Capstone-Project/Capstone_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning Engineer Nanodegree\n",
    "## Capstone Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform Exploratory Analysis and Preprocessing of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import visuals as vs\n",
    "\n",
    "# sns.set(style=\"whitegrid\")\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set_context(\"paper\")\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset has 37670293 samples with 24 features each.\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "try:\n",
    "    data_train_raw = pd.read_csv(\"dataset/train.csv\")\n",
    "    print(\"Training Dataset has {} samples with {} features each.\".format(*data_train_raw.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Dataset has 2528243 samples with 22 features each.\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "try:\n",
    "    data_test = pd.read_csv(\"dataset/test.csv\")\n",
    "    print(\"Test Dataset has {} samples with {} features each.\".format(*data_test.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description of the features as given on Kaggle.\n",
    "\n",
    "|  Feature |  Description | \n",
    "|----------|:---------|\n",
    "| date_time  | Timestamp     |\n",
    "| site_name | ID of the Expedia point of sale (i.e. Expedia.com, Expedia.co.uk, Expedia.co.jp, ...)|\n",
    "| posa_continent | ID of continent associated with site_name |\n",
    "| user_location_country |The ID of the country the customer is located |\n",
    "| user_location_region|The ID of the region the customer is located|\n",
    "| user_location_city| The ID of the city the customer is located|\n",
    "| orig_destination_distance| Physical distance between a hotel and a customer at the time of search. A null means the distance could not be calculated|\n",
    "| user_id| ID of user|\n",
    "| is_mobile| 1 when a user connected from a mobile device, 0 otherwise|\n",
    "| is_package| 1 if the click/booking was generated as a part of a package (i.e. combined with a flight), 0 otherwise|\n",
    "| channel| ID of a marketing channel|\n",
    "| srch_ci| Checkin date|\n",
    "| srch_co| Checkout date|\n",
    "| srch_adults_cnt| The number of adults specified in the hotel room|\n",
    "| srch_children_cnt| The number of (extra occupancy) children specified in the hotel room|\n",
    "| srch_rm_cnt| The number of hotel rooms specified in the search|\n",
    "| srch_destination_id| ID of the destination where the hotel search was performed|\n",
    "| srch_destination_type_id| Type of destination|\n",
    "| hotel_continent| Hotel continent|\n",
    "| hotel_country| Hotel country|\n",
    "| hotel_market| Hotel market|\n",
    "| is_booking | 1 if a booking, 0 if a click|\n",
    "| cnt| Numer of similar events in the context of the same user session|\n",
    "| hotel_cluster| ID of hotel cluster|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37670293 entries, 0 to 37670292\n",
      "Data columns (total 24 columns):\n",
      "date_time                    object\n",
      "site_name                    int64\n",
      "posa_continent               int64\n",
      "user_location_country        int64\n",
      "user_location_region         int64\n",
      "user_location_city           int64\n",
      "orig_destination_distance    float64\n",
      "user_id                      int64\n",
      "is_mobile                    int64\n",
      "is_package                   int64\n",
      "channel                      int64\n",
      "srch_ci                      object\n",
      "srch_co                      object\n",
      "srch_adults_cnt              int64\n",
      "srch_children_cnt            int64\n",
      "srch_rm_cnt                  int64\n",
      "srch_destination_id          int64\n",
      "srch_destination_type_id     int64\n",
      "is_booking                   int64\n",
      "cnt                          int64\n",
      "hotel_continent              int64\n",
      "hotel_country                int64\n",
      "hotel_market                 int64\n",
      "hotel_cluster                int64\n",
      "dtypes: float64(1), int64(20), object(3)\n",
      "memory usage: 6.7+ GB\n"
     ]
    }
   ],
   "source": [
    "data_train_raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "             date_time  site_name  posa_continent  user_location_country  \\\n",
       "0  2014-08-11 07:46:59          2               3                     66   \n",
       "1  2014-08-11 08:22:12          2               3                     66   \n",
       "2  2014-08-11 08:24:33          2               3                     66   \n",
       "3  2014-08-09 18:05:16          2               3                     66   \n",
       "4  2014-08-09 18:08:18          2               3                     66   \n",
       "\n",
       "   user_location_region  user_location_city  orig_destination_distance  \\\n",
       "0                   348               48862                  2234.2641   \n",
       "1                   348               48862                  2234.2641   \n",
       "2                   348               48862                  2234.2641   \n",
       "3                   442               35390                   913.1932   \n",
       "4                   442               35390                   913.6259   \n",
       "\n",
       "   user_id  is_mobile  is_package      ...        srch_children_cnt  \\\n",
       "0       12          0           1      ...                        0   \n",
       "1       12          0           1      ...                        0   \n",
       "2       12          0           0      ...                        0   \n",
       "3       93          0           0      ...                        0   \n",
       "4       93          0           0      ...                        0   \n",
       "\n",
       "  srch_rm_cnt srch_destination_id  srch_destination_type_id  is_booking  cnt  \\\n",
       "0           1                8250                         1           0    3   \n",
       "1           1                8250                         1           1    1   \n",
       "2           1                8250                         1           0    1   \n",
       "3           1               14984                         1           0    1   \n",
       "4           1               14984                         1           0    1   \n",
       "\n",
       "   hotel_continent  hotel_country  hotel_market  hotel_cluster  \n",
       "0                2             50           628              1  \n",
       "1                2             50           628              1  \n",
       "2                2             50           628              1  \n",
       "3                2             50          1457             80  \n",
       "4                2             50          1457             21  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2528243 entries, 0 to 2528242\n",
      "Data columns (total 22 columns):\n",
      "id                           int64\n",
      "date_time                    object\n",
      "site_name                    int64\n",
      "posa_continent               int64\n",
      "user_location_country        int64\n",
      "user_location_region         int64\n",
      "user_location_city           int64\n",
      "orig_destination_distance    float64\n",
      "user_id                      int64\n",
      "is_mobile                    int64\n",
      "is_package                   int64\n",
      "channel                      int64\n",
      "srch_ci                      object\n",
      "srch_co                      object\n",
      "srch_adults_cnt              int64\n",
      "srch_children_cnt            int64\n",
      "srch_rm_cnt                  int64\n",
      "srch_destination_id          int64\n",
      "srch_destination_type_id     int64\n",
      "hotel_continent              int64\n",
      "hotel_country                int64\n",
      "hotel_market                 int64\n",
      "dtypes: float64(1), int64(18), object(3)\n",
      "memory usage: 424.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "</div>"
      ],
      "text/plain": [
       "   id            date_time  site_name  posa_continent  user_location_country  \\\n",
       "0   0  2015-09-03 17:09:54          2               3                     66   \n",
       "1   1  2015-09-24 17:38:35          2               3                     66   \n",
       "2   2  2015-06-07 15:53:02          2               3                     66   \n",
       "3   3  2015-09-14 14:49:10          2               3                     66   \n",
       "4   4  2015-07-17 09:32:04          2               3                     66   \n",
       "\n",
       "   user_location_region  user_location_city  orig_destination_distance  \\\n",
       "0                   174               37449                  5539.0567   \n",
       "1                   174               37449                  5873.2923   \n",
       "2                   142               17440                  3975.9776   \n",
       "3                   258               34156                  1508.5975   \n",
       "4                   467               36345                    66.7913   \n",
       "\n",
       "   user_id  is_mobile      ...          srch_ci     srch_co srch_adults_cnt  \\\n",
       "0        1          1      ...       2016-05-19  2016-05-23               2   \n",
       "1        1          1      ...       2016-05-12  2016-05-15               2   \n",
       "2       20          0      ...       2015-07-26  2015-07-27               4   \n",
       "3       28          0      ...       2015-09-14  2015-09-16               2   \n",
       "4       50          0      ...       2015-07-22  2015-07-23               2   \n",
       "\n",
       "  srch_children_cnt  srch_rm_cnt  srch_destination_id  \\\n",
       "0                 0            1                12243   \n",
       "1                 0            1                14474   \n",
       "2                 0            1                11353   \n",
       "3                 0            1                 8250   \n",
       "4                 0            1                11812   \n",
       "\n",
       "   srch_destination_type_id  hotel_continent  hotel_country  hotel_market  \n",
       "0                         6                6            204            27  \n",
       "1                         7                6            204          1540  \n",
       "2                         1                2             50           699  \n",
       "3                         1                2             50           628  \n",
       "4                         1                2             50           538  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train_raw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value of 1 for `is_booking` represents that an actual booking happened and 0 represents it was only a click with no booking. Entries with no booking doesn't help us making predictions and can be removed. This also reduces the number of samples from ~37 million to ~3 million, thus making our algorithms faster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34669600\n",
       "1     3000693\n",
       "Name: is_booking, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['is_booking'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train[data_train['is_booking'] == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will try to see if there are null values for any of the features in any sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time                          0\n",
       "site_name                          0\n",
       "posa_continent                     0\n",
       "user_location_country              0\n",
       "user_location_region               0\n",
       "user_location_city                 0\n",
       "orig_destination_distance    1015179\n",
       "user_id                            0\n",
       "is_mobile                          0\n",
       "is_package                         0\n",
       "channel                            0\n",
       "srch_ci                            0\n",
       "srch_co                            0\n",
       "srch_adults_cnt                    0\n",
       "srch_children_cnt                  0\n",
       "srch_rm_cnt                        0\n",
       "srch_destination_id                0\n",
       "srch_destination_type_id           0\n",
       "is_booking                         0\n",
       "cnt                                0\n",
       "hotel_continent                    0\n",
       "hotel_country                      0\n",
       "hotel_market                       0\n",
       "hotel_cluster                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`orig_destination_distance` has 1,015,179 null values. We'll deal with this later. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the counts of each `hotel_cluster`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train['hotel_cluster'].value_counts().plot(kind='bar', figsize=(20,10), colormap='tab10')\n",
    "\n",
    "plt.xlabel(\"hotel_cluster\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Frequency of Hotel Clusters\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91    120972\n",
       "48     85377\n",
       "42     73123\n",
       "59     66609\n",
       "28     63978\n",
       "Name: hotel_cluster, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top5_hotel_clusters = data_train['hotel_cluster'].value_counts().head(5)\n",
    "top5_hotel_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature Engineering** is performed on some of the features to make them numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "srch_ci = pd.to_datetime(data_train[\"srch_ci\"], format='%Y-%m-%d', errors=\"coerce\")\n",
    "srch_co = pd.to_datetime(data_train[\"srch_co\"], format='%Y-%m-%d', errors=\"coerce\")\n",
    "\n",
    "data_train[\"date_time\"] = pd.to_datetime(data_train[\"date_time\"], format='%Y-%m-%d', errors=\"coerce\")\n",
    "data_train[\"stay_duration\"] = (srch_co-srch_ci).astype('timedelta64[D]')\n",
    "data_train[\"srch_month\"] = data_train[\"date_time\"].apply(lambda dt: dt.month)\n",
    "data_train[\"srch_ci_month\"] = srch_ci.apply(lambda dt: dt.month)\n",
    "data_train[\"srch_co_month\"] = srch_co.apply(lambda dt: dt.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_train.drop([\"date_time\", \"srch_ci\", \"srch_co\", \"is_booking\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108d73198>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y0qX/ZGMerl9iSkAnxs7dnilxnj2xn4g7YQyZsoVrF0NYOW8EfUatSJZu1dwRfNlhCL5FyzJzdGfOntxPqfK12b5xPkqZqjT+pD3bNs5n28b5fPJNT+M20OvYtHwiJcrVMFvWkmn+vN/iR0qUrU5szNN0HQd6nY4ls8fRd9g03Ny9GNr7O8pXqU2e/IUS0uzdvhkHRyfGz17P4b3bWL14Gl36jkKni2f270Po0HMo+X2L8fjhfawsX1QlOvYajm/R5BXljHT+5D4i7oTR7/f/cf1SCOsWDKd7QPIGv3ULhtOy/VAKFPVj3riOnD+1nxLlagNwP+o2F04fxNUjl9k8vsUr0r7PjDTHlhnlCZDiMr3z+LJk+kB+HjwX79wF2bJyOkd2b6ZGgxYErp9LHl+Fn/pO4s6/V1k1byTdh8xLc76ePnnIqrkj6eI/EzfPXDx6EPVG87+srAToPnQ+js450xzfc3qdjlXzRvHz4Nm4unkztt9XlE2y/Q/u2IC9gzPDpm0heP//2LBsEj+Ybf/1PIi+y5ThHRg6ZXNCjD2GzksWY8PmbWn6ZVcAdv21nL/XzOarDoPSnY+keUpr2Z47f2H8xy/H0tKK+9ERBPRqTdnKdbC0zLzq/9kT+7h7J4zBU/7i2sUQVs0bQe+XlI8Fi/oxc3Qns/KxWJmqNP7kB7ZtnMf2jfNp/k0vtm2YR56Cxfmxz2Tu/HuFNfNH0W1w2vdpyLzzP8Cuv5fjk8eXmGdPzNYZdvkMT588SlfcqdHp9EyevYAJw/zxdHenY+/+1KxSiYL58yakKepbkNm/j8bO1pZN/9vG7EXLGdK3BwA2NjbMnzQuU2J7ndgnzF3K5MG98XJ3o/2vw6lduRy++V7cpCzmm58F4wZjZ2vL+sCdzFi6moBfOnPg2CkuXAlj8W/D0Grj6TJ4DNXLl8XBPkemxaue2ktkeBi9JwRy43IIGxcOo8uwVcnSbVw0nBbthpG/iB8LJ3TgQsg+FL86XD57hHPHd9Jj1EasrG14bCpTra1taPxZN+7cvEj4zUuZEnt6j9Fc+YryQ++JrJwz3Cy9g1NOOvw6DRc3L25dv8iMkR0ZMXtHhsev1+lYOnssvYdNx83dm+F9vqVclTrkyfeiDrNv+yYcHJ0YO2sjR/ZtZfWSqXTuMxoAL588DJ+UPL8ZLSvqig2btSUuNob9/6zNdnErZapSplI9NBoN/4ZdYMHE3gyatBlrGzu+7ToSr1wFuB99l3H9vqCEXw3sHZxTiC5rYn/65CGr542ks/9M3Dxe1IEsLC1p0eYX8hUqScyzJ4zt9wXFy1YnV97CydYpkrOwythr+v8CGTZBZIro/cFoox9kdRgpql3NncCddwA4oz7C0cEK95w2ydJ91NCHpWuuA2AwwIOH8QCcOH2f2Fh9wvye7rZvKXIj9cIFcuXOTa5cubC2tqZunTocOnTYLI2fnx92dnYAFC9enMjIyLcaY2IXL6jkyp0bH1O8derU48ihg2ZpvL198PUthMbCvBC/fj0MnU5H+QoVAciRI0dCvjLKtUuhePrkx8M7L1bW1lSs2YSQ4F1maUKCdlO1bjMAyldrhBp6BIPBQEjwLirWbIK1tQ0e3nnx9MnPtUuhABQtWQkHR5dk69NoNMQ8NV7MxTx9hEtOzwzJx9VLoXjlyoenjzEflWu9z6mju83SnAzaTfX3jD3hK1ZvyLnTRzEYDOQvVDyht1ju/IXRxsWh1caZzRt+K4xHD6IpWrJChsSbah58XuShUq33ORVknodTR3dTrZ4xDxWqN+R8SnnIl3IeMkpI8C6q1GmKRqPBt5gfz5484sG9CLM0D+5FEPPsMYWK+aHRaKhSpykhQcb9KiRoV8L+VLVuM0KCdibMt+d/K/Cr2ggn5xc9Vm/fvIxep6NE2eoA2NrZY2Ob9ovSKxfP4O2TFy+fPFhZW1O1dmOOH91rlub4kT3Uqm/s9VS5Zn3OhgRhMBgIPXGEfAWLkN+3GACOzq4JjRVvy5ljO6lUuxkajYYCRf2IefqIh0m2/8N7EcQ8e0LBYuXQaDRUqt2MM8EvLiI3LR3Lx1/9goaMrThmRnmS2jKfPLqPlZUN3rkLAlDcrxonjvwDwJ2bVxJ6PPnk8SU64hYP779Zg2tiwfv/plzVBrh5Ghu7nVzcE6Yd3buFcf2+YlTvVqyYPRy9Tpds/peVlRnJuK3ymW2rpGVISNAuqtUzbf/qjVBNZcipoN1JYsz3yhhz2Dsm/B0bG5PhNxchfWW7rW2OhIbaeG0cZEJ8SZ0O3kWVOs1eq3z0NR2fVeo047SpHDwdtIuqdZsDULVu84Ry8/bNyyhlnu/ThYiO+JeH99NXt8ms8/+9qDuEHt9LjQYtzJal1+nYsPR3Pm3TM11xp+b8xUvk8fEmt4831tZW1K9dgwNHg8zSlC9bGjvT0xEllaJERKW9XMhIZy9dIa+PF3l8vLC2tqJhrSrsCzLvWFGxTImE2EsVK8zdqHsAXLtxi3KlFKwsLclhZ0uRAvk4fOJ05sZ7fCcVajVHo9GQv4gfz54+4uH9JOeh+xHEPntMgaLG/bxCreacOWY8Dx3esZK6H/+AlbXx+sPRVKba2NlTUKmIlXXmXVuk9xj1yVsI79y+yZabz7cELqZ6WK58RdBqYzOlHnbl4hm8cuXDy1QmVqnVmBNH9pilOX50DzXf+xiASjUacC7EWCa+TW+7rgiglKmGbQ6HbBm3rZ19wjkyNvZZwvnIO3dBvHIZO/S4unnh5OLG44f3slXswfv/xq9qA9w8zOtALjk9E3r22uVwwCePL/ej76YpdiFehzTepoOiKG0VRalr+vvNn60UWcLD3Za7kS+GObgbFYuHu3njraODsTHih298mT+pAgG/liSnq3WyZX3cyIcjx6IzN+AkoqKi8PTwSPjs4eFB1Esq31u3bqNSpUpvI7QURUVF4uHxooHS3cODqKjXu+D69+ZNHBwcGTViKN27dmTB/DnoUmgYSI/70eHkdPdO+Ozq5s39qLvJ03gY01haWpHD3pEnj+5zP+ouOd19zOeNDn/p+lq27cuGpb/j37ER65f8TrOvu2dMPqLu4pY4H+7e3IuOSCGNj1k+Hj+6b5bm+KF/yFdIwdra/JgI2h9IpZqNM6VxIiG+6Lvk9HixPXOm+Fvcxc3DPA9Pkubh8D/k8y2eLA+ZFaeru3eyytr96Lu4Jvk9nqd59CA6odHeJacnjx5Gm+YJ59TRndRu3MpsWXdvhZHDwYm5E3oypu/nbFj6G3p92o+De1ERuHm8iM3N3Yt7Ueb7yr3oF2ksLa3I4eDI40cPuHPrOhqNhvFDujG4Zxv+Wr/EbL55UwMY1ONrNq2an2kXSg/u3cXV7cX2d3Hz5sG98CRpwnF1806UxocH94zb/8yxnbjk9CZ3geLJlh128SS/9fuUuWM7cCcNvZ4yozxJbZmOzjnR6eIJu3wGgBOHtnM/0nhjMk/BYpw6YmwkuHbxNNERt7kf9fKy6WXu3grj6ZOHTBrSjjF9W3Nkz2bA2Eh87GAgv4xYzIAJa7CwsCBo/1/Jt8tLykoNMG1EB8b0bc3+7WnvLQQplCHuXjxIUiYnTpN4+z9I9LuA+TGr0cDUgI6M7vtFshg3rZjKgA6NCdr3Fx+3zviqYHrL9isXTjOk+2cM69mKbzr4Z2qvW0i5fHyQpHx88NLyMSpJ+Wis3+QpoHDKdHPi2iXTPv2K8+2rY82c8//aheP49JteaCzML7P2BP5B2Ur1MuymbVIRUdF4ery4seLp7k5EVOqNIH9t30WViuUSPsfFafmpV3869fFn3+GgVOfLDBHR9/D2eNEQ5enm9tLYt+zYS7UKxiGnihTMx+Hjp4mJjeX+w0ccDz1PeFTm1s0fpnAeephkf3wYHY6L2XnIm4em81DknWtcU48xfUhrZo/4lhtXMrexObH0HqOv4+SR7eTNpHrYvei7yeswKdTB3JIct48fGTsVRYTfYkjPrxjj/xMXziR/8jKjvO26YnaPG+DU0R0E9GjGrNFd+LqTec9tMJbt8fFaPLzzZavY794O4+njh0wa2o6xv76oAyUWdfdfbl49T8EiGTMUnhApkWET0kFV1UWJPnYG0v6spXhrUmx6StLGYGmpwdvTjtPnHjBt/mVaN89Ll3aFGfH7+YQ0jet5UbyIE137n8zUeJOFmkKDSGrtaTt27uTixYuMG5d1w1ekHO/rNQDq9TrOnjnN5Kmz8PTyYuzoEez4ZxuN3/8go8N8aXwptkFpNCTbcVKYN6m921bzWds+lK/WiGMHt7J85hB+Hjw3HdGaYkwpxGRpXh7vreuXWbd0Cj2GJC/KgvZvpV33EemM8hVS2tDJfouXp7l1/RLrl06mx+CZGR1d4iBSCEHz6jSvWOy6ReNo/nUPLCzMe7Lq9fFcPnecfuNWk9PDhwUT+3B49yZq1G+RypJeLuX9IEmaVOLX6XRcOHuSob8txsbWjrGDOlOwcHFK+VWhQ6/huLl78ezpE6aO7ceBXX8n9N7NSCnH9hr7CRAX+4x/Ns7hp/7Jj7m8BUviP2U7tnYOnDuxl0W/daPfxP+lO970lieplaEajYZ2PcaxbtE44rVaSvhVx8LUMNf4k/asXTiWUb1bkTt/UfL6Fk9XD2mdTsf1K2f5efBctHGxTPBvQ8GiZTl/+gg3rpxjbL+vANDGxeDkktI4x6kfM71GLMHVzYtHD6KYGtAB7zwFKVoyjTccX6MMSS1Nyl8b5/1lxOKEGKcM74h3Hl+KljQ+EdL8q240/6obgevnsydwZYY34Ka3bC9UrAzDJq/j9s0rLJwymNIVamJtk3m9+lIuo1+d5lW94Bt90p51i8Ywpk/LF/u0RcZfxqT3eD1tGus+f+GSXDjzogH0fvRdjh/aTo9h8zM44sRev364bfc+1EuXmTxqaMJ3q+dNx8PdjVt3wuk5KIBCBfKRJ1fGvmMgVS85/pIK3HOQ85evMT3AOBxY1XKlOXfpKh0GjMTV2YnSSmEsLTK3f9Kr6iKQ8nH5/GDQ63Q8e/KQzkNXcvPKaVZM7UXf37dl6g3yhLgy6Rh97vaNS2xePpHO/nPSEt6rpRj+q49bDeDi5sFvc7fg6OzKtUvnmDK6NyOnrjJ7iiLj4ny7dcUMk0lxA/hVaYBflQZcOhvMX6um0W3Qi7rYg3sRLJk6gDZdRmCR1uM3k2LX63TcuHqWboOMdaDfBhrrQM+ffIqNecq833rxWdu+mbMvCWEijbdvQFGUQsBSQAs8BY4CoUAewFdRlN3AHOAAMBOwA6KANqqqxqSwvDPAJqAWcF1V1W8URfEAVgDPb1V+rqrqXVPaHUBV0/86oL5pvi9NyxsPVMb4u/ZSVfVohm+Ed1SLD3PT9H3jow7nLj7Cy+PFhYuXuy2R0eaP9Tx4GM+zGB17Dxl7iO46EMHHjV9UYCv5ufLt5/np2v8U2vi3+xiOh4cHEYmGQYiMjMTNzT1ZuuMnTrBy1SrGjx2LjXXyXsNvi4eHJ5GRL3oJRaUSb0rcPTwoVLgIPrmMv1216jVQz5+DDGy8dXXz5l6iHmn3o8NxcTPvFZPT3Zt7keHkdPdBp4vn2dPHODi6GHtARd0xnzfny19Wc2T3Zlp9/ysAFao3ZsWsoRmSj5zuXkQnzkdUOK4p5CM66g45PbzN8gFwLzKcGWN70e7nALx8zO9437iqotPpKFA4c8cydXX35l7ki+15LzqVPETeIad7CnmICmfmuF58/3MAnj5pu2ufmj2BKzm4Yx0ABQqXMovzflR4sp5Uru7eZj0d70eFJzxO6OTixoN7Ebjk9OTBvYiEx96uXz7DwsnGfePxw3ucObEPCwsrXN28yetbHA9v47iFflXqc/VCiPEMkAZu7l5ER76ILTrqbrLt/DyN2/N95cljHJxccHP3onjpCjg5uxpjqViTsMsqpfyq4OZuzF8Oeweq13mfKxfPZljj7YFtKziyy9jjMV+h0tyPfrH9H0SH45zkuHN18zHrlfcg+g4uOb2ICr9BdMS//N6vRcK8E/1b8nPASpxdX2yDEuXrsH5hAE8e3sPhDcZizazyJLVlFlL86BWwGIBzpw4SfjsMMD7O36aL8UVVBoOBwV0+eOMX3O0JXMkO2PB1AAAgAElEQVSBf4z7fIUajSnpXBNbO3ts7ewpUqIi/4ZdAAxUrduM5kmeIDh5ZAd/r5kFwNedhr40b64Jx4U7flXqE3YpNM2Nt8nKkKi7ycrk52mSliGupt8lIcZEx3XSGK9dDE1ovH2ucu0PmDGqa4Y33qa3bH8uV95C2Njl4N/rlyhYpFSGxrg38I+E8jF/4dIplI/Jf4Pk5aMxT04u7knKR2N9IYe9I990Nt5ANBgMDO3aJF0vbYTMOV5PB+/mdPBuzpzYjzYulphnT1g0pT+Van5AxJ3rDO1mfJRbGxfDkK4fMWxa8p7qaeXp7k5E5IsnsSKiovBwS15+BZ8MYdma9UweOdSsfujhbjwX5fbxplzpkly8cu2tNd56uuckPPJFD72I6Gg83FyTpQs6dYbF67YwPaCfWextWzalbUvj0CFDJs4iXy7vZPOm16HtKzi62/huhryFyrzyPOTi5mPW89+YxjNhWqnKjdBoNOQrXBaNhQVPHt3D0TnjX/IJGXuMvsy9qDvMndCDNl1GZXg97Lmcr1GHyZm0DvPUWIfRaDQJvYELFimBl08e7ty6jm+RjKnfZmVd0a9K2t9J8TbiTqxIyUpETh/I44f3cHTOybOnj5k5pgsff9EN32J+2S52V3dvHJxck9WBvHMXRBevZe5vvahU+yPKVW34RrH/12msZczbNyXDJryZesA6VVXrAR8//1JV1cnAVVVV66mqugIYB/RTVbU+sBf4LoVlAdgDS1RVrQPkVhTFF3gAfGRaxx9Au0RpZwHVgFbAPlVVawJeiqL4KoryAWBpmu9TIPu9KSwLrf/7Ft93P8b33Y+x73AkTeobK6OlFCceP40n6l7yMZkOHI2ifBljxbGinyvXrj8FoGghR/p0KUa/gDPcf6B9e5kwUYoV49atf7lz5w5arZY9e/dSrZr521wvXb7M1KnTGDp4MK6uySu/b1PRYoop3ttotVr27t1NlWrVX2/eogqPHz/mwQPj458hp06avegsIxQoUoq7t8OIDL9JvFbLsQOBlKlUzyxNmUr1Eh6ROXF4O8VKV0Gj0VCmUj2OHQhEq40jMvwmd2+HUbBI6Zeuz8XNk4tngwFQQ4/g6ZP/pelfV8Eipbh7+zqR4f8Sr9UStH8rfpXN8+FXuS6HdhlfgHTs0D8UL1MZjUbD0yePmDqyG59+040iJcolW3bQ/kCq1E7bm1/Tk4fg/Vvxq1TXLE3ZynU5vNuYh+OH/qF46ed5eMi0kd349OufKVK8fIbHVrfJF/Qfv4b+49dQtkp9ju79E4PBwNULp8hh75SscuiS0xPbHA5cvXAKg8HA0b1/UrbSe4D5/nRkz2bKVjZ+P2x6IMNN/8pXa0TrH/zxq1KfAkVK8+zJw4THt9TQo/ik42UIvkVLEn77BhGm7Xxk3zbKV6ltlqZ8lTrs32lsVAg6sJMSZSsZ9/kK1bhx7RKxsTHodPGcDz1O7vy+6HTxPHpoPE7j4+M5GbSfvIlegJZeNRt/Ra/R6+k1ej2lKjUgeN9mDAYDYRdPYZfDMeGC+DnnnJ7Y5rAn7KJx+wfv20ypivXJlb8Yw2btw3/KdvynbMfFzZueI9fi7OrJw/sRCb2Nrl8KwWDQY+/0ZuVnZpQnL1vm85dmaLVxbNu4gNqNjI9RPn3ykHit8fx0cMc6ipSo8Ma9Qeo2+YIBE9YwYMIaylVpwKVzx9Hp4omLfca1SyH45PFFKV2VE4e3J8Tx5NEDoiJuUa5qg4R5CxQulWreYmOeJrzQKTbmKedOHSJXviJvFGdiBRLKkBfbqmzlJGVIpXoc3m3a/oe2o5i2f9nKdZPEeD3VGHPnN8Z419RYDsaxUX3yJB8HMr3SU7ZHhv+LTmccrz/q7i3C/72Gu1fuDI+xTpMv6Td+Lf3GrzWVj5sTykc7e8cUy0c7s/JxM2XMysdNABzZs4kypvLx6ZOHxMe/2KcLl6iY7h5OmXG8Nv+6OyNn/0PAjEDa9RyHUroKbX8eTemKdRgzbxcBMwIJmBGItY1dhjbcAihFC3Pz9h1uh99Fq41n576D1KhifiPk4pWr/D5zHqP8+5LT9UUD/6PHj4kzlRn3Hz4k9JxKwXx5eVtKFPHl5u273AqPQKuN55/9R6lVyfxcrl4JY+zsxYzr9zNuLi9eZqTT6Xnw6DEAl67d4FLYTaqUe3ldLC2qN/qK7iM30H3kBkpVbMDx/ZswGAxcv3QKO3snsxuAAM6untjaOXD9knE/P75/EyUrGBvYSlasz+WzRwCIuH0NXbwWB6f0v7QxNRl5jKbm6ZOHzBrThWZfdqdQJtTDnvMtWpK7ieowR/dvo3yVOmZpylepw4FdWwAIPriDEqYy8eGDewnjst+9c5Pw2zfw9E7fTaDEsrKumN3jjrhzPaGOdePKWeLj43FwciU+XsvcCT2oWqcpFao3zpaxl630HpfPJ68DGQwGls8agk8eXxp8/O0bxy7Em9K87cG732WKojgAA4G8GHvc5gBCVVVdqyhKqKqqpU3pTmPscQvG3rd/qaoakMLyzqiqWsr09yJgKnAT4/ALHoALxkbabknS7gZaqaoakWi++kBb4HkXRydVVc27haTiL2slw3eCckt/w71uFWw8chIbHsXF4VO5sTB9Y9klNbpJ2h/H6dWxCFUruBETq2PUZBX1krHSt3ByRb7vfgwAb09bBvUqjqODFfcfahk9WSU8IpZJAWUpVMAhocE3PCKGfiPOvPa6l05KfyXhaFAQs2fPQa/X07hxI7784guWLF1K0aJFqV6tGv0GDODatTDcTD0uPD09GTZkSLrXqyVtY1cFBx1h7uyZ6PV6GjZ+n9ZffM2ypYsoWrQYVavV4MIFlVEBQ3n8+DE2Nta45nRjxizjW6RPHD/GgnmzMRgMFC5alK7demL9hj2Jrz95eQ+M0OP7WLdoHHq9jurvfUKTz35iy8rp5C9ckrKV30MbF8viqQO4cfU8Do4utOs5LqEXZOC6ORzatRELC0taft+XUuWNjWALJvXl4plgHj+6j7OLGx993pkaDVpw6dxx1i4ci16vw8rahi9+GEj+l/RotbZ8/bFNTx/bx6oFE9Dr9dRs0JyPWv7Apj9mUKBwScpVqYc2Lpb5kwdy46qKg6MzP/Yag6dPXv5aM5f/rV+AV64XDck9Bs/E2dV4x3lAp4/p5j+VXHlfv2HCYEjb3dTTx/axeuF4Yx7qN+fDlj+y+Y8ZFChSEr/KxjwsmOKfkIcfeo415mHtXALXzzfLQ/fBs3BO8RHul9PqXn5f02AwsHr+KM6dOoC1jR3fdA6gQGFjT7bRfVrRf7yxZ07Y5TMsmzEQbVwsJcvVolW7/mg0Gh4/us+Cib2Nvf88fGjf67dkveSWTh9I6Yp1KF/NWJE9F3KIDUsmmF7OVpIvOwzBysr8OHCyTfaAR6pOBR9g+fzf0ev11GnQlGaft2P98tkULFKCClXrEBcXy5yJQwi7cgEHJ2c69x6Jl4+x7Dqw+39sWbsIjUaDX8UatG77M7Exzxg5oAO6+Hj0eh2l/KrwVbser/2ofsST13/BhsFgYMOiEainDmBta0frDiPIV8h4of57/xb0Gr0egBtXQlk5y5/4uFgUv1p82tY/2eNzI39uRI8Rq3Fwzsn+rcs59M8qLCwtsbaxo9k3fSlY7OUXoDmskx+fmVGepLRMgPVLfiP0+F4Mej213/+c+h+1AeCKeool0/yxsLDAJ29hvuk0DHvHN39zc2LbNy3k8K5NaCw01GjQImFdxw4EsnXDfAwGPZaWVrT+YUCKPWdSyltk+E3mjDe+6V6n01G51gcJeUuNRvPyKkvo8X2sXTgOvV5P9fqf8MFnP/LnyukUKFyKsqYyZNEUf25eO4+9ozPtE23//62by6GdG7GwtKRV276UqlCLyPCbzB5nesO3Lp5KtT/kg89+BGDO+F6E37qGRmOBm2cuvvppoNlYeklZWehfGntq0lq2H9q9hcANC7G0tEKjseDjz3+ifNWXN8CkJDb+9R/UMxgMrJk/MlH5OIL8pvJxTJ+W9BtvrAteTygfYyhRrhat2g1Ao9HwJKF8vE1Oj1y0M5WPVy+cZOk0fzSmffrrjsOwT+GloElZvMb+ktHH63MXzgSxY/NiOvWflmy9Pb+pysRlR1KNq5TNuVfmLSWHg08wbf5i9Ho9HzSoR5vPW7Bg+WqUIoWoWbUSvQYFcDXsBm6mXq3eHh6MGtiX0HMqv82ci4VGg95goGXTD/moUdoahGzin6ZpvoPHTjF54R/o9Ho+rl+bti2bMvePDRQvUpDalcvz89DxXL5+E4+cLqbY3RnXvzuxcVq+7zMUAIccdvTp8B3FfN/8xvjep1VfncjEYDCwafEILpzej7WNHa1+HEle03losv+ndB+5AYCbV0JZM2cAWm0sStnaNPvWeB6Kj49j7dyB3A47j6WVNR9+2YcipaoBMKZnQ2KfPUYXr8XO3pn2v87FO8/Lb2o52L5+XTG9x+ipoztYu2AUjx/eI4eDE3kKFqeL/2wC181m+8b5Zp0SugycbfaCy6QcbWJTnfYyp4L388eC39HrdNRu2IymrdqzYcUsChYpQfkqddHGxTJn0mCuX1FxcHKm4y+j8PLJS/DBHWz4YzaWlpZoLCz49IsOlEvS8Pu6nsS9/JooK+qKEwd/R/i/14iNeYqDkwtfdRxGyXI13yhfmRX39o0LOLL3TywtrbC2seXTNr0oXLwCR/duYdnMweRK1CmhTZcA8hZM/l6CrIod4J/NiepA9Vvw3kdtuHz+OBMHtyV3/qJoNMZrh2Zf/kypCrWTxdbIz1a6mSbxT94y2b4hsuHN09nqd5PG2zegKEoOVVWfmf7ejrGhdL2p8fa0qqplTNPWAANVVVVNn21UVU3WtTNJg+8iYBpQB4hXVXWK6SVoJVVV7Zok7W6gpaqqkYnm8wZqqara/2XrTElmNN6+DelpvM1KGdF4m1XS2nib1V7VeJudvUnjbXaS1sbb7OBVjbfZ1Zs03mY3b9J4m52k1HgrMterGm+zs7Q23ma1N2m8zW5e1XibXaW18TY7SGvjbVZ7k8bb7OZNGm+zk7Q23mYHr2q8FSIxabxNbmfBstn+BF3/Wki2+t3e3dpY1miqKEoX09+3gSuJpp1QFGUDsBjoDUwz9dQFGAHsfM11/AMsUxSlMfAvxvF1X0lV1b8URaltatg1YByP99fXXKcQQgghhBBCCCGEECKbkZ63QnrevmXS8/btk563b5/0vH37pOft2yc9b98+6Xn79knP27dPet6+fdLz9u2Tnrfiv0J63iYnPW/f3LtbG3uHKIriBqxP8vV8VVWXZkU8QgghhBBCCCGEEEK8bRrrbNUu+k6Qxtu3QFXVaKBeVschhBBCCCGEEEIIIYR4d7ybz4YKIYQQQgghhBBCCCHE/3PS81YIIYQQQgghhBBCCJHpLKxk2IQ3JT1vhRBCCCGEEEIIIYQQIhuSxlshhBBCCCGEEEIIIYTIhmTYBCGEEEIIIYQQQgghRKbTWMqwCW9Ket4KIYQQQgghhBBCCCFENiSNt0IIIYQQQgghhBBCCJENybAJQgghhBBCCCGEEEKITGchwya8Mel5K4QQQgghhBBCCCGEENmQ9LwVjG4yJ6tDSJP+gT9ldQhpomV7VoeQZqci8mZ1CGni5RST1SH858TEW2Z1CGmWwzo+q0NIk0sRzlkdQpqV8onK6hDSJDrGMatD+M+J1b67ZYv+He1lYm+tzeoQ0uyZ9t281Bn+Z8GsDiHNqlRyzeoQ0qSY9+OsDiHNnsVbZ3UIaXLzvkNWh5BmOe3f3XLxXaV5N0+hQmQY6XkrhBBCCCGEEEIIIYQQ2dC7eTtaCCGEEEIIIYQQQgjxTtFYSFfqNyU9b4UQQgghhBBCCCGEECIbksZbIYQQQgghhBBCCCGEyIZk2AQhhBBCCCGEEEIIIUSm01hKP9I3JVtMCCGEEEIIIYQQQgghsiFpvBVCCCGEEEIIIYQQQohsSIZNEEIIIYQQQgghhBBCZDoLS01Wh/DOkZ63QgghhBBCCCGEEEIIkQ1J460QQgghhBBCCCGEEEJkQzJsghBCCCGEEEIIIYQQItNpLGTYhDcljbcizbr/VJjqFd2JidUxarLKhcuPk6WxstLQq0MRypdxRW+AOUuvsudgJK2b5+Xjxj7odAbuP9QyerJKeERsFuTCXNm5o/D6sB5xd6PYW75pVodj5lhwEHNnz0Cv19Po/Q9o9fkXZtNDT4cwd85Mrl29Qt9+/tSsVQeAkFMnmTd3ZkK6mzdu0OdXf6rXqJmp8RoMBv5aPooLp/ZibWPHZz+OInfBUsnS/Xv1DOvn9UcbF0sxvzp89PUANBoNK6f3JPLONQBinj7Ezt6ZrgEbiI+PY9PCody6FopGY8GHXw+gUIkqGRZ36PEDrFowHr1eT62Gn/BBi3Zm07XaOBZOHkTYlXM4OLnw0y9j8fDKzdmTh1m/bArx8VqsrKxp+V0PipcxxhWv1fLHvDGoocFoLCz45KsuVKzeMMNizqzYY549YZz/i2Xci7pLtTof0rp9nwyN22AwsH7RGM6e2Ie1rR1fdxpBvkIlk6W7ceUMy2cMRBsXS8nytWnRth8ajYYTh7YSuHYm4f9eodfIP8hf2Hw/i468zehezfmgVWfqN22bobFnxv4StH8rf6+bj16vo0zF2rT8tkeGxpyUwWBg6x8juXTaeKw2azeaXAWSH6s710/k9KFNPHv6kH7Tjyd8H3YhiG0rRxN+U6XFT79RslKTTI03NSePHWbxnEno9XrqN25K81ZtzKafCz3J4rmTuX71Mj/3HUa1Wu9lekxnThxgzcKxGPR6ajT4lPc/bW82XauNY/FUf25cOYeDowvte43D3SsPAIHr53No5wY0FhZ83u5XSpYzltk7/lzKwR3rQaMhT/6itOkyHGsbW5ZMG8TFs8HksHcCoE2X4eTzLf5OxH4+5Agblv6OwWDA1i4HbboE4JUrf5pjT4nBYGDD4tGcO7kPaxs7vuw0kny+KZczf8waiDYuhhLlavPpd/3RaDT8vXoqocE70VhY4OjsxlcdR+Li5pXhMa5dOJYzJ/ZhY2tHm84BKZaF16+cZel0Y1lYqnxtWn7/KxqNhiePH7BgYh+iI27h5pmb9j0nYO/ozIUzQcwZ1z3h9ylXtQEftOwIwNMnD1kxayi3b1wCjYavOw2nUDG/NOfh9PED/DF/Aga9jtoNP+XDz743m67VxjF/8iDCLp/DwcmVjr3H4OGVmysXQlkyc4RxO2CgeesOVKhWH4Dtf65g7/YNgIE6jT6lUdOv0xzfy2TW9ge4cCaIdYvGodPF4+jkSo9hCzMlDwCf189BqUJWxMXDkr+fcuOuLlmari0dcHGwwMICLt2MZ+U/zzAYII+nBV81tsfWWkPUAz0L/3pCTFymhWrGYDDwz+qRXA7dg7WNHR99Nwaf/MnPRXs2TiT0yEZinj7kl8knkk0/fyyQjXO7813/teQqUCZTYj19/CArEvbzT/gohf183uTBpv3chU6m/fy5qIjbDPy5Fc1b/0STT74F4OmTRyycHsC/1y+hQcP3XYdQpHjZDIn3zIn9rF04Fr1eT80GLWicQnm+ZKo/16+cxcHJhfY9xyeUF1s3zOPgjg1YWFjQql0/Sparyb3IOyye5s/D+5FoNBbUavgZ7330DQDHD23jr9XGOlmf0SsoUDj5b5gWBoOBv5eP4kKIsa7S4odUriuuGa8r4uNiKVa2Dh+arituh51j8+KhxGvjsLC0pOm3g8lb6MX2vXnlNHMCvuDzzr9TuvL7GRJvZpUnAGGXQpng/w3teo6jfLXGAEwf2ZFrF09TqHh5OvWblu3iTq0cHNylCbZ29lhYWGJhacmvY1a+dqxrFo7lzHFTrF0CyJ9SrJeNscbFxVKqQm1aPY/1kTHWqIhbuHvmpn0vY6ypLfdC6FHWLhqfsNzwW1dp12McflXqs2TaQC6dDcYuoQ4WkK46mBApkWETRJpUq+hGvtz2fNHhKOOnX6B3p6Ippvv28/zce6Dly45BfNM5iJOh9wG4cOUxP/Q6Ttufj7H7QASdvy/0NsNP1c3F6zn68Q9ZHUYyOp2OWTOmMnT4KKbPmsfePbu4fj3MLI2nlxc9evWhbr36Zt+X9SvHlGmzmTJtNiNHj8fW1o7yFSpmeswXQvYSdSeMnuMC+eT7YWxePDzFdJsXD6P598PoOS6QqDthXAzZB8AXXSbSNWADXQM2UKpSY0pWNDZ2Bu9eA0C3kZtp23c+gX8YK6MZQa/TsWLuGH4eOI1hk9cRtC+QWzcum6U58M9G7B2dGDljMw2bfs36JZMBcHR2peuASQydtIbvuw1nweSBCfP8vW4eTi5ujJi+iWGT11GsVMZv/8yI3S6HA4N/X5Xwz90zF+Wr1U+27vQ6e3IfEXfCGDj5L774cQhr5o9IMd3qeSNo/dMQBk7+i4g7YZw7uR+AXPmK0u6XiRQukfJ23bB4HCXL1crwuDNjmz9+dJ+1SybRa+gshk1ex6P7UZwLOZLhsSd26fReou+G0WXUVj76djh/LxuWYrpifu/Rzn91su9d3HLR7PvRlK76cabG+TJ6nY4FM3+j37Df+G3Gcg7s+Yeb16+apXH39KZTD39q1m301mJaNW8UXf1nMGjiBoL3B3I7yf5xcMcG7B2cGTZtC/U//oYNyyYBcPvGZY4dCGTgxPV09Z/Byrmj0Ot03I8KZ/f/VvDr2D8YNHE9er2e4AOBCcv7tE0vBkxYzYAJq9N10fC2Y185dwRtu49mwITVVKr1IYHr5qY59tScO7mPiDvXGTDxbz7/cShr5wekmG7tggA+/2EIAyb+TcSd65w/ZSxn6n/8PX3HbaDPmHWUqlCXretnpjh/epw9sZ+IO2EMmbKFL38azMp5KZeFq+aO4MsOQxgyZQsRd8I4ayoLt2+cj1KmKkOmbEEpU5VtG+cnzFO4RAX6j19D//FrEhpuAdYuHEvJcjUZNGkz/cevxSePb5rj1+t0LJ8zlp6DphIwZR1H9gdy68YVszT7/tmIvYMzo2duplHTr1lrKhPzFCjMoAnLGDpxJT0HTWPJzJHodPHcDLvE3u0bGDh+CUMnruRU8D7Cb11Pc4wvk1nb/+mTh6yeN5IOv05h4O8baN9rQqbED1DK1wqvnBYMmfeIFVuf8mWjHCmmm7f5CSMXPyJg4SOc7DVUVKwB+OZ9ezbuecaIRY84eVFLo8p2mRZrUldC93Lv7jU6DN9Gk68D2LpiaIrpipR9j+/6rUlxWmzMY4J3LSW3b9pvQLyKXqdj2Zwx9Bw0hRFT1nJk/1b+TWE/d3BwZszMTTRu+jVrlkwxm75ywe+UKV/D7LsV88ZTpnx1Rk1bz7CJK8mdL+3HYtJ4V88fRRf/mQyauJHgA/9LVp4f2rkee0dnhk37i/oft2FjsvJ8A138Z7Jq3kj0Oh0Wlpa0+PYXBk/aRJ9Ry9i7dVXCMnPnK8JPvX+nSCp1srS6GLKXqPAweowNpHnbYfy5JOXrij8XD6N522H0GBtIVHgYF08bryu2rp7Ae590oUvABhp82o2tq14ch3q9jm1rfqNImYzr2JKZ5bler2PT8omUKGe+DzVs1pZvu47MlnG/qhzsPmQ+/cevee2GWzDelIi4HcbQqVv4qsNgVs5NOdaVpliHTt1CxO0XsW4zxTp0qnmsqS23WOkqDJiwhgET1tB9yDxsbOwo4Vc9YT2ftOmVMF0abv9bFEXpoCjKQUVR9iiKUiTJtBaKohxRFGW/oigfpGc90nibhRRF6Zzo7yaKorTOoOUWVBTlw4xYVmpqV3MncOcdAM6oj3B0sMI9p02ydB819GHpGmMl22CABw/jAThx+j6xsfqE+T3dbTMz3NcWvT8YbfSDrA4jmYsXVHLlzo1PrlxYW1tTp049jhw6aJbG29sHX99CL30E4cD+fVSsVBk7u8yvjJ87vpNyNZuj0WjIV6QcMU8f8uj+XbM0j+7fJTbmMfmLlEej0VCuZnPOHt9hlsZgMHD6aCBlq30EQMStyxQuWQ0AR2d37BycuXU1NENivnopFK9c+fD0yYuVtTWVa73PqaO7zdKcDNpN9feMvbIrVm/IudNHMRgM5C9UHFdTL6zc+QujjYtDqzV2VzmwY1NCj0wLCwucnHNmSLxvI/bnwm+F8ehBNEVLVsjw2EODdlG5TjM0Gg0Fi/nx7MkjHtyLMEvz4F4EMc8e41usHBqNhsp1mnE6aCcAPnkL4Z075YuckKAdeHjnxSdfkRSnp0dmbPPIO//inTs/Ti5uABQvW5Xjh8yPiYx24eQOylY3Hqt5C6d8rALkLVwOJ9fkPQ1dPfLinU9Bo8m6x58uXTiHT668ePvkwcramhp1GhB8eJ9ZGi/vXBTwLfLWHtO6dikUT598eHgb94+KNZtwKmi3WZqQoF1Uq9cMgPLVG6Ga9o9TQbupWLMJ1tY2eHjnxdMnH9cuGcs5nU6HNi4WnS6euNhnuOT0fPdj12iIeWp8eufZ08eZkqfQY7uoXNtUzhT149nT1MqZJxR8Xs7UbsbpYGM5Y2fvmJAuLuZZpuzvIcG7qFKnKRqNBt9XlIWFivmh0WioUqcpIUG7jPMH7aJqXeNvUrVuM0JMZWRqnj19zOVzx6hevwUAVlbW2Ds4v3Sel7lyMRSvXHkTysQqtd7nRNIy8ehuarxnvNFTqUYDzoUEGXtc2+bA0tL4QKBWG5ewfW/fvEphpUzCdKVURY4feXm+0iqztn/w/r/xq9oAN49cADi5uGdK/AB+Ra05fMZ4/r56W4e9nQZnh+T76vPetBYWYGmhwWAwfvZ2s+TiTWNP3fNhWsoXs860WJO6GLKD0tU+QaPRkKdQOWKfPeTxg+TnojyFyuHoknKv932bJ1Ot8Q9YWmXedcWVi2fwypUPL9N+XrVWY04m2V+CZxYAACAASURBVM9PHN2TZD83lo8Ax4/swtM7D7nzF05I/+zpYy6cPUHthp8AYGVtjb2DU4bEayzP85uV5yHBu8zShATtTth3y1drhBp6BIPBQEjwriTleX6uXQrFJadnQg9HuxwOeOfx5X608bfyyVsI73TcBErNuRPm1xXPUruueGZ+XXHOdF2h0WiIfWY8z8Q8e4xTzhf70OHtyyhVsRGOThl3bGZmeb7nfyvwq9oIJ2c3s+UpZaphm8MhW8adGeWgcV2vGatijLVq3aacOpooVlM9pmq9Zpw6uvO1l3vi8HZKlq+FjW3KN8jEq1lYarL9v9ehKIob0A6oDfwCjE40zRIYDtQDGgPDTd+lbZuldUaRIRIab1VVDVRVdVUGLbcgkKmNtx7uttyNfDHMwd2oWDzczRtvHR2M++UP3/gyf1IFAn4tSU7X5JXAjxv5cORYdGaG+86LiorEw+PFxay7hwdRUZFvvJz/Y+++45uq3geOf9K0pZSWTlr2hovMAmVPWSIoQ0FEUL9MWSpDkD0FZImyZaOgKBtRQdlT9gYve0PppIWuNOnvj6Rp08Fokw5/z/v14qVtTpLnnp773JuTc597YN9eGjay/aXCABGhAbh55Tf/nNczP+GhlidZ4aGPyevha/7ZzdOXiNAAiza31BO45PXCO39xAPIXKcfl07vR6+MICbzHg1sXeRLyyCoxhwU/xtMrMR53L19CQwJTaWPcLq3WntzOLjyNCLNoc+rIToqUVHBwcCTyWQQAW36ez6QhnVk0YyjhYcFWidfWsSd1/OB2/Ou1sMmERVjoY9yTjBU3L1+ehFiOlSchj3H3TLJ9nr6Ehab8UJdUTHQku7Ysp2WHvtYN2MQWfZ6vQBEe3btF0OMH6PVxnDm2h9Bgy33C2iLCAsjrWcD8c16P/ESE2fY9rS0kOBCvfIkfxDy9fQgJDnzOM2wvLOQxHt6J49rDy4cnIQFptkkYH88iwngSEoCHt+XYCgt5jLuXL83afMzovm8wolczcju7Uj7JyputP8/lq8EdWL9iRoovYLJz7F37jGfBlAGM7N2cY/u30aK9ZfkRa3gSEmCRZ9w9fVNs05OQANyS5BljLkps8/sv3zGhf1NOHvqdNzsOsHqMyfs9oe+St3H3Stm/ABFPQswT324e+YgITzy3unnlLFOHdmDBlL7GEglA8ON7uOT1ZPWCMXw97D3WLBpHTHRkBuIPxDPZuAkLtow/NDixTfKceOPKecZ81oFxA9/jwz4j0WrtKVS0FFcunuJpeBgxMVGcO3mQkCDb5Cdb9f/jh7eJfBrOt+O7M+3LThzdt9Um8QO4u9gRGpF4NVJohAF3l9Q/7n3aIQ8z+uclJjaeU1d0ADwI0lO5tHESvZriiEfezPuoGBEWgKtHYv+7ur/asejRnUuEhz6idGXbnueGhTzGM0mO8/DyJTQ4+bE/0Nwm6TiPiY7iz42raNOpt0X7wID7uOb1YPnc8Ywf/AEr5k8kJjrKSvEG4OGV7PwpOPm4TszbSfN5WPBjPJLlzbBkeTP48X3u3fyX4mVsU6IiQXhoAG6eSc4VPdL4XJEkh+f18CXc9LnizQ9GsOOXmcwY/Drb106nRYdB5te9fGonNZpYlqPLKFvlk7CQAM4e202DFh2tGq+t435eHtQA8yZ/wrQvO3Fw5/qXjvVJiOXnh5eN9ckLYn2Z1z156E/861suovzt57lMHvIu61dOz9A5mMhxagF7VFXVq6p6Aiib5DFv4KGqqlGqqkYCT4B0ryj6z9a8VRSlMTAUiAVKAGOBK8D3GHOECvQ2PfYjoAMiVVVtpShKZ6AH4AbsUFV1dIo3ML5HM4wz6Tpgn6qqY02raT82NZmsqupWRVFWYvxDlQE8gNZAc6CEoih7ga1ACOCtqupMRVGuAn8D1YHdqqqOUBTF0RR7MdNr91ZV9VpqbYHPgZqKolQE+qmqeikDXZmqVKdv4i1/1Go1+OZz4vzlJ8xbdp1ObQvTv3spvvrmX3ObFo19KFfalQEjzlg7xP+UhG/rk3rVSbSQkGBu3bpJter+1grrueKTDwgg+chJbbtItl3n//ndvOoWoFrDdwh8cJ2F4zvi7lWQoqX9sNOm+wssy3hS+V3yXk5tu5L+LR7cuc6GH+cwcNwCAPT6OEKDAyhVzo/3un3B31t/ZN2q2fT4PPVLe9LLFrEndfzgDrpbOebEwFKLK3mTVNqknonM/ly3gMatPySXk3OGwkuLLfo8j0teunwyksWzvsROo6GUUoXAgPtWjDqlVHfDF/Rt9pPxHGl1L5Hf0mqT+q81RD4N59zxPUyc/wfOeVxZMmsoR/dvo1bDt2jb5TPyunsTF6fjp0UT+Xvzclp17JPyhbJh7Lu2/Ui/kfMoUbYyf29ZyYZVM+nad3z6Yk/Dyx1Hn7/drTt9TutOn7Nz8xIO7PjJ+hO4LxNjqrnw+YqUeI1JC3aQy8mZi6cOsHjGQMbN2YZer+fuzct07D6c4mUqs37F1/y9eTlvvZ++7XqZY3qq+6ppC0qWrcSkOet5cPcGy+eMo1K1ehQsUpI33/kfsyb0I5dTbooUL4vWSsf8FGzU/wa9nrs3L/HpmCXoYmOYNfpDipepjG/B4umP1Qrmrn+GvRa6v+WMUtSef2/H8eP2SN5rmpvWdZw4d11HnD61I52NpOM4b36qwcCudVNp/fHUFzfOoJfJJake+9Gwee0imrf5AKfclucler2e2zf+5YNeQylVthI/LZ3B7xtX8M4H/VK8jjWkiDfVExoNLzq2RkdFsmTmYDp0G0buJFcn2MRL5JfnnSse372WNzsPp0KNFpw/9ieblo+m27AV/LFmKi06DsHOzsp5xUb5ZMPK6bTtMtD68T4vJhvnwUGTfsDd04eIJ8HM++oT8hcsTunyL/68+lKfDVI/yc3Q6z4JDeTBnWuUr5L45XnbLp+bz8F+/n5Cxs7BRLahKMp4YFyyX09QVXV8kp89gKQrc5KOsECgoKIo+Uy/r2pqny7/2clbk4JADcAFOAhcBwarqnpSUZQFQBvAC9igquo3iqIkfL28VVXVnxVF0QAHFUUpoKrqw6QvbGo7B2igqmqwoihaRVF8gP8BdYHcwFFFUbaZnnJZVdXPFUUZC7RVVXWFoihjVFVtbHq9/yV5+QIYJ5uDgX9Nz+kBnFZVtZuiKJWASUDnNNp+B3RQVdWqnyreaVWQt98wrs66fDUCH+/ES5J8vHIRFGL5DdOT8DiiovXsP2JcIbrnUCBvtUj8Fsu/ijsfvVeUASPOoovLxJPDHMjbOx9BQYnf6gcHBeHp+WqXmxzcv486dethb2+73f6fnWs4sc/4jWmhEhV5Epy4IjY85BF5k10K6+aZ+I04GFc9Jb0sW6+P4+LJnfSbkPgtrFZrT6suI8w/fz+pM16+xbAGDy8fQpKscgwLDsDdM1+yNr6EBD/Cw9sXvT6OqMin5HFxAyA0KIAF0wbT/bNJ+OQvAoCLqzuOuZyoWstYK7Z63eYc3LXZKvHaOvYEd2+q6PV6ipVKeROA9Dqw42eO7NoAQNFSFQlLMlaeBAeQ18Pykkjjt95Jti8k4IWXVt++dp6zR/9m65rZRD2LQKPRYO/gSMOWH1hlG2zV51VqNKJKjUYA7P9rAxobTFQc372G0weMNQMLFq9EeEjiIS489BEuqZRHyM48vXwIDkxcFRES9BgPT+8sjMi0EjsocVyHBj/GLZVxHRr0CA8vy/Fh/L3l2HLzyMe/5/7By6eQuayGX62m3FDPUqvhW+b9wcHBkTqvt2Xn1lU5IvbyVepy//YVSpQ13jimet03mDfZOhMWB//6mSO7jcePoiUt80xYSMo84+aZ32Kl7ZPggBTbDVCtXmuWTO9nlcnbfdvXctiUC4uVqmDR7wl9l5S7ly9hyfJOwo3TXN08eRIaiJtHPp6EBpovqU06qVKhWgN+WTaZp+GheHj54u7lS/Eyxr73q92cvzcvT/e2eHj5EJJs3KTMicY2nklzoqubRZuCRUri6JSb+3euU7x0eRo0a2e+nHzD6rkWqwgzKjP6393Llzyu7uRyciaXkzOlX6vO/dtXrDZ526iqI/UqG8/Jbz+Mw8PVDjCWPvBwtSPsadr3BYjTw7lrOqqUduDf23EEhBiYu+4ZAD4edlQsaduyCSf3ruHsQWMt9QLFKhERmtj/EWEvfyyKiXlG0IMr/PSN8eZfz8ID2bCgL+/2W2j1m5Z5ePlarP4ODQ7APdnxxjjOA1KM8xtXLnDi8C7WrZpD5LMI7OzscHDMhX+dpnh4+VCqrDFW/7rN+GOjdW5q5+7pa3EFT1hIAG6pnKuEBgXg4ZU/ZT5PljcTcqI+TsfSWYOp0aA1frWsfxNegKPJP1ckudLuSegj8rqn8rkiSQ4PDw0wl0c4fWgzrbqMBKBijZZsWT4GgPu3LvDrwiEARD4N48q5/djZac332ngVmZFP7ly/yIrvvgTgaXgoF08fwM7Onio1039PiqzOg+7m53pRuUYTbl27kObk7b7tazm00xRr6QqWx/XglGM71Vg9Xhzr81731OEdVKnZBK19Yn5Meg5W+/V27MrAOZjIPkyTtONf0CwUSHqgMR90VVU1KIryGfArxsWap4GHpNN/vWzCCVVV41RVDQOeAuVVVT1peuwwoAC/APkURfkR40pdgCaKouwG9gKlME4CJ5ewBDoYQFVVPcZVvGdN7xkBPDC1A0h43zuAZ/IXS+a2qqpBqqrGY/zj5gUqAh+bVurONf0urbY2sfGPB3T7/CTdPj/JgX+CaNnEOBFbQXHlaWQcwaEpLw84dCyYqpXcAahexZ1bd4yX45Up6cLQ/mUZPukiYU90tgr5P6NMWYUHD+7z6NFDdDod+/fvpWbtOi9+YhL79+2xecmE2s26mG8yVr5aU84c2kJ8fDx3r50hV27XFPUyXd19yOWUh7vXzhAfH8+ZQ1t4rVriycf1i0fIV6CExWVSsTFRxMYYx9G1C4ews9PiU8g69UyLl67A44d3CAq4T5xOx/GDO6hSo7FFmyo1GnFkz28AnDyyk3KVahhXlT2LYO7kT2nf9VNKv+Znbq/RaKjs35ArF08A8O+5YxQobP0b9Nki9gTHD26nZoOWVo23wRudGTZ9PcOmr6dSjSYc37+V+Ph4bl05i5OzS4oTRTePfORyysOtK2eJj4/n+P6tVKzx/PH8+YRVjJu3g3HzdtCoVVeat+9ltYlbsF2fh4cZL9t69jScvdt/pUGz9laLOUGNJl3oPW4zvcdtRqnalHNHjPvqvetncEplX83uSpUtx6MH93j86AFxOh2H9++iei3r36TuVRQzj497xOl0nDy0ncqmSfkElf0b889e46WDp4/8jVKxpjFn1GjEyUPbjXWQA+7x+OEdipeuiId3fm5dOUdsTBTx8fGo54+abzCVUI/NWHd2DwWLpj8vZmbszi55iYp8SsCDWwBcPnckQzfNSqp+i84M/XoDQ7/eQEX/Jhw/YMozV8+SO80848ytq6Y8c2ArFasb80zgw8SbhF44uQefNOpsv6pGLd8330iscs0mHNv/G/Hx8dy8cpbczq6px5g7DzdNufDY/t+o7G+MsZJ/Y/OlqEf3baWyKUeGhwWZVxLdunaeeIOBPK7u5HX3xsPLl4AHxpv7qeePkj8Dx6cSZSoQ8PAugaaceOzgDvySjRu/Go04vMe4ruHE4V3mnBgYcB+93nhfhKDHD3h0/xZePgVM8RtzYnDgQ079s4daVjweZUb/V/Z/nev/njLXer517ZzVxjjAvtOxTFkVwZRVEZy9pqN2BWPZoxIFtETFxBP+zHKBRC4HzHVw7TRQoaQDj0KMk72uzsbfa4A36zix/4xtL/2t3rgL3UdvofvoLZTxa8aFfzYTHx/P/RtnyOXkmmZt2+Sccrvy+ayj9Juym35TdlOwhJ9NJm4BSpQpbzHOjx7866XH+Ygpy5ixeBszFm+j+dsf0PrdbjRt1Qk3D288vX15eP8WAJfOHaOglc4Vjfn8tkU+r+Tf2KJN0rF7+p+/KWvK55X8GyfL57cpXroi8fHxrF44jvyFStD07Y+sEmdqajXrQv9Jm+g/aROvJftckdq5iqu7D7lyJ/tcUbWJ+bFb/x4H4Mblf8wLP4bM3MmQWbsYMmsXFfxb8NZHY9M1cQuZk08mzN/ORNO/qrWb06nnqAxN3GZW3GnlwZjoSKKjjF8YxURH8u+5I889f2nU8n3zTcGq1GjC0X2vFuvRfb+ZY6rk35ijpvOYo3uTxtr4ua97IpWSCUnPwc4d200BG9xr479Go9Vk+38v6SjQ2LSYsxpwNemDqqruUlX1dYxX/Uerqno7tRd5Gf/1lbfVTQWBXUz/LiuKUt00gVsX+AswmEoNoCjK34qibMK4qrUxEI5xkje1v1wg4KsoiqeqqiGmlbg3gSqKothjXHlbCEgoTJr0zCnh9dL6Kjz5MlQNcAk4p6rq96ZYHZ/TNhYb/22PnAihjr8nvyyuSXSMninfqebHVnxXnW6fG+eqF668wZjB5fisZynCwnVMNbXr360kuZ20TBpuXMkXEBjN8K8u2jLkl+L34yy8GtXE0duDJjf3cXXiXO6uePnaO7ai1Wrp03cA40aPwGAw0KzFGxQrVpzVP66kTJmy1KpdlytXVKZMGs/Tp085fvQf1qz+gQWLlgIQEPCIwKBAKlaqnGkxl63SiCvn9vPN0DdwzOXEOz2nmB+bN6Y9AyZtAqDNx+PYsGQEutgYylZuQNnKDc3tzh/9w6JkAsCz8BBWzeyJRmOHq4cPHT6ZZrWYtVp7Ovf8km8n9sNgMFCvaVsKFi3Flp8XUKxUefxqNqZ+03Ys+240o/q1IY9LXnoN/hqAPX+s5fGju/y+bgm/rzPeKX3g2IXkdffk3Q8/Z/mc0fyyfCaueT34eMB4q8Vs69gBThz+m09HzbV6zAnKV23ApdP7mfR5Kxwdnfigb2J5hunDOjBsunEffK/nGNYsGI1OF015v/qU92sAwNlju9iwYgpPw0P5flo/ChcrR99R39ss3gS26vNflk/n3q0rALR+rze+Ba2zsjwtpSs14tr5/cwf2QJ7RyfadEvcVxdPaEfvccaV4jvXzeDCsW3oYqP4dmgjqtbvQKO2n/Lg5nl+XTCA6GfhXD27h31b59F34ra03s4mtFp7uvUZxJSxgzEY9Lze/C2KFCvJr6uXULJMOfxrNeD6lcvMmjyCZ08jOHXsEOt/WsrMBWtsGlOnniOY91VfDAYDdZq0o2CR0vy2dj7FSlWgco3G1G3anpVzRjFuwFs4u+Slx6DpgPFu3dXqtmDSwPbYabW833MkdlotJcpWpmqd5kwd+j52Wi1FSpSjfvMOAKz4bgRPw0OJj4+ncHGFzr3H5IjYtVp7uvQZy5KZQ9Bo7HDOk5cP+0/I+B8gmfJVG3L5zAEmD3wTx1y5ef+TSebHZgx/l6FfG1fydOg+hp8XjUYXG81rfg14zZRntq2dzeMHt9BoNHjkK0jHHmOtHmOFqg24eOoAEz5rjYOjE137JcY4dWhHRswwrpbv1HM0qxeMRhcbY8yFVY1fVDRv14Pls7/gyO5NeHjnp8fgWYBxMubAX7+i1WpxcMxFt4HTzZfBduw+gpVzRqCP0+HtU9jiPV+VVmtPl15fMntCfwwGA/WbtqFQ0VJs/mkhxUuXx69mIxo0a8eSb8cwom8b8ri48ckQ42XuVy+f5s+NK9Fq7dHY2dH1kxHmm3sumP4FTyOeoLW3p0vvL8njYpt1Crbq//yFS1Lerx5Tv+iAxk5D3SbvULBoGZtsw4UbcVQs6cDEXq7E6uCHPxNrGI/82JUpqyJwdNDQt30e7O012GlAvRPHAdMkrX85BxpVNa7iPXNVx5ELmVe3sVTFRty4sI/vxzTHwTE3rT5OPBYt/6ot3UdvAWDPhulcOm48Fs0f3pDK9TrS4O1PMy1Ordaerr2G8c2EARgMeuo3bUuhoqXYZBrnVWs2omGztiz5dgzD+7Y1jfMpL3zdLr2GsXj2aPRxOvL5FqL7p+OtFu97PUYyf3JfDAY9dV435vNta+dTtFR5Ktd4nbpN2rNq7kjGDWhNHhc3uifN53Va8NWgdtjZaelkyufXLp/i2P5tFCxahilfGGuvtvngMypWa8CZo7tYt3wqT8NDWTi1P4WLl2PA6EUZ3o6EzxWzh72BQy4n3umR2Kfzx7Snv+lzxdsfjWPj0sTPFWVMnyvadZvIH2umoDfocXDIRZtuEzMc0/PYKp88z+yxHxNw/xYx0ZGM7tOMD/pMoLxfvWwRd1p5MCjgHktmDgSM5UP8679Jeb+X+/K9QrUGXDx9gPGftsbR0Ymu/RNjnfJFR0bONMb6fq/R/Dg/MdYKplhbtO/Bsm++4LAp1p6mWJ/3usGP7xMaFJBiZfDK74Ybz8GIp3DxcrzfK/3nYCJnMc0FrgIOYCyn2kNRlJaAm6qqvyiKMguoBkQDAzPyXppU61P9B5hq3g4D4oCiGGtVXMVYNxbT//cEOgD9Tb97CHQFvgA+wDhh6g0MNxUfTv4ezTHWvI3BWPN2nKIoA4APMU6iTlVVdZOp5u08VVVPmMojJNS2nQpUBrZgnHBN+P0FVVUrmt5jrynGcGABkPA17G+qqs5Oo20ssA14DIxQVdVi9j+5+m/vy5GDYMT23i9ulA2V+ffvrA4h3c4GFs7qENLFxzU6q0P4fydKl3O/G8ztEJfVIaTL3ZCce8fbCvmtfxO/zBASbeMafyKFGJ2N6vxlAgdt2pevZ2c5NSdCzj0WbfzLOjerygo1/d2zOoR0Kev7NKtDSLeoONuWt7CVkGc5M24AD2e5cjSzZfWtDDKiWeVcOTh62zjRqE62n4Py33ckW/3d/uuTt1av+/pfJJO3mUsmbzOfTN5mvpz6gRly7kSFTN5mPpm8zXwyeZv5cmpOhJx7LJLJ28wnk7eZTyZvxauQydv/lpOv18v2c1DV9xzKVn+3nHlGk8kURfEENib79TJVVX/MiniEEEIIIYQQQgghhBD/ff/ZyVtVVfdivOGYNV4rBGMNXCGEEEIIIYQQQgghhMgU/9nJWyGEEEIIIYQQQgghRPahsctWFQlyBLusDkAIIYQQQgghhBBCCCFESjJ5K4QQQgghhBBCCCGEENmQlE0QQgghhBBCCCGEEELYnJ1Wyia8Kll5K4QQQgghhBBCCCGEENmQTN4KIYQQQgghhBBCCCFENiRlE4QQQgghhBBCCCGEEDansZOyCa9KVt4KIYQQQgghhBBCCCFENiSTt0IIIYQQQgghhBBCCJENSdkEIYQQQgghhBBCCCGEzWnsZB3pq5LJW8GP3xbK6hDSRcffWR1Culwt1zyrQ0i3AmdPZXUI6ZLX4VlWh5Bu4bo8WR1CusTH59w6RgZDzozd21WX1SGkW04dL07auKwOId3iyZl9rjfk3JN9O7v4rA4hXfQ5dP/Myd5o7JrVIaRbFc9rWR1CutyILJzVIaSbIYfuo555cvJ5S1ZHkD459dgP5NxOF8JKcu4ZsBBCCCGEEEIIIYQQQvyHyeStEEIIIYQQQgghhBBCZENSNkEIIYQQQgghhBBCCGFzGrscXMIji8jKWyGEEEIIIYQQQgghhMiGZPJWCCGEEEIIIYQQQgghsiEpmyCEEEIIIYQQQgghhLA5O62UTXhVsvJWCCGEEEIIIYQQQgghsiGZvBVCCCGEEEIIIYQQQohsSMomCCGEEEIIIYQQQgghbE5jJ2UTXpWsvBVCCCGEEEIIIYQQQohsSCZvhRBCCCGEEEIIIYQQIhuSsgkiw06cOMHC7xdjMBho+UYLOr33nsXjGzZuYseOHdhptbi7uTFo4EB8fX2yKFo4eeI4S75fgMFgoPkbb9LxvfctHr9w/hxLFi/k1s0bDBs+inr1GwJw7uwZli5ZaG537+5dhn45ijp162Vq/GmpvGQKPq0aE/s4mP1V387qcDh/6hA/L5tJvEFPg2btafVuN4vHdbpYln03htvXL5PH1Z0+X3yNt09B8+PBgQ8Z81kH2nT6hJbtPkIXG8O0UT3RxcVi0OupXqcp7Tr3tek2nD55lBWLv8NgMNC0xVu079jV4vFLF86wcskcbt+8wcBh46hT/3XzY1+NHcJV9RLlyldixLjpNo0zQXr7/MaVC/yw8CsA4omnbadPqFa7CQB/bV3NgZ2bAQ2Fi5Wm+6fjcXDMZdW44+Pj2bhqKpdPH8AhlxMf9J1MkRLlU7S7e+MiPy0cjS42mteqNuCdj0eg0WjYsnomF0/tQ2tvj7dvETr3+QrnPHnNzwsNesjUIW1o2aEfTd7uluJ1M+Li6UP8umI6BoOBek3b07J9d4vHdbpYVs4dzZ0bl8nj4kbPwdPw9inE04gwFs/8gtvXL1K7cRs69xwBQGxMFItnDSXw0T3s7Oyo7N+I9l0/t2rMycXHx7Nx5ddcMvV/l75fUaRk6v2/ZsFodLExlK/agHf+N9zU/7O4cHIvWnsHvH2L8EHfSRb9b21nTv7DD0u+xWDQ83rzt2nb8SOLx3W6WBZ8M4mb1//FxdWNz4dNIp9vAeJ0OpbOn8aNa/+i0djxce+BlK9UzeK5MyYN4/Gj+8yYv8bqcZ8/dZifzPtnO1qnsn8u/W6saf90o28qOXH0Zx1p26m3OSd+PaqXOSf612lKu859rB63MXbr5pZH92+xaOZw8/MDA+7TrnMfmr/dJcOxxsfHs2HF11w8fQDHXE507Zf6eL5z4yKr5xvHc4WqDXi3m3E8P3v6hBWzvyAk8AGe+QrSfdBMnF3ciIqM4Ic5IwgJfohBr6fp2x9T+/X2AIQEPeTnReMIDX6EBg19RizAy6fQK8d+8fQh1q2YRrzBQN2m7XmjfQ+Lx3W6WFbNHcVdUz7pMXi6+X22b1zGkd2b0NjZ8V73LynvZzw3Gd33TZxyO2Nnp8XOTsvw3i3ftQAAIABJREFU6T8DsPSboTx+cBuAyGcROOdxZeTMX1855uQunDrEL8tnYDAYqN+sHW++kzInrvhuDLdvGMd57yHT8PYpyKUz/7Bx9Rzi4nTY2zvQ4eOBlKtUE4CZY3ryJDTIfOwZOHYhed09MxwrGMfL+hXTzOPlw36T0hgvl/gxyXjp0O1L83hZPnuoebz0GDQTZ5e8HD/wO39vWQ5ALidnOvUcTeHiCgCrF4zlwql9uLp5MmrWJqtsw9Yfp6Ce2Y9Drty813sKhVI5ht67eZF1349EFxuN4teQNh+ORKPR8PeGeRzbu548rh4AtHxvIOX8GqGP07F+6Vge3LqE3qCnev02vN6md4bjfRk56bNFevfbpxFhLJk5hDum84BOPUdmSrwZzZGnj+zgj3ULCbh/gy+m/EzRUhUsnhcS9JDJg9rSqmM/mrb5X4ZivXj6IOtXTDOdY71Di1T69oe5o7hz4xJ5XN3oMWiGOSfu2LSUw7s2YWdnR8fuw805EcCg1zNteGfcPX3oO2IeACu+G86d6xfR2ttTrHQlPug9Bq29Q7pjt1VuuXLxOIunf27eTr9aTXmzQ+Kx32DQM314Z9w8feg7fF6640/YBluMlWcRYSz7ZjC3r12gVuO2vNdjVIbiTBqvLfp859YVHD/wBwAGQxyP7t3k62X7yOPiZvV8/v+Jxk7Wkb4q6TGRIXq9nvkLFvLVxAksXrSQvfv2c/vOHYs2pUuVZM5337JowXzq16/HsuXLsyhaY7yLFsxl/MQpzF+0lP379nDnzm2LNvl8fBg4eCiNGjex+H3lKn7Mmfc9c+Z9z+SpM8iVy4mq1apnZvjPdW/VRo691TOrwwCMJ0VrFk9j0Ji5TJqzgaMHt/Pg7g2LNgd2bsY5T16mLtxK87e7sP6H7yweX7t8FhWrJp5o2Ts48sXE75kw+xfGffMzF04f4bp6zmbboNfrWbbwG0ZNmMnsBT9yaN9O7t65adHGO58v/QeOpH6jZime3/adznw6eLTN4ksuI31eqFgpxsxczfjZaxk0Zh4/LJyMXh9HaPBjdv2+ljEzVjNpzjoMBgNHD+6weuyXzxwg8OEdRn37B516jWfd0kmptlu3bBLv9RrHqG//IPDhHS6fOQiAUqkOX87YxJfTN5Evf3F2bl5q8bxNP0zjNb8GVo/boNfz89KpDBg1n3GzN3L84HYe3L1u0ebQrk0458nLpHm/0fStrmxabexzB4dctHm/P+9+ODjF6zZv8zET5mxm1IxfuP7vGS6cOmj12JO6dOYAgY9uM/q733m/1zjWLfsq1Xa/Lv2KTr3HMfq73wl8dNui/4fP3MTwGRvxKVAsRf9bk0GvZ8WimXw5fhYz5//E4f07uZdsv9zz12/kcXHl28XraNW2Ez+tXADA7r+2AjB93mpGTvqW1cvmYjAYzM87dngvTk65bRb36sVfM2jMHL6as56jB3dwP5X9M0+evHy9cAst3u7Cuh/mWDy+dvk3VKpa1/yzvYMjQycuYuLstYz/5ifOnz7MdfW8TWK3dm7JX6g442evZfzstYyduQbHXE5UrfV6am//yi6dPsDjR7cZO+d33u89jl+Wpj6ef1nyFZ0/GcfYOb/z+NFtLpnG89+bl1G2Ui3GzvmdspVq8ffmZQDs376W/IVLMmLGBj4bv5xNP8wkLk4HwI/zRtK0zf8YPXsrX0z9GVe3V59YNOj1/LJ0CgNGLWDM7E2cOLidh8nyyWFTPpkwbxtN3urKptXfAvDw7nVOHtrO6NkbGTBqAWuXTMGg15ufN3D8UkbO/NU8cQvQc/AMRs78lZEzf6Vq7ab41bI850kPg17PT0u+5rPR85jw3QaOH0glJ+7cjLOLK5MXbKXZ213YaBorLnndGTDyW8Z/u45un05k+XeWx88eAycz9ptfGPvNL1abuAW4dPoggY9uM27ONjr3HsvaF4yXcXO2EZhsvCiVajFuzjaUSrX4yzRevHwKMXD8CkbO3EDLd3vz8+IJ5teq3bgN/UcuTPV90kM9u5+gR7cZOms77/SYwKaVE1Jtt2nFRN7pMYGhs7YT9Og26rkD5sfqt/yIgVM2MXDKJsr5NQLg3LEdxMXFMujrLXw2aR1Hd/9KSOB9q8Wdlpz02SIj+62DgyNvv9+f9qmcB9hSRnNkgSJl6PnFbEq9lvpnoI0rp1O+av0Mx2nQ6/l12RT6j1rImNmbOXHozxR9e2T3Rpxd8jJh3u80eetDNqfIiZvoP2ohvyydbJET9/yxhvyFSli8Vo0GrRn73VZGzdqILjaaQ7s2Zih+W+UWgFKvVWPEjHWMmLHOYuI2Ydt8k21b+rfBNmPF3sGR1p0G0P7DL6wSZ2K8tunzZm26mfu7TefPKVO+Onlc3ADr53MhnidHT94qijJeUZQOVngdP0VR6ib5eUVGX9NWFEVprChKyq+Qsoh65QoFChakQIECODg40KhhQ44c+ceiTZUqVXBycgKgXLlyBAUFZUWoAFy9olKgYEHym+Jt2LAxR48ctmjj65ufEiVKPreI9qGDB6juX8O8XdlByMET6EKeZHUYANy4egGfAoXJl78w9g4O1Kz/BqeP7bVoc+bYXuq+/hYA/nWbcvncceLj4wE4dXQP+XwLUahoSXN7jUaDU25nAPT6OPT6ODQa2xU6v3blMvkLFMI3f0EcHByo17ApJ/6xnETz8S1AsRKlUx0rlfz8yW2KNzNkpM9z5cqNVmu8EEOni7XoV71eT2xsDHp9HLExUbh75rN67OdP7KFGwzZoNBqKl6lCVGQET0IDLdo8CQ0kOuoZJcr6odFoqNGwDedP7AagXJV65viLl6nMk5AA8/POHd+Fl09h8hcuZfW4b127gE/+IuTzNfZ5jXpvcO74Xos2547vpU5j40r4anWa8e/5Y8Y+d8pN6deqYu/oaNHeMVdulIo1ALB3cKBIyXKEBgdgSxeOJ+n/slWIepZW/z+17P/jCf1f19z/xcpUIcyG8V67eon8BQrjm78Q9g4O1GnYjBNHD1i0OXn0AA2bvglArXqvc+HsCeLj47l35yYVqvgD4ObuiXMeF25c+xeA6KhI/ti8lvad/meTuG9cvYhPgSL4mPbPWvVbcCbZ/nn62L5k++exFDmxYNHEcZxaTsQGKdFWuSXBpfPH8Mlf2GKVcUacP7GHmqbxXOIlx3PNJOP5/PE91GrUFoBajdpy7vgewNjf0dGRxMfHExMdibOLG3Z2Wh7eu45Br6dcZeNpZC4nZxxzvfqXALeuXSBf/iJ4m/JJ9XotOZsin+yhduM2AFSt0xzVlE/OHt9L9XotcXBwxNu3MPnyF+HWtQsv9b7x8fGcPPwX/vXffOWYk7t57QI+BYqYx0qN+m9wNvlYOb6XOq8bc2L1Os24bNqGoiXL4e5pXDlZsGgpdLGx6HSxGY7pRc6d2EPNhm+/1HgpWbaKaby8bR4X547voVYj49+kVqM2nDONo5KKH84uxisQSpSpQljwY/PrlS7vj7Ppg781XDy5m+r126LRaChW2rgN4cm2ITw0kJiopxQrYxzz1eu35eKJXc99XQ0adDFR6PVx6GJj0No74JQ7j9XiTktO+myRkf02l5MzpV+rZvWrmV4kozkyf+GS+BZMfXLw7LFdePsWpkDh0hmO09i3RS369tyJPRZtzh3fa97/qtZujnrhKPHx8Zw7sSdZTixqzomhwY+4cGo/dZu+Y/FaFas1QKPRGM+FSlfK8LmMrXLL84QGP+JiKtuWXrYaK7mcnClVrlqKc+CMyow+P3HoT6rXSzxeWjufC/E8OWbyVlEUW8bqB5gnb1VVte51tdbVGEgxeasoijbzQ4Hg4GDyeXubf/b29iY4ODjN9jt2/IW/v39mhJaq4OAgvL0TJ5+8vL0JDn71E74D+/bSsJF1Vgn9F4WFBOLpnd/8s4eXj8UHF4DQ4MQ2Wq09uZ1deBoRRkx0FH9uXEmbTp+keF2DXs/4Qe8z6H/NKF+lFiXLVrLZNoQEB+KVL/ESPE/vfOkaK5klI30OcOPKecZ81oFxA9/jwz4j0Wrt8fDy4Y22HzKsdysGd29B7jyuVPSrY/XYn4QE4OGVGLu7p6/FBGxCG3dP3+e2ATi6dxOv+RlXfMRER7Jr63Jaduhn9ZgBQkMe45Gkz929fAkNsezzsCRtEvr8manPXyTyWTjnT+ynXOVa1gs6FWGhj3FP0v9uXr48SbYdT0Iep+j/sFDLNgBH92ziNSusuElLaHAgXt6JcXh55SM02PLEPCRJG63WHuc8eYgIf0KxEqU5efQAen0cjx894OZ1leBA4xj6dfUSWrfvTK5ctvlCLizkMZ5J4vbw8k0Rd1hwoLlNypy4ijadUl6ubNDrGTeoMwP/15wKVWpTygY50Ra5JaljB3ZQs8EbVow35X6Z6nj28rVoE2ZqE/EkGDcP43mCm0c+IsKN5zQNW3Ym4P4NRn/ShKlD3uHdbsOxs7Pj8YNb5M7jypKZA5k2rCObf5yFwaDnVSWP28PLJ0WOSyufPAkJwMM79e3RaGDupD5MHfY+B/9en+J9r10+RV43L3wKFHvlmFNsQ/BjPJP1a2hI8nH+GE+v1MdKglNHdlKkpIKDQ+IH+5XzxjNxcCe2/brY/KWGNaQ2XsJSyeNpj5eQZOMlJMV7HN69kfJVbVdiKzz0MW5Jc7inL+GhAcnaBOCWJIcb2yRu55G/f2L2iHasWzyKyGfGhQCVarbAIVduJg9oxNSBTWnYqhvOLu42244EOemzRUb226yS0RyZlpjoSHZuWc6bHa1T0iwsJAAPr2TnHcHJ983E3Je0b8OCH6c4rwwz/V3Wr5hO+66D07xkWx+n49j+3zK8z9oyt9y8cpapQzuwYEpfHt69Zv79hpXTadd1MBqNdaZNbDVWbMXW+Tw2JorLZw7hV7u5rTbh/xWNnSbb/8tubF7zVlGUxkAHVVUHmH6+AMwBegBRwHpVVecpivI+0B/QAstVVV2qKMpKIBIoAYwGTj7nfSZjnNjUAENUVT2iKEpV4FtTE1VV1d6KonwN1AA8gImqqm4GPgfyKoryFvAOsF9V1YqKouQHVgFOQCDwEeADrAGuA5WBJaqqzk8jpiFARyDGFMcWYAlQBjAAvVVVvaIoygVVVSuanrMSmAe4AENMfVTetP17gP8BzxRF6Q30Bn4CbgNBiqL4AW+rqhqmKEpH4DVVVSem1WfWkNoJdFqLIXft3s3Vq1eZPn2aLUN6rtTjfbUdMyQkmFu3blKtetZNQmd3qX6wStHPqfwt0LB57SJatOliXlGWlJ1Wy/jZa4l8FsG8r4dw7/Y1ChfL+Lf7L8uGC30zLCN9DlCybCUmzVnPg7s3WD5nHJWq1SM2NoYzx/YybdE2cudxYeGMLzmy93fqNG5t7ehTCf0lYk/W5q9N32On1VK9vnEF4PZ182nc6kNyOdloBfRL5JOX+7ukpNfHsWz2CF5v1Zl8voXTHeJLeYk8nmruTLbM86+Ni7HTavE39b8tpDpnk6LPU2uioXHzt7h/9zajBvXA28eXsuUqodVquXXjCgEP7/FRr88JDHhoo7hfYqw8Jyc2b/NBmjlxwuyfbZoTbZFbElaaxel0nD2+n3c//NTG8b64TfLxnNzls4coVEzh07HLCAq4y7xJvSlVrhoGg57rl0/x5fRf8fAuwIrZQzm6dwt1mrziyqeX6ec02qQ15gGGfLUKd08fIp4EM2diH3wLlaBM+cRLWE8c/BP/+i1fLdY0pLp7pmjz/H3hwZ3rbPhxDgPHLTD/rsfAKXh4+RAd9YyF07/gn73bzKt3Mx70Sxx/Uh0vL+fKhWMc2bOJQRNXpSO4l/QSY+d5Oah2s/dp2r4voOGv9XP4fc10OvaezN0b57Gzs2PU3L1EPQtn4aQPKV2xDl4+RWyxFS+INfW2Wf7ZIgP7bVaxVY7849cFvN7ahudcpHaOlWoj0jpnPH/SWJu0aKnyXLl4PNX3WLt0MqVfq07pNMpCvDQb5ZYiJV5j0oId5HJy5uKpAyyeMZBxc7YlblvJtLftVdlqrNiMjfP5+ZP7KKn4mUsmCJHZsuqGZR8ArVVVDVIUxU5RFE+gL8bJ13hgn6IoCXdNUFVVfe6yKdMkbWVVVespilIEWA/UAuYDH6mqei3JytRJqqo+UxTFA9gFbAa+A7xVVZ1per2Elx4BLFZVdYOiKMOAXhgnYAsBTTBONJ8wvU/ymCoBbwD1VFXVm96/LRCpqmpDRVFqAtOA9s/ZNC+gPlAY+FFV1c2myd0LqqquVxSlOFAMaKqqapSiKH2AzsBCjBPNA57