdQkTAwAAza9/4/fU4uIN0bPPvRQAwDK+DAAD7JaXLscf/EDOWOvy5/afVsdP9iPBkbjqquew64I5fNixABdFOsvldnr2vKsXFgZQqaTx4GBojQFrzXA6cR57N9yw0Lrv/svOdzoxuUPs3XOu9vm/GwMA8F/5ymMAMKTn5x33hhvS6IknNqYf/BCwsdGjyeGTu9DUhV7vmWca6cf+KFVad3uedyQIgt1a6yKldFJrvd1aC1EUDRYKhTNRFMVpml41Nzd308DAwKMv9h3w02Q9WFpnnXUuY+bX3vkGnM3S5NSptyRHjt76/Od1FBfF3r0HEWddoFTdag2WUhcfPuKmf/XXlwdRQoB39dVHTRqr+PDRfdBxaAcAQK4rnf/ysYn67l1bIIpACDGZJMkWAAAp5SAAQJIk8U9aJ0Io9TxvnhCilFKTruvSKIqGpJRFY0zN8zzlM5syD0NimbKEG2zs/onlYM9coGGsjztKKRtEcWu0SA2lmBsDJkkSJoQ4Z60txEkaPzbZUg+eru5+aqIGzRTN3DrqOblcjkVRZJMkMZRSorVeVkp1t9vt8WKxeJpzvpQkyQVjDGGM+e12e+PqmpVSNk3TnJTSyWQy+5MkGaaUNsMw3AUAkKbptOu6F6y1mnOOjTFpkiTCGJPEcRwQQsBa68VxvBUhtMg5b6Rp6hBCiDEGIYS4ECJCCGWNMY4Qoqa1bhBCGtZauby8PIYQsp7nnZdSZtM07dFaa2tthlLaXp30k1KCtfYFUg8AAO12u4cQ0ri0tykzPXO+ffd3965OQsaf/fyPMy7f+z6wkeEltmFDDQmxhLO+To6fLK8GSgAA8b6rlhXgwQgRHD+3fx8AgHPNNYeca69xyciwT4qFCOZmW0l3+c8SN/sX8OGP6tLS3EzwsT/O2WPHh8nYWCrf/isGX3WFsrVarO6+Oy8feLioV1ZKAABkaEjGb3v7iI2iyz4TGxs9p267TZHHn8jI+QUBAGDa7QIZHKzo2dkS9jwfMUb14uLFdZ44udu56op55HuhPH5i2+p2+BVXnDVJhDBlhhQKSfjwI7su3U8KAHz7tjn3xhsPxYcOjdsg8AAA0lOnd5gongPGYFX5+8WInnx6zRoIhNhBBwZm1dzcIFgLam6+h28ZP4aE49tqRQAAoM3jVWNtN9Za2W/f3UtHN7XN/HzGtpo90XPPDbMt4/PY95tqfn6QjwzPJPsPXNQxq1SI2LY1lhcuSHn2XNG75eZz7e99f7Nz3bVnkoOHxpEQxjSbGKREiPOqe+ONiYljT85Mrx1XnMtFAABgDGAhUgAA22ig9LEn8q6UR6XnzWOMBQCAUmqAUgoY48Ray40xfpqmUaf8fRIh9CsA8DMPltbLcOuss84aF37xzX8YPfvcp5MTJ96s5uZ3vNhrxOaxSey5JHriyXG1sNCrF5d6abk0ldz93bWbAxsdneabNk4AwYQUi83oiSf3rY1nIwTOr7/zEPzKW4Nwx/a14Go1o3EpjLFFpdRa6r6TtVl2XXcCIUQxxiSKov40TUeSJOl1HCeSUjoAIDjnFa11aoyOCBOIECKUkt05l5KNJY91pv14oIAba6nDKAIAjDGWACALxWLXueWIVWNL//MdG9uv31Va3NLtMVcwbowJwjBknPN6EAQ7KaVVAIg64/h1pZSy1nLGmArDcBPGOLTWCtd1j4RhuMt13WlCSDUIgn0AgCmlc51+It9aW+jIF3AhBDHGUGNMo9VqbQGApjEGhBDC9/1FzjlL0zQMgmBnLpdr1ev1EUJINZPJ5OM4dtrtdleSJF0A4GGMF7XW457nTSCEGlEUjVNKzwshWkmSDFpried5F9I0LQMAKKXKnucd0loLa+1lRrnWWsf3/XOr5bd8Pn8+8L2N7JFHI1OvC3g+1gIbHFxKz57tVdPT3XJyqtsGwcVJMEKA7dlzil6Y9HJbNs8vd/f3FkqFKVyvcVhazCTnzpfEwEBIh4cwkfLZvpe+5FMT+fK7gaJUul5E+/pC9cMf9dtajcgHHyylX/5KWX3zW3166kKObx6bUPPzPQAANggI7emumGbLu2xp27acFzfe4Kuvfx2cDRsqyfHj3RbhDN+6paGmp7MmDDgfHm6YdmBtHAtQCpGu0ryu15VeWPyxuOjb7roQ/Yff3CFfdVuJDg/Nqx/8sA89T0JDr6xkdRTliOfVTbu9Jj2AC/kV02gWL81SkQ0jkW00GLwYWgPftPGsWlzqBwDQtVpZV6o9enm5IHZsr6lm00OMGf3SlzD/m98+gZK46H/kI0C7u5ty/4EMKMX1/Hzeu/WWCdJVqvLRUZOcPt0NxiAAADk97fMNG+fkhQsD/s03L2HXa9k0wUBI1hpD3euuPYw8Z9EmSXdy4OBmvbxc8G++ueLs3l1DjljB/b2C9Q3M4Yw3Y5XK65XKmiSFefuvxIlSW1avs861lu2oeS/7vl8JgmCj7/spY8xFCPVmMpnPvOhx+CmyHiyts846q6CVP//LT+vl5f4XE48EAHCuuOJYvH//Tmff3irOZVs6DLOkWFiiPT2Rbgd5d++eU7SnPA/GouTo0Z2m0fBNEGK+ZXOqFxZdIASct951qH3XW/aqcqn0YvsAAEAIJZ7nnXYcpyql7Flt9HZd91SSJBs7TeC+lDLTsTkpAQBIKd1CoXDCcZxau93mWusq53xVZ8lQSj1rQSMA1umDIh6nmBIMGKEkCILE8zynnWr+zIVmu5zl7r5CgBBzjedwHyPQSZI0GWN+HMdRGIZbV0fghRCnlFIDUsosIWT54nJkhhBSN8Z0O45zCmPscs6ntdZd1tqitRaEEItSyqzrulXXdZc7TedOoVAgHQFNaYyxnPMZpVRXJpOhxhjUaDR6MMYr7XZ7p+d5h5rN5jildDFJkn4URXPqQ39QYydPzoo4qaIwaPHhYYQxDrTWOI7jrd3d3ZOEEGi1WuPQ8euilK5JMnSOZx/n/MzqNN6lYIyXrLUOAAhrrZVKZdGttwR8evqcnp7pef7rSanUsmmCbRyvBVPittuOk994dyV621u36TvvyEcDI8s1TYZXtu4q25tvOeN99jMbnS3jj9NS6U8wIX9cfs+7/xkAIAzad/E0cuI43KV6e7LixhuOq+//oO9SaQPv+uuORU8+tVY6Qp4HrL8/0UtLlwV+mDEMYEOYnYPkwMG8d+MNF/TSsjbViue+5CUnaC63hDMZ34ZRbOr1HMrlJFgTYSFCvbT0YwHPySlkX30nsYQwWSz2ide99qh96OE8BMFlFRwxtqkuz52/rImdDQ5OXLot8Z//4Kj5rd9Mue8toWwuNZOTLxCvFDt3teXUVOnSAAsJYU2cxChNLaVkPrOy0jKnT9t0Zm4IY9Suvfxlo/a1r0E+YzOiv38h+NG9u519ewNdWZG0u3tezc6urYv29y+oubk+lM3My5lpLz1xctQaQ3Kvuu2MjWKbnjg5DBhr02xmse8FFuMguPe+cTU/X7Yf+6NZrjVTR4656ekzmy9ukFr3huuP0NHRSBUKRfu8ypZSqgtaLaKsVdl8vk4pdRFCYK31oyj6vu/7P1Pbk/VgaZ111gEAgNaPfjTS+H++9qeg1IsGSgAAyHMCU60V01OnympursiGhqp88+ZK9Miju5ydO4/JqQub5LnzI7pSWQuExPbt55IDBzfibCYSn/2fC8nVVw1YQhgAIIxQywK8IBPBOZ/PZrOYMXZKKTXCGJvgnC8YY/znZ6AYY9OrEgOEkKrjODEAOAihVhiGe13XbRgL9MRiFJR9Cl89WGU7+1wwxqiLnmqEIbBWSvkFxpgLAD2J1nQlQrQaSl3IZLTDKWrGGguKEgAwGGOKEAowxiuMMZmmaYkxVuk0mxOlVI9SqieTySwYYzzO+XQURdsppTOd9yeO40SZTGZJaW0c18Oe6zoIIWKtbVhrte/7NEmSEGNMEEIoCIINlNK5KIrKjuMEQoilVqs1nslkDmOMe6SUuUwUzaCPfHRZf+Obvtx/cJs+cbJfPfRwj7nv/i515x3CLRYJAMSU0qk0TXNxHHMhxOTq8UMIISHEkpSySCltYYwVQijRWr9ArFIp1SOEOO26biUMwxEAAMO5S3fulOZb385dehP377hjIj12rNc0mpeN5/Orr1qM7rxjhwUAQtlCnfleqC96tSHXg+I/fM5XCwsbabn0nb4/+9NH5ufnb47j+E9tGu9pt9vjAAAWgMuuYp97002H9Hf/9eLNXgjgmzevyIXFbuS6GpIEg5Rg0pR71193DPmZWdLVNYe7yw3R29OOv/WdcT0756r5+WJ65my3DYIsGxqcj596ZrtaWOyjheK8WljwbRT57t49B9XM7CgZH286t902r8+dz9s4ZrbVyoiNG0/IjRt7CUJLxnXL+LWvnYf7789Cu00AANimjUvp3ELJverKYziXXdIrlYtaYT09C3p5eS1Ywq++czYe6N8ud2wv0717Ju0993VDHCPnyn3HUSazSLu6qvH+/Vtf0C+lNTKNRs5GkcfHRhfTiSkOSiHMCEZDQ8tseaViPvyH3ekDD3XrVrNgUwk4l52Pn352h5yb73P27j2vlpaKYC3oMCyJ7dvr8MpXBE7/AEqefa6EHCHl9DRLjh4dEXv3nEonJsadPbtPAiYqPXFiTToEv/WXKzZf8MnJk21Tq5URY8a75aUnwwcf2q3u/k6fQ8gFdNWVqdb6suvBv/u7Z/x9+9rEdQxCSAOAppRySumXHMdZev41+NNkPVhaZ511YPrt7/hSvP/ADrWwcLVN0xftZeQ7ttfNSpXbKLpYxkAInF07psIHH9oOAKDm5/tsEPDL3jM2dio5dmwnAIC1llKwU3T37oh/81tNUSrPkaWliioWy3CxFFUzxriEkEVCSNtam1prB5VSjY4WS19nYgZfug8hxLLWuuD7/jnXdVuEkIzWOmi321s8z1sQQhBKCSq4tE0Jntk5kOunGEFHPmAJY5wopUBrvQlhfF8g7bNKw57lQNnPPTIN23qE7s05ZLoppTGmkXN5TkqZWGvddrs9Zowp+L5/1Frra619znmbUhq7rjvVbre3KqXyjLE5x3GaQRDsIoRUGGMlIYQLADHCOHe+qniXS6hSUnVMjLemaXoOIUQdx/Gq1Wq3EGIiiqKdnTJhJYqifiHEWUIIdRzHS5JEpAAlNjm1JB96ePulxwi0RkLJU9G+fSoMw+2EkIUoirZaa31CyAohRGqtc51sXTGTyZzGGFcAgLquG3HOI9d1awgh1CmNFjKZzAmtdSEMw42MsRCUIu7f/8NBPjNr9PkJhnM5bdOUgTEIZzNTemlpcC0QRwjY9m0ThAsnvfH6nEUIWWOyXhpkel1ypG1Y3q2tXMj+8xd7wVpQ7fbNzktvekj6/p8hhPbVarU+ACAE40YnuwWqq9iX6e7+X+y9Z5hlV3UmvHY+4abKqauqq7q6qzp3Syi1JJCQQJiMBBiDwSQbMPgDzIDBgHECjxk89tjGYIwxyeQBIcAyIgpQllqdu6uqu3K+FW46caf5UUGdhGd+fPA8UO+/qnvi3vves85a73rfE/KBB5tBa2BbtsyLPbvLen7B8m3bIj0/70EcYwBUlsPDe/TSUrtZXGoS/f0ryYmTzTaKLih58b5tRTU90wDGgJqZaRBPf/pI5jdfvJy89KWt+pWvyCY3XN+S7BxoFa98xWnqiFnqeWX5kpd0GkaF+2+fntPv/9NWJ4lD+fAjdchaYD09Z1S50iy6u88CpRZRgmlr8xJpaJikd7yI2Fe9cpru2L6kjh1vJk+9cU61tbUBACghWpxC/oS+/4EWnMkU5fDZ3Xp5uRF+DpDvpYAJloODW/XcfBOpbxhPHnhgl7rne83rgZut1Qjv7hpDlGo1OdUMxoAqFuuyd9wxKHbvmrdBbQkw9milIuSJk0gXiwW+dWvRrKxQnM2kaqHYCVGEgPEEu26oFxcb2Y7tZVMqO7y5aTndvr3Ajh5HwLnGnhfJqeluW60iSwigj/z1ii2XHX9kdCRpbGxxXfes4zhj9shRJ/nw/6jjL76dcMdpUEophNA/NjQ03Pnz7vcXgU2C9yY28WuOyl3faogfO/x8vbJyiSjf+UhPnS6I3btHTBLnwFjiXvWUc+FPfrrz4u2cA/tnrQWGKJliW7Yg3r9jMBkazsizZzvkj3/SStI0Se/6did88lMAAOC+7jXD5HWvlWEUbfU87wxCSAIAieM4r5Rq9n1/CGO8opRqXQuo1u0rtOM4R8Mw3GetpUqpIAiCAxjj2BjTLoSYXOMK5YwxwCnKa625MXIBr3a5aa21RQg1CiFAKaUfHKv+9tamjM8QqDBR0YHOHKsjEaKkHiiyZqYsvSw1ied52TRNq4VCYdQYk03T1PM8j1FKx6vV6g5rLUgpheM4Z6Io2hnH8Z58Pj+RyWSWhBAsCIIFxlgWAHyKscEI4iRNCcFIMsZca+3pOI73SymXjDFzsCre6AMAaK2za6T2eqVUPcZYRlFUVUoJQAjUS1+8hT740KQav6jz6tHHWtI0bQVYFZ90XfeIlLIxjuO9hJDZTCYzqJTihJCa4zju4uLiXgCAJEnWO/1OJ0nSaa3NCyGm4jjOCSHKmUwmKJVKjZn5hUnV0UHi5z1XottfREgch/DmP4hxNmewn2livb1LdN/eSfnc57RYABQ1NfVE1gKgCxOZulbZ20OCEp8f0+t63GZ+vqGe9ryUAAAgAElEQVSI0KeCYnFArModdIhacAL/2Z93ot9/4+NpX1+v+M53pqJ//NgucWD/mXRiog8AWHp6UOu5uWaxY8c5AGh0rrjieDI42Hf++Wr3/qQ/87znnAnu+f4OmyQbgbhVG6cH0tIyl77xd+tRY2M5qlab1zM6mJBqotR2ffuLXLj9RZCV8iT/6Mez6Y9/3IPiGOIvfWXLWn0T+I4+y1JZJXV1zapSjuIHHmxBhCrxpjeMVA9dt9tijKBzC9DbnrkAUeScf40MoSzc+b9Ppa969SXft3XgXK5MmpumEaUY5/MqfujhDQ6hmpnZ4uzYMRqfPt1rg+CJAee8Gj92eL976LqjplrVwFg2OXWyySZpkbW2pOHP7mtKjx0H2tk5CwCQDg62uoeuG5UzM9SOjee8m286Fx8+3MJ7e5cAo+Ppbb/hzj73xUs9C6MajLXJsWMC57IJzuXneVvbCnR2huI3nlUfCpHBX/7qRPjVr12Z/eQnjoZdna1a6z7n4IHD+tOf6YeFhXHV2ZkQQozruv/rye75F4nNzNImNvFrhCAIyMzMzGtKpdL+Uql0cHl5+dqopWU/c7xuqnWka9UMJAkBAPBvvvkEqaubBcE9Uy4LvnvXkrNnTzWZmMg4AwNjOgiyen7+Ai6Fc/VVR+XYeKtaWBBqfKIzHRxsTYfPNiLGsHv11YOglZ/e/0D7+fvox480wMteFkpjmqSUjVLKFill05o1BkEILRJCjJSyft0DDgDAdd1jURQdhCdMaSVCyDHG0MbGxhKlNE3TFAghFmOsyWrpjzLGGEKIYYxBKZUxxtS01pZSyup8x7fW1hYChdvr/eyzd9WPRgrniVUpQUhTZEzed3NxqhLOiIMQYpRSUy6XuznnFYxxHMdxE8CqBpLWupDJZEaSJGkyxpRd1w0qlcoWjHGlWq32R1FUlyRJ3JThFUoJpZSKNE0pxjiUUrZaa700TZsZY3Oc81Kapm2u6z6epmkjY4wCADLGMGPMBg9HC+Hx+fmz+tSpdgAA0ti4xAf6p7GfyfJHHgno9dfPxlr3KaVajTG5NdL8EqU0UEqhOI53GWOY1prDGpdp7X4s5zxQSmXWVLdznudVGGPc931IMhkLA/3G930uEYKIkFa/VD4tjx3Py6GhVl0s+npiMkOSeBL190va1JRSSpVS6gIO0dq5HPH1byT65MkCaW6uZF7z6iHe3Z2x//DRKj55krL5+Rn5vj/ZbSYnHf3t77Q5E5Nnki9+eR9ojWwtEGJg4Gx03/27rdaY928fwtkck6OjDaSleVpNTnYBAOC21oB3dZ3CQhigNKL1dfNqfqFlPRAijfWzeqHYghxHkb/+0ALu6gqttep8TpfjOKfTNN0ISunZcyX50X/aasPwguwnotSaJNF6oejqpWUu/+R9FL30JRbuuN2aPbvLlPM5rXUTACAD4CtKL9CCclpaizXfG8jU1Z2T9z9wWf8+tnXrghwe7rNx4gBj2CwtbXw3bZIIU6k4pJAv8127RkghX2TdXYukpQVsGLnp6dPdemGhDbseUtPT9Tjjr6SjYzt4Z+eQ2LNnjra3VeXIKs9KTU7VedffMJ4OD7fI8fF6XFeH0hMn2nVxsQWWVzDZ0lGkW7qE98UvVJKHHuoxy8s5PTvbpsbHG9T0dKu98YapzIH9rud5dXj/vpnyrp27MEKUaT3JHz8i7cioL174/LhmbRkhdLKxsfGLl7vfXzQ2M0ub2MSvAYrFYmulUnm3UuqQlPKqiz9Ht91qstNTk9nurtHKV7+2A+VyNhkcrFMzMx3uoUPHwEIr7+1dkROTCfX8NH74kX5xYP8RAFjlWRACzhUHj8aHH993Oc6Tnp/PJBjlsZ9RF3/Gb316McGoHp7EAcpai+M47gYAiON4u+M4j3POcaVSOXDRdnWO45yQUnpJkhDP83IYYwwAAmOcWmtBKSUBgBpjUkoprCmCO4QQUolUaoyq1bnUeJzrrEOnlEwz7TlGjIzQY4vYttY5/qlionc1UkQpRWmaWoSQdRxnljEWpmmqOedDaZruWLsmFoZhq+d5xxBCvFKpCKWUZ61tcxzntNa6dy0oLKVpCgihFQDA9mLRzzTdJqW0mUzmWK1W2w0AHCEEQohjWut9AAC5M2dOy09/1kDGl8RxgbS2Luq5uUZTqxVMEFo5OFgPAIDf/UcBISRkjI0ihKRSKpskyTbO+RHHcYSUUhNCpq21A+dfg9a62RgTrnXPZQEAHMdxrLVTSqlFrfU+KaUIw7CDcz7hLC7OpI8+ljMrKxsPflOpZPGVV1LZ1ZnnlFY557FSiq575V0wn0+/aQW+/OVuvr1vzFjTkP71h7PyZ/dtbHf+IrOLxUYgBEBrMLVaNn7o4b3eoetOqMWlbHL0+AG6ZUsJhAArZRcwBshxwP/TD4xU3/CmA0AIyLGxDgAAccXBYUiSADFuda3WCgDA3/7WU2b3bhVF0RWEkApjbEpKuW5fckHpLuzfscP7nx85m779Hb0gJQYAQK5j2Ef/cTLc0tENsBrZG2MA8Go8JQDiOI6vdF33dBRFOwE2PAM3gkjT2qKgWoXKM27tc/fvP4L+424/uetb21CthmnnljGSyy0DYxkAAFOrZUShUDYdHUrNz1O05pdnajXP1Gqemp5pAgCgnZ2zzHUrVsmNLJYcGakHxsBUax22UiF45wALH354AOIYxL69Q2AhJI2NSC0tobUvHejZuY0xcNtaQvfrX0SASM7U1y07T7ly0mq9pKam23Sx2AIAgGdmIR0ersa1WmjzeYoQ0s5HPzaffOPOHrmtd9G95qrx6H/8TQd9539LdZoOQ0fHxUvjl4LNzNImNvErinK5zOI4LszNzb2oXC5/W0p5szHmyX55EGttHY+/+OUu1tocknxhTI6M9AIA6KWlvI0iBloLeW6kTReLq+rAuXxVl0oNtKtznnd3j8aHH9+33np8OdhaLc+7u8+qubm28/+vR0Z9v6vrHN21c1FKeYm6sta6UQhxVGvdBqvk6TaEUEkptcHbwBgr3/dnarXaHq110xq/xllTs06NMSYIgiiTyfhaa2WM0RRjQRn1AABJa9HDkwHvKHgex0ZygqTR6s1SyseE6x2ShgwKRlsSaWi9R4BgQBisstYqhJChlOrl5eWeJElaXdddPL8t2lorpJQtnPNSkiTb1v7ncs7n0jRtb2pqKlJKaZIkidbat9ZihJBeI4tfMEcY4znGWHGdkG2tpY7jlAgh47QWmOSfP7HHjE+02jBstnFMbRxTUAqLHTuKamqq4PzmSyajKw52YYwjay1N03S7MaYeY2x83w8opa7ruktKqVRKeUlHGwAw13VH0zRtFELMYowjQgjRWicYYxrHcR8AgP+f94yk73nvdlMsXtDxSLf1Rt7vv0kHWjdZa2PGmAKAFULINGMsFEJMW2u1MSYLDfVYDA7NmnK53mq9ZPbtXaSv/p2IJPEUUkqbai1LujortKVZIt+f1dPTF4wXzuWm0jNndgIAmCBweM/W06S+fkmNj7eJv/nwUbl1q4vu/WnWlstkY63NzjWI3bvSdPhsk5qebua33nJcvuZ3WJIkewEAWWsFQohwzoeEEAAAOUrpnFJqw+pHNjTUO729J+HkSWD9/bPoj99dirZ292ysB4BVzhZjC9Zasa44TwgJjDGO53ljSinPdd0hQojwPG86DMNehJCx1mLl+63ywP6GHGOncSpTNTPTpaZn2vTc/BNNFc+4ZTR+/3uxuOlp5/Q372oFAHAPXTuoq7UsxDEFAGAdHefixw7vwdkM4tu2jbGuzhmSz095111Xix97bLVhopAf1bOz7QAAen6hQS8stGLXnTHVqjg/CAYA8J721JO2WuXxY4e3q/HxDBZiSS0utsjBoS6xb9+omppqAQAw585lk3//Qou8+z9b1XfvaRajoxVSq0nW2TmjJibqbZQIes01Ubpnl+t86SuPZm++6UeXWYe/cGxmljaxiV9BLC4u1pVKpX9J0/Q2a61rrSU/b3seJ1WldYHv3jWZPvxIk56f3+BG2DB0AAAQZXNwnmt8OjS03b/16Y9FjzzWG49P7L3ccS9GOja6HdXVGbuyggEASHd3bGo1Gt9wSMgw7PE877gxxiRJsuf8a7bWXtAxF8fxgOM4j8dxfBAAAGNcq1ar2wAAEEIQhmGurq4OpJQJIQSUUjaXy/nGGMAYc2OM4mqFJlKECnE7UdKEEpoLExk1wyS3+c7AsszhOI5fEIYhGl7WO7vrnRWOLBUE7FTFqC1ZoIIAUEo5Y4xkMpmT1Wp1r7X2YvFyIIQsGGM2BBxzudyItdbW1dUNKaU8hBBaM9xtzOfz1Wq1Wrn4GBjjQEq5wxgjhBDDSZJs11o3JEkiAaDFdHcZ2ts7r0ZGWmhT07Hk2PEr1vc1UbgEAN0mTgEBJFrrnO/7Y5zzZWOMq5RqIYS4q0Mbc0pp7cnmMAzDFiHEYc45JYQ41toPBkHwaoxxBgCApmmo77vft2l6QeDs/t7vHpe3PL1pcbW7sWatrQvDsEQIEXEcb13fDiEUO45zBFnr2fe/j4ectQDAE4HQu94JYC0IrWvkI38zkf70vj1i964MAIDFGJw3vfE06urU5rOf2zCvBa0BOS61tVo9aW9PVMcWklC6vfCpfzldfe4LdqL0iSmzUbSCc1ljarUG99WvyiSr84aeOJT2fd9naZriQqGgwjD0kySx5wt4htdds8c/dO2p0NpdhNIaBQgRQucIIQ8hhGIhxMNhGP4lxjiRUvYxxsqO4+BsNltdXl4WWutcGIZ71+Z9hnM+LqXMGGNaAADc5eWjwcc/8YRI5XkgXV2atbT6QilPdnUNuLc+fcYUF4vRw4/up21tZ8nWrQBGZ1hbK02GhkDPL2A9v7ARzBmry+tZOmDMu+QElGI9NdVDu7uX1fh4PevtnSV1hWr00MP9Nk2pc9VTjplKxVHzCw16fj4DABAfPbqX9fZMyZHRLaZSuaDsKh96pMCuvXY6fOCBXaAUQls6J6M7v9ma37GjbLu7Lnl5+mVhM1jaxCZ+9YAqlcqfxXF8x//V1tYCeee7Ujk41H1JjQwAwHWtMzBwNB0e7jG12sZvBuntHTGFAseZTM2Uy5eY6F4OZqWUFXv2rCQrK3UAAOy3XjaKb7nZrQFssdbiMAz3IoTA9/3BWq3WDwDgOM7MWlkiwBiXpZTrnKcGSunKWkebtdbmtdYon88P12q1eqVUQAjhAMDWMilGKSXR6lu9G6hcxDjLCoSgu8HKVAXlRhcQoHo3SNGEwPo+jLEiXDQhFK4ML6uWvY0oPVZUbhArk3c9wbEuGWNwHMdLGGMihBgJguAJbZ/VrNPE2jlRFK0KG2uta4QQ31p71hizBQBYrVbrE0IcD8MQEELU9/1jQRDsWz8WY2w4SZIDAABKqfy6gra1lmQymSGttYO2dERqZKSFZLMdvG/baDo+0SN27xpRc/N9AADpN7/Z6V5/6AgcuhbHcdyutW7EGCfGGFEqlSqc8xRjXK1UKhuCpBjjCsY4XpMKGAaAACGEAQCnaZoopd6apunVruuW8iMjZ+L3f6ApWVq+QMndf91rT5Vvf+He9dKTlDKzlpFpTZLkAjKztdaJ4/iA9+N7j6N8PkEHDyiMcU0bU2CMzUop2wAhII88Opr+6N69YC1YKTOkrW3ZufmmmehFL+jLFwrLwVe+usHZQdms0ktLjpqe7sS+b8TCvE+a9wwmxoD33/7wTPSh/75Rcoweeng/IASZN77hRK2zk+tVUc/zvg7uyTAMPa114/z8vLHWYs/zhsMw3I4xrgFAaoypN44jPIz/qbGx8b2c81AIIcvlMgKAASllUi6XO9cDLM65TNM0CsNwMU3T8+d8KIqiXiHEEOe8rJRqIQjNm3f/8baLv1vOFQdPm2rN00tL7ZWPfbwr27dtVr/7j1t1c5NOT5/ZDwCgJif7rFJlJISjlpdT99prjkUPPbwP1BPffDl0Ns+3bx+yWuew512i7wQICVOtOqy394waH68n+dxc/Njhg+sfxw89vO+SfZIEcCZz+ayzlKCr1XS9fJ+cOLHLveopg/L731PpqTMcXvuay+72i8ZmGW4Tm/gVw/j4+GuCIPjQ/8s+Wa0n04cfuaQdmbS1pqKv76QcHesz5fJGVonv3zcIv/s6ra8/ZPTnv7A986zbTlmZLplS+bItzYhzTXdsH2XXXTtp9u+LibXCag3qu99tlZ/7fEHc/sJhyfnGW6SUsoFzXnUcZ55znkopxVoQVYvjeNW+ghBsjBFKqdZ8Pr+UyWRijPFiEASNxph6z/OW1ywVYlh7McQYc4QQMsZIY0y0SmlChBHECi6GqiIOYe4jnue9WUr5qoVAs2JoM4GCpZGFGsoKKuZrsQqkiYwFHsRK6mClJ0mS5iRJmjjn4+slMgAASumstbbJ87yFKIoKnPMhx3GmlVIdhJAz2Wz2TxBCzVrr+wghhSiKDkgpW5VSLYyx4vmlMM75RunRWusLIUYwxpOO4yxaax2zvOzrj3+iYOPYkZOTGV2t1WVue+aZ6Cc/3W5rtY3MHLnj9vkwk9lvrfUAAHzfH7bWEkLIdBRFexBC00qpDoDVLF0ul/tYLpd7veM44wihnWEYHlRKtVprs3Ect63zd5RSDsrlSuqTn9pysXI1uv7QlBzov6D8yjmfWvfUu2TdITRv3/8nXfKub7eJRw9HzJhRtHuX73leiBAapZQuwQf/KmeWlvLAGPiHrquo2Tk3feTRDue2287ITIaogwfmxat+exZv3zHHy+VievrMKo9MSoROncb49hctJ2lan27trudRNGVOna5Hnheznq1F5xUvH1Evvl1HabrRUeY4zrzv+0tRFLXmcrkVrfUS53yZEKLiOO52HGcIABAhJGaMjVJKhxsbG1+TzWYjSqkGAFhcXHzW0tLS3dls9m8opdU4jm9eGzs3TdMGjPHyGtEbMMbLGGNsjCkghDghhBYKhRX00CMr6Ze/ckGno3P1VSdNuULS4eFtNooIckQo738w1vPzDbxv24QuLjaC1pht7wtoS/MSJGmgxsa71eRki3fTTadNECgbBKuBkbVAGurmaUNDRa+sKNrcItdL7wAAtKtzDhirRxQvgeu6SCmr115+nhTWAmvvGEGZDGfd3YsmjjOrMg4ApLW1YqNIm2p1IyhVMzONano661137Xeyz3n2vT/32L8gbGaWNrGJXzEQQi61m/h5QAiqtzy9le/bOwr/9pkwvf+B3esf0camSnzk6P7zPaucZz/rDHvPu72VKOrilC4hIaD2nf/Y5VxxxRE5Nr6xnfvCF5xJ77uvGWWyNfx7r6+FV17xhH3KC54H3tz80eRNb97v3vn10QhBB5z3gLXWImNMRWudaK0lIYQghGSpVNq+do8VhFDRcRwWBEGXUoorpTClVK+LViKEuLVWYoyJUkpprY3WOnVdlwEAp5Q6SZIECKEaQog8Oh2lT+nMMIKRLdXiP2ZI60YXNzoMSW2wvGV7ngBCTouEZLqqsvUO1h1ZwpLYnYmiqB1jvGittYyxcWNMFgAEpTSI47g9CIJeY4wjpWzyfX+MEFL2fb/fGPN1APh3Qsiz0jTt5JwX0zRtAgAwxmDXdY9baw1CiMRxvDEvAABxHO/yPO9kVKvt9H52/2lx9qwOSqWNjI571VOO1e7+zwve8lFjozatLReQktfkB5YAYD0YLgghDiuluoUQC1EUPd9xnEOU0tYoitzz9nMJIYFSyt84mNapuO2Zc3pouE6fO7exDrGWG91hnPMTlFK7Xma6HIQxYVILWpAxIE+d8vH0VLdXqS26b/o9LxkcMphzrebnVwNJKaH2vR80m2qVoMbGFOeyKEySgs7nOyylE/TM6Wr06GMbzQD8huuP67f+QTMGSNd4cAC//8YiT5PDYvfuTPrggzq540WYENJ4/rpXSrmU0lAIca5UKh2w1oLneUeTJOlfm4/1sbeu634zl8u9K5vNXtC2gBAacl33Z3Ec/wMAfPLi+5ZSDggh5pIkaXUcZ84YExNCIsdxNGPMMUmS2iNH8hfvZ4MwToeHNwynTbmShXIlCwBg0pRZKcna/ITy7Iinl5c3gvDoyJFe1to8dL6CON+5OxfcfXeHTVNEWltn3EPXHQOEjEWozX3WbZ698oqZ8m/99k7et20SE5KjUk6oidUuw4uBM34kDh4c15VqF9/WOxV89559Ys+u49DengGESogQHh87tvvi/aw2jDY3f/Vyx/xlYDNY2sQmfsVACLkPY7xsjLlsi/HloDwvz5dXZiCMNn4T2MCOCvbdBex5vimXXQAA2t09Fb/h99oNQsyJopPoYx/PyEpl1WpkdmZP5lnPHNKVSmTDCFB7u1Vf+HzBWltvLnNOuaWjC/u+NWNjFd3d1XPx50qpLUopIITMOI4TSClLjuNUEEJSa82klNu11qkQYkgpVVVKIcbYRkkniqIyQsgyxvLGmLhcLnf5vj+mtUZRFBmMcQwAnuu6ZqGmftLbnHkesqZsDBxQAPuV0mnOZQhJbYfmqg1OR85joOI6F8NKpBNsETWGhAAQ+r4vEUIrQRAMCCGmHcd5W2Nj40/n5+c/jzHu831/uVKptAshxhljodLGnalIHMeJrPfZC7MO9XK5nI1W63SjaZr2xHG8CwDAcZwjURTtuXh8AACstRnvQ399Kr733gs+x7lcakrlBtAaSFdnRP7+78bIj+/l8Wc+122t3ZgOzvnYGl9ogxOUJEkvAIAQ4rFcLve9Wq12e61W8wEAaa0tY6wmpcxYayGTycxLKUNrbcZau4w8j4dvf2srN2aU/N6bGpGUSlerjPT0PtHZZYxnjAmfbC0ypYbNu9/biIJg43+mXCnU/vVfC7V//VcAgBb3qqcMpdXqxlybapUAAPDnPnuiwthOh9KzrrXIfO3rUXLnXXvXfQlJS3Mxfc8f9RghsEnTdiHEFEJoSRvTDH/49rowSRr8W285orXuppSGAACU0ipjbEop1amUCimlMpPJPKK15mEYbvCGGGNFxtgYIeTvuru7v3C5e+vo6Dg3Ojo6yzmf9H3/R2mavjVN01dLKQ+unauotXYQQlprXb+ucQUAU5xzmn7lazL+7OcuCEr4y182QgihcPLkZccTYaLWX0RIY1MiR0Y3uFz+s3/jhJyYrE9PnNgr/uAtp8nUFFWnTxO9tEjXOWd6bq49TpJ2ZG0MjBma8RfMD36InIEdp6MHV0tubMeOaeS6sb1IHwoAQOzaPRT99Gf7AQBs0BfwXbtOJ6cHN+bkycA62u9u/sD7T/3cjX6B2CzDbWITv2LIZrMLQRBUjDG71gUc/2/gxnHVHj3KAKGqrVbzZnFJkObm0N23N+Tbt6+o4iK1f/+3Rmaz9VJKxtN0Efr7Q37tNYvq+z9otrUaTs+ea1CTU61qbr5VnThZx/fuHUmbmy4pzVGM5zNf+7pN773XVz/6cUu+f2A4aWu9rFfcWrmngRBSjON4z5o+UMZxnMEkSbq01g2U0sU4jg94nqcopeOc86U1fSVECMHLy8vbhBBjjDEdRVHdmmI3VkrlMMbLApvWxRiSepdypZQMJDgFh2JjDEJWxc6qphK051nCkNbtOY4asgIBAFJK0TAMe9YCOUEIqaRpujsIgquttTustVopVTDG0Pr6+pWRxZqIgMNSqMjQsjStPnE8jrWUUnHOWZIktfVSjBCiSgipSimbMcYJY+wxSukPKKUnPc9b8H3/5fCzn42kZ8/efsGYJQmhbS2jdMsWKa66aiY4sH+H2rnT58+8dTjOZvcAAFBKS2ulvkt83xhjxx3HeUs+n/8GAPw4SZI7KKXSdV2jlCoxxhYppROU0pyUEkVR1KuUalsvG2qE6rI33XRW3XIzIU+9sVTbtXP7+rEdx5mx1uaVUhdkSBBCxgmj0/Ytb+0wQ0OXZE8uukADWkubpg5tayubIFh9SJdKDnrB85f53ByX0zOadHcJ++ijkrS3r9C3v20Kv/61HBcKoTFGWGs9xthsFEX7tNZZzvlwJpMphmnar7XOGmOY67qnhRBBGIY71vwKT3ie5yGEVBAE2xhjRmvNPc87o5TKK6Wa6+vr3+Z5XvBkl14qlfq11p1CiDMtLS13G2M+gRBasdZuQwiRtTHEa7pWp4QQs9bayC4uhuHb37ETZ7OGdHWV+aFDZfwXfzoSXXftgIijBVIqF2l72yzKF3wxMDCGXUez3p4iwjhe93yzxjCEUGLTlPu/cdtY9Mijfc6ugVnnL/58Id3WE6tP/EurGhvvAGMD2tU1zNrb5klDwwIYq/XCQp0Y6D8T/vS+vXJsrFlNPdF9aJaWcmL3rrOmWvVBygsylySfn123cjHLy3ldLDa5N1x/Qk1NbYh7IiHg4uDJOXjwr/K3v+joz10Hv0BsBkub2MSvIAqFwqMIoX+z1qZKqWvgIj2YyyGpK9TRXG7GPn6E2XJ51X/N2BpgtEwaGzgQWqWeV0t7tjYBAEjOm1Qm0woN9Sv2C1+6NCDqaF8irW3ldMf2Sx7GxtqM2r0rRvd8nyOlCGtpTtI9e1z7hMDkJcAYB2sBw4i1lmuts4yxFa11DgCcXC43VqlU2imlS0EQ7LbWppRSpbW2hBAUx3EHIaRijNGMsUBrXbcmgul5nhdnOPG0MYJiRCiyFhHqWKPjSqQZIcQ8Phur/iZnHmPkE7yqy2ythSAIuhzHGZVS0iRJ2jDGy2maHtRa78xkMkthGHYVCoWJXC4XYYxJzhN+saZMmEjT1yBijbCdrCqvLSeQUioOgmArABAAAMdxhsMw3JPJZD7d1NT0vI6Ojr9raGi4s6Gh4euFQuFzvu8v5Z7/vBOVO795k15Z2Xr+ePHe3oX0zGB38sgj7e6znjWT+F6DFKIZAIBSOmmtbVxX9L4YjLEf9Pb2/h1jLF5YWHhzHMe3Ukr/2RjTwjmfBICjCDQN01AAACAASURBVKFtrusKzjlnjE0nSbKxBhBCGtcVUMR5m6qv38hw5nK5E1prEsfxpQRlxxlkjx9B5oEHEaRp5snMnAEATJp6/gtfMAK3Pn0meec7tjo3XD8Cjz2m+A3Xh/rqqwP+6c9q/eWvctHdaXFXdyV461v6ZEd7s6Q0SymdkFJ2CiHOSCm7rbUcAJAQolKr1foxxjUhxESapq2UUrPWGJCnlD5cV1f392ma3qqUapJSWqVUg+u6o0mSNBtjChjjZYSQzufzP32ya6+rq7s/n89/RwixDADg+z4UCoWHGhsb/1EptRCG4e0Aqx2gWus2hFCSJMmA1brEnvucaf07r1TmOc9GyTVXtSjXbQEAEN+5e1oOn21Jh4f7bLUq5MhII3KdAITwksOHewAAUFNTQm9/0ai7c2cRJek0ICRJRzsRtz5D2QMHqvwLX/zb9Mzgc9m23rI8e7ZNLyy0qbn5Vl0stphKJes/7zkn08GhHTaOCZhLc8V6fr7ROXDgNGlsmKdNTXOmXGkGrRHJ5xK9vHKBn6Oanm72rj90Cnlu2YRRvXfjDWOif8e0SeLIVCoF2t423fCWN79B7Nhx2Z6TXwY2g6VNbOJXFJ7npXV1dT+OomhUSvkc+C/K7t6Jk2fUX3yw0xSLzXTLljIf6B8j+WwUP/rY3vTU6To9M1OParWMesatFwReFJOa/fy/X/BjiDg3/D3vnqndeP0lXAQAAOfU6UHxr59ewWDnbBj6dqVE1W3PcCxCl31AIoQUQkgxxoppmrYbY5rWfM1mCSGx1roBIRQxxqbiOO6pr6+fjeM4a4yR1lrs+z4FgDlrbYOUMqu1dqy1ru/7p/P5/Ly1Nic4cwgCa61NCaUptgYTQhDFKM25jDmMcIqsLxihSqmUEOIZY2pSypyUsg4AtBAiFEJUrbUZx3FWtNZVjPHUmkEvtdZSDKCGFhPY5ie1HXBOu3Vbck0+w5QQo7Ves8NCxlrLlVJNhULhjLV2UCkV5nK5c7Am1XM+oqNHJ5Ph4VeCMQhlM0AaG5UJAq0XFuoBANAzn7Gk6uvqAAD8H/zoGM9keOw6l5RpMcZlzvk853x2aWnpoNb6CMb4WC6X+3h7e/tdCKFPNzc3f7FQKNxdrVZfQwjJWmvBWis451NJkqyT9DHnfFJKuRFAcc7LQghjrQ0opVWlVAHWgmNCyAJCiEbNzb32ec913Wc+Y059485VDhVjxv+T9w2qRx9zESWY/86rjuB3vgObvr5KuG/vAQsAsq5Q58fJGBzYL6PGxn6epLPpXXf1pfc/mLf/35u18v0sAEAulxuilPIkSeo557MIoVgp1YgQMkKIJYTQZDabpcaYGgBUtNZMKbUlk8lMep73Dkrp6Nzc3AeTJCms8dJAKVXHGBtf0wM7F8fxzQihT3meF19uLf88LCws3CKlvG39b8dxRpIk6QEA0IQ0KtdtNQB5C7DBE3PGJ0aTD39klwnDDN85UHV27zorR0abaUtrSc8vMOeOO06K97+3CK98RSXs37GTjIwuJ9/+zn41OdlKspmzwf/+Rr/8whcbbRJ3kauuDtlL7gAYGXX5zv5FvbKSETt3ztPGxmL04EPbEYB29u4ZV9Mzl6wd1NgY0kJ+MXn8yH69UGx1rrziDGBSUbOzW+te+5pzpLFhWZ4bWd3PWpATE016camedrQrQBBFP7t/Gy4UQr5t27jYs+fHTW976zf+X8fv/09scpY2sYlfcbS2tn5tcnLyXXEcX1aXZR2qpTnP9u6ZRGGk1MJCZ/zwIwMXb4M8b1YIUVorozQ5w2dH8ZGj/sXsA7Z370ht/97+i/fHxmjn8ccH5Z9/sFedx2/gL33xcYPQkxJ+rbWUUrqslMoxxqbWsxgIIZ7L5UBrPUYIEUEQCK11Vko5ba31kyTp8Tzv+OLiYk8mk6kEQVDwff8cAGjGGOacN5aCVC3W4mpbYyHrYRQzxtw0TatLy8vtvu+fcl03v7hYbG8vFIYoJWkYhkQIwQEglVJKx3Hm1lq/mTEGK6UwAEzkcrn6IAggDMMrKKWfdF33+9ba39Ra9169xW1FVuRqtCE0Ro8RQpi1lhljHNd1A6XUkjGmCgA0jmMSx/FbAOAtWuu/Y4yNaa27MMYOxrhbCPET8Sfvezgr5Tf18kqfmp+rN5VKvZqY2OCm2L/4YJPX1XXCVitucur0Pv6SFz8Or3/t1ovHmTFWTpKkO0mSbgAAKeVvcc6/0dbW9g4AgHw+bwAAqtUqY4wRY0xsrSWO42SDINjIClJKy1EUbfM87zgA5IwxJVjLxMlV0rSDMTZaa8I5n10z9j0IAGAIobXGxg7nU58cRxPjVeR6pHxg/87C615zSvb00GTfXodS+p+M8wkex7U0TW8AAKi+/GUDhJApQchocM1VO9w/fNsR29OTiQuFToDVbrZardaTz+cnfN+fl1I2YIxTAADPdY/FUdRPGZuKoqi4lnFyCSFnEELzaZoWAOCdWut7OeclxlgUBEEbAIDneUfXyd0YY6q1bqtUKn/Q2Nj450+2npeWlnY3NDRcQDKqfOvbmExP3yj27ikm+XwTIaSq1H+dWMELxRjXF2K2pavM+3csB/d8b7WT0RVFANuTPO/ZXWk+10wAYsdxzqpbnw7wD/+4tjfSNoqwjSKRPH70Wnj8KMCXvgxICMC+N0jq6k06PNxkw5ADAJhKRSSDQ62kqyvQExMbARvbunXKSpnG5xHp44cf2YmyWek945ZBVVoxJJe7QOuN1NfHJgypGhunJJdbAmM6yJ7dCHd15vXxk4/8lzf+C8ZmsLSJTfyKQwiR9vX1HZybm7uiUql8MU3T7ZfbLm1qakv/7ANt/he/NKz/7TOrmSJCIPOB9w+pb34rZ171ikra29uu1gjAXhhNpn/0nr1QqWw8JFF9vUKeh9ANh1TGcc7W1hSd15F99LHB4P0f2AUX2XnAzTdDJpM5DgBcax1ZawEhBHx8QkhKCmhpuYopDdGunYpzzpVS0nGcQcYYtdaiUwsJ3duWcbLZrArDENI0lQBAHMc5ghASAKvaRp7njUgpmZSyk3O+nKYpdrgDx2aTbKtfqcSE6DRNU601BQCIomiH4zizmUzmSBiGda2trTcppb6GMW6VUtYwxkwI4QdBkFhrEaU0qFQqO3O53JiUMpVSuqvDSHYFQTCWyWQewhiPEIJfoLVFGGO0pv00ZozZJqUkUkqhtW5HCEWc8zlr7UYsmiTJ6wkhE5RSRymVYIz9paWlp1BKzwnG3hM9+OCPbJpeUspUMzNZmJl5wlj1u/ds9a05Gr3+dfsI59OUUiKlJOf7nAEAKKV6EEKvKBaLn25qajoOADA/P99XKpW+JKU8mM/nz6x16pURQmk2mz0ex3GfMYYKIebCMNyLMZau6yYIIb9UKnUArGawfN8/q5TKR1HUDuu2ORsLCUHc0d5NujprhBAJaQq1Fzw/a62tY5T+Z19f32sAAGZnZ++sVCpflVJus9YSpVQ353w4oZTFz3n2Nq31+TpB0wghT0opgyBo8Txv3Fpb8Rz3nP3YP2fxD3/I7Wc/LSJrrySE1DjnowDgRlG0a239NAPAlQih2SAIdjqOcwQA6PkkbyllAwBAmqZvmJmZuau9vf3I+bcVRVGuXC6/MYqiVxhjbknT9Ab2mc+Ohj/56W+rpeWXqOnpbqAUsrfccgoaG7Rua9Vw4w0V5bqX2MGsI7zqyp2ZK54yBdksVnNz9Xxb7xwQPK6XVxqsVI4pFFZtUIzBCCGMXdddX1C4qbERMIZLymoIGTC2S01MXJJBoh3tCW1rWwknJrYBAJDWltiEodALC1su3tZWq8wsLmUsY4DqG6ru1VePWIwk7+62te//oFfs2TMIGEX+054W4Yx/Rt14Y7h43VMz9S8Izj7Z/f6ysFmG28Qmfk2QyWRmfd//eJqmhTRNr7ncNtzCGHzzW74ZH88AAJC6upq99ZYyfsHzM0F7e6ehlIO14MTxirWG8pHR2fN/UP1n3Pq32U9+Ip9u3+5IrbnWOg8AQGu1kn/nXaf0Pd/v0fPzGy3l/IXPH7Uf+fBC7Pt9lNKslNJJkqRHKdWqlGolH/7IAvrhj0Tymc/24KuvWolaWvbkcrlACFGO47iAEKowxngoZZqhFiVJklBKZRRF2zjnJkmSLqVUA2OsjDFWa0TkAqW06Pt+GEUR0krqHU1ujTFqKpXKNqVUPaU0oJTOMcaWMcbMGPOgEOLHSimQUj6jVCr1KaWU67rcWlsLgqBBKUXjON4CAIAQqhljwjXelE6SpDVN0+dYa/cZY9qMMdba1etN03RSaz0JAP1KKY4QIpTSSqFQwKVSaRshZEZr3SaEGLTWNhNClgkhlHOeAwBNCDnnuu7HWu+4/dvVb31nt15c3A0Yw3pASnt7x+iL7xghhfy8mZuvA6WoVco1I6O5XE/PUNLV6aRp2rHGu7qEXyaEOBsEwTOSJJksFovvp5Q+p1ar3QIAkKZpPQDMCCGcMAzbAAAjhKYJIYG11tNaZ621RAgxV6vVtgEA+L5/nBCyGMdxz/m2MJeDEGJoPcuFMa4ZY/LW2uYgCDKFQuHebDY7yxj7hDGmSyk1gDGOKaVLhJA5SmlFKdW8tq/lxpTYwkLJHDkiBedzqec1W2uBnxsJ4r/84E5bq2F75zfznuBjnu+Xk3yuHQAirXUDY6yKEKquZThrWusGpVT7+vHX4bruopSybi1jdXBlZaXPWrvgum5xZmbmmdVq9W5K6dVJkrTWarU3q0rl+uid736vmpk9ZKrVNZ6gAeI6U/EPfrhf3/9AK/6P/9Dsp/dN+MWFSeznismW7lLW94hMYhcAwDt5aij59y9sJR/+q8B0ds6rhx5plYcf7zXLK3nW3VWFa66uaM/L5vP5s0mStEtr8/T730e0pWUFYRIhxoqmVGpwr7n6KKmrW6SdW6q8f3sS3Xf/xr2h+vrU2TlwjLZ3FGlzc5ycONEidu0cUjMzLSAcAlIyu2bAfTH0SinjXHFwtvb1b+xS09N1amq6MTl5slHs3HmK9/bU8r/5km/r5SVe/fbdNzk3P7063bVDP2Wg+10/b138MrCZWdrEJn6N4DiOrKure5dSqi1JktuttRdmIdIk1sePbwEAwHWFKh/on7Cf/0I7ftpTZ/GzntlgEELZf/v0iegrX9uDXVfaHX3F9V358583qf7gzVhK+ajrus80Wi96p89EeGgootUaM2fPimRwMOvs338iPXduq1Yqg158Ry3lfC8AQBiGPDM1Pc7u+d6EBdBAKUJRXBADA2k6Oqbh4UckOXRdEoahMsbkjTE5a20xSZK0keksIU5SLpe319XVjYZhCBjjKQDot9Zizvm0Usqx1iJCyHQmk4nWNJESa6221tg01cm6xUqaph2c85NRFO221o4ghIoIoRYAeKa1llhrheM4NQBwy+VyixCiaK1VYRjWMcZWEEIyCII+z/OGlFJMKVUPAKC1rhBCUBAEXVprAgDcWrsbAMB1V/Wa1jzXKgsLC30AAAghCwCAMUaU0k87jtOLMd6eJEkFY4wppZ1KqbeUSqXv+7fe8iF4xct26q7uAv7c55fNqdNd5i//XMSNDVcAAOR+8IOT5p4fpFbLXhMlafrjH2Fz7dVb1q4tc7k1Y4zJSykPlEqluxljp5aWlja0mxzHObWmH5UKIWaq1eoAAGzJ5XLHK5VKGwAAY2xizfwXfN8/HQTBXoxxiVK6clHm5wJQSqvny19wzlestV4URS1Kqf2wxt3K5/NxqVSq8zxvIQzDJgCAJEl2IYRSIcQYISQiSWLVO98NZmFhq56aytDerYves35j2t54A4o//JHu9XPgzi3V5CUvbkgBFGUsCcNwBwBANptdrNVqeaVUPed8hjE2QQjRURT1ZbPZB7LZ7Ozy8vIhQoiklFYcx1mklLbXarW3FYvFNxaLxcVMJnNWax1orbnjOLMgpcWzc0maXuiOQ7Z0VOX4+EYJ3FSqnqkMbiOOOAcTU1ZgSoIDB4sAUMeVCtlDDwVpHCP6Dx+tRm/43V53oH84OHq00b/55hPBgw/ucb7/w6P6t1/OqtVqpzHG88rlk+lCcTe/snMqeviR/c6VVx6RY2NgrWXJiRO7kO+Dc8XBs+51156IHnhwDwCAjSKKhHCjBx7cuC69vLxH7NvzOBDGTLlETLm8Ey4DGwQkHRqOLv5/fPjw3vjwYYgefWxATkwUQGuQn/ykt/PGp/4lXIaX98vGZrC0iU38miGfz8f5fP4l09PTLy+VSp9ft1xgjM0iIebdP3zbA8lHP/YcnM8LtbTUJQeHsnD4cJ2zc2DEtLc1xF+/czcYAyYImFpYbBb79o2Szk6Ce3vidGLiaRVjmjRCrd6ZwcXkrW/fwfv758LpqbypBS4AQHz06B73+uvOJJNTW5K2ts71lmF/fmFS/tF78rpY3Hh48YGB4cpXv7Y999KXDEdTU0xrLYIg6Mtms8eEEKFSilBKkbV2JQzDbDabPVKtVvs9zzujtW4BAKCUjmutnXUTWyFE2VpLtdbWGON6nhdVq9WM4zgBY0zKtdZnzrlmjJ2KoqjfGPPfAAA8zzsWhuE+13WPcs7zpVLJ8TxvplqtDmSz2VNCiCkAqERRtMtxnOE0TbuklB4AQH19/RClVERRRDzPm6xUKgMAAISQ2trDFwFAe5qmLa7rFn3fP26txdbarOd5I8YYbYzp1VpnKaUMAGSapiHG2NNad6Zp+nnv9a/9si6XZRzHnfDOd3TiVU2lgn/Xt47AzCyK7/nePuS6Fb28nMeeF6F8rkKtLaUIFdI07fI87/j5YpGZTOZUrVbbBbDKG0MI5bLZ7CkpJVBK0VrXIUrT1Gaz2ROu6x5ljKFqtbqXc141xgTGmLZ8Pn9aKYUzmUwhCAIwxhS01pgxVpVSXhIwYYyl4ziVWq22YfwchuEe13WHAKCOc/4zAICFhYVmhNCHGGMDCCGLMR6qVCr7XNc9LqXsdF03IYQ4pSQp6P/+wTr/4YdP6vd9YDfJF8aDf/rYlXx25nH9v/5nxfna1+fUd+/poU+9cT6Qsg8AABNyGACaXdcdKpfLzetip1prSNO0x3GcQQAApVQzxrjHdV0Zx7EjhJhKkmRLrVbL5XK5k5VKZTfA/2HvPcMsO8pz0a/ySjt2zmG6Z6ZneqKkUUIgMgZbHMAc22CyAYMjxjZwnO89177GBtsHG2wfg22iscEkCwRCSEJZSJN7eqZnuqdz3nnvFSvcH727mRHyc35az6XfX91r1661qlatvb76wvtCa6lU2t8cBwAAON++92z8kY/+SB4ha2m9Ei4uHXvmcVA6qy5eIlOjh10V42Q0Mmfwu97Vz176kjREEYCUcWZtvVK5994RceTQSVmr5iEIQPf1EkKIr5TKU2Pmkvf88n5xcKyyXaGmg2AIcQ7Y3bKVTaMBwUMPj7DRPevIsmIThhyCAGPXvd6ASRIcnT1/DAAAZdKxfest54LHHn/WvENTbzwrLQhQCiAEtW88vgwGrdsnbvrf7f3df/+sbf+LsRuG28UufkyRTqfPMcY+bYw553neX3Z2dr6vo6PjU5njx75We+DBTHTq9Mv05qYAALB+/o1nGs+/Y0wSYqUKhaVk6nIaAMDUarZaW8sRz5vxv3XPOPrB0w4Pw4BOXFiL//hPDrLR0YKuVBJdKl2X+0Ay2Q3r+HEEPd1lY9tpjRDBliD6nz+d3jaenJe8eB4RErLu7grJZGj05JND1tTlS1zp1birs5U7jhFCWLVajSGE/CiKhsMw7GaMLYdhOKKUsgkha57nNXzfHwAAghCKPc/zLcvykiRp+L4/kMlkPg0AfyGEeLEQooYx9imls0EQHIrjuF0IMSGl7AAAYIwBAMgoigYJIYsIISSEYJzz1VqtNgYARmtNjDEpKWULpXRBCLGeJEkr57yKMQaMMW00Gmbba+J53oyUskMpFXHOVyzLqjcajTFKaUEphRFCIedc+b4/kiTJKABYAFCK4zjUWgtCyBez2ewfdHZ2/lkYhkGj0fgAQigwxlgGIWQLcUZ/+M87ksceH8Epr04+9ld1dvDgPPqFd+DwJ17RQ7c4groAAGGMY9u2V7fDYxjjwrbECsCWl0lKmUqSpLvJNL5dvYgYYxu+7x9OpVINpVSNUhpLKbOpVGo5iqJQCIGUUioMwxaEkHQcZxpLWWJ/8Eeb4uy59fimG9t5ubzuVGulyHVyjLH1JEmuWzdCiFXbtotJkhyN47hSr9c/RCm9Q0oZI4QgSRJFCFlOkqTLcZx1jDElhLBGo9EuhFi0CEWoWFqQly/3mCBwyet/ejMeGBhNDh5ohVffVbSqtQ1SKFR0W2tG1Ooq5iwrtF5O4rjbYEyQsFcE5yiJo7zW2rFtezqKorZqtZrnnAdhGKbjOO7knC9xzpeSJFFKqR+hZ6CELOGvfl3oK1d2+KTsW27eMHECqlTyTBheJzZLuzoLydxci6nXrdbK5vnVW17Q1/rVL1Xg/vt6gdJV2tmxoas1Ic9PAG5rq6ql5VZMWaw2NnI0n19ha+sJPTS+QX/wg1B+/RtdfGBwmQ8PefHMjKuKRcu+6cYptb4e0r7eEuvuXiUdHZVkdbWbtbUtqVJ5ixFfCKHrdbat4SbGDy6yoeGSXF7OQhgRms8ty7X163PPtu/bofG5ZH6+k/T11rFtEdMsBrBvvOF0dH6iXy4sphBj033/9Kn3/ic/V//l2PUs7WIXP8bI5XJXc7ncJ6891njwwdZ44sIbrkv6zOcBEAKOkAweerj9mf2I8XEPjDkfnj49Lj/5jxkAAGRZCjuOZF2dJqhWIxMEAgCAdnetqnqthSwurie/8O69JJ0u0I98WIVtbT1Wd/eGnJ3dKj8Pw0bw2OMHcu99z1zpE387AMZA9MCDB/DJU1WnWl2Bt7yJSClVNpu1fN/njuNMNwV1U1EUbV2XEOu+77elUqm5arU6KoS40vTEaAAwnuf9bmtr64ebwzhaLBb/MQiCVwRBsLPj11oLIcQGY0z5vp/VWlsAAEEQ7AEAiKLIZoxtWJY1GYbhmGVZ00qpbsdxzjRzkELLsk4JIdqMMSgMw80kSXb6r9fr+zzPu1Kr1UZTqdTper0+YNv2mpQyv62dFkWREUJsRFHUzhirGGMspdS653lv6urqmrvmWm9RSlmc8/NKqWOu6y75vj9mHzt6AWbnukwUcVNvxPXbbv2hpA2lO8ryCKHYGMMppQ0ppYsx/qHeBwA0BYl/RE4HYxwAgAIA8H1fE0LKQgg3juN8kiRBGIaHwzAEIcQV27ZntdZBvV4fxxhrMnkRm2IRUun0+fjee3vDSjXv/O6HJtSdd/4IjYRSSjUaje2Q3p8wxlQcxzWEkFRKub7vD3LOJxhjZQBA9Xq9g3Nea8rGsLC/D/Mbj2GIwrJ6rNCOLkxquON5W30b09a47RZLfOxvyuyv/lclKhS73NtuOx+fPDnS+Mp/XKhTyzQM6uzXgcZbc+34vr+/OW91rfUmY6zOOc8aYyxjjMcY8xFCNYTQglKqzjk/a4ype3HyudJDDz26PS5+YOxKsrjUQlrzM/Gly4doZ2dVrq7uJHVjz1uGldUWAADz9a8dObR331n48hfHIJuV4dMnDwAA2DfdeCmevpLn/QNFMrpX+A8+uLV2vvLVIySfrzsbG/PJxIRElqVpe7tqPPFkDpIE7Ftung0efWwv6ewsOSN7Fur3fW8/IKTsW24+4z/08A3AGECSAM7nqySOG3J6psu++cRMeOr0kHXjDeesEyfOhU88cShaXtlj3XDsbHx5elRXq9cZe2p9w7DBgRldqaaBMcRHRhaAsbosFp3tzRHtaP83eA6G37axayztYhe7AACAtT/4o6HgiSc/kKysvF5tbu7s6BHnSt56SwcAAE6SADo713WlskMqKI4emal+65v9zqHDl4ExDcmWDhgfHTkXnTlzFAAg8+Y3X4wmJhLEmSbptNV44snRqFZ3TRBwFQSd4m///pJdLM4mCwuDAAB0cLAu19c7xYED87V773XF+MGF6Nz5PgAA0tJSq3/hX/Zm3/TGBShXojjl+VEUDaZSqTXf98thGO53XXcaYwxhGLZjjOtxHDcAAJqVcQpjXLMs6/c7Ojq+eu0cxHH8MSnlXYSQUGttGWMgjuO9tm0X6/V6G1yPhBCyqZQabnpZ2mzbPg0A2/lJKo7jMWPMMgCoarWaAABHCFGMcUwImccYVy3L4pVKZRwAwPf9bsuyzodhePjafLJmqDSELa9WizEGMMYLAHCw0Wgsuq6rfN+3Njc3P26MoUIIzxhzxff9PcYYZI4eZXx19QJ95auS6kD/daEfrfXOewBjXGeMcQAIGGObQojcdtjIAEDDyU9aQXkYtM4RQhqc83nGGEYIsTiOc03v0kgqlZqVUlbT6fRiHMcghDiJtji0EABkEEKREOIixhjQb/y6Mp/+bCb4/Bd2qvXUJ//JE7l8JEdHY+Cs0jTGQowxsSxrPkkSKwzDCCFk6vX6HiHEWi6Xo9VqlSilehljBUII4ZzPYowlIcT1fd+xP/u5cvBvX9pr/9OnNtWvvi9QZ891eH/yp1dUpRqzF9yhkbB446tf29HXi+67b5z86q9Oz4F1BOTWLHiCViTnFzHGPgAAIYRgjJmUMhtFUYdlWRejKBqllC6FYThithLtD2Sz2X/u7e19V3MqodbRcS6emTkOAEDbWkP/wmQOAHK0q9M3BBO+b9+UXFocwOm0xqkU37lH6XSZ/NsXW7QxMWAc0gNj8zqMho2wYufmE7X6/Q8M86HhGXH0yCooGRipPeQ4vPaZzx4AACD9/RHYdsza26dxb2+ganULAIB1dCzV7v7mYdLbWxV79kja0Wl7r3j5ErZtP56fj5PpmTZAoMXY2JxuNAITxyg8PzGeesXLpxAl0whBFE5MDvE9eybDU6eOX7vGTBRmk6uzgzv3NFeuQAAAIABJREFUd23tR2hJrAMHTj7z2HMJu8bSLnaxCwAAiC9ffn549uy7rz2Gs1mfvf2tU42W/FEAgJDSlPOyl07DpUsAsGVIAcbabBZE4/77x8XRIyVVLlfk7Nwg6+3l0bnzAABQ/853RrHr1GSx5Ikbj886t91yJZlf6KF9vTNybT2NDUTIsgJ8YGwuOnd+QM7Oet5dP3Wl/q17RkhHh6+VMtbRo+dwLosQYyL5zr2o8t9e12NWVgj+xlcvJkLYxWJxUAhxEWDL62Pb9hVKaSEIggOpVOqCMaYihEiMMRthGNK2travwTU72Y2NjdYkSd5OKa1wzlc9z8vGcRwUi8U9nPM1AFi41uMkhNhUSu14XhBCGgA0QiiHENIIoVYAAK213WQLBwAAQggXQqwBQAZjbBljtrh+HOcSQigOgmBfOp2+UqlU9rque05rnTfGWAgh5jhO0RiDtdYrYRg+LwzDuyuVyqYQ4ttCiFNKKQsAIIqiNoTQ5k4+2h3Pw8Hz78hqQkooitR2IjsAgJTS45wvxHHchzG2q9XqUCqVulKr1XqDIEhZlnUxjuOBsts6tRzBDS7Lz/Zb4YoNKpBSph3HceM4joUQIUJIEkKWm5VwbUKILzHGXrad4N0c5xmtNWitc1EUdcCJm0CcuCmEV90F28K1am5uwP+VXwPr9a87F77nFwe2xYUppVPGmIznectRFMVCCAMAPAzDvs3NzTrnfCOKojbGGFQqlbwQYpFzbprevL301Jkqf9lLi+ov/qqq1tc7rePHV5KpqRbW19sIPv3ZcVOtkmtL6dnIyEL52ImdNcIJqgTV8ghnbCoMw+sMAgAA13XPY4yN1tqO4/g62oxyufyWKIpYNpv9xZaWlhogWASA4wAAwbmJvXxsbNXEsaQdHcXg0UcP63y+33vJS2bj1bWsLGy2AACQlpZN5LkSp1ObyexsDwC4qlhsSb3utedrX/73Q9ssmNG5cyNAKUCSABsamo4nJwe3n1eazdSTqUsmOnt2HwAAGx6e3nrYsQQASL3kReXql/+9TftblAnPlCFRK6s7f9OO9rD+zW+N6iAg2+1kKtVL8vlQFYsWAAAdHAyT1bVnFdm9FrS3t/p/avNfid2cpV3sYhcAAND43v0D0cWLP3ftMSMlM7/3OxkURaE1c3WebRaK1LJo8t37WgEAnFffNWlyeUdNTmYAAHSjYduHxkvYS6l4ZqYb57INsX/fReI467SlpWr92q8Wkeexxqc/O2rqdazW1nLWkcOXg4cfPiJXVjt1pZJ2brl5EhwHwkcf7XPvvHMyOnOmW5fKrlxd7SC53GbwyCNbu9J6HfNX37WMXvTCMiFklRASE0I0Y8wkSeIkSZJXSrV6njdTr9f3NQVKl2q12gmttZskyUQmk5kCAGg0GiQMwy9TSl9IKVUYYyql1EIIQildq1Qq+ymlgZRyR2vPtu01AIh0U2+Lc74eRdE+jPGcEKIAAFVK6bplWVprbbTWthBigjFWxxgzz/OUMYZJKZUQYj5JEhqG4QHXdWfjOE4BgI0xXovjuFdK6QKADMOwTWtNkiTp3rlHxliEEFGr1d7guu6FJEnapJQWbLFo1xhjVeY4GACSJEk6KKXzSqmdPCSMcc0Y02JZVrXpKfN83++1LGtSa91BKVVaa14hTiHQ0JkYyALhJU/62vO81FZRISNaa6jVav2EkAalVIZh2EkIsbXWBmNca1IyLMVx3C+l7HVdd1kp5RpjKAmjAH3968bE8XUbePHWt1SC9vadsRJCVhBCrhDCDsPQVkpFhJAa5zyKoqiFc74spWwhhExyzutBEAwRQpTaorFIi6ee3iB3viCO771v2H3+7VPhuYl+0NpVUZzifb2z2HE287/8ntgkyjdax2T/vtKFn/yZfdAklh/g6jKJ/E6lVJvjOGeVUinP8y7btl3mnBcbjcaIlJISQgKttQPPgBCiLfD9V9fe/d475eLSCRMEWQAAPjhQSK7O5tTGRk4uLHQAABDH8ZOlxdbk0lSGdXcVaUdHSRw6VAqf/MEw7ehsIIJB1+p287nDbO9orNbXLdAa3Je8eILk8xEbGJjDrhPLxaVO7HnSue3Wy6pcLkUTF7YqGhkDkk43dLmcMWBSrH9gunHvd0fE4cOX5PJy+7PJmgAAkI6OMh8dXTRJLNXaWmanHULAR0Yuk7a2TcQo6HLFs44fuyKXljIkmy0a33eftUMAsI8f+6R7+23