{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study: Modeling Bikeshare Usage\n",
    "\n",
    "In this case study, we'll use regression techniques to study some of the factors that drive the usage of bikesharing systems. The data set for this case study was collected from the Capital Bikeshare system in Washington DC. We use the aggregated version graciously provided by the authors of the following paper: \n",
    "\n",
    "> Fanaee-T, Hadi, and Gama, Joao, \"Event labeling combining ensemble detectors and background knowledge\", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, doi:10.1007/s13748-013-0040-3.\n",
    "\n",
    "This data set includes information about the season and time of year; the weather; and the count of bicycle users on each day for two years. This level of information gives us considerable ability to model phenomena in the data. \n",
    "\n",
    "In this case study, our primary aim will be **explanatory** modeling rather than **predictive** modeling. That is, we are more interested in learning something about the data we have than we are about predicting new, unseen data. To this end, we'll be prioritizing using a simple model with interpretable parameters. \n",
    "\n",
    "Some people would consider the emphasis on explanation vs. prediction to be the dividing line between *statistics* and *machine learning*, but in practice this is very fuzzy indeed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "def retrieve_data(url):\n",
    "    \"\"\"\n",
    "    Retrieve a file from the specified url and save it in a local file \n",
    "    called data.csv. The intended values of url are:     \n",
    "    \"\"\"\n",
    "    \n",
    "    # grab the data and parse it\n",
    "    filedata = urllib.request.urlopen(url) \n",
    "    to_write = filedata.read()\n",
    "    \n",
    "    # write to file\n",
    "    with open(\"data.csv\", \"wb\") as f:\n",
    "        f.write(to_write)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
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       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
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       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
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       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0          1  2011-01-01       1   0     1        0        6           0   \n",
       "1          2  2011-01-02       1   0     1        0        0           0   \n",
       "2          3  2011-01-03       1   0     1        0        1           1   \n",
       "3          4  2011-01-04       1   0     1        0        2           1   \n",
       "4          5  2011-01-05       1   0     1        0        3           1   \n",
       "..       ...         ...     ...  ..   ...      ...      ...         ...   \n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0             2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1             2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2             1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3             1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4             1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "..          ...       ...       ...       ...        ...     ...         ...   \n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "0     985  \n",
       "1     801  \n",
       "2    1349  \n",
       "3    1562  \n",
       "4    1600  \n",
       "..    ...  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  \n",
       "\n",
       "[731 rows x 16 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retrieve_data(\"https://philchodrow.github.io/PIC16A/datasets/Bike-Sharing-Dataset/day.csv\")\n",
    "bikeshare = pd.read_csv(\"data.csv\")\n",
    "bikeshare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are actually three columns we could aim to predict. *Casual* users don't sign up for the bikeshare service -- they just pay each time they decide to take a ride. In contrast, *registered* users pay a regular subscription fee. The `cnt` column is simply the sum of the casual and registered rides for each day. For this case study, we will focus on modeling the behavior of casual riders. \n",
    "\n",
    "## Exploratory Analysis\n",
    "\n",
    "Let's start by simply visualizing the total counts over time. For this, it helps to convert the `dteday` column into a proper `datetime` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "bikeshare['dteday'] = pd.to_datetime(bikeshare['dteday'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make a simple plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcfc80cedf0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (10, 7))\n",
    "ax.plot(bikeshare['dteday'], bikeshare['casual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few observations: \n",
    "\n",
    "- There seems to have been a slight overall increase in casual ridership over time. \n",
    "- There is a lot of variation in the data, with some days having very few riders and other days very many.  \n",
    "- There's some seasonal variation -- for example, we see noticeable dips in winter. \n",
    "\n",
    "I wonder whether there's much of a difference between weekend vs. weekday ridership. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>casual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1371.134199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>606.570000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 casual\n",
       "workingday             \n",
       "0           1371.134199\n",
       "1            606.570000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikeshare.groupby('workingday')[[\"casual\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! There are over twice as many casual riders, on average, on weekends. Let's take a look at how this breaks down by month. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcfc8707a60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = bikeshare.groupby(['workingday', 'mnth'])[['casual']].mean()\n",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "ax.plot(m.loc[0], label = \"Weekends\")\n",
    "ax.plot(m.loc[1], label = \"Weekdays\")\n",
    "\n",
    "ax.set(xlabel = 'Month', \n",
    "       ylabel = 'Number of casual riders')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we're beginning to see some patterns now. There's a clear increase in casual ridership on the weekends, and in warmer months in the middle of the year. So, we'd expect these variables to play major roles in our modeling.\n",
    "\n",
    "We can break things up further -- what's the dependence on the weather? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcfc87e59d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = bikeshare.groupby(['workingday', 'weathersit', 'mnth'])[['casual']].mean()\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize = (10, 3), sharey = True)\n",
    "weather_codes = {1 : \"Clear\", \n",
    "                 2 : \"Cloudy\", \n",
    "                 3 : \"Light precipitation\"}\n",
    "\n",
    "for i in weather_codes.keys():\n",
    "    ax[i-1].plot(m.loc[0].loc[i], label = \"Weekends\")\n",
    "    ax[i-1].plot(m.loc[1].loc[i], label = \"Weekdays\")\n",
    "    ax[i-1].set(title = weather_codes[i])\n",
    "    \n",
    "ax[0].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, this makes sense: cloudy weather doesn't seem to be much of a deterrent, but precipitation makes a major difference. \n",
    "\n",
    "## Data Prep\n",
    "\n",
    "We've now learned quite a lot about what we're looking for, so let's go ahead and begin preparing our data for modeling. Let's first grab only the variables that we want to include as a new data frame. \n",
    "\n",
    "Note that we've excluded, for example, `season`, because all the information in the `season` column is actually already contained in the `mnth`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"casual\", \n",
    "        \"mnth\", \n",
    "        \"weathersit\", \n",
    "        \"workingday\",\n",
    "        \"yr\",\n",
    "        \"temp\", \n",
    "        \"hum\", \n",
    "        \"windspeed\",\n",
    "        \"holiday\"]\n",
    "\n",
    "model_df = bikeshare[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to encode the `mnth` column in a way that an algorithm can understand. The easiest way is to use `pd.get_dummies()`, which will create a `0-1` column associated to each possible value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>casual</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>workingday</th>\n",
       "      <th>yr</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>holiday</th>\n",
       "      <th>mnth_1</th>\n",
       "      <th>mnth_2</th>\n",
       "      <th>mnth_3</th>\n",
       "      <th>mnth_4</th>\n",
       "      <th>mnth_5</th>\n",
       "      <th>mnth_6</th>\n",
       "      <th>mnth_7</th>\n",
       "      <th>mnth_8</th>\n",
       "      <th>mnth_9</th>\n",
       "      <th>mnth_10</th>\n",
       "      <th>mnth_11</th>\n",
       "      <th>mnth_12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.196364</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>726</th>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>644</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>159</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>364</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>439</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>731 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     casual  weathersit  workingday  yr      temp       hum  windspeed  \\\n",
       "0       331           2           0   0  0.344167  0.805833   0.160446   \n",
       "1       131           2           0   0  0.363478  0.696087   0.248539   \n",
       "2       120           1           1   0  0.196364  0.437273   0.248309   \n",
       "3       108           1           1   0  0.200000  0.590435   0.160296   \n",
       "4        82           1           1   0  0.226957  0.436957   0.186900   \n",
       "..      ...         ...         ...  ..       ...       ...        ...   \n",
       "726     247           2           1   1  0.254167  0.652917   0.350133   \n",
       "727     644           2           1   1  0.253333  0.590000   0.155471   \n",
       "728     159           2           0   1  0.253333  0.752917   0.124383   \n",
       "729     364           1           0   1  0.255833  0.483333   0.350754   \n",
       "730     439           2           1   1  0.215833  0.577500   0.154846   \n",
       "\n",
       "     holiday  mnth_1  mnth_2  mnth_3  mnth_4  mnth_5  mnth_6  mnth_7  mnth_8  \\\n",
       "0          0       1       0       0       0       0       0       0       0   \n",
       "1          0       1       0       0       0       0       0       0       0   \n",
       "2          0       1       0       0       0       0       0       0       0   \n",
       "3          0       1       0       0       0       0       0       0       0   \n",
       "4          0       1       0       0       0       0       0       0       0   \n",
       "..       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "726        0       0       0       0       0       0       0       0       0   \n",
       "727        0       0       0       0       0       0       0       0       0   \n",
       "728        0       0       0       0       0       0       0       0       0   \n",
       "729        0       0       0       0       0       0       0       0       0   \n",
       "730        0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "     mnth_9  mnth_10  mnth_11  mnth_12  \n",
       "0         0        0        0        0  \n",
       "1         0        0        0        0  \n",
       "2         0        0        0        0  \n",
       "3         0        0        0        0  \n",
       "4         0        0        0        0  \n",
       "..      ...      ...      ...      ...  \n",
       "726       0        0        0        1  \n",
       "727       0        0        0        1  \n",
       "728       0        0        0        1  \n",
       "729       0        0        0        1  \n",
       "730       0        0        0        1  \n",
       "\n",
       "[731 rows x 20 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df = pd.get_dummies(model_df, columns = ['mnth'])\n",
    "model_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can separate our data into predictor and target variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = model_df['casual']\n",
    "X = model_df.drop(['casual'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model time! Because our interest is explanatory rather than predictive, we are just going to train and evaluate the model on the same set of data. There are still potential problems with doing this! In this particular case, however, we're not too worried about overfitting because the number of data points far outnumbers the number of model parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LR = LinearRegression()\n",