Xb9bg7e1NYJJLlYKCgvD09ErR7tTp06z95RdmTJuGo0P6i79nlLd3PoKCEleBBKcR7/Mc3L+POnXrYW8v9/tLi4eXDyFBj8w/hwY/TnG5fUIbT29f9Po4oiKfksfVjZtXznPy8E7WrfqOyGcRaOzscHB0pGmrxBvLOedxRalYnQunD9ts8tbTKx/BgYnf1oYEBeLp6f2cZ2StjPR5UgWLlMTRKTf371wnMOA+3r6FcHUz3sSkeu0mXFPPWWXy9sCOnzmy27gKrGipioQGJ8YeFhJAXg/LG4+4eeY3r5pIrc2xfVu4eGo//UcvNZ+o3b52njNH/2brmm+IiozATqPBwSEXDVp+kOH4wbR6MkmfhwUH4O6RvM+NbTy8kvT5S5z0rVk0CZ8CRWn6VtcXtk2PAzt+5siuDYCx/8OS9P+T4JT9b1yZYNn/bkm21dj/++g/ZukrfyH2Kjy98xEclBhHcHAgHsn2Sy9TGy9vH/T6OCKfPcPFNS8ajYaPeiXe/G3s0N7kL1iEyxdOc+O6yqc93sGg1/PkSSgTR/Rn7NRUv5dNFw8vX0KSxB0aHIB7sriN+2dAiv3zxpULnDi8i3Wr5hD5LAI7OzscHHPRtFUn83ONOdHfJjnRFrmleGnjBUTnTx2iaMlyuLm/2nE4uf3bf+ZwkvGcfL90S208J7kkNiw4ADfTNrm6efEkNBA3j3w8CQ3ENa8xtn/2bKZ5ux5oNBry5S+Kl08hAh7cxN3Tl8IlyuHta5zQqlyzCbeunH3lyVv3ZPkkNPhxqnGnlk+Mv0+2Pab9M6EUgaubF1VqNuHW1QvmyVu9Po4zR3cxfPraV4o1LR5ePoQk69eUY8WXkOBHeHinzImhQQEsmDaY7p9Nwid/EYvXBXDKnYdaDd7k5rWLGZq83bd9rXm8FCtVIZXxYhlz6uMloV89k42XxHq8929f4afvx9N3xAJcXK27YvXw3z9xbM86AAqXrMSTpDk8JIC87imPoUlXhD4JCcDVPWHMJ+aimq93ZOUs48rJM4d/R6ncAK29Ay5uXhQvW5V7Ny7YfPI2J322yMh+m5msmSPTcst0zrVlzWyinkWg0Wiwd3SkUTrPudw9fS3KRoWFpIzBw5T7PLzyp8yJyc4r3Tx8OH9iL+dP7OXi6YPoYmOIjnrGyjkj+N9nUwH4fd1CnoaH0vmLsemKOTNyS25nF3P7CtUa8MuyyTwND+WGeibFtq2aM4KPTdv2sjJjrFhTZuVzgJOHtlPdCiWGhEivzCibYP56Q1GUhE90g4FpiqL8gLFcQSmgNMbJ1N0YV8Um3KbXsshR6somtFNV9S6Q8JWfq6qq10y/T7iG7TNFUQ4Am4AXnX2YXxc4DCTM6l5UVTVGVdVIjCtoU/MacCDhfU3/TRrnMYzbnVzST72nVVU1qKp6B0jr7gznVFWNMv3/z8D7iqIUABxUVb2dxnOsRilblgcP7vPo0SN0Oh379u+ndm3LS3yvXb/O3LnzGD92LO7utr/c6nnKlFVM8T5Ep9Oxf/9eatZ+tcvA9+/bIyUTXqBEmQoEPLxLYMB94nQ6jh3cgV+NRhZt/Go04vCebQCcOLyLcpVqoNFoGD5lOdMX/870xb/T/O0PaP1ud5q2ep+IJ6FEPosAIDYmmstnj1KgUHGbbUPpsuV4+OAeAY8eoNPpOLR/F/61bHc5eEZlpM8DA+4b62UCQY8f8Oj+Lbx8CuCVLz83rpwnJiaK+Ph4Lp87RsHC1rkJQoM3OjNs2gaGTdtAJf8mHN+/lfj4eG5dPUtuZ5cUJ1tuHvnI5eTMratniY+P5/j+rVTyN+6Hl88cZNfWZfQaOtei1uRnE35g3Ly/GDfvLxq92ZVm7XpZbeIWoFjpCjx+eIcgU58fP7SDysn6vLJ/I47s/Q0wXgqsVKzxwsnNLT/PIyryKR27DbVarMk1eKMzw6avZ9j09VSqkaT/r5zFKc3+z8OtK4n9X7FGYv/v3LKcXsPmpqvW56soVeY1Hj24x+NHD4jT6TiyfyfVa1rul9VrNWD/rj8BOHpoDxUqV0ej0RATHU10tPFwee70MbRaLYWLlqB5q3dYuGorc5dtZPy0RRQoWMSqE7cAJcqUt9g/jx7866X3zxFTljFj8TZmLN5myondaNqqE+HJcuKls0fJb4OcaIvckuDowe3UskLJhIYtOzN8xnqGz1hP5ZpNOGYazzefM56dcufhpmk8H0uSTyr5N+bovi3G+PZtoZJpnHt6F+DK+aMAhIcF8fjBLbx9ClOsdEUin4WbL6+8cuFoumpsJ+aTe8TpdJw8tD2VfNKYf/Yab7x3+sjfKBVrotFoqFyjEScPbUeniyUo4B6PH96heOmKxERHEh31DDBe0nz57BEKFk2c3P/33FF8C5WwuDQ5I4onz4kHd1ClRmOLNlVqNOLIHmNOPHlkp3msRD6LYO7kT2nf9VNKv+Znbq/XxxERHgpAXJyOcyf2U6hoxmqYN2r5vvnmM8bx8pt5vOR2dk09/1mMl9+obDFejH+To/u2Utk0XkKCHrJk5iA+GjAF34LFMxRvauo2/8B8g7EK1Zty8uAW4uPjuX3tLE7OruRNtg15TTn89jXjNpw8uIUK1Y03qUtaH/fiiZ34Fi4DgLtXAa5d/If4+HhioyO5c+0sPgVLYms56bNFRvbbzGTNHJmWQRNXMWH+DibM30HjVl1p0b5XuiduIaFvb1v0bSX/xhZtku5/p//5m7Kmvq3k3zhZTrxN8dIVadvlcyZ/v5NJC7bTfdB0lIo1zRO3h3Zt4PKZw3T7fBp2aZRUeJHMyC3hYUHm1a63rp0n3mAgj6s7bT/4nK8W7WTi/O10GzidshVrvvLELWTOWLGmzOhzgKjICK5dOmFuK0RWyIylg6EkTpLWMP33sqqqPRRFKQSsA94GLgMtVFU1KIrioKqqzrQC9mUKh10FugKYVt5Gmn4frihKKVVVr5tq5roDHQB/jKtaL5naxZJ6X1wFagMbME4yXzH9/mWKbV0CeiiKYmfaJjvT670OrDGtvE24XXO0afVxOFA1yWskfZ+Eo3zyWM39o6rqE0VRbmNc0fvDS8SYYVqtln59+zJq9BgMBgMtWjSneLFi/PDjj5QpU4Y6tWuzdNkyoqKjmTzVeADJly8fE8aNy4zwUo23T98BjBs9AoPBQLMWb1CsWHFW/7iSMmXKUqt2Xa5cUZkyaTxPnz7l+NF/WLP6BxYsMt49PSDgEYFBgVSsVDlL4n8evx9n4dWoJo7eHjS5uY+rE+dyd0XK+naZQau1p0uvL5k9oT8Gg4H6TdtQqGgpNv+0kOKly+NXsxENmrVjybdjGNG3DXlc3PhkyPNPMMJCA1k2ZxzxBj0GQzw16jWnSo2GNt2GHn0GMXnsEAwGA683b02RYiVYu3oppcqUo0at+ly7cpkZk0fx7GkEJ48d5tefljN7wY8AjBnWn/v3bhMdHcUnH79D38++xK+67WqXZqTPr14+zZ8bV6LV2qOxs6PrJyNwzeuBa14PqtdpysQhXbCz01K0pELDFta5CUJS5as25PKZA3z1+Zs45spN5z6TzI9N//Jdhk0zfqPesccYflo4Gl1sNK/5NeA1vwYAbFgxmThdLAsm9wKMNy17r6ftc4xWa0+nnsOZ81VfDAYDdZu0pWCR0mxdu4BipcpTpUZj6jVtz4o5oxgz4G2cXfLSc1DipZ0j+75JdNQz9HE6zh7bw2djFpLb2YU/Nywlf6ESTBlmXG3euOX71G9m/X5PUL5qAy6d3s+kz1vh6OjEB30T7847fVgHhk035pH3eo5hzYLR6HTRlPerT3lT/69fPoW4uFgWfGWsyVqsTGU69UrfqpUX0Wrt+V+fwUwdNwiDQU/jZm9RpFhJ1q1eQoky5fCv1YDGzd9iwTcTGdi7Iy4uefl0mLF6UPiTUKaOG4RGo8HTKx/9BtsmxrTi7tprGN9MGIDBoKd+07YUKlqKTab9s2rNRjRs1pYl345heN+2pv1zynNf80loEMvmjMNg0BNviKdGvWb42SAn2iK3AMTERHHpzFE+6jPKqvFWqNqAS6f2M/GzVjg4OtG1X+J4/npoB4bPMI7nTj3HsHpBQj6pT/mqxvHcvF0Pls/+gn92b8LDuwDdB88CoOW7n7B6wWimDDFeMNW2y0BcTNvS/sMhzJvYk/j4eIqULE/dZq9+L11jPhnBPFM+qdOkHQWLlOa3tfMpVqoClWs0pm7T9qycM4pxA97C2SUvPQZNB6BgkdJUq9uCSQPbY6fV8n7PkdhptUQEhfD99EEAGPRx+DdoRYUkdRxPHtqOfz3rlExI2IbOPb/k24n9MBgM1GvaloJFS7HlZ2NO9KvZmPpN27Hsu9GM6teGPC556TX4awD2/LGWx4/u8vu6Jfy+bgkAA8cuJJdTbr6b2B+9Pg6DQc9rlWvRwIr5sELVBlw8dYAJn7U2jZfE48/UoR0ZMcO4urVTz9Gm8RJjzH+m2t4J4+XI7k14eOenh2m8/Ll+Ec+ehvHL0smAscTJl18bVziv+HYYVy+d4GlEGKP7NKPVe/2o+6plNpIo59cQ9ex+pg9piaOjEx17TzY/9u3I9gycsgmA9t3G8uvikehiY1CqNECpYswXf6ydycPb/4JGg4d3Id7pPh6AOs3AEh6GAAAgAElEQVQ7s27xKL4Z3gbi4/Fv2J4CRZUU729tOemzRUb2W4DRfd8kOuqp+Tzg0zGLKFDE+jdYTSqjOfLssV2sXz6Fp+GhLPq6H4WKl6P/qO+tHqdWa897PUYyf3JfDAY9dV439u22tfMpWqo8lWu8Tt0m7Vk1dyTjBrQmj4sb3ZPmxDot+GpQO+zstHQy5cTnWbv4KzzzFWDmqA8B8KvVlFYd+6Q7flvlltP//M2Bv35Fq9Xi4JiLbgOn2+zLAFuOlXH93yA68ilxcTrOH99Nv9GLKZDBmwvbqs+N27KbclXqpigLYu18/v9Jdqwpm91prFn0PzWm1bbrMa4c3Ydx8vQExsv9cwMLVFX9QVGUThhr3uox1nl9C1gOzFNV9UQarz2exBICU4FGGCc5v1BV9ZCpnMIc02uqQB9gI5APOI1xslgxlR9YAzwEegIHTTVvC2KseesIBAMfmp47T1XVt0wxmOvVphLfFxhr3kaa4thi2qZSGCdme6uq+q+iKJ0xlmi4ZOqnkRhr3lrUCjbFVAuYBdwxtTPHYmrXyPQ+BZKsyH2um9ev2XYQ2IgO696hMrNcLZdzi5y7nT2V1SGkS16HZ1kdQrqF62x/Z2dbiIjJmfsngJN9XFaHkC4x+iy5b6VV+DhHZHUI6RIVl7l3Cbem+KyqSZdBkbqsK72UUVq7tC7Wyt7sc2jcALFxOTMvPovNuaW5qnjezOoQ0uVGpI3ry9uQ3pBj7kFuwU6TIz+CAmnU280BcuqxH0DzUuvnsqfmVXLl3I63EbXTG9n+D6r8siNb/d1sPnkrMpeiKA2Azi+qE5yUTN5mLpm8zXwyeZv5ZPI288nkbeaTydvMJ5O3mU8mbzOfTN5mPpm8zXwyeZv5cuqxH2Ty9r9GJm9fXY45MzDVxy2a5FcnVVUdklXxJKUoShMg+fWXn6qqej6T40i4Sdn7L2orhBBCCCGEEEIIIURmkrIJry7HTN6qqvpRVseQFlVVd2O80VpWx/EDmVTrVgghhBBCCCGEEEIIYVs58xoLIYQQQgghhBBCCCGE+I/LMStvhRBCCCGEEEIIIYQQOZfGTtaRvirpMSGEEEIIIYQQQgghhMiGZPJWCCGEEEIIIYQQQgghsiEpmyCEEEIIIYQQQgghhLA5O60mq0PIcWTlrRBCCCGEEEIIIYQQQmRDMnkrhBBCCCGEEEIIIYQQ2ZCUTRBCCCGEEEIIIYQQQticxk7KJrwqWXkrhBBCCCGEEEIIIYQQ2ZBM3gohhBBCCCGEEEIIIUQ2JGUTBDocszqEdDkbWDirQ0iXAmdPZXUI6fakSrWsDiFd3C/vy+oQ0s3DMTyrQ0iXKJ1nVoeQbnGGnPm9prODLqtDSLfgKJesDuH/HTu7+KwOIV005My4AexyaOw5NSfmZAVdn2Z1COl2JrhEVoeQLp55YrI6hHSLj8+Zlx87aPVZHUK6xcTlzGmUnHwMFeL/u5yZdYQQQgghhBBCCCGEEDmKxk6+GH5V0mNCCCGEEEIIIYQQQgiRDcnkrRBCCCGEEEIIIYQQQmRDUjZBCCGEEEIIIYQQQghhcxq7nFmrOyvJylshhBBCCCGEEEIIIYTIhmTyVgghhBBCCCGEEEIIIbIhKZsghBBCCCGEEEIIIYSwOSmb8Opk5a0QQgghhBBCCCGEEEJkQzJ5K4QQQgghhBBCCCGEENmQlE0QQgghhBBCCCGEEELYnMZO1pG+Kumx/2PvrMOjOto+fO9uAiHEnWABAgdNsOBWrFSgQCm0VJFCC7TFLbg7xVvcS4tToy3uEqCEBDg0uMYDCQmR3f3+2M0mGyO2kfeb+7p6lezOmfOb2WeemfOcEYFAIBAIBAKBQCAQCAQCgUAgKIKImbeCXHHJ7yJrflyJRqOhw5tv8UHPD42+D7jmz5rVq7h39w6jx/rSvEUrw3chISEsW7KQsLBQFCiYPG0mrq5uJtWr1Wr5fdssbl09gXkJC97/chbuHrXSpXt8N5A9a8eRmBBPNe9WvPPxeBQKBTtWDCPs2T0AXsW+wMLShiHT95KUlMD+DVN4ci8AhULJ2x+Pp3KNRvmm+9rl0/y0bgFajZqW7bvx9vt9jL5PTExg3ZKJ3L99g9LWdnw1cg5OLu6G78NDnzLx2x506TWQTl0/IzEhnrm+/UlMSkCjVtOgaTu6fvR1vunNDV5rZuHydhsSQsI5Ua9zoWpJyxW/86xfvQyNRkO7ju/QvefHRt8HBlxlw+pl3L97h+FjJtG0RRsAQkKeMX/GRDQaDUnqJN7u3J03336vQLVf9jvP+tXL0WjUtM9E+/rVy7l/9zbDx0yiWSrt82ZMQqNRo1arebtzN5No12q17Nowl8ArJylR0oJPB02nfOWa6dI9uHOdLSsmkJgQT616LenRZwwKhYKXMc9Zv3gUEaFPcHB2p9+wBVha2eB/8Si//bwchUKJUqWixxejqVK9PhGhT1izYBgajQa1OonWnT6iZceexUL7o3s32bFmBq/iXqJUKnmz+5c0aNYpV9qTCbh8mh3rF6DR+5a3uqf3LeuXTOT+nRtYWdsxYITOt9z9L4DNq2YkVwSdew2kfpO2AIwd+A4WpUqjUCpRqVRMmL8tTxpTY6o6B7gVeJHdG+ehVidhZW3H0KkbAJg0uBMlLSxRKlUoVSrGzNlRpHQD3A8KYIHvJ/QdNo96TToCEBH2lO0/TCEy/BkKFHw9bgWOLmVzrD3wyml2bpiLVqOhWbtuvNmtn9H3iYkJbFrmy8M7NyhtZUu/4fMM9zm4Zx1nj+xFoVTSs+8YatZtTvDje6xbPNpwfVjwI97tNYi2737Co3syP62eQfyrWByc3enz3WxKWVrlWDOYrs4vnvydf/avB6CkhSW9+k+gnIdE8JO7rE9VrvCQR7zTcxBvvPNprvQnE3jlNL9smIdGo6F5u2506tbX6PvExAQ2LpvAA3399x8+FyeXssRER7F6wUju3w6kSZsufNR/nOGapMREdqybza1APxQKJe/1HkL9Ju3zpDNZa37aCsCR37dx+tBu0Gpp3v592r77iVGe/+zfxN4ti5i3/hhWNva51l5cfUtq/C+fZcuahWg0Gtp0eI/OPT43+j4xMYEfF0/h7u2bWFnbMmTUTJxd3Tl97CB/7NtiSPfwXhDTF22hYuVqzJvyLVGRYWjUaqSadfl84GiUKlWedL4OrVbLr1tmIV89gXnJUnwwYBZlPdL/Fo/uBrJz9XiSEl4hebei86e6sTrA6b+3cvaf7ShVKqp7t+btj0aaVDPo+tOf189Ho9HQon1X3uqevq1u0Penpa1tGTBirqE/3bJqenLh6dzrK+rp+9P8JvDKKXZtmKv3J93pmEEb3bzMlwd3rlPa2pZ+w+Yb2uhfe9dy5vBelEolH/QdS826zYkMe8am5b68iApDoVDSov37vPGOro0+vHuTHWumk5iQgEqlold/Xzyq1slzGXJbz9f/PceerUtJSkrEzMycHp8PpXqdRryKe8k835Q8IsNDaNLqbXr1G5VnrWnRarXs3jDH4Gc+GTQjEz8TyNZUfub9PmNRKBRcOfsXf+xcRfDjO4yc9RMVquieYe8FXWPHj1N190DL2x8MwrtRuzxrNYVPPHRgAxdP/gGARpPEs0d3mbPuOKWtbPWfqZk39iNsHVz4euzyPJXBlOVIJqPxl0BgakTwVpBj1Go1P6xcxvSZc3F0cmL40CE0btKUChUqGtI4u7gwdPgo9u7eme76xQvn0rNXb+rVb0BcXJxhwGVKbvmfIPzZfYbNO8ij21c5sGkaX03+OV26A5um8l6fqZSvUpfNCwfyn/9Jqnm34sPBiw1p/vxpLiVL6R4q/Y7pyvfNzAPEvAhn84IBfDVlJ8p8WAagUavZtnouI6asxN7RlemjP6Fuo9a4l69sSHPy0D4sS9swe9UBzp/8i12bl/DVyLmG73esX0jtes0Nf5uZl2DktB+xKGVJUlIic8b3o0795lSRvPKsN7c82rSHeyu3Unf93NcnLkDUajVrVn3PpBkLcXRyZsywgfg0aU75Ch6GNM7OLgwZNo4De4wfvOztHZm1cAXm5iWIi4tl2KA++DRujoOjUwFqX8LkGQtwdHJm9LCvMtT+zbCx7N9j3A7s7R2ZvXC5QftQE2m/fuUUoc/uM3npb9z7z58da2cwatb2dOl+XjODjwZOplJVL1bNHsT1f09Rq15L/tm3DqlOYzp27cff+9bx9751dP1kGFKdxtRp2AaFQsHj+7dYv3gkE78/gI29M8NnbMHcvATxr2KZOaI7dRq2wc7BpchrNy9hwWdDZuJSpiJRESHMG/shNbybYVnaJgN1r0ejVrN9zVyGTdb5lpmjP8Hbx9i3nDq0D0srG2atPMCFU3+xe/MSBo6ci3uFKkyYvxWVyoyoiFCmDf8Qb59WqFS64cSIaT9inYdASmaYqs5jX77gl7UzGeS7CgenMkQ/DzfK77vJ6/IUGDKVbtA96OzftpgadZsZ5bV5uS9vdv+SGl5NiX8Vm6s+VqNW8/PaWXw76UfsHFyZO7Y3Xg3bUKZ8FUOaM4f3YlnahqnLf8Pv1J/s3fo9/YfP5+nD21w6fZAJi/fwPCKEpdMGMmXpAVzLejB+wS+G/McP7IB3Y12gYuuqqXT/bDjVajXkzOG9HNq/kc4fDcmxbjBdnTu6lGXolA1YWtkQeOUkP62eyqhZ23F1r8S4+bqxgEajxndg+zw/PGvUan5aO5vvJv2AvYMrs8d+jFfD1rinqv/T+vqfvvxXLp46yN6tS/hy+DzMzUvS5cPBPHkQxOOHQUb5/rlnDda2DkxbdgCNRkNszPM86UzWmt+28uzxHU4f2s2YOdtQmZmzfMYgajdoiUsZ3TgzIuwZN/3P4uBUJs/6i6tvSUajVrPpx3mMmbocB0cXJo38nPqNWlK2Qoo/P/7PAUpbWbPwxz2cPfE3P29azpDRs2jephPN2+heBD68F8TiWSOpWLkaAN+MnkUpSyu0Wi1L547l/OnDNG1l2gCFfPUEYcH3GbngIA9v+7Nvw1QGT00/Vt+3cRrd+06lgqc3GxYM5Jb/SSTvVty+fp4bl48wdNY+zMxLEJOmzk2Brj+dw7DJq7B3dGXW6I/1/WmqtnpoH5ZW1sxceYALpw6yZ/MSBuj7U9/52wz96fThvfBK1Z/mp8Zf1s3im4mrsXNwZd64j6iTpo2ePbIHSysbpi7/Hb/Tf7Jv6/f0M2qje3keEcKy6QOYvORXlCoV3T8bQYXKNXkV95K5Yz6kuldTypSvwr6ti3n7g6+oVa8lAZdPsm/rYoZOXZ/nMuS2nq1s7Bgy/nvsHFx4fD+IJdMHMW/t31iUKs2kRSn2NWNkb5MFz69fOUnIs/tMWvo79/7z5+e1MxiZhZ/xqOrNqtlfG/xMmfJV6T9yMTtWTzNK717ek1FzdqBSmfE8MpQ5o3pQu0HrPNmQqXxi+y59aN9FN1Hgmt8xjv6+xRC4BTj6xzZcy1biVdzLXGsviHJA5uMvQc5QKE0fA/pf439y2wRJkqZIktQjH/KpK0lSs1R/b8hrnnnU00mSpF6FqQHgv1syZdzdcStTBnNzc1q1asP5s2eM0ri6ulGpUuV0jfLBg/uo1Wrq1W8AQKlSpbCwsDC55huXj1C3+XsoFArKe9blVewLoqNCjNJER4UQ/yqGCp71UCgU1G3+HtcvHzZKo9VquXbhIF5N3gEg9MltqtRsAoCVjSMWpW14cjcgXzTf+S8AlzLlcHYrh5m5OY1avMmVC8eM0vx74RjN3ngXgIbN2nHD/yJarRaAy+eP4uxa1mgAr1AosChlCYBanYRanVQgwfOsiDjlR2JE3h8g85ugWzdwcy+LWxl3zM3NadGqLRfPnTJK4+JaBo9KVVAojF2pubk55uYlAN1MJ61WU2C6AYJu3aRMGu0Xzp02SpOsXZnm90+vXWsSjf5+R2nUqjMKhYJK1byJexnN88hQozTPI0N5FRdD5WreKBQKGrXqjP/Fo7rrLx6lcesuADRu3QX/i0cA3Yy4ZJuOj48D/b/NzFLKlZiYgFaT+9+koLW7unsYghZ2Di5Y2zoQ8yIy1/rvBgXgnMq3+LR4k3/T+paLKb6lQdN23Lym8y0lS5YyPBQkJiYYNJoaU9W536k/8G7czhAEsrZ1LBa6AY7/uR3vxh2wtnEwfPb00W00ajU1vJoCOpsqUbJUjnXfCwrA2a08Tq46G2nQvBNXLx4zLtvFozRpo9NWr2kH5GsX0Gq1XL14jAbNO2FuXgIn13I4u5XnXpBxv3jz2nmcXMvj6KxbKRLy5B5Va+rGBdW9m3LlvHHfmxNMVeeVpbqGWTeVqnoTFW48hgCQr53H2a08Ds7u6b7LCfeCAnBxK4+zvv59mr+Jf7r6P0bTNrrVKvWbtuemvv5LWpTCs0Y9zEqUSJfvmSP76aSfcadUKvMleGgKW3n26C6VqnlRQu9vqtZswL/nU2x/98b5dPt0WL74n+LqW5K5/V8grm7lcHEri5m5OU1aduTShRNGaS6fP06Ltrpxa6PmbQlMNVZM5uzJv2naMiU4mzzzXa1Wk5SUWCBjxeuXj1C/hW6sXsHTm7jYaF5EGf8WL6JCiY+LoWLVuigUCuq3eI/ASzp/ce7wDlq/2x8zfV9vZaI6T83doABcypQ36k+vZtCfNn1D11YbNG3PjeS2mqo/TTJhf6proxWM2qi/31GjNP4XjxnsuF6TDsgB59Fqtfj7HU3TRitwLygAW3tnKuhnMVqUKo1r2UpEReh8okKh4FWsLgD3KjYaW3vnPJchL/VcoXJ1w4t69wpVSExI0I1fUhH85D7RzyOoWrN+nrVmxDW/ozRq1SVbfqZStbp6P9OFa3p/4lauMq7uldLlW8JoTBafLyZkynFLMn6n/6RB87cMf0eGPyPw8gmateue9wIUQDkyGn8JBAVBsQ/eSpJkyjLUBQzBW1mW+2SRNsdIkpSj9UeyLB+UZTn9K+gCJjw8DCenlI7Y0cmJ8PCwbF37+NEjSpe2YtaMKXw35CvWr1uNWq02kdIUoiODsXVM2ZrBxsGNF5HGD14vIkOwsXc1/G3r4Ep0ZLBRmnuyH1Y2jji5eQDgVr46N64cQa1OIiL0EU/uBfI84lm+aI6KCMXBKUWzvaNLuofFyPCUNCqVGaUsrYiJjiL+VRx/7tlIl14D0+WrUauZMuxDhn3RnprejalcLe9Lmf4XiQgPw8kpZVamg5Nztu0cICw0hGGD+zDgiw/o2qN3gc26BQgPD8XRqI06ExEemsUVxui09+XLL3rSrcdHJtEeFRGCfSr7tnN0NQz8U6exc3TNME308wjDA4GtvTPRLyIM6a5eOMz0oV34YfZgPv46ZZZCZNgzZo18n4lfd6T9e31zNeu2sLQncy/oGklJiTi5ls+VdoCo8FAcHNP4lrT6w0Oxd0zvWwDu3LrGpO96MHVYTz4ZOD5lhodCwfdTBzN9ZG9O/L071/oy1GyiOg95ep/YmBd8P6Uvc8f04vzxA4brFcDymQOZO6YXpw7tKlK6oyKCuXrhCC07fmCUV8iT+5Qqbc2aBcOYM7one7csRKPJeR+bVre9owvPI4IzTZNsIy+jo3geEYy9U8blSebS6YM0bJGy9UeZ8p6G4OSVs38TGZb7ftSU7TOZM0f2UDPVqpbU5Ur9QJpbIjMoQ2QGZcio/jMj9uULAA7sWMHMUR+yesFIXkTlfWaiKWylTAVPgq5fIiY6ioT4OAKvnCIyXGcT/hePYevgQjkPKc/aM9JfXHxLMrpxYIo2B0cXItP09xERoTjq06hUZliWtiIm2vil+flT/9Ck1ZtGn82b/A2DP3uTUqUsadTMNDMSU/MiMgQ7h5TfwtbBlRdpbOlFRDC2DsZj9eTxfNize9yTL7Fici9+nPEZD+9cM7nmqPAQHNLYRmREaAZpMu9PJ3/3PlOHfcAnA33zfdYt6PoL+9QaHVzTPU9EpWqLqdtoVHiIYSxguDbNbxIe8phHd28atkbo8cVo9m5ZhO9XHdizeRFdPv4u72XIYz0nc/nsIcpXlgwv85O5eOogDZt3NNlLioz8zPM0fuZ5Fn4mK+7958/M4V2ZPaI7vb6clGcbMnUfmhAfx41/T1O3SQfDZ7s3zqPrJ8PTTYYpiuXIbPwlEBQEhbZtgiRJbYAesiwP0f8dACwF+gFxwC5ZlpdLkvQhMBhQAetlWV4rSdJGIBaoBEwALmVxn5lAG3RjpRGyLJ+VJKke8L0+iSzL8gBJkuYAPoA9ME2W5X3Ad4CNJEnvAt2BE7Is15YkyQ3YBFgAocBngAuwDbgNeAFrZFlekYEeD2A7cB8IkyRpErBWf99XwOeyLIdKkvS9Xs9VoK0sy9UlSfoCcJJleYEkSYOA5E2tZsqyfEBfL8+Bqvr83pFlOf1TRx7JaCZedjs7jUbN9cBrLFn2A84uLsydPYPDh/6m45t5f9jJCi0ZzR401pzhDMM05bp27nfDrFuA+q26E/rkNqumfICdozsVPOvm255g2dFDBuVSoGDfjh/o2OVjwyzb1ChVKqYs3kHsy2iWzxnBo/tBlKvomS+a/5fI0M5zcL2TswuLV2wgIjyMuTN8adq8NXb2BfSGNsPJstlXr9O+nojwMObMmGAa7dnxI7n8DbwbtcO7UTuCrvvx+8/L+WbiGgDsndwYv2A3UREhrJk/lHpNOmBjl4uZOYWgHXQzBDYvG8+ng2fkaWuWbPnDjHyLvoyVq9Vh2pJdPH10h/VLJ1OnfnPMS5Rk7KwN2Dk48yIqgsVTv8atrAfVajXItU5jQaapc41azcO71/lm4hoSE+JZOOFTPKp64eruwbDpm7FzcCH6eTjLZwzEzd0Dz5oNi4Tu3Rvn8d7HQ1EqjfsbjSaJ2zcuM3beL9g7ubF+8SjOHdtPs7Y5nM2Snf4nkzQZf5xybVJiIv5+x3kv1QP9p4On8su6Ofyx60e8GrbBzMw8Z3pfoyu/2ifArYALnD26l2HTNhl9npSUyLVLx+jSO++BiuyUIXtjhBQ0ajWR4cFUqV6XD74YyaFft7B78yL6fDsz37Xm1VbKlKtMh659WDZtICUtLClbsRoqpRkJ8XEc3L2Gbyb+kDfNr9FWLHxLsrQMfXXaRFmvoAmSAyhR0oLyFasYfT566jISEuJZtWgSgdf8qFO3ca40Zpfs2HRW/ZdGrSbu5QsGTdnBozvX2L5sOKMX/W3SWcOv701f359OXbKbp4/usGHpJGrr+1NTk96fZJiIDJ8zUl37Ki6WNQuG06PPaMNs7RN//8L7X4yiXpMOXDrzF9tWTebbSWvS5ZMT8lrPAE8e3Gb3lqUMnbwyXbqLp/6i73cz8qQxKzK27denUWSjZ/Ko6oXvon08e3SHLSt8qVm3Rd5syMR96LVLx6ks1TVsmXDt0nGsbR2oULkmtwIv5lRt5hTw+EuQcxT5sM3k/zeK2p63vdEFHMMkSVJKkuQAfI0u+KoFjkuS9Is+rSzL8qCsMtMHab1kWW4uSVJ5YBfQGFgBfCbLclCq2a/TZVl+KUmSPXAY2AcsQR8s1eeXnPU4YLUsy7slSRoNfAnsB8oCbdEFmv3098mIikA7WZbjJEmaC2zSB187A8MlSdoNVNDrrqSvl9TlcgG+QDcruBRwXpKk3/Rf35Bl+Tt9UPg9IN+3enByciYsLOVtZ3hYGA4O2QuAODo5UbmKJ25ldMvImjRthnzzBpggeHvu0Db8jutmNJStVJvn4SkzeV5EPMMmzTIe3dv7lLfJzyOCsbZLmZmnVicReOkQg6amzJJQqcx4++OUw0B+nP4Rjq4pe//mBXtHFyJSzT6KDA/BzsE5wzQOTq6o1UnExcZQ2tqWu7eucenMIXZuWkLsy2gUSiXmJUrQ7u2Ug+UsS1sj1W5AwJUzInibAY5OzoSFpbyhjQgLzdUMVAdHJ8pX8OBGoL/hQDNT4+jkTLhRG82b9uuB/oYDzfLC8YM7OHNYNyOzYpVaRrProsKD0y2ts3N0JSo82DiNfrasta0DzyNDsbV35nlkaIZLlzxrNiRsxQRiXkQaLQ+2c3DBrXwVbt+8lO1DBgpbe1xsDKvmDObdD7+hUjXvbGnODHtHFyLCX+9bIsPT+JZUe5MBlClXmZIWpXj84DYenjUNedjYOVCv8Rvc/S8wT8HbgqhzO0dXSlvbUdLCkpIWlnjWaMDj+7dwdfcwzMy2tnXEy6ct94ICshVgKQjdD24HsmHJGABiXkQSeOUkSqUZdg6ulKtUHSfXcgB4N2rL3Vv+upFJDrBzdDXSHRkegq29S4Zp7B2NbUT3eZrypCpz4JVTlK9U3ejFiVvZSnw76UcAgp/cI+Cy8bLv11FQ7fPx/Vts/3EKX49biZW1nVGe16+conylGrl7IZQG+zT1HxUejJ192jaacf1nRmlrO0qUtKBuI50x1G/agdOH9+ZZq6lspXm77jTXL6Hdv20pdo6uhD57RFjIY2aO7GlIP3v0h4yevQ1b++z3ccXVt2SEg6MLEanqMCIDf+7g6EJ4WLDBn8e+jMHKOsVWzqXZMiE1JUqUpH6jllw+f8Ikwduz/2zngv78iHKV6xCVavXa84hgbNLYkq2Dm9HMbl0aZ8N3tXw66LZIq+KFQqnkZXQkViZc2qzrT41tI31/6kpE+DPsX9OflrAoxeMHQXh4pj9QOS/YObgSmVpjRDC2GWiMDAvG3tEtfRtNNV6Iigg2tG91UiJrFw7Hp+U71G2ccvDh+WMH+KCPrn+q37Qj23+Ykucy5LWeI8OCWTl3OH2/nY6Lm/HKpYd3ZdRqNRWrpD/MKi+cOPiTwc9UqFI7Az+T3k+m9zPZ33LCTT8me/owyHCgWXYpyDHupdMHadAi5bn/jvwv1/yOEXjlFIkJ8byKe8mmpeP4/NvZOSpDQZUjs/GXdyPTr04QCAoz3G141SFJUvJLjuHAXEmSNqMLTFATFpoAACAASURBVFYBPNEFU4+gm02afGTyuWzco1pyOlmWHwLJ0xCtZVkO0n+evJ7wW0mSTgJ7gdetRzXkC5wBkqO6gbIsx8uyHAtktaGivyzLcfp/1wbGSJJ0DBgDOKAr8yW9vrtA2rXalYCrsiwnybIcDTwBkketybOQH+jzyneqVpN48uQxz549JTExkRMnjtGoSdPsXVtVIiYmhufPdctY/K/+a3TQWX7SpP3HDJm+lyHT91Kzfjv+Pb0frVbLw6B/KVnK2igwC2Bt50JJi9I8DPoXrVbLv6f3U6N+iiO+HXgW5zKVsE21pCshPo6E+FgAggJOo1SqcCmbP4HQSlVrEfz0IaHBj0lKTOTCqb+o69PaKE1dn9acOaqL2/udOUz1Oj4oFArGzlrPvNW/M2/173To3Jt33u9Lu7c/JPp5JLEvo/XaX3Hj6nnKlPXIF73/a3hWq87Tx48I1tv5qRNHaNg4/TLZjAgPCyE+Ph6AmOhobl4PwL1c7pe55xTPalI67T6Ns7epflgG2suWq5Avulp3+pBx83cybv5OvBq15cKJX9Fqtdy9dZVSltbpBlW29s6ULFWau7euotVquXDiV7wavgFAnYZtDMtQzx8/gJeP7vPQZw8Msxce3rlOUlISpa3tiAx/RkLCKwBiY15wR/4XF3ePYqE9KSmRNQuG0rhVZ+o3zfuBMR6etQhJ5VsunvoL7yx8y6Wzh5H0viU0+DFqdRIA4SFPePb4Ho4uZYh/FWc4ZCL+VRzXr56jbAXjWVw5pSDq3KvhG9y+eRm1OomE+DjuBfnjVrYS8a9iU5Unlpv+Z3GvkD3fXhC6p644yDT9f/WadKBXf1+8G7Wlomdt4l6+MCzvkwMu4FYu579DRc9ahDx9QFjwI5ISE7l0+iBeaWzEq2Ebzh3Tabty9h+k2o1QKBR4+bTm0umDJCYmEBb8iJCnD/DwrG24zu/Un/i0MH5hm3yYk0aj4c9da2jZIWfLEQuiziPCnrJmwTA+GzIL1wx8R9o9/PJCSv3r2+jpvzKo/9acPfYroFsKLNX2yXKGoUKhwKtBa24F+gG6fYfLlKucafqca81fW0m2iYjQp/x7/jA+Ld6ibMWqzFt/jBmr/mTGqj+xc3Rl3LwdOQrcQvH1LRlRuWpNnj19SIjeVs6d/Jv6jVoapanXqBWnjvwOwIXTR6jp1dBgKxqNhgtnjtAkVfD2VVwsURG6Rw+1OomrfmdwL2easXrTDr35buZevpu5l1oN2nH5lG6s/iDoKhaW1tjYGf8WNnbOlLQozYMg3W9x+dR+aurH6jUbtOX29fMAhD69hzopkdLW+X+AZmo80rbVU3/h7dPGKI23T2vOHtW11UtnDxnG6mFp+tPgx/dwdMnbftkZoWuj943aaJ2GxhpT2/GVc/9QTd9G6zRsk6aN3sfDszZarZatqybjVrYS7Tp/ZpSXrYMz/13X+Rk54DzObnkfQ+alnmNfRrNs5jd0++QbPGvUTZf3xVMHadSyU7rP80qrTh8xdv4uxs7fpfczBwx+xsLSKkM/Y2HkZw5QR+9nMiMs5JHBhiJCnxD85F6u9lwvCJ8IEBcbTdB1P0NagPd6f8eMHw4xbcVB+gydR7XajXIVuC2ocmQ2/hIICoLCnHkbSUqQ1Ef//xuyLPeTJKkssBPoDNwAOsqyrJEkyVyW5UT9DNjsbOL2H/AJgH7mbaz+8xeSJFWRZfm2fs9cO6AH0BBwBK7r0yWQcR39BzQBdqMLMt/Sf57dk31Sa78OHJJl+S+9zhJAHaCn/m8PUgKzydwFvCVJMkM387YsKQHe1BpMsk5IpVLx1ddDmDxhHBqNhvYd36RiRQ+2btlI1arVaNykGbduycyaPoWYmBgunj/Htq2bWfnDWlQqFX37DWDCuNFotVqqVK1Kx05vm0KmEdW8W3PL/wSLRr1JiZIWdO8/y/Dd8ondGDJdN/uky+eT2b1mHIkJ8VTzakk1r1aGdNfO/2G0ZQLAyxcRbFrQH4VCibW9Cz0Gzs03zSqVGR9/OYbFUwej0Who0a4LZStUYd/2VXh41qRuo9a0bN+VNd9PZNzXXShtZcvAEVl3dlGRoaxbOhmtRo1Go8WneQe8fVpleY2pqbtlIY6tG1HCyZ62d4/z37RlPNyQtz3g8gOVyoz+Xw9l+sSRaDQa2nZ4mwoVK/HTlnV4Vq2OT5PmBN26wdwZE3kZE43fhTPs2LaBJas28ejhfTauXYlCoUCr1dKley8qeuQtkJVz7d8xbeIoNBoN7Tq8pde+nipVJRo1ac5/t24yd8YEXsbEcPHCWX7etpElqzby6OEDNq1dqVsup9XyXvdeVPTI+wN+WmrVa0ng5ZNM/fYdzEtY8Mmg6YbvZo/6wHB6e6/+E9i6cgKJCfHUrNuCmvVaANChaz/WLx7J2SN7sXdyo9/whQD8e+4Q50/8ikplhnmJkvQdNg+FQsGzx3fZu3mB4Tdp1/lzylaoViy0Xz7zF0E3LvMy+rkhAPLp4OmU86ieK/0qlRm9+4/h+2mD0Wo0NNf7lv0/raJiFZ1vadGuK+uWTGT8IJ1vGTBc51uCblzhz70bUanMUCqUfDxgHNY29oQ+e8TKuSMAUGvUNG7Zidr1s/eyIzuYqs7dylWmZt3mzB7ZA4VSQbO23XGvUJWw4EesWTBUVx61moYt3qJm3RZFRndmKJUqun46gmXTvtQf1FKT5u3fz7FulcqMXv3HsXzG12g0Gpq27Yp7eU9+3bGCilVq4eXThmbturFxqS+Th7yLpZUN/YbNA3SnX9dv1pHpQ7uhVKn4sP94w3ZCCfFx3PQ/R++BE43ud/HUQU4c3AFA3cbtaNq2a441J2OqOv9z1w+8jIni57W6bQaUKhVj5uxIVa6zfDTAuFy5RVf/Y1mqr/9mbd/DvbwnB3aspGKVmnj7tKF5u25sWOrLxCGdsbSyof+wlPHH+K/f4lXcS9RJiVy9cJRvJ67CvXwVun36HRuWTmDnhvlY2djz+eCp+aQ1/21l9fwRvIx5rs9/vOGwuPymuPqWZFQqMz4bMIr5U75Fo9HQql1nylWowu5tP1LJswb1G7eidYcu/LB4MiMGdsfK2obBI1O2ypADr+Dg6IKLW1nDZ/HxcSyaOYKkxEQ0GjU1vRrStlP+HSSUGZJ3K27+e4L5IzthXsKCD75M0bnEtxvfzdSN1bt+MYmdq8eTmBiP5NUSyVs3jm3Yuju71kxg8dguqMzM+WDALJMftKZSmfFR/zF8P20QGo2G5u3ew71CFfb/tFLfn7bR96cT8B3UhdJWNnw5fA4A/924wsG9G1CpzFAolPQeMB7rfDhEMCONPfuNZ8XMr9Fo1DR9Q9dGf9uxggpVauLl8wbN2nZj07LxTB7yDqWtbOmbuo027ciMYV1RKlX00rfRoBuXuXDiN9wrVGXWSN3Lti69v6V2/Zb0HjiZXRvmotGoMTMvQe+Bk/OlDLmt56N/7CDk2UN+37mG33fqtm8YOmkVNna6OU5+Z/7hG99ledaYFbXqteT65RNM+/ZtvZ9J2aJhzqgejJ2ve+7p1X+i3s+8okbdFtSsp3sRc/XCYXatn0XMi0h+mDOIsh7VGez7I3duXuGffet0NqRU0rOfb54PojTluOXqhSNU925GSYv02/rlNwU9/hIICgKFqU4Sfx362ba70M0OPY4ueOqHbkuBUsBKWZY3S5LUC92et2p0e+G+C6wHlsuy7JdJ3lOAAFmWd0mSNBtojS6QOVKW5dP67RSW6vOUga+APYAzcAVdsFjSB063AU+B/sAp/Z637uj2vC0BhAOf6q9dLsvyu3oNAbIsp0w1SdHmkSadPfAjuqCxAt1euT/p97xtBFwDWmew5+0Q/X0VwGxZlvfq97xdLsuyX+q0r/stbt1+UDhGkEeuhpYrbAm5ooxN3OsTFVGee5vmFFZTU/HG8cKWkGsU2X4nVLR48lKcwFrQlDRLKmwJuSY+qajt4vS/j1JZPH2LRmP6U+9NhZkyq0VZRReNaeYCFAjF1V5sS74qbAm55vELq8KWkCscSscXtoRck6gunvtvmqtMf2i1qSiu45bi+lxR3OngXbJ4dkYm5NE3PYu8MZZb9kuR+t0KLXgryJpUs4w9gc2yLGdvzXMuEMHbgkUEbwseEbwteETwtuARwVtBThDB24JHBG8LnuJqLyJ4W/CI4G3BI4K3BU9xfa4o7ojgbXpE8DbnFE+vkwr9/ripN9O5JMvyiMLSkxpJktoCk9J8/I0sy9eycfkKSZKqA6WBYfkuTiAQCAQCgUAgEAgEAoFAIBAUaYp98FaW5c9en6pwkGX5CLqD1nJz7YB8liMQCAQCgUAgEAgEAoFAIBAUGgplkZrUWixQFrYAgUAgEAgEAoFAIBAIBAKBQCAQpEcEbwUCgUAgEAgEAoFAIBAIBAKBoAhS7LdNEAgEAoFAIBAIBAKBQCAQCARFH4VSzCPNKaLGBAKBQCAQCAQCgUAgEAgEAoGgCCKCtwKBQCAQCAQCgUAgEAgEAoFAUAQR2yYIBAKBQCAQCAQCgUAgEAgEApOjUCoKW0KxQ8y8FQgEAoFAIBAIBAKBQCAQCASCIogI3goEAoFAIBAIBAKBQCAQCAQCQRFEbJsgEAgEAoFAIBAIBAKBQCAQCEyOQinmkeYUUWMCgUAgEAgEAoFAIBAIBAKBQFAEETNvBTx46VrYEnKFi/WrwpaQK2zMXxa2hFxjd+N4YUvIFfdrtC5sCbnG8vK/hS3h/x2KYrp/vlpTfN/HljBTF7aEXJFUjOtcUPCotcJeBNkjOrFkYUvINdYWSYUtIVckqlWFLeH/HcW5D1WgLWwJAoHg/xnF12MKBAKBQCAQCAQCgUAgEAgEAsH/MGLmrUAgEAgEAoFAIBAIBAKBQCAwOQplMV3qWIiImbcCgUAgEAgEAoFAIBAIBAKBQFAEEcFbgUAgEAgEAoFAIBAIBAKBQCAogohtEwQCgUAgEAgEAoFAIBAIBAKByRHbJuQcMfNWIBAIBAKBQCAQCAQCgUAgEAiKICJ4KxAIBAKBQCAQCAQCgUAgEAgERRCxbYJAIBAIBAKBQCAQCAQCgUAgMD1KMY80p4gaEwgEAoFAIBAIBAKBQCAQCASCIogI3goEAoFAIBAIBAKBQCAQCAQCQRFEbJsgEAgEAoFAIBAIBAKBQCAQCEyOQqEobAnFDhG8FWRJ4JVT7NowF41GQ/N23enYrZ/R94mJCWxe5suDO9cpbW1Lv2HzcXQpC8Bfe9dy5vBelEolH/QdS826zQHYsnISAZeOY23rwIRFew15Pbx7kx1rppOYkIBKpaJXf188qtbJl3IEXD7Nz+vno9FoaNG+K29175uuHBuWTOT+nRuUtrZlwIi5OLm4c/3fc+zZupSkpETMzMzp8flQqtdpBEBSYiI/rZ2DHOCHQqmka+/BNGjaPl/0ZsSVS+fZsHoJGo2Gdh3fpdsHnxh9fz3gXzauWcr9u3cYOnoyTVu8YfhuxqQR/Cdfp3rNOoybPM9kGjPjit951q9eptf+Dt17fmz0fWDAVTasXsb9u3cYPmYSTVu0ASAk5BnzZ0xEo9GQpE7i7c7defPt9wpcf2Z4rZmFy9ttSAgJ50S9zoUth8Arp/llwzx9e+1Gp27p7Xzjsgk8uHOD0la29B8+FyeXssRER7F6wUju3w6kSZsufNR/nOGahZP68SIqDPMSJQH4duIP2Ng65FmrVqtl14a5BF45SYmSFnw6aDrlK9dMl+7BnetsWTGBxIR4atVrSY8+Y1AoFLyMec76xaOICH2Cg7M7/YYtwNLKxnDd/aAAFvh+Qt9h86jXpCMA3/Sqi3uFqgDYO7nx1Zhl2da6c8NcAi/rtQ6eToWMtN7WaU1IiKdW/ZZ8kKw1Wqc1PPQJjs7u9Buu05pZvuGhT1gzfxgajQa1Ook2b31Ey449SYiPY+3CkYQFP0ShVFGnQWu6fjI0N9VvElvZt30Z54//RuzLFyzZejZXurKDKfz5gon9eR6ZYudDJ63Cxi7vdh545TQ7N8xFq9HQrF033sygD920zJeH+nruN3yeoQ89uGcdZ4/sRaFU0rPvGEMfOuHrt7AoZYlSqUKpVDF23k9Gef6zfxN7tyxi3vpjWNnYFxvtR//YzvGDO1ApVdRq0Irunw7LlW5T+ZZnj++ydeVEHt29wbsffkP7Ll8Y8po0uBMlLfTlUqkYM2dHtrUWpG8BiAh9yrYfphAZ/gwFCgaNX4GjS1luXjvHvi2L0Gi0lLSw5NPB03EpUyFT7aYYH2aW59aVk3lwJxCtVotLmYp8OngGFqUsAbh05i/++GUVKBSUq1iNPkPnZqvuM/s9TGE7F0/+zj/71wNQ0sKSXv0nUM5DyrXOtOS2rcZER7FmwQge6P15r/7jDdfs376M88d/Je7lCxZvPZdvWnV689d2IsOesWm5Ly+iwlAolLRo/z5vvKMbI+/ZvJCAS8dRmZnj7FqeTwZPw7K0TTpNRU33ukWjCH5yD4C42GhKWVozfsHObGstSr4lq/abs/ovXnZuynHurcCL7N44D7U6CStrO4ZO3UDwk7usXzzakG94yCPe6TmIN975tMjohozH5wBxsTHMGNYV70Zt6dlvfLr7Fab2rHz40T+2cubwbrRaaN6ue47rWyDICWLbBEGmaNRqflk3i8G+q5i4eB9+p//k6cPbRmnOHtmDpZUNU5f/Ttt3P2Xf1u8BePrwNpdOH2TC4r0M9l3Fz2tnolGrAWjSpguDfVelu9++rYt5+4OvGL9gJ+/0Gsy+rYvzrRzb18zh2wnLmbpkNxdPHuRJmnKcPrQPSytrZq48QPvOH7Nn8xIArGzsGDL+e6Z8v5M+30xj/ZIJhmv+2L0Wa1sHZqzYz9Qlu6lWq0G+6M0ItVrNulWL8J26gMUrt3D6+CEePrhrlMbJ2ZXBQ8fTonX6APJ73T/im+ET0n1eEKjVatas+h7fqfP4ftUmTp04zMMH94zSODu7MGTYOFq2aWf0ub29I7MWrmDh8nXMWbSKvTu3ExEeVoDqs+bRpj1ceLd/YcsAdHb+09rZDPFdweTFe7h4KgM7P7wXy9I2TF/+K+3e/YS9W3V2bm5eki4fDub9T4dnmHffb2cxYcEvTFjwS74EbgGuXzlF6LP7TF76Gx8NmMSOtTMyTPfzmhl8NHAyk5f+Ruiz+1z/9xQA/+xbh1SnMZOX/oZUpzF/71tnuEajUbN/22Jq1G1mlJd5iZKMm7+TcfN3ZjtwC7oHttCn95my7Dd6D5zEjjUZa92h1zpl2W+EPk3R+rde65Rlxlozy9fWzpkRM7cwfsFORs3axt/71hMVEQJA+y6fM2nJAcbN+4U78hUCr5zMdjkM9WMiW/Fq2Jqxc7bmWE9OtZvCnwP0GzqTSYt+ZtKin/MlcKtRq/l57SyG+K5k4uK9+J06mK4PPaOv56nLf6Ptu5+wN10fuochvivZsWaWoQ8FGDplLeMX/JIucBsR9oyb/mdxcCpTrLTLARfwv3gM34W7mPj9Xjp0+SzX2k3lW0pb2fBBn7G07fx5hvl9N3kd4+bvzHbgFgretwBsXu5L+y5fMOn7/YyavR1rvU//ec1Mvvh2DuMX7MSn5Vsc3L06U92mGB9mlef7X4xi/IJd+C7cjYNTGU4c1NlOyNP7/L13HSNmbGbi4r306DOavGAq23F0KcvQKRsYv2A3nd4fwE+rp+ZJZ2ry0lbNzUvQ+cPBdMvEn4+Zsy3fdKbWm9+2o1Sp6P7ZCJ1Nz9rKib9+NuRZw7spvov24LtwNy7uFfl777p0moqi7n7D5zN+wU7GL9hJ3cbtqdu4XTpNWVGUfEtm7TcnFDc7B9P5k9iXL/hl7UwGjlnKhEV76Td8AQCu7pUMY9wxc3dgXsIC70Y5sxtT6obMx+cAv/+8HM+aeXuWLmgf/uTBf5w5vJtRs7Yzbv5OAi6fIOTp/TyVQSDIChG8BSRJmiJJUo98yKeuJEnNUv29Ia95ZnIfN0mSZuv/3VWSJHdT3OdeUADObhVwci2Hmbk5DZp3wt/vqFEa/4vHaNy6CwD1mnRADjiPVqvF3+8oDZp3wty8BE6u5XB2q8C9oAAAqtZsSGkr23T3UygUvIp9CcCr2Ghs7Z3zpRx3gwJwKVMeZzddOXxavMnVC8eM0vx78RhN39DNnGzQtD03rl1Aq9VSoXJ17BxcAHCvUIXEhAQSExMAOH14v2HGl1KpxDoPM5xeR9CtG7iVKYurmzvm5uY0b9UOv3OnjNK4uJahYiVPFMr0SxDq1G1IqVy85c4Pgm7dwM29LG5ldNpbtGrLxQy0e1SqgkJh7JLMzc0xNy8B6GY6a7WaAtOdHSJO+ZEY8bywZQC69uriVh5nfXv1af4m/hePGaXxv3iMpm10dl6/aXtu6u28pEUpPGvUw6xEiQLT6+93lEatOqNQKKhUzZu4l9E8jww1SvM8MpRXcTFUruaNQqGgUavO+F88qi/LUYPvady6C/4XjxiuO/7ndrwbd8DaJn8Czbp7ZVOrpNPauHVnrl5IpbWNXmubLly9cCTLfM1S231SAlqNzu5LlCxFtdq6maJm5uaUr1SDqPDgHJfHVLZSuZpXvvntzDCVPzcFuj60vFEfejVdPR+lid426jXtgKzXevXisTR9aHlDH5oVuzfOp9unwyCPS9EKWvvJv3byZre+Bru3tnXMtXZT+RZrW0cqetZGpcq/RWsF7VuePryNWq2mhndTACxKWVKiZCnDveLiYnT/j43B1iHztmyK8WFWeZaytAJ0s6oSEl4Z7Pv0od206tTLMKsrL3YDprOdylJdg8ZKVb2JCg/Jk87U5KWtlrSwxLNGfcOKg9RUMpE/N4Xt2No7G2Z/WpQqjWvZSoYXnjW8mxnarEdVLyJz0WcWhu5ktFotl8/+RcMWb+VIb1HyLZm135xQ3OwcTOdP/E79gXfjdoaXtBn5PfnaeZzdyuPgnPMQQWGMzx/cuc6L5xHU8E4f1C0K2jPz4c8e38WjqhclSpZCpTLDs0ZDrl44nKcy/H9CoVQW+f+KGkVPkYmRJMmUZa4LGLyOLMt9THETWZafybKcvFa1K2CS4G1URDD2jq6Gv+0cXNMNOKMigrF30qVRqcwoZWnFy+goosJDsHd0M742IusBU48vRrN3yyJ8v+rAns2L6PLxd/lTjvAQHFKXw9GVyIjQDNK4GZUjJjrKKM3ls4coX1nC3LwEsS+jAdj/0wqmj/iIH+aP4kVUeL7ozYiI8FAcnV0Mfzs4ORNehGagZkVEeBhOTrnXHhYawrDBfRjwxQd07dEbB0cnU8gs9kRGhGDvlKrNOboSGZG2vaakSd1eX8emlZOZMbInv+9cjVarzRe9URnoTfvQEhURgl2atpucJvp5hGHAbWvvTPSLCP01wVy9cISWHT9Id8+kxATmjv2QBb4fGx5EssPziBDsHHOu9flrtGaVb2TYM2aOeJ8JX3WkQ9e+hqBjMrEvX3Dt0nGkOk2yXY5kTGkrpsYU/jyZjcunMG14L377JX/sPK2N2zu68DxNP5hZPT9P1bcmlzPZNhQKWDb9K2aP/pBT/+wypPG/eAxbB5d8WY5d0NpDnt4n6MZl5o39mEWT+mYrUJ1d7fnlW7JCASyfOZC5Y3px6tCu16ZPpqB9S8jT+1iWtmb1/GHMHtWTPZsXGmZFf/z1FFbNGozvwPZcOP4bHbsaL0k21pT/48PX5bllxUTGffkGwU/u0eatjwCd3YQ8uc/CCZ8xf/zHBF4xfjGcUwrCds4c2UPNes3zpDMrzTlpq4WBqZ8twkMe8+juzQy3XTt7dC+16rUoVrqDblzCxtYRlzIVc6S3KPkWyLj95oTiZudp9UD++ZOQp/eJjXnB91P6MndML84fP5Du3pdOH6RB85wF/E2tO7PxuUajYc/mBXT7JOPVf0VBe2pS+3D38p4E3bhMTHQUCfFxBF45mesXRAJBdig2e95KktQG6CHL8hD93wHAUqAfEAfskmV5uSRJHwKDARWwXpbltZIkbQRigUrABOBSFveZCbRBNxYfIcvyWUmS6gHf65PIsiwPkCRpDuAD2APTZFneB3wH2EiS9C7QHTghy3JtSZLcgE2ABRAKfAa4ANuA24AXsEaW5RWZaBoBfADE63VcAZYDI4BOQB1JkgKBYOCwLMsH9ffcLMtyx4zyzC1pN5bO8PlWoQDSf/G6TalP/P0L738xinpNOnDpzF9sWzWZbyetyYNavcaMJKZLk7XeJw9us3vLUoZOXgmAWp1EZHgwVarXpWefkfxzYAs7Ny2m33cZL88wBcVlj++MgiA5ke7k7MLiFRuICA9j7gxfmjZvjZ19/syo/J8io3pO114zaA2vMaS+383C3tGVV3Ev+XH+CM4f/40mbfJhf99s6M0wzWuy3b1xHu99PBSlUpXuu2kr/8LOwYWw4EcsndYf9wpVcXYrnw2pGel4vdbXic0qX3snN3wX7iYqIoTV84ZSr0kHbOx0syvU6iQ2fD+GNm/3xsm13Gv1Z3Dj9PfNB1spCEzhzwH6DZ2FvaMLr+JesmreSM4d+80wezf3YrNRh5mkyfhj3bUjZmzCzsGF6OfhLJ32Fa5lK1GxSk0O7l7DNxN/yJvm1+jKTpqcaq9aswFqdRKxMS8YNXsr94MCWLdoFNNW/JG7wyxM5FuyYtj0zYZyLZ8xEDd3DzxrNnztdQXtW9TqJIJuXGbc/F+wd3Jj/eJRnDu2n2btunPkt618PX4Flap68c/+DezZNJ+Pv87+8v68jg8z1Jwqz08HT9ctXV8/m0tn/qLpG13RqNWEPn3A0CnriAwPZvGkL/BdtCfXe5qa2nZuBVzg7NG9DJu2KRfiMiEPbbWokF/PFq/iYlmzYDg9+ow2zPZM5uDu1aiUZvi0fCc/JKe7N5hGt9+pP2mQw1m3Oi1Fx7dAxu03RxRHOzeRP9Go1Ty8e51vQx3o2gAAIABJREFUJq4hMSGehRM+xaOqF67uHgAkJSVy7dIxuvTO5SSoAh6fn/z7Z2rVa2EUdM01BezD3cpVpsN7fVg+YwAlLSwpW1FClcHzh0CQXxSb4G0m9AbekWU5TJIkpSRJDsDX6IKvWuC4JEm/6NPKsiwPyiozfZDWS5bl5pIklQd2AY2BFcBnsiwHSZKU3CKny7L8UpIke+AwsA9YAjjJsrxAn19y1uOA1bIs75YkaTTwJbAfKAu0RRdo9tPfJ62mOsCbQHNZltX6+5dPLpAkSQeB5bIs+0mSVB2YChwEPgW2ZLMeM8TOwdXo7VFURHC6ZXT2jq5EhgVj7+iGWp1EXGwMpa1sdbOhwp8ZX2tvPHMsLeePHeCDPmMAqN+0I9t/mJIX+ak0uhCRuhzhwdhlUI6I8GfYO7kalQMgMiyYlXOH0/fb6bjoAz1W1naUKGlBvcZtAWjQrAOnDu/LF70Z4eDoTHhoypvDiLBQHByKxwxURydnwsLSaM/F7FkHRyfKV/DgRqC/4UAzQQq6tpiqzYUHY2efUXt9hr1jejvPKl/QLenzafkWd4MCch28PX5wB2cO7wagYpVa6fSmXbpm5+hqtC1AVHgwtvoZqNa2DjyPDMXW3pnnkaGGJVgPbgeyYYnOj8S8iCTwykmUSjO8G7U1zF51ci1H1ZoNeXTvRqbB2+MHd3D6kF6rZy2iwtNodciGVvustequeU2+Di6UKV+FoBuXqN9U9y5u+4/TcC5Tkba5PBTBVLZSEJjCnyfnCzo7b9zyLe4GBeY5eGuXpp4jw0PS9YN2mdSznb5vTV3O5PZhZ2gDjng3asu9/wKwLG1DWMhjZo7saUg/e/SHjJ69DVv7nPvbgtRetWYD7B1dqdu4HQqFAo+qdVAolMS8iDTsmfg6CsK3ZFlfqcrl5dOWe0EBmQZvC9O3qNWJlK9U3fDSx8unLff+8yf6eQSP78tUquoFQINmnVgx8+ssymua8eHr8lSqVDRo1olDBzbS9I2u2Dm64lHVC5WZOU6u5XBx9yD06QMqetbOVHtaCsp2Ht+/xfYfp/D1uJVYWdtlW9/ryEtbLQxMZTvqpETWLhyOT8t3qNvY+OyHc8f2E3DpBN9OXpPr080LQ7dancTVC4cZMzd7+2gXRd+SmrTtNycUFzsvCH9i5+hKaWs7SlpY6reEaMDj+7cMwdvrV05RvlINwwv/oqI7s/H53VtXuX3jMif//oX4V7GokxIpaWHJex9n70Dewvbhzdp2p1lb3UuKA9uXGM3qFWRNRls9CrKmOG2bYHhNIklS8i89HJgrSdJmdNsVVAE80QVTj6CbFVtWnzY7R0hWS04ny/JDIHmTUGtZloP0nyevAflWkqSTwF70wdTs5AucAZKjuoGyLMfLshwLZLaZZw3gZPJ9U90/HbIs3wSc9UHsbsDu1+jKkoqetQh5ep+w4EckJSZy6fRB6jRsY5SmTsM2huUaV879Q7XajVAoFNRp2IZLpw+SmJhAWPAjQp7ex+M1g2lbB2f+u+6nK0vAeZzdMj/lOCd4eNYi5OkDwoIfk5SYyMVTf+HtY1wOb5/WnD36KwCXzh6ieh0fFAoFsS+jWTbzG7p98g2eNeoa0isUCrwatuJWoE7vTf8LlClXOV/0ZoRnteo8ffKI4GdPSExM5PSJwzRsnLulXwWNZ7XqPH38iOBnT0lMTOTUiSM0bJy9JYPhYSHEx8cDEBMdzc3rAbiXe/1Myf+PVExr56f/wsuntVEar4atOXtMZ+eXzx5Cqu2T5YOMWp1EzItI3b+TErl26SRly3vmWmPrTh8aDlPwatSWCyd+RavVcvfWVUpZWqcbYNnaO1OyVGnu3rqKVqvlwolf8Wr4BmDse84fP4CXj+7zqSsOMk3/X70mHejV3xfvRm2JjXlh2N805kUkd+R/cStXJUutyYeFePu05fzxnGk9f/xXg6Y6Ddtw/phe67EUrV4N22SYb2T4MxLiXwEQG/OCO/K/hkH5rz8t41VsND2+yP3BPKawlYLCFP5crU4iWm/nSUmJ+PudoGyFzG0ju6TUc0ofmr6e23BObxtXzv6DpO9DvXxap+lDH+DhWZv4V7G8itPtDR//KpYbV8/iXsGTshWrMm/9MWas+pMZq/7EztGVcfN25CpwW9DaAbx83kAOuABA8JN7JCUlYpWDfeQLwrdkRtpy3fRPKVdmWgvLt1SsUpvYly+Ifq5bCnor4AJu5apgaWVDXGyM4XT7m/5ncStXKdMymGJ8mFmeWq2WkKcPAN2sv2uXjuFa1kNXTp83+C9QZzcxLyIJeXofxxyuRigI24kIe8qaBcP4bMgsgy/PL/LSVgsDU9iOVqtl66rJuJWtRLvOxocdBl45xT/7NjBwzFKj/Z2Lum6Am/7ncHWvZLTlQlYURd+SVfvNCcXFzgvCn3g1fIPbNy+jVieREB/HvSB/3Mqm+Gu/03/meMuEwhyff/HtHKav+ptpKw7S7dMRNGrVOduB24LSnpUPj34ebkhz9cJhGjZ/O9vaBYKcosiv/QtNjSRJXuhmu74nSVIjYD3gI8tynCRJZYGdQGfgZ6CjLMsaSZLMZVlO1G+bsFyWZb9M8p4CBAB3gKmyLHdOnnkry3JjSZJOo5t5e1u/Z64d8A/QEHAErsuy7CJJUm+ggizLc/T5Bui3TVgKHE818zYB3Uzd5bIsv5s6bQbaagMLgbf0ZVICFZKvlSRpNbrtIc7p0/dFN/P4lSzLA7JTt4f84zM1goDLJ9m9cR4ajZqmb3Sl0/sD+G3HCipUqYmXzxskJsSzadl4Ht69SWkrW/oOm2d463pw92rOHt2HUqmiR5/R1KrXEoD134/mv0A/YqKjsLF14J2eg2jWrjtBNy6za8NcNBo1ZuYl+LD/BCpUqZmpbnNVpnHsdFy7dJKf1y9Ao9HQvN17vNOjP/t/WknFKjWp26gNiQnxrFsygYf/x959xzdV/X8cf6XpYrW0pbTszWXvKVOWKEPZoKjIUAFRQXYLZc8qspGpgsguICiyp8je4FX27G6ZhbZJfn8kTZs2QEe6ft/P8/Hw8f3Snty87+nJOScn957cUMmV24V+Q6bh6V2Y7euX8Mem5eQvEL+Q/PXYhbjkdScs+D7L5/jy7OkT8ri48fEX4/DwfP03fLs5Pkl27oROnzjKj0vmoNfrebNlGzp1+4g1q5ZSqkw5atdtyNV/rzBzsg9PnzzGwdGRvG7uzFpgvPh6zPCB3Lt7i+fPo8iTx5X+X46gWs26Kc5gp0ndF4adOvE3KxbPRa/X06zlO3Tu/iG/rlxG6TLlqF2vAVf/vcL0SWMsss9e+BPnzpzgx6ULzLdUvt22A63ebp/i579VvsnrC6VCtZXf4tGkDo753HgRFMZ/E+ZyZ0Xy9ztMjpynzya77IXTh1i/YiZ6vZ43mr3LO536sXWNsZ1XrW1s5yvm+HDnpkrO3C70HTwdT9PrdXT/t3ke9RRdbAw5cubhyzEL8fAsyLdje6OLjUWv11GuSl26fDwUO+3rbwmK1b/6s0GDwcC6ZVO4cu4IDo7O9BwwkWKlKgIwdVgXRs1cD8Cta5dYtcCXmOgXVKjWkC69R6HRaHjyOJLls4Yar7LI502fId8mucJi5XxfKtVsTPV6rbiunuXXxROws7Mzvoba9DR/Up5Y0jusjFkvnz2Co6MzPQfGZ50ytAuj/eOzrpwfn7Vrn/isy76Lz9p3yLfkyuP60uNeOXeUTT/7m9t9k9Y9aNiyMxFhgfh+3gqvQiWwtzfu1drk7e40aN4JAG0KXp+2bisFi5Ri48pZnDj0h/mqhQbNO9Cu28uv3kvIzi75cxFb9+dOzjmY6dsHnc7YzstXqUvXXt/YpJ1fPH2IDStmoNfrqd/sPd7u1I/f1synWKmKVDHV849zfLh78x9y5nahT4Ix9I+NSzi6dzN2Wi1deg2nYo2GhAbd5YcZgwHQ62Kp1egd3u7UL8nz+vZ/m5HTV6doATQzs8fGxLBywVju3lSxt3eg40dDUCpbH6P0+le/GU+vvuVRZCgzRnbnedRTNBo7nJxz4PPdZp4+jmSJv/GNpk6no1bDt2nd0fr0K7P7FsDcv2AwUKRkBd7/zA97BwfOHtvD9rXz0djZkTOXCz0HTHjltizpMT+0dky9Xs+ssb14/uwJBgwUKqbQvZ8vOXLmxmAwsOknfy6fPYKdnR1vdepHrUSLFil5m5NebeeXRX6cPbYb93zGr6iw02oZMe3VV1OmpE9My2vVt//bPI96Yu7PB41ZRIEipdi0chYnD/1u7s/faN6Rtsnszw2GV79Gbd12rl45zayxvShYtIz5i2/bv/8llWo0wu+LNsTGRpMrt/FKuRJlq9Dj0zHJrtvMyg3w8zxfSpStQqNWXVOcNav0LXZa7UtfvwAaTfZt55k1FgHs3rqCv/dtQWOn4Y1mHXnTdCdW9IsofPu3Yvy838mRM0+y6zajcsdJOD9P6O/9W7h97RJd+4zOUtlf1YfPGvsxTx8/RGtvT8ePhr70uyhaVnXK/CsgspiwcX2z/EKkx7ilWervlp0WbzUYtzFwBw4AnTFuNVAMyAEsUFX1Z0VRumHc81aHcS/cthgXel+7eKuq6gZFUaYCTTBufzJUVdUjpu0U5piOqQKfA5sAT4z7z7Yy7mCgFMe4j+0DoC9w2LR4WxDjnreOQBjGLQ08Scbirel3QzHuefvMlOMM8Yu37wGDgbOqqn6lKEpu0/O3UlX1aHLq9lWLt1lZShZvs5LULt5mBaldvM1s6bV4mxFSsniblbxuUSsrywIXmKZKShZvs5qULFRkJdm5nWdXr3vDnJVl174lO8smb3OSyK59Irx+8VaIOClZvM1qsvNYJDKeLN4mJYu3KZdtFm9F8iiKkgvYrapq/eQ+RhZvM5Ys3mY8WbzNeNl5USu7LrDI4m3Gy87tPLvKzm+Ys2vfkp1l17c52bVPBFm8Fckni7fif4Us3iYVNuHTLN8BeIxdnKX+btn9C8tSzLQ/bsLNVE+pqvpNZuVJSFGUZsDYRD8epKrqhWQ+viYwF5hp62xCCCGEEEIIIYQQQoiM9T+3eKuqatId4bMIVVX3YvyitdQ+/hTGL24TQgghhBBCCCGEEEJkc/9zi7dCCCGEEEIIIYQQQoiMp7HLUjsSZAuyWZsQQgghhBBCCCGEEEJkQbJ4K4QQQgghhBBCCCGEEFmQbJsghBBCCCGEEEIIIYRIdxqNXEeaUlJjQgghhBBCCCGEEEIIkQXJ4q0QQgghhBBCCCGEEEJkQbJtghBCCCGEEEIIIYQQIv3ZaTI7QbYjV94KIYQQQgghhBBCCCFEFiSLt0IIIYQQQgghhBBCCJEFybYJAgetLrMj/E95FJMrsyOkmpvjo8yOkCo5T5/N7Aip9qxGtcyOkCrZuc7DnjlldoRUyZsjJrMjpFpMbPa8dcpOY8jsCKkWHZs9P7930OozO0KqZdc6z86cHLLnHLdB8LrMjpBqwUVrZ3aEVDkVUTazI6SaW87ozI6QKsWd72R2hFS78rhEZkdIFU02nreI/180djInSimpMSGEEEIIIYQQQgghhMiCZPFWCCGEEEIIIYQQQgghsiBZvBVCCCGEEEIIIYQQQogsSPa8FUIIIYQQQgghhBBCpDuNXfb8vovMJFfeCiGEEEIIIYQQQgghRBYki7dCCCGEEEIIIYQQQgiRBcm2CUIIIYQQQgghhBBCiPSnketIU0pqTAghhBBCCCGEEEIIIbIgWbwVQgghhBBCCCGEEEKILEi2TRBCCCGEEEIIIYQQQqQ7jZ0msyNkO3LlrRBCCCGEEEIIIYQQQmRBsngrhBBCCCGEEEIIIYQQWZBsmyCS7eLpI6xdPhO9Xk/DFu/xdsfeFr+PiYlmxewx3Lp+hVx5XPn0m+nky1+Qy2f/ZtOqOcTGxmBv70Dnj7+mXOU6PI96ygyf+GNEhAVTr/E7dOszTLKbXDh9hF+X+WPQ62jUogPvdPokSe5ls8dw69oVcuXJy+dDp5Evf0Gu/3uRnxdOAsCAgXe7fUaNes0A2Ll1FYd2bwY0FC5Wmt6DxuHg6GTT3ImdPnmM5YvnodfraNGqDR27fmDx+0sXz7F88Txu3bjGkBFjeaNhUwCCgwOZMWkser0OnU7HO+068NY776Zr1ktnjrBuxQz0ej0NmnegdYekbeXHub7cvn6FXLld6TtkOvnyF+LJ40gW+w/l1rVL1Gvanh59R5kf8+3YPjyKDDXX85djFuHi6p6u5/EqVZZMIf87TYkODuNg9XaZliNOdq5zg8HA9lVTUM8dxMHJmU79plCoeMUk5e7duMTGJaOIiX6BUrUxbXqORqPRsGbeYEICbwLw/NkjnHO6MGhSAHeunWfzCr+4J6FZh4FUrNUyTVkvnTnMhhXTTfXckVYd+lj8PiYmmp/n+nD7+mVy5XGlz+CZeOQvBMCfAUv5a08AdnZ2dOk9kgrVGhARGshP83x4FBmKRmNHwxadeLNNTwDu3PiHNUsmEhMdjVarpVtfH4qXqZym/HEMBgMbV0zj0plDODo503PAJIqUrJCk3O3rl1g135eY6BdUrN6ITp+MRKPRcObon/y+fiFB964zdMqvFC1l/HvdvHqBNT+MNz4HBt7pMoCqdZqnOev6FdO5dNqY9cOBEylqLeu1y6yc70t09Asq1mhEl09GoNFoePr4IctnDSMs5D4engXpM8SfnLldCLx3g1Xzx3DnxhXa9RhEi/a9LI6n1+mYPrIHed3z03/UvDSdQ9x5BPw0lStnD+Hg6EyP/pMpUiLpedy5folfF/kSE/2c8tUa0eHjUWg0Grb+4s+l0wfQau3J51WEHp9PIkcuF3SxMaxZ7Me9m1fQ6WKp3ag9Ld7rl+asG1ZMN7ePDwdMfEn7MNZ5XPvoHFfnT4x1Hh5yH3fPgvQZnKDOF4zh7o0rtO0eX+dB92+wfNZw83HDgu/SpusA3mzzYZrPIz3qPDY2hvVLx3Pn+iU0Gg0dPh5J6Qp10pQ1I3KnR1tJ7NKZI6xfMR2DXs8bzTvwlpU+8qe5PtwxjUV9hszAwzQWLfH/htumsahb39Hmx2xZPZdjB34j6ukjZq3626Z5rTly6RrT1+9EbzDQ4Y1q9HnrDavldp2+wtClm1g94hMqFito/vmD8Id0mPgD/d9pzMct66V73oROnDrNgsVL0ev1vN2qJd27dLL4/YaALfyxcxdarRZXFxeGfj0Ir/z5uXr9OnPm/8CzqGfY2dnxftcuNG3cMF2zGgwGflsZN/bnoMunUyhUPGk7v3vjEusXjyY2+jlK1ca0+9A49gMc2bmKo7tWY6fVUq5qE97pMZQ7186zabmf6TmgRceBVKrVwqbZL545wrrlxjlXw+YdaG3tPdEc05wrjyv9THOuy+eOEpDgPVGnjwZTrnIdol9E8YP/MEIC72JnZ0eVWk3o+OFXNs38KidOnmLR4iXoTO2mW9cuFr/fGLCZHX/uNLYbVxeGfP0VXvnzZ1g+MLaXTT9N5cqZQzg4OfP+K/rF1QtN/WL1RnQ09YtbVpn6Rfv4fjFnLhfz4yJCHzD1m/a07jyAZu0+SXLcNOX+cRqXTbk/6G99vnXn+iV+WWAcTytUb0THXvHzrR0bjPOtIZPj51uxsTGsXRw3DtnRsddIylSsbbPctj6PLau+5eKp/WjtHcjnVYT3+0+0qH+RSnZyHWlKSY2JZNHrdKxeMo0vfecxfvZGThzawf071yzKHNm9mZy58zB5wVZatPuATT/PBiC3S16+GP09475fzyeDJrB8ti8AzjlyMfa7teb/PDwLUN20wCjZjbl/WTydwWPmMnHORo4d3sH9O9ctyhzavZmcuVyYunArLdt9wAZT7kLFSjHGfxXjZq1h8Jh5/LxwMjpdLBFhwezZvoYxM1cxcc569Ho9xw7/adPciel0OpYsnI3v+OnMXvgThw7u5c7tmxZlPD3zM2jwSBo1tZygurl5MPXbeXw3bxnTvlvApvWrCQ8LTbesep2OX5dO5Quf+fjN2sSJw1bayp4AcuZyYeK832jeticBq4x17uDgRPvuA+n04RCrx+795RR8/dfh678uUxduAe7+tInjbftmaoY42b3O/z1/kNCgWwyZuYP3PhnP1h8nWC235afxvPfJeIbM3EFo0C3+PX8IgO5fzGLQpAAGTQqgYq1WVDS9SfMqXIYB49czaFIAHw9bzJYV49DpYlOdU6/TsW7ZFAb6LGTMrM2cPPIHDxLV89G9m8iZ24Xx87bTrO2HbF71PQAP7lzj1JEd+M4KYKDPQtYunYxep8NOq6XjR98w9vstDJuyioN/rjUfc/OqWbzT5XNG+6+nTbeBbF41K9XZE7t85hDBgbcYO2c73T/1Y+3SSVbLrV0yiR6f+TF2znaCA29x+exhAAoUKUPfobMoVb6mRfmCRUozbNoaRs7cwIDRi1izeEKa6hyMC+YhD24xbu423v9sLGuWWM+6xpR13NxthDyIz7pz8zKUynUZN3cbSuW67Ny8DIBcuV3o0nskzdt9bPV4+37/Be9CJdKUPaErZw8REnib0bN+p2u/cWxYNtFquQ3LJ9K1rx+jZ/1OSOBt/jlnPI+yleszfEYAw2cE4FmgOLu3LAXg7LGd6GKjGT4jgG+mrOOvPesJD7mXpqyXzxwmJPAWfnO20ePTsax5Tfvwm7ONkATtY5epzv3mWKnzT0bSLFGdexUswaiZ6xk1cz0jpq/BwdE5zYv+kH51/vfeDQAMnxHA56OXsGWVP3q9Ps150zt3erSVhPQ6HWuXTuELnwWMmRXAycM7kvSRf5nGovHzttGsbU8CTH2kg4Mj7boPpIOVsahKrSaMmPaLzXK+ik6vZ8raHSz4ojsBYz5jx8lLXHsQkqTc0+cvWL3/BJWLF0zyu5kbdtGwQqmMiGtBp9Mxd+EPTBk/lqUL5rLvwCFu3b5jUaZ0qZLMn/Uti+fNpnHDN1iy4icAnJ2cGD7kK5YumMuU8X4sXLKMJ0+epGte9Zxx7B/qv4OOvcezecV4q+U2/ziBjr3HM9Tfcuy/dvkYV07v5espmxky7Tcav2NccPMqXIYvJqznq8kB9B6+mIDlaRv7E9PrdPy6ZCqDfOYz7vuXz7ly5XZh0vzfaNG2J5tWmt4T5XFj4KjZ+M3aQK9BE1kxx8f8mFbtP2bC3M34+q/lmnqWi6cP2yzzq+h0OuYvXMSk8eNYsnA++w4e5Nbt2xZlSpUsydzvv2PR/Lk0bNCApctXZEi2hK6cPUTIg9v4fP873fqNY/1S6/3i+mUT6drPD5/vfyfkwW2umMYlpXJ9RswMYMSMADy9i7N781KLxwX8PJ3y1RrZPPfls4cICbyF7+ztdO/nx/pl1sfTdUsn0e1TP3xnbyck8JY5d4EiZej9TdL51tE9xnFopH8AA3wXs3nlTJuOQ7Y+D6VyfUb6BzBy5ibyFyiWpP6FyChpXrxVFMVbUZSpaXj8OEVROqfwMQMS/P9eiqI0ScXzpvkYrzm++bwURXnpKJEwR1Z24+pF8hcogqd3YewdHKjd8C3OHd9vUebsif3Uf9N4FV/N+i24cuE4BoOBoiXLkdfd+AlnwaKliImOJiYm2uKxQfdv8fhhOGUq1JDsJtf/u0j+AoXNues0fIsziXMf388bb7YFoNYbzbly/gQGgwEnpxxotcYL62Nios2f8oNxohMd/QKdLpboF1Hkdfe0ae7Erv77DwUKFsK7QEEcHBxo2LgZx/8+YlEmv1cBipcohZ3GcuNyBwcHHBwcAYiNicFgMKRr1ptXL5LfuwieXqa20uAtzp/Yb1Hm/In91G9qbCs16rfgH1NbcXLOQeny1bF3dEzXjLYQfvgkMeEPMzsGkP3r/MrpvVRv8C4ajYaipavx/NkjHkUGW5R5FBnMi6gnFC1THY1GQ/UG73Ll9B6LMgaDgYvHd1ClXhsAHBO8hmNjokGTtk39b169iKd3UfKZ6rlmg9acP7nPosz5E/up26Q9ANXrtUS9eAyDwcD5k/uo2aA1Dg6O5PMqjKd3UW5evYirm6f5KlLnHLnwKlSCyHDjuWs0Gp4/ewrA82ePcXWzXT9z4eQ+6jRuj0ajoUTZqkQ9fczDCMsFiocRITyPekKJstXQaDTUadyeCyf2AuBduCReBZMubDpa9Jsv0lrlAJw/sY+6TdolK2tJpSoajYa6Tdpx7vi++Mc3Nf5N6jZtz7njxnPI4+pBsdKV0NonvYEqIiyQi6cP8kbzjmk/AZOLp/ZRu5GxzouXqUrUs5edx1OKm+q8dqP2XDhpzFuuSgNz3RYrU4XI8CAANGh48SIKnS6WmOgX2Ns74JQjd5qynj+5jzqNk1nnZaua2kc7zp9IUOem10HdJu05fyJRnWtfftOaeuEYnt5FcPdMuiiWUulV54F3r1GmYl3zOeXImYc71y+lOW96506PtpKQsY8sYtFHnksyFu2jnun1WL1+S1TzWJST0uVrWL2LqUTZKjbt/17l4s37FPF0p3A+NxzstbSuWYH95/5NUm7+bwfo1bI+Tg6WbXnvWZXC+dwoVSBj8iak/vsfBQsUoIC3Nw4ODjRt3JC//j5mUaZalco4OxvruLyiEBIaBkDhQoUoXMj4msvn4U5eV1ciHz5K17yXT++lRsO4sd/Yzh9FWrbzR5EhvIh6QrEyxnZeo+G7XDplHPv/3rOGJm37Ym+a4+Z29QASjf3RLyzm77ZwI27OZXpvUavhW0na+bnj+6lnZc5l8Z6oSPx7IkenHCiVjVdN2js4ULREOSLCgmya+2XUf/+jYMECFCgQ124aczRxu6laBWdnZwDKl1MINbWbjHTh5D5qN05evxg3b6ndOEG/WDW+XyxepgoPw+Pr9/yJPXjkL4x3Ydt/6HLxRILcyZxv1U7GfCvw7jXKVk4wDuVysek4ZOvzKFf1jQTjUlUiM6h9C5FYmhZvFUWRByFzAAAgAElEQVTRqqoaqKrqqNeXtinzgqeqqj+qqnogk46RLKqqvur+hWyxeBsZFoy7h5f533k9vIgID7FSxhsArdaeHDlz8+RxpEWZ00d3U6SkYl6Qi3Pi8A5qNWhl80lKds4eGR6Cez5v87/dPPITGWa5KBQRFl8mce7r/15gzJed8fu6Kx9+Phqt1h43j/y89e6HDP/0HYb0bkWOXHmoVK2+TXMnFhYWgke++DcCHvk8CQ9LeiXIy4SGBDN4YG/69epKh849cPfIlx4xAYgID8YtQZ0b24plnUcmKBNX508TtRVrflrgx6ShXdm+fnG6L0JnJ9m9zh+FB+HqHp/fxd2bR4nyPwoPxtUtvg9ydffiUbjlxO+mepJcLh7k8y5u/tmda+eYPaotc0e/y7u9/F65aPQ6keFBuCXsB929kvQnkeFBuOUzlklYz5Fhwbh5eFs+NlH+sOB73L3xj3lrhM69hhOw8jt8Pm/Jpp+/o/0HtruFMtJKm3mYqM4fhgeTN1G/H5mojDU3/zvP5CHvMfWbjnTrNzZNdR6fwzJr4hyRVrLGnc/jh+HmhR9XN08ePwp/7XNuWDGDDj2HoLHh7WgPw4Msz8Pdy+LNY1wZV/cE7dwjaRmAY/sDKF/VeEtz1botcXLKgV//N5kwqCVN2/YiV27XNGW11j6SU+eRaajzOKeO7KBmg7fTEt8sveq8YDGFi6f2odPFEhZ8lzs3LhMZFmiTzOmZOz3aSkKJ242bR/4kmVI7FmWU4MjHeLvlMf87v5sLQQ8fW5S5cieQwIhHNKlcxuLnz15Es2LXUT5/x/ZX7yVHaFg4np7x87t8+TwIDXv5a++PnbupUzPpRRP/qP8SExtLwQLeVh5lO48igsmbYOy3Nq4/StzO3b14FGHsZ0IDb3JTPcV8v278MOkj7ly/YC53++o5vhvZju9Hv8t7n6Rt7E8sSTu3OhcITvLeInE7P/33boqUKJfkPdGzp484f/Ig5UwLc+ktLCwMz3yJ283LF2d37NxF7Vo1X/r79PIwPCjJPMpav5jX3euVZcDUL1Yz9osvnj9jz9bltO6cPssJkRGWcxjXl823EuWOjHj1fKtQMYWLJ+LHobvXLxNhw3EoMVuex7F9AZSvnr7bsvyv0Gg0Wf6/rOa1o4GiKHbAEqAMoAc+BQKAP4CaiqJ8AsxTVbWtoijNgG+BO0AM8KuqqhusHLMpMAu4C0QDF00/Hwx0NOWarKrqNkVRxgMtTcebCxQCSiiKsh9YDJQ1Pf4k8AtwDagCLFFVdb6iKG8CPkBOQFVV9RNFUb6ydgxVVTcoijIZaApogG9UVT1qKvcXELcZyzuqqsak4LwuqqpaSVGUT4E+QBSwAQhJkGMrcMZK1uIvOS93YBngDhiAdwBPYCHgDIQBH6qq+jxxztSwtuyRuDkbrJRK2Ojv377GxpVz+NpvQZJyJw7/Se+vrN/CkFbZNbvVxaYknYiV3KazK1m2MhPnbOD+nessn+NH5RoNiI5+wdnj+5m+aBs5cuVm4cwRHN2/nfpN29g8/ysikvQv8HL5PPMza/5ywsNCmTbJl/oNmpDXLZ22HbBS54k77uT9XSz1/moKbh5ePI96yg8zv+HYgW3mqxr+52XzOn9d3xFXykohi3+e/3s7Vetbvg6LlKrKV1O3EXzvGhuWjKJslcY23Z86aT1bLYTVfibBY59HPWOJ/xA6fzKcHDmNV8Id3LmOTr2GUb1eS0799Se/LPTjy7FLbJLbent4fRlNMvqd4mWq4PPdZgLvXmflfB8qVGuYpjpPVo5knE9yXTh1gDyu7hQtVYF/L51I3UGssHoeqWjnuwJ+QGunpWZD4x0jt65dQGOnZfyCvTx7+oi54z+mbKV65PMqkpawr89q9e+SNrGxMVw4tZ/279vmg4r0qvO6TTsQdO863/l0wz1fQUqUrYadVmuTzJDN2opl8NdmSlaZTGR9vhufT6834L9hFxM+SjoWLtx2kJ7N6pDTOXPuZLE+llovu3vffv69epVvp022+HlYeDjTv/ueYYO/wi6d91JMzrzE2jnF9TR6nY6op48YMG4Nd69fYPXcIQz/bqf5St4h034j+N411i0ejVKlke3G/uTkfk2Z+7evsmnlbL4eu9CiiE4Xy9JZo3izTQ88vQvbJO7rpGSs37N3H//9d5WZ01N9w3AapK5fTFxmZ8AP2Gnj+8Ud6+fT9J0PcXLOabOklpFe/7pMzXyr7psdCLx3nW9HdcfNswDFy1ZFa2e7cSgJG53Hzk2LsdNqqWWqfyEyWnI+ynsXeKaqamNFUeoA04FcwEpVVYeaFhfjTAdaA8HArlcccwbQFrgP7AZQFKUC0EBV1UaKojgDh4FtQDugnqqq0Yqi2KmqqlcUpZ+qqk1NjxuX4LiFgGaAFuNi7nzguKqqLUxlf1UUpZ6qqrOtHUNRlOpAFVVVGyiKUgTjAmvcR4cHVVUdrSjKcqAhYHm/6UvOK5H3gTaqqoYmOJcxCXLkSpwVCHzJeY0GAlRV/VlRlLieZQYwUlXV84qiDAI+Bn546V8hBdw88hOe4BaByLCgJLfbu3l4ER4WiFs+L3S6WKKePTFfFRERGsSC6UPo/eVE8ntbTrTv3FDR6XQUK5V04/D/5exuHvkJD43/FDIiLNhKbmMZ94S581heiVKwSEkcnXNw7/Y1QoLukc+rEHlc3QCoWa8ZV9Xz6bp465HPk7DQ+Cttw0JDUnX1rLtHPooULc7lS+fNX2hma24eXkQkqPPIsCDyuiVtKxGhgbh5JG0rrzouGG8tr93obW5cvSiLtybZsc7/3v0LJ/YbP5csXKISD8Pj8z8KDyRPovwu7l48jIjvgx6GB+HiFv9lGTpdLJdO7mbghCSfdQKQv1ApHJ1yEHT3PwqXrJSqzHndvSxuY4wMD8LVSj8YEWq8OiRhPef18LK4IiIyPAhXU35dbAxLvx1C7UZtqFY3fs/qY/u30uWTEQDUqN+K1YvGpSp3nIM7fuWvPRsBKFqqUpI24+pm+eUjeT28LG5riwxLer6v4l24JE7OOXhw56r5CzaS68CONRzZbcxarHRFi6sareWwmtV0Pnlc3XkYEYKrmycPI0LI4/LqD66u/3OWCyf3c+nMYWKiX/A86ik/zhlFry9T/mb18M5fOWraH7VoyUqW55GoDQO4untbXCX0MNHf5fiBLVw6c5ABPkvNb0hPH/ndeDuovQN5XD0oUbYad65fSvGC3IEda8zto1ipilbaRzLq3D11dR7n8pnDFClRHpe8HinKnlBG1LlWa0+Hj0aYy8we+wGe3sVSnTmjctuqrbxM3kRjUURYsNV+JaVjUUbyypuHwIj4K22DIx6R3zV+a4mnL15w9X4IfWetAiD00RO+WrSe2Z934cLN++w+8w/fB+zlcdRzNBoNjg5aejRNvy8RSsjTw4OQkPjvNAgNDcPDPelr7/TZc6xeu4Fvp03C0cHB/POnz57hO34SvT78gArllHTJeHTXao7vXw9A4ZKViUww9ice18FKOw8PwiXuqn53byrWbolGo6FIqSpo7Ox4+jiC3An6G1uM/YklaefhL3lP9JJ2HhEWxMIZQ/jky4l4JnpPtGrRRPIXKEqLtj1tkjU58uXLR0hoonbjYaXdnDnLr2vX4T99qkW7SU+H/kzQL5aqlGQeZa29JLyrKXGZ4we2cOn0QQb6xveLt65e4OyxXWz95Tuinj3GTqPBwcGJRq3fT1vuBPOthP35w7CkuY13riSaX75mqxit1p6OH8ePQ7PG9MSzQNrGocRsfR7G+j/AwDFLrSy8C5ExkvOxZFngbwBVVY8DpYAoVVXPWSnrrKpqkKqqBoyLjC+TQ1XVe6ZycZeGVASqm65C3QHkVhQlDzAMWGzaN7b8a7JeUlX1haqqzzBeJQxQRVGUnYqiHADqY1wITc653sF4BWycU6b/vY3xatfknldCQ4DpiqL8DFj7+teXZbV2XhUwLSCrqmowPWcFYI6pDj8AbPZVmsVLVyT4wW1Cg+4RGxPDicN/UrV2U4syVWs34ei+3wA4dXQ35SrXRqPR8OzpY+ZOHkSHnoMoXb5akmOfOLyDOo1a2yrq/5vsJcpUJOjBHUJMuY8f/pNqtS23Zq5Wuwl/7dsGwMm/9phzhwTdM3/BQWjwfQLv3cQjfwE8PL25/u8FXryIwmAwcOX8cQoWtt2X2lhTuqzCg3t3CQp8QExMDIcP7qV2XevffpxYaGgwL168AODJ48f8c/kihQoXTbesxRK3lSN/UiVRnVep1YSj+41t5fTR3SiVar9yENfpYnnyKML4/2NjuHDqEIWKlE63c8husmOd12vxgflLxsrXbM6ZI1swGAzcvnoWp5x5cMlr2fW65M2Pk3Mubl89i8Fg4MyRLZSvEf8Fh9cuHcWzQAmL7RfCQ+6aX8MRofcIfXADN89XDV+vZqznW4QG3SU2JoZTR3ZQuVZTizKVazXl2IGtAJz5exdlK9VBo9FQuVZTTh3ZQUxMNKFBdwl+cIvipSthMBhYtdAP70IlaN7uI4tjubp78t9l4zRAvXgMT++0vW4bt+7ByJkbGDlzA1XqNOP4wa0YDAZu/HsO55y5k7xRcHXzxDlHLm78ew6DwcDxg1upXOvNVz5HaHB8nYeH3Cfo/s1U7V3apHV3RvuvZ7T/eqrWbsaxA7+Zs+bImcdqVqcEWY8d+I0qtY1ZK9dqyrH9xr/Jsf1bzT9/mXc/+IrJP+xm4oId9B48A6VSnVQt3AI0bNWDYdM2MmzaRirVasaJQ8Y6v/nfOXK8pM6dnHNy8z/jeZw4tJVKNY15r5w9zN7fltF36FwcnXKYH+OWrwBXLxn3U3zx/Bm3rp63uj/e6zRp3d38pWHG9pGyOj9+8Deq1EpQ56bXwbEDr6/zOCeP/JHmLRMyos6jX0Tx4vkzANTzf2GntU/zXonZqa28TPxYFN9HJh2LmvK36fV45uguFFMfmVVULFaQ28Hh3A2NJCZWx45Tl2lSpaz593lyOHNg5hD+mPQFf0z6giolCjH78y5ULFaQH7/5yPzzD96sQ9+3GmTYwi2AUrYM9+4/4EFgEDExMew/eJj6detYlLl67Trfz1vAhDGjccub1/zzmJgYxk2aSstmTWnSsEG6Zazf8n2+mhzAV5MDqFizOacPx43953DOmQeXvIk+uM3raRr7je389OEtVDCN/RVqNuPaZePerCEPbqKLjSFXHjfCgy3H/pA0jv2JJX5PdPLwn1Stlaid127C3wnmXOUqxb0nesS8yYPo8MGXlC5X3eIxm1fPI+rpE7p+MsxmWZNDKVuGe/fuExgYaGo3B6mXpN1cY868+YwfO4a8CdpNemv0Vg+GT9/I8OkbqVyrGScOprBfTDBvuXL2MHu2LqPfMMt+8cvxP+M3byd+83bS5O2etHivX5oWbs25Z2xg+IwNVK6dIPcr5ltOzrm4+W987kqvGTcTjkP/nP8LrZ3W5nv22vI8rpw9zO4ty+k33LL+hchoybny9j/gTeAX05W314GXvQt+riiKF8Yrb2vx8gXcKEVRCmC8qjSu3BXgb1VVPwBQFMXRdLXtX6qq7lEUpQHGLQXe5yU3Yr/k56OAoaarUdcRf2ectbL/AT1Nz18EePaSY79spmbtvBK6oqpqH0VRCgHrMS7gJvxqxZRkvYRxe4eVCa68/QfwVVVVNZ2Dze590mrt6dF3BN9PGIBer6dB83cpWLQUW35dQLFSFahWpykNm7/Hstm++AxoT67cLvQbMg2Afb+vITjwDtvXL2H7euNts1+PXYhLXuMa+Mm/djHIZ66tov6/ya7V2vNBvxHMGj8QvV5Pw+btKVS0FJtXL6R46QpUq9OERi3eY8n3YxjVvz25crvy2TfGN+n/XTnDH5t+RKu1R2NnR8/PRpHHxY08Lm7UrN+cCd98gJ2dlqIlFRq3st2X2rzsPPr2/4oJY4ah1+tp3vJtihYrwa8rl1OqjEKdeg34799/mD7Jl6dPnnDi+FHW/vIjsxf+yN07t/lp6QLjvS0GA+927Eax4iXTNWu3viOZM6k/er2eN5q9S8Eipdm6xthWqtZuSoPmHVgxx4cxX7QjZ24X+g6ebn786P5v8zzqKbrYGM4d38eXYxbi4VmQOZMGoIuNRa/XUa5KXRq2SN86f51qK7/Fo0kdHPO50ezGAf6bMJc7K6xf9ZnesnudK1Wb8O+5g3w37C0cHJ3p2HeK+XdzfTswaFIAAO0/9mPjklHExrygTJVGlK3S2Fzu/N+/UyXRlgm3/j3FwW1LsNM6oNFoaP/xWHLlcUt1Tq3Wnq59RjN/cn/0eh3133yPgkVKs23NfIqWqkCV2m/yRrMO/DR3NH5ftCFXbld6D54BQMEipalRvxWTBr+HnZ2Wbn1HY6fVcvXKaY4f3EbBomWYMrSL8Tzf/5JKNRrx/md+bFgxHb1eh72DI+9/5pfq7IlVrN6Iy6cPMuHLd3BwdKbngPhta6YN68zImca23K3vGFYt8CUm+jnlqzWkQnXjXo7nju9hw/IpPHkUwaJpAyhUvBwDfX7g+j9n2LV5mbnf7NrHh9wuqa9zgIo1GnHpzCHGDWqDo6MzPQfGf8P0lKFdGO1vvIqrez9fVs73JSb6BRWqNaSiaT+1Vh36sOy7ofy1NwC3fN70HfItAA8jQpkxsjvPo56i0dixb/sqfGdtNm9bYWsVqjfmytlDTP76bRydctD9s/jzmDmyE8OmGa9u6dx7DL8uiqvzRuZvv97042RiY6JZOKUfAMVKV6FrXz8aturBr4t8mT7sPcBAnSbvUbBY2q6aq1i9EZdOH2L8l21M7SM+69RhXRg101jn3fr6mtqHsc4rmOq85Xt9WD5rKEdNdd7HVOePIi3rfP/vq/D5zljn0S+i+Of8UXp8OiZN2RNKrzp/8iicRVM/Q6PR4OruxQcDbHsLcXZqKwkZx6JRzDONRfWbGfvI39bMp1ipilSp3ZQ3mnfgxzk++H3Rlpy5Xehj6iMBfPu/zfOoJ+axaNCYRRQoUopNK2dx8tDvRL94zuhPW/JG84607dbfZrkTstfaMarbW/Sf9yt6vZ736leldEFP5v92gIrFCtA0wUJuVqPVavni836MGjsevV7HWy1bULxYUX5ctZqyZUrzRt06LF7+I1HPnzNxmrHe83t6MnGsDwcOH+HCpcs8evyYP3cbv2Bo2OAvKV0y/eaLStXG/HP2IDOHtsbB0Zku/eK3cJjt04GvJhvH/vd6jWX94tHExLxAqdIIpapx7K/VpCMblvgya2R7tPYOdPl0ChqNhpv/nmb/tiXGcUhjx3sfj0nT2J+YVmtP974jmT3R2M4bNHuXgkVLs/XXBRQrbZxzNWzegeVzfPAd2I5cCeZc+/5YS3DgbbZvWMz2DYsB+GrsInSxMfyxcSnehUoweVh3AN58u3uGzHW1Wi0D+3/O6DF+6PV6WrVsQfFixfhp5SrKlilD/Xp1WbJsBVHPnzNpqvG9XX5PT8b72a6vTo64fnHSV8Z+scfn8f3ijBGdGD7d2C926TOG1QuT9osbVxj7xQWTjf1i8TLGfjH9czfi8pmDTPzqHRwdnXm/f/x8a8bwzgyfYZxvde07hl8W+BIT89w4nlaLn29tXGGcb/0wfQCFi5Wjv88PPH4YzqIpn5vGofz0/CJ9t7JI63lsWD6F2NhoFkz6FDB+mWa3fmPTNfP/hHTe3ub/I83rvsTFtOftcoxX3Bow7nm7QVXVSqbfFyfpnrf3AR2wVFXVrVaO2RTj3rD3gRfAatN+s18BnUyPvaWqai9FUXYAThj3cfVTVXWn6crVPMBPQDXi97ydp6pqW9NzxO0z+yHGRdF/TE8f91xJjmH6+VSgCcaF06Gqqh4xXcna2bTdwbi4sik4r7gsPwLFgBzAAtOWB1Mx7mW7BeNeuBZZX3Fe7sAKIK+pvtpi3PN2HsZtLQAmqaq6N8kfNZEDl57JtydlIK0m+1a3m2P6fntvegmOyrhP2m3tWY2kV3xnBzlPn83sCKkW9sx2+8pmpLw5kmzFnm3oDVnn6rWUsMvG/Xl0bPacNDto9a8vlEXF6LJnnWdnTg66zI6QKg2C12V2hFQLLppxV+za0qmIrLu4/TpuOaMzO0KqFHe+k9kRUu3K4/S9czG9aLLxvCU7a13NMXtOdNPR47nDsnxjzDNoZpb6u7128TYlFEVxUFU1xrTguxv4QlXVyzZ7ApEuZPE2Y8nibcaTxduMJ4u3GU8WbzOeLN5mPFm8FSkhi7cZTxZvM54s3mY8WbwVKSGLt0nJ4m3KJWfbhJR4R1GUoRivLN2tquplRVF2Aglv39+uqupMGz9vhvv/el5CCCGEEEIIIYQQQqQHjV2WWhfNFmy6eKuq6haMt/8n/FkrWz5HVvH/9byEEEIIIYQQQgghhBBZg9y/JYQQQgghhBBCCCGEEFmQrbdNEEIIIYQQQgghhBBCiKQ0ch1pSkmNCSGEEEIIIYQQQgghRBYki7dCCCGEEEIIIYQQQgiRBcm2CUIIIYQQQgghhBBCiPRnp8nsBDajKMpnwMdADNBHVdWrCX6XG5gLFAXsVFV9M7XPI4u3QgghhBBCCCGEEEIIkUyKorgDvYE3gOrAVKBLgiJ+wDJVVQ+n9blk2wQhhBBCCCGEEEIIIYRIvrrAPlVVdaqqngTKJvp9Q6C7oij7FUX5Ii1PJFfeCiGEEEIIIYQQQggh0p1Gk/WvI1UUZRzGK2cTGq+q6rgE/3YDIhP8O/F+EDWAscDXwE5FUfaqqno5NXlk8VYIIYQQQgghhBBCCCEA0yLtuNcUiwAqJ/i3PtHvQ4HdqqoaFEXZC1QCZPFWpI7BkD03i34eq83sCKmSXesbICrGPbMj/M/JefpsZkdIlWc1qmV2hFRzPXk+syOkij4b9y0aDJkdIVUM2TM2AI72ieeWIr1JnWe87DrnOpK/a2ZHSL3nmR0gdXI7xWZ2hFSL1Wf9K9isufqsWGZHSDV7O+nPhRAAHAPGKoqiBaoC/yX6/WGMe+GeBmoBv6f2iWTxVgghhBBCCCGEEEIIkf7ssueHq4mpqhquKMpPwCEgBuijKEprwFVV1bXASGCJoig5gAOqqp5O7XPJ4q0QQgghhBBCCCGEEEKkgKqqi4BFCX50NcHvbgAtbPE82fMeCyGEEEIIIYQQQgghhPh/ThZvhRBCCCGEEEIIIYQQIguSbROEEEIIIYQQQgghhBDpTmMn15GmlNSYEEIIIYQQQgghhBBCZEGyeCuEEEIIIYQQQgghhBBZkGybIIQQQgghhBBCCCGESH8aTWYnyHbkylshhBBCCCGEEEIIIYTIgmTxVgghhBBCCCGEEEIIIbIg2TZBCCGEEEIIIYQQQgiR/uzkOtKUkhoTQgghhBBCCCGEEEKILEiuvBWpcvHMEdYtn4Fer6dh8w607tjb4vcxMdGsmOPL7etXyJXHlX5DppMvfyEunztKwKo5xMbGYG/vQKePBlOucp10z2swGNj04zQunzmEg5MzH/SfRJGSFZKUu3P9Er8s8CUm+gUVqjeiY6+RaDQazhz9kx0bFhJ07zpDJv9K0VIVLR4XHvqAqUPe5e0uA2jWrpdtc/80lSum3O/3n0yREtZzr17oS0z0c8pXb0THj0eh0WjYssqfS6cPoLW3J59XEXp8PomcuVzMj4sIfcDUb9rTuvMAmrX7xCZ5N6yYzqUzh3B0cubDAROt1vPt65dZOd9YzxWrN6LzJyPQaDQ8ffKQ5bOGER5yH3fPgvQZ7E/O3C6cP7GPbWvnodHYYafV0rnXcEqVq0F4yH2W+A9Gr9ej08XSpHUPGrXqmmVyx7l19SL+Pj3pPXgG1eu1AmBQt2oULFoGALd83nw+Ym6Kcyd26cwR1q0wvi4bNO9A6w5JX5c/zjW9LnO70tf0unzyOJLF/kO5de0S9Zq2p0ffUebHfDu2D48iQ3FwdALgyzGLcHF1T3PW1KqyZAr532lKdHAYB6u3y5QMmdFe5k/+nJv/XaBkuer0Hzkv1bk3rphmzt1zgPV+8Pb1S6xKkLvTJyPNuVfMGmrO3XuwPzlzu/LsyUN+WTiW0KA72Ds48UH/Cea27TfwLZycc2Jnp8VOq2X4tLWpzp6RfQvY5jWaXrlPHNrOri3LAXByzkm3vr4ULq4A8OzpI1YvGseDO1dBo+GD/hMoWbbqa7NeOnOYDSumm/qPjrTq0Mfi9zEx0fw814fb1y+TK48rfQbPxCN/IQD+DFjKX3sCsLOzo0vvkVSo1uCVxzQYDPz261zO/L0LjZ0djVp15c13PiDw3g1WzR/DnRtXaNdjEC3a90pxnafHeaxcMJaLpw6Qx9Ud3+8CzMfavm4BR3ZvIreLGwDt3/+SSjUapThzemWPCA3kp3k+PIoMRaOxo2GLTrzZpicAy74bRtD9mwBEPXtMjpx5GO2/Pkvkjol+wayxnxAbG41Op6N6vRa07TYQgBWzR3L72iW09vYUK12Z9z8dg9beIVW50/s8XlX/tnTpzBHWr5iOQa/njeYdeMtK7p/m+nDHNPb3GTIDD9PYv8T/G26bxv5ufUebH7Nl9VyOHfiNqKePmLXq7yyRF2DHpmUc3RuAxs6Orr1HmF+jz54+4peF47l/29jvfThgPCWVqmxbu5AjezaSx8U4b2n//qBUv0Yzeuy31RwXMrZtb/r5Wy6eOoDW3gFPryL0HDjB4r3H6xgMBtavmM6l06Z6HjiRotbq+ZqxnqOjX1CxRiO6xNXzY2M9h4Xcx8OzIH2GGOv5ZccNC7nPkpnx9dz07aT1vGjaIEKD71r0/8k5j4yeK4aHPmD1onFEhAWiQUP/UfPNf8fMzv2yeUvQ/RssnzXcfNyw4Lu06TqAN9t8mKLc2T27EMkhV96KFNPrdPy6ZCqDfOYz7vtNnDi8g/t3rlmUObIngFy5XZg0/zdatO3JppWzAcidx7M1GnwAACAASURBVI2Bo2bjN2sDvQZNZMUcnwzJfPnsIUICb+E7ezvd+/mxftkkq+XWLZ1Et0/98J29nZDAW1w5exiAAkXK0PubWZQqX9Pq4wJ+mkGFag1tnvvK2UOEPLiNz/e/063fONYvnWi13PplE+nazw+f738n5MFtc26lcn1GzAxgxIwAPL2Ls3vzUsvcP0+nfLXUv9FM7PKZw4QE3sJvzjZ6fDqWNUut1/PaJZPo8ZkffnO2ERJ4i8umvLs2L0OpXBe/OdtQKtdl5+ZlpvOoy6iZGxg1cz09+09g9aJxALi4eTJk0kpGzVzPsCm/sGvLciLDg7NMbgC9XseWX2ZRvtobFsdycHRi1Mz1jJq53iYLt3qdjl+XTuULn/n4zXr56zJnLhcmzvuN5m17ErDK+Lp0cHCiffeBdPpwiNVj9/5yCr7+6/D1X5epC7cAd3/axPG2fTM1Q2a0lxbte/HRF5PTmPsQwYG3GDtnO90/9WPta3KPnbOd4ES5y1auy9g52ylbuS67TLl3BiylUPFyjPLfxIdfTGbjj9Mtjvel33JGztyQ6oVbY/aM7VvANq/R9Mrtkb8QX49bwWj/jbTu9Cm/Lh5vPtaGFdOpUK0BY77fyqiZG/AuVOK1OfU6HeuWTWGgz0LGzNrMySN/8CBR/3F07yZy5nZh/LztNGv7IZtXfQ/AgzvXOHVkB76zAhjos5C1Syej1+leecy/928hIiyQMd9vYez3W6jVoDUAuXK70KX3SJq3+ziFNZ1+5wFQr2l7BvostPqczdr2ZLT/ekb7r0/Twm16ZLfTaun40TeM/X4Lw6as4uCfa83H7DNkpjl3tbotqFa3eZbJbe/gyJd+Sxntv4HRM9dx+ewRbvx7DoDajdowdvZWfL7dREz0c47s2ZSq3BlxHq+qf1vR63SsXTqFL3wWMGZWACcP70jyHH+Zxv7x87bRrG1PAky5HRwcadd9IB2sjP1VajVhxLRfbJo1rXnj63kTX/gsYM2SKebX6PrlM6hQrQF+c7bg478e78Lx/V6zNh8y2n8do/3Xpek1mtFjv63muBndtstXrY/Pd5vw+XYj+QsWY2fAsiSZXuXSmcOEPLjFuLnbeP+zsaxZYr2e15jqedzcbYQ8iK/nnaZ6HjfXsp5fdlzXvJ58M3klo/2N9bxzs2U9nz22GyfnnCk6B8icueLP83xo3r4XY2ZtYdjU1eRJxZw9o+ctXgVLmOdaI6avwcHRmap1UjceZefs/5M0mqz/XxYji7fZnKIovRRFaWLl5xfT6zlvXL1Ifu8ieHoXxt7BgVoN3+Lcif0WZc4d30+9psar4mrUb8E/F45jMBgoWrIced3zA1CwSClioqOJiYlOr6hmF0/so3bj9mg0GoqXrUrU08c8jAixKPMwIoTnUU8oUbYaGo2G2o3bc+HEXgC8C5fEq6D1N8DnT+whn1dhvIuUtnnuCycT5C5TlahnL8v91DL3SWPuclUboNUaL7AvXqYKD8ODLHJ75C+Md+FSNst7/uQ+6jRuh0ajocRr6rlk2apoNBrqNG7H+RP7TJn2UbdJewDqNmnPeVP9OznnRGPqQF+8iDJ3pvb2Djg4OALGqwYMen2Wyg1w4I/VVK3b0nzVR3q5Gfe69DK+Lms3eIvziV6X50/sp76V16WTcw5Kl6+OvaNjuma0hfDDJ4kJf5ipGTKjvSiV6+GUI1eacl84uY86pv7kdbnj+pM6CfrBCyf2UbfJu6bc75rP58HdayiV6wLgXagk4SH3eBQZmqasiWV035LVc5dUqpmvwClRpiqRYcY3mlHPnnDtyinqN+sIGPvI5FzxdPPqRTy9i5LP1H/UbNCa8yf3WZ7Lif3mLNXrtUS9eAyDwcD5k/uo2aA1Dg6O5PMqjKd3UW5evfjKYx76cx1vd/4cO9N+Z3lcPcz/W6x0JbT2qbsxLD3OA6BMhVrkyu2aqkyZmd3VzdN8xZpzjlx4FSqRZPHHYDBw+uif1Gr4dpbJrdFocM5hXCzR6WLR62LNr81KNRqh0WiM86LSlYkMC8IWMqv+bZO7iEXuxHPy8yf2Ua+pKXf9lqjmsT8npcvXMN9Zk1CJslVwdfO0ada05j13Yn+iei7CzasXiXr2hKtXTvFG8w4A2Dskr99LqYwe+201x83otl2+6hsW7z0iUvgaNdZTMutZMdZz3SbtOHc8QT2b2k/dpu05d3zvK49r7xBfz7GxlvX8POoZe35bSetOn6boHCDj28uDu9fQ63SUr1IfMM5vHJ1yZJncL5u3JKReOIandxHcPQumOHd2zy5EcsjibTaiKEqSv5eqqj+qqnogI3NEhgfjls/b/G83d68knVhkeDDupjJarT05cubm6eNIizKn/95NkRLlzANmumaOCCavR3xmVw8vHiaaQD8MDyavu5f533ndvYiMePUk+8XzZ+zZspzWnfvbNrA5UxBuCXLndfeyWICNK5M4d+IyAMf2B1DedHXwi+fP2LN1Oa07D7Bp3sRtI6+HV5I3KpHhweT18LJa5vHDcPObBVc3Tx4/CjeXO3d8DxO/bs+iqQP5oP8E888jQgOZMrQTY/q3osW7vc0fDmSF3JHhQZw7vpdGrbokec7YmGimj+yOv88H5ollWkRYOYcIK+fg9prXpTU/LfBj0tCubF+/GIPBkOas2V1mtJf0ym21H3xp7rBEucMAKFRM4dyx3QDcvHqB8JAHRJr7IA3zJ3/GjBFdObI7dbdkvyx7evcttniNpmfuOH/t3USF6sZbh8OC75LbxZ1VC8YwbXhXflnkx4vnz5KRMwg3j0TjX5JxPQi3fMYyCfuPyLDgJONUZHjQK48ZGnSH03/tYPqI7syf3J/gB7demzE50uM8XufAjjVM/qYTKxeM5dmTR1k2e1jwPe7e+IfiZSpb/PzqlVO4uHqQv0CxLJVbr9MxZWgXRvRpSrkq9SlRporFMXWxMRw/+Ju57adVZtV/2nMnmpN75E8yB0zt2J8e0pL3YYL6h/i+MjToLrld3Fg5fyxThnZl1cJxFv3egR1rmDSkMyvnp/U1mvFjv23muJnXto/uC6Bi9ZTdmWich6S8nh++pp5fddyI0EAmf9MJ389b0fK9+HretnYezdt9hKOTc4rOIS5jRraX4Pu3yJErD0v8BzNteFcCVn6LXq/LMrkTSjhvSejUkR3UbJC6DxKze3YhkkMWbzOAoihNFUWZl+DfFxVF+VRRlGOKouxXFOUL08+7K4pySFGUvxRF6Wv62Y+KoixQFOUPoLqVY49TFKWz6f9/a3rsQtJzP2NrizeJrlayusCToMz921fZtHI2PT/3tXU666zkSXyBlbXMGl59FdYf6xfQtM2HqbqdJnms5U6c6fVldgb8gJ1WS82GbQHYsX4+Td9Jh9xW6zlJRSctk4xDV63TnDHfb+XTYd+zfW38np9u+bwZ7b8RvznbOH5gK48iw1KaOt1yb/xxBu9+8DV2dtokv5uw4E9GTFtDry+ns/GnGYQE3klJ4qSScQ6ve11a0/urKYz9bgNDJ67g6pXTHDuwLU0x/1/IhPZiC9b//q8v87p+sOV7fXj29BHThnXm4B+rKVyiHHZ2xiFoyMSfGTF9Hf1HL+Tgn2u4evlkasMnzZXOfYtNXqPpmBvg34vHObovgHc/GAyATqfjzo0rNGrVlZEz1uHklINdm5enNLXVnFY/t9FoSN44ZfnzmJho7B2dGDF9DQ1adGLVgrGpypgc6XEecRq16sb4udsZNXM9rnnzsfFn/zQktRbLNtmfRz1jif8QOn8ynBw5c1uUO3n4D2qm8qrbl7FFbjutltH+65n8wy5uXr3I/dv/WZRbs3QypcvXpPRLtrOyhYyo/zRLzrieirE/3aQhr/Ufa9DrdNy5/g+NWnVhtP86HJ1ysDPA2O81fqsrE+ZtY7T/OlzcPNn4Uxpeo5kw9ttkjmtFRrTtHRsXo7Wzp3ajNinKlqx5SDLmMyk5rls+b3y+3ci4uds4tt9Yz3du/ENI4O1UbymT0e1Fr4/l2pXTdPjwG4ZNXU1o0F3+3r8lpakzfN4SJzY2hgun9pv3702V7Jz9f5DGzi7L/5fVyBeWZZ73gTaqqoYqimKnKIo70B9oinGkPKAoyjpTWVVV1VdeIqkoSg2gjKqqbyiKUhLokV7B83p4EREaaP53RHgQed0tb61y8/AiPDQQNw8vdLpYop49Md9yGBEWxMIZQ/jky4l4ehdJr5gc+vNXju7ZCEDRUpWIDIvP/DAsCBc3y0+vjZ+8xX+aHBke9Npbxm5dvcC5Y7vY+sssop4+RqPRYO/gSOPW76ct994N5twRCXJHhifN7erunSR3wjLHD2zh0umDDPRdah7Abl29wNlju9j6y3dEPXuMnUaDg4MTjVKR+8CONfxlqudipSpatI3IsKR1mNfDy+I2x8iwIFxNn3DncXXnYUQIrm6ePIwI+T/27js6iurv4/h7N703ktA7LDWhF+lNURGkCoIFBKWIFOkEQu+IdKQLCAiE0FRQehHpEBJgpTcJhBRCSd99/tjNppOyqb/n+zrHoyazs5+5uffOnbuzd1JdaqB8lTo8X+rFq/BQw0NiAByd3Shcohy3b1zI0MkzN3I/uB3AuoWjAXgVHkrApRMolaZ41mtp+FS/kHtxKlSpw6N7141qD07J2mVY8FMcnVK2y9A02uXb9gu6r8XVbfI+d2/5G5ZE+f8kr+tLVh3fv8WQu2S5aqnkTqUfTJHbVZ/bJVlu3Vfdraxt6TVQt66YVqtl0rdtDQ/ISDhmFzzrtuL+LX/KV6mToex53bdktY3mVu7H9/9l80+TGDB2GbZ2joCuvTq6uFNaf5dijQZtMjR56+jsnuSrrWEhCX/3eLr+Q/dtkMT9h6OLe4rzVHy9SmufTi7u1KzfGtBNnm9cmj2Ttzl1HGmxd3Qx/Hej1p1ZPuvbfJc9LjaG1fOHU7fJh9TQl3m8uLhYrpw9xOjZW/Nd7njWNvZUqFqHa5dPGR4g+Nv25bwKD6XHiOyb9M+L8s+W3MnH5MHPUu3XM3vuzynG5HXUl3+8+P7UUd/vlamo6/dqNWjDAX2/l7iNNm7diWUzB2cqb34592d2jJskUx7U7X+O7sb/wnG+816V7odgoCvnUwf15Vy+apJrtsTjEMMxpVbOTm8vZ91r0tmvsxtFSpTj1vULvAoP5eGd60wY2BZNXCwvX4Two3cfhk5O+5yal/XF0dmd4mUqUci9OACe9Vpy918/yMAQMi/HLfGuXTpJiTKVk7TZjCjI2YXIrPw3nfy/yfARj0qlij+DDQdmq1SqDcA7QDmgPHAIOAw4AfGPh8zIY14rAGcA1Gr1HSB7FxxMpHT5qjx78oDnTx8TGxPD+ZMH8KyTdNldj7rN+OfoXgAunj5IpWp1USgUvHkdzpLpg+nY8zvKV0pxI3G2avJeD0bN2cGoOTuoXrcl547vQavVcu/fK1ha26bozB2cXLGwtOHev1fQarWcO76HanVbvPU9hkz+Ge8lB/BecoBmH/SiTcd+Rk3cGnLP9mHUbB+q10mU++YVrNLMbc29mwm5q9fR5b5++SSH9qyh38jFSdY9+m7yBryX/In3kj9p9n4vWn/cL0sTtwDN2nY3LNbuUa8lZ4/vRavVcvffK1hZ26We18qGu/pyPnt8Lx76vNXrNOfMsT0AnDm2Bw99+QcFPjB8Yv7wzjViY2OxsXMkNDiQ6OhIAN68CueO+jJuRUvnm9yTl+5niv6fmg3a8Enf8XjWa8mbV+GGtZ5fhYdyR33Z6LWHSyVrl+dOHcCjbrJ2WacZpxO1S5W+XaYlLi6WV+Ghuv+OjeHqhRMUy4G1nQuCvKwvxmjatgdj5u5gzNwd+tx7DLnT6gctk+RO6E90uXfrc++muj73m9fhxMbGAPD3IR/KVa6NlbUtUZFviIx4DeiWarnh9zdFSma8/uRl32JMG82N3CHPn7Bq3jA+/3YG7on6PHvHQji5uPP0v7uAbg22wsXLpptZ13/c5/nTR8TGxHDh1H6q12meZJvEWS798xcVq9VDoVBQvU5zLpzaT0xMNM+fPuLZk/uULl/trfv0qNsStf9ZAG5eO49b0ax9ZT83juNtEq+ld+XsYYqWqJCvsmu1WjYt96ZwsTK0+ujzFO95w+8f3IuWSfK16PyQ++WLEN681n29PToqErXfP7jrH7x36pAP1y//Te8hsw1rJmeHvCj/7Mv9IEnulOf+5vxzVJ/79F+o9LnzgjF5Peo2S1bODyhdvhoOTvp+7/E9AG5cPUMRfb+XuI1ePnOYopkcw+Tlud+YMW5iuV23Ay6d5K9d6/hm9KIMr7narG13wwMUPeu25MyxzJXzmWN7DeVZvU5zzujrz5mjCeXsUad5qvsNDQ4kOippObsXLU3T9z5hxspDTF22n+FTf8ataKm3TtzGH0de1ZdS5asR8Trc8FV/tf/ZAjFuiXf+1B9ZWnagIGcXIrMUsn5hzlOpVB7AVLVa3UGlUtUD1gJ11Wp1hEqlKgZsBz4CfgXeVavVGpVKZaZWq2NUKtV6YIlarU71u6YqlWoS4A/cAbz171EGuKRWqx1Te01yR/0jMl0Jrl44wbZ1c9FoNDRq2YEPuvRjz5ZllCpfBc+6zYmJjmLtovE8vKvGxtaevsNm41q4OL/tWMX+nWtwK1LSsK8hE1dk6Qn2kbEZ/2qxVqtlx9rpXL9yCnNzSz4dMI2S5aoCMGdUF0bN0d3p+uB2AL8s8yImJpIqNRrTufc4FAoFV84ewmfdDF6Fh2JlY0fxUpUYMP6nJO/xx/ZlWFha0/KjL9PJkvEBs1arxWfddK5fPom5hRU9+k+lZDndxeSc0Z0ZNdtHn9ufzcu9iImOpHKNJobc04a8T2xMNNb6TwhLV/CgW1/vZLmX6nP3TjePqfLtD0vQarVsWzOD61dOYWZuSa+BUymlL+eZI7sydq5uzcv7twPYtMyLmOgoqtRoTNc+Y1EoFLx6GcbaBSN0d1wUKsxXw+djY+vAX7vWcub4XkxMTDEzt6DjZ8MpV6kW1/1O47thHgqFAq1WS9O2PWjcukuGyzencye2cakX1Wo3pWaDd7mjvsyWlVNQKpVoNBpafNiLd/QPGMpsmSd29eIJtuvb5TstO/BB537s2bqMUuUS2uW6ReN5eE+NdXy71H9CP27A+0RGvCYuNgYrazu+m7AcF9eizJ/Yh7jYWDSaOCp51KfrFyNQmqTf9t7UqpHh3JlRY+N8XJrVw7yQE1FPg7k5ZTEP1+3I1vcwP+/31t/ndn0BWDDxC54+vkdU5Bts7Bz4tP9kqtRIuuaWNp0vfmm1WravmZ4od0I/OGtkF8bMTegHdbkjqVyjMV376PqT14bcT3AqVIQ++tx3/73MxiXjUSiVFC5ejp79J2Nt68Dzpw9ZNW8ooFu/sk7jD3ivU+oP/VCk8tXM3CjztPqWzLTRvMj9ywpvLp85iHMh3YMxlCYmjJ6lu3vy0b0b/LJiEnGxMRRyK06vgVMND9pIUubJqov/xRP4rJ+DRhNHwxYf07bz1+zbupSS5argUbcFMdFR/Lx4HA/v3sDG1oE+w+YY7vDZ77OS00d2oVSa0KX3KKrWbJLmPkE34b9+4VhCnz/BwtKa7l9PoHhpFS9CnzNnTHciI16jUCixsLTCa8GuTH3VPCeOY+2Po7gZcJ5XL8Owd3Dmw24DeadVJ9YvGsfjezdAocDFtSg9vplo1EOesjv7resXWTDxS4qWrIBCoZvobP/pd1SrpTuuDUu8KFPRgybvdsty5pzI/fj+v2xY4oVGE4dWq6FWw/f4oGt/AAZ/UhNn1yJYWOoe4FijfivD74yV2+X/NgpFxofm/hdPsGPdHDQaDQ1bfsz7nfuxd+tSSpWriof+3L9+0Xge3buBta09XyXK7TXgfSIjXhnO/YMnrKBIiXLs3LiA8yd+N9yF9k6rTrT7JHue72BM3j98VnH68C6UJiZ0/XIUVWvp1lN9ePcGvyyfTGxsDIXci/P5oClY29qzftE4Ht1TAwpc3Iry6TcT0myjGk3659DcPPdnZoyb3lx8btZt728/JDY2Ghtb3bVHmYoe9Ph6wtsDplLO1y7rrtl6DUoo5xkjujJuXkI5b1yaUM7dvkoo5zU/JJRz3+HzsbFzSHO/16+cZmeicm7WtgeN2yQt5+Bnj1k+61u8fvBNlDNjx5GbY8X4OqN7SHgVenzjjampWYbLPidzv23cEh0VgdeAd5m85HesrO0ylbegZG/jaZFHa9XkXxEbp+X7iUirz7zy1d9NJm9zgf5u2x2AM3AM6AKcB0oBVsAytVq9QaVSfQIMAuKACKAduonedCdv1Wr1DpVKNR9oCFwFmqnV6koZyZeVydv8IDOTt/lJZiZv85vMTCSK7FFQyzynJm9zQ3qTt/lVepO3+Vl6k7ci++XVkpdCiPRlZvJWZI/0Jm/zM+nPc59MoYjMkMnblCI2zcj3rciq17h89XeTyVshk7e5TCZvRWYU1DKXydvcJ5O3IjPkYl+I/Esmb3OfTN6KzJApFJEZMnmbkkzeZp48sKwA0a+PWzLRjy6o1erv8yqPEEIIIYQQQgghhBAi58jkbQGiVqtz5mkHQgghhBBCCCGEEELkNGW+uqm1QMi+x7QKIYQQQgghhBBCCCGEyDYyeSuEEEIIIYQQQgghhBD5kCybIIQQQgghhBBCCCGEyHEKhdxHmllSYkIIIYQQQgghhBBCCJEPyeStEEIIIYQQQgghhBBC5EOybIIQQgghhBBCCCGEECLnKRV5naDAkTtvhRBCCCGEEEIIIYQQIh+SyVshhBBCCCGEEEIIIYTIh2TZBCGEEEIIIYQQQgghRM5TyH2kmSWTt4KYuILZcKzMYvM6QpZoNAV3fZdYTcGsK4qCW+QEv7HI6whZ4nDeL68jZFl0HY+8jpAlZuev5nWELIvTFsxGam0Wk9cRsiyugPbnkbEFd+hqbhqX1xGyRFtA2yeApoBmj40tmO0TwNREm9cRssREqcnrCFlWUNtodAGu5zYWBfP8X1DrCoC2YHYtQmSbgttjCiGEEEIIIYQQQgghxP+wgnv7ghBCCCGEEEIIIYQQouAoyF+NzSNy560QQgghhBBCCCGEEELkQzJ5K4QQQgghhBBCCCGEEPmQTN4KIYQQQgghhBBCCCFEPiRr3gohhBBCCCGEEEIIIXKeUu4jzSwpMSGEEEIIIYQQQgghhMiHZPJWCCGEEEIIIYQQQggh8iFZNkEIIYQQQgghhBBCCJHzFHIfaWZJiQkhhBBCCCGEEEIIIUQ+JJO3QgghhBBCCCGEEEIIkQ/JsglCCCGEEEIIIYQQQoicp1TkdYICRyZvxVtptVp2rJtNwKUTmFtY8tnAqZQoWyXFdg/uXGPjUi9ioqOoWrMJXXqPRqFQ8PrVC9YuGElI0H84uxblq2HzsLa1N7zu/i1/5o3vRZ9hc6jZ4F0AQp4/YfOKSYQGB6JAwYCxS3FxK2bUcfhfPMWva+ei0Who3Ppj3u/UJ8nvY2KiWbdwAvfvXMfGzoGvv59NIbeiXLv8Dzs3LSI2NgZTUzO6fDGUStXrAXDu5AF+91mDRhNH9dpN6PL5UKMypibg0im2rZuDRqOhUauOtO2YMvf6xV48uHMdG1sH+g6fTSG3Yrx6GcbKeSO4fzuABs3b06PvWACioyJYOX8kQYGPUCqVeNRpRsdeQ7Itb07VF79zR9j36xIUCiVKExO6fDmKcpVq8ejeDbaumkZkxGuUSiXvdepH7XfaZjjr9nWzCbiozzpoKiVTy3pblzU6OoqqtZrQNT7rS13W4KD/cHEtylfDdVnT2m9w0H+smjsMjUZDXFwszd/vQZN3uxEdFcHq+SN4/vQhCqUJ1Ws34+NeWatLWq2W3zbNQH3lOGYWlnTuN4Nipaum2O7x3QB8Vo0lJjoKlWdTPuw1DoVCwdYlwwgKvAdA5JtwLK3tGTzNl4e3/di1zjv+TWjZcRBV67TJUsbEWXO7b1k6vT/3bl6lbKWaDBizxKj8meWxagZuHzQn+lkwx2t+lKvvHU+r1eKzbpahzHsNnJZGmQewKVGZd+49xlDm6xaMMJR5n2HzsLZ14M2rF/yyfCLPnz7E1MyCngOmULRkBUKfB7Jx6TjCw56jUChp1LoLzT/olS3HsXP9LK5dOoGZhSU9B6R+HA/vBPDLMt1xVKnZhE5f6o7j0ukD7N+xnKeP7zB8+hZKltO1kdjYGH5dOZmHdwJQKJR0+nIMFarWNTpvPP+Lp9i6dh4aTRxNWnfk/U69k/w+JiaatfrzkK2dI19/P4tCbkW5e9OfDcunxR88H33yDbUatATgzeuX/Lx0Cv89vA3Al996U07lmW2Z42X3uSgy4jXzJiQcf2jwM+o3/YBuvUdla26tVsvOn2dyXV9XPh0wnRJlUq8rm5d7ERMdSeWaTej0xVgUCgW7N80j4OIxTExNKeRegh79p2FtY4/a72/2bvmRuNgYTEzNaN/zeypWq2903oBLp9i+bjZajYZ3WnXkvY5fJfl9TEw0Py8ez0N9OX81fI5hfLR/5xpOH/ZFoVTSrc9oqtRoBMChvRv5+9BOUCgoVrICnw2agpm5BRuWTODmtfNYWdsB8NmgKZQoUymLuU+yY91sff3oxLup5N6weDwP7lzDxs6Br4bNNeQ+4Luavw/5olQq6dpnjCH3xmUT8b9wDDsHZ7x+8E3xngf3rMd34w/MXnMMW3unLOWGnOsXD+5Zx/kTvwGg0cQR+OgOM9ccx8bWgaO/b+LvQz5otVreadWZFh9+luX88cfg+/NMrl8+gZm5JT3eUs+3rNDX8xpN6Kiv53t+0ddzk4R6bmVjT1xsDFtXevP43nXi4mKp26Q9rT/uZ1TW1LIbU/6XTh/g9+26/nzEjIT+/N6tq2z9abLuPdDyQdeBeNZrZVTWnGifXgPex9LKGqXSBKXSUEbCGgAAIABJREFUhDFztgBw8e8/+W3bcgIf32XUzF8oVT7lWO7tWbO/Taa1T61Wy94ti7n0z18olEqavNuNFh/0NLzX/Vv+zB2nG4/Vavhupo4jLcbW+d+3Lcb//GEUSiW29s582n86Ds5u2ZItPVm9Jr1705+Ny6fqNtJq+eiT/tTUjwVyJfelU2xbqxsHNG7Vkbap5V6kHwfYOdBPPw64duU0vomupTt/PsxwLZ2TsnvcAhAbE8PWNTP5N+A8CoWSDp9+S60GrXP8WISIJ8smiLe6dukkQYH38V60jx5fT2Tr6mmpbvfrqmn0+MYb70X7CAq8z7XLJwH4a9caVNXr471oH6rq9flz1xrDazSaOHb/soDKNd5Jsq8NS8bTqv2XTFiwm5EzN2Pn4GzUMWji4ti8ahbfeS1h8kIfzp3Yb7jQjXfq4C6sbe2YvmwPrT/qyc4NCwGwtXfk23E/MunH7fQePIW1C70AePUyjB0bfmT4pBVMXujDy7BgrvudMSpnarm3rJ7Jt+OX4r1gJ+dOppL7kC/WNvZMXbKXVu164btJl9vMzIL23QfR+bPhKfbbpv0XTF60i/Fzf+X2jcv4XzyZbZlzqr6oqtdn7NwdjJ27nV4DprB5xSTdcZpb8vm30/H6wZeB45bjs34Ob16HZyhrwKWTBD25z6TF+/j0m4lsXZV61q36rJMW7yPoSULWP/VZJy1OmjWt/To4uvL99I2Mm7edkTN+4c9dawkLeQZA6/ZfMHHhHsbO2cYd9SUCLp3I0DEk96/fcZ4/vc/wufv5uPdk9qyfkup2u3+ezMe9JzN87n6eP73Pv3669+v+7QIGT/Nl8DRfqtZ5l6p1dAMS9+IVGDh5O4On+fLFyJXsXjeJuLjYLGWMlxd9S+v2X/L5t9ONyp1Vj37eydl2ffPkveNdu3SCZ4H3mbjoN7p/7c2v6ZT5xEW/8SxZmVesXp+Ji36jYvX6/KUv8z99V1OsdCXGztvJZ99Ox2f9bACUJiZ0/GwEXgv28P30Xzh+YCtPHt1O9T0zdRyXTxAUeB+vhb/RvZ8329ekfhzbVk/jk6+98Vr4G0GB97muP44iJSrQ5/sFlKtcO8n2pw/tAGDMPF8Geq1k10bdxVV20J2HZjPEazFTFvpw9sR+/nt4J8k2Jw/uwtrWnhn685CP/jxUtGQ5vOZuwvuHrQyZsIRNK6Yb2t/WNXOpVvMdpi7eifcPv1KkeNlsyZs8e3afiyytbPCat83wj4trEWrWN25iJTXXL58g6MkDxv/4O5/0m8T21VNT3W77mql06+fN+B9/J+jJA0NdUVVvyOi5voye44tr4dIc3LUaABs7J/qNXMLoub70HDidX5aOTXW/maGJi+PX1TP4dvwyJizw5fzJ/TxJVs5/68t58pJ9tGzXC99NPwLw5OFtLpzaj9eCnXw7fhlbV81AExdHWPBTjv6xmdGztzBhwU40Gg3nT+037K/jZ8MZN28b4+Zty/LErSYujm1rZjBo/HImLNjF+VN/pMh9+vBOrG3tmbzkN1q2+4xdKXL7Mmj8cn5dPR1NXBwADZq3Z9D45am+Z+jzQG74/YNToSJZypxYTvWLrdv3ZszcHYyZu4OPegyhfJU62Ng68N+Dm/x9yIcRMzYzZu4O/C8e49mT+0Ydw/XLJwgKfMC4Bb/Trd8kdqxJvZ7vWDuVbn29Gbfgd4ICH3Djiu4YKlZvyKg5voya44trkdIc3K2r55fP/ElcbDSj5vjy/Yxt/H1oOyFBj43Kmpyx5V+kRAX6jkjZnxctUZ6Rs7YyZu4OBo5bwdaVU4wat+RE+4w3dNJqxs3bZpi4BShSsjxfj1xA+WTHldGs2d0m37bPf47uJjQ4kAk/7mbij7up06htkiy7NqUcjxnL2Drfsl1vRs3xZeQsH6rWasaBnan3NdnNmGvSoiXLMX7uL0z84Ve+m7CUTSumGT0Wz0zuLatmMnj8Uib9mPY4wMbWnmlL99K6XS92btRfS9s5MWjsQrwX7ODLwVNZt2h87uTNgWvoP3auws7BmSmL9+D9404qVsl8+xTCGDJ5aySVSlVYpVLNzOH3OKpSqQql8vN1+n9PUqlUXXLivf3OH6Fe049QKBSUqehJxOuXvAgNSrLNi9AgIiNeUbaiJwqFgnpNP8Lv3BHd688doX6z9gDUb9Yev3OHDa879sdmPOu3wc4+YXL2yaPbaOLiqOzREAALS2vMLayMOoa7t/xxK1IC18LFMTUzo27j97hy9miSbS6fO0rDFrq74Wo3bM31q2fRarWULFsJR/0nsUVLliMmOpqYmGieBz7GvWhJw8RyJY/6XDx9yKicyd275Y9b4RK4uutzN3oPv3NJc/udO0rD5rrctRq25oY+t4WlFeUr18TU3DzJ9uYWVqiq6e4kMzUzo0TZSoQGP822zDlVXywsrVEodF+tiIqKAP1/uxctjVuRUgA4Orth5+DMq/DQjGU9d4T6zTKYVaXLWr/ZR1w5myhrc33W5u25cvbwW/dramaGmZnu7xEbG41WPylkbmFFxWq6T6BNzcwoUaYyYVn8m1y/eJiajTqgUCgoWb4GkW/CCQ97lmSb8LBnREW8omSFmigUCmo26sD1i0nrrlarxf/sfjwafGjIaGKi+6JGbEy0ofyNkdt9C4CqegMsrGyMzp4VISfPExPyIk/eO97V80eo17R9hsq8TMUa+jJvz1V92V49d4T6zToAUL9ZB8Pf4smj26iq6+46LFysLCFBjwkPe46Dk6vhDipLKxsKFyvDixDj+xv/c0eoqz+O0hk8jrqJjqNw8bK4Fy2TYr+Bj25TUX8cdg4uWNnY8/BOgNF5QXceci1SPMl56HIq56F3WrQDoHbDVty4ek7XnydqfzGJ2l/Em1f8e+0ijVt/DOj6D2sbu2zJm1hOnIsSe/rkPi9fhFC+cq1sz371fKK6UsGTiDdp1ZXXSevKeV1dqeTZyFD2pSt4GOpv8TKVDXdpFS5enpiYKF3faIR7t/xxLVyCQvpyrt2oLVdSlPMRGujPOzUbtkGtL+cr545Su1FbzMzMKeReHNfCJbh3yx+AuLg4YqKjiIuLJToqAgcnV6Nypp67ZJLcfuePJMt91NBf12zQBrX/GbRaLX7njyTLXdKQu4J+sjM1O9bP4eNewwzjAmPkVL+Y2IVTv1O70fsAPH18h9IVPAzn1QqV6+B31rjxo/+FI9RtkrF6Xjq+njdJVM89Eup5qQoehOnruQIFUVERxMXFEhMdhampGRZWtkZlTc7Y8k+rPzdP0m9GGT1syan2mZYixcviXqy0EVmzt02+bZ8nDmzj/S79USp10wp2Di6G9zm6fzM1GqQcjxnL2DpvaZ1Qj6MjI7KlL8kIY65JLXJgLJ6p3IUTctdp/F6K+n/l7FEapDIOSHItXSLhWjon5dS45e/Du2mrv+NcqVQa9a0PASiU+f+ffCb/JSpg1Gp1oFqtNv6Wi6y9d+/0tzJOWMgznAoVNvy/o4u74W7BxNs4urinus3LFyGGCwUHJ1dehofoX/OUK2cP0+Tdrkn29ey/+1jZ2LFq3jBmjeqG78b5aDRxGCMs+BnOyfKFhgSlso3uOE1MTLGytuXVy7Ak21w8fZASZVWYmZnjWqQEgY/u8fzZf8TFxXL57JFsnQQFCE2l7ENTKfv4beJzv06WOy1vXodz9fxxKnkY/1XP1PLEZ86O+gJw5ewhpg5tz4qZg+g5IOUdpfduXSU2NoZC7iUylPVFyDMcXTKf9UU6Wd+239DngUz/vjNe/d+lzcd9DIOZeG9eh3P1wjFU1Rtk6BiSCw95ioNzwnvbOxcmPNkxhYc8w8Ep4ZgcnN0JTzahdk99Hht7FwoVLm342cPbV1g4th2Lx3Wgw5fehgFkVuV23yJSL/MXycr8xVvLPDhZmQcDUKyUiitnDgK6dhgS9MRw8R8v+NljHt29QanyHsYfR2jSNuaQ1nE4JzoOZ3fCQpNuk1yxUir8zx0hLi6W4GePeHTnGqHBgUbnBQgLDjKcYwCcXNxS1vfgIJzSOA/d+fcqE4d0YfKwbvT6ZhwmJqYEPX2Mnb0T65ZMYsr3Pfh56RSiIiOyJW9iOX0uOn9yP7XfeS9HLp5fhDw1lCno6kHyDxBehDxNUVdS+5DhzFFfKtdonOLnV878RfHSlTE1S3uCOiOSt08nF7cUOdIq5xchT3EqlLLdOrq407r9F3gNeI+x/VpjZW1HlUR3wO3Zsphpw7uwY93cLF9Mh4U8xcklWVsLTl4/EvIlzh0W/CzF3yd535Gc37kjODq7Uby0Kkt5U+bPmX4xXnRUBNcvn6JGA91SQ0VKVODW9Qu8fhlGdFQEAZdOGN3PvAh5mnTckUY9d0hUz3X9Zhr13FNXzz3rt8HCwgrvAS2YMrgNzdt9meaEelYZW/5vc++mH9OHf8zM7zvxSb+JRo1bcqJ9gm7+bfHU/swc1Z2Tf+3Icr6kObK/Tb5tn8+fPuTi3/uZPbo7S6cPMNxJHhb8lCtnDtOkTfaPx7Kjzv/260ImD2rFhVO/8X7Xb7M9Y2qMvSa98+9VvId0ZvKwrvT6ZrzRY/EM505e/1OtU89wTmcccPGfg5QoU8lwM0tOyYlxS/w3O/dsXcr0kd1ZOW8E4WHBaW4vRE6QNW+NpFKpSgNLgAtAGyAGWKxWq1OcgVUq1VHgCuAJPALOAh0BDdBWrVbHqFSq6UBzQAF8r1arT+tfPkalUtUEXgOfqNXqCJVK5a9Wq6sle49hQCd0f9vparV6n1EHqNWm+FGKC6zUtklntz7r59Ch51CUSpMkP9doYrl9/SJj5mzDqVBh1i4YyT9Hd/NOy06ZTZ4QL5WfJc+nTWWrxMf534Pb+GxcxFDvZQDY2NrT85txrJw/GqVCQTmVJ0FPs/erZBkpe20q22Tkk9i4uFjWLBhLiw964OpePMsRU8ih+gLgWa8VnvVacevaeX77dQmDJ6wy/O5FaBAbFo/js0HTDJ/8px81tRzpZ00v7Nv261SoMOPn+xAW8oyVc4ZSs0Eb7B11dyjExcWy7sfRNP/gUwpl8W+SXj2O3yqVjZL8r98/v+HZ8MMkPytRzpMhM/fx7PFtdqwaS0WPppiZW2Qppy5G7vYtIq3+Iv1tUrSLZNp8/BU+62cxa2QXipasQPEylVAqE4YXUZFvWDN/GJ2+HI2VdTbctZVq3Um+SeaPo36LjgQ+vsP8sd1xci1C6YqemGRTPUqtbSYv/Le137IVqzNl4Q6ePLrD2kXeVK/VCE1cHA/u3KBH31GUrVidrWvm8sfOdXz86cBsyZwQLOfORQDnTh2g9+DUvyptvKz1icm3+dP3J5QmJtRu3C7Jz588vMXezT8wYNxKo5Omfr7JyDlJkcaPFbx5FY7fuSNMWfo71jZ2rJo/kjPH91G/aTs69PwOe8dCxMbGsHnFFP7atZYPuvY3/jhIrX6kuhEZ+/skiI6KYP/OVQz2+snIhImz5Uy/GO/qhWOUVdU0THoWLl6WNh36sGTa11hYWlGslMro81Wq+bJw7v/L9ydMlAn1/P7tqyiUJkxedpg3r8NZPPkLKlZrkOEPyTMiJ8u/dAUPxv+wi8BHd9i4dDxVajTO+rglB9onwPfTfsbR2Y2XL4JZNKU/7sXKUCEHvoptbJt8Wx2LiYnG1NyC0bO3cvnMQTYtm8jwqT/r75AfitIk+8dj2VHnP/xkCB9+MoSDu1Zx4sDmXJnANfaatGzF6kxe6MOTR3dYt2gi1Wo1Mm4snlEZqP/pjQP+e3CLnRsXMnRiLixRkQPjFk1cHKHBTylXqQZdvxzBwb0b8dnwA72/y5vl2MT/TzJ5m30+Ahqo1epolUr1ttmjA2q1eohKpToERKjV6hYqlWoN0FilUoUBHmq1upFKpSoB7ADib4s8rVarR6hUKm+gF7Aq+Y5VKlUVoJFarW6iUqksgZNApidvj+3fyt+HfAAoVa4qoc8T7ggIC36a4it3ji7uSb7mHRb81PB1QjsHZ16EBuHg5MqL0CDD12Ye3A5g3cLRALwKDyXg0gmUSlMcnd0pXqaSYfLKs15L7v7rB0asx+7k4kZIsnyOzq7JtnEnJDgQp0LuxMXFEvHmlWGgHfr8KctmD6fPd1NxK5wwYPWs2wzPus0AOP6nD4psHpw4ubinKHtHp5S5Q58H4uSSMvfb/LJiKm5FStKqnfEPD8qN+pJY+Sp1eL7Ui1fhodjaOxHx5hXLZw2iXffBlKn49gf1HNu/lVMH9VnLVyUsOFlW5wxkdXp7Vt1r0tmvsxtFSpTj1vULhoc3bP5pCq5FStEykw8u+efgL5w7qvusqHiZarwISXjv8JBA7JKVv72zOy9CE47pRchT7J0S7gCOi4sl4PxBBk1J/Q4Qt2LlMLew4umjmxQvWy3VbdKSl32LZ73ce6hDfnJ8/xZDmZcsVy2VMk9693fqZa77u9g5uCQrc90HD1bWtvQaqJt802q1TPq2reHBJ3GxMayeP4w6TT6kRv2sP9ThxIEtnE50HInb2IvgpHXYcByJ7rAJC0lZv5IzMTGl0xejDf+/YEIvXPXLshhLdx5KyBwa/CyV85AbocGBOKdyHopXpHhZLCytePzgNk4ubji5uFG2YnUAajVsxf6d67Mlb9JcOXcuenRPjSYullLlUj5oJqtOHNjC6cO6/qtkuWpJ7moMC0lZVxycC6eoK4m3OXtsNwEXjzPIa3WSi7+w4EDWzh9Cz0EzKFS4pNG5HZOVc2jws1TbZ2rlrPt58nOVKzf8/sHFrZhhiaca9VtxR32F+k3bGdqDmZk5DVt04OCen7OW29k9yTePwkJSnvOc9PmcXAqnzJ3s75P8mBMLCnxI8LPHzBjZ1XCcs0Z9wsiZm3FwSrG6WJpyo1+Md/HUH9Ru/H6SnzVs2YmG+psS9mxemOSu0ow6+Weiel42aZ+YVj1PfNfhi2THefbYbgIuHWfg+IR6fvHU77qlQ0zNsHNwoUzFGjy8E2D05G12ln9GFNb3m08e3jI80CyzcqJ9AoZvYNk5uOBZryX3bvobPXmbU20yrX06ubhTU39+96zXio1LJwK68djaH5OOx0xMsj4ey+46H69Wow9ZNWdgrkzeGntNGq9I8bKYW1rx+MEtSmfyYXZZkaL+h6SRO41xQGjwU5bPGU7v76biWjj7PvxJS06MW2zsHDG3sKSGvv7WatiGU4dSPkxTZEIuLv3xv0KWTcg+I4GV+nVoK79lu3P6fz8ELif6b2egIvAPgFqtfghYJ3rdP/p/n9Fvl5qqQE39Hb77AVuVSpXpBfCate3O2LnbGTt3Ox71WnL2+F60Wi13/72ClbVdigtgBydXLKxsuPvvFbRaLWeP78WjTgsAqtdpzplje3TBj+3Bo67u55OX7meK/p+aDdrwSd/xeNZrSany1Yh4HW74CrTa/yyFi5fL7CEkUbp8VZ49ecDzp4+JjYnh3MkDeNZtnmQbz7rNOH1kLwAXTh+kUvW6urtWXr9k8fTBdOw1mPKVayR5TXiYLuPrV+Ec3b+NJq07GpUzuVLJc586gId+sjieR51mnD6qy33x9EFU1eqm+9XT3VuWEPHmFV17j8yWnLlRX4ICHxg+IX145xqxsbHY2DkSGxvDqnlDqd/0oww9wbZZ2+6Mm7edcfO241m3JWeOZS7rmWN7DZmq12nOmaP6rEcTsnrUaZ7qfkODA4mOigTgzatw7qgv4160NAB7tywm8s1LunyZ+SetN2jd0/CQscq1W3Hp1G60Wi0Pbl3GwtoOe8ekA1V7RzcsLG14cOsyWq2WS6d2U7lWwkD6dsBpXIuUSbL8QkjQI8NDEUKfP+b5k7s4uRbLdNa87Fv+v2ratofhoTm6Mt9jKHNLa9tUy9wySZnvoXqSMt8NwJlju6muL/M3r8OJjY0B4O9DPpSrXBsra1u0Wi2/rPCmcLGytGz3hVHH0eS9Hoyas4NRc3ZQvW5LzumP495bjsPC0oZ7+uM4d3wP1fR50xIdFUFU5BsAbvj9jYnSxOjzTzzdeeghQUnOQ0n78xp1m/H3Ed3nrRdOH0KlPw8FPX1saH/Bz/4j8PE9XNyK4OBUCKdC7gQ+vqfPfJYiJVKu/WisnDoXAZw7uZ+6jdumu11mNHmvB6Nm+zBqtg/V6ySqKzevYJVmXbHm3s2EuhJf569fPsmhPWvoN3JxkvX337wOZ+XsgbTrMZSyquxZqzehnB8RGxPDhVP7Uynn5vyjP+9cOv0Xqmr1UCgUeNRtxoVT+3Vr8j99xLMnDyhdvhpOhQpz718/oqMi0Gq1qK+eoXAxXR2JXx9StybnEYqWLG9E7vtJclev0zzJNon760v//EVFfe7qdZony32f0uXT/lCwWKmKzF5zjKnL9jN12X4cXdwZM+fXTE3cQu70iwARb15y69p5w7bxXr7QfdU25PkTrpw9SJ1GSSd3M6Lxuz0YOcuHkbN8qFanJedOZLKen9hDtdoJ9fzw3jX0HZG0njsVKsKtAN06kFGRb7h/yy/V9WUzKzvLPy3PnyWMW0KC/uPpf/dwdi2a5cw50T6jIt8QGfEa0H1D5fqV01luhymzZm+bfNs+Peq2RO1/FoCb187jVlT3oecUfTuduix7xmPZWeeDEj0k0P/CEdyyoV5nhDHXpM+TjQWePr6Hi1vW67Qxuc+fPIBnnWT1v24z/kk0DqhULf5aOpwl0wfTsed3lK9UM1fy5sS4RaFQ4FG7Gf8GnAfgxtUzOfKQWCHeRpHqLeMiwxItm9BVv5RBI2CQWq3+NJVtjwJd1Gr1c5VKtR5Yolarz6tUqkmAP3AHmKxWqz+Kv/NWrVbX179uoVqt9lWpVBOBJ2q1elX8sgmJXn8DGKtWq3vq389crVanu4jZX1ei0qwEWq2WbWtmcP3KKczMLek1cCql9J9azxzZlbFztwNw/3YAm5Z5ERMdRZUajenaZywKhYJXL8NYu2CE7pOtQoX5avj8FJ9qbVzqRbXaTanZQDf5dt3vNL4b5ukXOa9Cj2+8MTU1S5HN3DTja+FevXCCX9fOQ6PR0KhVBz7s0pfdW5ZRqlwVatRrTkx0FGsWevHwrhobW3v6DZ+Fa+Hi/LZ9FX/sXItbkYS7aoZOXI69ozOrfhjDo3v/AvBht6+pl8ELUI0m458yXb14gu3rdE88f6dlBz7o3I89W3W5Pevqcq9bNJ6H99RY29rTd9hswzII4wa8T2TEa+JiY7CytuO7CcuxsrZl7DfvUbhYGUzNdGXavG13GrfO2LIUsZq3f96TU/Xlr11rOXN8LyYmppiZW9Dxs+GUq1SLs8f3sWn5RIokmmD5bNBUipdO+tTs1M7F8VmvXT6FubklvQYlZJ0xoivj5iVk3bg0IWu3rxKyrvkhIWvf4fOxsXNIc7/Xr5xm54Z5hq+fNWvbg8ZtuhAaHIhX/3dxL1YGU1PdGlDN3u9Oo1adAQiLSFn331b+ezdM5ebVk5iZW9Kp7wzD3bGLvToyeJruE+JHd/zxWTWW2JgoKng04aPPvAwDlh0rx1KivCf1W3Y37PfSqd0c37cKpYkZCoWClh8PpErtt99F6WAZk27W3O5bFkz8gqeP7xEV+QYbOwc+7T+ZKjUapcgWXcf4dVmTq7FxPi7N6mFeyImop8HcnLKYh+uyZ327eGbnr77191qtlu1rpicq82mGu5BmjezCmLm6PA8MZR5J5RqN6dpnHAqFgteGMn+CU6Ei9NGX+d1/L7NxyXgUSiWFi5ejZ//JWNs6cPvGRX6c+AVFS1ZAoV/0/6Me31G1VtMU2TSZGIpotVp2rNUdh7m5JZ8OSDiOOaO6MGpOwnH8ssyLmJhIqtRoTOfeuuO4cvYQPutm8Co8FCsbO4qXqsSA8T8R/OwxK2b0R6FQ4ODsRo/+U9K92Lc2e3s9T+zqhZNsXTsPrUZDo1bt9eeh5frzUDP9eWgCD+7ewMbWga+Hz8S1cHFOH93HH77rMTExRalQ0q5bP2rW112APrirZsOyKcTGxuDqXpwvv52Eja19hvLEpdOfJ8mezeeioiV0fbbXwA/5dvwSw4RiRkTGZvxLY1qtFp9107l++STmFlb06D+VkuV0feKc0Z0ZNVt399+D2/5sXh5f55sY6sq0Ie8TGxONtZ0joPsadre+3vy58ycO7l6d5I7bAeNWJnlQT2rSG7f4XzzBjnVz0Gg0NGz5Me937sferUspVa4qHvpyXr9oPI/u3cDa1p6vhs0xfFPpD59VnD68C6WJCV2/HEXVWrp1S/f9uowLpw6gNDGhRJlK9BwwCTMzc36c1JdX4aFotVqKl1bR4+sJWFpZp5pLq337uMX/4gl81s9Bo4mjYYuPadv5a/ZtXUrJclXwqNuCmOgofl48jof6ut0nUe79Pis5fWQXSqUJXXqPomrNJgCs/XEUNwPO8+plGPYOznzYbSDvtEo6VpkwsC2jZ21560NjNOlkz6l+EeCfo7u4fvkUvYfOTfKeCyZ+wZuXYShNTen0+chU17mPjcv4WDG+nt+4oqvn3b9JqOdzx3Rm5KyEer5lRUI97/Sl7himD01az0uV19XzqMg3bFnhReCj24CWes0+puVHfdLNY2qS8Q7d2PK/cvYQO9Ym9OfFSldi0PifOHt8L3/tWoOJiSkKpZK2nb/Bs16rt2YxUWre+vvsbp/Pnz7ipznDANDExVKnyQe837kfAJfPHGLbmlkJ56nSKgZPWPGWckxaX3KiTaa2T9B9mLV+4VhCnz/BwtKa7l9PSLEm9YYluvFY8hstomOzdh+ZsXV+3YKhPPvvHgqFAifXonT9amKStc8zwsYi4+f/xLJ6TXr66D72+67T1WmFknbdvjaMBTIjvf78bbm36ccBjVp24IMu/dizZRmlyieMA9YuGm/I3XfYbN219I5V7N+5Jsm19JCJK7B3yPxD7DIzbZUT45bgoP9Yt8iLiNcvsbV34otBk3F2LZJYiI8OAAAgAElEQVShPC2qW8ltpslE7lma7yciLdsPyld/N5m8NVKiyVtTwAKwBLzVavWfqWx7lLdM3qrV6h0qlWom0AzdEjgj1Gr1Kf3rLgI1gFckW/M22euHAJ2BOOC+Wq3+Mr1jeNvkbX6Wmcnb/CQzk7f5TXqTt/lVQf5WRmYmb/OT9CZv87OcmLzNDelN3uZnmZm8zU8yM3mb32Rm8jY/yczkbX5TUMctWb3Yzw/Sm7zNrzIzeZvfZGbyNj9Jb/I2PyuobTSrk7f5QVYnb/NaQa0rkLnJ2/xGJm9TksnbzJPJWyGTt7lMJm9zn0ze5j6ZvM19Mnmb+2TyNvfJ5G3uK8gX+zJ5m/tk8jb3FdQ2KpO3ua+g1hWQydv/NZH7luf7v6hluwH56u9WcEfA+ZxKpfoTME/0o9/UavXctLYXQgghhBBCCCGEEEKIxGTyNoeo1er0n54khBBCCCGEEEIIIYQQaZDJWyGEEEIIIYQQQgghRM4ryOsa5pGCu9CMEEIIIYQQQgghhBBC/A+TyVshhBBCCCGEEEIIIYTIh2TZBCGEEEIIIYQQQgghRM5TyH2kmSUlJoQQQgghhBBCCCGEEPmQTN4KIYQQQgghhBBCCCFEPiTLJgghhBBCCCGEEEIIIXKeUu4jzSwpMSGEEEIIIYQQQgghhMiHZPJWCCGEEEIIIYQQQggh8iFZNkFgZxGZ1xGy5FaQfV5HyJJCdjF5HSHLrM0KZvY4TcH9nMrRqmCWuUaryOsIWWZ2/mpeR8iSmDrV8zpClinP+ud1hCx5EWmR1xGyLE5TMNuoqYk2ryNk2f0gy7yOkCVhLwtumVcrXTDPoa42r/M6QpY9fW2b1xGyRBNjktcRsszeMjqvI2RJPY7ndYQsOxrRMq8jZImpUpPXEbJMQ8Ect4g0KOTvmVkFd0ZDCCGEEEIIIYQQQggh/ofJ5K0QQgghhBBCCCGEEELkQzJ5K4QQQgghhBBCCCGEEPmQrHkrhBBCCCGEEEIIIYTIeQq5jzSzpMSEEEIIIYQQQgghhBAiH5LJWyGEEEIIIYQQQgghhMiHZNkEIYQQQgghhBBCCCFEzlMo8jpBgSN33gohhBBCCCGEEEIIIUQ+JJO3QgghhBBCCCGEEEIIkQ/JsglCCCGEEEIIIYQQQoicp5T7SDNLSkwIIYQQQgghhBBCCCHyIZm8FUIIIYQQQgghhBBCiHxIlk0QGeZ38TS/rJqPRqOhWZsOtOvyRZLfx8REs3LBJO7dvoGtnQMDR07H1b0oAA/u3WT9splEvHmNUqnEe956zM0tmDm+P2EhzzG3sABg5KTF2Ds65+hxaLVaDmyZzq2rxzEzt6R9n5kUKVU1xXaHdy7g6undRLwJZ8zSi4af3//3HH9uncnTR2o6fT2fKnXa5mjexLl3rp/FtUsnMLOwpOeAaZQoWyXFdg/vBPDLMi9ioqOoUrMJnb4cg0KhYPem+fhfOIqJqRmF3Evw6YCpWNvY51he/4un2Lp2HhpNHE1ad+T9Tr2T/D4mJpq1Cydw/851bO0c+fr7WRRyK8rdm/5sWD4t/qD56JNvqNWgJQBjvvkQSysbFEolJiYmeM39JdtzB1w6xbZ1c9BoNDRq1ZG2HfukyL1+sRcP7lzHxtaBvsNnU8itGK9ehrFy3gju3w6gQfP29Og71vCaXZsXc+bYPt68DmfhptPZnPckO9bN1uftxLsdv0qRd8Pi8Ty4cw0bOwe+GjYXF7diABzwXc3fh3xRKpV07TOGKjUaEfo8kJ+XjCc87DkKhZLGrTvT4sNeADy8e4Otq6YSEx2NiYkJn/QdT+kK1bOUW6vV4rNuFgGXTmBuYUmvganX5wd3Ati0VFefq9ZsQufeuvr8+tUL1i0YQUjQfzi7FqXPsHlY2zrw5tULflk+kedPH2JqZkHPAVMoWrICAN6D3sPC0hql0gSliQmjZv1aILKHPg9k49Jxhr9Jo9ZdaP5BryxlzwqPVTNw+6A50c+COV7zo1x737RotVp2/jyT6/q+8NMB0ylRJvW+cPNyL2KiI6lcswmdvhir7wvnEXDxGCamphRyL0GP/tMMfeF/99X8unoKURGvUCiUDJ++FTNzi2zLvXvDTK5fPo65uRWf9J9O8VRyP7oTwNafxuty12hKh891ueMd3beOfZvnMXnFSWzsnfA/f5gD2xejUCpQKk3p8NloylSqnS2ZE2ffs3EG6svHMbOwotvXMyiWWva7AWz/aRwx0ZGoajSl/WfjUCgU/OWzhLNHd2Bj5wRA225DqVSjGSFBj5k/qh2uRUoDULK8J536TMrW3Lt+Tijz7gNSL/OHdwLYuiKhzD/W15U/ti0i4PwRFEoFtvYudO8/HQdnNyLevGTz0tGEPn+CJi6O5u16U695x2zLndpxHNo2ndsBxzAzt+SDz2dRuGTKccvx3QvwP7OLyDfhDP/xUorf37i4n92rhvD5mB0UKZW1vjuzuf/eM50HN45jamZJ824zcS2eNHdMdAQHNw0lPPgBCqUJpSq3oP4H3wPgd3wd18/uQKk0wdLWmeZdp2PnVCxH8xrTt186fYDfty/n6eM7jJixhZLlkh5ryPMnTB/WgQ+6DqRV+y+zLfeVC6fZuPoHNHEamr/bnvapjM9XLJjM3Vs3sLN34NuR03B1L8qpo/v5zXeTYbuH924xbcEGSpWtyLRxAwgLfY65vv8bPXkRDjkwPs+pNnrr2lnWzRuMs368U71ua97tPDBbc+/eMIMbV45jZm7FJ9/MSL0/vxvAryvGERMTSSXPpnT4XNcn/umzhDNHEvrE9z8ZSuUazXj9MoyNC4fy8M5V6jTtSMcvvbItczz/S6fYtlY3zm3cqiNtO6Uc565bpB/n2jnQTz/OvXblNL6bFhEbG4OpqRmdPx9Gper1iI6K4Kd5IwkKfIRSqcSjTjM6fTYk23Mn9vfla8zfsAONRkOHFu/wZYd3k/ze568TbP/rOEqlEmtLC8b17UHZ4kUMvw98HkK3EdPo1+UDPmvXOkezQs7Vl3+v/s3vW38gLjYGE1Mz2n06gvJVG2Rrbt+fZ3L98gnMzC3p8Zbx1pYV+vFWjSZ01LfP37ctxv/8YRRKJbb2znyqb58XTu7j0J41AFhYWtPlqwkUK1Up23LHZ8+JvgXg1rWz7N4wi7jYWGzsnBjk/XO2Zv//QptobCsyRiZv06FSqQoDQ9Rq9dh0Nzb+vfzVanW1nH6frNDExbHhpzmMmrwEZxc3Jo34gpr1mlCsZFnDNsf/2oONrR1zf9rJP8f/ZNvPSxg0agZxcbH89IM33wybRMkyFXkVHoapSULV6z98CmUqpOxMc8qtq8cJeXafQTMO8PjOFX7fNJmvxm9LsV1FzxbUbdmTpeOTTs46OBehfe+ZnP5zbW5FBuDa5RMEBd7Ha+Fv3L/px/Y10xg+fXOK7batnsYnX3tTuoInP80awPXLJ6lSswmq6g1p12MIJiam7PnlBw7uWk37nsNzJKsmLo7Nq2YzzHsZTi7uTB/VC8+6zShaIqG+nDy4C2tbe2Ys28PZkwfw2bCQb0bMpmjJcnjN3YSJiSlhIUFMGd4dz7pNMdHXme+n/ISdvVOO5d6yeiZDJq7AydmdmWN64lGnGUVLlDNsc+qQL9Y29kxdspdzJ/fju2kh/YbPwczMgvbdB/Hfg1s8fngryX496jSjxfvdmTi4fbbn3bZmBoMnrMTR2Z05Y3tQvU5ziiTKe/rwTqxt7Zm85DfOn/qDXZt+5Kvhc3ny8DYXTu3Ha4EvL0KesXjq13gv3IvSxIROn39PybJViIx4zezR3ank0ZAiJcqxa9MCPujan6o1m+B/8QS7Ni1g6OSstYNrl07wLPA+Exf9xr2bfvy6ehojZqSsz7+umkaPb3T1efnMAVy7fJKqNZvw1641VKxen3c/7sufu1bz1641dOg1nD99V1OsdCX6jVxI4OM7bF8zg8ETVxv29533WmyNrD+5nV1pYkLHz0ZQQv83mTPmE1QeDSlSvFwq6bLfo593cm/ZJmqsnZ0r75ee65dPEPTkAeN//J37t/zYvnoqw6dvSbHd9jVT6dYvrb5waIq+MC4ulo1Lx9Br0EyKlarE65dhmJhm3zDphr4PH/PDHzy45YfP2ikMmbo1xXY+a6fQ5atJlKrgyeo5/blx5SSVazQBICz4Cf9e/RvHQgkXoRWq1adq7RYoFAr+e6Bm48LvGT1/X7blBlBfOc7zwPuMnL+fB7f98F0/mW8np/zww3fdFDp9NZmS5T1ZO/cb1H4nqOTZFIDGbT+n2Yd9UrzGxb0EQ2f4ZmveeDcun+B54H3GLtCX+ZopDJmWepl37asv89kJZd6iXR/e7/YdACf2b+Kvncvp0tebU39uwb1YOb4auYxX4SHMGv4htRp/iKmpeY4cx52A44Q8u8fXk//kv7tX+HPLJD4fvT3FduWqt6BW856s9H4vxe+iIl9x4chGipT2zJGMqXl44zgvnt+n+6gDPHtwhZO+k+k4OOV4y6Npb4qVb0BcbDT7VvbmwY3jlKzUFJeilen03Q7MzK0IOL2Ff36bR5teC3Isr7F9e5ESFeg7YgFbV05Jdf8718+hSs3G2ZpZExfHzz/NZcyUxTi7uDHx+y+pnWx8flQ/Pv9hpQ+nj//J1p+XMnjUdBo1b0uj5rox7sN7t/hh+khKla1oeN3A4VMoW6FytuZNLqfaKECZSrXpO2pZzuTW94mj5+/nwS0/dq6bzHdTUvaJO9dOoXPfyZQq78maOd+gvnKCSjV0fWKT9z+nebI+0czMnPe6Dibw4U0CH91KsT9jaeLi2LJqJkMnrsDJxZ2Zo3viUTflONfG1p5pS3Xj3J0bF/L193OwtXNi0NiFODq78fjBLRZNHcDsVX8B8G77L1BVr0tsTAwLJn+N/8WTVKuVvXU9XpxGw5x121gy7lvcXRz5YvxcmtaunmRy9r1GdejcRnfePHbejwUbd7J47CDD73/Y6MM7NVJ+AJZTcqq+2Ng50nvEMhyc3Ah8eJNVs/sxYcnRbMt9/fIJggIfMG6Bbry1Y81Uhk1LOd7asXYq3fp6U6qCJytnDzC0z5btevNBt8EAHN+/iQM7l9OtrzfObsX4duJ6rG0duH75BNtWTU51v8bIqb4l4nU4O9dOpd+Yn3AqVJSXL4KzNbcQbyPLJqRDrVYH5sbEbX5352YA7oWL41a4GKZmZtRv8i4Xzx5Pss3FM8do3PJDAOo2ask1v3NotVr8L52hROnylCyjGxDa2juiNDHJ9WOI9+/lQ3g07IBCoaB4uRpEvgnnZdizFNsVL1cDO0e3FD93LFQc9xKqJHdC5Qb/c0eo27Q9CoWC0hU9iXj9khehQUm2eREaRGTEK8pUrIFCoaBu0/ZcPXcYgEqe7xgmQEtV8CQs+GmOZb17yx/XIsVxLVwcUzMz6jZ+j8tnjybZ5vK5o7zToh0AtRu24sZVXX2xsLAy5IyJiYZcLOd7t/xxK1wCV3d97kbv4XcuaW6/c0dp2Fx392Gthq25cfWsLrelFeUr18TUPOXFe9mKHjg4ueZIXtfCJSmkz1u7UVv8zh9Jkbd+M92kcc0GbVD7n0Gr1eJ3/gi1G7XFzMycQu7FcS1cknu3/HFwcqWk/k4jSysb3IuVISxE1z4UCgWRb14DEPnmpVHHdPX8Eerp63OZDNbneonq89VzR6jfrAMA9Zt1wO+c7rifPLqNqnp9AAoXK0tI0GPCw55nOWd+yO7g5Gq4+8vSyobCxcrwIiTn2m9yISfPExPyItfeLz1XzyfqCyt4EvEmrfJ/nbQvPB/fFzYy9DGlK3gYylLt9zdFS1Y03P1hY+eIUpl956qAC4ep00SXu1QFTyLfvCQ8We5wfe7S+tx1mrQn4Pwhw+93b5xNu0+/R0FCv2hhaWM4H0VHRuTIuSngwmFqN9adN0uV19X51LJHRbyiVAVd9tqNOyTJnhf8LxymdqIyj8hAmddu0h5/fW5La1vDdtGREYbzkQIFURGv0Wq1REW+wdrWAaUy5+6HuHnl/9i77/imqv+P46+ke+/B3oS9hwhlCg5UBBkiDoaggCKgLMtG9lKRIdMBiLIRlA1ShuzVAmFvOuhkdSX5/ZG0TdoAHUlpf9/P8/Hg8fXb3jTve3LuuSfnnnvuLqq99A4KhYJiZWuR9DiBh/FZ+y3FytbC1SNrvwUgZNP3NGzzCbZ2lplJnh3Xz+2iYh19vQkoVYukJwk8SjDNbWfvRLHy+pliNrb2+BarwqP4cACKlX8JO3snAAJK1kz/ubXktW0PLF6WgKJlzP7t00d24RtQnCLFy1s085VL5wgoktE/fymoNccPZ+6f7yPI0D9v0LglYaf1/S1jB/dtp1FT09mL+cFax6i1hR3fTd2gdtlozx9SOq1NDGpH6PFnt4n2js6UUdW12nF6La2fa+if12vyKqcz9XNPH9nLS2b6uSXLVsLTMPOwaIlypCQnk5KSjL2DE6rq9QGwtbOjZJlKxFrxO0bY5euUCPSleIAvdra2tG5Uh3+PnTHZxtXZKf2/E5OSTe9eOXqaYv6+lC0eaLWMWTJbqb4UK10FDy/9ZxJQvDypKUmkpiRbLHfo8T3UD8pefyvt+KwflNHfynx8pn0OZSrWxtnVA4BS5WtYpU9rrbblxIEtVK//Cl6++ruL3Tx8LJ5diKeRmbfPoVKpSgM/AseB1kAKMEetVq8xs+1e4BhQG3gEdFGr1U9UKtU/gKPhX3e1Wq1WqVS1ge8ML1Wr1eo+Rn/nVaAv8AHQG3gD8AUWqtXq+SqVyh34A/3nFwpUUKvVb6pUqprALMAGUAOfqdVq095ZLsVGR+HtG5D+/719/LlyMcx0m5iMbWxsbHFyceXhg3jC795EoVAwfcwXPEiIo2FQa9p2+Cj9dYvnTECpVFKvUUve7tzT6oOiD+IicPfOuDrr7hXIg7gIswO1BUlcbCSePhkdDQ+fAOJjIk0G0OJjIvH0zvicPL0DiIvN+gXv8J711H4568wci2WNjsLbKKuXjz/XLoVm2cbLsI2NjS1Ozq48fBCHm7sXVy+e5ee544iJukfPARPSB1pQKPhuXH9QQLM279K0zbsWzR0bE4mXb0ZuT58Arl06a5rbaJu03I8exOV5NmduxMVE4OVj+nlfz5I3Ai/j49KQNy46kjIVa5i8Ni5T5yk68g63r11IXxqhY/eh/PjtZ6z7bSY6rY6vJv6ah+xZy9psfTbeP5+A9IHkB/HR6dt6ePnxIEF/5btYKRWnD++kXKU6XL98lpioe8TFRODu6QsomDvxUxRA49adaPxKp0KUXS/tMylVPuOz+18THxOR3naAvu7Gx0RkKv+ILG2huS8Hh/eup3Yj/cyzyHs3UKBg/qQ+PEqIpfbLr9Pq7awzRXOdOzYST2+jNtw7gPjYCNyNc8ea5vbwDiTe0IaHHd+Nh1cARc3cWnj26E7+XvUdDxOi6TVkvsUyp0mIjcTDxzR7QqbsCbEReJhkDyDB6PxzaMdKTuzfRPEyVWnbbSjOLvovbjFRd/g+uAMOTq682nEAZSrVs1hu/XGYqcxjMpV55rriE0h8TEbuv//4nmP7NuHk7ErfUcsAaPzq+yyd0Z9x/ZqT9OQRHw6YidKKT01+GBeBu1fGfrgZ+i1PG6jNLOLWOR7EhlO+eguO7Mi/u4YexUfg4pnR33LxDORxfAQu7uZzJz1J4Mb5PVRv8lGW3104uoaSlZpaLSvkvW1/mqTEx+zcuJTPRy1i16afLZo5NjrStH/u688Vdab+eXQU3r76MrexscXZ0D93c/dM3+bw/p0MCp5u8rqFP+j75/UbteCdLtbpn1vrGAW4cekUM4a1x8PLn7e6DSGwhOUGzhPM5TbTnmdpE41yH9y+kuMhmyhRtipvGrWJ1pS5jnt5m+/nej+nn3viv52UKFMJOzvTCQuPHyVw5tg+WrbtZrV9iIqNJ8AnI0uAjxehl69n2e7P7f+ycsseUlJTmT9SP4PySWISv/61gx+/+YLlm3daLWNm+VFfzh7ZTtFSlbG1s9wdIPExESa5n9bfMsntY9rf2mI4Ph2d3eg/Kuv55/DedVSqZflZ2tZqW6LuXUejSWXe+O4kJT4i6LUPqNe0ncXz/09QyDzSnJISy763gOZqtboZsO4Z2x1Sq9Wt0A/2pi1K2FGtVrcAxgKDDD+bC/Qy/L2+aS9WqVRdgY+Bzmq1+iH6AdvWQENggEqlUgJ9gK2Gn2csxqofDO6mVqubAw8Bi11C15F1DDhzHy7zVXwABaDRaLh47hSffTWB4CmLOP7fXsJOHwHg08HjmfjD73wzaSHqc6c4sOdvS0V+KjMxTWYxFVjmyjdbn4HpRtvXLURpY0O9Jm9aNJ5JDjP1hUw5zNcp/TZlK1Zn/PdrCJ72G/+sW0ZKchIAwyctY9TMlXw58kf2/PMnF8OOWzj40zNlbGKuAhWc+pM1r9mN4BnlD5D45DGLZgymY4+hOBmuPu/b/ifvdh/CxAU7eLf7EFbMH5PrnObL8fnbPO9Ybf1OLx4/SmDKkI7s+2clxctUSp8RN3jCrwyb+id9v5nPvm2ruHzuWKHJDvoBgCUzB9Gh+7D0z+R/0/OP0+xss339TyhtbKhraAu1mlSuqk/y4edTGTDuV84c3cXFs/9ZLnU26oTZugUkJz1h54aFvNrpc7O/r17/FYbN3Ez3wXPYtnpO3sNmlo12z+z+GbZ56ZX3GDprG19OXIebpx9bVkwDwN3TjxHf7eLLiet4s9swfp83lMTHD/M393P6N290+ZLRc3dRp/Gb7N+mv4VefWY/xUpVYsy8vXw1ZS3rf55o2dyZmM2YzX6LTqtl1+rJtOw4zNKxcucp50utJpVdK7+iWuMPcfcpYfK7iyc2EXU7jJrNepl9raVYq23/+895tGj7IQ6OznmJZ9bTz/HG2zy7T3ZZHYq9gyMlSmXcOt/vq3FMmbOSUZN/Qn3uFPv3/GOhxJlY6RgtXroKI+fs4Oup62nyajeWzfrCsrGzcx56xr41euU9hs/exqBJ+jZxs6FNtLpctuXG29y9eZl1v33PB5+Zrser0aSyePYIWrTtil9gcYvENedp3zcz69ymGRu+H8sX77dj6fqtAPy0ZgtdX2+Js2P+3YEA1q8v4bcvsWXVLN7tNdZSkQ2RctffMq4vbbt8yZi5u6jbuC0h20yXobkUdoT/9qzjra5WWMbPSm2LVqvh9rVz9Bo6j97DF7Jj/QKi7l23YHAhnk5m3mbfEGChSqXSATOAsKdsl/ZN7zDQSqVSOQNzVSpVWcAeSJuv76ZWqy8DqNVqjeFndsBEoIlarU6756GLSqXqjr5lLAJ4AeWB3wy/Pwp0Mfx3NWCVSqUCcEU/K9civH38ibmfcRUtJjoST28/s9t4+wag0aTy5NFDXNw88Pbxp1K1OulX+GvWbcyNK2qq1myAt49+JoCTswuNmr7K1Uvn0pdesKSju1dwMkS/PlzR0tVJiLmX/ruE2HBcC+is25Btv3No11oASparRlx0xi2D8dERuHuZ5tbPAMn4nOIyXR098u9Gwk78S/9Ri606w9nLx58Yo6yxZuqLl48/sdHhGfXl8UNcXE2vIhcpXhYHRyfu3LxC6fJV0v+Gu6c3tRu24NqlMCpWtdyDebx8Aoi9n5E7LjoCT6/MufXbePk8PXd+8fQOMLk1LS4mAo8s5RxA7H39TEXjvJ4+AcQafUb6uqKvT5rUFBbPHEz9oLbUapjxIIfDezfRqYd+AKBOozasXDA2R3n3bf2dg0b1OXNZe5irz8b7F52xf24ePsTHRuHh5Ud8bBRu7vrblpycXfmgn/6BdzqdjrGfv5b+gLa0Bw24efhQs34rblwOpXyV7M3ye9HZ9Z/JIOpl+kz+V4Rs+51Du/U3vJQsVy1L3c3cFnp4B2ZpC4230beF++g/MqMt9PQJoFzleumzi6rUCuL29XNUrJ77h38c2L6Sw3v0uUuUrUZcjFEbbia3Z6bc8THheHj5Ex1xi5ioO8wa3iH9tbODOzJgwircPTOO+XKV67Eq8haPEmJxyePdAAd3rOTIHv15s3jZ6sRHZ8rumbXM402yR+DmmVbnM2aPN2jRiZ9n6q9Z29rZp88SKl6mKj7+Jbgffp3iZXO//P/+7Ss5vNuozDPlznKsZq4r0eFZPheA2o3bsmRaX17r9DlH926gZbtPUCgU+AaWwtuvGJF3r1LSgjPiT+xdwekD+vVhA0tVJyE2Yz8e5KDfkpz0iPt3L7Jyln4266OEKNbN70uHvvOt8tCy0IMruHBYX2/8SlTnUVxGf+tRXDjOT5l1u2/taDx8S1EjyPRhW7cvHeTk7gW8/dlv2FhhTWFLtu1Pc/3yWU4d3sHGFbN58ugBCoUCW3t7mr32fp7ze/tm6p/fj8TL29fMNpH4GPpbjx89xNUt44G1/4XsoFGQ6XwP4/75y81e5erFMIJavpHnvJA/x6jxLc+Vazdl7dIJPEyIzdNdUvr2fLUhd/UsubPTJqbN+jNuExu26MTSGX3JD56Z+rmxMRFm+ucBxDylnxsbHcH8aYPpMWACfoGmF1mWL5iAf5GSvPKmdR+o6u/tSUR0bPr/j4iOxdfr6f3wNo3qMmWJfn3ZsMs32H34FHNWbuDB4ycoFQoc7Ozo/Gozi+fMr/oSFx3OL7MH8N5nk/ENKJnn3Pu3G/W3Mh2fT+tvmeQ2024C1GnclkXT+vG64SL03Rtq/lg4mj7DF+Di5pll+9xlt37b4uEdgIubFw6Ozjg4OlO2Uj3u3lCnP3hVCGuSmbfZd1CtVncHFgPBz9iugdH/XgReA2LUanVTYDwZFwcTVCpVOQDDbFrQL8nQHv0AbNo8/xFAK+BVIN7w+ktA2ohDfaP3Pot+lm9ztVpdD1iOhZSpUIWIe7eIirhDakoKh0O2U7tBkMk2tRs0Zf/uLQAcPbCbyjXqoVAoqFepPH8AACAASURBVF7nJW5dv0xSUiIaTSoXQk9QtGQZNJpUHiTEAZCamsqpo/spbvSABUuq37IbfcZsoM+YDahqt+LMoY3odDpuXzmFo5NbgV0yIejVrgydtoah09ZQvX5Lju7bhE6n4/rF0zg6u2ZZc9TDyw8HRxeuXzyNTqfj6L5NVKvfAoDzp/azc+NSeg+dg72Dk7m3s5jS5asSaVRfju7fRs36ph2jWvWbcXCP/qE6xw/tQlW9PgqFgqiIO2g0qQBER94l/M51fPyLkJT4hMQn+vVWkxKfcO70fxQradkHNpUqX5XIeze5n5b7wDZqZMpdo14zDu39C4ATh3aiqlY/39c/TqPPe4P7EbdJTUnh+IGtVK/X3GSb6vWac/jfTQCc/G8HFas10B+X9Zpz/MBWUlKSuR9xm8h7Nyhdvho6nY7l88cQWKwMrd4yvXXVw9uPS4bZqurQw/gF5qyT2PS1rgyfvobh09dQo0FLjhjq87Vn1GdHJxeuGerzkX2bqF6vhdF+bQTg8L8bqW6o548fJZCamgLAwV1rKVe5Lk7OriQlPjaqP4+5cOYgRUpm/xbKF5ldp9OxYsEYAouVpeWbpgMb/yuCXu3K0KlrGTp1LdXrGbWFl07j9NS20JnrlzLawrTyP39qP7s2LaH3ENO2sFKNxty7eZHkpCdoNKlcOX+MgGJ5a2Mat3mfwZPXMXjyOqrWa8WxEH3uG5dO4+jkanL7HoC7lx8OTs7cMOQ+FrKJqnVbUqRkRcYtCCH4hx0E/7ADD+8ABk1cg7unH/fDb6TPjrl97Rya1BScLfBF6OXW7zNw0noGTlpP1bqtOL5ff968cfk0js5u5rM7unDjsj778f0bqVq3JYDJOnNhx3YSULwCAA8TYtBq9devoyNvcT/iBt7+eZux1aTN+3w1ZR1fTVlHtXqtOG5c5s7my9zRMaPMj4dsopohd9S9Gxm5j+/B37CeqadvES6F6q/VP4i7T+S963j7mw5k5FWd5t3oEbyRHsEbqVjzFUL/24BOp+PO1VM4OLlle8kEByc3Bsw4TN+Ju+k7cTdFy9Sy2sAtQLWXu9Fx0AY6DtpA6aqtuHhCX28ibpzC3snN7JIJR7Z+R3LiA15+6xuTn9+/c46QtWN47eN5OLlaZ11BS7btTzNo/C+Mm7uNcXO30fyND2jTvrdFBm4BylaoTPjdW0SG3yU1JYX/QnZQp6Hp8hJ1GgQRYuifHzmwmyqG/jmAVqvl8IFdNGraOn37zP3zk0f3U7yU5fpb+XGMJsRFpbeLNy+fQafT5nmASN+er2fw5PWG3BuN2vOntIlOLka5zbeJocd2EmhoE62tdKZ+7rH926hZL1M/t34z/jPq51Yy9HMfP0rgx4lf0L7bAMpXqm3ymg0rf+TJo4d07jHE6vtQpVwpboZHcSfyPimpqew4dIKmdU0vnN28l3Hr+/6TYZQM1H82i8YOYtOc8WyaM56urzen+zttrDJwC/lTX548SmDpjL683mUQZVR1LJK7SZuuDJmyliFT1lKtXkuOhuSwvxWyiWp19W2i8fEZanR8xt6/x7LZA+nWfzL+Fhz0zI+2pVq9lly9cByNJpXkpCfcvHwG/2LWGb/4f0+hLPj/ChjF027RE3pGa97aAg7o160do1art5vZdi/6ZQxqoV+2oAv6mbKbgGj0s3UrGtanrQ38AGjQr3n7qUqlClWr1dVUKlUNw3t2BkahHwgOA2qiX3c3Gf2at3boB4gD1Wp1B8OatzMMWbXAALU608JXZvx3IT5bleD0sQOsWDILrVZL01Zv8Xbnnqxb8ROly1emTsOmJCcnsXD2GG5cvYiLmzv9vp6If6B+1tiBvf+wec3PKBQKatZ9mS7dB5CU+ISJ33yKJjUVrVZD1ZoNeL/nwGw/zOxylPvzNzJDp9OxdeUEroSGYGvvyNs9JlG0tP5LzMJx79BnzAYAdq6eTuiRzTyIi8TN05/aTTrSrN0X3L12lj/nfU7iowRs7exx8fCj7/jsP9nb1y0l17nXLJ3I+dMHsLd35P2+31KynP5JqdOGdmToNP2VxptXwlgxbyQpKYlUqdWEd3t8g0KhYMKAN0hNTcbFVd95LVWhBl16j85RBme77Gc/e3w/q5bOQKfV0rjV27Tt+Akbf59PqXJVqNWgGSnJSSz5fhQ3r13AxdWDPoMn4xdYnEN7N/PP+p+xsbFFqVDyZufe1G7Ygqjw28yb+hUAGq2GhkGv0bbjJ9nKotFmv/E9eyKE1cumo9VqebllO954tzebVs2jVLkq1KzfnJTkJJb9EMyt62qcXd35ZNBU/AL0gw3f9H2dxCeP0KSm4OTsxoBR8ylaohxrf5vN0ZB/0mdbNm7Vnre6ZG+WhUb37OyhJ0JY+/M0tFoNjVq8w2vv9mHzqrmULFeFGvVbkJKcxC9zvuGWoZx7DpqGryHv1rULObRnA0qlDR17DKVq7SAunz/B7NHdKVqyAgrDSevt9wdQrY7+d2uWTUWr1WBrZ897n4ykZLkqZnNpdc8e0NbpdKxeoq/PdvaOfNAvoz5PGdKR4dMz6vPyeSNJSU6kcq0mdOqpr8+PHsSxdPbXxN6/h5dvEXoOnomLqwfXLp7itx+DUSiVBBYvR7fPxuHs6sH9iFssmjFQn02joV6TN3i1Q5+n5itI2a9cOMF3oz82+Uze6jqAqnVMv6Cn1LPOYEyt32bi06wB9r5eJEVEc2n8HG4ty7Lse54oj2T/RhGdTsfaZRM5f2o/9g5OdP1sAiXL6WdqThv2LkOn6mfR3bwSysr5aeUflN4Wfvvl66SmJKcPcJauUIPOhqeTHwv5i50bFwMKqtQO4u1uXz0zS6o2+xdudDod63/+FvXpA9g5ONLl028pYZhhOmtEBwZP1q/IdOtqKKsWBJOanISqZhPadw/OcoFo4oDWDPz2T1zcvdi9aTHHQzZhY2uLnZ0jb77/FWUqPf+OBE0Os2/85VvUZ/Zjb+9Ipz4T02fHfvdNewZOWg/A7auh/LnwG1KSk1DVDKLdR/rsq+YP496NC6BQ4OVbjA49x+Lu5cfZI9vZvnYONja2KBRKWr/7OVXqPHsgzNYm+/1WnU7HumUZZf7ep99SwlBXZg7vwFdTDGV+RV/mKclJVKqVUeY/z/6SqLvXUSiUePkVoWOvMYZ18yJZtSCYhLgo0Olo+fYn1A1667l5IuNyd8ObTqdjx6rxXDsXgq29E298NCl98HXZxHb0CNZfDNqzbhrnjm7mYXwkrh7+1GzciSZvmt4uvnLWh7R4d2iOBm/jHuTuu4JOp2P/hgncVuv7W807TcKvhP5918x+h46DNvAwLpwVk5rj6V8WGxv9zNqqL3ejcsNObF7Yg5jwizi76b9wu3oW4bUeOVvTuVrp7Pdb8tq2nz6yizVLJ/EwIRYnFzeKla5E/+CfTN7j7z/n4eDoTKu3uz8zi5fj42znPnXsAMsXz0ar1dLslbdo17kHa1b8RJnylalr6J8vmDWW61cv4urmzudDvk3vn587e5w/fpnLuBkZa1EmJj7h2xGfoknV6PvnterzQQ765xGPsr+sj7WO0f3bVnBwxx8obWyws3fk7Q+HUqZi7WdFQZub9tzQJnb+dKJRe96ewZP1beKtq6H88ZO+TaxUM4h3Ptbn/n3eMO4a2kRvv2K8a2gTASZ9+QqJTx4a+pLu9B6+iIDnPOjO3TH7D6k6ezyEPw393MYt2/FGx95s+n0epcpn9HOX/hDMrWtqXNL6uYHF2bJmEVvXLcG/SMaF+y9HL0CTmsLwPq8SWKwMtnZ2ALR4/T2avNLhuVnqJO/Pdm5jB06GMevXNWi0Ot5u/hI927/GgtWbqVymJM3q1WDGL2s4cvYCtrY2uLs4M6R7Z8qVKGLyNxau2YKTowMfvpm7u5n2altme1tr1Zed6xew+69FJjNu+wxfjOszHqJlq9TmKPfaZRO5cFrf33rv04z+1vTh7zJkSkZ/6/cFGf2tDt31beKy2QOJvHsdhUKBl19ROvUajad3AKsWjubMkZ14+eo/E6XShq8m/fncPNocLHNorbYFYM9fSzn673oUCiUNW7xL0zeyrtOe2Zt1bAvOGnsFxJO9vxf4gUin5l0L1Ocmg7cWZBi87ahWqy37aPOs76MAbNRqdapKpeoBVFKr1blezCy7g7cFTW4Hb1+03A7eFgQ5GbwtSHIyeFvQPG/wtqB63uCtsDxrDd7mh5wM3hYkORm8LWhyMnhbkORk8Lagye3g7YuW28HbgiAng7cFSU4GbwuanAzeFiQ5GbwtaHIyeFuQ5HbwtiDIyeBtQZKTwduCJieDtwWNDN5mJYO3OVc4e5EFgEql2o5+Dds0W/Lx7W2BvSqVSgukAtZ7pKcQQgghhBBCCCGEEOKFkMHbXFKr1W3M/Hh6Pr13CtA4P95LCCGEEEIIIYQQQghL0L2gZ8YUZoXzflwhhBBCCCGEEEIIIYT4f04Gb4UQQgghhBBCCCGEEKIAkmUThBBCCCGEEEIIIYQQ1qeQeaQ5JSUmhBBCCCGEEEIIIYQQBZAM3gohhBBCCCGEEEIIIUQBJMsmCCGEEEIIIYQQQgghrE+heNEJCh2ZeSuEEEIIIYQQQgghhBAFkAzeCiGEEEIIIYQQQgghRAEkyyYIIYQQQgghhBBCCCGsTynzSHNKSkwIIYQQQgghhBBCCCEKIJl5K4h65PKiI+RK1cDoFx0hV3S6wrs4d/QT1xcdIVfsbTUvOkKupaQWzvqiQPeiI+SappAeo8ojoS86Qq5pG1R70RFyRXE47EVHyDUX+9QXHSFXCvPzLUr4FM5zUbmAwpkbICm1cH7VKax9cwAHm8JZXxSFs6oAkKotnPOx9tP8RUfINVul9kVHyBWlsvD2z5WF+LuFEJZQiE9TQgghhBBCCCGEEEKIwkJXmK/GvyCF8zKdEEIIIYQQQgghhBBC/D8ng7dCCCGEEEIIIYQQQghRAMngrRBCCCGEEEIIIYQQQhRAsuatEEIIIYQQQgghhBDC+hQyjzSnpMSEEEIIIYQQQgghhBCiAJLBWyGEEEIIIYQQQgghhCiAZNkEIYQQQgghhBBCCCGE1elk2YQckxITQgghhBBCCCGEEEKIAkgGb4UQQgghhBBCCCGEEKIAkmUThBBCCCGEEEIIIYQQ1qdQvOgEhY7MvBVCCCGEEEIIIYQQQogCSGbeimzT6XRs/HUy50/tw97eiS6fTaR4mSpZtrt9NYxVPwWTkpxI5VpNaffRCBRGV1b2bl7G5pUzGLdgPy7uXlw+d4SfZ36Bt38xAKrVf4U2Hfrlyz6dOv4fvyz8Dq1WS8s2b9Gu04cmvz8feopfFn3PzWtXGDB0HC81aZEvuYzz/broO7RaDS1av0W7Th+Z/D4lJZl5syZw7coFXN08+HLoBPwCipCaksLiuVO5evkCCoWSj/sMpEr1OiavnT5hKJHhd5g+d4XF8up0OtYsm0rYyRDsHRz5sN8ESpTNWkduXj3Hb3NHkpKcRNXaQXTsMQyFQsGjh/EsnT2EmKi7ePsVpdegGTi7ugNwMewoa3+ehkaTiqubJwPHLQNgdP/XcHB0Rqm0QWljw7Apq/K0D6EnDvDH0ulotVqavPIOr3foafL7lJRkln0/ihtXz+Pi5kGfr6bi61+Uc6f+Y93yH0hNTcHW1o6OHw+kUvUGAMwY9Qnxsfexs3cAYODo+bh7eucppzk6nY61y6akl/8H/b59SvmHsdyo/N/tMRyFQsHJQ9v4e/V8Iu5c5etJv1OyXFUArl8+y6qfxunfAx1vdOpHzQat8pzVGnXlzNE9bP7jRxQKJUobGzp2H0q5Svq6/0WXWhQtWQEAL99APhs2J0/7kLYf636ewrmTIdg5ONKtr/kyv3U1jBXz9PtRpXYQHbpnlPnWNfoyHzwxo8xTU1P4Y+E4bl0NQ6FQ0qH7cCpUrZ/nvCa5f5nMeUPu9/tOpISZ9vzW1TBWzh+pb89rB9HhY317vnH5DMJO/IuNrS2+ASXo+tm3OLvoj9W7N9T8sXg8SU8eolAoGTxxVXrdz081Fk3C/43mJEdGs6/2W/n+/pnpdDo2/JJxDn2vr/lz6K2rYaxakHEOfcdQ5v/8+QNhx/agUCpwdffhvc8m4uHtz56/lnLiwGYAtBoNEXeuMn5hCM6unnnOa632HODG5VBmBH9Az0HTqP1SG2Ki7rJoxiC0Wi0aTSrNXutKUJvOOc4ddnI/a5ZNRavV0rhVB9q072Xy+5SUZH6dE8zNq+dwcfOg16Dp+Bj6H9vWL+bgrvUolUo69RxOlVqN01+n1WiYOrwrnt7+9B3xIwDLvh/OzSth2NjaUqp8dd7vMwobW7scZ05jrTI/GrKFHRuXAuDg6EyXT0ZSvLQqY9+0GqYN74qHtz99h/+Y6/xg+XNo4pNHTAvO+Bux0ZG81PQNuvQakqec5ljrHProQRxLZg3mxuVQGjZvR+dewRbPvf6XyZw/FYKdvSNdn9Ge/77A0J7XCqK9oW35+885hB7bjUKpxNXdm/cNbcvZY7v55885KJRKlEob2n80nLKV6phJkPO81qjn4XeusXzeKG5fO8+b733BK293T/9bjx8lsHLBWO7dugwKBd36jqdsxZo5yp2fbcuFs/+x4bdZaLU6HByd+bD/BPyLlMxRXmtn/23eaEKP/4ubhzcjZ61P/1u3r6tZtXACSYmP8fYvSvcBU3Byds119jR5reebVhj6LTYZ/RYnF3eO79/M7s3L0l9/7+ZFvpq0mmKlK+U5s7Wzp6amsHpxWl9RQfuPh1O+SgOL5c68D3np725cPpPQ43uxsbXDN6AE7/edkN5vLIh5n9Y/j468w+TB7fAvWhqAUhVq0KX3aKvthxDGZOZtPlGpVN1VKtXX+fh+P6tUqnqW/JsXToUQFX6D4bP+oeMnY1m7dLzZ7dYuHU/HXmMZPusfosJvcOH0/vTfxUXf4+LZg3j6FjF5TZlKdRk8eR2DJ6/Lt4FbrUbD0vkzGT5uJjPnreDAvzu5ffOayTY+fgH0HRhM42at8yVT5nzLFsxg2NiZzJi7koP7subbs/0vXFzd+G7hat5o14WVP88DYPf2TQBM+3E530z4juVL5qDVatNfd+TgXhwdnSye+dzJ/USF32DMD5vp2mc0qxZ/a3a7PxZ9S9dPxzDmh81Ehd/g3Cl9HdmxYQmq6g0Z88NmVNUbsn3DEkDf6f5z8UQ+HfYDI2etp9fgGSZ/78sxSxgxfXWeB261Gg0rF01hwMgfGff9Wo6GbOXurSsm2xzYuQFnVzcmztvEK291Y92v3wPg6u7J5998x9jvVtPji/Es/X6kyet6DZzI6Fl/MHrWH1YZuAU4dzKEyPAbjP5hC+/1GcMfzyn/0T9sIdKo/IuUqMAnX8+mXOW6JtsXLVGeIVNWMXz6Gvp9s4BVC8ej0aTmMat16oqqekNGTF/DiOmr+aDveFYuGJv+t+zsHRgxfTUjpq+2yMAtwDlDuzjy+y2813sMq5eY348/F39Llz5jGPn9FqLCb3DeqMx7fpW1zA/tWgPA8Bnr6TdyIRt+m25yDOfV+VMhRN27SfB3f9Ol91hWL55gdrvVSybQufcYgr/7m6h7N9Nzq6o3Ytj09Qybth6/wNLs3LAYAI0mld/mDqfzJ6MYPmMjn49eho3ti7lOfPuXdRx585MX8t7mXDgVwv3wG4yY/Q+deo9l7ZKnn0M7fTKWEbP/4b7RObTFmz35etp6vpqyjip1mrFj3Xz9z9/qyVdT1vHVlHW88d5AylWul+eBW7DeMQr6wcKNK2ZTudbL6T9z9/Jj8Le/MWL6aoZMWsGOjUuJi4nMUWatRsOfSybRP3g+o2Zv4NiBf7iXqQ0/tHsdzq7ujPtxCy3f/JANy78D4N6tKxw/sJWRs9fTP3g+fyyeiFajSX/dnr9XEFisjMnfqh/UltHfbyJ45jpSkhM5sGtdjvJmZq0y9/EvxsCxy/hmxlpee7cPvy8cZ/L39vy9goBM+5Yb1jiHOjq5pJ87R8/6Ax+/ItR+qWWes5pjrXOorZ09bbt8TvsPrfM14PypEKLCb/LN7L/p3Hssa5aYb8/XLJ1A50/G8M3sv4kKv5netrR8swdDp61nyJS1VK3TjG2GtqVitZcYMnUdQ6aspeunE/hj0RiL5LVWPXdxdadTj+G0fOvjrPu+bCpVajVm1HebGDF9TZZj+Xnyu235Y9FEug+YwjczVlM/6HW2rl2Yo7z5kf2l5m/TP3h+lvdbsWAs7boNJHjWOmo2aMXOTT/nOruxvNbzitUbMXTaeoZOW49fkdLs3Kjvt9Rt8iZDpqxlyJS1dOs3GS+/YhYduLVm9v926/uKQ6et57NvFrFx+QyL9hWN5bW/q6reiOEz1jN8+jr8i5RK7zdai7X65wA+ASUYOm0NQ6etkYHbPNAplAX+X0FT8BKJAivs+G7qBb2NQqGgVIWaJD5+QEJslMk2CbFRJD55ROmKtVAoFNQLepuwY7vSf7/xt6m8+f5XKHjxa5xcvniewCLFCQgshq2dHS83bcWx/0JMtvEPKEKpMuVRKPM/7+VL50zyNWr6CscOm+Y7fjiEpq1eB6Bh4xaEnj6GTqfj9s1rVK2pH7v38PTG2cWVq5cvAJD45DF/b1hF+y7dLZ75zLE9NGj6FgqFgjIVa/Lk0QPiM9WR+NgoEp88pGzFmigUCho0fYszR/foX390Dw2bva3fn2Zvc+bobgCO7f+bmg1b4W0Y9Hfz8LF4doBrl0PxL1ICv8Di2NrZUb/Jq5w+stdkm1NH99KohX4WX91Gr3D+7BF0Oh0ly1bC09sfgKIly5GSnExKSrJVcj7N2WN7aND07WyVfxnDMdqg6ducNZRzYPGyBBTN+oXG3sEJGxv9AFxKSpJFliiyVl1xcHROn+mflPTE6usphR7dQ31DmZfOZpnXz0aZh9++QsXqDQF9fXdycefW1TCL5T57zCh3hZo8efy03I9Mcx/T565Us3F6nShdoQbxMREAqM8cpGjJihQrpf/i4+LmiVJpY7HcORGz/xgpMfEv5L3NCT2+m7pG59An2TiH1g16m1DDOdTRaOZScqL5un3y4N/UfvkNi+S11jEK8O8/K6nZsDVu7hkXsmxt7bCzswf0s8B0ufgCev1yKH6BJfEN0LfhdRu/xplje0z36+je9Fy1X2qNOvQwOp2OM8f2ULfxa9jZ2eMbUBy/wJJcvxwKQGx0OKEn9vFyqw4mf6tanSAUCoX+OCpfnbjoiBxnNslmpTIvq6qVPuu5TIWaxEVnDIrHRocTZmbfcsPa59CIuzd4EB9DhSp5n/1pjrXOoQ6OzpSrVAdbe3ur5A49vof6Qdlrz9PalvpBGe155rYl7RxqfD5NTnpisbzWquduHj6UKl8t/dyU5snjh1w5f5xGLfV13NbWLscz/vK7bQF48uRhen4Pb78c5c2P7BWq1MPF1SPL+0XevU75KvoBr8o1GnHqv525zm4sr/W8Uo2MfkupCjWIi8naXp88+Dd1Xn7dInnzI3v47StUqGrUV3R2s2hf0WQf8tjfrVTzZaN9qJnn86W18z6tPRfiRZJlE6xIpVLNABoBqcBWoLpKpVoPlAd6q9Xq/1Qq1SDgDcAXWKhWq+erVKruQBvAKdO2e4GDQNq9s2+o1eoUw9/ogP7znKhWqzdbY3/iYyPx9A5M//8e3gHEx0bg7uVntE0Ent4BRtsEEh+r/5IQdnw3Hl4BFC2V9WrmjUunmDm8Pe5e/rzVbQiBxctbYxdMxERH4ePnn/7/vX39uay2zgkvN2Kjo/DxzShLHx8/Ll88Z7JNjNE2Nja2OLu48CAhnlJlynP8cAgvN32F6KhIrl1REx0VQfmKVfhz+SLatu+Kg4OjxTPHxUTi5ZtRRzx9AoiLicTDqI7ExUTi6ROQZRuAB/Ex6dt6ePnxICEGgMh7N9CkpvLd2J4kPXlE8ze6pXcyFcCPEz9FgYLGrTvR5JWOuc8fHYl3pmzXLoWa2Ua/jzY2tjg5u/LwQRxu7l7p25w4tJMSZVXpAxEAP/84FqVSSZ2XWtG2U2+TpUQsxVz5x2cq//hnlP+zXL90hhXzRxMTdZePvpic5cuRJbJaoq4AnD6yi00rv+dBfAyfjZib/vPUlGSmDn8PGxsbWrfrRc0GeZ+9FRcbiaePUbv4tDI3ahc9vQOIi312mRcrpSL06B7qvPw6cdHh3L56jtjocEqVr57nzPpMEXgZ5fb0DiA+JiJT7ogsuePNfNk5vHc9tRu9BuiPVQUK5k/qw6OEWGq//Dqt3u6Z5TX/i/THXqZzaEymc2jmMvcJJN7o+Pz7j+85tm8TTs6u9B2VcZsn6AdXLpzeT4celrkl21rHaFxMBKeP7GbAmMWsmG/avsbeD2f+lP5Ehd/inQ8Gpw/mZT9zBF4+pnX2+qWzWbcxOm86Obvy6EEccdGRlKlYw+S1aV+Q1yybRvsPBpOY+Mjs+2pSUziy7y869hiWo7xZ81uvXUxzcPc6qtTOuGV77c/TeOeDwSQ+Mb9vOcpvxXMowNH9W6nXuI1Vzp9g3XOoNcXHRJi0LU9rzz2M++c+pu35FkPb4ujsRv9RS9N/fuboTras+p6H8dH0HjrPInnzo54bi468jau7N8vnjeLOjYuUKFuZjt2H4eDonIPM+du2dOs7lvmT+mNn74CjkytfT1qe7az5lf1pipQoz5lje6lZvwUnDm0nNjo819mNWaKepzm8dz21X3oty89PHtpKr68tc2eWMWtlL1pKRejxPdQ29BVvXTtHnAX7isYs2d89vGc9tV9+1eIZrZU3s5ioO0wb1glHJxfadvnC7OxcIaxBZt5aiUqlehPwVqvVjdVqdTMgElCq1er2QH+gr2HThWq1ujXQEBigUqnSPhNz2wLsM2x/C2iiUqmqAI3VanUQ0AIYa6190ul0WX6WeQatuW1A/6Vy54aF/KXx4wAAIABJREFUvNrp8yy/K166CsE/7OCrKetp0qYbP8/8wjKBn8vM/hSgpx6aLUpF5vI2t4mC5q3fxNvHn+BBvfh18XdUrFQdGxsbrl+9SMS929Rv1CzfQmcpU7P16Nm0Gg23rp2j7/Af6R+8gK1rFxJx9zoAgyb8yvCpf9Lvm3mEbFvF5XPHchneXI3Imk33nHpz9+YV1v72Ax98lrFsQq+Bkxj73WqGTlzKpfMn+W+vVa6vmD/+shT/849jc0pXqEHwrA0MmbyK7esXk5KclNuYaUGy5rBAXQGo2aAVo77bRJ8h37Hlj4z1G8fP28awKavoPmAqa3+ZRlT4rZymzsrsfmTeJOdl3rBFezx8Apg54j3W/TKV0hVrYmPRGazZaf+ev8329T+htLGhbpM3AdBqUrmqPsmHn09lwLhfOXN0FxfP/mex1IWa+QbbdBOzZZ7x3290+ZLRc3dRp/Gb7N+20mS7sBN7KaOqbZElE/RhrHOMrv15Gu26DTQ7I9vLN5BvZqxlzA+bOfLvJhLionOS2KzMmZ9+bjW/v2cNazqWLJd1rbw0qxZPpHzlupTP6xc4K7aLABdDj3Boz3radRsEkLFvZtYBzA1rnUPTHN2/jQZBWQdcLMWa51BrMpspG+25cePStsuXjJm7i7qN2xJi1LbUqP8KI2b+Rc+vfuDv1XlbD9ko8PPz5qGeZ6bRaLh17TxBbTozfNqfODg4sWPD0ue/8Dms2bbs3rycvt/MZeJPO3mpRTvW/TI9z3kzv7+xnGZ/lg/6jWff1lVMGdqFxMRH2OZhHXDTjHmv5wA71v+EjTKj35LmxuUz2Ds4UaREhbxGzcJa2Rs2b4+HdwCzgruw4deplKlYC6WNle52slB/d/u6hShtbKiXqfwtzkr9cw8vP8bO3c7Qqatp/9EQfp0zjMTHD/MU9X+WQlHw/xUwMvPWeqoAxvekaIDjhv++CaTdK9jFMNNWBxQB0qYemNvW3M99gdqGWbkAriqVys0yuwAHtq/k8B79ejolylYjLibj6ql+xpDprBhP70CTK7LxMeF4ePkTHXGLmKg7zBreIf21s4M7MmDCKtw9M66AVa7dlHXLJvAoIRYXo1kY1uDt4090VMbVtZj7kXh5+1r1PXPC29eP6PsZZRkdHZUln49hGx9ffzSaVB4/eoSrmzsKhYKPen+Zvt3oIX0ILFqC86EnuXpFzRe9OqDVaIiPj2X8iP6MnjyX3Pp36yoO7loLQKlyVYm9n1FH4qJNryqDYfaE0a0ycdEReBhmV7l5eBMfG4WHlx/xsVHpt9R6+gTg4uaJg6MzDo7OlK9clzs3LhJQtHT6zCw3Dx9q1G/J9cuhlK+Su+WevXz8icmUzTPTrWpePgHERIfj5RuARpPKk8cP028bi70fwbypg+k5YAL+gSVM/i7o1+5rGPQ61y6Hpd82mlf7tv6eXv4ly1UzU/6ZjlGz5Z/92/ECi5fFwdGJe7cupy/en135UVeMla9Sj/tzR/IwIRZXd6/0uuIbUJwKVepx+/p5/Iw+p+wK2fY7h4zKPM5oVkl8tJl20cd0pkpcTNZ9zczGxpYOH2fM4ps96gP8ipTKcdYsuQ3ro5UsV81kNkycmfbcI1N7nnmbI/9uJOzEPvqPXJz+JcTTJ4Bylevhami/q9QK4vb1c1Ss/lKeshdW+7ev5PBuo3NotOk5NMvxmbnMo8OzfC4AtRu3Zcm0vrxmdEH01MF/8rxkQn4cozevhLHse33dfpgQS9jJEJRKW5OZ8J7e/gSWKMeVC8ep/VKbbOf39A4gNjrTsWamDY+9r595btyGe/oEZDkmPLz8OXtsL2eP7SXs5H5SkpNIfPKIn38YQfcBkwHYsno+DxNi6fp17ta9y6928c6Ni6z8aSx9R8zD1U0/wH9VfSrLvv3ywwg+NuxbTlnrHApw65oajUZDqWcMoudGfp9DLWX/dqP2PFPb8rT23HgWX7yZfQOo07gti6b14/VMky3KVa5HdMSt9PNpTuX3+d+Yl08Anj4BlK6gn0Fa66XWOR68zc+25d2Ph3Dnhpoyhrx1X36NuRP7klvWyP4sgcXK8MWonwCIuHudsOMhz9z+WSxdz4/8u5Gwk/voF5zRb0lz4uA/1Lbgkgn5kd3Gxpb2H2X0Fb8f3Q2/wLz1FY1Zur+r7zf+S/9RWcu/IOY1x9bOHlvDXSElylbFN6AEkfdu5Pg7kRC5ITNvrScMMJ7eqMT0klpaizUCaAW8CsQb/dzctuZ+fh74T61WN1er1c2BGmq1+kGe0xs0bvN++oPEqtZrxbGQTeh0Om5cOo2jk6vJ7Z6gf+CIg5MzNy6dRqfTcSxkE1XrtqRIyYqMWxBC8A87CP5hBx7eAQyauAZ3Tz8S4qLSr3zdvHwGnU6Ls5uFZg49Q7mKlQi/e5vI8LukpqRwcN8u6jZsYvX3za5yFSqb5Du0byd1G5jmq9swiH27/gHg8IE9VK1RF4VCQVJiIomJ+vXJzpw8go2NDcVLlqH1Gx2Y/8sm5ixZx9ipCyhStESeBm4Bmr32XvpDoGo0aMmRfX+h0+m4dvE0Ts5uWU6CHl5+ODi5cO2ivo4c2fcXNeq1AKB6veYc/lf/sLXD/26iRn39z2vUa8GVCyfQaFJJTnrC9ctnCCxWhqTEx+m3eiYlPubCmUMULZn7JTdKl69K5L2b3I+4Q2pKCkf3b6Nm/eYm29Ss34xDe/4C4PihnVSqXh+FQsHjRw+YM/EL2n/wBeUr10rfXqNJ5UFCLACpqSmcObaPYiXL5TpjZk1f68rw6WsYPn2Nofw3pZe/o7Or2fJ3NCn/TVQ3lP/T3I+8nf6Aspiou0TcvY63X9EcZ82PuhIVfjO9Pbl19Rypqam4uHny+GFC+vqJDxNiuao+RWDx3H0OQa92TX9QQfX6LTlqKPPrzyhzB0cXrhv24+i+TVSr/+wyT056QlLiYwAunDmIjdIm13lNck9dy9Cpa6lezyj3pdM4PTW3M9cvZeROqyvnT+1n16Yl9B4yB3uHjIcfVqrRmHs3L5Kc9ASNJpUr548RUMxy9b2wadLm/fSHiVWr14rjxudQZ/PnUEfHjHPo8ZBNVKurH9SMuncjfbuw43vwN1qL7cnjB1w5f5SqdfO2FEh+HKPj5m5lvOFf7Zda0+WTYGo2aElsdDjJyYkAPH6YwFX1qfSnOWdXqfJVibx3g/sRt0lNSeH4ga1Ur9fcZBvjXCf/20HFag1QKBRUr9ec4we2kpKSzP2I20Teu0Hp8tVo1+1LJv60kwnzttJz0DRU1RqkD9we2LWW86cO0uPLqSiVuetS50eZx9y/x6IZg/jo80kEGJVpu/e/5NsFOxk/dys9Bk6jYrUGuR64BeucQ9Mc3b/VKrNu8+Mcag1N2nRNf8hStXotORqSw/Y8ZBPV6hrOm0ZtS6hR22JyPr12Dk1qCi657J/nRz1/GndPX7x8Aoi4q3/or/rsYQKLl81R/vxsW5xd3Xny+GH6HWYXzhwisHju1960RvZneRCvv2NCq9Wyde1CmrTplOvslqzn50/tZ/dfS/jka9N+S1rW04e3U7uR5QZv8yO7cV9RfeYgShvbPPcVjVmyv3v+1H52blxK76FZy78g5n2ahwkxaLX6h/bdj7hF1L2b+AQUt8r+CJGZzLy1ErVavUWlUrVSqVQHgSRgO5BiZtPt6NexDQOevWiT+fcJValUR1Qq1T70s3tvAN1zHfwZKtdqyoVT+5gy6HXsHBzp8mnGUxtnjejA4Mn6pyy/23M0qxYEk5qchKpmEyrVCnrm3z1zeDuHdv6B0sYGO3tHPvhiRr4sX2BjY0uPzwYxafRgtFoNLVq/SYlSZflz+SLKVqhEvYZBXLl4npkTR/Do4QNOHDnAmpWLmTFvhdWzpeXr/tlgJo8ZhFarofkr+nyrly+ijCFf89ZvMm/WeAb26YSrqztfDNU/vTwhPpbJYwahUCjw9vGj3+D8eRJm1dpBhJ0IYdyAtvrPsl/G01QnD+nEiOmrAejyyUiWzxtJSnISVWo1oUpt/aB063d6sXT21xzavR4v30B6DZ4J6Gd7VqnVmMlfd0ShVPByyw4ULVmB+xG3WTRjIKC/La5ek9epUiv3A/A2NrZ0/WQY343vh1arpXGrdhQtWY6Nv8+jVLkq1GrQnCat3mHJ9yMJ7vc2Lq7u9B48BYA9f68iMvwWW1YvYsvqRQAMHD0fB0cnvh/fH40mFa1WQ+UaDQl6Je8PhjGnau0gzp3Yx/gBbxjKP+MYnTKkI8On62cAdPlklKH8E6lcqwlVauuP0dNHdrFm6SQeJsSyYEo/ipWuRP/gn7h64SQ7NizBxsYWhVJJ517BuZp5kzmrNerKqf92cnjfX9jY2GJn70DPQdNQKBSE37nK7wvHo1Qq0Wq1tH6nJ0Us0MGtUjuIcyf3MeHLN7C3d+T9vhllPm1oR4ZO05d5509GsWLeSFJSEvX7USujzNcu05f5T1P7UbxUJfoG/8SD+BgWTPoMhUKBh7c/H3ye+wEV87mbcv5UCN9++Tr2Dk50/Syj/KcNe5ehU/UzFzr1GsXK+Wl1JYjKhtxrl00kNSWZeRN7A/plNTp/MgZnVw+at/2IWcHvAQqq1A6iah0rLdPyHLV+m4lPswbY+3rR8tq/XBo/h1vL1ryQLKC/s+T8qX1MHqg/h75ndA6dObwDX00xPYemJCdRqVbGOXTLqllE3b2OQqHEy68IHXtlPPn97NGdqGo0ztEajs9jrWP0acLvXGP9r/rzv06no9VbH1OsZMUcZbaxsaVzr2+YO7EvWq2GRi3eoWiJ8mxeNZeS5apQo34LXm7Znl/mfMOYz9vi4upBz0HTAChaojx1GrXh20HvoFTa0OWTb557++mqhd/i7VeEGcEfAlCrYSve6PRZjjIbs1aZ/7NmAY8exvHH4okAKG1sGDZlVa5zPo01zqHunvpZlccO7uCLYMuvRWnMWudQgDH9XyXx8UNSU1M4e3Q3/UYutMg5CDLa84kD9e35e59m1Jvpw99lyBR9e96x5yh+X5C1Pd+8ajaRd6+jUCjw8itKp176PuOZIzs4um8TNra22Nk78tEAy/TPrVXPE+LuM234eyQ+eYRCoWTv38sJnrUBJ2dXOvUcwc8/jECTmoKvf3GT98yO/GxbbGxsef/TMSyeMRiFUomzizsf9Bufo7z5kX3pd0O5FHaMhw/iCP70Fdp27sfLrTpwbP8/7Nv2B6BfxqpRi3dynd1YXuv5up/1/Zb5k/T9llLl9f0WgKsXjuHpHYBvQM7vxnqR2R8mxLBg8qeGvmIA3fpZtq9oug956++uWTqJ1NRk5n3bR78PFWrQpbf1vp9aq39++fxx/vlzLkqlDUqlDZ17jzL74D6RDQqZR5pTiqetUSr+d/x1PLVQVoLi7nEvOkKu6HQFb/2U7Ip+4vr8jQoge1vNi46Qa0mphfMam8Ls6oeFg6aQHqOFuW3RNnj2TJ6CSne44DzkMqccbApnu1gAlyDLtsLa5ZZzaP5L1RTeim5no33REXKlMLcthVVyqgze5DelspCeiAq512rZSwuTyYNjWwt8ZXSr91qB+tykxRRCCCGEEEIIIYQQQogCSAZvhRBCCCGEEEIIIYQQogAqnPcSCSGEEEIIIYQQQgghChWdrFWTYzLzVgghhBBCCCGEEEIIIQogGbwVQgghhBBCCCGEEEKIAkiWTRBCCCGEEEIIIYQQQlifQuaR5pSUmBBCCCGEEEIIIYQQQhRAMngrhBBCCCGEEEIIIYQQBZAsmyCEEEIIIYQQQgghhLA6HYoXHaHQkZm3QgghhBBCCCGEEEIIUQDJ4K0QQgghhBBCCCGEEEIUQLJsghBCCCGEEEIIIYQQwup0CplHmlMyeCtwstO86Ai5EpPo+qIj5IqjTeqLjvA/J1VbeE8OSoXuRUfIFV3hjA2As13Ki46QK/GJDi86Qq4pDoe96Ai5omhY9UVHyLWi5/e+6Ai5UvbIzy86Qq79V33gi46QK09SCu/XBWUhXVLP1qYQn