Part I - Exploratory Data Analysis.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "75602c79-e7a1-4540-9178-8dd41153bcf7",
   "metadata": {},
   "source": [
    "# Habitability Score of House Predictions\n",
    "*(Part 1 - Exploratory Data Analysis)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e0967ea-54d8-447f-950e-8bc54a6c800a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# For visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5860ec1a-c1b1-4b8d-b5ad-97048b97dd44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Property_ID</th>\n",
       "      <th>Property_Type</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Number_of_Windows</th>\n",
       "      <th>Number_of_Doors</th>\n",
       "      <th>Furnishing</th>\n",
       "      <th>Frequency_of_Powercuts</th>\n",
       "      <th>Power_Backup</th>\n",
       "      <th>Water_Supply</th>\n",
       "      <th>Traffic_Density_Score</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Dust_and_Noise</th>\n",
       "      <th>Air_Quality_Index</th>\n",
       "      <th>Neighborhood_Review</th>\n",
       "      <th>Habitability_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0x21e3</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Semi_Furnished</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Once in a day - Morning</td>\n",
       "      <td>5.89</td>\n",
       "      <td>Slightly below average</td>\n",
       "      <td>Medium</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3.86</td>\n",
       "      <td>71.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0x68d4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>733</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>1.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Once in a day - Evening</td>\n",
       "      <td>4.37</td>\n",
       "      <td>Well below average</td>\n",
       "      <td>Medium</td>\n",
       "      <td>96.0</td>\n",
       "      <td>3.55</td>\n",
       "      <td>71.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0x7d81</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>737</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fully Furnished</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Once in a day - Morning</td>\n",
       "      <td>7.45</td>\n",
       "      <td>Slightly below average</td>\n",
       "      <td>Medium</td>\n",
       "      <td>121.0</td>\n",
       "      <td>3.81</td>\n",
       "      <td>71.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0x7a57</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>900</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Unfurnished</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Once in a day - Morning</td>\n",
       "      <td>6.16</td>\n",
       "      <td>Well above average</td>\n",
       "      <td>Medium</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.34</td>\n",
       "      <td>31.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0x9409</td>\n",
       "      <td>Bungalow</td>\n",
       "      <td>2238</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Fully Furnished</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>All time</td>\n",
       "      <td>5.46</td>\n",
       "      <td>Well below average</td>\n",
       "      <td>Medium</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4.77</td>\n",
       "      <td>93.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Property_ID Property_Type  Property_Area  Number_of_Windows  \\\n",
       "0      0x21e3     Apartment            106                NaN   \n",
       "1      0x68d4     Apartment            733                2.0   \n",
       "2      0x7d81     Apartment            737                4.0   \n",
       "3      0x7a57     Apartment            900                3.0   \n",
       "4      0x9409      Bungalow           2238               14.0   \n",
       "\n",
       "   Number_of_Doors       Furnishing  Frequency_of_Powercuts Power_Backup  \\\n",
       "0                1   Semi_Furnished                     0.0           No   \n",
       "1                2      Unfurnished                     1.0           No   \n",
       "2                2  Fully Furnished                     0.0           No   \n",
       "3                2      Unfurnished                     2.0          Yes   \n",
       "4                6  Fully Furnished                     0.0           No   \n",
       "\n",
       "              Water_Supply  Traffic_Density_Score              Crime_Rate  \\\n",
       "0  Once in a day - Morning                   5.89  Slightly below average   \n",
       "1  Once in a day - Evening                   4.37      Well below average   \n",
       "2  Once in a day - Morning                   7.45  Slightly below average   \n",
       "3  Once in a day - Morning                   6.16      Well above average   \n",
       "4                 All time                   5.46      Well below average   \n",
       "\n",
       "  Dust_and_Noise  Air_Quality_Index  Neighborhood_Review  Habitability_score  \n",
       "0         Medium               90.0                 3.86               71.98  \n",
       "1         Medium               96.0                 3.55               71.20  \n",
       "2         Medium              121.0                 3.81               71.39  \n",
       "3         Medium              100.0                 1.34               31.46  \n",
       "4         Medium              116.0                 4.77               93.70  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the dataset into pandas DataFrame using `read_csv` method.\n",
    "data = pd.read_csv(\"data.csv\")\n",
    "\n",
    "# view\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3dc4f37a-f4b3-4ef1-833c-5e001ae87ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shape\n",
    "nrows, ncolumns = data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cf293f4-b65e-4552-b555-179b484bb5a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39499 entries, 0 to 39498\n",
      "Data columns (total 15 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Property_ID             39499 non-null  object \n",
      " 1   Property_Type           39499 non-null  object \n",
      " 2   Property_Area           39499 non-null  int64  \n",
      " 3   Number_of_Windows       37845 non-null  float64\n",
      " 4   Number_of_Doors         39499 non-null  int64  \n",
      " 5   Furnishing              38457 non-null  object \n",
      " 6   Frequency_of_Powercuts  38116 non-null  float64\n",
      " 7   Power_Backup            39499 non-null  object \n",
      " 8   Water_Supply            39499 non-null  object \n",
      " 9   Traffic_Density_Score   39499 non-null  float64\n",
      " 10  Crime_Rate              38712 non-null  object \n",
      " 11  Dust_and_Noise          38280 non-null  object \n",
      " 12  Air_Quality_Index       39499 non-null  float64\n",
      " 13  Neighborhood_Review     39499 non-null  float64\n",
      " 14  Habitability_score      39499 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(7)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Metadata information\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c632d935-5094-4dd4-8e40-633514b39598",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Number_of_Windows</th>\n",
       "      <th>Number_of_Doors</th>\n",
       "      <th>Frequency_of_Powercuts</th>\n",
       "      <th>Traffic_Density_Score</th>\n",
       "      <th>Air_Quality_Index</th>\n",
       "      <th>Neighborhood_Review</th>\n",
       "      <th>Habitability_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39499.000000</td>\n",
       "      <td>37845.000000</td>\n",
       "      <td>39499.000000</td>\n",
       "      <td>38116.000000</td>\n",
       "      <td>39499.000000</td>\n",
       "      <td>39499.000000</td>\n",
       "      <td>39499.000000</td>\n",
       "      <td>39499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1391.715638</td>\n",
       "      <td>3.923768</td>\n",
       "      <td>2.331375</td>\n",
       "      <td>0.484678</td>\n",
       "      <td>6.354772</td>\n",
       "      <td>121.352566</td>\n",
       "      <td>3.735945</td>\n",
       "      <td>73.482297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1953.287544</td>\n",
       "      <td>2.690992</td>\n",
       "      <td>1.179964</td>\n",
       "      <td>0.752207</td>\n",
       "      <td>1.219681</td>\n",
       "      <td>62.384669</td>\n",
       "      <td>0.796843</td>\n",
       "      <td>14.118753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>494.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>69.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>932.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.470000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>75.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1770.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>82.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28064.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.910000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Property_Area  Number_of_Windows  Number_of_Doors  \\\n",
       "count   39499.000000       37845.000000     39499.000000   \n",
       "mean     1391.715638           3.923768         2.331375   \n",
       "std      1953.287544           2.690992         1.179964   \n",
       "min       100.000000           0.000000         1.000000   \n",
       "25%       494.000000           2.000000         1.000000   \n",
       "50%       932.000000           4.000000         2.000000   \n",
       "75%      1770.000000           5.000000         3.000000   \n",
       "max     28064.000000          15.000000         6.000000   \n",
       "\n",
       "       Frequency_of_Powercuts  Traffic_Density_Score  Air_Quality_Index  \\\n",
       "count            38116.000000           39499.000000       39499.000000   \n",
       "mean                 0.484678               6.354772         121.352566   \n",
       "std                  0.752207               1.219681          62.384669   \n",
       "min                  0.000000               0.000000           0.000000   \n",
       "25%                  0.000000               5.600000          95.000000   \n",
       "50%                  0.000000               6.470000         113.000000   \n",
       "75%                  1.000000               7.220000         140.000000   \n",
       "max                  3.000000               9.910000        1044.000000   \n",
       "\n",
       "       Neighborhood_Review  Habitability_score  \n",
       "count         39499.000000        39499.000000  \n",
       "mean              3.735945           73.482297  \n",
       "std               0.796843           14.118753  \n",
       "min               0.000000            7.280000  \n",
       "25%               3.220000           69.660000  \n",
       "50%               3.850000           75.760000  \n",
       "75%               4.260000           82.260000  \n",
       "max               5.000000          100.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "data.describe() # prints only numerical stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40fbde34-17d5-466f-b95c-6828cdbee4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Property_ID</th>\n",
       "      <th>Property_Type</th>\n",
       "      <th>Furnishing</th>\n",
       "      <th>Power_Backup</th>\n",
       "      <th>Water_Supply</th>\n",
       "      <th>Crime_Rate</th>\n",
       "      <th>Dust_and_Noise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39499</td>\n",
       "      <td>39499</td>\n",
       "      <td>38457</td>\n",
       "      <td>39499</td>\n",
       "      <td>39499</td>\n",
       "      <td>38712</td>\n",
       "      <td>38280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>39499</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0x21e3</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>Semi_Furnished</td>\n",
       "      <td>No</td>\n",
       "      <td>All time</td>\n",
       "      <td>Well below average</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>14596</td>\n",
       "      <td>20357</td>\n",
       "      <td>29714</td>\n",
       "      <td>19511</td>\n",
       "      <td>19472</td>\n",
       "      <td>32996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Property_ID Property_Type      Furnishing Power_Backup Water_Supply  \\\n",
       "count        39499         39499           38457        39499        39499   \n",
       "unique       39499             6               3            3            5   \n",
       "top         0x21e3     Apartment  Semi_Furnished           No     All time   \n",
       "freq             1         14596           20357        29714        19511   \n",
       "\n",
       "                Crime_Rate Dust_and_Noise  \n",
       "count                38712          38280  \n",
       "unique                   4              3  \n",
       "top     Well below average         Medium  \n",
       "freq                 19472          32996  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For categorical stats\n",
    "data.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b17751f1-dc2e-46c4-8f52-404d7b386aad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Property_ID',\n",
       " 'Property_Type',\n",
       " 'Property_Area',\n",
       " 'Number_of_Windows',\n",
       " 'Number_of_Doors',\n",
       " 'Furnishing',\n",
       " 'Frequency_of_Powercuts',\n",
       " 'Power_Backup',\n",
       " 'Water_Supply',\n",
       " 'Traffic_Density_Score',\n",
       " 'Crime_Rate',\n",
       " 'Dust_and_Noise',\n",
       " 'Air_Quality_Index',\n",
       " 'Neighborhood_Review',\n",
       " 'Habitability_score']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prints the column names\n",
    "data.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07acf84d-8629-4d28-8380-11d87bd53e2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renames the column names\n",
    "data.rename(columns = {col: col.lower() for col in data.columns.tolist()},\n",
    "           inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49d7f618-7453-41ee-9607-34d090de4337",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "property_type: ['Apartment', 'Bungalow', 'Single-family home', 'Duplex', '#R%$G&867', 'Container Home']\n",
      "--------------------\n",
      "furnishing: ['Semi_Furnished', 'Unfurnished', 'Fully Furnished', nan]\n",
      "--------------------\n",
      "power_backup: ['No', 'Yes', 'NOT MENTIONED']\n",
      "--------------------\n",
      "water_supply: ['Once in a day - Morning', 'Once in a day - Evening', 'All time', 'NOT MENTIONED', 'Once in two days']\n",
      "--------------------\n",
      "crime_rate: ['Slightly below average', 'Well below average', 'Well above average', nan, 'Slightly above average']\n",
      "--------------------\n",
      "dust_and_noise: ['Medium', nan, 'High', 'Low']\n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "# Examine the unique values in categorical variables.\n",
    "cat_variables = data.select_dtypes(include='object').columns.tolist()[1:]\n",
    "\n",
    "for col in cat_variables:\n",
    "    print(col + \":\" , data[col].unique().tolist())\n",
    "    print(\"--\" * 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4c0defd9-72e2-489a-8cae-bb6462a9f644",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's replace 'NOT MENTIONED' & '#R%$G&867' values from dataset with 'NaN'.\n",
    "data.replace(['NOT MENTIONED', '#R%$G&867'], np.nan, \n",
    "             inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38fdeadd-d044-4c09-a09c-80b4eab58f2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_missing</th>\n",
       "      <th>%_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>property_type</th>\n",
       "      <td>510</td>\n",
       "      <td>1.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_of_windows</th>\n",
       "      <td>1654</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furnishing</th>\n",
       "      <td>1042</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency_of_powercuts</th>\n",
       "      <td>1383</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power_backup</th>\n",
       "      <td>828</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water_supply</th>\n",
       "      <td>345</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crime_rate</th>\n",
       "      <td>787</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dust_and_noise</th>\n",
       "      <td>1219</td>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        n_missing  %_missing\n",
       "property_type                 510       1.29\n",
       "number_of_windows            1654       4.19\n",
       "furnishing                   1042       2.64\n",
       "frequency_of_powercuts       1383       3.50\n",
       "power_backup                  828       2.10\n",
       "water_supply                  345       0.87\n",
       "crime_rate                    787       1.99\n",
       "dust_and_noise               1219       3.09"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values\n",
    "nan_columns = data.columns[data.isnull().sum() > 0].tolist()\n",
    "n_missing = data[nan_columns].isnull().sum()\n",
    "percent_missing = round(n_missing / nrows * 100, 2)\n",
    "\n",
    "pd.concat([n_missing, percent_missing], axis=1, keys=['n_missing', '%_missing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a12b77a2-45d1-4d0e-a652-042e3a2600ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data: (27649, 15)\n",
      "Test Data: (11850, 15)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and test datasets into ratio of 70:30.\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df_train, df_test = train_test_split(data, test_size=0.3, random_state=42)\n",
    "print(\"Training Data:\", df_train.shape)\n",
    "print(\"Test Data:\", df_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb96a7bc-215a-4075-a191-444d1859fc07",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f4663b-e51d-4e1a-8a03-90dbc2eceff9",
   "metadata": {},
   "source": [
    "#### Univariate Analysis\n",
    "Univarite analysis or `Descriptive analysis` provides an understanding of a characteristics of\n",
    "each attributes of datasets.\n",
    "Let's observe the distribution of attributes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cf1a9616-05c4-4578-bcb4-198e18ba07ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histograms of numerical variables to observe the distribution of data.\n",
    "# Property_Area\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.histplot(data=df_train, x='property_area', kde=True, bins=60)\n",
    "plt.grid(ls='--', c='#000', alpha=0.2)\n",
    "plt.title(\"Long tail right-skewed distribution of Property_Area\", size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02295543-e1f9-47b6-b6ed-9d1ee339c264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Neighborhood_review\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.histplot(data=df_train, x='neighborhood_review', bins=50)\n",
    "\n",
    "plt.title(\"Left-skewed distribution of Neighborhood_Review\", size=10)\n",
    "plt.grid(ls=\"--\", c='b', alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0248954-61ba-43eb-9129-aec2f8ba47df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Habitability_Score\n",
    "plt.figure(figsize=(7, 4))\n",
    "sns.histplot(data=df_train, x='habitability_score', kde=True, bins=50)\n",
    "plt.title(\"Bi-Model distribution of Habitability_Score\", size=10)\n",
    "plt.grid(ls=\"--\", c='b', alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7704f0-8939-467e-a65e-011ba724e45b",
   "metadata": {},
   "source": [
    "We can that the distribution of target variables(Habitability Score) has two models which means that it has two\n",
    "centers; This might be result of two groups present in the data. We should make distribution\n",
    "normal before using it in model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "032daadb-e3dc-4304-bf24-0a73821de1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    25331\n",
       "0     2318\n",
       "Name: habitability_score, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's examine the house with habitability_score greater than and less than 50.\n",
    "(df_train['habitability_score'] > 50).astype('int').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a537d1e9-3246-4a9a-9bee-7955edada81b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the distribution of numerical variables on these two groups of habitability score.\n",
    "num_variables = ['property_area', 'traffic_density_score', 'air_quality_index', 'neighborhood_review']\n",
    "\n",
    "low_score = df_train[df_train['habitability_score'] <= 50][num_variables]\n",
    "high_score = df_train[df_train['habitability_score'] > 50][num_variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4274d0e4-29aa-41ac-abdc-29552260eb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">score&lt;50</th>\n",
       "      <th colspan=\"4\" halign=\"left\">score&gt;50</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>property_area</th>\n",
       "      <th>traffic_density_score</th>\n",
       "      <th>air_quality_index</th>\n",
       "      <th>neighborhood_review</th>\n",
       "      <th>property_area</th>\n",
       "      <th>traffic_density_score</th>\n",
       "      <th>air_quality_index</th>\n",
       "      <th>neighborhood_review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2318.00</td>\n",
       "      <td>2318.00</td>\n",
