m03_v01_store_sales_predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d884834",
   "metadata": {},
   "source": [
    "# 0.0 IMPORTS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21302619",
   "metadata": {},
   "source": [
    "## 0.1 Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "200be1d1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:04.314617Z",
     "start_time": "2023-05-29T18:16:02.182416Z"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import inflection\n",
    "import seaborn as sns\n",
    "import matplotlib. pyplot as plt\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f7c71e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:06.309894Z",
     "start_time": "2023-05-29T18:16:06.266411Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def jupyter_settings(): \n",
    "    %matplotlib inline \n",
    "    %pylab inline\n",
    "    plt.style.use( 'bmh' )\n",
    "    plt.rcParams['figure.figsize'] = [25, 12]\n",
    "    plt.rcParams['font.size'] = 24\n",
    "    display( HTML( '<style>.container { width:100% !important; }</style>') ) \n",
    "    pd.options.display.max_columns = None\n",
    "    pd.options.display.max_rows = None\n",
    "    pd.set_option( 'display.expand_frame_repr', False )\n",
    "    sns.set()\n",
    "    \n",
    "jupyter_settings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3447b58",
   "metadata": {},
   "source": [
    "## 0.2 Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aea924e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:15.321927Z",
     "start_time": "2023-05-29T18:16:13.980761Z"
    }
   },
   "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>Store</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Customers</th>\n",
       "      <th>Open</th>\n",
       "      <th>Promo</th>\n",
       "      <th>StateHoliday</th>\n",
       "      <th>SchoolHoliday</th>\n",
       "      <th>StoreType</th>\n",
       "      <th>Assortment</th>\n",
       "      <th>CompetitionDistance</th>\n",
       "      <th>CompetitionOpenSinceMonth</th>\n",
       "      <th>CompetitionOpenSinceYear</th>\n",
       "      <th>Promo2</th>\n",
       "      <th>Promo2SinceWeek</th>\n",
       "      <th>Promo2SinceYear</th>\n",
       "      <th>PromoInterval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>445669</th>\n",
       "      <td>455</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-05-28</td>\n",
       "      <td>8756</td>\n",
       "      <td>549</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d</td>\n",
       "      <td>c</td>\n",
       "      <td>7660.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Store  DayOfWeek        Date  Sales  Customers  Open  Promo StateHoliday  SchoolHoliday StoreType Assortment  CompetitionDistance  CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  Promo2SinceWeek  Promo2SinceYear PromoInterval\n",
       "445669    455          3  2014-05-28   8756        549     1      0            0              0         d          c               7660.0                        9.0                    2010.0       0              NaN              NaN           NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales_raw = pd.read_csv('data/rossmann_store_sales/train.csv', low_memory=False)\n",
    "df_store_raw = pd.read_csv('data/rossmann_store_sales/store.csv', low_memory=False)\n",
    "\n",
    "# Merge\n",
    "df_raw = pd.merge(df_sales_raw, df_store_raw, how='left', on='Store')\n",
    "df_raw.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e23fe5",
   "metadata": {},
   "source": [
    "# 1.0 PASSO 01 - DESCRIÇAO DOS DADOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1286d684",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:18.828227Z",
     "start_time": "2023-05-29T18:16:18.549537Z"
    }
   },
   "outputs": [],
   "source": [
    "df1 = df_raw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc3ad7b",
   "metadata": {},
   "source": [
    "##  1.1 Rename Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a5ef7fb2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:19.990762Z",
     "start_time": "2023-05-29T18:16:19.985473Z"
    }
   },
   "outputs": [],
   "source": [
    "cols_old = ['Store', 'DayOfWeek', 'Date', 'Sales', 'Customers', 'Open', 'Promo',\n",
    "       'StateHoliday', 'SchoolHoliday', 'StoreType', 'Assortment',\n",
    "       'CompetitionDistance', 'CompetitionOpenSinceMonth',\n",
    "       'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek',\n",
    "       'Promo2SinceYear', 'PromoInterval']\n",
    "\n",
    "snakecase = lambda x: inflection.underscore(x)\n",
    "\n",
    "cols_new = list(map(snakecase, cols_old))\n",
    "\n",
    "# Rename\n",
    "df1.columns = cols_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa0122f",
   "metadata": {},
   "source": [
    "## 1.2 Data Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "01b33930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:26.690226Z",
     "start_time": "2023-05-29T18:16:26.685763Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows:  1017209\n",
      "Number of cols:  18\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows: ', df1.shape[0])\n",
    "print('Number of cols: ', df1.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71899925",
   "metadata": {},
   "source": [
    "## 1.3 Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5eb41dbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:28.704749Z",
     "start_time": "2023-05-29T18:16:28.582349Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store                                    int64\n",
       "day_of_week                              int64\n",
       "date                            datetime64[ns]\n",
       "sales                                    int64\n",
       "customers                                int64\n",
       "open                                     int64\n",
       "promo                                    int64\n",
       "state_holiday                           object\n",
       "school_holiday                           int64\n",
       "store_type                              object\n",
       "assortment                              object\n",
       "competition_distance                   float64\n",
       "competition_open_since_month           float64\n",
       "competition_open_since_year            float64\n",
       "promo2                                   int64\n",
       "promo2_since_week                      float64\n",
       "promo2_since_year                      float64\n",
       "promo_interval                          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['date'] = pd.to_datetime( df1['date'] )\n",
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e09b244",
   "metadata": {},
   "source": [
    "## 1.4 Check NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41c6fd12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-29T18:16:31.334570Z",
     "start_time": "2023-05-29T18:16:31.168825Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store                                0\n",
       "day_of_week                          0\n",
       "date                                 0\n",
       "sales                                0\n",
       "customers                            0\n",
       "open                                 0\n",
       "promo                                0\n",
       "state_holiday                        0\n",
       "school_holiday                       0\n",
       "store_type                           0\n",
       "assortment                           0\n",
       "competition_distance              2642\n",
       "competition_open_since_month    323348\n",
       "competition_open_since_year     323348\n",
       "promo2                               0\n",
       "promo2_since_week               508031\n",
       "promo2_since_year               508031\n",
       "promo_interval                  508031\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0686065",
   "metadata": {},
   "source": [
    "## 1.5 Fillout NAs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5403a1bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:45:05.571467Z",
     "start_time": "2023-05-26T18:45:05.571443Z"
    }
   },
   "outputs": [],
   "source": [
    "df1['competition_distance'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8a895b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:45:05.478405Z",
     "start_time": "2023-05-26T18:44:08.838540Z"
    }
   },
   "outputs": [],
   "source": [
    "# competition_distance\n",
    "df1['competition_distance'] = df1['competition_distance'].apply(\n",
    "    lambda x: 200000.0 \n",
    "    if math.isnan( x ) \n",
    "    else x )\n",
    "\n",
    "# competition_open_since_month\n",
    "df1['competition_open_since_month'] = df1.apply( \n",
    "    lambda x: x['date'].month \n",
    "    if math.isnan( x['competition_open_since_month'] ) \n",
    "    else x['competition_open_since_month'], \n",
    "    axis=1 )\n",
    "\n",
    "# competition_open_since_year\n",
    "df1['competition_open_since_year'] = df1.apply( \n",
    "    lambda x: x['date'].year \n",
    "    if math.isnan( x['competition_open_since_year'] ) \n",
    "    else x['competition_open_since_year'], \n",
    "    axis=1 )\n",
    "\n",
    "# promo2_since_week\n",
    "df1['promo2_since_week'] = df1.apply( \n",
    "    lambda x: x['date'].week \n",
    "    if math.isnan( x['promo2_since_week'] ) \n",
    "    else x['promo2_since_week'], \n",
    "    axis=1 )\n",
    "\n",
    "# promo2_since_year\n",
    "df1['promo2_since_year'] = df1.apply( \n",
    "    lambda x: x['date'].year \n",
    "    if math.isnan( x['promo2_since_year'] ) \n",
    "    else x['promo2_since_year'], \n",
    "    axis=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b98b4f9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:45:28.229151Z",
     "start_time": "2023-05-26T18:45:16.232730Z"
    }
   },
   "outputs": [],
   "source": [
    "# promo_interval\n",
    "month_map = {\n",
    "    1 : 'Jan',\n",
    "    2 : 'Fev',\n",
    "    3 : 'Mar',\n",
    "    4 : 'Apr',\n",
    "    5 : 'May',\n",
    "    6 : 'Jun',\n",
    "    7 : 'Jul',\n",
    "    8 : 'Aug',\n",
    "    9 : 'Sep',\n",
    "    10 : 'Oct',\n",
    "    11 : 'Nov',\n",
    "    12 : 'Dec',\n",
    "}\n",
    "\n",
    "df1['promo_interval'].fillna(0, inplace=True)\n",
    "\n",
    "df1['month_map'] = df1['date'].dt.month.map( month_map )\n",
    "\n",
    "df1['is_promo'] = df1[['promo_interval', 'month_map']].apply(\n",
    "    lambda x: 0 \n",
    "    if x['promo_interval'] == 0 \n",
    "    else 1 \n",
    "    if x['month_map'] in x['promo_interval'].split( ',' ) \n",
    "    else 0, \n",
    "    axis=1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c9a34f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:11.624802Z",
     "start_time": "2023-05-26T18:46:11.420591Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store                           0\n",
       "day_of_week                     0\n",
       "date                            0\n",
       "sales                           0\n",
       "customers                       0\n",
       "open                            0\n",
       "promo                           0\n",
       "state_holiday                   0\n",
       "school_holiday                  0\n",
       "store_type                      0\n",
       "assortment                      0\n",
       "competition_distance            0\n",
       "competition_open_since_month    0\n",
       "competition_open_since_year     0\n",
       "promo2                          0\n",
       "promo2_since_week               0\n",
       "promo2_since_year               0\n",
       "promo_interval                  0\n",
       "month_map                       0\n",
       "is_promo                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64433ec3",
   "metadata": {},
   "source": [
    "## 1.6 Change Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fdd61684",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:13.825775Z",
     "start_time": "2023-05-26T18:46:13.819201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store                                    int64\n",
       "day_of_week                              int64\n",
       "date                            datetime64[ns]\n",
       "sales                                    int64\n",
       "customers                                int64\n",
       "open                                     int64\n",
       "promo                                    int64\n",
       "state_holiday                           object\n",
       "school_holiday                           int64\n",
       "store_type                              object\n",
       "assortment                              object\n",
       "competition_distance                   float64\n",
       "competition_open_since_month           float64\n",
       "competition_open_since_year            float64\n",
       "promo2                                   int64\n",
       "promo2_since_week                      float64\n",
       "promo2_since_year                      float64\n",
       "promo_interval                          object\n",
       "month_map                               object\n",
       "is_promo                                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ded311a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:15.694124Z",
     "start_time": "2023-05-26T18:46:15.662669Z"
    }
   },
   "outputs": [],
   "source": [
    "df1['competition_open_since_month'] = df1['competition_open_since_month'].astype( int )\n",
    "df1['competition_open_since_year'] = df1['competition_open_since_year'].astype( int )\n",
    "\n",
    "df1['promo2_since_week'] = df1['promo2_since_week'].astype( int )\n",
    "df1['promo2_since_year'] = df1['promo2_since_year'].astype( int )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ce3dc1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:17.395894Z",
     "start_time": "2023-05-26T18:46:17.389318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store                                    int64\n",
       "day_of_week                              int64\n",
       "date                            datetime64[ns]\n",
       "sales                                    int64\n",
       "customers                                int64\n",
       "open                                     int64\n",
       "promo                                    int64\n",
       "state_holiday                           object\n",
       "school_holiday                           int64\n",
       "store_type                              object\n",
       "assortment                              object\n",
       "competition_distance                   float64\n",
       "competition_open_since_month             int64\n",
       "competition_open_since_year              int64\n",
       "promo2                                   int64\n",
       "promo2_since_week                        int64\n",
       "promo2_since_year                        int64\n",
       "promo_interval                          object\n",
       "month_map                               object\n",
       "is_promo                                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e90194",
   "metadata": {},
   "source": [
    "## 1.7 Descriptive Statistical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0172d9a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:20.202977Z",
     "start_time": "2023-05-26T18:46:19.855007Z"
    }
   },
   "outputs": [],
   "source": [
    "num_attributes = df1.select_dtypes( include=['int64', 'float64'] )\n",
    "\n",
    "cat_attributes = df1.select_dtypes( exclude=['int64', 'float64', 'datetime64[ns]'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d04eda6",
   "metadata": {},
   "source": [
    "### 1.7.1 Numerial Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f9839d62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:26.379910Z",
     "start_time": "2023-05-26T18:46:22.233970Z"
    }
   },
   "outputs": [],
   "source": [
    "# Central Tendency - mean, median\n",
    "ct1 = pd.DataFrame( num_attributes.apply( np.mean ) ).T\n",
    "ct2 = pd.DataFrame( num_attributes.apply( np.median ) ).T\n",
    "\n",
    "# Dispersion - std, min, max, range, skew, kurtosis\n",
    "d1 = pd.DataFrame( num_attributes.apply( np.std ) ).T\n",
    "d2 = pd.DataFrame( num_attributes.apply( min ) ).T\n",
    "d3 = pd.DataFrame( num_attributes.apply( max ) ).T\n",
    "d4 = pd.DataFrame( num_attributes.apply( lambda x: x.max() - x.min() ) ).T\n",
    "d5 = pd.DataFrame( num_attributes.apply( lambda x: x.skew() ) ).T\n",
    "d6 = pd.DataFrame( num_attributes.apply( lambda x: x.kurtosis() ) ).T\n",
    "\n",
    "# Concatenate\n",
    "m = pd.concat( [ d2, d3, d4, ct1, ct2, d1, d5, d6 ] ).T.reset_index()\n",
    "m.columns = [ 'attributes', 'min', 'max', 'range', 'mean', 'median', 'std', 'skew', 'kurtosis' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "20e7e8a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:27.825689Z",
     "start_time": "2023-05-26T18:46:27.805576Z"
    },
    "scrolled": true
   },
   "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>attributes</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>range</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurtosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>store</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>558.429727</td>\n",
       "      <td>558.0</td>\n",
       "      <td>321.908493</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>-1.200524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>day_of_week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.998341</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.997390</td>\n",
       "      <td>0.001593</td>\n",
       "      <td>-1.246873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sales</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41551.0</td>\n",
       "      <td>41551.0</td>\n",
       "      <td>5773.818972</td>\n",
       "      <td>5744.0</td>\n",
       "      <td>3849.924283</td>\n",
       "      <td>0.641460</td>\n",
       "      <td>1.778375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>customers</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>7388.0</td>\n",
       "      <td>633.145946</td>\n",
       "      <td>609.0</td>\n",
       "      <td>464.411506</td>\n",
       "      <td>1.598650</td>\n",
       "      <td>7.091773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>open</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.830107</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.375539</td>\n",
       "      <td>-1.758045</td>\n",
       "      <td>1.090723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>promo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.381515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.485758</td>\n",
       "      <td>0.487838</td>\n",
       "      <td>-1.762018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>school_holiday</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.178647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.383056</td>\n",
       "      <td>1.677842</td>\n",
       "      <td>0.815154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>competition_distance</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>199980.0</td>\n",
       "      <td>5935.442677</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>12547.646829</td>\n",
       "      <td>10.242344</td>\n",
       "      <td>147.789712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>competition_open_since_month</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.786849</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.311085</td>\n",
       "      <td>-0.042076</td>\n",
       "      <td>-1.232607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>competition_open_since_year</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2010.324840</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>5.515591</td>\n",
       "      <td>-7.235657</td>\n",
       "      <td>124.071304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>promo2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500564</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.002255</td>\n",
       "      <td>-1.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>promo2_since_week</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>23.619033</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.310057</td>\n",
       "      <td>0.178723</td>\n",
       "      <td>-1.184046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>promo2_since_year</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2012.793297</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.662657</td>\n",
       "      <td>-0.784436</td>\n",
       "      <td>-0.210075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>is_promo</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.155231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362124</td>\n",
       "      <td>1.904152</td>\n",
       "      <td>1.625796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      attributes     min       max     range         mean  median           std       skew    kurtosis\n",
       "0                          store     1.0    1115.0    1114.0   558.429727   558.0    321.908493  -0.000955   -1.200524\n",
       "1                    day_of_week     1.0       7.0       6.0     3.998341     4.0      1.997390   0.001593   -1.246873\n",
       "2                          sales     0.0   41551.0   41551.0  5773.818972  5744.0   3849.924283   0.641460    1.778375\n",
       "3                      customers     0.0    7388.0    7388.0   633.145946   609.0    464.411506   1.598650    7.091773\n",
       "4                           open     0.0       1.0       1.0     0.830107     1.0      0.375539  -1.758045    1.090723\n",
       "5                          promo     0.0       1.0       1.0     0.381515     0.0      0.485758   0.487838   -1.762018\n",
       "6                 school_holiday     0.0       1.0       1.0     0.178647     0.0      0.383056   1.677842    0.815154\n",
       "7           competition_distance    20.0  200000.0  199980.0  5935.442677  2330.0  12547.646829  10.242344  147.789712\n",
       "8   competition_open_since_month     1.0      12.0      11.0     6.786849     7.0      3.311085  -0.042076   -1.232607\n",
       "9    competition_open_since_year  1900.0    2015.0     115.0  2010.324840  2012.0      5.515591  -7.235657  124.071304\n",
       "10                        promo2     0.0       1.0       1.0     0.500564     1.0      0.500000  -0.002255   -1.999999\n",
       "11             promo2_since_week     1.0      52.0      51.0    23.619033    22.0     14.310057   0.178723   -1.184046\n",
       "12             promo2_since_year  2009.0    2015.0       6.0  2012.793297  2013.0      1.662657  -0.784436   -0.210075\n",
       "13                      is_promo     0.0       1.0       1.0     0.155231     0.0      0.362124   1.904152    1.625796"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f79b4c30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:35.142263Z",
     "start_time": "2023-05-26T18:46:30.593735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1258fb760>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot( df1['competition_distance'] )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1c64d8",
   "metadata": {},
   "source": [
    "### 1.7.2 Categorical Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "51496fb9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:40.473354Z",
     "start_time": "2023-05-26T18:46:40.264343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_holiday      4\n",
       "store_type         4\n",
       "assortment         3\n",
       "promo_interval     4\n",
       "month_map         12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_attributes.apply( lambda x: x.unique().shape[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28eef463",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:43.533973Z",
     "start_time": "2023-05-26T18:46:42.480845Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='assortment', ylabel='sales'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux1 = df1[ ( df1['state_holiday'] != '0' ) & (df1['sales'] > 0) ]\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.boxplot(x='state_holiday', y='sales', data=aux1)\n",
    "# sns.violinplot(x='state_holiday', y='sales', data=aux1)\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.boxplot(x='store_type', y='sales', data=aux1)\n",
    "# sns.violinplot(x='store_type', y='sales', data=aux1)\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.boxplot(x='assortment', y='sales', data=aux1)\n",
    "# sns.violinplot(x='assortment', y='sales', data=aux1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cbcb179",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-18T17:30:41.118721Z",
     "start_time": "2023-05-18T17:30:41.110324Z"
    }
   },
   "source": [
    "# 2.0 PASSO 02 - FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7362a089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:47.524692Z",
     "start_time": "2023-05-26T18:46:47.153863Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deaf6822",
   "metadata": {},
   "source": [
    "## 2.1 Mapa Mental de Hiπóteses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13d51497",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:46:49.227015Z",
     "start_time": "2023-05-26T18:46:49.215832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OY9s2hYWFWXX6+/uJxWIYhkFdXV1ms5ZwOEwqlcLhcJCXl6e3S0RERETkDGEn4yT7mkj27CfV34IVGTtN7YgReeonJDt3E9h23fxGK9o2/a37qTrnfDy+wJyqpBJx7v36jZTWreXd//pjnC43AGsveyM/veVDvHDf/7HlLX+OYUwPoNj70D28/6t3s3T52hfLXcMdN76HA4//jq1v/QBv/Osv0bnvGUpqV/Hmv/1Kpl0ADX/4Ne/4x29Rc+424MW1Hm+7iaq1m7n+lu9lrr3hqrdzxz++l6fu+Q5XvPdTejlFRBYhx9neAdFoNLOZSnrHZYBQKEQsFsPlcrF06dKsOunNWNxuN0VFRS/+b6hNT8/0IsgFBQXk5OTo7RIREREROY3sZIxE27NMPfzfjP3sc4Qf+wGJtmdPW5h4uGRvIxO//TKJ1meY68jIeDSMmUqSk1885+t07HmS6MQoF7zpfZlAD2D5pksprFhG85MPZpWv3XBhJkwEKK8/F4CJod6jX+TF71DF1fWZMBGmN29JRMOc97rrs65ddc4FFNfU0/rMI3pJRUQWqbN+hOLhax0ODQ0RDAaJRCIcPHgQ27ZZsWIFPl/22h7pn1OpFAMDAxQXFzMwMMDg4CAul4v6+nrtziwiIiIicjrYNsn+JhIHnyLR3QBW6sxtajJG+Km7SGRGKy45Znm3149hOEgl43O+xkhvOwB7H/4FB597NOuzRCxCbGoi61h+aVXWz0739KaRZjJx3GsVVC7L+nm0rxOAwoplM8rml9XQsXuH3lcRkUXqrA8UA4EABQUFjI6OMjw8zMjICJZlAVBbW0t5efmMOqWlpXR1dWGaJm1tbXR0dJBMJnG73ZxzzjkEAgG9WSIiIiIip5CdjBFv2UG85UmsqdCiarvhcmM4nMct53A6CRaWMNTRPOdzm6kkAN5ALr6c7GWZVl14ddbIQWBO7Tgajy97lpb14rWZZbCF0+XGTKX04oqILFJnfaBoGAZr166lpaWFUCiEZVnk5eVRXV1NcXHxrCMN/X4/69ev5+DBg0xNTWHbNqWlpSxbtkxTnUVERERETiErOkm88Q/EW3ZgJ2OL67uIJ0Bg6zvw1J7HXDdoqdt8ObsfvJvuAy9QtXbzrGW69j9L596n2XLtBzPToze85h0s33TpKb2/wJLptejDY8MzPotOjBLIK9ALLCKySGmXZ6anPa9fv35edQoKCtiyZYs6T0RERETkNLCTMWINDxNv/CN2KrHo2u+uPpfAlnfi8M9vl+Pz3/Rn7Hvkl9z/zc/x7n/9EYElRVmfTwz1ce/Xb8S2LLZc+0Gq1kyHjod2Pj4jUGx+6kHKVqwjr6RiAQ/g+Os+Vr4YeLbv2sGK86/IHE/GovQd3Evdpsv0IouILFIKFEVEREREZPGwLeItTxLdfR92IrLomm94/AS2vAPPsk3MdVTi4UpqV/Gaj/wTD9x+C9//m2s5/83vo3L1eVhmip7GXTz/2x9h2xbv/Kdv4/EF8FTUsvri1/HCff9HYeVyVl/0WizTZOf2u3jirlt54ye/xPorr51XG3zBJQx1NjPcdRBvIEhuUdms5Yqr66nf8mp2bf8J5fXrWbHlVYTHQjz8vS+RSiTYeu0Nep9FRBYpBYoiIiIiIrIopEKdRJ75GeZI96Jsv7tqHYGt78Lhzzuh85z32usoqlzOUz//b/74469hW+b0+b0+1lx6DZdc9zGWHLa5yhv/+t956Lv/xkPf/Te233YTADkFxbzmw5+bd5gIsPmad7P9Wzfx3b96Extfex2v/9g/H7Xsmz/1VR64/Rbu/fqNWOb0mon5ZTW8/cZbqVi9US+1iMgiZdj2HMaqi4iIiIiInC5Wiuie7cQaHp7TVNtT/qXK48dw+zBcHnB5MJye6c1NDAcYBlY8gm/VxXjqLmAhoxKP2TVmiqmRQQzH9IYthuE4atlUIs7kcD8ur++4ZY8nHpnEMk38uflzKp+Mx5gM9ePxBQgWLtU7LSKyyClQFBERERGRM5Y5MUj4sf/FHOs9Dd+WHDiCRThzi3AEi3HkFOAILMERWILhy8PhDWB4/NPBoYiIyFlEU55FREREROSMlGh/gcjTPz0lm64YLg/OwmpcxbU4Cypw5pfjzFsKDqcehIiIyBEUKIqIiIiIyJnFtonu+i2xhkdevms4XLiX1uEqX4W7bBXOggqNNBQREZkjBYoiIiIiInLGsFMJwjvuINm196Sf23B5cVetx119Lu6KNdNrHoqIiMi8KVAUEREREZEzgp2IMvXo/5AaOnRyv/SU1uNdsQ139bkKEUVERE7G/7aqC0RERERE5HSz42EmH7odc7Tn5JzQ4cRbtxXvmstwLilTB4uIiJxEChRFREREROS0shNRJh/+9skJEx1OvCsvwnfOVTgCS9S5IiIiLwMFiiIiIiIictrYqQRTj3wHc6T7hM/lqdmAf9ObcQSL1LEiIiIvIwWKCxCPxxkYGCAvL4/8/PyTeu5IJML4+DgAZWVlGIbxst/P2NgY4XAYr9dLcXGxHvAZpqenB8MwqKioOKPPKSIiIjJvtkX4iR+RGm4/odM4cgoIbHsX7vI16lMREZFTQIHiAsTjcTo7O6mqqjqpgeLo6CgNDQ3Yto3T6aSoqAiP5+VdNLq9vZ3u7m4cDgeBQECB4hmop6cHh8Nx0gNFp9OpQFFEREROq8gLvybZvf+EzuGtvxD/+W/FcHnVoSIiIqeIAsUzyODgILZtc+6557JkyalZ72VoaAiXy8UFF1yAy7W4X4cDBw6Qk5NDTU2NXqbjWL9+vfpPRERETqtE27PEG/+44PqGy0vgoj/BU7NRnSkiInKKKVA8gySTSQByc3NP3R9yiQTBYHDRh4m2bTM2Nobf79eLNAeBQED9JyIiIqeNOdpL+Om7F1zfESwi+KobtHuziIjIabKoUqShoSGmpqZYvnw5w8PDDA0NYVkWwWCQqqoqnE4nIyMjDA4OYpomgUCA6urqGWHZ5OQkQ0NDxGIxAPx+P6WlpTNCFoCJiQn6+/uJx+N4vV6WLl2Kw+GY/Q8j06S/v5/x8XFM08Tj8VBcXExR0bEXhY7H4/T29hKNRoHpaciGYVBTU4PT6cSyLIaGhhgdHSWZTOJyucjNzaWsrCzr3gYHBwmHwyxbtoyuri4mJiaora2dNaAcGxtjdHQU27aJx+McOnQIl8tFdXU1HR0dmbZ3dnYSjUZZu3YtTqcTgFgsRn9/P+FwGNu2M/0XDAZnXGc+ZWdj2zYjIyOEQiESiQROp5OcnBwqKioy9z4+Pp555mNjYxw6dIiCgoLjTkePRCL09/cTiURwOp3k5eVRXl4+4/mmy0WjUWzbxufzUVJSMuso0uHhYUKhEKZpkpOTQ3l5OfF4nOHh4Rnv2OTkJAMDAyQSCbxeL2VlZXi9Xrq6usjLyzvuezMxMcHg4CCxWAyHw0EwGKS0tBSv9/jTfTo7OzEMg+rq6gX3n4iIiMhC2Mk4U4/9EKzUwr7AFNUQfPWHMLxBdaaIiMhp4rz55ptvXiyN7e3tpa+vD6fTSVdXF36/n1gsxvDwMJFIhFQqRUdHBz6fj2QyyfDwMCMjI5SXl2fO0dfXx4EDB0gkEgQCAQzDYHh4mN7eXgKBQFbgMzIywv79+4nH45lQrrt7eve5qamprE1Z4vE4u3btIhQK4ff78fv9hMNhent7icVixwyHEokEQ0NDxONxLMvC6XSSSqUydfbu3UtfXx8ej4dgMEgymaS/v5+hoSGKi4szwVp3dzf9/f24XC46OjqwbZslS5bMOupsamqKsbGxTKjqcDiwbZuioiJaWlqIx+OMj48zPDyMaZqZoG10dJQ9e/YQiUQyIxtHR0fp6enJtC9tPmWPpqmpKRN++f1+TNNkYGCAgYEBioqKcLvdhMPhTOCYDgP9fj85OTlHPW8oFGLfvn3EYjH8fj+WZdHf38/w8DAlJSWZ8HRoaCjrHXA6nYyPj9Pb24tt21mhW1tbG4cOHSKVShEIBIhEIpnNT3p6eigsLMw8i1AoxP79+4lGo5n76ujowOVy0dnZic/ny5x7tg1UOjs7aW5uxjTNTD+m+yU/P/+4a282NTURjUapqKhYUP+JiIiILFTk2Z+R6m9ZUF1XaT3BKz+C4QmoI0VERE6jRTnPdXh4mPPPPz8Tgu3atYuRkRFisRibNm3KBGwNDQ2MjIwwNTVFMBjEtm3a29vxeDxs3rw5ExrF43Gef/55Ojo6sjYlaW1txTAMNm7cmAmCEokEu3fvntGmdAi3fv36rJCppaUlE34dLVQMBAKsW7eOffv2MTY2xrp16zK7O3d0dDA5Ocny5cuprKzM1AmFQhw4cIC2tjbWrl0LkAmD0v1zrOmrxcXFFBcX88QTT2Sun2YYRmbU24UXXpg5r2maNDU14Xa7Oe+88zKhlWma7Nmzh9bWVgoKCvB6vfMqezSTk5MMDw9TVFSUucf0/TU2NtLd3c3KlSszm9fs3r2b4uJili1bdsz3J5VK0dzcjM/nY8OGDbjdbmA6PEwHmCtWrCCVSnHw4EE8Hg/nnXdeppxpmuzbt4+uri6Ki4vJyckhGo1mQukNGzZk3sGenh7a29tntKGtrQ3DMNiwYUMmuJucnGTfvn3Hff8nJibo7OwkPz8/612JRqPs3LmT5uZmNm/ePOffp/n2n4iIiMhCJbv3kWh9ZmFfXErrCb7qQxgujzpSRETkNHMsxkZXVFRkQi7DMDJTT4+cApw+Ho/HM8fOO+88zj333EyYCOD1ejOhkGVZAITDYeLxeNaoMgCPx0NpaWlWe+LxOGNjY7NOE62trQWmR48txODg4Ky78RYVFREIBBgZGcm0Oa24uPiE18JLJBIsW7Ysa/pvKBQilUpRUVGRNQLO6XRSVVWFbduEQqF5lz2aQCDA5s2bqaurm3HvMD0VeSHSU5LLy8szISFASUkJtbW15OXlHbNc+h5gOoSE6dGs6Xfz8HewsrISn8+Xdf30u1VUVJQ1CjA3N3dOu2z39/dn3q10mAjTowqXLl1KJBIhHA7r324iIiJyRrETUSILXDfRWVhF8IobFCaKiIicIRblCMUj1zpMBzhHhmjp0DAduB0+bXZsbIxEIoFpmliWRSqVypR1OByZ9QxnW1fxyKmgU1NTmXaMjY3N7GSXK1NmPpLJJPF4nLy8vKzgKC0YDBKJRDJTitPSgdiJMAxjxpTkw+/hyPtM93G6zHzKHvUPR6eTQCBAIpHIBJSWZWFZFoZhzAhS5yodts02pbe6unrG/c42NTtdNx1qpqeOz3bO/Pz8zPt0eJ3ZyhYUFBw3fE63K5lMzujb9Ds/NTWlKcsiIiJyRonu/A1WbHLe9Rw5BQRf/WEMt1edKCIicoZYlIHi0TZFOXzUITBrCNfV1UVXVxeWZeHxeHC5XDgcDhKJRFY50zRnPScwY5OXdBg5NDSUGbE21zYfS/q8h4+Om60d6XJHa99CzHbN9HVmm8Kblt6pej5lj8ayLFpaWhgaGsIwDLxeL06nE8MwsG17wfeWbttsz3a2d2C2/kzXTZ/rWO/LkX2ZDkJnK3u8tQ8Pv1ZDQ8OC+1ZERETkVEoNHSJ+8Kl51zNcHoJX3IDDl6tOFBEROYO4zqabHRkZoaOjg7y8PNasWZMV3qTXL8z88fJiGDlbcHXkyLh02crKyqx1Dk/UkSMsj9aO4wVjCzFbGJs+tnbt2ll3jgaypqLPtezRtLe3MzQ0RHl5OcuXL88qv2PHjhO+t+ONcDxWufSxI+9hPu/LbOdNh4XH4nA4MAyDCy64YNbnBCcnVBYRERE5KWyLyLP3LKhqYNt1OAsq1IciIiJnmLMuUITpteeOHAl2+JRUeGlU2ZEjF2cre/iGLXMZYTZXbrc7a/r1kdLTbE/mNY8lfZ+pVOq415xP2aMZHR3F4XDMCBPTu2EvVHpNw8PX1kybmprC4XAQCASOWS59LF0mfY/xeHzGVOMj1zM81rs1l6nxPp+PaDSKbdvH3NRGRERE5EwQb30ac7Rn3vW89RfhWbZZHSgiInLUGytAAAAgAElEQVQGcpxNN5sezXXkqK6hoaFMQJQeYRYMBjEMg9HR0RmjzoaHh7N+DgaDuN1uRkZGZkw/Nk2T5uZmxsfHF9TegoICYrHYjKApkUgwMTFBIBA4ZaFSQUEB8NKmIIebmJigtbU1E37Op+x8n1dnZ2fWszrcXKZCp9t25PT0ZDLJnj17aGlpySp35PM+/Fi6THoU5ujoaFa5cDg8Y53Do71blmXN2l9Ha/9say329/fT2dmZFbjGYrFM+Hw8JzKVXERERGTG3xbJGNHd983/S0puMf7z36oOFBEROUOdVYFierOSjo4OpqamCIfDdHR00NnZydKlS4HpQCiVSuF2uykuLiYajdLU1MTExAQTExM0NzfPOoV12bJlmKbJnj17CIVCRCIRRkZG2Lt3b2YNwIWorq7G4XDQ2NjIyMgIsViMsbExGhoasCyLZcuWnbL+y8nJYenSpUxOTtLY2Mj4+DiRSISBgQEOHDhAKBTKjL6bT9ljPS/LsmhvbycSiTAxMUFjYyOmaRIIBIhGo4TDYUzTzIwQHB0dZXR0lMnJoy/4HQwGKSoqYmRkhIMHDzI5OcnIyEimT9MbswSDQYqLiwmFQrS1tWU2wOnq6qKvr4/c3FwKCwuB6ZDP6/XS19fHoUOHGBsbY2BggIaGhkyZtPS7FYlEOHDgACMjI4RCIfbu3XvU6eGHKysrw+/3093dnfUud3Z20trayuTkZNaIzt27d/P8888f85zz6T8RERGRuYo1PIIdm+/mhAY5F71bOzqLiIicwc6qKc8lJSVMTk7S19fHrl27gOmRZevWrcM0TUZHR2lubqa0tJSVK1eyYsUKTNNkeHg4MyKtsLCQ+vp6du3alTWaq7S0FJgOKw8cOJA5HggEOOeccxa883IwGGTdunW0trZmbcLh9/tZu3btjLDq5bZy5Urcbjf9/f2ZPjEMg/z8fFasWJG1dt98ys6mtraWaDRKT08PPT09GIZBSUkJ9fX1DA0NcfDgQXbu3MmqVatYunQpJSUlDA0NsX//fkpKSli9evVRz7169Wra2toYHBzMjAr0er2sWrUqq09Xr16duYfe3l5geg3D4uJiVqxYkQmKHQ4H69at4+DBg5n25uTkUF9fz9TUFKFQKOv69fX1wPRIx5GREbxeL5WVlQSDwVlHRB7O4XCwfv162tra6O7upqurK3O8tLSUurq6eT9Xr9c7r/4TEREROR4rNkm88Q/z/7tk9SW4SpapA0VERM5ghn0WznE0TZN4PI7L5cpa38+2bSzLmrHJSTKZzKyPeLxRdTC9jl56lOPJXN8wfV6Xy3Xa186zbZtYLJZZx+9YG8PMp+xskskkyWRyRt3ZNkZJJBIYhjGn55Q+RywWw+l0HrNPbdvOTNH2+XzH3FDGsiwMw8iEjYcOHaKnp4eNGzfOGIF45DsXCoU4cOAAy5Yto6qqas7vsmEY+Hy+BY+EXWj/iYiIiBxN5PlfEm/847zqOPy55L35Rgy3Tx0oIiJyBjsrt4J1Op0EAoEZxw3DmDXscrvd8wpYvF7vyxL4vVznXQjDMDIbr5zMsrM5Wv/PFurNN8BNb8Ayl3s4VjnTNOns7MTtdmcFgbZtZzaXObx+d3c30WiU+vr6rHcuvQbjkRu7zPddXqhTtcGPiIiIvLJZ0QkSLTvmXc+/6c0KE0VERBYBl7pA5MQ5nU4mJyeZmJjANE0KCwtJpVL09fURiUSorKzMCg5N02RgYIBUKkV5eTmGYTAyMsLAwAA5OTnk5+erU0VERGTRijU8jG2m5vfFpKgGz7Lz1XkiIiKLwFk55Vnk5ZBMJmlra2N4eDizvqbL5aK8vJyampqs6ci2bdPZ2UlfX19mZ3CHw0FhYSF1dXUaKSgiIiKLlh2bYvyXX8A2k/Oql/vav8JVslwdKCIisggoUBQ52X9E2zaJRAKYnkJ8vHUNE4kElmXh8XiOuTajiIiIyGIQ3XUvsf0PzauOu2odwStuUOeJiIgsEpryLHKSGYYxr7UuNRpRREREXinsZIx48xPz/esJ/8Y3qPNEREQWEQ2HEhERERGRkyLe8iR2MjavOp7ajTjzK9R5IiIii4gCRREREREROXGWSbzxD/Ou5lt/tfruFSIRi2CmkuoIEZGzgKY8i4iIiIjICUu078SKTsyrjrt6A878cnXePPS37megrWFmX3p85JVUsLRuLR5f4JS3K5WI85/Xb2LrtR/k1R/4Bz0oEZFXOAWKIiIiIiJygmxiBx6Zdy3/uqsW7f3ayQR2KgbJOHYqjp1KgJXCNlNgpsC2wLaxscG28Sw7H46zWd9cND/5IE/efRturx8OO52ZTGCZJv7cfC57z9+w6Q1/qtdSREReNgoURURERETkhCT7WzDH+uZVx122CmdR9Rl1H7aZxJoKYYdHsSKjWOExrNgEdnQSOzaJFQ9jxyPYich0YDgPqb5G/FvegeH2n5S2vv8/7qGoqu6ltlsmg+1NPHD7LTxw+80UlNew7LxL9HKKiMjLQoGiiIiIiIickPiB+a+d6F135Wlrrx0PY472Yo73YY71Yo0PYk0OYUXGXrZrJlqfJtXXROCid+OqWHvSz284nJTWncM1n/w3/ufj19C04wGWnXcJyXiUJ+/+NjXnbmXZxosz5VOJODt+elvmuGWmePzOW6k7/3LMZIK9D/2cc654C3WbLwOgt3k3TTu2Mzncjy+YR+WazZxzxZswDC3LLyJyNlKgKCIiIiIiC2aOD5DsPTCvOs6CStxlK09J+2wziRnqxBw6RGq4HTPUhRUeOS19ZUXGmHroW3jqL8Z/wdsw3L6Tfo1AXuH0tawUAKl4jCfvvg0gK1A0k/Gs47Zl8eTdtxELj7P/kV+xpLSK2g1DAOy8704euP1matZvpax+PZPD/dz7X5+h6cntvP3Gb+qXQETkLKRAUUREREREFmxBOzuf8yqyFgA8mWwbM9RBsreRVH8zqaE2sMwzqs8SB3eQ6jswPVqxfM1JPffeh38BQOXqTfOq53S5AWh49Ne847O3UXPutkx/PvebH1K1djN/+q8/ypQvvPMbPHHXrQweamTp8jX6RRAROcsoUBQRERERkQWxYpPE256bVx1HIB9PzXkntyFmimR/E8mOXSR79mPHJs/8vguPMvX7b+JZeQn+89+G4fbOq/7uB35KYElh5udkPEr/wX20Pf9Has7dxvor3za/Br24YUxxzcqXwsQXj3/4W/eDbWcVr1wzHViO9BxSoCgichZSoCgiIiIiIgsSb34CXpxaO1fe1ZeBw3kSrm6TGmwj0fYMyfad2MnoouzDRMsTpHoPkPPqj+AsqJxzvaYd23E4X/o6Nz7QTSC/kDd84l9Zf+W1OJwL6+OCimUz2xiLsOfBn9HT+ALhsRBmMkE8PAGAmUrqF0FE5CykQFFERERERObNTiWINz0+rzqGy4O3/sITu24yRqL1KRLNj2OODyz+jnS68K65HGd++byqXXfzd7N2eX7g9pvZtf2nlCxblRU0zpfHF8j62Uwm+L9/fC+jve1sfO111G2+DLc3wEjPIR77v//SL4KIyFlKgaKIiIiIiMxbovUZ7ERkXnU8K7ZhePwLup4VGSN+4BESLU9gJ+OviD50Fi8jcPF7cC4pO+FzXfG+v6Plqd/zu/+6kT//z1/gdHumPzDSa1VmT1mOTo7P6bwHn32Egdb9vPXTX2PNpW/IHG95+iH9EoiInMUc6gIREREREZkXyyR24JH51TEMfGsun/+lImNEn/4JE7+4mXjDw6+MMNHhxL/5reS+/m9PSpgI4A3kctWHP8dw10Ge+MlLOy+7vdM7SUcnRrPKd+57Zk7nnRjuA6CoekXW8f2P/mr6vxyxtqKIiJwdFCiKiIiIiMi8JNp3YoVH51XHU70BR7BozuXtZIzYzl8z8ct/Jt78+Bm3U/NCOYtqyX3TZ/Cuew0YJ/fr2JpLXs+KC17F0/d8h4HW/QC4PD6KqlbQ/NSD9DbtIjI+QvNTD7L3oZ8fNnrx6EqXrwVg533/Rzw8wfhANw98+xaMF+sOd7UoVBQROQspUBQRERERkbmzbWL7fz/vat61r5rrBUgceo6JX/4zsX0PgvkK2fTD4cS36c3kvuFTJ21U4mxe+xc34XR7uPfrN2KZ0xvmvO5jt+Bye/nR31/PN953EY/d8V+8/uNfwO3xYR9nU52ac7dxwVvez677f8LX3r2F73z8DTgcLt70qa9Ss34rT/38O9z5uffp90JE5Cxj2Lb+7yQREREREZmbRMcuwo//77zquJauIPfqjx+3nDUVIvLUnaT6ms7Mm3c4cXiDGB7/9D9uP7g9GE4PuNzT/+lwYjic0ztZGw4wDKzoON76S3AWVJy+tts2kyMD2LZNXnH5vKsnY1GmRgcJFi7F7X1pHcx4ZBKvPzin0Y4iIvLKoU1ZRERERERkbmyL2N7t867mO+fVxzsxiZYdRJ+7BzuVOD335nTjzC3BkVuMI1iEI6cAR6AAw5+HI5CH4c3FcHuARRqcGQa5RQsfGen2+Skor51x3BvI1e+FiMhZSIGiiIiIiIjMSaJjJ+b4wLzqOPMrcFeuPerndjxC5Mk7SHbtOWX34QgW4iyqwVlYjTO/Amd+OY6cQo2yExERmSMFiiIiIiIicnyWSXT3ffOu5lt3JUcb1WeGugj/4X+wwiMva9OdS8pwla/GVboS19I6DJ9G1YmIiJwIBYoiIiIiInJc8ZYdWFPzC/4cuSV4as+b9bNE2zNEnroTzNTJb6zhwFW2Cnf1BtxV63HkFOgBioiInEQKFEVERERE5JjsRJTonvmvnehf/5rpjUmyTmYRfeHXxBseOuntdBZW4am/CE/tZgxfUA9ORETkZaJAUUREREREjim6537sRGRedZx5JXiWn591zDaTRB7/IcnO3SevcYYDz7LNeNe8CmdxrR6WiIjIKaBAUUREROQsZpkm/a378PpzKKquB2B8oJvweIjiqhV4AsEztN0pHE5X1s+WmcLl8emhnmTmaC/x5sfnXc937uuyRifa8TDhR75NaujQyWmYw4l35cV4112tKc0iIiKnmENdICIiInJyjA/28Mwvv0fLMw8tmjbHwxP8+B+u5/5vfT5z7OlffIcf/8P19Lc1nHHtTUSm+MHfXsv/u24jP73lBvoO7gXgjz/+T5755Xf1Ep5stkXkmbvBtudVzVlQiad2U+ZnKzzK1PavnbQw0bP8AvKu/Tz+rdcpTBQRETkNNEJRRERE5CQZ6Wnj0R9+mZVbr2Ll1qsW7X0s23gJbl+AvOKyM65tz/32h1hWiq3XfpCmJ+7nR59+F8GCEqZGh/izr9ytl/AkizU+Rmq4Y971/JveCMb0zs7WxCBTv78VKzx6wu1xFlTi33Y9rpLlejgiIiKnkQJFEREREcmy6qLXsuqi156RbXO6PFzzV1+irH49l/7pJ9n3yC/o3v8ctRsvprz+XD28k8gc7ye6695513NXrMVdvmb6HCPdTD30TezY1Ik1xuHEt/EafOdcBQ6nHo6IiMhppkBRRERE5DRLxqIMdTSRiIYJLCmkpHYVxjFCk6nQAKHeQ3j8ORRV1uHx58xaLpWIMdrbQXg8hMcXoKR2NW6f/7jtSUSmSCZi+IJLcLrcYNuEx0PHqGGQk1804+jkcB8Tw32k4jGWlFaRX1ZzzOuGuluZCg2QU1BCQcWy6WsfYdvbP0xsapy+g3uJT01QvnIDG656xzH7a7Svg4nBHhwuN4WVdbO2VbLZZorwE3eAlZpfRcOBf/Nbpt+/wVbCD9+OnYydUFucS8oIXPbnOAsq9WBERETOEAoURURERE4Ty0zxyA++zK7778RMJTPH/XkFXPFnf8eG17wzq3x4LMTvvv4ZDu18LHPM7fOz9dobuOT6T2SVffJnt/PML79HPDyRVfbi6z7Otrd96JjtevR/v8Ku7T/hT77wv9Ss30oiHuWbH7j0qOUNh5NP/3x/5ueRnkPcd+tn6Wl8Iatc+aqNvPlTXyW/tDrreF/zbn73jX8k1N2aOZZbXM5VN/wjqy68OnMsGYuy/bZ/ovGJ+7BM86WyRWW8/uP/wvJN2W3saXyB7bd9nuHOg1nH6zZfzus/8a8EC0r0Eh5F9PlfYo72zLueb81lOJeUkuzZT/gP3wUzeULt8NRtxb/tegyXRw9FRETkDKJAUUREROQ02X7b59n70D2svvh1XPSuvyRYUMJwVyu//84XuP+bn8Pt9bP2sjcC0+Hj3bfcwGBHExe96y+p3/JqUok4O376LZ6461YcThcXvfOjAOza/hMeu+NrVK7ZxOXv/RS5RaWM9nXy8Pe+yB/+96sUlNXMa0qz2+PlrZ/+rxnHW59/lH0P/4Kly1Znjllmip/e8kEmQ4Nc+cEbWX7eJdi2TeMT97Hjp9/iF1/8OB/4z19l1tcbG+jiJzd9AMPp4vUf/xfK6tczPtDNw9//Er/+6t9w/S0/oHrdFgDu/+bnOPD4vWx4zTvZ9IZ34/YF6G3axe//51/4xZc+wYe/dT+5RdPrPoZ62vjJTR/E5fHwxr/5MrUbLsJMxNn36C954q5bufuWG3jfV38+6yjIs12i7TniLTvmXc/hz8N37utItD1DZMcdYFsLb4ThwL/lnXhXXwoYeigiIiJnGO3yLCIiInIajPS2s/eheyiqructf/cfLF22hsCSImrWb+Xaf/gGGAZP/OTWTPkDj93LYHsjW9/6AS57919TvnID1eu28LYbv4knEOTZX30/M8pxrL+DktpVvP7j/0L1ui3kl9WwfNOlXPWhzwLQ8Nhv59VWw+Fk9cWvy/qnbMU6Wp76PR5/Dm/5//7zpfvqaSe3qIzzXnc9F7z5/RRV11Ncs5JL//STVK7ZxFBHM8NdL40YfPLu20nEIrzpb7/Mhte8k6XL1rBy22u45q++iGWamZ2bLdMkOjVGxaqNvP5jX6C07hwKK5ax/tXXsvma95BKxGh5+qXdtZ/86bdIJWJc/Rc3se6KtxAsKGFJaRWXXP8Jzrn8zQx1NNP81AN6EY+QGm4n/PRPFlQ3sOVtxJseI/LEj04oTDS8AYJXfwLv6stQmCgiInJm0ghFERERkdMgPW159UWvm7H+X2HFMkpqVjLU0cxkqJ/cojJan3sUgJXbrs4q6/EF+NA3fofb68uMtnvV+/+eV73/72dcs7i6HoCpkcETaruZSvKrr/4N8cgkb/m7/6CgvPala9TU854v3jlrveLqlfQ07mRqZJDimpUAtD3/KC6Pj+XnXZZVtvqcC/jofz+CPzcfAIfTyXU3ffeo552+r4HMsbYXHsPpcrPqiP6C6U1nGv74Gzp2P8naS9+olzH9XCcGmXr0u2CZ867rqdlAqreRePPjJ9QGR24xwSv/EkfeUj0QERGRM5gCRREREZHTYLSvA4DCymWzfp5fVsNQRzPjA93kFpVlyi8prZpRNlg4M3wJdR2k6cntDHceJDY1jmWmSCXiANi2fUJtf+T7X6L/4D7Oe92fsObSa2Z8biYTND35AN0NzzIZGiAVj2FZJqO97S9ef3r0WiIyRXgsRGHFMhzOIzZVMQzySspnnLtjz5O0vfBHxga6SUSmsC2T8Fgo675iU+PEpsYpKK/B6fbM0rfTaziOD3TrRXyRFRlj6uFvY8fD865r+HIgNkW8/fkTaoOzqJbglR/F8AX1QERERM5wChRFREREToNUfHrnW5fHN/sfaW4vAMkXyyXj0RePH39ziud+80Me+f6/4/J4qDn3QgrKa3F6vFipJL3Nu0+o3U07tvPC7+5g6fK1XHnDjTM+D4+FuOuf3keou5Wi6nrK69fjLcvD4XSSiIaZGh3KlE0mpu/NOYd7sm2LX3/1b2nasZ2c/GKq12+lqKoOp8vDWH9n1oYumb46bt9G9SICVniUyd9/Cys8uqD6hmGQ7G86sS8lFWvIueJDGC6vHoiIiMgioEBRRERE5DTw+HMASERnHxGWiIWzyh1e3hdcctTzTob6efSHX8afV8D7vnI3eSUVmc+mRod44Xd3LLjNY/2d3P/Nz+Hx5/DWT38tE8wd7om7vkGou5Vtb/8wV/zZ32V9tv22zzPQ1vBSH/iO3QeHa3rifpp2bKd63RbeddP/ZF37wGP30vLMQzP7NhY5St9GssqdzcyJIaYe/jZWeGRhJzAM7AUGkWnuZZvJueR9cMTUfxERETlzaVMWERERkdOgqGoFAMOdLbN+HupuA6Cwcvl0+co6AIY6m2eUbX3+URof/x2JWIS+lr1YpsnKrVdlhYkAA637F9xeM5ngV1+ZXjfx9R/7Qta6iYfraXwBgPPf+GczPjvy+m6fn9yiMiaG+0lEpmaU3/vQPZm1I7tfPO/G114/I8jsb92X9bM3kEuwcCkTQ32zhpXp0YyFVXVn9TuYGu5g8oGvLzxMxMYwEyfUBs+qS8i59P0KE0VERBYZBYoiIiIip8GKC16Fw+mk8YnfkUrGsz7radzJWH8nlWs2ZzYlqd96JQB7HvxZVtl4ZJJ7v/b3PPDtm3G5vZlRd8l49ui8ZDyW2TXaenE36Pl4+HtfZKCt4ajrJqa9dP3s6cQHHruXgUONAJjJl65fv/VKbMtk78P3ZJXv2v8s9936j+zaftcR582+r5GeQ+x96OfT5z3svlZuew22ZbL/0V/NaOP+R345XWbLlWft+5doe5bJB29d0JqJML33smGbJ9QG3/qrCWy7Hgx9JREREVlsNOVZRERE5CQbbG/kgdtvmvUzw+Hk6o98nmDhUra+7UM89bNv87MvfISL3vlRAnmFDLQ18Mcf/wdOl5tX//lLOzWvuvBqKtdspuXp3/Pb//w0ay9/I4lImGd//X1iUxNc+YHP4HA6Ka9fjy+Y9+L04K2U1p1DqKeNp+/5b9Ze+kYmhvoIdbXStf/ZzE7Lx9O+6wl23n8nLreXgvLaWadNr7n0DQTyCll23iX0NO7kgdtv5pLrP45t27Q8/Xsad9zPJdd/nMfv/DpNT26noHIZRZV1XPSOv6Dx8d/x6A+/QnRynMo1mxjt6+DJu2/D6fZw0Ts/CsDy8y7lqZ99m6d+9m1y8kvIyS+i+8DzPPXz/+by9/4dD9x+Ex27dzDY3khh5XIueudHaXziPh75wZcxkwlqNlxIIjLF7gfvpn33Duq3vJrajRefde+mnUoQfeHXxFt2nOiJ4AQ29/Gf/za851ypf1mIiIgsUoZ9otv8iYiIiAgAh3Y+xt3//OFj//HlcPLpn7809feZX36XZ37xXSITL007Latfz5Uf+AxV51yQVTcemeT3//OvND52b2Y0nj+vgEv+5BNsfsN7MuXaXvgj99/62cwGKJ5AkK1v/SAXv+svefY3P+DRH3wF27Z47UdvZvVFr+Mb77+IyjWbec8X/w+AB26/iV3bf8KffOF/qVm/lV3b7+KB228+5n297ys/o6x+PalEjPu+8VkOPH5v5rPyVRu55pNfJJBbwI8/cz2jfZ3k5Bfz8e8/DkzvSP3At2+hq+G5TEhVXFPPVTd8ltoNF2XO8/Q93+Hxu76BmZyeZptXUsFVH/osK7dexS++9Alanv49GAY3fP23FFWtYKTnEA9+5wt07nkqs7O0x5/DhqvfxRXv/dScNoN5JTFHugnvuANzfODEvkBYKWCBXyEMB4GL34unbov+hSEiIrKIKVAUEREROc1s22J8oJtkLEpOYQmBvMJjlk/GoowP9uDxB8grLgfDmPWcE4O9mKkkS5ZWZoVnyXgM27bw+AIv2z0lIlNMDPfhzyskJ7/o8IYRnRrHF8zDOGKqa3RilG+8/yLe9plbWbntNbOe10wmGBvsxuX2sqSkIuve4+EJXF4/Tpc7q05saoLJUD9Ot4f80moczrNrvT47lSC2dzuxA4+e0KjC6S8PFixwqrPh9pJz+YdwVazRL72IiMgip0BRRERERM4IoZ42fvbPH8YyTS6+7mNsfO116pQTYpNof4HoznuxImMn4XTWgtdNdATyybnyozgLKvVYREREXgEUKIqIiIjIGWXHT7/Jrvvv4mPfe0ydsSA2yd5GorvvwxzpPkmnNKdHJy6As7iWnFd9BIc/T49GRETkFUKbsoiIiIjIGeG53/yQ8YFump564KzcMOWE2RaJzj3EDzxKKtR50k5rWCawsDDRs+JC/Nuuw3C69XxEREReQTRCUURERETOCE077ic8HqKoYjk1Gy6cscaizM6KjJNoe5b4wSexwqMn+cuCCQsZmeh0EdjyTjwrLwYMPSQREZFXGAWKIiIiIiKLjJ2Mkezex//P3n2Hx1Hd+x9/z8x2VUuyLPfee6ObYjAldAIkAZJcUiA37abeJDfJD27KJe3mppEQSiAhCYQOphiMKQbb4F6Fe5ct2ZZVt+/M/P5YIWNs2dqVZEvy5/U8fmzvzpk5+50deffjM+cktq8guXd9mxdbOeoXhSxXczbzS8k59zOaL1FERKQb0y3PIiIiIiJdgBOuIbnnPZIV5ST3bgDH7qAjuU23OWceJvpHziAw5RoMj08nTEREpBtToCgiIiIi0um4OI01pPZvI7VvC6l9W7Hr952Aw7pNKzlnFiaauSWEzrwJT9lwnToREZFTgAJFEREREZGTyLWTOPX7sOuqsGv3Yh/cTergbtx4+IT2w3AdcDMc9WhaBMZehH/8JVp4Rbq0RCyC5fFiefQ+FhFpDQWKIiIiInIKccFxcO0kAIY30LGHc2yceBg31oATrceN1mM3HsRu2I/TeBA3XIMTaySb24vbryROOkzMsA/efuMJTrsWM6+n3lYnUOWWdVRuLT/sMcMwySsqpWTAMPJLeqtIGUol4vzqY1M4/ZpbufDW76ggIiKtoEBRRERERLoG18GJNeJGa3EidTiRWpxILW68Eewkrp1KB4V28kO/Nz2eSuI6yeYFTKyCXoTO/jRWwQcCmMZqap79CdgtjNR7fwSfaaX/itG8hrRZsZcAACAASURBVLFrGJhNj2G4uIaJ0amXP3Q/ECa2nhnIwywZghEqIFb+ht6XrWGnCJ1+PbTDKM4Ni+ay4PF78PoDYBhNl4ZDKhHHMEzGz7yGS277Id5AsF26vm3VQvZt28Dp19yq8ygiIs0UKIqIiIhIJ+DiRBtwwtW4HwgLnWjdob9H6yDD8OtYnHgjDXP+l8D4SwmMngmGCbnF5Fzw74Rf/T2unTpGY+cDPf/Aw0e8KqM59MEwMAwjfRwMMI307ydBOkS0Mx4Y6QBOtAF2rdJbNkPJ6h3knvPJdlv9+jO/forifkOa/x6uq2bxsw+x6Mn7MEyTy7/8k3Y5zvoFc2io3qdAUUREDqNAUUREREROMBen8SB2zW7smor0nIE1u3FjDSe+K45NbNULJHetIXTmTVj5vfD1GYHvU7+n/rm7SB3c3cYQ020eEYl7lPzOMJuCRrPpz2YHZ4xNi664mSWJjqt3bVvZNRXUPf8LghMuJTj+4uZRru0lp6CYCz71TbatXED5Wy9w+Zd+3BxmxyMNrJz7BFVbynFdl16DRzFx1vUE8wqb2y+f8yi+QIh+oyaz6Kn7KeoziHikkS3L3sLyennz779hyJRz6D9mGgD7dmxk3Zuzqa3ajcfnp++IiYyfeW165ORx1Ozdyep5T1Gzdycef4ABY6cz7vwrMa1DX09TiTjlb71A5dZyEtEweUW9GH3OZZQOGnnYvvZt38CaN56lYX8luUWljDv/KsqGjjl8myz7umfjKt5b+DINB6oI5ObTb9Rkxp53Rfp6FRE5xeknoYiIiIh0HNfBrt9HYsdyoiuepfG1P1L35Pepn/0Twm8/RGzdXJJ73zs5YeIH2Ad30vDSr4i/91pzgJh/1ffIvfxbHbvYiOuAY+PaSdxUHDcZxU3GwU42jYJsnyTPcF0MN4XhpFodJrqkg0SFie17vqOrXqTuxV9h11R0yCEKe/UnGYuSSsYBqK3azX1fuYpFT96HPyefUH4PFj/3V+79yhXUVO5sbrfmtadZ89ozPPmzr7J99SJqq3ZTt6+CaGMtqUScun0VxMONAKyb/zwPfO1atiybT35JGYZhMu/Bn/PgN68nHjn2tbx56Rvc+5Ur2Lh4Hvk9ewMuL/3pDv7+/U9hJxNAOkz823c/wbwHf0EyFiWQk8+ONe/ywNeupfytF5r39d6COTzw9evYvnIRgdx89mxcxV+++VFWzX2ieZts+7r8pUd46Nsfo3LTWvKKS4k11DH7t9/jyZ99Ve9jERE0QlFERERE2pWLXVNBsqKcVOUG7JrduKlE1+i6kyK6cjbJ3WsInf4JzPxSfD0H4/vU72h4/uekDuzAdU9AuuY6uLYDpNKjFQ0Lw7TAbLpVOsN9Zbrgiuue1CViTgn2wd3UvfBLghMuIzjuonYbrejYKSq3lpNXXIbHlx5998q9PyEeaeC2PzxPXnEZAGfd8AX+/MXLePWBn3HD9/8IgOXxUbFxFadffSszPvHl5n3uXLeYngNGcNU3fglAIhrmpT/eQf8xU/jEf/+leVXkiRddx8Pfu4VFT97H+Z/8xlH7l0rEef6336NsyGhu+Z+/N7cdM+NyHr3zcyx76Z+cdtW/sWvdUiq3lPPxO+9nyORzmtu//OcfsXPtEsbMuJxkPMZLf/x/DJ50Fh/74T3pawT4149uY+4DdzH2vCtwbDu7vrouS2b/jX6jp3DzT//W/HDxI3/grUf/wL5t6ykdPEpvZBE5pSlQFBEREZE2cZNxUpUbSO4pJ7X3PZxofZd+PakD26mf80uCE6/AP2IGGCZ5V3yHZE0FDc/+NOPbhdtWXMC1cZ1Di8EYpqcpXGyZ4TqHFpBpzSGUIJ54jk105fMkd60i5+xPYhVmtjpzbdXuQ+fQdag/UMmS2X+jtnIXl9z+QyAd/m1ZNp9JF9/QHCYC5BQWM/qcS1n16pOkEnE8Pj8YYCcTx50rcduqhSSiYSZf8rHmgA6g/5hp9BwwjE1LXm8xUNy+ehGR+houvu0Hh7UdMvkcivoMYsOiuZx21b/hOOn5S/dsXHVYoHjJ7f+v+c87Vr9DrLGeSbOubw4TAS79wp2Ea/cDRvZ9NQxu/+NLR1wYfUdNAqC6YpsCRRE55SlQFBEREZHMs5CG/ST3lKdHIu7fAo7dvV6gnSK6/Bns+n2Ept8AgLdHXwzDxHVP4mt1msJFw0iHi5aHpmGM6bkYXQfc49zWbJB+HYDrGK0OHqVjpKp3Uf/CL8m/7BtYRf1a3e5fP7rtiMdyepRw8W0/ZOpHbgagZu8OXNdhz8bVPPfrbx+27f5dW3Bsm9qqXZT0HwZAblFPfMGcYx63Zm/6NumiPoOOeK6wbADbVy1sse3BPdsBWD3vaTYveeOw5xKxCLGK9H9GDJ50DiPPnMX8f/6e1fOeZvCksxk6ZQZDp56L5fWl97U3va/CXofXrKC0DwWlfdrc10Qswqq5T7B7/QrCtQewkwli4YamHw9JvXFF5JSnQFFEREREWsHFPriLxPZlJPeU4zQc6N4v1zAIjJ6Jf9ylzQ81PP9zXMfpJKfDBTuFa6fSoaLHD4aJa5iA5wPBYnol5/cXfUmvMp2+bdqAphnVLVzXwXGczvP6TiFmXgm5Z92cUZgIcMMP/kRhab/m96s/mEN+SdmhVcU5FHx5A0H8OfmHte83ajL9Rk3G6w81P+YLhI573Pf3aRhH3n5vebzYqZZXR7eT6bb+nLwj+jPyjFnNowhNy+Kj3/kdW5a/xfqFL7N56ZusePlf5BaVcs23/pcBY6c376sj+monE/z9v27h4J4dTL74RoZOmYHXH6S6Yhvz//k7vWlFRFCgKCIiIiLH4KbiJLcvJ755IXbN7lPiNVv5pYTOuAmreCAAif3bCL/0f7h25xyV5NopsFNgeTEsH5hNwaKRHsll4mAcZ1ZEwzCxLBMsmoNF11W42NECo84lOOVqDI8v47ZFvQdS3G/IMbfJKSwB0rf4XvCpb7RLn3MKigAI1x75nwrR+hpC+T2O25+JF330sFuZW3hTMnTquQydei4AFetX8tz//SfP/OqbfPXB+c3HOVo/2trXTUveoHJLOdd++/8Yfc5lzY9vfHee3rQiIk20yrOIiIiIHMGuqSC65HHqn7mDyJLHTo0w0TDwj55J7qXfbg4T65+7i8YXftVpw8TDT1oSNxHGTcWbV6oGcDCxMXFp3aIupmlieTx4vF5M08p4HRhpxZew3CLyLv4qodNuyCpMbK2C0r7kFZexdcXbRzy3q3wplVvWtWo/H1yMqN/oKQBsW3n47cLJWJQ9m9fQb/TkFvfzftuty4/sz4Z35lK3bw+Qnjux/O0XD3u+76hJjJ95DY0H9xGPNNC36Tgf3teS5x/mD5+7gLp9FVn3tf5Auh/F/Yce9vjaN559vyB6E4vIKU8jFEVEREQk/R3ZTpDcsTI9GrF6xyn12s28noTOuAlPySAAEns2En719+nRf11NKoFrJ9OjFS1fUyBokI4YjVaNWOT9bS0LAxMcF8e2cbX+c5v5R84gNPVqDI//hBzvrOs/z8t//jEv//nHnHHtZwnk5rFl2Xxe+uMdjDxzFld89a5jtg/kFLB/50YO7NqMP5RLSf9hDJ9+Acvn/IuyYeMYPv18wrXVvPqXn5FKJDj9ms+2uK+iPgMZddYlLHvpnxT3G8zIMy/GtW2Wz3mUtx79A1f+x88YP/Ma9mxcw9wH7iJSV8Oosy5uut14K+XzX6DngGH4Q3n4Q3kMm3Y+y1/+Fz0HDmfwpLPZt2Mjbz96N0V9B1FQ2hcgq772GjwagOUvPcL5n/w6scZ63n3mL+mpA4D9uzanQ0VDabuInLoM19V/r4iIiIicypyG/cQ3vk1i22LcZOzU+SAcyMGNRfCPOo/AhI9gWOn52+qe/Sn2wQroBuGZYVrp+RU/sAouvL+MS2uDxUNcx8FxbPQVInNmThE5Z9+Mt2xEm/bz5t9/w4LH7+H2u1887i3P71v83F9Z+Pg9ROprAPD6A4yfeS0Xfea76RWegb9//5NE62v4/O+fP6ztyrmP89Ldd+C6DpMuvpGPfOlHJKJh5tzz35S/9SJOU+jeo2wAF33uewyffsEx+5KMx5h7//+w9o1nSSXiQHoxmbOv/wLTrril6Y3m8sY/fsPyFx8hFm5aNd4wGDplBrM+91/Ni6zEIw28dPcdrF/0Mo6dXlxo+Gkz+cgXf0ROj/Tt1a3payoR5xc3TOT0a27lwlu/A8CrD9zFktkP47oOltfHlEs+xsxb/5NH7/gsO9YuZuC407j5p3/TG1tETlkKFEVEREROUU60jtjal0lsefewW2S7zQddywdefzooNK306CLTBMNq+t0kOOkqPD0HH2rUWE3Dmw+0OH+g03gQ1zCwgoW4GBg0zTXo2OkFTVwbbBvXSUDKBieVXpX5pBYCsPwt3FrrNo1YzGR/Jp7eI/EMmo7hD+lCas21Fg/j6zMKwxs4aX1wXYf6/XsByC0qbV4ApTXikQYc2yaYV3jY48l4jIbqSnyBELlFpRn1J5WIU3+gEq8/QG5Rz+bRf4f12bFprDlAKhknr6gUj+/o9UtEwzTWHCCvqBRvIHjUbbLpazIWpbFmP7lFpXj9gcPq4Q/maoSiiJzSFCiKiIiInGLcRJT4e/OIb5jfNeYGbGIG8zFzemAE8jEDeRiBPIxg05/9uRi+IIbXj+EJYHjTqx53Ck4Ku7Eap24fqdq92Ad2Ydfswq4/kA4gT9QHf9MD3sARIUi2oxUNjx/f2AvxjzoPTM2kJCIicipRoCgiIiJyinDtJImNbxErn4ebiHTOD6f+HKzC3lgFvTHzSjBzijFzizFzi9IjDrsTJ4VdW0lq31aSezeQrNyIG2vs2PpigD+YHqV5FCZ2xmuwmPmlBKZ9FE+vYbrIREREThEKFEVERES6O9chsXUxsbUv40RqO023zLwSPMWDsHr0xSrsjVnYBzOQy6m7rLCLXbOHxM7VJHeuInWgoxbGMdK33lqeo/bBxM14tCKAb+gZ+CdfeVJv6xUREZETQ4GiiIiISDeWrFhHbOVz2PX7Tm5HTAtPySA8JYOxSgbhKRmI4c/VCToGJ1xDYusS4psWYtdVtf8XAY8vvWDL0Z7DwcwiVDRzehA84yas0iE6gSIiIt2YAkURERGRbshNRIgue4rE9mUnrQ9WQRmespHpBTx6Dm1hURBpxdkktW8bsfLXSWxf3r4L6Hh8GO0cKmIY+MfOwj9uVueZx1JERETalQJFERERkW4mVbmByDuP4ETrTvAnSwNPz6F4B0zE23ccZqhQJ6OdOY0HiZXPI77+rfZbUMfytnibstF0CzRZBIueXsMJnnULRkAjUUVERLobBYoiIiIi3YSbShBbOZv4prdP5MdJPL2G4xswEW+/8RiBPJ2IE8AJ1xBd+SLxTQvbZcSiYfnA29JIxfRiLdkwQ4UEz70Vq0c/nTQREZFuRIGiiIiISDeQOrCDyDv/wGnYf0KOZ+YW4xtyGr7Bp2kk4klk1+whvOgRUlWb274zj/+Yt6VbWYaKWB6CZ96Mt/8EnTAREZFuQoGiiIiISFfm2MTWvkys/FXo6I91holvwCR8Q8/EUzoUDEP17wxcl/imhUQWP4GbjLXtFHsDYHmP/hwuJtmPhvRPvBz/mAs4dVfxFhER6T4UKIqIiIh0UU7jAcJvP4RdU9GxHxh9QXzDzsI/YgZmsECF76zvh4YDNM5/kNS+rW042WB4Q2BaLTzdtlDRN+xMAtOu02ItIiIiXZwCRREREZEuKHVgO+H59+PGwx12DDNUiH/0THxDTmtxJWDpZByHyPJniK2Z24ZvCAaGL9Ri6NfWUNHTbxyhsz4JlkfnS0REpItSoCgiIiLSxSR3rSK88O/gpDpk/2YwH//YWfiHngGmQp+uKLFlMY0LHgY7y/eIaaVDxZaexsEg+68RVulQQud+psXVpUVERKRzU6AoIiIi0mW4xNe/QXTFcx3zwdCfQ2DMRfiGn43Rwjx60nWkqjbTMPfu7OdV9PiOOTK1zaFij76Ezr8NI5CrkyUiItLFKFAUERER6Qpch+iyp4hvWtABnwhN/CNmEBh3CYYvqFp3I3b1Lupf+R1urDG7HfiCGC2NUjUMLLdto2TNvJ6ELrgdM6eHTpaIiEgXokBRREREpJNzU3EiC/5Kcs977b5vT6/hBKdeh1VQpkJ3U/bB3dS/9GvcRDSLbwtm03yKR1+Z2fD6MZORNvXPDBWkQ8X8XjpZIiIiXYQCRREREZFOzInWEX7zvnZfydnw5xCc+lF8AycBhgrdzaX2b6Nhzm9xU/HM3yuWD7wt3/rsKe6PW729be9HX4jQeZ/DKhmokyUiItIFKFAUERER6aScaD2Nr/4Op7G6XffrHTCJ0NSPau66U0xi52oa590DWcx7aPhywDRbepbg6PNIbHgDHDv7DlpeQmd/Ek/fsTpZIiIinZypEoiIiIh0Pm4iQvj1e9o1TDT8OeSc/Wlyzv60wsRTkG/ABELTrsnu/Zg61sIuLvHdawi2ddVmO0nkrQdJdMQ8oSIiItKuFCiKiIiIdDJuKpG+zblub7vt01M6lLzLvo13wCQV+BQWGD+r6Tb3DDk2rp1s+emGalKVW8iZ9RWMYEEb3vwusaVPEVv2dNtGO4qIiEiHUqAoIiIi0pk4NuG3/kLqwPZ222Vg7EXkzvwiZluCHukmDHLO+SRmblHmTVNxOMZsSbENb+G6kHPxVzDzS9vUy8TGt4m8cW/2q1OLiIhIh1KgKCIiItJZuA7hRX8nVbmhXXZn+ELknH8bgQmXg6GPfXLofZF77q1kvBiP64KTPOb7N7L0acxQITkXfRmrqH+b+pmq2kzjnF9j79+qkyYiItLJ6JOliIiISKfgEln6BMmdK9vnQ15eT/Iu/hre3qNVWjmCp9cwAqPPz/xdmoxzrEVdUvu3kdi5GsOfQ2jmF/CUDm3bVRGtIzzvj8TXvKxboEVERDoRBYoiIiIinUBs1YskNi9ql315SoeSd/HXMPN6qrDSouC0qzFzMr/12U0lj/l8dMULuHYSwxsgdP7n275qs+sSX/sK4Vd+i11ToRMnIiLSCShQFBERETnJ4psWECt/tV325Rs8ndwL/h3DF1Jh5ZgMj5/QaR/NvKGdOOZcik6klvjGppWaLS+hcz6Nd9DUNvfXrqkg/PJviC17BjcR1QkUERE5iRQoioiIiJxE9sFdRJc/3S778o84l9DpnwDTUmGlVXyDJuPtPTKzRsebSxGIlb+Om4g0feOwCJ7xCXyjzmt7h12HxMa3aJz9PyQ2zAc7pZMoIiJyEihQFBERETlJ3GSM8IK/tsvccIGxswhOvQYMQ4WVDBgEp1+X+Xs3lTzWVIq4iSix9974wGEMApOvIjD5yva5dhIRYsufpWH2T0lsmI+biutUioiInMhPEK57jPsVRERERKSDuITf/ivJXavavKfAxCsIjLlQJZWsNb5+H4ntyzP7IuENguVp+XnLS/5V38MM5B72eHLnSqKLHgGn/UYXGr4g3sHT8Q0/S3OHioiInAAKFEVEREROgvimt4kufbLN+wmMv5TAuEtUUGkTu7aSuqd/xDGHHX6Y6cHwBY/9/hx1HsHJlx95vAPbibz1EG6sod1fi9VzMN6BU/D2n4DxoTBTRERE2ocCRREREZETzK7ZTcMrv2nzrc7+UecTnHwVoNucpe0aX7uXxI4VmX2Z8OeA0fIsSobHR8FV30tv9yFOpJboWw9hH9zVQd90DKySwXj7jsHqPQqrsEzXioiISHv9M6tAUUREROTEcZMxGub8L07jgTbtxzfsTELTb0ABibSX1IEd1M/+WWZfJjxe8ASOuU1g7IUEJ7QwitZOEVv+DInNizr+i08gF0/PIVg9h2AVD8Dq0feYt2yfcuc/EQPA4wuoGCIicvx/VxUoioiIiJwoLuEFfyO5c2Wb9uLtO46cGbcec2SYSDbqX/xfUlWbM/k6gRHI4VjBtuELpkcpelsOqpI7VxJb8gRuInoCvwmZWAW9MAt7Yxb0xsovxczriZlb3KmDxqot69i3tfwDr8PA8njJLe5F7xET8fqDWe3379+6DtPycNPPH9OFICIix6X/khMRERE5QRJbl7Q5TLR69CV01i0KE6VDBMZcQGNGgaILtn3MAM5NRIlvfpfA6PNa3MY7YBJWySBi7/6LVOXGE/NiXQe7di927d4jnjICeZg5PTBDhRjBfIxAPmYgF8Ofk/7lC4I3gOENYHh8J/R63PTOXN594k/p4LApx03F47iugzcQ4oLP/BfjZ92Q8X4nX/5JDP1cERGRVlKgKCIiInICuPEw0ZXPtWkfZjCfnHM/h+Hxq6DSIXwDJmGGCnEita1vZCePO6IvvuFtAiPPAdNq+f0dKiR0wW0kty4htmI2biJy8q7XWAN2rAG7emcrL05P+vZvy4thWunXaVpgmBgY4NgEz/tsevRjO7nlf5+iqO+QdH8dm6qt5bz4628y9547GDDhTAp69ctof2MvuFYXgIiItJp155133qkyiIiIiHSs6PJnsPdvzX4HpofcmV/Ayu+lYkrHMQzcRCSz255dJz1K7xi3PbupOFZeCVaPPsfrAFaPvviGnoabiOLU7OkadXMdsFOQiuMmY7iJCG48jBtrxI014MbDJLctxvDnYBX1pS1zn+5c8w4V5UuZ/JFbCOb3aDptJrlFvTBMi61LX6f/+DOaw0aA+v17WPHCw6yZ9wRbFr9GbeVOSgYMx/L6mrdZNecR9m1ZR69h43DsFAse+R2Wz0ft3h0seOS3eP1B8kv7HvXxwt4DATiwYyPLZj/ImrmPsXXpG4Rr9lPcfxiWx6trS0Skm9GYdhEREZEOZlfvILHlnTbtIzT1WqyiASqmdDj/8LOyeJOnjrtJbP2bQOumbzf8uQRPu5GcS7+Op/eoblFXNxkntvhxIq/fl9kI0AzEGtL7LSg9NDqxavNaHvrq5ZS/+SzB3AIsr493n/gTf/3alcQjDc3brXn1cda+9lS6r47Du0/8ifXzZ/PsXV/kwI4NhGv2t/g4wHvzZ/O3b1zDtuXzySvpjWGavPnQz/nHt68/7DgiItI96JZnERERkQ5NERwiS56gtUHK0fgGTsE37EzVUk4IM68ET9lwUpWbMnibJzGsY49Cs2srSVZuwls2otX7tXr0JXT+57H3byW+du6Jm1+xA6UqNxB+8ZcEplyNd8h0sh2t2Fhdhcebnv4gEQ1TsX4Zi5++j/EXXU/PQSObt1v18iMYhsHNv3gcf04+AKPOuZzHfvhJ1r32NFOu+NSRdW8aUVj+5nNc870/0X/86U0n2j3q44lomFfvuYO+o6dy/Z1/aW4/duZ1/Ov7N7Pkqfs455Zv6OISEelGFCiKiIiIdKD45oXYNbuzbm/llxI87UbacoukSKb8Q0/LKFDEtsHrHvd9Gl8/P6NAsfk66DmE0AW3Y1fvJLFhPsmdq9K3GXdRbjJG9N1/kdy1muBpN2AECzLex+N3/NsRjw0/8xLOvulrhz128Zd+ysVf/AkYh85Nn5GTADhY0cI0DE3blvQffihMPMbjO1YtJBENM+HiGw+7vbnfmGkUDxjOliWvKVAUEelmFCiKiIiIdFRoEGsgtuqF7HdgWoTO/rQWYZETzjdoCuFF/wIn1fr3u5067ijF5N6N2HWVWAVlWfXLKh5A8KxbCEy+isTWd0lueRcnXNNl65za8x6NL/6SwNRr8Q6aQib/cXD1d+8mv7QvAE4qSf3+Paya8wgPfvkyrv7u3YeNKty46GW2LnuThv17SCXiOI7d3O5YevQZ1KrHayt3AFDUZ/AR2xaW9WfnqkW6qEREuhnNoSgiIiLSQaIrnsNNxrJuHxh/KVZhHxVSTjjDF8Lbd3RmbVoZPsbXv9X2/gXz8Y+dRe6V3yfnwi/iHXo6hi/UJWvtJqLElj2DneECNEX9hlA6eDSlg0dTNnwCI866lI/e8QChgiJe/fMdzdvNu+/HzP7V13BSSUaefRlTrvgUp137uVYdwxsIterx5mDSODIQtTxe7OMElyIi0vVohKKIiIhIB0jt20Ji+9Ks21vFAwmMnqlCyknjGziZ5K41rd7edWwMWnHb8/blBCZeihnIa3snDQOrdCjB0qEw7aOk9m0hVVFOak85TmN11/hC1m8cwenXY7RDPUzLQ3H/YWxePA87mSARDbPypX8w8dJPcNHtdx46B+28SEqwoBiASO2BI56L1tc0r0YtIiLdhwJFERERkXbnEl05O/vmloecM24CQzeTyMnjGzCBsGmC08q5Cl0XbAcs69jbOTbxjQsJTrikfTtsWnjKRuApGwFTr8ZpPEiqahN21Wbs/VtxInWdqr6GL0hg6nV4B02mveZITUTDVG5eQ6iwBMvro2HXZgCK+w09bLvyN55tOmVuuxy376gpAOxYtYDBU89rfjwZi1K5eQ2DJp+rC0pEpJtRoCgiIiLSzlKVG7Grd2TdPjDuEsz8UhVSTirDn4O31zCSezNYWdlJHT9QBOKbFhIYcwGGx9dRvcfMLcaXWwxDz0h3LVKLfWA7Tk0F9sHd2DV7cOONJ+dLWN+x6VGJwfys97Ft2Zvs2/YeAK5t01BdRfkbz9B4cB+zvvhjAAp7D8QbCPLe/OcZdsYsPF4/699+gS2L55HToyc1FdtwbBuzFefsWIr7D2Po9Jmsevlf9Bo2niHTzidSW80bD/6MVCLB9Gs+owtKRKSbUaAoIiIi0q5cYmtfybq1ld+LwKgLVEbpFLz9J2QeKHL8RYTcRJTE1sX4R5xzwl6LGSrEHDAJBkw61I94I3ZdFU79PpyGAziNB3AbD+JEanAT0Xbvg+ELEphyDd7BU2nrqMQ3HvzZof2aFnnFvSgZOJKLbr+DfmNPA8AXzGHWv/+YV++5g3s/lx45OGjyOXzka79k5Uv/ZNFjd/ObG8fz4TT5TQAAIABJREFU9cfXtPm1feTrv+LVP9/JnN99F8dOz6dZWDaAq7/7B3qPmKSLSUSkmzHc9hrnLiIiIiKk9m2mcd7dWbfPvfBLeEqHqZDSKdj1+6l78v9l9gXDn9Oq2/XNUCEFV34HTKtTvnY3GceN1uLEGnGj9bixBtxEBDcewU1E039OJSAVx03GcO0kODbYSVw7Be7ht4p7+owmeNoNGMGCE/5aHDtFw4G9+HPyCeQeOn4yFsHy+jCt9htnkoxHaayuwhsIkVukkdYiIt2VRiiKiIiItKPY2pezbusbNE1honQqVn5PrPye2PX7W9/ITkErbmV2IrUkdqzEN3hqp3zthteP4e2Fmd8r+524LuCmg0bLAk7OvKim5aGgV/8jHm9pFee28PqD9OgzSBePiEg3p5m+RURERNpJav9WUlWbs2prWD4Ck65QEaXT8fYdl1mD1i7iAsTKX28K3bopw0iP1rS8+uolIiLdiv5VExEREWknbZk70T/6fMyTcCukyPF4+47JaHvXTbV6W7t+H4ldq1VkERGRLkaBooiIiEg7sKt3kKrckFVbI5CLf9RMFVE6JU/Z8MzmOXRdXMdu9eaxta9271GKIiIi3ZACRREREZF20JbRiYHxl2F4/SqidEqG14+ndEhmbTIIFO26KhI7V6rQIiIiXYgCRREREZE2susqSe4pz+7DWG4x/iGnq4jSqXl7j8po+0xGKAJEV7+SXrhEREREugQFiiIiIiJtlNjyTtZtA2Muyux2UpGTwNt7RGYNXBsyuIvZaawmvvldFVpERKSLUKAoIiIi0gaunSKxbUl2H8RyeuAbPF1FlE7PUzIIw/JmcGG4uG6GoxTXvoKbiKrYIiIiXYACRREREZE2SO5ejZuIZNU2MGaWRidK12B58PQamtkXDU9m84K68QjRNXNVaxERkS5AgaKIiIhIGyQ2L8ruQ1gwH98QjU6UrsNTltltz2YoL+NjxDctxK7dq2KLiIh0cgoURURERLLkNOwntW9zVm19w2eA6VERpcvw9hqW0fZ2QzVWfmlmB3EdIu8+Aa6jgouIiHRiChRFREREshTPcjEWw/LiH3amCihditVzEFitD8HdeBjf0NMyPk7q4C5iG95WwUVERDoxBYoiIiIi2XBsElsXZ9XUN3g6hj9HNZQuxbC8eEoGZdbGAE/PQRkfK7pqDnZdlYouIiLSSSlQFBEREclCsmItbrwxq7a+keepgNIlZXrbc7JqC8HJV2Z+ICdFeOE/cO2kii4iItIJKVAUERERyUI8y8VYPL2GZT6vnEgn4SnLLFBMVW7GU9wffxYLENm1lUSXPgO4KryIiEgno0BRREREJENurIFU5cas2vqGnqUCSpflKR2avo+5lZzwQZzwQYITL8PwBTM+XnzrEuKb3lHhRUREOhkFiiIiIiIZSu5eSzajpgx/Dr7+41VA6bIMbwCrR9+M2qSqtmAEcglOviKrY0aWPUty7wYVX0REpBNRoCgiIiKSoeTu1Vm18w2eDqZHBZQuzdtreGbXS9UWAPxDpuHJcA5GAFyH8NsPk6repeKLiIh0EgoURURERDLgJmMkqzZl1dY35HQVULo8T6+hGW2fqtrc9CeDnNNvwPD6M7/uUgka37gfu6ZCJ0BERKQTUKAoIiIikoHknnJw7IzbWT36YhWUqYDS5WUaKNo1e3ATkfSXj5wehKZek9Vx3USUhtfuJXVgp06CiIjISaZAUURERCQDyd1rsmrnGzRNxZPu8QUiVIiZV5JBC5dU023PAL7BU/ANmpLVsd1ElMbX/kxyz3s6ESIiIifz84BKICIiItI6rp0ilVWQYeAdOFkFlG7Dm+FciMnm257T10No+nVY+aVZXodJGuc/ROy9N8lmcSQRERFpOwWKIiIiIq2UqtqIm4pn3M7TaxhmsEAFlG4j08VVUpWHzztqeHzkzPg0hjeQXQdcl+jKFwi//XfcRFQnRERE5ARToCgiIiLSStne7uwdMEnFk24l40CxescRYbyV35Ocs28Cw8i6H4lda6h/6f9IVm7SSRERETmBFCiKiIiItIbrkNy9NouGBt6+41Q/6VasglLMQF7rGzgOqX1bj3jY23sUoenXtakvTqSWxtfvI/zuY7jxsE6OiIjICaBAUURERKQVUgd24MYbM27nKRmIGcxXAaWbMfCUDc/sGtq78aiP+4eeTnD8xW3uUWLrUupm/5zY+jfBSekUiYiIdCAFiiIiIiKtkNqX3S2V3n7jVTzpljINFJN7N7T4XGDchQTGXNDmPrnJGNEVL1D33M+Jb1yIaytYFBER6ZDPASqBiIiIyPGlqrZk1U6BonRX3rIRmV1DB3bgJmMtLMRiEJx4KQCx8tfb3DcnWkdk2TNE187FP+wM/MPP0MJIIiIi7UiBooiIiMjxOCnsA9sybmbm9cTM66n6Sbdk9eiNEcjFjbVyKgDXIVW5EW//CS1sYBCceBmGL0R05Qvt0kc3Hia2bh6x8tfx9h6Bb/A0vH1HY1henUAREZE2UKAoIiIichyp6p24djLjdt7eo1Q86cYMvL1Hkdi2tNUtkhXrjxEopgVGn4cZzCf87mPg2O3TVdchuWc9yT3rMTw+vH1G4e03Dm/ZcAx/jk6liIhIhhQoioiIiBxHat/m7D5o9Rmt4km35u09MrNAcU95q7bzDZqMmdODxrf/1voRkK3kphIkdq4msXM1YGD16IO3bBiekoFYJQMzW726i0vEIlgeL5ZHIzZFRCTDz7kqgYiIiMixZTV/ounBUzpUxZNuzdsns1G4dl0VTmM1Zm7x8b+o9BxE/qVfI7zwn6T2be2gV+Bi11Rg11QcunRDhVg9+mAV9sYq6IWV3xMzt7iFuR9PvN3vLePg7m2MveDqowaBdirJutefpajfYPqNntryz7VknN98YirTr76VC/7tP/VmFhGRjChQFBERETkWx85q/kRP6VAMy6f6Sbdm5pVg5vXEadjf6jbJ3evwjzq3dfsP5pM38zaia+cRWzcPXKfjL/lILU6klmTF4aMpDX8OZqgQM1SAGczHDOZj+EMYvlD6d28Aw/JheHzg8WKYHjAMMC2Mpt/BaHP/1r72NKtffZKRZ11y1EAxFY8x548/ZMJFHz1moCgiItIWChRFREREjiHb+RM9vYareHJK8PYdTXx96wPFxO61rQ4UATBMguNn4es7mvA7/8Kuqzopr9ONh7Hj4cNGM2Yi99xb8fbVNAgiItI9KFAUEREROYas508sHabiySnB128s8fXzW39N7VmPm0qkR/JlwCrqR/6lXyO2/i1ia+dmFfSfTI3zH8Q3ZBqhyVdi+IIn7LiOnWLBo3czZOoMUskEa+c9zZjzrqD/uNMAMAyDg3u2s3ru49Tv30t+z95MvPhj9Og94LD97N24mg2LXqbhQCWB3AL6jJrEmHOvwDDMpuPYLHj0DwyZOoP8kj6smPMItVW7yCvqxYSLb6C475DD9rd/x0bK58+mrqoCj89P7xETGHfBtXj9AV1UIiJdgKkSiIiIiLQsm0DR8PjwFPVT8eSU4Ok9EszWj1Nw7STJPeuz/PZiERhzPvlXfBvfoMm0xy3EJ1Ji61LqX/w1yb0bTtgxXcdh0RP3UD7/eZ6+68vs27GBcM2B5udrK3fxyPc/ScOBSryBIKvmPs7fvnU9NXt3NG+zYs4jPPydj1G5eS25xb2INtby4u/+i2d+/tVDP/dMg0VP3MPa157hnz9I7y+U14Py+bP56zc+SmP1oZGl5fOf56FvXMfW5W+RV1KGYZq88dAv+Nu3ryceadBFJSLSFf79VwlEREREWvwmjr1/e+YfsHoOaZovTaT7Mzx+vL2Hk6x4r9VtkjtX4hswIetjmqFCcs78BIHR5xNdNYfknve6TL2caB2NbzyAf+hpBCdf0eGLvbw/z+K6N2dz3ffuZsD404H0oiwAm5e8xs13/ZPew9PnY/JlN/G3b9/Au0/fz6Vf/DG4Lsuef5i+o6bw8R//tXm/C/r+gQX/upt929dTOmhU80jFNa89zad++Tilg9ML9oya8RH++V+38N7bLzL96ltJRMO8cs+d9Bs9hRvvfKC5f+NnXsc/v38L7z51P+fe8nVdWCIinZxGKIqIiIi0wK7fh2snMv8Cr9ud5RTj7T8+o+0TO1aBY7f5uFZhb3LPu5X8S7+Gb+Ck9CIoXUR8y+L0aMXKTR17oKaalPQf1hwmflC/MdOaw0SAsqFjKR04kl1rFze3/9wfXuTm//n7Ye36jJwEwMGKwxetGjjhjOYwEaD3sPR7o37/XgB2rFpEIhpm4sU3HraoTL8xUykZMIzNS17XBSUi0gVohKKIiIhIC+ya3dl9wOo5WMWTU4pvwEQi7zzW6u3dRITk3g14+45pl+NbPfqQc9ZNBCdeRnzLu8S3LMaNNXb6ujmRWhpfvw//sNMJTrq8Q0crFvUZdNTHPzy3IUBBaV+2rXi7+e+JWIQ1rz5BxfqVhGsPYCcTxMPpW5PtVPJDbQ+f7sHy+pq2S//nTE3ljhb7U1g2gB2rFuqCEhHpAhQoioiIiLQgq9VcDRNL8yfKKcbMKcIq7o9dvavVbRLblrZboHioHz0ITriU4LhZJCrKSWxfkb4duh1GQ3boz5ravbixcKsCRdNKf4VLJRP4j/J8Mh4FDgV57/MFQsfc32GPeTw4Tirdt2SCR77/SWr27mDirBsYPPkcvIEgByu28/YjvzuyrXnsm+CaA8ijjCa1PF7sVEoXlIhIF6BAUURERKSlL74HMx+haPXoi2H5VDw55fgGTiaaSaC4fQWhMz+BYXnbvzOmha//eHz9x+MmoiR3ryNRsY5U5SbcVKLzFM30EJx4CYGRM8Bo3WxUuUWlANRW7iSnsPiI56srtgKQX9KnVfuL1B884rFYuJ5ATgEAW5a+QdXWcq761q8ZdfZlzdtsWjwvq5ccKkj3OVJbfcRz0foaQvk9dDGJiHQBmkNRRERE5KjcrG559hQPVOnklOQbPCWzKywZI7lzVYf3y/AF8Q2ZRu6MT1Nw3Z3knv9ZAqPOS48kPolzLnqK+pN/2dcIjDqv1WEiwNBp5wOw7PmHj1JUl3eevBcMgxFnzmrV/naXL8P9wAhOx7bZt/U9eg4eCRya+7C439DD2q1747n3f1RmpN+o9Ptk+6oFhz2ejEXZu3kNfUZN1sUkItIFaISiiIiIyFE4jQdxk7GM21klChTl1GTl98Iq6pfRyN74pkX4Bk87YX00LA/e3iPx9h5JkHSomTqwHbt6N6mDu7EP7saJ1ndsJ0yL4PiLCYzOLEh8X68hY5h0ycdZ+fKjJGMRxl94HblFpdRW7WbFS/+kYv0Kzrj+dnr0bt3PIsdOMvfen3DmDbdjebwsePRuog21jJ95HUDzAisr5zzKubd8nVhjHYuf/Uvzqs4Hdm0C1211OFvcfyjDpl/Aypcfo2zYOIZOO59wbTWvP/hzUokEp13zGV1MIiJdgAJFERERkaPIekGWogEqnpyy/EOmE8kgUExWvIfTWI2ZW3xS+mt4A3h7j8Lb+9CqxG48jF2/D7uuCrt+H05DNU64BidSg5uMt+l4VlE/cs64EaugrE37mXX7DynuN5ilzz/MM7/4j+bHi/sP5bIv/5TxF153/J9xyfSt31M+cjP1Byr58+0X4dg2lsfLGdd9njHnXgHAgPGnM+3KT7Hs+b+zYs4jWF4fky7+GFd8/RdE6w/y7lP3s3fjaj7+47+2uv9XfP2XzP3zj3jxd/+FY6fnTCwsG8C13/09fUZM1IUkItIFGK7ruiqDiIiIyOFiq18gtu7VzD5YefwUXH/XSb2NUuRkcsI11D72fTK5DzY48TKCU67qAq/OxU3EcKJ1uLEwTryx6fcwJGO4qQRuKo6bjOPaifSoPccG18GJhfEPmUpgzEwwrXbtVaT+IPFwA8G8HgRy87PeTyLSSGPtAXKLSo+6gEsyFqWxZj+5RaV4/YcWj4lHGvAHc7P6uZeMx2iorsQXCDXPDSkiIl2DRiiKiIiIHEW2C7IoTJRTmZnTA2/vkST3rm91m/iGtwlO/AhYnf2riYHhC2L5glDQeXoVyi8ilF/U5v34QrkUhXJbfN4bCNKj95EjsP2hvKyP6fUHKOozSBeOiEhX/DdfJRARERH5MJdUTTaBYj+VTk55vuFnZLS9E2sgvm2pCiciItKFKFAUERER+RAn2oAba8y4nVWkQFHEN2gKhi+UUZvY2lfJeLlgEREROWkUKIqIiIh8iFNflVU7q7CPiienPMPy4h+W2ShFu6aC5K41Kp6IiEgXoUBRRERE5EOchv2ZNzIMzPxeKp4I4B81I+M20VUvoVGKIiIiXYMCRREREZEPsbMIFM3cEgxL692JAFgFZXj7js2oTWr/dhI7NUpRRESkK1CgKCIiIvIh2YxQtArKVDiRDwiMnZlxm+iyZ8B1VDwREZFOToGiiIiIyIfYWQWKvVU4kQ/w9h2N1SOzeUXt2r3ENy1U8URERDo5BYoiIiIiH+Q6OA0HMv9QVaD5E0UOZxCceFnGrSJLn8GNh1U+ERGRTkyBooiIiMgHOOGDWd1yaWlBFpEj+AZNwcowbHfjYSLLnlXxREREOjEFiiIiIiIfkM3tzgBmXomKJ/Jhhklw4kcybhbf8BbJvRtUPxERkU5KgaKIiIjIBzj1WazwHCzA8PhVPJGj8A2ZjlXUL+N24bcfxk3GVEAREZFOSIGiiIiIyAdks8KzmddThRNpiWEQmnZt5tdiYzXhRY8ArmooIiLSyShQFBEREfkAW4GiSLvz9h2Nt++YjNsltiwmvlGrPouIiHQ2ChRFREREPsBpzHyFZ0vzJ4och0HojBvBtDJuGX7nUVL7tqqEIiIinYgCRREREZH3uS5OpDbzD1Q5RaqdyHFY+b0IjpuVeUM7ReO8e3Aaq1VEERGRTkKBooiIiEgTN94Ijp35B6pcBYoirRGYeBlWfmnG7ZxYAw2v/AE31qgiioiIdAIKFEVERESaONG67D5QaYSiSKsYHh85Mz4FGBm3tesqqX/5t7iJiAopIiJykilQFBEREWniRDIPFA2PD8Ofo+KJtJKndCiBsRdm1dY+uJuGOb/VSEUREZGTTIGiiIiISJNsRiimRycaKp5IBkJTr8ZTPCCrtqnqndS/8Cuc8EEVUkRE5CRRoCgiIiLSxM1mQZZQoQonkinLQ875n8PwBrJqbtdXUT/751r9WURE5CRRoCgiIiLSJJsRioYCRZGsWPk9m+ZTzPZ6raf+pV8T3/A24KqgIiIiJ5ACRREREZEm2cyhaAYLVDiRLPkGTiY45ao2XLQ24YX/oPH1+3HjYRVURETkBFGgKCIiItJEtzyLnHjBiZfiGzK9TftIbF9O3TM/JrFztQoqIiJyAihQFBEREWmiW55FTgaD3HM+hbfv6LZdv5E6Guf9iYZX/4jTcEBlFRER6UAKFEVEREQANxXHTcYy/zAVzFfxRNrK8pA783Y8pUPbvKvkrjXUPv3fRBY/jhNrUG1FREQ6gAJFEREREcDNYv5EADOgQFGkPRgeP3mzvoSndEjbd2aniK17jbrHf0hk6dM4WUxnICIiIsf4DKwSiIiIiIATb8y8kWFg+EMqnkg7MXxB8i7+Kt4+o9plf24qTmzNK9Q+/n0a5z9Iav82tCK0iIhI23lUAhERERFw45GM2xj+XDD0/7Mi7cnw+sm96IuE5z9IYvuK9tmp45DYspjElsVYBWX4hp2Of+hpmDlFKriIiEg2/167rqv/ohMREZFTXmLru0TefTSjNlZhH/Iu+7aKJ9IRXJfoitlEV73UYYewivvjGzARX/8JWEX9wDBUdxERkVbQCEURERERwE1kMUIxkKvCiXQUwyA45Sqswt6E334Y1062+yHs6l1Eq3cRXfE8hj8Hb69heMpG4CkdglXUF8Py6jycohKxCJbHi+Xxdqp9iYh0FgoURURERAA3Hs64jelXoCjS0XxDpmMV9aPxjfuxa/Z06M+AxM5VJHauSj9gmFiFZXiKB2AVlGHm98TKL8XM74nh8Z+0euzdvI6qreWHPWYYJrlFpfQcOIz8kt6d+nxuWvI6iUgjY8+78qjPV1dsY9e6pQyZMoP8krKT0sdELMIvb5jMmdd/npmf/lab9pVKxPnlDZM5/drPcNFnvqMLWkS6DQWKIiIiIoCTyHIORRHpcFZhb/Kv/C7RJU8Se+/NE3NQ18Gu2XPUENPwhTBDBZjBAoxQPmYgF8MbxPAF0r97/WBaGKYFTb+8ZSPb5ZbqDYvmsuCxP+H1B8F4v6sOqUQcwzCZcOG1XHL7D/EGgu1Shm0rF7Jv23pOv/Yz7bK/hY/fS83enS0GirvWLeWF3/+Aj995/0kLFD0eHxd8+pv0GzWpw+ogItLVKVAUERERIdtFWbTCs8iJYlheQmd8HO+gKUQW/AO7ft/J+3mRiGAnIti1e1vdJjjxIwQnXQZm+3wF++xvnqK435Dmv4frqnn3mQdZ9MR9GIbB5V/9absc570Fc2iorjqlgjTT4+Gs62875esgInIsChRFREREyO6WZ8Ofo8KJnGDeshHkX/MDYqteIrpmLjipLtHv6KoXSexaTe6MT6cXgGlnOQXFzPz0t9i2YiHr3nqBy7/yk+YRkfFwAyvnPkHllnW4LvQaPIpJF19PMK+wuf3ylx7BF8ih3+jJLHryfor6DCQeaWTL0vlYXh9vPPwbhk45h/5jpwGwb/tG1r75HLWVu/H6A/QZMZEJF16THjnZAVpzvFQizrr5z1O19T3ikTB5xb0YM+MySgeNBCAZj7LgsT8zdMo55BSWsOKVx2moriK/ZxlTLv04hb3S58VOJXnrkbsZMG4aQyafw/x//K7FOlRsWMX6BS9TX11JMDeffqOmMPb8KzAMUxeriHRr+iknIiIiQpaLsvgUKIqcDIblJTjlKgo/eie+oad1mX7bB3dTN/suoitfAMfukGP0KOtHMhYllYwDUFu1m3u/fCULn7iXQE4+ofweLH7uIe790uXU7N3Z3G71vGdY/drTPHHXV9i2ahG1Vbup3VdBtLGWVCJG3b7dxCINAKx7czb3/8c1bFk6n/yevTEMk3l/+Tl/+fr1xMMN7f6aWnO8VCLOX//zE8z7yy9IxCIEcvPYseYd7v/qNZTPf6HpBz0seOxPLHvpUf7xg08Trj2AaZosf/ERHviPa6nZuyN9nlJJFjz2J7avfiddwxbqsOzFR3joWzeyd/Ma8op7EW2o47nffJcn/+crukhFpNvTCEURERERsl2URYGiyMlk5haTe+6tpMZeSHTFbJK71nb+TjsO0RXPk9ixst1HKzqpFHu3lJNfUobHFwDg5T//mHikgdvufqF5TsKzb7yde75wGa8+cBc3/OBPAFheLxUbVnH6Nbdy7k2HArGdaxfTc+AIrv7mrwBIRMO8ePcd9B8zlZt+/JfmlYsnXnQdf/vuzSx88j4u+NQ3jv3z1rGbw7sPi9QdPOzvrT3eznVLqNyyjk/89wMMmXJOc/s5f/pvdqxdwphzL8cw0+Npyt96kc/+5il6DR4FwJ6Nq3nwWzey8PF7j3qr+FVf//kRdcB1WfLcX+k3egq3/M/DzdsW//P3vPXIH6jatr55/yIi3ZECRRERERHASWRzy7PmUBTpFF9qigeQd9GXsGsqiK55hcTWpeA6nbrP6dGKP0vPrTjhkvTiLRmordrd/GfXdajfX8ni5/5KbeUuLvnC/wPSYdyWpfOZfMmNhy1wklNYwugZl7Fq7hOkEnE8Pj9gYCcTnHGcOQK3rVxIIhpmyqU3Nod7AP3HTqPngOFsWvzacQPFSH0Nf7zt4la9ztYez7HTIz4rNq46LFC89N/vOGKfA8ZOPSzs6zNiAqUDR7Bj7eIM/gEw+MI9c8B1D3u436jJABys2KZAUUS697+9KkHHSdopDjY0EPT5yA917AiGSDyO12PhtXRKRUREMuXaSbAzn4fN8AZUPJFOxOrRl9xzb8WZejXxjQuJb1yAE6ntvB127PTIyp2ryDnvM1gFvVrd9NE7P3/EY7k9Srjk9h8y7fKbATi4dweu61CxcRXP/u+3Dtv2wM4tOLZNbdUuSvoPS7cv+v/s3Xd4VFX6wPHvnT6T3ntIIPTepIlgd7GjrB1s6zbdat9dV/25q+66rq69rIoNcMWGShHpgvTeWxoJ6ZlMr/f3R0I01JkhCQm8n+fh4cmdc+4598zcufe+c0oKBvPxn1sO9SxMzMw/4rWEjBz2b1hxwrqbomOZ+MDzR31t79olrPzs7bDL6zr4bHqNvoglH/yHTfM/oevgs+k6dCwFQ89Bqze0yJeU1fWIfcWnZbNv/bKw3j6v28mGeR9Tun0djvoaAj4vbkcDAAGfT05IIcRprcNGn7aVFLK9tPi4ac7u3Z+0+ISOeW8QDPK7N19kydaNRJvNfPiHv5CfltEmZTk9Hkbc9wtuv2ACv79iUqvue8P+Pazft5t6h50Ys4XclDTG9R2A8bCLsmhpj6sBFRUtGrSKgk5R0CoaLBotFq0OnUzSLIQQHUok8ycCKHqzNJ4QHZAmKhHz4MswD5yA78BWPHtW4CvZ0vjjQUd8KEvrhiYqPqw8P/3LK8Sn5TR9GYHRHEVsckbzQizwQ1BLbzRjioptkT+792Cyew9G/6Oe1gbTiTtBBPxNbfijcg7R6vQEQmhjrc5A/qDRR33NWnkgovI0Wi3XPPgf9q5byvbv5rBnzSLWzZlOTGIqV933LLn9hv+Q7yjPMlqdjmAYPywFfF7ee/BmassKGXzxT+k2dCwGk4Wa0v0s/uB5OQmFEKe9DhtQ/HbjOl6fNwuN5tiBl1d+8fsOG1B8cuYHVFnr+fShJ/hkxRLuef15PvzjX9qkp6JBp+N3l1/LwPyCVttnYeVBHpj6GttKCok2mUlPSKTB6aTSWke02cxD19zEFWeNkTPoMM77CHPoAAAgAElEQVSgn2kVe1ljq8Z+nJspvaLBotURpdERqzOQqDeSoDOSqDOSqDeSZjCTbjCjl8CjEEK0D783omzSQ1GIDk6jQZ/TH31Of1S/B1/pVryF6/CVbkFtWrTklFYvJpnosyejS+8edt7EzDySsrseN010QjIAuX2Hce6UP7ZKnS1xSQA46quPvBduqMMS27rPZ2GVpyh0G3oO3YaeA8CBHev5/Nn7+eyff+A3U5f+kO+weRoB3A4bpui4kOu1e/VCDu7dysQHnqP32T9p3r5r5bdy3gkhzggdfnzsZw890WY9+9pKncNOz6xc7p94A3qtjvsn3sDo3v0orKpgQJeurf8marXcceGlrba/ivo6Jj/3d3x+P09P+QUXDx6Otimwu6ushEenvcOf3n8Tg07PJUPOkrOoyWZHLW+W7aTW7yFaqz9uWp8axOr3YsVL2TF6xShAit5MhtFCltFCvimGbuZYkvUmFGluIYRoVao//MCCotWHPeeZEOLUUXRGDHlDMOQNgWAAf3UhvrId+Mp24K/aB8H2nXPR1Hsc5qFXo+iNbVZGXGoWscnp7Fu37IiAYsnWNeiMZjIK+obwJfnDPIE5fYYAsH/DdxQMG/fD/a3bRdnuzXQbck6rHkOo5R3YuRFr5QH6jJ3QnCar12AGnHc1iz94Ho/D1twzsWTbWlQ1iNL0430wEODg3m2kd+sTcjtYq8oBSMru1vKZYMHnjUlR5aQTQpzWOv2Ee/5AgFdmf87Zffrj9fv5bOVSLh02irN79weguKqSz1cuo7i6ApPBwLBuPbl02Ch02pYPAF+vXcmybZtQURle0IurRo7lsenvMKRrd64ccTZur5c35n3J8O69GNnzhwuNx+fj9bmzWmxPiIrmrO69eXX2Fy3KHdnjh3yBYJCXv/6Ms/v0JyMhiRnLFlBaU0VqXALXjh53RBB1V1kJs1Yt52B9LSlx8Vw+bDS9c7o0XkwDfl6d/QVDC3owule/5jybivbxzfrVVNTXEWuxMCi/OxOGjUSjHD8c9eznM6iz23jj1/e1OFaAHpk5vHH3fVz3j0fZXLSvRUBxd1kpX65ZwYGaakx6Pf3zunHlWWMwGQxHHHNiTCzTl35LTUMDvbJzuWnchei0Wj76biEb9u8hzhzFVSPPpk9OHkBz+4/p3Y+kmFhmrlhChbWO9PhEfjpmPFlJKSF9HiJtk+NxBf18ULGXxfXlrftwC1T6XFT6XGy01zRvj9Hq6WqOoZclnr5RCeSZolEkxCiEECf3nRtBD0XpnShEJ6bRokvthi61G+ZBl6IGfARqSvBXF+KvKiRQXUigobrpjqyVi45OJOrsyegzerbLoY669i7mvvo4c199nJET78QUFcPetUv4+qVH6DnqIi7/3ZPHzW+KjqOyaDfVJXswmKNJzimg+1nnsW72DDIK+tN9+Hgc9TXM/+9T+L1eRk68vVXrH2p5Zbs28c2bT+K01tJr9MXoTWZqSvexdcmXpOR2xxgVg9/b+OOR3+dh3mtPMHrSz9HodCz54AVctnoGXjAx5HZIz+8NwLrZHzJ+8h9w2xv4/pP/omga78urincfsWCLEEKcTjp9QDGoqrw+bxYNLgezVi0nKymFkT2sACzZupHf//dFclPSGNtnALV2G4/PmMrMFUt48+77MegaD//Zzz/i7W9nM6JHb/rm5vPFquWs3buLWauXE2VsfFhw+by8Pm8WQIsgm9fvO2J7KOUqisLr82ZRa29gxc5tDO5aQEJUDF+tWcGMZQv5+pGnSI1r7L4/b/1q7pv6Kj0yshmQ343Nhft4f9E3PHr9rUwcdQ4+f4DX583i9uCE5oDijGULeeKjdxnevRd9cvKoqK/lTx+8yTcb1/D8nfccsz1dXg/zNqw5InD6Y1FGE1/+5akW275e8z0Pvf8GBelZjOrVF6vTzjOfTWfakvm8//s/E202Nx9zZUMd6/ftYXhBL1xeD//+4n8UVVXg9nqodzjITUnl201r+Xj5Yr565CkyEpJQaXyfS2urWLtnJyN69EGraPho2UL+990ipt/7V3JTUo/7eYi0TY5nq6OON8p3UuNzt9tn3hbwsdFey0Z741CNaK2ePlHxDI5OYkh0MhZZmEcIIcIWSUARCSgKcdpQtHp0qV3Rpf4wmkgN+Ag2VBKwVhCoP0igoQLVUU/QWU/QURfRfIzGnmOxDJ/Yrj9IDLv0JoJ+P9999Aprvvqg8evLaGbA+VdxwR0PhZT/6xcf4bVfXcrgi3/KhLv/j6vufYbZrzzKrOcfJOhvnHcwISOXSX96kayeg1r9GEIpb/hlN+Oor2bx+88z97X/a3pjFboNGctFP/tTi/31H38FXreTF+84l2AggFanZ/S1d9F33OVhtcNZV05h9RfvsfbraWj1BoZccj1X/vEZnNZaVnz8BmU7N3L9o2/KCSaEOC11+siDvqmn4ZerV/Cfu37DWd0bfyny+Hz8+f036ZXdhXd++2Dz6sc/GTKCX7zyL6YvXcDkcy+iusHKuwvncv6AoTx3592NNw+qyoPvvoaqqsedw/FoQi33UI+4z1YuY9q9j9ArKxeAS4aOYMpzf2f22pVMOe8S3F4vj82YyuiefXnp579rrs8vX/03//hkGpcOG3nkQ5Gq8v6ieQzu2p237nmgeXv+7M94Zfbn7DxQQs+snKPWf0dpMf5AgBFN7RgKh8fN4zOmMqRrd17/9b3Nx3z1iLFMef5J/jv/K357+bXNxzx77So+e/gJspt6Ff7ylWf57PulTBoznqen/AKAy4aN4uZ//41vN67j5vEXomkajjBn3So+uu/R5vpvLtrHTc8+wX/nf8VjN9x2zM/DybTJ0biDAaZV7mVBXdkpPwfsAR+rGqpY1VCFVlHoF5XIWTEpDItNxqKR4KIQQoQkkiHPElAU4rSmaPVoE7LQJmQd5VUV1eMk6GpA9bpQPU5Ur5Og14XqczWuGh8MoAYDEAwQsNdi6nk2+uy+J12v8bf8jvG3/C6sPGddOYXhV9xCQ9Mw3ejEVLS6llP03PLke0fNO+iiSfQecwnBYABzTOPCMQZzFFf+4Z9M+PXj2Kor0JstxCSmhlSXKf+YdtzXB100iUEXtVxoMqTyFIXxt/yecTf9BntdNX6vh5ikVHSGI7+rFY2WCXf/H+ff/gCOumqik1IxmH68OI2FP83aecJ2uPDOhxl/8++x11USnZiK3ti4UNfNf38Pj8OG0RINinLEvoQQ4nTQ4aMNc9atIikm9qivXT1qbHPwqltGVnPwCGDlrm3UOew8NOnm5jQAY3r3o0tqOt9uXMPkcy9i9Z4dBIJBLhs+6kfXIoUbzrmAr9euDLu+oZZ7yIgevZuDiQD9cvMBKK+rbdrfdhqcDq4edU6L4OZffjqZapuVxpn21MOupQqz/vwk6mFd7AfmNS7aUlhZfszgWb3DDkBSbOgTEn+/cysOj5tJo8e3OOYh3XpQkJHFoi0b+O3l1zZvH9mzd3MwEaBHVg7Ltm9m0phzm7f1bGqTqob6FmUN7dajRd37d+lK94xsVu/e0XzsR/s8nEybHG6Xy8orB7ZT3Y69EkMVUFU22mvYaK9haoWWUbGpnBufQVdzrAyKFkKI44hoyLPOIA0nxBlLQTFGoTVGdZ4aKxriUrMiymuMijnqdr3RTGJWXrsdQyjlKRotMUlpx//Ob3p+MlqiG4N+J9EOepOZhIwuIbeZEEKcLjp8QPHVuV8c87UJQ0eiNzceQl5Ky4tGYWUFAJ+vXMbiLRtbvObyuNnvdABQXts4N11mYlKLND8OeIUj1HIPyTqsnEPDsL3+xiEURVUVTfVJbpEuMzGpuc5Oz5G9KpweD5+sWMKG/XuosVnx+v3YXI2Lf/j8gWPW39I0xPtQ+aEorqoEoEtq+hGv5SSnsmLn1hbb0uITW15s9fqm7T+s0HZo3sXD65F3lDKykpJZvmNLy3Qpaa3WJofLMFjoao7pkAHFH/MGAyyuL2dxfTm5pmgmJOYwMjYVrSKhRSGEOOLhUgKKQgghhBBChOy0WeX5UCDsEH+gcW6NaJOFWLOlxWvnDxiKTtc4NNYXaAwkHb5Iy6HAXrhCLfcQraIJaX/h8Pr93PqfJymurOCa0eM4u3d/zEYDhRUHefHrT4+bNyOhMUi5u6w05PJ8TXU8WpxKr9XiDwRCOmZtCMPLDTp9SGUc/nk4mTY5XIxWzz1Zffk+ppKpB3djj2D+nPZW7Lbzatl2Pq7az4SkHMbFZ2BQNAghhDh0wQ1/yDM6o7SbEEJ0IhqNlryBo0jKzJPGEEKIk3TaTrB2aJj01SPHMqZ3v2OmizE3znPR4HS22N44nPgHh2Jl6mHDi62H9TgMtdxQxUc1dsGvsTWEnGfxlg1sLynimdt+ycWDf1iFeeHm9SfMm5uSSpeUNL7ZsIZ7r76+eVGaw01fugCz0ciVZ40hMTr2mHWsc9hJaMXu/nX2I8tocDmJtUS1WZscy8jYVHpb4nn74C7W2qo7xXlR7XPz7sHdfFVTzLUp+YyOS0Mjg6GFECLCHooSUBRCiM5Eo9Nx0xPvSEMIIURrfKeergc2qGt3AL7bsfmI177dtJaypqHO3TOyAdhUuLdFmqVbN7X429w0BLfObm+x/dDcfeGWG/pxNM7x9932lkN6P1j8DRf99V7Kao8MZB2af7FrWmaL7bNWL298aDosKHq4KeddgtXp4K8fvk0wGDzi9e93buMfn0xj8ZYNAAxuOuYVO1oObXZ5PWwp2t98DK1h3b7dBH80D2IgGGRHafEJ5z882TY5ljidgd9l9+OXmb2J1uo7zflR4/PwWtkO/rJ/Lduc9fJNKIQ446mRLMoiQ56FEEIIIcQZ6rQNKHZJSeOiQcOYsXQB//tuEXUOO9UNVl6e/Rm/e/NFVu9pDAQO7tqdzMRk3pr/NUu2bqSqoZ6561fx6fdLW+zPqDfQNS2TbzetZVPhXursNr7dtJbPvl/avBBIOOWGqmtaJuf0Hcj/vlvIx8sXU1ZbzaItG3h1zhekxieQmZh8RJ5e2Y3BtRnLFmBzOTlQU8Xf/vdecz33lB84YnGSH5s0ZjxXnDWGuetXcf2/HufT75eyft9ulmzdyBMfvcsvX32Wgows/vLTyQB0S89kfL9BfPTdIr5aswK7y0VR5UHue+cVPH4ft553Sau9rx6fj6c+/oBKax21tgb+/vH71DvsXD1i7HHznWybnMjouDSe7DqcwdFJneo8KXbbebJoA2+U7+gUQ7eFEKKtRNJDkU70Q5IQQgghhBCtSXc6H9zfbv4ZT3/yIU9/8iGPz5gKQHJsHA9dexNXnjWmsQG0Wp69/dfcP/UVfv3ac0DjPIJPTb6LKc8/2WJ/j1w3mQffe52bnn0CgIKMLP51+6+47p+P4Q8Gwio3HE9P/jmPzXiHJz56l0BTj8Fz+w/mkeumHDX9Wd17c8v4i/hg8TfMWLYQg07HpDHn8tTku6iz23hr/tdsLtrHW/c8cJy2u5NhBT2ZtuRbHvnwrebtybFx3HHBBO688LLmhVMAnpryc5746F3+/MF/m+czzElO5fk772FAXrdWe08vGz4Kl8fDRX+9l0AwiF6r444LL2XCsJHHzdcabXIi8ToDf8jpz3JrBTtdVlRVxa+qBFEJqCqeYABn0I8rGMAZ8OMI+HEF/R3iXFlSf5D1thpuTe/BWbEp8s0ohDjzRDLkWQKKQgghhBDiDKWoJ9Mtq5Pw+HwcrK/FbDCQHBuP5iirh6iqSnld43Dk9IQkfH4/w/54F1POu4R7r7quRbpKaz2qqpKekHjS5YbD4XFT3WAlNS4es+HE8za5vB6qrI3pfxz8s7tcRJlMLXpWHo/b66WqoZ4Ys6V5Tsfjpa2or8VsNJEaF9+q7+GwP97Freddwh+vug6720VNg5WUuAQsxtDnsGqtNmkt3mCAer+Xer+XWr+HCq+Lg14XB71Oyr1OHIH2DzieG5/BzWkFGDRa+YYUQpwxHMvexleyKaw8poGXYepzvjSeEEIIIYQ44+jOhIM06vV0SUk7bhpFUY46fPho6dLiE1qt3HBEGU1EpZhCTm82GMlNST1ie3TTQjQhPzAZDOQkp4actktqepu9l4ei39EmM9Emc9j5W6tNWotBoyXVYCbVYD7qsdb43BS57RS6bex2NbDb1YA3GGjTOi2sL2eXq4HfZPUl02iRb0khxJkhgu9W6aEohBBCCCHOVDppAiE6JgVI1ptI1psYGtMY7ParQQrddrY56lhvr2GPq6FNyj7gcfBo4TruzurDgOhEeTOEEKc9NaKAotxGCSGEEEKIM5PcCR+DVqNhRI8+5KWmSWPI+9FxTlhFQ4E5lgJzLFckd8Hq97LOXsMKawXbW3m1ZlfQzzMlm7gprYCLE7PlQyiEOL1FMqet9FAUQgghhBBnqDNiDkUhzgRVPjfLrAdZWFdOnd/Tqvu+PCmXSaldUaSZhRCnKfv8F/BX7QsrT9SYyehzB0vjCSGEEEKIM470UBTiNJGiN3F1ch6XJ+WyqqGK2bWlFLptrbLvWTXFuIIBJqcXoEhYUQhxGlIj6aGoyOJVQgghhOgcvB4nWp0e7QlGWISaTgjto48++qg0gxCnD42ikGOK5tyETPLNMZR6nDQEvCe9331uG9aAj0HRSRJSFEKcfjfZu79DDfNHGEPeMLSxqdJ4QgghRCdUXbWfLeu/ZtumeRTtW0NdTTExcWkYTsOFKb0eJ//3wAACAT/deo45Zjq/z8Pj9/fH53NT0GusfEjEcUkPRSFOUwowODqJQdGJrLBWMq1yL/X+kwssLqgrw6LRcV1qV2lgIcTpRRZlEUIIIdqBihpwEAw0EPQ3oAasBAMO1KALNehu/ofqBzWAqgaAIDpTN8xJl7VKDTweB1/M+DOb13+JRqMjMTmXQMBHfe0BAEaOnczFVz6IRtN5RyLs3fUdBw/sYMy5dwCg1Rm48LJ7yc0bIh9B0WrkTliI05yCwui4NAbHJPFJVSFza0s5mYlTv6wpJlqr49KkXGlcIcTp83gTQUARjQx5FkIIIY64pqo+Ap4yAt4DBDwHCPoqCfiqm/9HDf+a67WvIeAtJyrtFhSNKeK6BYMB3nvtTor3r2Hchb/i7PPvwmiMAsDeUMW8L59h+eK38fu9XD7psU77HmzZMBtbfcUPAUWtjnMu+IV8OEWrkoCiEGcIs0bHTWkFDI9N4dUD26nyuSPe1/TKfaQZLAyLSZaGFUKcJk8/wfDzSEBRCCHEGX/59OB378Xv2oPftRe/ex8B70Ggldd+VVXcdXPx2dcTnflr9FF9I9rNuu//R9G+1Yy/6NecP+H3LV6Ljk1h4o1P43ZZqakuwud1oTeYAago38nGNV9QV1uCXm8iu8tABg+f2Px6MBhgwezn6dFnHFHRSaxc9j52WzUZWb0Zdc6taLQ6Vi+fRvH+dZjNsQwZcS2ZOf0A8HldLP7mFbr3HktUTDJrV3xEg7WCuPgMho++gYSk7Bb1rK0uYt3KmdRWF6E3mMnrNpwBQ69E2zRyYsHs59m9bTFanYH5Xz1L995jye4yiIVzXiCv21kU9Dq7eV+b1s1i9/bFAOQXjKD/4KP3Aj3R8QcCfhbO+Q89+ozD7/eybuXHDBx6Jd17nxNSnaFxuPWmdbMoP7ANj9tBbFwa/QZPID2zl5xoHZTMoSjEGSZJb+Kc+HRq/B5KPI6I97PeXsOg6CTidQZpVCFEp+fZvgDV7wkrj7FgNBpLnDSeEEKIM4aq+vA5NuOu/xZn5XQcFW/hqV+Az7GRgKcENWBvk3IVRQeqHzXowGNdRDBgQ2/p27g9DF/OfAy3q4Eb73gVre7oi470H3IZg4Zf1bwoyaa1X/Duq7fjcdvIyO6D3+9l+aK32brhawYOuxKdzgjAOy9PBmDp/NeIjU/H7ahn5bIPsNuq2LL+a8oPbCMmLpXtm7/h+yXvMvisiZjMMQQCft597Xb8fh+L5r5IVHQSiqKwcc3nrFkxnX6DLsFsiQdg57aFvPXCjTgdtWTm9MfrdbF0/qvs2bmMgcOuRKPRsm7VTMpLt6DRaDGb40hK6UJcfCbvvnY7sXFpzXMozv3iaeZ89ncsUQkkp3Zl17bF7Nu9gsryXeTmD2meQzGU4w8G/Ux99VZ0eiPzv3yGQMBHWmYPMrL6hFRnv8/DG8//lK0bZ5OQlIvZHEPx/rV8+/W/SU7NJy2jh5x8HZD0UBTiDGTW6PhFZm/yTTFMq9hLMIJfEL3BAM+VbuGJ/GFEyTxiQohO/4AUyZBnjTScEEKI014w0IDXthqvbQ0+xybUoOeU18ldOxuffR3RmXejt/QO8VqvUlayhS5dh4W88IrH4+CLj/5Cbteh3PrLqc1BxiEjruHN/9zA0vmvceFl96IojfcEm9d9yT0PziYhKQeAd1+7nXUrZzJ89A1MmvxvAAYNu4rXn5vEtk3zGDXuVjRNebes/4pf/vEz0rMaj6e0eCOv//talsx/lauufxK/z8MnH9xPRnYf7rhnWnNdBgy5jKmv3saqZR8wevxtXHPTPyncu4q09B5ce8u/Gp/dPM4Wx2VvqGL5orfoM+Airr/tJRSlcdnNz6Y/FNHxH9q+cc3n3HTna+R3HwkQcp0L966mrHQrU37xdovFYGZ9/Ff2711F/yGXyYnYAcmdsBBnKAW4JDGb+3IHYIpw2F61z82b5Ttae0CDEEKciqelCL5I5TZKCCHE6UkNuvFYF9NQ/Ddqd96JvewVvLbVHSKYeEjAW4G18BEcB98JqV5ej4NAwEd0bErIZezd+R0ej4Pho65vDoYBdOk6nNT07uzcuqBF+q49RjcHEwHSM3uhqkGGj76+xTYAW0Nli7xdug5rDiYCZOcOJC2jJ/v3rGysy+7lOB11jDrn1hZ1Keg1luSUfLZtmhvyce3fs5JgMMCAoVc0BxMBzhpzU0THf2gfqendm4OJ4dQ5GPQDUFK0oUX5l1/7GFdMelxOyA5KuhUJcYbrF5XAQ7mD+GfJJuwBX9j519iqmV93gAsTsqQxhRCdV0QBRUXaTQghxGnF7y7CXTcPj3UJatDVCWqs4qr9kqC/lpjs39PYbeLo9AYziqLB7ws9KFpbXQRAUmr+Ea8lJueyd9fyFtti49Nb/K3Tm5q2Z7SoB4Df722RNjm16xFlJCRls3vHUgBqKgsBWLdqJju3LmyRzuN14Kq0hnxc9XVljftPzD7imE7m+JMPSxdqnQt6jaXPgItZMPt51q+aSUHPsXTvfQ7de49DJ1NsdVgSUBRC0NUcw5+7DOJvRRuwRRBUnF6xl/5RiaQ3XRyFEKLTiaSrtSKLsgghhDg9LoJe+0ZcNZ/ic2ztXFVXtFiSr8WSPJHjBRMBNBotMbEpVJTvDHn3gaZnI+Uo+9Zq9QQPe3bSHGP0giaEaVKOFjjTaHQEA/6mujQGIE3mGEzmmBbp+vS/6JhzQh7vuDSHTV2l0ehO6vgNBsth+UOrs0aj5frbXmT3jiVs3TCHndsWsnr5NGLiUvnp5OfJ6zZcTtMOSAKKQggAsoxRPJA7kL8Xb8DZdNEKlVcN8kb5Dv7cZdBRLzZCCNEZHqbCfoaRHopCCCE6+bXPa1uHs2o6fvf+Tld7nakL0Zn3oDPlhZyne+9xrP3+I4r3ryU3f+hR0xTuWcX+Pd8z5tw7iYpOAsBuqz4indNRhyUqsdWOx2GvPWKb22XD3LQAXHRMMgBDR0xqMc9gJEymmOb9/9jhx3myxx9OnRVFoUfvcfToPQ6AksL1fPz+H/no3d9y/2PL5XTtgGTyHyFEsy6maP6Y0x9dBPOC7XJamd/UdV4IIc4IElAUQgjRSfldu7AW/oWGkic7YTBRgyXlWuLznw4rmAgwatwUtFo9n01/GIe95ojXrXVlfDLtftatmgnQHHTcu/O7Fum8XhcHijeRmz+k1Y6qaN8aVDXY/HcwGKD8wFbSM3u3qMvuHUuOyLtt0zzqaw+02KYe58fSQ6smlxZtbLF9766Wx3myxx9qnUuLNrJ5/VctXs/JG8zg4ROxWStxu21y0nbIM1EIIX6khzmOn2X0jCjvx5X7sR42F4gQQnQGamRjnqXhhBBCdCpBvxV72UvU738Yn3NHp6u/1phLfNensKRcD0r4Ay7TMnpy2TV/paaqkJf+cTlLv32Nwj2r2LdrOYvmvcQr/7oKj9vOdVP+g8FoITW9gF59z2P18mlsXPs5breN6qr9fDT1t/j9Xsace2erHZvf7+GrmY/TYK3AYa/hy5mP4XTUM2TENQAkpeTRd+AlrFr2AauXT8PpqMPeUMWCOf9h2lu/al68BcBsjqWifBeVB/fQUH/wiLJyuw4jLiGTpd++yq7ti7E1VLJ14xxWLn2/xQiMkz3+UOtcWrSRj9/7AyuXvoetoRKP205p8UY2rZtFanr35h6VomORIc9CiCOMjkujxOPgy5risPI5g35mVO7jrqaVy4QQ4rQmPRSFEEJ0Giru+kU4Kt5BDTg65TXXkjwRc8okFEV/UrsaNvp6ktO6sWT+K8z/6lmCTQuz6Q1m+g++lPEX3U1C0g+LlVw7+d/M+t8jfPrhAwSapoZKTM7lhjteISdvUKsd4sBhV+L1OPnXY+cQDAbQavWcc8EvGDD0iuY019z0T77+9Am+/vQJvvjoLwBEx6Zw6cRHGHzWxOZ0I8bewucz/swLT13CsFHX8ZOr/tSiLK1Wxw23v8RHU3/He6/dAUB8YjY/nfwc77x8S/O8ja1x/KHUecTYW7Dbqpj/9b/5cuZjTW+5QkGvc5gw8c9y+nbU01JVVVWaQQhxuICq8vfiDexyWsPO+3j+UPLlVyQhRCdSP+M+CIY3f2zsFY+giUqQxhNCCNGhBf312MtfxWtbc4proqDRJaDRJ6LRJaHRxaJoY9Boo1G0USgaE4piRNEYQTGgKFpQNDQOrFTQmbq0/jNPwI/NWoFGqyMmNjjjAhAAACAASURBVAXlOFM/+bwurNaDGA1RxMSltlod/D4Pj93Xl7PPu5OLr3gQj9uOzVZFbGwaBqPlmHms9eXoDeZj1tvtthEMBLBExR//Hqi2FFCIS8g87vzQJ3v8odQ5GAxgt1Xj93mIiUtF37RKtuiYpIeiEOKotIrCrzP78PD+1TjCXKRleuU+HswdKIMBhRCnN/mSE0II0cH5HFuwHXiOoL++/S6P2ih0xi5oTV3QGbPRGtLRGDLQ6pJA0XasZx6tjvjErJDS6g1mklPy26wuh/p6GU3RGE3Rx02r0xtJSsk7bppQhwnHJ2a3y/GHUmeNRktsXJqcuJ2EBBSFEMeUqDdyS1p3Xi3bHla+bY46tjhq6d+Kq54JIUTbkjkUhRBCnF7XNVf15zgqP4jwGhc6nSkPvaUPOnMPdJYeaPUpco0U4gwgAUUhxHGNjktjla2KdbbqsPL9r3I//fIT5VZCCCGEEEKI9qT6sZW/iqd+UZvsXtGYMUQPwRAzDH1UfzS6eGnzk2pPLV17jG7T3o9CtAUJKAohjn+BAyanFbDFXotXDYacb7/bJr0UhRBCCCGEaEdq0EVDyT/wOTa38kOBDmPMWRjjxqGPHnDSC6OIH2i1Om771bvSEKLTkYCiEOKEkvQmrkjuwsdV+8PK91lVEf2ipJeiEEIIIYQQbS0YsNFQ/AR+195W26fWkIYp8SeY4s5F0UZJIwshmklAUQgRkglJOSysL6fG5w45zy6XlT0uK93NcdKAQoiOTY1kfilV2k0IIUTHuIwF7DQUPYbfXdg6gQJTHpbkSRhih9O4yrIQQrQk3wxCiJDoFQ3XnGBVrqOZXVMqjSeEOE2f3qQJhBBCdIDLUdCJtejxVgkmag3pxGTfS3zXf2KIHYGEDIQQxyLfDkKIkI2JSyPLGN5QhzW2KqrC6NUohBCd6BFOmkAIIcSpvRKpPhpK/oHfve+k9qNojESlTSah23MYY0ciqzQLIU5EAopCiDC+MBSuSM4N+3F7QV2ZNJ4QooN/wUVwS6RKQFEIIcSppGIvexmfY8tJ7UUf1Z/4bv/GnHQFKDIrmhAi1PiAEEKEYURMKql6c1h5llgP4g9jhWghhGhvihLJLZEEFIUQQpw6rupP8ViXnsTFT0tU2mTiujyCVp8qDSqECIsEFIUQYdEqChOScsLK0+D3ss5WI40nhOi4FOmhKIQQovPw2tfjqJwWeSBAF09cl8cbeyXK8GYhRCTfI9IEoi0Ff/Sw5fR48AX80iingbPj0rBowxsOsai+XBpOCNFxRRBQVKXntRBCiFPxjOWrwXbgP0TaU15nzCU+/2n0lp7SmEKIiMkECWeI7aXF7DxQ3PLZSdGQEhtHt/RM0uITWrW8qoZ67p/6OltKChmcX8Bzt/+asX/6LbeeezH3XHp1q5alqip/+/gD1u3bzfh+A7ntvEtwejw88/lH/HPKz9u9rVXgu7pyAqqKRlHQoKBRFPSKgkGjxaTRNv9v0eqI0urRKp3rV0GjRsv4+Ay+rikJOc8WRy31fi/xOoOckEKIjieiHooSUBRCCNHegtgOPIcasEWUW2/pQ2zOAyjaKGlKIcRJkYDiGWLhlvX8d/5sTHoDSlPwKhAM4vX70CgKlw0bxf1XX4fZYGyV8j76bjGbivbx8DU30S83D4NOxz0TrqJ/l66tfmyrdu9gU9E+rhoxhi9WL2fa0oXotBruuGBCu7dzmdvB9PI91Ps9OMPojWnW6ojS6ojTGUjQm0jQG0nQG0nSm0g1mInW6TvcQITz4jPDCiiqwPKGCiYk5sgJKYTocBSNJvx+HjLkWQghRDtzVX+Bz7k9orz6qP7E5jyIojFKQwohTpoEFM8wH/z+YfJS05v/rrXb+GDxfN5ZOBeNovCXn97SKuWU1dYQbTJzzaixzdtuPe+SNjmmhOgY/u/G2+iZmcPN51zAsh1b8Ph8nD9gSLu1a1BVmV9Tyrc1pQRUNezhwK6AH1fAT7XXDTQc8bpZqyPNYCbDGEWOKYoccwxpBjOaU9izMc1gpo8lnm3O+pDzLLdKQFEI0UHJHIpCCCE6uICnGEfV9Ijy6i29JJgohGhVElA8wyVGx3DPpVfz/a7tzN2whj9PuhlFUfh4xRIsBiMD87oxddFccpNTuXnchQDsKT/A7HWrOFBbjVFvoH9uHpcNH4VJ3ziU9eU5n7OrrARfwM/Lcz4nLS6RK84axRvffMWQrt0Z2aMPAF6/jznrV7PzQAkOj5vUuHguHDiM7hlZzfULJU2PzGy2FO/nuS9nUllfR4wlioHH6Al5orpHotzjZHr5bg64HW32PrkCfgpdNgpdNlYcuilQNHQxx9DNEks3Sxy55mh0SvtOizouISOsgGKR206F10WawSwnnxCiY5Ehz0IIITq0ILayV0ANf056rTGH2JyHJJgohGhVsiiLACA7KRmX14PX33iB+nLNCr5c8z33TX2NVbt3cKC2cYXeOetXccO//8Z3O7aQFp+IRlH496yZ3PLck9jdLqCxd6LL60FVVcpqa6i2WfH5A/x3/mxW794JNAYKb3vhnzw3ayYur4cYk4U1e3Zx47NPMHfDmpDTAHy8fDFT/vM020qKSIlLoMHp4K8zpnLf1NdaHGModQ/rkt7UK/G5wo1tGkw8Fp8aZI/TytzqEl4u3sIju1fxzoEdrKqvwOb3tUsdhkUnY9Jow8qzylYlJ5wQouOJZFGWoAQUhRBCtA937Vz8rt3hP/BrY4jLfVjmTBRCtDrpoSjwBwJsP1BMWlwCRr0eAL1Wx+bi/dw87gJ+ftFlADg9bv728YcMzi/gpbt+g75pWO8VZ43mzpee4Z0Fc7l7wlU8cePtPPT+m6zctZ0nbry9Ka+nRZnr9u1mx4FiXvzZbxjVs0/z9qc+mca6vbu4eNCwkNKoqsqHSxcwMK8br//yD81p8uZ9yevzvmRXWSk9MrNDrnuoDnqcTC/fQ6nb3mHeR28wyBZbLVtstSjspZsljsGxyQyIScKsbZtT3aDRMjwmhaXWgyHnWdlQyeVJuXLiCSE6lkimkFAD0m5CCCHaXNBvxVE5LYKcGmJy7kOjT5FGFEK0OgkonmEO1Fb/cGFSVSrq65i2dAEHaqp54Orrf/RcpeDz+7ll3AXN21bu3oHT42biyLObA3IAg/ML6JaeyZJtm0IOygUCjb06thTvbxEsfHDiDWGlURSFTx54DPWweawGNA15LqqqoEdmdqvWfau9lvcO7MLfgYe6qcAep5U9TiufVOxjQEwSo+LTybfEtvriLqPiUsMKKBa57dT43CTpTXJCCiE6DCXM3taNX7bSQ1EIIUTbc1Z+iBp0hp0vKvUG9JY+0oBCiDYhAcUzzG/efPGIbUkxsdx/9fX8dMz4FtuTY2OxGH8I+pRUVwKQm5x2xD6yk5JZuWtHyPUY2bMP5/UfzKtzZzFr9QpG9uzN6J59Gd2rHwadLuQ00Nj78fNV37GxcC+1dhtevw+bq3EIs69pCHdr1r1PdCKXpOQyp6q4QwcVDwmoKusbqlnfUE260cK4xEyGxKagbaUFXfpYEojW6rEHQh9mvdZWw0WJWXJCCiE6jki+E4PSQ1EIIUTb8rsLcdcvCDufPmoA5uSrpAGFEG1GAopnmH/f/iuyEpOb/44ymUiLS0A5yoPUj4OJAD5/oOmZ68i0eq0OfxgPVlqNhn9MvovlO7fy7aZ1LNu2hZkrlpISG8ffb76TIV27h5TG6/fzs5f/RXF1JRNHns3oXn0xG4wUVR7klbmz2qTuCjA+MZM+0QlML99DscvWad7/gx4nM8r3MKeqmHOTshgZn3bSC7loFYWhMcksri8POc86e7UEFIUQHUskcyhKD0UhhBBtSsVRMZXG8UfhXNIsxGT+qunJRQgh2oYEFM8wucmp5KWmR5Q3MSYGgFpbwxGv1TnsxEdFh3ehUxTG9OrHmF79ANhUtI+/fPg2D7//JnMeeTqkNEu3bWbHgWKeuuVnXDhwaPO+F23d2KZ1B0g1mLm7Sz8W15Qxt7qkU/RWPMTq9/JZxX4W1ZZxSXIOQ+JS0JzEDcewMAOKO5z1uIOBsBd0EUKINhPJ95H0UBRCCNGGfPZN+Bybw84XlT4FjT5ZGlAI0ba3z9IEIlQD87oB8P2u7S22u7wetpUUMTCva8j72lK8n3k/WqkZGuc9vHzYSKoarNjdrpDSHKyvBSD/sCDp12u/B374La81697yBFI4NymL3+cNIMcU3ene03qfh+nle3ihcDPFJ7HATJ+oBAxhPIwHVJVtjjo5qYQQHYaiieA3VgkoCiGEaDMqjsoPws6lt/TBFH+eNJ8Qos1JQFGErGtaBuf0GcDM75cwe90q7G4XRVUVPPT+m3j8Pm4Zd1HI+9pSXMifPnyLGd8torrBisPtZmtxIXM2rKZbeibRJnNIaXpmZgPwvxVLsLmcHKit5qlPpqE0DV3bd7AMVVVbte5Hk2a0cE9ef36SkttqcxO2pxK3nRcKNzHz4F48ETwgGxQN/aMSwsqzwV4rJ5UQogPdEYUfUFSDMuRZCCFE2/Da1uB37wv78T46405kqLMQoj3IkGcRlr/ddDtPfjKNR2dMxR9oDDxlJ6Xwr1t/Qf8u+SHv57ox46mxNfDKnC/4x6fTgcbhzaN79uWPV04KOc2wgp7cOPZ8pi9bwMfLF2PQ6Zg48hyeuPE26uw23lk4l83F+3n9l39otbof+/KtcH5SNv2iEyl029HQuJJ2EAioQXzBIF41iC8YwB0M4Az4m//ZAl5sft8pfW9VYEV9BTsd9VyXUUA3S1xY+QdFJ7HWVh1y+s2OWlS53RFCdBCRrfIsPRSFEEK0zZ25s2pG2LnMiZegNeZK8wkh2uf+WVVVVZpBhMvt81JRX4fFaCQlNj7i/QSDQaptDXj9PlJi4zHq9RGlcXk9VDc0kBIXh0lvaN5ud7uIMppaLMbSWnVvbQFVpcHvpd7vocbrptrrptrn5qDHSaXHRZD2O1UV4PykbC5KyQl5bsVan4ff7lkRVjn/7DaCdINZTighxCnnWPoWvtLw5qmynHUdhm4jpfGEEEK0Kq9tLQ0lT4Z3/66NIrHgRRRtjDSgEKJdSA9FERGT3kCXlLST3o9GoyE1Lv6k05gNRnKSU47YHm0yt1ndW5tWUUjQG0nQG8k3x7Z4za8GqfC4KHHbKXLZKHTZqPK62qwuKjC/ppRCl42bs3oQrdWfME+i3ki2MYpSjyPkcrY4akk3yGrPQogOIIIeiqrMoSiEEKIN7sSd1TPDzmVJnijBRCFEu5KAohCd4URVNGSZosgyRTEyvjEYavV72eWoZ6ejnh32Otxt8GC7x2nlP4WbuD27N+lGywnT949KDCuguNVRxwUJElAUQpx6siiLEEKIjsDn2IbftSusPBpdIqbEn0jjCSHalQQUheik4nQGhselMjwulYCqsttpZWNDNRtt1XhbcaGAWp+HF4o2c1t2LwpOMK9iv6gEZteWhLzv7c56VFQUmUlRCHGqaSMJKPql3YQQQrQqV82nYeexJE9EUQzSeEKIdiWrPAtxOjwHKwq9ouK5LqOAvxYM57qMArJN0a22f08wwJsl29l6gpWZe1ri0Cmhf604An6K3HZ5A4UQp5wiQ56FEEKcYn53IV77hvAe6HWJGBPOl8YTAHg9TgIBnzSEaBfSQ1GI04xRo2V4XCrD4lIpctlYUHOAbScIBIZ0g6MGmVq6k5uzejAgJumYZReYY9nhrA95v9ud9eSZZL4XIcQpppEeikIIIU4tV82ssPNYkq9GUfTSeCEo3r+O6sq9J0yXklZATt7gTnmMf394EGeNuYkJE/8qb7hocxJQFOI0pQB55hhuz+5FqdvO11XF7HLUn9Q+g6i8X7aLKVk96RudeNQ0vS3xYQUUdzit/CQxR94wIcSpFcGQZzUgAUUhhBCtI+irwdOwLKw8Gl2c9E4Mw+b1s1i7YvoP13FVRVWDKIoGRflhCqbhY27qtAFFIdqTBBSFOANkm6L5WU4fdjrq+KxiP9Ved+Q3O6rKewd28fOcPuRbYo94vU9UPJ9Wh76/nTKPohCiA4hkyLP0UBRCCNFaXLWzQQ1vKg1z4mUdfO5EFTXgIhiwEvRbUQMNBAMO1IAdNeBADbpQg+7Gf6oHNegD1Y+qNv4PKqhBVFT05u5EZ9wFijbi2lw68a9c+qOee1vWf8XH7/+eSZOfo8+AS+RDKESYJKAoxJnysAz0ikrg3vw45lWXsqjmAEHUiPblV4O8dWAHd+f2I+2w1Z+7mWPRKRr8amgLwzgCfkrcDnJbcc5HIYQIWyRDnqWHohBCiFagBt24678J795eY8aUcNGpr7vqJeApJ+irIOA9SMBbSdBfTdBXQ9BX0xgcbAXegA1r0eNEZ9yF1pjVbsdXWryRbRvn0GA9iNkcR07eYPoPuRylad74YDDAorn/oaDXOKJiElm97APs9mrSM3sz8pwpaDQ61qyYTknhOszmWAaddQ2Z2f0iLiMuIYM1yz+krqaEmLg0ho68juTUri0/G4qGBmsFa5Z/SG1NMTGxqQwdeT3Jqfkt0lWU72Lzui+oqy1FrzeSlTuQQcOuRm8wy0kpQiIBRSHOtJNe0TAhJZe+0Ql8ULaLWp8nov24An7eKt3Bb/MGYPnRUEG9ogl7HsWdLqsEFIUQp1Rki7JIQFEIIcTJc9cvQA04wspjSrgQRRvVrvUM+q343fsJuAvxe4oIeEoJeCsgwk4K4fK791Ff+GcsyddiTvoJbb3G7OrlH/LVzEfJ6zaCzJy+NNQf5LPpD7Jt01yuv+3lxvsHRWHJ/FewNVRSvH8ted1G4PO6mP/VM9RWF+HzunA660lM7sL2zd+w9vuP+M1D3xCXkBl2GQ57Dft2LScnfyiWqAQ2r5vFmuXTuOehb4iNS2uut9NRx9sv3UhO3hDMlng2rvmU1d99yC/vm0VSch4Am9fN4tNp95OSVkC3nmNwOa3Mm/U0q5a9zx2/mYFJ5rgXocQWpAmEODN1Mcfwh/xBfFi2O+JFW2p8bt4v28WdOb3R/GjIcvjzKNZzYUKWvClCiFNHG8GE9tJDUQghxEkL4q79OrwsihZz4qVtXC+VgK8Kv2MbPucO/K7dBHyVp765VD/Oqul47WuIzvgZWkNm2xSjqqxcOpXc/CHc+qv3mrcvmvsCi+a9wMGyHaRn9mruRbhl/Vf86r6vSEhqnBv+/TfuZP2qmQwbdT3X3PwsAAOGXMF/X7iO7Vu+YeTYKWGXsWH1J/zstzNJz+oNQL/Bl/HWizewZf1XjB5/e3P+bZvmcMdvPmruCdlnwMVMfWUy2zbOYez5v8DjcfDlx4+Qmz+UW37+Ntqme6BBw6/h7Zdu5LsFr3P+hD/KqSlOSAKKQpzBTBott2X3YnZVMQtqSiPaxy5HPfOrS7ko+YeFVXpa4sLax06nFRVkFkUhxCmjRLQoi08aTgghxEnxNqwm4D0YVh5j7Gg0+qRWr4uq+vE7t+K1b8Br30jQV9Vh283v2oN1/58wp0zCnHgJrd1bUVEU7n5gLqrasvdldt4gAGoq95Ge2at5e373Uc3BRIC0jJ7s2bGEoaOub952KL3NWhlZGQWjmoOJAJk5/QGw1pe1yN+1++gWw6pz84c2lVsBwL5dy/F4HAwdeV1zMBGgS9dhpKZ3Z+fWhRJQFCGRgKIQZ/pDNDAhJZd4nYFPK/ZFNGDhm+oSulpiKWgKJBaYY9EqCgE1tL1Z/V6qvC5SZb4OIcSpEkkPRRnyLIQQ4iQ5az4PO4856YrWq4AawOvYhLfhe7z29ahBV6dpO1X146ychte2pnFuRUN6q+7f63GybtX/KClcj8NWjd/vxe1qACBw2I+KsfEty9brTY3b437YfmhuwoDfG1EZ8UnZLf7W6QxH7A8gPrFlukNBw2CwcdGf2uoiAJJS8o845oSkXPbtXi4npgiJBBSFEACMTkjHqNUyvWx32EFFFZhWtpt78wdh1uowarTkmWLY23QxDMVOl1UCikKIU0aJIKCoBgLScEIIISLmc+7E79oVVh59VF90pvyTLjvgLcNdvxCv9TuCAVunbke/azfuum+ISruZ1hrz5Pd7efvlm6itKmTIyJ9S0HMsBoOZ6qr9LJzz/BHpNcrRe0hqNJo2L+OIexrl+G3QHKg8SjKtVk9QRmCIEElAUQjRbGhsCqoK08t3h53X6vfyeeV+rs/oDkBPc1x4AUWnlbFx6fImCCFOjYh6KMoNtxBCiMi5IumdmHgyvRNVvPZNuOtm43NsPS3aUKNLIDrjZ+ij+rfqfndtW0h56VYm3fIcfQdNaN6+Y8v8TlXG0URFNw6Xd9hqjnjN6ajDEpUgJ6cI7fyTJhBC/NiwuBQuT82LKO8aaxU7HHVA+PMo7nJapfGFEKdMZHMoypBnIYQQkQl4SvHaVoWVR2vIxBAzJILSgngallO//2Fspc+cNsFEY9w5xOc/1erBRPhhXsLktIIW2zetbQwCq62wsnV7lHE0ufmNn6G9u75rsd3rdVFWsomcvCFygoqQSEBRCHGEcYmZjE6IrLfgzIP78AYDdA8zoFjudWKT7vVCiFMlolWe5TtLCCFEZCLqnZh0OeEN6VXx2tZQv/9h7GWvEPCUnhZtp9ElEJN9L9EZP0PRWtqkjPTMxsVP1iz/ELergbraUr765LHmFZerDu45YjGVjljG0aSkFdCz73msXTGdTeu+wO22UVNVyMfv/Q6/38vo8XfICSpCIkOehRBHdWVqPhUeF3vD7DlY5/Pwbc0BfpKSS6bRQpnHGXLenU4rw2KSpfGFEO0usjkUpYeiEEKI8AV9VbitS8LKo9HGYIwfF3J6v7sIZ+X7+Jw7Tqu2M8adTVTqzSjaqDYtJ79gJCPPuZWVS99l9fIP0ekMDB11PRNvegaHvZZlC16ntGgjt/7qvQ5dxrFMvOlffDXzUT6f/iCBpvuZxKRcrrv1JbK7DJKTVIR2/6y2RchbCHFasPl9PFu4AZs/vF44WkXhvvzBfFFTxML68pDzXZKYzU2HdfkXQoj2EGiowPbVU+E93JnjiL3qUWk8IYQQYbGXv4G7bm5YeSwp12NJufaE6dSgG2f1TNy1c4HO/KivgKJFQQFFQdFGE5U2BUN0+w7H9Xpd2BsqiYlNbV6lGcDttmE0Rp9wAZSOUsax+LwuGqwVGIwWYmJT5eQUYZEeikKIY4rR6bkxowevlYQ3z0pAVZlVWUjPmISwAoo7ZR5FIcSpemzRRDKHogx5FkIIEZ6grwZ3/bdhXqOMmBMvPmE6n3Mb9vI3CPqqO8CFVYtGl4BGl4hGF49GF4tGG4uijUHRRqHRWlA0FhSNCTRGFMWIotGDokdRtHSU2dkMBjOJyV2O2G4yxXSqMo5FbzCTlJInJ6aIiAQUhRDH1T0qjrMTMlhWVx5Wvq32WgbEJoWVp8htxx0MYNJopeGFEO18R2QIP48EFIUQQoTJWT0T1PCmzDDFX4iiPXZwSVX9OKtm4K6d0+7Ho9EnozNmozVkoTVkoDGkoTWkotHFI0s2CHGa3z5LEwghTuTSlC5ss9dR63OHlW9ZbTkJOiN1fk9I6YOo7HE10C8qQRpdCNGuIu+hqBLeBPlCCCHOVAHvwbB7J6JomxZjOcY+fZXYD7yA313Y9tdKbTR6c3d05u7ozN3QGfPabFEUIUTHJwFFIcQJ6TUark3vyusl28LKV+K2k262hBxQBNjhrJeAohCi3Sk6fWQZgwHQyO2UEEKIE3NWzQA1EFYeU9x4NPqjj/rx2jdiL38ZNeBsoxor6C090UcNRB/VH50pF/kRTQhxiNwBCyFC0iMqnkGxyWxoCG9OlgafJ6z025310thCiPan0TY9JIU3gb3q96EY5HZKCCHE8flde/BYl4Z7ccKcfPXRrj64ar7EWfU/Wn/hFQV9VB+MMSMxxAxD0UbLmyeEOCq5AxZChOzy1Dy22mvxBYMh5/H4w5sjZp/LhlcNYlBkzhUhRHtSULS6sBdaUQM+FMzSfEIIIY53tcBR8U7YuUzx49Aa0g/blR/7wbciCE4en0YXhzH+PExx447ZI1IIIX5MAopCiJDF6Qycm5jFvOqSkPNoAa2iEFBD+/XUrwbZ7bTSV4Y9CyHa/a7IEP5CK7IwixCik3M666mtKiQ2Pp3YuHRpkDbgsS7B59wRXiZFizllUotNatCJrfR5fM5trVY3rTELc+LlGGNHgCLhASFE6KQLkBAiLOMSM4kJa64xBb0a3lwr22TYsxDiFFC04c+jqEpAUQjRDurrDrB88X/ZsfXbVt/3vl3f8eaL17Fu5f+koduAGnDgqHgv7Hym+AvQ6lOb/w7667EW/a3VgolaQzoxWXcTn/8kxrgxEkwUQoRNAopCiLAYNVouSMoJK48+zK+abY46aWghRPvTGcLPIwFFIUQ7qK7cx7wv/8GG1TNbfd/JqV0ZPe52cvIGS0O3AUfFOwT94f1YrmiMWH7UOzHgrcBa9DgBT/FJ10fRWohKm0x8/lMYYkYgi6wIISK+dZYmEEKEa2R8GotqD1AX4oIrBkUJa77ova4GnEE/Flk5VQjRjiLqoej3SsMJITq19MzepGf2loZoA177etz1C8POZ066Eo0uHoCAp4SGkqcJ+q0nXR9j7GgsaTeh0cbKmyOEOGnytC6ECJtWUfh/9s47zorq7sPPzO11e2dh6b1XUbA37IpR7L2naV5NjIkaNcZo1FixxgooAhYQFRCQ3ntvS9neb28z8/5xl5WVBfbeLS5wns9nE5w758yZc6ac851fOSelHZOLdzXyQSMhSxJqI+MoasBWbzWDHKmiswUCQashCQtFgUBwghEK+Skp2koo6MVmTyE9sxuyrKu3TyQSJOB3YzRaMZqs9edkmkpF+V7cNSVIkkxaZhdstmTRsY1AVVx4Cl+PuZxsSMGScll0bPw7cB14AU3xNaktss6BLesOkXrLnQAAIABJREFUjPZBYmAEAkGzIQRFgUAQF0MS0plVcYDqxlopahKBGMwUN3irhKAoEAhaF13sgqKIoSgQCNoiihJh1oznWLF4IsohzymrLYlzxvyJQcPG1m3bunE2X3z6IGec+wBnnPfbuu3bNv3Id9/8k6qKn5PxSZJM34EXccnYpzEYzKKjj/x2wFPwWsyuzgC29BuQZBNh7wbcBS+jqU2zhDfYemPPuhdZnyCGRSAQNCtCUBS0GYLh6MvSZGh4Qbd5/168wQD98zpj1B/70vUFgxj0Ogw6cZm3yLpbkjgzOZtpJXsatb8RiUAM9a/zVKDRVUR1EQgErYZIyiIQCE4Upk/5O2tWTKFXv/MZffZ9OJxplJXs4tsv/8HXk/+KwWCm78CLj1i+tHgHn3/8W6y2JK656VUysrrj99fw05w3Wb/6G8yWBMZc/jfR0UfAXz6NkGd1zOUMtt6YEk4j5F6Gu/BN0JQmtcOSchnWtCsRqRMEAkFLIJQWQdzsKythybbNFFdXIgHJDicDOnahT/uOcdV322vPo6gKkx76+2G/Ld66kfveepk7zr2IIZ27HbMuXzDIiEfu47azL+QPl4xt1X6pjoT4rGw3YVVFkkBGQpIkDJKEXpIxSDJGScYk6zDLOkyyDqusx6bTY9cZsOn0OHUG9FLbf/EPS8xgVvkBPI1YUBslOaZJUVk4QHHIR5bRKm42gUDQKkj62AVFRAxFgUDQxqgoy2fNiimkZXRh7PUv1bk42+wpXHPTa7z2/AXMn/XaUQXF4sKtZGR1Z9ipN9Cz73l1268c9wLPP3EKG9dMF4LiEQh5VuMtnRjHS0iHPfNOAlWz8JZ8QkwByH9ZlWzCnnUPRscQMSACgaDFEIKiIGY0TeNfUycwaeFcEqw2OmVko9fJ5JeWUFoziZE9+vDirfdhNZma5Xgl1VU8PukD/nnDnYwZPLxRZYx6Pb+/+Cr653Vu1b75qaaYSaW7kCUZbxOtVqLCopEkvYlkg4kUvYlUg5kMo4UMgxmH3virW+8ZJJlRyVnMLDt2xjkdEnokIjFMjtZ6KshKFoKiQCBoJYTLs0AgOAHYue0nAHr1O/+weIkpaXmkZ3alpGg7rppinAmZDdbRb9Al9Bt0yWHbTSYbCYmZVJTvRVHC6OKw7D6RiQT34T7wEvGIgdbUsQRdC/FXzGhSG2R9Eo52D6E3dxADIhAIWhQhKApi5vu1K5i44EduPvN8fn/xVeh10YmKpmlMWDCH56ZO5N1Z0/ndxVc1y/EKKsv47+2/pVdu41+Kep2O288Z02p9UhUJ8kHxDtZ7KwGw6ZpuXehVIniVCEWhhoMwW2Q9OSYrOUYbuSYbHcx22pvsGOXWtWwcmZjJjxUFBNVjWx8aYxQUV7sruDA5V9x0AoGgVYgrKYuwUBQIBG2MyvLoh96U1Ia9hpJS2lNStJ2qygNHFBQBvN5KNq6dQeH+jXg9FSiREKqm4qoprZ37q6KzD0EJl+La+xSa6o99UW7ugBIqJuRe3qQ26Ey5OHP/D1mfJAZEIBC0OEJQFMTMmt07ALj17AvqxEQASZK4fvQ5hCMKHTPqT04KKyv4ZsVi9pWXIksSXbJyuOqU0djNlqMea8Pe3czbuJbi6iqcFhsDOnZmzOARyFLUNi+iKLz53dec1qsv4UiYL5ct4qLBIxjWrQfjv/uGwZ27MbJH77r6dhQdYMbKpRRUlmMyGOjXoROXDj0Vs9EYd38srClhYukufGqkVcfBr0bY6Xex0+/6eQyAXJOdLhYnXS1OelgTSdQbW7QdFp2eYYnpLKgsOua+JknGF8Pkc7uvBo8Sxi6+fgsEgtZAxFAUCAQnAOFwVNAyGBr2FtLro9vDoSNHt963ZxUT3r+bYNBD+7zBpKR3xGSyIUk6ykp21h1DEEUNl+Pa+yRqpCrmspJkQMLYZDHRYO2Bo90fkWTh3SMQCFoHISgKYiYjMRmA5Tu2csHAYYf9fstZ59f7703787n9tX+T5kxkVK9+BMMh3pk1nYkL5vDFw08eUVT8fNFcnp78CUO7dKdXbh4l1VU8NuF9Zq9bxcu3PxB9eWsa78yajsvvZfqKJeSkpDK8W0/CEYV3Zk1HUS+sExS/XbWMRz99ly6Z2ZzSvTc1Pi8vfPU5Exf8yMd/ePSY4uYvqY6E+KBkO+s8lW1mbDRgX9DDvqCHH6sLAcg2WultS6K/LZnu1oQWic04KimbhZVFx7Q9NCIj0XgnEBWNNZ4KRh3l67lAIBA0F/FYKApBUSAQtDWMJhsAoSN4uYSC0e2m2v0a4usvHiMQcHP97e/Qtcfoer9t2zwHn7dKdHQtSrgUV/4TKOHS+CrQWYiEDjRtzB1DsGffhySJj/ACgaD1EIKiIGYuG3YqH839gUc+eps561dzRp8BDOrUjayk5Ab3n7xoHhISEx58DIcl+sXsgkHDuf21f/PVskVcf/o5hy/QNI1P5s9mYMcuvPfAw3XbO36XyZvffc22gv10z8nFUGshOWPlUv57x28Z1rUHEE3KcijeYICnPv+IQZ268ta9D9Zlfr58+Knc8spzvD/725hctBe7SphQuguvEmnz41UY8lEY8jGrqgCzrGOAPYUhjlT625KbTVxMNpjo50hlnbv8mPuaJJlADFaKK1xlQlAUCAStQjxZnoXLs0AgaGukpUdjiJcW72jw9/LSXQCkpDfsEu331VBeupuU1A6HiYl+Xw3VlQWikw++AgL5uPY9E5dlYu2LB9SmfZgyJYzGnnlbtC6BQCBoRYSgKIiZFIeTCQ8+xpvffcWsdSv5fs0KALKSkhnduz/XnHomXbJy6vZ/4tpbePyam5Gkn1OIHEyWsqe0uOF3qyTx9aPPoGn1bdkOlssvLaZ7Tm5dnZ0zs+vExIZYum0z3mCAsaecXicmAgzq1I3OmdnM27Su0YLiKk857xZta0LetV+PgKqw1FXKUlcpVlnPMGcapydkkmd2NLnu01OyGycoIhOg8YLiBm8VPiWCVSceVwKBoKVnRcJCUSAQHP9063Um8jQdG9d+y5nn/67OxRlgf/4aKiv20T5vEFZrYoPlDUYLkiQTDgfQNK3eHH7OzBfrYicqkXC9uk82Qp5VuA+8HFfMxNoVD5LUtNBE5uQLsaWPg189VaNAIDgpp86iCwTxkJ2cwlPX3cbj19zMpv35rNy5jSXbNjN58Xy+WDyff4y7lUuGjowutjSNWetWsWDzeoqqKgmGQyhqdCISOYqFny8YZNrSBazN30mF20UoEsbtj76ww78o1yE946jt3V9eesT9clPTWbp9c6PPfbA9lUfa9+f9ou2UHsfxY3xqhHnVRcyrLiLPbOecpByGO9LRS/FNSNqb7eRZHOT73Ufdz4gUk9tzRFNZ4S7j9MQsceMJBIIWRYony7OwUBQIBK1IceFWpk95vOFnmCxz0RWP43Cmc+oZd7Lgx/F8+u6djDr7Xmz2ZIoKNjFn5ovodAbOvfjhIy8Q9UY6dBpC/q7lfP/Ns/QdeDE+bxUrFk8kEgnSs8+5bFr/HWtXTaNH73NIOOnmaBq+8mn4SidCE0wMmiomWtOuxpJyCUJMFAgEvxZCUBQ07QLS6eif15n+eZ25/Zwx5JcWc/eb/+HZKRO4cNBw9Dodz06ZwGeL5jJm0HDOHzgUh8WCXtbx4P/eOGK9oUiEW199jn1lJVx1ymhO7dEHi9FEfmkxr8/88vAXqtF81HYeFCClBl64Bp2eiKLEdN7dLQk8lTeYz8t382NV4XFprXgo+QEP7xZt44uyPZyf1I4zE7MwybG7TZyRnMMHBVuPuo+MhAGZUAxWiotdpUJQFAgELY6kj8PlWVgoCgSCVqS6qoCVSyc1PMeSdVx0RVRsPPvCP2K2OFg87z0+evuWun2y2/Vh7A0vk9th4FGPc+nYp/ni0wdZuuBDli74EFnW0bv/hVx15QsUF21l5/aFzPzyafbnr2Hs9S+eNP2vRqpwF7xK2Lu+ae8b2QjEG3pIwpZ5K+bEM8UNIRAIflWEoCiImdKaKsIRhZyU1MN+y0vPZMzgEbw3+1sKK8txWm1MWvgjvzn1TB67+oa6/TyBo1v2/bRpHVsO7OX5m+/h/IFD67bP3bAmrjYn250AVLhrDp+Yed0k2ewx12mUZW5I78IQeyrvF2+nLBw4/iepkRCfle1mRuV+Lk7J5azEbAwxxFns5UgixWCm4hh9YZZkQjHEUdziraIiHCDFYBY3oEAgaDnisVBUhIWiQCBoebp0H8UTz2+LqcypZ9zByNNvo6ryAOGQH7szDZstuXFz59QO3PX7KXjc5fh8VSQkZGEyR+fLHToO4ZEnlxMMeLBYE06SEdAIVs/HU/I/NMXbtKpkAxBfvENJMmDPeQCjfZC4KQQCwa+OLLpAEAv+UJDLn/0bf/zf64e5HR9k8/58dLJMssNJcVU0A3KnjPrWZd+sWBJ9NWsN2/YVVVVEy2X+otzKg+Via/eAjl0AWLJt82Hns3FfPv1rf4+HHtZEnsobzJmJWSeMw4FHCTOpdDd/2bOS5e6yRltgykiMTs4+5n6mBm1FjzaFg59qisUNKBAIWpR4sjwTERaKAoGgDT/XJJnklPZkZHU/opgYiUSTGeoaSExld6SSntG1Tkysm/PJupNGTFSC+6nZ+xTuwteaLCZKkh4pTpseSefA2f5RISYKBII2gxAUBTFhMZq4/8LL2XpgH7e++hzfrlrGlgN72VF0gPmb1vHH919nybbNjBt1Nnazhdy0dCxGE9+uXkpJdRXVXg8TF/zI3A1rSHMmkF9aXBdP8VC657QH4LOF83D7fRRUlPPMF58g18b321VccEQxsiE6Z2Zzeu/+TF48nxmrluIJ+NlbVsLDH75FMBLmljMvaFK/mGQdN2V05U+5/U4oK7qKcIA3C7fw/P71FIcaFy9yaELaMROoyEgYY5Rff6ouRjvuncsFAkGbXnjrhYWiQCA4eQiHA1RVHmDz+u8BSEzOEZ1yCGqkGk/RO1TteqjJLs4QtS5EMsRVVmfMJiHvCfSWLmJgBAJBm0G4PAti5vrTzyEtIZGP5/3Anz9+u95vHTOyeHTs9VxzajSmh81k5vFrbuapzz/i3Cf+BMDIHn149sY7+WzhXMZ//zWD/3Q3q194q149w7r24IbTz2XCT7P5fNFcjHo9V488g2dvvJMqj5v358xkw97dvPfAw41u979uuotnJn/C3ya8XxczMTc1nf/e/gD98jo1S9/0sibyz7whVEQCSEhotRKYomkomkpE0whpKmFVJagpBFUFv6rgVyN4leifWwnjVkLURMLUREKobUBE2+Kr5m/5q7g8tQMXJrWrE3YbwijrODUpi1nl+49apwUdQSKNbkN5OMA6TyUD7CniJhQIBC2DTmR5FggEJw/781fz0du3AlEX5x59zhGdQjROor/iGwKV36FpzfPRSJL0IMW39DbaB2DPvhdJtorBEQgEbQpJ0zRh8iOIm7ASodwVzcCc4nBiN1sa3C+iKBRXV+KwWEmw2uq2+4JBjHo9el3DcUT8oSBlrhrSnYmYjT8v9DwBPzaTGSmOjMSBUIiSmiqsRhNpCYlte0KDhisSpiIcoDwSpDTkpzjkpyjkozDkI6gqrd6mzhYnd2V1J91gOeI+HiXMMztXET5KnEQNKNfCMQmmfW3JPNy+n7jxBAJByzxzPRW4vnk6pjKyNRHnZY+LzhMIBMcdPl81e3YswWS2077jEIxGy0ndH5HAHvyVMwjWLACtGefYkiEqKMZeEEvq5VhTr0BkchYIBG0RISgKBMcpGlGrvX0BD3sCbnYH3OwKuAipaosf2yzruCWzG8MdaUfc58uSPSysKjpqPW5NwUdsE7bnOg0j2yS+0AoEguZHDbhxTft7bBMpo5WEq54RnScQCATH43xa8RB0LSJQNYdIYHdzL7Vr