0QLKUUh7W8VZg6F9DsogJLCWV8UhbRNBNDoCnF4ISyg8I5oCCGEEEIIIYQQQgghxP9jhfdSuhBCCCGEEEIIIYQQovCQZRNyTEpMCCGEEEIIIYQQQgghCiAZvBVCCCGEEEIIIYQQQogCSAZvhRBCCCGEEEIIIYQQogCSNW+FEEIIIYQQQgghhBBWp1MoXnSEQkdm3gohhBBCCCGEEEIIIUQBJIO3QgghhBBCCCGEEEIIUQDJsglCCCGEEEIIIYQQQgir0yn+/8wjValUnwIfAylAL7Vafdnodz8BVQAnYLlarf4ut+/z/6fEhBBCCCGEEEIIIYQQwspUKpU30BMIAr4CJmfa5Au1Wh0EvAT0ValUDrl9Lxm8FUIIIYQQQgghhBBCiOxrCOxRq9UatVp9DKho/Eu1Wp1s+E9H4DKQTC7J4K0QQgghhBBCCCGEEML6FIqC/y97vIA44z3LvIFKpfoV/cDtUbVarcttkcmatyKLsJP7WbNsKlqtlsatOtCmfS+T36ekJPPrnGBuXj2Hi5sHvQZNx8e/GADb1i/m4K71KJVKOvUcTpVajZ/5N9VnD7P+t5mkpqZQsmwVuvUdh42NLY8fJrB83miiIm5hZ+fAB/3GUbRkhRzuxwFWL5uKTqvl5VbtedXMfvwyJ5hbV8/j4upBr8HT0vdj67olHNq9HoVSSeeew9L3Y9dfv3Fw1zpQKChWsgIf9h+Pnb0Dv/44ikvnjuHk7AbAh/3HU6JMpRyWfFZnTxxk5ZIZ6LQagl55h7bv9siyD4u/H82NK+dxcfOg79dT8PUvmv776Kh7jBzQiXZd+vDaOx+RkpzElODepKQmo9VoqNeoFe90/SzPOdPodDrWLJtK2MkQ7B0c+bDfBEqUrZJlu5tXz/Hb3JGkJCdRtXYQHXsMQ6FQ8OhhPEtnDyEm6i7efkXpNWgGzq7u6a+7cTmUGcEf0HPQNGq/1AaAmPv3WLlgLLHR4ShQ0HfE3PTPMbusUVdG9n0dRydnlEoblEobhk/73eRv7tj4C+t/m8W0pXtxdffKUV5jOp2O1cumEnbCUOb9J1DSXJlf0Zd5cnISVesE0SmtzB/oyzw66i4+fkXpNVhf5uF3rrF87ihuXTvPW12/4JW3u5v8Pa1Gw9ThXfH09qfviB9zldsadeVoyBZ2bFwKgIOjM10+GUnx0ioAHj9KYOWCsdy7dRkUCrr1HU/ZijVznN1Y6IkDrFo6A61WQ9Ar7Xm9Q9ZjdOn3o7hx9Tyubp70+Up/jF67FMqv879NKwze6vIpdV5qacj5gF/mjufurSsAdP98DOVUecuZmU6nY+Ovkzl/ah/29k50+WwixctkLf/bV8NY9VMwKcmJVK7VlHYfjUBh1JnZu3kZm1fOYNyC/bi4exF6bDfbVs9BoVSgVNrS7sNhlKlU1+LZN/ySkf29vuaz37oaxqoFGdnf+Vif/Z8/fyDs2B4USgWu7j6899lEPLz9+T/2zjssqqOLw+9SFOkd7N1rw67YNaLGz1iiiS3ql8RoEjXFEo29x0ok9sRu7BU1GknsigXBDuo1Nqz0YgEEdvf7Y5eFhUUpi2K+eZ/HR7g7d/Y3w5k5c8+de+7RP1Zz4dQ+QGPf4Y/uMG35SSyt7Y2qPyfUWjET146tSY6I5kTdzm/8+1/FhaAAVi9fjEqlpG37D+jes6/e5yHBl1m9fDGhd28z4sdJNG3eWu/zhIQXfPf1p3g2ac6gwcPemO5Ttx4y1+8cKpWabvUqM6B5Lb3P91z6h18OBuFiYwlA70bV6F5Ps3mi3rR1VHLV2EFxO2sW9PEqcL0hF0+xbc1c7bqpGx26DdD7PCUlmbWLJnBf65cGjpiDs2tJnj+LY7n3D4TeDqFx6y70GThWd87uTYsIOL6PhBdPWbDhTIHoVqvV7Fo7m2sXT2Je1IK+g2cYnNsf3Alh41LN3F69bgu6fzYGhULBxTN/4bdjGeGP7jDip82UqVgDgNTUFLYun8qDOyEoFCZ0/2wMlWs0NIrenWtm63xRvyGG9d6/E8KGDL7oo8/H6HzRGp8fdL5owHBvLK3tSEx4xu8LxxIT/QSVUolX509p/F43Ht67wdYV00lKfIGJiQntu39J/aYdCo3uhOfxbFw2iajwB5iZF6Xv4Gm69ffGpRMJvnACGztHxv3sm/vOzqBdrBWbEf7oHqt8RuvOjwp/SKdeQ2jTqR8r548i4nEooFkTWFrZMM57W670vql2wOvXvIVJ65H9Gzl1aCeo1TRr+xFtOvUD4MHdG2xePoPUlGRMTEzpPWgc5Sp7GK0dxp7PF84YQnxsFCplKpWq1aPPwLGYmJoaRW8awRdPsW21Rndzr2506J5V95qFWt02dgzS6r52+Qy+GxaSmpqCmZk5H/13OFU9GgGwe+Mizmr90MKNBeOH4M3GAQT/XiRJmgJMznR4qizLUzL8HgtknCxUmeuRZfm/kiRZAEckSdomy/K1vOgRO28FeqiUSratmsnQ8cuY6LOboFMHeKINIKRx5sguLK1tmbp4P2069Wf3Bk3O5ScPbnP+lB8TfHwZOn4ZW1f+hEqpzLZOlUrF70sm8PmwuUyY74ujcwkCju0FwG/XCkqWlxj/807+++1PbF8zJ9ft2LpyJt+MX8pEH1+C/P2ytOP0YV8srWyZungfbTr1wzdLO3bxzfilbFkxE5VSSVx0OMcObOLHOZuZ6LMLlUpF0Ck/XX3d+o9gnPc2xnlvM0rgVqVUsmH5bIZPXMiMhTsI8P+LRw/u6JU5eWg3Vla2zF62h/ad+7L994V6n29ZPR+Puk11v5uZF2HUtF+Z5rOFKfM3cfXiaW7LV/OtNY1rF/2JDAtl8sJ99PlyEltWzjBYbuuKGfT5ajKTF+4jMiyUa5f8ATi4exWShyeTF+5D8vDk792rdOeoVEr2bPShWp2menX9vng8Xl0+Y6LPHkbN2oSNnWOuNBeEraQxbMpKxnlvy7KIjYkK48aVMzg6F8+VVkOEXPQn8kkoUxbt45OvJrFlheE+36Lt8ymL9hH5JL3P/9b2+ZRF+n1uZW1LjwFj8Or8qcH6jv65EfeS5fOsu6Bsxcm1JMOmrGGc9046fPQlm5dP1dW1Y80cqtdpxsRf9jJ23o586QeN7WxaMYfvJyxi2oKdnDvpx+NMY9T/0G4srW2ZuXQvbTv3ZefvCwAoUaYiE+ZtYPL8LXw/cTEbfv0JpTIVgC2r5lGzblOmL9rF5PlbKV6qQr50GuLGpZNEhoUyZv4BPh44hZ2rpxkst3P1ND7+Ygpj5h8gMiyUG5f9dZ/FRT/h5tXT2Gew48o1PRkxexcjZu2i51fT2bYi83rHONqjwkIZ63OAHoOmsHNV9tp7DJzCWJ8DRGXQ/l6nAfww15eRs3dRvV4rDu5apjneeQAjZ+9i5OxddOw9jIrVGryVwC3Aw3W7ONdp4Fv57lehVCpZsWwBE6bOYcGydZw8cYQH9+/plXFxceXb4WNo0bqtwTo2r19NjZrGvRnxOpQqFbP+DGBJ33bsGvohfsF3uR0Zl6Vc+xrl2fZ1V7Z93VUXuAUoamaqO/4mArcqpZLNK2fxzfglTPbZRaC/n+5mThqntH5p+uI/8OrUD98NmrnF3LwoXXoP5aP+I7LUW6tBK8bM3lCg2q9p55YJC/bTe9Bktq8yPLdvWzmDXl9OZsKC/USGhXJdO7cXL12ZASN9qFhN/6bPmcM7ABjj7cuQCcvZvX4eKlWW66Pc6714koiwUCYt3E/vLyez9TW+aNLC/URk8kVVPDyZtHA/VTw8Oaj1RSf8tuBeqgJj5+3kuymr8f3dm9TUFIoUsaD/NzMZP383Q8b9yq61c0h48bTQ6P7bdyUly1VlrPcu+n/zEzvXpq+/PVt3Zci4ZbnWmlW7WCuqlErcSpbTXTeMmbOZIkUtqO2puYk7cMQ83Wd1G3tRR3s8v7yNNW9h0vr4/j+cOrSTH2dvZNzP27l6/gQRTzRBct/1PnzQ42vGeW+jU+8h+K7P8zuFsrSjIObzQSPmMvHnbUzy2cnzp7GcP3PQKHr1dK+YxbfjlzDll+x1W1nbMmPJH7Tt1I9d6zW6rW0cGDp2AZN9dvDZt9NZs3C87pxaDVsxdk7B+qG3EQcQ/DuRZXmKLMuKTP+mZCoWALSWJMlUkqR6wD8ZP8yQ4/Yl8AJIzKue//vgrSRJ5SRJ2mekuqZIkvTxKz7/TJKkH7Q/DzHGd2bzPXmu+96tYFzcy+DsVgozc3PqN+vAlaCjemWuBB7Ds1UXAOo2boccHIBareZK0FHqN+uAuXkRnN1K4eJehnu3grOt88WzOMzMiuBWohwAVWs35mLAIQDCHt5BqukJgHvJ8sREPuZpXHQu21Fa7zsvBx7L1I6jNG6tbUeTdshXz6FWq7kceCxTO0pz71YwoLlwTUl+iVKZSvLLROwcXHLdxznlzj8huBYvjau7pg2ezdtz6Zx+Gy6eO07T9zoB0KCpF9evaNoAcCHgKC5uJSlRpqKuvEKhwKKYpbYtqZpgUY6fCHg9V4KO0qhlZxQKBeWr1CbxxTPiYyP1ysTHRpKU+JwKVWqjUCho1LIzVwI1NnYl8KjOtjxbdeFK4BHdeccPbKK2ZztsbNMX3E8e3kalVFKtVhNAs9OySNFiudJcULbyKnaunUe3/sNz8zhGtmj6LId9Lmn63LNVZy6fy9Dn2rZ5tu7C5XOaPrexc6JspZqYmmV9QCM2OozgCydo6tU977oLyFYqSHV0O3DKV65NXHQEAIkJz7l9/TxN2mg0m5mZY2llS364eysYl+KlcNGO0YbN388yRi8FHtON0fpNvLhxNRC1Wk3RosUwNdX0bUpKss4WEhOec/PaBZq3/VCj09wcSyubfOk0RMj5IzRo0QWFQkHZyrVJSnjG00z9/zQ2kqTEF5SrUgeFQkGl9B9oAAAgAElEQVSDFl0ICTqs+3zP+jl0+mQkigyTSFELK93O3OSkRL1dusYi+PwR6mfQnpgD7fVbdCFYq93C0lpXLjkp0eA4vHj6T+o27Wh07Tklxj+IlJj4t/b92XHr5g2KlyiJe/ESmJub07xlG86dPaVXxtWtOOXKV8TEQL/e/kcmLi6G2nUbvCnJAAQ/iqK0ow2lHGwwNzXl/RrlOXbj/hvVkBvu3QrG1b00Llq/1LDZ+1zJ4peO0aS1Zld2vSZtuaH1S0UtilGpWl3MihTJUm+FKrUKdN0CEBx4lIYtNeOz3Gvm9vLa8dmwZReuaudw91IVcCuR9cZa2MPbVPHQrAtt7JwoZmXLgzsh+dZ7NegojbR6X+eL0vQ2yqD3auBRPFt1BcCzVVedj1IoFCQlJaBWq3mZlICltR0mJqa4liiHa/GyANg5umJt58jzp7GFRveTh7eRPNLW3xWIiXzE07goACpVb4CltV2utWZGrBWzrhVvXA3A2a00Ti4l9I6r1WrOn/6bBs3/kyu9b6sdxqQgtIY9vEv5KrUool2DVa5en0sBGvtRKBQkJj4HNGsxO0fjzJUFNZ8X065lVMpUUlNTjL7eupumW7vGbdD8/Sz9f/ncMRob0F2mQlXsHV0BKFG6IinJyZq1Lm/GD/0b4gD/D6gVJoX+X06QZTkGWAecBHyAsZIkdZAkqZe2yC5Jko4BpwE/WZbv5rXP/q+Ct5IkFab2FljwNj91x8WE4+Dkpvvd3tFNF/zQK+OsKWNqakYxS2tePIsjLjoCByd3/XNjwrOt09rWAaUyldDbmsX3xTMHiYsKA6BkuSpcDtBcaN/75yoxkU+Iiw7PRTsicHBO1+Lg5Ep8THi2ZTK2Iz5D+wDsndyIi4nA3smNtl0+ZcLg9xk7qC3FLG2onuHO/t7Ni5gx4mN2rJmnc1D5IS4mAscMOhyc3IiN1l/cxkVH6sqkteH5szheJiVyYNc6uvT6Mku9KqWSycP7MOyzdtSo3ZiKVYzzSFCa5oz9ntZ3mcvYO2XtX4Bn8TE6R2jn4MKzpzHac8K5fO4ILdr30Ksr4nEoxaxsWOE9nNmje+K7/mdUKiW5oSBsBTTxoEXTv2bW6N74H9yhK3Ml8Bh2jq66R/nzS3xMBPZOue/z+Nf0+avYsWYu3fqNQGGS9ym1oGwlI6eP7KJ6Xc2jTtERD7G2dWTD0onMHt2Tjb9O5mVSQp71g3b8OenbTpY2REfq5sWMYxTgzs2rTPr+Y6YO70m/r8ZhampGZPgjbGwdWLN4CtNG9mHdkmm8TMrzDdpsiY+NwN4xXbudoxvxseGZyoRj7+iWoYw78bGa9oWcP4KdgxslymZ9yuBq4CHmjOzEqnmD6fnldONrz2Tzdo5uWcZsfIy+dnsnd53NA/y5dQHThnpx4dQ+OvT4Ru/c5JeJ3LjsTy3PdkbX/q4THR2Jk3P6xYqTswsxmfxSdqhUKtauWsqnAwYXlLxsiXiWgLutle53N1srIp5lHf+Hr4fSY9kefth2lLD4F7rjyalKPln+B/1X7uPIjdAC1xtrYH6MNTA/GvJLb5u42EzjM4O/SSM+JkJ/fDq6ERerXyYzJctKBAceRalMJTriIQ/vXCM2Oiz/eg30tUG92fqi6Ey+SLPRoGWHPoQ/usOEr9owa2R3Pvp8DCaZfOa9W1dRpqbg7Fa60OguWVbisnYjxb1b2vV3TM7X33nV/v+6Vkzj/Ck/GjTPmj7j1vUL2No56QL++eVNr3kLm9biZSpx69p5nj+LI/llIiEX/XXzyMefj8Z3vQ/jvmrPrt9/pmvf74zSjoKczxdOH8yoL9pgUcySeo0NP+2SV7L0v8GYQASOr9F94ewhSpevirl51gB0QfE24gCC/29kWf5VluWmsiy3kmX5lizLfrIsb9V+9oEsy61lWW4iy/LP+fmef13OW0mSKgDrgRQgAYjQ/l8emCBJkgR8h2bb8hbgAOCgTSJcC1ghy/KSbOo+gOYtcRbAZ7Isy5IktUYTYX+I5s1xwZIklQMWy7LcSXtesCzLNTPU0wsor43A70WztXoCkAT4y7Kc/mxB+jmOwCrAEVADHYGlQDxQGU2i5A+AdhnrlmV5fu56MCuZ7+SpDaVYVii0srKeqzZwgkKhQKFQMGDYXHaunUtqSgrVajfBRLsTrf2HX7BjzRxm/tCDEmUqU6p81dzl8TEkMvMdyWzKGD6sIOH5U64EHmXakj+xtLJhxc+jCDixD8+Wneja9zts7Z1JTU1h06/TOLh7NR175C+XbHb9plfGUJ+jYPeWX2nX5RPdLtuMmJiaMtVnMwkvnrF49kgeht6iVNlK+dKaQfRrNRss85pqd66dS9e+wzAx0bcBlSqV29cvMGbuNhyc3VntM4qzx/bQtE0udoQWgK0AjJyxDntHV57FR7Nw2te4lSxP2YrV8du5gm8n/ppzfa/BoJ1k7lGDQvP2fVfPH8fGzpEyFatzMyQwb5Vko8kYtpLGzeBznDnqy/Bp6wDN3fIHd6/TY8AYylWuxY41szm4ezWden/zmpqyx9D4y6zQ4BjVtrNCFQ+mLdjBk4d3WL1wMh71mqFSKrl/5wZ9Bo6mQhUPtqyax4Fda/jwE+Pe78uJ3RgqA5rg5qHdy/ly7AqDn3s0bItHw7bcvh7EX9sX8dX4VQbL5ZkcjFnD/Z7+c8de39Ox1/cc3r0C/7826QVwQy4co7xU962lTCjUGDSJnI1Kv/27qdegMc4urkaVlBNyMgW2qlKa/9SsQBEzU7YH3WDi7pOs+FQTTDkwvAeuNpY8jH3GoHV+VHZ1oLRj/nbu51Zw1rVYDnzX28Cg9sxFcuC3MuH5XjfCHt3h57G9cXApTrkqtTE1yX9+R8P9+Poyr9N7/fIpSpaV+HbSKqLCH7B4+pdUrFpPt1suPjaS9YvG0W/ojCxB3bepu92HX7Bz7Wxmj/o4ff1tYuTLRbFW1GtvakoKV4KO07Xv91nKBfkfMBjUzTNvcM1buXo+890XgNbipSrQ7sPPWTTtK4paWFKybBVMtfZ98q9tfPzZKOo2bsv503+xYekUvp+8PH9tyEajsebz7yYuIyX5JasXjONG8Dmq126SZ5lZyMla6zVlHt+/xa71Cxg2Kf/pVnLFG44DCARvin9d8BZoDeyUZXm+dqftakCWZXmIJEnOwAqgqSzLSZIkmQKlgZJAG8AUCAIMBm+Bj2VZfiFJ0vvAcOBrYC7QCXgMHMqJQFmWt0qSNFGW5dage/vcYFmWL7xid/A4wFeW5d8lSco4+1yXZfl7SZImAV1lWV6Tse7cYu/oRmyGHa5xMeFZHhtxcHIjNiocByd3lMpUEhOeY2Vtp7mTmGEXRFxMOHYOmou07OqsINVmxHRNcOX65dOEa/MOFbO0pv9QzY4ttVrNpKH/ydWLBeyd3IiNStcSGx2h05K5jIOTW9Z2RGXQGx2OnYMLN66cxcm1pC5PVh1PL+7Il/Fs2Um3A8DcvAhN3uvKob3rcqw1Oxyc3IjJoCM2Ohx7R+dMZVyJiQrH0TlDG2zsuHMzmKDTh9m+biEJL55hYmKCeZGieHXspTvX0soGqWYDgi+ezlfw9rjfFk4f3glA2Yo19Po9re8yYu/kpreLOi46HDvtozU2do7Ex0Zi5+BCfGyk7rG3+7dDWLPgRwCeP40l5OJJTEzMsHd0o1T5qji7lQKgdqM23L15RTOac0hB2Aqge1zIxs6J2o3acO+fYCytbImKeMRPP/TUlZ81ujejZ23EzkH/b/sqjvtt0bxsAShbqQZx0Zn63DEHfe7w6j7Pjjs3LnE16BghF/1JSX5JUuIL1i4cy2ffzcqR7oK2FYBHoTfZ9NsUBo9dirWNJgDn4OSGvZMb5SprXlJUp3E7Du5e/VrNr8LByZWYaH3bsc8yX7oSGx2mP0YzPXZavFQFiloU49H92zg4ueLg5EoF7Y74ek288Nu1Nl860zj19yYCjmp2xJSuUJO4mHTt8THh2Ga2e0d3vR1X8TFh2Dm4Eh3+gJjIR8wf0113rs/4j/lu+hZs7dPbX7FaA7ZEPODF01is8vFSPgD/vzcRcCSD9mh97VnGbCbtcdFhWdoHULfZB6yaO1gveHvp9IG3mjKhMOPk7EJ0VPpO2+ioSBydcjZ3yTeucT3kCn77d5OUlEhqSioWFsXo//lXBSVXh5utJWFP03fShj99oXsxWRr2lha6n7vXq8KCQ+d1v7tqy5ZysKFBOXduhMUUaPDWIZNfiosOx97B0Fosq196G5z8azNntHN7mYqZxme0gbnFyU1/fMZknf8zY2pqRvdPf9T97jOxHy553I14wm+zzheVqVjTgC8yoDeLL9LotbFzyuSLnAA4e3Q37T78AoVCgYt7GZxcSxL++C7lKnmQmPCcX2cPpVPvbyifi5dmvgndxSyt6TdEk4NWrVYz5ZsOuX6xlyHEWtHwWhE07y4oXb4qtvZOevUplalcCjjMmLlbci70LbXD0Jo3v8HbgtLazKs7zbRpv/ZsXKjb4X32+B/0GKCxn3pN2rNx2VSMQUHP5+ZFilKrYSsuBx4zavA2S//HhBtY47oRk43u2Ohwls0dweffTcfFPfdPFxhVewHHAQR5Q23M3I3/JxSmNALGYivgIknSemCU9thZ7f8VgIuyLCcByLKc9sxMiCzLL2VZTsDA2+EAJEmyBJZIknQCmIom4AtQTJblR7Isq4G0bWiZ79m8zjKnAl9KkrQByO610tWBo1rdau33AaRdXdxHsys3X5StVIOIJ6FEhT8kNSWF86f88GjQWq+MR4PWBBzXvFjs4tmDVKnZCIVCgUeD1pw/5UdKSjJR4Q+JeBJKuUo1X1nns3jNY1opKcn8vXs1LdppHnVKePGU1JQUAE4f3kmlaum7FXLejvt631mrYSu9MrUatOas9gVpF88cRNK2o1bDVpnacZ9ylWri4OzOvZtXSH6ZiFqtRr4aoHvhUVquLk2unKOUKJP/nazlK1cn/MkDIsMfkZqSQoD/39TJ1IY6DVtx+qgmZXPQ6cNU9WiIQqFg7MxVzFu+j3nL99Gu8yd88NHneHXsxdP4WBJePAMg+WUS1y4H4F6yXL50turQm7HztjN23nZqNWrDuRN/oFaruXvzMsUsbbIsyO0cXChazIq7Ny+jVqs5d+IPajV4D9C3rYDje6nVUHN86hI/pmn/1W3cjl4Dx1O7URvKVqpJ4ounukfm5OBzuJeqSG4oCFt5mZRAUqImUPAyKYHrl89QokwlSpatzNzVx5ix7AAzlh3A3smNsXO35Cpwm9bn47y3M857O7UbtiHgeO76POD4H7q+9WjQWveiwIBj6X2eHV37fs9Pvx1i+lI/Bgyfi1SzUY4Ct2m6C9pWYqKesMJ7OP/9ZqYunzaArb0zDk5uhD/WpBmSrwbgns8XgZWrVIOIDGM00P8var9ijJ4/cxhJO0Yjwx/pXlAWHfGYsEf3cHItjp2DMw7OboQ9ugfAjSvnKF46fy9WS6NZ+08YMUvzMrEaDbwIOrkXtVpN6D+XsShmjW2m/rd1cKFoMUtC/9H0f9DJvdSo34biZaow9deTjF94kPELD2Ln6Mbwn3Zga+9CVFiobifGw7vXUKamYGmT/x2szdt/onuZWM0GXpzPqN3SsHYLi3Tt50/upWZ9zZV65JP0x95Dzh/FNUN+zcSEZ9y+HkiN+sZ5Ocy/jUpVJJ48ekh42BNSUlLwP3GEhp45e2xw+KgJLF+7jd/WbOXTAYNp7dX+jQRuAWqUdOZ+9FMexT4jRankr5C7tJL0LyYjM6RROC4/oLyz5gL0aeJLklM1y8XYhCQuPYiggkvB7spO90vaueXUXwb8UivOHPsDgAtnDiHVbFggOaZzQov3+zB67g5Gz92BR8M2BJ7QjM97NzXj0+DcbmHFPe3cHnhiLzVf43uSXybqUt3cuHIaUxPTXPv7NFp26MOYeTsYM2+H1hft1fmi7PRa6PmivXjo+aI9AAQc34OHth2OzsW5eTUAgKdxUUQ8voezaylSU1NY6T2MRi07U7fJ+4VOd8KLp6Smpq+/K1arn6v1d3aItWLWtWIaQf4HaGggp+2NKwG4lSyvl3ouv7zJNW9h1Arp154xkU+4FHBY1/d2Di78ExIEgHz1HC7Fy+S7DfrtMN58npSYoLv2VCpTCb7gn++X8GamXCbdQf5/UbtBJt0NW3E2g+6qWt0JL56y+Kdv6db3OypVrWtUXTnhTccBBII3xb9x561KluWxAJIkHQQigbQg7W2gtiRJFtqdt2nBa8PPhurTAYiRZfkzSZI6kp5XNlGSpOJAGNAAzc7dOKCUVkMpwNAr5TMGiR/Lsvy1JElFgBBgj4HyIWh2Fa/PtPM2o/a043l+/a6pqRk9vxjHkp8Go1IpafLeh5QoXYl9W5ZQpmJ1ajV8j6ZturFu0Tgmf/MBVtZ2DBg+F4ASpStRr0l7Zgz/EBMTU3oNHKdLdWCoToCDe9YSfOEEapWKFu/31L0kIezhXX5fPB4TExPcS1Wk3+Dc3f00NTWj18CxLJ4xGJVKRZM2mu/8Y8sSylasQa2GrWnq1Y21C8cz+ZtOWFrb8kXGdjRtz/Rh3TAxNaW3th3lq9SibpN2zBrVGxNTU0qXr0rzdpr3061ZMJbnT2NRq9WUKifR58uJef0T6LWh36DRzJ/6DSqVkuZeXSlZpiK+m5ZRrlJ16jZqRcu2XVnxy0TGDO6KlbUdX42c+co642OjWLVwMiqVErVKTcNmbanTsGW+taZRo24LQi6cZOp3H2BexIJ+Q9LzXc4a1YOx87YD0GvgBDYsnUBK8kuq12lO9brNAc3jeqt9fuDMEV8cnN35YsSr08KYmJjyYf+RLJo2SJsgvzrN2n6UK80FYSvPomL4be5wQPMigQYtOlJDm3vV2NSo14KQiyeZ8u0HFCliQb+h6X0+84cejPPW9HnvQRNYvyS9z2to+7x9ty9YNf8HTmv7fKC2z+Njo5g7pjdJiS9QKEw4un8DE3x2G+UiDgrOVg7s+JUXz+PYuvInQJMm5MfZml0rPQaMZe3CsZocg66l9L4zL5iamvHJwB/5ZdpQ1CoVzby6ULJMRfZsXkbZitWp06gVzb0+ZNWCiYwb0gUrazu+HKEJdN+6fpEDvmsxNTXDRGFC3y/HYqPdndpn4I+s/GU8qakpuLiV4rNvpuRLpyGq1WnJjUsnmD38P5gXtaDXV+lv+54/tjsjZu0C4KMBk9jy63hSk18i1W5O1TotXlnvlXMHOX9yL6ZmZpibW9D/W2+jB5Sq1W3J9UsnmDVMo713Bu0/j+nOyNn62lOSX1K1Trr2/VvmE/n4HgqFCQ4uxfn4i8m6868GHkKq1YyiFllTzrxJ6qz/GadWjSji7ECbu8f5Z9oiHqwxTh7B/GBqasbAwd8zbeIoVCoVXu3+Q5my5dm8fjUVK0s0atyMf27eYM6MCbx4/pzAc2fYunEtC5atfau6zUxMGNOxMYM3HESlVtO1TiUquTqw9OhFqpdworVUhs0B1zl28wFmJgpsixVl2oeaueZOVDwz9p3GRKFApVYzoJkHFQs4eKvxS2NYqPVLTdt0pUTpSuzdspSyFatTu2Frmnl1Y83C8Uz8pjOW1rYMHD5Hd/64wf8hKfEFytQULp87yncTl1GidEV2rvch8OQBkl8mMebL9jTz6kbnXsbNQVy9bguuXTzB9O87UqSIBZ8MTh+fc0d/zOi5GjvuOXAiG5dOICUlSTO3a8fn5XOH2blmJs+fxvLbnCGUKluVweN/41l8DL/O/BqFQoGdoyv9vsnZTcPXUaNuC65dOMG07zpqfVG63tmjPmbMPI3eXgMnan1REtXqNKd6XY3eNF909ogvDs7FGaD1RR0++ooNSycwc2Q3ALr2HYa1rQOBJ/7g1vXzvHgWR8AxzXK/39AZlCqXNX/429Ad/ugO6xePR6Fdf/f9On39veaX0dy6FsjzZ3FM/NqLjj2H6l4CmlvtYq2ouT5KfpnIjStn+eSrrNcN50/50aCZEVMmFFA7CmrNW1B9vnzeSF48j9fWP073ktu+X09i+5q5qJRKzM2L0PerSfluQ3o7jDufW9vYs3T296SmpKBSKZE8GtGyfbbvTM+z7t4Dx7BgukZ3szZdKVGmEns3L6VsJY3u5l7dWL1wPBOGdsYqg+6jB7YSEXaf/TuWs3+HJvXE95N+xdbOkZ2/+3BO64d+HNSe5m2N74fedBxAIHhTKLLLafeuIklST2Co9tcnQCrwiyzLQdrP+6LJeZsAbEOT8zbb/LQZ6i2BJj9tNJpAahVZljtlyHn7GE0e3U2yLO+QJGkB0BDNbtz3ZVmuKknSZ4CzLMvekiTNQpNjdw+aHcGeaHLp7pZleQ6Z0Oa8XQPYowlGd0KT83axLMtB2dUty/Jrk/UcuvLynTQCheKdlI2FaerblpBnElLM37aEPGFi8m7aCoBa/W4+UvIuu5aiZu/mGI1PKvq2JeSZwpCqMy8oPGu8bQl5ptz1Y29bQp6ocG7t25aQZ856DHvbEvLES2X+c8u+LUze0bnlXUaRoz0xhY93ea0oePOYvKN2/q6utwCU7+g1EYCXh8W7K76AiAo+U+gHkXPNJoXq7/avC94Kco8I3r5ZRPD2zfMuL8hF8PbNI4K3b5539WJCBG/fPCJ4++YRwVtBbhDBW8H/AyJ4++YRwdt/F5EhAYV+ELnU8CxUf7d/Y9qEfCNJUhsg87MS38qyfPUNff9coFGGQ49kWe77Jr5bIBAIBAKBQCAQCAQCgUAgEBQORPDWALIsHwGOvMXvH/22vlsgEAgEAoFAIBAIBAKBQCAQFA5E8FYgEAgEAoFAIBAIBAKBQCAQFDzvcg6Pt4TJ2xYgEAgEAoFAIBAIBAKBQCAQCASCrIjgrUAgEAgEAoFAIBAIBAKBQCAQFEJE2gSBQCAQCAQCgUAgEAgEAoFAUOCoxT7SXCN6TCAQCAQCgUAgEAgEAoFAIBAICiEieCsQCAQCgUAgEAgEAoFAIBAIBIUQkTZBIBAIBAKBQCAQCAQCgUAgEBQ4aoXibUt45xA7bwUCgUAgEAgEAoFAIBAIBAKBoBAigrcCgUAgEAgEAoFAIBAIBAKBQFAIEWkTBII3jJp39xEBExP125bwf0dy6rt5j62ImeptS8gzStW72edK1bs7t1gVSX3bEvJEievH3raEPHOvWuu3LSFPJAQHvG0JeSYlyfRtS8gTavW7O7eYmirftoQ8kZjy7l6imZm8m/5f/Q77UHOTd9PO3+WnpuMSi7xtCXmi6Du8PleLy9B/FWrFu3m99TYRPSYQCAQCgUAgEAgEAoFAIBAIBIUQEbwVCAQCgUAgEAgEAoFAIBAIBIJCyLv7TI5AIBAIBAKBQCAQCAQCgUAgeGd4l1NJvi3EzluBQCAQCAQCgUAgEAgEAoFAICiEiOCtQCAQCAQCgUAgEAgEAoFAIBAUQkTwViAQCAQCgUAgEAgEAoFAIBAICiEi561AIBAIBAKBQCAQCAQCgUAgKHDUCrGPNLeIHhMIBAKBQCAQCAQCgUAgEAgEgkKICN4KBAKBQCAQCAQCgUAgEAgEAkEhRKRNEAgEAoFAIBAIBAKBQCAQCAQFjlqheNsS3jnEzluBQCAQCAQCgUAgEAgEAoFAICiEiOCtQCAQCAQCgUAgEAgEAoFAIBAUQkTaBEGuuBkSyI41c1EqU7G2sWf4tDW5Ov8v35WcPuyLiYkJPQaMoXqdZgBMHNIBCwtLFCammJqa8uOcLfnWGnLxFNvXzEGtUtHUqxvvd/tC7/OUlGTWLRrPgzvXsbK244sRc3FyLQmA365VnDnii8LEhJ4DftTpPPzHek4f3gUKBSXLVKb/0GmYFynKjSsB+K6fj1qtpqhFMfoPnY5r8TL5bsPVC6fYvMobtUpJi7bd6PjR51nasGrBREJvX8fKxp6vf5iNs2sJ7twM5vdlMwBQo6Zrr6+o17gNYY/u8av3GN35keGP+LDP17Tr3DffWsH4fR7+6B6rfEbrzo8Kf0inXkNo06kfD+/JbF4+g5dJCTi6lODz72dRzNK6UOgGmDD4P1gUs8TExBQTE1PGzN2sq+/on5s47rcFUxNTatRvSff+w/OkOzNqtRrfdbO4fukk5kUs6DP4J0qXr56l3IM7IWz+dQIpyUlUq9OCbp+ORaFQsHejNyEXjmNqaoazW2n6fD2DYla2KFNT2LJ8Mo/uXUepTKVhiy60/XBQrvWFXPRnx5o5qFQqmnl1p72Bfv590Xju37mGlY0dXwyfp+vn7OaO7OpUq9X8sXkRF88eRGFiQov2PXmvY1/CHt1lw5KJPLh7nc59vqVtl89y3Q79Np1i25q52u/vRoduA7K0ae2iCdzX2s7AEXNwdi3J82dxLPf+gdDbITRu3YU+A8cCkJT4Au+J6eM8NjoCz5Yd6fn5aIyJWq1m7/qZyJdOYF60GD2/nElJA7by8G4I238bR0pyElKdlnTpPw6FQsHBnYs5d2wHVjYOAHToOYyqdVoRE/mIn0d3wqV4OQDKVKpN9wFTjKJ3x5o5hFw8SZGiFvQfMp3SFbLqvX/nGuuXTCAl+SU16rbg489/RKFQ8OJ5PKt9RhET+RhHlxJ8MdwbS2tb3Xmht4LxHt+PAcPnUrdxe2IiH7PCezgqlQqlMpVWHfrQon3PfLcjjQtBAaxevhiVSknb9h/Qvaf+HBwSfJnVyxcTevc2I36cRNPmrfU+T0h4wXdff4pnk+YMGjzMaLryS60VM3Ht2JrkiGhO1O38tuVw+fwZ1q+cj0qponX7LnT5+FO9z1NSkvnVZyp3b93AxtaOb0bNwMWtBKeO+bHfd4Ou3IN7t5jh8zuu7iWZPvYr3fGYqAiate5A/0EjCrQdarWanWtm6+y/35AZ2dh/CBsy2P9Hn49BoVBw8cxf/Ll9GeGP7vDDzM2UqVijQLXuWpVIVdoAACAASURBVDeL6xdPYl7Ugk9e4Yc2LdP6obot6K71Q3s2aP2QWbofsrSy5cWzONb4DOf+7WAatfqQjweMN7r2glgLJLx4ysZlU3l8/xYoFPQfMpUKUm2j6lar1exeN4vrl05QpEgxeg/+iVLZ9PmWX8drfX9LPtT2+YFtCwkJOorCRIG1rRO9v/4JO0dXjv6xmgun9gGgUioJf3SHactPYmltb1Ttu9bO5prWXvoONmzbD+6EsHGpxrar121B98/Sbdtvh8a2R/yU1bZjop4wa0RX/tNjCG06f5ZvrfkZhy+ex7PG5wedHxow3BtLazsSE57x+8KxxEQ/QaVU4tX5Uxq/142YyMes9B6WwQ99QnMj+CFjr1sAdm9aRMDxfSS8eMqCDWfyrdEQwRdPsW21Rndzr2506J5V95qFWt02dgzS6r52+Qy+GxaSmpqCmZk5H/13OFU9GpH8MpHfvEcRGfYQExMTajVoRff+3xeI9oJac6URG/WY+T92pm33obT6YECWevOjuyDmc/nKaf7Y/AvK1BRMzczp0nckVWp6Gk13mvbCfE0kADUibUJuETtvBTkm4cVTtq74ia9/XMhEH18GjvTO1flPHtzm/Ck/Jvj4MnT8Mrau/AmVUqn7/Pspqxjnvd0ogVuVUsnWlTP5ZvxSJvr4EuTvx5MHt/XKnD7si6WVLVMX76NNp374bvglk85dfDN+KVtWzESlVBIXHc6xA5v4cc5mJvrsQqVSEXTKD4AtK2bw2fezGOe9jQbNO+K3c4VR2rBx+RyGT1zE9IU7CfD34/GDO3plTh7ajaWVLbOW7aVd577s+H0BACXLVmSi9wam+Gxh+MTF/L7sJ5TKVNxLlmOKzxam+GxhkvdGihS1oK7ne/nWmqbX2H3uVrIc47y3Mc57G2PmbKZIUQtqe7YBYMOyqXTt+z0T5u+kTqM2HNqzttDoTmPYlJUa7RkCt3LwOa4EHmP8zzuY+Isv7br8N0+6DXH90kkiw+4zzudPeg6awo5V0w2W27F6Oj0HTmacz59Eht3nxmV/AKp4NGH0XF9Gz/XFpXg5Du1ZCcClgL9RpiYzeq4vI2du4/Th7cREPsqVNpVSybZVMxk6fhkTfXYTdOpAln4+c2QXlta2TF28nzad+rM7Sz/rzx2vqvPssT3ERocx8Zc9TPplDw2adQDAytqWHgPG4NVZP4iTF1RKJZtXzuKb8UuY7LOLQH8/Hmdq0ymt7Uxf/Adenfrhu0EzRs3Ni9Kl91A+6q8f9LEoZsUE7226f04uxanr6ZVvrZmRL58gKiyUUT/70f2LqfiunWqwnO+aaXT/YiqjfvYjKiwU+cpJ3WfNO/yXYTN9GTbTV+8iwsmttO64MQK3ANcu+hMZFsrkhfvo8+UktqycYbDc1hUz6PPVZCYv3EdkWCjXLmls++DuVUgenkxeuA/Jw5O/d6/SnaNSKdmz0YdqdZrqjtk6uDBixnrGztvOqJkbObhnNXExEUZpi1KpZMWyBUyYOocFy9Zx8sQRHty/p1fGxcWVb4ePoUXrtgbr2Lx+NTVqGjcAZAwertvFuU4D37YMQDM+1/02j9GTf2Huki2cPfE3j+7r+9BjB/diZW3D/OU76dClN1vWLQGgWesOzFywgZkLNjB4+BScXYtTtkIVilla6Y7PXLABZ1d3GjYxjg99FdcuniQiLJRJC/fT+8vJbH2N/U9auJ+IDPZfvHRlBv7gQ8Vq9Qtc6/VLJ4l8cp/xv/xJr0FT2L7SsB/avmo6PQdNZvwvfxL55D7XtVoljyb8OM+XH+f64uJejkO7NX7IzLwIHXt+S9d+PxSI7oJaC2xfPZfqdZoxeeEexntvx71UeaNrv3HpJFFhoYz1OUCPQVPYuWqawXI7V0+jx8ApjPU5QFRYqM73v9dpAD/M9WXk7F1Ur9eKg7uWaY53HsDI2bsYOXsXHXsPo2K1BkYN3AJcu3SSyLBQJizYT+9Bk9m+yrBtb1s5g15fTmbCgv1EhoXq7KV46coMGJm9bfuum0v1Os2NozWf4/Dg7lVU8fBk0sL9VPHw5KDWD53w24J7qQqMnbeT76asxvd3b1JTU7B1cGH4jA2MmbeDH2Zu4uCeVcTn0w8VxLoFoFaDVoyZvSHLcWOhUirZvGIW345fwpRfstdtZW3LjCV/0LZTP3at1+i2tnFg6NgFTPbZwWffTmfNwvQbP+27fMq0RbuZ4L2V2/Ilgi/4F4j+glxzAezbOAepdguj6y6o+dzKxoFBoxbz4zxf+g75iY1LxhqsN9/aC+k1kUCQV/7VwVtJkspJkrQvj+e2liRpsYHjn0mS1Er7c3A2nxfMyjIHSJJUR5Kkpq8vmXuC/P+kjqcXji7FAbCxc9J9du7EPuaO+YSZP/Rg02/T9AJYaVwJOkr9Zh0wNy+Cs1spXNzLcO9Wli40CvduBePiXhpnt1KYmZtTv1kHLgce09cTeJTGrbsAULdJO+Sr51Cr1VwOPJZJZ2mdTqVSSUryS5TKVJJfJmLn4KKpTKEgKeE5AIkJz9OP54M7/wTjWrwULu6aNjRq/j4Xz+m34dK5YzR9rxMADZp6cf1KoGb3b9FimJpqNtanpCSjMJAQ/NrVc7i6l8LZtUS+tULB9XkaN64G4OxWGicXjd6Ix/eoXF2zWK9auwkXAw4XSt2ZOfnXdt7vNgBz8yKA/jjKL8Hnj9KwRRcUCgXlKtcmMeEZ8bGRemXiYyNJSnxBuSp1UCgUNGzRhatBRwCoWquZzm7KVq5FXEw4AAoUvHyZiFKZSkryS8zMzClaLHe7nDX9XEavn68EHdUrcyXwGJ6ttP3cuB1ycABqtTrbueNVdZ78axv/+fhrTEw0bi6tn23snChbqSamZvl/8OTerWBc3Uvjov3+hs3e50oW2zlGk9aaHYj1mrTlhtZ2iloUo1K1upgVKZJt/eFPQnkWH0OlavXyrTUzIeePUL95VxQKBWUr1SbxxTOeZrKVp7GRvEx8TtnKGlup37wrIUF5G2f55UrQURq17IxCoaB8FY1ew7b9nApVaqNQKGjUsjNXAjX2cCXwqM62PFt14UrgEd15xw9sorZnO2xsHXXHzMzMdWM0JSUZtUpltLbcunmD4iVK4l68BObm5jRv2YZzZ0/plXF1K0658hUxMTB33/5HJi4uhtp1GxhNk7GI8Q8iJSb+bcsA4PY/13ArXgpX95KYmZvTuEU7zgec0CtzIeAELdp8AECjZm0IuazxoRk5feJvmrRsn6X+sMf3eRofi1SjTsE1QsvVoKM0atklR/ZfXju3N2rZhataO3cvVQG3EsYPGmantWHLnPmhNK0NW2bwQ7XT/VC5yrWI1/qhohaWVKhaDzPzogWiuyDWAokJz7l1/TxNvboBYGZujqWVbeavzjfB549QX+v7y2r73NB8ntH312/RhWDtfG6R4aml5KREMDDvXDz9J3WbdjS+9sAM9pJD226YQ9u+EngYZ7dSuJeuZBSt+R2HVwOP4tmqKwCerbrq/JNCoSApKQG1Ws3LpAQsre0wMTHV80OpRvJDBbVuqVClllGufbLjbppu7TVRg+bvZxmfl88do7EB3WUqVMXe0RWAEqUrkpKcTEpKMkWKFkPyaAhoxmaZ8lWJjQ4vEP0FueYKCTqEo0sp3Eoax84zUlDzeany1bDT/k3cS1UiJeUlqSnJRtVemK+JBIK88q8J3kqS9EbaIsvyWlmWj+fmHEmSTAtKjwHqAAUSvI14HErCi6f8MnkAs0f3IuD4XgDCHt7h/Gk/Rs5Yxzjv7ZiYmBDovz/L+XHRETg4uet+t3d0yzARwuIZXzF7dC/8D+7It9a4mAgcnNO/y8HJVecwDJUxNTWjmKU1L57FER8TjoOzW7pOJzfiYiKwd3KjbZdPmTD4fcYOaksxSxuqa3dq9ft6CktnfsO4L9tx7sQ+2nfL/yMrcTGROGZqQ1y0/h332Oj0MmlteP4sDoA7N68y8buPmTysJ/2/HqdzQGmcO/kXjVq8n2+d6XqN3+cZOX/KjwbNO+h+L166km7BefHM38RGhRUq3QoFLJr+NbNG99az6Ygnody6foG5Y/oyf9IAo97AiI8Jxz7TGMvclviYcOwc0zXbOWUtAxBwzJdqtTW7VWp7tqNo0WJMHvwe075tR+tOn2FlbZcrbXEx4Tg4ZegrR7cs9hyXoT8z9nN2c8er6owKf8CF037M+bE3S34aTMST0FzpzQmxmWzH3smN2JjMbTJsOzkhyN+P+k3fN3jzJb88jY3ALkOf2jm68TQ2PFOZTLbi6MbT2PT2nTm4CZ+xH7J9+XgSXqQH7GIiH7FgfHd+nfFf7t4IMorezOPU0ByRNk8bKvMsPkZ3YWnn4MKzpzHac8K5fO4ILdr3yPKdsVFhzPzhIyYObk/brgN0F3/5JTo6Eifn9ItcJ2cXYqIjX3FGOiqVirWrlvLpgMFG0fJvJjY6AscM87OjsyuxmfpZ40M1f1dTUzMsrax5/kw/+Bzgf8hg8PbMib9p3LxtgYzPzBiy/8w78OJfYf9vkviY8CzztSE/ZO/o9soyoPVDRto1+ToKYi0QFf4Qa1sH1i+ZxMwferJh2RReJiUYXbvmb68/n7+2z53c9Wzoz60LmDbUiwun9tGhxzd65ya/TOTGZX9qebYzuva42Ezas7PtTPYSF/tq236ZlMDhPavp8LHx5sr8jsNn8dGZ/FA0AC079CH80R0mfNWGWSO789HnY3Q3nmOjwpj1Q3cmDm5H264DdAGvvFLQ65aCIsv4NLiGjMhyTZRZ94WzhyhdvqouKJ5GwounXAk6QVUP4z66n0ZBrbmSkxI4tm8VbbsPKRDdb2I+vxxwkFLlqmFmnv1mhrxQmK+JBBrUCpNC/6+w8U7lvJUkqQKwHkgBEoAI7f/lgQmSJEnAd8BLYAtwAHCQJOl3oBawQpblJdnUPRLooT33FyAWKCtJ0jagOjBBluXdkiRNAYJlWd6R4dwawBogEogBLmuP3wKOAraSJPUHfgPKak/7UpblW5Ik/QMcBOoDR2RZNvjcgCRJnxho20bgdqa2fa/9vk5Ad1mWY3LStzlBqVRy/841vpu0gpTkl3iP70+5yrW4cTWAB3euM2fMJwCkJCdhY+dooAZ1liNpFz0jZvyOvaMrz+KjWTT9K9xKlqNy9XzsKlJn/a4sOwmyKWP4sIKE50+5EniUaUv+xNLKhhU/jyLgxD48W3bi8L71DBm3mPJVanFwz1p2rvOm3+ApedcPWXb/GGyDoT7V5o+pUMWD6Qt38PjBHVYvnIxHvWaYF9HsWElNSeFy4Ak+6v9tvjRmEvx6vbns8zRSU1K4EnScrn3Tc1H1HzqVbatm8+eO36jVoDVmZuaFSvfIGet0Nr1w2te4lSxP5er1USpTSXj+lFGzNhB6K5hV80cxbcmfRgkAGLKZrPW+vr0HfX/D1MSU+s01u7pDb19FYWLK1KVHSHjxlEVTP6VKzcY4u5XOl97M2gz1p0ab4Xa9qr0pKcmYFSnKj3O2cCngEBuWTmLE9HX50puFHPR3zsaxYQJP/cXn3xp+LDPf5EDXq/q3cdveeHUbDCj4e8dC9m+cS48vf8LW3oWxvxzGysaeh3dD+N3nW0bM3qu3s8tYerPYtqEyr6l259q5dO07DBOTrPdYHZzdGee9k7iYCFbMG0bdxu2wtTfCTnlDdp7DvF9++3dTr0FjnF2ME0j+N5P9fJKxjMFCup9uycEUKWpB6bIVs5Q6c/Igg4dPyZ/IHGJ4Hnl9GcVbySeXNz+Uuczfvr9hYpruhwqcAlgLqJRKHty5Qc8BYyhfpRbbVs/hb9/VdO7zTdYT8kNO5nODfZ7+c8de39Ox1/cc3r0C/7826QVwQy4co7xU1+gpEzTCXq1LUyT3tn1g+1Jaf9CfohaW+ZL3Oh3GGIfXL5+iZFmJbyetIir8AYunf0nFqvUoZmmNg7M7Y713ER8TwfJ531OncTts7Z3z04is+oy4bikw8rhmyVjm8f1b7Fq/gGGTlukVUSpTWekzlvc+6IOLeymjyM1CAa25/t61mOYd/ktRC6uCUE1Bz+dPHtzij03zGTxueb6VZuZduyYSCHLCOxW8BVoDO2VZnq/dabsakGVZHiJJkjOwAmgqy3KSdrdraaAk0AYwBYKALMFbSZI8gPeBZrIsK7XntgCcgOZAKTRB493Z6JoFDJZl+bwkSRnf4FUSTdA3XJKkwcBFWZY/137fdKAPUByYBEQDNyRJmiTLckomfc7ADzls2wLAWZbl3CWkzYbjfls4dWgnAPWatqe6bTOKWlhS1MKSStXq8yj0JqDGs1UXvcAawKWAw/y5/VcA+g6eorm7G52+OzIuJhw7B80FaNqOJhs7J2o3akPoreB8BW/tndz0dmLGRkfovitzGQcnN5TKVBITnmNlbac9nn7XLS46HDsHF25cOYuTa0ldYLqOpxd35MtUr92UR6E3KV+lFgD1m77P4p/yfwfUwcmVmExtsHd0MVjG0TlDG2z07/6VKF2BIhbFeHT/NuUqaRK1X71wijIVqmJnjECEloLo8zRCLvpTunxVvcCJe8nyfDfpNwDCH98j+IL+47BvW3dmm773TzCVq9fHwcmNOp5e2sd4PFAoTHj+NDabGx6vx//vzZw5ormXVKZCTeIyjTHbTG2xc3TXu6scHx2u195zx/cQcvEEQ8av1C1yLpz6U/Pok5k5NnZOlK9Shwd3QnK1ULF3dNN7HC0uJhy7LPas6U8HJ/es/ZzN3JFdnQ5ObtT11OQLrd3Ii/VLJuVYa05xyGQ7cdHh2DsYalNW23kdD+/JqJSplK2Y9eUKeeX0wU2cO7odgFIVPIjP0KfxMeHY2r/GVmLCsbHXtM/GLv3isdF7PVj7s2Z3k5l5Ed3uiVLla+DkWpqosHuUqlAz13qP+23h9GGN/ylbsUaWvs78iKa9kxtx0ZnGo24cOhIfG4mdgwvxsZG6FAn3b4ewZsGPADx/GkvIxZOYmJhRu1Gb9HodXXEvXZHbN85Tt3HWHZi5xcnZheio9B2g0VGRODrl7GJcvnGN6yFX8Nu/m6SkRFJTUrGwKEb/z796/cn/Zzg6uxKTYX6OiYrAwdHZQJkInLQ+NOHFc6xt0h9rP3vyIE1aZP2bh969iUqppHylagWm/4TfZp39l6lY04D9Z/VTWe2/4B5jzsjJvzL4oYo1s8zXhvxQXEx4tmXOHd9DyIUTDJ2w0sDFdsFQEGsBeyc37J3cdOvDeo3b8dfu1UbR6//3JgK0fV46k++PjzFgH5n7PDosy98FoG6zD1g1d7Be8PbS6QNGTZlw8q/NnMlg23rao7Pai2b3aiZf/5pH9ENvXeVywEH2bvQh8cUzFAoFZuZFaNnhk1xpNeY4tLFzyuSHNGvas0d30+7DL1AoFLi4l8HJtSThj+9SrpKHrh47R1eKl67I7RsX8uWHCnLdUpBkGZ8x4QauidyIyUZ3bHQ4y+aO4PPvpuPirr923fCr5iXTbTv1M6rmN7HmenDrCsHn/ubAlp9JTNDYubl5UZq2z/uLqN/UfB4XHcbqn7+n79CZOLvn/yXf8O5cEwkEeaXw7QV+NVsBF0mS1gOjtMfOav+vgCY4mgQgy3Ja0tUQWZZfyrKcAGSXLKgacDLtnAznXpRlWSXL8n3gVZGVcrIsn9f+HJDh+D1ZltNmhJrAp5IkHQMWAWlXB6GyLEfJsqwGnmQ4npH8tC1ftOrQm3He2xnnvZ06jby4df2CLt/rvVtXcC9ZHqmmJxfPHuRZvObxnxfP4omOfEwdTy/duWUr1sCjQWvOn/IjJSWZqPCHRDwJpVylmrxMSiAp8QWgeczp+uUzFM9nfqqylWoQ8eQ+UeEPSU1J4fwpP2o11E/uXqtBa84e06R+uHjmIFLNRigUCmo1bJVJ533KVaqJg7M7925eIfllImq1GvlqAO4ly2NpbUtiwnPCH98D4PqVM7iXzH9uufKVaxD+5AGR4Y9ITUnhnP9f1MnUhjoNW3H6qCatc9Dpw1T1aIhCoSAy/BFKZSoAURGPCXt0DyfX4rrzAvz98DRiygQomD5PI8j/AA2b/0evrjR7U6lUHNixghbtsj72/LZ0G7LpEmU0Nl2r4XvIwecATdA5NTUFa1uHPGkHaN6+D6Nm72TU7J3UbNCGwJN7UavV3PvnMsUsrbNc4Ng5uFDUwpJ7/1xGrVYTeHIvNetrXrhz/ZI/R/5YxcAfFlGkaDHdOQ7OxbkVck6Xjy301pVc50/U9HOoXj97NGitV8ajQWtdOpaLZw9SRdvP2c0dr6qzVsM2un7+51oQriXKYmzSbUczRgNP/WXAdlpx5tgfAFw4cwipZsMcBSMC/f1omCFNiDFo2u4T3csuatT34rz/HtRqNaG3LmNhaYNtJluxdXChqIUVobc0tnLefw816muCmhlztYUEHcKtVGUAnj+NQaXSuKjoiAdEhYfi6Jq3nSytOvRm7LztjJ23nVqN2nDuxB+o1Wru3rxMMUsbw7ZdzIq7NzV6z534g1oNNLad0bYCju+lVkPN8alL/Jim/Ve3cTt6DRxP7UZtiI0OIzk5CYCE50+5I1/CtUS5PLUjM5WqSDx59JDwsCekpKTgf+IIDT1zlu1o+KgJLF+7jd/WbOXTAYNp7dVeBG6zoULlaoQ9fkBE2GNSU1I4e/Ig9Txb6pWp16gFJ49o0jydO3WE6rUa6ManSqUi4NRhmrTM+qj4mRMHDaZSMCYtO/RhzLwdjJm3Q2v/e3X2b5HN3G6hZ/978WhQ8C9TA2jxfh9Gz9nJ6Dk78WjQhsATufRDGbRev+TP4b2rGDRK3w8VNAWxFrBzcMbByY3wR/cATe7+4qUqGEVv8/af6F4mVrOBF+e1vj/0H419GJrPLSwsCdX2+fmTe6mpnc8jM6QVCjl/FNcM/j0x4Rm3rwfq5n5j0OJ/7N13XFPX/8fxV1giIggi4t5GceHeo65q696jtnW0arV1VK0i7o1U6m6dtVq1LtTaqnXvqrhBjXvUAcpwISvJ74+EyAgKgTB+38/z8fChws3N+96ce87JybknH/dkjPcWxnhvoVLNeOXlPWU7h20u7t14V14q1nx/2R42ZQ2TFu1l0qK9NP7kM1p0/CrVA7eQvtehrh3aAcDpIzuopD8GZ5cC3Liie/v4Mvw5wY/v4eJaOFE79II7qovkT2M7ZM5+izkVT5Tb//heqtRIlLtmY/6Nl7ucPnfEm5csmvEtHXt/R+lyVRM8Zvv6Rbx985pufUeT3jKizzV44jrG/rSfsT/tp8HHffio3ddpGriFjKnPI968ZNmcb2jTczgllen3vQ7Z5T2R0NGiyPJ/sprsNvNWE7esgFKp3IdumYK4gczbQBWlUmmrn50aNzBt9AbFRK4C/ZVKpYVKpdIk89j3vXr3lEplVZVKdQGopd8f8bLFPcdllUr1iz5/3MIuifMZe57UHFs0Znpd3QqXxN2jPjO/74LCQkG9Zp0oWFTXeLTtMZSF0wah1WqwtLSi+wBPwxdLxSlYpDTV6rZk+ogOWFhY0n2AJxaWlrx6HsqyucMB3dIMNRu0pkLVtK1xpsswjkXTB6PRaKjbtAMFi5Tmz42LKVaqApVrNqFes478umA8k4a2wc7egf4jvN/lrNeSacM7YmFpSQ99zhJlK1O1bgtmje6BhaUlRUqUo0GLLlhaWtF70ESW+3yPQmGBXS4H+gwx/i2iqT2G3l/9gO+UIWg0Gho0a0ehoqXYvn4pxUu741GrMQ2bd2D5TxMYN7gduewdGfj9LABuXrvA7m2/YmlphcLCgs8GjiO3foAwKuotVy+e5vNB49/39CblTe9zDvq11i7/S6+BExI839njezi6ZyOgmwVdt2mHLJP71fNQfvEeAYBGHUuNhp9QoWp9AOo17cjaJROZNqITVlbWfDF0Wrp1it2rNuLaxWPMGN4amxw56THw3Terzh3bmdGzdbNHuvSbwIafvYiJjqS8R0PKe+i+oXbbrzOIjYlm6cyvAChWujLdBkyiQcuebPjZizmjOwBaajXuQMFiylRls7S0olt/TxbPGIxGo6buR7rzvGvjYoqWcqdyzY+o17QjaxZ6Mmnop+Syd6Rf/PNspO4AjO4ToGXHfvw6fxyHdq0lh60dvQdNBuBF2HO8x/Yg8u0bFAoLDv21Di/f7eQ04bZ+XdkZywJ92anXtD0Fi5Rm58YlFCvlTpWaTajfrCOrF4xnwtC22Nk7MGDEHMPjPQe3JvLtG9SxMVw6c4jvJiylYBHd7dnnTv7D0PFJvjMz3ZTzaITq0lG8v2+FjY0tXb+eYfjdT54dGT7TD4COfSeyaZknMdFRKKs0RFlFN/j190Yfnty/DgoFTi6F6NRvMgB3r/vzz9aFurpHYUHHvpPS5VbbClUbEnj+GFO++xRrG1s+++Zd2Z41uivj5upmt3Qf4MW6JV7EREfh7tEAd31b0qJDf1b5juLUQT+cXNzoP/LH9z7f00d38fvNx7A8R7O2X1CoaNk0Hwfoys2AwcOYOmE0Go2GZi1aU7RYCTasXUWpMkpq1anPzRvXmTPdizevX3P2zCn++P1X5i/9NV2e35w81v5I3sa1sHFxoundI9ycupCHq9O+jr0pLC2t+GLgKLwnf4dGo6Fx87YULlqSLb//QonS5aleuxGNW7Tj53mTGfl1Z+xzOzB09LtlSq4HXsA5ryuuboWS7Pv08f2MnuSbYcdSoWpDrp4/ytTvPtGX/3c5Z4/uwti5unPcfcAEffmPpLxHA9yr6ur2S2cOsGXVTF6/DOPn2d9QqHg5hoz/xSxZ49qh6cN07VDPQe+uVe8fOjNmjq4d6tp/AuuXJm2Htq7WtUNLZujaoeJldO0QwJShLYl6+5rY2Biu+B9ksOcy3AonXdLCFObqw3TrP5bV88cRGxuDS/7CfD5karrkja981UZcu3iUWcNbY53D5xXZSwAAIABJREFUlh4D35WPH8d24vvZ2wDo3G8iG38eT0x0FOU8GlBOf87/2jiPZ4/voVBY4JSvAF36TzI8/srZ/Sgr10/X5Qfic6/akKsXjjJt2CfY2NjSa/C77N5jujDGW1e2uw2YwO9LvIiJidTV7R7vyvbW1bqy/cucbyhcrByDzVS203odxrVD/x70w8mlAP307VCrzgNZt8SLmd/rvtiufe/h2Ds4cf3ySfx+89Hdxq1vhwqmsR0yV79l61pfzh7bTXRUJGO/bkn9Zh1p2z391hu2tLSix4CxzJ+my12/aXsKFi3Nzg1LKFZal7tBs46sWjAeryFtyRUv96HdfxD89AF/bVnGX1t0t+cPm/gz6tgYdm9dgVuhEswY3QOAj1r3oEHzTumWO465+lzmZq76/PjeDTwPesjebT+zd5vuLt3BnsvS9Uucs/J7IiFMpTC+3lfWpFQquwFD9P99AsQCP6lUKn/973ujWxc2AtiEbl3YRSqVqo3+9wEqlcrovZtKpXIUujVvI4AF6Na87aJSqYbGf2z8NW/j/awiuiUcQoHnwEWVSuUT//n0g7VL0M2iBfhTpVL5JtrmsP45nxvJl6JjUyqVxdGthfsEGKBSqT64wvz+y1HZpxDEo1Bky9jksFR/eKMsKkqdkd+9JwCiYrLnObexMsvNABnCUpE9s7+ITN8ve8hIuWxiMzuCSQrmSrdl5TPcvfJNMjuCSVwDTn94oywqLNI8A2DmptZkvdknKWVjlT37XG9jstv8mnesLLJnG2qRfYs51hbZs5xn9lK6aRH+Nnv2uXJk4/55Nhq2SuKTatbZuLSbx4Ob17L8K1q0TPks9bplq8FbYR4yeJuxZPBWpIYM3mY8GbzNeDJ4m/Fk8DbjyeBtxpPB24wng7cZTwZvM54M3ma87DxsJYO3Scngbepl356BiZRKZVN0XxAW37cqlepKZuRJTKlUuqOboRvfDJVKtS8z8gghhBBCCCGEEEIIkR60iuz29VuZ739u8FalUh0EDmZ2juSoVKqrQJPMziGEEEIIIYQQQgghhMhcMtwthBBCCCGEEEIIIYQQWdD/3MxbIYQQQgghhBBCCCFExtOSpZaTzRZk5q0QQgghhBBCCCGEEEJkQTJ4K4QQQgghhBBCCCGEEFmQLJsghBBCCCGEEEIIIYQwO61C5pGmlpwxIYQQQgghhBBCCCGEyIJk8FYIIYQQQgghhBBCCCGyIFk2QQghhBBCCCGEEEIIYXZaFJkdIduRmbdCCCGEEEIIIYQQQgiRBcnMW4FCoc3sCCaJirHM7AgmUWuy72cmCrJnWcnOrC01mR3hf05kbPZsGq0ss+/1qcimH76XPPNrZkcwWUTA6cyOYJLgirUzO4LJcpy7nNkRTGKRjeuW7NrnymUdk9kRTKbWZs9znp3FZtNyrtZm08YfyGUTm9kRhBD/Y7LnO1QhhBBCCCGEEEIIIUS2os2uMzcyUfb8mE4IIYQQQgghhBBCCCH+n5PBWyGEEEIIIYQQQgghhMiCZNkEIYQQQgghhBBCCCGE2Wmz8ZrXmUVm3gohhBBCCCGEEEIIIUQWJIO3QgghhBBCCCGEEEIIkQXJ4K0QQgghhBBCCCGEEEJkQbLmrRBCCCGEEEIIIYQQwuy0Mo801eSMCSGEEEIIIYQQQgghRBYkg7dCCCGEEEIIIYQQQgiRBcmyCUIIIYQQQgghhBBCCLPTosjsCNmOzLwVQgghhBBCCCGEEEKILEhm3or3Crxwgs2r56DVaKjXrCMfd+yf4PcxMdGsWTieh3eukcvekf4jvcnrWgiAPdtWcuqgHwoLC7r1+wF3j/oAeA1ujW1OOywsLLGwsGSs9wYA/tywiEtnD2NhYYG9gxOfD51GHmfXdD8mrVaL35pZXLt4DGsbW3oOnkGREu5Jtnt4J5ANP3sREx1JeY+GdPxiHAqFgr83LSTA/yAKCwvsHZzpNWgGjumYU6vVsnX1bAIvHMMmhy2ffTOdIiWT5ntwJ5B1i72IiY6iQtWGdO47FoVCwZvXL1jtO4rQZ49xzleQfiN8sLN35G3EK35bMI7QkCdo1Gqatf2COh91BCD0+RM2/DyJsJCnKFAwaNwSw+uY2uxbVs8xZO/zzbRksl9lbbzsXfr+YMi+yne0IXv/ET7Y2Ttw9thf7NuxCoActnZ0H+BF4eJKgh7fZZXvGMN+Q4L/49Nu3/DRp32yRO6nj+6ybskE/rt7jTY9vqV5uy8N+5o4pBU5bPXXgaUlP8zemKrMmZU9vc554IXjbFk9B41GQ/1mnWhppG75beF4Hty5Sq7cjvQfMddQJvf6reDkAT8sLCzo2m+soW5Zu2QiAeeOkNvRGa95foZ9/bVpCSf2b8PewQmAdr2+o2K1hqnKmxytVsu2NbO4duEY1jls6fWe+mT9Un19UrUhnfT1yY51PgSeP4KllRUu+YvQc9B07HI5oLp8kj83/IQ6NgZLK2va9f6eshVrp0vmuNzb18zi2sWj2NjkpMfgGRROJvfGn8fr68FGdNDn3r1pAYH+h1BYKLB3yEsPfT34NuIV6xf/QNhzXT3TpE1fajXpmOa85igvABq1mjlje5LH2ZXB4xYBsHr+WB7cDsTSyopipSvR6+sJWFpZp/kY4jtx6z+895xBo9HSsVoZ+jWonOD3Oy7e5Kd9/uTLbQdAj1rl6VStLADVpq6htGseAAo42jO/Z7N0zWbMpXOnWLtiHhq1hiYt29GuyxcJfh8TE83PvlO4e+s6uR0cGTp6OvnyF+TE4T385bfOsN3De7eY7vsbrm6FmDZuoOHnoc+Dqd+kFX2+Gmn2Y0lO5eUzcf2kCdHBIRyt2jZTMmRkOY+zaeUsTh3aju+606nKqtVq2bx6DoHn9W3PkGkUNdb23Na1PdHRUVSo1pCucW3PK13bE/LsMXnzFaT/SF3bk9x+bwScYcuvcw37DXp8l37DvalSqynrlkziwZ1AtFotrgWK0WfIdGxz2qX4OMzR59q/czX+x/4CQKNR8/S/O8xaeZRc9o4c/nsdJw9sRavVUq9Z51S3n4kFXjjBptXe+nLTkVYd+yX4fUxMNL8u9OKBvq8+YOQcXFwL8fpVOMt8RnH/diB1mrSj54BxhsfExsSwceUsbgT6o1BY0L7XUKrVaW5ivvQv18ntc96EL4h8GwHA65ehFCtdkYFj5rNvx2rOHvsbAI0mlqf/3WXOyiPkyu2YaccREx2F78S+xMZGo1arqVqnOW26DwFAdeU0fmt/JDY2hqIl3ek9eAqWlqa/bTdXXzHO/VsB+Iz/jH4jvKlapyWhzx6z3GcEGo0GtTqWxq160rBlN5Pzxx3Dtl9nc1Xf3+o92Pi1+vBOIL8v0R2De9WGdPpSd61eOLWXPVuWEvToDiNnbKBoqQqGxzy6r2LT8qlEvn2DQqHg+5kbsbbJkea8Gf3e4pChboH6zTqZVLdkdFkBWDxjEPduXqFkuaoMHrsoyXNl1ew3As6wdU3CdqnvMF27JIQ5yMxbkSyNWs0fK2YydPwSJvj64X98D08e3k6wzckDftjlcmDKol00bfMZfut+AuDJw9ucO7EHL99tDB2/hI3LZ6JRqw2PGz55BZ4+mwwDtwDN23+J17wtePpsolL1Rvy9+RezHNe1i8d49vQBnr5/0+2ryWxZOc3odltWTaPbgEl4+v7Ns6cPuH7pOABN2/RljLcfo2dvpUK1xuzdtjRd8129cIzgp/eZuOAvenw9iT9WTDe63R/Lp9Nz4CQmLviL4Kf3uXpRl2/f9pWUrVSbiQv+omyl2uzbvhKAo3s24la4JOPmbuW7yavw+82H2NgYANYu8qRZuy/x8t3JqFkbyO3obGL24zx7ep9JC3bR8+uJbPxA9kkLdvEsUXZlpdpMWrALZaXa/KPPnte1EMMnr8bTZyutOn/NhmVTAMhfsATj5m5m3NzN/DBnI9Y2tlSplfqBDHPlzmXvQNe+Y2na9guj+xs2aaUuu4kDt5mRPT3OuUatZtPKmQwZv5QJvtvxP7E7Sd1y6uA27OwdmLLoL5q26cP2JHWLH0PGL+WPFTMMdUudJu0YMt749di0zWd4+mzG02dzug3cgr4+efKA8T/9TfevJrN5hfH6ZPPKaXT7ahLjf/qbZ08ecE1//pWV6vLDXD9+8PYjn1tx9m9fAUCu3E58NXoRP8z1o/c3M/h98Tij+zXV9YvHeP70PuN8d9P1q8lsXTnV6HZbV02l64DJjPPdzfOn9w314Edt+jHK24/vZ2/DvVpj9unrwRP/bCB/oVKMmuPHNxN/Zec6b2Jjo9OU1VzlBeDQ37/jVqhEgn3VbPgpE+fvZPyP24iJjuTEgW1pyp+YWqNh1t+nWdy7BduGdGBPwF1uPwtPsl3LCiXYNKg9mwa1NwzcAuSwsjT8PCMGbjVqNWt+mcuYST/hvXgj/x79h0cP7iTY5vC+neSyz828ZVtp1a4HG9csBqB+k1bMnL+OmfPXMXjEZFxcC1CsZFly2uUy/Hzm/HW4uLpRs+5HZj+W9/lvzTbOtBmQac+f0eUc4P7tQCLevDIpb+CF4zx7cp/JC3fRa+BENi433vZs1Lc9kxfu4tmTd23PP/q2Z/LChG1PcvstW7GWoQ4fNmkFNja2lK9SF4DOX47G02cL43/cirNLAY7u2WA0izHm6nM1b9eXsXO3MHbuFtr2HEZp9xrksnfk8YObnDywlVEz1zN27hYCzh8h+Mn9FOdNTKNWs2HFLIaOX8wk322cPb6Hx4nKzQl9X33aoj9p1uYz/NbNB8DaOgftegyhc5+kH5rs3rac3I7OTF24k0k/baOse3WT86V3uX7fPkdOW2MoJyXKVsajtq6ObNG+r+Hn7XsNo4x79VQN3JrjOKysbfhu0go8fbbgOXcTVy+e4O6NS2g0Gn5b7EXf4d54zfPD2aUgpw/vNOn8xzFXXxF0H07s+N2X8h71DD9zcMrHyOlrGTd3M6Nn/s6+HasIDw1O2zFcPMazp/fxmv8XPb6axOaVxo9h04rpdP96El7z/+LZ0/uG/laBImXo970vpconLMtqdSxrF42j24CJjPtxO99OWo2lVdrnt2V0/zyubhk9cz3j5m4m4PxRk+qWjC4rAM3bfcnnQ2ekOmtmZy9bsZbhPdF3idol8WFaFFn+T1Yjg7dpoFQqiyuVyl2ZnSMxpVL5TXrs596tAPK5FcElf2GsrK2pXr8Vl84eTrDN5bOHqNOkHQBV67ZAdeUMWq2WS2cPU71+K6ytbXDJX5h8bkW4dyvgvc+X087e8O+oqEgUCvNcMAHnDlGzYTsUCgXFy1ThbcQrXoQ9S7DNi7BnRL59Q/GyHigUCmo2bMcV/4MA2MbLGR35Nt1zXvE/RK1Gunwlylbh7Zvk8r2mhD5frUbtuHJWl+/K2UPUbtwegNqN23P57CEAFAoFkZERaLVaoiIjsLN3xMLCkif/3UajVlOusq4xymFrh02OnCZlv+x/iFqN2qYoe8myVfTZ2xoyXj57iNqN2+mzt+Oy/phKKj0Mn3yWKFOF8JCkHUDVldPkcyuCc76CWSZ3bse8FCtdMU2zJbJydlPPua5uKZqgbrnsfyjhcZ09bMhVtU4LVAGn0Wq1XPY/lKhuKWqoW8ro3xxnpCv+h6jZKGX1Sdz1WrPRu/qkXJX6hnNcvExlXoQGAVC4RHnDjH63wqWJiYkiNiZtg6DxBZw7SHV9PVhMn/tlotwvE9WD1Ru2I8D/AJC0HkRfDypQEPX2TaJ6Jm3l31zlJSzkKQHnj1KvWacE+6pYrSEKhUL3mpauRHhIUJryJxbw6DlFnHNT2Ck31paWfFyhBIevP0jX50hPt29eJX+Bwri6FcLK2po6DVtw7vTRBNucP32Uhk0/BaBW/aYEXjqLVqtNsM3Jo/9Qt1HLJPt/+vgBL1+EoazgYb6DSIHQ4/7EhL7ItOfP6HKuUavxWzuPjn1GmJRX13aksO1R6tqe2o3bculMvLZH33+s3aQdl84cTPF+L/y7D/eqDQx9lbj+o1arJTo60lAfpYS5+lzxnTvxN9XrtwYg6NEdipepjE2OnFhaWlGmfA0unzmQ4ryJ3bsVgKtbEfLpy03N+h9zOUlf/TB1m+hmk1er25zr+r56DtuclC5fFSsbmyT7PXlwB630M0vj7ogzNV96l+uU7DPy7RtUAWeoXDPpDDj/47up0aB1ph+HQqEwzBBXq2PRqGNBoeDNq3CsrGzIX7A4AOWq1OHC6f2pypuYufqKAEd2r6dK7Rbkdng38cPKyhpra125iomJRqvRpCk/QMDZeP2tFF6rNeNdq26FS5K/YNIPsa5fPknBomUpVFwJQK7cebCwsExz3ozunz99dDdB3VK6fA0umVC3ZHRZAVBWqkOOnLlSnTUrZI+TuF0Swhxk8DaFlEplupwrpVKZ9tbgw9Jl8DY8NBgnFzfD/53yuhoGFoxtY2lpRU47e968CudFaBBOLvkN2+XJm9/wiatCAQunDWLWmB4c37clwf52rF+I58CWnD32F226p8thJPEiNIg8ed8dVx7n/EmO60VoEI7O7/I75k24zV9/zGfKkGacO/EXrbsOTdd8ic97nrz5eZHo0+oXocHkyWv8/L56EYKjUz5dbqd8vHoZAkCjVj0JenQHr4FNmfV9Jzr3HYuFhQXBj++RM1dulvsMZ86Yrmxf+yMajRpTGMue+JP28PdmD02UPTTJc5w8uA33qvWT/PzciT2GN0dZMXdiCmDRjIHM+aE7x/dv+eD2WSl7HFPPeXhoEE7x8zjnTzIgHx6vDolft4SHBOOU6PoND/3w4NqRPRuZ8X1n1i6ZSMTrl6nOnJwXoUFJ8hirT/I453/vNgCnD/tR3qNBkp9fOr2PwsXLY2Wd9A226bmDE9SDjinJndctQV309x/zmTqkGedP7KKVvh6s/3Evgh7fYco3TfAZ04EOn4/DwiJtzae5ysuW1d50/GwkimTyqWNjOHP0T6P1TVoEv4rAzeHdm5T8DrkIfhWRZLsD1+7TdekORm06xNMXbww/j45V02vZn/RZsYuD102fsZdSYSHBOMdrz51dXAkLeZZom2c4u+g+bLC0tMIulz2vXyUcCD19fL/RwdtTR/+hToPmZvvANrvI6HJ+ZM8GKtdoYqj/UytxHZLStufFB9qelOz33Imkg29rF09g3FcfEfT4Hk1a90zxcZirzxUnOuot1y6ewKNOC0A3++/WtXO8eRVOdNRbAi8cIyzkaYrzJhZmJH+YkdfBWF89ORFvdG3kzo2LmTG6B8t8RvEyPCTZ7d/HHOU6Jfu8dOYAyoq1E0wMAd3rcfXiCTxqt8j04wDdhygzR3Xlh/5NKFe5LiXKVMbewQm1Opb7twMBuHBqH+HPTS8jumzm6SuGhwZx6cxBGrbsmuQ5w54/ZeaozkwY3JLm7fuleSm88LBE/ZbkrtVE/a3wsPfP+H32+D4KhYKlMwYy94duHNAv05ZWGd0/L1ikNLeuned1grol9R8+Z0ZZSS+Zmf38id0mvw8VIqVkzdt4lEplSWAtEANEAMH6v0sAXkqlUgl8B0QBG4HdgJNSqfwNqAwsV6lUi43stziwHrgPPFcqlSFAWcARcAVmAgOBAkBnlUp1y8g+nIGVgDOgBT4BlgAvgDKAE/Ap0AIooVQqDwM7VSrVPJNPSKJZM0DS2QzJbGP8x7rHfj99DXmcXXn1IoQFUweRv1AJyuhvx2rf61va9/qWPdtWcmTPRrMM4CaeDRQ/W7ytkj4w3jafdh/Gp92HsX/7co7tXZ+uA7jG8iWetW/0GD4wtf/apRMUKqbk24kreR70kEXTvqZUuWpoNGpuXzvPD96bcHIpwGrf0Zw+vIO6TTu9d3/JhE+aKwVlJqVv2W8EnOHUIT9GTF2T4OexsTFcOXeYdr2GpTTphzOlY25jRkz7zXAdLJo+ELeCxSntXiP1O8qE7JAO5zxxnkSZjV0GumswJddvQg1bdqd154GgULBr4yK2/uZDn2+MLxOQeqbVJ4m3+cfvFywsLaneoE2Cnz95eIs/189jsOeyNCdNGOnD9bvWaO53//6k+zA+6T6MA9uXc3zvelp1HYrq8nEKFSvHYK/VhAQ94JeZX1GyXPUEM3XTQ1rLyxX92shFS7lzI/Cs0efYuGIGpctXp3R5024XTk4Kqngaly1C64olsbGyZLP/dSZsP8byL1oBsHtEV1xz2/Ff2Cu+WrOHMq5OFHF2SLpTM+ZNUlaMb2T41y1VADY5bClSrFSSrU4d28fgEZPTFvL/KXOV8/DQYM6f2sfwKSuTPC6lUtQPSUlhT+V+X4Q94/GDW7hXSXjbbZ8h03S3tq+axbmTe6n7UYf3P9F7ni89+lxxrpw7QkllVcNdIW6FS9KifT8WTf+aHLY5KVRMmbZZfinoAxg/xuTza9RqwkKCKFXOg65fjmL/n2vZ+ts8+n6X9tuajeczuhHJleuU9OP9j+9OMtsc4Ir/EUqW80jVkgnJSY9+i4WlJZ4+m4l485Jlc0fw+MFNChYtQ7/h3mz91ZvYmBjKV6mLRVrv4DJTX3Hrr9607z3caBl2cnHD02cr4aHBLJ87nKp1WuCQJ29qUn84XzpcqxqNmjvXL/D9zA3Y5LBl8bQBFC7pjrJSHdOz6sIkzWLG/rmubumrr1vsKFRMiaUpdUsmlJV0k0nZk2uXxPtlxWUJsjoZvE2oCbBVpVLN08+0XQWoVCrVN0ql0gVYDtRTqVSR+hm0RYBCQFPAEvAHkgze6hUDmqlUqrdKpXIy8J9KpeqlVConAF1UKlUrpVLZB+gDTDLyeE/AT6VS/aZUKuOX9GsqlWqYUqmcCLRXqVSrlUrlBJVK1SRtp0L/6X28T3rDQoJxdHI1uo1T3vyo1bG8jXhNLntH/c/ffdoXHhJk+CQr7pPX3I55qVKrKfduBhgGb+PUbNiaJTOHptvg7fF/NnDqoG52Y9GSFQmPN8shPDQIh0TH5ejslmAW2ouQoCTHDlCt/qcs9/4mzYO3R/ds4OSBrbp8pSomOO/hRp47T978CW7lDQ8JwtFZd35zO+blRdgzHJ3y8SLsGbkddB2lfw9tp0WH/igUCvK5FSWvayGCHt8lj3N+Cpcoh0v+IgBUrtWUezcupXjw9siejYbsxUpVMJI94awe49njyoRzouzvbk15dP8G63+ZzOBxS7DPnSfBPq9eOE6REuVT1SnMqNzJiX8dVK7ZlHu3AlI8eJvZ2cG0c27I45w/wWyA8NB35TeOk74OccrrlrRuSXT9Grs244ufsX7zziydnbbr9djeePVJqYpJ8hirT+LPDk68zZkjOwg8f5QhXisSdDLDQ56y6sdh9B4yExe3omnKDHD8n/Wc1ucukqgefGHkPOZJnDvkaZJjA6ha/1NWeg+mVdehnD28nabtB6BQKHBxK4ZzvkIEP75D0dKVkzwupcxRXq74H+aK/2ECLxwnJjqKyLdv+HXBOL78bhYAf21eyuuXYfQcNdHk3MnJ72DH05fvZtIGvXxj+GIywzHb2Rr+3alaWebvP2f4v6t+28JOualR3I3rT0PNOnjr7OJKaLz2PPR5ME7OLka2CSavi64vEPHmNfa532X699g+6jZMOuv2/t0baNRqSpQub7b82UVGlvMa9Vvz7OkDJn+r+7AoJjqSSUM/Zcqiv96b8ciejZzYr297SldI2JcKSZrXaNvj9P62R/eY5Pd7/uReqtRqavRLBC0sLalerxX7d/763sHbjOhzGfKe2E31RLOE6zbtZOhj7Vw/P8EssNRyStRXDw8JIo+TsXKTtK+enFy582CTwxYP/ZfuVKvbghMH/JLd/n3M1d6/b5+vX4Vz/1YAX4/+KUmecyf2UMOE2XHm7rfY5XKgTIUaXL14goJFy1BSWYWR03QTFa5dOkmQCWuXZkRf8cHtQFbP/wGA1y/DCLxwDAsLqwRf2JTH2RW3IqW4ff2c4QuqUurY3g2cinetJui3hCTtb+lmUSZ6nT5wd0Ee5/yUdq9uWBrEvWpD/rt7zaTB28zun9dr2ol6JtQtWaWsmCIrZD9/ai+Vk2mXhEhPsmxCQn8A+ZRK5VpgtP5n/+r/LglcUKlUkQAqlSruvvJAlUoVpVKpIoD3LehzWaVSvY33/7gpEA+Bi/H+nVzN7A4c0j+3VqVSxX1sFPeO7sF7HmuSYqUrEPzkAc+D/iM2JoZzJ/ZQuWbjBNtUrtGEf/WL6F84tQ9lxVooFAoq12zMuRN7iImJ5nnQfwQ/eUDx0hWJiowg8q3uTWtUZATXLp2iYNHSAAkWVb989rDRL9cwVYOWPRk9eyujZ2+lYo2mnD22E61Wy72bl8hpZ5+kYnd0ykcOWzvu3byEVqvl7LGdVKyu+zKVZ/FyBpw7hKuR9ZNSq1GrnoYvtqhcqylnjury3b1xCdtk8tnmzMXdG7p8Z47upFINXb5KNZpw+sgOAE4f2UGlmrqfO7sU4MYV3bdJvwx/TvDje7i4FqZY6YpEvHlpuDXkRsBp3AonnR2VnMatehgWa9dl/9OQPaddbuPnNkH2P6mcIPtOffadVNZnD33+hOU+I/h86EzDGmDx+Ztwq0pG5E5O4uvg+uV310FWzx7HlHMeR1e33E9Qt1Sq0STBNvFzXfh3H2X1dUulGk0S1S33KV664nufL/56V5fOHKRgkTIm5Y7T8OOejJmzlTFztlKpRlPOHk1lfRLver128TgHdq7kq9ELE6yTFfHmJcvmfEObnsMpqayWprxxGrTsxfezt/H97G1UrNGMc/p68P5NXT3jkCi3g1M+bG3tuK/Pfe7YTipW13VU49eDgfHqwTwuBbgZoGs2X4U/J/jJPZxdi6QptznKS/vew5jxy36mLdlDvxHeKCvWMgzcnjiwlWsXT9J32Jw0L/lgTIVCLjwIecmjsFfEqNXsDbxLY2XCc/Qs3jIKR1QPKeGiG2h5+TaK6FgEotkPAAAgAElEQVRd9yMsIpKLD4MpmS/hh1nprWSZ8jx9/JDgp4+JjYnh32P7qFa7UYJtqtVqyLGDuoG/MycO4l65huGDCI1Gw+kTB6jbKOltyqeO7jO6lML/oows5xWrN2L2ikNMW7KHaUv2YG1j+8GBW9C1PXFf+lSlZlNOH0ld23P6yJ+GNqZSjSaGL2E6ffhd21O5RpP37tc/0ZIJWq2W4CcPDP++cu4w+QsVf+9xZESfC+BtxCtuXfU3bBvn1QvdEgShz59w6cx+kwYT47zrqz8iNiaGsyf2GumrN+bU4T8BOH9qP8qKNd97x4pCoaBy9cbcCPQH4PqV0xQoXDIN+dK3XH9onxdO/UPF6o2wtsmR4HnevnnFzav+Ke7nmPs4Xr0INSxRER0Vieryv+TXv/eJKyMxMdH8s30VDVuk/lbzjOgrTlm8h6n6P1XrtKD7gPFUqdWUsJCnuvWngYjXL7mjuoirkf77hzT8uCdjvLcwxnsLlWrG62+951rNYZuLezfe9bcqfuD1LlelHo/v3yQ66i1qdSy3rvqn6n1QfJndP09YtxygRv1Pskzu5MpKWmWF7OdO7E5TPS5ESsnM24Q0KpVqHIBSqdwHPAPiBmlvA1WUSqWtfuZt3Ds6YzfJGJN4EVFtMv9OrjcViG5m8NpEM2+NPTbtq8KjW6+p+4BxLJo+GI1GQ92mHShYpDR/blxMsVIVqFyzCfWadeTXBeOZNLQNdvYO9B/hDejW3alWryXThnfEwtKSHgM8sbC05NXzUH7x1n05hkYdS42Gn1BBv57g9nXzCXp8D4XCAud8Bej1tVd6HEYS7lUbce3iMWYMb41Njpz0GPju2+Hnju3M6Nm6T++69JvAhp+9iImOpLxHQ8p76L6hftdGX4If30OhUOCUryBd+6fvzKwKVRty9fxRpn73CdY2tnz2zbtvypw9ugtj5+pmznUfMIF1S+LyNcC9qi5fiw79WeU7in8P+uHkUoB+I38EoFXngaxb4sXM7zsC0L73cMOnzB37fM+iqQPQarUUKelOveZdTM4eeP4YU777VJ/93bmdNbor4+Zu1mf30mePwt2jAe5VGyTIfuqgH04ubvTXZ9+95WfevA7njxW6W/YsLC35YfZGQLd+2fXLp+j59QSTMpsz98vw53iP7UHk2zcoFBYc/nsd4+dt582rcJb7DAdArVZTo0Fr3I2sdZoVs+e0s0/zObe0tKJbf08WzxiMRqOm7ke6umXXxsUULeVO5ZofUa9pR9Ys9GTS0E/JZe9Iv/h1S92WTB/RAQsLS7rr6xaAVT+N4WagP69fhTN+YHM+7fYN9Zp1wm+tL4/uXQeFgrz5CtJzYPpds3H1yfRhuvqk56B359/7h86MmaOrT7r2n8D6pUnrk62rZxAbE82SGV8Bui8t6zZgEsf3buB50EP2bvuZvdt+BmCw5zJyO6bhlsN4yldtxLWLR5k1vDXWOWzpMfBdPfPj2E58P3sbAJ37TWTjz+OJiY6inEcDyulz/7VxHs/09bVTvgJ06a+7YaRFx0Fs/Hk8c8d0AK2WNj1HmvxFN3HMVV6Ss3HZdJzzFcBnfB8APGo345Oug9J0DPFZWVgw9pM6DF63D41WS3uP0pR2dWLJoQu4F8xLE2VRNpy+xuEbD7GyUOCQMwdTO+iu1zvPXzB910ksFAo0Wi396leilJkHby0trfhi4Ci8J3+HRqOhcfO2FC5aki2//0KJ0uWpXrsRjVu04+d5kxn5dWfsczswdPS78nQ98ALOeV1xdSuUZN+nj+9n9CRfs+ZPKY+1P5K3cS1sXJxoevcIN6cu5OFq09cjT62MLudpVaFaQwIvHGPyt59iY2PLZ0Pe1X0zR3XF00fX9vT4you1i9+1PRX0bU/Ljv1ZOW8UJ/VtzwB92/O+/YYEPyLseVCCu1S0Wi1rF3sRGfEaLVoKFVPS46uU9x/N1ecC3bqr5arUI4dtwpn1K34cScSrcCysrOjWfzx2afiiTV1ffSwL9H31ek3bU7BIaXZuXEKxUu5UqdmE+s06snrBeCYMbYudvQMDRswxPN5zcGsi375BHRvDpTOH+G7CUgoWKUXHPsNYvcCLzavnYu/gxBdDppiczxzl2tg+45w7sYcWHfolyXLxzEHKG3k9Mus4XoY/57dFXmg0arRaDdXqfkyl6rqB9307fiXg/FG0Gg0NP+6GslLtVGeOz1x9xeQ8fXQXv998DMtcNGv7BYWKlk3TMbhXbcjVC0eZNuwTbGxs6TX43bXqPaYLY7x112q3ARP4fYkXMTGRumPQ91sunTnA1tUzef0yjF/mfEPhYuUYPP4X7OwdadKmDz969gQUuFdtSIVqjYxFSJXM6J+v+HEkb169wNJKV17jvuw5K+R+H9+JXxD06B5RkRF4DWpOr0FTcPdI/fcNZEZ2Y+2SSBlZNiH1FMbXKfvfpFQquwFD9P99AsQCP6lUKn/973ujW/M2AtiEbs3bRSqVqo3+9wEqlSrJFDD9mrfxt5sMBKhUqi1KpfJLwEWlUvkolcom6JZQSHJPr37N29VAHnQDwW3QrXm7SKVS+Sfazyx0a/DuUKlUH1wo8cCVyGxZCKJiMuK739KflWW2PN0AKFL8WYX4X5edv3soRp09b0pRa7PvSbe1Mu1LEjNb/QDTl5XPbAHVB2d2BJMEV0zbIEZmynHucmZH+J+jyab1orVF9qwTAdTa7NmGZmfZ9e18du63WCqy6UkXmaJFlRzZt7CbybXbj7L8RVS+VKEs9brJ4K2QwdsMJoO34n+BDN5mvOz8JkgGbzOeDN5mPBm8zXgyeJvxZPA242XXt/PZud8ig7ciNWTwNikZvE09WTYhnSmVyqZA4ntyv1WpVFdSsQ9voFa8Hz1SqVS90yOfEEIIIYQQQgghhBAie5DB23SmUqkOAgfTuI8x6RRHCCGEEEIIIYQQQogsQZuNZ95nFrmvRQghhBBCCCGEEEIIIbIgGbwVQgghhBBCCCGEEEKILEiWTRBCCCGEEEIIIYQQQpidFlk2IbVk5q0QQgghhBBCCCGEEEJkQTJ4K4QQQgghhBBCCCGEEFmQLJsghBBCCCGEEEIIIYQwO1k2IfVk5q0QQgghhBBCCCGEEEJkQTJ4K4QQQgghhBBCCCGEEFmQLJsghBBCCCGEEEIIIYQwO1k2IfVk8FZgZaHJ7Agm0VhmzwvewkKb2RFMZkH2zK7WZt+bDKJjs2d2G6vsWa8A2FipMzuCSe4/s83sCCYrkjd7nvN/Kw3P7Agmi4m0zOwIJslx7nJmRzBZVPXKmR3BJE3/mZjZEUx23LlLZkcwSXbuK9ooYjI7gklyWGbP3ABhkXaZHcEkL99m36GIfPZRmR3BJJpsPGCWXd+HCpFesueogBBCCCGEEEIIIYQQQvw/l30/7hJCCCGEEEIIIYQQQmQbWm32nQWeWWTmrRBCCCGEEEIIIYQQQmRBMngrhBBCCCGEEEIIIYQQWZAM3gohhBBCCCGEEEIIIUQWJGveCiGEEEIIIYQQQgghzE6DrHmbWjLzVgghhBBCCCGEEEIIIbIgGbwVQgghhBBCCCGEEEKILEiWTRBCCCGEEEIIIYQQQpidVpZNSDWZeSuEEEIIIYQQQgghhBBZkAzeCiGEEEIIIYQQQgghRBYkyyYIIYQQQgghhBBCCCHMTquVZRNSSwZvhUkCzp/gj1Vz0Wg0NGjegdad+iX4fUxMNKvnT+D+nWvkyu3I19/PwcW1IHdvBrB26TTdRlotbbsPomqdpmbJqNVq2bJ6DoEXjmGTw5Y+30yjSEn3JNs9uHOVtYu9iImOokLVhnTp+wMKhYI3r1+wync0oc8e45yvIP1H+GBn78CNwLMs8x5GXtdCAHjUbkbrLoMAiHjzkvU/T+bJw1ugUNB78FRKlq2SqtyBF06wefUctBoN9Zp15OOO/RP8PiYmmjULx/PwzjVy2TvSf6S3IcuebSs5ddAPhYUF3fr9gLtHfQC8BrfGNqcdFhaWWFhYMtZ7AwAr5o0m+PF9ffZX2OXKjafPplTlfd9xbFrtjUajoX6zjrTqmLSM/LrQiwf64xgwcg4uroV4/SqcZT6juH87kDpN2tFzwDjDY2JjYti4chY3Av1RKCxo32so1eo0NymfVqtl8+o5BJ7Xl48h0yhqrHzc1pWP6OgoKlRrSNe48vFKVz5Cnj0mb76C9B+pKx/v22/osyf8/vNkwkKeokDBN56LyetaiOtX/mX72nloNFpy2NrRZ8g0XAsUNemY/NbM4trFY1jb2NJz8AyKlEh6TA/vBLLhZy9ioiMp79GQjl+MQ6FQsPN3HwLPH8HS0gqX/EXoOWg6OXM5EBsbw+YVU3h4JxCFQkHHL8ZS2r1W6k96PIEXjrNl9Rx9+ehESyPl/LeF43lw5yq5cjvSf8RcQznf67eCkwf8sLCwoGu/sbh71Cfs+VPWLBrPy/DnKBQWNGjemY8+/QyAlfNGE/T4HgBvI16R0y43nj6b05A9/a/RA3+u5eSBbaBQUKhoGfoMmYq1TQ5+WzSBm1f9yWmXG4A+Q6ZSpEQ5k7Mbo9VqObBpBrcDj2BtY8snn8/GrWiFJNsd3eFLwOntREa8ZORPF5L8/vr5PexYPozPx26hQLFK6Z7RHPX52WN/sW/HKgBy2NrRfYAXhYsrDfvTaNR4j+2Jo7Mrg8cuStMxmKNO3L5+IaeP7CLizUvmrzuVpnwppdVq2bp6tuG1+Oyb6cm8FoGsi/dadO47FoVCwYVTe/l781KCHt1h1MwNFC2VtKylRXrXLXE0ajVzxvYkj7Mrg8clLAubVs7i1KHt+K47na7HkhKVl8/E9ZMmRAeHcLRq2wx//vc5EXgL70170Wg0dKxflX6tGhjdbt+5q4xevoXfxw2gQrGCXLn7iGm/79L9UguD2jSmadX0rffiZEbdsm7JRALOHyG3ozPjf/RL8zGY2ie/evFftq1bQGxsDFZW1nT5YjjlKuna9jPHdvP31lUoFAryOOWj3/Dp5HZwSnPWxK6cP8n6lT5oNWoaNu/Ap537Jsm+Yv5E7t/WZR88ajYurgUNvw959gSv77rSvvvXtOrwueHnGrWaqaP7kMc5H8O95qd77kvnTrF2hS8atYYmLdvRrsvnCX4fExPNUt8p3Lulwt7BgW9HTydf/oLExsayYuFM7t5RoVHH0uCjT2jf9QtCngWx9KcpvAgLQaGwoOnHHWjVrnu65TVXOX/66C7rlkzgv7vXaNPjW5q3+xKAoMd3WeU7xrDfkOD/+LTbN3z0aZ80HcPfv8/kxuWjWNvY0mnATAoWT9p+PLoXyLYV44iNjqJs5UZ80tsThULBk/vX2LlmMrEx0VhYWtL284kULlmZu9fO8PuCITi5FAbAvUZzPmo/xOScxpij/Y+zZPYwngf9x0TfremaOS63Kf3d16/CWe7zPQ/0ubsP8DQ8Zsf6hZw+8idv37zEd92/6Z45fvb0PucLpn/Di7DnaNSxlC5fjZ4DxmFhaWm2YxAiMVk2QaSaRq1m/fLZfOe1iCnzt3L22B4eP7ydYJsT+7djZ5+bGUt20rxtb7b9pus4FSxaivFzf2fivD/4bsJi1v08HbU61iw5r144zrOn95m0YBc9v57IxhXTjW73x/Lp9Bw4iUkLdvHs6X2uXjwOwL7tK1FWqs2kBbtQVqrNP9tXGh5Tqnw1xs3dzLi5mw0DtwBbVs/B3aM+E37aybi5W3ArVCJVmTVqNX+smMnQ8UuY4OuH//E9PEl0bk8e8MMulwNTFu2iaZvP8Fv3EwBPHt7m3Ik9ePluY+j4JWxcPhONWm143PDJK/D02WQYuAUYMHIunj6b8PTZRNU6zfConT4D6Rq1mg0rZjF0/GIm+W7j7HEjZUR/HNMW/UmzNp/ht05XRqytc9CuxxA69xmZZL+7ty0nt6MzUxfuZNJP2yjrXt3kjIEXjvPsyX0mL9xFr4ET2bjcePnYqC8fkxfu4tmTd+XjH335mLwwYfl4335/WzSe5u2+ZOJPOxg9az25HZ0B+GP5DL78bjaePpup2bA1e7YuM+mYrl08xrOnD/D0/ZtuX01my8ppRrfbsmoa3QZMwtP3b549fcD1S7pjKlupLmO8/Rjj7Ue+AsXZv2MFAP8e3ALAGG8/BnkuZ8c6HzQajUkZQVc+Nq2cyZDxS5ngux3/E7uTlPNTB7dhZ+/AlEV/0bRNH7YnKed+DBm/lD9WzECjVmNhaUmnz7/XnduZ6zi69w/DPvuPnIunz2Y8fTbjUbs5HrWbpSl7el+j4SFBHN69nh/mbGCC7zY0Gg3+J/YY9texz0jDdZreA7cAdwKPEhp8j6+n/MPHvabxz4bJRrcrVekjPv/B+KB3VORrzh1aS4HiqfuwKqXMVZ/ndS3E8Mmr8fTZSqvOX7Nh2ZQE+zv09+/kT2U9boy56sTKNRozdva6NOdLjasXjhH89D4TF/xFj68n8ccHXouJC/4iON5rUaBIGQaM8qVUedPr7+SYo26Jc+jv34226fdvBxLx5lW6H0tK/bdmG2faDMi050+OWqNh1obdLB7ai22TvmHP2UBuP36WZLs3kVFsOHSGSiUKGX5WupAr68d9xSavgSz+rhfT1u8iVm16m/M+mVG31GnSjiGeS9Mlf1r65PYOeRjq+ROTf9pM32+nsmq+FwBqdSx/rJzL91OXMcl3E4WKl+HQ33+kS97E2dctm82ICQuYvmALp4/v5dHDOwm2ObZ/O7lyOTB76Q5atu3N5t8WJPj9xlXzqFS1XpJ979u1gQKFi6d75rjcv/7iw5hJvngv3sCpo//w34O7CbY5vG8nuewdmLdsC63b9WTDmsUAnD5xgJjYaOYs/J3pvms4uNePZ0GPsbC0pHe/75i75A+mzF3Bvr+3JNlnWpirnOeyd6Br37E0bftFgv3kL1jC8D7phzkbsbaxpUot0/teADcvHyUk6D7D5+yh/ZdT+PO3qUa3+3PNFNp/OYXhc/YQEnSfm1eOAbB3kw8fdRjCkGl+NOv4LXv/8DE8pljZ6gyZ5seQaX7pPnBrrvYf4MK/B8hhmzNd88bPbWp/19rahrY9htAxmX7LD7N/N0vm+NnNcc6/GunNhB83MdF3K69fhnHu1D6zHocQiWXrwVulUllcqVTuMuFxAanc3kOpVNaL9//VJjxnmveRzH6T7EepVH6pVCpHpcf+jbl7KwDXAkXI51YYK2trajb4mEtnDifY5uLZw9T9SDcDpHrd5ly7cgatVkuOHDmxtNRN+I6NiQaF+abLX/Y/RK1GbVEoFJQoW4W3b17xIizhG4cXYc+IfPuakmWroFAoqNWoLZfPHtI9/uwhajduB0Dtxu24fPbge5/vbcRrbl87R92mnQCwsrLGLpdDqjLfuxVAPrciuOTXndvq9Vtx6ezhhMd19hB1muhyVa3bApX+3F46e5jq9VthbW2DS/7C5HMrwr1bKSvqWq2Wcyf/oUaD1qnK+77jcHUrQj79cdSs/zGXkxzHYeo20ZWRanWbcz2ujNjmpHT5qljZ2CTZ78mDO2il/9TXwsIC+zTMAtG9viksH0pd+ajduC2XzsQrH/rXoXaTdlw6c/C9+33y8DZqtZryVeoCYJvTDpsc7zpcb9++1v0d8RpH53wmHVPAuUPUbNgOhUJB8TJVeBuR3DG9oXhZDxQKBTUbtuOKvy57ucr1DddnsTKVCQ8NAuDpf7cpU6E2ALkd85LTLjcP7wSalBHiynnRBOX8sv+hBNtcPnvYcP1VrdMCVcBptFotl/0PJSrnRbl3KwBHp3yGGc62OXORv1AJwkODE+xTq9Vy/tTeNJVzc12jarWamOgo1OpYoqPe4uhkWhkwxc1LB6hYpwMKhYJCJT2IinjJ6xfBSbYrVNIDe0dXo/s4tnM+tVsOwMo6h1kymqs+L6n0wM5eV0+XKFOF8JB3xx0W8pTA80ep16xTmvObq04sWbZyhpYVgCv+h6jVqF2KXosS+nqmVqN2XNGfc7fCJclfMO0D4saYo24BXVkIMFIWNGo1fmvn0bHPCLMcT0qEHvcnJvRFpj1/cgLuPaKIqxOF8zlhbWXJxzUrcPiyKsl2i3ce5suW9bCxenczYE4ba6wsdW9RomNiUZjxG6kzo24p7V4DO3vHdMmflj550ZLlyOOsq9MLFi1FTHQ0MTHRaLVatGiJjnyLVqslMuI1eUzsl7zPnZuBuBYogqs+e+0GLbmYKPuFM0eo91EbAGrUa8a1y7rsAOdPHyJf/kIULFoqwWNCnwdx+dxxGjXvkO6ZAW7fvEr+AoVxdSuElbU1dRq24Nzpowm2OXf6GI2afgJArfofEXjJH61WiwIFUZFv9W19FFZW1uS0y4WTswslSuk+nM1pl4uChYsTFpK0HTaVucp5bse8FCtd0dB3NEZ15TT53IrgnK9gstukxLULB/Go3x6FQkGR0h68jXjJq/CE5+hVeDBRb19TtHRVFAoFHvXbc+38AQAUCgVR+r525NvX5HYy3p9Jb+Zq/yPfRrB/11pad/7KbLlN7e/msLWjdPlqWNsk7ROWyIB+i7nOeU47ewA06lhiY2NQmHEc43+BFkWW/5PVZJvBW6VSmZlZPQDDwKtKper7nm3NuY8k0ms/qREeEoxz3vyG/+fJm5+w0GdGtnEDwNLSipx29rx+FQ7AnRtXmDSsM1NGdOWzgePf2+CnKWdoME4ubglyJh7MCQ8NJk+iY4nb5tWLUEPj4uiUj1cvQw3b3b1xiVmju7Bk5mDdEgnobgmyd3Bm3ZIJzB7Tjd9/nkRUZESaMjvldeWFfgDN2DZx5/bNq3BehAbh5GL8WBQKWDhtELPG9OD4vi1JnvfWtfM4OObFtUCxVOVNTpiRcx9m5NwbO47kRLx5CcDOjYuZMboHy3xG8TI8xOSML0KDyZM39eXjxQfKR3L7DX5yH7tcuVk2dwSzRndj228/GmZ19R48maUzhzB+YHPOHNlFyw4Jb0tK+TEFJXxu5/xJys+L0CAcnd8dk2PepNsAnD7sR/kqultcCxZTEnDuEGp1LCHB//Hw7lXCQ56alBEgPDQIp/jn1Tl/gje2hm305Tl++QgPCcYp0TGGJ8ofEvyI/+5ep3iZhLfu37p2Ls3l3BzXaJ68+Wne7gu8Bn/MuK+ak9MuN+4e72YS7dywkOkju7Bl9VxiYqJNzp6c1+FBODi9O6bcTm68Ck9aJpIT9PAqr8KeUrrSR+meLY456/M4Jw9uw73qu9vkt/7qTYfPRqJQpL37YY46MbMYey1eJDqWF+95LcybzTx1y5bV3nT8bCQKi4Rl4cieDVSu0STDB9Czg+CwV7g5vRugzJ/HgeCwhDOUrz94QlDYCxpVLpvk8Vfu/kenKUvpMu1nvHp9ahjMTW+ZUbekp7T2yeOcP7WfIiWVWFvbYGVlTe+vPZkyohuj+7fkyX93aNAs/QdCw0ODcY7XJjrlzU9YSOLszwzbxM8eFfmW3dvW0K7710n2u2HVj3T9YliS6zW9hIY8I6/Lu4E/ZxfXJLnDEuW2y2XP61cvqFW/KTlsczLkizYM69+eTzv0xj53woH8Z0GPuX/nBqWUFdMtc0aU8+ScO7GH6vXTPjnkZVgQjs7vjsHRyY2XYcGJtgnGIV4f18EpPy/DdPV4617j2PuHD3NHfsSejd607PLuQ7eHty6yaEIHfvvxa4Ie3Uxz1vjM1f7v3LiY5m0/xyaHbbrmNZYJUtffzWzm7HMtmDaY0f2bYpvTzuSl+4QwVZZa81apVJYE1gIxQAQQrP+7BOClVCqVwHdAFLAR2A04KZXK34DKwHKVSrU4mX3/CNQFLqE/bqVS6QSsAJyASOALIBbYgm5g2xJoDQwDHJRKZRugE3BUpVJVVCqVk4FCgBtQGOiqUqluKZXK2UBN/X6nqlSq7e/ZhxuwBrAFngGfA67A78DtFBxXgH4/FYDV+n2E6o/TLLRGfpb4cwmtka3iPp0qWbYSU+Zv5cl/d1i9YCIVq9U3+slc2oMmn+G923xgt0VKlGfakr3ksLUj8Pwxls0dzqQFu1Cr1Ty8e42u/cZSvExltqyezb7tq2jTY2iaMieZnZzMNsZ/rHvs99PXkMfZlVcvQlgwdRD5C5WgTLwlB/yP76ZGg1Ypz/khKTj32pQcazwatZqwkCBKlfOg65ej2P/nWrb+No++380wMaKx1z4l59q0/arVsdy6dp5xczfh5OLGKt/R/Ht4B/WadeLgrnUM9lxMiTKV2bdjNdvWzKX34ClG9m7Ccyc5px8+7/v8fsHSwpLqDXSzXWo36UjQozvMG98dZ5eClCjrke5rPCUtH0Y3wlj++I+NfBvBcp+RdOk7xvAJeRz/47upntbZ5Wa4RiNev+Ty2UNMXfx/7N13eBTV18Dx72aTENILEHroQw0dlC4IIgqIgKCCSlVAEZBO6NJBFAWUjoiglACigPQuvSXA0DskkEJP2933j9lsOqRsSPJ7z+d5fDC7s7Pnzt65c+fMnTv/4OjkwvwZgzi0ZyO1G7xL64/74uqeh5iYaH7/eRxb1y2iRfsvkq4oA5Jtr1N5tdlkNLJ91STe+XSSVWNK+kWZ057HuhBwmIM7/ek/bikAZ45p81EWLVGeC4FH0hptUpnQJmaV5ON8+TKZOXryRTLatljqQsmEdSE8NJjjB7fSb+zCJJ8TKfQV4/0URqOJaav+ZdynrZP9fKXihVk7uhdX7t5n5JL11K1Yilx2mXDa8orbFmvLaJ8c4M6Ny6xZNot+o+cAEBMTze4tq/GbsYK83oVZsWAKm9Yu4p321h3hl5o+S0rHp3Urf6Zpq49wyO2Y4L2TR/bg6uZBsZLlOB9w1KrxxgWVXNyJF0n+l7l8IRAbGxt+WrKRp08eMX7YF1SsUpN8+bVpQyKeP+P7ycPo3L0fjo5OmRyz9ep5SmJiojlzbBetPvo6g2siVf2vFx17juxYydsfDhpKBDEAACAASURBVKVCzWacObwJ/0V+dBm8mALFyvPNjO3kcnDiwqnd/D7rS/pP2ZLxeF8Qd0aP/zevnuf+vZt80GUQD4JvZzjEZGWgv5vlMrHP1XfkXKKjIln0w3DOBxymvPmOSiFehWyVvAUaAWtUVf3OPNJ2EaCqqtpbUZQ8wHygjqqqEYqi6IEiaMnTxmiJ1qNAkiSnoijVgNKqqtYxJ4g/NL81FFiqquoGRVFaAgOArUCgqqp9FUWJ3YN/APKoqjrdvL74qw9RVbWHoiifAJ2B0cB4VVWfmpPD24F1L1jHMGCeqqprFEUZDPQA1qemXIlMAnqpqnrMWlMypMTDKx+hIXFX3sJDgpLcTuXh5U1oyD088nhjMMTw/NkTnBLdIlagcAnsHXJz+8YlipWyzgNLdm9eyYHt