       "      <td>2318.00</td>\n",
       "      <td>2318.00</td>\n",
       "      <td>25331.00</td>\n",
       "      <td>25331.00</td>\n",
       "      <td>25331.00</td>\n",
       "      <td>25331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1092.01</td>\n",
       "      <td>6.63</td>\n",
       "      <td>128.02</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1421.70</td>\n",
       "      <td>6.33</td>\n",
       "      <td>120.63</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1903.28</td>\n",
       "      <td>1.23</td>\n",
       "      <td>59.27</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1990.86</td>\n",
       "      <td>1.22</td>\n",
       "      <td>61.23</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>102.00</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>389.25</td>\n",
       "      <td>5.81</td>\n",
       "      <td>98.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>508.00</td>\n",
       "      <td>5.58</td>\n",
       "      <td>95.00</td>\n",
       "      <td>3.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>693.00</td>\n",
       "      <td>6.68</td>\n",
       "      <td>120.00</td>\n",
       "      <td>2.58</td>\n",
       "      <td>950.00</td>\n",
       "      <td>6.45</td>\n",
       "      <td>113.00</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1262.50</td>\n",
       "      <td>7.52</td>\n",
       "      <td>150.00</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1803.00</td>\n",
       "      <td>7.20</td>\n",
       "      <td>139.00</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28064.00</td>\n",
       "      <td>9.49</td>\n",
       "      <td>931.00</td>\n",
       "      <td>4.04</td>\n",
       "      <td>28057.00</td>\n",
       "      <td>9.91</td>\n",
       "      <td>1043.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           score<50                                          \\\n",
       "      property_area traffic_density_score air_quality_index   \n",
       "count       2318.00               2318.00           2318.00   \n",
       "mean        1092.01                  6.63            128.02   \n",
       "std         1903.28                  1.23             59.27   \n",
       "min          102.00                  1.82              1.00   \n",
       "25%          389.25                  5.81             98.00   \n",
       "50%          693.00                  6.68            120.00   \n",
       "75%         1262.50                  7.52            150.00   \n",
       "max        28064.00                  9.49            931.00   \n",
       "\n",
       "                               score>50                        \\\n",
       "      neighborhood_review property_area traffic_density_score   \n",
       "count             2318.00      25331.00              25331.00   \n",
       "mean                 2.51       1421.70                  6.33   \n",
       "std                  0.68       1990.86                  1.22   \n",
       "min                  0.00        100.00                  0.00   \n",
       "25%                  2.10        508.00                  5.58   \n",
       "50%                  2.58        950.00                  6.45   \n",
       "75%                  2.93       1803.00                  7.20   \n",
       "max                  4.04      28057.00                  9.91   \n",
       "\n",
       "                                             \n",
       "      air_quality_index neighborhood_review  \n",
       "count          25331.00            25331.00  \n",
       "mean             120.63                3.85  \n",
       "std               61.23                0.71  \n",
       "min                0.00                0.67  \n",
       "25%               95.00                3.48  \n",
       "50%              113.00                4.06  \n",
       "75%              139.00                4.40  \n",
       "max             1043.00                5.00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "low_score_desc = round(low_score.describe(), 2)\n",
    "high_score_desc = round(high_score.describe(), 2)\n",
    "\n",
    "pd.concat([low_score_desc, high_score_desc], axis=1, keys=['score<50', 'score>50'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d427147e-bfbd-4c42-84ae-e68d6abf443e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['score'] = df_train['habitability_score'].apply(lambda x: '>50' if x > 50 else '<50')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "830fe699-0ba2-4124-9962-cbd80f9fb16c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplot\n",
    "plt.figure(figsize=(7, 4))\n",
    "\n",
    "sns.boxplot(data=df_train, x='score', y='neighborhood_review')\n",
    "plt.title(\"Distribution of Neighborhood Review by Score\", size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97d13eb-4bfc-4171-b3cf-38b358f92f6d",
   "metadata": {},
   "source": [
    "We can clearly see from the above boxplot that there are two groups in our response variable with different mean values. So, we need to transform bi-model distribution into normal distribuition, for that we can use log transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7cf45767-97cd-4ad6-a957-beae9f7879b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete the score column\n",
    "del df_train['score']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b42af54-d218-4c7a-8030-bf923e88ab5d",
   "metadata": {},
   "source": [
    "#### Bi-variate Analysis\n",
    "`Correlation analysis` or bivariate analysis helps to identified the relation between two variables; independent & dependent variables.Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d91d4c2d-efb3-4564-a0c5-20ee2b8710ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "property_area             0.061964\n",
       "number_of_windows         0.088146\n",
       "number_of_doors           0.070932\n",
       "frequency_of_powercuts   -0.240716\n",
       "traffic_density_score    -0.074858\n",
       "air_quality_index        -0.038011\n",
       "neighborhood_review       0.545207\n",
       "habitability_score        1.000000\n",
       "Name: habitability_score, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation\n",
    "df_train.corr()['habitability_score']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "227490c0-3e46-4718-8c86-4fa3a508aa47",
   "metadata": {},
   "source": [
    "Let's examine the relation between `frequency_of_powercuts` and `neighborhood_review` with target variable `habitability_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3c386e29-8755-409a-a07d-60b5f2ae044f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qxdy5c/Hcc89BLBZDrVbjypUrKCsrw/Tp0zF16lQsXrwYeXl5WLJkCa5cuYLZs2fjr3/9KyvirofrHZ9KpcL777+PL7/8EklJSfjXv/6Fw4cPIykpid0nMTERX375JU6fPg2FQuEzZXDmzJlYs2YNZs+ejVmzZuH06dNYvHgxnnrqKXbRQKFQKLc6mZmZmDp1KtauXct5fe7cuRg4cCAee+wxPPTQQwgKCsLJkyfx9ddfY+3atUhJScGIESPw8MMPs9kWTz/9NGQyGSs+unbtCrvdjrVr12L8+PEoLi7GO++8w7lOYmIiWlpa8M0336Bnz56Qy+U+LdF5PB6mTJmCd955B2fOnMG3337LbgsJCcEzzzyDJ598Ek6nE9nZ2WhqasKBAwcQHByMvLw8r/MlJiYiLy8PBQUFePPNN9GzZ09UV1ejvr4e999//6/6GX8j41OpVNi+fTsOHDiAiIgIvP7667h06RLnOz8xMREHDx7EuXPnEBwcjMjISK/zPP300+jXrx+WL1+O//u//8OPP/6IdevWtevKS7lNuSmVWhTKr4gvYwN3TCYTmT17NlEqlUQikRC1Wk0OHTrEbl++fDlJTU0lMpmMREZGkokTJ5LKykpCiG+jhWXLlpHo6GjC4/FYtzxfBas6nY5IJBIil8s5bkkMW7duJb169SJisZhERESQO+64g3zyySd+78PhcJAVK1aQhIQEIhKJSHx8PFm5ciW7/fjx42TYsGFEKpWSyMhI8tBDD3Guey0GENc6Ps/jzGYzyc/PJ2FhYSQ8PJw8+uijZN68eaRnz57s+err68nIkSNJcHAwAUC+/fZbn9f/7rvvSL9+/YhYLCbR0dFk7ty5xGazsdt9zfXEiRM5zoUUCoXye+Lr8/XcuXNEIpEQz6XWoUOH2M/CoKAg0qNHD/Liiy+y22tra0lubi6RSCQkISGBbNu2jXTs2JG888477D6vv/46iYmJITKZjIwaNYq8//77XkYLjzzyCFEoFAQA65bnbkDBUFZWRgCQhIQEjjEEIS7ThL///e+ke/fuRCQSkQ4dOpBRo0aR77//3u9cmEwm8uSTT5KYmBgiFouJSqUiGzduZLffyGe8PyOJ9sbneZxWqyUTJ04kwcHBpGPHjmTBggXkb3/7G+fZnT59mgwcOJDIZDICgFRVVfm8/n//+1+SlpbGfh+/8sornLH5muuePXtynAsptwc8QnwkeVIoFAqFQqFQbjoXLlxAXFwcW9NLoVBuLaiYolAoFAqFQrlF2L9/P1paWpCZmYm6ujo899xzuHjxIs6cOeNlaEChUG4+tGaKQqFQKBQK5RbBZrPh+eefR2VlJUJCQjB48GBs3bqVCikK5RaFRqYoFAqFQqFQKBQK5Qag9lgUCoVCoVAoFAqFcgNQMUWhUCgUCoVCoVAoNwCtmYKraVttbS1CQkK8OlpTKBQK5beDEILm5mbExsbSXmIe0O8mCoVCuTlcz3cTFVMAamtrERcXd7OHQaFQKH9azp8/j86dO9/sYdxS0O8mCoVCublcy3cTFVNwddEGXBMWGhp6k0dDoVAofx6ampoQFxfHfg5TrkK/mygUCuXmcD3fTVRMAWz6RGhoKP3ColAolJsATWPzhn43USgUys3lWr6baII6hUKhUCgUCoVCodwAVExRKBQKhUKhUCgUyg1AxRSFQqFQKBQKhUKh3ABUTFEoFAqFQqFQKBTKDUDFFIVCoVAoFAqFQqHcAFRMUSgUCoVCoVAoFMoNQMUUhUKhUCgUCoVCodwAtM8UhUKhUK4bg9GKhhYrmsw2hMpEUAaJESYX3+xhUSgUCoXyu3JTI1M//PADxo8fj9jYWPB4POzYsYOznRCCJUuWIDY2FjKZDEOHDkVZWRlnH4vFgtmzZ0OpVCIoKAgTJkzAhQsXfse7oFAolD8XtXoTZn1Ygjtf/x53v30Ad772PWZ/WIJavelmD41CoVAolN+VmyqmWltb0bNnT6xbt87n9pdffhmvv/461q1bh8OHDyM6OhojR45Ec3Mzu88TTzyBTz/9FB999BGKiorQ0tKCcePGweFw/F63QaFQKH94DEYrzta3oKRGh7NXWmAwWn3uU93Qirn//QWFFQ2cbT9UNGDe9uM+j6NQKBQK5Xblpqb55ebmIjc31+c2QgjWrFmDF154Affccw8AYMuWLYiKisK2bdswY8YMGAwGbNiwAf/6178wYsQIAMAHH3yAuLg47Nu3D6NGjfJ5bovFAovFwv7d1NT0K98ZhUKh/HGo1Zswd/txjkC6I1mJVff2QGy4jLNP/uBEFGq0Ps/zQ0UDGlqsNN2PQqFQKH8ablkDiqqqKly6dAl33XUX+5pEIsGQIUNw4MABAMDRo0dhs9k4+8TGxiIjI4PdxxcvvfQSwsLC2P/FxcX9djdCoVAotzAGo9VLSAHcSJP7Pha7M+D5ms2233K4FAqFQqHcUtyyBhSXLl0CAERFRXFej4qKQnV1NbuPWCxGRESE1z7M8b6YP38+nnrqKfbvpqYmKqgoFIpPbnejhYYWq5eQYmAiTTwe0DMuHPmDE9EhRBLwfCFS0W8xTMqvTOK8PTd7CNfFuVVjb/YQKBQKxSe3rJhi4PF4nL8JIV6vedLePhKJBBJJ4AUBhUL5c+IunoLEQhyt0WH57nIYra46TM/0t2s5X32zBXqTDUFiAYIkQoTLRL+5ILtWEdhktkEuFqAgOwlZceGw2J2QigQ4VqPDxqIqNJttCJGJUFKjw7r9GswaroJapUCxj1S/nGQllMG3j9CkUCgUCqU9blkxFR0dDcAVfYqJiWFfr6+vZ6NV0dHRsFqt0Ol0nOhUfX09Bg8e/PsOmEKh/G78VtEiX7VDapUCb07OwpwPS2C0Otj0t7WTs9q9Zq3ehLn/PY5CDfd8s4cnIyZUinD5byOqrqUGiiFcJsK2hwbitS9PYd1+DWecb07OQpBEiCU7S1nxtLGoCm9OzoJEyEdabBgrwMLlInQOl91WUTsKhUKhUNrjlq2ZSkpKQnR0NL7++mv2NavViu+//54VSn369IFIJOLsU1dXh9LSUiqmKJTblN/Klttf7VCxRotNxVUoyE5iX2PS39o9n4eQYs63dn8Fis824PPSSzc07kDOe9dSA+W+r9XhhLbFgmnZXTBruApysQBysQBZ8RGQiQTQm2ycbUarA/O2H8ecO7vhlxodpm85gplbj2HKuwexYEcptUenUCgUyp+KmxqZamlpgUZz9ZfQqqoq/Pzzz4iMjER8fDyeeOIJrFy5EsnJyUhOTsbKlSshl8sxZcoUAEBYWBimT5+Op59+GgqFApGRkXjmmWeQmZnJuvtRKJTbh/aEwrVEi/wRqHaoWKNFgTqJ85o/owUmaqYzWjE3NwVPOZxoMtlxuLoRG4uqYLQ62PN1DJVg5Z5yvHh35jWPu05vwndnrqBjiAQWuxM6ow2HqhoxtFsHxITLAt7HkWod9EYbtK1WEABLdpZynPnUKgXemtIbBAQbiqp8RqrmfFiCB/rH45UvT3ml+v0az4FCoVAolD8SN1VMHTlyBMOGDWP/Zkwh8vLysHnzZjz33HMwmUyYOXMmdDodBgwYgK+++gohISHsMW+88QaEQiHuv/9+mEwm3Hnnndi8eTMEAsHvfj8UCuW35VrMEvwt4ttLDWxqx4XO08XOl9GCvzTBaeoknKo1YMdMNaq0rRAJ+IiQi6BttmLOiG5obL02O3GD0YrqRiN2H6/lCBm1SoEkZRDkYgHnPtxroexOgiRlEBbsOIGe8REoqdF5iaFijRZjM2Pw+Yk6n9sAoCA7Cb3jIzhCyx1qj06hUCiUPxM3VUwNHToUhBC/23k8HpYsWYIlS5b43UcqlWLt2rVYu3btbzBCCoVyK9Ge4PEXLWqvhshgtEImEuDtqb0hFQlw/IIePB6QERvGGjIog8Vsmpsvo4VAaYIAkBUfgaW7y5DVJkRyVEosHJeG1XtPYuG4tGu6f73R5koR9CN0VkzMQGibyJOLBXhzchY2FVexxhHv/3gOxRot8tVJfsVQVKgURX76SBVrtJiXmwKTNbA9usFE7dEpFAqF8ufgljWgoFAoNx+D0Yomkw0tVgea2yI6EXIxokKlN2U8oe3YbktFApTU6DiRp0CpgYt3lmLx+HTM//QEZ3uOSomZw7pi+pYjrItfTrIS2x4aiH9+r8Gzo1K8Ii/Xkia4br8Gz+emYlRaFAAefjmvQ4+4cJhs7YiTtqia2e7w6aLHXKPFakfHECnuSFaiR1w4NhVXXRVzceGsgArUK6q9PlL1TRZ0asfJUC6mmQEUCoVC+XNAxRSFQvHJJb0JLVYHFu8q5Szgs1UKrLw7E/GKoN99TMpgMXKSlTharfOy8r5sMOObU5fxypdnAFyNPJmsDhRWNPi0/yaEYMmuUi8RVKhpgBMEBdlXIziFFQ0ghGDx+HT4arxwrWmC1Y1GzNx6DGqVArOGqdArIQJNASI57lG1fz88ELOGq9h7CBILYXc6wePxYLY5wG/7/9X39kBVQysn+sRcXy4WoGNbryhmTvrGRyBMLoJQwIfJ6sCm/H442maNzohJd0pqdMhJVvoUjzkqJQT8wO0rKBQKhUK5XaBiikKhcGD6IjW2WuEgBFnxESip0bOL6iKNFs9/egKv3d/rhiJUTJTFYLJCLnF9BBFCIBdfW/+l2cNVcDgJ1n2r4YiFnGQlFo9Px7DuHVFnMGPu9uOYt/04nhiR7JXyxh6jUqIgOwkZncOR2SnMq8fSkyO6efVeamixoEuHYK9xBYn9f5zKxQJ0jpBhQ15fhEhF2P7oYBRWXMG7hZUYmRqNAV0i/c4VI6TkYgEUwRKU1xoAAL3jIyAR8iECDwfOalnhk5OsxEt3Z0Iq4pq1SoR8dh7OXG7GiJSOeGBAPLYdrEavuHC8+tVprzosd0t4Zr4A4LWvz+DDhwZiyWdlXsfMHKaCRHDLGsVSKBQKhfKrwiOBipb+JDQ1NSEsLAwGgwGhoaE3ezgUyk0jkIGC+6IaAL54IgcRcjFazPaA/Z4Y8dRisSFMJsbCHaVefZemqZOw7WA1Hr6jKxIi5Yjxk0Z2tr4Fnx2vxZFzjT7retQqBbLiI/BzjQ5LJ2Ygf+MhvPu3vth9os6n4YJcLMCGvL5461sN53zMmIQ8HvI3H+a8vmhcGkQCvpegqmpowaKdZV7RGn/XyFEpMS07EQIeHx1DJUiNufrZw8xZo9GKZrMdJy7qkdkpDDKRAJFBYqzYXe7lwuf+jMZkROG50SlottjRYnYgRCqEkxBoLjdje8lFlNTosffxHDz/6Yk2sew9N+7zuW6/hvOcpgxIAAD8fF7Pik2JkI+S83psLKrCzsfUSI4K8TqfL+jnr39+y7lJnLfnVz3fb825VWNv9hAoFMqfiOv5/KWRKQqFAqB9AwX3lDe5WAAhn4+n/v0zZxHu2RiWsfGOCpVCLhJAyLfjqbu64cGcLqxVuLtBw9r9FRjXIxZjMqJ9RqiazDZkdgrDmn0VPu/BvTZp8c5SbJzWDyDA4C4Kn4YLBdlJWPetxq+hw9zRKV6vr9h9EovGp7FzxgjFcJkYM4d2BQjB0Ro9m1LYIUSCV7445SX+XIKSYN6YVLRY7OzrnoJWLhZgY14/vPVtRUAXPuZ+PjpUg8dHdMcLO7zTM1dMysTKvacAgGPRvrGoipM+6B6dm5ebgqy4cJSc17NizWJ3YumEDPx8Xo/ZHiIbAFrd7odCoVAolNsZmotBodxmBGroGoj2DBSy4sLZvwuyk7DUo5YK4DaGNRitqGk0Ys/xWhRsPoyCLYext+wSjFYHJEI+1F2V+OjhgVAGi9nzF2u0iAqVejXEZe7J7iQIkgT+DShIIsSs4Socq9HD7iCQCvkQC31/1DHX9HfPQj6fbVbLUKhpgNXhxJlLzfi89BLGryvC1yfrsWDHCUzfcgQLx6dhx2NqtqHtlWYLJ4rkTqFGCz6PhxCpkL1PT0FbkJ2Etd9WoFCjRe/4iIDjzYoLx+p7e2DZ7jKv/Yo0WizfXYaN+f3wfkF/NJls2JjfD8pgMdZNyUKJWwPegs2HUVKjw5uTs2CxOTH7wxKs269hRVOxRgu90cru42k44cs2nkKhUCiU2xEamaJQbiPaswAPxPX0WRrkJ9IDXO0zJBTwsG5/BYo0Wr81S9kqBd7L64cp7/7EMUgwWW0wGK0+m8tuyOvLHu/LVEIuFqC81oA3J2fhssEMqVIOu8N3NrPF7vRpwqBrtcLhJODzgL7xERg9Ixr7Tl7G+h8qYbQ6oG214m8bDrFNbpXBEmTFhWPyACecTmD5nqtipj13vFaLHaEy10exL0HLuPDJxQJEhUiwIa+vV/SIETkWuxNxkTKfgksZLMbjI7qhxWyH3mRjj0+IkGPXLxf9RrteGJOKrQ8OgM5o41yvocWKYo0WfAAP39GFjRbe4cM2nkKhUCiU2xUqpiiUW4D2Gspe6zn8WYDP234caydnBTxne7bjkrboTrZK4TfSw9Bsdi3WC93Sz9xtuhmKNFrwcBoF2Uns+QkhCJGKMevDEvSMC/dKays5r4dapUBJjd6PqYQCeW21PU+M6IZFO8uQPzgRapXCu2ZK5BJ5niYMjMAa3EUBqUiAK80WRIVI8NaU3nhs2zFEtM1jSY0eUhEfL39xkr3XvY/ncK4jaWeurHYnRHw+zmtbYXM48Y+/9IZcdNWlTyYSYFN+PyiCxXjj69PYd+oKe6ynSYREyEeL2dt9Ty4W4L28flj9xSkvw4gJPWKx+LMyn2Mr1mhhtDpgMNkwfcsRzvU6R0hZYZekdDk7nrigx9IJGbRhL4VCoVD+NFAxRaHcZP6XaJI7gdL0mGhRoEVusFTo3+46WYmuyiDsf3oIrHYnmsx2bMzv5xUZYZCKBWgxX62bce9x5EmhpgGPDO2KHyu1UKsUMFodWLDjBAo1WuQPTvQ6bmNRFd6cnIWJPS2obzKjQJ2EqQMSOJEaJ6qQFR8Bm8OJwooGTFcnYZo6CQA4YkIqFmDt/gpkxUewYs9fFI2xMl8yPg2M87dnzZVcLODUPwEu8ZejUnJMN9zPeaBSi1HpUVi195TX9T1rnvLVSThQ2chJt+ODh60PDoDJ5oDJ6kC4zPtjvSA7Ca99ecpn9Gn57jJOPZwnDS1WhMlE7PPedrAaC8em4cQFA+Z/Wsrul5OsxMKxabA7AkfiKJQ/A9Tgg0L580DFFIXyO+MehYoMEmPBp6VeC+0fKhowd/txvHpfz2u2H28vTa85wPZavQnFmgbMHNoVTkK8ohcPZifBaHPgpc9PernIedpnq1UK7D5eh8FdFOx+7aW6Cfg8lNcaME2dBB6Px17D13FGqwPzth/Hxw8PwiKPui338RSok2B3EMjFAhhtDizaWYqN+f3QYrGDz+PB6SQQCXgc0wq5WIC1k7Ow2UcUjfl76YR0WNvG5SkSC7KT2G0MG4uq8OnMwVi2u9xrrIwDX+/4q7VbgaJ4BMCMIV1gcxBOaqOAz8ODW46gT3wElk5Mx50pHfCNWwQrsJjVIr9NaPqiU4QU2mYrCjYfZsfcKUyKe9/5kXueigYs212GF+/O9HsuCoVCoVBuN6iYolB+RzyjUBvz+/mMWACuxenZ+hY4nASx4bJ2UwH9pekxKWtSkQAlNTqvY5n0wPzBiZi+5QjWTs7CzKEqGEw2SIR8nLhoQGSQBC9+fjKgi5y7ffacD0sAuKIpRRptu6lu4XIRpg1KwpP/+RnLJmawr/s77oH+8VjSJqQ866YYQWR3EjicBI8N6wqJkI8H+sdj9RenOFbgb0/tDeBq7dSbk7M46YmeFGu0MNkckAgF7HHuZMWFsxE2Zm6MVgfq9GZkxUegQJ3EsRFnRKj7swskfEpq9Fg8Lh1Ld5d5pTYyInLhzlKsmJgBi72UdRBsT8z6I0elROkFAzq2CXrmnp4d1d1nM98ijRYXdaZr6hdGoVAoFMrtABVTFMrvhK+aJiGTL+YHvcmGxTtLsXh8OuZ/esIrFXD1vT0gFwvQ0GKF3mTFnjnZAAF4PNcCusVsR4hMhFe/PMVZfLunETLpgZP7x7ML5KnvHWT3nTVchSazLaCL3NzR3vbZG4uqsG5KFng8Hlvn5K+XUUm1Dv2SFHj9vl4QCa/Oib/j3E0Z3prSG3UGE7vNbHOivsmCfkkR2HOiDiNSorC37BJ7DBOFAq42su0cIcNHDw/EK1+cwuQBCT6NLZgUQovNVeP00UMDESrnClirw8mmITJzAwBHanT4uUbnUySpPWrQAgmfguwkLPfh1Feo0cKJq6K2utGIaeokzM1NgdHiQIgscD1cdKgUOSoFR0RmqxR4ZlR3TH73J6yb0pt9vVijxbxc/+9bvcmG+mbLbS2mEhMTUV1d7fX6zJkz8dZbb4EQgqVLl2L9+vXQ6XQYMGAA3nrrLaSnp9+E0VIoFArlt4SKKQrld8JXTVNEUPumD91jQrFkVyl6xoUjf3Aiu7g/fkGP8zoj1n6j4US3hqd0wBMjusFkc0AZLMHKz8u9ehwxphSv3NcTjW3W6UwUyN1VryA7CaPSo3C+0YRA6E2uFMK0mFCsm9Ibx2p0+OhQDXjgYUxGNGLCZBjfIwYr9pzk9E9aODYNveLDYTDZ0GqxIypMArGAz9Zu+RIm7swY0gVSER97TtR5pdAldQiCkM+D0erA2fpmZHYKw6zhKgSJr37sldYasCGvL/6+7wweHapCoUaL6dld/NZMvTk5Cw6nE6P/XgjAJTSZ6BsjyoxWhyvNMDsJ07O7IFgihNPpxIjUKBCc8qqDenZUCuoMZs4z94VcLMBdaVFtroEJXm5+TLoiAJhsDjz6wTEAwNtTe6NQ0+BXzGarFJCI+Jg7OhXPgsBodSBMJoLdQTBt8yEYrQ6YbZ59pBx+XQUlQj77frhdOXz4MByOq3NSWlqKkSNH4r777gMAvPzyy3j99dexefNmdOvWDStWrMDIkSNx+vRphIRcWzNjCoVCofwxoGKKQgnAr+Gyx+CrpsnuIIEjNuf16BsfgV5x4T5d6wYkReJojY59TS4WYMqABNa1bUNeX78paz+0pRGa2hbKTBSIidYwgiItJrTdND0hn4e/bTnCGfvGfK57nFwswK5ZatQ0mhAsESBCLsbSz8ow/9MTV+8pWYnF49KwfEI6Fu0qww8VDawweT43FdWNRkiEfIS1RVqGde/o5VAHXBVe80angs8D/q9vPOIVcrz/4zlOvyxCgA2FlZg8IAGGNgHgIMRnzVKxRgsegCUTMlx1WG3RN0bs9YqPwIkLBvZ5rtuvwZMjkzEyNQp1BhsMZjvmjU6Fxe7AlRYLxAJXqp+u1YoQt95ZvqJxzPN45YtTAWvWGCHcIVjC7iMR8rGxqAo7ZqqxfHc5R3jnqJRYOjEdb35zBp0jg9hInMtRUcQ5hzsOJ8F0j+f95uQsfHSwGiXn9cjNiMbtTIcOHTh/r1q1Cl27dsWQIUNACMGaNWvwwgsv4J577gEAbNmyBVFRUdi2bRtmzJhxM4ZMoVAolN8IKqYoFD/8Wi57DExdjHsKmc3uxOLx6a5Frtt13GuP7nywI2vZ7Y5nahfa/r3tYDVbnyMXCwO67ulNNpTXNUGtUmBnyUVsmtYPhAD/mTEILRY7nrmrO+RiASx2p1caGENOshJhUhHnOsUaLZrNds6YjVYHrjRbcfyCHlEhEuz2iCYBrjqxpbvLsXBcKnIzY5A3OBFiIR8hEhFarQ7M3OqKtswaroJa5TK4CJR+yOcBEhEfUaFSLNrpqrHKio9gxUpmpzDYnS7xxER1eDye33MWabRotdjZOXePQo3NjMG9/zjAiquSGj1y02OwfHeZlwCapk7C7DYBtCGvLzpHyLyicby26zHP1Z/AY7av269xNUN2SxvMUSnQIUSCV+/rCScInh3dHfnNiQiSCNFqsYPPA36u0WHm0GQs96jDylYpsCGvH/7+zRmUnNdzXv+x0rfQXD4pA8t3l+PuXp18zt/tiNVqxQcffICnnnoKPB4PlZWVuHTpEu666y52H4lEgiFDhuDAgQMBxZTFYoHFYmH/bmpq+k3HTqFQKJT/HSqmKH8KrjfC9L/2bPJ1bYPJio8fHgCJSIhX3GqYXOluqZg3OgU1OiMbsWCiDWIBP6BgKHBzYvMXxfLlugdcjVq8M7UPOkXKUN9kZq2+mWjI619V4WhbTycn4JWm9sxd3XHfP3+E0ergXIeJ9LiLRwGfh7vSolxpaW622u4UVjTA6QTmf3ICcrEAO2aqsXR3Gec+GcFh8mGC4I7R5kCQWAALnKwwcY8mWexOtpaKEVmeKW2eWOwOTnTLaHVg3X4NclRKVlytnZyFmDFSVkj5M8mY/WEJokOlIITg+dxU1A42wWJ3QsjjYZo6CfNyU1HTaER8pNyvKQXzHlCrFKhvMmOaOgl1BjNyVAosHJ+OqoZWSEUCNJls+KGiAeW1BvxlYAKCxAIoQ6S4ZDBjqY86rCKNFjzeaayYmIFTl5uxMb8fLhvMiA6T4rFtx7zG4RKaDjyU0xXtlALeVuzYsQN6vR75+fkAgEuXLgEAoqKiOPtFRUX5rLNy56WXXsLSpUt/k3FSKBQK5beBiinKbc+NRJj+155N/q79xIhkHD3X6BWxmf9pKXKSlXh2VHc0tFg54zS1u7i/algQJhf5jGJ5RjCAq2mERqsDduLEwUotp/bIPRoiFwtQVmvA3NEpAABTW12NRMjH16cucfoeMccy6YLuBhENLRa0WATtpg02m+2YNVwFkYDHGi5kxUew0TFGtHwyc3BAs4gQqRB7Suug6sitUym9aMCTI7pBJhKgutEI4KrIkosEAccWJBZynhEDY/LAzEWdwcwKKd/NhZXYkNcXAIHJ7oSdODmpc4Cr3umZ//yCzdP6BxwTACyfmIG9pXVYvucktj86GLmZMZj0VjGMVgfkYgH++8hg7Cy5iC0F/bF4Zyl6trkaFqiT/Ir1wooGVGlbMeNfR11jTlZimjrR7xhMNgf4PJcRx5+FDRs2IDc3F7GxsZzXeTyuoiSEeL3myfz58/HUU0+xfzc1NSEuLu7XGyyFQqFQfnWomKL8IbnWSNONRpj+l55N/q4tFwtwZ2pHrNlX4XP/wooG1p48J1mJPXOyESQWotmt+a0v3IWJ8BqjWDnJSuQNTmQtzDuESEAIOFbjo9KjkBYTigezu0ARLMaafWfwxtdXx56jUmLmsK4Y1i0Ka77WcARVgToJJef1WDo+3adBxNYHBwS8pyazDb/U6LB0YgZSokMxLbsLTlzUY9H4dCz7zFX3Y7Q6wAOwMa8f1n5b4RWJ25jXD9+cvIwDZ7UYmRoNudglkhhhs2ZfBWYNV2FQWz8sRqDtnKUO2GRXLOKjc4SMY8BwyWBCsFiAbJUCpy41Iy5SDruD4MOHBiJcLsLPNTqU1Og552LOP75nDHp0Dkd9k4VjZlGQnYS4SBne+UsfhEgDf1R3Cpfh/n/+iIYWK3KSla5IV1vkjxFzLRYr3v1bHyzeWcr2llq3X4OpAxICntvdcbKwogFOQvw2+Q2WCHHJYEa4PLCxyu1CdXU19u3bh08++YR9LTraVS926dIlxMTEsK/X19d7Ras8kUgkkEgkAfehUCgUyq0FFVOUPxzXE2m60QiTv55NDCHtbPd17YLsJNQ3WQIccTXKVFjRgAU7SjGuRyzuSFay9TSe5CQrUd901QmutR3hFSIV4punhiBYKsSCT0+wAqjF7OD0WvKMomSrFMhXJ+Gnykb2mEJNA5wgGJcZ47W4louFyFYpEBUixYKdpV4C78dKLSscPGEiZoUaLRbuLEVWfATbw2psZgxeGJcCAZ8Pm51ALODjrW8rfEbi+OChT2IEijVaLN9dhgVjU1FrMHNqjzYWVeGutCh2LEarA5+fqMPMYV3hhHfz4lnDkiHg8fDyF6c4Y89JVqJbVDCWTEwHCLB4V5nXsb7SLAs1DchXJ4IAmLv9OLY+OBAvf3ESkwckcJ7BzsfUnJo192gcADgJwQP941Fea8DzY9Kw50Qtew0mwji4qwLZqg7sORyEYNZwFeIiZXh7am8vVz4GZQh3ce+ZWup+jzwAH/x0Divv6eG1/XZk06ZN6NixI8aOHcu+lpSUhOjoaHz99dfIynKlk1qtVnz//fdYvXr1zRoqhUKhUH4jAufaUCi3GO1FmgxGbvrVjUaYlMFi3JGs9LntjmQllMHtp/h5Xtu9zsYf7lGmYo0WHUMkWLKrDCsmZrCGCww5KgVmDVOhvsWCDXl98fbU3ghrJyIQIhWha8dgRIVKsXRiBnuPwVJX6p0/o4MijRZbiqvw8cODMGu4io3yFGu06Bgq9bo3o9WOB9YfRIvV7jNStrGoCvnqJOR4zDFjzrCxqIo9/11pUdiY3w9Z8RHQtVogFgiwZFcZxrxZiEaj1a9bYaGmAZmdwtr+rUVm5zAM6qLwSrEs2HwYz4xKYcfyz+8rYbY5MS4zhp3XDXl9MS4zFgIecPRco5cILKxowBv7KmC2Or2EFHMfm4qrUJDtLUKsDidaLXY0tFgx9b2f8Myo7tji9gzkYgHkEgFmDnOZbjCCt6RGh+lbjmD6liMY82YRfqnRY97oVNidTnSPCsXG/H6YNVyFvvEuQZkeE8axve+qDEZJjQ7j1xZj5tZjKNh8GCU1OleqY9vzzVYpIBcJ2L/9wTw3B3EZqkS1Nfm9nXE6ndi0aRPy8vIgFF79XZLH4+GJJ57AypUr8emnn6K0tBT5+fmQy+WYMmXKTRwxhUKhUH4LaGSK8ofieiNNNxphCpOLsereHpi3/Th+8IiArbw7E9pWKyobWgOmGHpe22J3ss55vgRGTltExvOYr0/WY25uCsb1iEWBOgkWuxMSIR8dQiR4YP1PnCjCpzMHB7Ra58ElSMPkYsSGy7B2chYaWqwQ8IHSJgMGdVH4NToo1GiR32xmF9zudtzupSBqt/totfiu92JS6j6dORhWhxPnG02QCLnGGwwX9SaU1TZhUBcFeDw+LjWZkRUfgZIaPeqbry3SBwBmqxMigffvRw0tVkx59ydsfXAA28eLB6BjqJRT45LeKRRhUhHyNx/2ea1ijRZ8vn8nQFez2xSkxYRyokBRIVLIRHy8P70/lMESEEI4ArEgOwnLdpXhaI0eBdlJmDs6Ba/4sIMv1DRg2e4y9GqL5AGuZzG+RwzkYgHC5CL2/guyk7Bkl3fE0L3m7ZfzekxTJ2LZ7nJ89PBAXNCZ2HFHBonYNEfmuW07WI2FY9MQrwjy9zhuK/bt24eamhoUFBR4bXvuuedgMpkwc+ZMtmnvV199RXtMUSgUym0IFVOUPxTXG2kKlgqx7cEB0JtsXmlM7UWY3MVGs9mGEKkIUhEfi3eVYd/JenY/fymGymAxXronEx1DJLDYnYiLlOOZ//ziswnt8JQOWDguDVUNRk7KFWOG0GK2Y0i3Dpi3/TiOVOtQkJ2E7K5KL6tzg9GGaW0pWJ5pZtPUSTBa7RzBGSa/KgR54OGSW8qgLyx2p087bqbvk7ulu1wsYF/3hdHqQEOLFbHhLttyX6YOANA5QoYPfqrGuv0aNr1tUBcFsrsqEdxOLZHUzUzC6nAiVOZ7f6PVgcZWq5cBhDufz8mBjVwVZ76ML1rNNswarvJphmG0OnC+0cTau6tVCmzI64sIuQgiPg+vfX0GD9/RBTxwTQoYt0EALtfBuHC/0bgijZZ9/oDrPbBi90kUZCdBLORDLnLVdfV2E1yeMKKvU7gM8ZFy/G1gIgDgmf/8AqPVgWyVApN6xWLqe4fY9x+TAml1OFGrMyI2Qu53Hm8X7rrrLhBCfG7j8XhYsmQJlixZ8vsOikKhUCi/O1RMUf5QXE+kyVdtFVO78vGhGiybmNGuI5+72DAYrZj1Yck1m1m0Wh34pvwyUmJDkRUXDl2rFe/l9cXhc43olxjJRpnkIgE6hkqwaEcpZ5Gco1JgZFoU5GIBQqQiVtw1Gq1YtKPUZ9rgkRodymsNbJ8p98jBR4dqkBUfDnVX7/RFg9EKAsIRH75g0hDd7bgvN5mRqAjChry+nMjSkyOTIRHyA5o5/Fipxc81OqybkoUH1h/02idHpcCJCwaOVbt7LdGGvL5+a69yVArwQDjXig2T+u2XdbnJ7HebWqXA56V1+KXNIn7e9uNYdW8PzljkYgF2z85GSY3Ory29ZxqnTCjA/DGpaLE58MiQrhAJ+BAJeGxTYIAbXfP1tyee2ws1DZg5rCsEPB5W7ClHvjqpXTfF840mzP/kBNQqBbLiI/CLWzSySKPF0s/KsWuWGrUGM+wOgssGM6x2B1754iyeuqs7giTX5nZJoVAoFMofHSqmKH8omFqmH3yk+rlHmvzVVrkaufLw6n09r7uuo6HFiqPVOr+RB/eIj8FoxaKdpXhgQLxfMwemaesr/68HNhRVei3iCzVa4IvTWDohnRNBW7TT1buop1vzWQbG3nuzpw13shLzc1Pw3Zl6r9TGWr0J35+5gj3Ha9ErPiKg+PFMQ1w6IR21OjP0Ju+ozrDuHbFyTzmmZScCPPhtSmy0OvDC2DSve1G39Uma9FYxAN+NawU8Hgqyk8ADjzNmtUqBmcOSoQwWY3hKB0wZkMA6F+6enY3Fu8q8xhMTJsO07CSvXlo5KgWeGZUCXasVaTGhkIsF2JjfD2v2neHs99iwrljsw2yD+XvB2FTO/MnFAjwwIB5LdnmI6GQl/vvIIPxt4yE0tFi9BO61Cl53BHw+HE6Cb05dwS8XDPhgemA3xc4RMrYJc9+2KJZ7g+jCigbYHAQmqwNCPg8EBD9f0GPfqSuYM6LbNbcOoFAoFArljw4VU5Q/FIFqmVbf24NdwAWqrSqsaECL2Y6o0Ou7dovF5tPpjok8tFquphg2tFiREhPq18yBx+Ph8znZMNuccBCCZ/973PdYNQ1YMC7V5325N59lrmG0OrDtYDVevDsTtXoTdEYbG5l69avTeD43lSPMGNGZPzgRRRotTl1qxocPDcSSz7zd6BjxwxAmE2HV5yeR0TkcozOifYrcfaeu4EBlI7Y/MphtSOurNspgsmHu6BQ0tFhhtjmQqAzC5yfqUNXQyu6TFReOjUVVHDGrCBZD12rFmMxo5KsTOeefvuUw+iREYJo6EbO2Xb1WRX0LnhrZja2PiouU48uyS3hs2zHIxQKsvrcH5uWmwGhxQBksgd3pxEW9CQAP5XVN2FhUhT4JEcgbnMhxN7yjWwe88uUZn8+xWKPFC2PSEBtmZtM4CSHY6OP9UVjRAEJO4t8PD0JNoxEdQiTISVbiaLUOD9/RBdGhUmzI6ws+jwcnIRDweDDaHKxFe2mtwev6crEAzRY75GIBVt3bAyU1uoC1dV+VX2YdFMf3iIVcLPBy8dO1WiHg89gfBT6bnQ2bg8Bsc8Dp9J3+RqFQKBTK7QYVU5RbimvpH+WrlkkZzN3v1+gT5Um4TIyXv/TfEHflpEzO9d1rXTwprGhArd6MKe8dxNtTewe8bpPJzppGuN8XY+JQkJ3EpvQlRMohFQmw/LMy7Dt1xetcNgfBujYBBlwVZ5P7x0MuFuCV/9cTdqcTc0enQMjnoc7gqqHyFD9qlQLfnbmCfaeuYHpOF/AArLq3BycaaGwzn2CaAgeqRwqSCLBq76k2Ywkd5o5Owbr9mramti7sTuIlZuViAf4zYxD+tsm3KURhRQOeGJHMqS0T8nnQGW3seGYNV6G81oAZQ7ogNz0Gy3e7zB7enJyFN/dXeIlKJt3Ns9+SyRo4/e5SkwkFm6/OwdYHB/g1qyjSaFHXZMaRtqjnP6b2xmNDVVj3bQWnTxkT5WTqmZimuu5pgmqVAsESAXi8q9G9krb7AzyjcEosHJ+GvaV1rIBasaecvU/3FMIQmRD/+E6Dh+/ogjX7KnBBZ0RJjQ539+qEdnrTUigUCoVy20DFFOWW4Xr6R7nXMvni1+gT5YnV4fS7+C3WaGF1XF1ohkpFrBDxh97kEkbt1a9IRHzM/rAEq+7tgVCpyKfxAZNq+Mmjg2G2OXwKKcAlLuqbLezcMeJMLhZgxpAukIr4eHHPSbZx7LaHBuK1L097pdC5R6lEAj54fJfIXX1vD1Q1tMJgsnEat7o3pPUkW6VAfZMFxRotZg5VoVdcOFtrVXJez0ZQOoVLcfyCAQXqJEwdkMBGdxjTDH/zQghhU9bKaw0oOa9HVlw4O56PDtVg20MD0dhqwZUWCyYPSMDTd3WH1eHEUyO7YfYwgiCJAAAP+09fxraD1ay4cI/UBLVjH+7pVWAwBRb0BpNLkButDpSc1/u0ZC/SaEHATb9zF3nZKgVWTMpEfZMJZ6+0ctwa53xYgofv6IJ5uSls/7OS83pMeqsYWfHhrGhkmkkDV9+rapUCdofLdfC53BSs/6ESHYIlKNZosWRXKV69v1fAe6NQKBQK5XaBiinKLUF7/aM8zR3a41prq66H9qJZrZarDXOVwWJcbgos2JiFqbtg8ITpLcXMw6v39cTG/H5Yu7/CK9Xwv48MAo8PNBsDN+5lFvEGoxVysQA7HxuMSLkY8RFy1DWZMWVAAqZnd4GTEEiEwLOjuyO/OdFvip7V7oSY77oXs80Bk82BrQer8UJuKttseO724/jPI4OwcEcpRxBkqxRYND4dU979CYCrkeyHB6vxl4EJeOqubjAYbRjfIwavfHEaMpEQe07UceZp64MDYLY5/DYbVqsUmNAjFn/bWIze8eFYPikDe0vrIObzka92Odz9dWACHA4n5GIheDweCCGQS4R47bMyTi2Tuq2vV49O4eC3hV7cIzUyscB/c2WVAh1CJBynxmupfWLOn9kpjBORcscz/a5Yo8Xc0SnIigtHyXk9nITg7/s1OH2pGf+Y2ofdz2h1wO4kWLXX22bd07HRYneyNXOMmDYYXe+j+iYLFoxNhbjt/Vyo0d5QGi2FQqFQKH9EqJii3BJcb/+o9rjW2qprpVZvAp8fOIIUJLn6n1OYXIwEhdxvNMbdzMFX7ROzz+zhySiscEWZfqhogMnmwFv7NT4Xvys/P4le8RHtNgcOkghRqze5DDL6x6OxxYIERRDW7a/giIdslQILx6Xh29OXcKiq0W9vrAOVWozrEQPAJdSY1MMgiQCPDe0KJyEo1mhxQWvC4gnpMNscaDE7ECoTwOF0ze2yiRmQigRQBomxaFw67n3nAFbf2wPTtxyBMliMDx8e6NUXSS4WwOEkKDmvx4KxqT7r04o1WizfXcaKggU7SjE2MwYdwySYva0E7/y1D1buKccDAxLY42cNV6GkRudXYIzNjEHPtjl2t34X8oDF49Ox2GOcOclKLB6fhrNXWlkhVV5rwF1pUQFFNBNBA67fwe+i3oRHP3BZsOemR7P3ZXNy9wuUiuou0sJlIiwZn45avSsKuO1gNdJiw9h9MzuHcSKxN5JGS6FQKBTKHxEqpii3BL9FjZN7bZXBZINcLACfz4PJ5mBrkK4FJmr27KjuGJ7SAWmxYV6pZOW1Bog9GsJ2ipBj1T09MP/TExyhmJOsRN7gRDZNzrP2KUgiRKvFjvomM2JCpRAJrxagtFrsPp32gKs9hgJFunKSlZCK+Ji7/Th6xoVjU3EV5o5OwWofTWCL2noU9UmM8Nu7inHbG9KtAwBXeqPR6sBHh2pwd69OMNqcmHFHV8zPdUUufq7RY8fPF9maHU8BxAgPJrWNseaub7JwhB4TiRLwedhYVIWPZwzE85+W+pyXQo0W+W7jL1AnYcXuk3j9/l5YvLMUPeMjOOPwFBie6YPxkXLIRQJ8+NAA1khEJOCBgAdtiwlZ8RGYnt0FQW2Nco9V6zBhXTGnhmmaOgn/+E6DZ0elgA/faZTbDlazr12vg1+HYAn7b3cDjwNntRy3xmsRaTnJSsRFyLC95CIyO7ne+6PTo7Hv5GUMT+mAkvN6pMeGonOEjK3VupE0WgqFQrlWEuftudlDuC7OrRp7s4dA+Q2hYopyS8DUOPmrewkN0Pw1EGFyMVqtDiz5rOyaarF8wUTN8gYnYu7oVCzbXeZldb5wXDqazVYAQQBcAqy+2QK9yYYXxqZCIuDDYHKZZfD5wIu7y70a7gKATCQAn8djbdtXf3EST97VHSv3nAYANJsDp/DZnQRCPo+tg+HxeGw9VVZ8OJZNTAePAEerdcgfnMjeh79asEJNA+blpqBGZ8RTI7tj7miXKYWQz0N9kwVfl19yLZ7bonLKYDHGZUZj9p3dvCy/1SoFXpyUiUSFDA4CnxG2wooGLN1VhoXj0rB8dznenJwFmUjgVV/EGClkxUcgKz4cF3SmgPPiLhosdicKNQ14QcBjhZb783Tf11/6YI5KiTx1IpvumJOsxMyhXXHoXCN+aZvvt6ZkeUX73Oc6Kz4CZqsdK+/OwHmdCXrTVefFDw9Wc+zcCSFslNPzv5FwuQgmq4MVMjkqBcLlIrw9tTfC5SKEyoSQiwXse2P+mFTkG0zg8XhQBos5ZhWehMlEWD4xAyDA0XONXuYXyyZmIG/jIQzq4uoHVpCdhOPn9TeURkuhUCgUyh8RKqYotwTKYDFGpnbE//X33Zfpgb5xN3TeX6MWi4madQyRYNnuMp8RnGW7y1g3v1q9CXP/e9wr2jB7eDKixAK0WG14bnQqLHaCQk0DlMFivJfXD699ecqr3meaOgl2B2HnQcD3b5OmDBYjuWMwPvjxHLvolYsFWDg2DZ88OhiNrVYYLQ4cv+hyrmNEA+O654/qRiNmbj3GGdPWg9V4OKcrYmxSDE/pwEZGwuRizB+Tinnbj/sUEQt2nMDKuzNxQWfy2RwXcEWSXhiXhgVjU8Hn8SDk87wiL0z0iIlwydsxf3A/nvl3U5sw9YzOuO/rq7eVa4wNAAjrZMcYP/RLjMSi8eloaLHASeD3HpkIGZ/Hg8NJEBchQ4hMCJPVgd7xEbgrLQpr9rls1mcNVyE6TIKF49LwyhenffYuY1wGPzpYg+k5XTiRsByVApvy+yIiSIL33d4bgCsSuDG/Hwo2H/YSVDkqJcw2B1bsKcfTI7t53UuRRovFO0vx7t9cjotWuxO94sLxl/7xtMcUhUKhUP40BC4CoVB+J8LkYiyZkO63L9Pzn56AwWi97vNeSy1WezBRM6u9fTc/g9HqJaSY7Wv3V+C7M1egbbHhSpMZL96dgQ8fGoD/PjIYr3x5yqf42FRcBR5ci958dRKKNA2sKYU7crEA/yroj4U7r0aDmKjK7hO1GP33Qkx57yDGri3C7uO1yE2PgbwtdUwoCOxj3TFEgg15ffH21N6Ynt0FV5otyBuUiIIthyES8rFwrCst71RdEw6fa0ST2e5XRByr0aPFYkewVIi3p/bGxvx+mDVcxRFDcrEAPAA9O4dDJhIgVCZChxAJhqd0YPdhhaDVgXnbj6NDsATZPuYFcIkJpj7NvVbN5dDnnSLHpBcCLtHmP2qnxZ2pHdmxF2u0yOwUBqPVgbe+1bTr1mexOyEQ8LBwVxkAoNlkB1PS9O3pevRJiMCOmWr8UqPDfe/8hL2ll/D4yGTwAEzP7sKZt2KNFlsOnMODdyRh+hauMDpaowcBD8s/8/4hoLCiAW/t12DhuDTO62qVAnnqRMz+sAT7TtbDX9eowjY3wSstFhAAv1ww+N2XQqFQKJTbERqZotwymG3+xcqNmFAA116LFai/lTJYjJfuyYS9rRGpv1REk9WOhhae35omJhphtDrQRSnH85+eQK/4CAzu4ru+iTmGAFg4Lg2T3ioGAC+zCrlYgI35/eAkwN8GJWLe6FRY7A4IBTy85qcv1vLd5chXJ0KtUqBI0xDAhU6JM5ebOfVIapUCKyZm4D8zBsFJCK40W7DWLZ3NX98sRty99PlJr/Q/xoabub8Vu8u9zDCWT8oAUI6fKhsRHSphm/dGBomxeu9J5KuTQMBNWXQdl4n/984B5KgUyGuzdFerFJCJBKzQcq8xczcEaa+mqL7Jwuk1ZbE7wedfrV8LRLhMBEWQGCfrmnBeZ8KU9w5y5mTu6BQs3V3GqS9zjyq5z5vR6mAtzN2FFDPnDicJEAlswPwxKdiQ19evY2NLgOhlVUMrJ3KZpAyCXCyg0SkKhUKh/CmgYopyy/BbmFC0128qVCZqt79Vq9WBz4/XIb+tGeqbk7NYc4CsuHCYbQ4M7qpAuFwMbWvgSBezOG+1OtgFt76dCEarxY4QqZBd2DJmFdOzuyBYIkCEXIxln11tNLvqi5Mo1mixIa9vwAX0zGFdMU2dhI8O1mDB2FSs2HOSa5ShUmDmMBWmbznsJSAvNZnB4wF8Hg8EBEdr9Oxx7pEe9+NEAj6EAh56xkfgaI2evR93G24AfqOTC3eUYt7oVARJBDDZHCip0bGNffeduoIDlY2cBsaMKLhkMGFjfj/XNTYfxoKxqegZFwYC4LFhKqwvrOQYbDCGIAvHpiJJGRTw2QDguCdKhHzoWl3PM5ARSLZKgWCpEMt3l2PrgwPRZLKyvbA2Frnuv9lsZ134/DkVMvPmLubcYdIUpw5ICHgPTWZ7wKbKgdIo3Z+3ewNrKqYoFAqF8meAiinKLcNv0Wi3vX5TQRIhnvnPL35rql65r6dLaGka0DM+HAvGpmJbmzmAr9ouz3QpTzqGSCAVCdBqtmNjfj9EBonQ2BpYTMnFAhyrubowN1odWLdfgydGJCMqRILdbb2XPBfd7UVVhHw+OoWLsGBcKsw2B+aOTsHzY3gwmGwIk4lgczjxwHpX/yd/Jgwzh3XF+UYjZ0HPiAj3iIqv+h73yAcTtZOJBH6tuos0WggFPPxUqWXv2f0+mXnxJC0mFK1WB8JkImx7aCCWfVaGWoMZJTU6lNTosbat5uqZu7pjXi4POqMNdgfBsRodBBf0HPc7d5iUwbSYUHY+LjeZER3mMjXxZ3mfo1LimVHdUbD5MBparLDYnVgyIQP3/fN7ztwwaYLXal8OeKcsMscWtBMlE/J5AW3a/ZXquadNuo+p1RrYKIVCoVAolNsFKqYovzv+Uup+i0a77fWbajHbA9ZU6Vqv1lwxFty1BrPf6MmxGn3AlLmKy82Y75Yyl5OsxLOjuvu3MlcpESwRss52wNWFebZKCYPJ5tfS23Nh7UmT2YY3v6nA82NSYHEQvOZWt8Wk0BmtDr+RkUJNA5wgmDs6BQo3K25GRIzN9D1PviIqABAsEQY02HCN2Y6OoVLOOdu7T6b5rVwswPLPytiIoPu1J7970OexcrEAu2dnY+FObu8oxohjzoclWDs5CzkqJWYNV+GnKi36JEQgR6XE0RodTl9qwtIJ6bDYnGi22CHk81CkacDkd39ihWRRW72d59ww93WtPaZykpWob7b43NZeY+giTQNmDVOBD26aao5KiceGqRAsEXqJSvc58KTVjzsghUKhUCi3G1RMUX5X2kup+zUb7TK495tqNrvsyZXBLgFXUqMLeGyTmxW50erABZ0pYKRg+e5yfD4nx2UE4Sdlzp3CigaIBTw8NyoFr4JrQpGjUuDZ0d1hsjm8elFZ7E44ibfltzsB+02plCg5r8fRGh0IgFe+5PaZYoQj0H5kxPPazFjfL+jvt/eTZ0QF4DbA9UeQRICGFq5guJb7HNxF4aor8jHeQGLFaHWg2WzD2MwYr/TBOR+WoE98OOIVcvRJjIDJ6kDfhEic15mwfFI66gxmvPWtBq98eYZNdxzURYGBSQr07ByOo20pfUarAy1uqZ7M3JScd0XFrkUsMlFRkUeESS4SYNZwFfrGR2B8jxis2H2SK5aSlVgyIR21jSYcOteIPokRyFcnsvfZIUQCXasV1Q1G9IwPxyNDu0LA5yFEIsTeskuc6KI7IVL61UKhUCiUPwf0G4/yu3EtNuWBhM//cl1/5hLBksD/CXguCsUCfruL7yaTFavuycSVFivMNlf/H7lYwLGrZpCLBUiNDYNYyMcjQ1WYm5sKm8MJg9GGuiYzdK1WhEiFbKTIYneCx+OhvK4Jo9OjfVp+A670xt7x4RiTGYMmow3BUiEuN5kxd/txpMaEYuG4NNTpzcjuqgSPx/MSIkarA9+crEdOsrLdyIjR6vBa8Butjmtys2PIUSlw4qIBA5Mi2X5KnuSoFODBOxL10aEabH1wIJbvLuMcl61SYNmkDLz65SmMTo/GeZ2R3eY5b/5MRTYWVUHXakOSMghvf3vWS4gsnZAOq93BmpMogyVYsaccA7tE4qezWhRptFAGi7Exvx+azXYYTDb23KdqDVg3JQuztpUg2COF1WJ34mStAcsmpeOns40BxKICHUIk6BUfgZf3nsL8sSlYODYVrVYHGlqsiFfI8W5RJdbt17D3yAgio9WBYzU6vLinHFMGJOCf31d6vT+/eDwHT//nFyybmIF1+zVYt1+DbJUCL92diV/c6t44Y0pWQtROhJFCoVAolNsFKqYovxvXYlMeJhez//s1aC8SJhbwA6Y/SQR8TuphyXk9BnXxbcHNIBMLMe+TE5xrvj21t08h5a+maJo6Cct3lyMrPhwrJmawZgsMI1I64P4+nXHigoUdPxOhOX2pGVsfHOjVEytbpcB/HhkEu9OJlz4/if2nrrBj88X6HyqxY6baKxLkiVQkQOWVFs5rapUCHUMlfo5wwQiaHJUSC8enwe5wwup04sVJmXhhxwmfwmjXL7XoECzhPLOC7EQYTFbMH5OKFrMdoTIRhAIevi6/hBf3lGPRuDR8drwWKdGh7Lwz1y3UNKC01oANeX2x7lsNNhZVsaIqKy4c/3lkEFotdkQGizGwayTy1YkAgE4RMpRfNCBv4yFsKeiPErZRb2/kZkQjs3MYXvnyDJTBYmxrE3qeDobT1EnY9lMNlo5Pg0jIFR/hMhEWjU/Huz+cxeT+8RjQJRKLd5Z5pdnlqZPwwPqrKYMF2a7eVUdrGmFzEPzrx3PsPDE1Zev2a6BWKZAVH8ExrvBMu1SrFBAJ+PjroASO+CzSaFGpbUWeOhFOEK96sFnDVBDxadcNCoVCofw5oGKK8rvxa9iUXw/XEgnTm6wcJzcGZrFbqW3FsokZbNrexqIq3JUW5Td6ckeyEmIB3+uavlK1/DWE9awpMlodXvukxIbhpb0n8dyoFCQq5QCu1ioFSwR+mwsv3FGKZRMyWCHlb2yAa/G9t6wOg7ooAtaBhUgEEAn4Xtba35ysDxBlUiI2XIatDw7Aj5VaTHrLFbVj0s5W3Z2JZqsDzWZXTyriJDDbHCAE6Bwux6xhKgDA6UvNyM2IwYIdpV7CceG4dGwsOodWqwOpMWE4VqPD8JQOmDIgAVsPVrNigBDg7W81fg0zcpKVWDQuDaqOIQCALsog2IkDYXIx3vlLHyxuq6eaNVyF94oqUazR4p2/9GEt65f6eBZsnVt8BHrEhaNWZ2LnL0IuQpBEiL/vO4PZdybDbHPiksGEFXdnoMlsw/lGk0/7cgDQm2zYerAa83NTQACOlbrn9d3TLD3/Zt7/V5rNGJkahc9LL3GOlwgFePrfx/BA/3j2OEWQBEFSASauK8bOx9Q+r0uhUCgUyu0GFVOU341fw6b8emgvEqZttSJcJoa21YYZd3TFfLcUuyM1OtZcoMlkxaJxaTDbHbjSbIHT6cTySRlYtLPMa5yr7+2B+maz1/VKzusxPKUD0mLD2FSyuEj5Nbm0tVi8ndEYO+7D5xoBAHNHp4DP48Fic0AuEfrtW1Wk0cJk50bIAtUcnbhgwLDuHV3ihXB7FeUkK7FsQjr+/s0ZPDGiOxbuOMHZPiKlI1ZMysQCjyiTWqXAM6O645UvT3FEHeCqIVu8sxQr786EkM/D2m/OeEV0+sRHIFQqwsKxaZAI+V5CirnP5bvLWJMRoYCHjUVV2P7IIKz43GUdf/yCAavb3ldr9lX4N9qoaMCyz8rw7OgUXGm2wGx3wmCyYXbb+4MZH1NbpgwWI1EhR0F2Emtv7gvmGTeb7VhfWOkl5heNS8fy3eXsHMnFAmx/dDDb08kXEiEfxRotJCIBarRGv/sB3rViQRJXI2V3ofbJo4NBQPDRoRrOvg4nwQP94znv3w15fWF3imC0OtDq4z1LoVAoFMrtCBVTlN+N/9WmfO3krOuKUAWKhMnFAhAAC3aU+nQo21hUhaz4cJSc1+OerE4Q8XnQtloQEyoDQGC2OrBkfDpMNtfCMVwuQnSoFGFysc86EqauZ9nuMnYB6i+9joFZ7Mp89Pix2J3IigvH7A9L8ObkLKz+4hROX2rGf2YMQkNL4F5XTSbuQtefhbdapcD83FT83/of8ddBCXh2dAqehStixbjSVWuNmNw/Ab9c0GPF3Zm4oDPBYLKxC/JXvjyF50an4MFWK/h8HhRBYuwtvQRdq9VLSDEUtdm/L99T7iVESmr0EAn4WLXX1fj3s9nqgMJxXm4qeACkYj6y4sNB4KoPk4sFWHVvD2wsrsKMO7oCCGy0UajRIr/ZwvZiylEp8ebkLLZWCrj6vF7+fz1wrEaHwV0V0BvbrxsLkghR4tanC2AaK5ehV3wEO09GqwNmmyNgWipjU25tp84N8I5ItlrsHKHmsrfXISs+Aqvu7cFGwdQqBX6s1HL6azH3wrz3bqSNAYVCoVAof0SomKL8bvyvNuVMTdW1EigSVpCdhCU7S72a2hZrtOADWDI+DZHBEuw4dgFiAR9LdpXigQEJWP/DKbbHlGetyIv3ZPq1eH+gf7xX6t21uLTlqJTgwbuYn7H7dnf5m5+bgvpmCxxuC3xfBEu54sz9HPNyU3BBZ0KHYAnEQj6sDpeRwRtfV+Cf31dyTBp6dA5HfKQcDhAcqW7Ef4+c99kk2GRzYM6d3fDKl6cwLzcF6/Zr2hWS/iI6BdlJWPttBbut1RzYgttkcyBUKgQhPCwZn46aRiN7HuYZzstNAXDtFuQA1xaegTGyiA6TYda2Emye1r/dZ8z08/KsVwLAWrgzyMUCiPh8zMtNQX2TBTweD8cv6MHjAX0TIiHg8WCyuazsWy2OdlwOuf2hsj3+drc9/88jg7Cp2FVLVlKjY1/fmN8Pb0/tzRpqyEUCKELEGJna8YbaGFAoFAqF8keEiinK78r/YlPe3E7NlSfKYLHfWp/BXRQBoxDzx6Th7W8rMH9MKhbvKsPkAQmQigTIH5wEoYCHrPgIlLi5mRVqGvD8J8ex+t4e6BQh9xKNWXHh2FhUxXHliwqV+q9FSlYiRCrE4yNUEAl5bD0Ns3AtrTWgb0Ikx4VOJhJg9d5T6Bkf4XcRna1SwOQjcma0Otj5Z+ZFLhZgz+zsgA14c5KVmDm0KwZ1UeCVL8/4nM+02DC89uUplNToIRcJrsnuWy7xjsYx8+h+/dB27NRFAj7kIgG0rRa89tUZ5LeJE/fz2B3EZTbSzpg6R8iwMb8f6/JXrNHCandyDEAWjE1Fk9EGo9UBmViAHyquBLRtD5UKoW2xekV5GJj+WDOGdEFueoyXkUVOshKPDe2Kgi1H2PditkqBcT1iAkYcF45Px6S3itlxPDOqO3StVq80P6PVgcZWK0d0Mq+7R7KyVQqMyYhG2UUDFo9P/9UMZCgUCoVCudWhYoryu+PPra+9mqobSR16fkwqGlutHEvq8lpDu81h6wwmLByfjlaLHQ/0j8fm4iqv2p03J2dxDACKNFpUa40IlghZ0VjfbIHBZAMP8BIjT45MxsyhXeEkxGuxO3OoCtVaI/omRuDVL05hT+llzvY5w5PRKUzKutCt26/B53OyUajR4mib8AHgFT1bPCEdPB5ptwGrXCzA2slZMNsdmD1MhcqGVq9onFwsQM+4cDgJ4HCCIzTcUx2z4sLx0aEafPTwQPB5PDw2rCsqG1oDCj6Z0LeY8oweCfgImPbG5wGNRhuCpWIUarSs0HQ/j8FowzR1Ei43mQMKnyvNFsz+sARZ8eHss7/SYsGzo1LAxylOby4AcDoJTtU2+TQ4yVYpsHhCGnStFvxyUY/kNnMLT6Qil+NjfZPZp5FFYUUDnIRwIltFGi1KqvXoEx/u1ZtMIuSjvskCAR/YkNcPIVIhQiQC7D5Rx7od8njc/zZsDlek83yjib2G2iOSVaTR4qW9pzCwSyRarLReikKhUCh/HqiYotwytFdTdb2pQzqjDSs86m5yVC63uPZ6IAFAi9kOByHX5LjHoDfZ2HTEVqsDy3aXo7CiAbtmqb3OkxEbhulbjngtdkvO6zF9y2GsnZyFhTtK0Ss+AsBlzrX5AFbenYm3v9Ww52yxuASMrwa/EiEfncJleGD9j3j1vp54elQ3PD4iGXYnQahUhM9L61hhyFi2by6uwslLzfhXQT8M7KJAbJgMz9zVHUIBH3qjFRFyMY5f0OOh969GRXyJTLuT4L28flj9xSkUqJMwd/txbJ7WD/2TIrF0V7lX76ZF49LQarX7FDae0aPqRmNAN8Y6gxlhMhGEbeKZidbIRFfF2pE2gd0zLhyLx6X7tDHPUydi68Fq9t6AKjx8Rxd0CpeBz+Ph4SFdoQx2WcHXN5mRrVJAwAem5yRhQ2ElsuIj2GcRJhMhRCrEA+t/Qmp0KPokRviMimWrFCBt77/p2V3aNbJwZ/mecuyenY3FO0t9RBJVGL+2mH3WG/P64qezWk5kkXmO2w5W41hbxJIZo6fwZiisaMDjdyZ71eRRKBQKhXI7Q8UU5ZahvZqq60kdMhitWLjjhPcv+ZoGLNpVitnDVX5T7Jhf3RVBYkjFgutaxEqEfDSbbTAYrVi0sxQ948KRPzgRQv7VxrhMal6QW8NgAY+HmDAphAI++idGok98BCKCRDhWo8fc3FQA4ER8jtbo0Wpz4GiNHk+OTMad3aMgEPA4NSyeEaJds9RoaLHCaHVAESzGm99UIC02DEO7dUBWXDjemtIbPABxkXJcbjJjysAEJETKQQCs3FOOBwYk4NWvTnuJFnfx5EtkxkXI8FKbg97UAQl4oH883vpWg5lDk/H0Xd3x2DAVgiQuQ5DTdU1wECeEAvgUSfVNFk5UTcDjYbYP4cikqa2dnAW5WMA6IjJCc+3kLPb5MwJrU3EV/vl9JdZOzsIjQ1UcIw3m/tz7MT2fm4qX9p7kOBUyjXxfujsTjUYbpm85go8eHogrzRZ2bD9WatlnU6hpwKNDu6K6keu8l6NSYvmkDFxqMqFYo2VNMvzhGbEzWh2oqG9Bz/gIzBuTimqtEYkKOX5pE+rufanWuglyhmKNFjwAz41OwQPrf0JOshKRQWLsmqXGV+WXvSzZGRxOgrB2Ui8pFAqFQrmdoGKKcksRqKbqemhosfo0QwBcC8WHsrvgMR923+6/uo/vEYMLOlPA67gvYhkRdnevTtC2WvFA/3g2re8ff3EZLrg36h3evQP763+vuHAvoZKT7HKMq9WbUFKj44iWguwkXDFY8O8ZAyEVCbB0l3c0xX1/tepqo+FwmQgVl5pZIw33GqkNeX2xZFcpjtboUZCdhA7BEhhMNjw6LBmnLzX5dJ0DuOLJXWS6Uu147NgkQj76xkdgRGoUVn1x0mc06dUvz+DBnCRsO1jNiehIhHyca2jFikkZrAtjyXlXOpuv+rcclQKNrVYkKuRwOJ3IUSlQ2OYUOPvDEnw6czBW7C5HoUbrZuLhEq7j1hb5fN7u93apyezVQ6uwogELd5ZiXGYskqOCYbQ6cEFnCmhnLhby0atzGL54PAd6kw1ysQA88MAjBAKeKxoUERT4/e8rsiUS8LFuvwZjMmIwc+sxfDZbjfmflnL2CeRgWKTRoqDFij4JEVgyPh3j1xVh7eQsv/sDrlTc9sZKoVAoFMrtxC0vpux2O5YsWYKtW7fi0qVLiImJQX5+PhYsWAA+37WAIIRg6dKlWL9+PXQ6HQYMGIC33noL6enpN3n0lPbw16D3fy1gb69BsNHmwMxtx/DfRwYh32D2imj0iQ/HsWodosIC97aStqWLMULg40M1UGYn4XKzhZPWJxa43qvuLnJLJojw2tdnkBUf4be/kZO4HOM8RUvf+Ah0ipThp0otPj9R57Wod9+/pEaHWcOS8e1pVxPdiCAxLHYH3iviXrMgOwnrAjSv9ZXCx1zLM0IXIhVhc34/HKnR4Zxbv6OS83rclRaFV744FbCRrdHq9BJ7ALDy7gy8/MVJzMtNQX6TGSFSIe7O6oQlu7g9v9QqBWYOS0bHUAkMRgtCZRLMHKaCs+06RqsDdXozBnZVYNnEDBitDhitDogEPPD5PGzO7wejzeEzytee61+xRovp2V3YVELG5c/dCdH9vCIBHzYngYAH2B1OBEtc6YJmhxMEwGez1QiVCFkx6Iln/RLgEpJyses9d7nJhByVkmPTzoynQ4gkYDQzSCzAsglXP0cDOQSqVQpIRXxI2zHyoFAoFMqtTeK8PTd7CNfFuVVjb+r1b3kxtXr1arzzzjvYsmUL0tPTceTIEUybNg1hYWF4/PHHAQAvv/wyXn/9dWzevBndunXDihUrMHLkSJw+fRohIb4Luyk3n1+zQa8nwZLAb22JkM8uoDd6CBl3t7N1U7KQrVJ4iRXAVdPSOUKGDXl9UXJej48O1mDRBJeTWV2Tme1nVJCdhKgQl3OfeyTA3JYWV6D2tsVmYBzjmH8zoiUyWIzFO0uRr07yOTZm/3m5KYgNk8Jsc+DEeQOeHZWCh98/gn/+tS9KavQcd0GmibC/5rX+6sQAb4EhFvDRCld/rY35/Vg3wiCxECIBP2DUcO7oFPB4wJVmC54e2Q2zhxE2DdDhdOL5T0txT584TN9yBG9P7Q25WIDcjGjkD070qjvLig/HsgkZ0LZYvOrTlCFi9BFFYJGHRX62SoF8dRKe+c8vbFTPXUQylvWeAsYduViA8loDclRKlNYaOEYhDGqVAhvz+iFEKkBjqw12J4FYwIeTEK9I4/CUDlg8Id27xkylxMxhKkzfcpjz2sLxabA5XGN/9r/Hse2hgayLo3t01D0quWBsKj6eMRAXdCZIhC5xJRMLQAjB4s/KsWOmGpebzBiRGgWAK4ZzVArkqZNwyWACIfhTuPldvHgRc+fOxd69e2EymdCtWzds2LABffr0AUB/5KNQKJQ/C7e8mPrxxx8xceJEjB3rUp2JiYn48MMPceSIq3kmIQRr1qzBCy+8gHvuuQcAsGXLFkRFRWHbtm2YMWPGTRs7xT8Go9VLSAHcBr0AfEat2uNCoxE6o9WvCHL/JT9IIvRKJSs5r0dVQyuMVgfCZSLkq5NA4G1wkK9OAiEEDkJYa+tln5Vh0bg01OpMnEWrSMDDrGEqtnYHAFot1xbpuNJi4UQSNuT1hUjAR8/4CFgdgY81WZ3IiouA3mTF5AHxmL7lMFbd2wP1TWavBTXT+ylQ6pevKBTATTNTqxQorzMgSRmEzdP6YdXeU+xzkIsF2DKtf8BIDZMWx4ga91TFJePTIRcL2AiJRMgHIcCKPSc55+sdHwFkt9WZ2ewIkgphtDo49/XEiGQcPdfoJeyKNFoQXBWNnlG++mYLlk1Kx9g3facCAkCIRIjaJjNmDuuK841GjlGI+1zywcOKu9NxpcUCscD13vu5Ruf1vmUa9z42vCseH5GM+mYLJEI+Tlw04EKjEe8X9Ed9swWdI2RwOAmqGlrhJARZ8RGYl5uCy3ozOkfKkJOsRM+4cI5Ydn+fPu+WBpitUuD/9e4Mi8OBnnHhWPXFSaTFhqG81oC5o1PQ0GIFIQQdQyX45mQ95nxYgn9N73/dLQz+iOh0OqjVagwbNgx79+5Fx44dcfbsWYSHh7P70B/5KBQK5c/BLS+msrOz8c477+DMmTPo1q0bfvnlFxQVFWHNmjUAgKqqKly6dAl33XUXe4xEIsGQIUNw4MABn2LKYrHAYrGwfzc1Nf3m90Hh0tBi9dug90i1DjqjDQt3ll531Oqizoglu0rx5F3dkK9OAo/H80r/Ymqi1G1uab6Ew7YHBwAArHbi0xnP3eAgITIIU9/7CQ0tVgCA2ebAEyO6cVL6Hsru4jJ+CJKw1xAKXA5z7Tfv9Y4kMPcyOj064LEysQA2pxPTtxzB+wX98ddBrtS5uaNTsNoj1Y5JRxMJAo8nSCLErOEqNiXMXZy6z29WfDheGJPKEQYF2UmwO50B0wiFbdbcvkTNst3lKMhOYk0jGlutCJeLAp7PZHX4tNXv0Tkca/ZV+LxHT9FYrNHisaEqTOgZCxGfh+NttVq+ImzZKgWsDicyO4Vj+pYj+Nf0/n4jcYWaBhitToTLRCg+qw0oZPefuoKpAxIAONgaLLVKgdxxabj77QMwWh3YkNcXACDk8xAdKkPnCBlazA50CJWgocWCZRPSUWswc67h/j51p0ijxcIdpegZH8426xXyeFi3XwOb3YlnR6fgq/LLnPeBWCCAXOzb1v52YvXq1YiLi8OmTZvY1xITE9l/3+iPfPS7iUKhUP543PLJ7XPnzsXkyZORkpICkUiErKwsPPHEE5g8eTIA4NKlSwCAqKgoznFRUVHsNk9eeuklhIWFsf+Li4v7bW+C4kWgmqaC7CQs3HHCb9TKYLT6PM5gtKJaa0RKbBguGSyY82EJ5o5KwWez1diQ1xcb8voiKz6CXehPU7sa8N6RrOSc545kJRIUctyRrIRcImAjGtO3HMEz//kFJef1yIoLx6v39URUqBTHqhvx+n092eOLNFqIhXwM6nK1tiRMLsJ7RZXYd+oyawZRpGlg08XcDSLcyUlWggff9uwlNXoIeK7Fuy/UKgW+LLuEV744jR2PqSEV8TEmIwYlNXpY7U6v8zHpaO314AqSCBEbJsVbU3pjRGpHLB6fjsxOYZz5ZZz9nIRg1nAVNuT1xdtTe2NUejSMVoffNMLNxVVwEMJ5zb2hbWFFA9RdFSjIdi3sByRFQiYS+D3fpuIqhMlEaLHYIRcLOGNpLxXUK3VRyMfPNTpYHU5EBkmwbGIGcjzeOzkqJfLVSTAYbRDyeW3Nbb1d79xpNtsRGybFR4dq2o1SWuxOBEmEeHtqb3a+6/RmVsxcbjKjvsmC+mYLymoN2H+qHkESAWwOJwAeLA4nxB7iPSsunE35ZOZmY34/zBquwtEaHfomRFydS7lLlBZqtLjSbMG6/Rr22tPUSWi12K67hcEfkV27dqFv376477770LFjR2RlZeHdd99lt7f3I58/6HcThUKh/PG45SNTH3/8MT744ANs27YN6enp+Pnnn/HEE08gNjYWeXl57H6ejSYJIV6vMcyfPx9PPfUU+3dTUxP90vqdCdSgN9Cv8z9UNLB9nDzNK5xOAr3Jxi6+jVYHvii/hPJaA9Jiw5AVF460mFCsnZyFkvN6fHyoBq/e19Ove+Cqe3tAb7SyBfe+ak0AV73IsokZeH5Md6zZ51pc1hnMCHO7R6HAZY1e4tZQd/0PlVg3JQvbfqr22yvpsaEqBEt927MXZCfhla9O+01DnOaWIrf0szKM6xGLLsogzBjSBVdaLF7nIwR4+1sN29jWn8nAl2WXUFKjw+xhyVgwJhVj1hb5tMmWiwWQioQoqdFxUgmlIv9280UaLQqyu3BecxcYcrEAHUKk+LntnBvz+yFUKgxoX99isSNEKvR6dkwUxx+eEUODyYbPfqlD38RIbDlwDk+P6oa5o1Lw3GiC+ibXfEYGiTH1vYMoyE5CblvUsD13uxCpEHqTHavu7QG+n88s9zG1WuycyBTz/7OHJSNMLgSPB7Sa7WixOBAXKfeKQG59cAAnzTJYIsTOWWocqWrkXIsRzEFiV7QpKz4CEpEAb0/tDZlIgESFHHsfz4bBZHfVwVVcweiMwJHS24XKykr84x//wFNPPYXnn38ehw4dwpw5cyCRSPC3v/0t4I981dXVfs9Lv5soFArlj8ctL6aeffZZzJs3Dw888AAAIDMzE9XV1XjppZeQl5eH6GjXlzfj9MdQX1/v9UXGIJFIIJFIfG6j/D4EatDbHk1mKy7qjKjWGqE32SAVCfDNqXr0jXc1P7XYnSiva3IV+Lv1EPJsXvqyW+8qX7VYseEyEEIwe1gy+AB6+nPd02ixYGcpxvWIxYcPD8T0zYchEvA5EQBdqysS595Qd+7oFNQZzJg3JhVNJhueHdUd83J50BltsDsIjtXoULDlMP4zY5DPeciKC8fGoir0jAvH3NEpEPB4aLbYIeTzUKRp4LjuuRtdzB2dgsvNZq/zZXYKw5p9FTjqJvgCCTQAWDExA2snZ3nVPTH27Ut3lXqlEpptgSM1ntvlIldEqXd8BOQiAa40m9ErPgLHavQw2xyw2AOfr8lkR4dgidezKzmvv2aHPObvQk0DFu8qQ8+4cBDwcLnZxPa5GtcjBjaH02VqUlSFsRkxyM2IgojPCyhO+TzAanegvsmMPgkRAfufXW4yo9bgenYjUjtiwdg0mGwO3JHcAUI+D6UXm9CjcxhaLQ5c1Juw50Sd13WPVDdyDDGeHJmM/omR2O2xr1qlwKxhKiiDJT7/G/KsactWKTCxZyy0rdbb3oDC6XSib9++WLlyJQAgKysLZWVl+Mc//oG//e1v7H7X8yMfQL+bKBQK5Y/ILS+mjEYja4HOIBAI4HS6fq1OSkpCdHQ0vv76a2RluRaAVqsV33//PVavXv27j5dybQRq0Ns5wn9NlFwsQIhEhHnbj3v1VZrQIxZ7y+rQLyGSFVFAFafmCQA6hctw4qKh3doOg9GKlXtO4vGR3fD8mFQ4gXaNGV794hT+VdAfDkLQYrGzJhh259XUNWaxPbRbB8z411G8PbV3wD5ErT6iPgBgdxJ2kfvG11drfxjR44nF7kShpgHPC3gcIeHLJvv4BT36J0XihTFpOKdt9Wpey9xzi9WO6VuOcK7NON8N7qLAuv0aThREJOAjOlSKJ0Ykg8cDMmLDvISYe0RoeEoHhAeJOdEt9+vweTwQt7RAX8SES2G02b1ExcaiKuyYqcbS3WWcbe4iwX0+mb8LKxqQPzgRrWa7K+Wzrc/VuB4x+LLsMjuvDS0WPDc6BZcNZr+Rx2ltUUVlsAQV9S2o05uxYGwqVuw56VXrN3tYMhQhYlReacWOmYMQKhXjot4EQ9sPCsdqdCivNSAuQoZOkTLYnMSngGMikMy2Yd07ekWv3Me6fGKG35oq95q2Io0Wi3aVYcn4tIDP43YgJiYGaWnc+0xNTcX27dsB4IZ+5KNQKBTKH5NbXkyNHz8eL774IuLj45Geno6SkhK8/vrrKCgoAOD65e+JJ57AypUrkZycjOTkZKxcuRJyuRxTpky5yaOnBMJXg95gqRBmm8Pvr/MLxqZiyWdlPvsqLd9dhgk9OyFEKkTv+HAv4wgAqG+2QMjnYfGuMvSOjwj4C/qlJjOSOgbjja9P48kR3VGrc/VL8udEZ3e6GgDrjDZc0BnRMUTKpuAdv6BHjkqJQk0Dmy5YeaUFK+/OQFykLGCvH7GA7zOyERMmDbgIfviOLrA7CTvORIUcT4xIhslqx9n6Ziwcl45VX5z02c+JWegbLTaU1zWxKZLrpvTmjNGzHoi59sKxqeDzeQFSI5WYOawrpm85wt6rWqXAhry+OHTOlW6WrVLgiRHd/N6jRMjHEyO6oexik9/IT06yEsfP69Gjc7jXNqPVgb1ldRibGYO5o1OgN9kQJhXBZLMjWCLEq/f19CkiAcDqcEImFriJdldqnZDPw9zcVDxisiE2TAqjxYGis1qU1xp8ukZ+dKgGL4xNhZOAjSIx769Hh3SFSMhHUJvo//rkZfzz+0oAwJ7Z2V6W7jkqBaZlJ2F9YSVeGJPmt/6KiUD6em6eFGu0MNkcAbe7G3UUVjRwfji4XVGr1Th9+jTntTNnziAhIQEA/ZGPQqFQ/kzc8mJq7dq1WLhwIWbOnIn6+nrExsZixowZWLRoEbvPc889B5PJhJkzZ7L9PL766itqP/sHwL1Bb63ehGf+8wuOVuvw6czBWLa73OuX/MzOYRz7ZncKNVrMy03Fum8r8OyoFLz61WmfAmHLj+fw5uQstFr8m2AYjFbU6c0Y2q0DAGDp7jI8lN0FT45MRm56DJbvLvM694QesZCLBeDxeEiLDYPN4cRD7x/BA/3jMSK1IwYmRcIJl131toPV+MvABGwq4tpRe/Y0ylEp8d2Zep+RDacz8CJ4Xm4KVu095SVixvWIwf/1jYfeaMXUAQnY7Me8QSLkY+G4NL9RoTkflkDow6yiWKPFkyO6ocls9+sUV6hpgBOE07OqWKMFn8fDC2NSkdkpHIQQ8Hk8n/coFwswZUAC3tx3BlMHJmLWMJXX/OSolJg1TIWfqrTI8DFHcrEAhACZncNgtDgQIRejvsmM6VuOYENeXzzzn19Y0fzqfT05Yjc2TIbLTWZktYn2GUO6ICJIjMPnGlmhsmuWGleaLX5TTdUqBZZPzIDV4cSFRhOmZ3dBVnwENha59lu3X8PuAwLclRaNQV0U6BAiwaIdpV7pia6/eeiTGAGz3eHXJdJTZBnbMchob7vn+fRGGwzG2zvV78knn8TgwYOxcuVK3H///Th06BDWr1+P9evXA6A/8lEoFMqfiVteTIWEhGDNmjWsFboveDwelixZgiVLlvxu46L8unj2narTm33+kn9BZwp4nhqdEV07hkDXasXyiem4qDfDYLJ5RRgsdidWTsr0ex5tqxWdImQw2RwYlR6FzE5hiFfIUWsweaWFAVcjYwXZSTBa7Zj87k/IUbms3Od8WIKsuHC2tmZUuivNZ0NR4Ma4JTU6LBiXirvfPsC+5j4fzQHEIADUN1l8ipiln5Xj+dwUtvbGn213WmwYFu0o9WruKxUJcMlgwtLx6SjS+K55szsJ5GI+m+rnC189qworGmBzOlGw2dWElul95Ym7SDtQ2YgZQ7pg7ugUAIDJ6oBcIsA3J+sxbfNhrJ2chVaznRO9co+YMeLH/VqBGu1uyOuLMKmQ7dkFVMHmIFi8s9Sn8HOvk2Oen1QkgDJYjJV7yrGvrYcUc353MV2s0aLVasf4tcXsuP/7yOCAVuuPj0iG0eqqwfIVsfMUWYxFvz9CZO03wHZHJhawJjG3K/369cOnn36K+fPnY9myZUhKSsKaNWswdepUdh/6Ix+FQqH8ObjlxRTlz4Fn36kjNTqviAjQvgObWMBn63Q+njEQU9876HO/Yo02YMNbByFoaLHg7W81KNRoMWu4Cu//eA4F6iS/0aBCjRaPDFXhx0pt298NIG3RF4v9qjHBiNSOGJUeFVBkLBibhqy4cOiMVja9zHP/jfn9APhPOxT4KXQvrGgAbywPQRIBa7PtK8WQMbgIFFVZ/c9TPq/RarHjw4PVmDFU5W+KAfhuWKwz2tg0T3/RFcbxkbn3jNgwXNCZ2PvoGx/BiiTGTtw9uucrYuZ+LUKADYWVHEHPnHtDYRWeHd2dI5J8PU+j1cHa3hdrtJzts4arUFKjCyimmf3dUykLspNQZwj8g4LdSRAuF6FvYiSSOgSDD65gvtxk5qTRMhb9hT6EcU6yEkEiQUADDU+jDsDlfHi7M27cOIwbN87vdvojH4VCofw5oGKKckvg2XfKvRbFfRFX32zxW0/F9GySiwV4a0pvEAJszu+HMLkIQgEfulYrHE6Co22iodVi9zkWg9EKsYAPJwEmD0jAtOwuUAaLsbGoCpYBgfsACfg8yMV87JqlBuBaUIfJRLA7CJTBYrz8/3rgtS9PY/KAhIDnOadtxbafarBofJrfha5czMedKR0w2UfNU7ZKgRGpUZCLBV625XKxAEI+H2/t13iZeLhHRSx2p980vWKNFot3luGB/vFeIoJZYO87dQVPjuwe8D47R8iwMb8fR8iJBTy8OCkD53UmOJwE2x4agANntRyhZ7E7veqxGGE1qIsCYgEfm/L74WiNDhFyEcRCHic61CFE4jXukvN6xIZJoVYp0KtzODI6hfmtJXPiquBZt1+DtJhQrzkOlYr8vo8HXUfEzj2Vsnd8RLuGGyESIaQCPsasLcLbU3pjyYQMnNO2shHN0loDnhvtei6FFQ1Y/0MlG5Vzf5/lqJRYMiEdZqfDZ5qpp1GHqw5OhW9P12NUO82kKRQKhUK5XaBiinJL4Nl3yjM1KkwmQoRcDGWwGEO6dfByAcxJVuLFuzOx/LMyzBjSBVIRH2/uO4MHBiTg1a9Oe9VevTk5C6Ey715XdXoTqhuNeGt/hUdxv5J1jwtEsESIrLgIL9OEnGQl/jV9AF7cU44ijRb5Ptz23OkQLEGhpgEv7T2JhePSsGw313RDrVIgRCrE434MGlxOa6c4EQ4GxrLcM1XMMyoSJhMF7PlVqGnAo0O7+hQbzAK7vtnsVwyqVQp8VX6ZrQ16c3IWPj16AR1DpHjh0xOc8WV7CL1wmYgj9PwZXWSrFLgnqxMuNZnY5suA7/TBjUVVeGtKb8wapkKQRBjQ3GPh2DT0jo9gX/eMoBVkJ6H0oh592poYe6ZotgcTsctJVnJSKYV8Hg6d0/k33FApwOMBjOQPlQnRarVjU3EV+/6RiwUY3r0jcjOi8dzo7qjVm9EpXIYxmdHIVyeyY7zcZAYIAZ/Hx9JdZZiY1YkTpUtSyHGpyYyN+f3gJAR8nivF8sQFAyb2jG33HikUCoVCuR2gYopyS+Cr7xSz+L0jWYm1k7PcekLBb6PdpRMzYDBaseLzk8jy0xeqWKMFD8DySRk4e6UFyiAx2wT4uzNXsPt4rX/DBHVSwIVsi8WGdW620+zxFQ3QtVrZBa17+hfATdUDgCCJELOGq7CxqAoO4sDKuzNxQWeC3q3+67LBAvgxaGDu88kR3bzcAa+ljilHpURIm7NiIJzE1QRWJOCj2WxDyXk95m0/joLsJPSNj0BshBSLx6dh8WdlXoLWXXQxz+SluzMx/9MTXm6NRRoteODhP48MAo/Hg5DP40R3/EXQijRaLN5ZiudGp3JeD/MhpI1WBx7bdgwzhnTBXWnRAefVQQin1qi+LXXuaLWOTfu7oDNhbm4KTlzQY/mek+wzyFEp8MLYwPbhEiEfOSollk5Ix8o9J9nXI4L8R7vUKgUWT0jHnhN1GJEahTcnZ0Eo4GPNvjOsq+TpS83YlN8PzWY7wuViWO0EYTIhXtp7EvvdarcYclQKjOkRiyUT0jkW+ADw8cMDUXxWi1O1BjyXm4q9pXU4fl6P58f6dxKkUCgUCuV2g4opyi1BoL5Tq92a67rv76/Rrs5o5