L/9nn/9XY9SztYhe7AACAZGn5lmceox3tFYmQK/7uf09Hd39zHAAgBgCg1Fjvfc/Z6itfcQRhbJw4Wo2+8R+dZHy8KG9/XmAeuN9T584xlEphaO/A4anThwAA0NTlyPnLj14FpUA3GhQAIJqcHEItLdIUCtREEfYfefQAzmTK4sjhq3JtLcSeJ02SEBNFSK6sdIkjh08Cwdno5OlhECLYZn4GAMAYSyHELAC0AmyFr7TWO96fbYLHJEny15alB0FwECE0qrVuGGOoMUY1v5fWWksAgDAMhxzHOeP7/hHHcc6HYdiHELoKAOlcLjdfLpc9znk5SZIhhNClMAyPWZa1GgRBlhBSz+fzK1prRyklAaBijMlqreuEECOlZHEc7+OczyKEpNYaA0ANAFyllNO85hYAAKXUdTlDQoiJIAi26RcOkC3ZFNzkkzodRdFhY8w8IaTBGMPbYreU0m0vVUZrbfm+b7muu5AkSaY5V0ZrjTjnMggCt8WETgGaerXS9LYTsVEoFHpzudy0MYYopQLG2EKSJC1NgsplznknQggajcY6pbRIKd2QUvYAANRqtb2MsQUhhOsD5FO//msT+jv3WtGTP9gDCIF49V1ngptu7LMpnQyCYMy27dNJkvRJKVu01n6SJI4QorJd5ZhKpa5IKRuWZS00+b3amrIxC3EcH8HGGLO8kvF/64M9AACRbWO1tpYFAHB/4hWnk9m59vjSpe7o0uVlI7hEAH5cqyuKAbQ2kJLh5dz5U7qxd8th1KyKvCClbLdtu6G1LhtjmFKqnVK6xBgrbN+z5n1aRwgVpJR7kieeOAFSAhACKOUBEoKZMNwJtQEA0Pb2K9Hk5PH8b/zabPETfz8o9u5dDR551AEAiM6e3eO8+IVnaUfnPHCGrIMH3fK/fHEPacmXaFfXLBCMwief7MOZTJ9Rqi6OHD6JCHUa931vjI+MXEf4KA6O1XW9VlSra3mczmQQ58rIpD31kz85Xf/ud7tNEFyXfwQAQPL5tej06X2ko8NHQmjS0rIil5Z6nDtfcN6//4GjAAAom1XuS196OpqcPEA7u2KSyRAjZVmXSlmcTjX4npHLyBJcFQoS53IYWfxHzvNcwq6xtItd7AIAAOTG+qufeUxXq5y87RfWo2vYn4EQsH7z/efrL7rzCEIIXIwX7Re8QOilpSJ7zWvWqkcPH7BecMcKes1PG1Otkujy5THr+PHTyBKOWt/Q8uvf2PlRZEND00CIpa5cySDLSlhP9wIYoPHMTH8yO8vE3n0EpTxNs9kL8eTFw3J1NSdXV3PW8WOnAQDUmbNtGCGpjaEAW/k3Wm9tcxFCsqlP1tX8XwOAbVnWWYyx2+Rhgo2NjVHf9z8ehmE6l8uFsEWsKKSUgVLKJEkSWJZ1Wko56vv+Ec/zziGEtG3bhSiKVBzHtFAo7OGcLxNC1hljCCFkEEITQRAczOVylwGgDgBdhBBLa50IIcAYQ+M4dgghpSiKugAAMMZhrVYbs217CiHEgiAYcRxnapvv59nQ9P5EUkrBGLsihIjCMBymlM4SQkgcx9IYI40xBmNcNsZ0WpY1H0VRL8ZYMcaCMAy3++IIIcU5rwkhiOu6U0mSaMuy5mOjt4hKEUQMQaWBednDOF0ul1uEEPNCCIYxLrmu68dx3Nk0xAKEENNaU8bY0jO5nCilDd/3+wAAanc87yC+43lglUorfG2tEQuRipMkj6TMNQkze6SU2wzZDiEk2PawCSFKtVptBADAsqxTSZIc8zzvAsbYBEFwmCBUsC5MLsal4qg4fOh0NHHhKHbsGACAH9h/rvG9+4+IEzedsW8+saFdF5lSOSeXloZhfd3cXlteqXT2NNCXvpFE/++fHnH+7hNn/KHBIwAAQRAccBznsu/7OYSQBABIp9OXhBBMStkolUottm1fVEqRKIpGoyhqd+bmT8fNcYiDB1aSufk2ubFh8fHxKsK4YLQuIaWAdLbrzPNuv4j6BmzzL1/6NP7YX/ioWHwzVCoASoGq1YHksinieUntq1/Lsu6uenJlOqfW1nNJNluhfb2brK+vpCuVDGDihqdO7QMAQM1xAwBAkkDj/gfHWG/v02pjM59MTXVnfvZnLlbv+fZIdPpMO9+3byq+dOm6tUdGRzbU2lo7YAxi/IAvV9dCbIlN7HnEf/iRnXCrKZdJsrqi5eIiBwCeAADbs2eVDw2eUfV6R3jq1FG+f/9aPD3TAUpB5q6fKv5na/y5gN0w3C52sQsAAAifPmmw5/m0tW2StLZQtbHRYuKYsb7eNdLaNktaWiJVKGT57beeb7z1LYdaVtdm0Ec+apHZWRVZ1jK+665afc/QOACAOHlqRt79za2S6SjCcmWlUy4stjh33LHpf+nLP8zlkJKxnp4V1tdXxJl0Jb54aUT5ftq++cS55Opsn1xczJh6nZC21jW1ubnDLs337tuUpVIepVOJfuVPEAOwk4PTLLlvSaVSU5ZlxWEYpiilPsa40VSYT+I47omi6C3VanUMY/yCSqXyKs55lRBSAwANABDHsYnj2Pi+v1dr7WGMKWNsEgBc3/eHpJT1JnllSUqZamrSdcRx3A4A9SiK9gMAcM7vxxg3oihql1KWlFLtWuuomayslFJiW4CWEFJVSuUAQDclXebjOO4SQkxJKXdK0AkhyXbekVLKtW17WWsNnPMAY6wZY1Xf9/c2y9grURT1JEnSQQhZbxodGQBATYmQHU9VOp0u27ZdsywrSZJEN0kvmWVZhCAsVyTOcIw2h0wN8hxrMHozSZIhzvlGEARDQoiGUkoSQqr1en3U9/0WhNBCGIZ7CSEhAKDm+EAIMYUxTjjnfpIkWQAAx3WntOuWZGcnClKp7Zc04pyXt7XzoCm+a9v2xWZ/Gc75zDabttbacV13OQiC3jiOewAA8UplOnnHOw9Do8HU2nonP3rkImCcYz3dM/GVmUOsuzvQhSJjvX2R+e+vx4jTgDT8KmJMx/d/L0P2j5WMkoG+97tduFZD9MUvaki9ZTwihNallK3GGCSEWI+iqN1xnCQIAsdxnEfq9fotSqmtnCOESm6pJONv3N0CAIBdLzGNOtGbBVutrwu1tpZT6+tdanOzSxYKnc6v/FJgUqmH6cjQCjz/eTd7b3nTx82TPxjEtlMwQZBiPd1+PDeXZ93da9G5831gDLB9e2vWofE5ks7Uo/MTHXJ+oVWtrrZ6r3j5Bey6ayTfomhr64pcXt5aT0kCtLNjRW1sdAMAIMZW5OzsFqHowMAyAICu1z3U2hpa4wcnoFbL4Wx2gfX3r5kg8HAmXdZBlOID/evxpUvXUQeI8fEEObbSSSxAKmSSxJOzc526UPT46OglwBipjY0MTqV0+//40AdJPnc9Q+dzCLvG0i52sQtY+8P/a39y9epx6+DBb9i33Hw6ePzxd+gk4cjzFKaslFy+PA4IhXxgYIqcuCmJx8a6A8/L4Re/qEBvuTmod3fvT1xnx5hBfb0MvnMvg3p9x4ixT5yYaTz00Oi1eRAmjoVcWekwUSS1H2RNo2Ehxwba3lHS1aq9HZqg/b0168jRFROGDV2t5uTKSnvqJ15RiOYXGNu/f9K05JEBSAEAUErrxhjheV5DSsk9LmpgdD2M4wGtdUop1dY0mojWeh8A5KWUWSml15QS4ZubmwOO42zCVgVWgVK6nsRx3nrg+5WopyenjckopXJNWY0G53xNa522bXuCc74ex/FeSmlFCHHFsqw/45w/0Gg0XoYxjrXWIWMMU0pZvV43GOO6lJI7jjOtlOpWSnHXdVcJIYFSKpMkSSfnfB0hVKWUloUQqxhj/9r8KSHELOc8aPaLfd/PWJa1lCRJq1Jqx5PHOV9NkqQToeur8oUQC4yxJQDgjDFCKRWEEKOUMgBAcaNBiVa6iEXVV6Yn51orKaRZkiS2EGI5CILRTCZTqNfrnQihjSAIjkLTqEmSpN2yrAuU0gQAQillp2VZE1LK/jiOe7XWglKKtNaYUlqLomg/IWReKbWTq5QkSY4QUnMcZ8lxnDXG2BohhBtjWjHGMWOsxhhbboa5+jKZTF1rvZ4kSRsAgLasjFWuLPH29prZis266de+Jo4mJrWJY4f198+KvSMxGx11gptuyKjBIQYXL3EIowx797um6oP9By0/2JT/cXeHWlryrL6exbi/vxUAQCnVRghZZoyVkiRxlFKZVColMcaztm3/gjFmxbKsR5pyLJalTYBf/rJlLJOYcV6Mr0z/CDeRffOJCRNFiXjta6UeGuw3MtmLP/NZIx95pCX8/kPH5Opqhy6VMvHVq21i374NRGkChHjWoYOXse34JJNxda0aJVOXB3eeSS9Vjs6cGUtmZzvkxkYnPzh2Tq1vbBG3dnetqrW1LmTbBqfSttrYEAAAcmWlQ4yPz8nFxXaxb+/Z6OmTx3S5YtPhYdClkm3CIGsfOuSreq2mq1WQKys/NOj7+2rE9dYQY43Ui1/ky4VFwUf2XKJtbQU2PFQQb3+rrd/xNqy+9S0PE5K0/86H/m9oblSei9gNw+1iFz/G2PyLv+qqffOb/yuenXuVaTRs/9HHQBw+vAycF0g6XRNj+9ejC5NtAABqc7NTbW52MtuegNe+BgAAIqU6sTGnn9lvLGUL+cw/1+2vfX02/NjfjFo3HL8SnDo1sl3ttM3dgoQAZFklnM+vQhIPQF/fOjLaDx577DrpBGI5UeOeew6ilGes48fP0e4uHE5M9PFcdhr+4VMetLbE5rd/09fGOBhjBluaXlgIweTTT0fhe39ljN399eXEmO5r+9Vac2PMDp+NlLLFdd0QAKBUKo1aljWjtRZxHO8XjM3IBx5soZ/7vEX//E8nItc9KKUclFKC53kXtNYsjuO9OkmEHcfnKMa8wtghjPF/N8bcLaXsIIRIhNBGHMepOI5rWusoCIIR13XXarXagWsuLfQ8ry2OY9+yrHPVavVQU6h3Y9sLxTmfIoQYjDEOgmBMKcXT6fQ059xyXbeeJElMKa0yxlaklLkkSdp93z/COZ8WQqBarTbcTFJuz2QyXGudopRyrXVUj5RUWlsOpUAITrTWGp06o9p6h6Oa1wphoqJSvTTqed65RqPRa4wRpVKpVwhRopTibZ4rgC1vmTHG1Ov1HZkUjLFRSrlbS4FdDMPwGABAGIa9hJBlhNDOu4lS6iulRBzHnRjjGiHExhjXa7Vav9baBgCI43gMAAAhpBzHeaxSqXTBDwkzwQBY2LKortfWTKXSxkZvXKl//RtVcOw8tqyyLBRG6e23XpTzC4tSyhup4JvwW+9PWXG83FA6BwDQGB4atV5452py/wOdprX1Ov4pKeVgUxLHZ4wV6vV6WWud9X3/A3v27HknAMDi8srJmDCocOv3ZTZzg3vnnWfh7m9d9w5GqZTiY/svJotLLXJpubP06+9rOH/4+7PQ28vM9LRQm4XDulazdr6gFKhyMaeK5ZRcWXFIOhWH587v56MjF0m+ZasSct++opydzZNMZicnCmUydaS06zz/jolkcYnyvXvT8aWpxAQBw4JXAeCHHE+WIAAAiHG2fSx68skOMX5gEQidBc5FsrjUB1FYu3YsvKcHwqefHgetNR8YmCH53GVs2cZ/4slx50MfvGgOjCl7esaE6xuEHTt6+dr79VzErmdpF7v4MYau1/oqX/y3j5habcf7oNbWUrpUyppGI6VKZYtks7EuldIAANjzfPbOd1Tinp4dziHGWFFK2db8O9BaMwAAYwzXB8aomySXcRJHanWtnXR1brL+/kvOzTdXsJeStLNDYi/ViC9c2EdaWgKcSi0kV65cbyjlcmGyudkFYYggjpFcWemIZ662076+tejU6X2su3uVUqpg/GDBeF7Gdd1VtQWBMQ7A94P4i//aSV/3mnmwLKvpVUIAAJTS5WbYbBtGKVW1bXs1iqI2KWVOKZVGCCkuhEpuu1XD5/8la+75Tkb09JyH7q6EWdYcpZSwhx8p4HK5hqUMzPt/uz36xN/1WcdvOB3msjdjQlJRFB2nlF4JguCw53mbvu/nAUBblrVUr9cPeJ53UUrpEkISAKjWarX+ZoVdqzEmNsbYWuudaiJjDKaUlpIkyUspM4yxutZ6GQDiJou4hRAqhWG4v+kxY1prppTKa625bdsLQRCMSilTtm1/QCl1XGutEELlc2tx2oChWQsHICVSn/u8jBfmQbhO4na1bTpRvS+TSs3EcWySJBkEAMAYx1JKDwCcZmI1o5Ru2ra97vv+Ti4LQsg4jlPCGG82+aIwQsg3xghjDHddd8kYk0UI1YUQa81Q4iZjrGZZFhBCDADwIAiu471CCJnmnDzged6bMcavTJKkv8lzBHh0dB4/+JDGlqiHTz09Sjo7y3J5pZ11d1WcV7y8GAeBS2695SFvdNQSQvQYY2qK0jBM4u1wIBU3n1iiyysVyw8sdfhQRQFkt89vWdZVKWUfQkiFYThojAmklCeiKJqdrKl3LSX4Z8oKv64KbNhL6izp7OygI8MBW14t8z3DC3xkZF3XahEkMq/W11tASgy1OoeLU1Lfe68XPvzoABvoXwApPe37DIwB2tlZTGau5rDnxbpctlQQ5okQkVxY6KH5/JJ1w7GKLlc8VSrZfGhwCXEekXx+AaI4ZQyI+MrlHrW+0R5fvJjPvvENc+LAgbJWqiavzraDEODccfvV6OqcbY3tm0UIMbm4tDPnan0jLQ4c8HXgSwSwllyZ3rczF7fcfElXKpFaXc2BUjiamGhRG5udOggw37OnGt1zzxCemFyNHn0sRdvbN3QcdaZe/KK/Jun0D63s5xh2jaVd7OLHFLOzs3+rarVfNxcv2oiLoq5UdnaTiHNwn//884AgwZxpnMn4Ys/wOjt+tKT27ycmnbIMpTyVSl3QWuc9zysIIeqU0mWlVKi1zgIAWL5/Nfyd3zsoq7UsbWtTcnY2gx2nrioVLzp7tlsuL1tqbS1NBwdqJggk6+pKEGNl2ta6TFpay6pQaDFRRJ0bb5xI5ud3jBrseT5JeVKtr3tyba09WVlpc97x9nU3n9eVSmVISpknhMg4jiNzfiLU372vi8wtIPPXH895vb3TUW9PDhBCGOOiEKJkWdYapXTNGKPCMBxECC0JITaUUi0YY19rbVFKZ+Ik2SP+26tn4Fv35OW/f6WH/OApjl71ShY3Gj36N3+b4/mFRvDqu4atp0+tycXFrLz//k7v5S+dCygd1gBphBBVSjlBEOQopSu2bVu+7/cKIVbT6XQGAFAYhp5t22vGmKrWOojjuJcxNr0tmyGEOG9Z1jwhBIIg2K+buTNaa66U6oqiqCOO45YkSXLN6rM2IQQIIda01sLzvNkoiloJIWtJknR6nncxlUq9Tym1EATB7UKI059+cuWJl42k22QSR0EYenJszPL37e2l3V0lkkSuSpIOAFiJougQpbTAOZ8xxric86IxxhZCXMUYLyqlOpuUAdMYY9u27SsIIay1pkEQDHHONxFCTCnVjjGuaa0dxpjv+36/1jpNKVVSSlcp5VqWVbBtWwCAxhirprEJAACO43w0l8u9g3P+eEtLy5+3tLQUtNZPtLW1/b7W+lGE0ArDxCInT0o2MJg22Rw2MlHy8pUOubSchzDUkM1swqteeQAQakEIBZxzprU22548BKAB4RrcdFMC992f1Z/9bIa87KVXFSGtQogrYRjub+ZVuc37keGcT4dh+DMAJrWSkJtCpXOdE6dmvT/6/Qq7+5tGtLRUgq99fTCZmemknR0b8YXJvbStdRN7Xp2k02AdGr8cPfXUiHV4fIOPjPj+Qw/v0Y0G4yMjc6pQyOJcdk2HYRan00VdqaQgDIn7gudP40xmFaVTyL/3voOqULDdV7z8HD9wQMTnJzLJ9PSAaTQsPjx8SS4sbHlajYHw9JlcdO58Ts4vtLsvf+kZkErHU5fb9MZGi6rV8jIIPN7ds64KhZ3fiWR2NqMbjRQiRKrNzR3DkQ30b8aTl/pByus8Z6bRcE0UYZJJSz40tBE89NCIWlvLIYyrmde//sMkl72e1Ok5BPx/brKLXezi/4dAcRwfqzjOUPIn/48nP/6xPOnsXAYAYMPDRfvmE6XG/fePx5MX90QTF4Z4f39Z1qpt/le+tj/+9d/Y605cWPQKhWUTx14QBJ0YYyOEkJZlZRBCdjqdviyEWMK+v8V7XCxayfT0FkldTzeKJy/2XHsxYnjPvFpbaw1PntynK5WOeOryeDw1tVccPzaTfcubL/uPPDJ+bXtdrTo4taW/igTX/IV3rgacp4MgqKTT6auc83oURXkhBLFSngcAkDz2WJupVFDjD/9oxFHqCgAApbSstcb1en2/7/sH4zjuBwDQWqeTJEkRQkLHcRZc1z0XBUFXarMwwa9elWpujgEAqEuXbPWan25NTUxO6lotm5w/v8/6xfeuho8/PtCcZFBLKxEihGOMlVbKTU1cmLAKxTNUynqlUulRSrlhGPaXy2VSq9XSnPNqHMcdcRyPhmF4hFIacM6ZZVlnHMc5F0XRuJSydVtu5dnguu5ZIcQpzjlCCIHrunWtdQohVKlWq3sppauMMWXb9kYQBENNzbtBy7KEUuq2D7yodwCM+t1arXYkjKIeQKgEABAEwT4hRDGXy800WcaREGIxSZJ+KWU6DMMurXUxDMOuKIqOa63dJtVBFgBihBBYlrUWx3ELAEAURf1hGPZQSsPtSjcp5eb2OMIwbL9mTEZK6QOAqT7KdKiCAAAgAElEQVRDTgMhdLG9vf2i9ZWvPkouTWkAMLlcbsK27UJfX9/XRkZGfiM3O/tKVSzOxFOXGuDXA9ba1rBvvfUMAADq7i7Hb3urTTn3jTFPaa0DALAJIZbjOBOWZZ3KrK1fMj/7hlb2yCNYXr5cwYTUzDveuce5NHWSnTzZQAA/4hWJomhMKZW1ZLRjSKT+9XOBnLw4nExdztc//re9iFINAKAbPgYAiKcu9yczM32yVEr733/oEABA4977BkwQFEFrAK0Bu44GAJALi4PW+PimfexYabv/+re/MxbPzHRBFG15IY0BHUWc/NSrHO+Dv/2HfO/e/wAAUPXadWEv5DgRGx5e4iN7JsJzE0PJ9HS3aWyJX5tKlbFcbiWZnf0RnTu1ssqQbRWuPRY88th+NjhQt264YfqZ7XW57MrFJSc8c6YHttYd0LbWf+VDg8/Z5G6A3ZylXezixxLr6+u3KKUOKsfZ+kFlDPBH/7xKP/Q/KsnMTF7XajW2Z08xmZ7OAwA0vve9g9YNN5xOjDkKSkHjAx/aBwDg/NqvzIrXvuZMqVQaTafT85VKZb/F2HoYBO24VltBU5f5M8+tCgXChoeLsljMWIfGF4KHHh5MVlaczJveOGOCsCWenV1QhcKWcRQnucb37gfW11sDSuvJ9MwPk2F7ezecb3w1wcWiUY89boTjRGEYdgBAybKsBWOMp5Qq+3v37qX/8bVLvFBMwre8bZwMD9fwlenQOnpkBWMMQRD0eZ53iVJKy+XyHgAApVS31tqyLGsGIZTlnCfm/MRi8O73HH3mePhHPjxR7u8fcT/yZxei3/uD/fLqbCdqyRtdLCFkDNCOdqE+8CHDPa+E3/hzS8H73n8EAIAfPzZD/uxP/W0epTAMc4yxiuu6fhiGBQBoAwCI4zgbx3H22nNGUTRo2/aZbUbxpvTIZYwxkVLKIAj2Nb00V4wxsLm52UMICTnnkwihzqb2GwOAJQDYt7q6+jkp5S2e5zUQQiqO44NJknwUYItyQUq5w8AchmFXvV7fSeZXShml1I63gTFWD8Owb/v/pqxLkTFmfN8f0VqnGGMLSqmdNlpr3Ew6l1rrjuZ5Q0ppBAANIUSpXq87xpg6Y8yO47gTAMA5fWYSFhfCVHfPxvrnvnCw8qUvf7n0yU+5QFkgRke/2Pf5z/ze9jniqanbgbHXkmxmJXrg+z1o/GABNQnck4WFFvfc+XVy+21zidbnGGM3UUp5HMc1SilvNBo5Viqts94eE37sr/eaIARkWYqP7T8X/94fHDTlsrBf+5qr6j3vJpEx17FVY4wjn3k+JABYayDLS1nZ/MzU6+42PTjCGPGxseV4crIbjAFTrV5nzGitW92XvHiStLbS+t139+6M6+LFnCqXd4xH5Loh6+1dCx5/Yif0ae3fbyvbVvL4sXfQY0fn4qkpoJksXJt4Zd94w4L//YdGAOC6jczWhzaA0nUTRc9qM+B0eiePig0NLhsDHslmFoGJrH3rLWdBSqGrNRIvLAzx/v5LpK31og7CYVVvZN1bbllh/X3ff7Z+n0vYDcPtYhc/ZlhYWHhlpVL59DaLc0tLy7z1xJMb+qN/SeTV2TYwBkwQCOvQoSuko71Eu7pW2MhIyTQaQ2p9fcf4IX19EfmJV6wGLS2HjTEMIRRiY0L729+J5Yf/PKP+5hOd8oEHW595flUs5VhPTwNxjtXqqksH+qZMGDnRzNUuTMhM8Pjjh37Y2iDr2NEZ2t6RSNfj7N3vXCCNxhppa60mv/TeDmhtqdWEGE7396+TXNYSlmUIIRwAsOM4vFKpDBsAwRgL6H33cXlpKkM++FsX/LGxI1LKVJIkna7rXsYYM8aY0FpHSinHcZwLcRx3SilzhJCNMAwz4DjSfO4LLdeOhb/tLVPxbbd5TIgG6esFeNUri/jnfmaDvennC8Kx6skPns7G37qn1cwvuLy750r4D588BEohAADynvcs64H+qEl1kAcAaJbGyyRJBGOsoJS6zki6FlrrvBCiQimdNsbEcRyPJUnSoZTqMsYwAAAhRGHbi2OMoUqpdsdxLkRRNAgACCHUwBjLMAxvalZ03Q0Ao5ZlpZuVdXNJkthKKY9zvoYxJk2R2J2XJiEk3r5Ozvl0FEU7vDyc81nOeU1rnQuCYB/nfIFSupEkSR9cQ/cghHiMMfb3lNIuhJAvpexgjFUxxiaO43ZK6bpSioZhOBZFUYtt2+d1krSj973fkQ98f8C/73s/G8/NvkGtb/TqRiOta7UWnEoVESUPWofG6wAAxU/94/OjqcuvVeVKxr7hhkuqUGgHbRL+82+oRL/8S5bf0tJPGSsCwNPGmBAARowx0rIsJ4qimHzmc1LOL7RCFCETxxikxGp1tdO58cZzyfx8l5q8mMNPPWV4b+8F3dmRMgAcmpO8RFK5xABpCaql3IP3hnxoqCYXlzLX3k+5utpG8rmCKpWztLd31b75xBwgCKyDBxb5wQNramklDp9+eiy5OmuzPcNTtLV1GQmBxfiBhejU6R0Dh6TTKllf7wK5ZZLZt9221LjvPk+trYWs4VOdyXok8Au0pYXH09OtAADi0KGL4VNPPSuXFz94YA1hIkk+h9nISFXOzW9dN2NgHTu6yPbvWyCWRUlbWwGMWfde9KKX6VrtxvDJp26Rc3NZubjYIZeXW8XBAxu5t77lqzqM5sT4ga/zoSELZzNz7i03D/GRPR8U+/Y2/rO1/lzArmdpF7v4MYNSqksptbMzLRaL/Zbrzsi5ua6dsn5jwH/wwUMAAKSzY0OMja3KUmmrik1rQIwp/Mf/82Kto/0INBNoCUJBpFSXzGUn1eUrvc88L85mi7S9bUUVigM4n93Ua1GX8/w7itV//beD6Z9+7bnGE09mg5MnD177HevY8Zn6N+85AsYA++w/n2m0tx+B4zuE3aCiyMtksxdMa6swAP/CMH6DlFJZluVGURS6rnvBGGNHYdgtbr99znrlKycbnI1uXzMAgO/7e1Op1OUgCEJjTNVxnPVGo7GjAbedY8NTqRh/+p9Os+VlEdx4Qx7VG5uxbWUQ59lUKrUupaSsvc0YY6pSyhV64uYpdPc9bxf/+A8VXq3WSaORCt74ZgwAQPeOXvVP3HgQpARCSNl13QmttRUEwZ4wDHMYY21Z1lQcP3tkglK6RAjZkFIOSyl3QpRCCB9jvGSMCQDAS5JkW+y1whibAwBJCCGU0hhthdaE1ppblnXasqxLPT09v7K4uDhXr9dfDwCTWusjSqkMQsg0CT8VY+yqUmrHoI2iqN/zvNNSyg6EkOGcn5dS9nPOr4ZheCSOY7Bt+zRCqFdKmWeMzRNCVhFCpFk993ee530mCIIbi8XiHwOAsm17TinVFkWR07xH4wgh5bruFSll1WwWROapH0zVisX92+tVrW9s5cnddMOECaMWEOzG4t984klVKNxh33B8Mb585Q1qZcVybr11TW1sdCYzM61scLCmNjeNblZEljWGFWMN77fUEbzF5h7Xl5YqrFRm3nt+sa3ws2/gYmTPRHj6zJZHr7NzM5ic3McPHpgArXPx5MVu9f7fOm795Kvm0fDQXHjXTw1ipRqtTzxUSI7ePOJend6MzpwbxZ6XOLffPuE/8kMSR6DUsMGBejK/AMhxeOPe7x507njeBaAU/IcfOeS+4AVXtO8vIUYr0ZmzWx7OttYIbxSKtLdnnXZ0lHS1BrrRaIdCQQBshdT54EASPPpoLv7K17L46tyEqlb6aCa7bjDayg8iBIBReu0zsXNJA/2rulRK5PJKh1xczJJ8vmDfeOM0sq1Yrq5a4clTQ3zvXkgqlSwbHFj2XvKi3+384/85vfrbH/igWlv/22Rxcedh9b//0L54etqVK6u9iLF3IcuquS+88wxYYiH9Uz+5/qwL/TmEXWNpF7v4MQO9OosEwafjfP6AAeDGGIhG9gzzjo6CnJtreWZ7Nji4kszNZ3lP1xX7da/N+4882sCWMPLe77rusWOn5fiBPMMY0Oe/wFMnbpqQjzzaAgCAMumY5vN17ft16/DhyGit/fu+d5ANDxdZRxcKHn08jYVdTL3tLdMokyVsdm4pqTcGlO9bAACkv78WPPlEz44xJpW2bfuMEMIpl8ujAABKKQ4AmFJqSSlfZoy5AADjUsqg0Wj4YRgeAtgiqmx0dgwCQCwYKwRBsMVGTcgy53yjWq0euWbIs5ZlnQUA2izXV5ZlNaIo6pOdHemgs2NLaNWxt0JRSQJJkhQdxxFRFBWNMV8mhCzSx5/4gfPOd7ysIuVYanh4Nbrn2/WdOf2Ft5eNbT9tjMkTQqrbxlk6nZ6OoignpcwbYySltCyl3PEu2bZ9xhjTizEu+L5/FACAMbbW5JZym3p4owAACKHYsqzNZpuZ7fL8KIo0IWRNSnnAtu3LTZLKDmPM0Nzc3JsHBgb+bHNz8+4kSdxyufxdznnNcZzNarU60ORCcrepARBCkW3bl2BLzHYnRCqEmOKckyRJDGx5ytocx5lMksQBAOM4Ti2OYzuKIieTyXwhm82Ws9nsfbVa7QdxHN8Ux7HHGLvCOXe11lkpZYsQ/x97bx5f11mei77fsMY9S3uSbA2WLc9jYmdw5gQykQAhpZQydqAtLZRCSNvT9lLO1N5ygFJobwegLXAopVCgKUMSAgmZB2JbtmVZkm3NW9LW1t5be+81feP5Q1vCTijn3PvPhV/0/OXf2mv4vrWWv/XqfZ/3eawhz/MuAwBI/u3fXWg++NDAS99V6/LLZsIfrgbcOBYTqtWire99//XeYz/oY+Pjd9C+3jqbnsLW3r3LKOYuGF1dwM+c5bGPf2JENRoErrgitvVNb7wdHn7cDxMJCY8+FuG77wL/Pb+9mzEGOopQNDm1w9q/b0R5nsTxOImGTu5iy8t7kGUJY3DbsNnTo8MTJ/rkN7/V68zOLdHNm+b1J//yQMf+A1Pg2JgDgPI8QwuWAQAg+XxTLi8nnCOHR2TLyztXHDmjwlAb1183R7I5g42edXSzBa1vfmubtW/fBM3nQ35hYnXCSxWLLVV2rZ6nMMdr1d3u1VeN40R8HicSzNy6Lbbyla8OAgCA1khWKzEABNo0N9uXXVYDgGHtecJ//IkDAAC0t/cCTsQC1fJTYmpqM01n5sOhofWAR1arnUG1ur5GINtmdGCL5z/83c1iZWXQ3LFjFwB8o/iRP3t+6p57TyHHLgNAF5+d26mDwBRzpc0AADqKkI6iZOs7D15nH9z/ly99jj+N2CjDbWADryDMv/++a/2/+uu/kF/4nzuMd7x9UWqdIABl55//pYS8FjOKxVnckZkzNm2qqCBwSEemxRcWN+GYK6Jz57eoWq0hSqVe2lVs8qeejvGvf31QfeWrKXN0tISbLa2feVYSIal71ZVLorLcVJVKh33w4Jz/ve/v5hMTWQAAVas5OJn0Ecah+dq76v5NN2bF9sEQNZuIP/lUwdq3d0XWara1fXCcT071rme7br5xPkynD4Vh2Om67mmMcR0hFLU/pE2tdTyKophSSjPGIoSQWhclVMpVShlKKVtKGU8mk2NRFGVN0xxfCyLWQCm9EEXRQSFETkqZM02zppRS7Vb2Bue8gxDCMcZyTUXbsqwppVSDUvrH3d3dn06lUifq//zlm2S9dhvZs7vMgkBySmP62w/GjF07z3lvf9uetgBlZk2ZGyGkCCHVMAw3U0rLjLEYQggjhIjWmpimeYYxtl0IkcAYN9ZUoS3LqpimKbTWVjvb1AEAQAiRlNJFhNAK57x/rTQHAMgwjEUpZYdhGMsYY880zXIYhpuEEDdijCcMw3jcdd3lVqv1q4SQiud5g1rrNZHJtOM4Z6SUadd1R4Ig2AMAAgAirfWatAHhnCdXp4WWtdYpxlinYRgLYRgeMAyjyjnPm6Y5CwCKcz7qOE7IOf+eaZoXGGO3cs57pJQdCCHPcZxxAEiszQ1uvGHSOHigAceOS/NVt9TxR/+sZF9xeFb+4HFb1WppAADN2GoWb1N3kk1OvYZs3hxiQsp8crKHnz+fFXNzeZxIzIfHjh0U584X4nfeGejJKdD/9CUNlMbUwoLQd98tW/ncgfjOHcfViSFXt1o2dpwVkowDsl0VDQ3tXH9xpMSqWs3zick8IiQ0enoWURhUg+88uB+kBLW4mFZzcx0AAMbWgQs4m2vQbJbRns3CLBamaKFg+N99pFfML+SQG3OjF4/1sJGRrFxaWhcfleVyhqTTC2Jh4VIhS0IgftONKHzu+TQfG8/L5eWcKM0Xo+HhTu+rDwwvvOc+cLf0T+Enn9hqbekvBU8+VWDnxm12/kKRJFOLolQqmrt3nhJLSwNyZq5LtVpJ95ZbXuSlUsG+7NA50tlZFfPzeXgJzG3bqnxiols3mxiEQMR1k6k3vfEzAACa82e6//zjf5v5pXf+DZ+cfJjmc+dkpXKlZmxdMZ6kUr575VX/7lx+2VP/u7Xr/29sZJY2sIFXEKKzZz8hl5d7jd6eWfr4E56+6caG8Y0HlqO//4ddL93Xvvyy86Qz2/Qefvggr9fzAADR8Jne1fOMZu3LLzvPxsZs0pldkY2mGzz1dN/6wW3RSQAAMTPrOkePDgVPP/2j0hbGK7SrS4f/8tVOtHdPECaTOzp6e8fRkcMjqtly3KuuWmDTU/k13gUAAAp/1Gzk+/5ex3EmACCGMZ5WSlkAoNvt3hCGoRVFUV8qlZqTUjZardb6/DKZzEy1Wt1p2/YwIYRorQPGmAMAEI/HxzDGzsWiigghFovFkmEYemtlrbb9iGWaJkgpRaPR2G+a5ng8Hk/Mz8//OmOsimq1Xwz+7YEB+NKXwf7lX3qRVJZCqXWW/N79Lcd1T2itMULIDYJgm+M4p6WUBd/3BwEA2m39+wAAbNs+DQA7TdNkrP2hYYxtc133jNY6rrX2pJQxjLHZarW2AQAkEokzURS5YRj2KKXci5+rZVmBZVkRAMwihAhjrBdjPJ9MJocbjcaeSqXyl6Zp/lIqlfplx3Ee8Tzv9ZTSOuc8AwDgOM6IlLLLNE3f87yDpmlWXddlQohyEAS6befiYIyllNIGgG7HccaCINiOECLpdHpEaw2u6862TXc/yDn/naWlpWOu6/5WMpn85Pj4+GEp5dtWXyVjxvf9ywzDqLQJ9w3OeT7avWtT7H9+7qShdNbw/Xjzbz5NxYWJPngJ+MLiPpJOe6CVQKYpzT27x9jwmVV+zqpCOdgH9o/Ivh6EOzICEYTIra8C6sbspZg7SDCuB//jowewkMvWFUdGdaNhRqeGtyDTVNa+vce1VnF2+szgxddUtXqK1eopY3DwhI5eLh1Es9lm8OhjBwAArAMHRqOhob2AV5vTaXdXVczMdLzsoLX3MZk0rKNHT6rGisvPjm4FIRDJZhvhyMiP1SiqpTqchoR849pX53NXXXe+8KprtgEAyPkFEwBALJX77CNHTvCpqUG90jDb9wWQ1o6YmtqkGg0b53PIuerKU8Gzz+27+Nw4HltmU1NZ0t3Nzb7eB7IffP+bAFZZ85m3vXVxbb+uP//Y8wDw/Pkrr/551WpdDgCATFNZlx160H/22UbHr/3qfzTdnxpsSAdsYAOvIGR+9Vdujd95xwdSb3rTvfIbD6T1696QiP7iky8LlMztg6dxIhl4P/jBQfvggeG17db+/VP2oUMnEMY1USqlSDrjRyMjObmwkDUGBqbdo0eHSU9PfV2pGwDYuXOD0fDwHmPHjmBtm//ss/uCp5/ej7duWY6KxR4AAPb880H4wg93sbNn+/0nnthBkqml9ZMYBqB6/ZL1SiuldRhWwyDYDkpZ1Wp1SxRFjHPutbMgyPM8EUVRVywWG8EYcwCAer3ek0qlRgGgMwzD3UopallWAwCg1WptZ4ytR2hty5FYtVotNpvN3YSQuOu6F4QQlDG2LQiCbYyxnQAAGGPu+/5/bTabH4nC8P8RjnNkfbDHjzu6XAH7zb8wGnR17QqC4GAURftc1yXtY03GWO6i664TSMIw3GsYxhTnPG9Z1jwAgNYa+b6/G2PcBIBNWutQKTXnuu7p9jwGlVIdLw2UbNueZIw5zWZzD0Kowhijtm0vmKYpEEIoHo+XCCE1Suk85zzBOW/E4/EVKWXMdd2T6XT6fNsAuCOKVtvhtdam4zixVqt1qG2ZArZtT7bJ6u3HZ0TJZPKMtYoE5xyFYZhHCCEhBEcImQBwg+d5zy8tLb3Ntu1HDMMYcxxnPIqig47jzFuWtYgQymGMXSHEJkppaBBi6b/9uyj8s48QMTq6zdq/f+Kl77Kcnk5oxqpsdKw7On16J5+Y7LH27jlubB0YUX6QNLZurQIhkRw6lVy56ca97OxoTP7956h/9mzT9PwXnanZWb1cJbJazetcNoAP3reCOju5ZgxHp04fIskUAkIALAvM3bvW33GyaVMFVoPFS2Bs3boQlZd2A6zqmZGOzGrGr51BNXp6a7rNVTN/7t4J68/+9BRAW9Lj8OGhaGioX95/X0/w8Y9us+69ZwgAQC4uJtnI2aJ7803DL71eYnpiXVkbS/EyHSPS2VnDiYQNlkGR68j2AwNZq0oAAFWrdVLHiYJnn9vn3nzTOeSsNt7RYnEFJZPIue8DY/YXP/9E7oP3vcW9+uqfpJOEwTB+dH86O5fZ2dHDgNDCTzjmpwYbmaUNbOAVBLlcRarRYLV//McvyKVVY1rkOCJ++21TbPycwMkE0kHIgZBO/7HHVlP97SADAAAh5IXHjx8EADC39J/2n3xq9a9UzwO5tBTjk5MQu+P2CfPOOzw2Pl5jU9OD/Px5S0cRxpYZAYADjgPQ/higaq3bra8sGv/+zapcribWB2oYQLuKkfa8KVIs1OVKo0CooQ3DKBNC5iilNvz+H5iSYNfat28u2LOnhrdt7Wx/yBNKqYzjOMOEENRqtfo45+l4PD4XhiHHGHuMMSMMwyIhpEUICQCgCQBJQkgdADKJROJsq9Xa5jjOVBAEvYZhlF3XbSKEZLPZ3L1WfmsHYIbjOBOe5+2mGFec0bEpuVSR7KGH1zNp4dDJ3fY3/vWC5zibMUJ+O0OSiKLIb++yHqDF4/Hhi61BAFZb6xlj3bZtX6Jb43neHsuyFjzP2wkAkMlkRpRSFa112TCMqM3p6ruIowVritbtwMNo27QUEULHgiDoBgCglI7VarXnLcsq1ev1AUrpNMZY1Ov1rZZljbUVujEAgOu6F8rl8pZYLDYNAL2ZTGZOSomCIFj7faTVam1RSsUdxzmptTYRQgnXdWuLAU5TBTJHZIgxVoyxoFqtfg5jXKKULnPO+wghLSmlsG2bGoaxVKvVtjuOczoMw8HQskKycycXTz/Tbe3bV2HT072J1732FJ+fT4fPv/Aj+YLOjohfuAAAANr3nej08HrpleTzgX3oEPUffSyesqyT/szsDv/cectaqS3pA5eHjWuu90wpASgFuPfetL95U3/8/e87ob72DYcdO7YjPHZ8q31g/6TyPC2mpvvMXTtPo3hc0HTa8L77yCUNC9bdd50Kfu1dW93p6bPhb713n2YMZK3WuOT/aGPFAM6B9veH9Jqjob9j+6bkr79rmJ8djYfPPb9PhyGmk5OjsGd3Rr7trQPk+4+tyKWllA4CK3jm2V32kcNz4Qs/3AQA4F577Wk0fR5YMnWSZHPpzF/9+SX6VNahQyVMqe0991x/57cemFypVpNGubxCVxq89ft/sA8AALmOkK1WDAAgGhvvxo7jyyBwrYMHSvD7v+tq2x5XWn/cvfqq/1B923/mGbLy9W98is/MXguwGiSaAwPPqFbzZmvnjrn/6LifJmwESxvYwCsAjW9+y659+jPfjsbHj6j6Svzi32ixOMFnZ3h0+vRuQAhe1hUjlUYdHczeMTganhhaJc8m4p5W+mWml0Zvb41NTnbIhQWfz84OOkcOV3UYIjBoAAhZtL/fAcaY0dMzG54+vQvbdiP6zfck2fLyrouvm7jzjrP+s89totmsF50e7lWNRkZPThaNm26Y4ZzvxlqX5ZmRvKxULPD8qnHdtRFpNidQLEbbTvNgmqa1VpYyTbNKCPEsy/I9zztIKZ0zDKOEMV5SSvW0gySglFbCMNxm2/acaZozSikmpXRt2z4fhqGNMXYsyzotpewxTXNOSmlyzrdrrUPTNOsIwEDxGIHvff+SjxLZvJkFrptWSplKCFMIkUYIQTwerwIACCHW294YY67jOKNaaxRF0aBpmnOU0pAxBmEYbo3FYqeklF1KqThjzBZCpGKx2BjGWNRqtTV/uWwURWAYxspamz8hpBa1hQpt255DCFXj8Xii1WolY7HY9Bp5GgBAKbUPISQYY5sJIb5lWU2llIEx5lEUbbdt+3QURTscxxkPw9DBGEee5/UCAEgpxy3LAsdxRiilvNlsrgd+QRDsZ4xVEUKOJFY032CMYoC0iSVe7bhDhmGMMsZ2Sik3WZZ1LIqiywAgGY/HzymlBKXUZ4wNxGKxMcdxOtDePbi6uGjJxUULACAcPqNIKlUDgPVgSYdRHicSoJrtJAtCYO7cccHo7/f59PSArC5jnE43vM9/Yb99+PJhmi9wHfiWm3Azcvi41QAAms95fndXv0HIUvSRjx403/mOYeu+959BH/s4ikbHdmrPQwAAbOTsXvvw4RH/8Sd+xGdaw47tSlmmC4uL67IJYq60E2UyWtdqq8ePjm2Kv/buc7xWy0YH9ucE5x28VCqz8XM23bx5mU9N5YjvcwAAHAQBbzXXM5I6CLAoLwrnmqNTQChjExOd8LGP5vsOHZqUBCf4iRNUYbyexUIEl4MfvnjQuebo2LIQRRmPJ0UqFcMtj+FYjKlWy8SWHaogMN3rrzvnP/7ENmvf3vP2FYcbWiojOXRKR1dd8c5Cb+9PDHjcq6+Wtc/+46PA+bsBAGI33/Q1kkodjcbGxp3Dh1/mLfnTiI0y3AY28ApANDxcUK1WEyEckUKBkelx0UcAACAASURBVEJhPZOBYy4yikUbpVLhj2sfBtfN0VRqOnjmuX06CDEAALLspiiXVzWUEAKcSnkAAHxyMmPmC/VoZCRv9PQ2g2eedRKvu7slJqe2REMnu2lnR1MsLSWi4dP9JJHwwqGTXapSyV5yXcfRYmHRAQRMa52xdm5fBACgO7aXXdNs5XK5mmHbzLr9tgsAAPz5Fzr8N/7CTvXpz0gpZcYwjEXLss74vr9t7ZSMsQ4AyGmtaSwWG0YIWW3ittvWF3ItyzqvlEoBADQajaJt28r3/f2maZa11gmlVG8URVvCMDwAAJwx1h2G4Q4pJfJ9fxdjLK20lq1Nm7awd/96zLzyipW168vpaZNWKrMX31atNbRarYxt26copcZFY90SBMGOKIq2JRKJkSiKNnuet14qxRjbpmkuUkpnXdcdopTOeJ63vS0yeQk456l2FgtglUsSWZa1ZFlWk3Oep5Qa8Xh8eS3QsSxrxnXdUd/3e03TnCGEnFNK2ZxzO4qiAUJIaBhGPQzDvYSQFd/3d0spc4SQ9bmFYTgYRVEUBMGuZrO5n1J6SZlFa20mEolJm2K6K2fCvoJl2JaVkFJy3/e3E0LWxRiVUt2WZZUAAKSUURiGnpTSUkq5QghdqVR6mgCRdfttp9aOQQY1ZXW5CI6taHd3yxwcHMXplATX0c6Rw2POlVeOuzdcf4aNnB3wHnxoL0kkZv0nntxN8zlk7tp5Ljo9vDOauLBTSxlnp04Frc9/oQAAIErzMfcjHz1p/dM/z+lGA+QN14GOolh47Pgu+8D+U9auXces/fvPWPv3z+tWS+koepkxLFlpUAQg1XPPdwIAkO7uRev977ugwxChYnFV7T4MSXjsuEuLBYUNwzK1DvixE4XYNUcjPjWVc6+68mT0Pz622S6X57DnRzqMLvmOi6mZPjYz3cGGhzeJmZku3WiQ4NiLA9j3pcbYco4cPgkAYOzYURFzpT5QCsInn9oeP3ZizrKsU7FYbCKxUi+rVssEANCMOdiNNYMXfthrDAysKMbyJJGiYnY23/jqV/+m0NPzf5QZYpMTdwPGkLznnoeN/v4Ynyv9bern7r0hftONP9XK3WvY6IbbwAZeAYhde81K5h1v/2eciD/oP/HkL+owtN0brx/h5y/kZXmpg42NZ2I3XHeWZLM1MTd3iZAkzeUCNjJyiSqx9v24eWD/BfzGn1tEv3u/ha44smRJOU1su6SkdIzu7jKfmuomHZ2z/qOPbY3ddONZPlfKidk519y+fVSUSkXVapnA+ap44r59o+bAwBLpyCCzv28heObZLbrlpYyBLefEzGwXyWUXrde/rix37iSGYVBCCJFKIf7dRzKgFOBstox/890iiMd7AAAjhDwpZSchRGQymSmEkC+lRIZhYMMwKABIpRRb9XBFFCGEOOeWEKIbAMB13XNaaw8hFDMMox4EwSbbtkf5qicaYIwb7Zb95MVBimVZo5zzzQogDq9+lefkczPi6WeyAAD43AUEt90aaK0v5hEhIUSBc553HGcMIcQopRe01haldFlr7Wutc21bkfPJZNLzfd/mnBMhRBdjbPNaV5zWOokxZhd1vQEAAGMsRyltuK7bpJSucM6TSqkExnhhZWVlm1KqtiaKSSmdDMNwDwAg27arQogiQijIZDJIKdWMoqhACKkppRJrfKhkMllFCHHGWA4hJGzbrhJCfM55HgDAtu2TnPMex3HKWmsbY9y0LEtYpuFEfmul2Wj0a62nDcOwwjDMCCGypmlOSinTjuOcB4B0m1cmYNV4eMG27abnedsBAIuYm7FGxxZgqZJUQWCQTEeNT0z2xm688WR08lSvXF7OiunppPY8JEqlTnNgoASgN/Hyko2z2QZx3RWzv6+MKPXDH764C4TAOggYSafnjMGtJHz6mXWLDzkxWSBC1GS5nHP7+1bUNx7Qcmamg3Z1L0ZDQwfl4mJOLi4mnCuONPjsbNo+sH8i8cvvbImpaYUzmSX5ljeb0JFR8A//aOt63Yp/8L7SypVHdib7eifcX3pHK/yXr6zywDiP0ff+Vpmn067znYemo4cf7kExd4IWu2LBD1/s1b5vo+deaNHR0Za5daBhDG6fM3o2M1mtOjiTadmHDs1EQyc3XfQSIDE754IfEFGpFBJ33jEsliqumJrKomRSpb74hQl89qxhXn65ZZpmUmYyvnnTTRVr756yPHfexZTMIEBpFYbEKHZNeN/97r7E6+5eQKbpGN2bvkILhZdlmV8KUV85H7/pxm6S7TxBU8nfK/7ff/Jt59DBn4lACWAjWNrABl4xaD783Xj9C198Oy+VbtOeR3UUhbS7u+recEMZGXRalBZiEIaa9vYwubi4zh/SpmkhIUBzfslfsKQzI/xfe9d2YVBHdHR0kK0DUnzhiwPYtn02M1NUlYpNstmqrFSy9v59ATs9nAIAcK85WiWZdAmZVs3s2TyN44kGMg0dvnhslzGwdZ6m0wKn0vNGf185fOGH+xTnjlqqpGFlJSFvu5UrpSJKKVUIIvngg1QHgU06O4LgHW/vBQCktTYppYxSOmmaZqXRaOyQUsZt2562bdtQSrFGo7HVNM261jprWVbUbDYH2xmPUQBYoJTSIAh64/H4dBRFoLVGSikDIWS2zVITGOOKZVmVKIrWrT8opb4QIgMAoLR25eCgE0doRU7PRMamriV1y80JpdQlJbr2cZNSyk0IIcE5H6CUriil0pzznmQyOdZWvY5jjGnbfy1NCCkRQtTa+SzLGuecdwGsdpDZtj3V9lvDSimLMZZijOWVUq5t2+cIIS7GeNIwjJAxtsZVWlgz7GWM5WzbniKEOO1MUT/GmNu2Pb0WNNq2PaWUYkopRwiRchznZBiGAwCAlFIxjHErk8ncBwC7fN/fihCSmUymGkURqdVqm9qGtQ2McSiE4EKIfHsuU6Zp1pVSThiGm2Ox2IV2MMYxxhFCSFuWtUQIKUspO/mB/V38ntcbsTA8redLSVlZTsrFcsLYOjBqHziwRLu7F8X0TAEsC0ApS8yV3NS9b7ggvZZD87mm/+hje3AmXZILi10kmy2RXC4wigWGAElZWUZgW6a9c9cI6eyYRW7M5rVaB3rvb82rXTsD/dTTOaQV0lLGoJ1NUpyb7pErxuO33+7iRJyQN70xYK97LYoymT4N4NorjXkxNJRWlfKyuO22zqinJ8lcl9grK1NmsbAASrl6dIzqL3/FgoWFCiKkZe/ZTVSrVRHT0wWNMehGIyHmSlkVhHbsqit182tf32z09XnW9kGPZNI0Oj2cBgAgmUyow3CdcmNu6b+AbGuFpNISWZZnvfpVIYpCxYrFprNta0prrQghpkylFNm106SJRMXs7LRpIb+c/NCHJvStrw51rWr6X/5KL9q92zZee5ewHee5n7T2rPzr1zpAydfqIPzr7Hvf8xXn8sv9n7T/TyM2gqUNbOAVAuy6uPLxT3xbN5smAIBqNFOyspxhIyNZuVjuUvV6h6zVsjSfHVdKJ0FrA4RAZn//kpiZSb70fOaf/LdlUihMtRWuY0Y2O42uv16hK68QdH5hWU5N5cCgAJZJxcwMsfbuOYeTqWWcToP//Uf3qFotKxbLXUqpOE7ETVmvOySZnA+eemqXKJUKYnauYB85fEpMThUAAHQYmvbb37qCMJaMMaQAkkatPokJXkAIS2NsbFYeOZLVqz3rCSllgRAyJ4ToopTOI4S053lUSrkCAJ1KKZNzniaENA3D8Nt+ZU4mk0m3+T5VANBRFBUQQpbjOCXTNJtRFOVs2x4RQgxGUVSglJYJIUgpZWKMywgh3jatBYRxYA8O1vHoWCV86pm99J57JoRh5F56Lx3HuQAADYQQkVKmlFJx27YvUEpXAICFYbhdSplijK0RtRuU0gXTND3GWB4AwDTNWYTQMiFknlLqBUFw0LKsBdM05wDAWOtOwxgHhJAaxlj7vr+fc56PxWJzGOMqpZRwzjvb9yPEGIcYY04ICSilzDCMWc/z9jmOMxaPx6tBEHRhjOuU0ogxlrMsa4lzXlBKxdrz+lwqlfqk7/u3EUJCIUQXxlgopaqmadZt2xZhGGajKOpRSrntIDGGMVZBEGwRQnQ4jnNaKcUAwNNa25ZlIUppIgiCXBiGvRhjpdpkc7R1AMnPfSEDQlDNmCmXqwUci7dILpfAv/nuSdOyAHExK0pzRfPWV3saEyXHzzXl0lKR5guhrFbTxsCWKT5ydhu/MJFnI2fzsVtunkUYVaLjJ/bIpaUuzbk0/+Ljk35n516RSnXRN7x+ki5VmpRQLObmXAAAVV8xrT17yuHpUzEIw5bs7NSmUlw7Tt2q10vRH31oOwAASaZ4bP/+WSMIKtYTT2j/818YENPTeVWtWXJ2ztXVqiHn5wvuNUeXgx++6NJsLrLe8ubQ+sD7y/r5F5Cq1x3teYYWvC4Xyyn7skPnwtGxjGo0XNVsmtbg4DTOZgkw5usgcEmhsCC9VhcfGe3hk5M57TgxqC5jaLWa/JprMjiVbBBCKAA8TyntoZTGWW+PMtIphGMxgvp6H+LPPLOPZLO+8XNvWGjdftsOLqXMZDJf+o/WndYj33NIInFH6p7X/7171ZXL/9F+P+3YCJY2sIFXCOqf+3w2eO7539JRZP3EHWPxyOrvmyfp1AIg5NDu7oqYmrqkNGfu3rWgfv6NyVDKvng8XoqiqJNSWgotcztPJdP6qivduOuOEqUDfna0qH3fFnOlPGiNzL5ezEbHfhR8RRGVS0uOc+TwRHjs+CX+VDiZjKyrr5pGfb0gR0bitCNTk9u3azwy4rO3v3MLHzrZLRfLXXJ5uVOOnytahfxJuXVrXgMgQAgIISHGmDiOU5FSEoTQCqWUMMaypmmOAwARQiQQQguEEGyaJiKEOEIIoZSKCCEZrfUCpVQppQJCSEJr7UVRtMV13RHLsuoAoG3brmKMlx3H0ZZl2Rhjn1JqaK01PXV62f/s3+8FAEDDZwjcfmuAMRZaawsAwLbtUhiGW4QQRUppQCk9TyktAYASQqSjKBpwXfcCxnjetu2FdjYpKaXsbJfVcgAAhJClKIr2SimLhmFUhBAFKWVCCJHVWtuu6w63BSzrnuftIYQ0pJSptmecpZTqYIzlTNOcV0o5WmsghEAYho6UMs4YKzLGVvk7q0KgfiqVkpZlUc/zXNu259o8MQIAEIvFPprL5T5ZLpc/yxi7WUrZ0SauR0EQ7NZaR0op2zCMmXg87gHAIgBwpVTMsqy5teyVECIvhChKKXNSyhQAzBBCMGNMY4wdKeV62TH+L1+9IGdnXe15NgCAffDAUDQ0tEvOLzjuvr3V6AePp4xCvooSCU+dO2cGP3i8RzUaKfvQoUWxsNBj7dwxCpgkZam07tvGFxY6ECCqVlYcAAAdRTHz4IFZtHVrJKVMS4Qy+vLLsP7SPzu6vrLunWj091XkYnlFeH7KTCZR4777t6JnnnXVt77TCYIT7Nih0d29zP71ax1kfDzgx050qWrV+HG8QZxKzqCurk7as8nTjuOJuTlsIYzY2Fgq/c53TPCpKSIryyl+YSJv7djeUJ4nVH0lRruKM9HQyX73uuuWSGdHyT6wP5S1ekzVaqt8pEaD0GJ+NHz6mV2J229bMHp6XKUUp5Se1FqX2+8kUVoj/2OfcFuf+svb2cPfLfBnni3Ar/5KlZtGTgixLR6P9xqG8cCPW07CMyN9JBF/xujp+TGEyJ8dbHTDbWADrxDIZrNDC/GyEtDL9pue7lcxdzg6PbyHFArzOgpta++eY5qxAhsb3wSUgvrwHyMSi0WO1otBEBQcxzkRi8VcjPG0lDIdASTloYMWnD4dWbt2nQDLMJBhJo3+fs975JFu+7LLhgEhLkqlvJif7wbLArmyErx0LNH58734P39oOOrosOz3vXeM2LYwHaejWSgIZDs1mi/U+NTUVoBV7zlrZi4p3/zWECjVOB6T8jN/FzdMc6rRaOyzLGsyiqKDCKEzWmsTY4xM01zQWjtKKdBaI4QQKKW41loqpdKMMYYQigVB0G2aZuj7vhGLxZYZY5t8398NAFhrDQihMcaYGwTBZkKIBAAtpaQAYAnHWZdekKdOdRgfuL+O4vGQ//EfVQXGfYSQita6u72LFUXRxdYrkMlkHsAYF3zf34wxXkEIzWut5x3HcZS6hCpiXvTvS4JbrTX4vr8HAMA0zXIymXyIc37AMIxyO3vzD4SQ+SAI3o0xriCEuJTSkFI2pZTbLcs6QSld0FqnAMDAGEeGYfAwDEW7DJfyPG/H+kBM8wdbtmy5f3Z29v5YLLatXq8LKWVKCGFjjBVCyDdN0/d9fxtCKCuEOCel7HIcZ9EwjLNKKQNWCekvJ0kTIoQQBsZ4EWOsOOc9GCEgQdDwv/6NQXPnzgVZLq9axLSFHlWrZUTfftCmhw4FeveudPShD2+D9sk1Y0508mQ3IIQhnwN6770NNjXF9dKSAQCAu4q+ffhwmU9NdQAAmIcOjTSvuvIyKuUSISSUUtpCqU76iY+f0W94426kFNiHDp7xf/D4Dh2GxNqze6b5139TAABwtm0rqVZL63w+wRcWrGhyot85eHDM//6ju186z0tw260Oec2dVcuyOptveRtnY+M95l13DSVe91ovmpyMs7Oj616MiJBF4LxgHzp0CrRKm9deu5D88IfclT/4w1TrW992rJ07RgTAwR/tbxDUVVDO1Vdl1KquBGZRtCdirNdxHBMAnoRHHzvEhoaKAAD2P3z2FCsW3ICQ3e13CzPGRlzXfemoAQAgeddrzv/YH37GsJFZ2sAGXiGIXXftUuvh7+4Q8wv7f9zvxtatnjmwpYHTmZKYnS3qKLK15yXE3FxahVEXKXZpa9vWcdVqgbm0VNO5nPZdd7NhGFWtNXccxyGEuJTSFVvrQP7X/54JXzzeKyuVog6juIoik09MZmkuvxKdOrVVzM8XzW1bL4iFxSJoDcaWLYtGT0+ZFrtKYnF1m/XZTw95udw+rbXNCekEyyo3m80BU6plY3TMRBgbcmkpDloDyeWWSTrFolOnitrzTFWrW/g1rzmHEwlNKdVhGPbEYrEZhJCnlErato1ardagEKLTcZxpAFBSSgNjHAghWBAEgwihRdd1ldY6EEIUMMYKAAIhROea1hIAAMY4Mk2zhTGucM7zaxpEmUzmHM5mXfnoDxxdr1MAAFUu22p2Nkanp33j5purXKlu0zSnXNddMk3TD8MwSwhZMgyjDADCdd0sQijVJraD1tpoK5ivSClDKWXCsqxRKWX2ovLX7MUilxeDEFI2TXMomUy+y3Gcj8Zisb/r6Oj4Fyml4Xner3DOu6WUqWQyOeP7fr/jOJNaa805L8bjceG6riKEsHagphhjHABqUsru9vnnE4nEm5PJZLndRecHQXCrYRj1VCoVraysDLYzTCkhRAIAsG3b84yxbsbYJsZYl2VZgZSSaq0vDgCBUrpIKTU550nDMALGWAKkdK33vX8evvKvpuLMAK2oXmnY7QejFWMJbNuSXH+tZ/3ObzO8Y7skff1LQIkkd981gSrLriqXbRAC+MJiTvzRf0LWVVdNomuvKTlveH0jeNtbc8GB/QV7eOSsnJ/P0jvvmON7dheVUjHTNGtSyhgAgDCMnHP11Sdgds6h1Fjm09Pd7UHr1L1vqGrfb2ilmuGzz+0UCwsx3WrZJJFsasYRynYi3Ta/fRkwBvThDzFkGAbxfIY7OwIjkVgOTp5Mh889N6DDkBl9fZ5cWopZ+/cv0+5uFB4/sZl0FaJo6FQ3iiKFpai3Hnyoy+zvL5tbt3GSzy0gwzBJPhshQpjRkeXeo48J1tVVRoxz/0P/2XRvffUZDLCCP/f5/wKNxnJ47PitqFgU/JfeYQuEugEAEl/80in6bw9Uwi99ucd//Inx5N13zfzvV6KfTWwESxvYwCsIsl5/RCyW98rl5R0om/VJR0egPc9yrrxiiJ071y3mSnFVr6e0511qkbFn9zl+ergoVlbSWmuXnzxVoI3GHL/u2rwQImFZVr3tbYa01mDGYgg2b67SRqMiFhc7SCZTxvF4IKamOmWlsk4ex+m0xslkRVVrGTFXypNkai4aGjpkXnFkghw8EMAdt7WIaTYJIS0jipg2DOasNJbkb7+vn58+naaFwqRcWMjH77pzBNmWiXbt8lHv5qaaK2XMP/qDITa4rZ9x3m8Yhk8IGVNKKUJIHGNc9jxvGwAAxtgXQmSiKOrnnGejKEpijKmUMo4xFmEYbqKUTjPG+pRSsbaS9hnHcSqU0pIQIiOlzHDOs4SQRcK5Z4yNTcQ9ryo6Ow1jfj4K/u7TxZc+CzU1nVBf+0aS3HTTMs2kLd/3u4IgKCKElGEYgjHW5bruhGEYMUIIAYApSukHqtXq7wCs6jEBAHccp+x53r61QAkAwDCMhbUy1hpisdipdvmqKwzDywGg2tXV9R3XdVcMw1DxePxCrVa7GWNciMfj59vZHWnbNtNao1QqRbXWHqXUbmfckG3bLufcy2az90gpzyGEtriu+4c9PT3fBwBwXXeiVqv9KcbY5pxnwjCklNJp0zRFGIYdWmvHMIyKaZoqiqK8ZVljjuNMeZ630zTNEdu2fUrpHOc8jzFeSSaTiwgh2/O8omEYZYRQw/ja1xf5t78zqFsty73mmiWaSlWAUF/VainVbCbsgweGcSq1ggcHOe3sIKSzk3r5XFFde40dbR3YbDVbk/L4iSwAgH3zTUvhNUdzLBEviEKhC4rFuhAiBwBA9+0NzTe/aZldeUVhjVRvmuayECIFsGqBo3M5rK+/LiD9fYzmcoH+v/5w1rz8snrrzz4yKBYX08iya6pWW8/6GXt2T7ALE1uc9713wnj3r1dotrMBgW+q8tKPgkStwahUfbRz+7IePqMoY1bw0EMpZ3CwyperCWvHzmWjq7vKLlwoyPl5F6dSU2JmpijnSmkQgqhGwwmPHc+QWCzgs7MdiOCZ8PkXDoIUwtqxfQkAxWm2Mwof+V6P/NevFdg/fSmrFxaytLr8iH7uhar3ve//rv/0Mz8HQmBotbA1M9s0Mpnz1ue+UGUPPTwozp3vkgsL/fFbX9WMXXfdg/+fFqafAWwESxvYwCsI7pEjETt/fkaUy/cijOpqZSUeu/boKf/5Fw7RQr6hanUbpHyZ/po5sHWRT07mIQgoBAEBANAIu9br7h6lth1IKbFt20QIobTWDC8sgDE5RVY++/eDydfefR4AGvae3SQ6dXrd84p0ddWMgf4SGx7ZAVIiAACS7VySlUrB+uSfLwQ33tAtlMpqKRUtlari3e/Z4t5wXUV+/d8sOTEZh2bTAEpp7Jqjk+HZsQwbOrlJLy5SAqgmJicL9u/evywotZRScULIWBRFl7UNY7dwzrvi8fgQQqiMEHLbQU8OAMA0zRmEkJBSptuq08NSypjW2ljjGZmm2TIMw4BVU9p5QkirbfJqGx/6sBKf/ux2eWYkpfv7ZvToOBFPPpk3775rRlWrSfD99dISdhxfvfUtiUiItG3bZwzDWKCUepTSFmOss52lalBKhZQyxhi7WSkFlNIlQkhDCNHNOU9fLBdgmuYFy7I0ALQAILBt+5xpmsT3/QEhRAwAUDqdfltvb++nXvqcV1ZWNkdRdFs7eOx0XfdCGIbpdlnOxxg7Qgjmui7RWv+V7/sHbduumab5eSklwhgnOOfbpJSPx2KxEACgXq8bQRC8Rmttaq0twzDqSimDEKIJIcu2bVd83++OxWLnwzDcBKv6f1xKWXQcp4kQIolEoo4xBsMwdKPR6Gn76i0qxnLw3/+kU7c8B8ViMnb0aLX5bw9so4X8nKws54z+/jIipBUNn9ktXnghi/buLQnOW3TkzAr57veW2f59Rdzft6C//JU8AAD+jd8YE13FdZNaSumKlDIDACDi8aSVy80kUynLNM0VhNCFMAwHAQA5jjPJGMsJITZLjDtZZ2cX278vIy2rgKvVOfXv31wVSt221ROlUhoAIH7/fTPsXb9C8RvfWI82dadC0+xXe/d6tmHOkTfe2zRuuaUin37GAsYsIx47H37ik/vRyooJCwtl0pH1NWPEyGXrwVNPDWopqXP1VUtGTw9no2M57fuXZqmUQtr3DTAMMLq7lwEhbu7cOS89X8j5EgmPHe+3du2aNga2SCBkhWQ7/fDJp25BhJy29+//RxKPn0aW5ctGY1AtLLg4DBT8ws/X8a5dFWwaSl2YSLBz5wvhseP15GvvHvo/XY9+lrARLG1gA68wNL/zYEbMld4sZme7gXNDzJUKNJ+fo8XCvCiVigAA5p49C7SreI50dFSMnTvLfHw8qz0vrjEG5w//0xl+/ETWuf++814+v1cIkTYpnSf1OtBk0jQIcYIHvuk3Pv6JzbGjV5/1Xvhhv9HXZ7CRs4ExsCWim7rnaVdxRjWbjlEocDY+/iNPtI5MpBkzjYMHq+m+Xow/81nE7//9nPraN4rgeUg/97zmTz61GZpNAwCAZjJBeOx4LzIMMLcOLBubN1fY2FiPceDAWOvG6/cqpeKWZdUIIVXDMMwwDPvWymeEkIUwDA8opRyEUKiUSrU7tnIAQGOx2FlCyGIYhntTqRTHGNcIIUtCiKxhGHOGYeBGo7FdKUUAwLQsq+ZoXVHf/o6lw9BW8/MWPnUqIX/4Ylp7nol/974LxA+5HB1dJw/T3bvPsVtfVQQAEEIUtNYuIUT6vr+FUjofi8UC3/eLAMCiKLKCIOiPxWKTURR1c84LWmtqmub0mk4SAIBlWSXDMIhpmrbWuhYEwQHO+XrWKZFI/FUqlfpb27ZfxhELgqDCGHujlDKBEFKu637U9/07GGNdGOO6bdtNhJCllPqY1vptlmV1EEK+jRA6EgTB7yCEtniedxtC6OpqtVqUUs5ijIcxxlcwxraYpjnGGBskhNRc19WUUmUYRhoAam3tqE4pZafrug3GWLotaIkAwA/DIaBayAAAIABJREFUkCqlkG3by5TSlmmaNSbEgHF8aE7OzmadgwfOBc8936GDwEGWhcztgxf49HRBzM1tXiNNi0cfy4uvf6NbPPL9opqY7ESvf10gXTdBT55q6sXFGH7vb1aFYeTX7odhGCUAIGuaUgghj1KqCCFaKaUxxjXbtithGBaUUi/rGMUY1+x8zqe203CuuqIUfvd7A2sSHGq5ahp798xHmfRmpXWmbX68Jerv62bZbCcrFLLOqdPTqtVKABegms0kdu06n5xMsNOntwDBCR2ETC4vp1St5pJUygOtfbO3t6oN09EA0to+OItdl6pa3UKxGKTuef1UODSUNft6F+l/+y+Z1BVH0s0vfqkLIQTG1i0zfHomGbv2aFk3mi2NCeHj49dEY+NvoLn8MaNn88n4nbd/PXbNNeNw6FC8ObjtCjS4LS6PHhVmoTiFSiWH3HvPkSCfF/F4/MX/9yvTTzc2gqUNbOAVhuW/+NR97OzZW9c3SIlUo5FUzaZr7twxTLO5eTE52SdmZzfJSiVP83nFx8eLAABIa4APfmDZufcNTH7qr3QiFl8mDz00q7/9nRz74hddXcgv+X/ypwn21a/1AWOG0dNTFqVSgSbii9Hp4e3Sa8VoOjMXHjt2ULdacR2EJikUWqpWcwEAaFe3J0qljuRb38JIR4dZ+537fuS+nklLq69/QczNda5tUp7n2IcPj/CxsU1ysZzgE5M5a8f2Ea2UwW+5eS1TNG0YhmNZlhRCtKSUKYQQaK1BKRXHGDdt215ECCWklJbruicJIdBWk15RSlFCCIqiyCGE1DnnBSllNgxD2zTN85TSmpQyzxjLR0rl+KtflXRvu3XcuPNOP3rrL3a6d9153shkfLlvr5JHryZGaY5jxzbNW26Z013Fhjiwfz2TYRjGVBRFWwBWdZOEEB22bc9prYFSWqWUVn3fHzRNc0UIEXddtxyG4bqtBwCAECLhOI4PAKjVag2+9PmbpvmZYrH4FPwY8nQikagEQVDCGAfpdPr9XV1dX/I8z9da73Ec502maW7VWm/2PO9GjDECAMw531mv14+kUqmYUkozxhKEkJbv+2/xPO+9GOObW63WNZZlnbdt25NSxoUQRcMwljHGyPM8A68SsVuu6zba8gT9CCGOECK+7/eFYVi0bXsOY2wKIWzLsojW+gfxeDzNM2kiv/2dTlEqdYJhGNaePUPktXczec/rBdY6VMNnOl86TwAAHUXEYfw8GdhC0LZtc9T3aPiqW/pfcq8WlVKpi8puDUKIFkIQzrkXBMGetc5KpVQnvMQVwzCMqYDz3Wz3rg53dq5JbrqhIp57PgVCELW8bIgH/r2QPHhgVmzq1pzznFLqEo6WkcmUzWq9ws6c2QYAoKq1uLl9+4yOIlOWl2LIsrBqNi0AAFEqJWlXcc5/6uk97pHD5yAIm2z4zDbsxgIUixnxO24/3/inLw2Qjo6WuXWrxx97PN781KfiOJ1R5pb+8+FzL+zRzaaDk6mF8IUX9mPXbdiXHaqQRBLzhYWrouMn7iQdHTfKlRUT3XvPX3Kt73Qcp+y4LtPbB6W+607R6uzYpZQ619HR8dCPu+c/y9johtvABl5hoPnc08iylI4utUlQzWYyOnnqsou3kT276/o1dyzH+3rrfGrKhHf9ajNwnG2q0QhJGKLGH3+4m+SyVbBtS87MxuQH7r+EVBwcO7adZDpmNOcBKAU4kayxCxfW1cDF/HzKHBwcd6+/rh6cPLWFdHZyFI/D8tvesfmScRQLgX3ZZVPe40/scK6+6lR47PgewAjhWLyqWXRJhx9fqvSbN1w/BQBACGFRFPVrrUeDILC11jbAandYWz9ollLKPc/bu3a87/vr3WhRFG23LKvk+35cSpmklDYAAEzTPKOU6maM7YKXIBaLnfQJGZTJpOM4zokVgIPwutf+aHy//3sKEMJa695ELNZylBrXWocIIYoxBs65chxnOAiCvcVisb68vJwnhCxprV3HcSylVIgQqgFAEQAaAJAHWCU/Y4yXEULM87zNAFCNx+PDnPMOznlaSum05/Se2dnZahiGv/G/2HvPMMmys0zwO/6aiMhIn1mVVVneu2611FLLgNQSUksI5BiQ4RkQAga/MzvADM/Mww5jdmHxZpnBjWGlndlZjCRASAIBUiOpW12tri7vq7Ky0meGvff4c/ZH3kiqWz2zu8P+GSnffxlx48a9J27k/eL7XpOm6dPDw8P/PMsyBQBoY2Pj6Pj4+H8KIfynPM99t9sdwRiPxxhVjDFJ0/S7er3ef86y7IRzDkIIi0mSfNha+ybv/cOEENRsNu/EGBOE0G3n3KjW+jDG2Gmt92OMnwshEMZYh1KKtNa5EGLJOTcppTwlpQQhxLkYI0vT9HKMMeR5vhBCQABAiqKYqdVqd6SUtTzPn1ZKTSTTU0dslvpYShK7XRqmJkfVm980HmNM0Yc+aPi733kO/cIvT7gvfnGrKBWveuVVaDYdEAzl8PA0HhlpiIcfWo9KAQBA1UWbK8vyFAAAY6xIkmTJe59ZayNjrG+t3XLI1lqfSJLknNb65IDcDwDgnDvIGDPWWr7xxjfsBwCofeNbz6n//HuncaPR448/fic++oqhRpKsaa1RCAFJKbdiesqHzhzNr994Dj7/+a3rRz937gg/cuS6abUb2atf3Xary3PgQyw/9+QJnOcEvIf+n3xiS13nFheb6WtePacvXOgBQiA+8P7l4hd/+TDfv/+uOH5iQ184vw8fPLA1xo1KITw8vOKXl6fk8jKgNIl8377bfnFxn75+PSbHj+0hWp+q1eu3YNO/q0YIoUKIP67VassY45958XfiqwHbnaVtbONrDI13fPOV9kc+8u2h2xv5r22HhoYC+s3fWJSzuw/bV7wcmcffMEqmp7ve+wnuvSZXrnTdrVuj/NDB5dBuN2JRUiAEGu9+1/WIsPWrqzWwloZud1gcO9qzt++M0NERBSGoWBRbJG+/sTHqVlfrycmTV+RTTx2kzSGZPHTmtp2bGwUASB975Tm3sFSzS4vjyezsHX3p8n66c6dGWR5wrUbMxUsTDx537HYTnOdr/rHHajzLrjrnprMsa6Vp+n8ihFY55x9DCE1lWRZjjFRrPZum6X3n3FeMUQAAKrXZzSpfjQKADCEMVRwlANh0nMYYB0IItdayqntlKrfv/EW73ORnEdLWxkx570ecc1PW2nFr7XitVrtUFW8EIbTIOV/v9/tHrbXDUso6Y2w+xlh3ztWttSN5np/jnLeMMbPe+wnv/ZT3PqviXha11nuFELc45wuMsTWt9UlK6Uml1MMxxlMhhPcaY57s9XrvWV5e/mOl1IxSaqjVan1dp9P5TSnlN4cQRr33pxhjz4YQ3kwImSSEpJzzmzHGs865QwihqfX19b0xxhVrLaoKnpUYI40x9kMIDULIBsbYU0pX6/X6uLW2Z4wZcs7VKaX3Yow5IUR670cQQqXW+ijGuKe1PliNFu8758YRQqsA8NcA8G0lY83aN739DrRanH7j2+7ob/9AIwA0q7UmPk2n6CtecS9+8lMjoBTO/uE/uIDOnFH9d7/ztHn4oQkAgAjACSFz3vtJAIBarXajKIqTAACc8xuEkA2t9R7vvYgxrhVFcWgwmhvAOTclhGgPilKATVk95/zagA8HABCOHc3oM2cX6OOvXyw+9MHTytohKeVorVbrG2OI935w7MC07sZf/lUcu92hB98Lj4wIhLFXX/rSKBkfX9fnL8ySsdHS3Ly1A6zFdNfMChkbvc/37VvGaWLo5FQHgs/d/PwIfPA7pXjXO1Q4e9a427en2K7d98C5mjh+bMneuj3qFhcns8deNU8nJ++GTjePRZkkp08t2Fu3J8SBA2vFZ/5iBwnhc+XJE6e994wxphljnHM+o7X+6enp6adf6nv03zu2i6VtbONrEP1Pfiq19+bf9F/bhu7dK+3bnsgjAK/yzGiSJB3OuSxCmE7arSVUlNYvLjF+6JAEjDw/ePBq8Zd/dQxnaT+02rWtnUXYQIlI3fz9Bt8ze9+vrr7ABwisI2RkdCUWhfBr63UyPrbilpfHycSE8yurIrTbI2znjnW3uDwsDh+6bi5e3BXabRrW118wtgCMAWIEKMsaX166lrzpjQ3O+QohhKRp+k+mp6f/zfr6+v9orT3qnIvGmJk0TZ9TSu1njBUhBAEAkOf5LUJIiTFG3nvhnBuvnKU7nPOV6sa4TCm9Qwi5b62djTHWY4yRc76KMdaEkCVr7exLrS1CKFJKb3vvp5IkuTY0NKTKsmwAAHjvQ5ZlcxjjNufcK6WAMbaaJMlKmqYrAOAppX2M8QJjbLUsy9OVMeULxmpZll1SSp2IMWLn3Ji1dpIQspznuZJSjgkhVimlknNeK8vyHWVZvgUAiDHm5caYdzjn3gIA9SRJLnnvR0IIMzHG8/V6/fe11m+ilFKllCCEPO69/0K73X4bAACl9J7W+uQgd68syx0P8Hlw5YK+31p7CyFUK8tyZzV+qgFAyPO8hTGe11ofBQBgjC167ycwxnNSysOEkOsxRpTmtdMeibGbHdej9Tqtv/lx1du9a+aBQmkLjrPxdP/+52m32y+/8zv2w/59NkmSlSRJJOd8ESHU5px7xtgyIWS9LMvjWZadAwDLOdfe+1HG2BIhZIhS2rPWvmC0Rwi5RwjZIIRo51zzgcdbnHNrrR0ePBYYE/TIkY08TXk5MzPI5JtLksRSSr1SamvfnJCAP/GnOLTaL+iehvV1nr3y0Vt27t5Y+sgjyxDCqltcmhJHj9yiu2bu+/WNUTd/f6dbWRlPX/7IUvHJTx3lhw8voLHRdfzoK/r9yclDCcRF0WxSvn9/TX7xi4EMNfpufWOcjoxcarznXf9y+md++of1jRv3Qr+3h+7c0UeEXGp+13f+Uuj1/hJaLT363m/7EWPMXgA4IoRYSdP0FWNjY1+V5G6A7THcNrbxNYnmd/zdXzC3b/+wW1za+V/ahj7xlnsWockHHYWdc6nWeifWpiRJxqyzPio5Errt63RsjPuNjWmwFnCadWFzTAQAAChLNc3zy8mJkyx6n8PFSy/xjjFHjBkAAPXcuaN0x46laI3wGxujZMcOQ+pDa/b6zePR+Rd3agAAgB88cBMPjwzhRPTR9A4ZPvC+UUrpWlEUH2s2m787MjJysygKQghpc843lFL7OOcLWuvDnPNFjHHunIMYI2itE+fcDoRQqNVq551zoSJR8xhjQgjpa613xBi31o8xttZoNHqtVmsqTdPrRVGcfqnjFEJcCyFMM8aw974jpTxirVUAAFWQriqK4hhjbK7dbm+NLAkhknN+U0p5PM/z81LKhwghturEpA/s/3qMcSSEYDnnG1rr0er1KxhjhDGmMUaQUk7meX6/KArqnEsopfMhhPEQws48z89rrcdjjHUp5ck0Tc9LKU8ihL5hdXX1/VmWRaVUv+oIUefcO9M0vSml3I8QGqvOZXEQTLz1GXE+J6U8wxi7V5blKYSQr9frl8qynGaM3eOcp91u9zDn/F6apivOORFCqDHG7lFKXQhh3Tk3lef5GgLYfX2tCBgAj9a4MFq5F4UUvwDykZcdoI++ohWsZVrrvdZaSwix1tpdAABaa0AI+TRNbwC8YBw7Twi5H0IY01qnQohJxtjiIIevIsJ3EEJMSjnCOb9sjDmaZdkFhBB5kXEoCCHm4fixTqH1ZC3PL0opZ9I07W9sbBwjhJg8zy8SQngIwdCzz3olkoPszJkOfdMbl9ynP70z9AvKRoZX9fw8b7zzHVfUtWtj5sLFKbZvXycSovXTX9q67sTRo1eLL509CLApILD/87/yAUHWyPPL7vHHx2izuZHP3VtNTp96D2hzMn/88RuI0ui/+ZvM3bt33zf0z37yUzuazQ/3+30etZ7CSXJPvPY1r6oKw9/csWPHt6ytrX1Wa/394+PjXyEY+GrCdrG0jW18DaLxjW+z3d//gx8v1jf+fTSGvNQ2/lWPah/CC36lI4TWYow7s9/67Zu9j37s5OBxff7iKVyrGTQyrAEAotEeAAA1GjF2u8hcunycHTq0IJ95RoROZzR7zWPP6+s3p/zy8gQAANkx3bdz9xqh1dr8Ve09uPn5rWKLTU9fUmfPbroOx6gAIXiwiENZJqNzTfX006MAMJb8618/bxv1XMT41L59+/6nwXbLy8u/Vpbl+wEA8jx/Vmu9hzHWttaOCCGu1Ov1Fa01GGP2bL5VxCEErLU+EWPcDEkNgTHGVqy1L+iO1Wq1NaUURgjJKkvuOaXUkQFPagBCSNRa1621RwghJed8FSFEKKXXQwh151wjSZJLlFLNGOuUZXkyTdNbCKHMWjtYE1qRimtVXtqVyuOKaq13hhCGAWC0UliNAmx2aMqyPC2lDANeTVEUB2KMkGXZc0VRPMw5v0MIWY8xDllrtwodKeXJPM+flVK+NoTQqNVqc+12ezhJko5Sqq2U2htCyPM8v1wVQBdDCKEoiq3RZpZlz3vvm2maXqhMNi+GECwA0EajsdrtdscZY7HRaFyXUpIYo0iSpNvr9fYAABhjIMuyC0mScCnlMMa4M1tHtJHlaQhB9/v9fS91HQsh7hBCOlrrScpYx4cwXbmrW0qpt3bLYB1ijCTG+IKQV0LIilLqYYDNwkhr3aCUslqtdrHf7x8XQpzv9Xqnsyy7lue5KsuyXtkhnEAImTRNr9fr9etVx0g752re+01OXr+/c2ho6JyUEnHOLxBCbAhhknMOxpiCPvqKLB4+vGIQ5IFzcG9+EyR/+NGr5nf/9/2h0635mZkb5vnzUwAA9tatoWRk5IWdVohFXF/HAADizJlgEAx774W19paMcUoAdIq9e3aE2d2fQQitFkVxBwB0aLXeZIw5EEL465WVFU8pPYAQupN7/3shhG8HAG6M+a16vW4opb88OTn57Eut/VcTtoulbWzjaxQzv/NbH57/0Peg/qf/7HfhgRvGAGSjhaH5N7VSvV6/2e/3TyUbrSV/7tzYi7enu3ddNZcunwQAcEvL09nrv/5q6HSH+b49q+r582Dv3TsUpWQAAOWTnz+VnDz55UGxBBhDNLr+4n2mj73qWlQqmOvXjw8ec63WvvyNb7xqbt7gQKiM/f4uNrvrlvzi06cBAOjxY/fs3tma1noEAHZLKf+5c+4wABQhhK39hBCGhRArIQQRQphWSj0UQrgzUKNRSjeccyPOOTQolAAAYow1QsgCY6x4UJLfarWOVOt03lqLlFJn0jS9WqnVkmazebMsy6wsy61YEO99BgDYe58AwBYHinPe7vf7xwAAarXa+coiQGGMKee8TSnFRVFsKd0YY/Pe+wlK6TJCSA8el1LmWZadV0rtds4dqPZ3zRjTJIS0ASBUsS0MALaKRErpyos/ixACHozTrLV9xljpnBtDCO0IIeSUUskYo9773HvfklK+wCneOYersQ1U63Qzz/Nmv99fK4piT5Zl16WU+xhjpTFmf5IkLV/5b1Vrslir1Ua89ybPcx1jrEdbsHZbCs75FQD4imKpKigbVcgxklIeT5JkhRDSppT6drt9DACAEOIppcta6x1KqVNVN9EDAKKUbhYbmxYUt7z3EQDqFR/pOqUUE0JsWZaHvPf3GGOuKIqx6lrhAEB6vd7Ber2+pJSiQoiVsiy3vljWWmyMOck3zVfvxhgN5zxljE20221nIO6GCBBC8JgQiE9+Pgudbg0AIEhFAQDEy152x9y8OU2mp7Lhv/c9d1q//W/3gLUAmCSxVgv81375fGfHjoMQIzSbzSvdbndvlmWXtNbNEMJ6jHECIUSVUm8YHFeapotSypeHEHhVqBLn3HdgjInW+vcnJyd/AQBgcnLyIy9e969GbHOWtrGNr2E0vuntz8svfSmxd+6+5sXPxefPj8I7vtkPfIkwxhve++HkV3510Tz75T0PbotrtR4ZGzN+eXnzhs+Y47OzHb+yXJZP/vVxv74xAQAkfeWj50O314xaU2/NUHL82EU6NbmSnDjRdssrw1HKrSDRofe994q+fGXM3ZsbD73+lsle7HQYwriwc/f24Dxv+Hab23vzOyAEwGNjS+LI4VL/7M/vza7f6PDpadWr5e/x3h/33p8BgGkAQBjjMoSQO+cmEUIEAEKz2bzrvW/AptHkFYTQsBDiOgDUH+ShxBgJpbSntX6BQ/bWWmB8X2t9JM/zm1LKw2maXkIItaSUexljSw+Sfav9fcWPVkppf+AMDQBdpdRhpdR4ZVQ5QiktrLXNB7Zfr0ZLk0KIG865KQCAEEKTELLEGOtqrWcYYytSylnvfc1aO2atHR8aGrpkrRUhhASqokkI0aOUbnjvG1U3Z6EoiocAAJIkuSClPOicm6SUFoQQqNfrSzHGVaXUBAD0pJTHOOdXQgi1wT4RQgQh5LIsW7fW1hFCq865egiBIIRiCKFlrd3FGLsfQmhWNgp5jDGvjulOjJEIIVKEEPfed7z3ijG2WK/XP6C1flUI4W8y0hCCNE03lFK7Qwj1EEINIYTGxsaKXq83QSntD7puQohV5xxJkuRONcorlVInKosDRyldQAitV527h7z3Y5TSPgD0pZSnOOftilQ/ZK1tCiGep5TeBwBCCAHnXNMYU8vz/I6Uck+tVrseQqBJkixIKY8AANRqtSVrrVBK7RZCLK+vr+9KkqRIkmRpaGhII4Tatl+Mx3/771iUMiWjo33fbmexKDhOkxYCZMjomNTPfrmZPvGWa4jSNtuz50n6Xd+pi717X04ZU2maLhVFURdCrGKMcwBoxxhxpeyrPWhdkGXZkrW2Tim19Xr9mnNurCiKYUrpzVqt9r1pmrqXuv6/WrFdLG1jG1/jSE6e+Gzxl599Y2i3X+DXI3791y7rNN0c+4QA6e/9/j3y27/T01986tDWNqdP36RjY7f5oYNr6otPbcrvMYbk1InL8sm/PkFGR7pACGe7d99hs7sL2mx6ffHS5j6VYm5xccotLU2ay1cmyfh4TF/5iisIoTWUZ1E9++UdKM9Fcur0Tb+xMZScOX2FTk5K3Kj3gRDwKytD4sjhi351tRmlYnhkZJX88A+upY88kunf/4Omu7+Q2I99fCLbt/+y3b2rgTEuCSF+dHR0nTG2LoRoY4zXAWDNOTdVq9V8t9udDCEwSmnbGLPTWjv5YKE0AOf8fgghHTh6PwiMMalummOMsdJ7D865vTFGRiltOedGhRCXGGN9AChfysyQUtqteEgEYywxxmhAPq9GgNODbhchZB1jnA0yyrz3Y0mSzAFADCGkjLG2UupInufXKKU9Y8wL1IMY4wWl1DEhREsIcSOEkGKMfZ7nKMsyVXXJRimlPUIICCF6GOO+EGIJANpa6zGlVGqtnUjTdKksy4PVcYxzzucAgMQYkyzLrlcSfGKMGQSwTjrnmlWHrc4Ym8MYl9baXc65Bue8NVh/hNB6WZaHGGNz1lpnjFFSyuMY41uc818MIaTOuTcihCJCCGdZdr4sy9kkSa4557bOWSm1xhhbpJRiSmlOCOkIIdpSyt3OuYkQgqCUzg8KTozxgtZ6n3NuDADqg8/BOTeMMW5SSlcwxksIIVQVh8A5R0qpA1mW3cMY940xk5TSOYQQt9aOGWPGm83mopQySZJknhCypLWeZowtGmOmpZQjsNnVWoBN7ybe6XT2JU89dcV94k93AwCw3buX3NzcONkxXSJMnFtf35E98rINsmt3t3zPu0ayXs+KH//RdTM+PmuMmUiS5FZRFIeQsVz87oeX5YH9e4z3Oyil/Rgje1ANKoToSil3NBqNG977xDlHvfc1IcQiQujJ6enpj7/4mv1qx3axtI1tfI2Djo6G8q8/3zM3bn7L4DFy5HBPv+fdtQiQAACIotzwP/0zQ+7W7RkAADI5sZ6eOX1TPXfuiF9a2oHzbMmvrk6TiYnV5MTxJfX0M0cBAOj01ILbaM34+/dHI0IoylL7tfWvtCxIU+B7Zi+wyUlefu7Jo6HbzcE5EtptYm/fHq9/09tvFJ/81FHf7TZDv8j9wsKwOHPmqrl2fXfo9WoAAOxVr7ou3/utp0i7O48ee+U98mP/UPNOx6jf+M09yd49l+jBgwXjfJ0xxjHGvt/v16SU+4QQq8aY6RhjSSlddM6NUErvDoJhXwrW2kmMsSOEaEJIyRhbwhjTEIKIMab1en2uGnGwJEnuD3LaGGNrWZYVzWZzuNVq7UzT9CYhZMl7PymEuMcYaznnms65ISHE9Soct4EQKrIsW7TWDmOM9UC2LoS4zRhbVkrtf+DwcIyxEULIhBAXlVLHAAA754YppRuVcg6yLHu+6hrkUkrunBuy1k4lSdK31gqMcdc5RwFAaq33YIw7jDFcluWMtXYshMBCCE3v/VDlq4TTNF0VQiwjhNY550sIIR9CwPV6/V6/3z8eQqDOOZckSds513iR/J5VqrMyhJDEGNM0TTeMMcMIIcU5X82yrN/r9fYopSYYY6uMMRdjjAihJ5IkeW2SJH9ECMlijIIQsm6t3e2cG0/T9Mve+1HYdONuOucmAWBOKbW74vAMjEoxAACldOmBYolTSheEEMsxRlQVskNCCFUpJRuVOvIGIYQBAKqc3kerwmiq0WhcNsbsrlRxqLqGKEIoVUrtJIRg51yTENLFGPM0TW957xsVX2q3lHIUACD5wz/cAGMTumOHEQcPrJLRkQW+b7+CGMr01Y/16NDQkDp7ts+vXBH4Va/0YXRsL3Q7ng8NrVBKCXnm7Br+yX9mzV/91SFx4dIt3mws1Q4erEtj8jRN24PzrDh8q865WI1C7fj4+HOEEIoQ+meNRmPp/+3/l68WbHOWtrGNbQDO82U6Pb1GmkO39OUrr6D79pU6xkmATe5Otmvmx92BA6/Vxr41dDpjfM/elfKvP79F8Pb9YjZ95JHrKM9i+Vef3eo8AaFDsdvdfA8hnFvfGOPvec89OHq4RRZrZwGKAAAgAElEQVQWR+LZswQwWXNLS2NR6ylcr2+SaxmD/A2vv4B27ybyTz7RKJ58UrCD+5ft9ZuTADAgpJeh293iORHGMgCA3t7Z47B3FtIkORdOneyhP/vzIfNT//JUunffDX/wgC3LsiOl3Fd1MyDGGCmlyjnXsdbuAQCw1h5IkuRZ7/3OFwfSbp2z/xtVXpIki1Uxc9dae6Tb7c5ungZbq7hT0Gw2b1hrlTFGl2UphRA97/2wUmoP5/wqQkgrpU4xxu5SSreOpXqvkRDCWrXPxTzPl8MmCAB8RXcrhICyLLvsnNtanzzPrwzOudrGKaVmQgjXQghbqj4p5Vi9Xn/eWrtTSjmKEAoAAM65mRhjwTmf01rv9d43OefXCSHUe1/jnN+uOFV1SqksiqJhjJkWQlzpdrvHKaX9aoS4jBAyAFAODQ1tAAAQQhilVJRl2e33+4ezLLtaluXhEEJZmW1yQsisMaZdjZJmq3GopJQ2MMbHYdMr6a3WWso5X4kxirg50kVSyocHqkZr7b4qzgVVzxNK6YLWequzGmPckrAhhJYrn6fLAGAopUmlnNuIMY4O1l8pdariMbUppecBYItTZoyx3ntBKZX1en2h1Wrtd87Vsyy74Jw7RgjxeZ5fllLuqT6jBCHknHN7CCEq63Zvw8/83FAoS5o+dKbV+6M/3mUuXz4mThy/ri9fmQjt9hA9fPiy3LnDwP/yrxo0zxXKsgxfuBjE8nKiZ2ZU+PJzzvzKrx4DpRAAgDl37hCcOwdxfeM5/8RbdpRlmQHAeJqm5zDGd2OMihAirLWzQoirAHCPUlo0Go12q9V6/fDw8F+81PfiqxXbnaVtbGMbUH/rE3dHvve7fx6n6WeKv/yrH4IIGL7xbY4L8bFGo/H2HTt2fNavrj2ZnDwxEo3erc6f3wveb70+dDqZW1gYtXfv/o3/DMbAD+yfc3P3JgEAQquV5t/7vfPZj/xg0h8fb5LPfCbqz35u2i0uTgZj6snRo0vl018a43v23EmOH1N+dbUGzvWj0am7dXs3nZq+L44cXgjes9jrpbg5lOI022Df9ndukg9+Z6942xOHYNM4EvI8f15KedTO7t5RZ/yGv3kzIY+9soNmZrBzLnLOi8oosVnxfOYwxumABxRjTJxz05XHz0vGZTwIjDHEGIWUcl8IYcvBOUmSVWPMTJZl13q93kFr7RSl1FprR40xU4SQ5RBC5r2frrodMOh8cM6XGGPPO+dmAQCcc6PNZvNnvfdHpJR7lVI7BjyZJEluE0Lag4w4jLFO07QPAEVVnAiEEFFKTVecoKGKd7Nord37onORCCGllJrBGFuEUPGA8zknhGxU7xMppcsxRsI5n2eM6SRJhqy1XgiBjDHnrLVHKofnrnOuWavVrmGMFUKIJ0mCtdaZEIIhhAghBBFCOEJoAWMsrLX1GCNwzjta6xljTDIY2WVZdst7nwkhFKWUVR2PiBBKY4ytsixntNZbBSAhZBUAalrrfRhjlef5LQCggy5bdYzugdzAtYFBJaW0nWVZnxBiEUKxLMs9AICqa2bJez9wBkcAAI1G4wONRuOnKjPPQe7fVJqm140xk4SQhSp4eZgxtjI8POx6vR6mlGKt9TgAQJqmSwCwaoyZDSFkhvNJvr5+R//JJ47Zdjvlp052IEsC3zmzDt714EPfvUC/64O1JBE1NDnpgNKIMcZ6emdfzM+3y5/7uXH3xafGxde9rpv943+0GG7fScLyMgOEAP/g93uX51sjOOfcFOd8QSl1plofPD09/R+ttWm/338YAN7pvT9cluWjeZ5/1cWa/Jew3Vnaxja2MYCnO3duiMOHrwHEsyMbrZ8defVjXx48yfbMdnCa/pS5cweA8Tepp576iqgPAADEOQBCATGmABCme/Yod+dOIl716Ep861u0cw4Txmpoaqq39SKtAUIIsdejutvdH/r9tr11axwApsjo6Eby0JlzdnXtMAJYYRPj9/z9+yPgPRXHj5W997z7zKANkOf5kjEmZYzRSokE8r3fmjS+/f2tKITAmxEbJs/zIaVU2Wq1AAAAY6yMMV/h0eO935kkyV2l1EuaSwJs3ky9901jDEvT9C4AlN77mjFmlxAiaK1XnHNbXQul1BYJufJ4uosQ6mqttzp1GOMiz/Pvazabn15YWPi4lPItjLH5mZmZH7t79+59KeUvAAAIIa5IKR+uDDE5pfR2mqYxSRLmvaf9fn9njDENIaSEEAkAQAjpOeciIcQyxkrG2JUByRhjvJEkyb0QAkvT9JZSal+SJPNKKRZjzCrOSj3P88sxRjuIA4kxDlNKL3Y6HV6Zd/66tfZdAABVrhqllC567wMhZIxS6gghtF6voxAjiyH0ACD1PthIWKilIvfe35FS7meMxUpcMAIAwDm/VxTFQUppJ8ZYGGMkpZRhjDEhxDvnMkrpsvd+HwAAQsgKIVYr1R9U7uoLIYRanud3vfcJIQSVZfmgwnOQBdcihPRhMwOPlGV5mHN+lRBipJR7Qwi1JEmeV0qdqta2EEI8MzQ0FLrd7j+QUv6sMeZhAABrbUII6cYYc2ttIoR4jlI67L03IyMjBCHEyrIEIcRqURT7hBAXBwfDOe+YN795hvzeHxTkJ//pDXLwQM0TQv3KKjLf+t5jycToebxw3/u5e1J0e/ze8Yf1+MJdSetDpKutDN1eDT/66E39uSd3k0deBiTLVvDpUxtk5wyyo6NDGKEyxJhhjDXGeAM2rSluOOf2Z1l2a3l5+dVCiJE0TX8ixjhprX1LURTfMjEx8Q/+S9+LrzZsd5a2sY1tbIHv3iWbH3jfrzY/8P7fT3fvegEvQRw6FPi+vbLxtrd+Wp47d8Pevv3NUesXjICSlz18BQmRkInxHmIM3Pz8cFTKp698xU23ujZKslTGnTMRn312Q//Gb+wTBw+1cZ71ydg4ilKBm58fAa0JP7i/4+4v1AEAopSpW1qawpRae+/eGJkYX/LLK1PJyRM3yz//zGHx0ENX7MTEWJ7n57z3GiGUAUDGOW9ZaxtJkszxPM8wIYkJSCFMawiCtdb2Mcb3kyTZqEwg1znndxhjJcZ4peILMYzxSghhBB5wyCaE6IGKreo4rGVZdr8sy4POuXHG2O3qF/qa975eqb9Cmqbn4ya23M29903v/WSaplcYY6tZlj0zPj7+dWNjY88zxsLIyMhH+v3+GCEkGR4e/p1er/c2QsjeSt7vEELYGDOFMfbe+/Fms1k65zRCyHvv16y1uwadFe994r0frdVqt51zaZIkLdgkEi8yxhaFEGVRFCcJIVgptRMAwDk3UavVPoYxfjJJklvOuVnnXK2yAUCc8zLPc00pVQCgKaXr/X7/zd77aULIUp7n17z3jcr4c3JoaAg6hS44xZgQwq117vqa9j0DZL7vWc4o187FZi3jGONF59xwlmVrxph6vV6/obXeV/lfJc65hBAiiqJIsywzhBDivbfee14pG0EIcV0pdfSBz84AwDLGWMYYnVJqL0Jo1Xu/5bKNMZZZlt1HCEWMMWaMDVtr+865yRDCGAAY7/04wKaVRJqm1xFCIIT45SzLbm9sbLyTEDI6Pj7+fdbaHRhjIIQEIUTHGDPqnJtIkmSDMRYIIdF7P2+MwVrrccZYr1LUBWttI03Ta1mWoch5S3R7XfSqRz0dHR0iSjnzwz+yM/QLLt77rWv20iUFn/50NKMTfqi19hvi5s1Xq90703J89Fht166L4kMfGkML9/uwse7D3Fyhz194iM/OKvVT/2IfvzfXYm96U1cI0aKUdrXWR+r1uqaUrvb7/f3OuR2EkAUAOAIAj+Z5ftQ59x+azeaf/a3/6fx3gu1iaRvb2Mb/V8T6Gx+/oa9dP2vvL7wlap0DAJDx8QXwIbW3bo341dUstNtJlJJHpQSZnFwy557fZT/35Kj58EdG/ZeeGc1f97ormNEy9HolGW6uAqAROj5+EzebSwhh65eXXyCxR5zbqBRDjSHLjx1Roddzfml5hDUa8+ixV0nvfcN7P9RoNApjjAwheMaYJ4RIQojAGAchBO5K7Un0Zb/fn7LW7okxJiEE4b1vOuemKiJ0uwqOvZwkCU+SpKWUGiGEqCzLbhBCCgBoEUIKxhg457jWeqv7FEKYEEIsKaWG0jRdxRjfJ5uhvgcwxsVANQWwKVtnjN1ECNWVUvudc0kI4dmhoaEbg22Gh4f/ZHh4+N8BACillvv9/oeMMbuqguyec26sUmkhtclJ6QghOACAMWYEABzn/FaMcRghtBZCGEmSZKMsy50IoY5zbrcxZtYYM5Fl2fnqBp7GGClj7E69Xn/37t27/6O19npRFN/rnBsXQtz03o9wzm9575VzLldK7ay4TNc45z1Kaassy+OU0vtKqaNCiMWAKe4rizNOcLvdrktZjo7XkxZnCC0UXl1bUYQTUNxLEULIvfdMaz2aJMmVoiiODUjYaZpeCiEErfV0rVabZ4zR6vzbaZoKYwwVQvyBEOJJrfXrBmuZJMklpdQpa+3UQCXHGJvz3o/BpvIMCCFFlmUoTVNBKWXGGE0IgTRN7zPGlqy1O5vN5rIQYsUYM1Jxsz7jnJthjH2/c+5lMcYvjo2NPaOUkmVZvpcQUivLch8hpJMkyW3G2BDG2McYUQhB9Hq96RhjKoRY8N7XMcYkhBAJIb0YoyylPKJPnxpLk7Snf/h/aOhf+pUddGLyDp/ZuRyfOcvhwgVjb93e0z9+aq2n7Ulx/pxQv/BL9dToFUEw9hgrKgS03/rEkaTXX3TPfnma7927aG/fHg937uZ8dGyhv2vmQAhBc877lNJ+p9M5CAAoy7ILADBUKRwppVQ3m833sspx/2sB28XSNraxjf8m1J94y01E6UeCdXvc/ftH+f79fXP16vRLbStOnlyKSrnQ7TZQCABSYrC2pc6ePYIeekjjf/IThJ051VL/4Xf3+aWlGbpndjk5uH+ZjI4uoSxN+L69q8CYo5NT99j4WGnn5gXftxcQF1FMTlBy+lRJsqzbaDQyjHFgjMUYowcAF2Ok1YiGOWtcQnGklOZCiE6MUcUYc0rp/QcDTL33I5RSr7XeqZQaUUqNMMZkZSuwXJblkRhjjhBKhBDr3nv+YGBumqbXpJR7Kq6PI4SkSqmZagyDBrEcSZLcq2TdupK5X7bWHuCcn202m198qbX03rfLsny99/4AwGYgL8ZYDmT2MUbBOV9ut9uHqptxz3s/wRgrAYAJIRaUUrur7kWBMW5rrbdiVay1k9baYc75Fe/9xPDw8Pumpqaeq95b9/v9vx9CSL33I/V6/YpSapZSuiilPBhjpEKIm1LKk4SQeWPMJOd8TgiRUUrnYowQQzTNWpIghKJzzsYY20rKvY08XRtKRZxvaxgTUQ7nrMs5V2VZ7kzT9HljzO4YYwqwGQsTY9TOuVlCSJkkSS/GSIqi2EAI1YuimBgeHv6d2dnZ7zLGJNbahxBCqnLIFiGEFxigUkpbGONukiRdznk7SRI5sCAIIRjvvQoh6F6vd9oYMxVC4CEEr7XOhRA3rbVThBCltX5tWZYjIQQaY2wVRVEnhGRpmlJK6SUp5ctCCHVr7WStVusAANrY2NgfYzTe+9HK8qBHKV1ECJUYYx9C0DHGOud8lVJ6jz/1NEZ373bSlz+yXv7FXx5xi4vj7v7CqF9emcp+8p/ejW94/XBy7rmCLi8pcK6b7d2L5VPPIPnhj0zCB7/zWSPEETw5AeH3/qAeigIgTSiUklJAq+xTny6y/fv7dnQEF0VxME3T8xhjo7WeZYx1sizTjLEGIeTv1+v15/7b/nP89wn8/7zJNraxjW28NEb+3vfcj97Npo+87JyZu/sC1RgSwouTJ86LU6eumPn5GXHwwN/YhBMCMXoOAMC+57s3+gjt97t2kaDUJm+oLCaLv/rcSXX22dP2zt2mOvvsLnvt+pS9N3fE93q5m5vbWXzik3vc/HxO3vdtZ9OpqZtpmo4DgLPWSu99rKT7bYSQbbVau1utFnXOGedc6Pf76xsbGwcAYN0Yk1FKNbwI1trkRX+njLErZVmeBACIMQrnXNbtdvcTQrYM+gghpXN/49dnjJlECLUANvkynHNdGRZC5STdbTabzXq9fgMhxAghvWazOQcAoLXmAACtVmuLT9Pv998ihGikafocIaTQWh+w1u703tcAAJIkuSulPJim6Tkp5ZQQQuV5vqC1nqaU3h9k1mGMDUJoTWu9Z5CH9iAYYxIhZI0xJwaP9Xq991prt6wfKmK1KcvyDGxK5lsIoeGhoaEL3vvUez+tlDpVFZ57pJQHCAazsrKya2VlZTaE0Pbej42Njd1USjEalDo1QUyTqOO9Xu90u93en2XZDSnlccbY3SRJnhVCPJvn+QXGWCdJkkXvfWatxSGEknNOyrLcl+f5jyVJ8pFqzbvGmGPe+52MsYXKuf025/wuAADn/IrW+rAx5kBRFHtijLLdbh9yzkUAQCEE3Ov1jiql9qdpOrd1fSNUhBAyrfVUlmXPY4yT6nHDGFuVUn5vURT/xhjzE+vr69+2sbHxPYN1T5Lky1prtbGxsZcxtoEQWm80GreFELeKoiAYY1EUxeEYY98Yc1hrvZNS6vDlK/XwzFlRfvZzJ0Kvj/Fws0tGR1XlZwbuTz81Tr77u4eSi+dR/+N/dCZ0Ontb/9u/PohTYXAIdfLlL99J0/QLfGhoiUxMlH5lZVzM7LokHnroqvrCF47qZ87uUT//ixnBWFFKe5Xh5kaMkZdluVdrTZ1zv9lsNv+vF18vX+3Y7ixtYxvb+Fuh/+k/y8rPPfl3QaotTg9Kk8gPH57TFy8d9ktLY3z37hLFULPz84n48R+9CD/wfYvita+z4u1va5XT07u4EItJnhPCecs9/aVhoMzz06cu4X/8j5boW59YRFr7cOt2A4xBuF5LolQUCdEX3/otz9nXvPoMYcIaYxvBOxNCCFLKer1e74UQTFEURyilXWvtqFJqlBBym1KKqjiToYGBZJqmd2KMrCJKr1V+Ri9Ie0cIScZY58EuFEIICCHY+83iL03TK5W30RY458sDCwLnXJ1zfss5N2Wtnay6NLeKojjkvR8aGxv7oSRJPrqwsPDB1dXVP+v3+1/f6XR+Wkqp19fX3+C9/7qiKN4WY0wRQjAwSYwxsiRJlgBg1Vo7E0KYyPP8srWWa613Vkq2eefcNADENE0vK6X2VWq8epqmN5MkWWaMLSdJslKW5dFmszkPAPeklCVCaGF0dPRsp9M54r0/AbAZwUEpbTPG7nLOVzHGG8aYCWstMMaIc67JOb8opZxKkmQuz3Pd6XT2Z1l2zTk37pybqNfr1yq5P0ExNrwqHiTTE875QgghN8bMOuemKy7UCgCM1ev1yBhbxRgb55wzxkTG2JOjo6M/0Ww255aXlx8zxrxWa/1G2ORmRWPMcMVPytM0XWaMuYGTd61Wu1SW5SEAwFrrUUrpWghBGmPGY4wcY7xYjevAOTeUpmnLGDNR8dSWhBBrQoi1siyPAgBKkuSG1no2xugwxp08z2/FGLHW+qAxZqRer5/XWk9aa2e01iPOueE8z+ecc8RaO0opVRXZvsCl7Pof/bEh/fkvzKBGI/qVFUGGhx0ZGcFkeFijNO2bZ58dIyPDa6RW04DAk6EhRSYn74u9+yydni6j98+VR4++XWE8m73mNVcEoW0y0nTu9u1aaLWbAAD0xPH5+Pqvz5Ik6SKEOggh5r0XMUaepumf12q1H0iSJMLXGLbVcNvYxjb+Vpj57d/8pVuve/27zY0br8GNhgzdbpq/4Q1zxRe+sHNgLxDKgnrGVPbxP1wkACg0mzWcJNDtdqcJIfcBQG5sbJxKHn3Fc/ArvwZ+YaEhvv/7kmL/vqMAAPjHf7SXnzl9EV++4vziYsPeuLkX1+vIvPMde0kI3Z52sw3OfIxeIIQYQkhbazmltAmbN9xFIcRi9Qv5oRgjqqJHtrphUso9w8PD81rrRSnl/hgjqsJwTwzI3M65WSHEJYTQBQAwCKHUGLNbCLEyyJTDGHOMcXfgzE0I6T1YPBFCPOcclFIAAJAkybzWOqkMD31Zlj/Q7/ffhTH+qPe+prV+Gee8r5T64IOKOSHE3YEarYr2mLPWEkrpYFRVVNEmW+NBhNAg5+x57332YKEVQmhYa8Xw8LDsdDrtEEIipSyttR9ACL0HAL691+vVEEIPZ1l2EwBKa+0sALSklGcopYsYY1yv15cqFV6n4rpohFBXSrnTObceY2RlWR7lnLcopXestYn3frzf7w9hjH2tVnu+KIqTMUaU5/lVrXUGAB4h9GD0Tua9p1JKApvEe00ppUIIbq3N2+32B9bW1l6vlHrf4DXV+a8DwBQAgPeeAUCv3+8frtfrzyuldgBA88H4GWNMxBhveS4xxrT3XvnNLD+QUo5mWXYeNjP2TgMApGl6eeCu7r2n1tp6mqbzzjkspRyDSmkHAKha9xcU5MaYGYyxFEK0tNYzWZZd8N5TMMaS97+v3ZyYkIoQ3f+hHznsV7Yi/PLkoYcuWoAJPrNro/zSM4chBmxv3cYAsFNfuAjs6NGb9WPHdrs0/echhB/MTp4YWpueSpNud85+9ON7BjvCe/cCFYJijJ0QwsDm6PZmlmUX6/X6d9RqtQBfg9juLG1jG9v424PSP8Gc70wfeui3yfjYLbe0nIb1teloDEVjozZ/7LG75vnzNbS8koY3Po4jQKfX6x1DCNk0TZcxxg2E0F1x+Qq3f/bn4/k3vu2q+4Y3esv5BABABBAMk3v25Y+M6be/fTJ//PWX3Z9+KudvemObjY/7hGLivfOMMR5CsN77HqVUEEJwWZZNa+147vxaIcs9EWMKABBjzIUQK865LbK1UqpRjZkGUSKaUmoppXOEkDnO+SpjTBRFcdQ5N+2cG3QcSkIIopTeizHWKKW9KtMsVJ46NYDNEFtCiC+KYl+WZZcxxh1CSBshVGOMdRBCdymlmZTyjBBiR4yxqNRsSAjRI4RsxBh9CKFWOVOf55wvYYy7SqmDIYQhzvmCtXYKAFiWZVcxxqsAIEMIzRijqMZ9DSnl/uoce2maXtNa702S5LaUklSu3DkAuBDCRAhhRAjxKcbY/9rpdA5aa0cqf6rrUsqB0ixJkmSh2+0eHh4eXmCMsW63u89au6OyHeg45xJKaavyhrqFMU6yLGOU0lCNHHkIocY5X0iSZAljjLMsC0qpOkIoVJErilLaK8tyj7U2nZiYcGVZBqUU8t73lVKv1Fq/0zl3Gh6gmgwNDV00xowxxm4wxlYYY32l1N4YI07TVBljEu+9DCEM4lUAIRQZY90YoxVCLBtjhjjn8865ScbYKiGEIISQlPIgAIAQ4qZSasuUNcaYZ1l2pyzLvSGEeoyxnmXZZYRQ8N43KKVLLzY9xRibWq3WSdPUFEWRCSFKKeUhx2iTHT2aQAgd4WyOxkbuo6/7+jX/9NPj/NjRNp/dHcy1600yOdHyC4uNqA0Tx47aob/zLVeTU6eejUb1QSlPn3r6Gn7Nqz9JKJ2llF7BH/3YUJybG07f/A1z9K1PzLMnnhjmw8MJxhjHGCPn3GVZ9lsTExM/yjn38DWKgYPpNraxjW38/4aVf/mvXpMcP36j9W///T8Ozr5Of/m5M4Pn8Cc/cVfUcmm9l9bavZzz2wAwo7UWSa83T//oE1F/7KPTAaGh+H98uOMqf53a1atfQrfvJr23fMNJipCsP3/+Dnn919cBAFNKod/vb+R5PrW2tgYAwBFCfQAQ3vtRgnFJf/YXluOzZ2t4ZETS06d7vQ9+x3FKaZdzfnfAQ3oxCCGRc35JKXVw4NtUq9Uu9Pv9Ew9uhxDysBkzgoQQZ7XWLyOEGM75XYxxGZ85y8mVq6T/rnfsGpCUAQCq/LKThJAOQqjlnNuDEPLj4+Pz3W63wxgLSqmd1trxZrN5XSmVMMYkpTRqrW2MMUgpTw32V6vVLvf7/c1uHMaFEOKWlPIkQsgkSXIJAGaklGNCiLvW2skYYyKEuKeU2pWm6VWl1P74omDfPM/XsizrVyM/ZoyR3W73QHX8l733VGt9EACAc37JGHMsSZJnMcaNEIJWSh2vCOgbjDFjjGFa/9/svWeUZdlV57mPP9c8GzYjfWalz6rMKnlXyCEz8iAkMSAQDMI1okEI0910M4CwLczQ0Jg2EpKQYYSQg+kCREkqmUEqlSorvTcRmeEjnrnu+PmQN5KsUtFo0VVfRu+3Vq6MePe9d8+75p0dZ+/9/6stSZIcz/P8TiHEqpRytSzL6RACttZShBBut9tzURSl1lpcVdWK994ZY1rGmEYdbB1TSh1ptVoXQwhOay1DCJVzLkEIOa31NoSQStP0EsaYZVm2o9vt3iCEcKVUtr6+fsdtx+1klmWHkiQ5lef5QQCAdrt9UUopiqIYGGOMc66jtd4mpTxFKSVlWXa895Mbc2gt0NnaWHXCGOfdbndldXV1mxDi2oZWlxDiLCHEFkVxqNFozA6Hw1saXI1G42ye57va7fYVhFDsnBuUZdlSSs3Ux/tUCEEi7weV1tt9CB2p9Lnwkz+52V24dGsFURw6eBmEiBsvfvG13vvev9Wtr0/T7dsrc/asRHFcjv/4215SvO41rSzL3k8pHfDZWTScnNzaaDSOcc7jWh1dcM4/L4T4niiKvmm63v4pRitLI0aMeMJJ7n3eNbF/f5b93d/tVV97+I1Ba7axjV254r3gK9Xk5N0hBOmc2zQ+Pt7DGA/yEHbTv/xYz5w5uzV+9387aSKZ+Np1nv2n318xX/jiRCPLl+Tc3JC9/GURMPZXGOOVwWDQIYTEw+Ewt9ZuDSEIKeUVzvmadw7xwWDevPNX94Y8T/zKSstdvNSQCI6jOAlVmu4UQly9XWdngxACstZOxnF8cmMFYKN1PYTAbnsqhno1ijG2aq2dCiGQ2h9smh07Nq/+r987GO+54yxs2+75scQgl3cAACAASURBVEfO0063V3p/SAjRM8a04zi+obWeAACM5hcuyX4/8e025pwDACx57621dpMQYsA5bzLGmPeehBCUEOI6Y0wVRbHrtrFz731DCDFnrR2vZRFiAIAkSWarqtocx/HxWnYgqo1ib63EYIw95/wKQggzxv7GObeZENKnlDYRQnMY4+tVVe2VUi4SQpZCCNZau3tsbOwM3BQ1RN57G8fxSghhCSHUKIpiq5TyqjFmijG2WAdsGgCWkyQBjPFiXV9F6pRdzjkXSik3HA73jY+Pa855TylFa4uUB+quvObNw+Hz+tjnjLEFKaXjnAeMMcMYz3nvkfdeE0IQxvgGY2wxTdOhtZYZY7ohhCSKorMAIKqqahJCVgaDwQFCyCoA5M65aWvtBMb4ujHmdj8+4JxfNcbMUEoHQojTcRxXvV5vJ+f8HGMswxgbIcR6WZZ3IIQGjDFMCFnQWk/Xry8IIStCCMM5/xrGuJNlmYPaEBkAWBRF80oppow5GDbSeYLL5ubNq9Ta2Oe5av/v35lVJ052m298w1f9ykpDnTk7iSgFt74u+ZYtX4qf+Yy3Tv3yL36Wc+4wxm8ihIQ+IRuB3BIhRCCEFGPsnd1u9+cZY9+0q0m3MwqWRowY8aSw9M5f2VIde+SH0NTkLvEr7zzHu92eOXZs3F27FvHveP0KYIx5kqwSzq8LIWg5HBrxe3+woD796UPJz77jD/zTnvoF69yhWhAS+GcfOGtOnTqsjz3SVf/vP3TR7GwfvvQPF4ujd82WSr1Oa90G7zFGqBJCXNDGbK2qaiexdgV+4qc2h/X1fwxurGXuaw9voteurZhvffEkY2yWEJJRSjPnXOv2z4EQUlLKQR3IQG1Qe/r2eqfbCSE0MMb29mCKfeKT1p2/0LT3f2YyfOjDKbv76Kreu2cH53wBAEqMcaSUGqulBSJUVcvmR9+2y73ohUJhLAghKUIoi+M4OOfyEILo9/tdrXUrjuOrCKGkKIptQohFQshFIURZm+5mCCHMOV+ilM5yzhdqy5W1EILCGOPa4oVCHexhjCsp5QXGmCGEMKXUpLV2Ik3TBG5O0Igxhvv9/gEAoNbaDkJIEkKGhBCLMUYYYzwcDiecc9NlWW6Komh1I5Cz1k42m81Hsiw7Uh8vaa2dLsuyTQhZS5JkGWNcGmMmCSHLCCFsjEHOOfDeL2ityzRN/5pz/mPdbvf91tq3aa0HdaqySwhZK4pit/e+3Ww2VymlnBAi6hStds4ppVSrXrHaF0VRNhwOd9bnljHGCGNsmVK6gBAiWusJSqkPITSjKLrGGBsihJwxZpJSusoYu+6cGyOE3JBSrte2Mnvq81lhjMuyLA+EEDBCCLVardUsy3YihJS1NhZCXIiiqG+tja21DYQQwxifGw6He6y1XWvtZinlnLW2gzHuI4QEpfRGFEVWa91gnF/y27aBeNELB/a1r1mtjh5ZjV76rZ8SeRGDUpvj5z3H+qI8G915+Ee3feTDP9d85SsuAQAwxtaHw+ErhsPhXkLIrBDiiyGE81EUPSiE+N5ut/vlJ+SL4P8njAq8R4wY8aSQf+6BX1GnTn07f/WrruTbth5If+itF5v3PveM+u3flfA/7jPsxo0i/6X/czs4B1EUzZHV1bj8+Mf30l27jhcvecmzbJ7vi6Low7Wg4b3uF/+Djj/04ZP6la8E/KUvufJP33swLC79oHzK3Y/A058GAADRH/1Jph/86ib8X/84cQBtAACD8Y74J378Yfe2f3306wb5utcpAABjzF7vPSOEzBFCTF38e/M9o+hMlmWHpJTnlVI76iDon/xrW0p54/YVHgTgUBTf0hJA7ZYLz3wGdc4lSZLc0FpL733AAIUwZrnAuEMJwcY5JD/+yXnz/W9BZVkeBIA2IWTVWjtpjOmHEJoIIVOW5SRCCNI0PRlCCHmeH3HOVUmSLHvvHUJove7O2gj8jnvvKSEEtNbTSZLMOuc2aa0d53zWGDPpnFMYY9BaT6Vpem4wGOxXSl0ghHAAMFVVgZTylJQSW2u7ANDP83xnu92+Sgjh1tpICDFXFMUhAADvvZZSPuyc24YxLjfUtR8HHkKoOOceIXQRIRQZYzIAEIQQnOf5ofqcvH1mZuYUAKD19fWq72hzEmNkjNFZlnUBbq78FUWhEUKcEBKqqmoQQjLnXEIpVZ1OhywuLsJwOIzrerFlhFCAm2a5VAgxprXGjUbj9GAwOAAAoLWeBABIkuREXfx/JITQppT2nXMzGONeVVW3tMacc5H3fnOapseMMS3Ouc/zvAcA2yill8uyPGqt7dSCoajdbpdKqarf77+CMVY65zpSytOc8wbcLKTfG0XRbFmWByilZ+I4XinL8pAKAbN2++K2LVs2LSws/Kki5LXxC5//CxNTU+8FABh76w88br3Npk2bXrZp0yYMAObxto/4R0Y6SyNGjHhSoL/8S//A/vt/+Rp//bdl9bJ+HK5cper4iR3Vxz5+jz5xckrOXb8MAGCt1eTc+RwAgD/j6SettUdDCElRFN9vjDkQx/EHWb/v/YEDvkriQ+FFL0x9XhAAgPJXf/1OkeUnGMaL6qN/uZXv2L7uGXuU6GC5f99R9oH3HSNH71p91CBrc3nnnKxTP9s552c3NgshThRFcSSEQKuq2iOlvCClPF5bfXwdQogrVVVFaZresooJACTs3DEAACBbtmh473tmCyn3J0lyAgAgSRIzMTFxpvnFL/6Bftkr9qbvee9s9d3fewg1m8jGMUXG3CpAxxgPAIBKKXWn01lIkuQaADBjzEyWZYfyPD+88XnyPJ+w1vIQQhzH8Tkp5RmMsauq6k6t9R4AaFBKV733lda6ZIwNjDGbKKULcRzL2ipmrqoqDADQ6/V29Pv9aG1tbRohpOM4TpVSlbV2USk1U9cr5caYQVVVg6IoDsVxfFkIcaEsy6NVVR0FgB7GuPDe357CBIxx1ul0zlVVtW0wGBzq9/s7iqK4K8uyvYPB4M4QAoRw82QhhGxd5wYAEBhj/zEJ1WpVFAOttQkh9GqFdaiqakcIwVdVVdZWL5uklEmSJFFVVRVCSFFK5+M4vm6t3Z6mKWWMUSFEI8sy2+v1NmutS0JIrw4Oj23suyzLoyEE5JwjQohrnPNlhBDinK886hILQWRZdoQQMsAYcyllq9vtnr9NYBMAIFhrm71er5Hn+XYpZRVFUVF7zVnv/bAsy079fpgQUuV5vt8Y09lICxtjiqtXr0oAeHm73f5Q92agFOp//xQORoHSN8QoDTdixIgnhRVVva6M4++o0nSy0+nMOudKd+Gis5/57E0bE2MouXS5b1/yrW2tdYd/8lPL5HWvWUQHD8Ry1673cs6/ghC6Ya19ijHmLpSmXk1OzAQACQCD6MzZNdxs3Ai93iQ5sH8uTE9T+PP/u936wbeu221bs7ow/Jb2k43jafqMZ1wMf/6RW+kzmuVD9/x7GwHjW+3l3vsmY+y8EGLBOTfjvb9lsGutnajTJPmGNMAGcRyfV0pt8963tdaNRp4fY+95r+F5Pmv+6E8OQFVh7z3mne4cu/PwMgCEOI5TznnDOmd8p3tIv//Pxu3xEy1kDAr9Pkle8C0F2bcPB+/Xk2ZzcTAY3ME5v4AxFkKIPkLIOuf0hm3HYxFCnNNaNzHG67WGlK3tVXre+yqKIl1V1Q7GmBFCZFJKHUVRe3V1dWcIgRtjWrXh7DGM8QAhBHXqsOG9LyilqKqq7YyxSwgh4b0fL4pip3Mu5ZyvAcCatXZvkiRz3nuEEDIIIamUmkqS5FzdDbnQbDZD7W23bK19lM0N5/wSxjg0m80QRdGK974khJxuNBonVldXX00p/TfGmGg4HO7VWne8950ois7U9VAMIbSqtd4VQqBSyrPGGIGYRCuFIw1BbtQF8KH2amMhBO29p5xzSNN0kOf5GMZ4VWu931o77Zzr1PpRJ+q0bbdWPZ+sNbNaGwX/AAC1Ejhqt9uD2muOIISUc+4rlNJICDFACBWcc1Urqq+VZTmplGpijEuMMRRFsQVuBj2Ec35Va70ZACCKouvGmAkAAELIgvd+ylp7IUmSt2VZJpeXl9+0urr6hhDC1+I4Lv8Ft/GImlGwNGLEiCeFlZWVn3HO7eacrznnbFmW42zTtMWzs4t+fqEDzuEwGCTR8eNL/IUvWEJ33smzO3bvVeNjW5RS92qtny2l/ECSJO+21j49ThK7Ye4qv/DFK+r9f3bAraxMyrvuWuTbtzk4d66yn//CVPnZzzXJ5SuDlJAltHfPshBimVI6pJTOW8q2sK886PzKCgcAcIuL3WjbttN6x45bwUYIgXrvu4SQFa31jsd+Lu99JKWcrSf1W8GYMWZMSnnCWjsNAOCVEuijf7luPvaJg9H3vPk8f8ELvuC+8uWd7rOfm2HPv3cxl/JOY4y21vbt9Ru6+IEf3A1ZRgAA0ObNRfxj/+pK+bGPTbIQlG23sUuSrG5Z71VVtYkQErz3lDHGkyTJEELXQwiuVqdeYIytA4AFAFBKHeKcz2qt91lru/XqBPbe47oGZ917j6y1myilJWNsTSnVAQCI4/hcCMELIWSe57uttePOuUAICVJKiTFeL4piH2MsQwj1CSELzrlpSulFAODW2olmszkUQlTOOUwIGVhrxznnKE1TtVJYdzVDiUAeKAreGDO2cUwppeuNZtNmjhCBQ6iLj9fyPP/2siwtxvhnQwhNay0DgHJDxZwQcqMW3wTG2JWNnzHGa977iSSSFQfbV0pNM8bW4Kbxch8hROI4HnPOeYSQzfPcIYTWtdaPEhkFAOKcm0YICYQQbGg5EUIGdTBrpJTXpZSF955IKZcYY4JSypxzllIaUUrPIoSuhBAmjTFEKbUNITRbluUhAKAhBJIkyVUhBMRxXMVx3MMYrwFAwxjTrDvXhnEcf9R7vxhF0Srn/PPdbvctnPPmysrKR4qieJu19nla6+fEcfw+xtg3pUbSE8GoZmnEiBFPCoSQMwDwEq11N03TGxhjibtdnf38v92f/vbvXtJf+Uo3fulLKxxFfh3j3V6KW6+VUl5njP37drv9flSWaYbQH/T7/S0IoYIC9Nz73n+zEykEUJcvN8Kn/moeNZockgSCUhAuX24Pf/anG7Ysb33HpUtLD6EvfPGGXV39xy4m78G++0+3w7fcC4DQ7fs/V5blo+QBNhBC3KgLdtHjbL5lkWIbjQ55+08Oo09+6rR/83ddjDi/X737PS9zeQHhyw9yeM2rQGs9obWegEhC+zWvOp1/4ENbw3CYsm953nz2wudPs7uPXnLjYw1tzA7inIjj+JjWeof3XjLGhl++Nrz/aduabwjeoSiKoiRJXK/Xm6h1lgAAIIqiywAAtVhnEUKIN4qNrbWCUroROAFC6EqWZUwpdUsryHvvqqq6uyxLkFJerKpqt3OuixC6WhSF4Jy3pZSzzjlird1eq4GfQQglGGOptUbGmAoAWJqmvt/vt+M4Ps4YayGE6NaxRksMKpVypAEEzvP81sFMkiTDmOCTSwXe1mJhaxNU7e2WhhB+GGPcMMbkxpgSIUSllItVVd3lvb+1UsgYizcEQOsVtBWEEPfeeynlegihQggNlFLtRqMBRVGse+/9cDicds6lCCFgjC0ZY75u9c5am8Rx/MiGOKi1dqxWCR9HCF0NIVjnHLLWBmvtKgA4xpjGGO9ACD2vKApfFMWtQAwhxG/7GbTWmxFCNwghmdaaNxqNrxhjxrvd7n1RFP2I9z5IKe1jx7W4uPh2pdSzN37XWj93OBzeHUXRVx7vmh7xzzMKlkaMGPGkMD09/fa5ubmXG2P2ZFl2uNFoXK6qapwy1gv/4ed94+y5LN88I6PPf6Hf/sQnL6+9+lU7AQA4519oNpvfOTk5Obv0K792OP/s5/67+Pl/U5hutyUf+to5/4d/vA84M+1P/80VsLayf/lx75eXIv7jb6MWoV6SZQv6oa9BidCjpAD8X/11x3zww19Xa2Tn5xvxVx86WTz1KYfq/V+6TWjx68AYD0MIMwAAlNIrjLGiLsAGY8xOjLH13lMAADWzaZv6obdCh3MgnD9NvOQli/rBB1P12le34DaNu+QjH304f+/77hIH9p+h3/piUj3j6caH0FDt1l2E8xPeWuSNGQeAOI7jKxhjZp2Lr6zpu5uRUqkgfEuTcGNMZa3dKaUcxnG8VBRFBwAkQgiKojhMKZ1jjM2GEExVVYeTJDlJKU0opY2yLMsQQiOEkEopzzvnHMbYVlV1BABgI5W2MWbnHKmq6mBRFJCm6XnnXHDONQAAyrLcDwDAGFvhnC+XZclDCBwhVBBCCkopV0oNOedjZVleHU/FVudcCDfNjwHgZm0SIURZZ8k905LGnGCMERoOh1ONRuN6v9/fjRCaQwhRSmlS29m0oyh6GCEkMMbHvPcSAByltKSUFs65xDnnEEKlc66dpimpvdjGyrJsee9PY4wDY4whhEp00yj5Yi1cOSjL8o76GlnRWo/Xx+GxkhMbJrxMSqlrbzVrjFkmhBQYY7DWNvI898658Uaj8bDWOgghUuecEUKcUkodRAh5Qkg/iqKoDjw/W1XVriRJ/ou19m+Xl5c/gjG+4Zz7gDHmKMb4eqvVWur1ei8riuKnbg/mMcY5pfQajPgXMwqWRowY8aTQ6/WeY4zZs/H7cDjcyTmfDd43YH29rxqNEsWxX3vuc45IKY9FCB0nhLy70+l8sNVqLc59/w98X/7AA38c8oLFJ0+fatxzd+bn5xOPwMs3vH7Vt1qxUgrYq15h2PQ0GGtDTMi6Zsz4Fzw/gdtWKAAA6AtfWKJnP+uYettPHHnsWPFt6TSEUO/xPg8hRAshLlprx5MkOeacc8aYXd77WwW9zjkuhDijlNp/+2u11jLPc6Lf8uZt4ge+b1UIsUysdeSjH1sW62vN4fs/cJTu2H6Df9trxfDI0a6J5K0JGGOMEUIhiqIVSukqpZTned6Oomj5jXdPbL8+MLwTY4sQIt57SwhZsNZK770nhJQIodU4jgtjDFhru0qpqTrgA2OMKctyptvtzlFKfb/f3ymEuEgIAQAIGytMSZKcV0o1hRC9Ou0ExpjdSZJcyvN8l7VWbRjuSinPGGO6zrlJ51wDYzynlNqdJMlZjDEbDoe7yrKERqNxdnl5eSJJkiXv/Tlr7Y4sy0iapo8456bqom4LIVhBQFtrhrVPWjEYDLakaXo6z/NNhJAlQojknM8jhAqt9W7G2LpSaj/GeM05h5xz0lobxXF8nnO+Yq3dQynNsizbXJbl5o1jrZQ6UJ/HNQCgaZpeB4BIaz0WQujddk6yJEkGCKFSKXVrxYlzfr6qqo1r3nnvYXl5eVsURbOEEG2t3SeEIJRSJqXUq6urU977vvd+ihAyiOOYZ1m2CgDgvcfGmLwsS6+UGieE3BvH8fL6+vrdmzdvfhnG+JcZY1d7vd7f53l+GCEUjDHXq6ra8thrl1J6qtvtLj7edT3iG2NUszRixIgnnOvXrx/Msux3vPfbbn9cXLh4hbznvax612/vZb3+gDzvuQmmdK6qqkNpmj6AMf7g6urqA1mWvdX/zd8+z56/MAEAYB54YIK+9tW5eepTqfvfXm7Dpk2Fo1Q0Gg2qCEFFUSSNRgOHEEytx2PSNF1ljC3WnW5CtVsTePNmFd/73OtomC2Q3/h1QA8+uEoPHpiH5z7HJtu3ryCEBrU2jrndLBcAgHN+uZ4cdxtjptlNOeoupXTBOXerXZwxdm2jRuY2qHMuDSEQ533snWshpZj/d/9+Rn35K+OI8yDe+Uuz9pnP9CxNB0KIFULIkDG2QAjxANBnjHGEkPXeE8ZYr9/vHwwhzElkh7HgCQBgrXUspVwGgL5zzoQQKowxzvN8v3OuG8fxea31jPc+ajQapwkhTGvN8zyfEUJkGOMe3OxYi4ui2M85n91IW7Xb7VAURRNj7OM4Po8Q6nvvVV1HtWCtncIYB875sDYLnk3TdA0hlFNKUVEUOxhjAyHEQhzHq4PBYF+j0biotW5nWbZPCDFvjJlyzhGtdcda25ZSDgkh1HtvAeAPkyTZAQiN50WlvHct51xijNlMKV2uV4MKjPEq5zwghJYopWvOuardbhdSyiWE8HUN9CjyNhsOh4fSNL0BAHN10bX23kuEkPfeRxhjFUK4TikdF0IMnHP9Wr+qHUXRNUqpGwwGh7z3SX19XKutbTYMk1uEEF2ri09FUaSKohDe+68459q1dhSz1s6kaXoRIcSVUpZSqhFCy977MQBoGmMiKeWq1noiy7IdxphnYYx3lWWpB4PBL2RZtqHgjiilQ2vt18kySClPdzqd9/0v3NLf9IyCpREjRjzheO/zoih+gTF2hVK6QCmd55QOw7veNWH+/jObkNYICwHita8pKOeUc/557/0n8zz/k7q9ex1xbu3fffpmZxRCwDbPzLoD+xPn/bQlZMI5R4UQuTEmQwj1vPe6qqqKMZZaaxGllFFKY0LIPELoBmNsMYQwWSbJZn7XnfNFq7UHXvoSJnbtWjD79kId/DQRQsZaK7z3jxWnRAghwBhbzvkFxpipUzGi7jZrAAAQQsrbUzOEkEwIseS9Jw2tb4hjjyySP/6Ttrzrzjn72c/6MBym4BxyD3x+zL3yFYPS2v1a6wlrbcdaO0EIuV5V1WFKqQkhRFprwBhrzvlyCIELISRCiF7tWz+eiqC1pgAwXlXVzlope35DndsYM5Wm6Ulrbdta26mqapoxthRF0dxwONzHGLtaVdXdWutuHMfHqqo6EEXRCgAk3ntqrWXopi1LUym1y1o7labpI9baLUmSLBlj2lrrpvc+SpJkgTHWYoxJQoguy7KDEFoBgKb3Xhtjxiil1+vgwGCMfW1auyyE8MaYtpQy996TWrBxSwghfnB2SH7hvtltL9/bgDiOZxljc4QQUEo1vPe5cy713k8IIfI0TbshhFRrXQghhEeEnlqu2ifm83xT5KeqqupYa6copYWU8obWerLRaJxoNBrGOVcQQqIsy2aqqmrVoqVOCJGFEFIAUFrrNgAQAADO+YK1dlO46X/C4ji+6pwbhBBaQojCe98TQiAhxK7BYNAJIcSU0hvW2klK6SoAlEVR7E/T1CmlJjjn6wAQYYxNWZZb6iA0RgiZdrv9teFweIgxxjfSgbWkgjK3SU1s4JybCSF8LEmS5Sf6Xv9mYRQsjRgx4glHSmmKoni2935GKbXfWjuDnCv8u987BllGAQDI+HjfvewlqKiqMcbYO40xz46iaBvGuMzzfIedmuw2m62L+qsPjcm3/dgj7ju+3Sml9kgpFzHGC1LKeWttBQBpWZb7jTGTcRz36hZxNBwOu3meT2GMbyil7qnbviMAwJCmGWPsCo2inp+Y2F1V1XYpZcY59957q5S6I03TWYyxs9ZGnPM1a23DWjtW+3wdDiEkaZoOjDGMc77AOZ9njK1hjPXtxcCU0qHWOnHOdby1y/bn/u1Oe+aM1H/x0XG6afqKX12bBAAg3c5s1G733Z472piQzHsfcc4vaa13p2l6VmudNJvNzFqb5Xl+ACG0sSJXDIfDPEKWUkrxYDDYFkIAIcRpQkjunAtSyrVWq1VRSk1RFJsopRsBFDjn2htii865sWazeQ4ASO2zNlGngOYRQrYsyylrbbcufHZxHF/13o8rpcbq92xuaChhjFe894hS6q21VQiBYIxXlVKbGWNrcRzPF0VxB0KoqlePgpTSRVHUQQhBkiRLWmuklOpgjK8rpe703g8SGnZ+4mSffMvu5kXs9GGE0DxjTCZJMkSEtquyFMaYCaXUeFVVg9o/bychZEVy1sIIwkcfWS72TUrXTaOelPKaMaZlrQXvfTuKoiFjjAIAHg6Ht1ZGa2+4rjEmoZReCSEwSmlFCLkGAGCM2cI5v0oIWa8DVAkAwlo7aa2NGGNDANAIoT7G+F21EnyFEBpWVbXfWjsphDhrjGlYa1vGmLZzjtXXLERRdBUhhGovv2aSJAjfFOIspZSVEGKhfs0/dkrUhBBoHMcfTdP08mO3jfjGGAVLI0aMeMLp9/tNhNA9WZbdRSldI4SAdm6KvO418+zMOeVv3EjAOcpf/cqTjcnJN87MzNyfDQY/7UNoe++ZtbYZEMLi7qPr7GUvUfCcZxNKKRBCes45bYzZSilt1hYfcxjjxTiO1xBCAgAs5zxSSoV6UidRFF02xkxijNcYY5ecc9ustduttdOU0hUp5SwhhCGEQlmWU957QSmdM8YkQohVjPGCMWYzAEAcx/NKqak4jnuMsb4xBllrU631bu99M4qiQQih52oDYO99LKU8E0JoIyEw/9YX3WDnLwz9YNCInvqUVX3u/CTfv/883761KP7sg0cio89XR+7alqbpQwAgCSHDuj2eaq1TIcRQa90ghFjGWBBCVN77zDnHQgjOGNPx3jMp5RKlNNTF5yjP824IQW9M9IyxOc75CsZYCyHAGCMAgKRp2pdSqjrlOA1ws8srSZJVIcS6UqqDELJJklwRQoAQAiilS1mW7WOMrUoprzDGVjHGglJaAQDp9/v7MMZea721Fv0keZ4fIoSswM0C7JWqqjZxzsN8X6koFhQFb/M8n4miKMMYI8bYDSllW8YJ+YuHl6KX7mutcuSnOOdLUspUKRWu58BShrIQAgwNunCxF4prfZNzLhapV1tOLZYrawqzU/NZhXnEBecMCCNB5ZtCCA0p5TlKqTXGYISQ0Vp34LZ6thoshFjL8/xAFEWLRVEcbDQalxhjfe+9rarqsLV22nvfFkKs1nIGpJZsWJmcnHzKxMTEV7rd7gd7vR4uiuKH4aYJM7bWTlhrkzRNj1NKVwkhS5zzvnMu1lpPJkly1RgDZVnu994XRVF0pZQrxhgmhPAIoZIQchUA9O0ro4yxG1EUvS1JEhjxL2NU4D1ixIgnnFarNZibm/u8tfYnAACiKLoAAB0DsNX/2jvX23//VyEWkQAAIABJREFUmRPMmt8dP3LkvwEAzH739/w42b5tC/6212Hbai3SsmyI//ruy8WnP30XbqQrjb/8CwSMNfr9/pY4jue11hIhNAsAzRCCoJQ2OOel9x5ba8ny8vJMraZ8FmPcLYrisJTykRBCQyl1KIqiMxsdW0qpGUrpUlVVUbPZlHWN0Jr3vmGtHY/j+EKe57cc6kMIEiEExpjZLMvuiaLoLGPMDAaDSUqpopSOO+eElPJEVVWHAQCstdsIIWus18vDb/7WuHrooQm2c2eeffrvDyTPf/656vz5cX3+fBcAwDz41QS+7y1grZUhhBzdlHgeJEkShxC0MQaklNcIIVprHed5nlJK+1rrXRjjZYCb9VV5nu+v03SzlNIhIaTHGCuHw+Fd8BgopXOEEOycS9fW1nYTQuYoffT0UJYld85FdXC5HEIQ+iZTQoirUsrjhBBXluW+Og13tiiK8Xa7bdI0vWCtlcaYBAC6CKHV+rhsqv+fiuN4rbKh7zFrnFyoQkfguCjwwqQb7mi32xejKNo6GAzm3/XAdTyoTPfSujF3jSNQSu0ihFzIsuyeh6+7U/0K8IOzQ31m4R8/J0IAR7Y01n/keVuj+SzAWMrj8wu9PBVden4xs8+cBqCUXkMIxQghEULIsyybejx5iCRJHvHecwAArbXgnF92zglrraKU8mazeWIwGBzudDpniqKIpZRXa/NciKLoQ0mSbHT8IYzxRH1/XPPeh/qaa4QQ6IZVDAAAYyyjlF5zzjWMMbK+brcmSXLcOZdIKfsAgLTW48aYnYyx9Uajcd17H/I839Jut83k5OT/TMl7xD/DKFgaMWLEk4L3/lbthHMOMcbWvPcghfjD1lPu+dX4Wc8sln7pnTvKB7/6g9Xx4z8d/v5+Cu//QODPfpYip04ZtbxyBADAFcUE/trDhXn602i73T5VVVW32WyeDiGwNE0v53m+I4TAnHPLAFBVVTUlpZz33jPv/UxVVQ24ueH2IMEhhHwIAYcQUK3AHC0uLnaSJDlhrW0yxnpKqS1VVY1viB0CABhjsjiOHzTGdAAAyrLcZ609lyTJSUIIZFkWJUliEUIRAByrVZsni6IYIz/375w5c+Zm0frlywkAQP6Zz+y9bVxAdu3KAW7aZCildgMARFH08GAw2CulPFZV1ZFOp3ORMdau64faVVVtTOoaAKCuodoJcHNSDSE8orU+qJTynPMzWutHdetZa7fEcXyiKIrDIQRECMEIIRbH8UNKqTucc82qqrYhhEKapsdDCLiqqiZjjEdRdNl7j0IIbYzxQAhxCWMMzrkJzvmS9z6NoigtisInSXLJe58657anaXoqy7KD9bUSZ1kWdzqdghGvL6zpdCkjLsWhnBQoDIfDlve+jxCKvv8Zm/R4wi5+ebacOjqZVt77pnNunFK6sqXN/ae+OL/9zi3tBUbRYlOyoh3z4b7pdLxU1hhgSaHzwfPu6J7fKYYvB1AAACCEOI4xRt77BsJYee+tEKIqiqKx0f2HEDJCiMX6GGEAgDiOSVEUFGNsGGPYWuuyLDscx/FprbWLoijv9/v7CCF5t9v9eLvd/g0AgCtXrrxLKfUShNB4FEXHa+X3VpqmxxhjqVJqe51CTeprLuWcL+Z5vjPcJjlRFMWhZrP5ibIsG977p20Ud1trm8PhsAMAwBgrtdaf+YZv3BGPyyhYGjFixBNOv9/H3W73fmvtnxdF8Qat9e5Go3GflPKHpqamrsKuXWj+J3/qe9S1az+iL1zYH5S6+V1kDNKf/dytiZxsmtbt7/6unr7//jJIOdT79t7BCbmil5Y2+TjxDkFDSnnZe283tI7SND2ZZdmhf2JoAACAMbZwc6aMAAAYY0NrbQQAUJblTJIk68Ph8K5Go3E8z/M9QohZpdRWAADv/UwIoae1viVuaYzZ65wr4jhe1lpvV0o9an9RFB0LIcygyYk1OHNm6z81Lv6iFxzP3/4TdwIAIIRu5UwQQhs+niFJklNCiNQYc5/3/ulaa0opjevn9VutVi+EwDeEGBFCDmOc1PpGNwghTmv9dfuuquoOIcS1EIJmjBUAQBBCFCG0whirjDGTIQSktZ5st9sSYzxb1xpt1CiJqqru8N4XGONgjOkyxiBJkrIsy14cx4nWOiilBnUxt200Gie01k2l1LZ6DKLVaqGJ2JlzC0Vxpq/Jx08U197xrMb2oigKznk0ljD9fzx7hoF3A10W2IWgq0Bzwpj+2/NLcPe29vWFfuEjTnOCPHnW7s6UsQElgpJ+oYudXSl/875Lz/r1l07ojRUihFBKGKdrRSDUWuW9d5TS+SiK1hljviiKySiKFofD4aP0t4wxayGEseFwuDeO4xMAQBljaxvGxUKINSnlcYSQzPP87iRJqBBChxC2W2v3x3F83hhzx8b5tdayJElQs9ks19bWSkIIEkKY4XDYopRaIcR5xlhfSulCCHY4HI43m81vbzabKcaYZln2dIxxZIzZUVXVa5xz3RDCmDGGw4j/JUbB0ogRI55wyrJ8VlmWP1WW5WsBACilx4UQNwMlAJj9zu/6V/kDn/9P4D2QrVvX47sOHy++9A93gnm0p2fr9a9fWP/Pf7TZV9Uk/h/3VTht+CrP7whKkfjVr7rS/4Hv75bO7WWMrRJCht77RlEU25MkueK9V2VZ7nvs2KSUZ7TWE5TS0hgTxXH8iFKq45zrRFF01XsfqqpqAgDUnVUSADJ005EeMMbrzrmJJEkuhRAyay1njNkQAsnzfA9j7MrtNilpmp7L8/wAAEB4w3fE8MDngX/H6y/Ci180dL/2G/vd5cu3VL/DxKQChKBO800A3NQ42ph8KaWcMRY751Cv13sj3DRCZUqpMQAArfUdhJArZVnuSZLkap7n20MIJIRgKKXLSZKAMeZx0zHee2mMmUyS5CIAdDnnQinVs9buIoTMMcYWAKAhhFgdDodcCCHX19f3pGk6G0WRdc69p6qqF9cdfD1CyJBSur60tHQUAEAIcb7X6+1JkuQS59yWZbkpSZKcMaaklI8ghBJAGM/3KrSSOzzZiZKAUX5kS2rXPb44heym0uHLkdYtEVPDpUjLbJggHoVTi67lgg+vOjLdHFZGr2WiLC2EJscmU5YnFDkLmJ2az4Yv3NOQP/vizesAehcAcEJILqXk51ZdMB4AB9/nhEpUt+R77x+qi9pnN44VY+xGvarHMcY+SZJZjDEpimJfq9W6UJblmtZ6l5RyTQgxTggRWutTZVm+eH5+/i2c8/sajUbbGLMPY7wYQqDOuS1VVR1sNBrnAaDqdrvrZVl+CQBkHMfWOQeccxdCQBjjy0qp/Yyx+7IsuxNj/EcY45wx9jPj4+MP1cP8nW/gVh3xDTIq8B4xYsQTTpqms71e7yjn/A6tdUMI8cFt27Z9GABg9ff/YHrwqb/6YCjLBAAgDAYRbrW5m5+PyFi3R8a6OfhAgtYUCX5NX7g4DSFA0Ib6LGNBa4xbrTX0fW8pzPj4rSLqWivHUUptVVVT1tpxSmkvjuM5rXUXAIAxtmitbTvnJhBCRZqmZ/M8PxpCaMJNnZo5pdRehFAuhDhb14PMGWMma7f5Smt9B6X0SlVVe2tdoHFCyFxZlncBAJZS3tgIdAAAtNZjURRdGB8fN2RmxqNnPXO2+JZ795p2ewZe9Yo19pznnGbPePqK/eKXpmB2Nkq63dP08CGstW4A3PScS9P0PKV0vqqqvVVVNYQQK9ZajhBi1toEAEBKedxaOyOEuFF3Y3nG2JxzbsJ73xJC9AeDwW6M8TJjTCGEDGOsv6G6jRCCVqt11Vq7qaqqprWWee8FxnjIOV9CCDUopSjP862NRqNXK3wPkiQJ1lpLCLlHKWWVUndYa6e895HWeiaO4xMIIaeUQiEEzBhbvZ6j+KvXy/7uMYmds9oYAwgTEYIPlUPzxxcK4R1AMxZRRzIxKZ1ct2z1D794Y+qemWjRQwg3BkZ9ebZc+9iJXnRkc6PXkiTc6BvCMCIRpyiWTE43GbU+gAfMAALuxMwT5LOuxGGi04IQwpKMk3UXEM0siR+e6/V+5/5rB+aHxj91RsRws67Ieu9bxphpIcQy3PTbs8aYXdbaiTiO1zjnXmvdbbVaq4QQjhDSdYq3DwAQQgDnnKoDpp8KIdwTQhhzzklCSF6n3DqtVuu4MQYwxr4oiqYx5i6EEM7z/PnGmD3W2n3GmJ15nj9TCCHKsnwVxngMIZSFEHrWWialXCKEDPr9/g6Mcbm4uPjc5eXlN6+vr7+dc/7/cM4fvew54htiFCyNGDHiSaEsy12c83sRQmcbjcY7kiTpAQCs/t7vv1GfPvOdtz/Xra1FrTe96Yy+fl3QqakKp4nje/dcImNjzFy+PI7aLd/4sR9dit/0hl553982o1955/XswL49t79HPTmRjdbp8fHxKwCwbozhzrkOQsgihJacczP182Ot9SbG2CJj7EoIQRBC+tbaqRBCYq3dVHc77e12u0tFUYxRSvu1gS6D23zgCCEVIcSHELD3vkEIGXjv443t1tpxjHHW6/U2ofHx3Do3DgAQABLb6czYLZubjYnxG/7CRaO+981jipBb0gMIIUMpLYqi2FcbtpI4jvvGmGEtZXBKStnz3ntr7STG+IYxZpvWeru1dlOapo8QQihCSBljuiEEYa1NhBBX4zgOWmtGCOnX4orzWusJIcQ5770xxmwSQlyua5R2xXE8q7VuRFF0bWhxe7WCShU93opF5AKUlFKw1lJCiMIYm3r8NoRA4jg2Qgitte784n1zZM9EXEWSsoAwOr/uxF88sqr2tsPmP31wNZ9uRuSOcYHPLOSDqRRREcfkl/76UjSeyusXVlXnwesl+tQjiw1HKDm3MBz7yEPzM0nEVyaaAktGuAueNSPBP316edhKorjQThOM7F0z6erZxeo3oyh5ija23U0lCoD5F2dLfGO96H3+wtokABq88e6xfKYtZxFCujYaVpzzK5zzDGMsMMbLtT8dB4AVKaUMIfQopdhai8uyDM65BmOsTykljDGqtfYY42dijNeMMVullPOcc885pyEETQgphsPhwdqvzwLAMkIoRwhhrfUEY6yquyzHGo3GRUKItNZ2AGAnAOzs9/tPr6rqZevr6/+6LMvG8vLyh/r9/tvzPH+rMeaFnPMvT05OfuiJur+/2Ril4UaMGPGkEEXRF4fDoWaMfXRiYuLKxuNkbOwNgDGA9yCf9tTLCCGMm02f339/6ubnJ/jWrdfKhx6agCiaiJ///OX0la88qxcWGHn1qygqCsvvuvN0tX3rNFNqYIT4OrXiDfBNYqXUdgAAIcSJqqqOPvZ5hJBVhJCtrUQiSmm5Ub/kvdchBNrv9zsY44AxxvV7DyilQSnVAQDgnHsppdVaL/X7/W0AkCZJcqmqqm2EkIExppPnOW+326d7vd6djx1DvN67mP/H3zpMd+64AVEkb98mpTxZa/icY4xlteq255xzjPEVpdRBADh/W+ddF2O8GEXRQu1gT7TWHCEkAQCccy0hxFxRFEcA4ALGmHjvkziOL4YQkBDiqjHmoBDiGiFkrq5T2pkkyWUACIwxGxCZXiuUH0uYELjtKqBUqUohZ5wQ4jqlNKqqSgHAGKW0CCEYjHFaK2xnv/nKLUJbzx68Psw/cXKVnV3Id957R+dkKx1ffflBicqAZWYANSWVFxazbEYa844XbvH/MFtKTsnq/k3p9js3t7MmR2oy5VdPzw/R9rFE7u5KPNcrc0BAr6zm9sjWVjOmCBwjQnnotWPyW/umk7lB5TY7D2gLpXxutVAoeLe1RcM7Xrx9bXFoYE/TNRBClfcel2V5MIqi00VRHKGUXuKc5wDQpJReyvN8exRFQwBYq1O2TClVEUJSxtgV51yklAKEUGWt3WSt5ZTSAWPsvJSyXRsaE8ZYyPN8GwAAY8wppTRCKAWANI5jxzk/gzGWVVUNsizbNBgMbi/QbxBCijRNz1ZV1XLOTVtrZ+prIQYAoJSeazQa7/if3K4j/hlGwdKIESOeFDqdzsk8z/+q0Wj85sZj13/4R9+Uf+6BFyHGQD7lnmN2vTcOxkT+0uXYraxIxLkHShEAAJmYqFi3M1RnzwU7O9stf/GdK3Z5aZrF8Sr82m8shYWFTvij/8wsY9Hj7X9paWlbFEXHEELbpZSnQgjdJEnOOOcMQghjjCe01gQAbFmWRwEAlFIQx/EJa+1hAADvfYtZmxuALsBN37gkSU7keX74tl0FjHGhlOLWWgsAEMfxyaIo9gMA8d53KaULnPPrvV7vnjiOH4Gb2j0YY0zCMKPVd735MAIA3O5UKEkqSemFeowIIcSUUsh7vzeKorNKqRwh1HDOWcaYbDQa55RSbc75MqV0qSiKQ1LK0xjjSAjx1RBClCRJbozpG2O2AAAwxpYJIauc82ZRFAkAQFVVOzjnN7TWY845GkIInPPCGLNJSjkvhCAhBEoIyXqlddtbxFqjK+QRmFKVgtIICM9DCE0hBK2qKqrrlwLGeK3X6+1stVoX4jiO+3m5+tGTg3EIvvqFF4xv/8CJ5NRkk7MoinDls+7fnVldPDgVi+fsismHH8zGll1HRZJFT90hYDU3aV5ZNZaKBoJAX7yvC8PSFptbXC4XliHAV4IHaAoQnKIJ7QPYgJYThtarovwZjpybaXKPAFlrLUKApQcMlCBzre/Irm6c9KwthTIEe+/HOp2rXMjp4v9j702jJEvP+s7n3d+7xZ57VWXW3t3Vi1pqurWDVowE2gU2uzfGBi/Y2J7RgMEcbBbNYebY40FCYMFIYBDYCCSBhJAEaiG6tba6ulvV1V1bVlXuGRkZEXd79/lQke3qRmCE1MczZ+L3LfPevO+NiPtGPudZ/n8LF/s1Csu9qGOtrRBCotls7g6Hw1t6vV7fWhuqqhqUZXkHAADnvNJaHwUAKIoC0jQ9CwChqqq7kiTZKsuyRggpAMAA4AGgyrJsS0opMMZxnud9xpiglOKyLDmlNGCMBWOsNMbENz1/QAh5xBijoih6VGsdK6VedtOxzVar9bd7vd5UvftrYFqGmzJlyrNGv99/M+f84SiK9jd/7Md/oPjYx/+D39uLwDlg3/Pdm+iH/wnNvvlVlD/nOcP6Dz7clHfddbb+zGdOAQCE4ZCqRx5pk5newFy8tOQ2Ntq8067VY1+e81ev9vxolMiF+XP6xPG5v2h9730nSZKLIYSEUkq997yu6xPW2tmJ5o8ihAwwxt451wC40dcUjOnJS5cu4Xf9co7e+94Gfv3r+iKKrjvnunVdrxBC8jiO1wgh16IoKo0xlhDi8jw/iRBSSZIMJ8KQW8aYzDnXQAhp51x7IlMwZ4yZ1Vr3sOCr5J57SorxKv/2twJdPmKVUssIoUZVVSta61nO+ePOuQWE0LpS6jSltJ4IYFpCiKSUOmNMWdf1LWmangOAWSGEI4RQY4wKIag0TTvOOWWtTRBCu3Vd32mMaR1oCYUQMKX0ojHmyMF7Z4zpIoRKQsieMab23mvnHGfghgjAV1V1jBCyLaWMOOcsz3PCOS8RQjyOY1BKjaIo2gshEISQiuOYV1UV7+QmvP3j11ZuXWrs37Hc82faYeFXP7fLA6DRHUsZawjkZ1tx67HruZ9pJ4wQKteGdSVxgNKGMJMJPa4tsg4ufubKiCGE6HxTxuPa5Vtj9RAhcFvCcVrqYBJBqwAQE4Raylg4PpO0AUIhOUqUNvbxvrWPro/3e82khQHwY5v5+A8e2/H/4ZPXT2MmLx/pJPLa0MCXd2r/pfXKZYKiZsSwVvVYa50YY3pFUTSdc82Jce/M5P2LoyjqU0qvZVnWxxg3KKWcUnqBECIwxs57D8YY45zTWZYVAIAJIRQAwBjDEEJ+NBoJ55zEGBeNRqOttbbOubLb7T5ECPk8xng1iqKiLMtXRlF0DWN8T13XswAAlNLVZrP57QsLCw88i9v8/xdMg6UpU6Y8a2CMP9RutwdXvu31v1ref/+/4CdO7tn19SYAgHz963bR738YyYWFdPwLv2Dkmduu0bm5WJ97vIN6vUKePnlOPv++fX32kROIsxIhRO216xHcSN4A7rT31b/4kYWAMTlYjzE29t4/Zfcw6fHZxhgz55yr6/ppY/shhMhau+C9l0KIVedcm/f7JnzP96f2v/xOz128OIe73V3z6ld1EWMBIfRHUspPxnE845wr67o+45yLEUIshDC21s4mSXJuPB6fIYRUnPNddCNFtEsIqSb9Tk+DMbZTNxq3mvvuXYClRRdCiLMs0xjj7QNHe8557r1vcs63GWPDEEI3hOCllAYAgBACZVk2Qggxxvia1vqIEGJDSpl4773WmhhjnFJqiRBiOOd7B1IAz7gdK4TYmKiPHwRRsXOOU0pDWZa3SCn3KaXMe18rpZaMMfNxHG8hhKI4jnEIAZVlSRFCJUJI1nVtQwgt5xwAQOCc5wlD9X1H0uqeQ5n50KO79IPn8+JVp7K9hJOwpwNdakapZMiWDhFlg2YU0yxizAaIUsHwTq4xBNi9a1H+1EwrfZN3PjBKKoxgGII/2U6jtCWQPd6Va7uFTQTFlTKOJMTu5pa0CCHpTuHRp67k1cleklXG48NNLnLtlfOB/vHjOwuzGVdvfd5hfnarUpwAOdyJOKegjnfjdG2/KgQK3juLJ0bKMwAAzrmZRqPxOABsSSlNCCHnnMdCCBlC0HVd76dpuhRFUWPS7C5Go9GpLMvKuq5bWutk8llrQggKIVTOOeG9T7z3kXNuCADrIQRqjDk9yWIdbrfbbUrpReccVFV1EmPsOOefaLVab5mfn3/ka9rEUwBgGixNmTLlWURKGXb+7/d+2/g3fvPn2PETe2xuduiti/xwyKnRm34wMObhh4M6e3ZBn39i0VxZbWVv+1cX3cNnI/3khWVz7XpX/s3vOOu/73stev3rStlur2GMDJbSize9abW+7ZYlAABCyFAI8TgARJzzC1LKPYzxLqV0Uyl1xlqbcs6vxnG8RykdHwhK3gThSl2ml6+U9P5Pjc0DDywAANDl5avyh/+JDsvLTGs9E8fx/5VlmQeAW6Io6kkpR9baLUqpVEodjuP4Mc55xBjbopTm1lqCEKowxmrSP/TnIISMnHNdAABrbdLtdkchBB1CUHVdHzjYdycmqW2E0A7nXHPOyzzPY4yxr6qKIoS4954hhAIh5LoxZn48HnestcwYc3iin3QhhMAppY4Qsm6tnb35XkIIEWNs0xiz8MzfO+daQojtqqqWms3mACEEdV33JveXM8ZUXdcmhFA454o8z29njG0SQioASAHAUkpLQkiita4jbA830ihvR1j94qfXT37vfUv9dsJlNyJ+p/S0Ms7lymGMwHcSuumMQ52IwlKD7e7kmlmAbHNkXqGth9o615L4Tyll47kEiWsDxZQLZKzCTMyRiDjd9gEa7/vijv/Gk60sJghvl45r48xiS5K1feUySchIWXSqF6lIsu2X3zZvwTuMMEJzKSUbI7v2nIW4LQnoTsoQRuAjKSkhRCmlZoUQjzLG8qqqjlpr540xTUopk1JWhBCKMQ6UUjEJghDcKN9SzvmYMeallJ5SOvLeIwDAdV1HcRyzJEm4ECKnlJYAUNV1vey9n4EbchaYUkqNMWMAGGCMmbXWUko/Mzc39/pOp9P/WvbvlP/GNFiaMmXKs8bOzk6n4Oxd8uSJvP7Qhw4jzsdsYWFs9wcZaTQN+57v5tUHP5QSSr3Pi4jecurJ6vu/b4n84UeVHw5jMAahI4c3qle98jabpU11x+0z7hUvT8xrXxOJ++4tjDGZEGKTMbZJKeVVVa1Ya+e11jMhhIhSyqIo2o6iaINznpVlCRhjbox5yjcLY6xDCIRVlWHv/lVSvf93n1LUpseP99npU4geOWIrY1pxHNcA8FZCSNN77zDGLISAGGO6LMsZY8wc53x7PB7f5r2vhBCqKIrbnhmU3AxjrDpQXkYIeQDYCCGwuq7tzZko5xzz3nMhxNh7X2qtG5PpvlJKCd575pzLKKWXlVJ3cc5HnPPywCQ3hJA65xa894kxpuOc6zUajfMHrvXNZvMKQmh9IoHwlUCc8wvW2nkp5S7nvKG1LkIIOEmSMaU0KoqiMylrIgDYRwjVVVXd5ZzjcRxvcs4jrbXy3qdCiF1nrR9pMEc70U5lvUgiSc/t1Kw0wZkQPADG1gU4Myfx2fWx3lcBM4xaj6yNyrsONfn6UH22rG1PUhIFQKcu7oySJ3dqds9yM5YM05gzem1Q7zdjVgGgHoKg+4WuU86Ycq50CCeru2W5OVR1OxNRW6BxLyPNiPLUhZD3UkpHld7LOKUZJ9lG7sRj2yps55YfapAKYZo+sDr2W4W/ZoG4lkANjDGfBIUDQsgWYywBADQR42QhBD9RONfj8XislJo5KJkqpYooilLvPWaMGTTBGGO2C5AUrE6T2FNKNwGgYIztR1E0mpRh6Xg8vl1K+e+OHTv2w0II+9ffuVOeyTRYmjJlyrPCcDiUg8Hg/XZ//7T9mbcTv7fXdNvbHbO62o2ed/c6WVgoqvf+ejPsDdryvvsu41tPr5NXvxqp5eVFccvpdf+xj7fBOUQ7nZH5pm/sHVw3IIQAIVBKdZrN5oWiKI5OeoC6QohLBwa2kwzKmBBS5nl+XCklGWNF8J7bSX8SpbTEGJeMsbrGeF6/5MXN5Ll3P4S+5W9skH/8Q2NsjfOEmBDHdTQ3t08IOVIUhRNCYOtcqJQDwYinlKbe+y1K6RoAYOdcJqXcKcvyeAjhLx2kYYxdOPBIgxvNvqUxhmutj32Fc9fqul6BG8M5XGt9wjm3qJTqUUovWGsXKKVlFEWrjUYj8d4PD7I/z4QQ0p9M6vXghp5QGUJACKFws+zBM/5mXQixHUVRy3vvMcZD5xwxxhDOOS6KojcRWWRKqduEEEPG2DXGWFEUxakQwlYIQRFCuHMu5pzbmHhzei4hvUbcoCjY0QSMAAAgAElEQVTYrbHux4JmysIVimHGBkDDUrNU8Lj2gdsAlmLiCuN2CApzgAhOIxIvZXR0Yq75k49v5fMUo2VOkLWAxplkOCYwSzB4Rom+sld/wCPc2ytt645ZDud39IVWwm0rEtc8wOF+6ffG2mHnQ4oAy+U2Jxf6dUDBIw/BUEK4sdZVjkiKvfuFT63B7zy8e+tnV8e9lx1vbILTi845OfFxmy3LssEYW1dKdRBC+yEENx6PJeecG2Mya22aJMlAKWWKolj03hfOuQalVDPGGSYk2s09jDzm14eBjw1EMykXEHxACNWU0qYxJscYF1LKf7S8vPyrADD1gfs6Mw2WpkyZ8qxQ1/V3Wmu/twboxnF0jdx55wY9caLPX/2qbX/hIqPLy5rcecfAb24J9PJvUsV3/q3b9aFDMwAAIY5pdOnSeTIzMxAveL6tTp38ipmZuq57QohrzrmmlHLdey9u9qQTQvS11m6incQF54598lNDNDvTxyEMRJKM67o+5Jx7aqKOLB/Zq9qtuywhXXPqZA+fOrWBmk3EOc8AwBBCjPd+LzcQheAhEgy890ZrjSZClTOT0swiY+yim2gqfSUQQtZ73wghPNVnxRi7HELg3vvWM84NnPNr1tpZSumG1vrQzccIIdY512SMXS/L8q6iKBKE0C7GeI9zvmatnT84P03Tx4UQBWOMWmu5EOLaJCvXIYTsCSG2nHPNKIrOOudmbyyBgHNeYIyVMcbu7+8fp5TmjDETx7Ebj8dpmqaXnHOkruszADd6qaSUqKqqmSzLtowxRGt9jDG2zhiD8Xh8rNlsaqWUcwHBv//UepGXtnvvkcTnyicrnYhhhNjRzIWRE3QmJcoHbI31Awww20pFGhEfccZQaQBtj9RzD7fjMy4ESCWLK+2fTCWOCSFIELgaMcruPpTeuVeqqwsZf9D5sJhGdOXMQgwNjlQdUGdYGoJRMAJ7NptxZ6ynVwbaXB/o+uUnW62MI3dhp1SnZ0X4lT9bI42I204qN0IIzdlWUi3E4cCTbY9S2scYW2OMkFL2nXPIey9DCG0p5fZ4PF4ghFTOuZExhjcajT7GeOScKzDG1aCGz53fVR+xPux4IL1MIFc5Vx5pRzGllHHOJcYYMMY5xvgtS0tLD/7Vd+iUr4apdMCUKVOeFYbDYQUAux6hXv2tr40JIRYARKV1F732NahwboUQMkju/YaLam6uefPfMhmN4O//PR4+/om6fMubBEx8zm5GCLHKOXd5nh8DAEAI5Qih7s3n1HXdZIxtxXH8JEJIl2V5pvOiF9TVm7/9EJ6bKf3/8b9vCM43UAim9v4IAICUklBKz00Up5t1Xd/JGLsuhLCcc+m9z7UFqazLZ1MmCSGtAzXsAz+2A1VtjDFFCLlJozlMfpcTQkpjzGwURRcmEgM3wyelvV1jTG8ihNlkjF2y1h6WUj7KGPM3+7tRSneVUocxxmNjzPLkdTxZVdWtAABxHH+ZUrqBMVYY47FSatkY0xBCPGGMad5clpRSjo0xcaPRuDgcDp8TRdHDdV2fklIOnHMjhBDSWs8AADDGYDgcHmWMXSGE5EopnyRJs/5vn9dOURupsDShKOcZJUNK6Z4xZqUsy6zRaJxbHZhktpH6h66MyUo3Tm6fFfkst73Dh/kwijh6YtcOP3pRWw+KrvTSGKFQLbdZeyn221/ccRxjnBBjPePU8oiIvdKomJL9ojRzmOGVWnsjYtw3HjU5RZ+9uqfveMnR1q/PzfZ+7b9+bvVxALA7YzOsLRzPlb0wk/LOsDRJOyYQ00CulIDnU0JediyNIgr1zkgNH98Yhz8+v7uS1yZ865nO7nOXmjRCWkaSSa3DyHufhRCU9z5LkqSf5zmVUgrnHAsh4L29vUYI4Viz2fx0XdfzVVXdyhjbFkLESqmKc97HGD+0kMofuXWlNXjG84GMMWlVVUeLovhO7/3dcRz/cLvdvvwX78YpXyvTzNKUKVOeFcqy/Imqqk4ghEYhhERrfZgxtocx3k7S1KZpOlBKWd1srpAo2pVS7jDGBtbabrPX6/PFxYi94PkcY4zruu7cfO0kSR6vquqEMaZNCLnuvW9MVLILxti2c64NcKMxeTKJJZVSh6LtnYfNj/34Ybd6lYXdPne//p874T3vzchnPpuGb32tmegIbVRVNZskSR7HsSeEbFdVdUxKueucs+PxuIFReKCVcEAI9QAAOedK771ljG0RQtYnDdvEWtuRUp49yOpgjLUQYlNrPZckyepEmfnL3vsuAJDJOSaEkEkpL1trO5RSL4S4iBDSSqkjE5XuAWNszTnXghvmreedcwuMsSestUcAACiluffeMcaGE9uSnBDC6ro+5b0XnPNxmqYBY7wJAMh7H03W53EcG621MsbMWGvn4zi+rLU+ZIxZAgBijJlljO2FEPJms7kfx/EDdV3faq3NEEKDTqdTG2OGXEbklz+zXb7z0+vzLzzevUhdfcx7nzabzetSyn0AIK1UPvTI9VF9uB3NFSp4QJjMNqVLI5E6a8v5JGRNgV0jTeNRbaCyzv/JE3v44U2NOMMMhSDTSHhOAqSM7DrAP7fUZIsGERNhG0nOfcTIw5lAEaOYPrKVi27Cwyef2DOA4J4Io44DQgKEZH2o6l4U3L1HskYnwtrWBV4b6v3jHWYXWrGsqqpsJ4Kl1Il7j3aGFEP5mlNp0m0mQmuNtdY9Qoj23gtKaYkQqpxzjTiOB0KIRggBTaYIW8455pyroih6AmN8HmOcTLJ3HGPcJoRcDCF8KI7jP+d6TAjRQoitLMs+1mg0fi2Kot1nbSNPAYBpsDRlypRnge3t7beWZflDGONd59zsxJYBms3mIIqiBsaYeu8dAFRSyhEAOM55prXWCCEy6aeJAgAihHBK6TYhhBhjJCHEYYy3D5qmOefb1toOQihIKS8AgMUY94UQQUo5sNYWzrkeo3RIfvrnuuj06S1y+FCV/aMf7Ie5uV35tn+1Xb7lzV0fAkMIBYTQjjHmKKVUAIApimKRMbZljDFFUZx0zqUY40NKKY0xTuu6rsbj8XwIgRxMQkVR9OhBgOSc61BK17z3zSiKHquq6ha4EXC0Jya9QUq5Pgn0LllrFzjn47IsT2OMwRgTTYKWOQCAScZp0Vq7EMfx2uS11oSQUQhhkRBylXN+XSl1Qkp5GWNcO+eaaZqWzjmglF611s5TSreLoliaNHijJEnOTYK4K5xzQgjxdV1HAMAIIVcngRIghBgAkCRJLnLOGQAISmmDc+7zPF9K0zQPIaAoitBW7sjxmTR9cqfMJaXm1oUkp5T2GWN0p3Dh/7x/vSaUtC/ume6odmxQGX3rYppe6Su7WQSylVt8vu9cL5N8M6+LQ01ZY0wSBKCziJXDQhvlQt5NedFJRFVrxxdbfDjU6JAy9oh2ECSnHzrWJuLayCJlQtWMWetwWz6ZV/ZV+5VdRAiQsZ4RgvGfnN9ufvDsbvcVR8W+kOLJEOBDH358eMs9S5J5ZwspZVMpxbox1U0eohuTc0IihApCSBnHcd85V0/kFfaccwsY4zpJEqS1rsqyjL33zhjTZoyNJmXSzSRJfsYYIxljq5TSd0RR9A9brdZvfaVAacr/GKZluClTpnxdGQwGaVVV/7gsy5aUcu1g0gsAQCllCSFDKWUWQmCEkFDXdUjTNAkhmCiK6GAw6Hjv90MIejwecwAorLUzQohcSnndWptVVfWUgjYhxEdR9CXn3EpRFHelafoYY4wqpbxzbkwpVYwxoJQa9/Nvj6qqOsIYuzRGKJG3nxkXgA557wgAQJZl77fWHp9c+vpoNDpDCFkjhOw755aEEEOlVNZsNrVzrrW3t9f23sdxHJ8NIUQAMAsAUFXVXXEcP1yW5V2TRvMBADSstU/TeZr0moDWusMYQ4yxqK7rrKqqDADAOffUdzRCyEkpL1lrQwihhzEeaa1jY8wsxvhLVVXdQilV1trTADea16Moau3t7R0GAKjrWnHOR1rrJI7jcwghQQh5NI7jRGvtQwiSMXZ9PB6vaK2vUkpls9m8vL+/f4Zzjg5Ka845nmXZBWNMc2LIi7XW5621RwEAPnstN6t72r7ydCv5oyf29WwjMj/wosNipcsFR4D7/f6KNqbet/H+5b3qUHo1v/S8lXbj/MZovxXTxqXdGjAgFbDHCwnhO/slWiUYYY+SUnuotBsstaJmO6XNQrlymKu1VLK9UW0OcUbHxkHn+l5VauffGQv6rYdY+IOtEqq90n5XJ6Z3zaX8kyiEuVaMf21tjD7bSvh/2RzWC2Xhr9y+1BZzjfhfX1HJ+WTg3veZK8VrdMC/vafIaA77v7W3t3dkIjb5OOecxJSyC31VnZwRjbVBHhZbiSeEFFVVLadpqhhj1xBCrigKTwgJjLGPSinf02q1Ntvt9lpVVSiKooOptS9+/XbhlK8308zSlClTvm4Mh0Pc7/ffgRA6pbWeE0LsAsBYSjlACG1hjB3GGLsbBMbYDqV0nhCyVZYlwhgLpVQLYzwYjUbHnHMN51w3hBAZYxrW2lnvfRMAgBCSc84f1Vp3tdYnGWMXCSFr1tpWVVXHvPeJtXZmMo4/nIhSrkRR9IhS6oxzrqFCmEtu2FA8GMfxe5xzqVLqVZzzS95732w294uiWLHWzkZRNKCU7mitZxljeVmWqTEmI4Q4hJDGGOvJZBkAABhj5tM0fYRzvuu994QQrJSaxxirKIrOAUCKMa7jOB557/t1XZ9ECO1+JeFKKeWAc36xqqpbJyU7kFKeU0odBwAIITQnb+tTvnIIIYcx3kII5c65dhzHG9ballJqcWKLcou1tsM537XW0hCCr+v6aAiBWmu7WZaN67pWzrmO9z5mjF2Z6Cv1lVJznPM+51wSQmRd1ycxxiVCaPePnhgnT+7W/huPpu5Fs4p0s6Sea8phUemEYbC5JcNWKgcpsU1K6JoNwXOMSa4crLQ4iiXPH1rdj9sxJWMTfG5hPCh02cuixCMgWltMCBkHANSSLOplnDIMaK8wOSf4PyGEP1Er/e0XtsuXSorfFjH8XWNlvw0QrhhBu5sj8/JmFr+2NGjrg19ae087kd9/9tp++76jre1X39L5Xx/bLLN7l+LbZmN06P4Lw4Wxsrfccyi6LaLwG0qpbxNCXAghCO99gjE2Qdf6wp7Rf7ZaD79hpdmIoihjjK0DQGKtDWVZ3soY27bWHmo0Gm+emZm5kOd5x1rbklJWeZ7Lfr//fErpIIQASinGGJuO/f+/jGlmacqUKV8XbL8/6zc3X6MQ+t4syy6kaXqZEJJmWfa+EEITIfRGAEDW2tp7f0Fr/Skp5bu993/Pe38nIeQbJppBD1trj0VR9FBVVXd/pbUmE3BUKfVcAICJX9YJ55yY/LwhhBhorZdCCJH3PjXGNAAAnHMLhBDjnGOc87X5+fmX7u7ufqtS6gVRFGVlWRLv/YkQAiCEHgMAIITsee8rhNBMFEUX9vf3T0wyWN4Y0xFCxM65XUJIzRjbQwgNvfdVVVVHKKU1AOxba3tCiC9675eNMSdardZOWZbj4XB4RgjxBQAASqmL4/hSVVVHD9S1GWOF1jrz3j/TgPep72/nXJQkyaWiKI6lafpEURQnhRDn8zy/CyHk4zh+nBDCq6pqe+/ZpAQKIQS2v79/SxzHj1lrn9YXppTaRwihbre7prVWRVEcq6rqyyGExFrbsNZSa+3GaDRKoih6Qko5kxeluP/CPgKA5M+u1tsfeGS/fceiy+cyKl5/pl1778liJ0HGGKrqOv6mYwm5OgZLMDajWpeppGkiCTvckY4RJGZS7O4/vx/GtbHa+mtHZ5P59X395Il5eSKikO9XVhKKUaVMo5fwfi/CL98p9d95yfHmzOm52G+Nzb8d6eCx91pK+oJejL64ua+vXtga/bNK2+9+4Ymef84c/fHVbfJ860Pz/sujcGIu+ebHd5TgFMTpuaQ82os6aYRKzFmdAPxQFEVjY8ybEEK3KqW0IGH+zsX4/ttmeVc7P4udL51znnOO67pGAABpmnYHg4Hq9Xr9tbW1vzMej/+3ieGx63Q6l/b29k6WZbkdRdG1PM+XpJQf895/zlp7D8Z4M47jB40xJI7jB3q93vWvda9O+eqZZpamTJny12bvXb/cHf7Wb/8zOj/3+fFH/vCNez/5Uz/bOnXiMjl27GBCjFtrT2GMT03KNTUhhHnvPx/H8SoA/EPn3GsYY0cQQqW11tV1fQshpBZC5ACwNhGcHMdxvKOU6iVJcn6SOcoO7kNK+aTWeoFSOhRCXFNKLWutZ5xzMo7ja3VdLx6cSym9ZoyZAwBwzjW01i+31r6BEHKoKIrnIYS8955NjneiKLomhNgtiuIW7z0AwNg51z3wbLPWLhhjEiHEnrW2Z4xpWWtnnHMLE6uQBqWUTwQ0a6XU4sSdvuGci733nHNeWGu7WutZhNDOgdwA5/xyHMeDA6+vAyYGwXOMsW1CyAalFKy1EaV0XSl1KIqi1bIsb5ucjjDGG0VRnIAbQoaXASA5aIKfvM6mlHJVaz0PAJAkySohRGOMJQD4siznpJSb3vs5jHHfWtux1kpCyBaldFwUxe1xHI8RgvJYl1WXBrZ2AfTxmbT+9IV+eyc3+huPJr4sigWE0GWEUIQQKgmCiycWO39DhuoN739kLyWM4fc9uDr/D148b9bGPsQYmUc382plJol7qUwQoPLVJ5PdmJOZs9fHdLkjoBej0npoM4LamNId8PaB87v69x+8Mtxa6YpDAIQmnG5UxvXmUsZLG5pj5Ybeh1mAMN8Q9E4NmJxd268WmtEdFCHJCFp7+PpIv/xUu5lr7yoLxBh3XybwsjHmxc65RwghZVEU3+S951JKxjn3G7m9+IXr4+6MCEKrWmOMiyRJhqPRqEsI2ex2u+/Y3Nz8RcaYNsbMMMYuTfzhutbaJIRQaK2XnXMr1tpXGWPusda+yDl3l3PuG/M8/6dKqU80Go3Nr/9unvKXMQ2WpkyZ8lWT57nce8cv/gp+yYt/FA4tXm7ed99H4nvu+VL+sY8vjN/9K69jb3jjgDYbqdF6JIRoTaaAAsaYY4wxACxaa+/FGC8DAHjvgTH2RFEUCSFklKapUUoZpdRd1toFa20XIbQ5KcPxOI7PI4T2D4IKSmkfACLG2CWt9dOEIIUQGyEEwRhb45wPGGNMa/1UoKC1PsI5X8vz/C5CiAMAwhi7MhG3xBjjDe+9cM51QgjcOddN0/RLhJCcMeYwxluEkOsAAISQrUkQ8rSsvRDikjFmJoSwF0JIAYBM3hNOCNl2zs2EEBgAAEKomJisRpPy5C03XefqpP9JVFV1yBjTttbOWGsT7308uUfGGFs7CAjjOH7EGHM0TdMLIYRcCDGPEBoYY2YBADqdzmocxzmlNMUY72itOyGEPsbYAICilJKyLJecc8RamxpjFicTfgvW2plJ5o6XZdmQUu7EoE6+7q75zecvZ3Ej4ckLT7TW3//Q9q0L7aw40qLYOUeUUi0A8ISQdywsLPxZu91+96WdYqOb8quZYNm+hk4nIqFfOXHPciPqNSS3LuBxpQdLDXp0riH/zGpF80CS9aFCzYhTD8hRhNYKE+6mFL8hpuSIZITkOshc2yMtSclWYWdw8DGjRAxKs7QxrEztsGIYM2WCV9bB9UGpX3mqKQhjLYKA75ZGD0rjs4hH73+kH6fMjyPsvoUQcjXLsp9zzhGE0DsRQlczyT/yb37/0ktecLzDlmZaqffeGmOUUuqIlPL3MMZ/qpT6X8qyPJZl2TmMsfHedw+kJiil15xz85Pn9TLnfK3RaFQIoQIAIq31Ee/9C7rd7ju/vjt6yn+PabA0ZcqUr5rt7e23QaPxQv+ZByO9ub2ijx39eJIke+zQ4sfLP/30W/gbX0+DELaqKqCUYuecpZQyjLE0xoy990ApbQCAwxj3McapEOKNGOMLlNKPWWszrfWZA4FJQkiOMU445+eNMYe11vOMsVVr7SIAgLW212g0riqlTlhrxc33GkXRtvc+UkodstZ2bg6UDhBC7GqtZ7333HtPvfctKeWXQwg+hNA8mASb3Mu2tXZGa72gtZ6z1s455xattfPW2oWbJ+EOwBhvOufmvPedOI6/fBDIAABMXtORm07njLFCCHEVY+wppVtCiH1K6a5zLtNaL8Vx3McYX/Xed27WcDpgMq5+kTG2qbU+SSndCSHUhJCMc14ghMBaG4QQO4wxRCnFGGNvjNHGmJ4QYqCUWm42mwxjTCilW8YYJoQYWmsbGOMBwA1pBiHEvjEmZYytTbJklwkhAgBYSlzZi2m7nfCdew9FleRUaa3nKKU7jLERxni11Wp9GgDC84/3zlGMH/zslb1XXtktm6+7vcMRJiLCeLRXmNz6QD57eUAbaSwHpQ6Y0qW2IP35ButigiHj8Gtbhbnb+zBrPSDJMOrEVCEEmdJ2nEg6sz1Sg5EKjFE0b5y/3o54Z6js9UzSnXaElmoXDCOYVg4lXREqSgjtF8bMxsS2EiFJCOHMYoqTOLLOuW5VVd+SJMnvU0qPVVX1DyD4I2tj/4GPPj44/LxFsUExXCaEPJgkyY/Ozc390nA4/Elr7Yr3Pq7rehEhlE4CxwMk53zHOdd0zvWEEJ4xdimEQPM8v33yLNFer/f2fr//i0VRtI0xGgDGlFL3V92/U756UAhTVfQpU6b89bj+fX/7R8j8/AMLP/czD2z+z297KRBy3Vy9+g7y8pcdGdx37+kQAqRpegkQQlmaNrz34xCC8CEQCEFNZAKIc84hhH40iqLfGQ6HLy2K4t8ZY57yKOOcP6y1vuvmtTnnj1prlznnTwoh2t77dDweP605Oo7jJxhjLoqiVlVV5XA4PH7zcVrX40atVst2K1MAh0MI+ObjjUbjS6PR6DnPWPdxrfUzhSSfghAyJISUWuunzGjjOL5UluWxyfF1hBCd6CVZhFDpJvYrAABRFH2pqqrnTNZat9b2JqWezbqu52867xyltFZKCYxxAABtre055w5lWfbEeDxeBgAxGWfXdV0vMsY20zQtCCEihICcD+U4z6HVaNSUEpPneUQpDVprFsex9N5rpRQOIaSEEJPn+QLc8Ig7CzcyeaNJk/ugqqr7EEIhTdPHQgjuIFMXRdH5sixvmZmZ2VRKVXVd8xBCRQhRZVXdXtLs/Eo7uvaZa+NfRwG94ond+gVzDRF/4Evr7gdetMxiSemTO2UZMyy6Kdc7ueFfujrsMxyStz53bvuh9WpOMOyRBwQIIkaxCsEzBAAJJ3RrXKlYSjquTOCEhGFR+1YqeCfhdaU9td7jcW37MzHOmhFV57bV2kzGzgwKa+dTbDEKZrew4XCLo5lEkFJbt9igdK90rh1hhxDC3vuggTIWTI0QCoyxR0MIVyilt2utt8bjcUQpPQMAUJblrYSQfSFEPhl2CHVdL0kpz2OMbV3XK5TS65TSrbm5udfHcby/vb39Dfv7+78XQhhwzt999OjRn19fX/+vjLEXaq1LjHEphPixTqfze3/Rcznla2OaWZoyZcpfibIs5fb29k81Go2Pl2UpGWO28cY3PJC96pXXrv/dv//28Qc/9Ev88OHUXFldtNevc/fiF7dIVY2h3w++Ox8VOhQxx+cxoW8fKf+KmBOBEKKEEIYxZs655w2Hw7+LMX65lDKtquqpDBBCqOacX2GM2QMpgjiOt733sdb6uDEmZoxtaa27AACEEBdCwFmW5YyxuK7r3Htfa63nIAQAhAAAQH7oD75c/ui/viv6m9+xSrOs0Fo/rcl5Mp32tH4hIcSOMebPTawdEEKQUspVxtgm53xHCLEOAO6g7BVCyAgh61LKaxjjghBSAoDy3mcYY0sI4dbaGACAUvqktfbQ5DX1JyKU0Gw2NzHG9Xg8vsM5N2OtnXXOzaVpes17n1VVtQA3SoHIGBNzzq8TQrYRQkmSJBIh5CmlbKw991TwTLLf+Py1fJdx+bxMkMA5IyEEhzGmzjnlnMuqqpoVQqx77+UkGDxpjFkMITSbzeb3AwCilAbv/RJCKA8huEmAt+y954yxdWttoZRakFKOtNZHKKXjf/mBK3MvOdFN3vPgxgubMTt5ai7BpfbmtvnEDZVH2noPAfitC6ndHpY7K0noLDbw/ns/t7FybU/bq4OqftGxbtirjEAQYFxb1Em5KLVHzvlRJ5PpJ89t73oAm0kadVKBauO2KcatQamdC8ESsIAA/NmNyp2ci5ZQQGhnrGqHEMOIhOfN47Qt8Ighi7uZlI+uj8azLZki70prbV2WZQMHlxBChs45Sik9wXC4w3sz4zwsVVV1D0Jo3RizgDEOB0KVAFBVVTUTx/EVIQRWSnFjzFwIodnpdP5zs9n8MNw4KU2S5F2HDx/+aUrpF6qq+mat9VuKojCMMYQQks65V+7s7Lx+d3f3W/I8f+He3t7p0Wi0Qim9yDmfTtd9jUyDpSlTpvyVyPP8Y8aY525sbPxT59xMo9H4BADA3rt+aXn0u7/38348bqrHz98t7jjjoaxqfv36enjnu7hwfo/de8+ok8W5MeYjY23vfODS4MhKm9tJOcgaYxTnPOOcB+99sNaaA0sNAADvfct7PzsZ2aYAAAihdSGEIoSMEEK7hBDKOd9HCFUTSYKrCCGYZDFoHMdt59wWRbjV/MIXLpErVzbJlcus/TM//ftVsznrnOPW2qfZpYQQDhS1nxrJl/29NS3E08pszwRjjOq6PmaMmSWE9BFCVAgxnNiKYO99N4QgtNZLE5XuPc75LsZ4qJQ6dPN1DkqR3vuUMVY3Go3VoigSzvlAKTWfpuljCKEaAKoQAmit526+lyiKHq2q6gylVAkhRhMphQAARBCkYoZDpe3dyqFbOxH6smS4xBh3EELUe29Ho9HJA+88jPGmlPKqMebWgyxcCAEzxj6wsrLys97736rr+oXW2mVK6bZzLrXWzkRRdK6qqkUatBKcVQFTXd0BJ8IAACAASURBVFuES8/1qdmo35IYpGTh+qAKxiOzX1k8Vk4ICmZjWNt2IriyZnTnUmP2xz98xfdLNz690FSc4ZG2nr7kWMKvD7SjBD9mXJilKNT7pXXdhozaEtFUCjGTSRRTRLX1KgDIXLsgKfKxwPRQKyowYzMEY/r42nD0nMMpW2xyuK1Lyh43URLHFWMUMcaEUsp0Y+rB2cJa2wQANfnMRnVdL2dZ1kcISR+QwoQJ770lhKx777sYY04I8d77ejIxqRhjyhjTyrKMWmsDQshKKR9HCP1mlmXnAQAPh8Of9d6fGQ6Hr7DW/kuMsSOE3GOMQdbahSRJ3m2t9XmevySKovZ4PH6NMeZbtNZvJYQ8N8uy3/iqN/zXkfF4HAkh/j8dsE2DpSlTpvx32dra+ueuqr6ZcJ6Ox+OVaHvn14t//x9fOv6DD/8L1O78BD99CunHz0fBGIKiaE2dPXu7vXhpUfzKf9oIz30OGmKZIIA0Euz5DMMdx3uRVMaH4J211uaMMQYAzntf53nes9bOcM6fxBhXE7HEJyil65TS3RCC9t43KaXrZVneOTF83SGEMCEEBgAWQtivquourXXHe19gjM1oNIq896340uXN8if+zbL58EfmUBIP3ZvftEwobZRlucQ5f4RSuk4IQc65FG6UmtYmzd4AAMB/9wNFWFkGz/lT5ruMsa2bDXw5509YaxcAAKy1s4yxEm406D5VbqOUrh9clxCyoZQ6ffM6URQ9obU+AgBo8vM6Qih47weMsXFd10eSJHmiKIrbrLXtyXSgTtN0FWNcMsZ2hBDrSqm5EELCOd+e9I+VxhiYaF1VWmtX1nUx20rThJM4hHARIbSstVZCCMY53wCAgbW2473vIYRCr9crAOA6YyxnjI2jKFrN8/x1AADe+5dYa+e01icwxtZ7HyOEaLfbLbqDz1PcOkwNFpm2btRIZXZ8NhURI6gwPrrvaFssNKRATpXtiJD7jnWyM7PCLTUZGSpIGtSX96y00J88uR+94fZ29fmrI7nQ4OnRXuwFZ2mubbMVsawVMxkx4gel9f3cDjFBBCBAM6Y1x+DakrInNvN8vi3PVQb4A5cGKK9NaEuCxtqjL17dH5+ciUVLYuuca1prtye9YZW11lhr5yilBQA0nXPDOI73qqo6EULA1trtuq57ALBLCGF5ntd1Xc9SSm0IATWbTU8I2aSUcsYYJoQUj2zbz2fU/l4jSx9eWlp6c6fTeeckUAIACFrrxaIovrvdbv9wt9v9jzs7O981Go2eZ4zpAEAJALvD4fA7GGPX6ro+DBPrHErpGqW01e/3j0opP84Y81+nr4SvSFmWYjgcziul7vvspf7Pj4vy54PKf9Ba+4N7e3tRs9n89LO5/rPJNFiaMmXKX8pwOGwMr177Rf65z+P6fb89oltba+jjn3hp/v73fxdbWSZ0bo76YysWymKPHzo0FGduRYCIp522J4cPF+rwYdROJSMoAAAIjJD3IWhndQEAGmPMrbWF1jrd399fds7FGOMxAFSUUuKcyyaTV/PGmHlCiOWc70opw2QSrRJCcEKINcbIScDEhRDXCCF7dV2fdM5hKeVOXdeHQErs3vPeCLwHt77R4Zxtk7ufQ7QxxBizMrGo2COEVGmaXimK4taD94Ji3Dc/9uNL8enTV9ShpacyX41Go08I2Z388wJCyIZz7qmepYlZ7VPBFCGkH8fxWCnVY4yZAyuTmxFC9CdWJEAIURPLFaaUOoExHmutF6WUBaU0N8a0AAA457vOOWqtXZwEcA1jzCxCCBNC+hhjNRqN7tBad5VSba31TBzHdSQEYgRLjDFGCHWstYX33oQQIuccIoT4JEn2hBBbzjkRQtBSyohzHhFCtA2o99C10d0LDfHWosiXMMZYSvlkCGHknJtPkuSKMUZB1COlZyXFSEmGkWQEG63VYLC3mCHdYIxtNiIqFzMSZmMcQgj12c0SLSTIcj1uIRFrybC7ezGhEcX6ZScye+ZQO728X1Prka+0q23ARFvrEQA+1uVkKzcs+IAppgwhstMvtG4ySBebIqq160SCyyNNltyxmKJRbd3VgaJ3H26ikQqhAcVKo9FYm1jfYK01QwhZANBRFEVa6110gxBCYN77SEq5EUWRrut6Ps/zFkJogzGmKKVEa91SSuEoilwcx12lVM05/+P7V6uP3XmoRa2uLzvntuI4LgBuqOHv7u5+c5Ikn6qqCgOATNP0XFVVXwSAOYRQVyl1WGt9RwgBUUpXD8q2k+fhQlEUZ4wxz6/reqHT6Xzwa/kuGI1G81VVLQGAo5TWxhiZ5/kP5nn+M+Px+Nucc29zzv1PAPDmpkBLJDiQnDFrrSmK4mXOuQ+kadr/Wu7hfxTTYGnKlCl/KaOP/OE/R3/0sTP1R/8o1J/441vdg59Z4MdWxr4shXnyQkZ73S00HEl58iRT586x4iMfPcoPHdoPnPfRm96IcJIYIUSCMXZa65y4ynvMHOdc2jwnRAhMCKEIIYUQ2pRSbimlFpxzc8aY1s0yAAAAIYQYY6yVUrMhBO69X/Dew3g8XmKMbed5fqBmzaqqOgEA4L2PjDFZFEWPBEoFuf9Te34waAEAmM9/oUdf+9rdipCntJgmPm6rVVme8Detz3Z2nwi/+b5594UvtMmb3lh7QlAcx1+WUrKwsWG5cxua0jmMMSKEKEppKYToT8pvAADQbDZXtdYzhJAiSZIRxnhda/20vqgJ+MDcFiFkEUJRkiQWIXSdMUYJITkhhJZlOSOlvDrpW0EAYDnna2VZ3mmtbWVZdnZijbJojEk55yXc6C/aZ4ztKqUihND/w96bR8mW1XW+vz3vM8SQkXPmvZl3nocaKRAbKEAFREVwom0VUdvXOAC2rT59uuzVbduKNE0D+hR5tDwRhwaFYixQmYqiqPHO8817b85TRMZwhj2/PzLyWlVIu+zV3Q+W+fkn14o4cWLHGfb55W//ft/vBqU0OOd+nzH2+o2NjdcqpY6FEFYqlUoFIYQRQhZjvIExPuW9n/TeNwGALGTualWQHe99ZJnfvbOiIsGAUsqMMUNJkti+hcpRrfVwoHJJSMnX19f3Y4zXAaDIsiwCgPiJVXT5RkupvQPUECb4F653il2DAqYGInF5TdtPX+2p6y1dIowTQDC3sxGP9izCtzZsb7FdhkQwtlE4EzMg65lVBGNQzkOprKklMh5KWNbKVYVhUgWMkGQ4HB5LGAne7RsSQmLb6+lgFjaUOjFZqVES3HBCl3u93s5KpZJTSqVzziOEus45TwgJzrm4KIqDnPPVKIrKJEm6GONYKdXmnPcQQopS6oqiOKS1rhFC1qvV6hpCKAaA9kZpr/dKN5s7/M2pID9Qi+jhsizvxRg3u93uYa3197fb7XelafqYUqo0xrysXq//ZZqm3TiOP661jpVSL3magGnbWjsEAIAxbmGM0ZaelnPuLmPM31ar1Zv/vfu91Wohznklz/P72+32iSzLvnVpaem7VldXf2NjY+MtZVn+WLPZ/JVms/l/WGv/ZVEU9yqlKoyxPd57W5ZlN89z0KpUglGjtXaUUiql1Iyxd20Fgt9obCt4b7PNNl+TxTf93PHeZz7zU67ZmgQAECdPXsRpgvIvfXkv5ryVvPzb1jp/9ufHyNBQD9cquc/LCj+w/1rx6KN7AWOQlfRKf/J2AIA5529BSPw79/CXldfGhOFBjA4ccCEEbIxJKaVN730AAA4AkCTJDCEkDyGwbrd7IIqihSiKbFmWkhCyWJblbinluS2vOGttYa2tMcZWyrKcBgAghARKaQchlMn1plTv/a+YTE2Vbm7OBqUoAID9wAe4+PEfmw3OWfLY4y306OPD+sknDxMpu/DOtzctwGYgNVCvkMlJ7ebnefT+P7lof/R1+8lTpyB793tCyHoVfePmRPzWtzwVBurIT00NKq13GGO8lPI0pZSUZTmZ5zlQSguEkEEIxWVZ7k2S5HyWZUcYY01CSAAA3RfprIUQMOe8ValUFCGEA4BACAEhJOOcNzjnS61Wa1IIcdM5F2utJymlPYSQ4pzf6PV6J0IIJI7jc2VZTkRR1LXWWufcegihYowZNsYMe+8vVCqVjzWbzfdqre8DAO29hzzPu4QQRAgRnU6nSwjZH0XRmzDGb2SMTZat7t7CBvy5y+u75pp5+53fs3+NUlICgCmKYhAhlPfP5dk4jmtZlrXiOH7Ue09CCDGjpEUwK75zul0xlR1EKV0+cGYNn17MGKac76hgdXQ0litd47QLqBHRDW3d0ErP2IsreTlRi2oR56BtcKMVUS+0bj96o4nv3Nmw1SjCg9XYOw92oaMDxWR1Zw13Lq4UUy5izhtFWrmFbmHUobEoPT4hwonJlC+0DU457XEOPoTQ7XQ6Ik1TE0WRWVlZuatvTmwwxiqO46t5nu8dHBxczPO8BACT5/kdlUrlnLW2kiSJTZKk5ZybLYrijo2NjZFarXbKe69CIIcvt9SRTmECQnHU7NmxgYSNzc/PP2dwcHAhz/NKmqYzvV7vp7TWAWO8CgBQFAVtNpuvZ4w9RAgxlNKN/jIpj+P4lFJqPEmS9U6nczsrSgi56b2/HwA+//R7fGNjY9h7f68xZkcI4VuVUiSE8M3W2qwvSqrjOHadTgdxzk/3RWVHjTHjzWYTAADSNL3Q7XaXnHNDxpin64KdCiEMhhA855xVq9Xm/9wZ6n8f25mlbbbZ5jYLCwsn1tbWfiRfW/+l9R947fMQ568zt2aPB6U262buuXuNHTkcm7V1wsfHM/DekJGRFRzHDCfpipmZmeRHD8+i6WkaveLlrd6B/bustWme5xXvXCbXm4f9k08S/YE/7cHUlO7s23sQIbTknLMhhC6lVGKMg1KqQSlVSZK0KKVCKVVpNBrdfreV2Wo9RwjNI4Q0IWRRSrkQQuAY43XGWOm9J1EUrRhjGtZaaa2tkPMXFvU73rXX3rgxFL/+dafctetxUEq6c+dr7KlTc/Y/ve2A/5u/HXeXLtWg3UZhfV2KS5evsr/525RXKpft9PQQ+sSnSGi1GGxsOPeKV8QYoRXzu7932DdbFeQ9uE9+asz/5YfHBCYz+tjRMdi0eBntK35viDNncycEL0LYyRhbCCGsGmMa3vtKtVpdxBgrrbVWSu1jjHU55+cAoEcIAQAgGGOXZVmNENKhlEpjjLXWAiEkKstyPI7jNYwx55x7Qoi21vJ+YXw1iqIVALDW2m5Zlkc55zPGmLF6vX4zz/NxSikviuI1IQQupTztvR9hjGUYY1QURa6Uep5zbiiE8K2c8zoAkEZM/YdPr5LHb3VqrdzK3Q1xa6JKeZZlOyila4QQQwjRhBBjjCmyLDvhnJNRFBnCeFwtZ/3A8meBrZ+vb1SPrCKEI8I4u7pWdFa6yty/S3YvNCE9ORnjesTIemal8cCNB7+eabRRBkdQoIV1Ltd2hXE6OD2Y+Lc9eGXq0HilXeVMAAbMccAVCbGxoTa/UeIKp0AFj5T1+vNX1jfumNxc9wsh+EpEbW4glti7siyHjTFxWZbCGFMyxm6GEGwIIWWMbXjvM+fcIACs5Xm+FyG0FEVRpy8psFoUxS7vvTPG1KMouh7H8UxZloRzPsoJYiEAC4SI0QgpZbSpRJzGcRwBwAjGOAohWGNMxTk3Rik1rVbreK/X+1Wl1PdgjE8mSfLOkZGRX69Wq78DAI9praFerz/lvb+hlLoXADBjbKbRaHznxMTEXzz9fl9ZWRlFCD1SluW3AMCLAGAqz/PDCKEmxtgDAO52uyKE0C6KYr9zbpxSuqS1foYERwghpGkarLVkq2MTAEBK2RZCcIyxs9bO1Wq1d/+vmrv+V7OdWdpmm20AAEAplXrvvy/Lsl/JACB55SvP9X7rt49G3/S8C77drgHGvHjkkbGBkyc79X/7a5l63/ujoBR1S8u5uXlzBEIYoZOTi6C0xVpnrtHohhAmMcJhRIoV9cDH0Ppvv+UuAADx/d97K7vv3tFYyit5nh9hjGV91ekkz/PxSqVytiiKvd57VFqviUxm57v+4CAzj/R6vVdyzlvOOfJ041qE0BNlWR4FAGCMPRlCqGCMl4QQ8/06Cwh3nKwAAEQ/+RM3uq959Qny7a/I+Kc/8yR8/gsxSdOvXh5gLEBZJvr0mYp0PkT33N3J5ueHEADY2bnx6M//4ilIkvjZH0NS6nBgP4vj+Gye58e2Xi/LcozM3FiG3/wtLd/1jiau1YhS6raeVFEUmDGWUUojYwwYY6rGmLsAAKIoukIppWVZKqXUEAB4a+1yCIEaY/ZEUXQRAIaLohhK0/RSt9udBIBBIcQTSqm7OOezfTPfRCl1gHN+yxgzXK/XL2RZNpQkyc12u/1jCKFVKeUVhBCTUs5SShPYfOASpVQ5MDCwqrXutlqtXc65GADAWHsOAGAgplZQhLIsGxNC9JxzsVJqghAyTykNIYSoH9TGC93S3Ngo9At2704uFPV8ZwWrQnvhMLhj4yker9D4yblujjAeqzOnODgUU++nh6La1TXtri1l5UsP1Gurme6sZ86nEEzp0QjFmGSlgZ944e4FglGrsK6sCDJ0dEKWKs+AiyihlMWHhrk5M9/eWMs92TlUqYcQPEIoUEp5CCFUeMgZZVgIcVUpNRxCMEqp3QAAUsoNpVSCMZ4riuJoHMcPx3E8B5tNCnGv1ztYq9Uu9mUgSkLITYQQZ4y5oih2Yow7RVHoNE3JoLDZaEVWgs56kcDIex8QQk5rrfsZQBxFkVFK1Zxz02VZPr/RaFz33neNMbwsy9etra29VUq5yDl/GGO80G63B0IIaa1W+0lrrU2S5GPDw8NNAEC9Xi8yxuwAgBcDwOv7mSTkvTdFUSTWWijLMieEYOccEEJWi6LYlabp2V6vd6woipNSynNb9xoAAMb4VpZlu7eMnDHGPc55Rgjh2oMXBFcRCr/9j56Uvo7Yzixts802AABAKdXe+1udTudnCSGFHB1pUiHy4hOf3A+AhLlxsyLvufda9vCXxhDna50ffO0En5xs0rLMzPnzowAAvtutIEopONe2X/jCZGXHzptizx6T/9zPV8tPPTgASiH5oz9yA/3wD3VUCBOU0jlr7Zj3njvnpPeecs6vlWW5R0rZJDxiV5ourQoS1SRyjJI/QwidtdYexBivOedGnzb+JWvtOGOsq7UeppQuRVEkOeeaMVYqpaoOIUIfe1zhEydaZs/uYc+YDMeOsuo/f+3LzX/7oLBLyy+6fUAQAnn33WfUmbNH5B13nCmfeOKE/fBHBiDPN9/HGKJjx/IgBDZPPjmANn3jgExOLtJ//eb54t579j5dMwkAgJdlJ7zlrdjNz+/AX/pSC8fJup6eul0r5Zyra61HrLUDjLGlrQ67fmebWV9f36G1jkMInDG2Xpblgb4MAcIYd621A7DZPbfRz3YA51xhjJeVUgeklG0AIJTSrtZ6h3OuqpQa6BfUd4wxkyGECgB47/2Q1noMY2ystYkxpjUwMGCiKKoRQli3220QQlQIgV5cD+pVJwZv/OiddTQ9GOM0TVW32x3HGDNrbeycG/TeawCIjTEVSmlei7jeNSAoQoRcbEE8WkvNqWWNnQcfMxQgBFjrFL19Y/VKwkA550oXMLm6pqj3rndgkDnvAx2IkN1RZUx5HPlgKQavh1OKGhEjR8djhgmtG2PkoHD5pXUTfvovLu/ZMSBX17MyH6lE7D984vrQv7hrMEiyWYNlrXUIIUYQ2KIoSu+9QwjVCSF4y5bEWisrlcpVzrnDGFtK6V8NDAz8bLvd/nat9fE0TW90u9193vtKFEVrSqldzrlxKWW336l5AiHUpJSGJBIpQd622+3daZq2EUIYY8zLsjTGmHFjzChjrOU2BZx8rVZrY4y5c25VSjlalqX23g/Gcdzq9Xp3KaW+yTl3wjl3SGv9wiiKbvV6vTeurq7+RrPZfHNZlm/Y2Nh4s9Z6PwAkSikoimJca02NMcNSypsY4xBCmAghGISQ8N5zY0wcQkiedk2tOucahJB2CEEIIRRCaN1aO8QYu4wx7nLOO4Ix2TFoZmpi5A3/k6es/63gf3iTbbbZ5p8CzWZzKs/zn4zj+KmGc6tyfHw0/rk3PVl91Xf9IKmkK0gI5xbmkbt2vaI/+/mh6PKV63DuXI8ePBAD+TvHDURZoMND4NabdfXe96LeC+8f1489HodOB6Xve+/l7LU/MGU2/cnCloMAxjiTUs5aa6uEEOe9j7Ism6TIm5PjwlYEpMhb3uv1XkkIOYgx7kG/Pfr29yIUpJSn4jhe8N7Hxphd3ntHKU3zPN8UeQyhze67b6H4nbceSjZb4sFaOxRCGKXDw1dv74wQiO6773T5la+cIKOjTl04fxwAIHS7f7fJ8PCc+vIjE/iVr3iGDQIdHtb5c+49yhjb6LeQ30Z+/ou33OzsTgAAOzs3od72tiOVjzxw5tnnIoqiM977Ac759Xq9flZKOW+MKSil3RACq1arFxhjXcbYKiHklpTyTP8YniKEbGzVCQEAeO+D1vogAEC73d4dQmgVRTEZQkAIIV+r1WYwxlXvfT2O41NxHJ+WUq4452pCiDyE0M2ybERrfbQsS9pqtZplWRbVavVCCIEkSXrutXcOlidHaK0xUMsoparVag0xxm4wxi4TQixjTFtrd4QQmlEUzSCEVnH/iWyttveMs97llax3aJCVU4kfRFZ74nV7f9Xd1e12TQgBO+d4IrA4NETVHz8yn1xp+ji3vmTeCAKuN91g5Z6hirpnIrHWIb93OKYjCX34gVOLy5RgizEOHUOqP3P/9K3cBHbf9MDgg+dW/HedHL7ZkKGNEHLGmCzP89ha27XWaq31TsYYeO8txniJMbaSJMkVQogty3KCUloXQuQA8GQURQ4hNO29T7331RAC6f/+nBCyxhi7bq3NvfcJAIBzbkhr7QGAUkrjOI5vYYwZAPgQggeA0ntPCCErvV5vjxCik+f5lDGmhxBCnPOKtdZHUcQZY7cAwCCEnnktUjqrtX55v8NxUmu9I8/zacbYjbIsD+d53gCArhDiz6WUbyOE5AihrCzLyaIoBkIIZZ7nuymlc865EULIkpTyYhRFwBibFEI8HsfxHGNsJYRg+t6KQAgpG43G/ePj4/dxzvZPj9Re/TUnnm8QtjNL22yzDbRarRFCyKPW2kNSygpijLAzZxW+evX/ZMPDjw2+4V/9ps+yr5DGwEl9+cput7wco9NnCD90EGlCe0Sr3M0vDAAABO+AjI9LPjU973s965utv1O7Rrhj77m7bp1LK5XKeYRQNYqiFYSQL4pijxDitPe+tlX3UBRFNY7jllIqY4zllNILhJCHMcb3IISIcy6K4/hWkiQthBDta91knPOcc77Y7XYPJknSwwh18crKPPzSL4+5Rx6pib37Fv2nHhziz73vUjo2tsQ5b6I9e6z+5KdeGpRibHp6Tp09ux8AIDpx/JK5cXMYnmUNxQ8fnvc//YbFIAXyH/jT278REYzsq75Leu9jIcQF59wwAIC8eOkKo5Tqz35ucGtfQRuGx8YWzHPuvS0z0NdXGnbOVRljSmsdlWU55b3HABA556J+B90u7z2x1o5aa0eNMSPW2rE4ji9FUZSkabrBOV+OoojkeX5bDT2EIDHGKxhjgxDCzrnIGFOx1taNMWN9BekhjHEPY7wIAJpzvsQ5XyeEoDRNKyEEQylFANCVUgJAgL4GVCaEqEopc0JIboyppWmaSSmbGONVzjkOIQDnXIQQrJSyihAyRmtciThBtph01iLoZ78QQm1KaVcphQghptPp7HeI3KpEvLxzMpYTKSGc8znO+ecwhCCQ45+40PIPXliPjo/HWiDbODheHZuo0oAhuEbKKaWEzzVLNlmP0HN3peUELw8ZY4bq9Tp0u92d1to6ACwXRbEHIeRDCAlCqCzLcq9zLjXGDEopL4YQGKU0a7fbU5zz87Va7fO9Xu+q9/6+KIpImqar3vumEOInCCETxph7EEJtKaVyzkWMsVnvPWaMadg0bNatVmtUKZUIIbr9OjPnnBusVqvni6KY6EsTrFtrc+fcZ5xzE327lAOU0rYxprrVPdq3w4lhU54jGGNuC6laa0eiKDqPEMoIIR9MkuS3JicnPxnH8e+XZXlPWZbPIYS0MMZOSrkaQqgyxuYAgHnvx4wxnaIoRpxzE1rrEUKI1lqPOOdiKeWFJElolmWVer3+eSllIIR8Q3bAPZ3tYGmbbf6JsLS09Hzn3LyU8qsMIaMoyrrd7uEQwv4soJCTaFUxdMO0O9+jPvCn98XPfe5fFY8/MVI8eepN0Z133ghFEdGpHTeKz39xH8tzjxCqiAMH5tzamiBDgz2X5QkbHZFgXdMuLo6JO++YwbVaYABCcHEJpqZUrtSBEAIKIbTLstwLAMA5b5Vlue9pQ0Na63Up5S8mSfJGa+23dLvdl3vvd0opgVK6wTkHxhj33ueEkA2Mcc8YMxxCSIUQV7XWhn78E2n5M2/cG1bXRFCaQwgl37nzGhkdoTA1hQml3+Q63R3lH71vPwAAeI/Y9NSyW2/W6MT4gp2de4ZiN5JSwy/+m6zct/co1mY5/Nmf3279x7umb9lv+9bB6MrVa+z/+SOQVq/Qbq+J3vtHuPzQX+4BrZ+R0XfXZ8bk8WOXzOjolqZSd8u413tPMcYt59wwpfSaMWYnwKYIplJqijE2wxhb7C9JjgAAUErX8zwfDiF0hBCVPM9jSumqtbaOECrjOL6slNrvvU9CCEwIcXXLimULSqnqB1AjxphxSilljGlKqdRal5RSEkLgCKE8iqJGv2MPaa231KkJAABCSOR5HvVFHVNCSNGvYxqQUmaMsUgppRdKxjc6XZIQPySlvA4A2jk3xDkvnXMV59yw977jvR+sJlErkUykgkbN3BnVbR0LIQwDQOS8T88u9tSnL63vPjoez+6oi7ieCHJqoWtjTsnbPztnVRz8RwAAIABJREFUdlRI794RP40gLEecOgBoSilX8jyvE0Ja1toaIWReCKE45y2EkMcY954u79BXJb/ebrcP9TNIilL6ccaYiqLo0xjjmBDyDs75742MjFzy3j/YN4LOCSFjAGC11jHGmGmtVbfbnTLGGOfcQBRFF40xPM/zXf0sFEYILVtrh4UQ5733IsuyQxjjWp7nRwkhS/1geUBKudDvBr2apilwzhOMMVFKdZ1zk/3MExJCzFprOcb4+vj4+L+q1+vd/v1XAMCX+wbBRkqpsiwbopQuFUVx3DlXlVJeK8ty/9OvF4xx21o77L2PNqW6UFmW5e6hoaE//AempW8YtoOlbbb5J0IIwVar1d7Xej/P8zfTmZkdptXu3Yhq1YlUDrel2EWeOjVYPPTQC/ixo19RDz/8HUjKTN53X2ZvzTbkoQNzzuik8pKX4I0/et8Oec89V+n4eK6+8ugkHh5eIAO1UdIYvIYp8/rKlR12YT4xn/7MOLvnnpti964OAMRKqSnO+Szn/CZCSBtjxp8+LoyxBoBsfHz8E5VK5eOEkPfDRrtpHvjYgfDBvwTYuxf5JEZlWdYwxhBFkTTGVJRSNUopoZQiNzqyDF94aAAPNcCXCodOJ0WMWihViZ58qsX27X0Uz86e9bduNdjU1CKupG22Y0durl0fY9O7VsA7gisVJU6eWMRJOiNf+IJV+ppXp0KIRWqMMe//wO1gikyMr9hveekwb7Wa6u3/5YD54kPD7pOfGnLLyw2w9nagREZGlvn9L7oGa6vS/viPDXhCOEIIpJRLvLWxohkdllG0qJTaBQDAGFu01o73H3iVEAJFCEX9gFEQQhY454tFURzrP2QDxngty7Ld1to0SZIzzjmGENJ9n7YxgE0rGc75Fefc7SVD5xyL4/jsVhAVRdEtKWXFOYecc11KadUY0+ScV5RSGULIO+cwQoh2u90DWmsjpTSMMeqc62yWsbmxJEmCMQZXKpVW30qGOufKRoR1TCFPkrgFAIFSGkkpPcbYe+9jACBSyg3n3EYIQaecsERyQlAgaRwtSim3lMcH9w9y9Jo7x1qj1biSSBZCCGyta3QrM+r77hiCiiDI6XIwODNgjHF9j7pVKWUXAOJ+cbLp9XoHrLWJcy7lnLe11kNpml4BgA0p5az3nlFKZ4UQa2VZHul2u2/c2Nj4hSzLXk8p/c2xsbEPx3HcBgBoNpv3EEJSAPjBfobQhhDAOVcjhLh+N2QKAIAQannvxxlj1yilhXOu5pwbpZQ2tdb7EEIqSZLZPM/3EkIM5xyMMVXOecsYM8QYuxBCaJRl2cAYL1trG4yxnBCyFEXRKgAYAOgaY/YTQk4RQh4pyzLWWsuNjY0dCKF/zxhbwRjvcs5p7z0CAGqtbfTthJi19rbAqpRytq80DwAQGGOzhBBpjJmOouhtnHP3j5qovk7Z7obbZpt/IlQqlcWv9V6WZaQoiooYHOyS02fx7tFh4j0vCSHKnTo1Jt/1zkOB4JdXllfe0n3w07+B0+QqEnzFLC7V+f59ebMfLBRf+tIRoBTknXcuqdNnJhACBITuiL7puTN8374Z21wfYd//ffPuzpMMIcRDCKFer1+11qooikaKolgGAJBSrmKMlzjn/1ZK+Zgx5nY2rF6v5+b3/+DP7LlzL4YkeVHQas02m92o0XCEEEIpFYyxBSGE7HQ60wAAgPFO9p4/WLEhjETt9mn/5p9PcVpR5V99+DgAQPGRB+6Lv+l5S+r0manbB4WQpwAA9PVrB93ikgAAIBPjPX3hwl1mZiawV79qKR8YOASUQvzqV1/XH/rQHgAANNjY9MB65CsdAADwf7/DBDlxbCn72Z8+Gb3uh6+ENA10s1C70uv1Dsl2+8nKH7//vHvzGy0A7CSE9IwxhwEAKKWLxpiJKIrOlGV5vFarTXQ6ncJaG5xz01uebUmStHq93lQIAfpdcLudc1UhxDIAiCiKrhZFsc85xzHG6d83RsbYEqV0GQB8r9czGGMbQojX1tZG4jhuSik5IcRhjGmv18sopSaEgJxzo0qpVYxxkqZppF2oSSnXEUIijuM2QigFgG6r1WqEEAbiOF4qi/wQBH+qLMuTSZJcCSHobrd7MIqiayEE0TeoJXEcx9571263Bee8RbmUxhhDCNlrrbUhBEtRYAhZp7ULANA5OMRkAOTKspQxY5Qlyc0sy6attSPVavVKmqZxu93uaa05ANT7HXxrnPM5hNBYnucTaZqeAwDOGAtJkoysra2NeO+JEOL0lvAjAEBfIPIFAPCh/kuo1+v9fqVSSRljVCnlCCFICGExxp0sy3ZtfTZJktNZlp0AAEAIHRRCXOrvE/oK7KsIIZtl2eEQAnfOgfd+SQjhjTFjSZJcyvN8h3OuBgBgrS2VUoxS6o0xhxBCOWNsrSzLI5u3BR5ZXl6+IqXMpJQrQgjrnIsRQrYoio0QApRluVMIcR1j3HPOpRjjVhRFN0MI6yGEvVJKZYwpkySZs9b2KKWx975nrd27vr7+7UmSbB2Hb2i2M0vbbLMNGGOwbTZ/SF267EO368Lv/l7uP/JRIpdXVPn4EwPw+ONN/5VHd7lu98X8wP526PZ88ZVHj7vllTowNoOdV3RycoHv2b3CduxcDkY7Ozs7GrSh8q47r+Sf/+JJt7TUwIwb9KY3dgoIh621aZqmt7z3hHPOrLVdrfUA53yVcy7jOF4bHx//uTiO25VKpb011uXl5QOdvXt/vXje815FXviCmz1KjxmExpIkWUcIsRACZYyFVqu16+m/0fc7eYjWq+4vPriP7d2zGtptjCMpyfhors9fiMPmw3KTEDCdnLD25q0kftELz+E0RerCxfHkpS85py9dGiFLSzfQXXcxJ3iE7ji5Bg98bACsRfzo0Xl689YSuXmTkDRdYruml+38wlcZ75LnPW/B3HFy3Aox2O8Wi8Om/xjwxaVl9db/dJxLuQK7dsdIira1tgEAQCm96pwbJ4TMMcZWrLWRtbblnBvru95XpZTtLMsmCSFFPwsS+g90hDHu9A166dZDPoqiFULILKXUcs5vcM4z733cN/od01qPY4wXiqK4yxgzJIS40dfPQbAps4N6vd4+7z3inC8RQhyltEUptRhj3M6NbtTSKgAQ55zRWgdjTB7HsfXet7Ms29//bYsAMKK1bgghWgCgCCGJ1nqw0WhwACgwxlGn0xm01qZ9Y9+6tbbdbDYPGmNIFEUGASgA8AghOL+sUD2mQFBICSEIIeQwxrYsywbAZmaEUvruLMuGjDEnQwgoiqLLCCGJMc7zPN8rhDjfX45scc6JtTYHgCVK6TpjzHLOm1EUNTnnK4yxnrV2dstaZGFh4V9HUfRC55wBAMo5l8452263R6IoKqIomqtUKh2EUEcpNdjPpIGU8lRZlse3rheEENZa77bWjgshLvUNpQVCiGutx0MIxFobcc7nrLXDAACEEGSMGfTeD0gp5621FWPMBACAEOKi937YOTdgrRXVanXDOTcPAIgQUjfGdI0xdYyxjaKoZ4yJYVOolFhrRznnxnsPCKGSMbaWZdm09z6tVCooz/O6cy4BgHRwcPBP/ocmpa8ztoOlbbbZBjrveOeR7q/9+i+5z31hAM3MZKHVHNZnzk7hAOtsdGTdWzuqnjq1EzHuzIULVVyvr9qFzQAAR3GGhGiHLBtRZ8/ts7Ozo2692Uhe8uILZKC+Vj7x5BHYtOGAUBScP+fepTAx7oUQK91u92Acx+tlWQ4qpQghpOm9H2CMZYSQL3vvP913K0e3bt36vvX19R/pdru/a425N+52rzohqlvF4NVqNaeUMnX23EaHkJ3+WTYpSZJckIuLC+gzf5PaL31p0M7MjCQvvv+WPHG8A9Yt6itXpp++PZ2YuGKuXNkFABCMQUGpnA4PzSFCkGuuD7hr18fh45/w8vnPX1T1+q4kik4Rra06fWaXe+ihnXZmZsytrIzhao2GssBIRhCUur0Mh3o9ZL/j2//+jM6Dn267p04N2UcfG0Wf/JQmL395j1ZS5b33jDGLELLee845d0VR7PDep865GkKojRAKhJDFrRoShNC6Mea2IS/nvO2ck1rraYDN5T1jTMUYs5tzvqCUGieErEdRRIqiuJ0xoZSuMsaAUnqzUqnIfnG2L8uypJRSYwxhjF3v1yu1+ktzcafTmYo4WTLGKGNMYYwhUkpKKRVaa4oQEv2WfNxfZlRpmt4MIRQYY+O9rydJssIYIyEE65yzRVFMpGl6KYTgGWNRvxNrmVKad7vdfRjjhV6vdwQhNAuE4IGECwih7b1X7XZ7n9Z6QAhxK4qiXySEfGpsbOy/5nn+fIwxI4QsGWPGKKXzSqlpSmkXYzx4vUvWO9r54ZQLa60riuKotXYkhBDKstzLGFuJ41hubGzskVK+p16vPwYAsLq6+kta67gsy2mM8UZRFO1+fdES5zxFCDGMscAYe4xx2xgz3O+gA2vtbZucrSAKYLNAm1LaiqLoplJqYquoO4Qg+oX2Wy3+rb7VDu0vx61QSjtCiHml1OH+NePjOD7rnIsppTWMsczzPDfG7MAYr3HO87Isd1lrK1LKi31BSsQYm9da7ySEZFmWHQghMNhc+t3I83xHf8x7KKUf6i//fUOzHSxts802AAiavlSl22ju0WfPHQ2lCuLY0XNkZFi4hcXglpcHyejoMlhrxNGjbSCE2ps3B8W991zTly/vcysrE3zPrnm7uLTZFeY9svPzwzhJWiAkhE5n87/lO+9YDo8/wdijj+X40MHCRNFQCGFZSikYY23vvUAIRf1OquVOp/MLvV4vWl5efnue528yxjwfA+TyfX+8oH/lV4/QH/6h3HkvAYBorRn50sNd9YfvGTYvvp+HZ0kLUEoNtDYK/MBHSzs/PwYAQHdMtouHvjQSrOWhLAUYc/sz/OCBtp2bG0JJ4hEm3i0tjbr15oidmxsBYze305qTS5ea9KGHQf3lX+2xi0s1KIpnBGlubS0mE5OaDg/NuZXVxu1DLoUm3/otheM8evbpEJevdO3jjw/wF99/Fn7x34AZHhrhnPcIIctFURxmjPWMMRMY47m+TYXYPOy+xjlfAgDknGtsvTYwMDBTlmWDMXajVqvNYYyJ977HGGthjLExZpQQojHGq4wx3RdG5EqpOuf8b6Io+ne1Wu1TIYRj3vtdQojCGFP096NDCBYACgCAoiiO9E2PJyilS/2MjOnXlFnOOSmKgjvn8n4wVGKMc4SQI4QsSylbeZ4PA8AGpXQ0SRKVZVkcQmiFEAY3NjYmkiS5YK3VCCEJm9kqRSnFCCGrlBqRUmYIIYMxzoPKdsRR1PHeI7NpltwAgCClnA8hrE9NTb0DAEKz2dyR5/mPE0IQ53yVMZYaY2qMsSXOeZAc04l6xINzuNvt7gMAhDZNdWkIgVNK22VZdr33jBCys9lsHl5bW/vlNE2rtVrtu/I8f74xZg/nvIkx7kkp/5tz7jAh5HwIIQOACqU0FkIsWWulc67w3t9WwyaELFJKHQBACIF67ysY41XvvWSMLW5lCTHGRQghRggZzvk8IWTDWjtICMmttUNSyq61Vgoh2oSQGUJIhzHGe73eNADc8N6bvhdguyiKo5zzdYRQ23ufxnHcAoAOY2wtz/MjUsolrXWVMTYjhAicc+ec6/azj1c45ytlWYaBgYHP/eMmpK8/toOlbbbZBtjOnaETR691GxtH3dlzDTCGu6WlcTs7NyCOH1vXFy+N+na76judKhkcXEDBc75vn8WveXVXPO+56/qznxuikzvW7cLC3+kKOQdubX1QHj50hU5OIjoxdlVfm9lhFxYGwVqnX/ayOoojhhAyURSZfq0RwhgHjLG11g7keX6nUuplzrnJrd0ihCh6x7vqod0m5PEnsdy165oeGhwhGCvWaXfp6FiBd03PsShad85VhBDnKaWqLMudoZI69NGPg19ZGQQA8HkWQQjB93pp8m3fdt3Ozoqg9WZ9Ur2+5FZWRuSRI6fM9evTX3XQAAAoDfzwoba+fHkE8uJr6tahajXY+YVhMOb2a2Rycgle9rKKo0R81QcOHFgP7/+TBj1ydLn4lpfsD94zY0zNWrsp/um9iOP4DEIIPb0lHAAgTdOVoiimoig655xj1Wr1d5xzcb1e/6wQouO9P6yUYn2dn1EhxFocx4u1Ws0TQnBRFFXnXENrXaOUdvuFz98LAP+Mcz7Sbrcbp5bt0icutixCAe0YiKhzDiGEMgDgWuvBpw1HxnF8CSEUIYTWiqI4iDG2hJC1PM93Sik7SqlYa70riqJrGOMoTdO02+0OpWnaxBgzxhjt9XoxQmjNGOOjKJrvdrvHKaUFxthQSnFRFBYAYmttJIRYM8ZgpdQOzvmi937IOZdJKWkIQWuthxBCHYQQAoA7siw7XavVrud5fjyKoqNSShRCMEVRNNI0XSvLcrQoipFqLBcEY7TVau2SUp53zg0TQhRj7DLnfCXP88PW2jEp5XXnXKKU+g7n3C5K6XKn03mZ934fpbTMsuwAxjjinB8KIXhCyGgIoaGUyhljvB+ENkMIMQCUIYQUAEBKeUtrvTNJknMIoUWMcUkI0f0M3pZuGRFCXO5fE6QvJzGYpuk5QgijlF4PIWghBEYIec657Aevlb5WEsqy7CgAzJVleahWq10HAOG9L0IIA0VR7Ijj+Ja1dsI5F1lrq0KIHufcUkpzhFCW5/lYHMdXnHONsiynGGNsYGDgvV/r3vhGYTtY2mabbaDb7VbWvf+/9YkT47GMZuwTT2zWx4yPz+mFhWHIcwEAwA4d6IhDB0uwLvdZNlQ5eRwXn/6MtjMzQ0FrV3nFy1fU+QsDz9g5oyWOk6J89LGjoSwpeA9s3/62+faXj/gQCOd8hVJKvPfae4+ttb12u30IIdR+eofWFgGAyLn55VCqyF27Jt0XvjhMf+D7F6O//puN4q67ZDa1c9qEMOa9H2SMtZRSe/nGxnzd2iIgVMJn/jq4frAUspzxfXtv1b77u1X+0EMxGxtbsGtro+A90MHBObpjsmXn5gZDUVSePQ46PbXCf+hfXOv9+OsPRUeOnHWf/NTos7e5PeZ2m6T3v+i822jjUBQxSmKN3/6fvUqTxrO3RQiV4ubNBffRj425q1dH2YOfztNDhy7asbEohCCklGeklCtZlt3hvReEkGxriSaO47O9Xu+wlPJyv9V8NkmSivc+AoA7EEJj/dqZqCiKwUqlMpNl2R4AmMvzfBIhtOqcK621k/3sRcQYa0VRhCilwlpb3uqhhX//yZljp+Y7I5+/sjHwmjuGM0aJ7XQ6k30F9ksYY8UYW+7rBfm+WOEE53wOAHKEEAaAPMuyA4yxNmPslve+7r2vI4RWvPdKCOG63e4YACxyzrvdbve4tbZhjIkwxpoQ4rTW4865FufcO+cqhJBmCAH1dZNmAYAWRTEupZwryxJCCIYxtiylbBtjJqMoWgSAF62urp7w3teNMXd779uwaQK8BwA2OOcb1trBvowAdc6tAQAXQnQ4513GWPDew1bnoHNuBCGUeO8ZAADGmBhjdvS7Di2l9Ia1ts4Y2wAA0FozjLGN47imtTZ5ng8YYyaNMYMAgPt6Sa1+UTaKoqiZ5/lxQkgZQgjGmJ0hBEoIWfPeV4QQ61tjoZSuCyFuGmPGlFJD1trRJEk2tNaR977Z6/UOO+cC53w9hJADgJNSdhhjHgDKLMv2aa3rhJDIGJMKIeb6ulOLjLEl2Kwp2+gXhI9Ya71zbsxa28AYrwoh1gYHB6cxxu8hhBRf6/74RmC7G26bbbYB51zinKsFAGZe9Z0B3r0pj0IG6pG7eet2rYS5cavKhkbWiiee2BvyPKGNxgoqCi/ve84Ne+tWambnegAAbHp6TRw8sGpWVog6c/aAvXHz9neRkZE2f+mLewohL6RsGmOGEUK6LMuhvu4SAgBQSh2M4/hCnueH4Vmon/3pNP3s5xaKP3xPwy0sprWnTvU2/sN/PFD75uefL2s11BfokyHLitq16039X965t3XxogyUQnT06FNb+xF33TlDalXIvvCFprl85aQBGI+ee9+Z4rHHj5vlpd0oiqhbX68AAPC777oAP/WGGMrS4stXMvXNz58sq5WTAADZ4UMn0ve8+4xvDBD1mu87gp7VAUcOH+7o17/OUe/n3Wf+ejBkOQdVGoDaM7fDuCP+89vb+qMfvwMAAEIAt7wc6wP7JSFkFSHULMvyuBDiyf55G5BSniaEbHDOi263exwh5Lz3lc3dYRlCuJMx1kIIPYwxhizLXjowMECMMdd7vd5kCAEQQjNCiC+HEF4IAPWnj8k5x7TWKooiYYwppiuA3v2aHaqt8eUPX8zMmWX1lbsm5N0IoXFCSKG1PsY5v0oIcd1u9/jQ0NA8AJQIocxvZsh6GGNu+kueSqlphJDrL9lVKKUCIdTt9Xo7OedNrfUE57x1+5wJMVMUxUnO+bwQIvPeU4RQiRBaDyEQpdRYmqYL/cLwdQCAPM8Pcs43kiQper1et9vtHqvX65c458OtVotQSnlZloc455d6vd4xzvlVAICyLPcghDxjbCmEUBpjNKUUyrLcp5SKpJRPlmV559bYGGM9hNCq934EY9yUUvYopU3n3CRj7HoIoQ4AqbW20rdVqVprBUJoiRDCCCGaMaYRQktZlh0PIcQhBI8x5gghnyTJRUJINDAwMNtsNqcAAOI4PgObPoENAICiKPakaXomhABa66GyLPdsLdMCALTb7QMAAEmSrEVRdNl7D1mWHXz6OeecX9Fa7+n/phlKqdFax1EUdQghdH19/Y7+e+ve+05RFMejKLpQFMXhzcs2sCRJDCFEKqUKABjnnK8/+z7+RmI7s7TNNv+D9P72s7z5+3/ww+lLX/LUP7z11y+zs7M/1uv1/lBKuaq1HmUItZI7Tm7oBz8z4FZWYnni2EW7sLhZi2Qt8EMH1xAXTTJQ7xVffmQvjuI1124PurKsgLcbiFBvFxdH7OpaioR0fm2t+vTvY3v3zJY/+M+HLOcxIWTdWjtKKZ2Pomg1iqIV733bOTeYXJ85ZX/+F/azF77gohXiGaKJyeLStfLXfn04ecE/W8dC5Oqhh6lvtWpo9+5lcewowhizTqezm62szpc//i+P+7U1CgCAvAcyOrLkllfG5N13n9YXLuw2c/MjgDHx7fam79fc/Kg4fPgGlqKgIyNN3+1G4nu/90L25jceM7Va3TQaDb1/36gX4hkGurpWHTWcD1cOHriEqpUcA3pQ/u47O+Sn3uDka7673SXkiABYt1/44jAAQDQ1Naf27x8GAGCMrTDGLvG/+kip/9/3P9PRPY6D+O5XrRQYH9iqYSGEdLaybtbaUedcgzHWMcYMxnF8QwiBAaDNN6k657y1dgIhNNzr9YY6nU6j7yNHEUIhiqJfBoA9UsqTAJCHEJJ+txUwxhYBgJZlKcqyrGKMN4wxOxKOk0olWXv+3sYfO+deXKlUZFEUFc65JoTkGGMTRZHu10KlfcV1RQiRnHMeQsi897ivdD6rtd4ppVwqimInQkhTSheLothnrY0AIAghrkspW845TSlddc7tUEpNWmsbUsp1xliNMRYqlQoAACrLspdl2QkhxGwURQZj3FRK2RAC7dfwLJZlqYeGhpKiKIwxZrjvLziKMdZbukewWcx81VrLi6I4Zq0dEkJcEkJYQkjWtwgpGWOzjLHVviRGnCTJzaIoJvvLhOOwWVuFKKUr/TohxhjzfVkMZq01IYTSOeeLojgWRdGMtXaAUroKACUhZCnP8xNlWaZlWW55AAIhpFUUxX4pZYdSeoNsGuB2iqK4w3tfSdP0snOOhRDkM66rTekBaoz5qiVmIcScMWZMCHEVABoIobYxZowQYowxbWPMGOd8xXtfZYwt9P8WAKCiKJqnlM6HECTnXHrvDef8z6WUy/+9eejrne1gaZtt/n5Q64/ed9wuLzOxb28PAFD7gx963vL/9as/pC9fHsXVqm2+63ff3P3IA7/TeeBjJ9of/OCd3Qc+OlWcPkPT+1+08P/34P8xIITmpZQ/o7VGSqmGRajhR0Y64uKlW6QxsESGh8GurzVAKcIPH75prl8fMTMzk2zP7luIiw3f7U66hYW62LP3qlldmyaVVOHBoZ5bWhoAVTJ59z0Ldm6uDgAgf/InTrvveKXQY6OTAACc854QYoFSCnmejyCESmPMUO3/Y++94yy5qnPRtVPFkzrn6eme6enJSXGUkJAIQiBsMMkYExxx4AKX4Gdjkw0mJ4OxMSZcnrlEAZIQGJBQTpOnZ3qm83Q+3eecPqHC3rXD+6NPt2Y0ku33fgY/uPP91X26qnbtXXW6Vq31re8rFGfEO9+1TU9OUfTAgw1ef98wKZVnvLNnK+zMyBI+chTLk0PN/NiJFpRJG1UoENzWCtbzbs7jrs4vCiE2CyE6CcFF8/X//YTdimWBe8klK8hxphGjVE6d7QQpQZfLPm1vO0Nb207rOOoCQCXS1HSPKpfbSS5XTd74hl7F2IXcIgDAGHOMcWyMsVzXPR20tg6Sa6+db3nda18d29bOII5uQIQUKaVR1Nm5MX3VgeNoeXkFXXlFBD09RCnlO45TjuN4i946aNjU1IqeOpsBAKA7d1TxZz8zpnI5rpTqeGIaVlFK2dTU1DRBCFkUQjTbtl0SQjRalpW3bdsql8ubfN+vcM6rxhiqlIoqlUovIaTAGDtLKZ3DGBds2x63bfvfgiB4G0JoQmtt4jjuAQBwHGdMKdUvpfTqQpYdrusWASDK5XK6LW17CMy1CKHUysoKKKUorBLri7BayurDGBcQQkhrLRljuE4K1+VyucN13UmM8SzGOKaU5jHGjuM4kxhjQAhxrbVr2/ZJsrp+HsZYYYwbwjDcQgipaq19z/OOhWG4mTFWtSzLBgCBECKcc4kQwq7rFpIkccIw7HccZwYhlFFKpRBCc3Ec70IITQTzEuPPAAAgAElEQVRBsJUxdiZJkk0AQBljNaVUijFWrJO35zHGnpSyKZ1OD0VRtEVK6SVJktFapx3HmQUACMNwt2VZlm3bBWMM4py3r2WYGGOAMVac8+YkSbqFEOkkSRLOeZdt2zNBEGyJ47hdStnqed4ipXQZY5xXSrVJKT3XdRXGuGhZ1iLGOC+lbPM87wSsBnORUioXx3EfxjgEALxG8JdSWr7v3y2l7Dg3YKpbp4yuiZOei7okAjfGRI7jhEqpZkppEEVRez2IOgkAJd/3kdbappQuhWG4VWudTpKkmVKqlFIOY2xFa+1KKe9Ip9NTTx7nVwkXy3AX8X8cgvvvb0GEFL0DB9aVZaPHD6ZYb68O7r13B06l7OI//tOfx4ePvBS7rlzZu+dfSUtrV+3HP75Or6zQ6OAhKH7hi7BG1hWnT6+aRNq2cSrVM6OXXWmRdPoe0tT4qLNr55B7ySVH07c8rwoABgDQ2Zf/9ruS6ZnLQasMzmQeST3zhi+3vP1tR/871gIAAD9+0It2bB/RWneufyZElp8c6jJhROHEEKRfeOtxPjwMulZrtjb1l6JiMUWbWwj2vDC8596Mc/llJ5Kps4OmWGSovX0BEyLInt1l7HsJ9v319nj18CMWueIKmrvvgdMr1149GEVRl+d5x2q12m5mzFlkWag5na6Fn/0cV1NnEQCAmp1l0VvetgMAAF93zWj23e9qkNmsRK47EX7964O4pXmSNjVh9eY3QdjctEWtrHzQcZxTrusOUcYCnsst65WVZgAAe+dOHvzkJ1tNFIOzb99x55L9Z+KTpzZjjMG94orPdH3u7z9d+cHtHTiVKqRuuD6Zf/Nb/ghufm654DgXaMVQSo85jvPpdDp9ez3T0x0EwXUA8JZ6+eFtxphbEUKRUsrmnHcDAIgd25ui97yrGwAASxnX27hTAABK6xb65jedNAcPdzovefEYetaz4prr7vQIedz3/VOEkAYp5XIURdsRQgavus9nAQCCIOh3XfdoHMdb4zjWqVRqGABKlmVtTJKE12q1bcYYopQ6j38lhACt9RsZY3e5rnu7lHJTGIb76+fdQAgZwxiLIAj2pNPpkxjjxlQqpSmlSMaxkkC8JEksjHFo23YchmGfMWYnAIDjOAVjDDbGiCRJACFUlVJm64Hcca11NkmSDqXUeiDquu5KFEWb6z+fiqJov+/7R9PpdLZQKGBCyFxDQ0MSRVGiV4U8OxhjZcdxmNZa1ccyCKEsYyxIkkRKKZ36XK16BgYIIZgQkl/zzatnw4YAwHYcByOEChhjwRjzpZQtCCHLcZyjWusMQijUWjek0+kxAMilUqmGOI4jABhJkqTVGBNTSm3LsqqNjY3+8vLyvBBiT71NfzGO49CyrEIURaumylJaa2KihJCSECJjWVY5juP18litVmvWWlsAAOl0+jgAHDbGdADAQhRF21zXPYYQ8o0xsWVZsRACAAB83/+r3t7evx8bG7tTSnmdZVlBEAStAOsCmutIpVIjURR11AOppFwub+ecQzabHa9ns8BxnDP1MmqjlLIihGgjhLBzj4MQ4lLKDZzzJWNM1XXdnqf63/OrBGTMBTZRF3ER/yXI/+0Hnq9XyrX2D33wnv/O8wjDsCcIgt+jlF7BP/npCnC+Qy4uIntg4C+R63ZVvvXtN6lqpQUQ8lR+iQHG56kuo1xWE89fkHNznf/OMEA3bgxUPm+bMDzvJQTncjX/6qt+nszOHVHl8lXJ5OQN55myMgZWT88h7y/e9p3259/yt7AaVP3SUL3zhwfCS/Z/TErZYIyxKpXKRuf2O4+KT3xy79o2zv79Q6pYzDl7d4dibLyHdnQs6yCI5PyCZht7w/jEiR06v2Qh31ektWVGTkyup/at7dtOiZOn1nlHyLKMtW/fcPTOd/RrSm1vbHyMTJ1N0PETVjIz3SSGTma9yy47FT300AVcpcYvf3Ea9uyhYnqmEPzWS7eTjo6KqVURtLZg89GP5BNKN9q2XcIYU0LIvzY2Nr4x/t4PnhONjP5d9O1vbyS2Fcj5hSdI44yZ1E03/pT19f5d2zve8ZOnW6PR0dEvxXH86nM/8zzvX/r7+1937mcrKyvewsLCCSllH8BqG3cqlRqpVCq717axbTvknHsAAPXsAXIcJ+Kc47pydOD98Ed5uWN7OerqXOOG5LPZbBIEgWGMJZVKpQ8AAK0ava534TmOcwIhpIQQg5TSqWw2e0epVHqRlHKj53nHkyRJA0DNGOMkSbLuwed53n1NTU3P5pzzKIp+n3P+akqpDsPwWsdxxhhjSRiGPbZtLzDGIkqpY4yBMAwjz/P8Wq1WM8Zk63PLUEojYwxOkqS9rt8zhTHW6XRaBEHAMcatYRi2+b6/oLVeEEL0K6WyuVxuNIoiwTnfXp/PoTiO9yOEkqampnlKqRNFUUlKKaMo2uQ4zpkkSQYcxykrpbTrulG1Wm3I5XK1OI6DarW62bKs4wAAnPP9ruue5Jx327Y9prU2Wutttm2PJ0niUkrTYRg22bYdIISKURT1Oo5zSmvdIYRY53EhhFTdSBcRQpht22T1trbsIAhqde+29fs/nU6fTJIkqitbMyFEs9aa1MnYZzDGAUKIcc7760a0Q3Ec73AcZyJJkm6l1HmBiO/7w2EYbq2vzzhCqJokyTaM8RnGGKnVatvqGbHY9/23ZDKZ71qWJYvF4se11ttrtdrVlmWNoVVrITdJkkEAAB+hlRAgRwgZtSyrQin1hBCMc96bTqdHpZQSIWTiOG5XSrU0NzdPBUGg6zxDWu8+nE6lUnEYhq5SypFSZo0xLJ1Ov6S3t/dbT/f9+lXAxczSRfzCYPX1NVW+9Z1bAOCeX+a41R/9+LoyIS8g27fVoji+hlLaZtt2jufzuvzlr/RaAwOH+IkT+8P7H7gt99lPT1ha0dpHPvYE0/bJ9hSUReaclu+ng5ye9mlLy4oMw/MIsnplJVW9485bAOCWp9wxSYA/+2aidux8lVUsdkRRdJvjOA81NTX9lzh1hw89bKtKJZN+zrPPE4Zb+Mt3bNK12qfln//pPcHy8pWZ735vDHV1xl4qdQLv38dkOhXram31jXx0tNvasWM+fOiRNrW46IjxiQ7ACJlqDSfj4+Ds3z8f55c6cCZTI36qAF1djmlqSrN3/c0UMxr477waULTaDEM39uZNUEsBxpQqLeTb3t6lUula6oZnlMI7jmURAMilfCdpba2ofD7DtgyUaWPToiwsd8iJSYU7u7T6zneQ4RzT5qZaPDnZbSpV8KfOTrNtW2eJZZVrtdpW13Wn0um0KF13Da7u3T3g3vyc08lrf2/72vyR63Jnz57h2g/vuin13Oc8lH/3e0da3/nXT1kqYIzdliTJLVrrRkrpOCHkbsdxPvTk7XK5XFgoFL4JAH9ACJkxxui1rNEaHMeZUEplpZTddZ6Lb9t2yvM8KJfLZzjnXeqFL6gZrR0HobNxHG9IkqR1eXkZXNcd4pyvq4yfGyhZljXOOd9ujMGU0iLnfLBarcZJkmwEAAjDcMfa9gih2LKs00KIQQAArfWJbDYb1w/1TwDwT7VazZqdnc0rpZiU0mKMjYVhuLuxsfE0IcRLkgTiON6MEDoihNgLAIAxPpQkybYkSSCVSp2M49gDAJBS9gIAJEkyadu2rZRa8TyvGMfxFqVUh+d5Q5xzH2NMMX5CgSFJks2EkGVjDC0Wi02u6y4JIVowxgGldDYMw90AAOl0etEYE0ZRJC3L0lJKrJSy6nyaRoRQuT7PUGudMcYYhFB7PVjYYdv2UYQQz2azS1JKhDG2oygCjHFS17LKrmVhjDEkiqIdjuNMM8ZqAEAQQqJSqTgIIS2lrKTT6WmlVAUhZCOENEKovVardbmuO+F53nIQBD0AAJzzLWtzTaVSR+qkbl6/noIxVqlWq0317GOX53lHgyDYU1/rklKKSim3AwDzfT9XLpebEUKQJEljQ0PDwa6urq8DgCkUCpeFYXgNpdRFCK0kSeIyxrS9siLYI48dhZkZiU4PbzDvfx9IKTdLKdevgeu6pyqVygUvLtVqNcM5b6iv/wnGWJlz7pfL5QylFCdJ0pxOp48mSbKBEBI+1ffqVwkXg6WL+IVBLS2DKpWetp36F4GlD334zcE9975M/eVfuGppyaFnRsLKtq27CCFca207/f1iLXGjazXgX/u/Y7Kxr8m/6cYhMTnVlIyOtrtXHZhUhWUlTo9sAgAwy8u+ddllE9HS0r+bWUKOIwGh/ziqumBHBPK6652c59aUUjdzzq+VUj7W1NT0+/9f1mD+rW8/AAAOJIkbHT78DlUs7TJKBflcdphkMkOpZz3rK0aIqyt3/eh/OIODp8G2X8OiKJTHj6eiz35u/Xo5l15ykp8Y2mzi2MKNDaX44Ye3rmXETBAQ0tS4QrYMzAIAkI4OCQAdan4+C5RusHp751LvfZeQjY0e0tq4L/mtmfgrX+1mm/oXTcyrVn9/gMbGuQGjTWurMIn0VKW6HqUmo2NZ59JLR3E6LVR+KZOcGdmSes6zZyof/NBGhLFcz94hVASAbiQE8Pe+f4B+6Z/PslTKzWazM6lU6mcTExM/VEo1GgBqBFemXF4lxba1TpOGBhk9/PBqN9s9P/8rsbF3u/5M4+ezv/e6u13XfeJpAQC9vb23FYvFcUKIzmazJyqVSnutVrsEAEafvP4YY69uIdEAAJDJZCaTJJHGGGzb9qlKpbLFsqxl27aPIISI4zigtZb1tnslhAgTKTvrreNr3U5Ia42TJGm0bbvMOb/gumOMK2vB0FpnFJzT2XZuYFXPLPUwxkaSJBnAGI/U/4QW3/E3B/jw8KVJsfQq/+bnzK3c/Nxtq0uNwHXdIUppDgAgSZIgnU6f1Fo7GOOabduza5pAhJCo3r5/SmstXdfNhGHI65IBslarrWVGppRSvVEU7UilUmNJkiRxHA/atn2cc75LKZXxPO8YxtgPgmBTEAQ+AIDv+3NBEOxcv19Wgx7DOe9jjMWMsdk4jtstyyrGcdxu23aSTqePq7qSPKXUrdVqnbZtH8UYs3o5qyZWNbaiOI4zruuWtNZNcRx3+b5/3Kwa36Y45331Y5TDMGzLZrMcY2wTQorVanUHAMDa9SGEROl0eta27UhKWdBai1qtttmyrDml1HppihBSjeO417btOWNMl+d5xyuVyq5MJjPsOM48QshzXXcIY6zqYy9alrUchuGOtWNUq9W0bdtVSum81roGAHYQBJnFxcV/qmevfKWUqOtDzQEAwQ8+nMQf/sgeAADa1VUCALCEqAnLeqJ0vmqQbIzWCFaNnh/zPO+vcrncQ8vLyy8wxrw2DMOrHMeZZIwFYRhu9DxvqC530GuMiXO53AMX3LC/YrgYLF3ELwzh4493GiXZf7zlfx3s7ds/X/vJT98AH/+4bd3y/CUQnDFCiigMF5Hr5uizbqqoe+9bJzQGP/q3bayvb5l2d2fkzEyztWPHyfjosX7W0zO8flCMITp2bKc1MDAiRkYGnm5snMkEOgwJSqel1d8/psvl9mRyMvvvbF+hl+yfxFI1lPo3Gz+oZJRSOaWUpZR65fz8/MGOjo7P/WfmXfnB7TgeOvn88Oc/v5GfGv5TIyV5UoYspVdW2nRLy97az352wCSJRyy2TH/j1uFStfosgzHAH/5+Ee67f32H+PGD270brj+ilpY0GIPZgSuPxGdGNpvl5RQAAO3ZsMyPHNkBACCXlvPW1sEJMXy6jzU3z0QPPrjXfOe7Y9YNN2hz551u/JWvdrONG5d0rcYMQpvo/n3D4Zvfssm59NJj0dj4HgCAsFSK/ZtunDFKcex6QXj//Rt1pZJx9u8fM1Li2j0/7wPOwdT/b6HGxljOzKyLVbK3v/V0TMhmD+MFjLFdq9W+ppSK4zjehRGqwIc/1g0AAAgBbWsHfuxY39q+Jo6xGD79Yrjv/s0rN93YTgi5nxByNpVKfby1tXV6Zmbmas75uznn18/Pzy9ZlmWSJEkvLi6eSpLkQ93d3d8CAMi//wNtuFDYgV77am1WycVHhRApy7KOKaX64jjeAQCgtZ5MpVKvSqVSc6VS6XZK6bYkSSIpZWLb9vK5cglRFG1DCEWEkKKUssPFuJxZXBxLGhozwnWa6g8zWfdpAwAA13Un6xpET1vSNcZ4GOPq6pKg2tz/eNOO+MiRL4mx8UtBayAdHXPB9c9InXPMoG6rgYQQAQDYUkoaRVGf4zijURQN5nK5E5TSIc55U709vuL7fgYhhDzPQ1EURZZlua7rTmitS5RSBwCAMbYSRVF73fYEMMbrpdIwDHczxuYRQlG9OwuSJHHr24W5XG4miiIjhBgghJTS6bQAAOW67lmtdRNatX8R1Wp1l+M4RyilC5RSbdt2PpPJZJaWlvoYY2GdkiI455sdxzkdRdGg4zgLANAVBMGu+ng113WPAkBznbxdiOM4JoSkwzDsf/IaW5Y1jTF2qtVqRinlWZaVRwgtM8ZCABjzfR+UUtIYEwGAp5SyKaWllZWVXXV+U6tSKgiCoCeTyQxXq9W9lNJlSmltLZg+Z6ypKIp2I4RS6XT6/iiKegHgPsbYMgDsrdVqA5TSiuu6KwihQErJ+e6dLdZVB06JBx/apqsVO/ONb57gt9/ZRf/ln4Uk2AIAEEIMZr/5reN6bKxB3XRThZVKQ6mBgXu859+SbNiw4V+jKPpmuVz+BOf8RqVUWL8+QAhJMplMTQjx3lQqVX7y2vyq4WKwdBG/MGSef8tdwQMPtoePPd7uXXbpwi9pzKDwsY8PJQ898rzkoUfawbIA3vM+MACNgDGQW28t8snJnHvgyrnooYc7ncsuO2HiyBHDw2kTx1QMDW0HADBx1AGEAMnlKt611+Tjk6eqpLUFnLbW06ZWi0zMDfY8lszODtL2tjHQJkYEI5zNAhiDw3vv28U2bxqCJwvp1IE8L2Tve89isHVwNwBAppYXktIZjPG8Umq7bduni8XiJ2u12n7btqtxHG/AGE9allVMp9P7PM97pW3boviP/7Sz8r3v/7FczN8KCDw5O9f0VOMBAJDm5nnW02PHhw7tsy6//Ij69CcXV5T6bVMPqsj8QpleezVC2pR0pSKQ61k4lW4K775n/Q3YufSSw7qlmepKpZ2fPr3OedGlUqtpbT5r79lzIqmreMd//7lN8rvfW0G5DAYAwI0NheTQ5NbMJz42gZ9xndfgp5bE8CkGD6y+dLKNvUtAaQhamfCBB7boSsUBAOAnT/Y7e/ccgydlU7z9+0f46GjW2r51DjseQvff3+gjPJp0tINqbalJhLo45xkAAGdiclwcPLi3vg6xmBg/j3CKPFfRDT3TcPNzmZSyTUr5YgAASimbmJhYDsPwr23bPsUYG9Ja54QQyPO8mSRJMkKId5fL5e9ks1kdPfbYK6NHH7vBzS+dMu95p6W11pzzXnOOTx1CyHie956WlpaxfD5/iTGmVq1WsRCiRynF1gKCNRhjqDEmzRhbchznEPzzF1n45a9utX7nlSPmNb/bbFnWVJ275DLGioyxYhRFmyilM0KIjecey1paXnC+/Z28nJyydLXikZYWDxcKk1rp14dx9Go5M3vp+nkSTBBjKJvNzimlisaYnOM4slqtBhhjyjnXUso+hJAmhESu604DgBdFUbNt23OEkCUpJalnc3CpVGo3xlgIIZ1OpydXVlb2CyFi13VPrlrLJQ22bY9qrT0p5YZz+VhKqcZUKjVarVa3G2NIKpU6Ytv2KUKIVavVDKWUU0oLnueV6zYlLJVKNUVRNJdKpWr5fH4t47MNYyyiKJrFGCuttQQAsCxrCQCcOI6zAAD4iVogBwCwbXuMEFLDGDNYzdi0GmNYnYS9iTE2rbV2bNseNcZECCFmWZYBACuO45JlWQUhRBPG2HDOjVKqMUmSFozxchRFzZZllTKZTEUIASsrK1t935+K47h9aWkpp7VuZIwtVCqVQQBAUspmKWVznYPWTghZdF13vq5LdgfG+P6GhoavRVH0Hq31jQAQSSkFAAAhZCkMw00A0GhZVlG0tTV6jlMCANCVqhd+4Ys7rb17T3OC14nlzuTURHzHnR1qbr4ZfnYPoBf9ZrJ4y/O+sXDmDEUIWbZt+5TSrGVZrhCiSgip1Eu/kTHmVGtr65fg1wAXg6WL+IUh+9KXHM6+9CWHf9njqiDcYO/Zs8iPHm2DekcIAABoDdHBgz0gpUSMFdzLL1+KHn10DwAAYAzWwMAoEFJBjLLk7Nl+75nXj/FjJ5rChx7uVYuLLDlzBoBSIE1NVbW4mAYAwL6v+dHFrU95Hvl8H+vvn0vGx88r3+FstoL/9n35YGDzepbKaG0JIfpt256xbft4HMe7AQA457+PMV7nhFiWdbJWq81xznvtH/04VfjUZ+5TxaIPAEC7u59WxwR5nrQGBkR4+HAHAgD7BbfUIilvZIzdhRACzvmt5md3K/7wo13O3j1L/PiJ/WzzppPxY4/1OHv2zMfDwx3AOZiYI3FqeJe1Y/sSzJ6vkEAbGml8+Mg2wzkBAKBdXXO0vT2fzM12O7t3n9RxbAAAKm98c5/9zr85aa6/jiS7dxL46tfA3rVzBBhTwY9+vNW//hkndLm83uJs4hiJMyM97lUHJlVpJS1OnWoibW1JdPBgly6VnlDAPnoU0G3f72DPuG4hfttbctqYdc81jNGqlxtjhvX0jMSHDq27udOurjGyYYNM/uz1adHSsvHcOVUqlT9bX8NV09h1ojbnfMh1XVtrjYrF4leWlpZmrUxmFwCAnp314yjqPrf0tT4epTM9PT131Y//xjiOb60fXzqOM6+1Rk/epz5ev3f4yMnoq1/bDgCALAZaa7RmiAuwWs5BCEV1FesLOpDooUMLanLSlyOjLbpazcmR9bhsIwAAu/yyInDO1Nlpjl/2sgUJsEkpVYqiaLNt22fCMJRaa2yMabdtO5RSgm3bJ4Mg2JXJZIaDIPAtyyqFYbi1oaFh0RhTQghZcRxXAcDUfetKQRD0OI4zFcdxbxRF2wEAXNc9HQTBnro+01CSJOsNBpZllZRS2LKsACFUS5KkjXPe4brumBBicK3zq1KptGitj1uWlSsUCjVKaSPnXLqueySKor3GGJtSOmdZFhFCZIUQtWw2OwMAEIahRykFxtjRc2xlNlJKFzHGJAzDPQAAvu+PE0IWpZTdrusmjuNUOee2MWYWALgxpj1JkhxC6EQcx9sIIVXbtqeTJNnAGBtbs0RJkqQlDMPmdDp9qlqtbpNSriCEbNd1R6WUyrKsCcYYVkpxYww919bGtu1hKWWj7/sjQogWznkrAFQZY1/o6em5bWFh4Te01tcopZTWug0AYtd1h4wxGcuyyvW5NwIA4F27NPzs7vV7xBCiaBiWpedlqRCBecffpFU+3wwYg3PTjSO1P/qDPSDEHkLIPCGkJoQQhBCGEKKO45SEENO2bZ/VWn/L9/2vuq77/56a8P9DXAyWLuLXAoVPfeYatqkvHz3y6AbsWG4yNtZk7951iGRzLh8d7VTz81lr29YTrKtLxIeP9IT33rfr3P2x70WGoOZkeHgzAADp7BThv/100wUDSQnItisAkAYA0EHwtH5gulL1wICw9+w+hDu7tNq/19IdHZ7c1N8tCNn8VPsghJbjON577mfYmJxVqRbcnm5RrVYHAaC/xfP/tPajH48jyzoLANtW5+AXAeA8jhjd2LtodXevqEo1Izlvce+6Y96p1cSK6+5Oed7PCCFfSafT3w/+4R8fjE6f6SENDWUdhgoAgKTSKjEGVLVaAc47AABIawuz9+8b5ocOrweIqLkpMcsFFh873uxeffUJEwQV0pDzavfet1/OznYCAKhzO9AAgL/7Pdud0d8+yRwHCwAASqr80OH96d984bHwoYcu4IapYrExevChRveqK48CQJO1beswkpKGDz3cCKquAKEUGKUQGdwS6VUOyhO4+55GAAD38stPRA88cP61T6fL/KGH9uMTJyrec59zSN/6/DbwPEukUk16lWOzZk7abtv2EYxxRkrZkiTJhkwmU4miKBuG4W9ZljUsTp7qBQBQMzPtJAhr0nMztm1PI4Rw3R0eAUAZAGB6evpazvlLAQAcxxnCGOswDHchhBTGuKy1Pj8raQzAd28joDWw5908Fb3i5W3wpG5mpVR6rU3+vDkCgHPw0EmytEyjQ4cH2MaNs6y3dzyZm92ki6Usbm4q26/53anwuc/dZGs9ikql9rChYQ8AQBRFTYyx2bqB77gQYtBxnMW6TQoAgHRd94iUkrquW0uSBNLp9FCd2J7lnAdhGA4qpUg2m60Vi8WdAACWZS27rnu4vpbdWusAAEBrzZRSyHGcQwghG1Y1g1ylVKPWmiGEPEJIfUnMuvExYyyPEAqq1eou13UnjDE2xrgUBMEOSmmKEBJZlnU6juPdWuvZJEk8zvlAnTTfp7X2fN8fWiNQAwAIIXKe541IKRvq3mtICNHKGDvred40ABjOeex5XhYhVOGcq1QqxaWUc2EYmvq1na0riOfjOO6rzzHruu4ExrgUx3EvAECpVOrL5XJjhJBkTQk7jmOeyWRmkiSJ6jpQW+r3YxxF0VbxxMtgLpPJvLunp+e2+u9/SAhxtdaRbdsjxpgNarUm3en7/okkSXatBfLh8567033s8dPxgw8OAqXAuru77JXygqJsCv3sHiIY82l7O/E/9YlKIZsZALQay9u2XQrDcLvrulN1qQQmhIDOzs49vu+vy7L8uuBisHQRvw7AYnz8j5c//olXAiHr5N9kfGIANvUv2FsGzuquTqWVao8OHnJ0qZR78gGQ60m9Uln/Pqi5OevJ26xvS8l/uktNV6s5fvTYfsu2h8NnXPeUGag1uMdPDNMwRHLf3po8h2Bp3/FDrr72NV+94c8Leu+eDgKwwL/8lRcF997Xc57Egeued872tm1TYnGhyyhViMfH261/+SzaCusAACAASURBVMJQTYidNcuCbCp1ularHchkMu/OZrOJevWr3lS98YbbrQ99dBmisDX1gucfDx98aBAAANt2AACAs5lqdOzYFlMoMmvr4EkjVRbbNlelkmPd+oI5CALGjxzNelcdeEvX5z/3k/m/+puPJeNjt5hEcgCj46NHt5kgfOIcz4xEamFhwLnssuNieHgn7e0thg890qwW8hf4wa0heuzgLv/ZzzoU3H3PPkgSRHt6FhBCFVWpdOqVlRTKZLS+8srzrg9GqJo88GADAIAqFCgbHFxKTp9eF6pEjKxdq4z45rf2wzdXO5ytjb0LeP/+eUinNJRWELnxmVZ129a9AACu6x5NkmQP53wJY7zIGNOc8z3+loEhOTeX00Fg2f/8xVP4f74ps0YIJoQsEUIWGMLL0//zrf+z9vuve6MxxqqLKu4GAKCUzjHGImOM4Jynz81MpR4/eEqcPNUPAGBe8fKSNuY85WVCSMWyrLk4ji8IxN3P/sMxftv31rNiyeRkFwB0kZ6eYuqP/3gIX3eNj1tbM8D5JOe8RTQ0rAfdSql2pRTUg4WUbdsTtm1HCCFdV6ZGURTtQQhpAMCe550Ow7CroaFhuVQqOZzzLGNsCAB6isXiuhK7ZVkySZK0MSZj2/YZhBBOkiTSWrtaayyEWA9aMMYhxlgopdzV5XfPcM5zCKG07/tTWusVpVRGCNHHGJthjFWiKNrDGMsDABhjfMZY1RjTWW91zyGEBABQzvkOSmkAABMAEFuWNYox5gihhFLaEIZhn23bI7Ztl6rV6gCldCxJko5UKmWklAxjYlciAURrAQANSikdx3GIEMq6rjsWx/GAMYYQQmQ2m52N4xhxzk0URX0A0Od53hHbts9KKVkURc1JkrQAANi2PcoYk+VyeWv93ijYtn1MCLEFAC4ot0dR9GoAeNfSB/6uCwn+ef17r3sXIcQVQuAoiqwkSQbqnXKtuVxuUgiRMoUChXIlTIaG2umllyzRj3xoGRCChFIUBMFuesvNtZ5bn39ZkMm8jSfJ1U2U/gPnfLdSqjOKosH6fRcKIXSSJIvpdLpi2/a18EvugP5l4GKwdBG/DtAdn/jY7/DRUTc+dPhFQOkq1+iGZ0zzo8dT4dF7BwAA7N27J0Ap76kOoPL5tHvF5UPRwsKOp/o7EAL2rl2jJo4ChMnTBlJPe4Kzsz3YGL2WqXgq0EcfU9H//sYea/++0/6ll6ryb71ou3NmZLT2+X9s0SsrjepDH8mm3v+e43S5YImHH25P33LLRHDfvc16pbxaErSsGgCANbB5EueyK/z0yC5TqRCwLNvp7T1DRsdodMl+AABACFHf97/T1dX1MABArPUrcTarUDYTx/ffv4kfPNTDNm9eYG1t8yiVsgAAWO+GUX7sxD4AADF8ervV3z8lRkb6jBAIudfMqVf/blcD5wdbrn/GTwAA5Pj4bPjze9dbo63+vsmkUOjEfoqrubk09jzXKIXYdde08jNnKPH9s3xqaj2rhhobYxNFFkTR+poh30/iY8eaIUlWu9qy2cDZtxdXv/8DDwBAaw1mclKj1ha5RgR37rt/QkxO7V497+FtyPeENbhlCKfSik9MDOBcwwWZGAAAOTnVDpNT7QAAdMvAVPynr9+w9jfO+Wbbto/Vy12+TJKB7HdvG9YIrQs+qrHRlBBinUSulGpRSrXQkdEjwTe++RHvmqtPkt4Nx+LRscStVA7B1kGH5nKqGkW7AADO9R1LfeozJ+I77ty5lkmyR0dScdt5DjBACEkwxjXLsiY55+sBk//1bxzh3//BXgAAe9/eAj98ZP1Bq6anG2FqqqAaXkASzqXWGjDGed/3cZ0EbGGMi8aYds/zJOe8EkXRJs75Kc55QyaTGa1UKnscx5mL47gTACAIgkHGWLFarSrP8xaq1eo2pdRux3EOK6XWTZaFEMz3fcw5X7FtO11v+x+vk+DPS5lprT1K6RjUu/vWgkiM8YwQotMY00QpLbuuO6OUqgAAAwDwPC+dy+UWhRBBrVbDQoiebDZ7UinlpNNpP0kSIaWkYRgKIUTfudfLcZx5AMgrpXqNMSyKImrb9qiUsquxsVGEYVjTWgvHcZBLia7VRDaKonat9TGlVDel9CxCqFFrzRFCbhRFnVEUge/7i+eKcGqtrSiKthBC5s9V09ZaZ2q1Wqvv+0eNMXYYhlvrnX5hkiQp3/dngiDoBlgt4XqetzTzh69/Hz9+7Ibk7PRVue6eqeTZN90dRdG1dWVysCxrgnPeJ4Qw2cnJ4fDNb93kXXP1ovWM61bkC18ga/Vy/xo/UBuD5hD6aJvn/YEOwxcBwMNCiM+ey4VDCAXGmMOpVOr9zc3Nv9Iq3f8eLtqdXMSvDeRy4WbA2AXK0qpU8shLX1JBpVJNjo+v8g+U9EkqXdSVSuap9qft7UU5P39BVsO96sAp0DpJzpzpVcuFduz7BqQEI8R/OmjS1arF7n9gxjpyZJJOz8xa4xOL7PGD87ilWap0Oou1VvDhj6ZMFFlqfqFZz84SduDKAl1aDpM7f7j6kA5DIm+/ow3m5jHJZorBT3/a71x66Rns2AXS3LyEfN+yt26ZiY8e2yqnZ9qBc0w2bIh0fjHD9uyuVV74gvXAJY7jRsbYl3O53CP5fP7zWutrXNfFiesidcedjQAAulhMqaWlDpOIjHfgquNi+uwWU6muz5lt2VKWZ8/mrBufueS89S2CNjaEYcrfEMfxD9Pp9HL19ttfLMbGrwIAIB0dBXvzpooqlhTJpCr+s55Vio8c7sS+z5NXvHyOZTMBKhS0nJ1rxblc5Fx66TCSMmC9G5aw6xatbVvn5PRMm7Nv71gyfHq9PKry+UaE0FQyNdUJAICEQOpnd7e7u3cfVx3tLkJIm796RxsEwXq5BpKEqEKhVc7Pt9FcToABqlZWUhfoa50D7PsieeGt69k+Y4yltW5RSkkpZS8gBMhPVdXXvpY1UeQCANDLL5/UVx1oJhhzQAjbnI/4taAQ/dkbtiPOkTlxPB1//gtd5vY7OtUDD3bQ5z57PnScnVD3/DLGZAkhs87QyTz/9Ge2nVtyQ4zK5Oqr/bXfPc87rpRqVko1waoTPHFOnzlL3/v+Mi4UE9LSPGvv2L7ADx7qdy+/7DjOZrBaWvJXx9FlNDNb0QOb3VCpTVLKjiRJUkopO0mSZoRQ0bbt2srKymYhREtdBTxjWdY0QohYlnWWEBILIdazUVpr13XdhbpEQQMAgJSyI5PJDGGMA8uywjAMe40xs1rrOEkSZYwJKKUIIRQAgFFKPWFTAwCMsVkAyFJKNSEkDwB5zvlOpVRKa21lMpllz/NsY4zCGNNMJhMEQUArlQpBCJUAINfQ0FCQUsZCCIkQSlzXzSilysYYvdZdRggR2Wx2MoqiViFEI8ZYo9W0mi+l7MQYx3ULF1bnOGW01oExpogxXqCUNsKql15/kiRZ13XHCSFls2qKS23bnvV93xBCppMkabFt+yyltIwxVlLKZoDVsq9lWRXLsiIACJRSjlKqkRBSMcaIehNCxvO8o4SQeYxxFIXhNnLXXZ3i+NBeAADT3TlZHRx8LkbI0cYwAADHceZYPq/ZRz4+iScmlDgx1KkqFSZn5zrQ1LQrr7/OXrv/6ucROI7DhBC3aq0PUEpvwhgXGhoaZgghf97U1PSBjo6Ov21sbPyB7/u/8h1v/x4uZpYu4tcGzW/4s9eOf/vbX0pGRgeN5xl64EoP4hhH//YTANfV7vbt04BxVU5Pdz/NIcRTfYgcx8iZmXUeTTI52Wpt3z6qFhezqlBoeap9ngpycrIHJid74L4nJEfQd74beC9/2SHa2EDCYnG97KAWFhvRH/2JhzZvkuc+JEl7+5yze2dYvevHfQAA0f3n829Ic/M87excklNTa8a3Uochin/60+6mfXvHC1de0U8A5qzxiQXzpS+/uvDBv/0TmU4zjLHBGGNYLlwg4EPb24fDRx7ZaarV82QgcMqPAADoDdcXsO/5ca2WhonJ6fjTn/n0/NatrxcTEzvTv/mbZ/jwSaoKpXRw9z1bAQBUPg/izCgg1xVOT894/La/GGSDAwWwbeYcOHACEaKx5+FobGwLjNUHGxsD7/pnnIwfP3ihdIO5MMjhb337HuuKyxfoK142Fi/m9z/tNZmf952uzhFIkvO4Xsj3I9rdXaR7dpfk7l2IBIEGgPNSObZtD8VxvL7+8YaevvTznnc8+ur/agAAsNrbXfmq11A9P89IYwPIYmlQwhNPIsSsInacnAlDz3/5y+Yrra2rpTgpI0mpW281J7i5SbK9ewvk9X80iyYmnfDDH9mCd+4snXs+xhiVJMlaMJcGAKBhOAtR3BgPnewHALB37zpkhADS0ODjTOasGDrZAgCQnBjqT04MAetoP2ZufOZ6aU8p5TPGTmcymd8rl8s/TaVSY4QQQAhBFEXIdd0HarXabzU1NblBEDwOAOfdiwghwBgTz/OOa63Bsiy7UqnsAFjP2gBCyJJSelprhzHGgyDoMcZ4nuedFEJA3UIljxCqGGOQZVnTcRxvEULsPHcsQkgAq5502rKskuM4bpIkFkIoUEoNGmNmGWOlpaWlAc/zTnLOd3HOASE0IoRoQwiVcrncyMrKyoBlWZMrKytb6ueXMMYE57zFsqyibduzGOMVzjmqq5P7juNUjTEO53ygfi4xY2y0bjnSvVauYozNuq5biuN4k5SyuKqm4B0PguDSuhxBxvO8E2EY7tRau8YYL4qiZgBoBwBIpVJHkySxkiTZVr/mYIxpj+N4/d412VwEAODe9MzTaHHR8f7uw8tqdCxlv/fdJwznEj/2OMO+VwruvXcXvvKKaWvr4KyzbVtQvefnm8nb35o3AI3nrOmi7/vLjLEmhJCvtTYIoQYAUM3NzdfCL9lp4L8bFzNLF/FrheIXvvg6vbIymHvVK6fkHXdiREgEQbhMctlafPBQv47jHGYWN3G8niFBlmW8K684pcPQV/l8w7nHSz3rppPRo4/1mFWxunWopaVG8H2wN286AUojE4bnqTT/pyGEpQ4d7pAHDzXZ27cdo+1tnLa2lgGMo8tlR+fz5/GrnN27p4O7fjQInD91OU8px9m/dwzlGhhtbT1tZKJVsdRiopglR46CffjwJP3hXQo/drCJpDOWmJujzo7tCXbdQhAEVby87NhGL+DfeOGyeviRFveKy6d0Ij01Pb3+T9TZs/sUUCrAmFAt5lsNQtS66kAs/+hPXNLRXuVf/8YBOT9/q65UNvITJ3rYht4agNG6WDw/oyclkbNzbay5cUaVKy6xnQoy2la1WgaEaNRxHJq6AjTt7Z0HrQI5M9PxpBmDoYyRbLb05Iyh3dkxmtx7f6/d2ztsbds6T3LZPE6ni6S5qUqamwvIcRIAQ3UcN5taQJFtwxpZ3Ln1BcPinX/dEO/bu1F2dbWKvo0XiKtijBeUUu0AAE2uO4rf9JaqePxgo4kiz3nlK2arr/ztjfDi3wxsSifUgw+fl7G09u0bjz7+0Q3u5s0j8p57WvSHPrCiALLemZER+Mu/lqmNvfOwYUPFGMO46/ZYtzxvUjQ2ZEXfxpz7utechV27QsbYPMbYoZQKznk7nPPyS8NwBX3045E8c2b9xYDkckuqUGjT1Rq1Brcoa/PmQJw+vU4iZ1u2VJJdO8/jwjDGfmzb9kOO49yklAqllC7GOFUvzX3f9/1Aa/0dQgirZ9pyAAC+7w9FUdTtOI40xvAwDHdyztePTQhZUUplkyRpsiyrQgiphGG43awavRJCyELdw20pDMPBJEnapJQdvu/PA8C81to253Q7Njc3L2GMrXoGyFZK2RhjVqvVjG3bec55t5SyhVLKGWOltSyY1nrKtm0ppZRCCN8Yox3HkWv2Jp7nDTPGlgkhiwBQ45z3CCHalFLNjLFQKeVKKW2l1HopFyFEbNsuUUrLQoiOc+ZcTpKkt24DkiGElKMoGkQIGdu2i77v20KIhBCyqLVucF13hhBSoZQKhFCYJEkmSZJe3/dPMcbmGGMFIUTGGLNOLSDbt3NHqqXqG/5sC+3pmROf/FSfKZep+u5treoHt7fLg4da5ZGjTSAlYh0dE2zDBlJ78MFeUyxStmPHQtLd1WLb9jwhpGjbdsW27awQImCMEYRQxbKsB9Pp9AcZY9NP/j78uuNisHQRv1Yo/fMXf0evrAzww0dypKtzjj/wYKc8e7aL9fXdZu3cfjfxU53Qv1G7/9dfTLPnPHuJURaztrbF8N77tqp8voF0dES0tTXUpZIDhABpbl5Ixsef2gQyDC21mO/AmYwmmXReV2tPK0D5H0JKohbzHWoxn1P5fA4RwtnGviGrvz+iG3oAKF1Elg3Yc0tyfuEpVdFxNluyNm1ajB96eJuJ48geGAgQQglpaCAqn/dMHLtqfr5F5fNNKp9PW4ODDzS/7KXPz+3Z/UHf978gpfyRGhl9hfjhjzrJa19Dnd6Ns+Lhh3MmjqW9e+dZOb/QhhobOOvumUWExLSttTkZH097V16+LO68yyK+Ox9/5X/tBgAwYZgznHtgDMi5uSzt7JhW+aXWpzpvyGRs2twU81OnNqZe+EIFbW0l/uijTbS5eVEVCk0AAPa2rYX40OHBp9odYSzoxg0F1tmZx7lc3sTcBkaR1qZRTUxk5MJCB86k5vnR4/tUsdiiCsUGVSg06jhOOfv2zSNKK6yrcx45jsV6Nkyx3t5Zsn+fz4wp84aGCzKH9Rb/KUppQgiJEEK1WKkuev11ZwlAYh04sBK+/GXdBoAYY2xqYFr/+N86SFPjCvud3z5JXvziYvyqV242GOOks6Mld83Vp6GpyUu0TqWOHy9CcxPw666VxLIMT5I+ACAGocCyrJgxthhE0R6ttQMAZc55X/1B/USgFEUV9p73zchjx89rKEApH7zrrh1BG3pWyM3PJdF3v4PZpk1V2tpSIg2NCVxySTHp2/hkB/qc67rfBYCXhmHYAgAFpZQXhuEmjHGv53mFKIpay+Xy6+r2Hwuu61bqpr1OHMcZjPHCWiv+GizLOrv2mZQyI6VschznrG3b0/X28wbOebcxxtZau+fsmsRxvIUxNqeUajzn8xpjzBhjhNYa4jgGY0yilBKc8011gveSMSaKomjA87wFx3HmMcZepVLZbFlWMZPJ2BhjE8dxjhDCPc8bjaJoK0LIi+O4VynVtibK6TjOEsY4opTOr5XOAAA8zzultXZt265KKTEhZIExNocQSlNKo7V2fQAAQoi0LKuSJElaCNGSJInDGKsCgMAYx2EYbpFSNiqlmO/703VRznlCCJJS5mzbplEUndc5ynK56WDv7m2AECTZbJtj2afV4cPnUwukRPbevUUgmKliyTZSUuy6yt6yuUz37Cl7nqds26bGmFOU0iNSyi87jvM1y7I+19DQ8KX/EwMlgItluIv4NQNOp4cB4HlsU3+FtbSMsSsu/5ShdIPzwlsfXY6iD/qV6k9IR/tO1/O6VX6J1P7lS55RyrF37y4CwAIghMXISL/7zBtOJ2dGsvGxY9v/ozHVwkKG9PRIa9vWIXFq+KkJ4gBAmpsLyLYTOTv75AfSBdCVqi8qp/f5N94wFJ8e6VYzMw3AGACYJ8jIhABpbFwySWLZg4NTOgotfuzYVgAAw7mrSkUNlFAxNX8htwpjwI59X3FDz7bizMzfM8Y+0NbWdujsbd/7qpyf/wB+9NET4stf7VNLS2kAADkz0+Pd9MyT8aOP9UT33XcJAABcdeBx7/rrC/LU6RzJZpbjY8d77G3bxvipU5sAAOzdu4aw68r45MldIJIU6emuqOmZDAAAymSkd+UVk7pa5WJsfAPr6FjSywVIxscrZqXsIYywjuMnLBfKZYZcNzZR5CDPE6yzc1yVy2m1tNTFensn+WMH99KuzikVxe20qamMfb/Ah4bWFZURsy9Ukk8SiB586MmB8GrA++ijgDZvilKf+uQplU5loyjqRAj9P+y9aXhdWXku+K1xT2eWdDTLlizLs+Wya65KqCqGKiCMYYabQAghISTc3O57k06HcGkSCLkkdDqhCSHhJsyEBgIUUAwVqgpqLrtsy7JlWfMsnSOdeU9ruj90JFt2FTfP0z94Ou33n7TPWXvvtdfZ+9vf937vaxBCNc55YIzhjUbjSCKRmGaMxWEYxg1Kj8EbXg8IIaCUrjic6ziOHUDIsPf85nn8xjewRhTtLAkiBKZYkOrDf0ZsKdfrlUoXv/FEJDjvjuN4+5gJIWVjjBuG4aFkMnmBUmpJKZ3nsj1xH3pkrjF6/jBwDphgjVPpGmlrDXSpDNHYhS5DWAXdekuoiht7WEvLeHD6zAHAGOxnTmr8wrvFFr8FAEAp1SuEeGPT12yhXq8fAQBIp9Pj1Wr14Nra2n7OeTmTycxgjFviOC4TQihjrCGE2OJUPRe3ryedTo/HcUwopVEQBANRFHW3tLSYYrG4CwDAdd2zAICFENsBBmOsSgiZJoTEUspIa20BAPi+3wUAl2zbJo1Go8QYyzcajSTGuOg4zhJCKGyave5pnleMMY611uC67kgURZ2NRqPVdd1ZALBs216s1WqHCCHVKIqueQlCCC0ihGzYVGUAhJBxHOdsFEW9SqmEEEL7vn+wuQ1SqdR4o9G4MstURggtN810L2KMI4wxaTQah8xVchCMsalarbbfGENt216yLKter9e7EUILsFkKQ1ccF94y4gUAgP7dOz3ZHAcgCIAkEtJ/8skeEAKQ60jrda87V773JccylI7GcfwD13U/0dnZOQnXsY3rwdJ1/LtC8r6XfBhhnCA93SV4+UtFvH//b2CMUUTpaxhjHaSn+5WUUj+qVCKWSbvOa1610fibT7ZEy8tZuKJebzY2knJh4X8a1GxBzc/nFEDOPn58RMexUOvFFrW8ss3/oF1di8YYlw/0l1SxmDdRhJHnhSaKLJDyGhFC+9ixEZxJ43D0/B61srr5sBEC1FphO1iyjxwZEYVCJ+/tmQ2efHK7LRxxru0jh8cBAKFMGkyxuKM8hVzXsN6ekfQb3/Dx2HVvMo3GYWty8hK0t2PW2zuqGo0x/YMf9QEll0uPxoBcWPR1pbrd7cXa2nj9m9/a4qn0AgDg/fsWAADAskCuFVpMGFqJl75srPbP/3zQPnH8gnfrrSvx5JSvG42MXFy02LEb3OQrX1Er/9EfHwGlAIzhOJWuWXv3ToRnzmwHO/GFsW77phOn4/Njh9jgnmn7yBEmlld87w/+cyH81x8n44mJCHkJZhaXLLHZnr4jixWNjBxxXvbSMVkut4nHHn9elfMrgYWkgcX3miiiqVTqnNZaEEISTYJwJpvNXgIAVi6Xd9u2fZ4QEkVRdMQYw7TWKc55USmlaXcX9Y8cblFR1J5IJEbr9fqOgDr6x8+1y9nZ7bf/6KGHPSeOyvIDHyjHaFMsknPu1Gq1/ZzzqUaj0ZNOp9eu7KjacX1TSZT5yhcncTJJylJ1CTAp4gdr6p2/vtssLjIgpB1/4YtTmOB68OTTm7YqWkP4ox+1u+/6tVHa1gYx51hrnYuiqC0Mw2GlFBPNDsREIjG3VQKzbfsCpdThnHOllBZC7NZaX5JS9ti2fRYADCGk3XGcEQAgSimGMQ4AQMVxnAqCYA/AJp+Hc56o1WrZTCYzHUUR3pJTYIzNCSH6bNuexBjzRCLhSimpUmp8y4YEAAAhhDY2NgYAADDGF6WUvcaYlub8TQohuimlVSllKoqiPmPMBEKIRVG0a3PJWtNa68gYkxZC2AAAhJBlpVQKIWQopaWtwI1SyqIoym+R123bHvV9fxghZLLZ7AxjLIcQWhZCFBFCg9VqtS+RSEwHQdAjpUwyxqbCMDxu2/ZprbUTBMHRRCIxe3WglEqlRpsaTytRFPXEcZyjlDKEUBwEQZ/neeNhGHZyzieNMSnf9w85jnN2e06ODTuGEEBKARsaqtNcdk03Gn5w6tReaJqDGz+gqFZjtm0/gTF+e09Pz8V/y+/j/2+4Hixdx78bFD7633YHTz311cyf/enbYGY2VfiD//1B59N/u1RLJru01gnXss7HYejIT/+DJxYWiHz7r67XP/zR/Ykbj4/wffuWxdJSSjQ5HkbpdQD4mca5z4UtZWjS2Vl2br75dHjmzFETRdg+fkOtfv93uv3l5axz553jIAUDgJRYWqrJufkdpR6c8AIDxvIfenjoufZBcrl1ks1UZamUw5YVhaeePXrldvvGEyNibq5XLq+0IMsKrNtuW44vXszT/t01cfJUJv2G17/WPnjgHLYtlcvlHl3+yEf/pHbu3OcKUZzRUZgCgFbr8OHZcG1t0DpyeEMHocGcr6hKeUc6n6RSDmAMV3aRse4uDrfccl7VaxCPnj+IOAcxOYEAALDj6sbDj7SqtbUhAADS09Wgy8uz0fSUAqW6AADk8kqKYhxH584dubo7LXz65DHr6JFK4u67WXBuVAWPPjro3/cQAqXaSDbrE8/l5MbjE+Gp04NXf9fEMahjx8Lo7he47le/NiEff7xdjpxLwvMBY+D/6T9eipQ6mEqlxjHGFgBojDEwxpKuu0mliqLIN8agIAgOAQBYlvVsFEU3KKXcUqnUZ1nWhOzqMioI2gEAoihyLcs6Fcfxsa32d/62Ny/R4eF1MjNLg4/9Za9aWODxY09kmFZFaIov+r7fl06nL1BKWb1eLwgh6luK0ldDvOiFChcK0qTTSGxscAAEsefaJggZAgBQCuKRc5tBRTpdtd7yphn5yE9SEMWu/qfPSj18zA3uuWsQIRTncrmZcrl8U3PodQDYjTFueJ6X1VpPb2WalFKThBBkWdasEEIQQhbDMDza9GWLrmyJB9jMtiQSiYWtvymltSAIuimlVSFENQiC4Su2iVQqNY0QYgghI4SIlFLBVpADsBmsNPlOAADQ7E67cn81Y4yNEFoFgBRCKI7jeNBxnFOu6wZa6w6lVAshZBIA6mEYcOdi4QAAIABJREFUDjQziXZz/AnbthkAlLTWfjPTIxhjRYSQK4TYCuyNMQY3Go013/cPZDKZSrlcdowx4Pv+YCKRqGutL22ptEdRNGxZ1iKl1A/DsAVjLLTWrLmWFqrV6iGMsXJddyqKItBa2/V63d5SJG80GkOu655RSvVuccKCIDiayWTGa7VaN21rI/anPnkWffJvmZia7g/Gx6/xr8MtuQ3zpjdUUqnUW/L5/L/b1v//t7jOWbqOfzeofOWr74kmJu/Ts7OvI7fdZtX/8Z9eIL/7gGcNH12mo6OL7Ps/YupLX1bhd7+3GweB7RAiMGdFQNiy+vupmJlNuLffXhBLSxmcSiFVKHh87+AF0ta2iNPpmLS01K8hKT8PTL1uy7W1fPKl9501Skmkje/ecUdJFovJ+MKFdrmwmDVxjBFlBV2pbGe0aG/vFN+zpxqevLbry7n15jOktS2WtVpaLS7m7aNHC6yrsx4vL1/WHWprW0KcGRMENmlp0biry8Qf+RNt3vA6ql78ojjpeRPhY4/1GaVlNHaxZuI4DB577APBk0+9UhWLu3W53Mn6+ubCU6f2OCeOX4rOjXayXX1r0enTR3Sttk02J21tAqfTq9i2sWw0XNR8S5Wrq4T19hZAKow811aFok3aWueAW4A9tybGxy+b11ZrXExPt/GB/gAlEku6Wm01UUR0qZy4WpkaAIDt2jUOhJD6/ff36iAgfM/ABslmZ3A6XUCWVY0nJndhxymo9fXtoM75w98fI+/97VW0vKLEr77Nsj1vTR053IZuuVnQM2cD6+jwLPZcn2Sz64Bxyvj+JnHeGMCvfc2iyGQ6CCGxMcaHTcswQwghWuuYMeYghJjv+9tlGs55XUrZ4nneNABQrXWIEPKVUp0AAFrrrFKq0/O8ESFEOxUi4JZdk7atRWsrwLfvz5lajQAAoNe9dklxngfYDCYIISXGWMq2XQ5glDHGKKW8K6YIKKWFWIiBgPPWIAi2mxU0QNrdv++s+tGDO7hD7H2/O9Z4+UuH5b0vydB7X6zNieMkGtxDNEDaGEODIMhijBWltMoYazDGNjDGThzHVQAIm+XBDKW0EIbhbmPMUhiGJ7TWW/tGlNKJprp2Tmu9XbbinK/FcdwCAOA4TiGO4zattUUpXdkKrmzbXqKUhlJKnzHGjTExxhjK5XI/IUQppTjnfFJK2Q0AEaVU27Z9iRBitsjViUTCxxiXhBDtnPN5KWW767qjhBBjjAFCiBFCZJvGv52WZU1JKTs45xNxHPe7rjtCKaWO4yQAADW1tWQikdjSO1qO4xgzxnxCyFIYhrlmIIdt264xxtYwxiqTydTjOK7U6/W9QohO13UnCCHLnucxjHFVCJEghJRt255DCFmU0lUhRLsxBmcymbDRaKRgcxFGjDFsWdYMxniZMeb6vn/Zv9G2zyulSDMb2SXT6bQdx8X4oYe3TacBAJx77l7C7/+jjeRb3rzOBvpf0d7evgDX8by4Hixdx//nsf6pv2ut/PNX/1iVSi90bjrhqfWNjfjpZ+bF1NQtJoqYeuD7OTg3mkCErEdPPbXp+l6vY7Wx4UXnRltAKl8HgY5HR7t0GFRoe/sS3zPQMEpWEMZOPD6+T5dKaWtoqILTqaoqFNIAAMiy4Gdp81gH9o/6P/npUb2xkZKLi3lNKTZRpEzTINY0fMoHBhbkymXCtnXk0ELw+BMHnms82tZWjk6f3gO+zwAAdBgyuV7MWAP9Y4Ag0rVahrS0aOfmmxqsp9cJR86l6af+74UIoZ6mGasDewc98defOBqdG30Fbc/fgmz77fWHH7oZAFGIos2Aq7Wl4t5622r9wQeH7VtungtHRoauNrG1Dh+6GI6c20OymSJ74d1l5z2/VSDFIhEzs2mcyZTDk6cOm4Zv8f37L7K+nkjOzbeK8UvbN3SSbyubhm8DAIjZuYwql9vso0fOmFjoxIteuIhdt+G8+IXLfO/eenzhQhYAgHa0L8VjF/cgz9O8f/d4ePLUXrVWaNcbG3mDcILlsvMkl5NyebkNAABls1K/5zfXglTqsL77BbYCyGUymUBKSYQxGfjcF3B8/nyXWitk1Pp6ju/bt6hWVtLs+A01928/OQn5No0saymO426llI6iaHcQBHnXdYvNMlQkhKhHUbR9/Sil0rbttSiKOppdWS2U0pWtYGkLQoh2x3Gm+filavDrvzFEl1ewvucu0K96ZcNCqIB/7R0zZHCQY4znOeerSql2Smmdc+4qrYBzngKARhRFO7olGWMXpZTXdAwCAOCebmU++7ntbJr9treebrz6lccAIQCEQBJi8VRKC603rWEcZ4QxVmGMlbTWNaVUSxRFA3Ecp5olqA6EkMsYO++6bgohtEwp5XEc78iUKqVaCSHrnudVMMYLjLFCky9U2jpWz/OkMWbZdd11SinSWjeaMgF7pJQphFBYq9X6hRA+AGxgjFc555oQMqe1zkgpWwghJUop8X1/T7MrjwFsi1/WwjDMIYQoQqhCKYUgCPqUUi2c80YURZcV3RFCjLFFpVSX4zgbWuuGlJJJKUFr7cdxTIUQfZ7nlaMokhhj0ey2S9i2vbSV8UIIgVLKDsMwY4yJhBDLzeGx4zgThBAvCIK9lFIlpSxYlgVBEPQIIfLGGMQYqwkhWgEAtNZzUsp8c+58rXUjiqJBKWX3lV2Gzf3W4zjeJaW0mtlLSlpatfnRg9yE4XY1ibzvd5ejob3Ubs+/sKOjY6fR43Vcg+tluOv4uaL67fvzqlzucW48MaartSTr6iqz3p5rWas/a4z/5+t/bUR8C9+3vxacv1BiLa3T9fvv/+3tD2gNan3dw7ncPtLWGqlC0QIAEHPzBABALi52k0zmAgCAnF/YBQBAUulLYnJqR3bHxNGGWl7ptIaPXBST093WwQPL2vczyLIX5dLSHl2v2/oKLSJk2zvUwk2lnNLF4o7fHPKc7bf/5GtfM+8/8eROC4tcrmYdODCnSqUYCLUBAJBlmdx7f3tl/S/+stM+dmw0fPqZYevI4VNycWmXfWx4pfa97+92DhyosP3712POayDl9nghxgl7cM+0HL/UrxsNOzp7lphSxbEOHzofXRg74N5265iJYlL73vcOQxhC8OOHBhMve9mF8NSpLrCsmlpd7UG2HepKpR8C3yaplAi+9JW9zr33Tut0OsQdHUDz7ZvBYBQhMTExRFKpFVUoJHHCCwAT0NWq4955x1rt6/9y+UEvJSDOMT90cF0HQVLV61n77rsNjI9va1+J6ZmDzi23nKGdHYnav3xzGAAAJxKKD+39sXvbbZ9hA7vHi3/1N1/a+rz7yU9MhZxzAIAt0nKxWGyhQRjwH/7wnFRqR3cdbs+X2NEjAfrV/2DKjn0QjAGIIiCESMuy1rfUkkul0u6WlpZFpZQMgqDf87xRIYSLMS5prXO+7/dallUKgiAPAIAQupZc3lwS5pvfjAAAoocfbrF7umv6t95di9/x9jYhxB7wd3JzLcvCSikhpQxFHEe1Wq0bYNNkNo7jLMY40Fo/n4YYREp1pX7zXafRxFRK7t1bq7/qFce2fL4uH5CZQQi1WZbVCIJgOJVKTURRlFVK2U1xRrNVPjTGAMZ4TAjRVq/XVRRFhx3HuYbvYlnWGKXUrVarXQAglFIOxnjdcRzOGFvBGNeEECaKoqEtwno6nR5rzjeyLGtcStnhed6olDJRr9ePAwCkUqkLTZsVSCaTC0qpCmOsgRBq11pbruue11rHGGOi1GWrMtu214UQBCEkCSG1LZ+5Jq/KjuO4gxCyopRKSClX4zgeTiaTZ5tlsHYpZRYAIIqikFIqwjDsSafT00II0Fq7zf1ijDEPgqDfGIOaopLtAACe550Pw/AQxrhGKQ0xxssIIVdrvZ1ObRLXt+YbGWMkpXQNAOiWHYrjOM8GQXDDlXN9Jbn7yjKkn8t2Jn7nvaPhh/5kmy9nSaF5KvWRjo6O6xmlfwOuB0vX8XNF8ORT7yh99nN/RtvaCqpUasOpZMnat+/D7q23PsT6+yfTr35l6Wd9f+m9v/uG6MKFNwFCYP/CnRfYS++1SG9PZ8LoCbG46IYj57rZrr4y5jyKzo502sePj6hC8cjV44jFxQGUyyqzUSLIcSK5sbFdGuMH9q8ZpV2cyTAdjbfibG7BOuisBE89tRVMtSHPA+e2W6tycakESmkxO5vnewdNePLU9j5oPj8uZ2Z3dNepUtnFyYRPOjqmVHFdqNXVXsAYaE/PIhiDaE93zX/00R2EYBNFqPqNb9gAAPHUVI81fHRJbpQG7RtPnFGFAlh7BuZJV+ccrK49lcjl/sb3/a9KKRPpdDolhBDxO97eSM7MLJY/+/mDyLIqfP/+ETE9M8j7d1eQ47T5Tz9Dwfcxsm3NBwdGgVGb9e8uBY8/sTv9px+6CMUiM6urmqQzdf+RnxxL3HvvePU//34vWBbCGFWDp5++XGqTEqPWljI/dKhq7Ruy6w891Ove9YIL/jOn+mlHR83EscCZTIn1dIf+T356FIwBlEpp0BpHn/r0om7UE/bNN63ItUJCzswkjBTcf+Zkm/eaV53mewYT8S03I+66z+RvOPYlAIDgiSff0QjDT6m1tUONN71lyGAM3qtesUhf+cpavbenFxtTRUJY8otf7tXl8mYwSynYb3vL6dqb3ngM8LXyVUopKqXcftpqrVmhUNi99TchJI7j+JDneVEYhrsBAMIwbEmlUrPVanWXMca7ZlAAUI1Gp3rsCQoAQLu7N+QbX58hCIVXustvwbKs+TiOVRzHjDFWbjQau5PJ5AUhBMRxvNt13QmlFNnyoXsuJKamp4LPfuEw+9AHJ/0jh4ebwdWVbfkgpcw7jrOulKKO45wH2BRm9DyvGsdxsiniOAQA4HneLEKIhmHYK5sBeRAE+1Kp1IVqtXoAACCRSJxvNBpDURTR5ncuNRqNA1rrlqZyNo+iaG8URZDNZudqtVrCtu3larU6SCktN+ff9TyvwBhzqtVqBSEkAUCFYbjFJzpTq9WGAaCHUhpgjBtKKa/RaGz/1hKJxIht289qrVONRmPLyHeCc07q9XqH4zgX4jjucBynaNu2klK2AABgjCsAAL7vDyGEYqVUCmOsEEJx89yHHMc5LYTAjUZjB3cwlUqNE0LWpZStlNIljPF6HMdHmuaz533fP0gpna1UKvubyt5lhJDeCkaFEB2JRGIUIWRLKRkhxCilfGg2ooRhOGxZ1uqVmc2ry7JbsGfnpsU/fXa7uxRxrsjufpTK5f7h+dbLdezE9TLcdfxcsf43n/jf5Nz8kK7XPZASTMN3xNzcS6JL46+PJibeFD79zDPJ++5dfK7vrv7R+w/Wvv2db5oosvi+fTONR36y173t1jrq7uH63AhEz5xyaUtuXS4t5UkmU2VdXbVwZqaP5rJLRun0VjcIyeV8zBl4d9wxi7OZJQDIycnJbW4SaWudltPTPWJisi3xSy+fdA4csKpf+9pOs1IhQMzOWWptzVPr6wnW07MePPFkj3PnHdNybi4LhAAYDbpW23EzQ4lEhBOJQExMDojZ2U7r8OFp2t1VFWMXe60TN8yKCxf3gFIU1E4Tb9PwncSLXzQGjNWjM2f7TbVqkVxLFF4Y2ydn53L2/n2fz/7aO/4cHn+iD/bt280Y208p9aSUikURk48+mrBe/rKV6MF/7eYHDmzEFy92q6VlBxCKeE/3MuvsKLt33L7ij5wdik+dzhuMSPK++yYan/tCfzx6HmhHR0NMTyd0qZyJJydb7MOHxiAKK7pcaaO9PXW1suLS/fvG8d99EowQ5fhb3z6EOZ+X0zOdYma2zVQqlPb1TmDOlZic2iNm5y53r0URgjgGubjYxvp618TMrIs5D5ybTkyGY+N79cqKB7//X1biG48n8Re/rBwp3+0cv6EOAJB86X3z/k8fDcT0zKuQZUm+e/cUlEpS/eSnKfOFL2Xsu16wEGTSfc56cRJ839BXvXIc/af/qPzbbh26OstyJTjnta0H6NWwbbsax3ErpVQopSysNSTmFyYo4zFaW1ui938nwI7jy2w2t+N7Tz19ST7w/Q4AALZnoBDcd2+7EKKVMbagtd7BjdNap6IoymutExhj2eyQ0mEYDhpjLErpXBiGz1m+tWbnZuxv/Mts9LG/OIAQ0vzI4Tly6GCVEGIcx1m4smymtU7GcdwupWxzXXddCMGllCoIgj1SygQhpLLl72aMcYQQGdu2Jwgh85RSyTlfCcOwlzE2pZRq1VorY0wSmu3tlFKplOIAQKWUWcuyxowxLqVURVGUyufzoRCCRFGUc113njG2RilFhBDc9GfrbFqpzEVRNAAAgBBKYIzLWmuPMbYghMg3r9ma53lFjPES5xzbtm1ZloV8388BACilcpzzWhRFrVLKtqYW06oxptEsoaZs2y4opZJKKceyrFkASGCMheM4NQDwAEA4jiOCIEgbY5KMsTJCSDqOc7Ferx8khCDXdUeaDQJESpkDAGCMrSqlbMuy1kVTPV4pZTuOs+o4zhRjbCkMw/44jvMAMNPsSEw0+UylpjzFtG3bDaWU0lonbdse3Qpkt9fYysoC/8hHl9UXvtitC4VWAADSnl93/5ffm/UPHzrd3t7+1edd9NexA9czS9fxcwXCOPdc/1frG1na0S4ajzzy9bUPfujF+Q+8//yV2wsf/fNdtQd+8AVdq6UAAGguV8bp1Ejt//yrHuuOO1YIxrZaX+9Q6+sAABCdG+0BALBvuukMdp1UdGGsQro6K/aJ4350btSL5xd6a9/81rVWGpvAJo6BHzp4jmYyKbG8DLitDXShcNVBXw5oSEf7CvK8mS2fMNKe9wFhAleBJLz1aOTcduAVjYxcNl5dK3JVrVru7bct+A89vKO8YuIY4rk5JWbn+kk2G6pSyY4nJnpYb09NXJpIVr/+jfdHExMvBoQH3aNH66KjPTTGEEop0f27NXK9IrnxRMq99yVnaSpJtwLH+MJYDnmebR3YP175568ec26+aYTeeWciOHnKq37lnw/Zx48t0u7ejfq3vn0k+ZrXjJNsbiQ8efJI8NjjRwAAUm9844XqV75ywH7xi8b0G15n+Z7X4h06tIjSKaNr9W29HWt4eEVtbKSw53HS0xOphYUdbfD80MFVhHERDADr7VvQ1WrSf+yJYdNoINLfH/KpyUT8sb+woK3Nr01N/17une/4/a3v2r/4C7Fa3zhtpMxGo6Pbc8tfdt+i39baZ5Ur51RLK8UDA8XwrW++QaOfESVtzfdzEM4JISuc87IQIk8pXRJCpDjnl8gffcANfvKTfcZxAAUBCABAX3RF6tOfOh11d+fiOO4GAMB332XxY8cm49On9+hqzWNhWBGOkwYA27Ksc1EUHb5i/wghZJLJ5MVqtbofYNO3a2u7lLKbEFJVSqWsUnlVZdItpOFXyaf/fl78+KEDUTPjZaSk6uQpErzwnv0AAEKItG3bI1rrpNaaKKXaPc8bM8ZQrTVXSllSyu21Ryn1AWAcY6yUUq7WOt/0KjtMKZ0Nw3DIGIMwxplkMnk6juM98gppjDAMWwkhq1tjxnG81/O8RaWUCoKg3/d9vSVRUK/Xt4M/hFBsWVYZY4yUUhBFUUc6nb6glMJbQpRSyryUMteUcQDf9/e4rhshhMAYIxFCNkIIp1KpiUajsUspxRBCO95CwjAcpJTOSSlzruuewxgby7JmfN8/GEVRnlJaAQDm+34rQijinAfVanWX1polEokLAADNTrcUAIBSKhBC5KIo2mVZ1mw2m50Lw1ASQhQhJKSUsua1jG3bHguC4IgxpiORSIwkk8kpKSXBGLcjhOYAoK61RlLKPsdx5huNxuE43qxS27a9ZIzZoWflPnv6vPjgh/ql72+a7XKu3Ve/aoL+zm9zbdvMRaj4P1v313EZ14Ol6/i5QpUra8+5gRDAydRaNDJ6uPS5z5+qP/iv3wWCy3xgoBZdGDusNjZu09XqdglBRyEKTzbb9o3epQob08+5v9JGV3RuyTNB4IKUduPhR4xaWX1O1/nLX1KG7993UczMHiqPnn/OBytKJgzvH5jR9XpaTE3lwief2n7QkXxbyAcGJrWUhmQyBjirgpAhzbfKeHL6OYNFAABEsA9xDKpUqj/Xdta3iwC3AuP7Dtu7d8yEgRPPzW/LHahSudX7pZc1TLGISXcXBQAi6vVYrG+EcGEsit76K/tAyk7r6JEdmTt7+OhU8NjjxwAAxNKyRzo66ojSViAEVKmiw5HRI6A1ROfO+WJh4ahz++1ndKMBqlzqgKQH/MiRFfxf/ldeN6YfAKCRbzvqfO2rBf077+ty77lrVW2UMOvuKdJ83mo8+GDvlSR55HnK2jc0omt1L7p06RAQAu7dd43J9XXXNBoIAIDlsmPyG9/K6rUC8kfP76NdXW8HgN9fX1/PxXF8j375y96Bxy66/pe/soP/FX/3gW588lmlVlcPbz0h+VvfMh329T5v6WoLxpg6AABGKNDGOJzzMaVUZxAEV5fMWukNxzbgJz8BFASXz4syoycmWdTa2uc4zkVCCK7V63vd/+vjE4mp6fGwq8uyPNe3MV6o1WqHGGOW67pn4zhulVJ2AQA4jjMWhiFrdofZV1psCCHaUqnUeLVSSaH/+sE6XlpO6zjOqCC4dn3ddON20K6U8jDGg1JKhzG2aNv2OOd8Lo7j/ubDXEgpt9vZ4zjuk1KmPM+7EIbh1vyaZmnMGGMQAICUsj2OYz+KoqTneZOMsVSlUmk1xjDOud4q21mWteT7freUMgEAgDEuYIxxLpcbL5VKQ1tBqjGGCyHSnPNly7LmlFIZIQT4vr+vOc4p13WntNbasiyoVqt7jTGsUqn0Wpb1IOd8jRBydxzHge/7rUqpzSaJBx4IvZtvvtBg7EBC66o+dXpRHz9GlTGuorSHEFI3xhQzmcwlYwyp1+u7KaUNjHHJGKN938+n0+mxOI5x03R4PgzD467rrjaPqxgEwVa2RwRBUGvKPwwnk8mL9Xp9X1P2ILOlK9W8LjQIggEAANu2Tyul8o7j6Gq1ephSWmw0GjsySGEYdiWTyZktzhc2xsDXvgHG9zfvkQiB9aY3jFT/w9uOOAhNYSEMpfTh51/x13E1rgdL1/HzBDJxfAMAAN+/b5q25+vhs2d24Wy2SNvzfvDY44cBAIzvW/HExGsAAMCYSTEzs+fqgbDjgHffvRcaD3z/QP17389ahw4aAADS0xMBAFaFAgMpgba2FMTEZBsAAE4ma+IKMUCUzWpTqeCrO9yQl0iClL599Oil4PHHh0hbm1SFwvZvx737rvPx2MW+6OzZfrqrbw4Qyu1ofRcSx5cuHfJuv2Oq+o1vtABACwAAOn7Ds2JmZkc77w4IqZvnfI3BL+3u3gjPnu1XS0ubZHWATWsLxiDx0vvG5PpGG+KMxafP+tGXvtzt3HV3WbS1rrFbbvEa7//jAau7u+jecftF7DoWct2qKpUjZPGAtLRoYGz74OXs7ECjUAh5f3/Bu/uucnhu1CItLXVkW0YuLu41jQYOHntsmA0frctPfwp8266Z1/0y0ZYFEIaXT0UpztKZyH/kp+0AAPGFsRb37rtGQGtgL/jFVdyaC00QUfHoo3lZXG+Tc3PdAACkpaUASvlydnY/AADbt68gl5dzcmm5b2tsXatlF975rs/HC4sHyEvv5ehlL3NR/y5/S/H7ynkzq6s7s3uMPe89kGJcQggpu9HYEJbleZ/9/DQM7qmre+5WURQd3eKWbIEQsgYAzFAitvfnOGDfeeckz6TjqKvTBdjk9RBCqrZtP+uH4Q1iV18RYyzwZnCCGWMCAKwoig46jjNOCCkLIYAQ0oIQKjqOsy6EqNRqtd1X7r9arQ4xQsp0//5GeGHseV8ATC67g3AuhHAAACilVUIIAoBDvu/3OY4zFUVRe7O0VInjuIdSOkcI6bzy3Bljp8mmIuf2NXEcZ9T3/cNNwnXUaDTYViCFEKqkUqmxWq22jxBiMcZKGOONRCIR+L6vOedIa20TQqpSytQV+5kPguByppDzVDKZXKnVah3JZDJTq9VshFBZCCFMk9BvjAEhxBGE0FStVhPGGAdjvA4AGQAAc+GiQw8drlmJRBn/5cf9+OSp3YhSnfj1d464b3x9otFoWJZl2VLKbL1eb0UIaYRQwXVdHMdxpJRqNcaoMAz3wSYZHQAAlFILANAOADZjrEEpXYiiqENrPYgQMq7rXorj2DabKcOWMAx3BN2U0mwzIwZNQnkcx7EA2CyVcs7HjDFpQoiWUnZIKYnv+zlCyLpSqsX+1v1n4qeeOrY1nv3GN5ypv+2tw9DUe7Jt+5LneVelxq/jZ+E6Z+k6fm6ofP0bGf+nj75Pl8sJe3h4KXjqmf04mw1oMlGgLS1AuzpX5fz8Dl0Yks8vq2Jxhzoz3dVXVrUaxhg35OJSB0hJAEA4d9x+Ua2tKl0qZWh7+7R99PBk8MRTw1uBjK5WXfcX7hyR9VrOPX7ioi4WU/bBgxvAqNKVynZZSK2spNXaWqsqlRy+d+8U6+0N+d7BgpiezgFCgDxv2z9OVypp5/bb5uX8wmWD0j17Znhv71r9+9/f2X2VTEW0u3vGiBhMcNnaAxgD2t1VYF2dgZibb6cdHRtqdbUNCAHa1VXStZoDRkPqdb+8CNxa4X/8/o3VP/ivQXagZ8W+9ebV4Jvf7iWJxALr7IgaP3rwkAkjS05NJQiYgnjqqVhdutQjl5ZSYna2le3eLes/+GG/feOJAiIEh08+dRARIq29gwU+OLgsZmfzIARVhUKatrbOkLa8H1+61Gvt2zcvpqa2b/B6dZWzF71wVCQSu61Eoqq1ZsYY2xhDAAC0MTa84BcrnNIFdfJUG21vLwKhtjXQP4tPnAgbb35Tv7n9Nk2++wDhu3cvIYJ9+8jhJbEwvwspFbGBgQV+6HCkZmfdK6UWAABMHBOcTNjR+QuH5U9+mocusb98AAAgAElEQVSHH0bht7/TZ584PqaKxSwodU35c2ue4d3vctSmWz0gjMuE0GVkDHh/9/cb4gMfzMNn/tElQRjzfL4efOhP9+nTZ1rRq18VYM5XtkxfHce5YFnWRiKRUL7vd5jBPdrq7Fwgv/ori+a3fwvpW28W4Yf/bJAtLBbJS+9b5JyvwuaLaqSUak+n0yXLsrTjOKxYLO5mjAkhhO153hRjjFiW5RFCQClVU0olmsFHG2Ns6kqxR4xxiAkp0fHxhnz29HPKBwAA0OPHl8VA/zX+gkKINqUU0VrXMMbCGLNl/OoZY2xCiB/H8W6llEsIWdrydnMc56Rt249prY0QYsi27QXOuRJCeIyxqTiOB9LpdDEIgixCyGitNUJIYIxFHMdtcRznmqa61BjTEgRBpzGmQghZufL8KKULV3rMKaUsSqnBGC9IKSthGA5JKdsIIZbjOHMIobpSKmOM8YwxG1prHgRBjx3HJf75L9bspeWN6B8+00+SyZp3z91KEdKIv/PdDlOvc/oLtxfxgQOMMaYrlUpHFEUZzvks57yGEMpgjBHG2ACA8TxPI4QWKKWlIAh2NY91RSnVKaVMAwBIKVua3KwaY2wqCIJ9SqkU57zBGFtotvlvZ8qNMVXHcdYAoCaE2KW1zlBK16SUHcYYqpRqRQjpVCq1LoTINcuRlmVZM1LKNnb/d1YwIGTiiFnvfMc5/y1v3g6cmmulnEqlVm3bfvT51sl17MT1YOk6fm5Y/+tP/CEgtJvm82uqWEyqlZWcqVYd99ab/cajj+0j6cyaXF6+fFNnDBBlXNdqm1YE+XyND/SPysWlvC4U2kh7+4paXukAACCdnb5a37Dk7FwvCIF0vZ4Fyoy+yvpDrqy0svb2ufDM2SETBJS0tCyI8UvdgDFcI4woBNNhmEKUrERnR4bsG24YZX09q9Gp0zu663SpTEhX5zLNt6+R1tb1+NKlvbSzaxnZVkJvbGzzCtT6ekatrnawvXunrQMHVtnAQGANH1vAuYyKz422i7nNQFEL4Vl7986TbMaI2bmcfeOJUbm80g4GtGaMRK99tdvqcp9mMqAPHsDRP32uS83N5dmuXavY8xZJa+sSyaSVrtXTulyyTP2y4a+YmnZBaxBTUy0knZ5ThUKnLpVScnEph3PZBbWy0gmUAh8YGDdCJqNTp4ZAa0CcGdBG2q9+VZHeeGLaLC467MbjjbCtrZtSWoiiKI0xjppt2mkAAGxMmadSDVvIEiBUis6fHxRTU+3yiSfb2Gtfc14wtou98pdmmOvi8OvfGBRzc20Qx0htlLJyabmddXbUkOv63otftI5tex6MsXSt5gAApF7/ywIIrci5uZQul20AALm4mLcOHphGjuPrej119fUk+XxdvvY1jgEAwq1LKzyLvdNnKs5Xv6Kjr3+jDzU5aMb3ufjW/e0QhhjCEFsJbyMYGtqPMY4cxxnXWqcQQtQYE0dR1KYBkvTQoSLKt2W1MbYwpiuxf/95PTHhoNZWHHPWw5PJFQBINIna9SAIvDiOo0QiUQUAJ45jnkwmV5VSCAAoxpgyxiwAqMGmH1hNCLGHMTZrjOEIIZ9zXhNC9LC5uVWYmKSktXVDVyo71jtOJqsslSrpw4dcvTneDmitXSllq9Y6YYxJX5GhYVprFwAAIaQJIbJpAxInk8kGIeSnURTdgTEux3G8FyFU4pyvNTWI+gghG019o4U4jvcIIVo456txHOebtjAuQkg3S4uIMTbFGHPiOG5ljC0zxkqEEME5X3Vdt8wYKzTNZy2lVA4hpLZI8ZzzlSiKejnnRcuyyo7jbBBCcOT7ewAA0TMja/FffnxQPvFEFgAAd3SoxonjbtTa2pUaHp4WDz+Sxel0bN9zD1FKRb7vdwIAKKUyUsq0UooKIVzLsoq+7/ekUqkGAPBqtTpICKlZlnUeY8wZY5VmkESg6SFnWdZUFEX7YZPwjpRSVhzHHbZtX3Rdt4wxXuScV+M43hXHcc7zvBWE0KoxJtnUn/IYYzOc8zXbttdt204ppWYBIDLGUCFEt+u6Z/nKCpXvemeS/+qvzDQO7B92XXc6nU4bv1mW45wXgiBYyuVyP7h6DVzHc+N6sHQdPxdUv30/Dp56+reCRx+9Sy4v59X6+rbmTjx+KQtxjEg+X1arq5ctNhAC765fnIgvjuedW289rwqFhJid7QchOAAAy7cvy9XVTgAA02gw981vmifve5+fOnHcj558yqG53LQqFDbfuBkD68jhZ42UWC4s9AJCQPt616yB/iKAiVj/7oJcXr6m+8k+fsNcdPrMECiF5NJSXi4sXvOGbuKY63Ilo9bXW9T6egtoDXJ+vp2258evzooBAOhKNU/SaRU89ngfzbeumjCqy6Wl7Tdq+8iR1ejUqV2qWHTAGDDGJJ1bb7kQPPzIgJqbS7O5uYZOp4r81Ckn+NOPdJq1AgcAQIRW4/MXDqtCoUsV19Ost3dSTE49p4UKAIB3110hWBzUyooDAGAdPrSMHHeZD/QXo9HRg9poF/xgMwuTTPl8eDhi731Pmd9zdw5eel9d9fW5jPMV3/f3GmMcx3Hm4zj2CCGB1tqzVtdmwne/Z1hcvNgql5c7rvTEsyze0MePA3WchujoSNiF9WU5MdGKM5kq3zMwSru7G2xokMrlFe3/4IcDcnGxw7nxxLp16OCimJ9vUeWKiE6evOZa6HrdZffcM4eqVVtXKjtKU/RFL7wgf+EFq6GVWFtDTmtFoY719u58dnJ8Dp0907FVjjWVKoUw3C47yWdO5tjrX3dBENIjhOhQSqUxxrJJsF3VWueklG1CiBQhpKS1Tpp8W4M+/jh3AdnywH6Lum4tjuMWrbUjpUxqrW2EUBljXLYsKwSAMI5jbVkWhstGqZoxxpVSglKaMMZUMMasSWqeCMNwLwCA2LOnwyoUxsVTTw9Zx46WeX//pCoW82AMWAf2Xwwff2LYBjQWDx99Xu9DSmmtKWT6XGVKZNv2rBCi3bKs7+dyuVdmMplnAOCHxhgRhuG9hBAfIRQghJJNUcmiEKJDCLEtTMo5LzHGVgkhoVIqs0k3sopaaxs2vePSjuPMEUJqYRgOCSHyQoh2SqnPGGNhGGYIIQI2ldU3GGOSMTaFMU5EUZS1bbuYTCYThBDb1Oux/vV3G5ZKTbCzZxnNZKpyfmFT48z3bfuuu6a45wb6M/9IxKWJtLo4njJ33jkjk0m0pWvUPOaLTUX2XBRFLVnXmw9XV+1KHHchhCLGWCGKov1SyjznvLLlHQcA0MwY5rfKkVdi01pPVYIgOMYYW9rqkLNtuySlzEkpM0KIFMZYSylbpJStCKG1OIp8vZnhDCzLKjPGkBAi5w8O9knLcrFtrzfXYnYrUGpe30Wl1GBra+vfPt8auI6duB4sXcfPBSSXc0qf+e8fVhsbmef7DPa8onXwQBmnU65a36D2seGL/iM/PUi7u4picqpH1+vboo+kpSUCAE+Xy5tvTkN7x/zvPXAYxi7aGtASajSWVbnUupVVsW+68Vx08tQwUGJZ+/aN28eG56NTzw7Gk5NdqlTKqvX1rH3ixAjt7NwgucwK7ego0vaOyAgR07Y2bA8Pz4jZ2TZAaFOn58iR87JYbHsum47tY8xkquoK/aYtsJ6eiXhmpg+kRDidiU3gMz40VMSuu4pcVwKYGnIdX1eqaQAAU68zXa15fHDPpC6VWtTkVFJ993ud0eNP5Ey5sp25wulkVZfK2zdr2tlZUKur1wQUtKd7lu8dmg/HLrSo5RVG+/qKemMjpYrFJOvuguCJJ4fAGHCOH5+krW2LJJtZort2+fgVv6Qrba37KaVzkTGthNJ13/cPpNPpUUppEIZhO0LIS6fT667rFjWAUp/93OX9UwrJz3z6IXbbrX7wFx8fSr7iFTMmmWxHFy8uR3//D3vd2247r8G4/DfeJeWpZ7uDHz3Yah87tiwmNzlnYmYmZQjxSDq1CAArrH93QS4tbY/PhoYWzd/8lQpvu3UP+8EPi3p906oGca6ij3xs/OJr3nxgDezOsibtCU7mW4haqGjcUTh6oqNjV/c589CPrwlsNycWA37H2wMFsJWhQ0opT0qZcF23IKWkWz5ltm1PSyFa7IceXkGY1IO3vplgx1mJoqiPMbYipdx+GeCczxhjdler1W4pZUpKmYvjOHZdN8IYc621iaLIJ4R4CCFKKeVKqQbGeJExxjzP87XW1LLtBbprV0SGj65HX/xyn8E4iZNJyfp6Z4wQHWp5xYEgiNQvvdzlnJ9njC1TSpe11u3QDMw45+NCiOfl03HOC0KIvGVZX21vb/8BAIDjONVyudweRdGbmmW1diFEznXdimVZmlK6FsdxHiEUep43hTGuMcYcQgjSWleMMYwQ0qCULsVxPGiMcZtK2gNNz7kW27bLjDERBEHGsqyLSqm04zjTGOMqpTQUQuSjKMpaljWvlAJjTIAQwphS3vjz/9ZhpdNrwQPf34dTqTm2d7BKh4bW9eKSg8+fXw8+/lf7aDZbkysrKTAGxLe+3U52714inR0NhVDGtu1xANCEkLqUshOMAf6t+xf0V7+GrR/+sErvekEt1Hq7qaBpRaIwxjOMsUVCSCWZTJaFELQZEEIqlbqklGKc80qTtJ+jlC66rlu2LGu9Wq3u01p7dr2+yOYXinR6Zo25rmbf+vYl9E+fbUEHD4Y+54cYY75lWX9uWdYTUspRjHEVY0wopbgpV4CuOK4aANhCiAEhxI9TqdR1P7h/A64TvK/j54KNT/3di0lrS8127GcNIARgMBiTpu3tQeNHD262NU9N7RdTU4Ac2yRe8pKzqlJyQSmQC4utV49H820XgVsOAGSdW285Y7RByLK0PH/ecfp3U23bimd2VUxn12kTRx4gZAAATKXKorNnDyCLn71yPBPHOHz66aOIc22i6BqlwujcaJYfPtQg6fSKWl9PhWNjB+1jw+fCk6cOX/3Zy4NqYt9ww0o0Pd1uyuXtmxdyXQlBgAEAsGNvBGfPDvOD+0fii+NHAADsm25cNUK22zeeGKX5fDq6MGYj14Fo5NxB68CBKe03mImiJFDWQIQE2HUrBjSw9g4QUzOXpQjKpbz7ghfM+Q8/vE3ERY6jaHtHFJ48ebmUSGlIensLulDIIcsqObfcInQU0eDxJ4ZAa/A+9tFL1qHDPPjHz/rO8RtOG2OYZVnLcRy3uq57TgihpJReS0tLo1laIRjjBAr8HZYKyZe/7GPWsWNY+f4vNwCg9ro3HHA/+Ynz6oEHUry3b9J/+OEj9q+/cyYePprgv/veOfGudx/AiaR2brl5VFWrwPv7tVhaNKAMiUZGjjt33jFy5fhiYqKbL69cilOpNvNHfwj4d3/PJ50dRbjx5vVLN//iDuXjujQ9LVgsdVrWGQ4aoltPDHvHbxiPTz17TSaOHDtao45TRlqzMAw7AQAQQtK27dF6vT5sWda0VqphALLYDxz7A/9HWa6tdbPbbl0GhJwgCPoBNgMpx3HOR1G0m3O+5rpugjHmCyEMQij2PK8RBIEdhqGo1+t7OOcbAODGcVy3LKsohPCCINgPAEAIOSOl9G3bLgohSC2XPZLt7Z0OAUDNzW0SuE8cJ9HYxU2Nobn53tTYxfPV/fuOEEICY4zFOa9gjFejKOpVSu0wGbZt+xwhBIdhmHNdtxTHMeOcP2zb9n+/8nNbKtVXQmtdamY18rZtjyGEhO/7B2zbngmCIN8kPw+m0+nx/8Hem0ZJdlVnovuMd4whIzMych6qKquypqySVJJKEhIYi8nIGOP5mWfzaE+Ythe2u9d67cXr7re6jd2An8GNjd0Y3HbT3WBsQGYyoIFBSEio5iGrKrNynjMiIzKGO5zx/cisVKWqsHu91Qb3W/X9yowb98aJc+6Nu+/e3/4+YwzdVq2ecBwHhBABQmgqiqKxMAwvtVqtw0KITBAE5ymlBdd115IkEXEcH/E8bx0h5AZBsEAppcaYVqvV6s5ms3H9x36yDwBATc9I5+feekl+5rMD9OTJRfSWH83yRnM5fv75YwgAkhde6OOjB5bFlavdSCmAv/zLUP2bf7s386lPnG9hvM9KyZ0wXEcISYpx2szkkNvRkaSf+cx+PjU9Cfte6j1J0/QQISQCgIwQIgAAkFLWt9XBcwBgtwn9SZqmHVLKPKV0Vko5JKUkSqmc67rzUsoOu7a+qt7xzru3r08ApUru298+JXq6hzxKP5TNZv9dsVi8pbO4VqsN1Ov1u6SUY1rrQ9bakSAIeL1eP4oxtkqpYQC40xX3P4A7wdIdfM/ReuZb2dbXvv7/pJcv3+KADQidfvlLNk6Q3qjQ+PkX9t3y/hvvMYbSIBDOkUMX4u+8eAy0BvfEifPJiy+ONT73+VHnrrvmSD4fyfl5m166vEtPyTl8eBwHAbh3HT+fzs7tsxsbPsqEwPfsLQOyND17/rbZL3HxUgAAW7+OrgtqaXnwdu8DAECua4AxB3FWIUHAbRh6emHBAwAw1Wo7yoTGOTB6NT59ZjtowS9ppkhl5eRkmwRocw4dWnKOHN5Qq6tNMKYjvXRpDzAGICXwkZF1MTExAgCA8nmlV9YaN49Bzc6V+MjIKb5/pCWuTRyk/X1rpLOkTb1uAAD4wdFJWiiktL+fm0JbC+3bV2785r84DgAQvPY1l1Ktge8fWREf/c+9Ubns2moVOQdGLiWvemW353mbGGOklOpECC34vl9N09ShlCoA4EqpikEo77zhdefTL315DDCG2Yde5/RvNvv1Rz+CUOBL24pY9I53HkK+n7rHj08CAMT/+S+G6NNfi6JGvQtlQpAz035y6vQwAAD2g/Pp2fNj7okTF4ExwGHGsOGhGZukRZskGIXhJi+Xk+jAfki6uvrc//Qnq3E+N4AQ6spTKNcU7ATdWQJzrhY9VCbEiDQDCIF+569y9Ovvimyrtcu2huQLKhXiICFkPQzDCaVUyxjjxnF8DADACMnNT/9Mj/uGN8ymn/v8Hru5uVW6VDJJf+6tW5w6QqJt5etCJpO5EEXRoWq1Ktva2pYKhYLeFnPEmUwWpdrmMhk0QymV9Xq9S2vdAQCXMMY7fihKKSqEOEQpvRiGYeB53lzLGOz/1E/VdKm4kv7hh0aTU6d3rjebplguLGg+dnQ1CILYGINrtdoAALR5nrdsjJGO45zGGBNjTE+SJEc8zztHCCk3m80DQRDMAUADY1xYXl6+VwhxIkmS92CMy57nnUuS5NiNtn+McStJkj0AAFLKTtd1z8GWwW4TY5zcUNSWUqptleyS4zgNY4zruq6q1WrHHMepRFE0um2x0oyiaI/neRsY4wQhxDnnOoqiXgAAxtialBJRSnlXV5fTqFQ2YGlpy6IliV2Sz4OijNjrU67zxJNJ48r4zgMFO7C/4Rw+XDZaC7tZd/VmvYvt3bOW/LNfGmaBr+k7f3Wiec/dBz3Pm9daB/TwEQNff4rSvXuW2dpakr37riuwxV/asx1AL7darZ0ISimV9TxvnlJa01pvKqVEEARJuVzuzOVy43EcB4yxtTRN+/P5/GqSJBWtdT8ZGmpnd921Ic+cKVgA4PffV0vf+jOh53m/ODAw8Fff7Xcnn8/P5fP5OQB4/KaXcZqmOUKIpJTeVpbkDm7FnWDpDr7nkLNzr0nHx28NlADACJHFmUz9htjkDcQXLh6ivb3LanFxV5cP6excZgMDq6bZKETPPjtw8zYxPn6QdHcpvbxC0wsXBhA9voZcN8GZMDKNrRIeyudTtVFpSy9dukEMXyZHDk+bSmU4PX++I3jNDy6k223GO0DoVvJ3kgDaUvXNAADgXLZBOztnUCYDpraZo3299fgb3zzijB3FiPNETU8X/EcfvUqLHaz+iU/uccbGymANAykxAAAttAkBAN6DD5yzQu5wDdLLl3sgm52HVOy0p4OUgPJ5bZqNnRKfe+hgOX72uVs4KWZzs0NMTA46x49tpONXOtX8AgSvemVZLi1Km4puHISq8fjj2cx/+YtVm83uCAWpWk1tr0+qrk/5oDWQUmeZ5nMo1tpLkqTmum4FIaSh2QqSt/58h/fhP1qB7i4qpWy0Wq09tFBYxW9/exd86ctAu7pqI2l5tPrYYw84R45cp6VSGx/ZNxk/8+wBfuTItfjZZ48CACClQF+/7qNCIeHDw1PJmbMjpFjUbb/4z1aibz2bAQAQ09NDmbe8ebzxyU8dAwAAjAEFPtj5eU8+/jmBH3zAGoRQks+VALY0ewLQlRqQDgQAex112UsanULKXeuc9vcNBW9/29n0j//kOGgNpKN9k/+r/3M+vfuubs91p13XzURRFCVJsrvTqL65YSsbvenH/+vQrtdLXRUA6ALYsvBwXbeSz+erSqkgn88vGGMIY8wHAIQxBiHEiu87p5DSDyHu5wjGLmOsur6+Hvi+72qtPYTQuSiKjgkhDrmuO2Ot7UjTtIoxZorSXvprvzoTRdEo/4FXXUIf/I85tS1uitvby3h0lEZpWvI870qj0djj+/6FKIqOxnHc7TjO2TRN7755/FrrImNsVQjBms3mXgDY22w2H7vR4r6NPACA4zh1jPESANgoinY1QKRpOtbR0XFtfX1917xxzoNtTlYUxzHa5mRlGGMVYwy4rns1iqJDxpiQcz5ljInjON5LCEmFEB6ltMoYW02SpFdrndnWJ9qA69fTHX9EYyD6wB8eskIALC/n+NDgpN2sb3Ee9+1bdvcfqKVra1j9/vsKQCkjq2ur5OMfT+S1ic7wXb8+Ia9cVXDP3RDHcT8AgNPdHol3/3an0Rph191Ims0Dxhjkuu4FpVSGUnqL7Eej0Tjsed44xli1Wq1hQsiC4zhXm81mP6V0OUmSPs/zZjY3Nw8TQrIAAALBIH3v7y561p5Njc0Zh6sgCH6jr6/vCy8//v8AjOM4f6+N1B3cijucpTv4nkPMzWWaX/7KL8BWp88umHKlwEf2jev19Zdu9ISAc+DAOh8cLJtGw9go2mmzd48eWU9e+M5BXa7kbj4OKRYrbM/wpNqodkIcYzAG1OpqQIrFCh8e3rihr4Qcrkk+v2Q2qkUAANtsZvTaeqdpNhkOAuHee6IBYJdpR8cCKXWus+6eSvDqH2iIq9fyN4/ff/Wrp8HomLYV5tjI3oqpNzpsmraDsVpOTQ3QQmGNjexbBa1cNb+Qpz3di6ynR8fPPddG+/sX+Y+9uc4OjjI9OSVMoxHSvr6Kmp8v2lRg1tMjcVsO6cqGFzzy8IX4+Rf2kmw21uXyzs09fPUPXE2vXOm/QZrGmXBar5dvKYkAIRso8JmcmMzdUByXM7Ml5+DoCtu3b671pb8bAqVQsnd0turnWDh1bYb19Sm9vJzL/NAPzZFie8KGBiskmwW1Uc2inp6WPXKYSSl70jQtGmOopaTAZ2ZScfK+DmEtTdM0k81mF1qtVj8FsG5n53X1jl/yk3y+w5mdW49PnTpiymVHXp8qks7ihi5XPL5neILv34/V3Hy4tU4OIEojs7GRx0EAtLNYNYAQ6iwtYmtE/Myz+50jh+f12loOrAUQApDjGPx772mIMMzdMg/cW0GErPejuEFamwcwxjFCSFtrd3WIyQP7u/x8/rydmib2Y3/GxEB/IZPJrG7bj1SttUgptYvbxJvNNfM3n75l7klXV52/6pE5J5vdJISsE0IoIUQyxrztTE5La82bEnnrTW09olFN0kMhRwZj7FlrbbPZVJ7nbXDO6baRay5N0ywhZBMhtKG1LlFKW4QQopSKtknfSHPeGeTzq7jR2CTd3WvmP/yub/t6Y0IIJoQ0kyTpopRuKKWKjuNcVUp1vtzT7kZpzlrrw3Z313eD1trRWucZY5tSypc3SiBjTHmb2H3zQ5GhlLbSNA2ttUJKuVdrrSil5TRNB7e/XwcAYELIOsaYYYyXCSGWMbaGEGqmaTpyw+9uW4Moi8+eremvf2PrYaitraHLlR0eH25rM2pxyQeEwH/zjyw3P/WpUUSJcDOZ9Xjf3m5nfm41/dM/O+C86pErzZ/48aH0+LEd0dcgCKastXWhVI/FmBDOpZQyC7AlymmMyQshsmEYXt3mamnP81YBAIstQnidMSaiKOqWUnYjhKTneWtSyiBN00Hf98fTNN15OMzm8+sSoTZpTcYPgl/o7+//0t+3BnfwPxd3Mkt38D2HjSIP/h6LCRyGu2T7nbGjG+m588X0woWic+zYJV0ubwVSW6an/OX7eydPXkkuXRpJT5/Z/SOtNaRnz+0BxoAODs6r2dl+u1l3jOv1kK7SvF5Z7d91nPvvn61/4UvD4b33Tjefeur4DUsQ3WrW+ZHD11mpZJtf/soIGAO21YB0/MoB2FbQBQDYKg2FGAAgOXP2CCAE/iMPX8bZjJEzs8NyZhZQR0fkHTiwwffuy5E4pvaVD9cgTmr1v/n0IQAAvbbWGa2tdQJsEbTl0pIDUmKbJruUM+MXX9zDR0aup+cv7ENBAKSt3QIhuyxYcD4fAQDjA4NL5MQJ0fzbz+3wq9LzF3rF9aku59DByfTK1X2Z1SVC/vLPuVhcvAsAwDl4cD4Zv9ynN+tYzc6G2be/bU5tVA3s29v0PE8ZY/LWmCjc3Fy0TzyZMQjLBOMipGng+/4VubGhLEJO7DhO/MY35MFa8P75ry/GE5Oju9b6wIFKfPZst1paHua53Arp7nb18nLO1mqUDA+3dD4vdLnM65/+bInt3bvCfutdGfHvfscBrcEq3XpZ1g+Rc+fK8NrX7FpXhFCa1ZHrJMlOWVcp1e553nIcx7u4OgAA0RvfcAS//rXCOo72fX8iiiKTpul+13UbNzrQbgaOE4P6+lp2YWFXsCFOndrH/+Yzp9XPvbXAORfGmOq2wazwPC9rjPlOFEUHliPUWIlsDnIOqoh4PU7wdG+O3SulTIIgYEKICCGUs9a2EEI1AOhljC0ihLIAsGCMSZvNJhZCHAYAIIS0OOdT8T13d6Kxo/n6vO4AACAASURBVIz6fsUKIZMkOZjP5z/juu6hKIrqGGMZBMGalJJprW9Lbt82kk211v7ttu+aB4wbxhjsOM4FACDGmPZtfz0KACZN010WPkmSFAFgcXtOu7PZ7FKj0ejCGK9sr9uOvYu11ieEEGNM3lprtdapEGJXZjlN0x7O+SrEW52MqFAQulzZeQhjQ0PLoLZU0MO3vOVi66//ZgQxZoP3vw8SKdrBWjAf/Rj2Xve6y43f+PVDAFt+c9t+bWCMEUmS7GTNkiTp5ZwvI4Ss53mJ1loLIZS1Vnmed01Kmfc8z1BK5xuNxui21cuUMcYBAGCMXcMY53K5XF0IsUYpJVLKDcbYmhCiRwiRMMa04zj/oa+v7+/+ofm/g/+5uJNZuoPvPbRZbH3zm79gqrVbbkwAAKpc7uD7R8YtQsXwNY9eUWvrWVrqrOi1tSxQShECa1PBwFqwcYKdo0cbanFxq6zmOAAEO3r1u1uYZB577IqcmyO22dxS8W21XNrTO2UqlV0lPuQ6yyClD0pq5HqbplbLAQCYjaqjl1cKYnKy3X/ooQtybq7E9+2ryemZtl3lOWOA9/VfV2trO8eVs7NFlMtS99Chq2phseTfd+KanLjmMULj+NvPexDFUkxNt3YkDgDAe+CBa7Sne4HvHY6SC5cG3bGjZTk90wtKvfR0byzOvOmxOsnnV8TVq0VVLpecI4ev2FYU8gP7r/DR0YqJoryen+9QCwvt2PcX1crKbuFCKbFz9EiZ5NsWoy996bCt118yE87lVqxIM2p5OeM/8vA4clwmLl3M4PtOVJsdHUcYY6tevT4bveUnjsvvvNhGctkq+8YzC+zaxCJ6/G8pm5kF/Km/3uDfeXGNdXdt2mzWQ5/8lHk5H0jOL7Tj9oKgnZ02efFUH9+zZ0qvrhb5odEFnG9r0e5u7b3utfP6V3/Fxm9582CayeS8paUFks3UWLEo1NpaEYxB/OGHz6N3/7aEhx5MpJS7ypGe5302TdMH4aYOIYSQwRhXbpjE7joPCGkxz7tijGlL03TQdd1VhFCLc06FEHnP8y5v23iUGWPX0zDc43R3X9dPPX1LGRRzVkGve60mhDBrbSil9K21Mk3TuF6vH8nn89cDap5/biZ64fRSc/ChwUyzJ8e7hdS+6zqBtRZba1OlVKK1xpRSNwgCghDy0jQ1Qohh3/cbaZrmfN+fJYQscc6rWmvQWhcMQkF7ezs0m02ns7Nzg1LqSCm/lqbp3jRN9xFCjBDiu0oKYIxTY8xL7vV/j60eY2xSaz0kpezVWndwzicBIEcIMUEQNIUQbdZalMlkLvu+XzPGNMMwdOI4bmOMleM4LjmOc81a204IaXDORSaTUZzzJWOMlyRJQUqZV0rdMNLdybT6vn9xOwOm6RNPUZLNGPrTPzmN29rW6OjoGi111nBfnyNXVp3MH37gOgwOgFlerrL7718hnovqg4MD7sbGsv7onw+g4aEF+/ArJOd8BiEUUkoXHMdZAwDYDo58QkjDdd1JhFBLSllQSlnHcdIoivYppUIpZTcAcGvtcqvVOhiG4ZUgCMpKKb2t0E1yuVyDc+5Zay3n3DXG2CAIjNaaMMZa28KTXx0cHPw333XS7+AfDXcyS3fwPYc7dlQBoFUA6LnddtJZbHj33ssbn/msbXz+C6Pu2NhUcubMHgAAvbJSoD09VVNv+AAAuloNaaNRRkFQsK0WsmkKJJ+ry23u0MuBslmlyuWMFWLXTRGBvUXlWU5NH3GOHr2SvPjiKD+wfxU5jrZpuut90enTh8PXvXa2dep08eU2KQAAOkn66ED/tJqb3yGRmrWyb7p7TPjDj12Jn/nWoK5WQ9J58QxwLuXmppGLiztPyM7Y0RpizAseeZhVPvRHQ9YYRPL5ab5/ZJkEoRM9++xhFARN5+DB+eZTT+9RC4vMGRu7kJ4/f1TNzhX9H3z1ROsrXz1ES53SbmzsXO9W3EKlAACAVjPJhyfvr8G5c7uyUuL69QOko32T9fbORc98azTze+8Zd48cim0mgwEAhBBdRMqdbpz0he/sA9gKXm2aAjp27LI4d24UAAA9+5z0/o+3XUjq9aMv+3hwDh1cSC9c7NNzC8D27plAuWzCDx48baO4K778zTEAAP6aH6wnpdJLWYlf+kWQUvbQZ5+b9X7urYswv9isn7h7jFK6oON4J3tGKZ1ljH0pCII/StP0jS9TTMaO44j05szgNsIwXEIIUULIkpRyI4qiUWOMizGe5pxPxXF8KAzDiSiK+owxW+M6e+7W0h8AoCDoAISqcRxzALCe51kppRJC9GxzckYxxnv/t7sLNSD8kdDjh9M0/VNKkBJCpK1Wy/N9n1lrHd/3N5IkUdbaL1lrRRiG47Va7T1KKYcQIq21Oo7jHV4Q53wFYzyzubkZUEoz1lq17cn2Bt/3N4UQ64SQ0Fobwk2BJADAdomSBEGwnCTJCsa4hTEmaZoeQAhJzvm0tZbe6M4DANBat2OMUwDwAABJKXswxnWtdYcQwvc87woA0EajcchxnJbjOFfTNM25rruMEEJBEExsbm7ucRxn01q7HMfxiLV2EQCcMAx1rVar7Mw3gEMprW9rOhmtdZfv+9OtVuso6Wi7rH70TTXV3Y3FIw8ftdYyBCAopUl4fWqGFjvaoLMTRftHCGGsstFs7gMAEO3t3d7+kU36tp/Pp9bmkiQZAAAIgmAjiqID1lpCCEk6OjrKGGO6vr5+kHN+1RhT0FqjWq0GjLE5KeUAAIAxhgMAYYzNaq09Y0xLStnh+/4VpVS7lNIQQhKEkEcIwdXE0hwxBgAUAHwsm83+t87Oztt6Xt7BPz7uBEt38H2BxdByjh+/in2Pxs+/sNc5NjYNxqSgVGyE7G1+/gsdptHY4t9kMzt3du+B+y/E337hqHP0yFmczZZosbgZffOZgnvy5JX4yScPAgAgQlvf7XP5QP9McvbskHfy5GVdreYRRgK23Nt3lUzoQP8a8j03efHFUQAAcfVaybvvxCUTxVJMXj9kk4QDAPgnT842v/LV4dvpK7HhoRWE0Yo1dlc50H/4FZdaTz51jDz66DW7TTw1SQJ6aSkLFjDOZmPEaIocN9FRJJyDB6Pk2jXMhofPYM559MJ3DtpGgzhjY1OAELhHj16Pv/3tYwAA7r33nEVhBvmvfOS8mJnpbH7ms0cBAOTUNPFf8dB49OxzB8EYuNn/DQC2BD//4x+Om5MPeqJW7qaf/8K8mpvbVb7S5UpOlys52tu7Afee6OS+35BPf01xgFkBMGgLhTYyOCj17CwjHe1rfN9IHTByUBBEiFJN1lbn9PLKgE1T5jNWipPkJY8yxoD19l4DQBn32NiyxXhTLSz2y28+s2tdAADw0rK6+f+E0py01hePPLzlUt/ZCZTSFUKIVVvWN+A4ztd6enoeDYJAr6+vFzDGK8aYXQa6GONbspH5fH5Oa20RQq4xRgshhm5suyEBAADQbDZ3ynGUkPX0bz49/PKci3VdgB96/YqUssAYC6IoapfyJZ6+UiqklNaFEDaKoj7Xdd+HrffLjuO8SSn1KWttXikVKqXqruuWkyTh1tqAMWa7urp+fm5u7jdvDA1jbKSURUJITWudB9gKaAGgy3GcdWstk1KmjuP4cRxvIoQCzjkDAOs4zrntkl7NWps3xhBKqZumab5er+8Pw/Bis9m8mZzNlVJHXNfdJb+hte55WbcV5pwvJknCW61WbyaTWW40GicAAKy100KI4jZXqWi3uG7tYRjONxqNPblcbqXZbKIoivZaa8EY86JS6gQAQBiG1wGg2Wq1juXz+ctaa9VoNMYIIYu+70/In/kZGobhJcbYL29sbHwgSZKfswBcKsVbI/uynHOM4pg4jlOt1+tHAACotYq/7/2X0+tTY0akM1rrPQAAnudduBEo3Tj9jDEaAFgYhnNRFIVhGE4IIZjWumCM2f27QqnCGAda65VcLtcmhKhaa3mapnlCSHN7rpUxpt4RUBXH8fuLxeIngiDQcAffV9wpw93B9wWtJ59+W3T+wj1qZqYQvPY1k7pa9cX5C8N6bb3bObB/Tlyb6AcAoMNDs+mly/tuZG1Yb98S7e5aT0+fOabm5zOyXPFpR0fC2gtMTF7PAkLgHD+2hguFBeL7dbZ3z6JaXe0Ca4END22YVsuajY08yWaX1NJSQa+stMmZmQHa3aVYX+8y8lxpqtXQbNYDPjg4rZaXd9rL1eJSp15b7/IfOHldzs21+4+84kp8+swwvEyHCYdhy73n7qusv78h5xcG1MpK183BFM6EK7re6JTXrnV4Dz54zSqlWF9vCNypymvXRky1mrPWUlOvu3RgYBGFoW199vEDem2tWy0vd4IQWxwM11X8wP655Pnnd7Inamm5ixTaGskL3zlkarXw5nGRYlGopaUsAAAtlSyiZMM0GnkAAH5wdJN5bsrvu4fYP/6TMskEsZy8fitBHABMo+GZZ75VxhtV3Xz/74+yI0emVVepm6yvT7MzZxpscKAhK5VuOTHZoeYXcnJqqkNOTpZob68EIZBNU0baCqticrI9+xM/PonzuXk+MLAKBBuxuNCvZufyulrtsJubt/DRtr/Hujx5/87YHMdZ3CYKEwAAQukSpdQzxuht/sgnS6XS28IwjAEAgiCIkyRZTdP0x28cI5PJXKrX6yNBEFzY5tVgzvmSUkq7rssppdgY0/r7SlQ782Nt4DvuVX369M65Q1/5yAr98IfW3P37c9qYohCCMcYaWuubs1s+pbSute6RUrYzxtoBwOns7Px8FEWfQQj9tNaaMcaaUspqEAQl2BLEjNM0HY+i6IOe522madqBEKqmabqXc76gtd4lhKq1DjjnDc/zMGyRrbXrui4hBGmthbWWSCkDIcQB2OqcKhNCOGMs5Zwvc869JEl2dQ5ijDeNMR3bKuZAKV1gjE0BgNJat1FK1zDGbpIkg5zzeWutyxhb1VqHAOBue7ytpGl6GAAwQijxPO/a1nLSZWMM2iauzzLG6pRSh1K6vL32AQCk2z5y69uaSxfCMGxzHMfxPM9LkqRgrT1sjDl+Qx17e9zGWhvFcdxNCFmhjSbnH/7TK/AHH8ypS5f7QSlwjh2rif7+ju1zJxFC+DdsYHK53DxCyJFS1mu12ojjOGtRFI24rrvEOW8ihMS2KCR4nncBIUQ554IxRjHGbDtjSQkhM4SQzzHGPuU4zkcLhcK/8n3/T3O53AXO+XdXur2D7xnuBEt38H1BtG/vzwYPP8RgcXmV9fZQXa1m9cqKCwDA+nqX1MJiF+npmXb2709BK3Xjxo8dp55euHj4pQNFTFcqoSVU0o6Oli6XAzl5vUjbO5i4fHlALS11hT/82Hm5sNhB2ttX5NT0EAAAGxlJxPh4nxViyxqEMZGOj/fyvXsnb1iN6Gq1w3/g5Dgw5ptqdet9bW3GVKsUB6G1qSjolZVbCeYPPTARf/NbR8TEZJdpNDz/lQ9fU0vLhRvdc3p1reTdf9+imp/PytXVIs5mUHrmbIdeXi55d999XS0tFUApIHv21PM/8mZja5taV6vcNJsOKZVW+J7h69j3W9j3FW4vZNTySnijNZr09lZNpRzYKLolS0LaC5N6bYsLpVdXM3x0dF7XakXscKE3qgFItaCffZbHf/u5/dj3t8x7bwNSKq3kHnujFhcvJ5izZcp5QCYmVsnzL7h2ZTWTnjs3cINUezNYf38ZrI35/pFpwNgC41RcvZYTV64OoMBfE5cuj2Xe8PqrOAhWWXd3DfmeoqXOml7f7eenr0+VvH37Lsm+vk4AAKVUh+M4F4wxHa7rXgQAjxCy6fu+4JyPd3Z2/mQQBNHNx2g0GlhK+dPGGI8xVlZK5Y0xrpSyRAipcM6vUUrT7XKWSNM0Y62V20atq5zz2BiDMpnMAsZYKKVChFDkv3jqEvvqkw3c0xWTjeoS7e1J9OpaG/rgB+YEZ4OpEIHv+zNpmmYRQunNmQdrLVNKFTjnG21tbSuu6wac8xP1eh2VSqUnG43GlxFCr8cYYwCQAJBDCDHOeWer1bqfUmqCIMhRSjc55xwAZhljEgAEIWSTEFLDGEtKaZNzHjmOQ5RSYpuvxbXWiRDCd10XMMYpQqhCCKkyxjBCiCqlVJqmPVLKwFqLgiCYd113M03TPGNsklJKOOeLlNIKY6wZx/Exz/MqUsqcMSaDMd4khKSu60acc6GUCoUQXQghQAhhQsjmTTYfMwghQynl29k3qbV2McZpHMdHtoPJVAjRrZTCxphcZ2dnAxBS+VxOcs5DADAIIWykUAgTAgDdxhhhjBEIIQwATGvtb/PUsFKqi7/3fdPiK18ds1tlUgAAcN70pnU00B/fEIlMkqQXIZQGQbAqhMgDwJIQoug4zhIhBDDGjSiK9m93hzqO4yxzzutxHO/XWkvHcSJCCGVb2d1JSul/LZVKb89kMk/6vn/ecZy52113d/D9xZ0y3B18P4DRxGSPKXZAevnyCN8zPGWT5CayLSEAALy/X6vV1RYbGJRyZrYLrAUxO7vfOXpkIr1wcVcXkrx6tZ30dDedY2PnWF+v0/zKE6POsWNNPjhYbX7lK0fY8NC0Lr/kP6cWFhz/B141HV+4WLLlsi/n5toAAJJz546599xzJTl1atQmCeh63ai1tZ12clutYv7AyRVrdAYQ3pDXr+/i3eBcTqbXJnZpSNlUgBVi5/uxPcOz8bPPbQlYxjFSM7MZAAB+8OBKfOrUjoCdnpzMxqdeXIqf+3avqdczzsGDc0apIL1w8RjOZGru8WMLemNjk3V1KRz4KL10uZcW26fTs+d36ePsAKE2tm/vJVCqQy4ulWzUyuGOQosEmaq1tkg72plYLxfZvSeqxHF8AAA2NFSmPd0t02gqnAmVTVNumi20+bkvtDkj+xri2sQYXJsAAADv5MlL8eTkLd1hNyCuX+9lPT3ryYunjvuvfOUkyWWrcmJir/+qRy7ZJC1699xztvHpzx4HYwB5XgoIMN+39wIAbGkDZTIpzuU2dLncjhHaVZZQSg1ul2zGAACstbMY46rruh/0PG9X2W5tbQ0lSfLHSqk2xljdGMNvbmHXWhe11sWtKUPadd3rcRx3BUFQdl33grU267pui3OehmHoLi8vl0LOL+oP/6kjPvlXu7SDgFLwX/+6Sf1HfxSwhx+eaN5/35EoisIgCKaiKNpVBtweN47juBcA5jKZjE2SRDSbzd9L0zTX0dHxgTiO356m6b/nnJcAAIQQKcaYWWsPeJ7XlFLGaMsLBVzXdRBC3PM8Y63lhBBirdXWWg0AVim1qbX+W875a9M0NZ7n5ZIkiTjnvpRS5/N5XK1WpVKKGWOaQohux3EmtNah67pZ13W1McZ3HOf6tieZwRg7WusWISTIZDKXtdaGEFImhKwZY0qMMbG9XvU4jsfCMLystZZJkoxRSjlCSFFKY8aYSynFnHOeJIm11oZa6zwhJMM5XyeEbHie1/R9H213xZlUSGhJFDjcYiFE6nmeY4wBTKlbT61ioJGU0svn81CpVFzXdS/cEBIFAHBWVqbSbz5zaNclk81atX8fbNvT1JrN5l7f9y8QQmij0Tjo+/6FRqNx3HGcDSllpxAiTyldRQgZay02xgTW2loURV2U0hnG2McYY5dd130im81ufrdr5Q7+6eFOZukOvi9Y/YVf/LfMWoU5XxNLy6G4eHGH18OGh9bk7Fwn6WifgzThyenTRzI/9paLulbL2Hqd0fb2WV0ud7/8mLbR5Hp1tUtMTHaAMcAHB8ZbTz29H5RCulwu2Cja6bwytVpGzsy0OXv2TOm1tZfsU4xBtK9vkZaKa7SrewW0cq21jm00dgImtbDQYYUM1fx8wX/ooRm9UfGcI0cusf6+MsnlsJyY3AnKkONYS5mDOG+wvr5ZUigssb6+ppyZ2UVuJ52dTe/++9blwkIeYWxZb+91nM0009Nn9tk0dXAQCOQ6TM7N5Uln55p//31l1tvjRd9+fo9eXW2jpdKkXlvrJoX2ml5fv21GSK+ttZmNaqcl1MeUKjU/X7CbdQdnMxU5M9vJ+nrX6egBagmTSKSROza2IWdmO8XUVDvr65XJ8y8MquXlNl2p5G2j4dLurjXkOoAo07bVcnS12k6LHXOm3ri9358QWFcqIQCArteNrlSyNk0dUiisJN95cR/p6Ej1ysrWvkpRkIp4998X42xmme/fX7OUesgaTnu659Ovf2Mv/pE3LRmEtvzyrPW27SLmjDF5Y0xeStlFKR3O5/MfvXkYtVrtp5IkeavrunMYY7ltlHpb3aBMFE3SrzyhMaV12t2FtdYFpVSH67pNSimz1qZpmmbJxOSq/L33Hnr5/qSzc1MvLlJ54WKv/NaznQF3ptCxMdnaUqO+bZlx6+urXKvVYo7jVLXWRgjxqNb6xymlXyyVSu+r1+s/W6lUxqSUAQBMUUpJHMd5jPFfE0JCY0xirf3GdoknL6V8AQCYlDJljOUwxixJEg0AF4Ig+JfNZvNVhJBTAHCGc/5FY8zdCKFlY4xECOEgCNYwxkZrzdI03e/7/iqlFDDGgBBSjUZj1BjjcM6rANASQnS7rpsihDTGWGCMixjjGqW0aa1NW63WMQAAjDFVShXCMJzHGBvP8zastesAEHqe5yCEGKVUEEK04zi1TCbDPM8zWuvIGCMZY45SquE4jsQICZdhoq2F1Ri5lZYW85sSl2NtV+tSdgWIIITKxhjmum6strBzrfhhZlF/9rOhTcUOl479wfvPRMXiGACAMcallFallB03ZA9c110OwzB1HCdFCGkhRNbzvBnHcbQQIpPJZK4CQD2TyXywUCj8793d3U8FQTDuOM6tnQR38E8ad4KlO/ieo/nkU0PRc8/9BKZMpJOTJXn16k6wwvaPbKqlZdc2m4FeXe3SlY1OAACcz62yri4iZ2ZCmyS+M3b0ilpa/q78EVQoRNh1q3ptvQSwFYzgTEbYZnPXDQpR4ltjGdxEtKWlzrXkzLkxtbLSpdfW21lvzzVaKi3y/SN1Xau2gxDIRhECpYhptaz34APTptHMJ6dO7VcrK7t4QjjwY/+++xYQxtg06oG8fn0fDoKVm6UBglf/wAxpL7RaX/jSPvfI4QlrwdXlcsm/98SymJwsAgB4J++/xgcHGmL8Sgfr7p63QhbE9IzWS0vtAACkvVDx7r+3rpaWM2ywf8o0mlmQksHtgVh31/wNE2Nn9OA8HxioxRMTA2ZqOuW53Eb01NMHxdVr7bbVorRYrIvZ2fabu+MAAEyzxRGhKWkvzBkhOnEup1hX9wpozU2r5W7NezHme/bMsIH+dSCUuGNjU6SjUNa1zRCHQZ0fODCTXB4fA6UQWONg7kh7E/FbLS0HcnqmS87MtrO+nmkxfqVXr651QKNJ2cZGTZ+834OXMuSIMTatte4mhLRc172cpmlPo9G4m3P+eK1W6yyXy/+i1Wq911orhBBDSqk2z/MuKqVuy88iv/3u9fjvvnwweOyHVjZddzSfz1cIIctKqc5WqxUopcpa6xJvNpbV45+7JYDnw0OX5fTMVmeg1qBOnW4jP/6WdUXILf6GL4fjODGlNAqCQGutN5rN5gHP854Iw/Biq9V62lqbIISGpJQDQgjX9/1Jz/M+57ruu5VSv5im6X5CyHt93/91jPGzQogyY6zLGONorZuMsaeUUsulUulL1tpvAcA70jStIoSeMcY8ghD6hrX2hWaz+bOO43yWUrqfUkp93z9vjHGUUsZai9M0TbaFOakQot1ayzDGSim1EUXRmOd5Ko7jkBCy3mw2j7quu0opFY7jbFBKG4QQMMbktsuQihAiPM/jSilhEEaEUHcjsjx0iRVCtAghwlr7NGPsY0EQ/K7v+//d87wPZzKZ3w+C4A8RwMcXaskXu3PuJxY2xSGprHNXt+sxRoXWOrbWekmSgOd5ijG2SghZ1lrnydzcpv7cF7puCLsCAPCDBzfF3r07a+W67oQQYhAAgBBS5ZxHruuGcRwjxlgrl8sprbWDMZaMsTUAuJDNZt9cKpWecl1X3bLId/C/DO4ES3fwPUd69Wp+879/4v8W4+MdZnPTBQBwjh+b4SMjK/L6VMFUKrdkJlh390r8rWf3AQBYIVxdrhSD1z56HmezK9h1sanVdnWdeGNHLidnzh4DAOCjo3O0sxiZWs2zrdaWANzIyCIbHqpg34/U/Pyuz7OU+MgCskIQAABdrpT0+no3YmxTr64WAABQR0fi7N170Tl6tJmePt0tZ2Z6g1e/6pKq1dogTnbkBWwqGHKcBWt0Vly52gUAQLtKy/qG9hJCELzm0Wbr6afbbJI4tKd3xT8+lhptrFpaiky1Vtzep9x66msHAWNAmUxEcrlNHIbENOqU9fZdEtcmjplmS6pKpaQXFrv58PCsrlR2EXt3oBTQvr4ZvbraBQCAw6CSnDlzEBoNHr7h9XPp5fGhGxytre1haqLIebk0gk0S1zQaGe/ee+umWq3x3p7l5Pz5Mb5nzxW1stLFDx6ctVEk5fTMIGlvXzJx0inGx3vV0nIRF9pAlytFU9vM4cBXthUx24qYM3rgslpZ3QmCrRDEOXH3nF5azpFsdpXt3VtRG9V2UAqZicm8O79QpXv3TKpMpgshlLiuu04IWdNa54UQg8aYjJRyzPf9z1er1b+Mouitxhh2szq1UqqUyWQuCiE6tzk9G5zzCqV0mr7m0aDtn79TbXpejjE2HUVRt1IqTwhZSNO0fztw6kKEbNhP/tUuIUeUywqbCrCt1k6JL/srv1S3nleFXK6pMb5F0wlgS8vIWku11k6wsrpuKhWDcjmNCJFa6zc5jnM/AHy+p6fnccbYR9M0zVBK857ntQHA0c3NzV9zHCcDAIkQYrRUKv2553mbc5XmG1OD3tKS2vEIYMbYX3V3d/8xAIDW+kc3NjYeEkKMMcbO1mq1X9BaI8/zPpKm6c9jjJc3NzdfmaZpFDxtYwAAIABJREFUO+f82/V6/dVSynZCSD1Jkl1mw4yxGACWjDEl13UrcRyrXC4HYRhm0zTdJIToTCbjc84Jxpj6vu8QQpqO42itdd1xnBzGmFBKmdbAzq0majDPIQy8ZWvtv+rs7HxXNpv9YhiGVznndcbYJiEkuXHKcM5bAx3ZxXzoLR3ozn18f3fuQ0rJP3dd9+40TU9TSkuc84BSGhhjEkppp+d5tYbDR3L3338Fzc9J59FHF+SFC0V1baKAf/InasZajxASa609a62/nTFbEUJ0AMBKEAQBAHCttWSMUUIIWGu/nc/nf7Wtre1Oue3/B7gTLN3B9xzVj/zZPcnp0z8PAACOAyiXE+7Ro6t6cTGrNzbyIF8SW8Rh2OCHDl7CmdCTs7MdQAjgbDZlA/0T6eXxg2pmtgeH4bJ3/30rOAgWWE830JGROdZR9EguN0eKxWU1O9cj5xc6vfvunWVDg3VjjKNmZ9tpsSjSc+f6Xj4+W284tK9v2lSru4INxJhyjh+7hjNhGZotR87M7NGNBiPFjmXaXkjk3HwfMraVeeyN8+mVq+1gLQBCwAcHVpLTZ3bUoq0QDBeL0tbrLikWG1brDTkx0QsIAeKc6lotkuvrXbx/YA3ncou0v09ZhDJ8aHAFhHT1xkbWRFGGtOVaCGDcO3n/X+hKZa9aXh5yDh2coAMD3H/oQQlKL6qVlVtLchiDe/SIAGsVINB8796yXF7pAq2BjYzEmJIVwFib2pZoqGk2Hfeu4+NqefmWYyHGgN97T+q845dBDg0L/fTTJdDa4QcPzuj1tV69utoGAMAG+itqfqEL0hQBANhGk4HWYNOUOfv2XVLbgRvt6tJqZXUXods9MLrJhgZSK1UbyWSa/MD+llpdy0CaYj0zG+DZOeS/7rWzZks7iAshuo0xu3S2rLUPxXF81+3OR8bYohBiMAzDy4wxxjlfEkL4AJDhQWAwIdZxnGaj0ThgreXGGIcQUlZKFa21WUrppqXUgf/y8V2f6R4+dFFOTu4OJI4da0KthtFnHw/RyZMLGKBhENoVrOfz+TVrrdBa+6nrFNjXv77qdveEdYBeIYSPMXaCIPg0Y6zsOI5oa2v7Msb4c1LKUpIk/dtZDeQ4Tuw4zhBau3xYOW1fmFprvXE1sseqsYJiyIFidDkIgmcAAKrV6o9KKY+1tbWdCYLg3xtjKsaY4cHBwd/RWreI1jXH90uc0r+VQgwZrQcd1x3XWrfDlpdd5LruFc/zVhFCSZqmByilCwDQxhirSCmFlFInSdKXpmmRELJEKdUYY4IQaqVpuoYQakopCUKoiTG+hjE+Swl+ujPjfKQs6K8NdOb+JAiCydut4T8Ez/NizvmnstnsVymlH7PW1gAg4pzvJ4QgQghFCC3ozk6PvfnNEcYY0OBAFf3r/6vCfW/TGEMdx4m01oG1liOEFEIIMMZISllwHKduttASQviO42z6vv/LuVxu8f/LeO/gnx6QvY0+zB3cwT8W1t//+6Obn/irT6ilpWMAAP4rX3k9vXypQ6+Xc4AQ8IOjsU0SJaemMzifT52Do5fi5759t3PinioIWQNGc/7dd280n3giK6dnbrFkYMNDK3xkX6KrtWby4qkjt9M/ov19i6S9Y43k8yb62tfuud04nbGxs+n587sNUrPZhJQ6lZyYDG/ZgVLwTtyzHH/7+W4AgOC1r7nEh4YDYzSoudlW6ytP7HTw4Uwm9h955Hr87LNDwWt+cL31zLMd3t131UlHR9z84hf7gDGjF5dusZNA+XzqjO4ft42WtVL0Zn74sf8GgP5T8bd+Y7z8Bx8oJufOfxjdd+9R9957M7Xf+peIdXetmji2Nk0LtKdnQ62uUpuKBDk8m549t99/9NXXQJuCnJ0FE8XWGdm3Ep8+c9S2WgCMgXv40CLuKDajJ544wIaGJmlvt42fe37k5gwTCgJwHnnFRXvvfZSsrQTRn3yk3xkbu445a8Qvnto1f969J84n16dG7MaGd/Pr/MCBs+Lq1ePb876Qnj+/K4D17rvvfDo+PmSazSxYCziXbbDh4XJ69twwAID/8Csu2DAcaP3mu3xjLXMc5xoAtIwxRYRQHSGUCCGO3OAIYYzB3PQdGGMil8staK21UooEQeBUq1WjlHKz2WyTEEK01rEQApIkKQBAm+M403Ec793ev6qVcp2PfGxNfPKTwwAApK+nZetNZer124pTAkJA2ttb8Jd/vpBSeuDlm/P5/HVCSFypVI64lE4TY4RmrCikNPl8/uGenp4rtzvs+vr6uxBCD0gpv+04zrRW6j0o2YjB73hvKuS7NwTOXFhuuvf1BcCteH97e/vvB0EACwsLf22M2WuMWRgaGvphALDNZvP1URRdqlarL7i/856Wnl+YMs3WiI3jXudDH1zTg4MtKaUrpexzHKeSJEnR9/3zmUym3VpLlVJVa20bpTSllGbr9bpUSl2llH4iDMMLGOML7e3t31dD1ziOabPZfJsx5i1KqRLGuEgpjZv1esxdNwsAXAihtzv1bBzHYwBbyuWEkBYhZIpzjl3XzSdJElNKDaXURQidQgj9SrFYXPsHhnAH/4vgTjfcHXxPsPLb7z6m1lZ/Mrly7R1qaakNAID0dDf58BCJvv71rRuKtSAuj3vu3XctmM7OxBkdrcu5uT2kUGimL55qA4A2VCgIs7ZelvPzt/WuIp2d662vPnn0dkHSDaj5hV41v9BL2ttXvXtPTAIhnt6sGzE+viPCiBgtZX/mp6+QTMZtPvFEjINAkZ5uazfrVq2sjNlGc5fmYPDIwxfjF76z0wmWnD7dqyoVGj70YFz/5reGvAcfmI+ffW7LqfzokcXmF75wBBUKaesbzxT1ykqYYJTS/v467usV8vTZXZkVAAA+emCJ5PPr8befPw4AkHnTD//r9h8sPoyyvXUAgI7feNd6s1x+Z12IJ+MokvwVDzaTJ58a1vMLW8aeG9UMAmiqxcUdTSbS1o6az3wzxFoj94EH51uPP/5SZ5+UkJw91+vee2IWOQ7ImZl9cn4egkdffRkBxuncLIU0zdg44eryeMmeOWfp2NE6AAAOfCauXRsixY5NvV7OASHgv+LBcb1Zz4IxhHR3V2lXVzk9c2YEAMDEUQ4cx25lnXYb2QIA6PpmaJXybqwpcpxNxHmVlEohwljqer2oJidDjFDZWFtK03Q/AIDjOOfTNB3b/rvOGJuD/5e98w6Q46jy/6uqzj1xJ23Ou9KutNJKVrRxxmAwmHSHwRgO7shwx2HjH+E4uDuOHO6IxxEPE0wwmOAccZQtK+xqJW3OcWZnJ0/nqvr9IWmtteQA2GDs+fyj3a7q6urqHs3bV+99HwBgjLnrup5lWe2yLI+wY7pHRNd1rVgslkulUl5RFO55XpkQIpmmWRRFkZXL5R5CiC1J0rgoipxSOi0Igl4ul49JGLzr7Y4SDvZb3/7uZmVjz2T5lltX11q7/PWDbmuLB6NjtlBTreD2dq/Y2dEryrIiUDrned4aAzGXy7XF4/FZAADL81p0XZ+1DaNKVdV/eTxDCQAgFov999LSUr3neS0Y48sZY4rsi0sY4+wdY6VUlUqqCEeIEFQED84qFos7U6nUek3TfI7jmIqiBA3DqHZdN+267qzjOP/mum61fOZZR+DGG5u9iclmeeeOcSORCHDL8uu6nkEIzQEA0XV9XJIkiVJqmgyHCJZCKuEDtm0PEEIOBoPB+6uqqqYA4FnzF7qqqp6qqt8BgO8AAFpeXt4hy3KM+/2HCCHXuK7bous6sm1bk2WZm6YJAACccxAEIet5XmOxWAxqmpbmsk8tu44XQugn8Xj8/X/RG6vwtFPxLFV4xinccGN98qP/2k9Ty1XKGVuPWPsPbBBbWxeQQEx3draVm9YpBaaE5mYLa5pLfL6C1d9ffXKZEWXrln7rwMHNjz0HAEDu6hp3Zmaqebl8ivLzEyFv6T1gH+xbk3Ivbdgw5AwOrgfGQN7QPWEfOdoKggBSW9uSMzy8Jrhcv+CCg0iW5NLNt6xmRGFdZ6xcxiQeyyJVtb3pmWoAAKmjY5oEg565b9+qTAAgBPK3/3eKaVrBvfyKTWsmp6ogNzfP2IODj5ZB2bjhK80//+ZVKNS8Jmh0ZmbmykKh8MWqg30DhQ9+eNX4Qbpuame/YKx8622rhqR+wQWHAQGTuzdEWHXctH93o20+9NAGoakxiVVtXu7sUMr33NvC8vk1niChvm5KWt+VNe64Y822FvbplrJ791HrwT2bxLY2gxWLhNvWirew2Ch1dhxBiooAuOPOzDazUikktrSk3dHRqFBbs0KqIrZ9+HAtCocZYsxl+fyq0YQCAQ8LgkczmVXtKGXr1j57dKQXABgvljDTdU6u+/mkS/Aa2QZZlo8SQpBlWXWapk24rqtgjIOMMZsQ4pNlOee6Li+Xy22hUGgMAATDMMKMMdfzvKiu64OiKKqWZeVP3sZTVXXGtu3ICZ0kRVHGCCGkXC63hB7eO+Rt3SKx2+8o4FKJI5+PwGWvDWKMnXw+3wEAEAwGx/L5/LHAb4Q8TdMGy+XyGhmKQCCwXxCEAYTQsmEYS5IkxRRF+WwsFsvBk7CystJj2/YLAOC9hBDH87w3/W64vKVKkz/ZU6eUCxa7La6RHwVVYRwh1FUulxOiKJ4liuI5nufJuq5fahjG36dSqauPl1aZILmcQTPZoKCpmUIgsPn4+s4rimIwxkRd15HneUXO+UEkqYuKJH4x5Nf/qj0rqVTqo4yxfygWi0FRFOdLpdKqASyKYpFz7gqCMKuqqjKVpVpHQrsnHou+CZ5FBmGFp4eKZ6nCM0r2B9e8Mf2FL/0PXVk5Zrxg4qln7R51RsY0urzc9njneVNTCgAo4vr1wik12Qh51PMiCJxEImmaTMZQMOAgSSrIPRs8d2pGp0tLp8QjPR7ccTV506ZFZ3KietVrxBgDxkDqWn/UmV84FnPkeSCv68zgWCzNCwWdW6YACLm0XI64R46sxlop27dN25OT1ch1RXn9+gXj3vtWt+HcublapMjjAABc1wFME3yvuHTCbGxowK5r65e9Nl/+2c9Xt2/U3s2HrAMHu078jn0+g4TD/scaSgAAjuMkRMomS1//xurWjtTZOS3U15es/v71+kUvnMI+H3bGxpEzPi5LLc1W5qtfrRPXrZvDsqyShoY0LxvImZ7ZiggZOlkfanXJIxEBq+opHiCk+wrmwb5uXi4L9qFDARSJUMxYFQCAMzK64ZT+qrIIAFFcVSXZhw9H9BddNF6+/Y42UleXZfn8qjEqt7Ykrb7+upPPxZpaxYslAAAsf/+7/W4irmNdM5nrFiilAUKIJQjCiuM46yRJmhZFccFxHL+qqgxjbJTL5YJpmo0+n89hjGU0TRuilBJJkghCKK8qCoFicQgRIhaLxaAgCDlBEJZPpJqbptmoqupRx3EioigWRFGklmXpAACF3buaZFmeMV/6kl5d16coY1m7XNYxxhoAgCzLi5xzwBibjDH1+JbVKe+q4zjrgsHgQ5TS/6qpqVl8bPsTEYlEBgBgYHZ2dh+l1GtoaBg4w1k5ur0lcs1put8fDocBAH558kHG2Cc0TUspivJaxlhtTpY3QE01kiSJwPEaerqu98uyzGzb7sAYf726uvpr8BwyFOLx+H8uLi7+VJKkKzDGU4qinOk4zmWMsQAA5CmldZ7nVVmWBetraw+6ILwPnkP3X+FRKgHeFZ4xUp/89Otz1/zo0zSZXE295Z6ncdMyvIWF5qcyBstmRbGtdZQuL0cBAEhtbQGLYoamUsdS6ndsH2CGoco9GyexJBftgYGN3tx8FCRJkBrqR+lK5rSaQydAwYAbfMMbJt3FhSq6tKSReKLMVlZUAABSVeXQlZUAiUaW1J4ecMbG/EhRKPcotw8ebOWmIdFMNkSTyQjy6RgY90h1dUFqbpoUGxvBm5kFsaU5jQBZQmNDzpubi5LqRFrdtatsHuqv19/3T7+nH/kQ1no2TqPubskKh6MUobC7fZutBgKzyHNFub1thBVKhGYycfA8JDY17Q1e9toLar/y3z9IJpPvyX73e59Kv/+qt5fvved16S/81+ViNmvgR/au8x586JikQDye8p1/nk3T6bBz9GhAaGpaEKqqBHPf/iDL52rEhoZlN5mMKes657lpFITqmhxdWQlww/DR5eWounPHPMtmEXecY1IAkcgSCKLszs4msCgWuGmuep3kjo5Jb2LiUf0ox8FiU+MITadPu2UqNjQu0kwmIVRVHZXXrV9wRkcVqbM9gxW16C0txQEASH29gTApA0KIG8aqgSa2tMySaCQvX3JJ0dq9s9HFOOF5XuJ4dfpJxphGCDEQQiXHcZplWU5yzqlhGB2O40iqqjIASIqiSAghKkJIRAi5+Xw+rijKskKZKD/0sI7a2wVJlq3jekXgOM6qESvLcoEQsmKa5gaE0IzjOJ0Ax5S4JUlaEAQBKYrieJ7nk2U5jxDKSZKU5pw7qqqqoigu2rZ9QrJAVlX1iKIoSYTQPbIsf1tV1Y/HYrHvqap6cn01tLCw8LNSqfShQqEQTSaTH7Rte4/f7z+ttykYDC4Eg8ElAIC6sPYHfYnLskzD4fBev9//f5ZlJSVJajRNs04UxSnP8xK6rve7rlsOBALfYoz9LBqNXveHjP/Xgt/vz4RCod8Hg8G+qqqq32GMv8c5T2KMd7quGwQAQAhxSZJeVROP/lEB6BWe/VSMpQrPGPnrfnmW+fDDbzjxOwmHLZJIzD02Q+gJ4RzE9rYlb24uDgAgd62fsA/2dQMASOs6x52pqXaWWg4iXded0dHa1eBj0xQZZUGpoWGMZjKn1bQhNdWe1N094jvzzBD2+fJsJbOobNtqiM3NS1j3Je3h4Q5gDGh6JQGCME7T6WqptXXCGRlpBADgtiOBdyxzj62syGJ7m0vTK+COjraxfN6UGhvStGwk7EOHmunyclDdsX1IbGnJ8TN3lZRLLrlGecWlX7Vc9wry698g8cwzLWF4OI8++WlD4pC0X3xRk1AsTtq33dFOTbNa3bzpKBChJHW0Haz90he+n1tZ6TFM8xp2zQ9rnL7+dVhRC87o6NleX/92AcE0zReqgVLQdu7IgkeJsWdPDbgueJNTMbGhYYl7tEgXF+Pu1FRC6tkwzi3bsA8NbPFmZmqVLVumAWPM8nnNm50NiW1tUzSdjgIASM1NY87wSCsvFARl48Zxb2EhTuLxlLJ92zK3bD9dWno0I4xzEBvqlryl5CkaRlJH+7gzObmemyamqeUa5NOz7sTkOnXHzqzxwAPrtPPP70eausxSqSi3bZGm15Y8caen497iYgS7jiEODhaE9V22qyg+xpjmeV6cc654nhcSBGGRUhp1XTdOKY1Fo9FZy7ICoiguc86p67qyKIocIUQJIRohZAXPzmJ46GHCt21TcDDAJUlSMcbC8QyzJCFkRdO0nOd5it/vjziOs4KOKYprgiAsYYwtWZYtTdMIIQQIIQwh5DuuSxTw+XwSxhhJkqQihL6NEBoXBOF6Xde/Wl9f/y+RSOQX4XD4Yb/fv3TyPafT6fDCwsIvbNveraqqIghCHcb4OlVVk6qqJp/qR+rofF6IBRT25D0fRdf1wwihH3mepyCEOhhjkiAIYJrmGaIoHkgkEj/9Q8b7a0bTtHI4HN7jed5dCKEHBUHY4/P5Pl1bW/vAX3puFZ45KsZShWeE1Kc+U2M+/PDHaDrdAgCAfT7H95KLZ2i5VIMEYZ7l86saM9KG7sNiS0uSxGI5sabGBVkusHx+9cuRcy5iRTa5YaheajkmtbaO0Ewmwl1XFCJVBsvlNKm1ddybnV3rRbIswmy7Su7uPizEIvNCTU1KiEUWhOqarNyzYc4bn4jRZDJSuu22oN1/KOItLtY44xNxqbkpb9x3f/fJIoxCXe0CTSZrtPPOSTuDQxE4DUIkMuROTLYDALBiKSjUJLL67t2ePTYW4oZBvLm5uDs2Vi1v6MpJr3pVqGB5SVmWXyDt3iVCKEhIc5Ps3HJb2L7ttgZ8/W8878E99eC6IpgmdmdmE1Jb6zzWfbp14OA7i9/+7ut4TU0O9fUXxabGRWd4uPNEnTuaWq6We3rG6NJSFc1mMXCex6HQCk0tR0hVlYnDYQ9RWvCWlmrUnTv6WSbnE6JR7s7MxAAASKRqXqqrs93JyZjQ3ppiK5ko9vk8bdfOKY5Qize/IAIAkJpqGyh11He/axlXVYnOPfeGuGmu2Z4TGhvzIIgu1nUHEDBuO5LQUL/MykaA5XKK0NCQQbLseQsL9erOnYcR59QZG4t72WyE+HyCWFc35c7P14Dnkceut3b+uUfs4dFmd+BwFb/jDoouvthgsrQ2vkoQTEppUFGUQYxx0nEcR9M0u1Qq1YZCIQoApqIoQYQQcl3XlCRJJQiJTqLGg7p6BIx6nDHmccAYwBFFUccYc8YY9/v9UqlUYrIsW7IsY13XPVmWLVEUzeOxoNyyLIkxplBKPYRQjnNeJoR4+Xw+ejzd/ouNjY1fqqqqusfn880+0Wcq++WvvtFsa329puv5XC7XijH+SkNDw5dVVU0uLi6+IpPJbCOEjMmyfFrxQ8uyREEQ+B9qKJ1AlmUvHA7fbppmM8Y4ijHmhJCcJElX+3y+wh8z5l8zuq4nQ6HQoXA4/GAgEJj6S8+nwjNLJWapwtPO/Lvfe74zOvo5e3BoGwCAum3bmLMwX1v42c87AADkLVsyvFTKcddV1V07V7z0Ssjau3c1ZkPdvm3Em55+dECMCHj02LvquiDU1TFnbAzkjRsmzf0HjtUCc93Tlg/g5TK2+/vXBM6iaMRFM9MWK5dXhRdP7E8oG7r7SzfcuCZ43P+yl416+VwCAKB86+3NpL7ewqpaxKI4x103Qi0rIDc1LJiP7F8NzFbO2HoEqZpM83mbFwpr4n6E1jYfpbRdwvhjeQf0gEgsv6IEC4XC7/nnPn2GUijM8G9+Kyr0bBx0r/+1HwmiSaoT2Lzv/h5l8+a50tCw7k5Ph2HvXgAAkDdtOshKpTUB7ZxREwCA5fI+O5f3CbU1GXX37gVzz55ad2ZGUrq7RNlzB+wjR9tZqaR7iwscAEBobExhUZKdmZmi72Uv+xQOB8M8X9xYuu++3eW77l5T983u628IvO+fJqG9I2B9+lMlUp2wmW0pwJgAGLu8bEg0nRa86elqAACxvX0OMHGBkPKJbVQSDE45xcIW7czdBRKNYHdsPKide27GuOeeKieXiwFATGxrG3THx7sAALRzzp7llGW9+bmEuW9/94n4MpbNBZV8frrk963RxsIYO5qmHTquKF1nWVZCEIRxWZY5QkgkhLiWZQ2JoliraZrf8zwPIhGumqZuW0aBE8IYxsEjixZqDAmCT3INRVE0jLFNKbU1TUOcc3w87miUc96vKIpk27ZFCOl2XTfGOW/x+/2mZVkJQohg23aWUhpyXfdoQ0PDXad7b0+Q+c53w6Wbb3mTO79wube8vMV37jmjJUI6JUmaRgidCQCQSqWacrnctzDGbGVl5XXJZHJCkiTVYKJflcWhpprofwAAVxTFfaJrPVVqa2s/uLCwUJYk6e0IoV9GIpG5p2PcChWezVSMpQpPK5nv/9//c6enX20fObrtxDEvk2F0YXFVN8g+eLBJ6upawbo+BAj5Wbm0xhtg9vV3Sus6x53hkTYAAGXjhmL59jtXA3yZaXBlS2+/+eCeVaMG+fSg3Lt5hOXyAXdq6nHLoAAAYE21eDZ/SvwGqU5kaNlYLbVBYrEkCYWKxRtu6JC6uo4CQB0rlSQiSY6bTMbgWE0xULZt7ffKhiq2tMwhRc5iRUHm/gM9cmfHQOH++0/J2qOOY6qyHCoWLSeuCRYAkSil4+Fw+JOzpdJ+1+835ZUV03zJxTXi9u2Ivvcf487ISAgAAEejBc5oWlq/LuMMDbcBQkCqwiqJxws0lVr1xiFKXez3U1YsEhwM5oRE9YozPd3ku+SlKY+zFLftoD1weDW77kRGItZ1JrY0L4f/4S1vC1z68tET7Stf+3pT6Y47L2OlUr23nH4FKxQauW1D+Yc/qpIGDhtyVze2+g4wXFVlAsIIOOPKzp1HvExm1Xhxx8bq1bPOHGXFYpls3HDAy+ZagVLguTwy7rk3AAh1+//m1fvKd9y1xrgFBMc8JQgBdz1i7tu3Sdm2bdKdml41Qkk0umJVJ9Zs9wmCkKGUIsdxVo1YSZKmLMuq1TTtKDB2BMrls/yJhGkYxiRjbKMgCDJjjHJNQyKlyviKNVjjF3jeYb0uElxRRIhSyjnnGABc13WXZVme4ZwboVDoHaqqOifPobq6GlKp1OZisXipqqqvRwhphUKhJhQKfU8UxU+e+nYCzM/P9xqG8f7aWAyUnp51K1/+yjbKOejvfc+w+68fkwNf/fJs3nXrOOfFycnJq0VR3K4oSgpj7BiGsTsQCPTIsixKHkWcuzunpqYukSRpSNf1jwSDwafFsKmtrf2P5eXl+6LR6N1Px3gVKjzbqUgHVHhaSH32cy+0B4681j5y5DIvuVaBWWioT3mzc6cN8gWEQL/wgvHyHXeuyYyTOjtmccCfwYGAYA+P1ivrOpPle+7tBEpBbGnOeOmVIC8WV7dm1B07JuyhoahQXz/vHD3aBQBAamosurioPPaSYktzhts29hYWjykniyJXtvQO0HS6BcnKojM42CnU1MxxjCIsl1eV9ev7mWMhe3B4E3in7nBgv8+UN20eNB94YK30QEfHsDM6uk5c35n30isKFqU0eeWlKXzhBbLQ2EgQQgoAlDnnkmmapizLVqFQ2A4AIHM+4TFWL6bTKemGm1L06CD2MpkWoSqctQcONyNd53JnxxEcDHk0n6+VznnBUvFnv+iGpSXhWAmVi/qN/ft7pLraWVYqIxwMmsA5Uv7n66qQTrvZN7yxlhUKa9ZGamu9X2xs/H7Dj3/4vSd61uV779MbNYOhAAAgAElEQVRYuRzJX/fLM5Ekvot5tNY+cDDhLS6uee5SR8eYMzrafvIxZdu2Pmvfvt5j6+YvyN3dY8w0IsA5krZtOyC+7x/vESi7m05OXVv+7GcjXioFYm1d0ltaahAbGxalzo5w6cab/LRU1k722Elf+NwB2LVTMAyjBwAQAIAkSYOO43Qde8RiyfM8XRRFQ1XVg5qmDeLJSdOR5QshHhc55/8qSdJWx3F2YYxbPM9jRBDEwWTZbowGLxeo1d5YX/uTXC7XJEmS6LpupFQqtRBCziSERDVNu0HX9R8/0boBAKTT6VdijKslSfqtz+dbOLktn8/jQqHwsWKx+EGMMSR8/lF63XURJMmBzE+uVej/fiMjvPu9Fs3la+Vvfn2UxuPLpmmeo6rqEEIIOOcCAADn3AaAuKIoGWAMFcvlmKqqSULIDxoaGj7zZHOsUKHCqVSMpQp/EtkfXHMxzWZf4UxMthr7HlnnTc00rTYiBMA5CHV1RW9+3v94Ywj1ddNyRwcr3/37lsfrg3QdsK7naCp1+or2AKCcsXXCGR5JkFhskUSqFO66K3b/oVM8O+pZZy6yUhm5ExM6Kxb9cs/GQ/bA4U0AACQWy0otLdO0XKpzjhw9tlUUj5dAFD1148ZFZ2aKYX/A45bNkKoKYFsMCaKPIUQRZwwhbAtNDVrpxpubsK4ZQk3NpDM2vlm78PyU/ZrXOFZdbSMAgCAI6apCwSkSQfY31Gcsy3IZY0SSpOZMJiP7fvaLfj43R+xbb3tURDIaTQntbWX7oYdbtHPPXTEHB4Pyd781gSYmHW1Lb8wbGXXzb317vdK7ud/q6z/lvoXWVkf56bW/LBaMg8pHPljHksltnHpBIZG4We7ouCv4t6+5Xdu9mz72vCcBzb/rPZdaBw5+xJ2d3bFmnXfvGuaOa9JMxudOHovlQsGAK8YTE87o6Cmq1UiWqf9zn5mEs8600G9v0PjYmGf29TXQQoGylYxPam1ZdsfGY6S6eo6m0/W8XAYAAOkNl4+ab35TgyzLg4wxlVLaqCjKQrlcbgcA8Pl8RznnVRjjcqlUaotEIt/WNO2j2Wz23xVFuUeW5RXHcdrj8fj/lEqlCwuFwhcxxlHXdR9RVfUT0Wj0wJOtwfF/n9J/pqZp1nPOr9A07TMAAKVSaffKysqHbdvuFUVRLJfL1YFAwHIdh7I3vUWgU1MyYAzojluLyoN7pvmDe4i4pVcjr3k1c11XzufztQDHMrJ8Pt+hYrG4GQBA339wkP7yV3GytTeNL325gaLRXDAYfGEwGPyjYpYqVHg+U9mGq/AnUfjt76429zx0AQCA2NqaR7pu8nJZJYmEITY15sF180hRbG9+/rQikgAA3tx8kze/AFJX15AQjzFz4HAbz2TWBArzchlwIlE8nbEkbdgwjgjWsaYbrFTSWanU7k5OAohivXLG1rS1/8CabDjzgQdrAADkbdsGwTZFZ3FpVciQZjJhLxyW3ZGR1W1Dmkr5kKqAPTVJ3OERv9BQP0XzhXpeKKz5/GjnnjNqDw83kEQC1B07Rsw9e7rs9MoxIcPGpiWvOrGqleR5XrQ8MdEvLy6FjJddohJFKWGMR1zXFQGglaSWfV4qpagXvXCezs5i7nk+d2w8DgTPSO3tI6xYYDyVigBCZbRzh+aYZq7wjnetQwDATJMDACBVBYQxxYFAmeayfqIqN9XX174BADj8eo2kzp8Cr/ufr/+mdPfvb85+69tvt44c+RBNr9QBAJh7HloHACB2tB9e7ZwviKixsXzagWybFP75ynYSixk0ldKQJFGpq2vInZvfgEIhygsFX/TDH1wq3nZ7EqmK5Q4Nt/vf/g8rxcsui3NKFQDgjuOsJ4SUHccJaZp2xHXdBlmWBYyxY9t2yefzPUII+UYymfwdY2yLrutHQ6HQTxcWFubGxsY+J4riPeFw+IWO43xZVdU3PkXD4g/6i1NV1TkA+AwAQDabjWKMDwLAtOM4LxcE4SgAVBcKBUUgJCOGQh4FkKUr3z9get6G4o7tGwMvvPCIrGma53mmZVmqpmmHDMPo4Zwj13U5AAAGAPbjH4ve4SMRb9++iE7pEfLOd6zkcrkbSqXSf9bV1T34h8y5QoXnOxVjqcKfApI7OwUSDg/aQ8MxhKAgJBLYnZho5pZFaCajufMLNXC8RMAJcCBgPXYLCDgHZ3BwvTM4CPKGDUt4Q1eOF0sWIIS89Irfm51tRapaBgBQdmzvZ9ks5q5XhQRC3fHxem5ZMgAcCyRubZn0kqmEsqlnnGO8JuD3BGJ7+5I3N5ug2Vz4RHFXAAAkSYyVSqufC6GhYVxIxItI98XNe+6pBQDw5uab5S29o/aBg2sCnlm+YNOlZFXphhsAAKrUXbv6rQMHNnLHIfb1v27mr7xUUG17CC8lbYqAwsiYVv7V9Y1VO7bP2k1NIUIIhd/d8GXfXXf/U/nBPW3qZz89SmuqNXrl1QEhEZ8AgB6aTDXSZAqQrhtSR8coPLBHKl14fnuNKPW5Gzd8AodCU/r55/UJoZAttrQkueMw/awzs/lfXV/DTQvgGRLM851/nuM7/7yvrXztGz8t3nzLR+zh4Xdyw1ABAIRoFNzRR+VnnJmZjcoZW4+wfIE4Y2Pr1wzEOdBUSgMA4I5DxIZ6xe7vB6m5adju6+9Of/6LCi+Xq6X29hEAAHNqpkQwlgRRnJNlWcUYJw3DSFBKddd1o4FAYMyyLCZJEjdNM+L3+3PFYvHDtm3vEEUxizE+s1QqlQghBGMscc6bTNM8R1GUf3iGPTAonU4/YFmWX1XVj2KMhwAAGGMOAABCyJC+892s09fXBgCAquOUH7OBoFAobHAcZwohlEcI6YZhbFJVtY8xpsi/v1fABw4cEKvCuHzkaC8AAInFDLu3V7ZzuZf5/f4px3HOA4CKsVShwh9AxViq8EdTvPW2qDU87LfHxtqxJDO6sBCVursGAACYYchyJDLvJVMB/ug2BSBZ5srmTaPMtpGXSvm8qenmx45LwsGMcd8D3QAA8o7tY/LZLygL42NTOBxBYm3NmHHf/T3csvDJ54htrYNIkQ0kSn67/1CnvHHDjHVooFtqa51Cus5xOGhIjY1JVjLynDGMOAN3LHmKt4ubJhY39QzRpaVNWNNs7nkRa9/+NfFU6s4dg8w0T1G29vK5NVuNzDA0wJgBAKGZTEgtlYY9jJn9tndsBgA4Ef1kfPs7XvEDV7WqqnpfyPV+Yk5O/RtQCvT//i9IGhpXPNsKIFGSTh6bl8uaMzraoQwc+mn89Ze9NByNzoRvvvFxn1Xw1a9aeNzGp5HIe9+djrz33Vcu/b8P/bD8yCNfJFXhmNXXt/HkPjxfkKz9BzYgXafypk0zKBxE7oc/5AiDw0XrQx/uRbYNIIpc3bp1oHTnXZuQ3+/xUlkEAODlMgIAQJJYAgDwbru9SWpoGDIv+9tOn8+3VC6Xq3RdnyyXyy0AAJRSSggRj5cmQZlMph4ANvr9fpMQslwul1sQQu9QFKVfUZRPWpa1y3XdQiKRsJ6pNZqZmTnftu13M8Y26rrejzG+s6GhoWTb9hkA0EswLivf+0HKvvZnbQAAXJLA2bKlHigF0TBy8szsMm5qdLCmiTnOIwAApmkeiwO79toZd3CokdXWFkFRQIjFbOUtb55zG+oVQRDSnPNPtLS0/OSZurcKFZ6rVHSWKvzRFH75q1c54+NneVPTdbxQENTdu/ql1tb9au/mb0SvfP8HSjff8ia6lFzVUyLhsKVdeMEczWTBemRft9zZOectLsVP1CoTm5sKQl39GDOMGppOH8+QQ7b8tr/HQmMTo+PjuvXIvggvldZq+dTVznHHjXgTk610KRkBAKCpVBBcFwuRaEHq6Jiiy2nBnZ6p9ubm6mkqVU2Xlx83Y85LJhNCIr4id3en3KnpWnCPZVyTaCQldXTOYl0Fd3ZWkzvaZ5Cu6yyblVAgwBBGNi+VfGJb25LYUD+l7d4V8JZTDsvmdKzrrpQrpIXGRuz+9ner24KktjYvXfV+z/L5gpzzCN686UZ+0y0aKEqvNzbu98bGYlJnZ4bX1ebEKy7PoFdcuiJHo4vewEBCam9/OPjSl1weecELnrIg4Z8L30UvXNJ37/6hMzIadCenzuC2fUqgPbgupslkEPf2Fr2zzkRePNaCf3U9AdtGQl3tLA6FFbGmuoBkac4ZHuk6+VShpjZLk8kYAAA9eDCqX3LJpCXLnBCSYYxpjDGFc05c1426rhtyXVdmjJ1scKYsy6rxPK+uWCxGbNsOqarqT6VSX8AYPxAOhx96JtZlZWVlm2VZ7/c8r0mSpJsB4CaM8YSu6wZjbB+/595NwuEjAeub31wNjJe3b1shM7MzMmM5sG3bufIDHe6Pr40L0UjSbm+LAzpmt2srmRlZ1zlnzNVe99oV4b3vmZMScdP50Y8X5Hzhp8LuXbc0NjefrtxJhQoVnoSKZ6nCH4115Eir3dffq53zggFnfKIVEGpUN/W8quptb51Mfvzft3nLj5a5QJJExc6OsdINN248IfbITBOwz+cB50zq7BxyZ2fbSVWV7Rw4WAWyzJXurgHAWGX9hxzU3OzHougJ8Xhe3L5ttnz7HY/WStP1ojMyekptLRwOFYSGhrzx8MPdPJeT1R07JgC46y4uBcSG+pL58N6Ok4UnV/E88BYWI95SMqKccUa/9cgjmwEAhNq6tD0w0H1CJZxlc1w95+y0WFszaR3saxGqawyprb3fOnBgHTeMandyirLjGXusUBB5Ic/od79PTwS+AwAIkaqZ0lve2qP3bJz1PvIhxRLF1/u+9PkHHIAL3f/3oQi+8IIZ+qpXioBQlWFZjQAAgWLxCACAvKH781Vv/Yfs0/Q4n3bkdZ2s9r+/9Omlj/zLb93Z+f+yBwc7vYWFppP7kLo6D9avyxFCiKIoGcPnq4JiUVZedknRvede2To62A4ADSf6I1XhUkfHQS+bbV49pmu2GQzUe5a1apD5fL5hSinmnJcQQtg0zc0ntR2xbbudUirTY89fNE2za2lpaT1CyAgGg//3TK2JoigDkiS90+/3rzy2LRqNzk/9+Fps9fV1AgCI57xgGRgATyaRd+j6jeTu31vSpz+Z946/O3TPHjkSjU0UezcnPIGoNBFXyi+7RPX19CSN1hYxEAgEUDz2m7p3vuP9UKlXVqHCn0TFs1Thj6J0++1d+Z//4susUAx42VxM3bp1TmpruyP2gau+P/umN3/IGjh89YkvRqSpntjUNGj3H1qteA8AgP1+ghSZgyRSIRwu0kwmxHM5WWpuHkGSZDjDw100mw0KgjBf/ua3urAip5zBoWYSDKZosejXzzl7BGvavBCJEPe4ejepqirJG7pHSSy2iCRJs/bubRUikVmhOpFx52Zj7vhEHSsU/N7sXETZvu2IN79wekkDgBMGjcSKRVVsaRl2l5ebwHhUhwlhzEkwNGvueWiDvGnTOABn3DAkEo2khEQ86S0t1ZwwrLCuc1osxtzBwWr1jDP6vcXFaqGpaZllMlW8VFbo0lIQbrxJlMcn5kJvvOLfjHvvP1t5x9ui0o4dsmEYmiAIeUVRFlVVNZTxcdkdHknFPnDlB6SmpqdFaPCZxHfhhcvguT9Xz9ha6y0s1tPl5dX6auT66xbs1pYWWZbnAGOuX/56Klz84lnS3q45N97kslxuTXA+9vkcpCghb2JyNdBf6Fo/7lz84lVPIULI5pxj27YbPM+r8TxvtU1V1T7DMDYxxk75Q5ExhgCACIJwo6IoKULIH5oZ+KRIkkRlWTYfr91dWHyILqd20kKhUXzxi+atd78zJNx3PyeKknWnpuL0rrtFbprHtiQBmChKJe+rX9PJr64HfPMthL38ZZxFqjxV00xBEIZidXV/93TfQ4UKz0cqnqUKT4n8L66LBv/2b9IAAMaePST/699+0pubrwMA4IUCxpr6HWX9ui/Pv+s9O62+/nfQdLoZSRKXN2wY4K5bJ9TVgjM8smZMd3x8tWwIr6lRaS6ngevqdGVl9cuNW5ZgPrKvW+7dfMCdnOwAACDxuCeYJivfedcpley5bUsAwJz5hXaeTusAAN78/GklCZhhnrYsxJrxKFVJbW2SWVY1z2S0NW2Og73FRR+KRkvWvn2rni5586Y++9BAr+/FLzpqj437uec5UmODYdx3fw8AgHn48Cbt3HMOWvv3d7NSWRYaGpalN12xTG3bU887d6PP5+POSy9+0HGcHQBACCErCKFwqVSqj8Vih/COHRP60cH/0M8523iy+T9bCL72b20AuIqEQnvdublLlj/1mTcCY8AYowyAMMYEjDG2Pc9wIlU1fkIOSa0tWazId3PbaXUXFi4CAMSKRVmor1uNJ5K2bh0nl78OTg4wEkVx0nGc9adMAgAcx2lVFGXasqw1Hi5VVccty2rhnJN8Pn8bpfTbjY2N//yMLMYTELv6qoXY1Veds/j+q862+/rfGGxpObcwPNIu19bMAUADy+ePvYOiCEpbe7p8xx0Rls0GAQDI2S+YQoQEJUXJcc4lSumv/9zzr1DhuUrFWKrwZOCFd7/3Z9bg0FmF3/x2ijuO4M4v1CNZWtEuOH/YuOvudQAA7szsZkN8ZDsrFv+ZZjLNSFVtuacnZe3duwkQApKIP6FyMJLlMrjuKUHTAAA4ELABkC61tI4hVRFoKkXdoSH9dH1ZuSxZ+/b3KLt2DljpdM/p+qxeE6PTXu9k6PKyqp937nj5rrs3nq7dm5trEFtbJlAsaq/WjOPQCoIApVtv6wZZBu2ssw5R11ndIiKhkGftP7CRHQ9aFnbvWiicc/ZmIgh5UJQ+AADO+QWCICie5xm2bfdwzo8Gg8FXxuPx30I8zuFLX3iyqT8rCVz68p8VbrhxvPZrX1EX//nKV8njE3nftjOmy4ZRoyhKESEkiqJ4XXVt7Tvghz9YdUOW7v59LSsUUOF3N1zMHOeVvFQKiLvPDJT//u966UneSoQQEEIe97kihPKUUg0AQJblcYyxJgiCwRizMcZlQojl9/u/JgjCV57RhXhieM1/ffFeALh38aqre6V47PNIEFezOqWuriVSU52yDw3EWTYbBwAQt21bdv71X4iA8axpmtU+n6+oadr1f7E7qFDhOUZFlLLCkzJ54UXftgeH3vrY49jno9q554yXbrypEwAAJIkFXvWKo15qGbylpTpncGg1uBv7/SUUCDA6Px947Digqtx34QUTpVtvaz3ZYELBgKNu7s1in7ZSuumW7hPHpa71R5zBoVO8SkJTUxr7fQuAMJVaW+TSDTd2nzYm6Thyd/cB++jRrY/bAQCwTzdRKETp3Lzv8foITU0LSFMi7uCwDAAg79qRQpQtCZGIVLrl1vVS1/oZ9NUvy/DRj/vshx7SSWOjwZZTCjePZfQhVXWVn187bWhaE8a4FI1Gr8QYP1IoFDZKkvQawzDKiqLsr6ur+9oTzfWview1P2zilvXh4k03C+hT/3mWVFWlUEpdQRAUVVU3BgKBp1KYFWez2a2lUukS13VbEUKjgUDgWsZYczab/RQc013aefIJsiwfsG17KwCAJElHHcfpRghxVVUXAcCTJGmkvr7+omfglv9ocj/+iVq6465vlW699Qp1164jzuhok9jcNO0MD7fLr3n1mPXz69rFzZtM95OfAJfSkKZpR0zTbKqtre0Mh8OLf+n5V6jwXKASs/Q8xzo0kBASCRMA+MI/vu9ce2hI0nbvzpzcx5maZtb+/Zc/9lzuONhbWpK1c88+SjPZEC+XRZDkrLXnoe5Hs9mOQSKRrJiIzXlLyVOy0JAsM26ZnrK+awpJUl7u6kqSSNWCvG7dsnHX3e3O6Fjs5P5Sa0tZrKtPA0COFYtVAABiS3MWK0rBOTq4nqZSNc7wcEzeuOEw8zwNTFN67DUBIZBaW5e9+fnEKW0nIdQ3THvTM4+bOafu2nlEiMcM+5H9tQAAgDH43v3urLihG/j8AjgjI1EUixlqR1veS6YsOjQUFj/xH0cUQrLu0HAM6bqhX/bakeKW3i6EUEkQBBshdHs8Hr89FAod8fv9v6iqqvpNIBDY+0Tz/GtD3bw5bx0auEf9m1fHzGCQipLUxjknhBAHYzykKMrIk48CXFXVhWAweE84HP51OBy+V9O0rK7rk5FI5DuRSOQ7APAIxnjecZyzRFGcYIxFGWO6LMsLlFI/51wHACTL8qwgCEiWZcW27UdUVf2zSC08FZRNPR7N524DxuaYaa5zx8fbcE21AZ/7DDV27GjxqeqgEIuVOOdpnEg4WBA8RVEWqqur/+svPfcKFZ4rVIyl5zlCIlEGAD7/rvdc4o6Pf973ohf9QF6/rnhyHyxLE87EhM4KxQ3ccdbWE+vsGDL7D22S168fFNvb5+3Dh7vBdddoIAEASB0d02463cILhVMNF9fFLJv1uTMzcZrJRNzZ2Zi3lKwWmxqXsa4vcssOcdcVTgSH07IREBLxEpbEsreUTMg9G0fd2bkquri0xqgi27d50sUvmkKZlQRLr6yZk3rmrn7zoYcfV1V89d59OmXlsg6n8cAKzU1TzuBQpzs7F5N7Nh4S6mpz6tatFpZFx9u71ynv2dNBdL2gtLYum9f+rFXcsW2Wf/hDaV5T7bM++elOZf26MSERy1j339+mX3bZeDiRIJTSaxoaGr74ZPN6LqBu6XVsn69fEISY4zhniKIoAcB1gUBgxLKsaUmS/mS3t67ro6FQ6E7TNMdEUSxTShEhJEgpFSilq8HjGONFwzA6BUHIRiKRjxNCnlUlQdTezW7wNa/eZ+7bt89Lpi5GsWjJu+D8KBJF0VrXWS384pdlEQFXCsU0Wb9uBGN8dSAQeNZJSlSo8NdKxVh6foLm5uZ+appm3DCMPHvgAb1w/W9+a03PtKEXnNkS6On5+cmdxYYGTgKBh6xDh15MM5mGk9uEuvoCnZ2NeAsLCaJrZW9hIQqnQWhsSHnJVEQ/95yjYk3tijszE0e6XpaamydYqRQGSk8xsIREddI6cHCzUFfLxcbGSbqUrAIAULdsSZp79jSQWCyFfT6RFos2y2TWZLVJu3YWjQ9/MMpaW0HfsiXlXP/rCAAAqanJS62tQ4zSBF1aetx6dSdghqErvb3D3uLiKfcld3ZOe3Nz1cAYoslkNV1citJ8oYhct8AsG8t/+zdlYtnLzuhYI02ldHjve1w7Fu1UrvvVMn14b1Roapq29x/YxB1H1AJ+U2xqGq9ub3/Dk83puYSiKEzTtP2U0kOMsUQ8Hn8nIWTi6TCUTiYUCh0KBoO3RCKRH1iWNWIYxlsIIWld16/2+/2fkSTpAUVRvidJ0ncCgcDy03ntpxP/i1+UpMupm5jjvCZg2orb0Y4kRTmCL7pwGW074yuRXTuvqqqq+nnFUKpQ4emlEuD9/IQrivLZVCq1V+S8z/7ZLwLOyEgNbmosiWed1fHYztlrfliT+/G1tzHbaQBB4OB5COBYKRBnfHy1rhpS1NPW/AIAcAYHW/Szzx4r33NvD8iyI9TVzQs11cvWvv296vbt/eZxLaMTSO1tE4CQCABAqhOT9tGhNrGzs6Ru2bJUvvuuqLp7V781OtYp1VTPuBMTpxRlJRdcsIAx7pRVNcMHBiLqju0jNF8oesvLtc74eIdQU/3U0sI9D4B6q0YVkiQqNDYuYkliSFMBybLHbXv1c8QNoxq3tJj2uedI+IEHs8a9924GANC++uVho7GhWTHNYeu731uHAIB7XkR96UsOoWIRuc3Nkqtp3wmfZgrPB8Lh8H0AcN+f41qqqv7esqz/lmX5h/X19U9WJPdZR/xj/zo6e8WbPm/cdntPoK31Yfmcs4+GQqHRv/S8KlR4LlMxlp6niKKYAACwOd9G3/WOgrSSOeLs3bvB/ea36Fxq+aNSY8MhWigs0my2Se7szGjnndtnPvhgnAT8aZrPm6xYDJFQyPFmZ8nqoOj0wndIU12htnbc3H+ggZfLGMplBZqagta+/XUAAG5qqUXeurUPPIchQSScUspdN2Du2dMFAGAfHWrznXP2ePHGmzrd0dF2IATUv+nNgShMc8PiOBxOs2x2rR7Pju2W3++fENJpn3nDjZwuJetpNnssC2pLbx8iBDxV7eani2c6eRxd97xcflUXSGxsHHUmJ9cDxgBHjzYqmzaN2sPDHdy2AQDAd/VVE8ijRGfMtfv7w0hRXGBMlFLLVbB926wHwAM/umbU/da3E9QwYso//SOlN9/CeWdHn6LrN/8xz/K5xv/ePfaLizYk3tYa9+eeifGrqqpyVVVV738mxv5z0fCja64FgGv/0vOoUOH5QsVYep7iOM7uE8J8nqIElCvfFyD/9M8Znl7xynfe+e9lAKxs2zpiPrCns3TzLRC++YZhsm3rSvnv/r4LBQOO/+Uvmy386CdrSlAApQkgBB6bgSYkqpPO8Mimk49509O+R3+eDQjRGFiHDp82M43ncsRLLbtiQ8O8OzVVB54HmW9+q1nZueMAzWSiUnPTklUqRUgkskyXluIAAHY+x7Saal7y+URUKosnDKVj82RgHezrVXbuHOBG2UWSTKz9+08bv4SDgUUAvmqIeclkK6mrs+nMjIwDgQJpbPSEUtHCPp9FQqHZwsf+rQdEkSu9m4/Y+/a3aeefO6Gfc65uV4VKhBBOFcUp19YY5N8/nlUUpeBhrEtvflMAEfK2SCTyrFXj/nOxtLRUc1a9SKG0/FuI+8+FivJ0hQoVngWcEidS4bmPPTIiur/9XatE2QQCsBXTGoQbbuYknkibe/du5baNuW0DN0wTAIDrOvBgUFVL5ZB60QunuGFK5Rtvrve9+EUZAADk94G8ZcsoYLJ8ulR9UlWVfrI5OcMjLWJL89Kag/jR19N86KENYm1tVlq/bhypqguMAViW4E5MNFqHBjYGr3jDnNzZkT/RX97zEJQvvqSJvO9Ka028EcYAGPkBAKyHH+6xBw5vtZhj0/oAACAASURBVPbv3yx1tA+ebl5iU1NRjMcmlO3b+kksmmemKenbt43gULAkreuYNO6/v9OdmFTsQwMh7rrH1L1dF2FFYUiSuPLmv5Pti19UUuKJgAAgM8YmAcAhojhHOQ8IglDFEfpKNBp93htKAACmab4y4pO2M8a68vl86MnPqFChQoVnnopn6XmIufeRK4uf/PQVIElM3bplxfn9PV0eACjbtg07qdSqhYKOF7pH5TLkL76kQdy9M6l1tEfN2+8Ams3KpTvulJUtW44gXaPm/Q9serzrPRXnADOMoNbdNQWKUkaUCcC5yV3Xh0PBlDM+vpUXS0BLJdUZHWvTztp90EuvCMyy/QAAgde9dgwBECGRWLXU+JGjAs1mRaGhAWNZppQQgcRitlRfN2o99PApApNIUe1Tjqkq91ZWwu7IaA0AgHLG1j4Si6tmX1871jTTW1xq0M4/P2k9tCfBCkUHiVJJ3rDhINJUyTxwcKPcu3nQisWoJMt+u7OjSAhRabm8wXGcdlEUB2zbDriua/n9/lufdIGeJ4ii2OI4Dg0EAj8JBoMVA7JChQrPCirZcM9DlE2bHij8+jdvpgsLYTo1vaqELdRUJ73FxdWsMiEe596JjDHbRnR8wufNzIpI1xEvlwE4B1JX69mHBprA88hpLgUAAGJTY9Kbm39crSKkqa7+wheOOUNDQbq4FJU62xetAwc3slwuQJeSNWpvbx/N5eLK1i3L2nnnmqgqQoFSBTD2SR3teXvgMEOYmKxYDGBdn+WeR3AgoCubN09bhwealS1bk/rnP5sRdu9OG9/4n+7TzUGorbHpUnKNJ0NIxFe8qelVHSYvl0tgWWbu5GRIbG2b4Y6DxLe/1STFYgksy7APHuymy8s1IAhFsabasg/2tZHampy4aZNIKRUYYxZjDHueFwYAz/O8oCzLj9TV1f3vU3luzwdSqdSllFK7sbHxzX/puVSoUKHCCSrbcM9DCr+7YR0JhzOkvm6NnhKnTD7xMwoEAIXDKygS4SgQWNWc8ZJJxBzHUXft6Jc2bhggPp9xorCn1LX+FE8AkiRghnHa7RShtmaWRCK2snnzkHGwbx2JRCgrlXzGvfdvPlaY9hjm3r29+nnnDtrz85qFIMM4I/bA4ahzeCAIjpsHSQ57waAgvOQlOeEtb3Kls8+aw6FQunTffet4viDBmbsseuNNZfaL6wjStFM8SAAAWNOKQlPjDPb7Vouc0pVMUOrsmFztZJrInZqKAwAggg2WzQacL/23BoZpulNTq7XGvNnZNqAeAkJAQBgtLy83ua6LyuWyhhDSAAAkScqrqjrFOX/kCR7V8w5JksKapv3LX3oeFSpUqHAylW245yHuzGzAnZxsZcWiKjQ1TmFVyyBCgLsOEjvbR7jjSrxYquWFQj1yHI5EsaBccP6Yt5JRnPn5RrW7a964977N2KdbUmvrmP9d7xgXmpp15847HXdxycdzOfHEtbjjAGB8SpV1qaN93F3J1GBNA3PPQz2ht/zdROH63zQAIaDu3H4I67p3slVTuvGmDdrZZx+k99wHxuJijGWzIQAA44EHukEUufqedw1lu9bvAADMN29GaiazKExPz9ClZBN90UWqsP8AEnfuEOwDB/LUMOKPnY87O1vDOMhiU9O8ffhIOwAAtywRSVIWANYW4sUYxJYWDRAyrSNHa53Dh2uVHTsO2X19PdxxkNzdPaKfd46MQ6EJ967fS2p39wyvqclr4ZAuCAJyHGdRFEUiy7LPsqz5p+epPjdobGy84i89hwoVKlR4LBXP0vMQ6/Dhl3nJVOj/t3ff8XaUdf7Av8/UM6eX23tvuT0kIRVCgEDo6K6IlB+IIkXX1V3cFVRWQd21IQqo6FpAwIYrHYSQSki7Pbf3Xs49/Zzp8/z+iIlJYLMW4Cbh+/4rd+7MnO88975e+dyZZ74PTcmcMTZepPX2NquHDjVrh7qb9P7BCmN0rMiSZUafnpaseJwxQyGvPjnlVNvbaxlCVHnvvjIAAFtD46wRDnuEs86SzKGhJJedDVLtsoFj71gRUQQrmXhL+yA2MzPFZ2RMcT7fsGPjxgG5rYNwJcWq64rLB+Q33qy3kkmvWF8/ZVu5opd43DoAgKWpHrW7u8kKh/3HnUzXCRkdESmlbE5OThQAQHU6vYzXJ3Pf+HpbyjByI40NpfKu3Yvm/PxxQYlxODTbyhWdZijssyYnnWwgTbY1NXXzRYXDRBQNYrO95fEicbsNPjeHl5Y3h0HXD4/pvn31QmXFMAAAUJoM/fyxQi4vH2w11S79/91cQPr7zUgkUiLLsuZ0OmVBEJy6rusMw0RPPD9CCKFTC95Zej8ixBCqqvq13t6Kt3wrLY3SYJA4Lzh/PP6HZ442nGQ9HgUAwFxYSAcA4LKyImYy4dZHRzn+hRenzXBYZAMBiwKRwTg8z5rY7bK0vHnITCYNoaoqpg8Pl1HLIvaVK7pMTcvUenszgGWBLG8KErstybs9lnzg4OE313iBqof254JpApeXN8hkZBAwrSTx+5NCYcE06AYPPBcivMBxV14eoevXTTGqWj41NeUFALAEQeL++Z8sJTOjBAwDKKVA1q1xwGOPH71WLi9vgs1ITyr79tcBANhWLD8k79tXQWVZtJ29qkWoqo4mX3qp6cQxopEIZyUTVvSxJypsDQ2dSktLHQCANjVVKK1d06G0tVc61q5ui//mt41AKbCZmQvKF7+0zP3V+ztJc5PEcRwHAMDzvAsAPhmJRN70er2zJ34OQgihUwNO8H6fib/yR2f0iSd/KJSVpYT8vHl9dCyTX96cst980wyzvGlOuOmmBf33v0/XJyccjvXrJ/SRES8AAJeRTo3ZORebl6fRWIwlTkdCKCqOat09WfroqM+KRFi1ozPLisUEvrAgxBcVTxFCFKWtvcqcmc0yg8GA8+KLuym1JLWtPd+cmj48sZxSECoqphw33sCR2lrZaGtPmnNzfmNy0m9fvbpTn5jItGIxvxkK+Y3p6UyxvKyb9QcCVFNjalt7vTE1lcnU1o5bP/1ZoevSS17TTLOWUso4VfV166FHSswHvxdwlJf38OHQtPYfXykXCguHWa8vLNbWzlvxuFvr6S0EALCd1dyutLTVga5zAACs1ztjJRKcOTf/tgvtKt09fvuqlV1GcCHTCocdAACEMFQfG8u2r1l9iMvJdvCZmTpxOAbFmqqE1tGZQ1R1LnHW8gqv12vwPG/XNM3keT4zGo2KXq/3tffkFwAhhNBfDe8svQ8kk8kqh8PRm9z9RpF84MDX+YZ6m7LrjVyLAOu4aHOb0tpWYuzYEWXv+Txn/PvnPVxOThg4LmkpytFHRNS0ZonHHbBVV43pXq8HAGaIQ2IAAKgss6Yse/iKiqRYWpKQe3ozzdFRJ19aetzyJ1pfn0A4VgaA49ZkMxdDrPz7ZyLytm2lViJxOESZJqR27aqzbzy3M/X6tjoAAKG8fJIRbXY24I8QhpAjc5qU7z64HgDAfuDgE7CshhFF8TX+Bz9yxLe+ng4AYP7mt3nqgYMuYllAvd5MfWDArQ38eXUIacWKLqWrqwqsY9ZOZVgnAEj/66CqKqR27qoDAGCzskaEokLQZ2fTWKcrQQ2Djz32y3Li9xk0FM7isrMPAQBwmzZRt9vdHY/H7Xa7fYcgCM0AIACAFIlEqr1eb/df/ENFCCH0niH0bVZTR2eGaDTKcMlknTw3fy3Ny61QFxYFsb9vmSGKWvK+r/qNkZGA2FDfow0MltJUSmDT00JmKOw/0liScbksYpfAnJtnxOVNrTQls1YkkmuZpt2aX5D40tIe4PkCRrINUFUDxuVMU/buy5POXtUut7Y1gHr8i2d8aemoPjRUdORrNiMjJdYuGzKDiybr87BWMsUpBw4e3xVcFMGxft2h5KuvLRMbGtrU9vZGoaa63wyFnebCQg6YJgiVFSkzuMjyuTnb4AcPh9lXt04oDz18izk/72MrK0fsH7+FGrk5CrcQFOJ3fqrs2NNLG8/tll/f9pZ2AsTltOxr13Ynt+9YBrJM/rcx5ktKEtQ0LGN8wg2UglBVNS411GVHf/UbHgBArKmZBrs9xVRXafw1/+jgcnM5juMmAOBiu90ep5QSSZKMv/qHixBC6D2Dd5bOUKFHf7xF3XLxWs0wmjmBT3fML2QBUC75wguK2defxaWnjRkjIwG1vaNaWrduWt61K8dcCP554jQhIFRV9ij7DywDAOACATHZ8loNUAq25qZuZX6hRh8aqgaeB9D1Rmnt2mlzYV4DADBCoXRb7bIBdWCwlMZiR18iYEQhIq1Y0W7pmslIEm/JsqnsP1AiVlXOprbvrCA+H3Vs2jRghkKy2ttbY6urPWTML2ToM7MsEUWLcdh5AACtu6dCWnP2lEZpinW7xxmnS3X/212m9ua+/xZeedUZe/yX33R/+d5FmJ1PpTZsENmW/Rqfl+9I7XnzuLfy2JycGMNx+tuNH40nGH1kxLBVVbUrra2N0prVPVRRdCBMhpVIgNbXlwU8bwmlxYv6yJjsOH9T0EwkqHT5ZQCx2CjJziqgM7OixbFE/8q9WQ6/f57heYkQEtN1/anMzMx3Zd0zhBBC7zwMS2coee++5Vo4fIVx9ipTCwS82sx0yDY+KbGGCYaiELWjswZ4HsAwgCYTLHAc8MubF+xr14TV119X+PIKRj7YIgmVlW1mLFZEFTWHzchImnNzDmK3u8RVq0bUvXuLj7wNZkUjkjY4lMPl5cV9H7mWi7/wIjk2KAEAgCDY5f37j04qJ3a7QVMpTp+c1EEUgYbDROnrK5AaG4ct0+glkpSuj45mA0A2AACYVpq0dvWEvHtPvhkMsjSV4nRVrXJsuWgmXl4OaWvWHEgmk687166JwKEuF0Mtzf7rpxRtYlJkW9t1LhpR7evWdQFQoJrmslKpqNp1KIukBVQaXBThRBxn6lNT2Y5LtrTK7e0F7p//TE7c8Um3OTYmOM7b2EIBnFZKVrSJiXptcBCcV1zelnziyTz3NdckhIyMRXVmNgdMUwaWzbJ3dEp6UWGKpqcLDMO438UfPUIIoXcYhqUzkNzSWkWczqvUhx5ZBg88CAAAJgCQNWu6qa7lGVPTTrGmetJSNZtQVrJIUzJDGCZdSE+PcVsulqIPPFhBTTpgzs+VGvEE2JqbOkCyi1YiXiLW1U4IeXmcMTcbMrKzNHNmVgAAUIeGfc7NFw6yXi+TamnNA8n21re7KD0uJNBU6vBCvjOzAWnt2m559+4ac3JSTExOVgMA8JdeMmBft+6QPjXl00dGcuS9ezPFZTVTxO83hds+Ma/X1QElRBQDASISQmVZvt8wDIXfvVvkfD6gAT9jGToPKdnO5uZGzUUHpJ59/i1Lndiam9uU4GLj242lOT+fk3z+hRzbmtVD1q9/o7OCoLBlZZzc0lrIF+ZPAmHcRx436iOjfi4QSCSfeSZuX7NaUDu7QO/uKbHf99XehZ27qjxPPTlg+v0SwzC4jAdCCJ1G8G24M5DW3w+x3z19nVBSOm9MTmYBAHCFBaNmOBzQe/u8QClQWbGEsrJJbWjIbkzPFHD5eQPODesdkfu+mink5XZrE5Ol/qeemNXe2CPoPT25jN87yefmJRnRZhCbmErt3F0Dkg1oPH64D5FhAJebqxKWTTFZmfPG0IjTnJ31AcsCUApiQ/2osRhy01hMBIYB4Hlg09PjQCkPuk649LQpY2b2z0ui8LzFZ2fNpfbuXcZnZITNUMgDlAKYBnFctHlc23SephJSBoQIDMNoPM8fNE0zZLNJdYY/QM38PEpKS6lRXp4SVJXQUIikrr4y0+F2T/LNzQt0ctJhJZI8AIARDGZIq1Z2GZOTmcTlAtA0IC6XwdrtUXMxlAEAYExM+tX29jShskIDTWP4oqJFMAybNjGRBymZAwAw5+Y8tsbGUHLHzip9dJR4P3zNLiuVNJWW1kqhuqqXTE6azLnnMBzH8YSQ54zDLQ14lmXfuvowQgihUwaGpTOQUFSUCj38g1vUrq5mobJiUKypHgPT8upDQxkAAIzLFbE1N06Y8ws5+shoLk0kWHFZ7WJq9+501uNRkh+/IxK68h/m2J//zCOsOCtl7N/vEpcti8rbtlcyHrcMAm+3n70qSBV1ypiezgIAIIJg8YUFcSIKyeSH/jHHvnLFbi4UymTdbmLG4gQ4ljfHJ9zE709ItbU9VFWdVFVALC/rN2ZmM5mCgikrEU+TVqwYZ+zSpFhXN5Xa+noDmCZYpmkTS0piri0Xz/O5uSnpggumSGlJFSFkp9frHaHf+PZvkt/45jpHRcXTanqaIHjcJZTjDMIwPMuydt7l5I2RUdXucSeZC84XeFV1ctd8KGFs36HRRNIOlkW4rIwocbkWvE//VhWyc6akDevDyd/+7riJ30JZWY+1GHJovX1Z+uhYujEzEzgSlI5g09OnrcVQhq2qMmWGw3ahsHCrpSg6TSaKjJHRdGdzk5YILuRFZeWfgOc+ZhjG2ng8voUQ8qogCG87fwohhNDSwrB0BgpNTRVpQ0O3mb29PnNx0c+4XfPqocNLeADHUVtz84C8a3c9cJzFcJxFFYUzpqcDXFFhOHn5VTO9Z29sIKLgjJ69lvFVl0W4C86bhv37GcJyVOvrz9aHR1xqX5/HXFjIcF/74QEAEhPKy8ZT27ZXaH39meTmm0Iph2OFLSW/mnr693VcZuYcl54xzXg8KmiaqfX3l9FkUqCKKgLLWZQhTj4ne5YBRlZbW0vN4GImTaXsQlVV1JiZcYIsc+b8vKQvLNhoLOaJPfZ4CVOQv80TXNyu/uzn08bUVIPjlpt/ymxYf5coimHDtAoBqCHrFiuwhAOXK6mUlQ4mJMkpiqLECILFRiJi6vf/4wLtcF8lsa5+Ttm7r5rG4nHGMhLW1Kyo9/UdfTOQCAJwGRkhIkl+c27u+PlNLAt8YeGkrbZ2QsjP45WDBwP69IxdHx31a6Njy+xnr+oUSktfsd3/lVfiHk+T+sEPFfD9AzH7ZZc4LYB0wzC+HggEet7jXxOEEEJ/IQxLZ5jQjx5Nt9LSHqHp6X6ro1O0olGBiDawYjEnAIB93dpD8q7ddXxpaYINBAb00dF8AACwLCLk5c+zC3Mkfcer04Fnnxb8zz0NJLgQTK1Z3cj8/DGOv+H6Gb66ala85eY5TlE1LpAWMYJBhmuoT4rnnGNjtlw8yzXWRy2vL26IYrYzHJ5idN1nzMz4Cc9ZxGaL6ENDJcfWa6qqS3rwgRZW1YArLwuAboQZUexwnLuhhypK2AyHsohoY6iuMwSAMaanOTBNEDkumXj6fz6o7D9wvn3tmq8z1183wXGcZZpmhFIrZ2RRtRMCIbvAmaZpjsXj8XN5nl9MxON5xifukOU/POPli4p6hLLSWcblDDJOB9GHRzKMQ91uGgpboKqcWLsspk/P8NLy5m6+pCRoTE9lcD7flCmn/CArRyevi3W1I2YonKf19WVygbRFfXQ0AEdachgGr/X3V+pTU8WwcSOXtEsrHdk5PfZrryGKzZbSNK3V7Xb/wGazKe/ZLwlCCKG/Ck7wPs0pHZ2crb7OAACIPv37lcl9+x4jA4MOayEoG/Pzhxs8AmWEFWf1EFVzyh2dVQAArNc7SDUt3dbc1CYUF9nVgSEw43GRUeQ47ehscP/6yanwHZ8KMDt2CT6Hsz8ly/nmzp1E+8LdhT6/f5FkZ9uUH/94gU0kAGbn0vSP3QKEkJ54NLqRJWQeKAWdED61Y6cPAMAMR3LcV16RVNva/1QRA9LNNw5BXUPSrKwQuPo6u9NuH5X+9V/WC4JwdA5P9HdP+7n0dKoc6i5yXXjBWPyll4qS23ecQ1XtPGN6uoHNzIwal2y5V00mRwghveL3HtrJ1i6bFjdsBsKyi5TScwkhZ6WnpwcBwK0QMq97PIY6MiJyxcVe+c29xWCaYMzMxInfp9NQmNdHRrL1kRHgi4sHbCtW9Mu7dzeArgOTnZ2ydF3iMzP6TF7IMOfm0gEAgBeIubDAAseBNjubTSTJpLJ83JpyViSaQx9+ZC/3lf8wEuedW50EsHhZnisuLr6e53kMSgghdArDsHSas9XXmeHHHq/nC/LF8H//9MdKR2eFrb52SmlpK+MKCgZZl1PUx8azuexsS+noODqBWh0aqrGVlY7LBw42qn39QJOHm22LzY0ym5YWjH75fglE0TAnJz1mb+8CX1I0qR1sqfYpyjTIMmeFwinGJtmV/Qep87JLd3KvbR3mr77qIVmWX9M0rcLj8YySmmrgcnI0MxgUqKaBMTd3NBQ4PnfXQGzjOeUAALyuBymlw6Io3uM5JigBAHg+cHUIAMCxYX0YACBwx+2hwB23t6T27HlwkWPvlfft+zRMTKjU464TXtuqxZ997la+rf1ZcfNV19bkeOTJyckmQRB8VjSqGa9u1eQfPZpmzs25icetW5Fw6shjNisccTk2X9CTfPmP1WxGxgJfWDintLTU6iMjQCSbRU2T4X2+BX1mptianWWktat7rKzMGFCwqW1tRQAA7quuHEjt3u2jsuwCABAbGwd0QdBNh1NjnU49+tnPmaKVmgSAIgrAcBy3E4MSQgid+jAsneYmrrvhW9rg0I3S6lUhpaW1jHE6VH1q5vAbXOPjZZbPNyeUlAxROeU/9jiaTApmPG7707+Pbldb2qr44uJZY2TERZNJCQDAikaTav9AvX3Duo7w5Vc1EJ6n0lnL+2xNjQ+67/3ik+4rLj/aYHF+fn4eACoopWFbT59pZGbMEFEkQknxNmDZEmKXDJqSOXZZteFwODokSfoRAFRkZWV9OhaLrfhLr9u+erVpX736CzOf/sxziQcefNJcWEg3vN4VtuXN/+JYu/bF4hyPDABgWdY54XBY5FU1zAV8JuNyRoSiQl0oKzukHDp0dN03+zkbupSOrlIAAKG6Kipv33G0xYDU3NxtJpMZalt7oVhbG1JnZ/3y7j3HdRqX1q/tS/zx1TQrEgkc2UZYViHhkMdYvmrRdu2H3D6vc7mSIhPRaLQIAIAQclw3cYQQQqcmDEunOTMY9Orj4342EDjc7IdhTcs0LQDgAQBoMpnO+H3B1Lbt6ccdqOvA+nxhoabGoJoq8Tm5cSuRkMEwGKW3t8ZxzjlDxsx0jHG5eUott33Dhj5GFArANIGaJtGnp8WCXz/1yIn1CIIQ9Pl8n+c47oeUWrnOzZuBdbkM30039gAALHzz2+cZU1PVUiK5O7e4uO3YY91u976/9vqzH/j23tnP33NV9OmndwmlJS+nferO79lXrzaTqtHEsWSRYZhf6rr+nwbLevj6+qj2/Qf9afv2/zbx8COVXF6eIS5b1mJpqgCEcZgzMxKXmztBDSMNAICIImUcDlUdG680Jyf5wxfIzwOA/8Q6aCKpHBuUAABYr5tTDh4s4Ae+V8B0tk4tfPHuXI/Pt9vlci0AgM3r9d75114vQgih9x6GpdNY7NnnMhifbxnjdhv6xISfCAIlohjns7MVXVMDfG7eIGUYnzE9DWJDfRuXnm5L7txVZWtomDAjYQ8RBVEbGCiiug7GQjDDikYJAQDvLbcMR3784+PuehCHw2Ld7iAAAOP1KsbMTOHUx269JvfRHz517H5paWnXORyOFABQuPGG0Ik1p//LZ7YCwNZ3chxsdbVetbu7lyqK05idWw8A2xwi1woAQAiZ4ThOZlk2LghCSNc0u9rZVWBMz9QZ0zPAOJ1JvrSkz5ibcwEACBXlrazXuwyWN4fMRNJjBINeOjl5dP4Rw3FH13ETm5pG1e7uIlBVUHr76m3NzW3a4GAJn5c3RC3Tq41NHG3CKb/xRq57584D/Ac+8Gpubu4v/rQZF2ZECKHTAIal01Tsf/6QFXvhxc+mdu1eaT9/06HU9h3LxPKyCQuIFwAi0rr1o8kXXjiuK7Xm96tiedmksn9/vlhVNaoNj7Kg62D7wFWTzJ13UPq1/3Qrr/zRo3R2JgEAWJ8vZYbDdhBFsDU3jctv7i3i8nLn2Ye/z4nTM88Iw8Nv6QvkcDiSJ257txlzc8P+2z6x2bX5wreEs9zc3MfC4XCfw+FoX+zu/rA7Ev0AzM+v4LKzFGNm1iYuqxmQ9+5rBgBg09PnzVA4K/X6ttITz8N4PGDJMliq6pBWrmjTZ2dztO7uAvuqVXOpHTsyQZaJtrBQxvh8mtrd3XTssUJZ6Va+oOAPebfd9n0AsN61gUAIIfSuIJTiH7enm6mP3fpJbXz8GiuZLAdKWRBtNt7vG069sadWWrf2kNLZWcFlZgWteJwxg8FMIkmUycqK2bZcNK88/ss0MxzxMXZJsZ+zYZiqGiS2ba8RGutD+v6DfgAANjs7yqWnzTFOZ6a8b7/Hce6GQaWtIxsYhnBfvW/erCgfTktLu8zr9aaWeiz+ErIsc5IkGQv/+V9Xxl965V6tr68BAEBacdZWPjd3m1hX22ZMTS1nvL6B0EMPP0ZVlRx7PF9etiCWl0+zbrdbX1hwKvv3S1Ys7mRcLp1xOWNCXe1s6tWty/iiooQxOemkf1r+BACAz8874L3++osDd94efI8vGyGE0DsEw9JpJPLEk3X62NiNoUd/8hmqKIQvKxtj/b6wGQ57jOBikWPNmu7ESy8tA9MErqBgVqis4EllpQwf+gdNNwxFcrk8zPQM0Q51R5Ofv7vasfHc+eTW1zOk9esGtf6BfNbn7eP8fib1xp5aAADbirM61PaOOrDZLFt11ag2MuriHvpeBHKy+4uLiy9b6vH4W6gDg77or3/9GSuVigsFBY/5b/34zJHvya2tvuivfnOLMTsrqj29t+sTE9kAAI5LL30ZVDlPPtCSzdhspjEzk85mZCTtq8+eSL76WoVYWztqBhcI8fp0M7QoGiOjhQAAjMcT8l73keaMu/99bKmuFyGE0N8Pw9JpIhKJ+BPPPvec9corHlpcQrnqSjZx9xfLqaKwxOFIk6eLLQAAE7xJREFUOi48fzj55t5KOjMrMF6v4rho85hJLY659ePUMC3TGfC7DQDNsizFjESo+dm7iDY0VGwlEoKtuakDgFClpaWBr6mZprpmGAODBWJdbbs2NFTL5xcMMX6fBoqazv3rZ5P2+roOv99/1VKPybsp/ORT54Z/8KMHmPQ0ai0uurW+/hIAAGnVinaq6ZatqcmpDg9prMNJEs+/UAMAYN+w/pAZjgBQagKlHsKxTxW9+Py/Le2VIIQQ+nvhnKVTUCgUuiiZTF7h8XgeUhSlmFLKRaPRe7Smxmb+rOULvv0HGVJWZlgCz9offKCbYVlWf+WPlr2uvsvIzUtnJCmqvLk3Xx8b84g9fSNAiBDs6Mh23Hl7F7ns0qD54stucVk1UdrbBQAApaW1njgclthQ3wamkWbpBiOtXNHFuN0G8Pyw2tJacaQ24aGHuszcvEx6+WXXBc7b+PjSjdK7S8jLi+T+6JF1c3d/4YDyp6AEAGDMB+36yEg54fkQcbvHE8//eV6YGVxU1e7uZgAAYrPRtH+76/mlqB0hhNA7i/m/d0FLIGIYxsbp6emD8/PzzywsLDzNcZxACLFsPK+lentUy+VS3XffPSStXOG1LIuSSMRtaqpXPXAg34pGLEvXCVAKakdHsdreXsDm5UWNnTt15ZaPr+QnJoLq4NDhydCEgGPzhUO2xoZptb2jUe3qztNHR/PkAwdrzWg0TR8YzDu2MG33nlraP9AHff27lmRk3iOO9evaxMrKBF9Y+DXW718AAGAz0mUgwAIAGPPzTqEg/wUAAFtT46xj03lT+vR0wZHjWb9vKvDxj53RY4QQQu8XeGfpPaSNj7vjz794qXHFZS5F10tFURwXRXHI5/O9eOx+kUjkmlQqVSkIwgjLsn7TND2pVKo2EAiMGIZhqjff5DWnpmQjHksIhLjI+IRu3vJRhfX7vNzUtKZ2dNYzPl/Eft7GFktRMsSCAoUrLLTrw8Ou1JtvxuXdb2wQq6u2AQB4b75pO1UVK7r19Y3HFWtZoA0Ne6QVy/9AZSVN7etfZ4ZCNmK3q64tF+0L3Hbr6Hs1bksp+1vf+Pn4wsL55vx8LePzmubMrN3W3NQhNTctEEk6mPa5u56NP/tcgzY2TvnCAlmNRNIAABib1AbYGgAhhM4IGJbeGyT4ne9myS2tz1iJuFtdv1Y0WTZXEIR5RVGeAICjYWn+2eeul0uKb2MIMTVNK5YkqcMwjMOdpQXBxvO8qev6bDIjw89+9/vpoTWrFc+GdW5JUXn5K1+NGgMDgvuaD82Yi4uJ1PYdDUJ5eTvJznaYNVVWYtVKP73tVsm1fcfz2nMvZNk3rNup9fRmqoMDRaAf3wWADfgjjnPP/VTO9777GACQyONP1MRfeOHfqWH0Be684y3NKM9Uwe8+WEKj0VK1o/P4Ngx9/Za0fl3CmJqqo6bpskKLHmDZCONyxax43M24nG3/2zkRQgidXjAsvcuS27YXJ1577Z7Eq1sv0MfG8m3Llw+y8/NRPTMzT1NVlWHZS2cmJ/dl5+X9dmRk5HZWst3kfPmVKZvXK0XWrPbIslxPCKEOh6NdHp8QmIDfIXR3M/r2HbLa3p7t/sXjY8r4uNMIhy1i6AHi96coQFhpay/x/r8b58Cy3GxNdTxaWMhompZn47hZMy3NonZ7emrHzgouOztMbLYYANiO1MxlZw25r7zicxlfuOd3f9pEvddde8iSU19Q2tvTlmYklwbr93upbhy3KK7jvI1dRBQYcVmtoTscYQCA2O/6fGYo7Lc1NrYrbW0NrNc7vDQVI4QQeqfh23DvovDPfnFH9Klf3aN0dWWBZQHr9ycsyxKYNL/MfOqTQ6Jpiuaevenc5gujyje/DY5vfyNKMzJcxsGDpvnfP9fomrN5c91asBwOh5BIhsizzznNUMjOOxwurbNrlBV4nhq6rvb01QtFhSNEFKm5GHJzebnTwDAMX1ToiP/qNyUAAPb16x7W7/9Kkcvt/kksFvu2EQy6hZ07x5TNm+sIy8quJ576Dp2YCCpt7V9zX33lrZn3fumMnbz910rteVOMPPHkRxNbt37dCkdc9nXruoSykgOJl1+50JiZzTl2X7GpaUBtbS23r1v7g4JfP3U74KM4hBA67WFYepfMf/2/Php/7rlv6sMj3iPbxIb6QbW94+gyIkQULVtDfYfc2dXo+sBVb3LV1RnxhnrNvOOTeYLHM3jk0Q+3rCYKFBgaDALr9Uwac/PZ0vq1o8nnXmgEQkBataJbfnNfzZHz2hob25S2tkYiSbpYu6wH7A4vd/55FjM69lDOV/7jm/39/a+ZpnmOaZosAIDb7b6noKDgfgCA+S/ft8V73Ud2CSXFsfdutE4P05/8p5uVzq47+cKCgFBZ+WUai/FqT+9mtbv7UiuZ5IBhwHnRRY9SOdXHuFylng9+4Cnn+Zt2LHXdCCGE/j4Ylt4h8sEWW+hHj/5EGx7JAssqMBaDaXxeXkTt6y+iycMrgHAF+WOs262oXYcqjx7IsiDW1U4JublU7e93AJAg8CwAYXTtUHcNAIC0atUASLaUvG17A3E4gPF6UlxaWp/a3tEEAMA4HBZxuyxzZpYjDocsrVo5pA0Ne9i0NBMy0hLG1Vc7fYEAaK++tjPnns/fMDU19e+pVOrfeJ63VFUNp6WlXRAIBIaWYtxOR/P33b/JfvbZbzjP3yQDAMz86+e+n9y2/R+l5c3/lfuDh78FeDcJIYTOKBiW3gGxZ59jkrt2PxF75pmraTTGAwDYGhva9eBisTk56T52X2K3y1x2dlQfGso6ss1+7jnd8t59NVSWAQCAcbuS9vM2juhDwxowDKNNTFaKRYV9Skvr4UnGkmSJ5WW9akfnn+8mnXVWhxWLuc1QyM3m5jBaZ5fX/cNHBpnlzZKiKEFVlvMsSqVAWtraVCq1XlGU+51O52wqlYKKiopKwP/g/2axZ59jzHA433fD9dipGyGEzkA4wfudQOna6FO/+pBj06YRYBgAXUvoCwuCFQ473rJrKiXxhQU9QkGBSkRBo4rKUcNIBwAdAHgAAEtWHOZiiKqdXc1HDywsPNoTS6yqGlBbW2uOPa9y4EC964rLu435eZe8500vv3HjrFxZofOaRu12u+R0Ol9MJpP7MjIy2gYGBr4kCMIYIYSwLDsIGJT+Lu7LLrUAAIMSQgidoTAs/Z0iv3xCSm7f8VWwLNBHR2TG4wFl7746AABbc2O30tJ2NNQwLpfMFhYuAscJyT++WggA4L7u2m61tc2ghhE4sp+tsWFK6empOuY4lSsqFERqtVBFlVifVzm2Bj4/v5/Ly1VT7e3FICsJyjBg/ss/xzRFqaYAvYIgSAzDnGOz2TyhUOgxn8/3oKZp1ycSicsyMjI+8u6PEkIIIXT6wrD0d0gdOOClhAiGYeQGnvn99OIll9ew+fkhxu2OWrGYR5+ecbDZ2WGaTIiuz90VZC68gFCWjes/+em4MDTUzZeXZyW3blvGedwRLjs7ZEXCdsJyjBmNCraSkqC8GMrmcnLiAJCygouy2tbeDACgT0xQobysTxsYrAQA4HJz7fKeNysAAGzLlw/abv1YLC4I+QQgZeh6uvmTn8bMG6//IS8ITzIMk85x3FA8Hs/zer01Xq93YQmHECGEEDrl4Zyld8DIeRf8ygC6xeztcwIA2Jqb25SWlkYAAFtjQxvUN3DwiY+xkiRJgiA84/P5/gkAIPb8C+ekdr9RmNi+/T4+KzNC7HZBaWnNktaunU++/Eq55x8+2K329KZRQ09RXY9rvX11QAiIDfWj+tCw34rH3QAAYmPjgNrWVg4AwFdVLlCLOilDTO7SSyesczYA+6V7JwO333a9a8vFc0s1RgghhNDpCu8svRMYZtHs7nYCAHD5+dNgWUcTKDUNhpmbYWgqZRc8npePBCUAAPclW3a4L9lCZ/75M6nUnr3/6th0Hq9PTCWTzz1fDgCQ3L07QFOyz1xYyLA1NHQDADgv2TJAqqp41ndwAqjFm7G4pQ0OlR85p33lynjij6+y5syM3+ztq7Z1d09nf/ubF4oVFRiUEEIIob8Be++99y51Dac9pbPLrnZ2/gOIIrAezxzjdOiM07kIoi2uDw1XmzMzfkd9XZBYlLHn5jx24vGuizZ3s5lZrTQWW2PFYy+yPu+gGY1Wi+WlU1r/QBYwDAAhAldePs5/+Ut2tq6WKL99OqCNjvmNgcFsUFUGAIBINioUF8VttbWmPjkl0FSKMycm7YzTtdOxdk3fez8yCCGE0OkP7yz9nVJ79rBmKJwmlJb2Wori00dGyvSREQAA4AoLQ2x6WsRcCHrVx55IJifGm6TvPmB3bFifOvE8nssuaYHLLjm6mO3U7Xdup5r2ab64qJWtrMrXu7s94HbJUdPM9/B8UPjgB+bg8V8G9NlZO5+XJ+uTkxKXlZ0CIGPAstNiZYXJrln9mhkKVUsrznrxxM9DCCGE0F8Gw9LfjgAAhB/75UPJ11+/hSrKceuHeT9603Dy9e3UWAiWAgCAZYq2tWt28rm5zNucC+CE1/dzH/7+T9X+/sfFigp9cnDwyy6WvWHx4ksr7LHYQILSNCG4oAmF+UHG4UhyaWlRadVK1UomVW1sTCKjo1doo2Myl5a2znvTjf/p3Hiu9m4MAEIIIfR+gGHpb5TYt09yrlwpa4NDq08MSgAASmdXXB8ebjjytdrXVyYUFX1GKC1J/KWfIVZU6AAAeWVlX5zv7u62X33VfUok6mHu/mJS6e6uBwBg/f6kkJsrpnbtzjDm5o6tQ+Lz8yD+8it3yfv2n5Pzve9e83dcLkIIIfS+9b/d5UD/B+fKlSkAoNJZy++UVp+99y07mKZEbDYgkg1sjY37fDfccEnO9x986W/9vIyamqc4Qr5j3n5HgdHdfXRCNxEEM7Vv34lBCQAA9NlZlzE3p5jBoCYfbLH9rZ+NEEIIvZ9h64B3QPDbD9wS/M4Dj4JpglhT0yFWV4X5woIfUVnulc5abjAO5+DbzVP6W4xsuuB3ak/v1Uc38DwIpaU9jN1u6FOTNUJh0YgZj1ms10vUnt5CKxIRAAAc52/6Rv4vfnbXO1EDQggh9H6Cj+HeAfbVq37qXfhIPuNydjs3bdoG1ErYzz47+W58Fl9UFFN7ev+8QddB6+2tZjMyUmJV5SH5zb11VNPIiccRQnLfjXoQQgihMx2GpXeAffVq07569Zfei8/yXvOhb1my3GQuLkrAMCkAwrM+r2XKsssMBunbBSVgGBCrq3/6XtSHEEIInWlwztJpxnnB+V2M3T6idnZVqO0djcbkZECfnk5T9+4rApvtLc9UuZycVums5XeLlRVvnVeFEEIIof/TKX1niUZGOUgs+IGaMnA2SrIb/+I3yc5ghPX7OgDgSgAALjtrWu061AwAoA0M1nG5OZPG1HTekZ2dF5z/rayv3f/LJaoVIYQQOu2dkmGJRkY5UKLpNDpdCp4cjoRHcyG9OgYAzy51bUst1dKaqXQd+vCRr4kgZh/9pqoyfGFdkBFtFtV1UZ+ZyeQL8meXpFCEEELoDHFKhiWQw/9BJ/Y1gK90B1jWXhoeqYDwcCWdOnAl03DtxwDAWuoSlwrDcUE+O7tPbWsvF6qr+9SB/j+vC7du7d35j//ia/KBg05tdMyR2rPnosBtn3htKetFCCGETnenVOsAKzblJsmFdLo48g0Ash9GX/0CabqxlDoy42S2rYFK6QowrMLkr+yGEzpev5+Ef/HY6sXvfPdZEPikGYnk03iCcHm5Ie+Hr9mc9s+fPrDU9SGEEEJnklPqzhJhBaCunDAkgw8Cw09CZlMBODIijLdAtky9F8DcALrG0LnOXpJZZy51vUvFd8P1e5TWtlZ9di4ttX17AQCAfcWKb2JQQgghhN55p9bbcJYhMc6MEBhKNQh2g1nx0dvowMurrG33/Q+JT6UxgYrfM1m1v3s/B6Ujsr/zrQvEqsob2YyMKPA82Net3bXUNSGEEEJnolPrzpIrew4AgKm69JGjG031EggPX0FFt5sUbThvyYo7BWV+6Qsd+sjIT4SysjmqaYeWuh6EEELoTHRKhaUTWQcevQds3npw5cyAJx/7BL2NvJ/992eXugaEEELoTHZKTfAGAELnD1WAK3sM1JgBakKi84fWk/SqQySrYRzex5O6EUIIIbQ0TqmwRBd6WarGryN6sgB0+RIAuJdUXPzSUteFEEIIofevUyIsWTNdaUx2bRAAgE7tZ8BTwMFsxwdJ2QVPAt5NQgghhNASOiXCEgCANdG5DmxiL7G5FeLKwmVNEEIIIXRKOHXCUmLBTghRiSPtfd8WACGEEEKnjlMmLCGEEEIInYpOraaUCCGEEEKnGAxLCCGEEEIngWEJIYQQQugkMCwhhBBCCJ0EhiWEEEIIoZPAsIQQQgghdBIYlhBCCCGETgLDEkIIIYTQSWBYQgghhBA6CQxLCCGEEEIngWEJIYQQQugkMCwhhBBCCJ0EhiWEEEIIoZPAsIQQQgghdBIYlhBCCCGETgLDEkIIIYTQSWBYQgghhBA6CQxLCCGEEEIngWEJIYQQQugkMCwhhBBCCJ0EhiWEEEIIoZPAsIQQQgghdBIYlhBCCCGETgLDEkIIIYTQSWBYQgghhBA6CQxLCCGEEEIngWEJIYQQQugkMCwhhBBCCJ0EhiWEEEIIoZPAsIQQQgghdBIYlhBCCCGETgLDEkIIIYTQSWBYQgghhBA6CQxLCCGEEEIngWEJIYQQQugk/j/fLgzgamggnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysal.viz.splot.esda import lisa_cluster\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# england_outline.plot(ax=ax)\n",
    "# wales_outline.plot(ax=ax)\n",
    "# england_outline.plot()\n",
    "lisa_cluster(moran_loc, df, p=0.01, figsize = (12,12))\n",
    "plt.savefig('img/moran_hot_coldspots_UK.png', bbox_inches = 'tight', pad_inches=1, dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression using weights matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.302585092994046"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_50(x):\n",
    "    return [x + 50 for x in x]\n",
    "\n",
    "## Tried to address heteroscedasticity by transforming by log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# f = pysal.lib.io.open(\"data/spatial_analysis_dataframe_20190818.dbf\",'r')\n",
    "f = pysal.lib.io.open(\"data/spatial_analysis_dataframe_norm_20190821.dbf\",'r')\n",
    "\n",
    "y = np.array(np.log(add_50(f.by_col['cap'])))\n",
    "y.shape = (len(y),1)\n",
    "X= []\n",
    "X.append(np.log(add_50(f.by_col['hage'])))\n",
    "X.append(np.log(add_50(f.by_col['hprice'])))\n",
    "X.append(np.log(add_50(f.by_col['dep'])))\n",
    "X.append(np.log(add_50(f.by_col['elec'])))\n",
    "X.append(np.log(add_50(f.by_col['empl'])))\n",
    "X.append(np.log(add_50(f.by_col['rural'])))\n",
    "X.append(np.log(add_50(f.by_col['detach'])))\n",
    "X.append(np.log(add_50(f.by_col['own'])))\n",
    "# X.append(f.by_col['x'])\n",
    "# X.append(f.by_col['y'])\n",
    "X = np.array(X).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :Domestic PV Distribution\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :       solar                Number of Observations:       30742\n",
      "Mean dependent var  :      3.9135                Number of Variables   :           9\n",
      "S.D. dependent var  :      0.0015                Degrees of Freedom    :       30733\n",
      "R-squared           :      0.3745\n",
      "Adjusted R-squared  :      0.3744\n",
      "Sum squared residual:       0.044                F-statistic           :   2300.4441\n",
      "Sigma-square        :       0.000                Prob(F-statistic)     :           0\n",
      "S.E. of regression  :       0.001                Log likelihood        :  163357.447\n",
      "Sigma-square ML     :       0.000                Akaike info criterion : -326696.893\n",
      "S.E of regression ML:      0.0012                Schwarz criterion     : -326621.893\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       3.8013150       0.0451243      84.2409210       0.0000000\n",
      "           House Age       0.0189290       0.0013357      14.1716780       0.0000000\n",
      "         House Price      -0.1590773       0.0116256     -13.6833183       0.0000000\n",
      "         Deprivation      -0.0491097       0.0021736     -22.5939360       0.0000000\n",
      "                Elec       0.0035359       0.0063948       0.5529332       0.5803132\n",
      "            Employed      -0.0504629       0.0045681     -11.0469065       0.0000000\n",
      "               rural       0.1034003       0.0017301      59.7646573       0.0000000\n",
      "              detach       0.1266135       0.0026440      47.8862165       0.0000000\n",
      "                 own       0.0347645       0.0030122      11.5410736       0.0000000\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER        27441.962\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2      497341.991           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                8        8112.348           0.0000\n",
      "Koenker-Bassett test              8         780.396           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "TEST                           MI/DF       VALUE           PROB\n",
      "Lagrange Multiplier (lag)         1          11.128           0.0009\n",
      "Robust LM (lag)                   1          10.737           0.0011\n",
      "Lagrange Multiplier (error)       1       13929.751           0.0000\n",
      "Robust LM (error)                 1       13929.359           0.0000\n",
      "Lagrange Multiplier (SARMA)       2       13940.487           0.0000\n",
      "\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "ls = ols.OLS(y, X, name_y = 'solar', \n",
    "             w=w, spat_diag=True,\n",
    "             name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'], \n",
    "             name_ds = 'Domestic PV Distribution')\n",
    "print(ls.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial lagged regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create small test sample\n",
    "df_norm[:1000].to_file('data/spatial_analysis_dataframe_LSOA_TEST.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_test = pysal.lib.weights.Queen.from_shapefile('data/spatial_analysis_dataframe_LSOA_TEST.shp')\n",
    "f_test = pysal.lib.io.open(\"data/spatial_analysis_dataframe_LSOA_TEST.dbf\",'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: ORDINARY LEAST SQUARES\n",
      "-----------------------------------------\n",
      "Data set            :Domestic PV Distribution\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :       solar                Number of Observations:        1000\n",
      "Mean dependent var  :      0.0180                Number of Variables   :           9\n",
      "S.D. dependent var  :      0.0152                Degrees of Freedom    :         991\n",
      "R-squared           :      0.3564\n",
      "Adjusted R-squared  :      0.3512\n",
      "Sum squared residual:       0.149                F-statistic           :     68.6018\n",
      "Sigma-square        :       0.000                Prob(F-statistic)     :    1.37e-89\n",
      "S.E. of regression  :       0.012                Log likelihood        :    2987.242\n",
      "Sigma-square ML     :       0.000                Akaike info criterion :   -5956.484\n",
      "S.E of regression ML:      0.0122                Schwarz criterion     :   -5912.314\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     t-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       0.0157956       0.0054610       2.8924317       0.0039063\n",
      "           House Age       0.0056631       0.0018941       2.9898839       0.0028598\n",
      "         House Price      -0.0321128       0.0097342      -3.2989475       0.0010050\n",
      "         Deprivation      -0.0059837       0.0029372      -2.0372371       0.0418916\n",
      "                Elec      -0.0126959       0.0081857      -1.5509863       0.1212242\n",
      "            Employed      -0.0106338       0.0072361      -1.4695443       0.1420026\n",
      "               rural       0.0291339       0.0085639       3.4019488       0.0006958\n",
      "              detach       0.0451599       0.0036251      12.4576733       0.0000000\n",
      "                 own       0.0205969       0.0038920       5.2921583       0.0000001\n",
      "------------------------------------------------------------------------------------\n",
      "\n",
      "REGRESSION DIAGNOSTICS\n",
      "MULTICOLLINEARITY CONDITION NUMBER           50.405\n",
      "\n",
      "TEST ON NORMALITY OF ERRORS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Jarque-Bera                       2        2842.256           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR HETEROSKEDASTICITY\n",
      "RANDOM COEFFICIENTS\n",
      "TEST                             DF        VALUE           PROB\n",
      "Breusch-Pagan test                8         160.344           0.0000\n",
      "Koenker-Bassett test              8          33.951           0.0000\n",
      "\n",
      "DIAGNOSTICS FOR SPATIAL DEPENDENCE\n",
      "TEST                           MI/DF       VALUE           PROB\n",
      "Lagrange Multiplier (lag)         1          72.214           0.0000\n",
      "Robust LM (lag)                   1          13.943           0.0002\n",
      "Lagrange Multiplier (error)       1          68.864           0.0000\n",
      "Robust LM (error)                 1          10.593           0.0011\n",
      "Lagrange Multiplier (SARMA)       2          82.807           0.0000\n",
      "\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "y = np.array(f_test.by_col['cap'])\n",
    "y.shape = (len(y),1)\n",
    "X= []\n",
    "X.append(f_test.by_col['hage'])\n",
    "X.append(f_test.by_col['hprice'])\n",
    "X.append(f_test.by_col['dep'])\n",
    "X.append(f_test.by_col['elec'])\n",
    "X.append(f_test.by_col['empl'])\n",
    "X.append(f_test.by_col['rural'])\n",
    "X.append(f_test.by_col['detach'])\n",
    "X.append(f_test.by_col['own'])\n",
    "X = np.array(X).T\n",
    "ls_test = ols.OLS(y, X, name_y = 'solar',\n",
    "             w=w_test, spat_diag=True,\n",
    "             name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'], \n",
    "             name_ds = 'Domestic PV Distribution')\n",
    "print(ls_test.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "No loop matching the specified signature and casting\nwas found for ufunc add",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-288-1f1c77214500>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m ml_lag = ml_lag.ML_Lag(y, X, name_y = 'solar', \n\u001b[1;32m      2\u001b[0m              \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspat_diag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m              name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'])\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mml_lag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pysal/model/spreg/ml_lag.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, y, x, w, method, epsilon, spat_diag, vm, name_y, name_x, name_w, name_ds)\u001b[0m\n\u001b[1;32m    561\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDIAG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0makaike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschwarz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDIAG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschwarz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m         \u001b[0mSUMMARY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mML_Lag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspat_diag\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mspat_diag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlag_c_loglik\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrho\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pysal/model/spreg/summary_output.py\u001b[0m in \u001b[0;36mML_Lag\u001b[0;34m(reg, w, vm, spat_diag, regimes)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__summary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;31m# compute diagnostics and organize summary output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     \u001b[0mbeta_diag_lag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m     reg.__summary['summary_r2'] += \"%-20s:%12.3f                %-22s:%12.3f\\n\" % (\n\u001b[1;32m    199\u001b[0m         'Sigma-square ML', reg.sig2, 'Log likelihood', reg.logll)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pysal/model/spreg/summary_output.py\u001b[0m in \u001b[0;36mbeta_diag_lag\u001b[0;34m(reg, robust, error)\u001b[0m\n\u001b[1;32m    723\u001b[0m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiagnostics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mse_betas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz_stat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiagnostics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt_stat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_stat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 725\u001b[0;31m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiagnostics_tsls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpr2_aspatial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    726\u001b[0m     \u001b[0;31m# organize summary output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m     \u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__summary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'summary_std_err'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pysal/model/spreg/diagnostics_tsls.py\u001b[0m in \u001b[0;36mpr2_aspatial\u001b[0;34m(tslsreg)\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtslsreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0mpredy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtslsreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m     \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpearsonr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m     \u001b[0mpr2_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpr\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpr2_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py\u001b[0m in \u001b[0;36mpearsonr\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m   3408\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3410\u001b[0;31m     \u001b[0mxmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3411\u001b[0m     \u001b[0mymean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         ret = um.true_divide(\n",
      "\u001b[0;31mTypeError\u001b[0m: No loop matching the specified signature and casting\nwas found for ufunc add"
     ]
    }
   ],
   "source": [
    "ml_lag = ml_lag.ML_Lag(y, X, name_y = 'solar', \n",
    "             w=w_test, spat_diag=True,\n",
    "             name_x = ['House Age', 'House Price', 'Deprivation', 'Elec', 'Employed', 'rural', 'detach', 'own'])\n",
    "print(ml_lag.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.16.4'"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error as mse\n",
    "\n",
    "#                      'OLS+W': mse(y, m2.predy.flatten()), \\\n",
    "mses = pd.Series({'OLS': mse(y, ls.predy.flatten()), \\\n",
    "                     'Lag': ml_lag(y, m3.predy_e)\n",
    "                    })\n",
    "mses.sort_values()"
   ]
  }
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