2oTtPiUrEPYgbnRgeEhQktEx7l7ehCcqi5v5FjIXN08eht3HzSMvD8Pu4+KqzU8aPyFUoVp9/lg4gSePwvDw8sbdy5tipX0BqPJaU7auW5Sm+N29vBPEHBYSjFui23pil/HwSrhttdcTlcVcXndLmbyoXKsx1y4GWJK3BkMMJw9tZ+jUlWmK9UU8EpUjPCQId4+kdSS5cqTEycUd+1wOVKmlzctb7fWm7N+etgd97N68kv3bzPWjVIUEo0e13z4V9cPjxfVD+0zS9RoM0RQpXpY83toDEXxrNubaxdM8fhjK7esqxc31pnqd5sye0CvVZdr37woOmuekLVqiYsLvDg3CNVH9cfPMn+DK+cN4ZQI4vHs9gSf20HvEAktHR6+3pc0nQyzL/DDqY/LmT//oVXdPb8Lib9fQpNvew1yfPbzyJ63nicoYG78hJpoFMwZQs/47VKmd8Gq4wRDDqcPbGTIlY/U8M/bR86f/wytfIcscyFVqN+GKeoraDd617MN2dva8/kZrtm2wTgLg+K7lnNqvPZwwv08lHoXFlelx2D2c3VN3O2FU5FMe3LnA799pD2t5+ug+a+f24v1eczP80LJX0Z4D3L5+gd9/GUOvYXNwdnEH4Ip6kjNHdxF4Yh/RUZFEPH/K0lnD+LRv+pLUmdEmvkp7Nq+w/BZFS1ZM5rdIug8k/S0yf3RqZrQtZ47uSlIXlswaRo26b3P/3g3GfKVd5IqOimD0l+8w9qe/M72cOYG3hwv3wuKmcwgKf0RedxfL308jI7l8J5ju32ltWsijJ/Sbs5Lve3ekgk/c7dUlCuQldy47Lt0JTvB6RmRl22JtGe2Thz0IYs6UAXTtO558+YsAcOvqBQDL3zXqNGWzv/VPLTy8vAmNd0wMCwnC3TNP0vI9CMIzfuwubly5EMDRA9tZtXQWz54+xsbGBjv7XISFBHPyyB5OH9tPdHQUEc+eMG+mHz37Jz/Ha3p45slHyIO4UXyhD4KTbHPPPFrcXnnyYTDE8OzpE5xdXDmw5198q72Ora0tbu6elCnry5VL58iXvxAxMTF8P3kYdRu+Rc06Gb+T5VXV8xc5e2IfRYqXw9XdK11lOLRtOUd3a33cQsUr8jA0rgwPw+7h6p6wDG6e3jyK18d9FBZkmR7hxP51tPhYe3BWxZrNWb9oJAAOuePO68pUbshfv47j6eMwnFys84C+zDj+X7lwmhtXzjG819sYDQYePwplxqhufDPOehcTM9LfzWqZ3eeys8+Fb82GnDqyS5K3GWAyZYNEfw6T3aZN+APIqyjKMmCQ+bXYxxCWAE6oqhoBoKpq7FMkAlVVjVRV9RmQ0tMMSgOHzJ+7Ajwwv14RGKIoyi5gCOAJ7AbumWOYyMsT3MfM/94wfx6gr6IoewF/tATzi5SJV8YDQGxWNzXliq+YqqqxsWTq446LlapA8N0bPAi6TUx0NEf2baFyzUYJlqlcsyEHd/4FwLGD2yhbqSY6nY4HQbctDygLCb5D0O1reOWzTmccoGHzjpYJ8n1rNebwnr8wmUxcvXCK3I4uSToqbh55yZXbiasXTmEymTi85y98a2gdpko1GnFo9wYADu3egG9N7fVH4Q8sV+uuXTqDyWjEycUdV/c8eHh5E3RHe8CAeuYQ+QuXSFP8PpZte4uY6GiO7d+Mb82GCZbxrdGI/3ZpcZ04uBWlYi10Oh2+NRtybP9moqOjeBB0i+C7NyhWqiKREc+IeP4UgMiIZ5w7dZCCRUtZ1nf+9CG8CxVPcKtpRvkkriP7tyRTjoYc3KXVkeMHt6FUrPnCuYN0Oh2+1RtyIVAbSXH+zCEKpHH7Nmze0fLgqso1G3Nod9rqx6Hdf1nqQaUajThk/h0O7YqrH741GiW7Xp+SFXn29BGPH2q3ml0IOEz+wiVxdHbl+bMnBN25ppXr9EHyF079fJD1mn3IoMlrGDR5DRVrNObI3g2YTCauXTxFbkfn5Mvk4Mi1i1qZjuzdQMXqWuznTu5jx18L6T7wxwTz8UZFPrdMAaKePoCN3pb8hRPOMZcWWv24nqCeV6rRKMEy8fe/E/9tpYy5nleq0ShRPb9OsVIVMZlM/DZ3NPkLFadJy0+SfOf50//hXbB4gtui0x+7dfdRjzz5uXbhNFGR2vyC6plDlgcjxc5Jp82ZuzPBvpsR1Rp9TJcR6+kyYj1lKr9JwH/rMJlM3L5ykly5XVKc2zaxXLld6Dv9EL0m7KDXhB0ULF7FKolbeDXteeiDu8yf3p9PvpyId8FilnW1/uhrvv15G+Nmb6ZLv6mUqVgr3YlbyJw28VVq0PxDhk5bzdBpq82/xQbLb+GQQjvjkOC32EClGpk3rUaszGhbWn/8NRN+2cb4OZvp2n8qSsVafNZ3EhWrN2Dygp2Mn7OZ8XM2Y2fvIInbeCr4FOJGcCi3H4QRHWNgy5FAGsabHsEltwO7Zgxi08Sv2TTxayoVL2xJ3N5+EGZ5QNmdkHCuB4VQ0Mt6yc+sbFusLSN98mdPH/PjhK9o0+krSpWrYlne3Ssvd25esfRXzp36jwJWeHBjYsVLlyfo7k3um2M/tO9fqiRqF6vUbMiBndrjTY4e2G6JfdjEhUybt5Fp8zbStOVHvNO2C01adKBd56+YsWAT0+Zt5ItvJlK2Uk2rJm4BSpQux707Nwm+d4eY6Gj+27uV6rXrJ1imWq367NnxDwCH9++kgm8NdDodefJ6c/a0Nv9tRMRzLl4IoGAhH0wmE/N/nEChwsVo8d5HVonzVdTzlzm6f1OGpkyo/ebHlgeJlavWhJP712Mymbh56SQOuV1wSXSh2cU9H7lyO3Hz0klMJhMn96+nXNXGlveundfunrhy7j+8vLVBCI/D71vO625dOY3JZMLR2XrtTWYc/xu+9QFT5m9l4txNDPx2Md4FfKyauE0Yd9r7u1ktM7Z5xPNnlj65wRBDwPF9aX4wuRAZld1G3hpVVR0GoCjKVrQpAGKTtJeByoqiOJhH3sYmnpO7LyWxi8BH5vUWB2Iv654FtqmqusX8nj1gr6rqRPPf84E3gChS3lbxv1+nKIon0A6oAXiZv4MXrOMi8BqwBm1O3AtpKFd81xRFqaqq6gmgVrzvtTq93pYPuw/h+3G9MRqN1G3SmoJFS7J+xRx8SpanSq1G1GvyHgt/8GNE71Y4ObvSY8BkAC6eO8Fm/8Xo9bbodDZ81HM4Lhl46NSLVKhan8Djexnb9x3s7B3o1Hu85b1Jg9ozbJr21PQO3f34bY4f0VGRlK9Sj/JVtXk+m77XjUUzB3Jwhz8eefLTbcAMQDvZ2/vvn+j1euzsc9Gl31RLY9++6zCWzBqGISaaPPkKJ/jO1NDrbenQfRg/fdsLo9HI643fo2CRUvy1cjY+JSvgW7MRdZq0YcmsEYz+8l0cnV3p1n8qAAWLlKJanWaM79cGG72ejt2HY6PX8/hBKL9M1eZ1MhpiqFG/BRXizb12bP9matS14pQJlnIMZZa5HHUat6ZgkVJsWKnVkco1G1G3SRsWzxrByC9b4ujsSvf+UyyfH97rbSKeP8UQE82pwzvpO3IuBYuUpE3nr1k8y49Vi6fh7OrBp33SPrVArArV6hN4Yi9jvnoHe3sHOvWJ+60mDmzP8Ola/ejYw49ls+PqRwVz/WjWphsLvxvIAXP96G6uHymt10avp03nb5g1rgeYTBQpUZ66Tdqi19vy0eejWTBdm0/R0cmVTr2Tf4Luy5Sv2oBzJ/cyod/b2OfKTcfP48o0bWhbBk3WRmC06zqSFT/7ER0VQbkq9SlXRTvpWLtkAjHRUcydqN0a6VPKlw+6j+bJo1B+nvQ5Op0ON09vPu6dsVvk9XpbPug2nNkTemE0Gnj9Da2eb1w5m6Ily+Nb8w3qNG7D0h+HM/rLd3BydqNr/Hr+ejO+7f8eNjZ6Opjr+aVzxzm8ZyMFi5Zm4sD2ALT6qC8Vq2llO7Z/s1UeyJcZ+2jxMr5Ufb0pkwZ1xEavp0jxstRr2g6AxT8M48mjMEwmE4WLKXzYc2SGy5BYiYoNuRywm3mjmmJrn5sWn0y0vLd4Qmu6jFgPwM61Uzl7ZCPRUc+ZPawBleu2p967X1k9nuRkVnu+afXPPH0Szh8LtOlXbPR6hky23l0IsTKrTVyzbCZH9m4iKjKCoT2bUbdJG1p2SP3I/fSoULU+Z4/vYVzfFubfIi4xMnlQO4ZO00ZJdeg+0vxbRFCuSj3KV9X2xVOHt7N60USePArj58m9KVSsLH1G/GKV2DKjbcnuqiybgVfDWtjn8aDx1d1cHPcjNxcnndv+VbPV2zC0w9v0mrUco9FE6zpVKFUwH3M27KS8T0EaVVZS/OyJSzdZtGUltnobbHQ6hn3YAg9nxxSXz4isaFsWfz+Yi2eP8uRxOH5fvEmLD3pTp3H6HoyYkT75zn9WEnzvJn+vms/fq+YD0G/UXNw989GyQ0+m+XVHb2uLV94CfPZV+vtbL4q9U4/BfDf2S4xGA/WatKZQ0ZL4/z6XYqXKU7VWQxq82Zr5349kaK/WODm78fk3E1++4kym19vy2ecDmTLma4xGIw3ffJfCRUuwevk8ipcqS/XaDWjUtCVzvxvLgJ7tcHJx5atBWr1q2qIdv/zwLUO+/AgTJho2eZeixUujnj3Jvp2bKOJTkmFfdwagQ+deVKlR50WhpFpm1fNH4Q+YOrQjEc+fotPZsOuf3xjx3TpyOzoTFfmc86cPWq3fUqZyQy6c3sPMwW9hl8uB97vF1YXZI9vQZ7x2N17LT0azdsEwoqMiKeNbn9K+DQB4r8s4/lk+EYPRgJ1dLlp10fragUf/5fCOFdjobbGzy8UHvWZYNQGZWcf/zJaR/i6AX6+3iXj+xBL3VyN/pkCRkqxdNpOje/8hKjKC4T2bUqfJ+7xr5X5LZmxzZxd35kz+mpjoaIxGA0qlWjRo1s6qcQvxMrrk5+TJGoqifAD0Mf95F23+2e9VVT1qfv9jtDlvnwF/os15+5Oqqu+a3w9QVTXZ2d3jzXl7BmioqmpZ87QGv6AlWXVo0zLcBsajjXaNADqizV273BxTd2BfvDlvA1RVXa0oSiO0pO1XwFogL3ACaKaqqqIoSrEU1lEQbc5beyAEbeqFvGkoV+yctxXRppkIRRtZfDJ2ioaX2R34LPtUgjSIisn+J1XJsbHJkZsbAJs0X1PIHgym7HaTQepFxeTM2O1tU3PDQPak0+XMen79fuY8tOJVKOIVmdUhpIutTc6t59HGHHoMzaH7J0Bkdd+sDiFdGv87KqtDSLd9njnz5Nre1vDyhbIpW13ObBdz6aOzOoR0C4vInIsbmS3smV1Wh5BueZ1zZr/FmEXzz1tDTj0PBXijUu6cu+EzyRE1PNv/oDUV92z1u2Wr5K3IGpK8fbUkefvqSfL21ZPk7asnydtXT5K3r54kb189Sd6+epK8ffUkefvqSfL21ZPkbdaQ5G1SkrxNu+w2bUKGKYrSGEjcy/xKVdUzWRGPtfyvlksIIYQQQgghhBBCCJG8/7nkraqqO4AdWR2Htf2vlksIIYQQQgghhBBCCJG8/7nkrRBCCCGEEEIIIYQQIvsxmbLVjAQ5Qs6cTFEIIYQQQgghhBBCCCH+x0nyVgghhBBCCCGEEEIIIbIhmTZBCCGEEEIIIYQQQgiR6YxZHUAOJCNvhRBCCCGEEEIIIYQQIhuS5K0QQgghhBBCCCGEEEJkQzJtghBCCCGEEEIIIYQQItOZTLqsDiHHkZG3QgghhBBCCCGEEEIIkQ1J8lYIIYQQQgghhBBCCCGyIZk2QRAZkzOrgaNddFaHkC6GHHyLQIxRrveI/3059Tae8MemrA4h3Up6G7I6hHR5Hp0zj5+Qc+u5jT7n1vPG/47K6hDSZUezcVkdQrrZH22X1SGkS05uW2xyZtOCiRwaODn33CIqJmfGDRBt1Gd1CP/vGMm5x3+RVE5uc7OKZGKEEEIIIYQQQgghhBAiG5LkrRBCCCGEEEIIIYQQQmRDOfeeHCGEEEIIIYQQQgghRI6RU6fvykoy8lYIIYQQQgghhBBCCCGyIUneCiGEEEIIIYQQQgghRDYkyVshhBBCCCGEEEIIIYTIhmTOWyGEEEIIIYQQQgghRKYzIXPeppWMvBVCCCGEEEIIIYQQQohsSJK3QgghhBBCCCGEEEIIkQ3JtAlCCCGEEEIIIYQQQohMZzRldQQ5j4y8FUIIIYQQQgghhBBCiGxIkrdCCCGEEEIIIYQQQgiRDcm0CSLVTCYTaxZPJvDEXuxzOdCp97cUKVE+yXI3rgTy22w/oqMiqVC1Pm27DEWn0/H0yUMWzxxI6P07eOYtSNf+03F0duPZk4csnzuKB0E3sbXLxce9xlGwaGmrxn7m+H5WLJyOyWig/pttaNG2S4L3o6OjWPjDSK5fPoeTiztfDJxMnnwFuXIhgF/nfquVHxOtO3xOtdcaA7D1r9/Zs9UfMNGgaRuatvzYqjEDBBzfzx+LpmE0Gqn35nu8/X7XJHEv/mEk16+cw8nFjZ7fTCFPvoKcPfkfa3+bRUxMNLa2drT7tB9lK9UCYPrI7jwMe4CdfS4A+o2ai6u7p1XiDTyxn1WLp2AyGqnTpA1vtemWJN6lP47g5pVzODm70W3AVLzyFQJg89qFHNzhj87Ghg+6DqF8lboA7Ph7Ofu3rQGTibpvtqXxu50SrHPr+qX4L/uOqYt24ezqkYZY97F68RSMRiN1m7xPs2Ri/fXHEdy4chYnFze69Z9miXWL/wIObPfHxsaG9l2HWmJNaZ2/zRnNjSuBmEwm8hXwoXOfb3HI7QjAsQNb+OfPuaDTUdinDF36TUl1GRIzmUz4L53EuZN7sbN34MNeEyhSPOk+evNKICt+9iM6KoJyVerT5tNh6HQ6NiyfTuDx3ej1tuTxLsKHX3xLbidXDDHRrJw3mtvXzmEwxFCzfivefK9HuuME62//6KhIZo7qQkxMFAaDgaqvvcm7HfoAsPiHody4HIje1hafUpX4qOdI9LZ22SZ2gGVzRhFwbDcubp74feef5Du3bViC/7LvmLJwd5rqeWqYTCYObJjAjfN7sLVzoNEHk8hbuELCMkU9Z9tv/XgUcgOdjR6fcm9Qu8U3AJzes5hzh1djY6PHwdmTRu0n4OJRyKoxxmftdjHi+VOmjohbR1hIMK81aEGHboOsGrfJZGLtksmcPbEXu1wOfNwr+WPozSuBLJ+jHUPLV63P+59px9ATB7ewefVcgm5fYcCEFRQtqf1GMTHR/DFvLDevBKLT2fD+Z0MpXaGm9WNfOolz5tg/ekHb8vtcc9tStT7vm9uW9b+Z2xbbuLbF0cmVp4/DWTyzPzcuB1Cr4Xu06zoi3fGtWjyFwONa/6Rzn/EUTa5/cvksy2b7ERUVSYVq9WnfZYjWP3n8kEUzBxFy/w5eeQvSbcB0HJ1dU1zvhYDDrF4yzbLeoDtX6dpvKpVrNX5he59e+wMvMfXPLRiNRtrUrUrX5vWSXW7rsbMMmr+a5cO6U8GnIGeu3mb88o3mjQRfvNuQxlXLZigWa/KdP5F8LRoRFRzCnqotsyQGk8nE6sVTLH3bzr3Hp9C31epObN+2XWzdeaLVndi+bbf+Wt25EHiEeVO/trT9VWo34e12XwCwY+MyDuxYi04HBYuUplPv8ZY+mTXKk5F2Zv1vMwg4tgu9rR15vIvwUa/xODq5WiW2+DG+6vOJ0X3eIpeDIzY2emz0egZP/iNDZUjveUWskPt3Gdm3Ha06fE7z9z4BYNGPYzh9dC8ubp6Mn7UqQ/GlVk6oLynFvWXFBC6d2YOdvQOtuk6igE+FJMvtWDuTMwfX8/zZI4bOPm55/fqFI/y7chJBt1Te7zmD8jWaZ3q8GanzJw5u4Z9V2vF/4MS44//Tx+Es/G4A1y8FULtRaz7olr5jaFaWwdoxZkZ7Huv6pQCmj+hE1/5TqfpaMwDW/TaTwBN7AGje9nOq18ncuvS/xIQuq0PIcTJ15K2iKMUURdmYjs8FpHH5Koqi1In39+J0fGeG15HCentbYz0vWH+SOBVFaaQoyk/W/q6zJ/YSfO86o2b9Tceeo/ljwbfJLvfH/G/58PPRjJr1N8H3rnP25D4Atq5bSJlKtRk162/KVKrN1nULAfjXfwGFipVl2PS1dP5yAmuWpD95lRyjwcDyeVPoP/JHxs9aw6F9m7lz80qCZfZuW4ejkyuT5m6gacuPWf3rDwAU8inJyOm/MWbmSvqP/Ilf507AYIjh1vVL7Nnqj9+0XxkzcyWnju4l6M4Nq8f9+/zJ9PX7ibE/rOHI3s3cuXk5wTL7t63D0dmFCXM28GbLj1lrjtvZ1Z0vh3/PmO9X0eWrcSz6wS/B57r1m8Co7/5g1Hd/WC1xazQY+GPBRL4cMYeRM/05um8zdxPFe2C7P45Oroz9aSON3+2E/2/fA3D35mWO7d+M38y1fDliDivnT8RoMHDnxkX2b1vDkMnLGT5jFWeO7SH47nXL+kIf3OP86YN45imQ5lj/XDiRPiPmMnLmOo7u35Qk1oM71uLo7MrYn/6m8budWZckVn/6jJjLHwsmYDQYXrjOtp8NYvj01YyYsQbPPAXYs3kFAMF3r/Ov/0K++fZXRs70p12XwWnb6ImcO7mX+/duMHzmP3zQYwyrF45PdrnVi8bzQffRDJ/5D/fv3eD8KW0fLVPpdQZP9WfwVH/yFijGtvULADh56F8MMVEMnurPNxP/5MD2VYTev53uODNj+9va2dN39AKGT1/N8Gl/cvbkfq5eOAVAzfrvMOqHDYyYsZboqAj2b1+brWIHeK1RK/qMmJvsd4Y9uMf50//hkcZ6nlo3z+/h4YPrdBy8hQZtx7HPf2yyy/k26EKHQZto+/Va7l07zo3zWmfVq2A53u+7mvYDNlCi0lv89/f0TIkTMqdddMjtZGkPR333B155C1DVfJHOms6e3Mv9e9fx++FvOvYYzaqFyR9D/1zwLR16jsbvh7+5f+8658zH0AJFStP1m5mULFc9wfIHt68GYOh0f3r7zWPdMi2xbU3nTu7l/t0bjPj+Hzr0GMOqBcm3LasWjueDHqMZ8f0/3L97wxK7Uul1hkzzZ8hUf/LmL8a2dVrbYmtnT4sPvqJ1p4EZii/wxD7u373OmB838tHno1g5P/ltu9LcPxnz40bu343rn/y7biFKpdqM+XEjSqXa/Gvun6S03jIVazF8+iqGT1/F16MXYG/vQLnKrwMpt/fpZTAambRiE7O//Ii1o3uz+Uggl+/cT7Lc04hIVuw8TKXicRdOShXKx+/DevCn3+fM7vsR43/fSIzBunUjI24tXcvhd7tnaQxnT+zj/r3rjJ61kQ97jmLlS/q2o2dt5H6ivq1SqTajZyWsOwAly1Vj2LRVDJu2ypK4DQ8NYvem5QyevIIRM/wxGo0cO7DZeuXJYDujVHqdodP9GTptLfkK+Fj2VWvKqvOJvqMXMXTa6gwnbjNyXhFr5aIZVKxaN8FrdRu3pP8oq5++vVBOqC/JuXRmD6HB1+kzcQvvfDKOf35Lvt9SpvIbdB3xZ5LX3TwL0KrLJCrWfjezQwUyXucLFClN94FJj/+2dva80+FL2nTO2DE0K8tg3Rgzrz03Gg2sXz6TclUs6SICju/h5tVzDJ26ioETlrNtwxKeP3uSaeUTwmrJW0VRsnIKhiqAZU9SVbXLC5bNzHUkJ1OTt1aM86XOHN1JrQat0Ol0FC9TmedPH/MwLOEJxMOw+0Q8f0LxMlXQ6XTUatCKM0d2aJ8/spPaDVsDULtha04f2QnA3VuXUSrVBiB/oRKE3r/No/AHVov7ysUA8hUoTN78hbG1s6NWvbc4cXhXgmVOHt5FnTe0A3iNOk04d/oIJpOJXLlyo9drA9Sjo6PQ6XTmmK9SUqlkeV+pUJ3jh3ZYLWaAq5cCyFegiCXumvXe4lTiuI/s4vU3tNEq1V9/k3NnDmMymShaoizunvkAKFi0JNFRUURHR1k1vsSuXQogb/4i5PHW4q1etzmnjiSM9/SRnbzWqBUAVV9vimqO99SRXVSv2xw7O3vyeBcmb/4iXLsUwL1bVylexhd783YuXb46J+Nt5zVLptGmc3/Qpe3KnRZr0QSxnj66M1Gsu6jd0Bzra01RAw5hMpk4fXRnoliLcu1SwAvXmdvRGdCuCEdFRVji3b9tDQ2ad7Bc1XVx80pTORILOLaTmvW1fbRY6co8f5bSPvqUYuZ9tGb9Vpw5qm3Tsr51LfXdp7Qv4aFBAOjQERn5HIMhhuioSGxt7ciV2zndcWbG9tfpdJbRbQZDDEZDjGU7V6xWH51Op22XUpUIDwnKVrEDlC5fAydnt2S/c/WSqbzXqb+l/bG2a2e3U6Zaa3Q6Hd4+VYh8/oinj4ITLGNnn5tCpV4DQG9rT55C5Xn68B4AhUq9hp19bgC8i1a2vJ4ZMrtdDLpznccPQyldvprVYw84spOa5mNosVQeQ2vGO4bmL1wC74LFk6z33q3LlDEfQ13cvMjt5MrNK4FWjf3M0Xixv6RtSRB7bNtSOa5tKVbal4fm0izQtwAAIABJREFUtiWXgyMlylbD1i5jow5PH9lJ7YYtU9U/KaFURqfTUbthS04d3hn3efOxqXajVpw6vCPV6z3x31bKV62HfS5tH0ipvU+vgGu3KZLPg8J5PbCz1fNWzQrsOq0mWW72hl181qwO9rZxN9XltrfDVq91z6OiY9Bls1EuofuOEh36MEtjOH10J7UapLLulKls7tu2tPRhtTpirjsNW3H6yMv7ggajgeioSAyGGKKiInDzyGu18mS0nSlbuU68fkDlDB0vU5JTzydiZeS8AuD4oZ3k9S5EoaIlEnxGqVAdJ5fk+wGZJSfUl+RcOLkd39e1fkvhklWIePaIx+HBSZYrXLIKLu75krzunqcw3kWUTOtXJZbROp/S8T+XgyMly1bD1t4+x5bBmjKzPd+96Xcq126Ki2vcoKd7ty5TunwN9Hpbcjk4UthH4dzJ/ZlaRvH/W5qmTVAUpQSwDIgGngHB5n+LA36KoihAXyASWAlsAjwURfkV8AXmq6o6O4V1zwBeB07FxqUoigewAPAAIoBPgRhgNVriWQ+8DXwNuCqK8i7wPrBHVdWKiqKMAQoB+YHCQHtVVS8pijIZqGle7zhVVde9YB35gaWAA3Af+ATIBywHLr+oXIqifA0UVxRlF7AI+Ap4TVVVg6Iog4AgczmaA87mWHupqvqfoiiVge/MZVSBL1RVTfJMPkVRAsxxFgJ+B54DNwCrZ+rCQ4PxyJPf8re7lzcPQ4MTdDofhgbj7uWdYJnwUO1g+vhhiGVZN4+8PH4UAkAhH4VTh7ZRsmw1rl06Q+j9u4SHBuHqnsdKcd/HM17cHl75uHoh4eDusJC4ZfR6W3I7OvPkcTgurh5cuXCGxT+NJeT+Xbp/PR693pZCRUviv3w2Tx6FY5crF6eP7aNYqaS3ZWQo7pBgPBNty6sXA5JZJvm4Yx0/uI0iJRTs7OIO7Et+GoONjQ3VXmvCO+17WKXzkrh+eHjl49rFMykuExvv08fhPAwNolgZ3wRlDQ8NpkDRUmxY8SNPHodjb5+LwBP7KFpS286nj+zCzTMfhYsp6Yg1CI/429bTO5lYg/DI450k1vCQYIrHj9XT25LkfNE6l80eSeCJveQvXJK2n2pXyGNHEc/w+wSj0UCL9r2oUDX522FT42FoEO5e8fZRT28ehgYl2keDcPOMi9PNy9uSSInv0C5/qr6m3fpTuXZTAo7tYHSvN4iOiqB158EpJhpTI7O2v9FgYPKQjty/d4OGzTtSvLRvgnUaYqI5vOcv2nUZku1iT8npIztxT2c9T62nD4Nwco8b1evknp9nD4Nwck16wgMQ+fwR18/tpFK9T5K8d/7IaoqWbZBpsWZmuwhwZN9matRtlikndOFhwQn2T7eUjqGeCetXeFjSE9L4CvkoBBzZSbU6bxMeco9bV84SFnIPn1KVrBb7w9AgPFLRtiSOPcW25XXr3lao9T0S9k/CE23b8GT6Jw8t/ZPQRP2T0FSv99j+TTRumXBfSK69T6/gsMfk94hrb73dXTlzNeGdD+dv3CUo7CENfMuwdOvBBO+duXqL0b/+xd3QcCZ81saSzBWa5Pq2qak74S+pOwBXL5xi0qB2uHnkpU3nbyhQpBTunt40afkpI3s1w97egbKVX6dc5biRXBkujxXbmUM7/ala5y2rxWaJMUvOJ3TMnvA5OqBu0/bUfbN9BuJP/3mFvb0Dm9Yu4Zsxc9my/td0x2AtOaG+JOdxeBCunnH9FleP/DwOD0o2UZsdZLTOZwc5oQyZ1Z6HhwZx6vAO+o5ewPK5cft6IR+FTat/pvG7nYmKjOBC4GHyF054UUakzGTKXheUc4K09uAaAWtUVW0ExN5noKqq+jZwHRgINFJVtSEwz/x+IaAH2qjWPsmtVFGUakBpVVXrANPQkq0AQ4Glqqo2BuYCA4CqQKCqqm8ADdGSxz8Ac1VVbaSqamii1YeoqtoSmAl0Nr82XlXVJkATYJT5tZTWMQyYZy7TYXNZUlUuVVV/AK6a1/krsB1oZn77fWCN+f8dVVV9F2gLTDW/9j3wsXlbP4n3uZQMAWapqtocSP/9zC8Qe8U4Ad3Ll3nZSI+m73Xj2dNHTB7Ujj2bfqdw8bLY2FhvOubk404cU8pxlyhTifGzVuM3dRn/rFlMdFQkBYuU4O33P2PG2N7MHPclRYqVQa/XWy3m5CNKsrkxJRd3vLLduXGZNctm0emLuGkTuvWbyJjvVzF4wiIunjvBf7vSPLNJCgGnYjunsEzyL+soULgETd/rwo/jPuenb3tTyKcMehtboiKfs3nNfFp2sN7A9sTJmuRi0srz4m2e0uud+4xn4i/byV+oOMcObAG0ZOP9uzfoN2YhXb6ewu8/j+HZ00fpLkOy+18q6nri32mr/y/obfRUr6c189cvn0Fno2fsnB34/bCZXX8v5UHQzXTHmRxrbH8bvZ7h01cx4ZetXLsUwJ0bFxMst3LBBEqVq04pK9+ylRl1B9Dq+dr5lrl7X6kU4jIaYtj++zdUrNsZV68iCd67cHwD928FUrlht2Q/aw2Z1S7GOrJvC7XqZ9J8Zcnun4kXSfsxtPYbbXDz8mbGsI6sXTqFYmUqo7ex7vEodXX35cv86/8LNvq4tsVaUrXdUtGHSet6H4bd586NS5RPlHxLrr1PrxSbEzOj0cS0Vf8yoG3y3cRKxQuzdnQvlg/tzsLN+4iMjslQPP9zUnPcTLYevFiR4uUYP2cLw6atpmHzj5g3rR8Az5484syRnYydvYkJv2wjKuI5h/dYqR+WUqzpaGf+XTsPG72eGlbeV1P6/sw+nxgw/leGTPmTXsPnsmfLSi6dPWrl+FN3XrFu5c80a/VxhufBtpocUF+Sk3wVyr6JoMyq869SjihDJrXna5ZMpfXH/bBJ1LcqV7kO5avWY4bfJyz+YQjFy1TGxsr5ACHiS2uG7A+0EbbLgNjLDv+Z/y0BnFBVNQLAPLoUtERrJICiKClNtFUaOGT+3BVFUWLvcakI1FMUZYA51kBgN/CaOYZbxCVfU3LM/O8NtNG2AH0VRWkBGIAiyX4qThniEqoHgI/M/5+aciW2CPjWXL5AVVWfmrdR/LLHDg2qCKw0v+9M3PZOSWkgdnKnQ8Ql1zNkz+YVHNiu5ZiLlqxI2IO422HDQ4Jw80h4hdPdyzvBLTPhIUG4eWpXsFzcvHgYdh83j7w8DLuPi6t2i3huR2c69TY/FMxkYsyXzS0PeLAGD698hMaLOywkGHfPvMku45nHG4MhhufPniS5dalgkRLYO+Tm9o3LFCtVnvpvvkf9N98DYM1vPyYYjWe1uBNty6RxexMacg+P+HGbR0SGPQhizpQBdO07nnz5iyRYL2jzPNau/zZXLwVabjHOCHcv7wT1IywkONn6EfbgHh5eCePVXk9Ub8xXPus2eZ+6Td4HYP3yWbh7eXP/3i0eBN9mwsAPLMtPGtyRwZOW4+bx8hHb7p7ehMXftqFx9TSWhzkmD6/8SWMNuZfws+ZyvmydNno91es0Z9uGJbz+xnu4e3lTrLSv+UEPhclXsBj3797Ap1TFl5Yh1r5/V3BwhzbnZdESFQlPFJtrot/AzTN/gtFwDxPtx4d3ryfwxB56j1hg6fAc3/+PdtuzrR0ubl4UL1OFm1cCyeP9suYzeZm1/WM5OrlSukINzp7cb3lYyd+r5vLkURgfDnzZISNrY4/v/r2bhATfZuIgbYRQeEgQkwd3YNCk31NVz18k4MByzh/SHoiSt0glnobftbz3NPwejimMut2zZhRueXzwrf9pgtdvXTzAiR0/0+qLZehtM+/2vcxqFwFuXlUxGAz4lLTeXRR7t6zgYLxjaPz982FI0v1TGwGSqH695JZqvd6W9z+NG00+c2Qn8hbwsU7ssW1LyYpJ6m5ybUvi2F0Tty3H99DHb4FVRjbv3rxSe5gl4FOqQsK2LyTpfpls/8Qcn4ubZ6L+iWe8z6S83uMHtlC5VuNkH4CYuL1PL28PF+6FxU0tEBT+iLzuLpa/n0ZGcvlOMN2/WwpAyKMn9Juzku97d6SCT9wDkkoUyEvuXHZcuhOc4PX/j3ZvXmnp2/qUrJBM3zYVdcfzxXUndvoMgArV6vPHwgk8eRTGhcAjeOUrbFmucu0mXL1wkloN0t9tt3Y7o+2ru+kz0jr7KmT9+UTc7+VF5ZpNuH4pgFLla6SrLBk5r7h64QzHDmxj1dIfePb0MTobG+zs7WnSomO6YkmPnFBfknNkx3JO7NX6LQWLVeJRaFy/5VHYPZyz2ahba9b5rJITyvAq2vMblwNZ/IPWz3ryKIzAE3uxsbGlcq3GNH+/J83f7wnA4h+GkC9/xvtfQqQkrSNvjaqqDlNVtTPwJmCPlgAFbQqByoqiOECCOXCTGzSQ2EWglvlzxYHYs9KzaNMaNFJVtR7atAP2qqpONMeQB3gDbYqAlBLR8b9fpyiKJ9AOaGD+N/b9lNZxEXjN/P91gAtpKFeC5VRVvYA2VcNAtERurPhlDzO/dgZoZy57DeC3l3yPZRvG+zfDGjT/kKHTVjN02mp8azXm8J4NmEwmrl44hYOjc5IG0c0jLw65nbh64RQmk4nDezZQqcYbAFSq0YhDu9cDcGj3eirV1F5/9vQRMTHRABzYvoaS5aon6PRmVPHSFQi6e5P7QbeJiY7m8L4tVKnZMMEyVWo25MBObeTD0QPbKVupJjqdjvtBtzEYtFEqD4LvcO/2NbzyabfpPArXBmiH3L/L8f92UtvKo7WKlapA8N0bPDDHfWTfFirXbJRgmco1G3Jw518AHDu4zRL3s6eP+XHCV7Tp9BWlylWxLG8wxPD4kVbFYmKiOX10D4WKlrRKvD6WeG8REx3Nsf2b8U20nX1rNOK/XRsAOHFwK0rFWuh0OnxrNuTY/s1ER0fxIOgWwXdvUMycwHz8ULsdLvT+XU4e2k7Nem9TyKc0Uxft4tu5m/h27ibcvbwZNnVlqhNaWqzXE8RaqUajBMto9dUc639bKWOOtVKNRolivU6xUhVTXKfJZCL4rvYwO5PJxJlju/AuVEzbHjXf4GLgYUDrDATfvY6Xd+E0bfd6zT5k0OQ1DJq8hoo1GnNkr7aPXrt4itwp7KO5HBy5dlHbR4/s3UDF6tq+eO7kPnb8tZDuA3+0zN8I4JGnAJcCtXlDIyOecf3S6QzNW5UZ2//xw1DLqOWoyAjU0//hXUiLcf/2NZw7eYAuX0/BxiZjtwxnRuwpKeRThikLdzN+zmbGz9mMu5c3Q6f+keHELUDFOh/Trv862vVfR7EKTbhwfD0mk4mg6yexz+2S7JQJhzd/T1TEY+q0HJ7g9Qe3z7J3zWiafzqH3M4Zm7f5ZTKjXYx1ZN9mq4+6rf/WhwyeuprBU1dTqWZjjpiPoddecAzN5eDENfMx9MieDVQ0HytTEhX5nMiIZwCcP30AvY2e/IUz3q7Xf+tDBk9Zw+Apa6hUI17sqW1b4h3/z53cx/YNC+kxKGHbkhENm3e0PDSscs3GHNr9l6V/ktvRJfn44vVPDu3+C9+a8fon5mPToV0bLK/71mj0wvUe3b+JGvXetvz9ovY+vSr4FOJGcCi3H4QRHWNgy5FAGvqWsbzvktuBXTMGsWni12ya+DWVihe2JG5vPwizPKDsTkg414NCKOjlnqF4/hc0bN7R8iAxrW+btrpzeM9f+Cbo25rrzu64uvMo/IFlFNq1S2cwGY04ubjjmSc/Vy+eJiryOSaTCfXMIbwLZew2W2u2M+dO7mPb+kX0GGy9fRWy9nwiMuIZEc+fAhAZ8Yzzpw9QoGipdJclI+cVQycuYuq8v5k672+atvyId9p2faWJW8gZ9SU5NRt/TM/R6+g5eh1K1SacPqj1W25dPolDbpdsN2WCNet8VskJZXgV7fnY2ZsZZ/6v6mtN6dB9BJVrNcZoNPDkcTgAt69f4M6NC5Q1P7xUvJzJlP3/y250yQ6BT4GiKB8QN0XAXbT5Z79XVfWo+f2P0ea8fQb8iTbn7U/mKQEs87OmsO7YOW/PAA1VVS1rHoX6C+CFNqJ9PtqUAOMBI9o8uB3REqLLzTF1B/bFm/M2QFXV1YqiNEJL1n4FrAXyAieAZqqqKoqiFEthHQXR5ry1B0LQpl7Im4Zy/Qq4oE3/sE5RlE+B4aqqKub3PwNaoI2uzQ/0jjfn7XS0hLIR6KuqapInkCQz520EcBOIUFX1y+RiSuzfU1GpqgQmk4lVCydw7tR+7Owd6NT7W4qWrADA5EHtGDpNG6Fz43Igv83xIzoqgnJV6tG+63B0Oh1PH4ezaOZAwh7cxSNPAboOmIGTsxtXL5xk2U8j0NnYkL9wST7+YiyOqZhP09EuOjVhA3D62D5WLpyO0WikXpNWvNu+O+t+n0uxUuWpUqsh0VGRzP9+JDevnsfJ2Y3Pv5lE3vyFObBrI5vWLkGvt0VnY0PLD3pQrbbWkE8e3pUnjx+it7WlQ5cBlPetnapYDGmY3+XMsb38sUiLu26T1rzTrjvrV8zBp2R5qtRqRHRUJAt/8OPmVRUnZ1d6DJhM3vyF+XvVfDatXUS+AkUt6+o3ai65HHIzza+b9kAno4FyvrX54LNvUn2LR4zxxYmvgON7Wb14Kkajkdcbv8fbbXvw18rZ+JSsgG9NLd4ls0Zw69p5HJ1d6dZ/KnnMycpNa+ZzcMc6bPR62n82mArVtLlfZ/h9xtMnD9HrbWn76UDKJrOd/Xq9zdApv+Mcb07L+JKbUyfg+F7WLJmK0Wjg9Tfeo3nbnmxcOZuiJcvjW/MNoqMiWfrjcEud6Bov1s1r5nFw5zpsbPS06zKYClXrp7hOo9HIzFGfEfHsCSZMFPJR6NjDj9yOzphMJtYunc7Zk/uxsbHhrbY9qFH37QRxRsWkPtloMplYs3gC50/twz5Xbjp+Pp6iJbWmadrQtgyarF2ZvnE5gBU/x+6j9Xn/M20fndDvbWKio3B00U7ufUr58kH30URGPGPFz37cu3UZMFGr4Xs0btn1hbHY2774hgRrb//b1y/w609+GI0GTCYj1V5/ixbttad7f9WhKp55C5DLwQmAKrWbWN5Lj8yoO4u+H8zFwKM8eRyOq5sn73zQmzrmEeexRvZuzpDJK1Ks56evpm/Eq8lkYt+68dxS92Jr70Cj9hPJW0SbL3X1zPdo138dT8LvsXxiI9zzlUCv176nQp2PKVe7PRvndSH03gUcXbQOsrN7AZp3mZumGKqXep7qZa3dLrq6ayMrhvd6l69G/EiBwqm/MPE8OvU3MJlMJlYv0o6h9vYOfNQr7hg6dXA7Bk+NO4Yun+NHdHQE5avUo20Xbf88dXg7axZP5MmjMHI7uVDYpyy9RvxCSPBtfp74BTqdDjfPfHz4xTg88758ZGVa5hqLbVvOndTalg+/iGtbpg5py+ApcW3L73Pj2pbY2L/9OmHbUqy01rYAjP2yGZHPnxATE01uJ1d6DZ/3wuSznT5p22Iymfhz4UTOntS2bac+4/Exb9uJA9szfLo2Wuv65UCWzfYjOiqS8lXq8UG3Yeh0Op48DmfhdwO1O0Py5Kf7gBk4ubi9cL0hwbeZ4fcp3/78r+Wi0Ivae4C6IatTvc3j23vmItNWbcFoNNG6ThV6tKjPnA07Ke9TkEaVE86H3W3GUga0a0oFn4Js/O80i7bsx1Zvg41OR893GtC4Stk0f/+OZuPSFffLVFk2A6+GtbDP40FkUAgXx/3IzcXp20YpsT96+oXvx/7GcX3buN940qD2DJsWV3e0vq1Wd9p3jas7Wt9WqzvdzH3b3ZtXsPffP9Hr9djZ5+L9TwZRQtEuGv3952yOH9iCjV5P4WLl+OiLMUnm305LXzFxeTLSzozv24KYmCicnM39gNK+dOiRtjtWbFIxHcmrPJ94EHST+dO1aSuMBgM16rXgLfNIufhexXlFfOtX/kwuB0eav6fNmf3LjGGogcd48igcV3dPWnf8wnKH38s8iUo6+j81srq+PHic/rg3/z6eywFav6VVl4kULKb1W+aNfY+eo9cBsG3VNAIOb+RxeDAu7vmoWq8dDVt/xZ2rZ/hzzpdEPH2ErZ09Tm556TUubdOX5HNNfX3JaJ0/dXg7qxfFHf8LFStLnxG/ADC6z1tEPNOOoY5OLvT2m0cBK1zAfZVlSC3dS8bOZVZ7Ht+y2X5UrN6Aqq81IzoqkilDOgDg4OhExx4jKVws+WNs08q5ss8cGNnEzjPPs2F6NKE3KuXOVr9bmpK3IuMURekMFFBVdar578+APKqqTs+qmFKbvM1u0tLJyk7S2yHPDl6WvM2ucvKE6GlJ3mYnL0veCutLb/I2O0hL8jY7SUvyNrvJqe1icsnbnCK9ydusllnJ21fhZcnb7Con9xVflrzNrnLqeQWkP3mb1dKbvM0O0pK8FdbxsuRtdibJ26QkeZt2r/ysQ1GUxiSdp/YrVVXPJLd8TpGacimKMgh4j3TMR6soSnlgTqKXJ6iqujWt6xJCCCGEEEIIIYQQ4lUzZqMH8uUUrzx5q6rqDmDHq/7ezJaacqmqOg2Ylui1Jalc/1mgUTrDE0IIIYQQQgghhBBC5DA5934/IYQQQgghhBBCCCGEyAKKonwOfApEA91UVb0U771SwELADu05WGmb7DmenDmZohBCCCGEEEIIIYQQQmQBRVE8ga5AfeAbYFKiRSYDA4AGQFfz8ukiI2+FEEIIIYQQQgghhBCZLqc+ODcZtYGdqqoagKOKopRJ9H5pVVWPASiKsguoBWxOzxfJyFshhBBCCCGEEEIIIYRIPQ8gPN7fibPS8f8OA2TkrRBCCCGEEEIIIYQQQmSEoihjgNGJXh6rquqYeH+HAZXi/W1MtHz8v92Bk+mNR5K3QgghhBBCCCGEEEKITGcyZXUEL2dO0o55yWKHgFGKouiBysDFRO9fVBSlGnAKaAhMTW88krwVQgghhBBCCCGEEEKIVFJVNVRRlKXAXiAa6KYoSnPATVXVP4BhwELADliiqmpoer9LkrdCCCGEEEIIIYQQQgiRBqqq/gz8HO+lS/Heu4Q24jbDJHkrhBBCCCGEEEIIIYTIdKYkz/USL2OT1QEIIYQQQgghhBBCCCGESEpnygkzBYtMte10ZI6sBFJ1xf8HNjY5s6KbTDn3aqoxB8cuRGrpbRI/DDdnMBhz7rgDHTmzPc/Jomr4ZnUI6eJw/FRWh5BuOfn4L14tnU7axFdN9s+s8aZvLtnwifx7KirbNwDNKttnq99Npk0QQgghhBBCCCGEEEJkOmO2T91mPzl3+IIQQgghhBBCCCGEEEL8D5PkrRBCCCGEEEIIIYQQQmRDMm2CEEIIIYQQQgghhBAi08n8y2knI2+FEEIIIYQQQgghhBAiG5LkrRBCCCGEEEIIIYQQQmRDkrwVQgghhBBCCCGEEEKIbEjmvBVCCCGEEEIIIYQQQmQ6kymrI8h5ZOStEEIIIYQQQgghhBBCZEOSvBVCCCGEEEIIIYQQQohsSKZNEEIIIYQQQgghhBBCZDojuqwOIceRkbdCCCGEEEIIIYQQQgiRDcnIW/FCgSf2sXrxFIxGI3WbvE+zNt0SvB8dHcWvP47gxpWzOLm40a3/NLzyFQJgi/8CDmz3x8bGhvZdh1K+Sl3L54wGA1OGfoi7Zz56DfspwTr/XDiJgzvXMfO3Q1Yrh8lkYvXiKQSe2It9Lgc69x5PkRLlkyx348pZls32IzoqkgpV69OuyxB0Oh1Pnzxk0cxBhN6/g2fegnTrPx1HZ1cALgQeYc2SqRgMMTi7uNNv7OJsGeuRvX+zdf0iAHI5ONKhux+FiykA/DZnFAHHd+Pi5smIGf7ZLnZIeTuP6tOcXA6O2NjosdHrGTJ5Zbrjz8wyvGj7W0vgif2sWjwFk9FInSZteCuZ/XXpjyO4eeUcTs5udBswFa98hXjyOJz507/hxuVAXmvUig7dh1s+s/73Hzm0+y+eP33EzN/+s2q8cXFbt50Je3CPpT+N4FH4A3Q6G+q92ZY33ulktXhNJhNrFk+21JFOvb9NoY4E8lu8OtK2y1BLHVk8c6CljnTtPx1HZze2bVjM0b1/A2A0Grh36wqTFu7BydmNXf/8xoHtazCZTNRp0pY33umcpWU4cXAL/6yaS9DtKwycuIKiJSsk+Fzog7tM6N+aFu1706TVZxmK9VXF/vRxOAu/G8D1SwHUbtSaD7qNyPJYU6orz5895tdZwwgNuYvRYKBJy0957Y023Lp2nj/mjyfi+VNsbGxo9n5PqtdpnuFypLdtAdi8diEHd/ijs7Hhg65DLH2BZ08fsXzuWO7cuAQ6HZ17j6WEUjnDsebk/TOnHv8zK+4LgUeYN/VrS12qUrsJb7f7AoAdG5dxYMdadDooWKQ0nXqPx84+V7riTyvf+RPJ16IRUcEh7Kna8pV8Z2JZsU9uXb8U/2XfMXXRLpxdPTIQu/XPLZbNGUXAMa0e+30XV4/X/jqDgGO70dvakde7CJ36jMPRyTVLY01pnSaTib9W/MiJ/7ais7GhfrMPeKPFx5bvun4pgGnDO9G1/1Sqvd4s1WWIXfeqxVMIPG7eR/uMp2hy++hlbR+NioqkQrX6tI/dRx9r+2jI/Tt45S1ItwHaPnrv9lV+mz2Sm1fP0fLDr3jTfLyPjopk5qguxMREYTAYqPram7zboU+aYta2lfXruV+vt3HIbT5vsNEzdOoKANb++h1njprrSv7CdE5jXcnK2AF2/vM7uzevRG+jp0L1BrzfuX864351ffLjB//l7z+1/tigSb/jk6gvmdWxQ8pti7VjFyI1ZOTt/yBFUd5TFKVgRtdjNBj4c+FE+oyYy8iZ6zi6fxN3b15OsMzBHWtxdHZl7E9/0/jdzqz77XsA7t68zLH9m/Gb6U+fEXP5Y8EEjAaD5XM7/1lO/kLFk3zn9cuBPHv6OKOhJ3H2xD7u37vO6Fkb+bDnKFYu+DbZ5f6Y/y1aKWFEAAAgAElEQVQffj6a0bM2cv/edc6e3AfA1nULUSrVZvSsjSiVavPvuoWA1tH9c8EEPh8yC7/v/Ok2YHq2jdUrXyH6jVnM8OlraN62JyvmjbWs67VGregzfG62jf1l2/nr0QsZNm1VhhO3mVmGF21/azAaDPyxYCJfjpjDyJn+HN23Ocn+emC7P45Oroz9aSON3+2Ev3l/tbOzp2XHPrTpPCDJen1rNGTI5OVWjTVx3NZuZ2z0et7/5BtGfb+eQRN/Y8+WP5KsMyPOnthL8L3rjJr1Nx17juaPl9SRUbP+JjhRHSlTqTajZv1NmUq12WquI2+26sLQaasZOm01LT/8mlLla+Dk7MadGxc5sH0NAyf+ztBpqwk4vpvgu9eztAwFipSm+8CZlCxXPdnPrV0ylfJV62Uoxlcdu62dPe90+JI2nQdmm1hTqit7Nq8kf+ESDJu2hr5jFuH/63RiYqKxt3eg85cTGfHdOnoP/5m1S6bw7OmjDJUhI21L3D66li9HzGHl/ImWvsCqRVMpX6Uuo2etZ8T0VeQvnLRPkB45ef/Mqcf/zIoboGS5agybtoph01ZZErfhoUHs3rScwZNXMGKGP0ajkWMHNmeoDGlxa+laDr/b/ZV9X2JZsU+GPrjH+dMH8cxTIMOxZ8a5xWuNWtFnRNJ6XK7y6//H3n2HNXn1fxx/J2HJkCm4t8aJe2+t1tZt6+hu1cfW0VatWveeuHetu45aF47WvffeqLHUXRWUoSArJPn9EQwEEAXC6u/7uq5ezwM5JJ/7eM6575yc+4ThM7cwfMZmPPMXYa/vsiRlMjNrSs95+vA2QoKeMnL2NkbN3kb1ei3NsmxdM4uyleu+c/6E/C4d59mT+4yZ9yeffjuK9UuS76Pr4/romHl/8uxJfB/dG9dHx8wz76MOjrnp1G0Izdp8ZfY8VtY2/DB6KcOmb2LYtA3cuHyCu7evpCpzRrVzgH5jljJs+gazyc8y3rUZMWszI2ZuwjNfEfZsefe2ktXZNdfPcvXcYYbP2MTI2b40b/tlmnNn5jV5/kIl6TlwJiXfcC2Z1dnhzWOLJbP/f2UwZP//shuZvP1vag+ke/L2nv918uQtjIdXQaysralWryVXzx8yK3P13GFqNWoLQJXazdFcP4PBYODq+UNUq9cSa2sbPLwKkidvYe75XwcgJOgp1y8epW6zjmbPpdfp8F09kw5p/KQwJVfPH6JmwzYoFAqKla5E5KswXoQ8MyvzIuQZUZHhFC9dCYVCQc2Gbbh67lDccR4yHWetRm25eu4gAOeP76RSrWami1knZ/dsm7W4urJpFWuxUpUIDQo0PV/JctWxd3TOttkzop4z+xhSqn9LMPbXQmb99cq5w+bHdu4QtRvH9dc6zdFcO4vBYMDWzp6SZasmu2qpWGlvnF3zWDRr0tyWHWecXfOYVpTY5XLAq0AxQoMtV9/Xzh+iZsO279RGipWuHNdG2nItri1cO3eIWo3aAVCrUTtT20nowomdVKv3AQAB/96haClvbGxzoVJZUapsda6ePZClx5C3YHG88ic/2Xbl7AE8vAqSr2DJdGXM7Oy2dvaUKFMVKxubbJP1TW1FoVAQFRWBwWAgOioCe0dnlEoVnvmL4pmvCADObp44OrsR/jIkXceQnrHlyrnDifpoIe75XycyIhz/mxeo26wDAFbW1ula4ZRQTu6fOfX8n1G5U6LT69DGRKPTxRITE5Wh56nEgo+fRxv8ItNeL7Gs6JObV04zXp8r0rdHYUa9tygV92FKYmUr1UWlMt5oWrSUNyFBAVmaNaXnPLZnAx98/B1KpfHtecJr3cO711G5dnOccru9c37znIeo1egd+6ja2EdrNWrDlbMJ+mhce6rVuC1Xzh40ZSxSsgIqK/ObeRUKBXa57AHQ6WLR62JT3XYyop2npFzl+LZSrLR3uq7TMzv7sT0beb9DN6ytjdcvaX2flNnX5HkLFscrmcVc2SU7vHlssWR2Id6VTN7mMGq1erparT6hVquPqNXqoWq1ep1arfZVq9XX1Gp1bbVarQZaAovVavVv6Xmt0OAAXN29TD+7uHklOZGFBgfg6mEso1JZkcvekVdhoYQGBeLqntf8b4ONF0ybVvjQ4fMBKJTmze/I7t/xrt44Qy7AQ4MDcfVIkMfdK8lkTmhwIC4JjzdBmbAXwaZczq55CHsZDEDgk/tEhL9k9phuTP25C2eObM+2WRM6eXAL5arUS/L77Jo9pXpWAPMnfsvUn7twfP+mbHsMCWVE/SfO7eruyYvggDeWSdhfs1JGjTOvBQX+y6O7tyhaqqIFMydtIy8StZEXKbaRoERtJMjsb2OiI7l5+QSVazcHjCtF/W9e4FVYKDHRkfhdOkZI0NMsPYY3iY6KYP+25XzQqVe68qUko7JnhIxqKw1bfkLAv3cY8W1TJv/UkY++GWJ6w//aPf9r6GK1eHgVsugxpGZseZGg7yY8tucBj3DM7crqBaOYNLAzaxaNIToqIl0535Q3J/XP/8r5x5K5796+wuRBH7NwUi+ePPQ3/q2bF83afMXIXi0Y3rMZuewdKVspbSsSc6LM7pNXzx3G2c3TIls9ZfQ5PyWnDvlSPhV3hGRE1pSe83nAQy6e3M3Un7uyYGIv0wr+0KAArpw5SIPmnd45e2LGMS/1ffRFKsaWxPQ6HZMGduLn7o0p412HYqW8U5U5I9o5GOeQ543/jsmDu3J8X/LvG04e3Eq5qmkfJzM7e+CT+/jfvIjPkM+YOarbWyd735w7512TZ1Z2IbIDmbzNQdRqdWvATaPR1NNoNI2AQECp0Wg6AH2AXhqNRgPsBr7VaDRpu2ciBYpEn5omu5xcoQCSPqBQKLgWt2dM4RLm+yyFBgdy8dQ+Gn3wiQXTphw08bEkW+YtT6vX6Xh49wa9hsynz/Bf2L35VwIe30t7zjflsEDW125fP8upQ760+8zyK5yzop77j/+NIVM30HvYQo7tWY//jfNpDJ9CvpxQ/8l1xnfInd5VNBkhvePMa1GRESyZPoCPvxlMLntHi+UzJFuPby+jeMdWcu3CEYqrq5g+6c9bsDjN23Vj/oSeLJz0HQWKqFEqVanO/bZ8ljiGnRsW0qTVF9ja2acnXooyuv4tKaOy3rxyggJF1ExYfJAh0zaxcdkkIiPCTY+/CHnG6nnD+KzX+CSTuqmWjrEl+V8rjGP6nVs0aNGJYdM3YGObi72+y9OX0xQlB/fP/9D5xxK5CxUry/iFexg6bRONWn7Kr9P6ARAR/pJr5w4xdsEuJi7eT0xUJGeP/pnW9DlPJvbJmOhIdm9eQpsuvS2TPRmWOuenZPfmX1EprajRoFUaEr759SyR9fXvtdoYrGxs+Xnqeuq99xFrFo4CYNNKH9p/3g+lKu3n/Xca895h7EwNpUrFsOkbmbh4H/f8r/P4wd+pe4IMaOcAP01YxdBpf9B3+AKO7P6Dv29cMCu3a/MSVCoVNdPTVjI5u04XS0T4SwZNXkPHL/qzbOag5M+FaZDdr8lTkhlji0g7g0GR7f/LbuQLy3KWckDC9f864PUZ5wGQtntp3sDFzcvs9qLQ4ACc3cxXxbq6exHyPABX97zodLFERoTj4OiMi7uX2cqT0OAAnF09uXb+MNfOH8bv0nG0MdFERb5i5dyhVK/3Ac+ePmDM960B0MZEMbpvK8bO/yvN+Y/sXs/JA5sBKFKiPCHPE+QJCkiywtfF3YvQhMcbFICzmycATs5uvAh5hrNrHl6EPDPdtuTi7oWDkwu2dvZxt55X49/7t/HKXzTbZQX49/5t1i0eQ6+hC3F0cklVxqzMnlI9u5j+1h3vGk2553+dkuWqZ7tjgIyp/4SZEuYOCQrE2dUz2TKu7l5m/TUrZcQ4A6CL1bJ0xgBqNGhF5VrvpTvn0d2/m9pI4RIVkmkjSes6aRsxHpeTs3uiNmJ+e9vFE7uoVv8Ds9/VadqROk2NW81sXzfHbHVMVhzDm9zzv8blM/vYtnYWka/CUCgUWNnY0Kjlp6nOm9nZLSUz2srpQ1tp3r47CoWCPHkL4+5ZgIDHdylasiKREeH8MqUPrbv2pVjp9H8BWHrGFpe4vmt+/HlwcffCxd2LYqWNK7Gq1m7Onq1pn7zNyf0zp55/MiN3wjf45as24I9lEwl/GcJtv3O4exY0latUqxl3b1+mZsPW6T6unCAz++Szp494HvgvEwd2NpWfPLgrgyevxdnVI/XZM+icn5LTh7dx/cJRfhi9JFUTMhmV9U3P6eruRZW465VKNZuxeoFx8vbBP34sn/0zAOEvQ/C7dAyVyopKNZummP/I7vWc2B/XR0uWJzQoUR91e4c+6vr2seVt7B1yU6p8dW5cPkH+wqXe+e8yop0DZu8bKtVsyr2/r1OqnHHf0tOHt3P9wlF+HP1ruibvMju7q7sXlWs1Q6FQULRURRQKJeEvQ3ByTt3UQE65Js/M7EJkJ7LyNmfxAxol+FmJ+UdFr88yMVhgYr5IyfIEPrnP84BHxGq1XDixm4rVG5uVqVi9sekW9kun91G6Qk0UCgUVqzfmwondaLUxPA94ROCT+xQtWYF2n/3IxMX7Gb9wN936+6CuUJOvf5hMhWoNmbL0EOMX7mb8wt1Y29ila+IWoFHLrqYvufCu2ZSzR3dgMBi4e/sKueydkryxcHbNg20uB+7evoLBYODs0R14V2+S5DjPHNmOdw3j772rN+GfWxeNe65FR3LP/2qyX8SWHbIGP3/Ckun9+bLvpFRPLmd19jfVc3RUBFGRrwDj7dq3rp4if+HU77OZk+v/NWN/fWDWX71rNDIr4129MacPx/XXU/tQx/XXrJQR44zBYGDNotHkLVCMZm0scwNCw5afmL6syNhGtpvaiJ29Y7JtxM6sjWynolkb2QbAmSPbqBjXRgAiI8Lwv3HeVPa1sBfGW7eDnz/hytn9VK9nPnmU2cfwJv3HrWLsgj2MXbCHxh9+TosO/0v3xG1mZbeUzGgrbh75uH3tDAAvQ58T+PgeHp4FiY3VsnR6P2o2bEOVOu9b5HjSM7Z412iUqI8+oGjJCji7euDq7kXAv/cAuHXtDPkKFk9zxpzcP3Pq+Sczcr8MfW5aPXbP/xoGvR4HJxfcPPJy9++rxERHYjAY0Fw7g1eBtLefnCYz+2SBIqXwWX6YCYt2MWHRLlzcvRjqsz5NE7fx2S17zk+J36Xj7Nu6gm9/nouNba4sz5rSc3rXaIrm+lkA/r5xHs/8xv3Lx8W9Nxq/cDdVajenS4/hb524BWMfHTZ9I8Omb6RSjaacOZK6PnrmyA5TX6xYvTFn4trTmcPxffRNwl4Em74sMyY6Cs3V06neHzQj2nni9w03r8S/b/C7dIK9W1fw3c9zUt1Wsjq7d40mprYT8PgesbFaHHO7pjF39r8mz6zsQmQ3CkstqReZQ61WzwRqA9HAXkCr0Wimq9XqosB8jUbTWq1Wtwf6A5c1Gs2Pb3vO/Vej39gIrl88xuaVPuj1Ouo0aU/Lj3ry5/oFFC5RDu8aTdDGRLNq3jAe3r2Fg6Mz3fr74OFVEDDeonTq0FaUShUffzOY8lUamD33bb9zHNi+il5D5yd53f6f12LWmjMp5k5N0zUYDGxYNombV05gbWPH573HU6REeQAmD+rE0GkbAbj/jx9rFo5AGxNNucr16dRtKAqFgvCwUJbPGmj8hNQjL90HzDCtWNy/fQWnD21DoVRQt2lHmrT64t2DZWLWtb+M5vKZ/bh5GL/LTqlS8fOU9QCsmD2Yv2+cJzwslNzObnzYuTd1m3ZMPmA2qufnAY9YMt14G6VOp6N6/Q9o2bFnOmo/a+o/JUrluzf06xePsWmFD3q9njpN2/PBR/9jx/oFFClRHu8ajdHGRLNy7nAe3buFvWNuuiforyN6fUBUZDi6WC257J34fuQv5CtUgi2rZ3H+2E7Tiou6zTrSusvb9zNNza0mlh5n/G9eZNaor8lfuBQKhfEzyraf/kCFqg1SimGif0t2g8HAxmUTE7SRCRSOayNTBn3MkGnGfcgemNpIFGUr16dTt2EoFApemdrIE1w98tEtQTs/fXgrNy+f4Jt+08xec9aor4gIC0VpZUXHLwehrlj7nes3I47hytkDbFo+ifCXIeRycKJA0TL0Gb7Y7DV2bliIrZ09zdp+na6smZl9dJ/3iYoIJzZWi72DE71H/Eq+giWyLOub2sqL4EDWLBxh+sKZ5u26UaNhG84d3cGaRaPMMn/eZwIFi5ZJkk2l1L/zcaRnbNm1eQmnDm5FqVLR6evBlK9q3HPy4d1brF00lthYLR5eBfmyzzjTl2qlRKdPed1Bdu6fimRuzUycPSee/zMq95Hdv3Ns7wZUKhXWNrZ0/HIQxdWVAfhrwwIuntyDUqWiYNGyfPrdGNOX9SQUUz11+2y+i8qrZ+DeqCY2Hq5EBwTx97h5PFyR/j33E7K7eCXFx7OqT47o9QFDpq5LcWLobef/jHhvsXz2YP72i2/HrTr3pm6zjozu24rY2BgcHI2rzYuV9uaTniNTzJfRWZN7ToCIVy9ZOWcoIc+fYGtnT9eeI5PsM/zb/BFUqNaQqnVavPMxQHwfvXH5BDY2dnzeJ76PThrYiWHT4/vo6gXxfbRz9/g+umxmfB/tMWAGDk7OvAh5js+QrkRFvkKhUGJrl4sRs7YS/Owxv80fgV6vw2DQU7XO+3zY6bskuRSKlMdES7fz5wGPWOxj3DZGr4uleoMP+eCj/wEwum9rtNoY050JRUtV5NNv372tZGX2WK2W1QtH8eieBisrazp+OQB1xVrJ5srs/pnSNfnlMwfYuHyy6XqsYNEy9B3xS7rqPLPGltRmf8/bNvvdg5/Ftp7TZfuJyPY1VNnq300mb0WKk7fZmTRd8f9BaiZvs5PsuE/Qu3rb5K0Q/wWpmbzNTt42eZudvW3yVlheRkzeZoa3Td5mZzn5/C8y19smb4XlSf/MGjJ5m5RM3qZezr0CFkIIIYQQQgghhBBCiP8wmbwVQgghhBBCCCGEEEKIbCjdX2olhBBCCCGEEEIIIYQQbyNbYKaerLwVQgghhBBCCCGEEEKIbEgmb4UQQgghhBBCCCGEECIbkm0ThBBCCCGEEEIIIYQQGc6AIqsj5Diy8lYIIYQQQgghhBBCCCGyIZm8FUIIIYQQQgghhBBCiGxItk0QQgghhBBCCCGEEEJkOL0hqxPkPLLyVgghhBBCCCGEEEIIIbIhmbwVQgghhBBCCCGEEEKIbEi2TRBCCCGEEEIIIYQQQmQ4g2ybkGoyeSsob3MzqyOkybgdRbM6Qpq839gpqyOkWX6n8KyOkCZhWtusjpBm9QI3ZHWENDnh2TmrI6RZbGzOvCklj8OrrI6QZs9eOWR1hDSxUuXcK89Ibc68BHSw1mZ1hDRTKnNme8mpbQXA7uKVrI6QJlFVK2V1hDSrdWllVkdIk0PhtbI6Qpq55MqZ46J7rpz5vgLgSZhzVkdIExsrfVZHEEKkUc58hyqEEEIIIYQQQgghhBD/cTn3o3QhhBBCCCGEEEIIIUSOIdsmpJ6svBVCCCGEEEIIIYQQQohsSCZvhRBCCCGEEEIIIYQQIhuSbROEEEIIIYQQQgghhBAZTm9QZHWEHEdW3gohhBBCCCGEEEIIIUQ2JJO3QgghhBBCCCGEEEIIkQ3J5K0QQgghhBBCCCGEEEJkQ7LnrRBCCCGEEEIIIYQQIsMZDFmdIOeRlbdCCCGEEEIIIYQQQgiRDcnkrRBCCCGEEEIIIYQQQmRDsm2CEEIIIYQQQgghhBAiw8m2Caknk7ciTc5cvMz8JSvR6fW0at6Uzz5ub/b4hm1/8tfeg6hUKlycczP4++/I65kHgKYdulKsSGEAvDw8mDRicKbn79w0F+WLWxETC7/tjOBhoC5Jmb4fO+DsoESpBP9HsazfH4nBAAXyKPm0hT221gqCXuhZ8dcromIyJqfBYGD76kloLh/F2jYXnXtOokCxcknKPbrrx8bFw9DGRKGu3JC2XwxDoVCwb/N8zh7ehIOTKwAtO/ejTOVG6GK1bFo6isf3bqDT66hWvy1N2va0aParF0+xeskM9Ho9jZu3o83HX5k9rtXGsHjWGO7+cwtHJ2f6DppIHq/8nDi8m51bV5vKPbznz/iZqylSvDQ+Y34gNOQ5ep0OdbnKfPXtYJQqlUVz+106wcYVUzHo9dRt1oH3O3RPknvVvOE8vHMTB0dnug/wwd2zAOFhoSyZ/hMP/vGjduO2dOkxzPQ329bN48yRHUS+esmsNactmvdNTvj9w9SNe9EbDHSoW5nu79dNtty+izcZuHQL637+hvJF8pt+/yT4BR3GL6bXhw35qnntDM2ak+vcYDDgu2oyNy8fw9rGjk96TaRQMn304R0/fv9lBNqYKMpWbkCHr4aiUCjYvnY6fhePoFJZ4eFViE++m0Auh9zoYrWs/3U0/967iU4XS40GbXmv/f8slvvKhVOsXjoTvU5P4xZtaZtM//xl1lju+t/CKbczfQdNMPXPv3zXxB/XPX8mzPqNIsVLM2FYL0JDnmNjYwvAz2Pn4uziZrHMr6W3zndumMf18wdRKJU45nbj0+8m4uzmybXzB9m1YR4KpRKlUkWHL4dQvEzVdGfdvGIKfpeOYWNrx+e9J1CoeNKsD+74sWbBCLQx0ZSv0oCPvhmCQqHgVfgLVswaSPCzx7jlyU+3/tOxd3QmIvwFaxeN4nnAQ6ysbfms1zjyFy4FwNqFI7l+8ShOzm4Mm+GbrvwJj2PrqsncvHwUG5tcdO01kYJvqPP1vwyPq/OGtI+r810b5uJ3/hAKpQLH3O50javzQzuWc/HEnwDodToC/r3DuF+PYe/oYpHcfpdOsGGFD3q9nnrNOtCyQzezx7XaGFbOG8GDuLGlx4CpeMSNLb9OH8j9uLHlkx5DTX8Tq9WyftlkbvudR6FQ0u7TvlSt/Z5F8iZ0/eIJ/lg+Db1eT/332vNBx6TZV8wZyf07N3FwcqbnT1Px8MzPjcun2bJmLrGxWqysrPn4q36UqVgTgLPHdrFz83IUCgUurnno1m8CTrldLZ79NYPBwJaVU7hx6RjWtnZ81iv59v/wjh9rFxrbf7kqDej4tbH9b1szg+sXDqOyssbDqxCf9hqPvUNui2ZM6/kHYPeWZZw66ItCqaRzt58pV7keABGvXrJ20VgeP/AHhYIveo+luLqS6Tn3bVuF7+qZ+Cw/jGMG1n9yvJdMwvPDxsQEBnG0SptMfe23OX3pKrOXr0Ov19OmWUO+6Nja7PH123ez48BRVEolLs5ODOvdnbyeHgAsXL2BkxeuAPB1p7a8V69WhmY1GAz8tXYSt68cxdrGjo/+N4n8RcsnKffvXT+2LB2KNiaa0pUa0uoz4/X5k/s32bZqDLHaGJRKFW2/HEXBEt5cPrmDY38tBcDGzp62X40mX+EyFsm7acVU07noi97j33AuusHqBOeij7/52XQuWj5rkOlc1L3/dOwdc3Pu2F/s27YcAFs7e7r0GEHBomrT8+n1OnyGfIKzmye9hsxP93G8dvnCaVb9Ohu9Xk/TFm1o1+kLs8dvXr/MqiVzeHD3H34YPJba9ZuYHps8agB/a/xQl/Pm59HTLJbpXWXUNYyl+F06zqYVU+POmx1pkcyY+Nu84Ty4cwMHJ2e6959mGhP3+C7l5AFflEolnboNoVzlemhjopk16htiY2PQ6XRUqf0erbv0AeC3+SPwv3EeO3snAL7oM55CxdLf3jP7OITILLJtgkg1nU7PnMXLmTp6KKvmz+TgsRPce/DIrEypYkVZPHMyy+dOo1HdWixeudb0mI2NDctm+7Bstk+WTNyWL2aFp6uS0UvDWLcngk+a50q23NLtr5i4KozxK8JwsldQTW0NwOfv27P1SCQTVoZx+W8tzWvYZVhWzZWjPH96n0EzdtOx+1h8V45NtpzvinF07D6WQTN28/zpfTRXj5keq9/yS/pN8qXfJF/KVG4EwNWze4iNjaH/lG38MH4jZw5uIPjZvxbLrdfpWLXYh0Gj5zB1/h+cOraHfx/cMStzZN92HBydmLF4Cy3bfsIfq4wXdfUat2Ti7LVMnL2W7/qNxcMzH0WKlwbg+8GTmDRnHZPnrefly1DOnDhgscyvc/+xdBJ9hy9k5Cxfzh/fzZOH/5iVOXnAF3uH3Iyd/ydNW3+O75rZAFhb29Cmax86fDEgyfN6V2/Ez1PWJvl9RtHp9Uz6YzcL+3bFd+S37D7vxz9PniUp9yoqmnWHz1GxaP4kj03btI/65UpkeNacXuc3Lx/j2dMHDJu1k87/G8OmZeOTLbdp+Xg69xjNsFk7efb0AbeuHAegdMU6DPbxZbCPL3nyFWX/NuObtstn9qKLjWGwjy8/TdrAyQMbLdZHjf1zGoNHz8ZnwXpOH92bpH8ejuufM3/dTMu2XVm/agFg7J+T5qxh0pw19Oo/xqx/AvQeMM70eEZM3EL667xp628Y7OPLoCmbKV+1EXu2LAKgdIXaDJq6hUFTNvPJt+P5Y8nodGe9cekYgU/vM2ruX3TtOZo/lk5IttwfSybwybejGTX3LwKf3ufGZWPWfVuXUbpiLUbN/YvSFWuxb+syAPb6LqVA0TIMnb6FL/pOZPPKqabnqtW4Hb2HLUp39oRuXT7G86f3GTprF53+N4bNy8YlW27z8nF06jGGobN28fzpfVOdN2ndjYE+vvw0ZQvlqjZiX1ydN2nTjZ+mbOGnKVv4sGs/SpStbrGJW71Ox+9LJ9N3+AJGz9rCueO7eZxobDkRN7aMn7+DZq0/x3fNHACsrW1p27UPHyUztuzasgQnZzfGzdvO6NlbKF2umkXyJs6+bskUfhgxn7FzNnPuWDLZ92/F3tGJiQu3816bz9jymzG7Y24X+g6bzZjZG/nm+3EsnzMCAJ0ulj+WTeOncb8yetYGChQtxaGdf1g8e0I3Lh/j2dP7jFQfNUoAACAASURBVJjzF13/N5qNy5Jv/xuWTqBLz9GMmPMXz57e52Zc+1dXrMOQ6b4MmbYFz3xF2L91qUXzpef88+ThP1w4sZsRs7bQd/hC1i+ZhF5nXAiwcbkP5SrXY/TcbQyfvpG8BYuZni/4+VNuXT2Fm0c+ix7Lu3q0agtnW/fIktdOiU6nZ8aS1cwYPoC1syex//gZ7j40P+eVKlaEZT6j+W3WBJrUrsGC1RsAOHnhMpo791k5YxxLpoxi3bZdvIqIzNC8t68eJejpffr77Kb9N2PZvir5MXH7qrG0+2Ys/X12E/T0Pn/HXZ/v/mM6Tdv1oe94X5p1/J7dG6YD4JanID2G/cb3E7fRpG0vtq1I/3kI4Mal4zx7ep/Rc//kk56jWP+Wc9HouX/yLNG5SF2xFqPn/om6Yi32xp2L3D0L0G/MCoZN30zLj3ry+6/m71MO7VyLV4FiSV4nPfQ6HcsXzWDI2BnMWLiWE0f28+jBXbMy7nm86NVvOPUaNU/y9607fkqfASMtmik1MuoaxhL0Oh0blk2iz/BFjJy1lfMndiUZE08d3IK9Y27Gzv+Lpq2/YGuSMdGXPsMX8cfSieh1Oqysbfhh9FKGTd/EsGkbuHH5BHdvXzE9X/svBjBs+kaGTd9osYnbrDgOITLDf27yVq1WN1ar1W/9aE+tVruo1epPLfFcaRH33OUS/LzCgs/d21LPlZxbf/tTIK8X+fN6YW1tRdMGdTlx9pxZmSreFbCzNa6+KqcuxbOgoIyMlCqVSllz2s+4VPbuEx32dgpyOyiSlHu9mlapBJVSYVra7+Wm4u9Hxgv0W/e1VCltnWFZ/S4cpFr9digUCoqUrETkqzBehphPwr0MeUZ0ZDhFSlVGoVBQrX47/M6nPKmpQIE2OhKdLhZtTDQqK2vscjlYLPc/f/vhlbcgnnkLYGVtTe0GLbhw9qhZmYtnjlC/aSsAatZrit/VcxgS3T9x6the6jRoYfo5l70jADqdjthYLQpF0n+39Ljnf508eQvh4VUQK2trqtVryZVzh83KXD13iNqN2wJQpU5zNNfOYjAYsLWzp2TZqljHrTpMqFhpb5xd81g0a0qu33tMoTxuFPRwxdpKRctq5Th85XaScgt2HOHr5nWwtTa/CePgZQ0FPVwpkS/jM+f0Or9+4RA1GrRFoVBQtFQlIiPCeJGoj74IeUZU5CuKljb20RoN2nLt/EEAynjXQ6Uy1n+RUt6EBgcAxj4anaCPWllZY5vL0SKZ//n7Bl75EvbP5lw4k7h/HqVBwv55JWn/PHl0L3UatiCzpbfO7ezj6zEmKtI0jtja2Zv+f0y0Zd74Xzt/iJoNjVmLlTaO4clnDadYXNaaDdty7Zwx67Vzh6jVqB0AtRq14+q5QwA8efQP6orGlWV5CxQn+Nm/vAx9DkDJctWxd3S2SP7Xrl84SLW4Oi8SV+fJnYsS1nm1Bm25HncuSlznJDN2Xzq5kyp1P7RY5nv+1/HMW4g8cWNLjXrvczXJ2HKYOo2Nqw+r1nmPW6axJRcly1bBysYmyfOePLiNlnEreJRKZYasnLzrfx3PfIXIkzcue/33uXLWPPvlc4ep08SYvVqd97gZl71w8TK4xK3Cyl+4BNqYGLTaGAwGAwYMxERFYjAYiIoIx8UtY8fI6+cOUSOu/Rd9x/ZfI0H7L1OpboLxsRKhQQEWzZee88+Vc4epVq8l1tY2eHgVJE/eQtzzv05kRDj+Ny9Qt1kHAKysrc1WC29eOY0OX/RPtg9khuDj59EGv8iS107JTf87FMzrRYG8nlhbW9Gsfi2OnbtkVqZaxbKm9xblS5fgWVAwAHcfPqZKeTVWKhW57GwpVaQQpy9dy9i8Fw9SuZ7x+rxQycpERbwkLDTQrExYaCDRUeEULlkFhUJB5XrtuHHROCYqFAqio8IBiIoIJ7eLsc8WLlWFXA7G8btQyUq8CH5qkbxXzx+iZsM273QuKl66Uty5qI3pnHP13CFqNTL2g1qN2nI1ro8WV1fG3tHYvouVqkRoUHwdhAQ9xe/iUeo262iRY3jN//ZN8uYriFfcNUzdhs04f/qYWRlPr3wUKVYShTJpP6tYuTp2uewtmik1MuoaxhKMY2JhszHx6vlDZmWunjtsagtVajdHc/0MBoOBq+cPJRoTC3PP/zoKhcJU3zpdLHpdbIaPf/+V4/iv0xuy/3/ZzX9u8jYVXIAUJ28zWGPANHmr0Wi+seBzZ+jk7bOgYPJ4uJt+zuPuzrOgkDeW/2vfIWpWq2z6OSZGS88BQ+k1aDjHTp97499lFBdHJSFhetPPIWF6XByT7wrff+zAtD65iY4xcPG2FoDHz3V4lzS+maiqtsE1d8Z1o5chgTi75zX97OzmxcuQgERlAnB280pUJv7i6dS+dcwa2p6Nvw4n4pXxgr1izRZY2+ZiYt9GTO7XjIYffmOx1U4AIUHPcPOIz+Tm7klIkPmFSXDwM9zjyqhUVtg7OBIeZv6G4szxfdRu+L7Z73xGf0+fL98nVy57atZtarHMAKHBgbh6xNe3q7snL4ID3lhGpbIil70jr8JCLZojvQJDw8jr6mT62dM1NwEvwszK3Hz4lKchL2lUsZTZ7yOiY1ix7xTffdggU7Lm9Dp/ERyAS4I+6uLmlST/i+BEfdQ9aRmAM4d9KVupPgCVajXH1jYXo3s1Ydz3zWnc+mscLDQhFxIUaN4/PZL2T2MfNr6ZfHP/3J9k8vbXueMZ9uPn+K5flmSy11IsUed//TGHsX2aceHEX3zQqa/p91fP7WfyT21Y4tObT75NfjVMaiRu3y7uXrwINn+D/yI4EBd3L7MyoXFlwl4EmT6EcHbNQ9hL4wehBYqouXJmPwD3/K8R/OyJaeI/Ixgzmp+LkqtzF7eEx5HX7Fh3/jGHcX2acfHEn7RMUOdgnCy/deU43rWSrpJKq5Bk6j4kUd2ndmyJePUSgO3rFzBxUFd+nT6Ql6GW/3A6NCgQt0RtIiT4WTJlzLOHJ8p+8dR+ChVXY21tg5WVNZ/1HMbY/p0Z1L0FTx7doX4z8y2vLH4cIYnazZvaf8J24+ZFaIh5GYAzh3wpW6W+ZfOl4/zzIjgAV4+k/fZ5wCMcc7uyesEoJg3szJpFY4iOigCMkwXObp5mt5ULo2fBIXh6xN+t4enmmuJ7ix0HjlK7qjcAJYsW5vTFq0RFRxP6MoyL128RmMGLRsJCAsyuz3O75TW79gbjNXxuV/Pr87C4a/gPPxvK7vXT8enfhN3rfWjeqX+S17hwZDOlvS1zLZbcuSg0mfHwzeei4ETnouAkr3Hy4BbKValn+nnzSh/afz4AhcKy75OCg57hnid+mwA3D0+Cg5LeXZZdZeQ1THqFBgfg6p5oPA5K3E7ix76EY2JoUCCuiY7r9XWJXqdj0sBO/Ny9MWW861CslLep3I7f5zHxp4/YtNIHrdYy+xBmxXEIkRn+q3veFlGr1RswTo6OAG4BiwEFoAF6Aj8CddRq9WFgHBAEzARUcWW+S+kF1Gq1G7AMcAMMwIdA4WRepxHwExCZIM8h4GvglVqt7qnRaFqo1errGo2mglqtXgm8AEoBrkArjUYTrFaruwJ94vIt12g0S5MrCzQHisUd13aNRjMzTTWYoqRvyN/0wdPew8fQ+P/DnEljTL/bsHQBHu5uPH4aQP+R4ylepBAF8uVN/gmy2LxNr7BSQbfW9qgLW3Hrfiyrd0fQuVkuWtWx4+o/WmJ1GfixTHKTH4kqO7kJktefwtZ+ryvNOvQCFOzdNJe/1vrQqedEHt65hlKpZPi8w0S+esmi8V9QskId3D0LWSb2u7SRt0zs+GuuY2NrR6Ei5rfuDx47j5iYaBbNHIXftfNUrGzBfc3eob7fqUwWS65mFcRn1OsNTN+0j3FfJt3vbtGfR/m8aU3s7ZKuOMsQObzOU+p/CUol/cNEZfb5LkalVFGtvnGPv/v/XEOhVDF24UEiXr1k3tivKF2hNh5e6e+jyXa9dxhXSNCGkuufvX8ai5u7J5ERr5gzZQjHD+2iQVPLraRMKVtq67xVlx9p1eVH9m9dwrE960xvfrxrvId3jff45+Z5dm6cT+/h6btNO9l6TNK8kzmexIUSad6+O5tXTmHKoI/JX7gUBYuVQanMwEu6dzkXvWXc/7DLj3zY5UcObF3C8T3rzCZw/S4eppi6ikU/REwuc+J2kvy/z5vrXq/TERIUQIkylen09UD271jN5t9m8s0PE9Md1yxXMr9L2sJTPr7HD/5h8+q59Bu9EIDYWC1H9mxixIzfyeNVkN+XTmXXluW06mS5vbSTSPbfIHGRt7f/vVt+RalSUb1+6yRl0yUd55/kf61Ar9Px8M4tOncbQrHS3mxYPpW9vst5v2N3dm9ewvcjf7FM9v+Y5Mf15MvuOXKSW//cZcF4417UtSpX4Jb/Hb4dNgGX3Lkpry6BSmnZ70NILLn+l7iXpjS+nD24ng8/HUL5Gi24dmYXvstG0O3n+Jsw79w8w4Wjm/nfiDVJnyNNgd/hvJlsX3w3t6+f5dQhX/qPWwXAtQtHcHJ2o3Dxctz2s/RCnXe5Bsi+MvIaJiMkPW8mW4iU/l2UKhXDpm8k4tVLfp3Wn8cP/iZ/4VK0++xHcrt4EBur5ffFY9m3dTkfdkpxCibNMvI4hMgs/9XJW3egPlAQWA2EAgM0Gs0FtVq9EGgLzAFKaTSa1gBqtfoQ8IlGo3mqVqtnAC2A6BReYxjgq9FoflOr1a9Hg6nJvM6LxHk0Gs3WuInX6xqNZlMyz31To9H8qFarRwHt1Gr1NqAXxtW6BuBI3OR0krIajWaFWq0eqdFoGqeyzt5ZHnd3nj2P/0T7WVAQHm5Jbxs8f/kqazZuYc7EMdhYx28t4OFu/GQ9f14vKlcox9937mX45G2jKjbU8zbeanX/SSyuTkrAuPWBq5OS0HD9G/82VgdX/bVUKmnNrfuxBATrmbfxFQCerkoqFLfstgkn963j7KGNABQsXpEXQfG3TL0IDjDdWvWas1tes09jXwQH4ORi/HTcydnD9PuaTTqxckYvAC6f/Au1dwNUVtY4OrtTtHQVHt25brHJWzd3T4Kfx2cKDgpMcnumm7snQc8DcPPwQqeLJeJVOI5O8SsLTyfaMiEhGxtbqtZswMUzRy06eevi7kXI8/j6DgkKxNnVM9kyru7G3JER4RZbEWkpXi5OPA2JX2kbGPIST+f426xeRUfj//gZPWYZ3xQ8fxnOj79sZM53nbh27zH7L91itu9BwiKjUCgU2Fir+KRxjQzJmhPr/Pje3zl10Dh0Fy5egdAEfTQ0OIDcrm/po0EBZsd49sg2/C4dpffwpaYLxIsndlKmUj1UVtY4ObtTrHRlHt7xs8jkrZtHov75PBBXN49kygTibtY/42/9PX1sX5L+6eZuPKZc9g7UbfQ+d277WWzy1tJ1/lrVeq1Y4tM7yRufEmWrExTwkPCXIam+Lf7o7t85eWCzMWuJCmbtOzSZHC7uXma3g4cGBeDs9noMd+dFyDOcXfPwIuQZTrmNd73ksnfk897GPQsNBgNj+rY0fdGGpRzfu44zcXVeKFGdvwhO5jjc8pqt/g0Neprk3wWgSr1WLPPpZTZ5e/nkLotumQDgmmhsCQ0KwCXRViquqRxbHJxcsLG1o3JN410fVes058QBy3wpnHkuT4ITtYnE51BXdy+Cg57i6pE0e8jzABZOHUC3H8bjmdc4Zjy6a9w65/XP1es2Z7evxXbsMjm253dOJWj/Zu0mKGlfNa7uS3CswQFmW96cPbINv4tH6DNyqcUnaNJz/jH+PlG/dc2Di7sXLu5eFCttXJFVtXZz9mxdzrOnj3ge+C8TB3Y2lZ88uCuDJ6/F2dV8/P3/yNPdjcDn8as5A4NDkn1vce6KH6s272DB+KFm7y2++rgtX31svAV6zKxfKJjPK8nfptfp/Ws5f8Q4JhYoVsHs+vxl8FNyJxpfEt8tZ7w+N7avS8e30uoz45esVqjZkq3L4/dgffpAg++ykXw1cDH2jmnfluXI7vWmc1GREuWTOReZ503+XGTM6+TsluhcFL9K+t/7t1m3eAy9hi7E0cn4AdwdzWWunT+M36XjaGOiiYp8xaq5Q/nqh8lpPp7X3Nw9CXoWv4oyuWuY7Cazr2HSysXNi5CgRONxMueekOcBuLrnTTomJjquxJntHXJTqnx1blw+Qf7CpUxt0NrahtpN2nNg+6oceRwibQyGnPOhS3bxX9024ZJGo9FrNJoHGFfGFtNoNBfiHjsJJHe/UgVgfdyK1UZA0m/wMVcO4wpaNBqNQaPRGFJ4ncR53ub1c7wuXwIoCRwADmJcZVvgDWUznLpUCR49ecqTgEC02lgOHjtJ3ZrVzcr8fecuMxctZdLwwbi6xL8RCgsPJ0Zr3H4g9OVLrt/UULRQwQzPfORSDJNWhTFpVRhX/LXULm9cVVgsn4rIaAMvX5l/ymZrjWkfXKUCyhe35mmwcbLXyd74ewXwQR07jl62zC0er9Vt/qnpC8bKV2vGhePbMBgM3Pe/gp29U5KLw9yuebC1c+C+/xUMBgMXjm+jfDXjG8uEexL6nd+PV0HjCcbFPR/+fqcxGAzEREXwwP8KnvmLW+wYipcqx9MnDwkM+JdYrZbTx/ZStab5rV9Vajbk+MG/ADh74iDlvKub3pjp9XrOnjxI7QSTQ1GREYQGG/d01OliuXL+JPkLFrFYZoAiJcsT+OQBzwMeEavVcuHEbrxrNDIr4129MacPbwfg0ql9qCvUzHaf+Jcvkp8HgcE8eh6KNlbH7gs3aOQd/6VSTrnsODJtALsm9GXXhL54FyvAnO86Ub5Iflb+9KXp9581qUmP9+tl2MQt5Mw6r9/iEwZN2cygKZupUL0p545tx2AwcO/vK+Syd0zyhsjZNQ+2dvbc+9vYR88d206FasZvPr55+TgHdyyjx8B52NjGf3miq0c+/P2MeytGR0Vw3/8qXvkt86UfxUuV5enjhwQ+fRzXP/dRtVZDszJVazbgWAr988yJA9RpGH+Lu04XS9hL4y3bsbGxXDp3nIJFLPeFd5as82dP7pvKXb9wCM+4en329IFpRczDuzfQxWpxcEr9StCGLT9hyLRNDJm2Ce+aTTl71Jj17u0r2L0hq10uB+7eNmY9e3Q7Fasbs1as3pgzR7YBcObINirWMP4+4tVLYmON59KTBzZTomw1057gllK/xaemLxOrUL0ZF+Lq/P7fxuNI7lxkZ2fP/bg6v3BsOxXizkUJ69wvQZ0DREaE8c/Nc6bzlqXEjy3G89C5E3uSGVsacerwDsC4xYC6Qo0UxxaFQoF3tUbc9jsPwK1rZ8hX0HLnzteKJs5+fA+VajQ2K1OpRiNOHTJmv3BqP2UqGrNHvApj3sTv6fD595QsG79llYt7Hh4/vEPYC+ME2c0rp8ln4S8SAmjw/icM9tnEYJ9NVKzRlHNx7f9eCu3f1s6Be3Ht/9zR7VSoET8+7t+2nP8NNh8fLSU95x/vGo24cGI3Wm0MzwMeEfjkAUVLVsDZ1QNXdy8C/r0HxLeRAkVK4bP8MBMW7WLCol24uHsx1Ge9TNzGKVOyGI+eBPA44BlabSwHjp+hfvUqZmVu37mPz+KVTB3yI67O8R8m6nR6XoQZ94/1v/cQ//sPqVm5gsUz1n7vM/qO96XveF/KVW3G5RPG6/OH/pexzeVkmph9zcnFE1s7Bx76X8ZgMHD5xDbKVjWOc7ldPLl7y7gi9c6N07h7Ga9nQ4Mes27eD3T6dioeedPXPxu17MrQaRsZOm1j3Lloh+lclMveKfm+aHYu2oG32bnI2A/OHNmOd1wfDX7+hCXT+/Nl30l45S9qeq52n/7IhF/2M27Bbr7p50PpCjUtMnELUKJ0GZ4+fmS6hjl59ADVall2SxVLy4xrGEswjon3zcbEitUbm5VJ2BYund5H6bgxsWL1xonGxPsULVmBsBfBpi2HYqKj0Fw9bfoSu9d7/RoMBq6ePUi+QiVz5HEIkVn+qytvE87EKYC7arW6WtzEal1gLxCD+fFfAzprNJrnAGq12hqox5v5YVwJuzrBytt7ybxOcnlI5vVTyn8HuAm00Gg0erVaba3RaLRqtfpNz/3mZaQWYKVS8WPPbgwaMwm9Xs8HzRpTrHAhlq/dgLpkcerVqs6iFWuIjIxitM8sALw8PJg0YjD3H/7LjEVLUCoU6A0GPv2oHUULZ/zkbULX78RSobg14/7nRIwWftsVYXps2FdOTFoVho21gl4dHLCyUqBUgOZBLMfiJmmrl7GmURXjKt7Lf2s5dd2yk7cJlancEM2Vo/j81BIbGzs69Yy/NXP2sA70m2Rc8dPhm1Fs+HUY2pho1JUaoK5knIjZuX46T+7fAoUCV48CdOw2BoA6zT9h46/DmTmkLRgMVG/YgXyFLbcHm0plxZc9BzFtzA/o9XoaNmtDwcIl2Lx2McVKlqVqrYY0at6WX2aN5qdvO+LolJs+A+OPTeN3CTd3Tzzzxq8ki46OZObEn4jVatHrdZTzrk7Tlpb9EgSVyoouPYYyf0Iv9Ho9dZq2J3+hkuxYv4AiJcrjXaMxdZt1YOXc4Yzu2xp7x9x07+9j+vsRvT4gKjIcXayWK2cP8f3IX8hXqARbVs/i/LGdxERHMaxnc+o260jrLr0smj0hK5WSoV3ep9f839Hr9bSvU4mS+fOwYMcRyhfJR+MEE7lZLafXebkqDbl5+RgT+32AjW0uuibYJ3XakI8YNMW46uXjbiP5/ZcRaGOiKFu5AWUrGz/M2LJyIrHaGBZNMt66XKSkN517jKZ+i0/4/ZcRTB3UHjBQs1F78hexTB9Vqaz46tuB+MT1z0bvtaFg4eJsiuuf1V73z5ljGNDzIxydctN3UPw3U99Kpn9qtVqmjv4BXawOvV5H+co1aNKinUXyJpbeOv9z/SwCH99DoVDgmic/nbqPAuDq2X2cO7odlZUV1jZ2fPnD9HR/SFC+SgNuXDzKuB8+xNrGzrRaFmDKoI8ZMs24EqdLj5GsWfg6a33KVTFmbd6+O8tnDeT0QV9cPfLRbcAMAAL+vcPq+cNRKJXkLViCz76L/4bvFbMH43/jHOFhoYz8rhkfdu5DnabpGyvLVmnIzctHmdzvA6xt7ej6bfxxzBjSkZ+mbAHgo26jWP/LcLQx0ZSpXJ8ycXX+1/qZPHt8D4VCiWuefHzcPf4b1K+d24/aux62dpb9Ahnj2DKEuXFjS92m7chfqCTb1y+kSIlyVKrRmHrNOrBi7nBG9m2DvWNuevSfavr7Yb0+ICrylWls+WHkIvIXKkGHL35kxdwRbFwxDcfcrnzVZ2wKKdKe/ZMePzN7XG/0ej31mrUjf+ESbPvdmL1yzcbUb9aeZXNGMLx3Wxwcc/O/AVMAOLRzPYFPH/LXxiX8tXEJAP1GLcLFzZM2XXoybUQPVFZWuOfJx9ffWz57QuWqNODGpaOM//FDbGzs+LRXfLvxGfwxg32M7b9zj5GsXTgCrTaKcpXrUy6u3WxaPonY2BgWTugJGL/Uscv/RlksX3rOP/kLlaRq3RaM79cBpUpF1x7DUKqMt+p37j6EFXOGEhurxcOrIF/2GWexzOlVefUM3BvVxMbDlaZ3j/D3uHk8XJHcTYCZy0qlon+Pzxkwfjo6vZ7WTRtQvHABlvy+hTIli9GgRhUW/PYHkVHRjJixAAAvD3d8hvYjVhdL7xGTALDPZceoH3tipcrYbRNKV2rE7atHmTnofWxs7ejYY5LpsfkjO9B3vPH6vO1Xo9m8ZCjamGhKezegtLfx+rxdt3HsXDMJvV6HlbUt7b4xtpFDWxcSER7K9t+MPyuVKnqPTf+/T/kqDfC7eIyxP7SKOxfFnzcnD+rE0GnGO/669BgRdy6KNvbFuH2mX5+LTh30xdUjL93jzkW7Nv3Cq/BQ/lhqvIZXqlT8PGV9uvOmRKWy4pvv+jNp1AD0eh1NmremUJHibFizhOKlylC9VgP+uX2TGROH8io8jItnT7Bp3VKmL1wLwOjBvXj86AFRURH0/qo93/4wlErVLLgF21tk1DWMJahUVnTuPowFE3uh1+uo08Q4Jv65fgGFS5TDu0YT6jbtwKp5wxjdtxUOjs50Szgm1mnBhP7tUSpVdIkbE1+GPue3+SPQ63UYDHqq1nmfitWMH5KtnDOE8JchGDBQsGgZuv5vZErxsu1xCJFZFBn1hSJZRa1WNwY+1mg0feN+vg50xrgXLcDfQI+4/78TCAdmxf3vdIwTqnrgByBPwudK9DpuwAqMX3ymA1oDRZN5nYaJ88TtbVsLmAE80Gg0nyba83a+RqM5r1arvwY8NBrNdLVa3QXjnrc6jPvntgaWv6HsZMAb2KbRaH59W509uXU5RzaCcTuKZnWENHm/sdPbC2VT+Z3CszpCmoRpbbM6QprVC9zw9kLZ0AnPzlkdIc2itRn7pi+j5HF4ldUR0uzZK4esjpAmVqocefoEIEaXM2++crDWZnWENFMqc2Z7idTm3LUe1qoMXc+QYaKqVsrqCGlW69LKrI6QJofCM28Cz9JccuXMcdHDPuzthbKpJ2HZa8u0d2VjlTPHxJzuPW/b7HWbZjbw25FkNw/PVr5s9M5bf2eK/9zkrUg9mbzNXDJ5m/lk8jbzyeRt5pPJ28wnk7eZTyZvM59M3mY+mbzNfDJ5m/lk8jbzyeRt1pDJ26Rk8jb1cu7VWCZSq9U+QM0Ev/pXo9F8llV5hBBCCCGEEEIIIYQQ/30yefsONBrN4KzOIIQQQgghhBBCCCGE+P9FJm+FEEIIIYQQQgghhBAZTp/tN03IfnLmhmdCCCGEEEIIIYQQQgjxHyeTt0IIIYQQQgghhBBCCJENybYJQgghhBBCCCGEEEKIDGeQbRNSTVbeCiGEEEIIIYQQQgghRDYkk7dCCCGEEEIIIYQQQgiRDcm2CUIIIYQQQgghhBBCKJxmFgAAIABJREFUiAwn2yaknqy8FUIIIYQQQgghhBBCiGxIJm+FEEIIIYQQQgghhBAiG5JtEwQ2sRFZHSFNalZ3yeoIaVLJzT+rI6TZ5aBiWR0hTZzsYrM6QpoFFq6R1RHSJiqrA6SdlSpn3scT8MoxqyOkma1Kl9UR/t+xUuqzOkKa6Aw5d92BjUKb1RHSRKnI6gRpZzDkzPC1Lq3M6ghpdqbK11kdIU3cLl7J6ghpptPnzHExOCrnXrfYWOXMc6hCkTOvcSHnjuciefqc2xSzTM4c6YUQQgghhBBCCCGEEOI/TiZvhRBCCCGEEEIIIYQQIhuSbROEEEIIIYQQQgghhBAZziDbJqSarLwVQgghhBBCCCGEEEKIbEgmb4UQQgghhBBCCCGEECIbkm0ThBBCCCGEEEIIIYQQGU6vz+oEOY+svBVCCCGEEEIIIYQQQohsSCZvhRBCCCGEEEIIIYQQIhuSyVshhBBCCCGEEEIIIYTIhmTPWyGEEEIIIYQQQgghRIYzGLI6Qc4jK2+FEEIIIYQQQgghhBAiG5LJWyGEEEIIIYQQQgghhMiGZNsEkSanL11j9vJ16PR62jRryJcdW5k9/vv2Pew4cBSVUomLsxPDencjn6cHAAtWb+DkhasAfNOpDe/Vq5Wp2Q0GA/s3TOSf60ewtrGj1VdTyFu4fJJyR7bO4vqZrURFvOSnOZeSPH7rwm62LvmRr4ZuIl+RipkR3eT8+fMsWvwrer2elu+3oEvnzmaPb97iy549e1CqVLg4O9O/Xz+8vDwzNWNCBoOBHasnoblyFGvbXHTqOYkCRcslKfforh8bfx1GbEwU6koNafPFMBQKBQAn9q7h1L51KFUqylRqxIefDLRYPr9Lx9m0Yip6vZ56zTrSokN3s8e12hh+mzecB3du4ODkTPf+03D3LADAHt+lnDzgi1KppFO3IZSrXI+Q509ZNX84L0Ofo1Aoqf/eRzRp9TkAW36bwfULR1BZWZPHqxCf9xmHvUNuix3La+cuXGThr0vR6/V80KI5XTt9ZPb4Jt9t7Nq7D5VKhXPu3Azs9z1enp7437nD3AWLiYiMQKlU8mnnTjRuWN/i+fwunWDjiqkY9HrqNuvA+8nU+ap5w3l45yYOjs50H+BjqvPdW5Zx6qAvCqWSzt1+plzlegBEvHrJ2kVjefzAHxQKvug9luLqSvz5xyJOHNiMU243ANp++j0VqjawyHEYDAY2r5iC36Vj2Nja8XnvCRQqnrRtP7jjx5oFI9DGRFO+SgM++mYICoWCS6f2sHPjIgL+vcPASb9TuIRxLLrnf431i8caXwMDH3bqTaWazSyS+XXurasmc/PyUWxsctG110QKFkua++EdP9b/MhxtTBRlKzek/VdDUSgU7NowF7/zh1AoFTjmdqfrdxNxdvPE/8ZZVkz/Hre4f6uKNd6jxUe9LZJ304qppnr+ovf4N9TzDVYnqOePv/kZhULBq/AXLJ81iOBnj3HLk5/u/adj75ibp//eZc3CkTy6e5PWXb/nvbZfm54r4tVL1v0yhicPje3ps17jKF66UrbI/dp9/+tMH/453fr7UKV2CwCCnz9h3S9jCAl6igIFvYYuMPWdtDIYDGxZOYUbl45hbWvHZ72Sb+cP7/ixdqHxOMpVaUDHr+Pb+e5NxnY+YGJ8O38t+PkTJg9oxwedetO0zdepzmfpMTyl55w58iuiIiMACH8ZTJGSFfh28Bz2bVvBuWM7AdDrY3n66C5Tlx3Bwck51ccDcO3iSdYtm45Br6PBe+1p9dE3SY5p6ZxR3P/nJg5OzvQaOAUPz/ymx4OePWHED51o16UnLdt/afq9Xqdj3KAvcHHLQ78Rc9KULbH0joOvwl+wYtZAUzvv1n869o7ORIS/YO2iUTwPeIiVtS2f9RpH/sKlABjd531s7exRKlUoVSoGT/kjTdkzou2sXjiK6//H3nmHRXV8f/hdeu/NLrY1FsCuscYWY+wlthQ1iUZNNNYoFuyxYG+xx1hi7/mKGntFRQWxrCKKlV4EpS77+2OXlQVEWHYtv8z7PHmSsHPv/cy5Z2bOnDt3bsAprG0dmDB/j/pc72L8v3gtiIXrtpCpis+/6dJO4/et+/1yxOff46aKz5dv3M75gEAA+nbv8M7j8/zwWD0Tl7bNSIuM4XSN9u9Fgz7iluP/bObcv7tAoaBhy640b6eMFR8/uMPfq6aTkZ6GgYEhPX/0pmxF3cw19BW3vEyMZ+38EYSFBFOvWUe++n68TvTmhbb3IikxntW+I3l0/yb1m3Wgxw/eetOoUCjYsX42N6+qxv8h0yidl53vK8f/tLRUqtZsTPes8T9ROf7HRD3D0bk434/IFrcsm8jjB7dp3+t13JLf3KMw6MPPJwz6AjNzVX9tYMjYOX8D6Dw+13V/np6WyoJJ/cjISEMul1Ojfkva9RgCwJ0bF9m7cT6ZmQpMzSz4Zsg0XIqV1lr7fxGxbULh+c+tvJVKpc2kUunSApSzk0qlvXVxLl0ilUqLPgsuInJ5Jr6rNzJv/HC2LJzBv2f9efD4qUaZSu6lWTdnEhsXTOOz+rVZvnE7AOcCArkbGsaGeVNYM2siW/b58fJV8jvVHxp8mrjIhwyceoQ2faZxeMvkPMtV8PiM78buyPO31JQkrpzYSHH3wk3kdYFcLmfZ8hVMnzqFVX+s4OSp04Q9eqRRpkL5cixetJA/li+jUaOGrF237p3rzI4s8DTREWGM8vWjS/8p7F0/Jc9ye/+cSpf+Uxjl60d0RBh3g84AcP+WP7evHufXmXsZMesATdr2y/N4bciUy9m+diZDxq9g4oK9XDl3iOeP72uUuXB8NxZWNkxZ+g/N233D3k0LAXj++D4B5/yYsGAPQ8avYNuaGWTK5RgYGtLl25FMWriP0TM3cfrwNvU5P/FswPj5uxk/bxcuxctwZM9andUlC7lczpIVK5k5ZRJrli/hxKkzhD16rFGmQvlyLFswj1VLF9Gk0aesXr8BADNTU8aMGMaa5UuYOcWHFavXkpSUpFN9mXI529bM5Ofxy5m4YA9Xzvrlsvn5Y3uwsLRhytKDNG/3NXty2Xw3P49fztbVM8mUywHYsW4OVbwa4rN4H+N9d+BW0l19vuZffoO373a8fbfrLHELcOvaGSLDw5i0+B96DvBh25rpeZbbtno6vQb6MGnxP0SGh3Hr+lkAipWqyA+jFlD+k1oa5YuXqsDoWVsZO3cng73/YOuqqcjlGTrTfef6GaLDwxi34BDdf5zMrrVT8yy3a91Uuv8wmXELDhEdHsadQKXuz9r1Z9ScPYyctZsqNZtydPcK9THulWsxctZuRs7arZPELcCta2eJCg/DZ/FBeg2YxNa32Nln8UGistn56N61SKvXw2fxQaTV63Fkr7LdWVrZ0L3fWJq3/y7XuXaun00Vr4ZMXLifcXN34lbCPVeZ96UbIDNTzr7NC/jE61ONc/21dDwtOvRl4oJ9jP59C9a2DoXWnase188QFR7GhEX/0PNHH3aszbse29dMp8cAHyYs+oeo8DBuZ/Pz/iNz+3kWezbMoYqXdg+J9NGH53fOEdM24O27A2/fHbhX8sCrnvKhSquO/dR/79h7GBWr1NI6cZspl7Np1SyGT1zM9MU78T97mKePQzXKnPl3L5aWNsxasY/W7fuw46/FGr9vXTef6jU0fQPg6MG/KVayrFa63kRR+8Gje9dSqXo9Ji3+h0rV63FU5edH9qyhRNnKjPPdzTc/z2DXn7M1zjfUZx1j5+7UOnGrD98BqN+sA0PGr8h1PX2P/3J5JvNWb2Te+BFsXjgzz/i8onsZ1s7x4a8F0/msfh2WqeLz8wHXkYWG8ee8qayeNYkt+w698/g8P55s2M2ldj+8t+vrI2559uge5/7dxW+zNuM9bwc3Ak4T+TwMgD0bF/Bl95/w9t1Ou56D2bNxoc7qoq+4xcjYhC97/Eznb3S3uCIvinIvjI1NaN9zCJ2/GaFXjaBMJEY9D2PykoP0HjiJravztvNWlZ0nLzlI1PPXdj6iGv8nL8kjbuk/lhY54pb85h4FRV/xOcCvk9fg7btdnbjNQlfxuT76cyNjE4b6rMHbdyfec7dz6/o5HtxVPuDatnoGfYfOwtt3B3Uaf4HfrlVaaxcICsp/LnlbCOyAfJO374k8Z8JSqdTwXQm4FRJKSTcXSri5YGxsRMtGdTlzWXNlaq3qn2BmagpA1UrliYyJA+Dh42d4VZViZGiIuZkpFcqU4uK1G+9KOgD3go5RrX4nJBIJJcp5kZr8gqSEyFzlSpTzwso279WqZ/Yvon7rHzA0MtW33FzI7t6lWPHiFCtWDGNjY5o2acKFCxc1ynh6emJmZgZA5cqViY6Ofuc6s3Pr6nFqNuqIRCKhdAVPkl8l8iI+SqPMi/goUpOTKFPRC4lEQs1GHbkZcAyAi8e20rTdDxgZmwBgZeuoM20PQ4JxdiuNk2tJjIyNqdWwDUFXTmiUCbp8knpNOwBQo34rZMH+KBQKgq6coFbDNhgbm+DkWhJnt9I8DAnG1t5Z/XTdzNwS1xLuxMcqfewTz08xNFS+9FC2ogdxMRE6q0sWsrv3KF6sGMXc3DA2NqZZk0acv+ivUcbLozpmZkr//UQqJSo6BoCSJUpQsoRy9ZaTowN2trbEJ7zQqT6lzUtp2Dzw8kmNMkGXT1C/mcrmDVohu3EJhUJB4OWTOWxeiochwSS/SiLkdgCftugMgJGxsV5WNOfkxpUT1G3SAYlEgnslT5JfJpIQp+nbCXFRpCQn4V5J6dt1m3TgxuXjALiVLIdr8dxJQRNTc7WfpKenolqArjOCA45Tq7FSd5mKqjaZQ/eLuChSkl9SVqW7VuMOBF9RtkkzCyt1ubSUZHQuMAdBV05Qt0n7Atm5XCVPlZ3bE3RZ2ZaDLp9Qt+F6TTsQpLK/ta0jZSpUU9s6i+RXSdy/HUCD5l0AMDLSzp/0pRvg1KEteNZrpV6xAvD8yX0y5XI+8WgAgKmZBSam5oXWnZPgyyeoo/LzsgX08zoF8HNlHY/h5FoSt1IVtNKmjz68IOdMSX6JLPgSHnWa59J05ewhajf6Qqv6AITeu4lLsVK4uCmvX69Ra65fOqlR5tqlU3z6mXJVZe1PW3A7SNlHAlz1P4GzawmKly6vcUxsdARBAWdp0rKT1tryoqj94I3LJ6jXtCMA9Zp2VPv/8yf3kVZXrv50K1GO2KinvIjXXTyjD98BqFilNpZWuRP3+h7/b4eEUtLNVR2ft2hU763xeVRMLAAPHj+jRrb4vOJ7iM/zI/bsFdJjE97b9fURt4Q/eYB7JQ/1eF+xSi2u+yvbhEQiITlZ+eA8+VUStg7OOquLvuIWUzMLyleuiZGJic605kVR7oWpmQUVPqmJsYn+52/K8buA479UOf7Xa9qewEvZxn9VHeo160DgpRxxi5Fm3JLf3KOg6MPP3xX66M8lEglm5hYAyOUZZMozNOJdfbVRgeBN/Fe3TSgjlUq3A1WACcAdYCUgAWTAAGAY0EAqlZ4EpgIxwHzAUFXmp/wuIJVKHYC1gAOgANoCpfO4TlOgm0wm+1l1XLBMJqsmlUr/BBKAioA98CXQCnBXadoP2ABlASfgqFQqrS+TyXqpznMA+EUmkz3U2kpvICo2Dlen15NFZwcHbt1785O9g8dOU7+m8lWfCmVLsW77Pnq1/5yU1DSuBt+hbKnibzxWHyTGR2Bt76b+f2s7NxLjI96YqM1J+KNbvIgLp4LHZ/