TSp9UWRRouqBiMKNh/GHckuowizzYGOIZKAi+iHsrv4THnKSVZi0bg0XNSZ/B6vd6sjcV8Ql7Q1yfWVUvbm5Cxc1FuQpBAiXC5uSyMU4J5enWCxO1ClNfq8FoPBZMO2hwbiwS2H0dBiRY5KCYEPBz53giVCPDOqO4o0VzC8e+CeOMESIRqNFrRa7Xj4/aOcCBEAlBTp0D8pEmMzY/DkiG5wOF39t3zZjRdptGixOryEFEOhpgH5BjNEAj6cTgKp6GqfsMARNC3m88GmE56qbUKkXOxTFButDvxyXo+7UqOxIa+vz2bEANDYakWHYAn2Pp4DANC2WLB8Qjrqmswc0wq5WICFY1PxyaODoTfaECwV4nKTGXw+/KerJiuRpAjC82NTYXM6MevOZJjtrnmxO4jfaFd9kxlmmwP//L4Sg7sqsam4CvNyU7D/1BX8VNmIWcO7YsWkDC8nwJxkJfIGJ+KnykavlNDCtihqx1DvRrLBEiHGZETjrrQoVDW0ol9CJIZ174jaRhOCpPSrhUKhUCh/Dug3HuWGMBitaGixoslsQ6hMxEZ3/hd89Z1iIk7XA2Pl3N6v40zU5YeKBszbfhwLxrX/i7qDEK8mwDFhMpy6ZAAAmOz+IzlMehcTheLzeJhxR1d0CpdhyS5vJzjm7xUTM3z0FFJi+aQMRMi9IyyevPrlKfz3kcFobLXg7JVWhLVzjFDAQ4hUiGKNFoQgYJpeiFQIB3HC4XSJFUIIth6sRrFGi+nZXbBuv4YVi29/p8HDOV29IhzuNJsDGxdYHU7EhstQqzeBgLBzGSQO/FFW3WjEox8cQ45KiWdGdUfh2XosHpeO5bvLcLRGj4fv6IJslRKEECiCJVjsMd+e9WQiPh88HlBSrUNMmAzhQSIQHrcZrruwnO9mf+96dumYM9xlzlHoka66eHwaTHY7mkwOhMmEcDgceHFSBoxWB/h8HpZNTMeiXVxrfua+8jcdhtHqYJ+f3uiaT6PVge5Rob4t1Ssa4CTEZ0ooM+etZu77mrHsH54SBYAgQi5GqEyI4+f1qGsyY1wmTfOjUCgUyp8DKqYo102t3sSxMQfARndiw2X/07mZiBMj1iobWhEqs16XWAtp+1W8vdoU9+0/VDTA6SQBj5GLBegcIcPHMwbCaHEgSCJEi8WGVosVEXIJVn9xCo8M6er3+JLzeoxI6YgHBsRzolAb8vr6tbou1mhRazD76CnUgBd2nMBLd2ciW6XwGc1hjCCKNFrU6k0IlYnQPToEcpHAb1RErVLAYLJhzddnUFKjx4PZXdA7PgJOEK9I3DR1Is43GpHfZmMOuBbZeepEHL9ggLwtcuRe/xbcTsQiRBpY6MVFyCER8BEkFkIocKX9/XJe3246oljgeq5MumZWfAQmvV2MpePTsKxNrK7ZV3FNLns/1+igCBHDbnfi65OXMWVAAl758jTm56ZynlOgHlsLdpRi8YR03J0Vi+dGd4fZ5oSAx0NkkAjLdpdzUu5yVErMGq6C0erAe0WVKKnRY+3kLDwypCvH9n/yuz+xvcmY+5W3NfyViwWIDpMGfJ9Nz+7ic1uHYAnkbmI1R6XE9JwkEAKs3FPuJfJnDVdBGXz72qJTKBQKheIOFVOU68KzHxTDDxUNmLv9OBaOS4OAz/ufIlX/q1iTiQXIUSlx4qIBOSqFzwWkp6Uz4Fr01zdbfKZ/ycUCbMjri+W7y72MIGYNU+FKsxkpMaGQi/3bSJ+qa8Lc3BQs8agfai8a5s9m2iW0THh2dAp4X57mzJdnTZLeZEOITIgrzRbwACybkO4d7WpL9wqXiVg7eGbx7plSdrnJjBMXDVB3VXIcAxmxsvreHnC4uc65mz8EstmWiwT+bbrbti/0iOKpVQo8OyoFw1M6+K77SVZynjWTAtonPgLdY0I58xAoXbBYo8XMoSr0igtHY4srypcWG8YKphYz1x3S077d07q+odkCVccQEAIog8SwE4IXdnhHKF1zQZCbGcOabZy4aMCdqR1htjnAczNFyVEpMC07EbpWKwDXDwYv35uJ1NhQr/F5IhcLvNwf1SoF5GIBpEI+3p7aGxIhH0lKOQQ8Plosdswcloy5uam43GR2/TeraQB4wLq2FFYKhUKhUG53qJiiXBee/aA8F4pWuxNflF3C6bomLJ2Ycd2RqkBibd724xwjCn+Ey0SYc6cKhBAMSIqEE971TdOzkzBz6zHOcWEyEYZ264AkZZBXb54FY1Px9rcarwgQc965o1OgCJag1WLH7OHJnG3MNZ8bnYKLeu+aquuJoHnS2GpDRVUj5uemIH+wmdPg2L0mSSLkw2x1orTWgG5RIYgOkWDRhHSYbQ60mh0Il4ugN1mx/odKPJzjiq65iwF3eDweag1mZKsUOHO5mZP+xtz3vNwUfFl22Us4MbViPIAzlzkqBRaOT4fF4cDySRlYuOOER8RDgeWTMrHCT8Nk4BTmjk6Bxe70mvdnR3XHA+t/4hwTJBHiqbu6obHVyrlOe8LW2Zbm+X5Bf6zYcxJrJ2ex4ssz6ubLvp1BrVJgfI8YNLZaESEXQ9tqgVwsCtjDLF+d5LPZsGt+lNg9Oxs1WiOe+e8v2PrgANyZ0gEigQCf/VKL57afaLdHm8PJTfVjBHmLxY4LehNmbj2GMRlReG50Kp7fccLLJn3rgwMx9b2fUFjRcNs37aVQKBQKhYGKKcp14d4PilnYbTtYDcC1+K5pNGJwVwXuSovCyj3lePHuzOtaVHmKNXd+qGjApSZzu+cLk4sRHSrF85+eYOthnhzRDXanq8ZGxOdBLOL+Cp+tUkAq4iMmXAa5WIDlkzJgtjlxUe9qkBomE+F5t7oXd4o1WvB5PNidBM0WBxqazVgxMQMtVjtMVif4PEAiFOCy3uzTWt3T2c+dnGRXhG3WcJVXZOOjQzXoHCFDXKQMFrsTmw+c85u6V3Jej1HpUcjsFAYJn4/IYAku6EwwmGyICpXiWI0Or399Bi//vx4IkrrEU3tiYFKvTnj2P8cRr5B71du0mB2scHIXpkarA9sOVmPu6FTM5RHUN1nYOZj0VjH6JETg8eEqzBuTiud5PLZ2TiLgw2i1Y5+PyBPzDBparJwmu3GRMshEQjyw/kcvY4VWix2tFkDsIVTdTS18EdH23mPeO+7iSyzkc1IuJUJ+wD5dK/acxDN3dQcBECITQd8auF4sUN+vQk0DFu4sxdjMGKydnAW5SIDHR3TDit1l7NyXnNcHrH/7sVKLUelRSIsJ5QjydVN6Q8DjQa1S4Mm7uuMFDyEFuITx8t1lWH1vD0zfcuRP0bSXQqFQKBSAiinKdeLeD6ogOwnbDlZjip+msc+MSoG29fp+ofZs3uvJBZ0J0aHSgOc0GK24oDfhaI0eM4Z0QW56DJa7LSoBl0jZkN8Pk9f/hKz4cOSrk/DS5yexcHw6Wsx2tFhsCJeJER8pR02jEQ5n4EaprRY7uiiD8HlpHd74ugJ7H8+B00nQMUSCWr3J1bQ3VAKLzbX4do/o2Z0EE3rGYvlnZV71J8+O6g6T1Y4392u8xMxHDw/CS5+X45tTVyAXC7DtoYEghHilIU5TJ+GjgzUYnRaNbT9VY8G4NCzw6OOkVimw6t4eOHWpCXER0chRKdsVA0t3lbGW2QUefbNCpUK2Vuqjhwciv9kCi92JjiESnLncjG9OXcbhc41e5z1arYPNSfD3z096je+JEd0CPgOzzcGZoz1zslGrN+GB/vEcJ74clRKXm8yoNZgxJiOG3V8uFkAZLPabGpqtUkDAA5aMT2NFl3vU0OZwsBbkxRotSs7rAzbnLaxowAtjUmG02qEIkiBYEvg9JhHy201DLFAn4e1vz2LZpHTojNyo28aiKnw6czCW7S73a1OfFhPKidjmqBS43GTGwKRIDO6qgNnm9Bs9K9JoMTc3BQC8opkUCoVCodyuUDFFuS7c+0ExPZH89ffh4TQWjQ/cT8cTz+a9vmgvhaihxYpmix1vTs5CfZMZS32khjERnF2z1Njxcy3mbT+OVff2wDP//pldgMrFAmye1h+zPyzBJzMHBxyTUMDDst3l6JPgauSqbbXAbHPi5S+5DYNX3p3h04SCsdB+YVwarjRbYHMQRAaJUFhxBT+d1foUM0t2laJvYiRSY8OQFReOhhYLlk/KQJ3ezOlH9dHBGswbk4I6vRkpsWFY6MPRraRGj4k9LRidFgPwgNnDu8IJIDc9OmDT3udyuwPgpsflJCsRKhWyJhdflV9mTR3kYgHemtIbveLCkdkpDFMHJHCsxwuyk3z26SrWaPHY0MApeO7CRq1SYG/pJU5D4DkflqBPfASeHd0dhRVXcGdKB/B5Vy3KC7KTsGbfGSxtM6TwFKX56iS88tVpvDAmDTaHEzkqZVu0xyW+tC02jtOj3Um8Il+eXNSb0EUZhKPVjQiTi/2aiTCiJrydHyYsdicKNQ0wWhyQCLmCxmh1oE5v5kTvPFNC3ecwp61vlYjPh8XhQEp0+3VXLWYH1CpFu/b7FAqFQqHcLlAxRbku3PtBWezOdvr7NLQb0fFEGSwO6DRXcl4PRVDgBaWDEMRFyFGrN6FH53BcabGgX2IkMjuF+ewbtG6/BrOGq7CpuAolNXpOSl2wVIiC7CQ0mWx+F7rZbQ54hRUNyB+cCACIDpVi8S5vEbdiz0lsf2QQVnx+krPNaHVg/qelUKsUGJsZg46hUogEPKTGhOGVL8/4vE8mErBq7ymOKFswNhW9EyJgMNowKj0K43vE4li1Dn0TI9HbGeH1vBiBIxXxsXBXKR7K7gInCDYWVWHKgISAc220OLAhry+CxEJszO+HSwYTunYIRmVDK2YN6woQ755aBAQv+Yg8vTk5C3wez+/76UCltt33BvNvz4bAfPDw0cMD8VX5ZazZdwZTBiTgofeP4l/T++OZUd0BcrVG7KmRdvSKj8A0P4Lj6ZEOyEQCzMtNweUmM8ZlxuBYjQ5OQjhGGwCweVq/gPMHAAaTHZlx4Zi8/idse2ggln1W5lFPpsT8sakQ8Xm40mwJeC5GDLVaHT4bEx+p0aGkRudzjnNUCnQIkbBGEx1CJGgy2VCrNyEtNsxnrylPQqVCTFMnUTFFoVAolD8NVExRrhumH1SdwYzKhtaA+3rWqrRHmFyM5RMzvOoy3BfId/fqBMB3r6tWq4OTLse48G0orITdSZAVFw6zzcHWdVnb+kJlxYWzi373iNGs4SoM6qKA0cJN4WJgjBP0rVZszO+HCLkIw1M6wGr3nQ5ltDpQ12QJ2Bi4QJ2EzcVVWDoxA8GSwNGYeh/nqjWYkdBihcNJ0Gp14FiNDuW1BvRNjIRY4L3ILchOQp3BhD0n6lCs0eKZu7rj1a9cEbVpHul7ngRLhPg/N3OHnGQlFoxNhc7ouvb4njF4fEQytK1WzBudiiCJwKuHE3PfADDjDv/W8huLqrBrVjYWe7j5sc/AaEVWXLjPhsCFmgY8bk9mBbTNTvCfGYPgJMCb35zCc6NTWHv1FrPDr6ADXM9w1d6TXgYZL96diZxkJY5W6zBjSBfcmdIRwRJhuwIws1MY4oRyNLRYse/kJcwarsLc3BS0mF39ooIkAry4uxwHKhuxdnJWwLonRlCGSIWwOZw+DUDWTXGJVk/3xzx1Eh5Yz9irK9AzPgKDuyigudKKpA7BEPL4EAp5AX9UIAA+PlSDV+/r6Xf+KBQKhUK5naBiinJDMGl2jW0WzH738/HruC/chZEiSIxxPWJ9piL1TYiAMljMsU9n6o9GpEZB22LBtOwu6BkfgY8O1WD1vT1AADyY0xVOEBw4q2UX1NkqBVZMyoAyWOxV3M+cs3d8BIR8HgRiAQo2H2ZTuKwOJzpHyHDiggGT3iq+Wo+TrMTSCemsNbUv2uuJ5ErV0sJid8LhDCym3AlkFjFNnYSVe05i/phUr+OYdE1m0S0U8Ng5APw37c1WKXC52cx5rbCiAct2lyMrPgK/1OiRp05Ei9mOGf86ilnDVchNjw7Y62hervf4GFyGD3Yvk4kvyy5j0lvFePW+nl4Oje4YTDY23a9Q04A6gxnRYVL8bVAi3th3Gk+P7M7efyCCJEIffb+0WLa7HEsm/H/2zjw8qvJ8/58zeyZ7ZiCEJRCYQBYWw2KAJCrIKiCgbb8CbQnBFQGXKhQVZROXWrWCWrFE6K+irXVjcUWtElRETRXCGokE2UJC9tmX3x+Tc5gzWxCxbudzXb0uMzlz5j1nJvS953me+86hrtmBD2i2u3n47QMsvTyXxa+G2rmLXw7887qhtDj8s4J9UhO4as0O6bi5Iy18UV0vvd6858rbqny+kMqeeL6iTDM6tcDJRiezCjJCDEBqmx1c1rcTs9rcAT1eHx8dqpMEqChO7399LwAD05OxOjw89s4B9p5o5tmrh7J8s7x6VmgxseTyvnz5TQPLJvdVnPwUFBQUFH4xfGcxZbfbMRgM52MtCj8xEo06upuMEb+pvijTfFbhncG5UkadmtLiITz+bqVsEy+KH0AmpFZPz/N/4x4kIJ69eih/emOvzAEucH6mrLKOxa9WUFo8hFaHW2YFHixK1s4cjNXpd6i77uKeXNYvjWUbK0I31Qf9rmpLL8+NeL1na4V+rM1JMFJlQ5zZEYlmFgGQl56MM0y1ItgOvL7VJd2DZ3ccZmZBj5DQXnHz/Nae4yHrEqtrq9+txIuP28b4Rcrg9GSs7QhJrUqIWnlptrv5IqBNTQzZDZ73icQz26sk58EGm4vURD0qAUZlp7blk5koq6yNnHWVacYboXV1694abhnVm69OteIDqdJ35wQfE/qlhf1yIC89iRitGq3Kv/bg9yK4jVY09bj2op4sGJ8lc0MUz7dsci7f1Nn4tK0iefu4LMkApHuKER8wcVUZIDdCeejXA9BrVKQlGvhvdT3T87sz/7lyLvxdCnGGMwJyxt8+5oEr+7NwfBatDn/1TKdRcdWaj/jH1fmkfcfgbgUFBQUFhZ8S5ySmvF4v9957L3/96185efIkBw4coGfPnixevJgePXowe/bs871OhR8pXZKNPHBFfz6orKVjvF6aSTrZZGdE7w7tfkMdLlfK6vRQsm4nd03I5qZRmdQ0O6QN6PLNe7hrQo50/LUX9eSZsqqw+U/LN1dwQXqyTEz552fgn9cO4809Jygtq6LF7qaHOZa9x5uA8KKk/EgDI7M68LuhPTBoVdQ0OaJWWJzuUNEicrLJ3m6YsFGnpkO8HrUK7pmUy9KNFbLNfVGmmWWTc5nwWJn02MD00HmowDXNLuxJk83F3RNzZZWFYBHibssbEu/Bx4dOy0J7k2K0xBn8luMPXNk/7OuJosBfbfJXehKNWmzttH02WJ3+GSaBkDa0eSMzSY3Xs3hSLss372HbwVpZblVUi/m2EOftlXVcU9iTuSMtdEuJ4VSzkwSDlp4dYnF7vcwdmcnfP/w67BqKMs3MG5nJO/tPRly/x+ejY4JBunaA2mYnHRMMlAZ9psRq0ulWJx3i9BRZTCHvRbjcK6vTw6NbD7Lmg0M8e3U+GrVATloCq6blUX6kgZONdjok6qV7c6rZwez1nwJ+h0OrwxOSDycIAnuON1FaVsXfSy7keJOdJZv2YHV6MBl1xOjOrKu2xSmdD2DLvEIcLi+/Hdod+7ds61VQUFBQUPipc05iasWKFaxfv54HH3yQa665Rnq8X79+PPLII4qY+oUhqARe+/K4bLN/UaaZi3t3aPe5kXKlrE4Pd7y8m7UzB4e0bt0y+oxFdqHFLAsvDeTz6gYWjMsKyWcqLauiptlOeXU9j03Lo9nhxu7y0DXZ/416OFON0rIqnr92KLuPNrJl13FmtGPM0Gx3S/NGwfM9+T1TGN7LzOJXd4cIhlkFGfzxxS9ZO3MwD76xj7KAlsPrL+mFWiVgdXowx+nQCAKD0pOl+65pZ+hfoxaIM2h44I29DMlIYcE4v421VqXiv9+cESKfV9fLLL2DTRUASouHUNvijBhyGygKGqwu5o60YNCqeWdfTdTKU9lXdRT2MnFPm0W9w32m8nGq2YHN5eHtvSe5piiDOZf0wuX1YYrVcs+kXDw+H1Mu6MLSTRVyEWQxM2eEhU++9t/LtCQD/y2TmzAUWcwsm5KL2+PlxpEWHn5rPwO6JVE8vIckILsmxyDg4/99dFh6XrAocbi8IffEqFczK6BFNLg69e8bhnO61cntbe9HoNCOVm2zOj1o1AJOt08SQlanhwn90thztJFB3ZP544tf8uzV+aydORiH24tWpSI+RojYDvrYtDyMOjWPvO3/myrKNKNRC5KlfzgOn7Yy59nPKbKYmdI2z6igoKCgoPBL4ZzE1N///nfWrFnDpZdeyvXXXy893r9/f/bt23feFqfw40eqLAVtjj84WMsfX/ySVdPyolan2suVivTNvIjbE77lSmxT+9Mb+8K6xrm9Z9rWFo7LosnmIsMcy0WZ5oivWdviJDXBILWxRUOtEnh+x+GQ+Z5399XQYvfwl3f2yjbriTFa4g0aStbt5NbRvXnivUqpciSKGdHmOy89mbxuSTy29SBLLs/l7o27Ka9uwNROS6XJqCNGo+bjQ6eZnt+dB97YJ7Mrnz/SwoR+aXROjGnX0lucKwq32Q80QgBIidVR0MvEqWbHWWUdXdK7A39+ax/vH6jlsWl5PPbuwZBjR2d3JEan4a5X/NcuioPy6gZKCjMkV8WOCXq8Xpj29Mesnj6QVdPyWLF5j3RvA8XQoVOtZJhiuWej3yAjOCC4wGJiYr/OPD49j48OnaZ/1ySMWrVsFu/vJReG3JOaJgcD08O7XhZaTJxqstM12YjV6abB5uLOCTmUV9ezfMvesNU2cc3Depqob3Xh9vronGjg8ekD+X8ff41eo+JIg435Iy14vLA86Hq3zCuM2A6qApZN7sumuQVoVAIqlYBBraImygygeL1icPDqdv7mFRQUFBQUfk6ck5g6evQoFosl5HGv14vLFX1zrPDzIlJlCfyCSsyECue8l2jUtZsrFW6znhijlbKujPrw4aDtzQ8tbKsCiC15KW1268sm95VmlYJxuD342rRb1JayTDM7quqYPrQ7z5R9LW2in5gxEJfHx0Nv+gXeu0Gb9SKLmX9eOwy318uKLXvDrmF7ZR1zLrHw0aE6PjxUR6PNyYUZKdwzMRe72xNxTQUWE2q1gNXl9rdGBtwbq9PD7f/+grXFQ3h913G2VdaxeV5h2NcXSTZqKbKYqQmy6g62JQ/Me3r26vyzyjpye3z06hhPr47xUd5Dgbtf8Zs6iLb24nHyipM/Kwn8QsLr88mcHsPNxkVr37ymsCc+n8BnX5+WVURFkW7QqDnQ1Cw9tr2yjoUvfhnRtOHeKf042exgycbdIe6Am+cVcvS0jcv7d5ZCp6OZjMwdYeHW0b1psDoZ0DWJ7V/V8enXp0NaYB0RnCYByfjk/9rCrOeOsJCWaCAhJvz/VQQL520Bf/MKCgoKCgq/BM5JTOXm5rJt2za6d5e3Or3wwgvk5eWdl4Up/DRor7LUbHeFGEyAvw3w/iv7y0KAgwneqInP6xivl7KuxOOCN4fR8q9EAXVmjW46JRiY+1w5nx2u99tPB5k+GHVquibHSAP/gdlJ8jY+MzdeYmHH13XM3eA3CvjDmD44PR5idGr0GhWlZVVSlpXT46VjvD9T6nijHZvLw65vGnh8+kBu3PB5WGt5nUbF6OxULuubhs/nw+XxsXRzBdcU9gzbWigKnJONDv627RALxvcJaY286sJ0HmyrVAFhbbUDz4fPb/6QGq9n87xCbC4PTrdX5goXLKw+OlRHocXEF0cb6JxoQC0IpCUa0KhVXNgjhUHpyRxvtLHj6zrJYTDwPQysIvl8Zxzqomed1dFi91BSmIHH65PlnoUT3JHaFkUSjVrJNj4Q8ecFY/uQlhiDUadi7giL9DvRtGHR+GxanW5idRp0WhV2t5tV7xwI6w5496u7uaDNlbK0eAjX292kxOpYuWVPxNf/47hsbC4vBo2afl0SZe+zUafmiRkDORrhywKRow02yaQDYGK/zhRYTCFzfsHvr0hzO/8mKCgoKCgo/Jw4JzF1zz338Lvf/Y6jR4/i9Xp56aWX2L9/P3//+9/ZvHnz+V6jwo+Y9ipLsXpNiJACeRugKIw+CBr2v2dSDl+daqW0eAifV9ez/3iTZLucaIRV0/JosrpYenlflmzcHdZRMBKnWs5UVDonGVj08i5pjfOeK2fzvELufvXMOUsKM9j1TSNwRrzNf648xJjB7vZQsn4nq6blYXV62Pn1afp2SSQ1wcCbFScYnJ4c1b78N099xKD0JO65PJcnpg9kThhBZdCquP/1fVJVRpxvyktPZs+xxrBVnw07DpPTOZFtlbUUN/UIuR/BgqTR6ooqzLQaFWqXh5PNdqpP2xiakcKL5UfJ65bEullDaLa7Q/KeSsuq+OuMQXRM0NNgdZIcq2PF5j1BFRkzswp74PLIhU9wReaJGQOl37UngI432Rje08SHh+oYm5sacs2BIq1DfPRgWtE2PhzbK+tQjRe4ccPnUs7Ukstzcbq8tDrdqFUCRp2a0y1OPjh4gn3HGrl1TFbESlhZQM6X2JK5cW5B1MoZgt/mfEK/NE5b5a15JYUZrN12iOJ2WlRBbpdfUpBBq9PDoB4pFAdZ0gfneQHEt/NvgoKCgoKCws+JcxJTkyZN4p///CcrV65EEATuvvtuBg4cyKZNmxg9evT5XqPCj5holaWLMs3o1Kp22wB7dYxj1bQ8aluctDpcxBu0NNrdHKxpQa/xm0bsO97Ekkm5MttlsZXoD//6ggvSk1k4Posjp23oNap28606xOkx6tQM6p6MQasOcRM8VNvKgnFZ3GB302hz0d1k5Nd//Yjnrx1KhjkW8G80RQFSZDEzd6SFXUcbsTo9xOk1rJ05mN3HGtl9tJEMk5F9x5uYckFn7tlYEbGyIFYE7tlYwcR+nSUbdynHymJi1zeN0vGlZVUU9jJL/x1OqBW1BbIGVxACEQWJKCziDBpa7G7+OC4bh9vDqRYHOvUZYTY6pxOpCXr+8fFhbh7VmyP1NvIzUpi9/lNKZw6Rub0FkhSrZfkWfw5VeXV9yH3YVlmLFx93XpbN8cYzGVbBVaTA9s/2LNF1ahW6torgpVkdJTHscHtDRNrckZaoFbn61uhVlwari0HpSTzy9kHJxEGkyGJiUI8UHt16kCKLmcWTcqLmkYH/fQkUulZHdLc8u9NLaVkVE/ulhfwNiOcZkJ7crptkTlqCbA3NNpesyrV25uCw1cCzjUNQUFBQUFD4ufCtxZTb7ebee++lpKSE999///tYk8JPiESjLmxl6aJMMw9c2Z+aoFDXYMSWIH+1Scc3p60sfOnLsNWQpZsqeOjXA2TzGLUtTrbuq2HrvhoAaYN+86jMyFlBFjOHTrVQWjyETvF6vF4fRp1a9g27AFy15mOpYtHq8GB1ejjd4uSLow0sHJeFSgC1SoXX5+N0qxOr08OIPh156v1DxBk0zFq3U9qoCwLMLsyg1eGJWtkoCagGlRRkUBqQi1TQFqY65fHtsufFtgk3h9uLWhAoKcjgmsKeWF3+7KUO8XquWvOxdH3lRxpC7o1eo4o4jzMyqwM3j+pNbYuTvG5JjMvtxNa9J3l7zwlyOieiFgTiDRp2H21iyaQc2RxbYNVHp1H587zSk9u1cVerBGqaHJKwCRQUYpjws1fn02hzkWTUsXJqX1Zs2RtSJSlqEwcT+6dhdXpkFTe9RhUi0iK1b4qfQXeEjCmRZKOOOSMseIOeL4Y517Y4KLSYKaus5f7X93L72Kyo5zNo1TKXxvYCheMM/n/SfT4fXZNjZBlwomAuLavilTkFLN1cEfYa5z9XzqppZ9q19RoV8UHCrFOCIUR0FrX9zSvzUgoKCgoKvyS+tZjSaDT86U9/YubMmd/HehR+gnROipEqS812f2XJHOcXR+FmfgIJbAlqtDpZFCSkQB46GzzcHjizFbgRFgSYM6JXSNhsgcUk2WRv+vI4Uy/oTOdkI9dd3FNWSSg/0sDQnimydZQWDyHRqOWp9w/x1PuHJOERbH1eWjyEeJ1GtlEvKchg9vpPeep3g6Lej8CWNdEo4I7LshnftxOv7z5BVW2rdE9F8fPAG3tlLY7ipvi2F74gLz2JvPRk2fsQbjNdfqSBxROyQ67HqFPLnP+CX0MjCLQ43Pzfmo8psJhYdnlfwOevcAS47AW3NIrVtEg02lz065rAoO7JLN9cIaucPTYtLySzqchiZu3Mwcxe/2lAFc/fMrhhRzVX5HWhKNMsBdnmpSfTIV7P8AALeDgTinvdxT1ZOC4LtSDQbHej1ajYdvAUXZJiooQom/jymwZWbNkbYoN+ssnOpi+PhVSr7rgsJ2LodaHFhDlOR7PdLYlSQfBXhQRBYNfRBnw+6NclEYfbS7JRi06t4pniwdS2ODELPm4bm4Ug7GfbwVqpgmd1eni94njEIOFB6UnSrGKBxURNk52ebdVY6bFmu2Ti4nB5STJq6RivV4SUgoKCgsIvjnNq8xs1ahT/+c9/KC4uPs/LUfipIlaWgmmvDTCwJai2xRl1HqSkICNkuD1wZkvcCJcUZjAyqyP/99THYbN9ZrfNND3y9kFuujSTR7Ye4M7LsmUb3ec/qebZq4eybHNFSB7R6ul5qBDQalTMyO/O7MKeUn7Vtso6EATuv6KfLKvJ4fZidXpkxhfhCNe+ZnV40KpVrH63krUzB0u/j+ZYqELg+WuHohIEtu49Kau8DeqezOsVx8lLT+aP47M5ctqKUaumU5KBRS/vlp2rPVfE28b0IUanxqhTs72yjiUbdzO7sCdzR1ioqm2N+Nw5l4S6gQYSb9CyZddx9hxrZEB6Mt1SYqKuZ1tlLQiweW4hp1ocqFUCZZW1/OPjwyyekMNpq5O5Iyw8ve0Q0/O788z2KkrLqlg/68Kwr5/bOTFEQBZZzEyZ0oUh3ZP9GWFBv1s6OZeJq8rC5nIBsvdO5GSTjeKCDARBCMkcWza5Lyu27GFAtyR/W917lbLq3NqZg3nivUpZ+12RxcyNIy2Y47TYXV5WvXuQ4uE9uOHiXiQatZIQFL8MCBckLFY/Cywm5o3IJC3JgM3pln4/qyCDGzf4W08vyjS3G3+goKCgoKDwc+acxNT48eNZtGgRu3fvZtCgQcTGxsp+f/nll5+XxSn89GmvDTBSlSkcDrc3ZLg9WKyJG9mctISIm1rxXICUN+VrW5N4nqsuTGf55tDZJrE17rJ+nWTCQ7TGnv9cOdsO1lJ92opaONOSJQqjXUcbo4bWBlYExP826tW8veckRRazzJI9uotdLcXNPZi9/lPZ2gZ1T2bFlL4s37QnpDXyheuGhZynPVfEO8arpGyn1e9Wsq2yjlmFPbG7vOSkJXJHkDgT+ehQXeQ2zEyz5HooVrbAX/mJes0Ha7G5/a6Jxxvt9Gsz/jhy2kqHBAO/W7uDl24YzokmGysm98UeQdhGE2x3vbKL5ZP7MruoJ4suy6HB6sTXdj0Ha1pCKrGBbY5xeg2b5hYAAu/uP8lT7x8iMUbHTc9/wtO/H+yv9KkFTEYdKpWAw+Xh3X2n6N81iSfeq5Stp6Qwg9VBj4lr9OJj+eS+gD8C4LGtB7ltbB9ONttZMimXJW2hxuIXD3MusUhh0Ceb7Ph8Pp6/dqi0fofHg07jz6d6veKENMMX7m9YQUFBQUHhl8Y5iakbbrgBgIcffjjkd4Ig4PFEb+1S+GURrQ0wkPacAZNitCHD7ZHEWnsGFKK4EedvbE6PbI0GrTq6UCnoIXss2ECi3uo/h4gogqK1H4rzKsH/HaNVs+aDQ6yenseGjw9LMz/hXOwCN+9GnUZyQnx+x2Gev3YoFceacLjd3Dy6N3NHZmJ3exjftxMC/r/d4PN0iNfzxIyBGLRqqfoWKBhanR6Wb94jm7Gxuzzc9sIXPP370EqMSKTw3kKLiUXjs3B5vLJK4/i+qVyR14Wq2lbZNTrcXgxatdTy5vH6ONXsoEtSDHuONVLQy8SjWw8wb2QmV12YzrJNFVyV351V7+zms+oG/z29Op8Gm0u6xmjzXGWVdVidHlJidfy+dAd/L8nnsse2AaGVp2iZUPNGZDImJ5UDJ5q56sJ0Trc6ZaYd4jEA/bsmhVjZtydybU4Pr1ec4PlPqvnbzCHc3VZJ2zyvgEKLmTsvy5Ys0j865A8czktP4p5JuTz4+j6m5aezYcdh/nhZNl4vaDQCCQYNUy/owqisjhH/hhUUFBQUFH5pnJOY8nqjtyop/HKIFMYbTKQ2wEDMcboo8yhmepiMYc8RKNYabS7sLg82V/QAW9GEwdAmqhJjtLI1llfXR11rOCETaCCh16j4vLpeWoNYZYnRqpm9/lNWTctjziUWmuwu/6xJjJZvTttYNS1Pml3xh6ZmSuf/8ptGrr/Egtvr4+6JOQR7IUTbvM8qyEAl+I0DDBoND75ewdaA0OACi4mVU/q1O+sU7C5odbqxOj2y+9HdZGTVtDySjZEFbWB4742XWNCoVX4jEL2a35d+wv+bnS8dJ64hv3sKnRL1IWuL1vI2sHsyt4zqjcfnY8oFXbDmpPKnN/bxWds1ri0LbXNrb56r2e7GqFdT2+Lk8OlW6fFgY4/2WiQn9ktjaC8TaUkGPjgYeswd47MBZAYUIu3ZwVtdHkrL/OYlf3pT3q7Yv2siD765j5zOieR1S6Jfl0T+ff0wBEHgZJOd6UO70ylBz8Lx2TS0OkiI0XG4zkqHRD1ZnRKivKqCgoKCgsIvj+iewgoKUTjWYGPuc+Vc+vD7TH3iQy798/vMe66cY+2EgkbjjsuyefbqfJ6YMZDS4iHMHWlhZFYHbhxpIVYv1/6NVidf1bTw+eHTHG+y4/H5SDFq6ZwUw3OfVDOrIIOiTPnGWBQWe441MmeEBZVaoKgtCDiQ9qpkkey4HW4vRZn+drzSsir/Gixmqcri8fokITLjbzu44R+fc+WTH3H56u182ibgctIS+Od1Q5nYL420RAPHG2w8Ni2PnV+f5ld//Yir1nzM2Ee3UV5dT5HlzPVF27w/s70Kl8fHrHU7uePlXVyV312qyonHLN+yh9vHZnFXGCOKwPOUFPoFY5HFxKeH62X3o8hiwqBRc7LJjtfbFvAbhFGn5r6p/UhNNHBRZgeSjP4qx2fVp7lqzcdtFvlu2XOe/6SarikxNNvdIWsTW95Cg29rWbqxAqfHR7PdzdKNuznV7GBbZV3Ue+X1RXfsSzJqcbj876FOfeZzUFpWxV0Ts6VrzuuWFNW5sWOCgWMNdjrGx/Bq+dGQY5weL0UWE8mxoZ/F9uzgEwwarE5PyBpONTt44r1K3t13itKyKnYfayTDFMt9r+1l/F+28bu1n1Cybif3vbaXuha/kEKAW174b7u27L8klixZgiAIsv916tRJ+r3P52PJkiV07tyZmJgYLrnkEioqKn7AFSsoKCgofF+cU2UK4P333+ehhx5i7969CIJAdnY2t99+O0VFRedzfQo/UhqtznbDeM+mBajR6qTB6sLqcqPXqFmxRd72VWQx84exfbh6/U7+ee0w6ZzHGmwhr19gMXH3xFwe3bqPvl0SSUvUs2RSLodPW9GoBZKNWtweH41WFzmdE5m9fif/um4Yy9uCgAOJViULnGcKJilGy7LLc7n3tb2SgFo1LY8bLulFg82Fqa1NMbiyEDzf9cJ1w8joEMfn1fV0TjLwzPuhG/8VW/a2tZb52NbODNX2yjr+ON5f4ShrmxETW/PEVrnPq+txuNxc0C1ZNusU3FaXnmKkc6KBIRkpTF69XbIfL2jLs5q0uoxB6cnkpScxf2QmKgSpWhNYRVr08i7pNYoyzcwKaJ0MFM5GnZrV0/O4+9XdzCrsGXIfos+O1WF3e2i2u5mW3106b7TnfHioLvp7X13P0J4mNs0rxOb08M9r82lxeBAEAZfHK4Umx+mj//PqcHtxuL0IwJLLc0OyuRqtLhZPzMXuDq2yBs7OhVujtk3kBX/OfAhsq6yTqpg1TfYQi3TxvnmBZZf3RRD85jDttc7+0sjNzWXr1q3Sz2r1mS8nHnzwQR5++GHWrVtH7969WbFiBaNHj2b//v3Ex8f/EMtVUFBQUPieOCcx9Y9//INZs2ZxxRVXMH/+fHw+Hx9++CGXXnop69atY/r06ed7nQo/MmpbnFHDeE802WUCJVw7oM3p4evTVla9e5C89GS+iBLietWF6TTaXNK5wgm57ZV1LN9cwQXpyTy69SBrPjjEulkXUrJuZ8TrsDk9JITZ9CYadSybnMvdr1bIXqfIYmLOiExmrw89Z5HFTLfkGFqcbmYXZmBz+TOlFr74JS9cNwwfcLrVyXPX5Eub3UgYdWp0WhUpsTq8Xl/YTbPV6WH2+k95/tqhLBAErAHVnHBzRSpBkFz9REe9GX/bIT2nwGJiygVdOB0QJBupdbDIYmJQ9xSG9zKxaHw2jTb/c8QWwG2Vtax8bR/3TunLjSN78cfxWRw+baVbspEH39gbWkU6WAs+uPainnx2uB4B+Pf1w2ixu+mcFMOpFn9FaVp+95D70F7LW6vDw/S26xTnmqI9p7Ssio1zC1m6Mdixz8SswgzmbijnuWuGMvnx7bL7s72yjrkjLZRX17P63Uo2zSuIui6DVo3P56PB5pLcCkUKLCY+ra6nn8dLhzidNCcXmIe1duZgmVAVnzerIEMKAw6c2/Nft7+6JFbmSgoyolbP7G4PdqeH0dkdlTDeIDQajawaJeLz+Xj00Ue58847ueKKKwBYv349qampbNiwgeuuuy7iOR0OBw6HQ/q5qanp/C9cQUFBQeG8ck5i6t577+XBBx/klltukR676aabePjhh1m+fLkipn4BtOe89029jU4JBhKNurBVpIsyzSy6LJtV7x6UZo2iV1WyUQtwvMHm36xHEHJllXXSxtPq9JDQFmIaTlx8Xl1PglFDg81JOnJHymMNNh56Yx93T8rB7vJgdXhocbjZdbQRu8tDXnpSSAXt9nF9cHq9gECDzcWto/uwaLyAVq2WDABE7pval6JMM58drg9Z14lGG3uONTE4Ixm700NtgLgJxur08E29v61S3DhHFkBm2cyTKE4D7/PSjRX88bIzQbKRne3qWLFlD0sn5eITfNRbXbIZJvGaTjQ50KnVJBg0bNhxmJtH9Y5of7+tspY/js/i8gGdcXq8tNjdfFpdT3nZIWa0tSUGt2NC+y1vMbozvxcrOtGeY3V68Hi9jO+XRnFQVpSAv7p3rNEW9v4EZp25PT6KLKaw11toMZGWoOetvSfp2zmRFvuZFrqiTDMzh/eQqprv7j8lZWMF2vx/8vVpLr+gM3dclk2DzUWcQUNNk52FL37J+pILKbKY8fl8shyrDnH++ydW5maEEaeBtDo8xOrU3DUxRzGbCOLgwYN07twZvV5Pfn4+K1eupGfPnlRVVXHixAnGjBkjHavX67n44ov58MMPo4qp++67j6VLl/4vlq+goKCgcJ44JzF16NAhJk2aFPL45Zdfzh133PGdF6Xw46e9mSLwV6+AiO2A17U6pU1oe9WFI6etPLfjMDeOzMSgCR3IDyTQxU6vUTGhbyeuubgXf35zn0xcFFpM/GpgV7QG+ca60erk7ld3+93fNlawrbJOCoT1n18dkl/VMd6AQatiyasVss3zyql9eX3X8ZAN9fIte9k0t4CaZocsPwjOhM06PV6+Pm2lc6K8ahGMKAw+r66nyGJiQHpyRGtvLz7JcTCcoNhWWctSrfrs7NcP1nLa6kSvVYWE6gYbRCyZlMPyKX2paXKEPZdIi8ONXqtCwEdKnE56/WsKe/LYtDwOnGz+Vi1vRRYzgu/M50UUOyeb7FGeY+Lzw/Vhbd0LLCZKCjOkWang+xPoQKjTCNw2Ngsf+0JClYsLMvjzWwcYk5tK+ZEGJvRLk7kBPrfjMFanh/IjDew51ihlYwV/fu+4LJsr//qhNIdXYDGxaloeKuDeqX1pdbgpLsjAh18s6zQqCiwm6f1qT4jGGzS4PF7uemU3q5U8KYn8/Hz+/ve/07t3b06ePMmKFSsYPnw4FRUVnDhxAoDU1FTZc1JTUzl8+HDU8y5atIhbb71V+rmpqYlu3bqd/wtQUFBQUDhvnJOY6tatG++88w4Wizx485133lH+4f+FcDYzRaZY/8YrUhUpsDKi16giVo9Ky6rQa1TSHMeKtgydSFidbilfadKANG4b14e7XtkdsnEuq6xj8au7eejXA2SP17Y4yUpLkAmSwA178HyTWJVatqkiRDSlJhjCViasTg8tDg+PR8kKuvOybPqmJeD2+qJmMnWI1+Px+th3rInFE3M51eKIWuUrKciIOvfl9nqZO8L/t92eyK1tcSIIZzblwZWaQHG1ZNMeXrg+NMsqkBidmqWb9nD3pBx0Gp/0+h6fj2e2V0lOg+K1gF8glRYPQRUUfFuUaWbB2D6caLZLj4li57qLe7J8cl/uCaoYFgaE1oZDbI/86FDkLwECPx+FvUxckJ7MrKDgaLE6eNPoTN6sOMH/DepKh3g9zXY3LQ43C8dn4/D4JPG3YcdhWWUqKUZLslHH70p3yKzqxcDmeZda0GpUnGpxSuKupCADm9PDrIIMYtqqmO0JUbUAta0uth2s9c9NKWIK8GctivTr149hw4bRq1cv1q9fz9Ch/oyuwKgB8Lf/BT8WjF6vR68Prb4qKCgoKPx4OScx9Yc//IH58+fz3//+l+HDhyMIAmVlZaxbt46//OUv53uNCj9CEo06lk/uy52v7IqYlzT1gi5R2wEDvxXffayRtTMHh1RpCiwm1s4czCdfnwb8m0WH2ysL2A0kUCRsr6zD54NjDfaIcyHbDtbSYneTGuD43GR3SRUHUeANTk9mUv80VmzeGzKjMrOgB60Od1jRFE2MqFTIKhaBiGt3uL1oVQJ3T8phyaaKkHu9eGKOtPHfcM1QvjhST+dkY8TXFBHfI5FAIdtgdZMSp2PeSAux+ugVSHOcDo1KxdZ9J4HQSs11F/ekpsnuFwH5XjQqgZVT+7Jiy96QgFux/W7bwVpqmhwkGbUktZkeCIIgXXugOBAFSpxezaDuyRQP7yE9lpZoYPfRRi5IT5ZmxcAvdh55+yADuyZTXJDBgvHZWB1uEmK0HGuwUX3aGrK2QNQqgdIyf5BwtMpOaVkVF2d2iChsAU41O7l7Ui4NNgd/euOA9Bky6tS8dMNwTrc6aXa4uXV0HzRqgRa7G5fHS0qsjhl/28FVF6aH/fIhIUaLIAhSGK+4hrUzB0vtg0UWs6wtMbhtdVZhDzw+H81O/yxeczutvb9kYmNj6devHwcPHmTKlCkAnDhxgrS0NOmYmpqakGqVgoKCgsJPn3MO7e3UqRN//vOf+de//gVAdnY2//znP5k8efJ5XaDCj5dko5aJ/TvLNrXit+6Duye3O7AemMvj88ETYao04jftg3okS4+1ONxhg3oDhZyIz0fIbFAwwZvEBIOW4432kJY1o07Nqml5XH9JLxptLtn1Ble3RKJttutbo6+rzurk6fe/Yunkvry2+zgT+qVJ97prcgxv7TnJ67uPMyg9mW2VtTRbXeR0TkCtit661TU5hj+9sU8SDBFnrDLN3Dulb8S5nwKLCaNOTYxWzb7j/kH5QPFojtMxqX/nkOpPUaaZ0pmDKVn/qaw9be6ITFwe//O9Ph8qQSAlTkeRxYzddUbcBFcGAZ763SDcAcFbgiDgcHvpGG9g+aY9UmujyMisDsQbtSEZTAUWE/dMzJWJr2C8vjP29tEqO4PSk4gzRP8ntnOiAbvbDYJKdo+sTg+/XbuD568dyj/e+prrL7FQ1+JCqxKI02vxeH2snj4Qr8/Hh1/VSWHKYhaY1eHmkbcPMLSXSVbVLD/SQF56EvOeK2f19Dwo88nEKUDHBD3v7K3hHx8fZuG4LOkzHH8Wrb2/VBwOB3v37qWoqIiMjAw6derE22+/TV6eX6g6nU7ef/99HnjggR94pQoKCgoK55tztkafOnUqU6dOPZ9rUfiJkWjUcXHvDiGi5qJMMw9c2V9qCYpURdp3vIlFl2XhfW0v/bokygJXA9lWWUtxgG12YoxWFtRb1+qg2e6WtU+JNLWJnmgEbxLNcTpqmrUhLWuB+VDBRHqNaJttjTp6y4/b47c8t7k8PPX+IR6blse67VXSDJco8F6eM5xlm/eQYNRy/+v7GNQjJaIAKrKYaLa7WDAuG7vbS1mUzKVtB2tZtqmCP4zNwkuo6JhV4HcsbLI5uXtCDreO7g0IPDFjILE6DZ2TDNz96u6Q6pvfuc/HxrkFHKxpQaf2mzvYXR6pkplg0DL+L9skoRcT5EoXiFGnJsMUy98/+jqk/XLxpGwG9Ujm0uyO5KQlYNCqOdloJy89ieVBNvxwxhFy8YRsFkWYmYrTaySBIlZ2BORVxiKLmZkFPXiz4kRUMVpe3cDA7kn4wmRb1bY4mfXMTv5xdT53vLxLanH8S5tpS+B5RGMR8fHlk/uy90QziyflMjA9GS8+WXi0QXMYAUFmsgFwssmOShDY/U0j1xb1ouJoI4fqrFyUaVbc/AK47bbbmDRpEunp6dTU1LBixQqampqYOXMmgiBw8803s3LlSjIzM8nMzGTlypUYjUbFnElBQUHhZ8g5iamdO3fi9XrJz8+XPb5jxw7UajWDBw+O8EyFnxuBoqbZ7iLe4A9gFYVUolEXtop0UaaZ5ZP7YtSpWTmlHzXN0Y0JxM1eYMBuorHtdWqg+JmdlBRmsGpantTytOtoA7F6DdsqayPPhYTZJCYadXRNNjKsJ7LNuVGnJiVWx9qZg0PaqiKJJsnCOnieR9qUR95of94W4mt1eKRZn5fnDPc7JSYapJm14w128tKT0WtVfFbdwOCMFBZPymX5pj0hLYlzRmSSGKPD5fFKszwd4vURW9G27jvFnBEWhvRICVuBfPr3g0kx6rC7vdz72l7p+ueOtDBMbYrYxritso6aJgedEgycbnVyrNHOii17WTUtj0KLCaPeL56sTg9/fPFLSouHRJzRu2tCNsvCZCV9Vl3P6VYXn359WibUizLNIW6MwWv74/jskPdTzMJ68j+V3DEhCxBosLpIiNGwYFwWV7c6sTo9GLRqOicZuOKJDwHYNLeQuzfujtgO+9Kc4by3vybsWibndeGul3dJtuuRgoYBqfomtsI+cGV/lm6qoLy6QdYWqREEbhvbhz+/uZ+szomkJhgAfzXvWKOdd/fW8IexfWixu0iO1bNv9wnZlyMK8M033zBt2jRqa2vp0KEDQ4cO5eOPP6Z7d7874oIFC7DZbMyZM4f6+nry8/N56623lIwpBQUFhZ8h5ySmbrzxRhYsWBAipo4ePcoDDzzAjh2h39wr/HwJ3GQ12V20uUdLj5+N4Aps0QqHXqMKqXiJmON0lBYPYdW7B2WiYOXUvuz6xu+GNrswAxWE5AbdO7Vf2E2i1eUmxXhGOMXqNCTHaqk42kjHts0n+Nu0Hp8+kNv//QX3X9k/JPcnLz0Ju8vLwnFZFA+3Y9RpsDrddIjXU7Jup789MEhoBbcrxhk0zB1pIa9bEl4vrNv+NXtPNPGv64ax+NXdfHG0gc6JBlodbqld76n3D1FSmCFV9NISY3hv/0lmr9/JoPQk5l3aW7pXT8wYGPXe11td9O2SyLygql+RxczOr09zaXZHVgaJmbxuSe22VzbYXNhcHsklscBi4mST3V8pcZ1pQfzTrwaw73gTiyfksHyzXCAWZZoZ0DUprPNeSWGGZL0fyLaDtRxtsEVdW3W9NcSKvKbJQW2zgz+MyeL+1/awdd8p6XjxPbvthS+wOj1smV8o3at6qyPkXIFV1Ga7m//3UXiXt8AZtPZCmcU2PfBXZDsm6KVrD37euuIhXBXGIVC8DpXKH5ysVgksnpBDWlJ0R8lfGs8//3zU3wuCwJIlS1iyZMn/ZkEKCgoKCj8Y5ySm9uzZw8CBoRuwvLw89uzZ850XpfDTIlKO1P1X9qdz2yZMqiJFwByni9gOWJRpxtIhjlVB1sxiELDX5+NvH3wl27AatP5so9+XfsLj0wdi1KlCcoNqmh34Iog4taBixWtnZn1uGZ3JhT1S2LzreEiFYe4IC78b1p0NOw5TXNCDWYUZxBs0xOo0CIDX50WnVfHcjsPMH9Ubq9NNg9XFY9PycLq8zL/Uwg0X96IhaA7L6vRQlGn2P9YWBCu2va3bXkXlqRYuzEjhsr5pLNtUwdCeJh5++0DYDXShxcSCcVk8/t5XbKus444JZ/7022uDBFi3vUo2d1RoMbHosize3nuSVocnpLom3uNo6DVnLNWL2lz0Xt99nBVb9rJu1hDmjrQwok8H3B4ffTol4PR4uG1sHxaMz6LJ5iIxRotRq6I2wuxZNPHRHjq1Kuxz37i5KERIgbw69EV1AwaNWmrji9Fpoq7DqFXzhzF9WPTSrqhras9ZMfD3apVAa0B2VTCJRi0PvbU/YpXr7ok5/L70Ex67Ko8kozIrpaCgoKCgEIlzElN6vZ6TJ0/Ss2dP2ePHjx9HoznnMSyFnyCNVmfEHKk/vvhliACKRLR2wAeu7B/yzXiggAv8lr20rEpypXN7/EYBX3zTwKdfnw7bclaUaQ7Jz2m0Olm6UW6aMKJPRx54Y1/Ezeedl2WT2zmRhS9+yf1X9uexrQdkz5/QN5W7Juay+JVdssdHZXXkplG9MWh92FwemXVygcXEssm5+HzIhKJaEJhVkEGnBANfftMoufw53N6IrWtllXXcYHdLgkhoO//2yrqoc12iO2JZZR0Lx2eRk5ZAt5QY3qw4SU2Tg76dEzHqQueZRFEYydJdPO/Y3FQ2zSvgnb01THl8O1anB6NOjSlWLwnIwOeIFbu89CTmjrBgdQq0OMKLhmji42zWFo5mmztESIlsr6zjzgk5flMJl4f5Iy34fILsXod7LZ1WRV56UkgbY1GmmS7JZz73ZyNOxXOWVdYyJrdTxGM1aiHiZ2V7ZR1eHzx79VBsTpfS3qegoKCgoBCFc1I+o0ePZtGiRbz66qskJiYC0NDQwB133MHo0aPP6wIVftzUtjgj5kh98C2zadprBxQJFnDit+zigL7YuiSGoEY1twizxtoWZ9g5pvY2n6IrWri5loyO8dz1yi6ZoDPq1FyVn879b+yVz+ZYTLwyp4DXK45zutVJalx4YbFwXBaFvUwAlBRk0OJwy14zOLcrwaBhTE4qpWVVuL1eZrW1hYmmBMFtkMHthkdO23i2Le9o9buVPDFjoN+WXBf6z4gYNvuHsX3w4QsJrZ1VkMFzOw6T1y2J0u1V5KUnS21xiyfkcM/G0Fyw4Nkg8dgPDoafiWvPtnzT3EKWbKoIETAzh/eQOUIG0p473/EGG3e8vJtbRmcyNieVe7fsZXBGipTbFVrVzEQlQL3NxWV9O8ms3WuaHOjVKmmu7mxEb+D6+3dNpNASfm6tyeYOeSyQZrubx945wH1T+0U9TkFBQUFB4ZfOOYmpP//5z1x00UV0795dsn7973//S2pqKv/v//2/87pAhR830XKk4Ntn07TXDghyAWfUqYk3aCgpyEB7kQqNWiAvPZn9J5oBePbqfFSCQGnxEMksItjyOniN4a7JGqH6If3e6aGkMIPCXqawLV3hWs4iuuhV1rF0cwVDeqSQFKOjxelhRn53Zhf2lK5he2UdT/6nktvGZLHnWCMAE/udybSJaHduMVFaPASQ5zW5vT6WTu5LVW2rtJnffayR3UcbJVOP7ilGFo7LomTdTsAvVrokxeD0eqQNf3Au12PvHGTBuCxusLslO/ldRxs53eLg5tG9aXV4mF3Yk/QUI1q1wK5vGslLT2LRy+Fb3sqrG1g4LksSiDqNin3HmyRhGHgva5rsFGWa+exwfUgY9MlGO18cqWdAtyQWjsuixeHG4/XRIV7P/a/vlSpkgc9LMmoxtIVLR8uhAvj3p98wNrcT2yrr2HuimfF905gYYG3vF0t2OiboUQFxei3Lt+yVvV5qooEGq5Nlk/tyrNFOk93FuNxOfPlNgyynqyjTzN0TczjWYGdivzSuePJDrE4PC/79JRuuGcpnX5+mY4JBuvYTjTYSY6K37hl1asoq62ht5zoVFBQUFBR+6ZyTmOrSpQtffvklzz77LF988QUxMTHMmjWLadOmodUq/fW/JBLayZ45m2wacfapye4iIUaLOTa6oBLFjigYlm6skFVURmZ14Nmrh4Y4vAVaSAduhoPXGHhN4oY6Pib6n4pKgC+ONDB5QOewm+1wLWftGQrcNSGHT6rqWB6wcQ68hj6dErjvtT1Mb2txHJOTKrWuRRNqCAKzCzPIS5e//stzhlPa9pxAMSZzwrOY+dvMITz1fiU1TXbqWhz06ZTA3ZNy+W91A1md46lpcuAFXt99gnkjMznRaCfRqCUhRkOjzcVl/dJ44PW9VNfbJOFwqtnOmOxOTO7fmf01LWHvibimP72xTxZuu3bmYNaWVclaIZNitHROMtAtxYhaJYSEQYuhtKWvVkjVse2VdawrHsL0fL8j2/QwBg1FmWZKi4dQsm5nyHtclGmm/EgD5jgdf5s5hNoWv0PlVRemc//re8npnCgzLznaaOe+1/Zy+9g+6DWqEPFr1KnZeGMBd4fJ6do0r5CvTrWgFgTKjzRw3+t7/esWYGB6EmWVdVidHo412Hht1/EQ45V+XRIjVrkK24xAAJrs0StYCgoKCgoKv3TOecApNjaWa6+99nyuReEnSDTjiLPJpjkb84pgRLETSTDkdE4Ma5Udrk0s3BrFa/r0cL20wYXIcy9FFjNllbVtuUyhAbEAhjA5Se0ZClTVtrJ513GZABRf/9qLenJpdkcEwV+BKSnIwOrwcMeEbP5bXU9qYkxEobbtYC03j8oMqeY0Wl3SY3npyRHEWC0+fKyY0o/qOivXP/sZL1w/FJUgsPGLo9z+onzTPnlAZ7442sBT7x/C6vQwd6SFPccawwqVQouJOy7LJj05/Pse7v22Ov1ugIsn5nBBtySa7W7MGhVqQeBXf/2I3w7tzqdfnw57HV58lBRmIAhn5oc+ra5nz7FGZuR3Z12E7C0gJIeqyGLmnkk5XLXmY0qLh7BicwULxmf572WbaH43wqzVH8b0wep0hVxbSWEGSzZVhM3puvvV3VK7pYjT7eW+qf24bWwWgrCfAd2SeHrbobCCWrv1ALePzYKg/LCiTDOLJ+Yw/emPAUhop61RQUFBQUHhl077Nl5hWL9+PVu2bJF+XrBgAUlJSQwfPpzDh8Nb/Cr8PBGNIy7KNMsej2RjHkh75hWNVmfY54liJ69b+KygSI+DXzjkdUuKukbxmhZPzJE2uKVlVcwqyKDAYpIdW5Rp5raxffjHx/7P/bbKWob1lB8D4PP5/PlJOjVzR1pYO9Ofz1Ra7Heti2TisL2yjmfanPQCr6HQYqau2cn43DS27DrO7PWfctXTHzP+L9t4bfcJ4tvZBLs8vjYjh2TWzhzMk78dSLcUI89/Uk1eejJjc1Ojmlkca7Dx+ZH6tuqMwJIgww7/vajjrld30zFOz2PT8jDq1OR1SyKnc2JYoVZWWcd9r+1D3+aEF0yk99Xq9LDopV0ca7Dxm6c+4qG39lPb6sDq9NC/a1LErKvtlXUMTE/G7jpTYSotq2J6fncMWnXYuTnwi5kB3ZLZMq+Qf147lNfmF3FZv068tecEq6bl0WJ3s62yDrfHR1GmuV3R3OpwgxBqCJHXLfraxc+xSFllHY12N41WFyum9OWyvmkR38N3952iodUpvf9rZw5my7xCFo7NYvrTH1Pb4qTQYiI5VjGfUFBQUFBQiMY5iamVK1cSE+P/9vijjz5i9erVPPjgg5jNZm655ZbzukCFHz+xOjXLJ/fltfmF/Pv6Ybx9y0WsmpbXbjbN2ZhXhEMUO5Fob/Nq1Gl446aiqGvsnBQjC3YVw2MXjsvi2avzeWLGQNbOHMyAbkn85Z0D3H9lf0kQqVVCiBgw6tTMLuzJ2pmDKa+ul8RPybqdlFfXS2JDJNBRLtzG2e31kZZkYHmYCty2g7W42rkHsXp/K+LqdyuZ91w5STFaHn5rHwvHZfHFkQaOnI6ew9Rgc9Gvi998Bh8Rhcf2yjo6Jhgka3WH2xtV7G6rrKXV6Q4rXNtDfN+3Haxl7TZ/zpZGJUR9jkYtyIwqxHBkTzu5Z812FxNWldHicHO80cail3fjdPt44r1KGtrytRqtLpZMym13PilOr8HqcEsi+4kZAyktHoJWHf2f53Cf8xaHG0EFd728i8OnW6M+36BTk5OWAPirmzXNDn6z5iNJSK2c2k8K9FVQUFBQUFAIzzn1cBw5cgSLxe9O9corr/CrX/2Ka6+9loKCAi655JLzuT6FHznR2vQSjdGf+13MKzonxfi/0Q9DexbSMVo1u442khCjjbrGYMezqy5MD2uPDv6NrdjeZ3V6ZPM73VJiqG91sed4Ix9/Vddu+2Gwi554/kDi9Bp8UUTMh4fqIlp/F1lM6NQq/n39MNxeHx3i9FJlacfX9fzr2qE4PO2HKDvcXoosZqwBlZ1wONxeytpsw70+H4frrFGPb20TNKI5htPjpWO8nlh99H+uuqUYmTvSQmlZFdsq6yguyMDUTmUl2ajlzYqTshZOq9ODyxNdjCbEaCXxK743omtkcVur5KfV9QB4fL4o74WZGJ2aWF1MiGPjs1fnhxwfSLjPuUYlEK/XSdcfjVi9mtOtfvGf2yURrUpg3awLSTBoSI7VKUJKQUFBQUHhLDinylRcXBx1df6Nx1tvvcWoUaMAMBgM2GzRv9FW+Plwrm16It/VvKJjvD6kvRD8ttyFEaoaBRYT8QYNxxptsjU2Wp18VdNCeXU9X51qodHqDJkXOZv2wQKLic/bNsWz13/KszsO82bFSdxeH/kZpqgVnDE5qaydOZi89OQQk4zAjXORxUy8XsOxhsh/a6VlVdxzeS5FFvn9KbKYuefyXFweL49sPcBVaz7m67pWaV21LU6qT9swaFQhzw28h+VHGkiM0XLXxGxiwsyDBSKuvcnmIkarJqmdSo34uRDv4Q3/+Jz39p+i/HB9xGpVgcXEmxUnZFW+OL0GjTq0Shj4HLfHJ7VwBn5mdh9r5L6pfWWVIrEds8Bi4r9HGigpzKBjgl6qPImiSrQwLy2rwhSvY95z5cws6BGyjgKLicWTcli+aQ/NDnfIZ+ujQ3VRP8fBWVhFFjNVp1rQqAXZOsJRaDHxZsVJ5jz7OcXP7OShN/eTZNRxYUYKWWkJipBSUFBQUFA4S85JTI0ePZqrr76aq6++mgMHDjBhwgQAKioq6NGjx/lcHwBHjx7lt7/9LSaTCaPRyAUXXMBnn30m/d7n87FkyRI6d+5MTEwMl1xyCRUVFed9HQpyzrVNT0ScfQrH2ZhXRJrX2neskcUTc8NuXmcVZFBvdTAmpxMDuydT3+rkaL2VimNN7D/ZTL3VxcvlR7nthS+IM2hkm9n22gcBZhVkUFp2xrBC/FkjgC96sYdTzQ5mr/9Uqm4FrlvcOBdZzNw9KYfjTTYSoogSq9NDk83J7eP6sHFuAc9fO5R/Xz+MSQPSMKhVPPz2AWnzHnhdRp2abilG7C4Pd0/KCRFU4jXtO95EgkFDTaMDQSCqYBHX3uJwM3FVGclGbUSRUGQxs/tog2xGDPxCdvmWvWHb/0ZmdWDhuCwGpiczI787Rp2aVdPySIrV4nB5wj5HvI4YnZpnioegaQtCLi0ewprfD+Ly/p15rW0Wbc6zn0vtmKUzh3BtUU+Wb97DsJ4m3tlbg93lochilkSjKM7y0pN4Z28Ng9KTZPNpYovohH5pvL77OFv31eAO01ZYWlZFcUEGRZmhgnjuiEzpcwZ+cbTosiyGZKRI7YHR5vyWTe7LyD4dePK3A9tteVVQUFBQUFCIzDm1+T3++OPcddddHDlyhBdffBGTyf9/1p999hnTpk07rwusr6+noKCAESNG8Prrr9OxY0e++uorkpKSpGMefPBBHn74YdatW0fv3r1ZsWIFo0ePZv/+/cTHx5/X9Sic4btmTIli6I8vfilzAzwb8wqR4KBfo85fjfjNUx9x1YXpslyf8iMNzH+unFXT8vjVXz9mVFZHplzQhYUvfhk2VPbht/azYko/KWy3vfbBLkkxnGpy8NCvB9AxXs/BmhapVa9ripETjfaoz++YoA9xDPRvfHOpbXYwrKeJTw+fZsuu4/TrkkiHeA1FmeawgrbIYmbfiWbuCHCcE6+rxeWROcuJ1yVaj4stf6It/PWX9EKr9ucruTxeHG4P8y/N5K//qeSaiy2oBB+zCjKihv6Kosrq9PC70k/4e8mF3Pf6PnlgrsXErMIezN3gf4+CEefWHriyP3dNyKHB6iIxRoNWrWLn16elaord5aWmyUHnJAMJMVoee7dS1nYpfhae31HNrWN6E6NT839rPpZe55niwSF25OCvHqoEgasLM7A6PahVAms+OATAY9PyqGmyS++f2KZ4QdckxvftxIote0NCl2cVZLBiy17p2sJd7/znynllTgE+fDRYXbQ43Ow62sgnX9dJ+V/i9dhdHmxOD5aOcZLDZmC7pMPtJTFGi93lYeKqMuk1X5kz/KyDtRUUFBQUFBTknJOYSkpKYvXq1SGPL126VPbznDlzWLZsGWZz+OrD2fDAAw/QrVs3nnnmGemxwOqXz+fj0Ucf5c477+SKK64A/G6DqampbNiwgeuuuy7knA6HA4fDIf3c1NR0zuv7qfJts53CPS8mjANdIGeTMRUohhptLow6NSqVgM3lodHqPKs1BQf9NlqdZKclhLUGD6yUZHVO4M5XdsnEi1GnJi89mRitmil5XXG4Pdw5IRufD1QqIaJ4Edv7jjXaWf1uJUadmtfmFzG4ezL9uyVxz6u7GZCeHNFevcDir3IEb/o7xOvRCAKPvXOQz6obZNlPovjx+XwyMViUaWbOJRZmr98pew3xdRdPyJE9LraDBduhiwYVq9+tpNBi4oIAK25RDKx65wALx2cz/7lyrr2oJwvGZ1HT5JDO66/IJMlmwGpbnPzqrx/x/LVDKR7eQyYI5m7wtzcGVssKLCY6Jugx6tTcf2V/KRMK4JbRmVzYI4Utu46HZIr1MBvZ29DE4ok53PXyrhAxM7swA6/Ph6bNMER8fqfEmKhOfndcls3ckRZi9Roe+vUADFo1tc12hvcyMbSXibtfrWDbwVrp9S7N6sA9k3L45rSNRKMWjVqgvtWFx+u3Zi8tqwoxyggMDG60uUiJ05Fs1DIrTL6VSE5aAj07xIZ8SRH8nrWXs6agoKCgoKBw9nyvISL/+Mc/uO22276TmNq4cSNjx47l17/+Ne+//z5dunRhzpw5XHPNNQBUVVVx4sQJxowZIz1Hr9dz8cUX8+GHH4YVU/fdd1+I8PslcTbZTuHEltXpYUHA8+aOtFBoMYW1bz6bNj2RRKOOVqeHJZsqvlXeVLTz3Tu1H3e8dKbiZNSpuWtCNv26JvJNvY3S4iEkGDSyDXZgUG1wwOuyybnUNNu5Z1JuW0jwmXUGblJXTx8IwODuySQbtayalsfxNoEliiFAXn2ymJlZ0CNkkwvw+k1FNDn8VttzR1pCxI5YebjhEgsenw+3x0cvcyxVta3SRv/z6npKy6qknKrghrLSsioem5ZHjFYdMZuqrLJOyqAKXH9eejJOtxer08OjWw+y5gO/i96wnibJvl4UVYHXZnV6+KbexpxnPw/7et1SYnjytwPpmhzD7m8aeWdvDUsm5YRkP43o0zGsKYj488JxWdhcHu6elIvH6+No25yZKNxG9DazaHw280ZYpOe1tBNU2+Jwh5hFFFlMDO6RQl2LnWWX51LX6sTrA7fHy4eH6niz4gQD05N56K39IaKvtHgIRxvOmHJE/BxmmsOGToskxWhJbvtSIfhLCrvLw4eH6kKe+23+ThUUFBQUFBRC+V7FlK+9IZGz4NChQzz55JPceuut3HHHHXzyySfMnz8fvV7P73//e06cOAFAamqq7HmpqakRM68WLVrErbfeKv3c1NREt27dvvNafwq0ZxqxaloerU5PWLE1Z4SFzw7XS4+Jm3BAJqi+TZve2a7p27YhuT1eLkhPZsG4LGxOD8lGHcs2V8ja3tbOHCx7TqQQ4G2VtSx+dTcXpCdTWlYltb6pVQJWp4fPq+ulTapG7a9ePdBm3e4XpP7NuTXIpU6syHRJiuG3a3eEbJCLLCY8Xh+2Nrc8Mfw1kMDq0fpZF9IhTsfRBhtWl0cSUnuONco24S12N0UWE59VN0jVD6fHG9EQRKySdIjX88SMgTKBdk1hTzQqgdLiIdjbXvNko42OCXocLi+/eerjsOcEIlqGF7SZI6x+t5JRWR1ZOD6LmiY7png9C17cFXJ8NFMQ8BtfrHqnkqWTcwFITTCQk5bAqml5qAUBq9uDSoAJ/dIoKcjAqI9ecTVq1WGDcO/ZWMFtY/pwqsXJb576iLUzBzN7/acA3Dwqkyfeqwwr+lSCwLLLc6XqWMTP4cFafD5f2FDoIouJrskxMuOIwIrtsQYbf33/qxAh9W3+ThUUFBQUFBRC+dHH23u9XgYPHszKlSsByMvLo6KigieffJLf//730nGCIG+T8fl8IY+J6PV69Hr997foHzHtmUbUNDtYtnlPWGHjCdrIBYqDuybkYHd5iDdoMcedXcvg2a6ptuXs2v0CabS5WP1uJXndkugQr2dpmDymYMKJFRGxMhMoXoosJm4fl0VOWgKrpw/k8+p6OsTrWd62aZ/7XLk/8yhAtInPD2TD1fksHNuHw/U28rol4XB7STJqSY7RoVL5aLL5W96iGWAYdWo6JxlYsqkipPIxqyCDDTsOS+9drF7N8il9aXV4ONFkRxAEKo41MTA9Oex5w1frTDwxYyCdkwws37RH1hZXlGmmS7KRTomhM2CBz++caAixDC+ymFhyeV+OnLb6M7mONDD58e2S6AvG6ohuy251ekiM0fJZdT12l4fZ6z/l7Vsuok9qPPVWJ6Y4HXe/slsSlv26JmJoCw2O1I6p04afndt2sJY7L8um1enGqFOTEqtj7czBbfb4Rh7dejDi81oC7PQ7xOujfg5vuMQitZKKVUCdRkWr0x2xNTZ4tvBc/k4VFBQUFBQUQvnRi6m0tDRycuQzHtnZ2bz44osAdOrUCYATJ06QlpYmHVNTUxNSrVJo3zSi0eaKKGy2V9ZREpRdI4qDUVkduSDMZvx8rKk9I4twiFWW8iMNjMpKDbsxFmeFwrnahSP499sq6yhudkitaoUWE1fkdcGo0/CHF76Q7mP5kYaos1Y7D5/m8v6d2fjF7pC2rrkjLOw8fJoCiymqAUZJYQbLNoUKxsB2vLxuSYzK7ohBq2bxK7tDzCLG5KSGiIjI1bo6EAQu758WMl+07WAt+GDepRbmjciUrQP8bY1zRlj4feknLLk8lwXj+9Bq95Bo1HK80caxehvF6+TzXuGEFCDZgEciRqum0ebi2ot60mx3c1GmmY7xehKNOr44Uo/D7ZXWv/rdSrRqgakDuoRdd4HFxNwRmTjC5GqJwsbl9aISBP59/TC+ONLA8i17sTo9PDFjYNR1ttjd0nvf3rEen491xUNISzKwYvMe2WcmWmts8GyhgoKCgoKCwnfnRy+mCgoK2L9/v+yxAwcO0L17dwAyMjLo1KkTb7/9Nnl5/pYzp9PJ+++/zwMPPPA/X++PnfaynYztmEpEEhzfdoj9fBtZBCParpeWVUXMSxLbFFWCwLaDte269YX7feD9KGtr9Voxpa9MOD3/STXPXzuMezbuDtmczy7MQICw7nF+UeJjwbhs8nuYSIjRUGQJn1U1rKcpYjUjUATfeVl2RKe6R7ce4PaxWcCZGaRo1bptB2u56dLM8L+rrGVWYQbmOB0T+6Uxu7AnGrWAOVbPF0fqmffc5yFmEuAXkEsm5WLUqWUtaQ63lz3Hm0LEXlllbeRA3EwzKUYt0//2OY/83wVo1Cqp/fKrmha/cUSLUxJCg9OTSUsy8MR/DnL9JRYm9ussa8esabLTMUHPmxXHZa8TqXpXYDFJ7ZXtfbbiDGf+Bto71uX2Un6kgfKy+hCR+11aYxUUFBQUFBS+PT96MXXLLbcwfPhwVq5cyW9+8xs++eQT1qxZw5o1awB/e9/NN9/MypUryczMJDMzk5UrV2I0Gpk+ffoPvPofH6LI+CBMleSiTDOxuugfiXAbvW87xB5sgPFdjCwiuRIGOppFql6IbYqvzS/E4wWvzxe1ghQckgqh92PbwVpsTo/U3mXQqvH5fDjc7hCnvl1HG9EIKszxOooLMpiW3z3EMMJf/bIze/2nGHVqSmcOgTbxF7g2MVsoEg63l47xelrbzhmOd/ed4vdDe5CXnszCcVk02FzEaKILXa+PEOEjYnd5KFm3k3WzhqASBNxeHy0ON6mJMTwQRkiJ92/JpgquvainrC1Or1FRWlbF6ul5Mgv2NR8c4pniISAgt1nPNHNPWx7XrwZ3xeP10S3FgNfrk9ov/339MJKMGkkIAXz1aTO3j81ixeY9ZHVOpGOCvx04Rqumb5dEHn5rP5f06Shbc6TqnfhzSWFGSBU0kEKLSXJAhNCKaSDi5zCayD3X1lgFBQUFBQWFb8/3KqZ++9vfkpCQ8J3OMWTIEF5++WUWLVrEsmXLyMjI4NFHH2XGjBnSMQsWLMBmszFnzhzq6+vJz8/nrbfeUjKmgmi0OqlrdXLP5bks2RjqnPfAlf0x6tQRxVZRppmaZofsse9qNmHUqdGoBBaOz2JWkwNBECQxMbh7ctRzt+dKKM6JnG51Rtyc5qUnISDQq2MsAA+Eyb0qtJgoDrD2FokksI422CTxU1KYwfCeJtSCii+qG2Qb4FtGZ6LVCNz32t6QlrtAwwix+mV1eihZv5PFE3JYMK4PVoff8CLFqONEU/QMq8QYLQdONtM12Rj1OKvL37Y5NjeVp97/ijvGZ0c93u3xRnSY02tUWJ0eapodlJZVycTys1fnR5xh23awlj+M7iMTU+VHGhjaMwUBgfH90igOEKXVda0smZSD1eWR2bJfvno7g9KTWTwxB4/Pi0GjkoSUf+0+tGq1JIRKCjIYlO63sd9WWcfWgCwu8LcnDuqRTL+uiTLRHU3YiOed91x5WCfHwrYZsavWfCQ9JlZMBQixvL9xhIWSdTt56NcDwr8hbZxLa6yCgoKCgoLCt+ecxFSPHj0oKSmhuLiY9PT0iMc9+eST57ywQCZOnMjEiRMj/l4QBJYsWcKSJUvOy+v9HAkUHuIm/4aLe6HXqkiK0cmG0aMF6Rp1ai7skXLOQ+yBZhOB7VGBG+eiTDOvzS8i2aiNeO6zdQBMNOr4uq5VsvUOZ87QaHMCfjEVblDfoFVxz8YKmVgItEMPR3Drl5QJxZlMqPZsvUXDiMDql9XpYdHLu3j26nxi9Rr+s7+GnV+fZkiPlIgtgIUWEylGHQkGDc2O6Lbfeo2KIouJXd80sr2yDo1aiFol+fBQHeXV9SEOc6LQLCnMYG1ZaNWm0RZ9s+/yeGWvW1pWxYvXD2fFa3vCrmXl1L68tut4SHVzW2Utyzfv4d6pfUOMTmL1apxur2xmrocpNnLGVGUtN43KpMXu5vL+aVJGVnA1NzAjSqwIlhRm8McXv+SqC9O5a0IOjTYXiTFaVALYXW6yOyVIrYpWp4cNOw5z52U5WF1uTjY50GtUWDrGkRSjZdPcQhzu6MYbSnaUgoKCgoLC/4ZzElN/+MMfWLduHcuWLWPEiBHMnj2bqVOn/mId8n7sBAuPQEe6izLNIfMV7Tl/fZf2oUCziWgW0He/uptVbd/kh+PbOADG6bVMe3pHiCW5mH+0aW6h7PnhBvUXT8xpE14uOsbrOXCyOWw1JlBEtJcJpVYJUW29SwoyIla/Gm0unvhPJbeM6s2aDw5xdWFP8jNS8ELIDNLSy3N5o+I4A7slk2LURRRdBW3tZvdcnsvlq7cD8P6BGuaNzESFEDFby+r0yIxJijLN3HiJhZL1O1k1LS9s1cagbcd+vC08OfD9srk8Ee9XaoIhbJso+EWQzeUJea8MOrWsvc6gVdPSjth0e30kGDQcrreREuf/9y7WcOaf0UARLdro6zUq8rolMS63E3uONaLTCJSs28mgNuv+hlYXt4/rQ3Fzj4D5LAdur5fr/t9n1LY4ZX+niUYdjVZn1HZdJTtKQUFBQUHhf8M5ial58+Yxb948vvjiC0pLS5k/fz5z5sxh+vTplJSUMHBgdDcqhf8t52I9/n05fwUaYHyXuY9v4wBojtMxuHty2Nc6242nzeVhxt92AP4N8+rpeQxKTw6x9Z7ZJjDCiYhAEbtlfiGt7dh6AxGrX3qNiu2Vdcwb6ber/1vZIcrb7L2DTRM2fXmMR94+yIar8/H6vMwqzAiZuyqymFg8KRe3x8upZgdWpwejTk1mxwTUAiGb/eAg3oQYLS/dMByjXs2uIw3YXB7y0pPCGpYYdWrMcbqIc3JFFjNGnZpLszqiUauob3Xi8foQhMjzWe05MTbZ3aQEfZaa26pDIj6fj1idOqSyFDjHFqv3/z4wtDdw5k8U0eVtAc3hgnf7dU3kuot78sjbB7ne7iIuRoNeoyJGq5au42ijjbf3nuD+K/vzz0+qWTa5r+xvIXAmMFwFWZmXUlBQUFBQ+N/wnWamBgwYwF/+8hceeughnnjiCRYuXMiTTz5J3759uemmm5g1a1bErCeF/x3fh/X4uRJogNHeBjjautpzJQxsczofG88W+5mKhdXpYdfRRsb360RxwRmB0SFez1VrPpbNOUXC6vCQYIj+59clOYYrnvgwYvULIE6vkYnScIJx49wC+nVJIs6g4VSzi7kbytk8r5BWp1s2YzTl8e0M72nitnF9AGQi7e8lF0oBtOGIN2hISzBIM0miIOmWEmrRXVKYwaNbD1BckIGP0BmiZZNzcXt9PPTW/hBL9WjzWdGI12tCzFdONjvYf6JZqtSpBQGjTs3amYNZ/V5lSNvi2pmDiddpWLH5TKuhUadGqxa4e2Iuy7fskd6LuSMtEauuK7fs5fZxWTzy9kEabS6SYrQ02lxMbxPrgbg8Ph769QBZGK+Ikh2loKCgoKDww/OdxJTL5eLll1/mmWee4e2332bo0KHMnj2bY8eOceedd7J161Y2bNhwvtaqcI58G+HxfRMobNrdAEdZV3uuhMHVpu+68QysYAD07ZwYIi7mjrSQl57E9sq69q8tRoNerYps620xo9eoGNQ9OcS5L7BaFduOrTzAN/U25jz7OUUWE3dNzGF4LxOnW538vvQTqQKTk5bAqml5lB9p4I3dJyiymBiYniy1qgGsnTlYZhAiCpqiTDPJRp2sAhoYThw8cyUKjo8PnQ7begmEzczaVlmLD1/IfBZATZM9ohOjWOkKFtU6tYo1HxzisWl5eAGHxy+An3ivMuwcmwqBe6fmSsYUgS19T71/iFXT8ojTa2TXGI5tlXXc3vbfeo0Kq8sTMXx428FaWuxuUiP4+CjZUQoKCgoKCj8s5ySmPv/8c5555hmee+451Go1v/vd73jkkUfIysqSjhkzZgwXXXTReVuowrnzbYXH940obBqsrogb4PbWdS7Vpu+y8UyI0cqET7jKk+jCBtHtrYsyzQiA0+tl8aQclm+qkM0w+VvucqhttnPvlL58U2+jweYKaa8ryjTz6denyep8ZqcdrkXNHKfDqFOzrbKOFVv2ct/UfrQ43TLBE4hRp+bVGwtosDrbzU8amJ7EovFZGDQqjtqcUe9JcDhypNcfn9spoglEWWUdN1xiCVlPp8QYZhX0wOvzhVSzlk7OxdZm2BAoqr0+H4O6JzP/uXKuvagnmR3jJDv6cGyrrMXq8kqthsFzcfOeK+elG4bLrjESVqdHqjCOy+3EqSCXzEAUZz4FBQUFBYUfL+ckpoYMGcLo0aN58sknmTJlClptaAUhJyeHq6666jsvUOG7833PV0TKempvTYlGXVgr8rNd1/fd5hR4XUa9mrsn5bCkrWISrvIUaDIxqX8aU/O6hFjQF1pMzBzeg6lPfEhJYQaHapopLshg4fhsrE4PcQYNPq+P+lYnMToNj759gJtG9+aJ/1TK5osKLSYWT8xhyuPbufainhRZTHwWaU4nQPxsO1hLi9ONVqWKahdfb3USp9fw6DsHI1RpYPO8QlQC/OqvH7HhmnxUquj35K4JOTTb3STEaPxVvDAzSVanh9YwM1GB6DUqnr06nya7iwxzLOWHG7hxw+cAskpXYowWtQqq66zE6s/8Uxcoqh+4sj/3vLqbvl0S8Xh97VrMH623SZWx4OqT1enheKONIoup3cqkQaNmVkEGG3Yc5tKsjlGPV5z5FBQUFBQUfryck5g6dOgQ3bt3j3pMbGwszzzzzDktSuH8830Jj/aynr7vdX1fbU7hruu5a/LJS09mdmFPOicZwrboWZ0e9hxr5P8GdcXl9XHXhGzcHh/NDjcer4+PDtVJlSWxarNhx2Gm5XcPmbHxC6ZcHG43f/7NBdS3Oml2uHG5vXx4qI6q2lasTo/Uqja+nz38nE5lHV7OWK0329z48IW1iy9qyz1qcfgrVxGzoCrrsDo91Lc6sTo9qAQVe47VhxVo4j0ZlZ3KX9+vZP6lvWUGDnCm2rVhx2F07QiRGK2a7V/VMj43jS+O1NOrYyzDe5nISkuQBFqSUUtyjI4YrYpfPfUR60sulJ1DFMotDhd3T8zhjpd3cfu4rAivKKegl4nV71aGrT7d/u8v+ed1w/jkUF3UymS91cGGHYeZnt8dh8sTkt8mojjzKSgoKCgo/LiJvmuJwIgRI6irC90kNDQ00LNnz++8KIXvh0Sjjl4d47ggPZleHePOS0UqWtZTozW07et/sa7vSqTr2v5VHV9UN1Cybiff1Nv4w9g+FFnMsmNGZnVg4bhs7nh5FyP//D5jH93GhFVleLw+Zvxth8wBbtW0PFweLwvGZbM+jAgqq6xj+eYKtCo1qQkGstISSDHqmN52Ho3Kb+4iVn96p8ZHtVrP65YEgFGvRiUIzH+unLz0ZNbOHMwTMwayduZgBqQn839rPsLp8XKqJXLrGUBNs51Eo5brLu7J0o27WbFlL7Pa7NwDKbCYuHlUb/705j5yOifypzfDZ2ut217FzaN685/9NRRlyu+rSFGmGafHi8vjY+nmCu7ZtAe708sfxvTmi+p6Zq//lDnPfs70p3ew8vW9VNfbuKBrIkadmq9qWiivrufAiWZe232CSavLmPz4hxyoafGLQ4dHas8M+9oWM+VHGtCoVYyKUE2qbXFSXPoJQ3qmsHxy35DPR1GmmcUTcxAQyOmcyHM7DtMtxcglvTtwUdA1K858Px3uu+8+BEHg5ptvlh7z+XwsWbKEzp07ExMTwyWXXEJFRcUPt0gFBQUFhe+Fc6pMff3113g8YeyJHQ6OHj36nRel8NPgXCzXfwpEuq7SsipenjOcZZv3IADTn/6YksIMmaMfwLLNoeYJYkhtcJgv+I0dos0I2QMqIIHOjIFzWVZn5OqGiMPtpchixqBRs+dYE4Mi2MWPyupIjFaNTh39u5YO8XoeefsAt43pwyNvH8SoU7P7aCO3jOrNvBE+4mM0eLw+3ttfQ22LU8rOimTMUFZZR0mLkzUfHGLj3ELu2bhbdh8vyjRzz+W5HKmzylrsPj9Sz6dfnw4N7D1Yi8/nY8WUfgg+H79e8xG1LX6BHzj3JVaYNGoh7IyXePySy3OZtLqMYT1NLBifhd3lCZvZdaTexv1b9nHnhGxuH9eH2+mD3eVFp1bxnwM1THl8uzTzdt/UfnRJNnK8wcb4fmnMHB6QNdXO+6nw42Dnzp2sWbOG/v37yx5/8MEHefjhh1m3bh29e/dmxYoVjB49mv379xMfH/8DrVZBQUFB4XzzrcTUxo0bpf9+8803SUxMlH72eDy888479OjR47wtTuHHzQ9pud7enNa5zHGJRLouq9PD8QY7Q3qk0N1kZFB6qBhZO3Nw2OqQKLTCBRW3bxF/xpY90JkxeOPf3pxOYoyWxZNyaHW6GdQ9mYHdk/jk0Gk6Jhik2aW6ZjtDMlL46Ks6fIQ68YkUZZr54MAp3t13ij+M7iMTiY9uPSgdJ7oPutqc8tq7VntbuK7d5Q4J7bV0iMPj8/Fpdb1UZQPo3zVJ9pqBlFXWcbzRxpHTVh64sr/kwChekxiq6z+2lkHpydKMlzyvy8EbFcfJS0/io0N1jM1N5ao1H0tOgMGtklflp3O4zkrxup2A3BjkoV8PkK6na4qRRquTBWEqoUDYUG2FHw8tLS3MmDGDp59+mhUrVkiP+3w+Hn30Ue68806uuOIKANavX09qaiobNmzguuuuC3s+h8OBw3FGRDc1NX2/F6CgoKCg8J35VmJqypQpAAiCwMyZM2W/02q19OjRgz//+c/nbXEKP25+KMv19ua0vuscV7Tr+rS6ni6JMax8bS8zC3rgxRextS4QsYoUzjK7PREUmEcV6MwYaO5QUpBBh3h9VHtwu8uDy+PB7fEiAD4Etuw6LqvoFGWa6Zpi5M9vH+BPvxrA3BEWIFgsmJk7wiIF+zY73GFFYuDzFrbNI7V3rXqN3xjD6wvNzHplznAyzLHsP97EuNxOgF+kGLXR7eHrrS56dohDr5W/tlgpE9+bNR8cYvX0PCjzhRh4zCrM4O29JyRb+gszUkLuf3De2OIJ2dJcXbBzoSiS4Odb4f0lcOONNzJhwgRGjRolE1NVVVWcOHGCMWPGSI/p9XouvvhiPvzww4hi6r777mPp0qXf+7oVFBQUFM4f30pMeb3+b5UzMjLYuXMnZnP4uQaFXwY/hOV6e3Naf/r1gKi/P5tv+aNd11c1zUzqn8ail3eFzUkKzqMSCawiBRPVRt1iIjn2zHojOTMatGpcHi9LJuWyZONuWetZgcXEzIIePPdJNeP7diI7LQGjXsNt//pv2NY4r8/HVRemc+OGz3li+kDuvCwbt9eH1elBoxIoq6xl1rqd5KUnUVKYgUYlMLynKWL73vbKOpxur2QFHulaCywmTjbZmTsik/f214T8Pt6gJdGoY9nkvjTaXBRZTAxIT8bj84V9XRG9RoXb68NlD9Oa7Pby/CfVlBYPodnupsnu4ubRfVgwTkWLw0WiUYfT7aGh1UVO50TJPCRwXk0uvMzcOqY3q6blkZqgJzXRAAIhwj5wFurHFKqtcPY8//zzfP755+zcuTPkdydOnAAgNTVV9nhqaiqHDx+OeM5FixZx6623Sj83NTXRrVu387RiBQUFBYXvg3Oamaqqqjrf61D4CfJ9W66Ho71v8etbv/u3/OKG/c5Xdsk2/SOzOnDzqD58U28FwuckzR1pCVsdsjo9bNhxmNvGhDrGRZvTWTalL6kJBtnxogOiaOO9YvMeaR1GnZrFE3L44/hsquut6NT+bKoNOw5z7UW9SInVYXO6aLG7I85pba+sY3ZhT1Y7K/H4fFz2WFnE4268xEJZZS2FluhfrLi9PhaOy+LJ/1RGdBFcPCmXL6rrsbs8PPX+IdnzA8W5Uadm8Su7mVmQgVGr5sNDdRHDj0UBNyYnFUEQQn5v1Kq5/8r+PPCG3BSjKNPMovFZ/H7tDmnOKvCccXpNyLyUKFpn/G0HeelJ5LWFHr8+vwi31xfRrfLHFKqtcHYcOXKEm266ibfeeguDwRDxuODPnM/nC/s5FNHr9ej1+vO2TgUFBQWF75+zFlOPPfYY1157LQaDgcceeyzqsfPnz//OC1P4afB9Zz0F0963+E0B80XhONtv+RttTtnMjlGrJiVOR22Lg1idhtLiIbJsJJHSsipem1/E3a/ulgnMokwzC8dloRKEELEltovdNSGbW0b1pqbZIQX0NtvCrzfRqMPu9nLrv/4rEwFWp4dFL++iyGLi9nFZfFNvk+aLStqqSSun9KOuNbq5gUbt3/C1N+Ok06ioONrIhH5pUY9rdbh5/L1Klk/uS4PVwe1j+7BovIoWh5tYvQa3x8vxBjv5PU0s37xHdk8DxXmj1cnxRjtb99Xw4aE6SouHUFpWxYZrhuLDJ6u0ifNaG3Yc5rK+nXB75RWsAosJj88X3k7+YC349nHr6N7c8fJu6fEii5nbxvahZN1Ofju0OwvGZ1HT5L+XYqByXnqS1A44uHsySUZt1L+HH1uotkL7fPbZZ9TU1DBo0CDpMY/HwwcffMDq1avZv38/4K9QpaWd+duoqakJqVYpKCgoKPy0OWsx9cgjjzBjxgwMBgOPPPJIxOMEQVDE1C+M7yvrKRztfYsfOF8UjrP9lj9Or5VVex6blsef3tgXUokQHeHEzf/g7skkG7UhAlOjEhj/2DaemD6Q2YUZ4ENWSclLT6JjgoHfl34iExJTL+gScY0tdnfUHKjiZgdznv1c9vj2yjqcHi9GXfT7lGz036f2ZpxidGrunJBDslEbURCI1aFtB2tZ/MouBoQx7hDZeONwHvr1gLDiXJyFm3ZhOuAXjrF6DVanh6vX76S0eAg32N002lySGH1+RzXXFPXCqFNzpM4mvU5Rppnlk/tidUa7h7XcNCqTtTMHS22c/sqem9oWJ49uPciaDw5JxhJ53ZL4e8mF/OfAKUlInU2F9oeo8Cp8Ny699FJ27dole2zWrFlkZWWxcOFCevbsSadOnXj77bfJy/NXnZ1OJ++//z4PPPDAD7FkBQUFBYXvibMWU4GtfUqbn8IPRXvf4ifHnp9v+QNfpz1zBTEMtyho8xvsLji4ezJWl4fbXviCksIMZhVmEK/X4PL4Q3gDRdnZrLe9Kl2kqlKrw41Rr4k6uySOIUWd58o0U9vswGP00sMcG1YQiNWh+c+VA20ir63NLxyxem1YcR44K1c8vIf0uIozjoNXrflYEjYOt5dhPU0UWsyY4nT8bu0n/OnXA3hixkDJdjzZqKW+nSy0miBBunbmYJIChKPY6nlRppmVU/vh9HgZldWRqRd0+VYV2v91hVfhuxEfH0/fvn1lj8XGxmIymaTHb775ZlauXElmZiaZmZmsXLkSo9HI9OnTf4glKygoKCh8T5zTzJSCwg9FolHHA1f25z8HTtExXi9Zep9ssjOidwdSEwzn5Vv+wGpBOAc+ke2VdSwcl0VetyQsHeJIi+AWKJ7v69pW2ayVOU7H32YO4YvqhoitbZFor0oXqaoUb9AiCISdXRLFj1Gn5u1bLqLJ7uKKvC7cs7FC1ppYlGnmmqKeXP+Pz9g0txA4IwiONtj4us4qVYeCRWIkigLEY7C1vUYQ+OxwPSAXeGq1jxWT+3Ks0U6jzYVBq+bz6nr2HWvizonZHKmzMm3Nx9S2OGl1uGXC6MIeKd/qHoqhvVMv6PK9CJ//ZYVX4ftnwYIF2Gw25syZQ319Pfn5+bz11ltKxpSCgoLCz4yzFlOBDkPt8fDDD5/TYhQUzgYf8NqXx2Vtchdlmrm4dwcg9Fv+hBgtsXoNLXY35dX1Z507JZ7nQE1L1OO+qbfx/CfVktV1tPOpVfKZqdoWpxT+e8MlvTBo1STGnN3mPFqVrrCttS6YwGrXfa/tDclyKj/SwD8/qWbF1H602N2oBAGP1y9YWhxurE4PBp2KDw6c4vp/fMbg7smy6lmiUUdtizOkvTCQtMSYEMOIQouJ+6b2k7XzBYu3x6blsXRjBYPSk5jYL40TjXYMGi13vbIrxN598YQc/rL1ABdmmLj/yv5s2HE45H40211kmGPbbU8U13fb2D6sfvcg5sIMRfgohPCf//xH9rMgCCxZsoQlS5b8IOtRUFBQUPjfcNZiqry8/KyOi+ZUpKDwXZHavYKc24KtzwM3u8cabNz2whfnlDuVaNSR0s6muVtKDHdNyDmr9acmGLj/in4crrPSYHMRq9Pg9nox6tRo1Cpi9WriDJqz2qhHm7VZMaUvyzfvkR0fXO1aOrkvC1/8UlZ1K7CYWHp5X+58aRdb99XIHhfb9USDhYJeJpZN7huy1mgiryjTzO6jDQxIT2J2YQaJRi06tQqby4PD4+Vkk527X90d4oYoWrb/veRC7np1N9sr65g70sLaskNh7d2Xba7gnkm5nGiw89S2r1gwLour1nwsO060Wg93D0V3waraVp69Op94g4Y1738V9noVFBQUFBQUfrmctZh67733vs91KCicFd824PRkk52F//4ixAb8XHKnPj1cL5vJMWjVnGi08e6+Gh55++BZCbRjDTb++NIuth2slYwtguexCi0mVk7tR7optt37EViFa7S5MOrUqFQCTo+X2UU9mZbfHbvLI80JBRKrU3NZvzSKh/eQKlMAK1/bw7v7TsmO3V5Zh16jklXfbh+XhVEXGpgbUaBkmrnxEgsl6/25PBdMS+Kht/aHWJLPHN6DD7+qC2kN3F5Zx7FGO+XVDcwdaWFsbmrE9suyyjpqmh10TYnh8+oGalucEefRgiuZsXoNOrWKequT7ilG1CoBtUrg3rbKmYKCgoKCgoKCiDIzpfCToj3ThXqrk0arU2oX+7q2NWKe0rfJnXrgyv4cPm1l1bsHQ0JaZxX2wKhTtyvQggOHIxlblFXWcefLu3ngV9GFmThX1OJwkRij45G3D8gqdmI16bYXvpCExEWZZml9tS1OFr0kdyRbO3NwiJACv6C8eVRvWuxuGtpmkzZ+cYz9x5tYOrkvnZNiZHNOiTFa/vTrAW3HO3G4vMQZNFy15mOsTg9zR1oiWpJ7fT7J1COYJrtLEqA5aQkR7w1Ao82F0Haf7a7o82jh2va6EytdU6PNhS/gWAUFBQUFBQUF+BZi6oorrmDdunUkJCRwxRVXRD32pZde+s4LU1AIR3uGAY02F/OeK+e+K/rxx5d2STbakTjb3CmjTs3j71aGbv4ra/FyZvMfTaAFV9WiGVtsq6zl69pWgLCCKnCu6JbRmXSI01Nc0INp+emSCUNpWRVQJRMm4voATodxsnN7fcwdaZFV33YdbeCSPh1Dgm1FsbZyyx7+OD6bRS/vCmmlXDm1H0/+5yu27q3hiRkDJVE3MIo9+vbKOkoiOP51iNPzyNYDsmOMOnVIxfDz6nqMWjUNNhd53ZLokhTDK3OGS1WnmmY7Vpcn6uxcuNmts20PVVBQUFBQUPhlcNZiKjExUZqHSkxM/N4WpKAQjWjzOKJhwAcHazlcZw2x0Q7H2eZO1bY4Q+a0RII3/+EEWqPVicPt4YkZA6XNvtMTPRC3weYKW+kKrHAZdWrG56axdHNFiNARM7CChUmDzcmSTRWSm5+IUacmwxTL3z/6WiZ0Vk7ty0NBQkq8boCF47KoPm1l2oXpzCrIkITcBwdrWfTyLoqH9+C3+d1JTTCwaV4h9a1ODNrQ9sBAwtm6F2Wa0WlU0uuWH2lgVFZHrspPZ8OOw4BfoNpdHob3MpGWYOBYvQ27x0taooFWp+esxVFwFVHk27SHKigoKCgoKPz8OWsx9cwzz4T9bwWF/yWR5nGC84wabH5BEy0n6dvkTn2bTKdggRauwlFgMTG+b2pIFUgUIlanB4NWHbbSFVjhKinMYHmQkAJ5BlawMHG4vHx2uJ7bx/ah0GKSDBxKCjNYFuZcqQmGiK2S2yvraLG7mf63HbJrE4XctoO13HBJL6xODyte2yOde+3MwVHuJiTF+O+hWHUa3tOETqNCJQjMHWmhtKyK0rIqXr2xgPte38v0/O48s71KJgILLSaWT+mLy+u//m8jjr7tbJ6CgoKCgoLCL5PvNDNVU1PD/v37EQSB3r1707Fjx/O1LgWFiJxNnpFY+Sgtq+KxNtOEYKODb5M7dbZ5RIUWEwbtmWyiSBWO8uoGBATKq+tD3PQem5