3ZxjT8Ai6xzYs+/BYBMf0gUCQdtFuDwLBMcpEpBmMJNmMDPYEc24rWga+QE3m33VbPBWsdNf0yIO0wFVYXzhFnYk1XBtWmf0DViKnpGczZLqYpSjfLOwIMcsKE6v2Mdd2T3EBSAQCJr/uRqHyzPC5VkgEAiOK1TFRci9ipBrMSHv+ua1Rqx7oUhIkpF4UhYYbH2wZ92FrE8SgyUQCNo0QlAUCE4gdJJEZ4uTzhYnl6S0x6OEWeepZLm7jI2+KtRmNkieU1XI/oCX+3N64fxFZsBEg4mhCeksrS458gNIkjAgH9U1+pcsdpVwZVoeqSdQ8huBQNA2kPSxB8vXlDBoGkjCHU0gEAjaIpoWJuLfSdi7kZBnNRH/TmjRGOVyrZgY23tBkk1Y067BnHQOwsVZIBAcF3Nn4fIsEJwcuJQwS1wlzKsuanTG5saSajDzh5w+5PzCFbkyHORfu1cfVcgMaCo1MSRnATgzMZvbsrqJQRUIBM1O9Wf/B2psz6SE3zwXn3WjQCAQCJoZDSVUQiSwi4h/NxH/diL+nWhaK1mTS/qom3OMGO39sWXcgmxIFUN4HFBUsIlQ0Eu79gPQ6U+c97+mqUiS/KsdPxTyodMZ0OkMbXIcW7t9xwO6J5544gnRDQLBiY9J1tHF4uTspBy6WJy4lDBl4UCz1O1TIyxxldLJ4iDtEMtBi06PKxLiQMB75IeQBP7abNiNZV/Aw8iEDOziYR43hZUV7Cktxm62YNRHjdW3Fx6goLKcjEThYiM4eQlumx+zG7O5+xlIeiEoCgQCQWuhKV6U0AHCvm2EPSsJVs/BVz4Vb8lH+Cu+JuRaSsS/DTVcDqit0CIJSTLFnHxFNqRiz7oTa9pVSDqbGNhWoKhgE7u2zkdVFRwJGQ3us27FFFRNweE8/Pfd2xfyyVs34UzIoGOXkSeEh4LbVcrE925n5rQn2LtrGf2GXNHqbQiFfDz/t0EoSoSOXUe2+PFiHcfWbt/xgnB5FghOMiSgjy2JPrYkdgfcTCvPZ6O3qsn1+tUI/zmwgbuyejDskGQt56TmsqKmjMgR3JolJKySjCeG+DUqGl+U7eH+nF5iQA/2iaaxatd2Vu3cTmlNFRqQnpBIz3btGdWrHzq5/tfGD+f+wGcL5/LWvX9keLeeADz84dvklxaz9qW3RYcKTt5npN6IFvLFuLANC+c0gUBwnKKhqX5UxYOmeFAVL5oaAC2IpgbRtBCaGoha+GlqdBamqdG4g1oYS/IF6Izt6tUYdq9HidQ0fLiIh3C4AL2xHehNSMhoKNG6tTCoYTQtgKp4QfGhaT5UxYemeKN/qg9V9YMWaTtdKOlrhcTGvwkk2YI56TxMiWciSQbUiFtcisfsMyOS3PSQR9s2zmLRj+Ox2VO4508zMVuch+0zY+rfGTH6NrLb9a233VVTzIwvHuOya/9N7wEXnzB9u2rJpxTsXcuFVz5Jdm7fX6UNep2RMy98kHbtB7b4seIZx9Zs3/GEEBQFgpOYTmYHD7Xry2ZfFRNKd1MQ9DapPkXTGF+4BXdGmLMTswFI0Bs5LSmLeZUFRyxnQcaLEpOV4lJXKWcnZdPDmnjSj+OGvXt4YtKH7CouBECWZXSSTFiJTrZTHE7+fs2NnN67/1HruXz4qVS6Xa3Wbm8gwCszpnHrWeeTmZQsbkhB2yAey+dISPSbQCBog6go4QrUcClKuBw1UlX7V4kSqUKL1ESFuyZY8AW9a3Bk3I7RPujnbcGdBMo+P6roFzwRuleKJ1aiBJIBDT3+qrn4q+aKy7SxwoWlI46sO5H1CU2uy2C0EAp6mT3jOS4e+0yjy9VUHmDsza+TldP7hOrb6soCTGYHg4Zf86u1QdbpGXnGXa1yrHjGsTXbd1xNm4XLs0AgSDNYOD0hC6tOz3Z/zVEzMzeG9d5KJAm6WxORgHZmO0trSo5qpahJGuEYA2TnBzyckZiFfBInQ1i1azt3v/ESZa4arjplNP+47hb+cuU47jzvIi4dOhK72czS7Vv4bvVyOqRn0iUrB4CFWzayaV8+lww9hXYpUYvSAR27cEqP1psgrd61neemTmLM4OGkJQhhWNA2CO1ehhaIzVLE2Hk4cgMWDgKBQNAqaApKqJCwbxNB12IC1bPxVX6Ft+wzAlXfE3QtJuRdS9i/lUhwL0q4FE1xoWkhmpycRAsTdC9DCRVjtPZAko0YrT2xpl2Gv2o+qP4TrrslZCTJUBsrUWp0KSQTyBaIqZyg7lJTAwRqFiAbktCbsuPuw/xdSykp3MJp59zPkvnvkddpOAlJOfX2WTDnDdp1GEjHriMJhwMsmP0amqbSofNwHM50ACKRID/Nim5PSslFVRXm//AKer2RSCTEwjlvsH7Vl1RX7COrXR9AYtXSiSxf+BF7dy3D5kitq+sgVRX7WLbgf6xZ/jm7ts4n4HeRltEVqdbTSFUizJ8VPUZlxV7m//AKBoOZ5NQOAJQWb2fZgg9Ys3wyO7fOw+MuIy2jKzrdke3I5n3/Mnt3L8fvqyYSDlBZsZfUjC5155yUklu379HOGWDxvHdYu3wyBXvXkpCUjdVWP4xSadE2lv70PutWTqFg7xqstmTszug6RFHC/DTrVVRVISml/c9ljnFOsbahYN86tm2aza5tC9m7cymBgIu0zK5ItevJI/VxQlJ2XO072G8bVn/N+pVT2Lx+JkUHNmK1JWFzHP8xU4WgKBAIAJAliS4WJyOc6ewNeKiINO3b8VZfDT41Qh9bMkZZxijLbPVWH3F/PRL+GL+Qu5QQZllPN2vCSTlmwXCY2157AZffx5+vHMf9Yy4jxeFEkiQkScJptTK0aw965eYxY9UyVu3awdWnno5Rr29QUKz2evAEA1hNpsOOVe31sGn/XvaXlyFLEg6L9bB9vIEALr8Ps8GAJEmU1lSzaX8+7oAPu9mCXqer27fS42bm6uWs2bOTs/sNwmIyY9Dr6rlmByNhNu/fy+6SIgLhEMl2R93L/jARKBJhf3kpe0qK8QeDWM1m9LJO3NiCmAnnr0T1VcdUxpg3FNkmYo8KBIJWkVVQQiWEvWsJVM/FVzENT9kEAtWzCXlWEQnsRAkXoykeWid2YBQldICgexE6QyY6Yxagw5pyMZJsJezd1KptaTmi1oWSbIDGJq6Q5EOExNjcogW/6EpJj6b6CXnWogQLMFi7IcmmmOvJ37WU/XtWcfXNb7Bj849s2zSLgcN+g3zIvPFQQTEYcPP5B/fiTMwir8uIn+eeQR+f/e/uetsnvHMroLF43js4EjII+GpYtWQCXnc5W9bPpKRwCw5nBts3zWblok/oN/gKTGYHADu3zuPjt27E560kq10fwmE/i+e9ze4di+g78BJkWYeqRpj43u3oDSbmfvciihImPbMbGdk92bR2OhPfu4NgwE1mTk+USIjlCz5gy4bv6DvwEvT6hvtq3copVFXsIxT04nCmo9MZyMzuGdM5y7KO+bNewWR2YLEksHndDFYs+oT+g6/AZLYDsGX9d0x473bCIT/JqR0oLtjE/Fmv4EzIJDOnF5FIkEnv34kjIaMuRmFjz6mxbVi1dCJffPQAOp2BlPSOeD0VLJr7FqWFW+nVfwzAEfs4Jb1jXO2LRIJ8+MY4tqz/jsSUXMxmBwfyVzP/h/+SkpZHWubxnWhUuDwLBIJ6pBnMPNK+H99W7Gda+V7UJny5nlVVgE+JcFtmN05JzGRJVTElR8gwLSNhRYcXJaZjfFG2h0GOFLKM1pNurKavXEJZTTVDunTn2lFnHnG/03r24dkb76BHTi4205Fjz9zyyr8Pi6Ho8vl4evInzF63ql627n4dOvHkdbfQMT2zbttr337JxAU/MvGhx/hs4Vy+Xr64rkyizc6T426uc7u+9JnH8ASi18J9b/0XgHfvf4ghXbqjahpvfz+dD+Z+TyD0sytpZmIyfxk7rp7rtqppvP3DdD6dPwe3/+e4d3azhRvOOId7zr9E3NSC2IgnW7MiXJ4FAkFLUSsg+jYR8m0i7N9eKxa2PdRIDa7C/2JyjsSedj2SzoYl5WIsKRdTueN+1HDJcapkyUjoQNLReEHQALJBLLdbiJBnHWH/Tuzp12B0DI79WtVUZFnHmKv+wQevXcOiuW8x+tzfNu0yqRWZN637lrsf/IbE5Khl36T372TdyqkMGn4Nl4/7DwB9B17KB29cy9aNsxh22k1EIkG++fwvZGb35MZ7PqnLIty7/0VMfO8OVi6ZwPBRt9Rt37jma35z8xt06Dw82h9BL99OfZzcvEFcd8f7dfv1H3IlH42/gcXz3uHMCx5ssN2XXfs80yY8yJ4di7ns2ucB8DUyvv7Bc163aiq3PTCZjOxoXPZeA8bw0ZvXs3ndtwwffSvhcIBvp/6dTl1Hcs0t45FqxdtJ79/FrG+epU8DcQwbe06NbQOaxoqFH9EubxA33P1R3XFSZr3GgtmvUVK0lYysHkfu41/E125s+/btXklxwWbG3f4unbqdVlf+uy//wd7dK+jV/6Lj+l6UxeNIIBAc/mCQuDilPQ+370dCEzOXLnKV8GrhZhQ0rsjsdNR9rZIu5u+2EU3lrcKtTXbTPh5ZsHkjAFeOOO2Y+144aBgdM7Ji00hUlfvf/i8/rF3JuFFn8cXDjzP9sWd4+Ipr2V50gDtfe4Ea389xNw9aIL741WTKXS7G3/tHpv3lHzxy5bW4fF4e+/R/BMPR7Lmv3vlbhnbpDsBjV9/Au/c/RPec6MTr5a+/YPz339A/rxMf/eHPfP/4c7x0231IksQf33+T1bt31B3zi8U/Mf67bxjYqQvvPfB/fPXoU7xz/0MM6NiF8d99w+TF88UNLYhtQRDHM0+LhEXHCQSCZkPTwoS86/CU/I/K3Q9Rlf9nPKUfE/KsbrNi4qEEXYup2vtXQt61dduSu76OPfOuWlHuOHkfSHI0EYhkapx1oaQHyQKSI2qRKMTElr1PFC/uovdxF72LGud9kd2uL0NGXs/iuW9TXrqrWdrVscuIOjERID2zO5qmMnDEz/EJ07O7LYWWAAAgAElEQVR7AOBxlQKQv3MJPm8Vw067qU6YAujU7TSSU/PYtnHWwYsSgNSMLnVCF8CeHYsJBb0MGn5NvfK5HYeQltGFHVtaNlZnXucRdULewX4FqKkurD2/pQT8LvoPHVsnJgJceMUTXHfH+w1mWI71nI7VBiSJe/40k5vv+bReuXYdBgBQWbbnqH0cb/tUNRpLtmDfunrlL7j871x4xRPH/X0onnICgeCIdLck8ESHQbxasIndgfizz631VPCf/Rv4XU5vBjnTWO0qa3A/GbBLOtxabFaKu/wuJpft5tr0zifV+Owuib4ge+V2aJH6v1+zgg1793DxkBH83xU/T4KuG30WEVXhxa8mM2nBXO4+P/pV8eBUoMbnZdKfHqyLbdkxPZOfNq1nybbNbCvYT7+8Tgzs1IVkh7Ou/QfPobi6kk9+mkNuajqv3fU7DLXxRzISk0hPSOSGl5/lzZlf8879DwGwdNtmAP585Tiyk1MA6JCWwYCOnfn31M+wGk3iRhbEtoCMIymLJiwUBQJBU8URLUTIs5aQZwUh73o09fhOW6JGqnEVvIzJeSr2tOuQdDbMyecR8m4k5F7chl8CMtFP6/oGRY5f7FwrIuoAERfx1yLkXkPEtwNb+rUYHbFn4D3j/D+wbeMsvp3yd26655NGjPvRcSRk1hdcDFHvIOch2w2125Ta+UNFWT4A61ZOY8eWefXKh0M+KsrqJ01MSc2r999VFfsASP7FdoCklPbs2dGy91xicv1M77raj7NKbdK6yvL8BvdLSMomISmayPOXFoCxntOx2nDwGGuXf8GBvWvwespRIiECfnftWISP2se/pLHt69TtNHr0OZefZr3K+lXT6NT1VDp1H0WX7qPr2ng8IywUBQLB0V8QeiOPtO/PcEdak+rZ7q/h2f1rGZWSje0oC3YrOvRxTMhmVOxntbv8pBobtz/qMpxos7dI/fM3rQei2Z9/yUWDo1/sFm7ZcNhvlw4beViinLxa1+gq79GF6QWbN6CqKhcNHl4nJh6kd/s8OqRlsGrXdvyh6EIrwWYD4Ovli9EOsVI16PT89erruWjICHETC2IjnsmdIiwUBQJBPGiEfVvxlLxH5a7f4y56g6B7xXEvJtY7Q9WPdki2ZzVU2gZHIRrnUMOBRgJgRztC4hQNGU0yoUl2VCkBFRuqZkbTxLL6Vx1DLYKm+oknyZDRZOO8yx5jf/4q1iyfDDRNGpaPYIUrHSXuplo7jzCZHZjNznp/3XufS99Bl9Zvs9FWfxpycB7SgBiq0xlQ1EiL9r98DMtjNY55UqzndKw2KJEQH4+/gfk//BdnQib9Bl/B8FG30GfgxQ1fF7/o43jbJ8s6rrrhFa697W06dB7Ozq3z+eKjB3j9uXPYt2fF8T9tFo8fgUBwzBetJHN3dk/Syi1Mr/0aEw8FQR8vHtjAmOR2fF925Hocko4qLfYX35uFW/h73iByTbaTYlxsJjNVHjeBcMtYR+0tjcY6+mr5YmatW334C0SnI7/08HhIBy0FD8VQ6w6tqlqjjrlmzw6enTLxsN9DkTCqprG/vIxu2e24+czz+HHDGsZ//w1fLl/EaT37MLxbT07p3gu72SJuXkHMxOfyLCwUBQJBDM8M1UegZiGBmjkooZIT8hwlnQ172vWYnKcQlWfCVGz/HVqkLXz8lQAdGobajMu6qAxVqwtoSKDp0dCBpCEhAXq0o7g9a0homhT9l3RwL01c7K2AwdoDe+b1yPrkuOvo3vscuvc+hx9nvkDXXmchH2L88HNCwPrjGfDXNN+c3h7N9jtg6FX14uw1Fqs9eu7eBowrfN4qrNbYEsc19zlbahPXeWMw/mjuc9qxZR7FBZu58vqX6Nnvwrrt2zfPieucYmqfJNG5+2g6dx8dXRPvXctXnz3MlxMe4nd//em4vv/EpxSBQNDoqddVqXncmNGlSV/tqiMhvqjMp4PVccR9jMiY4ng8BVSFF/dvoCoSPCnGJC0hmt16b2nLLEYOCpVr9+xi2fYth/21S0mra8OhNCW78sFj7i4pbvCYRr2BvPRMIkrULT4vPZMpDz/B7edciNlg5IvFP/F/H7zF2X/7E/+aOpGgiG0niHkRHIeFohAUBQJBI1DCZXhKPqRy1x/wlk04YcVEo20ASR2eweQcCUj4Kr6ifMtNaJEyfj2RTY8mmdGwo0kJaJIDJDOgO/rsV5PRNCnqBdGoeN1RYVHVpKgwKWi597VswpYxDme7B5okJh7kvMseQ1UVfvj6GYzGnz9KH3RP/mWikr27ljfbubTLGxSd/25feNhv2zbOoqaq8KjlcztEy//SDTgc8lN4YAPt8mJzBW/uc27XYWCD57di0ce8+uyZ1FQVtPg5HYylmPKLEFkbVn8FUM/TqTE0tn0F+9axed239fbJ6TCAfoMvx+0qJdiEsGJtAWGhKBAIYuKsxGycOiNvFW0hEmcilJCqstZfRYZsJKSqDe7jlPRUaCHUGOsuDwd4bt96/tphAI44YqEdTwzu3I01u3cyd+NahnfredR9D1SUYTdbYnKPtpmjk4lX73ygzmW5pbHVWhXefd7FjB05ulFlUp0J/PaiK/jtRVdQVFXBoi2bmLBgDpMWRIMh//nKceLGFcQwM4onhqIQrgUCwZFRggfwVc0g6FrKiWy1JslW7Ok3/MIq8QG0SEVrtgIkXdS6UDuYmVlf7+f4UEELgSbVZm8+9odvrVZUlAAkVciLzYjB2h17xvXIhpRmq9OZkMkZ5/+RH75+ul7iEL3BTGp6Z7ZtnE3/IVeRlJLL/j2rWLdyapPjLR4kObUDPfuez8olE0hO60iPPuehqgqrl05iwezXuOQ3/6Lf4MuPPBfO6ELXnmeyetlnZLXrQ9eeZ+D1VDB7+r+IREKMGH17jFOh5j3n1PTOdOlxBquXfUZaZlc6dT2V0uLtLJj9OslpeSQk5RwWQ7G5zymzNmHL6iUTOeOCPxLwu1j60/t1ruhlJTujHw0aeX6NbV/h/g3Mmv4sPm8VPfqeh8FgoaJsN5vWziAtowsms+P4njaLx5FAIIiVIY5UrLo+vFKwmaCqxFWHCpRpYRIPupn8Apmo63ONFnv9BUEvz+9bz8Pt+2E/gUXFCwcN473ZM/ly2SKuG3U27dPSG9wvoig89L/x7Csr5YuHHycnJbVR9XfOzGbTvnzyS0taTVDskhkNzJxfWhxX+aykFMaOHM2YwcO58Kk/M3vtKiEoCmJbiupjT+QjBEWBQNAQSrgMX8XUE15IBDDa+mPPuBVZnwiAp/QzAhVTQVOa+UhS9E+So9aDyLWioUzU2lCqv2uzo4Eaih5PblwiFg1q2woyWm0R4Q4d1+jLRqypV2JOPK1FBnjIyOvYuOZrCvevrz/nvvIJvpr0MB+8Hk1SmJbRhStv+C/vv3IVqto81/gl1zzHrK//yayv/8nMqY8DYHekct6ljx1VTDzI5eNeYOa0J/lm8l9QlWjoqKSU9lx902vktO8f+zqjmc/58nHP8+3Ux/lu2pN15bv1OosLr/xHq5xTh87DGXbazaxY9DGrlk5EpzcyaPg1XHbt8/g8lSyZ9w6F+9Zxw90fxXBOx27f0JHX4/WUMf+H//L9V0/VXsgSnbuN4rxLHz3+70lN08TTTCAQxMXugJsXD2zAq8Qf6NeMjO0ogaxriBDQ1LjqzjHZeKR9P5L0J26m3+e//JxP588mNzWdV+58gI6/EP68gQCPfPQ2C7ds5OIhI3j6+tsAeHbKRD5bOJe37v1jnXXj5c/+nfzSYta+9DYA8zet4/fvvs6I7r0Yf88f6tW7p6SIpyd/yg2nn8OZfQcA8OJXk/lo3ixeueMBRvfuV2//g7+9dNt9dfv/5eN3mbl6OR/+7hH6d4y6H1R7PZz7+MNYTSa+/duzdVaSAMFImIfef5MBnbpwxzljCIbD/P6918lMTOKJa2+uP3nXNM5/8hHCisLcp/4jblZBowntXIxvxeSYypi6jcIy+ErReQKBIPoOUrz4Kr7EX/NjCwhqbU3gMWFPvxGT89Q6gcdV+DqhmoVHdBHWUJCoFQElDTTQJAlJkw6xDoq6DkvI0d+QouJhW7Pzk3TRTM+xtEvWozekoNM7EJmhY7qzsGVcj86Q+isdXsPlKgFNw5mY1WKHiUSCuKqLMRjM2J1pR03m0hDhcAB3TTEGoxWHM73NnXMo6MXjLsfhTMdgtLT6OYVDfjzuMuzO9DrXboBgwI3JZI/LArMx7dNUBY+7nEgkiMOZXpf9+3hHWCgKBIK46WR28JfcAbxwYD3VccYQC6CihyPGTHSiJ0wYJY4vuQVBL0/lr+Gh3L7knKCJWn5/8ZVUul3MXL2cq//9JKf37k+f9nl1CVO+X7MCT8DPGX0G8Nerr4+p7tG9+jGyR28Wb93EXz5+l6tOGYXFaGLzgb289f103D5fo60dGyI9MRqs+JP5sylz1dAhPYOuWTncc8ElvDpjGne+8R/uOu8iMhKTOVBRxgc/fs+mffl1gqTJYMBpsfLlskWEFYXzBwwh0WanyuvmmxVLKK2p5sYzzhU3qiDGmVEcSVmEhaJAIKgVPAI1C/GVf46qtP24WJKkR9InIOucSLINWWdDkq1IOiuSbEaSTEiyAUkygWRAkvRRy0BkJElG08LoDNnojPUX0M7s+yH7/iZ2pYqm+lEVH5rqRQ27UCLlqOFK1HA1SrgCNVKNGqlB037FZ7CmgKZGRUWpkTGk1QiRYAmaJmFPvwqjrYe4dY4HJAlnQst77Oj1JpJTO8Rd3mAwk5ya12bP2WiykRzjuqw5z8lgtJCU0v6w7U1xPW5M+yRZhyMh48SbNosng0AgaAo5JiuPtR/ACwc2UBzyx1WHV1LRaRL6Br7SSkCCpKcyzsliWTjAE/mruTe7J4McqSdc/xv1ep698Q5O79OfyYvm8eOGNcxZ/3NG5l65HbjmtDO4ZOhI5Bi/uEmSxEu33ccr06cybdlCZq7+ORDzgI5deOi2q+mW3S7uto89ZTTfrV7OrHWrmLVuFY+OvY6uWTncfs6FOK1W3vlhBn947426/XNT03nqulu5ZOgpddueuv5WEm12ZqxayoyVS+u2J9sdPDDmcm49+wJxkwpiu+5FUhaBQBAHSqgIT8l7hP07287zTLaiM2ahM6ShM6QhG9LQGVKQ9UnI+kQk2UKbtZCTZCSdDZ3OBqTBEY15NNSIGyVURCRYhBIsJBLYTyS4D01trWezBlo4+v+SrtF9qoSKqTnwOkZ7X+zpY9EZksWNJBAIjq95s3B5FggEzYFbCfPSgY3siTNTlQw4NR26I0zC/Ki4tPhdqyXgopT2XJWWh146cRPchyIRSqqrUFSFrKQUTIbmiSEZURQKKsuJKArpCYk4LNZmq7e4upJkuxOr6XDX9MLKCrzBAEl2B6kO51HrKXfX4Pb7SbY7SLY7kCThRiSInXDRVrzz3oqpjKFdX2yjbhOdJxCclKj4q37AV/4FWhPmKU1DRm/KRm/qiM7cAb0xB50xG1nv5OR1qVWJBIuJ+HcT9u0g7NuOEi5vjeU1SMaY3SYl2Ygt9SIsSWfQmIQvAoFA0BYQgqJAIGg2gqrC+KKtrPXEl81Ph0SCJh/Mh3cYbhR8TYxF1NHs4J6cnmQbrWLABALBYURKd+GZ81pMZfSZ3bGfeY/oPIHgJEONVOIuGk/Yv711F3CyGYOlKwZLD/SWbujNHZAkoxiQY6CEKwh5NhDybCTs3dyyrtKSofEu0IdgsHTEkXkjOmOaGDCBQNDmEYKiQCBo3sk1GpNKdzOrqiCu8joNEhp0fo7mw6shQjDOJC11i39J5pKU9lyS2h6DJL4CCwSCQxaclftwf/9SbM+UtE7Yz/mt6DyB4CQi5F2Pu/htNMXTKsfTmztitPXFaOuH3tQxLrFKcMicUg0S8mwg6FpByLO+ZaxLJV1UWIy1mGzEnn415oThiKQtAoGgLSMERYFA0CLMqy7i49KdqHE8YqKWirojiorVWpgQTX90pRstXJPWiaHONDFdEwgEACg1Rbi//Xdsz6zkXBznPyg6TyA4KdDwVXyFr+LLll6mYbB0w+QYhtE+GFmfKLq+pUZU8RFwLSVQtYBI8EAz1y6DHJ/1qMk5BEfGOCRZWJ8KBIK2iRAUBQJBi7HdX8MbhVuoiSNhgQ5waHp0DU7lNapQCDfRUvEgHc0OxqZ1pK89WQiLAsFJjuqpwPXN07E9rxIycYx5RHSeQHCCo6kB3MXvEPKsarFj6AzpmBJGYXaeiqwXSTpaeYQJ+7bjq/iBkGd9M9Yr17pAxz7L1JuycGbfKVygBQJBm0QIigKBoEWpiYR4q2grW3zVcUy/JBya3KADtIZGtaYQQm22tuaa7FyY0o5TnOkndOIWgUBwZFS/C9eXj8f2rLKn4LzkMdF5AsGJ/GyI1OAqeJFIcG9LLMkw2gdgSTwPg7UHws311ycS2I+v/BuC7jXNNMS1omIcYyvJFpzZt2K09RQDIxAI2hRCUBQIBC0/CUdjesV+virfixqjq7IE2DUdxlYSFQEcOgOnJmQwOjGTXJNdDKBAcBKhhfzUTHk0pjKyxYnz8idF5wkEJyhKuATXgRdQwmXNvBLTYXaOwpJ0ATpjpujoNkjEn4+nZBJh/65mGG85mgE6vsLYM67GkjhKDIpAIGgzCEFRIBC0GnsCbt4t2kZhyBdzWSsyFq1hq0G3FsHXzKLiQXJNdoY6UxlsTyXXbBc2AwLBiY6qUP3Zn2KbTBktJFz1T9F3AsEJiBI8QM2Bf6MqrmZcgUWFRGvyJciGFNHJbR6NQM1SvCWfozY5CY8McnyWigCW5LOwp12OsGIVCARtASEoCgSCViWiqXxdsY9vK/ejxPj4MSDhOEKyFh8q7pbI0HcIqQYzvW1J9LQm0tOaSLLBJAZUIDgBF47Vkx6CWJ5Psp7Ea54XXScQnGhzluB+XAf+jaq4m61Oo60/trRxwiLxOERV3HiKJxJ0rWjiCjy+7M8HMTkG48i6AUnSi0ERCAS/KkJQbAWC4XD04W8wiM4QCGopDPn4pGRnzLEVZSRsmtygC3QIlRpNidmtOl4S9EY6mu10tiTQ0Wwnx2Qj2WBCFl+NBYLjmprJj6DFmEwqcdyLCIsRgeDEQQkVUr3/n2hNtkiLojOkYUu/CaOtr+jc4xqNQM0SPMUT0NRgE1bhTRMVjdbuOHPuRJLFx22BQPDrIQTFWmatX004EmHMoGFH3W/G6mUY9QbO7Teo0XXf+OpzKKrChN8/etz1y7r8XazN30WV14PDYqV9ajqje/YV4qigmaZksNpTzuSyPZSE/DGVNSFha8BaUUWjRosQ4td5tMlIWHQ6EvQmEnQGEnVGkg0mUg1mUg0mHHoTTr0Bp86AUdY1WX7Y6XNRGPKhQ0IvSZhkHVadHousx6k34NAbhMApEMRIzdS/oQVjExESfvM8kk5YiwgEJwJKuJya/U+jRqqboTYJS9L5WFOuEOLPiXSNBIuoOfAaSqi0CZeGISosxone3J6Edvci60S8b4FA8OsgZr61rM3fxacL5tApI4seObkN7rO1YD9/nfg/bhh9dkyC4vFIflkJj054j80H9mEzm8lKTMbl91FaU43dbOGRy6/hksEjxIXTANt8NXxeuhu/GhGd0UhkIEFnxKOEURopBAbRCEkRbJqMCfmQuiSSJD0+TcNDpNVlRRUNrxLBq0QokUCnSciAJEnIRP+NBrJ0cJkR/V+5tqXaEeuVuDGnO8MdaQCEVJUvy/ayw1fD/qD3KH0rkWAwkmowkWm0kmWy0M5ko4PZjknWiYtPIGhojaczxP7sUEIgBEWB4LhHVdy4Cp5vFjFR1ifjyLobg6W76NgTDJ0pi6S8v+IqGE/IuyW+SrQwSFLtTDh2IoF9VO97mcTc+5H1SWJQBAJBqyNmvrVcfcpoPl0wh6nLFvLoleMa3GfqsoUAjB0x+oTui5KaKm59/QVCSphnr7ud8/oPRidHX3Tbiwr4x+SP+dukDzDq9Zzff4i4eGoJqArTyvNZXFOCVdbjE4JizFhkHRFNJag1zmlZAzySSkBTsUo6DNpBSzwJqyRhwoCbCMEWNsTWSRJ6TcIAyJIUFRF/bkoDagVH3Cg1cI7IEi90OxVz7badfhcfFu2gLBSgncl29IURGlXhIFXhIDt8rnpHyzbZ6GFLoKctkW6WBIyyLC5CgQBAH7sVvqZEhC2wQHCco2lh3IWvoIRKmlyX0T4QR8YdSDqb6NgTFElnJSH3d7gKPyDoWhZfJaoKTZh/KaESqva9RGK7B9AZ08WgCASC1p0yiy6IkpeWwdDO3ZmxZhkPXnIVZoOx3u/BcJhv1yxnaOfu5KVl1G2v8rqZtnwRO4oKAY2O6ZlcNnQkGQkNfyUKhEO8O2cmQzt3Y3jXnj/XHwnzzuxv67YrqsqbP3zDaT36kGx38NnieVS4XXTPzuW6UWehl3V8sfQn1ubvxmmxctnQkfRq177esfaXl/HVysXsLy/DbDQyuFNXxgwchl53dKukl2dMpcrr5q27fl+vjQDdsnJ46+4/MO7lf7JxX349QXFHUQHfrllOQWU5JoORvu3zuHTIKXV92ZRzOthvI7v3JsXuYOryRZTWVJOZmMTYEaPISU4FIKIojJ81ndN69CEUifDVisWMGTSMU7v3BmDDvj3MXr+akpoqnBYb/fM6ceHAYchS05aB23w1fFqyk6pIUNxMTX0oSTJ6SSaiaYQ1tVEWixEJXCgYpKgb9MErXIdEIgaCkopbUxpt/XjUySOgR8JY62Ksp56p4eH/bgIR4OzkdlyRngdAWItaJf5YWdjkM9GAgqCXgqCXOZWFGGWZvrZkhjhT6WtPRi8JaURwMi8S4wjroYRFxwkExzUanuL3CPt3NHmmYE29AmvyJYi4qifDC0OPM+d23LKRQPWCuGZ7suxEVX1xN0ENV1G97yUS2t2L3txejIlAIGi9tbvogp/5zcjR/N/H7/DDulVcOuSUer/9sH4VnoCfq0/52TpxV0kht7/5InpZ5sw+A9DLOqYuW8SH82fxzt1/pFe7DocdIxCKCmNAPbEuFA7X2y5JEu/OmUmZq4a1+bsY0rkb/lCI/347jX3lpQTCIaq9XnJT0/hx4xqmLlvIN3/+B5mJyQAs2LKBhz56i/ap6Zzaow9VHjdPT/mUqcsW8vbdf8Sob3jo/aEgs9avZsgvBM9DsZnMfP3IP+ptm7lmOX+d9AFdMrIY0a0XNT4vL34zhUmL5vHRAw9jN1uadE6apvHunJkUVFawevcOhnXpjk6W+HzJfL5YuoAJv/sLualpqLX7ufw+ZqxaRk5yChXuHgB8vmQ+/5w6kSGdu9GrXQdKqqv422cfMnvDGl66+Z64rplgrVXiopoScQM198NJktBLOlQ0QpqG0girxTAa1VIEoyZhRsZQO5E3IWOSZPwouLXYZUUZMCJhlH6us2WXNCDLMq91G/nz88bv5qOiHTHHmmwsIVVllbucVe5yHDoDIxMzOCMxkySRyVpwMq4PdcbY71shKAoExzX+ypkE3Uub9uyQjDiy7sFoHyQ69OR6a+DIujG61otDVFQjlZicwwh6NkCcn4xVxUP1/ldwZt+B0dZDDIlAIGidNbvogp85s/cAUh1OpixbeJigOG3ZQlIcTs7qM6Bu27PTJqFpGhP/8ChpzkQA7jp3DFf/5yn+9eVnfPTAw3G35aDF3HdrVzDlocdplxK1wLv/3Vf5asVixo4YzbPX3Q7AxYOGc9Nr/2bOhrVcP+osgpEwf/vsQ3rk5PLevQ9hqI3pdMGAodz37it8vng+N4w+u8Hjbis8QERRGNal8bFevMEAT0+ZwMC8Loy/63d1x7ts6Ehue+MF/jf3e3574eVNOie51hXg+3UrmfT7R+mW3Q6AjfvzufHV53h/7nc8fvWNGGqtL2esXsbLt97L0M7R89A0jQkLfmRAXmfevefBurZ3/GE642dNZ3vhgbo6G8sOfw2flOykMiysElsSGQlzbXyZCCoRTTtmZMSQpBFCQY+EWZPqYixa0GGWZPxo+LQIyjGOa6g9tr4VLQwiwJi0XMakRD9IhDWVr8v2MbuyoNXiQbqVMN9XHGB2ZQHDnemMSW1HqsEsLkbByYOwUBQITirCvk14yyc3bb6ic+DM+QN6c2fRoSclEo6sG9AUL0H36phLhzzrsGeOw1syBU2L732iqUFqCt7EkXEd5oThYkgEAkErrNUFdeh1Oi4fdirr8nexu6SobvveshJW79nJFUNPrXMXdvl9rNy1nbP7DqwTEwGSbA7O7juI9Xt3U+31NLlNw7v0qBPeALpm5aBqGmNHjKrbdlAIK3NFg0cv37GVaq+H6047u07cAxjZvRcd0jKYs3HNEY9X7Y0md0h1JDS6jct2bMUbDDB2xGn1jjeoYxc6Z2Qzf/P6Jp/ToXUeKvz1yc2ja2Y2K3dtj77Ka0XLzhnZdWLiwe1fPvwk/7vvT/Xq69ehExBNQtPoF76qMLlsN68e2CTExNadp6GXZMyyDpuswyTp0B3DLTeChkdSqZIUfJKCAkhIWJFJkQw4Jf1hFoc6wC7JJEoydkluVTFRBV7rcVqdmLjb7+bpPWuZ1YpiYj19RNNYXFPCE7tXM6U0H7+qiOtQcHI8buJIrqKJuLkCwXGJGqnGXTQemvCmlfWJJOT+RYiJYmmNI+cODJbYrwNNDRKqWUZi+981LWuzpuIu/gRv+fQmXdMCgUDQGISF4i8YO2IU7//4HVOWLeT/Lr0agKnLFyFLElcdInjtLy8FoMMh8RQPkpsSzcJaUFlOos3epPZkJNaPxXgwHmH6IdsPbgsr0cXM3rJo275euZifttQX83zBAC7fkWN0WE1R98ZgpPFfxo7aF6mpLN2+tcnndJC8Bo6RnZzKkm2b621rqC2+YJBpyxexbu8uKtxuwkoYt9/f4HGOxu6Am1XucnwmLL0AACAASURBVPGK/jUX+5qEjIpe1ZBUDU0CDQlV1mgoXpFK1B1akVQMSOiIZl82SzJGnY4wEFEV9FrbeSiqaMyvLm4xF+dYiGgasyoLWO4qY1xGJwY4UsRFKDixictCUQjuAsHxh4q7eDyq4o67BlmfREK7P6MzZojuFCBJBpzt7qMq/xnUcGVMZUPeLZiCRSS2f5CaA6+jhCviboev4nuUUDGOzBuR/p+9+w6PozoXP/6dme1V0qrLsuVu494bxsHBEEwvAQKBAAnJDWk3yU2/v3tJQkgHkksSSAgtFNNtQjEGTHPBFXfLVbZsWb2utu/O/P6QLZAl2dqVZEv2+3keHierPbMz7+zsznn3PeeoMn2NEKJ3SIXicXLTMjh31Fhe3fAR0XichK7z6oaPmD1yDHnpGa3Pix3tOHRUu2Q2tVQxxnugc6EqHZ+iE1VmHUuOuWx23DZHm//mj53EJVOmd9o27+gcjHvKy7q8j7HW42y/T2bNRPy4qqZUjukYSwcrb5o1rd1rOCxtvzij8Thf/tsf+eubr5DjTefyqTP54twLuHjStKTPyShHGj8bNInxrgy5YE71bb9uEEvEicRjxOMJdN1AAVQDNMPAnABTwkDTjZbHoGXYMgomVFQgoRgkVNA1lYSmYAAmXcfWB5KJKvCN4hW8U3sEFYXb8obzHwWj8KSw4mxvaIxHebCsmMfL9xCRakVxJncIU6pQlCHPQvQ3wbrXiAWLU//e1tx4B/xAkomi7fvC5MFb8B+gaEm3DVQ9j6o5SRv4PUzW/G7tR8S/mfqDfyARrZCTIoToFVKh2IHrZs/jg51beX/HZsyamVp/E9fNntfmORkuNwC1ze1/0aw/OtS5w+rEY0mz48rbGoPBHtt/n9sDwJXT5jB75DlJtS3MzGJgZjbvbP2Y7192LU5rx/OmPbfqfewWC5dNndUai7rmpg5j0d0qzbbbax/vplAIj915wnYf7NzKzrJSfvfFO7hwwpTWx9/bvjml/XBrZu7IG8V6fzXPV5cQTMhQt95iGAYJXUc3dIwulIW2zLaoHJ2z87jhzIqKpqotjxpAQgdD71PHawZeqd7Pa7UHuXfELCa6fQx3eHmmch/rm2r6xD6ubqyiJOTnzgGjybbY5U0qzsTeYPJt5HtAiH4lHjlIsHZxyu0V1Yan4PtolnwJpmjfybYPxpX9eZorFyXVTo/7CdS8givnBtIKv0Nj2YPEQiUp70ciWkH9wd/jyv780XkVZeVxIUQP3jJLCNqbPXIM+ek+3ty0njc3r2+tWvy0Ab4sfG4PH+3e2a79ur27SXe6KczMbvc3+9GhvPXBtvMrrt+/u8f2f2JRy7wdq3Zvb/e35ds2UV5/4vL7W+YtoDEY4OfP/wtdb59sWbNnJ79/5fnWuRGPvd7q42IRikbYfuggE4/OU9gTPi7Zi/6prFJC1ykuKz3pgioVR495cHZum8df3dCymp+R4vjlqe4sfjZwImOdUq3Y03RdJxqPt1YKdymZqCioqnZ0EZ9PbphURcWsaphUFQUDdL2l89/HkomtxwHoeoJvFq9gaW0pTs3EV/JH8rWCUbi1vlGtWBEN8ZuDW9gdbJQ3qzjjpFShKAlFIfoNw4jhL38IjFSr7VXced/AZCuSYIpO2TPmY3GNTbpdqO5dEpFyFM2Bd8A3u71qs6FH8Vc8RVPZP9HjfjkxQogeIwnFjoJydL7ED4u38cGOLVwz49zWFYo//ZxbP3Mhu44c4s+vL6bW30RNUyN/ffPfbNi/m1s/s6BdGwCr2czg7FyWb93EloMl1Af8LN+2iSXrVrUuKNJdAzOzWTB+Ms+tep8XPvqQhkAzNf4mHlz2Kt97/EHW79t1wvbXzpzLZVNmsmzzBm76829YvG4Vmw7s48OdW7nnpWf4xj8fYGhuHj+75kYAhuTkMe+c8bzw0Qe8vnEtzeEQB6sr+dGTDxOJx7hl3oU9dm4isTi/XfwsVY0N1DX7+c3iRTQGA1w5bdYJ240saEk4Pr/6A/yhIGV1Ndzz8jMoR4df76s8gpFiVtFjsvC1/FHcnDMcuypFv92VOJpIjCUSXT4nigKqpqKqKp++jBQFzKqGWVVbrkfDaJnnTNf7RSxMwBvVpfzn7lWEgUluH/87ZBJT3Jl9Yv+CiTj/d2gH25rr5Y0rziypJO5lURYh+o1Q7SskokdSbu/KuRmLc5wEUpzsDhV33q0omiPZLxSaq15o2YJqwVPwNazuSd3vRzVvpu7A3YQbVyMLtggheqq/Kjpw1fQ5PLjsVcKJKFdNn9Phc24+7wKi8TiPvbeMR95dCoDH7uCbn7uCW+Yt6HTb/33NTfzsmUe45YHfAi0rEv/hlq/yhfvvIdFDiY5f3nArv1vyHL9f8hx3v/gUAJluDz+68noumzqrS+2nDB3BopXvctdzT7Q+nun2cNv5F/Hl+Z9rXTgF4J4bb+eel57hf557vHXuyEJfFvd96T8YP2hwj52XS6ZMJxiJcPE9PyWh65g1E7ef/zkunjT9hO2mDR3JTXM/yzMrlvPc6vexmExcO/M87rnxNuoDfh599022lpbw8H98L+V9m+7JYoTDy7NV+yiPBlFkREFSwokEkXiMRJKJ3ZYkYvtga6qKpigoxyoVdb1PJxJblpMxOry9M/QE/1W8gpsKhjHLncsdBSOZ4s/kmYp9p32/Y4bO38p2cueA0YxxpssbWZwZXcCUhjzLHIpC9AfxyEGCda+l3N7mnYfNe74EUnTtPtXkxZV9Hf7yx5JqF23eQiy4G7NjBIpiwpN/K82VTkINK7p3v5kI4q94mlDDSlzZV6W0IrUQQrTeMxuplmWJT/oQuk55fR2K0rKoSctwy5N8mBsGVU0NGAbkpvVeJzwSj1HZUI/NbCHT4+2wavJkwrEo1U2NuO0O0hzOkz63sqEeh9VKlietR49jxk++xZfmLeC7l15DIBymxt9IlietdWXqrghFI9Q0NZHl9bZJiDaHQzitth6rEhVdvKkBNvtreKFyP1VJrmbcMry5/TyJigImRfvUe/1oVeIp/qRTaKmOtKsm3GYLPrONTJONdIsVn9lCjsWJ22TGpKgpz2bjT8RY11RDwtBJGAYxQyesJwgm4vjjMRriUWpiYcKnYAEVs6LynwPHMtTulje26PfC294kvHVpUm3sU67COuI8CZ4QffrGI0FD6S+IRw6m1NxkG4y38GcoitRkiOTueBsO3pv0AkBm+2DSin7yqXtdg2DtUgI1r/fYnlmcY3BmXoLJViinSQiR/PeihKD7NFVlgC+5IYiKopDj7f1qHqvJzMAO5nJMhs1sodCX1eXnDsrqvZXujuWEnDYbTpst6fZ2i5XCzPbH4rLJwhKnWkUkyNMVeykOJDtctiWR2FHyV0XBpGifVIcaBui9n0zUUMi1OSmyuxnu8FBoc5NhtvX6tNduzcz89LyTXjPNiRhl4QCHIgH2h/zsCTbR3MPVVDFD58HDO/lx0QR8Zqu8wUW/pqQy5FnmUBSizws1vJNyMlFR7bjz7pRkokjl3YM79wvUlfw8qfm7Y6ESos1bsbjGt27H4bsY1ZSGv2IR0P2RN9HAdqKB7Vico7Cnn4/FORpZuEUI0VXyjSiEOKXihs5rNaUsrSlNenizgoKidVzRpykqpk9XB/fyEOcci4Pxbh8jnGkU2d2Ylb45Ja1CS+JxlDONUc6WqmEDOBDy83FzLesaq6mPR3vktfyJGA+W7eSHg8b32XgI0SUpDHk2TkElsBAidXq8gWDtSym3d+XcjmbOkkCKlGjWfOzp8wnVvZ1Uu0D1EiyucXw6yWfzzkI1pdN05BEMPdQj+xcNFBMNFKOZM7F5Z2HzTkc1pcmJE0KckCQURf/4ElZUZgwfxaBuVluK06s03MwjZcUciQSS79+rSusiOu0+yFQVTen9ZGKWxc4UTxaTPJnkWBz99jwowGC7m8F2N1dmDWJbcz1v1x3pkRWbD4UDvFB1gC/kDJE3vOi/10gKqzzLoixC9G2Bmmcx9HBKba2e2Vjd0ySIolucmZcSblyFkQh2uU08XHpclWILi3MU6YO+T2PZQySi1T22j4lYDYGafxOo+Tdm+1Cs7olYXOPQzD45gUKI9vfMMoeiEKK36RgsrTnEK9UH0FP4yOls4RUUMLeZL5EeTyYqwFiXj7npeQx3pp3Rg0B2BRt5oaqEQ+FAt7f17cIxnOOUX7ZF/xQtWUfwo6eTSziMno994mUSPCH6oFhoN42H7kmprWrKIL3obhTVIYEU3RaqW0Zz5fNJtTHZB5PeZi7FTxh6iKbyfxFt3tqr+61ZcrE4R2JxjMRkH4KqOeVkCiGkQlEI0bua4lEeLitOYa5EAAVNVehoyWwFMKkabQZA92AyUUVhelo2C3yF+My2s+JcjXR4+UnRBN6pO8KS6lLiRuqx/Ff5Hu4aMhmrqslFIPqfVFZ5liHPQvRRBoGqp1Ju7cq5TZKJosfY0s8nWPs2erzr98XxUAnRQPHR+Q2Pux9W7XgL7iBU9y7NNUuSmqMxGYloBaFoBaH69wHQLDmYbYMw2QZishZgsuajaHKdCHG2kYSiEKLX7A028tDhHTSmOEefqrVfxZmjj5i03ksmTvZkcXHmQLIsZ99iPSoKCzIKGOnw8lBZMbWxSErbqY9Hea3mEFdnF8mFIPodGfIsxJkj3LQy5YVYrJ45WJzjJIii575fFDPOzEvwVzyZVLtQ7RsdJhSP3RnbM+ZjdgyjqfxxEtGqXj+ORLSSRLQSmtZ+cg9pcqOZs9Es2WhmH6o5A82cjqp5UU1eFNXSJ85Bedl2Ksp2nvA5FquTMRMuljesECchCUUhRK/4sL6cpyv2JL3wSsttkYKqdr6oh1nVUD6dTDR6JpmYY3Hw+dyhDHN4z/rzN9Dm4keDJvDA4R2UhptT2sbb9UeYm5ZLlsUmF4ToXzRZlEWIM4GhRwjWvJBSW0Vz4cy6QYIoepwtbQ7B2jdIxGq73CYa2Ek8fBCTbVDnHXvbQNKLfkyw5jWCdctpWYbv1NHjfvS4n1hoX8fXlGpF1VwomgtVc6CodhTVhqLZUBQLimpp+VcxgaId/VelpZRAQbPkYLLmdXs/i7e9zYrlD6GcYBRNWnp+UglFf2Ml77/1AJde+0t5g4uziiQUhRA9ezOBwQsV+3m77nBqG1BAOWEy8bj5FA0DEt1LJqooXJRZyAW+QjRFkZN4lMdk5j8Lx3D/oe0pJRV1w2Bx9UHuKBgpwRT9iqKkMFRfEopC9Dmh+qXo8YaU2jqzrkfV3BJE0QtfMiYcvouTrlIM1r6Jp+CrJ/n+MuPMuhKrewr+ymeJhw/2mcM29AgJPQJJJFI/TTP7cOfehNkxvEf25z++t4TMrJ5ZRPBgyXq2bX5dEorirCMJRSFEj4noCR4u28lmf2o3CihH50zs7ANLPW4BFgxIdK8Tn262ckv+SAbbPXICO+DQTHyr8Bx+c2BzSsOfN/hrWBgppMAq8+qIfiSVuT9lyLMQfYoebyBU/3pKbc32Ydg850oQRa+xps0hUPNqUgnvSNN6EtlXo5kzT97JtxWSPuj7hBvXEqh5BT3e1P+v6USAhkN/xp4+D2fm5aduCLVhsG/3Skr2riYYqMfm8DJk+GyGjTgXFIXN61/m43UvkkjEePfNP+HLLGL8lCsACAbq+Hjdi1SV7wEMMrOHMGHqVXi8uW1e4vDBTWzZuIRwyE927nCmzrqRTeteINBcx2cXfh+AdSufQjNbmDz9823arl/9NKpmbvN4JOzn43UvUlG2A8OAnPyRTJp2DXaHLJgoeviWWUIghOgJzYkY9x7c3I1kIqhqx3MmAmiK0r56sJuViaOd6fygaJIkE0/CrZn5+oDRmJTUvjJerzkkQRT97O4o+fe6oesSNyH6kGDtEgw9lXmAFZzZN3d6PyJET1AUEw7fhcl+0xCqezup97LNO4OMwf+LM+vyM2bRlFD9+9Qf+HWnQ6t72uuLf8mix++kob4Mm8NLQ91hFj12J68vbqlGbG6uobmpGgyDxvoyAs0tfaHqyr387Y+XsXbFv7DanNidaXy89gUevPdyyg9va93+np3v8djfvsjB/evweHOoqtjN4w/ezPbNb7B/z8rW521Y8yxbNixpt38b1zzf5vGGusM8eN8VrHz3Yaw2Dw5nGms+fIIH772c+lq5Jxc9SyoUhRDdVheLcP/BLVREg6n33xWt01t3FQXT8RVDut4y3DlFc9JyuSZn6HEVj6IzA6xOrs0uYlHl/qTbbvTXUBMbRKZZ5lIU/aSjp8qQZyH6s0S0gnDj+ym1tXnPw2QdJEEUvc6WNpdAzasYia7fP4cbVrRU5yWRHFRUC46MBdjTziPU8CGh+nf7fcViIlZDQ+mfsKd/BmfmpSlVK8aiYaKd9F1MJiuqqhGPhdm49nlmn3c78y/+buvfN617iS0bXyEWDTHnM3dQW1XCjq01XHnD71qf88biuzEMg698+wXcnmwAzvvs13novitZ+so93Hbn0wC888a9eNJyuf0bi7DaXC33zmue47WX7iKvYEzSx7V0ya+IhJv5j++90loJee78r/HXPyxk2au/4fov/UUuPtFjJKEohOiW8kiQ+0u3UJ/iasAAqqpyoryeSTuuWsgwurUIy2VZRcz3DZDagyTNS89jvb+GvcHkbkIN4N36cj6fPViCKPoHmUNRiH4tUPM8kPx9gqLacGReIwEUp+arRrVhTz+fYM1rXb+n0iOEGj7E4bsohdez4si4AEf6+YT9Gwg3rCAWKunHETQI1b9LNLANd+7NmO3J3Wc+/OdrO/3b1Tf+gTETFpJItExnUlG+k3g8gslkBWDitKuZOO3qTtuHQ00c3L+WydM/35pMBHA4Mxg19kLWr36aYKAeXU9QXbmXaXNuak0mAkyYehVvvfq7pCMSjQTYu+tDJk2/ts2waqfLxznjL2bTuhfbHIcQ3SUJRSFEykpCfv5cuoVAIvW5wxRFabvIyvEfUqradkVn6FYy8YrswZyfUSAnL5VzBXwhZwh3l2xKet3A1Y1VXJE5CIsqM22IfiCFCkVZ5VmIviEe3ke0eUNKbR0Zl6NqMg2KOHXs6ecTqn0Tw+j6vXSo7m0cGRek9uMXgKJh80zH5plOPFJGuHEtEf869Li/X8YwEa2mofQ+HBkX4My6jK5OV3Dxlf+N25PT4d/yB4wFwGpzccHC7/PWq7/jvrvnMWT4bIaMmMPIc+bjcKZ3uu262lIAMrKK2v0tw1cIQEN9Wetoq7S0/DbP0TQzbm9O0rGoqy3FMHSOHNrC4kU/bPO36sp96HqC+trDZOUMlYtP9AhJKAohUlIcqOcvh7YT6UYnWlEUNFXtNDnVMm/icQmoROpDnS/LKpJkYjcVWJ3M8mazqrEqqXbBRJyN/hpmerMliKLPS2nIsyEJRSFOP4NA9fMptVTNPmzpCySE4pRSTV6s3pmEG1Z0uY0ebyDStB6rd0b3kwHWAlzZV+HKuoJoaA9R/yYizZv7XXLRZM3H6plMMnOfFg2b2aVVnmfOvZXBw2ax9eN/s2/XCnZsWcpS091ccMl/MW32TR22ScSjLfcTHfVvTOaW85iIYRzt06iauf0xmZIfxp2IxwAwWxxYbW1/HBkwaBIDBk3CYrHLhSd67tqTEAghkrWxqZqHy3YS78YchgAOk5lwJwlJpaN5Ew0DjNSqE89Lz+ezvgFy8nrAwsxCPmqsRk+yTnF1Y5UkFEU/6eGlMuRZFmUR4nSLBrYSCxWn1NaZ+XkUxSxBFKecI2NBUglFgGDdMqze6fTY4kGKisUxEotjJK6c64iHDxENbCca2E0sXNKHfzRTcfguxOn7XOoVm12QkzeSnLyRXLDwv2isP8LrL/+cpa/cw5Dhs/FltR9q7XT5AGhubr9YZTBQD4Ddmd6aeAyHGts9r9lfjceb98kpUpTWBOSnhUKNWKzOltd1t7zuwKIpbeZ8FKL3rkAhhEjC+/VHeOjwjm4lEy2qyghnWqfJRGgZ6txTHfYxrgyuzJH5+3pKptnGVE9m0u12BRupP3rjJETfvjtKYcizVCgKcZoZBGtSq0402YqwumdICMVpoVnzsbjGJdUmHi4lFtzVS3ukYLINxOG7mLSB3yFz2O9IK/wWzsxLsTjPQdWcfSJuJmse6YP+C2fmJb2WTGyoO8yGjxahf6rP4k3PZ875XwXDoKa65NM3Aq3/M91XiNPlo2TPqnbbPLBvLQ5nBhm+gWRkDkIzWSgr3dLmOeVl2wk017U9XrON0NFk5DF1taX4PzVqKC29AI83h3272yeoS0s2UF62XS440bOfX3fdddddEgYhxMlv02FxVQkvVXVv8mavycLnsgaxuqnzIbOqorRPKKa4EEu2xc7XC8diVuT3k56UbrayoqEy+XYmK0Psbgmg6NsScSLF7ybVRLW5sQ6bLbET4jSJNK0m3PhuSm3duV9DM2dJEMVpo5o8RBpXJ9VGT/ixeWf2+r4pioZm9mF2DMPmmYYj47PYvbMwO4Zhsuajam4URcUwIr1ayagoJjDigILDdyHuvFvRzOlJb+fAvjWUlmzA48mmvraUyiPFHf7n9uQQCjaw6PFvEPDXkJk9BE0z0dBwhJXv/p36ukN89uLvYrW5OLh/HaUHNjBw8BQMQ8fhSENRVbZsWEIiHiU7bwTRSIBV7z/Clo1LmLfgGxQWTUZVTdRW76d4+zvYbG68aXlUle/itZfuQlEUrFYXk2dcB0BF2U727V5Buq8QjzeH6oo9vPbSz1EVFavN1bpIjMlkYdO6lwgG6snOGYaiwK7t7/DyMz8gGgkycsxn5YITPUaGPAshTipm6Dx+ZBdrk5w373h5VgdfKTiHPx/eduIPpo5+ZUxhrkaTonJrwSisqiYnsYcNsrkotDk5FA4k1W6jv4YLMvIlgKKP9+xS+AHCkCHPQpwuhhEjUPtiSm0tznGYHaMliOK0sjhHYbIOIB453OU20eZtxCOHMVlP9ZQ+Cqo5Has5HVzjP30losf9JGLV6LF6ErE69Hg9eryp5b9EE3rcj2HEUn5lzZKLJ++LmGyDun0Uy5fef8K/3/r1JyksmszVX/g9y5fez8Y1z7X+LSdvJNff8kDrSsrjJl/OpvUv8+Q/bicjcxDf+MEbzJx7K4l4lFXv/5OV7/0DAJvdw/kX/SezzrutdVsXXf5Tmv01LPv3b1j2799gsTpZcOkP2bz+5dY5EQHmzv8aleW7WPLsjwFwONO55Oq7+Hjdi4SCDa3PmzrrRhKJOCuWP8T61U8DYDbbGD/lSi687EdysYme/TQwjG5OgiaEOKM1xqP89dB2SkJN3drOMIeXbxaO5bmq/aw5QXWipqgdVCfqLYuxJOnzuUOZk5YnJ7GXvFdfzqLK/Um3++2waXhTmGhaiFOWnIhHaHz+x8l1cry5uBfKjboQp0OofimB6kUpJUbSBv0Ck7VQgihOu3DjKvxHHk2qjdUzA0/BV/rXd6wRw0iEMPQgeiKMYUQw9DCGHgEjjqHHW/414rSMkdIxDANDj+DMuvS0zXUaDNQRDvlxODOwdTDaJhGPEgo14nJlgvLJ3Ja6nqCx4QgKCt60vE4Xfgs01xGJNOP15qKZLDzylxvQEwm+8u3nj9uP+qPPy0PVTCeIs05jQzkAbk82miZzxIqeJxWKQohO7Qk28vfDO2js5rx341w+vjbgHA6E/SdMJqJ0MHeiAejJ/+4xwpnGbEkm9qopnkyeqyxJenGWrc31nJuWIwEUfVYqqzwbusyhKMRpSU4kAgRrX0mprdUzU5KJos+weaYTqHoJPd7Y5TaRprUksq5As/SfIfuKYkYxmQEP/WkMkcOZgcOZ0enfNZMFl7v9eVBVjfSMk3/OOF0ZOF0ZXdiPdBzO9C7EWSUtvUAuLNGrZFIxIUQ7Ogav15TyxwObu51MnOnN4c7CMWiKwjOV+074XFNH8xwaeptJjrvCrKhcnzusp9a9E51wa2ZGOr1Jt9t63CTTQvS93k4KXRwZ8izEaRGsewVDD6Z0nTt8V0sARR/67jFhz0h2fjuDYO0bEjshxGkhCUUhRBvV0TB/PLCZxVXJV54db35GAbcVjEJTFD5oKKci2vkNv6K0DHfuiU765zIH4jPb5GSeApPcvqTbFAcbu7VKuBC936lL4ecISSgKccolopWEGt5Oqa3de74sxCL6HHvaPBTVmlSbcONKErFaCd4ZJr9wHAUDx0sgRJ8mQ56FEADohsHyujIWV5cQ1bvfMb4saxCXZhWhAMFEnFdrS0/4/E6TiUnmnXxmG/Nk0Y9TZrwrg6fZl1SbiJ5gf6iJEQ6vBFD0XYqSXHW0JMmFOOUCNc+ltKqsotqw+y6XAIq+99WjObClzSNUtyyJ7x+dYM1ruPNukQCeQT53+c8kCKLPkwpFIQTFgXp+uX8Dz1Xu65Fk4g25w7jsaDIR4M26wwQT8c5vnugkoZjCvlyeXdTx0GnRK9JMFgptzqTb7Qo2SvBEX+/WJfd0qVAU4pSKBXcSbd6QUlt7+kJUzSNBFH2Sw7cg6ak3wg0rSUSrJHhCiFNKet1CnMUOhPz8qXQr9x7cQlkk0O3taYrCHQNGMz/jkwmA6+MR3q0/cuIPog6rE42kqxMH2d2Md2fKiT3FxnRhYujjFQcaJHCib0ty2LMhFYpCnDpGgubqp1Lr/JjSsKdfJDEUfbeDbkrD5p2dZCudQPXLEjwhxKn9vJIQCHGW3YPTUpH4p9Kt3FOyke09tECGTdX4zsDxTPNkt3l8ae0hYieo3FEAU0cddz21uRNlIZZTb7QzLek2B8LNRGRVXNGXKVKhKERfFWpcTiJyOKW2Dt/VSc9RJ8Sp5vBdnHRXPdK0nlhovwRPCHHKyByKQpwl/IkYaxor+bC+nPJIsEe37TPb+NbAseRb2w59rYtFWNlYeZI+u9q+426Q9HxkA21uRqVQKSe6b6jdJqlIBAAAIABJREFUjVlRT5g4Pl7CMNgf8qeUjBTiVFBQkyuSlgpFIU4JPd5AsObFlNpq1gHYvOdKEEWfp1mysHlnEm5clVS7QOWzpBX9GOQndiHEKSAJRSHOYIFEnC3NtaxvrGZHoI5EL3R4hzm8fH3AGNwmc7u/La07dNLX1NQObnhSqPRZkDlAbp1O1xeJojLU4aY4kNy8iLuDjZJQFH2XVCgK0TfvbaqfwdDDKbV1ZX0BGaAl+gtH5iWEGz8Cuv79EgvtJ9y4OoUh00IIkUI/UEIgxJkjYRiUhv3sDDSwvbmOfcEmdHqvauYz6flclzuswyHL9fEIq05WnYiC2lEaMMnEp89sY4wrQ94Ap9EIhzfphOKeYJMETvRdySYUkQpFIXpbNLCZiH9NSm0tzgmYHWMkiKLf0CzZ2NLmEG74MKl2gcrnsbjGo2ouCaIQoldJQlGIfsoAamNhSkPNHAz7KQk1sT/U1COrNJ+MVdW4OW8E073ZnT7nrbqyFKsTjaQTiuel53ecmBSnzAi7N+k2B8J+YoaOWVblFn1R0hWKEjIhevW+Rw/TXPl4itezhjPrCxJE0e84My8j0rgaw4h3uY2eaCZQ+Rzu/NslgEKIXiUJxRTtOHSQYCTM+KKhWExnbhiDkQhmk4ZZ67lj1A0D9WhH7fjtn+z/n210DPzxGLWxMLWxMNXRMJXRIJWREEciAcKnYVGLwXYPXy4YRbbF3ulzmhMxVjRUnHRbqtL96kSzojIjLVs+lE6zIrsLk6IST2LYZ9wwOBBqZrjDIwEUfZBUKArRlwSqn0GPp7aQnD3tQjRLrgRR9DuqOR17xgUEa5cm1S7cuBqLezJW90QJohCi15yVWZodhw5SfPggI/ILGTtocIfPWfzRCobnFzBmYPu/ryrexjcfvJ8vX3gJU4aOOGPjFInFmP3DO7nl/Iv43pXXdXt71Y0NfP+Rv7KttITJQ4bz569+h9k/vJPbLriY71x2bYevN/uHd3L93Pn85Nqb+lXs6mIR4oZ+dG0RgwQGcUMnYRjEdJ2okSCi60T0BCE9TijR8m9zPEZzIoY/EaMxFqUpHu3VIctJfVgoCpdmFfE5X2HHicBPea++/KQLdKiKgtJRh11P7ngnejKxqfLbyOl/f6gMsbvZHUxy2HOoURKKom+SCkUh+oxoYAvhxvdTaquavDh8l0sQRb/lyFxIuGEFeqI5qXbN5U9gtg9GNXkliEKI3ukDno0HvXzLRh5e9ioZbg+Lf/orPA5Hu+f84tnH+dL8i9olFCsb6vn5M4/xq5vv4OIpM+QdlIRnP1zOlgP7+O/rbmHsoMFYTCa+fdk1TCgaesYco24YLK09xNu1h2lOxM6Y4xrpTOPG3OHkWR0nv+nXdd5rOHLS52kdDXM1ku+Vz/DmyMXVRwyze5JOKO4NNoFPYif6IJlDUYi+cW+VaKK54uGU2zuzbkRR7RJI0X+/jlQ7zuyr8Zc/keS146fpyMOkDfwushiREKI3nLVlPXaLlWAkzL1LnuWuL9zW5XZltdXc95VvcU7hIHn3JKmsrgaX3c61c+a1Pnb7BQvPnOOLBHi0rJjScDNOzXxGHFOG2co1OUOY6snu8uC/1U2VBBInnudFobPhznqS+2djqEN+de0rhjs8UJtcm30hf5tpEIToMxLxpJ5uGAmJmRA9zqC54p/oidQW8TI7xmB1T5cwin7PljaHUP0HxMMHkmoXCxQTqFqMM/tqCaIQosedtQlFi8nE7Qsu4f5XnueyabOZMmxkp88NR6M8/NZrTBs2khkjz2l9PBKL8Y9lr7Y+ntB1/vbGEs4dPY4Mt5tnP1xOrb+JkQUDuXHeBZg0jRdWvsemkr14HE6umHFuu8TkoZoqlqxZyaHqKmwWC1OGjmDh1JmYNA2AeCLBg0tf4dzR44jGYyxZs5KFU2cyZ/RYAPYcOczrGz6irLYGq9nMuEFDuHz6HGwWy0lj8saGNazcuRWAqcNG8bnJHd+AnWwfO/LAay+zq+wQsXiCB157mdy0DK6YOYeHlv6bKUNHMGtU56vuqYpCVWM9z614j8M1VWR507hm9jyKstvOhdOVY4/EYizduIbiw6UEImGyvelcOGkaI/IHpPxe0g2DN2pLea36IHHjzKhQcWpmFmYO5DMZ+UktmKFj8E592Umf12nyKMkFZSZ5MmUplj5kiN2NipLUMP2InqA0EqDIJisRij6WxohHOnhUaalcNAzaVSSeggWxhDjbhOqXEg1sTqmtolhw5XwJ5E5BnBFU3Hk3UV9yD8lWxAdr38BkK8DqkdF1Qoie/mQ6S0XiMb74mQWMyC/kl88+QTTeeSVCOBbl4WWvsm7vrjaPR+OxNo8risLDy17l5Y8+5Ft//xPhWIxQNMKf/v0Cv3nhKf77yYd5b9sm3HYHy7ds5Ev33UNF/SeTS3+4fQvX/Pr/8d7Wj8nNyMDA4O7nnuArD/yudf90w+DhZa/y+oaP+O7DD7DnyCFqm1qGGL6xYQ3X//7nrNyxldy0DFRF5d4lz3HjH39Jcyh0wnjc98rz/OSJv1PV2ECmJ41X163irmcebfe8ruxjR47U1RCKRjAMnSN1NdQ0NRCLJ3h42aus3bPzhPvWEGjm9j/9lvK6WjwOJ6+sXcX1v7uL0urK1ud05dgjsRi33v9r7l3yPKFoBLfdwbo9xdzwu7tYunFtSu+jI5EAvz7wMUuqDpwRyUS3ycw1OUP49fAZLPANSHr13a3NdVRHwyf/4OmhVX0nuTPlU7wPsaoaA23OpNvtCjRI8ETfu0GytZ/b03X+10i74Y+4Fny7/ZBomctViB4VCxUTqH4+5faOzKvQzLJomzhzmGxF2DMuSKlt05HHiAWLJYhCiJ79XDpbD1zXdTRV5X9uuIWb7/0V/3zrNb5+8RXd63wc7Vws3biWF3/yCwb4sgD4xoP3sWTNCq6d8xl+fctXAbh02ixuue8e3tmykZvmXUAkFuP/PfVPRg0YyD+//aPWVY0/N3k6d/7tPp5bsZwvfuZCzEerAF9b/xH33/Etpg0fBUAgEubuZ59g0pDhPHjn91rbXzFjDrf/+bc8+s7rfOvSazrc75qmRp58dxnzx0/mj7ffiXL0OH7+zGNtntfVfezIPTffwY8ee4iPdu/gnpvvAFpWcO6Ktzat54nv/qy1mnPBxKnc8cDvWbZpPV9ZcEmXj33jvt3sPHyQv379u8weNfaTfXv+STbs3dVpRWaH7x8MltUc4pXqg0mtattXFdpczM8oYLo3O+kk4qe9XXfy6sROhzsnWd3jM9sokKq2Pme4w8uBcHKThu8KNnKRb4AET/RpliEzMOW2jGYwZRZhHTmPSPF7EhgheuM+PVaL/8hfgdTusUy2wdjTLpRAijOOM+sKos2bSUSrkmtoxGk89BfSBn4Pk32wBFII0SPO+tlZxwwczA3nfZZH3nqdksryHtnmjBGjW5OJAMPzC9ENg2tnfzJ34Ij8QqBl5WOAtbt30hBo5sZ5C1oTYgCzR41lUHYO72zeCNCa7Buam9+aTARYs2sHgUiYa2fPa9N+8tARDM3N5/1tnQ8XWb93FwldZ+HUma3bB/j8uee3eV5X97GnzRx5Tpuh4ROHDGsTu64ee0Jvmd9q64H9bbb/089/kZ9dd3OX96ciEuS3JR/zUlVJv04m2lUTc9Pz+Ongyfz3kCnMScvtVjLxYLiZvaGTz3GkdPYaSVZ4jnP7ZBBTHzQihRWb9wSbzojEvDiDb5bsXuyTr2zzmG38QlR3lgRHiB5m6GGajtyX8ryJimLCnXsHKJoEU5xxFNWKO/92UhnKb+hhGkrvIx4qkUAKIXqEjM8BvnHJVbyzeQO/WPQ4j3z7R22SaqnISUtv2+kwtyzQkf2px4/N6xeLt6wEfLC6AoBX1qzkg+1tk3/BSISmYEWbxwZlt13Z9lB1y69Ug7Lar3hbmJnNR7t3dLq/FfUtqyjkZ2Qe165tRynZfewpx+/XsaThsQRhV4991qixfHbCZP72xhJeWbuSWaPGMGf0OOaMHofFlOyl0D9TWVZVY7zLxxRPFuPcGd1KIB7v7brDXeuYdxa6JBOKY1wZ8uHVBw1zeFFIbnafmKGzL+RnpCywI/oo+7TPo5htJBIJtmzZwoQJE1A1M44ZN9D89gPICs9C9BAjgb/8r8Qjh1PehCPzWjRLvsRSnLHM9qE4sy4jUP1K8peYHqKh9I94BnwDi3O0BFMI0S2SUAScVhs/vvYmvvvwA7y0+gOumT2vW+kiVe04SaOpnSdvYvGW5JjLbsdtd7T52/zxk9tU3gE4rLa27Y+tRtlBMtRsMhFPJE762scvqqKpWrf2saecLMHb1WPXVJU/3HYnK3du4+3N6/lw+xZeWPk+Wd40fvOlrzFl6Igu7U+u1cGPBk9kWc1hXqk+0Ocrq3KtDs5xpjPOlcEIZ1qPJhGPqYtF2Ojv2vK+Wkevn2QMbaqJIXaPfHj1QXZVY5DNlfSw5+2Bekkoij7JUjQVc0HLwmHFxcUcOXIEu93OqFGjMGUNwTriXCK7P5RACdFtBv7KR4kGtqS8BbNjDPb0iySU4oznyLyEaHA3sUDy8yIaeoTGQ3/CnXsztrQ5EkwhRMokoXjU+eMmcf64Sdz/ygvMGzsRU0fJseMqqBqDgR57fZ+7JTly5cxz28zv11UZR9vX+Rvb/a2+2U+as/O55lx2OwD+ULDN47XHbau7+9hbkjl2RVE495xxnHvOOAC2HNjHz/71MD9+/CHe+sUfu/yaKgqfyyxkgtvHI0eKORjy94lYKECBzcVQu4dhDi8jnWmkmSy9/rrv1Jd1aWVftbNUfZLFPSOdaWiKDHjuq0Y705JOKG5rrufqrCIJnuhTFJsb+5SrAKiurubAgQMA7Nu3j6ysLHw+H7YJlxI7sgM92CgBEyJlBoHqZ4g0rUh5C6rmbhnqLBOiiLOCiif/DupL7kaP16dwySXwlz9GPFKKK/vzoEhaQAiRyieRaPXja29C13V+99Iz2C2fJGGO/e/6QNsO8vo9u3rstY/NC7hq5/Z2f1u+ZSPldSeu/po4uKX96l1thzaHohG2lx5o/XtHhuUVtHToD7adT2PNrp09uo+9pavHvvXgft78uO1qzuOLhnLZ9NlUNzacdCXsjuRZHfykaBJXZg/GdIoTXDZVY4jdw7lpedyQO4wfFk3kz6PO5X+GTOGmvOHM8GafkmRiMBFnZWNl1z5wOhvvnORw55HONPnA6sNGp3B+jkSC1MTCEjzRdygKjqnXolgcRKNRNm9uO9XH5s2bicViKCYLjuk3dHu6FCHOXgaB6ucI1S/rzgWLK/cOVJPcH4izqCNv8uAtvBNFMae8jVDdcuoP/JpEtEICKoRImvwU8Sk5ael889Kr+e2LT7cZtmw1Wxick8fyLRu5Ysa5FGZm8fH+PSxZs6LHOhADs3JYMHEqz61YTlF2LhdMnEI8keCFle/x4NJX+OVNX+ay6bM7bT8kN595Yyfwwsr3GVNYxHljJlDrb+KPi58lEo9xy/zPddp20pDh5KZn8MjbrzM0N59RAwayqWQvz65Y3ub4uruPvaWrx77tYAm/f3kR9c3NXDBhCnaLhZLKCt7YsIahufmtlZpJf5krCgszBzLB7ePRsmJKk6zM6oxDM+HRLKSbrWSYrWSYbfjMVrItdrItdtwmS5/4Df6DhnIieqKLseqZBVlGOKTD0JcNsXuwqlqX3xfHfOyvZUFGgQRQ9AnmgRMxF45v+f7Yto1IJNK2ExYKsX37diZOnIgpZxiWYbMlaEIkzSBQvYhQ/Zvdu2fyXYHFOV7CKc6+zrytCHf+l2kqe4hU5/ONh0up3/8LHFlX4MhYAIrUHAkhuvgZJCFo6/q583lt/ep21Xr/ff0t/OyJf3DLfb8CWlZZ/sPtd/KFP/yidXGQ7vrlTV/mdy89w+9ffoa7n3sCgEyPlx9dc2OXEnX33PJV7nnuSf7n6Uda5w0szMzmvi9/k/FFQzp/E2ga997+DX70+EN886H7gZaFUH7zpa/xtb/8gfinjq+7+9hbunLsN8ydT21TI3957WV+88JTQMsQ6Dmjx/KDq77Q7X0osDr56eDJLK09REmwCUVRUGhJOKqASVUxKyompeVfm6ZhU01YVQ2HquHQzDg1E07NjNtk7pW5DntazNBZXn+kS89VlE4GISWZTEw3W/FZbPJh1Ze/WBSF0c40NvmTq1re0FQjCUXRZ9jGLwTg8OHDlJeXd/icsrIysrOzyc/PxzZhoQRNiGQYCZorHyXcjWHOABbneBy+KySe4qxl9UzBFb+e5spFqV+ORoxA1QtEmtbgyrkOs2OUBFYIcfI+vmEYsjRhlz9oDaoaGzAwyE3rvRVmI7EYlQ112CwWMj1pqElWQYajUSob6nFYrWR5k6vkOlJXg4JCbnrGCasvu7uPvaUrx67rOjX+RiKxGNneNKxmi7y5U/R+QzmLKvd16bmaomA6bqGfoyek5b8umubN5qa8ERL8Pm5VYxVPlO9Jut0vhkwm22KXAIo+IRQK8cEHHxCPxzt9jtlsZu7cudjt8r4Vosv31HoIf/lfiQa2dms7miWftIH/D0WV60+IYM2/U1r5uSMW9wScmZdjsg2UwAohOiUJRSFESuKGwf+WrKcuFunS882q2vGQ50QiqSrF63KHMTstV05AH9eciPGDPWuTHnxzsW8AV2QNkgCK084wDD766CPq6upO+lyfz8eMGTNkHkUhuiARq6Gp7D4S0bLudWI0J2kD/xfNnC1BFaLlm4tA9WKCNa/32BYtrgk4fBdidgxHFjwSQhxPJkgQQqTko6bKLicTAZROh3Anl3IaYvdI8PsBl2ZmpNObdLtVjVXE5Xcu0Qfs37+/S8lEgNraWkpKSiRoQpxENLCZhtL/7X4yUTHjyf+uJBOFaHtl4My6EmfWVT13zTZvpuHg76kv+SWh+vcwEkEJsxCilcyhKIRIWtwweKP2UBI3/kon8yeSVD7RoZnIsTrkBPQTU91ZFAcak2rTGI/ysb+GaZ6sfnWssViMpqYmpOj/zBCNRtm1a1dSbXbt2oXdbsdsNksAz4RuuaLg8XjkfPYUI0Gg9mVCda/2xNnBnXcnZvswiasQHVwfjsyFqJoTf8XTgN4jW42HD9Fc8RTNlc9idU/A6p6CxTVWphsQ4iwnCUUhRNJWNlYkVZ3Y+Rybyd3kDLS5ZLBFPzLZ7WNR5X7iRnLn+c3aMqZ6svrNuT5w4AA7d+5E13U56WcxXdfZuHGjBOIMomkao0ePZtAgmYahOxLRI/gr/k48fKBHtufK/TIW1yQJrBAnYEufh2rJpOnwQxh6qOc2bMSJNG0g0rQBFBMWx0jMztFYnOdgsg1AhkULcXaRhKIQIikRPcHrSVQnAqid3VwkWc1VaHPLCehHHJqJCa4MNvhrkmp3OBJgk7+WSW5fnz/Gmpoatm/fLidbiDNQIpFg27ZtuFwufD6fBCRZRoJQ/VKCtYsxjFiPbNKVcys2z7kSWyG6wOIcQ/rgn9J0+G/EI0d64RqPEw1sJxrYTgBQVDtm+xBM9iGYbYPQbAPQzBlIkrG9aCSIZjKjaVIFL/o3SSgKIZLydn0ZTfFoch80qoLeUe4wydGhA+0uOQH9zNy0nKQTigCLqw8yzpWBqY8vcnHo0CE5yUKc4crKyiShmKRYaA/NVY+TiBzusW26sm/G5v2MBFeIJGiWXNKKfkpzxdOEG1f16msZeqg1wXiMotrRrLlo5mw0SzaaJRPVlI5mSkM1p6GoNk5XwrGu5iD7dq+kqaECAKfbx8CiyeQXjuvV141Ggvz6Z5OZc/4dXHDJ9+VNKvo1SSgKIbqsPh7hrbrkJlJXlE6SiZB0heJAqVDsd0Y608ix2KmMJjfcpjIaYnndES70FfTtTnMsJidZiDNcJBKRIHRRIlZNsOY5Iv51PbhVBXful7FKZaIQqV1BqhV3/m1Y3BNoLv8XeqL5lL22oYeIh0qIhzpZuEwxoWpOVM2FojlQVCuKYkFRraBaUFBBUQENs2MoVvfk7u+TYbB08d2sXfU0DkcamdlDUTWN2uoDvNn4a4aOPJfrbvkzlh6at33/nlVUHtnFrHm3AWAyWfjswu9ROKhvTt1w/P4KcSKSUBRCdNlLVSVE9ERSbdJNVgJ6vNuv7dLMeEwWOQn97SYWOD89j0WV+5Nu+++aUia4M8ixyITfQgjRl+nxBkJ1rxFqXA5Gose2qygm3Hlfx+KaIkEWopus7smY7cNprlxEpGlt39gpI44eb0SPn3wRv0ijnUS0CofvQkBN+SV3bH6DtSufYta827hg4fdRtZaUiGEYrF35JEsX/4oP33mQzy78Xo8c4o7NS2lqrGpN0KmaiXPnf7XPvk+O318hTkS766677pIwCCFOpjjYwMvVB5JuN8juoqGjIdKGkVSF4mCHh2nebDkR/VCe1cGHDRXEklycRcdgX6iJWd5stD469LmsrIxgMCgnWYgzmNPppKCgQALR0ed0vI5gzYs0V/ydWHgvSc9lcgKK5sJT8H0szrESaCF66rpSrVg9U7A4RxALH8RI+PvRzmtEm7cSC+zA7BiOqqU2FdKG1YsoO7SVG277C1bbJ9tQFIUBAydgsTjIKzgHX1ZR69+CzXWsWfEv1q96hh1b36Smch/pmYVt2q9fvYiaqv1YrA7eeeNeaqsPsG/XCrZveZNIuIlAcy2aZsLlyeK9Zf+HrifI8A1E1xO8++afMZksJOJR3n/rr2xev5j62lLyC8cCCutXPcOaD5+gZN8aXO5M3J62faK6mlJWf/AoGz56jj073yccaiI7dziK2pJ4/fRrKMCK5f9g45rnOXxwE970fBzOdADee/P/2u2vNz0fgKry3az+4DE2rnme3Tvfo7mpmqzc4Wia1KidzSShKIQ4qaiu88Dh7QSTrDQcZHPj1+OdVDUml1Cc6MlkpDNNTkY/ZFJUdGBXsDHptk3xGHWxCBPdvj45pbckFIU480lCsb14eD+B6kU0Vz5GPLwP6NlV7jVLDt4BP8ZkGyjBFqI3kgDmTOxp89DMGcTDBzH0cJ/fZ0UxgZFAj9cTbliBopox2weT7ByMVRW72b9nNfkDxpKdO7zd3wuLJrVJJlZX7OUff7qWI4e2kps/CovVybaPX2X1B48xZPhs3EcLHt5Y/EtqKvezad3L1Fbvx+5MJ9BcS/nh7aiqCZvdQ0ZmEd60PJ56+A7cnhyGjJgNwL8eug3DMFj57j/weHMIBRtZt+ppmptq2L75DSrKduL25lC87W3WrniSCdOuwnZ0Kqg9O9/jsb/dTLC5jvwBY4nFQqxY/nf27V7J+MmXoapa62soisa7b/4Zm92N3eFl26bXWLPiSSZNuwqrzcWm9S+3219fVhFbP36VJ//xFSJhP7kFo4nHo3z0wWPs2LyU8VMuw2SyykV1tvbzJARCiJNZXHOAmljyNxoXpBfwZNXejv+YZBFDvtUpJ6IfOz89j3fqjtCcSH7OwTVN1XhNFq7KLpJ1AoUQ4jQx9BAR/1rCjR8cTSL2DotzIu68r6KoDgm6EL1JUbGlzcXqnUm4/gOCdcvQY3X94/PIiNFc+TyRpo24829Hs3R9FNPEqVez+v3HeOGp77Nz61uMHDOfgUOm4k3L6/D5r7/8CwzD4Kvffam1MnDeBXfytz9ezhuL7+bL31oEgKZZOFy6mVnzbuMzF36ztf2BfWvJzh3B1Tf+HmhZlKXNaVBaqgi3bXqdO//r36T7CgF46uE72LTuJabMup5rbvojAOOnXM4//+8Gire+xYy5txCPR1i86CfkFYzm1jufbF01euzES3jyH19h3aqnmXnera2vsWn9S9zxnefJzR999HkLefQvN7Ft0+vMmncbV97wmw72N8CrL/wvAwdP5uavPtL6GpOmX82jf/kiK5b/o8eGh4v+R5UQCCFOpDjYwLv1R5JuN96VgaGc8E4gqe3lWqVj0Z/ZVI2FmYUpt19WV8YLVSU9OJhOCCHEyXvtCaKBrfgr/kHdvu/QXPloLyYTFRy+K/EUfEeSiUKcQopixp7xWXxD78Gdf1u/qgyOhfZRv/8uQnXv0NVqBafbxx3feZ6JU69kT/H7vPT0D7j/7vO5/+7zef2lX1BVsaf1ueFQEwf2rWX0uAvbDDN2uDIYPf4iDh/cRDBQf+wjjEQ8yuwU5x4cMnxmazIRICdvJIahM3Xm9Z88lj8KAH9TFQAle1YTDNQzY+4trYk+gKEjz8WXVcTOrW+1eY3Bw2a2JhMBCo6uaN14gr7e/j2riEYCTJl5fZvXGDh4Klm5w9i94125iM5iUqEohOhUUzzKo+W7km6nKQpXZw3m/YbyE3RSup4aUlHIloU5+r3z0nL5oL6ciiRXfD7mnboj1MUifClvOLajwzeEEEL0LMOIEQvuJNq8gUjzBoxTsCKsakrHnftVzI7RcgKEOF0UDZt3NjbvLOKhA4Tq3yPi34Ch9+2V7luqFRcRC5XgKfgyXRkC7U3P54rrf81l1/6SI4e3cWDfWvbvXsX6j55lw0fPcvl1v2LC1CupqzkI0GYI9DEZR5N/DXWHW+cgdHmysKQ4qsrjzW3z/01mW8vjaZ88bj76WPzo/PS1R+e337TuZXbveK9N+2gkSCjU1Oax9IwBbftsRxe8jCeine5XXU1pSwwyO4rBQPbvXiXXzllMEopCiA4lDIN/lu+iKZ78ENUL0gvIsdjZH+5soufk6swyLTbMihRU9/svHEXhC7lDua90W8rb+Nhfy+FwgFvzhzPU7pGgCiFE97vjJGLVxILbiTZvJhbcgWFET9mrW1xTcOfchpLiAgtCiJ6mYLIPxm0fjEu/kWjzFsKNa4gGtvXoKu49yZY2B1f2dSQ7n6KqmRgwaCIDBk3k3Plfpba6hH89dDtvLL6bcZMuJXF0qh6lg8UBj1XrJRKfzDFv6caIKqWTH8s9AaxhAAAgAElEQVSVE/SBju2fzebGdtx98ahxC9pUFJ7oNU7YJ4yfOAaJJOfYF2dY/05CIIToyPNV+9mdwiIaGWYrF/sKCSTinVeiJTl3uwx3PnOMdHiZm5bLhw0VKW+jOhbm9we3Mtubw+VZA0k7+uuqEEKIrjBIRMuJhfYRC+0kFixGj5/6edNUzY0z+4tY3dOTTgIIIU6NllWhp2H1TMPQw0QDO4k2bybavBU93nTa9081eXHn3YLFNb7LbfyNlSQSMdKOq9YD8GUNZtzky1ix/O801JfhdPkAaPbXtHvusaHOx6oTTwenKxOASdOvYejIc3vnNdwZJ4zB6Tx+cfpJQlEI0c7bdWUnHq58AjfmDMOqauw5Np9IJ52ZZMhw5zPLNdlFFAcaqI51b0XBVY2VrG2qZk5aDvPT88iR94kQQhz3dZsgEaskHjlMPFJKPLyfeLgEQw+d1t2yembjyrpRqhKF6EcU1YbVPQmrexItP0xUEg3sIhbcRTy0n0Ss9pTuj807C1fODSha1wsPYtEQf/n9paT7CvnKt59tV8EHcOTwNlRVw+nKwGJ14nT7OhzWW7JvDQ5XBhmZXZlzsndmAR84eDIAe3etaJdQLN72FnkFY/Cm56fy5dH6vwqLWl5j/+5VDB89r00syw5tZdiouXJxnMUkoSiEaGNNUxUvVpek1HaGJ5sxR3+l2h/quV8tcyxSoXgmsakadxSM5HcHtxA3uneDFTd03q8v5/36cobZPUzzZDLelUG62dpnj3///v2Ulpa2e9xsNmOz2cjMzCQ/Px+z2ZzUdvfu3cvhw4cBmDZtGk5n53P46LrOBx98AMCUKVNwu92tf9u4cSNNTU0MHTqUwsKTL6Szb98+Dh06hNVqZdasWSd9fnl5Obt27UJRFGbPnt3l4wyFQhQXF1NWVobf7ycej2MymfB6vQwYMIDRo0e32Zbf72fDhg1Jn5/Ro0eTk5PT7vFwOMyePXs4dOgQfr+faDSK1WrF5/MxdOhQioqKOt1mXV0dW7Zs6byTpihYrVYyMzMZNGhQhzH59DnrqqKiohPuV0cOHTrE3r17qampIRJpmTfLbreTlZXFiBEjyM3N7fH347H4aJrG3Lld65icqnj0F4YeJBGtJBGr/OTfSBmJaBmG0XeGo5msg3Bm34TZPkK+DIXo1xQ0Sy52Sy729JYkk55oJh4+SDx0gHjkCIloOYlIBYYR69FXVk1e3Lk3Y3FPSLqt2WLn/Iu+xdIl9/DYX7/I9HNvJjN7CJpqoqH+CB+vfZH9u1cxc+6XsNpa7o3mfOYrLPv3b3nn9XuZOfdLGIbOutXPcHDfOhZc+oMTDklu+Q71UlW+m+rKvVhtLmy2npuyJyNzEOeMv4h1q54mM2swo8dfiK4nWP//2bvv+DjqO3/8r5ntTVpp1btsy73JtowbBowBUxxsA4FAAgFCLsmRXMpd2uWSX/LNES7hci0NAiEkJBSDDca4UGxwb9hyk1wkW1bv0hZt35nfH7IWhFV2Vqu2fj0fDz/80O58ZmffMzs7897P5/Pe/zI+fOc3WHPfk5izYI2idX56e1PTJ2Hy9Btw5MAryMqdicnTr4fL2YZ33noSwaAfS657lB+HqxgTikQUdsjRjBcazkXVNkmtw73pEz5OMvQ7fyIUV3hO17HnWbzJ05txf8Yk/KXhfMzWWeFxoMLjwEtNF5ChNaDImIgCvRm5ehPStQboxkghl6qqKuzbN/AE1mq1GjNnzsSyZctgtVoHTybIMrZt24bOzs5w+2XLlg2YjNmzZw8AYMqUKb0SisePH0ddXR30en1ECcXU1FRs2LABPp8PBoMBc+fO7XfZUCiEjRs3or29HQsXLow4mXjs2DG8++678Pu753UTBAEajSb899GjR/Hee+9h3bp14YSRy+UKv0clbDbbFQnFvXv3Yt++feEE26ft378fqampWLNmTZ/JyI6Ojoi3RavVYvny5Vi0aFG/+yzimy5RjDiB1tXVhQ0bNuDSpUsf33hpNAiFQpAkCeXl5di1axdmzJiBz3zmM1CpVDE7Hnvio9FoFCUUhzMeY4MMWfJCCjoghZyQQ93/SyE7pEA7pGAbQoF2SMH2Ue9xOGjsVQkwpqyFPvE6AJwTmSgeiSoztKYZ0Jpm9DqPhQLtkAKtCAXaIAV6zluXz2tBZ/f/ERZ+0SVcA3PGfRCH0Lv5mmsfhCUhDft3/Rkb/vbPvZ5LSZuA29b+GxYsuT/82OLrHkYoFMDenc9iz45nAAB6QwJW3PotLL7ukUFfr2Tp/dj82k/wu1/dgXnXfBa3fOb7MY37ms/9B7a98QS2vfkENr/+EwCA2ZKCW9f8SHEysa/tXX3Pz3DXA0/h7Q0/xRuv/ADS5Tkjk2x5uO+Lv0FO/hwe/FcxQZZlmWEgol2djXi5qSKqDvkCgG/mzsJkYyIAwC9J+P7Fw5D6O70ElfWWeHLyIuhF/v4Rjza2XML2ttoRea0EtRbJai0S1FpY1BroRRFqyY4uz1l4AnWwGQr7basWNFiZcROMan34sebmZpSWliIQUPbL+44dO7Bv3z5kZWXhuus+MXQkEIDL5UJtbS3OnDmDYDAInU6HtWvXYtKkSQOus6KiAi+//DKys7NRV1eHxMREPP74431OoN39EQziySefBAA8+uijyMzMDD/3/PPPo66uDitXrrwiqdWfo0ePYsuWLdDr9fjqV7/ab2+0Dz74AHv27EFSUhK+/OUvR5RQ7HlvgiBg0aJFKC4uRlJSEgRBQCgUQnV1NT788EPU1tZCq9XiK1/5ChISEuD1enslx3qcPHkSZ86cQVZWFpYuXXrF85mZmUhI6O49IMsyNmzYgPLycgDA1KlTMWvWLKSlpUGr1cLtdqO6uhoHDx5Ee3s71Go11q5diylTpvRaZ2VlJV566SWo1Wrcc889fe4Ph8OBsrIy1NTUAABWrFiBJUuW9LnPVq5ciaSkwecsSklJgc1mi2gf9uz39PR0XHvttZgwYQK02u75SR0OB06ePIkPP/wQkiShpKQEt9xyS8yOx574aDQafO9734toe4c7Hp+k1Woxd+5cpKamfvxgKAD7odch+/pP5AXqm6CyGiEmJQAqQFAJELQiBKMAwSADqiBk2Q9Z8gNyALLkhSy5IYU8kCU3ZMmL4RomN2I3GioTjEm3QW9dCUHUgYioT3IIsuS7fB68/D9CgCxBlkMAJEDyQ2spjunLhkIBuJytCAZ8MFts4V6JfZGkEOwd9RAEAYnWTEUFTnxeJyQpBIPROmwhDAZ9cHQ2QqPRw5yQOmjPyWi2NxDwwtHZCK3OCEtCGo9bYg9Foqv++xvAW62XsLWtJup13GbLCycTAaDK5+w/maiQWaVhMjGOrUnNR1cogD2dTcP+Wo6gH46gHyKCSJcrkI7z0Mmu8Jeh3dn3UP+AKgM35TwUTiaGQiGUl5f3maxSwmg0YuLEiVc8vmDBAnR1dWHz5s04f/48XnvtNTz44IPIyup/Dpyeob3XXHMNDh48iLq6OlRWVg6aiIyVefPmoby8HBcvXsS2bdtw1113XbFMS0tLuGfmHXfcEXHvxMOHD4ff24033tjrOZVKhcLCQuTm5uKZZ55Be3s79u/fj1tuuQV6vf6KxB4A1NXVdZ9bzOY+n/+kvXv3ory8HCqVCmvXrsXUqVN7PW8ymZCamoq5c+fizTffRFlZGTZt2oQvf/nLSExMvDK5Igh97vMeJSUleOedd3Do0CHs2rULJSUlfcYpPz+/VxJ4qOrq6lBXVwe1Wo0HHngARmPvaSYSEhKwdOlSmEwmbN68GR999BGWLVvWb+J4pI/HWMfj0/x+Pw4dOoSCggJMnTq1u3emSgPj1CXoeOtFhOr7r3oarLYD6KfAmUaCoA8CCQGICX4g0QchwQ/BHMT4TySaYbCuhCHpZggipy0hokFPGhBURkXzIcaCSqVBojWy7w9RVCHJlhvV6wyUqIxZYketQ3JKfkzW1d/2ajR62FILeLzSx8cdQ0B09fJKIfy54RyOu6KfRHmq0YrbUnp/uVZ6BhrurGz9qSy0Ed/XjwDuz+hOsIxEUjEBzZgoHYAWXYMuK0GN1IQbcUfOHRAvVyC12+04duwYurq6hnU7TSYT7rnnHvz9739HVVUVtm/fjocffrjPZe12OyoqKqDValFUVAS32426ujocPXp0xBKKAHD77bfj6aefRnl5Oc6fP4+ioqKPP/ayjM2bN0OSJMyfPx/5+ZFf8La3d1efzcnJGeAiWo01a9bA7/f3OeQ4Gh6PJ5wAvf76669IJva+IVHhzjvvRGtrK5qbm7Fz506sWbMmqtddtmwZDh06hGAwiIaGBuTl5Q37vuuJcVJS0hXJxE+aPXs2tFotUlNT+11urByPw6Gqqgqtra2YO3cuEhMToUnKQ9qDP4Tj0Gtw7ymD7FdYqTggQg5oAacWobpPJGd1IYhpHgipbghpHgjG4LiJkahOhiFpFfSJ17FHIhERUZzjJCZEV6mLHif+verYkJKJqRo9vpQ1NZxs6VE5YEEWSdlraPXcWXH/RSTggYxJuMWWM6yvk4oLmCrtiCiZGFRl46bC7+IzOashQoAsy6ioqMDevXuHPZkYjosohoeV1tXVob6+vs/ljh07BlmWMX36dGg0GsyYMQMqlQrnz5+H0+kcsf1otVqxYsUKAMDWrVvD8xsCwJEjR8JDXz/dy3AwBkP3jwp9FbL5pKysLBQUFISXH6qysjL4/X4YDAaUlJQMuvwnC4qcPXu21/tXQq/XQxS7L8+UDqePVk/MOjo64HK5Bjwmp0+fjtTU1H6HL4+V43G4uFwu7N27FxUVFeiZNShh4d2wPfIo1Dkx+p3ep4JUY0boaBqC2/IR3JED6bwVslc1ZuOiMU6DJetxJBf+6nKvRCYTiYiI4v8+joiuKgFZwputVXiq+gRaA97ob0BFNb6WMx0mlfqK9Vd5+78hVdpDMYU9FK8KAoC1qfl4KLMIakGI+fpTcBETpIMQBjkAJaiRYr0Nj075PvIM3UOM3W439u/fj7Nnz2Kkpx1OTU0Nz9tWVVV15fZKEkpLSwEgXAzFYDBgypQpkGU5/NxIWbBgAXJzc+FwOLBjxw4A3fPv7dy5E0B3L8aeefkiNX36dADAoUOHsH379nChj+HWk8CcMGEC1OrIEkWTJk2CKIoIBAKDJkD7U19fD0nq/uElOTl5RN5rfn4+TCYTgsEg/vKXv+Ds2bMIhUKK1zPWjsfhIssyzp49iwMHDsDj6Z5DUWPNRurnfwDzyhkQtFJsX69Th9BJG4Jb8hHakwW50Xj5rDnKNxEqCwxJNyOp4Akk5nwPOvMCQFCBiIiIrg4c8kx0FTnd1YFXmy+g2T+0apBqQcTXcqYjQ3vlkLdqrwtBOXY3Uyka9lC8mixOTEOOzoRn68+iyR+bqqUmuQ0T5EODLhdS5+LW3C8i2/DxkNmamhqUlZUhGBy9IYfp6eloaWlBR0fHFc+dPXsWLpcLKSkpvYYEFxcXo6ysDMeOHcOyZcv67U0Wa4IgYPXq1XjmmWdw5MgRzJgxA/v27YPf70dxcTEmTJigeJ0LFy5EfX09ysrKcPjwYRw+fBg2mw35+fnIy8tDXl5euIhKLPUkLnsV4hiERqOB1WpFe3t7VL3x2trasHnzZgDdSb7+Co2cPXsWjY2NAx/3JhMmT54c8XavW7cO69evR3t7O9avXw+tVovc3NxwjLOzs8M9J/szWsdjrOMRqfb29nDl6573a1mwDoaiReh4+y8IVse6h6kAqdkAqdkAIcEPVVEnhDwXIIzcDx2CoIbWXAxdwlJojbOYQCQiIrqKMaFIdBWo9rrwZusllHV1DHldoiDgsaypmGTo+wb+/IDDnQEo7OHFHopXn1y9CT8smIO3Wqvxfnv9kEoTiAhhEvZBiGCo/aOTvwvhcq8fv9+PkydPDpqkGAl6fXdS3eu9skdxT/GLnt5gPQoLC2G1WtHZ2YmKiope8xkOt+TkZCxfvhw7duzAq6++Co/HA4vFgpUrV0a3D0UR69atw6xZs3DkyBFcvHgRbW1taGtrw9GjRwEAaWlpKC4uRnFxccS9CQfj8/kAADqdLqr91dfQ+FAohE2bNvX5uN1uR11dHWRZhs1mw+rVq/t9jT179gy6HZmZmYoSaPn5+fjqV7+KgwcP4tSpU3A4HKisrERlZWU4DkVFRVi8eHG/81SO1vE4HPGIVDAYxPHjx9HU1IRZs2ZBq9VCnZiF1Pu/j4anfgoEh2cwkOzQIvhRGoSzSVDNboWQ4cFwFXIRRD20pjnQmudBa5oDQeQPfURERMSEIlHckgFc8Diwvb0WJ13tsbmpAPBo5hTMNvc/DK9i0ISistdkD8Wrk05U4e60QpQkpGJ980VUuB1RrSdLLoNedkX8mRHGYCx6hlR+ughGe3s7qqqqIIoiZs+efUW7uXPn4oMPPsDRo0dHNKEIAIsXL0Z5eTkaGhoAdA91VpqY+7SioiIUFRXB7/ejpqYG1dXVqKmpQV1dHZqbm7F9+3YcP34c9913H8xm89CPwcvbq7R3as/yfb1fSZJw4sSJ/s93KSmYOXMmSkpKBozX4sWLB+2V2V8F5sHarFixAitWrEBbWxsuXboUjrXdbsepU6dw+vRp3HLLLViwYMGYOR6HKx6Kvh+F0Tl7yC4NgvsyIaS5oZrbCsEcm16RKm06NMZZ0JpmQWOcDkHQgIiIiOiTmFAkijM+KYRjzjbs7KxHtdcVs/WqBQGPZk3FXLOt32X8soSL3sGG+UWeUTSo1DCqeJq6muXrzfhO3iyccLXj7dYaRce0Gj5kymciXv65s0/gppyHUGDKgVarxfz581FbW4vTp0+P6pDnnqHOFoul1+M9vcEMBgN27dp1RbueHnIVFRVwOBzDMiy4P4IgYP78+di8eTOsVmtMq/tqtVpMnDgREyd2Vwf3+/04ceIEduzYgcbGRmzbtg133333kF+nJylpt9sVtesZ6txXUlOj0eCxxx674vEdO3bgzJkzsNlsWLZs2aCvMX36dGRmZg7rPrTZbLDZbJg3bx4AoLGxETt37kRlZSW2b9+O3NzcXj0VR/N4HIl49HueUasxc+ZMZGdnhx8LtFejY/OLw9Y7sc9v1mYjgu/nQDW7HWKhA0p/vRPVVmgMU7r/mWZBpUkFERER0cD3W0Q07gVlGefcnTjibMVRZyt8Uiim69eJKvxD1jRMM1kHXO6Cx4FQDItW2Ng7kdDda3COORmzzck409WJDzsbcdzZNujtcrpcARGRJwLVoQa8f+lXSLbcgM/kroYKKuTk5CA5ORmlpaV9zmE43BwOR7i6c15e3sef+WAw3NOtq6sLR44c6T/RIMs4duwYrrvuupHdb5d7bA13zy2tVosFCxbAZDLh9ddfx7lz5yBJ0qDz/Q0mMzMTZ8+e7bMYTn9aW1vDPUr7S3D1VWhl1apVuHjxIs6ePYuysrJwIZqxJCMjA/feey+effZZNDc348yZM+GE4ng4HodDcnIy5s6d26uyuH3/y3DvOwcERqHHYkhE6FgKpHoj1PObAX0/1wKCGmpdDtS6AmgMk6E2FEGlScHY7KNNREREYxUTikTjVGfQjzPuTpR1deCUqwMeaXh6UCVrdPha9nRk6wYfLnbWPUhPHoXJRiYUqdc9MIBpJiummazoDPpx2NGCjxyt/VQVl5EqX1D8GiJC6HS+h+fOnMSKnC9gkrkARqMRixcvRmVlJc6dOzeilZ737dsHAEhMTOyVUCwrK4PH40FiYiLuu+++fttfunQJ27ZtQ2lpKZYvXz5qwzKjJUkSmpubIUkSsrKyBlw2Nzc33MbpdCIxMXFIrz19+nR88MEHaG1txcWLF1FYWDhom55eejk5ObBarRG/ltlsxg033IBt27Zh27ZtKCgouGKI+3ByuVxobGxEfn4+NJr+h7aKooicnBw0NzfD4XBcdcdj+FwkCJg6dSoKCwvD78HfdhGdm/+OUIOE0U7MyU1GBD/IgXpJA8QkPVS6bKi0WVDr8qHWF0CtzQIE3gIQERHR0PBqgsYMX6B73h+dRjMm1zeagrKERr8HVR4nLniduOBxxKwC7kCKjIl4LGsqLKrIYngm5glFHT8Y1CerWoubkrNxU3I2OgI+lLs7cdrViQqPA/agH2a0QYfoh/xrpCbsqv41Ss3XYk3OOqhFFSZNmoTU1FSUlpbC5XIN+3s8ffp0uKfX9ddf3+u5Txa/GKgKsc1mw65du+B0OnH+/PlhKUoxnHbv3o3du3fDYrHg8ccfh0rVf0XZtrY2AN1Jr1jMl5ecnIzJkyfj3Llz2Lp1Kx5++OFePdE+raamJrxfFi9erPj15s+fjxMnTqC+vh7btm3DunXrRiTGgUAAv/nNbxAMBnHLLbegpKRkwOV74vzJIfhXy/HY877nzp37iSHbMjr3/A2eAxeA4NhJkMpuNYK7JiDpzrugyykCERERUayJDAENp9q2Fmw4sAe1bS2DLvul3z2Fh//vlzF77Vivb7iFZBntAR8qPQ4ccDRjU+slPFd/Bj+rOop/Orcf/151DH9rqsB+e9OIJBNvteXimzkzI04mdgb9aPC7Y7oNNi17KNLgkjQ6LElMx2PZU/Afk0rwi0klWGH2DXm9AiQ4XR/iT+d+jrPO7t6OiYmJWLZsGQoKCobt/bS2tmLLli3YuHEjAGD27NmYNWtW+PmmpibU1dVBEATMmTNn4C95UQy37Un6jCfFxcXQaDRwOp145ZVX4Ha7+43Z1q1bAXT3LIxVpefbb78dRqMR7e3t+POf/4yamporlpFlGcePH8dLL70ESZIwZ84cTJkyRfnxJgi4/fbbIYoiysrKcPbs2RGJsUajQXFxMQDg/fffR1lZWd/fUaEQ9u3bh0uXLkEURcycOfOqOx4LCwuxbNmycDLR13IOTX/6d3j2XBxTycTwsekPoP21V+ApO8UvCiIiIoo59lCkYXWqugo/e/UvePILjyHHdvVM8H3M2QafHIIsywhBRkiW4ZdC8EkS/LIErxREV6jnXwCOkB/OYADyGNj2ZI0OD2YUYYrRqqjdGXdnJLc3CreFCUVSLkmthRysVfx1mGK9ud9nL7jOI9eQBaNaD5VKhRkzZiAtLQ2lpaXw+/1RbWdNTQ2ee+658N+hUAhOpzM8B58gCFi+fDmuvfbaXu16EjETJkyIqLBFcXExDh48iAsXLsBut0c8FPjAgQM4fvx4v8+npqYOey+6hIQErF27Fhs2bMCFCxfw3//938jPz0dSUhJ0Oh3cbjfa2tpQW9u9vzMyMnDzzTfH7PVNJhMeeughvPbaa2hpacELL7wAm82GzMxMaLVadHV1oaamJpzoXLhwIVauXBn166Wnp2PhwoU4cOAAtmzZgry8vD57RW7YsCGipKkoin0Wgfm0G2+8MTy0e8OGDXj//feRm5sLk8kESZLgcrlQVVUFj8cDQRCwatUqpKSkxPx4DAaDePrppwdsf+utt/Ya/j8c8fg0rVaL4uLi8HsGADnog7/qJPRTJwJT+24XqKuDaDRBNCYAQQmSNwipywfZ7Ydkd0PyBYb/hCjL6HzrDQCAYfpMfkEQERFRzDChSBRDnUE//t5UgQseB7pCwXG17QKAZdYMrEsthF5UKW5f1hVBQlFhxpRzKFJU98+Q0OapUdTGoknE6qzbFLVJTU1FYmIiWlpaotpOn8+HhoaGXo9pNBqkp6ejoKDgigQG0F3R+NSp7t5GPb3KBpOSkoKcnBzU1tbi2LFjVwyf7o/L5RpwaHesegEOZvLkyfjyl7+Mffv24dy5c7h48SIuXrzYa5m0tDTMmTMHCxYsGHBYdDRsNhseeeQRHDp0CCdOnEBbW1t42C8AqFQqTJw4EYsWLYponsXBXHfddSgvL4fdbsc777yDO++884plIi0QFOkchWq1Gvfffz9KS0tx7NgxNDQ0hI+zHlqtFtOnT8eSJUuQkZExLMejLMuDfp58Pt+wx+PTrFbrFZ9FQa2DpeSuIe3rkNOJYEsTAk2N8F+qgr+mGnIoNAwnxe6koiCqoJ86jV8SREREFJscgjySs8vTVWfbscP4/l//iCe/8BhWFX88L9OJqgt468h+OD1uTMrMxr1Lr8fXnvkfhEIS/v7tfw0v19DRhrcOH0BNazMEQcCkzGzctehamPS9E02Rrs/l9WDjwT0or62GLMuYkpWLNdcsgzUG820dsDfj1eYL8EhBmFTqcZVQzNObcV/6RBTqLVG1D8gS/vXCEfhlaeAFg8pi8qspS6ARODMDKeMKtGH9+Z8oapNiyMPqwu8qfq1Dhw5FnVCk6DidTrhcLkiSBK1Wi8TERGi12hF7fY/HA7vdjlAoBJ1Oh6SkpJgnMUdbMBhEZ2cnfD4fRFGEwWBQVGQm3qSlpQ06t2QsyMEg/DWX4Ck7De+ZMsiB2PZgFFQq2B54CJqsbJ5IiIiIaMjYQ5FG3K6yE/jmc79FfloGlk+fhYqGOjz8f7+CKApQf6JnXFnNJXzpd08hNcGKZdNmwRfw49l338ZLu3dg/b/8GGa9QdH66tpb8ehvnoIvGMDNcxdAJQh4cdd7ePHDd/H8N76H3CiHZNuDfvytqQInXe3jbl8ka3RYbcvHwsRUiEOoSnne7Rg8majwp4tEtZbJRIqKK6D8s2hQJzJw44TFYulVEGSkGQyGAYuzxMXFoVp9RY88Gn6CWg1d4UToCidCvvlWeMpPo+vgfgTbWmOyfjkUQvvrryDloUehSuA5j4iIiIZ4zcgQ0Ej7n80bkGZNwov/9INwT8O3PzqAf/3bnzA9Jz+83Pp9H0KAgBe/+QNYDEYAwKriEnzpd/+JNw/twwPLb1S0vic3vASn140N3/0p0q1JAIBHV96GNU/+GP/5xqv470f/UfF7OeRoxivNF+AeZ8Obk9Q63JScjWutGVDHIGl3oiuSBA7nT6SR4Qk6FUWngHoAACAASURBVLcxqa0MHBGNGYJGA+PsuTDOmgPv+bNw7voAwdah94aWurrQ8fqrsD34CIQ461lLREREI4vdf2hEtTodqGysx/Uz5vQatnxr8cJwj8MeP7n3Qex54n/CyUQAmF0wEQBwqblR0fq6fF7sLT+FW4sXhpOJAGCzJODmuQuwu/wkfMHIhxY5ggH8oa4czzecG1fJxEydEQ9mFOFnExbghqSsmCQTQ7I8TAlFHT8wFBVfyKW4jVHDhCIRjUGCAP3kqUh95MtIuPFmCDEY3h9oaoTzwx2MLREREQ0JeyjSiGrs6J7IPiu591AqURSRlWzr9Zgsy3jvxFHsLjuJxs52eAN+SFJ3UipwedLySNdX09oMSZZxsvoifvi353ote6GxASFJQl1bKyakZw76Hsq7OvFcw5lxM0eiShAwz5KC5dZMTDQkDGFgc98qPI7IkqqysldmQRaKlj/kVdxGpzIxcEQ0dokiTCXXQD9lGjo3bYC/tmZIq+s6dODy8OoJjC0RERFFhQlFGlE9iUB1H8Ns1J+qLPzkxpfx6t4PcOu8hbh57gJYDAaoRBX++c9/ULy+QLB7OYNW16vHIwDMKZyIOYUTYYjwV/8JBgtKElLxYUcDxmpFIwFAkTERJQmpKDanwKQavo/6R65I53ZSFi2blglFik5IVl7IQCOyRywRjX2qhATY7n8Qzj274Nq3e0jr6nz7TaQ99jUIOp7/iIiISDkmFGlE9QxDdnrcVzzX5nLAZk7ovsjtcuGVPTvx2SXX4Yd3PxBexuX1RLU+m6X7/3kTJuEbt68b0nvQiSrcmzYRxeYU/LXxPFoD3jERW60oYpoxCbPMSZhpSkaievirnvplCccjLkajMKHIHooUJUkOKb9JFzQMHBGND6IIy/LroU5ORueWtwBJiu5c6XLBuedDJNx4M2NKREREijGhSCMqLzUNWrUap6ov9nq8qrkRTZ0d4QRgY2d3kqrwU0OQNx85AACQLyenIl1fVrIN6dYk7DtTdkVC8eiF89Brtb0KuERisjERPyooxoaWi9jd2TjivRU1gohCgwWTjYmYbEhEgcEy4lWRT7na4ZMiTN4ovN9hQpGiJcvKb65FVhQnonHGMHM2RJMZHa+/AjkY3TQsXUcOwTBzNjTpGQwoERERKbuHYghoJOnUGtw4ex72lp/C33fvQIujEyeqLuD7f30WKQmJ4eVyU9Jg0Oqw9eghNHV2oLPLhZf37MTOU6VISUhEVXMTQpIU8foA4JEVq3Cmrhq/2PASGjra4PJ6sO3YYXz92f/Dy7t3Rvd+RBU+lz4J38idOaxFRAyiGhMMCbjWmoHPZxThXwuK8V9Fi/Gt3Fm43ZaHImPiiCcTAWC/o1nB0pGnXFWCgESNlh8YIiKiga5DCicgae3dgBjlNYAsw/HuNgaSiIiIFGMPRRpx31v7ObQ5Hfjlxpfxy40vw6TX49ur78YHp4+jqbMDAGDS6fHjz34BP1//Im752fcAAEumzsATDzyKV/d+gKff2YyS734NR375u4jWBwD3LrsBQSmEP767Ba/s6U4g6rVafGbBYnxnzWeH9J6mGq34t4J5eL35IvbYGxW3N4hqmNVqWFU6JGu6/9k0OqRqDMjQGmBRa2NeTGWoWgNenPc4hmXdSWodxDH3jmm8EASV4jbRDJMmIhoLdBOLYL3jTnRu2hhVe39tDXwXKqCbMInBJCIiosjvu2RZlhkGGg3tLie6vB6kW5OhVfed2w6GQmjsbIfFYESi8eMqrG6fD1q1ulcxlkjW1504kNHY0T2kOjUxEZoYFyw54+5EhccBrSBCFASoIEAUBGgFETpRFf6nF1UwqzQwq9RQj8PhlpvaqrGjoz6yhWUZCEWesJlqsuIruTP5IaGolLZswbGWLYraLM9+CBMTSxS/1qFDh9DS0sKgE8WxtLQ0lJSUjPntdOx8D10H90fVVpOegZQvfgkQ+GMeERERRYY9FGnUJJstSDZbBj5AVSrk2FKveNzYR0XCSNYHAKIgICvZNmzva6rRiqlGa1zvO78kYb+9edjWb9MY+AGhqKlE5cPlg5I/qtfSajk0nyje6cZJFeSE61Yg0NgA/6UqxW0DTY3wnj0D/dRp3OFEREQUEc6hSESKHXa2wCMpmQBeWUfoFC0LslD01EI0CUVfVK+Vm5vLgBPFuXHzORdFWO+4E0KUCVDXwX3c2URERBT5pQdDQERKSLKMnZ0NitpoFM6HyArPNBQalfKbab/kjeq1bDYbZs2aBZVKxcATxRm1Wo3Zs2cjKSlp3GyzypKAhBtvjqptoKEe/ppq7ngiIiKK7FqJISAiJUpdbWgNKEu+GEU17ArmUEzVcsgzRU8rKj9+/CF31K+Xl5eHrKwsOJ1OSJLEHRAH/H4/SktLFe1PURRRXFwMjUbDAMYBURRhsVigVo+/S2Xj7LnwnDoBf/UlxW27Dh2ANjePBwARERENiglFIoqYBBnvdNQpaiMACEjKKuiyhyINhValPKHoC3UN7ctUrR5XvZhocB6PB+Xl5REvP3XqVGRkZDBwNCYkrLgJrX9+VnE77/mzCDnsUCUkMohEREQ0IA55JqKIfeRsRaPfo6jNDFMS3KHI51u0qnXQijw1UfR0KpPym+iQi4GjXgoLC2GzRVbAKyUlBQUFBQwajRmajEwYZs2Jqq3n5AkGkIiIiAbFu3YiikhQlrC1rVZxu1kmZb22WJCFhkqvMitu4w06GTjqRRAEzJkzZ9AhrxqNBnPmzIEgCAwajSmWpdcCURyX7hOlgCwzgERERDQgJhSJKCI7OxvQHlRWCbdQb4FKYUGWNM6fSEOkiyKh6A46GDi6gsFgwMyZMwdcZtasWdDr+UMIjT0qaxIMM2Ypbheyd0Y1/yIRERFdXZhQJKJBdQR9eKe9TnG7m5Oz0apwiDQTijTkLzZBVNxL0Rt0QgYLqtCVsrOzkZmZqfg5orHAvGhJVO085acZPCIiIhr4voshIKKByAA2tFQhICtLtuTqTJhqtKKZCUUaBQZ1gsLjXIaHw56pH331Qoyk9yLRaFOnpEKbX6C4nffcGYBV64mIiGgATCgS0YCOOltxsqtDcbvbbLkQADT63IrapemMDDoNmVGjvEJpV6CTgaM+9cyT+Elz584ddH5ForHAVDxfcRvJ7Ya/pprBIyIion4xoUhE/bIH/Xi9tUpxuyJDAqYarZBkWVEPRbUgIlmtY+BpyIzqaBKKHQwc9euTlZwnTpyI5ORkBoXGBf3kqRANyn+s8547w+ARERFRv5hQJKI+SbKMvzRVwB0KKm67OiUPAoC2gBdBBUOl07UGiKyUSjFg0iQpbtMVZEKRBjZ16lRkZWVh8uTJDAaNo6t9Efpp0xU381ZWMHZERETU/yUGQ0BEfdnaXotKj/LKtwsTUpGn6y6IoXS4cwaHO1OMRJVQ9LczcDQglUqF4uJiiCIvn2h8iarac2cHgh08LxIREVHfeEVMRFc46mrDux3KqzrrRRU+Y8sL/13PhCKNEpNaeULRGWhj4IgoLmmzc6CyJChu57tQyeARERFRn5hQJKJeqrxO/L0puhuI2225MKs04b8bfF2K2jOhSLFi0doUt2FCkYjimW5SkeI2/qoLDBwRERH1iQlFIgqr97vxTP1ZRfMe9pigt2BZYkavx5QmFLN1Ju4EionuIc/K5uN0+dsAyAweEcUl/STlc3/6a2oAmedFIiIiuhITikQEAGjye/C7unK4JeVFWDSCiM+lT+yVvvFLkqIKz3pRhSSNnjuCYkIlaBRXeg5IXnhDXQweEcUlbX4BBLVaURvJ60GwtYXBIyIioiswoUhEqPF14X/rTsMVCkTVfnVKHlI/lQys93Up6uuVpTOB9Z0plqIa9uxvZeCIKC4JajU02TmK2/lraxg8IiIiugITikRXuXNuO35TV4auUDCq9rNMybj2U0OdAaDW61K0niw9hztTbFm0KYrbOPzsiUNE8UuXX6i4DROKRERE1Bc1Q0B0dZIB7OpsxJutlyBFOW9cklqHz6VN6LNnYa1PWUIxV2/mTqGYsmiYUCQi+iRtfoHiNoGGegaOiIiIrsCEItFVyCeF8FrLRRx2Rj+8UyOIeCRzMoyqvk8j1R4mFGl0JWhTFbdx+JsZOCKKW5qMTAgqFeRQKOI2wfY2yD4fBJ2OASQiIqIwDnkmuspc9Drxy5oTQ0omAsAX0icht5+qzD4ppKjCs0YQkaE1cudQTEWXUGQPRSKKX4JKBXV6huJ2gcYGBo+IiIh6YUKR6CrhlULY2HoJ/1t7Gm0B35DWdYctD7PNyf0+X+N1KRpEna03QRRYkoViK0GbpriN3dcERDkFABHReKDNylbchglFIiIi+jQOeSaKczKAo85WvNl2CY5gYMjrW5GUhRuTsgZc5pLHqWid+XoLdxTF/qZZZYBebYE3GPnxGJC88ASdMKgTGEAiikuajEzFbQKt7L1NREREvTGhSBSnZABlXR3Y0l6DOp87JutckpCG1bY8DNaX8ILHoWi9BUYmb2h4JGhTFSUUAcDub2JCkYjiliaKIc/BFs4vS0RERL0xoUgUZ0KyjOOuNuzsbECNgnkMB7MsMR13pRYOmkyUIKPSrSyhWMgeijRMErXpaHZfUNTG7mtGhrGIwSOi+Lz4t6UoL8zS2gLIMsDpSYiIiKjnmoIhIIoP7UEfDjtasd/RhM6gP6brXpmUjdttuYjkNqLe2wWvFIx43Va1DlYNK0fS8EjUpStuY/c1MnBEFL9EEeqUVASaIj/XycEggp0dUCclM35EREQEgAlFonHNEQrgdFcHPnK2okLhMONICADWpOTjOmvk8y0p7Z04gcOdaRglapUnFDv9TQwcEcX3DYAtRVFCEQBC7W1MKBIREdHH1xMMAdH4EZJl1PhcOO9x4HRXBy4prKashF5U4aGMIkwzWhW1O+vuULT8JGMidywNGyt7KBIRXXkDkJKiuE2wvR26iYwdUTzxu91QaTRQaTRjbv3DvW1EFIPrCYaAxrPy2mp0+byYnT8BQSkEjUoFjar7sHb7fb3+Hm9kAJ1BH2p9Xaj1uVHldeKixwm/LA37a6drDXg4YzIytAZlNxuyhAq3XVGbIiYUaRiZNSkQBRUkOfK5wlyBdgQlP9SilgEkovi8AUiOJqHYxsARjQP15WVoPHNmwGUmLVkKvcWCJ5YtwbIvPoyV3/inmG9H0O/DE8uWYMkXHsTN3/r2iLUlohG8nmAIKBLltdU4U18z4DJLpkxHemLSiG3TvnNl+Ppzv8GjK1ZhVl4Blv7om/jC8pX49h13we33YemPvomHr78Z37ht7ZiNa0CW4AgGYA/5YQ/60RrwosXvRUvAi+aAB12h4Ihv05KENKxJLYBWEBW3rfI44ZciT3gmqrVIUZi0JFJCFEQkaFPRqbDXYaevESmGPAaQiOKSKln50OVQRzsDRzQOnNmxA7ueexai2P+1/AP/91sUlJRg5de/gZzZcxg0IooKE4oUkR2nS/Hs+1sH/GL6zSOPj1hCscnegZ+tfxE/v+9h3FpcAn8w0Ot5rUqNb9y6BrPzJ4x4rPyyhO3ttfBIIYRkGZIsISTL8MkSvFLo8r8gukJBeKXQmNnHZpUG96QWYo45+vmRyruUDXeebLKC9SJpuFl1mVEkFBuYUCSi+L0BsCq/Xgt2djJwRJGSZUgeN0KuLsg+LySPF5LXA8nrhezzQZZCQCgEOSQBUgii0QTLtctjuglfe+11pBQUDrjMsocf4b4iouivJxgCUuL1b/8bCtIyRn076trb8F8PfQXTcvq+4VerVHj4hltGfLvOue14qfkCfFIIbik4bvbrNQlpuNOWB+MQh4efdCobDjXNlMQPFQ07q075OavTz3kUiSh+CVotRKMJkrsr4jYhhx2QZUDgT4FEACD7/Qi0tiDYcvlfRztCdjtCDgdCTgfkYOT3AiqTCYGmRlhvXw3RMDKjd0KBAD585mnkz5uPiYsXQwqF8MEffo9JS5chMTMDR9avR0dtLSxpaZi/bt0VycnaUydR9t57cDQ1wZCQgNw5czBr1a0QROWjnE5u24qKvXsBAAULFmDmLav6XK69pgalm95Ee00NNHo98ufNx6zbboNKPfA9TNDvw8lt29B45gx87i4kpKZhxs03I71osqL1fzJGpuRkHH71Fbja2pAxZQoW3X8/RJUaR15/DTXHS2GwJGDunXcia9p0RXEbzf1ApBQTijQsTlZfxHsnj6HJ3oEEgxFz8ifi1uISiJcvQkOShD+8uxlLp8xAstmCV/Z9iDaXA1OycvHAshugElV47cBuHL9UiQSDEXcuWNIreej1+7D/XBkmZ+VA1cfJMhAK4pn3tmBeYREWT54W8XZFyyeF8GZbNfbZu6vDGsXx8dHK1hmxJqUARYahV1pu9nvQ7PdEfjMDYIrJyg8LDbuoEoq+BgaOiOKaympVlFCEJCHkdEKVkMDg0VVH8noRqKuFv7YW/rpa+OvrELLbY/oa7uOl8FVWwnrnGhimThv29xQKBLDruWexLBTCxMWLIQgCdj33LLo62nHh4EHkzpkLo9WKk1u34Mj6V/H1N99CQloaAODw+lfx9i+eQMGCBciaNh2Opia88eN/Q9n77+G+//wvRdvx7v/8F/a+8AIKSxYia/p0HH97MyoPHLhiuXO7d+PVf/kOkvPyULR0Kbo6OrD5iZ/j6Bsb8OAfnoFa2/fc10G/D8998YuwN9RjyvU3QG+24OKRw9j9/J9w178/EU5eRrL+nhg5W1pQfbwUBfMXIODx4L3//R+0V1cj4PXCbe9Ecm4uynfuwEcbN+Abb76FxIyMiOM2WvuBKBpMKFLMrd+/C09sfAkLJk7GtOw8NNs78eNXX8D7J4/i1w99JXyifPb9rWixd6L00gUsmFAEr9+P/92yEdWtzfD6/bC7Xci1pWHHqVJsOLgXm773U2RYu4fjHjh/Bn/d9R4eu/HWfhKKITz7/lY8fH0onFCMZLuicd7jwEvNlWgP+MZPgkWtxe22PCywpMRsyPEpl7Leibl6C0wqVm2jETjetVEkFL1MKBJRnN8EJFoRqK9TloBw2JlQpKuC7PfDV1UFb+V5+CorEWgamZELIZcTbX/7K4xzi2G97Y4R660IINyjrXTTJjz2178hY8oUAMDMVavwp0cexqnt27DkCw9ClmUcfOnvyJs7F1985tlw+w+eLsQHT/8BjefOIWPy5Ihe09XWiv0vvohpN6zAZ5/6TwiXO3ls+tlPey0X9Pvwxk/+DRlTpuLhZ58LV36eecsqvPiPX8Ph9a9i8QOf7/M1Ln10FA1nyvH53/4OkxYvCT/+9pNPoOqjjzDzllURr78nRqe2b8PXXnsdSdk5AIAXH/9HHNv0JhbcdTfueuIXAIDZt92B5774IMp3vI9F9z8QcdxGYz8QRX0twRBQTL98ZRl/37MDcwsm4o//8K3w4wXvpuPpd9/GuYZaTM7MCfcI3Hb8CF77zo+Rc7na4D8+9xtsOrwPdy26Fr+4v3tOj9vnLcRDv/0Vdpwqxf3LVgzrdinhlyRsaruEvfYmyONk/ySrdbghKROLEtKgEWLbDf6Yo1XR8jPMHO5MIyNBlwYBAmQFn1RnoI2VnokorolRJAZDDjuAXAaP4pLU5YKnrAzu06fgr7oIOTR6c527S4/BV1mJpDvXQn85oaTUqe3bYbbZ+nyu+M41/bYrLFkYTmIBQNb0GQAAe2N3UlUQBDy+4Q3Icu/rqpzZswEAbZeqIk5kVR05AikUwqzbbgsnEwFgwT334OgbG8N/Xzh4CO7OTtz2ve+Hk30AMGnxEtjy81H+/vv9JhSly/ux7uTJXgnF27//w6jXX7jwmnAyEQDSi4pQsW8v5t91d/ixnhg4W1qiittI7geiaDGhSIpsP/4RbBZLn8/dWbIEGpUaG//l/7vixNZTHKWqualX4u6aSVPDyUQAmJyZjX1nT+PuRdd+/FhW9/ItjuiHFQiCoGi7BlPpceDvzZVoGye9EjO1RqxIysI8sw2qYZj7qMXvQY3XpajNbIuNHygaESpBA4s2BQ5/i6J2rPRMRHF9brQoTyhKTicDR3FF9vvhPnkc7uPH4au62D1P6BgRcjrQ+uILMBXPQ+Jtd0DU6xW13/XHZ/p9buaqW/sttmnNzu6dMLg8lDjk94cf87vdOPrGRtQcP46u9jYE/QF4L58fQoFAxNvYkxyzZmX1ejw5p/cPF23VlwB099o7t3tXr+f8bje8Dke/rzFx8WJMW3Ejdv7h9yh9axMmLlqMSUuWYtLSpeH3pnT9Cenpvf7WXN43CelpVzz2yXgoidtI7geiaDGhSIo88/6Wfp9bNbcEGpUabr8Pbxzai+OXLqDN6YA/GITT2z23XuBTv/SlfaoqtO7yL0KfrBat13SfPP3BoRU5UbJdAznR1Y7nG86N+V6JWkFEsSUFixPSkK83D2s15WNOZb0TU7R6ZOhM/EDRiLHqMhUnFDt89UwoElHcimbocsjpYOAoLgTq6+E6cgju46WQP5GgGYu6jh2Fr7YG6f/wNQg6XcTtBqvy7He7+3xcHKSYR9Dvx/NfehTtNdWYt3YdJi1dCq3BgNaqKuz8/e+UnVMuJ70+XVRFVKn6XE5nNkNv7t25ZdoNK3r1Krzi/ahU+OyvnkLFvr0oe+89nNuzG0defw2W1FTc/YsnkT9vvuL19xcjUVTFLG4juR+IosWEIikyWJVnfzCIL/3+16hubca6a5ZhyZQZMGi0uNTShN+989aVF7Ni32kuMcbDcZVu10Bmm5LxQPokbGipGnOVnFWCgCmGRMwx2zDHnAz9AF9qsSIDOGJvVhZDcwpYI5JGUpIuC9XOE4rasDALEcWzaHoohthDkcYzWYa34jycu3fBd/HCuNls3YSJSFq7TlEycTid270LDWfKcc9//BIzbro5/PiZDz5Q/t7MZgAI96rr4WrvPTe7Obl7ZFPxmjW9hi1HShAEFC1dhqKlywAANSdOYOOP/hWv/eD7+M72d4e8/pGO23CsjygaTChSTO0uP4nyumr8xwNfws1z5ocf/+D08bjargWWFEw2JOKVlgs43dUxqu/NpFJjsiER001WzDQlwTDCFaYvuO2KqjsDwNyEFH5YaEQl6TMVt+nw1TNwRBS3RJPykQKSiwlFGodkGZ7yMjh27kCgcfz8WChotUi8ZRXMJdcAwtj5Kb5nmHJKYe/ejyfe3nw53JGP40qbOAkAUHfqFPLnfXyPduHgwV7L5c6dCwCo3LfvioRf+c4dyJw6DdbMvq/1ak+dRGd9PWbefMvH65s9G3NWr8bO3/8OXpdrSOsfjbgNx/qIosGEIsVUQ2c7AKAwvXcvxs1He74U5LjZrgS1Bo9lTsEhZws2tlyCZ4R6K5pUauTpzJhkSMAUYyKydaZR7e23v7NJ0fKpWgNy9WZ+WGhEWXVRJBS9TCgSUfwSTcq/i0NdXQwcjSv+6mp0btsCf031uNpuXUEhktbdBXVS8pjbtozJ3YVCjqxfjxsf/zo8Dgf2/fUFCJdHnrVUVkaczMorLkZiRgb2PP8npE6ciIwpU1FzvBSHX3mlV5EWW14epq+8CYfXvwpbQQGm37gSUiiII6+9hg+e/gPW/PT/Ye7q1X2+Rt2pU9j+1K/g7ujAtBtXdg8LvngRp7ZtRdrEidCbzdCbzVGvP9ZxEyJMHsd6fUTRYEKRYmpKVvcEuuv378LXV62Bw9OFv3z4Xriqc2Vj/aic2IZzuxZaUjHFkIiXmy+g3N0Zs20WACRrdEjXGJCuNSBPb0aezoxkjW7MDBd2h4I4rnD+xPkJqRzuTCMuUZsOURAhyVLkx3fQDn/IDa3KyAASUdwRVCqIegMkb+SjDCQ3E4o0PoScDnRueRueUyfH1+dSo+nulbhw0ZjqlfhJhSUlWHT/Azj48ks4vP5VqLVazL/rbqz7+RPo6ujAnj8/j9pTJ/H53/x20HWp1Grc+9Sv8doPvoe/ff1xAN0FWu7+xZP4y1f+AaFPzKG/9mf/D9ue+iW2/epX2PzvPwcAmG0puPW73xsw2bfw3vvgamvDjt/9Flv+48nuOAsCJi1ZilX//C9DXn+s4/bFZ54dlfURRXXOktkXliLw2+2b8Oz7W7Hxn38y4ByKAPDUW+vx0p6dkGQZWrUady26Ft+6fR2+9uz/4UjlOSyYOBl//Idvofi7X8W9S67D99fcF277h3c34+l338aun/4aFoMh/Hjxd7+K+5etwL985h4AwK83v46/7noPB5/4X2jVGviDAVzzw2/gC8tX4tt33AW334elP/omHr7+ZnzjtrWKtmsoDjqasbH1EkQIA86vqBIEGEU19KIKiWotEtVaWHv+V2mRqtUjRaOHJsZzScbae2212NxSpajNjyYsQIpWzw8Vjbg3Kp9QPIz51oJvIsM4icEjorjU8sffI9im7IfBjO98H8IABRCIRpUso+vIIdi3b4Pk842bzVaZTFCnpCJp3d1QJyePi232ezxwtbbCkpoarmgMAF6XCzqTSXFHjc76ekAQkJiRMWDboN8He2MTNHo9LCkpEMTI7pckSYKrtRVBvw+W1DRo+pmTMtr1j1bcYr0+IiWYUKRh4fH70Op0IDUhMVylGQBcXg9MOv2ondhGYrvsQT8a/B5oRREqCFAJl/9BgF6lhkFUjflEYSRCsoyfVR6GPRh5ZbwJhgR8I382PyA0Kj6ofR4XHR8parM487OYmrScwSOiuNT2txcUDwVN+8rXobJaGTwae9emDjva178KX9XFUb7DFqAymSBaLFBZLFCZLRANBgg6PUSdDoJeD0GjgaBSAaIIQVRB9vtgmDFzzPZKJCLqC4c807AwaHXItaVe8bhZb4j77erpcRjvjjpaFCUTAWBJUgY/HDRqkvVZihOKnEeRiOKZaFA+pYPkcTOhSGOO50w5Oja8BsnjGbHXFDQa+0+9WgAAIABJREFUaDIyoMnIgiYlBWpbCtQpKVBZrd3JQiKiOMeEIhEpv5mAjPfaahS1MarUmGNhdWcaPVZdluI27az0TERxTDRGk1D0MHA0hi5KJdjf3Q7nnt3Df+NsTYKusBDawgnQZudAk5ICiCL3ARFdtZhQJCLFjjla0eRXdkOxMDE9LoZ60/iVpFeeUOzuoSgDLCVERHFINCgfoaGkiAvRcJK8XrSvfwXec2eHZf2CSgVd4QTop02DfvIUqK1JDDoR0ScwoUhEyi7eZBnbWpXNtyQAuDYpk8GjUWXWJEMj6hCQIp+kPSB54Qp0wKxJZgCJKO6IUUz5wh6KNBaE7Ha0/uV5BJqbY75uXUEhjPPmwzBtOkQ9CwkSEfWHCUUiUmS/vREtCnsnzrWkwKbhBRmNLgECrLostHiUTdbe4a1nQpGI4vO8GE0PRY+bgaNRFWxrQ8vzzyFk74zZOkWDAaYFC2EqWQh1EnsiEhFFgglFIoqYJxTElpZLitvdYMth8GhMSNJHkVD01SPXMpPBI6K4E00PRdnrZeBo1ASamtD65+cQcrlisj6V1QrLtcthmjsPglbLABMRKcCEIhFFbHtbDbpCQUVtJhkTkac3M3g0JiRHU5jFW8fAEVFcimY4p+TzMXA0KoJtbTFLJqrMZliuXwHT/AUQ1LwlJiKKBs+eRBSRGq8Lu9qVV7y9NSWPwaMxI6rCLKz0TERxStDpFLdhD0UaDSG7vXuY81CTiaIIy5KlSLjhRvZIJCIaIiYUiWjwizhZxksN5yFBVtRuismKicZEBpDGjCRdtuI2Dn8TJDkEUVAxgEQUV8QoEoqs8kwjTfJ60fLC80OeM1Gbm4ukNeugSUtnUImIYoAJRSIa1LttNaj3dSlud1tKPoNHY4pOZYRRnQh30B75jYwswe5rRJI+mwEkorgi6JQPeZb9fgaORo4kof2VlxBsGUI1Z1FEwg0rkLD8ekAUGVMiohhhQpGIBlThtmN7a7XidrMtNuQbLAwgjTlJ+my4XXZFbdp99UwoElHciaqHIudQpBHUuX0rvBXno26vslhgu+8BaPM4BQ8RUcyvIxgCIuqPKxTAX+vPKhzoDKgFEXemFTKANCZFU5ilg4VZiCgu7wRExQUpZCYUaYR4Tp+Ca9/eqNtrc3OR9tV/ZDKRiGiYsIciEfUpJMv4S/1Z2IPKhzbdkJwNm0bPINKYxMIsREQfE3Q6yMFgxMvLfiYUafgF29vRsfH1qNsbps9A8j33soIzEdEwYg9FIrryZgHA602VONelfPJrq0aHlbYcBpHGrGgKs7R7mVAkoji9GdAqG/Ysh0KKEpBEikkS2l99Oerh9ab5C2C793NMJhIRDfc1BENARJ+2s70O+zobo2p7b8Yk6ERWw6WxK1GXDkHh15872Al/yM3gEVHcEaKYR1EOsDALDR/nnl3w19VG1dY0bz6S7lzL4itERCOAZ1oi6uVAZyM2NV+Mqu3CxHRMMyUxiDSmqQQ1EnVpitu1c9gzEcUhQatV3IaVnmm4BJqa4NjxflRtjbNmdycTBYGBJCIaAUwoElHYIXszXmmsiKptglqLNSzEQuNENMOeOzjsmYjikKBhQpHGCFlGx6aNkEMhxU11BYVIuuse9kwkIhpBPOMSEQBgf2cjXmo4p7iiMwAIAL6QNQVGFeeqofEhmYVZiIi6bwai6KEoMaFIw6Cr9Bj81dWK26mTk2H73AMQVJxyh4hoJPHun+gqJwPY2nIJ77TVRL2OVSl5KDImMpg0bkTXQ7GOgSOiuBPVkGfOoUgxJnm9sG/fqvz41Whgu//zEI1GBpGIaIQxoUh0FQvIEl5trMBhe3PU6ygyWnFTSi6DSeNKUlQ9FBvQnYLn3ExEFD8EjUZxGw55plhz7voQUleX4nbWOz4DTXoGA0hENAqYUCS6SrX4Pfhz3RnU+bqiXodNo8dD2VMgMsFC44xZkwSNqEdA8kbcJiB54Qq0w6yxMYBEFDei66EYYOAoZkIOO1z79ypuZ5g5C6bieQwgEdEo4RyKRFcZGcAxRyueqiodUjJRL6rx5dzpMKs0DCqNx1vo6HopsjALEcXb2ZBVnmmUOXa8DzkYVHYTazIjafVnWNGZiGgUMaFIdDVdsAX9eL6uHC/Un4FPCkW9HlEQ8Ej2VKRrOV8NjV9JOhZmISJilWcaTcGODnQdO6r8O/yO1RCNJgaQiGgUccgz0VVAgowDnU3Y1FwFrxQc0roEAJ/PnIzJJisDS+NaNAnFdvZQJKI4E9UcihzyTDHi3PUhIEmK2ugnToJhxkwGj4holDGhSBTHZADlrg5sarmIRp976DcdAD6XORnzElIZXBr3ohny3OlrYOCIKK6IUQx5lljlmWIg5LDDfewjhQesCOsdHOpMRDQWMKFIFIdkAOe6OvFeWw3Ou+0xW+89GZOwMDGNAaa4kBxFD0W7vxGSHIIoqBhAIooLHPJMo8V14ADkkLIpeMwl10CdksLgERGNAUwoEsWRkCyj1NmKHe21qPN2xWy9KkHA/ZmTMZ89EymOaFVGGNVWuIOdEbeRZAl2f1NUw6WJiMYiDnmm0SD7/eg6ckjZsarVwnL9DQweEdEYwYQiURyo93XhoL0JH9lb4ArF9iJfJ6rwaPY0zplIcSlJnwW3q1NRm05fAxOKRBQ3oqryzCHPNERdpccgeTyK2pgXLYbKbGbwiIjGCCYUicYhGUCDrwunXO044WxDrdc1LK9jVevwWM50ZOtZRY/iU5IuE3WuMkVtOrz1KEyYz+ARUVyIqoeinz0UaSgXsjK6Dh1UfJxalixj7IiIxhAmFInGCUfQjwseByrcdpS5OtAe+P/Zu+/wOMpzb/zfmdmZrVp1WbJlWZZ777gQd4PBYFroLclJDyR5k5OTvDkneXOSE5JQAim/EyAJoVdjwMaAccPYBmzjXuUmq1jV6mXL1N8fa4SFJVmzKtZK3891+QLtPs/s7D2zO/Pc+5RQt77eSG8C7h04Cj5JZvCpz4pmYZYaLsxCRH0IeyhST1PPFEErL7NVxzvjMohe/sBNRNSbMKFI1As1GhpKw00oCQdQHGpCXrAOlWqox17/iuTBuDo1CyK4gh71bdEMXeZKz0TUl0S1KAvnUKROaPp0p82TVEDc7MsZuGhYFg6seRuWaWL81csgRfEDAhFRW5hQJOrJazoA1TTQZOgIGBoaDR21WgjVWhg1ehjVWhgVagAN+qW5UU+Unbg9fQRGcb5E6icSnOkAhHOfzo6pVyuhmyocIm/KiSj2iVEkGMxwmIGj6O6FVRWBw4ds1XGPHQcpgfem0cjfuRPv/s+vAUGAw+nEuKuuZlCIqMswoUjUxcrCAawsz0OTocGwLBiWCR0WdNNEwNRhWlav3O85Cem4Lm0oXKLEg0j9hiTI8CspqFfP2mkOoU4tQ7IriwEkotgnihAkCZZhdPxbkD0UKUrBo0dgqfaGzPtmzY6Z92cGAzCbmmAGgpH/DwZghkKwVBWWpsFSVZiaCugGLMsETAswTYheL+KvWgbB0bXN8z0rVyBtxEjIbjf2vfkGE4pE1KWYUCTqqhsIWNhYdQZrKwvhFCUEDD0m9nuA4sGXB+RwFWfqtxKdA20mFCPzKDKhSER9haAosGysuMs5FClagX177TVWU1LgHJLdq96D0dgAo7oaenUV9OpqGHV1MOrrYdTXw4pylJHo8aDqpecRv/RqyAPSu2Q/G8+excmtWzD329+F0+vFuocfRFX+aSRnD21Rbt8bKyG73Ri1aDH2rFyBsqNH4fT5MGLefOTMntPy+NXU4MDqVag4eQIAkJw9FBOXL0dc2gCe3ET9EBOKRF2gLBzAi6XHUdRNqy13B6/kwNUpQzA7IR2SwLkSqf9KcGagoGG/rTq1oVIgnrEjor5BUBTATkJRVQHLAnj/QDaYTU0InTpp73512oxLep6ZTU1QS4qhlZdDKy+DXl7WbUP+9aoqVL38IrwzLoNv1hwIUudGDe17601YloXxy5bB4XRh458exb4338DiH/17i3KH174HANi/6i3IbjeSsrJQ8Omn2Lvyddz44MMYtXARAKAy7xRe/M63IEoSRs5fCNHhwP5Vb2LnC8/hjr89gfQxY3mSE/UzTCgSdeYmAxY2VRVjbWUhdMuMiX1WRBFfSsjAkuTB8Ej8CiBKdGXYrlMbLmPgiKjPiHZhFoELPJANwSOHIonojhJFeCZP6dl7+3AYakE+1KJCqEVF0GuqezZIloWmnTsQPnXqXG/F6Hr+WaaJ/W+9iZzZs5t7D46YvwAH31mD+fd9H47zPruCKKJw7x5c8e//gWm33gYA0IJB/H/XXIWDb69uTiiue/hBwLLwtedehC81FQBw+de/gafuvA3rH3kY9zz1NE9yon6G2QSiKJWrQbxUehwFwYaY2F+P5MC8xIGYmzgQXiYSiZolOO0nFGu40jMR9SHRJAYtVWVCkWwJHDxoq7xr2HBIPl+375fR0IDQ8WMI552CWnzGXtKzm+hVlah6+QX4LpsJ78zZtnsrnti6BQ1nK3DFT3/W/Njk629E7ob1OLZxA8ZdvaxFednlwtSbb/n8b7cbydlDUV8W+QE11FCPwt27MfmGG5uTiQDgSUyMDJVe8RqCtbVwc/Econ6FWQUiuzfQAD6oPoP3zhZCi4FeiWmKG3MS0jE7IR1OLrhCdIF4ZQBEQYRp4/PcqFVBM8OQRScDSEQxT1Tsf5eZahgifAwedex8CTQhnH/aVh3PpMndtz+hEELHchE6lhtJIvbKRoeFxh3bEfqst2JaWoer7n19BbzJyRj+pbnNj2VfdhkSBg7C3jdXXpBQ9A8YAEEUWyYKFAWhhnoAQE1REQAgMWvIBa+VOCgTAFBbUsyEIlE/w4QikU2HG6vx7tnePcRZFkRM9qdgdnw6hnr84AxHRO00pAUJfiXN9jDmunA5UtxcmIWIYp/gtJ9QtLtSL/VvwWPHbPX8ExwOuEeP6fL90MpKETiwH6FjubD02FhAUa88i/p1a5F06+0d6hVce+YMTu/cAVEU8ecrFrV8/6EQakuKUXk6DylDcz6P90U6HRhaJFZCK/NZSrIMADBjJJ5E1HWYUCSyabwvCT/JntzrFmGRBRGjfYmY6EvGeF8S3BzWTNRhCc4M2wnF2nApE4pE1CeI0SQUu2lhCuqbQkeP2CrvGjEyqkR3W8Kn89D06Q6oxcWxFThBsL1Iy943VwKWhcU/+jEUj7fFc7oaxrqHHsS+N9/Akh//pMO74U1KBAA0VVdd8FygthYA2DuRqB9ixoEoCulOD340ZBI2Vp/B2spCGJdorpVE2YkRnniM9yVjtDcBCoc0E0UlwZluu06tyoVZiKhviGYuRJMJReogS9cROnnCVh33uPFd8trhvFNo+Ggb9MqzsddQT0pG/NKrIKd3fK5nQ1Vx4O3VGDx5CqbdenurZY5v3oxD77yDBff/oMXiLO3eJw3KhDcpCfk7dgDfb/lc4e5d8CQmIjFzME92on6GCUWiKImCgCuSB2O8Lwkvlp7AmR7orZgiu5Dj8WO4Jx7DPfFIkl08EERdIJqFWeq40jMR9RFRDXkOhxg46pBw/mlYmmbjJluEa9ToTr2mVlaKhg83Qy0pjsEPpADv9BmRXokOe8313E0bEaytxaQbbmizzKTrb8Dp7Z8gd8N6jF92Tcd2SRQx896vYNOfHsPm//0rZtxxFyzTxN6VK1C4ZzcWfv+HF8zBSER9HxOKRJ2U4fTix0MmYX1VEdZVFXVJb0WfJCNFcSHD6cUgpxeDXF5kOL1wsQciUbeIqociE4pE1EeITvs/ULKHInVU6PgxW+Wd2UMhuqL70dwMBtCwbSuChw7GZKwciUmRBVgyMqKqv3flCjh9PoxefEWbZUbMmw93QgL2vbmywwlFALjszrthqBp2PP8stj/7DADAFefH/O/eh5l338MTnagfEizrEo3VJOqDikNNeKn0OGr0MAKGDgGAQxAhCSIcggBZFOGRHPBKMryiAx5JhkdywO9QkCw7kSS7kCS7oPAXPqIeZVg6nj/6I1iwMWE8BNwz5jFIAn+bI6LYFjiwD3Xvvm2rTtzcBfBdPpfBo4sq+/Oj0CsrO1w+Ydm18M2eY/t1QsdyUb9pA8xQ7PWeFT0euMeMg2/O5bZ7JfY00zBQX1YGCEB8egZ7JhL1Y2wFEXWhQS4vfjJ0CjTLhEMQIHJ9ZaKYIAkOxCkpqFc7PseSBQv1agUSnQMZQCKKadH1UOSQZ7o4o7bWVjIRAFwjR9k7F0Mh1G9cb7snZJd/jrxeSL44SD4fRF8cRK8HossN0eWC4HJBlBUIsgxBdgAOOZKIEwVAEAFYUX0OL8n7lCQkDBrEk5uImFAk6moCAEXgL3VEsSbBmW4roQgAdeFyJhSJKOZFM7zUCjGhSBcXOmVvMRZHUhIcyckdLq+VlaL2nbdh1Nf3XAM6MRGO1DQ4UlLhSE6GIyERUnw8BFnmASeifoUJRSIiIgDxznSgwd6cS5xHkYj6AiGKhKIZCjJwdFHhU6dslXeNGNnhsoGDB1C/aQNgmt33BkQRysBBUAYPhpwxEEp6RlSLGBER9UVMKBIREQFIUOwvzFKnljNwRBTzRLfHdh0zyIQiXYRlIZRnL6HoHD6iQ9tt2LIZTXt2d8/nweOBa9hwOHOGQRmcxZ6HRERtYEKRiIgI53oo2lQXZkKRiGKf6HbbrmNyyDNdhFZRDrOpycaJKMKZPbTdIpZhoG7tu10+X6Igy3CNHAX32HFQBmUCAudBJyK6GCYUiYiIACQ4B9iuU69WALAALsBERDFMkGUIDgcsXe9wHTMYYOCoXeG8PFvllUGZ7c7naWkaat9ehXBBftc1hhOT4JkyFe6x49gTkYjI7ncoQ0BERATIogseRzwCel2H62hmGAG9Hh5HPANIRDFNdLthNDR0uLzFIc90EeHT9hKKrpxhbZ9vuo6aVW9CLSrsmmt+Whp8s+bAmTOMvRGJiKLEhCIREdE5fmearYQiANSHK5hQJKKYJ3q89hKKhgErHOYCFdTGCWLZ7knozMlp81yrfXtVlyQTpYQExH1pnq3FX4iIqHVMKBIREZ0Tr6ShrOmErTp1ajnSvSMYPCKKaaLHa7uOGQxAYkKRWqFVVMAMdHxYvCBJUAZnXfiEZaF+3VqE8093an8EhwO+WbPhmTodgiTxABERdQEmFImIiM6JV+zPo1inVjBwRBTzRG8UCcVAAFJCIoNHFwgX2EsAKpmDW53DsOHjbQjmHu3UvsgDByJh6dU8V4mIuhgTikREROf4nWm26zSoZxk4Iop5osdju47R1AguY0GtUfPzbZVvbXXn0LFcNO3cEf1OCAJ8M2fDN3MWIIo8KEREXYwJRSIionOi6aFYz4QiEfUBktdnu47Z1MTA0YWimD9Ryc5u8bdeWYm6dWuj3gXB6UTCNcvhHJLN40FE1E2YUCQiIjrHJydBgAgLZofr1KtnYcGEAPZ+IKLYJcbF2a5jNjYycHQBva4WRn29jZNPhDNrSPOflqahds1qWLoe1etL/ngk3vRlOBKTeDCIiLrz3oEhICIiOndRFCTEKcn2GtSWgSathsEjopgm+ewnFI3GBgaOLqAWFNgqr2RkQFCU5r/rN22AXlMd1Ws7UlKRfPudTCYSEfVE24khICIi+pxfsT+PYr1aycARUWw3Cnz2hzwbDUwo0oVsD3c+b1hy6OQJBI8cjup1HampSLrl1qgWGCIioijuHRgCIiKiz/mVVNt1uDALEcW6aHoomg31DBxdQLWZUPxsnkMzGED9hvVRvaYjMQlJX74VosvNA0BE1EOYUCQiIjpPdAlF9lAkotgmKIrtZIytefKoXzADAWgVFbbqKOfmT2z4cDPMYMB+g9bnQ+KXb4boZjKRiKgnMaFIRER0nqgSiloVA0dEMU+Kj7dV3gwFYWkaA0fNwoX25k90JCVD8vmgFhYgePSI7dcTJAmJ190AKc7P4BMR9TAmFImIiM4TxyHPRNRPSf5423WMuloGjprZHu6cnQ3LMFD/wcaoXs+/5ErIA9IZeCKiS4AJRSIiovP45CQIEGzViSzKYjF4RBTTJL/9Xl5MKNL5wvn5tsorQ7IR2LcXerX9VZ3d4yfAPXYcg05EdIkwoUhERHT+hVGQ4JOTbNXRzBBUI8jgEVFMkxISbdfRa5lQpAhLVaGWFNuqI6eno3HHJ7Zfy5GYCP/CRQw6EdGlbDcxBERERC35lBTbdRq1agaOiGKaIzHJdh2jtoaBIwCAeqYIMM0Ol5d8cQjl5sIKh+29kCAg/qplEBwyg05EdAkxoUhERPQFcXKy7TpMKBJRrJMSo+ihWM3vPoqwO9xZHjQIgYP7bb+Od+o0yOkZDDgR0SXGhCIREdEXxLGHIhH1Q1JCIiDYm0PWqOYq9xQRzj9tq7ylabZ6NAKReT59sy9nsImIegEmFImIiL4gTomih6LKRjURxTZBkiDF21vpWa+tgWUYDF4/Z+k61KJCW3WMKvvXzbj5CyHIHOpMRNQbMKFIRET0BT4OeSaifsqRbLOHtmVxHkWCeqYIlq53uLwgSYBorzeskpUF1/ARDDYRUS/BhCIREdEXRDOHYpPOlU6JKPY5UlJt19HPVjBw/Vz4dJ69CpJoe3i9f95CBpqIqBdhQpGIiOgLXA4fJMHekKomjT10iCj22e6hCEBjQrHfC+fZSyjaXaHZPXYcHKmpDDQRUS/ChCIREdGFTR345CRbNYJ6A0yL84gRUWyTo+mhWMGEYn9mqart+RNFOwlFUYRv9hwGmoiol2FCkYiIqBU+JclmDYvDnoko5jlS02zX0SrKGbh+LFyQb29hHkGIDHnuIM+EiZD88Qw0EVEvw4QiERFRK7yORNt1AhoTikQU2wRZhiPJ3jyyRl0tzGCAweunwqdO2j7HOjx/oijCO2Mmg9zN1EAAhqYxEERki4MhICIiupD9HopAQK9j4Igo5skD0qFXV9mqo5WWwpkzjMHrh0InTtgqL8gdb4J6xo2HFBfHINtQdvQIyo/lfh5vUYTT50Ni5mCkDh8BQWzZp0gNBPDYvDmY9ZWvYf73f8gAElGHMaFIRETUWiPGkWC7DhOKRNQnGggD0oGjh23V0UpLmFDsh4y6OttD3gWH0sGCAnsnRuH4B5vwyb/+CdnlAgQBlmlCD4cBAN7kFFx2z72YcefdzYlFh6Jg/v0/wKCJkxg8IrJ3v8AQEBERXcgr2x/yHNSYUCSi2KdkDLRdRy05w8D1Q6ETx+1VEEUIYseGO7tGjYYU33vnTrTCIRgNDTCbmiL/Ak2wQiGYoSCsYAhmOARL02CpKixNhaXpgGHAMs791zQBywIsC1JSMhJuub1L3+9XXngZydlDAQC6GkbZ4cPY+cJz+OBPj6LkwAFc/4eHIIgiRIcDs776bzyZicg2JhSJiIha4WUPRSLqp+SMgZE57iyrw3XUM0WR8h2dG4/6hOCxo7bKC7LS4XPEN/2yS/7+zIYG6JVnYdRUQ6+pgVFTDbOuDkZDPSxV7bLXsUJBVP/r7/AtWAz35Cld/jlyKE5kTpmKzClTsemxP+LTF5/HgVVvYdKNN8HQNHz0jycxeOo0DJ01u7lOyaGDOLZxAxrKy+Hy+zFo4iSMverqC4ZME1H/xYQiERFRKzyy/V4CTCgSUV8gKArk1DRbQ1mtcBhaRTnkAekMYD9haRrCp07ZPLc61vx0DsmGIzW1B9+MBb26CnppKbTyMujlZdDPVsA6N1S4p+LZsH4twsdzEXf1Nd22svW8792PQ++8jT0rXmlOKH7yr3/CMozmhOLe11/Duj/8DlnTpiN9zFg0VJTjnf/+JY5t2oCbHnmMJz8RAWBCkYiIqFWy6IIsuqCZoQ7XCemNDBwR9Y3vwEGZtufGU4sKmVDsR0KnTsKysTKwIAgQHXKHynqnz+jenbcs6OXlUAvzoRYWQCsphhUK9Yq4qgX5qP7XP+BbuBjuSVO6PgHgdCJr2gwc27QBWmvv2bKw6+WXkDlpMu548p/NDydlD8VHf38CFcePI23kSH4AiIgJRSIiorZ4HPGoUzvewAgaDQwaEfUJSuZgBPbutlVHzT8Nby8Ypko9I3j4kM2Wp6NDQ3kdKalQsoZ0+f5aqopw3kmop05CzTsFMxjstbG1VBUN77+H8LFcxF11DSS/v0u370tJASwLwbpauOK+sG1BwDdXvnXBlAeDJkwEAFQX5DOhSESR72uGgIiIqHVu2Y86teM9dMJ6IyxYEMA5xIgotkWT0AkXnIZlGBAkiQHs4yzDQCg3ivkTO8A7bXrX7aeuI3zyOMJHDkM9nQfLMGIqzmr+6cjciouWwD1xcpdtN9wYGVHhiotr/XUDARxY9SaKD+xHU1UVDE1DuCHyo6lho1cqEfVtTCgSERG1weOwN3+RBQthowkuycfgEVFMk+L8cCQlQ6+u6vh3oKZBO1MEZUg2A9jHhU+dhGlziLCoXHy4s+j1wjVqdKf3T6+oQHD/HoQOH4alhmM61paqQisqhGv0WAiK0iXbLM89Ck9iIhSPF2og0OI5Q1Xx0re+jprCQky68SbkzL4cstuN6oJ8bH3ibzz5iagZE4pERERtsJtQBCLzKDKhSER9gTIk21ZCEQBCp04wodgPBPbvs1VecEhAB1YH9k6eGn0PV8tC+OQJBHbthFZU2CfiLHq9iLvyajhHdN0Q46I9u3H21ElMvfW2Vp8/uW0LynOP4vrfP4TRV1zZ/PiJDzfzxCeiFphQJCIiaoPbYX/OopDRAICLEhBR7HMOGWp7HsXQ8Vz4F13B4PVhlqoiePSIrTqC7Lx4GYcD7okTo9ghC6EjhxHY/jH0qso+E2fX2HHwLb4SotvdZdusLizAO//9Szjj4jDz3q+1Wqa+rAwAkDx0aIvHD7+z5rOA80NARACYUCQiImr7Zt5hv6ehagQZOCLqE5xDcyK9ykyQ2y1LAAAgAElEQVSzw3WM2lpo5WVc7bkPCxw+ZGt1ZwAQnBcfquseNwGiy17yLHziOJq2bO5TiUTR40Xc0qvgHDGqU9vJ+2gbKo4fAwCE6utRduQIjq5bC0lx4oYHH4E/vfXP6ICRkdfd+/oKzL/v+wg11GPH889CECPzQ1eeOhVZsEXgfNFE/R0TikRERG01bqQ423XCRhMDR0R9guB0QhmcBbUg31a90LGjTCj2YU27PrV3HjkcEC423FkQ4J06rcPb1M9WoHHjeqiFBX0qtq4xY+FbciVEt6fT29r02B+b/192u+FPT8fUW2/HtNvvQFzagDbrZU2fgel33oXdr7yMva+/BklRMPmmm3Ht//wOgZoabH/2aZQcOog7nvwnPwxE/f0+wbIs9lkmIiJqRVWoCKvzHrRVZ8aAGzE+eTGDR0R9QtOnO1C/cZ2tOlJ8AtK+cz97MPVB2tkKlP/lT7bqiB4vRFf7Q55dI0ch4ZrlF92WpWlo+ngrAjt3RHrJxQpRhOCQITgcgCRG5okUREAUIEAARAHeOXPh7IIFabrsWAeDaKyshC81FbLL1fx4uLERTq+Xn28iYg9FIiKiNhs4USyuEjYCDBwR9RnO4SMAmwlFo64WalEhlKwhDGAf07Rju70KggDRefHVnb3TZ1y0jFZ8BvXvvg2jpuaSx0GMi4Pkj4foi4Pk80H0+iB4PBDdbohuNwSnE4LLBVFxQpDlDi1I09vIbjcSBw++8DvBx4XniCiCCUUiIqK2GtKS13YdlUOeiagvNRYSkyAPSIdWXmarXuDAPiYU+xgzFELT3j226giyI9ITrx1KVlb7Q+RNE00fbUXT9o97tFei4HDAkZoKKSUVjpRUSEnJcCQmQYyPj34laiKivnSPwBAQERG1cZEUZUiCA4ald7iOZoYZOCLqU1xjxtlOKIZyj8BccqXtRTao92ra9SksVbVVR1RcFy3jmzGrzefMxgbUrX4L2pmibn9/UkIC5MwsyJmZkDMGwpGcEpM9C4mIeqytxBAQERG1RYBT8iKg13W4hmaGGDYi6lPcY8aiYfNGW3UsXUdg3174Zs1hAPsAS9fR+PE2e1dQQYSgtD/cWc7IgJKV1fr19EwR6la9AbOpe3r+Cw4HlOyhUIYNh5KdAyk+ngeaiMgGJhSJiIjaoUgemwlF9lAkor5Fik+APHAQtJJiW/UCuz+F77JZ7OXVBwT27YHR0GCrjnCRhVgAwDdzdquPB/fvRcP69wHT7No3Iopw5gyDc+w4OIeNiMxvSEREUWFCkYiIqB1OyWOrPHsoElFf5Jk0BXU2E4pGQz2CRw/DPW4CAxjDLF1H/eYPbNURBOGiCUU5PQPOoTlfeDELjR9sRGDXzi59D5LfD/fkaXBNnAjR4+VBJSLqAkwoEhERtdfgEV22ymsGeygSUd/jHjMO9RvX2Z5Dr/GjrXCPHQ8IAoMYo5p27oBRV2evkixDuMhiLL45l7f429J11L+zGuFjuV3X2E1Lg2fWHLhGjmZPWSKiLsaEIhERUXttItFpq7xhaQwaEfU5gqLAPXY8AvvsrfKrV1cheOQQeynGKDMYRP2HH9iuJ7nb/zFOyRwM55Ds5r+tcAi1b7wOraiwS/ZbSkqGb958OEeMYjKbiKibMKFIRETUDlmyt0IpE4pE1Fd5Jk+1nVAEgMZtW+AaPRaCJDGIMaZ+4waYgYCtOoIiA1L7zcy4ufOa/99sakLtipehV1R0en8FpxO+ufPhnjyVPRKJiLoZv2WJiIjaodgc8qybTCgSUd8kp2dAyRpiu55eU93lc+JR99PKStG4c7vtepKr/R/iXCNHQU7PAAAYdbWoefG5LkkmOkeNRvI3vgP31OlMJhIR9QB+0xIREbXDISq2yrOHIhH1ZW2tynsxDR9tgdnUyADGCtNE9ZsrAcuyVU2QZcDRdu9EQZKaeyfqVZWoefE5GLU1ndpVweVC/HU3Iv76myB6ueAKEVFPYUKRiIioHZIg22uDWQYsWAwcEfVJzpzhcCSn2K5nqSrqN6xjAGNEw7Yt0EpK7Dcu3Z52n/dOnwHJHw+ttBS1Lz0Ps7FzSWZ5UCaSvvoNOEeP4UEjIuphTCgSERG1QxJl23UsiwlFIuqjBAG+2ZdHVTV49DBCx44yhr2cWlyM+k0b7Z8aigLB0fY8mZI/Ht7LZkLNP43aV1+EGQx2aj/d02Yg8Y67Ifn9PGhERJcAE4pERETtcAhRJBRhMnBE1Ge5x02IqpciANStfRdmoIlB7KXMcBjVr74MyzBs1RMEAZK7/bkT/YsWI3z8OGpffxWWqka9j4Ikwb9sOeIWX8G5EomILiF+AxMREbV3oRQctutYFhOKRNSHCQLi5i6IqqoZDKB29Vu25+ajHmBZqFm5AnpNtf1TwukC2lnF2z1mLIyKCtSvWQWY0V8jRZcbCbfdCdf4CTxeRESXup3EEBAREbVzoRQk+20y9lAkoj7ONWp080q9doXz89CwdTOD2MvUrV+H4NEjtusJogjR7Wr7Our1QjANNH64qVP7J8UnIPHur0DOHMyDRUTUG9pJDAEREVE7F0rB/qWSi7IQUZ8nCPAvWRp19caPt3E+xV6kcecONGz9MLrrpMcDCEKbzzucToQOHezU/jnSBiDx7q9ASkriwSIi6i3tJIaAiIionTZzFJdKAQIDR0R9npI5GO7xE6OuX7v6TaiFBQzkJRbYtxe1b6+K7hqpKBAUpe3GpsMBvbysU/snD85C4h13Q/R6ebCIiHoRJhSJiIjaaywJ0SQUeXklov7Bv3AJBKczqrqWYaB65avQOplwougF9u1F9Zsro7xAipA8bSf5BMsCgoFO7Z9z+Agk3HJ71OcYERF1H7Z4iIiI2m3x2h++HM28i0REMdmY8HrhX7gk+q/YcBjVLz8PrbSEwexhjds/QfXKFVEvkiL5vIDYRo98CxAMrVP75xo3HvE3fBmCw8GDRUTUC/HbmaiLnSo6irwzuS0eEwQRSfGpyEofhpTEAQwSUQyJZj5EQeCQZyLqPzyTpyJ0PBfhvFNR1TdDIVS9/DySvnwblCHZDGh3M03UrVuLho+2Rb0J0eWCIMttP2/q6Mx0wu5pMxC3aEm7czMSEdGlxYQiURfbvn8TVqx7Ck7F1TyPmmkZUDUVgiBi0cxr8c2bfwaX4u6T7/94zccwLR0CBAiCCAECRMEBSZQhCQ44BAUOyQlZ/OyfG4rkAjjnHPVSUSUUeT4TUT8Tv2w5Kv/5BMxQKLrvWlVF9Wsvwb90GTwTJzOg3cQMBlH92isInTwR9TYEhyOyEEtbzxt61L0eAcA7bwG8s+bwYBER9XJMKBJ1kz/+x0vIHJDd/HddQzVWffAC3tjwDARBxP13/L8+9X5rw2XYduY51IbLoZlBezemggin6IHT4YXbEQeXww+3ww+vIwFeJQk+ORE+ORluOZ6JGupxlmWvUeQQFTBBTkT9jeSLQ/zV16Lmzdej/741DNS9+za00hL4lyyFIHH6iK4UPp2H6pUrYNTVRb8RUYQY52v7ns4wIESbTBQE+K+6Bq4JE3mwiIhiABOKRD0kPi4J9173A+w/th3bdr+P+27/JQRBwNptr8Pl9GBMziS8seEZDEzNwvWL7gEAlJ4twqYdq1FaWQSn4sK4YVMxf/oySFLLj25+yQls3vkOKmvLkBSfivnTr8GwwaNblCkoOYkPd72L8qpiOGUXRmaPx8LLlsOpuDr1vizLxKHKDdhbsQaGpUMW3VFtI2Q0ImQ0oi5c3nZjRZThV1IRrwxAgmsgEp0ZSHJlIs6ZykQjdRvD0u1dWAWFQSOifsk1agy8M2ejaccnndpOYO9uaKUlSLj2ejhSUhnYTrI0DfWbNkSGOFudGIcsAI44H9DGYmWCaUAwjeg2rSiIv+HLULKH8oAREcUIJhSJetiA5EycKsqFpqtQZCc+2Pk2XE4PVm16HiE1AEmM/Bq/6/BWPPjUfyAjNQvTxl6OusYaPP7q77D+k7fwm/ufgOyIJC0+2rsef3z25xiSMQKjhk7A8fxDeHvzS7jv9l9iyewbAABbdr2HP73wS2SlD8Ok0bPQGKjD0289hne2vIoHf/QMPG5fVO+lLlyGrWeew9lgfo/EzjA11IRKUBMqAer3Nj8uiy4kuzOR6h6KNE8O0jzD4HL4eLJR15x3lmqrvCTKDBoR9Vv++Yugl5chnH+6U9vRykpR+fQ/4Ju7AL6ZszmXXpRCx3JRs2Y1jNraTm1HEASIPh8gtd58FEwTghFdMlHy+xF/821MHhMRxRgmFIl6kGHoOFV0FMkJA6DIzsiHUJJxPP8Qrl90N26/+tsAAFVT8ZcXfoWczFF44If/hEOKJCjmTl2KXz9+H97bugLXLbwLYTWEx199AJNHzcIvvv0XiGLkF+PfPPF9PPXGHzFv+tUwTB2Pv/o7jMmZgl/f97fmbS2eeT3+889fxxsbnsHdy++39T4sWDhcuRF7yt+GYWmXPK6aGUJZ00mUNZ1sfizRNRAZ3lEY5BuDDO8oJnkoarpp7xyXRSeDRkT9lygi4fqbUPnsv2DU1nRqU5ZhoGHzRgQPH4R/0RVwDs1hfDtILSlG/fp1nZorsZkgQPR621yEJZJM1KPatDw4C/HX3wjR4+VBIyKKMUwoEnWTiqriz2+ILROVNeV4+8OXUF5VjG/d8n9b3KRpuorrF93d/NCB4ztQ31SLb97ys+YEIABMGTMbA9OG4JP9G3Hdwrtw8PinaAzUY8nsG5qTiQDw3Vv/EzUNVQAE7D+2E8FwE5bOuanFtsYOm4KsjGHYeWiLrYRifbgCW4ufQ0Ugr1fH/7OejEeqPoAkyhjoHY3s+CnIipsIRfLwBKUOs5s0dzChSET9nOj2IPn2u1D5/DMwmxo7vT39bAWqX30RzpxhiJu3EHJ6BoPcBrW4GA1bP0Tw8KGuO55eLwSljek8TCPqnomeGTPhm78QEEUeOCKiGMSEIlE3+c0T37/gsUR/Mr5588+wbO6tLR+PT4Hb+fkvsyUVhQCATTtWY9ehrS3KhsIBNAbqI+XORsoNSB7UokxqUgZSkyI326XnygxMG3LB/qSnZGL/sR0dfk8F9fvw4ZmnYZhaTB0Lw9RQ1HAQRQ0HIQoSMuPGY3jCTAyOmwBR4ITvdJHGmWFvkSEnE9ZERJASEpF0252oevFZWOFwl2wznHcK4bxTUIZkwzdzDpw5wxhoRHpyho4fQ+PHH3V6qPkFx9HnazOZKJg6BMP+AiyC0wX/1dfAOXIUDx4RUQxjQpGom/zXt/6EAckDP7t1gtvlRUrCAAitzAHkdrZMQOhGJGHncfng/cL8hrMmLoTDIbco1x793BCU1l7XIcnNz3fE4LjxGJe8GAcr19le+ba3MC0DhfX7UVi/H26HHyMS52B00jx45QSetNQquwlFRWRCkYgIAOS0AUi69U5Uv/ZSlyUVAUAtyEd1QT4cSclwT5gI97gJkPzx/Su4lgWtvByBfXsQ2L8PRmNj125fiKzc3fowZyvq1ZzlwVnwX7O8/x0vIqI+iAlFom6SkZqFzAHZUdVNiEsCACyZdQOmjJndZjm/N5IEq22oarNMvC+xzTL1TbXN2+gIUXBg2oDrMMQ/CVvPPIfacGlMH6OgXo8DZ9fiUOU6ZMdPw8SUpUh0DeTJSy0brmbAVnmnxHmgiIg+owzKRPId96D61ZdgBgNdum29ugoNH36Ahg8/gJI1BK4Ro+DMGQZHckrfDKZpIlxUiFDuUQSPHIFeXdUtLyMIIqS4OMBx4SgOwbIi8yXaXC1acDjgnbcAnmkzuMBOX7pHCgQgyTIkWe6V2yOi7sWEIlEvNDpnMgBgb+7HFyQUtx/4AMMyRyM1KQOjcyZFyh39BNPGfqm5zJoPX8aqTc/jgR/+E2PObWtf7vYWZUJqECcLDmPK2Dm29y/FPQTXDf859lW8g4OV62O2t2Lz/bllIq/2U+TVfoos/yRMTVvOxCI1Cxt2E4puBo2I6DxyegaS7/4Kql95EUZDfbe8hlpYALWwANgISPEJcA7NgZwxEMrAQZHVg2MwiWVpGrSyUoTz8xHOz0M4Px+WqnbrawqSA1Kcr9V5DYUo50tUhuYg7oqrICVwNEhPKM09irJjue2WGTZ7DvxpAzr3mQsE8NCCL2HOvV/Fovt/0On91lUVDy34EmbddQ+W/PBHPJBEMYAJRaJeaGBqFuZMXoL3tq7AoLRszJ68GIZh4P2PXscr7z2JH9z9ayy6bDkyBwzF9HFz8f5HK5GVMQyTR89CQclJvLr27xiYNgRpSZGk2Izx8/D+RysxPGssZoybh9qGKvzrrUeh6mHcsOjeqPZREhyYNuB6ZPknYduZ51AbLusTsY8Mhz6A4QkzMS39engcHJLT3wV1e41ft8PPoBERfbHRkZyC5Hu+hpo3XoNW1r0jHIy6WgT27QH27QEACLIMR2oaHEnJcCQmQUpKgiM+AaLHC9HjaXvBkR5iaRr06mroVZXQKiqglRRDKy2FXldruydgZ4guF0S3+4LkqwALiGKIsxQfD9/CxXCOHM0PQA869sEmbHv6KQjtLHZzx5//2umEokNRsOi+72PQhIkMOlF/vbYzBES90w/v/g2eeuMRPPXGw3j81QcAfLaoy0+x6LLlzeV+fO8D+Nurv8WTr/0ehhn51fiyCfPx3dt+8XmZrzyAJ177Pf7y4n/DODdnYnpKJn7+jUcxKntCp/Yz1Z2N64b/J/aWv41DVRv7SPQtnKzdjvz6PZicdg3GJS/i4i39mP2EIpPQREStkfx+JN/9VdS9/y6CB/f33FVd0yJJupLiVp8XHI5IYtEhQ3A4AEmCIElwjRgF78yWI0Uat21F8OCBNl/LqG8AZAdEjxsCBJiwIJrnkoKWBdMwYOkaLE2FqemwNB2wzB5NHF5AFCB6vRDlCxOrgmlEkok2NifIClzjJ0AZPQaCJEFtI+503iFQnHCkdO1Q/e+88jqSs7O7b58dDsz5ytd48Ij6McGyLuXVi4guRtVUVNaWwam4kORPgSC0/mvja+//E6eKjuJH9/4WLqX1IZdhNYSq2nK4nB4kxad2+b5WBgtQEciDKEiwLBMWLFiWCcPSYVo6DEuHbqrn/oWhmSGoRhBhI4Cw0YSw0QizFw6fTnQNxNxB9yLZncUTsp8xLB3PHf0/tupcO/TfkeoeyuAREbUjsGcX6jeugxXFENqe5Jk8Ff5FV3zeizHQhNK//hlQFPvDqC0rkjw0LcC0YFkGYJiRGBgmLNPo8cSi6HRC9LiBL9xfRjVXogWYsGDy9LZN8scj/sqlcA0b0eltbX78f7Ht6afw3dfe6FBCsbqoCAfWrEb1mSLILheypkzFhKuWQXRE+h6Zuo4t/3gSw+ZcDkPTsP/tVRh/1TJkT5+Brf/8O7KmTkXOeYn34kOHcHTTBjRUlMMV50fmxEkYv/SqC3pMHn5/LU5+8hEAYMjU6Rh35VI8OG/OBUOeK06exKG176K2pBgOpwuDxo/HxGuWQ3a5msvoqorD769F2bFcqIEA4lJTMfaKK5E2fARPLqJuxB6KRL2cIisYmHrxRJbXHYeaurN47Nn/ws+/+WirZZyKCwPThnTbvqa4hyDF3ZntW1CNIAJ6PYJ6HZq0WjSqVWjUqtCgVqIuXG67t1hXqAmV4O28hzAp9WpMSr2KvRX7U4NXq7Xf+HRwjigioot+V06dDiVrCGrXrOr2IdCdug7s24Pw6VOIv3o5nNlDAY8XGT/7T1S/+ALCpcWAw0ZzShAAQQLO5VUEyOf+e96dkGkAugFL12HpOmDokQRkVxNFSB4vBEX+4q0YRFMHbA5vNi0LVqQ6RXMHrGuofv01eMZPhH/JFRCdrh553ZMfbcWKn/0HkgZnYficyxGoqcG7f/gd9q16C3f/7xOQFAWWaWLb008h1NCAA+++g4SBA5E9oxKGpmHb009hjmE0JxR3r1yB9x78PYZMm46M0WNQX1GO1b/+f8jdtBG3PPzH5tfd+Nc/45Pnn0X29BnIGDMWB997B6d3br9g/w69/x5W/eqXSM0ZhpyZsxCsr8OGPz+GT197FV976hk4fT7oqopnvvFV1JWWYtT8BXDG+ZC/exc+evZp3PCbBzDuyqU8wYi6CROKRH2AaZpYPHM54jx+/P31h2L4nQhQJA8UyYMEZ3qrJVQjiNpwKWpCxagOnUFlsBDVoTMwre7t4WBZJvZVvIPixiNYMPjr8MlJPPH6gQat0vY5zDkUiYg62BBJSUXKvf+Gxu0foWHbFttJrJ5i1NWh+pUX4JkyDf6FSyAoCpLuuhsIhVD658cARe6yRV8EUQIUqeW8joYOU9VhaSqgaZ3bviBAcLkhur+YsLIgGGZkiHNH740AWLC6Jd/ZXwUOHUA4/zTir1oG17Dh3fpauqpi9a9/hfSRo3Dvk/9sXll57JVL8fIP7sOu11dg5p13NT9+8L13cOsfH0X2tBmRe/JA4Is3y9j5yssYPGky7nn8780Pb8l+Elv+8STKTxzHgBEj0VhVhR0vvYBRCxbilgcfaf7srPntb1re8wea8O7vf4esyVNw51//1rwfk5Zfj+e+9XV8/NwzWPi9+1G4dzfKcnNxx1/+F8Nmfd5Tcu1Df0Dhnt1MKBJ153WcISCKfSVnC/HYc/+FhLhk/OSrv+vT71WR3Ejz5CDNk/P5fbap4WywAGVNx1HalIuKwOluSzCeDZzGqpMPYH7m15AZN54nXx9Xr9pLKPrkJPZgJSKyQxThmzMXrtFj0fDBRoROHOu1uxrYuxvhvFNIWLYcypBswOVCxs9+jprXXkWwMD8y/2J3kBwQ3Q7gXBLQUjVYajiy4nNHhyQLgOB0QXK7Wg5vtqzI6s02krmWBUTusphJ7A5GYwOqX38VngmT4F+8JOreiofXvw9vcnKrz01efj3yP92BQG0tlv7kZ83JOgAYNms2krKGIHfzRsy8867mhF9qzrDmZGLr55iA765444Jz8rNFW6oKCjBgxEgU7N4F0zAw4aplLRLx026+BftWv9X89+mdO6EGmjDlxpta7F/W5ClIzRmG41u3YOH37oepR87GkkMHWyQUr/rp/+XJRNTNmFAk6gMyB2Tjj//xYr99/5IoI907HOne4ZiMZdDMEIobj55bsXk/NDPcpa+nGkGsL3gcUwcsx6TUpQAEnoR9VIN61lZ5v5LCoBERRdMoSUpG4pdvhVqQj/pN66GVl/XK/TTqalH18vPwTJ0O/4LFEBQFibfehkTTQOkjD0XmVuxmgiJHhipbFixVjSQXtTbmOxSEyOrNLmeLRKJgmhAss8O9Qi0rkj40mUTsMYGD+xHOz0PCVdfAmTPMdv2tT/2jzefGX3kVqgoKAQAH1qzGiW1bWzyvBQOoKmg5zVBS1sWnNVIDAexb/RbOHNiPpupq6KqKUGND5LNzrndt/bnPdvzAgS3qJg7KbPF3dVFk/5Jbed3EzEyc3rkDAJAzazZGL1yED//+BPaveRs5s2Zh2Ow5GD77ckiXeAV3oj5/7WYIiKivkUUXsv1TkO2fAsPUUNhwACdqPkFx41F03a/pFvaUr0ZtqARfyrwHkiAz8H1QbdjevF5+JZVBIyLqBGVINlK++g2EThxD4/aP21yZ+VIL7NmFcN5JJFxzPZTBWYAoIeOnP0fpnx5Bj/3QKAgQnE4ITicsw4QVCgLhMCzLAiQRktMFOJ0QPusFdi6J2KHeiFZkOLN1bqVqujSMhgZUrXgFnomTEb/kSghyx+83L7bKs6FHEnxOnw+uOF+L50YtWNiiVyAAKB53+/uqqnjuO99AdWERptxwI4bNvhyy242qgnx8+OTjn5c7l1iUvtCjV5SkL+yf3nyef5Eky83Pi5KEm//wME5+8jFyN23AyW3bsOeNlYhLTcWNv/09sqZM5YlE1E2YUCSiPk0SZQyNn4ah8dPQoFYit3oLjlVvg2aGumT7eXW70KhVY8mQ78IpeRnwPqY6VGKrfBwTikREnScIcI0cDdfI0VALC9C4/WOE8072voZUYjKk+M8X4qpd+x4u1agFQRIheL2AxwPoGkRVBUwDlnpulIamwzIjqUFBFCE4pMiqu+claywz0mMx8t9zS6wIgCiKsAQJliTy3LwUx1aWIQ8Y0OVD6r1JkfnAJ113Q4uhwtE6sW0rynJzcdPvHsTYJVc0P378w80tyjl9keRlqKGhxeNN1dUt9y8xMfJ4VdUFrxWorYUnIaHFd8bwOZdj+JzLAQBnDh7Aql/9Am/+4uf44Tvv8yQi6ia8KhBRvxGnpGBG+k24ddQDmJJ2LRTJ3SXbrQjkYU3ew2hUqxjkPiRsNCGg21vlOdGZwcAREXUhJWsIkm69A2nf/QHi5i2AlJB4yfdJUBTEX30tkm67E5I/shBX6cN/QODo4UsfMEEAZAWm1wdLViAYBgTDAGBFUp2WBcswYIZVGKEwjHAYpqrCDIUiw6f1z1Z4Ptcr0QJgmBB0DWJYhajpkaHS7LXYM+f/4Cyk/du34J06vcsW/vnM4EmTAQB52z++4Lljmz9AXam9URp1ZZGhzCnZQ1s8fuC9d86dS5FzJnVYZPh2yeFDLcp9NoT5gv37wurPWjCIkiOHkTlxEgCg+NAhHFm/rkWZzAkTMfGa5Wg4exbhxkaeSETdhD0Uiaj/3ZxJbkxOW4axyQtw4Oz7OFz1AUxL79Q268MVeCfvEVyZfT8SXYMY5D6gKlRkuw6PPRFR95Di4+GbMxe+OXOhFhUidOwowqdOQq+p7tH9cGbnIH7ZtZD88QCA2jWrETh6BIIs97oZlS1FgSXLEFQVgq5FupKYFizTOLdEswUYVqTXogBEVm5pd4uAaUBQDQiCAEuSAEn8LFVJXUhwOOBfsKhbEomfSRqchTGLl2DX6yuQnJWN0YsWwzQM7HnjdWz5x5O47le/xsRrlnd4ewNGjQQA7F65Agu/dz9CDfXY/krcDfYAACAASURBVMLzEM7N3Xk27xRgWciaNAXx6en46NmnkZIzDOkjR+HMgf3Y9fqrLd5rytAcjJg7D3veWImBY8ZixJfmoam6Cuv/9Cj0cBiz774XAFBy5BDWPfoImmpqMGbR4sgw6/zTOPz+WqTmDGvuEUlEXY8JRSLqtxTJg+npN2Jk0uXYXvLquTkWoxfQ6/Du6UdxxZD7WqxCTbGpPJBnq7zLEQePw8/AERF19/V7cFZk3sIlS6FXVyF86iTCeSehlhTDCoe75TUFRYF/0RXwTD43H5tpoOyRhyIJO7kXz6MsCLCcTliyDDEcggATgiDBMq3I0OZzIp3HLAifJQcvlsOyLAi6DuhCJKkoSd2W+Op353fmYCQsWw5HYvf3xr3uV7/BukcfwbpHH8a7f3gAAOBLTsbSn/zUVjIRALKnzcBld9yJT199BbtXroCkKJh645dxw29+i0BtDT5+7hkUHz6Eex7/O25+8BG8+Yuf45X/830AQELGQNz4wO/xwn3f+XzuRAA3/s8DeO/B32P1b/4b5rnHEzMzcevDj2LQ+AkAgBm33IbGqip8+OTf8P4jDzaf98Nmz8HSH/+EJxRRd15iLMtif3UiIlg4WbsTO0pfg2oEO7UlSZSxOOvbGOQby7DGsPcL/oqSpmMdLj/INwZXZt3HwBERXbJLuQW9ugpq8RloJcXQysugV1d1OsnozB6K+KuXQ4o/1yvxrZUInDzZ5XPa9UjjT1UhaGpzvCzThGW2vjq03fygJUqwHEwsRkP0eGCpKvzzF8I7bUaPx1BXVdSXl0F2ueBLTonMrxklLRhEQ2Ul4lJTIbtczY+HGxvh9HpbvLfa0hIIEBCfnt7ue9ZCITRUlEN2exCX2vp81ZZporGqEnpYRVxqKhxOJ08sou6+pjChSET0uSatBlvOPIOyphOduzEUJMzL/CqGxk9jUGOQYel46dhPoZtqh+tMTLkS09KuY/CIiHoZs6kJenUV9OoqGLW1MANNMANNMJqaYAYCMANNsDSteY63ZpKE+Cuu+rxXIoCG9evQuHcX2uzCJ0mRRJ0FCLAiPQQFIbLS8mf/LnUD0DAghEPN77c5qXhBs1CAYDuvJEBKToZjUCYEJnQ6zFJVxM2bD0diEoNBRDGDCUUioi/e1Fkm9p19F/sq3u3sVyzmDLwdo5LmMqgxprTpGNYW/NVWnSuyvodM9kolIopdpgnLMGAZOqAbsEyzedGVrnwNo6kRWkUF9IpyaJVnYVTXwGish6npPfdeLQtCOHRuwZbI36ZhtpJUPJcDtZsIFSV4Z82Gb+58JhaJiPooJhSJiNpQ1HAIHxb9C5oZ6tR2pqRdi8lpVwOctDxm7Cx/A4erNtm5nOKu0Q9BEd0MHhERRcVSVWhnK6CWFCN86hS0ivIW8xx2S2MwHI4s2PLZPhgmLOvC3opRJRUBiF4f4q68Cu4JE3gfRETUxzChSETUjtpwGTYU/A0NamWntjM6aR5mZdzavNId9eomHVae/A3q1bMdrpHkGoTrc37O0BERUdddjQwDWmkJQnmnEMrNhRFo6p4GoaZCUD+f4sMyTVhGK4nM5kWgBdu5QSVnGOKvvR5SQgIPLBFRH8GEIhHRRYT0RmwsfAIVNlf9/aLBcROwYPDX4RAVBrUXOxvMx5rTj9iqMzZpAWam38zgERFR97AsqCXFCOYeRfDoUVjn9SrskkahrkfmVTzv9doeAi1E1dlQUBT4l14N95SpYG9FIqLYx4QiEVEHGKaGzWeeQmH9gU5tJ9k1GIuHfBdemb/Q91bby1bgaPWHtuosHXI/BnpHM3hERNTtzHAYwcOH0LR/L4y6uq5rGOoahPNXxLYsmLrRVmlEO+jCNXoM/MtvgOjmNCFERLGMCUUioo7ewFsmPil5GcdrPurUdtwOPxZlfRNpnmEMai+jmypePfELqEagw3UcooI7Rz0ESXAwgERE1IM3JiaCuUfRsP1jGPX1XdM4/EJPRctCZJGa1lqMAiAgut6KUnw8Em6+DfKgTB5HIqIYxYQiEZEtFnaVr8LBs+s6tRVREDE9/SaMS14IDvvpPXJrtuKT0ldt1cmKm4jFg7/F4BER0aW5MzEMBA8dRMPH22Ce38Mw2gaipkFQW/ZUtHSj1ZwihHNDoKO6GZLgX3YNPFOn8yASEcUgrg5ARGTvNhvTB9yA6ek3dmorpmViZ+nr2FjwJEJ6I8PaGxpkMHG46gPb9bL9kxk8IiK6dHcmkgTPpMlI/cq/wT16TOevh7IMS5bPewEBgkNq6+KJqPunmAbq16xG/btrANPggSQiirXrD3soEhFF53jNx/i4+EVY6NzXqNvhx+WD7sLguAkM6iV0onY7tpW8YKuOQ1Rw+8jfQxadDCAREfUK4fzTqH3/PZjBYOcaiqEgBOO8RF+7cyoikniMsrOikjMMibfcBsHp4gEkIooR7KFIRBSlkYlzsDDrmxA7OXdeUK/HhoLHsbnoXwjodQzsJWBYGvZUrLFdLytuIpOJRER9TMWJEyg+eDBm99+ZPRSpd38FyuCsTm3HcroA8bwMoSBAkCS0nTW0EO1vrGreKVQ9/VSXzQVJRETdjz0UiYg6qbTpODYWPAHNDHV6W7LowuS0ZRibvKDTiUrquN0Vb+NA5fu2612Z9T0M8o1lAInokinauwfFhw91uPyIufOQPCSbgWvH32+7GWfzTuHn23dBlKTYfSOWhfptW9C0e1f02zBNiMGWC5VZpgnLMNtpYUbfU1Hy+5F4171wpKbxRCQi6uXYWiUi6qQM70gsy/kx1hf8DQGttlPb0swQPi17A7nVWzAl7VrkJMyIrKBI3aYyWIiDlett1/MrqRjoG8MAEtEldeKjbfjk2ac7/t2VNoAJxf5CEOCfOx8OfzzqNm+KLNlslyjCdDohnrfYiyCKkYVazNa3J8ACrOhWfzbq61H99FNIvPNuyJmDeQyJiHrzZYY9FImIukaTVosNBX9DdehMl20zwZmOialLMTR+OkRBYpC7mGoGseb0I6gLl9uuOyv9FoxJms8gEtElFaipQbCu5XQZHz3zFA6+swbzv/M9jFl8RYvn4tJSoXi8DFw7+kwPxfOETp5AzbtrANOMrtHY2nyKhtlmklIQgKi7KQIQZBkJt9wO5/ARPCGJiHop9lAkIuoiXjkBy3L+HVvOPI3C+gNdss3acBm2nHkWu8tXYUzyQoxInA2X5GOwu4Bpmdh85l9RJRMV0Y3hCbMYRCK65DyJifAkJrZ4zB3nBwD4klOQnJ3dbn3LNHH21Ck0VlXC6fNhwMhRcChKizK6qiLU0ACXzweH04nGqipU5p2C4vUiY/SYSI81AIamoSw3F6ahIyUnB25/fIvtBGpqmvcZloXykycQqK5GXFoaUobmtLmPWjCIipMnEG5qgjcpCWnDRzS/Zlv7WHYsF3o4jMyJk1qUqy8vQ31ZGbRwGAkDByExM7NfnCeu4SOQuOzaqJOKltMFIRj4PIEoCBBEEZbR+iItlnWug2KUOUVL01DzyktIuPHLcI0bzw86EVEvxIQiEVEXkkUnFmV9G/sq3sW+ine6bLtNWi12lb2JPeVvY4h/MkYkzsJA72gIAtfWiqqhAhMfl76E4sajUdUfm7yAi7EQUcw7unED1v/xYTScrfj8OuZ24/L/n727Do/qSh84/r13JDMTd4gHdwlQrECxAjWoy1aod6tb2frub7fu3XbbbWlLlVKsxa24uwQNToKFEJdJMnZ/f0wyJUTIRCjyfp6nzy5Xzj1XMnPuO+ec99776Xvv/Z5l+1et4Nfnn2PY31/AmpvLqm+/wVUWSAqOieEv//uKvPQTTH3peQqzstwvGUYjw55/iS4jR3nK+f7+eyjJy+eesd8z+bmnyUo97FkX1a49N33wEf6nzZ3ncjhY9MnHbPp1Mk673bPcEhTEwMefqlD2niWLmfbqSwx/4WWOJG9h57y5+IdH8OQc9/y4WamHmfX6vzmavLXCNYju0JFRb7xFUPTFH1g0tWhJ8IiryZk90/udFQXNYESxnT70WUHTFKhm6LOmaSj16KWIy0nur5MJKCnG0q2H/MEKIcR5RgKKQgjRwBQUukZcTbg5nuVHf6DUWdRgZbs0B4fyNnIobyMWfSDxgV1JCEgiwtIMVYKLtbyGTpYf+5FD+ZvqtL+Pzpf2oYPkQgohLmh7ly/jt5eeJ6hpFLd89B8iW7chPz2dpf/7jKX/+wxFUegz+j4AdHr3K8OuBb/jcjq447Mv8PH3Z93P49gxdzbz33+HjP376P/Io8R07ER6Sgpz3n6Dee++ReuBAz09FXV6A6XWIn576XnaDBpM64GDsRdbWTd+HHuXLWXqyy9y99ffeuo45603SJ45nbaDh9D3/gfxCw0j89BB5r//LrPf+DcGk4n2w4a7yzYYAEjduJ6j25Lp//BfCYhwByddDgfjH/8rBRkZDH3mOZr16o3mcrFr4e+s/OZrJj/3DA+On1ivIboXClPLVgT060/+iuVe76sZDCgOB7j+6JWoqiouzVl9dmeDEUUDzWGrY4018mfPxGW14tevP8i80kIIcd6QgKIQQjSSGP8OjGzxMsuOfMtJ64EGL9/qyGN31lJ2Zy3FR+dLjH97ov3aEeXXBrM+QG5AFYrsOSw99h0Z1oN1LqNz2DCMqlkuphDiwqVpLPrkY1Sdjts+/cyTpCUgIpJbPvqEL24cyarvxtLj1tsxmM2UB3FO7t3LE7PmYvL3B2D48y+yc/5c9q1YzoiXXqHrqBsACG/egoNr17Bj3hxO7NpFs169AVAUBZfDQUznzlzx6OOe6sR26cqYW27kyNYtZOzfR0SLlmSnpZI8czphic24/q13PUOcfUNCuOm9D/jiputZ8fWYSgHFPcuW8uD4iRWGUGelpRIQEUnLy/tz2e1/8Swf0LwFh9e7A5CnDh4gvHmLS+L2+3brgSMnF+sO76dncRl9UEtOy/qsKCiqDs1V9XyKWmkphrh4XPm5OPPz61znwiWLcBUUEDDiKpAfUIUQ4rwgn8ZCCNGYjXZDMCMSn6Zb5MhGTapS6iziQO56lh/9ngkpLzJ13+usOf4LB3M3UGDLpPquA5fIuzMaB/I2MP3gO/UKJgYYw2kT0k8ebCHEBS0rNZXstFRiOnWulPHZaLHQqv8V2KxW0rZuqbCuWa9enmAigI+fH5agIIBKyV/K5yY8M2EMQMcRV1f4t6KqtOh7OQDHdmwH4MDq1WXlDqk0X2JIXDwRzVuQlXqY/IyK8+DGdOxUaT7G8GbNuWfs9wx/4aVKdQlv1hyAglOnLqlnIGDgIAyRkd7vqFPR9IYz7p9SY79BW1oqlp59MERF16vO1o3ryZ08Ee204e9CCCH+PNJDUQghGpmiqHQKH0asf0dWHRvHqeLDjX7M3NIT5JaeICV7BQAmnR+h5jhCzbGEmGIINkURYAxHVS72rwGN40V72Zwxs0Gue9+oO9ApBnmohRAXtKy0VACKsrOY/947ldZnHNjv3i71MM179/EsD4ioHIDS+/igMxoxBwaesdzk/hR2VU7aEZqQWGlZYNMoAAoy3PM55hxNAyAkPr7KcwiOiSFj/z7yjh2rUK/g2Lgqt3fabKQsXkTals0UnMrAXlKCy+kku+xaaNql9cObotMRfNU1nPr5JzSbl8ORDUZw2CuVpzmd1WZ9Lli2hLCHHqFg/lxK9+2tc71LUnbj/OFbgm+9A/W04LYQQohzTwKKQjSgklIrer0Bvc5wQZYvGlewKYqrmz3HnpyVbDo5HZuz+Nw9m85CjhXu4ljhLs8yVVEJMEYQ4BNBgDECf2M4/sZQ/Awh+BpC0KvGC/RKa+TZMjhSsIO9OavJs51skFJbB/eliaWlPMhCiAueo6QEgKKsLA6tX1flNqHxCejO7Immr7qnvarzrge+0Vx52gi9j/s7x1EW3LKX1VHvU3UCLF1ZJury7cqZAioHmYqysxj3yINkHjpEWGIzotq3Jzg6BkWvw2a1UpiZeUk+B7rAIAIHDyV3rndJ5DRVAYMB5fSegkpZkhZnNQlaSkooWLiA4FvvIG/mdIqTt9S53vbjx8j85kuCb7kdQ3SM/EELIcSfRAKK4qJz4MhuDh5LIdg/jO7tqx+auCVlDZm56TSLbkPz2Lb1Pq7dbuP2Fy5n5MC7GD3y6QY/r5JSK7e/cDk3DB7NXdc+KTf6AqUoKm1C+pMQkMTmjBnszV6F9icNR3ZpLnJL08ktTa9yvY/OF4shEIs+ELM+EJPeD5PeH5POFx+dH0adBR+dBaPOglFnQq8Yz2nWaasjlyJ7LnZXKaXOIvJK08ktPcmp4sMU2rMa9FgBxnC6R46SB1gIcVEw+voC0Kx3H65/851zfnyHzeYJCJazFbt/ZDOa3MFGo8VdR7u16h/f7OXbl51LTZZ/9SWZhw7R+557GfR4xTbUnLfeID1l9yX7LJhbt6FkTwolB72b61kzGFEc9gozqiiKUmOLpmTXTkoPHiBw5ChUX1+KVq+sexumoIDs78fiP2wElu49kGQtQghx7klAUVx01m5bzJQFYzEYjHz32gJ8zZV/qbbZS/ng+xewlhRy09D7GySg2NCS967j8LG9jBx4FwAGvZG7rnmC1gmdzttrX+osZGP6dFyaQx7EWooN6EhW8RGK7Dnn4f0sotRZRA7Ha7+TAiqqu6eCogIaCgpooCkKiqaVtfkV97Co8v+PQnXzPCqoDIt/inBLgmdZakEyu7KWkG7d3/hflKqRQbEPSiIWIcRFo3zewOzU1D/l+FlpqUS1a19hWd7xYwAERbuHPocluodFlw+/PlPmIfd8uGfOAVmVo8nJAPS49fZK69JTdl3yz0PAoCGUHj3i3dBnRUHTG1HstgrLVFXF5XJVu1v+3NmEPfoE/kOuRDWbKVi0oM711pxO8ufMwnb4EAFXX4dqlu9pIYQ4lySgKC5KFpP71+oVm+cxvO/Nldav3bYY0DD5WM7bc1i9dQFZuac8AUWdTs8NQ+49b+t7MG8j609MxqVp2F3F8hB6SUWHpji5EKZwUpQ/Qn8qijtwqCiUT8muVJXvSzktdnjaskobnfmygMbQ+Ec9wcRSp5W16ZM4mLeREFP0OTnfvk3vINgnSh5SIcRFI7BpU5q0aUt6ym6OJm8lpnOXCusXfPg+pVYrw/7+AgaTqcGPv2v+vAoBRZfTyb6VK0FRiEvqBkCLy/uj6nTsWjCfAY88iv60Ho1HtyWTc/QoMZ27VJq7sSpGi7u9Zy+p2D7ZOX8e6Xv2AOC8hBN96Pz88O/Tl/ylS7zb0WAA+xlBSJ0KNQQUHVlZWNevxbd3X3z79kMxm8mfNZP6JI8r2bUT25E0Aq8ZiU/LVvIHLoQQ54gEFMVFK6ltXxatnV5lQHHxuhl0ad2bDbuWV1q3N3UHa5IXkpl7Ej9zAG0SO9O/23DPUE6n08GEeWPo1q4vdoedReumM6DbVXRo0b3KeiTvXceOfRvplzSMuKYtADiReYTF62aQnnkEH6OJds2TGNDtKnQ695/kL3O/ZPOuVRj0Rn6e/Tld2/ahVXwHJs77ivbNk+jSpjdOl5MJc7+kW7u+hAU3Zd7KyZzMOkpIYARX9r6B6MiECvVYu20JW1NW49I0ktr2oVenQUxZMJaEqFY1Dg0/m2JHAWtPTCQt3/3rv0F6cdWNAgq6so57GuA6b+qFoqGWPf+KO4Tormuj0ggxRXNV4t89S9IKtrH6xC8UOwrO2en3iLyeZoHd5fkUQlx0hj7zHD8/+jBTnn+OK/76GE3atMGam0vyzOns+n0+7YcNb5RgIorC4Q3rWfbl/2g1YCAlBfms+/knco8dpd2VwwiIbAKAf3g4ve8ezarvxjLhqce5/L4HsAQFk75nN0s+/y86g4EhT9VuiplmvXpzdFsyc99+k/4PPowG7FmymN0LF9D/wUdYNuZ/pCxaQGhcPKEJCZfk8+DbqQvW5K04cmo/YkJTFNDpUZwVR6YoOhXNWX07pmDZUsydu6JaLFiSuqP6mMid+itUkcCntlwFBeT8Mg5T+44EXDlcErYIIcQ5IAFFcVFyOB306TKU9777O4eP7yMh6o9ECpm5J9m2bwPPjX6HNdsWVdhv3qrJjJn8Nh1adKd5bFuyck/y6fh/siZ5ES/e/6G7waK5mLJgLEXFBSzdMJvI0ChyWlU9mXfKoa28+fVT9E8aQWwT9/CiTbtW8O63f6dpeBxJbfuSX5jDl5PeYsGaabz22JcY9EZOZR+n0JqPr9mfjOwTWIsLcTjsTFkwFpfLSZc2vVEVhSkLxpJXmMO2vetok9gZf98glm+ay7xVk/nfq9MJDYwA4IcZnzBt8Q+0TuhEy/gOTJw3hr2p25mxZBxX97+tzgHFQ3mbWHdiMqXOInnoGuo9i/I5iHSgaWXzK56jbouK5qmEUtbj0B1A1Hl6H54bLobFP12hV+K69MkcyNtwTu9F1/Cr6RA6WB5KIcRFKa5rEnd89gUL//MRs998zbPcHBBI73vu5YpHHm2cA2saN7z7PjP//X+sHPu1+2tHVWk7eAhXv/LPCpte8ejjmPz9WfPTD/z86MOe5U3btuOGt94lumPtpoHpffdoMg8fYtfv8zm8YT0A0R068pf/jcEcFMS2OTPZPmc2h9at5al5Cy/NB0JV8e83gJwZ07y7nUYjSvEZAUVFcQcbteoTtBSuXEHAlcMAMLXvQLDJRO6kCWh2W71Oo2Tndkr37cG3bz98e/VBMUgiQyGEaCwSUBQXJU3T6NGhP/6+gSxaN537r3/Os27J+pn4mv3p0b5/WU+wP/aZtewX2iR24fXHv/Isj543honzxnD42F4Solt5Miwv2ziblx74iI4tewDupCynO5J+kDe//hs92g/g0Vv/gaIo2O02Pv35/0iMac2bT3zjKevypGG89uVjzF05meuu+AtP/uU1dhzYRFyTFjx91xvuBlKp9YzGmrvH2OL1M3j/mZ9IjG4NQL+k4bz86X2s3DyfkQPvIjvvFDOW/kTPjgN54b4PUBQFp9PB//3vEZwuZ52SaJQ4Cll7YiKp+VvlYWsk7uHB5cOINTStPHWL1oAHoCx4qJ22WIeKWjZs+VxPcK4Rao5nRMIfPU7SCraX9UrMP6c16RZxLZ3CrpQHUQhxQRr67N8Z+uzfz7pdfLfu3P/TeIqysynKysLHzw//iIhKWZtb9uvPKxuqzsr7+Iw5VS7vM/pe+oyueqqW4OgY7vnmOwqzsrBmZ+MfEVHt0OVed91Dz7/cRe7x49iLi/ELC8MSHFxpu5rqqPfx4fo33+Gql/9BfvoJLMHB+IaEetY/+ut0ivPzMZX1anto4pRL8rkxNWuOsWkUthNezJ2sqmiqinL6MGdFqTHjM+Ae9tyzF7qy++7TvAUh99xLzvhxuKz1+6Fas9koXLII64Z1+PXthzmpuwQWhRCiEahyCcTFSq8z0L/bVSzfOAeH8495cZasn0n/bsMx6CtmF1QUhc9e/o23nhxbYXl5EpRjGame7QBimzT3BBPPlJl7kn9/+Rit4jvyzF1voqruP7Vt+9aRX5TLtf3v8AQTAbq26U1UeDxrz+gxWRudWvbwBBMBWsS1A+BUjjtz784Dm3C5XAzuNdJT9/rMx3g4fwvTD7wpwcRzyj1PoaqoKIpaFmRU6loUqO4goqJq7v9FRYcBPSZ0GMqGM5/7YOKI+Oc8wUSb08ryYz+y6MiYcxpM1CkGBsY8QKewYUjGSCHEpcI3JISIli0JbNq0UjCxUT7xy37Q9QsNJaJly7POg6ioKsExMUS0bFllMLG2fHx9CW/eokIwsaxxhzkwEEWVVyO/Xr2938lgrPKeoVT/Pao5HBSuWFaxmKhoQu9/EF1ISIOci6uwkPz5czn1yYcULl2Mq6BA/tiFEKIhYy5yCcTFbEivUcxe/gvrdyyjT+ch7D64lROZR3iu58gqty8ptbJw7TRSDieTW5CN3WGjqNjd+Dg9KAkQFRFfZRmF1nxe+/IxnC4HL9z3gWdeRIBjGWmAu1fhhp0rKh7bZqUww/vASURoxcQU5YFSh8PdYzIz5yQAkWdsV5fM1ofyNrH86PfyYP2JlLLsJi4caDhRNAUUFUVTa9rJ3aav0BNRKeuN+GcED6t4sUAj1BLjfgHQnPye9gWnig+d0zoEGCO4IuZeQk2x8qAJIYS4JPnEJ2Bo0gR7enrtv8P1erApKGcMcVYUpcJooDNZN2/C7/L+6IKCPMt0wSGE3v8QuZMmYEs93CDn5LJaKVy+lMKVyzG1ao25c1eMLVqg6ORVWAgh6kN+hhMXtYSoljSPbcuitdMBWLRuOglRLWkW06bStnaHjVf++wDj53xBaGAkA3tcy3UD7qR/0ogqyzYZq04+snj9DIpLisjNz2LJ+pkV1pUHJS1mP3zP+K9nx4Fc0f0q7/+IzzJkufyY+jMaTQa9j9fHSgzsxuC4hzHrA+ThOsc0NFzYsVOMjSKclOLCgVOx46QUp1KCEzsunJQPi1YUDUXn7olYHkx0hxEN6PBBRc/50gtPQWHK/n8CdlRFx7D4x2gd3PecHbtj6BBGNX9JgolCCCEueX7denj/XaqvHJxT1LO0MVwuCldWTpComi0E33kPljrU42zHK0nZTc7E8WR88B5503+jJGU3ms0mN10IIepAfpYRF70hvUbx1ZR3yco9yZrkhdw+4q9Vbrdx5woOHk3huXvepW/XoZ7l67cv9ep4HVp059WHPuU/415l7LQPaJXQ0TMkOcjfPYRjcM9RdG3T+5ycv8XkB0ChteIwj8zc9DqVF+PfgVEtXmFd+hQO5m6QB6xRaWg4cWBHw3nWLVEc7lCiokennPnxrqBDfw6yM9eVQom9gJ92P0fPJrfRKrg3fZreTrx/F1adGE+RPadRjhrl24ZuEdcRZo6Tx00IIRrZwMefpLSwwDMVjDg/mZq3QOcfgLOg9iNnXHo9YKD2UAAAIABJREFUqt1+xld72RCJGnopFm/ZjF//K9AFVPyxWtHpCLj6WgxR0eTPmYV2RibperewSksoTt5KcfJWUHUYY2MxNmuOMS4eQ3Q0il7mXLzU2KxWdAYDOplvU4hak29zcdHr320EBr2BH2Z8Qqm9lP7V9AIsn3MwpkliheVLN852Nzy02iXDaBbTBqPBh8dv/z9CAsJ5//sXKC51Ty7dNrELAFtTVlfab+22JZzKPnFmc6fe5x/X1J1del/q9grLV21ZUOcyjToL/aLvZlDcg5j1/vKQNTgNJzZsWLFTctZgYjmdYsCgmioEE91TJhrQ43MeBxNPe4FAZX36JCbvexmwE+3Xluubv0KroD4Nepymvq25KuFphsU/LsFEIYQ4R1r260+HEVfXOLeeOB/eEFUsnbt4uY8OrYoeicpZ7rXmdFK0ZlW1681dkwh94CH0oWGNd74uJ7bUwxQuWUT2D99y8p03yfrqC/JnzcC6cT22tFRcJSUX3G3MTktj06SJLPnvJyz57FPWjx/H8Z075fmugsNWyvuX92bJZ582eNlFWVlsnfobKYsXyYUWFx3poSguehaTH706DWbF5rn07DiQAN+gKrdLjG4FwLyVk7nzmscptOYzfclPnizIR9IPoGnaWRtGpx/32Xve5qVP7uN/E9/g2bvfpml4HH06D2HuyslERSTQu/NgXE4n81ZPYeK8MTx5x78ZeNm1APiZA0g7sZ8j6QexmHzxNdctcNeuWVciQqKYOP8rQoMiSYxuzdaUNWzbu67e1zbWvxMRLVqw/sRkDuZtlIetnjRcOLHhwrtf4XWKHvWMHolK2Ue87gL9mC91FPPT7mdJCh9F+7BB9I26g4SA8t6KuXUq088QSougy2geeBkBxnB54IQQQojq2rHtO1CweiWcnr35rA0SA7gqDh9WVAVNq7mXonXTRvz6DUC1WKp+YY1sQuhDj1Cw4HesG9c3/sm7XNjTT2BPr/hDv2rxRR8aii44GF1gELqgIFQ/f1T/AHR+fu76nw+9bzWN+e+9y8ZJE7AEBhKa2AxVryM7NZWCjPdp1rsPN77/IcZqrrdoWBsmjGfVt2NRFIXHZ88lILKJXBRx0ZCAorgkDOk1kuWb5jC4mmQsAB1b9uDaAXcwa/kE5q2ajEFv5Mo+N/L0XW+QX5jDb4u+Z2/qDl5//KtaH7dlXAfuuuYJvp/+MR2ad2NY35t48i+vMXbqB4yd+j5fTnoTgKCAUB644XlPMBFgRL9b+GLimzz5zk0M7X0D9416pk7nrtPpeen+j/jvL//i3W+fQ1VVerQfwDN3vcVDr13tCZjWlY/OQr+Ye4gP6MKaExNxuhzywHnb7qtjIFFRdOiVysMy3Fmb9VzondAVdGw5NZMd2b9za6vXifZrx6jmr7A+/Tf25a456/5GnYUmlhbu/3xbEmKKKcuQLYQQQoiaqGYzpmbNKdm/r/btGb0exW47s7Fy9v1sNqwb1uM34Irq2wQGIwFXXYOpTVvyZk3HmZt7zq+Jy1qEzVoER9Kqv24mM4rFjOpjQjGZUH18UAyGsv+MoNej6HSg0/2RCVtR0AUEYmrfoUHquWvB72yc+As977qbQU88hVo+v6WmsWHCL/z+/rus+vYbBj7+pDzojf3MOBxsnTaVTtdex65589g69Tf6P/KoXBhx0VC02o7jFOISUWIrJic/k5CAcHyMJs9ya0khZh/fWvdQPBu73UZmbjpGo4mQgLAqA3vWkkKcTif+voENcsxSWwl6vQGdquNU9gkeeu1q7r/+Oa4ZcEeDlG9zFpNfmoGq6uRBqoViRz47sxeSVrDVPQdirT+5FfQYqnxmVPRlyVb+5BcRRYeq6NApBnSqAb3q/l8VfdkyPYqiQwUKSrNwaDZCzdUnRGkbPJBwS4Ln30cLd3EwfyMulxOD6oNeNWLUmfEzhBJgDCfAGF42HF8CiEIIIUSd2o2HDpI9fap33//F1kq9GjWXhuasefoW1WIh4unnUGoxf51mt1G4fBlFa1aDy3lRXGvV1xdLUnd8+w+od/bp+e++w8aJv/C3hUvwDQmptH7tjz8QmphIy379/7jXhYVsnTaV9JTdaJpGZKtWdBl1PebAP0Z2bf51CkazmZb9B7B+/M9kHjxAQJMm9Lj9LwRERrJ/xQpSFi0EoHnfvrQdemWF4x7bsZ2UhQvIP3kSc0AAMZ270H74CHdgFXA5nSz/8gta9L2cgKZN2DR5ErlHj+IfEUHXG24kNCHRq/JqsnPeXA6sWglAfPcetBs2nPf69KTnXXcz5OlnPdtl7N/HjjlzyD12FIPJRFSHjnS69joMJlOt7sWu3+cz9cXnGf39j6wb9xNHtyXzxJz5Fero7XnXt05CNCTpoSjEGUxGM03DKgc2ypObNBSDwUjT8JrnbmuIY2qaiw9/fIlAvxAevPEFz/JVW38HoEVcuwY7J6POTJglXh6is3BqdrZlzmNr5iwcLu8yC5YH6SpzZ29WzmGvRJ1iIMAngjBTLCHmOAKMEecskBfj144Yv3byMAkhhBCNxCc+AdVsxlVcXPt2p06HckZAUVHOPiu4y2qlOHkrlu5nz+ysGIz4Dx6KpWs3ChbOpyRl94V/sTUoXLGMkj27CRx5I4amTetcVEBkBACpGzfQ7sphldb3uvueCv/OPXaMnx64D4etlHZDh6HoVNb/PI7148Zx93ffExzjfi/aMWc2aBqbpkwisGkUBrOZjRMmsHfZUi674042T55EXFI3jm7bSvKMaVzvctFu2HAANk2exLy33yS+e3eatG1HwcmTzPjnq6QsWshNH35c9pworBr7NdacbA6tW0tM5y5YgoLZMXcOmyZP4tHps/CPiKh1edVZ9MnHrP3hexJ6XEbTdu3ZPnsmB9dWHvmyc+4cpv/jFcKbtyCxVy9K8vJZ9PGHbJz4C6O//wkfv7O/p22eMonQhASiO3Wm4zW57F64gH3Ll9HqioGn/X3U/rwbok5CNCQJKApxkVMUlZjIZkycN4aTWcdoEdee4xmprNzyO5cnDaNNYhe5SOfQsaJdrDz+A/m2DG/vJHql6l6JCgo6jDR2EM/PEEKkpQWRvi0INyfiZwyVIcRCCCHExUpVMbVshXVbshdvlwaoKtuzqoCr5rBi0ZrVWLp1r3XSHl1ICEG33I792FEKlizCdvDABX/JHRkZZH0zBr9+/fHtN8A9PNpLna4dybpxPzH1pRdIWbSQVgOuIC4piYAmVQcp57/3NqWFBTw0ZSoBkZEA9L3vAb68YSQLP/yAmz/+xH0bVZW0zZsY9cZbtB/hTnIZGh/P4k8/IXnaVO4bNx6d0YijtJT/DB3Ert/nuwOKmsaGX34mpksX7vxqrOe4oWO+YMWYLzm5dw+RrVp7eu0lz5jOfT+NJ7J1awDaDR/Oj/eNZtf8efS86+5al1eVwqxM1o/7idYDB3HTBx95nrXZr/2rwna2oiLmvPUGsV2TuON/X3oyP3ceOZIf77+X1d9/e9Yh41mHD5G6cSODn3oagBaX98M/PJwtv/1aMaBYy/NuiDoJ0dAkoCjEJeC24Q/TMq4da5IXsz9tFyGB4Txz15tcnjRMLs45YnMVsy59Aik5y71vz6OiqoYqg3cqKioGGiOYqKAQbmlGrH9HYvza428MkxsphBBCXELMLVt7FVDUVNU9PdAZs2qpqorrLMOTHVmZlOzdg6l1G6/qaIiOIeTOe7AfPULhqhWU7tkDXMCzemkuCpcvpWRPCoEjb8DQxLskHr6hodz703iWj/mClIUL2L3APSopoElTWvbvT7ebbiG8RQt3+7SoiAOrVtH1+hs8wcTyMtoOHUby9Kk4bKXojT4A+FgstB8+wrNdREt3UssOV1+Dzmh0Bxh8fAiNj6fgVNmP54rCI79Nr/RMxHTqDEB2amqFAGBCj8s8QTWAqHbtAcgrT5LjZXmnS9u4EZfTSYerKmabT7r5FrZO+2N4/6H167AVFZF0w42ewB1AbNckwps3Z9/yZWcN3m2eMhlVr6fjtde5q62qdLpuJKu/+5a8EycIPKMX6tnOuyHqJERDk4CiEJeIbu360a1dP7kQf4IT1j0sPfo1hfYsr/etfohz482XGGyKonlgTxICkjDpZeiEEEIIcakyxsSgWiy4rNZa76Pp9CgOe52OV7RmldcBxXKGmFiCb70DZ3Y21o3rKU7e4tVw7fON42Q6Wd98iV+/Afj16w9ezFEe2LQp1/7rNa5+9Z+c2LWT1E2bOLR2DZunTGbLr1O45v/+TcdrriX7yBE0l4tjO7Yz/dWXK5SRefAALqeT3KPHCGvWDAC/8IgKgTi9j3vevtODkQB6k5nSggLPv21WK1unTeVo8laKsrNx2myUlK13ntGjNSg6usK/ywOVTputTuWdrjw4FxgVVbHtG1NxuqucNHfinZD4ylM5BcfEcmjd2prvXWkp22bOoNWAKyrMY9ll1PWs+nYsW6f+xoBHH/PqvOtbJyEagwQUhRCikWiai82nZrDl1Azvkq6UNyQUA6pSdeOxoYOJqqIjISCJNiH9CTHFyM0TQgghBCgKpsRmWHfu8KIBowNH5WHPiqqinTG/4plshw9jP34cwxkBH6/aTyEh+F85HL9BQyjdk0Lxtq2U7t8PmuvCu/4uF4XLllC6by8hd49GKespWOv2nV5PdKfORHfqTJ977yPr8GHGP/oI8999m/bDR+Asu08GsxmTn3+FfWM6dSGmUxcMZvMf5emqnqu7pkQoTpuNnx64j+wjaXS9/gaa9+2L0Wwh6/Bhln3xeeU6nyVw6m15FfYtCzbq9BXb0OoZQ8vLr0tVw+91BgNOh6PG4+ycN5eSggL2r1jBB/37VlqfPH0q/R5+pMJxz3re9ayTEI1BAopCCNEIih35LDk6hmNFu+rWGFaNqNUkWNGhR2mgj2+9aqR1cD/ahPTDrA+UGyeEEEKICny8DChqOl3VE7HUcnaWojWrCLrx5nrXW9HrMbXvgKl9B1xWK6V7UijZvRPboUNozgsn+KIPCyNgxNW1DiYWZGTgtNsr9XgDCE1IoMOIEaz+7ltyjx/HLyQUgLiuSQx84qlGqf++FctJT9nNDe++XyHz896lS855eT5lQdOS03pPAhRlVxxFZAl29yosyqo8usiam4MlKKjG42yeMgn/iAiuePTxSusy9u9j3bif2Ld8Ga0HDqr1ede3TkI0yueTXAIhhGhYmSWp/J72KUX27Lo0f93JV6oJJqoNFExUFJVWQX3oEHZlWVZmIYQQQojKfOLiQVXBVcsefopS5faKotRqvEbxzh34D7kSXWDD/dCpWiyYuyZh7pqEZrdhO3SI0oMHsB0+iCMj4/y88IqCb++++F0xCEVfu7afvbiYMTddT3BMDKN/GFdhrr1yJ3bvRtXp8A0JwcfPj4DISA6uWV0poHhky2b0JhNN27ar12mUDzMOTUyssHz77FkAaJp2zsqLaO6eO/L4jh3EJXXzLD+0bl2F7WK7dHUvX7uWFpf3q3B9j+/cSfM+fas9RnrKbo7v3MnlDz5Ep+tGVlrvsJWSPH0aW36d4lVAsT51EqKxqHIJhBCi4RzK38jMQ2/VLZiogF6tOpOz+wO7YYY5R1qac03i8/RocqMEE4UQQghRc/PEaMQYFe3VPlpV2YkVpXYZnF0uitatabzzMRjxadWagOFXEfbI40Q89wLBt96Bb5/LMSYkovj4/OnXXB8aSui9D+A/5MpaBxPBPXS5/yOPkp6Swk8P3MfOuXNIT9lNxv597FuxnCnPPs2htWvofuvt+Pi558nufe99pKekMP+dt8k7cYLSwkJ2zZ/HxCcfZ+PECfU+lyat3HNibp48iZKCAnKPHWPeO295hkmfOnCgUoKVxiovtmtXApo0ZfV333Jg1UoKMzNJWbSQTRMnVHg2w5o1o2X/AWz+dTI75symtLCQ7NRUpr74PI7SUnrdfU+19ds0eRIoCl1GXl/1vTX60H7EVRxcu4a8Eydqfd71qZMQjfZZJZdACCEagkZy5lzWn5xc5xJ0GKvtmaigq3cw0aCa6NHkBpoFdqcxskILIYQQ4uLkExeH7eiR2reKVB0KlZNjKIpSqx5p1k0b8et/BarJ1Ojnplp88WndBp/yZDCahjMnG3tGBo5TGTgyTuLMzsaRnYVWWtrItVHw7d0Hv4GDUPSGOpVw2R1/wT88nHU/j2PaKy9VWBeakMiwF1+i+823epZ1v+U2XA4Hq775mo2T3AFEg8lEp2uvY8izz9X7jOJ79OCyO+5kw4TxbJo8CZ3RSNKNNzHyjbew5mSz5vtvOb5jG3d+NbbRy1P1em764EOmvvQiE55wJ0UJjIri+rff5edHHsJ12jyEo958m7lvv8nMf/3Tszw4JpabP/oP0R07VVm30qIids6bS2LPXpUSv5yuy6jr2TRpIlt++5UrHnu81teyLnUSolE/sTRv+xgLIYSo2GjWXKxO/5ld2YvrXIZONaCiq6ZpqaLDQH2CgOHmRPpG34mfIURuWCNzupzsT9uFxeRLbJNmckGEEEJc8GwnjpM18RdvGkeo1qIqFmtoDmetivAfMhS/y/ufTy0+XFYrztxcnHl5OPNycRUU4CoqxFlYiKuoEK24GFdxMZrduyzXqsUXxWQicOT1GGPjGq5NYrdTlJWJo9SGb2iop1dilWfncpGfng6AX3h4lcOl68NeXExh5in8wiMwnBYoLi0sxMfXt3a9VxuwvLzjx0FRCGzSpMZt7SUlFJw8icFiwT88/Lx4Es/HOolLkwQUhRCiPg01zc7io2M4nL+pzmXUlM0ZFPQYqU8wsV3oQLqGX1PtUOoLUUb2cVZvXVj5aikKPkYzUeFxtGveFb3OcM7rll+Uy92vDKJtYhfefupb+SMRQghx4XO5SP/yczSbrda7qFZr5czKmobL6arVEFfVz4+Ivz3r1ZDf84XmdKCVlqLZbGilNly2UnA40Mr+w+lE08qug0vDZS3C0qMnisEgz5oQ4oIhQ56FEKKO7K4SFqT9t86ZnAF0ir6GYCL16pmoU/T0anoriYHdL7prf/TkYb6f8Z8atwn0C+GRm1+id+fBF+Q5Tpr/NW0SO9Op1WXyxyaEEOLPpaoYm0ZRmnq41rtoOhXFcUZAUVFQoFbJWVyFhRRv2Yylx4X3Pajo9CgWPVh85dkRQly0JKAohBB1UOIoYF7aR5wqPlznMlRUVEVfw3p9tXMqno1B9eGK2AeJtDS/qO9Dl9a9ePTWV/94+XC5yM4/xcadK5i5bDzv//ACrz/2Fe2bJ11Q52WzlzJx/lfcdc0TElAUQghxXjBGR3sVUESng9PmpCunqCqas3bDngtXLsec1A1Fp5MbIIQQ5xkJKAohhJeK7NnMSf2A3NITdS5DQUGv+qBV8xu9glLnJCw+Ol8Gxz1MiCn2or8XPkYTESEVJ71uEhZDu2ZdCfIP5dtpHzJt8Y+egKK1pIhSWwmBfsE4XQ72pe0k0C+Y6IgEz/4ul4vDx/eSX5iD2eRLfFRLTEZztXVIO7Gf7PxMQgLDiWtSdQDXZi+lqLgQs8lSqawz1xUVF7Dr4BacLifFpVZy8rMw+5gx+VjqVEdNc3EqJ53svFMYDT5Ehkbja5bs3kIIIbzjbaZnVF11jaBac+blUbx1C5Zu3eUGCCHEeUYCikII4YXc0hPMSf2AInt2vcox6wOwu6rPFOge6uw9g2picNwjhJhiLvl7dVmHAXw77UPS0g94ln0/42N+X/0b7z39I5+O/ydHTx5mQLerePquNwBYsHYqP838L/lFuX98UeoMjLj8ZkZf9zd0uj++Nk/lpPPOt89y4Mhuz7LE6NY8eusrleqybNMcPp/wOndd8wQ3Drm3wrqVW+bz6fh/edZ9Ov7/WLd9KQAT53/FxPlfcdvwh7lt+MNe13HV1gWMm/UZJzL/yMypU3X07jyYx277B2YfGYolhBCilm2M8uQVtZyCX1NVlKq292LYM0Dh8qWYO3e5IOdSFEKIi5l8KgshRC2dKj7EvNSPKHEW1qucppZWZJakVbtehx7qMNRZpxgYGPugBBPLOJzuYVY69Y+vOkNZkpZpi3/EZLRw76hniI10Z2JesmEWn094nYSolvx99LvENW1OTn4WE+eNYeay8djspfz1FnewUNM03v/+eQ4c2c31g+/hqr63oKGxfNNcPv7pH/Wq91+uepTI0GhmLP2Z4X1v4vKuV3p6YXpTx7T0A3zww0tER8Tx8gMfEx0Rj7WkiBWb5zFj6c8APHfPO/KgCCGEqBVFb8AQFob91Kla76MpCkpVAUhVBZerVmU48/KwbtqIb89echOEEOI8IgFFIYSohSOF21h45HMcLludy1AVlc5hV7Mza1H1jXVUlDp+NPeJuoMISzO5WWVWJ7uzQLdO6PjHl57eCMDh43v5+O8T8DGa3C88moufZ3+OTqfnlQc/ITy4CeBO7PLcPe/w1zdHsWDtVG4Z9iChgRHsTd3B3tQdtG3WhXuufcpT/k1D7ycz9yTHT6XWud5xTVsQFR4PQJPQGDq06F6nOm7bux5Nc3HD4NFc1mGAp/yWce0J9AvB7rDJQyKEEMIrhsgmXgUU0emqDhx6mW+ucPlSLF26ovj4yE0QQojzhCqXQAgharY7Zynz0z6pVzDRoJoYGvskRwt3oOGq4UO5bsHETuHDiQ/ocsndm+KSItJO7Pf8l3piP1tS1vD1b+8xcd4YTEZzpSHGAP2ShnuCiQCpJw6QmXuSNgmdPIG6P96F9PTo0B+Xy8X2fRsB2HVwMwDd2l1eqew+XYY2yrl6W0c/SyAAq7YupKTUWmH7G4fc6xlCLYQQQtS6PdOkiVfba2rVr5uK3rupXVxFRRSuWiE3QAghziPSQ1EIIaprvGou1p2cwI6sBfUqx6wPYET8sxwp3F5jIhcVXZ2yOkf5taVj2JWX5D3atm8DT757S5XrYps04/Hb/klMZGKldU3DKiasKZ9jsLxn4JmahLqHkWdkHQPgVLb7PkYENz1r2Q3F2zr26TyY2ct/YdOuldz3f8Pp2qY3nVv1JKldX8KCIuUPXAghhNcMEd59fyjVJWZxOb2ajxGgaPUqLF27oQsOlhshhBDnAQkoCiFEFUqchSw+8gXHinbVqxx/YzhXx/8dDY1tmfNqanLXqXeiWR9In6g7ULwdO3SRaBbThmv631bhOvoYTERHxBMf1dI9GXwV/CwBFf5daisBwGiseiiV0eBeXmp3b2ezuxPqGAyVtzfoDY1yrt7W0Wjw4c0nxrJgzW8s3zyP1cmLWLV1AYqi0LVNH/56yyuVejoKIYQQNb48hoR6Nf+hpqrVtlB0QYE4c3JrfWzN4SB//lyCb7tDboQQQpwP3wlyCYQQoqJ06z4WH/2CIntOvcoJMcUyIv4ZLPpAfk/7Ly7NUe22KjqoQ1CwT9TtmHR+l+y9Cg9uwqDLrqt3OWYfC0ClocHlypeXb1cevLOVBe9OZy3xLmmP3WFvlDq662nk6v63cXX/2yi05rN93wYWrJ3K5t2rePObv/Hxc+NRFJn9RAghRO0oej36kBAcmZm136maAKQxsinFXgQUAUpSdlOyJwVT6zZyM4QQ4k8mbxFCCFHGpTnZlDGNWYferncwMdLSkmsTXsSiD+JIwXaOF+2uuYGOzutjtAjqRVPf1nLjGkBsE/ew6LQTB6pcf+TkIQCiy4ZPh5cNdT6ZeazStqlVlKErG/JVVSKU9MyjjVLHM/lZAujdeTD/fPgzurbpzeFjezl+Kk1uvhBCCK8YwsK92l6r5ocrZ2E+xljvpwnJnz0TraREboQQQvzJJKAohBBAZkkq0w6+xuZT09HQ6lVWrF9Hrop/FqPOgktzsCFjylk+iPVeD1k26f1JirhOblwDiQqPJ7ZJM/Yf2U3aif0V1llLili/fSk+RhOdW10GQNtm7gQ463YsRdMq9rpYuHZapfID/UIASD2j7BJbMUs3zqr8TJS9fDmc9jrXcezUD/j7R3dXGcQsT9jiquWQNSGEEKKc3suAIrqqXzkdJ0/id8Ugr4/vzM8nb94cuRFCCPEnk4CiEOKSVuosYvWJcUw78G+ySurfW6t5YE+ujHsSveoeErsnZwX5tlM17qPUYfaJpIhrMerMcgMb0L0jn0FR4I2v/8aqLQtIPbGfjbtW8K8v/kp+US53XPUoFpN7eHnrhE60jGvPgSO7eeubZ1i3fSmbd6/mwx9fxlrsHvJ8emC6bbMumIxm1m1fwpQFY9m5fxNLN87hhY/voU1C50rbhwZFALB883xWJy8kee86r+sYHRHPvrQdvPrZQ6zYPI+UQ8kk713HT7P+y8otv9M8ti2xTZrJjRdCCOEVQ2iYV9trNU2toWmY2rX3ug7FW7dQsnOH3AwhhPgTyRyKQohLksNlY1f2YrZmzqLUWdQgZbYLGUyfJnd45qSzu0pIzqz5F3R3ZmfvhJnjSQzsLjexgSW17cMrD/yH76Z/zPs/vPDH9Q6K5NFbX+XK3jd4limKwov3fcDH4/7Bhp3L2bBzOQBdWvfi+Xvf44F/jajQM9Bi8uP5e9/j8wmvM2725wCYfCzcOORe2jdPYs22xThOm0uxS5vetGvWlV0Ht/Ded8/Tt8tQd4ZmL+o4vO/NOJ1Opi35kQ9/fNmz3GgwMqTnSO685jG56UIIIbx/gQwL9W4HtfppXexH0gi4cjile/egORxeFZs7YxrhTaPQhYTITRFCiD+BommaJpdBCHGpsLmK2Z29hO1Z8yl25DdYuT0ib6JL2FWcnlglOXMuW07NqHE/HT5eD3e+Mv4JIizSs6wxZeVlUFCYi6/Zn/CQpjVum5OfRV5hNiEBYQT4Bde4raZpZOVlUFxSRGRotCe5S3XbZuaexMfgU2W5ta2jpmnkFWaTX5iD2ceXkKAIz5yOQgghRF2k/++/aDZbrbdXi6pOWGaIjSPk7nspXLYY5ahIAAAgAElEQVSEgiWLva6HPjKSsPsfQjEa5aYIIcQ5JgFFIcQlIaf0GLuzl7A3dxV2V8NN5K0qegZE30+LwF4VltucxUw58Ao2Z3H1H8Co6PCuARzl15ZBsQ/JDRVCCCHEnybzl5+xn0yv/UtnsRWlinl7FZ2O8OdedJf55efeZY8uY2rbjuCbb3VnkxZCCHHOyJBnIcRFq8iew6H8DezPW8Op4sMNXr5ZH8DQ2CeItLSotG5XzuIag4ngTsbirc7hI+TGCiGEEOLPfYkMCfEqoIiqQhUBRc3pxJF+AkNMLIHXjiTru7Fe16Vk9y7y580hYMTVoChyc4QQ4lx9F8glEEJcLFyai8ySwxwt3E5awdZGCSKWCzMnMDT2cfwMlecRsrtK2JW96CwlKChe5sVq6tuaUFOs3GghhBBC/Lkvkd7OW1hDYhZbWiqGmFiM8Qn4XtaTovXrvK5P0fp1KGYz/gMHy80RQohz9V0gl0AIcaGyOa2cKjnMKetB0q37OGndh81V3OjHbRt8Bb2b3oFOMVS5fnfO0lr0TvR+DrsOYUPkpgshhBDiz3+JDPYyoKhW33PQfiTN8//9hw6j9MABHFneD30uXLbUXcYVg6SnohBCnIvvArkE4lJis5cC1JgI4WJQUmpFrzeg1xkugrPRKHEWkW/LIK80nTxbOtklR8kuPUqB7dQ5rYlRZ6Ff1GiaBfSodhunZmdX9tknFVc0Hd7kYgkxRRNpaS5/xEIIIYT4818ig4O9a82parXNHtvRI6BpoCgoBgNBN91M1jdfoTmdXtercNlSXMXFBA6/SuZUFEKIxv4ukEsgamP/kV0cPJJS4zZmky/9koad1+fx4sejUXU6Pnh2XO0bQJrG5xNeZ+f+jfTsNIibr7yf4pIivv71PV564KMGqdeGHcuxlhQyoPtV9S6rpNTKLc/15sah93HPdU+dZ3dAw6k5cLhsOFyl2FzF2JxWbK5iShwFFDsLKHEUYHXkYXXkUmTPodCeicNl+9NrHuPXgf5R9+FrqLkBvS93DSWOghq3UVBRvPzlvHVIf0B+bRdCCCHEn08XGOTV9koNQ561khIcpzLQR0QCYGgahf+wEeTPmVWnulnXr8OVl0fQDTeh+PjIzRJCiEYiAUVRK2uTFzNp/teoNfzSFxESfd4HFK8ZcHuNDZqqJO9Zy55DyQztcwOL1k5n1rLxqKqOW4c92GD1mrJgLCdOHWmQgKJBb+Tu656iTWKnc359T1r3sfL4j9hcxWhogIZLc+LUHLg0B06XvWz5hcOk86Nnk1tpFdSXswX0NFzszF541jK9He5s1FlICEiSDyIhhBBCnBcUvR5dQADO/Pxaba8pinsUcjXNQFtaqiegCODb4zLsR49QvC25TvUr2ZNC5jdfEXzLbejDw+WGCSFEI5CAovDKZy//Rkxk4gVb/0E9r/N6n0D/EJ6++y2axbRm1MC72bhrBTZbKX27np/z2el0em4aet85PabDZWP9ycnszF6EUTWdk3kMG72hjEKb4AF0j7wRk86vVvukFSTXahi2t8lYmgV2R6fIx7UQQgghzqMXyaDgWgcU3Q0gHWhVD2O2p6VC98tO21Yh8NqRODJPYT9+vG7t01MZZH71BQHDRmDp1l3mVRRCiIb+HpBLIBrargNb2Lx7FYN7XkfT8DjP8iPpB1m2cQ49OvSndUInZi37BYvZj54dr2DuysmkndiPxeRHv27Dad+8Ym+svMIcFqyZSurxfWiaRmyTZgzuNZKwoD9+ydQ0jS27V5O8dx15hTn4WwLp0qYXSW37eoaXzl0xCUVVGd73Js9+h4/tZcmGWWTmpBMSGMEVPa6meWxbz/rE6NbsPbyd76Z9RGbuSfwsAbRN7FLluace38fSjXM4mXUUo8FE6/iODOp5HT5GU4Nd37Mdw+G0M2HuGNq36EbXNr09++09vJ1VWxdUOIcB3Ud43WPzTOnWvSw7NpZ8W8ZF8wzH+XemR8RNhJhivNpvR9bZeye6g4neNWhbBPWSDxYhhBBCnFd0gYFeba8pSvXzKKalVm4zGQwE334nWd+MwZmXV6c6anY7ebNmULJrB4HXjkLn5dyPQgghqicz1YoGlxDdiqUbZvHp+H+hae5xDS6Xi/+Me5WVm+cTH9USgMXrZzJ3xST+8d+HSDmUjI/RzKZdK3n5k/tYtXWBp7y0Ewd47M1RzFz6MxaTLwF+Qfy++jcef/N69qft9Gz35eS3eP2rJ8nIOo6/JZCTWcd4fcwTfDn5Lc82C9ZMZeHaaZ5/r9ryO39771aS96zFzxLAnsPbeeb921mwZqpnm7krJvHch3eyP20noYERFBbl8Z9xr/L2N89WOO9lG+fw1Lu3sGnXSsKCmqAqKt9O+5Bn3r8da3Fhg1zb2hzD4bAzaf7XbNu73utz8IbDZWNN+nhmHXrnogkmxvp15LrEVxgW9zevg4mnig9yqvjgWbdTvBzuHGKKJsinqXywCCGEEOK8og/ybh7FmpKkuIqKcGRWHuWh8/cn5M57UM3metW19OBBTn3+KQVLFqHZbHLzhBCiIb4H5BIIr76MbSWUlFqrXGcw+KBTdVhMvjx991u8/Mn9/L76V4b1vYmZy37m4JEU3n3mR0xGc1mbQmXP4W08fPOLXN3/dgAKrfk8+fZN/DjjU/p2GQrAmMlvoWnw8fMTCAl0z4Fy6/CHefLtm/hqyru898yP2OylzF81hRsGj+bu0xKRLFw7jSXrZ1JiK/Yc9/Rz+XzC63Rp05t/PvyZZ37If3/xGN/8+j4Dul+FQW9kxtKfadusC28+Odazb8ycL/hl7pccOraHxOjWFJcW8b8Jb9CuWRKvPf6lJ7vy4F4jeek/9zJl4bfcfe2T9br2dT2Gpmm1OgdvnLTuY9mxseTZTl7wz7Sq6Gge2JMOoVcSZoqvczm16Z0I3s+fmBjYXT54hBBCCHHe8TYxi6YqNY7RsB0+jD6s8nyH+vBwQu66h6wfvkMrLa1zfTWHg8JlS7Fu3oTf5f2wJHVHMRjkRgohRB1JQFF45en3bqt23d9Hv0u/bsMBaN88iRuGjOa76R8TH9WCn2d9zq3DH6ZVfIcK+xj0RoadNvzYzxJA786DmblsPJm5JzEZzWzft5Er+9zoCSYCBPoF06fLEGYvn0B+YS56vbsxcPDYHmz2UowGd0a3Ib1GMaTXqCrru23vegqt+QztfX2FZDN/vfVVcvMzAVAUhS/+Md3T07Jc68TOABw7mUpidGu2pqyjuLSIYX1v9AT6yq9DXNPmbNi+rN4Bxboeo7bnUBsOl42NGb+yI2vBBZdc5UwBxgjaBA+gVdDlmPUB9SqrwJ5JWsHWs27nbe9EUIgP6CofPEIIIYQ473gbUOQs0+zYUg9h6d6jynWGqGhC77qHrJ9+qFdQEcBVUED+3DkUrlyB72W9sHTrjmqxyA0VQggvSUBReOXhm18iNCiiynUt4yoGC/9y9aNs2b2Klz65n5Zx7bmliqzIESFRFYJjAJGh0QBk52Z45veLjqjcc6xJmHtI6smsY7SMb8/okc/w7dQPGP3qELq06UXXNn3o2XEgAX5VN3aOn0qtcLw/6tSUiJA/hpiWlFpZsGYquw9tJTc/G7vDRlFxAeCerxDgRGZaDfWMJXnP2npf+/ocozbnUBvHinaSkrPsgg0mWvSBJAR0p2VQbyLMzfB2LsPq7MpeXKtr4m0ylghLIhZ9oHzwCCGEEOL8e5H0cg7FswUU7YcPgaZVmzzFEBNL6Oj7yB73I66ionrX31VQQMGiBRQuW4KpQ0csXZIwxsdL8hYhhKjt94BcAuGNzq171jrLs15nICIkmoNH9xAaFFmhF6CnYWAwVlqm07kfS6fLCbgzwSlVfLGXByKdLndAbNSgu+jc+jKWbZjDpt2rWLn5d8YY3ubeUU97hlSfzuE4eyDN7rDx0if3cTwjjWF9byCpbV9MRgvHMg7z8+zPK5dVRT0NOgMOp6Pe176ux6jtOdRGvH9Xbmz+OsuOf8uJopQL4pkN9okmzr8zcf5diDQ3r3cSmjPZnFb25a6u1bbeDneO8+8sHzpCCCGEOC8pRiOq2YyruLh2O6g1B+pcJSXY009gaBpV7TaGplGE3vsA2T//iDMnp0HOQ3M4KN66heKtW9AFBWFq3wFz23YYomMkuCiEEDWQgKJoNIvXzWDd9qXcfe2T/DTrvyzdOJsrul9dYZu8guxK+5X3nPOzBHiCi7n5WZW2yy/KBcDf949sbYnRrUmMbs3oUU+TkX2CLya+wVdT3qVLm95ERyRU2D/AL7jasstt2LGcA0d28/y973N50pWe5eu2LamwXaB/SA31zCHAN6je17Oux6jtOdSWvzGcaxKeZ1f2YtadnIzDVXpePXeBPk1oYmlJlG9bonzbYtEHNerx9uSuqNU1UOqQAyvOv5N8kAghhBDivKULDKp9QBHcQUVX9aM6bIcO1hhQBNCHhRH24CPkTByPLTW1Qc/HmZtL0aqVFK1aiernh0+z5vg0a44xIdGd1foSCTA67XaK/p+9+46Pur4fOP763r7LJZe9ByTsvTeCgCjIUFxYV9WOn7O1tbZVW7WV1tZa6151T4YiIHvKkr0JG0II2Xvcvvv+/ghG0zDuQoAkvJ8+8sB8v5/x/X6+d998732fUVqKwWzGFBYmL3QhxOnvx9IE4kIoLM3j7VnPMfnK27hx7L0Uluby5oy/0TWjDzERPwwnLqssJrcom8SY1LptB4/vwWQwkxCTglajJTw0ih0HvgMeqVfH7oObsVkjSIxJoaDkJNsy1zF26A1oNbW9wGIjE7hx7L1szVxLTv6xBgHFTqfmENy+bz39ug6v2z7v28/4evlH/P3X71FUlgdASnz9XpkrN38DUDcvYZf02nnudhzYUK8sp9vBoeN76dNl6Hm3aWPrCPQcgqPQJXI0KdYefHvyXfLsBy7J6yzUEEOkMZlocxrRpjbEWjIwaa0X72FL9ZJZujLAFgsuoBhpSsaiD5ebiRBCCCGa74dJmw1Pfl7A6VVFi8KZR+64jxwmZMiwc5ajsViIvPNuqpYupmbDdxfk3PzV1Th27cSxa2dtnaGhGJJT0CcloY+LRxcXjzY0tNkFGfP2ZZK/fz/W6GjaD7/ijOmOfLeeyvx84jt1IqFzlx+ukd/PzEcf4fCaNRitVu756BOi2rSVF7sQouHfAGkCEYwNO1cQYYs+4/5+XYYTZg3nPx8/iS00itsnPATA3df/hi2Za3nxoyeZ9vA7dcNOQy02Xvnsae6/5UmiwuNYs20RW/auZsyg6+uGNE8Zczfvzf4XH819iUlX3o6qqixYM509h7fw0+seQVE02J3VvDnzbxw9eYApo+8iPCyK0opi5q36FL3OQEZqlwbHmhKfTr+uw1m0bhapie3o3WkwWbmH+GLhWyTFphEbmUh6cicAFqydwZ0TH6baXsns5R/WHX92/hFUVSUlPp0B3UawaO1M2qV2YUDXEZRVlfDe7H/h9rq4fvRd52xbt8fJ8o1zTrtvcI/Rja4j0HNQGvEwFGqIYULb37OnZBmbC2fh9bub9PWmKBpCdBFY9VFYDVHYDHGEGeKwGeKJMCag0xgv6fvhaMUmHN6KwM4lyOHOyaHd5IYjhBBCiGZNGxbkPIoa5fsZjU7/PJxzAtXlQjGe+xlP0WoJu2Y8htQ0yud+jep0XtBz9VdV4dyXiXNf5g/HYDSii4xCGxmJLjISbZgNTWgo2rAwNCFWNBYLik53UYOOB1asYO2776AzGPn10uWYQkMbpPG6XHz1+9/hqq5m2L0/rxdQXPzP56guKuKXM79i++yvmP7rh7nno0+lp6IQogEJKIqgfDTv5bPuf+7XH7By8zfsPbKVv/3qvbrVls3GEB6c+meefuN+Zi//kClj7gYgPiaZK/qO43cv3I7dWTu5cr+uw7l3ym/ryrxu1B14vW6+XPY+s5a+B9QOh75j4kNcP6o2iNY2qSOP3vUcH817mcXrZtXlbZPUgSd+/h+iw+NOe7yP3vUcr33xF96cPu3UnI0woPtIHpj6JwC6t+/PpCtvZ96qz1i4ZgZ6nYFrht7Ib+/6G5XVpXy59D0OZu1i2sPv8tuf/p03pk/jpU/+jO/UfIbx0Sk8/vP/0LHNuYeu2p01vPTJn0+7r+OTPbCYrY2qI5hzaByFblFXkRrak9Un36PcnYsOIwoKiqJBgwatRo9G0aFV9OgUPXqtCb2m9segtWDUhmDUhGDUhmDR2zDrbJi1YVh0tiaf87CpqKjsKVkaYAvV/heMZGsXueEIIYQQolnT2oILMqmK5uxPRH4/7mNHMXbqHHCZpi5diUlOoWLubFyHD1/c50GXC09eLp683DM/B+p0aMxmFIMRxaBH0df+oNGiaDSg0aC12Qi7elyTBR6NISEA7F20kL433dxg//4Vy0FVMfzP6tb28nLiOnTkqt/+Dq1ez9hHf0fGkCGUZB8nqVt3ecELIerf39TGjXcU4rw9+sLt+P1+/v27z/B43RSX5WMxh2KzRpw2vc/vo6g0D0VRiIlIOO0iLwAV1WXUOKqwWSMIMYcGdCzTF7/Nkex9/Oauv2EymBvsd7odlFUUEWmLxWgw/fBH11GN2RRSr3efy+2kuLwAs9FCpC3mgrRdY+oI5hzEuR2v2s7KnLcDSqtBiwZ94A/GulBuaP9M0EFIIYQQQoiLyZV9nNKvZgWewetF4zp7T0Jzj16ETZwc/MGoKvYd26lauhi/3d6i2lETEoI+Lh7bpOvQhp/flDerXnuVzV98RsbQYZTl5HDvJ581SPPpfb/EFBrKodWrGXTHnYx84MG6faUnstk1dy6lJ7LRm8yk9ulD9/HXotH90BfJ63axd9FC8vfvx223ExoTS5exY4lt30HeFEJcRjTSBOKSOhXP1usMJMSknjGYCKDVaImPTiYuKumMwUTg1LyKqQEHEwGs5lBKK4t44cM/nnZ/7ZyOqfUCcQAWs7VBIM5oMJEUm3bBgomNrSOYcxDnfOGyq3hRwKmDnT8xMaSTBBOFEEII0ezpbEEOeQ5g5Inr8MG6zwjBla1g6d2HmId+jaVf/xa3gIrr6BGK3ngV+9YtjTv/H/F5vXS5aix5mXspPHSw3r7KgnyyNm+iy9ir8XrqT1d0eM0a3rrpBg58uxJbQgKgsuBvz/LxL+7F565N63W7+OCnd7HsxRfxOBwYraFkbdnMO7fewt7Fi+RNIcTl9DdAmkBc7vx+P6MHTsZqsfH2zOekQcQ55VTvpcSZHUSOIAOK1k7SyEIIIYRo9rShYbWBu0ADYJpzB/n8djuenBPoU1IbdUwasxnbhEmEDBxM1Ypl9eY8bO5Ul4uKeXNwZu6t7a0YbMD2+3JUlfZXjMBsC2fHnK8Z++hjdft2zZuLKTSUDleMqHfdvG4Xc596kviOnbjzv++h1deOruly9TV8/sB9bJk5nYG33UH21m3k79/Hra+9QcbgIXX5Fz33N7K3bqHr1dfIG0OIy4T0UBSXTIe0bnRoc+nn4sgtOs7jL93Lt1sW8Ojd/5ALI871iMaOom8CTt2Y+RPjLe2lmYUQQgjRAj5NampXOg74wSiwZyLn/n3nfWi6mBgibrmV6F/8H6YuXVtUj0XXkcMUvf4K9m1bG91bUavX0238ePYsmI/P46nbvmvePLpdMx6twVAvfdbGTdjLyxnwk9vqgokAGYOHEJmWxv7lywHwn5rHPXf37nr5r/nD44x7/El5TwhxGZEeiuKS+cWNf2gWx5Ec15Z/P/a5XBARkOyqXRQ7jwfzpB1U+RHGREy6UGloIYQQQrQI2jAbvsrKgNOrGg2K33/WNK79mYSOGdskQUB9YhIRN0/FW1JMzYbvcOzaiepyNft2VV0uKuZ+/UNvxUasstz7uuvZ/PlnHPx2FZ3HXMWJHdspPZHNlMn/bJC2JDsLgF1z53Bozep6+zx2OyWnrnH64CF0GjWab998nZ3z5pA+aDAZQ4bSbuiwBkFKIUTrJj0UhRAi0Ac7/GwrmhNUnmDnT4wLkd6JQgghhGg5gp9H8dxBQl9lJZ7ck017nFHR2K6dSNxvfodt4mT0SUkton1dhw9R+tH7jQqCxrbvQELnLuyY8zUAO+d8TWz7DsSfZhXt73sxGq1WTNbQej8drxxN9/ETagMIWi03Pv8CU195jTb9+nN47Rpm/vYRXp04nuxtW+UNIcTldP+XJhBCiMAcLt9AuSsvqDxadKgEPlQlXgKKQgghhGhBtGFBBhQ1WvD5zpnMuWcX+qTkJj9exWjE0rcflr798JaW4ty9C8fe3XgLC5tl+5q79yBs3LUoRmOj8ve67noW/ePvVBYUsG/ZUkbc98Bp04VERgHQ87rr682NePpGVGg3dBjthg4DIGfXTuY8+QSz//h7frV4mbwphLhMSA9FIYQIgNfvYnvR3OAeWFGCCiYqKMRZMqSxhRBCCNFiaC9AD0UAZ+ZeOMfQ6POli4zEOmIkMfc/ROyvfkPY+AkY27VD+dEcgpfsg3pICBG33Er4DTehsVgaXU63cePR6vUsf+lFPC4X3cZfe9p0Kb16A3B0/foG+w6sXEFFXu2X6if37CZzyeJ6+5N79KTHxElUFRXhqq6WN4UQlwnpoSiEEAHYWbwQu7ciqDwGjRWf3xNw+khTMnqNSRpbCCGEEC1GsAFFVRPYcnV+ux3X4UMYO3S8OOcREUHIgIGEDBiI6vPhyc3FnXUMd/ZxPHm5+C9ioMzUtRu28RPQhIScd1lGq5VOo8ewZ+ECOl05Ckt4+OmfQ1NT6TzmKrbMnE5UmzZ0Gj0Gv8/LtlkzWf3Wm0x65q/0mDiJ3D17WPKvf1JTVkrn0VehN5spOXaMvYsWEpORgdFqlTeFEJcJCSgKIcQ5VLjz2Vsa/PCNCEMCxc7sgNPHSu9EIYQQQrS0D5TBDnlWAh8k59ix7aIFFOsdolaLISUFQ0pK7QZVxVdVhSf3JN7CArxFRbU/JcWoHk+T1auxWLBNmFS7KnUT6jX5evYsmE/PydedNd2kvzzLkn/9kyXP/5MF0/4KgDUqmqsf+wM9Jk4CoP8tU6kuKebb119j8T+eO9VgChlDhnL1o4/JG0KIy4iiqo1ch14IIS4DKiqLjv+bAvvhoPJZ9ZHoFTNV7pKA84xMvpfk0G7S6EIIIYRoUfJffQnV6w04vaYmwN5+ikL0g79u1ArHF+dBUcVfXY2vohxveTm+inL8VdX47TX4a2pq/7XbUd1u/C7XGYdwa0JCMKS1wXbtxCbplXi+vG4XlfkF6E0mrNHRKJqGQWDV76e6uBiv20VoTCy6Rs7xKIRouaSHohBCnMWBstVBBxMBukaOYWtBcHMuxlrSpcGFEEII0eJow2x4SwP/ElVVFJRA+rWoKo7tW7GOuLJ5nriioAkNRRMaij455dyn4/Wiut2oPl9tcNHvR/X7UF1u9ImJzSdIYDASmZp69lPXaAiNjZUXvxCXMQkoCiHEGVS6C9hc8GXQ+SJNKZh14UHlCTcmYNBapNGFEEII0fI+VIaHBxVQVDSagFZ6BnBs20LI0OEoupb/0VXR6VrFeQghBMgqz0IIcVo+1cOqk+/iU4OfF2dA3I0UO7KCyiO9E4UQQgjRUgW/0nPgH0P9djvOvbulkYUQopmRgKIQQpzG5oIvKXWeCDpf27B+xFs6UGg/GlQ+CSgKIYQQoqXS2oIbmaHqg+ulV7N+HcjU/0II0axIQFEIIf7HwfK17C/7Nuh8eo2RfrFT8PhdlDlPBpU31iwBRSGEEEK0TLrw8CDTRwSV3ldagnNfpjS0EEI0IxJQFEKIH8mt2c+G/C8albdP7HWE6CModmShEvi36CH6CCz6cGl8IYQQQrRI2iADhKpOH3QdNWu+lV6KQgjRjEhAUQghTilxZrMy5038qi/ovLHmdDpFXAEQ9HDnGHNbaXwhhBBCtFi6sDBQlIDT+6qrMLbvEFQd3uIiHLt3SmMLIUQzIQFFIYQASp05LMl+GY/fFfxDtMbAsMSfopy6pRbajwSVX+ZPFEIIIUTL/lSpQRfEwiyq2425R6+gq6n5diWq1yvtLYQQzeHWL00ghLjcFTuyWJz9H1y+mkbl7x97I2GGGAB8qpdix/Gg8ktAUQghhBAtnTYiuGHPijUUfVJSUHl8lZXUrF8rjS2EEM2ABBSFEJe1k9V7WXQewcQ2YX3oGDGs7vdS5wl8auDfnBu0FmzGeLkQQgghhGjRdBGRQaX3lpUSMvSKoOuxr1+Lr7xMGlwIIS4xCSgKIS5TKntKlrLsxGt4GzHMGSDUEMOQhNuBH+YMCnb+xFhzWxQUuRxCCCGEaNF0kcEHFI3tO6CPTwjuCc7no3L+PGlwIYS4xCSgKIS47Lh8NazMeYcthV8FtRrzj+k1RkYn34dBY663vcB+OKhyZLizEEIIIVqDoHsolpYAYL1ydNB1ubOO4di2VRpdCCEuIQkoCiEuKydrMpl7dBrHq7Y3ugwFhSuS7iHcWP8bdVX1U2g/FlRZsZYMuShCCCGEaPF0kVFBpfeWlAJgSM/A0KZt0PVVLVuM71RQUgghxMUnAUUhxGXB4a1kTe4HLM1+hRrv+c27MzB+KinWHg22lzpzgho+rdMYiDQly8URQgghRMv/YGk2ozGbA07vq6pE9XgAsI66Kuj6VI+H8i9nyqrPQghxqe770gRCiNbMp3rYXbKYr448xZGKjeddXs/ocXSKOP0E4vlBDneOMbdFo2jlIgkhhBCiVdBFBdtLsbaHoT4hAXPvPkHX5y0soGrRfGl4IYS4FPd8aQIhRGvk9bs4UL6GPSVLcXgrm6TMblFX0Ttm4hn3F9gPBVVenAx3FkIIIURr+nAZFY07Jyfg9J6SIvTx8QBYR47GtX8ffocjqDodO3egi4nFMnCwXAAhhLiY93xpAiFEa1LtKWF/2bccLF+L2+dosnK7RV1Fv9jr4Th/fzQAACAASURBVAwrMvtVX/ArPEtAUQghhBCtiD4qOqj03qLiuv/XWCxYR41p1ArOVcuWoLWFY+zUWS6CEEJcJBJQFEK0eB6/k+yqnRyu+I68mgNNXn6fmMn0iL6aMwUTAYodx/H63YHffDUGos1pcvGEEEII0Xo+XEYHF1D0FBfV+93cqw/OfZm4jx4Juu7y2bOIuPlWDBnt5EIIIcTFuOdLEwghWqIaTxm5NZlkV+3kZM0+/GrTT8itUXQMTbiNDNugc6YNNpAZa06X+ROFEEII0aroo2OCSu8tKmywLezaiZS89Tqq2x1c5X4/5bOmE37zrRjapsvFEEKIC0wCikKIFkCl0l1MkeMYRY4j5NUcoMJdcEFrNOlCuTLpF8RZAvuWO9iAYlxIe7msQgghhGhVFIMBrc2Gr6IioPR+lwtfZQXaMFvdNm2YjbBx11IxZ3bwT4xeL+XTPyNs8hRMnbvIBRFCiAtIAorivLk9LgAMeuNZ0zlddnQ6PTqtvknqberyxKWnouLwVlDpLqLSXUiZ6yRlzpOUuU7i8tVctOOIt7RnRNK9mHW2gNK7fDUUO7KDqiMxpKNccCGEEEK0OvqY2IADigCewsJ6AUUAU7ceuLOycOzcHvzzpM9HxVcz8Y+9Bkv/gXJBhBDiApGAYjOSW5TNroObKC7PR6PREBORQJ9OQ4gKj2vWx/3Hl+9Gq9Hxz0c+OmMat8fN1N8PZfKVd3D35N+cd51NXd6Wvaspqyo5a5rEmDS6ZvSRF+o5OH3V+FUfqupDxY9P9eHzu/H4XfhUD26fA5evpu7H4a3A7i3H7q2gxlOGT/VcsmPXKBp6RI+nR9Q4NIom4Hx5NQcBNeD0Jq2VcFOivFiEEEII0eroY+NwHj4UcHpPYQGmdg1HboRePQ5Pbg7eoqJGHUfVkkV4CwoIvWY8ik4+9gohRFOTO2szYHdW8/r0Z1m3YwlajY746CQA8opz8Pv9jOw3nl/e+EdMRsslP9aSikI+X/gGD059qm7btcNvDSr40hx9uex99h3bgUZz5vMY1X9SUAHFnQc3knXyIJOvvOOyeB2rqGSWrmBn8fwmXV35YokwJjIs8S6iTKlB582tzgwqfXxIB5SzLPAihBBCCNFS6eOC6wzhyc8/7XZFryf85lspfe8d/I7GPVs6dm7HW5BP2HVT0AW5ArUQQoizk4DiJeb1eXjq9fs4fCKTn4y/n4kjbsNkMNf+AXTVMGflx0xf/Db5xTlMe+jdswa8LobMI9tYu21xvYDiqAETW8W1CA+N4oO/Lmuy8tbtWEppeeFlEVCscBewNvdDihzH0GvMLesmqDHSK/paukSOatQiKarqJyfIgGKStbPc/IQQQgjRKgUbUHTn54GqgtLwy1ZteAS2G2+h7NOPwO9v1PF48vMo/e9bWMeMxdKn32nrEUII0YjP0tIEl9b81V9wKHsPd1/3GyaPrB94MhtDmHrN/6HT6vlk/qssXDeDa4dPrcun1xsYO3hKvTwL1k5Hp9XX215UmsfKzd+QV5yNomhIS2jHVYOnYDGF1KVZvH4WRoOZIT3HsHDdDI6c2IfFZGVAt5H06TwEgBWb5rJ0w9d4fB4+nf8aibFpXNl/AovWzURRFK4ecmNdeWu2LWLrvrUAdGvXj+G9rznt+R88vpv1O5dRUl5AiDmMTm17MqLvOJT/6fEYaHl5xSdYsXEuecUnMBpMdM3ow4i+49Fqm/al7va4WbNtIUdP7sfhtBNli2Vo77G0SawdrvH5wjfYlrkOvU7Pp/Nfo3fnwXRJr+3deDzvEN9uWUhB6UmMeiMd0rpzZf+JGA2m056zqkL3dv0YPXASr03/K13SezNqwCRcbiezlr5Lt/b96NlhYL1jm7nknQbbL0TbqKjsLVnG9qJ5l3SocmMoKGTYBtE7ZiIh+ohGl1PkOIbbZw+q5gRrJ7n5CSGEEKJV0pjMaMNs+CoDm0dRdbvxlpaii4o67X5Dahq2iddRMeerxj+zer1ULVqAc89uwq65Fl1cnFwoIYQ43/u9NMGltXzjHGzWSCYMv/WMaSaNvAOrxcaKjXPrti1eP4uVm+Y1SLvku6/qbT98IpOH/nEjq7bMx2qxodcZmLn0v/zqnzdhd1bXpft2y0KWrP+KZ956gN0HN2OzRrL/2A7+8tYDfLdrOQBllSWUVhSBqlJYmkv5qTkHl26YzfIfHduHc//DCx/9kdLyIiLCYli1eT6vfvF0g2NdtG4mj714J4ezM4m0xVJtr+Tlz/7Mc+89Wi9doOVtyVzDw8/dyMY9q4iJiEdVVd6YMY0nX/05Hq+7ya6Z2+Pmjy/9lA/mvojL5STEHMruw5t55PmprNm2GIDC0lyq7RW4PS4KS3OxO2rbevXWhTzy/FS27VtLdHgciqLh/Tn/5tF/31bvevz4nCNtMazcPI+XP3uK5RvncOzkQQBcHiczl/6XPYe21Ds+j9fVYPuFaJtKdyELs/7FlsKvWlwwMTW0J5PT/8SwxDvPK5gIcKJqd1Dpo8wpmLRWufkJIYQQotUyJAY3V7Q7L/es+03duhN6zfjzPi5PzglK3n2LqsUL8FdXy4USQojzID0ULyGn28GJgqMM6jHqrL3EDHoDndv2Ykvmanw+b1A9yhatm4mCwvO/+ZgQcygAw/tczZOv/pzlG+cyccRPAFA0CplHtvGzKY/V9YJ0uh3c8+exLN84l8E9RnPDmLs5WXiM9TtKeOSOaaetr6yyhLmrPmFQj1H8/u5/oZwaUvDqF8/US6eqKvO+/YzObXvx7IPv1G3/YlEbvlj0FlknD9ImqUPA5bk9bl7+9M+kJ3dk2kPv1q38PLzP1Tzz5gMsXDuDSSNvP2tbqaqK03XmnmZGgxlFUcg8spUjOft46v9eo3enIXX735r1d/Ye2cLwPlfzq9v+yp4jW0mLb1fXVg5XDW/MmEbn9N48c98bdcc4euAkHn/5Xr5a9j63T3io3jn/4Z4X6o7t3x8/jqqqQQ97b4q2qddOp+ZK3FY4p0UFEjWKhvSwAXSLGku4MaGJSlU5XrUzqBwp1m5y8xNCCCFEq6ZPSMCxf1/gz6u5J7F0637WNJa+/VHdbqpXnOcURaqKfctmHDt3YOk/EMvAwWgsFrloQggRJAkoXkJVNRWoqkp4aNQ500bYolFVlfLqUqJssQHX8eDUp3jglj/XBeIAOrbpAcDJwqx6aY16E+OG3lz3u8lgJiW+LcVleQHXt+fwFnx+HyP6jq9X57ihN7Nsw9d1vyuKwmuPz0ZV66+M++Nja5PUIeDydh3aSGVNOT+/4Q91ATOA3p2GkBiTxne7VpwzaFZRXcrU3w894/73/7KMiLAofH4fAAeP76kXUPzljX88a/k7D2zE4arh6sE31DvGLul9SI3PYNPe1dw+4aEfzrnf+Hrtde3wqazZtijo11lTtM33Kt1FrMv7iAL74RbzPrPowukYMYz24UOx6MKbtOwSxwnsnvKg8iSHdpebnxBCCCFaNUNCkD0Uc3ICShcyeCiKTkfVkkXnfYyqx0PN+rXYN23A1KMXlgGDzjjsWgghREMSULyEvl98JZAhpx6Pq/aCBTnfnaqqrN+5jK2Zaykuy8flceI/NaGx11e/d1l0RFyD3m86nYFqe1XA9X0ffIyNrP8QER+d3CCt02Vn6Yav2Z+1k/KqErxeNzWO2ro8p44t0PJyC48DtfM8btm7un49bjvVhZXnfkAxh/LwT545436rpbaHZ69OgxncYzSfL3yDFZvm0qvjYPp0HkKfzkPR6wxnzJ9XfAKAxNi0Bvvio5PZeXBj/XOOqH/OcVHJjXqdNUXb1D3s+e24fDXN/r2lVfSkhvYk3TaApJCuF2wV8qzKbUGlDzVEE26UOXuEEEII0brpo2NQ9HpUT2CjWXyVFfiqqtCGhp4zraX/QBSjkcpv5tYu5nKeVK8Xx7YtOLZtwZDWBlOPXpg6dUYxGORCCiHEWUhA8RIKDbFhNoZwIv/IOdOeLDyOxWQlLCS4+d7e+fIfLFw3gyv6jGNo76sIMYei1ej4x/uPNkjbmBVu/9f3gcD/HZat1dQv2+N188QrPyO3KJuxg6fQp9MQTAYzJwuP89nC14Mu7/vgqMVsrRva/b1B3Ueh0+nP/eCjMzCw+5XnTKfVaHns7ufZvn8963csY0vmGhavn0WkLYbf3vkcXTP6nDbf98eo0HBlOZ1Wj9fnPes56wM4h7PVez5t871oUxoT2z7OjqJ57ClZhoq/2byfDFozySHdSA7tToq1B3qN8YLWp6p+jgUZUEwJ7QHIyoJCCCGEaOU0GgwJibiyjwecxZ1zAnPnLgGlNffohTbESvnsWaguV5Mdtvt4Fu7jWVQtmo+xfQeMHTphzGiHYjLJNRVCiP8hAcVLrE+XoazfsZTcouMkxqSdNk1BSQ6HsvcytNdVPwz7VRRUGn4jV11TidlQOwdIZU05C9ZO55qhN/F/Nz1el+bHi380tRBTbcDq+56G3yuvKq33+5a9aziSs4/f3fUPhvYeW7d94+5VjSrv+2HjYwZOrjcM+UJRFIU+nYfSp3PtEOkDWbt48ZMneOGjP/DeM0tOm8dmjTx17CUN9lXWlBMWEl7/nO2V/3PO9fN9H5j839dBtb3igraNVtHRN/Z6UkN7sTb3Iyrc+ZfmOVXREWNuS7ylPQkhnYg1Z1ywnoink1dzEKe3Kqg8aWG95KYnhBBCiMuCISUlqICiK/t4wAFFAENGOyJ/+jPKZ3yGr6ysSY9d9XhwZu7Fmbm3NjiakoohrQ36tDboE5NQdPIxWgghZJXnS+z6UXehKAovffrn0y4I4va4eOXzZ9AoCjeOuaduu1FvorK6/txtecUnKKkorPu9uKw20JMSl14v3crN35z6S9nIP7BnyZiakAHAoeN76m3feXBDvd+LTg3rTY5vW2/7qi31jy3Q8jq1rQ3UbN//XYNj2rBrBUWleU12zQ4e383a7YvrbevYpgej+k+itKKoXsD2x23V+dQx7jhQ/9idbgeHs/fSqW1PANIS29XV82NbM9fW+91oqP2mtLKm/utg9+EtF6VtYsxtmZT+ON2irjptr8umFmaIIT2sP/3jbmBc2m+4reO/GZf2G3rHTCTe0v6iBhMBjlRsDCq9VR9JlClZbnpCCCGEuCwYklOCSh9M8PF7uuhoIu/+OcaOnS7cifj9uI9nUb16FWUff0DRv56j9P3/UrlwPo7tW/HknmzSXpLnK39fJju//grXGVaxdtXUsPPrr8jbu1depEKI8yJfrVxi7VK6cM91j/Lu7Od55PmpTBx5G+lJtX8Qs3IP8s3qzzhZmM19Nz9Bm6QOdfkyUjqzaN1MVm2eT98uw8gvOcG7s/9FTER8XZqEmBRMBjOrty1gUI8r0euNrNm2iE27VxERFs3Jwix8fl+D4cNnE2IOw+V2suvgJmIiE0iIrv+g0CW9NzER8Xy5/D1SEzJom9SR/cd2smDNjHqLqrRN6gjAwrUzuWPCQ1TbK/l65Ud1QaHs/COoqhpweYkxqQzpOYaFa2eQFJvG4J5j8Pl8LF4/ky8WvcXDP/kLowZMPOu5uT1OVmyae8b9iqLhyv4TOHR8L+9+/TwV1WUM6TkGo8HMycJjrN62kNT4DCwmKwBWcxjZeYc5kX8UiymElPh0+ncdweL1s2iX2oX+Xa+gvKqE977+N26vi+uuvAuoDTzGRiby5fIPSEnIICO5M5lHtrFs49f1H9L0RpLj2rJh1wpGD5hMQnQymUe3s3zj3CZvmzPRKnr6xU4hra63YsF5vBsULLowQvRRWPWRtfMNGhKwGeOxGeLQXeAhzMFw+qo5UbU7qDxpYb2R4c5CCCGEuFwY4uKDmkfRX1ODp7gIfXRMUPVozGbCb7wFx7YtVC1djOr1XtDzUn0+PLkn8eSexPHj47BY0EZGoY2IQBsahsZqRWMNRWu1ophMKEYTGqOxdm5G5cI9Ex5YuYL1775Dcq/eGK3WBvvtpSUs+OszDPrpPSR07SovVCFEo0lAsRmYcMWtpCZk8NWy93lv9gt1c94ZDSa6t+vPg1Ofruu99r2bxv6MrNyD/OfTJwEICwnnvlueZNmGr6mqqR3yajaGcP8tf+KNGdO49+lrgNqVfR+5YxoL185g+uK3uenRAcz61+aAj3Vk/2tZvnEOf379lyTGpPL6E3Pq7ddqdfz+nhd44cM/8Je3HgRqF1T57Z3P8dTrv8R3ap7A7u37M3HEbXyz+nMWrZuJXmfg6iE38Mgd06ioLuOr5e9z8Phunn3wnYDKA/jVbX/l3dnP8+7s53ljxjQAIsKi+PmU3wcUMLM7a3j5s6fO/LCiqQ0ojh9+C2VVxXy24HXe+fIfQO0Q6N6dhvCzKb+rSz9++C28Pv1ZHnruBsYOnsL9t/yJ39w5jTdn/p2XP3uq7tjjo5P5473/pmOb7nVt+Njdz/PCh3/g2bcfBiAmIp5H7pjG4y/fW++Y7rv5SV78+Al+/587AUiNz+Cxu5/nty/8pG416qZom3OJMaczKf0Jthd9Q4nzOAoaFEWDRtGioEGn6NFrTegUI3qNEb3WhFFrxawNxaizYtKGYtGFoVFaxi3paPkm/KovqDzptv5ysxNCCCHE5UOjwZCcguvY0YCzuI4dDTqg+D1zn37oU9tQOX8unpwTF/10/XY7frs9oLoVnQ60WhSNBrRadJGRhN90q8zVKIRoURRVbYKlsUTT/SHy+/lq+ft8Mv9VHrljGiP6jj9r+sqachzOaqLD4xss4vE9n89LcXk+IeZQrBZb3Xany45eZzhjvjPxeN1U2ysJD42q1xPufxWW5qKgEB0Rf8Z0TreDsspiIsNi6obwQu08j2ZjSL18gZQH4Pa4KS7Px2gwERkWjXKBhsL6/X7KqorxeNxE2mIw6Bv2oLM7q/H5fISG2Oo/LLmdlFQUYDJYiLSd/qFJVdW6oeExEfF4vF5u/t1AJl95B3dP/k29dKUVhaioRIfHn/WYL1bbtGaq6ufrI89S4wl8rp4ocyrj2jwijSeEEEKIy4p95w4qVi4POL0+PoHoqT8534c1HNu2UrVyWbMainw2GosFtFrCrp2EMaPdeZX17euvsv7dd/jFl18T1aZtg/1lJ7J587qJDPrpPVz50K/qthcdPsSehQuoOJmDzmgisXt3uk+YhP5UkNPv9bLm7TfJGDoMn9vNrrlf03XctaQPqZ1TPnfPbvYvW0pVYQGm0DCSevai6zXjaoOmQohWSd7dze2CaDRcM/QmQsyhfPLNK+QWZZ81fVhIOHFRyWcNCmq1OuKikusFEwFMRkvQwUSoXQ05Iiz6rEE9qO1JGBOZcNZ0JoOZhOiUesFEAIvJ2iBfIOUBGPQGEmNSibLFXtCAmUajIcoWS3x08mmDid+fx/8GE6G292liTNoZg4lQ2+sxNjKR2MjEs56HoihEhcedM5h4MdumNTtRvSeoYCJI70QhhBBCXJ6MbdoEld6Tn4e/pub8KlUUzH37Ef1/D2Lu3feCDi9uSv6qKsq/+JTKb+Ze9EDo3kULePfWmzm6bi1hcfEoGg0rXnyBD+64tW4uRtXvZ/2777B34QJm/ebXFB46RHVJMQDbZs3gw7tuJy9zL6ExsTgqK/jmqSf56ne/lTeBEK2YDHluhqyWMH5121958eMneOBv1xMZFs3Q3mO55zq5IQtxaansK1kZVA6toifd1k+aTgghhBCXHa0tHF14ON7y8oDzOI8ewdK9x3nXrbFaCRs/AcuAQVSvXIbr4IEW0WaOndtxHztC2LWTMKRnNLqcyvx8NFrtabf/mNtew6Jpz5LSuw9TX3sTrV4PQI9Jk/nkZ3fz3QfvMfLBh+u2713wDTe++BJp/U59Ya6qbPnsU5J79uK2t9+tK3dNmzdY+/abFB48QGyHjvJmEKIVkoBiMzWg2wje+tM89h7ZhtfnpV1qF2mUy/2BTKOhR4cBJMWkSWNcIgX2IxQ5soLK08bWG71G5sMRQgghxOXJmJ6Bd9vWgNM7DuxvkoBi3Qfe6GjCb5qKtyCfmvVrce7LhGY+65evspKyzz/B3KsPoWPGohiDX5zwiwf+L6B0WRs34rbX0GvKDXVBQ4CU3n2Izsjg8OpvGfngw3U9PaMz2v0QTARQFH7x1ZwGbZrUo3YNgJLjxyWgKEQrJQHFZizMGsHgnqOlIQRQO3T9L/e/JQ1xCe0pXhp0ng7hQ6XhhBBCCHHZMmW0oyaIgKI75wS+6mq0p1mh+Lw++MbFY7v+RqwjS7Fv2ohj985mP8eiY8c23EcPEzZhMoa26UHlvfapZwiLazglUlVREd889WTd76UnaqfYikxt2GkhIjmFrI0b6m07XTq33c7OObM5uXMHNaWl+NxunFVVAPgCXOVbCNHySEBRCCECkF9ziLyag0HliTG3JcqcKo0nhBBCiMuWITEJjdmM3+EIOI/z4AFC+vS9IMejjYgk9OpxWEeNwbU/E/v2bXhOZDfb9vNVVlKzdjX6pGQUgyHgfEk9ep5xUZYf858K+J1unnqtXo/P661/PS2W+sfndvPpz++hNDubXlOmkD5kKHqzhdLjWax+4zV5AwjRiklAUQghzklle9E3QefqHDVCmk4IIYQQlzdFwZTRHvueXQFnsWfuuWABxbrD0usxde+JqXtPfBUVuPZn4tyXiedkTvNpOp0O66gxWPoNuGCLy1giIwGoKS1peB3KyrCEh581/+E1q8nfv4/rnnuezleNrdt+aNVKee0L0cpJQFEIIc4hq3I7JY7gvrkONUSTEtpdGk8IIYQQlz1zx45BBRS9xcV48vPRx8dflOPT2mxYBg7GMnAwvspK3EcP4z56FHfW0aB6VjYlfUoqtgmT0Z4K+F0oyT17A3BswwYyhg6v2+5xOMjL3Ev6kLNP31ORnwdAdNv6vSF3Lzj1ZXwzn69SCNF4ElAUQoiz8PhdbC2YG3S+rlGjUdBIAwohhBDismdITkEbYsVXUx1wHvue3dguUkDxx7RhYZh79cHcqw+oKp6CfDwnsvHknsSTexJfaekFrV/R6bBeORpL/4EXrFfij0Wnp9P+ihFs/3ImCV260