gfffcrWmNiYnB2clL/v5OTEzKZ7I3lDx8+Qu3atd+FtDfyIi4SO4fXNrd1cOVFbAQ2dq8HuhexEdg6uGqWiVMGHdHhD3koC+DIjkUYGZvStvdoSpXTzetj8bER2Du+vq6dgysP793IXcZJWcbQ0AhzCyteJsYTHxOJeyUPjWPjYzUnYzGRT3ny4E6er7tdOLGHWp+20Uk9shMdE4uzc3YfceSO7N4byx868i91a9XM9fc7srukZ2RQvJhbHkdpT3xsJPZOr89p7+iSh81fl8lu84TYCMpmt7mjK/GxkRibmGJlY8/GZZN48lBG6fJV6N5vDKZmyqDrlN9W/E8doEz5KnT9bpTGq+e6rIudoysJsZHY2r/27YTYSOyy+5hK89t4eC+IzSsmERv1jG9/+T1XgrEoKDVptsmE2AhsNHRHYOeQXbcbCdl0/2/bIq6c3o+5hRWDJq5X/z3s3nV8f+uMrb0L7fuM1jopl5287Byfw87x+dg5MSFWXdbW3pnEF7H5Xi8m8glWNg5sWj6Rp2F3KVXuE7r1/U3tT+9bd3xsBIGXjjPUZw2bV7yeHEU+C8Pc0prVvsOJiXyKtHo9Ovb5FQODoj3fjY/L4S9v8nMHzb40Pi5/P09NecWxfesYPGE1xw/8qZ02PfXhbztn4KVjSKvVwzzbgwyAtNRkbl0/x1ffa/86bnxsJA5Or69v7+hK6F3NSXB8TJS6TFadkhLjMTEx49DuDYycvBy/fRs1jvl73Ty6fzeMlOSXWmt7k96i9IOJCTE5/Fz5MLFEGSmB/v9SvnJNHobcIDbqOfGxEdjYOQESls0YiARo2Ko7DVt210K3fsf//NDH+B8VG4dLtvjcxcGem/dC31j+wLHT1K+prEOFsqVZv30vPd9jfP4ho4+4pVjpCuz/e4mq3Zpy89pZSpdXJt+69RvD0umD2P3XfBSKTEbN+EtvddFl3PIuKMq9sLKxf2c6c8ZZBR3/E7SMW7KT39wjP/Th56DMdy6Z9hNIJDRu1Y1Grbqpy+kqPtdXf54plzPrt55EhT+iaZueuFdUluszaDIrZg7B2MQUM3MrRs3cpJXu/zKZYtuEQvNfXXnrCPREmVAdDswGRshksiZAOtABWARckMlkzWQy2XFgIdBHJpM1A5KA1m+5hjewRyaTNQU+A5LfcJ38uC2TydoCh4COMplsG/BApWm+qkycTCZrJ5PJFgHFpFKpvVQqdQNM9JG4BZSp6BxI3rDqyu/Uee7cf0ifjsoVKPW8qtGgpgcDvWfgs+APqknLY2jwjt0wjw1WJBRs1ZgiM5NjO36nedffdK2qwCjy0v8G+ceOH+fevXt069Y17wLviLw05xStyMuxVPclUy4n+eULBk/eStteo9iyZETe59QROf05z0tJJOTVGLIfm5L8itW+I+jWb0yuCb7frlUYGhhRp/GXOU9RZPKy5Zt85N8TJ7kbEkL3rp01/h4TG8vs+QsZ9esvGOi6jRbAH95UJu8/S8iUy3kceofGrbvj7bsdE1NzjuxRPlxp8vlXTF16EG/f7djYO7Nrg68OKpElMy9Bby9TkD6nbEUPxs/fy+jft3JkzxrS01K1lZkbLdtk9iJtewxj0rJj1GzYjrOHtwBQsmwVJiw5yqjZe2j0eR/Wz/9Fb3pzjTt52lk75HI5jx/cpnHrrxg7ZzumpuYc3avFwzo96d7155w8k7KZmRncv32Vzt+MZPTvW4iOeMLFk/sKqzo3BRh3tPHzQzuW0+zLbwqdFH8buurD8/v7m1bX3rhyinKVvbTeMgHeNM4XoH0iYe/WP2jVobd6tVAW1y+fxsbWnrLlP9Fa15vQVz/YqtP3vHr5glmju3H60BZKulfGwED5EGvEtL/4bfZ2Bnmv4PThrYTcuqK1fg1NevCdnOhr/C9MfHj41Hnu3H9A71zx+XR8FvxBVWl5DIv40Of/FXqIW4qVLEerTv1YMnUgS6cPpkSZShiq/PvM4e106zuamSuP0K3vaDYtn1z0Oqhl6i9ueScU4V68SwpkwwLci8KS39zjrejBzwFGTt/AuLnb+Hn8Mk75bePerQBAv/F59uvnJ/1t/bmBoSHevjuYsfIoD0OCefZIuRjm+MFNDPJexoyV/1L/s47s3jBXp9oFgrz4r668vSaTyTKBR6oVsvYymSxA9dt5QAoE5jimGrBVKpUCWAHBwIN8rlEFZQIYmUymAJBKpe55XEf9LrNUKs3ZXWeVfYQy4ZwX2d+X34xyqwcLYGPexYuOs6M9EdGvn/5Fxcbi5GCXq9zlwJts2HWQZdPGYmJsrP57327t6dtN+bEBnwV/UKqYa65jdU3Ayc0EnlXu61WsTHUS48LVvyXGh2NlV7BVt6mpL4l+dpct85Uf/3j5IopdywfRdfCKd/bRMicnJ6KybYMQHR2Ng0Nu97h67Rpbt21j7uzZGvZ/V1w4uoVLJ5V7BpcsV5342Nc2V67w07S5rYMbCdlWrWRfBWjr4EbVOq2QSCSUKu+BxMCAl4lxWNkUfS9HOwdXjVcX42Mjcr36Yu/oSlx0BPaObsjlGSS/SsLSyhY7R1fiYsI1j1XVS56Rzpp5I6jT+Eu86rXUON/Fk/sIDjjNUJ/VBZ7sFQZnR0eiorL7SAyODrltdfV6IFu27WTerOkaPvLy1SsmTJlO32/6UKWyVOf67BxdiYt+bbe4mEi13XKWsXd0zW3z6Gz3KyYCW3tn7BxdsXN0VT85r1m/FYdVyTYbu9fto1HLLiz/vWgJxdN+f3P+2C4ASpevplEXpZ7cdYmPyaG5EK9XuZUsh6mZOc8fh+T60FNhOHtkC/7HdwJQqlw14mM022Qu3Q5uGivJ4mPCc7VbgBoNv2TtnEG06f6zxnYKn9Rowq5100h6EafVapdTflvVdi5Tvmoedta0Yd52Vuq1tnUgIS4KW3tnEuKiNLYayAt7lT+VVa2w8KrfqsDJ23eh+9H9m6xfpHyImPQijpvXzmBgYISdgysl3Svj5FoSAM+6zXlwNwhyv9n/Vs4c/psL2fxcw19icvfhylU2OfpS+/z9PCzkBoH+R9m/eQHJLxORSCQYGZvQpE3Bd63SVx+e3zmTEuMJCwlmwOjc+1AGnPOjdkPtt0zI0hubrZ+Li4nAzsEpRxkXYqMjcHDK1kda2xJ6N5gr54+xY8NiXr1MxMDAAGMTU+JiIrl++TRBAedIT08j5VUSqxZMYMDwvPdhfBu67AetbR1z+Lmyzza3sOLrwUp9CoWCyT+3UX9c5nUbccSzTgvCQoKpUKVwbxnpy3fyQ5/jv4ujA5HZ4vPI2DicHHL3vcr4/ADLpo3TGPu/69aB77op15VMXvAHJd9BfP6xoI+4BaBhiy40bKHcnmff5sXqVZgXTx2ge39lH1+zQWs2r8j7WxEF5V3HLfqkKPdC35zy26r8AB1QpkJVjXEzLxvmaWd77eIWyH/uURD05ed22fvrus15eC+YilVq6TQ+13d/bmFpQ8Wqtbl1/RzWto48DZOpV+HW+rQNy2YM0lq7QFBQ/qsrb7M/XpEAD6VSadau658Cd4E0NJPbN1Bub9BMJpPVBt62Nv4m0AyUSVlVYjav68QBpVR/q/MWnQCZOcrIs/33VuAroDOw6y36tOaTCu48eR7Js4go0tMz+PfsJRrVrqFRRhYaxuyVG5gzdigOtq9ff5DLM0lIVO4PE/LwMSFhT6jrVU1fUtXUataH/hP20X/CPip6tST44l4UCgVPQ69jamZd4C0TzMytGTbPn8EzjzN45nGKu3u908QtgLRSJZ49e0p4eDjp6emcOn2a+vU1vwgccv8+S5YsZfKkSdjZ5U6svwsatOrNsBl7GDZjD1VrteDq2X0oFAoehQRiZmGtsWUCgI2dM6ZmljwKCUShUHD17D6q1FRmHKrUas79W8rnHFHPHyLPSMfSWjevPpWpUJXI52FERzwhIz2dgHN+VK/dTKNM9drN8D+1H4BrF49SqVpdJBIJ1Ws3I+CcH+npaURHPCHyeRhlK1RDoVCwaYUPbiXcadH+W41z3bx2lqN71zPwt8U62YsyL6SVKvL02XOeh0eQnp7OydNnaVCvrkaZkPuhLFy6nKkTvbHP5iPp6elMnv47rZo3o2mjhnrRp7T5Iw2be9RpqlHGo3YzLp5U2fzCUaQqm3vUaZrD5o8oW6EatvZO2Du6EvH0IQB3bvhTrGQ5AI09xq77H6d4EV/jb9KmF2Pn7mTs3J141G3OpdP7USgUPLgbiJmFVa6Ela29M2bmljy4q/TtS6f3U732Z/leIzryifoDZbFRz4h49hAH56K9wtqodW/1h8Sq1W5BwBml7rB7St02OXTb2DtjZmZB2D2l7oAz+6lWS9kmo1QfVQG4GXACF9X+dy/io9SrTR6FBKFQZGJprV0f1LRNT8bN3cG4uTtUdj6gtrO5hXWedjbVsPMBPFR2zt6G/U/tx6NO/va3sVP50zPlM1rZDX/cVP70IeiessyPqap/atRvRY8fxuNZtzllKlQj+eUL9euVsuBLuJXU3Pe0oDT+vBdj5uxkzJydVK/TnMsqP3+Yj5+bmlnyUFWPy6f3U+0tdh42ZQM+Sw/js/QwTdt+TavOPxYqcQv66cPfds5rF45QrVaTXPsmJr9M5N6tK2/1r7fhXrEKEc8fExXxlIz0dPzPHsErRx/pVacp508cBODK+WNUrl4HiUTCuJlrmbvqIHNXHaRV+9582bUfLdr2oNs3vzBvzSHmrjrITyNnUrl6Ha0Tt6DbflB5f5QrxP1P7aO6yn6vXr4gIyMdgPPHdlH+k1qYW1iRmvJKvfVDasor7gSdp1jpwvfr+vCd/ND3+F+5gjtPnkeo4/NjZ/1zxed3Q8OYs/JPZo8dhn2+8fnjdxKffyzoI24B5ZYhALFRz7nuf4w6qtX8tvbO3LupXE0uu3EJ5yJ+xf5dxC3viqLcC33TtE1P9YcrPes0x/9U4cZ//1MH1ONH9drN8FfVwf/k2+OW/OYeBUUffp6zv74deIHiqv5al/G5PvrzxIRYXr1UfvcjLTUFWdBFXEu4Y2FlQ/KrJCKePQTgTtAFjY8kCwqGQqH44P/50JB8iKL0iVQqbUaOPWZRJjxXqorcA7I+Z/o/lFskLFD92xdlQjcTGAo4Zz9Xjus4AOtRfvhMDrRDuT9tzusogJ0o98Y9pTpf1p63S2Uy2RWpVNpJs2GmAAAgAElEQVQXcJLJZL5SqfR3wAPYBxQHgmUy2c5s110PpMlksoEFtUlM8PlCO8H5gEAWrf8beWYm7Zo3pm+39qz+ew+VK5SlcZ0aDJ08l/uPnuBkr3zK6erkyJxxw0hNS6ff6MkAWJqbMXrgd1Ry1y4g2R+V+yvKBUGhUHB061RCb57B2MSctt/NVCdf103vSP8JygnEiV1zuHX5IIkJkVjbuuDRsDuN22s+Edw87xuadxtTqORts9IhWunOzqXLl1m5chWZmZm0bt2KXj178tfGjVSsWJEG9esz1tubhw/DcFCtuHB2dmaKj0+Rr3s9RruBSaFQsG/DdO7eOIuxiRndf5xByXLKwHXR+M4Mm7EHgCehwexY5U16eipSj8Z0+HY8EomEjIw0dq6ewPOwOxgaGdO212gqVK1f4Otbm2Xk+3vw1TPs+nMOmZlyGnzWiTZdB3Bw6zJKl6+CR53PSE9LZcMSbx4/uIOllS39h89Rr2zz27WKCyf2YmBgSLd+Y6haozEht6+yYFJfipeuiESifEbWofdQqtVsjM/PX5KRkYallTKh5V7Jg14DJr5RWyXzN+9Xlx/+l6+wYvU6MjPlfN6qJX16dOfPTVuoVLECn9ary5jxk3gQFoaDvdJHXJydmTZpPP+eOInvwiWUKV1Kfa7Rw4dSoVzBEldZ3EvJ31eCr55h5/o5ZGZm0qB5J77o+iMHti6jTPmqeNRpRnpaKn8uHs+Th3ewsLLh+2w2P7RrNReO78XA0JDufcdQtabyK/WPH9xh84opZGSk4+Rakm+HTMXCyoY/F3vz5KEMkODoUpzeAyfmuyJQnlnw55oKhYIda2dwO/AcxiZmfD14unp17KzR3Rg7V9k9P7p/k03LJ5CelsInXo3o3t8biURC4KVj7Fw3k6QXcZhbWlOibGWGjF/JpdMHOLp3LYaGRkgMDGjTdSCedVvkqyVNXjjdu9dPRxZ4DmNTM3oOnE6p8so2OW9sF0bO2g3A4/vBbP1jPOlpqVT2akTnvso2+eeCYUQ9e4hEYoC9czG6fe+DrYMrZw9v5vzRbRgYGmJsYkaHb8bgXqlGflIAMDWU5/u7QqFg+9qZ2ew8jTIqO/8+ujvj5ipX+Yep7ZxKFa9GdO8/DolEQlJiPOsWjFKuFnFy4/sR87C0suVFfDRzxvYkJfklEokBpmbmjJ+/F3MLK548vMPmPyYjz0jHyaUkXw+eVui92PSlOzsbl02gWq0m1Kiv3M3pdtAF9vzli0KhoHS5KvQa6IORUe63L+SKgk9kFQoFO9cp/dzExIzeg177+Zwx3Rgz57Wfb14+gfT0FKp4NaJrv9d+vmv9az8vWaYyg8av1LjGoR3LMTWzoHn7vvlqMTLIHbLoug9/0zmzWOjTn1ad+lO1RiMNHRdO7OP29XP0Hz4nT+3mRun51i07QQFn+XvtPDIz5TRq0ZH23b9nz5YVlK1QhRp1m5KelsrqhRN59ECGpZUtA0fOxMWtpMY59m5diZmZOW06aU7k7wRfwW/vRn6dsKhAWl6m5/8BoqL2gy/Vfv4ce6di9Ff5+YO719m4dDwSAwPcSpanz09TsLCyJTriMat9fwWU2yrVbtSWz7sMyFObgST/EFcfvrNu4Rju3bxCUmI8NrYOfPnVYD5t0aVQ47+XwdV8db+J8wGBLF6/RR2ff9etA6v/3k3lCu40rlODYZPncP/RExw14vNfSU1Lo78qPrdQx+dltNLgX6OvVsflh9fGeTg2rYuJkz2pETHcm7qEx+t3vv3AQmB2NedLl5roI26ZN6EvL5MSMDQ0out3o6jsoVyMEXL7KjvWzyFTLsfY2ISeP45X74ebFx9C3ALgM+RzUl4lkZGRjoWlNYMnrKJYPg8QDQ1yrkkqGEW5FxMGfUFKchLyjHTMLaz5ZeIfFCtV+IecireMoVnj/63rynHz6yGvx/+Zo7rj7ft6/N+47PX4/9X3r8f/tfNfj/8/jJiHpbUtCXG545YJC/byNOzuG+ce2ZEUoE/UpZ9HRzxh5ZzhAGTKM6jduC1fdP0RoNDx+dtsruv+/GnYXf5aOoHMTDkKRSY1G3xO2+7Kzx5d9z/GP9uWITEwwMLShq8HT1WfKy9aeph+IPuOfDgs/d+Hn4j8ue073m/lLfznkrf/35FKpWuBlTKZ7FJBj9EmefshoG3y9n2ji+Tt+0Lb5O375m3J2w8ZbZO375u3JW8/ZAozCfqQKEzy9kPjbclbge4pTPL2QyKv5O3HQmGStx8Sb0vefsi8LXn7oaJt8vZDQB/J23fB25K3HzIfa9yibfL2Q+BticQPlbclbz9kPlabg0je5oVI3hae/+qetzpFKpXOAbK/k/xUJpP1eQ86NgOSwiRuBQKBQCAQCAQCgUAgEAgEgnfBh5+6/fAQyVsdIJPJxrxvDQDvI2EsEAgEAoFAIBAIBAKBQCAQCPTDx/mOhUAgEAgEAoFAIBAIBAKBQCAQ/D9HJG8FAoFAIBAIBAKBQCAQCAQCgeADRGybIBAIBAKBQCAQCAQCgUAgEAj0TubH+73C94ZYeSsQCAQCgUAgEAgEAoFAIBAIBB8gInkrEAgEAoFAIBAIBAKBQCAQCAQfIGLbBIFAIBAIBAKBQCAQCAQCgUCgdxSK963g40OsvBUIBAKBQCAQCAQCgUAgEAgEgg8QkbwVCAQCgUAgEAgEAoFAIBAIBIIPELFtgkAgEAgEAoFAIBAIBAKBQCDQO5li24RCI1beCgQCgUAgEAgEAoFAIBAIBALBB4hYeSvg9Kt671uCVlRyTXrfErQi9FXJ9y1BaxwsU9+3BK1Ilxu+bwlaExBX6X1L0Aor04z3LUFrDA0y37cErchM/3j9XPKRRiMSyce7bECRKXnfEv5zmBqmv28JWqHg4/WVlIyPs3M5kfRxxuYADlcD37cErUip6fm+JWiN9fWr71uCVrxINX3fErTGzEj+viVohULx8fbnH3PMJRDogo8zohEIBAKBQCAQCAQCgUAgEAgEHxUKkYsvNGLbBIFAIBAIBAKBQCAQCAQCgUAg+AARyVuBQCAQCAQCgUAgEAgEAoFAIPgAEdsmCAQCgUAgEAgEAoFAIBAIBAK9o8j8GPZN+LD2iBYrbwUCgUAgEAgEAoFAIBAIBAKB4ANEJG8FAoFAIBAIBAKBQCAQCAQCgeADRCRvBQKBQCAQCAQCgUAgEAgEAoHgA0TseSsQCAQCgUAgEAgEAoFAIBAI9M5HseXtB4ZYeSsQCAQCgUAgEAgEAoFAIBAIBB8gInkrEAgEAoFAIBAIBAKBQCAQCAQfIGLbBIFAIBAIBAKBQCAQCAQCgUCgdxRi24RCI1beCgQCgUAgEAgEAoFAIBAIBALBB4hYeSsoMAqFggMbZyILPI2xqTndB8ykRNkquco9eXCTHau8yUhLQerZhPbfeCORSAA4d2QTF45uwcDQkMqeTWnbaxQvE+PZvORXnoTeoFbjznT8boLOtd+4ep4ta31RZMpp3LITX3btp/F7enoaaxZNIuz+bSytbRk0ahZOLsXVv8dEPWfC0O507DGANp2+BeDVy0TWL5vG00chSJDQ72cfKlT20Ln2LG5eO8eO9bNRZGbyaYvOfN75+1x12LBkPI9Db2NpZcv3I+bg6FKCpMR4VvuO5NH9m9Rv1oEeP3jrTeObCL56jm3r5pKZmUmjlp34okv/XNrXL5pIWKjS/gNGzsbJpTgP7gWzccU0ZSGFgvY9fqJG/eY613fz2ll2rp9NZmYmDVt0oXUetv1ryXgehd7C0tqW74fPxdGlBACH96zh/LE9GBgY0L3/WKp4NSQuOpwNS8fzIj4aicSARi278tmXXwOwdv5oIp49BCD5VSLmFtZ4++7QST301UYf3w9i9zof1TWgZZchVKvdsshad66fzc1rZzAxNeObwdMoVS631keht9i4bALpaalUrdGYbv1+QyKR8DIpgXULRhMb9QwH5+J8P9wXCysb9XFhIcH4jv+a/sPnUKN+a2KjnrHadziZmZnI5Rk0bdOLxq2/0kq7tm0RwG/3Wi4c34PEwICv+v9GFa+GAEwY9AVm5hYYGBhiYGDI2Dl/A3D1/BH+2b6C8KcPGPP7ZspUqKqV5pwoFAr2/TWTO4GnMTYxp8fAmZR0z9tXtv3hTXp6CpU9m9DxW6WvHNm1FP8TO7G0tgfgix6/8olXU14mxrNx0a88Dr1B7Sad6dxXN/25rttoFplyObPH9sLOwYVB45YCcOfGRfZunE9mpgJTMwu+GTINl2KltdStW1+JePqQtQvGqI+PjnhCux6Dad7ua9bMH03kszBAOT5ZWFrj7btdK92g9JFd62ep2+jXg6e/oY3eZFO2Ntq131h1G12/YJS6jfYf7ouFlS3JrxL5a/E4YmOekymX06L9d9T/rDOxUc9Y4/trtjbam0ZatFFd+0p6WioLJvUjIyMNuVxOjfotaddjCACyG/7s2TiPjIx0SperQp9BUzA01E1oHRhwgY1rFpApz6RZ6w506PZtrnqsWDCFhyEyrGxs+GX0dJxdi5ORkcGaJTN5ECojU55Bo8/a0rH7d8RERbBi4RQS4mKQSAxo/nkn2nTooROt2blx9Rx/q+OtzrTNI95au2iiKt6y46c84q2JQ7vRocdAdby1bslkgq6cwdrWgWmLizZe6qMvedM5FQoFB/5ewrWLR5EYGNC49Vd81raP+lphIcHM9VaOUzUbtNa6TgqFgn82z+Ru4GmMTczo+uNMipfNPVY8fXCT3WvGkZ6WSiXPJnzZR9mfPw+7zb4Nk8lIT8PAwJAO306iZHkPrp8/wJl/1gBgYmZBh+98KFa6stY6s9DHGHr8n82c+3cXKBQ0bNmV5u2UMdfjB3f4e9V0dd16/uhN2YrVi1yHwuCxeiYubZuRFhnD6Rrt3+m18yL46jm2rvMlU9VGv+iSu42uU8XkVtZ2DBg5Sx2T/7ViurKQQkH7HgOpqYrJX71MZMOyqTx7fB+Avj/7UF7qqVPdCoWCvRt+5/b105iYmNNz0Iw845bHoTfZ+sd40tNS+MSrCZ2+G4dEIuHQ9sXcvHICiYEEKxtHev40A1sHFwBCbl1i31+zkGdkYGltzxCfDUXW+y7nFge2LiXo8gkkEgOsbR34Zsg07FR1+5B1Z/Hv/j/Zs3E+s9eewsrGXivdSu3vLj4/8PdSAi+fxMDAACsbe779WXubCwQFRSRvBQVGFnia6IgwRvn68fh+EHvXT2HIlG25yu39cypd+k+hdAVP1vsO5G7QGaSeTbh/y5/bV4/z68y9GBmbkJQQA4CxsQmtu/5C+JN7RDwJ0bnuTLmcTatmMXLychwcXZk65hu86jalRKly6jJn/t2LpaUNs1bsw//MYXb8tZhBo2apf9+6bj7Va3yqcd4ta+ZSvUYDhoyZQ0Z6OmlpKTrXnr0O29bMZOikldg5uDJ7bG88ajejWKny6jLnj+3BwtKGKUsPcuXsIfZsWsgPI+ZibGxC+55DePYohOePdW/fgmjfsnoWw31WYO/oyswxffCs05Ti2bSf+3cvFlbWzFi+n0tn/dj91yIGjJpN8dLlGT93M4aGRsTHRjFtRA886jTR2eQ4S9/2tTP5ZeIq7BxcmTOuF9Vz2PbC8d1YWNkwZek/XDl3iL2bFvL9iLk8f3yfgHN+TFiwh4TYSJZMG4DPogMYGBrS5duRlC5XhZTkl8z+rSeVPRpQrFR5vh8xV33eXRt8Mbew0lld9NVGXUtW5OepOzA0NOJFfBSLvDvzSY1mRboPt66dJSo8DJ/FB3l4L4ita6YzeuaWXOW2rZ5Or4E+uFf0YMXvg7l1/SxVazTm6N61SKvXo3Wn7zmydy1H9q6l09fDAcjMlLNv8wI+8XrdZm3snRkxfSPGxiakprxixsguVK/drNCBVlHa4mt/2U1CbCSLpw5k8uL9GBgaAvDr5DW5gtZipSswYPQCtqycViidb+NO4Gmiw8P4bZ4fj0KC2L1+CkOn5vaV3eum0vWHKZSp4MnaOQORBZ6hslcTABp/8S3NvtR8EGNsbMLn3X8h/PE9wnXUn+urjQKc+N9m3Eq4k5L8Un2ubatnMHDMItxKluP04a347VrFtz9P10q3rn3FtURZdUI2Uy7He2ArPOspJ88/6LhvuXXtDJHhYUxa/A8P7wWxbc10RuXTRstW9GTF74M02mil6vVo3ekHjuxdw9G9a+n49QhO+23FrWQ5Bo5dSuKLWKYPa0/txu2wsXdm+PRN6jY6c2Rnqtdupp5cFwR9+IqRsQlDfdZgZm6BPCOdeRO/o2qNRpSpUJ2/lk1g6KTVuBYvy8Gty/A/uZ9PW3Qpkt2z6vHnSl/GTV2Mg6MLE0f2o2bdxpQs7a4uc/LofiytbJi/aicXTh/l7w3LGDpmBv7njpGekcbsJZtJTU1hzJCefNqkFUbGJvTpPxT38pVJfvWSCSP6Us2rrsY5daF786rZjJy8HHtHV6aN+Rqvuk0pniPesrC04fcV+/E/c5idfy3ip1Gz1b9vXTePajUaapy3YfP2tGjbgzWLJhVZn679A3jjOS+e3EdcTDgTF+7DwMCARNWYmqVl7ybNcUpb7gadJiY8jOFz/HhyP5D9G6byk0/u/nz/hil07DeFUuW9+GveQO4FnaGSZxP8tvnSvOMQKnk2QRZ4Cr/tvvww7i8cnEvyg/dfmFvacjfwNPvW++R53sKgj34x/Gko5/7dxW+zNmNoZMzS6YOpVqsxLsXKsGfjAr7s/hNVazYi+OoZ9mxcyPCpa4tUh8LyZMNuHi7fhNe62W8vrGeUMflshvso2+iMMV+rYvLXbfTsv3uxsLJh5vL9XDp7mF1/LWKgKiafMHeTOiafOqInnqqYfOvauVSr8SmDxszV25zozvUzRIeHMW7BIR6FBLFr7VSGTd+aq9yudVPp/sNkylT0ZM3sn7gTeJZPvBrzWbv+fPHVUADO+G3i6O4VdPvBh+SXL9i9bho/jl2JvVNxjXaqLe96btGyQ1/a9/wZUMY1h3aupNeAiR+8boC46HDuBF3E3qlYkW3+LuPzlh370r6Xyub/bOZ/O1bSe2Dhbf5fJjNT7JtQWP7T2yZIpdJmUql0aQHK2Uml0t66OFdRkEqlwQUsV1YqlbbV9fVvXT1OzUYdkUgklK7gSfKrRF7ER2mUeREfRWpyEmUqeiGRSKjZqCM3A44BcPHYVpq2+wEjYxMArGwdAeXT/LLSWhgZm+paMgCh927iUqwULm4lMTI2pl6j1ly/dFKjzLVLp/j0s3YA1P60BbeDLqFQbcRy1f8Ezq4lKF76deef/CqJu7eu0bhlJwCMjI2xsLTWi36AhyHBOLuVwslVWYdaDdsQeFmzDkGXT1C/WQcAajRoheyGsg6mZhZU+KQmxib6se/beBASjEuxUjir7F+n0ecE5rD/9csnafCZcjVCrQYtuZ2l3dRcnSDMSE8D1epQXaK0bWkN2wZdOaFRJujySeo1Vdm2fitkwf4oFAqCrpygVsM2GBub4ORaEme30jwMCcbW3pnSqhVqZuaWuJZwJz42UuOcCoWCqxcOU7vRFzqri97aaPb7kJaqXqVbFIKunKBuk/ZIJBLcK3mS/DKRhDhNrQlxUaQkJ1GukicSiYS6TdoTdFl5b4Iun1Dfk3pNOxB0+bj6uFOHtuBZrxXWNg7qvxkZGWOsqld6ehqKzEytdBelLQZePpnDX0rxMCT/br1YyXK4liirldb8uBlwnFqNlb5SpqInKa8SeZHD/i9U9i+r8pVajTsSrPKVN2FiZoG7jvtzfbRRgLiYcIKvns4z2ZacnKT896skbB2ci6Bbf75y54Y/Tq6lcHQurvF3hUJBwPkjRe5bblw5Qd0mHQrURt0reanaaAduqNrijcsnqNe0IwD1mnZUt12JREJKyisUCgWpKa+wsLLFwMBQo41maNlG9eErEokEM3MLAOTyDDLlGSCR8DIxHiMjE1yLlwWgsmd9rvn/W2jNeXH/3i1ci5XExa0ERsbG1G/cigD/0xplAvzP0KS5MtSr2/AzbgZeQaFQIEFCakoycnkGaampGBkZY25hib2DE+7llasmzS0sKV6yLHExkbmuXRRC7wXjUqykeryv2+hzruUc7y+dzBFvXc4Vb5UoXU7jGGnVWlha2xZZnz78I79znjm8nS+6/YSBgXK6Za0aUwFO+m3Bq77mOKUtt68ex6uhsj8vVcGLlFcvSIzXvLeJ8ZGkpiRRukINJBIJXg07cuuqsj+XSCSkpij7vJRXSdjYKR+YlK5YA3NLpd1LVfAkITa8yFr10S+GP3mAeyUPdaxSsUotrvsfV9dNF/15UYg9e4X02IR3ft28eBASjHO2Nlqn0ee55kTXL79uo7UatODOjcu5YvL0bDG5ck50lUZ6nhMFBxynVuMO6rgl+Y1xy0vKVsqKWzoQfEXp52bZHmimpSSr9V899w/V67TE3kk5lmZvp9ryrucW2R/WpqUmfzS6AXb+OYdOXw8v8tziXcfn2W2empqik7mRQPA2/tPJ20JgB+SbvNU3UqnUsBDFywI6T96+iIvEzsFN/f+2Dq68iI3QLBMbga2Dq2aZOGUHHR3+kIeyAJb59GDl9G95HHpD1xLzJD42Egen15rsHV2Ji9Ec7ONjotRlDA2NMLewIikxntSUZA7t3kCHHgM0ykdFPMXaxp51SyYzeURv1i+bSmqK9oNlQepg7/Ta9vaOLiTksH32Mll1eJkYrzdNBSU+JhIHx9f2t3N0JS42p/0jcXDU1J6k0h569wY+w7oyZXh3vh44XqerbgHiYyOwz67PwZX4HBPa+NgI7HP4x8vEeOJjIrF3dNM8Nsd9iYl8ypMHd3K9phdyOwAbW0dcipXRWV302UYfhQQyf2x7Fnp3pFM/nyLfh5w+befomivBHR8biV0O38kqk5gQi629chJma+9M4otY1TERBF46TuPW3XNdMy46nJmjujJxUGtaduyv1etNRWmLCdn8KGd9JBJYMu0nfh/Tk7NHdxZaV2F5ERuJnaOmryTEadYjIS4PX8l2j84f2cK8sZ3Yvmo8r17qb2Kqrza6c/0cOn89AomBZijUZ9BkVswcwviBLbl06iCtO2m+dldw3frxlSwCzvlRu1GbXNcNuX1VJ31LXm00IYeGhHzbaEyONqpczdSkTS8inoYyYWBzfh/Zha79xqqTW3HR4fw+qgsTB7WiZcf+hVp1q9SsH1/JlMuZOao7v33fjMoeDXCv6IGVjT1yeQZh928CcO3CUeKji57cAoiNicLR6XXdHZxccsUtcTniFgtLK5ISE6jbsDmmZuYM+a4dw77vyJed+mCVI/EZFfGMsNC7lJdW04neLOJjo3DI4fM57a/UnXu8V8Zbf9Khx0CdatLUp3v/yO+c0RGPuXrej9m/9WTZjEFEPlduaxIfE0Gg/3Eat8o9TmlDYlwEttm02Ti4qcf1LF7ERWJjr9mfJ6r6/LZ9xuG31Zc5wz/Db+scWnUfnusaAad2UcmjcZG16qNfLFa6AiG3AkhKjCctNZmb184SF6Nsi936jWHPxgV4D2zN7r/m0bHP0CLX4WMmPiZKHW+Dqo3mjL1iotS+nldMPmlYN6YM/4qvB3pjaGiknhOtXzqZqSN7sUFPc6KEvOKWHL6TEBuBnUN2H3HTGLf+t20RU4e04Oq5g7Tprlw1GfX8Ia9evmD51L4s8O7OldP7iqz1fcwt9m9ZzPifWnH5zD/qrX0+dN1Bl09g5+BCybJSrfRq6nr38fm+LUvwHthaZfPBRa6DQPA2xLYJUEYqlW4HqgATgDvASkACyIABwDCggVQqPQlMBWKA+YChqsxP+V1AKpVOBioBtoALMBMYCBQDuspkshCpVDoLqAPYA1NlMtle1XFlASeVpqzzfQ4MAr4GyuehZRhQVyqVVgMGy2SyW9oaJzuKvD4JmOMpk4K8lr8ry2TK5SS/fMHgyVt5EnqDLUtGMGb+Eb0/qcpLd85r5qVbgoS9W/+gVYfe6lU3WcjlcsJC79D7x9GUr1SdLWvm8s/u9XTpraeOuwC2L1CZ98CbPSJ7mTffo3KVqjNl0S6ePwll/eJJVKvZUO+riHP5R56VkJBX7bIfm5L8itW+I+jWb0yuV5ivnD1ELR2uulXq1F8bLV3BkxGzDhD59D7bV3kj9WhctPtQgHaZZ5m3nHbXn3Po2OdXDAxyP++yd3LD23cX8bGRrJ77KzXqt8LGrpArLIrQFvP+s/LYkdM3YOfgQmJCDIun/oRrCXcqVqlVOG2FIL8297rQm+vaoGVPWnYeBEg4vHMxBzfP4asBM/SgNG+K2kZvBJzC2taB0uWrcPfmZY3fjx/cxCDvZbhX9ODovvXs3jCXPoOmFF6knnwFICM9naArp+jYZ1iuclfOHsozqVtY8u5P3l5G8pZWejvwHCXKSPll0lqiIx6zdNoAyleuibmFFfZObozzVb62uGruMLzqt8LGzqko1dBJf25gaIi37w5evXzBqrnDefboHsVLV6T/r3PY9ady66RPPBtgoKuHi3n2jzmL5N2X3797EwMDA5b+eZCXSS+YNu4nqnnVwcVNua9fSvIrFs4axzc//IqFhaVu9OanKVcc8uZ4q3WHPrniLX1TVP/IL8ZMT0/DyMSU32Zv5br/v2xaPokR0zaoVpr9qn4lt6jkN66ry+Rzby4d30rb3mOpWqc1N/wPsWftBPr/tl5dLPS2PwGnd/HjhE06EKv7frFYyXK06tSPJVMHYmpmQYkylTA0ULbFM4e3063vaGrUb0nA+cNsWj6ZYT6ril6Pj5QC+cpbYvKpi3by/Eko6xb7UL1mQzLlch6F3qHXD2MoV6k6W9fO5dDu9XTS9ZxIyxg3e5G2PYbRtscwju1dzdnDW2jT/WcyM+U8eXCLn8avJT0tlSU+vSlT0RPnYmV1Kl/fc4sOvYfSofdQDu9Zwym/v8depgEAACAASURBVLVO4L4r3WmpyfjtXs0vE1bmOk4r3kN83rH3L3Ts/Qt+u9dyym+rSOAWkjx9SZAvYuUtOAI9Ua5UHQ7MBkbIZLImQDrQAVgEXJDJZM1kMtlxYCHQRyaTNQOSgIJ8ZeCJTCb7EtgPdJPJZG0AX+Ab1e/TZDJZC6AFkH1TrziZTNZOJpMdAJBKpb2A74CvZDJZ0hu0LAL2qPQWKXF74egWFo3vzKLxnbGxdyE+2ytTCbER2NhrroqxdXDTeMqlLOOs/q1qnVbK17rKeyAxMOBlYlxR5BUIe0dXYqNfa4qLicDOwSlHGRd1Gbk8g+RXSVha2xJ6N5gdGxYzekA7jh7Ywj+71nPsf9twcHTB3tGF8pWUTw5rf9qSR6F39FYHO0dX4rKt6ImLicQ2h+2zl1HXwarorxkWFXtHF2JjXts/PiYCuxyvrdk7uhIbk7/2YiXLYWJmztNHut23187Blbjs+mIjcr1WZ+/oSlxO/7CyVdo8JlzzWNV9kWeks2beCOo0/hKvepof95LLMwi8dIxan35eZP3vuo26lCiPiak5EU/uFVrrKb+t/D66O7+P7o6tvbOGT8fHRKhX6WVh5+hKfA7fyVqJZ23roH6FOyEuSv3q6aP7N1m/6DcmDWnDtYtH2bZmBoGXjmue18EFt1LluX8noNB1KEpbtMvmRznrbKeulyOedZvz8F6BdskpFOeObGH+uM7MH9cZGzsX4mNy+IpdwX3F2tZJ9fEGA+p91p1H9/X3JoU+2mjonevcuHKSiYPbsG7BGGTBl/hz8TgSE2J5GibDvaLy45O1Pm1DqCxQO9168hVQflCklHvlXA8f5PIMrvsfo1ZD7ZK3p/3+Ztbobswa3Q1be5c82mhu/bnbaJaPOOZoo0qtF0/sxbNeSyQSCc5upXF0KUHEswca57V1cKFYqfLcv3O1UPr11Z9nYWFpQ8Wqtbl1/RwA5aSejJi2gTGztlChSi2ctfywXU4cnFyIiX692ik2OjLXuOngpBm3vHqZhJW1DedPH8GjZgOMjIywtXOgUmUPQkNuA5CRkcHCWeNo2PRz6nz6mU60ZkcZS2n6fO7x/nWZ7PHWg7s32LFhEWMGfKmKt9Zx7H+597MsCvrwj/zOae/oSg3V+O9ZtwVPw5Tj5qP7N1m38DcmDn7zOPU2Lv67maUTO7N0Ymes7VxIyKbtRWy4uq/OQvmWjWZ/bq3q86+d3UuV2q0AqFa3DU+zvXUT/kjGnrUT6fPrUiystP+QUBb66hcbtujCuLnbGDFtPZZWtuq2ePHUAbzqtQCgZoPWhL3lVej/7yhj8re30bgCxOSmZuY8fXQfe9WcqJxqTlSzQQudzYnOHtnCvLFdmDe2Czb2zrnilly+4+CmsdozPiY8VxwMUKPhl9y4dBRQto3Kno0wNbPAysaecpVr8yxMViTd72NukUXtRm25ruUWPu9Sd1T4Y2IinzJzdHcmDm5DfEwEs8b0ICEuWjvt7zE+r9P4C65d1M22SQJBfojkLVyTyWSZMpnsEeAAuMtksqwZ/Xkgr3X81YCtqpW4TYHieZTJSdbSnsfA9Wz/nbXZ1VCpVHoG2AOUynbcxWz/bQzMAEbJZLK0ImgpMA1a9WbYjD0Mm7GHqrVacPXsPhQKBY9CAjGzsMbGTrNDt7FzxtTMkkchgco9Pc/uo0pN5cdUqtRqzv1b/oDyFRV5Rrr6S+X6xL1iFSKePyYq4ikZ6en4nz2CV52mGmW86jTl/ImDAFw5f4zK1esgkUgYN3Mtc1cdZO6qg7Rq35svu/ajRdse2No74eDkyvOnDwG4FXSJ4iXL5by0zihToSqRzx8RHfGEjPR0As754ZGjDh61m3Hx5H5A+fqmtFrdD2L/nbJq7Ur7Xz57GM86zTTKeNZpyoUTyg9/BFz4V23/6IinyOUZAMREPiPi6UMcXXTq4irbhmnYtnptTX3VazfD/5TKthePUkll2+q1mxFwzo/09DSiI54Q+TyMshWqoVAo2LTCB7cS7rRo/22ua94JuohrcXeN14u05V200djIJ+r7EBf9lKjnD7B3LlForU3b9GTc3B2Mm7sDj7rNuXT6AAqFggd3AzG3sM6VvLW1d8bU3JIHd5VaL50+gEdtZcIh+z3xP7UfjzrKv09Z5sdU1T816reixw/j8azbnLiYcPUHNF4lvSBUdh0X1V6VhaEobdGjTtMc/vKIshWqkZrySv3BrNSUV9wOvEDx0hUKre1tNGzdmxG/72HE73uoVrsFAWeUvhJ2LxAzc+tck30blf3D7intH3BmH1VrKX0l+z5zwVf+xa1kRZ3rzUIfbbRjn2HMWPkv05b70X/4HKTV6tJ36O9YWNmQ/CqJiGcPAbgTdAG3ktp90EkfvpLFlbOHqJPHyv07Qf64lnDXeO2xMDRp04uxc3cydu5OVRvdr26jZhZWebZRM402up/qGm1U+fqp/6l9VFe1UQenYty9oexnXsRHE/nsIU4uJXO00QRCZdfV+8kWFH34SmJCLK9evgAgLTUFWdBFXEsofSLrwzbp6Wkc2btOZ6/Bl6v4CeHPHhMZ/oyM9HQunjlKrXqar6zXrNuY08f/B8Clcyeo6lEbiUSCk7Mrt4KU+9+mpCRz724wxUuUQaFQsHrJDEqULEvbTvrZCcy9YlWNeOvS2cMFjrfGzlzHnFX/MGfVP6p4qz8t2vbUqT59+Ed+5/So0xxZ8CUA7t26gktx5VYmU5f7MU31T/ZxqjDUb9mHn6ft4edpe6hSswXXzyn788ch1zE1t1YnZrOwtnPB1MySxyHXUSgUXD+3j09UY7+NnQsP7iinKaG3LuLoqtQZH/OMLUuG0n3gbJzcdPNhO331i1ltMTbqOdf9j6n7R1t7Z+7dvAKA7MYlnT1g+VhRxuSv26gyJn9zGw24cAypqo1G5YjJw58+xNGlGLb2Ttg7uRKumhPdCbpEsVK68ZdGrXszctZuRs7arYpb9r+OWyys8oxbzMwsssUt+6mmiluiVNuWANwMOIFLcaXGarWbE3onQLVPeDKPQoJwKVG0Od27nltEZqvbjSsncS1elLjl3eguUaYSs9eeUveFdo6ujJ2zDVt77d62edfxeXabB10+iVsJ3X38UyB4E2LbBM01/hLggVQqraVK4H4KHAHS0LTVDZQrX6MBpFKpMaD5adz8r6NxTalU6gB0A2qjXAmcfbWsPNt/pwNfoUzWfiWTycLfoKUOeri3Us8m3Ll+mrmj2mBsYkb3H1+/IrtofGeGzdgDQKe+k9ixypv09FSkHo2Reiq/TF67aRd2rp7AgrEdMDQypvuAmerk4qzhLUlNTkKekc7NgGN8/9tqXEvoJnlhaGjE1z+OYf4U5asxjVp0pETp8uzZsoKyFapQo25TmrTsyOqFExk7qCOWVrb8H3v3HR1V8TZw/LtphCQkpJCE3rmI9CZVQBBREER68wcIKGADpSZ0pIZepXeQkoCigtKLSG8BvIJUBUJIoUOS3X3/2M0mmywhZTfl9fmc45FkZ+8+M5k7M3d27txPvp7wyuN26T2YRTMC0cbFks+vID0/H22VeF+Whw69hjF3fF90Oh213/qAAoVL8eOGeRQt+ToVazSkTuPWrJgdwKjPWuDi5s7HA6aY3h/Y912eG8v37LG9fD5iodnTN23J3t6BTr2GMHNsP3Q6HXUbt6JAkZJsWz+foiXLUblmQ+o1/oClswIJ6NcSVzd3eg+cBMDlS6fZEbIce3sHNBo7OvcZTh53607429s70P7j4cz7ti86nZbajQxlu33DPIqULEfFGo2o81ZrVs4ZzqjPmuPq5kFPY9kWKFyKqrWbMn7AB9jZ2dOh13Ds7O25cukUxw5sp0CR0kz4xnAx37LzF5Svarj4NuxVad0tE8B25+j1v06xb/ti09/hg/+NyPAXL69Xqc+FUwcZ80VzHJ2c6dpvnOm1iYPaMWzqJgA69ApkzfxAYmNeUK5yPcpVqQfA2x98zLIZ33BkTwiePv58PHBaip93999rhKwKMt3m2vj9/1GwSJk0x52Rc7FA4VJUrdOUcV+1xs7eno7G+vLofiTfTTHsMajTxlG9/nu8bnza+pmju9m4dBKPH0Yxf+JnFCqm8PmIhWmOO6myld/k0pkDTBrYDCcnZ9p/klBXpg9rzcCJhrryYY+RfP/dcGJjXlC2Un3KGuvKT+uDuH3jT9Bo8MpXkDY9R5veP+HLJqb25sKJ3fQeuhi/Qulvz21xjqb0WZ0/GcWSIMNeuC6u7nTtNzbdcVu7roDhYSR/nvvD4lONTx7eQfV0rrpN6vUq9bl46gBjv3jPeI6ON702aVBbhk417P3WodcI4zn6nNcq16NcFUM7F3+O/rEnBE+f/PQ0nqPN2nzCmvmBTPi6NQCtunyFm7snf577nZBVQYbbHI3naIE0nqO2qCsPo++zam4gOp0WvV5H1drvUKGa4YLwt20rCD11AL1OR/132qNUeCNjhZ4oH90/+YbJo79Ep9PRoEkLChUpwea1iyheqizV3niThm+/z4LpYxjYpy2uedz5fJChDX37vbZ8N2s8Qz7rjB49DRq3oEjx0qgXz3Bo7y8ULlqSYV8abvrq0K0vlavXsUrM8XF36T2EGWP6o9PpqNe4JQWLlGSrcbxVuWYD6jf5gMUzRzCsb0vjeGviK4/73bRhqBdO8vhhNN/0akarjp+aHhib1vhs0ZZYOiZA09Y9WTFrGHu3ryaXswtdPh2d5phTo0ylBvx17gDTB72DUy5nPuyVMIadO6I1n40ztOct/zeKLYuHERvzgjIV61OmoqE9b9VzLD+vmYBOp8XBMRetehjavL1b5/P0cTQ/rDL8bGdnT78xGduT3Vbt4qKpX/Pk8QPj8Yfj4uYOQJdPR7Jp+RR0Wi2Ojk50+WTkS2Ozlcqrp+HdoCZOPp68dW0/l8fO4dZy2+9tb4m9vQOdew1h5tj+6HU66hrP0W3rFxjH5A2MY/IRDO9nOEf7DDSco1cuneaXkBXY2ztgp7GjS59hpjF5p15DWDIzgLi4WPL5FaL7Z6OtHvtrVQzjlolfvYtjLmc6fpLQJ00b+iFfTwoGoE3PkWxYGGAYt1SuR9nKhj7ppw3TCb99HY3GDs98+Wn78SgA/AqWpGylekwb0hqNxo43GrUhf+GMfSGd2dcW29bOJMyYN698+enUO/n4IDvGbU2ZPT7fumaWWZl37hNo1fz8F8i2CWmnsbxn1n+DoigNMWxh8Jnx51AMk6Pxm69cBnoZ//0zhm0JZhj/H4RhglQHfAHkS3ysJJ8zGghVVXWzoijdAR9VVYPiPx/4HAg2HuM00FRVVSXx++LjU1W1vKIoFYG5xlj9LMRyC9gO3AOGqaqa4v3NIce0ObIS+LrZ7gFhtvRcm3O/M3GwS/vTv7ODWK119pXLCo+e58z64pYrLqtDSDc7uxzZJPLkhWNWh5BuLk45s75oNDmzrgBodTnz5iu7HFzmeXM9zeoQ0uW51imrQ0i353E5sw+Neppz23Mv15hXJ8qGnletlNUhpFueM2nbYia7ePjCts+wsCVnB+2rEwmrysljrsYVnLP+dths5tsN2X8OKqCjfbb6u/2nJ2+FgUzeZi6ZvM18Mnmb+WTyNvPJ5G3my8kXEjJ5m/lk8jbzyeRt5pPJ28wnk7eZTyZvM19OHnPJ5G1yMnmbdjlzRJONKYoyBaiZ6Ff/qqraJaviEUIIIYQQQgghhBAiO9DJItI0k8lbK1NVdXBWxyCEEEIIIYQQQgghhMj5cuY9c0IIIYQQQgghhBBCCPH/nEzeCiGEEEIIIYQQQgghRDYk2yYIIYQQQgghhBBCCCFsTp8zn4OepWTlrRBCCCGEEEIIIYQQQmRDMnkrhBBCCCGEEEIIIYQQ2ZBsmyCEEEIIIYQQQgghhLA5vV6f1SHkOLLyVgghhBBCCCGEEEIIIbIhmbwVQgghhBBCCCGEEEKIbEi2TRBCCCGEEEIIIYQQQticTpfVEeQ8MnkrcM2lzeoQ0uVZnGNWh5AuOr0mq0NIN30Ojj2n8nSJyeoQ0iVOl3Nv7Mip9dzdOWfWFcjZ9SWncrTLmX1/Tq4rUc9dsjqEdNHm0DYRwMEuZ+6plzd3bFaHkG7aHHqO5jlzKqtDSLdHlatmdQjpkvdszi3z53EyjZLZcur4XAhryZm9qxBCCCGEEEIIIYQQQvw/J18ZCSGEEEIIIYQQQgghbE6vz5l3xmQlWXkrhBBCCCGEEEIIIYQQ2ZBM3gohhBBCCCGEEEIIIUQ2JNsmCCGEEEIIIYQQQgghbE73H9g1QVGUUsBSwBFYqarqd0lebwEEArHAQ6CjqqqPXnY8WXkrhBBCCCGEEEIIIYQQ1jEJGAi8CfRUFMUryeu/qqpaS1XV+sBJoENKB5PJWyGEEEIIIYQQQgghhLCO0qqqnlRVNQ7YB9RM/KKqqjGJfnQGQlM6mGybIIQQQgghhBBCCCGEEICiKKOBUUl+PUZV1dGpPIQm0b+jgKQrb1EUpTMwBHgOTE3pYDJ5K4QQQgghhBBCCCGEsDl9Dtj01jhJOzqlNIqi5AdCLLw0AdAl+jkvcMbCZ6wD1imK8iUwCBj8ss+SyVshhBBCCCGEEEIIIYRIJVVV7wC1LL2mKEo3RVGqAmeBBsCUJK/nUlX1hfHHSCyszE1MJm+FEEIIIYQQQgghhBDCOoYBSwFHYIWqqpEAiqIsV1W1B9BHUZQ2xrTRQI+UDiaTt0IIIYQQQgghhBBCCJvTZ/9dEzJMVdUrGFbcJv19D+P/5wBzUns8mbwVqabX69myfBIXTh/EKZczXfuNp3CJcsnS3bx6gTXzAomNecHrVerTpsdQNBoNp4/s5OdNCwj79yrfTFhPkZKvA/Dnud/5Ye1M4uJicXBwpFW3r1HKv5HheC+cPsTm5ZPR6XTUbfwhTVt/bPZ6bGwMq+YEcPPqRVzzePDxgKl4+xYEYGfIEn7fHYKdnR3teg6lXOW6RN2/y8q5ATyMvo9GY0e9Jm1o1LwrAKeO/MpPGw15GzRxHUWNecsoW5V5vMj7d/h2QCvea9ePxi27ZzjezCzzW9f+ZMPiccTGxGBvb0+HXgEUK10h1bHq9Xo2LZ/MhVOGsu3WfxxFLJXt3xdZPS+QmJgXvF61Pu16DEGj0fDk0QOWzRhERPhtvPMV4OOBQbi4uad43MjwO6xdOJqoiLto0NBv+Dy8fQuyZv4obl69gF6vxzd/Ubr1H49zbpc0l3/o6cNsXDYFnU5HvcatafZhz2Tlv3x2IDevXsI1jwe9B07Gx7cgF88eIWTNbNM52OajAZStUJOYF8/4LmgQ4Xf/wc7OjorVG/Bhty/THJclmVlXgldNI/TkfuwdHMnnV5iu/cfi4uqeZbGmdEy9Xs+P6+dw+o/f0NjZUb9pexq918X0WTeuhDJ1eFd6DphC1dpN01jqCXJSXQHb/A1Wzx9J6Mn95PHwInB6wlZV/1xX2bBoHC+eP8XLtwDdv5hEbhc3K+ThMJuWT0av01GncWvesZCHlXMCuHX1Eq5uHnw8cIopDzuCl3JkTwgaOzva9xxiykNg33dxzu2CnZ09dnb2DJ2yPsNxviz2jcunGMu/Nc1aJ68vK+YY64ubB72M9eXxo2gWBX3Djb8vUKthSzr1GmZ6z9Z1czi6fztPnzxk1pojVotVr9ezeflkU7/Zrd+4l/SbhrY9vt9sG9+2Pza07ZHht/HKV4CPBxja9ng3roQSFGA4B6vUakpk+G0WBw1Ap9Oh1cbRoFkn6jdtn61iv/vvNdbMH8E/1y7RouPnNDH292G3r7FsRsLWahH3/qF5+340at4tXfFbyk/wiklcPH0Qx1zOdOlreQxz6+oF1s435Kdclfp82N0whtm2ZhqhJ/dh7+CIj19hOvcdl6a2O7Ux2mIscPffa6yZN4Jb1y7xfqeEMo+NecGMkT2Ii4tBq9VSpVYTWnTon664bVFXjh/8id+2LQMgl7MLHXoFUqiYYjqeTqdlytBOeHj50nfo3DTHbSkfthjnPnkUzdLpA7lxJZQ3Grai/ccBGY41sdBTh9mwLAidTkv9Jq1590PzxVKxsTEsmzWCG1cv4ZYnL32+noSPbwGuXQ5l1YLx8Znn/Q6fULXWWwA8ffKIlfPGcvvW3wB0/2wUJZVKVo07LSounoDvew2JuRfBgSrvZ1kclpw/dZj1S4PQG8v/vTbJy3/prBHc+PsSrnny8uk3hvKPFxF+hxFftKVlh09o9sFHVokpK64rPmtfmQJFSgPg5ePPp0MNc0AZua6Q8bnlY/55/g+2rp6OTqcnl7ML3fqPwzd/ES5fPMGWFVP498Zlenw1OUNjcyFexS6rAxA5x8XTB7l39wYjZ/9Exz6j+H7JeIvpvl88nk6fjGLk7J+4d/cGF88cAiB/4dL0+mYGJV+rZpbeNY8nnwyZy/BpIXTt/y2r5wzPcKw6rZaNSyfQP2ABI2Zs5cThX7hjHAzFO7InGBc3d8bM/Ym3WnRj65qZANy59TcnD+8gcEYI/QMW8P2Sb9FptdjZ2/PhR18zcuY2Bk1Yw4Gd35uOWaBwKfp8M51SSfKWUbYq83jBK6ZQrko9q8Sa2WW+dc0M3mv3KcODNtG8Q3+2rpmRpngvnD5E+J0bjJ6znc6fjGTDYstlu8FYtqPnbCf8TkLZ/rp1KUqFNxg9ZztKhTf4devSVx531dwAmrTsbsjPxHXk8TBsa9Om+yCGB20mYNoWvHzyc2BH2idgdFot6xdP5POAeYyeGczxQztMFwDxDu8OwdXNnfHzfqRJi64Er54FgFseT/oPm8WoGZvp/vk4ls9OuMhp2vJ/jJ2zlcCg7/lbPUPoqUNpjs1SrJlZV16rVJuA6cEETNuCb4Gi/BqyNEtjTemYf+zbRlTEXUbM3MbImduoXreZWSxb18zgtcp10l7oSfKUU+pKfLzW/hsA1GrYkv4BC5J93tqFo2nV5SsCpgdTqWZjdv2wwip5+H7JBD4LmM+IGSGcOLQjWR5+3x2Ci6s7Y+Zu560WXQlJlodgPguYz4bFE0x5APhq9BKGB2202cStTqtl/ZKJfBYwj1EzXl5fXFzdGTf3Rxq36ErIGkN9cXTMRcuO/WnTbWCy41as3oChk9ZYPd6Lpw8RfvcGo2Zvp1OfkWx4Rb85avZ2whP1m78Z2/ZRs83bdjBMXG1ba34OunvmY+D41QybuolBE9by27ZlREfey1axu7q5067HUN56/39mx/ErUJxhUzcxbOomhkzegKOTM5VqNk5X7Bbzc+Yg4XdvEDjrJzr2HsWmpZbzs3HJeDr0GUXgrJ8Iv3uDS8b8KBVqMzQohKFTg/HNX5RdW5dYLbZ4thoLuLq5067nUBonKXMHRye+GLWE4UGbGT51IxfPHObaX2fTHLet6oq3b0G+Gr2c4UFbaNamD+sXjTE73t6f1+JXsHia4315PmwzznVwdKJ5h89o3e0bq8UaT6fVsm7xZL4MnMPYWVs4dnAHt29dNUtzaNdWXNzcmTD/B5q834UtqwxtYoEiJQmcuoZR0zfw5Yi5rFn4LVptHAAblk6lfJU6jJsTzKjp35O/UAmrx54W/6wM5liLXlkagyU6rZa1iyYzYMQcxs3ewtFDycv/4K6tuLi6M3HBD7z9fhc2G8s/3oZl0yhfpa5V48qK6wpHp1wMD9rE8KBNpolbSP91hYzPX37M7xd/S/cvJjE8aBM16r/Lji2LAPDyyU+3/uOpXu/dVMcuRHr95ydvFUVpqCjKK786VhQlr6Iona1xrETpRyuK0ja16Y3v6a4oStqX5FnB+RN7qflmSzQaDcXLVOLZk0c8iAo3S/MgKpznzx5TvExlNBoNNd9syfnjewDwL1QCvwLJB3yFi7+Gh5cvAPkLlyI29gWxsTEZivX6lVDy+RfBx68QDo6OVKvbjHMn9pqlOXd8H280aAlAlVpvo4YeRa/Xc+7EXqrVbYajoxM+foXI51+E61dC8fDMZ/qm0zm3K34Fi5su0PwLlbDqYDaercoc4Oyx3fj4FSJ/oVJWiTWzy1yj0fD86RMAnj99hIdnvjTFe+74Xt5o8H6qyraEUgmNRsMbDd7n7LG9Ce9vaMjLGw1bcvbYnhSPe+fW32i1Wl6rVNuYHxeccuUGMK3q0+v1xMQ8B40mTXkBuHYlFF//wuTzN5R/9XrvcPb4PrM0Z4/to1ZDw+qJqrWb8Of5Y+j1eoqUKEte4zlYoHBJYmNiiI2NwSlXbpQKNQBwcHSkSPGyREWEpTm2pDK7rrxWqQ729oYbTYqVrpimPNgi1pSOeXDnRt5t+yl2dobuOY+Ht+lz9u1YR+Vab5PHPcW97F8pJ9UVsM3fAKB0ueq4unkk+7x7t69TqpxhIuC1irU588cuK+WhsFkekpb5ueN7qWVsU6rUfhvVWOZnj+9LkofCpjxkhuvx9cUYe42673AuWez7qG2hvuRyzk2p16rg4OSU7LglylRMc7udGudO7KXmm6ls28tUMvab73PueKK23ViX3mjQknPG/hRg/y/rqPSG+Tno4OCIo6Mhf7GxMeh1iR8snD1iz+PhTdFS5U3toCXq+aPk8y+MV74CL02TVqHH91LDOIYplsoxTI1EY5iyidruoqUrEW2lNiUxW40FTGXuYF7mGo3GtAJOq41Dp41LV59vq7pSQqlsWmlevHQloiMSvoiIirjLhVMHqNP4wzTH+zK2GufmcnahZNmqFtuejLp2JZR8+QuZ+tAa9d7hzLF9ZmnOHN9HnUYtAKhWuzF/nj9uaBNz5TbV6djYGNPf/tnTx/x18RT1mnwAGPpRF9c8Vo89LSIPnSA28kGWxmDJ1cuh+CYq/5r13uF00vI/llD+1es05tI5Q/kDnDq6l3x+BSlYxLqT45l9XZGS9F5XyPg85WM+e/bY8P+nj/HwMoxfvH0LUrBoGTSa//y0WprpdPps/192I7UsXSjvzQAAIABJREFU9fICKU7eZpLuQKombxVFsbfmB0dH3sPTx9/0c15vPx4kWV3yIPIeeb39zNKkZQXKmaO/Uah4WdOFUPpjDcMzcRxefmYDUFMaH0Mae3sHcru48eRRNNER9/D09jd/b6R5ZxJx71/+ufZnmm7TTw9blfmL50/ZtW0Z77bra8VYM7fM23YfTMjq6QR8+jbBq6bTskvabtE2lJt52SYtt2gLZRtf/o8eRJomHjw88/HoYWSKx7135wYurnlYNHUAEwe1J3jVNLPVc6vnjWBY70aE3b5Ow3c7pSkv8bEmriueFsv/Hl7GNInLP7FTf+yisIVz8OmTh5w7cYCyFTK+pUlWnp9H9obwehpWm9si1pSOeT/sFqd+38HkIR2Z921f7t25YfiMiDDOHt1D/bfbpTr2l+cp59QVQyy2rS9J5S9cinMn9gGGLXGiIu5aIQ9JytzblwdJ4kicJnEeHiTKG5i3VRoNzBn3KRMHd+TQb5szHKclURb6oSgLbaWl2LOCpX4zNW179Cva9ujIMM4e20P9psnPwaj7d5nwTRtG9G1Kk1Y9TV9wZJfYU+Pk4R1Uq2vdlUPRUeb9ocfLxjBeSc7vqORjmKN7Q3jNSncKJft8G4wFUqLTapnwTTuGfNyQshVrU7x0xTTHnRl15fc9wZRLtDpxy4opfNB1oFUnKTLj2sLaoiPC8fI2b8+TlX1EuKnviW8THxvbxKt/nWfkl20ZM6A9XT8Zjr29A+Fh/5LH3ZPlc0cz9utOrJw3lhfPn2VepnKQ6Mhw0/gEjOWfZEwQFRGebAzz+FE0L54/45fgFbTs8InV48rs6wqAuNgYJg/pyNThXUyTvfHSc10h4/OXH7NL39EsmNCfgE+acGz/dpp+YL5FgxCZQfa8NSiqKMpGoBwQCPwJfAdoABXoA3wJ1FYUZR8wFogApgP2xjSfpvQBiqJ4YXjSnBegB95L9FoxYK6qqi2MP4eqqlpeUZT3jfE8Bw4BvwKVgW2Kohw2vvYdUNR4qD6qql5RFOUKsBdwBzqku1SS0FvaVVrz6jSapIle4s6tK/ywdgb9AhalJ7xX0iT51tHiJtkaDYY/z8vf+/zZUxYHDaRtj8FW2QcxJbYq8583zqdR827kcrbtIm5blvmBXzfSpvsgqtR6m5O/72TtglF8MXJxqmNLVbmlovxTe1ytNo4rl04xbOpGPH38WTZjEH/s22ZawdKt/zjD7TrLJnLy953UbvRBqvPy8liTln/KaW7fvELw6ll8NdL8VnKtNo4lM4bRqHkn8vkXSltcqZQZ5+eOLYuwt3OgRv3mWRqrxTpiPGZsbAwOTrkYMnkDZ47uYs38kQwct5LNK6bwQdevsLO3wndyObyuGEKxTn2xpGu/sWxaNolfNi2kQo2GODg4ZiDSFAJMGsdL0lj+teG9X49fSV4vXx49iGD22E/xK1ic0uWsu32PpQCSl38q8pdZUhGvxTSvOOyWFVNo1eUr7OySn4OePv4MD9pCdOQ9Fk/9iiq13sY9r7eFo7yCjWJ/lbi4WM6f3EfLztbbpxp4SX6SJnl1X/xr8CLs7O2pXq+FVcNL7eenZyyQEjt7e4YHbeLpk4csmjqA2zcvm/atTDUb15W/Qo9xZG8IA8auBOC8cX/wIiXK8deF42mLNQW2vrawBb2FfiVp0JbSxP99SpSpwNhZm7nzz1WWzR5Fhap10Wm13Lz6J516DaZEmQpsWDqVX4KX80HnfrbIQo6Wuv7Gcp3ZumEhTVt2SddzJdITly2vKwDGLdhJXi9f7of9w6wxvShQpDT5/AsDVriuiP8sGZ8DsGf7GvoOn0fx0hX5bdtygldOpUvfMcnSC2FLMnlr4A3UAwoBq4FoYKCqqicVRZkPtARmAaUTTbDuBTqpqnpXUZRpQFPgRQqfMRwIUVV1laIoqR1xtAP6qqp6SlEUO1VVdYqinAHaqqp6X1GUvsBpVVV7KIpSARgHdAIKAoGqqmb4/rIDO9bz++4tABQpWZ6o+wkrkKIjwvDwNF9dktfbz+y2tuiIMNNtBSmJirjL4qCv6NZ/gqnTyYi8Xn5mt15ERyaPw9Pbj6j7YXh6+6PVxvHs6WNc3TwMq4oSrbSKjkzIpzYuliXTBlKjfnMqv9Ekw3Fakhllfv3Kec4c/Y1ta2fw7MkjNBoNDk5ONGiW/sXlmV3mR/f9QLseQwCoWrsp6xaOfmWM+3ds4PAuQ9kWLfU60RFJyjZJvBbL1hhXHg8vHkSF4+GZjwdR4abbaA3vSX5crTaWwsXL4uNnmNCqWOMtrl8+Z/Z5dvb2VKvTjF0/rEjzICuvt59ZXYmKDCOvhfKPvH8XT28/s/IHiIoIY8GUgfT4Ylyyc3DNQsOm/E1adE1TTC+NNQvOzz/2bSP05AG+GLX4lZN3mRHry47p6e1HFWPslWo2ZvW8kQDc/PsCy2Ya6vvjh1FcOH0Qe3sHKtV8K9V5MeUpB9UVsN3f4GX8Cxbn8xHfARB2+zoXTh7MeB6SlnnEPYtteZSFMs9rzJspDxFhptU58Ss883h4U6nmW1y/HGr1yVvPJLFHR4SR19NS+VuuL5lh/44Npn6zaMnXLfSbqWjbvVJu22/+fYHls8zPQTs783Mwr5cv/oVL8vefJ6lSK3UPLcmM2F/l4ulDFC7+WvomnJM4uHM9RxKNYRL3hw8iwnC3NIaJTHJ+J8rzsf3buHBqP/1HLElT252SzBgLpIaLqzulX6/OxTOHUzV5m1l15d8bf7Huu9H0HTYftzx5AbiqnuH8iX1cOH2I2JgXPH/2hJWzh/G/LyamOr/xMuvawlY8vX2JjDBvz5P3ob5ERdzFy+flbWL+QiXI5Zybf2/+jae3L57evpQoY1iRWLV2Y3YEr7B5XnIiT29fIu+/uvwj7ycp/zweXPvrPCd/38WmlbN4+uQRGjs7HJ2caPxex3TFkpXXFZAwBvDxK0TpctW5de2S2bgsrdcVMj63PD5/9CCSf2+oprskqtVpxrxvrXf36n+VxS9iRIpk2wSD06qq6lRVvYlhZWxxVVVPGl/7HVAsvKc8sMG4ErcB8KpNwsphWA2Lqqp6VVUT19akNTe+5RoD9FEUZQ1g6TGf5YH/GWOYg2GlLcB1a0zcArzZrBNDp25m6NTNVKz5FscO/IBer+faX2dxdnFLNlD08MyHc25Xrv11Fr1ez7EDP1CheqMUP+Ppk4csnNSflp2+pETZKtYIm6KlXufenRvcD/uHuNhYTh7eQYXqDc3SVKjekKP7fwDg9B+/UaZ8TTQaDRWqN+Tk4R3ExsZwP+wf7t25QbFS5dHr9axZMAr/gsVp/L51nkxqSWaU+YCxKxkzbydj5u2k4Xtdadq6d4YmbiHzy9zDKx+XL54AQA09Sj7/Iq+MsUGzjqaN/SvVeIuj+380lW1ulzwWyzZXorI9uv9HKtZolJCXfYa8HN33g+n3Fas3tHjcoiXL8/TJQx49MNwG9VfoMfwLlUSv13Pvzk3A0ImdP7kPv4LF0lDyBsVKvc69Oze5H/YvcbGxnDi0k0rVG5ilqVijAX/s+xGAU0d2UbZ8DTQaDU+fPGTut5/TussXlEpyDm5dN5dnTx7TvsegNMf0MpldVy6cPsRvW5fzyZDZpn2GszLWlI5ZscZbqKHHALh88QS+BQw3Voydv4Nxxv+q1HqbDr0C0jVxCzmrroBt/gYpefQgAgCdTseOLYuoZ+E2+fTl4aZZHirWSFLm1Rvyh7FNOX3kNxRjHirWaJAkDzcpVqo8L54/5fkzw77fL54/5dLZIxQoYp09zC3Hbqgvxw/vtBB7A44kqi+Ksb5klgbNOpoevGXoN9PWth878CMVqydq24116ej+hLZ9zLwdjDX+l/gcjIq4a9hTEHj6+CFX1TP4FiiWrWJ/lROHf7Halgn13+nE4CmbGTxlMxVqvMVx4xjmegpjmFzOrlw35uf4gR8ob4z70plD7Nq2jN6D56S57U5JZowFXubRg0iePnkIQMyL56jn/kj1MxMyo65E3r/D4qABfPTZBPwS1eNWnb9k/MJdjJ23gx5fTaFM+ZrpmriFzBnn2pKhD71FeHybeGgnlZK0iZVrNOD3vdsBOHlkN0oFQ5sYHvav6QFlEfduc/ff63j75sfD0wdPHz/u/nsdgD/PHSN/Yes/S+P/g+KlXycsUfkfO7STyimU/4nfd1PWWP5DJyxjyqKfmLLoJ95+vzPN2/RM98QtZO11xdPHD03PiHn8MIqr6hnyZ/C6Qsbnlo/p4ubOs6ePCbt9HYA/zx3Bv5CcnyLzaf7rM96KojTEsJL1M+PPocBVYEyilbe/AseAZaqqNjOm2wO0V1X1vvFnR6Bu4mMl+ZypwDlVVVcnWnk7CggFfgP2q6paWVGUQsZ0Xoqi5FZV9ZmiKE7ABVVVSyuK8ivwkXHFb38gTlXV74yf4aSqakz8tgupLYNfz8akqhLo9Xo2Lf2WS2cP4+jkTNd+4ylS8nUAJg1qy9Cphv32bv59gTXzA4mNec5rlevRrudwNBoNZ4/tZvOyCTx+GEVu1zwULFaW/gHfsWPLd/y2danZ5Fv/wO/MHtRjiZ0m5bBDTx1ky4op6HRaajf6gGZt+rB9wzyKlCxHxRqNiI15wco5w7l17U9c3TzoOWCKaVXkji2LOLJ3K3Z29rTtMZjXq9TnyqVTzBjZnQJFSpv2+2rZ+QvKV63PmaO72bRsoilvhYqV5bPAhRbj0ulTf0FrqzJP7OeN88nl7ELjlt1fGU92KvMrl06xeflkdDotDo5OdOwVSJGS5dJUthuXTuDimcM4OTnTtf84ihrLdsI37RgetAmAG39fYPW8QGJjXlCucj3afzwMjUbD40fRLJ3+jWG1mY8/vQZOwzWPR4rHvXT2CMGrgkCvp3CJcnT+ZBR29vbMGNmd508fo0dPwaIKHXsHmm4pcrBL/cNvzp88yMblU9HpdNR9qxXvte3ND+vnU7RUOSrVaEhszAuWzQ7g1jUVVzd3eg2YTD7/Qvy0eTE7gpfimz/hHPxy5EK0cbEM7fMO/gWL4+BouHW80bsdqdfk1Q8ridOl/N1gZtaVUZ81Jy4uBlc3wwqi4mUq0qnPiFSXq7VjfdkxwfBl1opZw4i6f4dczi507DOCQsXMvz9cNTeQ8tXepGpt85V9ObWuQObXF4BlMwdz+cIJHj+Kxt3Di+bt+1Gn8Yfs/cnwNGQwrH5u1eXLl05Eal7RJibNw+blU9DpdNR+6wPebdObHzfMo2jJ16loLPMVswP45/qfuLi583GiPPyyZTFH9mzFzt6edt0H83rVetwP+4fvpgwAQKeNo3r993i3Te9Ux2Nn8dZfy86fOsgmY32p81Yr3mvTmx82zKdoyYT6snx2ALeuq7jE1xdj7MP7vsvzZ0/QxsWS2yUPX4xYQIHCJdmyegbHD/5iWmlUt3Fr3u/w6pUsr6or8W1wQr+Z0AZPHNSOYVMT2nZDv2lo29v1TGjbl81IaNs/Hjgt2Yq51fMM52CVWk25dO4IIauCTLdcvtmsE/WapOlZtDaP/WH0faYM7cjzZ0/QaOzI5ZybgOlbye3iRsyLZwT2bcqYuT+T28XyA5K0aRi3JM3P5mWGMYyTkzOd+yaMYaYMbsvgKQljmLXzA4mNfU65yvVo08Mwhhn3xXtmbXfR0hXp0HtkmmJwsEu5nttqLPAgKnmZB87YSmT4bVbNDUSn06LX66ha+x3ea5d857VXXZ7Zqq6sXTiKM0d34eVjWJNiZ2/PkEkbzD77rwvH2f3jSvoOtfyMZn0atjSw5Th3VP93eP70MXFxsbi45qFf4CLyFyr50licHWJTHff5k4fYsCwIvU5H3cYtad62F9vWL6BoyXJUrtmA2JgXLJ01gpvGPqnPwInk8y/EkX3b+SVkBfb2Dthp7GjRvjdV3jBM0t28prJq/lji4mLJ51eI7p+NxtXN/RWRGDyqXDXVsadW5dXT8G5QEycfT16ERXB57BxuLbfu3uoeZ0+l633nTh5iw9IgdDod9Rq3pEW7Xmxdt4BipRLKf/HMEaYxwSdfT0y2ldO2DQvJ5exCsw/StyDneZz5DcyZfV1xVT3D+u/GorGzQ6/T0ah5V+o0/hCdTpfidcWryPjc8vj8zNHd/PT9PDR2dri4utO131h8/Apx40ooi6Z+xdMnD3F0zEWevD6MmBGSLNYmFXNl3V4v2dSQRc+y/UTk5D65s9XfTSZvLU/etsewlyzAZaCX8d8/A4+BGcb/B2HYekIHfAHk4+WTt17AcgwPPtMCLYDBQKiqqpsVRZkF1ACOA++oqlpWUZRJwBuAM7BVVdXJiqL0Az7EsIp3KjAfiH9c5o+qqs6w1eRtdvOqicTsKi2Tt9lNTi3znCwtE3LZyasmWIT15dS6Ajm3vqRl8ja7ScvkbXaSU+tKTpbeydvs4FWTt9lVTr48S8vkbXaSlsnb7MYWk7eZIb2Tt9lB0slbIVIik7fJDV6Y/Sdvp3wqk7cim5HJ28wlk7ciLXLqhJxMsGS+nFpXIOfWF5m8zXw5ta7kZDJ5m/ly8uWZTN5mPpm8zXwyeSvSQiZvk5PJ27STVscGFEWZAtRM9Kt/VVXtklXxCCGEEEIIIYQQQgghch6ZvLUBVVUHZ3UMQgghhBBCCCGEEEJkJ7qcfItJFpF7z4QQQgghhBBCCCGEECIbkslbIYQQQgghhBBCCCGEyIZk8lYIIYQQQgghhBBCCCGyIdnzVgghhBBCCCGEEEIIYXN62fM2zWTlrRBCCCGEEEIIIYQQQmRDMnkrhBBCCCGEEEIIIYQQ2ZBsmyCEEEIIIYQQQgghhLA5nU62TUgrWXkrhBBCCCGEEEIIIYQQ2ZBM3gohhBBCCCGEEEIIIUQ2JNsmCNycXmR1COnyT7RrVoeQLl6usVkdQro52muzOoR0idPl3O+pijnfyuoQ0uXK06JZHUK6xcTlzPpSkwNZHUK6HaJhVoeQLrkcc2abCKDRZHUE6aPV59DAgYfPcuawOyYu55Z5/rw5c4zrnftxVoeQbpHP3bI6hHR5+CJXVoeQbnnPnsrqENLlQaWqWR1CummOhmZ1COmSU/t+gDhdDg5eJKOXXRPSLGdeoQohhBBCCCGEEEIIIcT/czJ5K4QQQgghhBBCCCGEENlQzrx/SwghhBBCCCGEEEIIkaPodbJvQlrJylshhBBCCCGEEEIIIYTIhmTyVgghhBBCCCGEEEIIIbIh2TZBCCGEEEIIIYQQQghhczq9bJuQVrLyVgghhBBCCCGEEEIIIbIhmbwVQgghhBBCCCGEEEKIbEgmb4UQQgghhBBCCCGEECIbkj1vhRBCCCGEEEIIIYQQNqfXyZ63aSUrb4UQQgghhBBCCCGEECIbkslbIYQQQgghhBBCCCGEyIZk2wSRLudP/c66JUHodDrefPsDmrfpbvZ6bGwMi2eO4sbfl3DL40Hfbybi41eA+2G3Gf55O/wLFAWgpFKe//UdbvN49Xo9P6+dwF/nDuDo5MyHvSZQoNjrydL9e/0CwUuGERfzgjIV3+S9LsPRaDTcuXGJH1aOJi42Bjt7e97/aCSFSlQ0ve+fq+dZNK4j7ftNp3yNdzIU64XTh9i8fDI6nY66jT+kaeuPzV6PjY1h1ZwAbl69iGseDz4eMBVv34IA7AxZwu+7Q7Czs6Ndz6GUq1zX9D6dVsvkoZ3I6+VL32FzAVg+ayg3/76AvYMDRUtVoHOfEdg7OGYo/nihpw7z/bKp6HQ66jX5gHc/7JksH8tnjeDG1Uu45vGgz9eT8fEtwMUzfxC8ZjZxcbE4ODjS9n9fUbZCTZ4/e8KUgIRjREXco9ab79Hh40FWiTfehdOH2bR8MnqdjjqNW/OOhfJfOSeAW1cv4ermwccDp+DtW5DHj6JZHPQ1N/++QK2GLenQK6Feb1s3h6P7f+TZk4fMWPOHVeNNjeMnTrJw0WK0Oh3vNn2bDu3bmb2+JWQrO3b+ir29PR4e7gz86kv8fH2tHoder2fT8slcOHUQp1zOdOs/jiIlyiVLd/Pvi6yeF0hMzAter1qfdj2GoNFoePLoActmDCIi/Dbe+Qrw8cAgXNzcX3rciPDbLJ46AJ1Oh1YbR8N3O1G/aXuzz1o46XPu3/uHwOkhVslfyMqJXDpzEEcnZzr1/ZbCxZPn79bVC6xfGEhszHNeq1yf1v8bhkaj4eeNcwg9sQeNnR1u7l50/vRbPLys/3dI6vczF5m2ajM6nY5WjerQvVVTs9e3/HaQTb8dwM7ODhfnXAzv1YkShfKbXr97P5L234ynd9v36Naiic3jzWg5/7A2iAun9mNv74CPX2E6fTqe3K7unDy0nT3bl5vef+fmX3w9YRMFi5VNd6zpbU8AdgQv5cieEDR2drTvOcTUnu/5aS2Hd20BvZ66TdrwVouuhvxe+5P1i8Yb+ik7ezr2Hk6x0hXSHXtioacPs3HZFEN73rg1zSy157MDuWlsz3sPnIyPb0Eunj1CSKL2vM1HAyhboSYxL57xXdAgwu/+g52dHRWrN+DDbl9aJdbE9Ho9wSsmcfH0QRxzOdOl73gKW2hzbl29wNr5gcTGvKBclfp82H0oGo2G00d2smPzAsL+vcrAb9dTpGTCuOHfGyobF4/l+bMnaDQavp6wAUenXFaN3RbjlmuXjrF2dn88fQoBUK56Exq16m+1uJPmYef6b7ly3pCHlj0nkr9o8jzsCZ7B+SPbePb0IUPnnTL9/sZfx/l1w0TC/lH5sM80ylVvZvUYbXGOBvZ9F+fcLtjZ2WNnZ8/QKesBCF41nfMn9mPv4Eg+/0J06z8WF1d3q+bnzMk/WLloJjqdjreavk+rdt3MXr8UeoaVi2dx89rffDF4DLXqNTK9NnHkQC6rF1DKVWTIqKlWjSs1bDH+shW9Xs/WlRO5dOYATk656dj3Wwq9pB/asDDA2A+9yQfGfuiXjbO5cGIvGjsNbu7edEzU31+5eIxtqyahjYvDNY8n/UettFk+zp86zPqlQeh1Wuo3ac17bXqYvR4bG8PSWSO48fclXPPk5dNvJuHjW8D0ekT4HUZ80ZaWHT6h2Qcf2SzOtKq4eAK+7zUk5l4EB6q8n9Xh5KhxS9K4g1dO5JKxD+2cQtzrFhjjrlKfD41xb1tjjNshIW4XV3eePIpm+YwB3Pw7lJoNPqBtzwCrxJs09m2rEs7RDp9aPkf/uXqBDd8lnKOtPjLEHm/f9uVsXxfEmIWHcHX35NSh7ez9cSkATs4utOk5ggJFrVPe/zWybULa/edX3iqK0lBRlORncsaPG2qFY3RXFMXF+O/KiqJY/8omHXRaLau/m8yAkbP5ds4mjh7cyb+3rpqlOfjbNlzd8jB54VaatuzMxlVzTK/5+hdk7Mx1jJ25LlMmbgEunztARNgNvpq8g1bdx/DjqrEW0/24cgytuo/hq8k7iAi7weXzBwHYuTGIRh/0p/+4EBq3/pyd3weZ3qPTafl10zRKVahr8ZhpodNq2bh0Av0DFjBixlZOHP6FO7f+NktzZE8wLm7ujJn7E2+16MbWNTMBuHPrb04e3kHgjBD6Byzg+yXfotNqTe/b+/Na/AsWNztWjfrNGTnrBwKmBRMb85zDu4MznIf4fKxbPIkvAucyZtYWjh/cwe0k+Ti8aysubnn4dv4PNHm/C8GrZgHg5p6Xz4bPZPTMTfT4fCzLZgUC4JzblZHTvzf9550vP1VqvWWVeBPH/f2SCXwWMJ8RM0I4cWhHsvL/fXcILq7ujJm7nbdadCXEWP6Ojk6837E/rbsNTHbcitUbMGTSWqvGmlparZZ5CxYyfsxoFi+Yx94DB7hx86ZZmpIlSjBn5nQWzptDvbp1WbJs+UuOljEXTh8i/M4NRs/ZTudPRrJh8XiL6TYsHk+nT0Yxes52wu/c4OKZQwD8unUpSoU3GD1nO0qFN/h169IUj+uRNx9ff7ua4UGbGDRhLb9uXUZ05D3T55w5uotczi5Wy9+lMwcJv3uT4TN+pn3v0WxeOs5ius3LxtG+1yiGz/iZ8Ls3+fOsIX9vtejB4CkhDJq0hderNmBn8AKrxfYyWp2OKcs3MmtIPzYGBfLr7ye5+s8dszTv1K3OhikBrJs0jG4tmjBjtXk7MX31FupUTj4hYysZLecyFWozeEoIg6eEkC9/MXZtWwJAtXotGDRpC4MmbaFLv4l45iuYoQugjLQnCe15MJ8FzGfD4gnotFpu37zM4V1bGDJpLcOnbeL8yQPcu3MDgJDVM2je7lOGB22kRcd+hKyeme7Yk+Zj/eKJfB4wj9Ezgzl+yEJ7vjsEVzd3xs/7kSYtuhK82tie5/Gk/7BZjJqxme6fj2P57IQLtKYt/8fYOVsJDPqev9UzhJ46ZJV4E7t45iDhd28QOOsnOvYexaalltucjUvG06HPKAJn/UT43RtcMrY5+QuXpufXMyj5WjWz9FptHKvnDqN9r5EMm7aVz0ctx97BuushbDluKVqmGv3HhdB/XIjNJm4Brpw/QOS9G/SfsJPmH43l5zVjLKYrU6kRPQM2Jvu9h1d+WvaYSPk3WtgkPluco/G+Gr2E4UEbTRO3AGUr1iJwxhYCp2/GN39RdgYvtXp+li2YxtAx05g2fy2H9+/in5vXzNJ45/Oj71cB1G3wdrL3t/iwM/0HjrBqTKllq/GXrfx55iD3795g2IxfaNd7NFuWWj4/tywbS7teoxk24xfu371h6ocatejJN1NC+HpSMOWqNuA3Y3//7MlDgpeNo+c3cxkc9AMffTXdZnnQabWsXTSZASPmMG72Fo4e2sHtpNdzu7bi4urOxAU/8Pb7XdhsHKvH27BsGuWrZPz6x9r+WRnMsRa9sjoMk5wybrEY952bBMz8mQ69R7NpieW4Ny0dR/veowiY+TPhd26a+lClQm2GTA1hyJQQ8vkXY9dWQ9wOjk681/5zWnX9xmqxJvWnsf8fOv0yO9VxAAAgAElEQVQX2vYazZZlLz9H2348mqHTfyE80TkKEB1xh7/O/05en4QFC16+Bek7YgVfTw6hSetP2bRktM3yIERS//nJW6AhYPXJ29RSFMU+hZe7Ay4AqqqeUVV1VgppM83VyxfwzV8YX/9CODg6UrNeU04f3W+W5tSx/dRtZBhsV6/TmEvnjqHXZ923K5dO76Fy3VZoNBoKl6rMs6cPeRR9zyzNo+h7vHj2mCKlqqDRaKhctxWXTu0GQKPR8OLZYwCeP3tMHs+E1XB//LaG16u9jVse7wzHef1KKPn8i+DjZyjbanWbce7EXrM0547v440GLQGoUutt1NCj6PV6zp3YS7W6zXB0dMLHrxD5/Itw/YrhO4SoiLuEnjpAncYfmh2rfNX6aDQaNBoNxUpVIDoiLMN5ALh2JRTf/IXJZ6wjNeq9w9lj+8zSnDm+j9qNDN+GV6vdhEvnDXWkSImy5DWuPihQpCSxMTHExsaYvTfs9g0ePYikdLmqVok3nqH8C5uV/9nj5nGfO76XWg2N5V/7bVRj3LmcXSj1WlWLK6+Kl6mIh2c+q8aaWupflylQID/58/vj6OhIwzff5MgfR83SVK5UEWdnZwBeK6tw/36ETWI5d3wvbzR4H41GQ/EylXj25BEPosLN0jyICuf5s8eUUCqh0Wh4o8H7nD22N+H9xrJ/o2FLzh7bk+JxHRwdcXR0AiAuLga9Tmf6nOfPnrL7x9U0a9PHavkLPbmXGvVbGs6n0pV49vRl+XtCsTKV0Wg01KjfkvMnDPlwdnEzpYt5/szsm39buXDlOoX9fSjk54OjgwNv167K/hPnzNK4ueQ2/fv5ixjzFQnHz1LQ14cShfxtHmu8jJZz2Yp1sbc3TLQVLV2R6Mjk7d7p33+map13MxRnRtqTs8f3JWnPC3P9Sih3/7lG8TIVccqVG3t7B0qXq8aZo4Z8aTQanhn7qWdPH+PhZZ0259qVUHz9E9rz6vXeSZaPs8f2UauhoT2vWrsJf1pqzwsntOdOuXKjVKgBgIOjI0WKlyXKSv1PYqHH91LjTWNdeUWbUzy+rrzZkvPHDWXqX6gEfgWKJzvun+d+p0CRMhQspgDgmicvdnYpDefSzpbjlszy15ndVKxtyEOhkpV5biEPAIVKViZP3uTx5fUphF9hxWZtoS3O0ZSUq1zH1PYUL1OR6IjkZZERV/66hH/+Qvj5F8TB0ZE6bzbmxB8HzdL4+uWnaPFSaOySl2mFytVxzm29LzTTwlbjL1sJPbmHasZ+qKixH3qYpG15mKQfqla/JaEnDOdn0v4eYx0/dfgnKtRogqePYXVrHo+MX1u8zNXLofjmL2Rq22vWe4fTScfqx/ZRx+x67rjpeu7U0b3k8ytIwSIlbBZjekUeOkFs5IOsDsMkp4xbkjp/IlEf+oq4zfrQ+LgrJcRdrHRFHhjjzuXsQomyVXFwtN05e+HkHqonOkefp+IcrV6/JReM5yjAttWTadH5azQktJfFylTBxc0DgKKlEvIkRGb4z22boChKCWA1EAvogeLAE0VR+qiq2lRRlF8AZ+N/3YFwIFhV1YbG988DvldV9YCFY08DagNnMZatoiijgVBVVTcritIQaKuq6meKolwAfgGqKYrSDlgHOBkP1R54DagMbFMU5TDwc6L3tgaGGuNfrqrqd8bPKQj4A4WAdqqqXrFOqZmLiryHl4+f6Wcvb1/+vmw+YI1OlMbe3oHcLm48fmToRMPDbjNqQGdyu7jxYee+lHm9ii3CNPMwKgwPr4TJBQ9Pfx5G3TO7WHgYdQ93r4R8uXv68TDK0CC/23kYq4J6s+P7qeh1OvoErjMd99KpXfQYsoJ/lwZmOM7oyDA8vRNiyOvlx/XL55OnSVK2Tx5FEx1xj+JlKpq9N75z37x8Cq27DuT58ycWP1cbF8uxAz/StseQDOcBIDriHl6J8+Htx7WkdSTiHl7e/mb5ePwomjzunqY0p47sonAJxTQBF+/4oR1Ur9vU6hd00ZH38PRJqCee3r4Wyj8hTeLyd0sUd3YSERFBPh8f088+Pt78qf710vQ7fv2NGtWrvfT1jHgQeY+83gnlm9fbj+jIe2YT29GR98ibpO48MK6WffQg0pTWwzMfjx5GvvK4UffvMn9if8Lv3qJ1t4GmiaTt38+l8fsf4ZTL2Yr5CzOPw8uPB5FhZvl7EBmGR6J2xsPbz2zg99P3szhx4AecXfLQf8Qyq8X2MuFRD/DzTqi7ft6ehF65nizdxl/3s+6nvcTGxbEg8AsAnj1/waoff2Pu8M9Zs32XzWONZ41yjnd0XwhVaiW/Ffv0kR18/M2cZL9Pi4y0Jw8iwyiWuD031un8RUrxw/o5PH4UjZNTLi6cPkSRkobvn9v2GMzc8X0JXjUdvV7HN9+uylD8L82Hlx/XLOTD6xXt4qk/dlG4eNlk7fnTJw85d+IAbzXvYpV4zeKKMm8bPIztiXlduUdeL/N+Nzoq5Um18Ns30Gg0LPj2Ex4/jKJqnWY0btUzxfekla3GLQC3rpxh7ogPcM/ryzsdB+FXsLRVY4/3KDoMd6+EFUvunv48ig6zOFGbFWxxjoJhHm7OuE9Bo6H+222p93bbZJ/9+56tVKubsW22koqMCMc7X0LZevn4ckW9YNXPsJWcNv5KOu7wMPZD7kn6IbO2xdvfNJ4B+NnY3+d2caPvCMMdT+F3rqPVxjF/bHdePH9C/WZdqf5mK5vkIToy3NRug6HMr/1lPlaPighP1rYb+h9nfglewdejF7Bzm3X6mv/Pcsq4xVLcnqmIO2kf+tK4a1t/65uXeRB1j7xeSc7RqCTnaJR57B5e/jww9v8XTu7Bw9MvxS0Rju0Lpmyl+jaI/r9Bdk1Iu//iytuGwBbjZGxjYAUwSlXV+E3+2qqq2ggYDQxQVTUSuKsoymuKouQCagAHkx5UUZSqQGlVVesAUzFMoqbEFVht/KwHQHNjTOuBnqqq7gfOAK1UVR2c6HPsgG+NsdcHeimKEt8KRaiq+j4wAzDf5MqaLJxoib+RArC0yFYDeHj5MG3xdsbMWEfHHgNYOD2QZ08f2ybOVwaUNObkaeLzdXzPBt7tNJRB0/fybuehhCwzTNT+vHYiTdt9bfUVN+ZhvrpsDXmxEL9Gw/mT+8nj4WW6wLdkw5JvKfVaNUq9Zp1JO4shJktjOd54t2/+zZbVs+n6afJJ8eOHdlKzvg0GAKmoJ6lKk42kVK+T2r1nL5cvX6Ftmw8tvp4psbys8UjncT19/AmYtoXRc7ZzdN8PPIyO4Na1Pwm/e5PKbzROdeypYTGOZHUj5frTvMOXjJq3m2p1m3Nw57rkaa3Mctkl175pA7bOGs3nnVuxLGQHAN9t/olO776Fi3PmrXYC65QzwG8h32FvZ0+1eua3ZN+4cg6nXLnJXziDE1oZaE8s/1pD/kIlePuDHswZ+wlzx/ejYNEy2NsZvoc/uHMjbbsPYsJ3v9K2+yDWzB+dsfhfEaN5kpTT3L55heDVs5K151ptHEtmDKNR807k8y9klXCTBJZSWMYkqW8j4+l0Wq7+eZpun0/iy7ErOXd8N+p5K+9nbqNxS/5i5fh62m4+G7eVWk26sG72Z9aN2yy+5L97VdlmKhucowBfj1/JsKnf81nAPPbv+J7LF0+apftly2Ls7e2pWb95eiN/idS0jdlUTht/peb8tPj3SPj3ex2+ZOS83VSt24JDxv5ep9Pyz7WLfDx4Pr2HLuK3kIWE37luxcATxZeq8rTcxmzdsJCmLbtk2UrtnCbHjFuSSV/cSdP8GvIddvbJ47al1PTtL7srOObFM3ZtXcQ77V7eP165cJRj+4Jp3inztmsR4j+38hb4HghUFGU1YPb1onF/2XnG1blOGFbdAiwFegLHgB9UVbV0ppcGjgKoqnpVUZT7xt8nTpu4xXimqupZ47+9gPmKovgAHliYHE4kH/CvqqqPjTGfxbB6GCB+dHgTwySzTXh6+xJ5P+EbtciIe+RNcntmfBovHz+02jiePX2Max4PNBqNadVNsVKv4etfkLu3b1K8lPV3rji6ay0n9m8GoGDx8jyIvGt67UHUXdzzmsfs4eXHw0TfFD6MCjPdZnj68Fbe62LYn7d8jWZsW2bYE+zf66FsXPA1AE8fR/PXuQPY2dlTrlr6HtqT18vP7NbR6MiwZLe+enr7EXXf8E2oqWzdPMjr7UdUxF3z93r6cv7EPs6f2MeF04eIjXnB82dPWDF7GN2/mAjAT5sW8PhhFJ2+GZmumC3x9PYlMnE+IsIs1BE/IiPu4pm4jhhvQ4m6H8b8yQPp+cU4fP0Lm73v1jUVrVZL0RQmo9Mrr7cfUfcTyjAq4h4eSW41jU/j6Z087uzIx8eH8Pv3TT/fvx+Bt7dXsnSnTp9h/fcbCZo8ESdH6zy0DmD/jg2GhysBRUu9TnTiOhqRvH7n9fYz274jOiLM9DfI4+HFg6hwPDzz8SAqnDzuXone84rjevmSv3BJrlw6yeOHUdy6eokR/Zqh08bx6EEkM0f15KsxaV/peujX9RzZY2hnipQobx5HZBjuSeqPh5e/2YqEB4nyl1jVus1ZPKUf76YwcLQGX6+8hEVEmX4Oi4jCx/Pl9blp7WpMWvo9ABeu3GDP0TPMWbeVR0+fYafRkMvRkfbvNLB6nNYu52P7t3Hh9AH6BSxJdpFx6vdfqGKFWw8z0p7kNbbzpjxGJKx0qdv4Q+oat8DZtna2aaX6H/t/pF1Pw90TVWs3Ze0Cy/uLZjgfkS9pz1/SLkZFhLFgykB6fDGOfEna8zULx+GbvwhNjA9ds4aDO9dzZLehzSlS0ryuPIhIXlcMKyaT9Luv2OYmr5cfpcpVM634K1elPv9cu4RSoVaGYs+McYtz7oTbtctUasCPq8by5FEUrnmss3rx+J61nD646f/Yu+v4pq73geOf1JUKVPCiwd1lMGAMNsZwG2zDNmwCDC0yhhcdui++wXAoMGC4y6BYoR0EdyrQQpHSpk1+fyRNlVJJKr8979drr9H25uY5J8899+bJybkAFPCqSERY/BraEeFBOOSQWbdgumM07hsejk55qVyrCXdvBFCqnO7D8X+O7CDg/DF+GL/E6IVV17zuPAuNn9kZ9jQEF9d8qTwi58gN118n9q3ljP48VDjJeehFWPJzubOrZ+Kx5VlQsvEHoGr9T1nu058WHQfh5OqBvaML1jZ2+q+W1+DxPRVu+b2M3h7de7XEfZ7y+7mgZO/n7ly/wvlTB9j0+6+8ef0ShZkZllZWNP2ki9HjzK1y43UL6M+hcXGXqJDsvWVKcSc9h+ZJGveFYwwckzxuYzu5by1nDic4RsMSH6PJzv9JYn8RFoSTizvPgh8QFvqI2SPbGR47x7sD309cTx5nNx7fV7Fp6Xj6jPgNe0dnk7ZJiIT+izNvNSqVapRKpeoBNAM0xBexWwBhKpXqA+AX4outB9DNcu0FvOuWnzeAWgBKpbIYEHe1FA7EvVtJWFCNTfDvL4CjKpWqEbAkwfNGk7zA/hQoqFQqHZRKpSW6pRXi7kbwrkKxURUrVY6QJw8IDX5EjFrN2RP7qFrrg0TbVK31AScP7wTg3KmDlK1YE4VCQcSLcMMNHUKCHhL85AFuHgVNEmftZl8YbshRtlpTLp3cjlar5cHNS9jYOib72p6jszvWtvY8uHkJrVbLpZPbKVu1ieFvd6/5AXD76j/k9SgKwNCZBxg66yBDZx2kfI3mtPpyXIYLt6ArboU8ucfT4IfEqNWcP7mHijUaJ9qmYo3GnDm6A4CL/+yndIVaKBQKKtZozPmTe1Cro3ka/JCQJ/fwKlmBz7/4gcn/O8DERXvoNdgHZYVahsLtyYNbuHrpFD1/mI6ZmfGGA6+S5Ql5cp+n+hzxO7GXyjUTt6NyzUacPvwXAOdPH6CMPkfevH7J/Mnf0bb7d5QsWyXZvv1O7DHNrFvi+v9+ov6vVDNxIapSjcb8c0Tf/6f3o9T3f06lLF2KR48eExQUhFqt5sixY9SpXSvRNjdv3WLegoVMGDcWZ2fjXoQ0atGF0TM3MXrmJirXbMKZo3+h1Wq5c90fWzvHZEUSJxc3rG3tuXPdH61Wy5mjf1Gppu5u2BVrNOaMvu/PHNlh+H2lGo1T3G/4syCio94C8OZVBLdVl/Ao4MUHH3dmypKDTFy0hyETf8e9QNEMFW4BGjTvarhZRIUaTfA7vgOtVsvdG/7Y2jmk3D4bO+7e0LXP7/gOKlTXtSNUf+Mp0K2P5p7CWpvGVq5EUe4HhfIo5CnqmBj2n77AB9UrJdrm/pP4YsCJi4EU8dS1aenPg9kx/xd2zP+Fri0b83Wb5iYp3IJx+/nqpRMc+ms5fX6aj5W1baLHaTQa/M/so2rdzL8Jysx4UqlmoyTj+X28SlYA4OUL3ZrUYaFPuHTmIDUbtDS0+UbgOQBUV87ilr9IptsAycfzcyf2UrlGknbUbMQ/R3Tj+YXTByhTIW48j2DB5O9o+8X3lCyTeImkbWsXEPn6FZ16DjNKnHEaftyV4T6bGe6zmYo1m+B3TJ8r1/2xeWeu2HNXP+b4HdtBBf3Y8i5lKtfj8b0bREdFEhsbw81/z+FZqESmY8+K65aXz0MNs40e3r6MVqvFzsF4437NJl/wzfhtfDN+G8qqTbl8WteGh7dSbkN2MsUxGvX2DW8jdUtURb19w1X/0xQoUhKAwIsn2bdtJf1G/Jps7DGGEqXLEPT4ISFBj4lRqzl17CDVazcw+vOYQm64/mrQvBtDp21l6LStVKjRlPP689C9G7qxJU+SsSWPixs2Nnbc05+Hzh/fQYXquuMz4fk+MMH5vkKNJty+dp7Y2BiioyK5f/My7gVNs6ZssVLlCU70fm4vVZL0eZWajTiV4P1c3LX6yCkr8FmyC58lu/jos2582r6XFG6TyI3XLaA/h07fwvDpW6hYI8E5NK1xH9tBxRrxcR/csZy+w5LHbQr1m3djyNStDJm6lfI1mnIu4TFqm/Ixam0bf4yeO76D8tWbkL9IaSb8dhzvefvxnrcfJ1cPBk/eTB5nN8KfPub3OT/QdcBUk3yo8l+i1Whz/H85jSI7byKVHZRKZScg7ta6T9AtMTAL3WzVn4AdwDMgECitUqla6R83HqinUqneuUBVgjVvrwCNVCpVGaVSmR/Yjm4W711Aq1+3NkClUlXQP64SsEYfwyNArd9mANAOOAycJH7N2/bACHTF2lUqlWrxu9bWTUufnLr6Mt1J4H/uBOtWzEYTG0vDZq35rGNvfNf+hlfJslSt1Qh1dBRL5o7j/m0V9o556Dd0Cu6ehTh36iC+6/6Hubk5CjMz2nb5lipJCr9p9fC5fZq31Wq17Fw9kRtXTmBpbUO73lMoWEz3Rnjh2LYMnOgLwKM7AWxdNgp1dBSlKzXk0+5jUCgU3Lt+nt1/TiFWE4ulpTWtvhxHQa/Ed1ffunQUpas0pkLN1Ncwc7VXp/r3gAvH2bLKB40mlroftqFF+2/YuX4hRUqUo1LND1FHR/H7/NE8uHMNewcneg32IZ+H7uume7Ys4fThbZiZmdOh53DKV028Ds/1QD8O7vid/qMWAPBd56q4uuXH2kbXl1VqN+WTjv3eGZuleew7/5bUlfPH2bBiJhqNhvpNP+fTDn3Yvm4RRUuUo0qtxqijo1j+6xge3FFh75CHvkOm4eZZiF2blvL31hW4Jyg8/DhuMXmcdbMsR/dvxXfe88lfKO2FrRhN2gvTAReOs3mlDxqNhrpN2tCyfV/+Wr+QoiXKU6mmLu5V87x5ePcadg556J2g/8f0b8nbyFfExqixtXPku7G/kb9wCbaunsO547sNs0brNW1Hq8790xRPCdv7aY79Xc76neO3JUvRaDQ0/6gZ3bp05vfVayhdqhR169RmxOgx3L13D1cX3cwrdzc3JozP3B2nb74pmux3Wq2Wjcun8O+lk1hZ2dB94ESKltAdR1N+6sjombrZWvduBbJ64RjU0VGUq9KATr1HoVAoePXyOctn/6SbeZPPkz5DZmHv6PTO/V71P83WP2aiUCjQarU0atE12dqDz0IesXjaIMbM9jX8LjomYx9kaLVatqyczDX/E1hZ29Ll24kUKaEbZ2aMbM+wabrZgPdvBbDutzGoo99StkpD2n09GoVCwco5PxLy+C4KhQIXtwJ07D0u0Vpc79OAIxmK++TFQGb/sZlYjZbWjevQq20Lftu0k7LFitCoRiVm/r6Zs1euYWFhTh57O4Z93YkShfMn2seSzbuwtbGmR6uMfXh1gsZp3jaz/Tz5x5bEqKOx08+WKFqyEp36jAfg5r9n2bluLj9OTNuSFdaWqY+JmRlP/t6ylNOHtmFmbk7Hr4dTvpquCDNrzNe8fvUCc3ML2n/1E2Uq1dbFfvUCm1b6oImNxdLSii59vVNdLsdckfZT/5Xzx9m4coZuPG/yOZ906MuOdYsoWrIclfXtWDHP2zCe9xk8XTeeb17Knq3LE43nP4z7jdgYNSO/+RjPgsWw0M/0/7BlFxo0e/+SLW9j0r5MkVarZfOKyVz1140N3fpPooh+zPEZ3oHhProZOvdvBfLnojGo1W8pV6UB7XvqcsX/7EG2rJzCq4hwbO0dKVS0DP29/weA3/G/OLBtOaCgXNWGfN79/V+djIhM+xfeTHXd8s+BPzl7aB1m5hZYWlrTsutIipRK/d4D0TEZK5BptVr2rJ3IrYDjWFjZ0LrnFAp4VQRgyYQ2fDN+GwAHNs0g4OxOXj7XrelbtUEHGn3+HY/vXGHjokG8fR2BhaUV9k5u9P9lZ7piyO8clerfjX2MPg1+yP98BgOgiY2hRsNPaNm+LwDjB7VCrY7GQT/2eJWqSLdvUz7XutpkbEmxi36n+H3pPDSaWD78qBVtO3/FxjVLKV6qDDVqN+TW9avMmjyK169eYmllhbOLKzMX/amLb3h/Hj+8z9u3b3B0dOLb70dRuXrtdMcQ9tbh/RulwBTXX+kRqU7f8bl15SRU/iextLahy7eTKKw/D80a2Y6h07YC8OBWAOt/80YdHUWZKg1o+7U3CoWCVXN+IPTxXRQKM1zc8tOh93jDeqeH/1qB31FfFAozan/Yng8++fK98TjbpJ7n73L5/AnWL9ddqzdo2ppWHfuwbe1ivEqWo4r+/dzSuWMN7zm+HTo12RI329f/hrWNHS3avD/OpF5UNu6Nh+NUWT2LvI1qYZXPhajgZ9z4ZT4PVm426nMozqR+g8KEctJ1S3o+74iL++olXdxd+8XH7TOiPcOnx8e9dnF83HHn0Ek/JI7bq1R83BMGNScq8hUxMWps7fPQf/SS934IGqNJe/BarRbfVfHHaOdvJ1G4uC722aPaMWSq/hi9rTtGY6KjUFaOP0YTmvz9R/w4aSP2eVzYuGQcV87ux8VNdx1sZmbBj5M3vjeez6pb5NyZPtmk3/TwHF+I/G2ES4563f5zxduMUiqVYwGVSqV6/9GZy2SkeJsTpKd4m5O8r3ibk6WneJuTpKd4m9MYo3ibHVIq3uYWGS3eZreMFm9zgvQUb3OS9xVvc7L0FG9zkvQUb3Oa9BRvc5KMFm9zgvcVb3OqjBZvc4KMFm+zW3qKtzlNRou32c1UxduskJ7ibU6Sg78s+F7pKd7mNFK8TU6Kt+mXe89SWUg/o7YCMFX/cxMg6QKh36lUqitJHyuEEEIIIYQQQgghhHj3DePEu0nxNg1UKtXQJD8fAg5lUzhCCCGEEEIIIYQQQoj/gNz53VAhhBBCCCGEEEIIIYT4f06Kt0IIIYQQQgghhBBCCJEDybIJQgghhBBCCCGEEEIIk9NoZM3b9JKZt0IIIYQQQgghhBBCCJEDSfFWCCGEEEIIIYQQQgghciBZNkEIIYQQQgghhBBCCGFyWq0sm5BeMvNWCCGEEEIIIYQQQgghciAp3gohhBBCCCGEEEIIIUQOJMsmCCGEEEIIIYQQQgghTE6rkWUT0ktm3gohhBBCCCGEEEIIIUQOJDNvBa+jrbI7hAxxsVNndwgZkpvX5o6KyZ1DhoLc2+lXXxbL7hAyxMJMk90hZJi9de4cW45ENsnuEDIst+aLWS4eW55H5s5zv71VTHaHkGFuDlHZHUKGqDXm2R3Cf86Tl07ZHUKGWVnkzvHcxiI2u0PIsLe59fr8TEB2h5Bh2toVsjuEDGm0qGN2h5Bh5g6O2R1CxlUfkd0RiP8HcudIL4QQQgghhBBCCCGEyFVk2YT0k2UThBBCCCGEEEIIIYQQIgeS4q0QQgghhBBCCCGEEELkQLJsghBCCCGEEEIIIYQQwuQ0uflGQNlEZt4KIYQQQgghhBBCCCFEDiTFWyGEEEIIIYQQQgghhMiBZNkEIYQQQgghhBBCCCGEyWk1smxCesnMWyGEEEIIIYQQQgghhMiBpHgrhBBCCCGEEEIIIYQQOZAUb4UQQgghhBBCCCGEECIHkjVvhRBCCCGEEEIIIYQQJqfVypq36SUzb4UQQgghhBBCCCGEECIHkpm3IlVarZbNK6cTePE4VtY29BgwkcLFyyXb7v7tf1m9cAzq6CjKV21Ih54jUCgUvH71ghVzhhEW+hhXtwL0HjwTO4c8hsfduxnATO/u9BrsQ9U6zQFYOLkfd29coXiZqvQfueA/F7up4r4e6McSnx/I614QgCq1m9KyQz/D/jSaWHxGdsXJ1T1T/Z6wHVtWTjO0o/uASe9oRyBrErSjfc+RKBQKLp7ey+5Niwl+dJufpqyjSInyANy9eYX1/5ugew60fNJxAJVrNc10rKbKleuBfmxZ5UNsbAwOjs78OGElwY/vsGLOcMN+n4U85NNOA/jw0x6ZbsfW36dy9eJxLK1t6NZ/MoWLJW/Hg9uBrF08BnX0W8pWbUi7r0ahUCjYvmYmgReOYm5hQT6PwnTtNwk7+/icD3/6hKlDW9OiwwCafNYz07Fm9fEZ9vQJa3/7mfBnQShQ0H/UQrOjGoYAACAASURBVMPxYAwBF06yYcUMNBoNDZq1oWW7Xon+rlZHs/LXsdy7fRV7Rye+GTqdfO4FuHMjgNWLJ8Z1DJ917kfVOk2MFldKtFot2/+YwjX/Y1ha2dL52ykUSiFXHt4JZMNvo1Gr31