bH8zuqEdqmdIIrXYHC7tu2yxVlmvnwUB0lhRk8uvUAxQUZ+NqOjdR2GCn8V0QUroGvC2fCjN1eX8h8VDSRW2Ax0TnJwLt/uBgfsOTV3WHv0fznymm2u8npnBhx9uzuVypYObXvtxZH7Ynns20PVVBQUFBQUPh5c05iqqmpiRtvvJHnn38ej8e/eVWr1fzf//0fjz/+uNIGqPC9Ey3PyKhTY47TSVWX+c+VU1KYIeUpJcZo6WEyRgzYDYc5Tsd9V/STBQXvOtqAzwdDeqTg8fp47pp84vQa7nttr+T8FmkTX1KYwb2v7Q1bURKAOy/LwefzYdSpSTbq+KqmRQqwTTHqJBc9YzvtcnF6DT4fPFM8hBNNdvIzUpi4qoxV0/IoLauif9ckbhnVm3kjfKhU4WMNIoX/Rvt9oJBLNuokBz6xCher0zA6J5WH3tgnq3oVWUzMKszA5fHRMV7P3OfKQ6pigWLN6fa2O3vW4vTIDCjCESyO2hPPZ9seqqCgoKCgoPDz5pzE1NVXX81///tfNm/ezLBhwxAEgQ8//JCbbrqJa665hn/961/ne50KCiFEmp8qKczgr/+pZPHEXJZvrqCssk7abBdaTKyY0g+ry/2tXqvV6ZEFBRt1atbOHMwT71Xy6NaD0nFFFhMzCzKoa/VXOgIrHIFGCbE6DQPTk8lLT5ba+kTKKus43mSnc5KBv5cM4a5XdocEFJcWD2FHVR0en49otDjczF7/qX9tmWaG9TQBfqMJ0RXv0a0HMerU/L3kwrDnaK+KFC4cGPwVrQKLCY2A9Fri+2DUqXmmeLDfmj0gNPhkk51YnRqtSohaTdpeWccto3pj1GnCGmkE0mx3YYrVRz0mWBxFm837Nu2hCgoKCgoKCj9vzklMbdmyhTfffJPCwkLpsbFjx/L0008zbty487Y4BYVoRJqfGt4mGB54Yy8XpCczK2CzXn6kgRWb93DnhOyzfp1Gq5OF/5YHBZcUZrD6vXDufnV4gSWTcoEzFY7ATKlAQXHXhGz+ed1Qvqm3odecmZmyuzwcrbfROdHAZ9X1stf44GAtPuDOCVk43T42XJ0v2ZUHzlwFC51tB2u5+9XdLJ6YQ5fEGO5/w18ZE9d26FQLRRZziNFGaVkVpTOHoGq7PpEii5mZBT2kObVgkmK0zCrIQK9VUdNkp6Qggxn53TFo1fh8Ph4L444onveuidkhOVPBuL0+vD6fNF8ViYQY7bcWR9ECkb9Ne6iCgoKCgoLCz5tzElMmkylsK19iYiLJycnfeVEKCmdLcOBqvEFLg80ptX6Fy0wCuGVMb7461RLVGlukptnBgPQkigt6SC1+5jhdm/V4KNsr6/B4/RUjcRPfv1uSbK4nUFzd8fJu6blFFhOrp+ehFlS4vV5OW11hM5f2Hm/CoNGwYvMuyUACAmeuDrNgfDav7z6OUaeWhMkHB2u5c0I2Xh8y44lntldxYUYKc0b0wotPJnLy0pMQBP9xgVUkgOd2VIcVPUWZZnqYY1m5ZQ+3jekDgFoQSEs0oFGrsDs9XF3YM2xlbltlLV5f+612rQ43O6rqGJOdKjPSkK3DYkKn9q/124qjcJ8tc1z7nxcFBQUFBQWFXw7nJKbuuusubr31Vv7+97+TlpYGwIkTJ7j99ttZvHjxeV2gwk+HwNDWhBjtWQmV80Fg4OqxBhsnm7ztmiYcrrMy59nP280NOt5gQxBCjSKKAkwQwomJwMfunJCN0+OVPT9SYK+/8iNw54RsNu86Tk5aAnndkkLO/8CV/bnzlV1hZ65UCBQX9GDK49vJS08KWWeLw02zzS3NMIktu1i2jAAAnUdJREFUhwkGDb8v/UQ2XyZW82at28nq6QOZvX6n9FpGnZqX5wzH5vbI1lFoMXHf1H50TorhjsuyqT5tZevek0zP785Db+2XCcrFE7J56YbhNFhdxBk0nGyys/DFL2lxuOndMS5sNUms6CXGaMnPMFHT7OCeSbk88Po+tu6rkY4rsJhYPCmX+lYnakHA7fVx86hM7piQjVoQUKsETO18RsOF+SooKCgoKCgoiJy1mMrLy5NypgAOHjxI9+7dSU/3h6JWV1ej1+s5deoU11133flfqcKPmu8j4PTbijPROW9AtySp1S8SYmUlWm5Qo9XJfw6c4rUvj0Vs5wtXNQJIiNHwdW0ri1/ZxbbKOp6YMVD2+/ZMEwQBnv+kmgeu7B9WGHZM0IdtkROfv2B8FiWFGWGDe406DfF6LeVlh2RrWDtzMFanR/aYOOe1aloesTo1m+YVgE/g3f0neer9Q7Q63OSlJ0viK8McS7xBQ9dkI+Cfm1r1XiV56cmSeDTq1Fx7UU8uze5ITZODY412qT1xYHoSz149FEHwhW21C5xVC6zoFVhM3D42i98O7Y7V5ZFE4Nd1raSnGLmr7X0QET+bilBSUFBQUFBQ+C6ctZiaMmXK97gMhZ8y30fA6bmIs9oWJ58drmdwj2S6JMVEbP0KniWKlBtU2+KkY7w+asZSsPU4+FvcalucrHr34JkqjFbN3JEWBqYno1EJdIjX8/Kc4TTZ3Ow8fDqk1e2behuPTx/Ix1V19O3sb6kNNLCwOqLPEx05baW8uj4kuLfAYsKgUbH41d0RxZiIOU5HafEQWh1uDFo1GrWK+lYnWrWKMdmpDO+Zgl6jlomvJ2YM5PlPqrn/yv7E6tS0ON3SfVr9bqXU3riuzfhCJNDufPnmCu6b2s8fdOzysHRyLg63lyarC4NOzYNv7At5X/3Xso+8IKv4LfMK2XO0MeQ9VMJ3FRQUFBQUFM4HZy2m7rnnnu9zHQo/Yc53wOm5irMWh0uaQ/p/Hx3mbzOHILBfZqgQHO4rUm91Ul5dL6uANdld7bYLBlOUaWbZ5FxaHR5ZO1tSrI7/hsmUmlWQwZ5jjWFbBle/V8nUCzpT0+wIMbDYNK8g6jr0GlVIcG+Rxcyswh44vd6wIrP8SINkQGHUqSktHsK+401c0C2Z5ZsrZIKk0GLijsuyEYLc1DvG65k5vAd7jzfRJSmGVrsHo06Ntm1u6bqLe1LTZGdWQQbT28woxKpUYAWtxenhm7YWS6/PR2q8HrfXx6lmR9i1wxmHP9F+Pcmopdnhpk+nBNncmMgHB2s53mjnUG3r/7QtVUFBQUFBQeHnw3cK7VVQgPMfcHqu4iwpRseDb56ZyZn+9Mdtpgk9iNNraHG4Q8J9RRptLslCXKyAJRi01Fujrz3JqGXtzMHSfFFNs4NPq04TG2CeUFKYwZ/e3Bd2vgmQWuBEISFWzrYdrOWOy7KJ1ap5Y34R27+qlRzxdGp1WOc9o07N4ok5pCXG8Py1Q9GqVejVKjQageVTcqlrcfDNaVvYayktq+LlOcNZtnkPF2akYHW4cXt9LN1cETYQ977X9rF0cq40ewVIokktCNRbXeg1Ao9Ny0OtEjDq1IzPTQs5X2BVSqygNdpcTHt6h3TMK3MK+PBQXdj5sUAC30fx3Esm5XLdxT155O2DIccfqm2Vssq+a1uqgoKCgoKCwi+PcxJTHo+HRx55hH/9619UV1fjdMpzXk6fPn1eFqfw0+B8B5yeqzhzeryyalBJYQaD05NJNGrRaVS4vT4GpSdDIbK2unBtf3988Uv+9OsBJBjUFFlMYVv9Ciwm3tt/KmT2ae3MwbKfo81HBbbAlRRkhFTOWuxuPB4PcQYdm3cdl11fafEQEJCEZ+A80aKXdkmvUZRp5u6JOZxsspNg0GJ1hK+2WZ0eTjQ6yEtPZkK/NO7ZWEFJQUbU2SynxxtizlFoMVFckAEuDzaXj2e2V5GXnsxdE7JZHkaYBVfQAGL18jBiu8tDaVkVL90wPOxaIrG9so5lmyq4fVxWWDEVGDistP4pKCgoKCgofFtU7R8SytKlS3n44Yf5zW9+Q2NjI7feeitXXHEFKpWKJUuWnOclKvzYEe2/w3EuAafnKs5aHP4gXrElbs+xRtw+Hw+9tZ8Jj5Ux4287mLVupzRLZNSpJfESbHP+wcFabA435jg9c0ZYKLDIDS2KLGZmF4Y+D/ymC2LQrfhzNM4ICA156cmyyplRpyYxRsfiIOc+q9NDybqdTOqfxqZ5BTx/7VBenVvAE+9Vhgi/bQdrWbLJ36Z372t7SU00MDKrQ8g6jDo1HeP97YhWp79N0emJvvbTLc6wVatntlfhxUeMVs32yjpKy6ro1zUx6vxZXrck9BoVhRYTp5od8rXp/W16To+/XTEckQKEI71muOM/OFjL8SY7je0EASsoKCgoKCgowDlWpp599lmefvppJkyYwNKlS5k2bRq9evWif//+fPzxx8yfP/98r1PhR8z5DjiNM2gimkcUWkzEGcJ/bEURJtqOBzvIieYNDreXWJ2a1+cX8coXRyPamyPA4ld281l1Q4hd+MkmO7uONoZ9nlGrRqMS+OP4LGqaHHRMMES9XrE6olEJ5HVL4qFfD8CgVXOy0UacXk2r0xNREHSIN7D7m0b/a/hgVmFPBoTJbgqsgC3fvIfbx/XB4fbKhNDiCTn8+a393DY2C1vbczvE6aOuPTk2/Hu7vbKOOy/LprXNKMPq9PBNffj2wkBqmuysmNKPd/efCPndyKwOxGjVYbOw2gsQtgW9T5Fm5wAOnWpl5Za9Z9Xy90PFASgoKCgoKCj8ODgnMXXixAn69esHQFxcHI2NjQBMnDhRyZn6hRIt4PTbbjhbHW6KCzLwQchsTXFBBq1tFSgR8fyNNifPXZOPTqOS2uYCHeQ27DgM+NvuxFmoEX068v8+OhxWFNlcXknEnLEVPyPKeprjKC1OkgwUrE4Po7I6kBSrY+fXpyW3urkjLe06CxZZzBw42SwP8M00M7SXiRZH+LbG6y7uiUGrkrX/iecMZ2ghVsC2VdYyqyVDZmmu16jokhzDopd38duhPTDF+98fnUZFgcUUttVPdAY0x+mobQmt5HxdZyUl4H0Ww3Mj0SU5BrUgsHxzBXdNyOEh3UFp/e/tr+H2MVks21QhE7dxeg0atUBSjI5Jq8vCi2Ig0ajlnVsvptnuwqBVs3nX8YgiWq9RnVXL3/cRB6CgoKCgoKDw0+KcxFTXrl05fvw46enpWCwW3nrrLQYOHMjOnTvR66N/k60QmZ/6t9zhAk7PZcPp9vrQCAK3jenDH8erqLc6cXt8fF5dz/znytlwdb507DenrSx66UtZ5ebZtt+L4qGkMIMNOw4zPb+75IYnUmgx8beZQ5j+9MeyjfVFmeaQ2axgRz0RUbxs2HGYuybmcOcrctvx0rIqHpuWByATVAUWEwvGZmFzuimymLE6PcwdaZGE2baDtdz9agXLLs8Ne59G9OnIA29ENrYIzsAKnA+yuzwhIcTzR/UGwOry8N+9NRRZzJxoc94LPK+49lkFGRyus/LAlf1lpg8iBq2aWL1aEpJi62M4YVZkMeFwefH4fLyz7xS3jfHwwnXD8AL1rU58Ph96rYrPqhtkWVgb5xZwqtnBtoO1DEpPClvBK8o0E6NV090UC/j/zr480hBWSAW2/kUzO/k+4gAUFBQUFBQUfnqck5iaOnUq77zzDvn5+dx0001MmzaNtWvXUl1dzS233HK+1/iL4Of4LffZbDgBmYA0aFT85e39ZHSMl1lcJ8Voef6TaqxOjzQzdbTeysKXvgwxnUiO1fHEjIF0S/EHx4oOcGLLXyBllXUIwv62TbmTeIPf9S8jxUi9zSWrRGnVKnQagZKCDGYX9sTu8kjW3s9/Us31F/fCHtQ6B/4Wt/nPlVNSmMHC8dnUW52YY3XE6NTcu3kPW/edko4NriptO+g3eYgkQiKZQwRnYAXPBwUKqwKLiZkFGTTb3dLv1nxwiNXT8zDF6pnW5ooYWMUSXRFXTx9Ix4TQL1AKLCZ8Ph+Pbj0gVRkDRWVwi95tY/vQZPVb0Rt1alQqgRWb98iPyzSHrbgBrPngEI9Ny8NLqOi7Z1IOSTHyObs7J2Qzs95vvS5WFvPSk0Ja/yKZnZzvOAAFBQUFBQWFnybnJKbuv/9+6b9/9atf0a1bN7Zv347FYuHyyy8/b4v7pfBz/ZY72obz08P11FtdLH51t+yYQouJxRNzeeCNvWErSI+/exBznL918HCdVSakgqtGYnudw+2N6qi37WAth+usUnWl0GJi5dR+JBk0/Pv6Ydz32l5Zu2CwKBOrNIlGLc02d9jXEKspOWkJzHn2czbOLWDxK7sjhM/Kq0pNNnfY6lDwHFAwWo1KchZMTTCwde9JjDo1g7on08MUy7NX55MQo8Ht8dFkc+P2eiXRlZeexNwN5bxxUxED08Pfu8I2wdQaFCAs3g+1IPDOvlN8dOg0JYUZ3DKqNzaHhxVT+nK8wU6DzSUJs1XvHGDeqN542649WEiJ75PX55Pdm/f21zA2p5NMsAaLPrfXJ/39hPvSoshi4pU5BbxeEdr6F8ns5HzHASgoKCgoKCj8NDknMfXBBx8wfPhwNBr/0/Pz88nPz8ftdvPBBx9w0UUXnddF/tz5uX7LHW3DWVKYweJXdoW0ZZVV1rF8cwUXpCfzbkDFpqyyDoH93H9lPxKNOr6qaaHB5pKdL1jkiJUQo1YtOzYcgY57ZW122ndPyuXe1/bKBE646pb488JxWWjamQsKrAhFC58NrCrFG9Q0O1zccVk2LXY3CTFaYrQqWhzRxZRGEPh9UOZSafEQOsXroS1s95t6Gx3jDSTHajnd4uTuiTl8eaSBgelJvPDpEVqd0efX1IJAvEHD6zcVUVXbKqtaPfTrAcAZIVlaVsVr8wtZ/MrusO14DrePheOyGNbT1K6VvMhT7x9iXE4nijLNbDtYG/K8IouZy/unAZG/tNhWWcfSzRXkpSeHtHpGcqI833EACgoKCgoKCj9NzklMjRgxguPHj9OxY0fZ442NjYwYMQKPJ/omT0HOz/Vb7sQYrRTo6nB7ZS1xl/TuEHHDXFZZJ1ViAtlWWYvd5Rc9TXaXTJiEqzxZnR7++OKXvHDdsHbXGngugKzOiXxTb5MJiPbyogTBnzXQntkEgLUdISSKu/F9UzFoNawtk4s4sXomiohgCi0mPjwUKvpUwL1T+7Ho5V0hrXZ3TczmRKMDt9dHeoqRReOzOdHkiFjxmf9cOaum5dFsd9M50cDzn1TL3ByD76nV6ZGZegSzrbKWO9QCakGIem+Cz4ng48ZLeuH1+UIE340jLGjVKmkeMdKXFsEirT0nSjEO4IMw5zuXOAAFBQUFBQWFnybnJKZ8Ph9CmA1PXV0dsbGx33lRvzR+rt9y69SqkEDXkVkdePbqoXxTb4363EjZTKKwTDBoeWdfjTRLFO54o07N/Vf253CdlZQ4XUSRUxQmbyivW1JINau9vKgGqwur00NxQQYCAtsq5e2LxQHzODE6daTTAH7zhiKLmdvHZHFnUMYU+AXn0k0VLJ6Qw/LNFTKBEs0mfFtlHS0OT2gLXWUtyzbvIS89mfLqenqYY0mO1RGn18gMH4LpkhyDUa3C6fGG2OP7XQrlgcfiXFYkWuxu7O7oQjMtMYbS4iHYXR66Jsfg9QmUrP80rOArWb+TF64bhkpwtfulRWKMllfmDJc5UUY89jzHASgoKCgoKCj8NPlWYuqKK64AQBAEiouLZc59Ho+HL7/8kuHDh5/fFf4C+Dl+y91odYZUPwByOieybHOFrAoQjuCqhogoLM1xOvYfb5IqWOGOF9vySgoyKFm3k7/NHILAfpnIKbCYWDwplymPb5c9V9yQn82aRJKMWrxeuOHZz3j26nxmFWagUQukGHXE6tQcrrPy0K8HoNeoUAmRK1iFFhM9UozcONKCwxNqaCHyzr5T3DqmN7ePy6K42SGtOSVWx4y/7YhoEx6p0hmYRaVC4M4J2cRoVRRZzLJ7JlKUacaoVeHxgsfrI1anZsXkvri9PmxuD60ODxP6pfHf6gaWb9mD1ekhth0RadSpabK7otqx//dIPcca7ax+t5J1s4YQq4su+JodbgSh/S8tko06enWMi3pMINHiABQUFBQUFBR+GUTfHQaRmJhIYmIiPp+P+Ph46efExEQ6derEtddeyz/+8Y/va60/W8RvuS/KNMse/zF8y91odfJVTQvl1fV8daqFRmtonlA4IrVU5XVLYnuATXY4CsNUikAuLBONOpZO7ss/P6kmLz2ZDvF6ioLOF/hafTrFM/3pjxmQnsTamYN5YsZA1s4czMR+aby++3iI8EiK0YasMdqaCywmdn/TiClWR0lhBjq1iji9Bq1K4I2KExyus1K8bidznv2c2es/pa7FSXFBRsj5xFkkl9dLslFLUzuzXq0OL+/tr6F0e5V07tOtzohCCqJXOgOzqFocbg7XWbltXJ+Qe1tkMTGroAfLNu3B6wONSsUfXviCA6daWLxxNxMeK+M3T33E+L9s47Vdx9g8r5B1xUOI0aqj3kONWiBWp2ZWhHszqyCDFVv2ktctiSKLiQxTLMazEGitTo/0pUU4zvVLi8Q2AXZBejK9OsYpQuoXwpNPPkn//v1JSEggISGBYcOG8frrr0u/9/l8LFmyhM6dOxMTE8Mll1xCRUXFD7hiBQUFBYXvi29VmXrmmWcA6NGjB7fddpvS0nce+TF+y/1d7NojtVSJm/XINtn+StH9r++VPa8ojLDsnBTDQ78eQG2Lk1aHi+VT+nH3q7ulCl/oa1UFZSuZWTG1L8s2yTc5hRYT3ZJj2HusUeaiJ55HIDQvSrTUfumG4SGtjQUWE6OyUxmZ1UEy1bC6PNz2whcRZ5FKi4cQp1eTEBO9muL2eMnrlszkAZ25q80dMFqeU6HFFLXCFvg7r89HhwQdPh/cPi6L2/HPKGlUAmWVtczd4He+u2WMh/tf28uA9OSwBh3bKuu459UKBqQncaCmiXkjM6V7GniP5o3IZOvekwzqnkKXRENbzphAvdUlyxkTheKiy7JZurmCOydks3JqX1ITDLLZPNHuXMBvkqG05imcL7p27cr999+PxWIBYP369UyePJny8nJyc3N58MEHefjhh1m3bh29e/dmxYoVjB49mv379xMfH/8Dr15BQUFB4XxyTjNT99xzz/lehwLhQ29/KL6rXXuklipxsx7JyrpjvIGte09w86je3DyqNzanB6Nezb7jzWErEMH3bNW0PGqaHVSftkr5R5Feq0O8HqfHw8or+lPf6qTJ7ibBoCE5VkdqgoG7J+WyfJN/jkh8XpxOzT2X53K03obD7SUt0YDT7eVUi4PV0wfi9Hgpr26QrdEvGvaxaHwWo7JTSU0w0CFeH7U1zeP1EaPVRG0HLLKY+OTr03z2dT2TBqQxJCOFBeOyEICLMzswd4SX7V/VSSHAhRYTSy7vS73VHvY1g7OoUuJ0OF1e7n99r2zuSRSPIlanh8+qG5hd2BNAuleBomZbZS2zCjPYdbSBTvF6JvZLk70XNU12HG4Pj7/3Ff+8rgMIAlOf+DDsOgFMsXpqmhx8fOg0GkHF67uOh6xx7czBqAQBm8uNUef/jPwYv7RQ+OkxadIk2c/33nsvTz75JB9//DE5OTk8+uij3HnnnVJr/Pr160lNTWXDhg1cd911P8SSFRQUFBS+J85aTA0cOJB33nmH5ORk8vLywhpQiHz++efnZXEKPxzf1a490hxY+ZEGSRwEi4kCi4mF47LYXlnHg28ckB4XDRXqWtu3iE806qhtcTJ7/afMHWmR5n3CvVZeejIX9+5AZscEUhMMsvM0Wp38ZesB7piQTVVtq7TpN+o1TF7tn696bFoeD7yxL8QVL1ywbHl1AzqNmtd2Haessk62NqNOzXUX92REH787ps3pIVbvF1K1LXZWTOnL4lcrZO9HgcXEnBGZ2F0e1nxwiJtHZ3JBt2TJjEIMGx7e08TFvTsQp9fg8niZ9cwnPD1zcIgxhCiQlm6sYO3MwXRONKAVVJy0OpiW351ZhT0lYRSchSWuPy3JQHlZaFVOvB9Gnd9Uo9Huwhf0vh1ttLN8y14GpSch+PytfsFrBH/L3uIJObi9XvRaNf+8dhh3hbHYF50LF4zLRqcV0KrOVNx+TF9aKPz08Xg8vPDCC7S2tjJs2DCqqqo4ceIEY8aMkY7R6/VcfPHFfPjhh1HFlMPhwOFwSD83NTV9r2tXUFBQUPjunLWYmjx5smQ4MWXKlO9rPVG57777uOOOO7jpppt49NFHAX9v+tKlS1mzZg319fXk5+fz+OOPk5ub+4Os8efCd7Vrj9RStedYI4sn5rJsc0VIe99tY7N48j+VskqQZKe+4zALx2eHfS3R9rrR5sSo93+kX5tfiFaj4oq8Lix+dXfYkN35z5UzNjeVRqtfpDVandS1OnF7fXi8Pq66sDsuj5eaJv9G3+r0+K3e05PIi9jSVosXebAswHUX9+Szr08zqyCD6fndidVpmJrXhXu37OF3Q3tg0KpChVmmmQVj++Dz+bisbyeKh/eQtQPOXr+TvPQkSgozsLu8PPy2/54adWpWT8+jtCy0rXH5lL4021wMSE9mVmFP4nRq3F4fHx6qY+nGCv42cwgPvLGX6fndWb+9KkRwicJINKsotJiI1am5NCs1bNBuoPDyen1odGpOtzjplGjgmbKvQ8xA5ozIJE6vRgXMGWHBG3AOo05NabE/uHnby/7H1s4cHMVqvY5ZLQ4SDBrUxrO3W1dQOBt27drFsGHDsNvtxMXF8fLLL5OTk8OHH/orqqmpqbLjU1NTOXz4cNRz3nfffSxduvR7W7OCgoKCwvnnrMVUYGvfD9Hmt3PnTtasWUP//v1ljyu96d8PZ2vX3mh10mB10ep00+r0kBSjpWO8nkSjTtZSVW910mhzUX6kgRl/+5irLkwPablrbHVx5aBuPLO9KqS6MasgA29IPSP8XJd4/HM7DnPHhGwmBLWUiXNJeelJ4BOoa3XS6vRw9yu7uSo/PUQkFVnMrJ05mNnrP5XmpmK06qiZU2LLG/hFwPjcNJYGCEijTs3SSbncdVkOO6rq2LzreKgwO1gLPlhyeS6LXt4d8bVKCjJIiNFIz7/2op48U1YV0hq4rbIWBFg2OVdau1GnZvHEHCb2S+PKvC7c9eruiEIxuCIFUNxmCPGHMX0iiprtlXXMucTCh4fqGJubyqfV9aQnxzC+XyeKC0IF4qDuyVzWtxPLt+yVtWZ2TY7hT2/s47PqBm7+/+3dd3gU9fY/8Pf27KZnQ0KANNgAKSQEQk1CEynS9aqUqwlBUboigghIFQQbV4qK0vx9Ker1goCIdCT0EloIJRBIkEBISCHZbJ/fH5sZdrIlhYRQzut5eB53Z3b2M7MrzNnz+ZzTPQRxKm8YTdbfCUsavRFOYiEEoGCK1KxmzZrhzJkzKCgowG+//YaEhAQcOHCA215+9oa9liKWpk6diokTJ3KPi4qK4O/vX7MDJ4QQUqOqtWaKdfLkSaSlpUEgECA0NBStW7euqXHxFBcXY9iwYfjhhx8wb9487nmGYWhuei2pTLn27IJS3LyvxpK9V60yKgvLilSwU6pSMvMxYu1Jbp/ygcjKhBh4OUvx3c7Ldm/ip/RqzmWRAPvrug6l50EmFmJYu0AUa4wI8XGBc1m/pHvFWrQK8ESDPk5Q1XNBfokWMokTpvzXXCDBXrYJYLBxZHvcyi+FWCCAROS4EKZY9PCmKSkuGHPLBVLfDInG6kMZ8HKRwsfNyW7584PpuRX2XQIAg5Hhjv1CqA8W775q+3hXc3n9stQ6I6b+7zy2josDYzBxgaCjQPHteHOg6Ocux7++Owy1zoh3OjVxOD6RUIBVyRloG+yFi7cL0TPcF/2WHLK578GruUjsGGQ1NXNlQgxOZRZg6dBorE7OwOLdV7EyIcbh+7rLJTh8PQ9nswqwtIJ1foRUhVQq5QpQxMTE4MSJE/jPf/6DKVOmAADu3LkDPz8/bv+cnByrbFV5MpmM13KEEELIk69KpdFZt27dQnx8PNq2bYsJEyZg/PjxaNOmDeLi4pCVlVXTY8SYMWPQp08fdO/enfd8RXPT7dFqtSgqKuL9IQ+x091m9Q9HvJ1y7QCw/8o9q0AKMN8MT/ntHO4WPSx0UFGmq5GnHFKR0G5QcSg9DwKBeS0Xy966LoVUhKHtArHmUAaG/HAUhRoDPt2ehn99dwSj/u80ktacwJ/ns+HjJkNDLzmMJnPQwpZSt+Vgeh7uPdBi9LrTSFxzAnKp4/91PBUSKKQijO2mQu/w+rysTVJcMNYfu8mVdFdIxViV2AZju6lsFtlQax0HUz5uMhSVGrggLadI63D/B6XWjXOz7qtRoNZDIRVBIXFcatxFJsaaxDYQCs1ZMIVUBIXM8WskIgGWDImGVCTEhz2bwVUmxtZxcdjwdntsGxeHlQkxvNLkthokaw0mJMUFlxW0MF/PikrsuzqJzftfzUV2oabKJf4JqSyGYaDVahEcHIz69etj165d3DadTocDBw5QH0ZCCHkGVSszlZSUBL1ej7S0NDRr1gwAcPnyZSQlJWHEiBHYuXNnjQ1w48aNOH36NE6cOGG17c6dOwCqPjed5qXbx06bO3UzH8uGtkLvcmt1ch6Yb9TzSnTwcZXZDz6u5uJaTjGMJgYNPOQ2M12WRRJKtAYYHc/YQr5aD6Ps4U721nWxzXoPlRV6YAOX8uuw5m27iJGdm3BTwGzdwFuy3G4wMg4by0qEQmwa3RHztl1EmJ8bb3tMgCda+nvYnM5oq3iFq5PYfjGGvmHQlX02G95uj9TbhWjg7rhsva2ATSYWwk1uDsZcnRz/tSCXiLD/yj0sXZOOeJU3lg6NhqKsf1RKZgGS4oIR7e/BXeu7hRoYTSbzFCfGBIVUjOmb+EUj4lRKrHurPYb9eBS5xTqb5dtlYiGi/T1418xuif0Qb3zYsxmS1pzgruX13BKMXmcujlPZEv+E2PLxxx+jd+/e8Pf3x4MHD7Bx40bs378fO3bsgEAgwHvvvYf58+cjJCQEISEhmD9/PhQKBYYOHVrXQyeEEFLDqhVMHTx4EIcPH+YCKcA8f3zJkiWIjY2tscFlZWVhwoQJ2LlzJ5ycnOzuV9W56TQv3TbLaXNju6nwY/J1m8FCpxBvTO8bVmHwUVCq55VRtyxIYTnVjb05rmjKlsHI8BrO2sp2KaQi9AjzRbS/B4a1C0SQUoHuob74/K9LNtdhucslYJfdOOq/VH67Rm/EiLhgCAFeUBCvUmJ4XDAEAmBOWUGGRIsy4gDgrpDgCzvTGYUAfh7ZAX9dvINVyRloHegJZ6kIU18KBbM9jVsHpZCKsCohBsv2pWNquSIRH/ZszutpZSlWpYRTucwTWxK9h6svVh/KwKQezRwGijqjCdH+HgAeToFcMKgFJrwQAqOJwdJ96fzCFyHeGNtVhcn/PYsNb7fHx5vOWx07OT0Pc7elYuErkVhz+AYXtFs6/08hWjbyMBcBsQjWLt0pxIi4YHzUuznUWiNcnSQo0uhQUKLnBaWWn19lS/wTYsvdu3fxxhtvIDs7G+7u7oiMjMSOHTvw4osvAgAmT56M0tJSjB49miuMtHPnTlrHSwghz6BqBVMBAQHQ662zAgaDAQ0bNnzkQbFOnTqFnJwc3loso9GIv//+G0uXLsXly5cBVH1uOs1Lt81y2lz5DIClv6/mwmRiKhV8WJZRtyxIYWIYzNnKr+jnqNlsrEqJu0UatA7w4J4rn+1iA7TPd1ziApyx3VRIycx3uA7LRSbmAgpH78/2YIotmz4mFAC9W/gh0aK4xd0iDZylIkhEAu445Y8rtthW3sH0PCQ+ME9HW5XYBvXdZLhbVIpirQktAzzxcZ8w3MgtQSNPORbtuGRVZILtaTWlV3Noy9ZAseLLquVl3Vdz12t6n1C0aOSOW/mlEArM43o7Ts9rVmx5DYbHBqNQrecF0gfT81CkNaChhxwf/XbO5rRPE8Ng4SuRuPdAa/fck9PzMLV3KOYOiICTWIjP/xWJYG9nGIwMnJ1EcJGKAQHwQ/J1XgGNlQkxWL4v3ary4NiuKrzTuTG+3nXVqocWYP4e5zzQUjBFqmzlypUOtwsEAsyaNQuzZs16PAMihBBSZ6oVTC1atAjjxo3DsmXL0Lp1awgEApw8eRITJkzAF198UWODe+GFF3D+/Hnec8OHD0fz5s0xZcoUNG7cmJubHh1tnurDzk1fuHBhjY3jeWE5ba6irJNaZ0TOA22lgg/LMupsQYprOcVW09bsTdmKVSkxrlsIgrwUvBvf8tkuy+l9LEdBIbsOS2swYVy3EKz4+5rNICI+xBsz+oYhI7cEKxNicOF2Ie6X6CAWCuGhkEIgEOBidhHXHDc+xBvTXnpYxr38eeWXOC4rzwZBQoEAE15QwV0hQ05xMZbuTUdsE2+MXncaKxNibDbyZd8jt1jHm9ooEwvh5+4EZ6kYGqMRaxLbIECpwCe/X8DHZZUClw9rBQA4mZmPS7eLrF6fklWA9cduIqyBO5eZYpXojJCJRQ4r+n3Uuzmy7pc6PPcSnQFGkxFSVzm2nLnNK52+YFALbD9/m/fZJMUFY+m+dIfB8skb+UjoGITxG1Ks3i/zvhrOMjEaeMi5EvtFGj3c5BJ4O1M/KkIIIYQ4VulgytPTkzd1rqSkBO3atYNYbD6EwWCAWCxGUlJSjfWhcnV1RUREBO85Z2dnKJVK7nmam15zLKfNVZR1cnUSo2NjJYK9nSGEgHfTG69SYka/cPx5IRsKqYg3NY9la72TWmfE+A0pSCqbsqXVm+AsE0MgAMRCAeQ21vpYZru0BqNV4FRRUJiv1sNZIkKQlwIjOzWBm5MYM/qEgQFQrDGgSKOHl7MUA5cdglpn5Ho4/fD3dbs9mA5ezeWV7LY8r/e7N7WaZlcee+0PXs3FlF7NYWIeZgFdnESVOi+N3mg11e7Dns2Q80CDIo0BKVkF+DH5Oi8gY8e1KjkD699ujy/sTI3ceOwmeobVx9huKi6AdHUSV9ibrFhjrPB75SwVw0UmxpT/nuN9pwBzoY3ywVpFwbJIIMC0PqF4eflh3pQ/SzN/v4CZ/cIxddN5XkETWldFCCGEkIpUOphim+Q+aWhues2xnDbnaMpbfIg3Tt7Mx9xtF/FO58aY1T8MGr0Jd8qq96VkFWDgskOIDvDAqsQ2vCptLHvV/dhy2L3C6+M/u6/wbp4d3dwaTCaU2LhZrujm3WBk4O4ugcZgMjeDtXi/+YMi8Of5O0iMDeJuxNlqcran1z3swZSn1vGuH3te0f4e2H/lXqUyegBwt0iDRp7m8133VjuUao3YPj7ORsct++cdr/LG8NggDF5xFL++2wElWqPNIIRhGMSplEhOz8Nba09gVWIbjNIYUFiq52WmhrYLxOsrjiA6wAPfDInGhmM3IRMJIXFyfK1dnEQ4dC3X4fcq54EGQqHcKpAC7Ff4c+SB1gB3ucRmIMVe62Z+bpj6v3NWgVpV1lVRVosQQgh5PlU6mEpISKjNcVTa/v37eY9pbnrNsZw2Z2/KXacQb4zuquKqpH296yoYBjh1477VzSg7VW1p2XEsebtIER/izWUC2Mp+7PQxkVCA1kFeOJVZwN0I27q5vV1QymUx/pwQb/U+bFBoq8rcncJS5BVroZCKbJZ4n/dHGlYmxOBGbgkXAFSUCUkqmyZoMDJWUwYVUhHqucrQKsATcU3MRRkOXcvjsjts5sdyOppAIIBUJMSqQxlIySzAN0Oi8Z+9VxEd4OkwKAlQKrBxZHuIhQIkp+di7PoUtA7wQInWAA9nKVe23vK6uzqJMWdABOb/cRHNG7jjfrEO7goJPMqCkZhATwDgqg0eSs+DAMDMfuHQGk0QCgS8z9RSrEqJnCItLt0utDmVMk6lxNSXQnEkPRfOMtt/Ldmr8OeIa1lms/y4LK/1kiHRDtcHVrSuylbjaMpqEUIIIc+HR2raCwClpaVWxSjc3Nzs7E2edJbT5kq0eswf2AI6owklWgNcnSQQCwXo/c1B3i/9LRq6O2wSyxagsOSukGLugAhM33wep8uChPKlwuNV3tg8OhYZeSWQiIQ4nZmPVckZ3PHY6oOnMvPNPZokIi6zwtp4PBNrhreBTCzCnK2pVsefOzACN/PUNoMStc6IEWtPYsu4WLRrrMTMLanQGR1nQnRGE+JV3txYk+KCkRQbDIOJQbDSGXO3pfKr/4WYz/F2QSlOZubzyqLHh3hDAAaf/H6BK/POrgljAyvAOiiZ0TcM3+5NR5+oBvBxk6FdsBIvhvlCLhEhu6AU3x24hve6N7VZUdHbRYqNI9tj9pZUm1P82MCPlZyeh3vFWgz94Rh3PIZheJ9BfIg3xnRVYez60/jlnQ6Y/0cabz2Wu1wCL4UU94q0aN/EC0KB7QApJavAqkR8RRlUVyfzGrHZ/cKRkVfCW//FXuuKsluW66rKs9c4mqoFEkIIIc+HagVTJSUlmDJlCn755Rfk5VnfxBiNjpuMksevKtOQ2CIRtqRk5ltNmapwqpWdtTRaoxEtAzwxuVdzfL7jknUVuPRczN6WiugAT3PxhbJ1Sezxcot1OHUznwsIYgI8kRgbDAbmAEMhFeGzVyJx/lYhtp3Ptnn8GZsv4IOeTe2OXa0z4sqdYkz69Sxm9AlFsNLZ4bn6uckxe0A45m9P46b2Aeaqgj8duWGz0t3cPy7ipQg/q0BvbFcVnGUPizpYZsUs12GxQUmwtzOEAgHyizV478WmyMovRdb9UjhJzNPr0m4XYupLoWjf2AsysRD/fbcDFmxP441pcNsAzN6SilOZBXj/xRB0bebDvZ9cIsKyodEYs57fB6tArbca06guKkhEQuiNJpy4cR/HMvKw6F9R0BtN6BbqA183Jy6wOXLdnJ2LDvDAjD5hkIqENgOkVckZWJ3YBhAIuOBlVXIGVibEQGjxHGAOpOb0D8fiXVcwrnsIBAyw9vANXp8zlofccUNpAHYDI3uNowHwKlkSQggh5NlUrWBq8uTJ2LdvH5YvX44333wTy5Ytwz///IPvv/8en332WU2PkTyimpyGZGutU8XFKmzfrBaqDdw6IkdV4JLKTQubN8BcfKRIo+dV8BvWLhCTfj3LBRhezlJ8tfMyEmODHZQiz8VHLzV3OH6ZWAi1zoipmy5g+/g4h+udnGUiSIQCTO8ThlK9kdvP0fTAg1dzMeGFEKxMiIHWYIKHXAI3uQSJq49j9fA23H7lg1bLYA0wV+Ob9OtZbB8fj6nlejmxmaUF29Mwo28YZmy+gMTYYJsFHVYlZ2DZ0FZwkgixsFyQGx9ibtI71iKgsvz82TEt3ZuOdW+1w7Afj/GO/+eEeK56YHmH0vPAALieV2JzKmB0gAfUOiPaN/bChBdCYDAxcHUSw0UqQv9IP15z6btFGuQ80CAxLhif/pGG6X1CMW9gBD7edJ6XNYtTKeHvKbdqKG153VKyCuwGRhUV3bD3QwIhhBBCng3VCqa2bt2Kn376CV26dEFSUhLi4+OhUqkQGBiIdevWYdiwYTU9TlJNNT0NqXxvJ8DxVKtOId42C1AAgJuT+etXUWZLIRVjVWIbbuocu7+bk4QXpLBBj2UT4IPpeRjSLtDh8Uu0xkoXhMjKL3XYg+mBRg+BQowSjRFtgrzwfvemMJgYCB00kQYAY9k+bIn11gGe+Hf7QGj0D69NZfp6jezUGDM2WzfF5YK6AE+o9Sa710VrMCEpLhjZhaX4w1Y272ouwDBcoQ1b/ZtYhaXmQMJyXdYDjZ73WZbPcpZoDRAAGGdR/bBYY4CXixQ6gwn3irUI9XPH/iv3cPF2Iab0ao5pmy/YLBMfq1Kib2QDRDR0h9Zgwud/XUbLAE8ML8vkKSQiGBkG/xRoMP6FEIzq0sThGjZbgRH744LlObJr8k5n5sOtElkvQgghhDy9qhVM3b9/H8HB5htKNzc33L9/HwAQFxeHUaNG1dzoyCNjp8KN7aayutGzXH9UWeV7OwEWU63KlUiPUykxf1ALu8d3k0sQp1JWGCSodQaMWHuSm+ZXojWgUK2Dt4sUN/JKuP1SsgrQrXk9rg+SQmr+eld0fLlE6DBAsiwIIRYKuBv98j2Yxm9IwYa320OnZ+AsEyOioTu+3n0Fh9Lz8N93OzgcQ7GWf47jN6RgQvcQuDiJuUDP4foglRL1XGXoEeZrd/0am+V7UBbk2CvoEBPgCS8XKXzdnDCsXSDv+6LWGXEwPQ8TujdF2u1CTOkdiqE/HLX5fjKx0Oa6LPbasudpGVA5y8TYf+UeWgeap3ZuPJ6JdW+1x5xtqTbWhoVDbzQ67LeVFBuMiIZuKCrVY3daDnan5QAAb1z8xsbe2DS6I7ILNFZr2CwzrOy0WSPDoHuoDwa3DbA6xziVEoNj/G2OjRBCCCHPhmoFU40bN8aNGzcQGBiIsLAw/PLLL2jbti22bt0KDw+PGh4ieRTFWr3Dm9kSbdWnIVkWqchX61BYqsfxG/fROsgTibFBvADDUcEGrd6IGX3Dcfrm/Uplhtjtc/pHILdYhyY+LlzZcADcjffCHWkAgJ7hvgAqLlIgEgrw0W/nsPCVSHzUOxSlOgPkUjFyijSY8ts53s1+SlYBWpet4bI6lsobAgHw6fY0LBzUAmstbtSlYqFV8QRH55gUFwyGAZxEQnzYszmEuGy3wmK8ylxhcfCKo1iV0Mbq+Ja0BhPqu4sxtpsK9VxlVmO6cLsQvcP9rApllA9+Ckv1GPdCUyzedRmD2wZYXY94lTdSsgpsNlIuf57sa2NVSggAXMouwtTezWFizIUqygdSgLnwxZxtqZj+UliF53vvgQ7+XvzprCM7NcYaG+M6mJ6LOdsucuv0WJYZVstpswqpCBtHtreaDsmO8eNN56kIBSGEEPIMq1YwNXz4cJw9exadO3fG1KlT0adPHyxZsgQGgwFfffVVTY+RPAIPuRSL/rps92Z2/sAWVTpe+UIWbnIJ3lx13G5D1O7Nfeweq6BUj7d/Ookv/hWFeQMicDTjPnxcZbzS5X7ucoxZf5o3bp3RBL3B/H713Zy4steD2wZg4Y40DG0XiNWHMgCYb9AdBSGfDmoBuViIpUNb8cqjK6QiTO8TitXD2+BWfilkYnN25lrOA0zq1QzYwfCr8qmU+LBXMxRp9Lh85wGK9Ube9uxCDWb0C8fcbRf5hRJUSgyPC8bY9Q+zX2xGxWAywcgA3x9IR2JsEN7t0gTFWgOm9GoOsVCIIo0eYqEAV+4+wIi15lL1CpnjhsCeCgmcxCKklGWazNflYUaRYWAVSFleN8vg5/O/LiE6wBMdGyutimdM6tkMS/ZcxZB21oFW+fNkP6fhscFgGAbT+oTi1e+OYHDbALzUws/h6yEAr3lweTKxEH7uTrwmygqpCC+E+lSYwWN1CvHGwlcieRUk2c9QrTMit1hnd00eFaEghBBCnm3VCqbef/997r+7du2KS5cu4eTJk2jSpAmioqJqbHDk0emMJqsbPcv1HTnFWhgYplJNRv/JV+NmnhoFpXo4SUTYcykHl7KLbJYvtzU1qjw3Jwlyi3UYvf40lg6NxvZzt7mbeIVUhBl9QuHtKsWXr0Vxwcyq5AwUlepR380JgHna4bwBEZi2+TzXo8q6fHgGr/IdAPi4ySCAALfz1fD3UmDZ3nReIMVm8yyLJcSplPikXziKSvV4qYUfEi2m+eUUaZFfooNULMSPCW1wp0DDO1eZSAiAwUsR9a0KJQhgez2VWmfErC2piAown5dlMYex3VRo4O6EQKUzb4xODjJgcSolGnrIkZKZz50r22fp3S5NUKTRI1DpXGGQEa/yho+rE6IDPBET4AmJSIit4+KQdV8Nd7kEPm4yLNt7FVN6N0fmfbXNY7EUUjFWJsRwDYGnvxSGEq0BucU6LN2bjrZBXg5fn5FbgjOZ+TanDMaqlOZCFEUatA1WckF3Ulwwcoq0Do/rLpdg8+iOcHWSwNvl4f8btqr3afSOq5dSEQpCCCHk2VXtPlN79uzBnj17kJOTA5OJP5Vr1apVjzwwUjOKtQbeY3trWCqq7nfrvhpT/nfO5pqiz3akIayBO698+fgNKYgJ9OSmRtkqzc4Ws4j098DK5AybwcxUi0CBPbZEJOBNHyzS6DC5V3OU6ox2y4e/HdcY7goJZBIRijUG5D7QwcdNhkKNAaY8NW+tl72pacnpeTh1Ix/bz9+2G6wMjw3G8n3XMLkXv0KgkWEwd+tFu9P8LDM+AODnLkfeAy0OpudyUyctg2CDiUHjes64X6zjXqOQiiARCTH1pVAw29N4a4liVUokxgZjzraLGP9CCPe8WmfEuA0p+GZINApK9JCKSq3GV15CbBBeX3EE0QEe6BfpBxPDoERrgLtcgiPX8/D35RwsGdoKaq2hwmqRluvhxnYNwQOtHkKhgDtXT2fHAb5MLOTOs/yUwbFdQ6DRGzFm/Wn8NqojFgxqgY83PQy6HfFUSNHEx8XqeVvV+6pbzZIQQgghT79qBVOzZ8/GnDlzEBMTAz8/PwgqqFRG6k75Uub2AgVH1f0K1TpMLRdIAfwKcTEBnlyRCwDY8HZ77hd9R6XZF74SiYzcEl4g4WidjaBsu2V1PIVUgn5Lk/FTUlub5cNXJWeg5RAPfLGTP90xPsQbM/qEIcOiiAXAL2Nevkqbv5cCUzedhy3J6Xl4K74xogI8IBYJsO6tdnCXS2A0MZCKhZUq/w6YA4EzWfloHegJhVQEZ5kYzjLgl3c64NytAowry8AopCJseLs977p98vsFjIhrzKtaV75J7fgXQjC2mwoxAZ5wV0ggFglRoNahZYAH9EZz0GZv2qa7xbTOQ+l5mLctDfMGRkAmEmHr+dvoE1Efr7RqiOmbzuNgWaNh+0UzvOHlLMXKhBjcLdJAozdCqzdCJBJgVUIMlux7GBhVtJ7udGYBZvePQO/w+ijRGeHqJMadwlJ8+F/zmrcHGj1C/dywZEg0ruQU48CVew7X0dmvQGkdGFW3miUhhBBCnn7VCqa+++47rFmzBm+88UZNj4fUsPKlzFvZKZ4A2F/fkVuscxgIjIhrDD8PJ6Qk51tlu+YPaoFZW1MdlmZ3kvB/2XfUk4kNWCx/7fd2kaJ1oCeS03NtTguzF5wdvJqLudtS8WG5LBIbkNnK4i0f1spuGexVyRlwkpjXI/HWEIWYG/A6wr6nZQXB1gGeSIoLRklZpT92u+WUtv1XcrBgUAR83JxQz1WGpXvTMaRdoN3rBwAavQkXbxeipb+NAFOlxKqEGCStPWkVUMWqlNh/5R7v+YPpuVDrjRAJgRYN3XEmqwDbzmcjJbOAC9j6Rfph3rY0XvYvXqXEjH5hyMgtgVgowO1CDXan3UVYA3d0bVYPi3dd4U3VFAJWBTHY68R+TrO2XLDa57NXIjF+Qwr3fXFXSOGlkNpdRxerUmLugAi7U15ttQZgjyUAeNlAy7VWhBBCCHk2VSuY0ul06NixY02PhdSC8qXMxULHWUS2N5ClihqTikUCzNt20Wa2a+qm84jy9+BKUpffnlusg7tcygtQ2JLm9khFQqtf+8d0VSEzrwR+7k6IUyl5N7UOG+am52GyANx6GuDhtC02CGMDg2h/D/i6yRxWR2QYxmbQ9m7nJg7PKcBLwfVfYgOlg+m5GNWlCQ5ff3g8y0IQq5Iz0KKhO1YnZ+Bgeh6+/Xcr3vgdXb+wBu62A8z0PEAgwPQ+oby1WLbKxLMeaPRwl0uwOjkDibHBXAC0+lAGViVn4J3OjfFhr2aYLGgGjd4EZ6kYF/4pwMBlh7jAjD3+xmOZeCmiPhcUsVM1N45sj4+EgM7AQCwSIL9ED6PJ3O9KLBQ4rBg4o08oFNKHhTm8XaSICfTkraPj1r490MJTYX9anq3WAGqdET8fz8TCVyKh0ZvwQKO3WmtFCCGEkGdTtYKpt956C+vXr8eMGTNqejykFggA9G7hh6TYYPi4yhzua3nTybI1tcmSUiG1m7k6eDUXiR2D7L62RKtHfXc5ViW2wZK9V7F0bzpWJsQ4fD/PcsUycot1SFpzAj8ltcWt+6WY1LM5BLjMZUIqagp874EWCR2DYCoLhNhpW9H+HlzWgQ2eVibEOJyCONxiup6lI9fzeAGbpViVEjtS79gM+KRiIVYlZ1i9V1JsMJLignlrzeq5mD/bikrB77+S4zjAvJqLCS+EYGVCDLQGE4K9nfHH+WyrAg8sVycJTAy4RsBJccFYf+wm2gR54aPezZFTpEVesQ4mhoFIIEBRqR4tGnngl5Ed8E9hKVxkYmj0Rqw/dhMf9GiGUr3RKvtnNDFQSMWYvz3VKpP0Ue/mDotmzOgTBlhU87MMiMpnUiuTSbJsDUCBEyGEEPJ8q3QwNXHiRO6/TSYTVqxYgd27dyMyMhISCf9mm8qj1w5bRRwquoErVOsw+bdzSMsuwqrENjAxjMN+RyIbmStbU5tY8SpvyCUih+ts7AUzCqkIbnIpDly5hy1n/uFukh0FA7EqpVV2rUijh1pnRF6JDmKhAEv3XuVKiReW6q16DJXHMOBlKQwmBgOiGiDzvpoLDKIDPJEUG4x6rjK7ZbCT0/OQFNfY5rZVyRnYMjYOs8tNRYtXeSMhNshmxoe9Rrauq9ZgsgqIpGKh41LwId6YMyAcfb5JxhevOq66qbaoUCcUCHA2M9/mOOJVSjiJhbhXrMWqxDbwdpHCVSpGS38PrDmUwQty4sqKYLBBWbxKiYTYYLz900n8+k4HTO8bhj8vZKNrUx+r7N/YbiqcsahCyDqUnldhZb5irQEQ8H8keNSAyF1BwRMhhBBCqhBMpaTwb/ZatmwJALhw4QLveSpGUTscFXFwVDEtt1iHUzfzsf5tc2NRdgqWCeV7LplvbMsHU4VqHQrUeozu0gTGclPYYlVKJMQGYf72NJulqVkectuZrRl9w/D5jkt4r0dTNPdzxWiNEW5OYpgYBr0jfHGnUAuBQMCtR2od4ImE2CCrMbo5SaCQihDs7YzP/kzD9D5hyC7UcKXEHRVBYIsYsIUq2GxI+r1iBCmd4evqhLaBXrhVYC7xXaJ1XAbbXplstc4IvcGID3s1R+IDLZylYrgpJMguKMW6YzdtByoh3jDZSarJxEKrIDW7UFOWGbNdCl4mEkJvMkGtM1Y4FVAkEODNsnVa3i5SbBzZAbO2XOBNn4xTKTGzfzhe/f4IcsuqCsarlJgzIALTf79gsxoig4dV99ig8ueRHQAB8Op35uP0DPOzyv45yqRVRCEVl5Wm56OAiBBCCCGPqtLB1L59+2pzHMSB8o1CWY4q8LGKNHokxQXjy78u8XoLWd5ou8slkIqFWLLnKi9jcbugFAeu3MO2c7eRklmAJUOiMbqLCoWleqsKcRqD0aq8N2AO+AKVCqvMVqxKiZb+bmgV4IlZW/hTt6wzGN7YPDoWpXoDlu5Nt8qqeLtIMaNvGOZuNTebfbeTltek1VGmZkRcMEavO223ZHx8iDdm9gvDmiMZ2HvpXoVTEO0Fjt2a14OTRASjzgAvZymcJOZMyYbjmRjaLhBag8nqGkzt3Ry70u7aPBYAq4ybWCjAuHLrgABzpm9VcgZ+facDnIQixId4IyWrwGE/qiMW67QGtw3A/O0X0SbYiyv5rtYaIRELcfpmPt7oEIivd5kzUAfT85BdqLGbvStfudC8Zo3BnUIt1gxvi91pd6E3WfdGczRV09G5xKu84SwTQSFx3MyYEEIIIaQ6qt1nijw+thqFsuxV4GO5OUmsftVX64y8x2yAMMeiitndIg1u5JYgWOmMEXGNcTozHyKBgNc41lJyeh5Gd1HZXIPi5yHH569G4VpOMQosAjGxUIRZW1MrkcHIxextqZj2Uihm9gu3Old3hRStAjww9X/mkuUikQB67cObb8t+U2yQEahUIPWfQjT0lEMhFWFw2wC7Ff9mbUlFywBP7L10r8L1SI085VZro7o398G0PqH49I+LGNwukHsfNoDbYDGNUGswwV0ugZ+bEyBgcP5WIe89uof6YHqfMMzYfB5RAZ68saRkFSA6wPxZW645CvNzw48JMTAyDAwmE+b0D8f87WmY0Tccs7ellstQWk87ZNeODW0XiIU7LlllJ2f2Dcf3B65z2bUCG0VMLJUPjLLul2L0utNl769E38gGVtMbHWXSViVnYPPoWKtziVUpMWdgODJz1Wjia90zihBCCCHkUVEw9RSoqJreAwfbvV2kuFGuj1J5WoMJIT4u8CubLni7oBRT/nvWqsx0v0g/q5tcy5t2kVCAbePiYGIYOEmE8HV14gKfYo0BQ388xttfZ7TOQLDKZzDM+/Gb9Voq0Rq4Y0vFQjhJRFj/djscvpaHVckZvAAyXuWN3i3qw8fNCZ9tT8Oif0WCYeCwHPvw2OCyprgCzOwbjrnbUq2uz5wB4RCYGHzxahTuFmmgM5igN5pQz1WGGb9fQMsAT17AZhnkxTVRwkMhBQPzGh+t0Yi9l3Iw/aVQfNCjGYq1BugNJpTqjZi2+TwOpefhVNmUTfb6sBk4J7EQQ8qCtvIVB2f2DYeeMWHeoBa4dV/NC+JkYiG8nKUY9uMx3mesNZgc9v6auy2Vl5WsaAph+e2Wjw+m52H21lSrLKejIDY6wAN/pmZbnUtKVgFyirTQmUwVZnAJIYQQQqqDgqmnQEXV9FwdbHdXSNHI03EBBne5BG5l09O4KYU2bprnbUvj3eTamxoXq1JiXLcQ+Lo6cc8VafRW+y8f1srhuMpnMDLvq1HfzboaYaFaB7lUjFUJbWACg79S73IBVLzKG5vHxOLPC9n4/sB1tA7wxCf9wjBg2SFEB3ggOsAT9d3kFQacOqOJG/v3B64jKS4YiRbrkfak5SCvWAcfVxnUWgNyi7VYd/QmhrYLRE6RlgvIbAVsEpEAXs4ymwHai6H1sfPiHRzPuI9D6XlYmRBjMxhjg4hGnnJ8/FKo1dRJgA18LmLOgHBM+uUsEmODrMazMiHGav2WTCyssLx8okXga552583rK2V5TmyjXVuPgYel5C3fb+PxTKx7qz3mbku1Wrf1Sb9wDP3hKLduy1Lv8Po4mJ5bYQaXEEIIIaQ6KJh6CjiqptcpxNuq51J59d2c7JbljlMp4eokRonWAMDxlMKD6bl4t8vDm1xH2QoAmD+wBXfz6uYksdq/qhkMqVhoFTjaKsxh2dj2VGY+Tt64j85NfdCxiTdcZCIIBcA7nRubg6LYYBRr9Qj2dnY4lnouMny9+wo39vLBY3SAJwpL9fjPnquYMyAc64/exJ5L9zCsfRA37a18cMgGlzlFGqspaux1nLvtIj7s1Yxbk2QwMVzPK8uGwePK1pf931tt4Sl3UKq+rMluVIAHPBQSrEyI4RX5sBUIpWQVINrfw+H1sTy3i7cL8UHPZjCBsVoHlmjRq8pR7yqxUMAbx+C2AVi4Iw0tAzwxvFz2aeGfaRjcNsAq2ItTKSGVPCwt7yiDSwghhBBSHRRMPQVsNQoFKt8Xx10hxaz+4fikXIW1eJU3JvVshqQ1J/Djm+Z1UxVNKRQJBdx0K0fZikPpeSjRGbjH3i5SdGysrPTUrfIZi/gQbwQrFfByfniu9gpzsMd7p3NjhJc1py3fgHZsVxUiG5qnGro5SXA6M9/uWOJUSkjFQodTEsd0UeH4jfs4eDUXn/yeisSOQRjSLhAuMjEYxlwMo3xwyAaXI+Ia2z32wfRcTEYzAObgK1jpjJ+O3LDZMHj8hhS4O0lxp0hj81isUp0RKZn5No/x0W/nsP6t9rzgji1e4Qh7brEqJYa2C8Rba09gcNsArs+Ur5sTch5o4CGXYMmQaPi4yfBX6l27FSDdFRKM6daEV95+6d507L10z+b7J8U15p1PXFlVwYRVx7njO8rgEkIIIYRUBwVTT4lH7YsjAGyuKRnyw1GodUbuRrOiKYUuMjHmDYjAfbUOOsPDinnlm6w6SUS8EubuCqlVMMGu8RECvExK+QwGW1HvXrEGUpGQO2dHWbRD6XmY0qu5VcEEdhsA9Gnhh5b+HpCJhZj3R1rZWAS8rEz35j6Y3jcUxVojlg9rxWWC2GmELIlICLYrADtNbdiPx7B1XCz+vlrABYeWARsbjL7TqYnDa16qN2d9kuKCMcdOBgsAZvQJtdknrLzylQPZYwhhLlNeWKrjvisSsRBioQAMA7vZzXiVN7ycpViZEMOr8JiSmQ/AnMVjs3fsZ37hViFS7PSuilUpIRMJkXW/FK0DPfHd/nQMaRfo8JyMDIP1b7WDWCSEXCpETpEWr1mUbK9MBpcQQgghpKoomHqKPEpfHKWzFOeyCmxmkixvNB026A3xRj1XGe4UluKVb49wVQAdlRVfaNEHy8WJ/3Vj1/xsHNkeiQ+0XBDGMAxEAgG+eDUKMrEQ9Vxl6L/0EFoHeGJMNxV3nPtq6zUyloQCfgBpGQg9LHAhQF6JjhvLkiHRXDakvpsMThIRpm3mZ/QsM0FsMOAiEyOqoQe3T2HZ1D6DkcHF24UYHhuMjccyy/pAmYMXdmqcp7PjANZZJjIHIxVkAj/q3RxZ+Wqc/6fQ7pqleJU3r+y5pYPpedAaTdh7+R6XufJ2kWLdW+2xcEcaEjoGwWSj19jcgRGYsy2VlzUqP4WPvd5qnREbj93EJ33D0b6JEjM2W/eu+qRfOApKdfD3UqBYY0BUgGeFjZdFAgE0BiMEBhP+s+e6VQn8ymRwCSGEEEKqioKp50RlpwpWtJ+vmxOKNebpe2ymJbpclTrWQYs+WABwOtN6PY5aZ8St/IelsW1ZPqwV1DojDqbnwgQGCwa1wLTNF5DYMcjuaxRSEZwkYrvT2cZvSIHWYELmfTVXoEOtM2LchhR8MyQa647dxJRezfHp9jS7mSC2GEesSokzWfloFegJbxcpBrcNgL+XHMuHtYJMLMR73ZviUnYRJvZoimKNAbP7h0NvZGAqm/5nMDIOpzsC4IIwR24XaBDopcD7P5/B5jEdcfJGPnzdnLhA8k5hKWKCPDFw2WG7xyjWGLjgDzAHo2w27Oj1+7xiF+5yCTR6I7RGI97sEIQPXmyGm/fVVj3IWC4yMZe9KtIY8NXuyzbXQH32Zxqm9g7FsB8PYdPojlyGy25JepU3NHrzZ8d+LpYFOeQSEVepkhBCCCGkJlEw9Ryp7FTBivZjs1fslC25RGQ3Y8JWUQOAL3dexsaRHTBzCz/T426n0S3LcnrgofQ83MovxcGruYjy97DbrHV6n1DMLvc+7OsB8w23rKyE+t0iDXejblkhTyQQVFi6PU6lxIc9m2PID0fROsATqxLbYOGOS7yKh6sT22Dr2duY/Nt57vXdm/tgZr8wxId4o1Ct52WsWGx2B2AwfkMKfkpq6/A6NfBwwunMfLRv7AVAgO3ns3lZn3iVEjFBXg6PoZCKMLRdINaX9b7qGe7LnUv5/mQAsP6tdpAIhWjkKYdWb3IYFCtkIry+4igAoEvTeth76Z7dNVBjuqqg1hlxI0+N4bHBWH/sps1rFB/ijYSOQVwBDoBfHGRlQgyClI6LixBSVQsWLMD//vc/XLp0CXK5HB07dsTChQvRrFkzbh+GYTB79mysWLEC+fn5aNeuHZYtW4bw8PA6HDkhhJCaRsHUc6ayUwUr2m9an1Dcyi+FUCCAh1xi1X/K0gONHgzMFdnuPdBYrd3S6I2VLkQBAAaGwcqEGBhMDF6ObogZ5QtrhHijVYAnr+iEpUNlDYYz80oQp/LGwh2X8GHP5hDgEpLLAqpVyRnoFFLP4TVydZKgZYAn8sumCR5Mz8W7mia8sSTFBeObvVetzq15AzfM3JqK4bFByC7UYFfaHZtr2tYfu4kPezbHF69GQSoW2g0eY1VKKCRizPsjDRtHtsfcrfwS4oB5Gt/cramY0ScUU21cmziVEneLNFh/7CbCGrgj2t8DxRrbnylLKhaCAQON3oS7RRrEqZRW7wuYAzmpSMitOyvVOz5uzgMtFFIR/NydkFusQ2LHYHi7SDGzXzh0BhPuq3UwGBkEKxWYvz3N7tqrnAdaxAR6OnwvQqrqwIEDGDNmDNq0aQODwYBp06ahR48euHjxIpydzcH7okWL8NVXX2HNmjVo2rQp5s2bhxdffBGXL1+Gq6trHZ8BIYSQmkLBFLFSqNYht1iHIo0ebnIJvJ0fBlbmhr7neFP14kO8rdYQWWKLW0T7e+DQtTyrqXfsmisBwLsRt1c6WyQQ4M21JwEA37/RmgtCdEbztK5Ltx/gXrHW4TlKxUJE+bvDYGLw6aAWuFOkwfDYYIzqokKx1oBgb2eotY5v+IUCcxbk238/7JfFrpVi2VvnxD5/9Pp9/PJOe7QO9MTcbalWUxKHxwajUK3D6HWn4e0ixY8JbQBctrr+M/qG4X6JFmqd0VyYw251wDxM6xtmVUwitizDVqTWY0qv5pi3LQ1L96Zz6+LscZaJodGZoNUbMeW3czZ7QcWrvDGmqwqDlh/mvh/r3mrn8Lgysfk7sWjHJbvfiZhATywZEo3ZAyKgMViXxx/XLQRBXgpaK0Vq3I4dO3iPV69eDR8fH5w6dQqdOnUCwzBYvHgxpk2bhpdffhkAsHbtWvj6+mL9+vV455136mLYhBBCagEFU4THVt+mTiHe+OyVSDhLRVaBFGBeG8UwDK+hr+Vr2eIWN/JKuKmBwMPpWmqdEeuP3cTcARHIV+uh0RvBADhyPc8qQItXKXkFFMRCgVVgNqZrE7Ro5O7wPA1GE+RSKUo0egTXc8H1XHPT3mE/HsPYbir8vyM3EBPk5bCQQ3LZ8/VcHjYSLl+xsHxvqfLPq3VG7Ei9i4u3CxEV4InEcpmpjcdu4pN+4dj5fjzACLBoRxqiAjyQGBsErcEED7kEDT3lyMxTw9tVhvdfDIGXs7l/VPmiG+x1zC7QYN7ACKTnFMNZZu4xZlnZ0dtFilWJbfCupgkUUpH9axDiDZOJwWc7LuHj3s2RW6zDsB+PYuErkZjSuzmKNUa4yyU4m5WPpLUneJ/jket5drNYnUK8oZCKsMRGRo+rXNg3DF2a1ivLoAJLh0Qj54EWhaXm5tDOUjE8FBIKpMhjUVhYCADw8jJPo83IyMCdO3fQo0cPbh+ZTIbOnTvj8OHDdoMprVYLrfbhD0FFRUW1OGpCCCE1gYIpwrHXt+nvq7mY8ts5zB0QbvOmGjBnlEZ1UfECm/LFLRp5ynlrkt6Oawx3hQRikQD5JXqU6IwQiwQY//9S8NkrkVals2NVSszoF46hPxzlGtdKREKsf7sdDl/L4wKGUr3JYd+oeJUSh67loUNjJQLKik+4OUmQXWjuz8RmjU5nFmDp0GhAAN41iVcpMTwuCGPXpyC2rAcVYA4uyk9JVEhENpvsKiQibh82wCxfDTFOpcSs/uEAY87GzdqaioPpedhdbp2RZdnx1Ylt8OVfl3mZqfLVBxmYC02MWHsSY7upcPF2IcIauGPJkGhoDSbUd3NC6j+FmPtHGgDgmyHRVg1441XemNyzGW4VmNevGXo9LKIxoixrCJjXLNmaUsies6BcKXr2O/NAq3e4Xm1Wv3BeUYlHqXRJyKNgGAYTJ05EXFwcIiIiAAB37twBAPj6+vL29fX1xc2bN+0ea8GCBZg9e3btDZYQQkiNo2CqljiaKvekctS36eDVXDzQGmxuYxkZBlvGxuJWfimClAo09JDzzrm+mxNXdn1VcgZaDvHAFzsvl7tJV3LVBAe3DbBaQ5R5X43PXom0GXiwAUO0vwdXlQ+wLuowo184Bi47hDA/NxQ7PyyqcbfIPB1RZ3yYNRq7PgUb3m6Pyb2aIafI/ItxSlYBxq5PQXSAB4bHBiO7UGNugNyjGb7Zc4V7L4VUBA9nqc2Kgt1DffFC83rYc+keL8BMKiuy4C6XYP+Ve5i/PQ3dQ33RrL6r3al7bDGMkZ0aY+neq1b7WRbdOJtZAAEYqPVGjO2mQptALwxs2RCzt1ywGuP6t9ujoEQHrdGEj3qFQmsw4l6xFgGeChgZBsPXnMDCVyLN10prtFkgwh72nP83qiMMJgZZ+Wq4O0nQxMcFvm5OyL5Z6vD1DzSOv4uEPC5jx47FuXPnkJycbLVNIOD3fWMYxuo5S1OnTsXEiRO5x0VFRfD396+5wRJCCKlxFEzVAkdT5Ro8wSWaizR6h9tLKlhDpDeYIBAAk349i61j46yCR8uy65H+HrbLqafnwQRzsQpba422jI212Yg3OT0PAoEA695qB73RZBWgWAZkGbklUOuMkImFKCzVo1Ctg7tCikClAnEqJW/annkNkhbjyo4V7e+BMD83LBkSzZX//vmd9pjeNxTDfjyGz16JhNZgQnJ6HpLigvH5X+axlm9qrNYZMLNfOBiY+zOxlfLYNUFvrjrOZeVGd1HBZHu2IEdrMCFO5Y3Fu6/a3M4W3Yht4g1XJxHEIiFXcnzFwXyb0+kEuISWAZ7c58COLb9Uh5E/nUJ0gAeXiZNLRRi+5oTV9XZUqVGtM6JEZ8AbKx+e656JneHrBiikjv9qUkhFDrcT8jiMGzcOW7Zswd9//41GjRpxz9evXx+AOUPl5+fHPZ+Tk2OVrbIkk8kgk8nsbieEEPLkoWCqhjmaKsf2XHpSM1RuTo5LlIuFAodV95xlIhiNwA9vxsDEMFyQYoktu55dqHHYgDbJRl8l8xod+5mPg1dzkdgxCD5u5psRW6W8AXNAtv7tdijVGeHmJEZusXmcLjIxpvUJhUwkwrq32qGwVM81EW7f2MvmsWJVSpy/VYiIhu7ILdZxAdyU3qEQwlygwm5TY5U3PujZDB/0aIqbeaV2+zMVafQoa0lll0wshMHoeCcjw+Dtn07i53faI+VmPned7X0Oyel5vP5W7HWf3T8C0/uEws9djjHrTyNWpYRYKLB5vRcMirBbgTBepcS+y/d45/qgLKAXVvBdEwnt/7pPSG1jGAbjxo3Dpk2bsH//fgQH8/++Cg4ORv369bFr1y5ER5sz5DqdDgcOHMDChQvrYsiEEEJqCQVTNczRVDm259KTGkyx/aP+tjH+WJUSyem5GB4bDGG5dS5xKiXmDWyBedtSeet57GXj3BVSruBDZcWqlBjbVQV9BSkaZ5kYTmL7RRNiVUrsvHgXS/emI06lxNyBESgpu4HPK9Ehu1CD1ckZvJv/eJU3Zg0IhwAXscfi/GJVSozrGoJSvRF3iszrrdiAIszPDV5ln3NSXLCdLJx5fLMHhONidhGX9Vo6tBWvaEQ9Vxn2pOVUWD6+R5j9X7wBc+ZQrTNCozNx66HsFchgWW5XSEWIDvCE1mCEr5sT6rnKMKtfGAKVzjAx1o2H40O8Ud9djuFxwTDB/nRLS84y819JYqHAYe8tCqZIXRozZgzWr1+P33//Ha6urtwaKXd3d8jlcggEArz33nuYP38+QkJCEBISgvnz50OhUGDo0KF1PHpCCCE1iYKpGlbRVLkHFWyvS+w0vPKZtfIlyjeObI/EB0HcVC4AmLMt1aoBq6NsXEVZMHf5w4p0/l5y/JV6F8PXnMCPFZTqLtEa8OPB65jZPwyzt6RaFWKwPI/k9DzM2HwBCwa1AAAYTAxWJtsOemb+fgHzBkZgTFc9DCYGcqkQLlIxdqXdweLd6fjfqI54/8UQfH/gOjeFUCwy3/DbK4/OHltnNNlcV/XNkGisP3YT528VQiCAw+Bi/IYU9AjzrVS/LmeZmMsGOUkcT5djP19H2bXZA8KRsOo4RndVYVa/cJRoDXB1ksDIMFyw5Gi6peUYpSLz+ymdpViwPc1m762fj2fii1ejHI6bkNr07bffAgC6dOnCe3716tVITEwEAEyePBmlpaUYPXo017R3586d1GOKEEKeMRRM1bCKggTXCrbXtQYecnzxahSu5RSjoFRvNfXMMrPDWpkQYxVIsexl4yrKgu2/co97j+XDWnH/ffhansOsU0pWAXan5cDEMJjeNwy38kt55b/LT6FLTs9DSdljk4mxO4UwOT0P/xRokJmnRptgT9zMU2P4byeQW6xDrEqJ05n5qOciwzdDorHh2E2kZBVALBQgPsS7wuzP7YJSXtDAVvzbcOwm3uveFINXHMWSIdHcui22p1aQ0hkpmfncOe27nIOxXVUQAnaDyHiVN4QCc9NjXzcn8xgruJ6A4+zazN9TMbqrCl2a1uNV2LuWU8xda1vBpGUPK66nVqkOgDPcFVLMHhCBj34757BCJCF1galo3i3MxSdmzZqFWbNm1f6ACCGE1BkKpmqYoyDBsueSPU9CFUBfNycYTQw++u0c7zziQ7yR0DHIqoluRcGCrWycZTGKvx1kwQB+76ZVyRnYNLoj5my7aDdDAwB7L93DyPgmGLH2JJYPa4XR607bHV9RWWW4El3F1QpbBnjgak4xZGIRBrcNwMXbhRjaLhDjN6RgSVnmZsGgFpi97SIOX8vD8mGtuGyLPfVcZTYzU8Njg5FfoodaZywrWsFfk7RtXCwiGrrjp6S2uFeshVwsgreLDP2iGmBy7+a86oPjN6SgdYAHpvUJhVwiwqqywIjNODFgeD2f4lRKJFpcz4qya5/0C+MFUoDj/xfiVUrUc5Vh+bBWvIB969g4bh92fV1usQ4PNHq4Okng7fLkV8UkhBBCyPODgqkaZi9IqMwv6k9SFUBbN7JioQC9vznIy+wA1o1qy7OXjWPfI+eBFpn31QAe3vgDwNhuKnRorITRxGBVYhtuHVF+iQ5tgrww7aVQFGkMUEhFEECAvZfv8o6vkIkqOT4xsgtKodU7DgpFAgF6/+cg9zhe5Y0ZfcOQXajBF69GwcdVhpYBnsgu1CCioTsmdA9BUYke/p5yh41vz98qtNucdlKPZjbPIValxI7Uu1YBzprENsjML4WPqxO8nKUQiwRoG+yFVgGeOJ2ZD2epCB9tOs9rmMwWzRjVRQUjw8BgZODtIsU3e65yn3VFAXOJRdl8yx8EpvcJw6nMfMzddpE7VpxKiYTYYAxecZT3XbL1YwP1jyKEEELIk4yCqVpQnV/Un8QqgOVvZAvVOsQEeuLkzXxemW8PhRQLBkVg7h9pVoFWRdk49j2cZWIuALW3PidWpcTKhBj4e8hRv2VDzNh83mFzWoVUhPiyqWr2G/h6QywUIPO+Gr5uTogP8bZZQCROpcSR69ZT3OZsS7UqH94v0g8r/r6O/zt6E+vfao/5f6Zh7sAITN98npf9iVUpMbt/OPouse5PA5gDqo96CxBnMd0OMAdgn/QNg85gQpem9XCvWAuZ2Dw10MgwXAVBy8+IXRtVrDNaXQc247V0bzpWJsRgxNqTUEjNFQ2HtA3g1q05wgbM9n4Q2D4+HkWlOjjLJHCSCDFrS6pVIEXT9wghhBDytKFgqpZU9Rf1p6EKoLtCioWvROLmfTWW7L3KL0QQ4o1ViW2QtOYEd5NclRtkNgC9U6QBBMDpG/lIig3GsHaB3BqiVckZWL7vGhb+KxIzfjvnuDltVgFO38zHjL7h+GxHms3iDfEh3pjZLwzX7pVAIhLi6PnbmNq7ORgmjRf0xKu8kRAbhI9+O4ex3VS8AOV0Zj7aBXlhqcUY5m1LQ1Kc+f3mbjMXwYgO8MDYbiH4uE8YbuSWcFPbbheUWgWglopKDfj4pVAwYBCn8obRxODEjfu4lV+KH5Ov884nTqVEnxZ+DoPRzk3rOfwc2AyUWmfE/RIdRqw9CcCcJbSXXWMDZkc/CHzy+wXeDwJfvBpF0/cIIYQQ8tSjYOoJ8bRUAVRIRVi2N926EMHVXAgA/Dk+HvlqXYU3yJZTwVydxJCJhCjWGfFAo4e7XAIGwDiLYhGWWaeiUr3dwJNtTts/qgEGLjuEr19vibAG7hALBJjUoxk+6i1AgVoPpbMMZ7Ly0X/pId57RDfyxPQ+YbhfouMKcHg5S/H2Tyfx2SuRNgOUgS0bwt9Tjqz8UvO1SM9FYmwQgIeFFxbvTsfSodEo0Rp467daJbZxeL2dJEL867sjaB3ggd4t/PDxpgsY201lFUgB5iIZZ7IKMKNPmM1iEYfS8zCmi+Ppej6uMq4whIdCirHdVFiVnIFLtwsxd2AEZmy+wAuoLAPmaznFlf5BgKbvEUIIIeRZQMHUE+JpqQKYW6yzmZ0AzDfMBhODlgGeDo9hORXMMotSvqCE5ZQ9y6wTWzDCHiPDcGW3xUKB1bqisd1UOJuZbzez1TbYCy8084WXs/lmnwHw1WstIRYJEB3giZTMAi4AO5Seh9lbUrFsWCv0X3qIm15Xz1WGEq2Rt9Zr7PoU/JTUlveepzPzHZYzZ5vaHkzPQ2JZds1RMYi52y7if6M6Yuqm8za3H75uvxpivMobV+4+wMebLvDGsCqhDZxlIizefRkf9mqG4cXBYBgGjTzlqO/mxAVFT8sPAoQQQgghNYWCqSfEo1YBfFwe9Ya5/FQweyW3LYMnNnA4lJ6HpNhguMvFNqfbsU1ufVxkYGAuqe6pkGLBoBaY+8fDAgiOgpFD6Xl4O64x5DIR7hVpsGRfusMgDzBnoqaKQh1Or2Nfk5yeywUzCqkIYqEAH5VV3hMIBNx5RAd4WFU1ZKfgOSoGodYZUVhq/zNYlZyBrWPjMHPLBZzOLODWVQFAfXcn7Lp4FwqpiBcsCgG0DvLC5jPZyCvRY96ACHgoJFXuHfak/CBACCGEEFJTKJh6QjxKFcDH6VFvmMuvDasosEkqy8awRAIB5FKx3Sa3G49nIiWrAFP/9zAzE6/y5gorsGXGHXFXSHD8eh62nc+uVJAHAA9K9RUGhiM7NUZUI3f0DK+Pu4UaNPSUY87WVCzefZU31s1jYvHnhWyrnlhsRb+KqhOyVQxtUeuMEAiAeQMjYGKAmb9fsBv4PQwWH2bFDpZlH219H5+WHwQIIYQQQmoKBVNPkCepr469flePesNcPrNVUWBTfnugUoFpFqW9WYfS8yAAMK1PGF759jBvm3lKG4ONI9vjVn5phZXppCIhfNyc7DbwtRXkucolFQaGH/VujkV/XsLB9Dy81z0EK5OvW001PJiei7lbL6J1kCcvkIpXKXG3SAMADqsTxqqUyCnS2q9eGOINpbMUBWo9Zvxu+zoC1sGi5edgL/v4tPwgQAghhBBSUyiYegJYBS4uUjTxcamz8VTU78ryhpldI9SxsRIysRC5JToAsHvjXD6zVVGWxXJ7fIg3DCbGbpGD5PQ8ZBdqbFbHO5ieh8QHWoxedxpju6nsBhtxKiVKKpG9stwep1LCWSKqMCuX90CHqABPJMYGw9fNiZeR4o81FxO6h3Db41XeGB4XBAEE5jVMyRn4Zkg0AFhNQRweG4wpv53Don9FQlh23qx4lRJju6oAADqjqUrBouXn4Og8n6QfBAghhBBCahsFU3XsSWrUC1Su3xV7w5xXogMDYFa5qWKOxl8+s1VRloXtrxSrUmJMVxWKHKwHAgCN3n6ZcTYAsheMxKu8MalnM+SrdZUO8uJUSswb1AL//vEYZvUPt7u/QipCAw85UpKvY+nedCwf1srh8U2Mec2Xu1wCjd6IsetToJCKsPCVSPi9JEdRqbkhrlQkRPq9YoiFAl7DYwEE6N3CD4mxwdAaTJCJhbhbZA4080p0KNY6LuJhGSxafg6VyT5SpT5CCCGEPC8omKpDT2Kj3sr2u2LHNXZ9itVUNUfjZ6eCsefNBjYCoFxvJyVmD4hA+r1irHurHVydxEhacwLfDI52OH5HQRC7Ta0zYvyGFEzvE4r3uzeFwcTAWSbCX6l3MeSHo0iKC0ZDdye7QV73UB809nbGnxPiUawxIL9EhwHRDXHhdqHd18zoE4Y521K5bRUFa04SITYcu4kxXUMwrixA+uyVSKwqtyaL7YFlWUbeXul0wBwYzeoXXuHaN3Z8bLZr/IYUxId4Y/6gFhQoEUIIIYSUoWCqDj2JjXqrUq3vTpHGYZn08uNnpzMWa/WYPzACWfmlKCjVQyIUYnKv5vhIIMADjQFuTmIYGQavfX8EucXmaYOxKiUGtw0wl/YO8bZ53eJVSjjLRFiZEAOtwQS5RAQTw0AkEMDIMLy+SdEBHvBxc8Kbq47ji1ejkJxehJTMfKh1RqxKzsC3w1phXNcQAPzsVffmPpjWJxTTN1+wmmI3Ii4YkQ09rF4Tq1IiOsCDV668ooycs1SM4XHBqOcqRUygJyL9PWwWtziYngsGDG+NU0Vrt4wmBj7uMvvXMcQbDT3k2DEhHgzDIDO/FEuGRCMlqwCzt6Zi9oCIOsmaEkIIIYQ8aSiYqkNPYl+eylbrK1TrcKusSa09BaU67r9vF5Tik98voLmfG6L9PXCnSIsm9Zzx7YFrvBv6eJUSM/qFY/eFbN7aJ3Ydz7gNKdg6Lg6zt6TyihzElmWy5v9xEbsv3eOej1MpkViWWVHrjIhXeWPbuDhk5qkx6b9nodYZ4SGXWE39G7XuNMZ0bYK5AyJQqjOiRGeEq5MYConQKpBiXwMAbYK8EB3giWkvhSK78GHBiPK9sTYez8S6t9pj7rZUXkYuTqXErP7h+OtiNqIaeUIuEWHJkGhkF2rsBkjJ6XkY1UXFba9ovZdaZ4S7Qoq5AyIwbfN5q8AvoWMQFv11CSPjm6BUb8T7P5/hfRZaQ91kTQkhhBBCnjRPfDC1YMEC/O9//8OlS5cgl8vRsWNHLFy4EM2aNeP2YRgGs2fPxooVK5Cfn4927dph2bJlCA+3v4blSfA4+vLYq8pnT2Wr9bEZI0e0ehMK1eb9Pvn9Aga3DeD1YFJIRZjVLwzT+oTin7LALCWrAAOXHUJ0gIdViW6twWQu7Q1wRQ7y1ToUaQzw95Jj3h8XcfT6faseVHcKS/FO58b4etdVHEzPxYzfL6BPCz989kokfj6eiUClAjGBnhi/IQVJccFc8QUfNxn+KSjFGyuPc+P9Kamt1bRGFhvwjVh7Er3D62PE2pPctp5hvrx9B7cNwMIdaWgZ4InhFuuaUrIK8Nn2S4ho5A6ZWIgGngoAwPXcEofXWiYWYtf7nVCiNcBJYr80OgC4y83fK0+FBOO6hWB0FxUKS/Xc+7PXXGswoU8LP6vKfnWVNSWEEEIIedI88cHUgQMHMGbMGLRp0wYGgwHTpk1Djx49cPHiRTg7OwMAFi1ahK+++gpr1qxB06ZNMW/ePLz44ou4fPkyXF1d6/gM7KvtvjzVKW5R2fLWRRq9w6lq8SolDl/Pg6+bEwCguZ+b1TQ1tc6IzPxS/H72dqVKdMvEQrwY6gNnmZgLEN3lEhhMDHIfaHH0+n2bTXPZ9VcA8P2B61zQs/pQBha+EomGngou+8MGLSlZBdh4PBOrEttgZUIMdEYT/D0VDhviAuaAr1OINzQGfiEMBuBdK3Yq3l6LLJqlIe0C4GkRrFQUeHsqHlaALFTrKvW9cldI4amQoOfigzaPyV4n9jO0VBdZU0IIIYSQJ80TH0zt2LGD93j16tXw8fHBqVOn0KlTJzAMg8WLF2PatGl4+eWXAQBr166Fr68v1q9fj3feeacuhl0ptdmX51GKW1RU3rpQrYNcInJYontGv3AMXHYI3Zv7gIH9dTyVbdobq1LifokOM/qGYdKvZ/lTA0O8Mb1PKN7p3NjOuqI8LhvFZru0BhOS0/Og0ZunxLkrpLieW4LR604DMGehvhkSjUU7LvGm4a17q53NsbI85BIsfCXSqjz7vss5XFnyQ+l5FU7FA8ALpqsSeFfle2WrjLwle+OsiawpIYQQQsjT7okPpsorLCwEAHh5eQEAMjIycOfOHfTo0YPbRyaToXPnzjh8+LDNYEqr1UKr1XKPi4qKannU9tVWXx5HxS1O3sxHgVrvcPqfvfLW2QWl2H/lHlo0dMOMPqEQCwSY1KMZPuotRL5aB4ORwZ3CUvxZtuaJvelm1w+VV5l+TvEqJeYMjIBCKrYKpADg4NVczNuWhg97NcPXu2z3bmIDs1WHMpAUF8xVq7PMsFhmf5Ligm0GZkeu5yFOpeQFWKx4lTf8PeXw85BbZYe+P3AdkQ09zNPmYoNRz1Xm8LwbecqtPo+qBN6V/V5VtqqfpZrImhJCCCGEPAueqmCKYRhMnDgRcXFxiIgwT9u6c+cOAMDXl78mxdfXFzdv3rR5nAULFmD27Nm1O9gqqI2+PPaKW7AZl+mbz/PW/lSmt1WhWoeb99XYdfEOfN1k2H4+m3eM2LKmsH7ucsz7I4130323yPZNe0UlwoO8ndE6yAsPSvUwmWA3QDyYnovJgmY2t7G0BnOj2tFdVDhy3TxuZ9nD/wUssz/2MmYPS7kLeJUM41VKjO6qgkgoAGAd/Kh1RoxZfxoz+oTBx00Go4lBvMrbZjXEeJW3zXVPlckYlg+Q7TV/Zvc1Mozdqn6xKiVyijT4xyIQromsKSGEEELIs+KpCqbGjh2Lc+fOITk52WqbQCDgPWYYxuo51tSpUzFx4kTucVFREfz9/Wt2sHXMXsbBXsalMtP/CtR6LNl7FdEBnliZbH2MQ+l5EAJoHeSFVgEevJ5EgUqFzYxOSlaB3UxPrEqJs1kFkIgEcJZJKqx+WKpznOViAzeRUIBVyRmIVSkhFT0M5iwDIHsZM7ZH1bq32iExNogrHOHn7oS7RVrM3JKKL16NgrtCigYecnz+ahSu5RSjoKzAAwD8Z9dVHL6eh2+GRMMEplzfKCVm9AszF+5QOlu9v73Auyrr4yz3VUhFWDo0GmDAC+xiVUqM6xYCfw85tEYTujf3qbGsKSGEEELIs+KpCabGjRuHLVu24O+//0ajRo245+vXrw/AnKHy8/Pjns/JybHKVrFkMhlkMsfTrJ529tbYOFqjVFGVthKdAYfS8zAirrHdYxxMz8OU3qEwmBjojA8DkoaeCix8ORJ/p+fCx1XGVdrLfaBB/6gGmL011WrdFdss9rdRHSs1rUwmFiJepcTB9DwopCIkxQVzVf08FBKU6oxQSEWQiATo2FiJwe0CUFiqA/AwaGGzP/amJQLmgOp+iY6r1hev8kaiReNcy2tYrDFg6I/HuNcqpCKsf7s9NAajzeqBabeL8OeFbPRt0aDC82VVZX1c+X3VOiPGrk/ByE6N8UHPZtAbTXCWiuAsFcNDIaHAiRBCCCHEgSc+mGIYBuPGjcOmTZuwf/9+BAcH87YHBwejfv362LVrF6KjzcUQdDodDhw4gIULF9bFkJ8I9tbYVMRRlbaSsmIFYqHtjB/rvlqHpXvT0b25D+95gVCA7edu86YGxquU8HVzQpsgLyTFBkMhFUOtM/BKdN8uKEUDd6cKizDUc5VhZr9wfLbjklUZdsAcoK1MiMH1nBJM7tUcQ388ip9HdrA6FhtA2HuvWJUSKVkFAMx9oSb1bIYhPxzlijlYXsPy2TS1zoi31p7AqsQ2XBlzsUiA/BI9ijVGNPdzw4Er9+DSpvL/a1al+bOtfdU6IxbvvorFu69iz8TOdqcGEkIIIYQQvic+mBozZgzWr1+P33//Ha6urtwaKXd3d8jlcggEArz33nuYP38+QkJCEBISgvnz50OhUGDo0KF1PPq6ZWuNjYlhHL7GUZU2D7Y/kXNFZbolVsfiMiI2Ku2ZAEQHeGLE2pNYmRDD68/Eyi3WoYmPi8MiDKV6I/ovO4SlQ6Ox2u40RAFaB3li89l/8EGPZnYzXvaCUbapcEZuCVYmxCAlqwD5JTpeVTzL87Y13TK3WIekNSfwY0IbfPnXJd41iVMpMaNvOOZuTcXHfcIcrmFjVaX585PYKJoQQggh5Gn1xAdT3377LQCgS5cuvOdXr16NxMREAMDkyZNRWlqK0aNHc017d+7c+UT3mAIcN9StarNde8qvsalsDyJbfFxliA/xhsHI2O0vFatSwmBkrI7lKHvCVtqzzPhYHi8lqwBKZ/OxHBVhSMnMh1pnBMPAbmPdg+m5SIwNwuLdV/FJ3zCH15R9r38KSnEjT801tR247BAveFo+rBX33+XP2zKbZjn10MtZahVIAUByeh7mbEtFdIBnhWvYWFVp/vw4GkUTQgghhDwvnvhgiqkgkwKYi0/MmjULs2bNqv0BlVPdoMdRwQABgMlVbLZbWY/S28pdIcXCVyKRmVeC4WXrfGytc9LqjVbHqigjAgBju4ZgxNoTVscbvyEFg1o25I3D1jjZQKEy5dYBoETruMcS+165xTqu/5QtbGEJW9eQvd4zf7+A1y2mHq5MiLEb8LHB5dK96Q7XsLGq0oOqthtFE0IIIYQ8T574YOpJVpUKapYqKhjQu4VftZrtVtaj9LZq4CGHSCjAtE3nER3giaTYYK6iXUpWATYez8Sng1rA182J97qKMiKNPOU4l1WAJUOieccbvyEFMYGelbrJZwOFisqts9s1+rL1WBUEqI4CkPgQbwR4KbBnYmeb17BQrUOpzoh3uzSBzmBCdIAnUjILKh3wVWbaXVUC5NpsFE0IIYQQ8ryhYKqaqlJBrbyKCgYkdAyyu60ymYrKeJTeVr5uTpgzIAIf/XaOV+CBvSEvH0gBFWdE6rs5wbmJN6ZuOm8VnFb2Jp/NnN28r7bbw4mdNhirUuLw9Tx8d+BahQFqRQGIn51gzFawHatS4psh0RDbKdvPYgO+yk67q0qAXFuNogkhhBBCnjcUTFVTVSqolVfRlDdHWYsnpUBAVW/IK5MRcVdIsfQRb/IZAD/+fR0JsUFWPZzYaYPrj93kpg+WL2VeU+drL9hmx8OuEbO37iwlq6DK0+6qEiDXRqNoQgghhJDnDQVT1fQoVdEqmvLmaJpadQoE1FQxC3tMDAO9yYS7D7TIK9FBaef4lQlIHuUm3zKAOXw9j+vhpDWY4CGXwNtVhtv5pQhr4M4FUkDlA9SqjK2ighsj4ho7XHf28/FMmnZHyBPs77//xueff45Tp04hOzsbmzZtwsCBA7ntDMNg9uzZWLFiBVcYadmyZQgPD6+7QRNCCKlxFExV06NURatoylvOA63N11WnQEB113VV57hsILBgexpmD4iwefzKBCSFah1yHmhRUKo3N5CVieEhr7iBrGUAo9YZrRoL2yu77uizqm4gWlGwrdEbMenXs0iKC8boLirIxEK4yMQQCQUQCQX44tUoCqQIeYKVlJQgKioKw4cPxyuvvGK1fdGiRfjqq6+wZs0aNG3aFPPmzcOLL76Iy5cvP/GVZgkhhFQeBVPV9ChV0Sqa8sb+96MWCHiUdV3VOS6bYYkO8MSU387hi1ejbK6fchSg3C4oxZT/nuOtd4pVKTGuWwgCvRR21ycB1Zs+yX5W7JiKtXp4KKTQGUwoLNVDbzTh0LU8rErOgFpnrHQgWpnso1pnREpmPlr6e8DP3QmN61GzXEKeFr1790bv3r1tbmMYBosXL8a0adPw8ssvAwDWrl0LX19frF+/Hu+8887jHCohhJBaRMFUNT1qVbSKprzVRIGAR1nXVd3jWpb1vpZTDKOJ4QUejjJlzlIRpvz3HE5l5mNsNxWi/T2gNZjgJBEh414JhAJAIRXZHXNFAYy7nL89VqXE3AERKNEZMeW3czh1Mx/fDInGor8uW029+2ZINMZvSKl0IOqwAqDKG17OUq7p78/HM/HFq1EOx04IeXpkZGTgzp076NGjB/ecTCZD586dcfjwYbvBlFarhVb7cGZCUVFRrY+VEELIo6Fg6hE8alU0R1PeaqJAwKOs63qU4+qM5gxQQameF3hUlCmb0TcMpzLNAQ3bj4kVq1JiVr9wFKj1dq+LowAmVqWERm/EyoQYXul1ndGE6b9fwMGruRjbTYXVhzKsikJwRSPizEFiZQJRe8F2nEqJhNggDPvxGJfporVRhDxb7ty5AwDw9fXlPe/r64ubN2/afd2CBQswe/bsWh0bIYSQmkXB1CN6kquiPcq6LkdcZI6/NvVcZADMU9ksA4+KMmUFpXokxQXbDWjmbE3FS5EN4KGwvX7KXgDDruUaZ1F0gtXXoqdXtL+H1Tory/dPKisYAVQuELUVbLs4iVGiNWD9W+2oJDkhzzhBuRYIDMNYPWdp6tSpmDhxIve4qKgI/v7+tTY+Qgghj46CqWfYo6zrckQqEjos6y0VC7ny3sDDwKOijJazVOQwoDmYnofE2GCHWaHyAYyzTIyTN/N51ftYnUK8odYZuMeVbaQLVD4QfZKDbUJI7ahfvz4Ac4bKz8+Pez4nJ8cqW2VJJpNBJpPV+vgIIYTUHPs1uMlTj83UdArx5j3/qFPLCkp1GF7WJ8kSmwG6X2zevio5A8DDwKOiTJmztOLYXmswVZgVcldI0cTHBS0DPBHi64rOTeshJtCTtw93DeQPr4GjkvSW2x8lECWEPPuCg4NRv3597Nq1i3tOp9PhwIED6NixYx2OjBBCSE2jzNQz7lHXddniIpNgyA/HeH2c2DVI4zek4Kektnhz1XFuTRAbeFSUKZOIBKhvo/qfJZlYWOXpiY6uQaFax40pJaugUo10F5VVXLyWU1xrvbsIIU+24uJipKc/zKJnZGTgzJkz8PLyQkBAAN577z3Mnz8fISEhCAkJwfz586FQKDB06NA6HDUhhJCaRsHUc6Cmp5p5u0gRE+hpczperEqJ/Vfu2Syu4KgC4vxBLfDJllRENHRHvMqbVxrd8tg5D7RWWabKsHcNLMe0KjkD3wyJBsBvpBsf4o3Z/c2NNt+OC0aJzoixG1JqvHcXIeTpcfLkSXTt2pV7zK51SkhIwJo1azB58mSUlpZi9OjRXNPenTt3Uo8pQgh5xlAwRarMUVA0Z0AEikp1GNSyoc0MmL0sUV6JDrvTcnD4Wh6WDjUHNOV7TY3tGoIG7k41ngGyHFOJVo/5A1tAZzShRGuwyuTVVu8uQsjTpUuXLmAYxu52gUCAWbNmYdasWY9vUIQQQh47CqZItTiePuhs93X2GvZezy0BAKh1Roxdn4KRnRpjQvcQGEwMFFIR5FIRzmQWIMyvdn7VrWz2rrZ6dxFCCCGEkKcPBVOk2qo6fdBRw17LhrpqnRGLd1/F4t1XuefWv9UOcSrvOg9Uaqt3FyGEEEIIefpQNT/yWFQ0Pc5ZJraqOsiKD/FGEx8X+D0B65Fqq3cXIYQQQgh5+lAwRWpMoVqHaznFSMnMx7V7xShU67htFU2PK9YY7JZxX/RKJHwrqPJXkxydB1uR0BYqmU4IIYQQ8nyhaX6kRjiawtfAQ16p6XFNfFxqvIx7VVV0Ho6KbzxK7y5CCCGEEPL0oWDqOWWvEER1j1VRhbvKTo9j12Gx47ueWwI3ue6x9HGqbKW+2ujdRQghhBBCnj4UTD2HKsq+VFVlKtxV1LDXcnpcTY+vsqpSqa+me3cRQgghhJCnD62Zes5UlH2xXB9UWZWZwsdOj7O1JspyelxtjK+yqFIfIYQQQgipCspMPWcqk31h96vsFMDKTuGrzPS4uuzjRJX6CCGEEEJIVVAw9ZypKPtSUKrDrK2pVZpiV5UpfBVNj6vL7FBVzoMQQgghhBCa5vecqSj7otWbqjzFrrJT+GpifLWZHarJ8yCEEEIIIc8+ykw9ZxxlX+JDvHH4ep7N11U0xa6mKtzVdXaIKvURQgghhJDKoszUc8ZR9mVW/3CsSs6w+9qKpti5K6Ro4uOClgGeaOLjUq0A5EnIDtXEeRBCCCGEkGcfZaaeQ/ayL3klOqh1Rruve1wFGCqbHarJXlmEEEIIIYRUFQVTzyl7hSCelAIMFRWqqKteVIQQQgghhLBomh/hPAlT7CqjLntREUIIIYQQwqLMFOF5Ggow1GUvKkIIIYQQQlgUTBErFU2xq2t12YuKEEIIIYQQFk3zI0+duuxFRQghhBBCCIuCKfLUYXtR2fK4C2UQQgghhJDnFwVT5KnztBTKIIQQQgghzzZaM0WeSk9DoQxCCCGEEPJso2CKPLWe9EIZhBBCCCHk2UbT/AghhBBCCCGkGiiYIoQQQgghhJBqoGCKEEIIIYQQQqqBgilCCCGEEEIIqQYKpgghhBBCCCGkGiiYIoQQQmrJ8uXLERwcDCcnJ7Ru3RoHDx6s6yERQgipQRRMEUIIIbXg559/xnvvvYdp06YhJSUF8fHx6N27NzIzM+t6aIQQQmoI9ZkCwDAMAKCoqKiOR0IIIc8X9u9d9u/hZ8lXX32FESNG4K233gIALF68GH/99Re+/fZbLFiwwGp/rVYLrVbLPS4sLARQO/82mbTqGj9mbXra/n2m61u76PrWLrq+Vfu3ScA8i/+CVdGtW7fg7+9f18MghJDnVlZWFho1alTXw6gxOp0OCoUCv/76KwYNGsQ9P2HCBJw5cwYHDhywes2sWbMwe/bsxzlMQgghDlTm3ybKTAFo0KABsrKy4OrqCoFAUOXXFxUVwd/fH1lZWXBzc6uFET7Z6Pzp/On86fyre/4Mw+DBgwdo0KBBLYyu7uTm5sJoNMLX15f3vK+vL+7cuWPzNVOnTsXEiRO5xyaTCffv34dSqazWv02P2/P+/0Jto+tbu+j61q6n7fpW5d8mCqYACIXCGvlF1M3N7an4gtQWOn86fzp/Ov/qcHd3r+HRPDnKB0EMw9gNjGQyGWQyGe85Dw+P2hparXne/1+obXR9axdd39r1NF3fyv7bRAUoCCGEkBrm7e0NkUhklYXKycmxylYRQgh5elEwRQghhNQwqVSK1q1bY9euXbznd+3ahY4dO9bRqAghhNQ0muZXA2QyGWbOnGk1PeN5QedP50/nT+f/vJ6/IxMnTsQbb7yBmJgYdOjQAStWrEBmZibefffduh5araDvQu2i61u76PrWrmf5+lI1P0IIIaSWLF++HIsWLUJ2djYiIiLw9ddfo1OnTnU9LEIIITWEgilCCCGEEEIIqQZaM0UIIYQQQggh1UDBFCGEEEIIIYRUAwVThBBCCCGEEFINFEwRQgghhBBCSDVQMPWIli9fjuDgYDg5OaF169Y4ePBgXQ/psfn777/Rr18/NGjQAAKBAJs3b67rIT1WCxYsQJs2beDq6gofHx8MHDgQly9fruthPTbffvstIiMjuW7mHTp0wJ9//lnXw6ozCxYsgEAgwHvvvVfXQ3ksZs2aBYFAwPtTv379uh4WIYQQ8lhRMPUIfv75Z7z33nuYNm0aUlJSEB8fj969eyMzM7Ouh/ZYlJSUICoqCkuXLq3rodSJAwcOYMyYMTh69Ch27doFg8GAHj16oKSkpK6H9lg0atQIn332GU6ePImTJ0+iW7duGDBgAFJTU+t6aI/diRMnsGLFCkRGRtb1UB6r8PBwZGdnc3/Onz9f10MidcBoNOLu3bvIycmB0Wis6+EQUiX0/SWPikqjP4J27dqhVatW+Pbbb7nnQkNDMXDgQCxYsKAOR/b4CQQCbNq0CQMHDqzrodSZe/fuwcfHBwcOHHhu+8h4eXnh888/x4gRI+p6KI9NcXExWrVqheXLl2PevHlo2bIlFi9eXNfDqnWzZs3C5s2bcebMmboeCqkjmzZtwhdffIGTJ0/CYDAAAMRiMWJiYvDhhx8+1/8e1CSj0Yjc3FwIBAIolUqIRKK6HtIzgb6/j8fz8P2lzFQ16XQ6nDp1Cj169OA936NHDxw+fLiORkXqUmFhIQBzQPG8MRqN2LhxI0pKStChQ4e6Hs5jNWbMGPTp0wfdu3ev66E8dlevXkWDBg0QHByMwYMH4/r163U9JPKYfP/99xg8eDAiIyPx888/Izk5GQcPHsTPP/+MyMhIDB48GD/88ENdD/OptmnTJsTGxkKhUKBBgwbw8/ODQqFAbGzsczetvqbR97f2PU/fX3FdD+BplZubC6PRCF9fX97zvr6+uHPnTh2NitQVhmEwceJExMXFISIioq6H89icP38eHTp0gEajgYuLCzZt2oSwsLC6HtZjs3HjRpw+fRonTpyo66E8du3atcNPP/2Epk2b4u7du5g3bx46duyI1NRUKJXKuh4eqWWff/45li9fbjMLPXDgQLRp0waffvop3n777ToY3dPv+++/x/jx45GUlIQPP/wQvr6+YBgGOTk5+OuvvzB48GAsWbKErm810fe3dj1v318Kph6RQCDgPWYYxuo58uwbO3Yszp07h+Tk5LoeymPVrFkznDlzBgUFBfjtt9+QkJCAAwcOPBcBVVZWFiZMmICdO3fCycmprofz2PXu3Zv77xYtWqBDhw5o0qQJ1q5di4kTJ9bhyMjj8M8//yAuLs7u9o4dO+L27duPcUTPFrrZr130/a1dz9v3l6b5VZO3tzdEIpFVFionJ8cqW0WebePGjcOWLVuwb98+NGrUqK6H81hJpVKoVCrExMRgwYIFiIqKwn/+85+6HtZjcerUKeTk5KB169YQi8UQi8U4cOAAvvnmG4jF4uduIbOzszNatGiBq1ev1vVQyGMQHh6OFStW2N3+ww8/IDw8/DGO6NlCN/u1i76/tet5+/5SZqqapFIpWrdujV27dmHQoEHc87t27cKAAQPqcGTkcWEYBuPGjcOmTZuwf/9+BAcH1/WQ6hzDMNBqtXU9jMfihRdesKpeN3z4cDRv3hxTpkx5JhfZOqLVapGWlob4+Pi6Hgp5DL788kv06dMHO3bsQI8ePeDr6wuBQIA7d+5g165duHnzJrZv317Xw3xqsTf7X375pc3tdLP/aOj7W7uet+8vBVOPYOLEiXjjjTcQExODDh06YMWKFcjMzMS7775b10N7LIqLi5Gens49zsjIwJkzZ+Dl5YWAgIA6HNnjMWbMGKxfvx6///47XF1duSylu7s75HJ5HY+u9n388cfo3bs3/P398eDBA2zcuBH79+/Hjh076npoj4Wrq6vV+jhnZ2colcrnYt3cpEmT0K9fPwQEBCAnJwfz5s1DUVEREhIS6npo5DHo3LkzLly4gG+//RZHjx7l/v6rX78++vbti3fffRdBQUF1O8inGN3s1y76/tau5+37S6XRH9Hy5cuxaNEiZGdnIyIiAl9//fVzUxZ7//796Nq1q9XzCQkJWLNmzeMf0GNmb23c6tWrkZiY+HgHUwdGjBiBPXv2IDs7G+7u7oiMjMSUKVPw4osv1vXQ6kyXLl2em9LogwcPxt9//43c3FzUq1cP7du3x9y5c5+L9XKEPA43btywebPfoUMHutknT7zn6ftLwRQhhBBCCCGEVAMVoCCEEEJIjUpISEC3bt3qehiEVAt9f0lVUDBFCCGEkBrVoEEDBAYG1vUwnll0s1+7GjZsSN/fWvSsfX+pAAUhhBBCatSCBQvqegjPtAYNGkAopN/Da8v8+fPregjPtGft+0trpgghhBBSZbdu3cK3336Lw4cP486dOxAIBPD19UXHjh0xatSo567vHiHk+fTshIWEEEIIeSySk5MRGhqKTZs2ISoqCm+++Sb+/e9/IyoqCps3b0ZYWBgOHTpU18N8ZmVlZSEpKamuh/FUKy0tRXJyMi5evGi1TaPR4KeffqqDUT070tLSsHr1aly6dAkAcOnSJYwaNQpJSUnYu3dvHY+uZlFmihBCCCFV0qZNG8TFxeHrr7+2uf39999HcnIyTpw48ZhH9nw4e/YsWrVqBaPRWNdDeSpduXIFPXr0QGZmJgQCAeLj47Fhwwb4+fkBAO7evYsGDRrQ9a2mHTt2YMCAAXBxcYFarcamTZvw5ptvIioqCgzD4MCBA/jrr7+emXVTFEwRQgghpErkcjnOnDmDZs2a2dx+6dIlREdHo7S09DGP7NmwZcsWh9uvX7+ODz74gG72q2nQoEEwGAxYvXo1CgoKMHHiRFy4cAH79+9HQEAABVOPqGPHjujWrRvmzZuHjRs3YvTo0Rg1ahQ+/fRTAMC0adNw4sQJ7Ny5s45HWjMomCKEEEJIlTRu3BgzZszA8OHDbW5fvXo15s6di+vXrz/mkT0bhEIhBAIBHN2iCQQCutmvJl9fX+zevRstWrTgnhszZgy2bduGffv2wdnZmYKpR+Du7o5Tp05BpVLBZDJBJpPh2LFjaNWqFQDgwoUL6N69O9fM92lHa6bIM2/WrFlo2bJllV7TpUsXvPfeew73EQgE2Lx5c7XHZc+NGzcgEAhw5syZGj92RdasWQMPD4/H/r6W9u/fD4FAgIKCgjodByHEvkmTJuHdd9/F2LFj8fvvv+Po0aM4duwYfv/9d4wdOxajRo3C5MmT63qYTy0/Pz/89ttvMJlMNv+cPn26rof4VCstLYVYzC9ovWzZMvTv3x+dO3fGlStX6mhkzx6hUAgnJyfevYWrqysKCwvrblA1jEqjk2fepEmTMG7cuLoeBqmkjh07Ijs7G+7u7nU9FEKIHaNHj4ZSqcTXX3+N77//nvsFXyQSoXXr1vjpp5/w2muv1fEon16tW7fG6dOnMXDgQJvbK8paEceaN2+OkydPIjQ0lPf8kiVLwDAM+vfvX0cjezYEBQUhPT0dKpUKAHDkyBEEBARw27Oysrj1ac8CCqbIM8/FxQUuLi51PYxK0el0dT2EatPr9ZBIJI98HKlUivr169fAiAghten111/H66+/Dr1ej9zcXACAt7d3jfw98Lz78MMPUVJSYne7SqXCvn37HuOIni2DBg3Chg0b8MYbb1htW7p0KUwmE7777rs6GNmzYdSoUbwpkhEREbztf/755zNTfAKgaX7kKdClSxeMHz8ekydPhpeXF+rXr49Zs2Zx2wsLCzFy5Ej4+PjAzc0N3bp1w9mzZ7nt5af5GQwGjB8/Hh4eHlAqlZgyZQoSEhKsfgE0mUx235OVnZ2N3r17Qy6XIzg4GL/++itv+/nz59GtWzfI5XIolUqMHDkSxcXF3PbExEQMHDgQCxYsQIMGDdC0aVNu2/Xr19G1a1coFApERUXhyJEjvGP/9ttvCA8Ph0wmQ1BQEL788kve9vz8fLz55pvw9PSEQqFA7969cfXqVd4+a9asQUBAABQKBQYNGoS8vDybn4Et7HVdtWoVGjduDJlMBoZhHH4ely9fhkAg4Eqlsr766isEBQWBYRib0/wOHz6MTp06QS6Xw9/fH+PHj+duNJYsWcKb975582YIBAIsW7aMe65nz56YOnVqpc+NEFJ5EokEfn5+8PPzo0CqhsTHx6NXr152tzs7O6Nz586PcUTPlqlTp2L79u12ty9fvhwmk+kxjujZ8u6776JPnz52t3/66af48ccfH+OIahcFU+SpsHbtWjg7O+PYsWNYtGgR5syZg127doFhGPTp0wd37tzB9u3bcerUKbRq1QovvPAC7t+/b/NYCxcuxLp167B69WocOnQIRUVFNtc+2XtPSzNmzMArr7yCs2fP4t///jeGDBmCtLQ0AIBarUavXr3g6emJEydO4Ndff8Xu3bsxduxY3jH27NmDtLQ07Nq1C9u2beOenzZtGiZNmoQzZ86gadOmGDJkCAwGAwDg1KlTeO211zB48GCcP38es2bNwowZM7BmzRru9YmJiTh58iS2bNmCI0eOgGEYvPTSS9Dr9QCAY8eOISkpCaNHj8aZM2fQtWtXzJs3r0qfS3p6On755Rf89ttv3BovR59Hs2bN0Lp1a6xbt453nPXr12Po0KEQCARW73H+/Hn07NkTL7/8Ms6dO4eff/4ZycnJ3HXs0qULUlNTuV/GDxw4AG9vbxw4cACAOXg+fPgw3XgQQgghpOYxhDzhOnfuzMTFxfGea9OmDTNlyhRmz549jJubG6PRaHjbmzRpwnz//fcMwzDMzJkzmaioKG6br68v8/nnn3OPDQYDExAQwAwYMKBS78kCwLz77ru8fdq1a8eMGjWKYRiGWbFiBePp6ckUFxdz2//44w9GKBQyd+7cYRiGYRISEhhfX19Gq9Vy+2RkZDAAmB9//JF7LjU1lQHApKWlMQzDMEOHDmVefPFF3nt/+OGHTFhYGMMwDHPlyhUGAHPo0CFue25uLiOXy5lffvmFYRiGGTJkCNOrVy/eMV5//XXG3d2dqYyZM2cyEomEycnJ4Z6rzOfx1VdfMY0bN+a2Xb58mQHApKamMgzDMPv27WMAMPn5+QzDMMwbb7zBjBw5kne8gwcPMkKhkCktLWVMJhPj7e3N/Pe//2UYhmFatmzJLFiwgPHx8WEYhmEOHz7MiMVi5sGDB5U6L0IIIYSQyqLMFHkqREZG8h77+fkhJycHp06dQnFxMZRKJbc2ysXFBRkZGbh27ZrVcQoLC3H37l20bduWe45dMF3Z97TUoUMHq8dsZiotLQ1RUVFwdnbmtsfGxsJkMuHy5cvccy1atIBUKnX4/uxCTfb909LSEBsby9s/NjYWV69ehdFoRFpaGsRiMdq1a8dtVyqVaNasGW98tsZfFYGBgahXrx73uDKfx+DBg3Hz5k0cPXoUALBu3Tq0bNkSYWFhNt/j1KlTWLNmDe94PXv2hMlkQkZGBgQCATp16oT9+/ejoKAAqampePfdd7nrsH//frRq1eqpWTdHCCGEkKcHFaAgT4Xy8/AFAgFXItbPzw/79++3eo2jEt/lp5MxNqoi2XvPirDHZhjG5rS18u9vGWzZe392f/b9bR3b8hxsnU/519nbpyrKj70yn4efnx+6du2K9evXo3379tiwYQPeeecdu+9hMpnwzjvvYPz48Vbb2OpAXbp0wYoVK3Dw4EFERUXBw8MDnTp1woEDB7B//3506dKl2udICCGEEGIPZabIU61Vq1a4c+cOxGIxVCoV74+3t7fV/u7u7vD19cXx48e554xGI1JSUqr1/mx2xfJx8+bNAQBhYWE4c+YMryLToUOHIBQKeYUmqiMsLAzJycm85w4fPoymTZtCJBIhLCwMBoMBx44d47bn5eXhypUrXCnYsLAwm+N/FJX9PIYNG4aff/4ZR44cwbVr1zB48GCHx0xNTbU6nkql4jJ67Lqp//73v1zg1LlzZ+zevZvWSxFCqoRhGIwcORJeXl511vPvWbNixQr4+/tDKBRi8eLFdT0cQmoUBVPkqda9e3d06NABAwcOxF9//YUbN27g8OHDmD59Ok6ePGnzNePGjcOCBQvw+++/4/Lly5gwYQLy8/PtZpEc+fXXX7Fq1SpcuXIFM2fOxPHjx7nCCMOGDYOTkxMSEhJw4cIF7Nu3D+PGjcMbb7wBX1/fRzrvDz74AHv27MHcuXNx5coVrF27FkuXLsWkSZMAACEhIRgwYADefvttJCcncwUyGjZsiAEDBgAAxo8fjx07dmDRokW4cuUKli5dih07djzSuCr7ebz88ssoKirCqFGj0LVrVzRs2NDuMadMmYIjR45gzJgxOHPmDK5evYotW7bweodFRERAqVRi3bp1XDDVpUsXbN68GaWlpYiLi3uk8yKEPD927NiBNWvWYNu2bcjOzrYq60yqpqioCGPHjsWUKVPwzz//YOTIkXU9pCfGmjVrHM6iIU8HCqbIU00gEGD79u3o1KkTkpKS0LRpUwwePBg3btywG7BMmTIFQ4YMwZtvvokOHTpwa3CcnJyq/P6zZ8/Gxo0bERkZibVr12LdunXc2h+FQoG//voL9+/fR5s2bfCvf/0LL7zwApYuXfpI5wyYszW//PILNm7ciIiICHzyySeYM2cOEhMTuX1Wr16N1q1bo2/fvujQoQMYhsH27du56YPt27fHjz/+iCVLlqBly5bYuXMnpk+f/kjjquzn4ebmhn79+uHs2bMYNmyYw2NGRkbiwIEDuHr1KuLj4xEdHY0ZM2bwGv4JBAIu+xQfH8+9zt3dHdHR0XBzc3uk8yKEPD+uXbsGPz8/dOzYEfXr14dYzF8R8TT3A6wLmZmZ0Ov16NOnD/z8/KBQKOp6SI+MYRiuui4hVM2PPPeMRiPTtGlTZvr06XU9FEIIIXUoISGBAcD9CQwMZDp37syMGTOGef/99xmlUsl06tSJYRhzldXevXszzs7OjI+PD/Pvf/+buXfvHnes4uJi5o033mCcnZ2Z+vXrM1988QXTuXNnZsKECdw+AJhNmzbxxuDu7s6sXr2ae3zr1i3mtddeYzw8PBgvLy+mf//+TEZGBm/MAwYMYD7//HOmfv36jJeXFzN69GhGp9Nx+2g0GubDDz9kGjVqxEilUkalUjE//vgjYzKZmCZNmvAq3DIMw5w/f54RCARMenp6hdfs5s2bTP/+/RlnZ2fG1dWVefXVV7mKtatXr+ZdTwC8sdvCVuD97rvvmEaNGjFyuZz517/+xVV4ZRjzv9uzZ89mGjZsyEilUiYqKor5888/ue0vv/wyM3bsWO7xhAkTGADMhQsXGIZhGL1ez7i4uDA7duxgGIZhTCYTs3DhQiY4OJhxcnJiIiMjmV9//ZV7PVtldseOHUzr1q0ZiUTC7N27lzEajcxnn33GNGnShJFKpYy/vz8zb9483mssx52SksJdA3a75Z+ZM2cyDMMwy5YtY1QqFSOTyRgfHx/mlVdeqfBzIHWHMlPkuXPz5k388MMPuHLlCs6fP49Ro0YhIyMDQ4cOreuhEUIIqUP/+c9/MGfOHDRq1AjZ2dk4ceIEAHPfQbFYjEOHDuH7779HdnY2OnfujJYtW+LkyZPYsWMH7t69i9dee4071ocffoh9+/Zh06ZN2LlzJ/bv349Tp05VaTxqtRpdu3aFi4sL/v77byQnJ8PFxQW9evXiZcj27duHa9euYd++fVi7di3WrFnD6zv45ptvYuPGjfjmm2+QlpaG7777Di4uLhAIBEhKSsLq1at577tq1SrEx8ejSZMmDsfHMAwGDhyI+/fv48CBA9i1axeuXbuG119/HQDw+uuvY/fu3QCA48ePIzs7G/7+/hWeN9vDcOvWrdixYwfOnDmDMWPGcNv/85//4Msvv8QXX3yBc+fOoWfPnujfvz/XmL5Lly68Qkjl+w+eOHECGo2Gq4o7ffp0rF69Gt9++y1SU1Px/vvv49///je3P2vy5MlYsGAB0tLSEBkZialTp2LhwoWYMWMGLl68iPXr11d6Gn/Hjh2xePFiuLm5ITs7G9nZ2Zg0aRJOnjyJ8ePHY86cObh8+TJ27NiBTp06VeqYpI7UdTRHyOOWmZnJdOzYkXFzc2NcXV2ZDh06MAcOHKjrYT1xwsLCGGdnZ5t//u///q+uh0cIIbXi66+/ZgIDA7nHnTt3Zlq2bMnbZ8aMGUyPHj14z2VlZTEAmMuXLzMPHjxgpFIps3HjRm57Xl4eI5fLq5SZWrlyJdOsWTPGZDJx27VaLSOXy5m//vqLYRhzZiowMJAxGAzcPq+++irz+uuvMwzzsJffrl27bJ7v7du3GZFIxBw7doxhGIbR6XRMvXr1mDVr1ji4SmY7d+5kRCIRk5mZyT3H9kU8fvw4wzD8bExlzJw5kxGJRExWVhb33J9//skIhUImOzubYRiGadCgAfPpp5/yXtemTRtm9OjRDMMwzLlz5xiBQMDcu3ePuX//PiORSJh58+Yxr776KsMwDDN//nymXbt2DMOYM4hOTk7M4cOHeccbMWIEM2TIEIZhHmaZNm/ezG0vKipiZDIZ88MPP9g8j4oyUwxjztyV7+3422+/MW5ubkxRUVFlLhd5AlBpdPLc8ff3x6FDh+p6GE+87du3Q6/X29z2qAU0CCHkaRITE8N7fOrUKezbt89m/7pr166htLQUOp2O17vPy8sLzZo1q9L7njp1Cunp6XB1deU9r9FoeL0Uw8PDIRKJuMd+fn44f/48AODMmTMQiUR2q5r6+fmhT58+WLVqFdq2bYtt27ZBo9Hg1VdfrXB8aWlp8Pf352WbwsLC4OHhgbS0NLRp06ZK58sKCAhAo0aNuMcdOnTgejQqFArcvn3bZq/Fs2fPAnhYlOjAgQOQSCSIiopC//798c033wAA9u/fz12PixcvQqPR4MUXX+QdT6fTITo6mvec5fcgLS0NWq0WL7zwQrXO0Z4XX3wRgYGBaNy4MXr16oVevXph0KBBz8Ras2cVBVOEEJsCAwPregiEEPJEsNVTr1+/fli4cKHVvn5+ftx0s4oIBAKrnn+WP2KZTCa0bt0a69ats3qtZcN0R30R5XJ5heN466238MYbb+Drr7/G6tWr8frrr1fq5p2x00/R3vPVxR7L8pi2ei1a7sc2c5dKpejSpQsiIiJgNBpx/vx5HD58GO+99x6Ah/0b//jjD6vKsjKZjPfY8ntQ0XUVCoXcuFj2fqC05OrqitOnT2P//v3YuXMnPvnkE8yaNQsnTpygyn9PKFozRQghhBBSBWz/u6CgIKv+d87OzlCpVJBIJLzeffn5+bhy5QrvOPXq1UN2djb3+OrVq1Cr1bz3uXr1Knx8fKzex93dvVJjbdGiBUwmk9X6H0svvfQSnJ2d8e233+LPP/9EUlJSpY4dFhaGzMxMZGVlcc9dvHgRhYWFXE/D6sjMzMTt27e5x0eOHOF6NLq5uaFBgwY2ey1avie7bopt3C4QCBAfH48vvvgCpaWlXGYrLCwMMpkMmZmZVtfY0fqukJAQyOVy7Nmzx+Z2Nti1/HzL9yyTSqUwGo1WrxWLxejevTsWLVqEc+fO4caNG9i7d6/dsZC6RcEUIYQQQkgVjBkzBvfv38eQIUNw/PhxXL9+HTt37kRSUhKMRiNcXFwwYsQIfPjhh9izZw8uXLiAxMRELlvB6tatG5YuXYrTp0/j5MmTePfdd3lZpmHDhsHb2xsDBgzAwYMHkZGRgQMHDmDChAm4detWpcYaFBSEhIQEJCUlYfPmzcjIyMD+/fvxyy+/cPuIRCIkJiZi6tSpUKlUvOmJjnTv3h2RkZEYNmwYTp8+jePHj+PNN99E586draZGVgXbo/Hs2bM4ePAgxo8fj9deew3169cHYC7usXDhQvz888+4fPkyPvroI5w5cwYTJkzgjsE2cz9//jzXMqNLly5Yt24dWrVqxbXMcHV1xaRJk/D+++9j7dq1uHbtGlJSUrBs2TKsXbvW4RinTJmCyZMn46effsK1a9dw9OhRrFy5EgC4YGzWrFm4cuUK/vjjD3z55Ze8YwQFBaG4uBh79uxBbm4u1Go1tm3bhm+++QZnzpzBzZs38dNPP8FkMlV5iih5fCiYIoQQQgipggYNGuDQoUMwGo3o2bMnIiIiMGHCBLi7u3MB0+eff45OnTqhf//+6N69O+Li4tC6dWvecb788kv4+/ujU6dOGDp0KCZNmsSbXqdQKPD3338jICAAL7/8MkJDQ5GUlITS0tIq9c/79ttv8a9//QujR49G8+bN8fbbb6OkpIS3z4gRI6DT6SqdlQLM0+k2b94MT09PdOrUCd27d0fjxo3x888/V/oYtqhUKrz88st46aWX0KNHD0RERGD58uXc9vHjx+ODDz7ABx98gBYtWmDHjh3YsmULQkJCuH0iIiLg7e2NqKgo7lp17twZRqPRav3Y3Llz8cknn2DBggUIDQ1Fz549sXXrVgQHBzsc54wZM/DBBx/gk08+QWhoKF5//XXk5OQAME+93LBhAy5duoSoqCgsXLgQ8+bN472+Y8eOePfdd/H666+jXr16WLRoETw8PPC///0P3bp1Q2hoKL777jts2LAB4eHhj3RNSe0RMOUn6xJCCCGEkBrXpUsXtGzZEosXL67roVg5dOgQunTpglu3btVpkaFZs2Zh8+bNVlPiCHlSUQEKQgghhJDnlFarRVZWFmbMmIHXXnuNqrUSUkU0zY8QQggh5Dm1YcMGNGvWDIWFhVi0aBFv27p16+Di4mLzT3WnnYWHh9s9pq2qhYQ86WiaHyGEEEIIsfLgwQPcvXvX5jaJRFKtFho3b9502MOwfE8tQp50FEwRQgghhBBCSDXQND9CCCGEEEIIqQYKpgghhBBCCCGkGiiYIoQQQgghhJBqoGCKEEIIIYQQQqqBgilCCCGEEEIIqQYKpgghhBBCCCGkGiiYIoQQQgghhJBq+P/386rvJu1zLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data preparation\n",
    "freq_grp_data = df_train.groupby(by=['frequency_of_powercuts'])['habitability_score'].mean()\n",
    "\n",
    "# plots\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "\n",
    "sns.scatterplot(data=df_train, x='neighborhood_review', y='habitability_score', ax=ax1)\n",
    "ax1.set_title(\"Positive correlation\", size=10)\n",
    "\n",
    "freq_grp_data.plot(kind='bar', ax=ax2)\n",
    "ax2.set_title(\"Negative correlation\", size=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38e47bfb-b107-420f-9f9b-d8ecaa7f9c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's examine the relation between target variable & categorical variables using barplots.\n",
    "# Subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(12, 3))\n",
    "\n",
    "df_train.groupby(by=['property_type'])['habitability_score'].mean().plot(kind='bar', ax=ax1)\n",
    "ax1.set_ylabel(\"Habitability Score\")\n",
    "ax1.set_title(\"No Relation\", size=10)\n",
    "\n",
    "df_train.groupby(by=['furnishing'])['habitability_score'].mean().plot(kind='bar', ax=ax2)\n",
    "ax2.set_title(\"Positive Relation\", size=10)\n",
    "\n",
    "df_train.groupby(by=['power_backup'])['habitability_score'].mean().plot(kind='bar', ax=ax3)\n",
    "ax3.set_title(\"Positive Relation\", size=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f006110-032f-40a3-beb1-eb45eb903621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subplots\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(12, 3))\n",
    "\n",
    "df_train.groupby(by=['water_supply'])['habitability_score'].mean().plot(kind='bar', ax=ax1)\n",
    "ax1.set_ylabel(\"Habitability Score\")\n",
    "ax1.set_title(\"Positive Relation\", size=10)\n",
    "\n",
    "df_train.groupby(by=['crime_rate'])['habitability_score'].mean().plot(kind='bar', ax=ax2)\n",
    "ax2.set_title(\"Negative Relation\", size=10)\n",
    "\n",
    "df_train.groupby(by=['dust_and_noise'])['habitability_score'].mean().plot(kind='bar', ax=ax3)\n",
    "ax3.set_title(\"Negative Relation\", size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d8f487-c2ba-4be2-bd23-3c4085ab9655",
   "metadata": {},
   "source": [
    "# Data prepration for Machine Learning Algorithms\n",
    "Its time to prepare the data for your Machine Learning algorithms. First, let's separate the predictors and the labels since we don't necessarily want to apply the same transformations to the predictors and the\n",
    "target value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d38c0bf-4ebd-4b29-81be-66c04caa191e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the features and label variables.\n",
    "X = df_train.drop(['habitability_score'], axis=1)\n",
    "y = df_train['habitability_score'].copy()\n",
    "\n",
    "X_test = df_test.drop(['habitability_score'], axis=1)\n",
    "y_test = df_test['habitability_score'].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e098100-2efa-402e-8ca7-cb2820b74c62",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "\n",
    "Let's remove the outliers from the dataset using Interquantile Range values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a33cbbce-bfcb-440d-96d5-a5ef72c1c8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliers from numerical features.\n",
    "def remove_outliers(col):\n",
    "    # find quantiles\n",
    "    Q1 = np.quantile(X[col], q=0.25)\n",
    "    Q3 = np.quantile(X[col], q=0.75)\n",
    "    \n",
    "    # Inter quantile Range \n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # Upper range & lower range\n",
    "    upper_range = Q3 + (IQR * 1.5)\n",
    "    lower_range = Q1 - (IQR * 1.5)\n",
    "    # Replace the outlier values with upper and lower range value.\n",
    "    X[col] = np.where(X[col] > upper_range, upper_range, X[col])\n",
    "    X[col] = np.where(X[col] < lower_range, lower_range, X[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a6cef55-d272-457a-b8b5-41b83a4a7748",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in num_variables:\n",
    "    remove_outliers(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "16e36b5d-4cb4-4d89-8c89-269a7cf4c1e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features\n",
    "num_features = ['property_area', 'number_of_windows', 'number_of_doors', \n",
    "                'traffic_density_score', 'air_quality_index', 'neighborhood_review']\n",
    "\n",
    "cat_features = ['property_type',\n",
    "                'furnishing', \n",
    "                'frequency_of_powercuts',\n",
    "                'power_backup', \n",
    "                'water_supply',\n",
    "                'crime_rate',\n",
    "                'dust_and_noise']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e1e821df-4071-44b0-ae66-0199bea9fe9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='most_frequent')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle missing values in data using most simple imputer \n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(strategy='most_frequent')\n",
    "\n",
    "# fit model on training data\n",
    "imputer.fit(X[cat_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "20489353-ac5f-45ae-b1a8-c2d6cd299d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the data\n",
    "X_transform = pd.DataFrame(imputer.transform(X[cat_features]), columns=cat_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7de60367-da4d-4132-8287-1f04fd3cd93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<27649x25 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 193543 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the categorical data into numerical data using OneHotEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "cat_en = OneHotEncoder()\n",
    "X_transform_1hot = cat_en.fit_transform(X_transform)\n",
    "X_transform_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78c8ce49-1d6e-47fe-9e2a-1fa29bc63792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       [1., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_transform_1hot.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde146f5-75ba-4651-8963-55632644f1b5",
   "metadata": {},
   "source": [
    "### Transformation pipelines\n",
    "As you can see there are many transformation steps that need to be executed in the right order. For that we will use scikit-learn `Pipeline` Class to help with such sequences of transformations..\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "44f58635-a966-416d-8956-04570a78fbc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a pipeline for numerical attributes.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "num_pipeline = Pipeline(\n",
    "    [\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('std_scaler', StandardScaler())\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Pipeline for categorical attributes.\n",
    "cat_pipeline = Pipeline(\n",
    "    [\n",
    "        ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "        ('cat_en', OneHotEncoder())\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a43d3f1-1ed0-404b-99ea-28cabbda1008",
   "metadata": {},
   "source": [
    "Let's use column transformation, to apply all these transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "83d9bab4-88c5-4a5f-8d32-4d2acdfb9de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "full_pipeline = ColumnTransformer(\n",
    "        [\n",
    "            ('num', num_pipeline, num_features),\n",
    "            ('cat', cat_pipeline, cat_features)\n",
    "        ]\n",
    "    )\n",
    "\n",
    "X_transform = full_pipeline.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cb8a7f-212a-4959-b912-72749dfb642f",
   "metadata": {},
   "source": [
    "In this example, we specify that the numerical columns should be transformed using the `num_pipeline` that we defined earlier, and the \n",
    "categorical columns should be transformed using a`cat_pipeline` \n",
    "\n",
    "Finally, we apply this `ColumnTransformer` to the data: it applies each transformer to the appropriate columns and concatenates the \n",
    "outputs (the transformers must return the same number of rows.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0fbf3e6e-0e36-4859-8cc4-18f2f93e61b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27649, 14)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "976de5eb-ffac-4c60-8466-49577badccc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27649, 31)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_transform.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ef5c6ed7-f68e-41c8-9036-80b643f4905a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_transform = full_pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b863556e-c388-4a27-ac7c-240ec41efee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label data transformation\n",
    "y = np.log1p(y)\n",
    "y_test = np.log1p(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f1f9dbc0-4e3e-40e8-9fd6-600c981c8bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the pipeline & data\n",
    "import pickle\n",
    "output_file = 'pipeline.bin'\n",
    "\n",
    "with open(output_file, 'wb') as f:\n",
    "    pickle.dump(full_pipeline, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6056f739-cc3b-437a-a044-de555342e606",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the data into csv file\n",
    "def save_to_csv(data, filename):\n",
    "    df = pd.DataFrame(data)\n",
    "    df.to_csv(f'{filename}.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0827f41a-39ce-45e3-a9bb-a1c084315a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save transform data into csv\n",
    "save_to_csv(X_transform, 'X')\n",
    "save_to_csv(X_test_transform, 'X_test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4696f6d-26db-49b3-919f-0fc2adab6db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_to_csv(y, 'y')\n",
    "save_to_csv(y_test, 'y_test')"
   ]
  }
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