Credit Risk Prediction - Vantage Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8197763f",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9829c685",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0cc719d",
   "metadata": {},
   "source": [
    "# Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9e7eb422",
   "metadata": {},
   "outputs": [],
   "source": [
    "application = pd.read_csv(\"../dataset/application_record.csv\")\n",
    "credit = pd.read_csv(\"../dataset/credit_record.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f541eb9b",
   "metadata": {},
   "outputs": [
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       "      <th>OCCUPATION_TYPE</th>\n",
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       "             ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
       "0       5008804           M            Y               Y             0   \n",
       "1       5008805           M            Y               Y             0   \n",
       "2       5008806           M            Y               Y             0   \n",
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       "...         ...         ...          ...             ...           ...   \n",
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       "438556  6842885           F            N               Y             0   \n",
       "\n",
       "        AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
       "0               427500.0               Working               Higher education   \n",
       "1               427500.0               Working               Higher education   \n",
       "2               112500.0               Working  Secondary / secondary special   \n",
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       "4               270000.0  Commercial associate  Secondary / secondary special   \n",
       "...                  ...                   ...                            ...   \n",
       "438552          135000.0             Pensioner  Secondary / secondary special   \n",
       "438553          103500.0               Working  Secondary / secondary special   \n",
       "438554           54000.0  Commercial associate               Higher education   \n",
       "438555           72000.0             Pensioner  Secondary / secondary special   \n",
       "438556          121500.0               Working  Secondary / secondary special   \n",
       "\n",
       "          NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
       "0             Civil marriage   Rented apartment      -12005          -4542   \n",
       "1             Civil marriage   Rented apartment      -12005          -4542   \n",
       "2                    Married  House / apartment      -21474          -1134   \n",
       "3       Single / not married  House / apartment      -19110          -3051   \n",
       "4       Single / not married  House / apartment      -19110          -3051   \n",
       "...                      ...                ...         ...            ...   \n",
       "438552             Separated  House / apartment      -22717         365243   \n",
       "438553  Single / not married  House / apartment      -15939          -3007   \n",
       "438554  Single / not married       With parents       -8169           -372   \n",
       "438555               Married  House / apartment      -21673         365243   \n",
       "438556               Married  House / apartment      -18858          -1201   \n",
       "\n",
       "        FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
       "0                1                1           0           0             NaN   \n",
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       "...            ...              ...         ...         ...             ...   \n",
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       "\n",
       "        CNT_FAM_MEMBERS  \n",
       "0                   2.0  \n",
       "1                   2.0  \n",
       "2                   2.0  \n",
       "3                   1.0  \n",
       "4                   1.0  \n",
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       "438553              1.0  \n",
       "438554              1.0  \n",
       "438555              2.0  \n",
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     "execution_count": 3,
     "metadata": {},
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  {
   "cell_type": "code",
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   "id": "1ca1e440",
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       "              ID  MONTHS_BALANCE STATUS\n",
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       "1048572  5150487             -27      C\n",
       "1048573  5150487             -28      C\n",
       "1048574  5150487             -29      C\n",
       "\n",
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     "metadata": {},
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   "source": [
    "credit"
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  },
  {
   "cell_type": "markdown",
   "id": "bfe306da",
   "metadata": {},
   "source": [
    "# Vantage Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b3ea1f0",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>45985 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "MONTHS_BALANCE  -60  -59  -58  -57  -56  -55  -54  -53  -52  -51  ...  -9   \\\n",
       "ID                                                                ...        \n",
       "5001711         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN   \n",
       "5001712         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    0   \n",
       "5001713         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    X   \n",
       "5001714         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    X   \n",
       "5001715         NaN    X    X    X    X    X    X    X    X    X  ...    X   \n",