    "LR.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7307281905763694"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize how our model did at replicating the casual usage of the bikeshare system. For convenience, let's add the model predictions as a column of the `bikeshare` data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "bikeshare['preds'] = LR.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize the modeled values against the true ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcfca254ac0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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849K7u9fAXoLs7LtjhWvuUW+hzIsBXKFmGiYArpFS/pcQ4i4A1wgh/heA9QDeDgBSygeFENcAeAhAE8AHpJSpqussAJcDWALgJvXHYDAYjBlAdCGkOC8Kl5eHKz9/24RLtfuxTWMVJRcnJDr77vQRrG/NHSoJl5TyfgBHENtHALyu4JgLAFxAbF8NoMz/xWAwGIw2IbpgmpdzOElQP+SpPFztRrj6XaiRUs5MrWLGNWfgTPMMBoPR4+hGSDGdQ4VLnykjTPOdKlz9io4Vrj7/3OYDTLgYDAajx2FISo+EFPWpSNN8uwpXn/OGGZvmWeKaMzDhYjAYjB5HdzLNLwyFK23TAd7vhEt2aJpnzD2YcDEYDEaPI+mGh2sOH9o6nOVnms/ft0v8ZJ+7uDpd2qe/P7X5ARMuBoPB6HFYhavzOuZD4fJN85lqR3t1sbozMz8Wp+GaOzDhYvQt2DTKWGyYWeLTLjakCupc1OLV7YcU+/s+lp16uPr7Y5sXMOFi9C3a7dgZjIWKboQUXfIjpcTX7ngCz+2YnGHLaOgwoKtwNZ3zZ23cm/1+G2cz9HCxwDV3YMLF6FvM5TR4BmM2YROfdv6bdg9d89RWfOLGtTj/Bw/OsGXl50q93F9O1vl2rqPPb2OJmc1SZMwdmHAx+hbcRzEWC/TU/m5lmn/0uTxr+9LBVhYj6fxcmadq2f3tqM+9RjYe2zSGA86+Efc9va0r9XVumu+tz20xgAkXo2/BIUXGYkO30kI8vjknXAfuvWzGbaJA5eFyCUA719Frd/HPHt4EAPjBfc90pb5OE59qVJnmm2mGTbMUWu43MOFi9C04pMhYLNBkZWYKl32vCdfSwdpMmlUIKg+Xe349GHp+bAqbRssf9r2mcHVbWcrzcHWSab61cv/0Xw/hmE/egh2TjbbPwfDBhIvRt5jLteMYjNmEfnjOLA+XPXrD1gmv3m6DVLicc+nw4lGfuBnHXHBLS3X1GrqXjkHO0DRf3pCfPvQcAGBsstn5SRgAmHAx+hiscDEWG7qVFiLtMAlp64hTQLhtb+fe7Pe0EJmc3c9Az4DtNSVxIYIJF6NvwR4uxmKBCdH1SuJTnYfL83BZtHNv9tpd3O2POc/D1cFxLZazM2DbPwfDBxMuRt+CR2yMxQKtcMxM4YqPnbWQonpNPQ8XrXZVod/v405N8/qQqtCmVrh4gDpzMOFi9C36vaNmLD7MLPEpsW2W7hG7lqK7zb5vL6TYrVb1JnRaiHbJtjbvVxOu/JX7y5mDCRejb8EjNsZigTHNdzmkOFt3iL713HO67xdzHi4N0SXXvFU3O25J6d4k0R6uTutnaDDhYvQtqBE9g9HL6FYeLo25DCm6p2rn3vRnNy58VtDtFlJ+uG7CLBvVo8R2IYEJF6Nv0asjYwYjRLfzcIX1dhv64V3k2yoLKb7x3+7AP/3gIec4u6/RQ6OobmWF6HTCRKu/FRtSbK9+RgwmXIy+BaeFYCwW2Dxcnf+mKQVjtm8Rn3DZ7WFI0W3bQ8/uwDfufNLuc665kfbfPU2R13bQqmmeB6gzBxMuRt+iF8IPDEYr6I6Hy61v5rMey0C11wsNBudtltyr7q7p5sJXuLqeFqLDeltPC8GEq1tgwsXoWzDfYiwWdPrQdTG3Hq44BFpmmi8jUi4pbKQLn3B1GzP1cFWFNhPOw9U1MOFi9C14liJjsaFbpnmrasy4SSSoEGhZ4tMywkUpXFkm8ZkfP4znx6Zm3NaFDv0ZzrZpnvvLmYMJF6NvwRI5Y7Fg5qkBwvCeqm/WTPPF5wTie3O6VLmSUblfrt+Ki3/2OP7PNffNuK2zhi655m2KjTYPbPHHYtNC2PJPbxnHXY+PtHlCRn2+G8BgzBeYcDEWC0xIcQYEaV5CigXn6lTh0iHFpYP5o23j9omZNbQXYMjrbJnm81f393HCZ36GTALrLjylo3P2K1jhYvQtWCJnLBp02zQfvM4EI2NTOPf7D3ikKSNO4CdB9euYKvVw2fdNNUtxaCB/tG0bb3TW6FlEt1VDG1Js97jWYEOKdht3nZ2BCRejb8EKF2OxoBs+HipFQzdmKV7ww7W44q6ncNMDz9qNhNHbPVMUUixVuKh2568LkXBpiBZiimd8YxX+8br7S8vMfuLT/JUHqDMHEy5G36IPJzQxFjlm8kikPFTdeIbrOpqpS67ikKL7PI9CiiU3q+cDC7S5cu/X/KCdz/Tnj27GVaueLq9PvXY+S7Gc+HFaiO6BCRejb8EdCGOxwKocndeREQtJdyP8ZUJSFQqaR/ja8nDF9faTGNMKOW6mGY6+4Gb84L5nzLZ2M82zwjVzMOFi9C048SljscD8krsWUuyewlVTTxn3fqM8Yp5pvo2QogtT7yK+tdc9vxM7Jm2otJWQ4o7JJjaPTuGc7z8Q7asyzWsFjFfmmDmYcDH6FtyBMBYLrCLVOSjTfDfGJLWEUrhiQichC9WU6TQtrJ8kihWfxGObxvCNlU9WN34WUUV0inDiv96Gt/37L6LtZd9VbQa5tBLFEtI+XDap2+C0EIy+BQtcjMWCWTPNdzGkWKVwZRlQTxJMp5lpixB5W8ozzTvviW2NNMNAzdcW3nLxnRidauLdf3iAyTNVhUaaoSZEy+VnE48+NwagPAzrgcgWr4+tzjTPCle3wAoXo2/BIUXGYkF31lKMTfMuI3rPZatw5T1PtV2vUbiymNC5DZaQRk3RmwcVUSpLC0F7uOy2iUasjo1ONdU5W8dBH70JHyNCcvOJorUoi0DNChUVUhtFmBmdgQkXo2/RaybQiekUE9PFoRUGo9t5uNwH9M8e2YyPfq99wmFDik79JvTnn1+HvkLCVapwEf9VERFBJPNsBf9xz/q2ypehSllqtjDDkiTJFAgi2rZpnhWuGYMJF6Nv0WuzFA8778c4+OM/mu9mMBYgZpoaAAhnDPqvM4H1D1kCQZrbZbyMzEBdEa7StBAVJKLkGlr9vLqRj6xdlKl6Gj5htf/9av1WPOtk2df7Ohlj8lqK3QN7uBh9i14jXE3u8BhF6HBWYdH6hZQC1SksiXLPq17hn18/3PXWlhSuCg8XdZ8LVbbVz2s+uoqWCBdx7QDwli/npnq99I4h5B30IZyHq3tghYvRt1iAOREZjI5gTejtPRTd56+Xh0u/tvmMfWzTGA44+0bcsvY5s430cFGhP6es3l6v5f+3upYi5eEqu4RWr6+bZKNVtWyqWW0fcL/vsnqlUbha+1xc2JmjLR7AKAQTLkbfgj0JjMWCTkOAVQpXu0Tjvqe3AQD+6367jA+VkoBqr3QULk0HqJQSZddAe8OKj231+mZDXK5KCzHVaE/hKmtjJovLVC9ezab5boEJF6NvMR++DAZjNqF/0c00w/u/tRr3b9hWWp5Sh8Lt7UArUm74OymZpRiZ5hP73gWVmZ5qq1Xmyn1d7YbJur3gdCugQoplfRZ1LTpBKtX+Vvs/TXrZ0jBzMOFi9C3YBMpYLAgVqae3TuDHDz6Hv73qV+XHBR6qsL52ByV1ldfBnWFXI8gNSYxg1RS9WQTlqOb4YTW//qJr0PW22gV0c2zWal1USDFsrzdLkRDEnnp+XJ20+DzVaynmrxwRmDmYcDH6Fky4GIsFnXquilQt64Vqrz5K4dKqla9w0YqLNc3LYJ9uD0UKnXLGG9aqh6vVkGL3+4oqokMpXGGfVTU5YPPYpNrXQQMVOKTYPTDhYvQteMDGWCwIQ26tEomiB3anJvy6Dj85CldS4sMK82WFpnkb+oP3ClhSR+XcqiIidl/xtRS1c64wSSRsDa/FV/LiOrTqRYcUW2tH2eLVbMtoD0y4GH2LViTyTaOT+OlDz1WWYzDmE7LgtSqLeNEst04Xr6b8PjVCIdHEIQxp1hLaW0UtXaQJAL0kEaJtFOZT4aoCZZoPm1E06cHsN/vi+t3lk8qQlPjdmG+1ByZcjL6F7rDLOpx3fvUe/O9vrkaD50QzegDhQ7Eqm7mXFoJ43+7zVHu4Ui+kqE3zthw9S7H4XqQIg1G4nG1UugkyD5fJNE+fr5Xzd4pWq6JCimUKF3WdhtiWkLEqiJLEp5ybqz0w4WL0LVpZvPWpLbnptJku/o7lic1jOP8HD7JXowdhTPM6hNRyqIxWSEIvVKtKEOXhouv3X/X+cGkfGTAztw4dtqxaS5GcpQh9nlY/qNaKtYPKtBCkaT4gXJm7L66jLF1I1kL/B5Qv7cM9RXtgwsXoW+gRW1LS82lPSoOaArTI8J7L/xuX3bkOG7ZOVBdm9AYqnqZFClf4oG6Vl+jTuR4ufSxlmg8Jj/FwacIXtI3ycLlPfUPkiGuh0LrCNQ8hRVLh8v+vTnxaHBpu3cNVbJpngas9MOFi9C20aFVGuPSIux8UrrHJJgBgeIC7hV5DWZ6q8gPt28wjRP5r6/mqcqSEX8tVSIoULvtwD9pBeLj0PUkvSRSrdRTava5uoOW0EIRpPvx+i2aZhttmkkcsKcnDNR/5yXoZ3LMyFix2TjXJmTrdgum8SlSAhJh1tVgxoT5r7kJ7D6Ei1Cq8PE5ESNGa29urr5HG5Cp1By0E45ISUBawaJakIQ6ewpVF22T0pioDe7yzkWYmYWhZuZmiKpTXmsLl7itWoOgZjCqkWNWQoDxVP6M1MOFiLFi84twf443/dses1W9DisVlaiakuPh7lvHpnHCxEbZ3Yb+74u/wnidG8NAzO6JS9OLSYb0VIMKH+tj2PFx+KIxS8KhZirpC/1qItgu/XhdnfXsNVpz3E29bq9f/2KZRbJ9oVBdsAZpwuf1T5OGqmKVIffbmWPVaNZNVl6xK68GoBhMuxoLGE8/vnLW6dQdSGlLsI4VLgxPC9h4kQTQAWkU57dK78cYv3KHK0w/srIDwVEGf3/U8Gg8XmXbCJ2EiMM2H5d3r0yqaW9Qqfe61xO20GezjfTev3RRvbPH6X/+523HqV+5qrXAFqiYeANXpL8rod6umeevBo+rnvqIdMOFi9C2MpF5SRo+4G33g4dLgUWvvwTxYKwhSGCrzzeWUAlWskNDtUGoIYZD3VS//PLotkWm+RGnT9VHrJvrNLW77bCxe/chzo6X7WyUpmVHwYsXPtosiyRSxJdrRqmhpyDxFAFurg5GDCRejb6E7i5YUrj6YpajBClfvwT4LfdUnDBc9sjEnA4kJqdFKUKezFHUd7iQTaoYhlS9LEmkhTB1EVnnSw0UQOVLhMnm4SsgYERadS2TEdxO1Q4Io4+wmQrHhoVVXRpFoWz/3Fe2ACRejb2E6kFLTfP4637MU57JjYw9X7yFShAq+woefzb1bL3/hruo4izKFpGWFi1SzVB3ULEhvNiFhmg9M+27b7CxF5/zBOd1zUSgbW3hh0eJiswY/BUbelmgtRa98/P2VTXpoNceaDSkWkzZGa2DCxehbtPIQ0Zmzp+fZwzWXohMLXL2HMOxTFLbaNDoFAHjh8mHvuPxYRNtksK/VdjQJDxe1zT9/vLSPDMpTebiotBAI6i1pceEeT6Xr4k1hmlO17JJLnAoS2lIhYXoRckqdouuMyqlXWuEqP7aRZjjvhgexWf3u+h1MuBh9CypxYggdeplvhWsuQ5qscPUuDEEq+LmERIYiK1Qoq+XFsFXNzSyut+qBLaUN74dkgCKSxsPlnR9ROXqSor8oNgX3nu/mLRFmzy+C+3mlxHcDBJMD1HdeNvnBRaspP6hJD3Zf+dE/efA5XP6LdbjgxocqztIfYMLFWJCYCx/RlrFpAOUdzkKZpTiXFjImXL2LLFB9Qg0l9PSQISmvfPEDmz5//tok8nB5ubkKFBdDuMyxfjlP4Uq1hysmRqFyVtjekn1+SLH6A2idlPqvRXCv1abAKC5TlvqBNM2X7PPLqXN3oHDp3H5JWe6dPgITLsa84+4nRvDaz97mJTmdi8WiN+6YBFDeUeoHwHzn4ZpthcsllGya7z3EXie6XGi+9o3h8bGUElbaDvXq/l71sU3CExX6xWqJnyArVLjc9prFqyvCahSMab7ktqKM/2UoKhP2L2W5sahyQEHOsaBu0lhPbTPH6iJ231QzxX+u2eC3WStcHXi4dJ8+PFCrKNkfYMLFmHf8ZtMYnti8E9vG7ZT1uSBcz2nCVVKmXxSundOW7Jw5I98AACAASURBVLLA1XsoCsGFNiGrVsR1kETA1NdaO8wyPgSR81SvTGII09FsQrO0jyED6pUgg5rAkQlVibBaWXsp+J6z6g+Aqmt0soED//GHuPzOJ526/NdW6qNSYIR1UGSz7Poo9fLiWx/Dh6+9Dzc9sNGW023oIKSoCddQnakGwISLsRCgO2nn5p2LvFdW4SouYzLNL3IP1/h007xnhav3EBKNwgdtqBgR4TgX1Mw3F6OTDXzrrnXRw9v3cMXb/iq9Eo8MvxuDmTVTS8goXUWZh0sTuNAH5lxm4XWVJT4N68/PH+8/7ZK78HdX/8opQxGu/L666JbfRHVV3WWUF6t8mSJVL0lA4/JUVTpL/ibVN7r1USHFqq5CZ8tnhSsHEy7GvMNI4W64YJYVpclGahS1Mn+GHnHPdx6u2SZB/sOFCVevwoYA81cRuLjKMshTYSuKwLj42PUP4JzvP4h7ntyiyheH+dx7+pTsZwCAXWGThGbSTXzqX1F4XUDBLEUiv1e5ypMC41vIfVUK1z1PbsH19z5jz0N0Ebr/cNX7VpPJ0h6uYoWLImWlS/sQZHrJYB0AMO7YO6zCFbexyttmQop1JlwAEy7GAgAVhpjtNAwbt+cjuL13GWxJ4ZrvWYqUnN9NtPqAYixMhEpQEUUKfTskuXLJUgU52LIzn3iilQyqlPVw2b1TGAAADEiHiEhrrg7bQil3ZWoWpfJQ2Pu+S4BPHwjssMRJq2zUAtxlKFvL0EXFV2SPdWcpasIVdIvVmeaLT0UpbUsHc2I0QVgMyNQYLStcTDUAJlyMBQB9Q89lSHHreP6g2GvZUEservnOwzXbhK9q7TnGwkao7BR9h2E2cuntk9G2qvr0/rIQnQkpOr/haQwCAAYx7ZSzmeaL8nCRST2pEJp3/rhROgP/smd/kW/Y+Guzz6jaLaq+ZWFckugQ6SwotGSa99rhv7ptK5oVGpZfMkAQLvVKqfxVfQV7uHzwp8CYd1AzkGbbNK/PWa+J0p6vvkAUrtlWnbwwBDOunkOoZJh1QotM8yUPYnJJmYrfRNlSOdSC1tOUwgWrLpltMmxv3E6aYADvqv0U64b/EmjGSTf1aaaX7Zu/2f602WdnJjszLUu6ox0TTdWeeJ/7uWnyEX5HRfBCisS15v/HpJCaREC2jfDFDWuFyw0pmghEXEerIcVBDikCYMLFWEBwFa7p5mwTrvy1lojSTmOhrKXYnGUS5I2mOaTYc5DBmypFikoBQSfJLP8thPcOVdokQ3UGLTqkOARLhjIp45BiQbtd0JEuiY/Wr8z/aUzGBXQ7lr04f7PNEi5NHr0FuEs+h+dGi9PLuJu0qt5qwlGP+LagcFFrKVpS19o9XaZwlYZHCzDZ0KFm7lMAJlyMBQDKwzXbCpc+Vy0RpZ2GGe3Ot4drlgkXNdOL0XuomlUYhpioZWBAbCtSWG1IUROl4oey+xueVCHFYTnllTOLVxckPM2kxGm1n+H45NekQuMSsyViurBNGmkylL/ZvsFsszOT46WIKOj0MmUqkrufCuVRoHKOhYMhkjBHUw4KSClBsGuKEYw7hGsmebimmmnh+fsR9fluAINhTZl222wTHN051YToiTxcs0+4HIWLe8feQ6CaFCpcZn/8cKZCUq2kIyCa4YEiazqkOBwoXHYtRb8+16P2LwNfBQBc1zwtapst55KwkntX7yNCiq16uPQ6gVWmeePdIj57ElkTe2E7RrBbYR4ueGQzJlCUkhntc7epj8MNKZbNdKwKixqFi0dxAFjhYiwA6HvWN83PLsHRp0oSUdoZ2LQQi1vhajVZImNhIiRSNvEpnRaCWgyZCj6VhZMoUGszUoZ3TbiGHIUL0rY3PIZS2vbefHd0EVQzpVTkYWwzcO27gbFN1sSVqX3TO015s34qsdg2BU3MqkzzRWHSIvzFs/+CNcNnIUFWSHz9+1a/Ut8Bsa0khYbv4cpfO+mDtIeLfaE5mHAx5h3zkRaidYUrf52LzPdlmHWFi1A6GL2DMExV9A1G5vrKh7P/WlSf5nWkupKl+F2xwWvTFKFwSViyY8hA1DZbR7250ztPYXt1gXW3Aw9+D/jPM212MqN+WWKaEMmOW8lWT5O9YgWxSvX5g7GVAIBdMO6khfCPoawAZWoWQK3LGH/fvocr7p+p81OYLEkX0o9gwsVYMHA7tcZcmuZb6A3me5birJvmM/o9ozcgg9BSoefKKZO/2n36e/dDdH75IhjBiCBwR49cj5uH/i9WpGvNvkmZe7hchcsNKYZ+MnqdQGLxanNuV6FRFzawLH9dd4dTh+NVUqiRIcWoWLSvbIamu7/VTPOTyVIAwHIxXrh4dashYS9Df7AGJaVwuStPlBO58quYarCHywUTLsa8Q4/afNP8bBMMpXBVrGJPTWmfD7DCxWgVmSwmSGVpFqyqFJOmYk9YoLgE7QCAfSbXAQB+Tz5m9mmFawnsDMJD5ON47bNfxz7Y5oS9QL7m/+i1FOPrq0/tcIopUkWQK6nva6cbEOTqEtUKV7um+ar7bCLJCeJujsLVylqKlOrlNj9MjkuFlbX3yt1Gec6qegqTEJf7FABsmmcsAOhbcT7ycNkRtYz8Lno7ADSa89thzOUsRSZcvYfw+yN4hFfOqizFhMAvX0Tg4J2ICqGN1XYHAOwm7TI+TeTpB4adxKeniptx4sZb8Tf1p7FdHuPVR+aYyuJwlX5fb2x3NmrpLiZchoQ5lRgPV4sKV+lag65yHIQSq26zCaNw7TT+1kjh8r53/zz+NlvOLImkfXzEYIvycJXNdCyC8XBxnwKAFS7GAoDxcLmm2lknXPlrEph0i8rNdx6u2TfNt/ZwYSxM+MpScUgxJC5V/qdW1/2zaSHi+nbWdgMA7C53OOXznW5aCIH8HttFTMSKlmmbewJCPTGjN3eWnSZcNkymB1fCsA57f1Prp5ZdftlMQIrMVPnsNCaEIlwYd8K9/lH00j7x+d12pKm/jSJtbkjRKmHtK1x2vcuKgn0CJlyMeQc1C0YrXITo1BVYhUu1oaLcvOfhIrwx3YRbJaeF6D2EREd/hVGmeRm+xr8rjyQQsxm9+oL/Kc+SVmp2h0O41D7XNJ9oEobpqJ1k2M4QrvicwiFQFKmyJv802lfr0DRflRg0JE2VCpdYAiBXuDT5K29H3FZKnQrVMu8jNWpdfA2dKFxlSz71I5hwMeYd1FpkWs6vV3isOoWW/+tJEp3bK6cVrnmfpdjaaLtTUCNlRu8gTv5Jf4dGACIIDKl6ESoIVaEIZhe69Wkys5ujcCXIiY5HuFS5JZiKzN6kcVvGJMS8y1zTd1lIUd9Xtg5hQorO0j4tEB1qhYZMSpycrMqXGJra7p2piqyYkCLGC0ka5eGiPG1Unj1S0SRVuuL2ttpVcJ+SgwkXY95hOiwv8Wn+TzJLEpc7SxEoGcGrHfOdh8v1k8xGSzgPV48j+P6KFK74wR3/rvRv/k3JL7A0HfWOqwI1w1GrTXuACCl6CpcmYY0oVxXp4dIKl3N+s9tRrIwx3jHNR2khCIWrSV0MgbIFojMJ/E39egDAwPZ13jVUfaSTGAagZynq+vyDckIq8d7ajVgysTFqLKVimVmKRLjR1O/8bsIJDP75yyEIb18/o5JwCSH2F0L8TAixVgjxoBDiQ2r7nkKInwohfqNe93CO+UchxGNCiEeEECc7218phPi12vcFQbmUGX0HKs+L9nDNmsJlQor0UiKmbWrzfOfhmn0Fyqmf00L0NMo8XDbNAqVwqSIS+B3xDL4w+CWcte2zXvlKuIQo89+4hEuHD5fA9XApEiamTT2GPznXFp7AV7h8VS1vkiJalMKVxSFFajmvctN8cRkpJWqKSGZqooD1cJV/pnrvcuwsTgshgYPEb/GxgStx4kPnRGWo0HEWKFwgfgNUO6oy6ZeBXQo5WlG4mgD+PynlwQCOA/ABIcQhAM4GcIuU8iAAt6j/ofa9A8ArALwBwJeFEHqp8H8H8D4AB6m/N3TxWhg9Csp7oGcFJrNMuKpN80rhWkB5uGaDcFEeD0bvQAbvix5wkXGbDD8BuyFPKrpHtiWqn4LNwxWfy5jhMe6UVwMq2NBforYNY7rYLO82RM9SpEJipAnJNYLrfcX+rtZDirH6ZvcBdXVdqdCEiyZPIbQy6ObhCv2VEsDeQs3IpFRAIiRs83DFylVZ2JAMN7bPw/salYRLSvmslPKX6v0ogLUA9gXwZwCuUMWuAPBm9f7PAFwtpZySUj4J4DEAxwghXgxguZTyLpl/q990jmH0MfTNOJeLV+tz1iruAGuan28PlxtK6X79mVc/9469Bs/8ntn/RZAYIlQrKIO1hMxVJgBTapHpwrxeYR4uSokl1jLUpvnECcfVNOES04UeLto07xIM/cYhV0QerjikaOuglvMquyPMgDEDjk9+jRdhxB4npSGSmUi88lW3mSFcGDeDoPB7yKTEHsjDvpMDu0X1UudKjQHfttG2N26H/c0Q+1rsKtimkKOtPFxCiAMAHAHgHgAvlFI+C+SkTAjxAlVsXwB3O4dtUNsa6n24ndHnoEzzmuDM1hpcmsC4Hi4pJZ7dPomX7L7ElCszxM4lZptwecIB9409B/8hW5JpPlCTw+PyV5sfqyEGzbaq84blzHuCcGkSAmHP2dosRVfOihUuSo6xpnnHp6XPT8xSNKp2Fm+jTDBu2749+CmMyiUATjfXVVchxVCkq1y8WrVpCNPYWRJS3EOMAQAm6rt57XFPShNr/zU61pSnyV4r10CFNPsZLZvmhRC7APgugL+T0pluQhQltsmS7dS53ieEWC2EWL158+ZWm8joUegfgUsqyrI3dwOUh+vrK5/EH154Kx7ZaBM0tpqkcLbhj7a73xi3o+W0EL0HGbw3iU8LTPNh4tPHh96JU0Yuz7dJiV0xAcAqXFUDH+rhre+dJHzCw/q1XIWLDCkG9fqEIO4j9FvhLu1jLtZuM4SPyHuh33oTVYgO4H/WfoJTkrujPmJXMWHKZFKiJhQxTP0koJUKl2rjkGg4+aziT2Z3+ISLVLi8I4qvyUy2INrTSUiRSrzaz2iJcAkhBpCTrSullNepzc+pMCHU6ya1fQOA/Z3D9wPwjNq+H7E9gpTyUinlUVLKo/bZZ59Wr4Uxz1g/Mu4tetoqqKV9TJhxlpiOCSk6Hq5VT+Z+lSefH7NtC0bY84XMI6Pdr5/y8jB6E5kspuThw1bK/MFeExJ/uvWbpsyuIvdbTYkh77ii+qgFma3CpRQed4FoRSaEQ5pqDuGyIUW/Lj/Jqa5X4i3JHdgXmx0juNsPadO8DTPWRejdigccjTTuj+z/Ev88cDkuHvxCaR8hnevKbGr3+FoI6JDiMKbN/R/NUpTAHiIfIE7VlkZtpZOb+tuofeE5qvYVQ3+P3KcArc1SFAC+DmCtlPJzzq4bAJyh3p8B4PvO9ncIIYaEEAciN8evUuHHUSHEcarO051jGIsAr/nMz/C+b61u+zhq9Ep2sF2EMc0bhQsYrOe3w1QzDiXMNweZbdO87/HgzrHXUBRSjJf2iR+2uzjrGeoyuyqDe0OteVgUUgvN7OTDm0jfoAmXfpWwHq4lYtp0AGE4S7rhSa3WZRk+P/jv+MHQR217UkLhylwPl7qelkOK/nXT+csoQiJNSFGTwJBMFsEoXGg4Hq6gftiQIkWMqH40XNLHU79Kwobk9VVcBStcPlrxcL0awP8E8GshxL1q20cAXAjgGiHE/wKwHsDbAUBK+aAQ4hoADyGf4fgBaZduPwvA5QCWALhJ/TEWAfSNesdvnm/72LIkjLP18Dd5uIzCJQ3hmm7GHe28K1xEyGO26ufOsfcQJj4tXrw6f3VJwnI1I3FKDGNIlVmuFK5Mjcn1cUVzhqlBk34nKNO8JjyOAiOca0jktFeH4W7u5BXFHBJFpPYUY25yE6dxsWk+J3cJqFmKGalw+Z9nJiVqQXnqvsmVO6XEBdniK0OKjofL/b5OTlbhUbm/ObcOKZJpMnw+7O230dTyvoWqw72+MlD+3H5GJeGSUq5E8X32uoJjLgBwAbF9NYBD22kgozcwk4e0GZQSM+Vm6+GvR4w1R+Ea0oTL6dTJDuN7fwU8cB1wzibMFar8JDMF1SEzegd+SLja5O56e7TnaCJZmhMuwChcddlQ5bXCRT8KKNUms/JK0AZpDPJG4ZKOkR1APZ30DrUqixsq1McSqzBkMTFztxlyZ8hYPOBISxUuu2GwmVuaZZiKQghkUpq0EFnmm+er7jNDuETD+94uGbwIqRQA3gsJYDexU52TSgRbrU75937cDir0GNZfhFZnZPYLONM8oyuYCQmgOgVP7eoC6xqbauL79/42Oqc7S3Gono9Zp6tCivddBaRTmEvMtgLFS/ssHpTNUgzDX5kTPpxUa/dlUhoPVx054Sr6SYQKFKWu6Azytg12RqJRuGDN5QCQBPeXqdYLKcYZ5I2qhpiY+ab5gAk49erBWNkgx/13l8lN6rqc8hPbzDZr0G+qbXlLq+4yN6Ro1zjMt9WE7ZdsyFJvi1VGKtRrP9N4nwvrA6P2lcOqadynAEy4GF3CTO4nO6KkH/rdIAAfvuY+fOjqe/Gb59RSJUFaCMgCD1fZ4r3NmZOujdsnccDZN+LOx8pDsf6DbBYULqfOeU45xpgh3KV9QtNV9ACW1iA/KazpWs9SHAgVroJz6upqzQlcPHARXowRx8OlSZUN31sPl1Wy3ZBiPZ00v8nDxWMYSlXYzPFhmaV9ZKxIe+Uyn/AAMOSO8nDpOholebjc+3HZ9CZVha0/G9tsjqsFStz+U49i3fA78Xs716AMnmlenU5kDa+MhHTUOj+/FmD7OUrFCmeq5m2My4eqKIh9hZDeS9+DCRejK5gJKaKW9ul25vNHN+VESz9/dP0m0zwkBmpqSQ9C4SKvb3zLjNu15qmtAIBv3/1UaTkyv1EXQY2ARycbOPu792N0slFwFGOhIPThFHu4YoVruVa4kmFz/HIVptIhRePhikzz8Op72ZaVOKW2CucMfCsyzZtj4KSFgISU+Z8XUpRTkBLYEztw/dDH8b5tF+XnccOCql6RUaqXc/0ECUsCkuIpXJnEKcndeNn4r822kHC6fdXS6RFVva0/3akIl7RL++i0EAdOrgUAHD76c5RBf0ZDaNjPMhzkSTcUS2Te16+K5K4Qjzu/jVi9l9Eb+7YsR1cRSvvPPgQTLkZXMJP7iTKmVxk528XOqXz0OVireedyQ4p63zQh8Xgdhl6panwkKtcujMBWcY2zrXD566/l/3zr7qdw9X8/jUt+/kTXz8foLkJFwhCksJyMX7XCNaEUrkxKDKoldwYQKlwh48qwn9hkGjBe2x0AsJfYYVUZBITLIQkCmfGc+R6uKUgALxPPAQBe0NyojiWW7HFChQPTeSjPy8NFmOZNmI9IC5FJ4OLBL+DDT/+Nt82F+79QypZ0FbGmCh9mdmkffS4duh3O7FJHFDShrIsMMs3rS1J/Rqn3uTnk5s+SlThMPOF9z/9Q/w5uGDoHgyMPu8XJwZafPDUuF+wqRKSo9jmYcDG6ghkpXOrQlOhL8+35P5ONFGNTxHpoLWDnlM3ZAxCECyiYpWhDHgZL1Drt4+3PyAxhFbfyz48chXYRfuLT/FV72jr9zBlzh/ChWZhp3imjX7WHa0LYFRY0IRkIFK6Qb711/FqsHPo7LNnxGACgmeSJUvfGdkPcjRLl+LUSV+FSDRPIzPE1FVLcV+T32OaaWsikQs1aMq1UZ9dcT6SFMB4yKi0EISHHHi73htShPFt/ZvxaEon2W6nzjytiO1RBuBKHgGpPm2jmod7cNK8/S5/QZVLi3wa/jB8MfcyZzCDxJ4kKYTpt0/tMu4nB72A2EShj7udQegmscAVgwsXoCmZGuOKb0iMA6v3rP/dzHHrujzs6hyYNoX/BKlyyIA+XaqNb2dI989cuKFz6CVb16fmJT7vfeVGj3GWDOeHayYRrwcN/aEoUhZ2jpJcAlqtZik01aT1PeZAXqPJwHT79y7ycHnwoUrG32O4Y2DPvWCnhebikIog1ZGiq5J11mStcLzGEax/Vjphwud6pJJvWF+qU04lP3bQQAdHy+p5Y4Q5vOXJNR9fD1dQDPLdYvm1KLZdURbjcdBpa2UpUSHFa5Ufz1EJzLbaOJc18YessA/YXKsyZ1Lxi1L3vtvvMHV/GDUPnYK8sHmC2OlBkvpWDCRejK5iZaT4/uMjDpfuRDVsnMFOEIy6ThwuAsnB5IUUqg7ZRuHbOXUjR3T07Hq6Y0C0byh/A4x2sHsCYP0iUebjyV/d3rfNwCccDpN/X4Q9UQg/XoFpzMa0NqRPkv5XdxHjk4XJDi24eLqnaXINEqhSuJGtCSuAlIr/HxrFUNdxJXkqEA+3N4QwSiNmMWnUSmT/DDwCGpB+2y6uVxf+btRqde0iHGT0rgCZG+bahLD6PC3cSgVAKlyZeDUWOJeLwqNs/vGTsQVNuSCi1MvXLeaSQIEgvSPOw7oFyfdTGyn6LUMz6GUy4GF3BzNJC5K9UHi6gO6b53TCGd9V+amYs6SoTd/FqVdYNKZIdxtDy/NVVuFZeBHz9ZGCjNdq2AuEkXi3DXHq49LmWDnJIsVfgPf+dkGLxWopW6Voq8oe4JgI5+VEhRZMWQhMkv8IBlaC0mQypOtywGrx6dRgvV7gcwiVh0idkyYA6pgkJif2EXktXMwGH/BuiEy/j4xrpJVEusfqOXxeApTJWnmKFyyFDOoN85l57TH7M+fXsQ+I8LtzP0oQUQ8IlnckGROLTRMbETyt41Dqxkuhnnqm9BACwX/Zs1MbKxaudWagMJlyMLqEbaSGKQordGB19ceCL+MTAZahveTSvU6eFMFkhbBiG9HC5UQb9FJvcZrfddzXw9N3Ak7e31S4TZqko55va2zpFS3A7Ti3waTI6Ps2Ea6EjVEDLFiHWZfRxroE932c9VsbDVVDPoCJcOiO98LxQfuoFQ7jgJz6VinT5hCs30y9RCppR3yjpmzDSe0Z9SuGC3za3jmVEqC9SuNz+QK/p6KaWCHxS7jn0OV3T/G+eG8VffvVuby1aL/O+Ilw19eoqXCZ/GREO1G0T7uxGGSrWdGRBv9fh3P1lTLiqOq7ZXqKt18CEi9EVzIQU7Tm9AR+vfxNZ6ngg3Bs/YHOdLPfzu0me9FSHPlIp80V7nTxculOgTfNUCMHpuIz7vb3wmzAhxYqRojfybOsULYGapahf9YQDxsJF6MMp+o1QSpjN4+QqUL7ClUmJ02o/w2HiMa8+HVK0PqY4/5VJASG0XwtIhOvhskpNJga8+gwxI+q38a84VYR3H5ptjtcrmNnnMoelaE/hMp+bc8401esmus1oeu0ZzqxF4p9vXItfPD6Ce560qjllmtchxWlH4UoIwqxhJixIJ7VLyRJDpJKuXvaXGxGi1aV9mG/lYMLF6ApmQrhOfeYzOLP+I7xwNM57k7/3y080ygnAH37qFnzp1t942/aETniaM5y9Rh/Bk8PvwktG7gKgfS95Wc/DRSU+zeJRMdX5twKdB6yq45rtTPCUh0tvYoWrF+AT8qKleMJM8H54z3qiasEsxUwC/zLwVVyTfNSrT+83SoobUjTbXOVHeiTBql5K4aqpWYoqFJYY9YbKrxWoVF45QuFyZKmXZ0/hr2vXRwqXlBK7KMI1JYZsFfA/Tyqk6Cdb1aqXN3LMy+tFup2QIuXl9E3zU94rZZpHZq8hvHaPCBtFTl+bU5xQuDRhXqa8fi5aX7yaGRfAhIvRJczkfpoSecLFJdM2ROdWF3q4qgjAM9sn8a8/edT8n2USw9owqjqbfXf8CgCw/6bb1HbbKfgeLhsGsRuJTt3sa1MNsgJbKSiPVTfhVqn5pj7nGCtcCx6xwkX/RsJyenYg4CoklgiFHq4QWuEy2dxdcmUUa99PlZMrTWBcD5dEJnLlRocU40Wu3ZBbZuoML9AneXFI8RPp5/B/B65BEqhOUsJZ6mjY+az86/Y+DsI7pZOg+p93MJvQQUIROUg01IxGG1IMPVyuEub7U71zeWtLEqq9ua544GXTesQo64r8uorL9ROYcDG6gpmQgLHabgCAoYZDuNybNbhbx9skAGMOQdMekEx1H25uIH2WaSItBDUNnCZc7Slcdqp81UjRUSbaOgOw7vmd2DRaPiOKUtB0m1jh6i3kBIbeFyrHzVTGYTsn5DeAptlGwSpcsdqkz5V45Cf1VDXXw1UTGWSiQ4oppKO0kcShJNO8p3rpO4YI95s0Euq4VErsohfzVvmyqOunwnbSM82ncTlF7gQxKNP9gPu9JUgxneS50WpZiYcrSnzqVEwkfZWZ/51WESMREHIXZf1+GL5mQH1rDMYMMZMRjCZcSxrbbX3egCyvfDl2YhBNP03B5HYgqQODywrr9xQr1RGmyGfgCUde1+eZItJCkB4uSs3K2iRc4TSyAhR2XmObgIGlwNAuhcee+K+3AQDWXXhKS/WHyV45LcTCR2gpqkp8CuRltJcRoE3zNWRA2iysb9AoYLF6o+8t90GdZRkgE7PNeLigTfO5oiMCYmZIDRE+pLPPux1I8f2aGMJo7/NdiZBibJqPSR5tmnebpq8l/iypUGUCiUZtGEi3G8IVpYWQMSnNvNQZsaHftFf/614XpXAFhNxFWbcvC973M1jhYnQFM1kNfqyWp1kYblqFi/JwrRz6W6wePstXXC58KfDZg7368s6cVsgM4ZKhwmX7akrhoj1cxDV3rHCVl/MTn+avzTQD/vUg4PI3tnVOsn7SI8bdZK+g1cSnXmRc5hNQLKmRZru7zA7SqeoBFWFWzwgDpFTKVZhpPlPtyJK62p6HHi0x0yqSS+h0HURaCDJ9RKzU1rTClc+aQZYBuyqFaxKOhyu4QdOmrYv2cMXkR+8XIIgfMXlGSImGWt+yFsxS1B4uN/xLedX0ZySIti1Lt2Pl0N9iv6nHv/Z7ogAAIABJREFUbROD3wcAJGaWKeURiy7FqYvqU/obTLgYXUHh/fTITcDFxwFp8QLIWm1a0nAJl7Nf/aMzYkeKy9R2798nh9+Fzw78uz1+2po9tY9ChxQTMwK0vpfpZhyOoBUuwpjbpodLOGkpykB1hB/61p35m2fva+ucFMo8XIyFjyIPV1nCzkxKn3DpByqc5WIAoDlV+cAURChPL9bsKlwyyyKjvm5rDRmkVrhkU5nr9XmpMH5MwigPF0VENGqZ0y8pT9syqLxk7kSE8MAsTqzqLl6t3yfOzGtKBdSgJs8kSJGq/GY69KmVrqbqMwF30W8idEqkvdDbDpv6FfYTz+N/7LjKaUmxwlUjPGJl/RZlwO93MOFidAWFHfK17wE2r81DfwXQHdDSpi1DJeDTaMVT9NbaSvuPQ7h0Z5SZn74d1RvliFCTKJMs1Ym1q3DpeqsikZmUeFXyIF6VPGg+6yceyWd1Gt/LDOB2nDyVu/fgh4Vcf05ZOQmdcNQtrI3Yqb5H0kal2CmJe0JvSwLyk4cPA4VLholPA3M9ESoUpQOf1gZDVuECgDzEWjcqWaySi+B/tx1en6UJmVO/IGZt6s9cp6cJTfOpMs3rtmvTvHD6LTvpQZFsN8FrSbqOhsxJW81JGbF88jl8beAz2BN28XHdxsQJOdvPBYVghSsGEy5GV1B4OzVbWI6HIFy7N57DHyf5TMJ4lmKxikSFNrPUfQjk+/XDxJvNRFwE7eEiyFUWhxVaQauLu8osw1WDF+CqwQtM2QNFnogw2/2Ats5JtyN/FcIqitxJ9ipspvlwsBJOvmimNg9XEjzEddgK6bTvWaKgw2WeOTu+T2TmkzydaV6fUxrClUZKWH58ObkSFLkiM9LncMkGZAaZOUlRXe+ZBI5LHsKe2KGKxiFF119mkr6mlnDJLFb8TKejmJybYzBBhlR9Hok61ni5HB+WCNU/Wa5waSLcVDNC69Jey6Hbb8Xra7/Ch+rfNZ16FNa1tZd7uDxVnvsSgAkXo0NMTKf4q2+twYatucG08uFcovzoTnLQybz8lm2X47LBz+C1yS+jUdROinCVEBdvUVmTH0eFFB2ZnEwEmBFqT6sj6xYgVUdOzVxy8aLxR7xjAOB3a/kaZ9O7vrStc1LQ115PBOfO6UGE4Rty0fWgnMxApoWQyElYQxOurOknHCUbEIfLwsSn+flTo6ABilBJa9TXClciU0DGD3ufTBSHD+mQYpmHKy+XK1xamXNzV0lcPfgJXFn/p7wqL6QYh/LM55X6hC5vW0yIdEixmTqES1rFT7dde7hIhYtQswTxuenz6+/XJZ1bBl4EAHhtcq8TUvS/g1aXGfNV88JifQUmXIyO8JOHNuJHD27EhTc9DKCFEUwZEdFEx8n0MiHy6dAfql8XPfgnJwnVbHoMAD0rKE3djjN/3wwUrnxqujTvg6b5bSByDnVKuHbdvAZXD34Cbx/9Vmm5/cfuBwBslHuYNu03kCuCUzrsMAPoOmuJ4NFoD8IzzWfFHq4wpNjMpF3I2RyTP8QbOut7lvqzA6nzEzMBjaITkLDYNC/NLEWpEp8K+OWMod81hIfL8+QF4nOWhhRjD1cNvkk8/wxywnOQWrHCM6ar8m7btH9NenUQk2102FV1fY3UrVeazPv6eozC5eQgq4kg3EqqezERbsq8D3QVLn3s/slm81vRahrl4SrzZs127sBeBBMuRkdoqJHYYC3/CZE33tSofV+mcBH5XbSR/g+SJ1Df9ri3rzFJLPo6vkW1wzZkampSndqdKaQfKnqWou1E9JFUSorKpX1KwhZlqCnD/wHNJ0rLCdUpTskB05ZhtRBksznzPFn6AV1PEhPC5U6yhxApXIQyC39gpE3zNrmo9SLVkKHpKFx0zjmPvuV1eEQnJi5SZkq50ucMlvapCCnS4cP43nT7FKOwZRnC9J3eDEeZIctixS8/1p/04w7irIfLvU6Vc4voIxKCEGmFSxMunY3fXczbPdaqTUXXCa+8F+rV54QmUm54NPZdRSFF7zdVonB5dRUW6ysw4WJ0BJ06YbCuCRdxR+183r4vISJUx+l2EMOb/Fl42TShcE1sjdoxsTVf+4ta1iI1sxStTJ5Jif3FczgotevF0ab5Eg9XmwpXU83KGpRTpeWE6SStljGkJio1mu2RPAr6OmtOSJH5Vu/A99XYtBBlGdIzqdNChCFFneVcpWnMmvT927TJdIU2lRMPdhEQLq1mATb9gyFXOi1ElnrlbMoKwv9EJRJ1t5l7MwVqJRNMZJare3qdR5fMBOFI38MVEy7tG5UE4RKE6qVnK0+nlii7aTJEQJwsuYpnS1a1zfrtVJiS8nzBIVyBab7VUCEVbeh3MOFidAQ9EhvQChfFM6iQGwm9z/cvGAQpJWopRbiUwuU0ZHpbbipPPTnd9324D5lMAncM/T0ub/yDbZkTZomuhTTSt0d+9LTvAVmcNsOtVwhriB6q5710WpJyo1VIKXGkeBR7iJ3Gt8aj0oWLiekU9zwxQu7z/Yj+lxj6b/y0EPaYGjI0hePhSgkV1TWEE0qKfbA75wzSQiRwFrRGBinqyCBMughLzOIBjSBUNVp9djxWtZLwu/Jw0QpXSLhcpU1PmHH7mTjMCCLEWqRwmVmbQUhRL0UUZeB33ldNLLCzR/P21KUfVrXvVb1BWogsLkLDI1xlBfsHTLj6HF/5+eP46u3l4SwKEeGi7igqDEfAJOfz1klzvSAh4SLUIK1wOR3t5M54RpEMiZEZAQJ7jj9ZeAnUgrDd8HClqkMdqFC43KnZuila4UpbVLiaaXHbBidHcN3QebgtezcS5RMpS83BmF/843X347RL78bTW/LwehgqtL9b/7jYXF+wtA+kT7io3zVBavxZikE4EABID1eumtSQAUkNKWpIZNNLC2EvKFa4qCzqAsT9mjXLFS5IFVKMQ6FC9UE6YTJI07wzOSfVy+fE6pFPuLRilUNHDjIJJEJCJjWkSJxlgdSrWbC6yiBf3FdZwhV7uHQb3HprigRT6y1S4LQQMZhw9TkuvOlhXPDDtW0fN60Jl1JZyPuJMm8SsHK5G1KUxtSJZgsK19SYOqU9z+R0I9qmR6Gh0gVIvHDs4aha0sNFhQ9nmBZiUE6XFzQPEvtw1SPOZtraOSebxd9B3flMdQJaj2Nyf7mg8PDG3B85Oqkf7BKHi8cAxxMF+OGf/H/3fW6ajxavVkb6hkO4qBl+VB463yCviIu7TQ0Y9FqNQv2fpcq8LxJkSMzi1S4xU40zdVEKF5WA1TOTVyhcmTdL0fFC6fUHQeXL0jOf3QFKsfrmmfHVMUua2/CB2vVoNPWSQErdEzX1eejPsmk+DyklqFmbINW3eJaiJlyuh8v/rvxQoh7seb+hkn7B3cVqeQ4mXIyOoEdiQ6UKV2shxdC0C+SdwpQx7fqEq54SCzGr+lOnY9FExFW9zFT1YJ23/CEVt7E8pEj5SYI6fvvLil5JeeEqCFfidNZ25KnaRoV7Ahwi1mFyquQcTsddV1Plw+VizPtMzmgpJ8bMob8O7f05dHQlrh/6ON5e+7m3tE+5aT7/LvUst9AX1MSg+Z/Kjk7d3xThijLNI0x8Kq0SlFjClV+f3zd4KQ8IcmX6EjLMmAFlSYLV51ZzFB0DFbbXhMv3ScmobZIagJWoTW/ceAn+YeAavHTkjvwwrTyKJFe4IpKU9wPSNe8TqldCfEZ6sKmVvLqMU1fkbfDryM8p/eajGKxwxWDCxegIrYUUW/RwEWEAgQzTTofvggwpavM70cFRpvnwASEB8qFC5qQiO05C9XrsZuCrfwz899fi9pr2aMJVFVJ0R5k+uavK4fUH4jH8cOgjGLj7iyXVu4QrJ7T+tG77/g3/djsOO+/H5e1lzAk04dqtmfu5/kA87nm4orQQ7neaqbQQYUhRPbhTxzRPpoUgyQRlEnc9XKlaN1H/npVkoj1PQoUU0YS7xFBC1EXn3Iq9ZF6orVaPr8Mcm3vayJCi1AqXripWjNwP16SF8DxthMKlrltPUNhlQiUz1sRPJCrEWqRwuW3U/YL7Xelrd9U3/zOqoyCkaELCVunKZD4Q+32xHm9KflGeh4s5VgQmXIyOoNNCDNSL00I88qyznE/ZLEUiNJDIzCpcanQ5DZUZOYtDillGkCtqW+A1cX0rYedRuEhrycPFI1ybVbLSET+tRdBwAMAAqozvebm6o3DZHq28ZzskeSp/s/XJ4kJOu2vGw+Wc3fnn0efG6OSzjDlDGCocr+0CAFguxn2FKzgukxJvSu7EHyX3Q0o1SzFSuDQJcBOfUgoXlfLAUUiy+L62KQnUQ1zkxMHUL2rIhFK4nJCiVaJjNYuauSggoddLNfuzZmVIUU8YcM9pjgUg1SOTUtr8xKeqPBHeo9S3bbW9AADLpjYBgEnXIQsULqFnohIeLhn0o2Hb9GekZyd6Hi6iLzE5wFRutEwCPxo6G18Y/FK5eN+iwnXZnU/ixvufLa5oEaGE7jMYxdAhRa1wUSMdz1vUimney52TYVr4WZanMYhBNFEnFK5mmmIQfgdnssoTM4WsWVWVgYzygRURjpY9XHrafH0oaq89hwrNtqhw1ZBa0khkxKawl1qOZHpwj+JCzmc0oAgXhwQWPoQiFeNiGQBgN+xEw/VwhSFFAF8YvBgAsE7+LZkWQv8W0sQmPiUHTJUKV0FIUQaql5SWwBhFJ1hL0ZlBaa6dULjcMGMTNQyiaZWfLAUGlsTXIWp5W80sRco0HyhcFUSnzMNFbRtD/v3tMr3ZXGcNGUSSfx5hWghNfnyvWhzatPtj4pcQIUW3beHi1UngD8w/jxKFy3lftjLU+T94CABwyopTigstErDCxegI2jRf05N2iPtusOb849zIn/7Rwzjin34S7RNBZ5qinicoVZ1dQ40Palns4WoqYz0VUsy8TPNhOM4qXCEp9AkHcS2eAkaQsKYiUWWEyyhc5T4sK/9npiOjHjgU9hI54Zoc3LO4Ha6HS3nkOHFh7yAV+c2mFS5ydi3C7zRfrDkypqvfU1OvYJA2QKY78QYcscIVqln6GDdUqMuFHq5ENpVp3ieDXiJRIhmq66XUyZPtPZ/SsxQTrTtIZBlQF7GHS5vmM0LhMtdM+NcoAuonW/XL7dLIcxfaNBk1ZE5IsWZCioq4eve+r94DtK8rTAtRg0vQ3O8F5lzuOb20ECUdAw/YYjDhYnSEhlK49NJf1A1VFwQhAfDl2x7H1nFnVEWY5hOkkBBooOaEFPPOsk4RLm2QT+OOpXRqtqMExDmL3PdVHi5ihN+GwlUFTUZrSKMM0C0TrmRpSf223TpFhTdC5Q5zQSH8OhK1YTl2epnmKxOfptQsxfy34KaFEJTCRao3hIcrKOfOPsw3ZZYQqJBiAp+YJcFAKa/Xeoucq1Llc4XL3VZomtckTM1SdAmGPZevcFE+Kdo0T31GRF+iCN3yRq5w6ZCiEAlSkUQKl5kxmBEzDKuM+qpNOqToDfZIhcsS0ExKT9UKU/a44FnOMZhwMSrRTDMc9NEf4upV6802dwkKoMg0T3QsBKLsycg76UwZaG1IUeWs0rMUnfqbzTh8SHq4gnUQ3bUUQ+LiJ4l0rytQs1zzKqlwEWEMc50Fytb0OLDTSWyp6q0jtbMUjZG4gnCpkGLpEkDO51bXIcXM7Vjz18kGe7cWEuzDL/9elotxAK6HK1C4gv9SGa+lqB/YWVt5uOLfop2l6N5HfqZ5vc0kLBYJMhVC89NCxPcXNXPRTUDWhM7S7pAaysOV1EzdeVoI398JwOThKlO4vFCb7nuIRLCUaV4T2sHMqss1ZJDO5wG4CpciPt73Eoc2E2pQRilt+jqpPFzquuoi89RTAEiaxVYIr/+s8Jn2C5hwMSox3kjRSCU+caPN16VN80VrtgFARiUHJEBN5U5khgwJmqiZzq5hTPOKcDmdXlqSAoIKM0aZ5omQopTAcozhzfVf4IV4PqqDCi3Kdj1cRZL8114HfOZ3onPWhPW7tBtSbJTk63I7ae3hohSuHRMzz2rPmDn0d2N4ivr+lmM8n0mmB0LBTyMMk2dZHFI0C7x7Hq6qPFzEb5GclZeb5D2FS6Z24JHUVEgx9HD5ZKKJGrncj1F94ShcrvpcFlKUmedpS7wBoFW4pJTeLEWrUpUrXHZ5rpggCuWjsgPY/PxCJz4NTPMJZZoPCLN7TnqCg0sGi1X7OPxri+gkyRRCNZXBhIvRAnQWZHcEN+VkRM5fJfbEDk+eJs3qVP2G/DgdnBrdNVCDSJvefj012u1E0jSeFWQ7QmLUTc4YCkOKEn9ZuxUX1b+Ej9WucHaEhMu2Y3TC6YD06K8kuzU53R4ANj3k/esvdaQ/4zgUS0ETLllCuNyRbV1q07zdrx/U25hwLRgIFeIB7MN2SDTMuqAA8NttE/jaHU+YY8KJIG5aiDBEnQonLQupYFMDmZh0+EZsn0ipjfEsRZX+xF3k2j1P7s9S74mFpBPHw+URolLClZOYOhVSzGwertx+EKtI5IQBMkUDQVT1BB/zkeg8XAKZcNNCqFeh0sMUpb/QbSuZGekRP7VMk9sHh6Z5AJDN1FOramlxbj8pgdcna7BCPM6WBAUmXIxKCJXsx71ldEgxzazC9cvhv8JFA18yZShliawfMeFKkCFDLR+lqs7OGmfjjkt7uFLCHEqpXuHCuvnMqTikuETkHcowHCIVjmid+neM23L3Pbkx2h+h5Y7IJY16mQ/7cCmDJsFlfjF33yCxtI8mX9vGmXAtBPz51PV4cvhdwFSeesUnNT5ZdpXpxAlhZxk807xNC6Hum8TNNF8xS5EMXfnKWb7JrpGY6sePzMx9KczSPjqk6N+nuv5M1JxQoavAWGKmFS4vPxUZUvQ9XNpETpnmDQUkVSSXRKbmesNyfqZ5bU/QC0nnr0ZpU2khqDxcmQwM8tL//vQ1eeeBJXe+wqX6WBn3lW57s6zp/bZEk0hCrY+HxNcGP4sbhs5hhUuBCRejZbj9SeThUjfxKbVVtjx14xOgzK9CZoAQaKBupmRbP4J6aBAhRVR4uKKQotQjSxl13JlDwhLpJhwNPVyxggYAm7Zu98sDWPXkFuyYdEiL27ayudNeqMb3xlQlPiXPFcA1RWvTvEgbOKP2Ywxh2lz7dla4FgTOmLoqfzM5qraEv136CVdzTM6ZlEhTG0KrmcGHGrwkWuGqnqVIhcvMwCDwLDWVIVwnVpVZZs3fammfxKRmyK+jpn1SOmUFanalBXLZmiwOKWaZMyPRgevhcpY6SoS0IT8zS1GobOuxsuSlhdD3MnF/ewMk/bll2qOliRqcpX1qJn2Nm2kegQ2CVLjIxKfKk+X6R1NNuFx7RPz9ySz1BmJlIUWXZCVUSLoPwYSLUQnj03JGqnZVe10o7pAzj3CVPOwNkfIVLilqaMqaGf2ZkTgxarMeLmr0SHRwgdSen9p9aMFLgqhz0Hj1lfgjAGBIJzNVbZqYTnHqJXfhf1+x2inv9Eol8rzfEerOMVYGKejlSMpIr7tPe7j23XInzh+4AufWrzDfMxOuhYFlyJP/hiFFQP12C44bxLRXLpUlebhERR4uYrkYb/BBDKSkzDDVyHKFxhCi1A42EjVLMZjNGIYUcyM5NaDS5VOTKqPaNG9JWOoQLnPtsMqSDSnGpnl/hqbue1wfWDyJwAyeAiN7JtWSS0mC1Akp6nUPTTiZ8nARRJiePeqSwWa0LTME0feoSgk0ZP7ZJtSqH/p457iXNJ8uLNdPYMLFqAQ1WLar2utRZvwwp5QlF2bpCGKGUyIzZEKb5nVnoMtTCpf2cDkeMt0hE6kiQj+FDNqos3WbEbaw+a8iszxB6IDcT6MaAABoqs/oV+u3Oe0hOmkCws2VE4w8/WnxxShNQeGcW6/r2BDDAIC/rP/Mdp7jW/GXtVtQ/EhndB3//ALgZ58id9l7zP/tFi25MuCs2SkhVegqCP1FCleT9gl6C0kTYSpy9rHEVDPLLQOKEOXJUNVxOu8UUi8thGuGB5CnSqAULif0mJ9F2M+mMKRoTfPNgHDpukOFi8rwTqrQlFkdcX+hCV3dhBTV8VrxI0zzMqifXEuR2BaSu/yEeiDlkqu4X9aEy4RrSwiX+xPcv/lUYbl+AhMuRiWorNVTzQwnJ6sglDFcEiPgopDi7hjFQWID0iDPi+/hSgFR82YphuuquefMtOJGhRSJmVPaB2YVLhkQLm1ctZ2kIR1hpnnKzwFgSKsJOq+Rmtmpk8aa89qLQBG83D0RAe1CSNFVuHTW+4CAAsCRay/EJwe+jqPFI62dkzFzpFPAzy+0/zcd0pTqe8Px22WyMDo9IN2QIjzCZRdhVvdS4i7tUx5SJGcpOgSjadIpZJhqNPPZtjqk6KQ3EEkSKFxWYQbsfZ6KuqNcEQqXzD2gGUS+LUvzSSyDRC46Jw9XM8tQF26/otR1M5DTywUp4ofEUdoodb2cENnPTXm49MoXXl4yq+bVjNJFKFwBYdafg7fPOSft4SL6So+ENZFJaWaMlylcbt/m/u76GUy4GJUwQo6z7RWTv8QlgxfhVeu/ku8jYvSead7pjH40dDZ+OvR/o3CI5+GCdNJChB4uHT6MQ4oUyaNULxjypmcfAW6eVj3TyyhcuoPL80d49fv5xmybTEhRbWtQT0Gv8y0z1zskLfUVrihEcd5uwC/s5IUwHENW77abIFxhOHV/sam4rYzZxbhNUSJD8g8A6qFIYcBZQkr/xkOl1NxfrsJF/XaoMBWIB7aUNut7lmJK5XKTQj1+stTxcNnM6hJ5GhS3bWYijOPh8tYrdBSx3CmW5G2b2AZAYgTL4+swHi6JZupnwU913yN1Hi6lcCni10CdzBFm1290w3sp0kx6sypD8hMTrjwPlyVa7uLV9vimE2ItXLi74Jz5hfoTcfJr8K0ceTX599JQ32etLC2E836gaumyPgETrj7HScl/43XJmtIyGcG4lmd5WGwXtdgqpZ4UhRRfJLbmh6hNVGgskRkQhhSNn0M9EFIifEjMsqFImOmc9Oyj0MOV5UaYyMNFhQ+J9cwAYDhQuFJqqg4xoqTgT1H3/WeeCXdKmah//mniXGWm+VjhotZV2zm4DwD7HTLmAR759n8LAIAsT467D7biH+pXe78dd5F0aQgG7ZMyaymm1bMUBfEQ1/d/PiNRES5kmG6oey5xlt4xHq4kV3SQeveSHpTpQVYqak5mfJfUZMDGX+OQsbuRiTykKGQGTGwBAHzuzi3RZTyzQ6eZyUOKdbcfSH0FKvRwpaiTpEZQRFhKjE01URNUf+RMEshSG8pMcoUrUZ9HzekrpbRrKeYEVPd3TqSAmNhDK1yEQV4PhD0Slq90oUOKtQoPl/Z61VnhAsCEq+9x6eDn8fXBz5aWkQD+MHkAyzFqtunpy9aH0XpIUSNWuPyQosnDFYQUzdRwItEgqXCRoQ/fm4Ega3MmMzVL0Y6wsyDsaDpYwh+RZTL2cKUx4ZIF6lgIb+QZebjscQ8//Vz+xlmkV5vmZQmhMw9ZJMbD5S0Erpq5c3BvAMCLRPzgYrSH1eu24LZHKpRCMv+V+1vQ3h7/9yGlxAUD38AH6jfg2MSmhRgMQ4pSOr9x6dUpEz2LsEGro8RAg04L4cwYzDJM6XVPlSlfyswPoenEp64XSfiDJ9c0H7XjK8fnb/PFcQBkwHi+asPzMla4fjuq71Pt4Yq9oW5aiMwhiKmoO6b5/HqbWlUL2iZkirGpZoGHy/dTpXoWuEoLIWQKN/ms9nBJQ0DrJl+ir3Dpfq6KcMUhRUP6CA+XDilWebj0986EKwcTrkWIRprhwpsexvYu5UzKpsfwH4OfxCW1z5htNfOAJghXI8/NUpWHq9zDleUeLlk3hlKb+FQ9EAhp3iNhhMfD5t/SdTkKl5t3Km2qnDzKO4GsROEKRtgAmpm0SWBVZ9dIMwyiAU8qJJb+8GDa5CoY/hRu93P7wGU/z98or4qbPLJ8lmJ+7qlkiRNSdLwsWmFQ3zcTrpnjbV+5C+++7L/LC1HfmetdNA9hP3QlAfMA3hXjZp8XUsykNysvMn8nNUzLWn6fVaSFsH4t916z961ZEkdmmNbLQyXOLEVdv1K4akiDHFb+5Jxc4YpDiu7nsEROIkOSHzue/15HCMLlJkhNs9z5lZlBim9+N4YCPYgSdUdZsqoXrX5nGJ1sBElfVajQTdGQNax3LEmQiXre53mES3ncpPt5UCSP6D+I78qmhfCV0ryc2y+mAGQ+exxALSueWZ17vcoJV9ZnCbqYcC1C/OiBjfjKzx/HBT98qLpwCxBqDb6XCzu1N1FkwhAu90ad3BZtMw+Gpt/hA5YweIkG4WSaz5re/sSoSK0pXLRJVY/8HA+XNzspXMhWRibVok4V8M3IZtZXYxqPDp+Bs+tXOeVjM6uHYPSctzlQuJzjlkElIhzICZc707LcI6Z8Zskw6rAhFtsM/1qZcM0RSn4TAJxQUGxsHke+pNQSJ2nvIGLTvHB+4/kOdW+YtUwb9MSMklB9/t4qZy6pmW6oNujZgZm0v02lcAmZef2H6RvMOo90WoiaQ0qWYTxXuKRVuEawW3QZTakfgxINtZi3Vt+yVCuIepZiApn55CoM2+XJmuNQr5AZRiebftqJgvBeQ5HSWr0OKZJcdVOkaErWHcXdUbiCST2pk89MBEobYA34+pxAgQcvSJOROcpVuWnequtFpvlUMuFi9Di0V2iyUaJotAGdf8vkc4I/sgLgE5EJRbicDrmhF04e3Wi2WcJFhCFkBpmoWYqyCXf9NRu2i1e595Q23TlTM2+Mh0uvYRaG7fxM13kHB/9hV6pwZc5DQpHT6Tx30l/V/8upo8I0b2R9qnOMJxssE0pdrA+r63JMwCUKl+6s3RCJp/gFmbN3x1hhXYwuwv3OGhNqm0uuYqKjH4rjMidcS4V9KLpKg04LEYYUjRqiPJQyK1K44oe4kDaDvJvBYKOPAAAgAElEQVSp3PQTUhqFy4QsZWZCaLVaDVIkeUIHwvydmZBinVRu95leb94vkxORh6ta4coJl07KqtMzuIk781mVrsKl+kfnHjJtd0L1QqZK4YptCaGBfVr1l/VazYZPVRumMWBCioZweV4yFdoUAwU5wvT3Xa5whdGAfJsfUkzSkkzzjiXD9Q66IH2tixhMuBYhhM5z2aX6MmIGojviA+DPUmzkIQyX6DSb6uYefdZsS83okRgpQ5oOP1HLSYSLXMs07hwk0WGQIUU9skynjQHV83AZhcuqb+HaZQhGlHm91q9lZlbpEWeTkN+r0kKYB1lcjspfphWuaZVDSyL25pAwygFtAjbfb1lYktF9uJ+3Cot5v1P9oPRUiHyAMqEUrqWuwiWDxKcZketK/07Uwskya0JUTfgwYaoMTe3vMdniM08J14Mv4S4arQlGvY4UNeyebcX/c+lBpvp80oo0ZvVMhdCyzL9vd21Y5XUX7MxnKUIC4yNIkwHsQJwWws1I30wz1JCaHGR6RrCepSggsX2iYfqzTNhZisKQn1o0GGsqgjg62fRDilR4L2sYFbBWc0zz6nuZRh11kV+7XeqoHg1IU9TITPOQOqN+7OHyJw4pJSxSuKRJ81EvCSnmedTy8xaFFJlwMXoeeu3DqgVDW46f65QLziZ9I1sPFxXKcwmMurmndzrbCBnb6bhtHq6m92DQHYU7S9Gci1xHjCJ0ltjtgdEo8alUKoE7K6glhQta4bKznbTxliJclcsfEcbVSOHyQoq5CjIp8oetGxYlp/brdnsdN6FwaROvCTMw5gSechwTLjsbLnigFoYU7W/QpIWQfkjfDErUgAdpsLQPsWyNCR9Km+Hd/lak109MK9O8naWYmnukVqtDihr2yka8j0GoWcLS9XBJiYarJAMYlL7iYhSu8RFMD+wO6pdr13TU921qUmKEebhqyHDCZ27DDb96WrWjbtUpo/pbD5cZgKGmFK7WQop6Jme9Xkcmkjz8p0jRNHTesCCkGAyUqHbk7zOk0p+NCUMs3T4wHtBlaaqUqxzlmebtYG8ANDHjkCKj55Hou6Hit9zqj12m8ehE+3yaxCxFrYa4HivznhqdEz4mN/FpIn0DuzGwEiHFjPBTla2lCAD7iO3qeeWH0DIpzewo45moIHS6XteM3FSj96xpP0edqd9fXqPYmEwmPiXSaexZzzs2HU5yw6Ll6pTupOl8Pib3V6tJVhndgfud6cGKp8TShDzLgIbMFSQ3pKhnKTZkLffiOB4uoVQkBAoXsiYd+i7Iw6UVLlettiHF1PiTdOJTZNIsPp8TjFr0MSTIvFl5mVJvGqn0Ql4DmU+4pAkpbsP0YOzfAoDn5W6mvWmaoSakSfq699ePBhqTUZ4sndohFXFo0w0pCm9bphQu97vK8Mv1W33TfGoVrnqtbq5V3/c6nJdlqbkvs8TxcBnCNUDf+yofGKVw+Wkh9G/LDWGn/mSisjxczmBvIGPTPMCEa1EiaVXhapVwaf+O5+Fy5HPAW/w4M0lIXRIW+y10OUrhqiGDTHLTfCKbnh+ASgthyVU8QqPTQriEaxskJPz1CjPPN0bOUtTnN14xEXi4dChRKVwN2zntnJiMPo8yn4w3Qo1mKTrXMpSfayzTU+5dM3QJ4TIK10CkdOS703hbn41O5wUViiq1fI5OfKoHC1r1BKxpvoE6nt4y7i/WHOSaE6KWP9zDxKdUjilnAkdqiFS+0HE+68/m3NL+JNfDpZWk3LMUP5Z04mGTFkLNUsxDgGobBAb/f/bePd6SoywXfqqqe132bfZcksnMhGSSkAt3gYSLBxAExXNU4ETldkB+giIo4scnR+SIyoeiHNGD+MlRERUONxFEBA6C3AOSQwiIuRACCQkhk8tM5rov69JdVeePqrfqre5aa++ZzCSZYb+/3/z2ml69unv16q5+6nmf93kbgMsI6Y5tvIxKzba2CwA32+3h+9Th2Mq4wupdIaWoGpMczRlhxvo3LStqEOCqHKBD7G966f/8UsI2mbpqMFy+l6KOv537qI6Gz4zhCvYUSbozvY60Z/JGlrzWpk1++bKYJgYA2UwpHt4TOiFwGcgkDVe9Abg24mQPgkVrpxTXuUHd1nBRhUscSDNAKnlgt/sfRsf03E1uoi0EPUCCuDcD6KawWdOqFAHgNBwCGmDKNFOKwjZ0XqL1AHQl0MRqGRQ0C9Zthmt48Lb0GJvHGQ+kdY4I3MYUa9zGonKgbugHUgsbjoPrM5rBRfO5ZuJNR3MhLL7Pxsp7J7KAK8c8pteRBVD6+2RBRFsI0nDVUHjNh67BvuUIUIhFCvuREtpKQNcN0XU60XDLCPxrZ4vglxHDGlgrEzVclrFeXMMVHOhZuPsvHpv1gGusTQCWBnICw2WB0TKqItPWB8B3CXDBBpmDVQxwDQ6F8a5ACkJMcr+wlGJLwO7WWxrW6EgLQ6CUmDPWTkjrCpXXiBVKwXqbDDrfFfmXGR3GWq7h4inFIJr3xzO2CoD1KUWNIaUnwySuPUmW1iQgjzNckjNX1RB40wOBj7w8nE+ZA1zXfxw44rS8GwzXRpz0ETVc09fT60RcNlgnMIaL2Jsgms+lFNtsU8pwpS17+HaEtVHDZetEi5TrpRjTHNMZARpEBCyWlatYOk0c9rYQXLOkM6192H5k22vHpT894GIeYSGlqONscLzsnNpT9m0awxWrv5q6Ln7cfbvqt0tC+TVE+T5CmlZyDVdONM/O0QbDdeJjLWYpY3wqjE5E0fNYDdcDMVx0La0M472thE2sBoSvEm5VKWaOIzilW4OaeiR6PzAJy4TpOgjOo7GqDcBBFWUEIyyEt2WxDEwIa0JxCjWq7tom4IoM11jlAdcSCekZ0xbaGgHA6v5QJNSc5BhZtmwhDOvzyAXsxHB1pI0soF+v4GardYXKT86KQoUCgZhSjIaxBBC1ZKnNkHZtg8EaBYTR0NppuEbo0E7DeQ7njrVU44ArkSnwyfLwsPv77U+6/bP1EluI9z4L+Ksfdrv9PhtDNgDXKRihSnGNi1nXeZq3GbnqttDzi3bBTTIz6aeo4eKpx7QJs1svDmiCpRSNsayvGonm20JekxXvc8EoPRg0hnIWuph1KcUGw+UGs6YtBGO4VNnafs2qkzRjBek4+fnWUx6eOW8up40hsWx63iRs+EzPDpJtJWnXaaK+4FhdZgdT0xBKC28psBEnONacQLTT8tZqjGqTMFz0W1EvvmZqLHaMiJVv5HLumldnJgb8vmLbIzAhvHhfwMbts5QiiMmyJkxQlJLZlGKstI3HK2FRaePeExK2wXAdFvNRND9eQSXzgMuy9J4h9k114woMcBEwiuctTlCioD9jC+EZruVRjVLG8w1rMN8rEpZa1+NgnUEpRS6a15zhoslcJn3IU4p8jBJwonkldGDC0ejmAcTxW3DA7NlT1dgXgFCdTh6A1sZemIHhorFtyTH8329jyAbgOgXDGIsFLGe7giTrVZM9VJLIiOZLEnlmBt8wOJv2gyF9WLQZLvLjIeNTqgLiQCo4zd8dDZcvWq76p+E0cThp4kvbTtOYJJrnDBdR+O4vb2Sr2TkL4ItVKebAZjg37LP8HNWNNEQqvnXLeoa8mui7c+3XNIaLz85ta/2mLUTQtG3EiY0s4MqwlglwrzGoNGO4BhO7OsTUmHugGqPjPSKLYHyabUSfObZEw+UZKAXDBPJMJyVi6lFVrm1Y0VvIphRVY8JDrE/le0Fa3zeRgMCv3+89eNHCX8HCO82PlloM1+2bL8H4aX+eAi5i5xMN1xoMV8N2xSTtfvx44BmuI8MaJWe4jMZ8t8CWnkAtnZWLrsYh7VooZnxqqFCJQJIJv4vJivfLVkqRLCtIwzVEbFDOj5dvxxnXEsPlC5hE5hoIgKvvDy9eM6GlVGMM2gBcG3HSR//Q9biq92I8dunjU9ezHABMufBzDBfNWGJJNGe46tbnbIMhcS/bs3O6SR3g8n3VTJ3cvE1/K/8fv4F25eIkR2wrJEzZRx8jl1JMGDmDiglyW6L5CSnF8ECoOcPVtoUgsWs2VcN0EVEPZ8NA22zmzb9Xz6dUQopQt797Nui4ZZxtJwJ5nT7YwwNwI05sZDsbcBaifS0IqzEY68BwKehwCyj2+/HPEfixxsQ0pRTOb6nZvDpbpRjZ58RVvnbJPrKAmLnjy3jagb91bzNbiPlVZ7OgtuyOn/dBrAy3hSANl2O4jGe4PHASEvvlaRioOafhgs6mFK855+dQPvw5DHDZoLNMRPODWEWY03AFtouOTSqWyvNjgyhDlWIpuIbLYFQbdKSB9qya0TVqYsOFhEHhGC6dAi5rdUyBivY+tVAt1qvy4n2qog6Aa4KfGy2rmQaPpxSTCSNV0fq2YnzMpudFUlmOCLgEDDA69c2UNwDXKRjdZUfXXrLy+anrcU3R1Icn9Rtki0JOngZdlt6jh3Na5daenYcmzGwZpdqUZ7hqUUDZev0Ml+UPnikzcd8c28oSBbQXxKdVirxkvgW4eEox6CPiAFdzwEUpVs5cEevF9vm///1W7D0yTBguzUBpG3C10zyq0ZYnZ52Ri+Dez1MTGW0dZwi/3/QX90qskVJEZtIijMaw0slkQQdAlAKuVkrRRoYLUjmLB9sAXI3qXCDea5Jdp7AGI60dm+wBxmnX/g2esPop97aMflILgz3YaxeBzkwU09Pu4NKHTsPFmFiQhosYLv84K/qwQkAIwAjhzDlNjVEjpbg0exaEEFAqAsRwr6omw+VBqHBjQpj0yUwvRVFiXNXYtzRibJMzL10aVigS0bzFqDZQVkfAVUfRPISE9cCJtLQBcDGGy4oCPnvHxPsl8+YihksmDBeZ4yKTbSARcMpwxcpT+nVCjBxLSSlF3jCbAJdpTN7pufOK4gPAH+yKOrBTNDYA1ykYw2IeADBnjkxdzzCbgmnMbtIk2gfdQEHvkRFYr63haqfGoq+QE81rFA4cMRCichqurJCebCTa6wnrfZBl4QAXUpbAmjphuEi0Gx9GJZrp1MrGcvDUlJUAV5v14g/Pd17+Hbz03V9rpBTjOWoBrinu83RsWQCaicB0yHaLELf9NIWsYKYSZrl46Gs/gae+6bKj+9D3e6wlms92InApxUJEvZY2KeBqAi8bUoomblMWnuFqiOYzBSqcXdEs9T2qvMZKtr21rIgVzgvDPbhVbPfL08dS7SvwLGKa21kluCpF1WS4iq5v2i5gIdE3jnkZy36y3dWZXQCAkgEuukdtQzTPxxDXbCemFFUDcFnPEl/y+k8Fdl3DpeqJ4UrOUe3AsaaUYj1OGC7X6ijqtYILvjVhbDaZYhee2pS+6MYIl2IlhmuaLYRNQDQDXOAMF7sWieGilCLbVijWaDxLyBbiWepzbsEpznJtAK5TMGg2O6fXAFz1OhkuAlCsSrETABexTRlwtUYVoQ00dlodCCAMojQT5OnPcJPneiny7WdSYzmGqyNqNG0hrI2zZzqeFHBNat/hB1iue6NzlDTubgMuAYuDq+MkpaiZ1ivqPjKzUfa9/BfwizMpqVyEQTo+QFINV/pdjyWleGRY4/o7l47qM9/3kZkspGxTvunwoNIh/RXaUqENuEJqTMYHaqiwE9JruOo0LZ8Ffuw+8UBKcEAk25WHsXm1xubRrbjNAy6DJsMlA8AP9i9CBh+ukFIkoFb2HTgTbszqecA1bIrm/XEWBe3PBhbJKAa4Vu5KnOATwMW0U9TKbKhlOK+37F9m62ksj2rXlkdE81LXMFuH/qdGV/jlG3/RH6MbAzuood71dABALTzgMnWY3OY1XKlHmIO+LsVKthAjQQwXiebZPc8AV0wTuwrYaEHBU4rL4fzTuhQlqFI7nRDTRKBLovoMMD+VYgNwnYJBF/WCWYOe5QCA3zhffy9w61fDf23Gh6vT0HA1Gz8DTYYroz/Jto5gDwmhgnbAsGOVGSAVQVguBcNnVXGfRiiAUopIRfPGGNTGJEyAsWiI5tOZLbeFMBmWKqfN4udDwTjgxz7LncS1H2hDpWUmpSgaD2UOuJIG2I0ITKXMu1M3bSEC43ecY1Rr/NAbP4vPXb/3uG97arx+J/C+59+z+1xP5IoqcvcQW6+ux7A2doOQgjFcBJiFhYMjUYsEEMPlty8kaisBWyNrwUIpNCsSZ3WqprVGew2XzQIu7X2xrKmxUO3DXnG6+3+D4SI3dwuXUtRQgG9wTU7zTu/JGS5nj8MZrmZKkaq5yyKm9wyl8jjgWt7bAlyCAVUVxgH3d2kcLWxIWqBFAWsMHo2rccbKN8P5Jv8xaWsY5fufjlbSnpfNFKuk82uxY3STW5b0UmwDLmENjE+GutSpA3njpi1EjjWHbTFcCu2xOzJcpOGK75FoPunLOzwchusetf6ZUthzKsQG4DoFgwZH8mSauF49IaX4oZcAb/vh+P8MwxVmJGHwjTdSSEHmqhQT0TzNqriGqw4tISxnuFhFZTBd1W2GK9tuhD0YYmWhASB8StE72Tc0XJVOqxSBvC0EfbcxZ7hqdhw0C635OWozXIE14oCL+eNomTJcqbt4+uCl48i58ecipEKlG7hdEUX796NzyXVBxzNu2b+K7+5fxes++o3jvu2pUa0A1334nt3neiKTPsymFNlvFdrCNDRcvNUK4O67MHnh7vD0G/tG0tB1IhloVimSi7rbJqtShMW4do/5HOAaLOx2xzk6BAmLJe+Lp7lgHVH8HXqZeiAlwHopelsIt8G+rzp2Gq6+cePg0KcUXzF+KS4dvTZ05ChVtKcIcgh+DEu3T04pko3KzV/EcwfvdauP3ftnir14fvEv7hyJEtJUeE/n9/13cueDtFoKGsZruIqDNwIArigfBZz/IzAyD7gues+j8KTVj/vjjaL5AITZ5ElY7ZVn0oFiY9EVFcai44BqrrWPiSwoabj6B67D3B1fzk7KooarzXB1chqu1f1hDOmJqvWZUzE2ANcpGCbDSGXX41VzofVO5iFq29tra7h4Oq7NcGUfFg3HdLeaiQ7vXsPlDjCCkKa3jANSBDDS1Aodh7HCtfkIIMxASAUrS5Sghqz8OFzDbCXioDPJ+NRkGa62hgu8SCEMPPF8B6fvDBPmHmRuoJVZ0XwzpZhL604GSNH4tIwWGLnfr1lEQHHrlcC+b03c/npjWLnt94pTO7Ww7lhLNJ8xPiXAVQrGzhr383O/J+WTTEBkUdIqRWd8ClM17ALSa6yGNxdFw4fLaOcEDwuh2oBrOHe2O46hMwFe9YDrpv6Dk/VINE+dHiwkIIRjuGruwxUZLoBSipHhIsD1j+bx+Jq9IEwfE4YriOYZwzU4kDTFVtBhXCBBOz7/38P7D9q1Bb0C+Kfe63CacLKOFTWPWRP1STSRrEPTaA3rU4rlAXcf/eP8c4Cyj2a7bS27aEYt+zE9zO0pEobLpxS9aH4RKzgi5mODcjRYc/abUpp419f+CA/+l+cwH65MlSJ5mPlnSg0VARdPKQ4Pt823NxiujTjZIidyz67HGK4grqabhgdVHbJF3aYtBNf7kBVBwnARoOMMV6ZK0RoYYyCF9foF1TrWaLranmFPEvIaIcLszhhf7SQVIAuUqN13SwSjruScWKRCNDVcrH1JYiroBz2u4QqAq12lyB9kWYaLPVB5KblbZh3YZOe1Va6diOanXBdBG1MEcDm1tU9Tw/W2JwNvuWTy9tcZIz/j75YbQxOAPGCekPahqD3g6ggG1q0TnQf/JL8858MVJkrS91LUVUM3lt7zrhtETFnSdSpgMKp8i6sMw2V86qkYHQAADDzgun7mEel6VKUHOMAlBEAaLmPbthBl30+gHCtP7vNDkYrmieEKGi5rIlPPAReAxXp/eO0YLg0NCSs94BpGvezubXO432IXW+2BsGxZbcIion5R+RRbXWv8jPocutXhYLbaOfhtAMD+3pkAgDmT6h4HviiK4pfP/iiMLGPhi6590lMmPlwWdI4sbDXAjBhhScy7StRMIQ4v2NGNtGa2SpE0XGES7rY5Ft0AuJJnk9HQDby1Abg24qSLRG8xhe2yzPk8pBRzZbkN7xRrLTqgG1Qnf93+21WK8YHdtoVIGS7NSp1luNFtnfZ849usmP+VzWq4YusPWIOhrwoSUsGqTrCFaNogkGljOA3aRBCpYuk77dNVKU5Od9oMkGqlFA2yDJeATZr90rIaMRUEMKaDZrk50XUmBJzGw0oZgF/6m6ZAjle+Hc8YeIfte43hGk4vNDkRMbUjxHqrFNl1WhHDJd2yAtpPMtKU4tPUlxJ7A3cshrGv0qXJTdVI1af6wAqR7S1QoSIhtjXBVV6oNE3oFwKIDNeKB1yQJX6l/4b4tb2rvPEu+BZUuRed5oXkDFfP2RoLkejBBg3AhZaGK7b2sQ3AtdXcFV4rGMxghCF6nuGywIhdN1K1gMOgWMSMiJPGrjco1rrGG8u3uu9ZuOPrHb7BMU+dzQCA+fpgsq2h2pT833Zm/PlwxRGW2KzEh8vC+M4Bwhpg4LZ5RDYZrvZ4IWEDux6+ImPOwuq+wvC2g27SbrW7rsei6zSDum6Ni1qbMIH2H8JRxZ3XAlf81dF95l6MDcB1KsRt/wb8+eNiSe06vZeQ8+GaCrjcCGUtr1JspxRzYCKn4crNzg3TkECqyOr49Ke2ouXDxQf8SSkY4xUrsAbDykAJSikWLqWIxuBhdSKad8dWtxkuo4MQtEYUrqbVOG3RfE7DRQPmJNF8YA6YXqZiJpO0LDmn7DjkNMDlNR4IehnbYPzSNJKiZsLHOVaGY/xR+Re4yN54/De+njhy2z2+y6nncY2UImnq+LVLgKsT2tC4ajDejB0A3li+Fc8rnCdW0EpallIsPMNVj7L3Ld3zGjIAvq4dYaRib8IIuFKG6/KzXxJ6vhYDxx4NCwe4hACukheFdbXv4FBRdZyQgN9npX3a35skuw32YEysUqQYopccA73TSRgufz69birX1/GvO2/EC4uPYyD7Qbwf9Evuy7bGXTGzJfl/xwOu8Yh1+/ApRWEN9sgd6Bbu+8zpFHBV5Wx4/Tn1WHSU9MdhvTks3csijAPu/nYAVFgDOXDs24rcFLoJuHPSZk+5hosiXEfs4q0GDnR+6QYqePETUUHC/HFDU6qx+4rfxo09VqxytAzXW58IfOyVa9xE953YAFynQnzs14E7rwbuuArA+oXSiWieuF0PuJYsmw02GS7EMt8wI8oNyDylmHlYGMZwUYWRMWyWKVTsG+aPtQLrOxaAThSMprqxKJCPDJfFoHJmjEJIV6UodOt+tb5cOzUCNJmUYvTCqZjxaU7DJTjgyjwoQyVkJvXI3bo5wxUaWjeYDpHV0U0DXK5OE37GrllPPf4d0t/v+NP/oyN34afVZfid23/puG97XXHk1nt8l1OLD9bwv0LGaT40PmYaLpdSbFzPAJ6nyIQ0arjo2uyVJUa2APQYXerRScdxxV8Bg0Nuf95jCtaihxFG5HdlTdCTcYbrA/oJuOrcF4N6KRYjByiG5Sb/XRpZU1lACYuqNihROxAkBCQsvnTjXRBwfVdtKDvs+apjEYX0AEYiZa0opdgpo8g/mjG78zSa3YlmPETe7Lc3AwjlzjNnRqVsAYdy/rT0/9SC68ANYZlg4vjrxqeHSfC+7tnpcfv1xv3T8Wr1SpRKQnjAZbzEwXg5RuIjyDRc0rOKS8RwsUIc6ptIzKuAbacURVqcAwDGT/hJDRC1ZNx2I3027fjWu5PtNp81awaRBjkpzH0wNgDXqRBH9ri/vUX3d50Ml01E3CngWmGzQduoUuS9tGTmIRDThywllasi5BV4YBoS+jzTcEFHwNVsFlsjGo7mWqEI42d3EBAwGIxdSlEqYrhq/+34dzComfGp+w4aiWje78Ow48gCLjq2TI/EFsMFmzBhJCqVNrp1czaraqYUG9VDnGkTmAK4qCVLSCkCiT4jAxB1fXSD4xYcwc295zrbkQkxGB/lgHu8oxqsvc5xjqmp2SzDxSc3UaMTtleTaJ4q4CjVlDJcADAH9315ax9yCO+WJSoUKFb34mfG/xQ/dPMXgY+9EvL//BkAoKZUuh5DwaCSkanRYz+pK+J4MrKlY2+I4Rp54BYYLmcCEb6iHxvGtcaiWMaoXAhMzQe/tgfnbu1DqSJNKVoAAhGEAdFV3Qe91S3jfdWtHWgItg6dNH3HYyj7cSM6Tl7BinMoZhZPT/5fanfeuwdioUlZxezCzeYMPHCH2/c/7/gl/K28NLxHgMsUM6iMRaeQXtPm7ltn3SBDmhFwEzAHtxUAznAt+JSiN8m1vPm4DmnoHNPn1mGAy193HZlqDXmjbpOZmCdxtClFrwNMGMb7cGwArlMhCHAFrdA6U4o5I04CXJbR75l2DJGqThkVt8v04WysiJoi0wAwIMpahvfDOlLFEnNvC1GJIormieGyKv8wCt+dGC4JGBPanghZAKrjRPO2kVL0rX24ZYXVOm4z0XCx1Kb7EoloPpybdYjm7QSGS4A1AGYzzzqkFFOfpfC7sAfsVKd5a30aQkIK6xjPLIhmA+yU7b3w7V/BQ37nE8myi+QtbhNff9fEz62O4iSAX5/3WEy7X07ULqemFKf7cHXqJeBf/zT0+gOi91PJKwcNsoCLQFmsUtRB99frFhgjo71ibI6xwreLsaF58Vj2XOWiNahHbpnoxDTYEB10SxU0XOXoAJbFLJTXUgmRfm16YI+rCpuxhFFncxDNA8DmftGwhejCApC+ShEAoLrQNn3cEcPVZSnFZxx5Nw7JRQy3uJSmVmkaMjkNst2GyH0BlQIwAPNbtqf79ud48dC1YRkBT8C1HfrVp5wfjuFr4oFx8wFw9TCqjbO18IwfMVxWSFikvRSJ4ZqrD+HCL/wKAGClWPCi+WgLYRDBN10zkwBXMh74yV1BPYZozGeyh+xElEezapHFdbcfwc+/40qMa7YOXVcbgGsj7vGY0k9lpAEAACAASURBVGMQd1wDvPH+wDIzlOQMl04B1zKmpBRt9M7KMUtN49PaAx33XtuvizsZG81mQULGmbc/Vs0YLjCgEyp0GFvAzVCtZ7gAB7gEDKSUsCrf2scEHy4OOlhPOZ5StEy875fR4MPBpuDf3UQwSKEEpRTb9hESFlbK5HtJGAa44mwUiEBr/T5c2rEBMjKNOSuAhCWbUg37mW/uxdIovW42wdH+prs48XMrHHDdC3qqe1IL8jvFO/CX5f9YI6U4Pa37qNveCXzyt/DY4b+GZTqkFCPDpU0+pRh2w9PV/trslJ04ieDBrmNNaSqYwA5Wsh/0kmbslokyjidDeIbLX8/F8CCWxFzwwxIQDcDlLRTqGpvFMsadRQDRzd1VKTLjU8Vb+8TKxbrBJBI51enESd2F1XX4zOxPYLzlQgDA/jMeF9Yf2xRcjb2GqxVSBVE6xeZtO9rrASjMKFQac8B1WxnTiFKkGQflz5NRPVTaeLYwarhgNAwUrLfOAEgD6lr7nDX8ZtjWqlps2UKEQiVrwrhYiwzwRmPM9M+Q0jNcse8lS1fr9piSxJSU4qv+4Sp86ro78Y3bWfp2g+HaiHstAtDJ6D6+/BfAyj7g+o/F9xImxQtgvS6DM1wiC7jooZ/22ANSNqS2buYZbj7TADBIfV6s1bGyUqjQckR4EJJouAjQiVgdmGW42OyONFwupVhEp/kGwwVrfNsQLhyfoOGi8ngb+6PRdxszywphmHt0w7mdzoO1NmF2uIYLkNBWJMxgRQ8BoxNRdM55fzrDZQLDBTgD2rRdzN3XcG0SDnDp7pQ0zYi3RLrn2aZ7cp8/V3wCT1VXrtFWK389N2PRxoc1tYMqSDQvnL9UUzTPw/oHqjFxAtHrlBivAbhIG3nh8hXAge8AACrZhfFaIetBmOgyhst20C0UhAdD5fgQbhfbUXqRuGO42IPcjw2VNlgUy6g6iwADE9IaJK19VOGd5pkuqjPrzXzb0fXMmj5wEwBgX2cXxlsfiCeM3oTvXPjisF4TdIzkTB5wZZZtO207VmzbPwsA3qOfDAAY7PrBsOz27rnhtRQCy5b6EwpI3/tRqz7GnOESsd+rFbEAhs6RbWjaAMCqTkPDZZIUoJvE2fWlFA0BLtp4yp4aoxsTwMzvMSWl2PQjAwB05tzf0cnR9HoDcJ3swRzYw0CcaLj8RU3UK9OoWN1OKRoPuAy7vANjResyJqWZwgLigO+AjnDbCkL6fJUi2RsYZgsBoeLN76sUa+EBFzMppMaw7uuacPyCze6siC7LQ99QV0rpfbic91DavFqjNqkthDV5DRfXkoVl/hw4QTEVFnCGK+80b4EGw8W/g6/EYm7ywRbCOhYjMo9t0fx0DRcBLg98efoU8XfjPj1HDbg8w1VN0cUMhozhujc8eY4FcF3/z8Bnf/+YdzkJCLSOJ+fDxYLS8vTbF6zcXmvXwWEi4JIxHU4gvdctMbZrM1wG3lz07T8OAKhkz1cMRsAlO7GtzhAdz3BFxugy+WhXbQcvmue7I8BV1diCJVTdzYBQTLhtkfhwqY5jZgRQlA7k2LLfYhIppdjreECw31XG7u/sgpTALXa7M0sWxCilgGmkZsL9kkR/S2vRptke/uSB78NXnutSiHUvrnOFuQi3/dyVOPCDr3HbtSW6nXjepQCW/QRYQ6Lwx61VF8bCa7hi8/GeWXEpRabhirKKOK6/tvpZFEq4MYR1/dC86wD8hDjXCxNNhstfd8G4nxiu2Dcyae2Tu7/Xcc8nNipdAlwNhutr7wTu+vaa27qnYwNwnezBBNZZUEMDNlGvvJqDpYQC4Bq59xXdSIOD2HpT2vLE3YSNlGIipo4Pewvn/RKaziapR2JvbDReZIwRZGztA+2ApeYicQJcwglB+XHUzJsLhnxpnA/XoPKPCeV8uKSwQCOFFmwhBLu5ya4baGi4CFxFtikyXGUAaTIz2GSNTznDxc6Ra2kSXfUlrHf5dusZayEFAWCbfB5YyxbC/VbCP1ys1dm+ajwFuh6DXT44EsNVlfOTVsfquN30+7iHtcCnXpt3xj+WlOJ7n504jR9tTLUzy6R1J4FCYmAKGCz0otYRcOmeqQxXSCnWYT/9bpnXcHHbEojWNivZC9VwhiaEZQNwlTJhgv5V/ABK5cCAFGlKMTAko2XMiBHq7iLz1/KTP+7DJUvHzAgBMePS11pNSSmW/jvudwzdgc6OAMaMsWFyNVRz7e+ZY7h2Pry1SAiB33zWk3DJBWcCv/AZfO+ZUd84QBfF5vtBlV08f/wbeNzoT9ArVfLZJQ+4DCS29d33WNLuuEsVz6U9/D08yVyOb88/BpaqFHWFzfU+GM4CAvi8eRgKKUI3ASDNNjiGy1UkrkfDReM2dTgItiEyAq4sY8vjaKsUcylFa4EPvwz4yx86um3dA7EBuE72aPiatJf5i7rTBlw5Y1LKw9ODGx98Mebv+rpbh1UpRv1EO5VnTIbhCoCLp6lMMBwlDx2rdWILYfxDRHgQUouYtqNtatF2mq9RhIenhAb5cAlE0bxLKdL26gbDZdrGp4axPnKKaN7qMNur+bGZKswwA2PERe3e3DEtZmDVh0JG5s7/BsSqGeMYrlgGTuA74QowKSRcKTmxDrrW4M2ug+t0Tkg/IXbiLowGsZ0JMVxNTx8ew3F7AnHc4/CtwBffBLz3We33jpbhOg6ar1aVosmALGBqShFAYDslDHYu9hMhvWMsU6d5HrFK0YZxoVdOSCkyE2IDGSxiKFxKUSYpRdMEXIUKPlwAsF/PoCCGSwB3HGH7oFSe9+uqu5sDWpKwUGYM8KbrqgwgtjPrmKSR6MIYi0LGfQbRvK9SlIduxgBdDMqtUJLGOoR7/Y7e/ZPvKZnmMYldj2gvS95/JDqbz0zOR6eQKKTAF8xDsQ+bgwcX7YdSiv/bPBo7ZtyXu2vk9t3hFZ83X4YOanz5jOfACukMqv/HA3DR6Gr07DCMswAwtgUKKd1EUZOvIgNXTJ9aZ9oJAen4FXpw+lMcPBqZhstMuranLWvuk103WdE8TQiq+55VxAbgOtmjoTsCkLrLB4aLUoqsoXVGd0U3RAAah76X3SW933Q2B9opRQvBts9TinUQ4OtAO5sIBIWMqQ4mmqf9EcOiRWxrEUCYH/DdMhuqFF1K0Ul9lVRhMBW6QpLIsN5UMQFhXDTPZoG5lCJVUIoypHikqQNjwJ33STQbU4qswomL5kWstKQHfSw20NAsZRRb+8TzLaaBA/LhYuJpvn6OyTRTBscuxvhS7+UQH/l/wjJiuOqkvYcBrv1Q+J41b/p9ohiuABhEPIawz6MEXAdvutuHk2i4Dt0CvG5ztM7IieYnAFG6hwpo7FrsR8sWuPtuXaJ5o4OZar87QTTPJm0aEh1RJW9Xsh9T357hsgxwjWwnEc0DwGHdiSnFhliH0lly1bm9696WwOgoGPQHtwMLOyOjp0qfUhTob9oGAPjekgO2HMjQbnoecKnRIewTW1EUMgCu2hjAm7Yud7YlxyVhUBUZtnbOW0Awg9JmzHbjeT1j6yI29UsUKn7xLmO4pHCC/f/zjC/gVdWL0bVufLjTq0M6SoSUojhwI7QVWJrdjUr5QoWVfQCAbWZ/YgQ7RulTilHDxS0gLBvbRnIGTx29Addc/Prke+RaAQWGizIuAcw3NVw5husoU4o5wNW8v+9DsQG4TvbIpX1ys2KqEmKAKzEm9SCJmJkAuBgFTTerBbOFQDs1FtrRsJRirFJMU4rElvFebpTqFNxpngBX0BfUINNHDcXc50nDFc1Q4SvwiGkbjF1KUakC1qcGha0bthCUUkwZuXA+s7YQKqwXl5Xh2KStMKa2JyylSMxEsIWoUw1XaDskVBD+03FUgeGqYQyvUmw/nKf1UpRWwwgVUoqmrpMBMcfITWO4HiOvc9vde01YtkAMF/fvuuIvgfe/ALj6/a3jPWEMFw3IRWxBE+JoAZfX/dydSADXnd4m4Bsfah/PGgwX3RuKGK4kpbiWaD4+ZKUZw0CgLDvZ6jTLxhADEfqqUlSqBysEhNUQtff5KjjDVfqUYgQVq1qFlCI9KGkiQmPD6Xs+CQCoZ7aHiViJGjOre4DNu6GCvrIErButFjY78LN3aYSb9684Noj2Qj5cTC+1hDkoKTDjdV0rIx1YcNvQMSlhsDxzJrLx6zcBL/pE/j0AM2yflz7q/hBCBJAHpK2tpBQwFhj0znBFQ8YBrttWfNGBkoH1kQduxG3YhqLTxWrR1pLxlOIIBZRsari4D1cc76yQuN6ehZr9jm6d9qS9WTFODJfWUdsK4Og1XE0kDsTnE2+t1Ly/70OxAbhO9lhL49EECOM84DINBioM1mwWGkw9eZViEM1zhiseh0spRiPANKXomkYrXqVoohgeQsYqxZBS9N/jTQ/CBVf/sduMLNoMF2uvET1oKKXo/6dUGEyFrtK2FpZsISLF7hguD/I47U4Ml2XLSDQvyijeNzXG3u06gpXYnie29uFVitqfbycMNsJZW4TUKe3TNEXzaRGBtiL9fq1wLKBYyxaCz2in9Ol8vHRdD1a3XxyWLVCVIv/czV90f7nOI+zgGATs6wm6B3KAa+o5ysTRak4yoXmpPKWT6X7NsG9JWoYF3RsBcFkdrAysmS6a54xtYcYYowMhZdLE+Q+rZ7oXjOEykOiK9BwY5RguARMefjaTUuQP0ErbkFIk3EHpL+nPxf2/8y58Rv8AVk7/AWif4tol7oLSQ2Dx7BbDJQVQzrl+hF1RYf/yOHpuIaamemUEP0cwi1LKwECtjusoHxAKeMgz4ymDxdIkwDWzBehNLg7hwO+s7VsBAAUba3nzdiEcKCcNWr1pNwDgKnNu3Jb/rDx4I24229FREgPfi5EHTylWcClFZ9tThYrE4C1mdRjbhN++kalbf64VUJQu+DE/3NsG2V63PI6W1aZtDDOAq9EP874QG4DrZI8k5UDpPVa2rRtMB2e4OIOhm4CLGC420wqAKz7YVU7DFfZpYIPQm4T0KYNBJqqWmBqrMXPV2926sggzb7JUCE1U2YzGJBoun2aECuxfbHjLbCGEcY7N/qYUZuyr/qLeIDjN0wzX2DAT/MsveY8oxnBxT6wo6GcpRVvHvmIMwFBaMDi8M40MsYAKxovmZTLzJNG8MWlKUQYNFx1bkaSY8L7nA1e9P/xXWu14y5BS1Eg0X4GRYzNalrJtxg7h9TZMbEu9/TRPKe7znkC+S0LSiuhEMVxjrysj9/O7w3AdB1BoGaMZKlRVjn3z9+iEhxIZTCqhsXOxB2VrjOCuNxLNT04pUqrbQJkxKrrPWGXeZ+HBM6t01pChryqF6PQjE+sBJDVmBoBl20e3kIkWpza2lVIkvWOnE1m2a+xulEURNEXnCX8fbj67oeGybvt9D7hQ4fCgSoCMClWKcfuHMQulBGY9A7U8qqPOUxbApW/FRx73jwCAaxefhGE3tuz5B/14rMyfE0+EWh/Dsn2Lu/Z5SjFhuHwRAXWdWD33xzD8+S/g4+LxAIBdi/0wThcHv4Pv2u3oFBKr5XSGa4ySieY1RrXvHRsqVm0cr8kupsF45iZlsjG55qa6qWVRZnIzZQIjrcZucXs6JaL985RitQG4NuJERY7hYg+qmmbPtB6vUsyYldLsOQxeTBRa0IyFDdxZbU+4aQxcL/hYpZiYZRodthXSIcPDmP+3t7pt++bSQPThMpkybCsiw0XHw4X0ZAtBYGVYadf/jGu4TJ2AH2uj07wNAvk6sE8jy1KK9CBMrCK4hsufU1OhJsDFvcpEFDu7KkXu16UDwLWC+kGa8FuTxsalFPnvYunH8OspxgJa4LoPAx/8+bAfaU2SUrRap1WNYaBsl4HngBEB8pqxWQXStDUAYP8N4TzQ923v8zhHAFwEau4Gq3YcAJfh1i7EcGXTnZ619Pd01TDitEKgsq4ybddiHxIaI5C/Vu2nNZNSinHCU9hRmBjIMj5gBV3fXAdKwmwexYzXS2pI3Wa4ljDjzTrT4ycwRP5cJGEoy/jgHNsSvVIGi4b7C99lY/Gs+N1Ux/lwAQHId1AnoA6IOipeEXjYzqKQLr3XKyVWRnXQcEE6Vq7acgF2D9+DQ6c/GgUDRr9WvRRfe9on4xdS7XRsLsquOzdc0N/jDBfcJLfSNqzXO/Oh+KeX/Qf8yyuegEefuzWgVGEq7McmlEpiJQO4KF1rIKGhoJRwLLmugraVZBaWMVwEFbRogpicpCXNetAYro1Grko9iSmTrGetvhef6/4auodvbK/Pt3UyM1xCiL8RQuwVQlzDlm0RQnxSCPFt/3cze+/VQogbhBDXCyGeypY/UghxtX/vT4XIJWQ34qhjDY1HmFHQskQ03wZJlCbKabhUAFy8SjHdp7FRIA/rzPaSKsWGLohE88SsLOz9Sjw+IdsaLpkOYrWVsCL2UowaLhlTeZ7hogo/p+FyNgui8A8kXUEgOt6D+XCFAciYcBxDMOBEYJX717DUY8eOgM//IbbWd6KWKeDi+8yJ5o01/uFhvYbLp2cZsATcQzgRzQc2kpgwpmljPl9YuoP25G0hmJYuy3Dx369tektB10e0CLHBF0rnUpE5wHXCGC4/6ciySPcGw8WKJALDFQF9c1/EQjcF7dYXhhQw2LXYQwEd0oxUpcgnN4momPlwFWYcAJdgLI3whpvJpC0zAZKdfkjfR8AVBeRH7Ay6ZQT3FE2GywaGKz44T1ucx0PPXITxDNdZwnfOmN8RGVxZBuNTYriIheMM13zPfecuY7j26z7mPBCb6xZYGeuo3fJ/n/4Du/BbP/FAvPzJ5+P0+R7+rn4i/r52FgQlA3Tr1hCVjmlNNFxlynC5lKL7/Sn1+oAdC7hguxPt83M5sgU6hcSw0wZcxIJSapDbQgwrE1ueAeByCUpZ6oY9BB8PhE3HiPDZxGmeebjlUuNT7qf71a41WP+uqAvN6hqDhuskBFwA3g7gxxrLfgPAp6215wP4tP8/hBAPBPBsAA/yn/mfQoQ78s8BvBjA+f5fc5sbcSyRrWJiF7VuAK7xhJSi1we0qhSZUDT0MEQEWiqI5uNDIDAY1oaHAPcIIzGssJqJ5t1+FvddEfYnRGS4aOBuUtrv0k8BZARXoXpPxJQirIkeNNZitdKu35dQYfZcV2N/HORD5Z3mhWWmkHVgnwLgshZivAxjBQbSp00O3eJE5wBq0cGcWQI++3qcq2/CSM35dj/USzFWaCphvC0EY7g0nSMTbCG4aJ7MLq3R0JpruEg3FtOdYXDkgOuQG8Rc4+xmax+uaWuL5kPtfWamGgAXFWNYoCOI4Von4DpRDNeoyXDdu4ALWcCVYd8I2DMQzcNAoIYzuzx9zt0nmxfm/WFGH67A2Hbno38TE82XdswYrgga5ASGqxmdUnnRvEWhG4wdgCPEcMl0zk0VhMEDK8NwXXTmNpRKQvvt7RD73djR25SkFKlKkarYCHBxhouAVY8J2A+aGeze5j4z2y0cw+WPg/SNSgq86HHnoFcq7Fzs4TfqF+PX6190x8oB13oZFp/aLrmGK7GFcPcPZ7hawX6HCg5w1arfWo2aedPEVUnhbSFqDCoNISwEA/uf++btfvvkbt/UcJn263BPkIbLbU83qhSz4wCNJUt3An/7n4Br/iG8tVe5fpTlkVvYF8oALkp5rzOle0/GmoDLWnsZgAONxU8H8A7/+h0AnsGW/521dmStvQnADQAeJYTYAWDBWnu5dTWd/4t9ZiPuTmRF83kBO4DJVYrEcLGUorU2mcESCDPGBJfnIJr3nxujAHdRt0I4gMFmPTQzt140Ty7qANBfuTV+HynDzRpTinFwvHXnU/F79fNAfcT4d+dVisJqp+GCwAXDqyBX96OLMSALFB13U9bjEYSNbJM1OvRSpGMwjOGiVA2sgRwexhL6uFY+ACvoA5/5vXDemwDx1pkHQgvJfiPvAG1dq5LaWNh6iFXfBsSlFP25927aXMOlWcm15h0AAhuZYbjqtpu7oJQiDfpeNF9xo1k0qlHpd84wUaX014ofVLVhDNe0mW1SVHGCU4pUuZuAvKMUzZ+wlCIB+jYYbFXF0ts+TVRABw8uq6hIw4SUYriHpHKgi1779QozCqlvyYCSJDAxzgMus9nplzqFm2RZ67ZVoYzXFZwnViEFROP4uZDcfR+fWmSyhk0EIH1T6Z1iP+qe8+WSXDTvqxTJouEfzeNa+yCGq88A12HM4hwPuGY6DnCFKyLjubVjUwpqEjA0wZ29Ff43khMYLkoG1R5wJaCO1mGfrVCgW8gAXAHguXN/jdfsfk9o7aM9w1WSD5ehlKKBULHrx2v+8WragfscG39d+6a2dQyNM0HDxa4t/szR06oU77wa+O6/Ah94YXjrgHLFBZ0lZlUUxg22rVB4cnIyXLnYbq29HQD8X288gl0AuHHTrX7ZLv+6uTwbQogXCyGuFEJcuW/fvmM8xO+TyMyABZ9FNO0Y+OyU59NDs+UomteGeHmEZW6deJPJBsNVowjbiqk8VqVodTRTtIbpkzzLU8d0hTLjVi9Fw1KK3931k6hRQIg0fQg4hisOAI65EgA2mUN48/d+CnN2BTj7B1H6dAU1/I0uy47Cd4wAM1ullKL1N/P7noet33g7DttZ3KrOxGXiEmDpNmgvrjeNQXrP/EMSxk9YB6Q0ZEzZ1iMMQsqyjoUFXofmUorE5EUNV2p8mg5ENVTUZHGGi/VQs4h+Pq5yziTeYvz8ukVtgE9B+l8drqsIuLLC2FwF3gmrUvSAi/3Wx7zP5LPHaILKfdfItFHlAJdnifWElGK4jnQ4x7Yg4B5F84ZX3XU8gKFlJmW4FGO4RKgYjqJ5wwDX8jPfjwuHb3csknDXuDIjjEUn6LIAoONNTzkIAxAqCJuiebB7fsuCd3v3rNAOcSC0yQnXN2m4BIDuPOyr9+BP9U+FfVPMBcDFRPN2Fru3OsA111XOFiIcRxtAbZ1NH+orvGH7elUzmfW4houAU6UppdheX7CJcYUCpZLodxReXb0I1c+8E7earVju7QwMF5nJKmrts3oA3W9/1KUU/bX3vf3LcSJLoI9NIDWfxAHtNm/0NxQdmeSZk9jDUOTMuxvRXcoxXBxw+evzJE0pHk3krjA7ZXk2rLVvtdZebK29+LTTTpu02kYAySB/812+UiNhIdyF+JWbnGlgPZqUUkyr2qSwGGuT13Cxm6Gp4dJChvdFw/+KPksPCuGNTwthAjBRrFVRb7w/I5qPg95YeSGujGAitBCCAl1i7j0RDAMBYCBmgIt+HKV/INXVyD2MAsNVo9bWDR5BdxUZrgAa77oeAHAIcyiVdECpGoTBZNb68336gwAAdyw8zFdQmnBsZGhKJqumGoWUpTMfdCk6QSlFZgthGCAyrBVR83zUlonm+QOeVRZZxnBZU0Na1qux4bHj1smwUj6IATUMcJEjeda/KzdwnmgNF11rGWH6uiOX0j/KSBgu+m0oHZKzhfB/q0afQwsBDYnt8yWzvpjxhxZtIQLg4gyXZzXKw99BacdBa2gVTynmClbisrHsY+T7JFohYYx2DJfsJpiCGJpmWxxin5qiee5/tcUzXCSad61+HOAiecPYSu8V6FOB3TnMdonRYeaiAeCxyr1yAdvm3LoznQIr4zoch8gALtlI7513+lxrnWOJXsP4FADGtQdcmZQiB69jFOgoidmOwnv1k7G0+8cwro1LM/r7ufbXQCFdoQX0COd+5qXYLg5Bel3rTfuWokSBJmIMcDmZQm7ynQdcWqdVill7E5qM6ar1Fj1rOqt3xoVZDdepx3Dd6dOE8H+9chG3ArgfW+9MALf55Wdmlm/E3Q12oVHjX8FaelBK545DbgC2GQrYr+g3FzVcVW2Th4jrOZjSwpIJJWtnJxrz9F7DZSFw/t5POJNIBricuJyEle3BzKg+hF/eHTnAaFnlz1i5maiQ3OTUQEM4RovdjEYodBCZnb2d+wFlP+gVdO18uDRjPSrtyqTDPk0N6DFqFEE7RXHYzqJQwqUCqwGMHzAWjO9i/6T/hgfU74bpb/YMV6zkJGZCMoaLGLRYpehTiiK1hSAmwtajRIweU6wecIkiplz4YBYYLs8CcsdxLrA+SoarIMBlooaLGC47JaWY+vScYA1XTvR/dzRcx8jICZ7ibfpwZVOK/rdvpRQFts7P4Knqq8CerwIAxtse4N6kAhVhU4bLN/89su3h+JbZhR1X/iFKW6Eig17GLknVvkctuw/GwmuRlCtksdagNCNUIgVcQRxP7XP89+hM8OHizFLR8VYejHnTfZdqognhB/99b2S4fJBeqwmQ/IGEl2fu2BFSeHPdAsujOrBC2TY+LG5+w49j+0Jv6jprxenz7nslGi5/zOPAcLUf25JruLxofq7nfrvlYY2xdoBrKN2YScDJ+XCl34t+5y32QPDOI9F8zaoUaxQpwzWpcp3pQhPRfI7hCpO4HOBKx7TkNZ/sBA3XqQO4PgzgBf71CwD8E1v+bCFEVwhxDpw4/gqfdlwSQjzGVyf+LPvMRhxN/NklwKd/N/6fXXxKpoJpIJoqxhuDMRT84WnbgGusTcqGAHBmePFGia1kvE6KM1weTFBLF7zzGU6MDuV1XRFw8W70S2c9BS8evwK3nXNpuFnnD7tGw0ZkAJcQiVjceJDHWRMhJDo2PtiOdHwW3N+Uuhp5HROJh3WwhaCHjjMlrVCLMmmRATjtRyGFZ7hWA9Bd0Ifccc/vxKAW6BUqlMy747XBjT+mFIdYRXSkn/mnFzrGiBr0MtH8XunajcgjexK7BdlgI5PZKBdpk2bPupQizeKptQ+1UgpeYmyAtVM0XPR70DFxDde9z3B5wKVzDNdRgqZjFPnziY/VnOEap8eRTIravykPCQOxfAfE8p3AB38BADDe/nD/UQe4nIarzXDpchafMo+EGh1KGC7DHloiy3Bx53K3LvX265gRLqmu9K23sLu7XwAAIABJREFU4v0SNUhue5WvOAwMVyOlyFm24J3GfL2MB1wUtx6uMapNAloofZgXnMdlj3zoQ8Pr2a7C6kgH1lNkACcAXPmap+CK33xy9r2jjV2b3fdKDVrd39EUhouDQRLNz3XdsuVR7RgupTDwgIuAU6FEC7hLn4r75eLD+Ez3lQAik8k1XDUKpFWKeQ1XrIDVyXVvsuOAW7YyyI9RrWhYUbgDO4kZLiHEewFcDuBCIcStQogXAXgDgB8RQnwbwI/4/8Naey2AvwfwDQAfB/DLNnKILwXwNjgh/Y0A/vk4f5fvj7jrW8AX/ij+nwMutB9asflxbnbQXi+kFAlwMRNOt2IN3hCZpxTJzZ1bUVghMQ8/4zh8q7/pXDWV4FUrDHCZcgb/Yi6BkArNyRzXcI0C4OIeUw44WCakt9YAUiUpxZXeGf6keQ1UVXmWJ7bnqYnhChqEGqhHqEUB07h1VmzfpRRtFzB1qKqc8wzXaMbtr1eqqGk79D3mESbiuaxHGCCK5rvf+oh7LVS0u/DnbUks4LCdgTx8SzKANSuGNJgbf9KrkaoltUspksZDO1uIOpyPXEoxLVTg0dSQWWtRNgFXwmbZ9L0J2z0uMW4yXBMA1/K+bGojiWMEa7xfta0yxqd28mw+9u501+XIRh+3EONlYOv50VBWRx+uoNcSEug4hksIhYHtQJoKXTsMomrDGGWVY7h4Os54G4fCabgeW1+BBSxjS31nmlIsfIrOs6CVZEANUSQeAFfJ0nT0EGVift1LHdXvGhisjnUihp9dJ8P1tMc8KLyOovm0SrEZ2+a6OH3+7jFbFDsXHeA6PIjXHWm4KKWYE80nDBeUB1ye4RrVGNUa3VJiRfrf22oI4cBcE3Cpou0fJoItBGO4RJRtAPxZ0BgXVCw64vd34scXFrp78is3srShZ7voeskanyaAizRcJ2eV4nOstTustaW19kxr7V9ba/dba59srT3f/z3A1n+9tfY8a+2F1tp/ZsuvtNY+2L/3MmuPVWG6EUlwwCVoJsDE8NSaIcw+WGSqFOkmKaBR1SataAOARmlvFGlrV20nuCDcsTddEZkEa3m7nzqK+pkWxPhyZilEUmkDALWKA9uIyp6ljFWVHvi5Szs+9IWQKJkj9qC33X/WpxT12Gm4kpSiT+WFlKITzVeibKUUC1H7lKIbkLpe/H/54k+6Y/U6k17pztFDlr4I/MmDcfHoy0F7EwYsPQ4pRVVHzZ0QkbkjUCukxC32dHT2XgUMD4d1ZQPwaKHyKUVWLWmFCrN40nAFzRyzhYhC+skMV8v5v66DvixXTXuPMlykmaLCBp05DqOBP7o/8KFfmr6tY9Rw8cITwRkuut+mnCOq7urBAecDWADQAFwAsPW84J1F1a6JaF4qoOs+K6QMusF5uxxa5yDnw8Ui0XB5QNBRMtFF/f3OVyUOEGR/IH33iIEHAd2g4Qp7BACYDgNc9BAt4jhgZlKGa//AYnVcY7bDBPLdaQxXO31Hn+EarnVXHd6NeO6jzgLgPLbCMTENlxCpX1cIJhAfo0CvUJj1DNfSsEKlnenrQDpNnzJjKCEgIKDtOgCXoJRiPAe6kVJsdrkIMgTW2iexLJpSpbipy74jNda2DUDH9pGmFMn4dH3Gs/dkbDjNn+zBKd0ArlglSIvhmlCR1dBwSRhXFZNluHjqKmq4dCOlCJh2BY5x62lfqRfYMDaYaZ8uEGjPSPfMxhlo5VkgIZUzdDSWMVysZNma1uxUerPBMPuqU4brode/GYX2jXf9A+qxX/p5YHAQNdopxS5qFFJihQCXcUDpoztehid034+hH2d6pYKGwqJ2rW/cIJWmFFFH0Xx/lUkdg4bLgpsK3mG3onvHV3HeJ342fr+GLYSGitdAnWG4jHGDKp0nY/1v2kgpsmXTNFxN53/DQF449hw7dBw0UWtGqIQiy4oc4PI/2NV/v8a2jpXhYvch/z3ofpuiaaNl9xOugvvbxhV8S6uBZ707rt/fEryzrG+CnlTdyiJouCAY4MJKTCmy3nk5hos/Qkhj1ClkAGJfFQ/CNVt+BASePqCfEBgaPXcm3lI/DX9x5hvC54A2w6UzDJcoY0rRNlKK+1YNBpUODaiBaAGhspWD+WrC2W4BYyMbKTOA83jHf7j/Ntz0B/8JF54xH5bxKsVS5h/Zlp2PMUrM94rwnQ+ueg+yQmLowW1hRlBSZBmuUmSuY7KFYBmGWjRF85M0XMRwpU7zWYbLX+M9xe4P1li7vX6O4aJn1n3PW30DcJ3skdFh8QcglZDLcBNN0HCF1E/UcI1qk1oI0P6C11VMg0mrfUpRMYbLJKJa96HaMzrKpbFCnj/e9AS4pESL4RqVceZHA7KULn2orU0aVXftEHjf87HL3tkqQa9nKaXo9Vn1CAIGxoOJ/ng/Lh1+MFlHWg1c92Fn7tq4mTuoUCrhUooAeh5wSVnglsMVnvXWywEgVHAlp1Q4xi8MWHoUNFyzq8xNhfpSwsQKHyHDgDlz8JvhvMhGax8tCmYLwUXzsaIUMjITxtStIgLAM1zkHJ2bXdKhNhgrm9knGkDHeJDXWm+0DHzgRS4lfTyiAapSLUkmlbeebR3NZ5DOe7KAi0xOM2CQ7ulSuL/XW1enJKwBHvATwPwOt/7MlshKmQoWSKsURdRwCamimS+iTxOl3MdWQRU5hosBrjoCrqWuu7/24DQUyj3Ydw/fg1dWL4kpRSnwxvrZ2CN2AmAMV0M0bzrRpZ6YLcHSRbbBcB0YufPLU4pbZ9362ZQijT1nPCRZvND3FX3kjCDvGcak2YRFsJRizhICSM9HZR3YojTqgRV3fXULGTRcAXCh3SKqGB9BK0j4zsa9JsMVJt+BXffjGWNZ+T2S1XCFsaE9XkhkkmI5ZrwxaWlt+16MDcB1sgcHTQSuuDbLpoLDxBk4ST2mYE1NYbgMda2HYqJ57ubOqhQbg0e5cnvCcA3G7sbiNHZguIRAc3xRUuFnRr+NwVPeEFgCIRx7QwyX8RquC+pvAdd9GNvFwVBGDgB/WD0T+878UX9AbgCS9QC8xRAAnKn9A74x0DrAFW+d4ewuvLm+NGG4enYAA4XCP0S+u98BsKDhYhFF8/776HEAbnOre+KKUgXjUwJcQkr8fv1cAMDqpgvc+YNiegpKKbLBMdFwRTZSCJkMjsKnWKnAAUAipM8CJx/BF42AQ922okjTZWnz7eT9274GXPMB4O/+S2s/xxQN4Em9CZP3TjDgMtaith5g8EkNVVjZFHBpKyIIa+znJusAVtDX0cO3v5kxXNq35Iomw5AqaLhmuiVGlgMuzx6rEm+uL8V/Hv8uZAZwWKlwWe+JuKPYGRiubiHx9Z3PBgDcaHeikDKZnhDDRctGvv1Tx197tJwer7wtEH23QqnQz9T2tyXHVHsAQSk1wOmsAHfef/fpD8J7f+Ex8QOqBF7wUfePxWLfnY9DniHKVWmuGb/wWeClXzr6z7EIKUVt8ilRAJYVEVRwYIvSqPtX3PXVKSSGigEukWe4ZAZwVYX7nLXAEty+FAzLItg4foXZBE2mY0ox6aWY1XDRBI1VMJoGw5WVImQYLr7sI78KvG4LcMOn2/u8B2MDcJ3skRHBC9OeRYQqPn7Bc+VuQ8jsUoquzcyenT+Kv6h/MqxHeq8KBRS74C2kmzXz1GaDzSmGB2AgnWjeGgxGbjBIANcUDVe/W+Ir9iKMHvGimJaR0rExxgKwnlVLP8cHpPfrJ2KmR+1N3AOnrJcBa5Pm2Kcb73bS0G40Ga7Lf/JzuNaeg7KQQcM1h1UY4aucWDgNV7vpsIYMGjyhx6Hia27IU4okuLdx0BESt9rTsbLrcRDGgZpalJmUIhfNswc8738mVfDbsUZ7DZ47tlBVyawiwoA5JaVI16epWUpxQvpQG5uI8kPKgfQ6t3893clNlwH/9m4cdYRrnVKKbHAPzOA6Z8PHaJpqbKwyTHopBsCVphRrFGyZDmANAAb+mgsTKLr/Z7aENBj1LVUwkUERMjBcW2Z7GCLegzRBUVLgTfVP41q7GyqTUlstFvGnm16F/3f73zINl8LerY/Cj49+H2+rfgydQiasTdnomTisIjMGgLX28X5dRWx8TSlFJUXs9jCbMlxkO9NnXlbb5t3nDq9WeP5jd+Ox56WfwTmPB/qLyaJNfbf9QwOfdl/DFiIbux4BbH9Q/r3nfwj4xS+suQk6c47hyj+yBdO0iaJEqSRmPcN3YNkDLiWxqtzvrewY3VJCQGDcYLjE4FBr+1XhDXKtxX81vwoA2KN2Ztr5OD0vrQsgWO+4ZtiZST6PnA+Xf92cxCX7zWm4+P14nSs8olZm91aceBXgRpzY4OCKHN6h2bKU2ZIwCCY1WYbL3SQKxg2g9RBLs7vxHUtl/lXYZ40i9CgTlFKUEtDsYd8EXNURGGzy1gg1BmN3gxWsX1oEXO0UwEy3BFCn2grpmbYr/gLbRt8LDFdymso4aK+ghxkajMsZGEiUetUxXAwMbTd73Win2gxX0HCpbgB+pRQ4QswUBrBS4bbDg+Szzhai3ZLFWIGzxJ1Q0BB6hJHouHY/Ij7QpZROm4bohSbJqFQWkJ65qlG41kVAIpqPGrGM07w1gFDR3NIYCGjWCzNeP2YdKUVpDSAYCMikFB2LRifBhGbhFFpX7pE7Cci8w08CHn6UzFc4bhLNr8FwVYPYBmjSttyG1n0IxlpUKNBD1RDND5JtRQsIiW6o5HQs7rPHr0EfI8z5KuAWA9DfEm0+DLWI0gjckVSAT8dt2rSAISILTNotrnkqlGpdk3tnzoewbtjgKcVuKXGt3Q1o+DY+McpAW7u/xHBRSpH+WisAAdiSVQB6YFGoCLjETBNw+RQ7SykSw3VwtSGRmBIEuOjbiuI4Py7Pe9K6VqMxcFRPZrjAzlHpvcqkFJjtKBzwDFe3jD5cha0x2y0gRNteBMM24Ap2Esbik/XD8EdP+DIu+Prv4cHayRj4tS8ak5Ygmtc2eeZMs4VIU4pujIpjQ4YoyDJcU1KQ91JsMFwne2Q0XDJzUSeCw4Zeiy+jB6QSFlVVuVSPLKOeQuuQ0tCiiN5R3gICSUqxDbjKagmuya7TcA1G7sYqWZXNqk8VzHWLZn9bdEovwLTEaDkx64wYYeYzr8EDVq7wZquNS5sBrgE6mKF0gxAYyhl09YpPocVBdbs46F40UgljlOj7CjGceXEAfop8uOABl1C47VCaku2WKqvh2iaO4FHyevzX4u8htes/19SJzRYOnAnLzGdJcyVLCM+UaFE48GhtGHSMmGQLoaGNg1VCRtG8sY7hMt6UNfpw2aR/o3vBZ5xR6wUw0XzdrozUjepA3nzbHVqmCvJ4FDeHlKJ3vl8rRXFkikfz3dBw0YNO8N+jMTs3YXKjkjSjhcQV9oH4rHl4YHTCfU/nizFcMDrYQgTORCjgoh8HnvcPKLedi7IX7xGt2pqnQolWde7+/jkQQsBYi7H/PTuFTHykCpX29WsyXCMG1ABgwQMduv4TTVNBDJfE0HZw2M5AsckaEBmumUxKkQTk64nFGQJcpBW9d6re6Nhv3r+StYQAAMEAV6cbX892CxxYJYZLBcAFOEAqkAFcqwdb218W7nP7l8fQxuL0TT0A0VyaezM27SFC5TPX7KLx/IE/z2GiEbdnA8NFKcs17tdcB4nm+vdSbACu+3KMloHX7wC++bHJ62RBE7uoKb3HZwU2tx6rLPShfQsULbtBF2FMnTBc5D4Po50lhFDprKORPpNWQ0O6VjNGY+Q1XEUZB7Mj2g2gm2c6rRLoIOq2NlDWnYaY1x1HA6kxwGUhk9nvSM7gHH0zzsKdSRuREKo5oCscsr5y6hEvCMCvLGTwz5oVQ1ihWiJXsoXgYSExI9xD9/HyKgAO1BWNaqE5VfuGJTaAXsEZLp9SNKJw7ZIs4kNaMF1Xo5fiuDboooJV3cCYwWu44C08uKmhabjP5xpOy8bgaJN9esDOdBrWGJiDN+NFRbTnM83G62z7645qCDRTJC2Gq26/x/c5Wpq8/UxKH8t7ga++Iz0vzY/x6q6kv2nqDZaYnNIyr1MkgEKMTrPNFvqbg+7IGg1rrDPQ5c7pqgTu/xQAwMxMrAaklCJnVEolW/rD/bPnQcBxDpzh4k2iS18Nx7cDcA1XCriouo60adyagl4r4RiuA3a+bR1DDBdLKZ52DAzXQr8BuO6BKsVcPPJs5zN2w97lKaL5yMB2OhFwzfWKwHB1ColKxvXmugoQIrSIGshZ/Jt6MPDTf93a/grc+Plnn70BALB9oefZdu+lxlzjmwUz8Ro0yf3brFK0RTc+w9g9WdeURUkncX6jtLG4LFcFHXfaXnYPxgbgui/H3m+4ZtNkdLoGRWobLBUQZ8g5his1noyiaArh9SRaRIZLfunN6N9xJQDWyNTn5i0UwJpGW2Na1YGAG8BqUUCaCque4erwBrK1e704U7YqdkoCFSamFLuddOZpIVo6KdGZSf6/2GeO9cUsHmGuQSl0klKkkA3AZS3wNXsBntH9K+BhzwoPz1KK4J81jwEgFf7mBZfgD38quld3C+X7PPKD46aFviqM6WkOzp0PAJiVI1+UwAYuAqCihGz0m9TahOvDsV7u9Z67ol8XrDO43SRWUHUWYgpKm9DuR0Mm14dhOi+/MtteCubDtZB4f3n2hg24Va0x/4Fn4eHyhrAsALK703rnnc8A/vvZ6bJWleIaKcVmpW5uW/z1198NfOTlwKd+Z+LHyBMLAFS1zLaRslRZwOVTitRvr25Yd0TAtSUKvW2NV/yda/cz6vn+tGcx4TiA2TkGuGSG4ZKM4XrQpXh5/XIcmj3HgSmb+nBxl/em7ii09hExpShEBHcLXl/58upleNH416Dnd8YPNzRcB7DQmpQFhounFOdTAfx6Yt6LzkNK8V7yddq+0MNZW9z4NTmlGEFptxtfnz7fDQU7nUJCSIl39Z6LV216I2a7BZaHdQCoBhKvnv8D4IKntja/KtLxc/tCz7Ht/hquOeBqsNvRh0s32OoG4JKdsMyylGLtJRAkys9OcnL3YbLsKKuPT1BsAK77cqy4/oGY8VU4yUMgQ5sSu5Dz4UrW86kUGFf9BP7wZOJHb7qpZTc0ay6+/k6c9dmXAWCgoB65lAX5OFkdTEghJP5z+Ra8+by3xd1DohYllB1jPHYplLIbb+iDlUslLvTKlm9Oadz6xtqgneqWjYEww3AJXlqOmC4AYosgALCZno7dbgq4CuvO/R12qz8Wt1xJGewcusIxRru3zeKZl9wvVAxlRfMsdUhsxZjJK/fMuH54fYw9w2VCqlhRClAWkN6IVIdmsXV8cBPguuNq7Pry6+LOTY1xpbGAFdTlQmTMrPYDpgRvRZS0hplg72BtFL8HAa3mM+C0Ag8AxnUNMWJAEOyavDvu87dc3t4GvfbHlGq4MgNz3WhvlR5ke7uUevrWJyZ+zLBzlAAuDmIPfQ+zl7nfqkLBpABOL3mG79vXYrgufqH7O7MlCL03rdyCPz/0EgDA6sK5wEsvB374t5Njmp9jlitFZJIoCiUj4Jo/Ax/Rj0EhXWGLsTYyVSpNKZYqZbiIpaFF3zswQCmjsJ6YpSOYxafNI9PEur8+CyXwRfNgfF4/rDVG0OSQpxQJfP3aj1yA9UZTP3pMVYrHKe7vm2JPSikqNqHs9iLDtXtrHNucIS3wrt5z8BVzAWa7BfavjML1U1uBMzblHfOlUgGAAnDXnpDhGuaAq1mwI6ggirUkAzIMF8+OcIPUse8RHIBcRmKQmfTlU4obDNdGTIpVD7hm/Yw0EfKutpbZDJs1LaUIY8LsOKaCGmJhOHZkwAS1FEfUJre51f0BXAnptFlj7TsaCok75Q7c2j0vflC4rvXSVNADV4JczmwKbx+sCmzql5BSQArgNmxDjQIf7j8DB7c+zB2u13AJAXTLdCB0rXLSS1t10xnafC8CrrpggCtTiSQaDBf1ZKwpNRZSmyI5T4ZVV128e7NfJ+fD1b4NK9Yz8qbiXACA1MOW8SkxcpatHxgu5nujZeG8uT7xm+mOjEY1WkFX1NDdTUlVm7MQSBuBC5jY97LBFNH2HHuTloibTGUk13CNqhrN4Sik+nKVgFVajLBm0OQlOW6fUlyr6qnZT5RHdhZN7ONk40XDLDBShovNzj/2yngIVkaGS2tYCJzp++5Ruj9Mqp7034DX7AXKfjArfezNb8E50rVLMZDA9ge2tIkzM/E+GJWuM4JKUooi6KqsKFzVo08XWqTGp5zhKtX0KkWwzwIxpUiRO41KCvxB/V/wp/pSTPACTYxPAddc+leefH5+5alBGq57J6UIxIrLiSlFpuHqd2Pa8GwGuLqlDJrYlVGNuU6B/cvjwHBVRuDcbcxkloU2Fl//nR8N/9821wGECONCzTSaojFpCVWKRseqaStaGi5IFYt4GCM+9gxXy1iV7aM56Uv+8riXGa6NKsV7Im66DFjZBzz4p47ucyvOSTqUPSelrwOgt6mVUrSWHMKdc7nNASmWehyjQBdV/iL1F7qRBVZtG3Adlm5Qtst3OVZL+pQiLFZH2gEuKSEpFVHOAtUKIBUqCHRtBTN0+phyNpZk7xt3sHnGgRwpBZ6Kt+C8bXOY75d4emiujNCgtlM2L2PREuur7hw+8Zh34j2XXeP+zx4kvNw7x3A1qxQLX5lZe2qLUoqFlLFUHUjcn///5zwcV9x0AKfP93CkZQsRj3URTjdHHkMAsK/226kGQTRPvxNprnjzb2KgdF0jari83q7ZX8zUqFecSNb0FuO5INE8VMMWYu2UIonwAa7h4ilFf94Y6zWu6nZlac52gvaZKV3PBl1zS7cB876dUxDNV+n3gE+D83WA9TNcLQA6GXBZBkqLHMNldGJH0tRwaUicuXkmvgd2jwsRfmdiZfj9f2A1P8sXnXi9DrqOVVcNDRcxXMZfJ8RwWWtbVYoUhWpWKZKGK39+FvrN1F17PZ5aa6YUN/VLHB5UmGmNC8cWUcN177WKofOpJqBLfmy9fgRf52yLk76OL14w1mJlpD3DNcb2kFIUOPe0NBNAMapNcp4L5Sa1MjBcrEqxIZqna9BoHe7lmjG2FKmHI8vSVA3RfE4ek5uUbWi4vk/jHT8JfOCF09cxBvi97cDlb4nLVlz7F2pAuxbDBatRG4sCOuirplUpwrK+eDnBvS+v1ZBYRZtqPqwca2NX9gXPLSEd0DsyrCCFM9OUQjizVO/5ExmuGnrkHjayFxmu20Yz2ORTfu6zwMpYJ1WLxlqMKo1uodoaLtFmkYreLKqdF+Pz5mGt71Faxr7kZrENIX3pU4pau5ucRPNKCljICE6ZUH++V+LJD9gejo8H7zu3Vfhm1wxw7R/596uBH/xtYnwKRJAFIAj/3QDnwU0wu2w8NKyG8VVJtrsIGcCU63hJLvgB4MFGNq1ZFedfG8sAV7Ci4DNgz3DxlGLFWDI6tGnNpQftSqpszJ3u/i7dkXxnftxJu5HsxGMa4Moxx2sP6u4c+evG991MtmFtct1VKEI1JWm4iOEizVLyIKLIsKcPvt+W/EGx63XszUTTlGLUcNH1RADAeA2XkgJKiqDTAuDThXE3HUopTsCj62W4KJqi+R0+Ldbv3H1G6n0vfkzIDdxbonkgMlzlBA2XYue7x1KKCcNVuN9BG4uVcY25rsL+5ZhSNBA477Q8w0Vg+jf+40V4wWOdJpIsaoDIcFVWtSZbQRfKtKcVFJo+XFYUQVcsmIZL16nxaZbhyk3KEsC1oeH6/o6lO4HVA/H/e650/iFf+OO4jFKKNGjyiyoYJLJWPUaj1hZKxEqybErRxFRNTQOo1/8kTvRMhL2aTSm6gdus7IMytbMm8J5YhweVtxpQUFJAWwSTUSEUahRQtnKVmADQi/qRPcMyMFxKChjrKPDZbhFNET3D1SslyoY/Ti06aM6Ki+5scI5uBlUIAhMYrsYDl7zHKn9uScNFVVYrHpzaMk1jUjSHTCkl8BN/AiAyXKtG4bXVz+KZo9/Cv+8j5kJ63YQJ14JgGi4Kk+ldFgBZE1AaBrh6iw2neQuu4SKn8rCvCT0RHcPlZ7cNhqu2rJScpRQ1aQDBN5XXiAE4CsDlWS1u7dBKKaYsces7TRHN37XMUpu5FOuE4KL5omKAi7OGjLUwkFFrZlPARZMmlQNcmQnEfL99LwNAUfA0u5sccR0T7+NH1iCFFBBChCpFAlpdViFYqNTAeJJ5J8VcpwG4csfKjqXJcJ172iwWekVSKXms8ehzt6JUBC7v9uaOOXprpBT5OeASC85YkQHt6tiZ4M50C7zgB3ej9lWKGhJnb41j1i3mtPCavNJe8kPn4f97+oPdQq7h8mx1zXu2NoxPYaNovoZC4rUFPxGle4druCqfUrQpkAPYBCmbUtzw4doIij++APjjC+P/v+nbSpz35LiMdCe5WfeYGK4U2dfGQDI38MCEZCo7hNVMw2Vb65EDtuGsDYth16U6q8N70bcDjNVsaCR9ZFDHlKJAynBJCaM6EGYMjD3g6kbAdWBggqhdCKeRWh7VmPNGfQDwvLd9GUPPcMkGzV6pXuvhXc7MBSPDZsxL5oKeqVKkh/b7zRPdtjzg0sZi//IIy8N04FiyvjXRBMA170GVpYorVQAX/xyWLvzpYCo5Rom36x/DFf+XvTcPl+wqy8Xftdbeu8Yz9Dwn3Ukn3UlIQiYwwQCJEAhNGCSAgCAKKAh6nZhEIv5EERX4iYIMctWLcnG46PWKE15AEJBByQxoJjJ10sPpPmMNe6+17h9r3ntVnTrdnT5NUt/z9HOqd+3atatq77Xe9X7v937yHHxJnIfPb/4xYN97dVNuv7WPBlweUJRWNO80XLKsu1KvVoDLpOfqTsMFKUDBIQlRFhWip9NgrvmxjDE6UoB77I2rWM3t53Ki+dAWoqy7i6csVwi4WrrgJGC4jB+ZSSm68xAxEDmE4bro/K5TAAAgAElEQVT1Pu88ygBxiEBXeGnXtBhQpehpBzmI1bSZ72rrtGG4hgEud108LDVTHrvGAaQl7yygmrqzbXf09ca0zlJKiT4XFuRUNFz++0Q0XMEpV6xgCPDj/wK81DUS9wGGYeH+kj8ZXZni9Vftxl+9/knxgx9DUEern7BjrjQswzVINF9K/ZrwixeyRGUb5rvqOmrVErz+qt147iWasQKx7OJTe+/Bs/u/jjv3/Djekr/aMlxBEJ1SlNKyUDmY0xJaDZc3F0kBIbUPIy8DLuc1R7x7ssjLonn3OxycCzszBI/HKcVxBOGvnGd1zzzfvsBMKrELKCaal4rhSgKvpOVSiqFo3mfCqDAMF4umFNPWNLoyRW/2YdTRQZE0QFjiUoqQANEMl5C2bxuhFDTJQHgftF9luPbPdmwZNNPpyMU+R6vG7Er5gaMddHOl4SqnTXLaAC+NjWljMOBK2l4vttgs0N6IP77qq/jt/vUAgAy6tRGXuOSd/4x3fvpbAIBDC2piNgwXsgGASyrdWm/NHn08PfAYQIqwSlGC4tazfxJob9Cl2NJR74bx81OF+nFQpWgmXh+oGJGqAVzN6UhrH4pZMoFmMWuBlANvcY2VEBJEA0daSikqwKWFs55QWgpR6bsZZZvMtnKPz0Fhjmn0kP4xRA5IaXuDAgMA1xCGK/BKKzNcQ5guKZW5MAAkPuDy399juDioPTfjw7WuXcMbn7EHb/iBcwa+jw+uDhrANUAH5DNCBmT4E3mWOEbDpBaNPktKZblgFkoh4CIBTeVSim7jF9802HWdAMDWxwd2BT4QNOf6C/lrsbf3x1jTzAamxo4lzJjTjaS9T1bUU5OJiD8fpH5LgNUwoRmjYARY6KnP0dZVnKatmpDEVnNectFl+JUXX4GjV/wiPsmvxrY11U4LdlErJXjhtFlOw6XnFN+HS3LVCQSkArjUeFQFXJyHKUXqdVLpax/HZc2Lh4Gwkxhj0fypEnaQHkKN+n5BetUdtEcRqj2Kn/bh0ZSieQ+BwgzIEZd65Nq5HCyaUpxo1HAYk0jnD6ApO+glLVAqNMOVYysEKFWrKi6lZbEIZWAsA+nloCad4jFcjBL80GWnqfMhxIrT/ZQiADw831VUe2nFXrAGlvJwZKrX21YXVonrP4bF912KlpiPal5AKKanJiyQypAjS6hd9ZnzO6R7li3q5q50AOCq5aoyc3/9TOzCLWgVCgQRD3T6Gi7AuU1LQkGlcCXXmuHiEYZLcG4Hv6OJApXywO2OpaAJSPcodn/jvQAA1lwDyjSbo41PJaGYJZPYwI9Y3ZE04M5cjyXbBu616KkwXCR1rJuXUlTgqlTez0uMkdqxus2Ph25VQGXDnvAcfYBWGoj9KkW5QoYrmNssOxW5l0th3rMnE9RErt4jqYUg1gPREtS+xgCulBK8/qrduOnWpcrx3Qm6e+OQ1DrJQQyXx4yYSZuWJnIzjgir4TIpRYmZxT7WtRQrF6QUKQ0E8mXjUwDYsTa8VxLq7vtYlNOIfpyIVKIfW9c0gYPAtjVxy4STESalmPM4YAg7AoSf/y9eezk+ffN+rGvXgudMr0Wm3fsFqP1e3/Mip3X9w1dehit2l3pPqndVf6SwoKggql2V2q5ZbuoBLqGMrzmoynB4oWwh1L1DfcBVhKJ5dbJC9dA1baZii7JgW0TrtQoxZrhWI2IO1NFBunQxRdKCBQ8nu0KIkleSCP96r4WU4KaqzpTjQtrVK7HO5Qy8bNYJYLKR4bCchFg4hDbpokhaoIwFDJfRcAkhAe2FRSkFy+qgMkdSLOoKRreCOmvjhPWD8QeSZsqCCe6+mU6U4eIRwJXU29ZH5gcv2hZ+kInNuGnLC9VrY2M8oVjTzLCIOubWX4SfyV+PemRQN2aqC1Kde9n7ywTTvfO+JRSobPZ1cYTHcPnVjgCwzgAu7TRvq0+taN77fXQqSggnmn+ormw5iM9wEQbsv9m9LKhSVLYeEhRzmuESIkwplh2l1ZtyzYSVVqPcMFyZfS5IKcpYSjFmC7FMuu5DTwI+8AT/IOqv7+Ze8rPzgV9UEzKETUuIf25mYbS8eN6Apznt4G21jP5rg5QiDTRt/uRIk7g2EUAArhbNommAvUHGKH62/zq8sHeDPXa5Mo2WAFeiU4pCKIZ3bUv3EvV0WEnJhytNhqcUAeA/33mt+wiR/QZpmYATD7imtPazlca/t5MRpgBgEAgNOwKUiwgaePWVZ1SeM96ApsuHRPw7vWrvxiA1aYM4wFV4DJdbbOkxKnHNq5X+kEKABMJ49f7Um+sKdKWpttYMV4TFsp5fy9lCnCIM1xhwjRq9eeVhtFL/n1j0I61Chuk+hlRiBIDLiObBLQthBna/obVrWMxRlFKPylLC9HjTflMDiNDJZg3zsgnZnUMTXfC0BUp1SlFruCilWjQvHaiiCdKshkQWoPkiurQZTAybJh2b5gOsRsaCNMShhZ5muMLLuEga6OSlGyttglKCG294Ot59/QUoR0t7EHV7ffw5rsEr+2/CfekZ+rsRWNPMIEHxmcv/BJ8Rl0YroN74jD3Yu3nCpl9pLQ64THx5YTMAoNZTgIt4lZp9pLjyLJfqXN/Wk6oWzZcZLj+laESqvmj+4fouCG1ye1BO4o35j6uJVzeqvSH/EWRZar2GjGjeMFwtPgshFAgzzMsgY1IFzIxoXv8OdgXsGC5frC4ED1KKXJLhGq5RtRgWcPkMl38v5EE6YqWieRYArlJKcaiGS+07J/U10psLXyNF4JMlQBwTJ5QOxgjHGRsCuLz0YRfuGopFmhD8lbgSX5d7o4ArZY7h4lbDRWG4h8OLfXud+pWGZQ2XAQeDbCHUaXu2LZH9ymanfmQnXN1uOeETfNzRo64BTzEKwzXImAxlcb3aLzX9aQdcFwPD/AYew8WJl1I0Ok47XmgNl2kXVgJcgjqrCCoK62lo+7BGFl5WtxgzR44QFGMN1/dK/MtvAl/5PeDGTxz/sbqz1W0x3Ye9wAa3NBBlwKXTOdKncYGQ4fIsIGx7Hq9BqBlMXZVifGXXrtewhDpIfwEt0oNIFcNFIHWVogSlCiQVXIJrI1BKKbJaHSkKyN48erQZTAIbJxx17w+s9ZRZs1ETtYQCaUj1c9ZAr3xfabA33cyiwtOJtpr4Or0ePr7uv+Hz4vH45vrr1JO9Oaxpqe/poNZp1SOr3XYtwbMv2OKcvwekFE38wwFlq2H9ZeqhhuuJu1z5vk0pwhifatCsAdI/fdtVvFqjQc4tCyVYA/dKZZHwN/xJ+Av+VEXha/3WV8R5SBnxWsFwUKlAUCeZRir74P1FtaLU19aRRQ1igsFOggdO82FKMSeZBWEyuNZDhst3Vn/oaKyKb8SVqhloi0g1IaAYrqBKcWUpxQBwlQHisMFdf/Z5y3DNl44hSilFUtJwUTDNVrBypwU/vHu3p9tODfo8mWd7EGW4qANOZlGWaONTISSOLPaxru3880yUjU+zERiu4CMsYwtR/RwneFp73geAC18C7HjiiT3uCmI5hqts3zEofDBmHmd6U7lP5nIhPYbLzEMczGOd9KLQVJFLl1IUoGHTdgCAq1IkktsFgjFNjvlv0ejcGLmHVzpuPEIxBlyjxpwRtS8jxrzzc1U373IMBVxeekNfsHcdmK88Zx5zEV5oBRcacBkad3A6ElJqCwV3PAInpLeAK5JOBADKKIqkYVNiMm2BUj+lqHopLvYK/Osdh/ChL6vSfEoZskwBrproIGdNm+a4SZwRaK38gbueMhxZDNmGWkqBc54TbBNJA+fvUGDmK/xc/PyGjw4UsJuYnlCAi8nCikzv2PkS4AUfAy58qbWpODCnAVeEYqeEqAa/UjMPyzBchzER/J/WQg2XP0mZicx43xDN2Jgmww/Mea01Ek80LzmEJGCM4Wtirzovw3Z6DNeCbChRtJmshNAMFwNpaoPbxcNqmwZct9w3g7sOLlSYV78Cz4rm9fVd0MyyrSLQcIlSi6PEsXOzEbA0qhbDMlzhMWxRieCBhkusmOGKvNcIonkj1Lesk1ntD6pSlNQVQGjhsWGKknQYw+WZp9LhgMtPN5kJ3P7VqUOUUoqmG8TRTh+FkFjXqmo9lS2E/z5VDddKYxiLU65yPO5Yewbw/A9VDJBPZhjRfBHVPKjfx1SSl1OKfvjPGWBmyPpBKcWBEaQUdVsxkloQ5GQPhnEXILotlQJcJYarVKVoUoqiMCCsCqrIqIBrWLufkxhjwDVqGIuGxprh+338eYoJGxYjMlzGGHK+owfICGIfyHCxUton8lolijbGpwVw0yexRhypMFx8AMNFKINIW5gUatKWWRuUJWBEYHGpg+3kIGR7I+44oPQpi3pAOG06Q71eR4YcbXSQJy2AMvzJeR/By/tvtdoCIBTt1lOGwyXAVU8Y0FwLvPLT+C5TmiiRNnHeVlWRdRQtHGzsjJ6/H2snFZC+7LQJtPT7r23XgfOvB9I6mhlDxqhjuCIpRULVZGJWiiQdILK97ndw25YfrGiWSCNMKVJC8J4XXogLd0x7jXgVw2Umza3r12BtK7OsGgA7UeeF0kxwUCSU4I+4qvIy1Wq+SHURdVU27jnNK7BEkE7o1ObSTADmKQSOLOUlpioUzVvvHN21oPAYrrJY3bfyyMFsqq/l44lYSjEm3rfP6W1eSpFzjo4w132J4YoBumEMV7Rl1jI6M7j7si+9KuGyW7b3fQgQ77MrcDqShsvTawmmr8ciLotIg4bTIcNlWviYyTRguEBwWBeMmIVBcNwBovlR5/cowzUEVDwawzYqj+l/oX6fJ/Y+gIu6H1ompRjadQCAbCtpw/8hgytFo+EBLtOmS9DEWkVY6xqzOJXCjkcypuHyqhSpLKoMVwRA2ZRiVCA/ovv8SYwx4Bo1/F5sxxsGcPnai4geRnopBPWgmqf23bqlFFrD5dI+VusTbe2jAFcuGSjvAX/1E+qYGnBZW4gBDBchFDL1WJysBZYoSnnz/K1oky7y05/iPrbOybcYR61WR0Y4WqRrU43fSc/DHFoB4PLFoI2U4YLtDpQAToeAnd+P21NlyCcTl6JcQt2WoQ8NzRQ1qLCeNz59TwjBmlaKA3Nq4o6J5hNKkCXU9psbeHNf8krc96R3AQCu792Awz/6ZXUKpZQiJcALLtmO/+15Cll3Z83YZPUm/vonn4Rcuu/MpBTp0btx9n9+BCnhaGQMt8ud+PST/wYf5fv0sXxBdR01xkC9VLTRcNWmVCqyP38w0HBRqHYuspSuLoRLKVrRvGW4al6aMbx2/bV7gQSwVYrLrV71415EG2me80BGwblrvM7zEvALTRvVPoMBV7QcfaQqRW0tYm1ZinB/yYP7XUAzXEfuwdmHPgOhQTQAJCOK5kViANeglKJnC0FCwOV0V9ro0nh/aSBm7pVBDBcChmt5DZf/3iSCuMrWB4/2iI1JfjBCMI8mjmBydIbLAPbWOpzV/R/4BL1uZScVpBSNhiu128oMl9SAS4Kq/qwx0bydmwo7X5gWYCRCGji/vyGi+YicZrViDLhGDeP6fiLKSg3g8qrSYkJbA5Yc4KrqRXyGi+gqxZQUkHo1G9WkCO8CJqpdRz13veks4OLOFiIahJUA1wRo1kIbHZy5dJN6251X4ieerMTn1lqi6CGtqfNbj1nkqUqlGZfjsza5tG2z5t67nlJcvXcTbrzh6XjcNvUav3rGrnbSpk2VfUdsx9PP3RQ/fz+YS61ee75a8T1hV9gCZU0zswxXTDTfSBULZrUQQ1ZTF52mWKZvyL1obtGpPq+Bdw9ZdGUvjbuzrnSUrK48cjyBNUvV97z1G++221r6e7yHbLNgwwCunNbBwZAlFMw4zUvnwzUxpUrCZw4dACUSTGsy3pZ+AmJppsJw+W1rbErRDMjUq1KspBTdcFSAeq1sqqn0qFVEFHDpe8ZPKQrhfM5EEQzI0RT8qL0URQguh2u4jMGtdx7lohgRAi4pBfDRqwEACeEWiCTDqvI8hosYwDWg8CdLquyH78dFPIj04S/eq96bEpy9yY1j5r4EHDOVlqsUS8anjQHVfyadGYMPwzRcj8awDNeAlOKoovkkwnDVEoocSdAeaKSIMFy+wbIFXNaHi9uUIkBBK07zCXxbiJ5luIy+2F9kheOL6i9rFkulrE6EqFitGPtwLRxQbT+2Pn74fobhGqFtx7JhAJevB9PH7fX7zvGqLHiPieY9Px/TS7GBHjra5Vx6bFb5tdApxQIJGrkTXZcZro986bsASlYKUAampOY+A6m1QVtnYJJ0sK7/IDoyQ9Zai7c+axM+950D6Bw0GpIuahpwbSZHcFddAZsffdIuPH7HNC7d6YCOz3aZgXm6mdk2PX6TXFuOnGTAQ7cBAN7w0h/ExLk7KudeicRpaa44cz3u+LVrK342a5oZvnq30quVNVymbUaaEPQswzW4qmnTpEs3Gn0G8TRcOVilR5wKqgYZbVUgkhoYJZhutwA9j5pGtu2Z23B0cg9+9/ClWKtTksacFXA6nD5ToFmJ5l2VIgUHCMG6KfUbH5qdxx6EKazW/n+DmPAguSy19jG8lb6+Bc20j5issEPCm1qFt9oVMXAVG0T7noFo+TkPZBBwxyx5E4P53OpNR0spBuxe2RZiWEpRf47BDJcoMVxE/X9JX39wqfVhE6xBNQfllCou6Qz+PH4xSTNzKUP7HHEM14FF9XsySvC6p56Jbz80h2dfsBXTTXdttLME871ioNO8+boG9Tw0l380pRjZ+KvPPQ9fvXumuvOjIBrLpBT9GCqaj2m4kmpXgZGCuHHOdkEItJHeeAx9rxjRPKFglSpFBkh1bVJZoE/UuCSNhiuSvmdlEEa81Lv++4+3PAhrmTtOKa5y/OWPAf/zh5ZPGZZNDWMRCHOHlBB3dQl4BHDdfK8bMCqr7chK31zUBRggBXIuUEcOqT2glmO4pGa4GgHDpVNG1hZC3ewv7f8iPs09j6My4Kq3wdbvBgCcJu5FjsQConYtsWW+KLpIM/W4RnJw0yiXkgBsAc6cDwgrA6eso7XHcGnXb8oYcPDbAIDJXZdGUxKV0ClFw8TE+r0947xNtruHAUlmjFqjzydlFN+RGuBNbR/6lj9y+emYanjieM14KtNTEj1vSZSGy4rmNWtRq3lWGl7V2r3br8PH+D47gRqtjTqW2tajDWSMKp8lzxVa+XAxrJ1Qv/HcvGKQmCfS7ktaASw8JprXvTrBlKg25zI0Pi1VKXLpPHkCnWLsXhgppeg0XBQShXRN24V3b0TvlyGieRlbyIziNF9JKYYpRKN3MRFouAA0PMBl0kTG/60cnzzjXXhO7534xppnAY+7HnjyG6P7+ekmw4j6qUUCB6ALq+GiqKcMH375pbjuwq3B8Qw7bcxRy+/TzdXnHchwDbGPiAnjX375TvzeSy+OHut7PZZjuPwYVqWZRJgwM4auXBdXrVK09jTStR8zjDiRQrUlU/XroDIimjcMl+TIqUkpaoYrYosUA2HlisT/9sl/r7xutWIMuJ75LmB+P/DvfzTa/sN+sPn97vEwwGWQvTcImRUC9ys3zIRjqdIIw6Uv9AIMRHDwokCN5JBpW59GhOHy+ysSCk4YWrkzxLQu0lKL5vXg+mXxOPxx4dprEMrA6g5w0doE2HplsHkmHkAOZm/+dj31AFcPxNOviWbMxVhFqxYHXC0NIOoRhouxBHjJnwHnv0gJ6kcJU4E0ZJJ8xeU77WMDyIzRo2HcMkbxJ/xpeGHvBmDPtZVj+PErz30cbvrla4JzWJI19PQkHF1wmirFogsuifW4YR7rRD0/r06mPr8FXIuO3TjaVd/XIprYNKV/G50mkKLQFYkEmQZzebdTeS/SnQ0AF+cFhPQ1XA4gFZIChIESgUJUHd79O0YZfWpWzE+t8Qh7ZAHXXPX7sqL5JXtPEkiP4QpTd8eVUiyDQT/NUT4tfQ/25BCGq5RS9DUvNeIAF6MEL+m/Ddf0fjP6Xnevvwr7sQ5pYxK4/mNAe0N0vyxguFyvRMA1qUZEwzUojL1LzmW0SnHrdAP7zt+CD74sDpJsSvGxlT2Mhq1SHKGf47Dm4P5zqWW4zG98jLYQf/oCXHrzDWobrWq4SKAnVot8QSgSWZSO5wOuApxmEJJ4gKt6r9GoLYu3AJt7sMqCrWKMU4qbz1fC0lENTYcBrgWvX5sUGIhnI2kLznNQhBRpZbUdIHxT6eWlJiSH0MwHMZYEHrgqJFW936RbHUhQFEgwVTjAZRkunVIsZMkfSQchFIkn9KaNKWD6dHBJMEE6eEiucRoQAsxLl1IMCgaaXi/DUvgGij64Mqtif5KwgCtJgD3PVP9GjUQDjiGAy19VGxBsNpkecqrSi+Drcu/o7+3FAhquSWvkedtLkffQRQaiBz0/zUfW7rSPuzX13ZoJ9NC8m6iPdDg2U2BO1rBlUpvS6t/lqvs+CAC4GxSJ1oRxfY8kSQrJ6iC8C9qbhRSOWeGcgxNXpcg8EMLBQHQngpzLUjouZLhUSrEqri9EoZQdMSHsMIYLsO1zGIQFDP7EkEsWYbjIcIYrJuj3ryHBA31d+XUhw1X6TAHDRdFYcgu6kOGi+Io4b+A5mr6G7frw4T6WUjSbDMNVFs0PS1995BWX4C++cT92rmui6xkRm/NhlOADA8AW4O63Md5yi81zt0wus+fw38RnMc3vbbxUV6yLM4Br/02wy26zcJXcLl6oTSlyy3ABFJkMOzgohstUKXKAMBSeN1fQD9j2CJbgkig/vPJiaeEh4L3ngOGjldetVowZLkAJS0f9IYalFEtGjupvBFFH2pMYr5GMVgfw6Kq7tPrnxhCzryZF01bGpTxEqBeBFiES1deqJhzgFFbDpYXOnmjeb6pMKEPW9ESy7U1AkuEImdKvCwf4DrwqKQ9w0fZgwDWI4TK2DD2vi73VDR2LX45NKQ6eXAGndzA+SpefuQ5X792IX3u+qpA8XtPFedmwovaonxBRGi5SdNBDagGfn+aja3fZx51MsYcmReQzXIW+/Wd5DZt0KyVQFmipKCSSTD3HdW/NNGGY+ek71fO9OVtFBCimNhDNE1UiDlGoa5QawCUCsb3pD2iCg9r7IwBcRcTFvZxSTJvV5wCg6HgpeOM35iaGAqxaEZw2Q4ar6AEHvhWct/fh9d+wiCAao1Qp+gsyEKRFRKOG5SdK09ewXRt+X/iiecdwaTdyRkG8YkMuHWgaFFumGvjpHzgLhISi+VFb79hDjxEX6inDX772cnzo5Zcsu286otO8AWZGDnH13o0rOicScaa3Cz8/pcjc4sa0pRKEYgKq3daNF/4yruq9R807ms1isgAnCQokjuGK9AOm/rw2wGsrTDuONVyrH4SO/kMMA1zD0gtARFSrV7lcoNNTg3oWaRVCBjQJVpv06lwzXLKvXLkNw2XdfqV0Xk2BLQStVCHath2G4So7gOsghKLWdAyXGdjNPkXJv6sDj+HyGJmkPfhGb3lVisEKPFXv0em77+Tf2qqCa37d+QOPNzBYCEYHxcYJ3W5C/0yT9RT//ZWXYffGico5HkvMo2G9meIaLqJ8uPKuZrjUdt/8MvN+k06mwGwjNaJ5ByjN7344r2GLAVyEBL/xRHHYgTnNcKVJglq9jkVZA+vPVVKKnEtQ4npymoq7AhSJ7kTQzZ2oVu1TBD5cgWjea2fChzW0Nv0I/R6WQTP2DpZ6pXZVktt7JARc+m/aCAHXJ14MfPD7nK/XcoL+AYy4YUjzQVWKJdF82bPNj+XEzoZRmlgBw2XuO9/4VPlweZrREd47fj6j9SR8rFUiLheX7lyLqcbyi8mhDJdfpagfb5ys40tvuRpvfuYKWfkI4PIb20then6aQhwJAsdw2dfsfjrulluClCKTBQRJAmlBLKXIAsAVISZQEtaPNVynQHhUZjQCO4YRAZetzqpqsspg7A+/dDcWu5o9oFXAZSYBHkmjGMFvIRmI5JBaHGwbJxvQJnmF4aIQIIRWWvcY4aMBXD4gK7zHhFLUW47hMitX415f7sFonJDLDFc6GdeUAGGVoh8N3Y+ik7vf7ZbWk7Cz+wksNYeL1aNhypn5aIBrSQO9Wmm1fryNc+dl0zFcUQ2X6qVIeA9dmTlrAA9wpYkblLuZsp/wgasBaQYQzYl6UDWZe42z1/YftAUO5tpKkgQZo5hDC0k+HwAuwYW9Tu11oz2lBCjSRPXanO9WNUu+PCVguDzgwYvIKtY8znULIOZ5Qfn3bt7BUrevPyOzz0uPJa6YJqaN0Ifrrs/pE+lHziMCBv3x4lM/Dnz9D/TL1D59P6VYLoopi+YBoD6NcixXFGJTigPuJRM+O2uqcM38nJTSe6NouMJz9N5nZIZLvWiEwrxxeDHMh8sHYz6Dvm26sXKAG2HShPXcUtevAAEx+xnRPKHB4mpynbLh4cTrpQgOQRkKkoD4GRkTnj4yWNj5f3Ww8utWMcaAC9AM1zCRe2QVG4vYJBARwZcZrvlugUQbRKZksIC34NVUhb86J1IocTBgKyANICORlQCR0tpC+MF1C5BMpxmLASlFEIZWWwGuo7KFNdoS3DTENkyZCb9K0QdctcnBKcVBFUzbplXayHe2NhMPH0FYWgm7MsuH7rZXayhMn8N9F2wJnh822I0S/1dcjM8IlTaI2UJIUFXjU3TRQ2onQKOzAhQgMvGlO1XVa9MrvTerZPO7LqDuGC6ELOaa3oNIE4ZCUpDCpRRTRjAnm0jzuVDcLQqX5iam4rEAERwFGNI0BYPAQq8IRfMybO3DPYbLT1nyWGNoe1/pv/73FqQUu1jq656OnsN7NKVozs1nuBYPe8eNAb/QAqNynjf/GfDpn7ffCeClYGIMV5BS1EP1ad+HlYZhlFrLAK40qU7GtkG29uEyImXnND/aFOJXGpYXKYPCAAC+ykLn77UY6sN1IvtMxsYnvXAVmuGSWkagnuRgoq/ShBpwLfsVM7sAACAASURBVKAJovvchgwX1wwXQ95X9x8p+1Aa7aWnxwz+6ohpv1YrxqJ5QCH1YT9ERDsV3y82CZQFtGnlwtgy1bAoPPFSioZCtQxX0MbHiObdRZdKDpmHGi5/xZ7LRC1RbUmt0nCpHlbu0EuJWkW3tZCe+ylF39GcUjS0KebHimvx83Xdw4+EfwFgw0TNAS4g6EvWrA9ogYPBq/dnnb8ZH3jpxbjmPGdqatsAHssAbc6HDwdcb993LvZunsAPP/F0vEYbuvpxvBquP+JO6B/95Nr4lPAuesgsKEv9fnqE4Rr5e2j2Z3DjzUpo3fTsNaYbKY4u5bbn44JshAwXSe31cKBxBjYyZYxIeRegQJKohuQLpIV1+XypirBwFbckAaROe8sCAhRZmoJAYr6bY50Ir2e/ea4AdY23PYqjyCOr2Jh2yn8ubaqFSN7BUhFanfj2C9yYi5rtgGLL+noRs+gVxUTtViLnYbb1vQbcgM1JJ2kdyBHRcKmUYs6aeOLie/Gr6X9X29eeWf2My4RvzTIsYtcu8xgu/1Y0MoNRL/fjY7jGgGslMbx59QlM00ZavpmUIi8K2HZdpvJZCqSii3nUkejXzrFp9zsbwNVfQk120KFtSJLgwZkF3P7gnOtaAWjW3CyUyinFcC6nEe3XasWY4QLUBTE0pTggRVDZLyLOizFcpdRDIQRSOP8Rd7iwzQgvqivnwBZCClANuGzjZFMpAh66WkOLCQmppBSXUtUvcqJQ7IjPeIQpRYZNGzbhZ3f/HZ72E7/t9jGAy9v3huvOwy8950L3JprhWpS1gH0ZNQgh2HfBlkB38qNPUmLxi09bpt9lLMwqbJkbspExvOLynQMb5B5vSjE4pRjYJBQbMYOJ+/8FXWQu5ZP5YDbD4WQzbpS77aaUuVYwU02T8lUvXkQjYLjMAPaQXIdPnPX/I6FK19WQxhZCvdcCaSEr5kMGqnDaLMNwFVy5uRegyFKXUiybhg4SzQdMj2W4ImxyLKUnhdN05R0s9XL9Gd2q27BNBVgVSDHnfh1lrqRA33h6Hbgd+OqH44Br/iH4IXVJvLHcUAxXCbwJgTydwAwmsY7ogoB1VZAPAG9+5l78xWsvjz43qoYrllIy12CZGXEpxZWBJ2D0e8QyXGPAtaIYpiM9kYArJpo3KUUhlau8BAkc6Wuigy5pWAS+lK5xgAsacD18Gygk7s3ORGt6HdaROfzXgfmK+N1IDXxPPfOcH8afUT23utfSGHABCqkPSxUO0mSUIyqa9wFXaULQ+/QLUWn2C3g56yjDVU4pJgCE7RlnDUm9aslyNYcqva2K5nPWRBc11HXZrj8RBqJ5qswy3/fDT8KFHsgxEy2njsVq1xK84opdwCWvBH74f9mbcLG1Y+QUw3LxfWeswz2/sQ8bJwczZgPDpDhX6EVTjuMVzfsRJfe8jV2Z2VRN5gOu1vrKeRDiHL2ndUrRMEpLqGPDhHu9Acy34gx00ykQLaSfhElXKwCzSNqoFfMgnsZJeilFU+0qCg7iMVwOcIXAqOzDZa9x754YKpqPphu5q1oseuiYlKKnnTL3EJe0WqXIMu++rQIpKYW7f278U+Dv3xTqAM1r5x5Uf23KRX3aTFeA5nle1YNJbs1p10N3p5g+HbF43VPPxGU7455z26abYJRg+5pG9HkTMTY5bF7tnl+paN7fa9T7/WeffhaAsCvDOJaPYb/JiRyf5JCUouQca8QMFpI1AeBKRQddUrPXdb+2zg65HFpLvf9GAMC96W6QDXuxmzyATp+HqUHBkeem0reUUiyJ/k6llOIYcAFawzVqSnHE/QyS9hmucssP/bfPhdVwUcSsJSKAq9Tax2m4FEhidVOl6NKHdlXvM1yUVRguEIY5qrRKuWTwh8vAFiJCKQOA0BO2IJEV9XW/A+x+mhUdbzznytGc4B/pmNgCPPlNwMv+8rgOcyIHtEEaLhMCLs0jJzbjI8U+vGnLHwKEoFuE1yklxGuNFGq4SH0yOG+jweuKxE6OORJMEZ0Ws4CrhTpfgCy8ykfu+qpZ4M1zKONThlqWgmoNV+B/JUspRelSimEVZOke8h/HVrhSBN5AHS2aty1IvJSiqvQtmQyzzGO4Iu8pvfuqo73sCr9vo36tMUXWnnPmM2V1BYLuPjiLp73ns+rlkiomzvtO1hMNuNoj9AUtxfnbp3DTL1+D09e1lt+5FIHxqbedW4brGETzI94jz79oO+75jX0DW/+MIx7DdFonsvIzxnDZlKLg2C4exOH6Ds9MWXoMl97WWGftfnJQRVA8dAvmyQRms43Ahr3YQQ6i310AlcJ5QkqOvPCYab1Nv1H4mU8h0fxYwwVoH65hYviIXkRK4HO/Blz8I8D0Dr0txnBFAFTJ5iEvpMdw+YN6OPhzf+Wsj2v1XSQBkQWoHuxpzTjNaz8kKUKTRZiUIq0CI0IwRyaxEQcD/RZQZbhiYUT3voarEjuvBK57P3DBiwbvo+P3X3bxwBTeCQtCgKvfdtyHOV4Nlx9xhssdfy2Zx6zeqZ6leHPxMlyZqAl9sRcysQSDGa5aKzRTNFWmPSSupQhJMCU14NKMUYe10cgXsOgBDOExRtICrgJEqh5qacIU4PIYLpU+FMH9w03vQCDw6zJp9ZmFDiyfUwZJZfbLFkRwy3DRJAEE4NtCxDVcqafN8hdPhk2TlYWM1Xz552IYrpYuENGfra71i7ffPwMGvchBgoVOD03Brbh42oDdibBIY9RYTr81KEKGy203E9/IDJf34lNigfUojlGrFI87ooDLMVw7sB+31y92A5kUyEQXPVYHiLJwoa21diHYF9rk9PAduJedBsYo6MY9oESiMXs3CNS9lkBVKOZWThPOa1UN14iEyUmIMcMFaA3XMMAVsYWYvR/4wm8BH3+et9+IVYolJN4vClud6Je+ktLgH00pmh5WJAGVyhATABLTcsdzmu+XxIXUF837QSnmqTIvLUrpRh+A2eqTUtg8Ph0yyBMCXPIjqgpsmbj2/C14xnmbl93vVIiToeEysZ7MWuuIcq+1vNRzjRAHBqdLGq5GO7QaMMxUXyaupUiE4eqyCVAI0K7XFop7KUWTPuMqPWaNT4nEfKdvNRiKWRKBpksSZnun+dvNsW9/wL1nRaxe7sjgM1xaw2VbSwlh9+eBhkt/fywbLobX2rQg8gjgWjjgjgdAam+xNFWAq9fvYyd5WH8fCpRC8or/1v395e+XExnGh0s1oPY1dsY2YgyeTrUYVqV4Ihn4OODS99XCw5hAB0cbpwFwC6ia6KBLGzajk7XX2LGrL7WGa+YuPMi2gBGCdOPZAIDGwj2hyalwDNdyVYqnEsM1BlzAsWm4zMV2+A733LIaLhH9W3hAinmTRVnDJXiVLTMrckESEMlVJRkAVmvqhreOzXLiQiPQF1rDFQIjQhjmmVptlxkuP704iOGyXiyxlOKjPI7XFsKP5RiudZizoMysEgdVaBJCrPjY2EKY37ZeAlyG4eqXGK5JooGEZri6TIF6tnTA9gRUVYqhhotzrvp8gtq+agu9fsXSxG9O61cpBsyXvgeSiEFwwD77zzHHcHVzDbiMV5kH9ArDtPnHYukA0bw5J1GxVUG+5DSB5jMZFlAvwAzYpNrOY0v3Tnw4e596ORJVWSU4hP69fzF/Fe6duAiv+fg38UjHes9qxWe4/DCT3LiC8NSLoQzXiUwpxhbc2kCazNwFAJhvKE9ECQom+khQoE/qaGjLoebEWjBKkDGK3DSsn9+P+8kWMEpAJ9RCO+seDmUxkivdI0oVx/5fHWm5unEV47E3I8aCkOE/REwvEts/JuTlsVVxyHAVuQNlocAvTJUUQ0Tzik1Q/kwAkGRNcBAszR5Et5+Dgg9IKTIrYDRBKMXCAIbLDzqA4TIpRUGHpBQfpWEmJtMu43giquHyts2haeGvbTk0EHABP/UDu3Hz/bN47uO3Yu/mCRz8Y/X7pZ4zPeCAUh8pprSHUwDKMwW4eol6HVs6iEXUUEMRMFxWz2FTiswO0ovdHLLumCVZSilKT1cZarjUPnHANUBrRR3D1ddCWxrp+cbBqoP2ANH8V+86gJ/7g/vwfs3chV9gAdQmlU7RnIvRuekFmLlvmQZ+63JXxZgjQR3q+zD35if4D6Cx59W4/+v34Yre+5Ejwddx4uMrb706sBAJm1e7/Xasn8D22qT13luNeN+LLxzo0/dYjmEp25U2qF7mjezDjszw8JanolNXXUM+87Wb8XwAeaYW7gIEGVcLNp42Uddp99a0shWqpRQ97o73gAZcaK6FAEHamwGTArmnvcwLt2Az29SbhXNz6uuiV9lFdwy4gBE0XJEcsL8t76jU2MgMVwi88sIDXBGGy+iw/BYnuO1TwJ5nor14j96ZgWqGqy8TZCxBAoHn5n+Pz/3RL+JMv0rRbwZKKUQZOBGKJbZ8k9QYpQy4iVYOSyk+SoMQgt99yUV4/I6qI/hKY7mU4ov7b8e7SMhADCqhp4Tg+Rdtx/MvUivOK3bX8Le6x1laD39rU12aI7EeToXnzYVUpRQN4BLzBzAj12OaLILki1jq66IMDWoE51bDZVjRpW4fqBlxvWG4vNQh8Rkuj/nSC5g0KPWOF6Ooxz7DJWwaQni6LmvPEmvtY0TzUgYD+a/9n1vxQL4dvUauCkvKPxXzDE0B50xvHerVsZIkRSEp2sI13i7AQMDhi+ZTphjKRsbwYG+wUfDxxpapMGVJvevL/4hP2bMFv3Dd4MbTJyPMtTyO0eNEarh80fy1/Xfh7U+5DvMPfxcAsPXwlwEKUG0hI0CRcaMBbaHeVYCrPaGUmPWUoS/d8e4jm5BSAlCGObRR782AIGxPV2hrlcAWQkoA4RjIECFCVinGKUVgeR+uWErR/+FMddKyPlxlhkunXjTgEqABw2WcdZ0BpIfUFw8CH38+znzo79X/KQOBQMI76CJcde469DntNB9WcxBI3dqnBIwoQ4+pgbeGwUaggzRc0ky0j0GGCwCuu3ArdqxtLr/jMhHFW3qQO7zjGbhHbrH7OFfuAceKbMv1AJe1poLtjuFKrEt50DVAM1x5qgBXjRQ4rAXfpDeHpa4RpjuGC8JpuACg0+97ovkExujTBEcCagpDvFWpaRsUzBvlYpQhVYqmlDxWpVgEDJeXUjT7efefaaId2EL4kdTD4xjrDJNSNManiXLTnpAOcPVlosYBwa0lS8YoOn2Ow4sKsG2bPjlaLnNdmebVdnvy2Ly3v9fjREoefOPTjqxhz+YJdJub8R2xHU+k31a7aI2iAEVNAy6ZtdCQCnDRplqYNlIWMFxzsmXTn0fJFOr5ETAI2zTdv5eFx2DHAJXfveXew/M4utSv7HOyYgy4gOU1XAHDZdIWy7BeK9FwacCVkyxA407DpY1P+WBQyGmKRBYgvI9eCXAlLKkIDqVUlZGE0GpKkTD0qGIx6hh8cdIB9LRtYPoYBVzHE0akDgzupQi4QcYyECVX7ldesTPwXYqBt1xS9GSKRiOcvE1Koi9Tez5BxanWcBWpY8YWZAM9mYD257GkG7Ebhotz5cPFQW0amheFvYc4YQBkwHAVJAWVVRG8MVmlsVVrLKUoXCp9bqlrU4rSNCv3Bmk1mEdsIcwxPcBlFkZCeAsZPxLvdYBLKVqGS2vREoYCFJNwTvQ5VAGM8uHSoDihuPvwIriQePuzz8U//MyV1fd8BCIJNFzuImLp6qUSx3HsMapR7UjhDVCs1sK26QYoJbhFeua8euGRoMC5C19RL8taqOv0IupqsVdPqapS1NGRiS3ImGfTaOZHQIjvJSlw473KmNtkUvb9zhdsc3o/Em+sODTfWdWm6GPABYzgwxWzhYilNGJVikM0XCJkuDhNg4oK15LAiOYHn6OgNaSyD8p7qjWLF5QxEK98XfACQurjR3y4CKHoMzWpJmQwEB3McOlmx2PAteL42tuehr2bFXMU02JIC7i0OFXvsnfLBJ6way3e+bzHAQDe8Zzz8K9vvtq+LpaenBUNHMQUJmrlogkNuDzRvE0zksy68heZ0371kGEeTYjOnK0ENIBbCmUBIYjTcOWF6x2oWgCVU4oJmNTHCRiusDo3eD6aUpSY02Pw/TML6Btmirg0oxQcQhKlj6s4zXupQQ9wmfuUDGK4WAlwWYZL+4DpRVSmGS7/PivA0BLzwIFvK/dtqH6IM5rdOmNDCxP1k3Nv0QEarjR57MkFHg1xYp3m3XW/feNaEKLSzh3pwLgpCsk8HRWttd09qgFXI2XoCs/vUTB7rvNsGq3iaIk0EPiTr9wNAGg3FKijEDgwq4Gcl+40Gq4CDCmRJ+3eicUYcAFawzWseXWE4VoWhJlGtnl1m91PAlJab6GCZGogN95ZZaf5IWlPrgGXyLu2ysyeHggIBITUF/R//A/wvA+iGS6UtFaEEvTZ8iaJgwCXScNYFmEcI8dkPbUNuYdpuEyKz6zWagnDn//E5bhwBdqx3yueh5f331ppamzGZB9wmTRjzhwbxj3A1UeCednAjXfciwOzqgLJpJZ5oaoUQZhNQxSFa+0jjGkvfIYr8RguD4jxqpA+7r/l7jXTekfwwqUUWZhSFLYFSQlwUY8J845vffMgq7YQAKC1K/jCb6nxwzJcuTsegCRNKq+3k8rc/U7DlRDM61TtyRSKs5KG66BUE+SJtD8Zx8mLE2sL4canNRNqvljoFYGkhabVLgFJ3ZtbNOCqpQxdn+ESiR3bFpNpTIhZMK9KseA5ZpdUgZjpssEgMNdR91lPunvEMFy5ZKidyJTqMcR4RgTiGq75h4HWBt3YesT0Yax5dUzDVdqPc8Nwmb5q3PX2g6fhGspwpchkDlH0IFgIuLikSOEEuHTxYeDmTyqGKyZ8pww5q2qQvvimq9Qq+2P6vAaI5mHTXeNB+VjCAK14Zx/t/C7Vd1xPRpt8owwX2piVbbTrgwBXNaVYeICrltWwIOtoky56MsU8aWICS7jpvhl18vo62PFPr8JDYkqxSialqL25AM3WlRguTlKP4eLa8JDbwpEQcA3wyWIJILkdfAV33j0IdB9SVRoSCpRZtQEMl1kMMRKxhQBU02sAuOMzwJ2frYrm9fmnmuHyw+/mIEBBCZBSihntkn9SARfzNVwEz+m9E3vpvbh6lSeucRxbPFLGp6keh2YW+8iWAVzMeEQCNuXYSBm6nobLB1ydZA2m5Bw42moxL4GZha7LAOnFE4XA0QW12CskRU0fzgIuMGRs3Etx9YOUQNVdnwfec7aqBARKbNaw5rkxDdeQlKJ+bFKKBfW8eyJsmW1ULas/W0FrSNEHKXoQrBY8x0F0Gx/3Os5qSIhQ/RB9KwooqjjGcO1Y2wwYFDJgtWS0O+Mh+djCpPSicgs9yPVNqikd7RYeZu5ddiE3++YysYDOaMYKD4i3agnm0NTnk2JBNjBBlqybu2GHsoUHsLl7h9IKaqPbrFhwKXWiWuqEKUUG5jFcZmUrrZB+cAss9dgtjPrCgLzCpRSp0zPCa7JLyiz0AA0X04O9cb8ux78/4JmfShGmFKW07F7KWOX1hbc6F4SAEgJGCRb76jOdzFY3rFQFux/r8Dlx0Zjh+h6NE5lS9G2BzPVwaKFfSilGGK7mlLJNAexgU08petxdU13J7LXX01rRKSxaH78Ds0sWcJl7mUJgZlHdZ/4ixvhwFWBY7S5R47sG0ClFDwT94y+pv4uH1N/YQF4CTdX9YgxXHJgZM0dBvdW0tw/xBLpA3BuL0wwUEilfBCkDLklAIcC8G8RUSYGyijibUIo8Wb7Kjg6wfXBi+rEp4rHEsDHR+HDlwqQSRwRcQ56rAC44DZcBdGZQ44ljuFpZglmpLSKQYh5NtNGxA2EtP2r3zWRfAbANewEAu/h37TUuiaqw9Y1POUmRSJd+Mz5grpfiMosbL83YFY7hMtWFhHopRaHZX3/hZQGXbx/hMVzEpRRF5H5cKLzfhaUupajP0wKuNKn4ePn3twAFpSQQ+p5UhmtAL8UTmpoax0mLYX0WVxzeKs50sXjGeZuCoq20VFzxK/nLQae3A6//KvCaz9rtjZSh402pXDiBf6G9vBiRILoY5eDckpPc6HnzL2v/Hxbm1ZiTB4DLONInyOjqzknjlCJQZbiO3qv+GnS+nC3EMNF8xDRVcE+1ITmkTSl6gMsHM4bh0u/dR4J6ya7BpCMbfBFIwzL/QlJQKUATBlOwIfqqKoqQ0IoCAEAZimQEDdeAlKER2pIx4DqmMOm/mKzQpRR1H8TjSCmaGKTh4qAew6XNbBOf4WKYg7pO+hpwTZCOvZ4m9jwFvf1fQ81UuhIKbDwXAhS7cQ8gJ8ElUYUAQgTXi0opavsSybWwHtahPbrgGbAIMtoQX8PlW0UYvytJiKf9MrYQfkoxpuESqvBEElDPjLUPT5jLUsdwAWoRpseHLEmVxmzAzyOgVvp+Kqh+EgFX4MPlneOY4Tr14k9f/UQcWcby4JFwml+UNQvAn7pnIy54+uOAz6t9ysD8VrELl2cJMLkZmNxqt9dLKcVCSHvN+1pRY1h8aK4LCj0/e1phMnOneo0HuBgxKcUEdTJOKa5+EFbScBnBe0RzFWOzBrUUAaKr7rJjvDFz5JqZUr3nquDN9qiL/GxJTV18bbIEVqJxOQgoJChleErvveqQBnBRGvaa0tvyEUTzg5zmpZ09xoDrWMKMiTEPUzPpGZPARyalWJ3cbUqxvjZ43ZzUrvNIceb2LVifOG0F23AW/uF5N+JOoRsu0wTImjja2IG9+C4EV95cUqfygpQidV5UkFJbRywjmo92hPAAl+AouEkpZt5rhS4siYjm/RY9kSpFo4Mss1Q9H3BJETJcvB+I5suvbRAHzgQoGCVBOf/JTCkmg6oUxwzXKRdP2r0ez75g69B9TiTgMvKGObQCAF5ruLmjrBnrIY02Uq+nDEu+hivntnpaZo5AMHYkh+Y7OIcoYsRIWAAgnVXGq3kspShVH9fVjMf8XfP3t+zHgcV+CHBMxFKAK2W4glW3rjYs9USUpZRiXvQD8GZW/nKIaL7dUkLESSxV8uaFVBouxhgO6Soj6NYKhFQvQgriqqyGxKBeikNn93EsGwbwxFzjqb6u+itNKQ75Tcq+NFYbAWKPb8bN7pTz2PE1XD2ZYMvGjajzBbyIfV6/KcO26YZjezRAn2/swFZyCEKY5swK6BAI3JOdBfzkvzkzXg1OzP+ltwiyWsZolaKp9pVY4o7hKgrNmjHjTu0ZjBJS0XAdWPIWThHRPIEEodQalJro+ckD3ndieQD47Dtx1l0fB6DExmWJQBMh4KIknCjrJ5FdopTgLdfuxb4LtgbNq4+V4Xre44cDgnE8snEiPagM2z4rWwEA9wFXGVz1kFYYdUABroUSOTel26PJhgNcm9eoee7w/BLen/2eOg8PcLUXFODins7ZieYTO36uVjzmAddf3/gA9s/lJcBlKIYhJqexEvToqrsKwowNhNlmGC4jdudFEX2dGNIHarKtLsQ26SLJqoCLQoAxZlfesregnqTMmThq2whK5EBTUz/IwJtXpxSHWW2MY2Awm1KMAC6EgCs7DqbhWedvjm434CxhxKaH1xQHAQD5mjPtforhchou3lR90V6d6O4HhGLrdMNV3ek0uWAZMhQQBbfVgUQKUCnwQHYmsPEc6/sFniuvKw24zD1ApDM0ldGUorD3TYf7DFeYKhSiQDOfwSwmAmmBST2+//P3uON5ekw/pUgIrbDOfekxXLxQKUWjf/v6R7H5yDcAOB8uPxoe4Lr3SBdz3cICrozRE6vDGSFe+5QzsWfzRJD2PJbr7p7f2If3vfjxJ/DMxrHSGLbwWmkYjeUcmsg8JiupOdnBxaetCV7TRRatlKSkqk2eaqh7aPPGTXZbq6GOPbvYsdsChmtOeXPl3oLH9+EiY4ZrdaOZJSgESoCrnFJchuEa6jTvgytt9OgxXNJLKZo0R1EUTqsCXzSv0ykRwcfUhCu1bTTDdGAhqAZcCQokKCTF3fvVBEootYyGoWEZESNRz4N1QeOU4vGEwbqxlKLxpuoLilpCj2sA/cBLL8adv/6s6nvoQ6Ye6F6rmyuLNXGGq48UR/f8EJZe9GfuQIRi40TNDn7Wt43VkCEHF9xqp4hUDJdhwYTxkrOAS7eL8pq2m+s1LyK2EJ5v1lKhASwvbFcH3wV/Xfc+fJdsUcBP32s9vd987i2+BvhwGYZrQbqFTr/McBV9oOaVw+tIIz5cTS+laNKNZpKqj5hCfiTCv9KOleE6kRP+OI49ztiwvGRkuahxtWifk83wekjcfUBL80hWa+C0SNuz2/fPVQDXdFONAddcfJY7nm4pNeV1ZoAHuLaL/QAQpOl9Wwi/V/FqxGMecNVTpgTIMVPRmG/WyCnFwayX36Knl+dWNO8Yrtx2QgccUyRj56hjzaQTFk62SoBLu8oznUbpIsPCgurdRj2Gy1zwSu91PIBLn/eyRxhHLMykJIYwXD1BRk4nDnufWIrBvL/PYM7VFBtG1jqGq5mxQMNFshaa5z7TO5BmY4wOSjNcMqkhIwU4LyCItmOAtM3UAc+2YUBKkXhWEcbqoeKDp++3RQ24VJWiSSmaxtoF1vbuw31kqxLvW4ZL7WfToZGUIqNKG0kIgwDFjPSNYD2GS+SK4cqqk1zMh8tPKXIY0br6Xk6mfmtYHA+zOo7Vjb/76SvxqdddcdzHMeBlv1wXavrSwRXun3njM6JO72+4andFy2gYLmshAdgxZA1xvUf9Lg3biXIW8L3xEs8Wgq4yCfCYv2uaGdMMVwTMDK1+GtEWwi91N0yWN3B3ernVcEld8sqLAr3cpS+IFMBn34kNs7eo/0cummkPcJGShktVRQrUUnURdpEh4YqSrWeJXdWbC55AIqEEr+7/PF6f/Wrlvez7DNguI4/GMXrQIYDLeFP1OEHtEapWu2/99wMA7qXb7ba/PePtuL53A9KJ9XZbX7g3cQAAIABJREFUu5bYKsWeTKvgzVSx6oUENbqppIYacsXuGg2XMT4t9YqEUAxXFXA5q4g8dwapNoQrPOkUyhqF88IthMyqeO5BZKKLh5JtIJ4PlzFItavuiGh+qpEGGi7zXQBAveFNOnlXnYtXbWW/IsIqDFe9pOECnIarma1eYbnPTo1F89+7ce7WScseHU88uPlq/Gb+YryreGkJcFW9t5Z77tKda3HVOaG+b7rhaT8N6NL37Ros2P2a3YP28QTRxqfePZV6VYokyGSd/HjM3zWNlCEXBDIqmi8BKZYdg2jeDdJ9PTH4Wqxuv+/20RNTUfRd+TqAaTkLfOG3cPaBfxj4OZLMa0BccpoXRQ4KiUZNXaxdZEgKJZqvZanNXdnJRQhQSvDP4hJ8K7tg4HsOzijqJ8YarmMKV6U4WDR/IhiuQXHntufjgu5HMdN06UNRm8I35N6AYWl5VYp9pFX/MJOS1GkA431FdEpRcG79r5QKStj+bMLXcEHY3pHSaricrsukFDt+41rpnOyXcuUkn+eF888y5ryH7wAAHMq26+tWgguJmYUOhCTePVFluKYaqTpvqs5+XjqQlbW8Fku6IjiWUgSlFYbrHc1ftI/N4sqA2ZNpCVGOE5FSHMejJyhj+CB/LpZQL6UUG4NfxAYXYyVpyHxNN73/G8Clx4Vp4gDX0rrzKsfyi1isD5dkllxYrXjM3zWNjIGDxNN15aa4LHOPY0L6KOBy22wfN++9lrq5PabwbCH6nrD+dDy4/AfxqwqTcBWRQjFcBnD1ZAqqAVcjS+37WxpWOg3XsIF1kB7j/nWKIbll7TXLn/c4KjHMh4tqerzHHznAlTCKObSwYaIWbANC082A4UJaTUNrtoroa5MEDFfhqhSptoXwuiGEKUXHcAkhUHAFzgwoy7Xe6sEjbhCeW3KVx4UkEKDoF4WrLjSLkt6cOm5t0or33/eZ/8Q/3/4QODwxvBAVhosSl6rnoJiHm2jkml3ue+jr84qkFEFopXNEb/35+PX8Jeqr0udr7sfGamq4jlM0P45HV/jjfzYqwzWkv27mmaQSgjD1WNPssF4oXbNLP/eqf8bC5u+LnZ17S7j5bcxwrXI0UqW/iDfDDdmsRc7AizzcB0C1KXXkGPC0Jt57HZhbtDlmx3DlVkMycvggKwkZroRwMCLR9BguA7jqWeK0ZfpykEIgY4NX0i/s3YDfzl848Pn6lj3Y2f0EsO3SlX2GcQBwE1tcNK+ui64gI5meXnPupmX3KYcRaG+ccNeUEWs3PYarnlLcLk7HF/j5uFXsqmr6NOCiBnCZHptpDTWSA6LQDJdqrm4q/gBA6n1F3oPg3IplIQos9hUUMqDMMFx+i6r3/OPtrrpXH/3BI4tWw0ESPfDnqgFumtWsluxf7zikHeSJS02URPMJ4bjz4CIIJDZMNiBBAsBV2+iEvssBrjLDtWN92xYaGHG+YbhWU8N1ImwhxvHoCf9+TxPv3h/GcA2J1GO4JmpJKFHQTa6NhivpHtE7bkYmXcVi9LjwU4qrK5p/zDvNNzMFuAT3hr2yTkuzVAsFQdHpYQoYPaXo/cA2TSg4uCRgROKW+44441G9MuC8sJWLI4fPcJVo25p2pW/U1PYuMrRlByBAPU2R6wqwvkwAAkgpsHO9So/cf6R6MX9d7sXX+V78woBT2Xf+FqQvp3jaOSuf7MfhabiiPlwacBUEtdbyk94HXnYxuisE74s9de35DNf1F2/HznWtIKVFCMEs2nhF/lYAzs7C7WA0XGoBYDRchvGivAdBlIaLQNlCmCpFlqp95pY6IBBIkxQCKvV/ZLEPBqFaWBVOb5UQYWWD3z00b+9bAWVMyiAcgDEaLq4AV5bVlIYLAo1UFZJIEAeGSinFn0k+hTsbF4D2Bda163h38Rx8W56GF7B/BQCs23ya+x6MBUtEwwVS9eF64pkb8Omvuf5wgNNMncy2PuUIjU/HJTGP9aAB4+ldl8MYriGReAxXRWPW1IbL5r5dmlF/61NWevHdZBdOL5QthF/J71cpRv02T2I85pcpLqUYAVAl4KWM01YmmvfNSvuF6w1X6BTJrfcfwQRRbJNIlcZDFAX6QybJmC1EmFIMAVddt1bJMpdSbBE10SRJgj/b/Av4YPEcfEWep09dKN8dAAu9FQI/qIn4GedtPqEme4+lYEOrFLVeacSUYspotCpoWByYU6JtH3BtnKzjWedvqez79mefax9XMsw2paiLQTSwMEUdpOiohtZarE4hrJlukqn3PjK/CAaBNGGQIJCcY2apr5J9+riFXsgwCOS68bMQHB/+l//S76tE7RQCTz9nAwCgliXgkgC5+qxZVtP2FBKNTFUzcVBsmNSr9ZJofhs5hE8Vb8D2qQyEMvwhvxZfEU5LsmbC6bn6HVVR1WOR6q36VKU667JdG1DTnSNqTOLWX3nGWMM1jlMuAoaLHT/DlWUOZO1aX2KDGxpwGW1nZ0Z1iKlNYG7tRXhT/hq8d/07oscNfLjGgGt1o5EyyHJK0aYSQyDVk6mrMByR4eKeWaJJExIpbIXV7Q8exVlNXVnR2qRfU1hdysgRpBRDwNXWlRu1mroRusicuSKhmKFr8ZvFD0Ead2/Jccb6iMB3HCclXvPkXdg23cDTIulAA/gV4HpkJt8+V9fulqnlV6qv+n6nVRpUpWgYLWPWatirtlxS/UOpYpYIJIi+BmsacM0tqCa1WZpCgKFfFJbhMivinLtFkPG/eujoEv7gC0oQr5gqxXA1EpMazRTQKcy9UQchDBvkIezl/6k0YqDYulazUobhKjVsT+fvi5oET7XcPXj/w6qK6pv7u9UvsNauMFxJkmDDGvW+GZVo1xLbdWCivopJiXFrn3F4ETCePgAfotMaFplOKRaS4pwtk+GTzTXhm/K+SjMSAg7gz/lVmE/W2d39peqY4TqFQjFc1BmNSlkRvBtn+BzJAFfrIYDLE78XhQNrnDqNxun1RSBtQepKDMFzZ+aoo+s5V1eaTQNDRfMtqIG+1mjim29/OhrNlvP6IRSFnmBtqx4p7Ao21vdqHI9s7N44gS+95Wqsb1cresxvv1Q8cqL5n3v62fjpq3djX4TRGhZ2xWtWobri0AjU+7qnoQFck2QJktXAKAOkVOk+fQ1mmuE5Mq90V2maQBKCfj/HTAlwFb42UqcctDITgGG4GBgETOFTvZZCgILoptJZrW5FwG+67ydBISFBsLatV+u6BVCsyspvcbWv92u4qvceTHtGxKYi+Ha+AzjzauCqXwpeX2a4QChS/R2luqrya3erFMp1y/TKO1kxFs2Pw2e4arHrYftlKzpeLTNMOMU5W0rpd8NwdWe9bQqE7dmk9n3BZc4jMEgpGlsImcT9Nk9iPObvGqXh8qoUI8xVV6cs+kiithBHF3voFwIxIX3habFy/ZhIYSclBoFNbA5obwDVK4OiyIMqRQA4CjeAR83bfJBVsoVoQa3ik6yBNa0MJG2gQXQJPaHIuTreLFWl7D3duPrLb7kan3/jU6vvNY5Vi1QDru4j6MM13czwc9fsWXELGYs7DPi3onktbte3h+n1OYlFyKQOxpiuB5TWjd4Ars/edj8YBLI0gSQMvaLAkaU+KBFIdQuroijQzTkYuAV3VPHWALRoXltPNLS4t6kBF9WAq16veR8AmuEimGopwJUXyr6lK70FiNZkUY/1uk3uwt1yCybbbbzp3M+hJ1N0FtQk8Z25BHj5XwE7nhB8bwWYbaulvjeGRAOuRLciueG6c/HiS3fg8jPXYbXCF82PYow8jkd3hKL50ljxs7cDr/jfKzpeVtPttkCtpMWGbhsWAK6Wkgdsnqrjnt/Yh30XusWIzzrb5tVjhmv1o26rFCOtePTjfq7AST4AcN3w1zfjzf/r5ijDJXyGKy+QcyXhNStxAom18ijQ2mhF7b1ebh2xTRyRPuCKXDR+qsOAryt+CgCQaYRvtks/x04oCv3Z/2f2Arwt/zHcsXkfAGDrdCPKsoxj9aLGXEXpI8VwHWvYAdgAfg24dm1QzO2FO9bop9V1OEE6QFJHkiTaZsEBLnMvHDi6AAqJhCXqWs0LHF7og0Ei1QzX/UcW8bwPfEkJ3S3gEmCaHZLax55BoJHqar+aSikyoe7tRr1hKyTV65WGa6qlznVuqQuIAgvc+851msNnuK67cCte91S10v7BS09HHwmavQMAgAfmheqPqasVe0R3lgDDglfhCMqshs2YNj7jvM149/UXrGprnHFXnnH4EThBlBdnU9viVblDwsgIBAi2rynpHZsew2WKT1rrMSjWtRzp4Devjhqcn8Q4tUbsVYhmlqjVZVko7z3u9ZWeKkcCEmHCKAT+8baHBrTx8RiuIsdCtwCDsH0TGQQm+VGgvRGthhp0lzqdwPgUAGY9hovFAJc/Gpoc+jXvBC55pduumYdFkQT7Fprh2rFhGn/Kn4b1k8NFjz/xlDOqJpfjOClRZy5NdqoBLlulaBku9f9WXf1/Y1uXdBvAhSXQVDFcBhwZ8FKrq2swQ4FNE6kCb7qK8J7Di0iIE83/+92H8O2H5pESqewdoO4R20FBKv6MEYGGJgUVw+WlRGp1V1UJx3BNa4ZrfqmnbCyoxx7rNIff+eF3X3IR3vzMvQBU0UEOhh3kAB6Ua/GN4gx0cm4noj5Vxz4kp/CA9CYPwrzPceqYB49v+XH4QQaJ5mPxxNcCV/z00F1MSlGAVqUsJqW4NANMaiZLM1yxaNWcBCfxRPOq5dfq3VOn1oi9CtFIlYarYnLqPe6ZlKL0fDw8wMWgNU/+D2kYLu5ruDjmusr13deaNPMZoLUBraaaiDq9XkTD5ZnCDRqEr3mn+ju5zW2jXoWaZrj2bt8QPP+Wa/ficdsm8cGXXYyPvPySZbU7b732HNz1rn1D9xnHIxNk33vw1/wKfFFc8IiJ5o81bJqpxHAZqwezIDEMV0IEWNZAkiSW9rcMV0PvgwLTdQpQBqLTgnceXERCJFjiWOI3XLUbkxlBpo/9W+mHkemB1thCUAjUPYZLeMNfs1EPFhFbJjOsa9cx3TaAq1sFXGbVncd9gNa3a7Yy8+/Ik9FFDUeXcntP9qhaxf/fza/Ci/tv979IpLrAxVSlngphJthVFe6P45SJQMO13OLv2ncD1wxuEwfAtp4TMWi/5QJV/XjlzwMTuphoGMPlZWZ8Hy4AY8C1mtHQGi4DkD70uf90T2oQ1vcZrggTRolQItJYSpGHovl5zXARzUIl4Eh7RxTDpZmAh47M4+iS0pYY8Z/fG2pgA84rfgp46wPAhj1um6/n0szD9vVTwfOP2zaFv/2pKzFRT3HNeZtXNW0xjuFRW78TbxQ/hT5S19z1FAkLWGyzag20DPDS907q+fSwrIGUMesGTUspxZRwdb0TBbooJO46uKDvIW2kCoHrL9mujq/f+yz6AD6p+4A+4Yx1KHRKsW5OiTgXeS4JWvUaKHetgR6f3whKEwu4FjoKcAVNqc2qu78U/T4m64n9XO1pNTkcXcrxzaPq8//T5tcAAP7ktU/Gl97x/OC1xhaCrnIKxA8zLGyePDafpXE8usJfoJyIqtW6vuZvEmdWn6xNAL/0EHD2Nc52YgjD5YdJKV7/BF1RvYr31BhwaQ2XAUgf/rwHuEoMF0trng9XyHDV0hBwGRG+8DRhBeeYWQw9hCaJcqtGfcpu+9qdB/GxL96pXq8rvXw36mhK0US5XxuLaLt8EHaMJbzjWL0wRQ4Xnz69zJ4nN6wthEkpmpVkieFKa27CbjRbSBIWYbjUoJpCO7wTBkJVpaGQUClIfR0/+/xN2Lm+pfSVzAGiDUS17Xnp9+0ClyQQzZsUJaBafkw1UnDT8xDAunw/MP+grVL87sF5HJhdDEXzluFyr/ODEIK6Nh3esFYBrj/7+r14/h/chJ3dT+Db61Xrq1rClFfaGU+1r61ppm61fYP8WOiqsWzzCHYh43j0R+jDdfxQotaexA/23oHX5T8zfEdzj+sqxXhUqxQ3Tmnt1yreU495wJUlFO1Ghn6eY7aTh5oJPUFYJ+u05ih+DyVTSGSMYq7Ts9uW+uo1nHP0pKk+1BVWEGAaXP3+C8/WJ9J2bQuUFat6Gw24AoaLrIASDVKKeiL0JqVyReM4vnfiotOGDTgnPyqieeNBp69hM9ARr6K23WohTRwTZABX0wdc2oGeUOaaOUPYxcITDPAUPH49636FDAJ1A7gog9SAq48Euze2IfvV1KDRlfzHdw/hv/YfxdG+t6w3lVOa4Yr5lhnT4S2bVRrkL/79fnfschrmJZ8Efu5b6jnNdq92s10/HppT9jJjhmscQKm35gnQk9ZThv+QZ2PT+sGpQgDuHh8GnHwwqKUF5YXfasSY3gBwxVmbgNsE3vE3t9mBH4CrUtQpxaRWB+vGRfMJpZhd6sPYtS12+2gBkLxAjgQ1FFjo9nF0Qbcl0WXfWa5cqJG1LOBKSQGq2QHDcG2abgPz8fP/r/N/AWfFnyqlFCMMF10+LfX+l1yE3kp7O47jEYvXXLkLX7t75pTzSKNl0by2XKgMdCXPuDThkKWUYqsMuAgFo9SyuxTOWsUKYSUPFxMmiGKxGQRquugAhKrFjAQESTBRS0DySGpQn08CgYRwcOnp5kxKMV/CF990FSYjjv5mcbRxg5pElvruPmJlw9S0of7BpVRPpZTis87fgr+7ZT9+5ulnr/apjOMUiBPNcE3WU7z7BefjyWcvkyp8wmuA2z4FnH5F5KSMk4A7tzq0btJU8q8iw3VqjdirFBsnGugmwF998wFs9VeUerAznli1egNsVqBfCCTCSW4ZBJbyAt2+04AsdPrYCKDX71ux3pGFDo4s9MCIBDV9o4yvSG0iatoodCqx3ahFAdeX+HmQF7xuCODyU4p6oqMrY7iec+GpYbY4DhVv23fu8jutQjgNl+lTaLzeDMNlmrSHi4Ak6VnANa0d2ltNA7i4TilSEJpYAEOlSylCcDeIDmC4mvUMtCOQUrdNWkZOne8Z0xQ4Wv5Q6v55/LY2JmcIDvV8Wwij4VrEjrWRtj1etCbWAlD3+vp2DYcWeji61B+4f11XWZ1KKcWpRoqPv+qJq30a4zhFwgdcJ8oI98WXnbb8TqdfAbxjNv4cq4XWTgAa6CqyoaQlXY14zKcUAQCU2YGYEm+AK6UUG/U6KJH47Lf248+/do/djUFgoVug23fteBa6fSz2Ctx9YNYO6DMLPRxZUrS8MWhET+lMfIYrAcf1lyiQk+ufyKx4Y9HIhlSrRaoUw5TiGHOP48SE1XCd8xz1d1oPnsswXJRQMA2kNk0pywTjtP7G9M+BzhF1zRJindcJhLuOpXDHHgC4dqybwJVnrnXAjDALBI3n1cZ6zN9O7fOii7diskYCLaXVkMSYsVLU265Q5ZmPU+nFg/O9QbujWTe2EKcOwzWOcfgRiuZPkUKrxFRIu/NpkL5qqE1DacNqxBhwAQChoFLg4tOmccUuT4isfxjjiWVKzt/wp9/At/c7hD1dp1joFVZcDwB3H5zHC37/y+CcWz3KYreH/Ue0wNZMFt044PrhJ+xQj3Xp+/qpNvC6rwA/9InK6TeGOY4vl1Ica7jGcYLCVrde9mrg/7V370FuVfcdwL8/SSvtene9ttf22l4bY2M7BoPBD6iBQEhc48bhjT11aAqkhMQl0JSBTkmHBtqBhhCaB6EtJZTSR4ZkSpnwaBpIIQweEgg0AykOYJsMARsKtlObXdv7kk7/OOfonnt1pV2t75WuVt/PzI6kqyvpHstH+ul3zv2dP/01MG2huSPwyzK47qdT/yrbUjyN0PuxMHIYaJ+pC4LaH0Yq7wwROAtLhw0pptJIpdJ6ONEGZpLCVDMh3g5fhpZ3sNm5wjDaZNg3l9LLcI0ecEmrF3BduEKXbTlcYZg+m0lehovI5a42UO2qFLEJWXqrDYP+DFeBAVd9SQqiCnjoqtNx+4XLipvtGYZ2IWm73EgaBaya5y2uuXr+FAznFQ4NehmuZ15/H6/9b5+u0eUUY3x2h646XfyyGLRzuDqL29YtnVbMuNnMVntrDug5Dlj6CeCyx4BubxBxUqUMl5vBssFVlXO4iKoiArQ5P1yCGa7gjwAn4CoGOADw+ee96x0z9RqDoryJ5MUMlwofriw+py4poYcevTlcLWnzWrYPhAVctp8+cSOm9e/wl2Sxk+bLnKXok+vEF9Yuxjc3n4QV86bic2cuxJcvOqHs7q2TdTD3Svua0Z+bqA5SSSwfZPv/go8UN+mAK6f7cqqFGa66S6W9D2xnfPfNPX3oHxzRVabhVb/OII+zlnhnUtjq3wcHhopF2+zQ5JIZbcXhwyltGW9ZnrAhRfMFcvrRXd5/Cvtl5S7ds+AM4BN3FG9WHFJsdb74ihkuNwhjwEVHZtOquZV3CJyl6MtwtbT6T3dyg6/JztzBjh5A0silnTp0qbEPKUJslWnbr1Je37KPqTBp3vpoyzbvhllsHkd/uPRxQbnJuHbdEpx/Ui9SKcEXNxyL+d3llz7p7p6BZ8/bipO3fHv05yaqg0SuNmKnKyzdAPzBEwDM0naZVuDkK4Av7QU6xla/Kw4MuAD9gWw/sJ1TRnfv68PxNz2ON/f2AwBaW22GK4/2rPdPlzOfyYeHRoqT3AUK3/3sGsztyhbHlTevmoNje8yHbHFI0QxNZtu9LxB3ErANtFLhc61ymVTlAphuNV4baPmGFBlw0ZH5ysXLsf2Wj5ffYZ6ZaH3c+foyE8hwZZ3acW6A0+IsMdU5C5AUVh3VhU+fOs+/byHvzA8bJcNVPHMy4wWC6TFkuOxTdS9yjj0HXP3fwOYHSh9X8jzVrwpw+srl6OqsPBmfqF7sFIJEFWAu1gAs+LPsmfJzoGup5gGXiPyOiLwuIjtF5IZav34oSQNQerjBOcNhiilLbU9Fb2v1hhTd07U7TfCVRqH4AX39ukVYs7Db1BDS/yFndLTgoS3myyd0Dpc3X8Q3uRco/cA2E5JXr92I1kpzuNpnlm7jHC6KUCollevwzFiizypasr70vkzOm+sF+DNcrg49h6ujRXDj+oXeY4uZK9Mfw4bIbZFTlQf2vaGHAnOd3mvZvnjB34Y/1rriR8Blj3q301lg+qLSYsNETcBmuLo7EvQdYvvyyJC/72aSUTuupqeoiUgawN8AWAdgF4AXROQRpdQva3kcpQdmJ/UWfEOKy2a345wFs7GptRf4BZDJ2iHFgm8ceO4U/R9OV5BvAYYGMNP+JyyMOIXa8t6EPXdIsWWSf+iwMDJ6hmvaQuC67fqLqJKw+92sVpnMGVGsWqcAA/v1Mh2TnSFJKfPjoaNH/7/NDwGDOuOMXKfuU1vvAGzmKewHRCrtZbj2vA7MWGq2p/yPOf4iYPpi4G5niNAd7uxd7T3GPu9ozrge2PWz0fcjajCHTU256R3JyB4B8CbN5wcDAVcyjrHWGa5TAOxUSv1KKTUE4LsAzq/xMZRKOQGXcwZDShVw1yUrsWCav5zCCXM6/IVPVQH3f/pkLJvdiZQdtrP3F/Lhk3tTTobLN6SSAbb+NfD4n3m33UtXZ4//CyGMLc7oay8rzVOd2aHuTA7orpDhsvOkMjn9K3VkABgyAZfbb76/xewX9v9ZTCYsD+x5zVtrVELmR7pVqE/Y5H+aYKHSsVj75/6sGNEE8RtTR25GkgIu+8Mrk/N/NyYkw1XrgKsXwNvO7V1mm4+IfFZEXhSRF/fs2RP/URVPF3VOL4eUTqQ3wcnfXbLcP+SnCjjrQzOxsLvNOVVdeY9Nh8zNsoHZUJ8eTrRsMPTuy+b57VIk48xEhX1JcA4X1dskE3CJ+NdEOypQWPOq54Ar/ktfz7QCI4P+DFdQ2A+I/JDORvW9p7Nq023AlSp9zNT5+vKibwMX31tdm4iayNqlPTjvxDn40rkJKsS84avApvuB3lX+bHlCMly1Hk8KS8eULAyolLoHwD0AsHr16ioWDhwn9ywqN7iyv3bN0iI26MmlnHo+qYwTmBVQsnxAYcQEUVL+bKpyk4Zd45h0WxaHFKneOmfpS1sWZcMdetjQFku1unr1H6ADrkP7nDN7O4BF63Tf2P5DvS3sB8TIgO7jfe/o28GCrO5j2qaWr2JNREVt2TTu/OSKeh+GX3YSsOxCfd03pNgWvn+N1frbdheAec7tuQDeqfExlHILM7rVsG0gZZYWKQYnNlMlztwQQGe1SgIuvfBucdJu2ORe92yKchmnKAMjN9hLYi0Vmvg23KEnry/6bX37lCtHf0wm5x9SzHUAn3oQOLDbC7jCJs2PDPgzvZ260nvJWYrlLLsIWLR29OMjouRI4ByuWgdcLwBYLCILAOwGsBnAJTU+hlJuyf9ixWo3w5X3givAm9Tu1vex+wWXD1AmWEu3APlh7zndIKvLiUHLFSI9koDrmp/roRiLw4hUbx0zgHO+Vt1j7ByuwcAcLrdeVzakttXwgP/Dt8MGXCFDimE2/WN1x0lE9eeOCiVkDldNAy6l1IiIXA3gcQBpAPcppbaN8rD42Q/ewX7giRv19bAhRXEDLpv1Svn3Cx1SzOizJ/JOuYe2aXpC8OAHQJd7llZwzpUt8ngEb1X3Mf7bDLioEbWYOVzFDJeZUC8CnPknun8dfUbp4xacCex4wrvdbgofFs9SZH8gmnCY4QKUUj8A8INav25FNpB65qvA+6ZCRbrFGVI0w4LFIUWT4UqlAxkur+aW7yzFVNo7pb049yuto+5gwBVcdsDOYIt0DhfPTKQGVMxwmXlfbv2rj5kfSsMDOmN8wJybY+dj2f7TNtX78LUfyFzeimjiSWAdLlaaB7x5TG9u9bZlcoEMlxtwmfIRbkFFu19YhkvSOsjJDzlnN4oX+LgBl1N41SeuOVxEjSKT0wHVYJ/uU2Efoi2twLWvlG63P6o6ZpVuY38gmngSmOFiwAV4v3737fS2pbP+shAiIXO4gpPmCyFzuEwTUav7AAAN/UlEQVQQZjNc9jkl7Q1l2Dkl7uNKjjHCgItnJlIjyrTqgoaDfTq7Vc0JH7ZfuoWAw85SJKKJgRmuhApbTiTd4hVBtcOCJQGXU1ARKJPhyuu5IjbD5Q4pbrwPWHoOMH2J97pOpXsfDilSs7Mfmof2AdmQGlyV2D7u/rgZOqgv3TpgRDQxJDDDxVQHEL6cSDrnDe8F6nD5J82nvcDMVxZCeftKWr/h+WHvOVMZoHclsPk7/tctBAMuVf4Yx4sBFzWiYsC1N7zoqevsW4GDTtHksAyXna85a3l1x3HZo9H2RyKKXgIzXAy4gPAMVyY4pOjM4VJ5Z9K8M4erkC/NcKm8f0hx+JDe3jIp/FhKAq4KxzheUWbLiGrF/ko9uDe8/IPrtKv9t22A1OnM4Tr8f/py1gnVHceCM6vbn4hqz/3ObElGwMUhRSA8AAmWhfCdpZj3iqFKKjBfKzCHq3iWYjawLIlzhpUrOGneZsqiLFDKYqfUiOyv1MP7gZYqK0cXM1w9pfe5QRgRTQzu93qaQ4rJETqHy8lwuWckApUnzQeDMJv1Smf1kGLYwruucnO4osxwETUi+yt1YBwBl4QMKX7uGT3syB8gRBOP+52ZLTOiVGMMuIDyAVfBGRYsmcPlVpqvEHC5leaHD3s1hMoFXOWGFDkMSM3OZriGD1U/JyMswzX7xGiOi4iSxzekOMoUhBph2gQYPcNVMmk+72wbS4YrbSrND3lnRpUbUixdy9scIwMuanLumUbVBlxhZykS0cTly3Ax4EqOsOxRJuvNpyqMlKk076ylmB/RGaySgGvEmTTvDClWG3FzSJGanRtkVTsJtn26XiybJSCImoM7VSAhQ4r8Fge8YMYd5ks7leaHDuoIuViHy5k0nzLB1b9cAOx5tcyQoq00b4o2ZtqAdJWjuQy4qNll2sKvj8VvbQGueo7ztYiaEYcUE6T/PX153AXeNndIcbBfL5RrA64PdgO/fFgHUmIWr7bLAhVLRShvDpg9SzE/rIO3ssOJFUQdcB2zFjjtj6J9TqI4uUOK1Wa4Mjn/hHkiah4JGVLkpHkAWHou8NZzwPpbgJf+VW9Lt+gslVLAUB8wea43pPjkX+jLwrB/0jxgqs+bDFexyKmzePVQ//je/KgDrt9/KNrnI4qbO6RYbYaLiJpXubqXNcaACwA6e4CL7/Vvs7+mVcFkuDpK1yAcOgR0pL3aWoAZahQTrDnrJmbMpPnB/iqXJYmhDhdRI3JLQSSkkCERNYBUMgbzGHCVY5e/KeT1vKtsSMA1fEjfv/tF/7bQDFcWGDEZrvEMKbIsBDW71sne9YQs1UFENFbJCPuSyD0jcahfr90WHNYbPgQceDuw7bAXcB3er7els8kbUiRqNO5JLQy4iKjB8Fs8aM5KfWkDrvwgMDKgA65ghksVvAn3lpvhesksTL34bFNIddjLlpWz8tLAa9ghRb5V1OTcYfVqK80TEdUZv8WDLv0+sOVZbwhv4IC+DBtSDONmuN74MTD3FKD7GJ3hAvSCuZWGFM/7FnDpw6XbGXAReZjhIqIGw2/xoNYuYNbxQOsUfXv/W/oybNJ8mCEnwzXY552KbhfPPLTPe+5y7PwxAFjxe/py2sKxt4FoomOGi4gaDAOucibP1pd7t+vLXGf4xPXTrvHfdocUh/q8+VpuENU2WsDl1BtadTlw036gc1ZVh080MZlhRbcmFxFRA2DAVU7nHH25d4e+zIZMmgeAs2/xr89WDLiUKQFhhg/tkCIwhgyXs6+kWBKCyLKBFutwEVGDYVmIckoyXB3lAx93qDE/BECAlx/QWS47X8uX4RplPbf26d51zt0i8qRz+iQW1uEiorFYfHa9j6CIAVc5ucl6/SWb4cpVKFYanNt14C3vui1yWs2Q4uQ5wPq/Al59zD+8SNTsMllgEMxwEdHobtpf7yPwYcBVjojOcu3bqW9Pml5+X3cIMMjO4co4AVfrKBkuADj18/qPiDz2h0ulPkdEBCRuOg7HqyqZ3KsvWyZVXvg2VeHDP3RIcZQMFxGF6z5GX3LlBSJqMAy4Kpl5rL7smlc5UrZDipIC/vAn/vvspHn3NPbRJs0TUbhN/wRcdC8w5ah6HwkRUVU4pFhJz7Ly9136iLcCedr8M554iX7MF3cDXzbZMRtw9a7yHtvaFf2xEjWDSdOA5ZvqfRRERFVjwFVJz/H6cqi/9L6FH/Gu2yFFG3i5leTtdTfISvOfnYiIqJnwm7+SGUv15RnX+be3z/DftkOK7tmKkgZU3r9u4pVPAe+/Fv1xEhERUaIx4KokOwm4+YB/27XbShefTocEXJmcLoLqZrt6V/mHFomIiKgpMOCqVtfc0m12SNENuNJZHXDZeV5ERETUtHiWYhTChhTX36ovR6sqT0RERBMeM1xRSIdkuFZ8Sv8RERFR02OGKwphGS4iIiIigwFXFGxRVAZcREREFIIBV5RYX4uIiIhCMOCKBDNcREREVB4Drigx4CIiIqIQDLiixICLiIiIQjDgikJx0ny6vsdBREREicSAK0q24jwRERGRgwFXlDikSERERCEYcEWCQ4pERERUHgOuKNg5XEREREQhGHARERERxYwBV5SUqvcREBERUQIx4IqEHVJkwEVERESlGHBFgXO4iIiIqAIGXEREREQxY8AVJc7hIiIiohAMuCLBOVxERERUHgMuIiIiopgx4IrCUWv0Zffi+h4HERERJRIX/4vCqsuBhWcB0xbU+UCIiIgoiZjhioIIgy0iIiIqiwEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcxEKVXvY6hIRPYA+HXMLzMdwN6YXyOp2PbmxfY3b/vZ9ubF9sff/vlKqRnBjYkPuGpBRF5USq2u93HUA9venG0H2P5mbj/b3pxtB9j+erafQ4pEREREMWPARURERBQzBlzaPfU+gDpi25sX29+82PbmxfbXCedwEREREcWMGS4iIiKimDHgIiIiIopZQwZcIjJPRH4sIq+KyDYR+YLZPk1EfiQiO8zlVLO92+zfLyJ3BZ7rVhF5W0T6R3nNVSLyPyKyU0TuFBEx288UkZ+LyIiIbIyrzea1EtNu5/6NIqJEJPbTbJPUfhH5uoi8ZP62i8j+uNrtHEs92h+6n4jkROR75t/leRE5OtrWhh5LJO0XkUki8h8i8pp5ntsqvGbd+33S2u7cX5O+n6S2N3K/r7L9E67fm/t+KCIvm+e5W0TSZV4znn6vlGq4PwCzAaw01zsBbAdwHIDbAdxgtt8A4CvmejuADwPYAuCuwHOtMc/XP8pr/gzAqQAEwH8C+LjZfjSA5QD+GcDGZmm3cwzPAHgOwOpmet8D+1wD4L4J2v7Q/QBcBeBuc30zgO81SvsBTALwUXM9C2Br2Pta6f1HDft90truHENN+n7S2u7s01D9vsr2T7h+b+6bbC4FwL8D2FzN+48j7Pex/kPV6g/AwwDWAXgdwGznTXo9sN/lwTfAua/sF495rtec258E8PeBfe4fzxvQyO0G8A0A5wB4GjUIuJLWfmf7TwCsm2jtr7QfgMcBnGquZ6ArN0ujtd/c/00AV47n/a9Hv09C2+vZ9+vddmd7w/b7Su0P7DMh+z2AFgCPAvjd8bz/4+33DTmk6DIpzRUAngfQo5R6FwDM5cyIXqYXwC7n9i6zrW7q3W4RWQFgnlLqsYheqyr1br9zHPMBLADwVESvOSY1an8lvQDeNq85AuAAgO4avC6A6NovIlMAnAvgyZC7E9fvgfq3vZ59v95tdx7f0P1+lPZX0vD9XkQeB/A+gD4AD4bsElu/b+iAS0Q6oNOCf6yU+iDOlwrZpmJ8vYrq3W4RSQH4OoDrYnztsurd/sDtzQAeVErlYzwOnxq2v+JhhGyrSZ+Iqv0ikgHwAIA7lVK/CtslZFvd+j1Q/7bXs+/Xu+2B2w3b78fQ/ooPD9nWUP1eKbUeOouVA/CxsJcKe9h4X8/VsAGXiLRA/+N/Ryn1kNn8nojMNvfPho5ix/PcaWdi5F9CR7hznV3mAnhn/Ec/fglpdyeA4wE8LSJvQo/3PyK1mTifhPa7NkN/eNVEjdtfyS4A88zjMgC6APxmPK9b5TFG2f57AOxQSn3DPDax/R5ITNvr0vcT0nZXI/f70dpfyUTo91BKDQB4BMD5tez3mSiepNbMGQP/AOBVpdTXnLseAXAZgNvM5cPjeX7zq+WkwGv2icga6FTmpQC+NZ7nPhJJabdS6gD0iut2n6cBXK+UenE8rztWSWm/c9+HAEwF8NPxvF616tH+Cuxr/hTARgBPKTO5IS5Rtl9EboH+sviM3ZbUfm+OIxFtr0ffT0rbnfsatt+Ptf0VNGy/NxmyTqXUuyZY3ABga037fZST2Wr1B30GggLwCwAvmb8N0GPJTwLYYS6nOY95EzoS74eOYI8z2283twvm8uYyr7kawCsA3gBwF1Cs0n+yedxBAPsAbGuGdgf2eRq1OUsxUe0HcDOA2yb4//vQ/QC0Avg3ADuhz+hZ2Cjth/7FqgC86jzPZ6p5/1HDfp+0tgf2eRrxn6WYqLajQft9le2fiP2+B8AL5nm2QQdRmWrefxxhv+fSPkREREQxa9g5XERERESNggEXERERUcwYcBERERHFjAEXERERUcwYcBERERHFjAEXERERUcwYcBERERHF7P8Br1HOUH8XguQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize = (10, 7))\n",
    "\n",
    "ax.plot(bikeshare['dteday'], bikeshare['casual'], label = 'observed')\n",
    "ax.plot(bikeshare['dteday'], bikeshare['preds'],  label = 'modeled')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad! The model does a fair job of replicating the true data. The fit isn't perfect, in at least two ways. \n",
    "\n",
    "1. There are some visible discrepancies between the modeled and observed values. \n",
    "2. There are a few days on which the model predicts negative ridership, which obviously can't be correct. \n",
    "\n",
    "So, this model has room for improvement. Still, considering how simple it was to put together, it's a fair start! Recall that one of our main aims was to learn something about which conditions make a difference for ridership. Linear models are great for this, because we can just investigate the coefficients directly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>variable</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>weathersit</td>\n",
       "      <td>-113.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>workingday</td>\n",
       "      <td>-822.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yr</td>\n",
       "      <td>287.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>temp</td>\n",
       "      <td>1615.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hum</td>\n",
       "      <td>-550.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>windspeed</td>\n",
       "      <td>-1164.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>holiday</td>\n",
       "      <td>-299.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mnth_1</td>\n",
       "      <td>-262.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mnth_2</td>\n",
       "      <td>-275.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mnth_3</td>\n",
       "      <td>87.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mnth_4</td>\n",
       "      <td>226.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>mnth_5</td>\n",
       "      <td>241.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mnth_6</td>\n",
       "      <td>45.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>mnth_7</td>\n",
       "      <td>-92.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mnth_8</td>\n",
       "      <td>-15.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mnth_9</td>\n",
       "      <td>147.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mnth_10</td>\n",
       "      <td>166.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mnth_11</td>\n",
       "      <td>-50.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mnth_12</td>\n",
       "      <td>-217.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      variable  coefficient\n",
       "0   weathersit      -113.62\n",
       "1   workingday      -822.77\n",
       "2           yr       287.47\n",
       "3         temp      1615.43\n",
       "4          hum      -550.08\n",
       "5    windspeed     -1164.64\n",
       "6      holiday      -299.52\n",
       "7       mnth_1      -262.63\n",
       "8       mnth_2      -275.11\n",
       "9       mnth_3        87.13\n",
       "10      mnth_4       226.14\n",
       "11      mnth_5       241.25\n",
       "12      mnth_6        45.13\n",
       "13      mnth_7       -92.17\n",
       "14      mnth_8       -15.67\n",
       "15      mnth_9       147.51\n",
       "16     mnth_10       166.83\n",
       "17     mnth_11       -50.74\n",
       "18     mnth_12      -217.65"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"variable\"    : X.columns,\n",
    "              \"coefficient\" : LR.coef_.round(2)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what we observe: \n",
    "\n",
    "1. Recall that higher values of the `weathersit` variable correspond to more severe weather. The negative coefficient indicates that, for each step up the severity scale, roughly 100 riders are lost. \n",
    "2. After controlling for the other variables, there are roughly 800 more riders on weekends than working days. Wow!\n",
    "3. The `yr` coefficient accounts for the slight overall increase in riders over time: a day in year 1 might have nearly 300 more riders than a comparable day in year 0. \n",
    "4. Warm temperatures bring out the bicyclists! \n",
    "5. Similarly, no one likes humidity, or high winds. \n",
    "6. There tends to be a large drop in casual bicyclists on holidays. \n",
    "\n",
    "The various month results deserve special attention. Higher values indicate that, all else being equal, the model predicts more riders that month. However, we should be very cautious in interpreting these results, since certain months are likely to have lower temperatures or higher humidities, for example. So, it's not usually the case that \"all else is equal.\" Still, these results do give us suggestive and potentially useful quantitative insights, such as rider's preferences for warmer months over colder ones, especially in the spring as the snow melts. \n",
    "\n",
    "## Next? \n",
    "\n",
    "Depending on our goals, we have many possible next steps. We might aim to improve our model accuracy by using more sophisticated models or collecting more data. We might aim to deploy more complex models in order to handle dependencies between the month variables and the weather variables, for example. Rather than the explanatory angle we've used here, we might instead choose to look at this problem through a predictive lens, and aim to predict the bikeshare usage in unseen, coming months. As usual in data science, each model opens at least as many doors as it closes. \n",
    "\n",
    "<figure class=\"image\" style=\"width:100%\">\n",
    "  <img src=\"https://d33wubrfki0l68.cloudfront.net/795c039ba2520455d833b4034befc8cf360a70ba/558a5/diagrams/data-science-explore.png\" alt=\"A diagram with the words import, tidy, transform, visualize, model, and communicate. Import points to tidy. Tidy points to transform. Transform points to visualize, visualize poitns to model, and model points to visualize, thus forming a directed cycle of arrows. This cycle collectively points to the final word, communicate.\" width=\"600px\">\n",
    "    <br>\n",
    "    <caption><i>The cycle of data science. Image credit: Hadley Wickham</i></caption>\n",
    "</figure>\n"
   ]
  }
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