VAR (Multivariate Analysis).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "pd.options.display.max_columns = 15\n",
    "pd.options.display.max_rows = 20\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20,8)\n",
    "plt.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Real Estate Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "rent_df = pd.read_csv(\"data/real_estate/fresh_rent_data.csv\", parse_dates=True)\n",
    "rent_df = rent_df.replace('None', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "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>date</th>\n",
       "      <th>event_description</th>\n",
       "      <th>price</th>\n",
       "      <th>address_city</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>area</th>\n",
       "      <th>status_type</th>\n",
       "      <th>home_type</th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>living_area</th>\n",
       "      <th>lot_area</th>\n",
       "      <th>borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>Listed for rent</td>\n",
       "      <td>1500</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-03</td>\n",
       "      <td>Price change</td>\n",
       "      <td>1300</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>Listed for rent</td>\n",
       "      <td>1350</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Listed for rent</td>\n",
       "      <td>3000</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-23</td>\n",
       "      <td>Listed for rent</td>\n",
       "      <td>3200</td>\n",
       "      <td>Flushing</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>SINGLE_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>Listing removed</td>\n",
       "      <td>3600</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>Listed for rent</td>\n",
       "      <td>3600</td>\n",
       "      <td>Maspeth</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-04-22</td>\n",
       "      <td>Price change</td>\n",
       "      <td>1800</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-04-16</td>\n",
       "      <td>Price change</td>\n",
       "      <td>1300</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-04-12</td>\n",
       "      <td>Price change</td>\n",
       "      <td>2400</td>\n",
       "      <td>Rosedale</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48311</th>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2350</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>TOWNHOUSE</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48312</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2100</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48313</th>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2200</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48314</th>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2450</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>TOWNHOUSE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48315</th>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2400</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>TOWNHOUSE</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48316</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2500</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48317</th>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2400</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48318</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2299</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48319</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2274</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48320</th>\n",
       "      <td>2020-01-21</td>\n",
       "      <td>No price history</td>\n",
       "      <td>2500</td>\n",
       "      <td>Bayside</td>\n",
       "      <td>11361.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FOR_RENT</td>\n",
       "      <td>MULTI_FAMILY</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>queens</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48321 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date event_description  price address_city  zip_code    area  \\\n",
       "0     2021-04-16   Listed for rent   1500      Maspeth   11378.0     NaN   \n",
       "1     2021-03-03      Price change   1300      Maspeth   11378.0   350.0   \n",
       "2     2021-02-17   Listed for rent   1350      Maspeth   11378.0   350.0   \n",
       "3     2021-04-10   Listed for rent   3000      Maspeth   11378.0     NaN   \n",
       "4     2021-03-23   Listed for rent   3200     Flushing   11378.0  3000.0   \n",
       "5     2021-04-22   Listing removed   3600      Maspeth   11378.0  2000.0   \n",
       "6     2021-03-20   Listed for rent   3600      Maspeth   11378.0  2000.0   \n",
       "7     2021-04-22      Price change   1800     Rosedale   11422.0     NaN   \n",
       "8     2021-04-16      Price change   1300     Rosedale   11422.0     NaN   \n",
       "9     2021-04-12      Price change   2400     Rosedale   11422.0     NaN   \n",
       "...          ...               ...    ...          ...       ...     ...   \n",
       "48311 2021-04-10  No price history   2350      Bayside   11361.0     NaN   \n",
       "48312 2020-05-27  No price history   2100      Bayside   11361.0   750.0   \n",
       "48313 2021-03-11  No price history   2200      Bayside   11361.0  1100.0   \n",
       "48314 2021-03-25  No price history   2450      Bayside   11361.0     NaN   \n",
       "48315 2021-03-20  No price history   2400      Bayside   11361.0     NaN   \n",
       "48316 2021-03-13  No price history   2500      Bayside   11361.0  1100.0   \n",
       "48317 2021-03-12  No price history   2400      Bayside   11361.0     NaN   \n",
       "48318 2021-02-01  No price history   2299      Bayside   11361.0  1000.0   \n",
       "48319 2021-01-04  No price history   2274      Bayside   11361.0     NaN   \n",
       "48320 2020-01-21  No price history   2500      Bayside   11361.0     NaN   \n",
       "\n",
       "      status_type      home_type  beds  baths  living_area  lot_area borough  \n",
       "0        FOR_RENT      APARTMENT   1.0    1.0          NaN       NaN  queens  \n",
       "1        FOR_RENT      APARTMENT   0.0    1.0        350.0       NaN  queens  \n",
       "2        FOR_RENT      APARTMENT   0.0    1.0        350.0       NaN  queens  \n",
       "3        FOR_RENT   MULTI_FAMILY   3.0    1.0          NaN       NaN  queens  \n",
       "4        FOR_RENT  SINGLE_FAMILY   3.0    2.5       3000.0    2500.0  queens  \n",
       "5        FOR_RENT   MULTI_FAMILY   3.0    2.0       2000.0       NaN  queens  \n",
       "6        FOR_RENT   MULTI_FAMILY   3.0    2.0       2000.0       NaN  queens  \n",
       "7        FOR_RENT      APARTMENT   1.0    1.0          NaN       NaN  queens  \n",
       "8        FOR_RENT      APARTMENT   1.0    1.0          NaN       NaN  queens  \n",
       "9        FOR_RENT      APARTMENT   1.0    1.0          NaN       NaN  queens  \n",
       "...           ...            ...   ...    ...          ...       ...     ...  \n",
       "48311    FOR_RENT      TOWNHOUSE   2.0    1.0          NaN       NaN  queens  \n",
       "48312    FOR_RENT      APARTMENT   2.0    1.0        750.0       NaN  queens  \n",
       "48313    FOR_RENT   MULTI_FAMILY   3.0    1.0       1100.0       NaN  queens  \n",
       "48314    FOR_RENT      TOWNHOUSE   3.0    2.0          NaN    1489.0  queens  \n",
       "48315    FOR_RENT      TOWNHOUSE   3.0    1.0          NaN       NaN  queens  \n",
       "48316    FOR_RENT      APARTMENT   3.0    1.5       1100.0       NaN  queens  \n",
       "48317    FOR_RENT      APARTMENT   4.0    2.0          NaN       NaN  queens  \n",
       "48318    FOR_RENT   MULTI_FAMILY   3.0    2.0       1000.0       NaN  queens  \n",
       "48319    FOR_RENT   MULTI_FAMILY   1.0    1.0          NaN       NaN  queens  \n",
       "48320    FOR_RENT   MULTI_FAMILY   2.0    2.0          NaN       NaN  queens  \n",
       "\n",
       "[48321 rows x 13 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.datetime(2020, 1, 22)\n",
    "end = datetime.datetime(2021, 4, 1)\n",
    "rent_df = rent_df[(rent_df['date'] >= start) & (rent_df['date'] <= end)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Smoothing and Interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_ts = nyc_ts.interpolate(method='spline', order=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yAFOmclgu9V9TpoxAgXY5RoxI7PUZlpX1bBsjIiJyXwqVEsaWsxJCBraMMSFEROTCso8XIjGRLWPUf4WFBQAAystre33GDz/8iTHpD6KkpEqosIiIiMiFeMhkkCsUbBk7BxNCREROQqVSoKT0cyxaNN3me3JySuHv742gIF8RIyOSTliYP4AzVT69UVZWjT//zIZebxQqLCIiInIhCpW5EogtYx0xIURE5CQGDYpBWFgAamsbbb7no49+gr/fdaisrBMxMiLphIb6o7a20aY2yq54eamxcOGlSEuLEzAyIiIichUKlQoAuHb+HEwIERE5idTUWABAZmaBzffU1TWhrq5JrJCIJBca5t+n6iDAXH33wYf3Ytq0UQJFRURERK7Ekvg5u0LIaDBAxpYxIiJyBmlpcTAaTTh2TGvzPR4eHnjppVswa9ZYESMjks5nn27Fs8+s7NMZ1dUNMBpNXD1PRETkpiwVQqazKoRajawQcu90GBGRExmSGocTJ4rtmnPS1taGBbdNRVCQD777bpeI0RFJ4+ef9wtyjk5Xyy1jREREbkpumSFk6Fgh5O4zhNz7V09E5ER2bD+CvX9m231fTk4JErhpjPqpUaMGQKvV9bltrLy8BsFMCBEREbklpdr6DCFuGSMiIqfwxhvf4eWXv7b7vtzcUiQlRYgQEZH0/tj5LzzwwOw+n6PT1SI01F+AiIiIiMjVKJRWEkJGY3srmbty73QYEZGT0GhUkMk80NjYYve9uTmlmDt3AuRyGUymVhGiI5KGv783VColysr6Vh0EALfe8m+0tBh6vpCIiIj6Hatr5w3mzwUyuRytJpMkcUmNFUJERE5g9uyxqG/4GkOGxNp9b05OCRoamhEeHiBCZETSCQszV/T0tV0MAAoKylFWVt3nc4iIiMj1tK+dN5zdMmb+sTvPEWJCiIjICaSmxsFkMiEnp8Tue5cv34LAgHkoKqoUITIi6VhavISoEEpPT8ZTT82DTMaPPkRERO5Gbq1CyGiuCpK78aYxfioiInICqWnmDWO9aWlpbWWbGPVPlgohISp7xo4dhGefuwFBQT59PouIiIhcS/sMoZbOLWPuPFiaCSEiIicwevQAZGTk9vr+jz66D4sWTRcwIiLp7dmTjRtveKVXlXPn0ulqAYCr54mIiNyQtRlCxvaWMVYIdWv69Ok4evQosrOz8eijj3Z6PzY2Flu3bsW+fftw4MABzJgxQ/BAiYj6q/DwACQkhGP3rmO9PmPipDRMvniYgFERSa+wsAIrVvyKurqmPp9lmUPETWNERETux9oMoVaj0fweZwh1c4FMhmXLlmHGjBlITU3F/PnzMWTIkA7XPPnkk1izZg3OO+88zJs3D++8845oARMR9TctLQbcd+/72LBhb6/PyMnh6nnqf4YOjce4cYMEOYsVQkRERO7rTIXQmYSQ0WBOCMnYMta1MWPG4MSJE8jNzYXBYMCqVaswe/bsDte0tbXBz8/8Acvf3x9FRUXiREtE1A9VVzfg7bfX49gxba/PyM0pYUKI+p2HH7kGK1Y+IshZrBAiIiJyX5aEkKnDUGlLhZD7toz1mAqLjo5GQUFB+8+1Wi3Gjh3b4ZpnnnkGP/30E+699154e3tj6tSpVs9atGgR7rjjDgBASEhIX+ImIuo3Lr54OI4fL0RhYUWvz8jNLUVoqD98fT0Faa8h8ahUCshkMjQ363u+2M0FB/uioqJOkLOKi6sQEvwXVFXVC3IeERERuY72odJ6rp0/W48VQh4eHp1ea2tr6/Dz+fPnY/ny5YiNjcXMmTPx+eefW73vgw8+QHp6OtLT06HT6foQNhFR/yCTybDu2yfw2GNz+nTO8eOFOHQoDwEB3KDk7O6++3Icz36flSo2CAoSLiHU1taGysq6Tp9hiIiIqP9TqC0JIW4ZO1uPCSGtVovY2Nj2n8fExHRqCVu4cCHWrFkDANi5cyc0Gg0rgIiIbJCaGgtfXy/s2nW8T+esW7cTI4bfi4KCcoEiIzH4+Xnh8b9dhyNH8ttbmKhrwcG+qKwUJiEEAA88MBsLFlivYiYiIqL+yzJU2tImZv6xCQC3jHVrz549SE5ORkJCApRKJebNm4fvvvuuwzX5+fmYMmUKAGDw4MHQaDQoL+cXJUREPRk71jwwd1cfNoyR63jooasREuKHL7/4BRs2PoPBg2OkDsmpBQX5olKgCiEAuH7eJMy9boJg5xEREZFrUCiVMLS0dHjNUiHkzjOEekwImUwm3HPPPdi0aROysrKwZs0aZGZm4tlnn8WVV14JAHjooYewaNEiZGRkYOXKlbj11lvFjpuIqF8YOzYFlZV1yM7u2zB+Dw8PbPvlJdxzzxUCRUZCi4oKwgMPzsbq1duxdesBXHbZaFx11Tipw3Jqc659Ce+8s0Gw88rLa9iqR0RE5IbkKmWH+UHAmRX07rxlzKZf+caNG7Fx48YOry1durT9x1lZWZgwgd9xIyKy15ixKdi9u2/tYoB5PsqgQdEYPjyh70GRKC6/PB0qlQJPPfk5iooqsWvXMcy+ahxefvlrqUNzWv/732FBz9PpajF0aLygZxIREZHzU6hUHeYHAUBr+5Yx900I9VghRERE4pk963k8/NDHgpxVUKBDdAzntzkbb28NAOCTTzZj/IV/xYkTxQCAb9ftxNixgxAVFSRleE4rIMAb118/EZGRwv3+6MprWSFERETkhhQqZaeEkNFgTghxhhAREUni1KkyZGbmC3KWVqtDbCwTQs4kLi4U5bovMXfuBBiNJuzde6L9vXXrdgIAZs0aK1V4Tm3QoBisXPVXjByZKNiZOl0t5HIZNBqVYGcSERGR8zNXCHVsGeOWMSaEiIgkM3RoPO6/fxb8/b0FOa9Qq0NMTLAgZ5Ew5s2bBI1GhT17sju9d/SoFp98/DNaWgxW7qSgIB8AQGVlvWBnvvLKN/DUXIPmZn3PFxMREVG/oVB2rhCybBxz5woh902FERFJbMqUEXj934vwxRe/CHLevn0nMWBgFJRKBQwGY883kOj+csNF+P33LOTllVp9f+HCNx0ckesIDvYDAFRU1Ap2psnUKthZRERE5DoUKlX7EGmL9gohzhAiIiJHS0qKQG1to2Bf8C5fvgWXz3yGySAnMXRoPIYPT8SKL3/p9jqlG38I6U5wsC8AoELAtfNxcaH46OP7MWrUAMHOJCIiIuenUClh6tQydrpCiC1jRETkaAmJ4cjNtV45Qq5v/vxJMBpN+Oqr37q85pZbpqCx6WtERAQ6MDLXEBzsC5PJhOrqBsHOVKuVWLBgKlJTYwU7k4iIiJyf3MpQabaMMSFERCSZpKQI5OSUCHZeZGQQTuZ8iBtvvFiwM6n3Pv54M25b8AbKy2u6vKakpApyuRxJSREOjMw1LFv2A8Zf+Fe0tbUJdqZOZ67GCwnxE+xMIiIicn5KlZpDpa1gQoiISCIJCWHIE7BCqLKyDomJ4UhICBPsTOq9kyeL8cUX27q9xpIQZEKos9LSauzefVzQM6urG2A0mrh6noiIyM0oVEoYDeeunecMISaEiIgkEhZ6I557bpVg57W0GFBWVo2YGK6el5K3twb/XfcERo5M6vHaU6fK0NraiqSkcAdE5lquvfZCzJgxWtAz29raUFFRywohIiIiN2Nt7Xyr0WR+jy1jRETkaE1NLaipEW4+CgBotRWI5up5ST355PWYPXsc1OqeP1zo9UZotRVIZIVQJ489Phd3Lb5c8HNzc0sFbUMjIiIi5ye3snYeMFcJydy4Zcx9f+VERBKaMCEVs2ePw/PPrxY0KaTV6hAfz5YxqQwcGIkHHpyN5cu3YNeuYzbd88a/v0VRUaXIkfXNCy/chFOnyvGf//zosGcGB/vi8OFTgp974QWPCH4mEREROTdrW8YA8xwhVggREZFDTZo0FA89fDX0emFXxG/6cR+2bM4Q9Eyy3Q03TIZCIcfjjy23+Z7XX/8Wq1dvFy+oPho5MgmP/+06LL57pkOfGxzsi6rKeoc+k4iIiPonhUoFg5UKIZPR6NYzhNz3V05EJKGkpHAUF1eiqalF0HPffXeDoOeRfZJTonHqVDlKS6ttvkculyEmJgSFhRUwnu5ldyZPL50PAEhLi4O3twYNDc2iP1OpVMDX1wsVFbWCn71gwVTMmHk+rpv7suBnExERkXNSWFk7DwAmg9Gtt4y576+ciEhCCYnhyBVww9jZPDw84OHhgdbWVlHOp679/NN+7N930q57rr9+Ir748mGkDrkLR49qRYqs9zb/vB81NQ2oqqyHp6fKIQmhoCAfAEClCBVCCQnhuOaaCyCTyfjfCBERkZtQKDsPlQYsFULu2zLGhBARkQSSkiKwY0em4OdecMFg/PLrS7hs+lJs23ZQ8POpe59+usXue85ePe+MCaF33tkAvOPYyrPy8lokxN+G2tpGwc8uK6uGTCZDcLAvystrBD+fiIiInIuHhwfkSgVMViuEDG7dMsYZQkREDiaTyaDRqJAnQoVQeXkNlEoFYmO5et7R1GolIiOD7L4vJ8f85yDJyTaNRUYG4ZZbpiAw0Fyto1QqkJAQ7pBnt7a2Ij+/HNXVwm7hA9DezhcW5i/42UREROR85CoVAPNGsXO5e8sYE0JERA7W2tqKqMibsXTpCsHPLikxf7EbGsovdh1tzJgUFBZ9iksvHWXXfWVl1WhoaHa6hNDUqSPwyfIliIoyJ7k+Wb4EW7e94JBnp6XF4fHH5yIoyFfws8vKzFVBYWEBgp9NREREzkehMreEsWWsMyaEiIgk0tbWJviZ9fVNMBpN7TNYyHFSUqIBAMePF9l9b05OCRISHVN9Y6tx4wajtrYRWVnmNra9f2YjISHcIYmUsWMH4YUXb4aPj0bws4uLK3H0qBYymYfgZxMREZHzUVoqhKy0jBm5dp6IiBzp6qsvwMpVf4Wvr6co51dW1rW3+ZDjpKREoaXFgPz8crvv/ftzq/Cek22IGzM2Bbt3H28fvLx7dzYAID09WfRnBwebK4MqKuoEPzs7uwipQ+7Cli0HBD+biIiInI+iPSFkrWXMALlC7uiQnAYTQkREDjZr9lhMnjwUdXVNopz/zrIfsG3bIVHOpq4NTI7CiRPFvdpc9fXXv+Gnn/aLEFXveHqqMWJEInbtPNb+2v79J2E0mjBmTIrozw8K8kVLi8EhG82IiIiof5NbWsYMVoZKu3nLmPtOTyIiksjEiWnYsSNLtPOfe26VaGdT11JSonH8eGGv7vX398awYfHYu/ckmppaBI7MfsOHJ0ChkGPnWQmhxsYWHD58CuljHFMhVFkpfHWQxeo1j+JolhZLl34p2jOIiIjIOXRfIWSEUq12dEhOgwkhIiIHiooKQlJSBN5+a71oz1Ao5PDx0YiyoYm69vzfV6Gysr5X91588TB8898ncP7oJdi376TAkdlv165jiIy4CbW1HavYnvjbZ6JVtp0tMMhXlHYxi+TkKKjV7vvdQCIiIndimRFkbe28sUUPT1/3HbXAhBARkQNNmJAKANi+/Yhoz/j4k/tx4YVDMHDAItGeQZ2tXr291/da5g7FxoY6RUIIOLOe/WwbN+51yLP/Mv9f8PYW77t1paXVCA/nljEiIiJ3YKkQMrRYSQjp9e3vuyPOECIicqC2NmDnzqPIyMgR7RlVlfXcMuZgkZFBSE9PhlLZu++zWBJCcXGhQobVax98cC9mzjy/0+tyuQyXXTYaw4YliPp8g8EoaoVbWVkN184TERG5ifa184bOLWNGvd6tW8aYECIicqCvvtqBCy94BCaT/YOHbVVZWYeAAB/IZPwr3lGuvfZC7Nr9Wq8TcTpdLZqaWpwiIRQdHYyFt1+KpKQIq+9/9fVjuO22qaLG8OqrC3HppaNEO7+stBphYf6inU9ERETOoz0hZK1lTG9of98d8asFIiIHkctlDknSVFWZ59gEBHiL/iwyS0mJQm1to9U2K1sVFOgQ6wQJoXHjBgEwzxE6l8nUir17TyBdxE1jnp5qPPDgVTjvvAGiPePw4VP444+jva7oIiIiItdhaQkzWRkqbWhpYcsYERGJb+rUkaisWokRIxJFfY5lsHFgINvGHGVgclSvN4xZLL7rHbz04hqBIuq9ceMGo7lZj4yMXKvv79k2DyIKAAAgAElEQVSdjfPOGwCFQi7K86OjgwAAhYUVopwPAJ9+ugWXTnsKBoNRtGcQERGRc7AMlbZeIcQZQkRE5AATJ6bB01OF7OwiUZ+zd+8JPPnE56itbRT1OXRGUlIEcnJK+3TG1q0HceCA9SSMI40dNwj79p3sMlmye/dxaDQq0eYIxcSEAAC0WvESQkREROQ+5N2snTfqDVComRAiIiKRjZ+Qin37TqKxsUXU52RlFeDFF9egvLxG1Oc4UkJCOB555Bp4eHhIHYpVsbEh0BaU9+mMuLhQzJs3SbTKG1u1trZiRzdb8HbvPg4AGDMmWZTnR0cHAwC0Wp0o5wNASko0jh1/H5ddNlq0ZxAREZFzUJ5O+BgN1tbOt0ChVDrtZ0yxsXmeiMgBVCoFxo5NwTvLfhD9WTKZDNHRwaitbURNjXibmhzp6aXzceutU1BYWIEVK36VOpwOPDw8cMXlz6GkpKpP50ydOhIffnQfdu48hry8vlUb9cXkix7v9v1Tp8owfNg9yMoqEOX5gYE+MJlMoraMNTa2IDk5CjExwaI9g4iIiJzDmaHSViqETm8ek6tUMLaI+01bZ8QKISIiBxg9eiA0GhV27MgU/VkhIX44lf8xbrjhItGf5QhyuQyXXDIcAPDCizdDo3Gust62tjZs23awzwkSy+r52NgQIcIS1eHDp0TblPf22+uhUV8jaiVdWZl5+DdXzwPPP38Tbr/9UqnDICIiEo1CaWkZ61whZGgxv6Z007YxJoSIiBygtLQaLzy/Gtu3i58QsmwZ6y9DpU2mVqQk34G5c15CfHwY7r33CqlD6iApKQJz5oyHt7emT+dYEkJSrp5/993FWL3m0R6vGz48AW+/fSd8fDxFiUOsZJOFXm9EdXU9wsPdOyHk6+uJvz56Ld59bzGmTh0pdThERETdSrkgHdc88TCueeJhTLvzNpvvk3dXIXQ6SWQZPO1umBAiInKAnJwSPPXUF6ioqBX9WQaDEfX1TQgK8hX9WY6i1xuxdu3vuPee9/D559ukDqeD6dPPw5qvHoOvb9+SIwUF5pk5UiaELpkywqYZRlFRwVh89+U4//yBgsfw6qsLce+9Vwp+7rlKS6sRGuYv+nOc2eTJw6BQyFFZWY+Vqx5BQkK41CERERF16ZLbbsa4a2dj9BWX4bK7FyEwMsKm+9rXzhu6SQixQoiIiMTg4eGByZOHwdNT7bBnVlbWI6AfVAilpsbhePb7SE83DzBetuyHPs/qEVpsbAgMBiNKS6v7dE5TUwt0ulrJEkLBwX5ITo7Crp3Herx2376TAMyVQkK7ft5EjBgh/LnnWv/9HhzIkH6rm5S2bj2IKy5/FpMvehy//34Uzc2dS+mJiIichdrHC8f+2IXVS18EAKi8bPtmnEKlhKGL+UDG0y1j7rp6nkOliYhElpoai63bXsStt7yOzz7b6pBnVlXVIyjI9RNCCxdOQ1xcKHJzzwxZvvHGi+HhAaepFIqJDUFhYQXa2tr6fNb0S59GcXGlAFHZz7I1bKcNCaGysmpUVtZhyJBYQWNQKOSIiAhEYaH4vwePPPKx6M9wdg0Nzdiw4U8AwOxZf5c4GiIiou6pvbxQqS2Cvqnp9M9tTAgpVVbbxYCzKoSYECIiIjFMmJAKAA4ZKG3x0otrUFfX5LDniUEmk2He/ElYv34PdLozrXY333IJ/P29nSYhFBsb2t7u1Vf7958U5JzeGDduMEwmE/78M9um6zMzCzAkVdiEUEREIGQymagr58ksIiIQixZNx8cf/4zCwgqoVApceOEQ5OWVSbrljoiIqCtqL0+0NDZB32hJCHnZdJ9CpbQ6UBoADHoOlSYiIhFNmJiGoqIK5OSUOOyZa9bswMaNex32PDGMHz8EkZFBWLN6e4fXDx7IxdChcZDJnOOfsNjYEMESQuedNwAPPDBbkLPsdfSoFu+9u9Hm7V5Zmfnw87Ptg5itoqPNa+DFXDlv8cADs1FX/xXkcuf4c+Ro06efh2efu6F91pinpxpbt72I666bIHFkRERE1qm9vNDS0IiWxkYA9rSMqazODwIA0+nKIVYIERGRKCZMSMWOHVkOfWZ4eACio4PbZ724orlzx6OpqQU//PBnh9cPHsyDp6cayclROHZMK1F0Z0y/9GlB2sUA4JJLhuOf/7oNH330M2prGwU501YrV/6KlSt/tfn6u+56F62twm4D8/JSIz+/XLAEW3eamw3w9tYgJMSvz/OfXNGUqSNQWlqFw4dPAQBqahqQm1uKESMTJY6MiIjIOpWXJ1qaGtHSaGfLmErZZcuYwc1nCLnnt8WIiBwkNNQf8fFh2PnHUYc+98EHr8KO3/7p0GcKbcOGvXjqyS/Q0NDc4fWDB/MAiDPQuDdOniwWrPpLqtXzMpkM3t4au+4ROhkEANu2HURC/G04dChP8LPPVVZmTgKFhbnn6vmpU0di8+YDHZKZGRk5GDVqgIRRERERWadQqSBXKKA/q2VMZWPLmFzZdcuYUd/Sfr47YkKIiEhE1dUNGDf2Iaw+p+1JbJWV9dBoVNBoXPcftx9/3IvXXlvX6fXMzHzo9QYkJIRJEFVHsbGhWLJkNiIjgwQ5z5IQio0NEeQ8Ww0aFI26+q8wZ854m+/x8fHE2m/+Ztc9zsRSFRQe7n4JoSFDYhEREYgtmzM6vH4gIwcpKVHw8nLcRkQiIiJbqL3NyZ+zW8bUnrZVCCnV6m6GSp9uGeMMISIiEprBYMTu3ccdvjmqqqoeAFx209jUqSMxcGCk1ff0eiOCAufjX//6xsFRdTZ69AC89vrtiIgIFOQ8S6uUoyuEBgyIAHAmIWWLhoZmTJs2EuPHDxEsjuefvwnvv3+3YOd1p6ysBgAQFubvkOc5k9jYEDQ2tmD//pwOr2dk5EImk2HYsARpAiMiIuqCpT2spbEJ+qbmDq/1RKFWwWjoYqh0e8uYUoAoXQ8TQkREIpowIRU33nixw59bWVkHAAgMdL2EkIeHBz5ZvgT/+OeCLq+xdfCx2GJjzYmbggLbEyndKSmpgslkQlRUsCDn2SopyZwQsqf1ra2tDUePajEkNU6wOMZPSMWgwTGCndedoqJKfPjBJuTllTnkec7kp5/2w9dnbnv7pcUvvxxC+vkPSLrtjoiIyJr2CqHGRrS1tkLf1Gxzy5hfaAhqy6zPJzRZtoyxZYyIiIR2661T8I9/3urw51oSQpYNQq5k+PAEREcHY91//+jymgkTUrHu2yfh7+/twMg6i40NQVNTC3S6WkHOM5lakZhwO55/frUg59lqwIBI1NY22v3ryMwswJAhwiVwoqODHLJhDADq65twxx1v4/ffHTvw3Vm0tbV1GoZeW9uIvXtPQK83ShQVERGRdWrPMy1jgDkxZGuFUFBUJCqLiq2+Z1k7r1C7Z7s0E0JERCJKSAxHbm6pw5976NAp3PCXV3DsWKHDn91XEyemAQB+/fVwl9d4e2swa9ZYDBsW76iwrIqJDYFWK2wCQ6vVwWBw7BfkSQMicPKk9Q9K3TmaVYDY2FD4+tr2gawnMTEhKBT497Mnnp7u9wHwnXfuwoMPXmX1vUsuGY777rvSwRERERF1z7Ji3jJQWt/UZNPaed/gIKg8NagstP45xzJsWqFkyxgREQksMTFCsA1U9igvr8HKlb+2b1JyJRMmpiE/v7zbeTaWVpcRI6RdkR0bGyJYu5jF1VdfgMcemyPomT1Z8eWveOvN7+2+b//+HOzceRTBwX59jiEoyBcajcphFUIAsHPXq1jz1aMOe56zmHvdBCQnR1l97/LL0/HSy7dALudHRCIich6WlrHmRkuFUBPUNrSMBcWY/72r1BZZfd9omSHkpkOlFVIHQETUXykUcsTFheDLLxxfIeTh4YGJE9NQWFjRq8oPKV144WD88suhbq8pLq6ETlcr+er5aVOfEqw6xmLq1JGYe90EvPzy14Ke252VK3/t1X2bNu3Dpk37BInB21uD337LdGhVW0VFndutnQ8K8kVwsF+Xv88ZGTnw9FQjOTkKR49qHRwdERGRdZbkj/50Qkjf2GRTy1hQlHlJSWWh9YSQyWhEa2sr184TEZGwYmNDIJfLJakQamtrw+Ytz+PWW6c4/Nl9NWzoPfjb45/1eF1GRg5Gn5/sgIi61tysR3l5jaBnFhZWICTED2q1Y0qXvbzUGDw4BkqltN8jKigox8QJj+LHH/c67JllZdVut2Vs0KBoAMDx49YTQocOnQIApKUJNyyciIior87eMmb+30aobFg7HxRtrhCqKu7687hJb+BQaSIiElZeXhmio27BN990PRxZTNXVDS45VLqmpgFarfVNEGfbuuUAqqvrIZNJ809ZaKg/Xn11IYYOFXaOkaVlKioqSNBzuzJmTAoys97FpElpvbr/8y8ewpcrHhY4KscoK3W/hFBKSvcJobw8c0WjZYMeERGRM2jfMtZwpmXMlhlCQdGRqKuobF9Vb41B3wI5184TEZGQ2traUFxcidraRkmeX1lZh4BAabdw2WvJktm4917bBtq+/PLXmDrlSbS2tooclXUjRiTigQevQnCwsEm3oqJKAEB0tGNWzw8YYC6lPnmyd5Vsra1t7YPA++Kuu2Zif8abUKkcV6lUVlYDT0+14G1/zi4rqwB5eWVW36uubkBdXaPD/vwRERHZQuXlCZPR2D4E2uaWsZioLucHWRj1Bii5ZYyIeisw0AcPP3w1FAq51KGQE7n66gvwyCPXSPb8qqp6l6sQumvxTFwyZbjUYdjE0lKTmVkg6LmWCqHQUMdUrgwYEAGDwdjr4dgZ+08iJiakz/EmJ0chKSncoSvPt28/gueeXemw5zmDTz/dgrTUxTAaTV1eExd7Gx555GMHRkVERNQ9tZdX+4YxwI6WsW5WzlsYW/ScIUREvXfXXTPxz3/dhvnzJ0kdCjmRuddNwKI7pkv2/PLyWpdqh4mICERychR2bM+0+Z5Pli/B9+ufFjGqrqWmxkKnqxV8hlBWVgFUyqvw3/86ptUwaUAk8vLKYDL1rtJq//4cAMCoUUl9iiM8IgAlJY7dird793E888wK1NU19XyxG6mpaZA6BCIiog7UXl5oaTxTda+3YcuYh0yGgMjwLlfOWxj1eijYMkZEvXXV1eMAAPcvmS1xJORMEhPDkZvr+A1jFs8+swJ33fmOZM+3l6XtaPv2Izbf01DfjEmT0iRZkZ2aFocjR/IFP7etra3b6g2hDRgQ0adNdGcSQgP6FEdERCBKSqr6dIa9PDw8EBYW4DYtYx4eHjiS+Q4WLeo+UT1r1li8/fadDoqKiIioZ2ovz/aB0gDQ0tQElacGHt3MkvQPC4VCqexyw5iFUa+H0k3XzjMhRNRHISF+GDEiEb/9lom1X//GtjFql5QUgdwc6RJCe/eewO7dxyV7vr1GjEiE0WhqTzDY4rffsuDr64VhwxLEC6wLgYE+yMoUPiEEAE88cR0eeMAxCeYnn/gcr/zrm17fX1PTgP+8/yOys7v/sNWTiIhAFBc7NiEUGRmIktLPMX/+RQ59rlRiYkIwZEhsj9cNGxaPxXdfDo3GPT8cExGR81F7d6wQsvxY5anp8p6gGPOGsZ4SQoYWPeRu2jIm7Y5Zon5Ap6tFRPhNaGtrQ3U1y+zJzMfHE6Gh/pJWCEVFBeGii4Zh/frdLtESI5fLsG/fSRgMts+Q+e03c3vZ+PFDkJFheyJJCEPT7hatMmnK1JGQy2V4/fVvRTn/bJs27evzGXfeuazPZ/zxexYOHszr8zn2KC+vBQCEhwc49LlSSUkxfzDuasOYRX6+eZ5UTEwwTpzoffUYERGRUFRenu0bxgC0zxNSe3l1eP1sQVHmxRk9towZ9Fw7T0S9V1VVj+rqBigUclx//USu6yVERwfDYDAiN7d3m5uEkJ6egi9XPIyBA6Mki8Eejz/+KcaNfciue/Lzy6HV6nDh+CEiRdW93s7d6UlRUaVD1s6HhPhh2rRRgrRMBQb6QKns/feZbr/9Lbz55vd9jsMeBoMRlZV1LjVrqy/OrJzv/julloQQ/y0jIiJnofY8d6i0+cfdrZ4Pio5Ea2srqoq7/wYth0oTUa8EB/th85bnMW7cIADmlocvVzyMG2+cLG1gJLljx7Tw1FyLb75xzGBga4qLzevLIyMDJYvBEf79+rfYsvmAQ585f/5FWPPVY6K11BQVVjhk7feECanY9NNzfU4aTpkyAhWVK3HBBYMFisxxSkurERrmHhVCAwdGoqGhuf3vhq4UFOgAAHFxTAgREZFzOLdlTH/6x92tng+KjkJtWTlMBkO3Zxv1BiaEyH21tn2P5567QeowXNKVV6bjkktGoKXF3OKi1epQUKDDoMExEkdGzqC1tdWhw4HPZZnHEhkpfqVJX8XFhWLX7tcwefIwu+997bV1+Pjjn0WIqmsXXTQUF188DM3NelHOLyysgEajQmCgjyjnW8THhwEATp0q69M5luHavd00NmZMCsp1X2LSpKF9iqM3yspq3KZCKDu7CGtWb+/xOq1Wh/r6JrcZtk1ERM6v01Dps1rGuhIUE4mKHuYHAae3jHGoNLkjSzn4k0/NkzgS13Tp9POg1eqwf//J9tdOnChGcrJrtOiQeG6//VK8/vrtksZQWmpJCDl/hdCQIbFIT0/udQtWSIgf0tLiBI6qa0NSY5GZWSDa+VqtDsXFlQgK8hXtGYA5IVRf34TKyro+nVNSUoXi4kqM7GVCKDIyCMHBfpLMunrrze+x7O31Dn+uFN57byMWLnyzx+v0eiP8fK/D227y+0JERM5P5eV5zlBpG1rGoiJRVdjz+AZ3XjvPodJuLjW1520j1LWkpIhOa6dPnijG1ddcIFFE5CwunX6eQxMU1uj1RlRU1LpEhdCgQebZJseOdT/stisbf3wWjY0tuGjSY0KG1aW0tDh8tWaHaOevXfs71q79XbTzLeLiQ9vnxfTV/v05va4Qiogwt2w5eu08AHzzjfi/z0RERNR7Hh4e0Hh7nzNU2tIyZr1CSK5QwD88rMcNY4B5hpBSpRYmWBfDCiE3p9GooNPVIjzsRqlDcUlxcaEoOOeLqRMnihAa6g9/f2+JoiJnEB8f1uc2HCFMm/oUnntupdRh9GjQoBhUV9ejrKy6V/ev/343xo8fgtBQ8Vt/wsMDEBTkK2qFkKOY/5wKkxDK2J+D1NS4Xg2WjogIRGtra6///+8Lf39vnHfeANE2xjkLmUyG2ro1eOihq226fvHimfhk+RKRoyIiIuqZUmNeLW9tqLTa03qFkFeAv/nfPl1Fj+cb3LhCqH9/+qEeffvtToSF3oDy8hqpQ3E5Hh4eOHQoD/v2nezw+vLlW5CYsBC1tdbXH5J7iI/vnCyUQkZGDkpLHf9Ftr1SBkX3ujoIANat2wmZTIYrrxwjYFTWBQb6YM+ebFHX3Ht4eODrtY9jwYKpoj0DABbe9gae+Ntngpz1zTe/456734NM5mH3vZGRQSgvrxFta1t35s2biD/3/hth/XywdHh4AHx8PG1uyxswIBJz504QOSoiIqKeqb3NVUDWZgh11TJ25p6evyZz5xlCbBkjDB4cgyVLZuPll79GXl73K/nojLa2Nky/9OlOr+t0tdDpaiWIiJyFp6caYWEBglVe9MX48akYPjwB7767QepQupV9vBAVFb2fY3PgQC7y8koxa/ZY0QdMHz2qxdgxD4r6jLa2NkyYkIryshp88slm0Z5z8GCeYGft23eyU4LcVrt2HUN1db1gsdijrMz8DZGwMP8et2+5spgY89Y6rVZn0/X5+eXw8lIjKMi3zzOmiIiI+sKySaylsaH9tZ5axjSWhFCDDQmhFj0USvdMCLFCyM3tz3gTjz42B3f832WSzzvpTx588CrMnHm+1GGQREJC/JCXV4qcnJ6H2Ilt1qwxeE3i4da2WLz4XTz11Bd9OuO7b3dh2rSRoq2CdzSttgIxsSGinR8WFoDbbpuGiAhhho7L5TKMGJGIqCj7Z1Z98slmPPbYp4LEYS9LBV14eP+uEIqJMf9ZsichBHD1PBERCUOuUODR71ZhxPQpdt9rSfqcXSFkMhphNBi6rhDysiMhZDBArlRAJpfbHZurY0LIjUVFBWHEiETk5ZqrgpKSIiSOyLXMmzcJR4+9Z/WLn/uXzMLc61hq764KCsqRlHg7Vq36n9ShoLi4Cmq1UvT15c7g1VfXIXXIYtFWwVus+eoxfPTRfaI+AwCOHy/E4MExop0/alQSPvzoPgwYECnIeRqNCvsz3sRNN11i971qtXR9+5a5Rf29ZexMQqjnWQoAE0JERCQsL38/hCXGY+IN19l9r6X96+wZQpafq7tsGTPPc7WtQqgFANxyjhATQm4sLS0eAPDLL4fQ0NCMxMRwiSNyLQMHRiIlJRqVlZ3bHE6cKMbAgcJ8kUXUF5YWGGfeNHbddROgLVze57+DCgrKHTLIe8yYFCh6MTjZXlmZBUhMDIenpzhbLyxf6Av1e9bQ0IyqqnrE9qKqqbpmNZ577gZB4rCXpUIoLEz8geRSOnpUiw8/2GRz+9epU2U4dkzbq5lQRERE57IkdRJHDUdwTLRd91qqgJrPSe60NDZ22TJm3wwhAwBA7oZtY0wIuTHLyvkjR/KRk1OCxCQmhOwRGxuC0tIqq9UIJ7KLmBByY3fffTnWffuk1GEAMFcIAUBkpDBtQWIYPDgGERGB7bH2xU03XYw775whQFTWeXmpERcXiuPHtKI9w2L//pPYsycbISF+opwfHx8Go9Ek6NycgoJyu9vcAgN9oFYr+zRDqi/q6pqw4NZ/Y8OGPyV5vqP8/PN+3HHH2zZfr9PVYsjgu7Bu3U4RoyIiIndhSdAAwOgrptt3r5elQujchFBTj0Olm+sbrL5/NoPe/PWc0g0HSzMh5MZSU2NRXl4Dna4W2dlFUDmoRG7ChNT2GR9itkOILTYuFPn51mcxnDhRjPDwQPj6Wv8Livq39DEpGDEiUeowALhGhVDKoBjk55cL0up11dUX4J57rxAgKuuSk6MAAMePF4n2DIv16/fggnEPo6BAnOHkcfGh0Gp1gm72ys/X2V0hZJlhVFLS94Rgb3366RYcPSp+kk9K/v7e8PBgtQ8REUnDktRpbmjAeVdc1qt7W+xoGdPYM0OoxfwZlC1j5FaOHtVi5YpfAQBzrn0Jl898RvRnhoUFYPOW5zFv3kRce+2FyMx6F+efnyz6c8UQFxfa5RdqJ04Uw2QyISGBVVfuKD4+zCGtS7bIySlBfNxtTjHPqCtpaXHIyhLmi/GszAIkJ0dBKVJL16BB5hLnYw6oEBKb+c+psMkmbUE5YmPtmznjDAmh1NQ4XHjhEMme7wj79r+Bjz+53657/vnPBVix8hGRIiIiIndiSers/f5HhMbHIm54mh33WraMdW4Z66lCSN/UZPX9sxn1loSQ+1UIce28G3v99W8d/sy//OUiqFRK7NmTjcLCChgMRsyZcyH+/DPb4bH01S/bDuHIkXyr7/3wwx54e82BXm90cFTkDOLjQ7F9e6bUYQAATKZW0SpMhODlpcbQoXH47ttdgpyXmZkPpVKBgQMjkZVVIMiZZ9PpavHf//6B7Oxiwc+2ZuWqv6K5WY8Ft/5b8LOvveZFwasY//OfTVi/fo9d91jaGUtKqgWNxR5/f/5GpKREY9jQuyWLQUweHh6Ijg5CcZF97YHBIX6YMCFVpKiIiMidWBI0f363AWOuugLnXzkD+QeP2HSvqouEkL6xCf7hYV0+r7mhAW1tbT2eb2xvGRNnbqMzY4WQm5LLZZDJzvzfn5YWh/U/LMWwYQmiPvfmWy7B7t3HceRIPqqrG7B58wFcO2e8qM8Uyz33vId3391g9T293shkkJuSyWSIiQlBQb7zJGHuuOMy3HKL/Ss+HcHTU4233lyPTZv2CXJeZqY5CWSZkSa0rVsP4tprXkRTU4so559LpVJgzJgUUc7W6WqRe3rLpFAyMnLsnsVz7Fgh/vXPtSgstG37lRiKCisQHe28bZV9FRrqD5VKafPKeYuC/HJERQVBoXC/NbxERCQsS0KopqwcJ//cj/jhQ22+V+PtBUNLC1qNpg6vtzR1s2XMy8umdjHgTEJIzpYx66ZPn46jR48iOzsbjz76qNVr5s6diyNHjuDw4cP48ssvBQ2ShDd58jDUN3yFsWMHtb82c+b5on0RBQDDhydg5MgkfPbplvbX1n79GwYMiMTIkUmiPdfDw6N97odQzk6mdeWpp+ZhyZLZgj6XnJ+fnyd27jyGw4dPSR1KuxtunIxbbnXOhFBFRS0efPBD/P57liDnHTtWCIPBiOjoYEHOO5ej16NnZRZg4MBIwVvgQkL88PTT8wT/u9HbW4MrrkhvX3Fui717T+DRR5ejvr7nkm6xFBZWICDAB15e/fM7gzEx5v8ebF05b5GfXw6ZTCbaf09EROQ+zmz9akJ9ZRU8/Xxtvlfl5dVp5bz5rO5bxmxNCBlOzxBSumHLWI9f1cpkMixbtgwzZsxAamoq5s+fjyFDOvbZDxw4EI8//jjGjx+PoUOHYsmSJaIFTMJITY2DRqNCXp75u8OW7xInJUWI9sx58yZBrzdg1art7a99++0uGI0mzBGxSugf/7gVe/58Hd7eGsHOnDFjNOobvu52cPDFlwzHtXMuFOyZ5BqqqxswaeKjTjWzp7i4ymm3jCUkhAua7GhqaoGvz1y8+eb3gp15tpLSz/HiizeLcrY1WVkF7S1wQhoyJBbPPHuD4HPOwsMD8N33T2Pq1BF23SPk38+9YalO6q+JD0uCzu4KoQLz9XFx9s2FIiIiOteZTWFNaKyphZe/7VtU1V5enQZKW87qau28xsfbppXzAGA6vXbeHWcI9ZgQGjNmDE6cOIHc3FwYDAasWrUKs2d3rHpYtGgRli1bhupqc/9/ebnztEqQdampsaioqEVpqfn/s8bGFpSWViExUbwhyEuXrsCkiY+hslI21JgAACAASURBVPLMauGKilrMuGwp/vGPr0V55v/932W4beE0+Pl54YYbJgt2blxcKLy81O2/f9YczSpAamqcYM8k6q2S4sr2wb3OZsvWF/DpZw8IeqZY7Zrh4QHw9/dGkZ1zWPrC0gI3ZIiw1Zvx8eZ+e6GHn1sSDvYMll7z1WP4fv3TgsZhr/6eEMrOLsKzz6xATk6JXfedPFmMzZszYDCwBZqIiPpG7eUFfVMzWk0mNNbWwtPXBzIbW5LVXp5Wkzvdrp23o2XMoDePAlBw7Xxn0dHRKCg4M5hTq9UiOjq6wzUpKSlISUnBjh078Mcff2D69OlWz1q0aBH27NmDPXv2ICTEvrW0JKzUtLj2LzQscnJKkShihZDBYMTu3cc7vb5lywHU1YnTKjBl6khUVzdg//6TuPOuGYKdGxsbAr3e0G1C6ODBPAQG+tjVOkGu7777rkTGgTdF23LVG8XFVfD393a6dpjQUH8kJoZj758nBD135szz8d33T0MuF3ZM3qBBMQDMbWmOcvSoFmvX/t4hkS6EhARzQihf4FlXer0RpaVVdq2ej44OlnR+EADs3XsSl057ChkZOZLGIZasrAI8++xKVFc32HVfTk4JLp32FHbuPCZSZERE5C7U3l7tSZ3GmloAgKevbW1jXSWE9I2NkMlkUGo6f8a1p2XszNp5JoQ68fDw6PTauZO6FQoFkpOTMXnyZMyfPx8ffvgh/P39O933wQcfID09Henp6dDp7CtbJmGlpcUh65yE0L69J1BcLM53vh99dA6eeOK6Lt9fvHgm7r9/luDPHTs2Bbt2HcP77/2IkSOTOsxM6ovYuFAUFOi6nVp/8GAeAPPsJHIfQ4bEIjIyyKm+o56fXw6TyYSoKOcampuengwAVhPFfRES4ocrrkjHgAHCtllJsXK+qakFc+e8hF9+OSTouSmDonHqVBmam/WCngsA+fk6xNiREIqKsn/7ldBqahqweXOG3QkTVxEXF4qQENtL84mIiISmOStBY0kI2do2pvb27mKGkPk1lWfnKqGzE1A9MbJlrGtarRaxsWdK1WNiYlBUVNTpmm+//RZGoxF5eXk4duwYkpOThY+WBCGXy/Daq+vwzTe/d3j93nvfx803vSbKM+9aPBPnjR7Y5fsXXzICj/9trqCbTCIjgxAbG4rdu47jyy9/QW1tI268cbIgZ8edTgh159ChPJSUmCsz+rPRowfi8JFlCA3tnAR2R3HxYYJXXfTV2rW/wctzDk6ccMyqdFuNGZMCk8mEfftOCnquWJvGUlKi0djY0uN/+2IQesZOQkI4jh4VJ7FVUFBuc8tYUJAvNBqV5BVCgLmybOLENKnDEMXyTx/A2m/+1qt7v177ONZ89ZjAERERkbsxzwHqXUJI5eWJZivVPpaEkLVNY2ovLzTX2/aNHkP72nkmhDrZs2cPkpOTkZCQAKVSiXnz5uG7777rcM26detw8cUXAwCCg4ORkpKCnJz+WXbdH5hMrXjxxTX46af9Dnne0KHxiIsLxYYful5F/NmnWxAWFoDLLjtPsOeOHWte1bxr1zE0NDTjokmP4YEHPhTk7P9+8wdWrvi122vq6poQFXkzVq7s/jpXt3jxTKSmxuFWJ91i5WgDBkTg5En75nSITa83OlXFkkX6mBQcOZKPhoZmQc/NyrIkhISd4bV16wE8//dV3VYGiuHpp+ehrPwLm7Yb2uqiSY/h+uv+Idh5Z3vqyS9sPtsys8cZEkL/euU23HvflVKHIYqYmGC7B0pbyGSy9uo4IiKi3lJ5eXauEPKzsULIy9NqhZD+dIJJZWWwtMbb2+aWMZPe0jLGtfOdmEwm3HPPPdi0aROysrKwZs0aZGZm4tlnn8WVV5o/OG3atAkVFRU4cuQItm3bhkceeQSVldKWf1PXIiODrFZz+Pp64sDBt3DnncLN2gGAyy9PBwBs2NB1Qmjjxr0oK6vGzbcIl1RobjZg69YD2L/fnJw8cCAXRqNJkLP//e9v8cEHmwQ5y9VVVdUDAKZOGylxJNKTy2VITAzHyRNFPV/sYH//+424++7LpQ6jg5deXIPHH/tU8HMbGpqRl1eK1DRhE0IbN+7Fyy+LMwC/O/n55fD0VAs69L+trQ21tbZ9SLJXVlYBMjPzbbq2rKwa99/3H+zZky1KLPYoLKzot0OlY2JCUGjnynmLgvxyu2ZCERERWWNthlB3FUID0s/D3zauRWTKwA7VRWfrqkJIJpdDqVGj2caWMUP7DCHnmrfpCDZ9u3Hjxo0YNGgQBg4ciBdffBEAsHTpUnz//Zm1vg899BDS0tIwfPhwrF69WpxoSRBPPnk9jh1/r9PrdXVNiIgIxOhuWrt6Y+bl52NvD/OJjEYTVnz5K668cgwCA30Eee6PP+7F1ClPoqXF3BMaGRmEV165DcOGJfTpXE9PNYKDbctmz507AQcPvQ21uv9mm5ua9DAYjLj1ln9LHYrkvL01+Oqr3/D770elDqWTKVNH4KqrL5A6jA527MjExo17RTl7+/ZMNNQLN6x+4MBIwTd92Sory9zaJVQL3OjRA/Hhh/eKNvA+MjIId945w6bNdqWl1Xjrre8F33bWG/01IRQc7AeNRtXrCqH8/HIEBPjAz8/6Wl8iIiJbnF2xY0tCKDI5CcExUbj9nVeh8fXpYqi09YSQ2tv8b5bNQ6VZIUT/z959xzdVtn0A/zVpVtN0L7oodA9GgZYCDrYgsgRxgIDCAyq8ggwHoCDgREBFZMl0ACIIIoLsvSqUQveGbrrTnaTp+0dIaGnTnJN10vT+/vN8nvac+1yitMmVa3QkwSFeiI9v/dPb2NhM9OjZRW/PsrCwQG5uCX7ff0njtbt3n8Hlywl6mUVjYWEBPr95DyiLZYEFC8fjqadCdDp7+PBwFBX/Silx1tjYiLCwzma9fv7jj38BjzveYAPJ2xOxuAZTJn+DY8eimQ6lhdTUfPj56XfIsi4CAjzw/PN9wOUaZhvbtKnrMHv2Rr2d98EHE3H12hqDxdsWZQucvhJSffsG4M0Zw9HQINfLeU/y8XHBj5veQXh4VwrXuqq2tzEtL7cEnTrZt7pMoz3z9FQkuXK0rBBSzkQjVUIEQRCELpquga+rqoJcLm8zIaQcFC0QWcOSw1FVAzVVX/uoZUzQ/EMLnhW9hJC8oQHyhgaydp7oGEJCvFpsGFO6G5uJsLDOelvX3NjYiNdeXYM1aw5pvDY2NhPDhi5DSoruK51DQ71RId6PF16IUH0tN7cE5eVVCAvrrNPZyk/pqayejo3NBNAxNo2NGxeFlSsnMx0Go/Q5FF3f0tPy4OXlZDLVai+//DT+OvqxSf+ZKVlasjFufBT++usmJBLjz2ISi2uQm1uCYD1VCAUFeUIsrjFYElc5QysgQPPcmY8+mojzFz43SBx05eaWgMOxhIuLeQ3Iz8srxcwZ32u9zS8u7j527jytqrYlCIIgCG0oWsYUSZ1GuRx1lVUaE0JyuRy731uCBqkM4sKWS1vUtYypKoQotowBirYxSw5JCBFmzsnJBi4udm1WCPH5XEov5KnQZs37k5U92oiMDACHY9kiaRMf/0DnuSJBwV7Izi5CFYV2lPT0AtTU1KNHD/1VXZmaK1fXYPr0IejbNxAffvQSHBxETIfEmK+/fgMPsncyHUarUlPzwWKx0LWrG9OhAAC6dfdBWlo+amrqDXK+nZ0Qd2K/x8yZw3U+a+DAbnB0tMHBP67oITLtfPH57zhy+LpezgoM8jTYhjFAMReooKAM3Sn83HP3cERurmlUF/7++2UEB72N4mIx06HoVVFRBXbsOKV1y1hiYjZmvPmdyW0pJAiCINoXrpWgWYKmpkKsISHEh7SuDslXb2Dl0DGIPnKsxTXKljGu2pYxalvGAKBBKiVbxgjzp2xdSlBTIXTjRgp+/vkc5HLdt+jMnDkc165/g8GDu1O+Z9myl1Fc8pvOFUp9+vihvLyqxQvY+LgHCNUxIRQc7Kma6aGJXC5HXNx9dDPTCiEHBxH69QuCra0QBw5chqUlG6NHRzIdFmN8/TqhtLSS6TBalZKSi9zcEpNJ2PXo0QV372YZ7Pzy8mp4ejohIsJf57NeemkAKitr8O+/t/UQmXZ+/PEfHNZTQijIwAkhALh7N4tSZaSHh6NJbBgDgOJiMZKTcwzWSseUwEBP9OypuX1PE1OpLiQIgiDaH66ADxaL1SxBU1MhhqCNhBCHz4ekVrGJtqq0rNUtr9I6xQeLXD6/2df5NGcIAYC0vh6WXJIQIsxccnIO3pj+LW7dSmv1+ykpuZg2dR2Sk3V7s9C5swvWrpuBM2dice7cPcr3ZWcXw8qKB19f3Wad9Inwx61b6S1+cMTHP4BcLoednVCrcy0sLBAU5IlEiht0AOCvIzcQdy9Lq+eZuqYro2/fTkdtbb3OCbf2zM+vE1JTTfNT9Fu30uDlOR1XriQwHQqEQj58fd1wz4AJIQC4fTsdPcN9dT5nxMjeOHo0mtGWGUtLNkJCvCEU8jVf3AYOxxLV1fVIUFMlqi93YzMREuKlMbnv4eGIPBNJCHG5lnj33dGIiqJf2WrKPvhwIg4fWabTGXHxG7Htp//TU0QEQRBER6Oc6VNX/USFUBtr57mCxwkhdR4Pg26eyOEJhS2ep4lMIiUJIcL8FRaWY/fuMxqrGHTd9LV121w0NgIz3vyu1WyuOvceJU66ddN+zg+Xa4nu3X3wXytrjDduPAY319dRXk69fLApNpuFee9uxb59modkK33++e94772ftHqeqWuaEAIUw0e9O7swGRJjlO1Yprhy3tSEhnqDxWKpZmwZyp2YDHTv7qPznKKw0Dn48INd+glKS/37ByMufiMGDAjW6RypVIaQ4Lfx9dcH9RRZ67766iBcXV5vs9qGy7WEs7OtyVQINTTIsXbdDIwaFaH54nbE09NR63YxpeJiMby9nfUUEUEQBNHRtDbTp0asqWVMAElt2yM6GmSK2Y5Pbgfja9EyJpNIyFBpwvw980yYxgqOTZvewb24H7R+BodjiWHDwrHxh79V20moSkjIhlwu12k1PIdjiaVLfm61vUIu160VQCZrwM6dp3HjRrJO55gLd3cHAI8TQgkJ2ZDJGpgMiTGeno7g8TgmPWfj88+nYuvWuUyHgVu30tAtbA7Onbtr0Ofcvp0OHo+j87r2yspand9Q6yrhUVWivjaNGVpJiVjjnLXGRmDCi5/j4MGrRoqqbQ0NchQUlMPdzFbPe3o6ab1hTOnBgyKSECIIgiC0pkwISZpsCtM0Q4hHoUIIULZ6NU8I0V07DwCyeglZO0+Yv02b38HyFa+1eU1KSi7c3R3h5KT+L2hbbGyskJqah5QU+pUSdXUSpKXlI1SHTWDV1XVYu/ZPtUmbjRvfxrJlL2t1dkCAB+0B0X5+nVBWvg8vvfSUVs80ZZWVtbh+PQn5+WUAFG/upkz+huGomCGVNuDrr/7A9eummyz08HTCsOHhTIeBhgY54uMfoLJS82B2Xdy4kYxffjkHmUz7RPCUKYPwySev6DEq7RQXi1FUVKFzcmvevDH4+9hyPUXVtk8+eQVTpw5W+32pVIY//7yGxMTWZ9oxITe3RFX5aC68vJyQq2NCMzfH/P5cCIIgCONpbQ18TYUYAhsRLCwsWr2HSoUQoGj1Yj/ZMmalTcuYBBzSMkaYOx8fF9zPetjmNbquSi8pESMwYDZ27z6j1f3r1h7WaZtPjx5d4OZmr/b7wSFeGDGyt1ZnL1o0HidPraJ1T3GxGLa2Qnh6mt+L6QMHLqN/v8WQSo2/itvU5OeX4sMPdyMu7j7ToahlKqvn588fixdeMHxbTmZmIaa+vk5VXaONSS8/jRcn9NdjVNpLTMxGcIhuM7r6DwiGn59uM9qoGv9if7z8yjNqv+/j44qhQ3uCy7U0SjxUmFtCyM5OCKGQr3OFW1FRBTgcS9jaajd/jyAIgujY1M0QYrFY4ItaH1XSdKh0W2QSCSw5LSuEZFIpGqTU5z9KJZIWiaWOgCSEOhBXVzsIBDxkZha0eV1cnOLNU5gOVTq62Lr1BPbtu6j1/bt2v4ftO+ap/X5CvPabxoKCvWh/ml1eXo3q6jp4ejpp9cz25OmnQ3H+whfo3AHnCLm62uk88NfQTGX1/CfLX8HIkX2M9jxtqx0BICzMW/UzkWmJCdk6VwiFhHgbfMOYUmxsZpsfLIwfH4WTp1bByopnlHioyMstgYeHA9Nh6E1trQQjnvtE5w11V64kYvWqfbRmAhIEQRCEUmtr4GsqxACgdrC0cu28Jg0yWStDpa1otYsBQAOpECLMnY+PKwAgS0OF0MOH5SguFmudEBoxojdOnV6NTp20e1HNYrHg7++u1WBrgYCH0FBv3Pqv5UBppfj4B7C1FcLLi/48hJAQLyQm0G9vyMkphocZJoROnlqF776bpfr/lpZsPPNMGOMJByZs2jwH12+sZTqMNqU9Gnjt7+/OWAze3s6ws7PG3buGHSit9NVX05GesU1tOXJbRCIBfHxcEW8iVV/btv2Lqa+v0+qfBVD8fAwK8sBdAw/zVrobmwkPD0c4Orb+Qs/DwxE1NfVaD/k3hI8//gVenm8wHYbe1NdLcfJkDDIzC3U658aNZHzyya8Qi+m9uCYIgiAIoPWh0rVixZIjdXOEuFYC6hVCT84QsqKfEJKSodKEufPxUVRtaEoIAcCypT/jDy3btgIC3DFkSA9IJNq1Efn5dUJyyhaMHh1J+96ePbvA0pKN6FY2jCkpv9e/f5Daa1prOXNxsYODg0ireRc5OSVm2TLWvbtPs/aj+/cV/2117tzxho8qVs6b9oax1NQ8XL+exGiLn7Ji5K6BV84rpabmQSSy0mogbsij9ixTaQO8fTsd//zzn9ZVGt26dQabzcbt2+l6jqx1yn/H6qqE3D0ckZdnGhvGlJQVneYiJMQbY8b01XnTnoWFBZycbEy+CpIgCIIwTY8TQs2HSgNtJIQEfMozhFqtEKqhlxAia+cJs3fyZAwGD1qC9HTNW5C2bj2BkydjtHqOs7MtGhoaNK62VyctLR91dRKtNo316eMPAPjvvzS118TEpOPGjWS1n7I7OdngTuz3+PmXhc3mJSjnSiQm0m+3+OPAZfx15Abt+0wZl2sJFxe7ZiujlXMqOto2GgsLC/j6uiHdhDeMAYo3u/37Lcbx47cYiyE83BdyuRz37hknyZKcnAsACAz0oH2vq6sdKiqqjRarJiwWC88910uVqKJ/vwXOnbtrtIRQbGwmysur4OysvkIoN7fUKLFQZWsrxNdfv4GnngphOhS9mDTpKRz6c4nO53h5OeFh0a94+eWn9RAVQRAE0dEoZwg13zJWAaCthBC1odINEinYnObzCPlatIwptoyRhBBhxsrKqnD+/D3U12sersXjcdCnjz9EIgHt5zg726KkpFLrT7HlcjkSErIRGkb/TU9k3wDk5ZUgP1/9m4yGBjn6RS1qMafIxcUOKalbEBHhjx82/I2XX34a/91ar/p0OyYmHbP+twHR0Sm049qy5QS+/vog7ftMmbIlsGlCSCKRIS+vpMPNEPLxcYFAwENKSi7ToZg8ny6uSEzM0biSXF+U/04CAz1p3/vXXzdgb/cKsrJ0a7fRl8bGRhw+sgxTpw7S6v7r15MxZPBSPHhQpOfIWldUVAEH+1fx+++XW/2+r6+byfzZKtXXS7Fo8YsYNKg706HohaenIwoKyiCTNeh0TlGR4lNcZ2dbfYRFEARBdDA8KytIausgb3j8+6hGrL5CiGXJhiWHg3rKQ6Wf3DJm1WxeERVSScv19R0BSQh1IBMnDqD8IrdPH3/cjF6n1aekTs42qheP2oqPf6DVDKOlS/Zg2tT1lK5lsVjgNMkmT5s2GH5+7sjMLMTq1fvxzNMfgM/n4k7sBrzxxlAAwE8/ndR63oVIJNC5bN+UKCummiaEAOD8+TgUFpYzERJjwsN9AQAxMRkMR6LZBx9MREbmT4w9f8ab3yEyYoHRnldYWI6KimoEBDA3N0lfGhsbkZlZiC5azuhis03rV/6o5z/F11+ZVqK8rk6CBw+K4MfgnC198vB0Qna2bhvGAKC2th7V1XVqq70IgiAIoi2ttXDVVii6SQStJIS4fEWLMqWWMam05QwhoRWtlfOAotKIwzOdRRfGYlqvDgmDWrV6Cma/NZLStfHxihYJbZIyuTkluHmTfhVNU8lJOfD0dKI9r+DBgyKcOROr8bquXd1QXPIrXnyxHwBFy8//Zj2HixfjVBt4rl9PRu9e83HyZAxenzqY/j9EE6NGRaBC/Dt69uyq0zmmpLq6DgcOXEbaE21SUyZ/gyVL9jAUFTNiYtIxf95W3LuXxXQoGtXW1sPHx5XRT/pra+uN+rylS/bgiBYtmyf+XYk33xxmgIi0l5lZqNXQdjabhZLSvVi0aLwBolIvMjIAd+/90GqbW2xsplYz2QwtLS0P/v6dmA5DLzw9nZCTo585TcXFYjiRCiGCIAhCC621cDXIZKirrm61QogrUHSpSCm8ZlQMldZ9y5i0XgI2h1QIEWbKwsICPj6uuE+xPL+8vBo5OcUI1SIhNG/eVsx48zva9zV18OBVjB/3Ga0y94kTB2D69CGUrs3KeggLCwtVxdSgQd3g5+eOrVtONLvu4cNyjHjuEwwepNsMhrw8RQubOQ2Wjo3NxMuTvqI0k8rcZWYW4vvvj1Jqx2SacgZWcLBu68u1MWJEbxz44yOjJ6N+/PEfnD59h9Y9Tk42GD48HDY2VgaKSjuZGQXo0sWV9n1BQZ6wsbFS/Swylry8UoSFdcbIkb2bff3pp0MxbdoQrTemGVJaaj6jm/j0ydPTEbk5ulcIAYoWQCcnUiFEEARB0Mezan3Ic02FuNW181wBjQohiRTsJyqE+EIh6mi2jMmkEnDIljHCXLm52YPH49BaPRsXd1/r1fO6Sk7OwZEj12m9wV64aDxmzR5B6Vq5XI6LF+Px7MAwAMBHSyahtLQSBw9e1SpeTZTDlj3NaPW8ujdy48f3Q3rGT3BxsTNyRMwZPToSrq7t459XWQEXFER/po6uBg/ujhdeiDD6mnErKx769PEHl2up+eJHQkNNa8OYUmZmIRwcRLCzE2q+uIlevZhpa8zJKUZc3H08N6JXs69Pnz4EX341TetZc4ak3BZobU1/hp6p6d9vMdavP6KXs9avO4Id20/p5SyCIAiiY+FaCVqt2KmtqGyzQohaQkgCS07LljHaW8bqJWCx2WCZ0YgPKkhCqIOgs3JeKe7efYSEeIHFov6fCZvNQnzCj6qZO7p49tkw1dYwTZycbBAR4Y/j/1DfnnTh/D0EBnqiUycHrFq5D2/N3miwCo/iYjHq66VmVSG05+cFuBm9rsXXZbIGdOni2mE2jbm52ePIXx+3m+07OTnFqKqqRXCw8RNCEZEBiIlJN/ra+1GjInAzeh2tJFi/fkEATC8htHfvBYT3fBeVlfSGcoeH+6Kmpl61dc2YThy/haefDm3WAtytu49qLb2pWb/+CJydJhtt8LkhJSZm4/596r/327J37wUcOmSYD00IgiAI86YuQVNTIdaQENI8VLpB2nxdPIfPA4vNpr9lTKJ4H/jkgGpzRxJCHYSyxYDORpcdO05h5IjloFPR7+hog+BgLwgEuv9F2r5jHhYsHEfp2uee6wUWi0Vrnfb58/cAAP37B+HixTj88ccVreKkorGxEbm5JfAwowohb29nVFe37OtVvvno3LljJISUlRfGWuWtq8bGRmzZfMLolSJsNgt9+vgh+maqUZ8LKCoOAeqbxgQCHt6dNwZnz8aioKDMkKHRlpdXitjYTDQ0yGnd1zO8K+7ezYRcTu8+fTh+/BZ4PA4GDeoGQPHfQmioN+7GZhk9FiqY+DMyhJAQb8yZMwq2tvSqydSxsxOqtm4SBEEQBB18obDVBE2NWF1CSDHcmWrLWNOh0jyhot2ffkJI8b6mo7WNUa+fJ9q1Aweu4MaNFFqfFCYl5ajaS6hSbiDRdcsYACQn5yIw0IPStSNG9sbDh+W4dSuN8vl37mTizJlYZGQYZ+3xmq8PmtX2LR8fF5w9e7fF15UrrTtKhVB4uGJQeGxsJsORULd48Q6jPzM42AtCIV/ngfPaUA4+p/rzxMqKh3+ORWPHjtOGDEtrM2YMR0pKLi5diqd8z+/7LzE24+rKlQT88ss5FBcrfi/4+blDIODh7l3T/Tvz8y8LceH8Pfz000mmQ9HakCE98N33s7B//yW9nDd79kh88eU0CK0mGn0wPEEQBNG+KdbAt14hJLARtfi6qkKojsLa+ScqhHhWig9CaA+VrquHpLauww2WJgmhDkIqlWk1/Hf06EiUl1dTfuOhHBZbVFRB+1lPSknOwTPPhMLCwqLFnAkOxxJdu7qhqKgCpaWVyMkuxvHjt2jNo5DL5Rg2dJnOcVK15YmB1e0Zh2MJd3cH3G+lBbG8vBqVlTXo3NmFgcj0b8CAEFy/noQtW+bg3Ll7+PXX882+3zPcFykpubRbeJhmY2OF6uo62pUm2rK25uPGjWRGEkI1NfV48KAI/gHUEkIlJWLMnLnBwFFp74svp+GPA5dpJYQ2bz5uwIjaJpHIMPX1x+2lISGKgeam2jIGAAMGBKOxsbFdJ4T8/DpBLK5RJeJ0pTzH2dlGlfgnCIIgCCoULWMtXyurbxlTDpWmkBB6YoYQX1khRHOG0I1DR3Hj0FFa95gD0jLWQbz99vOYNOkp2vd99vlUfPDhRMrXu7joLyGUlJQDoZAPD4/mc3fWr5+Jmto/kJi0CWPGRAIANmw4ihXLf9P5mYZkZcVjZLOTIXh6OoLNZqttQdy39yIjs0q01a9fEI7+/Ql27X4Py5e/qhqIHRbWGZcuf4X33huLLl3d8PMvCzF+fL9m9/bq5Wv09itdjR4difKK/ejevYvRnnn9ejL6RS1SDew1tpQUahWHL77YDOxuSgAAIABJREFUn/LsMqZkZBSgC43V8w4OIri7OxgwImq6dnXDs8+G4c8/r8HN9XWTm8/UVFpa+9805uvXSVUdpw/K3+vG3hJIEARBtH9cK4HaGUKWHI6qIkh1PY2h0g2SJyqEtGwZ66hIQqiDmP/eWIx74o0sFdevJSEqKpDyauCysmqcPBmDggLdW6OUCYWmb+LefHMY5s0fi337LmHq6+tULUt5eaV6G5xpKDNmDEd8wo9msbZXIpFh/brDuHWr9bk5s2dvxKZN/xg5Ku0EBnri6N+foE8fPzz7bBiWr3gN77zzPABgzpxRqK2tx44dpzHiueXIyCjA7LdGNrt/+LCPTT4Z+aT09AIAMOpgaabXi6/8dC8WLdyu8bpv1r6J1Z+9boSItJeZWYiuNBJC06cPQU7ubjg4tCzJNqadu+bjl18XQSjk4+HDcqNVp2kjLTWv/SeEfN0MkhAyh99hBEEQhPFwBXywWCzUt7IGvlasqD59skqIzlBpmbT52nmelSIhVEcSQpSQhFAH4e3tjAdaJEyuXUuCg4MIARRbLU6disGI5z5BSYnuJerR0amI6PMerlxJBAB07+6DHza+hVOnYjBt6nr88su5dlW2rlw9/2TFU3uUm1uChQu34969LLXXWLaTlY1jxkRCKpWhX9RidPGZAX+/WVix4jfY2Fhh8pSB2LfvEkpLKyGVyrD3twsYMqS7qoIIANLT81VDi9uLtLQ8yGQNRls9z+dzUVq2F7NmjTDK81pz+XICLl9OaPOasLDO8PFxxYHfLxspKu1kZhSgc2dnyhsgg4O98PBhOUpLKw0cWdveX7wTHh6OqKw6gDFj+jIaiyapqXlwcBAxnkTTFovFQpcursjQolVcHVIhRBAEQWijrQSNMuGjbBFT4vAfDZVupc3sSTKJBCwWCyy24r3H4wqhlgkooiWSEOoAnJxswONxkJNTQvvea9eSACg2cRlbdXUdbt1KQ12dBACQlfUQu3aexpTJa9vlFhhlQsjTDDaN2dtbg8dTP3Bt4sQBqKo+oNpuZ8rWrDmE7t3+T9X+ppy19emnr8HaWoAfNx5TXbt370Ww2WxV++Wnn05u0ULWHkgkMmRkFCDISC2M4eFdYWsrZHRjl7W1AOPGRcHLS/2w89GjFS2ox45FGyssrWRmFoLL5cDTk1pyOSjYE4mJzCctb9xIxuHD1wEAffsGMBxN2+LjH+DOnQw4OFgzHYpW5HI5nBwn4+uvD+rtzNzcUsx48ztcvZqotzMJgiAI88drY6aPcmg0h8dr9nWuQIAGmQwNMpnG81Xr4h9VCbX1PKIlkhDqAJSzI/LySmnfm5yci9LSSvTu7Ufp+h075+PfkytpP0edkSN7Y/r0IQAAsbgG77yzSS/ziZigTMhRfRNnytatn4mk5M1qv5+bWwIul2PSM5OWL39V9d91a/9NhXXzwenTd5ptrktIeIBpU9fh998vo3t3HyxdNgn9+wcbLWZ9SkrKQUCAcVpiIiMVb/6ZGCit5OBgjUN/LlXNHWvNqBciEB2danKr5p+0d+9FODm+RrlCMjjYC0mJ2QaOipp33v4RZ8/G4rffLjAdSptOnoxBr/B5em25MraqqlqUl+vv09Ha2nrs3HkaGRkFejuTIAiCMH/KBE1r1T5SZUKI37xCiCvgU2oXAxQVQgDA5ijmCHH5yoHUZCMmFWTLWAfg6moPAMjLo18h1NjYiJ493kVuLrV7fXxcwGbrL8/42uSBeOGFCMyaPQJTJq9t1y9ECwvLIZXK2qxQaC98fFzbnNmU+OjNZ0iIF/755z9jhUXZ9OlDsHzFa+DxOM0SPk0NH/Zxq21vP/98TvG/vyxAWVkVPvtsv0FjNZQd208ZbRZIRGQAsrOLGE20PHhQhNTUPIx6IQIbm1R9KQkEPPj7u2PjD38zEB09VVXUN9o5O9vCwUGEpCTmK4QAoKCgDEOHGG+7Y0c1bFg4hgzpjhUr9qqqbPWhR48uaGiQm/RAcIIgCMK0KFvGWhvyLK1TJG24guYVQjyBgNJAaUAxQwh4XCGkbDeTUlhZT5AKoQ7h1KkYCPgvIjo6Vav7c3KKKa9zd3a21WsFT0pyLmxthfDxcWV8/oWu5HI5Zs/6AQcPXmU6FJ35+Lggq5WV80rl5dXIzy81yQohW1shNv74Nk6fvoOPP/5F7XWNjY2QSlsvU923/wMMGxaOVSv36fUTeGP6668b2LHjlFGeFRHhj5s3tfv5o09HDl/H4MHdYWNj1eJ7tbX1cO80Fd988ycDkdG3bNnLmDChv8bramslmDL5G5NMzJq63/YuxoYNs5kOQyvDhvXEu/PGoL5eqtdzd+95DytXTdHrmQRBEIR5a3OGkB4qhBoeVQipEkI8HuRyuapyiGgbSQh1EPX1Uq03uri62mHLljmIigrUeK2zsw2KHuovIRQbmwkAmPfu1nb7xrupXbvOICam9c1c7YWlJRueno6430ZCCFBUCRlrRg0dgwd3h0DAw6cr9mr9dyI8vCsAYNOm4/oMzajYbBZCQryNMiD25z1nsdcEWoSOHLkBLpeDESN6tfr9hgY5amraR3nx9DeGYvyLmhNCVVW1+O23C0hNzTNCVObFwUGEyL6af++ZIl+/TkhPL6D8YQ5VRUViODuTLWMEQRAEdW0NeVZVCD2REOII+NQrhFQzhBQtYxw+X3UuoRlJCHUAM2cOx6efTtb6/pqaesyYORzDh4e3eZ2FhQUcHUV6rRA6evQmuoXNwYEDpr31hypPTyc8+2wY02HoxNPTCWw2u82WMQDYvesM9u29aKSoqBs2rCfE4hrcuJGs9RlRfRfC2Wmy2gqi9sDZ2RZx8RtVA7INafXq/Th0iPnKuGvXkvDwYTmefjq02dc5HEvcuv0tXnrJ8H8W+hIf/wA9e3bReF1EhD969fI1QkTmR7F6vhPTYWjF19dNNSBfn4qKKsiWMYIgCIIWvkixoKGtLWMtK4QElJM6sicrhPg8yOpJQogqMkOoA3hhdCS8vZ2xfPmvWt1fWVmLuLj7iOrX9qYxHo+DPXvOad2a1prGxkbExz/Q23lMmzNnFN5bMBYC/gS9f3JrLJWVtVjw3k8aV3grZ+2YmitXEpGbWwKZrEHrM8rKqvQYETMKCspQWVmDgAAPgz7Hx8cV5eVVJlHhJ5fLEd5zHvLzmw/YHzasJ8LDfVFd3X56zW9cT8bo0ZGwtRWiokL9n+3KVVPg6ChCZMQCI0ZnHlJT82BnZw0nJxsUF4uZDocWX99OOHM6Vu/nFpOEEEEQBEGTyFGx4Ki6tOUsSeWcnydnCHEFfEor54EmFUJNhkpLSUKIMpIQ6gA8PBy12jDW1KWL8Zj+xlAIBDzUqpnYXlcnwYw3v9PpOeYuO7sIXC4Hrq52Jr/JSJ2SEjG+/fYIpWvd3R1QWysxqQTKr7+eZzoEk5GcnIuAQMMmhL77fhZ8fd0QFjrHoM+h6slkEABMevlplJVV4dSpOwxEpB1lhVtkZABOnYpRe11QkCcuXYo3VlhmRdlm5+fXqV0lhBwcRJBIZAbZkFZcLIa9vTUsLdk6JdUJgiCIjsPGyRFVpWWtrpBXP0NIgKoSau9fG2RPDJXmcSnPHyJIy1iH4O7ugDyKW8LUOXToGoRCPkaO7K32GgsLC52e0REo10R7eTkxHIn2fHxc0bWrm8brXF3tkJO7G5MnDzR8UBT5+naCi4sd02GYjJSUPINXCEVGmsZA6aY2bJiN1atfB6CobBw3LgqH/7zWrloAo6NTUVlZAw8PB7XXCIV8dO7sgqRE09gw1t4kJGTj77+jIZNpN2uMKaWllXB0eBVbtpzQ+9l7917EyBHL222FK0EQBGF8IidHVKpJ7sjqFe1eT84Qord2/skZQjxSIUQDSQiZOTabBVdXO50rhC5ejENMTDqsrflqr5kzZxRq6w5RShZ0VMqEkLd3+109/8nyV3Hu/OcaryssLEdZWZVJbRr78qvpuHFzLdNhmIyU5Bx07uwMPp9rkPN79fKFq6s9blxPMsj52hJaC7Bg4Th07eqGESN6w8bGCvv3X2I6LFrE4hrY272KXbvOqL0m8FH1V2JitrHCMitZWYUYM3ol/vvPtBKaVMnl+k9kpabm4d9/b2s9kJ8gCIJov/gia0SMG0X7PpGTAyqLWy9OaGxshKS2rmWFEJ+PespDpRVJJbZqhhBpGaODJITMnIODCMXFYuTqWCHU0CBH717zsWfPWbXXzH5rJO7dy0JGRoFOzzJnjyuE2m9CSNPK+aYSE7MRHGIaCSEWi4XBg7vjzOn20xZkaPv3X8bYMasN8sYRABa/PwEVFdXYa2LDxZcu2QOpVIZ162ciN7cE2386ibNn7zIdFm2a/r0pk7FJSaRCSBcsVvt6qTRr1gjs2v2eQc62sbHCuHFRcHdXX5lGEARBmKeeI4bilVXL4BvR+rZWdUSOjhCrSQgBijlCXEHLljHaFUKcx2vnpWpGnBAtta9XOQRtRUUVcHN9Hdu2/auX8ywsLGBnJ2zx9YEDuyE01Bsbfziml+eYq/Lyaowdswp//HGF6VC0RishlJCN4GBPA0dETe/evrC3t25Xc2IMLTk5B8eORUMi0X+rlLOzLcaPj8KWzcchFrfcKsGk/PxSrPx0H8aM6QsXF1v8738b2uU8lH79gnD9xlr4+ra+Cevvv6Mx8NmPyMp5HezeswBXr61hOgxaBg/pgaioQIOc7eHhiEN/LsUzz7TvbZkEQRAEfTZOjgCAHsMH076vslh9t4qkrg4cfsuh0lLKCaFHW8Y4j7eMkQoh6khCiKAl5s53+H7D7BZff2fOKJSUiNtd2wUTjh69iZycYqbD0IpAwIOnpxMeaFg5r5SYmA1XV3s4OIgMHJlm/R5tyTt//h7DkZiWYcPC0bu3n97PLSqqQLewuVi79rDez9aH778/ivv3H2La9CFMh6K1yspaREYGtPrmn8VioaKiGhcvxrXLZJepEFdUq1rv2ouAAHekpBgmCahsP+/Uyd4g5xMEQRCmS/QoIdR92CBYUKye5YusweHzIC5W/95HWlffbIaQJZcLFptNvUJIqqgQYqtmCJGWMTpIQsjMjR/fD78f+BBCofrZP3RE30zFmDF9MX58P4SGeoPP58LFxQ7jxkVh+08nUVcn0ctzzFnv3n4YNy6K6TC08vrrg2BpycaJE7cpXX/s2H94fcpa1NdLDRyZZkFBnigrq0JhYTnToZiUnbvm4Z059PvBqUhNzUNRUYVBztaVVCrDpJe+RFlpVbtrCVJKSMhGZWVNi4SQQMBD7N0NmDqV3id4REupqXmwtRW2m1XrFhYWCAjwQGpKrkHOr6ioRm1tPdzdHQ1yPkEQBGG6bJwU7cIiRwd07d2T4j2K3xdtVgg9MUNI2T4moTxD6MktYzyyZYwGsnbezCmTDy/X6CdLumfPWUybPgQHDy0BAOzceRoz3vwOUX0Xtts16sY2e/YIvDA6EocPX2c6FNp27z6DkhIxrlxJoHR9cnIOkpNNY37J2rWHcfDgVabDMDmKTWPuej1z0aLxiOoXhEkvfWWw+UT6EB2diujo9jkwGFDMELp5MxV9n0gILV06CaGh3sjMLGQoMvOhbLfz93c32eRmUx4ejrCy4iHFQAkhAMjPL4MbqRAiCILocKwdHZAZcxfugf7o+dwQpEdr/oBY5KhIIqkbKg08miHULCEkAEA9IdSgbBkjW8a00j4/FiUo6+TugPz8Ur2tiL10KR6ODq+iT+/5eO3VNdix/RQA4PbtdJ03mXUU2dnFcHOzB5fb/vKx9fVS2kmVsLDOePZZ5udNpKfn48yZWKbDMDkpybl6Xz0/8vk+6NrVzaSTQebi+rUk9OzZFf37BwMAXnyxPz78aCJ27TqDS5fiGY6u/WuaEGoP7OyEiI3NREKC4TbL5eeXkpYxgiCIDkjk6IjS3DwkXryCbkMHgsVma75HVSGkPiEkqasDR/B4htDjCiHthkpzScsYLSQhZObc3R30nqiprKzF7dvp2LfvIuVKEeIx5aYxT08nhiOh5+ChJZg06Sna933+xTRs+OEtA0REnUDAw8yZw+Ht3X63uxlKSkounJ1t4ehoo5fzhEI+BgwIxsl/qbUVErr5/vuj+PPPa0hNzUNUVCB+/mUBrl9Pxjtv/8h0aGYhK+shNv34D9LS8pkOhZK4uPsI7/muQZOBb7/1I2bP2miw8wmCIAjTpBwOfeffMxA5OsC3T7jGe5QJIXEbLWMtK4QeJYTqaA6VVlYI8XiUB1ITJCFk9jw8HEnljonJzlYkhNpTcmLIkB4YP76fVrOo7sSkIyjIE3w+1wCRURMU5Imt2/4Pffr4MxaDqbp1Kw0AEBGhnz+bQYO6gcvl4N9/Y/RyHtG2oqIKvDzpKxQVVSAiIgDZ2cUYN3Y1meemJzJZA+bM2UQ+/GgiLu4+0tPbR4KMIAiC0A+e0AocPg+VxSVIvnIDAODdLVTjfTbOjpBJJKgVi9VeI6mrB4fXtEJI2TJGLanTIFNsy7XkcsCyZIPNsSQVQjSQhJCZe/iwHIkGLB0n6FNWCHl5tZ8KoRkzh6OkRIxffz1P+947dzJhaclGWFhn/QdGkXJLkKnMMzIlN26koHev+Th9+o5ezhs+PBzV1XXkDTQDNmw4ivCe81BcrP5FF0Efi8VqNxWdu3a/h23b/s+gzwgM9MTcuS+Ax+MY9DkEQRCE6VC1fpWUQlJbi/qaWljZaq4uFzk6QtxGuxig2DLGEWg/VBpQVAlZcjmqxJK0jiSEqCIJITM3dMgyLFv2M9NhEE1kZT1E717z8eef7WOotK2tEOPGRWHf3ouQSGS074+JSQcAhId31XdolAUEuEMul7ebtg9jqquTICYmXW+ryRMSsrHpx3+0+m+F0F1tLXkBpG9ffjkNySmb28U2uv79g2AtEhj0GVFRgfh+w2y4uzsY9DkEQRCE6VBtCytRJHdqxWJKCSEbJ4c2N4wBug+VBhRzhNhcLjh8RUKIarsZQRJCBGF0MlkDYmLSUVVF/YcckyZNegp8Phe7dp3R6v7MzEJUVFQzmxAK9ERW1kPU10sZi8GURUYGYM2aN2FhYaHx2gkT+uONN4aqvXbz5uN4//2d+g6RIBiTkpILgYBn8m2+HI4lunRxRUqy4TaMAYqh0gDI6nmCIIgOxPrRtjDlLKCaCjEENiKN94mcHFVJJHUkdXWqRA4A1Qp6SQ31pI5MIoElh6NKLMlIyxhlJCFkxvr08ce169+ge3cfpkMhnjB6dCSmTBnEdBiUpKfnY+uWE6pZM9oYMngpli5lrlItMNDDoGuY27uwsM5YuGi8xk1KAwd2w77972P7jnk4c/YzdOni2uz7Hh6OEDTZEkEQ5iApSdFqGhTkyXAkbeva1Q1sNtvgP+vy88sAgGwaIwiC6EBsnBQJoaqSxwkhSi1jjwZRt0VaWwcOjweLR5W4dIdKA4BMKoUll6tqGZOQljHKSELIjPn5dULfvoGQSvXTCkLoz7TpQ/DBhxOZDoOSs2fv4q23dNsoc/t2OsrKqvQUEX3Dh32Mt2aTrTjqXL+eDADo2zegzev69g1EcnIu3nn7R4SHd0XMne+bJYV27pqP2zHfGjRWgjA2ZUJIOYvMVAUEKBK6hk8IKV7Yd+pEWsaYNGRID/j7u4PFYmHr1rl47bVnmQ6JIAgzJnJ0RINMhuryCgDUEkIsNhtCe7s2V84Dj5M3ymQOT6uWMQnYHEtVpRGZIUQdSQiZMWV5u3KIMWE6cnNK4OFh+i+mn346FB4eurcFeHo6Ydmyl+HlxUzLRVlZFfl70IakpByIxTWIigpq87qvvvoDEX0WYPPm4wjvOQ+frd6P+/cVf64LFozD0KE9sXXLCWOETBBGU1wsRkmJ2OQrhCora3HsWDRSUvIM+pySkkpIJFJSIcSgwYO749Tp1fj4k1fA5VrC168Tdu95DxMnDmA6NIIgzJTI0QFVpWVolMsBPEoI2bSdELJ2sAeLxYK4SNNQaUUlkLIy6PFQaTotY48qhB61jJEtY9SRhJAZ8/Z2RkmJGNXVZKiWqcnJKYadnbVWa9yN6Ysvp+G3vYt1PsfBwRorV03BgAHBeoiKntBQb6xY8RpcXOyM/uz2Qi6XIzo6FZFqKoR8fTth6NCesLRkq4YWZ2UVYs2aQ5DL5Rgzpi++/Go6/vjjCtavP2LM0AnCKBYv2oG9ey8yHUabzp+/h9EvrERFRbVBn9PY2IjQkDn47LPfDfoconUsFgvr1s9ERkYBZs/aiLo6CcaMXoXo6FRs2TqX0iw4giAIukROjqgqKVP9/xoKQ6VFj9rMqMwQAqCq7uEKBJDW16uST1Q0SKTNt4zRSCZ1dCQhZMa8vJ1JVYSJys1V/GDUR/WNIYWGeuNubKbO5yQkZKOiohrDhofrISp6nn46FJ8sfxUcDtvoz25PblxPhpOTDdjslr8W3nxzKP45vqLVBGZQkCcOH1mGvLxSvPnGd8YIlSCMbteuM7h4MY7pMNrU2t9dQ0lPzycfNjFk2rTB6N69Cz76cLcqQV9dXYctm0/A3t7a5FsbCYJon0RODhA3SezUVIjB4fNgyVM/O1K5ql7j2vlHyRvlQGiOgE+rOghQDpXmqqqLSIUQdSQhZMbS0/Jx/tw9psMgWpGTo/jB6OXlxHAk6nl6OsHWVoj4+Ac6nyWTNeDQoWt48cV+4PE4eoiOusBAD1RX1yEvr+2Bdh3dihW/oYvPDDQ0tPw05vlREbh8OaHVyoO0tHx88P5ODBr4UbvZnEcQdIlEAjzzTJhJD03Pyd2FL76YZpRnjRoVgffeG2uUZxGPCYV8rFo9BdeuJeHAgcvNvnfjhnIWXCAToREEYeZEjg7NZgHVVIgBoM0qIRvHR6vqqc4Q4j9uGZPSXBuvGCrdpEKIJIQoIwkhM7ZgwU9YuHA702EQrbh+PQkuzpNx5kws06GoFRbWGQAQF3dfL+ft23sRtrZCjBzZWy/nURUQ6ImUlFw0NjYa9bntjUymGD7v7t58tpWnpxN69OiCf45Fq71vzZpDyMwsNHiMBMGUgQO74fyFL9CtW2emQ2lVly6ucHW1R0ZGgVGeN2pUH3y0ZJJRnkU8xmaz8MeBK1jUymu75ORcvDzpKxw/fouByAiCMGcWFhawdnRQbRgDgFpxJYC2E0IiZ2VCSMOWsRYzhATaVQg13TJGWsYoIwkhgmCARCJDcbGY6TDaFBrqDQB6qRACgLNnY5GZWQgfH1fNF+tRSIgXEhKyjfrM9qpbNx8kJW/GjBnDVV97/vk+AIBjx/5jKiyCYJypr55XVoXcvJlilOfl55fByckGHI6lUZ5HKIjFNZg/fxuuXUtq8b3GxkYcOHAZDx+WMxAZQRDmTGAjgiWHA3GTxA6lCiEnR9SIxZBJJG2e/3iG0OMKoXoaG8YAxVBpNpcDDmkZo40khMxUSIg3cnJ3YejQnkyHQqixaNF4TJ48kOkw1Pr553MY8dwnelsX39AgR4D/LHz7rfGGDvP5XDg4WCORJIQoiYu7j2vXkvDd97MQHOwFAHjm2TBkZBQgMZH8GRIdV2ZmISQSqcnOZ4mMDEBNTb3eEviaKFtw3dzIpjFjsLYW4Ng/KxAe7tvmdV5ezpgzZxT4fK6RIiMIoiNQzgJqtWWsjU1jIidHjdVBwONqHu6jodICa2vUVdJ7/6GYIdSkZYysnaeMJITMVOfOznB3dyQzPUzYa5MH4qVJTzEdhloPH5bj5MkYvZ6pnE8jFPIRFOSJPn389Xr+k+rqJLCzfQVr1/5p0OeYi8bGRkybuh5VVbU4e+4zvPTSU5j6+joMHrSE6dAIglEyWQPS0vIRaKIVQhGR/rh9O13V+mlo+fmKF/hk9bxxfPvtTAwf3hMCQduJnvDwrtjww1sID+9qpMgIgugIRI7KbWH0KoSsHexRVVqm9vtKypYxZXWP0N4O1WX0qh0bZLJHa+fJDCG6SELITHl7OwMA2TJmwnJySuDpaZpDpS0sLDB//liDtEccPLQElVUHkJC4CTej18HXt5Pen9FUY2Mj6uulBn2GOSkoKMPQIcuQnV2M/b9/gMGDu5OfIwQBxYwWU20Z27XzDDb9+I/Rnpefr3iB7+pqZ7RndlQ9e3bFmzOG45s1f+Lq1cQ2r1W2DEZGBhgjNIIgOog2K4TaSAhZ2dqgurxC4/nKodLKLWNCeztK9zWlmCHEAZfPg0wiobWyvqMjzd9mytvbGRKJVPWijTA9ebkliIw0bIWMtrp0ccW69TMhFteoZmfoy7at/yItNQ9lZVX4/ItpiIjwR3p6vl6fofT2288jNNQbc+duNsj55iou7j76RS3CxIkDcPr0HabDIQiTsHrVfqOudqdj+/aTRn3e3buZsBZORE0N+QTW0FatnoLS0kp88cUBjdcWFJThwYMiRPYlCSGCIPRH5NSyQkhSWwuZVNpmQkhoZ4sHd+M1ni9tMkOIxWZDYCOiXSEkk0hhyeXCkscj7WI0kYSQmfLydkZOTgnZrGTCcnKK4eJiBy7XEhKJjOlwmtH3hrGmTpy4hRMnbsHSko1XXn3GoG+wnh/Vx2SrsExdQ4Mc+/dfYjoMgjAZMTHpTIfQqq5d3SCXNyIry3ib/hoa5CQZZAR9+wZi1KgIfPThbojFNZTuuXkzhVQIEQShVyJHB8gkEtVmMaWaCrHmCqEKChVCqhlCfAhsRGCxWKgup5sQejRDiM9TDakmqCEJITP1X3Qqskmbh0nLySlGXZ0Erq72yM42rX9XyoSQIbdzyWQN6NnjXYOdDyg2pV292nIbC0EQhDZeeukpVFXVmtRq7yVLXsKYsVFwcZ5s5OdOQlFRBbZt+9eoz+1Ibt9Ox/9mbsDevRco3xN9MwXjx0fBzk6I8vJqA0ZHEIS58wgOgK2LCzyDA5tVBym1lRDiCgSw5HJRQ6H1q0EqhbyhARwBD0I7WwBAVSnNGUIw55W1AAAgAElEQVQSKdgcDrh8PpkfRBNJCJmp778/ynQIhAY//3wOu3adYTqMVoWEeiMrq7BdDyUXCvnw8XHF9p+M20pBEIT5+mjJSyguFhs1IeTgIELMne+weNEO/P775Rbfj4gMMNq6+aaGPxcOCwsLkhAyEAsLC0ilMtrtgFu3/ovNm4+jsrL9/v4mCIJ5ljwe3v31J1hyOACAtJstf+/VVojVbhlTJnaqy8WUnietrweHx4PQ3u7RfTRnCEmlsOQqtoyRljF6TLMZntAZh0NyfaZOuXHLFIWEeCEuzvDri4cPD0de/h6DDJZWrk031hpmgiDM38ULcejfP9iov2NLSyuRm1uC3XsWoH//4GbfEwr5CAnxQjQDCaHkpBzVz1lCv2xsrHA75js8/3wf2vdWVFSTZBBBEDoTWAthyeHg9LbdWP/ydOyc/2GLa9qqELKys3l0DbXEjqS2Dlw+H9bKhBDtGUISsNhs8IRWJCFEE0kImSFPTyfU1h3ElCmDmA6FaIOlJRvbt7+LCRP6Mx1KC/2iFuF/M783+HMePqyAm5s9evf21fvZIpEAyck5JCFEEITeXLwYDysrnkF+Zj1p8eIXVb8fRj3/KbKyCnH4yDIEBHgAePQ7ZMc8sNlsnDt3z+DxPCkxMQdOTjZwclI/P6IjCwjwwO49C/DfrW9p37t27QyEhXnj4UN6n5ArrVjxGnkNSBCETrhWVgCAwoxM5CQko66yqsU1NWIxBLaiVu9/XCFE7eeYtK4eHD6/SYUQ/aHSACAQiUjLGE0kIWSGvL2dwWKx8PAhvb9IhHHJZA2YMHEAnnoqhOlQWqivl6Kw0PD//cTHP0B9vRS9e/vp/exz5+4iOOhtpKbm6f1sgiA6posX4wAAzz7bzaDP4fO5WLT4Rbw+dTAAoKysCi+MWgkAiL27ARMm9Ie7uwOeeSYUixZuV8VlTImJihlzQUGeOp0THOyFFSteQ48eXfQRlkmwthbg2D/LMWZMJG7fSoOVFQ++vp3wzjvPa7x36NCemDFTsWb+v/9StXr+xJcGYOy4KK3uJQjCfE1YthhvfP8VpWv5QkVCqL5a/UD7NiuEbBUJISozhABAUlcHroAPoZ2yQoj+2nkAENiIyFBpmkhfkRny9nYGADwgQ6VNXm5uCTxMbAvWxIkD0LdvID78cJfB29qkUhnu3ctCLwMkhAiCIPStuFiMhIQHCAn1Nuhzpk4dDGdnW6xfd1j1tfT0fPTpPR9LlkzC1atJyM8vRUjw24wNDk5MzEZ5eZVOFUIREf64em0N2Gw2ln38MnbuOI2PPtqN4mJqMydM1YYfZsPHxwUDn12CK1cSAABz547CvPljUVVVhz17zrZ6n7W1ANt++j8kJeVgxYrftH5+RkYhfH3dtL6fIAjz1CnAD25+XSldy7USAAAkNepbUGsqxOALhWBZsiGXNTT7nqpCiGLLmLSuDhw+D0J7W9RVV6sSPFTJpMoKIWvSMkYTqRAyQ8qEUHZ2McOREJrk5BTD09OR6TCaeWnSU3hxQn+jzTi6fSsdvXrpv/3iwsUv8eGHE/V+LkEQHduA/u9j2tR1BjvfwsIC7y0Yi1u30nDhQvPKnwcPivDWWxuRn6/Y9sLkFqkHD4rgYP8qDh++rvUZ//2XhqVLfoa/3yx8u/4IXp86CKdOrwab3X5fno4f3w/Tpg3B558dUCWDAGDx4p04f/4e1q2fCQeH1lssXnppALy8nDDjze9QXy/VOoaM9HySECIIogWhnS0EImvYubpovJanrBCqabtCCECrg6UFjyqHaisqW3yvNZI6xQwhoZ0d7eogAGhQVgiJRJCRljFa2u9vXEItb29nlJSIUV1NyuVMXU5OCTxNrEJowIBgXL2aaLTnHT16E7t3nQGfz9XbmaGh3hgwILhdv6kgCMI0VVQYNgkzalQfBAZ6Yt3aw5ovbqf4fC5CQrzR2NiIr78+iPT0fCxatAPPj1yBTz7+xaSXLmhy+vQdfPLxL1i5cm+zr8tkDZg7ZzNsbKywatWUVu/dufM0evZ4F9euJekUQ0ZGAUQiKzg72+p0DkEQ5kVZtePqq7lFl2dFrWUMQKttY0JbW9RVVaNBJqMUm7T20QwhBzva84MAQCZVPIfNsYSklrwHpoO8WzJDZ8/exdpv/mQ6DIKCjPR8VFXVwcLCgulQAAA+Pq5wd3fE1SvGSwgdOxaNBQt+Ql0dvdJQdeztrXH4yDIUFpZj+/ZTejmTIAhCicOxxC+/LsIbbww1yPk1NfX4668bOHCg5Yp5U/PWWyNx5K+Pad83enQk4uI3IioqsNnXz569i6NHbwKAQbZPGhKfz4VAwENlZS1Wr97falIrIeEBNv7wN2bNfq7ZzCQrKx4CAxWzmOLi7uscS3p6AcrLq+DmZq/zWQRBmAcLFktVtePqRyEhJFC0jNVraBkDWq8QsrKzobU6/vEMIVvaG8YANGsxI0Ol6SEJITN06NBVfPnlH0yHQVDw2We/IyT4bTQ2NjIdCgCgf/8gAGhW5m4MXK6lXjbVsNks7N33Pjw9nTDhxc9RUFCmh+gIgiAek0pliIjwx+gxfQ1y/tmzdzFu7GrInpjHYIqcnGwwenQkhEI+rfsmTxmInJxi3LzZ+tDkYcPCERe/EZ06OegjTKPY+OPbuHzlK3C5bY/nXLFiLz5dsbfZwoPPP5+K2zHf6u2f99ixaDjYv4p797L0cp45c3S0wbp1M5GWvg0+Pq5Mh0MQBiMQWYPFUrz1d/PVPEeITsuYoLUKITtbyivngSYzhLRsGVNuGQNIQogukhAyQx4ejuBwyLxwgj6RSID09HzExRl3Vfut299h0+Y5Op8TFRWEyEh/zHlnE65fT9ZDZARBEC1duhiHp58O0Wt1J5vNwsKF4yESCfR2pqEpN40FBHhQvsfR0QYjR/bG3t8uQC5vvTXs/v2H4PE4eOWVp/USp6FNnToYb7wxFEf/ugmJpO32iIqKaqxevR81NfXw8nLGsGHhmPt/L2DH9lOq2VCE4bm42OHTTycjLX0r5r83Fl27urWrv3sEQZdynTsAuFFoGaM0VFqsvmXMytaW8oYxoMkMIXtbrVrGGqRNKoRIyxgtlBJCzz33HJKSkpCamooPPvhA7XUTJkxAY2MjevfurbcACXpYLBYys7Zj+fJXmQ6FoMDNzR7/nlyJkSNN4+/Mli0n4O83S+2LdENJTs5BqB629ly5koBOblOxYwdpFSMIwnAuXIiDo6MNwsI66+3MmTOHY803b2LgQMOutNenxMQcAEBwMPXV85MmPaVou/vlvNprUlJyER2dislTBukaosH5+7vjh41v4fz5e1i5ch/l+3g8Ds6e+wzHT6zA/ftF+PDD3XqNa/36mVi6dJJezzQnZ899hqXLJuHMmViEBL8NlsVoUlFFmDXhozXwDzPvU54hJJNI2pwB1OYMITtbVFdQ3xgpra2HwEYEvlBIKoSMTGNCiMViYePGjRg5ciRCQkLw6quvIjg4uMV11tbWePfdd3H9uvbbJgjdOTnZwNKSjby8EqZDISiorKzFsGHh6NbNh+lQGJWYkA0/v04aS+3bohxKrctmFoIgCCouXowHADzzTKhezhMK+fh05WScP39PNUOnPUhNzYNM1oDgYC/K94wbH4W7dzM1vvn+9Zdz6NXLl9bZxsbhWOLX3xahvl6K16espfVhSn29FJ98/CsePqzAjDe/0/sikJ7hvnhuhGl82GSK/m/uFoQEv4OJE75AUpIisenl5QxbWyHDkRGEYQjtFQmhjFt3wLcWatw0xhNatTlQGgDqq6ohl8tbrxCys6FdIcTh8QAAVWX0Rz6QGULa05gQioyMRFpaGjIzMyGVSrFv3z6MHTu2xXWrVq3C119/jbo6UqLFJHd3Rf95Xh4pO24PqqvrUFIiRufOmtc/GtqwYeFITNqEoCDqn/TqS3z8A1hasuHv707peisrHpYunYTw8Mfr6j/77HXcif2ebBYjCMLg7t9/iBMnbqGmRj8vOkeN6gMXFzusWP6bXs4zFqlUhuPHb6GsrIryPRNe/ALTp32r8br9+y+hoaEBkycP1CFCw3JysgGLxcLMGRuQm0v/g7i9ey/AvdNUnD9/T++xZaTno2tXMhOnqdBQb3z11XRwuZY4d+4uUlJyVd/r2tUN9x/swKuvPsNghARhOFZ2jxNCAODq1/YcIZ6VoM2B0gDQ2NiIWnFli4SQBYsFKxt6Q6WlTXIIulYISWpJQogOjR/He3h4IDs7W/X/c3Jy0Ldv80GKPXv2hJeXF44dO4ZFixapPet///sfZs2aBQBwcjKtVdvmgiSE2p/794vQ2Yf5hFBoqDcCAz1RVES9vFNfEhIUM4tCQrwRH9/2/KI+ffxx+MhSuLs7AgBiYtIhEPAw5fVBuHAhrl2vKyYIov14fuQKvZ016oVIFBVV4PJl4w7014exY1bRur6qqhZ37mRovK6wsBxTX1+Pq1eNt/WSKjabBTabhfz8UkRGLDB6mzUVGRkFcHd3hEDAQ20HfXNkacnGtGlD0K1bZ5SUVGLSy0/DyUmEb775E0VFzd9wZmQUICUlF2PHRWHz5uMMRUwQhiO0U8wQUiaE3Hy7IPmK+s4enpVVmwOllWoqxC0SQlY2ItX3qJI0TQhps3aeVAhpTeNH6a0NTGy6EcnCwgLr16/HwoULNT5s27ZtiIiIQEREBIqLi2mGSlCh3FCRn0+2K7UX9+8/NIkKIR8fF1RW1qCkxPgJoeTkXCxauF3jmwSRSIB9+9+HVNqApwa8j88++x1DhvTA/Qc74Oxsi51kdhBBEEbEYrHA43H0ctafh66ZZGKBCgsLC43VmZ07uyDmzveIiPCnfO7evRdw//5DXcPTu20/vYv9v38ACwsLk/13lp5eAAAdukpILm/E0mWT8OaMYfh05WR07eqK6dO+bZEMUjpy+DoGD+5O2sYIsyS0t4W0vh5l+QUQF5fAjVKFEMWE0BNr55XVSLS2jDVJXGu1dl7aZIZQHUkI0aExIZSTkwMvr8f9256ensjLe7wqUyQSISwsDOfPn0dmZiaioqLw119/kcHSDLl5MwUfvL+TrNtuR/6LTkVGRgHTYcCniysyMwsZeXZ9vRTr1h1utoa3NT9sfBve3s547dU1qk+NMzMLwWazkJtbgpMnY4wRLkEQBNzc7FFS+humTRui81nTpq7DW29t1ENUxhcQ4IH8gj0YPTqyzevmzRuDkBAv2hXM06YNweDB3XUJUa9cXe0wdeogZGYUNvuA1NQkJ+fi7t1MWFnxmA7F6Pr1C4JIJIBcLkf/fothI5oEHnc8nBwn499/b6u97/Dh6+BwLE1m0QdB6JPQ1hY15YoPfQvTMjUOluZaWbW5YUyptZYx5QDr6nItK4S0SQg1HSpNRtjQojEhFB0dDX9/f/j4+IDD4eCVV17BX3/9pfq+WCyGs7MzunTpgi5duuD69esYM2YMbt26ZdDAidbdu5eFNWsOQSpte+0pYTq++OIA7ZJ7Q+jCYEIIULzIfuaZMLXft7RkQy6XY9XKfbh2LUn19YyMAvTvtxgjRywn7WIEQRhNQUEZqqvr8OxA9T+3qNBXhRFTMjIKwONx2nwTbWsrxIyZw7Bv3yXas3ZWfPoaZv7vOV3D1JvRoyPBYrFMfptlTEw6evZ4F9HRqUyHYlQeHo74+9hybNk6FwBUH5BKpTKNrXM3bqSguFiMYcPDDR4nQRhb03XuBekZcPX1afN6ntBK4wwhAKivqQFXwG/2NVWFEJ0ZQo/avORyOWrElZTvU2ogLWNa0zhDqKGhAXPnzsW///4LNpuNHTt2ICEhAZ9++in+++8/HD161BhxEhQFBXmisrJWq+GGRMd26WI8EhOzNV9oIO++OxqLFr8IodVEyGQNLb4vkzXgjemtDyJNTs4xdHgEQRAtnD17F8OG9YSFhYXW1SI3bq7F5UsJmDt3s56jMw6ZrAGnT8diRBsJofnzx0AkssLabw7RPj86OhV9+vjpEqJejR0XhYyMAsTF3Wc6FOIJFhYW2LFzHjgcNj5e9gvt++VyOV4c/xmjH44RhKFY2dqqhjwXZmSBLxTC1tUZFYVFrV7PsxJo3DIGAJLaOnD4zRNCQjtFxVA1rZYxRVVPbYUYjVq04javECIJITooreM5fvw4AgMD4efnh88//xwAsHz58laTQYMGDSLVQQzavWcBtm77P6bDIGjw9e2EhMRNGDUqgtE45s7djI0bjzH2/ISEbHA4lvDz69Tie2FhnREZGcBAVARBEOqdOR0LV1d7hIV11ur+zp1d0L17F6Sl5es5MuM6cfwWvLycERrq3eJ79vbWmP/eWBw8eBV372bRPvvWf2nw83OHvb21HiLVjVDIx5AhPfDXkRtMh0LJTz/9H7Zvf5fpMIxm/Ph+GDYsHIsX7UB6unZ/py5fTiAfqhJmSWhvp0oIVRYrWnet7e3VXs8VUJshJK2ra1khZEu/QkjZMlalRbsY0HyGkIS0jNFC9jObGXd3BxTkkw1j7UlZWRWCgjwpr1w3BBaL+R8FCQmK6qSQkJZvKJZ9/AqO/bMcXK7GokaCIAijOX1asa1l6NAeWt0/alQfAMDff9/UW0xMOHFC8UFga21jFRU1mPPOJnzyMf2KDQCIjk4BoNgwyTS5vBFz52zGrl2nmQ6FEjt7a0T2DWQ6DKN5+53nkZVViG3bTmp9hkgkwNy5L6B7dx/9BUYQJsDK1kaVoKkVK2b7CB5tA2sNXyikNENIUlsHrkDQ4lkNUhnqqqopx6ec+6PN/CAAaGg6VJq0jNHC/LtAQm9YLBZcXe3Iyvl2prS0ElVVtejc2ZmxGCZM6I/KqgMICPBgLIakpBzI5XKEhHg1+7qTkw3GjeuLn/ecg0RCZmMRBGE6cnNL8P7iHTh37p5W948Y2QdpaXntvkIoN7cES5fswcWL8S2+J5fL8dtvF7RuSb51Kx0AWvxuYEJtbT127DilVaUTE7IyC9GlS8fYMsbhWKKmph6bNx3XafNbYyPw7Xf/w7hxUXqMjiCYZWFhAaHd45axmkcJoSeHQSux2Gxw+DzUUagQktTWgmclaLaZ3MrOVvUMqiSP2ryqaVQVPUm5el7ZfkZQQz5uNyPOzjawtGSThFA7lJX1EN4Mrp7v0sUVQiEf+QxWl9XW1iMr6yGCn3jRP23akP9n774Doq7/P4A/b7Du4O5YxxQREHCBA7fmXjlzm5WVLUsr+9YvR5laWVqZpZZp2XRb5ig198atgMhQhrI3d3CDG/z+ODhA1t1x8OHO1+MvuXt/3u+XZca9eL1eb9ja2mDLlqMMRUYIIfX78st9Jj1nZ2eDIUO64OdWPpzYUJ99tgcA9POUhEI+ftg8H1cux2Pt2r9N3re4uBTubrORn2/chwtz43DYmDt3JP7+OxI5Oab9BLulJSdng8ezg4eHCNnZlhGzqVQqtVku6CgpkSMmJhW9+4SaISpCWgd7J0ewORx9skVerBvaXF+FkC1PV/FjWIWQbo2NvR3KKhIxfJEQsmLj/s6uTOKYWiEE6OYIcW1tqULISFQhZEW8vV0BgBJCFig1NQdtGUwI+fuLkZ8vgVTa+F/8zemF59dh+Ufba7w296WRuHAhltGB14QQUh8ul4MhQ8IQGFh7/llD2Gw23nt3K3777VQzRdbyunYNQOK9zVi8eBquXF2LSZN6o7i48Z8wN4bpZBCgu8p80w9voH//DkyHYrDK4cjWXiVkb29r1u+hLkfGo0+fkBoVD4RYMr6o8hp4XbKlsQohu4qEkLK08ZavyiRQ9cHS1dvTDNXUGUJAtQohGiptFEoIWZHU1Bw8PesLXLmSwHQoxEjH/ruJixdiGTvfn+Er5yudO3cHiYkZ+q8DA73g7i7Apu8PMxgVIYTUj8ezw3/HVmLOnKFGPSeXK7Fp02Fcu2Y914JzuRykpeXh01XPwdHRHkOHLMVPP5k+z6VSjx5B2LX7fXh4iMwQpWmGDQuHRqPByZNRjMVgrLi4NPzzz1WoVLVv7rQmM2YMxP2kLejSxd8s+0VGxsPZ2ZHRNnpCzOnRa+DL5AqoVSrw6qkQsuPxAMCga+crEznVB0vzRUKjbhgDAEVJCcrkChSkZzS+uB5qlQoatRoaNY2YMAYlhKxIQYEUO3eeZbTth5jm228P4s03NzN2frtWkhACgNGje2DjxnkAgPv3MxHQ7iXs3HmW4agIIaRuEokMV64kYtjwrkY9N3Vqf3h61n/DiyW6di0RgwctRliX+QgPW4CLF++aZV87OxtMmzaA0dsmhwwNw40bSSguNnxIKtOSkrIwftxKXL9+j+lQmtVzc4YiISED0dEpZtnv0qU4aDSaVjG3ihBz4It0yfTSwqokjVwihYOgvgohIxJC8sqEUNVgaZ5ICFmRkS1jCiXWTJqFq/tNv/FYXVZG7WImoISQFenY0Q/9+3dkOgzSBEyVJ//x+yn89edFRs5+VHCwN+a9/iQmTuwDFosFqVQOjcb0AZGEENLcThy/hV692kMg4Bm03sfHFbv3LMLs2YObNzCGxMSkIi/PfG1et24lQaPRoGdPZm4ac3CwQ58+ITh18jYj57d248b1xNq1LzFytlgswhNPdMLuXefMtmd8fDqcRbOwb98ls+1JCJP0LWPVqnZkxZJ6W8YqZwgZcu185Zyh6gkhvlBo0nDowowsaNWmVzSqy1TULmYCSghZkQULxuHPvxYzHQYxQXh4O0hL9tR5ZW9L+PTT3dhlxm+mmuLnn49DIpFh399LsWfvIqbDIYSQRh0/fhscDgeDB3cxaP2oUd0BAEeP3mjOsKyGTKZEVFQK+jE0v6d790DY2tqYfJsck/b+uRiHj6xo1jP69g3F2wsnMpIUmjy5LzgcDvbsOW+2PcvLy1FSwuxMRULMSZ8QqjafR1ch1HDLWJmBt4wBVS1jXDs72NjbGT1U2hw0lBAyCSWErIiXtwsNlLZQOTnF4PPtm22wdMeOfvX+ZJXPt4eLS93/Q2CCVCrHLz8fBwD8ubd1VC0RQkhDLl2KQ2mpwvCE0OjuSE/PR0xMajNHZj3OnI5B376hsLOzafGzL1yIhYf4GZw6ZTnzgyopFCq0b+/dLHt36eKPjRvn4dtvD+Kbdfvx9sKJWLRoarOcVZ8pU/vj7t2HuHPngVn3HTmyGw798xFsbelCZmL5+M4iaFRqKEurEjyyYkn9CSG+CS1jFUOlHRz5AAC5VNqkmE1BLWOmoYSQFfH2dkFmZiHTYRATZGUVorRUgeDg5vmm7dz51di8ZUGd702a1Ad5+dsREuLbLGeb4uOPd+GN179vNVVLhBDSEJVKjZ4RC/Hee1sbXctmszFsWDj+o+ogo5w4cRsxMQ/g5eXCyPm5ucUoK7O8QaXJSVnw83MHh2P+b/nnzx+LOc8Pg1KpwsKFP2LHjjNY+fEziIhouda+2U9/iTnPfW32fUUiPp58MgIdO/qZfW9CWhpPJNDfMFZJJpGAV+8MocpbxgyoEKocKl3xjD6ZZMANZeamVlGFkCkoIWRFvL1dkJmRz3QYxATl5eW4e/chOnYy/zceAgEPTk4OOHTwSp3v+/vrqpJSU3PMfrap8vMl+P77f6HV0uwgQohliItLM2jeWadOfnBxccKxY7daICrr8c8/V9G71ztISWnZCxAEAh72H/gQ/fpZznXz1SUnZ4PL5cDX182s+wqFfDw9ezB2bD+DoiLdB7/X532PzMwCPPlky7W/5+QUNctNfbdvpwDQtfQTYun4IlGtmT5yidSAW8aMbxmzr6gQMiSZZG6FmVkoyMhs8XMtHSWErASLxYJYLEJWVlHji0mrdOfOA3RqhoTQoEGdweVycPbsHbRr51Hr/XbtPJCZWQCFoszsZxNCyOOCx7PDunUvY9y4ng2ui45Oga/PHBysJ0lPGsZmt+y3rgMHdsL48b0stnWo8gbRgABPs+47Z85Q8Pn22Lix6kag4uJSdA1/EytX7jTrWdWNHt0DEuluXL+xDmnpv+Cpp/o2yzmJiRmQyZQIC/Nvlv0JaUl8Ue0hz5UtY6w6/k6trBCqbAdryKO3jNnxdQkhRUnLVwjt/ugzbHv/oxY/19JRQshKsFgsDBu6FD9XzF4hlmffX5fw45b/zH7T2PDhXSGTKbFo8VT8+deSWu8Hh/ji3j3KphNCSFPI5WWYMXMgps8Y2OjajIwClJY2/o02qem118YgO+f3Fp0jNHp0dygUZbh0Ka7FzjSnu3cf4odNh8166xsAzJz1BK5fv4dbt5JqvF5YWAIACArygr29rVnP9PAQ4Zdf30ZGRgHy8iSwt7eFn5+7Wc+opNVqEROTijCqECJWgC8S1hgoDegqhADAwcmx1npbHg9KmRzlBlTq16oQ4hteXWRuGpUK6jL6AbexKCFkJbRaLc6du4P79+mDvaU6cOAyli/fjvLycrPuO3RYOM6ejcHhf6+ha9cAtGlT9c0Ti8VCeLg/bj/yDR0hhBDjlJeX48SJKAwf3rXeNXZ2Nti1+33079+xBSOzHmlpeXB1FaB375AWOc/Ozgaznh6Ev/+OhFKpapEzzS0rqxDz5n2H6OgUs+3JZrMRdTsFP289Vuf7gwZ1RkLiZoOHrBvKzU2ArKxCPDXpU4wauQxurk/jm28OmPWM6s6dvaNvhyPEkvFEwlq3flV+7VDHHCE7Pk+f6GlM5cyeyqHSdo7MVQgR01BCyEr4+rph1qxBcHauneUllkMk4kMsFpl1z/lvfI9PP9mNAwd07Qnjx1e1M3C5HCx8+0ds337GrGcSQsjj6MTxW/D0dEbnzm3rfL9//w6YNm0ABAKHFo7MOpw7FwuNRoPBgzu3yHmTJ/eDi4sTfvrxvxY5r7mw2Wyzfn+o1Wrx2msb8d13/9b5/uXLCZDLlRg1qpvZzgR0rfXdur6Fu3cfmnXf+rz33lZMm/pZi5xFSHPiC2tXCMkqKoR4wl4T3fsAACAASURBVDoSQjwHoyp8lDJ5VctY5fwhBmYIEdNQQshK9O0bim3b34W3NzO3bxDzSH2wFUuXTjfrnmfOxODChVgkJKQjPj4N48b30r+nUqmxdesxREbGm/VMQgh5HB05cgMajQYzZz5R5/vDh3eFSqXG2bN3Wjgy61BcXIqbN5MwyMyVJ/UpKZHjwIHLOHnS8q6br+7wkeU4eGiZ2fYLCvJq8H2FogxnzsRg5KjuZjlvwYLx+PHHBWCxWGavoibE2rn5+YJjw0VRVs3LYypbxuoaLG3HczAqoVMml9caKq1g4JYxYhpKCFkJd3dddjcnp7iRlaQ1i401701j06YNwIABVa0Jhw5exZAhYXB01GXxw8L86UpVQggxk8zMAuzadb7eD61Dh4UjMjKe5gc1wZnTMejbN7RF5ggdPHgFkyZ+YvFJiIcPcuu8VMIUQiEfd2K/wwcfzGhw3X9Hb6JDhzY12tRN8dprY/DNt69A5OwIDqdlP7bY2HBx4+Y3+N//nmrRcwkxpw4D+wEA4i9drvG6rFj3mdGhzoQQH2Uyw1rGAN1g6aqh0jxo1Gq6/t2CUELISojFImi1WuTnS5kOhTRBrBlvGmOxWPhq7Vy8MX+c/rVNmw5j6JAlkMl0f0l//Mmz2LX7fbOcRwghBHhm9pf48MM/ar3u5iZAREQQTp64zUBU1uPPPy9g1ae7mz0h1LNne/0PTyxdcnI2vLxczDLk+cknI2Bjw8WxY7caXHf06A0AaFLbmJubAOu+eRn//nsNs2Z+AbVaY/JeplCp1BAK+egREdSi5xJiTh0G9kV2UgoK0jJqvC5voGXMlucAhREtYzUqhPh8RgZKE9NRQshKiMVC5OVJoDVgGjxpve7ceQBPT2e4utb+y9lYkyf3ha+vG/7ed0n/2v37mbh0KU7/5yQ8vF2tG0IIIYQ0XWiob42v3dwEiI9Px59/XmQoIusQGRmPTz7ZBYmk+T5wtG0rxuEjK/DjT2822xktKSlJd/W8v7+4yXtNeqoPMjMLcOVKQoPr7t59iKlTPmvSn/fZswfD1tYG7//fz1Cp1Cbv0xRRUckIp5vGiIWydbBHYM/uuHu29n+HlTOE6qwQ4vOMrhCyqdYyRgOlLQslhKyEu1hE7WJW4M6dBwDQ5CohNpuN5StmIzb2AfbsuVDjvS5d/LF69fNwcxPAz8+dbhgjhBAze/PN8Yi5s7HGldhxcWno0nk+YmJSGYzMOjg5OaBPn+a5acze3hZ//rUEbDYLHyz9rVnOaGnJyVkA0OS2MRaLhZEju+GfQ1cNaqP766+L+mvoTTFz1hO4fDle/70RE6JupyA42Nss1VWEtLT2vSPAtbXF3XO1E0IalQpKmRy8um4ZM3KodPUKITsejwZKWxgu0wEQ83j7rc0QCvlMh0Ga6Nq1e3j1lQ1ITMxofHEDZswYiE6d/DBj+upaVWMdOvjivf+bAqlUl/m/fTulSWcRQgipad++SKz9+iX8/sf/cPTIDQiFPHz00XYoFGVMh2YVli2bhfkLxsFZNNPs/0y/3/Q6uncPxPhxK3HvXqZZ92ZKXFwaPlj6e5N/P0FBXhAK+bh0Kc6g9SIRHy++OAJHjtxAbKzxSZ1RI5cxflnKrVtJ4HA4CA9vh8uX6QIOYllCB/aDorQUyTfqblWWS6X13DJmXFJHpVDCyd0VgK5CiBJCloUqhKxEenq+Sf+zJa1Lfr4EW7YcRWZmgdHPTpnSDxs3zoOPjyscHe1x9mwM9u69UGvdkSM3oFKp8cGHuoGQt28nNzluQgghVR4+zMWyD7fB19cNn3z6LF6bNwY+Pq5Mh2U1Tp2Kgp2djdmrhN54YyzmzBmGFcu3459/rpp1byYVFZVi1ardTf5hU0ZGAcaNXYEjR24YtJ7D4WDNFy/gqaf6mHSeRCJDXFyaSc+ay4ULd7Fz51mzJx7ph7ikJXQY2BcJl65Co6675VJWLKlnqDQPSqNaxuSwq3btPLWMWRZKCFmJt96agF69gpkOg5hBQIAnhgwJM+oZOzsbbPxuHl59bTQUChW2bDmKwYMW11nSLZHIcOZMDIqKSjFq5DLk5BSZK3RCCCEVVq3ajcCAlyASzkBgwMu4f986qk1ag/PnY6HRaDDYzNfP//XXJXzy8U6sXLnTrPu2BmKxCCEhvo0vbEBpqQL//nvN4B9a5edLcOtWMoYN72rUOQ4Odjhx8lMMGxZuSphmlZNThKdnfWHWH56NH98LhUU78dPWt+Ds7Gi2fQmpzjMoAM5enoiro12sklxSu0KIY2MDjg3XhFvGKlrGHGmotKWhhJAVsLHh4ut1L2PkSNNvciCtx+LF07Bt+7tGPTNz5hMQi0UYM3o58vMlja4/eOAKxGIRkpKyTA2TEEKIASQSGfLyGv97mRhOIpHhxo0kDB5inoRQaKgvOBw2MjMLsGzZNou/Zr4uP219E9t3GPe9xaOmTx+Anj3bG/XMieO30K9fKHg8O4OfGTOmB4YMCYNW23r+Pfj5ucPGxjyTNpYsnY6CAimeeWYw+vfvYJY9CXlU27BOAIDEK9frXVNXhZAdT1fpo5QZXuVTJpfDxp6GSlsqSghZAXd3XWaXhkpbh/j4NHh6OhtVTvzmW+MRE5OKY8duGrT+4MErAIDu3QNNipEQQghh0pnT0ejdOwQODoYnGurz/aY38N1388wQVeuVkpzd5KHSGzbOwyuvjDbqmePHb8PW1gYDBnQ0+Jknn+yBwsISnD0bY2yIzWLUqO5ISd1qlhbFAQM6onfvECxd8huC27+KQ4d0rYkBAZ5N3puQ6pzcdG3Kxdm59a6RS6TgPZoQ4vMAwOiWMRoqbbkoIWQFxGIRAFDrj5WIj08HAISE+Bi0vnfvEHTrFoj13x40+IyUlGxMnPAxTp+ONilGQgghhEnfffcvevV8p8mzXfz83DFoUGc8eFD/hyZrkJycDZHIESKRabNr2rRxh5ubADdu3DfqufPnY1FSIkdwsGHf0wDAmCcjcPToDWg02sYXt4DIyHhoNBoMHWpcO39dAgO9cP9+Jn777RRSU3MAAF27BiD27nf46KNZTd6fkEpObq6QFUugUanqXSOTSODwyC1jdjwTEkIKJWzs7MDmcGDH50FRShVCloQSQlZALBYCoAoha1E5QNGQhBCLxcLly/EYNnQp/vjjtFHnHDx4Bbm59GeGEEKI5UlJyUZ0dArKy8vB4Zj+7ezs2YMBwOj/h1qayhbxdu1Mq0Tp1i0AAHDzpnEJIblcCXe32diw4ZCB5wTCy8sFh/+9ZnSMzaW4uBTXr9/HUDPMNPr11xMICX4NcrlS/1p0dAq2bz+Lj5Y/jeFGzlsipD5Ori6Q5OU3uEZWLIEdzwEcblU7pG1ly5gRVT6V84YcXZzBZrOpQsjCUELIClCFkHVJTs6GSqVGaGj9wx+Dg30QefkrHDy0DIDuxpXq31wQQggh1o7FYmHX7vexdu1LJu/xzLNDcPZsjL5aw1olJ2cDgMltY926BUCj0SAqKsXoZ5XK+isUHsXlsvHffzdx9KhhLfAt5dTJKPTpE2LULKRHVX6/rtXWrHzSaLR4fd53uHv3IX786U04OTk0KVZCAF1CSNpIQkgukQIAHIRVbWP2FS1jZUYMhi6TK3RnurkAAFUIWRhKCFmBXbvOoa3fi0hJse5vZh4XarUGI4Z/iA0b/ql3zeo1zyM42Btnz7SO/npCCCGkpZWXlyM7qxBvzB+L6dMHQCDgGfV89+6B6NChDbZZeXUQACQmZmDOc2tx5UqCSc+Hdw1AfHw6ZDLjf/jk5eWCU6c/w6RJjV8/f/VqIkaPan03oJ44cRs2NlyjZiE96sLFNfj5l7frfE+hKMMLz6+Dj48LvvpqrslnEFLJyc0VJfkN3wgoK9ZdeMCr1jZm62DaDKHKMwFASUOlLQolhKyASqXGw4e5UKs1TIdCzOTs2Zh6r3Xt2jUAEyf2wdqv/saaNX+2cGSEEEJI67F06e9IScnBzl3vo6BwB44d/8TgKo47dx5g+rTPsWfP+WaOknkymRK//34KaWl5Jj0/c8ZqPDlmuUnP5uQUISzMH3NfGtngOgcHO7i6Chpcw5QLF+5i9tNf4urVRJOeb9fOA4GBXrjWwPNXriTgs1V7kJHR8Id4Qgzh5OoCSSMJocoKoeoJIf1QaWNaxioSQgI3NwCAglrGLAolhKzAzJlP4PXXn2Q6DGJGISG+ePvtiXXORfjgwxkoKirBt0YMkSaEEEKskVQqR3jYAowc8SG+WPMXbly/B4XCsBYlpVKFvXsvoKjo8fhpdqdOfnjiic4mPVtWpjZ58LZGo8Vnq3Zj7NieGD26R73rJk7sjeyc39G5c1uTzmlOcrkSO3acQWFhiUnPV84GOn78doPrli3bhuXLt5t0BiGVbB3sYe/Ib7RlTKZvGatKCAncdVU+JQWFBp9XptBVDla2jCmpZcyiUELICjw9ezBeeHEE02EQM+rXLxRrv34J/v41e/05HDa02nJ8vXY/JBLKvhNCCCGlpQocP34Lixf/ivff/wVarRZ8vn2Dz0yc2AdLlkyHrS23wXXW5KPlT2PTD28Y/VxERHt89dVc/QwcU3zzzUEkJKTj63Uvwcam7n/m06YPQF6eBLGxD00+p7nNmjUIf+//ACwWy6jnhg3virS0PMTHpxm0fuzYnpg+fYApIRICR1ddYsbwlrGqGUIiDzHkEimURs0QqqgQctdVCNFQactCCSErIBYL6YYxK1PfTWMajRbTp32Ojz/eyURYhBBCSKsXFOSFuPhN6N+//nkvSz+YjqdnD0JZmboFI2NWSnI2/P3FRiczBg3qjIXvTGrSaAKVSo2Fb29BSIgvXnxxeK33AwO9MHFib/y45b9aQ5dbEzs7LiZM6I1nnhls8DMsFgvDhoXh2LFbBj/zzv8mYcPGeXB2djQhSvK4E7jqqnwkeY21jFUkhKpXCIndUZRt3Fxa/QyhikSUgmYIWRRKCFkBXUKodQ3fI00TH58OADVuGlu0aGqDN48RQgghBEhPL4Cjoz1efmVUne/36ROCiIj22LDesKvQrUVycjbs7W3h6els1HNdwvyRlpaHggJpk84/fPg6pk39DFu3Hq/13sKFE6FSaQy+np4pv/56EpGRcVi95gWDbwNjs1mY++K32PT9vwaf8/ZbW+DszMfixdNMDZU8xiorhAy5ZUylVEIgdtO/JvIQozjbuPbQylvGBJVDpY2oLiLMo4SQFRCLRcilCiGrUlAgRV6eRF8h9Moro7Hqszl49tkhDEdGCCGEtG5yuRI7d5zF1Kn96/zQvuDN8SgqKsHvv59iIDrmJCVlAQACAjyNei48vJ1J183X5c8/L0KlqlmVZW9vi6dnD8K2P04hK8vwuSVMKC8vx5sLNkMsFmLZslkGPaPRaHHgwGWjBlJHR6dgx46zeG3eGLi4ODX+ACHVVM7ykTbSMlZeXo7CjCy4+HjrXxN6uKPY1AqhivlDVCFkWSghZOH4fHvweHZUIWSF4uLSEBzii1GjumP9hlfx77/X8OGH25gOixBCCGn1fv75OHg8O8yYMbDG615eLpg6tT9+3nocpaUKhqJjRkKCrvr40Xb0htjYcNGhgy+ibiebLY6xY3vi8pW1cHDQ3QanUJShS+f5WL58h9nOaE7XriXi563H8eZb4+Hr69bo+lmzBpk0KHv153vh6OiABQvGmRImeYwJ3Fyh1WpRWtj458PCjEy4+HgBANhcDpzcXE1oGVNWnOsGtUoFdVmZ8UETxjw+k/SsVGmpArY2k+q8jYpYthnTV6NdOw8cOboCMTGpmDVzTavuqyeEEEJaiytXEhAb+wDPvzAcP/74n/51R0d7HD16Exs3/sNgdMxITc3F8GFLcfNmksHPeHu7oKCgxGwVQgBQXFyKnj3bY+7cEUhMzMCZMzFIT2+4taW1WbLkN+zdewFpaXkNrgsM9MLWn9/Cxg2H8O67W406486dB1j/7UHcv5/VlFDJY8jR1QWlhUXQahqf+5WfnonwjqEAdAkdNpuN4hxjW8Z0FUK2DvYoLaKuFUtDCSEroFZrmjToj7ROmZkF+ObbV5CXJ8HYJ1dAKpUzHRIhhBBiMRYv+hUaTc0fpCQmZmDC+JUMRcQsrVaLkyejjHomNTUH3l7PGT2IuiHnz8fi3Lk7+Hz18+Dx7LDo/V+wZs2fZtu/JeTmFuPo0RsAgBkzBuLUqeg6q/XXb3gVSqUKX331t0nnvPXW5ibFSayTuF1b2NjZIT0uoc73BW4ujbaLVSpIzwDfWQQ7Hg9CD3cAMLplTKNSQaNWg8PlUruYBaKyEgvXo0cQNmx4zegBgcQyPPvMVxgyeEmr76knhBBCWpuDB6/g33+v6b/u0SMIbduKGYyIeRER7TFv3pNGP1deXm7WOJZ/tB1cLhtrVu/F11/vN+veLcnFxQlbflyAmDsbMXx41xrvTZnSD6NH98CyD/9AZqZhH87rIhLxMXPmE00NlViR2Z+vwPQVS+p938nVtdGB0pUK0jMBAM4+XhB66P5+LDJyqDRQNViaBkpbHkoIWbiuXQPw+htjYWNDxV7WSKlUITXVuCw9IYQQQnS4XA62/vw23n33KXz3/es4eGgZ0yExaty4nvh2/Suws7MxaP3337+OTz991uxxnDoVBUf+NCxa9GutIdOWpKBAil4930FGRj72/b0UPXu2BwDY2dng63Uv4+bN+01uT5wwoTe273gPPXoEmSNkYuFc2/jCt2OI/or3uji5uULayJXzlSoTQq4+XhBVJISMrRACqtrGlFQhZHEoIWThxGIhAF3pKiGEEEIIqaLRaOHgYIs1X7yInj3b4/vvDL/62xrFxaWBw+EgKMjLoPXjJ/SCl7drs8RiLeMO4uLSMGrkMmRnF+HQPx+hfXtvKJUqREbG4/V539dqWzTWwYNXoFZrMHlyXzNFTCxZ+MihAACeSFDvGidX41rGAMDFxxtCD3coZXLIJVKj41IpdIOlFVQhZHEoIWTh3N2FkEhkUChomjshhBBCSHXl5eWY89xanDoVhZycIvz220mmQ2LU3bsPAQChob6NrnVzE8Db2xXRUea7YcxaZWcXYfSoZVCrNfpr4mdMX43Ll+ObvHdhYQlOn47G5Cn9mrwXsXxdRw0DANjY2cHWwb7W+/aOfNjY2xncMlZaWASlTAYXH2+IPMQmVQcBVCFkySghZOHcxUK6cp4QQgghpB5lZWqMGP4hOoTOe+yumn9UQoKuGqBDhzaNrg0L8wcA3DbjlfPW7N69TAwZvARlZboWOHPOXdr31yWEhPga9O+NWC83P1/4dAhGZuJ9AABPKKy1xslNV9EnzTf85r6C9Ey4+HhC6CE2+oaxSpUzhGiotOWhhJCFs7HhIiPD9EF1hBBCCCHWTqvVorCwhOkwGCeXK5GSko0QAyqEwsLaAYBZr5y3dgkJ6bh5877Z9/3770gAwODBXcy+N7Ec4SN11UGRe3W31vGd60gIVcwWkuYbfiGNLiHkDaHYHcUmDJQGqlUIUcuYxaFJxBZu5ozVTIdACCGEEEIsxMAB7yM7u/Hq8qKiEpw8eRt5eZIWiIo0JDOzAH5tXkBaWh7ToRAGhY8aipRb0UiPSwQA8EUNVAgZ2DIG6OYIBfbsBhtbOxQ1sWWMKoQsD1UIEUIIIYQQ8phIT883aKDzL7+cwPBhH7RARMQQlAx6vNk78uETGozYMxcgK9JdJlRny1hlhZBRCaFM2PP54NhwmzBDqOLa+VKqELI0lBCyYCwWC3/+tQSTJvVhOhRCCCGEEGIBOnb0w/r1r8LT07neNRwOGywWqwWjIo0Ri0XYtft9jBzZjelQCAOEFVfC5z9MQ2mxLiFUX4WQRqWGrNjwyr7Km8YA066cB6rNECqlCiFLQwkhC+bs7IinnuqLNm3cmQ6FEEIIIYRYADc3Ad6YPw5duvjXu2bcuF7IL9hOQ4xbkeLiUjz1VF888URnpkMhDBBVJISKsnP1yR5eXQkhVxeUFBQaNdS8ID1T/2vTW8aoQshSUULIgonFur8EcnOLGY6EEEIIIYRYgri4NABAhw71D5bu1y8UDg52SErKaqmwSCOUShViYx8gvGs7pkMhTWTvyAeLbdzHcKFYVwBQnJMDrVoDubQEPKGg1jonVxdI8427cKhGhZDJt4xVzBCiCiGLQwkhCyYWiwCArp0nhBBCCCEGyckpQl6eBOFdA+pd07dfB1y/fg9KpaoFIyONuXUrGV0b+PdGWj+OjQ2WHvkLvSePN+o5oaeuQkiSo5slVVpUBEdnUa11PJEApUXGfTZUlJRCViyBWqVCaYFpnyv1FUI0VNriUELIglVWCOXkUIUQIYQQQggxzKlTURgxomud79nachEREYRLF+NaOCrSmFs3k+Dj4wp399qtQsQyiDw9wBMK4NrGx6jnhB7ukOTlQ6NWAwBkRZI6h0rzhUKj5gdVyk/PQHF2rlGtZtVVVQhRy5iloYSQBWOxWEhNzaGEECGEEEIIMdjRIzdQVqbWV5tX161bIOztbXHx4l0GIiMNuXYtEVevJsLNrXarELEMzl4eAAB7R0ejnhN5iGsMfC4tLgZPVPvPAU8oMCkhFHPyLGJOnTX6uUqKkhIAgFwiNXkPwgwu0wEQ0+3Zcx579pxnOgxCCCGEEGJBfvnlBLZuPVbne7m5xVj16W5cuEAJodbm/PlY9O71DtNhkCaoTAg5OPKNek7oIUZBWrr+a1lRMcT+fjXWsFgsOAicTEoIHf/hZ6Ofqe72f6egKClFUVZ2k/YhLY8qhAghhBBCCHmMaLXaet9LSsrCBx/8TjMqCWkGIi9PAIC9k/EVQkXZVQOfS4uKa7WM2Ts5gc3hoLSo5btHyuRyRJ840+LnkqajhJAF+/zzOfjuu3lMh0EIIYQQQizMrFmDkPpgK3g8O/1rHA4bY8b0ANvIG5BIy/nsszmIvPxVi5/bpo07/tq3FLNmDWrxs62Js6fxLWM29nbgCQUofiQh5ODkCA63quGn8tYxUyqEyOOL/ra3YH36hiIktP4rQwkhhBBCCKlLdnYh2rRxx+DBXfSvjRnTA//8uxxjx0YwGBlpiEJRhoiIoBqJvOb28sujEHNnA8LC/HHmTHSLnWuNRPoZQoa3jOmvnK82Q0hWUQVU/ep5fsVMIRkDFULEclFCyIKJxSIaKE0IIYQQQox2/nwsSksVGD26u/61V14dg8zMAhw+fJ3ByEhDbt5MApvNRpcu/i1y3jPPDMEPm+fj8uUEDBu6FBkZBejTJwRDhoS1yPnWxrmiZczBiJYxoYfuyvmiuhJCoqq2MX2FkIQqhIjhKCFkwTw8RMil/m5CCCGEEGKksjI1Tp2KxqSn+kIo5KNNG3eMGdMdW386BrVaw3R4pB63biUBALp2bdfsZ/XoEYTNW+bj9OloPDlmOVJTc8BisbBh4zzs2PkeXF3ptjNjiUxoGRNVJIRq3jKmS/rwqyeEKn7NxAwhYrkoIWShbGy4cHZ2pAohQgghhBBiktWf74VYLMT6Da9i7twRYLFY+PHH/5gOizTgwYNc5OVJ0Kt3SLOflZ6ej7//jsT0aZ/rk4Tl5eV4fs7XcHV1wpIl05o9BmvCFwlh62APmUQCe0c+WAbO6qqsECrOqZohVNUyVi0hJKCWMWI8SghZKEdHe1y8eBeJiRlMh0IIIYQQQizQhQuxmDF9DZYs/g3Dhofj6NGbSE3NafxBwqj13x7E5cj4Zj8nK6sQT8/6Anl5NVuQYmJS8esvJ/D6G2Ph5+fe7HFYC2dvXbtYZuJ9AIAdn2fQcyJPMUqLiqFSKPWvlRbqukT4zlUJIb5ICK1WC7m0xFwhk8cAJYQsVGFhCQb0/z/s2nWO6VAIIYQQQoiF2r8/EmlpeRj0xGLMeW4t0+EQA3z88U5s3nyk2fZ/9tkhOHxkBYTC+gcfL1++A1qtFitWzm62OKyNyFOXEMpK1LX9OTTQNubs7Qlxu7YAAKHYrUZ1EACUFuuqgPiPzBBSSEtQrtWaNW5i3SghRAghhBBCyGNOq9XWqgQhrZdQyIeHh8js+/r6uuGbb1+Bvb0tJBJZvevS0vLw6Se7cScm1ewxWCvnihvGMhN0FUL2TvUn3J794hPM+2kDOFwuhB7iGvODAEClUEKlUNZsGRMJ6cp5YjRKCFmop57qi5u3voWPjyvToRBCCCGEEEJaCJvNxoOHW7FkyXSz7/3lV3PB5XLw4gvrUF5e3uDaVat248sv95k9Bmsl8vJAmVyB/LQ0APUPlnb390PbsE4QuLshfPQwCD3ENW4Yq1RaXFyzQkggoIHSxGiUELJQ/v5ihIe3Q0mJgulQCCGEEEIIIS1Eq9XiypVEDBjY0az7tmvngSlT+mLD+kNITs42+LkpU/phy5YFZo3FGjl7eaIoKxtyaSmA+hNCPcaNhlajQX5aBgbPeRoCN1cUZ+fWWicrKgZPVHXTG08koCvnidEoIWShxGIRyspUKC4uZToUQgghhBBCSAs6f+4OwsL8IRAYNpjYEK+/PhZabTk2bDhk1HNBQd6Y+9JI/O9/T5ktFmsk8vRAYWYWFCW6oc8OdbSMsVgsdB87ComRV3Fq6x/wCQ0GABRn1VEhVFQMvrDmDCG6YYwYixJCFsrTy5munCeEEEIIIeQxdO7cHXA4HPTtG2q2PVeu3IFJEz9BRkaBUc+tXr0Xf/11ESs/nk3jLBrg7OWBosxsKCpuAaurQsi/Wxhcfb1x7dARXD90WF/xU2fLWFEx+M5Vc6T4QpohRIxHCSEL1a1bAKKjaYgbIYQQQgghj5vIyHioVGoMHNjJbHtKpXIcPnzdpGffWfgj2Gw2li9/2mzxWBOOjQ0E7m4ozMqGvKT+lrEe40dDKZMh5sQZlMkVuLz3AACgKKt2C5+sqBg8oa5ljM3hwEHgRBVCxGiUELJQ164m4tDBK0yHQQghhBBCCGlhMpkSsOJM2wAAIABJREFUzz7zFX799UST9+Jw2Dh4aBlGjepu8h4PHuTi++/+xfMvDEPbtuImx2RtRB66fyZFmVlQK5VQq1S1WsZYbDbCRw5FdEUyCACObf4Z25esRE5y7UKA0mJdQojFYsFB4FTxGlUIEeNwmQ6AmOall9YzHQIhhBBCCCGEIbt3nzfLPpMn98PYsT2x+YcjTdpn1ard+Pffa0hNrd3e9LgTVVw5X5ipq/RRSEtqVQg5ODmCJxDgYcxd/WvKUhmuHzxc556yIgnYHA7snRz1t41RyxgxFlUIWSBHRwemQyCEEEIIIYQwbMaMgRg/vleT9njnf5OQmJiBQ4euNmmfvDwJjh+/1aQ9rJXIU5cQqmz9UpSUwt7pkYSQQNf+JZdIDdpTmpcPABB6iMGreJZaxoixDEoIjRo1CnFxcUhMTMT7779f6/2FCxfizp07uH37No4fPw4/Pz+zB0qq/PzL27h8ZS3TYRBCCCGEEEIY9O57k/HhspkmP9+3byh69w7BN+v2o7y83CwxrVgxG7/8utAse1kLgZsLAECapxvYLZdKYe9Ys2XMoSJBJK8YOt2Y9LgEAECbjiHgUYUQMVGjCSE2m42NGzdizJgx6NixI2bNmoUOHTrUWHPz5k1EREQgPDwce/fuxZo1a5otYAL07h2MxMQMpsMghBBCCCGEMGj7ttOIiGiP4GAfk55/e+FEFBRI8csvTZ9FVInNZuGZZwYjJMTXbHtaOkdXF5TJFVDKZAAAhbQUDo+0jPGEujlAcolhSZ3clAdQlJSiTeeO+uHSpcVUIUSM02hCqFevXrh37x6Sk5OhUqmwc+dOTJw4scaa06dPQy6XAwAiIyPh60v/8TcXb28X+Pq64eqVBKZDIYQQQgghhDBo585z0Gg0mD17sEnP7/87Eh8s/R0ymdJsMX3zzQEoFCr83/tTzLanufiFdUL/mVNgx+e16LlOri6Q5ufrv5aXlNRqGbN3qkgIGVghVF5ejrTYOLTp1AE8UUXLGFUIESM1mhDy8fHBw4cP9V+npaXBx6f+DPTcuXNx+HDdg69efvllXL16FVevXoWbm5sJ4ZJevYIBAJcvxzMcCSGEEEIIIYRJmZkFOHkyCk/PHmTS89u3n8GmTXV/djNVXp4EP245imeeGYw2bdzNundTjZr3EiYvfRcfHtuPMQteBYtd/8dhDpeL9/Ztw+Sl7za4zhC6hFCB/mtFSUmtljFexU1hMgNnCAHAw5i78A4JgsDVFRq1GgoDk0mEVGr0TzaLxar1Wn39pbNnz0ZERAS++OKLOt/fsmULevbsiZ49eyIvL8/IUAkA9O4dgrIyFW7eTGI6FEIIIYQQQgjDtv1xGkqlCu7uQoOfEQh4eOedSRAImqdS5ssv96G8vByLFk1tlv1NJfLyQMrtaKTcjsbwV56HuF3betd6BQfBMygA/WdOwbNffAyOjY3J5zq5uaKkekJIWlr7ljGBcS1jAPDgzl1wbW0R2LO7wcOoCamu0YRQWloa2rRpo//a19cXGRm159cMGzYMS5cuxYQJE1BWVmbeKIne4cPXsej9X6FUqpgOhRBCCCGEEMKwbdtOo0vn+cjNNXx+zEsvjcSXX81FUJBXs8SUlpaHea99h/XrDzbL/qZy9vJA6u0YnPt9FwDUmuNTXdvwzgCAU1v/QPjIoXj5u7Ww45mWQHN0cYY0v1D/taKkBHZ8Xo3iCwcnJ6hVKqgUhrfvPYyJBQD4dgqldjFikkYTQlevXkX79u3h7+8PGxsbzJw5EwcOHKixpmvXrvjhhx8wYcIE5ObmNluwBDh7Ngbr1u1nOgxCCCGEEEJIK6DRaFFeXg4+3x5eXi6Nrudw2Fjw5nicPh2NGzfuN1tcP/98HHFxac22v7EcBALY8XgozMyGvETXWmXvxK93fduwTijOycWhrzdi+5KVCIjoitd+Wg++s8ioc9kcDvjOohotY/KSErDZ7BqzjBwETkZX+RRmZKGkoBBsNhuldOU8MUGjCSGNRoP58+fj6NGjuHv3Lnbv3o3Y2FisWLEC48ePBwB88cUXcHR0xJ49e3Dz5k3s308Ji+bg6ipA794hsLXlMh0KIYQQQgghpJVgs9mIit6AtV+/1OjayZP7oW1bMb5e+3ezx9W2rRg7d70Pf3+PZj+rMc5euhiKMrOgLCkFANjz608I+XXphNSoOwCA6wcP45e3F8MrKBDzf92kb+8yBF8kBJvNhjSvaqi0QlpxfrUKJZ4JCSEAeHjnLgAaKE1MY9B0rMOHDyMkJARBQUFYtWoVAOCjjz7CwYO6EsARI0bA09MT3bp1Q7du3WrdQkbMY/To7rgU+aXJ10oSQgghhBBCrI9Wq8XuXecwdWq/BtvAuFwOPlo+CwkJ6Th06Gqzx6VSqTFhQi8sXTq92c9qjLO3JwBUVAhVJGSc6m4Z44uEcG/bBg+iYvSvxZ45jy3zFsLNzxdPvvmawec6uuqqth4dKv3o+Q5OjqYlhGIqE0JUIUSM17Rx6aRF9egRBJlMibt3Hza+mBBCCCGEEPLYWLduP1QqDT5a/nS9a1xcnJCdXYT33t1a70VB5pSRUYCtPx3D7GcGw9m5/nk9LUFfIZSV3WiFUJsuHQEAqdGxNV6/f+0mzm3fgz7TJsEvrJNB5wrcXAGg5lDpioSQQ7WbxhwEAsikxieEHsRQhRAxHSWELEiPiCDcupUEjUbLdCiEEEIIIYSQViQ7uwhfrPkLs2cPxhNPdK5zTU5OEYYNXYqDB6+0WFybNx+Bvb0tZs8e3GJn1kXk6QmVUomSgkIoZTJotdp6K4TahnWGVqNBWkU7VnVHN2yBJCcX05a9DzaXU+fz3caMgEDsDqDuCiF5HS1jDk6OUJjUMhYLrVZbY39CDEUJIQvBZrPRvXsgrl+7x3QohBBCCCGEkFbo88/3Ijk5GxMm9Kr13ssvj4KPj2uLxxQVlYJr1xIx96WRLX52dc5eHijKzNZ/rSwprTHUubq2YZ2QmXAfZXJFrfeUMhn2fbYW3iHtETH+yVrvO7o445k1KzFw9jQAgJOhLWMCJ8hMSAiV5Bdi86tvI3LvgcYXE/IISghZiJAQH/D59rh2LZHpUAghhBBCCCGtkFyuRK+e7+Ddd7eCxWKBw2HD1paLTZvewA+b5+P112snMFrC12v348jh67CxYe5yHJGXBwqzqhJCitJSONRRIcRiseDXuSNSo+/Uu1fMybOQS0vgHRJU6z2vYN1rnoEBAHQJoTK5AspSWdXZ0oqEUI2WMSfIK143VmLkVcgl1DJGjEfXVVmIpKQsDHpiUau6upEQQgghhBDSuuTn6xIDgwd3wR/b/ofc3GKEhbXDqk93Y9mybYzEtGPHGezYcYaRsys5e3oi/uJl/deKklLY1TFDyN3fDw4CpxoDpetSlJUNkWft29O8ggMBAB6B7QAAjq7Otdq5KodaOzjpbiuzdXAAh8s1aag0IU1BFUIWQqlU4dy5O8jNpenxhBBCCCGEkIZJpXJcuhQHPt8e06d9jg8++B1aLXOzSNlsNsaM6QEHB7sWOU/cri2e/eJj2PF44HC5cHJ3RWFmlv59RUkpHBxrVwi16x4OAPor5+tTlJUNkVfthJB3cHsAgKuvN2wd7CFwc60xUBoA1Eol1CoVHJx0CSlexTX2VOVDWholhCzEyy+PQv/+HZkOgxBCCCGEEGIBrl1LxNQpn6F90CvYu/cC0+Ggf/8O+Off5XXON2oOTy1+B11HD0eHgX0h9HAHm81+JCFUAjvH2jOEgvv2QnF2LnKSUxvcvygrByIPca3XvYODoC4rAwCI2/nD0dUF0oLaA5/lEql+qLSDsCIhZGLLGCGmooSQBeBw2Fj79UuYMqUf06EQQgghhBBCiNHOn49FWloenm6B28aC+/ZCcF9d4il0YD84e3kCQI2h0nVVCLHYbLTvHYH4S5fRmMLMLDi5uoBrV1XxxOZy4BHoj7vnLgEAPIMC4OTqUucNYIqSUv1Q6crWMWoZIy2NEkIWIDTUlwZKE0IIIYQQQixWeXk5du44i9Gju8PFxanZzmGxWBi38A3kp2Xg9n8nETqgD1x8vACgVsuYnWPNGUK+HULAFwmRcOlqo+cUZeUAAEQe7vrXxP5twbW1RfSJM1CXlcErOBB8kRDSvLoSQiX6odIOlS1jUkoIkZZFCSELMHSoro81MjKe4UgIIYQQQgghxDTbtp2GjQ0XU6f2b7Yzuo0dCZ8OwTi8/gdEnzgDJ1cXdBk2GABQlJ2rX6coKYX9I0OlK6uKEiMNSQjpqo2qD5auvGEs/W48cpJTERjRDWwOp9YMIQBQSKsqlCorhEy5dp6QpqCEkAWYMLE3YmMfICkpq/HFhBBCCCGEENIK3b6djNjYBxg5qluzndF11HDkPUjDrcPHEH8hElqtFh2e6AdpfgHUSqV+naKkBLYO9mBzOfrXgvv1QvrdBJQUFDZ6jr5CqFpCyDskCGqVCjkpqci6nwyfDiEAUGfLWElhIZzcXAFUqxCihBBpYZQQauVsbbno0MEX+/+OZDoUQgghhBBCyGPK0dUZrr4+Td5nzOjlmD5ttRkiqptQ7I6c5FSUl5dDVizBg6g7YHM4NdrFAF2FEAB9lZCtgwP8u3YxaH4QABRnVySEvGpWCGXfT4ZWrUH2/WSw2bqP23UlhHJTHsDFxwtcW1v9LWOVMRHSUigh1MqVlanh1+ZFrFq1h+lQCCGEEEIIIY+pCe++idd/3tjkfR4+zIVWq4WdnY0ZoqpN6OGO4tyq1rDYs7ob1qoPlAaqJYQqBjsHRnQD18YGCRevGHSOuqwM0vwCiDyrbhrzbh+EjPh7AICse8n61+tKCGUnpYDN4cCtbRvYOzlCLpGiXKs16GxCzIUSQhZAq9WitFTBdBiEEEIIIYSQx5SbXxuIPD3gExrc5L1GjuyGlNSfEBjoZYbIqrC5HDi5ukBSbVZQXMWNX4VZ9SSEKiqEgvv1gkqhRPLNKIPPK8rK1reM8UVCCD3ckZlQkRC6n6RfJ83Lr/VsdlIKAMAzwB88gYDmBxFGUEKoFbO15eLW7W8xffoApkMhhBBCCCGEPMYqK2FCB/Zt8l7R0amwt7fFd9/Pa3QtTyjAmDdfA99Z1OhagZsbAKA4N0//WnpcAi7u+gtR/52qsVZRUgKgqkLIMygAGfGJUJeVGfz7KMzMhnNFQqhyoHRmoi4hlP8wHSqlEiqFEspSWa1nc1MfQqvVQhzgDweBExTSEoPPJebTu3cIxo7tyXQYjKGEUCs2ZEgYwsLaQSqVMx0KIYQQQggh5DHF4XL1A5A7DOzX5P0yMwuwZPFvGDGiG2bNGtTg2u5jR2L4y3Mw/9dNNQY410UgrkgI5eTWeP3PT75Ayq2alT+PVggJxe4oqpgLZKjqFUIBPbpCq9Hg4R3dzdDlWi1yklPrbBcDALVSiYK0DHgE+MPByREyicSos0nT9eoVjGPHP8aKlbPBYrGYDocRlBBqxSZP7ouSEjlOnjS8bJEQQgghhBBCzEno4Q42m42CjEy0DesEnlDQ5D1/+OEIIiPj8P2m19G3b2i967xDgqEoKYWTqwsW/LG5wcHWQrE7AECSk1fvmkpVFUK6hJDA3Q2S3MafAwA2m40uXfzR3o2NMG8unn52GIaOG4jUqDuQV0vs3Dx8DDEnz9a7T3ZSir5CSE4VQi2qSxd//Ht4ObKzizBu7AqUl5czHRIjKCHUSvH59pgx8wns3XsRSqWK6XAIIYQQQgghj6nKKpjIvfvB5nAQ0q+3wc+6+fmibXjnWq9rtVpMnfIZsrIKMWH6ECw6tBvidm1rrfMJDUZqVAw2vvA6HF2c0WfaxHrPEtZTIVQXRWlVhZCtgwMcnBwNeg4A4hM24XbUeqx5dwjG+knxx29vY1J/L9w9d7HGulNb/8D+Nevq3ScnKQVifz/wnUV05XwLcnFxwpGjKyCTKTFi+IfIyipkOiTGUEKolZoxYyAEAh5+3HKU6VAIIYQQQgghj7HKq9VjTpyBNL/AqDlCTy3+H17bsh4uPrUHSGdkFKBnxEIcjMyGe9s2mPLh/9V4n8PlwjOoHdLjEpCZcA8FaRmNVgipVSrIioobjUtRopvrY+/oqG81q6+yKDDQC59/Pkf/9bqv9+PZZ77C5Oe+x6+JInzwSxwuZvMQd+4SevZsj48+mtXo+QCQnZwCrq0tBG6ulBBqQc89NxTu7kKMH7cSKSnZjT9gxSgh1EpFR6dg3df7cfHiXaZDIYQQQgghhDzGRB66hFBhZhbiL1xGhwF9wWI3/lGSxWbDv2sX2DrY46kl/6tzjVQqh1DsDi8HFZZMD8CQWZP074kD2oJra4uMuEQAQH56Blx8ves9T+DuDklOnkHtP2qlEmqVCvaOfAjd668s8vAQ4dTpVXj1tTFo31539saN/2DbttM49d9VFCi5EIb3RkpaIdLjEjBlSj98tPxprFv3cqMxZN+vuppeLqWEUEtZt24/ekYsxO3byY0vtnKUEGqlrl5NxDvv/Mh0GIQQQgghhJDHnMhTjNKiYpTJFYi/dBl8ZxE8Ats1+pxnUADsHflIvhmFjk/0R9iIIXWuE3q4Q1pQCA97Fb5fPxd8kRAA4BMaAkB3UxiAigqh+hNCQrG7wXOAAEBZUqpLCHlUzB565FkbGy727F0MFxcnDHpiERITM2q8L8nNh0atBtfGRn+9/aJFv2LtV/vw5lsTsHjxtAbPz0lO1f+aKoRahlisu62OkkE6lBBqhZ56qi9CQ32ZDoMQQgghhBBC4OzliaIsXWtNQVomgKoBzg3x79oFALDzg4+RfjcBkxYthK2Dfa11QrE7YhKy8eHHexHsChw98yV8fd3gHdoeSpkcuakPK87OAE8ggIPAqc7zBGI3g+cAAYC8pAT2jnwI3HW/l+Lsms9+++0rGDCgI158YR2iolJqPV+u1eqTSNXnB7333s/4/fdT+HTVc3jxxRH1nq8oKdWfSRVCzW/27MG4d38zwsMbT2Y+Ligh1Mqw2Wxs+XEBPvhwJtOhEEIIIYQQQh4zdbWCiTzFKMrSXckuydMlQATuro3u1a5bGCS5ech7kIbDGzZDKHZHm04daq0TiN1RnJ2L1St+w57zWejZyRN3475H994dkZl4D+VaLQAgPy0dAOqcRwToEkvGJISUJTLY8/kQit2hKC2FUibTv+fj44rZzwzGmtV7sXv3+Xr3KMrMhkalRkLkVf1r5eXlmPviNzhy5DrGPBnRYAzZySkAABlVCAEAbG25cHR0MPu+3t4u+Hb9q4iKSkF0dGrjDzwmKCHUyoSG+sLFxQlHj1xnOhRCCCGEEELIY2T8/xZg0cFdcPOr2a0g8vTQVwhJcvMBAAI3t0b38+/aBck3owAAGfG6tq+6Ws1EHmJ9ImfZop/w2z1n/LHvFrie/siIS4Sfn66CJz9N17JV12BpOx4P9o58SIytEHLSDZV+dKB0eno+OoTOw7Jl2xrcI+rEaVzc/ReUpbIar6vVGkyd8hmmT/u8wedzklJ0sTzGCSEOpyot8fnnz0Mi3Y2U1K3Ys3cxAgPrTv4Z66etb8HWlos5z30NbUWCkVBCqNXp3TsYAHD5cgLDkRBCCCGEEEIeJ76dQuHm54v5v/0An1Dd5xJbBwfwhAJ9QkitVEIukcLJzaXBvZzcXOHq64OUW9EAdO1YipJSeAT411j3aCIn4dIVpKTmIMmpExwEAihz0hEdswH7D3wIkZ0GAOqcI1R5U1ixsTOEKiqEqlcWdejQBoAuKVRWpm5wj7O/7cTfn39d53symRLl5eVo08YdX301F7a23Fprsu7pZtnIiiUGx20t7OxssHDhRNxP+hF9+ujmRW3efAQfLP0dZ8/GYOjQMNy89Q1eeGF4k85ZsGA8Ro3qjnf/9xPu3880R+hWgxJCrUzv3iEoKiqpNbCMEEIIIYQQQpqTyEOM5JtRUJeVYd7WjRCI3SHyFAOAPiEEANL8AgjcG64QatctDACQcitK/1pOcirEjySEKgc6F1XM0inXanFl3yF4VlQSxV6JxsoVOzF4cGdcv/IlBrgUILRTQK3zKuN5dA5QQ3QVQnwI3KtmD/n5uePqta+xbJn5RngMHtwZC9+ZhAsXv0C7dh413rt+6DB2LP0YuSkPzHaeJRCJ+Pjv2Mf4au1LSErKgkajq9qJi0vDqlW78dyzaxEetgBXryaiW7fa/76N4e3tgoMHr+CHH46YI3SrQgmhVqZ7j0BcuZJo0FWJhBBCCCGEEGIuQg93pNyMwpbXFsLByRHdRg+HyFOXwKicIQTobuMSuDU8Q8i/axeoFEqk363qfMhOSoZHO/+aZ1YMp65eoXNl30FoNRpoNRo8jEvAV1/tQ2jIPGz74xS6eWjw7ds94eGhuy3KyclBH3tlbIZSlspg7+gIodhN/9ynq54DAGzdetzgfRrz+++nMGniJwgI8MT1G+swalR3/XtlcgWuHfjXbGdZAm9vF5w5+zl69QrGzBmrMXTIEly9mlhrXVpaHoYP+wDvvrsVABAR0R6TJvUBi8Vq9AxbWy78/XV/dpcs+Q3Tpn5m3t+ElaCEUCvzxMBFmPviN0yHQQghhBBCCHmM8J1FsLGzQ1F2NrKTUvAwNg7hI4dWSwhVVQhJ8vLhVMdQaRaLhTadOyKgR1e07x2BB3dioVFXtVxlJ6VA6OEOe0e+/jWBuPYNX8XZuYg+cQZpsfFQKZQAgMzMArz00nq8uyUW+2JUyM4uAgDs2v0+vvnmFbh56SqZHp0F1BC5tAR8kRBcW1sUZ+eie/dAzJ49GOu+3o+0NMP3McSBA5fRo/vbSE7OxoGDH9ZICj1uxoyJQNu2Yjw5ZnmDA7sB3YDuyra9Dz6cgb/2LUVC4g94773J6NLFv87k0LBh4bgdtR6XIr+Aq6ugxh6kJkoItTIKRRnS0/OZDoMQQgghhFgROz4Pk5e+Czs+j+lQSAtycnPF1GXv13tNe3VVrWG6xEzUfyfRNrwzAnqEQ6vV1kjYSHLz4ORaOyHUedggvL3jJ7zxy/fwDmmP+1dv1ni/coBy9baxuiqEAGD7kpXY9PKCWmfci0tFstZdfxva7VvJWPDmePyw8knw1dIaN4U1Rllaqv91cU4uPl/9PHJzi7FmzZ8G72GMlJRsDB60GFs2H0VkZHyznFGf6dMHYN26l/Hee5Mxa9YgDBzYCQEBni12PpvNRpcu/gCAn376D2Fd5uPUqaiGH3rE1CmfYcb01cjIKMDqNS/gdtR67Pt7qf79ESO64cjRlTh2/BNwOGw8P2cd8vMfv9lMxqg91YowZurU/oiICMKSJb/T5HNCCCGEEGI2gRHd0X/mFMSdj0TsmYZ/Ik+sR/jIoeg7bRLAAvauWN3gWpFHzVlBt4+exNi3X0e3MSMgzcuvUekjzSuAHc8Bdnxejdu1gvv0hKKkFL+8vQhajQap0bE1zsiuSAh5BPjjQdQd3bmeYsiKJVArlTXWqpVKqGu+BAAoSM8A18YGQrE7irKysXjxrzh9Oho7//wAz7kq8PfoHjhi4I3NcmmJ/te2GgVCQnzwyce7IJEYnlQyllQqx/z5mwDohiqPG9cTf/55sdnOqyQQ8PD6G2PB5XJqvM7lTIRWq8Xzzw8Dm83Gr7+e0M/zMRcXFyds2/4u+vULRXD7V5GdXYQHDwyf9VRJrdZgz57z2LPnPNq0ccfg/2fvvsOqLPsAjn/POWyQrYAggiAqS4aiuPfeO2e5S03T0lzlyEp9sVJzlKbmyJUr996ICxQnKqAiQ5ay9/P+ceAosk3F6v5c13O9ec5zP899DuDr+fEbzZx5/jxFdY+/9s4kJiaBL6esZfHiv0hLy3ijr+PfSGQIvUd692lEr96NRDBIEARBEARBeKP0jJT9VvKyQIT/hqquTgB49+qGjZtrsefmlYY9j1L2CooNe8Ljm7dR09DI1z8IICFGWU71amPpap5uhARc557fZR5c9i8Q5Il7EkFmenq+PkIGlUwLZAcV58Xo+ReTxg4dusoPh+KIfp7BipVj0NRUL9W1Xs4QenA7hBoOo1mx4kCp9/J3jR/fhW3bpzJpUvc3fm2ZTMbo0e0ZNKg5AKtWHUZDvRsG+n1wrPUxrVvN4MMhL0aw9+jZkFWrP+Xa9SU4OFi+sX24uVXj0uUfaNbMhUkTV6tK/f6ux4+jWb/+BHv2+AHKJtVDP/qJarbDWbDgTxEMKiUREHqP1KtXAz+/d5s6KAiCIAiCIPz76RrnBYTMSjhT+DexdnXiztkLxEdE0uurychfyQ55mYFZJbIyM0mKi1c9du3wcSB//yCAxGhli4uXG0vrGhpgbl+N4MsBRd4jJzub6IePMcudIAbKHkJlmQwWG/YEABOr/EGLbF1jvl5zg7ZtviI9PRO5XI66evEFMamJyYCEo2Ea6QnPSUvLIDPz3fWaWbRoF1u3nmXh/4aycdPnGBuXXNpXGnZ2Fhw5Opdlyz+hc5d6+Z5LTEzlzp0wjh27xu+/H1c93qXzHHp0n4eJSQXOnV9Agwa1/vY+BgxoxrnzC1BTk9Ok8RRWrTr8t69ZlODgSDZtOiV6BZWRCAi9J8zNjbC2rshFv6CSTxYEQRAEQRCEMtAzMgLAyEIEhP4rdI0MMa1ixb0Ll9gxzweL6nZ49+pW5PmG5pV4HvU037TjogJCeRO5KrwUELL1qA1A8JWiA0Kg7CNUqVpV1Z8NKlVUZSWVxrPIKLKzsjCu8iJDSE1DA/1KpkSHR3H3bhgA8+d/yLHj81R9awqTnpyMs1E6ba2SGNC/San38KZkZWUzoP9CZkxfT8+eDQi8sZSGDR1f+3oaGmrMmNGXwBtL8fS0Z8TwJfTp/X2p1+/adYEG3l8QE5PAocNzVBPcXlfjxk74+QVRx/OzQqeICeVPBITeE/Xr1wAQGUKCIAiCIAjCG6ebWzJmIErG/jOsXZTlYg8Db3Lr1Fki7j3AqVnPc1AgAAAgAElEQVSjIs83MKvEs1cCM3Fh4WyaOpuzf2zP93hCTByQv2SsmqcbmenpPL55u9h9RQWHYmxZGTVNTeRqCiqYmpSpZCwnK5tnkU8xsXwREHLv0Bo1dXXunr+oeuzy5Xu4uFTl2vUl/LF5Mo0bOyGXv/j4q66uxsdDGtKichJBUZmsXn2k1Ht4k7Kzc/j226141Z3IrVuP/9aAoQ4d6jBrdn927/bDsdYnrF5d9oyckJAoGjaYzJdT1pKYmApAq1ZuaGlplGq9mZkhNWtaAfDppytp3WoG0dHPy7wP4d0QAaH3hLFxBSIi4vD3Dy7vrQiCIAiCIAj/Mnp5JWNmIkPov6JqbSeys7IIu3UHgPsXr2DjXhuFWuFlVIbmlXgeWTBT58reg8Tl9u3Jk5qQQGZ6er6SsWqebjy8fpPszMxi9/U0OBS5XE7FqlXQNzFBLpeXKSAEykDVy2VnjQf0IeLeA+5ffNFMesuWM9jaDGPeN1vo0MGTU6e/57vvBgNgYWHM9cAlTBrXjqDnmvz8V2i+zKjycP16KK1bzSA0NAqZTEa/fk3Q0CjbDKhduy5gV20EH/RbQERE3GvvJS4ukZ9/3geAi4sNh4/MJezJWhYuHFrkZDJLSxNmzx7A9cCl/LF5MjKZjIyMrDfeoFp4s0RA6D3x229HsKw8hPT04v8CFQRBEARBEISyyssQMjSrpBrXLfy7VXV1JjzoPplpysbO9y9eRVNHG2uXwkuSDAvJECpOYkycqmRMU0cHy5oOJZaLAUS+NGlM3yx35HwZeggBBB4/hWVNB7z7dMeujjuWNR04s2FLgfOePUtm5swNWFb+kL595rN+/QlAOXErNTWDPh/4cDCsAhFhUQXWlqfu3b3Z9McXBFxbQrt2nsX2QjIy0mP16k9p08YdgIcPS/81LI3AwFCaNZ3K0aPXGD+hC0H3VnLw0Bx0dDQBZWPssCdrefjoN6bP6MPFi0EMGuhT7gE2oXTE2HlBEARBEARB+JdR09AgOzNT9aEsr4eQQl2NCibGqh4wwr+LupYmmWnpyORyrJ0dubL3oOq5B5f9ycnJwc7LkxD/6/nW6RkbFTpNrDgJMTHoV1QGhGzcXJArFKUKCEWHPiIrIwOXlk0JOHQMoMwZQuc3/4ljk4Z0/WI84UH3SY5/xpV9RZdHJSWlsm3bWdWf794Nw8N9PHKFAo/PUol9FFam+79tO3acp327r1mydBT7D8wiPT0TP7+7NGs6FYAxYzpSs6YV6emZjBjZlgoVdLhzJ4zDh/3fyn5On77B6dM3sLAwZuTItgwa3IJKlQwJDY0iNPQpB/Zf4eHDp2zceJKQkPcruCYUT/x64D1Qp051Am/8jIeHXXlvRRAEQRAEQfiHU6ipMXn3JlqP+kj1mK6RIU9DHgL/ndHzVo418ejUtry38U5N27+dYUv/h1WtGmjp6fLw+k3Vc6kJCYTfuUd1L88C6wxUmTqvlyFUzdON7MwsHl4LLHFddmYmR35Zi1u7VjQd1E953zIGhCRJ4o8Zc0hLTqaqqxO+23YVGHFfGjnZ2SzqM4Qzm7aXfPI7dujQVVycx9Kn9/f89ONukpNfvL7q1SszeEgLJnzWlf37r+DiPIaFC3e89T1FRMQxe/Yf2NuNIDRUGfjZvfsCI0Ys4Ztvtohg0D+QyBB6DzRsWAsnJ2siIuJLPlkQBEEQBEEQiuHYrBEmVpZUrukAKLNGNHW0eXL7LpVsq2JoYc6jwFvlvMu3r+XwwTi3bEpE0H0igh6U93beOj0TI/QrmuLY1BQbNxcAHl6/ke+c+5eu0LBfT9Q0NfMFUAzNlb2lXp0mVpyE6BiqeboB4NS8MY8Cb5KRmlaqtcd+XYdD/brY1XEnKyOD5Phnpb5vnqTYeDZO+Zq2nwwv0PS6LGIePn7ttW9benom27efY/v2c/kenzDhVyZOXE2FCto8f55cTrsT/g1EhtB7wLtBLR4+fPq3Gn8JgiAIgiAIAkD9nl0BMLJQNn/VNVT2Dwq7HQT8dzKEKtpYI5fL6fjZmPLeyjthZGEBgN+Ov9DQ0SbleUKBYMd9vyuoa2piU9s53+OGZsrvibL0EEqIiUXX0ABrF0csqttxZd+hUq+VcnLY9OUsUp4nlDk76GX3/C6zdMhoEmNefzLXP1VOTo4IBgl/m8gQeg94e9fg7Nl//29pBEEQBEEQhLfLqLI5Dg28yMrMxMhCmfWRN2Es+uEj0lNSVNkg/2YyuRxTaysSY+Oo1cgbey/PfBOo/o2MKisDgGc2bsV32y60dHUKnBN8NYDsrKwC74eheSVlpk5c6TN1EnP7UDX7cABZmZlcy+0HVFrPop7yy+jP0NGvUKZ1giC8OSJDqJxZWZlSpUpFfM/fKe+tCIIgCIIgCP9w9Xp0AeDijr/QNTJEQ1sL3dyG0slxz3gW+RSj/0BAyNC8EuqamhxZuYa48Ag6TRyDTCYr7229Vca5GWHxEZE8vnGLe36XC5yTnpxC2M07VK9XJ9/jBmaVeP40ukyToRJilNUNLq2acfv0eVKeJ5R5z49v3OLueb8yrxME4c0QAaFypqGhxu+/H+fkyZIbsAmCIAiCIAhCUeQKBV7dOnH33AVC/K8Byt4werkj55Pi43kWEfmfyBCqWNUagPC79zi49FeqONWiTtcO5byrt8vQwozUxCTSEpOKPe/W6XPYuLnk+z4wNDcr04QxgMQYZYaQXC7nyl8Hyr5hQRDKnQgIlbPg4Eg+HPIDN248LO+tCIIgCIIgCG+AXKFAQ1v7nd+3am1nDMwqcnHnXuIjlM2BDc3N0DXOCwgpM4T+Cz2EKtkqA0LRDx9xde9BQq5eo/PEsegaGpTzzt4eo8rmxEdElnje1dxePx4dX0xgMzCrWKYJYwAJ0cq+PSnPE7h1+nyZ1gqC8H4QAaFyZmPz7/8NjSAIgiAIwn9Ju7Ejmbx7E3I1xTu9b17PoMj7wcSHR6oe0zMyIiszk7TEJJ5FRqFf0RSFuvo73du7VrGqNamJSSTFxiNJEtvnLkBLT49Ok8a+1vX+CeVmRhbmqq97ceKeRBB8JQDPzu0AZTDIyNycuFKsfVlSXDyZaekEHDxKdmbma+1ZEITyJQJC5cjMzJDgkFWMG9e5vLciCIIgCIIg5HJq3hgzO9vXXu/SsilGFubY1/V4g7sqWQUTE0A5/SkhOoac7GwMLczRMzJUjfXOKwsyMKtY7LXMqtng1Lzx293wW1TRxpro0EeqP0feD+bU75vw6tZJNSq9tNzbt2b2qf20HD7kTW/zjSpthhDAlX2HMLezxbKWA92mfEZOdjZ+O/aU6X452dksGTySvYt+fp3tCoLwHhABoXLUqpXy/4zEhDFBEARBEIT3g1yhYOD8OXT4dNRrrTeyMKeSbVUAardp+Sa3ViL9iqZkpqWTlphETnY2z59GY2ShLBnLCwjFR74oJStOm0+GM2jBXOSKd5vlVBy5mgKPTm2RyQt+hFGoq+d7rmJVa6IfPsp3zuEVv5EYG0eDvj1KdT8NbS0GLpjDwAVz0NTTpdGA3u8866u0NHV10NHXL1WGEMC1Q8fIysig11dTcG3dnMMrfiMuLLzM931yO4j0lJQyrxME4f0gAkLlqFVrN2JiEggICC7vrQiCIAiCIAiAmZ0tGtpaVK3t8lrrHbzrAvD41h1cWjZ9pwEE/YomJOQ2+gV4FhGFkYU5ekZGJKkyhJQBoZImjVnWdEBdS5OKVau8vQ2XkVu7Vgz4bhZOzQpmLnl178SA72bh1rYlapqaGFqYEf3wcb5zMtPSuXP2AtXr1SlVCViP6Z9Tu00L9v+0gt8nTUff1ASnpo3e2Ot5k4wqWwDwrJQZQqkJidw6dQ5rZ0dl9tS6TW9ze4IgvKdEQKgctWrlxrFj18o03lEQBEEQBEF4e6ydawFQwcQYU2urMq938PbieVQ0R1euQdfIEPu6nq+3D1enMq+pYGqiavQLymwgQwszdI0MSY6LB14EhIrLENLU0VEFgirXdCjzPt6W6l7KUem1GnsXeM6zk7IfjkentphaWyGXy4kOKTi0JejCRfSMjahcs3qx93Lv0Ia6XTty9Je1HFu1jtunz/MsMor6vboVuUbHQB9jS4uyvKQ3xih35HxcKQNCABe27yYrM5Ptc+aTnZX1trYmCMJ7TASEykmtWlWwtDTh6JGA8t6KIAiCIAiCkKuKsyM52dkA2Lq7lmmtTC6nev26BF24yJ2zF0hLSsatbdnLxhy86zJ+4yoa9e9dpnUVTE1IjHkREMobMa9n/CJDKDMtnYSYWMzti+6RZOFgr/pvy/coIGSX25OpVuMG+R43sbLE1t2VhJhYajSoRzWP2gAFMoQA7vleAqBGg3oFnjO2qky1Ou7UatyAXjMnE3L1GkdWrgGU/XIu7tyLQwMvjCqbF1hrbl+Nz3dsYNyGX8ulzM44d0+lLRkDuHvej+nerQnxv/62tiUIwntOBITKyePHMfTp/T17914s760IgiAIgiAAoKGtTa+vpqBnYlTeWyk3VZxqcf/SVVISEqjqVrayMcuaDugaGnD3/EWyMjK4efIMzi2blnmiVzVPdwDajxuFfqXimz+/TFky9lKGUEQUaurqaFfQUwWEAO6e86NmI+8iy9ksaymDQM+jorF8KZOm1aiPVEGZt00mk9Fu7EhVUM7Y0gITq8o8vnkbA7OK+YJWnp3akpOTw+bpc1GoqdFi2CCg8IBQYmwc4Xfv4VDfK9/jCjU1Jm5Zy5g1yxi+zIecnGw2fjlLFRwEuLhzLwBe3fMPhLGp7cKYdcvRMdBH39QEmzJ+37wJRhbmZGVkkBQbV6Z1Wenpb2lHgiD8E4iAUDlJT89k+/ZzREU9K/lkQRAEQRCEd6BGAy+8e3d7rayW95lMJitVLx81DQ0sqtvxKPAWoQGB2LqVLUMoL+vk3gVlFor//iPoGhow9+whRv+6hGp13Et1HRs3F+KeRKBQU6PblAmlWqOmqYmOvj4J0S96CMVHRKn+Oznuxb85b544jY6BPrbutQu9lmWN6iTFxXPn3AVVhpChWSXajx1JsyH9S7Wfv6vp4A9oPeojes6cDKAqvdv3wzIgf5aQR6d2PLh4lbvn/Qi7dRcjC3OeRT0lIzW10GvfPX8RWw9XNLS1VI9ZuziirV+BfT8uY9nQMSzo2r/AxK74iEjunvdTlaeBsgn50CULSI5/zo8fDCMrI6NcprMpJ4xFiVYUgiCUiQgIvSVaWhpUqKCd7zEDA10+/LAlp05/z9ZtU8ppZ4IgCIIgCIWr4uwIKDMe/k3afzqaL//aUmIj4co1q6NQV+PxjduEBgRibl8NbX39Ut+nRqN6PLkdRFJuv57bZ86zeuwXXNz5F5a1HFSZK8WRKxRYuzhx8+QZDq/4jdptWuBYikbG+qbGACTGvMgQeRb5IqCRFB+v+u+75/3ITEvHuUWTQq9VuWZ1wu/e48mdIHSNDDEwq4hT7rm27q6FTvl6lVYFPVoMG4SahkaJ576qilMtOoz/mPiISCyq21G9Xh3svDxIjI3jnt9lHt+8reojZO3qRMWqVbiy7yAAV/Yq//flkfOvCvK9iJqGRr7x8/b16pCTk4Pvtt08uHQ1X+ndy26fPo+JVWVVzx7Lmg7oGhlyYMlKIu894J7fZZybF/6+vk1GFqUfOS8IgpBHBITeki+/7MXtO8sxMtKjWTMXLvj58DR6A7+tmUClSgacOX2zvLcoCIIgCIKQT5Xchso2Zeyd8zbI1RTU69kFEyvLv3UdTV0dGvbriYmVpaoUqih5DaUf37xFaG5flaq1S9fcuVH/Xth5uhNw6Fi+x2+dOsuu73/g6v7DVPOoXWKmUuUa9mjqaBPqf51T6zYRHnSf3rO+RM+4+DK+CqYmAPmmjOXLEHqpZCwjNY0g34uFBi7kagosqtvx5M49wu8E5e7JAefcrBdt/QpYVLcrdi8AfWdPo+OET6jVpEGJ575MU1eHgQvmkBATw0/9h5MYG0fjgX2p7lWH+xevAHD7jC82bi5Y1nSg6+TxZKalc/3ICQD89x8mOyuLp4U0lM4TfPUamenpOHi/KBuzr+tB+J17pCYkFLu/4CvK/p+2nsrsqrygUt7jN06cwdTaCnP7amV63X+XUWXzMvUPEgRBABEQeivs7S2YPKUnJ04EEh+fRJs27ujoaPLDol141/+cWjU/ZtGiXeW9TUEQBEH4x9MzMeKLXZuwcqxR3lv5x5PJZFRxrElGahpGFuYYmlUq1/14dGhLn1lT+XLvFgb7zCu0kW9peHXvjJaeLkC+AEBhqjg5khAdw/OoaB7fvE12VlapysacmjWi6+QJBB47xYk1Gwo95/7FK2jq6GDt5FjstWxy7xcaEEh2VhYbv5yFdgU9+s2bWWyGk35uQCjxpSljaYlJpCUlA6iylvLcOHEGY0uLfL14ACrZ2qCmocGTO0GE370PgL2XB3Z1PLi6/zAA1TwLLzXLU79XV1xbNwfK1phbrqZg8P+UX+uNX84iMSYW3227cGrWCAOzity/dBVQZl7JFQombP4Nc/tqbPlqHunJKcrXHxvH6jGfc3z1+iLvk5WeTsjVa9Rq3ACZTIaapiY2bi6qgFNxIu8Hk5KQoAoEVavjRnToI1VG0a2TZwFeq2zMspYD0w5sp/OkcRiYlb53lJqGBvoVTUWGkCAIZSYCQm/B4iWjSE/P5IvPfwNg2rTfcXUZy5dfrsPP7245704QBEEQ/j0cvL0wt7PFuWXTv30tTV0dKtcofhT1P4m1i2OpSnvymFhboa1fgct79gOoGuNq61d4rUlTLYcP4aOfvi9V757C1O/VlejQR5xYs5Gajbzp8vmnJa7RMdCn1aiPmLh1HTUa1kcml9Oofy9C/K/z5E5QoQEhXUMDVRCminMtHt+4DSizaJ7cCaJ225YM9pnHpO2/U8m2qmqdtr4+TQb1Y7DPPAYt/IawW3fZ+OXXSDk5he7tQW4ww75e8WPobdxciI+I5FnUUwAi7z1gz8LF1GrkTeOBfYtcp1/RFCBfDyFAFSR4OUMIlJlLOTk5uLRogkkVK+y9PJErFFjm/gyE3wkiPSWF6IeP8e7dDYW6Gmc2biXuSYSq6fXLrBxr4t6+NV7dO9N18gTunvcj5Oq1MpUf9pz+BTUb1efPuQsIuXoNgPNbdpCVmQnAfb/LADy+cZunIQ8JvnoNn56D8D9wJN917p7341lkFMW5vOcAlWyr4ti0ITa1nVHT0ChVQEjKySHk6nXsPN2RyWRU83AjOHevoHz/H167UWQ5XnEa9u2JgVklGg/sw/QDO6j9Si8vaxfHQoOChubK4K3IEBIEoaxEQOgNa9TIkXbtPJn19SYiI+NLXiAIgiAIwmvL+y39m+h503rkR4z/YzVaFfT+9rXKW5NB/Ri/aTWtR35Y6jV55VJ+O/aQnpKqmrDV++sv+XTjr+gaGZbqOjK5nB7TP6fD+NE4t2hKve5dijzX2NKC4csXFcj+MbOzxdbdlQvbd7P/p+UEHDyKvZen6sOwnokRw5f5YFLFSrXGrV0rZhzeRfuxI9GvZMqwJQvpOfMLTKtYcWbjVoJ8L2Hrnr+RsEJNjeHLFjFu/Uombf+dijbWPLp5W/V8kO8lKlatgpVjDUyqWNFm9FDVc4MWzqHr5PFYOdbE/+ARVo/7nMy0oic2pTxP4MmdIFVz5KLYursSGhCY77HzW3Zw69Q52o4ZjkJNrdB1FUxNyMnOzjdNDOBZZBQ52dmkPM9fCpUUF8/DgEDajhnBtP3b+Hj1Ugb97xuq1nYmIzWNp7k9eJ7cCUJTR4eE6BgeB94i+EpAvt47efce+/sKBi6YQ98500hNTOSPaXMI8b+GpWMN1DQ1i33NAA369qB+r64c/WUtfjv+Uj2eGBPLlT0HeBrykJhHYYAyKLOg6wcsHzqGuCcRJV67MP4HjhAb9oSWIz7Evp4n2VlZBF8NKNXakKsBVLKtir2XJzoG+qpysTw3TpzB2tmRnjMn49mpHRra2kVc6QU1TU1c27TAf/9hvuvQm9iwJzT6oJfq+TpdOjB+02rajRtVYK2xpQUA8eGv914IgvDfJQJCb5i7u7KmesOGk+W7EUEQBEH4D7DLzVSwdnF67UwU1bW8PFBTV6e6V/Ef2IuiqaNDv29mYGhu9rf2UZRWoz6ifu9uJZ5nWcuBjp99QnpKKi1HDMG0apVSXb+KkyMZqWmE373P4xu3sHV3pZJtVVxaNUNNQ4O6XTqU6jp950yjYb+eHP9tPcFXAmjzybBCPxDL1RQMnD+HWo288ejYNt9z9Xp2ISszk0u52Ur3L11Bx0BflcHl3q41tRo3oMvnYwFldlf3qROJCg5hQbf+fN+pD6HXAvHu1Y34iEgCj54kyNevQCPhduNGYu3iyMm1m5DJ5cjlcoIv+6ueP/Tzr3zVuB3ftu/F2U3bqN2uFRVtrHHw9qJGg3rsWbiYb9v3ZMvMeSTFlvyLwPsXr2Dj5qJqtKxf0TTfSHpDs0oYmpsRGnC9wFq/HXvQ0tVVBepepW9qQlJcfIEMpYh7D4gNCy90+tSh5avx2/EX2+bMZ/9PK3Bt1YyG/XoSce+B6jrhd+4BcPPkWSRJIviKPxVMjKloY626TrMh/VGoqbF0yGi+79yX7zv1JTE2jhD/QNTU1aniVLPE96bhB70IDQjk4NJfCjy3/ZsF/ND3w3yP/d1pWjnZ2RxfvZ6qrk407NuTsJt3VGVnJXmQGwBqOWIIAMFX/PM977djDzdPnMG9fWv6f/c1bT8ZXuI1nZo1QruCHlf2HiI+IhL/A0ewcXNR9Y5ya98KgFYjhlCvZ/4ga63GDcnKyCA86H6p9i8IgpBHBITesCVL/sLcbBCxscU3pBMEQRCEN0VdS5OOn32CeSkavb4tVo41aDduZJHZC2+DnrERlWyr8vjmbTR1tKnskL/cq1odd7pPnahqtlscTR0drGop+xCV1GemKI7NGlG3a0e8+3R/rfXFqdWkIe3HjqT3V1PoMP7jIs/T0NZm0IK5JMXF89MHQ8lMS6fXjMmlukcV51o8uRNETnY2IQHXqVyjOu3HjSIzLZ0nd4IKfAgtjHOLJtTt2pEjK9ew74dl7P3hZ/RNTWg65IMC57b9ZARVazuT8jwh3whxNQ0N6nRuz41jp1RlTvcv5pZb5QbrnJo3JicnB+cWTbFxc6X5RwPRMzbiz7kLiXoQQlpSMr+M/ozTG7aw6/sfycnOJvjqdWUj4dzR8A7edWkxdBDnt+7kL58l+PQcxLx2PfJle+RkZ5P87DkAp9dvJis9g5bDh9DpszHEhoVz9o/tpXpv89y/eBV1LU2qujrRZHA/vj7+F/N8jzB23Qo6fvaJqiQsxL9gQOj+xStkZ2Xh4F230GtXqGhCQnTByViHlq1mycARha65d+ESW7/+lgvbdnFs1To2Tp1FdlYWD6/fUJ3zKFA5CCWvaXNeMCQvsKZraIB3n25c3X+YkKvXiA59pBr3/vCaMtMpr4+QTC4vNFvI3L4a5na2XNl7sNBAT05WNhmpaYW+hr/j0u79PIt6io6BPvdKUS6W58mtu6SnpFK9Xh2eRUYVyFJKjn/Gb59OZmajtoRcvVYgo6ownp3a8TwqWlW2dvPEGeQKBY5NGqKtr49DvbqcXLuJ22d96TnjC6rXqwMop9K5d2jNrVPnSE1ILMOrFwRBEAGht+Lp02clnyQIgiAIb0ib0UNpMXQQY9cuL1MD1zdFx0CfjxYvoPXIj+j19ZTXvk5JI8FfZeuhbGyb1zw2r+eNmZ0tn25cxZg1y2jUvzfevbqWeC0bNxfkCgVJcfE4NHi9gFDeGGy3V/p+vA4LB3uafzQAdS1NNLS16DF9EpH3g/HdtouWwwczfJkPPWd8QaeJY/OVcnWaOAYTays2fjmLqOBQ9v24nOr16+DRKX8GzqvvtVyhwLKmg6p/TmhAIAo1NVxbN8d3605Or99CJduqxX6w1dRRZumE373H4RWrAXh47QbXj5yg2Yf90TN5MSXLrq4HLYYN4sL23ZzdtI2qrk7oGCjHu7u0aoauoQEX/tyjOj/haTRPQx6qSnSqebpxZuNWnj+Npse0STQZ1A//A0cIu3VHtSYrPZ3d83/kxvFTqj8HXwmghrcXHh3bMPh/84i8H8yehT8ByoyT4sqPkuLiubB9N3W7dsCylgMHlqwkO7e3TWkFX/EnJzub9uNG0XnSOG6dOse5P/5EppDTZFA/mn3Yn7SkZCKCHhRYm5aUzKPAW9TwrlfotfVNTUmMLRgQykpPVwW1SnJ17yG+69ibA4tXqh6753eZRb2HEOR7EYCYh49JiIlVfS80HtgXdS0tjq/6vcD1kp8952nIQ1WPpv7ffsWkbesKnFe7TQtysrMJPHqyVPt8U7IzMznxm7IJeF5/olKteylo9mq52MuknBweXAnAsqYD6lpFl83pGhlSs2F9ru47pMrMenIniLjwCJxbNMGlRRMU6mr4HzjM+kkziH38hO7TJiFXKHDwrksFE2Ou7D1Y6v0LgiDkEQGhN0hNTcHv6yfSrNnf72MgCIIgCKVhbl+NpoP7c/3oSRJj4xj1y2KaDO6HhYN9mRoK/x1950xDz9iIS7v34dWtE61f6rNSWtYujsw9d5ieMyeXqt8GKDMU0lNSuXniDHHhEapgWI/pn2NaxZLtcxfkNnctueF0NU83sjOzOPX7H5hWsSrzqHOZXE7NhvVJSUjA1NrqtZow59HWr8CwpQvpNHEsn21ZS+9ZUzGubMH2OfPZPmc+B3/+FcuaDri0akaTQX0Z8P0sZDIZ1q5OePfpzpmNW1VlTxe27yL0WiBdPv8UbX1lwKVaHXe+PrGXGg1eBBbM7GzR0Nbi8c1bgDKQA5CZns7JdZu4dvgYqQmJ1C8muNZ2zHD0K1Vk25z55GRlqx7f99Ny1DQ06DxxHKDsldJn9lRiH4Wxe8GPqv/byHkAACAASURBVIlReftpOuQDoh8+LvAB/f7FK1TzdMO5eRMUamr47zvMoWWrsKzlgEJdLV8QoyhB5y9ibl+NAd/PJvJ+MKvGTCq278+rTq7bSFZGBo9v3SHglUbGpZGWlEzYrbvYetTm0fWbrJs0nb98lrBk4Eime7dm6eBRLB82hpzs7ELXB/lexMqppupr+bIKpsaFZgiVVXx4pCrDJ8+T3PHzeYKvBODerjVjf19J4wF9CDx6kqjg0EKvFxoQiI2bCzUbK0sDK9lWzdecG6B225Y8uOxPYmzc395/WZ3b/CcrRnzKvTIEhABCcgNBwVeuFXteqP91FOpqVHFWTpezqe3CPN+jtBk9FLmaAplMRqP+vVGoq3FlX/6gzs0TZ3Dw9qJOtw7EPA4j7NZd0lNS2PfjMsyq2eDVvROenduT/Ow5t0+fL9P+BUEQQASE3igHB0sGDmxO5crG5b0VQRAE4T1h7eqEW9uWb2WEt0wmo9dXU0hLSmL77O9ZOmQ0T+4E0fWL8Xz+53o+37FB1avkbZDJ5bQYNgjnFk3Z9+MyNs/4hst7DtBuzAga9e9V8gVyaenpMnDBHKScHOr36sqk7b+rPjwVx87TnUfXbyp/W5/7odPWozb2dT04snINvlt3cv3ICSxrOWBkkb9pcc1G9Zl2YLuqBKmapxtht+4QeEyZTVJS2ZhMJmPokoWq8i1rF0d0jQw5sHgl2VlZqulALYYNYs7pAwxfvoiWw4cU+kH+Vb2//hJ9U1N2frcITR1tPDq0we/PPaoyoiMrfmN2i87MataRHfP+R40G9WgxbDC9v5pCwtNoDi39VXUtSZLYPmc+Ogb6dPzsY3QM9Bnw/SwqmBjT5YtPkSuUfZfyGko/ys0QSk1I5ObJs5xct4nEmFgy09K5uv8wrq2bF/oaKtlWpfGAPlzYtotH12/mey7m4WNOrNlAnS7tsffypNXIIZhWsWLbnPlkpKbx+OYdkuLiqdnYm5qN6lPFsSbHV/1eoHTo/qWraOnp0nLkEJ5HRRN26w6Xdu0jyPciR1b8RmzYkxLf2+vHThLzOIx9Py7j548+KfNUpudR0fwy+jPWT5rx2j1sru47RNitu/w27guy0l8Eo7LS0wnxv07YraIn0gadv4hcLqf6K5PKZHI5FUyMSYiJKWLlm7X/pxWc3rAFmUxGZno6R1b+VuS5If7X0TU0oP+8r1QTz14ORppXt8Osmg3XDh9/6/sujJSTw70Ll8q8LvD4KWLDwrlz1rfY80Jzg6u2uVlSXj06o6GtRdsxI/h04yom7/6DNqOHcs/vcoHMsBvHT6OupYmdpzvXX3p/bhw/TYj/ddp+Mhzn5k24dugY2VlZZX4NgiAIIiD0Brm62gBw/Xpoue5DEARBeLe6T53IyBU/UKNh/XyP61eqyKiVPzHof98w8+huvti5MV8j1jxaero4t2ha5mbEdbq0x9bdlb8WLSX52XOS45+xZNBI5rXrwf6fVmBuZ1viiOvXVa9HZ6bs2UzHCZ9w8+RZzqzfAsDWr7/lxvFTdJ86qUAjVRMrS8Zt+KVA2VHPmZMxNDdj9ZjPWT50DHKFgk9++5mauSVYhdGqoIdFDXtVM9eQgEAMzc3o/uVEEmPjuPDnbgBunDgNgFPzRqq17h3aMHTxQkysLOn25WdoaGth7eJI8JUAokMfERceUWSfljxu7Vvj1KwRzT8agLl9NWo1bkB2Vhb+B45w3+8ytdu2wNajNu3HjSL2STiGZpXoMH40X+zYkO/D8Ku8+3SndpsW7F+8grObtrGwx0B2fOvD7tyypldd2L6b60dO0GH8aCrXqM7O734gPSV/Y9yIoAecXr8F717dGLH8B/SMjTiycg3m9tWo06UDesZGtBwxhGeRUcTmTnEC+G3cFxxc8qLBr++2nahratJkUMHR5/V7dyMnJ4dDy1YVus+jv6wj5lEYfedMp/lHA7m854BqDLuUk8Odcxeo2bA+rUcNJS48otDyl7zzTatYcePEaSRJIic7m5Ujx3P0l7VFvqcviwsL57sOvTm+en2R4+FL8uDS1VIFn4pyZuNWfuj7YanLuF726MYtUhOTCpQ16hkZIlcoSHwDGUKlEfs4jL2LlrJk0EhmNetYaIlbnrwG2bpGhmyaNofo0Ef59q8qFzt28m1v+42KCHrAt+17qoJcRUlNSCDyQYiyLFVNgUvLpvgfOMK6SdPRNzEhPSWF9Z/P4JdREwqsDb4aQEqCsi9pwKFj+Z7b67MU/YqmaGhrceUvUS4mCMLrEQGhN8jV1YaMjEzu3n39fyQI745cTcFgn3nUbFS/5JMFQRCKUMHEmAZ9e2Dn5cnIFT8wYfNvqmygblMmoFBT49dPJrLzu0XoGhkydt0KrBzzT9zpO2c6H/30PTOP7GL6wR2lyo6RKxS0GvkRj2/e5tKuffmei3sSwcl1m0hLSsa5RRPV4+3HjaLVqI9KvO7Lo7wL06BvD/rMnkZqQiJrP5vKmvFTVNkS2VlZrJs4nYs799Lm42H0mP45MrkchZoaAxfMwaa2C33nTFc1lq3XswseHdpwePlqQq8FEnwlgJ/6DyMqJJShPy2gyaB+ODZtVOBo2K8ncrlc1eA270OnZS0HTq/frCoDinkURuT9YJybK8vGvLp1YuD82YQEXGfrrO+wqG5H96mTUNPQIPiqsvTjnu8lqtero8qeeZVCXZ3240YRce8B6ckpdJzwCTUbexN6LZDUhEQCDh3HtIoVQ5csIDYsnBXDxvG/HgNZ1GcIqYmJjFz5I00G9ct3TXUtTbpPm0SvmZO5e+4Cp9ZtAiAtMYlzf2wvdvrR1lnfE/M4jGuHj6v65bzq8PJVxIVHYO3iyL4fl3Fw6S+EXguk3ZgRDF2ykAomJqydMLXYrJeIoAcEHDpG08Ef5GvUrWoCffw0SXGFT9rKSk/nz28WYmxpQUZKKn/5LMn3/O3T59EzNsLGzYWTazYWmu2QFBdPxD1l4OHmiTNF7vPfLCc7m/sXrxToI6Rf0RSAhJh3ExAqi+jQR8Q9ieDC9t0EX/bnru9F7Op4oFBXRyaT4d6uFQ8u+5dqSts/VV4Go4O3FzoG+lw7dIzrh48zp1UXfuw3lIBDxwotE8zJyibg4DHCg+7z5Hb+sr3Qa4H4HzhCxL0HhOY27xYEQXgdUnkcly5dKpf7vs3jr71fSf4Bi8t9H+JAkslkkrGlRbHneHZuL/kE+kojlv9Q7vsVhzj+K4dcTSEZmFUs9328yaPJoH6ST6CvZOFgL9Xt1lH65vwRacbhnVLjgX0ln0BfqeXwIapzTa2tpOkHd0jzLhyV7L08JUCq1aSh5BPoK3X54lOpUf9e0lfH9kjjN60u8b4eHdtIPoG+knOLJkWeM2jhXOnrE3slmUwmmVpbST6BvpJPoK/UZHC/Itd0mjhWWnjtnFStjnuhz5tXt5O+v3xSGr7Mp8Q9dvpsjOQT6CsNXDBH6vLFp6rX6RPoK7UdM0Jy8K4rLbh6Rhq54gdJJpfnW6upqyONXrVEtefCjnkXjkrqWprK7y2FQvrW75g099whSVNXJ9+12n86Wlrgf0Zya9dKWuCvvJ+ahoYESOM3rZZ8An2lhdfOSdr6FSRAqt22peQT6Cu1GDZIdf2Xj7yvrYO3l9Ri2CDVfloMGyQBko6BvrTA/4y04OoZycqxZr61ahoa0sgVP0hzzx2SNLS1JEDSNTSQpuzZrHx/Jo+X1DQL3rOkQ6GuLslksmLPqeLsKLUa+aHqPFuP2qrX7tS8canuY1LFSlpw9YzUc+Zk1WPu7Vvnvh91S1zf8INequ/9lw9tfX1pYcBZ6evjfxX7+jtO+FiadXKfpFBXfyc/3+/j4d2nu+QT6Ct1nzpRcmnVTNLQ1pJqNqov+QT6Sja1Xcp9f4Ud6lqaqu87p+aNJZ9AX8mujrvk2amd5BPoK7m1bVnue3ybR91uHSWfQF9p3IZfpG/OH1H9/VOaQ65QFHl+cc+JQxzi+G8fpY23vLvZsP8BMpmMK5fvlfc23hmtCnqkJyW/dg3963Jq1ggrx5oc/219oY0gjSzM6TdvJvZ1Pdg2+3subN9d4ByZXE7L4YMBsK/niZaeLmlJyW9974LwvjGrZkOdLu05uPTX1+4/oKmrU2z2gupedrb0//ZrLKrbsajPECLvB7/W/V6Hpq4O7caM5PzWHUSHPirTWm39CqQlJhX5d51Hp7Y8vnmbiKD7ROT+FnfEih/oNmUCkQ9COLl2o+rcmEdhLBk8ipErfmDE8kVs/fo72o0dSeT9YPb9sIzsrCzSk1Po981MardtybVXSgTyyGQyWo74kIh7D4rNlLhx4gxu7Vph7eqEe/vWZGVmEuR7kc6TxpH6PIGH12+SkZrGs8goQJnp4dW9E3K5nAHffc3/eg4mNbdcAZRZLIMWzCE1MYnNM74p8b3b+8PPJMU/o/OksQCc37qTPQsXo2dsRIthg8ga2Jeo4BDWTZpeoHwnPTmFX0ZNwNy+GnJF4QnNibFxqv8fyMnO5tDPq0iIiS3w/Xjj+GlajRjCoIVzeXI7iHUTp5OVkaHc46KlfLJmGZH3HqhGNt8+fZ4Hl/3pOOETmg7+gIfXbiDx4utv5+lOkO9FgnwvEnz1Gg0/6IWhWSVun1E2dU15nsDBpb/y/Gl0vqlXAFkZGRxZuYaxv6/Es1N7fLftpPXooZhUsWTlyE8J8i17LxOgVNOuHt+4xeMbt1R/Drl6jf2LVxAfHlHqjJvYx2H4btuJd5/unF6/mejQR9Tr2YXYsCfcu1ByU95zRYxpT01I4NCyVUQ9CMnXV+dVB5f+yok1G8s83evfJODgMZybN6Fejy406t+b8Lv3CDio/LviXfUQKquX/712/+IVsrOycGzWCNfWzXl883a59Q96V0IDlBk8NrVduPzXAdXfP6WRk51dZJPx4p4TBEEoDQUwqzxuPHLkSH799deST/wH2bTpFHv2XHxn95MrFK8VjHnddXlkMhlNBvVjxPJFVK5RnetHT8I7Cgop1NQY9etinHL/EfEo8BYJT6NVz7u0bMrIlT+hX9GU8Lv3qdejM49v3ibmpZ4IAK6tm9OwX09Ob9iCrbsr4XeCiHwQ8k5egyC8TK6mQMrJ//Pzd39GS31vhYKRK3+kdpsWJMXH8yjwVsmLXuHg7cWk7b+ja2DA3XMXCjyvrV+B6vXq4t27G31mT0VNQx1JkqhY1Rr//YdLvL5MLqcsg8gV6uoFAgtyNQVDFy/As1NbrF2cuLhzb76/sxRqarQYOoiE6Nh8wQ+7Ou70nPEFfWdPw6VVMzJS04h6EJLv+mbVbOjw6WhO/LaBR4HKRrqJsXHcOHYKI0tz9ixYzLOop/n2k56cgv+Bo9h7edJ4YB+09SuwdsKXxIUrx11H3HuAc8um1GxYnwt/7sGlVTPc2rbk4bUbqn/4u7RqRqMPerF7/o+qEprCPIuMotng/kg5OdTp0p7rR06wadocHOrVoVH/3jT6oBdNB/cjKS6exzdvU7tNC+p0bs/eH37GsVkjKtlWzReU6jRxLM7NG7N2wpfF3vdloQGBxD5+QlZmJjvmLSQnK5uQgOt49+5GRkoay4eNITm+8F4qkiSRGBtHQnRsocergZ+H124QWci+EqNjqNutI+nJySwfPo6Ul3q3xIdHoqGtze1T51STlLKzsri0ex/3/C6jX6kixpUt0NbTUx3xUVHs+OZ/JD97Tk52NtGhj8nJycZ36y7VdUP8rxERdL/Ir4tj04ZU83Tjzjk/+n0zg4s793J247ZSvadvUsjVa6X+WuYJu3WHBn174Nm5HXrGxnh2asfJNZsIvlr0+O3SCL4SwNOQh8WeI+XkkFlMwOi/ICs9nav7DnFizQbCbgdRr0dnauX229r34/L3PkCQnZlJjQb1cG3dHF0DAzZNnU3ck/Dy3tZblfLsOQ379URDW5sDS34h+mHZfjEhCIJQVmWJt7zXKUziKHjI1RTSR4vnS1P2bJa09HTLtG7o4gXS5N1/lGndy4dlTQdVCv/EretU6f+vcy1rV6dSp6jnHe4d2qhKDWYe2SUt8D8j1enSQQIku7oe0vyrp6VPN/wqGVU2lzR1dKTPtqyV5l04KlWuUT3fdSZuWydN2bNZkqsppFkn90kD5s8u96+rOP47RwVTE6nXV1OkL3ZulBYGnJV6TP9cksnlkkwmk7p9+Zk099whqaKN9Ru/r0JNTWo8sK9kVs1GAqSmgz+QfAJ9pRmHd0qzT+0vUGZT0mHhYC/N8z0qfXP+iOQT6Cs1Htg33/M2bq7St37HJZ9AX2nB1TPSYJ95kp6xkdR86EBVuUDeuTKZrEC5i6aujjRhyxrpsy1rpQomxiXux7lFU+n7SyelsetWSE7NGklyhUKSyeVSn1lTVWVLPoG+UsN+PfOta/hBL8kn0FeadmC7pGdsJMkVCqnP7GmST6CvNOvkPqnzpHHSpD/XSz6BvtJHP32fb22H8R9LC/zPSHomRmX+emhoa0n9v/taajd2ZIHnajb2lnwCfaW5Zw+pypFGr1oiaerqSHZ1PaSZR3dLX+7dWqDMqrBj1C8/qa6RV76koa0tubZuLrm1ayWNXrVEmnfhqKRfqaL08eql0rQD2yWZTKb6/mg2pL8ESJa1HKSFAWelnjO+eCPfjyZVrKQKpibv7OfO2NJC0jMu+9fpbR15pTJT9myWvvU7/k7fizdxVHGqJQ37+X/Kn2//M/+4/f+bjipOtaQ5pw9Ic84cLPe9lPZoPXqo5BPoKw1fvqjc9/Kujg9//F765tzh/3S5ozjEIY53d5Q23iIyhN6QDz9syaIfRvAwU59BP8znvt8VEmPj3sq9es6cjGfHtmjrV8DEypLrR04om/K1b42Uk0Ny/LNC1/WaOQWPjm3QMdDHuLI5gUdPlvqeDt5e9PpqMp0mjkXHQJ8d3yxk+9wFaOtXoOmgfijU1UmKiy/y3q+ycqzBx6t/xqNDG26cOENSKd+rvrOnkZ6Swu+TpuO3cy/WLk40G/IBGtradJ44hvjwCJYPH0tSbBzZmZncOnUWz05t8ezUjmuHjpGWnEyzIf2p06UDuxcuJvxOEJVsbXBu3oRT6/5AS08P93YtC2QBvEqupsCjQ1s+mDeTul07EnDwmCp93bVNC1KeJ5CRklqq1/RfpVVBD+8+3bGv64mNmzMJMbGqco3i6Bjo49yyKVEPQihLZlol26p4de9ENU93qro6ERv2hIzU/F8jLT1d3Nq1RpJyStXc0qyaDY5NGxIfHkVWejoymYxajRtQybYqMY/Ditzfh4u+xal5Y8Lv3iPs5h3q9eiMmZ0tzi2aUL9nF2RyORb2dlz+60CpX1+BvdnZMvxnH+r36srDazfISE1l6OL5NPqgF149Ouc2JB7C3XN+/PnNQpoO7kdOTg5JcfEMXbIQp+aNlRNNingNFg52DF+2iKzMTH7s8yFGFuY0HtiX51HRhN+9h6m1FaN/XUxSXDxrP5vKjm998N9/mIzUNMJu3aVul/ZUre2CTC5n4PzZdJ86kTYfD6PRB714/jQ6t6HwfKq6OqOtXwG3ti25dfpckd8jXt070/+7r4h6EIJebpPnNqOH0ubjYVg51uDwit/Y+a0Ptu6ueHZuz+U9B0hPSUFTV4chi74lNuwJRubmVK9Xh1qNvfHo0Iajv65j7YSp3DlzHt+tO5GrKfDu1Y1bp86REB2DTCaj75xpPLx+o9DS1JJkZ2UReOwU9y9eKfBczKMwzKrZkJWRwe75P3Lr9Hka9++NZ+d2NBnYl8SYWNZ/MZOE6JLLQ7R0danVpAEh/tdVk5iys7KICg4l8n4wwVev0fiD3ti6u2JXx12V6fHo+k0qVbOh6eB+PA0OpcP4j5GrqbFmwpdlKncoSmrCu/17MjUxiYzUtHd2v5I8DXlIvZ5dMLa04Pjq9dw6dba8t1QmCdEx+O8/zPUjJ7h2+Ljy72ShXCRExxBw6Bh3z10oceLV+yIpNo4a3l5s/fq7IhuR/9s8vHaD60dO8PyVrFFBEIS3obTxFhnKyNA7d+nSJerWLX6k6z/JX3u/omETF9Y8tARkBF24xMoRn5Z6va6hAbYebty/eLlALxtdQwMcmzZEoa6ORXU7GvXvzdFf15GRkkqH8aPZ9+NyajTwwt7Lk6yMDA4sXsmZTduwqG5HZQd75GoKKteoTsN+PTnyyxqy0jNoP24U2+csQK6moH6vrsQ8CuPEmg08un4z371dWjal9eihWNZ04PnTaM5s2ILvtl2qPcrkcgYtnEvtNi0ASH72HN9tuzi7cWuRATEjC3M+3fgrWRmZqGtpEhcWzpLBozC2rMwQn3kYW1oAyik5p9ZvJuDAEbKzsqha25lPN/zKn98s5PyWHYCyPGTA97Oo3aYFCdExLB4wosA/hsyr2zF23QqeRUZx+8x5WgwdxLXDx9kw+StysrNxbNqIYUsXsnnGXJp9qBwffPfcBdZ+No2M1FSsHGugqaPDg8vK0cZ2ddzpO3c6JlaWRAWHYmptRdCFS6z5dArdp03Eu1c3HlzxZ/lHY5AkCQ1tLWo0rM/dc34FAhBvkpVjTSQpp8AUiveRTCZj2DIfajV6MVI6PSWFPQsX5/tgrVBXp3o9T8Ju3yUpNh51LU0+Xv0zVV2dOLV+M3sWFD6G+WU2bq40HzoA5+ZN8j0ece8BSwePIi0pGW19fVoOH4x3725o6ekCcPusLyd+26Aac5xH19AAG3dX6vXoglOzRqq9Bxw4iq1HbSrZVgWUH+gDDh7F3N4Wc3s7zm35k9O/b8atXSsGLZzLzu98OLtJ2UujyeB+dP1iPKDsZ5KalEzvr6awaepsHlz2p9dXk0lPTmHj1FnkZBVeCmBoboaDtxdyhRxDczOafdhf9XOqpadLbFg4lWys2fO/Jdh7eeDcvAnpKaks7Naf+IhIBi6Yg3PzJsjkMrIyMtHS02Xfj8s5vvr3fPexq+NO86EDqdW4ASkJCSz76BMigh6gpqnJ6F9+wtajNk9DHqJQV0dTR5vFA0YUOp65btcO9PtmJgCPAm9x+8x5JEmiZsP62Li5EPMoDFNrK7bMnEdkcAjDf/YhOyuLLTO/4c7ZF6VpFg72tBg6EI+Obbl91pffJ04jKyMTl1bNVF+LZ5FRqilcJlWs+GLnBsJu3mH95JnU79WNNqOH8mO/oRiYVWTID98hl8vzfX3yaOrqMOPwTu5fvMq6z6bS/KMBdJo4lrWfTS1TgP111WzszYDvZnF1/2H2LlpaaA+1wuhXNGXitnVsnvkNd874FnpOi2GD6TjhY7KzspjbuhuJuZOK1DQ0GL1qCbburgBsmPwV/geOvJkXJNBkUD8aftCTRb2HlKoPlyAIgiAIQmmUNt5SqoBQ27Zt+emnn1AoFKxatYr58+fne15DQ4Pff/8dT09PYmNj6du3Lw8fFl8D/m8JCE36cz1edjr0rq3BmUgdVq49Tfjd+3SeNJZfRk3g7nk/1bkKNTUcGtRDXVPjxQVkMuzquOPVrRMa2lqkJSXju22XshfFK8/lubznAJtnzAWZjNG/Lsbey5O05GT2/7ic6vXr4tKyKdmZWSjU8/cMv7R7P5tnzEUmlzN61RLs63oA8OjGLUyrWKFjoE+Q70W2zvqO+PBI2nw8jLafDCcqOJSTazZyZd+hIps4mlhZYuPuinPzxji3bEpOVhYn127i8PLVqka1MpkM5xZN6DRxLLqGBiwZNBJLxxoM+G4Wp9Zvxr19axQKBVf2HQLA3suTyg72JETHcM/vMsaWlTG3s2VOq675AisyuZwGfXtw3+8yUcGhhe6vev26jFi2CIW6Gr7bd/Hn3IWqDCA1TU3mnD6Apo42aUnJnN+6g2ZD+hN26y4ZaWmq9yk86D5hN+9Qp2sHYh+FsXvBT9w+c556PTrTZ/Y04iMiMbIwJ+jCJRzq12XLzHlc/usAHy2ej2OThqQ8T+D81p0A2Lq7YmBWEYCsjEzCbt4hNCCQwGMnVb8p09TVUY09liSJ6NBHyswlScLU2gpzezvkCjmaOtp4de9MNU83AM5s3Mq+H5eV6sOiTW0Xmgzuh7qmJmc3bePueT90DPSxdnFEQ1sbZDKMLcyxcXdFx1Cfnd/6EBFUcq8JU2srFOrqRf7GuMmgfnSdPJ4d3/pwYftu9Cua0Gf2NBzq1yXs1l3uX7pCWlIy3r27YVCpIsnxz9j+zUI8OrTBqXlj7vlepEbD+uz8bhG+23ZRxbEmNu6u2Lq7YGxZmSd3gggPuk/t1i2wcXMhOf4ZZ//Yju/WnaQkJGJXx43hPy/iwRV/zm7cSs+Zk6lgYkzAoWOc3/wnth5uNB7YB31TEx7fvM3V/Ycxt6uGrburKsiQFBfP2T+2c8/3EvV7d8OjQxvC793n5G8byM7OpvlHA6nq6kT0w8ekPHtO1drOnF6/Bbf2rXgWGcXiASPyZaE5NWuETK7gxvFTyGQyxq5fSSWbqqpx3RraWvhu38X22fn//jWvbkfzDwcof35e+pm/ceL/7d15WJVl3gfw71lwA/QcdgMGEc0lpShH0CSXUmEmU9MU99Tc05p63+CtJqm3JslMJy1tFJdUEhH1RUtUXpncUjEPmwgcUOAAAQYuLKks9/xBnPEMIjvnwfP9XNfvKp9z82x+r67Oj+e575MIC1wFAHhlpT/6DPXCLv8PkHjiJADgybHP405JqX7eH2snR7wVtgPa8xex76MgTAj4C9xHj8SGOYtRlJ2L3l5/xHMzp+IPA/ujuLAIp0PCcDZ0P8pu/XvOHblCAfcXRmDE3Bmwc+2BbxauQGZc4gMzIJPL4T1zCnKSUvTN1prtI+fOwNilr+FE8E4c/XoLgOonvGav+QTde7vhfHgEfisugevTT8LF/QncKS3F2dD9iFz/jwZNjO3hOxqvBAagqqIScqUSST+exq53PgAADBj1HCrKy+tsnIxd+hrGLJmPPX/9a3DnKQAAEcFJREFUGK98EIDE6JP49u336j2m1CmUSrwREoyCaxnY5b/S4DNzVTcs3b4RhVnZ2LriHSOdIRERERE1VIs1hORyOVJTUzF69GhkZ2cjJiYG06ZNw5UrV/RjlixZAnd3dyxZsgRTp07FxIkT4efn1yInKHWLv/gAny/xQFbBb3g98AdEb98NuVIJ/4jvcKekFGunvAohhH51Haf+fWrto6K8HJcOH0X88Wg88+JYPDn2ecgVCv1nPx+KxJnv9qG4sAiiqsrgyRtLG2sMn+WHs3v3oyinekJSD9/RcHqiL3QJSdBdTkb53bu1fq6rrQ1GzJ2B+GPRyIiNR4fOneE1eTzGLn0NAJAW8zMGjHwOFw4eRljgqkZNUGjt7IQxi+dh0Eu+yElOxande2Hn6oK+w4bgscd74XpGFvb89RNkxMYDABZt/hKPe/0RN/Py8c3CNwwmlOw7zAuDJ46Dq4c7utra4P+3fIsf/r6xEX9D/9Z/+DBYOTrU+q0/AEz72wfoM9QTW5a+heykFAwY9RxmfvYRSm/cxI/f7kHZrdsY8ep0dO/thjN7wnH4iw0Grx74LF+I0Qvn4mDQOpzevRdLt30NezdXXDl5FoNe8sXxb7bBvmcPDHh+OERlFXKSU3E9MwtCCHSyMIeL+wBYWluh/O5dxPzfD7hTXIwhU15GZ0sLg/Msu30bleUVsLS2MthelPsLTu4MhZVjdzw3cyquZ2Th2DdbERsZhaqKSti4OMPVwx09nhyI7r3dIJPL0aFLZzi4uaL05i1U3L2Hbva2KC4sqrVvoPppl47mXaAwUyJ42X/r/+6A6i+L9r16QiaToWOXLvCcNE7/NI723EX8/H0k7Fxd4Ny/H27mFyA//Sp8li9C8qmz2PZGgH4/MpkMXpMn4Ok/j4HzgH4w69gRqT9dwIWD32P4bD84P9EPAHBw1Vqc/m4f5vz+2lVleTnMOnYEAFzP1KFQlwOn/n1gYaVGYXYOftzxHS4cPFyrQTbopT9h2ifVT6j8ok3Hd+9+pJ9QFqh+MuKZcT4YMWc67FxdUHrzFjJiE5ARG4+M2ARkJSQZvDajUCprNSM6dO6Ee7/dgUwux8T/eQvP+k1CVWUl1k2bV++TXN0fd8MbIcHISkzCnvf+F56TxuOFBXMQtXkHUn+6gM6WFvCaPB79vIfiblkZzocfwk9hB3CnpBRVlZW1HsFXduhQ72s+919DJ0sLvLV3B7raWhvc33/uCMHFiCMPXQUIqG601jfmoediZlarAa3s0AE+ry/E8DnTUFVRAV3iFSSdPIOfwg426HXD+1k7OWLa3z6AY9/H8fnLMx/4FNODdOnWFe8d3Y9O5ua48Use1kye3ehjS1XNBOcPel1WrlBAVFW1+aqSRERERNR4LdYQ8vLyQmBgIHx8fAAAAQHVX+BWrVqlHxMZGYnAwECcO3cOCoUCeXl5sLW1bZETlLoNGxZj1uyReNJ9BTIy8vXbPXxHY+ZnH+GaJh73ysrQc5AH7pSU4uCqtbWWWi7+tRCl9614YmltBXO16oGftTb1Yw7w+7h6yfbobbtx+IsNTd7XEyO98crKAFhaW6GivBy5yVqcCtmL2CNRBg0m9WMOeP61OYj6x3b90scP0tXOFiWFRa2yeobCzAwymczgC7O5WoXfiosNXtHp3NWyzi9/Xbp11T8tYe/mirfDvoXCTIkTwd/i+3XVTSwLazXulpY98OkdezdXeM+YgkEv+UKhVCL+eDTOhO5H2c1bkCsU6P54L7h6uEPZwQwZsQnITkpGxb1yiKoqXM/S6c+zt+cgTAj4Cxx69cSt/OuQKxX6Jk/ZrdvIuZKKivJ7EFUCKWfP48KBQ6gsr8DTfx6DPkM9kZuajoy4BP0qPCU3bqCk8AbUjzlg0Td/Rzd7O1z7fSUZ9WPd9U/M1Kh5Gqf8zh14z5iKbva2qCgvxy/adKjs7WBpbYVb+dexZvKsOrOtMDODuVqlX0FOrlRgxJzpqKqoxD93hACoXv7ad8ViiMoqXNPEIyMu3mDeH5W9HW7/WvjQvAyZMhGW1lY4EbyzzmaJTCZDN3s73MovaPaXYe8ZU1B+7x7OhR2sfzCAzl274k5xMYQQkMlkmL4qEE//aYz+8+LCIpzavRdnQw8YrI7VUpz698GIOdORk5yKa5oEZMYnPnRurbZiYaXGb8UlzV52WiaToZOlRaMbOr4rFmPk3BnYOG8Zrmni6/8BIiIiIqI21GINoUmTJsHHxwcLFiwAAMycOROenp5Yvny5fkxCQgJ8fHyQk1P9G9a0tDR4enqisLDQYF8LFizAwoULAQA2NjZwdXVt1EVJkZmZEu7uPfDzz4ZLy8pkMkx892049at+IqggIwuHv9jQLibOk8lksO3xh3qXfm2IThbmUHV3wPVrmQ16leNR4jV5PKydHfHDuo2NaiSYq7pBYWbWoMli6yKTydB32BB4vTIeZbduVz/ZoolHwbXMZjU1LKzUePm9/4LK3g5A9WtTGXEJyE5KQUV5dXMqOylZ3/BSKJWwd3NFQUaW/mkRa2cn3C0rbdCkzWRIJpfjDwP7Vy+tXlkJ3eXkFpnclxpHJpPB0tZG37AkIiIiIpKShjaElPUNkMlktbb95xfKhowBgM2bN+tnuo6Jian35NqD8vKKWs0goPr693/yuRHOqPmEEC3SDAKAOyWlyNPWP+fMo6gpqw4BaJEnwoQQuHLqLK6cOtvsfd2vpOhGo+ZLqayoQG6K1mBboS67Rc/JlIiqqjrn5KG2I4RgM4iIiIiI2j15fQOys7Ph7Oys/7OTkxNyc3PrHKNQKNCtWzcUFbXOkutERERERERERNQ89TaEYmJi0Lt3b/To0QNmZmbw8/NDRESEwZiIiAjMmTMHADB58mScOHGidc6WiIiIiIiIiIiard5XxiorK/H666/j6NGjUCgU2Lp1K5KSkvDhhx/i4sWLOHToEIKDg7Fz505otVoUFRXVu8IYEREREREREREZT72TSreWR2WVMSIiIiIiIiIiqWhov6XeV8aIiIiIiIiIiOjRwoYQEREREREREZGJYUOIiIiIiIiIiMjEsCFERERERERERGRi2BAiIiIiIiIiIjIxbAgREREREREREZkYNoSIiIiIiIiIiEwMG0JERERERERERCaGDSEiIiIiIiIiIhPDhhARERERERERkYlhQ4iIiIiIiIiIyMSwIUREREREREREZGLYECIiIiIiIiIiMjFsCBERERERERERmRgZAGGMAxcUFCAzMxM2Njb49ddfjXEK1A4xL9QUzA01BXNDzcUMUVMwN9QUzA01BHNiOlxcXGBnZ9egscKYFRMTY9Tjs9pXMS+sphRzw2pKMTes5hYzxGpKMTesphRzw2pIMSes/yy+MkZEREREREREZGLYECIiIiIiIiIiMjEKAIHGPolLly4Z+xSoHWFeqCmYG2oK5oaaixmipmBuqCmYG2oI5oTuZ7RJpYmIiIiIiIiIyDj4yhgRERERERERkYlhQ4iIiIiIiIiIyMQ0uiHk5OSEEydOICkpCYmJiVixYgUAQK1W49ixY0hNTcWxY8egUqkAANOnT0dcXBzi4uJw5swZuLu76/c1duxYJCcnQ6vVwt/fv85jzp49G6mpqUhNTcXs2bP12z/++GNkZWWhuLi4sZdBbURKeTly5AhiY2ORmJiIjRs3Qi5nP1SqpJSb6OhoJCcnQ6PRQKPRwNbWtpWumppLKrmxsLDQ50Wj0eD69etYu3ZtK145tRSpZAgApkyZgri4OCQmJiIoKKiVrphagjFyc+TIEdy4cQOHDh0y2L5s2TJotVoIIWBtbd0KV0stpSVzExwcjPz8fCQkJDz0mHXli7mRLinlZMuWLYiNjUVcXBzCwsJgbm7eCldMxtCodeodHByEh4eHACAsLCxESkqK6NevnwgKChL+/v4CgPD39xerVq0SAMSQIUOESqUSAISPj484d+5c9Xr3crlIS0sTrq6uwszMTMTGxop+/frVOp5arRbp6elCrVYLlUol0tPT9fvz9PQUDg4Oori4uFHXwGq7klJeLC0t9eP27dsnpk6davT7w5J+bqKjo8Uzzzxj9HvCal+5ub8uXrwovL29jX5/WO0nQ1ZWViIzM1PY2NgIAGL79u1i1KhRRr8/LGnkBoAYNWqUePHFF8WhQ4cMtj/11FPCxcVFXLt2TVhbWxv93rBaPzcAhLe3t/Dw8BAJCQl1Hu9h+WJupFtSysn936XWrFmjPz6r3VfzdnDw4EHxwgsviOTkZOHg4CCA6uAmJyfXGqtSqUR2drYAILy8vERkZKT+s4CAABEQEFDrZ/z8/MSmTZv0f960aZPw8/MzGMOGUPspKeRFqVSKiIgIMWXKFKPfD5b0c8OGUPstKfz3plevXiIrK8vo94LVvjI0aNAgcfz4cf32mTNniq+++sro94MljdzU1PDhw2s1hGqKX+zbXzU1NzXl4uLy0C/6DckXcyP9kkJOAIivv/5avPPOO0a/H6zmV7PemXFxcYGHhwfOnz8Pe3t75OXlAQDy8vJgZ2dXa/z8+fNx5MgRAICjoyN0Op3+s+zsbDg6Otb6mYaOI+mTQl4iIyNRUFCA4uJi7Nu3r8WujVqPFHKzbds2aDQavP/++y12XdS6pJAbAJg2bRpCQ0Nb5JqobRkzQ2lpaejbty9cXFygUCgwYcIEODs7t/QlUitoi9zQo6c5uWko5qv9k0JOtm7diry8PPTt2xfr169v4pWQlCib+oPm5uYIDw/Hm2++2aA5fEaMGIH58+dj2LBhAACZTFZrjBCi1raGjiNpk0pefHx80LFjR+zevRujRo1CVFRUYy6D2pgUcjNjxgzk5ubCwsIC4eHhmDVrFnbu3NnYS6E2JIXc1PDz88OsWbMaeuokEcbO0M2bN7FkyRKEhoaiqqoKZ8+eRc+ePZtwJdSW2io39Ghpbm4aivlq36SSk3nz5kEul2P9+vWYOnUqtm/f3qj9k/Q06QkhpVKJ8PBw7N69GwcOHAAA5Ofnw8HBAQDg4OCAgoIC/fiBAwdiy5YtGD9+PIqKigBUdxvv/22Xk5MTcnNzMXjwYP1EnOPGjatzHLUfUsvL3bt3ERERgfHjx7faNVPzSSU3Nf8sKSlBSEgIBg8e3LoXTs0ildwAgLu7O5RKJS5dutSq10wtSyoZOnz4MLy8vDB06FCkpKRAq9W2+rVT07VlbujR0RK5qYuTk5M+N4sWLeJ3qnZMajmpqqpCaGgoJk2a1FKXSEbW6PfMduzYIdauXWuw7bPPPjOY2CooKEgAEM7OzkKr1YohQ4YYjFcoFCI9PV306NFDP2FV//79ax1LrVaLq1evCpVKJVQqlbh69apQq9UGYziHkLRLCnkxNzfXv2erUCjEnj17xLJly4x+b1jSzo1CodC/S69UKkVYWJhYtGiR0e8NS9q5qfn8008/FYGBgUa/J6z2mSFbW1sBVM8BodFoRO/evY1+b1jSyE1NcQ6h9l8tkZuaqm9umIbki7mRZkklJ25ubvpxq1evFqtXrzb6vWG1SDXuB5599lkhhBBxcXFCo9EIjUYjfH19hZWVlYiKihKpqakiKipK/z80mzdvFkVFRfqxMTEx+n35+vqKlJQUkZaWJt599906jzl37lyh1WqFVqsVr776qn57UFCQ0Ol0orKyUuh0OrFy5Upj30yWRPNiZ2cnLly4IOLi4kRiYqL48ssvhUKhMPr9YUk7N126dBEXL17U52bdunVCLpcb/f6wpJ2bmkpPTxd9+vQx+n1htc8MhYSEiMuXL4vLly9zVUyJlzFyc/LkSVFQUCDKysqETqcTY8aMEQDE8uXLhU6nE+Xl5SInJ0ds3rzZ6PeH1fq5CQkJEbm5ueLevXtCp9OJefPmPfCYdeWLuZFuSSUnMplMnD59WsTHx4uEhASxa9cug1XHWO23ZL//CxERERERERERmYhmrTJGRERERERERETtDxtCREREREREREQmhg0hIiIiIiIiIiITw4YQEREREREREZGJYUOIiIiIiIiIiMjEsCFERERERERERGRi2BAiIiIiIiIiIjIx/wIfHnubA7KqpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nyc_ts.index, scaler.fit_transform(nyc_ts[['count']]))\n",
    "plt.plot(smoothed_df.index, smoothed_df['mean'], ls='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Covid Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc_covid_df = pd.read_csv(\"data/covid/covid_nyc.csv\")\n",
    "nyc_covid_df.set_index(\"date\", drop=True)\n",
    "nyc_covid_df.drop(['city_pop2019', 'cityname', 'statename', 'statefips'], axis=1, inplace=True)\n",
    "nyc_covid_df['date'] = pd.to_datetime(nyc_covid_df['date'])\n",
    "nyc_covid_df = nyc_covid_df[(nyc_covid_df['date'] >= start) & (nyc_covid_df['date'] <= end)]\n",
    "nyc_covid_df = nyc_covid_df.set_index('date', drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "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>new_case_count</th>\n",
       "      <th>new_death_count</th>\n",
       "      <th>new_test_count</th>\n",
       "      <th>new_case_rate</th>\n",
       "      <th>new_death_rate</th>\n",
       "      <th>new_test_rate</th>\n",
       "      <th>case_count</th>\n",
       "      <th>case_rate</th>\n",
       "      <th>death_count</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>test_count</th>\n",
       "      <th>test_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            new_case_count  new_death_count  new_test_count  new_case_rate  \\\n",
       "date                                                                         \n",
       "2020-01-22               0                0               0            0.0   \n",
       "2020-01-23               0                0               0            0.0   \n",
       "2020-01-24               0                0               0            0.0   \n",
       "2020-01-25               0                0               0            0.0   \n",
       "2020-01-26               0                0               0            0.0   \n",
       "\n",
       "            new_death_rate  new_test_rate  case_count  case_rate  death_count  \\\n",
       "date                                                                            \n",
       "2020-01-22             0.0            0.0           0        0.0            0   \n",
       "2020-01-23             0.0            0.0           0        0.0            0   \n",
       "2020-01-24             0.0            0.0           0        0.0            0   \n",
       "2020-01-25             0.0            0.0           0        0.0            0   \n",
       "2020-01-26             0.0            0.0           0        0.0            0   \n",
       "\n",
       "            death_rate  test_count  test_rate  \n",
       "date                                           \n",
       "2020-01-22         0.0           0        0.0  \n",
       "2020-01-23         0.0           0        0.0  \n",
       "2020-01-24         0.0           0        0.0  \n",
       "2020-01-25         0.0           0        0.0  \n",
       "2020-01-26         0.0           0        0.0  "
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bf88170f98>"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nyc_covid_df = pd.DataFrame(scaler.fit_transform(nyc_covid_df), columns=nyc_covid_df.columns, index=nyc_covid_df.index)\n",
    "nyc_covid_df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonal Decompositions of Covid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "result_covid = seasonal_decompose(case_count.drop(case_count[case_count.isna()].index), model='additive')\n",
    "result_covid.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_covid = seasonal_decompose(new_case_count.drop(new_case_count[new_case_count.isna()].index), model='additive')\n",
    "result_covid.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seasonal Decompositions of real estate data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result_price = seasonal_decompose(smoothed_df['mean'].drop(smoothed_df[smoothed_df['mean'].isna()].index), model='additive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tcanR15akXQGAEftv6lgSEB0JRxdnHN66A1kn/sCcB++lIj8hhBBCyBhgdcVniUSC0tI//1goKyuDRCLRe25AQAACAwPx22+/6Y45OTkhJSUFp0+fHrRovXr1aqSkpCAlJQUikYi5T4Awzs1TiKa6/rvVmyN2w8nNFc5urgAA72Dr7lQTSMSQl1eg6GIagJHLfU5cPB8uPB6Ofv41sk+eQX1ZOSYvWzIi97ZlXA93tDY29jnWUF0Dvo+3hWY0es24eyW6u7uRl3IB0TfeABabzci4PrJgVOYMvYxfUVGle4hFxpaghDiou1QovpyuO1aVVwAA8LbyB5qjiThMhsrcfHSr1UZfW3olE93d3QiIHm+GmY1twYnx6O7uRv65VPz4zgdwcnPFjavvsfS0CCGEEEKImVld8Vnf7tc9PT16z12+fDm+/fZbdHd3644FBAQgMTERK1euxLvvvougIP0FxG3btiExMRGJiYmoq+vfVUusB08khFLP16iloRHdajWjnc9XF4ysuVDAYrPB9/FCfXkFWhoaUVNYDGms+YvPdiwWpt+5DEWX0lB8KR093d04/c0ehCTGwzs40Oz3t2VcPh8tir7F58bqWvB9KHaDSa4CD0y8dSHO7zuAE19+AzehAMGJ8cMe18HZGSJ/v0E3G9RSVFZBIKHYjbEoKCEWpRmZ6Gxr1x2rytUUn32s/IGmIbh8d/C8rH+1hiRMhkoj85612ptbUFNYTMVnMwhJTEBldh7alEpU5uTj3N79mL5qOaauvM3SUyOEEEIIIWZkdcXnsrIy+Pv76z728/NDRUWF3nOXL1/eL3KjslKz2VNhYSGOHDmCuDjaNMaWOfN44Dg4QFnbP/O5p7sbLQ2NjHY+C3qXytcWl1p1oYDv4wU2hwN5We/3e+plSGOj9T68YVLo9UkQBfjh2Be7dMfO7vkRXR0dmLKcshsH48LnoaWhoc+xxuoaOLq4wMmVa6FZjT5T/3I72A72+H3Hl8g88QfaW1oQkzxr2OP6yDTvB4YUn+XlldT5PAbZOznCPyqiT+QGAMjLK9DZ1q77HrJVbkIBHv/2c6zd9m9LT2VQPC9PcD34KM/ONXmMkrQrCIiOZHBWhG1vD2lsNPLOXdAd+/61N5Fx5DhufeZvuOXJ9YxuEEsIIYQQQqyH1f2Wl5KSAplMBqlUCnt7eyxfvhz79u3rd15oaCg8PDxw+vRp3TE+nw8HBwcAgFAoxJQpU5CRkTFicyfM43kKAQBNejKfAU30BpPFZ21Oa9aJ0xBIfOHg7MzY2Ezy6N3MTFGhKT4XpV4Gl+8OT2mAWe/r3bsJY87pFN2xloZGZBw9icgZU8x6b1unr/O5oaoaAOBOmw4ywtHFBVOWL0Xa4SOoLSqBqqMDGUdOYMLsG8DiDC96w1cWDMCw4rOisgpOrlw483jDuiexLQHR48Gxt0fBub7F556eHlTlF9j0poNse3vc8+4b4Ht7wTtIatUrXSRhvZsNDqv4nAE3oYCy2xk0LiYK9k6OyD97Xnesq70DOx57Fsf+uwszVq3Akmf/ZsEZEkIIIYQQc7G64rNarca6detw8OBBZGZm4ptvvkFGRgZeeuklLFy4UHfeihUrsHPnzj7XRkRE4Ny5c7h48SJ+//13vPHGG8jMzBzpT4EwiOepyeNW1vfPfAY0mw4yGbvB9/FBV0cH8lM0nTlegeMYG5tJQokYAFBfrlkVUHjxMgDz5z7zREJ0dXSgTansc7w8Kwcevj5WW6y3BlwP936dzw3VtQAAPhWfGRE2dRJceDwc//Ib3bGLB38F14OPkMSEYY3tHxWB1kYlFOWVQ56rPUdAhasxJTghFt3d3ShMvdTvtao82y4+L33+SUhjo7Hn9bcBANFWvMmsuLf4bEg++0C0mw6Oo+gNxoRo856vWRnQ092Nvf94Fxd+Ooi4eXMsNDtCCCGEEGJOHEtPQJ8DBw7gwIEDfY5t3Lixz8cvvfRSv+tOnz6NCRNGZtM1MjLcRJrCsnKQzueAKOaWxnqIfdBQWY3K3g2ifEKCUJaRxdj4TBH4idGtVus6Z2uLStDR2gpvMxc3eF4ivREoNYXFAACvwACUZWSbdQ62iG1vDycuFy0N12Y+1wCgzmemBMXHoKO1rc9mb9knz6CtqRlx8+Yg5/RZk8eWxkSj6FLagHsQXE1RWQVAs0KhPCvH5HsS2+EmEmLS7YtRmpaB9uaWfq9X5xUiafECuLjz0Nqo1DOC9UpavAATlyzEoa3bceKr3Yi7eQ6iZs/A4f/ssPTU9BKHy1BfVq7362Coytx8dLV3wD86EhcP/srg7Mau4MR4lGfloL2pWe/reSkXED8/GUI/CerLykd4doQQQgghxJzM0vmsVCrR2Ng44P8IMRRPpOl8btJT8AQ0nc+Mxm74+kBRWQV5WQVUnZ1Wu+mgQOKLhqoadKvUumPy8koI/cRmvS9PJEJTnZ7ic0ERAOvtFLc0Lt8dAPoVn5W1deju7gbf2/o38LIFgXExKLl8pc/PhaqzE+m/HUXSrQvw98P7cN+//2l016aTmyt8ZcEouphm0PmKit7is6+3Ufchtoltb4973nkdTq6u2L3pDb3nXP1A05bYsViY/eDdKL6UjoObtwEA0n89Cv/IcKvNNReHhqA8y/TIDQDoVqlRlplNnc8M4Tg6QhoThfyzFwY8R/ug3298+EhNixBCCCGEjBCzFJ95PB7c3d3x7rvv4v/+7/8gkUjg5+eHp59+Gq+88oo5bklGKZ6nEB2tbehobdX7enO9Ak6uXHAcHRm5n4fYB4qKKnSr1agpKoF3kHXmWgokYsjL+27EKS+rgEBi5uKzl0hvF3pdSRnUKhW8ejOhSV9cj97is6Jv7IZapUJzvZw6nxngyHWBOCwEBRcu9nttz+tvY+8/30Pu2XPwGx+OJc8/adTmnOMmRAGAwcXn1kYl1F0quDIYCUQsS+AnxrMHvsOs++/qtynarc88DmlsNHa+8Aoqc/RnglflaY7bWvE5cvpkiPz9cOSzr3Rd/2m/HgUARM2absmp6eXg7ATROH9UDiPvWask7Qr8IsOHnRdPNJFgHAcH5F2V93ytqrxCqLq64BcZNoIzI4QQQgghI8Gsmc/JycnYsmULmpub0dTUhI8++ghLly415y3JKMMTCfV22mo1yxUAADcGup85Dg7giYSQ9y6Zr84rgE+ItRaffSG/Jnu2vrwCAomvWe/LEwn1Fp/VKhXqS8vhJaXOZ324fD6A/p3PANBQXQN3Hyo+D9e4CVFgsdkoTL3c77WOllYc+2Invn52E/a/9xF4IiF8ejcQNIQ0NhrdajVK0w3bQ6CnpwfNcgWjefTEsmQTr4PQT4z5Gx7GQ598AN/QEMTNm4O73nwF19++GIe3fYbLv/w24PWN1bVoUzbZXPF52p3LIK+oRPpvx3TH6krKUJmbb5XFZx9ZMFgsFsoZKT5nwN7JEb4hhr9XEP0ipl2v2U/j3MCdz+quLlTm5sMvgorPhBBCCCGjjVmLz2q1GitXrgSLxYKdnR1WrlwJtVo99IWE9HIboNip1dS7ESET0Rv83iXy2iXzVQVF8BD7wsHZadhjM4nj6Ah3L0/dZoNa8vJKOHG5uogHptk7OcKZ56Y38xkAaoqK4U2dz3pxPXqLz9d0PgOaohRtODh8QQmxUKtUKLl8ZdDztLnPYZMnGjy2NDYaFdl56GxrM/gaKj6PLpLwULQ1NWPn8y9DEhGKJ777Anf+cxNCEuNx9Iud+PmD/ww5RlVeAXxktlN89g0NhmzidTi18zt0X/O7W/rvxxCUEGu2/96YShIWCgCoYKjzGQACKHpj2CKmTUZ+Sio629oHPa8sIwt+kRS7QQghhBAy2pi1+Lxy5UrccccdqK6uRnV1NW6//XasXLnSnLckowzPU6QrMOuj7XxmYnm7Nr9ScVXnM4vFgqc0YNhjM0kg1syzf+yGZoMegZ/ELPfV5m8ra2v1vl5TUATROH+w2LRE+VraAk2rns7nxuoauFPm87AFxk1ARXbugBE9WsraOlTm5iNscpJB47LYbARER6LokmGRG1pM59ETy5KEh6IiOxcpe/fjrdtWYc/rb+PdFffjxRvmY98/30NPd/eQY1TmFdhUF+20lXego7UNf3z3Q7/X0n89Chabjeg5My0ws4GJw0LQpmzSPUQeDnl5pWZT42jmNjUei4R+EngFjkPm8VNDnluWkQ0Xd57ZV3ERQgghhJCRZdbic3FxMRYvXgxPT094eXnh1ltvRXFxsTlvSUaZoTqfm3sL00zEbuiKzxWaOIvq3k30fIKtq1NN+0eZvOza2A3Nx0Iz/dHG8xQCwMCdz4XF4NjbQ2DmTQ9tkYt2w0E9G642VNfAhceDg7PzSE9r1GBzOAiIHo+CC5cMOj/71BkExsfA3mnorHifkCA4cbkG5z1rNdXL4Sqk4vNoYMdiwTc0GOWZOQA0+fonvtqN0vQMXQ6yIaryCuDizgPPU2SuqTKG68FH/IJknP/hANqUyn6vl2Vko6awGLf//Wk8vvszzF17n1V0QUvjJqAiJ4+x8UrSMqj4PEwR068HAGQePz3kueUZ2QBA3c9EL7a9vaWnQAghhBATmbX4LJPJcPjwYaSlaf5oj46OxnPPPWfOW5JRxN7JEc5urgMWOwGgWaHtfGag+Cz2QbdajcYaTWdvXWkZVF1d8A62rtxn7aaC+jYcBGC24q9bb8FEWaf/YYC2WO8dSLnP1+Ly3dGmbEK3qn/sUGN1DQBQ9/MwSCLD4ODshEIDi885p87C3tERQfGxQ547Lka72WD/LOnBNMsVcBOM7diNkKQETFm+FI4uLpaeyrCIAvzg6OKCiuycYY1TlWs7mw5O+8sdsHd0xPEvvxnwnA/vfRh7//UeOlpaMeeh+3H/B29atDgUvyAZ4tAQnNu7n7Exi9OuwCtICkeubX8PW1L4tMmoKSxGfWnZkOdW5uZD3aWChHKfyTW4fHc8u3835j26xtJTIYQQQogJzFp83rZtG5555hl0dXUBANLS0rB8+XJz3pKMIm4iTadt0wDFTgDoau9Ae0sLY7Ebyto6XYGwW6VGbVGJ1RUKBBIxujo6+m3E2NnWhma5QlecZpq7tvhco//rUVOoWdXgRbnP/bh68PVuNggADdWahx2U+2y6oLgYAEBhqmHF54ILF9HV0YFQA6I3pLHRUNbWGb2Mv7leDnsnxzFZtPKUBuC+9/+Fhz75AEueewL/99M3mLj0FtixDP+Vw5oexkjCZACAsszhFZ8re4vP4t7xrJWrwAPT71qG1AOHdO/r+jTVy3Hs853YfM9D+PzxZzEuJgpLn3tiBGf6J0cXFyx47BGUpGUgZe9PjI1bmpYBFosF//ERjI05ltg7OSIkMR6ZJ4buegYAVWcnqvIK4B9JxWfS1+JnHgffxxvjZ06z9FQIIYQQYgKzFp9dXFyQkpLS55hKpTLnLckoossYrhs48xkAmusVzMRuiH36FZiKLqYhODEOHAeHYY/PFIHEF4qKKr3LvevLKiA0U+czz1MIVVfXgEXU9uYWNNbUwos6n/vh8t0H/HdrrNJ2PlPx2VSB8RNQW1SC5nqFQed3tXeg8MIlgzYdlMZGGx25AQBN2jz6Mdb9HD17Bp78/ksEXxeHH9/+AB+sWoP6kjLc8eIzuPe9fwx5Pd/bCw9ufRd/P7wPkojQEZjx0CQRoVB1daGmd3WHqVoblVBUVlnN5zWQWQ+sAsfBAT9v3mbwNWm/HsWh/2zHxKW3YNLti804Ow0Wm91nM+A5a+6Fu5cn9rz+llFRKEMpSc8EQJsOmiokMQH2jo7IPDZ03rMWbTpIrhU1awbib56LupIy+MqCKdKKEEIIsUFmLT7X1dUhKChI94fA0qVLUVlZOcRVhGj8mTE8cOczoFnezsTGXh6+PrrNBrXSfj0KJy4XsonXDXt8pgj8xKi/JnJDS15eYbbOZzeRaMivRU1BMbwDpWa5vy1z4bujpaFB72vamBdr6vS0NYFxMShMNS4WI/vUWfjKggfN33UTCiDy9zOp+KzLo2dgVYa1Slx0c5+YHw+xD5Zteg7lWTl4fcHt+H37lyhMvYwP7l6LQ1u3Y/wNU+EbOvCGe9fdcjOe2PMlpLH4J6+NAAAgAElEQVQTAADB18Wb/XMwhCQ8FFW5BVAz8PC8PCsHknDrLT7zfbwxZdkSnNu7H3XFpUZde3Dzx8g8fgpLnvkbnj3wLV4+cRAvHvkJPC9m3tt4Xp5Y8drf8bfvvsDrZ3/DKycPYfWWdzB91XJMv2s5zu75ESVpGYzcS6tNqURtUQnGTaDcZ1NETJ+MjtZWFJy/aPA1pRlZ4HrwdftwkLGNy3fHbX9/CmUZ2fj62U0ANA81CCGEEGJbzFp8fuSRR7B161aEh4ejrKwMGzZswNq1a815SzKK/Bm7MXDmM6Dd2Gt4BR47Fgt8by/Ir+l8zjtzDm1NzYi+8YZhjc8koUSsy3e+lry8Eh6+PkYtbzeUu5cITYPkbwNATVExdT7rweXz0aLQ3/ms6uxEs1xBsRsm8hD7gOvBR/HldKOuyz51BgAQev3A0RshSZo/cIsumVB87u18dhulHVrjYqKw/JUX8NcvP0ZAdCRYbDbu/McmwM4OXzz5fL8u9KOf70RXewcm37FE73jBifFY8eoLqMjJxZtL70RDVTX8o5iPOnAVeCBi2mSjrhGHh6IiO5eR+5dnZMNTGtCna9eazFlzLwDgl48+Nfranu5ufPl/L+LMnh9QmHoZWSdOw00ogLQ3N324EhYk47qF89BQVY1j/92FY1/shKfUH4ue/Cs629vx03sfMnKfaxWnXaHOZxNFTJuM3D9SoO6N3zNEeaZm00HKfSYAMP+xR+DMc8POF15GcdoVtCmbEDKRis+EEEKIreGYc/Di4mLMmTMHLi4uYLFYaG5uNuftyCjDEwmhVqnQotDfMarVLFfoNgUz+V6eQrDtOf06n9UqFTKPn8L4G6aCxWajW91/w7iR5OTKhYs7D/Jy/SsI6svKwbbngO/t1e9zGS43kRC1Q3TCVRcUwZnnBjeRcMiHBmMJ12PgzmeAmQcoY5XI3w8AhvzevFZVbj4aq2sx/7GH4ejijD++29evQHLdLTdDXlGJkstXjJ5XU2/n82iN3UhYcJMmc7+5BQ99shnZp85AGhuN/z71d73vT21KJVJ/PoT4Bcn48Z3N6Ghp7fP6zevXoqG6Bv95cANUnZ0oSc80S87uwr89iutumYcXZy4w6D2K5ymCm1CA8qxsRu5fnpUDFosF39AQFF8y7oGJufnIgpG4eD5Ofv0dGqqqTRqjTdmE717+JwBN3m/svDnwCQnC5UO/D3t+0tho1BaV4JNH/syV/vGdzfCLDNc8xDMwdsdYJWkZuG7hPPC9vdDQu0EsGZrQTwKBxBe/ffKFUddV5ORDrVLBb3wY0n87aqbZEVsRNjkJaYd+R2WOJjM//9wFq1qNSAghhBDDmLXzubCwEFu3bsWkSZOo8EyMxvMUobleMWR+Y7NcAVcPPuzs7Ey+l4evLwDo3VQs7fARuAo8II2bYNSYbHt7BCXE4sY19yJ+QbLJc7uaNlJDPmDsRmXveb6M3O9q7l6eQxZrdJsOUvezDsfBAY4uLgN2PgN/fg8T42m/1wdaDTCQnp4efLr+KdQWl2g2xfthFwIm/Nnd6O7tidDJSTi374BJGbLNitHb+czisBGbPBvpvx/D+3c9iMrcfETPnoGze35E6oFDA153auf3cOJykbDgpj7HI6ZNhjQ2Goc++hSqzk4AQGl6JjzH+cOZx2Ns3i7uPMQkzwIAyAzsnBOHazYHLM9iqPO5d9NCa4vecOS64O63XkWLogGHt+1gZMyu9g7Ul5YztmmvNCZa7yqEsowsVOUVMHIPfbRRHle/P5ChBURrokqMXTmi6uhARU4eZElUYBzrnFy54Pt4oyInT3cs98x5iPz94CGmWBZCCCHElpi1+BwWFobDhw/jkUceQWFhId5//31MmTLFnLcko4ibSABl3eAZwwDQomgAi82Gk5ubyffS/hKrqOjfsZd14g90dXQgevYMg8eLnj0Dr546hEd2bMG8dQ/itheeAovDNnl+WkMWn3sLcAKGNx3kODjAxZ03ZOZzde+GXN5BUkbvb8tc+O4AgNbGgYvPLYoGRnLLxyKBnwTqLpVJHYllGVn48J6HsfXBvwJ2wLJNz4HF1vycJiyYBxaLhXN795s0r26VGi0NjaOyoz1s8iRwPfg4/+NBNMsV+OiBddj5/Mv4/rU3B72u9EomSq9kYvKyP6M37OzsMO/RNagrKcPZ//3457npmoKf/3jmNh5LXDQf9o6O6GrvgGxiokHXaIvETMVuNFTXoEXRAD8rixRYtuk5CP0l+OLJF4ZcbWSMqrwCRorPQn8/uAo8TMpfH66K7FyoOjsREEW5z8bwj4pAZ1s7qvMLjb427fARSGOjKY5qjPMJ1rx3VOX9+T2Ud/Y8gD9jsQghhBBiG8xafG5vb8fu3buxdOlSxMXFgcfj4ehRWkJHDMMTiaAcImMYgC7OgOvhbvK9BGJN96S+pcadbW3IOXUWUbOmGzxe8iOrIa+oxKfrn8LOF16Bo4sLxk0Yfu6ltsuzvkx/7IaiqgrdajXjmw66iTQFNGXN4MVnZU0t2ptbqPh8FW5v8Xmwgg5Tm2baGo6jI2Y/cDecXLkmjyH0E0NRWYWe7m6Tx8g5fRZ7//EufIIDMem2RQCApMXzkX8uFfVl5SaP21QvH5Vf14QFyWhRNCD71B8AgM62dqTs3Y+u9o4hrz2183v4yoIRlBALAJgwdxYkEaE4uOVjdKv+jDUqzcgCAMZyn+3s7HD97YtReOESMo+fMjgzVBIeirqSsn4xIcNRnpUDcYT1dD5PX7UcMXNn4ad3PjRqYzhDVOUXQBTgB7a9/bDGkcZGAwCKLBBVou7qQnlmDnU+GykgKhLlmdkmxZVd/PlXAMCE3pUKZGzyDgkEAFTl5euOVeUVoKleTtEbhBBCiI0xa/EZAKZPn47NmzfjwoULcHJywh133GHuW5JRws3TsNzgZrmmqOfqYXqRRyDxRVO9HJ1t7XpfT/v1CARiX/hFDt2tJo2dAF9ZMI5+9hWu/H4cab8eRbdajdBJhnXaDTXPtqZmtCmVel/vVqnRUFUDIcOdz+6engBgUCd6SdoVxN50I9xGYcenKbRxGi0Ng8RuKBrg4s5jpDvelkxZtgQ3/3UtEhcvMHkMgUQ8rAKxVvpvx5B39jxuemQ1IqZNhqc0ACl7fxrWmM1yBVxHWeyGI9cFUTOn4+LBX/sUiw2V+vMhtCqVeGTHFrx88iCWbXoWVXkFSN3fN66jvakZNYXFuqX7wxUy8Tp4SgNwavce5J45B4HYF0I/yZDXScJDUZ6Vw8gctMoys+EbEmQVP+9CPwkWPPYILh/6HUc//5rx8atyC8DmcOAVGDCscaQxUWhrajapi5YJJekZ8IsM162MIINjsdmQRIShpHcFg7HqS8tQmpGF2LmzGZ4ZsSU+wUHoaG3rF4mXd+YcdT4TQgghNsasxeeCggJs2LABx48fR1RUFJYtW4bvv//enLckowSLzYarwANKA4rPjHQ++4kHzYy9cuQE1CoVogyI3rj+jsVoa2pG6oHDADRFlNL0TMgYKT6L9UaDXE1eXsF857OnEADQOETnMwDsef1tODg7Y9nLzzE6B1tlaOez5lzbzH0ef8NUPLZrh1EZjBxHR9xw718AAFEzp5l8b6GfGPVG5j0PZO8/34OzOw93/msTOlpbcengb8Mar7leDrdRtuFg9OwbYO/kiPM//mzS9V3tHfj00afw8+ZtuPDjQVw5cgK7X/qH3s71kvQMxjYdvP72xWhRNODyL78j98w5AIBs0uCdc45cF4gC/BgvPpdn5oDj4ADvoEBGxzXFtDvvQE9PD75/7S2zjF/VWyz2CQke1jjS2GiUXE4f1gqH4ShJuwJHF2d4B0stcn9b4x0cCAdnJ11etikuHfwV42KiKNt3DPORBaG6oLDfvgu5Z87B3cuT9hchhBBCbIjZis8sFgvbt2/HkiVLsHPnTrS2Gr5kNTk5GVlZWcjNzcXTTz/d7/W7774bNTU1SE1NRWpqKu6//37da6tWrUJOTg5ycnKwatUqRj4XMvK4HnywWKwhM4YB6DZyG07hTiDxHTBHGQBaG5Uoy8hGYFzMoOO4uPMQM3cWLvx0EJ1tbbrjOWdSEBAdCUeui8lzNGSegGbTQaY3HOR5igDAoE70msJi/Pj2B4iYNrlPtutYxdV2Pg+S+awtPttqREPyI6vhFxmG+97/FxxdDPsen7R0IXgiIfJSLiAwPsakjeUcXVzgKvAY8mfCUBXZuUj5309w4nJx6Zff+vwMm6JZrhh1KwASF92MutIyFA8j/qDwwiUc+uhT7Hn9bXz59EYUXbys97zS9Ey4e3mC5+Vp8r0AQDTOH1GzpuPsnh+h6uxEbVEJGqtrh1y2LQ7TbjbIcPG5dzw/C0dvOPPckHTrAqTu/8Wg93ZT1BYWQ92lgk+w6YV2R64LfGTBFonc0NIWUaUxxm08PFZpVyyUpmeaPMalg5rojRjqfh6zfIID9W4mmnf2AgAg6Lq4kZ4SIYQQQkxktuJzd3c3Zs6cafR1LBYLmzdvxrx58xAZGYkVK1YgIqJ/59OuXbsQFxeHuLg4fPLJJwAADw8PbNy4ERMnTkRSUhI2btwIvo12Eo51vN5O26Y6+ZDnajufXQWmfa3tWCx4+PgM2T1Zmp4Bv8gw2LEG/rG5btHNsHd0xOnde/oczz2dAjaHg5DEeJPmqCWQiFFfPnjnc31ZOdy9PMFxdBzWva7G8xRB3aUyeCOqkzu/Q+aJ07jlifVjvjPFzVMItUqle0iij/bf1RaLz0EJsZCEh+LcvgPwDpJi5RsbYWdnN+g1bHt7zLzvLhScv4gf394MNoeDyOmTjb63wE+bgc5M8RkADvz7I+SdPY9jX+wa9lhN9XI489yGnXdrLRY89ghCkhJweteeoU9mQOkVTeEqwMTcZ2eeGxY89gie+O4LqDo7ceqq9+XcM+cQnBg/6PeqbrPBLGY2G9SqKy5FR2srxOGWLT5PWnoLHF1ccPTznWa7h1qlQm1xCXxkpm86GBA9HiwWyyKbDWrVlZShuqAI0+68w2ajN4IT47F6yztY9/lWPL77Myx57gmzRb8EREWipaFxWJFI8vJKlKRlIPYmKj6PRS7uPPA8RajO6x+1U19Wjo7WVngHSkd8XoQQQggxjVljN06dOoX3338fU6dO1RWK4+IGf0qdlJSEvLw8FBYWoqurCzt37sSiRYsMul9ycjIOHToEhUKBhoYGHDp0CDfddBMTnwoZYTyRptPWkIzhrvYOdLS2mdz5zPf2AtueM2T3ZEl6Jpy43EGLqdffthiFqZdRmZPf53jRpXR0tLYNK3rDVeABRxfnQeNBAOg+DwGDS1V5nkI01df3W/o4mF3PvwK1SoXpq5YzNg9b5O7piaZ6+aDLxW2583nqytvR0tCIb1/+B/b96z1EzZyOmx5dM+g1iYvng+/thUNbt6PsSiYaa2ox3oToDW1mr5yBzGetpno5tty/DpU5eYyMBQBuNvh1vVbyww9g5n134uTO73Dks69G5J7lWblQd6ngH2V87rObUICn9+3EjHtW4uLPh/HPRSv7vHfmnjkHN6Fg0KKoJDwUTfVyg1bgGKOnpwcV2Xnwixh6DwFzYXM4mPqXO5DzRwoj3+uDqcovhE+w6cVnaWw0uru7UZJ2hcFZGe/Avz+Cd5AU191ys0XnYQq2vT1WvPoCxGEh6OroQLNcgSnLl+Led//B6INqLf+oCJPznq928efD8B8fYVA+OxldfEI07xlV+f07nwGgtqgUnlL/kZwSIYQQQobBrMXnyZMnY/z48di0aRPeeustvPXWW3jzzTcHvUYikaC0tFT3cVlZGSSS/r90Ll26FJcuXcLu3bvh5+dn1LUAsHr1aqSkpCAlJQWi3kInsR5uot7O51rDlgK3NDSYnPks6N2cTz5ER3Fp7x9SA22AFTFtMrwCx+H07v/1e03d1YWCCxeHVXzWRmkMNU95meZ1AYObDvJEIigN/FpoNdXLkZdyHrKksb0jOc9TBOUQWdm64rOHba3U4Pt4I3r2DJz5bi+62jtw4qtvceb7HzDrvjvhHSTVe42DsxNm378KxZevIOf0WfT09ODKkRMInzoJHAcHo+6v/R6vLxv8Z8JSdF9XG990cPpdyzH3oftx5rt92GOmbGB9VB0dqMzNN6nzOWrWDLgJBdhy/zrsfP4VNFRV93k990wKAEA2ceD3ZEl4KCoYjtzQKs/MhjhcNuQqAXOJSZ4FvreXWTYZvFZVXgEEfmLYO5lW5BwXE4Xq/EK0N7cwPDPjpP16FMWX0pH88P1mKdiaU9KtC+Dh64Ovn3sZW1evx3/WbMC3m/6J8GnXY/WWt4cdCXY1B2cn+IQEDStyQ+vSL5rc/WgD9tsgo4t3b1RPVa7+4nNdSSlEAVR8JoQQQmyFWYvPs2bN6ve/2bMHXz6n7w+xa7stf/jhB0ilUsTExODw4cP47LPPDL5Wa9u2bUhMTERiYiLqDOiuJSNLF7tRP3TsBqCJLeCaWLgT9m7OVz9E53NtUQnampoRoKcLz1XggTteegbVBUW6nMJr5Z5OgU9woMn5pdpNBIfq0K4rLQMAiPz9TLqPPjwvEZS1tUZfl3fmPEQBfuD7eDM2F1vj7u2JxprB/+3alE1Qq1TgmhgdYylTlmsyvU/u/HMj2R/f/gAdrW2Yt36t3muWvfw8+D5e+OndD3XHrhw5DkcXF4QkGRdLI/SToE3ZhDal0oTZm5/2/cvVhnOf7VgszH5gFbJO/IHdm/5h1OoHJpReyYTf+HCji7Th0yahvqwCBedS9b7eWF2LmsLiAXOf2RwOvEMCGc971irPzNGspBngIY25Tb9rOaoLipB94g+z36sqNx8sFmvAB1KDsbOzg3RClEUjN67207sfgu/jjanLl1p6KgZj29vjxtV3ozD1MnJOn9UdP717D776vxcRGDsB8zc8zNj9JOGhYHM4w9psUKuhqhqNNbW6QiQZO3xlwWhrakZDdY3e12uLSyGQ+ILN4YzwzMYeZ56b2SJ6CCGEjB1mLT57eXnh448/xv79+wEAERERuO+++wa9pqysDP7+fz7J9vPzQ0VF32KbXC5HZ2cnAE0ROSEhweBriW3geYrQ2qiEqvfrPJQWRaPJsRsCPzG61ep+nXHX6unpQdmVLPhf04VnZ2eH5a++AGeeG7548vkB55zzh6bTLtTE7uc/i8+Dd3k2yxVoUzbBUxpg0n304YmERnc+A0Du2fMAgJCkBMbmYmt4nqIhl+339PSgpaHRpmI37J0cMXHpIqT9erTPz05roxJHdnyJ6NkzEDBhfJ9rZt1/F2KTZ2P/e1uQn3JBdzzvzHl0tLZi/MzpRs1BIPFlNO+ZadrOZ1uO3ZDGRsNV4IEze34YNDrGXMozc+DC48Hd28vga9j29pBNvA5ZJ04Pel7G0ZMImzJRt7z7at7BgeDY26Oc4bxnrayTf6CrvQMz7/2LWcYfjKc0AP7jI3D6mz0j8jChKl+T2eoTEmz0tV5BUjjz3FB8yTqKz/nnUpF54jRmr74bTm6ulp6OQSYtvQV8H28c/PDjfq+lHjiE3DPnII2NZux+2pic0ivDLz4DgLysgvFNlIn18w4ORHV+/7xnrdqiErA5HEZX+ZH+BH5iPLv/W6x8baOlp0IIIcTGmbX4vGPHDhw8eBBiseYXg5ycHGzYsGHQa1JSUiCTySCVSmFvb4/ly5dj3759fc7x8fkzy/aWW25BZqZmad/Bgwcxd+5c8Pl88Pl8zJ07FwcPHmT4syIjwU0kNCpnc1ixGxJfKCqr0a1SD3luSXoGxKGyPvEAM1atQMTU67H3n+/1y3q+WlVuPprq5ZBNMi2GQuDni6Z6OTrb2oY8t7a4FJ7jmFmOyOZw4CrwQKMJuafVeQVolivGbPGZ4+AALt99yM5nQNO97+phO0XKiOlTwOW74+TO7/q9duyLXVDW1ffppouYPgXz1q9F6v5f8Pv2L/ucr+rsRNaJPzD+hqlGdbgK/SRDrgSwpOZR0PkcNWs6VJ2dI9Ihq492JYcxBYag+Bg4urgga4g5//rxZ2hvbsGtzzze7zVJuAwAzNb5rKytw4mvdiNh4Ty9xW9z0kYYpB0+MiL3qy8tR1dHh0mfZ1B8LACg4MJlpqdlsp/e+RCOXBesePUFi8WmGIrj4IDZD9yNgvMXkdv7APxaZZnZ8AkOMjr2aCABURGQV1SiuV7ByHj15RVUYByDfEOCUJWnP3IDAGqLSwAAnuOYa7QgfXEcHXHP26/DxZ2HuHlzIA6TWXpKhBBCbJhZi88ikQi7d+9Gd2+3lFqthlo9eIFPrVZj3bp1OHjwIDIzM/HNN98gIyMDL730EhYuXAgAWL9+PdLT03Hx4kWsX78e99xzDwBAoVDg5Zdf1mU5b9q0CQoFM7/8kpHFEwmhrDO807Z5GIU7gURscAGrJC0DbHsOxL2FCa/Acbj5rw/h8qHfcfqbPYNe29PTg9wz50zufBZKxEN2PWvVFpcw9gv5n/nbxhefe3p6kJdyweg4hdGC59W7caYBxefmeoVNdT4Hxcego7UNhamX+r3W2daGw1u3IyQxHrc8uR6PfvEfPLD5TVRm52HXxtf0jpdx9CTcvTzhGxpi0P3t7OysvvO5s60dHa2tcBuB4vNN6x7Ew9s/BNventFxo2ZOR+6Zc+hobWV0XEPV924mKTSi+BQ2ZRJUnZ3I6115MZDWRiX2//sjhCQlIDa5bySYODwUHa1tqCspM37SBvr1ky/Q0dyCeesH36CTadGzb0Dx5SsDLmdnWrdajZrCYviEGB+dEJQQA2VtHepLzfd1MFZlTp5uc9WB4oWsRfz8ZLh7e+rtetYqy8gC255j8HvvYOzs7DAuJpqRvGcteXkl+N5etOx/DHEVeoDrwdetmtCntljznsBUowXpb+nzT8A3LAT/fXoj2pRNmPvQ/ZaeEiGEEBtm1uJzS0sLBAKBblnnxIkT0djYOOR1Bw4cQFhYGEJCQvDaa5pCxcaNG/HDDz8AAJ599llERUUhNjYWs2bNQnZ2tu7a7du3QyaTQSaTYceOHcx/UmREuImEaDKi+NyiaICTK9ekwotA4mtwUVe7jFS7Adbctfehq7MD3276h0HX555OAc9TZFJ+oTFF8triUvB9vRnZFInfu9zdlM5nQBOp4OHrAyGDGdS2wt1TU3xuHGLDQQBoVihsasPBoPhYFF9OH3DFwB/f7kVdSRlmrFoBR64Lfnz7A2x54FF0tXfoPb/gwkUAms3FDOEmEsLe0VFXnLRWzXLzP1S4/vZbMWfNvQi+Lg5TGMyi9Q4OhCjAD+m/H2dsTGM1VFVDrVJB6Kd/82B9wqdOQsGFSwatEjnz3T6UZmRh4ZPr4eDsrDsuiQhFZU6eWaNG2pRK/L79S0TNnA5pjCb2wI7FgqvAA+7enhD4iRnf2I7v442A6Eik/XqE0XGHUpVXYFLnc2B8DAou9H/AZWknvvoWp77Zg9kPrEL8gmRLT2dAEdMnQ15ROeiDmLKMLACAX0TYsO8XN38uBBJfpP16dNhjacnLysFis+Fx1apHMrr5BGveKwbrfG5TKtGiaIBoDBSffUOD8fKJgwNueG4OE5csRNLiBTi8dTtS9/+CY1/sRPTsGZCEh47YHAghhIwuZi0+P/7449i3bx+Cg4Nx4sQJfP7553j00UfNeUsyCtjZ2cHdSwSlAQU7rZYGzUMNLt+46A2OoyPcvTwNLuo2VteisaYW/lGR8A6SIuamG3Hiq2919x+KLvf5+iSj5mnHYoHv621453NRCVgsFkT+hhdsBuLd261WU1Bk0vV5KZo/emUTbTt6I35BMm7+60NGLbPWbi5pSITMSBQpmeLkyoVvWAgKBykKqVUqbLnvEfxryZ14c8md+H37l4NuDCgvq4Cyrl5XhBuKthPW0J8JS2mql5u18zn0+iTc+uzjyDh6Ejmnz2LOmnvhzOOZNBbXg6+LZAA0kRsAkHHkBCNzNUW3SpPHb2jnM9/bC76yYGQdHzzvWaunuxt7XnsLfG8v3PjgPQA0/w2ShIWaLXLjase/3AVlbR2WvfwcHt7xIV49fQgvHd2Pvx/eh+cOfIe12/7N6P20X1Mmi4OGqMzNh4evD1zcDf/e9PD1gYevDwp7H0xZmz2vv4W8s+dxx4vPIHLGVEtPpx8Wmw1ZUgJyTp0d9DxFRRVaGhrhFzm84jPH0RHzHl2D0owsXDxwaFhjXa2+9z1e6E/RG2OF9kHVYMVngNmIOWs2edlSuLjzcOPqe0bkfmx7e8xbvxZ5KRfwy0efAgCO/XcXWpVKzH1o8L2bCCGEkIGYtficmpqKGTNmYPLkyVizZg3Gjx+PtDTr2DSGWC83kRAcBwfUG5Hl2qJoAKApnhhDINZ00hiTG1uanoGAqEjMWXMvOtvacOzzrw2+tqGqGrVFJUZHb7h7eYJjb29E57M2C2/4v5T7BAeho7UViooqk66vLSpBY3Wtzec+z7znL5j9wCosfHK9wdfwjOp8boAzz81sO7fbsVgYf8NURnI9x8VEg8ViDVp8BoCG6hpU5Q6cg36t4kvpBnc+C3o7Ycdy57NPSBBWvfUqqvML8d+n/o59b/4bTm6uuPHBu00ab9lLz+Ked9/ATeseBKCJ3Ci+lG5U/r451JcZnvkaPu16ABhys8GrFV9KR8renzDj7hUQjfOHQCKGkyt3RIrPnW3t+OndLXATCcFmc3B2z4/4/rW38M2Lr+P4l98gMG4CZCZGNekTfeMNqMzNR11xKWNjGqLoouZ3v6CEWIOvCUyIAQAUnLe+zmdA82Bkx2PPojI3H/e8+zoSF8+39JT68BsfDmeem+6h92DKM7PhFxk+rPtN+8vtEIh98cOb7zO6kaW8VPMer910mYx+PrIgtDYqh1wBWVtcwujm2tbIwdkJ8TfPRXtzC8bPnAbvIKnZ7xl7041wEwrw67jcR7AAACAASURBVLYdutU/7c0tOPr5TkTNmgFJBHU/E0IIMZ5Zi8+33XYbnJ2dkZGRgcWLF2PXrl2Ii4sz5y3JKCAQa3Y1V1QY3tGoLT4bG1ugLWgYkxtbkp4Jr8BxiLnpRpz8+juDu561cv5IQXBinFH5hQJdl6dh86zrzcJjYjmijywIVXmFw/pjMvfsOZsuPju5cuEjC4aisgoz7lqO2asNK+65e3miq71j0I5frRZ57wMUMxUqk25dgPve/xfu/OcmsNjDy84Mio+BWqVC8eUrDM1Oo/hSGjzH+RtUrBVKfNHd3W3yQ5GR0lQvN8uGg0J/P6z5z3vobGvDJ488gY7WVlTm5CPlfz9h6srbjd6gKzA+BuNnTkNtUQnmrLkXCx5fh4DoSItGbmjJyyoMjt0In3o95BWVqDZypcZP73yIrvYO3Pp/j+v+sC7PNH/xGQDO7duP5yfPwft3PYj/vfEOTn79Lc58tw8/vPUBGqtrdR3Zw+Uq8EBQfMyIbTR4tZK0DHS1dyDoOsN/BwyKj0WbsgmVRjzAGmltSiW23LcOeWfOYfnLz2PW/XcZfK0kPBRz1t6HtR+/j5eO7scdLz7DaGEp9PokdHd3D7jR4NXKMrLgIwsyOTOey3fH7AfuxpUjJ5CfcsGkMQbSWFsHVVeXUbnvxmLb22PC3FmYs/Y+rHx9I+7f/CaWPPcEpq9aPqJRB9bCw9cHS557wuQHXxxHR0xcshAbdn6KWfevMvp6aUw0StIyhjyvtqgUfG+vPpFJI2HCnJmYuGThiNwrZu4sOLly8fVzm9DZ1o4b7vmL2e857S+3oyq/EDmn+753HP/vLnS2tSNxkXU9aCOEEGIbzFp8fuGFF9Dc3IwpU6YgOTkZn332GbZs2WLOW5JRwEOiKT4bs5xe1/lsZOyGsLeTRm5E8bk0XfMLcVd7O45+9pVR9wOAnNMpcHRxwbjo8QZfI+z9N6kvM+zfpKO1FY01tQNuOugTEoTpdy1HxLTJEPiJB42S8Blix3FD5J+9ADehwKSsa2swbkIUWCwWvtn4Gs79cAA3r1+L6265ecjr3L1EaKwderNBAGiWywEY/wDFEHZ2dph+13K0NDQievYMLH3+yWGNF5gQg/LMHIMydY1RdCkdADBuwtA/GwI/CZQ1tVB3dTE6B6Y1yzVZ3sbEtQyF7+ONtR//Gyw2G1tXr++zcdzPH/wH6i4VFjz2iFFjLnx8HRqqa/D2Hffg4s+HMfNezR+4V34/xti8TVVfVgE3oWDIAgOLzYZs4nXIOvGH0fdoqpfj4IcfI3zqJMy8706oVaphv+8Nl7qrC7/v+BIhifGQxk4Y9njjb5gKFpuNyxYoPqu7ulB8OR3BCUYUnxNiUXjxsllzt5nQ2daGT9Y9idT9v2D+hocNKrD7R0Viw67tmPvQ/XByc0XumXOIu3kuntr7NVa99SojK2DCrk9CWUYWWhuHfvhZmpENjr09fGXG53IDwI1r7oWDsxN+fPsDk64fTE93NxTllWbrfOZ7e2HdZx/h7rdexdyH7kdgXAzcPT0Rd/McLHryr/jrV5/g3vfeGPUdtgDg4OyMm9Y9iKf37cSU5Uux6s1X4O7tadQYcfPm4IVf9uCOl56FpzQANz54j1FxO848N/jKgvVuZnyt2hLNCg5RwMjtKeIVOA5/eeNF3PHSs1jy3BPDfpg/lKRbF6KmsBjpvx3D2f/9iPgFybpVdeYwLiYK/uMjcPLrb/u91tHSivzzqQibPNFs9yeEEDJ6mbX4rFZrNqKaP38+tmzZgn379sGBgSXfZHTTdT5XGt7RqMt8NrJrVCARo7OtHU31coOvKUnPRFd7B479d5fRXc8AkH/uArrVaqNynwUSMbq7u9FgxL/JQFl4diwW7vrXy1j01F/xwIdv4bkD32Hl6xv1jsH14IMnEg67CJN75hwATfHDHNxEQrOMqyWNm6Dp9L10Bbv+/ipKr2Ri+l3LhryO52l4dnmztntfwHzxOWzqJPgEB+J/b7yNQ1u3Y9Jti3DTow+aNBbb3h4BUZG6DQKZVHolC+ouFcYZkPss9BMbtWLBUprr5WCx2XAx8sGYPq4CD8QvSMbabf+Gs6sr/rN2Q78OX2VtHX779AvEzJ1lcNfahDkzMS4mCgc/2IbOtjZ89cxLuHzodxSmXja6g9gc5L3RKkN1c3sFSeHkyjWoaKHPyZ3fojI3HwFRkagpLIaqs9OkcZh05ru9aJYrTI5S0WJx2EhcvAB1pWWozMljaHbGyT+XCnG4DE5urkOey/XgwztIOmS0j7VQq1TYtfE1KCqrsPjpDbBjDfzrtZ2dHZY89zc01dXjxRvm491l9+K/T/0dr8xdjCOffaX52b1+eFErji4uGDchql/n4kB0mw6aEL3x/+ydZ1RUVxuFH3qTIkhHmhRFRFDsXaOxd429pmg0GmNMoiaaqInRGEvUxFhj7DX23hUEAVEREBDpICJdevt+IERkgBmYAcx3n7WyVtbcc+85gwxz7z7vu7e8ogKug/vx4MIVXoRFSHy+OCTFiG+9IwnWri58fmgXBlYW/P3ltyxs25Mf+w5n7ejJfNfpfb7r/D5n1/+BTVtXFvyzj/dnfST1NdQ1KurqtB7Yl0m//sjS66fp/clU/K7e4I/ps1FQUmLsiiVib54qKCkxbOEXpDx/we9TP+W38R+hoq5G57EjxV5PifVWmO+jKscmhBdbzNVW6KCcvDwfLFtMTmYWd/YfodOYEUxZtxIVdXWZzGdgZYF1a2fu/XMagJt/H0BeXp4uE0bLZD6AzuNGkZX+Cu9T50UeD3LzxMDKgoYmQgCogICAgIBkyFR8jomJYcuWLYwePZpz586hrKyMfCU35AICALqmxqQnJpGXnSP2OZmpaRQWFkpc+axrZkKSBPYeANnpr1g5cBQXN2+X6LwSstLSifJ/IlE7o2ETK5KiYynIzxf7nIq88FwH98PIxpqD3y5n44SPCbztTtMuHUQ+XBi9rlSuqficHPecUB9f2g6Tfpuifcd2fH/9DFPW/yyzm2ErFydig5+Sk5lJYX4BflduYtrUjgZ6lW92aBvok/pC3MrnZACZ+AN3nzSOlPgXPLh4lQubtnL3yAl6fzyVZl06Snytxs2boaSiQtj9qh8MJSU/J4eYJ8Fi+T7rmZlK5NVeV6QnVu/fVVFFBfMWDnQYNYyRS75m3qG/+OHmOcav/B5ldTW2fzq/QluIG7v28TIymuGL5ldZQamgqEj/OTOICwnF69Q5oFhI2/3FIjZPniHRmmVFySZDVW33pva2QPXtMgrzC/hn5dria9SC37M45GZlc2vPIZp16UiLXt0wtmtCI3Ozcn+vHXt2Zcb2jSLtSeTk5Rn301KsXJy4uu3v2lp6OUK9fZGXl8fKpWWVY0vG1Fe/Z1HkZedw+tdNmDa1o92IwRWOaztsIOaODpxZu6m0awuKN9HPbdhCdkYGzbt3qdFamrRphYKSIsF3Kw8bLCEpOpbMtLRqic9Wzk6oa2nJ1M4lMSautFNNWjRp04oZ234jKy2dDeOm8/DiVfJzyt53ZqamcW3H36wcMArf85fpM2Mag+b/d4LTje2aMP/Y34xbuRRL5xbcP3uJDeM/ZN833/P0ng8nV63Dtr0r3SaNFet6Tr17oNFQhzPrNhPq7Ut8aBiPr9+i8/jRKKupinUNK5eWFOTll3YZVkZiVLHFnKxCB+07tWfimhVYuRR3nnQaMwJL5xacXLWef1au5fiPa2jWtSPL7lxgzr7tDF4wR+LnkMpoO3QgBXn5pUJwUnQsjy5fp8OoYag20JDaPCVo6TeiZe+e3PvndIWdbUFuxZ1F9p3aS31+AQEBAYH/NjJVgkePHs3Fixfp27cvqamp6OrqsmBBzdq9Bd4tquMfqGtqLJHlBkBhQQFZaekSBw7qmZpIZLlRQmp8Qo1agYM97mHewgEVDfGqJUyb2REdGCTRHC/Do9DU0y1TZaaookLfWR8R8cgfr5PnCH/ox8OLV1HX0sLAyqLcNYxsmwA1F58BPI+dRt+iMU0k8PwUB0sXp9JK8q9OHCi1C5AW8ooKmLdoTvgbVTglD/S2bV0rPVfLoJHYYW2vSjyfG0pXfDaxt8W2vSt39h2mML+4G+Wfn34lLiSUEd8tkLhix6pV8UNYdatLqyL8oR+NmzertJVVVbMB2ob6vAiLlMkapMmr110VVbXJyisoYNO2NSO++4ovj+/lJ48rzN2/g5FLvqJln55kpaVzfuOfrPtgCsvfG0L4w4rDe/Nzc/ln5a8YWFnQddIYkWNUG2jQbdJYvjl7GH1Lc86u+73c3zRphobVhJJQyap8n02b2ZGXnVNaDVcdQr3uc/Db5Vzfubfa15A2bgePkpmWxpT1P/Plsb0sPHuE2Xv+xMim2CKh87iRTF63Ett2rnz85/oyGx1ycnKM/mEhLv16c3rNxtIKurog4pE/+bm5Yn0HWLduSV5ODlH+gbWwMunx8OJVnnrdp/9nn6CmpVnuuJqWFgM+/5RnPg+4f/ZSueMFeXkEuXnSvFvnGln12HVoQ05mVmnQozjEBARXK0iseY8u5OfmEuQuntBdHZKiY9BoqCP2PVNVNNBtyIRVP/AyMpoN46ZXWbH9KimZA4uWcWf/EbpPGcdgCYKH6ytOfXry2Z5tKCops+XDz1j+3hCOLV9N5BtZDp7HT/Po8nX6zZ2BsV2TKq/ZYfRQEiKiePq62w3g2o49aOho0254xRsyb2Ll4kTMk2Bys7KrHJublU3K8/gKLeZqgkv/Pkzf+AtO73Vn9t9/8uEfa+k/dyaBt93xOXMBALeDx9g0eQY3dx8gPzeXzuNG0W+OdDZt5RUVcB3Sn4BbbmW6M6/t3IOaZgM6SVBNLi4dRg9DTkEetwPHKhzzIiyC5LjngvWGgICAgIDEyFR8zsrKIjw8nH79+jF79myMjY25fPmyLKcUqEfoNTZjhdsl+n32iUTnNTQxrlZFY0ZyiuSBg6bVm6umhNz1QkFRkSauraocq6rZgEaNzSSu5kuIKBZg9M3/rQjpPGYEOkaGnF23ufS1sNcPp5bO5a0OjGyKE8fFFVAr49Hla2SlpVdaEVYdTOya8CI8ktWDxxDk7snAL2bj0q+3FK9vi4q6WhnxOTowiMzUtEqtU1Q01FFRVxfbdiM7PZ2C/HyJKmTlFRVEChxv0m3SWHIyM7l79GTpawX5+Rz+fiXahgb0nyvZg5J1K2fin4WXqdiTJhEPH6Oirlbpg66JnQ0AsUH1ozq1Mp6HhgHFFeMV0fL9Xiy5eoqZOzbReuD7JMc959qOPeya+w0r3h/Gd53fZ8tHn3Fl619EBwSJJQo/ueOB39Wb9P5kKjqGBmWO6RgasOj8MQYvmENy7HO2z/qSwNvuNXujMiQzNY2s9FdVVz43tSM2+CmFry2/qovXyXN17vf8JtmvMtgwdjrbZ33JX59/wz8r19KosRlfHN7NR3+sY9jC+QTcuM0f02ejpd+I6ZvXoKKujm07Vz78fS1thw7kwuZt3KhGRoE0yc/JIdIvQCzx2apVSyL9Auq9p7soTvy8DjUtTcav+oE+M6fTe8Y0en00mZ7TJzH2x+9Q09Lk+E+/Vni+//XbaBvqY9rMvtprsO/Yjmc+vhL9/KIDnmBiZyOx37RDt86E3PORegbAmyS+LkiQhu+znJwc435agpqmJnsWfEv2qwyxz/1n5Vpu7T1Et0lj6TJedtYHsqbPjGlM/vVHYoNCWPfBFEI8vSv8Xjnyw8/kZWXz3kdTKr2mYRMrmrR2wePIiTLXinj4mFBvX7pNHitWJ465o4NEm9sJ4VHoW0q38rnTmBGMW7mUsAePWNZrMGfWbcaiRXOKCgs5umx1mbERDx9zbsMf/D71Ux5evIpT7x5S8Wy3cHJEU0+X+2cvlnk9JjCYgJtudJs0Vqp2Hyrq6nQeO5KAG7dLN3wrIsjNE9t2rhIFpwsICAgICMg8cHD37t3o6enRqFEjdu3axeLFi2U5pUA9wq5DG5TVVHnv4ykM/mquWOfIycnR0MSIZAmtMKBYfNbQEV98VtPSRE1Lk8Q6EJ/DHz4mJzMTp97dqxxb2kouYRt4QkRxEEvJTbmqZgN6fTSZwDt3CfX2LR33MiKKjOQUkYFWxjbWxD0NlWjeisjLzuH+uUs49e6Bmpb44TNVYWxnS1zwU1LiX7D7i0WEP/Rj+OIvpRbIUtJuGfbgX/G5qLCQEE9v7Crx5dQ2KA7pSRVTuC8qKpJoA6Vx82Z8deIAK9wuMffADt7/9MNyIqNmIz1c+vXG8/hpstNflTkW+cifO/uO0HHMCCzF8FiG4s+npUsLmfqwhpduhlQcsGZS+pkIkdk6pEVGcgqxwU+xaVvxRlO/2R+TkZLK7i8WsbRbf3bM+pILm7by+NpNkmPF93l/m5Or1yMnJ0/ftzYA240cgpqWJhsnfMzvUz8l8JZbteeoLZKiq/Z8NWlqW2/sMqTNy8hoAm+54Xf1Jnf2H2H1kLHcP3eJpp3bc+fAUf6at4in93z4+8tvMW1qx5Krp5ixfSOmzew4uXoDl7fsrOu3AECojy+mzewqFU1UNNQxbWonE1/52iAu+ClXtu3GvkNb3v/0Q/rO+oj+c2Yw4POZNO/emWs79lTqux14253CggKa96ie9YaOoQEGVhZi+z2XEB3wBEVl5dKKenEwsLJA36Ix/tdvS7pMiUgq7X6oufjc66PJ2Hdqz4lV64gLlvz+5uSq9YQ/8JP6RnptIK+gwOjvF/L+rI+4d+IMf0yfXWXmSWZqGneP/INT7x7ovg6/FkWHUUPJz83F6+TZcseu7fibhsZGtBrQp9K5TB3sUVJVEcvvuYSEyCipVj7bd2zH8MVfEnDzDttmfkF6YhLXd+7lx77DWT10LCnP4ys89/65y2joaEuU6VIRdh3aUlhQQLBH+c/x5T93oqGjTccPhtV4nhLajxyCurYWV7dXbc0U5O6JmmYDiYLTpYGSqorUuh8EBAQEBGofmYrPY8eOpU2bNnz//fd8//33tG/fnvHjpdsSL1B/sXJxIi3hJbf2HKLbxDGM+LZqyxVN/UYoKimRFCO54JKRkoJGQ/G91kpuopOiJRe6a0pBXh5eJ8/h0r9PlSKpqUNx9VPME8lsNxKjYigsKCi9Ke/98VTUtbU4t/6PcmPDH/iJrHw2tLHieYj0KgA9jp5ESUWF1gMrfwARF9UGGuiZmRAbVPwgX1RYyIFFy1BUVmb0skVSmcOqVUuSYuNIjS/r3Rzs4YWOkaFIuxJ4Q3wW0/MZikMHq/KRlpOXp9eHk/lsz1YUlZW4vHUXBXn5vPfxFD78Y22ZVu0OI4cgpyDPnf3lU8sBzm/8k5S4eEYu/Vqs9RnaWKOupcUzGYrPKc/jSY1PwLIS32fTpnakJyaR/jJRZuuQJk89fbByaSnShsjAygJ9S3PcDhzl0eXrEnndV0Vy7HM8jp6kVf8+aL3+fZSTl6ftsIEEuXtWat1R30iMjqnUdqOhiRHqWlrEvgMbEtIgIyWVg98uZ0mXvvzz06+llimBt9w49N2PRAc8Yf+iZazoM4xbew7W8Wr/5Zm3LwqKili6VLy5ZNe+DQqKihKLp/WJi5u3scC5M/NbdODLlp34yqULX7fuxjdtunN+45+VnpuZmkbYg0fVDui1alXsl/30no9E55VYe5k5iF9xXbLGwJuy3cBKfH2fVt3QQQMrC/rPncnCc0fo99knPLhwBY83uoEkxff8ZYxtm1T4/S9LzJ2a03ncKImrTpVUVZj62yrajRjMpS07OfTdj2JXxt/ef5SiwiK6TBAdtKysporroH48unxdZBD3kzsexAaF0GPaxErtZKxfe72HSyI+R0SioaONurZ0ihp6fTyZ5Ljn/P3F4jIe4NmvMsrdB75NsLsnGSmpVYrs4mDfoS2RjwPKFQ4ARPoFEOTmQbfJ41BSVanxXApKSnSbNJYQD28i/ar22g7x9KawoKBWfZ/l5OT45M8NLL12mvc//VAQoQUEBATeQWQqPoeHh6Oq+m/AhIqKCqGh0qmiFKj/WDo7Eeb7iJOr13N95146fjC8yoAzXZPXgnC1Kp9TJfJ8LmnfrKvQstLU6vGjKh1n2tSO1PgEXr0OLhOXgvx8kmLi0LdojLGdDV0mjObukRPEBpUXZ8If+mFgZVHm5l3LQB91LS3iX9sGSIPYoBCi/ANpN2KIVK5nZFNszfBmFdnLyGjOrNtMs84daD+y5vNYOrcQ+SBU6vtcQXBkyaZCmiTic1IyDarwfO40Zjj9587A7+oNfh05iQsbt7Jp0icc+HY5xrZNcOjWCShuX+0wehhBbh6loTxvk5uVxZWtuzC2bSKWn6P1a1EjTMYVieEP/SoNHTS2txH5e1xfeerlg7KaKhZO5auEHHt2BcD/hmwqB2/vP4ycvBydxowAwL5TO3QMDfA8dkom88mKxOhYdE2NKxQuTJtWb5PuXUeU0ONz5gJ/TJ+Nz+nz5Ofm1sGqKib8gR8FefmVWm8069qJrLR0wh9IP9S0LigqLKQgP5/83FyxN5cCrt/BtKkdOkaGEs9n2tSOvJwcnodKtnGcGBVDemISTdpUbQdWgkO3zkQHBJES/0LSZUpEVloaWWnp1QodbNalI18c3k33KeN4GRHNoSU/sX/Rshqt59HrcEWnPj0lOs++U/saBS/LycnxwbLFDFv4BbP/2lJpJfLb9Jg6AYeunTiybBUXN2+TaN60FwncP3eJdsMHiexcc+7bGzUtTdwP/1PhNa7t2IOhtWWlFf2WLk68jIyushr7TRLCi7v8GkkhdNCipSNNWrtwc/cBiQK+SyjIz+fR5es079FF7IBFUahpadLYsRnBlfioX9qyC009XTqMqnn1s+vgfmgb6nN1h3iBtFlp6UQ88q9V32fXwf2watWS5yHP6DNzOovPH6PFe91rbX4BAQEBgZojE/H5t99+Y8OGDeTk5ODv78+uXbvYtWsXjx8/5tWr8ju4Av89tPQboWdmUtpCf27jFlLiX9BlQuUeebqmRgDVst14lZwikfjc0KR4ruoI3dIgKTqWR1du0GHUsEpbkE2b2lW7lTwhIhIDa0tGLfmazNQ0zq7/XeS48IePgWKPuRKMX7fexknZ+9Tj2ClM7GykctNaIpi+3cLsfvAYwR5eDJr/WZWVxJWha2qMtoG+yBbQpOhYXkZFV2i9oWXwWnxOEL86NyMpuUrPZyuXliRGx7JnwXdkpaWXvv7g/BUSo2Pp+eEkAFr06oaWfiPuHBBd9VxCwK1ir99mXTpVuT7rVi1JjU+QOBBUUiIePkbPzFTkv528ogLGNtbvVIVrqLcvhQUF2LRtXe5Y8x5diPIPrLKiqrokRcfy+NotOo4ehrKaKu2GDyY9MYmAG3dkMp+sSIyOQUlFBc0KOkVMm9lRWFBAXIiwwV2fyc3KJiogsHLxuUsHgu7eKw1I/X/k8evNqOpUP5s2s+P502cS//yKiooI8fDCrkNbscIONXS0sXRuQcDN2vlbkhQTJ3Hls1OfnkzdsIrnoc9Y3nso22bO494/p2vsJZ72IoGw+w9pKYH4rNpAgwmrfuCDZYsYt3IpiiqSV6w27dwBoyZWeB4/jYGVBV8c+VssexbVBhp0nfABj67cwOPICYnnBbj5935U1NXpMGpomdcVFBXp9dEkYp4EV2rJ9fDSNRKjY+g5fVKFYyydW0gcZlyabyIF640eUyeQmZqG5/HqB7PeP3cJFXV1HLpVr3MBwKZta+QVFEqLHEQR/uARIZ7edJ88Djn5qh/nDa0tWXT+KBNWLyvz91deQYGe0yYS+TiAEBEWHxUR5O6JWfOmaOiI33FaXVQbaDBg3izCH/qxceLHrB8zjcToWMb//H21QlIFBAQEBOoGmYjP3t7e+Pj4cOHCBX7++We8vLzw8PBg8eLFnD9/XhZTCtQzSlpqS0S7wvwC3A4cxb5ju0r9BEurkavhc5qRnIKikpLYrVi6JsZkpaWLbGmrLW7s2oealibtRor2DlRSVcHQ2rK0HVZSEsKjMG1qh0VLR06t+a2MWPkmUY8DKMjPL2O9UfLvFC9l8dn71Hnin4Uz+oeFqGo2qNG1TOxtyUxLK1d1VVRUxLEVv6CkokL/z6qfPP727/HbBN/1wqZNa5Htr9oG+mSlv5IohCk9KbnKDRQTe1uRVb+FBQXc+Gsfli1bYO3qQsexI3gZFU3QHY/K53yZSOTjgNKK6cqwau1cKz6skY+L2z4bN3cod8zA0gJFZWVig98d8Tk7/RXRgUHYtCsrPmvq6WLeojmPZeyXevPvg6hra9Fj6gSad+uM98lz1arqqkuSoos7VCryfDW1t+VFWIRUbUsEZEPQHQ/MnZqLrOo1bWaHln4jAm/V3wDM2uBlRBQvwiKqJz43tav25twTN0809XQxfh3qWhlNu3REXkFBZl0bb5MYEytR4KBL/z5MXL2MiEeP2fLhZ1K3aXp46RomdjboW4onenadOAZ1bS3uHjlB64F9mbXrd4mzKbpNLvYcPrp8Fb+OmkRCRCTjf/6hUksigM7jRqGmpcmVP3dJNN+bxAWHEuTmQedxI8tYSHUYPZRGjc04K8LS7U0KCwq4vmsfFk7NRW4+NbJojKaerkR+z1DcvViQn1/j0EEDKwua9+jCnQNHaxSeGebzgJT4F7j0r37wtV2HtmS/yiDCz7/ScZ7HTqFtqE9jx4oDjaHYFmXSrz+ipqmJfcd2fLrrdxZfPM6Xx/fyzZlDNDI345oYXs9vEuTmgby8vFT8rauiz6cfotFQp9hiqqiIKP9Ats+aT0ZyCpPXrpRqjoyAgICAgOyQifi8f/9+mjdvzooVK5gyZQoffvghK1aswNHRkf376zZtXaB2sHJ2Ijcru0wLtMfRk+RmZVeaEK5rYkxawssyPmviUtJ+XJVtwZtz1VXVcwlR/oE8vedDt4lj/3q2AQAAIABJREFURAqYxrZNkFdQICaw+pXPACEe3tw/c7HCcXnZOcQ8CS4rPttak5bwUmRbd03Iz8nhwKJlaDbSY9jCL2p0LRM7m1K/57d5GRHF7X2HaTNsoEQelm/SrEtHMlPTeF6BAB989x6qDTQwdyxvp6Cl30giv2co3kBR02wg0hsYijcjGpmbVRhWde/EWdITkxix+EuatHbB/eDxChPs3yTwphsWLR0rrWBpaGKEjqGBTMMGS4gJDKIgPx/zFuXFZ5Om707Y4Js8veeDhZNjGX9Gh+6dkZeXx//6LZnOHf7gERGP/OkzczoKSop4/lP9qq66IrE0cEy0yGLazO6dsmL5f8b79Hnk5eVxHdyv3LESa64nbndre1n1Dv8bd2jStjXKampin6NjaIBGQ51qd0uVVFrad6xaUGo9oA8p8S+IDqgdq5uk6FixbTfkFRUYvGAOkX4BbJs5j+xXGVJfz6Mr1wHEqn5W09Ki68QxPLpyg6PLVrFr7tcYNrGU6B7ItJkdtu1cub33MIX5BSTHPuevz7+hMD+fD1YsrrD6VUVdnW6TxuJ//XaNA1mv7dyLtoE+Y1Z8i5y8PCoa6vT+ZBohnt4EuVW+0Q3gdfIc6YlJIqufm7R2BpD4HqMwv+C1xVzNKp+7TxlPfk4ud/YfqdF1ioqKeHDhCk07d6i2KGrfsS1PvXyq7F544uZJYUEBDl0rLx4Y8e1XGFhb8veX3/JDr8HsX7SMqMeBJIRHEvnIn2s79/D4mmT3IVH+T8hIScW+k2ytNwybWNF57Eg8j50q87cmIzmF3V8sQttQn3E/LRGrW0NAQEBAoG6Rifi8evVqGjZsiJWVFa6urrRq1Qpra2u0tbX55ZdfZDGlQD3DqpUTkY8Dytw4Zaam4X36PK0H9a2wurOhafUF4YzkFACxQwcbmhpXy95D2lzftRcdI0Omrl+FmUPTMsdq6mMa6nWfmCfBHF2xusqx4Q/8aOzoUCqCG9lYVyi61pQo/0Cu/LkL10H9cOrdo1rXkJOTw8jWukIhFooTwTOSUxj69TwAGug2pPO4kbQfNRQTe1vkFSoO7FFtoEGLnt3wPX+5NMzrbZ7eu09hYWG5ilYornxOS3gp0Xt69fp3uIGu6M+HURNr5BUUKhTZ8nNyuLXnIEY21uRl53DvRPnUeVEE3HJHXl6epp07VDjGulXxg6EswwZLyM3K5vnTZ1iIEJ9N7Yv9TBPCI2W+Dmny1NMHRSUlrN4IWnPs0ZXE6FjigmVvFVESOhfq4/vO/eygODyxsLBQZOWzho42OkaG1d6kE6hdkmLiCPHwps3QAeUEg2ZdOxLpFyBxxsF/kSA3DxSVlCq1KHkbk6bFLejV/Sykv0wkNvgpdlXYYpk52GPfqT1uB45Va57qkBQTi5KqCpqN9Koc26xzB7Qa6XF1+9/kZmXLZD2p8QmE+T4SS3zuPmUcKhrqXPx9OwCPr93C6+Q57Du1F9t+o/vkcWS/ysDj2L9BianxCfzz8zqatHap0Nau09gRqGtrcbkGVc8lPL3nw5l1m2nVvw/DF39J9ynjaaDbkLPrRFu6vU3xPcohmnZuX+Z+V05Ojs7jRvEiLKJa308JEZHo18DzWUNHm9aD+nLvxJnSZ4ma4HvuEopKSji9103ic/XMTNEzM63U77mErLQ0wh/4VZqn0274IFwH9+PyHzsI8fAiPycHn9Pn+Xv+YnZ/sYi9Xy/l7LrfxSpUeJOiwsJSmx5Z0mfGNHIzszj/25ZyxyL9Aji5aj0O3TqVWs4JCAgICNRfZCI+Dxw4kI8//riMv3N6ejozZ85kwIABsphSoB6hrKaGib2tyJC223sPoaSiUmEQnK6JMcnV9JMtEe40dMTzfS6ufJbc3kPaPLnjwbkNW7B0acG8Q7v46I91pb6/pg52ZKamkVzNdcY/C2ftqMm8jIiqcmzEAz9U1NUwsbPByLYJhtZWPH8qvbDBt7myfTeRjwMY9f03IkWIqtA1NUFVQ6NS8Tn7VQbnf9uCVauWzNy5me+unGTYwvmMWvI184/+zXK3ixUGBjr17omSqgrep85VeP2stDQSo2IwEdGirGXQiLQXEorPrwWXiqr3TeyL56mo2hvA/dBxMlJS8Tp1jqy0NLHmjQkMIi3hZaXWG1atWpKVli6zDYm3iXwcQGNRlc/2NsV+pgXvlh9smO9DCvLysWnrChT/nbRt74q/jC03Snh05ToPLlzh8paaCxB1QUF+PinP40W23ZcKbjWs6hOoPe6dOE2jxmZYv652hGIByLxFcwJv/39bbpQQ5vuI3KxsiSoLTZvZUVhYSGwl34tVEex+D+tWLct0abxNrw8nk5WWjvuh2hOfS7sfxKh+bjt8EGkJL3lyR7YV9A8vXcPE3rbSsDuNhjp0GT+Khxev8vwNT/qAG3dQUVfDVkQWwNvoGBnS8v1eeBw7Wa6K2+f0eR5fv0X/OTMwtLYsc0xdW4tuk8YSeNudKP9Ayd5cBVzfuZcr23bTcfQw3vt4Cg8uXJHo2u6HjpGemMTQb+aV3vc179EVE3tbrmz9S2IRFCAhIopG5tUXny2dW6CopITvucvVvsabRAcEkRAeiUv/PhKfa/e66yCoEr/nNwm8fRczB3uRFi6WLVswbNF8gtw9ubz1L4nXUhVBbp5oG+iLFVhdHTQb6dGiV/fiTYEKujDdDx3H58wF+s7+uFwGi5y8PHYd2jD+5+/54shuBsz7lMbNK7coqS0aGhvRc/rEcp9ZAQEBgf8yMhGfK7pxKCwsrNZNhcC7hXkLBxQUFQkTkVT/IiyCwDt36TRmRDmbCTl5eXSMDastCGekVF41+iZqWlqoNtCotqgrba5u382KPsM4s24zTVxdmLB6GfIKCsVhg7VUzVcSDjntt19YcHwv8gry+F29IbP5CvML2LPgOxLCIhmz/Fs+P7gLq1YtxT6/xJMytoqq0XsnzhLx8DEmdja4HTzG6iFj+bHfCPZ+vZTMlDTenzld5HmuQ/rxIiyCSL+ASq8fH/qs3M2jnJxcNW03XovPFYQOGtvZkJ2RQVJMbIXXyH6VweqhYzm5ar3Y8xYVFRF4yx37Tu1F2r9Asfgc/tCvwipwaRP5KAB1LS0amZuVed3E3vadChssITcrm0g/f2zatkZTT5euk8agpKLCYxlbbpRQ8nmTJFCovpEUHSuy8tlUEJ/fOR5duUlWWjpthw0qfc2+Uzvk5eX/7/2eS8jPzSXU+75E4bymTe14GRFVI8/aIHdPFJWVsW4tuuLa0NoSp949uHPgqEzsLCriZVSx+GxgZVHpOE09XZp16Yj36fMy36R8dPkahQUFtB8huqACir2elVRUuPTHjjKvP/W6T3ZGBg5i+Hq3HzUEOTk5bu89LPL40R9WkZORyay//qBp5/ZA8c9p7v4dqDbQ4OLm7RK8q6o5/9sW7uw/Qm5WFuc3/inRudmvMji3/g+sXJxoPajYeqfPjGkkREThe7564m9CeCQq6mpoGehX63yLli3Iz8urdr6KKO6fu0STNq0k9vW269CWpJg4sYpGAAJvuwHFQa1votfYjGkbV5MSF8++r5fK5N4t6K4nAPYd20t0nrjFJu2GD0JBSRH3w/9UOu7oslXEh4YxYdUydIwMUVBUpNOYESy+cIxPtv5G0y4dyH6VQbeJY/n84E5m7tgk0XqlTQPdhnyy7TcGfP4pX508wNz9O2gzpH+drklAQECgNpCJ+BwQEMDEiRPLvT5+/HiePHkiiykF6hElIW0RDx+LPH730HG0DfSx71D2gUpLXw9FJaVKhbXKyJCg8lnXxAgobv+tL+RkZHJ9516OLl+NbTtXBn4xC2PbJrUmqKTEvyAmMJjcrCxO/rKBZb0G88xHtuFySdGx/DbhI/Z+tQSNhtrM2L4RazFbjE3smlBYWEh8aOWVuEWFhWyeMpPvuw/g1OoNxD8LJyk6Ft9zl7i9/zBWrVqWC1fSNTOhSWsXvE9VHZD6PDQcfQtzFBQVS19T19FGUUmJtATJxOf0pGLxWaOCDRQTe1uehzyrchPvVWIy+bm5Es0dcMsNNc0GWLmU3wDQ0NHGqIkVz3xkb7lRQknooLnTv37aWvqNaKDb8J0KG3yTkHs+WDg15/sbZ+k3+2PiQkIJ8629n+m7TmJ0LAZWFuW8TU2b2ZEc95zMVPEq/QXqnvycHHzPX8apd4/SkGCHrp1IT0wiOkC4TywhyP0eBlYWNDQ2Emt88YZ1zQS0Z/cfkpeTU6Hvc88PJ5GTmcXtvYdqNI+kJEZG8yopucp7BNfB/VBQVOTeP2dkvqbU+AQeXLhCxw+Goa5d3t9XQVGRdsMHEXDzDi/CIsocK8jLI8jNk+bdOlcpxrXq34cQDy9SnseLPJ6emMSmyTNIfZHAR3+s44Pli5mzbzsqGur8Pm2W1Kqe3+SflWtZ2m0ALyOjJT7X6+RZwh/6MfCLWbQe1A/TZnZc2fpXtTcLSoTa6lpvWDg1J/ZJSLXyZirC9/xl5OXlce77ntjnyMnLY9u2tUSbxHHBoaQ8j6fZG77P6tpafPT7rwBs+3S+1LNbSkiNTyAuJFSi7gx9S3MWnT9G/7kzKx0nJy9P+5FDCL57r0ohPjcrm7/mLURBSYnpm9fw1akDDF/8JUkxceyev5gfegzi96mfsrT7AC5v3YVN29YSbepJE2U1NaZvWoOOoQE7Zi/g5OoNKCorMWbFd0xcswIVdfU6WZeAgIBAbSAT8XnWrFnMmjWL69evs2bNGn755Rdu3LjBnDlzmDmz8i8bgPfff58nT54QEhLC119/Xe74vHnz8Pf35+HDh1y5cgVz839DJvLz8/H19cXX15eTJ0+WO1dA9lg5OxEXEkpWWrrI40/ueJCRnELrQX3LvF7STl1dQTgnI5P83FyxPJ8bmhgD1AvP57fxPnUOt4PH6DZpLEoqKtX2e64Oa0dP5udBH3Dr74O1KuT4nr/MmhETSYyKYcq6leg1NqvyHGN729cVXlX7ORbk51OQn1/uda8TZ8nNyqbT2BFlXncd1I/CwkJ8TlctPseHPkNBSbFM26326+qbVAltN0o2ULT0RHtaGts1kVmoWvBdL/Jzc2kuogqrZEMp7L5sNyPeJD40jJzMTMwd/7XeeFfDBkvwOnmW++cucWbtJtaPmcbaUZOrDBQS+Jcnd+6i0VAHu7escswdHYSq53eQeyfOoqymyujvF/L5oV249O+D//XbQofcGwS7F1cW2okRAKimpYWuqXGNPwv5OTk883kgUpzRNTPBpV9vPI6ekJmgVRFFRUU89bqPrYiMhTdpO2wQYfcf1pq3/ZWtf6Girk7XiWPKHWvRqxuaerq4Hz4h8lz/G3fQNtTHtFnFocjmTs3RMzOtsio4ITySDeM/wv3wP7QdOpCk6FjWj5lWYSGINKiuWFtUVMTxH9egoaPNmOWLeRkZzf2zFYdiV0VCifhsKXnooLyCAo0dHYh4JN2fU0J4JNEBQbj06y32OUY2VqhpaRLq7SvRXAG33LHr0AYFJSXUtDSZvnkNOsaG7JzzNYlRkm8OSEKQuyfWrVqirKZa5Vi9xmbM3L4JHWNDekybUCYD420cunakobER7oeOi7WOlxFRHPx2OSZ2NuRkZLJ1xjx+n/opjy5dKy3GyEpL4/IfO0l9kUC3SeU/r7JGXkGBSb+uwMzBnj0LviXg5h1u7TnIryMncfrXTTi9153PD+4UrDgEBAT+s8hEfI6NjaV9+/YsW7aM8PBwIiMjWbZsGe3atSM2tvKqVnl5eTZv3ky/fv1wcHBg7NixNGtW1p/J19cXV1dXWrZsydGjR1m9+t8wtaysLFxcXHBxcWHIkIrb4ARkg5y8PBYtHQkT4fdcQkF+Pr4XruDYoyuqDTRKX9d9LQhXN3AQICM5FY0K/HLfRNe05nPJkpOrN5Q+MNRWknxdk53+ih2zvoSiIj7cvAY1Lc1Kx5vY2RAXUrOgtqy0dHzPXaJV//dR1WwAFLcDug7ux9N7PqTEv6jyGiW+2IZNrEpfK9lISRXj/LfXkxz3vEy1bwk6Roaoa2nVyMuzMnKzsghyv0fnsaMYvGBOmZ+/dStn8nNzifKvvYrEosJCovyflBGfTe2L7RUq8/muzyRFx7Lv66Vc37WPKP/Ad863uq7xv3GHjJRU2gz9NzvCpm1rGpmb4X+tdryzBaRH1OMAYoNCcO77HkWFhZz4eR0nV2+o62XVK+KfhZPyPF6sKj1TKW7OBbvfw8jGGm3DsjYGHUcPp6ioiBu7D9R4jurw1NMHHSPDcnZMJVg6O2FgZYHnP6drbU3xz8J5eOkanceNKr2PKKH9qKEkRseWbiK8zZPb7hQWFIjc9C3BpV9v8nJy8Lt6s8q15OfkcGz5ataOmszGSR9XWCldH4gJDObukRPIKyhwZVv1q56h+F4rLzunWpXPRjbWqKiryUSk9z13CfMWDmIVVEDx7y8g0rawMgJvuaOiro7z+734dNfvmDk0Ze9XSwmX8DrVIcjttU1PFR0JumYmfLpzEwpKimyc8BEpcfGM/mFRhYGbHT4YTmp8Av437oi9Fr+rN1n+3hDWjZ5CkJuHyDEF+fnc2X8U+07tMbKVjVd1RfSZOZ1mXTpybMUv5d7Xjb/2seXDz1DVbMCHf6xFUVm5VtcmICAgUBvIRHwu4fr162zatImNGzdy7do1sc5p27YtT58+JSwsjLy8PA4ePFhORL5x4wZZr/3sPDw8MDMT70tdQPaYOTRFTbNBlXYNPqfPo6SqglPvf1PCG74WhFPiqn+znJGSImblsxHZrzIqrM6uawry8tg59ysOffdjuVbN/zKJ0TH8NW8humYmjPh2QYXjlNXUaGRuJhUh1u3gMVTU1WgzpFjQaj9qKHpmpnifrDho8E1ehEdSWFCA0Rvis6VzC/JzcysNBqyIEA9vbNu5lrMWKLEGiavGNcXl0JIf8Tp5li4TPmDhmcMM/WYeXSZ8QNMuHYj0C5DYyqOmRPkFYNrMDgUlJaA4bDAxOqZWfUYF6g8FeXn4nruEY8+upZsj3SaNJT0xifvnLtXx6gSqw9YZ81g5YBQbxk7n9r7DNfIq/q8S5H4P2/auyCuI9uMvQZre54Gvg/oce3Qt83rLPj0JvnuPNAnzDKRFyD1vAGzauYo83npQX3IyM3l4UbxnDmlxddtu1DQb0GnMv11UjSwaY9vOFc9jpyqs5s9ISSX8gR/Nu3cReVxeQQHnvu8RcNNNou+9mCfB5GVLz0JCVpxZu4m/5i0Uy+KsMoqKikiIjEK/GqGDFi0dAaRe+Qzw4MIVAFz6i1f9bOXiRFrCS5KiJbMffHrPm7ycHMatXIqemQnbP53P42tVb1ZIg2f3H5KXnVPpBpmalhaf/LkBJVVVtnw0h0i/AA5/vxIDKwve/7R87koji8bYd2yHx7GTEm9KpMS/qLJ75u6RE+RkZtFNRLeCrLBycaLXh5O4d+IMHkdFd2aHevuy7+ul6JoY02X8KImur6alhUu/3ti2c6WRuVmF+S0CAgICdYlMxefqYGpqSlTUv95O0dHRmJqaVjh++vTpnD//702LqqoqXl5e3L17t9LK548++ggvLy+8vLxo1EiyMAiBiinxCAyuIqU50i+AF2ERZaw3dE2MSX2RUCOB61VyCg3ErHyur1XPJbxKTObeCdl7FtY3nvk8wOvEWZp27lChD2JJMKE0vEFjngQT/sCPLuNH89merYz87ivCH/jx6Mp1sc7Pz8khMToWIxvrf9fn4kSU/5Nq/S6HeHqhrq1VWsVWgon9a/G5htXelZGRnMKRH35m/QdTiQ54QtthAxn69ecYNbEixNNbZvNWROTjABSVlTGxs8HS2YnmPboQdl/2lTwC9Zd7J86gpKKCc9/3MLCywKFbJ9wOHqv1jREB6ZD+MrFanrH/TwS5e6KupUVjx2aVjjNpaktK/ItS+6aaEB8aRmxQSGkgHEBjRwd0TY15dKl2hd03eRkRRUr8C2zbirbeaNq5PcF3vWp9EyPmSTABN93oNnFMabV4h5FDKcjL514VVdj+N+5g2swOHSPDcsds2rZCq5Eevv/RzbXcrGz8rtyQShheQnhktWw3LJwcSXuZKJMMmJT4F4R6+9Kqfx+xxls6O1XaOVoRuVnZBN5yJyM5hT+mf1arwcL5OTmEevvStJPo0EF5BQUmrv4BHSMDdsz+srRzLcTDC4+jJ+k+eRydx40q9f537vsec/ZsJS87B49jp2Sy5qy0NLxOnqXVgD5o6unKZI43UW2gwdiflpIUG8eJlesqHfv0ng8BN93o9eFkNHSqLqaCYuF51l+/M2H1MmZs38jCs0f4fP/OMlk0AgICAvWBeic+ixKbKtrBHD9+PK6urvzyyy+lr5mbm9OmTRvGjRvH+vXrsba2Fnnutm3baNOmDW3atOHlS8l8WQUqxq5jW6ICnoj18ONz5gI2bVrR0NgILQN9LJ1b1PjmLzE6BgNriyrDW3RNjEmuR2GDAmUJf+CHmmaDClPtW/bpSVb6K556+khlPreDR9EzM0HXzISD3y5n06RPJKoaig99VurRpqisjJmDPeHVeIAACHn9nmzf8rU1sbflZVQ0OZmZ1bquJMQ8CWbrjHksateLJV378evISVzd/rfM532bSL/i0MFWA95n2m+rSImL5+Tq9bW+DoH6Q0xgMLFBIbQdOpAuEz4gLyeHu4f/qetlCQjIjBAPLwoLC7HvULnvc3HYoPS8z71OncPCqXnp93DLPj3Jz8vj8fW6tbh56ulDkzatyt3nGVhZoGtiTJCbaIsLWXPx920oq6ux8MwRBnw+kzZD+vP4+i3SE5MqPS/gZnH7vWPPruWOufTvQ1b6KwJv35XJmv9LJEREoWdmWmWHwNtYtnQkUgZVzyX4nruMobVluWDrt9HSb4SemQnhD/yqNc+Bxcv4se8Iol6HNdcmT9w8MLCyKO2+eJP+c2Zg36k9x39cU87a5PSvG4l45M+whV+w5OopPtuzlYm/LOdlVAzrx0yVaYfFrT2HkFdUpMuED2Q2RwnDFs5Hx8iA/Qt/EOse/szaTahoqPPeJ1OrHKuspsqHv69B36Ixu79YxO9TP+Xchi2YNrOj7fBB0li+gICAgNSod+JzdHQ0jRv/2zZlZmYm0ie6V69eLF68mMGDB5P7RsVTXFyxoBgWFsaNGzdwcancg0pAeqioq2Pp1IJg98qrnkvwOXMBgNHLFvH1yQPomhjjduBojdYQ8cAPdS2tCkXLEhoaG9X7yuf/ZyL9/IHiipS3kVdUoEWvbvhfvy21akffc5fZMXsBPw8cjdfJcxIHXj0PDUffwhwFRUUaN2+KorKyxJ59JaS/TCQuJBTbt9qKTexsqmXjUVMyklOIDQqhIC+v1udOeR5PWsJLuk78gKKiIrZ9Or9WgzAF6if3TpzFvIUDbYcNxOf0BV4lJdf1kgQEZEZmahrR/k+wr6CyEEBRRQUDKwupBm/eP3uRgvx82gzpDxSLzyEeXnVuV/b0njeaeroY2pQtLilp+w9yF+31KmuiA4JYPWQsDy9fo/vUCWg01MHjqOigwTd5ERZBlH8gncaMKCOoKyor06JXd/yu3BA6O8TgZWQUCkqKpYHi4qCurYW+pTnhMgxl9Lt6g8KCAlq+37PScaXBztUsXMjNyq6V4gRR+Jw+T3piEiOXfl1G/G81oA89pk3A7eAxPI+X7wDIfpXBpkmfsH7MNB5dvo6WQSPOrN3EpkmfyNxyMDEqmgfnL9Nj6ngce3aT2TzOfd/DdXA/rvy5S2xf8fhn4XgeP02nD0ZU6heuoKjIlHUrMXd0YO9XS3h0+Tqh3r5c3b6bZz4P6DNjGkqqoj21BQQEBOqCeic+e3l5YWtri6WlJUpKSowZM4ZTp8q23Tg7O/Pnn38yePBgEhL+3RXV0dFB+bVBv56eHp06dSIgoPZ3gP9fadKmFQpKigRVEKzyNsmxzwn19sWufRvCH/jxy7DxVaZ5V0XJDaSlc4sKx6hqNkBNS1MQn+sxCeGRZKaliQzes23XBnVtLR5KsfW3qKiIgJt3yMmo3o17fOgzFJQUaWTRuPQBorrVKwAhnt5YubQsDRxRUlWhkbnZOxu0VxPCHz4mLzuHnXO+knlqu8C7wf2zFynIy0dRSYlbew/V9XIEBGTOEzcPzFs4oKalJfK4dSsnFBQViXzkL7U5XyUm8+SOB60H9sOipSO6psZS/d6tLk/v3QcoZ71h36kdCeGRMrFPEJekmDgOLFrG2lGTOfz9SkI8xLOruvHX/mIboTeCB10H90NNswG+5/+blhvSJiG82LJR31J83+eSe8wIKX5u3uZVUjJPve7j3KdXpeOsnJ3Izcom5sm7FzKemZrGiZVrMXd0oOvrSuLm3TszZvl3PPW6z8lVlXesRfkHcui7H/nx/eFc37Wv1sKYj/zwM5GPA5iw+ocqAxOrg46RYamV35VtuyU69+LmbeTn5TJ+5dJyQaYlDFs0H/tO7Tn8/cpygaTnNvyBln4juowfXe31vyvoNTZDS1+wUBUQeBeod+JzQUEBs2fP5uLFiwQGBnL48GECAgL44YcfGDSouH3kl19+oUGDBhw5cgRfX19Oniw27m/WrBne3t48ePCA69ev8/PPPxMYGFiXb+f/CvuObcnJzJJIdDv03Y/8+fFcts2cR2J0TI3XkBAeSUZKamlitCh0X1dFJMc+r/F8ArKhqKiIKL8ALESIz87v9yIr/ZXYmxy1QXxoOACGTaywcnbiRVhEjXw3Qzy8UVZTLQ3CMbJpgryCQp1UPtc1/6xcy/px02WSRC/wbpKRnILn8VN4nzpPfGhYXS9HQEDmBN52R15BoTRX420cunUmNyubkHvSsaIqwevkWbQN9Rm28Itiy41rt6R6/eqQHPecl5HR2LT7V3xWVFamiWurenNfEBf8tNKgwbd5dPk6STFxdJ88DoDGQuDQAAAgAElEQVQGug0Z8PmnhHr7Eny39vx732USIiIBJAodtGzZgoL8fKL9Zfus+PDSNfQtzSu13rB0aUGknz+F+bUjvEqbBxev8vjaTfrO/pgOo4cx6dcfiQ4MYudnCyjIz6/r5YkkNyubHbO+JDE6lmm/rcbMwV5q15aTl2fsT0uQU5Bn38LvJRbU0xOT2L/wB0wd7Jmx7TfUtctuPLYfOYQOo4ZyZdtuvE6cLXd+mO8j/G/coce0CaUBzf81rF1dmL55DYvOHWHRuaN0nzJeYtsdAQGB2qXeic8A58+fx97eHhsbG3766ScAli5dyunTxS07vXv3xsjICBcXF1xcXEqDBe/evYuTkxPOzs44OTmxc+fOOnsP/4/YdWhLqPd9idrzE6NjqgwnlJTwB36lop0odE1LxGeh8rk+E/HIHyMba5TV1EpfU1BUxLFnV/yv364TG4iKiA+LoLCgAGMbayydW9So6hko/hzl52Pb3hUFRUUGfD6T/NzcUjuS/yfSXiTwXIYhiwLvJsdW/MKBxcvqehkCArVC1ONAMpJTaNalo8jjDt06EeLpTX6O+FkF4hBw043M1DQaN29WLyw3Sgi5500T11alQoNVq5Yoq6nypI78nmtKYUEBt/YcxLq1M+ZOzRm8YA7K6mocXbaqrpf2zpCRnEJmWppEoYMWTs2JCw4lNytbhiuDx1dvVmq9oaymhmlTu2rbtdUXjq1YQ35uLiO/+4r40HC2zZxX7Y7C2iIzNY1tn3xOVno6s3f/SftRQ6Vy3e5TxmHTphUnVq4lKbq8fag4PL52i7/mfoORjTUzd27G0tkJRRUVLFo6MmzRfJ7c8eDCpq0Vnn9+4xZUGzSg5/SJ1X0b9Zbhi79k1q7fMXd04OLmbQS5ezBo/mzm7NtWpb+6gMCbNDQxov/cmXyy7TeGfjOPNkMH0MhC/E1MAcmol+KzwLtHQxMjDKwsxPZ7liXhD/wwtLYst0tcQokfXF22ZgpUTYSfP/IKCjRu3rT0Ndv2rqhra/Hg4tU6XFl58nNySIyOxbFXNzQa6hB2/2GNrpeTkUnU40Ds2rVhxLcLsG3nyqGlP5GWIISjCggICPy/UVRYSJC7J/ad2pUL2jNsYoWemWlpcJ00KcjL4/65YtuH+mC5UUKIhzdqmg1oM3QAUOz3nJ+XR6jX/TpeWfXxPH6azNQ0Pli2mNYD+3Jt+98y9739r/EyIhp9MUUDOTk5Gjs6ECHDsMESXiUlE+rlS8veosVn8xYOKCgqVjuour6QlvCSQ0t/IuCmG39+MrfebFZVRUr8CzaMnc4zH19GLfmaiWtW0GboADqPG0nHD4ajrKYq9rUUFBUZNP8zBs6bxYOLV/E6ea5Gawu87c6O2QvQMzPlsz1/8tPdK3yy9TdSnsez9+ulFBUWVnhuXHAoPqfP03XimNJQ9P8Czbp2otOYEbgdPMbyPsO4tGUnu+Z+w+75i9ExMmTewV30nf0xCkpKdb1UgXqMrpkJ0zb+wqLzx+gxdTwa2tq0GTqAMcu/ZeGZw8zcuRnnvu+hoKhY10v9TyH8NAWkgt3rFPb60PIY/rC46tTCyZHA2+7ljjc0MSI7I0MILqvnlHhXWrR0JNTbF4CWfXqRlZYu9Wp5aRD/LAzHHsVp9dKoXgnx9Kb3J1OxaOnI5T93cf/MxRpfU0BAQEDg3STwtjutBryPmUNTot6wCWj+2ic44Fb5+x1pcGvPITR0tHl0+bpMrl8d/K7eIMjNgxGLF5AcG4d9p3aE3X9IblZWXS+t2uRmZeF+6DjvfTyFF2ERXN3+d10v6Z0jISISq1YtxRrbyKIxapoNynyWZMnDS9cYueQrjO2aEBdctpvL0sWJwsJCmQYf1hZ+V27gd+VGXS9DYl4lJbNt5hf0mDaBvrM/xvn9fz26m7i6sGfBd1Veo5G5GRNWL6Nx82a4HTzGqTUbpbK2EA8vfuw7HCsXJxo7OmBobcmFTVvJSqv6Ofb0r5tw6NaZkUu+5vepn0ocqF7fUNfWYvT33xAb/JSTqzeU6YJ9dOkaTz29GbxgLr0/mYpT7x4cXvJTqS4gUL9QUFLCdXA/GjdvRkFeHvl5ecQGPeXhpWtS7+J6G+vWzkxZtxI5BXmubtuNx5ETpMS/QE5ODj1zM1r06kaHUUOZ+Mty0hOTuPfPGTyOniA59jmqmppo6jWkgZ4uDXQbotVIDwMrCwytLTGwsmDN8AlkpKTKdP3vMoL4LCAV7Du2Iznueb2o0oj2D6QgPx9L5xYixWddE2PB7/kdIDM1jYSIKMxbFPs+q2lp4tirK4/rmeVGCc+fFovPr5KSSQiPrPH1gu/eo/cnU3lw4QoXN2+TwgoFBAQEBN5Vgtw8KSwspGmXDmUEM4dunYkKeELai4RKzq4+iVHR7P1qiUyuXV0K8wvYPX8xs3dvYfK6lahqaHBm7aa6XlaNub3/MOYtHLi4eTv5ubl1vZx3joSIKFoP7IuiikqV4oWZQ3FXXXTAk9pYGn5XbzB88Xxa9ulVTny2adOK+NAwstNf1cpaBERTVFTEtR17uHfiDEoqKuRmZtF+1FD6z5nBs/sPcTtwtMJzLVo68uHmXykqKmLX3K+l7o+fkZzC42u3JL5uRnIKZ37dxAfLF9N22EA8j58W+1wdQwP0Lc1pZNEYJRUVkuOekxz7nLiQULGew+QVFNDQ0SY9MUmiNVfGsEXz0dDRYdvML0SuITM1jYPfLsf3/GVGLvmKWX9vwe3AUc5t2PJOb07+l1BUVqbd8EH0nD4RHSNDMpJTkJOXR1FZGWU1VYZ+8zk+py/gfui4THSlNkP6M3LpNyRFx7J91pdlwuyLiop4GRHF9Z17ubFrH3Yd29Fx9FB6TB1Pj2kTKCooREGpvHyalZZOfFg4T+54oKCsLPU1/5cQxGeBGqOspoZ9x3Y8uHClrpcCFAdIxAaFYOncQuRxQXx+d4h49Bi79m2AYn8vFTV1bu89VMerEk186DMAqe2wP/N5wNYZ8wj1uv/OVyoICAgICNSMjJRUovwCaNa5A5e3FGeaaOhoF3fHbPn/yzjJychk+6z5zNm3HVUNjXfW7/lNXiUm8+fHc+t6Ge8sJQG0xjbWVVY0mznYk5edUxoYLWtKrTf69OTi5m2l93VWLk7YtnPl3G9bamUdAlXzKjG59P+vbf8bCydHBi+YQ9TjACL9AsqNt+/YjsnrVpKW8JKtn8ytd7aO906cwXVIfwbOn43/zTtl3p8oFJSUGLviW1z69xF5/EVYBDvnfFVhoY2WgT7tRwym/YghaBvq8/SeD3f2H8H/xh2JgxffxLFnV1r178P5TVuJDQqpdGyQmwdrhk2g/9wZdBo7Eqf3evAiLIK83Byy0tJ55vOA4Lv3SIqJQ1WzAQ2NDSkqLCIhPLI0IFNBSQk9MxMyUlJrFCJfU+QVFLBt34bGjs1o1NgMXTNjUuMTeHrPh1BvX7LTXyEnX+ykm/3qFXnZsq0ari6KKiq0HzGYntMmom2oT9j9hxxa8mOZUF1rVxc6vA7T7DJ+NAG33Lj190FCPL1rPL+cnBz9P59Jz2kTCb57j7+//LZSa6CioiKC3DwIcvNAx9CA1oP6oaymyquk5NL/0hOTSv9fQDwE8Vmgxrj0741qAw2Rabt1RfgDP9oOG4S8gkK5L7qGpkbvfKjH/wuRfgG4DupH9ynjS284Yp4E1/WyRPL8afFDjzQ9+4LcPKR2LQEBAQGBd5snd+7Se+Z0NHS0yUhJpWmXjsjLy8vE7/ldIDU+gT8/moNtO1figp/W9XIE6pg37dqqFp+bEhsUUiMxTFI8/znNhFU/0H3KOK7v2gfAoC/nkBqfwK09B2ttHQLiU1RUxIHFy5h36C+mrP8Zr5PnCPd9RHLccwybWGHmYE/XiWOIfxrG1pmfVyns1hVHl61i/rE9jF2xhB2zv6zw915FXZ0pG37Grn0brm7/myB3TxIiosjLzqGhsSFGNlYMXjCXuft3sO+b7wl290TfygJTe1ssWjpi6dwCY9smICdHkLsnnsdP4TqkP1PW/0xSbBzuB4+V+ttLgpy8PP3nzuT502dc2yGeJVFOZib/rFyL7/kr9Jw+ETXNBqg0UMfU3o7WA/sCxQVrb3p6F+Tl8yI8AgVFRfQam5b6/abGJxAbHMKL8EgSo2JICI8kyj9Qpr7mBlYWdPxgOM5930NTTxeAlOfxJMXE0cTVhVYVbA7k5eTwKimZl5HRJEREEfnoMT5nL1KYX3t/695ESVWFDqOG0WPqeLT0G/HU6z77Fn4vMqPhmbcvz7x9ObFqPR1GD6Pz2JHM2L6R2KAQbu05yP1zl6vV/ayspsb4n5fi2LMbbgePcWLVOol+HinxL7i6fbfE8wqURw74vy+p8/Lyok2bNnW9jHeWzw/uREFJiV9H1J80Xee+7zHxl+WsHT2ZmMB/xUo1LU1WuF3i9JqN3Ni9vw5XKCAOZg5NmXdoFwARDx+zafKMWn1QkJROY0Zw/9yldyZkRUBAQEDg3aFx82Z8fnAnh5b8xONrNxn9wyLMWziw/L0hQoeMgADw3ZWTPPN5wL6vl1Y4Rk5OjhXul/E5c4HjP66pxdXBpF9/xLFHVzZN/gRdE2MmrlnBwW+X1ziYTkC2GNvZMHLJVzR2aFam7b6woIAgd0/2frWE7FcZdbjCqmk3YjCjv1/I3SMnOLpsVenrcnJy6JqaYGJvQ6+PJmNib8uhJT/hc/q8yOvoGBkyZcPPmDa1o7CgAMXXwX7ZGRlEPvLn2f2H+Jy5QFJ07P/Yu++wKss/DOD3GUwFQRBRtmxEFBQEBbfiTM0RqLkxK7Vl2jLTSsuytNQyyzSzzDT3wIEDEOQogrJBhiAKCgLKXr8/DH+5Geecl3F/rutcV5zzvs9zczyc4Hue9/sAuL9y16GvJzwnjYd1zx4oLynF2d//wpHvfqz1/7dcRnpj8spPsPXtD+Sy/4CBhRlsPFzR1tgI+TezcedmFiRSKQytOqGDVSdUVlQgKzkVt1KvoXVbXXS0tUYHG0vom5pATVPjwTg3Eq/i6oVLuHwsAMnhkc/cALK21DQ1MXjuTPSZ8hKqqioRcyYYFw8eRUJI2EOrmg0szGDezQkqaqqoqrw/r7pWK7Rq0wZa+nrQNzOGgbkZNNtoIzslDYfWbGhQOxgVdTUY2dnCyM4aBhZmMLAwQ3V1NbKupuLm1WSU3CuESCSCWCKGSCyBWCJGG4N28Jw0AVp6bZEYegHHNm5G8r/7ONWGVFUVzsMHo8/LPuhoY4W7ObmIPBaAKydOI/liRK1qAobWlpi8cikMrTph36o1CPrj6e1zqH7qUkvlymdqEGMHO5h0tlf6L2/Pk/bvph3m3ZweFJ/VtVpj+pov7j9+JVqwbFR7NxKSUF5aiuqqavzx4fJGXXgGgOAdu4WOQEREzVRGTBzu5uTipeUf4KXlHwAAQnbtZeGZ6F9pkVEwc3J85jF6psZQb90KGdHK6ff8X38v+wImne0xZdVyACJkxifiwoGjSs9BdXMjIQnfT5kDFXU1mDo6oE37dsi6moqs5NQm05/9/O790DM2wsDZU5GTcR0pFyPhOmYEnIb0h6a2NoD7BeRfFyx+4p5JNfJuZmHd1LkYOOtlSFRUcCPxKm4kJCErOfWJxdeqykpEBZxBVMAZGFpbov+MyRg4eyradjTEnx9++qDNxdOIJRIMmTsL1+MS5LaZZXZKWr37CWvptUV7SwuYd+sCc2cnuI4eAU/f8cjPvoWEEBnu5d5BUX4B7mTewPW4BNxKS69VUVoslaDHyGEYOm8O2rRvh/O79+PQ2h+e2vKjtt+DfZ/eGPn265ix9ktkxMTj4qGjiDh68rF9IkRiMbT09aDZRhsaWq3RWlcHBhbmMOhkhg7WljC06vRgJXjJvcL7c4vuf6jx34L8o+LPncfxHzcjpR5XBleUlUG29xBkew/B2t0VvSaOhduYkfD0HY+7ObkI/H0ngnfseuIHP2KpBANnT8OgOdNRXHAXP7++kFcUNwJc+QyufH4efVNj6HYwRHJ45GOXOkxY+h6chw/B8oGjGt0nvh8d2wNVDQ2E/XMA0acDMW7JIrQzN8WOD+9vREBNQ9+pvsi9nokrJ88IHYWIiEhQnbp3u787fEUFKssrcCXgdKO91JtI2fpM9cHod9/AJ/1GPHWjM+dhgzFl1XKsHj/1ub1jFcGsqyNe3/IDJFIpNs5Z8FDPUyJFEolEmPzFJw/6OZcWFeHy8dNIuRSJzPgk3Ey6qpSewf2mT8aod+Yh/tx5bH3rA5QWFT31WLcxI/HSpx9i84JFiD4VqPBsdaWqoQ6HPr3hPHwwTDo7QFNHGypqag8eLy0qQnZKGm5fy8Dtaxm4kXgVGTHxyM24DomqKnQMDWDZvRsG+k2DnrER0i5HY+8X3zyxv3h9iSUSuI4ZAY8JY2DS2R5VVVXIz8pGaWERSouK0Uq3DXQNDZ+4mV7ezSzcTEpBenQs0qNikBEbj/ys/xeuRSIRdDq0h6qGBqqrqlBVWYnqqmpUVVXebwEi599PVNTVYNfbHT3HvQB7r14ovnsPkf4nUVRQgLLiEmhotUb7ThboYGOJNgbtEH74GPau/AaFeflyzUH/V5daKovPYPH5SfTNTDBgxhRYe7iibccOAICiggJcPnYK4Yf8kXwxAqqaGlgacAARR05g5ycrBU78OGMHWwz0m47O/TwhkUpRfPcetrz5HpLCLgodjYiIiIiI5Mi8axfM//0n/PrGe4gKePKihVHvzEdv33H4wH2gYH1QXUcPRztzMxxe+4Mg81PLJVVVhfdrs5Cdeg2R/gEoKy4WJIfr6OGY8Mn7yIiOw09z33ziIjaJVIrFB/5CYV4e1vrOEiBl/aioq0Hf1BjG9rboaGcDA3Oz+4v5Oho+WD1cXlr6UJH6WlQMjm345ZmrzuWhnbkpunoPhL6JEdQ0NaGmqYHC/ALkXr+BOzduoigvH8V376EoPx+30zKe+cGA0IzsbDBg9lRY9+wBNU0NSFVVUVZcgqyUVGQnpyLi6MkWuyeGMrH4XEcsPv+fVE0Ng/ymof+MyagoL0d88Hkkhl5AfvYtOA3ujy6D+kK9VasHn4LZebpjjc/M527sISTtdvroNnQQ4s+df7ATNhERERERNR9SVVV8HnoCgdv+wsFv1z/xmFd/WQdVDQ2sndR0illEzZHjgD54+evPkBmXiJ/mvvnQnjkikQjjP14M9/GjsenVtxAX1PRbJtT0lTayt0X7TuYoyi9A3s0s3LqW/mDDVKo/sUSC6qoqtiJTMvZ8pnoxtOqEGd99CX0TY1w4cAQHV6976JK1mDNB2P3ZKnTu5wWXEd6w6+2OtMvRjbrwDAAFt25zF2kiIiIiomasoqwM12PiYdb1yX2fRSIRjOxtcenwMSUnI6JHRQWcxZY338f0b1fglU3fYdvCJchJz4BILMZLyz+E6+jhOPHTlmZReAaAyooKXI9LwPW4BKGjNEuNfW8oYvGZ/tWphzNmrv0SZSUl2DDzdVyVhT/xuPKSUkQcPYGIoyegoa0l2OVqRERERERE/5V6OQq9JoyFRCp9bDOztsZG0NBqjYwY5W82SESPiz0bjM0LFmPGmi/wweG/kRmfiMK8fFj37IEj32/EiZ+2CB2RiORELHQAEpZaK024jRmJVzauQcHtHHw/Zc5TC8+PKi6426j7ABERERERUcuRFhkFFXU1dLCxeuwxEwdbAEBGTLyyYxHRU8QHh+KLkROx98s1KLlXCDMnRxxYvY6FZ6JmhiufW6iRb8+DjbsrOthYQiyRICU8Er/MX4TiggKhoxEREREREdVZWsQVAIB5N8fHVjgbO9ihoqwMN5OShYhGRE+Rl5WNwN//QuDvfwkdhYgUhMXnFsrQygKF+fk4vvFXpEZcRpIsnC00iIiIiIioycrLykZ+1i2Yde2CoD92PfSYiaM9MhOSHmvHQURERIrF4nML9fNr7wgdgYiIiIiISK5SI6/AvFsXiEQiVFdXAwA62FjByq07jv24WeB0RERELQ97PhMREREREVGzEBVwBm07dkDP8aMf3Dds3hwUF9zF2W07BExGRETUMjXa4rO3tzfi4uKQmJiIxYsXP/a4qqoqduzYgcTERISGhsLMzOzBY++99x4SExMRFxeHIUOGKDM2ERERERERCST80DEkhl7AyLdeh7ZBO5g6dUbn/l449et2FBfcFToeERFRi9Moi89isRjr16/HsGHD4ODgAF9fX9jb2z90zKxZs3Dnzh1YW1vj22+/xZdffgkAsLe3h4+PDzp37oyhQ4diw4YNEIsb5bdJREREREREcvb3si8gkUox7qOFGD5/Lu7m5CJw+06hYxEREbVIjbIq6+bmhqSkJKSkpKC8vBw7duzA6NGjHzpm9OjR2Lp1KwBg165dGDhw4IP7d+zYgbKyMqSmpiIpKQlubm5K/x6IiIiIiIhI+XIyrsN//SY49u8Da/ceOLlpK8qKi4WORURE1CI1yuKzkZER0tPTH3ydkZEBIyOjpx5TWVmJ/Px86Onp1epcAPDz84NMJoNMJoO+vr6CvhMiIiIiIiJStrO//4VrV2KQk5GJkL/3Ch2HiIioxZIKHeBJRCLRY/fV7FT8vGNqcy4AbNq0CZs2bQIAyGSy+kYlIiIiIiKiRqaqshLrp78KFXU1VJSVCR2HiIioxWqUK58zMjJgYmLy4GtjY2NkZmY+9RiJRII2bdogNze3VucSERERERFR81ZRVsZNBomIiATWKIvPMpkM1tbWMDc3h4qKCnx8fLB///6Hjtm/fz+mTZsGABg/fjwCAgIe3O/j4wNVVVWYm5vD2toaYWFhSv8eiIiIiIiIiIiIiFqyRtl2o7KyEvPmzYO/vz8kEgk2b96MmJgYLFu2DBcuXMCBAwfwyy+/YNu2bUhMTERubi58fHwAADExMdi5cydiYmJQUVGB119/HVVVVQJ/R0REREREREREREQtiwjA4w2RWxiZTAZXV1ehYxARERERERERERE1anWppbL4DCA7OxtpaWkwNTXFtWvXhI7TbLVp0wb5+flCx2gW+FwqFt8LFIOvW8Xi86sYfD9QLL5uFYvPr2LwfUGx+LpVDD6visX3BcXi67dh+PwpVkv7+dfX18ft27dhZmYGAwODWp9Xzdv9W3Z2tuAZmvNt48aNgmdoLjc+l4q98b1AMTe+bvn8NsUb3w8Ue+Prls9vU7zxfUGxN75u+bw2xRvfFxR74+uXz19jvrW0n3+ZTFbncxrlhoNCycvLEzpCs3bgwAGhIzQbfC4Vi+8FisHXrWLx+VUMvh8oFl+3isXnVzH4vqBYfN0qBp9XxeL7gmLx9dswfP4Uiz//z8e2G//B3s9EBPC9gIj+j+8HRPQovi8Q0aP4vkDUcrW0n//6fL8SAJ8oJE0TFR4eLnQEImoE+F5ARDX4fkBEj+L7AhE9iu8LRC1XS/v5r+v3y5XPRERERERERERERCR37PlMRERERERERERERHLH4jMRERERERERERERyR2Lz0REREREREREREQkdyw+ExEREREREREREZHcsfhMRERERERERERERHLH4jMRERERERERERERyR2Lz0REREREREREREQkdyw+ExEREREREREREZHcsfhMRERERERERERERHLH4jMRERERERERERERyR2Lz0REREREREREREQkdyw+ExEREREREREREZHcSYUO0BhkZ2cjLS1N6BhEREREREREREREjZqZmRkMDAxqdSyLzwDS0tLg6uoqdAwiIiIiIiIiIiKiRk0mk9X6WLbdICIiIiIiIiIiIiK5Y/GZiIiIiIiIiIiIiOSObTeIiIiIqEUwtLaETvt20NDWRllxMaJPBQodiYiIiIioWWPxmYiIiIiaPa/JEzHmvbceum/jnDeQEBImUCIiIiIiouaPbTeIiIiIqFmzcOmKUe/MR/SpQHw3xQ+rRvsiJ+M6Rr79OkRi/jpMRERERKQo/G2biIiIiJotLX09TP36M+Rez8QfHyxDWmQUspJTcXjtjzCys4HLCG+hIxIRERERNVssPhMRERFRsyQSi/Hy159CrVUrbHn7A5TcK3zwWKT/SVyLisGw+XMgVVMTMCURERERUfPV5IrP3t7eiIuLQ2JiIhYvXvzY4yYmJggICEB4eDgiIyMxbNgwAVISERERkdBcRnjDsrsz9qz4GjcTrz70WHV1NQ6uXgfdDobwmjReoIRERERERM1bkyo+i8VirF+/HsOGDYODgwN8fX1hb2//0DEfffQRdu7cCRcXF/j4+GDDhg0CpSUiIiIioYilEgx5dSYyYuJxYf+RJx5z9cIlxJwJRv+ZL0MslSg5IRERERFR89ekis9ubm5ISkpCSkoKysvLsWPHDowePfqhY6qrq6GtrQ0AaNOmDTIzM4WISkREREQCch09Avomxji67idUV1c/9bjQ3fvQSqcNLLs7KzEdEREREVHL0KSKz0ZGRkhPT3/wdUZGBoyMjB465pNPPsGUKVOQnp6Ow4cPY/78+U8cy8/PDzKZDDKZDPr6+grNTURERETKI1FRweBXZiAtMgqxgeeeeWxCSBjKikvgOKCPktIREREREbUcTar4LBKJHrvv0ZUsvr6+2LJlC0xMTDB8+HBs27btiedt2rQJrq6ucHV1xe3btxWWmYiIiIiUy338aOh2MMSRdT8999jyklLEnzuPzv29lJCMiIiIiKhlaVLF54yMDJiYmDz42tjY+LG2GrNmzcLOnTsBAKGhoVBXV+fKZiIiIqIWQiKVYuDsqbh64RISQ2W1Oicq4Cx0OxjCyN5GwemIiIiIiFqWJlV8lslksLa2hrm5OVRUVODj44P9+/c/dMy1a9cwcOBAAICdnR3U1dVx69YtIeISERERkZJ1GdgXbQzaIeCX32p9TsyZIFRVVsJxQF8FJiMiIiIianmaVPG5srIS8+3i3noAACAASURBVObNg7+/P2JjY7Fz507ExMRg2bJlGDVqFADgnXfegZ+fHyIiIvDnn39i+vTpwoYmIiIiIqXp5TMOt9MzEB98vtbnFOUXIDk8kn2fiYiIiIjkTCp0gLo6cuQIjhw58tB9S5cuffDfsbGx8PT0VHYsIiJqQURiMaqrqoSOQUSPMLTqBMsezjiwet1j+4I8T1TAWYxZ/Cb0jI2Qk3FdQQmJiIiIiFqWJrXymYiISEga2tqY8+O3eP/QTugZGwkdh4ge0eulF1FeWgrZ3oN1Pjf61FkAQOcB3HiQiIiIiEheWHwmIiKqBQMLM7zxx8+wdHWBhpYWXtuyAfqmxkLHIqJ/qWlqovuooYg4ehKFefl1Pj/3+g1kxifCsT9bbxARERERyQuLz0RERM9h7GCHBb9vgnrrVvhh1nysn/EaJFIpXvt1AwwszISOR0QAXEZ4Q71VK5z7a3e9x4gLCoF51y5Q1dCQYzIiIiIiopaLxWciIqLnGL7gFZSXlWGt7yykRlzGzcSr+GHWPIglEkxZtVzoeEQEoOe4UciIice1KzH1HiMhRAaJihSWPZzlmIyIiIiIqOVi8ZmIiOgZ2ltawLa3O4L++Bt3btx8cH/W1RQcWrMBRnY2sPFwFTAhEWnp68Gksz0ijwU0aJyUS5dRXlIKa/5MExERERHJBYvPREREz+A1ZSLKS0oR+vfexx4LP3QMBbduo+/USQIkI6Iadp7uAO63zWiIirIyJIdHwMadxWciIiIiInlg8ZmIiOgpWum0QY+Rw3Dh4JEnbmBWWV6OoD93wc7THYZWnQRISI2JSMxfq4Ri5+mB/OxbyIxPbPBYiaEydLC2hJa+nhySERERERG1bPwriYiI6CncJ4yBiroaArf99dRjQnbuQWlRMfpO9VViMmpMTLs4YOE/v+PNPzdDRV1N6DgtjlgigY2HK+KDz8tlvIQQGQBw9TMRERERkRyw+ExERPQEEqkUvX3GIT44FFnJqU89rii/ALJ9h+AyYghXSrYwKupqeOHdBZj/+ya00mmDjnbWGPve20LHanFMu3SGprY2YhvYcqNGZnwi7uXegY2Hm1zGIyIiIiJqyVh8JiIiegLn4UPQxqAdzjxj1XONs9v+glgqRa+XXlRCMmoshi2Yi75TfRH69158MeolBPz8G3qOewEuI4YIHa1FsfNyR2VFBRJCwuQyXnV1NRLPX4C1ew+5jEdERERE1JKx+ExERPQIkViMgbOn4npcAuKDQ597fE56BhJDZXAZzqJjSyGRStF9hDcijp7A7s++QmlhEfw3/IyrFy9h/MeL0c7cVOiILYadpweuXY5Gyd17chszMVSGNgbt0N7SQm5jEhERERG1RCw+ExERPaLLwL4wsDDDyZ9/q/U5kccCoG9qDCM7GwUmo8bCztMdrdvq4sKBow/uq6qsxPbFS1FRWoZJK5dyA0Il0NJrCxMHO7m13KjBvs9ERERERPLBv4qIiIgeMXD2NGSnpOHy8VO1Pifq5BlUVlTAacgABSajxqL7qGG4m5OL+HMPr4zPz7qFf1ashqmjA7wmTxQoXcth29sdABAn5+LznRs3cSstnX2fiYiIiIgaiMVnIiJq1kwcHTBw9jRottGu1fF2Xh4wdrBFwOZtqK6qqvU8hXn5uCoLR1cWn5s9da3WcOjbG5eOHEdVReVjj0ccPYGYM8EYOm8OdDsaCpCw5bDt3RMFt3OQGZco97ETQsJg6eoMsVQi97EJkKqqQl2rtdAxiIiIiEjBWHwmIqJmS7eDIfw2rMbwN+biw6P/wPt1v+cWOwb5TUdu5g1cPHj0mcc9SeSxALQzM0FHW+v6RqYmoOuQAVBRU8PFA09/jez+7CtUV1dh3JJFSkymHGqtNNHBxgoSFRWho8DK1QVJ5y+gurpa7mMnhsqgpqkJMydHuY/dUqlqqMNz0gTM3rAanwb544NDf7MATURERNTMsfhMRETNklRNDdPWrIRYKsXPry9EXHAohsydiXf3bIexg+0Tz3EdMwIWzk44/ev2J65ofZ4r/7be4Orn5q37qKHISk5FRkzcU4/Ju5mFI99thL2nB5yb+EaUYqkEnft5Ytq3K/HJ6UNYEXoSC3dvw/zff4JUVVWwXAYWZtBup48kWbhCxk+ShaOqspJ9n+VELJFg6urPMfb9t6FnbIRLR46jla4OevuMEzoaERERESkQi89ERNQsjV+yCCYOdtj+3ieIPRuMbQs/whrfWaiqrMS8rRvhPGzwQ8ebODpg/JJFSAgJQ8jfe+s1Z+GdPFy9cInF52asrVEHWHZ3fuaq5xrBO3YjLTIKYxa/iVY6bZSQTv5cRnrj4xP7MfP7r2Dh7ITYs+dwaM0GHFqzASYOdhi1cL5g2Sx7uAAAksIUU3wuLriL9Og49n2Wk1EL58Peqxd2LV+FL1/wwc6lKxBzJhh9X/aBqoaG0PGIiIiISEFYfCYioman29BBcB09HP7rNyH2bPCD+9OjYrDWdxauRcdgyqrlmLRyKSxcukJLry2mr1mJ/Ozb2PbuElRV1n3Vc43IYwFoZ26KDjZW8vhWqJHpPmoYqqqqEH7I/7nHVldVYeeyL6ChpYVRCxcoIZ18OQ8fAt/PP8btaxn4Zd67WD7oBfz18ecI+GUbAn7ZhtNb/4Cn73jBNtm0cnNBXlY2ctIzFDZHQmgYTBztod66lcLmaAl6vfQi+kx5CWd++xMhf+95cP+JTVvQSlcH7hNGC5iOiIiIiBSJxWciImp2+k71RVZyKo5v/PWxx+7l3sHG2Qtw6tft6NzPC/O2/ogPj/4DTW1t/PrGYhTlFzRo7qiTZ1BVWYkuA/s2aBxqfEQiEVxHj0BS2EXcuXGzVufcTLyKU7/+DtfRw2Hj0XTaN3QZ2Be+ny9B8oVL2DhnAWLOBD3WiubQmg1IjbyCl5Z9AD0TY6VntHR1QVLYRYXOkRgig0QqhaWri0Lnac4MrS0x5r23EH06CAdWr3vosbTIKCSGXkD/6ZMFbeFCRERERIrT5IrP3t7eiIuLQ2JiIhYvXvzEYyZMmIDo6GhERUVh+/btSk5IRERCMnF0gGkXBwT/ueupm5BVVlTg4DfrsGzASPz54adIkl3E9vc+wY2EpAbPfy/3Dq5diYGdp0eDx6LGxdLVBXrGHRG252Cdzju+8Vdkp6Rh3JJFUFFXU1A6+bFw6YopX32K9KhYbJ6/COUlpU88rqqiEtsWLgEADJk7U5kR0b6TObT02uKq7JJC50mNjEJpUTH7PjeA25gRqK6qwo6PPkV1VdVjj5/YtAXa7fThOmaEAOmIiIiISNGaVPFZLBZj/fr1GDZsGBwcHODr6wt7e/uHjrGyssL777+P3r17w9HREW+++aZAaYmISAievuNRcq8QF/Yfee6xZcUluLD/MH5+7R1EBZyRW4bYoBCYONqjdVtduY1JwnMdMwLFBXdx5WTdXisVZWX4e/mX0DcxxoCZLysonXyIpRKM/3gx8rOysem1t1FaVPTM4/NuZiH8kD+6DhkADW0tJaUErNy6AwCSZIpd+VxZXo7k8Aj2fa4nsUQC5+FDEHP23FOvKkkKu4i0yCh4Tpqg5HREREREpAxNqvjs5uaGpKQkpKSkoLy8HDt27MDo0Q/3iPPz88P69euRl5cHALh165YQUYmISACtdHXQbehAXNh/+LlFM0WKCzwHsVgM2149BctA8qXeuhW6Dh6AS0eOo6L0ySuBnyX5wiVEHD2BvtMmQbudvgISyoen7wQYWlpg7xdrUHL3Xq3OCd29DyrqanAZ4a3gdP9n6eqC3MwbyM3IVPhcCSFhMLAwg057A4XP1dxYu7tCW1/vuRt0XjpyHIaWFmhr3FFJyYiIiIhIWZpU8dnIyAjp6ekPvs7IyICRkdFDx9jY2MDGxgZBQUEICQmBt/eT/xDy8/ODTCaDTCaDvn7j/SOQqCmw8XBD95FDhY5BhJ4vvgCpqiqCd+wWNMf12AQU3M6BvRdbb/yXTnsD9HhhODraWgsdpc66DRsMFXW1Orfc+K9Da3+AREUK79dmyzGZ/GjptYX3a7MRczYYMWeCan3e9dgEpMfEwX3cCwpM938ikQiWPZwV3nKjRmKoDABg3YR6djcW3Ud6oyi/ALGB5555XM3j9mxXRERERNTsSIUOUBcikeix+x7t5ymVSmFtbY1+/frB2NgYgYGBcHR0RH5+/kPHbdq0CZs2bQIAyGQyxYWmBusysC+sevZAB2tLaOm1xfGNmxF+6JjQsQiASCzG0HlzMMhvGgDA2r0Hdi1fhYqyMoGTUUsklkjQ66WxSAgJQ3ZKmqBZqqurERcUAsf+fSCWSFBVWfn8k5oBda3WsHF3RTtzU7QzM4W2fltUVlaiqrIS+ibGMLTqBOB+G4o/PliOSP+TAieuPbcxI3Ej8SrSo2PrPUZuRiaC/9wFr8kTEbh9J24mJcsxYcONeOs1SFVVsO/LNXU+9/yu/Rj/8SKYODogPSpGAen+r72lBVq31cVVBbfcqHEj4SoKbt2Gba+ekO09pJQ5mwNVDQ04DuiLiwePorK8/JnH3r6WgVtp6bDz8hD8w0MiIiIikq8mtfI5IyMDJiYmD742NjZGZmbmY8fs27cPFRUVSE1NRXx8PKytm94KK7rPyq07pq/5Aj1GDYNEKkVZcQkmf7EMY99/GxJpk/rspNnRbKONOT9+i0F+0xC6ax+O/fALXEePwGu/boC2QTuh41ELZO3uCt0Ohgje8Y/QUQAAcUGh0GyjDdMunYWOonAOfT0xdfXnWHb6EKZ9swLDF8yFtXsPaGhro3VbXegaGiI/+xb2f/Ud1k6ahWtXYjBl1XJ4TXlJ6Oi1YuxgBzOnzgjbW/9VzzWOb9yCksJCjHz7dTkkkx8jOxu4jh6B01v/xO1rGXU+P/ywP0qLipWy+tnKzQUAkCQLV/hcNeLPhcHGww0icZP61VlQXQb1g5qmxnNbbtSIDTwHa7cekKo1/k05iYiIiKj2mlT1TiaTwdraGubm5rh+/Tp8fHwwadKkh47Zu3cvfH19sXXrVujp6cHGxgbJyY1rZRHV3uBXZiA/+xZWDBuPirIyiKUSjHjzNfSbNglGdjb4Yfb8566mIcXwXfExOnXvhr8+XoGwPQcAANfjEjFp5ceYuOx9/Pzq2wInpJbGsb8XSgoLn3t5t7IkhIShsqICdl7uSI24LHQchfGYOBbjlyzC3ZxcBP/1DyL9T+JGwlWUFRc/9ZyNr7yJySuXYsziN1FeWorQv/cqMXHdiKUSTPzkfRTczpHLqtfiggKc/GkrRi2cr5RVwrXV22ccSouKcWrztnqdX1pYhEj/k3AePhj7v/pOoT3XbTx64nZ6Bu5k3lTYHI+KDw6F6+jhMOlsh2tXGse/WWPXfaQ3bqdn1Pr9Ly4wBH2mvAQrV2fEBYUqOB0RERERKUuTWr5RWVmJefPmwd/fH7Gxsdi5cydiYmKwbNkyjBo1CgDg7++PnJwcREdH49SpU3j33XeRm5srcHKqDwtnJ1i5dcepX7c/aONQVVGJA19/jx1LPoOFS1d0GzpI4JQtk9OQAXDo0xuH1v7woPAMAFEBZxD0xy7YuLuilU4bARNSS9S5nxfig883mg+kigvuIjXyCuw9ewkdRWHamZvihYULEB8ciuUDX8D+VWuRFhn1zMIzAFSUluK3hR8hNeIK+k+f/MS2Wo3FgFlTYWRvg92ffoXigrtyGTN09z5UlJWh29CBchmvodRaaaLbsMGIOHIcJfcK6z3O+d37oaapiS6D+skv3CMkUims3FwQH3xeYXM8SUJIGKqqqmDb212p8zZVrfV0Yd2zR53apF29cAllxSWw92q+75lERERELVGTKj4DwJEjR2BrawsrKyusWLECALB06VIcOPD/Atg777yDzp07w8nJCX/99ZdQUamBBr8yA3dzchG66/EVcbK9h5CVnApP3/ECJGvZ1LVaY+x7byE9Jg5B2/9+7PFI/5OQSKVwHNhXgHTUUhk72KJN+3aIPl37TdKUIS4wBMYOttBu1/w2thVLJZi0YinKS0uxY8nnde5rXV1VhaA//oa+qTFsPNwUlLJhDK0tMfiVGbh05DiiAs7IbdySe4WICw5F1yEDGkXh3WWEN9Q0NRDSwBXoqZFXkJeVjc79veSU7HHmzk5Q09REfLByV8YW5uUjIzoOdiw+14qDV2+IJRJcPn6q1udUlJUh8fwF2HGjViIiIqJmRanFZy0trWfeiGqYdnGAbW93nNn6B8pLSp94TPCfu2DaxQGmXRwUnqdmY73ZP3yDV39Zh1c3r0enHs4Kn7cxGr5gLlq31cWuZV88sdh0PS4Bt9LS0XXIAAHSUUvVuZ8XqiorEddIWm7UqLl03LpnD4GTyN/gOTNg2sUBu5Z/iYJbt+s1xuUTp3E3Jxe9fF6Uc7qG026nD5/PPkJxwV3sWfmN3MePPBYA3Q6GMHUSvie4x4QxyIiJb9BmijWiTwXCtldPSFVV5ZDscXae7qgsr0BSmPL6PdeICw6FaRcHaGjzd9bn6dzfE3du3MSNhKQ6nRcbeA76JsZoZ26qoGREREREpGxKLT5HR0cjKioK0dHRuHPnDq5du4b09HTcuXMHUVFRyoxCjdygOTNQeCfvmRuHXdh/BCX3CuE5aYJCs4hEIkxc9j4GvzID2np6EEnEaGdqgilfLmtxf4Bau7vCY+JYBP7xNzJi4p96XOSxAFi5dWfrDVKazv28kBJxGYV5+UJHeciNxKsoLrgLi+5dhY4iVxYuXTFoznTI9h2u08rGR1WWl+P87v1w6NMbOobt5Ziw/lQ1NOD92my8d3AnOlh1wq7lX6LwTp7c54k+FYjy0lJ09Ra29YZpFwcY2dkg5AlXGdVH9OkgqGlqwKpnd7mM9yjbXj2RcilSoT2lnyY++DzEEgms3V2VPndTIlVVhbW7G2LOBNf53LigEABg6w0iIiKiZkSpxWdTU1OYmZnhwIEDGDt2LHR1daGjo4MxY8awPQY9oKGtBfs+vRDy995n9g0tLSqCbN8hdPUeiNZ6ugrLM27JIriNGQn/9ZvwzcRp2DD9NfwyfyFat9XFmMVvKWzexsbCpStmrP0SWVdT4L9u0zOPZesNUiYdw/YwsrdBzKnG1XIDuN9aIiXiMjq5dBM6ity00tXBy6s+RU76dexZubrB44Xu2geIRHCfMFoO6RpGz8QY7+7ZjiGvzkLs2WB8OdoXUQFnFTJXaWER4htB6w2PCWNRWlSES4dr35v3WZLCLqKksBCO/fvIZbz/0tLXg5GdDeLPKbffc41rV6JRXHAXdr16CjJ/U2HVszvUNDXq1QbpTuZNZCWnwsaDBX4iIiKi5kKQns9ubm4P9Wg+ePAg+vfvL0QUaoQse7hALBY/WP3yLEF/7oJURQXu48coJMuw+a/AY8IYnPhpC479uPnB/ddjE3By01b0eGEYOvfzVMjcdaGhrYVR78zH9DVf4JWf1mLOxjXoYGMpt/HNujpi9obVyLuZhR/95j93xVlmfCJupaWjm8Ar+qhlqPkZjD7T+IrPAJASHon2nczRSldH6CgNJhKJ4LviY2jqaOO3hR+htLDhq0/v3LiJ2DPBcB83GhKpVA4p66d9J3O8vmUDVDXUsW7aXGx7dwlyMzIVOmeEfwB02hvArGsXhc7zNOpardFt6CCEHz4ml39L4P5q9vjg83Do21vuRXXbf4u+yt5ssEZVZSUSQmWw9WTf52fp3NcTpUVFuCqrX2uU5PAImDk5Nop+6ERERETUcIIUn3Nzc7F48WIYGRmhY8eOWLRoEe7cuSNEFGqEbDxcUVJYiLQr0c899nZaOuKCQuExQf7F51a6Oug7zRcXDx7Fke83Pvb4iZ+24HpcAsYvfU/QopKdlwfe3bMdXpMnQs+4I1TU1WHsYIcpqz6VS89N3Q6G8PvhW9y9nYsfZ8/HvZza/axG+p+EpatLsyi4UePWuZ8nslPScCv1mtBRnij5YiQAwMK56bfe6D9zCuw9PbD3yzXIjE+U27jndv4DLb22gl0t0cHGCq/9ugEikQgbZr6OlPBIpcwbczro39YbwvTIdxs7Eqoa6gj5a49cx406dRZtDNrB2MFOruPa9u6Jgts5cn3t1VV8cCh02hvA0KqTYBkaO4e+vRF/LgwVZWX1Oj8t4go022iz7zMRERFRMyFI8XnSpEkwMTHBkSNHcPToUZiamsLX11eIKNQIWffsgeQLEaiqeHwzuye5fPz+yjEDCzO55nAfPxoqamo4uWnrEx+vrKjAnx9+Co3WreH3wzdQa6Up1/mfRreDIXr7jMOod+Zj9g/fwG/DNyjKL8DaybOwevxUrJv6CrYvXgpDSwsMnTenwfP1mz4JKupq+OmVN+q0qVjksQBIpFJ0YesNUiB1rdawdOter8u7lSU9OhblpaXo1MT7PnewscTQ1+cg4ugJhP4tn/7ANeLPhSE/6xa6j/CW67i1odvBEK/8tBYVZWVYP/1VZF1NUdrcpUVFiAsKRdfBym+9IRKL4ek7HskXI3A9LkGuY8eeDUFlRQU6D/CS25gisRi2Hm5IOBeG6upquY1bV7H/biLqOED+bUWaAyM7G+gYtkdMA65ESY28vw+MeTcnecUiIiIiIgEJUnzOycnBvHnz4OTkBCcnJ8ybNw85OTlCRKFGRsewPQwszJAQKqv1OVcvRgC435NYXiRSKXr7jEN8cCiyklOfetyNhCRsffsDdLSxxqx1X0Oqpia3DE/Suq0u5m/7CS9+uBC9fcahbccOOPHTFnz70gxcj/1/8SD+3Hmc++sf9J3m26DnRbONNlzHjMSlw8eQe/1Gnc7NjE9ETsZ1OPQVvi2JkAwszDBq4Xx0GdgX6q1bAQBU1NVgaNUJWvp6Aqdr+nqMGgapigoijh4XOspTVZaX49qVGLm+RymbSCzGhI/fQ/Hdu9j92VdyH7+6qgqXjhyHnacHNNtoy338p1HV0MDM71dBqqKCjXPewO1rGUqbu0bE0RNo074dOnVXbl9we69e0DM2QuAff8t97OKCAqRcuozO/eRXfDa2t0UrXR3EnwuV25j1UZB9C1cvXIKLAB+UNAUO/TxRVVWF2LPn6j3GrdRrKMzLh3k3YdrREBEREZF8CVJ8trS0xPr163Ho0CH4+/s/uBFZu/cAACTWofh8Oy0dd3Ny5fqHu9OQAWhj0A5nf3/+RpixgefwxwfLYOHSFdO++RxiqURuOf5LJBZj8hefQFNHG99N8cP7bv2xarQvjny/EZXl5Y8df2D1OtzJvAGfzz6Cinr9iuK9XnoRapoaOL3lj3qdHxcUCiu37pCoqNTr/KZOXas1Zn7/FfpNm4Tpa77A8sCj+PjkfnwhO41392zHkuN7MfmLT2BkZyN01CbLY+JYpF2ORkZMvNBRnik5PAJGdjZQ1dAQOkq99PZ5EWZdHbFv1RoU5RcoZI7wQ/6QqEjhNEQ5LShEIhEmf7EUhlad8NvCj5CdkqaUeR8VfToQJYWF6D5yqFLn9Zo8AXk3sxAVcEYh40efCkRHGyvoGRvJZbyu3gNRWV4hWL/n/wo/fAztO5mjo6210FEaHYe+vXHtSjTu5TasnV5qxBUWn4mIiIiaCUGKz7t27UJsbCw+++wzLFmy5MGNyMbdFQW3c3AzKblO5yVfjEAnF/kVn/u8/BKyU9Jq/UduxNET2P3pKjj06Y0XP1wotxz/NXjOdNh4uGHPitVIi4x67mXHZcXF+GfFauibGMPGw63O80lVVeE5aQJiA8/V+d+jRmxgCNQ0NZS+oq8xEIlEmPT5x2jbsQPWz3gN66bNRcDmbUgICcORdT/h90UfI3D7Tjj088Tbf2/FC4veEDqyXIlEIkxauRSfBR/Dh0f/wVt/bYHLSPmuFLTs4QxDSwuE7PxHruMqQsrFSEikUph1dRQ6Sp3ptDfAsAVzERcUivBDxxQ2z/W4BNy8mgKXEUMUNkcNkViMsR+8A8cBfbFv1VokhIQpfM6nKS8pxZUTZ+A0uL9c+vTXRvtO5rDxcEPwjn9q3eKqri4fC0BVVRV6jB7e4LHEUgm6jxqK2MBgFOblyyFdw1w+FoDK8go4Dx8sdJRGRUNbC8YOdogLavjq9NSIK2jfyRwa2sq7EoKIiIiIFEOQ4nNVVRXWrVuHkJAQhIWFPbgRWfXsgaTzF+p8Xkp4JNoadYBOe4MGZzDv2gWmjg44+/tfdeorGbprH078tAUe48eg37RJDc7xX1Zu3TH41VmQ7TuM8/8cqPV5iSEylBYV16v43OOFYdDSa4tTv26v87k1rsouoqKsDHae7vUeo6kaMGsqOvf3wv6vv0PyhUtICY/E0e9/wo6PPsOJjb/i0pHjOPD19/h08Bic27kHfV/2gdvYUULHlpu+0yah+8ihiA8JQ/LFCIilEry0/EO5bkDWy2ccivILcOnoSbmNqSipkVdQVVmJTk2w9ca4jxdBLJZg92erFD5X+CF/WHZ3hm4HQ4XNoaqhjunfrkBvn3E4veUPBCmg7URdhR/yh4a2Fuz79FLKfL19x6O8tBTnd+9T2Bx5WdmIP3cebmNGQCRu2K+bdr09oK2vh7C9h+SUrmGK8gsQf+48nIcNVnqv7sbMvGsXiMViJP/bDq0hUiOvAADMunZu8FhEREREJCxBis/79u2Dn58f9PX1oaWl9eBGLZuhVSdo6+vVqd9zjeTwf/s+y2GFbd9pvigqKMDFA0fqfO7RdT8hwv8kRrz9OhwHyG+jvaHz5uBO5g3883ndeq1WVlQg+eIl2Li71uk8kUiEvlN9kR4di6uy8Dqd+19lxSVIvhgBu94tq/hsZG+DofP8EH742HMLWyV37+Gfz79G/LnzGPfRQpg4OigppeKYdnHA8AVzEXksANsWfoQ/P1yODTNex93bOXj5q0/lsjmnlr4eugzoi7C9B1FRWiqH1IpVWliE6/GJTa7vc6+XY2hMXQAAIABJREFUXoRDn944vPaHOvd9r49LR+737u42bJBCxtfSa4tXN6+HQz8v7Fn5DQ6s/l4h89RVUthFFNzOUUofYZ32BnAdPQKXDh9X+CrisD0HoWPYvs7/D3qU65gRuJuTi9jA+vcRlrfww8eg28EQ5s7cFK+GhUtXVJZX4NqV6AaPlR4Vg8qKCrbeICIiImoGBCk+z549G0uWLEF4eDiio6MRHR2NqKgoIaJQI2L97x+niaF1X/mcGZ+EknuFDV5V2N7SAk6D+yPoj10oKy6p8/nV1dX488NPkX4lBi9//SkGzHq5wSu+jOxtYOHshMDtf9crU0KoDAYWZnVaFW7erQsMLMwQ+PvOOs/3qNigEBhadYKOYfsGj9VUuI0dhcryCuz+tHYrRaurqvD7oo+Rn30b09esRGs9XQUnVBx1rdaYsupT5Gffws5PVj64v7igAL8vWgrdjoaY8PHiBs/T88VRkKhIEbJzT4PHUpaUi5Ewc3KERCoVOkqttLe0wAsLFyA2KASB2xv+XlAbuRmZSI24opAibHtLCyzY/jPad7LAljcWN4oVzzWqKitx6chxOPTpBQ1txX4YP2rhfIhEIhz74ReFzgPc7/tceCcPbi/W/6qOVro66NzXExcPHlVYi5D6iD4ViLLiEjgPY+uNGhbOTsiIiUN5ScM/ECwvKcX1uASYd2XxmYiIiKipE6T4bGpq+tjNzMxMiCjUiNi4uyI7JQ15N7PqfG51VRVSI640eFXhwNlTUVpUhMBabDT4NBWlpfj59XcQfSoQI958DfO2/gh9U+N6j+fpOwGlRUWQ7T1Yr/NrNm+s2cyxNroNHYTyklJEBZyt15z/Ff9v78eW0npDLJWgm/fA+5uI3Sus9XlF+QX49Y3F0GrbFn1f9lFgQsUa/e4b0DE0wLZFS1By995Dj6VGXIb/+p/hPHwInIfXv6+vWCqBx4QxSAgJw+1rGQ2NrDTJFy9BVUMdpl0a/+p2qaoqpny5DCWFhdjx0adKnTv8kD862ljB2MFWbmNa9+yB+b9thFRVBRtmvIro00FyG1tewg/6Q6qqCqfB/RU2h5Vbd3QbOggnf/kNd27cVNg8NSrLy3Hh4FE4DuiDVjpt6jWGywhvSFSkkDWSlhs1yoqLEX3qLLp5D1TYRsNNiVRVFaZdHJAcHim3MVMjrsC0S2eIJXx+iYiIiJoyQYrP6urqWLx4MTZs2AAAsLS0xNChyt3lnRoXkUgEc+cuuHrxUr3HSL4YgQ7WltBsU7/NafSMjeA8bDBCdu5FUX5BvXMA9wuJv73zIX5fvBQGncyw4PdN9SpAt9JpA+fhg3Fh/5E6FTL/62ZiMu7m5D5YWf48IrEYToP7I+ZsMEqLiuo1539lJaciN/MG7Dw9GjxWU2Dr0ROt2+ri4kH/Op97IyEJKRGXYdtE25QYO9iix+jhOPvbDly7/OTLrgN++Q3pMXEY+rpfvQsKzkMHQ8ewPc7KYWW+MiXJwlFVWVmvHuzKNnjuTHS0tcZfSz7HvZw7Sp374iF/FBUUYMirs+UynpVbd/j98C3ysrKxdtJsZMTEy2VcecuIiUN2Shp6jBqmkPHFUgnGvPcWcjKuN6iXf12F7TkIqYpKvVezu40ZgWtRMfXe+FaRLhw8ila6OnAepvhNMhs7k852kKqqIuWS/IrPaRFXoKapgQ42lnIbk4iIiIiUT5Di8+bNmyEWi+Hl5QUAyMzMxIoVK4SIQo2EgYUZNLW1kXrpSr3HeND3uZ6rn/vPmoLKigqc3vpHvTM86tLhY1jjMwvV1dWYveGbOq/86jnuBaioqSF4x+56Z6iurkbi+Qu1Lj536t4N2u30EeEvv03c4oPPw9q9R5NpN9AQLiO9UZiXj/jg0HqdHx8cCiM7G2jp68k5meK9sOgNFN7Jw4lNW556THV1NY7/uBn6psb1Wv0sEonQf+YU3Ei8itizwQ1Iq3zFBXdxLSoGNr0ad/G5la4OvCZPRPjhY4L02C25ew+nf/0Dnft5wtSpYZuNSdXUMGHpe8i9nol1U1+p15U1yhS6ax86de8GS1cXuY/d22c8OlhbYt+qtUrtk34z8SquXYmpV+sN865d0NHWGhf2HVZAsoaLDwrF9dgEDJk7s8WvfrZwub/nRuqly3IbMzXyfks+827sq01ERETUlAlSfLa2tsbKlStRXl4OACguLuZu4S1czYYyqRH1/6MlPSoWFWVl6ORS900H27RvB9fRIxC25yDu3s6pd4YnyUnPwOYFi6BjaIAZ362CVFW1VueJJRJ4TByLxNALyLqa0qAMiSEyaOvrwdCq03OP7TZ0EEqLiuVa2IsLCoF6q1bNfmMmNU1NOPbvg0j/k6isqKjXGHH/timx7dVTntEUrsugfrDs7oyj6zc9d5V+9KlAXI9NwOA50+u8+tnOqxc6WFsiYPO2hsQVTMK5MJg6Oii8r29D9HnZByrqajj+42bBMgRu34m7ObkYNm9Og8YZ5DcN+qbG2PXpqnpfPaJMwX/9gzs3bj7oyywvuh0NMXSeH2KDQhB9KlBu49ZWyN970dHGCvZevep03oi3XkPB7Rxc2F/3DYCVobq6GkfX37+yqceo4ULHEZSFixNuXk2R6yaWeTezkJNxHbZN4GoRki+JVAoLl64Y6DeNfdWJiIiaAUGKz2VlZVBTU0N1dTUAwNzcHGVlZUJEoUbCvJsT7uXeaVD/1oqyMly7ElOvlc8eE8ZCLBbj1K+/13v+Z0mLjMIfHyyHhbMTxn30bq3OcejbG207dkDQnw3fFCvhQd/nZ69+FkskcBrUDzFnguSyYVCNxNALqCgrg0Of3nIbszFyHNAHqhrqCD9U95YbNW4kJKHgdg7sejed4rNUVRWj3pmHzIQkhP1zoFbnHPtxM9qZm6LbsEF1mmvAzCnIzbyBiKMn6hNVcPHnwiCWSGDl1l3oKE+koa0NT9/xuHz8FLJT0gTLUVZcjIBftsHGww2WPZzrNUb7TuboP3MKZPsOIynsopwTKkZFaSmOfLcRJg52cB4un4KLSCTCS8s+BKqB3ctrtwmqvF04cBi3r2Vg+Juv1rqo3rm/Fzp174ZjG36RSwsoRYk5E4RrV2Iw+JUZLeLqnicRicWw6OYk15YbNWLPnoNVzx6QqqnJfWxqnAb6TcOnwccwb+uPGL5gLqasWo4Z332J1m2b7mbMRERELZ0gxefly5fj6NGjMDY2xpYtW3Dq1Cm8//77QkShRsK8WxekRta/5UaN1MgrMLK3qfXq4hrdhg5CUthF3MlU3AZMl48F4OTPv8Ft7Eh0qkUxpdfEsci7mYWYMw1fgZx3Mwu3Uq/B5jnFZys3F7Ruq4uIo/JruQEApUVFuCoLR+d+nnIdt7FxGeGNnIxMpEbU/7VcXV2N+ODzsOnVEyKxIG/RddZvxmToGRth/1ffoaqyslbnRJ86i8z4RAyeM6PW36d5Nyd06t4NZ7b+iaqK2s3T2FyLikbJvcJGu7Lda/IEqLduhRM//Sp0FJz76x/kZWVj2IK5df5ZEIlEGPfxIpQWFuHA6u8VlFAxwg/5IyMmHsPmz63z/8uexH3CGFi798CB1d8rZZPBJ6mqqMSR7zeio41VrYrqYokEI996HdkpaTj/z34lJGyYo+s3oa1RB7iNrXtrkebA0MoCGtpaSLko/+JzzNlzUNPUgJVr/T6EoqbFfcIYDF8wF4nnZfj1jffwsddQ7Fu1Fra9emLhP7/DoplfQUdERNRcCVLZiIyMxIQJE+Dn54c9e/bAzc0NAQEBQkShRqCVThsYWJg1qGBXIy0yGlIVFRjZ29T6HGMHW7QzM8GlI4pfSXl842bkXr+BFz9455n9Idsad4Rtb3eE7t5f62Le8ySEymDp6gyJispTj+nmPQgl9woRFxQilzn/K/p0ENqZm6Kduancx24MWunqwNq9By4dPvbgqo76ig8ORSudNjB2sJNTOsUxtLbE4Fdm4NKR40j8d4V9bVRXV+PYj5thYGEGjwljnnu8WCLB0Hl+KLyTh7A9tVtd3RhVVVQiKexCo9x0UK2VJrymTERUwBncSLgqdBxUlJXBf90mWDg7YfLKpXVaVdrbdxwsuzvj4Op1KLyTp8CU8lddXY2D36xDW6MO6D9zSoPGamvUAaPemYf4c+cRumufnBLWT6T/SVyPTYD3637P/bd0e3EUDCzMcPDb9XL7f6AixQeHIuXSZQyeOwMGFmZCx3kqkUiEjrbW8Jo8sc4tUJ6lpt2ZIlY+X5WFo7SoGPbN/MopAuy8PDDuw4WIORuMrW99gKiAMyjMy8fZbTuwxmcmyoqLMXHZB/XerJiIiIiEI0jx+eDBg7h9+zb279+Pffv24datW7U+19vbG3FxcUhMTMTixYufety4ceNQXV2N7t0b56XN9H+mTo4AIJfi87Ur0ffH7FL7Taq6DR2MivJyXDl5psHzP095SSn2fvktOlhbwmvSxKce5z5uNCorKnC+li0MaiPmTBDUNDVh5+n+xMclUim6DOqHqFNnUaGANjjRp4MAAJ37ecl97MbA2q07JFIpok83vJ9qQkgYqqqqnvpv1ViIpRL4fPYRigvuYs+K1XU+/8qJ04gPDsXIt+dB39T4mceO/eAdWPfsgcPf/Yiy4pL6Rm4U4s+FQc+4I/RMnv09K9vIt+dBU1sbxzcKv+q5Rtjegzj4zTo4Dx+CGd+vgqqG+nPPad/JHCPfmoeYM8EI23tQCSnlL/H8BVw+fgpDX/fD1NWf1+tyc1UNdbz81WeorqrGzqUrFZCybqqrq3Fo7Q/QNzFGz3EvPPU4Lb228H5tNpIvRgjSn7q+9qxcDbFEgjd3bG6UPWpdRw/H8sCjeGfXbxjz3luYsfbLem/Q/CgLZyfkZWUj9/oNuYz3XxVlZUg8L5NrsZwanw42Vpj69We4Hp+IbQuXPPah082kZBz4+nsYWJjBZYS3QCmJiIiovgQpPoeFhcHZue6Xz4nFYqxfvx7Dhg2Dg4MDfH19YW9v/9hxrVu3xoIFCxAaGiqPuKRg5t26oLK8AunRcQ0eq+DWbdy5cRNmTrUrPotEInQbOhDxwedRXFDQ4PlrI/pUIGLOBGPIa7OgbdDuscclUincxo5E7NlgFGTX/oOZ50kIlaHwTt5T/yi29nCFZhttubfcqJF3MwvXYxOabesNq549UHz3HjJi4hs8VmFePjKi42DXu3EXn/vPmAITBzvs/nRVvTeZ2vHxClSWl2PSiqVPXc000G8aek0ci5M//yb46k15iD8XBgCw7VX71c8ikQhGdjboP3MKpq7+HOOXLsaQuTPRzXugXFaBDZk7E70mjsWpzb/L5TUsT6d+3Y6dS1fA1sMNr25ej/adzJ96rEQqxeQvlqG0qAg7l65QXkgF2PbuEhxaswGd+3ni3T3b4TZ21DOvmPkvsUSCl7/+DMYOtvjj/U+QdzNLwWlrJz44FEmycAx/49Un7kGg1koTfj98CzVNDfxTjw+0hHQ9NgHfTJyOzLhETFm1HL4rPoZ51y5CxwJw/2qqFz98F9mpafjj/WVYNWYScq9nYto3K9Cm/eO/h9SFWCqBTa+eCu2rHnv2HPSMO6K9pYXC5iDhSFVVMWXVcpT8j737Dovi6uIA/NtC772DqKAiiCBYUcSGxIK9t0jEXmOJGmPvEnvXRI01ErsiiogiLoqCsIIIKiIIgtIFQcr9/jDhi7HQZlmQ8z7PeRLZmTNnx2XcPXvn3pxc7J88G+/fvfvsduJrN5AQFY1uE8fW2fnVCSGEkNqqWpvPgr8/IDs5OeHu3buIjo7G/fv3ERoaivv3y37T2rJlSzx58gRxcXEoLCzE8ePH4e7u/sl2y5cvx7p165CfX7tHx9UV9ZrbIPHRYxQVcLPAXXxEJMz+Hk1dFjNbG2gY6OPB5aucHLu8Tq/5FQKBEMPXLPlkGgzrzs5Q0dKE6OQZTo9ZUlSMB77X0LRje8gqKHzyeHPXLsjLzkbM7TucHvffIgMCUa+5DZTU1SR2DGlp6GiPZ/cfcHaLeHRQMExtrKCgqsJJPq4ZWDZEt4keCPO5WqW7BrJTX+OvFethZmuNTh4jP3ncadhAfDdtAu6d88GlzTurUnKNkZaQiLTEl+VuPmuZGGOBz1+YdfIges6cDKMmlrB26QDXyeMwcsMK/PjXH1UaJd9mUF+4Th73YZTxxu2VziNJd06dx8FZC6BlbIRZ3ofgOumHz04h1H3KOBg1scSfS1YjJy1dCpVyp6S4GP77/8CvA0fjzYtEDF62AD+dP4GWfXqW2YTu9/NsWHVoh79Wbii966SmODZ/KTJfpWDczl8/GgEtEAoxZuNq6FvUx8FZC5Ec80SKVVZOdupr7PCYDP/9h9CsiwumHt6D+ZdOotfsqWjW1eWzXzhXhwE/z0FJSTH++PFn3L9wGSlP4/D79HmQkZfDmI1rqjS3uEVLByipqyHiiuSmz3sUeBsAYNVB8qOfBTIykFdWkvhxyP91nzwO+g3McWLxKuS8Sfvqtpe37oaWsVGdnV+dEEIIqa2q9Wvju3fvokWLFujTp+z5PT/HyMgICQkJpX9OTExEq1YfL9rUvHlzmJiY4OLFi5g9e/YXc40bNw6enp4AAG1t7UrVQ6qOLxTA1NoKIm/uGq3xEQ/R3LUzVLQ0y2w+2Ll1QWF+ASKvV++H8/TEJJxYvAoj1i7FkBU/4+hPS0rnCW4zsA/SEpNKR0dyKcznKtoN6Y+mLu0RdulK6c+FsrKw7tQBYr8AFBcVcX7cf0QG3EK3iR5o3L4t7p/3kdhxqpu6ni506pni9p+nOcv5+FYwuk0YC8s2LRHuK5nR6JUlp6SI0V4rkZueWanpNv7rwWU/WLu0R7eJHlDT04X/vkN/z+04H9adnBEZcKvWj2L9r8e378K+RzfIKSqiIC/vi9vJKSli7NZ1kFNSxNEFyxAjulv64VwgFKJx+zboNWsKxu3ciITIR3gTn4DMlNfISv0Q2SmvUcJKIK+kBDlFBSRERpeOgpVTVESX8WPQccxwRAbcwskla6rluVfWQ/+biAuLgPvc6eg20QPNu3fBn0tWIy40HEI5OfSePRXthvRHsPfZWjVdQ1lSnj3H1pGeaNK+LbpN8sDg5QvRZfwY+O05iHvnL320+Kasgjzc585A6wHu8NtzAMEcf4nJhcyUVGwd6YmRG1Zg0JL5aNGrO/JzcqGipQlTGysc/3k5HgfV3jvXSoqKcXHTTvjtPYhmXTrCvmd3OA0dgI6jhwH40Eg9uXQNslK4u7Ppa+x7uqJRu9Y4tcoLmSmppT9PefYcxxYsw/eb16LPTzPhvWxtpfLbunZG/ttcibxn+UdWymu8fBSDJh3a4frvRzjPb25vi7aD+8HQsuGHdSkYEHb5Km4cPIakx7GcH4/8X73mzeA8ZhhEJ8+U6/c++tbf86uP/x4h5y5xNnCFfCCnqIii9+8l+lmAEEJI3VStzWcejwcAePbsWZX2/7d/L+zF4/GwceNGjBkzpsxce/fuxd69ewEAISHlXySLcMvQ0gKyCvKczPf8jxfhf8/73KzpVxsQfIEAtq6dEXUz6KvNH0kJu3QFGgZ66DFjErJepSIm+C6admz/YV7bzbvASko4P+bzsAhkvkqB/XfdPmo+N2rXCgoqynhwWbKLLr589BhZKa/RtKPTN9V8btDyw9zysXfucZYzXhyJ3MwsNGnftsY1nwctmQ9NY0Ps9JhS6ek2/uvksrV4l/MWLfv1Qsu+PVHwNhdySoo4u24zAg+fqPIijjVNyJkLaDuoL9oM7IOAg0c/uw2Px8Pw1UugY2aCPeNnfHJbe3FRESKvByI6UIS2Q/rDprMzTKytYN1JBzLycp/NWVJSgqd3Q/HkXijaDe4HVR1t3D19AadWbagVC7vlZmTi6PyluH/+MvovmospB3fhzqnzMLWxgoFFA1z//Qh8tuySdpkS8SjwNh4F3v5/E3rZAnTxHIPwK9cQHShCcVExhixfCC1TY1zbdwg+W3dLu+QvKsjNw29T5qD7FE80cLSDqq42BEIhTq/2QsjZS9IujxMFuXkIOXsJIWcvQSAjA8NGFmjUtiU6eYzCnFNHcHb9ZoScuSjRGpTU1eA+Zzqeh4tx+8SpTx5/6H8T1/YdQucfRiEmOKTCo5f5QgFsOjtLbK2If4sKDEKnsSOhoKqCd9k5nOTUNTdDj5mTYO3SATlp6YiPeAix/w3IKSiiZb+ecOjlhgeX/XBk/pKPvuQh3JBVkMfQlYuQkfQK5zdsLfd+Plt2YdLvO+DQ261GfsFWG8kpKmLE+mWw+nthz5LiYsSHP4T38nV49aRyn9sJIYSQf6vW5rOOjg5mzpz5xcc3btz41f0TExNhYmJS+mdjY2MkJSWV/llFRQXW1tYICAgAAOjr6+PcuXPo3bt3uab1INWvXvMP8yHGh3PXfE6MjkFxYRHMmll/tfncsKU9VLQ0Jd5w/Rr//X9AXV8PLmNHwGXsCBTmFyDi6nXc/vPTD4lcYIwh7NJVdBg5BIpqqsjL+jDPdfPuXZCbkYnYu9w1T790/Mgbt9CipyuEsrIS/7BaXSxatUBuRiZexT7lLCcrKfm70dQGPD5fIl9GVEbbwf3QvHsXXNi4HXGh4ZzlLcjNw18r1uPa3oPo9MMoaBob4uLGHbXy1vvyeCGOQmzwPTiPHopbx7w/+7vgOmUcmrq0x6lVXl+dT7W4qAiBh08g8PCJ0p8pqqlCTU8Haro6AI+Hgre5KHpfiEZOreHQyw1uUzwRHxGJ36bNQ8LDKIk8R0l6fPsONvQbDtdJ49Bh5GC8zcjEnvEz8FiC0wbVFP9uQjuPGgrnkUPRaeyHKWvSk5Kxy2MKnt4Lk3KVZSspLv5mptIpS3FhIRIeRiHhYRTCLl3F4OULMWT5zzBu0ginV/8qseN2GDUUimqqOPnD2i/+G3J5+x40cLTDoMU/IeFhFDKSXpU7v0WrD2tFhPtKbsqNfzy6eRtdPb9Hkw5tEXrBt8r5rJydMGbjahQWFODipp0IPHIChfn/H0Xru3MfnEcOQbeJHiguKsKxBcu+uS9B/yGUk4NBw/owbNQQ2a/T8Pj2nWr5MnLAL/OgaWyIXR5TKjQI5Om9MLx6Gofmrp2rrfmsYagPO7eukJGTg1BWBnFhYkTdqFlTGlWWirYWftjuBQPLBvDffwj5uXmQV1JEy769MPPPA7i25wCu7TtEo6EJIYRUSbU2nwUCAZSVlT87grk8QkJCYGFhgXr16uHly5cYMmQIhg0bVvp4dnY2dHT+P5/e9evXMXv2bGo812ANHOyQnpTM6e2nRQUFePk4BqY2Vl/dzqH3d3iXnYOoG0GcHbsyzqzZiNS450hLTMaTu/c++vAjCWE+V+EydgSadXVBsPdZyMjLoWlHJ4RevFItI3siAwLRdlBfWLRyKJ3HsbZr2LIFnoSEcv7B9NGNIDj0coOpjRXiwx9ymrsyLFo5wH3udETdDEKABG59Bj7ckn9q5QaJ5K5p/PYewMT92+DYpwdE/5myxWnYAHT1/B7B3mcRdMy7wrnzsrKRl5WN5JiPvxBJiHwEv92/Q8NAH5mvUmp1M+X9u3yc99oK0cnTyM3MrrZFY2uKf5rQckqKsGjlCC1jQ9w5dQ75b3OlXRr5irTEl9g5djJ6zpqCjmOGoeh9Ic57lX/UZ3nxhQK07NMDj27e/uoXoyVFxTgybzFmnTyEEeuWYfuYieV+L9DctTPe5bytli99XkREIj0pGfY9XKvcfDZp2gQj1i1DUkws9k6chdyMzE+2yc95C98d+1D0vhDfTZ+At+kZOLd+S5WOW9PIKyuh/6K5sO3W6aMF/DJfpeDOqfOIvB6I5NinHzWiZeTloKarAzU9XQhlZPAkJBTFhYUVPrbTsAFo0bM7fLbtqdSXZRFXr6PLuNFQ1tTA2/SMCu9fEbIKChi/Zwt0zD4MgCouKkInDyHOrd+CG4eOSfTYkqZpbIiJ+7ZBSUMdv02dg+hb/5/6JODgMfSZNwOuk8ehYWsH7J88GwW51X+nKCGEkG9DtTafk5OTsXz58krvX1xcjClTpsDX1xcCgQC//fYboqKisHTpUty7dw/nz5/nsFoiaQIZGVi2bYnQi1fK3riCXkREwrFPjy+OGJVXVoJN5464d+6S1EfflhQX49bRijeXKutldAxS4+LRYeQQ5Oe8hayCAuQUFattBHhs8D28y86BrWunb6L5rGViDA0DfVzbd4jz3NG376C4qAhWHdqVu/ksEAoxdOUimDazhrySIgQyMhCdPAOfLbuqNGrF3N4W329Zh9fxCTg6/9sdAVadnty9j+fhYnQaOxJ3Tp0rbfi07NMTfef/CPG1G/hr5XqJHDsjufyjG2u6Ny8SpV2CVBXk5uGhf+UX/STVjzGG815bIZARouOYYSgsKMDlbXs4PYZVh3ZQ1dFG8F/nytw2/WUyvJeuwcgNK+A6aVy5pq4RCIWw7twBD/1vVqr5WFGMMYRe8EUnj5HlWtPjSzSNDOCxfQPepmdg/+TZn208/9u1fQehrKUB51FDkZmSipuHjlfquDWNgWVDjP51FTSNDHDrmDfi7j9AUsxTGFg0QOuB7ug6YSxcJ/2A9+/ykRQTCxk5Oajr6UJJQ/2jPLkZmbh34TLueJ9FyrPn5Tq2uV0z9J49HZHXA3Ftz4FK1R9x1R/dJoyFdWdniY9+7j1nGrRMjLDTYwqe3L0PgYwMhq1ejN5zpkFRXa3WTvXEFwgwYs1SyCsrYcf3k5AYFf3R47kZmTjy0xJE3byNoSsWYcK+rdg7YWbpXZOEEEJIRUhlzueq8PHxgY/Px3PFLl68+LPburi4VPl4RHIaOtpDXkkJkQHcLwwVH/EQTsMGQr+h+Scj/wDAtlsnyCrII+SsZOdbrKl8tu5G/5/nYOSGFQCA7Ddp1XabdnFhIcT+N2DTyRkCGZlq+dAqSRatPsz3/DQvSumtAAAgAElEQVQklPPc+TlvERcaDquOTuWev7X7VE/YfdcND3yvITcjE0rqanD5fjgatrTH4bm/VKpRZ2JthR+2eyErJRW7xk2tcyNMJclvz0H8sH0DOgwfjKSYJzC2agy3qZ54HBSMP+YsonlGCfmGnV27CTJycug6/nu8in2KBxzO7996gDuyUl4j+paoXNs/8L0GyzYt0cljJJ7cuVfmGgYWbRyhqKparWsS3L9wGV08x8CuR7dKNYGFsrL4YcevEAiF2DdpVrkb2OfWbYaarg56zpyMFxFReP4gosLHrknsvuuGwUsXIC87GzvHTkZc2P+fT1pCIh7634Cang7M7Wxh1swaho0tkJuRhecPxMhKeY3MlFRkvkqBrLw8HNy/Q7sh/dF++CA8uOyHy9v2Ii3hy+8zzO2aYZTXSqQnJePowsp/kZ0c8xSpcfGw7eoi0eZz045OaDOwD/x/+6N0+qviwkIcnvsL3mXnoMu40SgpKoLvjn0Sq0FSOo4ZBjNba/wxZ9Enjed/C7t0Bfk5bzF64ypM+n0Hdo+bVukvfwghhNRtrLpCQ0Oj2o5VkQgJCZF6DXUx+i74ka2648+EsrKc59YyMWZeYhFr1b/3Zx+fcnAXm3PmqNTPgTSDx+ezes2bMbdpE1izri7VeuzGTq2Zl1jEmnRoJ/XzUNUYsW4Z++XaOYnldx41lHmJRUzDQL/MbS1aOzIvsYj1XzT3o59bd+rAlgVeZqvuXGN2bl0rdHyTpk3Y8iBfNv/SSaaqqyP18/0txqyTB5mXWFQaE/ZtZTLyclKvi4KCQvLB4/PZjBO/s0V+Z5msggInOdX19dj68CDWfYpnhfaTVZBnc88eY79cO8eUNNS/uu2YTWvYiqArTCAUVuv5mn50P5v158FK7dtx9DDmJRaxRm1bVXhfOSVFNv/iSfbzldNMQVVV6q+byobL98OZl1jEJv2+gylrcfO5TEldjblNm8BW373O1oUGskFLFzADy4YfbaOgqsoGLJ7HvMQittD3FNNvWL/Kx3WbOp6tCwtkSupqEjlXyloabEnARTbrz4NMICPz2W2GrvqFrQsNZLrmZlL/u61I6Desz9bev8FGea0s9z4NHO3ZqjvX2MwTBzi7VlFQUFBQ1O6oYC9V+gVLO6j5LJ34+cppNmbTGonlX3bThw1d9csnP9c2/dCYdvl+uNTPQV0NgVDIlt/yZUNXfvr3U5tCIBSyJQEX2bDViyV2DJ16psxLLGJtB/f76nZKGupssf95Nuf0kc82LtX0dNjkAzuZl1jE3OfNKFfDoJ6tDVtx+ypb4ONdruY3ReVCy9iI2bl1Zeb2tkzDQJ/xeDyp10RBQVF9Uc/WhnmJRey76RM5yddtogdbHx7ENAwrft02sGzA1twLYB7bN3zxWtSo3YcvkDuPG13t58pp2ADmJRYxfYsGFdpPQVWFLb/ly37Y4VXpYxtbNWZrQ2+y77eslfprpqLBFwhYn59mMi+xiI1Yt+yLzdSqhIq2Fuu74Ee2+u71Dw3uAzvYsNWL2YwTv39oTIcFsp6zpjBZBXlOjmfU2JJ5iUWsZd9eEjlnvedMY+tCA5leA/MvbqOkoc6WB/my8Xs2V/vfqUAorNQX1XyhgM08cYAtCbhY5pdM/43GTq3ZurBA5rF9A+MLBNX+nCkoKCgoalZUpJfKByFSYNjIAhoG+ogKkNxK0RF+AbDt2gmKaqof/dzRvQdKiotxn4PV0knlFBcVQewXAOtOHSCUlZV2OZXm2LcnVLQ0EXqJ+3nL//H6+Qu8jk+AlXO7r243YNFcKKiq4PC8Xz67aGVWymvs/GEKbvxxHB1GDMbkAzth5ewEHv/z/ww0dmqNcbs34m1aOraPmfhNzRFc06QlvkSYz1XEhYYjI/kVzadNSB3zPFyMkLOX4Dx6KLT/XtSssnh8Plr27YlY0V1kJFX8up0c8xTnN2yFVYd2GLlhBeRVlD96XCgnh34Lf0RqXDwCDhytUq2VEebjh+LCIjj07F6h/TqNHQF5FWVc3LSz0sdOjIrGBa9tsHbpgG4TxlY6T3Xi8fmw7+mKOaePoP3wQQg4cBRH5i2WyJRnOW/ScHqVF5Z16Y1z67dARVMT5va2yE3PhMj7DDYNGYsLv27D+3f5nBzvZXQM3iQkolk37qdZlJGXg2OfHojwu46Up3Ff3C43IxOXt+6BZZuWaNatE+d1/JdlG0eM37MZCy+fwpp7AVhx+yo8d21Em0F9P5mP+0u6TfCAsVUjeC9bV+ac5/8VfSsYp1ZugFWHdui3cHZlngIhhJA6qlrnfCbkH1YdnVBSUoKowCCJHSPwyJ9oM7APWg9wh//+PwB8eBPu0NsNj2/fQfbrNxI7NinbA99raNW/Nxq1a4XI69zP+y1pQllZdB3/PZ4/ECM6sHxzalZW1M0gtB3UF7IK8p/90FbfwQ7Nurrg4qadn53j/B8lRcU4t24zXoQ/hPu8GfDYth6Zr1IQcTUAOWnpeJeTAx0zE9h06QhNQwOkPHuOnR5TkPMmTZJPjxBC6ryLG7fDprMz+sybgX2Tfqx0HivndtAw0Me59VsqnSPo+F+QkZfHd9MmwNiqMf6YswgJD6MAAJ09RkLbxBg7PaZIZc2G3IxMRN8Swb6HKy5u3vnZRaX/S11PF+2HD0boBV8kxzyp0vEDj/wJoyaN4Dp5HBRUVXFu/eYa+YWhsVUjNO3YHnZuXaFTzxRJj2Px+/R5eOh/U+LHfpedgxuHjuHGoWMSP1bE1etwHjkUCqoqeJedw1leu+5doaiqitsnTpW57e0/T6NVv95wnzMN0YEivH/3jrM6/sEXCuA2xROdPEYhLTEJzx9EIC0xCUJZWVh36oABi+ai++Rx2D91Dl5ERH4xj7ldM3T+YRTunrlQ6YVqg73PQsPQAF3GjUbS49hynSNCCCGEms9EKpp2dMILcSTepmVI7BgpT+MQI7qLdkP6I+DgUZQUFaP9iEFQ19fDmTUbJXZcUj5PQu4jNyMTzV07S6T53Lx7F1i7tMfbjEzkpKUjOlCEl9ExZe5nbNUIzqOHQV5JCTLyckh/mYzTq70+GU3ceoA71PV0cXzhcs5r/6+IK9fhPHIIWvbtiVtHvT96jMfjodePU5CR/Ao3D58oV74HvtcQcS0AVh0+LKTTZmAfyMjLAQAKCwoQc/sufLfvg9gvAAV5eZw/H0IIIR/LSUuH7859cJ8zHVbOToi6Ubk7w7p4jsGbhMQqNxkDDhxBXFg4RqxbhhnH9iM1Lh7xEZGwc+uC0Iu+pYuvSUPIuUto6tIe9j1ccf+8T5nbu04eBx6fh8vb9nBy/BOLViAvOxvOI4dAWUsDx39eUWMWT7b7rhvcpnpCy9gIJcXFiHsQgYubduCh/80a2SSvKrFfADqNHYnGTm0QxuFdaG0G90Vy7FM8u/+gzG1ZSQlOrdyAqYf3oNMPI3F5Kzevs3+oaGthzKbVqGdrA5H3GZxdu+mj96QXft0GY6tGGLlhBSbt347D83757O+/vLIShq1egvSkZJxZXbXPQZe37oZho4boPWca4sIiqvylDiGEkLpB6vOESDtozufqDVUd7Q9zBf4g+bkCrZydmJdYxGxdOzMDywZs7f0b7PvNkptnmqJiMWDxPLYy2I/zxdX+WVRo8fULbEXQFeYlFrHVd6+z+g52X93Pvkc3tiYkgC276cNmHP+NTTm0m60PD2Ljdm78aH5EGXk5tvj6BTZx/7ZqO1eTDuxgi/zOfjJPo51bV+YlFrEWvdyqlF8oK8uUtTRoERkKCgoKKQVfKGBzzhxl8y+drNRizP/MxdyqH3dz4CqoqjCXsSPY2C3r2NIbl9jSG5eYiraWVM8Tj8djUw/vYUtvXGKKal9f/M+0WVO2PjyI9fpxKud1dPIYybzEIvbzldOsVf/ejC+U3hy4Jk2bsKmH9zAvsYjNOPE7c+j9XYXn862NwePz2fIgXzZo6QJOz6WXWMTaDelfof2Gr1nC1oQEMDU97hZn1jYzYQt8/mKr7lxjtq6dv7qtsqYGm3Zk34fFRqd6fvS7IZSVZcPXLGHrwgKZma01J7UpaaizX66dY/POHedsHu/KhLKWBpNTUpTa8SkoKCjqctCCg5I9YRRVjDaD+n5YLIaDla7LCh6Px+ZfPMmmHd7LZp86zBZfv1An3ozXljC3t/2wWEyfnpzldJs2gXmJRWzk+uWli+qpaGmyOWeOslV3rrF6tjaf7MMXCFjPmZOZl1jEJv62/aPXSKt+vZiXWMRGea1kfIGAKaiqsB4zJjIvsYiZ2zWrtnNl0dqReYlFrM3AvqU/E8jIsAU+f7FZfx6kReooKCgovoGwaOXAvMQi1sVzTIX3nXJoN/v5yulyLShb2ZBmg/XfYWDZgK0LC2SDlsz/aq0/eh9ii66eYXKKkmlONWzZorTpO//SyTIbhJIIm87ObG3oTbbY/zxz7NOjzr0fGP3rKrbQ9xRn+QYvX8hW3blW4YamhqE+W3MvgA1Z8TMndZhYW7GlNy6xJQEXmbFVo3LtI6sgz4avXcq8xCK26o4/67dwNhu+dilbKfJjXmIR6zZhLKfnvoGjPVsfHsQGL19YrX/n8irKrFX/3mzSgR3MSyxiXmIR+8XvHBu3c2O5zxUFBQUFRdWjIr1UAYAlqOM8PT2xd+9eaZdRJ/AFAoxYuxTpL5Pht+f3ajkmj89H6/69oaKliUM/LqRbw2qQzOQUNOvqAsPGFrjz17kq5+s+xRNdPcdAdPIMTvyyEiXFxQCA9+/eQewXANuuLmg9sA9S4z4s4gfGoG1qDI9tG9DcrQuCjv+FIz8txvu8/8/X9/JRDPLf5sJ51FC0HuAOt6njYW5vi8jrgbj+++Eq11xe6YlJaNS2FRq3b4Pbx/8CAPSYOQlWHdri6IKlSEt8WW21EEIIkYz0l0nQb1gfLfv2QugFX+S/fVuu/Rq2bIGu47+Hz5ZdiP/KnK9VxUqYxHJXxNu0DMgpKsJp+EDE3rmHzOSUT7ZxHjkUDr3dcGzhcom990t/mYy7p84j4eEj1G/RHE7DBsKytSOSY59Wy9oiLXq5YfiaJUh4+AhbR41HfPhDiR+zplFSV0OLHq64f8EX77Kzq5RLUU0VQ5b9jHvnfCC+VrE5kfNz3kJOSRFtB/dD5PVA5KSlV7oOfYsGmHJwJ/KysrFz7JSvLnr4b/8s6B1+xR+KqipwdO8BNV1tRFy9jkubd+Hu6QuVrulzMpKSwRcI0GHEYLxJSPzquiNcMGxkge5TPDF0xSI06+qCwvwCBB4+gRjRXeTn5qFec2u0GdQXCZGPkJaYJNFaCCGEVLyXKvVuubSDRj5XXzj0/o55iUWsqUv7ajumnJIi+8XvHOs1m/tbLimqHu2G9GdeYhEzadqkSnmadXVhXmLRV0dBqenpsJ/Onyi9Tbb/z3PYqjv+bHmQL2vevctX87cd3I+N8lrJOnmMYhatHD6Z/qI6orHTh1uqu0/1LB1pNXhZ9Y42oaCgoKCQbKjr67HVd6+z0b+uKvc+E/dvY79cO1ep6Tpqa8gqyLOFl0+xOWeOMnllpY8e0zDQZ6vu+LPvt6yttnp4fD5r1a8XW3z9Alv/4Bazc+sq0eM5DRvAvMQiNn7P5jo9ZZa2mQnzEotY64F9qpzrn7szDRtZVGp/eRVltuymDxu/d0ula5BXVmI/XfiT/XLtHFPV0a7S85FTVGR8gWTvVuALBGzSgR1sZbAf0zY15jS3prEhs3PryvotnM1mnzpcOo3ewMU/ffZzg6qONvvR+xBbG3pT4r9/FBQUFBQ07YakTxhFJYMvELD5F0+yWX8erPZjS/IWVIqqhZySIlt151qVbtnTt2jAVt25xqYe3lNmU5gvFDCbLh2Z5+5NzEssYp67N3E6P5+kY/qx/cxLLGLLb/kyu++6Sb0eCgoKCgruo5PHKOYlFjH7nq5lbtvctTPzEotYh5FDpF53dYdFa0e2LjSQTTuyj8mrKDPgQ+N5+tH9bNWda0xdX6/aa5JTUmQT929j6x/cKvOL7coEj89nvedMY15iERuzaU2d+sLhS7Ho6hk2ymtllfNM/G07m3PmaJVyOA0byLzEItbYqXWl9h+zaQ1bFxZYrVO7VTXU9XTZssDLbMaJ36s8OKNJ+7Zs/N4tbHmQb+mUGitFfmzczo2s/fBBpb/nXwp5FWU26fcdnM9/T0FBQUHxaVDzWbInjKKSIY1RzxS1I/ovmsvWhAQwBdWvLxz03+ALBayBoz2bf+kkW+x/vsIjRKS5QEplw6RpE9Z3wY9SX/CJgoKCgkJy8e/RhFomXx5NqGVsxFaK/NjUP/bUmPmYqzuaurRna0NvshnHf2OOfXqwFUFX2EqRH7Pu5Cy1mmQV5NnE37ZXeAS0nKLiV5t3sgoK7PvNa5iXWMTc585gPD5f6ue/JsTg5QvZssDLVZrvWlVHm60PD2LdJnpUqRaBUMh+uvAnm3P6SIVHHbuMHVFrv0hq2vHDIu+9506v1P4q2lps5IYVH+ZPv3iS9V80l7Ue4M6MmlhW+DwKZWXZDzt/ZevDg5hjnx5SPzcUFBQU32pQ81myJ4yiEiHNUc8UNT8MLBt+eLM9quw32zwejzVq24qNWLeMrbh99cOIiGC/zy4kSEFBQUFBUVtDXU+XLb/ly6Yf2//ZO7gEQiGbfmw/Wx7kyzQM9KVerzSjSYd2bO39G8xLLGLTDu9lmsaGUq9JVkGeTfp9B1sfHsScRw396rYNHOzYmE1r2PrwoNLF4hZePsXGblnHuk30YO1HDGYe2zew1Xevs/UPbjGnYQOk/vxqUtj3dGVeYhEzamJZ6Rz/jFjWNTercj02nZ0/jLzt37vc+zTr6sLWhwexkRtWSP18Vjbc581gXmIRs3J2qtB+DVu2YCuCrrA19wJY53GjObljVSgryzx3b2Lrw4OYQ+/vpH5uKCgoKL7FqEgvlff3/9RpISEhcHR0lHYZ37Tec6fDeeQQ/DZtLiKvB0q7HFIDTTm0G2q6Ovh10Gi8y8755HFZBXk4DRuINgP7QtPIAG/TMxB5PRBRN4MQIwrB+3fvPpOVEEIIqb2sOznj+81rcO+cDy5u2lG6iJ2ckiJ6zpqCtoP64sCMnyq8ONq3qL6DHYwaWyLouDdKioqlXQ4AQEZeDkNX/gLbbp0g8j6DUys3lNbG4/Fg09UFXX4YDaMmlsjNyMTd0xfwLuctFNVUoaqrDcNGFtA1NwOfz8fr+AQ8CryNMJ+reCHBRSVrI1UdbSz2P4/zXtsQcOBIpXJMObQbckqK8Oo/kpOaphzcBS1TY6z+bmCZ71EbONjBc/cmJERGY7fnNBTmF3BSQ3UTyMhg2uG90DDUh9eAkchKeV3mPo3atcb3m9bgTUIiDsycjzfxCZzVI5STg8fWdWjYygFnVv+KoL8X7CaEEMKNivRSqfkMaj5LEo/HQ98FP6LdkP4IPPInzqzZKO2SSA1lbm+LCXu3IF4ciT2eM1D0/j2AD68h+x6u+G7GRKjr6SI2+B5E3mfw0P8migsLpVw1IYQQIlnfTZ8Il7EjwIpLEHHVH+DxYO3SATLycrh5+ATOrt0k7RLJV/B4PHSf4okunmOQ/jIZSTGxSEt4iUbtWkO/gTlS4+IRcOAI7l+8gqKCT5uOsgoKUFRVQWZKqhSqrz3mnD6CrJRU7Jkws8L7quvpYpHfWVzasgvX9h7kpB5TGytMP7ofV3buh++OfV/czsCyISYf2ImslFRsGz0R77KzOTm+tGibGmPmnwfwMjoGuzymoqT4y18ENe3ohFFeK/HqSRz2jJ+O3MwszusRyslh5PplsHbpgKu7f8flbXs4PwYhhNRVFemlCgAskWg1tYCnpyf27t0r7TK+OXyBAIOWzkebAX3gv/8Qzm/YKu2SSA2WmZyCN/EJ6DhmOHTNzRAdFAyH3m4YtHQB2g3pj9Tn8fhj9s/w23MAKU/jwEpKpF0yIYQQInGxd+4h9IIvAAZb1y7QNDTA/Yu+OLNmI+78dU7a5ZFyeHL3Pl49jYOiqgp0zEzQ2Kk13mZk4szqX3FqlRcSox5/sUlXXFSE/Nzcaq649tE1N4N1J2cEHDha4feILfv3QuN2rXFy6VrOmr9Zqa+hV78eHNx74Nn9MGS+SvlkGwPLBhi/ZwsK8/Oxc+wUvE1P5+TY0pSXlY3MVylwHjkUQlkZxAaHfHa7Vv17Y+jKX/AyOgZ7xk9HXpZkmu4lxcWIuHIdajra6DByCDQM9REbfA/FRUUSOV55KKqpon6L5tCtZwZdczMoqqt+uKuF1fkxgYSQWqYivVQa+Qwa+SwJyloaGLl+BRo62sN3xz5c2blf2iWRWqLDyCFwnzsdRYWFEMrIIDn2Ka7/fgShFy6D0ZsyQgghdRhfKAAYvjqakNR8PB6P3tNwrEn7tvhhhxf2TJiJx0HBFdp32pF94AsF2DT4e05rUtfTxYR9W6FpZIiz6zZ9NO2DURPLD43nggLs9JjC6XQTNcHAxT+h9QB3PLx+EycWrSxtLvP4fPScNRkdRw/Do1si/DH7ZxTk5lVLTa6Tx6GL5xhkJqfg5NLViBF9vjEuCQKhEI59esDWtTMaONhBIBR+9HhOWjqiAm5BfO0GYoJD6O5OQkitQNNuVBA1n7lVz9YGo7xWQkFVBd7L1uL+hcvSLonUMp08RkHX3BTB3ufw/EGEtMshhBBCCCE1mFBWFktvXkLYpavwXra23Ptpmxpj/sWTuLBxO67/dpjzuuRVlDFs1WI07eiEqBtBSHn2HIX5+XAaPhD5b3Oxy2Mq0hJfcn5caePxeHAaPgg9Z01Gbnom7p33gbKGOgwsG8LUxgqBR/7EufVbqv2LtHrNm2HwsgXQNTfDzcMncG7dZol/EaSgqorRv66ERSsHvH7+AuFXryPm9h28zy8AGIOWsSGsOzujSfu2kFdWQn5uLqIDRQi/eh2PbgbV2jnACSHfPmo+VxA1n7ljamOFSb/vQFbKaxyY+ROSY55KuyRCCCGEEELIN27EumVo2LIFlnbqVe6pN3rPnQ6nIQOwvFsf5LxJk0hdPB4PXcZ/j7aD+0FBWRky8nJIjYvHnvEzkJH8SiLHrCmMGlti2OrF0KlnirfpGch5kw7RydMI9j4rtZqEsrLoMXMSOowYjNt/nsapFesl1oDWNjPBD9s2QMNQH38uWYP7532+uK1ARgYWrVrAurMzrF06QEVLEwV5eYj8Z0T07TvIf0tT8BBCag5qPlcQNZ+5oaqjjRnHf0PR+/fYPNRDIotGEEIIIYQQQsh/2bp2xqgNK7Bt9ATEhYaXub2sggJ+8TuLR7dEODJvcTVU+AFfKAArLqlTU6/UxKlm3KZNQJdxoyXWgDZqYokJe7eipLgYv0//qUJ3c/L4fNRv0Rx2bl3RrEtHKGmoo7iwCM9CH+D2iVMQ+wXUuPNJCKl7KtJL5Uu4Fs65uroiOjoasbGxmDdv3iePz5w5E5GRkQgPD4efnx9MTU2lUGXdI5SVxZiNqyGvrITfps2jxjMhhBBCCCGk2kQHilBYUACbLh3Ltb19T1coqKog6Ki3ZAv7j5Ki4jrXOKyJz9dnyy747T2ItoP6os9PMznNrd+wPsbv3oz83FxsHv5DhacRZCUleBoSCu9la7HEpSe2jRqPgINHoWGgj9G/rsLs00dg39MVfIGA07oJIURSalXzmc/nY/v27XBzc4OVlRWGDh2KJk2afLRNWFgYHBwcYGtrC29vb6xbt05K1dYtfefPgpmtNY4tXI5XsTTVBiGEEEIIIaT6FOTlIeb2Xdh0di7X9k5DByAx6jGeh4slXBmpqXy27ELAwaNwGjYQnTxGcZJT19wME/ZtRdH7QuzymIr0xKQq5SspLkZcWAQubd6JNb0G44/ZP4OVlGD46iWYd/44WvXr9ckChoQQUtPUquZzy5Yt8eTJE8TFxaGwsBDHjx+Hu7v7R9sEBATg3bt3AIDg4GAYGxtLo9Q6pb6DHVoPcIf/b39A7Bcg7XIIIYQQQgghdZDY/wY0DQ1gbNXoq9s1cLCDgUUD3Dp2spoqIzXVBa9tuH/hMnrMmAhH9++qlEvL2AgT9m4FAOwax/1ikqykBA98r8Gr/0j8Nm0u8rKyMWjpAsy/dBJtBvWFQEaG0+MRQghXalXz2cjICAkJCaV/TkxMhJGR0Re39/DwgI/P5yf1HzduHEJCQhASEgJtbW3Oa60reHw++sz7sFjGlZ37pV0OIYQQQgghpI6KCriF4qIi2HTu+NXtnIYNRG5mFsJ8/KqnMFJjMcZwYtFKxIjuYuCS+WjSoV2l8qjr62HC/q0Qysli17hpSI2L57jS/2OMIfJ6IDYP9cCeCTOR+SoVAxbNxYJLJ+E0bCAUVFUkdmxCCKmMWtV85vF4n/zsS/NHDR8+HA4ODli/fv1nH9+7dy8cHR3h6OiIN2/ecFpnXdK6vzuMGlvi3IatKMwvkHY5hBBCCCGEkDoqNzMLz+49QLOuLp/97AgAxlaNYd2pA4K9z6KogD6/EKC4qAgHZszHy+gYjNm4Co3ata7Q/qo62pi4fxsUlJWx23NatU5D+TgoGNtGjceucdOQkfQKfefPwmL/8xi+ZglMbayqrQ5CCPmaWjU5UGJiIkxMTEr/bGxsjKSkT+dQ6ty5MxYuXAhnZ2e8f/++OkusUxRUVeA21RNPQkIRccVf2uUQQgghhBBC6rg7p89jxNqlaDOoL26fOPXRY3yBAAMWz0NOWjr89x+SUoWkJirIy8Oe8TMxcd9WfL95DfZPmYPY4JAy97N17Yzes6dCXkUZuz2n4+WjmGqo9lOxwSGIDQ6BUWNLtOzXC/Y9usG+hytCzl7ExY07kJOWLpW6qotQTg4CgQAFeXnSLoXUUkoa6tAxM4WShhqU1NXBFwpQkJuH93l5eB2fING7GeoCHoCat/TsFwgEAsTExKBz5854+fIlQkJCMGzYMJ0Bzi0AACAASURBVERFRZVu07x5c3h7e6N79+548uRJufKGhITA0dFRUmV/s9znzoDTsAH4ddBoJMfQIoOEEEIIIYQQ6fPctRFmzW2woe8IZCS/Kv15++GD0OenmTg0+2eE+16TYoWkplJUU8XE37ZD28QYNw+fgOjP08h8lfLRNjweD2a2Nug+ZRwsWjngZXQMTi5Zg4TIR1Kq+lOyCgroPG40Oo4ZhsL8AlzZuR+3jp1ESVGxtEvjDF8oQKM2reDQ2w1NO7aHjLwcSoqL8S7nLZ6HRUDsfwNRAbeQm5kl7VJJDSSnqAirjk5o3K41zGytoWNm8sVtbx3zxulVXtVYXe1QkV5qrWo+A4Cbmxs2bdoEgUCA3377DatWrcLSpUtx7949nD9/HlevXoWNjQ2Sk5MBAC9evPhkUcL/ouZzxanp6WDBJW/cO+eDk0vXSLscQgghhBBCCAEAaBjqY87pI3gWGo59E2cBAFR1dTDv3DHEhUWU/oyQz1HSUMeAX+bB2qU9AODx7TtIf5mM3IxMKKqpwqZzR6jp6SA3Mws+W3cj2PssWEmJlKv+PG0zE/T5aSaaOLXBqyfPcGqVF56GhEq7rCrh8Xho3r0L3KZNgJaxIXIzMhF22Q8ZSa8gr6IEZU0NNGrbCpqGBiguKoLYLwA3D59AfPhDaZdOaoBG7VqjzcA+aOzUGjJycshJS8fzB2LEh4uRFPMUb9PTkZuRhZLiYsgpKUJOURF52dlIT/x01oW67ptuPksCNZ8rru+CH9FmQB+s7jkQGUmvyt6BEEIIIYQQQqqJ07AB6Dv/R9z84wTAAyxbO0LL2Ajr+g6jJgIpFw0DfbQZ1BfWnTpAWVMDCqoqKH5fiEe3RIi4eh1RN26hILd2TPPQtKMT3OfNgJaxER5c9sP5DVuRmZIq7bIqrF7zZnCfOx2mNlZ4+SgGV3btx6Obt1FcVPTJtkZNLGH/nSta9esFBVUVJERF46H/TTy+FYzEqOgvrh9GJEdWQR7q+nrQMNCHkoYahLJykJGTRUHeO2QkJSM9KRmZySmc/90IZGTg0NsNHUYOgX4Dc2S/foMHl68h4qo/nj8Q02uhkqj5XEHUfK4YVV0dLPTxRsi5S/Beulba5RBCCCGEEELIR3g8Hib9vgP1WzRHQV4eUp4+x41Dx/Dgsp+0SyO1FI/PB5/P/2yjszYQysnB5fvh6OwxCiUlJbi27yBCL/p+dTAZj8eDur4edOvXg16DetAzrwcdc1OwEoa8zCy8zchEalw8kmOeIOlxLPKysiVSu5KGOnrOnIyWfXsiMyUVlzbvQuiFy+VqGsoqKMChtxsc3XvAuGlj8Pl8vMvOwcvHsXgZHYOUp3HITE5BRvIrZL5Kwft3+RJ5Dlzg8flQ0daCmq4OCnJzkf0mDfk5b6Vd1mfxBQIYWzVCAwc7GDdtApOmjaFlbFTmflmprxF1MwiR128h5vadKv2+8YUCOLr3QBfPMdA0NEBCVDRu/nEc4Zev1drf45qEms8VRM3niunz00y0HdSPRj0TQgghhBBCaiwZeTkoqanVyhGehEiKhqE+es+ZjmZdOgIAMlNS8SIiElmpr/E2PQOMMeiam0Gvfj3omptBTlGxdN+36RlIjYsHA4OSmhpUtDShpKEOACgpKUFcaDhCL11BxBV/ThrRfKEAbQb2RffJ4yCnqIgbh47i6u7fK90gVtJQh2WblqhvbwvDxhYwtLSArIL8R9vkZmQiIzkFr+Nf4NWTZ3j1JA6vnj5DWsLLap9eRVlTAw0c7dHQ0R71HeygY2oCgYzwo20K8vKQEBmNuLBwPA+LwPPwh1JrSOvUM4Vla0dYtHZEQ0d7KKiqAADSEl8i4eEjJD1+8vfo5lfISc9AUX4BigoLIaekBE0jA2iZGMGytSMatW0FeWUlvE3PwL3zPrh76jxSnj0vdx2yCgpo2bcHOowcCi1jQ8SHP8Tl7XsRI7oroWdeN1HzuYKo+Vx+qjraWODjjdALvvhzyWppl0MIIYQQQgghhJAK0m9YH/VbNIe5vS2MmzSCspYGFFVVAQAZya+Q+uw5Up7FI+VZHFKePUfqs+efXbxPWUsDhpYNUa95MzTv3gV69euhuLAIj0V3EOZzFZH+gSjIq/j0JDadndFjxiTo1DNF7J17OL3Kq0INyPLg8flQ09WBhoEeNAz1oa6v/+H/jQygW88MWsaGpdsWFhQg9Vk8Xj6OwctHMUiMeoyEyEcoLizkrB4FVRU0cLBDw5Yt0LBlCxhYNAAA5L/NxbPQB0iOeYqMpFfISn0NOSVFqGprQdPIAKbNmsKosSUEQiFKSkrw6skzxIWG4/mDCMSFRny08CpX5JWVoNfAHKbWVqjX3Ab17JpBXU8XAJD+MhkxoruIDQ7Bk5BQvE3PqFBugYwMLFo7oGWfnrB26QCBjBBxYRG4c+ocwn398f7du0/24fF4MLGxQrMuLmjVrxcU1VQRFxaBa3sP4lHgbU6eM/kYNZ8riJrP5Td4+UK06NEda3oPprnSCCGEEEIIIYSQb4RARgYCoaBKU08YNrKA/Xfd0NytCzQM9JH/Nheik2dw8/AJZKe+LnP/Ju3botskD5haW+HVk2e48Ot2qTUPZRUUoFe/HvQbmkOvQX0YWDSAYWMLqGprAQAK8wvwPFyMp/fC8PReGF5ERKLo/fty55dTUkR9++Zo2KoFGjq2gGFjC/D5fLx/l4+4sHA8uRuKJ3fvITHqMUqKi8us1dTGCub2tjBvbgMzWxvIKysBALLfpOFldAySomOR8jQObxIS8SY+4bNfJsgqKEBJQw1K6mpQ0tCAqrYmVLS1oKKlBZW//1/bxAjq+nql+6QnJSM+/CGehIQiVhSCtMSX5T4HZVHW1IBDLze06t8buuZmpV8CJD95ipzXaVBUU4WiuhpMbaygpquD4sIiRAYE4sbBY3geLuasDvIpaj5XEDWfy8fOrStGrFsGvz0H4LN1t7TLIYQQQgghhBBCSA3E4/FQz64Z2g3uB1vXzigpKYHYLwARV68j+lbwR6NXVbQ00ayrCxzcv4OptRXSEl/Cb/cB3DvvU2bTVRpUtLVg1qwp6jvYoUELu9KmcWFBARIjo5EQGY2EqEfIeJmMdzlvkf82F0rqalA30IOmkSGMGlvC2KoR9OrXA18gQNH793j+QIwnIaF4cvc+Xoijqjyimsfnw8CiAcztbWHStDEMLS2g19AcQhmZ0m2KCguRl5WN/Jy3kFVUgJK6GmTk5D6bL/9tLnLepCE7LQ0ZSa+Q8jQOKU/jkPjoMbJSyv5SgQvmds3QtGN76FvUh37D+lDW0EBeVjbysrORGhePh/43EHXzdo2dB/tbQ83nCqLmc9m0jI0w6+RBJMc+xY6xk1BSVPP+ASCEEEIIIYQQQkjNomlkgA4jh8DOrSuUNTVQmF+AtJdJpQ1WA8uG4PP5SI59ipt/nMC985dqVc9BQVUF5na2aOBoB7Nm1jBqbPnJXNL/lv0mDYlR0UiMjMbTe2F4Hv4QRQUFEq9TIBRC09gQ2qYm0DY1hoqWJhTVVaGgooKC3DzkZmYiLzMLuRlZyM368N/sN2/wNi29Ri/ESKSDms8VRM3nrxMIhZjyx25omxrj1wGjJTJfECGEEEIIIYQQQr5dfIEA9eyawbpTB6jp6kAoKwOBUIgX4iiEX/FHytM4aZfICb5AAJ16plDT1Ya8igoUlJWQl5WNjOQUZL5KqfAcyITURBXppQrL3oTUZep6uhi66heYWlvhwIyfqPFMCCGEEEIIIYSQCispLsaze2F4di9M2qVIVElxcem0FIQQaj6Tr2jW1QUDF/8EgYwQx39eDvG1G9IuiRBCCCGEEEIIIYQQUktQ85l8VrNunTDaayXiIyJx5KclSEtIlHZJhBBCCCGEEEIIIYSQWoSaz+SzIv1v4vRqL9z+83StmuifEEIIIYQQQgghhBBSM1DzmXxWcVERbh31lnYZhBBCCCGEEEIIIYSQWoov7QIIIYQQQgghhBBCCCGEfHuo+UwIIYQQQgghhBBCCCGEc9R8JoQQQgghhBBCCCGEEMI5HgAm7SKkLTU1FfHx8TA1NcWLFy+kXc43S01NDVlZWdIu45tA51Ky6FogGfS6lSw6v5JB1wPJotetZNH5lQy6LkgWvW4lg86rZNF1QbLo9Vs1dP4kq679/mtra+PNmzcwMzODrq5uufdjFB8iNTVV6jV8y7F7926p1/CtBJ1LyQZdCyQT9Lql81sbg64Hkg163dL5rY1B1wXJBr1u6bzWxqDrgmSDXr90/mpy1LXf/5CQkArvQ9Nu/EtmZqa0S/imnT9/XtolfDPoXEoWXQskg163kkXnVzLoeiBZ9LqVLDq/kkHXBcmi161k0HmVLLouSBa9fquGzp9k0e9/2WjajX8JCQmBo6OjtMsghEgZXQsIIf+g6wEh5L/oukAI+S+6LhBSd9W13//KPF8BgCUSqaaWCg0NlXYJhJAagK4FhJB/0PWAEPJfdF0ghPwXXRcIqbvq2u9/RZ8vjXwmhBBCCCGEEEIIIYQQwjma85kQQgghhBBCCCGEEEII56j5TAghhBBCCCGEEEIIIYRz1HwmhBBCCCGEEEIIIYQQwjlqPhNCCCGEEEIIIYQQQgjhHDWfCSGEEEIIIYQQQgghhHCOms+EEEIIIYQQQgghhBBCOEfNZ0IIIYQQQgghhBBCCCGco+YzIYQQQgghhBBCCCGEEM5R85kQQgghhBBCCCGEEEII56j5TAghhBBCCCGEEEIIIYRz1HwmhBBCCCGEEEIIIYQQwjmpNp9dXV0RHR2N2NhYzJs375PHZWVlcfz4ccTGxiI4OBhmZmYAAE1NTfj7+yMnJwdbt279aB97e3tEREQgNjYWmzdvrpbnQQghhBBCCCGEEEIIIeRjQmkdmM/nY/v27ejatSsSExMREhKCc+fO4dGjR6XbeHh4ICMjAxYWFhg8eDDWrl2LIUOGID8/H4sWLYK1tTWsra0/yrtz5054enoiODgYly5dQvfu3XH58uWv1pKamor4+HiJPE9CCCGEEEIIIYQQQgj5VpiZmUFXV7dc20qt+dyyZUs8efIEcXFxAIDjx4/D3d39o+azu7s7lixZAgDw9vbGtm3bAAB5eXkICgpCw4YNP8qpr68PVVVVBAcHAwAOHTqEPn36lNl8jo+Ph6OjI1dPjRBCCCGEEEIIIYQQQr5JISEh5d5WatNuGBkZISEhofTPiYmJMDIy+uI2xcXFyMrKgpaW1ldzJiYmfjUnIYQQQgghhBBCCCGEEMmT2shnHo/3yc8YYxXeprLbjxs3Dp6engAAbW3tr9ZKCCGEEEIIIYQQQgghpGKkNvI5MTERJiYmpX82NjZGUlLSF7cRCARQU1NDenr6V3MaGxt/Nec/9u7dC0dHRzg6OuLNmzdVeSq1kl4Dcxg1seQsn6quDuQUFTnLp66vBxl5Oc7yaRoZQCAjw1k+LRNj8AUCzvJpm5l89suTytKpZ8pZLgDQNTersfl4PB60zUzK3rCc+AIBtE2Ny96wnARCIbSMubsDQygrCw1Dfc7yycjLQV1fj7N8coqKUNPT4SyfvLISVLS/fMdLRSmoqkJZS4OzfErqalBSV+Msn7KWBhRUVTjLp6qjDTklDq/NerqQVZDnLJ+GoT6EsrKc5dMyNgJfyOG12dQYPD53b5Xq0rUZ4Pb58vh8Tq/NfKGA02uzQEYGmkYGnOWTkZeDhgF313pZBQVOr81ySopQ1eFu8IaCqgr312YNdc7yfbg2q3KWT0VbC/LKSpzl+3BtVuAsn4ahPoRy3L0P1zI2gkDI3ZgnSVybuXwfXueuzVy+DxcKoGXC4ftwjq/NQjmur83yUNcr33yp5cH1tVleRRkqWpqc5VNUU+X22qxZs6/Nano63F6bDbi9NmsaG3J6baYeCSkLk0YIBAL29OlTVq9ePSYjI8MePHjArKysPtpm0qRJbOfOnQwAGzx4MDtx4sRHj48ePZpt3br1o5/dvXuXtWrVigFgly5dYm5ubmXWEhISIpVzIM1Y4OPNPHdv4izf3LPH2Ih1yzjL94vfOTZg8TxOcvF4PLbi9lXW68epnOSTkZdja0ICWNcJYznJp6SuxtaFBbL2IwZzkk/DUJ95iUWsZZ+enOTTb1ifeYlFrFlXF07y1bO1YV5iEbNs05KTfI3atWZeYhEzs7XmJJ+ta2e2PjyI6Vs04CRfq3692LrQQKZpZMBJvg6jhrA19wKYsqYGJ/m6TfRgK0V+TE5JkZN8vedMY0tvXGICGRlO8g1aMp8t9D3FeHw+J/lGbljB5pw+wkkuAGzczo1s6uE9nOWb+sce5rlrI2f5Zp86zEZ5reQs389XTrNBSxdwlm95kC/rPXc6J7mEsrJs9d3rzHXSD5zkU1BVYetCA5nzqKGc5FPT02Hrw4NYq/69Ocmna27GvMQi1ty1Myf5TKytmJdYxBo7teYkn0VrR+YlFjFzu2ac5LPp7MzWhwcxw0YWnORzdP+OrQsLZFomxpzkcxo2kK29f4OpaGtxkq+L5xi26s41Jq+izEm+njMns2WBl5lQTo6TfP0XzWWL/M4yvkDASb7ha5eyeeeOc5ILAPPYtoFNP7afs3yTDuxgE/Zu5SzfrJMH2ZhNazjLt8DHmw1ZsYizfMtu+rC+82dxkksgFLJVd/yZ29TxnOSTV1Zia0NvMpexIzjJp6KtxdY/uMXaDOrLST5tU2PmJRYx+x7dOMln1MSSeYlFzMrZiZN8DRzsmJdYxBo42HGSr2lHJ+YlFjFjq0ac5LPv6crWhQUynXqmnORrO7gfWxt6k6np6XCSr5PHSLbqjj9TUFXlJJ/btAlseZAvk1WQ/x97bxokZ3WlDT6VS+VSuZW2Yl+MBIh9EzS2aQyYzTTG003QeOaHe0w4ov3hbs94YsLM9IQ9/iY+wu4JNzHhsCM6sMdh+0fTDN2OENiSMIvxgjGS2BFLSZRAS5VUpdor98yaH2/eN7Oy3vfec+49pRKQN6Ij2iAdzpv55nPPfe7znCMS77/73/+XxW89vXUxEpPB5i/+t28t/m+/+v8W+/r6ROL9j//Pdxe/8ejPRGIBWPz7H/9g8b/89Edi8f6nR/7fxS//4P8Wi/fA4/+++D989/8Ui/ftZ59Y/Jv/438VidUXiSz+txeeWrzjf/4vIvH6U6nF7+767eJNX/mSSLzMmsHFf37594uf/u/vFom35tSTF7//+p8Wr/r850TinXzuOYvff/1PixfdeL3Y9/th/D8Ol7pqyudGo4Gvfe1r2LFjB9566y08+uij2LNnD77zne/gzjvvBAD85Cc/wdq1azE8PIxvfOMbeOCBB/y/PzIygn/5l3/B3/3d3+HAgQPYvHkzAOCrX/0qfvzjH2Pv3r3Yt28ftm3btirPd6Kv3U/swKZrrhK7Gc0PrcfFN10votjri0SQH1qPy2+7WUT9nBhII5XN4Mo7bxO5iRvI5xFPJrDlrs85xwKAzNo1iMZiuPoLd4jEy6/3lE1bpOK1buPF4p3kxZN63kJLybXlLqnnXY9IJIKr7rxdKN4GROMxXPFXt4nEKwxtQDyRwGW3fVYsXjIzgEs++xmRePmhDcisGcQFf/lJsXhrTjkZG6++UiReYWgDTtr4CZx+0QUi8fJD63HWpReL3Xznh9Zj07VXiykUC0MbcOEN1yGdd1eF9PX1Ib9hPS677SYR9XN/KoV0Locr77hVRK2czufQn0riqrs+J6KSyK5dg2g8JoZ9ufUetlz9hb8SidfGZpl4PpZKxxPD5g0eNn9eEJtjMVz5V7eKxCsMbUCsvx+Xf+5mkXj5oQ1IpNO49OYbZOKdtAEDhTwu/MynReIVhjagMLQBm/5CZmB3fmg9Npx9Js669GKxeGdcdAFO2vgJkXjes14l5jwqDG3ABX/5KTEFYH5oAy695UYRF2Ksvx8DgwVcccetIq7BVC6LRDrlYbOAWnlgcBCxeFwMS3Pr1yISjcph8wZpLF2ZOlz6nHDV52XORQWFzXfK1M35oQ2IxeO44nO3iMVLpFO47NabROIVhjYgncvhwhv+UijeeuQ3rMd5n/wLkXj5ofVYd8ZpOPuKS4XibcCpm8/FKedtEolXGNqAc666XMzJlB/agM3XXSum9s4PbcDFn/2MiJo6Eosit24tLr/9ZhHXYDKTQXJgAFfdebsMRzKYRzyREMPS7DqPI9lylzQ2y2KfFKfxcVirRj4DwLZt23Deeedh48aNePDBBwEA3/72t/H4448DACqVCu655x5s2rQJ11xzDUZGRvy/e/bZZ2Pt2rXIZrM4/fTT8dZbbwEAdu/ejYsvvhgbN27EP/zDPxz/h/qQrN1PbEckGhXZiCPRKJIDA4j19+PSW250jqfAOZkZwIWfuc45XirrEeLZtWtw7ievdo6XbBHsa087FWdffolzPJXfKedtwsnnnuMcL5nLAAA+ceVlIjazVNaLd94nrxGxxKp4F914vUg7APX5XXrrjSIbsYp3xeduETkkqXhXCZHPKp5UUZ5sfR9X/pUMoaO+3yuFyHs/ntDnp573KqHPT124SeWXymYRiURwxR3uhFhfXx8SmQHE4nFcKnBI6k+nEIlGkUincdGN7oekVAurMmsGcf6nrnWP1/pu15xyMs6+8jLneOpdOXnTOTj1fPc2VSq/sy67WMRSrOKde+0WkdY0Clsu/Myn/Wd3WclWvEtuuVHEIqp+a5d/7haRQ5KPpVLY0nqfr/qYYGlKGkuF97aViieBzYCHL9F4DJff7n6RHE8mEIvH0Z9K4mKBi2T13abzOWy+zv0iWeFJYWgDNm65wjmeym/D2Wfi9As3C8TzvtszLr5AxJKtsGrj1VeKXCQrLL3g+k+JtBdQz3vxZz8jIvJR8S677bMiF8k+Vt1xm8hF8ophqRi2yMZT74s4Np/w5xgZbE5lM4hEo7hcgCOJtnA5nkjgkpvdOZJUxnvWVC6LCwQuklUdnlu/Dpuuuco9Xuu7XXfGaTjz0ovE4p12wXkYOuds53jqt7FxyxUirXNUfud/+lrRVjIf5bWq5HNvrd6aeP8A3n/tTRHg77zJk9iYUh2HXpH8OuJJEIBL83MvZJbEEzi0KiAEIKK2VfGisRguv81d0aWeN55M4BIBRZc68KdzOWwWUNuq/PJD67HpGne1rdrY1591Bs642F1tq97nMy66QERtqw5J51x9hcghSb0vm//ykyJqW5XfJTd/RkRtq75f6UPSFX91q/MhKRKN+ngqgaWJzAAirQsUCSV/J7ZIY5UE1nfGk8F64fxynZ+f+yFJvXveIckdmxWWxhMJEbWtyi+VzYiobVW83Lq1ImpbFW/dGaeJqG3V+3Lq5nNF1LYq3jlXXS6itvUPSZ/6C5FDktqLLrrxehG1bSc2i6htFTbfcavzRXJfX5+PzRJY2p9K+T02T0gsza0cNp+Y+cmeO3xsjkRERD4qXiweF1HbqnjJgQFcdOP1YvGya9eIqG0VQbTm1JNF1LbqfTl50zkialv1Pp99+SUialu195537dUialv1fVx4w3Uialt1jrn0FimRTwubpS6SlQhEAJtj/f2Ity7LT0xOQ7rOXRnsA+Q/P4nLFPUuAzIXySq/aDwm5kj+qK8e+fwxXruf2C6itlWb8Ni+ERG1bWc8CbWt2pTG9o2IqG0785NQ2yogHNs3IqK2VUA4tm9EZmPqyE9q42zU6xh//4DQxpRFaXYOs+MTMhtTNovZ8QmUZueEDoUZHDt4GLVyReyyYnz/B2g2GmIb+5H39oupbVPZDMb2jYipbZOteGJq22wWY/tGRNS2kZinAh7bNyKitk0MeIeEsX0jImrbTiyQUNt2YoGE2rYzPwm1bbIjPwm1bWd+EmrbznhS2KfiSahtU9ksms0mjo68L4RVWZQXFjB95KjY884dm0RxZlYE65PZDCYPj6JaKosdusbfP4BGvS4W7+jI+wBkDkkKS6XUtqmch6VyalsvnoTaVrk+xvaNiKhtletjbN+IiNq2E0sl1Lad2CKhtk12xJNQ23bmJ6G27YwncZHciaUSattkZzwJLG2dY468t1/sMqBaKmPy8KhMXZ/LYH5yCgtT00J1eAbTY0dQKRbF6tyJAwdRr9VEsbnZbApdJHvYJ6W2VfHE1LateFJqW7UXSahtletjbN+IiNq2E1sk1LadWCqhtu3MT0Jt24l9F914vfNgxE7O5bLbPus8yLATS6+4w13kswTrBS9Wper6j8Pqkc8f4/XKtt+gUavjyjvcfiyqiH7+3/8TgLvaVgH1H//tMRG1baojnoTatjOehNpWAdcf/+0xEbVtZzwJtW0qm0G9WsWf/2MrTr9ws7PaNpXNoDQ7h5ee2I5ztlzhPOE5lc1gYWYGL/36SZx/nbvaNpXNYG5iEq8++Qwu/uz1zmrbZDaL6bEjePO3v8flAhtxKpvF0f0f4J0/vSiitk1lszj01jsYefk1kY0zmc3gvd2vYHR4n5DaNoO3fvc8Jg+NOl8GKPvbqzuextyxSefCQ9nfdm39NcoLC86XPQpL//wfW1GvVt3za2HBnx79T5FDUie2SKhtkx3xJNS2nflJqG0740mobTv3Dgm1bTKXQbPRwJ8e/aWI2jaVzaA8P49dj28TUdumchkUZ2bx0q92iKhtk9kM5o5N4pUdT4uobVO5LGbGjuKNZ38norZNZbOY+OAA3vnjn0XUtqlcFofefhf7dr8shqUjL7+KQ2+/K3ax+vYf/oSJAwedsUq5Pl77zbOYnTjmHE+5PnY/vh2luXkxLP3zf25FrVKRxeZGQyze84/8h4jathP7JNS2ipD44789JqK27cRSCbWturj84789JqK2TWUzaDabeP7f/1NEbeth8wJ2bf21iNo2lc2gODuLl371pIjaNpXNYn5qGi9vf0pEbZvKZjFzZByvP/WciNo2lc3i2IFDePv3VoK3AgAAIABJREFUz4uobZPZDEaH92HfzpfERCUfvPYmDrz5lhjZ/u6fXsT4/g+csUW5Pt54+jnMHBl3rnOV6+OlX+1AcXZWDPt2/vIJT+TjXIcrbP4lGvW682VKJzYD7hfJnVgqobbtxNJEOuV8kdwZb6CQF+BI2vEKJw3hHMeL5M54Q584C6ddcL5zvGqpjJ2/fAJnXnKhSNumj/rqkc8f47UwPYO3//An50OSAtbD7wxj786XcKUrsLbijbz8Gg7ueQdXCBEmb//xBU9tK0S2v/bUs5gdn3De6BTZvvvx7SjNzuEK1/yyGdTKFbz0qx0iG3Eym0Vpbh4v//pJEbVtKufF2/3EDkQiEVx+h/shqTQ3j92PbxdR2yZzGZTm5rD7ie0iattU1ou36/HtGBgs4LxPuR2SUiq/x7eLqG29/LzPT0Rtm/OU6Lsf3+astlX2t+LMLHb/aruz2lYVHQtT03hl+1POaltFns4encDrT/3WWW2r8jt28BD2/O55Z7Wtijc6/B72vrjb/bfbivfB62/ig9f3OKttFZa++8JOEbWtyu+NZ34norZV+b306ydF1LbJbBaNWh27H98uorZV2Pfytt+IqG09F8k8XvrVDgACh6RsBuUWtkiobRWW7n58u4jato1920TUtp3xJNS2yQ5sFlHbZrMoz3rxXNW2yvXhXSTvcFbbKtfHwtQ0Xt72G2e1rcKCuYkJvPabZ53VtgoLpg6NYs9zf3RW26r8jrz3PoZf2IkrPud2kazI0w/eeAv7X33dWQSi8tv74m6M7X1P4OLSi/fmb3/vqW2F6vqXtz2FhalpkXjNRgO7tm5DpVgUOXdU5hfw8q+f9NS2zt9HFqU577fWbDad80u2RCC7H9+GSDSKy253F/mUW9gXTyRwyWfdRT7eOWGbp7a9/lNO8RSW7np8O3Lr1zkPsE7lst7zPrEd6844DWdccqFbvNb3u/vx7c5qW+X6KM3OYdcT27FxyxUonDRkHU+5PnyRz6evxUAhbx1PYen8sUm8uuMZZ7WtwpapsSN445nnnNW26kww/v4BvPP8n53Vtiq/Q2+9i/d2vyJQp3nx3tv9Cg6/MyzGubz1u+dx7OAhZyxV5yIl8pEi23c/vh3l+QURLK0Ui3h52288kY8rR9I6k78kxJF8HFaPfP6Yr11PbEd+aL3TRqyAYckh6SJ7tW26FU9t7K69bf38lNrWsbetOhAVp2fx0q+fdO5tm87lUCmWUCkW8eqTzzj3tlVAWJ5fwJu//b3IIak0O4e5Y5MialtFIBw7eEhEbZvKZlCencfhd4ZF1LaK0Bl56dXWIUkg3uw83nn+BcxPTokQROW5ebz57O9QKRadVQjqEPLqk087WxKV/c3fiB3VtqrIUocuV7VtsiPe7id2OKtt2/l5lymualtVBJbm5vHSE9ud1bapDix9qXVIclHbqqKyNOths6va1n/eWe+yx1Vtq+IVZ2bx8q+edFbbKtdHeW5eRG2rsK9SLIqobVW8halpEbWtIhCmDo+JqG3VxeXY3vdE1LaKPN3/ymsialt14H/3TztF1LYKS9/47R+E1LYZlOfm8OqTzzirbZXrozQ33z4kOcRTro9Sq05zVdsqQkKR965q204s3f34Nme1bXc8V7VtsgP7dj2xHWtPOwVnOQywbufnEVinnLsRJ5+70T1eC5vPvuJSrDntFId43vfrOSGexLnXXu3UUi+Zy6BRr6M0O4uXtz+Fi274Sye1rVIWV0slvP7Uc7jslpuc1LaeSGAexZlZvP3753H55252wmZFnk4fOeqpbR3rvnQuh/LcPI6OvO+pbV2xKudh6Qev7xFR26p4w3/ejZkj487x0q14e577o5jatjQ3j9d+8yxq5YrTXqlcH0sukh2+32Q2g0gk4teRgNtFcrp15i3PepcBrmpbdYZWWJpIp3DxTQ7YrOK1sHSgkMf519m31EvnlmJp4aQhfOKqy2XiPbG9pbY9zzpeqiu/My+9COvOPN05XnF2Fruf2IFN11yF3Pp11vHSOa99W3FmFi//+je48DOfXtKj3ya/8vwCKsUiXvvNs7jk5hscL5K9325pdg5v/vYPuPxzNzuJfNK5HEpz85gdn8Dwn3eJtAb5qK8e+fwxX3t++wfUyhWc98lrrGOoorI8O4fXfvMMAOC8T9nHS+baBNErO5724knkN7+AV3Y8jUgkgnOvvdopXnlhAc1GA6/ueBqxeNzJBqIO/ADw6pNPI5FO46zL7AkiVRQBwCs7nkZmzSBO2+yw0XXEe3XH01hzyslOiilFnqp4J286B7kN9pcByRYBA3jPe9ZlFzv19VaEzuLiIl578lls+ournNWnpbk5NOsNvP7Mc07vsrK/lebmUS2Vsee5Pzr91pT9TR2Shl/Y6fhbax/QZ46M4/1XXnfKr5M8PTryPg6/uxfnS+Q3O48Db+zB5KFRJyV6ZxG498XdWJiaFsGq0uwc9vzueVRLZbfPr4Nsf+03v0Wz0RCL96okNrfIXQDO2FwrV1CvVvHKjqcRjcewyeFiNdmFff2pJM6+woEgyi2Nl87nnNSs3VhfGNrgpJhSF5defs9gw9lnOvUnTGWXxjvj4gvc1KwtQgcAXnvyGWzccoWTwkmp65qNBl5/6rdO77KXn/d91CsV7HnuD07xlOujNDeP8tw83n3+RRlsmZvD3MQxvPfSq07xkh1k58QHB3FwzztOZGwbC+Zw6K13MfHBQRmsmp3De7tfwdyxSbHP7+0/vIDywgLOd9rb2mT7G08/h0atjvNd9qIOLFXYfP6nXfa2dn4SdXgym0W9VkOtXMGrO55GNBbDuS4Xq111ZDyZwCeutCeI1EUU4GFpKpd1UrOmltSlzyC/YT1OcboMWIql68883an1RrIDS1/d8TROv+B8NzVr51705NP4xFWXO5L3XrzFZhOvPfUszrv2GkfRi3dx2ajV8Oazv3d6l6OxGBLplHeRvFDEW3/4k9tvo3VpUpqdw/zkFPbtelmorprD5KFRfPD6HrczeQf2jb67D0dH3hfC0nmMvPwaZo6MC9Wl3j5Zmp0TOceU5+bxxrO/R71adcL6ZIdA79UnWxyJ017ZPseIcCQ5z/VRLZbw6o6nEYlGnUQvyWwGlfkFLC4uehxJfz/OcSDvU11n/GRmAGdeYt/XWwnqAODVJ59Bdu0ap7ZInRzO7se3Y/DkIQwJDJz+KK8e+fwxX/VqFVOjY05DAjsJp/L8AmaOjmPNKW7xms0mKgtFzB4dR6VYxKBjfqqImThwEM1Gwzk/BTRH938AAE7xOsnToyNevEGHeOqGHwDGBfLz7GWteK38nN6XDgJG4vNTt45A+3ldPr+l+b2PaCyGvCU53heJ+PY8wPv8UtmM9S1wYiCNSDTqb5zj+z9AbsN6awKm80Ye8L4P188O8C6iVDzXd68zv3HH/NIdRaAX7303pW1HvMVmE+MfHHB83pbCZM47JE0eOiwSrzQ3j0qxiNnxCZHPrzy/gLljkyjNzTtjQaVYQqNex+TBw2jU687vSzf2uewd6VYLGaCNVe7YorDeGyS35lS39688236XAUks9eI57b05z/UBeFjq5ef2vJ1YFYlGnezEnXvv+P4PkEinrJXy/akkYvG4v1ce3f8BcuvXWRMwbcdWe68cPOUkawImtQz73LA53ZXf+P73MejwLqc7sArw3hcp7FtcXPSe1+ldbsdTeCWCpXNzqJbKmD5y1BlLVX4L0zNYmJ5xxtJaxbvImzo0inq16pRfJ5aOvy9V53ZhiyOWlgSxNBWEpY7YXO7GUqdzUWYJlrrm10nojI98gEgkgsLJ9tisXB+Ah1XxZAKZNXZK+Vh/P+LJRPt9GfkA2bVrrB2myQ6yU+VXOHnIHpuzAdgsdCYHJLB0eTzXMzmAJXjgll+bHG82Gpg4eMjp80t2XFzWKxVMjR5x/m2o/JSL2GWvTGU910e1VML02BFUS2VHTsNzfSwuLmL8/QMAXLFlOTa7fX7ZZVgqxuGMuGNpZx352lPP4v+6+QsYG95nHe/jsHrkc29h6vCok7pJDT2qFItevNExR+u0N/RocXGxld+Ym/qqQ83VrDdaBIzD82bbioFyi3B3JbBUvNnxCTTqdbH8pkbHAMD5+/DjjbXinSxTpE4L5NcJ/P7zWr4vkWgUyYEBnzydOuyWn69o6P4+LPPrLiqnDo8hEokgf5Ld0MZkbnm8RDplrYBZlt/omBsB01XkO2NBRxEIAJOjMvE63xfX31pnflNS+c0viOSXzGV914dMfu0isNloYObIuBiWVopFFGdmBZS7Xry58QnUazU38rRDITE9dgSAAJbOyWBVd7zJw25Y5cXLiuXnuT4yS7DAJV4inUY0FvMJHVdsTgZgMwBrAiYIC+IJewJmWbzDY8isGbQmYNSBv9yRX+EkewImGZCfGxasDJYqgtIZS9X7Mt9+/1x/a9VSGY1aTSS/zgP/4uIipseOOn9+Cvtq5YpHwAjVffPHplArV8SwdHrsKAB3LC0LYnNgXe+MzTJYuixeKz/bvTKWSCDW3+/Hm3Stw7OqDl96TrC9uAzC0lg8jqxlq4JlWHp4FOl8zrrFVyd5CnjPmx9ab+3gDHpemTq8A/sE2q2Vxc4Jy88xrvkp14fKb40Tp9GumwGvlpTC0kathpmj42KcS3Fm1hMQCtWl00eOotloOJ5jVg5La+UKZscnrGN9XFaPfO4tj4ARKjoAGeBXBT4gRGaL5tcmT6Xy88nxRsMd+LNZnwyrLBRRnJ0VUwLPjh9Do+ZGjgcVqbbP27a/LS2ibYteebJ4edEGuBTRyw/UEvlJHZLCnrdgSY53KySmRkc9BYxlr8ggsj27do11/7CgIrVw0gYndaJyfaj8JA6si82mn58UgSCRXyf2SeW3JJ4AQaR+Gx4Bc0SsSK2WypifnBLD0vnJKdQqFbeL5A6Cw5WA6YtElrwvrliVGEi3+liuLFa5x5MhYLrVZu75KVeKym8UgD0B0022T7oSMNmlSuqp0TEnAiboolaCgKksLPj5uWJVpVhEs97w8zvRsFQS65O5rnPCqCMB033ucK3DO/KrV6ueSEUISxemZ1AplpwvU9T3OzN2FM1mU+x9cRWp9KeSiMZjy+tI17q5+2LQ8hwThAUi8bqw1PZ9Ds9PLl40FrPu4xsUL5XNWA/rXk62eyIV29lEQWS7629NuT5UfvKcgetFWSc2uwkI5TkXeU5I1S0SAsJO7CvPzaM0N+8er+P77S3z6pHPvYXp0SPIrl2DWMKWgFn6w5sePYJBJwtSN1AfcSpSk9ksip3xHG8JPaDuIjicgV+WzC6K5pdBaXYWALDYbGL6iP3n59vfWvlVS2UsTE27k7GzSgEziXq16qwsVp+fr050LQJVUTTmWJTnuslYR4LDH9Cmntc1v9ZQodnuQ41dobW8qHRTi7YPNd77PO3Hsz8kNep13/UxPXoEsf5+ZNettY5XnutwfYwewUAhbz0JfDm2HEFhaIP14KNUF5aKkLGC2Jfs3jvG3LGv2L23OR4aiq13z8vP7fPrfF5fnWj5+SnXh4pXr3jqRNu9V13kqc9vYWoa1VJZjDydPtIix23z6yZ3HQmYbqyflr64dCZguuOpvU0mnsJSewImOJ7LZUCz2URFuT7GjjgRMMnW0KO26+OIGwHTdUCfHjviRMCkAshdyQO1BJZ2kuMSWFoS3Yu6yPZRiXNClzrRMr9u10ejXsfc+DEn10ckGvWxtDQ7h/LCgpjrY2Z8vKVOlMIWKTJb1X2OWJ8LqUsdzzF+3TzmWJd25SeBpZ3xXPNb5hIaPeK10BqyF6mo9m2A97yJdBrpvN18ie66dHp0DPkN662HQweRz67twpbUkc5YFcThSO4d7srsYtc5RvLc4aoc747XW+bVI597q+OW2taClF2muIj19yOzdo1VvM6hRyq/gcGCvT20w/6m4hWGhuwJmFxGvsjvviU8SVYRYmv97ba/tfOzVUsttb8BqvWBjP1tcXHRI9hs43XdyCt7qG285eS4socKHdBb6sSCc7xuq7gjQdRN6FjbG0MUIQ5YtcT+5mq/7LK/Tfqfn/370o2lgMPzBmBpNB5Dbr09Ob403ihSuaxPNHJXMgBL8xtc1IkBigshNZzKz62PZVvBofKzjdc59KgzP2ts7hh6JJFft6sC8Ip8e6XtUuxztYd2Y59vD7Xce7vjKXvoiYKlQa4PwA1LgeXqRPu9tz30qDM/FyxVQ48647nUGqLY3H1APzyKSDRqPV9iOXk6hkQ6bT3gMxmApW4ETACWOhAwnS2bVH4u2By099r+1pTrY5kLxzK/bteHyk+qbYTKz17EsBT7mnU3B2e366M8v4DS7JyYEnhmfMJzcDqTp1KumaWzUuSUzzLK9qD2bYCL6CWzrH0b4Ha5sBQLPCW6y/N2YxXg4BLqriMPj6E/lXRooZVdhvVOAsJcNhCrXFpoLcMWoTYewApxLpb5KddHZ7zeMq8e+dxbIsBfCiBgXDbOIOB3UbN2A40bAdMN1KNI53JIDNj35+oGVlt7aLf9zcvPHli77W8qP6ki0D2/pQfgFcnPwR4aFk8qP1d7aDfZ7moP7R7E4moPXWZ/k8CqLnINkC9SXd6XUhc56ZZfN5a2inJr5fhy8hRwu/zoLlJdCZju7zc5MGA94DPo4jK3fp3VgM/uoUeAmzW++7em8pPGPqn+9F5+ow5YsNSVIpZfp4LI5fPLLVXX+fZQx+9XKZxc7aGp7FJ1nT9fwsX1UfOGHgFyWKrI4kmBuq/YRe665dfteHPH0m41nJefvUuo2PUuA24E0ZLfhiJgrNWJy7G5P5W0HvDZrYabHB1Dbt1aq/kSyvXR7Ri0HSIXeJEnglVL3z/rOi0US13bUAhhaZdSuZ2fjHLXc3AeFSPbq6USFqZnxC5C5yaOeQ5OV9fHQnsOE+DmElravs0dS4PJYsv3r5s89Z/XXnm/RCAlQbYH5id07hC5WF1a97kICIM4F1sBoXJ9LLtotBQQKteHlOOy+yKqt2irRz73lvuhIRdcRNsSMOncckWDe34yBExfX9+SXm5L8+PH6x56pOLZ2kO77Uwqnq09NEwhYWsPDYznQsbmlqvrXMjsbnWdH8+6KAp5XsnLAIciv3vokXt+S4ceNep1p/5c3UVMaXbOacBnt0Ji5ui404DP5Tf8EoqLznfFleBYTp665NeNfRIEUTCWrv7FZZjrw3bAZ5Drw2XAZxiW2g74XDEs7SKI3PvTS5HtIReromS7+2VAeW5BLL96tYp6xXN9uA747D7wuw747MZS1wGfqdxSrHcd8Bl64LcmAENEGw7fr3jdLI7NMgRMoOvDQSmvyOJugsh2wGc3majysx3wmQzBKtsBn+ncUleFys/1YlAMS1tqfTEsDYrnkF/785OZL5HKe64P1b7NdcBnKpdFeb59kec64DPdhfWuAz67scV1voQ4VuVyogK4dHe8FeJcXN6XILLdJj9/1odQfonMQMv1ISMg7HZVePHsBYTpgHi9ZV498rm3nAmY8FtMIULHhSwOsb/Z5hdkf3MhYMLIXdv8Aotel/wCgXrMuj9Xd99OFc+2P1cg2e7QnytMSW1vb5QmJJYOPVL5uRA6nUOP/HhC5Cngrrwvd23qTrfUXUWq64DPbrLTdcBndxHtOuBz2UWZc5EajKVO6johQiLM9QE4YumsEJaGYIF9fgFY5TDgM1j5bD/gM+x5bQd8dluJvfzsB3wGXgw6XeQFkONOBEwGtXLb9eGaXzd56pxfUDxHsr0znuuAz26y03XAZ3c81wGfQT2QAXkXjri6ziK/9qwPmXhhvzXAbi/SYakTNgecY2xqyXYdvpQgsh3wGXZxaTvgMwxLbR2coSIV1x7IQnV4MpttuT7KMvl1YZ9rft0OOpWfSz9+yTq82wXh7ODswlJXB2c3eerq4BSvwwVFG5FYFIl0WszBmcwE/9Zs8wvGAntOKAyrvPws4gW0l+st8+qRz73lTMB0A6sLAeMPPRIiYHz7mxQZG0KeAnZFb3cfxiX5ORXRMgRRWBEInGhke4ACxqbID4ln25+re+gRAKf+XN1Dj1R+tv25wope6yI1FxBPsEhV+UkVqSdifp1FvuuAz+5DiMuAz+6hR4DbgM/uoUcAnAb3hF1EAXJY5WK/1BfRNheDQUple/tqmOvDy0+oyBf5PpbuvbYDPtX7Uu7CZtsBn91DjwDvfbEd8NlNdqr8pFoOAe5k+/L8Rt2wr5swcXEdCZPt3nDPLnLcYdBTt2ijXq1iduKYY8umDgLGYcBn2/WxtF97s9m0+vwCZ32I133uWBrYosoJS2UckoGuDxeCaMXI9qX52To4A10fjg7OTteHys/FwRkospAUbThi/TKy2AFLu/u/u+cnT7YHkuNCnIa7g3Pp9+vi4IzG4+hPJZfEcxEQpjK6OlIYS23qyBXCqiWzehzq0iAs7S3z6pHPvQXAnoAJsr8B9gRMUK80FwIm6FbKhYAJOqC7EDDBSmX7ScJa5bMUUDsU0cFku/TG5MWzIoi6hh55+blsTEuHHnn52ffnCitSbftzhREStv25wope2/5coUWvkLpO5SdldXbNL5DQscwvyPUBtFofCLk+1IBPKQKhXmnZQ4Wwb35yypqACXJ9TDtMedddXNoRHNJk+3IsdSOINEpvh+93CVnsSGB1Dj0SiReABdF4DHlLdWIQVtkO+Ox2faj88hvWWxIwy7Fv0tG6L0nAhGKpQxsP8csAIQImbOiRbX5BWNqo1zE3fkysjizNzqK8sCCGVTNHx9FsNCzJ3QClshNWhYtAxJTU4mpCAWwWVDt2uz5cCaKg35q1gzMES20dnMF16ai1gzMZglUuAyo/dFjq1M5R7pwQ6+9HPJEIVKK7cRoyDs4g8tRFQBiELS4CwuMpgLPjDJbH6y3z6pHPvQWgBawWU96DbviB1kZnRa4F98/x8pOL5/WedLG/LVXATI0ecXve2QACxgYI88uLVJ+AsVKvLc/PJ2As8ksH2AeditRcFo16e+gR4LgxtZTFgWSx1fPmAn8btvmlA4oiJ4IoF0ye2uYXSCA49Ofq7jXnxbPvzxVGcOQ3WNpDBYvUSGy560PlZ+f6CMFmy/wUFixTJ9oSHHkN1rtgS9dliq11P8jq3KjVvCLfpigPiFecmUWlWLTGgu5400eOtggYIWx2UNelctnlrg8nrPKwVA096szP7vPLBV6U2ecXjH2i8RwuktPZ7DJr6NThUesBn2EEjDfg04KACcHS7Pq1VgM+w/Zeq4u8gKFHKj/ri7yuoUcu+QVhi0t+fk/b45CfrUjAi9dBwNTtCZigeOX5BZRm59w+v875EuMTHgEj2P4OsMcWIPgiz/Yc0+36aGOzRQ/uIHLS8eIy6LcG2A1LDiZjHZT3Qa4PhwGfYSIL2wGfYW08bB2cgaKNw/YttJJhrh4LziCeTCAWjweT95aCISAEm504FyFsDuNwDttdVgRxLr6A0IFz6XxffAGhDbYEkO1KQCjJWfWWfvXI594C0CJgLPpzaYFVyEqs8nNSKgtZ40Of1/EWU6r1QZDCBGgRMELqNZ+Asfz86rUaauW2/c0nYITUZtNH7PtzhSmLAYeiV5CQSIZYsQFLgiikSLXNLzCeg7K92/62ND+7W/kga3c0FmMTMEH2NxUvmRnwiyZybplwrMqtX8cmYLRY6tSzWIbgCL1otMwvSAns5TcqptZzyU97sSrUk7pZb1j3TkzmMstcH+W5eZTm5q3z63Z9zI5PePZQy9pAFkuXY4vT/IZA8tTBvhqihvPys7v8CL0ItcTSIKwC7NvSBO1tkUiEfQiORKNIZgaWk6eWBEzQ0CPAe19y69ayB3zqsKBg0UJLGkvD83PD0uD8ZC7yvPwc1YRCZHsyQA3nETBHnVo2dZLF1VIJC9Mz9mRnl+tjbuIY6tWqdX7hIgY7JXpoHSmOpXbxQp/Xei/qcqg5KseP1zmGO1/Cu8gLFlnYDPjsT6UQjQW4PiwHfGqx2WLAZxB5CrhwBsFtHlxFJeKcxgpzLrYOzqB4voNTML/e0q8e+dxbANoEDLc/lw+EM8uBxqY/l4pXDCFguPbQ0HjOwN+lrrMFLlWkzs4G5OdgeZFSrATY3wBY9zpMdvUlbOdnr07s/m59Asby++2O59KfK5lbfkB36s8VeOB3689VXPbuubRBCSckrNV/s8uLDi8/XjxlfwvCFpv8dL81m/ySIdgyddgjYPLMIl/lF/S8iXQKA4W8TLxW70Q+AROCfY4Xl93xJh0VIct+H7YERy7887Ml272hR6Ul/9zlMqA0N7+ELHbJLwhLm40GZo6Mi100VopFFGdmxdRwc+MTqNdqYuRpu02L7UXZCpPtjq0FpC4ug4YeucRLDCyf9QHYY7NOZAHw1aJhVl1bAiZs6JEtARNEnqr8rAgYHSEhKdo4YS4GW66PhaJYft2uD9f8urHUG/B51Lq9XPe7Uis7ODgD4s0fm0KtbDfgMwjrp8eOArDH0tA60vbiMqyOtN3LhfaOvr4+JDIDoflx98r+dAqRaHRZXWVLtuvO0AB/9o8Oq2wGfCZD6/BRqwGfunOHk4BQjCwOOcdYCwjDOSEnB2fHRZ5Lfp7ro4hmvWH+w73lr1Uln2+99Va8/fbbGB4exje/+c1l/76/vx+PPPIIhoeH8cILL+DMM8/0/90DDzyA4eFhvP3227jlllv8fz4yMoLXXnsNL7/8Mnbu3HlcnuOjsKwJmIDm8oALAROuBLbpz9VW/y2PZ9OfK1ypbNefK2joEdAu8rkryP7m5WcJ/AFqM5WfVBHoxxMqUgG3W9ZuZaxbfssP6C79uYLsb279uZYfQpwGfGrsjdznDbW/WarrdK4Fm/xC7W+uZLYYYRJ+oLbKL0zBYamACRvUMTU6ingygcxaLgET/rzZtWsQT/LsoUFDj7z87OyhQUO2Wra/AAAgAElEQVSPVH6i2OeCzWFYKkQWu+QX1LfTLb/l8TwCxlYBsxxLq6Uy5ienxMji+ckp1CoOBEw3OW454DPU9WGLVQFDj4AVqEutsTkMq2z3Ij2WcvPTYSlgQ8CEtKuzJGB0ymcXAibofbEhYJTro1KUIoszKM8HXOQ5XFxKYn0QtrTzk3FVuOQXhM1+PKEWRvVqFbPjE5aOweXfx8L0DCrFksNevvR5Z8aOWg34VK6PcJcL8yIvxPVhS7YHzb5Ymh/v/TPXuZbxlp1jWrOErOtwqb0tPJ6dgDD8HGMjIAy9uLQWEGaWtW8D3DiD8vxS14fKz76u6rXc4K5VI58jkQh++MMf4vbbb8cFF1yAL37xi9i8efOSP3PfffdhamoKmzZtwkMPPYTvfe97AIDNmzfj3nvvxYUXXojbbrsNP/rRjxDpGGZ1ww034PLLL8eWLVuO6zN9mJftxqR6+0kRRLpec4DFRqcBasCBgAkAQoBf5Kdz2WX2N5VffypppYAJO/Db9OcKLQIPez2zbeyhYfFs20aEEhxC9jeVnyihcwKQ7WH2N9sBn2H2N9sBn2FFpe2Az3D7mx0BYyIkpLBq0pVsDymi5dR/SnnPLfIN5D1TLRo0iAVwUzsGuT6mDnsDPrPreD3MQ8nT0TEMFPLoT6V4+YUc+CcPj6EwtIE94FN74Le0nkuT2cFYOirWxqOdn6XrQ5KACfh+FxcXrVxHYUOP6tUqZieOWRyog39rC1PTVgM+w8jT6bEjVgRMqFXX+WJQps9r+EWoJZZqDvxW8QwXtez3RfgyoO36KC/Lz9bBGVaX2jo4u99llZ8tARNY5zr09A4VgQjN+nDJL5XLLjsDuucn+fnlwkUqXLLYd30sfd5GvW7l4FTDaLu/X9/Bycyv3b88zMHJxaqwM7kllgb0Qwc6sVQoniVWBfV/B+wdnNL5+RyO0Dkmncsta9+m8rMTEGaXtW9T8awEhGFY1Zr9wxUQBrkWesu8Vo18vvrqq7F3716MjIygVqvhkUcewV133bXkz9x111342c9+BgB47LHHcNNNN/n//JFHHkG1WsX+/fuxd+9eXH311cf9GT5KyydgrItomVtWneLCi8e/xQy2v9m1Fgizv9kTROHkqZcfX40ZVgR68fgWpDCgjvX3I7N2DTtedxGj8hsYLPDtoRoFR2FoiE/A6BQcQuo6lZ8NWZzIDIiR7cr+FngIschPN/XXpj9XWB9Gvz8X+8AanJ8/4FMI+/wBn0LK57Z1X1pdx8O+0J7KjkV090WePUEU7vqwyU9HntrlF+L6cIkXgs3ReAx5CwVM4IH68BhSuax/qKXHC8fS/Ib1Vi20ggidSUHrucrPWk0YhqXM/MJcH7b56foS2uQXhqXu+S39Phr1OubGj4nVkaXZWZQX+ARM2MXlzNHx1oBPSywN6NsJnAAuF1N+FlgV6PpwuBgUx+aQusU2vzCy2IaA0V1c2jk4w+rSUasBn9Iul9CLPCcXjgxWxfr7EU8mxM4JuoFlVnW4Lt4of8BnWDxbB2cYedp2cNpi1dLv13dwCp1jbDkN3dwpm3hGB6dFvEY9uH0bYCd6KQeRxQ4OzrDfBmC3t+ni2biEgrCvt/Rr1cjnU089FQcOHPD/98GDB3HqqaeG/plGo4GZmRmsXbtW+3cXFxfx5JNPYteuXfjKV75yHJ7ko7F8AsaWIOruz6UIGCa5G2Z/8wkYKyAMtr9ZxdPY3wBbslgQWDXkqU1+YUBt359LOr/wA380HkNuPU+dGH6oGUU6l0NigG4PDRt6BHQQMAx7aJj9zctPvkg90Yto0XgWh5owBQcAK+t+GNnuD/jkEhwhRbQ/4NM2vy6yePrI0RYBY4HNQa4PW3Wd5sAPCBapJ1ARLfm8oReXDoeaMCyNRKPsAZ+6lk3JgQFfwUNZyvXR3UfQy2+MTcD4rg8htV7YgVrlZ41VwvmFfX72+c0u+3dOZHZQvMM2BExw//dm3ZKACekDWp5fQGl2ToyQmBmfsCNgshlUS2U0arUl/9xFqRz2rtjEC1ObuYhedBeD3PclGTBLY0l+VnVu0Ltsp+4MvSizHPAZ1N/fy89uwGdoPIsBn7FEArH+/nCRCtPBmcyqfvJCog3TRZ5ly6EwkQ/7TB5CnlrnZ7oIFbq4VA5OqZZIvoNT6NzhOzgt8qvXaqiVuy/yLEUqOYMATqiOtBYQhnIutgLC8LoUsMP6XtsN/lo18jkI7LsJwrA/o/u7n/rUp3DllVfi9ttvx/3334/rrrsu8L//la98BTt37sTOnTuxbh1PIfRRXTatD8Lsb0CLgBE68PsEjM2BP0At5RMwNoRJQH5+fy4pNZxq4yGlVHZQPoeRp148WbK9IEXoqI3pJHq8sKFHnflxnjds6JGKx7WHmoq2ZGaAZQ/VKZVt+nOFXUQBikDgvXth5KnKz+a3AUgSMMFFr5cfvxWAiWznP2+w68PLzy5ekOvDH/BpES8IS8tz8yjNzbPzCyt6Z8cn0KjXTwws1anrGFgF6LC01efV4hCijcfMT5K894ceCX0fvusjBKu4Az5NWNqfSrIGfOqwZdKCgAmbpaHys8XSMOu59ZAnaSwNzY9f54bnZ/H5tcj2ysLCsn83NTqGQQvyL2jokUfAHLXae4Pe5WqphIXpGavvNwjr5yaOtVpo2WCzBlvYWBUWzxJLNcpi0XjWriMTQSTnwuHm159KIhqPhZKnAO9c1K77gj8/7oBPk4iBO+BTi1WH+QM+P/SiEnGy3dbVE7z32uRXqyxv3+Y7OIUEVy4CwqBntRUQhnEu9gLCFRBthGABIOfC6S39WjXy+eDBgzj99NP9/33aaafh8OHDoX8mGo0in89jcnJS+3dHW8XC+Pg4fvnLX4a243j44YexZcsWbNmyBRMTE6LP9mFdHvDL3CIBtocGTTzLW9YwS4TdxhRCjlv25woDVtv+XGGEjm1/LhPBIWaXtOjPFTb0yMuPvzGFDT0C7OyXYX0OrfMzHKjZ+YXc8Kv8IpEIyx4aZiVW8bj2UFORyu3PZSqibQmTsPeFrRgIcX2o/KysvwGuD8C+yA/HersiP0wxYJdfMJY2Gw3MHLG8uAyIVykWUZzhD/gMy29ufAL1Wk3s4tK+TYuebOcoQnSuj0mbizyD6wOww9KwNiPc/ExYZZufTo3JGfAZNvRI5RdP8AZ86lwfNgM+Teo/7oBP08WlzW8taOiRys8Om5e7Plzy02KpEPbZ5xesLPYGfB61zG95vFrZjoAJUwLPH5tCrcwf8Bl27pgeOwrAtmWTDFZ5rg8Dec/IL5FOIxKN6rGUEU970WMx4FOHLTZkuwmrAJ5130Secgd8alsiHR5lD/jUXqyO8gd8ai8DLM/kAFCalyPbg1wftvlpsXQFOA2+gDC8rrcTEAbHsxcQBos2bAWEYecOewdn+PfbW+Fr1cjnnTt3YtOmTTjrrLMQj8dx7733YuvWrUv+zNatW/GlL30JAHD33XfjmWee8f/5vffei/7+fpx11lnYtGkTXnzxRaTTaWRaBFI6ncYtt9yCN9544/g+2Id4WREwIQdqwLbPq6HoFerbCdgDvyQBoyV0bPpz6QgYC+BP53LBBIxFf65YIhE49Aiw68/VLtqEDvw5PXnKjZfWHdBd8hMiiLTxLMhs7fNKf34W/bnCes0B3uUHd8BnKpcLdX0oAoYz4DPst+blxx/w6VmT5YrUMHuen5/FIJawQR12WBpMSNjmFzb0yC2/5Z+fR8Dwi/yw96VaKmN+cooVT+f6mJ+cQq3CI2D8oUdBRb7FgM+woUeA5cXbimGpEJmtxSq+fTVs6NHS/OjxTFjqxeM8b/DQI8AjiLgDPsOGHnn58Qd8hg09Alp1M3PApxGrbLBFh1UW2KzFeiHsa+cnc5Fnm186lxPd28K+33q1itnxCdbe2xeJhMZbmJ5BpVjikcUD6dZF3vJ4voOTdfFmJmPtyN0TE0tJF5eMeHqyvdUKwOYiVOgcYxJt2Do4y0FkscWAz1Q2E+j6UPFsHJwntADOhH0Wc7HCsdlCQGjMT1CkIsi5+A5OQU6ot8LXqpHPjUYDX/va17Bjxw689dZbePTRR7Fnzx585zvfwZ133gkA+MlPfoK1a9dieHgY3/jGN/DAAw8AAPbs2eP/+e3bt+P+++9Hs9nE0NAQ/vCHP+CVV17Biy++iF/96lfYsWPHaj3ih25ZETAhigHAjoDRxrMhYEJ6h6n8+H1FNUW+LdmuJTj4SvSgXnM2+Sn7W1CvuXZ+FkVlkALGoj+Xru+kTX8uXTyb/lxa9ZpFfy59PPuiMuh5bfpz+QqJwPz4/bmSWoUEvz+Xb3/rGnrkxbNRO+p/a148nj1UhwXcAZ8mrOIO+NRh6aTFgE95LNV/fjaKi9C9Y3TUMj8ZLPWHHgl9fjpsWVxcxDRzwKcOW+rVKmYnjokpixemptkDPnWExPQRGwIm/PNzwlJNb0LWZYC25RDfdWRqAcXNL2zokZefHYEVNPRoSX7M91l3oOYO+DSJLLgDPnVDj2wGfOrUcPbKbEGyXTg/owuHkV8kGkVyYEBMOa67yLPJT0ee2jg4kxostXFw6rDUZsCnvv+7xUVZSP93wA1LpXqi685ZVg5OnevDRtmeC27fBtg7OHV1H3fAp+kij+/g1J8T+A5OfR1p4+AMe17bQfGydbiGw2Hm57s+hPJTro/STI985q5VI58BYNu2bTjvvPOwceNGPPjggwCAb3/723j88ccBAJVKBffccw82bdqEa665BiMjI/7fffDBB7Fx40acf/752L59OwBgZGQEl112GS677DJcdNFFfszeoi1bQizUsuHHo4NhmP1NxeMTMJpbx9EjFgRMsP0NUAoYOgGjhh5J3RLq7G8qP96BNbyotMlPd2C1yc8Uj7tx6uxvNvZQnf3Npj+XjpCYn1T2UJuiPKBNyxG+PVRnf7NRO2rtbxatBfQEgk1+4dgybYGl2gO/ZX7GeMzfW9BvA/CelzvgU3fgnx4dYw/41H6/Y/wBn6b3hUUWa4YeAXx7o27okU1+RmxmDtDUYSkANpmtI08B/oBP3fM2ajXMTRyzIzgCCRj+gE9dS6Q2ASOkXmthAVddFzT0qDMe9zLgeGGp1V6kdagJY7NFb0zd0KPp0TH2gE8TlnIHfJrUcNwBn2F9OwE+FkRjMSTSKe37YkUWhxEcllgljqW6eFZYGv792rXJkHFw6rCvslBkD/jUke1WDs6sztVjh6Vh7dtssC+pad9m08eXUpdy35dwrLKrc3VYCvDPRaZ4LAen4SKULyAMJ9unR4+wBYSmvZcjIOyLRBA2dwrw3hdO3dJ2fYS/L1Iujd7Sr1Uln3vrxFpWt6yGohLgWvfD7W9W/bkM9kZOfjr7m4rHIWASA+nQoUcqHqc/l4lAmB49wurPZQJWdpGqKdqAFmEi1LfTJj8j2c5UrOjsb0CrKBcqolU8m40zaOiRsodyi7Yw+5tnD+X159IVqTNj/P5c+osoPlZp7W8roK4DmASRcH56dRhf2W7qIe3lxyMAdfGisRiZgNENPVLxkpkBH9MouQEaLGjZQ6kEDAkLhKy6Kj8pstMpP509dDXzM5DtbLWjjoDxB3xauD66hh4B7QGfbGwOORDOjk9YEDB6ay1ggaUh77Kd+k9vTbbJL1wkYIGlBmWxlx8znkYdBvAJotDfRmvAJ5WA8V0fmvelP5XEwGCBFE/3WwP4Az7NWMUbUCmNVWlTHc7EUj+e1rHKePfy4S2RvPy4ohJDPMHPTw345LZsCmvfpgZ8cs9ZYd+tP+CT9f3mwrHUwuWib+ljgaVapbKKx2spZeYMeC2vwsjTSRssFeZcSOcEIl5FYgbXxyjPwZnMGC62DvMcnEYsYAoITVjVW+GrRz73lr/mp6YBgG0pCfvhzU9OsePpCI6FKV48k/2NG89kf1tgfn4m8nRhkhdPdyMPeN9vNBYjqwl19jcVL5XN0IHaQI4vTE3z1DSGePNTU+x32RhPML+FqWmkmPmF2d9UPE5+yVw2dOgR4H2/3AGBuhvghakZ3uenKYoa9TrK8wt8rAp5l0uzc2jU63LYx8QWU34LLSzlvi9hRb5dfuGEzjwTq3RDj7z8ePFMrg8uNpsuovz8iO+zGVumEYlEyL0OjdgyOY1EOkW2c5rI0wUmlprUdfOTTGzWuD4AG6zSXwYsTPLi6VwfdvmFuz4A7/cmi838zy8MW5qNBoqzsxZ1X/B3WykWUatUxLB5waIu1ZGnXGwB9HWuXR0e/v362MyqXcLjcZ/X5Prg7h0m1wf386NgKQD6RSMBS+OJBJkwMbk+2HWugYDhYpVpr/TqXNpnB+hdHzb5teMF1802e0e9Wg1s3wbY1fU68soGm8PiLS4uYmGaWYdrsL5eqaBSLLLfl/B3ZQaAHFbZYmnou8fEKsDEabSwRWxv4+UXjcfRn0rKYWlGj33seDk9+exzOOQ6XC+ymJ+aQjTuOWE48cLel94KXz3yubf8pfrtsQaxaBQc6iaXGs9kf+PGM9nfqkVePJP9jfv5mQ7o7XjEWz2DgkM8XpEbTw/U1VIJiXSKbMkxke3VUpn9LgPhG1O1VEY/cVMC2va3oKFHNvkls5nQoUdevBK/hYym6LWKF/Jbs42n29S9eJzPT0/A2LwvYfGUHZ2NpZp3GQASzPdFCkuV/U0Kq3RDj2zimQiEFcPSNFdxIYXN+otL/l6kz69SKiPW389wzRCw1AabxbA+3PXhxeNjVZjrwzaeNDabsZQeT+f68OLJYalVPMPFZaNWZ++9ut8GwMR6jeuDiwWA/n3hYpXJ9WGNpUas4mGfNJaa8+MREjqsEo1XLCMSjTKU2aa9zQ5Lw9+XMqtuoey93N9GrRzs+rCJZ64jmfkZsLTCrHNJWMo4xyQ1WOXHY36/oeR4s4lauXICYKkMtnj5hX8fKl6CGC/W3494IhGKVRVLbNadyb14zDoyDEuLXCw11fUtLCWTxcR4Qvn1VvjSejwvv/xy7V9++eWXRZPprdVdtdYPL56k9fcx2d9qZfVDpsUz2d+qKl5SqEgtq+eVilfh5WciJFrx2PmFEIA17vMa1Gad30dlYXl/se5lUnCoeLFEf2AvyWX5GYC/ViqT32UVL2zoEeB9ftTvVsULG3oEtIo2VjxDUVmuICs09AgAaqUKOz8twVEuM4v8jK8MCIxn8f1OHjoc+u9r5TLiRKxS8XRFR6VYYn1+OoLDxyri52dyfXCxwOT6qHGxz0SeMuOZL6JWGUsJWAVwPj8iNqcSKAXPprHKL55MkLDeGK+FBX19faH42B1P5/qoMrHZ5Pqolivk35qXnwFLyxXyuwKYCYlauexbUmn5GS4Gy2Vefgbsq5X52Dw7PqGNx957TXsRd+8Nwap6pYJms0n+/Eyuj1qJh30m1wcbS4nkqRj2Mfc2ajx6HW4gT63PMfp41N+HqQ7v3IvCCNal+RkIonIZsXgckVg09DJtaX5614dXp/F+azrXBxdLTeRpjV2Hm7E0y5xLZMqPi6VhWAAoLOXEy+LYgUOh/97mHHP4nb3h8cpl9t4b6sKpN9Co1Xnvi+b79bGUmF88mUAsHidgwepgqQmr2lhKxCoyh0ONR+VwqFiqF8DVmHubCUt7K3xpyefvf//7of9ucXERN910k3hCvbV6i6sIMdnfuIoQcfWaqahkKkKM5ClbCUx8Xvatnox60qxUFlZwdMSjkc/hQ49UvGgshmg8HlrILomn6UXmxeMqJITVa0YFgry6jmUvy2UwN35ME4+vhpv44KBcPIJaj6fQCXd9ePHo34fJ9bHYbKJWqdCtukbXh61CQhhLhVwkdNfHiYml3L2S4vrgxKMolVU8EvlsdH14/zyeTAT2uuxeuqFHKh4X+8Im0NvGk3Z96OJVSmXkGAPkktms4Xn52KyGV4XG47iENEOP2vnx9l6p79d3fWji1RgXq77rw3AJz8XmYlg8S+wLj8fEFkNPYHusknE0KgJB7HlVfjPH6Xk71JO6d7Qdr7WXh5LFrfclmUQ55LKvO155YcHo+uBcNMpi6Qnu0jCJNkplcssXwMO+8f0fCOZ3/M8dZGzu60MyEy6y4MbrT6UQjcVC46mLRknXgvrvkuIZyNOVrPs4+Ykri8WweaUckj3lM3dpyecbb7zxeOXRWyfI4gC1CVitgTrUsrFyZKdMPGl73gqR7UK95vjW8/ChR935LUwR4hGKVC9eCiUK+axRngJLredhirml8cLVUl5+q1/0qmnVwfH4ReXR9/Zr47EVIeJtPGTimVwfXjz692FyfXDjmbCFe9Foise+aFyhNhSSWOXld5yxdBXbjOhcHzYElsn1oeJRyGcjlnZYzynqP8qBn43NBvKU2zJHzcwIjsfHvsmDOvVaCQOFPCuepPVc5/oAeNZzk+vDy49OjptcH348NpbKWM+TxgO/bXu044tVVOu5iZCwtZ4ft4tLC+t5s9kMvfTjW8+9OnKx2dTHS6WI5LMZSwFPTRi2v3Qus2hDljytrGIdCXjPO3N0XBsvP0S/aDSLLFagjQe1fVtfHxKZAbG9tz+dQiQaFdt7TXWfF4/DkbTO5AYHHbuOPF7t5awFdaZ2dVICONn2cmxRiboMIOBkby1dtNHqAC688EJccMEFSHbI0X/xi1+sSFK9tXqLBdR+ESijMPGBXxiozYoQmfzaRZtMEc3ucS19y5rNolIsoVGvi+RHOaDz4tHIZ6oihKKW8vKjK0Io5ClHEXLsYHjbCJuit/iuxv62AgSMdDyqIkQNPQrDAm5+yvUhpa473ljKjWfCUv5Fo8lFstoKiRVS6wlehNarVa3rgxVPMzHei8cn26lYSsvP3LNYxaORz2bylGM9T+YymD0a3jaiWuQf+CfeP6DJT971wSck9FiaXb+WFMvk+vDiMbCqRRZLEUSmAzU7P0MPZD8e0/Gms57XazULLD3+jjfKWinXhxjZ3nJ9VIphZDEfS02uD15+ZuUpNz/TbwPwLn4p5DOFPI1EIoglEqFD/5bEy2axMK1v38bF0snDo3LxjHulBdlu2MszawZJsSLRqKcsFqojE5kBRCIRQx1uIYA7rvH4F41hz7u4uCgar+2a4bpSgt8/a/d6SDw2tuTUuUMmnrrIM5LZVJFKS2RBEaP11tJFGjj4rW99Cz/4wQ/wgx/8ADfccAP++Z//GZ///OdXOrfeWoXF6bdkKioBXn+upEkRsrjoKULYfUXNPYs58Si912jxlP0tvI8lJ790LuvZ30L7WPLySxvaUPB765kPwLx4OWMPZIDe/yqdy+mfl9s3Nqc/oNcs+lVpDw3MPpZpQjxqD+S+vj4jocPpA9qfSiEaD7e/qfyoRUfacGBV8ei/Da8dif73Qe8bm2rF0xESLOxT8YTel7TBOu3lR/9+0wa1Xq2i+pTyerlR+tNz4pnsjRws1Q09Yvc9NfUEZmOpuZ88QC/y01RsFsZS3vPqfxuceCQsTSbIw3TThJ7PrD6ghPeF+qy+60NHnjKwxfRb8/Kj15HyWKrHFqBFSHCxlPC+SMXj9I1N5fXx6tVq66JR5vOzwVIvnmGeASO/Rq0e6sBgzzOgYimj1tD/1rjzDPQXedy+pyayk43NBOWzF4/eh9aEpdF4DJEYbZiuiWzn95A2tG9j1LmRWBSJdNqALXQsTQzo27cBzDrX0L7Ny4+zdxCUyixOw3zRyNl7kzlaPKkBgf5FI7uHtMxsGJN73WY2jL59Gz9eRefIs3jeXssNu0Uin++++27cdNNNGBsbw5e//GVceumlSCTohW9vfXgWT10ne+tIIogYihBTPLYiJJfV2t/41vOcwf7Gt0uS1GGMvqySCglKry9uPJO6iROPrODgqAkpZDtHOa6Lx5h63rO/ucUz2d9s85OyntPUenTrucn+5scTcmlwrecm+5sNlupdH3wFBwX7ONZzfU/glcJmRtsNbZsMfosqEpZyrOeGlkjc/KjWc8rSDT1S8ai5mYYeATzrOaXP4YmOpRzrOUVkwVKvEch2Kyw1vM/c/vRSSu/23hGMzVxHYyqbQb1aDVW9itelFtZzEpYylO001wfv3BEej2c9N1002rTkomCpnKuHF88T5ei/33gygb6ImV6JxuPoTyXFf2v6c4L8mZzzrpjjcbB0heIJ7b2Ui0HW3ivMuZguBuvVKpqNBqNdXZbYvo0hgNO2b1vlM7khXm+FLxL5XCqVsLi4iHq9jmw2i6NHj+ITn/jESufWW6uwJPuK8uOZb0U5QJ3MZbT2Ny8/3qFGZ3/zreeMvqL6opfbdsNEnsoCqzh5uur5UXtIH3+yXdnfTOQpNZ6yv1Gt56ZFL3pXC1tO8Hgkdd0qku0rdRmgaV/DtZ7rXB821nPRA7A09q1APGpbC8r6MFxcSsWjDj0CaBe/pqFHKp6ynpvWiU6emtRStvmJk9li1u6VItuFsVmwrte5PrjW8+RxriP51nNz2wgvnuzFpTw5vlpYL/v50eOZv1/Vvk3KwUkTWdAdeTQs4GApQanMGAZrmn0B8MjxlcNS/TmGLhJYob1SMF69Vgtt38aOZ3RLKwcdh9MQxJbjjKW9Fb5I5POuXbuQz+fx8MMPY/fu3XjppZfw4osvrnRuvbUKy6qvqBBQJ7N6+5uXHw9YTbdS7HiaA5IXj6dOJLW1EFLDnQiKEBJ5Sh0EJK1UJh5qKLf8bfubzKHGt78JHbqoRSo5HpGcpCpCSPY8afLUpug1vM/s/vkncJGqc33YxNO5PgCuGlNf9Hr58dSYuneFrQhZNddH7+Kye5FcH4yLX6rrgxqPiqUATSlPvYiKxmOIxsxjYFZPqSwZj4+lx8v1wc6PMPSI+7w614cXj7f3mg7o3O9X91trNhqoV6s8F8nxdH2sgOONE89zVUhivfRFrdn1QY3Xn0oiGo+JOSRN/dABnuuI6jKIRCKkVg+0izxhJfVqukjEsZRKtmq0w/wAACAASURBVNN/a+b8eJxLrRJ+kQfwzzG63xo7P0Mdubi4iApThGRqnwUwHW/Sog0DJ9RbwYtEPt9///2YmZnBv/7rv+Lmm2/Gl770JXz5y19e6dx6axVWjdNvyWB/A8Dqo0q5ReL0WyLFK5VZvcNMBAenl6BJweH3uJYqUrnDCExqOGY8Y680G7KdRJ6a47Xtb4Q+oATCKZUxFzE1Rn6Uix7O50cqUhnfL+mAzuj3RbESc/rJk4rUsnDRa9XLTf++SLo+uL3mdK4PAC1rt4wCwcuPjn0mbAG8vrGSdjru+2KajQDIkbvSF5ftHtIye2+VkR/J9cHo1UdxfXD6lFKxQDQeY/4AdTYHQOuTS4pXKiOeIF40Ensgy7o+bPp26vvairo+mHW4zvWh4nH6nlLqXF5fTAqWysbj5Kd73kathmajQZ5/Ybq4bGOLMJYK9SnlYKnn+jD3QKbGS6TT3kWe0N5Gu+ih16UU0QYPS2nYQs6PQp6Wy4jGBC8aOWdyIpZyzviA6ZxAx1L/fZnX7+UcbKmWymjUapr8ZLGUNxfLjPWcvciEfV5+chyOumjkcTiUfvIyrTp7K3yRyGcAOOWUU3DttdfijDPOQKFQwHXXXbeSefXWKi1J+xs3HunAv4rK5xVRcEjGM+RnM4yAckvIGxolY1eL9fcjnjAMPeLY30hWU0/RQHleWh9GejyqvQxgKjgo8UiKC3o8yiGOqv6jW8+JdkT2oBP9+8cZtkpxfYge+Esl3oGV4PqQGnrUjscoUikKDsGLS9b7YnB9KOs5qycwBZuFCB2OS4Pi+mC1CCK5PlYI+ygXjQylMic/KbUetWWOF4/hmqGoE1l7mwyWrp7rg/db07k+eOq14+v6UPlJKYu9/HhtPHS/DW48CoEgeY5hXzQKtqHoi0QILaU42Jz2LvIIjkaOS8PUI5eaH9VV4cWjuFJoSmByPCXa0M5HoO+9VFcFQFOLUlsikS8aV6hVZ2Uh/CKPO8+gUiyiWddc5K3mGZ9YN9M5jRObc5HmhExY36jV0KjXxTiX3gpf5qsvAN/97nfxt3/7t9izZw8ardv1xcVF/P73v1/R5Hrr+C8uUMtbNszxBgYL5HgmSwTXMnTswCFDPB5QH35nr/bPrLr1XPP9cqznlKFHK0YgkIo24TYUxAM1PR6jqGQU0VLWc+rQo3a8KX1+RLsf4B1qdO6LznhhQ49UPGU911mOvXgE18cqF6nyB35zEZjfsJ4UT/ziMpvFzNFx0Xjj+z8wxOMSOnLYTFWEUA78JNcH50BNcH3YtaGQwSrqgZobT8p6zsdSWn5y5L05XidBpHNfePFoZLuynuv6UwLHf9ZHOx7d2i2JzVTyNLtuLTE/eWxemJkxxOMNyZoaHdPHW3Xruf6ikWo9p836oJOJyYx0+zbZujRJIndtsF7GIbmqIpAViGd670nxOtov6i7oVLxms4mKYdZHIp1CX1+fFnMB730pzxtcHytBnlLbPBCxdPAUmZY0XrwSkq0ajBRP+NwxOz4hGk+SbKeJaGjvC8X10Vvhi0Q+f+ELX8B5552HqqbPTG99NBZ3kqs0UFMIDjJQ57I4OvK+IR4dqNO5LA4SFCGcIlrqEEIZesSJl0injUOPOPEoU4k5ihDa0CN60eZPJSYolUnx8rShRwBREUKYcsxShBCHMnnxzN8HZaqzjcKESo4vTOsPt6lcVjv0yMuvfYgzFuWGCe8qnlKE6FRuAANLBRUD3CnbkkVqOpfF5OFRYzzyRWMui7F97xHyWyUXjmC8WCJhdH1w4rWtujKKkJQ0lrKwRQb7xLG0uEJYSqitaPHo8wxo6j/O5UKO5PpQ8Uzkc5qIzdFYDNF4XGuJ9vKjzvqgY6mkSCCdy8qSp7ks5iaOGeOl8zlyvGMHDhrirZ4ajvq+SM368OLRvg8KWbzYbKJWqawAVgnXkQSRStqv+2TrSDnlc66Vnxn7aFhqPhdxXEe0cwwDm/NZNBsN/50wxTOSz7ksKvMLxos8wBMs6fYEQNXNstgnjaWSWJ/K5VCcmdX+mUqpjBxRBJLO5TA9dtSQn9zAQT8ei7yncCR014fU+9Ju39ZTPtssUtuN9957D/F4fKVz6a0TYNUYvfDShTyKBkUDp9/SQD5vJJE4Vud0IW9WXJQrZGtyOp9HcVoP/NT8IrEoUtmMcSMhF6nZLCLRqPnzI5Lj6YJXFBWN3wct3gAhHkcRMlDIA4D2eTnDCNIqnuZ98XvDUYrAvBdP97ycnsrqeXW/txonXp7y+dEVIelCHs1mk9b3lPK+5PMozy/ohx5x4hGwgPW8+Zzxt8bp6Z0u5M2/tXLZV4SY1kDeHI/TAzldMGNzrVwhF5Xe8xKwlJjfACUesa9oXySCVC5L+n6p1uRYPE76fllYatzbaJ8fBQuAVm9C4rsM6LG0XvEugSh7JQtLCfE4WEpp9cDCUkJt5X9+mve5ahFPV2twWmgNFPKolspa14f/+RFrDSM2M9p4pPM50m8NoO8d5t9aBbH+fkSiUXM8Qp3L6QNKqcM5fTsHKHUuo2+nt/fq43H6gKZzhL2X+LzxZALxZIL0+XGwwLj3Er9fCpaqeCTsI+TXqNe9i0YOlhKwmfS8FCz1scr8vnDy6ye0g1N7ry4/Dpb65yzN74OHpXnUq1WfwNXFo74vxZlZPVnM3HspZ3KA/r6YsKpWqiASjSJK4LA8rDdj1epxGjkS50LGUsJeWWPkR/t+afGi8TgS6TTp86O9K7S62Zs1w8FS/fvSW8GLpHwuFot45ZVX8PTTT6PSUXB+/etfX7HEemt1FkcRMlAo4Mi+/YZ49Fu9gcECAWgY8Qp5LEzJxIsnE+hPJbEwPa2PVy6TFCH+gXVKH49apA4MqqLNHI9WJBRI8agbiR/P+Ly0jVMpInX5cYYRtPPTFZWqBzLl+1D5EYpU0gGYkF+RQSAM5lGv1Yx9LAH6Ab00M6vvY8kgEAYG8+Z3j0mYmN49Tg9uD6v08Sod74tJETJQKJDsagAQS/Qb1X8DgwUcO3TYGI9FIFCwmVOkGr8PepGazAyQ3heK9TydyyISiYjlR8EqLx71Io+GpdUi7fulYJVdfkIXjQwspVx+0LCecZGn9l7BeI16XaxtyUAhj9LsnKGPJQPrSdhMV08OFApkbKblR6sjVTyT0s3Lz3ypD3h1ohHrBws4/K6+3RrHej5QyOPA63sM8RgXecS9l/Jbi0Sj3kUtAUspjsZkNoNoPEZ6/yiORjKWUrFPGEszBKxqx5PBKi8e8eKSKAJpNpviWEpRFrfzC/9+eVhaQLPZNFzk8bCqvLBgcORxsZSOfZT8yFhKULZTz+QqvwV9dz7mXpRCychp5I1u6Uqp7F806tp9qPwOvfOuIT+OyKKAeUpdSnFLRyIkEQ1ZCZxOI9bfT/p+s2vXGOPJ16VU7KNiPY3D6a3gRSKft27diq1bt650Lr11AiwWUA/mMT+l3x2oQO0XqZP6eFRgTWYziMZiNAKBWHQAwPykbJFq3kho1nO/SCXlRz/wS2104vHURmJ8Xmp+aiMJf/841vOBlhJYrkjNyxephs+OYz0fGCQURQzr+UChQPqtAUTCaZBCcPAONeaewDzC6fC7w+R4RvK5kMc8AUspF419fX1k8p7yrvSnUognEiTs4xSVYvEG6YQE6aKRvHesLpZS3hfKbyNDwFIvHvP71eTHsZ5TDg0c6/lAoYBauaJVm/EO6AWz2oxhFSdhMyce8QAMELF00IwtHOv5QCFv7gnMxOaj+03t2+jWc95FqNl6Tv1+Ke9yLJFAIp0Wq3MVPoqRu8Q6l2o9V1iwGlhKq1uoWEp1zai9QwibB2nxamRXj/miluVaGCygXquhbOgx7MWjYSlZZEF8X6h1KRWrjN8tYz7CQIEQj9MWZLCAuXFTSx/eZcDkIXP7NsBrb1ia1atUBwYLmH/pVVK8/lRS+16p/Ghn8qTxojEaiyGVzYhhaSqbQSQapWGV0BmaFc8/45vjqfY6+vyk62ZavN4KXiTy+ec//zni8TjOPfdcAMA777yDumEgU299OBcVqFWRyrnF1AG1X6QSb7lMQC2vaKApi9nAKqQIoedHVIQQb/XYt45S6j9FEJEG2dA2kkatbiwmOARRaXZOe+HC6XFNUzQwD/xEZbFcEc0j2+eOTcrFK+QxRegxTM+vgP2vvE6LRyScpBQhkViU1DaCetHot/QhEESUi0ZxLCVjFVfNdbz3DqIihLx3rI5aj6Is9uLRsbTZaIj1eR0oFFCam9e29GnU62jU6uT8zC6IVcTSAkMdRlSvFUXzK+DIvhG5eIMFHNzzjmh+HCzVrb5IBCmiEljF05HPqqWP2QVRRiQaRay/X3uB3W4rcPzrXGrdQstPGqtK/rPo81sdLK0wRSWkVgAkF4m5pY8fj/j9Vool7eX6YrPZssavFpYKK4uJdR9lNgwFq7iikvH3D5Dyo34fY8P79PE4IhXCxSXVFdXX19dSAtOxWXde7E8lEU8mWA5O3QV2mqzc5YkYKO8L72JLyPXBwNL8EOGikcUJcT6/Hvlss0g9n6+//noMDw/jhz/8IX70ox/h3XffxXXXXef8H7/11lvx9ttvY3h4GN/85jeX/fv+/n488sgjGB4exgsvvIAzzzzT/3cPPPAAhoeH8fbbb+OWW24hx+wt/fL7LRnAgVqk1kq0/k3UIrXdi1FvJyYXqey2EWYgjFN6h61akUotKhlFKgOoSUUqMT9TkdrOT8ZKDHAUHOaiCEDLei5kdWb1sTQXvawe0gR7njjZzolHsg9y8jN/v9TPj9rSp0YksKgtfdT7YjrUkFv6EK37VKxSvzVTj2syVlF/u4NErCcfgKnPS4xH3Dtq1J7PrfwofXLJLX2qVVSKegUoucVSqw+jaWgntRcjFevptYHZSlyveNZzeksfc792gNpnk4GlQvlJt0Ti9D2lkO3Uvqd+Sx8ilpr6xvotfYh7ubnOpWOBF08Gm6vlMmLxOCIxfY9rDtlJe1foWEr9rXn5mfc2yfZyZGxh7L1ULPVa+ugv8jwspZ1jirOz2pY+QKsOp2IVEZtJWEDAqma9gUatTn7/zN+tqiOFLgY58wdWFUtp7Q0p2EKJF4lFSf3fqVjadkvT5g9IYSl11gy9fVsZkUgEsf5+Yn6m75c260O+9Sf9HCMqAmHUpZT8eit4kcjn73//+7jlllvwmc98Btdffz1uvfVWPPTQQ27/4UgEP/zhD3H77bfjggsuwBe/+EVs3rx5yZ+57777MDU1hU2bNuGhhx7C9773PQDA5s2bce+99+LCCy/Ebbfdhh/96EeIRCKkmL2lX9RbWw4Z68UzECaMIpUUj5pfhyKElp95I+YREgSFiWjPZ2pPZWKRSi4q87QiVZpAYBxqTO+eiifVK82LR+uTS8lPWc/pRblJLdW2/prz4yhMaO8fWa1n+PzofSdpPZ/9IpWKVUJFaoUaj4mlpveF3neSWJQzeiB78YgEDEXRIFn0ki8a6Wo96m+t2Wwae9VWGBdvppY+Xn70vc30rvjxqC1zJLGZnB9dOX68Ly551nNeX0xKftS2G0YlsGrpI1T3qZY+lLqPEo/S05aTH/lii13n0r5f80UjD5vNexuvJZLpojEjrnwWds0M0gYOcrDUa+mjb73CEYGYWvrw8jNfvHnxOCILwXhEEQgvP308ddEoJrLgtMlgXAxKOy4pIotEOiXmuKS39OFivYxSnlP3keIx6lLp/EgXjWTOZQVcGhzORWjQuWrpY2qz1VvBi0Q+x+NxvPtuu2n68PAw4oTJobp19dVXY+/evRgZGUGtVsMjjzyCu+66a8mfueuuu/Czn/0MAPDYY4/hpptu8v/5I488gmq1iv3792Pv3r24+uqrSTF7S7/YRbQwsNJ7ExoIk1UC/kqJqgg5wYtUQt9JLx7jgC5ZpJLJYkZRKVmkEhQDXjz6oYaWH+PQYPj8+NZzInlqiGfT0ke3OC19ALP1nHvg/zgVqV48095BP/CT4lEPDdyLRmNLHzqWUlv60Kzn5pY+XjwGlooTCMJYSnGRMIZukbGZaD0XxWYClvKs53JDqPgtfUwXebSWPnTylIYFVOs5p/2YF08Kq5hYSiV0DO8zh4yl5ce8CJVSZgu39CFbzwfNLX28/Ijx1hCVwCtCxsq0HPLiMc4dxxlLVTzarI/jv1dSlbGRaAubheq+ZGaANtyTOB+B7MgjznKhu3OZAjih9oZsUcmJKtBbAU4onkygL6KnH+ktfei/tUqxiHrF4JamupGJWNpbwYtEPu/atQs//vGPcf311+P666/Hww8/jN27dzv9h0899VQcONDuJXTw4EGceuqpoX+m0WhgZmYGa9euDf27lJi9pV/sIlpo8Ay9/9DqASut7yT18+MVqUbrObkIlC5SOWSnbDzRIpWlfKYOuDsxyXZq30lqPGrfSaoihNx3kqteEztQMw/8q1YEHu8ilemaESKw6C196PnRWvoQFSFUspOqCBEnO6WxmaF8FiUQVotsl86PQRAZDujUlj7NegP1Ws2MBWQC4SOCpSuEVeJYKhWP0Q6Ok59ZZEG8+CW29KmWSp71PGFwzRBb+lCxKrMCZOzHC0s//MpnL575+4j19yM5MGD8/BYXF0nxUn5LH2JbixOWPOViqZRogxmPgFUAMP9xEZUQW/qQHaEtl4ZZZMFwN68ClvZW8CKRz1/96lfx5ptv4h//8R/x9a9/HW+++Sb+/u//3uk/HESmdassw/4M958Hra985SvYuXMndu7ciXXr1lHT/sivNjDoi7bMGuJGQuwv5RMIxl6HRIXEmgK57yRAUVIXyEUqYAbWDFnRQLeySwJrZs0gCVipipDM4KBskbpGtojODJp7uQFo9ZeSO4RQ1GvUvpPUeOl8jtR3EvCe13RxlBkcBGDGAi8/s/Xcj2fqlVah9dYbWEPLj2plp+bXJjjMWODlR1TAmBQhxKKc3EOamJ/qdWh+Xwq0IpXY0ztD7DtJ7cFNvsgjxssM0rBU9ZozKUKo8chtHohFNLnFEuPzo8SjYnOtRO/zSsZmU0ufGK2lD9Daew3xUrksqaUPAFJvQipWAbS+sVQsrVerrYtGWSw1fb9cLDW2HKJiKbFnNpkwIcbLUNVmZdrslcxggdTSh1OHU1r6UHtwD5DrSOo5ho6l0XiMYD2ntaGgYymjPz3ZgUiJR8RS4jmG2jc2MzhodL96+Zk/v75IBGmCw0/FM+WXGEgj1t9P+34J2Ewl/1Q8yhkVMGOLf9FI+O1S8iP3kxfGUjL2Eecj0LGU+rzEcwx5b2vFMzjy6JzGIKqlsllkQezBTT6T+3uvuZ0e7cxbJl00kjkX4bq0t4IXiXyuVqt46KGH8Dd/8ze477778PTTT6NqKCZM6+DBgzj99NP9/33aaafh8OHDoX8mGo0in89jcnIy9O9SYqr18MMPY8uWLdiyZQsmJiacnuWjtDiKkGazKac2G6QVqZxbTCrZSc2PekCn5UcsUsnDCOg38rRhBDTFAGcYgdSB38uvQC9SCUpgb8ox7X0xFZWJNL1IpfSNpSoGgNZlAHm4J+0QZ744og9eoNxSU4ty33ou2HcSoByAeYoBupWYmh/tfTFd5FWo8cgtfVrPS8BSUkufIvXzo/ad5GAph+Cg5Me4aDSp9Vai3z25rcVqKIHl4vl9J8kXocS+k0KuGc4QG1I8BsFBsZ7z8jN/H5xZFcAKqNekeiBTreet/ObJSmDzRVm9ViO09KFbz2ktfajWcxo209WTdOwjxWNisxEPGA5EmvWcSBaz2sERsIplPadhqeld8UUWQljqD/cUw1IaFpDjEbEKoO29nHMCCZuJyud6tYpmo0FQAq+Mq0Ju7hTtXMRx9TQbDZTn9Bd5HKwqzc6RhntS86M58uickCSWcupIgNCSi8EJReMxRGMxkfx6K3iRyOdnn30W2WwWg4ODeOWVV/DTn/4U3//+953+wzt37sSmTZtw1llnIR6P495778XWrVuX/JmtW7fiS1/6EgDg7rvvxjPPPOP/83vvvRf9/f0466yzsGnTJrz44oukmL2lX6wilaMEPs5F4MBgHvNTU3LxCtR4tM8vTVUgMDZiEhnLOHRR41GHESxMyljPo7EYUtmMWFGZymYQiUYxP0l7X+hFjFA8deAn5UcvUqnfL/lALRxPynpOVVyQrefqeQ3vM7ulj0Ftxi1SzS19pLGUbh+k/dYYF4PCWErCqhXAUoDWc5yaH8l6LthPXvWdlMJS1XeSupfT20YIYak09q0QNlPfFymrsxePTsCYsJRqPad+ftx+8kXDkCIOeUrqO8nAUlnsY8YjEE4flTrXiyd8jiFcdNPyM180quGeUljaHu5JjUcURZDPCatDxs5TCKxiiTHc88S8uKTOTZLOr0Z0vLUvLuXI7Fql4v95iXgL0zOkuUkAbRgsVYBEzo8l0COQ2cJYSsIqKjnO5YQI8xF6ymf7RSKf8/k85ubm8Nd//df46U9/iquuugqf/exnnf7DjUYDX/va17Bjxw689dZbePTRR7Fnzx585zvfwZ133gkA+MlPfoK1a9dieHgY3/jGN/DAAw8AAPbs2eP/+e3bt+P+++9Hs9kMjdlb9MUqUhm3UjQCQfiWSxKoGW0tvHgE4CJs6ixFiBB539fX11ICy8TrTyURTybEFCFplnJXuEilFL2cIpWgCMmIF6nCBIK0IoShpCYpQgq0ItXLj6bWoxSpZEVIC0upRSrFfsnDZoqiQRD7GEOA6PnJXlzKKkLkL1apWAro90pq30kVz6QIofadVPGk1E3keNJYukJkrLTy2dR3kp7f6iifyfGIfSebjQbq1SrpwE/rO3mCY+lqqdcYsz7I8VYlP1lCh3LuiMSiSOfojrxIJKJ15bWHe8rVQQAHm2VdGie6i+T4YymjDmecYyTzazabKBpEFouLi6gQREgDhQLK8wto1GraP+eT2QSRimxdynTQnbDYR3cdrSYndDzz48xN6q3gpdeVqz8Ui+Gkk07CPffcg3/6p38S+49v27YN27ZtW/LPvv3tb/v/f6VSwT333BP4dx988EE8+OCDpJi9RV8KqI3WaXKRSuyFRwVWag/pQh6H3n7XGI/cr6qQx/5XXzfn5wOX2couBazcItUUL5nNeH0nmbeiYQc+HoHQVoSE9evmkp1SbQ8AkHo+s4pUQlsQTpFaK5XRf9KQWH61cpl0A0zNz7N2m78PSpHq5WfucT0wSCtSAa9vLOmATrRb0d4XYksfqiKE3DJHtqUPp+XQkff2G+MprDcTRDSsp/b0pmIzpYc0q6UPIR6vpU/7fQn77/sXecSWSIC39zZCsZ6HVfGEd9EY5qASx1J2iyDzwDJqfpS+ndznVUru8PwYey+h76m/9xr6TgKtllyEvZfSd1LlJ9UeTcWjtfQxfxeNWg3NRoPQQovafozYZ5NZN1OUtpOHglsVBsWjfL9H33vfGI/cQ5q8V8piPeUc47f0YWJp2PJb+nD28mQy9LfExZZozLtoDHNQtYdx0uKZsZReh9N6KvPimc+oDBdJuYxULkvKj6Jsp2IpQOzvT2lXp4Z7LujnJgEg9ckdKORRnJ4xuqW9/Gg9sylKVnXRSMJSkkOIPjBPltMoYObouDEeB0uPHTxkjkfGUupexOCESNhinmfAm5tk/vz8lj6E/HoreJGUz//1v/5X7NixA3v37sWuXbtw9tlnY3h4eKVz661VWHRFCFcx8FFQhMjkxylSKfE4RSolHudATSGzOUUvRRHCVQyYFCF8RYOkWo+jLBZWPhusxF48imLF6ztJKlKJ8SgtfdrxZIoYcjyiy8CLR7tcoORHtZ6nuQoO4+UC1UWyMr3c5NR65nh+Sx8h7FMtfaSs51ylqDEeE0u9eJq9g4lVgP4QwsVS8gFdXPlMjSe4dxCt55S+k15+NCyl9J1U+UnVkV5+NDUhD5tlsIqeH/Vii2o9F1abic9KkXNpqJY+VEeUKT/V0kfqHMNVipricQY5U84xGRsRiKY24GKfumg0xluFNhS0nsryLg3KcE9yvME8aW5SO54MVnnxTlws5eVHu2hs1OtyZOwKYSmp/Z0QlqqWPlT3sBdP55ZWLX0Ye5EOq1juV/M8Aw729VbwIpHPjz32GC699FLcf//9AICRkRHcfffdK5pYb63ekgR+MlALAiunSCUpgTlFKuGWsN13kq4m1B2qOQdqyi0mr6g032Jyb+QB/S0wp0ilTFHnWruVIsScH+39o6rrTMM9yfGIfSe9eDRFA+fAT1KbMYpUklqKU6QSCCx6foQBkKz8aO8L5d0jK0K4vdw0z2vT0keHBayWPkWCooE5jFPlELZWioyltfQxPy+vpQ+FzKYTMKSLRiaWki8axXoqc1v6mF0zlJY+XjwiGUto6UONR23pQ8+Ph32iymei9Zy+txEvLokXjTTrOdOKbbSey7XJ4LX0McfjtfShiCwsyFjSxaAUltpcDBLyE45HaulDGMjJfd5EOoW+vj5NfnSXBhWrGrW6cbinH4/Q5oEy3JOTnyyWCos2WOeElcjvxLy4rJUraDab4gI4HXkaibU4EiEsbbf0OUFFFtLYxxDo9VbwIrXdSCQSuO+++3DhhRci2XGQue+++1Yssd5avUU9JEkpVjhFKkURYlOkam+5mOSkKT/uIAwvPwpZLKUsllVw8PpOUhQmjI2kQxES9nlzlc+Ad4gLUyxQ+06qeGbrOa3vpIonWaRSp2yzyFjhA796/8PjcYtec5FKafPgxaMRRJSWPvR4cooQTksfykWjXUsfuYsjYzwG2SmOpQRFiFVRTlGEMAgsvSLEAkulsL4jXqj1nNHSp1pqWc/j8dCWPZyWPmTsk8RScWzmYalyZYXnJ698prT0IccjtvRR8UzzGyQJIk5Ln8VmE7VKRRuP1dKHQCZyWvpQBhjKuzRsCA4p5TNDXSc0G4bVJmPFyPtU02CNsAAAIABJREFUqEOORxZ730c8mfBjB+VXKZZoLX0+ZmRspVRGbsN6Q37yyuJjB8xtHvx4BEfe2N4RRn4n7l5paskVTybQn0quigBOHktXDluM8YRcJD3ls/siKZ9/8Ytf4KSTTsKtt96K5557DqeddhrmCDa+3vpwLlOvQ06RCrT6N2l64XGKVACeIkSrDmMAK6Hfkg2wapXKzL6TgGSbDAqw2sRbBYKIUaSa8qP2naQqx6lFDKVPLqtILZcJihB6kUrrgcwpsipi6itgBchY4b6iNaLVXio/TkufdjyZlj6AuQc3B6vqFc8+KoZVZcpFI8OVQujRzO2v7sUTwlKOy4VxUUtz4ZhdGjXS3ktv6eNjqXbvZbT0IX0fDKwqVxDr70ckGtXkx8NSmouEgQWGeNSWPioepc8rGUuJfV458UguF8beq+07yWjp4+Wn/345LX1UPP1vg45VjXrdu2iUrsMNv10vP4JIxccqjQvCCkt1rooVwlLG/AHSOYYzz8DwvpBb+pAciIyWPoQ+r9w6NxKNIhqPa/ITxhaui0TQkUfHUurnJ+yQJH1+vHOC7l2JRKPeRR6rziWILIS+Xw4WqItGMfcwAUs5LX1qLCxlkO2UvUNqL2Lsbb0VvEjk88aNG/Gtb30LCwsL+PnPf4477rgDF1988Urn1lurtEyEzsAaOjCoeLoDf4YBDF48/a1jZg29VxpFETKgeq8JKUIGOL3cCIqQDCc/Uq85YaX3mkJLCUxTmwF6giij7GqEIpWiCMmsoRdZVGU79V2ukD6/QdaNPP5/9t48OM7qzP/9qhe11C21urV1y5ZsjLEBg7GNdyBhMV7YTVhC7sxAliG/ye8mcyeTWz8yk5phZjKVSpjfVGpuVSqLk8pSde9QQIJxAsZACIQEbGRjdoxlxwELr7J2davV2/3j7fft7d3OOU9bknk+VV2JZfH16X7f/r7nPOd5ngOHiUI0Slqe1yQ4PqpTjjU95+Cu2PW1z7jweL2k5XkNzU2uW/poekmHTFZBL3UoPRfpO2mMz42em0m0UXpO5M0uNnpEnh1uNvLEvNRFMFvg2etuo1HTc9fSx83nFxFo6eP8rGwS8FK3G5eiXup0P7v2qoRLbxYYn5vSc8rstSaBgISb7DWx62s/PpGWPpqevbfUNza6bunjRk80W4pynlvUs/MCTc9NSx9Nz/56iIzPTem5yDrB1TyS2ktr1SaDqo1HNCrQ0sel97lu6eNmHUPrzXJeb3//uZ/XTzpuNAp7qZs2D9PW7/7sVuH4AgEEgkEyLw1GCkkWZPNc0ZiGk5eKx1zcZe663GhMZ8jmpa7W5EJe724e6XZ8rqpw9Gely/uFqcZV8DldKCkcHh7GJZdcgpaWFpx33nm1HBczjThOovW+ky5OhnWlJ3Dqr6bnYKyFSeD4oPNJuG70moyMAWc9IyOEaHzuMkJkekjbZ4SkJ11OUt3sYka0thFu+05q47O/X9zeK64yQiLu9Vz1ZRU8kM5RL+LuVOdSPaf7RUTPOSNEsHzQRUaI++thr6dPUkW8yu67IZoJ7DQ+kRPUNT2HDAm9xQ1RD245L3XjVW71HD4/4/0667nKCIm413MTTBTtWQw4e2k27balj3MPbn1B6K6lj7vSc/ffNXdtPEQW/M7jaxG6lx31BL3UzfgmXI/P3f1HWtotdH3t9fS+k0Jeb5tt1qxlm7kcX8ohOC7jpa7ajwncL+5a5kyjN7vxUpf3s1Ppucg6wV3Vgvtnr9ukg1w267qlj6NeJILk2LjLlj7uvMr9prRLrxKd5zp8frReGkFCcOPSqb0haXBcogWPc0UjTfBU5NwkTc8hyaIpBJ/fTxeMFayWdo4Z0AaLReZ9mp67mAtVsF0k81lkfG7mBpmU80ajkNe79CoASLioyHPrpW6TLBhzXAWff/SjHyESieCf/umfsGPHDrz77rt46KGHaj02ZppwO4mmM36xEobpMlaqbEx9kpp0EUBwkxFiTFIzGVdjA5x7zYlcW4Bykuqu2b94prK9nugk1XFSKTpJtd1lFZuk1mR8Fte3zuNBo0C5mlNGSCAUJJ2khoyMhpnqVaJeem5MUqmyMUV6ubnSE8gEdpURItB30lXwVLDvpKZH1NLHlVdRj4/Y+6Y5gKDpOT173d/Ljnoi1zehbTT66utN/16k76Q+PtuxCfSd1PWo+o1remfZS4W9ijaTOuWU+SzjzbbBe2JvLowv4bai0akFVLQFmakppBLOLX1czyPdtvRxmfks6qW2wVPBsz6cx0ebWSzkpS7PRxD2UofNHtpgu1jbCMA6qaR4bhKNNxfPTXJfwTmTva8281xiL53RMZez/ex1f26S21aY7s9Nop33Mea4Cj7/5Cc/wfDwMH7/+99j4cKFiMVi+OEPf1jrsTHThFNfUWFjdeiFJ2r86Umn7DX3k1Rdzym7zu0kFXCT7eh+kqqNzzljRaSvI+AmIDGTF9RipcS043M+8FL0oAlXegK91+z0vH4/GpoEJqlGn1zz+yUoOEl16nEt7gWTthkhMl5FOkl17IEsNj7nBbVYxkXaoeezcEaIk57gJNWN97mdpLrSi7QgMTrqqqUP4OZ+cf/ddZURItLX0Y1XCfXwLeg5ZPKLer3T3EDkXgGcqlwkvJmor6hTlZBo38liVZS5nmjfybRDlZWMl/r8fnh85huNMsFJu3tFpO8k4NxTWdhLHatSJLyUsO9k2uEsFylvtq2ga0Eul7M8mLkSN1U9kxMTyExNudObnHRcJ7i9V3KZLLLpjP39J9J+zDg43anlkPvvGkDYA7lmXmp//wl7qUMFp+uqBQcvFTncEyitMDX//GTOTQKs75fiuUnuvdTj8VhuNBa9hWhNLlCdq+tR9acH3MQ0BKtcHHpmy3ip3blYUvNwF88itzESN17q9twkwN2z1+29nM/nne8XwX7jAN28jzHHVfC5s7MTP/7xj/HUU08BAC6++GJ8/vOfr+nAmOnj7GfriU5SnccnMkl1kxEissvlJiPErbEaekTjc5cRItMDmTi77qxmhLQIlf7aja+urq4QLKYJjtc3NsIfCAhkrNjrhSQmqYB1Roh4BoLT+ETL3/TSc6dgtohXuWiZQ9WfvgZeCrj//FIuS7FdT1JdeJXW0sf8hHoZPbctfVzpCSz4Nb2kwwGGYl7vaqNRdBJNXkVCVNrtNnOXcuNSJNjuUM4p3nfSXs9o6UP0fmW91OpZKTPvA6wXcbJVJJYbjdReKhgwcZ1tRt2308XBy+7GV2jpMz5BNr7k6JirbDNNzzl7UtSbqeaR7sbn3vvcbDQGRebhrkrPxXoCO+rN4KSS4uGeNHqih3s6rROkvdRKT8JL7ccnPm+23WiMCOol3GYWE3spcZUGXeaz+2ppTc/Zq9xWS7vSm0YvNfSIYi65TBaZdJo0JsRU4yr4/LOf/Qy7du3CnDlzAAAHDx7E3/3d39V0YMz0MfMnqXTBWE2P1ljdZNeJGatzRoiQ8TvuYrofn5uMEKnsOqpSZ4eMEL3vpPCBfhbja2hulpukWuyiyy/4HfSEg7sWesILaqdgsfgCHSCclCccMkJksvWo21A4fNdoJ6nuW/roeo49aAUnqfYZIWJemnJRlSI2PmcvddvSx9Ajyiw2NhqdvHnaW/pYbOQJtvRxCp6KtvRxKj0Xb+ljH7wX9yoHPVkvtdpolPAq+/GJeylAmJlN3NLHsfRcoKWPNj5nb3bb0seVnlQAgaYCTNNzrpoR9WbqpBLndnWE6w6BCkR3pediPYHt9IzDPYW9ylyvvrFB+HBPwNqrgtLBWCevEvNSq/tF1KuckkqaJL3UeeOS2OsF5hr+hgDqPOYhKpHDOHU9yqQNp4O6Zaql7ZMYxKqlnc+xEp2H03sp1cYW4K7ti3Awm/BZyVTjKvjc3t6ORx99FLnCjZ/NZpF1GShkZh/kk1Qno5bY5XI8pOgcMtaznxFSi0k0TUaIcN9Jh4wQmb6TtnrSwdOzNAmU6JFrqycxqdT0qAIIbjM4aMfnPhPYISNEdJLqmBFCO0ml91L3C2p342MvddRz6mPp8vo6ZYQIt/RxygQWbOlTq2AnnTdPj1fRj48qW08uM9uq57jMRq3H44EvYL1RK9LSx433CbX0mY5gLKVX1cKbycc3ndl6bq6HO718Pm+r5/F5EQyLt/Sx0hNt6eNckVcjr6LKBBY4UK1Mz8Gr3B+w6DLzWfCwWqfMbPHx2W+sCh8oabPRmMvlBDbynFt/To5PuDrcU9dzPHhZ2Avsxyek5ybm4vJe1sZHHxNyqkam37icnmpzxhxXweeJiQm0trYapa1r167FiMtSL2b24diPR3QS6KL/kKiebXadaKmzYy88iUkqUf8hTe/sLUJE+07qelabAaKTVE3P+kEn3iOXtsewUym7aN9Jx8xdiR6+gJs2GS713LbxcL2o0bK0LDNChHu52fe1FW3p49SDW6bvpP34BIOdBe+zzgghbvMgOL60i0m0aJWGU0aIiFelk86HUIlVuTj19Hbf0gew92bRlj4AbHsdirb0AeyflaItfTJTU4WNRqcAAnVmrJiXWmW2i3uzfc9s8b6T9nrCLX0Ketafn3iPYVs9wZY+Tj24ZVr6aHpW97NYS5/05CS8fp9tj2thL6WehxO19NH0nNYJ4l7q1DdWbHzOPbOF1x2EwfG0zTrG3xBAINgodr/YXF/Rlj7GRiOVFzj1kxfukevOS90Hnx30JHoMA3RVGs5eKraOcVwXGUkqLoO7Dj24Q9EIMuk0UhMukyyMZ6+1XlLw3CTbjUbRijfHmIa4V5F7qY1eUGadQPwsoqqW1sfnGBMSWidYn2dQbOnDmc8quAo+//3f/z127NiBhQsX4g9/+AN+8Ytf4Ctf+Uqtx8ZME04ZIdpBGHTBU/ISEOLxkWdwEOqJ9p100gtGxCapTnpNgsFOTc/6ejS1ii34nTJC5DMkLPQEx+ekJ/r5OWU0NAkGTBxLz6NRAMC4aLaeRUZIUzSKTDrtvqWPQzlnU2tUuKWPnZ54T2CH4HOruPcBdgEnce+znQQKjs+xf77w+Bx6Zgsc8uRKLxol81KvzyfU0kfTm7TMCGkMi7X00fWovLQ4Phov1fRsNhplN96IvM+1lxKVnodao4XxiXqp1fUo6BEdHtzUGtVa+ggcIAfYbTSKtfRxzJ6U9VKbZ6/od81RT7TlkMNGo1hLHxfeJ+JVDtl1Ml5K29bCZiNPsKWPk15DUwhev49Mz6jIE372Ws+rADpvNvTcHqY7NYVcNmuTCSzrVdbzPm18RN5HfbaJ8DrBXVWPSCawnV5TawSpRAKZlMuWPg4VptReKrOGBqxbcsl4ldfvg9fns9ajjhkQVuRRx1woY0JGSx+i8dU3Ngq19HHSE02KYMyxDT6vWrUKsVgM+/fvx9VXX41//Md/RCqVwjPPPIP+/v6zNUbmLON88IxoSYm9UYciEfoSEKJJlser9QQed1nOpOnZT1J9fr/r8iNdzyrjosnY4Rccn8OOvGi2nlN2mOj9Yp1dVzB+ovtFNGDinBEiWp7nLqPB/QF3TtlheraZ4CTVJiNEaJLq1PNZoq1ALfTsg88Sejb3s+h3A7DPCBHVc8wIIe5PL+wtFnr6JFXYSy30jL6TQnrWVS5G30lBr6LygqKevZe69Spd72xtNIqOz+1Go7CXOlVpCBzuaatHfmhUC9KplFDfSVu9Qqao+8M9nTYaJb3UJltP9F621ZPwUk3PpspFaHzOz14qL9XG10I2z9XHJzwvtUqyqK9HQygk9vnZBMeDLWF4PB7h6+vYlkGwNN5ujQVQeqlYVYWjnt6WQfB+dtRzeb+kXVS8AdN7AJ+tXiQi2NLHeU2eGBl1nWThfJ6BeLBT07O+vjLBYrvrK/TdcGq/KFE9DNidjyA+b/YHnCoaCRPqBK+v3caWfm4SlZc2NDdr1dJka3wZ77NOohH1KsYc2+DzD3/4Q0wVyouvuOIKfOMb38D3vvc9DA0N4Uc/+tFZGSBz9nE0agljBewydCQOebIwamOSStRXtNHoOymqR1NuBWgZXY7ZZsLll4TZa3Z6gqXOgH0pu2H8oiVSRAfSAe4edGQZIRHRvpPOk2iRSaqrBTVlJrBEdpPz+AgzLkTL82rlpVTjc1rUSCxCrDYajb6TRBkhRksfoowQGW92lSFBlXEh4aVuFvx045teL3XUExxfLptFZmrK9ruRy+WQcJ0J7HyolWjfSVs90VJYxwU6tTcTe59sAIFsfM6bAaKHKNnqSXizx+Mx3Yyq83gK2WY0XhoIBuGrr5fwZocD5KbVq2z0JKtISOfhhFUkgH3wvtiWgTZ47/Z+yefzSNmcf2G8X5ftQY1gts1hq1PJSYHDPd141bnopXZeRZwEIjHPpQ+2241PrIrEfnyiZ6UkEQg2oq6ururvRM9N0vRcVGmIeqlNPAioxbyZeJ0gcH2ZamyDz16vF0OF6P6nP/1p/OhHP8KvfvUr/PM//zMuuOCCszJA5uxT7FdFM0m16/clM0k1+tCa6ElNUicnEbDZwQTEjZWqt5mu57hAJzqMICQ1yScOINhlhNQqQCTQw96ub6zoJBVw3gwQ+645Z4SIbQQ4taEQ7EXmOEkV75UG2GWEiPdysx+fYN9Oxx7S4r3SbPWEJ9HW94vRd1Kw1xxgvogz+k4K9l6z2miU8irb7xqxN0s8O+z6dkqNz3bjTcJLbfqeymyE2vUmFO07qY/PbkEo0ndS17PLRBfpO5lNp5HLZq2rFoT7Ojr12ZTzUruqGRkvtfr8hPtOuujpLTMvJQsgOPWNFe07qffgNhmfTN9Ju57Zjc1N4i19Jifh9ZlvNMp6lWM/eUEvtdST8irnikHx8xvOspcKzV1Szj2QBd+vnV4um8XkmLuNPG189vdLcnTM9eGe+kajrZcStkcTDsa6OH+A3Eul5rlEXuqip7fYvedwnoHwWR/Wn5/UuUk23lw8N0lczxeor/o7qYQwu7M+iL1U9NwkwL7iUmrjzeb8AZmNQaYax+Cz16sdnrFhwwY8//zzxt/5LFooMLMfu11RqUmqzS6hzCTVLiNENvsKMA+2S2UWk2fX2ezwS+zCUfczot91tB+f6CTVKRtOZJLqqCc4idH07ANYIveym9JzqYwLm4wQsfKt2vRyowvGuikfpPE+vaUPlZcafSeJxieX0WDnzXJeZakn6VVWGSG1y3wW1LP5rgmPz012GHFmsdu+k/r47DaORFr6aOOzz3YUXTA46glmvzh6s2BwCCDMfHbVcohQj7pKQzDYbjePlOs7af3sMFr6EOkZSRZCXmD97JX1KsB8blBsQyGmZ7XR2CTrpYRVJPY9lae3SsNRzxgf3bojnUoZ96g7PfvrIdLSx1FPcB7panwC93I2nUY2kyGrgnDlpeRVGjReKjW+hHOVkJSXmniV3LlJ1ucZiB7uWapn6s2S81JLPSkvKGw0+v0m45vhFW8yCXpuvJkPHFTCNvj83//933jxxRexfft2JJNJvPTSSwCAhQsXYkQgU5CZXdgZtcok1cyoZSepgLmxyk5SrfRkS50tjZp6wV8zYyUKthf6TopNUu0mvWJ9JzU9m0O3pnmSqulZH7QjN4m23/wQuVeMjBCqzGLySbS+ACYan80CXbalj5WebEsfwN5L6SapchtvjnpSASIqb7bbaJRo6TMtAQS6zGyRlj5FPWuvEmnp46gn6aVU2VeAU5WQmJc6jk8wGOtYek7dHk04WExdpWGtJ9fSx1pPrqWPzUaZZNKBpZ7kPNJ5fNOXVGJXei7rpZYbjTUr7aYqPRdr6eNmfCItfTQ9Yi919D5iPSlvpvE+Tc/p2SZWBZHL5ciTSizXHYTebBzuSRRsD4SC8Pn9dF4a0YPFVF6qkLRhso6RDcZaj0/F62nWCankJHz19fAUklvNx0e80UicoCeSZMFUYxt8/ta3voWvfe1r+NnPfoarrrqq+B95PPjKV75S88Ex08OMn6TaGbWK8VONz6Y0Xmp8kylboxadpNqW5wn2ndTGZ1+OKDypdCiXFNVzKs+TmqQSlW9pejbll5LZevblZRLBdqLyPCMjxERPtu8kQNfSxy44KdPSpxg8NQl2ynhBwvr9qnipWYmZ3MaWdfmgXEsfGz3JAAJg8flJTno9Xq/9RqPARr2tt8i09LH1qhoEECgX/NJVJGcp81lyfFQLfk2PLkCUz+WQTqXIWg7ZBRNlWvrYzftkWvrYLqgl249Z6il4lflGo7g321UdqYzv7GTryW+Emm40yrT0cdgYFG7p41SRJ9DSx834RL0qZVuFQ+2lYpmshp5dRZ5UUsnMDbbbtuSSbFdnpuf1+9HQFKKr0tCTLIh6UsuepWGpJxPTqFUVyQz3UrOqo5kSE6pvbLDcaBRPsrD/7iZGR4WqpZlqbIPPALBnzx5s374diZITs/v6+rB//37pfzQajeKZZ57BwYMH8cwzzyBSuHkruffee3Hw4EEcPHgQ9957r/Hzyy+/HG+++Sb6+vrwX//1X8bPH3zwQfT392P//v3Yv38/brjhBukxfpwhX/An6Bf8lnoqxm83PsFJKmARcJLpO5mwy9YT6zupj8+69Jx4wS9YCmvoEWVfOY1PtO+kpueQDUepN82Zz0W96s9Ppu+kpmd+PWRa+tiVns+EBX/tMovPQnadVMaAi2w9mfGZbjTSZxPKtPTR9MzvF9GWPrYZITMlGEsczLbeGJQLxlJuXDptBsiNr/YblzItfXQ9076TEi19spkMsmnzjUaZlj4pNxtHVMFJFa8y3WiU16Ob59psNKp4qe34JAISNhuXQi19bNcJEi19EjYbjVLzKmovpUsS0PTOppeK9fB11JMKttt4s3Sw3bylj/Q6xmR89Y2N8AfEWvrkczktCck2c5fYS6XmkdZeKlYt7carRDJ3bbxZJQHORE+qpU/C2Utl1jF2SSoJiZiL1bM8PZkyfkdMz/x+SYyMildL23mzoFcx1TgGn2vB17/+dfz2t7/F4sWL8dvf/hZf//rXq34nGo3iwQcfxNq1a7FmzRo8+OCDRpD6+9//Pr74xS9i0aJFWLRoEbZs2WL8d9/97nexYsUKrFixAjt37jxr7+lcwi5zt0lmUm6XCSxxSrTe7N80QCRREmHbxqM1ItV30lIvKjdJtdKTnQQC5kbdFI2SZl81RaP0wVjS8UlkXEymLIPjMtfX6gBDmb6T2vjMg/f1jY3CfScB60m0zAIdsM7gaGqNanqC90vKojQ+FNX0hCapNhkhMuOzO3Cw6KU0wfYmCS+dsg3ey2Q02IxP8mASOz3Rlj5pF14qNEm1OfCyqVXc++wO5JTxZv27ZrrR2CpXlWLfs5jQS1vFvTk9aX0YrJQ322WbSYzP6kBEmZY+duMLtoSFW/oU9cy+GwXvE332Wum1intBJmVdei7lpQWvMptHyujZemnh/Yq19LHzAplnh92zTX4ebjU3yKYFs810PbONxtaoeEsfu/cr6aWanvlGt4yXWlc0EmcWE3upjNenbTKLtWcljZcC2vUdH6QZn9fnQ2O4GeNEz47GcLOWZCE4Pqvrq393Rcdn6c1RXW/ItVYuk9U2Gm3nke717Ly0SUrP2UvHB93rpR28ChC7HvZeWnj2CtwvTuuYXDYr1tLHweuTY+PIZjKu9Wzn4VJeRR9z8Xi98NVXH9goEzNgqpmW4PNtt92Gn//85wCAn//859i6dWvV72zevBnPPvsshoaGMDw8jGeffRZbtmxBPB5HOBzG7t27AQC/+MUvTP97Rp6a9ZqzyF4TnqQ6jG9ieESw76R99p/oJMappEQm81TTM8+uE3loanr2k2iRSQdQzAgxM+pQpAUTEuMjDXA4lDrLXF/TA+R8XqlJqpVeQ3Oz1ndSapJqvUMtPj6rSaquJ3F9TQ8p0nfkRfXMM7OLbSPc6xVLz63HJ/J9s+1jGRVfNLjKVBaZpNqV0xUygcX6TtqPLzk2Lth30iFYLHgv25aeSy74AatDtyS81HF84t8NwDrDhLRncTRC5n36+Kj0jL6TEveL2bXV+06KPzvOspdKPXvtWvDIeLO1VwkHiCwCOlJearegpu6LKdkT2FJPqqWPfWl3KpEUa+ljW3oul9lpPb4ZMM+1fVZGJeZp1s/KptaotPdZVTRSzXMBhYpGuyQLIi+VOdzTTi8okSlqpyezhtb07L2UKilHJkmAenx2G41SVSkOXgVQtsmQzwS2Gl9magqphEhLH/vxJYSrpamDu2cz5iI3j7Qbn6hXMdVMS/A5FovhxIkTAIATJ06gs7Oz6nfmzp2Lo0ePGn/u7+/H3LlzMXfuXPT391f9XOfLX/4y3njjDfzkJz+xbOfB2GNr1DKTVAdjpZ6k0mYWi4/PNkNCpvzN0DMvaZLJuACsS1RkM0KsykOlMkL8fnh8JhkhLXQ9kP0NAdQ3NpCVOsuUEmt65uWDMm0jbPUUJqn2fRNlyiVpSp01PfMSKfn3O2lRXib+fvXSc7tScZFJajF4an6/iE9S7csHxSep9j2kZe5lAOb3i2SprqUecRWJbKmzpkfkpbaHoEl4qdNGI9GC3+g7SbTRaPSdpFrwz4AFei3Gl7LayFPwUvuDq4kCOhLBXb30nK5lk33SgUzfSUs9ib6T9sFTuTYF1uOT91KyCr+E/WaAtJdabPbMlHWM5UYj0XfN4y0c7im8kWeVZFE43PMc81LLpAgVLyXyKls9hWC7eTWywrrIri0IVZVG4dykJFWSRTSCyYkJZKam3OvZHrYqP4+0evaSerOCV1ndLzL96WsxPqoqF6aamgWfn332Wbz11ltVr1tvvdXVf2+2e5vP5y1/DmjtOBYuXIjly5fj+PHj+M///E9L/fvvvx+9vb3o7e1Fe3u7y3f18cAwapMFdbijDeODg0J6dv2Mwu3tGDsjqGdz6Fa4ox1jkuMzC5iE29sxfkY8Exgwf7/NHW0S47OvFp+zAAAgAElEQVQ2wnB7m/jnZ6FXV1eH5vZWjAvrmS9qfIEAgi1hifGZ3y+haARev09KzywjJFz43su8X7NJQrhDVs98EqjrjYnef456VONrA0D3fpvb2yTHZ3U9iPXa25BNZ4RPObYbX3J0TGiSapcR0izlpTbe0iGhZ+tVEl5qo9fcQed92viI9TrapL3U7H5plhqfuZ7X50MoGpHWq3z2Noab4Q8EpPTMNhrD7ZLekjDv29ms4KWm84x2BS81nbcoeKnp+OS9z2wjRclLzfRUvN7i88tls1KbC2bzvuaOdqQSCeP77YZcJotMOm15v4wPDgn3nQTMvSDcIe+l5uNrk9ezeLbJz/vM7xdSPQVvNv1+dMo8K8291OP1oqk1Kr2OqbxfGppCCAQbpd6vx+Op2mhsbm8FIOmlFvMqTY/G+5qlvcq8qkL6WVQDL/WbJCDJP4vMvbRZeh1j7qXGukOicsFqjZ+eTGFyfMK1Vj6ft71fJoaG5Vr6EHmp3XkGM91Lm2sQc6HyUqDwrBS+98y9tM7jQVNbq/D4mGpqFnzeuHEjli5dWvXasWMHTp48iXg8DgCIx+M4depU1X/f39+Pnp4e48/d3d04duwY+vv70d3dXfVzADh16hRyuRzy+Ty2bduGNWvWWI5v27ZtWL16NVavXo2BgQGqt31OkM/nLTNCIvEYho6fFNKzK2+MdsUwTKgXiYvrFXtIm+h1xTB0/ISQnlVGSF1dXWF8YnpWu47+hgCaWqPin59FRkioNQJ/IIChE4J6FhkhkVgHAIjfLxYBnWhXDAAwLDm+ygdTpKAncz+bfjeU9Ey+G3FdT+x+sSpNNj4/0fvPQi9S8HDR8Vl9ftGuODLpNMYGzgjpWXlVtCuOxMio8f1R1Yt0xTBy6rRQJjCgvV+zHf5IV0z4u6aNz/r6CntfSu+tZ+59wt81O2+W8FK7AEy0K46hE+LfDcA8IyTaFRf//Cy8yuv3I9zRLu6lk+Z9aIMtYQSCjRLeYt47sSXWAY/HQ/asjMR1b5G8X0i9uXqjcaZ4adpi4y3SVfBSwfvZ+lkk581W5w9Eu+LIZbMYPS02V7bz0snxCaFMYMA6QBTpimF04IzwCfR2z17Rew+w7hsr482ZqSnLjUZtHi7rpeb3i8x3w0ovGpfwUps+rxGpdYK5nsfr1byZSK+huQkNoZDCs6P8fgl3tMPj9Up7c6VeJC7ppRbji8blvCptUdGoexWVl0Z1PSIvjXbJPTumbLxP0xP/fljp5XI5jJyojqPYj8/aq6aSk5LnQZh76ejAGaF2a4D1s1dmXmromc374nFhPX2j0er+o/TSSDwmfC+Te6nNWS5RiZiL1TqhzuNBS6yDzEvrGxsRbAkrXI9yvea2Vvj8fmE9ppppabuxY8cO3HfffQCA++67D0888UTV7+zatQubNm1CJBJBJBLBpk2bsGvXLpw4cQJjY2NYu3YtAODee+81/ns9oA0At99+O95+++2z8G7OTaaSSVOj1oyQzqgj8RiGTwo+NK0yd3XjEn0IW+jVNzYgFGkh02tqjcLn90vomQezW2JauxrxYKy5XiQmG9y10IvPML0gkV5iUssICZTvAsuPzyKAYOiJ33/mep3ITE1J7QJb6U0MDQu14CnqmQWzOzFy8pRQdpitXqxTapJgVR4aiceEF1za+Cwm0THxSVtRzyIgIXjv2ZWea5+f7HfX/H4Rn/Sa6/nq69Hc1qqgV1FVEWmBvyFAttHYUth4k95otPIqUT2Lck4jIEG10agc4DDXI9tolA2O23ipNj4xb7b2lk5kMxmMnhbbeLPz0uToGFIT7lvwOOmNnDotlB1W1KMJnmp61t5H7aWyeqYbjRLerOlZXw+q4KShRzRP8/i8aO5oEw/WWXiVHtwV3pixKD0vBndpkkBUvbTyfpH1UqvSc8OryLy5Nnoycw1/QwB1nvIwRiQmr2c178vlchg5dVpMz+Ig7Eg8hlQigeSoeAWdlbeMDZwROvBN07PxPimvsn5W0npzp5SX2j17ZTYabb1ZcHyZqSnkslnTc7GkEv6svFnfeJP10orrEQgF0Rhulp6nVeo1t7fB6/ORxVxk52nUekw10xJ8/va3v42NGzfi4MGD2LhxI7797W8DAFauXIlt27YBAIaGhvDNb37TaI3xb//2bxgqHBDxpS99CT/+8Y9x6NAhHD58GDt37gQAPPTQQ3jzzTfxxhtv4Nprr8VXv/rV6Xh75wRmRi27KwWYG7XsrpSVUcvuSjkHO4kmqUY2kqRRVyyoo0a2FJWe/AIdqF7wS+sl7AMIdJ9fYXwnBSeVFiVI0XgM6VRKKrjr9ftMMkJiGB8cQiYlGNxNmJfTRbriGJYK7lplSMSlHsJW5XRaRoOknmWGhIye+ecX7YoJZ5foembji0pmcJiVc2pVFeKTXl2v8vr6AgEtuCu70Vjx+TW1RuEPBDB8ksZbihtvkpNKi0xbKr2o9MaReflgVHp85noR6SoS82elfFVKjfQqg/ddMWTTGalyTtONxq44EqOjQv3VNT2L7Lp4XK6qwjL7T3xT305PC0hIeqmV90mNz0JPOmBiEUBQ8GbLijyigI7X50Nze5uwnlF6XqHXGA4jEAySLdBbOgtVFcIbM04VbzRJG+R6xEkR1F5qZBbPGL3K6ytXVWH3LJ+ckKuqsJqXjp2WqKqw9Hr5ea6ll8ro2QTHab1UPLNYH59ZNbJMprI2PuuKVSo9Pbgrf30rqiqaQmhsbhLWs8p8DksHdx2SGIiC40aVgWTMpfJ+iUpWVVgllUQkqyqYanzT8Y8ODg7i+uuvr/r5vn37cP/99xt//ulPf4qf/vSnpr+3dOnSqp/fe++9tAP9GGP2IJbdldL1qiYJCrtIlHq6UVcHO2X19F54lZmxcpnKViUlsnpTFqXn0tlmFiVDLbqe4CKzWNpdPb70ZEr88ACL8UXiMYydGRQP7pYskkr7/2rBP7l7WdcrnTDL7vCXZoSUBjMiMdkMBOsd/sFjx8X1rCa9sRiO7H9DanxWGSF/fv0tCb1J4/BInbq6OrRIZALremYtc0LRCJleqDUCX3291PU122iMGJm7NPeLbHaT1UZjUW+GbjRKtnlwygQWn5Rb6ElXuVhvDGbSaYkezdZ6E8Mjxr8nOj6zucHwyVMSwd3iRmPpc0I+m6u40VgazFDRM9soa5HOLLYOSPS/856EnkVAJx5D/7vvS+mFO6vPaInEY3j7dy9J6VV+fl6/H+H2Nslnb/VGaLAljPrGBunrW7nRqLfMocqklvXSXDaLzNSUyXdNzUurvED3KlkvtUxiEM3MdtKj8lLZikZrvWwmg1HBdmZWbTci8U4kx8aFeu5qeuZJG5F4J0ZPDwhXVegBIn9joGwTUL5qobjRWO71shVvFkkgEq0XdT0rLz3ed1hCz8qbO3HgD7ul9JrbWs31/iinV9UPvVBVQeWlsi1zAD3Jp1xPr6qQfvZaVmyJ6eXzeaRMzr+Q1TNiJJbzZqIkEEW96vGpJalQxYSYaqYl85mZ+Zj1W5LdlQLMe+HJ7koBMO3PJbsrZZURIt3T1jKDQy7z2TIjRC8Hk83crdrVk+z1ZZMRItPryy4jRHZH3mx8Kr3DzPUkd+QtguPRrjhGpPQKu94mGSEyD830ZIo2U9nku1vn8WjBcWm9yqoKvWWOxPU18aqmQlWFdK85q0mbYCYwgEJpd+V3TS4bSRtfdduNqGR2E2DeNzaiMD6z+6U4PpqMENlsOGsvkKuqsNxo7IohMzWFiUG5jbfqjJCYXMscS6+KSbXMsdtopPZSue+axf0Sl6v6sLr/IpKZwOnkpGnpuXR2nWV/f/mN1aqqCr1ljqSXVt4roWgE/oYAmbfoZftyz147r6Lx5oik9xXHZzEPJ9OT89JsOm2+0Vgrb5bokavpVX93s+mM8FkV1npxqZY5VucPRONxqeCuVR9a+Xm4hfdJepXds1xOz+JZqeClXr8PXl95jp/avJkuU9lsXipbVVHUs6qqoPHSYlUF1TxXLkHKUY/o+sp6lTY+Oj19o9FsHgkobAyaxDSk9Gy8NJfNYuS07Dy82ksnJyaQHB0T0mOq4eAzY4p5MFbBWE31VDKfrTM4KPVken1ZGXUk3olUIinV6wswn7SNnxmUCO5aZ9fJlUcR69mOTy2zuExPNthpeT1U9Sozfuj06jwetHR2SAfvPR5PWdZAIBhEMBxWKFerqKpoa4XX7yMrf5PN4nfWk/WWWuvNfC+V1zML3hcCRIKLBsuNxphsyxzrKhKZljnWfTtjki1zrJ8dlF7Voup9JpntpF4a75RumWOlp7YRWrwedXV1Ba9XeFaWbA76AoWDiGUzd4la5uh61Ydd1sYLqErPZ5T3mZSey2bGauOzm4fP3GevTMscy42eeAyJUfGDiG295bRMyxwbb5FsSQOYHQYr56WW5xlInNNTOj66dQKxXq3WMSVzA72qQtqbA+UbjUZVBdGa3DiIWPJ+qb2Xqs6bLaoqpKtIzta6Y+bq5bJZ4aqKbDqNbCZjOo+cHJ+Qr6owuf+kDiK2nfdx1jMFHHxmTDEzatldqaIeza6UpmeSYaKwK2Wq1xWX6vVlpSc7ibEy6kiX3InsdpnK5JnFkm0ArPSkyt8S5pNUmVOJAfNJpcfrlQ/uJqqz//SDHGTbMgDl7zfcobXMkS3VrdSTPXRG1/P6fPD6/UU9yf7guh5VLzIrPdkTzzU9k+wwJS+1rvqgOsTLyK4j6hsb7YpLtczR9KrbqkS6Ci1zpqak9MyrIMTfq1F6bnK/qH3XTLxZxQtMsgnlvNQ6I0TJS80ylYk2LlVb5lTqqbXMqb6+Ta1RpZY5lXqq3ly50TizvLR6HhlV6MN4Nr1U/rDa6u8aoBIwqb4eMi1zAKvgvVzLHLvxybTM0UvPzbLr1LzUxKuIvEUbH62XavNwQm9W/fxMAjpUXqpXVVA9e42qCqLxGYchSvZArtSLKnlVEoFgI+rq6orjI/ZSperm5KTJd011Hl793dD0iNYdkv3arfSi8ZhUyxxLva6YVMucol51TEPmIGJNz/x+kbm26ckUcrmcqZeqeJXZukNGj6mGg8+MKWa98GR3pTS9pGkPZJldKV3PrHeY7K6UVcaKzEME0MpAzPotyeqlJ1Om/ZZk9DIpLWBj1m9JtlzIXE/uehg9s0v0PD5vIWNAXq/0fmloCqGhKSSVDWcs+Ev0wu1t2gnqCpPe0vertkOt98Ij0jPpdai6gw5UBkzU2lD46uvh8RYPbFQdn2WvNNmMEJPvhkzLHG181b3rIvFOTCUny3qQK+nFOqVa5uh65l4g6aUW12Mm6Zl5qUzZvrHRaPLsVckENutdp5RZXKJXrKqQ99LS8ekHEcseUlSpp9IyxzSAoOhVAL2Xlnq97AFotRqf11deeq6avWbmfTXRk/RmM++TaZlTHF+1nkzLHE3P3PtkWuboeubzSFovlfmuaXpm11dunpvP5ZBOpczXHTKbtIlqLzX0VLy+tOLNqKqgeXaoVVWYb7z5A5Itc0y8qkWhZY5edVQ+D1erCrDWU6jgJNPTrocvUE+mR+3NVD2B7fRkWuYA2v1itiaXaZmjjc/c+2Ra5hh6lDGNQouvaj35mItZTEjGS4FCmxGidYce06CKuTDVcPCZMYVyVwoApsz6I0nuSgEWfUUVdqVM+y3F5bLXAPOMkKjK+CyyO2WM0CwjxOvzobmjjSwTuDHcjIZQSDrbByjPuGjp6NCCu0QZHMVsJPHPz6wXXkSyn3e5XmlGg0ImsJlenFZPJWPA9P0qZNeljUl+cSIT7YpJtcwBtPdbnRESQyqREG6Zo43PvG/n2MAZZDMZcb3JlHn2lcqC36xvIqWXSmYCAxbnBcTlqj6sxifbx7KoV50RIju+yl6CenBXxkuNjcbSqgq9ZY7kWQuaXvHza25v01rmEHmB7OGUpXoBEy+Vym6i9mbTZxGFl5roSfa7r9QjeRYFq8cn2jJH00tVlZ5H4nGpljmAdb97mYOItfGZe9XwCcngrpWXSnuLxTxXSY/62VHtpdLrBNOe2XKHcZrp6VUVMl6azWSqKhr1qgqqeZpeVaHmBTTebDqPJEliqPYqJS8Nluqpz+sDpusElWdvtZ7cs7y6LY3S2SHJVFVFo15VIRPctfKqxIh4yxzAogKxS8vcFa2qsBpfVCFmkLbIzKb1Uup1Qlzq3gM0LzU960M6RkL3bDM2Gkv0VA4iZqrh4DNjimUmsOSuFHlmsUUJCKWxUup5/X6EO9qV9EqNWjvIoZEs0zvc2S7d68vMqFX7HAIWpcREfUWVMhpMSs8pMiTK9dQn5QFivcrx5bJZjJ4ekNAza+MRw+TEBCbHxoX1UhZ68i1z9IyfktJzyewhXY+yd5h57zq1jAbTvo4qeiY9kEm9WSVYnExWtczRvFnyelRsNDY0hdDY3ETnzYWWOVLebGw0EmdfBWe299VUT6lPJHUAxjozW7aqwkwvPZkSPojYWq9TvmWOxdxA5btWtdGoNM+twTzSzEulM4Gpx2eRFEG0TqjzeNASk6uqMNMrHkSsolf8/IpVFTT3i4oXmJWeU88jSaoqyMZnU5VCpqdXQdDqSW28WaxjMum01Mab1fhUWuZoeuXXd+TkaamNt5RFReNMXeOr69UiRlK7dYe28Ubnzb5AQPogYk2v/HqoHERspqdyEDFTDQefGVPOnlHTGJdxkAPRpDcUaYHsQQ5mei2xDgByC34zvahCbylNr/x6qJzIbqYXUdhBz2YyyKYz5jv8CuV0VDv8ZpM2leuRMptUdin0+jJboCv2+qrUi3aplYOZjU9lx1vTK8+uk8kUtdJT2eGfSk7C4/XCV18sb1TLVDbrn6+QCWzSU1m2Z3FxfBUtczrayao+Gpqb0BAKkWVIhDva4fF65at6rAIIKnpBE6+S1kuaeilVRkhUoYrEvI+lWv93oNpLAVWvL/cWlZY5lXrRrhhSiaRkyxzzANHo6QGpljlmz0qVTFFTL5XsaWupJ9lzV9Mz2WhUyr4y7wOq4s1U/d/N9IzgrqReqsKr9JY5VF7aXAjuKukFTbxZYW5g5lVU1zeqULUAVGdjKp2lYVPxptYDudz7ANmeu9ZVH3Itc8yC41pVhVTLHJMklWhXHOODQ9Itc6r0Cmty2ZY5lXpq8/DqAy9Vg7Ha+Mq/H6rftfKNRuJgseI6puogYpXq6wo9f4PWMofKm0OtEemDiM30IjWLkdDoRRT1mHI4+MyYUmnUxq6UivGX9OPRd6VUjLCsp61+grqKnlmfQ+mSkknTvonSD5JJ8/GplEuWL7jks9d0PdNeZColQ0S9zTIpLSOksvdaNpPB6GmZgxzM+2Kq9Pqq0oupBHfNe0irTgLLv29qG0dAde86Zb1GukxloDIgQaFXuhkln9GQSk7C5/fD49MyQrw+H5olT1DXxlfuLY3hZgSCQTK9lg79BHWaZ0ex567CpNK0p61C7zpTL1UZH6WelZcqLBoaTJ69KhuNFdcjl8tJHURs3rdTb5kjcxCxeXBStmWOqVcVeu7KYO71BF5VmVmsOD5yr6/UU1gAA4TPyoS20aiXnhtVFSoL9JKx6QcRU313m9vlqyo0vVp4H828rxZ6qYoe3CqZttr4KoLFClUVhh7R+PK5XNVZMyrPXisvVTmIGKj00k75ljkW3iKfuVsbb668vioVW+Z6il5KFnwm9uaK9nzFqgqa75pqVUWqogdyU2tUsaqi3JtbYgReauJVKpsB5jENopgQxUYj4fiYcjj4zJhSPPRNMy/lXalJ84wBKj3VXSnLjAHpB0ll9hp1pjKBXllGCEXmc3k2XDadwZjECeqaXuUuZhyJ0VGkEuLBXcD8+sr2+rLKCFHJVtH0yq+vSp/DSj2lNgUWevKTBJNec11x6Uxl889PpZ98uZ7eMkdl4wgoZoQEW7SWOdIbRxWbFS2xQnBXsQ+ovtFY3HhT2Dgi6g+u6ZX32YwqTgIr+3YWM20VNhpN+8lLfj+s9JTGV+6lsi1zinrlXjA5LtcyBzB7v4WNN4mWOfpGY+X9Iv9d07wqQOTNpl4ap8gsLv2+yWcWm/fjV/BSi2elircAxcx2Pbir0v+9dHxGyxzl96td37Ae3FUYX2npuWrSgdU8Uv7ZW+mlalUalf39ixVvKl5aGkBQ9NKq8akngVTqyVZVAObXdyo5KdUyB6j25mg8Jn0QcS6TNalopJ2XRlV62pr29yfw0rI2GTHpe7lmXlpx9orqPFfX04O7yvPcwvVVOYgYqPbmYlWFvJeWVjQWE8xovFSln7euFyD0ZusYDs06QTUmVOWlqkklld6nUFXBVMPBZ8aUypIc5V2phLlRq5W/VZfTqSzizHrkypfuV/bZVA0WV/ftzKTTGCcK7qr0+gK0XdvKHsjDJ09JBXe18VUEx2Od0pMsQ6/i+srqFUvPS8enki1l3ruOslyNIhM4UHG/qGcqa3q++vpCry/5zE5NT7seoUiLUq+vytLzYssctYwL/X6myJYqHV8xG0kxs70QzKbxKrNSZyrvIy6nU86Gq5z0yldVaOOr9KpOJMfGpaoqrPRkqyqKejRepelVX18VPbNFiGyfPqtDspQzgSuflUTeZ+gp9Cwu1fP4vGjuUKiqqCg9N1rmEHmf3jKH3EsVn736s5JKr75KT837qjYaZ4iXmnkVtZ7sQcTmelrLHJmDiK30xs8MSgV3NT1aLzW7vvR6ctfWbKNRyUstznKh9WaVisEKbzbOqqDxPoqqilK9YlUFlfepVmxZ6dGsYyj0SisalfUS1WtytfFZtZej2QhtMc6qoBuf7EHEml61l8pWVTDVcPCZMaXSWCn655TqUUx6S41adVeq0qij8RgyU1NSvb608VVnc00MDUv1+jLXi2PkpNwJ6mZ6Kj1oNb3q/kiywTBLPYVeS2b9oGQX6JpeRWa7QgZCZY9rrddXp3QGQmXwVG+ZI78xUx4waWqNar2+FDZmSvWMcjDFXnOGtxD0YSzViypPsiy8dMa83/IMHeU+lolJeDwe+AKBMj2ySW9XTKuqkOiHbq6nVlVRGWyPxuPSVRVFvQqvIg7uyl5boHqjUaU/uDa+6ioh0o1GhT6Reuk5XaZy+XfXaJlDpKcdRBwk69vZ0qlVVch+fikL71PpJ6/pNZbpSb/fRLleRHl8FRuNVN5c9X7VMuX1jcbis40oCaRQVSHTMgfQvaU8635yYkKqZY42vuoKOtmDiE31SIK7FV5KnWSh6s0V11fVmwMVz0rZBB9Nr3qdQFX1oR7cLfc+vapCfl5VrqdXVSh7aWVShPK8lMibK84ziCqcVQFUr4tUK8qqNy5Vz+ao9HrV8WltN+o8WpjP8Hqic7EiXfIHEQOoOqg7qgd3JQ4i1sdXVaGmFDOonkeqxAyYcjj4zJhS2TuRYlcKKJaKq+9KVeup7EoZRm1khGg73vLB3epeeEqTykSyqs+m2iS1uhee6qSyrH9TjCCA0FAZMFGZpBb1tFN6O6UnCZpe8fr6GwIIRSPKwXFdL9Qaga++niAjpPDdUG2Zkyj3AooddKDYG46iBy1Q7gW10FMNSFBmFpfpKfeJLO/9F4nHlKsqNL2AoZcYGTV+LqPn9fvKMkKUgruVXqqQ3aSNrzrzeURRr6xnNkEAgaoPo6ZX+WwjCMBU6SluNBY+P9WDiHU9/fMLtoShchBxLpNFJp0uzqv0ljnK2XC0XkrnVRVeT+ZVRW+ZiXp+cr3i9chls1IHEZfq6fdLS7xTa5kjcRCxrufxeEoqGjsxOqAS3K30KgJvrvJ61WDszPZSfyPNWRW6Hu2zsuilxaoKmmelXlUh++zN5/Nl94tyVcUk7by0ck0+U72vSk+1Io8485luHVOd+azWMqdyHt6JqeSk1EHEml7hfgkUr4dsyxxdr7JHM9WanEqP6uBgQFsnlOkpVl8z5XDwmTFlqpAFFopGAKB4kIPsrpSJnsqkQ89SC0VK9BQWmKnCrnIw0lLQU5wEFjKpSxchKpnAqWQSDc1NJQEYtfGlEkkEW8Jl5ZdKC/REEqGo9tkVD3JQ0UsY11b1IAdAu1/0e0/1IAdNL2mMT8/cVcngSCWTaNK/G/oCXeVBnEgiVHIvq+ilC8Fs/fqqtrgpfncr9SQn5Ya3VLxf6VLxSq9S7P9u4lWZqSnpjTfdq5pKvFSlqkLXKx2fUlWFrmfcz51Kk8rq8cWUv2v+hkAxY0V1oyyRRENTCF6fr2R8Kt6cKPdmxY3GVCJhfNe08SluNCaSxrU1Wuao6hWurd4yR/V+0b3AaJmjqkfkpUD5s1J1YyszNYVsJlP23QDUqz6qvU/RSyvGJ3u/mHmVNj5Zb658v2otc8zGJ3sQMVDqpfr9om28ybbMKY6veP+ReHPJ/azqBT6/H4FQkEgvgUBTyDgAksSbw2Ejm1DVS1MlXgqoHUQMlHupXlWh7KWFe0U/iFjp2VuiZxxETDzPVb2+ld4n66W5TBbpVKraq8i8WVHPwkvl5+HV8z4SvZLrq9Qyx8SrtIOI5YK7ZuOTPYi4TC9KE4Oo1lOd9yXg8XjQGA4X9VRiLskE6oONZRuXqmv8xnBzyfkI8gcRA9r3rXzerHY9mHI4+MyY8sGb7yA9mcKKLdcDKJRHKUwSDu97HdlMBstvKOgplm8devU1ADD0VCeph17dBwBYoesplr/1vboPHo8HyzZtAKBeSnxozz4Ego1Y8smrisFdpfe7F81trbhgzUrjIAeV69u3Zy/ae7rRc8nFJQc5KOi9ug9zL16MzgXzSxbA8nqH9uzDghWXIRLrLCm3Urkee7F4/WqEIi3FgxwUHpx9u/diydVXob6xUblNAaBdj6XXXwOvz6esl8/lcPjV17Bs0wbUeTzqwdiJBD58+12suGEjgGJ5nmxVxcjJ0zh15AMs31LQU6yqOP3BUQyfPFX0lq4YxgeHpKsqjh3ow8TwSCpYb+EAACAASURBVIm3xLV+6JLB3Q/fegepRBLLt5R4qcK98qfXXkc2nSl7vyp6h3r3IZfLGc8OZb1X9wJA2f2iklnct1vTK35+apPKvj298AcCuOS6Txotc1T0Du3ei1CkBYvXrzFa5qiNby9a53Rh/rJLiy1zFCblfbv3Ys7iCxC/4HzlE9T18c1fvhTROXHlg351vUXrVqOpNVpy0K+a3pKrr0QgFCTz5ss2XANffX3J+1WbuyzfsgEer5fkIOIP3ni7yptl9cbODOLEoT8ZekbLHMmqijNHP8LgseNYcaOup9Yy53jfYYwPDhXfr2LLnKNvv4fJiQljfKrtvY7sfxOZqani56dYFfCnvfs1b75xE8n4Du3Rvbmgp7hA79O9vjA+lQPpAKBvzz74/H5cdv01mp7i+A69ug+N4WZceOVa4yBiJb09+xCJdWLBisuMg4iVvOXVfYgvXICuxRcoV1UA2vudv+xStHbPUZ736XoXrF6J5rZWGq9/dS8uumo9GpqblA9118d36XWfhC8QUPY+XW/Zpuvg8XpLDs+U08um0zjy2hsl8yo1vYnhERw7eKhsTa5yEPHQR8dxpv8jY14V6YphckL+IOKTh49g7Mxg2ZpcpWVO/7sHkBwbx4qSdYLKvffn199COpXC8pJnpZI379uPXDZrrGOUvapXj2kUn5WqXqrpFWNCqjENr8+HyzZdWxifegynIRTCxZ9Yr3wQMaC933BHOxauWqF8EDFTDQefGVOSo2PY//SzuPzmzQgEg8q7UmMDZ/DO717Cmq03w+v3K+9KnTnaj4OvvIp1d95mBMRUjPWjAwfxwZvvYP1dtxd7fSmM70979+Pkn/6M9XdvVT7IAQAO/OEVDJ84iSvu3lpykIP89Xjz2RcwMTSM9Xffrpw9BACvPbkLqUSCTK93+5PIpNNYd9dWEr3dv3wCqKvD2jtuVS7fAoBXHt0OfyCAVbfeWJKBIB8c3/3odjQ0hbDixo3KmcX6+JrbWrF0w9VGyxyVReYrj21HW/ccXHjFGq1lzmQKE0Ny/dAB7f12LVqI85ZfVmyZI1lVoY9vwYrL0LV4obJX5bJZ7PnlDlx4xdrCoku+/zagZSfu3fEUlm64Bk1tUeXyrcnxCby+81msuHETGppChfHJ3yvjZ4bw1vMvYvVtN8EXCCASU8uGG/zoOA6+/CrW3nmbtuhS9ObjBw/jz6+/hfV3bS1svKllAv/59TdxvO8w1t+91WiZo6L3/suvYvCj47jirtuLLXMU9N767YsYOzOoeamRuSuvt/+pZzE5PoH1dxW9WeX97t3xFDJTU1hf4s0q98ueX+4A8nmsu+M2ko3G3Y9uh8/vx+qtN5F5aSAYxOU3bS7JrlPTC0UjuGzjNcptKADglUceRyQew0VXrVdumaON73HEzj8P569agUg8pnQQsaa3HfMvuwRzL15sZEvJBnfzuRz2/HIHFq9fg/Z53crZXNl0Gr3bn8Ql134C4Y52gkzWBF578hms2LIRjeFmtChmFk8MDePN517A6ltvhL8hUEiykNcbOn4CB156BWs/dQs8Pq9y0sbJP/0Zh/ftN7xZtZ/8h2++g48OHMQVd99utMxRefb27e7FwNF+rL/79mLLHIXxvf2732P09ACuuOt2o6pCZXyv73oOydGxwrxZ3av27tiJ9GSq4M3qXrrnV79GNpPB+jtLvFnFSx/bDq/fhzW330Lyfnc/uh2BYCNW3rxFOVMZ0Lw02BLGsk3X0awTHnkcLbEOLLn6ymJVhWTLHAB4+dHH0XHePFywZiX0g4hlW+bo4+u55GJ0L7kIqgcR5/N57H7sCSxau8pIGlLy5kwGe371ayy5+iq0xDqUK96mkpPY95unsWzzdQi2hJW9NDk6ijd2PY9Vt9yA+sZG5XnuyMnTePfFP2Dtp26B1+eDaqvO03/+EH179mLdnbo3q61j+t89gKPvvIf1d9+uVVUoHEQMaMHiU0c+wBV33a58EDEAvPviHzFy8jTW3327csscAHjjmeeRGBkt82bOfKaDg8+MJa888jgaQiFt0aWYgQBoi5qm1iiWb95QyBhQa97+8iOPo3VOF5ZvuV7blVLUe+XRxxG/4Hws27xBObgLaJ/fecuWYsnVVwFQC57mslns/uUOXHjlOlyw5vKCnlpArPeJp3DptZ/EvKWXKOulJhJ47alnsOKGjehafIGy3vjgEN567gWsvu1GdJw3X1lv6NgJvP/H3Vh7x61o656rrHfi0J9w5LU3sP6urVrGgEKvLwD48xtv4djBQ7ji7k8h0hVT6vUFAAdf3oMz/cew/u7bEVXs9QUAb5cExFQzRQFg/87nkBwbx/q7t5J4y94nnkI6ldICbIo7/ACw51fFgFi0K44RZe8rLLq23qKcCQxo3mcsugj0XnnkcYQiLVixZYMWHCfw0kisE8s2byi0zFH7/F5+5HF0LpiP5VuuL7TMUb8e8y5dgkuvuxqAfF9CQAuI7X7sCSxatwqL163W9BTGl81k0Lv9N1hy9ZWYv2ypst5UMol9v3kay7dswJwLFyvrTQyP4I1nnsfKW25A7PzzCnry98vwyVN47/cvY82nbkFbj+7N8nqnjnyAQ72vad48t6vwb8h789G330X/u+/jik9r3qxyEDGgZYue/uAo1t+lealKyxwAeOfFP2Dk1Gmsv3urVhWgUFUBAK/v+i0So6O4ohDAUv2u7fvN05hKThbeb1zpuwYUA2Lr7tyqZV+petVjT8Dr82HNp24hOVRo96Pb4W8IYNUtNyhn1wGaNzeGm7Fiy0bljVVA89JwRzuWb96gXFWhjW872ud1Y/mW6+FvCJDozb1oMS7bqGXEqTx78/k8dj+6HQtXrcBFV60DoOZ9uUwWex7/NS7+5BVYsPyygp5az+K9v96JZZuuQ/fFFyrrJUdH8fqu57Dyli2Inb9AWW/01Gm888IfsHrrzWif162sN/DBURzc3Yt1d96G1jldynr9776PD996Vwu2Kx5EDACH9+7XAmJ3a/NIlZY5APDeS1rS0Pq7bkc0HlcK7gLAm8/8DhPDI2Tz8H2/eRqpREJ7vwRe9er232hJQ3feppwJDAB79KShT91K46V60tBtN5LNm0uThtTnuVrS0LItG7SDiAnmuW3dc7B8y/XaQcTK3vw45iy+AJdtuq5QVaGo99h2LLh8GS7+xBUA1BKktBjJE7jwynU4f9VyTU/h+mZSKfQ+8SSWXn8Nei65SFmPKYeDz4wlH771Lj567yCuvu8z2q6UYslB3+69GPiwHxv/5vPweL1KO94A8M4LL2H09AA2/8+/BqC2Iw8Arz+tZSFQ6e39tZaFoOup7GICxUXXpr/5gqZHYPxevw/Xf/GzANSy6wBt0VXf2IBrP/eXmh7Bgy4YDuMTf3G3cnBX12vp7MC6O29DKpFUCu4CxSyEy2/arNTrq3R83UsuxCXXfEL5s9OyELbjgjUrccGalcrX1shC+OSV6Ll0ifK9pwfElm26Dl0XnK/83UiMaFkIK2/egvZ53cqfn56FsOb2mxHtiiuPT89CWH/XVrR0dihlcADFLIRP/OWnEQyHlb1Kz0LYcP9n4fX7lO8XPQuByvv0LAQqvX2/3olUIlni9Wp6rz7+a2TTGWz6Es349IDYxv/xOU2PYBHiDwRw3Rf+CoD6+33lkcfR2NyET/7VPQDkW+bovPzo4wi3t+GKuz+l1DKndHxt3XOx6pYblA4iNvQe1RZdSzdcA5WDiIFiQOz8lcuxaN1q5Xsll8liz69+jYuuWo/5yy5V1ktPprD3iZ1YuvFazLnwAuV7JTk6hteffg6X37QJHefNU9YbGziDt5//PdZsvQnRuWrt4ACtiu79l/do2Z2KBxEDhSq6N97GVf/HXWhqjSq/3z/tex0nDh/BdX99L/yBgLIXHPjDKxg6foLMq9589ncYHxwqeqni+F57chcmJybIvP7VQhUd1fvd89gOoK4OG//m8wU9tfvllUe3w1dfj+v++l4A6uuOlwtJQ1ff9xlNj2Ae3tzWiivvuQOZdFopuKvrtc7twuqtNykdRFyq17VoIZZtuk6pZY7Oy488jvOWL8WFV6xV/q7pSUMXXbUO561YquwFmakp9G5/EkuvuxpzLlqsfG31pKHlN2xE54L5yvfy+Bk9aegmtM6dozzPHfzoON7/426jKkr18zvRdxhHXnsDV95zh3JVBVBMGrr2c3+J+sYGZW/Rk4Y2/c0XCv3Q1fT0pCGqea6eNETlzXrSUFFP7X7Rk4b0GImql75SqKLbcP9ntfEp3s9MEQ4+M7a88uh2dMzvAaC2KwUUA2KGnuIukp6FQKWXnkxh7693kuklR8fw+q7nSvTUjFrPQqDS07MQOub3aMHd02rBXT0LoWN+T+EghzElPT0LoWN+D0lwV89C6JjfoxwsAYpZCB3ze0h2RPUsBE1PvbxHz0KgCMYCxSyEtu45JHp6QIzqIAc9C0HL5lK/HnoWQkNTiETvlUe3o3VuF0lVBaC9XyqvArTNKCpv0bMQqPT0LAQqPb11CZXe2JlBvPX8i2R6g/3HcOAPuw091Y234wcP4c+vv4WO+T1ay5zhESW9I/u11iUd83uUW+YAwPt/3IPBj46TeaneuqRjfo/yvAUoti6h8ubeJ55EZmqqMD4Kb9YWXW3dc4m8+XH4/P5CpjKF9z2OQDCIcHsbyfXd/ZjWuiQYDpN5cyQe01rmEHlpMVNU/XrsfpTOm/O5HHb/cgeZXjadRu8TT5HppRIJ7H+Kzpsnhobx1rO/K1nHqOkNHT+BA394pcSb1e6Xk4eP4E/7XkfH/B6tZY7ixtuHb76DY+/3oWN+j3LLHKDYukSbN59W2ngDCq1LBs6Qeenru7SAGJXe3h07kU6lyPT0pKH2Hpp5uJ401Dqni8ZLC61LWjrVDok3xvfodgRbwsqHxJfqtcQ6SKoqdL32nhnqzYXWJVRelc1k8OrjvyEb31Qyidee3EWmlxgZxRvPPE+2jhk5eRrv/v6PZHqn//whDr26Dx3ze5QOImaq4eAzY4uehQCo70oBxSwEQH1XCtCyEHKFnW6KXalXHt1u/H+K9/vyI48DgNJBDqW8UtCbHJc/yMFMb/T0gPRBDmZ6qjuiOvrnR9HoX89CAGiurZ6FANBMYvQsBIDm89OzEAD1zEmgmIUA0FwPPQuBSk/PQgBorq+ehQDQeJWehUClp2chADT3i56FANDcL3t+tcMoMaW4vqXeTPF9M7yZoKoCKHofRVUFoAXsAM2bVVrm6LxseDNN9oZ+PSj09NYlAM29ovXy/Q2ZXiqRwL7fPA2A5rs2MTyCN5/9HQCa8Q2fOIn3XnqFTO/UkQ9weO9+ADTXV6+io9LTq+gAtZ62OnoVHUBzffUqOoDGS/UqOoDm+r5aCIhR6e1+rHTeTLORrKPSMkfn5YJXpVMppZY5Oq88oumNDw4ptcwx9ApeP3zipHJwF6CdN+sBMYDmuZvLZPHqr34NgOZemUpOYt+vdwKgGV9ydBSvP/1bTY9gfKOnTuPdF/8IgMYLBj44ahyaTOGl/e++jw/ffhcAjZce7n0Np458oOkRfH7v/f5lYwOU4n5549nfGZvvFOPTkoaSZHqvbteq6ACa97v7l08UYyQk8/ASbyZJotG8VLVlTlFPG5/KQcRmehRVFUyRaQk+R6NRPPPMMzh48CCeeeYZRCIR09+79957cfDgQRw8eBD33nuv8fN///d/x4cffoixsfLMyvr6ejz88MPo6+vD7t27MX/+/Jq+j48D+gEqAI2xTgwNFxddBMalH6CSTdPsSp08fASH9+3XjEuh15eOfoDK8MlTSr2+dPQsBKoAgn6AytAxGj39ABWq8ekHqFDp6VkIVO/3lcKii0pvN2FAByjZDKB6v8R6Lz9KHBAjHJ9eqQEAgwTj01uXADTj01uXADTBbL11CUCziNNbl2QzGeWqCqDYumRyfEK5qgIoti4ZPT2gXFUBFFuXUN3LeusSqu+a3rqEanx66xKq8emtS8i8oBAwIfM+Ym9+uVbePAO9FKD1+nw+X7weBOPTW5cAwNDx48p66ckUenc8pekdU9fTDwAHaD6/0dMDeOd3LyGXzZJktg98qB0APpWcVDqIWEc/AHx04IxyyxygeAD48HGa4O57L72MoeMnyL5r+gHgVN81/QBwinsZKB4ATjbPLQTEqDdCB8m8/uPjpUAxAEj+rCTwvlw2iz36s5zgftar6ACa+2VyfAL79aQhguD9+JkhvPXbFwpJEerePNh/DO+/vAeZqSmlg4h1jh88jCP738TE0LByyxygeAD48MmTJMHd919+FWf6j5Hdy3oVHdV3jSmSP9uv73znO/kHHnggDyD/wAMP5L/97W9X/U40Gs0fPnw4H41G85FIJH/48OF8JBLJA8ivXbs2H4/H82NjY2X/zZe+9KX897///TyA/Kc//en8ww8/7Go8vb29Z/0zmE2vUKQlf/lNm8j0wh3t+WWbN5Dptc7tyl9y7SfI9DrOm5e/6Kp1ZHrxRQvzF6xZSabXveSi/IIVl5Hpnbdsab7n0iVkegtXrcjPuXARmd7i9WvysfPPI9O7+JNX5tt6usn0lm64Oh+Jx8j0lm/ekG9uayXTu/zmzflgS5hEq66uLr/6thvzgVCQRM/j9ebXfuqWvK++nkTP6/fn195xa97j85Lo+RsC+bWfuiVfV1dHohcIBfOrt95Edm2DLeH8ypu3kOk1t7fllxN6c7Qrnr/0uqvJ9Nrndecv+sR6Mr34BefnF61bTaY39+LF+fNXLifTm3fZJfl5S+m8+fyVy/NzL1pMprdo3ep8bOECMr2LPrE+3z6Pzpsvve6T+eicOJneZZuuy4c72sn0Lr9pUz4UaSHRqqury6+69cZ8Q1OIRM/j9ebX3H5L3t8QINHz+nz5tXfcmvf6fCR6vkDBmz0eEr1AMJhfs/VmsmvbGA7nV95yA5leU1s0v+KGjWR6kVhnfun115DptfV05y/+5JVkerHzz8svXr+GTG/OhYvy569aQaY3b+mS/Pxll5LpLbh8Wb57yYVkeovWrsrHFy0k07voqnX5jvPmkeldcu0n8q1zu8j0Ltt4bb4l1kGmt+LGTfmm1iiZ3spbbsg3hptJtOo8nvyarTfn6xsbSPQ8Pm0e7vX7SfR89fX5tXfcSubN9Y2N+TW330J2LRrDzflVt95IptfUGs2vuJEwRtLZkb9s47Vkeq3dc/KXXHMVmV7ngvn5C69YS6bXtfiC/MLVl5Pp9Vxycf685YQxkuWX5XsuuZhM71x9CcZSz/4ADxw4kI/HtQVBPB7PHzhwoOp37rnnnvwPfvAD488/+MEP8vfcc0/Z71QGn59++un8unVa0NDr9eZPnz5diw+MX/ziF7/4xS9+8Ytf/OIXv/jFL37xi1/84he/PpYvkVjqtLTdiMViOFEoTzhx4gQ6Ozurfmfu3Lk4evSo8ef+/n7MnTvXVrf0v8lmsxgZGUFbWxvhyBmGYRiGYRiGYRiGYRiGYRg3+Gol/OyzzyIej1f9/Bvf+Iar/76urq7qZ069ukT+m/vvvx9f/OIXAQDt7e2uxsQwDMMwDMMwDMMwDMMwDMO4o2bB540bN1r+3cmTJxGPx3HixAnE43GcOlXdVL2/vx/XXHON8efu7m688MILtv9mf38/enp68NFHH8Hr9aKlpQWDg+YN1rdt24Zt27YBAHp7e53fEMMwDMMwDMMwDMMwDMMwDOOamgWf7dixYwfuu+8+fOc738F9992HJ554oup3du3ahW9961uIRCIAgE2bNuEf/uEfXOnu3r0bd955J55//nlX45k/fz56e3sxb948fPjhh+JviHFFS0sLRkZGpnsY5wT8WdYW9oLawPdtbeHPtzawH9QWvm9rC3++tYF9obbwfVsb+HOtLewLtYXvXzX486stH7fvf3t7OwYGBjB//nyh/+6sN6VubW3NP/fcc/mDBw/mn3vuuXw0qp0ou3Llyvy2bduM3/vc5z6X7+vry/f19eU/+9nPGj//zne+kz969Gg+m83mjx49mn/wwQfzAPKBQCD/yCOP5Pv6+vJ79uzJL1ggdgr7qVOnpr1h97n8+uEPfzjtYzhXXvxZ1vbFXlCbF9+3/PnOxhf7QW1ffN/y5zsbX+wLtX3xfcuf62x8sS/U9sX3L39+M/n1cfv+ixw0WPKa/oHPlNfBgwenfQzn8uvmm2+e9jGcKy/+LGv7Yi+ozYvvW/58Z+OL/aC2L75v+fOdjS/2hdq++L7lz3U2vtgXavvi+5c/v5n8+rh9/2WCz3WF/8NA6/28evXq6R4GwzDTDHsBwzA67AcMw1TCvsAwTCXsCwzz8eXj9v2Xeb9eAP9Sk9HMUl577bXpHgLDMDMA9gKGYXTYDxiGqYR9gWGYStgXGObjy8ft+y/6fjnzmWEYhmEYhmEYhmEYhmEYhiHHM90DYBiGYRiGYRiGYRiGYRiGYc49OPjMMAzDMAzDMAzDMAzDMAzDkMPBZ4ZhGIZhGIZhGIZhGIZhGIYcDj4zDMMwDMMwDMMwDMMwDMMw5HDwmWEYhmEYhmEYhmEYhmEYhiGHg88MwzAMwzAMwzAMwzAMwzAMORx8ZhiGYRiGYRiGYRiGYRiGYcjh4DPDMAzDMAzDMAzDMAzDMAxDDgefGYZhGIZhGIZhGIZhGIZhGHI4+MwwDMMwDMMwDMMwDMMwDMOQw8FnhmEYhmEYhmEYhmEYhmEYhhwOPjMMwzAMwzAMwzAMwzAMwzDk+KZ7ADOBU6dO4YMPPpjuYTAMwzAMwzAMwzAMwzAMw8xo5s+fj87OTle/y8FnAB988AFWr1493cNgGIZhGIZhGIZhGIZhGIaZ0fT29rr+XW67wTAMwzAMwzAMwzAMwzAMw5DDwWeGYRiGYRiGYRiGYRiGYRiGnFkXfN68eTMOHDiAvr4+PPDAA5a/d8cddyCfz2PlypVncXRMrfEFAtM9BIZhGIZhGIZhGIZhGIZhXDCrgs8ejwff+973cMMNN2DJkiX4zGc+g4svvrjq95qamvC3f/u32L179zSMkqkVnQvm41uvPIfuJRdO91AYhmEYhmEYhmEYhmEYhnFgVgWf16xZg0OHDuHIkSNIp9N4+OGHcdttt1X93je/+U089NBDmJycnIZRMrWiY34PvH4fFq1dNd1DYRiGYRiGYRiGYRiGYRjGgVkVfJ47dy6OHj1q/Lm/vx9z584t+53ly5ejp6cHTz75pK3W/fffj97eXvT29qK9vb0m42VoaQyHAQA9ly6Z5pEwDMMwDMMwDMMwDMMwDOPErAo+19XVVf0sn8+X/f13v/tdfO1rX3PU2rZtG1avXo3Vq1djYGCAdJxMbWgMNwMAei6pbrXCMAzDMAzDMAzDMAzDMMzMYlYFn/v7+9HT02P8ubu7G8eOHTP+3NzcjEsvvRQvvPACjhw5gnXr1mHHjh186OA5QrAQfG6d24VQNDLNo2EYhmEYhmEYhmEYhmEYxo5ZFXzu7e3FokWLcN5558Hv9+Oee+7Bjh07jL8fHR1FR0cHFixYgAULFmD37t249dZbsW/fvmkcNUOFnvkMAD2XcvYzwzAMwzAMwzAMwzAMw8xkZlXwOZvN4stf/jJ27dqF9957D4888gjeffdd/Ou//ituueWW6R4eU2Maw80YOzOIXDaLedx6g2EYhmEYhmEYhmEYhmFmNL7pHoAoO3fuxM6dO8t+9uCDD5r+7rXXXns2hsScJYLhMEZOnsb44BB6lvKhg8zsY/nmDbjkuk/i//v6v5T1q2cYhmEYhmEYhmEYhjkXmVWZz8zHm8ZwM5KjYzj6znt86CAzK7nwynW4/MZNuHTD1dM9FIZhGIZhGIZhGIZhmJrDwWdm1tAYbkZidBRH334PzW2tiHbFp3tIDCNEIBQEAGz+n3+NOg/bL8MwDMMwDMMwDMMw5zYc/WBmDUE98/nt9wDwoYPM7CMQDCKbzqBr0UIs37xhuofDMAzDMAzDMAzDMAxTUzj4zMwa9LYbxw4eQiad5uAzM+sIhIL402uv43jfYWz60hfg8Xqne0gMwzAMwzAMwzAMwzA1g4PPzKzAFwjAHwggMTqGbDqNY+/3cd9nZtYRCAWRHBvHru9tQ+eC+Vhx46bpHhLDMAzDMAzDMAzDMEzN4OAzMysIhpsBAMnRMQDA0bffQ/eSi1BXVzedw2IYIRpCIaQmEnjrty/ieN9hrL3jlukeEsMwDMMwDMMwDMMwTM3g4DMzK2g0gs+jAICPDhxEY3MTIvHYdA6LYYQIhIJIJRIAgBN9hxFub5/mETEMwzAMwzAMwzAMw9QODj4zswI98zlRyHyeGBrWft4SnrYxzQRCkRbUNzZO9zAYlwRCQaQmtODz+NAwmqKRaR4RwzAMwzAMwzAMwzBM7eDgMzMraAxrQWa97cbE8AgAIBhpIf+3rvzMnfj8//MQuW4t+OIP/wu3/q+/ne5hMC7w+v3w+f1lwefGcDO8Pt80j4xhGIZhGIZhGIZhGKY2cNSDmRU0VmQ+J0a09hu1yHxeuGoFFl+xhly3FrTP70Y2k5nuYTAuaAgFAQCpxAQAYGJQy94PRSMYPT0wbeNiGIZhGIZhGIZhGIapFZz5zMwKghU9n43gc5g++NzS2QF/IIBAIVg4U6lvbEBDKIS27jnTPRTGBYGmEABgclzPfB4CoAWfGYZhGIZhGIZhGIZhzkU4+MzMCvQM5+TYuPa/hQzoWmQ+hzu0Q+CaWlvJtSlpatPG19QanfGBcgYIBAuZzxOFzOdC33Lu+8wwDMMwDMMwDMMwzLkKB5+ZWUFjuBnJ0THkczkAQGZqCqlEskbB5zYAQHNrlFybknBbm/H/W+dy9vNMp9h2o5D5PMiZzwzDMAzDMAzDMAzDnNtw8JmZFTSGm41+zzrJ0VHy4HOwJQxffT0AoKltZgef9cxnAGjvmTuN84zzyQAAIABJREFUI2HcoGenTxYOHDQyn1s5+MwwDMMwDMMwDMMwzLkJB5+ZWUEwHDZabegkRkYRbGkm/XfCnR3G/2+a4ZnPzSXB57ZuDj7PdAIhredzqhB8ToyOIZfNIhSd2fcZwzAMwzAMwzAMwzCMLL7pHgDDuEFvu1FKYmQUjcSZzy2Ffs/ALAg+t7cil8shNZFAKx86OOMx2m4Ugs/5XA6JkVHu+cwwDMMwDMMwDMMwzDkLZz4zswKt7cZo2c8mhkcQamkh/Xf0fs/ALAg+t7UiMTyCgQ+PcubzLEDPfJ4sHDgIAONDw9zzmWEYhmEYhmEYhmGYcxYOPjOzgqBZ5nMNej6HO7S2G0PHT8z84HN7G8bODOJM/zG0cc/nGY/e83kqkTR+NjE0POPvM4ZhGIZhGIZhGIZhGFk4+MzMCszabiRHahB87mxHYmQUQ8dmQfC5rVULPh/9CK1zulDn4a/zTCYQDGIqOYlcNmv8bHxwiDOfGYZhGIZhGIZhGIY5Z+FoFTPj8QUC8AcCSJj0fPbV16O+sYHs3wp3tGPk1GmMDw7N+OBzU1sU42cGcab/I3j9PkRindM9JMaGQCiIVCJR9rOJoWHu+TzLueEr/wOf+sb/Pd3DYBiGYRiGYRiGYZgZCQefmRlPMNwMAKYHDmp/T5f9HO5ow9jAmVkRfG5ua8PowBkM9h8DAD50cIbT0BQyDhvUGR8aRjDSwlnrs5jlN1yPK++5A+vuvG26h8IwDMMwDMMwDMMwMw6OeDAznkYj+Fx+4KAefG4kbL2hZT4PGAfBzdSgYH1jIwLBRiPzGQDaue/zjCYQDGJyfKLsZxNDQ/B4POTtY5izg9fnQ+ucLmTTGWx94KuIL1o43UNiGIZhGIZhGIZhmBnFzIys2bB582YcOHAAfX19eOCBB6r+/qtf/SreeecdvPHGG3juuecwb968aRglQ4me+WzWdgMAaeAu3NGO0dMDGB/UgoKhSAuZNiXNba0AgLEzQxg+cQrZdAat3Rx8nsmYtd0YHxwGAG69MUuJzu2Cx+vFU//1fSTHxnDv//530jZADMMwDMMwDMMwDDPbmVXBZ4/Hg+9973u44YYbsGTJEnzmM5/BxRdfXPY7+/fvx6pVq7Bs2TI89thjeOihh6ZptAwVjYW2GpVtNyaIg8+hSAt8fj9GT2s9nwHM2MPgmtvbAABjA2eQy2YxdPwE2rjtxowmEApWtd2YGNKCzzP1PmPs6ZjXAwA48vqb+H+//i/oOG8erv38X03zqBiGYRiGYRiGYRhm5jCrgs9r1qzBoUOHcOTIEaTTaTz88MO47bbyPpsvvPACkskkAGD37t3o7u6ejqEyhDQ6ZD5TZSeHO9sBAKOnz2D8zCAAzNi+z81t2rjGCuM80/8R2s5y5vPSDVfjr/73v5/Vf3M20xAMIjVR3nZjfGhmb3Iw9rTP14LPAx8cxaFX9+HEoT9hzoUXTPOoGIZhGObco3PBfGNNwDAMwzDM7GJWBZ/nzp2Lo0ePGn/u7+/H3LnWAbcvfOEL2Llzp+nf3X///ejt7UVvby/a29vJx8rQEXTo+UyV+Rzu6AAAjJ4aMDKfm2dq8Lkk8xkAzhz96KxmPgdCQdzxT/8LyzdvQGzhgrP2785mAk0hTCYqM59HAABN0Zl5nzH2dMzvQWJ0FBPD2nUcPn4S0Xh8mkfFMAzDMOcWgWAQ/9d//wQ3f/X/nO6hMAzDMAwjwawKPtfV1VX9LJ/Pm/7uX/zFX2DVqlX4j//4D9O/37ZtG1avXo3Vq1djYGCAdJwMLcaBg2PjZT/PpFJIT6YQDNMEn1s6tE2IkZK2G01tMzMo2NzWilwuZwS9zvQfQygaQUNT6Kz8+xv++j6j7/TidavPyr852wkEbdputHLm82ykfV43Bj7oN/48dPwEIl2xaRwRwzAMw5x7LNu8AQ2hEBbxnJNhGIZhZiWzKvjc39+Pnp4e48/d3d04duxY1e9t2LAB3/jGN3DrrbdiamrqbA6RqQGN4WYkx8aRz+Wq/i4xMvr/s3fe4U2VbRj/NelM23TvSTeUUUYLlb33EkFQARUEUWSouPjECU6cuAeIqICIggjiYO+yZ2np3nvvpHx/pAktzWwblud3Xb2+z5MzXtrk5Lz3ez/302bOZ1sXhZu4NK+AypJS6uVybBwd2+TcbY2NkyMVRcXUy+WAInYDuCHRG45eHvSfMZWYLdvJS04lJDrK6Ne83TERibCQWFFT3jR2Qy6TUVVaJjQcvE1x9vUhP62p+Gxtb4e5ldVNHJWAgICAgMCdRdTEMQA4eXvi4ClUGAkICAgICNxutLn4XFpaSklJSbMf5fbWEBMTQ3BwMP7+/piZmTF16lS2bt3aZJ+IiAi++OILxo0bR15eXquuJ3BrIJFKmzUbVFJZWopVG4nPdq4uVBSXIK+r4+rVq5QXFd+ymc9SZ0dV3jNAYbpiEcbxBkRvjF78OHKZnO0ffU7ckRgCI7siMhUb/bq3MxYShRh5fewGoHifCeLzbYfYzAwHDzfyU65FQRVn5QDgILifBQQEBAQE2gQXf1/ade1MzJbtAARFdrvJIxIQEBAQEBAwlDYXn6VSKXZ2ds1+lNtbg1wuZ/78+ezcuZNLly6xceNGLl68yCuvvMLYsWMBeOedd7CxseHnn3/m1KlTbNmypS3+WQI3ESuprWbxuQ2dz1IXZ0rzrkWwlBcW3bLis42To6opIqByXxrb+ewT3p6I4YPZvXodpbl5xB+JwUIiwa9TuFGve7tjYS0BaBa7AYroDetb9H0moBknb09EYjF5qdfE56LMbADBlSUgICAgINBGRE0YjVwm448PPqW8sIjAyO43e0gCAgICAgICBmJq7Au4uLhgaWmp+u/GDQNbwo4dO5o1EXzppZdU/3/o0KGtOr/ArYdEakvldc0GlVSWlOLk0zaCq9TZidLca275W1l8tnVyIin1jOq/ayoqqamsRNoQHWIsvMPDADi6+XcArsScpF4uJyQ6iqRTZ4167dsZS2tFFrc68bm8qAhHT48bPSSBVuLsq4iAaux8LspWiM/2HoL4LCAgICAg0FpEYjE9xo3i0v5DlOUXkHD8FIGRXW/2sAQEBAQEBAQMxGiZz2PHjiUuLo6kpCT27t1LcnJyM9FYQEAftDqfi0uwbqWjXonU1ZmS28T5bOvkSHlBUZNtN2K8Spe5slFeVWkZaRdihQYwOtDqfC4sxlqI3bjtcPbzBiCvUcPB0rwC5HUyHATxWUBAQEBAoNWE9u6F1MWZmN/+AODKsRM4enrckJg5AQEBAQEBgbbDaOLza6+9Rq9evYiLiyMgIIDBgwdz8OBBY11O4A7mRsRumJiYIHV2pjSvQLWtvODWFJ8tJBLMrSybZD5Dw3iNLGJa29tRU1mJvK5OtS3+SAy+nTqoBFaB5lgonc8aMp8F8fn2w8XXh4riEqoaVWVcra+nOCdXyHwWEBAQEBBoA3qMG0lZQSEX9ynmkAkxJwEIEqI3BAQEBAQEbiuMJj7X1dVRWFiISCTCxMSEPXv2EBERYazLCdzBSKRSKks0xG6UlmJmaYGphUWrrmHtYI/YzLRZ5rOVrQ2m5uatOndbY+vsCEBZfkGT7YoGiY5GvbbETkpFcdPGoXFHYhCbmhLYQ2gAowkLiUKYry6vaPZaeVERpmZmWNra3Ohh3dGYmJhgbmVltPM7+/k0idxQUpSVLTif24BbceFPQECg9YjEYtyDA2/2MARuE7zCQkiIOUm9TA5ATmIyZQWFQvSGgICAgIDAbYbRxOfi4mKsra3Zt28fP/zwAx988AEymcxYlxO4QzE1N8fM0oJKLc5noNXuZ1tnRVZy08xnhbPY2G5iQ7F1ahCfr3c+35DYDTuqSpr+LZJPn6O2qpqQaCF6QxO6Yjfg1nuf3a50HzOC6e+8xit7t/Pynj+wkrZNQ9Lrcfb1btJsUElxVg72gvO5VQR0j+Cl3dvwah9ys4fSYkYueJQnvv+SWaveZerrLwpim4BAA1ETx/DUprW4tvO72UMRuMUxMTHBwcONwsysJtsTYk4SFCU4nwUEBAQEBG4njCY+jx8/nqqqKhYvXsyff/5JQkICY8eONdblBO5QlKKyttiNxvu1FDtXZ4Bmmc8ANk63lgPPRiU+X+d8LizC2tH4sRsVJU2dz/K6OpJOnSGwx53tQomePJFJLz6Dicjw26aljebYjYpihfj8X4zekLq68Pz2n/EKaxuRMWL4YO574yXade1CxuV4LCRWeIa0vehnam6OvbsbBanpzV4rysrGztUFkVjc5tf9rxByVxQikYiQ2zhLPnryBOzdXbF1caLb6GH0vFt4/rkVEInF+HXpeLOH8Z8mKKo7IpGIDv373OyhCNzi2Do7YWpuTlFmdpPtV46dxN7NFScf75s0MgEBAQEBAQFDMZr4XFlZSX19PXK5nLVr1/Lxxx9TWFio+0CBOwonb69WHS+xVzQTvF7wVNJW4rPUxQWAskaZz2VK8fkWK/+WNri0mzmfb0B8g8ROfQRKTmLyHd/8pdvoYdw1ZSJjn37C4GNVsRtqnM/l/2Hnc2D3CJx9vOk2enibnC9ywhgKM7N4beh4flr6GgBuge3a5NyNcfLxQiQSkadBfBabmiJ1cW7z6/5XaBfRWfG/3W7PqC5LWxus7e3Yv24jH9z7EDkJSTgLIslNx9zKkoc+eosF676i89CBN3s4/1kCGj7XHQb0vskjEbjVcfBURFg1cz4fV+Q+3+mmBwEBAQEBgTsJo4nPpaWllJSUUFJSQlVVFTKZjBINAqLAnYlbgD8v7NhE9JSJLT6HdYOoXFmsQ3xuZWm9nZsL9fX1zTKfgVbnKNu7ueIeFNCqczTGxsmRermciqKmv5PyGyCWS+ykav8WRVnZWFpbGy3i4FbA0duTmspK+k+fSu9p9xh0rIW1BLlMhqymptlrFUVK5/OttchxI/AMCwagfb+7Wn0uqasLIdGRHN+6g6tXr1Kam0dVWblRxGcXPx8AtZnPxVk5AELTwRYiMhXj2ykcgHZdO2NiYtKm57dxdKB9P+OKXs4+ikXX/LQMxf+mpuPsK4jPNxMbRwfmffMJYb17UVVaRvTkCTd7SP9JHDzdsXNzoTgnl3YRne/oZwaB1uPo6QHQzPmcl5xKbVU1boH+N2FUNx4LawmjFs4TGnsLCAgICNzWGE18lkql2NnZYWdnh5WVFZMmTWLVqlXGupzALYhPxw4ADH9sdosfmFTO54ZogutpK+ezvZsr5QWFyBvlkpcXtF7MtXVyZP73XzD705WtGl+Tczo7Ul5UzNX6+ibbVeM1kohpYmKiaDioZhFJOTFw9LwzG62JzcyQujizd+16Luzez4RnFxHau5fex1taS9TmPYPCsQ5gY+TIlFsRr1CF+OwW4N9q53yPsSMQicUc37JdtS03MRm3AP9WnVcdSherusznoizFZ8HhDv0sGBuv0BDMrSy5tP8QEjsprm389xs2bxYPf/y2KgrHGCjfHwXpCmd8QVoGjl4eLYrsEWg9pubmPL7mMzyCA1mz+Hn2rP2JkOgooWT/JtCuWxcA/vr0a0RiMe376v89KvDfQ/k9er34fPXqVfJT03D1/2/khveeOonBs2fQZdjgm3L9Rz57n0GzZtyUawsICAgI3DncsJnQli1bGDRo0I26nMAtgHtQAHKZDFsnRwY+9ECLzqHMwa0obh71AFDZIIRa27fS+ezuSnFObpNttVVV1FZVt1h8NrWw4KGP3sbBwx0HD3fs3VxbNUYltk6OlBc0j7BROmiN5Xy2sLFGJBarjd1QTgxuhuDm7OfDvG8/UTWNNAYOnu6IRCLyU9NZ9+wyirJy6PvAFL2Pt7CWUF1Rofa1uuoaaiqr/pOZz55hISScOAVAh1a6nyPHjybhxCkK0jNU23ISk43ifHb286G8sIjqsvJmrxVnK5zP9u6C+NwS/LsqIjd2fbsOuCZWtRXhA/ogEolwMaJooRQ1C9IyAchPz8DU3Bw7VxejXVNAM97tQ3Ft58fGl1dwYfd+jv26DblMRq9JQg73jSagWwRVpWXEbNlOaX6BkPssoBUHTw8qioqprapq9lpuciou/r43YVQ3FpFYzF333g1AUFS3G359n44dCOvTiyFzZgqVCgICAgICrcJo4vPEiRNVP5MmTeKNN97g6tWrxrqcwC2IR3AgWXEJnNr+F/1nTEPagom3tZ3C+awpdqO2qhpZbW2bOJ+Ls3ObbS8vLGqxmDv11Rfw6diev79cDYBvGzU5kjo7U5pX0Gx7mZEbJF77W6gRn5VuT48bL7iF9IokKLIb/abfa7RrOHkpXLmFGZnUVlWTfjFWtU0fLKytNTqfQeHst7a/fcXnjoP6MffLDxGbmup9jNTFGVsnR87+tYu85FTa9215FIJv53Bc2/lx/LftTbbnJCQhdXZq8wmTs683+WrynkFxTyovLBKczy2kXdfOFGZkkXj8FKX5BbRrEKPbAq+wEOzdFXEorkYULZx8vCjNL1AJJgUN8RtOPq3rgSDQMjxCggBIPHEGgNLcPC7tO0jkhDEG3bMEWk+7rp1JPnOOermcS/sOEda7FyJToTmrgHocPN2b5T0ryUtOxdHL447/DIcP7IuDhzsluXkERXa/4dePHD8KWW0tFhIJfe4zLHJOQEBAQECgMUYTn8eOHav6GT58OGVlZYwfP95YlxO4BfEIDiT7SiLbP/ockamYEY8/YvA5JPZSaiqrkNXWatynsqQUq9aKz+5ulOSoF59tnQzPfI6aOJauo4ax/cPP+Puzb6mrrsGvc3irxqhE6uJEaX5+s+0VSvHZSA5apcCvzvmscKZUY38Tcm6Vgk705IlGy8NzbCQ+AxSmZyrK6PXMo7W0llBTqVl8riotw0pqvEaRxsTB052pr79ISHQU3uFheh+nzHvOiI3n4v5DBEZ2xdzKskVjiBw/mprKKs78tavJ9uzEJIA2j95w8fMhT03es5Ki7Bwh87mF+Ed0Ivn0WQCSTp6hXde2cz6HD+ijaIQsk+HazpjOZy+V4AyQ3xDPcifkPts6OaoE/NsFj5BAqkrLVFUJAIc3bcHWyZHwQf1u4shuDW6UeCexk+IeFKBaBLi49yBWUlvVZ9y7QxieDVFMAgKgMDRcH7mhJC85FbGp6R2/qNf3/ikUpGfy71ffYefmgnNDz4kbgam5OV1HDuXs37u5sHs/fe+b3OLnNAEBAQEBAaOJzw8//LDqZ86cOaxYsYK8vDxjXU7gFsNKKsXOzYWs+AQKM7I4+NMmIieMNnjSam1vrzHvWUllSWmrGg5aWEuwsrVR63wuysrG0cvD4HMG9+xOcXYOu79dh1wmI/1iLP5dOrV4jEpMTEywdXJS63yWy2RUlZYZLXZDmb+tyYVenJ1zU5zPTt5eVJdXYGVrQ69JxlngcvT2QFZbS2muQvQvSM/E1NwcqauzXsdbSDRnPgNUlZVjaXP7ic8isZj7VryEiUghwgd0j9D7WK/QEACy4q5wad8hzCwsCIrqYfAYxGZmdB0xhHP/7Gkm8OckNIjPbdiUyMzSAnt3N5WgqI6izOyb8lm43XH08sDO1YWkU9fEZydvT+zc2iauosOAPqSevUBBWobB5do2jg7M/uw9vcbifJ34XJydi6yuTtWI8HbFxMSERz5/n4c/eluv/X06duClXb+3WeRUS/EMCSIz/kqTbZcPHqUwM4voe/4bpgiRWMzgR2by2OpPmfr6iwx99GHGPvUEC374mjeO7Wb04seMPgb/CEUVQ9Iphfgcd/gYstpaoiaO4f43X2bxhtXMWvWO0cchcPvg6Omhqqy7ntzkVIA7OnrDIySIwB5dObT+Fy4fPgZAUNSNcz93GNAHiZ2UmC3b+febtVg72NPTSM/ZAgICAgJ3Pm0uPn/00Ud8+OGHGn8E/ht4BAcAkBWfAMCp7X8jEonwMcAZCWBtb6c25qExlSWlrYrdUE6MG7uilOQkJuPs643YzMygc7oHB5J5+dpkN/nMebzahxh8nuuxdrBHbGZKaV5z5zO0LiZEFyrnc6n6v0dRZtZNiRpw8vEiIeYkV46doN/0e41Swuvo5UlRZrYqOkiZK+zkrZ+YZKGl4SBAdVkZVrbGF5+92oe0adOzQbNnENA9gl9ef4ecxGQCuukvPnuGBZOflk51eQWJx09RXVFB+xbkPjv7eGEltSX24JFmrxVn5VBTWdWmuc/KPF9NsRvK696MKoDbHf8IxQKdSnxuEKnawv0sdXXBJ7w9F/bsJy851WDnc1BUd9r3iabLcO0NnxSLUi4UpF17f1ytr6coI+u2b3DXYUAfvMJC8AgNwkKiu8qkx7iRSF2cVX/X1uLk483U11/EzNLCoOM8QoLIiktosu1qfT1Hf9lKSHQUwb0i22R8bUG7rp2Z8NziVj8rNMbe3Y1536xi1IJHsbS2Jrhnd0Y8/gi9p01CVlerKOe/AYJWu26dkdXWknb+EqDoqxF/7AQ9xo6k05ABxB2Jwd7dDffgQKOP5WbRa/IEhregCrAxnQb3Z9TCeW00IuNiIhK1+JnD2sEecytLCjM0xW6kAMaNULrZ9L1vMrVV1Rz9dRv5KWmU5OQRFHnjcp8jx42iOCeX+KPHSTlznoTjpxgwc9odH3UiICAgIGAc2lx8Pn78OCdOnMDS0pJu3boRHx9PfHw8ERERyOXytr6cwC2KR8PkIfuKYsKXdSWRernc4JJKib1U1VRQE5WlrROf7RrEZ3WxG7mJyYjEYlwMKHMTm5ri1s6fzLhr4nPK2fOYWVjgFda6klJlUz2t4rODkTKfG5o6VmhwPhdm3Ry3p5O3J/npGexe8wP27m50HTG0za/h6OWhitwAhfNZeW190NZwEKCqrAIrW9vWDVIHIdGRPLnxOyb9b0mbnM8twJ9hjz7MyT92cnLbThJPnqZd1856TzS9QoPJuBQHKFz7cYdjWtR0UPn5LVbjjrp69Sq5ySm4t2HshouvUnzWFruRjaW1NVZS4/5N7zT8IzpTVVZO9pVEADIvX6GmsrLFTQctGy3odOivyBS/sOcAucmpOPt6GySKKKNbQqKjtO7n6OWhaE6a1nRxIj89Q+/FqluVoXMfQlZXh0gkwqtDqM79wwcomsm5h7SNmNh72iQix48ixACx2MHTHUsba7LirjR7bd/368lOSOL+N182asNafek2ZjiPfrOKvvdPMejfqA2f8PY89ctaPMOC+eH5l3lvykxeGzqBZ7v3Z2n0UD598DHO/r0bj+BAo2cvB3SLIO1CbJMYtV1fr+Xg+l94a9xUfnrhVQDa9+ll1HHcTIbOeZBhjz5Mx1bEvYxaOI/Bs2fc8iJ9cK9IXtr1O2OffqJFxzt6KqoONTmfq8srKM0vwMXvzhSfLW2s6TZ6OCe2/UlVg+njSswJAm+Q+Gzr5Eho756c+P1PrtbXA/Dv12uxd3cjYsSQGzIGAQEBAYE7izYXn9euXcvatWsJDg5m4MCBrFq1ilWrVjF48GAiIvR3xQnc3rgHBVBZWkpJjiJqRVZTQ15Kmqrxj75Y29lRUaRH7EZrnM8NUSDqYjeyVWX7+jsnXQP8EJuZkh1/zWmVcuY8AH6dW9d00K4h5qFMTewGQHlRsdEaDkoaGg5WlZapfb0oMxtbJ0dMLQxzpbUGWydHLCQSCtIyiN1/mKz4BAY8eF+bX8fJy5OCRu6b4qxs6uVyHPUUkyx1NBysugHO59Deigl99OQJDH9sdqvPF9YnGrGpKVvf/RiAxBOnsZLa4h4UoPNYC4kEJ19vMi/Hq7bF7j+EvbubwSW0qsoFNYtHoIjeaEvnszJvUWvmc+aNb8A5+JGZLN6wRu8c8luRdl07k3r2vGqiWy+Xk3LmfIuaDroF+PPq3h1Mf/d1LG2sCR/Qh/y0dHISkshNSsHMwsKgSg3XBvE5sHtXra5UlTO+UewGQEFq+m2dTRrWpxc+4e35c9WXAPh27KB1f6+wENX73zPYsO99TXQa1B+AkLt66n2MZ8MzR6Ya8bm2qpq1Ty3F3MqKB956BZH45jW+GzZvFve/8TLJp85SXVGhWixpLSPmz0FWW8d798zk5Ladqu2y2lrkdXUAZMRexszCAtd2/m1yTXWYWljgHR6mqmZQknjiNJuXv0tRZjaleflkxMYR1ifaaOO4mXiEBGLv7kZtVTX3LHsW64YoM0Pw69JRVbXRe+qkth5im2BiYsKQuQ8x54sPsHVypMuwQS06j/L+rMn5DIrc5zs1diMoqgdmlhac3P6XatuVYyeROju1eR8LdXQbPRyxqSnHt15r5Hz54BHKCgp1LsIKCAgICAiow2iZz56entg2cvLZ2Njg6amfS1Dg9scjOFAVuaEkK+4KnqEGis8O9lSoaXDXmNaLz67U19dToiaTPC8ljXq53KAHPQ81k93SvHyKsrLx69I68VnqrBCf1TUcBOPHblSVllGvoYKhOEsRW3IjG60phR5lDMb+HzbiGRqMV/uQNruGhUSCtYN9E+ezXCajODvXIOez9tiNcixsrI0qHAZH9eDKsRMc3fw7w+bN4q57727V+Xw7h1OQnkFZvmIhJKmhiZQ+uc8ewYGIRCIyYq+Jz8r/7xZgmFBs564Qn5V53NeTk5CMvbtbmzWjdPbxpqygUOvf80Z/FqyktgyaNR3vDqEG5W7fSlhYS3APDiTp9Lkm25NOncUjJAhzKyuDztdhQB/EZqZ0GtyfxRvXENyzBxf2HABaVq7tFuBPdXkF5laWWmMklLnOBdeJz/lpGVjZ2mBtpIawxmbo3IcpzMxi39r1FKRn4NtJu/isbO4Yf/R4mzg0vcJCcPTyoK66hlADhA/l93F2fKLa13MSkti8/F2CorozbN6sVo+zJQx48H6GPzabY79t48u5i4g/crxFEUTX4xbgT1ifXhz8aZPqO1IdygoUr7C2+968npBekZiamamaDWoi9sDgdnPPAAAgAElEQVQR2nXtYrTmwTeT9n0Vf9PVC5/FSmrLpBefMfgcPcaNoraqmjN/7aL7mBFNqjtuFcY9s5CR8+dwavtfbHn7Q+zdXFvU4FWX8xnaVnz2CAmiXbcu+Hbq0KKIJFNzc8Y+9QQebVTpEXpXFNUVFaQ0+k68EnMCuDG5z12GDSL13EVyk1KabE86eYZ23QxfEG4LRKbim7pIKCAgICDQOowmPr/55pucOnWK1atXs3r1ak6ePMmKFSuMdTmBWwz3oIBmk73My1dw8vbSe1JhIhJhaWujscGdksriUiwkkhZnJNq7uVKWX0C9rLmoKqupoSA90yDnpEdwILLaWvJSUptsTzl7odXOZ1sXZeyGBudzYRHW9nZ6iZiWNtb4delIz7vH6jXptLa3o0JLBIpygnAj3Z5O1wk9Z//eg7xO1qYlgY7eDROg69w3BXqW0ZtZWiASi5s1w2tMVVk5IpEICxtrvcbk4OHOkLkP8fwfP7Pwx290/r2t7e3wah9C3JEYNr36Fhd272fiC0+1aEKoxK9zOClnL6j+uygrm6KsbAL0iEhQLg5kXo5TbctraB5k6Jjs3FwozS9ALpOpfT0nsaF6oY2cQs5+PuRrcT3Dtc+C/Q36LPSeOglLa2vqamroOnrYDblmW+MVFoJIJCLt/MUm2/NT0xCJRNi7G9a0LjS6J5mX4/n0wccQm5piZmHBxQbx+VqjKv3eayKxGBd/X05s+xO5TKbV9eXko2iAen3FjvIedTu6n4N79sA/ohO7vvkeuUxG6rmL+HRsr/WY8IH9SDl9jvijx3Hy9tQrI1obHQf3p14uZ8/aH3Hx99W7EbBHSBD5qenUVlVp3Of41u3EbNnOoFnTW3VPbAlBUd0ZvWgeZ/7axYYXlyOXybi07yAOHu6tFu37Tr+XuuoaDm/8Vet+ucmp1FZVt+mi7fUMnj2Dwsws4g4d1bpf7IHDiM1M2yx25FYirG806RcvE3f4GDs/+ZouwwbpzJBvjKm5OREjBnPu3z38+/V3WEisiBw3yogjbhldRw7lzF+7+PH5Vzi/ay+gO65IHQ6e7lSVllFdVq5xn7zkVGydHFsdceXs58PTv3zP/O8+Z+GP3/DC9p/p+8C9eh9vYmLC1Nf/x4AH72PkE4+2aixKQnv35MqxE02ebQrTMynMzDJ69Ia5lRXe4WHENTQ5bEzS6bM4eXshddGv4bY6fMLbt8j5/8Bbr7Ls361ETRx7W1d5CQgICPxXMZr4vGbNGnr27Mmvv/7Kr7/+SnR0NGvXrjXW5QRuIezdXLGS2jZzPiudwB56luBKpLaIRCKNGcNKKhuc0S11P9u7u6qN3FCSk5hksPM5JyG5mZidcuY8jl4ercqWlLo4U1lS2iQzsTHlhUWIxGKdv4uBD93P8sP/sGDdV0x55QXGP7dI57Uldnaq37U6VFEDN7DpoJO3J/X19aqyzKrSUuKOHKPLMP0ndLpw9FK4mwuuE58L0zNx1MP5rFxs0RW7AegVvRExYggv/PkLI+fPoa6mBt9OHXSWKQc2uGSuHDtBvVzOhpdWIK+ro+/9U3ReTx1SVxccPNxJbSQ+g8IRo4/z1jM0mIqiYlUsD0BNZSXFObkGiz/2bq5q89qV5CS0rfjs4utDnpa8Z4CKomLq5XJsnRzb5JraMLO0oO/9U7i0/xBn/tpFl2GD2rRZWWvoNGQAYxY/rldzImWfgMzrGsOVNDjaDblvmlla0K5bZy4fOkbymXOsvGcmqxc+y5VjCtdYRVExlSWler/XnHy8MDUzI/XcRVLPXiAkWrMw5uTj1cz1DNcywp1vQ/G534yplOblE/PbHwCknr+Io6eHxve3vZsr3h1CubBnvyp+yj1YdxyPNjoN7k/iyTOq6Ah9xSzPkCC1kRvXs+29VdRV19zQRm72bq5Mf+c1cpNT2fDictX2S/sPA7QoA1+Jtb0dPcaM5Pi2HTqfoa7W15MVd8VozufAyG74R3Ri97frNC4SKkk+c46qsvI7LnrDSmqLf5dOXDpwCIA9a34gLzmVqAlj9D5H+MC+SKRSjm/dTsalOJJPn6P31Em3lAjnFuCPrZMjl/Yr/p2FGVnkp6a3UHz20Op6hkYLiQb0ZVGH0pW+ZvHzfDVvMef+3cuEZxfRfcwItfsPmjWdB956ReVyHjF/Dl1HDiUnMZn2faNbJcyCoqrPyduLywebL9YkxJwkKLKbUf/u/hEdEZuaknjidLPXkk4qGgL7tyAOCxTC8xPrvmT0oscMOs7Ry4NOQwZgYmLCva++wMKfvrnlc891YWjzXAEBAYHbnTYXn0NDFU1ounbtiqenJ2lpaaSlpeHp6UnXrl3b+nICtyDK5kLKplFKshryXfWN3pA0rIrrFJ9LWyc+27m7aRWvchOTcfH31bvUyzM4qJnwDoqmg9C63Gepi7PGZoOgEJ8BndEbwb0iyU9N55v5S7i496AqN1cbEjsplcWaxeeS3Dzq5fIbKz77eFGcnaPKrgQ4/ee/OHl76iwL1xel+Nw4dgMUTQelzk6YW1lqPd5ConAzV2sTn0sVzh59mg76de5IXXU1rw+fyHtTZlKUla0z5zo4qjvV5RWknb8EKMS3k9t20mPcKKykhn9u/Bp+t8r3tJKEE6eRujjrLFn1DAsmo1Hes5LcpJQWOJ+1i8+FGVnU1dQYHOehDnMrS+zcXMhPSde639WrV6koLrkhEQs97x6LjaMD/369lpN//IVEKqV9X+MLN8pcT3ULMGaWFtzz0rM8+P4bDHz4Ae5782Wd90+PkCAqiksozW0af6S83ynz7vUhoHtXTM3NiTusmLhXlZZyfte+JvvkJqXoHbuhXLjISUgi7vAxvDuEafy+cfbxbtZsEBTvw/r6+haVc99MHDzdCesTzZFftqoWPdPOKdzpPhpynzs0NBq8sOeA6rvQoxUigbOvNx7BgZz/dy+5SSkUZ+foJWaZWVrg7OutevbQRnlhEbu+/Z5Og/vjH2H8knITkYgZ763A1Nyc7xY/36QypjQvn7SLsSpBrCVET5mImaUF+7/foNf+6Zcu42VgQ2h9GTLnQUrz8jn26zad+9bL5MQdPkZYwz3M0taGMU/ON7hZ9a1GSHQUYlNTYvcpFhbq5XKSTp/F04Am1D3GjaQ4J5f4o4pFtIPrN+Hi70uwkVziboHtdD7fXI8yDiIh5qRqW9zhYwRGdjW4oaWDpzuFmZrzngFyGyKU9K1i0URo757kJCZz7p89xB44wvdLXiT+yHHufW0pHfr3abKvjaMDwx+bTddRw3j6l3XM+2YVQ+Y8yOFNv/HtE0sQicX0GDeyVeMJ663Itb98qLnzOO5IDNYO9vi00TOuOgK6d6VeLif5uhgsUGTE11ZV064F90lTCwumrViG2NSU9v3uMkhA7zlpPFy9yvtTHmTdM8uwd3fjvhXLDB7DrYBILObe15by2oGdzd5fLcU9KIBJ/1vCmCfnM/iRmTckmkVAQEDAUNpcfH7yyScBWLlyZbOfd999t60vJ3AL4tHQcOx68bk4J5fK0lK9mw5a2yuEG52xG611Prtpdz5nJyRjamamV7m0xE6KnZsLWWqcVukXLyOrrcWvS3iLxgkgdXHSS3y21iE+u/j5knL2PBf3HiAnMVkvl4bEXkqlltiNermcktw8HNxvnPjs7OPdzGV4fvc+ZLW1bRa94ejlQU1lZfMy+oYMTQdP7eXfljZK53OFxn0McT5LXZ0pycmjKDObepmcfes2EBTVHe8OYRqPCe7Zg8QTp5vkde/7YSPmVpb0umeczmtej1/njshqa5tkNgMkNbhkArprjt4QmYrxCA4kM1a9+GyoQ9nezZXinOZ57Urq5XLyklPbpOmgUjjU5XwGhcBvY2TxWWQqpv/M+0g6eYakk2eIPxJDWUEhXUcZP3ojqGcPRs6fw9A5DzXZbufmwqKfvqXn3eP456vv+H3lKiKGD2bKK89rnWh6hASqvW8q73dSFxe9xxZ6VxR1NTUkNji01JGbnKJ3VqiyEVtuUgpxh2MQiUQE9ezRbD8TkQhHb0+1+bqy2lpKc/P0iuq5lejVMOE/+stW1baM2DjkMpnGBb6OA/uSm5RCblIKRZnZVFdUtEp87jRY0WhQuYBw+eBRgnv10Lmg4RbQDpFY3MxNr4n96zZQkpvH2Kfmt3is+uLXuSN+ncPZ+s6HzTJVAS7tO4R/RKcWPdeIzczoPXUSsQeOkJOYrNcxGbFxWElt9armMQS/Lh0J6RXJ7jU/aKzYup7YA0ewd3Ol68ihLPrpWwY+dD+97hnfpuNSh4nIaMWgtO8bTUVxCSnnrlULZcbGI3V20qtCxtbJkdC7enLi9z9VDVnP/LWbsoJCoqdMbPPxDnzofp7evI773njZoOMCI7tRmJHVpElg3OFjWFpbG2y8cPT0UFXUaaIwIxN5ncyg/P7rMbWwILB71yYuY3ldHasXPktGbBzT33mtyX271+QJmJqb89EDj/D3F6vxDg8j9sARNi9/l/zUdBKOnzLI0a6OkLuiyE9Lp0DNQubFPQfa9BlXHQHdI0i/dFltXFy9TE7q+Ystyn0etWAubgH+xGz5A6mLs95RP2JTU3rePZaLew9QnJPLqR1/s/2Dz/AKCzHa4ouxMDU3Z+Z7y4maMIaKomJmvr+C8AGtE6BNTEy497WlRN09lt5TJzFqwaPMWvVuq/oh3eoMfOh+Zn+6km5jhgsOcgGB24g2f9KaO3cuAIMGDWr2M3hw25XCCxiflpZ0uQcHUpydQ1VpWbPXMi/r33TQ2l7xpak7dkPxeku+ZC1trLG0saY4O0fjPtfK9nWLV9dKx5uLKPK6OtIvXaZdV92ZuJqQOjtrzHsGKG8QSLU5n00tLLD3cCOvIbe2NC8fMwsLnZl5Ejup1tgNUERv3Gjnc2F6U0dydVk5lw8epcuwQW1Slujk5am223pBw3V1iUkW1grns66Gg4BeuYV2Ls6UNHKHHv1lK1Vl5QyYOU3t/vZurrj4+xJ/9HiT7dnxCcQdiaHPtHsMdiT5dgkn/dLlJo5zgJzEZCqKirVGb3i3D8XMwoLU67J9QSHuWdpY612yam5licROqtX5rByXf0Qnhs2bRcTwwS2Ow1CW9hakanc+g+KzaO1oXPG5x9hROHp68O/XikirermcMzv/Jbx/nxY17LK0sda7YWGPsQpnV5fhg5o0Axwy5yGcfLz46tFF7Pjoc/as+YE/P/mKyPGjGbVIfaSBiYmJYkFCzX2zpqKSmspKpC76x26EREeReOI0spoajfvkJadi5+qi1+/JLdCf4uwcaiorST1/kaqycrXRG/ZurpiamamN3QBF00FnX/2dz2MWP87oxYaVJrclIlMxUXeP5dK+Q02+I2urqsm+koivmtxnr7AQAqO6q5o7Xr16lewria0qj+44uD9pF2NVJfiXDx9DIpXiHa55wQ0UkRuA2kUNddRWVbPzk6/wj+ikEryNRfjAPsjrZJz5a5fa1y/tO4hILCa0dy+Dzx3SKxKpizP7f9yo9zHGajo45JEHqSgq5sjPv+l9TOzBIwA88ParWEisKMrKNuhzYyjWDvZMW76Mt0/u45ktP3Hvq0vpc99kIsePovOwQYT1jSagR1e8O4QR2KMr3ceOZNCsGXqPycTEhLA+0Vw+eEQlHAOq6h9PPX7nXYYPQmxqyvGt21Xb5HV1nP17NyHRkXpFGzUbl0iErZMjnqHBeHcIxcbRAbGZGfe+upQxT86nIC2DToP76+2gNDExIbBHVxKOn2yyPb4h7suQ6A0rqS2WNtY6xed6mZyC9IxWNR0M6NYFcytLLh860mR7TWUlqxc+x9Wr9Yx7ZgGgEEF733s3lw4cJuXMef5c9SUv9R/N148/pYraO/brNlz8fVvc/FdsakpQVHfi1LieAarLK4g9cJiIYYONEr0hNjPDt1MHtZEbSpJOncEzNNigRsABPbrS94F7Obj+F35fuYr6+nra9+ut17GdhgzA1smRQxuv3UdO/LGT0vwCBszUXvnXGAuJhFEL5zH+Gd1Rg8bAQiJh9qcr6TioP5tXrOSdux8g41IcM95bQcdB/Vp83h7jRuLbsQMbli3n+aiBvDtpeoO5ZEIbjv7WoeekcYx5cj7+XTpx/xsv89KubfSbMfVmD0ugBYhNTVud2S9we2G0Zf577rkHGxuFi2/p0qX88ssvRES07ItY4MbjFRbC//7+jfHPLMK/SyeDHnA8ggPJuqK+s3xW3BU8ggP1Op8ydkOb21bxesudz/bubgDaYzcaXElugf46z+ehY7KbePwUvh07GFzKqETq4kRpvjbncyEAtlrEZ2dfb0QiEXkN/65rzkLNgp9ILEYilepcCCjKyr5hDQctJBJsnRzJVyP0nN75D/bubvh16dTq6zh6ezYTuOFaDIeTDqeYstGW1tiNBuezpY1+zufG7veaikqO/PwbnYcNUiv8Kx2a8Udjmr227/sN2Lu70XnIQJ3XVSISi/Hp0J7Us83FY4DEk2cI6Kb5Xq9slNO4LFeJ8rOmb/SG1FXhhi3WIT6f2v4XlSWlDH30Yaa/+zoPr3pHr/Nfj7OvQnzO10N8VjiftVcgtAYLawkjF8wl5cx5Vb4mKCZkZpYWdBo8wOBz3vfGy8z7ZpXO2B4LiYROQwaQdjEWC4mELsMU7x+JnZQeY0dy4vc/iTt87f329+ffcvrPf4iePFGtu9DBywMLiUSVD3w9pbn5ei9ISF1d8AgO1DhxV5Kb1NDgUo9ybbcAf9UiZL1cTkLMCUJ6NRdSrm+Aej0Fqel6Nxx08vGm/8xp9Js+Veffw1h0HNgPqbMTh35u3rAu9fzFJrEbFtYSxj+ziEXrv6WqtIyjm685pbPiE1rsfJa6OOPfpRPn/92r2hZ/JIb6+npC7+qp9ViPkCBqKiubRSZpI2bLdrKvJDJywaNGzVQNH9CXhOMnqS5XXxGTdv4SZQWFLcp99gkPo76+nsTjmsWj68m+kohcJmvTpoP27m506N+b/T/+TG1Vtd7HlebmcfngEeKPHue9KQ+SeOI0Ln6Gi4tTX3+RpTs3c/fSpwmJjlLrlO8+ZgTPbvmJiJFDOPbrNgrSMggf2JeJzz/J1NdfZObK5Tzy6Xs8vvpTFm9YzWOrP+W+FcsYvWie3vngXu1Dm+QgK8mM1V/wb9ctgsLMrGYueZWruIt+rmJF08IhzP3qI946sZeX9/zBU5vWsnjDGl7Zu503Y3YTNXEMOz/9mncnTacwI4vxzy7SyxXuFhSAjaMDV441/W6vLisn7fwlrVn51+PYUFGmK3YDFAuJrRGfQ3v3pK6mhoTjp5q9Vpqbx9+ff0vHgf0I69OLLsMHKRZ21l1b2JHV1DRZVDj79y6qyyuImji2RePx69IRS2trLmtpznlqxz/YubnQTo8Gz0rMrawY+ujD2Dhp/z7x7dgeMwsLreJz8qmziE1N8eusfyXn+CULKUzPZNt7q6goKibt3EW9o4Wip0wkPy29ScNSeV0dB378mbA+vXAP0t1ToMvwwTyz9ScGz55Bv+n3GrXBqjqspLbM/fJDArpH8MNzL3Hwp01Ul5Xz5dyFZFy8zMz332DE/Dl6RzwqUQrqKWfOc+qPvwDFHDTu8DH6TLunRQtTbUWf+ybz2JpPW5T5romwPr2Y9L8lXDpwmJf6j+LThx4j+cw5xi9Z2GoHucCNJaB7BM9t28ir+/9k/nef03/GtBvSK0cbIrEYJx/vVvXnEtCO0cTnF198kfLycnr37s3w4cP57rvv+Pzzz1t93uHDhxMbG0t8fDzPPvtss9fNzc1Zv3498fHxHDlyBD+/G9u5/E4i/cIloqdM4Il1X/L05nV6OcREpmLcAvzJ1lDmmnn5ChYSCY56lB4rYzcqivQUn1uQXWvXkHWsLXajtqqKwows3PUo2/cMCaK8sIiygkK1r185dhKxmSn+EYaLohI7Kabm5lqdz5XFpdTX12vNmlW6Nxs7n0F7pqpyVVIf57Odm4vBD09KrO3tCOgeQa/JE3ROppTlwepK3C/sPkBdTQ0RI5pXWzj5eOPdIUznA7jqOl4eFKgRLyqKiqmuqNBZpmxprU/shuI1vWI3XJwpzW26ALH/x41wFbUNBIN79qC8sIjs+OYLQrH7D5GXkkY/A7q6ewQHYm5l2SzvWUniydM4+3prFAsDI7uRfSVRFRHTGGWJuL7iszKrvETL5xcU2bNvjJ7M81GD2Pv9erzbh7bIGezi50NpXr7aUtTrKS8s0jt2o8vwwcz65F2mrVjGuGcW6uU0G/LITKTOTvz65vtNtqeevUB+WjrdRhsWvRExYgjhA/ogEot1OrY6Dx2AhcSK3954n7yUNHqMHw1Ar3vGY25lyf4fmjsuL+w9gJWtjVoRUulQ1RSPUJpfgFTPzOfQBoHj8mHNE3eA3KRkAFzaaRctTExMcG3nR07iNdEn9uBRtbnySmFZXeazYntGQ068brfYwIfvp76+HlMzM41Nr9oCkakY/y6d1E5Qo6dMpDAjS23Tq7RzF5HYSXHy8ca/Syee+e1H+tw/mSObtvDWuKnkNTQCA0WVhbW9XYuacIXepZi0Kp3UoPgeSr8QS6iOCa1HSCBZ8QlcvXpV7+vVy+X8/cVq3AL8W+VE04azrzduAf5c2LNf4z5Xr14l9sARQnv3MjgOwqt9CHnJqdRWVel9jKy2lpzE5DZ1PisXGs81WjjQly8fXczns5+gLL+A/JQ07D3cMDU31/t4qasL3ccMR1ZTS49xo5j75YfMfG95k32iJozhvjdeIjc5lfcmz+TnV97km/lPs6zvCF7qP4rlI+7mnYn38+F9s/hs1ny+mb+Ezx9ZwBujJ3N08++E9eml15jC+kZTX1/f7HNUXV5BQXoGXnrkPvt36UiKmvzdK8dOIJfJCLlLt7jjERLEsn+2MP2d13D28Wbf9xvYvPxd1ix6jtULn+PXN1ay57sf+Wb+Ev767BtkNTX8/t4qPEOC6DlJdzxXkJaF5bgjMfh27IClHs84cK1xtS7nMyiaDrr4+rR4sSj0rp4knTxDXbX6Spl9328gNymFCc8upt+MaYr4JS3CcG1VNad2/E2XYYOwtLFu0XjkMpmqQa46Lu49QE1lld7RGyYmJkx9/X+MePwRBj08Xeu+Ad0V/ZmSTp7RuE/ymfPU19fr3XTQ0csD7w6hHNqwWbUQdXH/IXw6tte5uOoW4E9QZDeO/Pxbs3v54Y2/UlNZRX8NlX9KRi+ax4x3X6e8oIgv5y6iprKSPvdN1mvsbYGNowPzvlmFV/sQvnvyBU42iMSguA98/sgCjm/ZztC5D/HY6k8NMvEMmj0DqYszv731fpPfz97v12Pn5kKX4YNaNGYrqS3tunVp0efK2sGeWaveZeLzT+IZHKS4/77/RqsrYz1CApmxcjlZcQl8/9T/kMtkJBw/xZqFz5F24RLTli+77aLNjIHY1JT2fe+i/4xp3LPsWUYvfqzNoknEpqZY29u1anFebGbGmMWPM+/bT6iXy9n19VrMrawYt2QBT29eR7CaWDtjYuvkyOhF83hu20bejNnDC9t/5n87NzNywaMtNgsKaMZo4rO8IVt09OjRfPbZZ2zduhVzAx4c1SESifjkk08YOXIkHTp0YNq0abRv37Tsc9asWRQVFREcHMz777/PW2+91apr/lfJiI1j9cLneKn/KDa9+jbuQQFENggM2vDuEIapuTmpFy6pfV3pCPYM0e2CsraXIqur0yn01FRUIq+TtdD5rBSfNcduAOQkJukVu+EeHEiWlnzJpFNnkdfJCIw0vBGEcuKuLfO5Xi6nqqRU68Oc0umnFJ9LGoRMW2fNwoDyd6vLhV6UlY3Y1LRFIsPAhx/g1f1/8viaz5i87FnuefEZrfsrHzDUZeLVVFZyad8huo0a1uTh39bZiSd/XsPiDat5Zc923ji2m7A+msuarR3ssZBI1MZuABSmZ+oRuyFRjUkTSmFal/hsJZViZmFBaX7TBYiSnDwuHThE+IC+zY4J7tmDK8dOqBVgrl69SsyWP/Dr0lHvxoO+DU4XTeJz0gnFhCVAjSNHZComoFsXrqiZnILCZVRdUaG3+KxaPNKS+dwYWU0NsfsPIxKL8e1kePa6k6+XXnnPoFicsLKT6iUcRU+eQED3CNp17UKvSeOZ+9VHDHjwfs3j8Pai3/SpxGzZTpqa+JJT2/8muGcPvR0EEjspE55bTNqFS9RUVhLYQ3tz4O5jR5KXkkby6bPEbPmDoMhuuPj70nvqJOKOxDTL+4dr7wt1Ti2PkCDq6+vJSVBfMVOam4dUy/2pMSF39aQ0v0DrfRgU7mS5TKbT+Wzv7oaFREJOg1gNcHLbTiqKihn22Owm+zr7eCGrq9O4mKlcKHPy0b5gJXVxJnLcKI5t/p2kU2f1En4MxTM0mEn/W8LLu7bxxLovmfzy801ed/H3JaRXJEc2bWni6lOijM0Z/8xCHlv9KbLaOj6ePodfXn+nWeSW8m+hb7+HxgT17EFZQWEzV3zc4WP4dg7XOjHwDA1W2/xXF2f+2kVeShqDH5lp8LH60LghozYuHzyCtb0d3u1DDTq/V/tQMhpctYaQcSmuTcXnoKhuVBQVk6OhEk5f8lLSEIlEelcNAESOH4VILOarx55kWb+R7Pj4CzoO6q8q0Xdt58eE558k7kgMnzw4T1XZoKS8sIjCjCyyrySSeu4iV46d4OLeA8QfiSE/NZ0zf+3CQiLRK282oFsXsuKuqK0cy4iN19lM0d7NFXt3N5LPNP/OrS6vIPXsBUKjtVcBANx1792YWpjzxZwFrBg5iW3vreLg+l849+9ezu/ay4EfN/HH+59yce+19+XZv3aRcOIUI+fP0SmkBkZ2oyA9QxWP05i4w8cQicUE6fnsq+ylUaSX8zkFM0tFnJyh2LkpKmXULbApkctk/Pbm+7j4++LTIYz9P2zUuaB1dKXR8ZoAACAASURBVPPvmFtZtqjxYMhdUaScOa+xKgIUAvelfQfpPHSgXmaPIXMfosuwQZTm5dN9zAitbtiA7hFkxSdoNZtUl5WTfSWRdnqKz8pn0/O7ry24Xdp3EJFIRFgf7Q2SoyaORVZXx7Hf/mj2WmVJKTG/baPb6OEanYrdRg9j0KwZHP75Nz6Y9jCXDx3l+NYddBs1TKfwHdq7F89s+alVDcylLs48tvpTXPx8+faJZ7iwu/miY21VFRuWLWfdM8vwCA5k9mfv6fXs6OjlwYCZ0zi+dQep55o+C14+cITcpBT6TTc8jsLS1oZ536xi/nef8/z2nxn48AN6P092HNSfpzatJSQ6kl/fWMnLA8fwxwefEXpXT57dup4xix9vcczCuCULqa2q5pv5TzeZU8lqa/nuyRe4evUqM99fgamF8TKgTUxMiJ4ykee2bWTJbz/yxPdf8sDbrxpUhaALGycH+k2fypMbv+O1gztZvGENM1YuJ3ryRMRmZlqPtXVy5LHVnzL705WMW7KAzkMHMuDB+3ls9actmpuDYu7Zf8Y0Zn/2Hq8d3Mmr+//kzRN7WfqnorLIkN+3SCxm5srlDHz4AY5s2sLKe2aw4+MveG/KTN6ecB9lBYXM+eIDBj6keQ7UVkjspNy99GmW7tzMgAfvJy85ld2r17HhxeWc2vEPQx6ZyTNbfmpV82eB5hhNfM7IyODzzz9nypQpbN++HXNzc0StbOYRFRXFlStXSEpKoq6ujvXr1zN+fNMmJOPHj+e7774DYNOmTULOdCupqajk8M+/knz6HH3vn6LzyzCwwTGXeKJ5+RpAdkIi9XK5Xp3LJXZ2OpsNKqksLVXFdBiCvbsb9XJ5MzHvenISk3Ft56f1328iEuEeFEBmvOZ8ydqqKtIuXFI5RAxBH/EZoKywSOsDlYu/D8U5uSpXVFm+bueztZ3id1tRrMP5nKUQ8R0aJgH2bq56lXxZSaUMmfMgcYeP8eWji4nZ8ocq4kATziqXofoS93++WoOVnZTBs2eotg1/fDam5ub8+MKr/PrGSurlcsIHana3OXopRCJNZdsF6Zk6YzeUgnJ1meaJRL1cTnV5BZY6HsiUubeluc3F1itHT+Ds661yA4Nigm3n5tIs77kxqWcVDZB8dOSnKvHr3JHS/AKNjqSM2DhqKitpp8Y9690hDAuJRK0zSkluUorhzmc1vw9NpJ67oHDstKD6wMXXh/wU3ZEboMh8FolEWOtxX3L19+Ps37tZMXISL/Ufydm/djH2qflMW75MrbNuzFPzkctkbP/wM7XnO/nHTkRisd6uqHFLFiKRStmwbAXJp88RcJ34PPyx2Ux55QUkdlIcPNwJ7tmD47/vAODE1h3U19cz/e3XsHd3Y9/3G9Reoygrm6KsbLWLEh7BgRSkZWgszS/J0y92w8TEhJBekTojN0AhKBRmZOks13ZtaICZ26hxW01lJbtXr6N9n2j8G6J9RGIxAT26UpieqVashWsLZc4+2rNi+8+YhkgsZveaHzi6eStuAf46J/lWUim9p92jlxBhJbVl/trP6T52JJcPH+PIpi1Ejh+laupm4+TAgx+8SU1lJcd+/V3tOXISkqmprCJ8QB8u7jvI+1MfUt1LrkcpALckeiOkVyTxR483E3uSz5xHbGqKlwZh1t7NFWt7O7WNTXVxtb6e3avX4RPe3qCoAH0JH9CXzLgrOl2d8ccU921DGmopP6PKDGdDyIiNw87VRe+qIF0E9uhGwvFTBjnP1aFcKNc3esPExISed48l/uhxCtMzkdXU8M+Xazjz1y5GLZpHUFR3Hnj7Veqqq/nphVc1fl61ceXocarKyumkwx1vYmKCb6dwUtQIxwCZl+Nx9vPRWg3h1/Bdpekclw8fwzs8TKsBw0QkotPg/lzaf5i4wzEG/U22vv0hNo4ORN2tOUZClfcco/7ZP+XMeWoqK/X+PDl4ulNTWaUz6g0UzmfA4GbFgEq0j9XiZAa4fOgoZ//ZQ0VRMce37tB53rTzF0k+fY5+06cZVAlobmWJd/tQra5nJad2/IOtkyNBUdrnEx0H9WfE448Qs2U7G5Ytx8bRQbUAdj0isRj/rp20Rm4oSTp5Br8uHfX694UP6Ev2lcQmZpGMS3GU5ObRXke0UIf+vbly9ESzpt9K9n2/AZFIRH81Iqt3hzCmvPwCV2JOsnnFu6rP+oEff8bU3Fzrwm7HQf14+OO3cQvwZ8JzT+r8N6rDwdOdx7/7DDs3F76at1hrlArAqR1/s/HlN3APbEfnobrj8AY+9AAAf6h5Frx69Sr7vt+AT3h7g4RRM0sLZq96F7fAdmz/8HOKsnIYs/hxXt7zB0t+/YG7lz6tdiHZyduL2Z+u5KEP36SiuIQPps3iwI+bkNXWsuubtbw9biqn//yH/g/exwvbNxm8qO4f0ZmQXpHs+vZ7tfPgosxsfnjuJTxCgnjks/dUsZqNsZLaMvzxR3ji+y8Z+cRcgxdbHb08mPvVR9zz4jOUFxSSk5BEbVUVwT17MP+7z3l8zWdEDB+Mk7dXi5zBTt5eTFu+jGX/bGX8MwuRy2Sc2v43ZQUFeLUP4Z5lz/D8Hxvpdc94tXNr7w6hLFr/LR4hQfz4/Cv8r/dwlvUdwZqFz+IW4M+in77V2pxeHV5hITy5cQ3jlizA0dODmC3b+e3N99m39ifSLlwiespEFnz/pd7Niu9e+jThA/vyy+vv8Mtrbzep0MpJSOKj+x/h7N+7GfPkfGasXK6Krmxr3IMDWbT+W3pOGsfx33fw1ripfDP/aXZ8/AXHftvG+v+9xsfT51JVWsbsT1cybN4so0ax/Zcwmvg8ZcoUdu7cyYgRIygpKcHR0ZElS5a06pxeXl6kpV1znaWnp+Pl5aVxH7lcTklJCU5OQm5La9m3bgPOvt468wcDenQlJzGZ8oLmJfUAddU15Kem6+WAktjb6fXgCYrV7xY5n91cKc0voL7Bqa+JnIRkzCwttJYMOXl7YiGx0tnc6ErMSXzC2xvUqAMai8/ahfJyXeKzn2+Tkujaqmqqysq1ijtWKuezrtgNhUvFwdOdjoP68/yOTUxc+pTWYwD63HcPltbWbHnnIy4fPELa+UuYW1lqHZOTjxcVxSWqZn3Xk3EpjhO/76Df9Kk4enngHhRAz4ljObj+F078voMDP24i5ex5/LQ4YJ28GnIHNYrPGSqBWhPWDg7U1dTodPBXlZXpdD4rFwhK1Dx4Kd3EgY0WNsL6KhwlsQeONNtfSdrFWAB81DQPU4df53BSNbieQSGkJ58+p1ZkVC66aJvYGCI+27m5UFFUrLWx3PVUl1coHDsGis8WEglSF2fy9XU+N8SK6HLWmFtZYefmQl6y4rx11TV8v+RFdqz6kh7jRjLv21Uqx4mJiQmDZs2g85AB/PvVWo0LUblJKaRfvEy30cO1XltkKmbI3IeIHD+K3avXkRV3hYTjp/AMCVI54S1tbRg0azo97x7Lkt9+ZPyzikY9JxrE5+KcXOKPxChK/VPSiL0u17QxSafOqm246hkSpPW+WZZXgIXESqfzzt7dDRtHB5LVlKerIzcpRadgocz6v94ZeXD9L5QVFDJi/hwAJjy3GP8undj97Tot10ulvr4eNy35lFZSKdFTJnD6z38oTM/kzE5FfqiuidqEZxdx9wtPqY0aup4eY0diIZHwycxH+eHZl9j06ltcOnCYic8/SVjfaOZ9vQoHD3e+fvxpjRFS9XI52z/8jF9ef4c1i57TeB8GxfdGSU6eweKzW2A7pC7OxB9unlefek4hdGu6fyubuLXEAQxwfOsOSnLyGDy7bd3PEjsp7bp21hq5oaS8oIjMy/GEGCA+K8X4jEuXDR5bhiqD2DCntTocPN1x8vZs1nyuJSjvuS5++jX4C4zshpO3F0c3N1042fjSCoqyspn75Yd4hYWw/n+v61zM14RcJiN2/yE6DOij1ZTgGuCPla0NKRoWZjJj4xCJRKroIXX4delIXXUNmZfVL6RcPnQUkUikdZGiXdfO2Do5cu7v3Rr30UT6xctkXIqjs5Y+Au7BgVjb22kUTeUyGQkxp/TOfnVr56/3d62y0qYllRWhvXtSkpOnsd9AY3547mXeufsBveNsdq/+ASdvTzoPGaD3eLzCQhCJxaSeV1892pjYA4epLq8gYsRQjfsommm+SMqZ82x69S0uHzpGcU4uURPHqN3fMzQYS2trvcTn5NNnsbS21nlft5LaEtAjQm2lR+yBI4RGR2lseu3k7YVrO79meemNKUjP4NSOv7lr6qQmz1s2Tg489NFblBUUsvappaqGkKD47r986Ch33Xt3s2uLTU3pMW4UM1YuJ+PiZX576wP8OocTMdwwM5uznw+Pr/kMiZ2Uz2cv0Ot3CnD2793kJCYzZM6DWsUuGycHIieMJmbrdrWGFIDjv2+noriEoXMe1OvaFtYSZqxcjl9EJ3547mX+/fo7Pnv4cd4aN5Vt762iKDuHHuNG8tjqT5o8p4dER/L05nW069aFLW9/yPtTHmz2TFeck8v6/73Oe5NnknEpjikvP8/IBY/qNS6AYY8+RFlBodbmtbEHjvDT0lfxbh/K05vXETl+FF5hIYQP6MOIJ+awdMcvDHv0YcwsLBj48AM8+fN3LD/8D8uP/MNbJ/by1Ka1TSIfnHy8GbdkAY9+9THPb/+Z57ZtxCe8PRtffoNVMx9l7VNL+WLOQl4fPpFf31iJg6c70999nRd2bOL1w38z870VepntbJ2dmPzSczz7+3o6Dx3IgR9/5u3x0/jwvllsXv4uXz/2FG+MmswXcxZQkpvH5JeeY+nOzQyZ+xBSF2dC7+rJtOXLmL/2C+rr61k1Yy4ntv1JValizn5hzwE+nj4HuVzGgnVfMezRh/UyhnUbM5wnvv8SkVjEh/fN4u3x0/h1xUr2/7CRPz74jLVPLeWb+U/j4OXO4vWr6dBfe+b24EdmEj15Av989R2HNmxWu09tVRXfL3mRre98RMdB/Vjw49ca54QmJiY4eLgT2rsX3UYPI6xPL3zC2+t01ncaMoAF677E1NycT/7P3nkHNHX3XfwQEjaEvffeghvcu9paa9211drabcdTfVpf29qne087bF3VqrWO1roHKgqKsmXvGQgbEkhCyOD9I9xIyLoJAbTez1+ahJsLSW7uPb/zPWft8zj63mcqu3yqsnPw3WPrkfbPacx7cT3WfvPJkInh9xNDlkIvEAjQ1NSEyZMno6ysDGKxGKWlujtQ+qPqIDxw9Z7MYwDgmWeewbPPyi4aHUmO897P5CYkop3dgCmPr1A7KmpEo8EvdhSyzl7UuK364lJSQpel3TCIz67OWiM3AFnsBgC4+PupLJ8D7lz0aRv3Lk/LwOxn1sIvNlrrKnh/CNdrp4bCQUA27q+pfMPJ1xu3L1xWuI2rxVlIuDe1OdGJv+WEJYvgFxMNqUSCcQ8vwIWfdqm9yDMxN8eU1cuRd+Wa/AKA+BJw9PZU+3MOXh4q8577c+b7XzBq7iwsePUFmFtbo5vHw8Xte+T31+QWYObTslZoVa5LYiW3jaU+dsPE3AzWDvZqRRpLO6Zax0Z/BJ1dMLfW5nyWFewNzHwGZLmqfA4XAeNGI+PUOQBA2OQ4NJRVaHyPd3d2oamymtRYobmNDZz9fJD2zxmNj6vIvI15L66HuY21wgh+wNjRYJeWq8x7JmiqrMbYhfNhYm6u9SLP1sVZa9mgKqqychC7YC6MaDTSrjdHb5nwQbjwtNHV95pryl8H+mew1yjcnvDLHjSWV2LVR1vx2qHdOLjlfUx9YgUiZ0xF1pkLSNx7UON2M0+fx8P/fQWO3p4qT6pcA/2x8qN34BUeiqyzF3HxF9nnoqKvdMl/zCjkX0lC9KzpoJuY4PC7H2PyY8sQNWsaytIyFVybacdPIyR+ApIPah5HrsjIxugFcxVKPBlmpnDw9kTm6fNqf47TrxRV0ygy4WLuH5GhCVZBEcKmxMHMylLtdl38fNHV1q70XdQj6MblXb9j0RuvYtXHWzF24Xxc2b0fqcdPqX2+HoEArbV1GoWm+OWLYWphgUu7fpf/TNbZixjz0AM4/uk36O7iKX2ufGOiMfbh+ZBKJJj+5GqFPElVTFz2CKpv58nFxt7eXhzc/D/85/BveOanr9Ej6MbOF1+XvxfUkXzwiMb7+6NP6SAhupbcVBafee0daGXVyWOABuIRFgypVKr1+1gdEpEIiXsPYtEbr8JnVKRa16muhE6JgzGdjvwrmiM3CEpS0jD5saVgmJmqzaTtj2dfiRZLD+czUYDnFRmG4uvqFyxVYWRkpPDZDxgrW2gsU+OE1YXuLh46W9tIO58nLHkYfC5XKWu6u4uHfRvfwku/bUfKkb81ClpkyLt8DbEL5sI3JkptPq5vX3dF1W3VC2KEQ909NEjtY3yjI1GTXwCJWKzyflZ+EfhcLkLixuP2+UsqHxM1azpEQiEKk1I0/k7qyLmUiHkvroe1owM6VUwKEj0FmqaaSlJSET5tEuzcXbW6/j3CglF4jdzrI+B2oq2eTUrk6Y+RkRGCJo4jtRAEyGK7OnVY6M5PTEJTZTWmr1uNbDWvy0A8I2TXRiw10YUK+9PTg7zL1xA1exqOvPepynOZGU+uhom5GQ698yHEPT0AgLR/TmPW02tg4+ykJFr6j+2bXtWQ90xQmZkDAPAbHa1xkS90suyYl3flmtJ9hdeuY8KjC+EXE62y8DFsqsxAoe19e2H7bsTOn4PpT67Gqa9/AACseP8tWDKZ+G71epXn4EkHjmD9j19ixpOPg8fhwMXfF14RYfAMCwHDzBTl6VnY9dIm9HR3Y9zDC7DgtReRe/kaJCKRxn2hGRsjbtkjmPfSM5BKJPhp3UtaTUn96ZVKkbDjN6z+5H8Inz5ZZUwHICvzM2YwkPib+nNBUbcQF37ehcX/9zrGL16ocpKJRjfGpJVLETljCvxiR8GYQcfh/32CnH7XiU2V1WiqrMaVPQdg5+6KVw7sxPqfvsb3j6+HW1Agnvr+CzRX12DHixvVCuEE7JIy/PLcq1jy9n8x+5m1sHawx9H3P9NoAvOOjkDIpIk49fUPWstrM0+dR1VWDlZ+9A5WfviOwn05CYm48PNOsEtkPRSRM6fCPSQIYpEIUokE0XNm4Pmd25Bz8QqkUimiZ0+HVCIBq7AYNbkFyDpzEbeOnVCKFhJ1C5F88ChuHP4bHiHBcA8JhEdYCGIXzEH0nBnISUhE/pUkdHd1QcDtRHN1LbjNLTCi0RC/4lHMf/k5MExNcOPPv3Bp5z6Vx1hAdj5QkpKG4LjxmPrECszf8Czm9xkg+Fwu0k+exbltv6q8xmKXlOOb5U9i8f+9jnkvPYOo2dNx4ovvFWIZzW2sET5tMoImjEXQhDGwdXVBWVomft/0ttrrtqKkFHy74ims/fpjPP3DF0g5ehwnPv9e4frNmMHAvBfXY9b6NUg/cRZnv9feAXd13x9gFRbjiS8+wKsHd6EsLQMdDU3gd3Bg7+kOF39fOPv5qBSDxSIRipNvIvPMBXCammHr6gI7N1f4RIfDNyYaVvZ2qM7Jx2+vbda6AC3u6cGhtz9EXWEJFm56GY++vQl/bHlf6/5TqGfIxOetW7di7NixCAkJwW+//QYGg4H9+/dj8mT9m0hZLBa8vO6M4nt6eqK+vl7lY+rq6mBsbAwmk4m2NmVRaMeOHdixYwcAIC1N+cKGQhGpRILkg0excOMGuKlxqXmEBsHc2krr6i67tBwxD8zWejFlyWQqNXurg8/hgunsROqx/WG6OKvMJx0IUYTmGuCLwmvXVT4mJH4CBNxO1JdoXmSpys6FWCRCwLjROonP1o4OEHR2af3i1eR8tmDawNKWqeB8BmRipibx2cK2z/nM1ex87hF0o6utHYHjRqMy8zaOffQlXj/8G6asXobT36qOCIhfvhiWtkxc2rFXfhshljl4eah9Pzl4eaBWizuE29SMK3v2Y15fNus/X3wnXwkGgOqcfBjT6fAMD1X5PJ7hoWhnN6h1LcszXD091IrPVnZ2WkszAXLOZ7n7XcUCRG9vL8rTsxAwThaZYGJuDv8xMUg+eFTrc9fkFZAqePCKkC2wqMoZ7k9FRjZoNBp8Y6Llnxca3Rh+o6O1CtfEZ97J10vr6DjTxRkcknnP/anKzkX8ikfhEuBHyvEEyFwsAEi7sbpIOp8HFoD2JzchEa21dVj3/Wd4cfePkIjE+PuTr0i9plnnEvDQxg0YvWAuLmzfrXAf08UJr/2xG908Hva+vgU5/dxwNXmFEHULETA2FvlXkhD74Fw0V9fi1l8nkX7iLCYuXaR0YZp9/hIkEgnyLmsuFqvMkl2s+o+OkYvPrgH+oNFoassGgTsxMzZOjhq/Ewh3RBNJ8bkiI1s2ZhwTpXY6wMXfV62YfePw35i+9jGMXTgfOQmJOP3tT1qfk11SplGEDZsaj+qcfIX35a1jJxC37BFs2PcLbBwdYGlni5yERPyx5T2IhD14dMtGdDQ04sqeA1j8f68jOG4cSlS4hQFZnqdrgB8OvfOhwu18Dhf7Xn8Lj769Cae+/lGlEDAY2KXlCBw/GjS6sYIDTRNBE8ehuapG7eJZTU6+2sIrj9BgtFTX6lS6N5CbR//B7GefxLQ1q7Bv41saH2tEoyE4bjzs3F01OrMipk8Bt7mFlLgEyIT36U8+Br/YUShJ0R4n4xEajLZ6tsL3HFm6u3hgFRQjcNxoJPyyR/sP9GFEo+G5X75DV3s79r+xFQAQMC7WIHnPBM1VNfJjsCbMbWwQNWsabh07oXIipq6wBO/NeIhUaaw2CpNTIO7pQeTMqWrFZ5/oSPA6OGhRs2jZ0dgEXgcH7mpKB+kmJvAID8G13w+p3Q+pRILSm+kaSwejZk9DyY1UvX/v3IREzN/wLCJnTEXKkb+V7g+bPBFNldUaF4OJ929w3HjcOnZC7eOYLk6wdrAHSwf3fn1xqcZFPVXYe7jD0paJqr7vJUPTK5Xi6r4/sOzdzQgcP4ZUlIZXRCg6GhrVnk8OpCg5BWMfng+P0CCwChT/XlYOdpi0aikyz1xQ+N5MO34Gc55dh7EL5+Pyrn0KPxMzdxYayiu1CoiALEqro7EJvjHRGs9JImdMAbelFbW5yueNJSlpEHR2YdKqpSq/c0KnxKOpslplt0t/WqprkXn6AiatXIKrew8icuY0hE+dhL8/+Uqt8FuUdAMtNSwseFXmvhXyBagvLsWNw3+hOicf+VeS5IL9ya+24fmd2zB51VJc3feH2v0IGBuLR9/aBNdAf5SlZuDI+5+p/exrIvtsAua9sB5znlunUnw2MTfHpJVLkHfpqtbtX//jKCJnTsWiN19FWWq6UofNojdew+RVS1FXVILEvQeRd/mqUn50f9rrG7DrpU14cc9PeGHnD3DwlHWhbF//MmnDWK9UiqPvy1zpMieyCQ5s/p/ax895bh147R24fki1W3YgbXVs/PzUBkTOkGWNdzQ2o72erSCg8jo4StMx53/cielPPoZZ69dCIhbjyp4DSNr/J+nPo1QsQW1+IWrzCwGcxJnvt2Pq4ysw9YmVShMQXW3t6Obx4OjlieIbt/DXR1+qNIqooiQlFSUpqXD08ULs/Dlgl5SjMOmG1oURPoeLA5v/h+zzl7D0nTfw/M5t6GhoxO0Ll+Ho5YmQyRNBZzDQ1daOsrRMlGzfjbR/Tms9Z2tl1eG71evxwEvrMX3d4wiaMBY3j/4DVn4RRN1CLHnnv3APCcKtv07i2Aefk/odAdli5rcr1uGhjRvgEuAH/zExsLCxQUdjExrLK3Hrr5NorKhCU0UVOlvb+jQOW/iPiUHsgjmImKHYg9RcXYuCq9dRkZmNrDMX5Z9vMiQdOIz6kjLS14AU6hky8Xnx4sWIjY1FZqZsFZzNZsNai6tPG2lpaQgKCoKvry/q6uqwcuVKPPbYYwqPOXHiBNauXYubN29i6dKluHz5spqtUejKrb9OYO4LT2Pq4yvw59aPlO4nckK1XbS29okOdm6uGoUEC1smeFoK7gj4HK5eeZK2rs4oStbuBOnu7AKnqVme/zkQIyMjhE2JQ/GNW1oP0j2CbtTmFshFQrIwnZ3Urob2p6u1DZa2TNCMjZVWkgln4EDxmdPcrHIcnsCCaSPLJdYwWk1QnZMPhpkp9rzyJnoEAuQkJCJu2WIk7NgLIU/xwoduYoJpa1eh5GaawslOO7sBEpFYbTYqjW4MOzdXZJ9N0Lo/ib8dwMQliyDqEeL6H8cU7iNGt72jIlSKzwFjYzVGVhDvZXsvd7WuJUtbJrra1Tt9Cbq5XSozyvrDdHYEn8tVu2hTnpaJqFnTYOvqAveQINBNTEi9v2vzCjF24XyVTpj+EFlh2i4Ka3ILIBaJ4D9mlFx8JvKetV2AEccEF39fEuKzk7z4TBeIWAa/mGjS4rNbUACkEgla1WSMD4R4za20OJ8d+z6T6k4664tL8d2qpzHn+aeQdfqC2vfZQLhNzShPy8ToB+cpic9BE8aBYWaK7x9/RmmUWyISoTonD/5jY2Ht6IDA8WPkQpRELMb1Q4qfIUB2IZFzQft3bWNZBfgcLvxio5F+QrYIQYxKayqGkzufNeTSAzLxWcDtVBv7NJDqnDzZQqCGz7lLgJ9a15pYKMSxj75AzAOzcfjdj0nlqLJLyhA5a5rKxVeGmSm8IsOUhKba/ELcvnAZdu6uyL10FUI+H1NWL8dLv21HwdVkeIQFY+/Gt5B/JQmz1q/B9CdXqxWf45Y9AgG3E9nnlI+dtfmF+G7V01p/B32ozsnDjHWr4RMVIV+E0ASNboyAcbHIOHlO/TZzCxC7YK5KN6Z7SJD8+K4vPQIB0k+exaSVS2DBtFEZO0U3NcW0NSsxccki2PfFNHV3dqn8+459eAGiZk7Drb9OkM7crczMhrinR5ZlTkZ8DgvRK++ZoPRWOqasXkbaaQ3I3lNBTmCs4AAAIABJREFUE2WLl6l/n0RJSprB8p4JmqtrETZFczEZICsXY5iaKokK/TGE8AzIOlFKb6UjatY0nPxym8rH+IyKVFvOS1BfVAqPENXZo57hoaAzGFqd9yUpqRg1dyac/XyUzqu9IsJg5+aKcz/s0LgNTTSWV6KpshpRs6cpic9mVpYIGD8GSWry/uXbqKgCp7FZq/hMFGzWFegiPpchfOok0E1NScdweYQR0TyDm8rVRPqJs3hgw7OYvm41SfE5DLX5RaS3T1xvBYwdrSQ+z1j3OIwZdFwc8P3fWstCWVomxi9+SEF89ooIg8+oSPz9yVekn78y87bGPGFjOh2hk+OQfS5B5bFAyOcj+Y8jmLV+LVwC/BTirUzMzRA4bjRuHFZe7FDFxV92I3bBHCzeshGhk+NQfP2m0jl/f3p7e/Hr8/+Bo7cnmiqq0NHQqPZ4VXorHYVJNzDnuXWoup2r8vM4+sG5WPnhO2hnN2DPq28i77Ky05ssUokEl3buw4r3t8icswPiciYseRgWNja4skd9zBdBb28v/nznI2z6az9WfPA2tj+9Qf57jlu0AJNXLcWVPQfkjnEysAqKcODNrXjy20/RWFGlk/Dcn/M/7oBY2IMFrz6P5upaXPh5l9JjQuInIHzqJJz+9medFpN7pVKl6RdtiHt6kPDrb7JSUalUq9lLG92dXbjw8y5c3r0fNk4OMLe2ggWTCRd/H7gHB8HOww1nv/9F5fkCGVqqa5U+32TIv5KEkpRUREyfgjEPPYDJjy1DZ0srkg8cQfa5BLAKinT+7paIRDj97c8ouHodj779Xzz0n5fk93FbWrFrw38VymzJ0tHYJF/UBkDKwFBwNRmnv/kRvjFRYJiZoaOhER0NTYMyIwCaJ3soyDNkmc89fasJxJvXwgAZKRKJBBs2bMD58+dRWFiIw4cPo6CgAO+99x4WLpQVYezatQsODg4oLS3F66+/js2bNw/6eSlkCLidyDh5FrHz56jMKw4YE4OWGpbWFfP2vlVXbeH0lkwmKcco0Fc4qGPshrmNNUwtLEiP7bfW1sHBU3XbukdYMGycHFFAckywLF2W+6xLdpCNowOpfEL5uL+KojNnQnweMOLf2dyqtXCQz+GS+jLa88ob+OWZV+QH+cQ9B2BuY42JSxYpPXbGU4/DxslRwfUMyE682tkN8qiDgdi5ucGYTiclBPYIuvH96vX4Yc3zSqvCvPYOtNSyVEZOuAT4wcreTuOXDTE6qu59AQCW9rakTsoEnV0wI+F8VhW5QSDPfR4bi9DJEyHk80mNT9b2ufC8tcTheIaHoqWWpTDyrwqxUIjavEL49ysdJFrutU1GtNSwIJVI4Oznq/FxdBMTWDvYg6NH7EYrqw7clladSgeDJo5FTV4B6ZNRfl85J5nYjbZ6tsYL5q62dvz98VekhWeCrDMX4OTrrVQw4hsTBQG3U60jqDw9Cx4hQYhbugg0Gk1rlBJZent7UZmVo/C+cAsOgJAvQJuGCJ3Ovpx7Gy0RWc6+ysKLJkTdxPtU9UKglYMdLJg2CmWDA8m/koQDb75LWqirLykHjUaDa4ByNJLvqCjQGQyVC7j7Nr6F71Y9jSPvfYoTX3yP3S+/AUcfT8x94WmU3kxHzoXLkIhESDpwBCHxE1SOoFvaMhE9ZwYyTp0jvb+Goiw1A1KJBMHxE0g93jsyAmaWlprLUonc5wHRG+Y21nDwdNc777k/acdPgc5gYPSDc5Xuo5uY4KnvP8OCV55HSy0L+za9jZq8Aix68zWlY/mc55/Cqo/eQXlGFimHPEGPoBtV2bmksnJNzM3h6OM1qN+79GYa6CYmGhej+2PtYI8FrzyPstQMtLLqsHDjy7D3dDdY3jNBS00tbJwctZ4zBY4bjebqWrX5yIYm99JVOHh6wC1Y2fxgZm0F10B/rcJxXXEJ3IICVBa3aYvtICAWJkImTVS6L3rOdEhEYrWReWTJvXQVgePGyPsACEInx4HOYJAS20pupiJowliNOdkeYSGQSqVapwj7U19cCpqxMVwD/Ej/jHtoECRiManpR30R9/Qg+eARhE2OU3s+S2BmZQlnPx/5+RgZuM0taKqsVuj6AGSTkpNWLEHm6fMqF7ZT/zoJJx8vhVK7yY8tQzePp3U6rT9V2TmwdXGGnZvqPpyAcaNhZmWJPDXREQBwbd8hiLq7MXtALnHAuDFgmJqSjl9pqWEh8/R5jJo7ExKRCIe2al8Qbq1lofj6TbSzG7Q+9vin30DQ2YUNe7fjgQ3PKmRFT1q5BKs/fQ+Vmbfx9bK1gxKeCTJOnkVzdS3Wfv0xXju0G5NWLUXMvFmYtGoppj/5GMrSMjU6lPvTzm7AP599i8Bxo7Hm648RNiUePqMiseSdN1ByM01tebUm8hOT8fXyJ/HDmudIRQuq49LOvUg9fgrzXlyP2AWK37MRM6Zg3fefgV1ajut/aJ/4MxRCHn/QwnN/xEIh2lj1qCssQenNNCQfPIrD//sEvzzzit7C82ARdQuRfS4BuzZswltxs/Hh3MU4+dU21OYXDmrRuDIrB18teQJvT5qHX559FUff/xxfPPKYXsKzKshOzhHXGyUpqWiqrB608ExhOIZMfD58+DC2b98OW1tbrF+/HgkJCdi5c+egt3v27FmEhIQgMDAQH3/8MQDg3XffxcmTMpeDUCjE8uXLERQUhAkTJqCyslLT5ih0JOtcAhhmpvIiMwIjIyP4jY4hVajQ1ldKZ+/upvYxZlaWMGbQtWYME/A5XJhZWaotrVAF08UZANDRQFJ8ZtXDQY1gHjZ1EqRSKSmXKQCUp2XBmE5XOzKsCmsnkuIzMe6vorHe0ccbEpFYaeyK09wCuomJWgFfnetLFQO/tGrzC1GWmoGpT6xQKDgImxKPuS88jczT51U6QlpqWHBQc7Ie0OeyryF5kt7R2KTWNV6TW6AkXgB3yvHKNFxAi3t6wOvgaIxWsLKz05hxTCDo7IS5jRbx2dkRXA3u94bScvA6OAgcPwahk+NQditD6xgWIHP+SERieEVoE5/Ju+oqMrLhFR4GhpkpACBowhiwS8u1nqRKRCK0suq1lg4SLlh9xGdA5n4mKz6bWVnCOzIcpTfVC2EDkUokWt8bgKwAVJ+xTDLkJCRC3NOjJJz5xkShKidP7QlmeXoWaMbGmPHUE6gtKNJJ0NVGZdZtOPv5yEV5t6AANJSWazzZFfL56ObxtDqfnfy80TRgqkMbFRnZfQWwZsrb68uY1XWbmiAEf1XlWP5jYyGVSNSO8PenMOkGfljzHHIuXsHRD++MMaYc+RtCPh/zX3kOMQ/MxvjFCzF+8UJEz5mB2c+tA93EBCkaYiGGCgG3EzV5BQghWTgWNHEspFIpylLVH3+J45b3gNJBYvy+3gCORnZJOVgFxRi76EGF22l0Y6z58kOExE/Aobc/wC/PvILb5y/h6PufwcrOFg+++gIA2Xfn45+9hwdeegZp/5zGzhde15hbroqSvkJPbQtZ7iFBoNFoSg5IXajMug2xSCR3Mmtj4aaXwTAzxdEPPseZb3+Ge0gQlr0rM31oeu10hZjWctRSOugS4KdxisLQ5CcmQSwSYcrqFUr3EWWY6soGCeqLSsEwM5VPpilsY1QkWmpZWqc52urYYBUUY+rjK+TfuQRRs2egLC1DryiW/uQmJMKYQUf4tEkKt0fOnIrO1jZUk5g0KElJhaUtEx6hqp3egMz5LBMLyIs/xGddl9xnj9BgNFZU6TR6rQ/pJ2TlvJEzpmrenzAi1oy8+AwA5RlZ8B8ToyDoz3z6CdDoxgodJ/3JOnsRtQVFWLxlI8xtbGBlb4eYB2Yh/cRZpQlFTfTPfVZF5MypEPIFGhcR+Rwurv9xFDEPzFb4DIRPjZcZKEgW9QGy7Oemymr8ufUjUtEhutBSw8KXSx5HxqlzmPPcOmw5cxSvHdqNVw/uwqNvbUL+lSTseHGjTn8/TUjEYny/ej2Of/oNaMbGeHTLRjzx5Yd4dMtGmFtb4fxPumkqqcdP4fKufQgcPxrrf/oKr+zfgc6WNuz/7zsa85Y1wS4p0/n7TBVH3/sM5elZWPnBW3jwtRcQM28W4pYvxtqvP0Z9cRl+WveiwSZWKJQRdQsNNqVEIOByUZKSipQjf5PWDyjuD4ZMfP7qq69w9OhRHDt2DCEhIdi6dSu2bVM9lkZx71CZeRudrW2InjVN4XbXIH9Y2jJJ5UR2trRCJBRqFJ8tiII7krEbgr4Dmy7uZ1tXQnzWXjgIyNySNs5OoJuYKN0XNiUetbkFpFd/q7JzIO7pQehkZZeKOphOTuA2k4jdIMRnO2XRy8nHC62sOqUTDW6/Qi9VWNgyB/XlcWXPfti6uuDJbz+VlwSs/uw91BeV4vD/PlH5My21LDiqcRTr0lCujZqcfNi6usBmQGZ4wLjRaGc3qC2YJOB3cFS6zAFZyYKZlSXpwkEzS0uN7dbanM9E7nPU7Olw8HRHIcnFELFQiIayCo1FoOY2NnDw9ACrgNxIaEVmNowZdPjGROPhN15FcNx4tcUpA2mqrNYqPtsSi0d6ZD4DQHV2Lhy9PVUu0gwkcPwY0IyNSY2994fX3qHd+ezrRbrEUFcE3E6UpKQhcuadi14zayu4BPhpzLmsyc2HWCSCibmZxiJAfajMkAmrwXHjMeHRhfCKDEN9qfYyHm5TC5gaculNLSxg6+Kss1Benp4FYwYdPtGRSvfJJ0WqDCe+t9XVQ8jnq3RKBoyNRV1RCemLV3ZJOfa+vkVh8ULA7UTK4eMInzoJT3zxAVa8vwUr3t+CtV9/jKmPr0B5RtaQOv00UXIjFV6RYUrOSVUETRwLVkGRRsFMLBSivqRUaXKFEHHqigfvfAaAtH9OwSs8VP6a0YyNsfqT/yFixhQc/eBzBadgXWEJkg4cRvyKRzH3hafx5olDiJ4zE2e+345Db3+otjROE6V9hYtBfaVu6rgTI6D/7004rYP6yh41ETRxHMY89AAu796P5qoaZJ+/hOqcfARPHIeutnaFEfrB0tzn3nTyVp/7bMxgwNHbU14QPRx0tbYj+eARjHvkQaXPtE90BKRSqdb4F+L18lCR++wbE0W67PKfL76DvYcbZq1fK78tbOokOPl4ITdBt/FzVdTmF6KjoRHRs++c/xszGAibEo/8K0mkynuJ8lBNTn6P8BDU6ZD3DMiOq908nsq/odrnCQ0eVEQNWToaGlFXVILwGZo7j4jJM7LnWATlaVkwt7aS/+5m1laYuGQRMk6dU1vKLZVIcHjrx7BkMrHojVcwYcnDoJuY6OwuZZeWo5vHg2+MavE5YvpklKSkao1CubrvEMTCHoX3buiUOJTeTCNloCBoY9Xjs4dXGsR5rAohj49Db3+I317bjJrcAnS2tqGbx8OVPQfw2+v/RzryhSx8DhdJBw7j62Vr8enCFfh80Sq8O20B3o6fq7UQWBWnv/0Z/5v2IHa+tAnXDx3Drpf/q1dchqGRiMX47bXNqLqdh2lrHsMTX36Ipe+8gYqMbPzyzCuUeElB8S9iyDKfASAhIQEJCbJxAhqNhsceewwHD6pvZaW4++mVSpF35Rpi588B3cRE7hgIkOc9a3e69Pb2or2+AXYe6sVnS6ZMzON1kPvCIb48LWxsSOd9Evm6ZJ2Traw60Gg02Lm7KmQmW9nbwSsyTKdVaFG3EHmXr2Hsw/Nx5ruftY5Am1lbgWFmqpPz2drJQek+Zz8fpbxnQLHQS5UwYclkgjMIF0FR8k2c+voHzHrmSWz6az/4HC7EPT3Y8+qban/3lhoWzG2sYWnLVDg5MqLREDxxnMrmbH0g3Do+UeEK+WD+Y2JQfF17ISSPo158JoRHMvEx3Z1doBkbw8TCXKX4ZGRkJBOfmzW/DuVpmfJiC7JOfACoyS/AqLkz1d7vGS4TdMheGFVl50IqleKJLz6ApS0TV38/RPoz0lRZjZD48TCi0dRe0BKTC/o6nyuzZeKr76horUV5QRPHQcjnkxYBCLra2jVmPlvZ28HCxkblZ9JQ5CcmIXzaJHmeok90JGg0mjz3WhWibiFqcwvgExOF7HOq8471pbZAVkDy+GfvAZCNS5MpUOS2tKo8phE4+fYVN+ooFFdl50AqkcB/bKySO8vJxxsioRDtbHILlGTo7e0Fu7RcqRyLbmIC76hwlZnaunLmu5+Refo8REKh3D1oZm0Fc2srjREiQ03xjVTMfeFpBE0Yo5Rh2R9TCwv4Rkfh6j7t54s1uQUYs/ABhWOFe0gQOE3NpM8FtJF15iIWbnoF4xY9iFPf/IjVn76HmHmzcOKL75GiIo/03A87MGruTMx7cT2qb+fh8HufDmqhtDa/CAJuJ4LjxqvNHwdkjtHO1rZBO/5Kb6Vj3ovrNU48Rc+diZUfvI3mqhpc2nknN/bkV9uwYe92g+Y9A3cy8R1VuIMJnHy8YEyno7FseCceE37di/GPPISFG1/Gr8+9Jr/dZ1QUGsoqtC4mNVVVQ8gXwDcmGpmnL8hvt3N3hY2TI+nvnYr0LKSfPIsZ61Yj/cQZmNvY4IkvPgCroBgZp9Rnp+tCTkIi4pY+AhNzc/QIBAgkYhVIin1dre2oLy5FcNw4pbI7QDaxZ+virFPZINB3XC0ph1sIudJBKwc7MJ2dDBLNQ4b8xGTMfmatxs+UZ0QYWll1OgttA3Ofxy16ECbmZkg+cETjz9UXl+Lynt8x59l16O7iofjGLZ0Xb6USCapv56nMffYIC4atqwvO/fCr1u10tbXjxuG/MGX1cjDMTMFpaoa9uxsSfv1Np/0ZLnIvXdU5T3iwGOo8USIWo/DadXkfy90Cn8PFz0+9BGMGA85+PrB1cUbprfQhn0ygoKAYXgzufLa2tsbmzZuxbds2zJkzBwDw0ksvoaKiAsuXLzf001GMALkXE2FmaangXPAfE4u2erY8A1cb7fVseTmPKiztCPGZnJOYL3c+qxYBVWHr4gyJWEzKTQwAbbUyB6yDl6IbN3RyHGg0ms5f5NcPHYOFjQ1i5ytnSQ7ExlEmumiKXCAgnNx2rooZbEZGRrJiDVXic9/fQF3usznTetCr41f2HMDH85fg2u9/QtzTg32b3tboOicuNgdGb3hFhsGCaUNKGCZDfVEpxCIRvPtFb7j4+8LawZ5UuQC/g6v2fUeI0mQKBwV9ZY7maopZLWyZoDMY4GhwPgN3FoAaK6pIfx4BoDa3ABY2NmpzCe+Iz+QuCrs7u1BfVAozS0scef8znPj8O9Kjfa2sOlmms6N6sdHWReZU5+jpfGYVFEPc04O5LzyFRW++hhnrVsPawV7lY4MnjkN5epbOrsUuLc5nJ58+wbR66MTngquy41LEdFnrs29MFKQSidaswCt79uP8jzsMProqyyX+E1lnL+KHtc/jq6VrSAlz3OYWMJ2c1N5POOV1vXgW8vhgFRYr5FATOPl5o6WGRcrRpwvsknKl2A3v6AgwTE31cjMNRCIWo66oBE2V1X1FK41oKC1HZebtEXU51eTlQ9DZheB4zdEbgeNHw5hBRxGJY3x1Tj7MLC3h0q8M2CMs2KCiEq+Dg4LEZIx+cB7WfPkRYubNwj9ffIer+/5Q+fgegQC7X34DB7e8j21rnhv0hE6vVIqytEwETtDifDaQk7P0ZhpoNBoCVTitjWg0zH/leaz96iOwS8rw47oXFdx+lZm3ceS9T5V6HAaLWChEO7tBfsxUBZH322BAxzUZBFwuLmzfjZD4CfJpNiMjI/hER2gtGwRkGZZlqRlKk3DE359MQSfBqa9+gEjYg1UfbcX6H79EV1sbdr74usHyLrP7ovdWffQOaMbGiJgxBUI+X2OswkBKUtLgFxutFA8CAB6hupcNEtQXl8I9mJzzmXie+mESnwsSk0EzNkbYlHi1j/GKCNWpbJCA29SM5qoaBIwbDSMjI0xa8SiqsnNJHQMvbt+DhvJKmFlZkloEVkVVVg5cA/2Vcu4jpk+BVCIh3YVzeec+5Fy8As/wEExZvRwioRCFSeQNFBT/DiQiEdglZShMukEJzxQU/0IMLj7//vvvCAkJQW5uLtavX48LFy5g2bJlWLRoER555BFDPx3FCFCWmgEBtxNRfaN3Tr7eCJ08kVSTM0FbfQOp2A2yF8p8PWM3uM0tpIUFYnxtYLlc2NR4cJqadb7oq8jIBru0HJNWLtH6WCIOg4wQ1CPoRldbO+zcFcVnW1cXMExNVQpd3BYidkO1uGNpyyQdgaIJPoeLk19twwdzHtEqsrTW9jmdBor98RMglUp1jkBQh7inB/VFpQq5oQEk8p4JeB0cWNiqft8Reb/kCgdlJX7qcp9t+lyf2tzvDaUVaGXV4bYGh5wqavpyBr0ilcsXAVnZYCurXidXzoHN7+KbletwU8eM2XYSufBMF2cIuJ1658BJRCJc3XcIdBMTjFv0IB56fQPmPP+U0uNsXV3g7OejU94zAa+9Q2PmM5Ep3Fw1NLEbgOz9UpNXgIi+kV/fmCjUF5dpFSPyE5OHzHV0+tufsf+NraSyjQm4TS1qY4EAwNnPFxKxGC0kSkgHUpGRDZ/oCKVIJScf7yGJRGGXlMHSlqkQ9RMwNhZSqZRUQei9CiGyhcRpLh0MmSQrS9UUDUNARBoQx2+6iQlc/HwNkvfcn9Tjp2HtYI+oWdPw18df4dq+QxofX1dUgoyTZw22cFGZeRsOnh5qF8iMGQy4BPoZRHSvzStEdxdPZfTG5FVLMfuZtUg5ehw/PfWSyi6Fm0f/GRJHaXN1rcbYDZcAP0glkiGdJFHHjUPH0Fxdi4UbX4b/mBgEx0+AuY01addyYdINOHh6KGTeRs2ajnZ2g07liZ2tbTj3wy/yPoNfn3sNna1tuv0yGqi+nYfjn36D6DkzsOrjrYicMRVFyTd1EolKUlJBNzFRWfTqKY/M0f3zW19cCnNrK43GFgIiokKf59EHVkERuM0tCJ+uOnrDgimLNdM175mgLD0T/mNiEDJpApx8vXH9EDkhWSISYd/Gt3D2h19RmEROJB5IZVYOaDSavByTIHLGVFRl55KOI+R1cLD/ja34ZMEybJkwEx/MecTgi98UFBQUFCOLwcVnf39/rFu3Dr/++itWrVqFsWPH4qGHHsLt2//eC6r7DYlY1podOWMqzKytsPbrjyHqFpIarSJoq6uHlb0dTMzNVd5PxG6QLhzk6iE+u7jo5JrsbG1Dj6BboXSQRjdGSPwEFCXfJL2d/lz/4xg8w0PgM0o5b7Q/RNEWGeczICt1HNg+TVzUqBJTRN1C8LlcucDZH2MGA6YWFsOeudVWx4ZUKoWjl6ITN2TSRNTmFRp0f2py8+EVESovbCGb9wzIcsnVxm4QiygkCge7tTifiYUBTZnPgGz89LOHV+HCz7u0Pmd/Gssr0SPoVpv77BkeonMWYVNltbxgTRcIx/bABZT+MJ2d0KFn5AbBme9+xueLVuHt+DnIvXRVqUgJkLmeAei12NHV3g4Lpo3aHG8nXy+IRSK0s8k71PUhPzEZ3lERsHF2gk90BKpuq4/cuFvhNrfAxNwMZlaWKu938vVGWx1bp3xIgor0LDBMTRXe+zRjYzh4eQyJkFXf95lw75cRGzAmFuySMgi4nQZ/vruJ4hu3YO/hBkcNDtbQyRNReiud1KRBS3Ut+FwuvKNli2augX4wZtANLn4WX7+J7POXcOjtD3TORTUExGfWZ5TqklTvqHDQGQzU5mmeaCCDVCJBeVomgiYolw4Gx49HQ3kljr73mV6ftcHQUl2rspSPwCXAD621dSPilpOIxTj51TY4+/vipd9+xrPbvwEA0uIzEZFFOGNNLSwQEj9er6zmG3/+jfM/7sAvz74qnyAzJEkHDuPUNz9i9IK5YLo4ke5yIKjIzIa4pwfBccqLGx5hwWipYcnPh3SBEOnJlA56hAajlVWn1/PoQ29vL/KvJiN00kSF4m0Couy5lmSJ9kCI3OdH3vwPOlvbcPuC+lijgTSWVyLhlz16L5RV5+RDIhYrFKjbubnCIyxY5/cGgahbSFq0pqCgoKC4dzC4+CzqdzIqlUpRWVmJrq7h+XKnGD5yLyXCgmmDF3f/CJcAP+x/812dhNy2OpmzUZ24ZGHLhFQikUcRaEMf5zPTxYl02SBBK6tOQXz2jgiHubWV3o6BjFPnIOjs0up+tnHsE59JZD4DMvFu4N9WLj6rGUtX5ywk/qbDPa4t7ulBR0OjQuyGuY01vKPCUXzDMJEbBNU5eTC1sMD4Rx6EEY2GgLGxKE8jN/7O6+DA1MICxgyG0n1W9rLIhS5ShYN9zmdr1c5nomyNoyXzGZC5WXTN25RKJLJSnCnxMLdRFMDNbazh6OVJOnJjsBBirDbns76RG6ooSEyGnZur0oVrcNw4cJtb9Cpp47V1wJhOV/p7Ejj6yGId9G0aJ0v+lSTQaDTMevoJmFpYkHKU3m1oK0V19vPROXKDoCIzB1KpVN5dAAB27m6gMxhDEonC7othIKI3jOl0+IyKJFXYe69TckO2iBMSr9r97OjtCQdPD9KxSr29vajJKUDkjKnwjo6ARyhRumdYR6NUIsHvm95WKBccToiYIMLROpDQyXGQiMQ6xR9oovRWOpx8vBTOI4yMjOAbE4XKrJExkzRX18KCaaP2PM8lwG/YIzf6k38lCZ88uAzbn3kFB/7vf9i78S3Sx6T2+gY0lFfKozfCpsaDbmKCnATyIiKBVCLBhe27dXJM68qV3ftx5vvtaKlloUDHyDlRtxCVmTkqSwc9w0N0znsmYJeWQyqRkBafh6NssD8FiddhZmWJgHEqHN8RoQCgc9EiAfHd4eTrjVvHTgzrwlCPQID64lL4xd7JfSYc3nmJ+onPFBQUFBT/TgwuPo8aNQocDgccDgdcLhfR0dHyf3MMMLZPcXdQdP0WhHwBPEKDcf7HHfI2drK0EWP1Hu4q77e0ZULA7SS9Ei/s4kEqkegYu+Gis3PE/M/IAAAgAElEQVSylVUH+36xG96jZKO++oo5PQIB0k+cwai5M2HloH48n+niBCGfr7W4hqCd3aDsfPbxQncXT+0IJrelVe6w7g/h3hWMQNtwa02dQgZx0ISxoBkbGyzvmaAw6SbYpeVY/t4WbD75J+m8Z+COKK/K/WxpZwepVErKpS3gyhZazNQ5n/tem84Ww43QDuTiL3tg5+GG53dsU/gsEaOwujqf9UVddEx/bF2d9S4bVEVB0nVIpVJEzJgiv83IyAiBE8aiRMfjGwGx6KAuesPJxwstQ5j3TMAuKUM7uwETl8mirzSVDd6tcPrEZ6azcjSQEY0GJ28vvV3KAi4XDaXl8rgdAHCWL9YZ/vXp7uxCWz1bXjroFRkOE3Mzg+Q93+20surQUstCiArhCZBNtgBA0XXy00SnvvkBPYJubPhtO6auWYXuLh7aWLrHr9zNSEQi1OYXqRWfw6bEoTI7B91dPIM8HzHpQbwegEzctbCxQVXWyBw/iKktVe5nYzodTt5eaBxB8RkA2lj1KL2ZhsxT55Fz4bJOP1uUlIKAsbEwMTdH9JwZ4La03tXH6ks79uKTBcv0mtYouZkK9+BAhRgZcxtrOHh66C3AirqFaKlhaRWfTS0s4OTrPWxlgwSlt9LQI+hG+DTl6A2fqAg0VVbr/fklcp+lEglSdIw5MwSVWTnwjgwH3VSW4x05YwoaK6rQMgSxVRQUFBQU9y4GF5/pdDqYTCaYTCZsbGzAYDDk/2bqUAZHcXcjFgpx49AxpJ88q1exTHsdIT6rdjZa2jJ1ctr29vZCwO0kLT6b29jAxNwMHQ26is/1Cs5nn6gItNWzB5Wpd/3QMdCMjbH60/dgYm6m8jFugQFoLK8ivc32+gaYmJspiF7Ovt4aXXzqnM+EiM0Zgey1llqWQsZ2SPwECLid8pxPQyHgcvHV0jXY/8ZWiEUiiEUi0g4yIhrGQpX4bMuEgMMltYiizfls4+QIXgdnSEeKi6/fxJ5X3oBLgC9e2P2j/P1zp2xweMRnQPUCCoGphQWYzk5ordMei0KWrtZ21OYWKERvuIcGwdrBXq+8ZwDg9RVNqhKfjWg0OHp7Dmnec3/yE5NlhZWNzUMe8zEUEM5naxXRQHZuLmCYmaKpskrv7ZelZsIvJlo+wXAnpmhoFgeI0kEHTw88+tZGiHt6UJGRPSTPdbdRlJSC4PjxKss4QydNRHN1LanIIwJ2STm+Xr4W+YlJcA3wQ31xqc6TH/cCVdm58IoIVZqysXF2gkdoMIr0nMBSRWNFFVpZ9QjvV5BGjNXrktVuSJoqqgDciSjoj6OPF4wZ9BF1Pg+WouQU0E1MED41HqGT45B36arBy07vFojFjaB+0RvE1MJgJqzqi0vhHhKo8THE/YaejtCGqFuI0ptpiJgxBTRjY/ntHqHBCJsar/cEJcGVPftxYftunSc6DUHhtRswMTfD64d/Q/i0yQgYOxr5V64N+35QUFBQUNzdGFx8prh/OPXNj/hjy/t6XeR1trZB1C1UO1ZvYcvUOdOXz+GSFp9tXZ0BQOeTtDZWHUwtLOQuZa/IcL0LQgiaq2rw59aPEThuNJ75+RuYWlooPcY1yF8+qk0GQlzqL945+nhrdAZym5tVis+uQf4AALYesQODpbWWBWsHe5hZWYJmbIyQSRNQeit9SGIKeqVSZJ29iC8ffRwfzFlEWqCTO59VvPes7O1IL6IQjhd1EQ1MZ0fSsSuDoSj5Jna9tAmOXp7Y9Nd+jFk4H54RYWirYw9r7ndbHVvt4pRLoB8AoEGHzwQZ8hOT4R0ZLv8cPPDSsxDy+fI8Tl3papM5n1WJbHZu6gtAhwIie/FezHsGgM5mWd49U8UxysnXBwDQNIh85tJb6WCYmcpLk5x8vcHr4AxZ3BC7pAzOfj74z597YOfmit2vvDns0UYjxfVDx8AwNUX8ikcVbjdmMBAwbjSKdXA9E3R3dmHv61vw+6a3ceLLbYba1buKquxc0E1M5IuBBKF97uTCJP2OU+oovHYdQRPHyd2MfrHR4La0ysuXh5tWVh1YBcUYt+hBpftcA2XnKSPtfB4MFZm30c3jYcFrL8DUwhw5CYkjvUtDRl1hCXjtHQieeGcCwn9MjOy+QTiS64pK4eDpofY8CgDc5dE8w+t8BoDU46dg7+6GBa88D0A2XfXo25vA6+Dg4i97BrXtW3+dxMXtuw2xmzpTkpKKHS/8B3QTEzz9wxcwZtCRfyV5RPaFgoKCguLuhRKfKUaMtnq22rF6SyZT57IJHoejg/jsAgA6j+231srcWA6eHrCyt4ODpztqcgbvwk0/cQb733wXPtGReH7HNgUHtJWDHawd7HUSnwdmatNNTGDn7qpRfOY0tYDOYCjFR7gFBaCjoXHYiln6Q5TlOHh54MH/vAg7N1dknDo/pM/ZK5Wiq1V7QSABvy9OSJXAaGnLRBeJskFAltPYzeNpcD47DVvzd+mtdGx74lm0surw2MdbETNv1rC6ngHNzme3wKFZEMm/KrtYCpsaj4gZUxA+bRLO/7iT9Gs4EHnshp2y89nJR30B6FBQnp4FVkExbus4Cn63IOTz0d3Fkxdv9sfZr0981jPzGQAqMrIglUgQ2Fey5uTjNaQLA3XFpTCm09FSy8LXy9fqJbjeqzRVVqPg2nVMWrkEdBMT+e3+o0fB1MJc7wJfAMg+f8kgpXt3I9V9C0e+A0oHw6bEoZ3doFcuvSYKrl6HibkZAsfL4mj8YqNHPC8+9e+T8AwPgUdYsMLtLv6+kEokg1qAGmkkIhFKb6bDwdMDvA4OytPJRX/di/T29qL0Vrq8dNDUwgKTVy1FYdKNQRXNlaXKppRGzZ2p9jEeocHobG0btvOp/uRdvobrh45hxlOPI2rWNIxf/BB8R0Xh5Jfb7vmy2aLkm/hi8WO4vGsfci9dRbWBJxQpKCgoKO59KPGZYsRoq2fD3lNN5rMdEzwdM8J1cj67EM5n3TOfAcDB0x3eUbK8Z0OdYN0+fwl/vPU+vKPCET71zui/W5BsRFAXl+dA57OjtydoNJpGoUte6DUg99ktKEAn4duQtNTKxOdZ69di+trHkHTgMPIu697+PpTwOvrKLlVmPtuSKhsk6O7iwVxD5jNnGJzPBPXFpfjhiefw5zsfoZ3dgIKrw+tiaa9vAMPMVGUWumtQAIR8vjy+x1A0lJajrY6NmAdm45E3/wN2aTmSDh7We3vERbSlvfLChJOvFwDonVOsKxKRCN+seBK3z18alucbCrjNLbBREbvh7OcjcykPQrTo7uKhNq8QQePHyLbp6zOkr01uQiL2vLoZP6x5Hu31914MymC5uvcPWDvYY/SD8+S3hUyaCHFPz79adBsMna1taKllKeQ+G9PpCI4bPyjBXh3l6VkQ8vkInzoJNk6OcPD0QOUIi8+ZZy5AJBRiwqMPK9zuGuiPVlY9xELhCO2ZYSCmbPITkyAVD20R7UhTkpIKprMTXAL8MGnVElja2eL8T7sGtc2a3ALUl5TJ+w1U4TMqEvUj4Hom+Ofz71Cdk4+VH76DB//zEsrTs5Bx6tyI7Y8hEXULcfrbn/Hba5v/tZExFBQUFBT6Q4nPFCNGex1bZeyGEY0GK3s7nd2GusZuSERinbOaCUexvacHvKPCIRGL9S5HUcXti5fRI+iGTz9nk1tQAADoJAB3d3ZB0Nkljy1w8pEJXU1V6p2B3L6x9v7OQhrdGM7+viMmPrfWysT+UXNnojwjCye+/H5E9kMTfHnshmrxmddBXhATcDthZmWpdLsRjQZrB3twm4ZPfAZk7qTU46fw4dzFSPvnzLA+d3tfKamdm/Ixwi0wAA1llUOS61pwNRnBE8fB3sMNf3305aAEAIlIBAG3E1YqXPEeYSHgdXAGlRd/vyETn5VjN5x9NUcKkaU0NQPeURGwdrAH08VpSPO4e6VS5F2+OqQZ7nczZakZqCsqwbQ1KwHInKuj5s1EReZt9Ai6R3jv7l6qsnMVxGffmCiYWVkOOi9WFeKeHpSkpCF82iT4jR4FACMuPgu4nchNSMToBXPlcSCA7P3TWD780WCGJv9KElpqWbh17ORI78qQU5IiK/KNmjUN09c+hoJr1w0ytXDzyHF4hYfCMzxU6T7fmGi4Bvgh59LImRgkIhH2bXwLEpEIZpaW+OujL0dsXygoKCgoKIYTSnymGDHa6uphactUyji2dXEG3cREHrlAFj6HCwsb8rEbnOZmnVfmxT096Ghs6nM+h6OhrMKgF8pSsQQ1eQUKF5duQQHobG3TWYzvH1tAlGe1VKv/m3KbZSOI/Z2FTt5eoDMYIyY+9wi60dHYhI6GRuzb+NZd6QQS9/RAyBfAwlbxvWdkZCQrzmzTwfnc2aXS+Wxpx4QxnT4smc93C219blBV0TyuQf4GHzEnyE+UObzT/jljkAK4rvYOleKzX+woVGaNTHHXvQq3uUVpMsPIyAhuwYFo7CsjGwxlt9JhzKBj/KMLAWherKMYPFf3/gHXQH888cUHeP3oPphZWuLyzn0jvVt3NVVZubBxcpQvLIdNiZcV5OpZiqqNgsRk2Lm5In75YvQIulFXZLjFdn259ddJmNtYI3r2NAAy97eTjzcadChlvlvpbG3DJwuWoSp7ZEX+4aCd3YDmqhrMeW4dLO1sceFnw+QVZ5w6ByFfgDgV7udJq5ZA0NmFzCGOb9NGR0MjfnrqJezasGnIzmUoKCgoKCjuNijxmWLEuCMuKTobHftcui06ZqEKOFyY21grtEirg+niBI6OkRsEraw6OHp5wjsyHDW5hs+WrMrOhUdoMBhmMlePrmWDBO31DXLhzsnXG5ymZgj5fLWPv+N8viPuyF3XJSMjPgPA3te34Md1L+qUwzzc8Do6lLKyzaytYUyn61QiJujsgpmNcuYzs8+Nzhlm5/NIQkTHDJyOsLLXPQNdF0pvpuHo+5/jn8+/Ncj2eO0dsLRXjA6xdrCHk48XKjMo8VkXmqpqYO/hrvBZ8wgLhqUtE2VpGYPefmV2LkRCISYuXQRg+PK471eyzyWA09iMmAdmI/tsAj57eCVKbw2NiPpvoeq2TJQMHD8WIZMmInruTFSkZ6FHIBiS5yMc1YHjx6AmN/+uWAAuT8tEK6sO4xfLFokcvT1hzKCjseLeLRu8Xym5mQa6iQkKrhrG9QzIIpSyzyUgdsEcBXOLlYMdoufMQNrx00P2edGFhrIKFN+4NdK7QUFBQUFBMWxQ4jPFiEFEWDh4DBCfvTwB3Mn7JQufK8veVVfY1h9bVxd06Fg2SNDGqod3dATMbawNUjY4kKqsHBgz6PCKDIcRjQbXAH3FZ/Yd57OP9rF0cU8PeO0d8r8/ALgGB0AiFg+qyGuw1OTko41VP2LPTwZ+BxcWA2I3LO1k/+9qJy+aCzo7Vb5/bZxl4jO35f4Rn7s7uyDgdio5n4kFkaFyC/X29iLlyN8GK//pam9Xcj77jYkBAFRkDt5ZfT9RlHwTNBoNIZMmyG8LjhsPACjtG+EeDGKhEFXZubB3d4NUKtV5+oZCNyRiMXa8+Dq2Pf4s/njrfb2LPe8nGsoq0d3Fw4r3t+DZ7d/A2sEeyQePDNnzdba2yRfZK+8SN25vby9S/z6FoAljsWHvdizeshEA0FhGic/3GrmXrkIsEuH8TzsNut2UI8dhamGB0QvuZMpPXLIIdAYD1/88ZtDnoqCgoKCgoCAHJT5TjBjyTNeBzmdvT4i6hTrn2/I5feIzidxnWxdnncsGCVpZ9aAzGAAMVzbYn+qcPACyRnsHT3eYmJuhQQ/ncXt9A8xtrGFmbQUnHy9SLr7S1AyETY2HEU12aHALCkBzdS0kIpHOz38/wedwlJzPVnYyt6susRsCNbEbLv6+AIDW+0wMa6tXzoV3DfQHALBLy0Zil3SG19YBywHis//oURDyBWAZMC/+foCVX4jO1jaETYmX3xYcNx71xaUGy84uS5U5qNvrG+758rJ7AXZJGapu5470btwz9EqlOLvtFyT/cRQ7XvgP3pnygDwqaKgouHYdgGxh/G4h6cBhJB04DKlUCmc/H7TVsdE4govkFPpRejMN70yaC1ZBkUG3W5tXgLrCEkx9YgWcfL1BMzZG3LJHUHzjls5TlRQUFBQUFBSGgT7SO0Bx/9LV1g4hXyDPLiRw9PZASy1L5zIxQny2tGVqPLm0tGWCYWaKjoZG3XcaQGufI7ubxxsSRzCfw0VjRRV8Y6LQ3Jc5Wl+iu9DW1hdb4BEaDCt7O1KFXLkXryBm3iz4xUajIiMbbkEBqM0r1Pm57zd4HRzYuroo3EY4n3UqHFTjfPYMC0ZbPVunCI9/A+3sBjh4eijc5hYUgK629rs6hqU/Xe2qxOcY1OTcHSPs9xK9vb0oSr6J8GmTYESjgWFqAr/Ro5C0/7DBnqP0Vjrmv/wcWqoHX2BIQTEUDKXTWRU3jxyHubUVylIzh/V5NSHk8XH8029GejcoDMBQFYye+3EHHv/8fbxx/CAqMm/D1tUFf3381ZA8FwUFBQUFBYV2KOczxYjSXs9W4Xz20mvcmdchE5+1lQ4SIiFHz9iN1r4IiNq8Qp0LC8lCNNq7hwRBKpXqlWXY3pepTYylN5EQnwuTUiASChE1ezpMzM3h4OkxYmWD9xJ8DldJYCSiFrp0KRzkdoFmbAxTC8USTo+wENQVlgx+R+8x2uvvlGYSuAbqF0MzUnS1t4POYMCsb1HBzMoSbiGBqMjIGuE9uzcpTLoBS1smfKIi4D8mBnQGAyUphsvNrM0vBK+9A3XFpQbbJgXFvUxnaxtOfPE9xD09I70rFBSkKbiajI/nL8G1/X/CJyoCraw6FFy9PtK7RUFBQUFBcd9COZ8pRpS2ejYcPNzl/zcyMoKDlwcKk1J03hbhfLbQErth6+oMAPrHbtTWAcCQlA0SVGXlYMKjCxE1ezpaa1gQdes+/k3EmgTHjQMANJNw8gn5fBTfuIXo2dORdfYiANxTQt9IwevgwNz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      "text/plain": [
       "<Figure size 1440x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_price.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pearson Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "sns.heatmap(corr,xticklabels=corr.columns.values,yticklabels=corr.columns.values,annot=True,annot_kws={'size':12})\n",
    "heat_map=plt.gcf()\n",
    "heat_map.set_size_inches(10,6)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "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>new_case_count</th>\n",
       "      <th>new_death_count</th>\n",
       "      <th>new_test_count</th>\n",
       "      <th>new_case_rate</th>\n",
       "      <th>new_death_rate</th>\n",
       "      <th>new_test_rate</th>\n",
       "      <th>case_count</th>\n",
       "      <th>case_rate</th>\n",
       "      <th>death_count</th>\n",
       "      <th>death_rate</th>\n",
       "      <th>test_count</th>\n",
       "      <th>test_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            new_case_count  new_death_count  new_test_count  new_case_rate  \\\n",
       "date                                                                         \n",
       "2020-01-22             0.0              0.0             0.0            0.0   \n",
       "2020-01-23             0.0              0.0             0.0            0.0   \n",
       "2020-01-24             0.0              0.0             0.0            0.0   \n",
       "2020-01-25             0.0              0.0             0.0            0.0   \n",
       "2020-01-26             0.0              0.0             0.0            0.0   \n",
       "\n",
       "            new_death_rate  new_test_rate  case_count  case_rate  death_count  \\\n",
       "date                                                                            \n",
       "2020-01-22             0.0            0.0         0.0        0.0          0.0   \n",
       "2020-01-23             0.0            0.0         0.0        0.0          0.0   \n",
       "2020-01-24             0.0            0.0         0.0        0.0          0.0   \n",
       "2020-01-25             0.0            0.0         0.0        0.0          0.0   \n",
       "2020-01-26             0.0            0.0         0.0        0.0          0.0   \n",
       "\n",
       "            death_rate  test_count  test_rate  \n",
       "date                                           \n",
       "2020-01-22         0.0         0.0        0.0  \n",
       "2020-01-23         0.0         0.0        0.0  \n",
       "2020-01-24         0.0         0.0        0.0  \n",
       "2020-01-25         0.0         0.0        0.0  \n",
       "2020-01-26         0.0         0.0        0.0  "
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyc_covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2020-01-22</th>\n",
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       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.008563</td>\n",
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       "      <th>2020-01-24</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.051180</td>\n",
       "      <td>0.005792</td>\n",
       "      <td>0.169553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.047678</td>\n",
       "      <td>0.004907</td>\n",
       "      <td>0.167256</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>0.049551</td>\n",
       "      <td>0.004381</td>\n",
       "      <td>0.167420</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>0.051812</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.164447</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>0.073788</td>\n",
       "      <td>0.003801</td>\n",
       "      <td>0.173571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>0.100018</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.186176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>0.127532</td>\n",
       "      <td>0.003508</td>\n",
       "      <td>0.203466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-23</th>\n",
       "      <td>0.113125</td>\n",
       "      <td>0.820328</td>\n",
       "      <td>0.040681</td>\n",
       "      <td>0.181921</td>\n",
       "      <td>0.040252</td>\n",
       "      <td>0.866505</td>\n",
       "      <td>0.182249</td>\n",
       "      <td>0.039874</td>\n",
       "      <td>0.865979</td>\n",
       "      <td>0.943460</td>\n",
       "      <td>0.943475</td>\n",
       "      <td>0.981264</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.959071</td>\n",
       "      <td>0.959067</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-24</th>\n",
       "      <td>0.111647</td>\n",
       "      <td>0.841201</td>\n",
       "      <td>0.037871</td>\n",
       "      <td>0.611040</td>\n",
       "      <td>0.071698</td>\n",
       "      <td>0.863019</td>\n",
       "      <td>0.611834</td>\n",
       "      <td>0.071354</td>\n",
       "      <td>0.863168</td>\n",
       "      <td>0.948609</td>\n",
       "      <td>0.948659</td>\n",
       "      <td>0.983128</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.963550</td>\n",
       "      <td>0.963549</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-25</th>\n",
       "      <td>0.109417</td>\n",
       "      <td>0.856615</td>\n",
       "      <td>0.034964</td>\n",
       "      <td>0.666525</td>\n",
       "      <td>0.070440</td>\n",
       "      <td>0.862187</td>\n",
       "      <td>0.666272</td>\n",
       "      <td>0.070094</td>\n",
       "      <td>0.862231</td>\n",
       "      <td>0.954227</td>\n",
       "      <td>0.954242</td>\n",
       "      <td>0.984991</td>\n",
       "      <td>0.986111</td>\n",
       "      <td>0.968025</td>\n",
       "      <td>0.968025</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-26</th>\n",
       "      <td>0.112108</td>\n",
       "      <td>0.876279</td>\n",
       "      <td>0.033134</td>\n",
       "      <td>0.718178</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.879348</td>\n",
       "      <td>0.718343</td>\n",
       "      <td>0.067261</td>\n",
       "      <td>0.879100</td>\n",
       "      <td>0.960279</td>\n",
       "      <td>0.960323</td>\n",
       "      <td>0.986755</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>0.972589</td>\n",
       "      <td>0.972590</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-27</th>\n",
       "      <td>0.115710</td>\n",
       "      <td>0.895770</td>\n",
       "      <td>0.035196</td>\n",
       "      <td>0.750390</td>\n",
       "      <td>0.069182</td>\n",
       "      <td>0.901198</td>\n",
       "      <td>0.750296</td>\n",
       "      <td>0.069255</td>\n",
       "      <td>0.900656</td>\n",
       "      <td>0.966603</td>\n",
       "      <td>0.966604</td>\n",
       "      <td>0.988585</td>\n",
       "      <td>0.988889</td>\n",
       "      <td>0.977267</td>\n",
       "      <td>0.977266</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>0.120984</td>\n",
       "      <td>0.911978</td>\n",
       "      <td>0.038323</td>\n",
       "      <td>0.846034</td>\n",
       "      <td>0.083019</td>\n",
       "      <td>0.904571</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.082581</td>\n",
       "      <td>0.904405</td>\n",
       "      <td>0.973731</td>\n",
       "      <td>0.973781</td>\n",
       "      <td>0.990782</td>\n",
       "      <td>0.991667</td>\n",
       "      <td>0.981962</td>\n",
       "      <td>0.981962</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-29</th>\n",
       "      <td>0.129576</td>\n",
       "      <td>0.932714</td>\n",
       "      <td>0.043641</td>\n",
       "      <td>0.949766</td>\n",
       "      <td>0.101887</td>\n",
       "      <td>0.899050</td>\n",
       "      <td>0.950296</td>\n",
       "      <td>0.101574</td>\n",
       "      <td>0.898782</td>\n",
       "      <td>0.981735</td>\n",
       "      <td>0.981757</td>\n",
       "      <td>0.993477</td>\n",
       "      <td>0.994444</td>\n",
       "      <td>0.986629</td>\n",
       "      <td>0.986628</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-30</th>\n",
       "      <td>0.139651</td>\n",
       "      <td>0.956041</td>\n",
       "      <td>0.050627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101887</td>\n",
       "      <td>0.881563</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101994</td>\n",
       "      <td>0.880975</td>\n",
       "      <td>0.990161</td>\n",
       "      <td>0.990131</td>\n",
       "      <td>0.996173</td>\n",
       "      <td>0.997222</td>\n",
       "      <td>0.991204</td>\n",
       "      <td>0.991202</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>0.145912</td>\n",
       "      <td>0.978159</td>\n",
       "      <td>0.057075</td>\n",
       "      <td>0.582801</td>\n",
       "      <td>0.072956</td>\n",
       "      <td>0.860264</td>\n",
       "      <td>0.583432</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>0.860356</td>\n",
       "      <td>0.995072</td>\n",
       "      <td>0.995115</td>\n",
       "      <td>0.998070</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995669</td>\n",
       "      <td>0.995669</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>0.150360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062985</td>\n",
       "      <td>0.584930</td>\n",
       "      <td>0.072956</td>\n",
       "      <td>0.834366</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.072613</td>\n",
       "      <td>0.834114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>436 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean     count    median  new_case_count  new_death_count  \\\n",
       "date                                                                        \n",
       "2020-01-22  0.000000  0.004907  0.210890        0.000000         0.000000   \n",
       "2020-01-23  0.008563  0.004907  0.176442        0.000000         0.000000   \n",
       "2020-01-24  0.041442  0.006382  0.164960        0.000000         0.000000   \n",
       "2020-01-25  0.051180  0.005792  0.169553        0.000000         0.000000   \n",
       "2020-01-26  0.047678  0.004907  0.167256        0.000000         0.000000   \n",
       "2020-01-27  0.049551  0.004381  0.167420        0.000000         0.000000   \n",
       "2020-01-28  0.051812  0.004038  0.164447        0.000000         0.000000   \n",
       "2020-01-29  0.073788  0.003801  0.173571        0.000000         0.000000   \n",
       "2020-01-30  0.100018  0.003673  0.186176        0.000000         0.000000   \n",
       "2020-01-31  0.127532  0.003508  0.203466        0.000000         0.000000   \n",
       "...              ...       ...       ...             ...              ...   \n",
       "2021-03-23  0.113125  0.820328  0.040681        0.181921         0.040252   \n",
       "2021-03-24  0.111647  0.841201  0.037871        0.611040         0.071698   \n",
       "2021-03-25  0.109417  0.856615  0.034964        0.666525         0.070440   \n",
       "2021-03-26  0.112108  0.876279  0.033134        0.718178         0.066667   \n",
       "2021-03-27  0.115710  0.895770  0.035196        0.750390         0.069182   \n",
       "2021-03-28  0.120984  0.911978  0.038323        0.846034         0.083019   \n",
       "2021-03-29  0.129576  0.932714  0.043641        0.949766         0.101887   \n",
       "2021-03-30  0.139651  0.956041  0.050627        1.000000         0.101887   \n",
       "2021-03-31  0.145912  0.978159  0.057075        0.582801         0.072956   \n",
       "2021-04-01  0.150360  1.000000  0.062985        0.584930         0.072956   \n",
       "\n",
       "            new_test_count  new_case_rate  new_death_rate  new_test_rate  \\\n",
       "date                                                                       \n",
       "2020-01-22        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-23        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-24        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-25        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-26        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-27        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-28        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-29        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-30        0.000000       0.000000        0.000000       0.000000   \n",
       "2020-01-31        0.000000       0.000000        0.000000       0.000000   \n",
       "...                    ...            ...             ...            ...   \n",
       "2021-03-23        0.866505       0.182249        0.039874       0.865979   \n",
       "2021-03-24        0.863019       0.611834        0.071354       0.863168   \n",
       "2021-03-25        0.862187       0.666272        0.070094       0.862231   \n",
       "2021-03-26        0.879348       0.718343        0.067261       0.879100   \n",
       "2021-03-27        0.901198       0.750296        0.069255       0.900656   \n",
       "2021-03-28        0.904571       0.846154        0.082581       0.904405   \n",
       "2021-03-29        0.899050       0.950296        0.101574       0.898782   \n",
       "2021-03-30        0.881563       1.000000        0.101994       0.880975   \n",
       "2021-03-31        0.860264       0.583432        0.072508       0.860356   \n",
       "2021-04-01        0.834366       0.584615        0.072613       0.834114   \n",
       "\n",
       "            case_count  case_rate  death_count  death_rate  test_count  \\\n",
       "date                                                                     \n",
       "2020-01-22    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-23    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-24    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-25    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-26    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-27    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-28    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-29    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-30    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "2020-01-31    0.000000   0.000000     0.000000    0.000000    0.000000   \n",
       "...                ...        ...          ...         ...         ...   \n",
       "2021-03-23    0.943460   0.943475     0.981264    0.983333    0.959071   \n",
       "2021-03-24    0.948609   0.948659     0.983128    0.983333    0.963550   \n",
       "2021-03-25    0.954227   0.954242     0.984991    0.986111    0.968025   \n",
       "2021-03-26    0.960279   0.960323     0.986755    0.988889    0.972589   \n",
       "2021-03-27    0.966603   0.966604     0.988585    0.988889    0.977267   \n",
       "2021-03-28    0.973731   0.973781     0.990782    0.991667    0.981962   \n",
       "2021-03-29    0.981735   0.981757     0.993477    0.994444    0.986629   \n",
       "2021-03-30    0.990161   0.990131     0.996173    0.997222    0.991204   \n",
       "2021-03-31    0.995072   0.995115     0.998070    1.000000    0.995669   \n",
       "2021-04-01    1.000000   1.000000     1.000000    1.000000    1.000000   \n",
       "\n",
       "            test_rate  \n",
       "date                   \n",
       "2020-01-22   0.000000  \n",
       "2020-01-23   0.000000  \n",
       "2020-01-24   0.000000  \n",
       "2020-01-25   0.000000  \n",
       "2020-01-26   0.000000  \n",
       "2020-01-27   0.000000  \n",
       "2020-01-28   0.000000  \n",
       "2020-01-29   0.000000  \n",
       "2020-01-30   0.000000  \n",
       "2020-01-31   0.000000  \n",
       "...               ...  \n",
       "2021-03-23   0.959067  \n",
       "2021-03-24   0.963549  \n",
       "2021-03-25   0.968025  \n",
       "2021-03-26   0.972590  \n",
       "2021-03-27   0.977266  \n",
       "2021-03-28   0.981962  \n",
       "2021-03-29   0.986628  \n",
       "2021-03-30   0.991202  \n",
       "2021-03-31   0.995669  \n",
       "2021-04-01   1.000000  \n",
       "\n",
       "[436 rows x 15 columns]"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf=pd.merge(smoothed_df, nyc_covid_df, on='date')\n",
    "cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf_diff=cdf.diff().diff().dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Optimation - Lag Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tools.eval_measures import rmse, aic\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag Order = 1\n",
      "AIC :  -26.030772180141888\n",
      "BIC :  -25.971345226508802\n",
      "FPE :  4.954268269804052e-12\n",
      "HQIC:  -26.0072475686244 \n",
      "\n",
      "Lag Order = 2\n",
      "AIC :  -26.29471249677942\n",
      "BIC :  -26.195483301693894\n",
      "FPE :  3.804981639061132e-12\n",
      "HQIC:  -26.25542801966559 \n",
      "\n",
      "Lag Order = 3\n",
      "AIC :  -26.310838423072937\n",
      "BIC :  -26.171658499588695\n",
      "FPE :  3.744131698377709e-12\n",
      "HQIC:  -26.255732195905104 \n",
      "\n",
      "Lag Order = 4\n",
      "AIC :  -26.36829379123361\n",
      "BIC :  -26.189013727164987\n",
      "FPE :  3.535102884366687e-12\n",
      "HQIC:  -26.297303538835536 \n",
      "\n",
      "Lag Order = 5\n",
      "AIC :  -26.386365766157493\n",
      "BIC :  -26.166835216066552\n",
      "FPE :  3.4718343712481663e-12\n",
      "HQIC:  -26.299428819255752 \n",
      "\n",
      "Lag Order = 6\n",
      "AIC :  -26.389037384036826\n",
      "BIC :  -26.12910506113172\n",
      "FPE :  3.462635059563203e-12\n",
      "HQIC:  -26.286090675845152 \n",
      "\n",
      "Lag Order = 7\n",
      "AIC :  -26.454299575442352\n",
      "BIC :  -26.153813243385738\n",
      "FPE :  3.2439540748535237e-12\n",
      "HQIC:  -26.33527963820847 \n",
      "\n",
      "Lag Order = 8\n",
      "AIC :  -26.576555470084664\n",
      "BIC :  -26.23536193471095\n",
      "FPE :  2.8707417713479137e-12\n",
      "HQIC:  -26.44139843159914 \n",
      "\n",
      "Lag Order = 9\n",
      "AIC :  -27.012106541998197\n",
      "BIC :  -26.630051642938422\n",
      "FPE :  1.857185273229239e-12\n",
      "HQIC:  -26.86074812206476 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = VAR(train_diff) #recall that rawData is w/o difference operation\n",
    "for i in range(1,10):#[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]:\n",
    "    result = model.fit(i)\n",
    "    try:\n",
    "        print('Lag Order =', i)\n",
    "        print('AIC : ', result.aic)\n",
    "        print('BIC : ', result.bic)\n",
    "        print('FPE : ', result.fpe)\n",
    "        print('HQIC: ', result.hqic, '\\n')\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Mon, 03, May, 2021\n",
       "Time:                     22:41:48\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         15.0000    BIC:                   -228.706\n",
       "Nobs:                     425.000    HQIC:                  -240.472\n",
       "Log likelihood:           45727.3    FPE:               2.39394e-108\n",
       "AIC:                     -248.156    Det(Omega_mle):    3.71967e-110\n",
       "--------------------------------------------------------------------\n",
       "Results for equation mean\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000057         0.000628            0.091           0.928\n",
       "L1.mean                    0.249731         0.092473            2.701           0.007\n",
       "L1.count                  -0.419710         0.922509           -0.455           0.649\n",
       "L1.median                 -0.068193         0.076602           -0.890           0.373\n",
       "L1.new_case_count         -7.593400         9.041403           -0.840           0.401\n",
       "L1.new_death_count        -1.310384         1.634108           -0.802           0.423\n",
       "L1.new_test_count        -33.063901       114.443026           -0.289           0.773\n",
       "L1.new_case_rate           0.070804         2.562305            0.028           0.978\n",
       "L1.new_death_rate          1.266025         1.938718            0.653           0.514\n",
       "L1.new_test_rate           0.970996         2.387596            0.407           0.684\n",
       "L1.case_count            905.470151      1081.456012            0.837           0.402\n",
       "L1.case_rate             -14.787426        22.780977           -0.649           0.516\n",
       "L1.death_count             0.233631        46.562741            0.005           0.996\n",
       "L1.death_rate             -0.227837         0.804378           -0.283           0.777\n",
       "L1.test_count           5665.398044     22050.478301            0.257           0.797\n",
       "L1.test_rate             517.587464       474.766278            1.090           0.276\n",
       "L2.mean                   -0.094265         0.071139           -1.325           0.185\n",
       "L2.count                   0.714934         0.874390            0.818           0.414\n",
       "L2.median                  0.061899         0.055138            1.123           0.262\n",
       "L2.new_case_count        -18.802648        19.015616           -0.989           0.323\n",
       "L2.new_death_count        -3.857468         3.172793           -1.216           0.224\n",
       "L2.new_test_count        -30.647810       242.266398           -0.127           0.899\n",
       "L2.new_case_rate          -2.180389         4.980993           -0.438           0.662\n",
       "L2.new_death_rate          3.270666         3.806004            0.859           0.390\n",
       "L2.new_test_rate           2.682468         4.629794            0.579           0.562\n",
       "L2.case_count           1621.335147      1435.250088            1.130           0.259\n",
       "L2.case_rate             -23.502313        41.550097           -0.566           0.572\n",
       "L2.death_count            22.168007        64.492278            0.344           0.731\n",
       "L2.death_rate             -1.150704         1.300747           -0.885           0.376\n",
       "L2.test_count          -1385.003905     28857.239708           -0.048           0.962\n",
       "L2.test_rate             586.636136       899.912763            0.652           0.514\n",
       "L3.mean                    0.027850         0.071776            0.388           0.698\n",
       "L3.count                  -0.133446         0.761714           -0.175           0.861\n",
       "L3.median                 -0.030256         0.055092           -0.549           0.583\n",
       "L3.new_case_count        -26.856628        27.114719           -0.990           0.322\n",
       "L3.new_death_count        -4.774360         4.399700           -1.085           0.278\n",
       "L3.new_test_count        -49.203131       350.258194           -0.140           0.888\n",
       "L3.new_case_rate          -3.429760         6.802132           -0.504           0.614\n",
       "L3.new_death_rate          2.057925         5.180720            0.397           0.691\n",
       "L3.new_test_rate           3.664825         6.269790            0.585           0.559\n",
       "L3.case_count           1121.649871      1401.536487            0.800           0.424\n",
       "L3.case_rate             -13.901197        57.106706           -0.243           0.808\n",
       "L3.death_count            82.296020        65.125870            1.264           0.206\n",
       "L3.death_rate             -2.408211         1.660778           -1.450           0.147\n",
       "L3.test_count           2795.921201     28160.795593            0.099           0.921\n",
       "L3.test_rate             586.216926      1233.794196            0.475           0.635\n",
       "L4.mean                   -0.021135         0.071005           -0.298           0.766\n",
       "L4.count                   1.226808         0.780117            1.573           0.116\n",
       "L4.median                  0.023472         0.055196            0.425           0.671\n",
       "L4.new_case_count        -34.347885        31.698307           -1.084           0.279\n",
       "L4.new_death_count        -7.269784         5.165089           -1.407           0.159\n",
       "L4.new_test_count       -160.954390       412.013700           -0.391           0.696\n",
       "L4.new_case_rate          -5.157447         7.940071           -0.650           0.516\n",
       "L4.new_death_rate          3.957496         5.996605            0.660           0.509\n",
       "L4.new_test_rate           3.343654         7.241420            0.462           0.644\n",
       "L4.case_count           1095.951339      1272.531680            0.861           0.389\n",
       "L4.case_rate               1.943220        67.886352            0.029           0.977\n",
       "L4.death_count            25.424784        57.345319            0.443           0.658\n",
       "L4.death_rate             -2.624498         1.897599           -1.383           0.167\n",
       "L4.test_count          20656.602469     25094.704974            0.823           0.410\n",
       "L4.test_rate             930.250981      1458.421894            0.638           0.524\n",
       "L5.mean                   -0.032519         0.070498           -0.461           0.645\n",
       "L5.count                   0.113943         0.779730            0.146           0.884\n",
       "L5.median                 -0.000254         0.054475           -0.005           0.996\n",
       "L5.new_case_count        -21.164954        32.017325           -0.661           0.509\n",
       "L5.new_death_count        -8.763027         5.457528           -1.606           0.108\n",
       "L5.new_test_count       -212.597837       416.611997           -0.510           0.610\n",
       "L5.new_case_rate          -6.753180         8.196338           -0.824           0.410\n",
       "L5.new_death_rate          5.003814         6.186850            0.809           0.419\n",
       "L5.new_test_rate          -0.063667         7.599357           -0.008           0.993\n",
       "L5.case_count          -1366.371562      1221.819353           -1.118           0.263\n",
       "L5.case_rate              -6.166918        71.857426           -0.086           0.932\n",
       "L5.death_count            20.812947        50.991996            0.408           0.683\n",
       "L5.death_rate             -2.985659         1.963319           -1.521           0.128\n",
       "L5.test_count           9733.862943     23067.791048            0.422           0.673\n",
       "L5.test_rate             876.227410      1520.297194            0.576           0.564\n",
       "L6.mean                   -0.024017         0.070048           -0.343           0.732\n",
       "L6.count                  -0.143468         0.793832           -0.181           0.857\n",
       "L6.median                  0.032774         0.053580            0.612           0.541\n",
       "L6.new_case_count         -0.889410        27.805701           -0.032           0.974\n",
       "L6.new_death_count        -7.934338         5.198835           -1.526           0.127\n",
       "L6.new_test_count       -151.786492       363.976615           -0.417           0.677\n",
       "L6.new_case_rate          -8.020763         7.804914           -1.028           0.304\n",
       "L6.new_death_rate          4.395688         5.697390            0.772           0.440\n",
       "L6.new_test_rate          -3.234928         7.236941           -0.447           0.655\n",
       "L6.case_count          -2258.650152      1270.469557           -1.778           0.075\n",
       "L6.case_rate               2.234891        66.754425            0.033           0.973\n",
       "L6.death_count            -4.935701        55.651326           -0.089           0.929\n",
       "L6.death_rate             -2.195164         1.924567           -1.141           0.254\n",
       "L6.test_count         -11969.873626     24759.330430           -0.483           0.629\n",
       "L6.test_rate             861.583943      1440.749710            0.598           0.550\n",
       "L7.mean                    0.108583         0.069834            1.555           0.120\n",
       "L7.count                   0.466599         0.802326            0.582           0.561\n",
       "L7.median                 -0.101925         0.053438           -1.907           0.056\n",
       "L7.new_case_count          6.606192        19.790227            0.334           0.739\n",
       "L7.new_death_count        -5.701414         4.432213           -1.286           0.198\n",
       "L7.new_test_count       -113.035062       255.365634           -0.443           0.658\n",
       "L7.new_case_rate          -8.470368         6.572459           -1.289           0.197\n",
       "L7.new_death_rate          3.641218         4.716278            0.772           0.440\n",
       "L7.new_test_rate          -5.303465         6.237304           -0.850           0.395\n",
       "L7.case_count           -845.722460      1438.664452           -0.588           0.557\n",
       "L7.case_rate               5.840016        56.009171            0.104           0.917\n",
       "L7.death_count           -56.047657        63.933382           -0.877           0.381\n",
       "L7.death_rate             -0.700703         1.692228           -0.414           0.679\n",
       "L7.test_count          -8177.120348     28750.437423           -0.284           0.776\n",
       "L7.test_rate            1108.057724      1205.783754            0.919           0.358\n",
       "L8.mean                   -0.602588         0.069923           -8.618           0.000\n",
       "L8.count                  -1.092940         0.930108           -1.175           0.240\n",
       "L8.median                 -0.054463         0.054929           -0.992           0.321\n",
       "L8.new_case_count          9.121641         9.918565            0.920           0.358\n",
       "L8.new_death_count        -5.643777         3.195247           -1.766           0.077\n",
       "L8.new_test_count         12.576114       121.885595            0.103           0.918\n",
       "L8.new_case_rate          -4.328804         4.699580           -0.921           0.357\n",
       "L8.new_death_rate          4.455026         3.305213            1.348           0.178\n",
       "L8.new_test_rate          -4.769814         4.595978           -1.038           0.299\n",
       "L8.case_count           -775.556927      1458.123060           -0.532           0.595\n",
       "L8.case_rate             -19.202600        41.395103           -0.464           0.643\n",
       "L8.death_count           -32.998135        65.218932           -0.506           0.613\n",
       "L8.death_rate              0.014685         1.330432            0.011           0.991\n",
       "L8.test_count         -25119.201107     30405.404603           -0.826           0.409\n",
       "L8.test_rate             806.652022       887.673315            0.909           0.363\n",
       "L9.mean                    0.151487         0.088796            1.706           0.088\n",
       "L9.count                  -0.556253         1.011267           -0.550           0.582\n",
       "L9.median                 -0.024588         0.074552           -0.330           0.742\n",
       "L9.new_case_count          2.316287         2.429955            0.953           0.340\n",
       "L9.new_death_count        -3.008522         1.639640           -1.835           0.067\n",
       "L9.new_test_count          1.864163         2.387849            0.781           0.435\n",
       "L9.new_case_rate          -2.347829         2.433725           -0.965           0.335\n",
       "L9.new_death_rate          3.078842         1.642711            1.874           0.061\n",
       "L9.new_test_rate          -1.902897         2.386337           -0.797           0.425\n",
       "L9.case_count            580.967701      1099.720524            0.528           0.597\n",
       "L9.case_rate              -4.881046        23.234438           -0.210           0.834\n",
       "L9.death_count           -47.112194        49.013984           -0.961           0.336\n",
       "L9.death_rate             -0.192614         0.827246           -0.233           0.816\n",
       "L9.test_count           1116.767038     23434.667032            0.048           0.962\n",
       "L9.test_rate             393.309419       467.004859            0.842           0.400\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000038         0.000040            0.953           0.340\n",
       "L1.mean                    0.009352         0.005845            1.600           0.110\n",
       "L1.count                   0.430753         0.058305            7.388           0.000\n",
       "L1.median                 -0.007707         0.004841           -1.592           0.111\n",
       "L1.new_case_count         -0.739024         0.571443           -1.293           0.196\n",
       "L1.new_death_count        -0.051679         0.103280           -0.500           0.617\n",
       "L1.new_test_count        -14.108217         7.233133           -1.950           0.051\n",
       "L1.new_case_rate          -0.043196         0.161945           -0.267           0.790\n",
       "L1.new_death_rate          0.027616         0.122533            0.225           0.822\n",
       "L1.new_test_rate           0.219017         0.150903            1.451           0.147\n",
       "L1.case_count             89.657742        68.351174            1.312           0.190\n",
       "L1.case_rate               2.292096         1.439824            1.592           0.111\n",
       "L1.death_count             0.923028         2.942901            0.314           0.754\n",