       "...             ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "5150482         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN   \n",
       "5150483         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    X   \n",
       "5150484         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    0   \n",
       "5150485         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN   \n",
       "5150487         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    C   \n",
       "\n",
       "MONTHS_BALANCE  -8   -7   -6   -5   -4   -3   -2   -1    0   \n",
       "ID                                                           \n",
       "5001711         NaN  NaN  NaN  NaN  NaN    0    0    0    X  \n",
       "5001712           C    C    C    C    C    C    C    C    C  \n",
       "5001713           X    X    X    X    X    X    X    X    X  \n",
       "5001714           X    X    X    X    X    X    X    X    X  \n",
       "5001715           X    X    X    X    X    X    X    X    X  \n",
       "...             ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "5150482         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "5150483           X    X    X    X    X    X    X    X    X  \n",
       "5150484           0    0    0    0    0    0    0    0    C  \n",
       "5150485         NaN  NaN  NaN  NaN  NaN  NaN  NaN    0    0  \n",
       "5150487           C    C    C    C    C    C    C    C    C  \n",
       "\n",
       "[45985 rows x 61 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table\n",
    "pivot_tb = credit.pivot(index = 'ID', columns = 'MONTHS_BALANCE', values = 'STATUS')\n",
    "pivot_tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2e0bc54",
   "metadata": {},
   "outputs": [
    {
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       "MONTHS_BALANCE  -60  -59  -58  -57  -56  -55  -54  -53  -52  -51  ...   -6  \\\n",
       "ID                                                                ...        \n",
       "5001711         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN   \n",
       "5001712         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    C   \n",
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       "...             ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
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       "5150487         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    C   \n",
       "\n",
       "MONTHS_BALANCE   -5   -4   -3   -2   -1    0 open_month end_month window  \n",
       "ID                                                                        \n",
       "5001711         NaN  NaN    0    0    0    X         -3         0      3  \n",
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       "5001714           X    X    X    X    X    X        -14         0     14  \n",
       "5001715           X    X    X    X    X    X        -59         0     59  \n",
       "...             ...  ...  ...  ...  ...  ...        ...       ...    ...  \n",
       "5150482         NaN  NaN  NaN  NaN  NaN  NaN        -28       -11     17  \n",
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       "\n",
       "[45985 rows x 64 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add columns open_month and end_month for each ID. We use a grouped function by ID to find the smallest and biggest MONTH_BALANCE\n",
    "grouped = credit.groupby('ID') # convert credit data to wide format which every ID is a row\n",
    "pivot_tb['open_month'] = grouped['MONTHS_BALANCE'].min() # smallest value of MONTHS_BALANCE, is the month when loan was granted\n",
    "pivot_tb['end_month'] = grouped['MONTHS_BALANCE'].max() # biggest value of MONTHS_BALANCE, might be observe over or canceling account\n",
    "pivot_tb['window'] = pivot_tb['end_month'] - pivot_tb['open_month'] # calculate observe window\n",
    "pivot_tb"
   ]
  },
  {
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   "id": "2e9ef5f8",
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      ],
      "text/plain": [
       "MONTHS_BALANCE  -60  -59  -58  -57  -56  -55  -54  -53  -52  -51  ...   -5  \\\n",
       "ID                                                                ...        \n",
       "5001711         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  NaN   \n",
       "5001712         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    C   \n",
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       "...             ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
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       "5150487         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...    C   \n",
       "\n",
       "MONTHS_BALANCE   -4   -3   -2   -1    0 open_month end_month window       ID  \n",
       "ID                                                                            \n",
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       "\n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
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   "source": [
    "# create new column ID with the grouped indexes \n",
    "pivot_tb['ID'] = pivot_tb.index\n",
    "pivot_tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be09f6d4",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>MONTHS_BALANCE</th>\n",
       "      <th>ID</th>\n",
       "      <th>open_month</th>\n",
       "      <th>end_month</th>\n",
       "      <th>window</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5001711</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5001712</td>\n",
       "      <td>-18</td>\n",
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       "      <td>18</td>\n",
       "    </tr>\n",
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       "      <td>5001713</td>\n",
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       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5001714</td>\n",