Km8gd8/uVoFAoF+7Ys4Mzhzdg7ugDQsvOPlK3SiOtXTrF7/WxiY9SYW1jSqttPlCxfx+ix+/4+lauXjmNpZUPXVPJ83W/6PK/SkLb6PN/xpz7PzePz3NY+DzExajYtm8CD24EoFArafjWSkuVqGS3uwIsn2bjSB41GQ/2mbWnRNnl+rJo/hvu3r2Lv4ESfIdPJ516QVy+fs2TmT9y7FUidxq3p2meU4THzJg3gRfhTNLExlCxbja59RmFmbm60mONotVp2rJ6C6tIxLK1t6fTNFAq+I182/W806ui3KKt8QOseunzZv2UBZ4/E50uLTj9Spkojw+PCnz5m9ojPaNZuII0+7ZVsv+mN1RRjS9CjO6xZNJaHd67Sqst3NGv9tWFfh3ev4dTBLWi1UL9pu0yP5WCafImzaNoPPA1+yLg5WzIdZ1KmOve/fvmc5bOHcO9mALUbf06n3t5GiTfw4kk2rZyOVqOhXtO2fNy2d6K/q9XR/D7fmwf6fu49xMdw3tizdTmnD/miMDOjU68RlKtSH4Ax/VtiY2uHmZk5ZmbmjPRZZ9jf4d1rObpnPeZm5pSv/gHtegw2SjviZHZ83L1xPgHnDqEwM8Mhjyvd+k3GydXd6DFuWjmdwAv6Y3TgRIqklCO3dMdodHQU5as1pGPcMfpSd4w+C31MXrcC9B6S4BhdOJYHd67yWdf4YzT8aRC/L/Am4vlTFAozGjRrz4efdk933IEXT7B55XT9MdmO5inkyh/zvbl/+1/sHZ3oPXiGIVf2+i7j1EFfzMzM6NhrJOWq1E81rr/WL+Cy32EUCjMcnVzpMXAizul4HUzVx6ntd1CnKhQoUgoA13ye9Bs5H4A1i8Zz/3YgWq0W9/xF6TFwEja2dunuf1Od+8+f2MmhnSsNj39y/zpDp2yioFeZdMeYWZWWTsH9k8ZEhzzjWNXPsvz5U3PyThAzD10kVqulbcXi9KyduH92BNxl7lF/3B1sAehctSRtKxUH4Nejlzlx+wkAfeqW4+MyhbM29hsPmb7nHzQaLW2rlaZ3w8qJ/r794g3m7PfD3VGXl11qlaVddaXh76/eRtNm4VaalCnK6E/rZmnsQiQkM29Fqv69eILQoHuMn7eTrt+MY/2ySSlut2HpJLp+O57x83YSGnSPfy+dAGD/tuUoK9Zm/LydKCvWZt+25YbHaDSxbP9zDmWr1Eu0r2atv+bLQZP/s7GbMu4SZasxasYmRs3YlKhwC3B49594FCyWqdgTt+M4IUH3GDdvF12+Gc+G97Rj3LxdhCRoR/7Cpejz0xxKlK2eaPsChUsybNp6Rs7YzIDRv7F+yS/ExsZkMlbT9Pmb1xFsXDaZb0fMY8xsX3oPmQmAR4FihtdhxPT1WFrZZLoADXD10nFCn9zHe+5uOvf9mU3LJqa43ablE+nUdzzec3cT+uQ+V/XtUFasy4gZvozw8cXN04sD25YlepzvH9MpW6VhpuOE7Dk+/1jgTdPWXzN2znaGTV2Lo5OrUdoCoImNZe3SaXw/ZgETft2C3/E9PH5wK9E2Jw9sw87BkcmLdtDssy/Y+sevABQoUgLvGX8ybvYGvh+7kDW/Tcp0Tr/PNf9jPA26x4hZe+jQewJbV05IcbutK36hfZ8JjJi1h6dB91D5Hzf8rWHLLxky1ZchU30pqy/E2Ts60/OnRQydvp0u/aaybvFIo8d+9dJxQoPuM3rObjr1/ZnNy1PO880rJtKpz3hGz9lNaNB9rvnrcqd0xboM9/FluI8vbvm9OLBdl+f/HNoMwHAfX/qNXsr2NTPRaDRGiVkTG8u6ZVMZ5L2Q8XO24ncihfw46IudfR4mLviLpq2647tGlx+Wlta07jKQ9j2GJNtv3yE+jJ21kXFztvAqIpzzp/cbJd6kVPp8GTZrD+16T8B3Vcr54rvyF9r1nsCwuHy5HJ8vDVp8yY9TfPlxim+iwi3Azj+no6ycs8cWe4c8dOw5kiaffZVoP4/v3+DUwS0Mm7KWUTM2EXDhGCFP7mWqDabKF4CL/xzE2sY2U/GlxlTnfgtLKz7tPIi2PX4yWqya2Fg2LJvCIO9FjJ3jy7kTe3iSpJ9P6ft5woKdNGnVHd81cwF48uAW50/uYcycrQzyXsT6pVPQxMYaHvfjz8sYPXNjosKtKuAsl/2O4D1rM2Pn+vJR6y+N1pY4mR0fm7TqyXAfX4ZN20L5ao3Yu3Wx0WMMvHiC0Cf3+Hn+Trp9O471S1POkfX6HPl5/k5Cn8TnyD79Mfrz/BSO0V4jaZrkGDUzN6fdl0MZN3c7w6as4djeDcle5/fRxMaycfkUBnovZuycbZw7+XeyfZw+tBU7hzxMWLCLJq16sC1Zrvgy0HsxG5ZNRhMbm2pczVp/jfesLYyeuYkK1T/g783/S1e8purj1PZraWXN6JmbGD1zk6FwC9D+62GMnrkZ71lbcM2Xn2N71pERpjr3V2/QimHTtjBs2ha+GDAVF7eC2VK4BXj4+1bOtuqTLc+dmliNlukHLjC/fUO29GzBnmv3uf00Itl2zZWFWf9Vc9Z/1dxQuD1+6wnXQsJZ99VH/PFFU/7wU/EqSp2FsWuYsvs0i75oju/AduwJuM2tkPDksZcvxsb+bdjYv02iwi3AwsMXqFHUM6tC/s/QaLQ5/r+cRoq3RqBUKr2USuXObHjeKkqlsl6CnwOM/RyXzx2m1gefoVAoKFa6MpGvX/IiPDTRNi/CQ3kb+YripSujUCio9cFnXPY7rHu832FqN2oNQO1Grbnsd8jwuKN/r6Vy7Y9wzJO4gKKsWAdrW/v/bOymjPtdwp8FEXjhGPWatstU7AldOXeYWh+0TlM7ipWuom9Ha67o4/UsVByPAsmLyVbWtpib6740oFZHoVBkPlZT9fm5E7upXLsprvnyA+DolDfZc6uunMHNszCubgUy3Y4r5w5TU9/nXqUqE/nmXe14bejzmh+05so5XbxlKtc39K1XqUq8CAuO7yO/g+R1L4RnoRKZjhOy/vh88vAWmthYylbSfWJubWOHlbXxChh3bgbgnr8wbp6FsLC0pGaDj/E/eyTRNpf8jlD3Q91Miup1m3H1ylm0Wi3WCXI6Rh2NUZL6PQLPH6J6w89RKBQULVWZt29eEpGk/yP0/e9VSpcr1Rt+TsD5g6nut6BXOZxcdLODPAqVJEYdpWuTEQWcP0zNhmnLc6+4PG+YIM8rxed50VKVeK7P86CHtyhVvjagO1Zt7Rx5cDvQKDHfvRmAu2dh3Dz0+VH/Yy77HUm0zWW/I9RtrMuPanWbcS0uP2xsKVm2KhZWVsn2a2vnAIAmNoaYGDUKE+VO4PlDVG+gz5eSuuM1pXyJinxF0bh8afA5gedSzxeAwHMHcHUrhEfBkkaJ1VRji6NTXoqWrGDInThBj+7gVaqS4dxUsmwN/M++v92pMVW+vI18w4Gdq2nZvm+m4kuNqc791jZ2lChTLcV2ZdTdmwG4eRYmn76fq9dvgX+yfj5Mnca6fKha9yNU+n729ztC9fotsLS0Ip9HIdw8C3P3ZuqX4cf3buLjtr2wtNS1IaVrgszK7Phoox9TAKLfRppkTNEdY2k8RpW6Y7R2o8/wP5vgGNW/JrUbt8b/bJJj1CLxMerk4maYHWpja49HwWI8DwtJV8y6XCmSKFcunzucpF1HDGNH1TofoQo4g1ar5fK5w0lypQh3bwakGpdtwtchKjJdsepiMU0fp2W/ScW1RavVEh39NsPXOKY69yd08dRuqtVrmaH4jCHsxDnUYS+y7fnfJSAojEIuDhRydsDS3IyPyxTmyK1HaXrs7WcRVC/khoWZGbZWFpR2c+LUnSATRxwv4NFTCrvmoZBrHiwtzGlRoThHVPfT/Ph/Hz/l2atI6pYw3jf1hMgoKd6mk1KpzEl9VgWo996tMuF5WAgu+eI/aXLO65Hsgud5WAjOeT1S3OblizCcXNwA3cXTy4gw/WOC8T97iIbNO0rsWRQ3wJ3r/kwd1oFFU/rz5MFNw++3rPKhTfchKBTGS++U2vEiSTtepNKO1Ny9cZnJQ9owdWg7Ovcdl+zNtDFiNUafhzy5x5tXEcz9uRfTR3TmzNEdyZ77/Mk9VK9vnAvFF2HBuORN0A5Xj0QF2LhtnF09Ut0G4MwRX8pWaQBA1Ns3HNyxghYdBhglTsj64zPk8T1s7R1ZOnMw04Z3wnf1LDSaWIzl+bMQXJPEGh4WmsI2ujabm1tga+fAq5fPAbh9/Qrjf2jPhMEd6f6td6Zz+n0iwkJwTpArTq4evAhPkivhwTglyBUnVw8iErxGp/atZdbINmxc4s2b18nfbFw5u48CRctiYWm8YgvoczgNeZ4o9ryp5HllXZ4XKKok4PxhYmNjeBbykAd3/uX5M+O8wQhPId/DU8j3uG3i8uO1Pj9SM29if4b1boKNrR3V6jQzSrxJRYSH4JQkXyKS5EtESvkSHt/G0/vXMmdUGzYlyJfot284snM5zdrl/LHlXQoULsnNqxd49fI50VGRBF48Tviz5LmWHqbKlx3rF9Lssy+xsrbJVHypMeW539iSxuqS1z3ZOPGufn4RFoxLvpTboFDA/In9mDq8Cyf2bzZsE/LkHjevXsBn5BfMHtfrvcXejDDG+Lhrw69MGNiU8yd30bLjIBPEmPj8k9Zj9EUGj9GEnoU84uGda3iVqpiumJ+HBeOSN/G10/NnSWOOz4mEufL8WUiya7OkhcOU4tqxdh7e/T7C7/guWnUemK54TdXHqe03Rh3N9BFdmDH6C0OxN87qhWMZ1fdDgh/fpXHLrulqS3ybTHPuT+ji6T1Uq/dJhuL7/yz0ZSSejvFLXbg72BHyMvmHCoduPKLTqn0M236KoIg3AJR2d+LknSAi1TGEv4ni3INQgl++ybLYQyJe45knfmKVex57giOSP//Bq3fpsMiXoRsOEfTiFaCbGTpr71mGNK+ZZfEKkRpZ8zYFSqWyOLAaUANvgBD9/4sBY5RKpRL4HogC1gN/Ay5KpfIPoBKwVKVSLUxhv17ABuAuUAGYAnwClAeWq1Sq+Uql0hP4HbABQoEvAXfgT+BWkv3/AORRKpWtgHaAtVKpXARUBw6pVKrkC52lVwp3AUz2KXxK27xnt1tW+fD5Fz9iZmb8tfkMcmvsJoq7cLGyTFy0F2sbOwIvHGfJjB8ZP28nV84fxdHJlSLFy3E90C8TgScNMYWvGiRrRkrteP8n8l6lKuE9extBD2+zeqE35ao0wNLKOqOhmqzPNbGxPLjzL9+NXYo6OopZY3rgVaoSHgW8AIiJUXPl/BFad/shg4EnlYZ2pGGbfb7/w8zcnOoNWgGwZ9NCGn/SA2ub9K9R9k5ZfHxqNDHcunqBkT4bccnnyYo5w/jnyHbqNTHObPOUvliTvOff3ebipSsy4dctPHl4m5XzxlGhWv3M5fR7pBZL/EYpHcO6beo260Kztv0BBXs3z2Pnnz50+iZ+yZighzfYtX42fUcuNWbY+rAyludJZ/vs9/0f5mbxeV67cVuCH91mtndnXPMVoFjpKsZbPzYNMac8Zr5/PPx+7GLU0VGs+HU01wLOUq6yCdZjS0Nsqb0udZp1oak+X/ZtnseuP33o+M1k9m1dQIMWX2Jtk/lv26QWqzHGlnfxLFScjz7vyYJJ32BtY0fBokrMM3t9YIJ8eXDnGqFBD+jUcxhPQ9I2YyojTHnuN7q09OE7tkn517rHDp30O86u7rx88Yx5v/TDo2AxSpWrTmxsDG9eRTBs6hru3Qxg+exh/LJwt1FntxpjfPy08w982vkHDmxbyvG9a41ewE3T65+GPEqvt5FvWDpzCB16Dk80szWjkh+TKW7E+6673hVX627f07rb9+z1XcbRPevSVcA1VR+ntt+Ji/fi7OrO0+CH/DqhDwWKlMLNU7e2aY+BE3VLT6yYyvlTe6n7YZu0NeR9z22Ec3+cezcvY2VtS/7CpdId2/93ablm/KBEflqUKYyVhTmbL91i3N9nWdK5MXW9PAkMCqfn2kO42FlTqUBezM2ybi7cOw/LBBopC9OyYnGsLMzZ6HeNMb7HWfZ1Szb4XaVBqUJ4OmV+vBDJaXPgsgQ5nRRvU9YY2KJSqWbrZ9quAFQqlWqAUqnMBywF6qlUqrdKpdIcKAwUBJoA5sA5IFnxVs8TaAS4Af8CRdEVhi8A84FRwBKVSrVFqVQOB/oC29+x/1+BfCqVaiaAUqnMD4wDngHXlErlOJVKle5FZY7uWc+pg7qbWBQtUZ7wp/Ezj54/CzZ8EhvHOa8HzxPMMnn+LNhwcwNHJ1dehIfi5OLGi/BQw9eY798KZOWvIwB4FRFO4MXjmJlZULlW5m7Sk1tjz4q4E14Qlq/WkA3LJ/MqIpzbqktcOXeEwIsnUEdH8TbyNb/PG8VX309NdzuO7VlnaEeREhVSaEfimy2k3I7EbU2NZ6HiWNvY8uTBTcNNTdIqK/rcOa8H9o7OWNvYYW1jR8my1Xl077qhePvvxRMULlaWPM4Z/+rk8b3rOK1fq7NIiQqEJ5gp+DwsmDxJ+tzJ1TPRjI+k25w9up3AC8cYOGaZ4cLs3s0rXDqznx1/zibyzUvMFAosLa1p2KJbumLNzuPT2dWDQsXKkM+jEACVazXhzvXLulHVCFzyuhOWJFbnJLnskteDsGdBuOTzIDY2hsg3r7B3cEq0Tf5CxbGyseXR/Zt4lUxfTr/PyX1rOXN4EwCFi1dMNKv0RVgweZyT50rCGSsvwoLJo3+NHJ3yGX5f+8OOrJjZ3/Dz82dB/D7ne7r0m0o+jyJGif3EvgR5XrxCotjfleeJYk8y/pw9up3Ai8cY4B2f5+bmFrT9coRhm1/HfYGbZ1GjxO+S1yNZvju7JM+P8KdBuOR9d368i6WVNZVqNsLf74jRiren9q/lrD5fChWvyIsM5Iujc/J8qfVhR1bN0uXLg5uXCTi7j7/XzyLyzUsU+rGlXvMv0hVrVowtqanXpJ3hg6Ada39NNJstI0yRL7evX+b+7auM7t8STWwsLyPCmDWuN0N/Wf7Ox6RVVp/7jcU5ST+HPwtJMdaU+ln3+yRt0L9GzoZcykvlWk24eyOAUuWq45LXgyq1m+q/9l0RhcKMVxHhmV5/3djjY5xq9T9lqc8AoxRvj+5Zz8kD+mO0ZPnEMabw+qeYIy4ZP0ZjY9QsmzWEmg0/pUrt9H9DwdnVI9GM+udhyWN20eeES17P5LmS5DWJa0ta4qrR4BMWTx343uJtVvSx7jEp7zcu7/N5FKJUuRo8uHPVULwF3drD1eu14MCOVWku3mbFuT/OhVN/UzUbl0zIydwd7QhKMFs25NUb3BwSf4PD2TZ+wkHbSsWZd+yy4ec+dcrSp05ZAEbv/IciLllXDPXIY09QxGvDzyERrw03JovjbBfflvbVS/PrAd1kpssPQ7hwL5iNftd4E61GHavBzsqCHz+Smbgie+SkJQBykg2Am1KpXA0M0//uH/3/iwMXVSrVWwCVShX3vdtAlUoVpVKp3gCp3eEkQKVSvVWpVA+A+yqVKky/r7jHlE7wXKeAuBWz07L/eyqV6qlKpdICT4A879guVY1adDHcTKlSrSacPfYXWq2WO9f9sbVzTPYmyMnFDWtbe+5c90er1XL22F9UqvEhABVrNDZ8VfzM0R1Uqqn7/YSFe/hF/1/VOh/RuY93pgu3uTn2rIg74vlTw6fWd29eQavRYO/ozOfdfmDSbwf4ZeEeev7oQ+kKtTJUuAX4oEVXRs7YzMgZm/Xt2GFoh42dQ4rtsEnUjh1U1LfjXZ6GPDTczCks9DHBj+9maL3YrOjzSjU+5Na1C8TGxhAdFcndm5fxTHBTuHMn/870kgkNP+7K8OlbGD59CxVrNMFP3+d3b/hj+44+t7ax4+4NXTv8EvT51UsnOLhjOX2HzU+0Huz3E/5g/IJ9jF+wj0Ytu9OsTd90F24he4/PoiUrEPk6wvDVP1XAWaOt3wvgVbI8IU/u8zT4ETFqNX4n9lK5ZuNE21Su2YjTh/8C4PzpA5SpWBOFQsHT4EeGnH4W8pjgR3fJ6575NZCTqt+8m+EGYxVqNOX88e1otVru3fDHxtbRUJiNk0ff//f0uXL++HbKV9eNdQnXOw04dwDPQrqZKpGvI1gxsz8tOw+mmLKa0WJv0Lyr4YYiFWo0we94OvP8+A4qVI/P80N/LafPT4nzPDoqkqi3ujcnqsunMDO3MFqOFE2aHyf3Uqlm4pt2VarRiNNHdPlx4fQBlBVqpjoj723kG8N6f7GxMQRcOJFofMmseh91M9xgrHz1ppw/oc+Xm/7Y2L0jX2zsuXdTny8nUs6XwHMH8NDnS/9xaxg59wAj5x6gwcc9+LD1N+ku3ELWjC2pefniGQBhT5/gf/YgNepn7mu3psiXRh93YvrS/UxZ/Dc/TVqJR/6iRincQtac+00hvp8fEqNWc/7knhT6uTH/HNHlw8XT+1FWqIVCoaBSzUacP7kHtTqap8EPCXlyH6+SFYh6+4a3kbpCQdTbN1z1P02BIrr1nCvV/BBVwFkAgh/fJSZGjUMel0y3w5jjY2iCm+0FnD+MewrrD2dEoxZdDDezqlyzCWeOpu8YPXP0L8OxWLFGY87oX5MzR95/jGq1WtYsHo9nwWI0/SxjN4nT5cq9RLlSsUbjRNskHDsu/rOf0vpcqVijcZJcuYdXyQqpxpXwpodXzh1JcR3opLKijyvVaJzift+8ikCtX98+bmJI/kIl0Gq1hDzRrS+q1Wq5cv4IHgW90tjrWXPuB9BoNPif2UfVulK8TUl5TxcehL/i0fPXqGM17L32gEYlEl+nhr6KX0bh6K3HeOXVlSFiNVqeR0YBcD30OTdCX1DHK3MfcKYr9gL5uP/sBQ/DX6KOiWVPwG0aKRNPLAhNUJg+orpPsXzOAExt35i9Qzrz9+BODGlei1aVS0rhVmQrmXmbMk3ckgNKpXI/uuUL4oq0t4DKSqXSRj/zNq4AntZ539p3/DvODaAOsAXderbXU9k2msSvYdJtMv09rPJVGxJ44TgTvv8USysbug+Iv7Pn1GEdGTVDNyunc58xrFk0BnV0FOWqNKBcVd06Qh+16c2KOT9x+pAvLvk86T1k1nufc864rwh+dJeot28Y068Z3fpNoFyV+v+Z2E0V98V/9nN830bMzc2xtLKm548+Jru5TVw7/r1wjF++/0Tfjvg70k4b1oGRMzbr2zFW3463lK3SgHJVdXcc9z97kM0rpvAqIpzfpg2goFcZBnr/j9vXLrJ/23LMzS1QmJnRqbd3pt/8mKrPPQsVp1yV+kz9qQMKMwX1mrSjQBFd0SI6KpJrl0/T9ZuxmYo9oXJVP+DqpeNM+qElVta2dO0X3w6fEe0ZPl03I6Nj77GsXRzX5w0pW0XX51tWTiZGHc2iybqb2XiVqkSnPuONFl9CWX18mpmZ06bHUOb/0hetVkuR4uWo36y90dpjbm5B1z4jmPvLADQaDfWbfk6BIiXYvm4RRUuUo0qtxjRo2oblv47Be0Br7B3y0HfINABuXL3IHt+VupxWmNHtm9E4GuENfWrKVPmAq5eOMW1IC6ysbOj0bfySB7NHtWXIVF8A2vUcx4b/jUYdHUWZyg0pU/kDAHatm8nje9dAocDVrSDte/0M6Gb3Pg2+zwHfxRzw1d2l/JuRy3Aw4o154vJ88o+6PO/ybXzuzBjZnmHTdHneoddY1v2WPM+3rtLl+eIpujwvWlKX568iwvht6rcoFAqcXD34YkDGPsRKibm5BZ37jGTepP5oNBrqNfmcAoVLsmO9Lj8q12xM/aZtWTnPm7GDPsPOIQ99Bk83PH50/5a8jXxNbIwa/7OH+X7sYhwcnVk07Qdi1Go0mliUFWvxQfMORos5oTJVPkDlfwyfobp86ZhgiYy5o9vy4xRdvrTtOY6NS3T5oqzcEKU+X3avn8kTfb645CtIO32+mIKpxpaI50/xGdmFt5GvUSjMOLJ7Dd6zt2Fr58CyWUN4/fIF5hYWdOo9GjuHDH12bmCKfClQ2HgfVqXGVOd+gPEDP+btm1e6JYf8DjFgzBLyZ+IDFl0/j2KBvp/rNmlDgcIl+Wv9QoqWKE+lmo2p17Qtq+Z5M35QK+wc8tB7sA+gW+u4Wr3mTPyxLWbm5nTpMxozc3NePg3jfz6DAd2NBGs0/ITyVXXXgfWatGX1onFMHNwOCwtLvho00ejXYZkdH3eun0PI47soFApc3ArQsfc4o8YHum9/BV48zs/ffYqVlQ3dB8bHOOWnjoyeqTtGu/Qdw+qF8cdoef0x2rxtb5bP/olT+mO0j/4YfRGe+Bg9vGsNY+Zs49G965w9tpMCRUox5Sfdevitu31PhWoN0xyzubnu2F44uT8aTSx1P9Tlys71CylSohyVan5IvSZt+X3+aMYP+hR7Byd6JcyVus2ZNLgNZmbmdNbnys2rF94Z1/Y/5xL8+C4KhRmubvnp2jd914qm6uN37Tfo0W3W/e8XFGZmaDUamrfpRf7CJdBoNKxeOIa3b16hRUvBokq69B2TrrbEMdW5H+D2tXM4u3qQz6Mw2anK6lnkbVQLq3wuNLlzlBu/zOfBys3vf6CJWZiZMaJpVQZuOYZGo6V1xWKUyOfE4hMBlPN0pVHJAqy/cJOjtx5jbqbAycaKCS10Rc4YjYbe63Q3wrO3tmTSp7WxyMJlEyzMzRj1SV36r96LRqulTdVSlHR3YeGhC5QvkI/GZYqw9sy/HFHdx8JMQR5baya2SfvYIDIuxWWWRKoU0mnJKZXKTkDcd1OeADHAXJVKdU7/9y/QrXn7BtiIbs3bBSqVqpX+7wEqWllDtwAAIABJREFUlapCCvv1etd2cf9WKpUF0K15a4Vu+YMe6JZYSPY4/f7+1MfYBziRYH9HgA4qlerp+9q73z9KkkCkiTY71qQzAkWaP1vJeWI0ufMLEhZmqX0BIWezsjDejcyy0otI062Ra2q5NV9sLWOyO4QMe/HWuDeSyyr2Vrm3z3Nrnqs1Jrw/gYmZ59I+j1Ln3j63ssidfS6yXnRM7rzGBdDWTvZWP1dovMh0Nws3NXMHx+wOIcNsuo7InW+iTajbyIc5/g362mmFctTrJsVbIcVbkWZSvM16UrzNelK8zXq5NV+keJv1pHib9aR4m/WkeCv+C6R4m/WkeJs9pHibnBRv00+WTTARpVLZBN3NwxL6TqVSXcmOeIQQQgghhBBCCCGEyE5ajXzQl15SvDURlUp1CDiU3XEIIYQQQgghhBBCCCFyp9z7XQUhhBBCCCGEEEIIIYT4f0yKt0IIIYQQQgghhBBCCJEDybIJQgghhBBCCCGEEEIIk9Nocvz9ynIcmXkrhBBCCCGEEEIIIYQQOZAUb4UQQgghhBBCCCGEECIHkmUThBBCCCGEEEIIIYQQJqfVyrIJ6SUzb4UQQgghhBBCCCGEECIHkuKtEEIIIYQQQgghhBBC5ECybIIQQgghhBBCCCGEEMLktBpZNiG9pHgrRBbTosjuEDJMgQyyWU2hkD7Palpt7jxGLcw02R1ChpmZ5c48V+TOVAHA2iL35ktupcnF5//cKreO51a5+PjMrdctuTVXcrPcfA5ttKhjdoeQIUcGbMruEDLMuYJDdoeQYfW7jsjuEMT/A7JsghBCCCGEEEIIIYQQQuRAMvNWCCGEEEIIIYQQQghhcrJsQvrJzFshhBBCCCGEEEIIIYTIgaR4K4QQQgghhBBCCCGEEDmQLJsghBBCCCGEEEIIIYQwOY02996YM7vIzFshhBBCCCGEEEIIIYTIgaR4K4QQQgghhBBCCCGEEDmQLJsghBBCCCGEEEIIIYQwOa1Gm90h5Doy81YIIYQQQgghhBBCCCFyICneCiGEEEIIIYQQQgghRA4kxVshhBBCCCGEEEIIIYTIgWTNWyGEEEIIIYQQQgghhMnJmrfpJ8VbkSqtVsvmldMJvHgcK2sbegyYSOHi5ZJtd//2v6xeOAZ1dBTlqzakQ88RKBQKXr96wYo5wwgLfYyrWwF6D56JnUMeLvsdZueGBSgUZpiZm9Ph6+GUKFONh3evsX7pJN5GvsbMzIyP2/Wler0WOSp2gOuBfmxZ5UNsbAwOjs78OGElAOMGtsDaxg4zM3PMzM0ZMW19hmJP2IYtK6cZ2tB9wKR3tCGQNQna0L7nSBQKBRdP72X3psUEP7rNT1PWUaREeQBev3zO8tlDuHczgNqNP6dTb+9MxZkwXlP0+YEdK/E7vhsAjSaGoId3mLb8KPYOTvrfxeIzsitOru70H7kgx8Qd597NAGZ6d6fXYB+q1mlu+H3km1dMGtyGyrWa0Kn36HTHnVI7tq6axr8Xj2NpbcMX/VPOlwe3A/lzka4d5ao2pN3X8fmyZ7MuX4ZMjs+XmBg1G5ZM4MHtQBQKM9p9PZJS5WtmOlZT9Lnf8V3s374CAGsbOzr3GUMhLyXBj++wYs5ww36fhTzk004D+PDTHplqR0IBF0+ycYUPGo2GBk3b0qJdr0R/V6ujWTlvDPdvX8Xe0Ym+Q6aTz70g//qfxnfNPGJi1FhYWNL+y8GUqVjLaHGlRKvV4vv7VK5eOo6llQ1d+0+mcLGUc2Xdb2NQR7+lbJWGtP1qFAqFgt0b5xNw7hAKMzMc8rjSrd9knFzdOX9iJwd3LAd0/d+h91gKFi1j0nZkJue3r5lFwPkjmFtYks+jMN36T8TOPk8Kz5R5psiPbX/O55+jO3nzOoJ5f542Sdyg7+ffp3JV38/dUsmXtYv1+VK1Ie30+bJ9zUwCLxzF3MKCfB6F6dpvEnb2eVBdPsVf6+YSG6PG3MKS1l8MpXSF2pmONavH84WT+3H3xhWKl6maoXNQSgIvnmTTyuloNRrqNW3Lx217J/q7Wh3N7/O9eXD7KvYOTvQe4kNe94K8evmcpTOHcv9WIHUat6Zzn/hzy/a18zlz9C8iX0cwZ80/RokzNaa6hjGmwIsn2LxyOhqNhvpN29E8hX7+Y74392//i72jE70HzyCve0EA9vou49RBX8zMzOjYayTlqtQn/GkQvy/wJuL5UxQKMxo0a8+Hn3YH4MLpfezaqGvPsKlrKZqJ9hg7bnV0FHPG9SQmJprY2Fiq1mlGq84DAfhjwRhu/nsOGztHAHoMnEjhYhkf1zOa2wB7ti7n9CFfFGZmdOo1gnJV6gMwpn9LbGz1199m5oz0WQfAzg2LOXlwC455XAFo3e07KlRrmMG4sy5X4hzYsQrf1bOZvvwoDnlcMhR3Vse+9Y9ZBJw/irmFJW4ehek+8BejnFdNdR56/fI5K+cM5v6tAGo1akOHXsZ5T/QuJ+8EMfPQRWK1WtpWLE7P2omPpR0Bd5l71B93B1sAOlctSdtKxQH49ehlTtx+AkCfuuX4uExhk8aaHpWWTsH9k8ZEhzzjWNXPsjucRJzr1aX4sJ/AzJzgbdt4tHJVor9b5/ek5PjxWLq4EBPxguveY4kOCcE6vydlZs4EczPMLCx4sn4DQZu3ZE8jhECWTRDv8e/FE4QG3WP8vJ10/WYc65dNSnG7DUsn0fXb8Yyft5PQoHv8e+kEAPu3LUdZsTbj5+1EWbE2+7bp3tQrK9Zm1IzNjJqxie79f2Htbz8DYGllw5eDJjNmti8DRi9myyof3ryOyFGxv3kdwcZlk/l2xDzGzPal95CZifb3w/jljJqxKdOFW10bjhMSdI9x83bR5ZvxbHhPG8bN20VIgjbkL1yKPj/NoUTZ6om2t7C04tPOg2jb46dMx5g4XtP0ebPWPRk1YxOjZmyiddcfKFWuuqFwC3B49594FCyW4+IGXWF5+59zKFulXrL97dqwgJLlqif7fYbbcek4oUH3GPPrLrr0Hc+m5Sm3Y+OySXT+Zjxjft1FaNA9ribIl15Dk+fL6YObARg505cBY5awbfUMNBpN5mI1UZ/ndS/Ijz+vZPTMLbRo/w3rlkwAwKNAMUMOjZi+HksrGyrXapqpNiSkiY1l3dKpfOe9kJ/nbsXvxB4eP7iVaJuTB32xd8jDpIV/0axVd7au/hUAB0cXBo76lfFzNvP1dxNZOc+0bxwArl46TmjQfUbP2U2nvj+zefnEFLfbvGIinfqMZ/Sc3YQG3eeav67/m7TqyXAfX4ZN20L5ao3Yu3UxAK7uBRk0bhXDfXxp3q4fG5dOMGk7Mpvzyop1GTnTl5EztuKevygHti0zSZymyo9KNRsxavoak8Sc0NVLxwl9ch/vubvp3PdnNi1LOV82LZ9Ip77j8Z67m9An9xP184gZvozw8cXN08vQz/aOLvQdtoARM3z5YsBk/lw4KtOxZsd43qz113w5aHKmYzc8T2wsG5ZNYZD3IsbO8eXciT08SZIvpw76YmefhwkLdtKkVXd818wFwNLSis+6DKRtjyHJ9lupRiNGTPvTaHG+j6muYYxFExvLxuVTGOi9mLFztnHu5N/J+vn0oa3YOeRhwoJdNGnVg236fn7y4BbnT+5hzBxfBnovZsOyyWhiYzEzN6fdl0MZN3c7w6as4djeDYZ9Fihckm9+mk3JTLbHFHFbWFrx/fhljJ65mdEzNvLvpZPcue5v2F+bHkMYPXMTo2duylThNjO5HR/7VgZ5L2L90iloYmMNj/vx52WMnrnRULiN0+TTHoyeuZHRMzdmuHCb1bkCEP40iGuX/8ElX/4MxZxdsZetXBfv2VvxnrUF9wJF2ee7PFlMGWGq85CFpRWfdPqOz7sb9z1RSmI1WqYfuMD89g3Z0rMFe67d5/bT5O9zmysLs/6r5qz/qrmhcHv81hOuhYSz7quP+OOLpvzhp+JVlNrkMafVw9+3crZVn+wOIzkzM4qPHEngoO+52L4Dbi0+xrZ44veMXoMHE7JrF5c6d+HBkmUU/W4QANGhT7n8dU/8u3TDv8dXFOz5NVZu+bKjFUIAUrxNkVKp9FIqlTsz+FhPpVI51dgxveO52iiVygL6fzdWKpXGme6RwOVzh6n1wWcoFAqKla5M5OuXvAgPTbTNi/BQ3ka+onjpyigUCmp98BmX/Q7rHu93mNqNWgNQu1FrLvsdAnQzshQKBQBRUZGg/7dHAS/c8xcFwNnVHUcnV15FhOeo2M+d2E3l2k1x1V9MOTrlzVB8aXHl3GFqfdA6TW0oVrqKvg2tuaKP1bNQcTwKJC9qWtvYUaJMNSysrIwar6n6PKFzJ/+mev2Whp/DnwUReOEY9Zq2y5FxH/17LZVrf2SY9RHn/u1/iXgRRtnKyYu6GRXgd5ia+nzxSmO+1ExDvgQ9vEXpirrZcI5OebG1z8OD24GZitVUfV5cWcUwS65Yqco8fxaS7LlVV87g5lkYV7cCmWpDQnduBuDuWRg3z0JYWFpSo8HH+PsdSbSN/9kj1Gmsm41QrW4zrl05i1arpUjxMji7ugNQoHAJ1NHRqNXRRostJQHnD1OzoT5XSlUm8s27+v81XnG50rA1V87p+tnGzsGwXfTbSMN4Xqx0Vez0H6wULVmJF2HBpm1HJnO+TOV6mJvrvoRUtFRlnj8zTbymyo/ipSvh5OJmkpgTunIuQT+/J18S9fO5uH6ub+hnr1LxeVGoWFmc9G3zLFQStTqKmEzmfnaM58qKdbC2tc9U3AndvRmAm2dh8nno8qV6/RbJ8uWy32HqNNbFWbXuR6j0+WJtY0fJstWwtLJOtt9iWZQvcUx1DWMsun4ukqifL587nGiby35HDPlQtc5HqALOoNVquXzuMNXrt8DS0op8HoVw8yzC3ZsBOLm4UUQ/u9jG1h6PgsV4HhYS355MfNBsyrgVCgU2tnYAxMbGoImNMVybG1Nmctvf70iS2Atz92aA0WN8d9xZlysAm1f50Kb7YMP5NbfEXjbBedWrVCXCjXReNdV5yNrGjuJlqmFhmXzMNLaAoDAKuThQyNkBS3MzPi5TmCO3HqXpsbefRVC9kBsWZmbYWllQ2s2JU3eCTBxx2oWdOIc67EV2h5GMY4XyvH3wgKhHj9DGxBC6dx+ujRsn2saueDFenDkLwAs/P1wbNwJAGxODVq0rkJtZWaFQSOnMmLRabY7/L6f5z2egUqk0ah+oVKoglUqV+akjadMGMF71IQXPw0Jwyedp+Nk5r0eiC4u4bZzzeqS4zcsXYYY3Ck4ubryMCDNs53/2IBN/bM1vUwfyRf9fkj333ZtXiIlRk88jY18JMVXsIU/u8eZVBHN/7sX0EZ05c3SH4fEKYMHkb5k+ojMnDmzOUNzva8OLJG14kUobspop8wUgOiqSq5dOUqXOR4bfbVnlQ5vuQzJ1QjVV3M/DgvE/e4iGzTsm2pdGo2HrHzNp2z35zKjMeB4egnPe+HY4vStfXBO0w9WD5+Gp50vBokoC/A4TGxvDs5CHPLz9L+HPMnfBaOpcATh1aCvlqtZP9vvzJ/ck+gDAGJK2x8XVI1nh+HlYCK76bczNLbC1c+D1y+eJtrnwzwEKFyuDpaVxP1hJ6kVYcKJccXb1SFZofREWjFOCXNHlU/w2uzb8yoSBTTl/chctOw5K9hxnjmylTJUGJog+njFz/sxhX8pWNU28uS0/knoRFoxLGvIlaT+nVLw/c8SXsinkhf+Z/RTyKotFJtuW1eO5KSTLl7zuyfoy4TbvypfsltOvYZ6HBeOSN8nYkOy4DMYln26bhP38/FlIsmPieZLX6FnIIx7euYZXqYq5Im5NbCxTfurIiN6NKVOpLsVKVTJs99e6+Uwe2p7Nq3wy9eFiZnL7RYI2QeJcUShg/sR+TB3ehRP7E19/H92znklDOrB64TjevMrYt/myOlcu+x3G2dWdQl7KDMWbnbEndPqwL+WNdF7NivOQqYW+jMTT0c7ws7uDHSEvI5Ntd+jGIzqt2sew7acIingDQGl3J07eCSJSHUP4myjOPQgl+OWbLIs9t7Jydyc6OD4HooODsXZL/CHm6+s3yNtU92081yYfYuHggIWTbiKClYcHVTasp8bfu3m4ahXRoU+zLnghkvhPrHmrVCqLA6sBNfAGCNH/vxgwRqlUKoHvgShgPfA34KJUKv8AKgFLVSrVwnfseyjQUf/YucBFYIFKpWqVwrZfA5+j6/fiwAignz6OPiqV6rRSqawPzAS0wH6VSjVe/7jm/8fefYdHUXUBHP5tNoQQShJa6E3gIr0EFARBUERFFCxgwQaoFBWQXkRFqiiKAgoItk+RKthApINIL9IuTYoIoSTUQNru98dMkk1vm+yOnvd5eJLdnd2ce7lT9sydM0ABoCrQA4gA2gF1lFL7gNlARaXUPKAmMEJr/X12+yxBKmccUpwBTm2ZTHx0vSZtqNekDUf2b+On7z7mlZEzE167HHGeLz8aRtfe7+Djk82kXC7F7oiL49Rf+3ll5ExioqN4b0RXKlWrS0iZSvQb/SVBRUty9fJFPn7nJUqVqUTVmqHZix9SP+OTogmptcH9syUyJRfHC8Cf29dSRdVPKJnw5/a1FA4sSoUqNTm0b2tWo00/JjfEvfDziTz0VF98fOxJnl//63fUatA8yRcYt0i1HckXyfp4ue2ujpw9fYz3hnYhuERpKlWvhz1Zm7Isl8fKob1b2LR6Mf3e/iLJ87GxMfy5fQ0dnnwts5FmTqrrqi3ZIukv88/JIyz66kP6vjHdvbGlItVxkGJ2T/rxPtD5NR7o/Bq/fT+T9cu/SZLAPbxvC3+sXsSrb37lrpBT56Yx/+uiGfjY7YQ2T7Hrdg+LjY+Usjdeki/z6+JP8bHbaZSsn8+cOsIP37xPz2EzchxpXm/Pc0UmxkumlvEwyx3DkHKspDrxxmYjo/F+80YkMyf159HnB1HA5UqF3OKOuH3sdoZNmk/k9SvMeLcf/5w8TJkK1XjoqdcoElSc2NgYvv30LVZ8P5v7H3s5e4HmYGyn/rTx3tff+SLh+HvK2y8TUrYy1Wo24s57H+f+R18Em40f5k5l4ReT6No75YSR7MitsRIddYNli2byyohP3RJnavJinC9bOAO7jy+NWzzgjpAzjCWzy6S1H8oLzkzEd+ctpWlXozx+vnYW7DrKG79sYUbnVjStVIp9ZyN4/ptVBAfkp26ZYtiz+x35PyXl/iT5/8PxyZOpMngwJTu058qOnUSFheE0S7JEh4Wxq3MX/EoUp8b773Hxt5XEhKecMCJEXvhPJG+BVsBCrfX75kzb2YDWWvdSShUHZgLNtNY3lVJ2oDxQFmgN2IFtQIrkrVKqDnAvcIfWOs7lvemJ0Vp3VEp1BYYALYDmGEncTcBkoKPW+rRS6ielVH3zfT7m++4Eemqtn1VKLcNIFG9TSrUCipmfVQ4jWZ2t5O3aZXP5faVRjLviLbWIuJA4w+7SxbAUl9wFFQtJcqnppYthCZdBFg4syuWI8wQGl+ByxPkUlxoCVK0ZyoWpI7h2JYJCRYK5EXmN6eN7077LK1SuXs/rYg8qFkLBwkHk9w8wL09sxOkThwgpUynh0tbCgcWo27g1x4/szXLydt2ybxPaUOGW2qm0oWQm2pB3l0Xm5XjZvnEZjZonzpg8pnfx57Y17Nu5gZjoKG7euM4XU4by7KsZVy7Ji7hPHt3HnA8HA3DtSgT7dq7Hx8eXvw7t5uiBHaz/dR5RNyOJi40hv38ADz3VN8O4k1u//Fs2uYyXSy4zYi9fDKNIauPFZRbCpfCUbU3Obvel07ODEx5PHvk0JczyJlmRV2Pl9IlDfPPpm/QcOo1ChYOSfOb+nRsoX/lWigS5t9xJULGQJO2JCA8jKNl6GFwshPALZwkuFkJcXCw3Iq8lnIiIuBjG9In9ef7V0ZQolTs3oNjw67dsWmXMSKpQJelYuRSecqwEFi2VZMbK5VS2PwAN73iAmRN7JSRv/zmh+W7GG7w45BMKJut/d3D3mN+ydgn7dqyl98hZOb48NS1WGB/JrV/uMl5uqZ1ktn1a4yV5P7suY/TzOnqPSNrPly6eZfZ7r/FU77EUL1UhW7F6cnter0nrbMWcnhTj5eK5VPf9EWmMF0+y0jFMUNGQJJd0XwpP+beDi4UQccGY8efaz0HFQlKsE/Fti4uNYdZ7/Wnc4gHq33a3ZeKOF1CwCNVqhbJ/10bKVKiWsP7ky+fH7Xc9zMqlSU+KZin2HIztILNNCbG7rNuux9/1mrTm+OG9VKvZKMm+vvndnZg27pXsxZ2HY+X82VNcPHeasQMfS2jn+EGdGTjuGwKDs15v0xPj/I81S9i7fR2vjpqZo/1qXu2H8krJwgGcdZkte+5aJCUK+SdZJqhAYvmGjnWrMGXdnoTH3W+/le633wrAsB//oEJw7p8Ysrroc2H4hSTOxvYLCUkxezb6/AUODhgIgE+BAhRr05q4a9dSLBN59BhFGjbg4m8rcz/w/4Cc3j/lv+i/crrmO6CEUuorYKD5XPxtdqsAO7XWNwG01vGV7/dpraO01pFAWiPrVmB9/Htc3pue+OmBp4DdWmun+Xt85qGA1jq++M0moLr5+3bz50mXZZPbqbV2aK3TWyZDLdt1SbixT90mrdmy7gecTid/HdpNgYDCKb4EBQaXIH+Bgvx1aDdOp5Mt636gbuhdANQJbZVQVmDz2qXUbWw8f/7syYTZFqeO7Sc2NpaChYOIjY1h5qS+3HbngzRs2tYrY68behdHD+4gLi6W6KgbHD+yh1JlKxN1M5KbN64DEHUzkoN7NlGmQtUst+HOdk8w5N0FDHl3gdmGpQlt8A8olGob/JO0YSl1zDbkhbzoc4AbkVc5sn9bwrIADz35Gu988htvT13G830nUr12k0wlbvMq7remLuNt81+D2++hc/fh1GvSmudeHc/o6b/y9tRldOz6Ok3ufDBbiVuAFvc+waCJCxg0cQF1GrdmqzlejqczXvL7F+S42Y6t65ZSu3H64yU66gZRN42DzYN7fsfuY6dUuVuyHGte9Hn4hTPMnNSPZ/qMJaRMpRQxJK+Z7C6Vqtbi3JmTXAg7TWxMDNs2LKdeaMsky9Rt3JI/1vwAwI5Nv1GjdmNsNhuR16/w8ZhX6PjUq1St0cDtscVr3vYJBo5fyMDxC6kd2pqt682xcng3BdIcKwEcP2yOlfVLqd3I3IafOZGw3N7tqylp1qWMuHCGOZP78lTvcZQsXSlX2uHOMX9g1wZ+WzKbHoM+wi9/gVyJF6wxPpJrce8TDJqwkEETFlIn1KWfMzteXPZFB3ZtYOXSz+gxMGk/R16/wowJvWj/RF+qqIbZjtWT2/PcUDFhvPxNbEwM2zcuo27jZOMltBV/rDHi3LlpBap2E48kI5Kz0jGM0c8nkvRzndBWSZZxHQ87/1hBdbOf64S2YvvGZcTERHMh7G/OnTlBpaq1cTqdfD19FKXKVqbNg89YJu6rl8MTbhAcHXUTveePhPq88XVFnU4ne7asonT5rB/bJo09e2O7buOWyWI/SaWqtVMcfx/YnXj87VoTddfmVZTJZux5OVbKVqzOhM/WMnraMkZPW0ZQsRCGTPwuW4nbvI4dYN/ODaz4fg4vDZ6S4/1qXuyH8lKtUsGcirjG6UvXiYlzsPzgKVrekrQC4vlriWUU1h79h0rFjPs4xDmcXLoRBcCh85c4fP4yt1cKQaTv6r79FKhQnvxlymDz9aXEvW0JX7M2yTK+QUEJVwCUe+F5zi0x1gW/kiXxyW8k0+2FC1Okfj1uHD+BEJ7yX5l564ivQ6uUWgGcB+ITrUeBekopf3PmbXxCOzMVivcD3ZRSPlprRybr5zrT+D3+iPuGUqqsmcBtCvwI1E9j2WiS/h+mtkyO1GrQgn071vPWqw+Qz8+fp3sl3tlz3MDHGPrufAA6dx/B19NGEBMdRc36zalp1je65+FuzJ48gE2rFhNcvBTd+r8HwK4/fmPzuh+w233J55efF/pNxGazseP35Rw5sIPrVy8nHLh17T2acpWyfmfb3Iq9VLkq1Kx/B+MGPIrNx0az1p0oU6EaF8L+ZuYkIwEXFxdHaPP7qJnDekq1GrRg/451vP3q/WYbEu/UPH7gowx5d4HZhpFmG25ya/3m1Gxg3E1395aVLJg9lmtXIvhkfC/KVqpB7+HGZVijet/LzchrxmXkW1fRa8QMSmcjIZc83tzoc6Mtq6hRrxn5/QNwt9yMOy/VbNCC/TvXMfq1+/Hz8+fJnonjZeKgRxk00Rgvj3cfyf+mjSAm5qbRjvqJ42XhHGO8fDqhF+Uq1qDn8E+5ejmcT8a+jM1mI7BoSZ7uk/N7MuZWn/+y4BOuX7vEd7OMO7/72O0MHj8XMJLQB/ds4okXR+Y4/uTsdl+6dB/Ch6N74nA4uKP1Q5SpUJWl306jYtWa1GvciuZtOjJ7ynBG9H6QgoWK0L3fBABW//Id586e5KcFM/hpgXHZ+GtvfEKRwGyfg8tQzQZ3cmDXesb0vQ+//AXo8lJi/7875BEGjjdmzj36wki+/SR+29KCW82x8uPcyZz75zg2m43gEmV4rNsbACxfNJ3r1y6zYLYx9nx87Lw+dl4utiNnY37B7LHExkYz7Z0XAahYrS6de7zh9jhza3ws/HIyW9b/QnTUTQb3aEvzuzvyYOeebo8/fry885oxXp54OXG8TBz8CIMmGOPlsW4j+WZ6yvGycM4YYmOimTamB2DcLObx7qPYsPxbLoSdYvmiT1i+6BMAeg6bkaMbgXpiez75jWcJO32cqJuRjHj5bp58+S1q1k9Zbzuz7HZfOncfysfvGOOlaeuHKVO+Kj/MnUrFW2pRt3ErmrXpyOdThjOqT3sCChWhW7+JCe8f0fM+bt64RlxsDLu3rOaVkZ9QuvwtLPpqMtvW/0x01E2GvXgPzdp0on0uH7QaAAAgAElEQVQujJd4uXkM4w52uy+PdxvG1DE9cTjiaHqX0c8/zp1KhVtqUrfxXTRr3ZEvPhrGqD4PULBQIC+Y/VymfFUaNm3LO/0exsfHTufuw/Cx2zlyYAdb1v1ImQrVGDvAmDnZ4clXqd2wBbs2r2T+7HFcuxLB9HG9KVepBn1GfOIVcV+5dIEvPx6BwxGH0+mgYdN7qdPISKp+/uEQrl2JwImTcpVq0KVH9vehORnbZcpXpWGztozu2xEfu50uZuxXL4Tz6cR+ADjiYgltcT+1zHr3i7+azN/HNWCjWMkyPPlS9mLP67HiTnkd+7zPxhEbG81Ho18CjBsluuO4K7f2QwBv9WlL1A3zO9G2VfQcNiNbkxQy4uvjw+A2Dei9cB0Oh5MOdSpzS/FApm/YS81SRWlZtQxzdxxh7dF/sPvYCPT34612jQGIdTjo9q1xo7mC+fPxzgO34etFZRPqf/UexVo2wa94MK3/Wsvhtz/i1Jyc3/8lx+LiODZhIrWmfQw+ds4tWcKNY8eo0PNlru3fT/jadQSGNqLiK33A6eTKjp0cHTcegAKVK1O5fz+MFIuN019+ReSRIx5tjvhvs3njXdTcTSn1ONDbfHgGiAU+0FpvM19/CqPmbSQwD6PmbULdWqXUXq117TQ+ewBGzdtIYAoZ17wtrrWeZJY5eFRr3UcpVSn+PUqpFsC7GLN9V2mtRyR7n+uyDwP9gF3A4vjPyyjm5Fbsjvr3DwIv4vRgLbecsmXqnIZwpzinNceL3WbdsZLPbs3LeCKjrXs+1sfHmuPF3zczF9x4pxsx1hwvvj7WXD/BuuM8zuE9CYKs8rHwvsiqbBbtc6dFj7esLCbOutuWO7e95ekQsmVNr/meDiHbgmpbt0zEHTu3ywYmmfY99nv9zuLHmTW96v/tP5G8FemT5G3ekuStyApJ3uY9Sd7mPasmtSR5m/ckeZv3JHkrskKStyKzJHmb9yR56xmSvE1JkrdZZ80jdw9QSrUGkl9L+YrW+s80lu8PdHB5Kk5r3Sa34hNCCCGEEEIIIYQQQvy7SPI2k7TWq4BVWVj+feD93ItICCGEEEIIIYQQQgjrcDqteyWVp1j3WgUhhBBCCCGEEEIIIYT4F5PkrRBCCCGEEEIIIYQQQnghSd4KIYQQQgghhBBCCCGEF5Kat0IIIYQQQgghhBBCiFzndDg9HYLlyMxbIYQQQgghhBBCCCGE8EKSvBVCCCGEEEIIIYQQQggvJGUThBBCCCGEEEIIIYQQuU7KJmSdzLwVQgghhBBCCCGEEEIILyTJWyGEEEIIIYQQQgghhPBCUjZBWJbN5ukIsskplwgI4c2suoo6sOpGEXywZqfHOa3b51Yd51Zm1XHusGjcwjOcFt0u2mzWHedW7fNYhzXjBrAXKuzpELIlqHYhT4eQbZf2XvN0CMKNHE6Hp0OwHJl5K4QQQgghhBBCCCGEEF5IkrdCCCGEEEIIIYQQQgjhhaRsghBCCCGEEEIIIYQQItc5HdYtVeMpMvNWCCGEEEIIIYQQQgghvJAkb4UQQgghhBBCCCGEEMILSdkEIYQQQgghhBBCCCFErnM6HJ4OwXJk5q0QQgghhBBCCCGEEEJ4IUneCiGEEEIIIYQQQgghhBeS5K0QQgghhBBCCCGEEEJ4Ial5K4QQQgghhBBCCCGEyHVOh9PTIViOzLwVQgghhBBCCCGEEEIILyTJWyGEEEIIIYQQQgghhPBCUjZBpMvpdLJgzgT27VyPX35/uvYaTfkqNVMsd/LYfr6aOoKY6ChqNWjBo88Pxmazcf3aZWZPHkj4+X8oWqIM3fpNIqBQEQAO7dvKws8nEhcXS6HCQfR9aw4RF87y5dThXLl0AZvNhzvufoS77n8607HOnzOBfTvMWHuPpkJqsR41Yo2OjqJWwxY8Fh/rVSPWi+f/oViJMnTrb8Sa1uce2ruFBZ+/m/C5Yf/8xQt9J1KvSWu+/HgER/Zvwz+gMABde4+mfOUaHu3z35bOYev6nwFwOGI5+/dfjP9sLQULBfL1tDfYu2MthQOLMvy9xZmK01PtiHfiyF4mDX+aF/pNpMHtbb0y1q3rf2LFktkA5PcPoHP3EZSrpABY/fPX/L5yIU4n3NGmE3c90DVHbUitTYs+H8/+nevJl9+fp3q+k2qbTh3bx/+mGW2q2aAFnZ4bgs1mY8nX77F3+xrsvvkoHlKeJ3uOJqBgkVT+Uvbjy8s+D/vnL2ZPHpTwuRfP/c0Dj/fKcb/v27mReXMm4nA4uKNNR9p1fCHJ6zEx0Xz+0QhOHjtAwUKBdO8/geIly3Lt6iVmTBrAiaP7uL1VB57oPjThPbExMcz9bByH9m3DZvPhoSf70PD2u3MUZ3JOp5PvvxjHgV3r8PMrQJeeYyhXOfXxMfeT4cRE3+TW+nfy8LNDsdls/DJvCvu2rcbmY6NQkWJ0eXkMgUVLAnBk/xaWfDmeuNhYChYOpveoL9wee07G9s5Ny1m2YDphp4/Rf8y3VLilFgAXz51mXP+HKFmmEgAVq9Wlc4833Bb3vp0bmT9nAk6Hg2ZtOnJvx25JXo+JieaLj4Zzyhwr3fpPpFjJsgAsW/QZm1Ytxubjw+MvDKZm/TsAWPnDV/y+chHYbJStUI2uvd8mn19+t8Ucz+l0sviLcRzYtZ58fv480XMM5dMYL99+MsIcLy3oaI6Xpf+bxL4da7HbfSkeUp4nXn6HAgWLEBcbw9wZozh9/ABxcbE0btGBux/ukeNY83Lfc2jvFhZ+kfRY4PnXjGOBnMiNbcuUd3pxOeICjrhYqt7akCe6D8XHbs9RnOCZ/f33X09m3851ALR75CUaNWuXrdj37dzAgjkTzH7uRNtU1ssvPxrOyWP7KVg4kG793k1YL5cvnsXvKxfj4+PDYy8MSVgvv5r2Bnu3G8dUI95PPKbaselXfppnbHsGjvuGiua2J6fc3YaY6Cgmv/E8sbHRxMXF0eD2u2nfubdbYs3reA/++Qfff/U+DoeT/P4BdO09mpKlK2Qzbvdvw0f0vA//AgH4+Njx8bEzZOK3APzw7cfs3roGHx8fChUJ5pk+owky97Ge6OP0PjOtPj68fxsLP5/I6ROHeb7vBBo2zfmx+pIvE49bOr+c+nHL38f2MffTxOOWh54x9kPx1vw4hx+/mcRbn2ygYJFgdmz4kdU/fAaAn38Aj7wwkjIVM/edLTs2Hv6bCcv+wOFw0rFhdbq1qJfk9SU7DzN5xVZKFg4AoEuTW+nUSCW8fu1mNA9PXUTrGhUZ9kDTXIszuaBmTakycAD42An7/ntOz/k8yev5S5ei6qhR5AsOJvbKZQ4NH0n0uXPkL12KGpMmgd0HH19fzsz9jrMLFuZZ3BmpO3MsJe9vRfS5i6xr8KCnw/lPcTodng7BcmTmrUjX/p0bOH/2BKOm/MgTL77B3FnvpLrcdzPf4YmXRjFqyo+cP3uC/bs2ALDi+89QdW5j1JQfUXVu49fvjZ1j5PUrzJs1hpcGT2HE+4vp1n8SAD52O526vs7IyUsYMOZr1i3/jjN/H81UrPt2buD8mRO8+dGPPPnSG8ydmXqsc81Y3/zoR86fSYz1VzPWNz9KGmtan1u9dhOGTZrPsEnzeW3ULPz8/Lm1XuJO9OGu/RNez2ziFnKvz+/u8DxD353P0Hfn0+GJ16hWsxEFCwUCcHurDvQeNj3TMXqyHQAORxxL/jeZW+s38+pYi5UsS9835zBs0kLaPfIi3854C4B/Th7m95ULGTj2G4a+O5+9O9Zx7swJt7QloU271nP+7AlGfPgTXXqMYv5nqbdp3qx36PziKEZ8+BPnz57ggNkmVacpQyYtZsi7iyhZuiK/fT/LvfHlcZ+HlKmcMP4HT5hLPj9/6jVpk6M2OOLi+HbWOPoMn8qoyYvYumEZ/5xKur3auHIxAQWLMPrjH2jT/mkWf/0hAPny5adDl9480rV/is/9ZdFMCgcW5e2PljLqg0VUr9koR3Gm5uCu9Vw4e4Khk3/hsR5vsvCzt1NdbuHst3ms+5sMnfwLF86e4OBuo//vav8CAyYu5vXxi6jZsCUrFhnbjxvXr7Bo9mheGPAxgyYt5Zm+77s99pyO7dLlq/HC65O55daU/VospDyDJi5g0MQFbk3cOuLi+G7WWPoMn8bIyYvZtmEZZ5KNld/NsfLWxz/Suv3TLP76AwDOnDrK9o3LGDF5EX2GT2PuzLE44uK4dDGMNb98w+AJ3zJy8iIcDgfbNi5zW8yuDuxaz/mzJxk2+Wce7/EmCz4bnepyC2aP5vHuoxg2+WfOnz2ZMF6q12nKoImLGTRxMSVKV+K3Jcb2ZNfmX4mLjWbQxMW8PnYev6+cT/j50zmKNa/3PdVrN0nYtryayrFAduTWtqVH/4mMfG8eb0xeyLUrEWzftCJHccbL6z7fu2Mdp/46wJCJ8xkw5n/8tvRzbkRey3Lcjrg45n02lt7DpzNy8vds2/hLivVy06pFBBQqwlsf/0Tr9l35PsV6uZjew6fz3awxOOLiAPOYanjKY6oy5avy4oD3qZrKtie7cqMNvvn8eHXULIZNWsCwd+exf9dG/jq025LxfjdzDM+9Op5hk+bTuMV9LFs4I9txu3sbHq/vm7MYNmleQuIW4O6HnmPE+wsYNmkedRrdyc/zP81SrO7u4/Q+M60+Llq8NF17v0No8/uy0NNpO2ju+4e8/wuPdn+ThbPTPm55tNubDHn/F867HLcAXLp4hkN//k5Q8dIJzxUtWZaeIz/n9QmLubvjy8yf9aZb4k1NnMPB2J83Me2ptizu3Ylle49x9FxEiuXa1qrMvJ4PM6/nw0kStwBTV+8gtGKpXIsxVT4+VBkyhH19XmXnI49Sot29FKhSOckilfr149xPP7GrcxdOzZhFxVf6ABB9/gJ7nnue3V2eZHfXZyn7/HP4lSiet/Gn4+8vFrGlfXdPhyFEpkjy1qSUqqSU+tHTcWSFUuo5pVSAy+8D3P039mxbTZM7H8Rms1G5ej1uXL/K5YjzSZa5HHGemzeuUaV6PWw2G03ufJA9W1cb79+6mttadgDgtpYd2LN1FQDbNvxMvdvaUNTceRYOLAZAYHCJhNka/gUKUqpsZS6Fn8tcrFtXc1vLTMaqjFhva/kgu7e4xNrKjLVVB3ZvWZXpz935xwpqNmiOX/4CmYo13XbkUp+72rbxFxrdkXgwVbVmKAFmItddcrMda3/5hnq33UPhIkW9OtYqqn7C7KHK1epx6aIxls+e/otK1eril78AdrsvVW8NZfeWlW5pS7y9W1fT+M4O2Gw2KmXQpsrV62Oz2Wh8Zwf+NGOvUa8ZdrtxcUbFavW4dDHMrfHldZ+70n9upkSp8hQtUSZHbTh+ZC8lS5WnREg5fPPlo/Ed97Jn65qk7dy6hqatjDP5DZvezcE/t+B0OsnvX4CqtzbA188vxef+vmoJ7cwZLfGzbtxt7/ZVNGphjI+K1epxI/IqV5L1/5WI89y8cZ1K5vho1KIDe7cZ49Q/oFDCctE3b4A5q2XHxp+o0/hugosbfRu/bXdr7Dkc26XKVSGkTOXUPjrXHD+ylxKlylPcHCuN7mjH7hRjZTW3m/ugBk3vQZtjZffWNTS6ox358vlRPKQcJUqV5/iRvQDExcUREx1FXFws0VE3CAwukSvx792+msbmeKlkjpfU+zxxvDRu0YE/t5nbk7p3uGxP6nIp3Nie2LARFXWDuLhYYqKj8PXNR/4ChcgJT+573HUskFvblgLmeuuIiyU2NibJbLScyOs+P/v3UarVDMVu9yW/fwDlKioO7NqY5biN9bJCkvVyz7bVSdu2dU1CbA1uvwe9dzNOp5M921YnWy8rJKyX1WqGJpwcd1WqXBVCyrp325MbbbDZbPgXMGb9xcXF4oiLTdjGWzHeGzeMxP6NyGsEFs3eNjK3tuFpKeCyj42KupmldTU3+jijz0ytj4uVLEvZitWx2dyTbti3fRWhLsctNzNx3BLaogP7tiUeXy/5agLtn3wdG4n9Wal6g4TvQBWr1uVyuHuPd13tPX2B8kWLUK5oEfL52mlXuwpr9MlMv3//Pxe4eO0GTW8pm2sxpqZw7VrcPHWKqNOnccbGcn75rxRt1SrJMgFVKnN58xYALm/dStFWLQFwxsbijIkBwMfPz23jwV3CN2wjJvyyp8MQIlO8a+3JI0qpf0u7nwMCcvMPXAo/R3DxxLN7QcVCUiRTL4WfI6hYSKrLXL0cnvBlMjC4BFevhANw7swJIq9d4YM3X2DC4M5sXrs0xd++eO40f/91kEpV62Qq1svh5wgqlvVYL2cQa2Y+d/vGX1KcWf7h248Y8/ojLPh8IjEx0ZlqQ3yMudHn8aKjbnBg10bq335PpmPKjtxqx6XwMHZvWUWLto95fayufl+1iJoNjEvPypSvypEDO7h29RLRUTfYt3M9EW5Ojl6KSDpuA13GerzL4ecIKurSpqIhXIpImezcvHoxtzZo7t748rjPXW3fuCzJyYvsikilDRGptCF+GbvdlwIBhbh+9VKanxl5/QoAS+dOZczALsyYNIArly7mONbkkm/XAouGpPjCcjk8LOn4KFYqyRj6+bsPebt3G3Zs/JF2jxkzLM6fOU7k9StMe/s5Jg97jG3rlrg9dneO7eTCz59m4uDHmPLmcxw9sN19MScbK8HFSqbo77TGyuXwMIKLp1wPgoqFcHeHZxnR816G9ribAgGFqemmqxGSuxwelnQ/mMZ4CXTpc+P/JeV2bfOaxdxaz9ie1LvtHvLnL8Connfx9iv30Kr9c6kmvbLCk/ueHclOjGZXbmxb4k0Z3ZOB3VrjXyDAbeVY8rrPy1ZU7N+1geioG1y7EsGhfVuIuHg2G3GHEVws2XbiYvK4E9c/136+dPEcwcnWiUu5mPRJS261wREXx9gBjzG4Wytq1G1K5Wp1LRnvUz3fZPrY3gx/6W62rP2Rtg8nLR+Q+bjdvw0HI8f80eiXGTeoCxtWLEjyeUu++YhhL7Vl6/qfaN+5VxZidX8fp/eZ7urjjFyOOEdQ0WTHLRHJ9kMRSY9bAouW4rK579+3fRWBwSHplkTYsmYRNeq1cHPkic5duU6pIgUTHpcsUpCwK5Epllt54DiPTlvM69+t4uxlIzHucDh5b/kW+rdtnGvxpcWvZEmiwxL7OjosjPwlkp4IuX7oMMXaGFe0FW19F76FCuEbaOzP/UJCqP/dXEJ/+Zm/P/+c6PMX8i544bUcDqfX//M2/9qat0qpKsBXQAwQCZwzf1YGRiilFPAqEAXMBX4BgpVSXwJ1gZla66lpfPbrwGPmez8AlgAzgWqAA3hRa30olfe9CVQHAoGSwFjgJaA08IjW+ohSqiMwBHACc7TWn5rvKwuUAsqZf7ssUB9YopTaCOwHGiqlFgNVgR5a6z+y1XmunCkHbYqzv6ktk8HHOuLiOPXXfl4ZOZOY6CjeG9GVStXqEmLWGoy6Gcms9/rzyHODkpx9Tj/U1OLIONaMgs3ocy9HnOefk0eoWS/xS/NDT71GkaDixMbG8O2nb7Hi+9nc/9jL6f+hdGJ0R5/H+3P7Wqqo+jn+gpyhXGrHws8n8tBTffHxyXmdvnTjcGOfH9q7hU2rF9PvbaP2Z6lyVbjnoef5+J0Xye8fQNmKCrs72wNptCn5IhmvM78umoGP3U5o8/ZuDS+v+zxebGwMf25fQ4cnX8tspGnLRBtS6+P0ZjA54uKIuBjGLTXq89hzA/jth69Y+OX7PP/qmByHmyywDONykv4Yur/za9zf+TVWfj+TDcu/od1jfXA44vj7r/28PPwzYqKj+GjUk1SsVo8SpSvlauzZGdvJBQaX4M2pv1KwcBCnju1j1qTXGDrp+ySzjLMtM+MgjWVSf9pG5LUr7Nm6mren/kxAwcLMfG8gm9f9yG13unldJY3+TDGOM27jisWfYvex08jcnpw4+ic2HztvTVtF5PUrfPTWs1SvfTvFQ8rnJNiMY82FfU9qxwLZlgvblnivjpxOTHQUsz8cxsG9W6iZwxIPZjCphJJ7fX5rvWacOLqX90Y8Q6EiwVSuXs8ttXshtX5OdSFSG+/umsmcU+5og4/dzrBJ84m8foUZ7/bjn5OHKVOhWi5Em7vxrvrxa3oOm0rlanVZsWQOi754l6d6vpX1IHNhGw7w+jtfEFS0JFcvX2TK2y8TUrYy1cxSSQ89+QoPPfkKyxZ9xtplc7OUwE3r76UXanp9nN4+wG19nIHM7NdT3S5iTFz57fsZvDh0Zpqff2TfZrasWUTvUV/lLNB0pNntLlqq8txXpwp+vnbmbT3IiMXrmfXcfXy39QDNq5WjVKAbjkmyLOW2Lfkx4vHJk6kyeDAlO7Tnyo6dRIWF4TTLg0SHhbGrcxf8ShSnxvvvcfG3lcSEp5x0IYRI3782eQu0AhZqrd83Z9rOBrTWupdSqjhGsrWZ1vqmUsoOlMdIiLYG7MA2IEXyVilVB7gXuENrHWe+9yEgUmt9p1KqCTAB6JhGXH9rrZ9USo0EHtVat1NKdQW6KqXeAsYATTASw78rpRaZ77uote6hlHoG6Kq1HqWU2mV+xgWl1HOAj9a6o1LqTqAnkK3k7dplc/l9pVFIvOIttYi4kDib4dLFsBSXZQYVC0lySfWli2EJN64pHFiUyxHnCQwuweWI8wmXvQUVC6Fg4SDy+weQ3z+Aqrc24vSJQ4SUqURcbAwz3+tPaIsHqH9b+rNC1i6by8bfzFir1uLSxWSxFs1ErMEZx5re5+74fTn1mrTG7psv4bn4PsqXz4/b73qYlUvTv2FPXvR5vO0bl9HITfWnPNGOk0f3MefDwQBcuxLBvp3r8fHxzfLNYfKqz0+fOMQ3n75Jz6HTKFQ4KOH5Zq070ax1JwCWfvNhkhlJ2bV++bdsMttU4ZbaScbt5YthFAlOerMLY/aHS5vCk7Z7y9ol7Nuxlt4jZ7nlS6mn+xyM2ozlK99KkaCcX84fXCwkRRuCkrUhfpngYiHExcVyI/JauidOChYOwi+/P/XN8dyw6T1sXOmemwhu+PUbNq8yZvaUr5JsfIQnbgvjBRUtlXR8XDybYgwBNLjjAT6b2JN2j/UhsGgIBQsHJ2zbq9QI5Z8TOsfJW3eP7dT45vPDN59xqXn5KrUoHlKec2dOJNzQLCeCko2ViIvnUvZ3GmPFeD75fqsEB/f8QbGSZSkcaIz9+re14Zje7bbk7YZfv2WTOV4qJBsvl8JT9nlg0VJJZqJdvph0TG1Zu4R9O9fRa3ji9mTHxp+pUe8O7L75KBxYjMrV63Pq2L4sJ2+9Yd+zY9Ny6iY7Fsiu3Ni2uMrnl5+6jVuye+uabCdvPd3n7Tq9SLtOLwIw58PBlCxVMcttCCoakuSql0vhKY8bg831L7hYqZTrZbJ1Ivk6nRdyuw0BBYtQrVYo+3dtdEvyNi/jLRxYjNMndMIs3EbN2jF1TM/sxZ0L23CjP+LXgWLUa9Ka44f3JiRv4zVucR/TxvbJdPI2t/o4tc+8ejncbX2cmo2/fsPm1S7HLeFJj1tS7PuTHbdcDj9LYHBJLoadIvz8ad4f0inhvZOHP8qro+dSJKgE/5zUzJ85iu6DP6FgsuNGdwopUpCzV64nPD535XrCjckS2hDgn/D7I42q8+FvWwHY8/c5dpwIY97Wg0RGxxAT5yDAz5e+9+T+TNzoc2H4hSR+T/ELCUkxezb6/AUODhgIgE+BAhRr05q4a9dSLBN59BhFGjbg4m/uLRcnxH/Bv6V8QGq+A0oopb4CBprPxSczqwA7tdY3AbTW8VXj92mto7TWkRgzaFNzK7A+/j3mz+rxn6213gLckk5cW82fp4BdLr8XBUoAp7XW17TWMcBujJnCAPHXb540l01NZpbJUMt2XRJuvlG3SWu2rPsBp9PJX4d2UyCgcIoD8sDgEuQvUJC/Du3G6XSyZd0P1A29C4A6oa0SSiJsXruUuo2N5+uG3sXRgzsS6vQdP7KHUmUr43Q6+d8noyhVtjJt2j+TqVjjbwpWr3FrNq/NWqyb1/6QEFOd0FZsXmPGusY11lbpfu62VEomxNd6czqd7NmyitLlq3q8zwFuRF7lyP5tCcu6W160462py3jb/Nfg9nvo3H14tu7qnRexhl84w8xJ/Ximz9iEWeXxrl6+mLDM7i0rCb3j/iy3IbkW9z6RcLOlOo1bs3XdUpxOJ8cP7cY/oFDqbfIvyHGzTVvXLaW2GfuBXRv4bclsegz6yC21nMHzfQ4p6z3nRMWqtTh35iQXwk4TGxPD1o3Lqdu4ZZJl6oa2ZNOaHwDYsek3VO3G6SbCbTYbdRu15NC+bQAc/HMzpctVcUu8zds+yevjF/H6+EXUDm3D9vXG+Dhx2BgfRZL1f5HgEvj7B3DisNH/29cvpXYjY10773KDvX3bV1PSrCFbO7Q1xw5uT9i2nzyyh5Jlcx6/O8d2Wq5dCcfhMA4HLoSd4vyZkxQLKZfj2MF1rPxNbEwM2zcuS2WstOIPcx+0c9MKVO0mxnho3JLtG5cRExPNhbC/OXfmJJWq1ia4eCmOH9pDdNQNnE4n+s/NlHJjPc3mbZ9g4PiFDBy/kNqhrdlqjpfjh3dTIM0+D+C4OV62rl9K7UaJ25NVP3xG9wFJtyfBxUtzZJ9RFzLqZiQnjuzJVj1ib9j3bN/4C6FevG25eSMy4dgkLi6WvTs25Gi8eLLPHY44rpklIk6fOMQ/Jw9RIxtJaKOfTyRZL+uEtkqyjGtsO/9YQXVzvawT2irZenmCSlVrZzmGnMqNNly9HJ5Qwic66iZ6zx9uq9Wbl/EGFCrCjchrhP1zHICDezZRqlz22pEb2/Com5HcvGEk8qJuRnJg9ybKVDC+L7jexHbP1jVZWldzo4/T+kx39nFq7mj7JP3HLaL/uEXUCm3DNtfjlgKpH7fkL5B43LJt/VJqNWpN6QrVeeuT9QyfsoLhU1YQWBxOVecAACAASURBVDSEfmMWUCSoBBEX/uGLya/xRK9x7r1KKBW1yhTn5MXL/B1xlZjYOJbtPUZLVSHJMuevJpZRWKNPUrm4kUwe90grlvfvzC/9Hqd/2ya0r1c1TxK3AFf37adAhfLkL1MGm68vJe5tS/iatUmW8Q0KSphGXO6F5zm3xBhffiVL4pM/PwD2woUpUr8eN4679ybNwpqcDofX//M2/+aZtw6t9VAApdQK4DwQn6Q9CtRTSvmbM2/jk9iZKWyxH+imlPLRWjvM9x4G7gL+Z868PZbO+51p/G4DLgBllVKFMGbe1gf+SmNZgGiS/h+mtkyO1GrQgn071vPWqw+Qz8+fp3sl3mF63MDHGPrufAA6dx/B19NGEBMdRc36zalp1se85+FuzJ48gE2rFhNcvBTd+r8HGJeN16x/B+MGPIrNx0az1p0oU6EaRw/uYMu6HylToRrjBho1zjo88Sq1GmZcf6hWwxbs27meN195AD8/f57unRjr2AGPMWySEWuXHiP4ampirLXMWNt27MZn7w/gdzPW7mas6X3uxXOnibgQRtWaoUli+fzDIVy7EoETJ+Uq1aBLj5Ee73OA3VtWUaNeM/L7Jz3LO+eDQRzev41rVy8x4uW7uf/xXgmzQrMrN9vhbrkV6y8LPuH6tUt8N8u45N3Hbmfw+LkAzHqvP9evXsbu68vj3YYl3GTLXWo2aMH+nesY/dr9+Pn582TPxLt/Txz0KIMmGjMZHu8+kv9NG0FMzE2jTfWNdW3B7LHExkYz7R1jZlPFanXp3OMNt8XniT6PjrrBwT2beOLFzK+P6bHbfencfQhT3umJw+GgWeuHKFO+KkvnTqPiLTWp17gVd7TpyJwpwxnZ50ECChWhe78JCe8f1vM+bt64TlxsDLu3rObVkdMpU/4WOnZ9jTlTRjB/zrsUKhLMs73df/nhrQ3u5MCudYzrex/58vvT5aXE8fHekE68Pt646OORF95g7ifDiYmOokb95tQwx8dPc9/n/D/Hsdl8CC5Rmke7jQIgpOwt1KjXnPcGd8Rm8+G2ux6hdHn3Xmqb07G9e8tKFs4Zy7UrEXw6oRflKtag5/BPOXJgO7/Mm4qPjx0fHzuP9xjptvIyxlgZysfmWGna+mHKlK/KD3OnUvGWWtRt3IpmbTry+ZThjOrTnoBCRejWbyJg1Mhu2Kwto/t2xMdup0v3YfjY7VSuXpcGTe9h3MAu+NjtlK9cg+b3POqWeJOr2eBODuxaz5i+9+GXvwBdXkpcX98d8ggDxxszMB99YSTffjKCmOib3Fq/Bbeafb7o8zHExkQzfWwPwLgpzOPdR9G87RN8+8kIJgx8GHDSpOXDlKmoUvz9rPDEvietY4Hsyo1tS6HCQUwb/xqxMTE4HHGoOk24s617xkte93lcbCwfvPEcAP4BBXn2lXEJN8TLCrvd2P9OHdMThyOOpncZ6+WPc6dS4Zaa1G18F81ad+SLj4Yxqs8DFCwUyAuu62XTtrzT72F8fOx0NtdLgNkfDOLwPuOYavhLd/PA471o1qYTuzavZP7scVy7EsH0cb0pV6kGfUZ8kuW4c7sNVy5d4MuPR+BwxOF0OmjY9F7qNGqZQSTeGe+TL41i1qT+2Hx8CChYhKd7vZ3tuN29Db96IZxPJ/YDjJsIhra4n1pmnf7vv/6QMHMfW7REaZ58cYRH+xhI9TPT6+MTR/Yy492+RF6/wt7ta/lp3nRGTs7+lUS31r+Tg7vWMb6fcdzS2eW45f2hneg/LulxS2x0FKpe4nFLWlYs+oTIq5dZNMfYbvn4+NJ3zLxsx5keX7sPQ+9vSs+vluNwOnm4QTWqlgxm6qod1CpTnFY1KvDN5v2s0Sfx9bFRpEB+Rj+cezV4My0ujmMTJlJr2sfgY+fckiXcOHaMCj1f5tr+/YSvXUdgaCMqvtIHnE6u7NjJ0XHjAShQuTKV+/fDSFPYOP3lV0QeOeLR5riq/9V7FGvZBL/iwbT+ay2H3/6IU3MWZPxGITJBKVUV+AzIB3yhtf40lWW6A0+Yy4zWWq9I6/NsadWGsTql1ONAb/PhGSAW+EBrvc18/SmMmreRwDyMmrcfa63bm6/v1VqnegpdKTUAo+5sJDAFo+btbIwZt06MmrcHU3nfm8BerfUCs8xBca31JKVUK4zyB32UUo8Ag83P+VxrPT3Z+1yX7QV0AlYDp10+r5JrWzKyYneUJQeBl5QWy7J/6Sonckmc05oD3W6z7kD39fG+M62ZcT0m55dre4pV+zyf3ZpxA0TFuLnOdh6xcp9bdZzHOqx7oZ5VjxVF3rNZ+LjFadFjxRsW3Q8B3HMo9yaV5KbtE3MnMZ0XLu29lvFCXuqBGG3NlTQXtXp0k9dvdNcsaJqj/zel1AJgHMYV9RuB+7TW4S6v1wH6aK1fyszn/WuTtyLzJHmbt2SVE1khydu8Z9UEiyRv856VE4mSvM17Vh3nkrwV/wWSvM17krzNe5K89QxJ3qbUstPvXr/RXbuoWU6Tt7u11vXM3ycAq7XWy1xefxMoiVGGNQzorbW+lNbn/ZvLJuSYUqo1kPw64Ve01n9m4r0TMW48Fu+01vopd8YnhBBCCCGEEEIIIYRwHzO5OirZ029prd/M5Ee4Jn8jSHlfqtJAQa313Wb5hCHmv1RJ8jYdWutVwKpsvneQm8MRQgghhBBCCCGEEELkIjNJ+2Z6yyilSgOpFfQeC7hechUE7Eq2TATwu/n7MmBGen9LkrdCCCGEEEIIIYQQQohc53Ras5RUclrrM8Dtqb2mlOqqlGqIUfO2JTAx2SJrgfuAL4BQ4HB6f8u6RayEEEIIIYQQQgghhBDCuwwFJgPrgc/jb1amlJpjvr4c8FFKrQF6YdzcLE0y81YIIYQQQgghhBBCCCHcQGt9BGPGbfLnnzd/OoA+mf08mXkrhBBCCCGEEEIIIYQQXkhm3gohhBBCCCGEEEIIIXKd0+H0dAiWIzNvhRBCCCGEEEIIIYQQwgtJ8lYIIYQQQgghhBBCCCG8kJRNEEIIIYQQQgghhBBC5Dqnw+HpECxHZt4KIYQQQgghhBBCCCGEF5KZt0IIIYQQQgghhBBCiFy34YeWNk/HYDU2p1Pu8iaEEEIIIYQQQgghhBDeRsomCCGEEEIIIYQQQgghhBeS5K0QQgghhBBCCCGEEEJ4IUneCiGEEEIIIYQQQgghhBeS5K0QQgghhBBCCCGEEEJ4IUneCiGEEEIIIYQQQgghhBeS5K0QQgghhBBCCCGEEEJ4IUneCiGEEEIIIYQQQgghhBeS5K0QQgghhBBCCCGEEEJ4IUneCuFCKVUj2eNbPBWLEOLfSSlV0NMxCJEblFJByR4X8VQsQgjhDeS7hRBCCHfw9XQA4t9JKRUKTAQKAc2A0VrroZ6NKlOmAa1dHo8DHvdQLJmmlBoBfKG1PuXpWLLK/LL/EFAUsAFord/3aFCZoJRaDnwJLNRa3/R0PJmllPIDngNKABOA5lrrNZ6M6b9CKXU/8BYQoJSqB8zQWr/g4bAypJS6BygDfA1U11of8HBIaVJKNUzrNa31jryMJSeUUn211h+4PO6utZ7lyZgyaRFJ96GzsMA+FKzX50qp/mm95s37UKXUR4Aztde01q/mcThZppT6XGv9nMvjyVrrfh4MKUusNs7jKaUqaK1PejqObLLcdwullA/wP631E56OJauUUlWAriT9XuH125Z4Sik7UITE2MM9G9G/m1KqCdALCMaY2OjUWnfwbFRCpE6StyK3vA90AuZrrWPNDaPXUkrdB9wPVFdKTTGfzgeEeC6qLDkITFVK+WIkFBdrraM8HFNm/QT8Algt8fwMxsHhSqXUn8BsrfUWD8eUGV8B64EHtNZjzMT/Gs+GlDGzj50Y+61SwAWMBPQZrXVNT8aWBcOB5sAyc7tYycPxZEgpNR24Dtyptf7C3D7e4+Gw0vNKGs87Aa9PlLvoAHzg8vghjESoV1JKNcU4UVveJamYDyPpbxWW6nPgovmzDRALbAaaYPS7N1tg/nwSOE1i3OU9FlEmKKVKAWWBWi4nifIBDTwXVbZYbZzH+1gplQ/jGGahFY5xrfzdQmvtUEpdUUoV11pf8HQ8WTQfY4yv8XAcWaaUGovx/fkfjOStk6SJf6+llOoA9CdpErSuZ6PKlJlAH8CqJ4fEf4gkb0VuidVaX1BKxc+u8PYSHfuBSIwD84Xmc9HAXo9FlAVa6wXAAvPLxQRgmlLqG2CS1vq4R4PL2CWt9TueDiKrtNZhwCSl1GcYB4m/mQnGiVrrJZ6NLl3FtNYfK6U6mY9tHo0mk7TWdQCUUtOAd7XWfymlKgODPRtZlsRpraNctot2j0aTOUpr3Voptdp87NUxa62fd32slCqotb7uqXiySinVE2MGSGWl1B6M9TMO+NmjgWUsEuOEylUSk4rRwFMeiyiTrNrnWusvAJRSj2qtHzSf/lQp5e1xrwVQSg3WWr9kPr1cKfWLB8PKjNuAh4FyJJ4kigYmeyyiLLDqOI+nte6glAoh6Unzz7TW2zwcWnos/d0CI2l4Uin1F+DAOsm401rrrzwdRDa11lrXyHgxr/QOxsQQq03GOaq1Xu/pIITIDEneityyTSk1CSiplBoD/OHpgNKjtT4BnADWJrtcxdtnsAAJdQWfMP+dwPiC4QTmArd7MLTMOKyUehHYjnkppRUub1ZK3Qs8DxTHuJy8l/nSr4A3J2+jlFK1AZRS1TBmVVpJXa31XwBmAre2pwPKgkVKqflABaXUVyR+mfNmDqVUScCplCqKMcPP61m1RIXWejowXSn1uNZ6nqfjySyt9W5gN/CFp2PJKqv2uYuSSqn6WutdSqn6QDFPB5RJAUqphzFm3t4GFPBwPOkyT8ouUUpV11of8nQ8WfUvGOcANzGSobFAfuBlpZS/1vppz4aVumTfLZKUHwK8tvxQPK11NU/HkE1nzRmsrt8rFnk2pEzbppQK1lpHeDqQbDhgpcSty1VCN8wJT67jxWtLD4n/NkneilyhtR6klGoHnAX2a60tcWbfTDQ/gvUuV/kB+B/QQWt9Jf5JpdRUz4WUaYFAU/MfWOfy5tbAKK21dn1SKZXWZdveoidGPeriGGfJ+3g2nCzbrJT6nsQv/F59YsiV1voDs1ZyLYyD3H2ejikT+mOcBKoNzDMfW4HlSlQk86tS6lmsVwu8J8Y2JQ5zH2qRmVpg0T7H2F+ONcf4X1hj/wlG2YTBwIvAIfOxFQQppeaQdJxYqT6iJce5mVwphXGs215rfc18/muPBpYJFiw/BCTUjn0H4/4lnYDeWusPPRtVppw2f8af3Hdi1GP3Wi6lwfyBZ5VSx82XvH4f6lLHvIBSagOwk8QkqDfXGo6/SujXZM+nWpNdCG8gyVuRmw4AYYBNKdXQCrMpgTZWu1zFvKnAbq31jOSvWeGyofjLnC1YVyvENXEbf9MSC4zzc1rrLvEPlFL+ngwmq7TWryulGgDVMBJzOz0dU0ZcSlTEcxhPK+Xts0G01nuwxgms5KxYosLVT8AyrFeDrQcQqrW+4elAssGSfW6eBHrI03Fkldb6H+A1T8eRDVavj2i5cW4e5/6htZ6S/DVvnXWbjKXKD7mYAfQFppgnQR8EvD55q7V+SylVDKgM/KW1vpjRezzNpTRYklm3SqlAz0WVaQvSeN6rk6AupYfGa62HxD+vlLLCDdbFf5Qkb0WuUEr9DyiJcROq+BmsVpgNYrnLVcybChRTSvlpraM9HU9WKaXaY9TpPa2UKgsM1Vov9XBYafoX3LTkZ5Im434E7vZQLJmmlGqhtV7vkgiNxajdV9nbE6BAnXRe8+rYlVI/kPQAPBo4DHyotT7rmagyxYolKlxd0lqP9nQQ2fAnEOPpILLJUn2ulPpMa93NZcZWAm+eqZVG3FaapW31+oiWGueQcJx7N5AieWsRliw/BKC13quUin9oiXskKKV6AN2BPUA9pdSs1Ca4eKmFJD1Gnwk87qFYMsWljvnnWuvn4p9XSk0G1nkqroyYk1cCgDuUUsEY49sXuAsY58nYhEiLJG9FbqmotW7u6SCyoS0Wu1zFVAk4at6EIv6mAla5jG84cJvW+ppSqjCwAvDa5C0Wv2kJKW8eaJX9QBVgPSkToV5/OZzW+i1Px5ADh4EdwFagMUYNbY1x6WobD8aVLouWqHBlyVrgwC3AIaVUfH9baV9kqT7XWnczf6Z6ckgpVcsbx30m4m6jtV6Zt1FlidXrI1pqnLu4qJR6D6NUUhxYqo6pVcsPnVRK9QEKmwnRY54OKJOeB+4wZwvnw0ggenXyVinVFGgGlHepxZoPo06yV7PwpJbOwHMY6+VCjORtFLDYgzEJkS6rfGkX1rNKKdUG2EXiwWG4Z0PKmNa6uqdjyCar1IpLjY3Em2Z5/c2zrH7TEiBcKfU0sBrj7PIlD8eTKfGXN6WVCFVKvau1Hpi3UWWOxWea1dJax3+R0EqpZ7TWfZRSXn0lhXnjyZIYX/ItUaIiGavWAn/K0wHkgFX7PC0fYc2SJ8MBb07e/gmcw9i2+GBsZ6zEquP8uPmzpvnT60/cujiitU5YF81ZuFbwEtAN4x4Dvhj3TLACH4zkYaz5M/mkBW8UCVwArpJYizUaa+xTLTmpxfxe8YVS6k6ttdfOEBbClSRvRW6xA9OBv7HQjb8sXJz/U611u/gH5g0crFAHDOBL4A+l1C6gHuDVdXqVUmO01sOB91zqaQKWuWlJN2AY8ASwF2OGwr9BI08HkJaMZpp5uTjzJnzbMGbexpiJUW//MvQzRtme+DsfW+mLPkBPrfXN+AdKqQKeDCYL2gEztNZOpZQN42ZUn3o4psyyap+nxRKXOKfC2+O+V2t9V/wDpdRKjNJPlqC1fl4pVQgoq7XW5npqBZe11h/EP1BKdfdkMFn0tVLqMa11nJm4nQe08nBMmTE6lVqgVricfAJGGbzTGDNX3/BwPBnSWu8GdgNfeDqWrPoXTGr5Wyk1iqQ3cfTmG62J/zBJ3orc0tqis1gtVZxfKVUD47Lgsi61QPMBFT0XVdZoraeZtSnjbyxw3tMxZSC+5lofj0aRTWY9Z6+cofpvp5TKj3FSpTrG3dW/1lpHeTaqDHXGSMA9g1FCoYv5BbSVR6PKmE1rbaUv98klr039AxaoTY0xPj4FMBO4j2Od5K1V+zwtXn2zmHR4e9z5kj221E0/zasmngMCzUuclwDtPRpU5nQAPnB5/BAwy0OxZNWnGDP8BgDf4eXHj2Yt0IJYtBao1nqxUup7oARwQWvt8HRMmaWU6okxPuKwzhVa8YKUUnNImgS1wqSW+RjbljUejkOIDEnyVuSWLeZlqtrTgWSVxYrzh2DU6ilEYi3QaOB1j0WUSeYshPkutZ0AmiulvLp+nNY6zPy1PrDUZYZZB+CE5yLLHKVUL6A31jwwtLrvMOoMrgKamI8f9mhEGdBaX8WYZR7/BS6f+by333Blq1LqHox6vZYp3ePCqrWp/ZVS+bTWMebJioKeDigLrNrnafH245e0eHvcm5RSszBKD7XCqMFqJS8ALYBV5om4AE8HlB4zmdUL4walezDGRxzGyRZL0FovN/ehG4COWus/PR1TBlKrBRqNl9cCVUq9orX+SCn1ES4ngczvFVaZSdkDCNVa3/B0INkwEyPxfNLTgWTRaa21V1/1KUQ8qx+YCu/VBnhQKRWJsQO1SoLIUsX5zTt8rlVKTdZaX/F0PFkUH+/FdJfyXn3NS4XiZ5i9ijGDxdt1x6IHhmaSvL7WemcqL59K5TlvU8jlLt/LzcttvZpS6mXgVaAUcBkIx4tLVLhoZv6LZ4nSPS4sWZsao87qH0qpHRhlcLy65l0yVu3ztLzt6QAyYp5gKQN8DVTXWh/ASNR5La31QKXUAxi1VxdrrS2TRDTFz0KMT27ZPRVIZmitpwPTlVKPa63neTqerEilzn0s8I1Syqu/E1m4Fmh8rAs8GkXO/AnEeDqIbDqqtV7v6SCy4axSaixJb+JopTJb4j9EkrciV2ita3s6hmxKXpzfq79EuGhlzmAN5v/t3Xu0nVV57/FvAsEQ9SApUBSwcpFfuV/Foqio3BRBSykqiggIHBAoRPBeaSjUWqAKHAgE8chFKwSUANKIVkJBjUC5CBR/eGiCgIUjVz1yM7DPH3OuZO3Nvqy1k535zrWezxh7rMveGeOXPdZe632f95nPTN1DjT4whNSJkG+rm++ULVkqmYuKtXSYVXtgmIvkJwN7DvO9AwtE6oik6fnu7ZI+QHp/2QEYrgjdNIeSinA/BHYHzi0bpzPtMykrVeVsatvflvQDYAPSidwTAJLebbvpFyuq/J2PtMzW9vVlk41O0izSJqVvt32hpDOBXXMBt9Fsfx/4fukc43Q26QLFRpKuy49rcJ2kAxm8JLuxq7QgzbmXNBl4r+1rSucZh12Bf5e0N3AqcL7t0wpnGlGeGwvwnmFm9d5QJlXXNgTuk3RPfjxQyegBgGclfZvBRdBG/41mD+fbVu2itj0SQh+J4m2YEJLWIC3NWoeKhn/npZ7nA/+DlPvVpE6zpjsZ2NN2Dd2Hg0i6lzT+4TFgDeAR0u/8s7ZvKpltDHMkzQNuBHYibUBRg5oPDAEW5Q20FpCKFdi+rWykMV1BOhicBGzf9nzTZzsCPJ7fF1tdQ5uO9Q+aQNIOpJ2xa5u9Bow8m1rSxbYPKBCpY7Yf5+UrKr4ANLp4W/HvvNZltrL9LkmtInOjO0B7he1Lc9F2Q+C/Khon831gHpUtybb9kqTDgBqLtzvl231Jha35QGOLt7XP6s0+UjrAMriudIDxsD1T0p+wdO+VWleEhj4QxdswUS4DvgN8iDQDp9FdoC152cRfAv9N7mChjqW299ZYuM1+DJxqe5GkNwCfJV3h/w5pd/tGsv3P+QRoE+BK2/cASNqsdb+haj4wBJgGbJu/IP2NHlwuztgq7wK9VtKqpB2Q7wV+UDhPp2qdvTaWdUsHGKemzzEdTdN/57WupnhJ0lrAQF6d0PQ52j1B0mtIM/qnA29v+j4DbZ5qGztUm8clnc7gi841dPatKmlr4A/5Im4NG6x+nMGzep+n4bN6h3iUdEFOpI1tzy8bp3N5BcWW5E15bf+idKZO5DGJnwB+AWwl6eu2ZxeOFcKwongbJozt2ZI+bHuWpKtK5+nQu2xvUjrEOKwq6SbSMuzWUpXGdzpnW9peBJALuJvbvj/PS24023eTlte2O4sGF/xtP5C7Epd0xVPBRmsttg+S9CpgHdvOHaFVkPRm4AxgVeAZ4DjbTd/sZnbu6PsG8I1cbKlBrbPXxlJDt/Zwas0Nzc9e62qKGaSLtJuTLvjPGP3Hw3JSZQcr8Kvcwdq+JLvpq25aFuXb9pUrNRRvTwGOA/4ud7X+vHCeUVU8q7fdRcDNwL+Q5vZfTOp8bjxJJ5JWly0APi7pP2yfWDhWJw4C3mp7saQppNnJUbwNjRTF2zBR/ihpJdLV5qOAPysdqEO3Slo9L5+syemlAyyDWyVdTjog3x64WdLKwC/Lxhq3RhcTJV1AKh7+BXALaalqDScRAEg6mNRZsZqkbUmbxL2vaKjOnQ7sa/shSeuRChdvLZxpLJfkjWIW58LtZaQd1puu1tlrY2n0+8soas0Nzc9e5WqK3JXV2AudPazWDtbVgB3zF1Sw6qbF9szSGcbD9tXA1W1Pfa5Uli79p6RPU9novmzNtrnCP2kbK1ODXW23Rm2Qm4pqKN5OBqaQVn9MyY9DaKQo3oaJsn++PZJ0YvHRglm6sRvwMUmtTsTGb/yV/b50gPGyfZyk7YCNgB+0dVIcXjDWsmh6l5Zs7yRpvu0P5sJ5TQ4G3gb82PaLkqaVDtQN2w/l2wcllY7TifOAb0o6HriUNIqgBlXOXuvAzaUDjCVfuG3NjSfP1Kxl88/hNP13XuUy27wC5FTgNcCTwKdtN/133Quq7GC1XcUGgsPJG36dSBrBMgA8Ybvx8+Ml3UXKOxl4LfCI7c3KpupIlaP7sufbNrbdkbpG4kyWtLbtRyS9lnrmmH+F1Ej0MPA64EuF84Qwoijehglh+3FJf0aaqXUD6UpW49neuHSGcTo6304CtiHN7N2jXJzO5SHxfwWsDlwh6YO2Ly0ca1k0vUurtanN85I2J83srclL+bZVJK/l4BBgvqS5pCVlO1LB7se2f5A3/rgJ+Evbd5XO1Im8fJK2TUuqIunvbf9tPuk/jTS+4jTbnymdbTR5bvw+wG9omxtv+96iwTogaQPgAAZvcndM03/n1LvMdhZpJcJCSesDcxi8oWOYGFV2sLYVEpeopLkCUjHo3aTZq/tSRzcitrdo3Ze0LnB8wThdqXR0H8CBpA7nw0gX4w4sG6crJwBz8z4Jz1DJ68X29yRdCawJPGb7pbH+TQilRPE2TAhJF5GWq7RmajX64FDSX9ueI+llM9dqWGrb3pGQRw5cVDBOty4EziV13SzOg+NrLt6eVDrAGE7JB1YnkuaZ/UPhPN06G7ge2ChvGHd24Twds/3FvPnHxsA827eXzjSSthPlVuFzMfBtSVWsRpB0OPA3wNrA08ATwHZFQ3WnfZfvzWj4Lt9t3mX7z0uHGKcrSKNN5hfO0a1al9n+xvZCgFzAfbR0oH5QawfrkELiDsB7Csbp1pO2n5I0OTe3bF06ULfyuKdaLq7UOroP248Cx5bOMR62fwK8uXSObg29cJs3caxlzEboM1G8DRPlz2y/o3SILvwu3z4+5PmmL4EfzqtIJ/u1mGr7muEK500m6QjSEvIXyR1mtre03fQT561tzyd1f75f0icK5+mK7Utz0XZD0qZU1cynzicTq5GWwa0vaf2m7jjdfqJcqcOArYAfAruTLhDVpLZdvltqnRsPaUnwJaVDjEOty2z/X+52+jmwA/D71nFADRfNayPpAtuHVN7BCoDtmyV9uXSOLtyaL5rPk3QrlWwWJ+lqll7ESYTAWAAAIABJREFUXYe0kVMNPpxvaxvdh6RPknIPOrcom6ozeVPe/8ng1Ss1bJ5Z64Xb0IeieBsmyo2S3mj7V6WDdML2D/Ld1W1/rfV8LYWtIV1yzwI1HdQ+Jel9wBRJu/LyAnpTHQpsb/vZMX+yWfYGvtb2+P3A1wtl6ZqkI22fI+kVwNWSZtn+VulcHZpH2nX6ofx4gIZvFifpXOAI2wOSJgHn2D6idK4OPJ6LnpNIXcONny84xMlUtMt3m92AAyUtyo+rOfEEzpN0MXAHdW1yV+sy23lt968slqJP2D4k31Z5YU7SWSwtOr+eNCe5CrZbG319WdK5FV3cas24HyBtdPe70X64CSRNBubYfjfwf4GvFo7UrUOAbW3XcsG23deBI4AHSwfpUq0XbkMfiuJtmCgfAPaX9If8uJYTuCoLW7UejGefAD5LWtq8C/VsVHYX9XQ4tTqFjyR1fP6CVOh/Ebi2aLDu7QucQyqe7wdcDtRSvF3J9qGlQ3RJtluFrAFVsssacG3udLoQuJf6NjDbyPaSIpyk+0uG6VTFc+MhFUCvBh4rHaQbFS+zfRG4osILoFWTNM/2Hm2PL7FdQ2dia3PVAdIYgirmr0N9y7Il7TPC8zR1tVCL7Zck3SZpc9t3l84zDv8JTAVqLN4usn1T6RDjUOuF29CHongbJoTtzYd7XtJHmtglV3thS9JGwD8BGwD/BXzW9n1lU40tXyGfaftvSmcZhw2B+yTdkx8PNHl5kO1ZwCxJ+9m+rHSeZTBN0nRgse3fSHqudKAufE/SIQw+QGz6Lt+T8niHhfkEtIrjhrYVFN/IX7Wp8kJifo2cTBrfsw/wSdtnlE3Vsd/aPrl0iE6NsAy+pmW2awP/Julu4Bu2F5QO1Msk/TlppNY6bcW5KVQyD9T2DZL2AkTqMK+meEt9y7JHawhpdPE2ew+wr6RnSJvc1vKeCOm880FJD+THNWU/V9K/A3ey9Bi3sRcp2lR54Tb0pypOwkJPOYQGdsn1QGFrNvAZ27fkjRxmAzuXjTS2fIV8DUmr2H6hdJ4ufaR0gHFaA0DSW4GvADWNHQC4gNSBMyOPTlhUNk5XdgBey9LNqBq9kWN2LHCJpFeTuuOPLpynI/l98EhgdSqavVb7hUTSZ8+xwJl5A8q9gFqKt09L+kfgFpaeeDa2UNFaBg+8vX0ZtqTVCkXqSt5k7bS8CdLxkrYivb+fb/vpsul60p8Cm5MurLSKcy8AnyqWqAuSziOtdvoZsLukvW1XMdqMypZl254JIGmq7SUXyPMIn8YbqYGoEp8DNrX90Jg/2Tz/SDqvqG1sQlUXbkN/i+JtWNEmjf0jRV0n6UAGD1uvYenESrZvgSUbOUwuHagLbwDuz4WK1hXyxhdZgEdJS/dbXSDnl43TsZrHDgC0ruJfVDTF+Kxr+52lQ3TD9h3AW0vnGIfzSfP6qtgYpqUHLiRi++626RpN/8xv11qt0jrxb/xM6uwK4F1tj88nvbc3Wp5HvRvpAtbqpFn9LwHXAG8rGK0n2b4BuEHSV2uYXToM2d453/+WpBtKhulSrcuyr2Xwe8s1pPFmjSbpzKHPVdIBCnAP6fyiRgtrukjRpqoLt6G/RfE2rGgDY/9IUd8nbaJR1Qk/8CtJZwALSLtNVzEfMdt/uCclbWb7nuG+1xAXATcD/wK8BbiYVBhtuprHDgya75y7K/cY5ceb5k5JuwO3svQA8YmykUYn6Y2kjbPWoaIOVuB+2zeWDtEtSTOGuw/VnOz/WtJRwKslHUoa49N4+YLn9JpG+EjakfTZs17ba2UK8Lpyqbryc+BHwBfbN7eVtHa5SH1h5/x6WR2YTEXLsiVtbfsOSdtS14WhWpdlD20EqaVucEW+nQS8ibTBXS3eQBrJ1prXW0tDC8BiSd9hcBG0huOWWi/chj5Uy5tw6B1NP9h6yvbflw4xDoeSZiJuDFxPRTs3235ghG+dxeAr/k2zZl72CfATSdcXTdO5b7B07MBUYKTff+PlLvMvl87Rha3yV8sAzX6NA3wTmJm/TgLeXTTNGNqKWM9K+jbwH9R1EvF46QDL6HDSeKSfk44xjywbpzOVjvB5hlQM+j1LXzcvUM9In7NsX9x6kJfBX9X2uRomxsnAnrZrW9p8OPAVSesDC6lnc1uod1n2E5I+SjqveCfwVOE8Hcld5i3zJV1dLEz3PjjckxU0tADMLR1gPFpjQoaSdLHtA1Z0nhBGE8XbMCEkrQJ8HFiTNP9mJ9vzSRuYNNmvJB3G4BP+pm8oBGlzm7l5N/hJrceFMy2rphf6n5f0AVKRYkfSLLbGsz2bNJey5aBSWcZD0lks7eB/PfDkKD/eKCONTJD0Kdunr+g8HXrO9nWSPm/7XyU1felhq4j1C+C3pHmxk4G1iiXqgu0LS2cYr9y9epHtD5fOMk5voKIRPrbvJG0MU+tr5iDSipWWA4GrCmXpJ/dWWLgFeMD2B1oPapm/mtW6LPsQ4PPAh4G7qeR4sW1DPkjHiVV8/kPVDS0jHr9I+qHtXVd0nuVg3dIBQhgqirdholwM3Ei6un+KpC8C89s31Wio1UiFuB3z4xo2FAI41vZcgFzAPYb6i7dNH7FxIGkp3GGkJTcHlo0zuh7Ymbzl8nw7ADxpu6Ydp0eyJ2kn6iZ6XtKqwEJJp5E3vGuq1smDpOvbi+WS/o10IbEKbX+nk0ib3D1qe7OyqUaXu1d/J2kN27UtD4ZKR/hI2hs4kXSi2Xpf3KRsqpGNsCnfS8DtRYP1j1Ul3UT6fde0I3yV81ezKpdl53O2E4Y+X0FHYmu81gDpAv/7C2ZZXpre0DKaWutNTT8PDX2o1j+m0Hx/Yvt/tV39rOJDx/ZBABWefC7pQMidt68smGV5afRrxvajpF3Vq9Dambx9ZmyNhiyH6xWNfa3bfi+ApCNJ84WbWmQeasqQxzV1aQ2d7bwucHzBON14N/BRSYtY2r1axcWhijuevkT6vX+PNHf9xLJxRte2Kd8xtl+2sVDTi+U9oJb38KFqnb/ai8uyG92RONLvu3I1FxJrzd7YY/PQv6r54AvVeV7S5rBkw5s/FM7TEUnvI3VnPSxpHeBztmtYxjdH0jxSt/NOQJW7lA9xUukAw6m1g3XIMrJBKlm+18sae2Ar6RXAR0nztO8DGr3BWpufSfo6aVbfzqTNHKtk+yFJ25fO0QnbG5XOMAGafgL3pO2nJE22/bikrUsH6sRwhdus6cXyqtm+QdJegID7KjnGhUrnr46h0UXQUTTymKXtuHxlYG3STPA1gEdsb1oy23LQ9M+h0dSa/ebSAUIYKoq3YaIcAfwT6UPzZOCTZeN07AvAm23/P0mvBn5IBTPYbP+zpOuATYArW10rNXSw5CWUR5FmUy4pgtpu5AZgrQ5W4O3tY0AkrVYoUqe2aLt9DWn22vbA01SwfK/HNXle8qWkGeA/BnbIjz8w6r9oANsnSNoT2BT4nu1rS2fqRt5gpTU2YR3g38smGl3bRnEvU8lGcaNpZKGiza15tMk8SbcCvy4daBnVeqJfBUnnkT5zfgbsnjeK+0ThWJ2ocv7qGJr+3jKSRv6NtlasSDoHONX2wrzB3WfKJlsuGtnQ0qFGn0dL2ohUs9gA+C/gs7bvs90Lr5vQY6J4GybKW2x/qPUgz2SrYYOESSztEq6iW7jF9t2kA9p2NXSwHApsb/vZ0kG6dAWDf7fnA/sVyjKm1jIySXNt75LvT6LhB1W9RNI+tr8raVPSSeg3bf/I9u6ls43iVbb/Pt//QZ4dWwXb3we+XzrHOB2VbweAp2z/rmSYDrQ2ins3sJi0keMOvHx8RY0aWahosf25fPfLks6tYG+BsdRa0KqFbO+c739LUhWjiCqevzqaRr+3jKLpHYlb2l4IkAu4m4/1D5qitoYWAEmn2P5C20VnWJp9b9tfLRivE7OBz9i+RdIO+fHOZSOFMLwo3oaJchjwnbbHtewifBFpue2dwFYM3gm5RjUcGN5FszsPB5G0I/AWYL22brMpwOvKperKepLWtv0IaQfedUoH6iNHk7qcPwWcA5wK/KhoorHdLukDLC3G3S5pOoDtWkYoVGOk8SaSGj3epG2juH1t75WfPk9SVR3PI2h0x5OkDYADgOnApPxaqWEDqpHUcNxSNUlb275D0rbU//uudfQANLQIKuksRriIYvuYCjoSF0i6kqXHLTWNTaqxoaU1AueoUX+quVayfQuA7ZslDZ2vHUJjRPE2LFcj7CI8ANxWNFiHbJ8jaQ6wPrDQ9m9LZ1pGNXSwbAjcJ6k13mHA9t4lA43hGdIcrd+ztNvsBeAjxRJ1ZwYwNy+zfQY4rnCefrKKpJWBybZ/Kun50oE6sH3+ancF6b2l6V39Nap9vMlabYWhrYE/KR2oUzV2PGVzgK8B8wvnWF4aXSzvAYcDX8nLyRfmxzVr7HFuxUXQy0sHWBa2j5e0DfBGYJ7t26GOUXJU1tACSzZwBtgauMr2QF7Ztzcw0kagTfIrSWeQivw7AvcXzhPCiKJ4G5arsXYRbjpJfwfMtP1bSZMknVj5rqU1dFTUUvQEwPadwJ3AhaWzjIft+cCbhz4v6VO2a92FuhY/JB0cnpA3AnuucJ4x2X7ncM9LOn5FZ+kHPTDe5GDgHyS9gVQYOrhsnK7U2PEE8LDt6lYJVVwsr90DtpfMLZc0tWSY5aDJx7lVFkFtLxmlIWlX0sqyS0gbl1YhF2xvH/J0Y0fJtY0cmE5dDS3tjrU9FyAXcI8B5hbO1IlDgfeTXt/XA1eWjRPCyKJ4G5YrSW+zfSPw8NDln01e8tnmHbYHYMkHzztKB1pGNXSwPEr64BRpN/vzy8bpTJ7jfCJpyd4AacfvTcqmWiZ7AlG8nQCteWDAdsB/k7qdZ5Bmg9bqvcBppUP0sCrHm+SupveXzjFO1XU8ZY9I+gfSxoKt45cajrdqLZbX7loGF7CuAXYplGV5aOToAai/CCppFmn/j7fbvlDSmcCuhWMtiyYX+msdOdBuyYWgfNH5lQWzdGNvYO6QjuEais6hD0XxNixvGwA3Au3D4d9I2r2xhpOJVSS9xvZTklan7YOoySrvYLmIdPD9L6RZshcD+xZN1JkvkTbn+R4p74ll4yyzJh/U1q72eWDDidfLxKpqvImku0iFw9aoJGj7LCoWrAM90PH0cL5tP+6q4Xir1mJ57YbOc2z0uWDFoweWqLgIKtvvktQ6l1ipaJpl19gRG7YfAJB0ODC7rZB4GHBe0XCdmyNpHqkOsBNwWeE8naq1Yzj0oUZ/YIf6tDYtAd4E7EXaGXYtXn6w2FQnAj+R9BvgT2n4CXObmjtY1rTd6uD7SdtBYtM9mYv8k20/nuc71qyxB7W1a80Dax2c94h4vUygkcabNJXtLcb+qcaq+qJKxaOdapt33yuekPRR0vLgdwJPFc4zlipHDwxRaxH0JUlrAQN5o9KaVwtBHRedP2T7PFhSSNyPSoq3tv9Z0nXAJsCVrfnCFcwarrVjOPShKN6GiTItf+hsZnu3Wgpytn8kaXNgjfbNyiR9xPa3CkYbS80dLM+37Wa/I/X8P27NXXHzJN0K/Lp0oGVUy+89NEO8XiaQpDeSLh6uQz7hbHJhqzUyaei4JGj+Ev5aO54kXWD7kLauZ6ik2zmrat59DzkE+DzwYeBu4KCycUZX++iBrNYi6AzgO6Su/suopKFF0k62bxrmWzWMkpsqaYrtP+a9EaoqJNq+m/S+0q6xs4azWjuGQx+K4m2YKKtIOhZ4KD+u4WonkK50Ar8d8vQhQJOLtzV3sBwIfI50onxfftx4tj+X735Z0rm2nywaqEOSViIdnExnaVHou7Z3LxosNJKkfWx/V9KmpBP+b9r+UbxeJtw3gZn56yTSiJYma41MGq4Dt9HF2zZVdTzZPiTffXv754+k1QpF6laV8+5rl18rJwx9XtLFtg8oEKkjFY8egEqLoMB020uKbpJ2LBmmCx+SdBppo88LbT8MUMkouTOBBZJuA7YCvlo4z/LQ6BpAxR3DoQ9F8TZMlAOAnYHZeSfbs8vGWWaN/uCh4g6WvKT82NI5uiXpXOCIXOx/StIs20eUztWBa4EH8xekjq1aiithxTua9Pr4FHAOcCrwo6KJ+sNztq+T9Hnb/5pnsDVWa2RSxUv4od6OpysY3NV0PrBfoSzdqHXefa9at3SAMdQ6egDqLYJ+CZjf9vg44GdlonTO9lGSViFtPDVb0mTgAuC7tl8qm25kecXHL4HdSBdE77f9RNlUy0Xjx2xV2jEc+lAUb8OEsL0QWNj21JxSWZaTpn/wVNfB0gNLPpULt60uLZUO1KFJtj9ROkSoxiqSVgYm2/6ppOdLB+oTz+exLAtzB9EapQN1QtLepC6z1Umz7mt5P4fKOp5yAegtwHqSZuSnp5CWldeg1nn3varpx7m1jh6Ayoqgkj4M7A9sKemq/PSUgpHGY31gB9Iqs38F/hz4PvCekqFGk88lTra9J/B46TzLUdMboEZSa+7Qw6J4G0Jnmv4GXl0HSw8s+ZwkaX3bCyVtQD3vp7fkuXG3kU/WeuTKfpgYPwQWACfkbsTnCufpC7bfCyDpSGAP4PT8uOnL+E4G9rT94Jg/2SCVdjw9AzwG/J6lJ/ovUM9KnFrn3feqph/nVjd6oOIi6LXAT4G/Ac7Iz70APFIsURfyhaD/Br5h+9Ntz5828r9qjEWSjiYdd70IYPu2spGWWQ2zhofT9AtaoQ/VUmwIYYWR9KekzpXWPNDbgJdtwtIwNXew1Lrk81jgEkmvBp4mLS+vwVvyV8sAsSwoDCHpFNtfALYjnQQdRzp5rqnbqXq2nwW+1/ZU05fx3Vtb4Rbq7HiyfSdwp6Q5tp/JHfI7A00vOrdUOe++h91cOsAYahw9UGUR1PbTpOPaGZL2Iq/qs33V6P+yMfYZbh8K28eXCNOlacC2wDbAKsBLwMeKJuqQpCOAo0hF5yUrKSuZNTycpl/QCn0oirchtJF0EunEeAPgAVJHy24VbEZVXQdL7Us+bd8BvLV0jm7ZfmfpDKEKZ+bbo4qmCEM18mRC0lmkC0GrSroJuJ2lnf2NntfbptaOp2tJRduZwJqkCy17lgzUiVrn3deq7W/0ZWwfY/szKzhSt6oaPQD1F0ElnUc6n/gZsLukvSsZu7WGpFOAdVjaiFPLJs5rAnuRNhXchbS6ohaHAtvni869oNaO4dDDongbwmC7236zpPnAO4FvF87TqRo7WKpe8inpzaROilVJ/5cZtht9IgHD5j7O9oKyqULT5MIKth8onSUM0tRlfJeXDrAc1Nrx9GK+Xdf2gZJ+XDTNGHpg3n2tqvwbrXj0wBIVF0Fle+d8/1uSbigZpgvfJF3MmkkqwL27aJruTMsrQTazvVtlKynvooLmoaF6sGM49LAo3oYw2O/y7YvAq4FNC2bpWI0dLK0ln8CFpbOM0+nAvrYfkrQeaRZbDZ24teYOITS089b2DQCS3g9clU8+J5F2+65FrR1PA5K+BtybH08uGWYsPTDvvkqtv1GAPPf+dcAlwMbFQnWmytEDQ9RaBEXS1rbvkLQtDf38GcZztq+T9Hnb/yqpltUfkDaJPRZ4KD9u/O9c0tWkC3HTgfsktebyD1TS8dxrHcOhh0XxNoTBLsg7fH8NuIGGd972QgdL3p38RGBd0v/hSdublE3VGdsP5dsHJZWO07Fac4cQGr+M71jbc2HJHNljgLmFM3Wq1o6nfUndwvMlTQX+tnCeTtU6775qkmYBfyAVzy+UdCawa+FYI6p99EBLpUXQw4GvSHoDsIi0uq8Gz+dzuYV5k7I1SgfqwgGkMTiz8/v52WXjdKT28VpVdgyH/hTF2xDa2P5Ovnu1pF8C95fMM5Ye6WD5EmlJ0/dIJ6Enlo3TsfmS5pLmI+5IKvbXoNbcIfSNipfxTW3dyZ23ryyYpVvVdTxlK5GKb38NHEPD58bXPu++B8j2u9ouTqxUNE2HKh49APUWQQW8htTNvyFwGdD4xhDb7wWQdCSwB3Da6P+iOWwvBBa2PTWnVJZOtcZrSTocmN228uYw4Lyi4UbRAx3DoQ9F8TaENpKusf0+SZ8mLZv8LXXMYK25g+VJ209Jmmz7cUlblw7UCdtfzFk3BubZvr10pk7UmjuEPlPrMr45kuYBNwI7kU72a1FjxxOk0UPnAp+2vVjSocClhTONpup59z3gJUlrkcZtTAcWlw7UoWpHD1BpERQ4GXhva7VWLSTNs71H/vz8nqRLgI+WztUHPmT7PFiy8mY/Gly8pf6O4dCHongbwmDT8m1r2eT8kmHG0iMdLLfm5U3zJN0K/Lp0oNFI2mfIU4uB9SWtb/u7JTJ1QtLbbN/Ylr+K3CH0qSqX8dn+Z0nXAZsAV9q+ByCPIrhn9H9dVo0dT9lU29e0HQM0Wg/Mu6/dDNKs+81JRcTjysbpXKWjB6DSIihwb02ZJf05sBmwbtux7hTgz8ql6itTJU2x/UdJr6DhK29q7RgO/S2KtyEM1lo2+XDpIB2qvoPF9ufy3S9LOrd9/ENDbTHC8wNAk4ugG5C64Ybmb3ruEPpGLyzjs303cPeQp89i8OqQsPw8Jel9wJS8EdXjY/2DJqh53n3lptte8reYmwBqUOvoAaivCHoW6W9yVUk3Abfnx9hu8uZff0q6KPHKfDuJNHqoigtbPeBMYIGk24CtgK8WztOp2jqGQx+L4m0Ig7WWTZ5Xw7LJXuhgkXQucITtAdJJ6CzbR5TONRLbM0f7vqRTbZ+wovJ0yvaFS+8ume3cOoEOITRDry7jq6lLrjafAD5L2tRpF1KRqwa1zruv3ZeA+W2PjyPNkW266kYPVFwEvbx0gPGwfQNwg6S3A38PnEB6T9wU2L9ktl6XO1Z/CexGaha53/YTZVN1rKqO4dDfongbQhvbCyVtAXyStJttFcsmK+9gUS7ctq54qnSgZbRd6QBjOIy0ZLLlQKCqXZtD6FU9vIxvoHSAXmX7KVLxtjZVzruvlaQPkwpYW0pqfeZPKRipWzWOHqi5CFqzSfmzs4oReL0g/75Ptr0nlaz+aFNrx3DoQ1G8DaFNxbvZ1tzBMinPXV0oaQPifWlC5N3rjyTNuf0FeQd7UidICKFZem0ZX3TeLmeS7iK9h68MrE0aobQG8IjtTUtm61BV8+57wLXAT4G/Ac7Iz70APFIsUXeqGj0APVEErVVrBF5Vr5cesEjS0cAC0rgKbN9WNtLoKu8YDn0oiiQhDFbrbrY1d7AcC1wi6dWkZZ9HF87Tk2zPAmZJOsb2maXzhBBG1WvL+E4qHaDX2N4CQNI5wKn5Auj6wGfKJutMhfPuq2b7adIx1gxJe5HGENxnu9ErbyoePRDKaY3Am13DCLweMg3YFtgGWAV4CfhY0URjqLxjOPShKN6GMESlu9lW28Fi+w7graVzdCNfqd3a9nBdqw+u6DzdsH2mpB2Adcivb9uxYVkIzVLlMr7c4X8UqetmEmmjtS1tX182WU/b0vZCWDL6afPSgTqRV9ocQNqcb5KkKMatABWuMKty9EAoJ78fLmx7qooReD1gTWAvls4afqxsnI5V1zEc+lcUb0MY7HDgHyVtCtxLJbvZ1tzBIunNpCV8qwLPADNsN3rzjNaVWmDPYb53YIFIHZN0Ael3/RfALcBKQBRvQ2iIypfxHQpsb/vZ0kH6yAJJVwI/B3YgnYDWYA7wNQZvnhUmXlUrzGL0QAjVmDZk1nAtF22r6xgO/Wty6QAhNMzGwEbAE6TdbDcqG6czks7NJ/wAT0maVTRQd04H9rW9FbAfcFrhPJ1aJOloSW+StG3u1K6BbO8P/Nr2B0uHCSEMljdwPNn247ZvqahwC3AXqasvrCC2jwdmAvcDJ+XHSNqsaLCxPWz7Yts3tL5KB+oXrdFala0wCyE029BZw7W8t6wJHEy6aL4W0dwYGixenCEMNhPYyfbTklYDrgeuKZypE8on/K2uUJUO1I3WJhS2H6woeutKbatoO0D68G+6Vkfc83l57SYlw4QQhlXVMj5JV5PeA6cD90m6J39rwPbe5ZL1hzzCZ+gYn7OAdxWI06lHJP0D8B8snWEaq0Am3uHAVyS9AVhEJSvMQgiNV+us4Vo7hkMfiuJtCIMtAn6X7/+Ohs8vbTNJ0vp53t0G1PW3PV/SXFKRYkegiu4b2wdJehWwjm23dT433Sl5PvLfAacAXy4bJ4QwjNqW8R1VOkB4maZ/Jj2cb9tn9EbxduIJeA1p9eWGwGXAlkUThRCqV/Gs4Vo7hkMfmjQwMFA6QwiNIeka4LXArcB2wKOkpYiN3kgjL4E7G3g1aTfho/NGYFXI+TcGfjXCJmCNI+lg4OPAaqQiy1zb7ysaKoTQE/Jn0aCNP/K4k0aTdDgwO3exTAIOs31e6Vz9SNKPbTe58zYUIOkXwHtbK55CCKGfSVqf1DF8KelC+V62ayk8hz5TU3deCCvCqW33v10sRZdyofatpXN0Q9I+Q55aDKyfO4hr6L45GHgb8GPbL0qaVjpQJ4bsBg+A7ei6CaFZal3G96FWsTbn3w+I4m0ZjeweknSB7UMk3UUel0DKOhCfRSvEvVG4DSGEpOKO4dCHongbQptaN8yQ9GbgDGBV4Blghu2flU01pi1GeH6AOpZOvpRvWyefK5UK0qXYDT6E5qt1Gd9USVNs/1HSK4BXlg7Ux04qHWA4tg/Jd99u+8nW83mfgTBBJJ1FOl5ZVdJNpBnJrVnDjV1ZFkIIIYQkirch9IbTgX1tPyRpPeA7NLwT1/bM0b4v6VTbJ6yoPONwNmlDu40kXUc9g/ljN/gQmq/WjT/OBBZIug3YCvhq4Tw9b8hqiiUdrLab3q19BYM3VDsf2K9Qln5weekAIYQQQhi/KN6G0CNay+BsPyipdJzlYbvSAUZj+9JctN0QuL+9g6jhNiR2gw+h0Wpcxpdn3P4S2A3YgPS++ETZVH2hqtUUknYE3gJUYzWuAAAKeElEQVSsJ2lGfnoK8LpyqXpfrSvLQgghhJBE8TaE3jBf0lxgAbAjEAfpE0zSkbbPyUuDr5Y0y/a3SufqwEdKBwgh9J484/Zk23sCj5fO00dqW03xDPAY8HuWvk5eID6bQgghhBBGFMXbEHqA7S9K2hrYGJhn+/bSmfrAvsA5pK6n/UhLEhtbvJX0Nts3MnxH8wMrOk8IoSctknQ06ULiiwC2bysbqTdJupo0s3Q6Fa2msH0ncKekObafkbQyaURIdGmHEEIIIYwgirchVEzSPkOeWgysL2l9243f9Csvs916hGLzgys6T5emSZoOLLb9G0nPlQ40hg2AG3n5RnG1bBAXQmi+acC2wDbAKqSNHT9WNFHvOqp0gGV0LaloOxNYEzgO2LNkoBBCCCGEpppcOkAIYZlsMcLX5iVDdcr2AHDyCN87cAXH6dYFpG7b/5VHJywqG2d0ti9cetczW1/AHSVzhRB6yprAwaTZt2sRTQITxvYDth8A9gB+ne//Oj+uwYv5dl3bhwGrlgwTQgghhNBkcVAdQsVy8W1Ekk61fcKKyjNOtS6zPSbfXlQ0RfcOA77T9vhA4KpCWUIIvWVann27me3dJF1fOlAf+JDt82DJ3OH9gPMKZ+rEgKSvAffmx9FQEkIIIYQwgijehtDbhptv2jStZbbb5scDpM6tRrO9ZPyApB1oeLeTpCOAI0ljNX4BTCL9rmM+cghheVlF0rHAQ/nxpJJh+sRUSVNs/zGvAnll6UAd2pc0XmO+pKnA3xbOE0IIIYTQWFG8DSEUZfsgSa8C1rHtPAe3KrZvlvTl0jlGY3sWMEvSMbbPLJ0nhNCTDiDNMZ2dC3Jnl43TF84EFki6DdgK+GrhPJ1aCdgV+GvSSpbXlY0TQgghhNBcUbwNIRQl6WDg48BqkrYF5gLvKxqqA5LOInWuArweeLJgnG48BCBpE+ALwP+2/W9lI4UQeoHthcDCtqfmlMrSD/LFzl8Cu5E2pbzf9hNlU3XsQuBc4NO2F0s6FLi0cKYQQgghhEaK+VIhVE7SJEnbjPDtB1domPE5GHgH8ITtF0ljFGpwOXBFvv1b2/sWztOpo/Pt8cA5wEkFs4QQQhin1qafth+3fUtFhVuAqbavARaXDhJCCCGE0HTReRtC5fIGJScDew7zvQMLROrWS/m21cW6Uqkg3bB9Q+kM47SKpJWBybZ/Kun50oFCCCGMW62bfj4l6X3AFEm7Ao+XDhRCCCGE0FRRvA2hN9R68gZpJuL1wEaSriNmJE60H5FeJyfkmZRRvA0hhHq1Nv3cBliFdEH0Y0UTdeYTwGeBp4FdgMPLxgkhhBBCaK5JAwMDY/9UCKHRJP3vIU8N2D64SJhxkLQ6sCFpXl8ts2OrJumVtv9QOkcIIYTxk3QNsBdwAqkI+pjt/cumCiGEEEIIy1N03obQA2wfJOlVwDq2nTcxqYKkI22fI+kVwNWSZtn+VulcvUrSe4GZwDRJWwGzayr0hxBCGGRaHp+0me3dJF1fOtBoJN1FGpO0MrA28BiwBvCI7U1LZgshhBBCaKrYsCyEHiDpYOBa4DJJKwFXF47UjdZGX4cC+wGfLJilH3wB2An4v7YXA28oGyeEEMIyWEXSscBD+XGjL97a3sL2lsB8YDvbGwPbAf9eNFgIIYQQQoNF8TaE3nAw8A7gCdsvkmbg1WKapOnAYtu/AZ4rHajHvWj7eSrbIC6EEMKwDiDNjT0lzzGvZW78lrYXAuTbzQvnCSGEEEJorBibEEJveCnf1liQuwC4HJiRRycsKhun531X0hzg9ZIuBq4oHSiEEML45MLnwran5pTK0qUFkq4Efg7sQNpIM4QQQgghDCM2LAuhB0j6IHAEsBHwn8DXbV9WNlVn8vw7WFp4Ji+pDBNE0ibAZsC9tu8pnSeEEEL/kbQN8EbgV7Zvz89tFp9LIYQQQgiDRfE2hB4haXVgQ+B+20+WzjMeknYA9rB9UuksvUbSPiN9z/Z3V2SWEEIIYTiSfmz7XaVzhBBCCCE0Scy8DaEHSDoyF2xfAVwt6SOlM42H7ZtJs3vD8rdF/tofOBJ4E6lbe/+SoUIIIYQ2jd5wLYQQQgihhJh5G0Jv2Bc4BzgU2I80Q/ZbRRN1SNJZLB2Z8Hqgyq7hprM9E0DSXNu75PuTgKuKBgshhBCWiiWBIYQQQghDRPE2hN4wTdJ0YLHt30h6rnSgLlyebweAJ23fNdoPh2W2nqS1bT8CrAWsUzpQCCGEkEXnbQghhBDCEFG8DaE3XEAqgs6Q9ApgUdk4nbN9Q+kMfWYGMFfSqsAzwHGF84QQQggtMfM+hBBCCGGI2LAshB4gqdWtuuQP2vaWheKECkn6lO3TS+cIIYTQ+yQdARwFvEjqth2I45YQQgghhOFF520IPcD2Fq37knYA9igYJ9RpTyCKtyGEEFaEQ4HtbT9bOkgIIYQQQtNNLh0ghLB82b4ZeEfpHKE6MWcwhBDCinIX8MfSIUIIIYQQahCdtyH0AElnsXRkwuuBJwvGCXWKGTohhBAmlKSrSZ8304H7JN2TvzVge+9yyUIIIYQQmiuKtyH0hsvz7QDwpO27RvvhEIYRHVAhhBAm2lGlA4QQQggh1CY2LAshhD4iaZ8hT70A/B/bvyyRJ4QQQv+RdDgw2/aApEnAYbbPK50rhBBCCKGJYuZtCCH0l/2BI4A35dtPAGdLOqVoqhBCCP3kQ7YHAPLtfoXzhBBCCCE0VhRvQwihv6xse1fbnwN2A1YCdgF2LRsrhBBCH5kqaQqApFcAryycJ4QQQgihsaJ4G0II/eX1ktbI99cAXpe7np4rmCmEEEJ/ORNYIOl84EbgjMJ5QgghhBAaKzYsCyGE/nI8cK2kqcCzwPGSVgZOKxsrhBBCP8gzbn9JWv2xAXC/7SfKpgohhBBCaK7YsCyEEEIIIYSwwkj6vu09S+cIIYQQQqhBdN6GEEIfkHSK7S9IuhoYdNXO9t6FYoUQQuhPiyQdDSwAXgSwfVvZSCGEEEIIzRTF2xBC6A9n5tujiqYIIYQQYBqwLbANsArwEvCxoolCCCGEEBoqirchhNAHbD8qaTLwDdvvLp0nhBBCX1sT2As4AdgFeKxsnBBCCCGE5ppcOkAIIYQVw/ZLwG2StiidJYQQQl+bZnsA2Mz2bsBrSwcKIYQQQmiq6LwNIYT+8h7gryQ9y9I5g1uWjRRCCKHPrCLpWOCh/HhSyTAhhBBCCE0WnbchhNBf/hK4A1gM3A/sWzZOCCGEPnQA8DRwiqSpwNmF84QQQgghNNakgYGBsX8qhBBCT5D0Y+DTtm+VtAPwT7Z3LhwrhBBCCCGEEEIIw4jO2xBC6C8r2b4VwPbNxOdACCGEEEIIIYTQWDHzNoQQ+suvJJ0BLAB2JI1OCCGEEEIIIYQQQgNFx1UIIfSXQ4HrgfXy7cFl44QQQgghhBBCCGEkMfM2hBBCCCGEEEIIIYQQGig6b0MIIYQQQgghhBBCCKGBongbQgghhBBCCCGEEEIIDRTF2xBCCCGEEEIIIYQQQmigKN6GEEIIIYQQQgghhBBCA0XxNoQQQgghhBBCCCGEEBro/wPJ3M+dcf4aSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data_train.corr()\n",
    "plt.subplots(figsize=(25,15))\n",
    "plt.title(\"Feature Correlations\")\n",
    "sns.heatmap(corr, annot=True, cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the correlations seen are between:\n",
    "* srh_adults_cnt & srh_rm_cnt\n",
    "* srh_destination_id & srh_destination_type_id\n",
    "* srh_destination_distance & hotel_continent\n",
    "* is_package & stay duration\n",
    "* srh_room_cnt & srh_adults_cnt\n",
    "\n",
    "There is no distinct correlation between hotel_cluster and any of the features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now look at the `destinations` dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Destinations Dataset has 62106 samples with 150 features each.\n"
     ]
    }
   ],
   "source": [
    "# Load the destinations dataset\n",
    "try:\n",
    "    data_destinations = pd.read_csv(\"dataset/destinations.csv\")\n",
    "    print(\"Test Destinations Dataset has {} samples with {} features each.\".format(*data_destinations.shape))\n",
    "except:\n",
    "    print(\"Dataset could not be loaded.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>srch_destination_id</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
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       "      <th>d9</th>\n",
       "      <th>...</th>\n",
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       "      <th>d145</th>\n",
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       "      <th>d147</th>\n",
       "      <th>d148</th>\n",
       "      <th>d149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-1.897627</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
       "      <td>-2.198657</td>\n",
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       "      <td>-2.198657</td>\n",
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       "      <td>-2.181690</td>\n",
       "      <td>-2.082564</td>\n",
       "      <td>-2.181690</td>\n",
       "      <td>-2.165028</td>\n",
       "      <td>-2.181690</td>\n",
       "      <td>-2.181690</td>\n",
       "      <td>-2.031597</td>\n",
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       "      <td>-2.165028</td>\n",
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       "      <td>-2.224164</td>\n",
       "      <td>-2.189562</td>\n",
       "      <td>-2.105819</td>\n",
       "      <td>-2.075407</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.118483</td>\n",
       "      <td>-2.140393</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.196379</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.192009</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.224164</td>\n",
       "      <td>-2.057548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.115485</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.161081</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "      <td>-2.177409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-2.189562</td>\n",
       "      <td>-2.187783</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.171153</td>\n",
       "      <td>-2.152303</td>\n",
       "      <td>-2.056618</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.145911</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.187356</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.191779</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.185161</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.194008</td>\n",
       "      <td>-2.188037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   srch_destination_id        d1        d2        d3        d4        d5  \\\n",
       "0                    0 -2.198657 -2.198657 -2.198657 -2.198657 -2.198657   \n",
       "1                    1 -2.181690 -2.181690 -2.181690 -2.082564 -2.181690   \n",
       "2                    2 -2.183490 -2.224164 -2.224164 -2.189562 -2.105819   \n",
       "3                    3 -2.177409 -2.177409 -2.177409 -2.177409 -2.177409   \n",
       "4                    4 -2.189562 -2.187783 -2.194008 -2.171153 -2.152303   \n",
       "\n",
       "         d6        d7        d8        d9    ...         d140      d141  \\\n",
       "0 -1.897627 -2.198657 -2.198657 -1.897627    ...    -2.198657 -2.198657   \n",
       "1 -2.165028 -2.181690 -2.181690 -2.031597    ...    -2.165028 -2.181690   \n",
       "2 -2.075407 -2.224164 -2.118483 -2.140393    ...    -2.224164 -2.224164   \n",
       "3 -2.115485 -2.177409 -2.177409 -2.177409    ...    -2.161081 -2.177409   \n",
       "4 -2.056618 -2.194008 -2.194008 -2.145911    ...    -2.187356 -2.194008   \n",
       "\n",
       "       d142      d143      d144      d145      d146      d147      d148  \\\n",
       "0 -2.198657 -2.198657 -2.198657 -2.198657 -2.198657 -2.198657 -2.198657   \n",
       "1 -2.165028 -2.181690 -2.181690 -2.165028 -2.181690 -2.181690 -2.181690   \n",
       "2 -2.196379 -2.224164 -2.192009 -2.224164 -2.224164 -2.224164 -2.224164   \n",
       "3 -2.177409 -2.177409 -2.177409 -2.177409 -2.177409 -2.177409 -2.177409   \n",
       "4 -2.191779 -2.194008 -2.194008 -2.185161 -2.194008 -2.194008 -2.194008   \n",
       "\n",
       "       d149  \n",
       "0 -2.198657  \n",
       "1 -2.181690  \n",
       "2 -2.057548  \n",
       "3 -2.177409  \n",
       "4 -2.188037  \n",
       "\n",
       "[5 rows x 150 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_destinations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimentionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 150 features in this dataset. We will reduce the number of features using PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "srch_destination_id = data_destinations[\"srch_destination_id\"]\n",
    "latent_features_data = data_destinations.drop([\"srch_destination_id\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
       "      <th>d8</th>\n",
       "      <th>d9</th>\n",
       "      <th>d10</th>\n",
       "      <th>...</th>\n",
       "      <th>d140</th>\n",
       "      <th>d141</th>\n",
       "      <th>d142</th>\n",
       "      <th>d143</th>\n",
       "      <th>d144</th>\n",
       "      <th>d145</th>\n",
       "      <th>d146</th>\n",
       "      <th>d147</th>\n",
       "      <th>d148</th>\n",
       "      <th>d149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.597617</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.344165</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.495544</td>\n",
       "      <td>-2.496930</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.620769</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "      <td>-2.671613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-1.596004</td>\n",
       "      <td>-1.341325</td>\n",
       "      <td>-1.917826</td>\n",
       "      <td>-1.814585</td>\n",
       "      <td>-1.607558</td>\n",
       "      <td>-1.209058</td>\n",
       "      <td>-1.474441</td>\n",
       "      <td>-1.879678</td>\n",
       "      <td>-0.977219</td>\n",
       "      <td>-1.791073</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.855317</td>\n",
       "      <td>-0.960356</td>\n",
       "      <td>-1.357408</td>\n",
       "      <td>-1.775218</td>\n",
       "      <td>-1.790435</td>\n",
       "      <td>-1.799341</td>\n",
       "      <td>-1.335962</td>\n",
       "      <td>-1.816892</td>\n",
       "      <td>-1.718778</td>\n",
       "      <td>-1.500309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           d1        d2        d3        d4        d5        d6        d7  \\\n",
       "min -2.597617 -2.671613 -2.671613 -2.671613 -2.671613 -2.344165 -2.671613   \n",
       "max -1.596004 -1.341325 -1.917826 -1.814585 -1.607558 -1.209058 -1.474441   \n",
       "\n",
       "           d8        d9       d10    ...         d140      d141      d142  \\\n",
       "min -2.671613 -2.495544 -2.496930    ...    -2.620769 -2.671613 -2.671613   \n",
       "max -1.879678 -0.977219 -1.791073    ...    -1.855317 -0.960356 -1.357408   \n",
       "\n",
       "         d143      d144      d145      d146      d147      d148      d149  \n",
       "min -2.671613 -2.671613 -2.671613 -2.671613 -2.671613 -2.671613 -2.671613  \n",
       "max -1.775218 -1.790435 -1.799341 -1.335962 -1.816892 -1.718778 -1.500309  \n",
       "\n",
       "[2 rows x 149 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_features_data.describe().loc[['min','max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "      <th>d6</th>\n",
       "      <th>d7</th>\n",
       "      <th>d8</th>\n",
       "      <th>d9</th>\n",
       "      <th>d10</th>\n",
       "      <th>...</th>\n",
       "      <th>d140</th>\n",
       "      <th>d141</th>\n",
       "      <th>d142</th>\n",
       "      <th>d143</th>\n",
       "      <th>d144</th>\n",
       "      <th>d145</th>\n",
       "      <th>d146</th>\n",
       "      <th>d147</th>\n",
       "      <th>d148</th>\n",
       "      <th>d149</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      d1   d2   d3   d4   d5   d6   d7   d8   d9  d10  ...   d140  d141  d142  \\\n",
       "min  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   0.0   0.0   \n",
       "max  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  ...    1.0   1.0   1.0   \n",
       "\n",
       "     d143  d144  d145  d146  d147  d148  d149  \n",
       "min   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "max   1.0   1.0   1.0   1.0   1.0   1.0   1.0  \n",
       "\n",
       "[2 rows x 149 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler() \n",
    "\n",
    "dest_features = [\"d{0}\".format(i + 1) for i in range(149)]\n",
    "latent_features_data_normalized = latent_features_data.copy()\n",
    "latent_features_data_normalized[dest_features] = scaler.fit_transform(latent_features_data_normalized[dest_features])\n",
    "latent_features_data_normalized.describe().loc[['min','max']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cumulative Explained Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dimension 1</th>\n",
       "      <td>0.5414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 2</th>\n",
       "      <td>0.6149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 3</th>\n",
       "      <td>0.6555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 4</th>\n",
       "      <td>0.6778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 5</th>\n",
       "      <td>0.6979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 6</th>\n",
       "      <td>0.7152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 7</th>\n",
       "      <td>0.7295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 8</th>\n",
       "      <td>0.7428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 9</th>\n",
       "      <td>0.7533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 10</th>\n",
       "      <td>0.7627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 11</th>\n",
       "      <td>0.7713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 12</th>\n",
       "      <td>0.7793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 13</th>\n",
       "      <td>0.7868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 14</th>\n",
       "      <td>0.7940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 15</th>\n",
       "      <td>0.8006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 16</th>\n",
       "      <td>0.8068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 17</th>\n",
       "      <td>0.8127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 18</th>\n",
       "      <td>0.8184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 19</th>\n",
       "      <td>0.8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 20</th>\n",
       "      <td>0.8288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 21</th>\n",
       "      <td>0.8337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 22</th>\n",
       "      <td>0.8385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 23</th>\n",
       "      <td>0.8430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 24</th>\n",
       "      <td>0.8473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 25</th>\n",
       "      <td>0.8516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 26</th>\n",
       "      <td>0.8555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 27</th>\n",
       "      <td>0.8594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 28</th>\n",
       "      <td>0.8631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 29</th>\n",
       "      <td>0.8668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 30</th>\n",
       "      <td>0.8703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 120</th>\n",
       "      <td>0.9943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 121</th>\n",
       "      <td>0.9947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 122</th>\n",
       "      <td>0.9951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 123</th>\n",
       "      <td>0.9955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 124</th>\n",
       "      <td>0.9959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 125</th>\n",
       "      <td>0.9963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 126</th>\n",
       "      <td>0.9966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 127</th>\n",
       "      <td>0.9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 128</th>\n",
       "      <td>0.9972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 129</th>\n",
       "      <td>0.9975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 130</th>\n",
       "      <td>0.9978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 131</th>\n",
       "      <td>0.9980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 132</th>\n",
       "      <td>0.9982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 133</th>\n",
       "      <td>0.9984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 134</th>\n",
       "      <td>0.9986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 135</th>\n",
       "      <td>0.9988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 136</th>\n",
       "      <td>0.9990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 137</th>\n",
       "      <td>0.9992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 138</th>\n",
       "      <td>0.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 139</th>\n",
       "      <td>0.9994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 140</th>\n",
       "      <td>0.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 141</th>\n",
       "      <td>0.9996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 142</th>\n",
       "      <td>0.9997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 143</th>\n",
       "      <td>0.9998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 144</th>\n",
       "      <td>0.9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 145</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 146</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 147</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 148</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 149</th>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cumulative Explained Variance\n",
       "Dimension 1                           0.5414\n",
       "Dimension 2                           0.6149\n",
       "Dimension 3                           0.6555\n",
       "Dimension 4                           0.6778\n",
       "Dimension 5                           0.6979\n",
       "Dimension 6                           0.7152\n",
       "Dimension 7                           0.7295\n",
       "Dimension 8                           0.7428\n",
       "Dimension 9                           0.7533\n",
       "Dimension 10                          0.7627\n",
       "Dimension 11                          0.7713\n",
       "Dimension 12                          0.7793\n",
       "Dimension 13                          0.7868\n",
       "Dimension 14                          0.7940\n",
       "Dimension 15                          0.8006\n",
       "Dimension 16                          0.8068\n",
       "Dimension 17                          0.8127\n",
       "Dimension 18                          0.8184\n",
       "Dimension 19                          0.8237\n",
       "Dimension 20                          0.8288\n",
       "Dimension 21                          0.8337\n",
       "Dimension 22                          0.8385\n",
       "Dimension 23                          0.8430\n",
       "Dimension 24                          0.8473\n",
       "Dimension 25                          0.8516\n",
       "Dimension 26                          0.8555\n",
       "Dimension 27                          0.8594\n",
       "Dimension 28                          0.8631\n",
       "Dimension 29                          0.8668\n",
       "Dimension 30                          0.8703\n",
       "...                                      ...\n",
       "Dimension 120                         0.9943\n",
       "Dimension 121                         0.9947\n",
       "Dimension 122                         0.9951\n",
       "Dimension 123                         0.9955\n",
       "Dimension 124                         0.9959\n",
       "Dimension 125                         0.9963\n",
       "Dimension 126                         0.9966\n",
       "Dimension 127                         0.9969\n",
       "Dimension 128                         0.9972\n",
       "Dimension 129                         0.9975\n",
       "Dimension 130                         0.9978\n",
       "Dimension 131                         0.9980\n",
       "Dimension 132                         0.9982\n",
       "Dimension 133                         0.9984\n",
       "Dimension 134                         0.9986\n",
       "Dimension 135                         0.9988\n",
       "Dimension 136                         0.9990\n",
       "Dimension 137                         0.9992\n",
       "Dimension 138                         0.9993\n",
       "Dimension 139                         0.9994\n",
       "Dimension 140                         0.9995\n",
       "Dimension 141                         0.9996\n",
       "Dimension 142                         0.9997\n",
       "Dimension 143                         0.9998\n",
       "Dimension 144                         0.9999\n",
       "Dimension 145                         1.0000\n",
       "Dimension 146                         1.0000\n",
       "Dimension 147                         1.0000\n",
       "Dimension 148                         1.0000\n",
       "Dimension 149                         1.0000\n",
       "\n",
       "[149 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(latent_features_data_normalized)\n",
    "\n",
    "pca_results = vs.pca_results(latent_features_data_normalized, pca)\n",
    "display(pca_results[\"Explained Variance\"].cumsum().to_frame(\"Cumulative Explained Variance\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~70% variance in the data is explained by the first 5 principal components.\n",
    "\n",
    "Visualizing this provides a clearer view. Visualization code is based on [this](https://www.kaggle.com/fabiendaniel/customer-segmentation) Kaggle notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 5))\n",
    "sns.set(font_scale=1)\n",
    "plt.step(range(latent_features_data_normalized.shape[1]), pca.explained_variance_ratio_.cumsum(), where='mid',\n",
    "         label='cumulative explained variance')\n",
    "sns.barplot(np.arange(1,latent_features_data_normalized.shape[1]+1), pca.explained_variance_ratio_, alpha=0.5, color = 'g',\n",
    "            label='individual explained variance')\n",
    "plt.xlim(0, 100)\n",
    "\n",
    "ax.set_xticklabels([s if int(s.get_text())%2 == 0 else '' for s in ax.get_xticklabels()])\n",
    "\n",
    "plt.ylabel('Explained variance', fontsize = 14)\n",
    "plt.xlabel('Principal components', fontsize = 14)\n",
    "plt.title('Variance explained by principal components', fontsize = 14)\n",
    "plt.legend(loc='upper left', fontsize = 13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>srch_destination_id</th>\n",
       "      <th>d1</th>\n",
       "      <th>d2</th>\n",
       "      <th>d3</th>\n",
       "      <th>d4</th>\n",
       "      <th>d5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.035081</td>\n",
       "      <td>0.114494</td>\n",
       "      <td>-0.050162</td>\n",
       "      <td>-0.097483</td>\n",
       "      <td>0.065729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.429997</td>\n",
       "      <td>0.039974</td>\n",
       "      <td>0.068071</td>\n",
       "      <td>0.025081</td>\n",
       "      <td>-0.031307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.054116</td>\n",
       "      <td>0.015300</td>\n",
       "      <td>0.073246</td>\n",
       "      <td>0.168669</td>\n",
       "      <td>0.132129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.473205</td>\n",
       "      <td>-0.024389</td>\n",
       "      <td>0.019545</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>-0.025236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.208739</td>\n",
       "      <td>-0.037167</td>\n",
       "      <td>0.014076</td>\n",
       "      <td>-0.029207</td>\n",
       "      <td>0.005117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   srch_destination_id        d1        d2        d3        d4        d5\n",
       "0                    0 -0.035081  0.114494 -0.050162 -0.097483  0.065729\n",
       "1                    1 -0.429997  0.039974  0.068071  0.025081 -0.031307\n",
       "2                    2  0.054116  0.015300  0.073246  0.168669  0.132129\n",
       "3                    3 -0.473205 -0.024389  0.019545  0.000167 -0.025236\n",
       "4                    4 -0.208739 -0.037167  0.014076 -0.029207  0.005117"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=5, random_state=1)\n",
    "destinations_reduced_data = pca.fit_transform(latent_features_data_normalized)\n",
    "\n",
    "destinations_reduced_data = pd.DataFrame(destinations_reduced_data, columns = [\"d{0}\".format(i + 1) for i in range(5)])\n",
    "destinations_reduced_data = pd.concat([data_destinations[\"srch_destination_id\"], destinations_reduced_data], axis=1)\n",
    "destinations_reduced_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_final = pd.merge(data_train, destinations_reduced_data, how='left', on=\"srch_destination_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill NaNs in orig_destination_distance with the mean of the values\n",
    "data_train_final['orig_destination_distance'] = data_train_final['orig_destination_distance'].fillna((data_train_final['orig_destination_distance'].mean()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_final.isna().sum()\n",
    "data_train_final.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y=data_train_final[\"hotel_cluster\"]\n",
    "X=data_train_final.drop([\"hotel_cluster\"], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) # 80% training and 20% test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   6 out of  10 | elapsed:  1.0min remaining:   40.4s\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:  1.5min finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1, verbose=1, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "importances = model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame({'features': X_train.columns, 'importance': importances })\n",
    "feature_imp.sort_values(by='importance', ascending=False).plot(kind='bar', x='features', figsize=(20,10))\n",
    "plt.ylabel('Feature Importance')\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_final = data_train_final.drop([\"cnt\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating Model Performance\n",
    "#### Naive Predictor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from average_precision import mapk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = data_train_final[\"hotel_cluster\"].tolist()\n",
    "target = np.array(target).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = [list(top5_hotel_clusters.index) for i in range(100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.033666666666666664"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map5_naive = mapk(target, predictions, k=5)\n",
    "map5_naive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4971"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "del data_train_raw\n",
    "del data_destinations\n",
    "del data_test\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supervised Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the large size of the dataset, pick 15,000 user IDs at random create a reduced dataset with those user IDs only. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced dataset contains 54670 samples and 28 features\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "unique_users = data_train_final['user_id'].unique()\n",
    "sampled_user_ids = random.sample(list(unique_users), 15000)\n",
    "data_train_reduced = data_train_final[data_train_final['user_id'].isin(sampled_user_ids)]\n",
    "\n",
    "print(\"Reduced dataset contains {} samples and {} features\".format(data_train_reduced.shape[0], data_train_reduced.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mapk_scorer(actual, predicted, k):\n",
    "    predictions = []\n",
    "    for p in predicted:\n",
    "        predictions.append(p.argsort()[::-1][:5]) # Pick the 5 most probable \n",
    "\n",
    "    target = actual.tolist()\n",
    "    target = np.array(target).reshape(-1, 1)        \n",
    "    return mapk(target, predictions, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "def map5_scores(clf, data):\n",
    "    predictors = [c for c in data.columns if c not in [\"hotel_cluster\"]]\n",
    "    scores = cross_val_score(clf, data[predictors], data['hotel_cluster'], cv=3, scoring = make_scorer(mapk_scorer, needs_proba=True, k=5), verbose=1)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_classifiers(classifiers, dataset):\n",
    "    results = []\n",
    "\n",
    "    for clf in classifiers:\n",
    "        clf_name = clf.__class__.__name__\n",
    "\n",
    "        start = time()    \n",
    "        scores = map5_scores(clf, dataset)\n",
    "        end = time()\n",
    "        print(\"Classifier: {}; MAP@5 scores: {}\".format(clf_name, scores))\n",
    "        results.append({\"Classifer\": clf_name, \"Mean MAP@5\": scores.mean(), \"Time taken (s)\": \"{:.2f}\".format(end-start)})\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will initially use the default parameters for all the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.4s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: BernoulliNB; MAP@5 scores: [0.13138947 0.12998446 0.12848437]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    5.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: DecisionTreeClassifier; MAP@5 scores: [0.09235967 0.09334065 0.08932604]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    6.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier; MAP@5 scores: [0.16603941 0.16292284 0.16287071]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    5.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: ExtraTreesClassifier; MAP@5 scores: [0.16172528 0.16323459 0.16144562]\n",
      "Classifier: XGBClassifier; MAP@5 scores: [0.21636993 0.22100475 0.21743557]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 15.2min finished\n"
     ]
    },
    {
     "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>Classifer</th>\n",
       "      <th>Mean MAP@5</th>\n",
       "      <th>Time taken (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.129953</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.091675</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.163944</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.162135</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.218270</td>\n",
       "      <td>914.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Classifer  Mean MAP@5 Time taken (s)\n",
       "0             BernoulliNB    0.129953           3.43\n",
       "1  DecisionTreeClassifier    0.091675           5.85\n",
       "2  RandomForestClassifier    0.163944           6.04\n",
       "3    ExtraTreesClassifier    0.162135           5.07\n",
       "4           XGBClassifier    0.218270         914.93"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import time\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "clf_NB = BernoulliNB()\n",
    "clf_DecisionTree = DecisionTreeClassifier(random_state=42)\n",
    "clf_RandomForest = RandomForestClassifier(n_jobs=-1, random_state=42)\n",
    "clf_ExtraTrees = ExtraTreesClassifier(n_jobs=-1, random_state=42)\n",
    "clf_XGB = XGBClassifier(n_jobs=-1, random_state=42)\n",
    "\n",
    "results = evaluate_classifiers([clf_NB, clf_DecisionTree, clf_RandomForest, clf_ExtraTrees, clf_XGB], data_train_reduced)\n",
    "\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Grid Search Cross Validation to find the optimum hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "def tune_params(clf, parameters, X, y, random_search=False):\n",
    "    scorer = make_scorer(mapk_scorer, needs_proba=True, k=5)\n",
    "    \n",
    "    if random_search:\n",
    "        print(\"Randomized Grid Search CV\")        \n",
    "        search = RandomizedSearchCV(clf, param_distributions=parameters, scoring=scorer, cv=3)\n",
    "    else:\n",
    "        print(\"Performing Grid Search CV\")\n",
    "        search = GridSearchCV(clf, parameters, scoring = scorer, cv=3, verbose=1, n_jobs=-1)\n",
    "\n",
    "    start = time()\n",
    "    search.fit(X, y)\n",
    "    end = time()\n",
    "    \n",
    "    return search, (end-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    3.1s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: DecisionTreeClassifier\n",
      "Best params: {'min_samples_leaf': 5, 'min_samples_split': 2}\n",
      "Best score: 0.11734345466739833\n",
      "Time taken: 3.77 s\n",
      "\n",
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:   10.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier\n",
      "Best params: {'min_samples_leaf': 5, 'min_samples_split': 2}\n",
      "Best score: 0.18638192793122368\n",
      "Time taken: 11.92 s\n",
      "\n",
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:    9.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: ExtraTreesClassifier\n",
      "Best params: {'min_samples_leaf': 5, 'min_samples_split': 2}\n",
      "Best score: 0.18690323760746297\n",
      "Time taken: 10.21 s\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y=data_train_reduced[\"hotel_cluster\"]\n",
    "X=data_train_reduced.drop([\"hotel_cluster\"], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.90, random_state=42)\n",
    "\n",
    "param_grid = {\n",
    "    'min_samples_leaf': list(range(1, 7, 2)),\n",
    "    'min_samples_split' : list(range(2, 6)),\n",
    "}\n",
    "\n",
    "clf_DecisionTree = DecisionTreeClassifier(random_state=42)\n",
    "search, time_taken = tune_params(clf_DecisionTree, param_grid, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_DecisionTree.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))\n",
    "\n",
    "print()\n",
    "clf_RandomForest = RandomForestClassifier(n_estimators=51, random_state=42)\n",
    "search, time_taken = tune_params(clf_RandomForest, param_grid, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_RandomForest.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))\n",
    "\n",
    "print()\n",
    "clf_ExtraTrees = ExtraTreesClassifier(n_estimators=57, random_state=42)\n",
    "search, time_taken = tune_params(clf_ExtraTrees, param_grid, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_ExtraTrees.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: XGBClassifier\n",
      "Best params: {'colsample_bytree': 0.4, 'max_depth': 3}\n",
      "Best score: 0.17769343332723614\n",
      "Time taken: 255.22 s\n"
     ]
    }
   ],
   "source": [
    "clf_XGB = XGBClassifier(n_jobs=-1, random_state=42)\n",
    "parameters = {'max_depth': [3, 6, 9], 'colsample_bytree': [0.3, 0.4, 0.5] }\n",
    "search, time_taken = tune_params(clf_XGB, parameters, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_XGB.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    3.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: BernoulliNB; MAP@5 scores: [0.13138947 0.12998446 0.12848437]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    4.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: DecisionTreeClassifier; MAP@5 scores: [0.13701837 0.14079813 0.14053851]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    7.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier; MAP@5 scores: [0.22417735 0.22670872 0.22520162]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    7.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: ExtraTreesClassifier; MAP@5 scores: [0.22562452 0.22647285 0.22513564]\n",
      "Classifier: XGBClassifier; MAP@5 scores: [0.22732826 0.22468276 0.22538308]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 20.6min finished\n"
     ]
    },
    {
     "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>Classifer</th>\n",
       "      <th>Mean MAP@5</th>\n",
       "      <th>Time taken (s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.129953</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.139452</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.225363</td>\n",
       "      <td>7.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ExtraTreesClassifier</td>\n",
       "      <td>0.225744</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.225798</td>\n",
       "      <td>1235.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Classifer  Mean MAP@5 Time taken (s)\n",
       "0             BernoulliNB    0.129953           3.24\n",
       "1  DecisionTreeClassifier    0.139452           4.77\n",
       "2  RandomForestClassifier    0.225363           7.94\n",
       "3    ExtraTreesClassifier    0.225744           7.02\n",
       "4           XGBClassifier    0.225798        1235.90"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_NB = BernoulliNB()\n",
    "clf_DecisionTree = DecisionTreeClassifier(min_samples_leaf=5, min_samples_split=2, random_state=42)\n",
    "clf_RandomForest = RandomForestClassifier(min_samples_leaf=5, min_samples_split=2, n_estimators=51, n_jobs=-1, random_state=42)\n",
    "clf_ExtraTrees = ExtraTreesClassifier(min_samples_leaf=5, min_samples_split=2, n_estimators=57, n_jobs=-1, random_state=42)\n",
    "clf_XGB = XGBClassifier(n_jobs=-1, colsample_bytree=0.3, max_depth=9, random_state=42)\n",
    "\n",
    "results = evaluate_classifiers([clf_NB, clf_DecisionTree, clf_RandomForest, clf_ExtraTrees, clf_XGB], data_train_reduced)\n",
    "\n",
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForestClassifier and ExtraTreesClassifier have better scores and relatively lower times. Hence, tune these further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 384 candidates, totalling 1152 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    7.2s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   38.3s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  3.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1152 out of 1152 | elapsed:  6.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier\n",
      "Best params: {'max_depth': 29, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 131}\n",
      "Best score: 0.1933113834522285\n",
      "Time taken: 364.95 s\n",
      "\n",
      "Performing Grid Search CV\n",
      "Fitting 3 folds for each of 384 candidates, totalling 1152 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 2 members, which is too few. The minimum number of members in any class cannot be less than n_splits=3.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    6.8s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   35.0s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  3.2min\n",
      "[Parallel(n_jobs=-1)]: Done 1152 out of 1152 | elapsed:  5.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: ExtraTreesClassifier\n",
      "Best params: {'max_depth': 13, 'min_samples_leaf': 3, 'min_samples_split': 2, 'n_estimators': 131}\n",
      "Best score: 0.19149137247728798\n",
      "Time taken: 302.07 s\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [51, 91, 131],\n",
    "    'min_samples_leaf': list(range(1, 7, 2)) + [50],\n",
    "    'min_samples_split' : list(range(2, 6)),\n",
    "    'max_depth': list(range(1, 32, 4))\n",
    "}\n",
    "\n",
    "print()\n",
    "clf_RandomForest = RandomForestClassifier(max_depth=None, n_estimators=51, random_state=42)\n",
    "search, time_taken = tune_params(clf_RandomForest, param_grid, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_RandomForest.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))\n",
    "\n",
    "print()\n",
    "clf_ExtraTrees = ExtraTreesClassifier(max_depth=None, n_estimators=57, random_state=42)\n",
    "search, time_taken = tune_params(clf_ExtraTrees, param_grid, X_train, y_train)\n",
    "print(\"Classifier: {}\".format(clf_ExtraTrees.__class__.__name__))\n",
    "print(\"Best params: {}\".format(search.best_params_))\n",
    "print(\"Best score: {}\".format(search.best_score_))\n",
    "print(\"Time taken: {:.2f} s\".format(time_taken))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now obtained a newer set of hyperparameters which give a better score. Use thse to get scores from the redcuded dataset (records with 15,000 randomly chosed user IDs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier; MAP@5 scores: [0.22610752 0.23079722 0.22799223]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    9.8s finished\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Classifer</th>\n",
       "      <th>Mean MAP@5</th>\n",
       "      <th>Time taken (s)</th>\n",
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      ],
      "text/plain": [
       "                Classifer  Mean MAP@5 Time taken (s)\n",
       "0  RandomForestClassifier    0.228299           9.80"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_RandomForest = RandomForestClassifier(min_samples_leaf=5, min_samples_split=2, max_depth=13, n_estimators=91, n_jobs=-1, random_state=42)\n",
    "\n",
    "results_final = evaluate_classifiers([clf_RandomForest], data_train_reduced)\n",
    "\n",
    "pd.DataFrame(results_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this model on the full training dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 30.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier: RandomForestClassifier; MAP@5 scores: [0.33050607 0.33015664 0.33100882]\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Classifer</th>\n",
       "      <th>Mean MAP@5</th>\n",
       "      <th>Time taken (s)</th>\n",
       "    </tr>\n",
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      "text/plain": [
       "                Classifer  Mean MAP@5 Time taken (s)\n",
       "0  RandomForestClassifier    0.330557        1806.65"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_RandomForest = RandomForestClassifier(min_samples_leaf=5, min_samples_split=2, max_depth=29, n_estimators=131, n_jobs=-1, random_state=42)\n",
    "\n",
    "results_final = evaluate_classifiers([clf_RandomForest], data_train_final)\n",
    "\n",
    "pd.DataFrame(results_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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