       "L1.death_rate             -0.023730         0.050839           -0.467           0.641\n",
       "L1.test_count           2643.592582      1393.654541            1.897           0.058\n",
       "L1.test_rate              32.192622        30.006613            1.073           0.283\n",
       "L2.mean                   -0.002180         0.004496           -0.485           0.628\n",
       "L2.count                   0.013203         0.055264            0.239           0.811\n",
       "L2.median                 -0.001674         0.003485           -0.480           0.631\n",
       "L2.new_case_count         -1.876228         1.201842           -1.561           0.118\n",
       "L2.new_death_count        -0.189185         0.200530           -0.943           0.345\n",
       "L2.new_test_count        -23.146691        15.311943           -1.512           0.131\n",
       "L2.new_case_rate           0.098548         0.314813            0.313           0.754\n",
       "L2.new_death_rate          0.065688         0.240551            0.273           0.785\n",
       "L2.new_test_rate           0.375704         0.292616            1.284           0.199\n",
       "L2.case_count            113.338560        90.711992            1.249           0.212\n",
       "L2.case_rate               5.593102         2.626087            2.130           0.033\n",
       "L2.death_count             3.897176         4.076100            0.956           0.339\n",
       "L2.death_rate             -0.011944         0.082211           -0.145           0.884\n",
       "L2.test_count           1710.379350      1823.861715            0.938           0.348\n",
       "L2.test_rate               0.630014        56.877111            0.011           0.991\n",
       "L3.mean                   -0.003725         0.004536           -0.821           0.412\n",
       "L3.count                   0.153988         0.048143            3.199           0.001\n",
       "L3.median                  0.004659         0.003482            1.338           0.181\n",
       "L3.new_case_count         -2.522675         1.713729           -1.472           0.141\n",
       "L3.new_death_count        -0.267113         0.278074           -0.961           0.337\n",
       "L3.new_test_count        -48.741649        22.137339           -2.202           0.028\n",
       "L3.new_case_rate           0.172027         0.429915            0.400           0.689\n",
       "L3.new_death_rate          0.009573         0.327437            0.029           0.977\n",
       "L3.new_test_rate           0.222049         0.396269            0.560           0.575\n",
       "L3.case_count             63.118546        88.581194            0.713           0.476\n",
       "L3.case_rate               5.579721         3.609310            1.546           0.122\n",
       "L3.death_count             4.938882         4.116145            1.200           0.230\n",
       "L3.death_rate             -0.022015         0.104966           -0.210           0.834\n",
       "L3.test_count           4965.993954      1779.844415            2.790           0.005\n",
       "L3.test_rate              -4.743658        77.979392           -0.061           0.951\n",
       "L4.mean                    0.001496         0.004488            0.333           0.739\n",
       "L4.count                  -0.084855         0.049306           -1.721           0.085\n",
       "L4.median                 -0.000524         0.003489           -0.150           0.881\n",
       "L4.new_case_count         -2.188807         2.003425           -1.093           0.275\n",
       "L4.new_death_count        -0.379578         0.326449           -1.163           0.245\n",
       "L4.new_test_count        -69.647152        26.040468           -2.675           0.007\n",
       "L4.new_case_rate           0.178426         0.501836            0.356           0.722\n",
       "L4.new_death_rate          0.024948         0.379003            0.066           0.948\n",
       "L4.new_test_rate           0.113438         0.457679            0.248           0.804\n",
       "L4.case_count            -45.525207        80.427714           -0.566           0.571\n",
       "L4.case_rate               4.935744         4.290615            1.150           0.250\n",
       "L4.death_count             3.572536         3.624391            0.986           0.324\n",
       "L4.death_rate             -0.052401         0.119934           -0.437           0.662\n",
       "L4.test_count           3992.319764      1586.058546            2.517           0.012\n",
       "L4.test_rate              56.343291        92.176517            0.611           0.541\n",
       "L5.mean                   -0.002471         0.004456           -0.555           0.579\n",
       "L5.count                  -0.091335         0.049281           -1.853           0.064\n",
       "L5.median                  0.000371         0.003443            0.108           0.914\n",
       "L5.new_case_count         -1.522521         2.023588           -0.752           0.452\n",
       "L5.new_death_count        -0.550401         0.344932           -1.596           0.111\n",
       "L5.new_test_count        -63.475145        26.331093           -2.411           0.016\n",
       "L5.new_case_rate           0.085439         0.518032            0.165           0.869\n",
       "L5.new_death_rate          0.138254         0.391027            0.354           0.724\n",
       "L5.new_test_rate          -0.096307         0.480302           -0.201           0.841\n",
       "L5.case_count            -71.105967        77.222547           -0.921           0.357\n",
       "L5.case_rate               3.652501         4.541599            0.804           0.421\n",
       "L5.death_count             2.308497         3.222843            0.716           0.474\n",
       "L5.death_rate             -0.098354         0.124087           -0.793           0.428\n",
       "L5.test_count          -1190.925043      1457.951674           -0.817           0.414\n",
       "L5.test_rate              41.888385        96.087217            0.436           0.663\n",
       "L6.mean                   -0.003236         0.004427           -0.731           0.465\n",
       "L6.count                  -0.015111         0.050173           -0.301           0.763\n",
       "L6.median                  0.000933         0.003386            0.275           0.783\n",
       "L6.new_case_count         -0.996139         1.757401           -0.567           0.571\n",
       "L6.new_death_count        -0.470080         0.328582           -1.431           0.153\n",
       "L6.new_test_count        -50.530535        23.004384           -2.197           0.028\n",
       "L6.new_case_rate          -0.248088         0.493293           -0.503           0.615\n",
       "L6.new_death_rate          0.055682         0.360092            0.155           0.877\n",
       "L6.new_test_rate          -0.127384         0.457396           -0.278           0.781\n",
       "L6.case_count            -22.834551        80.297381           -0.284           0.776\n",
       "L6.case_rate               0.562499         4.219074            0.133           0.894\n",
       "L6.death_count             0.193147         3.517326            0.055           0.956\n",
       "L6.death_rate             -0.085064         0.121638           -0.699           0.484\n",
       "L6.test_count          -2515.660369      1564.861896           -1.608           0.108\n",
       "L6.test_rate              26.015366        91.059584            0.286           0.775\n",
       "L7.mean                    0.007238         0.004414            1.640           0.101\n",
       "L7.count                   0.505698         0.050709            9.972           0.000\n",
       "L7.median                 -0.004284         0.003377           -1.268           0.205\n",
       "L7.new_case_count         -0.782054         1.250800           -0.625           0.532\n",
       "L7.new_death_count        -0.341847         0.280129           -1.220           0.222\n",
       "L7.new_test_count        -28.588978        16.139853           -1.771           0.077\n",
       "L7.new_case_rate          -0.151822         0.415399           -0.365           0.715\n",
       "L7.new_death_rate          0.035852         0.298083            0.120           0.904\n",
       "L7.new_test_rate           0.157079         0.394216            0.398           0.690\n",
       "L7.case_count            -37.821043        90.927789           -0.416           0.677\n",
       "L7.case_rate              -0.120943         3.539943           -0.034           0.973\n",
       "L7.death_count            -4.048797         4.040776           -1.002           0.316\n",
       "L7.death_rate             -0.093949         0.106954           -0.878           0.380\n",
       "L7.test_count          -4315.629031      1817.111499           -2.375           0.018\n",
       "L7.test_rate              35.483106        76.209050            0.466           0.642\n",
       "L8.mean                    0.001489         0.004419            0.337           0.736\n",
       "L8.count                  -0.647716         0.058786          -11.018           0.000\n",
       "L8.median                 -0.000084         0.003472           -0.024           0.981\n",
       "L8.new_case_count         -0.641668         0.626882           -1.024           0.306\n",
       "L8.new_death_count        -0.158052         0.201949           -0.783           0.434\n",
       "L8.new_test_count         -6.704249         7.703525           -0.870           0.384\n",
       "L8.new_case_rate           0.109891         0.297027            0.370           0.711\n",
       "L8.new_death_rate          0.100024         0.208899            0.479           0.632\n",
       "L8.new_test_rate           0.086348         0.290479            0.297           0.766\n",
       "L8.case_count            -47.178899        92.157630           -0.512           0.609\n",
       "L8.case_rate              -1.525647         2.616291           -0.583           0.560\n",
       "L8.death_count            -9.077736         4.122027           -2.202           0.028\n",
       "L8.death_rate             -0.113973         0.084087           -1.355           0.175\n",
       "L8.test_count          -4220.422364      1921.710252           -2.196           0.028\n",
       "L8.test_rate              16.214661        56.103543            0.289           0.773\n",
       "L9.mean                    0.008455         0.005612            1.506           0.132\n",
       "L9.count                   0.048044         0.063915            0.752           0.452\n",
       "L9.median                 -0.006695         0.004712           -1.421           0.155\n",
       "L9.new_case_count         -0.036941         0.153580           -0.241           0.810\n",
       "L9.new_death_count        -0.046077         0.103630           -0.445           0.657\n",
       "L9.new_test_count          0.044989         0.150919            0.298           0.766\n",
       "L9.new_case_rate           0.034647         0.153818            0.225           0.822\n",
       "L9.new_death_rate          0.050834         0.103824            0.490           0.624\n",
       "L9.new_test_rate          -0.047397         0.150823           -0.314           0.753\n",
       "L9.case_count            -62.018863        69.505545           -0.892           0.372\n",
       "L9.case_rate              -0.254158         1.468484           -0.173           0.863\n",
       "L9.death_count            -2.206205         3.097827           -0.712           0.476\n",
       "L9.death_rate             -0.037875         0.052284           -0.724           0.469\n",
       "L9.test_count          -1281.245197      1481.139306           -0.865           0.387\n",
       "L9.test_rate               6.696468        29.516069            0.227           0.821\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation median\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000197         0.000762            0.259           0.796\n",
       "L1.mean                    0.037471         0.112270            0.334           0.739\n",
       "L1.count                  -0.051139         1.120005           -0.046           0.964\n",
       "L1.median                 -0.033404         0.093001           -0.359           0.719\n",
       "L1.new_case_count          0.291244        10.977041            0.027           0.979\n",
       "L1.new_death_count        -2.157435         1.983947           -1.087           0.277\n",
       "L1.new_test_count       -126.533932       138.943676           -0.911           0.362\n",
       "L1.new_case_rate           1.248620         3.110859            0.401           0.688\n",
       "L1.new_death_rate          1.416141         2.353770            0.602           0.547\n",
       "L1.new_test_rate           1.073880         2.898747            0.370           0.711\n",
       "L1.case_count           -187.045422      1312.980612           -0.142           0.887\n",
       "L1.case_rate               3.598803        27.658065            0.130           0.896\n",
       "L1.death_count            25.529710        56.531172            0.452           0.652\n",
       "L1.death_rate             -0.737571         0.976584           -0.755           0.450\n",
       "L1.test_count          23999.594574     26771.177169            0.896           0.370\n",
       "L1.test_rate             163.734687       576.407095            0.284           0.776\n",
       "L2.mean                   -0.130635         0.086368           -1.513           0.130\n",
       "L2.count                   0.193724         1.061585            0.182           0.855\n",
       "L2.median                  0.088236         0.066943            1.318           0.187\n",
       "L2.new_case_count          8.773565        23.086594            0.380           0.704\n",
       "L2.new_death_count        -2.789036         3.852044           -0.724           0.469\n",
       "L2.new_test_count       -393.448371       294.132245           -1.338           0.181\n",
       "L2.new_case_rate          -0.666499         6.047354           -0.110           0.912\n",
       "L2.new_death_rate          1.174186         4.620816            0.254           0.799\n",
       "L2.new_test_rate           3.133249         5.620968            0.557           0.577\n",
       "L2.case_count           -791.854862      1742.517049           -0.454           0.650\n",
       "L2.case_rate              13.470074        50.445392            0.267           0.789\n",
       "L2.death_count            37.971070        78.299173            0.485           0.628\n",
       "L2.death_rate             -2.289758         1.579219           -1.450           0.147\n",
       "L2.test_count          50774.548248     35035.170950            1.449           0.147\n",
       "L2.test_rate             252.266675      1092.571494            0.231           0.817\n",
       "L3.mean                   -0.014446         0.087142           -0.166           0.868\n",
       "L3.count                   0.163232         0.924786            0.177           0.860\n",
       "L3.median                  0.035924         0.066886            0.537           0.591\n",
       "L3.new_case_count         15.969491        32.919601            0.485           0.628\n",
       "L3.new_death_count        -2.886553         5.341614           -0.540           0.589\n",
       "L3.new_test_count       -514.393393       425.243572           -1.210           0.226\n",
       "L3.new_case_rate          -2.903152         8.258374           -0.352           0.725\n",
       "L3.new_death_rate          0.974606         6.289839            0.155           0.877\n",
       "L3.new_test_rate           3.241731         7.612065            0.426           0.670\n",
       "L3.case_count           -611.919474      1701.585838           -0.360           0.719\n",
       "L3.case_rate              24.486077        69.332452            0.353           0.724\n",
       "L3.death_count            13.938906        79.068408            0.176           0.860\n",
       "L3.death_rate             -2.641823         2.016327           -1.310           0.190\n",
       "L3.test_count          22874.266039     34189.627895            0.669           0.503\n",
       "L3.test_rate             394.731366      1497.932269            0.264           0.792\n",
       "L4.mean                   -0.043508         0.086206           -0.505           0.614\n",
       "L4.count                   1.240635         0.947129            1.310           0.190\n",
       "L4.median                  0.083720         0.067012            1.249           0.212\n",
       "L4.new_case_count         27.377117        38.484471            0.711           0.477\n",
       "L4.new_death_count        -4.293674         6.270863           -0.685           0.494\n",
       "L4.new_test_count       -529.603011       500.220068           -1.059           0.290\n",
       "L4.new_case_rate          -6.939951         9.639928           -0.720           0.472\n",
       "L4.new_death_rate          3.454704         7.280395            0.475           0.635\n",
       "L4.new_test_rate           1.063980         8.791707            0.121           0.904\n",
       "L4.case_count           -911.121517      1544.962907           -0.590           0.555\n",
       "L4.case_rate              42.832477        82.419870            0.520           0.603\n",
       "L4.death_count           -36.562731        69.622149           -0.525           0.599\n",
       "L4.death_rate             -2.456959         2.303849           -1.066           0.286\n",
       "L4.test_count           3032.050593     30467.130177            0.100           0.921\n",
       "L4.test_rate             324.651246      1770.649615            0.183           0.855\n",
       "L5.mean                   -0.126413         0.085591           -1.477           0.140\n",
       "L5.count                   0.307986         0.946659            0.325           0.745\n",
       "L5.median                  0.028248         0.066138            0.427           0.669\n",
       "L5.new_case_count         46.001959        38.871786            1.183           0.237\n",
       "L5.new_death_count        -4.467378         6.625909           -0.674           0.500\n",
       "L5.new_test_count       -258.074341       505.802796           -0.510           0.610\n",
       "L5.new_case_rate          -8.983718         9.951059           -0.903           0.367\n",
       "L5.new_death_rate          5.107794         7.511369            0.680           0.496\n",
       "L5.new_test_rate          -2.632694         9.226273           -0.285           0.775\n",
       "L5.case_count          -2007.944969      1483.393780           -1.354           0.176\n",
       "L5.case_rate              42.163930        87.241095            0.483           0.629\n",
       "L5.death_count           -53.777430        61.908669           -0.869           0.385\n",
       "L5.death_rate             -2.229056         2.383638           -0.935           0.350\n",
       "L5.test_count         -51737.282647     28006.282340           -1.847           0.065\n",
       "L5.test_rate             130.537585      1845.771550            0.071           0.944\n",
       "L6.mean                    0.088971         0.085044            1.046           0.295\n",
       "L6.count                  -0.085513         0.963781           -0.089           0.929\n",
       "L6.median                 -0.024661         0.065051           -0.379           0.705\n",
       "L6.new_case_count         53.560391        33.758513            1.587           0.113\n",
       "L6.new_death_count        -3.486974         6.311833           -0.552           0.581\n",
       "L6.new_test_count          5.607956       441.898916            0.013           0.990\n",
       "L6.new_case_rate          -9.419130         9.475837           -0.994           0.320\n",
       "L6.new_death_rate          5.057660         6.917122            0.731           0.465\n",
       "L6.new_test_rate          -5.322328         8.786269           -0.606           0.545\n",
       "L6.case_count           -878.067158      1542.459313           -0.569           0.569\n",
       "L6.case_rate              28.861402        81.045613            0.356           0.722\n",
       "L6.death_count           -32.418651        67.565496           -0.480           0.631\n",
       "L6.death_rate             -1.376689         2.336590           -0.589           0.556\n",
       "L6.test_count         -50797.712922     30059.956635           -1.690           0.091\n",
       "L6.test_rate             513.017788      1749.194064            0.293           0.769\n",
       "L7.mean                    0.170289         0.084785            2.008           0.045\n",
       "L7.count                  -0.246986         0.974093           -0.254           0.800\n",
       "L7.median                 -0.099041         0.064878           -1.527           0.127\n",
       "L7.new_case_count         42.925968        24.027038            1.787           0.074\n",
       "L7.new_death_count        -0.351091         5.381088           -0.065           0.948\n",
       "L7.new_test_count         63.042003       310.035843            0.203           0.839\n",
       "L7.new_case_rate          -8.509816         7.979530           -1.066           0.286\n",
       "L7.new_death_rate          1.819564         5.725967            0.318           0.751\n",
       "L7.new_test_rate          -7.900522         7.572624           -1.043           0.297\n",
       "L7.case_count           1126.206719      1746.662381            0.645           0.519\n",
       "L7.case_rate              26.655216        67.999951            0.392           0.695\n",
       "L7.death_count             2.517625        77.620624            0.032           0.974\n",
       "L7.death_rate             -0.592321         2.054511           -0.288           0.773\n",
       "L7.test_count         -11402.705831     34905.503790           -0.327           0.744\n",
       "L7.test_rate             816.948642      1463.925184            0.558           0.577\n",
       "L8.mean                    0.052055         0.084893            0.613           0.540\n",
       "L8.count                  -0.263891         1.129232           -0.234           0.815\n",
       "L8.median                 -0.744170         0.066689          -11.159           0.000\n",
       "L8.new_case_count         20.413679        12.041991            1.695           0.090\n",
       "L8.new_death_count        -0.625949         3.879304           -0.161           0.872\n",
       "L8.new_test_count         38.279582       147.979595            0.259           0.796\n",
       "L8.new_case_rate          -5.118121         5.705695           -0.897           0.370\n",
       "L8.new_death_rate          0.937518         4.012812            0.234           0.815\n",
       "L8.new_test_rate          -5.712219         5.579912           -1.024           0.306\n",
       "L8.case_count           2249.826160      1770.286805            1.271           0.204\n",
       "L8.case_rate              18.066871        50.257215            0.359           0.719\n",
       "L8.death_count            42.104391        79.181393            0.532           0.595\n",
       "L8.death_rate              0.142752         1.615259            0.088           0.930\n",
       "L8.test_count           3741.791376     36914.776287            0.101           0.919\n",
       "L8.test_rate             608.641560      1077.711752            0.565           0.572\n",
       "L9.mean                   -0.024075         0.107805           -0.223           0.823\n",
       "L9.count                  -0.258254         1.227765           -0.210           0.833\n",
       "L9.median                  0.032550         0.090512            0.360           0.719\n",
       "L9.new_case_count          2.957101         2.950174            1.002           0.316\n",
       "L9.new_death_count        -1.373403         1.990665           -0.690           0.490\n",
       "L9.new_test_count          1.342424         2.899053            0.463           0.643\n",
       "L9.new_case_rate          -3.037648         2.954751           -1.028           0.304\n",
       "L9.new_death_rate          1.466264         1.994392            0.735           0.462\n",
       "L9.new_test_rate          -1.392298         2.897218           -0.481           0.631\n",
       "L9.case_count           1810.768199      1335.155300            1.356           0.175\n",
       "L9.case_rate              15.252646        28.208606            0.541           0.589\n",
       "L9.death_count             6.531852        59.507192            0.110           0.913\n",
       "L9.death_rate              0.103368         1.004348            0.103           0.918\n",
       "L9.test_count           6182.997018     28451.701338            0.217           0.828\n",
       "L9.test_rate              94.675790       566.984065            0.167           0.867\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_case_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000432         0.001313            0.329           0.742\n",
       "L1.mean                   -0.102234         0.193438           -0.529           0.597\n",
       "L1.count                  -5.993913         1.929742           -3.106           0.002\n",
       "L1.median                  0.003620         0.160239            0.023           0.982\n",
       "L1.new_case_count        -10.130437        18.913176           -0.536           0.592\n",
       "L1.new_death_count         0.247496         3.418293            0.072           0.942\n",
       "L1.new_test_count        423.921480       239.396588            1.771           0.077\n",
       "L1.new_case_rate           6.013031         5.359934            1.122           0.262\n",
       "L1.new_death_rate          0.602740         4.055489            0.149           0.882\n",
       "L1.new_test_rate           1.164339         4.994471            0.233           0.816\n",
       "L1.case_count            377.029108      2262.233784            0.167           0.868\n",
       "L1.case_rate              11.180448        47.654176            0.235           0.815\n",
       "L1.death_count           -26.762384        97.401840           -0.275           0.783\n",
       "L1.death_rate              1.951087         1.682631            1.160           0.246\n",
       "L1.test_count         -79456.182620     46126.089644           -1.723           0.085\n",
       "L1.test_rate           -2440.752089       993.135460           -2.458           0.014\n",
       "L2.mean                    0.082064         0.148811            0.551           0.581\n",
       "L2.count                   0.541793         1.829085            0.296           0.767\n",
       "L2.median                  0.019356         0.115341            0.168           0.867\n",
       "L2.new_case_count        -45.333336        39.777642           -1.140           0.254\n",
       "L2.new_death_count         0.416987         6.636978            0.063           0.950\n",
       "L2.new_test_count        388.538188       506.782731            0.767           0.443\n",
       "L2.new_case_rate           5.239342        10.419444            0.503           0.615\n",
       "L2.new_death_rate          2.512124         7.961554            0.316           0.752\n",
       "L2.new_test_rate          -2.015402         9.684791           -0.208           0.835\n",
       "L2.case_count           4198.218807      3002.314658            1.398           0.162\n",
       "L2.case_rate              78.276501        86.916188            0.901           0.368\n",
       "L2.death_count           -77.551015       134.907577           -0.575           0.565\n",
       "L2.death_rate              1.019978         2.720956            0.375           0.708\n",
       "L2.test_count          11950.235390     60364.750708            0.198           0.843\n",
       "L2.test_rate           -4477.120993      1882.474213           -2.378           0.017\n",
       "L3.mean                   -0.103589         0.150143           -0.690           0.490\n",
       "L3.count                   2.234075         1.593383            1.402           0.161\n",
       "L3.median                  0.043659         0.115243            0.379           0.705\n",
       "L3.new_case_count        -95.322100        56.719674           -1.681           0.093\n",
       "L3.new_death_count         7.678343         9.203472            0.834           0.404\n",
       "L3.new_test_count        340.971035       732.684372            0.465           0.642\n",
       "L3.new_case_rate          -2.475644        14.228978           -0.174           0.862\n",
       "L3.new_death_rate         -3.989832        10.837241           -0.368           0.713\n",
       "L3.new_test_rate           1.551732        13.115403            0.118           0.906\n",
       "L3.case_count           6696.931878      2931.791173            2.284           0.022\n",
       "L3.case_rate             165.253919       119.458136            1.383           0.167\n",
       "L3.death_count           -35.386180       136.232951           -0.260           0.795\n",
       "L3.death_rate              0.996042         3.474082            0.287           0.774\n",
       "L3.test_count          15283.776702     58907.900509            0.259           0.795\n",
       "L3.test_rate           -6819.045204      2580.901008           -2.642           0.008\n",
       "L4.mean                    0.068834         0.148531            0.463           0.643\n",
       "L4.count                   3.179562         1.631881            1.948           0.051\n",
       "L4.median                 -0.128531         0.115460           -1.113           0.266\n",
       "L4.new_case_count       -125.312023        66.307811           -1.890           0.059\n",
       "L4.new_death_count        13.357112        10.804544            1.236           0.216\n",
       "L4.new_test_count         39.150708       861.867058            0.045           0.964\n",
       "L4.new_case_rate          -4.833927        16.609363           -0.291           0.771\n",
       "L4.new_death_rate         -8.318111        12.543944           -0.663           0.507\n",
       "L4.new_test_rate          -1.374362        15.147898           -0.091           0.928\n",
       "L4.case_count           3679.403100      2661.933657            1.382           0.167\n",
       "L4.case_rate             186.311435       142.007439            1.312           0.190\n",
       "L4.death_count           -51.988213       119.957276           -0.433           0.665\n",
       "L4.death_rate             -0.372345         3.969476           -0.094           0.925\n",
       "L4.test_count          67166.602977     52494.127131            1.280           0.201\n",
       "L4.test_rate           -8454.250427      3050.786388           -2.771           0.006\n",
       "L5.mean                   -0.085449         0.147471           -0.579           0.562\n",
       "L5.count                   6.387080         1.631070            3.916           0.000\n",
       "L5.median                 -0.003872         0.113954           -0.034           0.973\n",
       "L5.new_case_count       -113.176349        66.975145           -1.690           0.091\n",
       "L5.new_death_count        14.244004        11.416280            1.248           0.212\n",
       "L5.new_test_count       -281.476702       871.485962           -0.323           0.747\n",
       "L5.new_case_rate           6.550695        17.145433            0.382           0.702\n",
       "L5.new_death_rate         -6.322428        12.941906           -0.489           0.625\n",
       "L5.new_test_rate          -4.164669        15.896644           -0.262           0.793\n",
       "L5.case_count          -3053.538049      2555.851543           -1.195           0.232\n",
       "L5.case_rate             270.162485       150.314293            1.797           0.072\n",
       "L5.death_count          -108.362180       106.667137           -1.016           0.310\n",
       "L5.death_rate             -0.313872         4.106950           -0.076           0.939\n",
       "L5.test_count          70356.753383     48254.145929            1.458           0.145\n",
       "L5.test_rate           -8073.848694      3180.219661           -2.539           0.011\n",
       "L6.mean                    0.022297         0.146529            0.152           0.879\n",
       "L6.count                  -2.855015         1.660571           -1.719           0.086\n",
       "L6.median                 -0.071596         0.112081           -0.639           0.523\n",
       "L6.new_case_count        -65.070338        58.165100           -1.119           0.263\n",
       "L6.new_death_count        14.252091        10.875135            1.311           0.190\n",
       "L6.new_test_count       -476.069964       761.381126           -0.625           0.532\n",
       "L6.new_case_rate           4.596524        16.326638            0.282           0.778\n",
       "L6.new_death_rate         -5.404048        11.918034           -0.453           0.650\n",
       "L6.new_test_rate          -2.574565        15.138528           -0.170           0.865\n",
       "L6.case_count          -5792.866181      2657.620025           -2.180           0.029\n",
       "L6.case_rate             335.119824       139.639628            2.400           0.016\n",
       "L6.death_count           -42.242172       116.413712           -0.363           0.717\n",
       "L6.death_rate              1.238324         4.025889            0.308           0.758\n",
       "L6.test_count          45190.099140     51792.576984            0.873           0.383\n",
       "L6.test_rate           -7967.581290      3013.818992           -2.644           0.008\n",
       "L7.mean                   -0.074756         0.146082           -0.512           0.609\n",
       "L7.count                   0.151361         1.678339            0.090           0.928\n",
       "L7.median                  0.065400         0.111783            0.585           0.559\n",
       "L7.new_case_count        -60.338373        41.398004           -1.458           0.145\n",
       "L7.new_death_count        12.987064         9.271484            1.401           0.161\n",
       "L7.new_test_count        -90.148121       534.184246           -0.169           0.866\n",
       "L7.new_case_rate           3.396758        13.748537            0.247           0.805\n",
       "L7.new_death_rate         -7.514442         9.865703           -0.762           0.446\n",
       "L7.new_test_rate          -1.328897        13.047447           -0.102           0.919\n",
       "L7.case_count           -752.235653      3009.456965           -0.250           0.803\n",
       "L7.case_rate             234.137886       117.162267            1.998           0.046\n",
       "L7.death_count           122.382583       133.738454            0.915           0.360\n",
       "L7.death_rate              2.000127         3.539872            0.565           0.572\n",
       "L7.test_count         -67676.729001     60141.337333           -1.125           0.260\n",
       "L7.test_rate           -6968.705338      2522.307623           -2.763           0.006\n",
       "L8.mean                    0.032135         0.146268            0.220           0.826\n",
       "L8.count                  -0.640881         1.945639           -0.329           0.742\n",
       "L8.median                  0.010935         0.114903            0.095           0.924\n",
       "L8.new_case_count        -36.598950        20.748058           -1.764           0.078\n",
       "L8.new_death_count         9.341737         6.683947            1.398           0.162\n",
       "L8.new_test_count          6.758573       254.965258            0.027           0.979\n",
       "L8.new_case_rate           3.231690         9.830774            0.329           0.742\n",
       "L8.new_death_rate         -7.459498         6.913979           -1.079           0.281\n",
       "L8.new_test_rate           0.728312         9.614053            0.076           0.940\n",
       "L8.case_count          -2892.998520      3050.161273           -0.948           0.343\n",
       "L8.case_rate             120.432519        86.591964            1.391           0.164\n",
       "L8.death_count           142.672635       136.427622            1.046           0.296\n",
       "L8.death_rate              0.544175         2.783053            0.196           0.845\n",
       "L8.test_count         -13412.451120     63603.265164           -0.211           0.833\n",
       "L8.test_rate           -5658.494718      1856.871237           -3.047           0.002\n",
       "L9.mean                   -0.038683         0.185746           -0.208           0.835\n",
       "L9.count                   1.125954         2.115409            0.532           0.595\n",
       "L9.median                 -0.030870         0.155951           -0.198           0.843\n",
       "L9.new_case_count         -0.403290         5.083078           -0.079           0.937\n",
       "L9.new_death_count         3.513588         3.429867            1.024           0.306\n",
       "L9.new_test_count         -0.417565         4.994999           -0.084           0.933\n",
       "L9.new_case_rate           0.420381         5.090964            0.083           0.934\n",
       "L9.new_death_rate         -3.374900         3.436289           -0.982           0.326\n",
       "L9.new_test_rate           0.355000         4.991836            0.071           0.943\n",
       "L9.case_count          -3943.489746      2300.440234           -1.714           0.086\n",
       "L9.case_rate              33.214514        48.602744            0.683           0.494\n",
       "L9.death_count            67.093433       102.529450            0.654           0.513\n",
       "L9.death_rate              1.210954         1.730468            0.700           0.484\n",
       "L9.test_count           3876.456382     49021.592072            0.079           0.937\n",
       "L9.test_rate           -2399.930969       976.899808           -2.457           0.014\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_death_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                     -0.000337         0.001011           -0.333           0.739\n",
       "L1.mean                   -0.409145         0.149018           -2.746           0.006\n",
       "L1.count                  -0.936522         1.486601           -0.630           0.529\n",
       "L1.median                  0.404382         0.123442            3.276           0.001\n",
       "L1.new_case_count        -17.483609        14.570003           -1.200           0.230\n",
       "L1.new_death_count        -8.483964         2.633325           -3.222           0.001\n",
       "L1.new_test_count        110.323608       184.422177            0.598           0.550\n",
       "L1.new_case_rate           0.712093         4.129093            0.172           0.863\n",
       "L1.new_death_rate          4.849897         3.124197            1.552           0.121\n",
       "L1.new_test_rate           3.865398         3.847554            1.005           0.315\n",
       "L1.case_count           1943.303791      1742.740293            1.115           0.265\n",
       "L1.case_rate              50.027595        36.710995            1.363           0.173\n",
       "L1.death_count           100.641607        75.034735            1.341           0.180\n",
       "L1.death_rate             -2.153251         1.296236           -1.661           0.097\n",
       "L1.test_count         -22186.515283     35533.814207           -0.624           0.532\n",
       "L1.test_rate             172.902710       765.074412            0.226           0.821\n",
       "L2.mean                    0.012161         0.114638            0.106           0.916\n",
       "L2.count                   0.283802         1.409059            0.201           0.840\n",
       "L2.median                  0.111307         0.088854            1.253           0.210\n",
       "L2.new_case_count        -33.332892        30.643207           -1.088           0.277\n",
       "L2.new_death_count       -14.905716         5.112879           -2.915           0.004\n",
       "L2.new_test_count         33.436529       390.406461            0.086           0.932\n",
       "L2.new_case_rate           0.340788         8.026750            0.042           0.966\n",
       "L2.new_death_rate         11.341571         6.133283            1.849           0.064\n",
       "L2.new_test_rate           6.317867         7.460801            0.847           0.397\n",
       "L2.case_count           1850.912646      2312.870917            0.800           0.424\n",
       "L2.case_rate              83.773488        66.956980            1.251           0.211\n",
       "L2.death_count             5.896730       103.927752            0.057           0.955\n",
       "L2.death_rate             -1.473316         2.096122           -0.703           0.482\n",
       "L2.test_count          15088.682454     46502.746122            0.324           0.746\n",
       "L2.test_rate            -742.614776      1450.187723           -0.512           0.609\n",
       "L3.mean                   -0.020072         0.115665           -0.174           0.862\n",
       "L3.count                   1.622546         1.227483            1.322           0.186\n",
       "L3.median                  0.005719         0.088779            0.064           0.949\n",
       "L3.new_case_count        -57.935412        43.694716           -1.326           0.185\n",
       "L3.new_death_count       -14.531643         7.090010           -2.050           0.040\n",
       "L3.new_test_count       -111.923057       564.432635           -0.198           0.843\n",
       "L3.new_case_rate          -0.500109        10.961472           -0.046           0.964\n",
       "L3.new_death_rate         13.466285         8.348605            1.613           0.107\n",
       "L3.new_test_rate          10.975854        10.103616            1.086           0.277\n",
       "L3.case_count           2908.139549      2258.542262            1.288           0.198\n",
       "L3.case_rate             120.324854        92.026080            1.308           0.191\n",
       "L3.death_count           -94.564055       104.948770           -0.901           0.368\n",
       "L3.death_rate             -1.342170         2.676303           -0.502           0.616\n",
       "L3.test_count          27885.947091     45380.443219            0.614           0.539\n",
       "L3.test_rate            -778.115803      1988.229603           -0.391           0.696\n",
       "L4.mean                    0.103723         0.114423            0.906           0.365\n",
       "L4.count                   0.373518         1.257140            0.297           0.766\n",
       "L4.median                 -0.189172         0.088946           -2.127           0.033\n",
       "L4.new_case_count        -67.758614        51.081058           -1.326           0.185\n",
       "L4.new_death_count       -11.534789         8.323417           -1.386           0.166\n",
       "L4.new_test_count        -40.195201       663.950145           -0.061           0.952\n",
       "L4.new_case_rate           1.688412        12.795232            0.132           0.895\n",
       "L4.new_death_rate         12.731960         9.663385            1.318           0.188\n",
       "L4.new_test_rate          10.285120        11.669374            0.881           0.378\n",
       "L4.case_count            722.893950      2050.654125            0.353           0.724\n",
       "L4.case_rate             175.332316       109.397220            1.603           0.109\n",
       "L4.death_count           -75.962596        92.410598           -0.822           0.411\n",
       "L4.death_rate             -2.235692         3.057935           -0.731           0.465\n",
       "L4.test_count         -14052.758750     40439.512102           -0.348           0.728\n",
       "L4.test_rate             370.181827      2350.211724            0.158           0.875\n",
       "L5.mean                   -0.139051         0.113606           -1.224           0.221\n",
       "L5.count                  -0.521737         1.256515           -0.415           0.678\n",
       "L5.median                 -0.049179         0.087786           -0.560           0.575\n",
       "L5.new_case_count        -70.614614        51.595147           -1.369           0.171\n",
       "L5.new_death_count       -10.193850         8.794675           -1.159           0.246\n",
       "L5.new_test_count       -138.622865       671.360189           -0.206           0.836\n",
       "L5.new_case_rate           6.502015        13.208200            0.492           0.623\n",
       "L5.new_death_rate         16.427694         9.969960            1.648           0.099\n",
       "L5.new_test_rate           8.911862        12.246180            0.728           0.467\n",
       "L5.case_count           -370.300927      1968.932432           -0.188           0.851\n",
       "L5.case_rate             148.835757       115.796509            1.285           0.199\n",
       "L5.death_count          -182.998786        82.172373           -2.227           0.026\n",
       "L5.death_rate             -1.297294         3.163841           -0.410           0.682\n",
       "L5.test_count          18380.877885     37173.189173            0.494           0.621\n",
       "L5.test_rate             831.180328      2449.922277            0.339           0.734\n",
       "L6.mean                    0.174218         0.112880            1.543           0.123\n",
       "L6.count                  -1.421152         1.279241           -1.111           0.267\n",
       "L6.median                  0.053422         0.086343            0.619           0.536\n",
       "L6.new_case_count        -55.188589        44.808217           -1.232           0.218\n",
       "L6.new_death_count       -12.440773         8.377797           -1.485           0.138\n",
       "L6.new_test_count       -212.281228       586.539541           -0.362           0.717\n",
       "L6.new_case_rate          10.629427        12.577431            0.845           0.398\n",
       "L6.new_death_rate         20.060371         9.181207            2.185           0.029\n",
       "L6.new_test_rate          15.254781        11.662156            1.308           0.191\n",
       "L6.case_count          -2459.282044      2047.331064           -1.201           0.230\n",
       "L6.case_rate             132.425773       107.573147            1.231           0.218\n",
       "L6.death_count           -47.035457        89.680770           -0.524           0.600\n",
       "L6.death_rate             -1.442469         3.101394           -0.465           0.642\n",
       "L6.test_count          11511.450512     39899.064109            0.289           0.773\n",
       "L6.test_rate            1476.194473      2321.733426            0.636           0.525\n",
       "L7.mean                   -0.092712         0.112536           -0.824           0.410\n",
       "L7.count                  -0.424466         1.292930           -0.328           0.743\n",
       "L7.median                  0.307110         0.086114            3.566           0.000\n",
       "L7.new_case_count        -36.144295        31.891474           -1.133           0.257\n",
       "L7.new_death_count        -9.668480         7.142405           -1.354           0.176\n",
       "L7.new_test_count       -228.946563       411.515563           -0.556           0.578\n",
       "L7.new_case_rate           7.949786        10.591359            0.751           0.453\n",
       "L7.new_death_rate         14.207329         7.600169            1.869           0.062\n",
       "L7.new_test_rate          16.687772        10.051266            1.660           0.097\n",
       "L7.case_count          -2021.247940      2318.373083           -0.872           0.383\n",
       "L7.case_rate              86.065195        90.257429            0.954           0.340\n",
       "L7.death_count           102.573189       103.027103            0.996           0.319\n",
       "L7.death_rate              0.249066         2.726985            0.091           0.927\n",
       "L7.test_count           2312.826094     46330.636813            0.050           0.960\n",
       "L7.test_rate             607.909981      1943.091451            0.313           0.754\n",
       "L8.mean                    0.034721         0.112680            0.308           0.758\n",
       "L8.count                   0.611741         1.498847            0.408           0.683\n",
       "L8.median                 -0.062543         0.088517           -0.707           0.480\n",
       "L8.new_case_count        -12.990132        15.983528           -0.813           0.416\n",
       "L8.new_death_count        -7.863330         5.149063           -1.527           0.127\n",
       "L8.new_test_count       -235.015777       196.415698           -1.197           0.231\n",
       "L8.new_case_rate          -0.019841         7.573260           -0.003           0.998\n",
       "L8.new_death_rate          9.947759         5.326270            1.868           0.062\n",
       "L8.new_test_rate          13.493380         7.406307            1.822           0.068\n",
       "L8.case_count          -1822.139726      2349.730160           -0.775           0.438\n",
       "L8.case_rate              21.800888        66.707210            0.327           0.744\n",
       "L8.death_count            99.088195       105.098737            0.943           0.346\n",
       "L8.death_rate             -0.134293         2.143960           -0.063           0.950\n",
       "L8.test_count           1082.764395     48997.576528            0.022           0.982\n",
       "L8.test_rate             708.961665      1430.464148            0.496           0.620\n",
       "L9.mean                   -0.448595         0.143092           -3.135           0.002\n",
       "L9.count                   0.586103         1.629632            0.360           0.719\n",
       "L9.median                  0.308527         0.120139            2.568           0.010\n",
       "L9.new_case_count         -4.723149         3.915813           -1.206           0.228\n",
       "L9.new_death_count        -8.799907         2.642241           -3.330           0.001\n",
       "L9.new_test_count         -8.510889         3.847960           -2.212           0.027\n",
       "L9.new_case_rate           4.643700         3.921888            1.184           0.236\n",
       "L9.new_death_rate          8.936265         2.647189            3.376           0.001\n",
       "L9.new_test_rate           8.503778         3.845524            2.211           0.027\n",
       "L9.case_count          -1528.860277      1772.173113           -0.863           0.388\n",
       "L9.case_rate               4.736837        37.441736            0.127           0.899\n",
       "L9.death_count            84.854906        78.984853            1.074           0.283\n",
       "L9.death_rate             -1.447077         1.333088           -1.086           0.278\n",
       "L9.test_count         -43561.733069     37764.400976           -1.154           0.249\n",
       "L9.test_rate             877.963731       752.567073            1.167           0.243\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_test_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000962         0.000687            1.399           0.162\n",
       "L1.mean                   -0.132656         0.101263           -1.310           0.190\n",
       "L1.count                  -1.020632         1.010198           -1.010           0.312\n",
       "L1.median                  0.072316         0.083883            0.862           0.389\n",
       "L1.new_case_count         15.024094         9.900838            1.517           0.129\n",
       "L1.new_death_count         0.722086         1.789439            0.404           0.687\n",
       "L1.new_test_count       -105.569816       125.321457           -0.842           0.400\n",
       "L1.new_case_rate           0.007255         2.805866            0.003           0.998\n",
       "L1.new_death_rate         -0.489824         2.123003           -0.231           0.818\n",
       "L1.new_test_rate           0.288641         2.614550            0.110           0.912\n",
       "L1.case_count          -1786.555846      1184.254284           -1.509           0.131\n",
       "L1.case_rate               8.566218        24.946432            0.343           0.731\n",
       "L1.death_count           -10.327719        50.988783           -0.203           0.839\n",
       "L1.death_rate              0.998199         0.880839            1.133           0.257\n",
       "L1.test_count          19470.008341     24146.496112            0.806           0.420\n",
       "L1.test_rate             769.018529       519.895394            1.479           0.139\n",
       "L2.mean                    0.004467         0.077901            0.057           0.954\n",
       "L2.count                   1.737277         0.957506            1.814           0.070\n",
       "L2.median                 -0.026407         0.060380           -0.437           0.662\n",
       "L2.new_case_count         26.136509        20.823154            1.255           0.209\n",
       "L2.new_death_count        -0.939498         3.474384           -0.270           0.787\n",
       "L2.new_test_count       -189.279153       265.295136           -0.713           0.476\n",
       "L2.new_case_rate          -2.413613         5.454463           -0.443           0.658\n",
       "L2.new_death_rate          1.195443         4.167785            0.287           0.774\n",
       "L2.new_test_rate           1.675303         5.069881            0.330           0.741\n",
       "L2.case_count          -1041.851969      1571.678410           -0.663           0.507\n",
       "L2.case_rate              11.625869        45.499660            0.256           0.798\n",
       "L2.death_count            -2.407212        70.622620           -0.034           0.973\n",
       "L2.death_rate              0.907526         1.424390            0.637           0.524\n",
       "L2.test_count          14988.225345     31600.277186            0.474           0.635\n",
       "L2.test_rate             797.862828       985.454362            0.810           0.418\n",
       "L3.mean                   -0.048629         0.078598           -0.619           0.536\n",
       "L3.count                   1.410946         0.834119            1.692           0.091\n",
       "L3.median                  0.032995         0.060329            0.547           0.584\n",
       "L3.new_case_count         37.069959        29.692120            1.248           0.212\n",
       "L3.new_death_count        -2.366336         4.817915           -0.491           0.623\n",
       "L3.new_test_count       -216.142988       383.552139           -0.564           0.573\n",
       "L3.new_case_rate         -11.169117         7.448712           -1.499           0.134\n",
       "L3.new_death_rate          0.287542         5.673175            0.051           0.960\n",
       "L3.new_test_rate           4.093378         6.865768            0.596           0.551\n",
       "L3.case_count           -266.030356      1534.760148           -0.173           0.862\n",
       "L3.case_rate               9.049333        62.535009            0.145           0.885\n",
       "L3.death_count            86.100618        71.316438            1.207           0.227\n",
       "L3.death_rate              1.387862         1.818643            0.763           0.445\n",
       "L3.test_count           4108.964286     30837.632273            0.133           0.894\n",
       "L3.test_rate             673.649816      1351.073040            0.499           0.618\n",
       "L4.mean                    0.008666         0.077754            0.111           0.911\n",
       "L4.count                  -1.059210         0.854272           -1.240           0.215\n",
       "L4.median                 -0.028759         0.060442           -0.476           0.634\n",
       "L4.new_case_count         40.704124        34.711403            1.173           0.241\n",
       "L4.new_death_count        -0.854462         5.656059           -0.151           0.880\n",
       "L4.new_test_count       -129.992468       451.177842           -0.288           0.773\n",
       "L4.new_case_rate         -12.479273         8.694817           -1.435           0.151\n",
       "L4.new_death_rate         -2.432481         6.566615           -0.370           0.711\n",
       "L4.new_test_rate           5.146066         7.929757            0.649           0.516\n",
       "L4.case_count           -250.283859      1393.492733           -0.180           0.857\n",
       "L4.case_rate             -25.949184        74.339319           -0.349           0.727\n",
       "L4.death_count            44.138383        62.796303            0.703           0.482\n",
       "L4.death_rate              0.476738         2.077976            0.229           0.819\n",
       "L4.test_count         -17235.791990     27480.093076           -0.627           0.531\n",
       "L4.test_rate             367.775810      1597.052822            0.230           0.818\n",
       "L5.mean                    0.060871         0.077199            0.788           0.430\n",
       "L5.count                  -0.032890         0.853847           -0.039           0.969\n",
       "L5.median                 -0.044201         0.059654           -0.741           0.459\n",
       "L5.new_case_count         35.069974        35.060745            1.000           0.317\n",
       "L5.new_death_count        -1.160031         5.976296           -0.194           0.846\n",
       "L5.new_test_count       -275.580587       456.213231           -0.604           0.546\n",
       "L5.new_case_rate         -11.354355         8.975444           -1.265           0.206\n",
       "L5.new_death_rate         -2.045550         6.774944           -0.302           0.763\n",
       "L5.new_test_rate          10.004384         8.321717            1.202           0.229\n",
       "L5.case_count            574.402564      1337.959923            0.429           0.668\n",
       "L5.case_rate             -42.639461        78.687865           -0.542           0.588\n",
       "L5.death_count            -2.774731        55.839063           -0.050           0.960\n",
       "L5.death_rate             -0.327837         2.149943           -0.152           0.879\n",
       "L5.test_count          26986.652058     25260.509964            1.068           0.285\n",
       "L5.test_rate             167.377655      1664.809705            0.101           0.920\n",
       "L6.mean                    0.043315         0.076706            0.565           0.572\n",
       "L6.count                   0.240524         0.869290            0.277           0.782\n",
       "L6.median                 -0.004401         0.058673           -0.075           0.940\n",
       "L6.new_case_count         21.758999        30.448784            0.715           0.475\n",
       "L6.new_death_count        -0.877102         5.693013           -0.154           0.878\n",
       "L6.new_test_count       -270.260662       398.574571           -0.678           0.498\n",
       "L6.new_case_rate         -14.551294         8.546814           -1.703           0.089\n",
       "L6.new_death_rate         -1.531777         6.238958           -0.246           0.806\n",
       "L6.new_test_rate          11.828558         7.924851            1.493           0.136\n",
       "L6.case_count           2015.726941      1391.234594            1.449           0.147\n",
       "L6.case_rate             -53.852692        73.099796           -0.737           0.461\n",
       "L6.death_count           -28.463053        60.941287           -0.467           0.640\n",
       "L6.death_rate             -0.929060         2.107508           -0.441           0.659\n",
       "L6.test_count          -1341.998019     27112.839359           -0.049           0.961\n",
       "L6.test_rate             -70.653092      1577.700801           -0.045           0.964\n",
       "L7.mean                   -0.049608         0.076472           -0.649           0.517\n",
       "L7.count                   0.499461         0.878592            0.568           0.570\n",
       "L7.median                  0.048933         0.058517            0.836           0.403\n",
       "L7.new_case_count         11.909211        21.671396            0.550           0.583\n",
       "L7.new_death_count        -0.835031         4.853519           -0.172           0.863\n",
       "L7.new_test_count       -209.914373       279.639526           -0.751           0.453\n",
       "L7.new_case_rate         -15.845459         7.197207           -2.202           0.028\n",
       "L7.new_death_rate         -0.195693         5.164586           -0.038           0.970\n",
       "L7.new_test_rate           9.090967         6.830194            1.331           0.183\n",
       "L7.case_count           1331.030061      1575.417328            0.845           0.398\n",
       "L7.case_rate              -6.827350        61.333147           -0.111           0.911\n",
       "L7.death_count           -51.050935        70.010597           -0.729           0.466\n",
       "L7.death_rate             -0.683039         1.853084           -0.369           0.712\n",
       "L7.test_count         -10768.950043     31483.322763           -0.342           0.732\n",
       "L7.test_rate            -383.095125      1320.400053           -0.290           0.772\n",
       "L8.mean                    0.029417         0.076570            0.384           0.701\n",
       "L8.count                   1.775603         1.018520            1.743           0.081\n",
       "L8.median                 -0.002343         0.060151           -0.039           0.969\n",
       "L8.new_case_count          1.064225        10.861378            0.098           0.922\n",
       "L8.new_death_count        -1.471481         3.498972           -0.421           0.674\n",
       "L8.new_test_count       -103.865065       133.471483           -0.778           0.436\n",
       "L8.new_case_rate         -12.237284         5.146301           -2.378           0.017\n",
       "L8.new_death_rate          2.112502         3.619391            0.584           0.559\n",
       "L8.new_test_rate           5.633255         5.032850            1.119           0.263\n",
       "L8.case_count            852.479585      1596.725582            0.534           0.593\n",
       "L8.case_rate               4.572651        45.329932            0.101           0.920\n",
       "L8.death_count           -62.435402        71.418346           -0.874           0.382\n",
       "L8.death_rate              0.183405         1.456897            0.126           0.900\n",
       "L8.test_count         -19695.018763     33295.603569           -0.592           0.554\n",
       "L8.test_rate              -2.639077       972.051489           -0.003           0.998\n",
       "L9.mean                   -0.121418         0.097236           -1.249           0.212\n",
       "L9.count                  -2.615034         1.107393           -2.361           0.018\n",
       "L9.median                  0.025419         0.081638            0.311           0.756\n",
       "L9.new_case_count          6.614814         2.660935            2.486           0.013\n",
       "L9.new_death_count        -1.947979         1.795497           -1.085           0.278\n",
       "L9.new_test_count         -2.539835         2.614826           -0.971           0.331\n",
       "L9.new_case_rate          -6.571237         2.665063           -2.466           0.014\n",
       "L9.new_death_rate          1.990554         1.798859            1.107           0.268\n",
       "L9.new_test_rate           2.600273         2.613171            0.995           0.320\n",
       "L9.case_count          -1338.599270      1204.254937           -1.112           0.266\n",
       "L9.case_rate              17.257591        25.442997            0.678           0.498\n",
       "L9.death_count            22.732702        53.673029            0.424           0.672\n",
       "L9.death_rate              0.248003         0.905881            0.274           0.784\n",
       "L9.test_count         -19012.718443     25662.259504           -0.741           0.459\n",
       "L9.test_rate              71.303648       511.396210            0.139           0.889\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_case_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000435         0.001314            0.331           0.741\n",
       "L1.mean                   -0.098920         0.193657           -0.511           0.609\n",
       "L1.count                  -5.988551         1.931923           -3.100           0.002\n",
       "L1.median                  0.000955         0.160420            0.006           0.995\n",
       "L1.new_case_count         -8.358097        18.934552           -0.441           0.659\n",
       "L1.new_death_count         0.195868         3.422157            0.057           0.954\n",
       "L1.new_test_count        427.751320       239.667166            1.785           0.074\n",
       "L1.new_case_rate           4.318018         5.365992            0.805           0.421\n",
       "L1.new_death_rate          0.629570         4.060072            0.155           0.877\n",
       "L1.new_test_rate           1.142774         5.000116            0.229           0.819\n",
       "L1.case_count            368.770173      2264.790679            0.163           0.871\n",
       "L1.case_rate              10.179637        47.708037            0.213           0.831\n",
       "L1.death_count           -25.851972        97.511929           -0.265           0.791\n",
       "L1.death_rate              1.949362         1.684533            1.157           0.247\n",
       "L1.test_count         -80175.223312     46178.223758           -1.736           0.083\n",
       "L1.test_rate           -2455.387707       994.257954           -2.470           0.014\n",
       "L2.mean                    0.079411         0.148979            0.533           0.594\n",
       "L2.count                   0.569046         1.831152            0.311           0.756\n",
       "L2.median                  0.020988         0.115471            0.182           0.856\n",
       "L2.new_case_count        -42.948334        39.822601           -1.078           0.281\n",
       "L2.new_death_count         0.329281         6.644479            0.050           0.960\n",
       "L2.new_test_count        396.030861       507.355523            0.781           0.435\n",
       "L2.new_case_rate           3.173151        10.431220            0.304           0.761\n",
       "L2.new_death_rate          2.566711         7.970552            0.322           0.747\n",
       "L2.new_test_rate          -2.127563         9.695738           -0.219           0.826\n",
       "L2.case_count           4172.385092      3005.708031            1.388           0.165\n",
       "L2.case_rate              75.579273        87.014425            0.869           0.385\n",
       "L2.death_count           -77.232640       135.060057           -0.572           0.567\n",
       "L2.death_rate              1.028766         2.724031            0.378           0.706\n",
       "L2.test_count          11277.886090     60432.978100            0.187           0.852\n",
       "L2.test_rate           -4493.301271      1884.601883           -2.384           0.017\n",
       "L3.mean                   -0.103539         0.150313           -0.689           0.491\n",
       "L3.count                   2.245666         1.595184            1.408           0.159\n",
       "L3.median                  0.043254         0.115374            0.375           0.708\n",
       "L3.new_case_count        -92.848980        56.783782           -1.635           0.102\n",
       "L3.new_death_count         7.560056         9.213874            0.821           0.412\n",
       "L3.new_test_count        348.409706       733.512490            0.475           0.635\n",
       "L3.new_case_rate          -4.757210        14.245060           -0.334           0.738\n",
       "L3.new_death_rate         -3.875751        10.849489           -0.357           0.721\n",
       "L3.new_test_rate           1.403623        13.130227            0.107           0.915\n",
       "L3.case_count           6714.917403      2935.104836            2.288           0.022\n",
       "L3.case_rate             162.254828       119.593153            1.357           0.175\n",
       "L3.death_count           -36.431594       136.386929           -0.267           0.789\n",
       "L3.death_rate              0.982380         3.478009            0.282           0.778\n",
       "L3.test_count          15298.244561     58974.481293            0.259           0.795\n",
       "L3.test_rate           -6815.422332      2583.818077           -2.638           0.008\n",
       "L4.mean                    0.068616         0.148699            0.461           0.644\n",
       "L4.count                   3.177778         1.633726            1.945           0.052\n",
       "L4.median                 -0.128181         0.115591           -1.109           0.267\n",
       "L4.new_case_count       -123.244100        66.382756           -1.857           0.063\n",
       "L4.new_death_count        13.242626        10.816756            1.224           0.221\n",
       "L4.new_test_count         47.751365       862.841186            0.055           0.956\n",
       "L4.new_case_rate          -7.084342        16.628136           -0.426           0.670\n",
       "L4.new_death_rate         -8.200029        12.558122           -0.653           0.514\n",
       "L4.new_test_rate          -1.527597        15.165019           -0.101           0.920\n",
       "L4.case_count           3727.897822      2664.942314            1.399           0.162\n",
       "L4.case_rate             182.218669       142.167944            1.282           0.200\n",
       "L4.death_count           -52.312438       120.092858           -0.436           0.663\n",
       "L4.death_rate             -0.368313         3.973962           -0.093           0.926\n",
       "L4.test_count          66927.207014     52553.458733            1.274           0.203\n",
       "L4.test_rate           -8438.345004      3054.234546           -2.763           0.006\n",
       "L5.mean                   -0.082633         0.147637           -0.560           0.576\n",
       "L5.count                   6.363193         1.632914            3.897           0.000\n",
       "L5.median                 -0.005005         0.114083           -0.044           0.965\n",
       "L5.new_case_count       -111.276235        67.050844           -1.660           0.097\n",
       "L5.new_death_count        14.089418        11.429183            1.233           0.218\n",
       "L5.new_test_count       -275.507611       872.470961           -0.316           0.752\n",
       "L5.new_case_rate           4.498748        17.164812            0.262           0.793\n",
       "L5.new_death_rate         -6.177932        12.956534           -0.477           0.633\n",
       "L5.new_test_rate          -4.198173        15.914611           -0.264           0.792\n",
       "L5.case_count          -3053.601054      2558.740299           -1.193           0.233\n",
       "L5.case_rate             266.607314       150.484186            1.772           0.076\n",
       "L5.death_count          -107.860172       106.787698           -1.010           0.312\n",
       "L5.death_rate             -0.289156         4.111592           -0.070           0.944\n",
       "L5.test_count          70813.378113     48308.685284            1.466           0.143\n",
       "L5.test_rate           -8046.136255      3183.814112           -2.527           0.011\n",
       "L6.mean                    0.020203         0.146694            0.138           0.890\n",
       "L6.count                  -2.869827         1.662447           -1.726           0.084\n",
       "L6.median                 -0.069632         0.112208           -0.621           0.535\n",
       "L6.new_case_count        -63.419911        58.230841           -1.089           0.276\n",
       "L6.new_death_count        14.121833        10.887427            1.297           0.195\n",
       "L6.new_test_count       -469.351018       762.241678           -0.616           0.538\n",
       "L6.new_case_rate           2.984245        16.345091            0.183           0.855\n",
       "L6.new_death_rate         -5.298401        11.931504           -0.444           0.657\n",
       "L6.new_test_rate          -2.477515        15.155638           -0.163           0.870\n",
       "L6.case_count          -5812.783629      2660.623806           -2.185           0.029\n",
       "L6.case_rate             332.365077       139.797456            2.377           0.017\n",
       "L6.death_count           -41.728253       116.545289           -0.358           0.720\n",
       "L6.death_rate              1.268364         4.030439            0.315           0.753\n",
       "L6.test_count          44992.187383     51851.115657            0.868           0.386\n",
       "L6.test_rate           -7939.995537      3017.225368           -2.632           0.008\n",
       "L7.mean                   -0.074463         0.146247           -0.509           0.611\n",
       "L7.count                   0.156543         1.680236            0.093           0.926\n",
       "L7.median                  0.066081         0.111910            0.590           0.555\n",
       "L7.new_case_count        -59.102591        41.444794           -1.426           0.154\n",
       "L7.new_death_count        12.885855         9.281964            1.388           0.165\n",
       "L7.new_test_count        -84.078531       534.788009           -0.157           0.875\n",
       "L7.new_case_rate           2.320048        13.764076            0.169           0.866\n",
       "L7.new_death_rate         -7.425161         9.876854           -0.752           0.452\n",
       "L7.new_test_rate          -1.177804        13.062194           -0.090           0.928\n",
       "L7.case_count           -764.452254      3012.858410           -0.254           0.800\n",
       "L7.case_rate             232.095473       117.294690            1.979           0.048\n",
       "L7.death_count           121.921037       133.889613            0.911           0.363\n",
       "L7.death_rate              2.012626         3.543873            0.568           0.570\n",
       "L7.test_count         -67584.079666     60209.312211           -1.122           0.262\n",
       "L7.test_rate           -6946.502445      2525.158467           -2.751           0.006\n",
       "L8.mean                    0.031864         0.146434            0.218           0.828\n",
       "L8.count                  -0.617014         1.947838           -0.317           0.751\n",
       "L8.median                  0.010899         0.115033            0.095           0.925\n",
       "L8.new_case_count        -36.111916        20.771509           -1.739           0.082\n",
       "L8.new_death_count         9.319048         6.691502            1.393           0.164\n",
       "L8.new_test_count          7.989673       255.253433            0.031           0.975\n",
       "L8.new_case_rate           2.680458         9.841885            0.272           0.785\n",
       "L8.new_death_rate         -7.440362         6.921793           -1.075           0.282\n",
       "L8.new_test_rate           0.814803         9.624920            0.085           0.933\n",
       "L8.case_count          -2865.489681      3053.608725           -0.938           0.348\n",
       "L8.case_rate             119.320216        86.689834            1.376           0.169\n",
       "L8.death_count           142.382992       136.581820            1.042           0.297\n",
       "L8.death_rate              0.536461         2.786198            0.193           0.847\n",
       "L8.test_count         -12482.837777     63675.152893           -0.196           0.845\n",
       "L8.test_rate           -5643.911896      1858.969970           -3.036           0.002\n",
       "L9.mean                   -0.036158         0.185956           -0.194           0.846\n",
       "L9.count                   1.083807         2.117800            0.512           0.609\n",
       "L9.median                 -0.034356         0.156127           -0.220           0.826\n",
       "L9.new_case_count         -0.192956         5.088823           -0.038           0.970\n",
       "L9.new_death_count         3.530405         3.433743            1.028           0.304\n",
       "L9.new_test_count         -0.432831         5.000644           -0.087           0.931\n",
       "L9.new_case_rate           0.211784         5.096718            0.042           0.967\n",
       "L9.new_death_rate         -3.390739         3.440173           -0.986           0.324\n",
       "L9.new_test_rate           0.372652         4.997478            0.075           0.941\n",
       "L9.case_count          -3951.111919      2303.040312           -1.716           0.086\n",
       "L9.case_rate              33.372912        48.657678            0.686           0.493\n",
       "L9.death_count            66.882628       102.645334            0.652           0.515\n",
       "L9.death_rate              1.209032         1.732424            0.698           0.485\n",
       "L9.test_count           4130.146309     49076.998834            0.084           0.933\n",
       "L9.test_rate           -2399.070395       978.003951           -2.453           0.014\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_death_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                     -0.000345         0.001009           -0.342           0.732\n",
       "L1.mean                   -0.407951         0.148634           -2.745           0.006\n",
       "L1.count                  -0.960067         1.482773           -0.647           0.517\n",
       "L1.median                  0.404829         0.123124            3.288           0.001\n",
       "L1.new_case_count        -17.197594        14.532485           -1.183           0.237\n",
       "L1.new_death_count        -6.775107         2.626545           -2.579           0.010\n",
       "L1.new_test_count        107.444941       183.947289            0.584           0.559\n",
       "L1.new_case_rate           0.809977         4.118460            0.197           0.844\n",
       "L1.new_death_rate          3.201013         3.116152            1.027           0.304\n",
       "L1.new_test_rate           4.049183         3.837646            1.055           0.291\n",
       "L1.case_count           1896.245350      1738.252732            1.091           0.275\n",
       "L1.case_rate              51.464102        36.616464            1.405           0.160\n",
       "L1.death_count            98.379094        74.841520            1.314           0.189\n",
       "L1.death_rate             -2.118202         1.292898           -1.638           0.101\n",
       "L1.test_count         -21645.445330     35442.314539           -0.611           0.541\n",
       "L1.test_rate             151.150502       763.104344            0.198           0.843\n",
       "L2.mean                    0.005549         0.114343            0.049           0.961\n",
       "L2.count                   0.302696         1.405430            0.215           0.829\n",
       "L2.median                  0.116583         0.088625            1.315           0.188\n",
       "L2.new_case_count        -32.622310        30.564301           -1.067           0.286\n",
       "L2.new_death_count       -12.875543         5.099714           -2.525           0.012\n",
       "L2.new_test_count         26.232313       389.401163            0.067           0.946\n",
       "L2.new_case_rate           0.566026         8.006081            0.071           0.944\n",
       "L2.new_death_rate          9.462377         6.117490            1.547           0.122\n",
       "L2.new_test_rate           6.657775         7.441589            0.895           0.371\n",
       "L2.case_count           1783.756824      2306.915268            0.773           0.439\n",
       "L2.case_rate              85.527396        66.784566            1.281           0.200\n",
       "L2.death_count             2.476859       103.660137            0.024           0.981\n",
       "L2.death_rate             -1.463262         2.090725           -0.700           0.484\n",
       "L2.test_count          15938.854924     46383.001424            0.344           0.731\n",
       "L2.test_rate            -788.656655      1446.453486           -0.545           0.586\n",
       "L3.mean                   -0.023660         0.115367           -0.205           0.838\n",
       "L3.count                   1.571935         1.224322            1.284           0.199\n",
       "L3.median                  0.006233         0.088551            0.070           0.944\n",
       "L3.new_case_count        -57.137884        43.582202           -1.311           0.190\n",
       "L3.new_death_count       -12.435344         7.071753           -1.758           0.079\n",
       "L3.new_test_count       -119.324980       562.979220           -0.212           0.832\n",
       "L3.new_case_rate          -0.235745        10.933247           -0.022           0.983\n",
       "L3.new_death_rate         11.554804         8.327107            1.388           0.165\n",
       "L3.new_test_rate          11.440174        10.077599            1.135           0.256\n",
       "L3.case_count           2891.407277      2252.726509            1.284           0.199\n",
       "L3.case_rate             122.266130        91.789112            1.332           0.183\n",
       "L3.death_count           -95.829715       104.678527           -0.915           0.360\n",
       "L3.death_rate             -1.336085         2.669411           -0.501           0.617\n",
       "L3.test_count          28001.299335     45263.588455            0.619           0.536\n",
       "L3.test_rate            -879.551450      1983.109906           -0.444           0.657\n",
       "L4.mean                    0.104974         0.114128            0.920           0.358\n",
       "L4.count                   0.389413         1.253903            0.311           0.756\n",
       "L4.median                 -0.189349         0.088717           -2.134           0.033\n",
       "L4.new_case_count        -67.672312        50.949524           -1.328           0.184\n",
       "L4.new_death_count        -9.512225         8.301984           -1.146           0.252\n",
       "L4.new_test_count        -51.995230       662.240472           -0.079           0.937\n",
       "L4.new_case_rate           1.966671        12.762284            0.154           0.878\n",
       "L4.new_death_rate         10.965954         9.638502            1.138           0.255\n",
       "L4.new_test_rate          10.696837        11.639325            0.919           0.358\n",
       "L4.case_count            806.057591      2045.373685            0.394           0.694\n",
       "L4.case_rate             174.835218       109.115522            1.602           0.109\n",
       "L4.death_count           -78.685370        92.172641           -0.854           0.393\n",
       "L4.death_rate             -2.239511         3.050061           -0.734           0.463\n",
       "L4.test_count         -13060.170676     40335.380249           -0.324           0.746\n",
       "L4.test_rate             235.368835      2344.159923            0.100           0.920\n",
       "L5.mean                   -0.138717         0.113313           -1.224           0.221\n",
       "L5.count                  -0.547436         1.253280           -0.437           0.662\n",
       "L5.median                 -0.049898         0.087560           -0.570           0.569\n",
       "L5.new_case_count        -71.212394        51.462289           -1.384           0.166\n",
       "L5.new_death_count        -8.442616         8.772029           -0.962           0.336\n",
       "L5.new_test_count       -154.567864       669.631435           -0.231           0.817\n",
       "L5.new_case_rate           6.706980        13.174189            0.509           0.611\n",
       "L5.new_death_rate         14.918503         9.944288            1.500           0.134\n",
       "L5.new_test_rate           9.180397        12.214646            0.752           0.452\n",
       "L5.case_count           -278.772327      1963.862426           -0.142           0.887\n",
       "L5.case_rate             147.247044       115.498333            1.275           0.202\n",
       "L5.death_count          -182.346033        81.960778           -2.225           0.026\n",
       "L5.death_rate             -1.364229         3.155694           -0.432           0.666\n",
       "L5.test_count          19339.310788     37077.468110            0.522           0.602\n",
       "L5.test_rate             698.715629      2443.613721            0.286           0.775\n",
       "L6.mean                    0.172590         0.112590            1.533           0.125\n",
       "L6.count                  -1.401076         1.275947           -1.098           0.272\n",
       "L6.median                  0.056252         0.086121            0.653           0.514\n",
       "L6.new_case_count        -56.716186        44.692836           -1.269           0.204\n",
       "L6.new_death_count       -11.015140         8.356225           -1.318           0.187\n",
       "L6.new_test_count       -233.846624       585.029200           -0.400           0.689\n",
       "L6.new_case_rate          10.783957        12.545044            0.860           0.390\n",
       "L6.new_death_rate         18.791888         9.157566            2.052           0.040\n",
       "L6.new_test_rate          15.465911        11.632126            1.330           0.184\n",
       "L6.case_count          -2340.737590      2042.059181           -1.146           0.252\n",
       "L6.case_rate             130.372292       107.296145            1.215           0.224\n",
       "L6.death_count           -43.819401        89.449841           -0.490           0.624\n",
       "L6.death_rate             -1.572424         3.093408           -0.508           0.611\n",
       "L6.test_count          12708.066173     39796.323911            0.319           0.749\n",
       "L6.test_rate            1373.641019      2315.754956            0.593           0.553\n",
       "L7.mean                   -0.090815         0.112247           -0.809           0.418\n",
       "L7.count                  -0.404607         1.289600           -0.314           0.754\n",
       "L7.median                  0.306586         0.085892            3.569           0.000\n",
       "L7.new_case_count        -37.675091        31.809353           -1.184           0.236\n",
       "L7.new_death_count        -8.729898         7.124013           -1.225           0.220\n",
       "L7.new_test_count       -246.548134       410.455910           -0.601           0.548\n",
       "L7.new_case_rate           8.032229        10.564086            0.760           0.447\n",
       "L7.new_death_rate         13.376024         7.580598            1.765           0.078\n",
       "L7.new_test_rate          16.809443        10.025383            1.677           0.094\n",
       "L7.case_count          -2010.329408      2312.403266           -0.869           0.385\n",
       "L7.case_rate              83.992390        90.025015            0.933           0.351\n",
       "L7.death_count           104.440812       102.761808            1.016           0.309\n",
       "L7.death_rate              0.145430         2.719963            0.053           0.957\n",
       "L7.test_count           1649.462989     46211.335298            0.036           0.972\n",
       "L7.test_rate             524.603767      1938.087985            0.271           0.787\n",
       "L8.mean                    0.033611         0.112390            0.299           0.765\n",
       "L8.count                   0.582999         1.494988            0.390           0.697\n",
       "L8.median                 -0.062605         0.088289           -0.709           0.478\n",
       "L8.new_case_count        -13.823701        15.942370           -0.867           0.386\n",
       "L8.new_death_count        -7.396328         5.135804           -1.440           0.150\n",
       "L8.new_test_count       -245.520495       195.909927           -1.253           0.210\n",
       "L8.new_case_rate           0.047099         7.553759            0.006           0.995\n",
       "L8.new_death_rate          9.545905         5.312555            1.797           0.072\n",
       "L8.new_test_rate          13.518158         7.387236            1.830           0.067\n",
       "L8.case_count          -1901.464414      2343.679598           -0.811           0.417\n",
       "L8.case_rate              20.070009        66.535439            0.302           0.763\n",
       "L8.death_count           100.792048       104.828108            0.961           0.336\n",
       "L8.death_rate             -0.234816         2.138439           -0.110           0.913\n",
       "L8.test_count           -214.930086     48871.407635           -0.004           0.996\n",
       "L8.test_rate             658.461813      1426.780699            0.462           0.644\n",
       "L9.mean                   -0.450821         0.142724           -3.159           0.002\n",
       "L9.count                   0.545822         1.625435            0.336           0.737\n",
       "L9.median                  0.310789         0.119829            2.594           0.009\n",
       "L9.new_case_count         -4.870412         3.905730           -1.247           0.212\n",
       "L9.new_death_count        -8.639358         2.635437           -3.278           0.001\n",
       "L9.new_test_count         -8.489863         3.838052           -2.212           0.027\n",
       "L9.new_case_rate           4.791650         3.911790            1.225           0.221\n",
       "L9.new_death_rate          8.772759         2.640372            3.323           0.001\n",
       "L9.new_test_rate           8.485831         3.835622            2.212           0.027\n",
       "L9.case_count          -1618.261418      1767.609762           -0.916           0.360\n",
       "L9.case_rate               3.205040        37.345324            0.086           0.932\n",
       "L9.death_count            87.493023        78.781467            1.111           0.267\n",
       "L9.death_rate             -1.500112         1.329655           -1.128           0.259\n",
       "L9.test_count         -45546.754763     37667.157542           -1.209           0.227\n",
       "L9.test_rate             843.519715       750.629210            1.124           0.261\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation new_test_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000964         0.000687            1.403           0.161\n",
       "L1.mean                   -0.130445         0.101209           -1.289           0.197\n",
       "L1.count                  -1.049814         1.009660           -1.040           0.298\n",
       "L1.median                  0.071115         0.083839            0.848           0.396\n",
       "L1.new_case_count         15.046578         9.895561            1.521           0.128\n",
       "L1.new_death_count         0.661480         1.788485            0.370           0.711\n",
       "L1.new_test_count       -108.032023       125.254673           -0.862           0.388\n",
       "L1.new_case_rate           0.003592         2.804371            0.001           0.999\n",
       "L1.new_death_rate         -0.456721         2.121872           -0.215           0.830\n",
       "L1.new_test_rate          -1.452067         2.613157           -0.556           0.578\n",
       "L1.case_count          -1788.019173      1183.623187           -1.511           0.131\n",
       "L1.case_rate               7.871497        24.933138            0.316           0.752\n",
       "L1.death_count            -9.372558        50.961610           -0.184           0.854\n",
       "L1.death_rate              1.025588         0.880369            1.165           0.244\n",
       "L1.test_count          20272.292323     24133.628283            0.840           0.401\n",
       "L1.test_rate             776.611237       519.618338            1.495           0.135\n",
       "L2.mean                    0.005455         0.077859            0.070           0.944\n",
       "L2.count                   1.764534         0.956995            1.844           0.065\n",
       "L2.median                 -0.026470         0.060347           -0.439           0.661\n",
       "L2.new_case_count         26.237153        20.812057            1.261           0.207\n",
       "L2.new_death_count        -1.031718         3.472533           -0.297           0.766\n",
       "L2.new_test_count       -190.105741       265.153759           -0.717           0.473\n",
       "L2.new_case_rate          -2.436017         5.451556           -0.447           0.655\n",
       "L2.new_death_rate          1.182205         4.165564            0.284           0.777\n",
       "L2.new_test_rate          -0.383345         5.067179           -0.076           0.940\n",
       "L2.case_count          -1047.429582      1570.840852           -0.667           0.505\n",
       "L2.case_rate              10.094769        45.475413            0.222           0.824\n",
       "L2.death_count             0.379802        70.584985            0.005           0.996\n",
       "L2.death_rate              0.969449         1.423631            0.681           0.496\n",
       "L2.test_count          14707.488327     31583.437188            0.466           0.641\n",
       "L2.test_rate             824.220418       984.929207            0.837           0.403\n",
       "L3.mean                   -0.049377         0.078556           -0.629           0.530\n",
       "L3.count                   1.397358         0.833674            1.676           0.094\n",
       "L3.median                  0.034983         0.060297            0.580           0.562\n",
       "L3.new_case_count         37.251510        29.676297            1.255           0.209\n",
       "L3.new_death_count        -2.482096         4.815348           -0.515           0.606\n",
       "L3.new_test_count       -213.602722       383.347741           -0.557           0.577\n",
       "L3.new_case_rate         -11.121100         7.444743           -1.494           0.135\n",
       "L3.new_death_rate          0.234390         5.670152            0.041           0.967\n",
       "L3.new_test_rate           1.984212         6.862109            0.289           0.772\n",
       "L3.case_count           -281.493397      1533.942264           -0.184           0.854\n",
       "L3.case_rate               6.434807        62.501683            0.103           0.918\n",
       "L3.death_count            88.378517        71.278433            1.240           0.215\n",
       "L3.death_rate              1.489193         1.817674            0.819           0.413\n",
       "L3.test_count           3429.557484     30821.198693            0.111           0.911\n",
       "L3.test_rate             713.775863      1350.353044            0.529           0.597\n",
       "L4.mean                    0.007939         0.077713            0.102           0.919\n",
       "L4.count                  -1.047519         0.853816           -1.227           0.220\n",
       "L4.median                 -0.028047         0.060410           -0.464           0.642\n",
       "L4.new_case_count         41.005245        34.692905            1.182           0.237\n",
       "L4.new_death_count        -1.030959         5.653045           -0.182           0.855\n",
       "L4.new_test_count       -121.961253       450.937406           -0.270           0.787\n",
       "L4.new_case_rate         -12.403009         8.690184           -1.427           0.154\n",
       "L4.new_death_rate         -2.481746         6.563116           -0.378           0.705\n",
       "L4.new_test_rate           3.184467         7.925531            0.402           0.688\n",
       "L4.case_count           -263.965975      1392.750131           -0.190           0.850\n",
       "L4.case_rate             -29.818529        74.299703           -0.401           0.688\n",
       "L4.death_count            46.279954        62.762838            0.737           0.461\n",
       "L4.death_rate              0.574596         2.076869            0.277           0.782\n",
       "L4.test_count         -18380.409174     27465.448752           -0.669           0.503\n",
       "L4.test_rate             426.338815      1596.201742            0.267           0.789\n",
       "L5.mean                    0.059761         0.077158            0.775           0.439\n",
       "L5.count                  -0.039840         0.853392           -0.047           0.963\n",
       "L5.median                 -0.043714         0.059622           -0.733           0.463\n",
       "L5.new_case_count         35.581153        35.042061            1.015           0.310\n",
       "L5.new_death_count        -1.388055         5.973111           -0.232           0.816\n",
       "L5.new_test_count       -264.118099       455.970112           -0.579           0.562\n",
       "L5.new_case_rate         -11.310334         8.970661           -1.261           0.207\n",
       "L5.new_death_rate         -2.071687         6.771334           -0.306           0.760\n",
       "L5.new_test_rate           8.343138         8.317282            1.003           0.316\n",
       "L5.case_count            557.834310      1337.246915            0.417           0.677\n",
       "L5.case_rate             -47.069782        78.645931           -0.599           0.550\n",
       "L5.death_count            -1.697573        55.809306           -0.030           0.976\n",
       "L5.death_rate             -0.262112         2.148797           -0.122           0.903\n",
       "L5.test_count          26204.144480     25247.048470            1.038           0.299\n",
       "L5.test_rate             231.028419      1663.922517            0.139           0.890\n",
       "L6.mean                    0.043041         0.076665            0.561           0.575\n",
       "L6.count                   0.283226         0.868827            0.326           0.744\n",
       "L6.median                 -0.005310         0.058642           -0.091           0.928\n",
       "L6.new_case_count         22.310701        30.432558            0.733           0.463\n",
       "L6.new_death_count        -1.082628         5.689979           -0.190           0.849\n",
       "L6.new_test_count       -263.926060       398.362168           -0.663           0.508\n",
       "L6.new_case_rate         -14.516536         8.542259           -1.699           0.089\n",
       "L6.new_death_rate         -1.543334         6.235634           -0.248           0.805\n",
       "L6.new_test_rate          10.494272         7.920628            1.325           0.185\n",
       "L6.case_count           2017.220648      1390.493196            1.451           0.147\n",
       "L6.case_rate             -59.029275        73.060841           -0.808           0.419\n",
       "L6.death_count           -29.902386        60.908811           -0.491           0.623\n",
       "L6.death_rate             -0.876691         2.106385           -0.416           0.677\n",
       "L6.test_count           -474.851804     27098.390746           -0.018           0.986\n",
       "L6.test_rate             -12.856282      1576.860034           -0.008           0.993\n",
       "L7.mean                   -0.046987         0.076432           -0.615           0.539\n",
       "L7.count                   0.504364         0.878124            0.574           0.566\n",
       "L7.median                  0.046122         0.058486            0.789           0.430\n",
       "L7.new_case_count         12.086007        21.659847            0.558           0.577\n",
       "L7.new_death_count        -0.989540         4.850933           -0.204           0.838\n",
       "L7.new_test_count       -208.629404       279.490504           -0.746           0.455\n",
       "L7.new_case_rate         -15.783032         7.193371           -2.194           0.028\n",
       "L7.new_death_rate         -0.176207         5.161834           -0.034           0.973\n",
       "L7.new_test_rate           8.186603         6.826554            1.199           0.230\n",
       "L7.case_count           1377.422944      1574.577778            0.875           0.382\n",
       "L7.case_rate             -11.881920        61.300462           -0.194           0.846\n",
       "L7.death_count           -54.186960        69.973288           -0.774           0.439\n",
       "L7.death_rate             -0.632933         1.852096           -0.342           0.733\n",
       "L7.test_count          -9937.761696     31466.545091           -0.316           0.752\n",
       "L7.test_rate            -324.395305      1319.696403           -0.246           0.806\n",
       "L8.mean                    0.031531         0.076529            0.412           0.680\n",
       "L8.count                   1.789642         1.017977            1.758           0.079\n",
       "L8.median                 -0.003437         0.060119           -0.057           0.954\n",
       "L8.new_case_count          0.953208        10.855590            0.088           0.930\n",
       "L8.new_death_count        -1.567907         3.497108           -0.448           0.654\n",
       "L8.new_test_count       -103.875761       133.400355           -0.779           0.436\n",
       "L8.new_case_rate         -12.147489         5.143558           -2.362           0.018\n",
       "L8.new_death_rate          2.173885         3.617462            0.601           0.548\n",
       "L8.new_test_rate           5.229467         5.030168            1.040           0.299\n",
       "L8.case_count            887.179616      1595.874676            0.556           0.578\n",
       "L8.case_rate               0.756412        45.305776            0.017           0.987\n",
       "L8.death_count           -66.245927        71.380287           -0.928           0.353\n",
       "L8.death_rate              0.184771         1.456121            0.127           0.899\n",
       "L8.test_count         -19582.900369     33277.860120           -0.588           0.556\n",
       "L8.test_rate              38.797677       971.533476            0.040           0.968\n",
       "L9.mean                   -0.119395         0.097184           -1.229           0.219\n",
       "L9.count                  -2.655225         1.106803           -2.399           0.016\n",
       "L9.median                  0.024921         0.081595            0.305           0.760\n",
       "L9.new_case_count          6.572603         2.659517            2.471           0.013\n",
       "L9.new_death_count        -1.985469         1.794540           -1.106           0.269\n",
       "L9.new_test_count         -2.446905         2.613433           -0.936           0.349\n",
       "L9.new_case_rate          -6.529049         2.663643           -2.451           0.014\n",
       "L9.new_death_rate          2.027841         1.797901            1.128           0.259\n",
       "L9.new_test_rate           2.505767         2.611778            0.959           0.337\n",
       "L9.case_count          -1339.747389      1203.613181           -1.113           0.266\n",
       "L9.case_rate              15.889651        25.429439            0.625           0.532\n",
       "L9.death_count            21.382761        53.644427            0.399           0.690\n",
       "L9.death_rate              0.228244         0.905398            0.252           0.801\n",
       "L9.test_count         -19106.264560     25648.583915           -0.745           0.456\n",
       "L9.test_rate              84.949782       511.123683            0.166           0.868\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation case_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000004         0.000011            0.328           0.743\n",
       "L1.mean                   -0.000862         0.001630           -0.529           0.597\n",
       "L1.count                  -0.050432         0.016260           -3.102           0.002\n",
       "L1.median                  0.000032         0.001350            0.024           0.981\n",
       "L1.new_case_count         -0.069110         0.159360           -0.434           0.665\n",
       "L1.new_death_count         0.002139         0.028802            0.074           0.941\n",
       "L1.new_test_count          3.573388         2.017120            1.772           0.076\n",
       "L1.new_case_rate           0.050788         0.045162            1.125           0.261\n",
       "L1.new_death_rate          0.005055         0.034171            0.148           0.882\n",
       "L1.new_test_rate           0.009589         0.042083            0.228           0.820\n",
       "L1.case_count              2.233302        19.061242            0.117           0.907\n",
       "L1.case_rate               0.095035         0.401527            0.237           0.813\n",
       "L1.death_count            -0.226724         0.820693           -0.276           0.782\n",
       "L1.death_rate              0.016432         0.014178            1.159           0.246\n",
       "L1.test_count           -669.725213       388.651503           -1.723           0.085\n",
       "L1.test_rate             -20.569849         8.368010           -2.458           0.014\n",
       "L2.mean                    0.000688         0.001254            0.549           0.583\n",
       "L2.count                   0.004489         0.015412            0.291           0.771\n",
       "L2.median                  0.000165         0.000972            0.170           0.865\n",
       "L2.new_case_count         -0.361640         0.335160           -1.079           0.281\n",
       "L2.new_death_count         0.003764         0.055922            0.067           0.946\n",
       "L2.new_test_count          3.291954         4.270075            0.771           0.441\n",
       "L2.new_case_rate           0.044163         0.087793            0.503           0.615\n",
       "L2.new_death_rate          0.020980         0.067083            0.313           0.754\n",
       "L2.new_test_rate          -0.017549         0.081603           -0.215           0.830\n",
       "L2.case_count             34.901406        25.297052            1.380           0.168\n",
       "L2.case_rate               0.659661         0.732343            0.901           0.368\n",
       "L2.death_count            -0.654725         1.136711           -0.576           0.565\n",
       "L2.death_rate              0.008592         0.022926            0.375           0.708\n",
       "L2.test_count             97.606830       508.624322            0.192           0.848\n",
       "L2.test_rate             -37.790660        15.861445           -2.383           0.017\n",
       "L3.mean                   -0.000872         0.001265           -0.689           0.491\n",
       "L3.count                   0.018845         0.013426            1.404           0.160\n",
       "L3.median                  0.000369         0.000971            0.380           0.704\n",
       "L3.new_case_count         -0.782217         0.477911           -1.637           0.102\n",
       "L3.new_death_count         0.065223         0.077547            0.841           0.400\n",
       "L3.new_test_count          2.900898         6.173488            0.470           0.638\n",
       "L3.new_case_rate          -0.021114         0.119891           -0.176           0.860\n",
       "L3.new_death_rate         -0.034037         0.091313           -0.373           0.709\n",
       "L3.new_test_rate           0.012597         0.110508            0.114           0.909\n",
       "L3.case_count             56.386949        24.702832            2.283           0.022\n",
       "L3.case_rate               1.391334         1.006536            1.382           0.167\n",
       "L3.death_count            -0.299990         1.147878           -0.261           0.794\n",
       "L3.death_rate              0.008508         0.029272            0.291           0.771\n",
       "L3.test_count            127.005824       496.349122            0.256           0.798\n",
       "L3.test_rate             -57.576207        21.746284           -2.648           0.008\n",
       "L4.mean                    0.000574         0.001252            0.459           0.646\n",
       "L4.count                   0.026753         0.013750            1.946           0.052\n",
       "L4.median                 -0.001077         0.000973           -1.107           0.268\n",
       "L4.new_case_count         -1.037255         0.558700           -1.857           0.063\n",
       "L4.new_death_count         0.113201         0.091037            1.243           0.214\n",
       "L4.new_test_count          0.373833         7.261962            0.051           0.959\n",
       "L4.new_case_rate          -0.041168         0.139948           -0.294           0.769\n",
       "L4.new_death_rate         -0.070541         0.105693           -0.667           0.505\n",
       "L4.new_test_rate          -0.011677         0.127634           -0.091           0.927\n",
       "L4.case_count             31.303421        22.429053            1.396           0.163\n",
       "L4.case_rate               1.568408         1.196533            1.311           0.190\n",
       "L4.death_count            -0.441716         1.010742           -0.437           0.662\n",
       "L4.death_rate             -0.003009         0.033446           -0.090           0.928\n",
       "L4.test_count            562.921903       442.307630            1.273           0.203\n",
       "L4.test_rate             -71.382216        25.705468           -2.777           0.005\n",
       "L5.mean                   -0.000721         0.001243           -0.580           0.562\n",
       "L5.count                   0.053875         0.013743            3.920           0.000\n",
       "L5.median                 -0.000031         0.000960           -0.032           0.974\n",
       "L5.new_case_count         -0.938969         0.564323           -1.664           0.096\n",
       "L5.new_death_count         0.120632         0.096192            1.254           0.210\n",
       "L5.new_test_count         -2.316210         7.343010           -0.315           0.752\n",
       "L5.new_case_rate           0.054823         0.144465            0.379           0.704\n",
       "L5.new_death_rate         -0.053561         0.109047           -0.491           0.623\n",
       "L5.new_test_rate          -0.034763         0.133943           -0.260           0.795\n",
       "L5.case_count            -25.264287        21.535221           -1.173           0.241\n",
       "L5.case_rate               2.274676         1.266526            1.796           0.072\n",
       "L5.death_count            -0.917617         0.898761           -1.021           0.307\n",
       "L5.death_rate             -0.002560         0.034605           -0.074           0.941\n",
       "L5.test_count            590.624926       406.582186            1.453           0.146\n",
       "L5.test_rate             -68.138397        26.796053           -2.543           0.011\n",
       "L6.mean                    0.000189         0.001235            0.153           0.878\n",
       "L6.count                  -0.024039         0.013992           -1.718           0.086\n",
       "L6.median                 -0.000602         0.000944           -0.637           0.524\n",
       "L6.new_case_count         -0.538497         0.490090           -1.099           0.272\n",
       "L6.new_death_count         0.120657         0.091632            1.317           0.188\n",
       "L6.new_test_count         -3.964970         6.415283           -0.618           0.537\n",
       "L6.new_case_rate           0.038385         0.137566            0.279           0.780\n",
       "L6.new_death_rate         -0.045739         0.100420           -0.455           0.649\n",
       "L6.new_test_rate          -0.021219         0.127555           -0.166           0.868\n",
       "L6.case_count            -48.237706        22.392707           -2.154           0.031\n",
       "L6.case_rate               2.825615         1.176583            2.402           0.016\n",
       "L6.death_count            -0.357370         0.980884           -0.364           0.716\n",
       "L6.death_rate              0.010476         0.033922            0.309           0.757\n",
       "L6.test_count            382.561378       436.396474            0.877           0.381\n",
       "L6.test_rate             -67.196265        25.393986           -2.646           0.008\n",
       "L7.mean                   -0.000633         0.001231           -0.515           0.607\n",
       "L7.count                   0.001272         0.014141            0.090           0.928\n",
       "L7.median                  0.000551         0.000942            0.585           0.558\n",
       "L7.new_case_count         -0.503271         0.348813           -1.443           0.149\n",
       "L7.new_death_count         0.109771         0.078120            1.405           0.160\n",
       "L7.new_test_count         -0.733304         4.500956           -0.163           0.871\n",
       "L7.new_case_rate           0.028329         0.115843            0.245           0.807\n",
       "L7.new_death_rate         -0.063413         0.083127           -0.763           0.446\n",
       "L7.new_test_rate          -0.010778         0.109936           -0.098           0.922\n",
       "L7.case_count             -5.796374        25.357232           -0.229           0.819\n",
       "L7.case_rate               1.975857         0.987192            2.001           0.045\n",
       "L7.death_count             1.035695         1.126860            0.919           0.358\n",
       "L7.death_rate              0.016866         0.029826            0.565           0.572\n",
       "L7.test_count           -566.302151       506.741875           -1.118           0.264\n",
       "L7.test_rate             -58.773496        21.252585           -2.765           0.006\n",
       "L8.mean                    0.000273         0.001232            0.221           0.825\n",
       "L8.count                  -0.005446         0.016394           -0.332           0.740\n",
       "L8.median                  0.000090         0.000968            0.093           0.926\n",
       "L8.new_case_count         -0.306424         0.174820           -1.753           0.080\n",
       "L8.new_death_count         0.078900         0.056318            1.401           0.161\n",
       "L8.new_test_count          0.066736         2.148299            0.031           0.975\n",
       "L8.new_case_rate           0.026977         0.082833            0.326           0.745\n",
       "L8.new_death_rate         -0.062943         0.058256           -1.080           0.280\n",
       "L8.new_test_rate           0.006331         0.081007            0.078           0.938\n",
       "L8.case_count            -24.005417        25.700201           -0.934           0.350\n",
       "L8.case_rate               1.016840         0.729611            1.394           0.163\n",
       "L8.death_count             1.207571         1.149519            1.051           0.293\n",
       "L8.death_rate              0.004672         0.023450            0.199           0.842\n",
       "L8.test_count           -109.747139       535.911559           -0.205           0.838\n",
       "L8.test_rate             -47.724080        15.645718           -3.050           0.002\n",
       "L9.mean                   -0.000324         0.001565           -0.207           0.836\n",
       "L9.count                   0.009516         0.017824            0.534           0.593\n",
       "L9.median                 -0.000261         0.001314           -0.199           0.843\n",
       "L9.new_case_count         -0.003248         0.042829           -0.076           0.940\n",
       "L9.new_death_count         0.029622         0.028900            1.025           0.305\n",
       "L9.new_test_count         -0.003666         0.042087           -0.087           0.931\n",
       "L9.new_case_rate           0.003392         0.042896            0.079           0.937\n",
       "L9.new_death_rate         -0.028454         0.028954           -0.983           0.326\n",
       "L9.new_test_rate           0.003137         0.042060            0.075           0.941\n",
       "L9.case_count            -33.026610        19.383164           -1.704           0.088\n",
       "L9.case_rate               0.281101         0.409519            0.686           0.492\n",
       "L9.death_count             0.568915         0.863898            0.659           0.510\n",
       "L9.death_rate              0.010234         0.014581            0.702           0.483\n",
       "L9.test_count             34.592965       413.048572            0.084           0.933\n",
       "L9.test_rate             -20.228136         8.231211           -2.457           0.014\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation case_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000004         0.000011            0.380           0.704\n",
       "L1.mean                   -0.000821         0.001654           -0.496           0.620\n",
       "L1.count                  -0.050781         0.016499           -3.078           0.002\n",
       "L1.median                  0.000055         0.001370            0.040           0.968\n",
       "L1.new_case_count         -0.030585         0.161708           -0.189           0.850\n",
       "L1.new_death_count        -0.000413         0.029226           -0.014           0.989\n",
       "L1.new_test_count          3.851747         2.046840            1.882           0.060\n",
       "L1.new_case_rate           0.038031         0.045827            0.830           0.407\n",
       "L1.new_death_rate          0.005982         0.034674            0.173           0.863\n",
       "L1.new_test_rate           0.010449         0.042703            0.245           0.807\n",
       "L1.case_count              0.682706        19.342090            0.035           0.972\n",
       "L1.case_rate              -1.419574         0.407443           -3.484           0.000\n",
       "L1.death_count            -0.158093         0.832785           -0.190           0.849\n",
       "L1.death_rate              0.017182         0.014386            1.194           0.232\n",
       "L1.test_count           -722.499164       394.377889           -1.832           0.067\n",
       "L1.test_rate             -21.637597         8.491304           -2.548           0.011\n",
       "L2.mean                    0.000367         0.001272            0.289           0.773\n",
       "L2.count                   0.008178         0.015639            0.523           0.601\n",
       "L2.median                  0.000316         0.000986            0.321           0.748\n",
       "L2.new_case_count         -0.329772         0.340099           -0.970           0.332\n",
       "L2.new_death_count        -0.000656         0.056746           -0.012           0.991\n",
       "L2.new_test_count          4.222692         4.332990            0.975           0.330\n",
       "L2.new_case_rate           0.028469         0.089086            0.320           0.749\n",
       "L2.new_death_rate          0.022372         0.068071            0.329           0.742\n",
       "L2.new_test_rate          -0.009169         0.082805           -0.111           0.912\n",
       "L2.case_count             37.833203        25.669779            1.474           0.141\n",
       "L2.case_rate              -1.129185         0.743133           -1.519           0.129\n",
       "L2.death_count            -0.601829         1.153459           -0.522           0.602\n",
       "L2.death_rate              0.005968         0.023264            0.257           0.798\n",
       "L2.test_count            -28.416204       516.118386           -0.055           0.956\n",
       "L2.test_rate             -38.822953        16.095147           -2.412           0.016\n",
       "L3.mean                   -0.000929         0.001284           -0.724           0.469\n",
       "L3.count                   0.020141         0.013623            1.478           0.139\n",
       "L3.median                  0.000398         0.000985            0.404           0.686\n",
       "L3.new_case_count         -0.783943         0.484953           -1.617           0.106\n",
       "L3.new_death_count         0.053696         0.078690            0.682           0.495\n",
       "L3.new_test_count          4.446600         6.264449            0.710           0.478\n",
       "L3.new_case_rate          -0.042151         0.121658           -0.346           0.729\n",
       "L3.new_death_rate         -0.029778         0.092658           -0.321           0.748\n",
       "L3.new_test_rate           0.030522         0.112137            0.272           0.785\n",
       "L3.case_count             62.945731        25.066803            2.511           0.012\n",
       "L3.case_rate              -0.554922         1.021367           -0.543           0.587\n",
       "L3.death_count            -0.129316         1.164791           -0.111           0.912\n",
       "L3.death_rate              0.001727         0.029703            0.058           0.954\n",
       "L3.test_count              7.760878       503.662323            0.015           0.988\n",
       "L3.test_rate             -58.702388        22.066694           -2.660           0.008\n",
       "L4.mean                    0.000796         0.001270            0.627           0.531\n",
       "L4.count                   0.027562         0.013953            1.975           0.048\n",
       "L4.median                 -0.001068         0.000987           -1.082           0.279\n",
       "L4.new_case_count         -1.082680         0.566932           -1.910           0.056\n",
       "L4.new_death_count         0.100768         0.092379            1.091           0.275\n",
       "L4.new_test_count          1.844657         7.368960            0.250           0.802\n",
       "L4.new_case_rate          -0.063730         0.142010           -0.449           0.654\n",
       "L4.new_death_rate         -0.061158         0.107251           -0.570           0.569\n",
       "L4.new_test_rate           0.015836         0.129514            0.122           0.903\n",
       "L4.case_count             38.464995        22.759523            1.690           0.091\n",
       "L4.case_rate              -0.232208         1.214163           -0.191           0.848\n",
       "L4.death_count            -0.594584         1.025634           -0.580           0.562\n",
       "L4.death_rate             -0.010813         0.033939           -0.319           0.750\n",
       "L4.test_count            576.726831       448.824585            1.285           0.199\n",
       "L4.test_rate             -72.617371        26.084212           -2.784           0.005\n",
       "L5.mean                   -0.000488         0.001261           -0.387           0.699\n",
       "L5.count                   0.051293         0.013946            3.678           0.000\n",
       "L5.median                 -0.000073         0.000974           -0.075           0.940\n",
       "L5.new_case_count         -1.004588         0.572637           -1.754           0.079\n",
       "L5.new_death_count         0.114047         0.097609            1.168           0.243\n",
       "L5.new_test_count         -0.709271         7.451202           -0.095           0.924\n",
       "L5.new_case_rate           0.042181         0.146593            0.288           0.774\n",
       "L5.new_death_rate         -0.042682         0.110653           -0.386           0.700\n",
       "L5.new_test_rate          -0.004367         0.135916           -0.032           0.974\n",
       "L5.case_count            -22.562821        21.852521           -1.033           0.302\n",
       "L5.case_rate               0.789665         1.285187            0.614           0.539\n",
       "L5.death_count            -1.194347         0.912004           -1.310           0.190\n",
       "L5.death_rate             -0.008290         0.035114           -0.236           0.813\n",
       "L5.test_count            566.680990       412.572762            1.374           0.170\n",
       "L5.test_rate             -70.960841        27.190866           -2.610           0.009\n",
       "L6.mean                    0.000023         0.001253            0.018           0.986\n",
       "L6.count                  -0.024507         0.014198           -1.726           0.084\n",
       "L6.median                 -0.000556         0.000958           -0.580           0.562\n",
       "L6.new_case_count         -0.580488         0.497311           -1.167           0.243\n",
       "L6.new_death_count         0.117772         0.092982            1.267           0.205\n",
       "L6.new_test_count         -3.107171         6.509806           -0.477           0.633\n",
       "L6.new_case_rate           0.034492         0.139593            0.247           0.805\n",
       "L6.new_death_rate         -0.034645         0.101899           -0.340           0.734\n",
       "L6.new_test_rate           0.010831         0.129434            0.084           0.933\n",
       "L6.case_count            -50.979157        22.722641           -2.244           0.025\n",
       "L6.case_rate               1.709254         1.193918            1.432           0.152\n",
       "L6.death_count            -0.496928         0.995337           -0.499           0.618\n",
       "L6.death_rate              0.003112         0.034421            0.090           0.928\n",
       "L6.test_count            528.027233       442.826334            1.192           0.233\n",
       "L6.test_rate             -68.701299        25.768141           -2.666           0.008\n",
       "L7.mean                   -0.000498         0.001249           -0.399           0.690\n",
       "L7.count                   0.000060         0.014350            0.004           0.997\n",
       "L7.median                  0.000400         0.000956            0.418           0.676\n",
       "L7.new_case_count         -0.524179         0.353953           -1.481           0.139\n",
       "L7.new_death_count         0.108124         0.079271            1.364           0.173\n",
       "L7.new_test_count         -0.530493         4.567273           -0.116           0.908\n",
       "L7.new_case_rate           0.032871         0.117550            0.280           0.780\n",
       "L7.new_death_rate         -0.051818         0.084352           -0.614           0.539\n",
       "L7.new_test_rate           0.014575         0.111556            0.131           0.896\n",
       "L7.case_count             -8.553879        25.730845           -0.332           0.740\n",
       "L7.case_rate               1.251599         1.001737            1.249           0.212\n",
       "L7.death_count             0.970969         1.143463            0.849           0.396\n",
       "L7.death_rate              0.012286         0.030266            0.406           0.685\n",
       "L7.test_count           -437.924144       514.208203           -0.852           0.394\n",
       "L7.test_rate             -59.617273        21.565720           -2.764           0.006\n",
       "L8.mean                    0.000255         0.001251            0.204           0.838\n",
       "L8.count                  -0.004131         0.016635           -0.248           0.804\n",
       "L8.median                  0.000014         0.000982            0.014           0.989\n",
       "L8.new_case_count         -0.323790         0.177396           -1.825           0.068\n",
       "L8.new_death_count         0.074893         0.057148            1.311           0.190\n",
       "L8.new_test_count          0.042662         2.179952            0.020           0.984\n",
       "L8.new_case_rate           0.038970         0.084053            0.464           0.643\n",
       "L8.new_death_rate         -0.050921         0.059114           -0.861           0.389\n",
       "L8.new_test_rate           0.020039         0.082200            0.244           0.807\n",
       "L8.case_count            -24.910614        26.078867           -0.955           0.339\n",
       "L8.case_rate               0.608623         0.740361            0.822           0.411\n",
       "L8.death_count             1.287289         1.166456            1.104           0.270\n",
       "L8.death_rate              0.002364         0.023795            0.099           0.921\n",
       "L8.test_count            -62.065769       543.807673           -0.114           0.909\n",
       "L8.test_rate             -49.493285        15.876242           -3.117           0.002\n",
       "L9.mean                   -0.000426         0.001588           -0.268           0.788\n",
       "L9.count                   0.007822         0.018087            0.432           0.665\n",
       "L9.median                 -0.000157         0.001333           -0.117           0.907\n",
       "L9.new_case_count         -0.017615         0.043460           -0.405           0.685\n",
       "L9.new_death_count         0.030927         0.029325            1.055           0.292\n",
       "L9.new_test_count         -0.006995         0.042707           -0.164           0.870\n",
       "L9.new_case_rate           0.017755         0.043528            0.408           0.683\n",
       "L9.new_death_rate         -0.029628         0.029380           -1.008           0.313\n",
       "L9.new_test_rate           0.006827         0.042680            0.160           0.873\n",
       "L9.case_count            -33.452326        19.668755           -1.701           0.089\n",
       "L9.case_rate               0.094595         0.415553            0.228           0.820\n",
       "L9.death_count             0.867566         0.876626            0.990           0.322\n",
       "L9.death_rate              0.009347         0.014795            0.632           0.528\n",
       "L9.test_count             32.910638       419.134424            0.079           0.937\n",
       "L9.test_rate             -20.533900         8.352490           -2.458           0.014\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation death_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                     -0.000009         0.000027           -0.345           0.730\n",
       "L1.mean                   -0.010597         0.003939           -2.690           0.007\n",
       "L1.count                  -0.024639         0.039298           -0.627           0.531\n",
       "L1.median                  0.010619         0.003263            3.254           0.001\n",
       "L1.new_case_count         -0.442140         0.385151           -1.148           0.251\n",
       "L1.new_death_count        -0.176910         0.069611           -2.541           0.011\n",
       "L1.new_test_count          3.026409         4.875109            0.621           0.535\n",
       "L1.new_case_rate           0.023671         0.109151            0.217           0.828\n",
       "L1.new_death_rate          0.135486         0.082587            1.641           0.101\n",
       "L1.new_test_rate           0.109735         0.101708            1.079           0.281\n",
       "L1.case_count             48.340041        46.068476            1.049           0.294\n",
       "L1.case_rate               1.402795         0.970437            1.446           0.148\n",
       "L1.death_count             1.595378         1.983506            0.804           0.421\n",
       "L1.death_rate             -0.057281         0.034265           -1.672           0.095\n",
       "L1.test_count           -608.163596       939.318765           -0.647           0.517\n",
       "L1.test_rate               3.589943        20.224363            0.178           0.859\n",
       "L2.mean                    0.000010         0.003030            0.003           0.997\n",
       "L2.count                   0.006572         0.037248            0.176           0.860\n",
       "L2.median                  0.003172         0.002349            1.350           0.177\n",
       "L2.new_case_count         -0.824511         0.810038           -1.018           0.309\n",
       "L2.new_death_count        -0.332617         0.135156           -2.461           0.014\n",
       "L2.new_test_count          1.047342        10.320201            0.101           0.919\n",
       "L2.new_case_rate           0.015245         0.212183            0.072           0.943\n",
       "L2.new_death_rate          0.312457         0.162130            1.927           0.054\n",
       "L2.new_test_rate           0.178942         0.197223            0.907           0.364\n",
       "L2.case_count             44.283806        61.139596            0.724           0.469\n",
       "L2.case_rate               2.352098         1.769975            1.329           0.184\n",
       "L2.death_count            -0.578432         2.747279           -0.211           0.833\n",
       "L2.death_rate             -0.039418         0.055410           -0.711           0.477\n",
       "L2.test_count            389.414655      1229.277044            0.317           0.751\n",
       "L2.test_rate             -21.281747        38.334994           -0.555           0.579\n",
       "L3.mean                   -0.000720         0.003058           -0.235           0.814\n",
       "L3.count                   0.042151         0.032448            1.299           0.194\n",
       "L3.median                  0.000257         0.002347            0.109           0.913\n",
       "L3.new_case_count         -1.486951         1.155048           -1.287           0.198\n",
       "L3.new_death_count        -0.317582         0.187421           -1.694           0.090\n",
       "L3.new_test_count         -2.832450        14.920497           -0.190           0.849\n",
       "L3.new_case_rate          -0.008048         0.289761           -0.028           0.978\n",
       "L3.new_death_rate          0.369649         0.220691            1.675           0.094\n",
       "L3.new_test_rate           0.302189         0.267084            1.131           0.258\n",
       "L3.case_count             78.299061        59.703445            1.311           0.190\n",
       "L3.case_rate               3.321759         2.432664            1.365           0.172\n",
       "L3.death_count            -2.736207         2.774269           -0.986           0.324\n",
       "L3.death_rate             -0.033619         0.070747           -0.475           0.635\n",
       "L3.test_count            747.205155      1199.609523            0.623           0.533\n",
       "L3.test_rate             -23.476105        52.557864           -0.447           0.655\n",
       "L4.mean                    0.002906         0.003025            0.961           0.337\n",
       "L4.count                   0.009295         0.033232            0.280           0.780\n",
       "L4.median                 -0.005043         0.002351           -2.145           0.032\n",
       "L4.new_case_count         -1.779301         1.350302           -1.318           0.188\n",
       "L4.new_death_count        -0.241274         0.220025           -1.097           0.273\n",
       "L4.new_test_count         -1.099402        17.551193           -0.063           0.950\n",
       "L4.new_case_rate           0.050864         0.338236            0.150           0.880\n",
       "L4.new_death_rate          0.349422         0.255447            1.368           0.171\n",
       "L4.new_test_rate           0.278337         0.308474            0.902           0.367\n",
       "L4.case_count             22.806093        54.208025            0.421           0.674\n",
       "L4.case_rate               4.691054         2.891861            1.622           0.105\n",
       "L4.death_count            -1.869067         2.442828           -0.765           0.444\n",
       "L4.death_rate             -0.056343         0.080835           -0.697           0.486\n",
       "L4.test_count           -335.631933      1068.998458           -0.314           0.754\n",
       "L4.test_rate               6.421102        62.126682            0.103           0.918\n",
       "L5.mean                   -0.003800         0.003003           -1.265           0.206\n",
       "L5.count                  -0.015283         0.033215           -0.460           0.645\n",
       "L5.median                 -0.001297         0.002321           -0.559           0.576\n",
       "L5.new_case_count         -1.872590         1.363892           -1.373           0.170\n",
       "L5.new_death_count        -0.216954         0.232483           -0.933           0.351\n",
       "L5.new_test_count         -3.860091        17.747074           -0.218           0.828\n",
       "L5.new_case_rate           0.178584         0.349152            0.511           0.609\n",
       "L5.new_death_rate          0.445481         0.263551            1.690           0.091\n",
       "L5.new_test_rate           0.235132         0.323722            0.726           0.468\n",
       "L5.case_count             -7.700978        52.047753           -0.148           0.882\n",
       "L5.case_rate               3.906453         3.061023            1.276           0.202\n",
       "L5.death_count            -4.359863         2.172186           -2.007           0.045\n",
       "L5.death_rate             -0.030918         0.083635           -0.370           0.712\n",
       "L5.test_count            520.045909       982.654830            0.529           0.597\n",
       "L5.test_rate              19.752296        64.762481            0.305           0.760\n",
       "L6.mean                    0.004588         0.002984            1.538           0.124\n",
       "L6.count                  -0.037267         0.033816           -1.102           0.270\n",
       "L6.median                  0.001450         0.002282            0.635           0.525\n",
       "L6.new_case_count         -1.492283         1.184483           -1.260           0.208\n",
       "L6.new_death_count        -0.289970         0.221463           -1.309           0.190\n",
       "L6.new_test_count         -5.843063        15.504882           -0.377           0.706\n",
       "L6.new_case_rate           0.283710         0.332478            0.853           0.393\n",
       "L6.new_death_rate          0.536649         0.242701            2.211           0.027\n",
       "L6.new_test_rate           0.401136         0.308283            1.301           0.193\n",
       "L6.case_count            -61.205658        54.120182           -1.131           0.258\n",
       "L6.case_rate               3.428480         2.843643            1.206           0.228\n",
       "L6.death_count            -0.549837         2.370667           -0.232           0.817\n",
       "L6.death_rate             -0.037903         0.081984           -0.462           0.644\n",
       "L6.test_count            312.064614      1054.711983            0.296           0.767\n",
       "L6.test_rate              38.483094        61.373872            0.627           0.531\n",
       "L7.mean                   -0.002424         0.002975           -0.815           0.415\n",
       "L7.count                  -0.010367         0.034178           -0.303           0.762\n",
       "L7.median                  0.008127         0.002276            3.570           0.000\n",
       "L7.new_case_count         -0.995976         0.843035           -1.181           0.237\n",
       "L7.new_death_count        -0.232550         0.188806           -1.232           0.218\n",
       "L7.new_test_count         -6.103634        10.878210           -0.561           0.575\n",
       "L7.new_case_rate           0.207546         0.279977            0.741           0.459\n",
       "L7.new_death_rate          0.378416         0.200907            1.884           0.060\n",