       "      <td>-14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5001715</td>\n",
       "      <td>-59</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>45980</th>\n",
       "      <td>5150482</td>\n",
       "      <td>-28</td>\n",
       "      <td>-11</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>45981</th>\n",
       "      <td>5150483</td>\n",
       "      <td>-17</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>45982</th>\n",
       "      <td>5150484</td>\n",
       "      <td>-12</td>\n",
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       "      <td>12</td>\n",
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       "      <th>45983</th>\n",
       "      <td>5150485</td>\n",
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       "      <th>45984</th>\n",
       "      <td>5150487</td>\n",
       "      <td>-29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45985 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "MONTHS_BALANCE       ID  open_month  end_month  window\n",
       "0               5001711          -3          0       3\n",
       "1               5001712         -18          0      18\n",
       "2               5001713         -21          0      21\n",
       "3               5001714         -14          0      14\n",
       "4               5001715         -59          0      59\n",
       "...                 ...         ...        ...     ...\n",
       "45980           5150482         -28        -11      17\n",
       "45981           5150483         -17          0      17\n",
       "45982           5150484         -12          0      12\n",
       "45983           5150485          -1          0       1\n",
       "45984           5150487         -29          0      29\n",
       "\n",
       "[45985 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the created columns to overwrite the pivot table\n",
    "pivot_tb = pivot_tb[['ID', 'open_month', 'end_month', 'window']]\n",
    "pivot_tb.reset_index(drop = True, inplace = True)\n",
    "pivot_tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "526ec749",
   "metadata": {},
   "outputs": [
    {
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       "<p>1048575 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  MONTHS_BALANCE STATUS  open_month  end_month  window\n",
       "0        5001711               0      X          -3          0       3\n",
       "1        5001711              -1      0          -3          0       3\n",
       "2        5001711              -2      0          -3          0       3\n",
       "3        5001711              -3      0          -3          0       3\n",
       "4        5001712               0      C         -18          0      18\n",
       "...          ...             ...    ...         ...        ...     ...\n",
       "1048570  5150487             -25      C         -29          0      29\n",
       "1048571  5150487             -26      C         -29          0      29\n",
       "1048572  5150487             -27      C         -29          0      29\n",
       "1048573  5150487             -28      C         -29          0      29\n",
       "1048574  5150487             -29      C         -29          0      29\n",
       "\n",
       "[1048575 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the information of the pivot table to the original dataset\n",
    "credit = pd.merge(credit, pivot_tb, on = 'ID', how = 'left') # join calculated information\n",
    "credit0 = credit.copy()\n",
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b40156e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>1048575 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  MONTHS_BALANCE STATUS  open_month  end_month  window  \\\n",
       "3        5001711              -3      0          -3          0       3   \n",
       "2        5001711              -2      0          -3          0       3   \n",
       "1        5001711              -1      0          -3          0       3   \n",
       "0        5001711               0      X          -3          0       3   \n",
       "22       5001712             -18      0         -18          0      18   \n",
       "...          ...             ...    ...         ...        ...     ...   \n",
       "1048549  5150487              -4      C         -29          0      29   \n",
       "1048548  5150487              -3      C         -29          0      29   \n",
       "1048547  5150487              -2      C         -29          0      29   \n",
       "1048546  5150487              -1      C         -29          0      29   \n",
       "1048545  5150487               0      C         -29          0      29   \n",
       "\n",
       "         status  month_on_book  \n",
       "3             0              0  \n",
       "2             0              1  \n",
       "1             0              2  \n",
       "0             0              3  \n",
       "22            0              0  \n",
       "...         ...            ...  \n",
       "1048549       0             25  \n",
       "1048548       0             26  \n",
       "1048547       0             27  \n",
       "1048546       0             28  \n",
       "1048545       0             29  \n",
       "\n",
       "[1048575 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename status credit score (more than 60 days = bad)\n",
    "credit['status'] = np.where((credit['STATUS'] == '2') | (credit['STATUS'] == '3' )| (credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 1, 0) # analyze > 60 days past due \n",
    "credit['status'] = credit['status'].astype(np.int8) # 1: overdue 0: not\n",
    "credit['month_on_book'] = credit['MONTHS_BALANCE'] - credit['open_month'] # calculate month on book: how many months after opening account\n",
    "credit.sort_values(by = ['ID','month_on_book'], inplace = True)\n",