G74CGpKSlj+4r/wulwMvOOus+aP69gJgG0zZzDiwYdxVlay8eMPUZTa5+Cio0dqg4oX4VyEEBeXBBSFEOIsdhUtwuGtCCpPiD6CdFt/aTwhhBBCCKgd9typEzVbtwScxXFgH6HDr0DTiBWOm/K49fEJ6OMT6jb5HQ68+Xl4S4rxlRTjLS7GW1KM/9QiJOdDn5yCbeJktJFRF/U0J037O4v/Po1vnv4z/lNzJkYkp3DDC/8h6Rwrbqf160//n9zOli8+Y9usGWgNBnpPuZFJz/4Ne1kpGz54j9zdu7jt7XflfSBEa7u1q6r0QRZCiNMpdhxncdZLqAR3mxwYfxPtI4ZIAwohhBBCnOIpKqL404+CyhM2/ApC+raML2lVrxd/dRW+ykr8VVX4Kivw2+2oTieqy4nf6UR1OvG7XOD3gd+P6qv9V9HrsQwYhGXAoEvak8/jdFJVUIDBYsEaExNcXoeD6uIirDGx6E2muu2u6mqMISHSQ1GIVkh6KAohxGn4VA/rcz8LOpgYZoihXfggaUAhhBBCiB/Rx8Sgj4vDU1AQcJ6aHdsJ6d0XNM1/GhlFp0MbHoE2PKLlXiOTici0tMblNZuJSEltsN1otcqLX4hWSib4EkKI09iSP5tKd2HQ+XrFTqibM0YIIYQQQvzA0r1nUOl9VVU4Dh6QhhNCiGZIPvUKIcT/yKrcxqHy74LOF2tJJ1VWdhZCCCGEOC1zx04oBkNQeao3bpCVgoUQohmSgKIQQvxIqfMkG/KmB51PQcOA+BsBmR9GCCGEEOK0z0t6PebOXYLK4y0rxXFgvzSeEEI0MxJQFEKIU+zeCladeAev3x103o6Rwwg3JkgjCiGEEEKcRUjvPkHnqd6wHvx+aTwhhGhGJKAohBCA22dnRfZb2L0VwT8Y6yPoGTNeGlEIIYQQ4hx04RGY2rUPKo+3vJyandul8YQQohmRgKIQ4rLn8TtZfuItyl15jco/OGEqeo1RGlIIIYQQIgAhffsFnad6w3f4HQ5pPCGEaCYkoCiEuKy5fDUsy36DEkd2o/J3iBhCfEgHaUghhBBCiAAZEhIxJCcHlcfvclG1fq00nhBCNBMSUBRCXLbsnnKWHH+l0cHEcGMCfWInS0MKIYQQQgQpdPDQ4J/ddu/CnZMjjSeEEM2ABBSFEJelYsdxFma9SIWroFH5dRoDw5PuRKcxSGMKIYQQQgTJkJSMMa1N0Pkqli1G9XqkAYUQ4hKTgKIQ4jKjcrh8A0uOv4LDW9noUgbG34zNGC/NKYQQQgjRSI3ppegtL6dqrQx9FkKIS00CikKIy4bLV8Pqkx+yIW86ftXX6HK6RV9FW1tfaVAhhBBCiPOgj4/H3LFT0PlqdmzDeeSwNKAQQlxCElAUQlwGVE5U7eabo8+TXbnzvEpKDetJz5hx0qRCCCGEEE0gdPgVKDp90PkqlizCV1kpDSiEEJeIBBSFEK1ataeElSf+y7c57+HwVpxXWfEhHRiaeDsKijSsEEIIIUQT0FpDsQ4YEHQ+v8tF2bw5qB6ZT1EIIS4FRVVVVZpBCNHaOL1V7ClZxsGydec1vPl70eY0xqTeh05jlMYVQgghhGhCqtdL8acf4S0rCzqvKT2DiImTQZEvfIUQ4mKSgKIQolWxe8rZX7aag2Xr8PrdTVJmtDmNUSm/wKC1SAMLIYQQQlwA7tyTlMz4olF5Q3r2JuzKUdKIQghxEUlAUQjRCqgU2o9xsGwdxyt3oOJvspLjQ9ozIvle9NIzUQghhBDigqpctZKaHdsaldfabwChw4ZLIwohxEWikyYQQrRUVe5isiq3crRiC1Xu4iYvPzW0B0OT7kCryK1SCCGEEOJCCx06DOexo/gqyoPOW71lE2g0eHy6fAAAG05JREFUhA4ZKg0phBAXgfRQFEK0GKrqp8R5gpzqPeRU7aHclX/B6uoWfRU9Y8bJAixCCCGEEBeRp7CAkumfo/oaNwd2SO8+hF0xUuZUFEKIC0wCikKI5vtA6XdS5jxJkSOLAvthCu3H8PpdF7ROraJnUMIttLX1lQsghBBCCHEJ1OzcTuXKFY3Ob2rXnvBrxqPoZJSJEEJcKBJQFK3CkZx9HMs5UP/FrShE2mJIic8gOjyuVZ63qqq8OXMae49sY2D3kUwZfTcOl533vv4Xj/30+ZZyFrh8dqrcxVS6i079W0CpM+eCDGM+mwhjIsOS7sRmjJM3lRBCCCHEJVQ2fx7OQwcbnV8fn0DEtRPQhoZJYwohxAUgX9mIVmHjrpXMWvYuRoOpboiqz+/D43WjKBqu7D+Bn015DJPB3KrOe9ehTRzI2sWYQdexYtNcvln9OVqtjpuuuveSHVNu9T68fjd+fPjV2h+v343H78Ttc+Dxu3D7anB4K+t+fKr3krdlx8jh9ImdJPMlCiGEEEI0A+Fjr6GkogJPYUGj8nvy8yj+9BPCrxmHsU1baVAhhGhi0kNRtAqfLXidWcve5ZU/fElSbJu67RXVpcxd9SmzV3zAmIHXcf8tf2pV552VexBVVWmb1BG/38/WfWtxe1wM6TkaRdFc1GNxeqvZXPAledUHcPsdLaYNwwyxDEq4hVhLuryRhBBCCCGaEV9NNSWff4qvuvq8ygnp24/QwUNlCLQQQjQhuaOKVs1mjeSOCQ+x8+AG1m5fzH03P4miKCxePwuT0UKnNj2ZveJDEmNSmTTydgCO5x1m9daFFJaexKA30j6tO1f2m4DRYKp9sPH7mL7oLfp0HorNGsHCdTMoryqlbVIHJlzxEzQaLUvWf8n+rJ1YLWGMGjCJjOTO9Y7rXHXUpcs9xKqt8ykuKyDSFsOIvuNJT+5Ut79NYgcOHt/Dh/P+Q0l5IVZzKB3b9jxtWwRaZ2Mcr9zOpvwvcflqMGhaRi9QraKja9RoukaPkV6JQgghhBDN8XktxErE5OspmfEFqsfT6HJqtm7BdeQwtjFXY0hOloYVQoimuEc//fTTT0sziJZu96HNZB7dzvhhtxAWEt5g/86Dm8jKPciU0T9Fq9Xx36/+SU7BMVZunkduYRahITb6dh7Gmm2L+MvbD2B3VtM2qSMer5t5337K+h3LGNFvPHqdAQV4+s37UFGZveJDosJjqbZXsnDdTMorS1i/cynHTh4g0hbDpt2rWLBmOlf2n4DFbAUIqA6A9TuX8cxb9+NyO0mISeVozn4+X/QGUeGxdUHFRetn8Y/3f4tepycxNo3yqlK+XP4eWbkHGdb76rrzD7TOYDl91azP+4zdxUvwqbUPeVpF3yyGMJ+ZQkb4AEYk30NyaDc0F7knpxBCCCGECOIDa0gIhuRknAcOgN/f6HL8TieOzL34q6rQx8WjMRikcYUQ4jxItxzR6vl8Xo7m7CMqPA6D3lj7wtfpOXR8N5NG3s4tV/8SAIfLzpszp9G5bW+e+r/X0Gn1AIwaMIknX/0Zs5d/wG3XPlg3lHjt9sW89NhM4qKSAPjr2w+xfNNcrh48hUdunwbAiL7j+ePLd7Nh90omXHFrwHW43E7enDGNnh0G8cTPXkKjqa3z2Xce5v2vX+CKPuPQ6wzMX/0Zndr25C/3v113vtMXt2H64rfJyj1Im8QOAdcZrOzKnWzKn4XTV91CXgkKqaE96B4zlghjorwxhBBCCCFaCENiEpGTr6N0zmxU7/l9cW3fuwfHgf2E9OmLtd8AFAksCiFEo0hAUbQqBSUn6/7fr/opKSvgmzWfU1Bykp9P+X3dPgUFj9fDpJF31G3bdXAjDpedsYOn1AXdALqk9yYlPoPNe1fXC7z1aD+gLpgI0CaxPdv3r2fskBt+tK0DAGWVRUHVsfvwZqodlYwZOLkumAjwyxv/SFllCYqioCgKr/zhK/53GtSOaT0AOFl4nDaJHYI+r3Nx+WrYlD+L45U7WsRrQlE0pNv60TVqNGGGWHmTCCGEEEK0QIaUVCImTqZs3pzzDiqqXi/VmzZi37UTS89ehPTsjcZikUYWQoggSEBRtCrPvvNwg23hoVH8bMpjjBt2c73tEWHRmI0/PDjkFZ8AIDEmtUEZ8VHJ7Dq0sd62qPC4+g85p3o/Rtl+2P79/IQerzuoOvKKsgGI/VHAEiAmIoGYiIS6350uO8s2zuFA1k7Kq0rxeN3UOKoA8Po8jTqvs8mrOcC63E9wept/r0SrPop24YPICB+AWRcmbw4hhBBCiBbOmNaGqBtvpvTr2fid578IoN/ppHrjBmq2bsHcuQuWHj3Rx8gX0EIIEQgJKIpW5Y/3vkhcZG0QTlHAbAwhKjwORVEapDUZ638L+X0AjtOk1en0eH31vwn9cc/BQLYHU0ddurPweN08+drPySvK5qpBU+jdaQhGg5ncwuN8vuiNRp/X2USZUoi3dCCrcluzvP56jYnk0G6k2/oTH9IeBUXeFEIIIYQQrYg+PoGoW26ldPYsfJWVTVKm6vVi370L++5d6GNiMXftirljZzRmszS4EEKcgQQURauSGJNKUmybRuW1WSMBqKgqbbCvqqbstIu9XKg6Qk/9e7p039uSuYajOfv57Z3PMbTXVXXbN+1ZdcHOy6C1MCzpDtLCerExfyZOb9Ulv+ZGbQhJ1i6khfUiPqSDrNgshBBCCNHaP8RGRBB9y08omz8Pd+7JJi3bU1SIZ1Uhld+uwpCUjCk9A1NGBlpbuDS8EEL8+F4sTSBErU5tewKw48AG+nQeWrfd6XZwODuT3p2GXLQ6OrWpTbd9//p66eav+YI5qz7m2QfeobgsH4DkuLb16vh2y4La/zk1t+KFOK+U0O7EWtLZnP8lWZXbL+5NS2MgxtyWhJCOJIR0INyUKD0RhRBCCCEuM5qQEKJuvJnKtaup2ba16StQVdw5J3DnnKBy9Sp0EREYkpJP/SShDbNdVu3tdblqn8WNxrOmczvsaHV6tHr9RT9Gn9uNCugu8kI7l/KchbiUJKAoxCkpcen073oFS777knYpXejXdTjlVSV8MOdF3F4Xk6+846LVkRzXlr5dhrHku69ITWhHzw4DOZ53mBlL3iYxJo3YyETaJnUEYPG6mdx27YNUOyqZs/LjuuHd2flHUVX1gp2XURvCsKQ7SQ3rxab8mRdkXkWtoiPMGEeUKZVocypR5lTCDfF1K20LIYQQQojLmEZD2BUjMSQkUr50MarbfcGq8paV4S0rw75nd23VISHoY2LQRcWgj45GFx2NLjwCpZkElUpPZJO1cSOVhQWAQkhkBMk9epHQpUujyvv0vl/i9/m4+8OPz9xGbjcvjLyCgbfdzqiHf127UVVZ+NzfyN62lQ4jRjL4rrtx2+0se/EFpjz3zyY9549/8TPiOnZi3B8fP2u6nJ07ydm9E0dZOTqjEWt0NOlDhmCLTwj+dXG6cxbiMiEBRSF+5Ne3T+PtWX/nlS+exndqbsH4qGR+f/cLdEjrflHreOT2abwxozatz+8DoH/XEdx38xMAdGvXjwlX/IT5a75g0fpZ6HUGxg6ewq9vf5bKmjJmr/iAQ9m7+cv9b1/Q80oN7UGcJaPRKz+bdKFYdDYs+nAsOhthhhjCDHGEGWMJ0UdI70MhhBBCCHH258n2HYiJj6di2VJcx7MuSp3+mhpcNTW4surXpzGZ0IaFoQ0NQxsaisZsRjGZ0BhNaExmNEYjaLUoWi1oNChaLYpe33TzNaoqS154nq2zZmK22Yhu0xaNVktpdjbLiv5N+qDBXP/cPzCYL86q1sc2b+Lk7l30nHwdu+bNY8v0L9BodQy9594mrcdRWUn+gf0MuvOus6b58rFHObF9G+GJidgSk/B53BQdPsyifz7HkLvuZsR998sbSogAKap6alykEKKOy+2kpKIAk9FCZFjMJa1j5tL/cjRnH7+67VlMhoYPGk63g7LKYiLDYupWlQawO6sxG0PqLUhzoc/rRNVuiuzH0GkMaBQtiqJFc+rHoDFh0JrRa8wYtGYMGjMmXSgaRSsvOCGEEEII0STse3dT+e2qC9pbsalpw8OJGD8Rfez5rzC9b+kSvn7ycQbedjsj738Qje5UHyJVZcuM6Sz9978YfNfdjLz/gaDK/fCenwbUQ/H54UPq9dYrPHQQVYW4Dh1Q/X4Or1uL1+Wi06jRKJqmG3W0b9lS5vzpCX61eBnmsLDTpln0j7+z4+vZXDft73QaNfqH43a5mP/XZ8hcuoSpL79K24GDAq73dOcsxOVCeigKcRpGg4nEmLRmUUeIOZSyymL+88mT/OGeFxrsNxnMJESnNNhuMVkv+nmlhHYnJbS7vICEEEIIIcQlYenaHWNaW6rWrcGxL7NFHLPqdlH8xadY+w/EOmBgbe/FRjqxs3bE0KA77vwhmAigKPS7ZSo+r4eotDb18lTk57Fn/nzKck6gaDREp2fQ67rrMYaEnLWuzCWLObJ+HQCpffvR5aqxDdLEtu9A7t49rHj5JaoKCzCFhZHUvcdpyys6cpg9ixZSkZuLzmgksWs3ul87Ab3JdM7zPrZxAwmdO58xmAiQs3MHkalp9YKJUDsv5FW//R0x7dphDq+/+E7u3j3sX7683rF3vfqacwZDy3JOsOubeZTl5KA3mkjp3Ztu14yrd028bjeZSxZRcOAAbrsda0wMncdcRWy79vJGFi2CTEQmRDPm9/sZ1X8i1w6/lcyj26RBhBBCCCGEOAet1Ur41eOIvvV2DIlJLeXBn+qN31Hyxad4iooaXUxYbBwAx7dsOe3+gbfdQbthw+t+z9uXyX+n3sKeRQswhdnQ6vWsf/893v3JVFzVZ54jfeUrLzPnT09QVVSENTqaPQvms+DZvzRIt+3/27vz6KrLO4/jn+zrBbIHk0AS9kISFiUkCkNQFhFaCxYQsKW0laFW2/G4zYCWcWrdahVqq+ByREEUCFZRy65CkCSSMARIQMCQEElISCQL997cdf5IiN4kxF+m09Mwvl/n5OTkd5/n3uf3nPz+uJ/zPM83e7PWLl6kypJimaKjZWlo0PuPrtCWhx7waHds+za9snC+vvj0U/WKiZGXl7f2rHpOry36cZfjuKw0P0+JY9O7bGOKjlHj+fP6quJsh9eCw8KUuWixYocM7fbY2zu1P0cv3T5XJ/d+ot4xsZLc2vbk41r/yyVytq6cddhseuPni7Vn1UrZLBYFhJpUXnBQr96xQMU7d/AQ46rACkWgB6u8UK7n1i1Xb1O47r3jcSYEAAAAMMgvJkYRc+bJevqUmvJzZT9/vseP2V5Towsb1smUPk6h16VL3dwWnDJjpvLeXK+/PrxMJz7+SIPGT1DCyFHqFRvbaftDW7IlL+knr65VoMkkSfrelKlav3SJij7Yquvm3t6hz6XaWuVvWK/BE7M0+4mnpNYjlj587L88G7rdOvj2BsWnpWnBC6vbLu/rn6icl9eo+uTnih40WDazWdue+L0SRo7UvFV/bquWnDpzptYt+YUOvL62yy3atWfOqKGqSklju96qnL5goUrzcvXqwgVKnTlTSeMyFJ+aqkBTJ6saDY69PYfNpvcfXaHYwUO04MU1bfcybPIUvf3ru1WQvVljb5+vs4cKVXXiuOau/JOSx2W09d/+9JMqLyzodLUn0NMQKAI9WFx0op6+dx0TAQAAAPwvBQ4YqMABA9VcXqamz/JkO3u2Zw/Y5VLjgU9lPX1KfabcLN/ISMNdQ8LDtei1tcp5aY2O796tkl07JUm9YmM18IbxGj3rNkUNGNDWfvqyhzX9P5a3hYKSFDei5QijurKyTj+jrLBALqdTI6bd7NFv9OzbdPi9d79u6OWlOzdmS+3KNsSltLx/bVmZogcN1pn8PNnMZo28dVZbACdJCSNHKTI5Wady9nYZKJbm5covKEjxqaldzk3idWO18MU1ynnlZRVs3qSDG9+WvLwUPXCQhmRlafSs2QoOC+/W2Ns781m+LBcv6tr7HvC4l+RxGQrv108nPtqjsbfPl8vZUnTz3LGjHoHi1Psf5IHFVYNAEQAAAADw/15Av/4K6Ndf9urzMh8pkuV4idx2e48dr726WjVvviHTuAyFXjvW8GrF3rF9dcvDv9XN/75MlSXFKi8sVGl+ng5tydZ/v7NF05c/opTpt7Q0drt1fM9unf50v+qrquRobpbb1RJ2Oa8wNw1VVS2f07evx/U+cfEd2tosZh1+7119WVSkS3W1ctrssjY1erx/XesW5PB+/Tr0D4uL15nP8ru839K8XPUfPcbzzMgriE8bqXmrnlfzpUuqOHxY5YcKdHr/fu1bs1oHN76t+c//pS0oNDL29urKyyVJR97fqlM5+zznwmyRtbElpE1KH6chWZO0b81qHXl/q5LSxyk5I1MDMjLl4+/Pw4qrAoEiAAAAAOA7wy86Rr1vnKxeEybK8vkJWY4eka3yXM8crMslV3Oz3C5Xt6sie/v6Ki4lVXEpqcr4ySLVlpXprXvu0s4/PKXhU6bK29dXO/7wtAqyN2n41GkadtNkBYaGytvXt8tzAi+Hae0DPG9fz2IyTptN65fcqbqz5Rp56w+VnJEhv8Ag1ZWVae+aF7++RbtDkuT1jdWOl/n4+cnpcFx5ehwOlRcWasLSpd2am4CQEA3IzNSAzExl3XW3Tu7bqy0P3q+P/vy85j63yvDYO4yndW4CQkMVGGryeG3IxCz5+LXMmbePj2Y9/qROHzig43t26dT+HB16Z4tCI6N06+8eU8Ko0Tyo6PEIFAEAAAAA3zlefn4KHj5CwcNHyNnYKOsXp2Q9dVK2iooOW13/KV/Ww8LUe8o0+fe9xnCfxppqOe0O9bmmY5+I/v01fOo0HVj7mi5WnlOQqZcKNm/U6Nm3aeoDD7W1+7YiKAGhoS3tGhs9rl+qrfP4+9T+HFWdOK5bH3tcw26a3Hb95N5PPNoFh4W19K+r6/BZ5osXFdyu8vI3VRQdls1iVtK3FGSR263asjIFh4d3Wgl60PgJ6jvse6o+ebJbY28vOLxly3TqzO97bGXu/B/Qqy3UlKQvjxRp64pH9Nfly3T3B3/jAUWPR5VnAAAAAMB3mo/JpJC0UYqYPUcxS5aqz7TpCh6RIt8uwqx/pJDRYxS54MfdChPtFotemjdHWx66/4pbcqtKSuTt46OQsHA1tBapiUhM8mhz9G8fSpLcVwhVL5/BeK74mMf19luT61u3Rkcmeb7/kQ8/8Pg7Pi1NklSal9fhfipLihWXcuWzEb/IzZUpKlqRScldzk1FUZHWzL1Ne1e/0OnrNrNZdeVlMkVFdWvs7bXdS+6BDq99/vFHqq+qbJm7Y0dV0q6ac1xKqlKmz1DThRpDla2BfzZWKAIAAAAA0Mo7MEhBQ4cpaOgwSZKzqVG2igrZKs/JUVMj+4UauW22f8wX9D59WlYlXhPX7b5+QUEaf+cS7frjM1r/r3dqzJy5ikhMlI+vr+orK3V463sqzc/TdfPmKyA0VGEJCfILClLx9m0aMjFLvv7+Kt65Q5/v/VihkZGqKy+Ty+mUt4/nVuaEtJHqFRurA2tfU2TSAMUMHqwvjxSpYNNGjyItMYNbziIszN6sf1l6l6yNjcpb/0bb1u2aL05Lbrcik5I1aPwEHXonW32HDdPAG8brUl2ddq98Vo7mZqUvvOOK91yal6uk9PRvnZv4tDQNnpilws2bZG1o0PBpN6t3bF857TZdKC1V/ob1sjY1KXPRT7s19vbCE/pp6KQbVZC9WeH9EzU0a5JcTqcKt2Qr5+U1mvHICqXcMkPnjh3TrmefkfniRQ3JmiS/oCDVnjmjYzu2KzI5uW0VKNCTebndPWAtNwAAAAAAVwO3W86GBtlrquWovSBnQ4Mc9fVyNtTL2djY7e3S3sFBcpktChk1Rqbrr5eXr9/fNbzju3cpf8Ob+vJIkcf1iMREjfnRHI2Z/aO24O/Y9m3a9sTvZTObJbVUI57x2/9U4eZNynnlJXl5e+vB/bl6/eeL5XI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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('image/MindMapHypothesis.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8d8e24",
   "metadata": {},
   "source": [
    "## 2.2 Criação da Hipóteses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c590d9",
   "metadata": {},
   "source": [
    "### 2.2.1 Hipóteses Loja"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b14025",
   "metadata": {},
   "source": [
    "**1.** Lojas com número maior de funcionários deveriam vender mais.\n",
    "\n",
    "**2.** Lojas com maior capacidade de estoque deveriam vender mais.\n",
    "\n",
    "**3.** Lojas com maior porte deveriam vender mais.\n",
    "\n",
    "**4.** Lojas com maior sortimentos deveriam vender mais.\n",
    "\n",
    "**5.** Lojas com competidores mais próximos deveriam vender menos.\n",
    "\n",
    "**6.** Lojas com competidores à mais tempo deveriam vender mais."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d6cfa57",
   "metadata": {},
   "source": [
    "### 2.2.2 Hipóteses Produto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8671d16a",
   "metadata": {},
   "source": [
    "**1.** Lojas que investem mais em Marketing deveriam vender mais.\n",
    "\n",
    "**2.** Lojas com maior exposição de produto deveriam vender mais.\n",
    "\n",
    "**3.** Lojas com produtos com preço menor deveriam vender mais.\n",
    "\n",
    "**5.** Lojas com promoções mais agressivas ( descontos maiores ), deveriam vender mais.\n",
    "\n",
    "**6.** Lojas com promoções ativas por mais tempo deveriam vender mais\n",
    "\n",
    "**7.** Lojas com mais dias de promoção deveriam vender mais.\n",
    "\n",
    "**8.** Lojas com mais promoções consecutivas deveriam vender mais."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e18ab42",
   "metadata": {},
   "source": [
    "### 2.2.3 Hipóteses Tempo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bcafc74",
   "metadata": {},
   "source": [
    "**1.** Lojas abertas durante o feriado de Natal deveriam vender mais.\n",
    "\n",
    "**2.** Lojas deveriam vender mais ao longo dos anos.\n",
    "\n",
    "**3.** Lojas deveriam vender mais no segundo semestre do ano.\n",
    "\n",
    "**4.** Lojas deveriam vender mais depois do dia 10 de cada mês.\n",
    "\n",
    "**5.** Lojas deveriam vender menos aos finais de semana.\n",
    "\n",
    "**6.** Lojas deveriam vender menos durante os feriados escolares."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c299494",
   "metadata": {},
   "source": [
    "## 2.3 Lista Final de Hipóteses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef6dfb8",
   "metadata": {},
   "source": [
    "**1.** Lojas com maior sortimentos deveriam vender mais.\n",
    "\n",
    "**2.** Lojas com competidores mais próximos deveriam vender menos.\n",
    "\n",
    "**3.** Lojas com competidores à mais tempo deveriam vender mais.\n",
    "\n",
    "**4.** Lojas com promoções ativas por mais tempo deveriam vender mais\n",
    "\n",
    "**5.** Lojas com mais dias de promoção deveriam vender mais.\n",
    "\n",
    "**6.** Lojas com mais promoções consecutivas deveriam vender mais.\n",
    "\n",
    "**7.** Lojas abertas durante o feriado de Natal deveriam vender mais.\n",
    "\n",
    "**8.** Lojas deveriam vender mais ao longo dos anos.\n",
    "\n",
    "**9.** Lojas deveriam vender mais no segundo semestre do ano.\n",
    "\n",
    "**10.** Lojas deveriam vender mais depois do dia 10 de cada mês.\n",
    "\n",
    "**11.** Lojas deveriam vender menos aos finais de semana.\n",
    "\n",
    "**12.** Lojas deveriam vender menos durante os feriados escolares."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022b2100",
   "metadata": {},
   "source": [
    "## 2.3. Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75c406a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:47:48.356726Z",
     "start_time": "2023-05-26T18:46:59.747874Z"
    }
   },
   "outputs": [],
   "source": [
    "# year\n",
    "df2['year'] = df2.date.dt.year\n",
    "\n",
    "# month\n",
    "df2['month'] = df2.date.dt.month\n",
    "\n",
    "# day\n",
    "df2['day'] = df2.date.dt.day\n",
    "\n",
    "# week of year\n",
    "df2['week_of_year'] = df2.date.dt.isocalendar().week\n",
    "\n",
    "# year week\n",
    "df2['year_week'] = df2.date.dt.strftime( '%Y-%W' )\n",
    "\n",
    "# competition_since\n",
    "df2['competition_since'] = df2.apply( lambda x: datetime.datetime(year=x['competition_open_since_year'], month=x['competition_open_since_month'], day=1), axis = 1)\n",
    "df2['competition_time_month'] = ( ( df2['date'] - df2['competition_since'] ) / 30 ).apply( lambda x: x.days ).astype( int )\n",
    "\n",
    "# promo_since\n",
    "df2['promo_since'] = df2['promo2_since_year'].astype( 'str' ) + '-' + df2['promo2_since_week'].astype( 'str' )\n",
    "df2['promo_since'] = df2['promo_since'].apply( lambda x: datetime.datetime.strptime( x + '-1', '%Y-%W-%w' ) - datetime.timedelta( days = 7 ) )\n",
    "df2['promo_time_week'] = ( ( df2[ 'date' ] - df2['promo_since' ] ) / 7 ).apply( lambda x: x.days ).astype( int )\n",
    "\n",
    "# assentassent\n",
    "df2['assortment'] = df2['assortment'].apply( lambda x: 'basic' if x == 'a' else 'extra' if x == 'b' else 'extended' )\n",
    "\n",
    "# state holiday\n",
    "df2['state_holiday'] = df2['state_holiday'].apply( lambda x: 'public_holiday' if x == 'a' else 'easter_holiday' if x == 'b' else 'christmas'if x == 'c' else 'regular_day' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "64398397",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:48:04.026525Z",
     "start_time": "2023-05-26T18:48:04.008811Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>store</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
       "      <td>2015-07-31 00:00:00</td>\n",
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       "      <th>sales</th>\n",
       "      <td>5263</td>\n",
       "      <td>6064</td>\n",
       "      <td>8314</td>\n",
       "      <td>13995</td>\n",
       "      <td>4822</td>\n",
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       "    <tr>\n",
       "      <th>customers</th>\n",
       "      <td>555</td>\n",
       "      <td>625</td>\n",
       "      <td>821</td>\n",
       "      <td>1498</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state_holiday</th>\n",
       "      <td>regular_day</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>regular_day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>school_holiday</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>store_type</th>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>c</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assortment</th>\n",
       "      <td>basic</td>\n",
       "      <td>basic</td>\n",
       "      <td>basic</td>\n",
       "      <td>extended</td>\n",
       "      <td>basic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_distance</th>\n",
       "      <td>1270.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>29910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_open_since_month</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_open_since_year</th>\n",
       "      <td>2008</td>\n",
       "      <td>2007</td>\n",
       "      <td>2006</td>\n",
       "      <td>2009</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo2_since_week</th>\n",
       "      <td>31</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo2_since_year</th>\n",
       "      <td>2015</td>\n",
       "      <td>2010</td>\n",
       "      <td>2011</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo_interval</th>\n",
       "      <td>0</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_map</th>\n",
       "      <td>Jul</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Jul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_promo</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_of_year</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_week</th>\n",
       "      <td>2015-30</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2015-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_since</th>\n",
       "      <td>2008-09-01 00:00:00</td>\n",
       "      <td>2007-11-01 00:00:00</td>\n",
       "      <td>2006-12-01 00:00:00</td>\n",
       "      <td>2009-09-01 00:00:00</td>\n",
       "      <td>2015-04-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>competition_time_month</th>\n",
       "      <td>84</td>\n",
       "      <td>94</td>\n",
       "      <td>105</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo_since</th>\n",
       "      <td>2015-07-27 00:00:00</td>\n",
       "      <td>2010-03-22 00:00:00</td>\n",
       "      <td>2011-03-28 00:00:00</td>\n",
       "      <td>2015-07-27 00:00:00</td>\n",
       "      <td>2015-07-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promo_time_week</th>\n",
       "      <td>0</td>\n",
       "      <td>279</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                0                    1                    2                    3                    4\n",
       "store                                           1                    2                    3                    4                    5\n",
       "day_of_week                                     5                    5                    5                    5                    5\n",
       "date                          2015-07-31 00:00:00  2015-07-31 00:00:00  2015-07-31 00:00:00  2015-07-31 00:00:00  2015-07-31 00:00:00\n",
       "sales                                        5263                 6064                 8314                13995                 4822\n",
       "customers                                     555                  625                  821                 1498                  559\n",
       "open                                            1                    1                    1                    1                    1\n",
       "promo                                           1                    1                    1                    1                    1\n",
       "state_holiday                         regular_day          regular_day          regular_day          regular_day          regular_day\n",
       "school_holiday                                  1                    1                    1                    1                    1\n",
       "store_type                                      c                    a                    a                    c                    a\n",
       "assortment                                  basic                basic                basic             extended                basic\n",
       "competition_distance                       1270.0                570.0              14130.0                620.0              29910.0\n",
       "competition_open_since_month                    9                   11                   12                    9                    4\n",
       "competition_open_since_year                  2008                 2007                 2006                 2009                 2015\n",
       "promo2                                          0                    1                    1                    0                    0\n",
       "promo2_since_week                              31                   13                   14                   31                   31\n",
       "promo2_since_year                            2015                 2010                 2011                 2015                 2015\n",
       "promo_interval                                  0      Jan,Apr,Jul,Oct      Jan,Apr,Jul,Oct                    0                    0\n",
       "month_map                                     Jul                  Jul                  Jul                  Jul                  Jul\n",
       "is_promo                                        0                    1                    1                    0                    0\n",
       "year                                         2015                 2015                 2015                 2015                 2015\n",
       "month                                           7                    7                    7                    7                    7\n",
       "day                                            31                   31                   31                   31                   31\n",
       "week_of_year                                   31                   31                   31                   31                   31\n",
       "year_week                                 2015-30              2015-30              2015-30              2015-30              2015-30\n",
       "competition_since             2008-09-01 00:00:00  2007-11-01 00:00:00  2006-12-01 00:00:00  2009-09-01 00:00:00  2015-04-01 00:00:00\n",
       "competition_time_month                         84                   94                  105                   71                    4\n",
       "promo_since                   2015-07-27 00:00:00  2010-03-22 00:00:00  2011-03-28 00:00:00  2015-07-27 00:00:00  2015-07-27 00:00:00\n",
       "promo_time_week                                 0                  279                  226                    0                    0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff01681",
   "metadata": {},
   "source": [
    "# 3.0 PASSO 03 - FILTRAGEM DE VARIÁVEIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ef27a4a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:48:08.707437Z",
     "start_time": "2023-05-26T18:48:08.180150Z"
    }
   },
   "outputs": [],
   "source": [
    "df3 = df2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4ee3a42b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:48:09.901896Z",
     "start_time": "2023-05-26T18:48:09.879489Z"
    }
   },
   "outputs": [
    {
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       "      <th>promo_since</th>\n",
       "      <th>promo_time_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>5263</td>\n",
       "      <td>555</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>basic</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Jul</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2008-09-01</td>\n",
       "      <td>84</td>\n",
       "      <td>2015-07-27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>6064</td>\n",
       "      <td>625</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>basic</td>\n",
       "      <td>570.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2010</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>Jul</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>94</td>\n",
       "      <td>2010-03-22</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>8314</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>basic</td>\n",
       "      <td>14130.0</td>\n",
       "      <td>12</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2011</td>\n",
       "      <td>Jan,Apr,Jul,Oct</td>\n",
       "      <td>Jul</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2006-12-01</td>\n",
       "      <td>105</td>\n",
       "      <td>2011-03-28</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>13995</td>\n",
       "      <td>1498</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>1</td>\n",
       "      <td>c</td>\n",
       "      <td>extended</td>\n",
       "      <td>620.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Jul</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2009-09-01</td>\n",
       "      <td>71</td>\n",
       "      <td>2015-07-27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>4822</td>\n",
       "      <td>559</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>regular_day</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>basic</td>\n",
       "      <td>29910.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>Jul</td>\n",
       "      <td>0</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>2015-30</td>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-07-27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   store  day_of_week       date  sales  customers  open  promo state_holiday  school_holiday store_type assortment  competition_distance  competition_open_since_month  competition_open_since_year  promo2  promo2_since_week  promo2_since_year   promo_interval month_map  is_promo  year  month  day  week_of_year year_week competition_since  competition_time_month promo_since  promo_time_week\n",
       "0      1            5 2015-07-31   5263        555     1      1   regular_day               1          c      basic                1270.0                             9                         2008       0                 31               2015                0       Jul         0  2015      7   31            31   2015-30        2008-09-01                      84  2015-07-27                0\n",
       "1      2            5 2015-07-31   6064        625     1      1   regular_day               1          a      basic                 570.0                            11                         2007       1                 13               2010  Jan,Apr,Jul,Oct       Jul         1  2015      7   31            31   2015-30        2007-11-01                      94  2010-03-22              279\n",
       "2      3            5 2015-07-31   8314        821     1      1   regular_day               1          a      basic               14130.0                            12                         2006       1                 14               2011  Jan,Apr,Jul,Oct       Jul         1  2015      7   31            31   2015-30        2006-12-01                     105  2011-03-28              226\n",
       "3      4            5 2015-07-31  13995       1498     1      1   regular_day               1          c   extended                 620.0                             9                         2009       0                 31               2015                0       Jul         0  2015      7   31            31   2015-30        2009-09-01                      71  2015-07-27                0\n",
       "4      5            5 2015-07-31   4822        559     1      1   regular_day               1          a      basic               29910.0                             4                         2015       0                 31               2015                0       Jul         0  2015      7   31            31   2015-30        2015-04-01                       4  2015-07-27                0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c30e19",
   "metadata": {},
   "source": [
    "## 3.1 Filtragem da Linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de3b2152",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:48:13.420784Z",
     "start_time": "2023-05-26T18:48:13.262234Z"
    }
   },
   "outputs": [],
   "source": [
    "# a coluna open indica se a loja está aberta ou fechada, e quando a loja está fechada as vendas é zero, é uma relação direta, então não haverá aprendizado, não quero as linhas cuja a coluna 'open' é  igual a zero.\n",
    "df3 = df3[(df3['open'] != 0) & (df3['sales'] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fa033f",
   "metadata": {},
   "source": [
    "## 3.2 Seleção da Colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a64d2f00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:54:35.579360Z",
     "start_time": "2023-05-26T18:48:17.062332Z"
    }
   },
   "outputs": [],
   "source": [
    "# a coluna customers é uma restrição de negócio, pois não teremos ela disponível no momento da predição.\n",
    "cols_drop = ['customers', 'open', 'promo_interval', 'month_map']\n",
    "df3.drop( cols_drop, axis = 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac1991e",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b59ee269",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-26T18:54:59.169692Z",
     "start_time": "2023-05-26T18:54:59.164483Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844338, 29)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37bd4637",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0313c18f",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dda5c329",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
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   "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05c4c30e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40298bdf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "829b0b8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9444d31",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ecde816",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef720941",
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   "source": []
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