       "L7.new_test_rate           0.440624         0.265700            1.658           0.097\n",
       "L7.case_count            -51.890174        61.285043           -0.847           0.397\n",
       "L7.case_rate               2.205929         2.385910            0.925           0.355\n",
       "L7.death_count             3.444111         2.723470            1.265           0.206\n",
       "L7.death_rate              0.006148         0.072086            0.085           0.932\n",
       "L7.test_count             25.824301      1224.727419            0.021           0.983\n",
       "L7.test_rate              16.404939        51.364659            0.319           0.749\n",
       "L8.mean                    0.000887         0.002979            0.298           0.766\n",
       "L8.count                   0.016013         0.039621            0.404           0.686\n",
       "L8.median                 -0.001624         0.002340           -0.694           0.488\n",
       "L8.new_case_count         -0.357245         0.422517           -0.846           0.398\n",
       "L8.new_death_count        -0.197333         0.136113           -1.450           0.147\n",
       "L8.new_test_count         -6.241480         5.192152           -1.202           0.229\n",
       "L8.new_case_rate          -0.004455         0.200195           -0.022           0.982\n",
       "L8.new_death_rate          0.262443         0.140797            1.864           0.062\n",
       "L8.new_test_rate           0.355215         0.195782            1.814           0.070\n",
       "L8.case_count            -51.116375        62.113952           -0.823           0.411\n",
       "L8.case_rate               0.521624         1.763372            0.296           0.767\n",
       "L8.death_count             3.224431         2.778233            1.161           0.246\n",
       "L8.death_rate             -0.004709         0.056675           -0.083           0.934\n",
       "L8.test_count             23.882048      1295.226649            0.018           0.985\n",
       "L8.test_rate              19.297072        37.813611            0.510           0.610\n",
       "L9.mean                   -0.011807         0.003783           -3.121           0.002\n",
       "L9.count                   0.015908         0.043079            0.369           0.712\n",
       "L9.median                  0.008170         0.003176            2.573           0.010\n",
       "L9.new_case_count         -0.124424         0.103513           -1.202           0.229\n",
       "L9.new_death_count        -0.229743         0.069846           -3.289           0.001\n",
       "L9.new_test_count         -0.223259         0.101719           -2.195           0.028\n",
       "L9.new_case_rate           0.122273         0.103673            1.179           0.238\n",
       "L9.new_death_rate          0.233297         0.069977            3.334           0.001\n",
       "L9.new_test_rate           0.223244         0.101655            2.196           0.028\n",
       "L9.case_count            -42.513738        46.846518           -0.908           0.364\n",
       "L9.case_rate               0.110266         0.989754            0.111           0.911\n",
       "L9.death_count             2.627322         2.087925            1.258           0.208\n",
       "L9.death_rate             -0.039499         0.035240           -1.121           0.262\n",
       "L9.test_count          -1157.681391       998.283221           -1.160           0.246\n",
       "L9.test_rate              23.503117        19.893738            1.181           0.237\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation death_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                     -0.000001         0.000053           -0.019           0.985\n",
       "L1.mean                   -0.004611         0.007832           -0.589           0.556\n",
       "L1.count                  -0.014554         0.078129           -0.186           0.852\n",
       "L1.median                  0.007010         0.006488            1.081           0.280\n",
       "L1.new_case_count          0.040059         0.765738            0.052           0.958\n",
       "L1.new_death_count         0.178883         0.138396            1.293           0.196\n",
       "L1.new_test_count         -3.828358         9.692451           -0.395           0.693\n",
       "L1.new_case_rate          -0.208605         0.217008           -0.961           0.336\n",
       "L1.new_death_rate         -0.021194         0.164195           -0.129           0.897\n",
       "L1.new_test_rate           0.038149         0.202211            0.189           0.850\n",
       "L1.case_count             20.331626        91.591073            0.222           0.824\n",
       "L1.case_rate              -0.241898         1.929375           -0.125           0.900\n",
       "L1.death_count            -4.544579         3.943509           -1.152           0.249\n",
       "L1.death_rate             -1.361517         0.068125          -19.986           0.000\n",
       "L1.test_count            772.999824      1867.507273            0.414           0.679\n",
       "L1.test_rate             -43.970707        40.209081           -1.094           0.274\n",
       "L2.mean                   -0.000125         0.006025           -0.021           0.983\n",
       "L2.count                   0.083006         0.074054            1.121           0.262\n",
       "L2.median                  0.001668         0.004670            0.357           0.721\n",
       "L2.new_case_count          0.466109         1.610478            0.289           0.772\n",
       "L2.new_death_count         0.211159         0.268711            0.786           0.432\n",
       "L2.new_test_count         -6.618635        20.518116           -0.323           0.747\n",
       "L2.new_case_rate          -0.567845         0.421852           -1.346           0.178\n",
       "L2.new_death_rate          0.332009         0.322339            1.030           0.303\n",
       "L2.new_test_rate           0.308515         0.392108            0.787           0.431\n",
       "L2.case_count             -6.398529       121.554732           -0.053           0.958\n",
       "L2.case_rate              -1.118460         3.518976           -0.318           0.751\n",
       "L2.death_count           -12.977146         5.462004           -2.376           0.018\n",
       "L2.death_rate             -1.442280         0.110163          -13.092           0.000\n",
       "L2.test_count            552.134186      2443.988031            0.226           0.821\n",
       "L2.test_rate             -66.638528        76.215745           -0.874           0.382\n",
       "L3.mean                   -0.007704         0.006079           -1.267           0.205\n",
       "L3.count                   0.041617         0.064511            0.645           0.519\n",
       "L3.median                  0.006955         0.004666            1.491           0.136\n",
       "L3.new_case_count          1.210585         2.296410            0.527           0.598\n",
       "L3.new_death_count         0.327090         0.372621            0.878           0.380\n",
       "L3.new_test_count          3.149628        29.664197            0.106           0.915\n",
       "L3.new_case_rate          -0.983847         0.576089           -1.708           0.088\n",
       "L3.new_death_rate          0.520810         0.438767            1.187           0.235\n",
       "L3.new_test_rate           0.431451         0.531003            0.813           0.416\n",
       "L3.case_count            -38.411000       118.699447           -0.324           0.746\n",
       "L3.case_rate              -0.646889         4.836502           -0.134           0.894\n",
       "L3.death_count           -10.145985         5.515664           -1.839           0.066\n",
       "L3.death_rate             -1.388509         0.140655           -9.872           0.000\n",
       "L3.test_count          -1858.096049      2385.004528           -0.779           0.436\n",
       "L3.test_rate             -45.526145       104.492955           -0.436           0.663\n",
       "L4.mean                   -0.001522         0.006014           -0.253           0.800\n",
       "L4.count                  -0.006572         0.066070           -0.099           0.921\n",
       "L4.median                 -0.000016         0.004675           -0.003           0.997\n",
       "L4.new_case_count          1.687074         2.684605            0.628           0.530\n",
       "L4.new_death_count         0.390796         0.437444            0.893           0.372\n",
       "L4.new_test_count         21.009955        34.894417            0.602           0.547\n",
       "L4.new_case_rate          -1.228546         0.672463           -1.827           0.068\n",
       "L4.new_death_rate          0.647993         0.507867            1.276           0.202\n",
       "L4.new_test_rate           0.416556         0.613293            0.679           0.497\n",
       "L4.case_count            -27.524641       107.773724           -0.255           0.798\n",
       "L4.case_rate               0.020050         5.749456            0.003           0.997\n",
       "L4.death_count            -5.890944         4.856711           -1.213           0.225\n",
       "L4.death_rate             -1.241581         0.160712           -7.725           0.000\n",
       "L4.test_count          -3406.539906      2125.330046           -1.603           0.109\n",
       "L4.test_rate             -34.025066       123.517207           -0.275           0.783\n",
       "L5.mean                   -0.004023         0.005971           -0.674           0.500\n",
       "L5.count                  -0.067671         0.066037           -1.025           0.305\n",
       "L5.median                 -0.004081         0.004614           -0.885           0.376\n",
       "L5.new_case_count          1.207849         2.711623            0.445           0.656\n",
       "L5.new_death_count         0.429265         0.462211            0.929           0.353\n",
       "L5.new_test_count         20.842108        35.283857            0.591           0.555\n",
       "L5.new_case_rate          -1.046235         0.694167           -1.507           0.132\n",
       "L5.new_death_rate          0.749287         0.523979            1.430           0.153\n",
       "L5.new_test_rate           0.125831         0.643608            0.196           0.845\n",
       "L5.case_count             36.048918       103.478777            0.348           0.728\n",
       "L5.case_rate              -0.838903         6.085776           -0.138           0.890\n",
       "L5.death_count            -4.032032         4.318633           -0.934           0.350\n",
       "L5.death_rate             -1.064857         0.166278           -6.404           0.000\n",
       "L5.test_count             83.412202      1953.665901            0.043           0.966\n",
       "L5.test_rate               5.571391       128.757573            0.043           0.965\n",
       "L6.mean                    0.003989         0.005933            0.672           0.501\n",
       "L6.count                   0.018624         0.067232            0.277           0.782\n",
       "L6.median                 -0.001937         0.004538           -0.427           0.670\n",
       "L6.new_case_count          1.008629         2.354931            0.428           0.668\n",
       "L6.new_death_count         0.382034         0.440302            0.868           0.386\n",
       "L6.new_test_count         16.381039        30.826042            0.531           0.595\n",
       "L6.new_case_rate          -0.737009         0.661017           -1.115           0.265\n",
       "L6.new_death_rate          0.656162         0.482525            1.360           0.174\n",
       "L6.new_test_rate           0.162911         0.612914            0.266           0.790\n",
       "L6.case_count            -12.335671       107.599078           -0.115           0.909\n",
       "L6.case_rate              -0.499675         5.653590           -0.088           0.930\n",
       "L6.death_count             6.201890         4.713243            1.316           0.188\n",
       "L6.death_rate             -0.811391         0.162996           -4.978           0.000\n",
       "L6.test_count            845.663064      2096.926381            0.403           0.687\n",
       "L6.test_rate               5.996745       122.020508            0.049           0.961\n",
       "L7.mean                    0.011329         0.005914            1.915           0.055\n",
       "L7.count                   0.008003         0.067951            0.118           0.906\n",
       "L7.median                 -0.000297         0.004526           -0.066           0.948\n",
       "L7.new_case_count          0.295607         1.676081            0.176           0.860\n",
       "L7.new_death_count         0.284624         0.375375            0.758           0.448\n",
       "L7.new_test_count          2.875652        21.627521            0.133           0.894\n",
       "L7.new_case_rate          -0.791295         0.556637           -1.422           0.155\n",
       "L7.new_death_rate          0.370550         0.399433            0.928           0.354\n",
       "L7.new_test_rate           0.165236         0.528252            0.313           0.754\n",
       "L7.case_count             90.267822       121.843903            0.741           0.459\n",
       "L7.case_rate               1.468725         4.743549            0.310           0.757\n",
       "L7.death_count            14.569160         5.414670            2.691           0.007\n",
       "L7.death_rate             -0.513991         0.143319           -3.586           0.000\n",
       "L7.test_count           2643.702261      2434.942692            1.086           0.278\n",
       "L7.test_rate             -40.392537       102.120684           -0.396           0.692\n",
       "L8.mean                   -0.005142         0.005922           -0.868           0.385\n",
       "L8.count                   0.034291         0.078773            0.435           0.663\n",
       "L8.median                  0.003008         0.004652            0.647           0.518\n",
       "L8.new_case_count          0.549167         0.840027            0.654           0.513\n",
       "L8.new_death_count         0.100937         0.270613            0.373           0.709\n",
       "L8.new_test_count         -5.123355        10.322780           -0.496           0.620\n",
       "L8.new_case_rate          -0.920805         0.398019           -2.313           0.021\n",
       "L8.new_death_rate          0.192912         0.279926            0.689           0.491\n",
       "L8.new_test_rate           0.304485         0.389244            0.782           0.434\n",
       "L8.case_count            -18.048337       123.491898           -0.146           0.884\n",
       "L8.case_rate               2.744025         3.505849            0.783           0.434\n",
       "L8.death_count            14.117952         5.523546            2.556           0.011\n",
       "L8.death_rate             -0.322217         0.112677           -2.860           0.004\n",
       "L8.test_count           1562.075917      2575.105785            0.607           0.544\n",
       "L8.test_rate             -49.510880        75.179157           -0.659           0.510\n",
       "L9.mean                   -0.006958         0.007520           -0.925           0.355\n",
       "L9.count                  -0.077188         0.085647           -0.901           0.367\n",
       "L9.median                  0.004262         0.006314            0.675           0.500\n",
       "L9.new_case_count          0.377469         0.205799            1.834           0.067\n",
       "L9.new_death_count        -0.007822         0.138865           -0.056           0.955\n",
       "L9.new_test_count         -0.203946         0.202233           -1.008           0.313\n",
       "L9.new_case_rate          -0.378833         0.206118           -1.838           0.066\n",
       "L9.new_death_rate          0.012692         0.139125            0.091           0.927\n",
       "L9.new_test_rate           0.203623         0.202104            1.008           0.314\n",
       "L9.case_count            -43.322934        93.137938           -0.465           0.642\n",
       "L9.case_rate               0.324066         1.967780            0.165           0.869\n",
       "L9.death_count            11.432911         4.151110            2.754           0.006\n",
       "L9.death_rate             -0.176886         0.070061           -2.525           0.012\n",
       "L9.test_count           -917.331290      1984.737498           -0.462           0.644\n",
       "L9.test_rate             -10.972038        39.551748           -0.277           0.781\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation test_count\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000005         0.000004            1.399           0.162\n",
       "L1.mean                   -0.000689         0.000526           -1.310           0.190\n",
       "L1.count                  -0.005299         0.005244           -1.010           0.312\n",
       "L1.median                  0.000375         0.000435            0.862           0.389\n",
       "L1.new_case_count          0.077984         0.051391            1.517           0.129\n",
       "L1.new_death_count         0.003755         0.009288            0.404           0.686\n",
       "L1.new_test_count         -0.538036         0.650494           -0.827           0.408\n",
       "L1.new_case_rate           0.000032         0.014564            0.002           0.998\n",
       "L1.new_death_rate         -0.002550         0.011020           -0.231           0.817\n",
       "L1.new_test_rate           0.001492         0.013571            0.110           0.912\n",
       "L1.case_count             -9.272631         6.146995           -1.508           0.131\n",
       "L1.case_rate               0.044404         0.129487            0.343           0.732\n",
       "L1.death_count            -0.053577         0.264663           -0.202           0.840\n",
       "L1.death_rate              0.005181         0.004572            1.133           0.257\n",
       "L1.test_count            100.148289       125.334892            0.799           0.424\n",
       "L1.test_rate               3.991616         2.698571            1.479           0.139\n",
       "L2.mean                    0.000023         0.000404            0.057           0.955\n",
       "L2.count                   0.009016         0.004970            1.814           0.070\n",
       "L2.median                 -0.000137         0.000313           -0.437           0.662\n",
       "L2.new_case_count          0.135680         0.108085            1.255           0.209\n",
       "L2.new_death_count        -0.004864         0.018034           -0.270           0.787\n",
       "L2.new_test_count         -0.969660         1.377042           -0.704           0.481\n",
       "L2.new_case_rate          -0.012538         0.028312           -0.443           0.658\n",
       "L2.new_death_rate          0.006189         0.021633            0.286           0.775\n",
       "L2.new_test_rate           0.008687         0.026316            0.330           0.741\n",
       "L2.case_count             -5.409091         8.157960           -0.663           0.507\n",
       "L2.case_rate               0.060298         0.236171            0.255           0.798\n",
       "L2.death_count            -0.012399         0.366574           -0.034           0.973\n",
       "L2.death_rate              0.004711         0.007393            0.637           0.524\n",
       "L2.test_count             77.243805       164.024516            0.471           0.638\n",
       "L2.test_rate               4.141466         5.115103            0.810           0.418\n",
       "L3.mean                   -0.000252         0.000408           -0.618           0.537\n",
       "L3.count                   0.007325         0.004330            1.692           0.091\n",
       "L3.median                  0.000171         0.000313            0.546           0.585\n",
       "L3.new_case_count          0.192406         0.154120            1.248           0.212\n",
       "L3.new_death_count        -0.012272         0.025008           -0.491           0.624\n",
       "L3.new_test_count         -1.108638         1.990867           -0.557           0.578\n",
       "L3.new_case_rate          -0.057988         0.038663           -1.500           0.134\n",
       "L3.new_death_rate          0.001479         0.029447            0.050           0.960\n",
       "L3.new_test_rate           0.021239         0.035637            0.596           0.551\n",
       "L3.case_count             -1.377504         7.966332           -0.173           0.863\n",
       "L3.case_rate               0.046969         0.324594            0.145           0.885\n",
       "L3.death_count             0.446865         0.370175            1.207           0.227\n",
       "L3.death_rate              0.007203         0.009440            0.763           0.445\n",
       "L3.test_count             21.235620       160.065929            0.133           0.894\n",
       "L3.test_rate               3.500086         7.012885            0.499           0.618\n",
       "L4.mean                    0.000045         0.000404            0.111           0.912\n",
       "L4.count                  -0.005497         0.004434           -1.240           0.215\n",
       "L4.median                 -0.000149         0.000314           -0.476           0.634\n",
       "L4.new_case_count          0.211222         0.180173            1.172           0.241\n",
       "L4.new_death_count        -0.004426         0.029358           -0.151           0.880\n",
       "L4.new_test_count         -0.662517         2.341885           -0.283           0.777\n",
       "L4.new_case_rate          -0.064787         0.045131           -1.436           0.151\n",
       "L4.new_death_rate         -0.012638         0.034085           -0.371           0.711\n",
       "L4.new_test_rate           0.026708         0.041160            0.649           0.516\n",
       "L4.case_count             -1.293739         7.233069           -0.179           0.858\n",
       "L4.case_rate              -0.134588         0.385866           -0.349           0.727\n",
       "L4.death_count             0.229112         0.325951            0.703           0.482\n",
       "L4.death_rate              0.002473         0.010786            0.229           0.819\n",
       "L4.test_count            -89.267153       142.638273           -0.626           0.531\n",
       "L4.test_rate               1.913503         8.289668            0.231           0.817\n",
       "L5.mean                    0.000316         0.000401            0.789           0.430\n",
       "L5.count                  -0.000171         0.004432           -0.039           0.969\n",
       "L5.median                 -0.000230         0.000310           -0.741           0.458\n",
       "L5.new_case_count          0.181957         0.181986            1.000           0.317\n",
       "L5.new_death_count        -0.006010         0.031021           -0.194           0.846\n",
       "L5.new_test_count         -1.420585         2.368022           -0.600           0.549\n",
       "L5.new_case_rate          -0.058952         0.046588           -1.265           0.206\n",
       "L5.new_death_rate         -0.010632         0.035166           -0.302           0.762\n",
       "L5.new_test_rate           0.051933         0.043195            1.202           0.229\n",
       "L5.case_count              2.984232         6.944820            0.430           0.667\n",
       "L5.case_rate              -0.221178         0.408438           -0.542           0.588\n",
       "L5.death_count            -0.014383         0.289838           -0.050           0.960\n",
       "L5.death_rate             -0.001705         0.011160           -0.153           0.879\n",
       "L5.test_count            140.529725       131.117297            1.072           0.284\n",
       "L5.test_rate               0.873039         8.641367            0.101           0.920\n",
       "L6.mean                    0.000225         0.000398            0.564           0.573\n",
       "L6.count                   0.001249         0.004512            0.277           0.782\n",
       "L6.median                 -0.000023         0.000305           -0.074           0.941\n",
       "L6.new_case_count          0.112874         0.158048            0.714           0.475\n",
       "L6.new_death_count        -0.004536         0.029550           -0.153           0.878\n",
       "L6.new_test_count         -1.396139         2.068843           -0.675           0.500\n",
       "L6.new_case_rate          -0.075538         0.044363           -1.703           0.089\n",
       "L6.new_death_rate         -0.007970         0.032384           -0.246           0.806\n",
       "L6.new_test_rate           0.061397         0.041135            1.493           0.136\n",
       "L6.case_count             10.460734         7.221347            1.449           0.147\n",
       "L6.case_rate              -0.279431         0.379432           -0.736           0.461\n",
       "L6.death_count            -0.147738         0.316322           -0.467           0.640\n",
       "L6.death_rate             -0.004825         0.010939           -0.441           0.659\n",
       "L6.test_count             -6.357548       140.732005           -0.045           0.964\n",
       "L6.test_rate              -0.363935         8.189220           -0.044           0.965\n",
       "L7.mean                   -0.000258         0.000397           -0.649           0.516\n",
       "L7.count                   0.002588         0.004560            0.568           0.570\n",
       "L7.median                  0.000254         0.000304            0.837           0.403\n",
       "L7.new_case_count          0.061810         0.112488            0.549           0.583\n",
       "L7.new_death_count        -0.004321         0.025193           -0.172           0.864\n",
       "L7.new_test_count         -1.086316         1.451498           -0.748           0.454\n",
       "L7.new_case_rate          -0.082253         0.037358           -2.202           0.028\n",
       "L7.new_death_rate         -0.001033         0.026807           -0.039           0.969\n",
       "L7.new_test_rate           0.047186         0.035453            1.331           0.183\n",
       "L7.case_count              6.901043         8.177367            0.844           0.399\n",
       "L7.case_rate              -0.035331         0.318356           -0.111           0.912\n",
       "L7.death_count            -0.264924         0.363397           -0.729           0.466\n",
       "L7.death_rate             -0.003551         0.009619           -0.369           0.712\n",
       "L7.test_count            -55.243070       163.417452           -0.338           0.735\n",
       "L7.test_rate              -1.985319         6.853673           -0.290           0.772\n",
       "L8.mean                    0.000153         0.000397            0.385           0.701\n",
       "L8.count                   0.009223         0.005287            1.745           0.081\n",
       "L8.median                 -0.000012         0.000312           -0.039           0.969\n",
       "L8.new_case_count          0.005538         0.056377            0.098           0.922\n",
       "L8.new_death_count        -0.007628         0.018162           -0.420           0.674\n",
       "L8.new_test_count         -0.538075         0.692798           -0.777           0.437\n",
       "L8.new_case_rate          -0.063525         0.026712           -2.378           0.017\n",
       "L8.new_death_rate          0.010952         0.018787            0.583           0.560\n",
       "L8.new_test_rate           0.029233         0.026124            1.119           0.263\n",
       "L8.case_count              4.422430         8.287970            0.534           0.594\n",
       "L8.case_rate               0.023885         0.235290            0.102           0.919\n",
       "L8.death_count            -0.324101         0.370704           -0.874           0.382\n",
       "L8.death_rate              0.000949         0.007562            0.125           0.900\n",
       "L8.test_count           -101.802649       172.824284           -0.589           0.556\n",
       "L8.test_rate              -0.011610         5.045534           -0.002           0.998\n",
       "L9.mean                   -0.000630         0.000505           -1.249           0.212\n",
       "L9.count                  -0.013577         0.005748           -2.362           0.018\n",
       "L9.median                  0.000132         0.000424            0.311           0.756\n",
       "L9.new_case_count          0.034337         0.013812            2.486           0.013\n",
       "L9.new_death_count        -0.010107         0.009320           -1.084           0.278\n",
       "L9.new_test_count         -0.013180         0.013573           -0.971           0.332\n",
       "L9.new_case_rate          -0.034111         0.013833           -2.466           0.014\n",
       "L9.new_death_rate          0.010328         0.009337            1.106           0.269\n",
       "L9.new_test_rate           0.013493         0.013564            0.995           0.320\n",
       "L9.case_count             -6.947138         6.250810           -1.111           0.266\n",
       "L9.case_rate               0.089555         0.132065            0.678           0.498\n",
       "L9.death_count             0.117869         0.278595            0.423           0.672\n",
       "L9.death_rate              0.001286         0.004702            0.273           0.784\n",
       "L9.test_count            -98.487015       133.202620           -0.739           0.460\n",
       "L9.test_rate               0.370054         2.654455            0.139           0.889\n",
       "=====================================================================================\n",
       "\n",
       "Results for equation test_rate\n",
       "=====================================================================================\n",
       "                        coefficient       std. error           t-stat            prob\n",
       "-------------------------------------------------------------------------------------\n",
       "const                      0.000005         0.000004            1.398           0.162\n",
       "L1.mean                   -0.000707         0.000526           -1.344           0.179\n",
       "L1.count                  -0.005547         0.005245           -1.058           0.290\n",
       "L1.median                  0.000392         0.000436            0.899           0.369\n",
       "L1.new_case_count          0.077647         0.051406            1.510           0.131\n",
       "L1.new_death_count         0.003683         0.009291            0.396           0.692\n",
       "L1.new_test_count         -0.571328         0.650685           -0.878           0.380\n",
       "L1.new_case_rate          -0.000423         0.014568           -0.029           0.977\n",
       "L1.new_death_rate         -0.001922         0.011023           -0.174           0.862\n",
       "L1.new_test_rate           0.001656         0.013575            0.122           0.903\n",
       "L1.case_count             -9.178418         6.148804           -1.493           0.136\n",
       "L1.case_rate               0.044160         0.129525            0.341           0.733\n",
       "L1.death_count            -0.074183         0.264740           -0.280           0.779\n",
       "L1.death_rate              0.005181         0.004573            1.133           0.257\n",
       "L1.test_count            108.170809       125.371774            0.863           0.388\n",
       "L1.test_rate               2.353244         2.699365            0.872           0.383\n",
       "L2.mean                    0.000025         0.000404            0.062           0.951\n",
       "L2.count                   0.009219         0.004971            1.854           0.064\n",
       "L2.median                 -0.000141         0.000313           -0.450           0.653\n",
       "L2.new_case_count          0.134648         0.108117            1.245           0.213\n",
       "L2.new_death_count        -0.004799         0.018039           -0.266           0.790\n",
       "L2.new_test_count         -1.015099         1.377447           -0.737           0.461\n",
       "L2.new_case_rate          -0.013299         0.028320           -0.470           0.639\n",
       "L2.new_death_rate          0.007349         0.021640            0.340           0.734\n",
       "L2.new_test_rate           0.008724         0.026323            0.331           0.740\n",
       "L2.case_count             -5.289647         8.160360           -0.648           0.517\n",
       "L2.case_rate               0.060264         0.236240            0.255           0.799\n",
       "L2.death_count            -0.037758         0.366682           -0.103           0.918\n",
       "L2.death_rate              0.004655         0.007396            0.629           0.529\n",
       "L2.test_count             81.587470       164.072782            0.497           0.619\n",
       "L2.test_rate               2.163679         5.116608            0.423           0.672\n",
       "L3.mean                   -0.000249         0.000408           -0.611           0.541\n",
       "L3.count                   0.007368         0.004331            1.701           0.089\n",
       "L3.median                  0.000165         0.000313            0.526           0.599\n",
       "L3.new_case_count          0.190242         0.154165            1.234           0.217\n",
       "L3.new_death_count        -0.012361         0.025015           -0.494           0.621\n",
       "L3.new_test_count         -1.163689         1.991453           -0.584           0.559\n",
       "L3.new_case_rate          -0.058858         0.038675           -1.522           0.128\n",
       "L3.new_death_rate          0.003337         0.029456            0.113           0.910\n",
       "L3.new_test_rate           0.021011         0.035648            0.589           0.556\n",
       "L3.case_count             -1.224539         7.968676           -0.154           0.878\n",
       "L3.case_rate               0.041137         0.324690            0.127           0.899\n",
       "L3.death_count             0.426499         0.370284            1.152           0.249\n",
       "L3.death_rate              0.007118         0.009443            0.754           0.451\n",
       "L3.test_count             25.243327       160.113030            0.158           0.875\n",
       "L3.test_rate               1.391206         7.014948            0.198           0.843\n",
       "L4.mean                    0.000050         0.000404            0.124           0.901\n",
       "L4.count                  -0.005453         0.004435           -1.229           0.219\n",
       "L4.median                 -0.000153         0.000314           -0.487           0.626\n",
       "L4.new_case_count          0.209006         0.180226            1.160           0.246\n",
       "L4.new_death_count        -0.004638         0.029367           -0.158           0.875\n",
       "L4.new_test_count         -0.699208         2.342575           -0.298           0.765\n",
       "L4.new_case_rate          -0.065801         0.045145           -1.458           0.145\n",
       "L4.new_death_rate         -0.010363         0.034095           -0.304           0.761\n",
       "L4.new_test_rate           0.026528         0.041172            0.644           0.519\n",
       "L4.case_count             -1.264012         7.235197           -0.175           0.861\n",
       "L4.case_rate              -0.141041         0.385979           -0.365           0.715\n",
       "L4.death_count             0.217754         0.326047            0.668           0.504\n",
       "L4.death_rate              0.002465         0.010789            0.228           0.819\n",
       "L4.test_count            -90.782776       142.680246           -0.636           0.525\n",
       "L4.test_rate              -0.114047         8.292108           -0.014           0.989\n",
       "L5.mean                    0.000318         0.000401            0.792           0.428\n",
       "L5.count                  -0.000275         0.004433           -0.062           0.951\n",
       "L5.median                 -0.000233         0.000310           -0.753           0.451\n",
       "L5.new_case_count          0.180656         0.182040            0.992           0.321\n",
       "L5.new_death_count        -0.006776         0.031030           -0.218           0.827\n",
       "L5.new_test_count         -1.441789         2.368719           -0.609           0.543\n",
       "L5.new_case_rate          -0.059813         0.046602           -1.283           0.199\n",
       "L5.new_death_rate         -0.008027         0.035176           -0.228           0.819\n",
       "L5.new_test_rate           0.051463         0.043207            1.191           0.234\n",
       "L5.case_count              2.869126         6.946863            0.413           0.680\n",
       "L5.case_rate              -0.232622         0.408558           -0.569           0.569\n",
       "L5.death_count            -0.005966         0.289924           -0.021           0.984\n",
       "L5.death_rate             -0.001814         0.011163           -0.162           0.871\n",
       "L5.test_count            139.339149       131.155880            1.062           0.288\n",
       "L5.test_rate              -0.866336         8.643910           -0.100           0.920\n",
       "L6.mean                    0.000233         0.000398            0.586           0.558\n",
       "L6.count                   0.001315         0.004513            0.291           0.771\n",
       "L6.median                 -0.000031         0.000305           -0.101           0.920\n",
       "L6.new_case_count          0.112748         0.158094            0.713           0.476\n",
       "L6.new_death_count        -0.005587         0.029559           -0.189           0.850\n",
       "L6.new_test_count         -1.404580         2.069451           -0.679           0.497\n",
       "L6.new_case_rate          -0.076469         0.044376           -1.723           0.085\n",
       "L6.new_death_rate         -0.005611         0.032393           -0.173           0.862\n",
       "L6.new_test_rate           0.060454         0.041147            1.469           0.142\n",
       "L6.case_count             10.343770         7.223472            1.432           0.152\n",
       "L6.case_rate              -0.293660         0.379544           -0.774           0.439\n",
       "L6.death_count            -0.127892         0.316415           -0.404           0.686\n",
       "L6.death_rate             -0.004751         0.010942           -0.434           0.664\n",
       "L6.test_count             -7.492580       140.773417           -0.053           0.958\n",
       "L6.test_rate              -1.595226         8.191629           -0.195           0.846\n",
       "L7.mean                   -0.000273         0.000397           -0.687           0.492\n",
       "L7.count                   0.002525         0.004562            0.554           0.580\n",
       "L7.median                  0.000259         0.000304            0.854           0.393\n",
       "L7.new_case_count          0.060970         0.112521            0.542           0.588\n",
       "L7.new_death_count        -0.005438         0.025200           -0.216           0.829\n",
       "L7.new_test_count         -1.093185         1.451925           -0.753           0.451\n",
       "L7.new_case_rate          -0.083285         0.037369           -2.229           0.026\n",
       "L7.new_death_rate          0.000711         0.026815            0.027           0.979\n",
       "L7.new_test_rate           0.046813         0.035463            1.320           0.187\n",
       "L7.case_count              7.009589         8.179773            0.857           0.391\n",
       "L7.case_rate              -0.047091         0.318450           -0.148           0.882\n",
       "L7.death_count            -0.239188         0.363504           -0.658           0.511\n",
       "L7.death_rate             -0.003515         0.009621           -0.365           0.715\n",
       "L7.test_count            -54.841406       163.465540           -0.335           0.737\n",
       "L7.test_rate              -2.801840         6.855690           -0.409           0.683\n",
       "L8.mean                    0.000151         0.000398            0.379           0.705\n",
       "L8.count                   0.009522         0.005288            1.801           0.072\n",
       "L8.median                 -0.000008         0.000312           -0.026           0.980\n",
       "L8.new_case_count          0.004367         0.056394            0.077           0.938\n",
       "L8.new_death_count        -0.008443         0.018167           -0.465           0.642\n",
       "L8.new_test_count         -0.529577         0.693001           -0.764           0.445\n",
       "L8.new_case_rate          -0.063808         0.026720           -2.388           0.017\n",
       "L8.new_death_rate          0.011808         0.018792            0.628           0.530\n",
       "L8.new_test_rate           0.029135         0.026131            1.115           0.265\n",
       "L8.case_count              4.383527         8.290409            0.529           0.597\n",
       "L8.case_rate               0.012210         0.235359            0.052           0.959\n",
       "L8.death_count            -0.301666         0.370813           -0.814           0.416\n",
       "L8.death_rate              0.000903         0.007564            0.119           0.905\n",
       "L8.test_count           -104.371214       172.875139           -0.604           0.546\n",
       "L8.test_rate              -0.454711         5.047019           -0.090           0.928\n",
       "L9.mean                   -0.000640         0.000505           -1.267           0.205\n",
       "L9.count                  -0.013890         0.005750           -2.416           0.016\n",
       "L9.median                  0.000140         0.000424            0.330           0.741\n",
       "L9.new_case_count          0.034128         0.013816            2.470           0.014\n",
       "L9.new_death_count        -0.010516         0.009322           -1.128           0.259\n",
       "L9.new_test_count         -0.013174         0.013577           -0.970           0.332\n",
       "L9.new_case_rate          -0.033900         0.013837           -2.450           0.014\n",
       "L9.new_death_rate          0.010739         0.009340            1.150           0.250\n",
       "L9.new_test_rate           0.013481         0.013568            0.994           0.320\n",
       "L9.case_count             -7.111950         6.252650           -1.137           0.255\n",
       "L9.case_rate               0.082251         0.132103            0.623           0.534\n",
       "L9.death_count             0.119296         0.278677            0.428           0.669\n",
       "L9.death_rate              0.001365         0.004703            0.290           0.772\n",
       "L9.test_count            -96.701459       133.241816           -0.726           0.468\n",
       "L9.test_rate               0.202249         2.655236            0.076           0.939\n",
       "=====================================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                       mean     count    median  new_case_count  new_death_count  new_test_count  new_case_rate  new_death_rate  new_test_rate  case_count  case_rate  death_count  death_rate  test_count  test_rate\n",
       "mean               1.000000 -0.017015  0.784316        0.044592         0.113421        0.033670       0.044014        0.115074       0.033529    0.044586   0.041764     0.115955    0.018687    0.033683   0.033290\n",
       "count             -0.017015  1.000000 -0.001255       -0.035316         0.016690       -0.013463      -0.036163        0.018359      -0.013192   -0.035516  -0.047769     0.019983   -0.018722   -0.013435  -0.011806\n",
       "median             0.784316 -0.001255  1.000000       -0.008432         0.112461       -0.000674      -0.008580        0.113565      -0.001108   -0.008370  -0.008428     0.113906    0.041999   -0.000679   0.000351\n",
       "new_case_count     0.044592 -0.035316 -0.008432        1.000000         0.220864        0.004717       0.999940        0.220566       0.006719    0.999995   0.990172     0.218449    0.043644    0.004735   0.004557\n",
       "new_death_count    0.113421  0.016690  0.112461        0.220864         1.000000       -0.032345       0.221794        0.999780      -0.031145    0.220680   0.206933     0.999445    0.542559   -0.032263  -0.030902\n",
       "new_test_count     0.033670 -0.013463 -0.000674        0.004717        -0.032345        1.000000       0.005211       -0.033116       0.999748    0.004741   0.010619    -0.033647    0.004784    1.000000   0.999780\n",
       "new_case_rate      0.044014 -0.036163 -0.008580        0.999940         0.221794        0.005211       1.000000        0.221513       0.007229    0.999941   0.990108     0.219385    0.045442    0.005229   0.005037\n",
       "new_death_rate     0.115074  0.018359  0.113565        0.220566         0.999780       -0.033116       0.221513        1.000000      -0.031943    0.220384   0.206623     0.999607    0.542952   -0.033035  -0.031691\n",
       "new_test_rate      0.033529 -0.013192 -0.001108        0.006719        -0.031145        0.999748       0.007229       -0.031943       1.000000    0.006742   0.012139    -0.032416    0.006369    0.999748   0.999554\n",
       "case_count         0.044586 -0.035516 -0.008370        0.999995         0.220680        0.004741       0.999941        0.220384       0.006742    1.000000   0.990182     0.218269    0.043471    0.004759   0.004579\n",
       "case_rate          0.041764 -0.047769 -0.008428        0.990172         0.206933        0.010619       0.990108        0.206623       0.012139    0.990182   1.000000     0.204495    0.038110    0.010629   0.010532\n",
       "death_count        0.115955  0.019983  0.113906        0.218449         0.999445       -0.033647       0.219385        0.999607      -0.032416    0.218269   0.204495     1.000000    0.541002   -0.033565  -0.032220\n",
       "death_rate         0.018687 -0.018722  0.041999        0.043644         0.542559        0.004784       0.045442        0.542952       0.006369    0.043471   0.038110     0.541002    1.000000    0.004812   0.005540\n",
       "test_count         0.033683 -0.013435 -0.000679        0.004735        -0.032263        1.000000       0.005229       -0.033035       0.999748    0.004759   0.010629    -0.033565    0.004812    1.000000   0.999779\n",
       "test_rate          0.033290 -0.011806  0.000351        0.004557        -0.030902        0.999780       0.005037       -0.031691       0.999554    0.004579   0.010532    -0.032220    0.005540    0.999779   1.000000\n",
       "\n"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fitted = model.fit(maxlags=9,ic='aic')\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Durbin Watson Statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean : 1.97\n",
      "count : 1.94\n",
      "median : 2.01\n",
      "new_case_count : 1.96\n",
      "new_death_count : 1.96\n",
      "new_test_count : 2.02\n",
      "new_case_rate : 1.96\n",
      "new_death_rate : 1.96\n",
      "new_test_rate : 2.02\n",
      "case_count : 1.96\n",
      "case_rate : 1.97\n",
      "death_count : 1.96\n",
      "death_rate : 1.99\n",
      "test_count : 2.02\n",
      "test_rate : 2.02\n"
     ]
    }
   ],
   "source": [
    "#Check for Serial Correlation of Residuals (Errors) using Durbin Watson Statistic\n",
    "#The value of this statistic can vary between 0 and 4. \n",
    "#The closer it is to the value 2, then there is no significant serial correlation. \n",
    "#The closer to 0, there is a positive serial correlation, \n",
    "#and the closer it is to 4 implies negative serial correlation.\n",
    "\n",
    "\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "out = durbin_watson(model_fitted.resid)\n",
    "\n",
    "for col, val in zip(cdf.columns, out):\n",
    "    print(col, ':', round(val, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Co-integration using t-statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.8653530124235614, 0.9255566996756764, array([-3.92179396, -3.35021226, -3.05421426]))\n"
     ]
    }
   ],
   "source": [
    "#  check for co-integration using t-statistics\n",
    "\n",
    "import statsmodels.tsa.stattools as ts \n",
    "result=ts.coint(cdf['case_count'], cdf['mean'])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "#get the lag order\n",
    "lag_order = model_fitted.k_ar\n",
    "print(lag_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Granger's Causality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "maxlag=lag_order #becuase we got this value before. We are not suppose to add 1 to it\n",
    "test = 'ssr_chi2test'\n",
    "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):    \n",
    "    \"\"\"Check Granger Causality of all possible combinations of the Time series.\n",
    "    The rows are the response variable, columns are predictors. The values in the table \n",
    "    are the P-Values. P-Values lesser than the significance level (0.05), implies \n",
    "    the Null Hypothesis that the coefficients of the corresponding past values is \n",
    "    zero, that is, the X does not cause Y can be rejected.\n",
    "\n",
    "    data      : pandas dataframe containing the time series variables\n",
    "    variables : list containing names of the time series variables.\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
    "    for c in df.columns:\n",
    "        for r in df.index:\n",
    "            test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n",
    "            p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n",
    "            if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n",
    "            min_p_value = np.min(p_values)\n",
    "            df.loc[r, c] = min_p_value\n",
    "    df.columns = [var + '_x' for var in variables]\n",
    "    df.index = [var + '_y' for var in variables]\n",
    "    return df\n",
    "\n",
    "o = grangers_causation_matrix(cdf, variables = cdf.columns)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_x</th>\n",
       "      <th>count_x</th>\n",
       "      <th>median_x</th>\n",
       "      <th>new_case_count_x</th>\n",
       "      <th>new_death_count_x</th>\n",
       "      <th>new_test_count_x</th>\n",
       "      <th>new_case_rate_x</th>\n",
       "      <th>new_death_rate_x</th>\n",
       "      <th>new_test_rate_x</th>\n",
       "      <th>case_count_x</th>\n",
       "      <th>case_rate_x</th>\n",
       "      <th>death_count_x</th>\n",
       "      <th>death_rate_x</th>\n",
       "      <th>test_count_x</th>\n",
       "      <th>test_rate_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_y</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count_y</th>\n",
       "      <td>0.2390</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1214</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1249</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1246</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.3194</td>\n",
       "      <td>0.0127</td>\n",
       "      <td>0.0126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_y</th>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0271</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0962</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0960</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_count_y</th>\n",
       "      <td>0.1328</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.0427</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0106</td>\n",
       "      <td>0.0115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_death_count_y</th>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.8249</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5745</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.5751</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.7237</td>\n",
       "      <td>0.7237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_test_count_y</th>\n",
       "      <td>0.6011</td>\n",
       "      <td>0.2318</td>\n",
       "      <td>0.6671</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.7954</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.2408</td>\n",
       "      <td>0.7842</td>\n",
       "      <td>0.0546</td>\n",
       "      <td>0.1969</td>\n",
       "      <td>0.1146</td>\n",
       "      <td>0.2858</td>\n",
       "      <td>0.2826</td>\n",
       "      <td>0.2204</td>\n",
       "      <td>0.1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_case_rate_y</th>\n",
       "      <td>0.1331</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.0930</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_death_rate_y</th>\n",
       "      <td>0.0011</td>\n",
       "      <td>0.8218</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.5744</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.5751</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0.7226</td>\n",
       "      <td>0.7226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_test_rate_y</th>\n",
       "      <td>0.6023</td>\n",
       "      <td>0.2288</td>\n",
       "      <td>0.6700</td>\n",
       "      <td>0.2410</td>\n",
       "      <td>0.7789</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>0.2420</td>\n",
       "      <td>0.7683</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1975</td>\n",
       "      <td>0.1107</td>\n",
       "      <td>0.2857</td>\n",
       "      <td>0.2824</td>\n",
       "      <td>0.1451</td>\n",
       "      <td>0.0658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_count_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1294</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0378</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>case_rate_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1325</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>death_count_y</th>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.4071</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.4652</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>death_rate_y</th>\n",
       "      <td>0.0013</td>\n",
       "      <td>0.4520</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0251</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.0445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_count_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.2461</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2415</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2147</td>\n",
       "      <td>0.1913</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test_rate_y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.2469</td>\n",
       "      <td>0.0204</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2423</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2147</td>\n",
       "      <td>0.2147</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2945</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean_x  count_x  median_x  new_case_count_x  \\\n",
       "mean_y             1.0000   0.0034    0.0163            0.0006   \n",
       "count_y            0.2390   1.0000    0.1214            0.0000   \n",
       "median_y           0.0103   0.0271    1.0000            0.0962   \n",
       "new_case_count_y   0.1328   0.0000    0.1098            1.0000   \n",
       "new_death_count_y  0.0012   0.8249    0.0000            0.0057   \n",
       "new_test_count_y   0.6011   0.2318    0.6671            0.2397   \n",
       "new_case_rate_y    0.1331   0.0000    0.1099            0.0116   \n",
       "new_death_rate_y   0.0011   0.8218    0.0000            0.0057   \n",
       "new_test_rate_y    0.6023   0.2288    0.6700            0.2410   \n",
       "case_count_y       0.0000   0.0000    0.1294            0.0000   \n",
       "case_rate_y        0.0000   0.0000    0.1325            0.0000   \n",
       "death_count_y      0.0006   0.4071    0.0000            0.0000   \n",
       "death_rate_y       0.0013   0.4520    0.0000            0.0000   \n",
       "test_count_y       0.0000   0.0000    0.0394            0.2461   \n",
       "test_rate_y        0.0000   0.0000    0.0394            0.2469   \n",
       "\n",
       "                   new_death_count_x  new_test_count_x  new_case_rate_x  \\\n",
       "mean_y                        0.0089            0.0001           0.0006   \n",
       "count_y                       0.1249            0.0251           0.0000   \n",
       "median_y                      0.0000            0.0000           0.0960   \n",
       "new_case_count_y              0.0000            0.0068           0.0073   \n",
       "new_death_count_y             1.0000            0.5745           0.0057   \n",
       "new_test_count_y              0.7954            1.0000           0.2408   \n",
       "new_case_rate_y               0.0000            0.0070           1.0000   \n",
       "new_death_rate_y              0.0014            0.5744           0.0057   \n",
       "new_test_rate_y               0.7789            0.0403           0.2420   \n",
       "case_count_y                  0.0000            0.0010           0.0000   \n",
       "case_rate_y                   0.0000            0.0010           0.0000   \n",
       "death_count_y                 0.0000            0.0121           0.0000   \n",
       "death_rate_y                  0.0000            0.0251           0.0000   \n",
       "test_count_y                  0.0204            0.0000           0.2415   \n",
       "test_rate_y                   0.0204            0.0000           0.2423   \n",
       "\n",
       "                   new_death_rate_x  new_test_rate_x  case_count_x  \\\n",
       "mean_y                       0.0088           0.0001        0.0000   \n",
       "count_y                      0.1246           0.0234        0.0000   \n",
       "median_y                     0.0000           0.0000        0.0001   \n",
       "new_case_count_y             0.0000           0.0074        0.0274   \n",
       "new_death_count_y            0.0005           0.5751        0.0000   \n",
       "new_test_count_y             0.7842           0.0546        0.1969   \n",
       "new_case_rate_y              0.0001           0.0075        0.0322   \n",
       "new_death_rate_y             1.0000           0.5751        0.0000   \n",
       "new_test_rate_y              0.7683           1.0000        0.1975   \n",
       "case_count_y                 0.0000           0.0010        1.0000   \n",
       "case_rate_y                  0.0000           0.0010        0.0000   \n",
       "death_count_y                0.0000           0.0120        0.0000   \n",
       "death_rate_y                 0.0000           0.0250        0.0000   \n",
       "test_count_y                 0.0205           0.0000        0.2147   \n",
       "test_rate_y                  0.0205           0.0000        0.2147   \n",
       "\n",
       "                   case_rate_x  death_count_x  death_rate_x  test_count_x  \\\n",
       "mean_y                  0.0000         0.0041        0.0146        0.0000   \n",
       "count_y                 0.0000         0.3196        0.3194        0.0127   \n",
       "median_y                0.0001         0.0000        0.0021        0.0000   \n",
       "new_case_count_y        0.0427         0.0000        0.0000        0.0106   \n",
       "new_death_count_y       0.0000         0.0000        0.0046        0.7237   \n",
       "new_test_count_y        0.1146         0.2858        0.2826        0.2204   \n",
       "new_case_rate_y         0.0930         0.0000        0.0000        0.0108   \n",
       "new_death_rate_y        0.0000         0.0030        0.0045        0.7226   \n",
       "new_test_rate_y         0.1107         0.2857        0.2824        0.1451   \n",
       "case_count_y            0.0378         0.0000        0.0000        0.0000   \n",
       "case_rate_y             1.0000         0.0000        0.0000        0.0000   \n",
       "death_count_y           0.0000         1.0000        0.4652        0.0094   \n",
       "death_rate_y            0.0000         0.0000        1.0000        0.0471   \n",
       "test_count_y            0.1913         0.0000        0.0000        1.0000   \n",
       "test_rate_y             0.2147         0.0000        0.0000        0.2945   \n",
       "\n",
       "                   test_rate_x  \n",
       "mean_y                  0.0000  \n",
       "count_y                 0.0126  \n",
       "median_y                0.0000  \n",
       "new_case_count_y        0.0115  \n",
       "new_death_count_y       0.7237  \n",
       "new_test_count_y        0.1030  \n",
       "new_case_rate_y         0.0117  \n",
       "new_death_rate_y        0.7226  \n",
       "new_test_rate_y         0.0658  \n",
       "case_count_y            0.0000  \n",
       "case_rate_y             0.0000  \n",
       "death_count_y           0.0094  \n",
       "death_rate_y            0.0445  \n",
       "test_count_y            0.1414  \n",
       "test_rate_y             1.0000  "
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coint Johansen Cointegration test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "def cointegration_test(cdf_diff):\n",
    "    res = coint_johansen(cdf_diff,-1,5)\n",
    "    d = {'0.90':0, '0.95':1, '0.99':2}\n",
    "    traces = res.lr1\n",
    "    cvts = res.cvt[:, d[str(1-0.05)]]\n",
    "    def adjust(val, length= 6):\n",
    "        return str(val).ljust(length)\n",
    "    print('Column Name > Test Stat > C(95%) => Signif\\n', '--'*20)\n",
    "    for col, trace, cvt in zip(cdf_diff.columns, traces, cvts):\n",
    "          print(adjust(col), '> ', adjust(round(trace,2), 9),\">\", adjust(cvt, 8), ' => ' , trace > cvt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name > Test Stat > C(95%) => Signif\n",
      " ----------------------------------------\n",
      "mean   >  113.73    > 12.3212   =>  True\n",
      "case_count >  46.45     > 4.1296    =>  True\n"
     ]
    }
   ],
   "source": [
    "cointegration_test(cdf_diff[['mean', 'case_count']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VAR Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_diff(actual_df, pred_df):\n",
    "    df_res = pred_df.copy()\n",
    "    columns = actual_df.columns\n",
    "    for col in columns:\n",
    "        df_res[str(col)+'_1st_inv_diff'] = actual_df[col].iloc[-1] + df_res[str(col)].cumsum()\n",
    "        return df_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cdf[['mean', 'case_count']]\n",
    "train, test = X[0:-30], X[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_diff = train.diff()\n",
    "train_diff.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Mon, 03, May, 2021\n",
       "Time:                     23:59:45\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         2.00000    BIC:                   -26.6301\n",
       "Nobs:                     396.000    HQIC:                  -26.8607\n",
       "Log likelihood:           4262.60    FPE:                1.85719e-12\n",
       "AIC:                     -27.0121    Det(Omega_mle):     1.69102e-12\n",
       "--------------------------------------------------------------------\n",
       "Results for equation mean\n",
       "================================================================================\n",
       "                   coefficient       std. error           t-stat            prob\n",
       "--------------------------------------------------------------------------------\n",
       "const                -0.000045         0.000803           -0.056           0.955\n",
       "L1.mean               0.967966         0.043443           22.281           0.000\n",
       "L1.case_count        -5.181653         4.956486           -1.045           0.296\n",
       "L2.mean              -0.070850         0.057263           -1.237           0.216\n",
       "L2.case_count         4.021303         8.199537            0.490           0.624\n",
       "L3.mean               0.003125         0.057288            0.055           0.956\n",
       "L3.case_count        -0.071264         7.898112           -0.009           0.993\n",
       "L4.mean              -0.021027         0.056851           -0.370           0.711\n",
       "L4.case_count        11.571922         7.863471            1.472           0.141\n",
       "L5.mean              -0.014146         0.056935           -0.248           0.804\n",
       "L5.case_count        -1.298983         7.826636           -0.166           0.868\n",
       "L6.mean               0.028097         0.056899            0.494           0.621\n",
       "L6.case_count       -18.417837         7.905096           -2.330           0.020\n",
       "L7.mean               0.003356         0.056866            0.059           0.953\n",
       "L7.case_count        13.266141         8.081841            1.641           0.101\n",
       "L8.mean              -0.632071         0.056334          -11.220           0.000\n",
       "L8.case_count       -14.390559         8.425666           -1.708           0.088\n",
       "L9.mean               0.533365         0.042568           12.530           0.000\n",
       "L9.case_count        10.526386         5.066653            2.078           0.038\n",
       "================================================================================\n",
       "\n",
       "Results for equation case_count\n",
       "================================================================================\n",
       "                   coefficient       std. error           t-stat            prob\n",
       "--------------------------------------------------------------------------------\n",
       "const                 0.000015         0.000008            1.904           0.057\n",
       "L1.mean              -0.000180         0.000428           -0.420           0.674\n",
       "L1.case_count         1.461899         0.048850           29.926           0.000\n",
       "L2.mean               0.000656         0.000564            1.162           0.245\n",
       "L2.case_count        -0.522169         0.080813           -6.461           0.000\n",
       "L3.mean              -0.001408         0.000565           -2.494           0.013\n",
       "L3.case_count         0.254461         0.077842            3.269           0.001\n",
       "L4.mean               0.000687         0.000560            1.226           0.220\n",
       "L4.case_count        -0.084218         0.077501           -1.087           0.277\n",
       "L5.mean              -0.000215         0.000561           -0.384           0.701\n",
       "L5.case_count        -0.213434         0.077138           -2.767           0.006\n",
       "L6.mean               0.000554         0.000561            0.988           0.323\n",
       "L6.case_count         0.346831         0.077911            4.452           0.000\n",
       "L7.mean              -0.000266         0.000560           -0.475           0.635\n",
       "L7.case_count        -0.702033         0.079653           -8.814           0.000\n",
       "L8.mean               0.000458         0.000555            0.825           0.409\n",
       "L8.case_count         0.783270         0.083042            9.432           0.000\n",
       "L9.mean              -0.000944         0.000420           -2.250           0.024\n",
       "L9.case_count        -0.330135         0.049936           -6.611           0.000\n",
       "================================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "                  mean  case_count\n",
       "mean          1.000000   -0.042632\n",
       "case_count   -0.042632    1.000000\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = model.fit(9)\n",
    "display(results.summary())\n",
    "z = results.forecast(y=train_diff.values, steps=30)\n",
    "df_pred = pd.DataFrame(z, columns=train_diff.columns)\n",
    "df_pred[\"new_index\"] = range(433, 463)\n",
    "df_pred = df_pred.set_index(\"new_index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let’s inverse the differenced prediction, as shown here:\n",
    "res = inverse_diff(cdf[['mean', 'case_count']],df_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation & Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
    "    def mean_absolute_percentage_error(y_true, y_pred):\n",
    "        y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "        return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "    print('Evaluation metric results:-')\n",
    "    print(f'MSE is : {metrics.mean_squared_error(y_true,y_pred)}')\n",
    "    print(f'MAE is : {metrics.mean_absolute_error(y_true,y_pred)}')\n",
    "    print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
    "    print(f'MAPE is : {mean_absolute_percentage_error(y_true,y_pred)}')\n",
    "    print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast accuracy of CASE_COUNT\n",
      "RMSE:  0.85\n",
      "MAE:  0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "rmse = mean_squared_error(test['case_count'], res['case_count'])\n",
    "mae = mean_absolute_error(test['case_count'], res['case_count'])\n",
    "\n",
    "print('Forecast accuracy of CASE_COUNT')\n",
    "print('RMSE: ', round(rmse,2))\n",
    "print('MAE: ', round(mae,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast accuracy of Rent\n",
      "RMSE:  0.01\n",
      "MAE:  0.11\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "\n",
    "rmse = mean_squared_error(test['mean'], res['mean'])\n",
    "mae = mean_absolute_error(test['mean'], res['mean'])\n",
    "\n",
    "print('Forecast accuracy of Rent')\n",
    "print('RMSE: ', round(rmse,2))\n",
    "print('MAE: ', round(mae,2))"
   ]
  }
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