    "credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d95c2405",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    open_month  sta_sum\n",
       "0          -60      415\n",
       "1          -59      406\n",
       "2          -58      440\n",
       "3          -57      400\n",
       "4          -56      470\n",
       "..         ...      ...\n",
       "56          -4      874\n",
       "57          -3      919\n",
       "58          -2      771\n",
       "59          -1      686\n",
       "60           0      370\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate new accounts per open_moneht\n",
    "denominator = pivot_tb.groupby(['open_month']).agg({'ID': ['count']})\n",
    "denominator.reset_index(inplace = True)\n",
    "denominator.columns = ['open_month','sta_sum']\n",
    "denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a8e27664",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      open_month  month_on_book  due_count  sta_sum\n",
       "0            -60              0        NaN      415\n",
       "1            -60              1        NaN      415\n",
       "2            -60              2        NaN      415\n",
       "3            -60              3        NaN      415\n",
       "4            -60              4        NaN      415\n",
       "...          ...            ...        ...      ...\n",
       "1886          -2              1        NaN      771\n",
       "1887          -2              2        NaN      771\n",
       "1888          -1              0        NaN      686\n",
       "1889          -1              1        NaN      686\n",
       "1890           0              0        NaN      370\n",
       "\n",
       "[1891 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vantage = credit.groupby(['open_month','month_on_book']).agg({'ID': ['count']}) \n",
    "vantage.reset_index(inplace = True)\n",
    "vantage.columns = ['open_month','month_on_book','sta_sum'] \n",
    "vantage['due_count'] = np.nan\n",
    "vantage = vantage[['open_month','month_on_book','due_count']] # delete aggerate column\n",
    "vantage = pd.merge(vantage, denominator, on = ['open_month'], how = 'left') # join sta_sum column to vintage table\n",
    "vantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfd37a2b",
   "metadata": {},
   "outputs": [
    {
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       "<p>1891 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      open_month  month_on_book  due_count  sta_sum  sta_rate\n",
       "0            -60              0        0.0      415  0.000000\n",
       "1            -60              1        0.0      415  0.000000\n",
       "2            -60              2        0.0      415  0.000000\n",
       "3            -60              3        0.0      415  0.000000\n",
       "4            -60              4        0.0      415  0.000000\n",
       "...          ...            ...        ...      ...       ...\n",
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       "1888          -1              0        0.0      686  0.000000\n",
       "1889          -1              1        0.0      686  0.000000\n",
       "1890           0              0        0.0      370  0.000000\n",
       "\n",
       "[1891 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for j in range(-60,1): # outer loop: month in which account was opened\n",
    "    ls = []\n",
    "    for i in range(0,61): # inner loop time after the credit card was granted\n",
    "        due = list(credit[(credit['status'] == 1) & (credit['month_on_book'] == i) & (credit['open_month'] == j)]['ID']) # get ID which satisfy the condition\n",
    "        ls.extend(due) # As time goes, add bad customers\n",
    "        vantage.loc[(vantage['month_on_book'] == i) & (vantage['open_month'] == j), 'due_count'] = len(set(ls)) # calculate non-duplicate ID numbers using set()\n",
    "        \n",
    "vantage['sta_rate']  = vantage['due_count'] / vantage['sta_sum'] # calculate cumulative % of bad customers\n",
    "vantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d7b9007f",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "month_on_book   0    1         2         3         4         5         6   \\\n",
       "open_month                                                                  \n",
       "-60            0.0  0.0  0.000000  0.000000  0.000000  0.002410  0.002410   \n",
       "-59            0.0  0.0  0.000000  0.004926  0.004926  0.004926  0.007389   \n",
       "-58            0.0  0.0  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "-57            0.0  0.0  0.000000  0.000000  0.002500  0.002500  0.002500   \n",
       "-56            0.0  0.0  0.004255  0.004255  0.008511  0.010638  0.010638   \n",
       "...            ...  ...       ...       ...       ...       ...       ...   \n",
       "-4             0.0  0.0  0.000000  0.003432  0.003432       NaN       NaN   \n",
       "-3             0.0  0.0  0.001088  0.001088       NaN       NaN       NaN   \n",
       "-2             0.0  0.0  0.001297       NaN       NaN       NaN       NaN   \n",
       "-1             0.0  0.0       NaN       NaN       NaN       NaN       NaN   \n",
       " 0             0.0  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "month_on_book        7         8         9   ...        51        52  \\\n",
       "open_month                                   ...                       \n",
       "-60            0.007229  0.007229  0.009639  ...  0.028916  0.028916   \n",
       "-59            0.007389  0.007389  0.012315  ...  0.029557  0.029557   \n",
       "-58            0.004545  0.006818  0.006818  ...  0.025000  0.025000   \n",
       "-57            0.007500  0.007500  0.007500  ...  0.025000  0.025000   \n",
       "-56            0.017021  0.017021  0.021277  ...  0.036170  0.036170   \n",
       "...                 ...       ...       ...  ...       ...       ...   \n",
       "-4                  NaN       NaN       NaN  ...       NaN       NaN   \n",
       "-3                  NaN       NaN       NaN  ...       NaN       NaN   \n",
       "-2                  NaN       NaN       NaN  ...       NaN       NaN   \n",
       "-1                  NaN       NaN       NaN  ...       NaN       NaN   \n",
       " 0                  NaN       NaN       NaN  ...       NaN       NaN   \n",
       "\n",
       "month_on_book        53        54        55        56        57        58  \\\n",
       "open_month                                                                  \n",
       "-60            0.028916  0.028916  0.028916  0.028916  0.028916  0.031325   \n",
       "-59            0.029557  0.032020  0.032020  0.032020  0.032020  0.032020   \n",
       "-58            0.025000  0.025000  0.025000  0.025000  0.025000  0.025000   \n",
       "-57            0.025000  0.025000  0.025000  0.025000  0.025000       NaN   \n",
       "-56            0.036170  0.036170  0.038298  0.038298       NaN       NaN   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "-4                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "-3                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "-2                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "-1                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       " 0                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "month_on_book        59        60  \n",
       "open_month                         \n",
       "-60            0.031325  0.031325  \n",
       "-59            0.032020       NaN  \n",
       "-58                 NaN       NaN  \n",
       "-57                 NaN       NaN  \n",
       "-56                 NaN       NaN  \n",
       "...                 ...       ...  \n",
       "-4                  NaN       NaN  \n",
       "-3                  NaN       NaN  \n",
       "-2                  NaN       NaN  \n",
       "-1                  NaN       NaN  \n",
       " 0                  NaN       NaN  \n",
       "\n",
       "[61 rows x 61 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vantage_wide = vantage.pivot(index = 'open_month',\n",
    "                             columns = 'month_on_book',\n",
    "                             values = 'sta_rate')\n",
    "vantage_wide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c32ca6f4",
   "metadata": {},
   "source": [
    "This is a standard vingtage analysis table. The rows represent months of opening accounts, columns represent months after openning acounts, and values are accumlate past-due rate. As open-month closes to 0, the observe window shortens, thus the bottom half triangular field is filled by nan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1afd2536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ld0iCK4QQQogB5fS88laCO0MS3GGu057cp59+mrS0NO6++27sdvuJx8PhMD/72c94+umn+eQnP9nvQQohhraW0HHWlP8UTdO5oOz/kW4vTnZIQgghhBgplMLlWYmr5VVCqTPx5X4UtE77+sQQ1+lPd/v27XzqU586KcEFsNvtXHXVVWzbtq1fgxNCDH1NwUOsOv5jTLqVC8rulARXCCGEEANHKVyel99KcGdLgjtCdNqT29jYSGlp6WmfKy0tpbGxsV+CEkIMD/X+Payv/CV2cwZLR92Oy+pOdkhCCCGEGCmUwtX8Ii7vGkJp5+DLuUwS3BHijIWnzObTb2I2m09aO1cIId6txreT1yrvI8Way9JRt+OwZCQ7JCGEEEKMFEqR0vw8Tu86gmlz8edcIgnuCNJpkhuLxXjsscc6fD4ej/d5QEKIoa+ydTOvV/+WdFsJ5436JjZzarJDEkIIIcRIoRQpTf/D2bqBYPo8/O5LQDrnRpROk9xFixbR3Nzc4fMLFy7s84CEEEPbMe86tlT/kWzneBaXfg2ryZnskIQQQgjRR0zRRiyhY8kOo1OWcAUO3zaC6Qvwuy+SBHcE6jTJvfnmmwcqDiHEMHDIs5LttY+Q55rCotIvY9btZ95JCCGEEEOCJXSc9JqH0VUk2aGcUSBjMYHsD0iCO0KdcU6uEEJ0xf6mZ3mj/p8Ups5kQfEtmHRrskMSQgghRB+xhI6RXvMwhjmVlvybUKbBeyNbaSaUKSXZYYgkkiRXCNErSin2ND7JnsanKUmby7ziz6Nr8tEihBBCDBeW4FEyah8mYc7AW/QZDHNaskMSolNyJSqE6DGlFDvr/8HB5ucZnbGEOYWfRpfKhUIIIcSwYQkeJqP2URKWTLyFn8GQYpJiCOjS1ajX6+3W40KI4U8pg221D3Ow+XnGZ13IOZLgCiGEEMOKNXiQjNpHSFiyaCm8URJcMWR06Yr0S1/60mkf/8pXvtKnwQghhgZDJdhU/QeOtLzKJPdFzMz/FJokuEIIIcSwYQ0cIL32L8QtblqKPoMyyxxXMXR0abiyUuqUx4LBILouF7VCjDQJI8brVb+lyreVabkfY3LOJckOSQghhBB9yBrYT3rtX4lbc/EWfRplciU7JCG6pdMk96abbgIgGo2e+Pptfr9f1skVYgTaVvswVb6tzMy/mrOy35fscIQQQgjRh6yBfaTX/o24LR9v4Q0oWe9eDEGdJrlf/OIXUUrx4x//mC9+8YsnPZeRkUFhYWG/BieEGFyiiSDlrRsZm7lMElwhhBBimLH695Be9w/itoK3ElxHskMSokc6TXInT54MwJ/+9CdsNtuABCSEGLyq27ZiqBijMxYlOxQhhBBC9CGbfzdpdf8kbit6K8EdvOvgCnEmXZpU+/LLL3P8+HEADh48yE033cQtt9zCgQMH+jM2IcQgU9H6Oi5LLlmOsckORQghhBB9xObbRVrdP4nZS/AWSYIrhr4uJbnPPvssubm5APzjH//goosu4vLLL+eRRx7p1+CEEINHON5KfeBNRqXPQ9O0ZIcjhBBCiL7QtIm0+n8Ss5fSWng9SpcEVwx9XaquHAwGcTqdhEIhjh8/zre//W10XefRRx/t7/iEEINEZetmFIrStLloiVCywxm54gF5/8XpSdsQHZG2ITpgC+xFa3iCmGM03oJrQbcmOyQh+kSXktzs7GwOHDhAZWUlkyZNQtd1WUJIiBGmvHUD6bYSSltexRaUqQpJcwxykh2DGJykbYiOSNsQnVBpk/C6PyEJrhhWupTkXn311fzf//0fZrOZr33tawBs376dcePG9WtwQojBwR9toDl0mBlZy7EF9hNKnU3cVpDssEYkl8tFIBBIdhhiEJK2IToibUN0ROlWUsqWgact2aEI0ae6lOTOmjWL3//+9yc9Nm/ePObNm9cvQQkhBpeK1tcBmBxrJWFKxZdzKeiWJEc1MrncbkJNTckOQwxC0jZER6RtiM6kSA+uGIa6lOS+LRQK4fP5UEqdeCwvL6/PgxJCDC4VrRvJsZWQHa3F575YElwhhBBCCDFodSnJraqq4r777qO8vPyU5x577LE+D0oIMXh4w5W0RqpYaB9DwuQglHZOskMSQgghhBCiQ12qHPXggw8yZcoU/vznP+N0OnnooYe48MIL+cIXvtDf8QkhkqyidSMaGhONEIHM86UXVwghhBBCDGpdSnLLy8u56qqrcLlcKKVwOp1cffXV0osrxDCnlKKidSPFpjRslmzCabOTHZIQQgghhBCd6lKSa7FYSCQSAKSmptLU1IRSCr/f36/BCSGSqzl0mECsiQma3t6Lq3VrGr8QQgghhBADrktXrBMnTmTjxo0sXbqUefPm8aMf/QiLxcKUKVP6Oz4hRBJVtG7EhEaZtQCf9OIKIYQQQoghoEtJ7le/+tUTX1955ZWUlJQQDodZsmRJvwUmhEguQyWo9L7GaN1OLPtC0EzJDkkIIYQY9pSvDWKRATtfggTK4xmw83WbbkLLyEp2FGKI6TTJ9Xq9PPLII1RWVjJ69GiuueYaUlNTJbkVYgRo8O8lbAQZax9FOHVmssMRQgghhjWlFOrZx1DP/H1AzzvoV1DOLcR09++SHYUYYjpNch988EGCwSAXXnghmzZt4pFHHuGWW24ZqNiEEElU1fw8VjRy3JcQk15cIYQQot8opVD/+jPq5WfQzlkMk6YP2LlTU1Px+XwDdr7u0hzOZIcghqBOk9z9+/dz77334nK5mD9/PrfddttAxSWESKJEIkxlYC+jzZnERshcXKvPj8PbmuwwzsjU2Ex6OJzsMMQgJG1DdETaxuCmDIPgs38jsmMDtnOX4nzfx9C0LtWG7RN2ux3zYG8fldVd3lQBjYEYezUHjSZb/8U0xCwfm46uackOY8B0muTGYjFcLhcAaWlphAf7L4AQok80Nj9LlAQlmeePiLm4dm8rmeWVKE1D6QN3YdETus+H3VDJDkMMQtI2REekbQxeKhGn5cXHiBzaRcq5y0iddyFa28CuXjLc2kc4bpAWN9hQG+SltsH9N30gLRuTDiMnx+08yY3H46xatQql1InvX3311ZO2ueCCC/ovOiHEwFMGlS2rcWhmMt0fSnY0/c7ubSXzeAUxp4PmsaNRpsGd1LvdbpqaBv0MKpEE0jZER6RtDE4qEsH43T1waBfax64ntOLDhJIQx3BqH68c8XLf63VcMCaTq5blcEWyAxpE9BGU4MIZktzx48ezdu3aE9+PGzeOdevWnbSNJLlCDC9a2xbK417Gp8xC1y3JDqdfnUhwXU6ax5QN+gRXCCHE8KBCQYxffx8O70P71BfQl7wv2SENeTtrA9y/qY7p+U5uPrcAi2mEZXXiJJ0muXfdddcAhSGEGBRUgvrG/5AAioZ5L669xUtmeSVRlxOPJLhCCCEGiPK1Ydx7F1QdQ7vx6+jnLE52SEPesZYw96ytpiTdxu1LioZtgqsSCYjHB+58sdiAnauvdWmdXCHEyGD3vcGRaD0p5nSyneOTHU6/cbR4yZAEVwghxABTLc0Yv/wONNWj3/wttLPPSXZIQ15TMMYPVlXhtOh8+/xinJbh+Tdd1VZi/OLb0Dpwaxr7L/0kXPSJATtfX5IkVwjRTiWg+UUqVYSJGSvQhmkFPoenhYyKKqIuF54xoyTBFUIIMSBUQy3G/30bAj70L92FNmFqskMa8gLRBN9fVUUobvDjC0txO4fnNCvlbcb41V1gJNAuv3bACkjZps8hMjCn6nOS5AohALD7dnI8WoMCStPnJzucfnEiwU1x4RldhjJJ1UUhhBD9T1WXY/zyuxCPoX/th2hlw3e01ECJJRT3rKumqjXCd84voSzTnuyQ+oUKBjDu/R4E/Ojf+BHaqLEDdm6r2w1DtCiZXOEJIUAlcHleYb+Kk24rIcNekuyI+pyj+V0J7hhJcIUQQgwMdewgxs++BYD+jR9LgtsHlFLcv6mWXXVBbplXwIwCV7JD6hcqFsP47Y+gthL95tsHNMEd6rp0lVdVVYXX6wUgHA7z+OOP8+9//5tIZKh2YAsh3s3etp1ArJH6hJ/S9HnJDqfPOZo9ZFRWEUlNaa+iPMjXwhVCCDE8qAO72+dROpzot92DVlSa7JCGhb/vamLVsTY+ebabC8akJzucfqEMA/XnX8KB3WjXfQlt8sxkhzSkdOlK79577yUYDALw6KOPsm/fPg4ePMgf/vCHfg1OCDEAVBxXy6vs16wAwy7JdTZ7yKysJpKagmf0KJAEVwghxABQb2xpn0eZ5W5PcHPykx3SsPDyYS+Pv9nM8rHpfHxqdrLD6RdKKdTjf0JtXY/20evR5y1NdkhDTpfm5DY2NlJYWIhSii1btvCLX/wCq9XKLbfc0uUT7dy5k4ceegjDMFi2bBmXXXbZSc8rpXjooYfYsWMHNpuNm2++mTFjxhCNRvnud79LPB4nkUgwb948Pv7xj3frRQohOmZv24Yp7uWgipLtGEeKNTfZIfUZZ1MzGVU1hCXBFUIIMYCMTWtQD/0Kike3F5lKTUt2SMPC9ho/v91cx8wCFzedmz9si2Sql55CvfJftOWXoK24LNnhDEldSnItFguhUIiqqiqys7NJS0sjkUgQ6+LaSYZh8Kc//Yk777yT7Oxs7rjjDubMmUNxcfGJbXbs2EFdXR333Xcfhw4d4sEHH+RHP/oRFouF7373u9jtduLxON/5zneYMWMGZ511Vs9esRDiHUYMV8sq6ixuWvw7mZX//mRH1GdOJLhpqXjKSiXBFUIIMSCMNS+g/vYAjJ+CfsudaA5nskMaFo56wvxkXQ2jMmx8c3EhZn14JrjGxlWofz+Mds5itI/dMGwT+f7WpSR34cKFfP/73ycUCvH+97dfBB87dozc3K71+Bw+fJj8/Hzy8vIAWLBgAVu2bDkpyd26dStLlixB0zTOOussAoEALS0tZGZmYre3V0tLJBIkEgn5YQvRRxxtWzDFW9lnL0NDpyT93GSHdBLNMDCHuz/33+r3k15TJwmuEEKIAWW88ATqiUdg2hz0z9+GZrUlO6RhoTEQ4/urq0ix6nx76TBeC3fPDtQj98GEaWjXfxlNrl96rEtJ7nXXXccbb7yByWRi6tT2Nb00TePaa6/t0kk8Hg/Z2e+Mmc/OzubQoUOnbON2u0/axuPxkJmZiWEY3HbbbdTV1fG+972P8eNPX5Vu5cqVrFy5EoB77rnnpOMNNmazeVDHJ5JnwNqGEUUrX4uRMp6KwD6KM2dQnD+IqvaFI1i270ALhnq0u5HjRp82Bfcw+wMhnx2iI9I2REekbfQ/pRSBv/2ewBOPYlu0jPRbv4NmGRprtg729uGLxLn7hV3EEor7PnY2Y9zDs5Jy7Mh+Wn53D+aS0WR++xforpRkhzTo20ZnupTkbt26lZkzZ2IyvXPXZOzYrl8MK6VOeey9vbGdbaPrOj/72c8IBAL8/Oc/p6KigtLSU6vTLV++nOXLl5/4vmkQr+vkdrsHdXwieQaqbTi8r5Eaa+VQ6nzamlcxMfuSQdMm9WgM9+GjqHgcb2kxhqmbd2x1jUhKCng8/RNgEslnh+iItA3REWkb/UsZBuqff0Cteg5t8QpiV99Ec2trssPqssHcPmIJg++tqqKyJch3zy8hjRBNTT27+T2YqYZajHu+Ca5UjC/8PzyhMITCyQ5rULcNgMLCwg6f61KS+9hjj/HAAw+wYMEClixZ0mFPakeys7Npbm4+8X1zczOZmZmnbPPuN/F027hcLiZPnszOnTtPm+QKIbrIiOJsWU3UMZajkQp0zUJx6pxkRwWAKRol+/Ax9Hic5rFlxFzD846t6J2XDnt5eEcD8cSpN0hHKk07eNobxkJI2+g/upHg8/seZ3Hddv5beh5/tyyHxw8nO6xuGcztw1AQMxRfWVDA2fnD83pAtXkx7r0LDAP9y3ehZQzPitEDrUtJ7s9+9jOOHz/OunXr+MUvfoHNZmPJkiUsXry4S/Nyx44dS21tLQ0NDWRlZbFhwwZuvfXWk7aZM2cOL7zwAgsXLuTQoUM4nU4yMzNpa2vDZDLhcrmIRqPs3r2bSy+9tGevVggBgKN1E6aEH2/mJ6is/CWFqTOwmBzJDuutBPcoejxB89jRxFxSrEOcanOVjwc21zHR7eAsd/Lb7WDhcDgIhYZfD4foPWkb/UOPx1jy8u8prdvJjnMvo2XWh/jAEKwbM9jbx1luOwtLh2d1ahUOYdz3ffA2o3/1h2j5xWfeSXRJl5JcgLKyMsrKyrj66qvZvXs3f/nLX3j88ceZOHEiy5cvZ+HChegdzH0zmUzccMMN3H333RiGwfnnn09JSQkvvfQSACtWrGDmzJls376dW2+9FavVys033wxAS0sL999/P4ZhoJRi/vz5zJ49uw9euhAjk2ZEcHnXEHGMp9oIEY63Upo+P9lhYYpEyT5yFD2RoHncaGJOSXDFqQ41h/j5+hpGZ9r5zvklOCzDa851bwz2YWUieaRt9D0VDmH89kdw/A20T3yWOcsuYnCMh+o+aR/JoeJxjN//BCqOon/hW2hjJyY7pGGly0kuQF1dHevWrWPdunVomsYVV1yB2+3mhRdeYNOmTXz961/vcN9Zs2Yxa9askx5bsWLFia81TeMzn/nMKfuNGjWKn/70p90JUwjRCUfr6+iJAIHs5VQ0v4hFd1CQMj2pMZkib/XgGgbNY8cQc0rvnDhVvT/KD1ZXkW438+2lxZLgCiGSQgX8GPd9D44dQrv+y+gLLkh2SGKIUUqhHv01vLkd7Zpb0KYPrtUthoMuJbkvvPAC69ato66ujvnz53PLLbectE7t3LlzT5ugCiEGF80I42xZQ8Q5gYg1n6q2rRSlzcasW5MWkykSaZ+DaxjtQ5QlwRWn0RZJ8L1VVSQMxXeWF5Pp6NY9WiGE6BOqtQXjV9+Fuqr2JYJmJX8kVE+pg29i/On/aAiHBu2c3GHLUBAJoV3ySfTFK868vei2Ll0l7Ny5k4suuohzzjkHs/nUXWw2W6e9uEKIwcHh3YhuhPBlns+22keJGUFGpS9MWjymSAT34WNgGDSNHU1cElxxGtGEwY/XVFHvj/H9ZSWUpMu6k0KIgaeaGzD+79vg9aB/8dtok2cmO6QeU1XHMX5zN6Sm41i2bFDPyR228ovRlrwv2VEMW11Kcm+//fYzbjN9enKHOwohOqclwji9awk5JvBa87NUtG5ksvtS8lxTkhJPe4J7FAxF87jRxB2S4IpTGUrxqw217G0M8fWFhUzJlbnaQoiBp+qqMH75HQiF0L/yfbRxk5IdUo+p5sb2ar42G/pXf0DqhElEZE6uGGa6PN5r69at7N27l7a2tpMev+WWW/o8KCFE33O0rsdIhHghVktVYC9n517BpJyLkhKLKfxWgouiedwY4g57UuIQg98jOxp5rcLHtTNzWFw2PKtrio6pNzZjrHsp2WEMeS1WK4loNNlhDG1H9oOmoX/9brTSMcmOpsdUwNee4EYi6N/8MVp2TrJDEqJfdCnJ/de//sXLL7/MggULeP3111m+fDmvvfYa8+cP3XkIQowkWiKEpWUt/zHCVAeqmZV/DeOzL0xKLOZwmOzDxwBoHisJrujYswdaeHqfhw+elcGHJ2UlOxwxwFTAh/GnX4LVCumZyQ5nSDPMZojHkx3G0FZchn7V54f0Ei8qGsH4zQ+hsRb9y99DKy5LdkhC9JsuJbmrVq3izjvvpLS0lNWrV3PdddexaNEinnjiif6OTwjRB8yeV/lfpJpaFePcwhsZnbkkOXG8O8EdN5q4XRJccXqbKn08uK2ec4tT+MzsPLQhuPak6B31/L8hHGzvbZKL8V7JliViRjxlJDD++As4sh/9s99AmzAt2SEJ0a+6lOQGAgFKS0vbdzCbicfjjBs3jr179/ZrcEKI3otG61nV+BSNKsa84i9Qmj43KXGYQ2Gyj0iCK87sQFOIn79Ww9gsO19fWIhJH1kJrhaPo/Wk0mkkgh6L9X1ASaC8HrSNa9AXrkDPK4Jh8rqSZhi1jaFGaRrqNEVbBzQGpVB//z3sfB3tE59Fm7MoqfEIMRC69FuXn59PZWUlJSUllJSU8NJLL5GSkkJKSkp/xyeE6IVQzMu6Yz+kTUVYUnAD+X2Q4KbUNeDwtnZ7P1M0itJ1msaNIWGX6rji9Gp9Ue5eXUWmw8ydS4uxmUfQWriGQVpNHSlNzT0+RH4fhpN0n/pa+/979ic3jmFiWLWNISSUlkbLmFFJjUE9+zhqzQto7/8I+rLk1OIQYqB1Kcm94oor8Pl8AHzyk5/kvvvuIxwOy9q4QgxiwVgzq4/9iFC8lfelzSU16/xeH9MUiZBaV0/cbidu7d7aujGHDV9+HgmbJLji9NrCcb6/qhJDKb5zfjEZ9pGzFq4pEiXzeAXWUIhAdhaxHsxVT0lJwe/390N0A0u1eVH//QfahGnS49RHBnvbCMY8RBK+ZIfRL6LmJlrbGru9X45zIjZz7zuTjHUvoZ75G9r889Euv6bXxxNiqOjSFcSsWbNOfD1+/Hh+/etf91tAQoje80XqWV1+D7F4K5dacrDlfZxEHxw3tb4RNI3msWUYFksfHFGIdpG4wd1rqmkMxPnBshKK00bOzRBbaxuZFZWgwFNWSjgjvUfHcbrdBIfBvMvEv/8Ih3ahX38LWlpGssMZFgZr21BKsb/pf+zy/AvowRD9oaIHgzOWj/4uNvO4Xp1WvbEF9dffwpSZaNd8UWobiBGlS0luVVUV+/btw+/3k5KSwqRJkyguHrrV5YQYzlrD1awuvwdDxfiwJYe0tNn4rLm9Pq4pEsHhaSGQky0JruhThlL8ckMtB5pCfGNxIZNGylq4SpFWW0dKQxNRh52WslEkbN0bITHcqGOHYNsGtIs+IQnuMBdLhNlc8weq2rZQkjaXSe6LQXKwE1Kteb3aXx3Zj/GHn0DJGPTP346W5HnBQgy0Tlu8UooHHniANWvWkJ2dTWZmJh6Ph5aWFpYsWcJNN90kd4WEGEQ8oeOsLf8pmmbiovTF5Ab24sm8oE+OnVrXgNI0/Lmypt5QUtUWYdXRNlRPChkNkGpflNcr/dwwK5eFpSNjLVw9GiOrvAJrIEjAnUVrYQHoI2j+cQeMpx6FlDS0FZclOxTRj3yROtZX/gpfpIbpeVcyIfsDcj3Zh1RdFcZvfgDpWei3fhvN7kh2SEIMuE6T3JUrV7J3717uvvtuxo17Z8jE4cOHuffee3n55ZdZsWJFvwcphDizpuAh1pb/HIvJwQXFt1BY+wjh1JkkrO5eH9sUjuBo8RLIcUsv7hDSGIhx58pKWsNx9EF8Aalr8JHJWVwycWSshWpr85FRXommFJ5RJYQzM5Id0qCg9u6EfW+gXfFpNMcI6c0fgWp8O3m96gE0TWfJqG+SnzI12SENK8rrwfjVXaDp7Wvhpo2Mz1Uh3qvTJHft2rVcf/31JyW4AOPGjeO6667j6aefliRXiA60hMrxRiq7vV9TPAVfNwuExBJBdjc8jt2cydJRt5HXugGUQSCrj3px6xtQuvTiDiX+aILvr6okGje494OjKc0YOXNcBy2lSK2rJ6W+kbjdjqesVCqNv0UphfHko5CVg3beB5IdjugHShnsa/ovuxueIMNeyqKSL+Gyyt+UvqRCQYx7vwf+NvSv342WW5DskIRImk6T3KqqKiZPnnza5yZPnsxvfvObfglKiKGusnUTG6seQPVJuaeuSbeVcN6ob+LSNBxtWwinzcawZPX6uOZwGEeLF3+uG8Mic3qGgljC4Mdrq6nxRfnu+SWS4A4CeixGZnklNn+AYFYmrcWFKBme/I7tG6D8MNp1X0KzjOx5ycNRLBFiU/UfqPZtZVT6AuYU3oBZl8+lvqRiMYwHfgy1Fei3fButbHyyQxIiqTq9YjUMA4fj9OP4HQ4HhmH0S1BCDGXHWtaypeZBsp3jOafwM+iaqVv7Z2Zm0tLS0u3zOi1Z6JoZZ+MzoBSBzN4vGQTt6+IqXScgvbhDglKKX79ex5v1Qb6yoICz813JDmnEs/r8ZJZXoiUStJQUEcru/c2nd1NGAvXPP9J08E0SiYG7sdZdUZOThH6a6Q5KgacJXDmw8iV45eWBD66XLIkQZiOa7DA61GQyJa1t+FINNiwK4U9VTN9pZdzBN9D48gDeAh4hwmHwNqNd/2W0qbPOvL0Qw1ynSW4ikeDNN9/s8HlJcoU42aHml9le9yh5rqksKv0SZr37a12mO9zEAj2b96rHvDhatxBKm4Nh6f08HHMojMPbij83B0MqMw4Jf32jiTXH27h6upulo3u2FI3oI0qR0tBIam09cZuNlrGjifdg/dvOT6FQj/0Jteo5TLPmY5gG3+9pHAv77edQbp0Mg3hueG9MCa2nLLov2WF0yGyzYkQGPgmvyWxj01kV6Epnyd5R5IZSoGTAwxg5ps5GX9A305SEGOo6/WuYnp7OAw880OHzaWkjowqmEF2xr+l/7Kp/jMLUWSwo/gImfeCH3LlaVgEQzFzaJ8drn4ur48/tffEq0f9ePOTl33uaWTEunY9OyU52OF1iikZJr6rBlIQL8P6mGQbmWIxgRjqtJUUoU/dGdXSFeuFJ1Kv/Q7vwUjJvvo2mQbYWan1NjF3bgoSDirJxVtIzT34PVDyOeuov4EpB+8DHhmwOnOn+AHraRckOo0MZbveAtg2lDPY0Ps2exqfItI9mYcmtuGbI3xEhxMDpNMm9//77ByoOIYYspRRvNj7J3sanKU2fz9yiz6JrA9+bosc82Nu2Ekqfi2HJ6PXx3u7F9eXloKQXd9DbWu3nd1vqmF3o4vPn5A+J5ThsrW1kVlSBUkTSUpMdTr/wpaYQysrslx5MY8OrqCcfQTt3CdpHr+/z4/dGJGywZ0eI6ooYqWk6c5a5yHSf+jlivPgs6vBz6F//EdpYmaM5HEQTQTZV/44a3w7K0hcxu/B6zEm46SuEGNnkylWIXlBKsbP+7xxsfoHRGecxp/AGdC05xWRcnlWg6X3Xi1tXj6Hr+HPk7vtgd7g5zM/WVzM608Y3FhVh0t9JqFrC5VS2bkIxeNbJ1RSM9+VSGHDTZg6xI6uKoDmW7LD6Rxxo6IfjNtahDm+AS0rQZhdC479w+h0EQ6F+OFnXKQXB+gJaDk/ESJhJLztKWulRKhKKivr3bByLoppegMtGoWXsgfo9pxzPYuhkRGU5od5qrrVSpXsG5FzVbVvxRxuZlX8N47KWD4kbbkKI4UeSXCF6yFAG22of4mjLasZnrWBm/lVoSUpwTdEm7L7thNLnYZh7P43AHArhaG3Dl5crvbiDXL0/yg9XV5JmM/HtpSU4LO+0wWPe9Wyr+TOGSqB1swBaf3EpFx+If5Ai5Wa3vos12moSrVKCpluUAYk4zLCCOQjelQBokNxbGdFMLNUfw+SfiOE8Rqzocert9dR3VEcvkYCpGpj90PziKU9blY0rY1eSgazz2Rdetf5jQM7jMKeztOx2cl0TB+R8QghxOnL1KkQPGCrB5uo/UN66gcnuS5ia+9Gk3q12trwKmqkPe3EbpBd3CPBHEnx/VRVRQ/GD5aVkOto/0g0VZ2fd3znkeZlc1yTmF9+CvQ9ufvRWe5XhCjRN0VJSSHbWNC7nqmSHNaSohhqMe24Dqx399p+iZbxTqdk9wPMuT8RkKI4djrJ/X3sv8qSZDsrGzUDTZ3a8j9eD8f8+izZjHvqNXz/NBoqsY+XYYr729YStPSvGJ9plZGTwseifkx2GEEIMGElyheimhBFjY9X9VPu2MS33Y0zOuSSp8Ziijdh9OwllLMQw935eoznY3ovblp+LMg+O3j9xqljC4Edrq6jzx/j+BSWUpLfPZwzHW9lQ+Wsagwc4K/v9TM/7RLeXsepzSpFS30BqXQNxu42WslLi9r6tMjwSqDYvxq/uAmWgf/mukxLcZPG1Jti5OYjXkyC3wMy02U6crjOPaFHPPgaJBNqlnzzt86l1DdjbfHiLCwlnSJXw3lJpaTDIipIJIUR/6lKSGw6Hqa2tJRQK4XA4KCgowC4XKGIEihsRXqu8lzr/bmbmf4qzslckOyRcnldAMxPIOK9PjpdaV49h0glIL+6gZSjFvRtr2dMQ4msLC5mS1z5nsTl0lNcq7iWa8DO36POUZSxMcqSgx+Jklldi8/sJZmbQWlyEMiVnWP9QpsIhjPu+D60e9K/+EC2/OKnxJBKKw/vCHNoXwWzWmDnXSdEoS5dGtKiGGtS6l9AWvw8tt/CU5+3eVlLrGwhmZRLs4zWFhRBCjAydJrmBQIA//vGPbN68GbPZjNPpJBQKEY/HmTt3Lp/+9KdxuVwDFasQSRVLhFhX8Quaggc5p/AzjMnsm6SyN0zRBmz+XQQzlqDMKb0+niUYxNHmoy0/r1+WOxF94y87G1lX7uOaGTksKWsfhny0ZS3bah/Gbk5n2ehvk+koS26QgNUfIPN4BXoigbekiGA/VRkerJob4vjaej/fWCUM1JrnwBiF9rHPo6lRcDhyynZNda34/ac+3teUAcePRPC3GRSNsjBlhgObves3LtQzfweTGe1DHz/lOXM4TEZFFVGHA29xIX6/QVN9vC/DH5F60jYam6rwB1r7KSIxmNhtNsKR/v/sECfTNZ1RpZO6vV9GlomMLBmMeyadvkMPPPAAVquVX/7yl+Tl5Z14vL6+nscff5wHHniAr3/9NHNphBhmInE/ayt+RkuonHnFN1OaPi/ZIQHtvbhKsxDMXNwnx0uta8AwmQjkDI01Vkeip3bV8uReDx8Yn8Hlk7NIGHF21v2Vwy2vkOeawvziL2Drg2HrvaIUKQ1NpNbWkbBZaRxbRtzhSG5MAygSNtizM0R1eR9WjE5fBulAPVDfUQXlgausbHdqnLvERV5B9+bKqoojqM1r0T74sVOGW2uJBJnHKlC6hmd0KW1tig2r/MSig6cy+NDVvbbhDx2lsW19P8UihADQMNHWWNbt/SZMtUuS2wWdvkO7du3ij3/8IzbbyWvX5eXlceONN/LZz362X4MToiPheCvHvOtQyhiQ81W0vo4vWsvCklspSps1IOc8E1OkDpt/N8HMpShT70dUWAJB7G0+2gqkF7e/KaVoDSe6XQl3b2OQ/1tfwzlFLm6ck9c+/7bq1zQFDzIx+4NMy/t40uffavE4mRVV2Nt8hDLS8ZYUjZj2pJSiujzGnp0hYjHFWVNsjBpr61XntfHs46hX/4f2/svRL7ys022zsrLweAZmmRiLVUPXu//CjKf+As4UtPd9+OQnlCKjvApzJELz2NG0RUxsXO3HZIIF70vFZh85IwD6Q3faRk1NNc8+t4HCwiIuuGAFGvLeD3eZmRm0tHiTHcaI5HR2f4k0k0l+J7ui0yQ3NTWVY8eOMXHiqWXgjx8/TkpK74dHCtET+5r+x8HmFwbsfBbdyeLSr5GfMnXAznkmLs8rKN1KMGNRnxwvta6ehMlEwC29uP0pYSh+8VoNr1X4erT/xNwUvr6okJbwETZU3kc0EWB+8c2Ups/vuyANg5TGJkzR7vdE2tp8mOJxvEWFBN1ZI2Z4cjBgsHtbkIbaOJnZJqaf4yQ1vXfJvfHK/1Av/B1tyfvRLvrwGee7OpxmbMHBO99ZHdgNb25H++j1aM6Trx9S6htxtLXRWlSAV3ew8RU/APOWppCaNjJukvSnrrYNj8fDSy8/R3p6OhdffNEpnRxieHK7M0CTaQFieOk0yb3yyiv58Y9/zOzZsykrK8PpdBIMBikvL2fbtm3ceOONAxWnECcYyqCi9XWKUmcxv/iLA3JOTdPRk7QG7umYIzXYA28SyFyGMnX/LuB7WQIB7D4/bQX5I6bXLRmUUvxpewOvVfi4eGImRanWbu1v1jU+NKOMfRVPsb3uERzmbJaP+S4Z9tI+i9EUiZB5vAJrKEzCbIJu9uIYZjNNo0uJ9eDu9EBQLc1QXd53x1Nw3JPG/ob2obdT8j2UZbWhVYKq7MVxD+1BPfcvGDMBZpwLe3acsec/kp6Gam3r+Un7mfHM3yAjG+38D570uK21jdS6eoKZGTS7Mtm4KkAiDvPPlwR3IAWDQf7zn/+g6zqXXnqpJLhCiCGt0yR30aJFjBo1ivXr17N//34ikQg2m42SkhJ++MMfUlyc3OqOYmRqDO4nHPdSmr4Akz4y5yS4PK9g6HaCfVQ9N7WugYRZenH723/2t/DsgRYumZjJp2fnnXmH90gYMbZV/Ja9tc+TnzKNeUU3Y+uDgmNvs3tbyaioAk2jefQoIunJX1u3L6mAD+OuL0LQ3yfH87kK2T3p03gz3LibdjFt/0M4ws0o6PZQ9A4dPYC67/tdOp63r87Zj7TrvoRmfSd5MkUiZJZXEnfYacgtYOPqAJGwwfzzUkjPlAR3oMTjcf73v/8RDAa5/PLLSUsbXr/7QoiR54wZQklJCVdeeeVAxCJEl1R4N2LW7RSmzkh2KElhDldjC+zFn7UcZep9MR+rv70Xt7UwX5Z26Uevlbfx5+0NzC9J5fpZud3ePxRr4bXK+2gOHWaS+2Km5n6070YXGAZptXWkNDYTdTpoGVVKwta9XuahQD3/BIQC6DfdDuk9X5rGMOBwnYtDtSmYTYoZJV6KZueivf+23sfYUIv66wOQmoZ29c1ojq73iKenp9PaOoir4dqdaEXvjDrQEgmyjpWDplFfVMrr60IEAwZzF7vIdI/MG5jJoJTixRdfpK6ujg996EPk5+cnOyQhhOi1Tv+K1NXVkZqaemKZoFdffZXt27cDMGfOHJYuXdrvAQrxbgkjRpVvC0WpszHrI3MolcvzMobuINRnvbj1JMxmgtKL22/2NQT55YZaJrodfGVBAXo356k2Bg+yofI+4kaY903+Fhla95cc6IgpGm0fnhwM4Xdn01aYD/rwu9mhWprbCzjNPQ9t1oIeH6elKc4bW4L42gyKSi1MmenAZu+btVxVcyPqd/eA04n+jR+jZed0a3+r243W1NQnsfQ7pcioqMIcjtBQVsaGTVHaWhOcs9CFO697FZtF77z22mscOXKERYsWMXbs2GSHI4QQfaLTK5mf/vSnBAIBAB5//HGeeeYZJk6cyMSJE3nmmWd48sknByRIId5W599NNBFgVF8W2RlCzOEKbMED7evi6vZeH8/qD2DzB/Dn5qCGYWIzGFS1Rbh7TRU5LjP/77wibOZurCeqFIc9K1l17EeYdRvLRn+XcTl9s1wUgK2tjZwDhzGHI3jKSmkrLhyWCS6A+u8/wDDQLvlkj/aPxxRvbg+y/hU/sbji3MUuZs13dWt92E7jC/gw7r0LIhH0L93V7QR3qElpaMLR2kZrfh7rd2q0NCeYNc9JXqEkuANp9+7dbN++nWnTpjFz5sxkhyOEEH2m057cxsZGcnPbh9WtW7eO73znO+TktP/hnT9/Pt/+9re5/PLL+z9KId5S0boRmymVvJQpyQ4lKVyelRi6i1BG3yT5b/fiBtx90xMlTuYNxfn+qip0TeM755eQZu/6EMyEEWVb7SMc866lIGU684pvwtoHS0UBoBSptfWkNjQSs9vxjC4lkaQiM5GwwYE3w4SC/bgcWCSM8k2DpReg7UuBfd2fk9vmTRAOKcrGWZl0tgOzpe+qRqtoBOPXP4DGWvQvfw+tuKzPjj0Y2dp8pNbWEUxPY/UhB031cWac66SwZGgOkY8lDCKJwb2Wry0Sxx9NnPRYRUUFL69ZT8moMcyZv4hAbGCW5BODz+naR7+dy6RjkSVwxADo9IorPT2dmpoaCgsLicfjJy0Z5HK5TvTyCjEQYokw1b7tlGUsRtdG3nwtc6gcW/AQ/uwPoPpgqLbV58fmD9BaVDBse++SKRw3+OGaKlpCcX64vJSCblRSDsY8vFZ5L57QUSbnXMbUnA+j9dH8Wz0WI/N4JbZAgEB2VtJ+/kopqo63rymbiKteL7fTqUYP2NIhowDCPUtGUtJMzJ5vJyunbz97VCKB8cefw9ED6J/9BtqEaX16/MHGFIm2F5qy21hTl0V9TYKpsxyUjB6aCe6Gijbu3VhHOD7YE8RDp38483zww1+fODKw4YhBpoP20Q9uW1zIglIpbCb6X6d/rT/4wQ/yy1/+kmuvvZaLL76Ye++9l8suuwyAZ555Roa2iAFV7dtGQkVH7FDlFM9KDFMKwfR5vT+YUu0VlS1mAtnSi9vXEobi5+trOOIJc/viIia4u14grCGwnw2Vvyahoiws+RLFaXP6LC6rz09meQWaYdBSWkwoK7PPjt0dwUCCXVtDNNb13ZqyHVHHD2H86+toF12B/v4Z/XKOnlJKof7+e9i5Ce0Tn0Wb0zdrXg9WmmG0F5pCscGXR0VFgoln2xk9fujVV1BK8a89zfztjSYmuO0sGjW4L9rf3TERiUTYsWMHADNnzpSlgsSAdlyVZfR+qpUQXXHGJNdms3H//ffj8XgA2LFjB2azmQULFnDDDTcMSJBCAFS0vo7Tko3bOT7ZoQw4S+gY1tBhfO4Pgd77Hg+rP4AtEMBbNHznYCaLUoo/bq1nS7Wfz87JY25Japf3O+xZyY66v5FizWFR6bdIsxX1VVCk1DeQWtdA3G6jpayUuH3gLzSUoTh2KML+3WHQYOosB2XjrGjdLMTVHcaTj0JKKtqKD/fbOTpiCkcwRyIdPq+2rEWVH0O7/Hq0OUugl2vcaqp9nvVg5fS0YA6H2aEK2H9UMW6SjfGTht4FbzRh8JvX61hzvI3zytK4ZV4+1kFemd7tdtPU1EQ0GuXf/36BklArH/3oR09MQRMj29vtQ4jh5IzjrpYtW8YFF1xAc3MzHo8Hq9VKYWEhVuvQHFokhqZI3EedfzcTst/fZ8M2hxKXZyUJUyqhtLm9P5hS7XNxLRaC2cnpyRvOntrn4flDXi6blMWHJpz+/dXjcaz+AKj2obOGinO0ZTX+4H7OtV/IuKwLMQetEPSeum/CwN7NRMbpacHu8xPMzKC1uCgpS0W1eRO8sSWI15Mgt8DMtNlOnK6ex9EcPMIx7zoUncwja2lGFR6ERRPQWv4BLT0+XbeYlM6EQBFjg/nodJLAu0fBRde0f32svE/OPdhrpG/XK1jfup60STr+bDNbapIdUfdE44odtQFC8TifnG5nTKadN+qTHdWZ2T12QqEQFRUVRN1+JswexfHYfzh+hve/IGV6n44mEUKIgdKlyUWapuF2u3G73f0djxCnVdm2BUWC0hE4VNkSPII1dBSf+2LQe1951Ob3YwsE8Q7jSrrJsu54G4/saGRhaSrXzuy4hyS9shrHe3rt3EwAJkAACHR+1dzdAeaGplGbXUBrSgb4FHSWGPYxw1CUH4lSeSyK2awx6Ww7Oflm4jFFm7f7cSRUlENtT3PM/zwmzYZZ67gnUAu1wVgrKsUDrQOT4ZYmSlgSX0QaqezR97HPtB/Fe+YBJ+IQCYPJDDYbdJYId4OuaxjG4C2AFFExmuIt6BkaIatGqPv1v7pIoRlRtPe+771kKIglFHlORXGKhknXqBu8Hecna9NIxOPoLoPMNBMB400CXVhSOSvaREqk+3ci/Nkf6JO/V0II0VMjr3qPGJIqWjeQZiskw16a7FAGllJv9eKmEUo7p0+Ol1pbT9xiIZik+ZjdoaqOoQ7sSXYYXVLni7L/YAs3uCx8MC8DXt3B6UrR6PE48foGmlwuvNYgVW1bURgUps4izZZ/xvM4HE5CoWCX42qsi9PYZBAz9nb9xfSRsDUdT/YUYpYUXIEaMlsOEDwSo6d9lqGMFmrO3kk0xU9GRSm5+ydjip/+QjrNV0FJ7Vqq8+fjTev/tT+tVhOTJuRRkJ+O3x9h077jtHghhYn9fu6hIhUYm2mipMwK/TJEXWF3NpOadRTdHEMZfXeJk1CKuFJgAouudXut62QzlEFCS6BbdEx6N+a/R/wQ2dLt8/l2Gyg1/C4xtXnnobm6NgVFCJFcw+8TSAw7wVgzjcGDTM25vF/n7g1GltBhrOHj+HIu6ZteXJ8fazCEt7ho0PfiGuteQv39dxCPJzuULskDTlQp2EmHfUgJwPvW1zbgnfTrebrySn3djCvzrX8DKW6yc2Dcx6jPOwd72MOMNx8gp3l3j4+XMMGe+XaOz7Li8CsWPRkiv2I3cOZjFtVtpKhuY4/PfWYarilzyJj3PjSTmdZNK2nbvpZCI0FhP551KFOb+v6YutNE2opCHKPSiNaG8DxXTbyp4/nQor+9kewA+oU2eTpIkivEkCBJrhj0KlpfB9TIG6qsFCnNL5Mwp/ddL27d2724Gb0/Xj9R8Tjq8QdRq56DSdPRr/0i2Pq3OE08EWZH3V+p8+/q9r6K9iGMABaT1unAU4uyclX8UxzTjvOq6QUKUmYwu+BazKauv77srGyaPc1d2vbQ3jBHD0RYsCyFlNSBuanR0Ai7dyvCYSgrg4kTcjCbb+vx8ZoiR9nS9Fd88XrGpCxk+qgPY5nWebVqY+MqePxPaDd8GW1a/80nNEeiZDR6sIUjRBw2vDnZxCd+Gv2qT5+yrWptQd37PYhF0L50F5o7r8/iMAzF0QMRAj6dWDzWZ8cdGhSFKQeYkr0WkxZjX9NcNh0vJjo+jnVy76oGK6ANKyFlwa7FSSPC4L412DGrzUJ6WjroI+tGcV9L7HfCwX4bZ580FkuEWKx7nx2uFJ1ps539FJEQvSdJrhj0KlpfJ8sxhlRb310UDgXW4EEskUracj4MfbAusK3N196LWzJ4e3FVmxfj9z+Bg3vQVlyGdvm1aKZ+XD8VaIvU8lrdr/DF6xhTsBSL3vXlfuKGYm15G75IgiVlaWTaO/85lQWysfsy8GSnMj3lRkZnLOl2ITU9LR0teuaLkVhUcbjSwJJuY806Rcd9y30vJU1n4UInWe6et9u4EeXNhn9zoPkFnJYszht1G/kpU8+4n4pG4KWnYfRZaPPO75/RH4ZBan0DKQ1NKF2npaSYUFYGaKe/yaGCAdSD/wdBP/rX70Yr67sK8ZGwwbaNQZobrGTn2DD0oTHyoS/YTT5mZr9AgfMQzeEitjV/iMoGE2EiZOZmYO9FBfG4oahpixKMG7idZrKdlj6aOZ0cZrOZyBAZFTOoJYDE4J333mPKIB7v3utKSHMSg1y3rkBefPFFGhoa+NSnPtVf8QhxkrZIDS3h48zIvyrZoQwspXB5XiZhziScNqtPjpda10Dcah20c3FV+WGM3/4IfG1on/4q+ryl/X7Oat92NlX9Dl0zc17ZbeS5Jnd534Sh+NGaKrbXBvjWkmLOKU7pfAelyN17gIjLwuiSy3oX+Bkopdi5OUAi3r5sz+jxVizWgbmxYbNrlIy2YjL1PCVoCh5kc/Uf8UXrGJt5AdPzPoHF1LWbD2rVc+BtRv/MV/slwbX6/GRUVmOORglmZtBWVIBh7vhPqYrFMB74MdRWoN9yZ58muF5PnC2vBYhGFDPmOpk5p3BkLAOiFHbfNlKankVTcXxZHySRsRD/axs4VrudRYsWMWtWz9/nitYIP1xdhScW59b5BSwpG9xr4HaFLBEjOiPtQwxHXU5ylVI888wzBAIBLrvsMlJTZU6C6H8VrRsBjdK0eckOZUBZg/uxRKppy/1I3/XihkK0lBT1U8GX3jFeX4V69H5ITUO/7Sdoo/q3UJBSBnsan2ZP41Nk2stYWPIlXNauV49XSvGHrfVsrQnw+XPyzpzgAvbWNsyxGG1FBb0J/Yz8vvalejyNCSxWjUXLU0hJ7d/e8L4SNyLsbniCg80v4LRks3TU7eSlTOny/iroRz33L5g6C23CtD6NTY/HSauuxdniJW610jR2NNHUzn/uyjBQD/0K9u9Cu/7LaFNn91k8lcci7NoawmbXWHhBChlZI2Nglh7zktr4FLbgQaL2Mny5HyFhdbN79262b9/OtGnTmDlzZo+Pv73Gz8/W12A1adx9YSkT3F0f2SGEEGLw0JRSXRqfsGvXLv72t79RVlbGqFGj+OAHP9jfsfVaTc3gXYBP7pqdmVKK5w5/E6cli/PL7kh2OAPGnZ1NYuddaEYYT+lXQetlgqIU7oOH0RMGDZPOGlRJrkokUE88jHr5GThrCvrnbkNLy+jXc0YTQTZV/54a33bK0hcxu/B6TJqF5w952VjZtbJODf4Ydf4YOS4zBaldWzP8tjQ/mbrBHd5UVC8GPlotFqKnmTulKcgPWSkK2jCAvUYQX3oci23w/Lw747SUU5T+BDZzM82BudT734ehOplTGQ6Dp+HEWsMnHouG2wvD9OEw94WZVj5WYMdh0nihIcL/GsLEuvKXMx6HoB9yCsCd2yexaApKA3bywlZaLXGOpIaI6+3BdNQ2hgfF4pS9fCRzIxqKp7zzWOObikIjHArT1NSI3W7H7c7p8YpMSsGehiCjMmz8v/OKyXENnyVw5JpDdEbah+jIYG8bhYUdl3js8q3f1atXc/7551NaWsrDDz88JJJcMbS1hI/hj9Yxyf2hZIcysFp2YonU0Jb7sd4nuLT3IFpDYVpKiwdXgutvw/jDz2DfG2jnfwjt459G62TYZ19oi9SwvuJX+KP1zMz/FOOzLiRuwP2b63j5SCul6VZc1s7f89ZwnDp/jDSbiWyn5UTRqc6MNic4y5Lgbz4b0QT0an6spk45pyuuMy7oJCVhoskSY2fUR8wEaWYTdvNBMhxv9vx8A8Ckh8mw7yWaSOdQ4w34o2PeeqaD9ykWRVWVt2clprfajFLtSaXJCnGj/V8v5dtMXF+WwuQ0Kwd9Mf583Ed1uJtr+2YXoGXm9Mk8PouhcVbASXrcTLUtwnFHuP0tejuk07SN4cBtbuO6nDVMdlSzN1TEw43n0RRvH0Ici0VpavZgtthIzcgi1st1gpePTeeGWXk4LIOzboEQQoiu6dIVZTAYZMeOHdxwww2kpKQQCAQ4duwYo0eP7u/4xAhW3vo6umaiuC8qCyeDiqMZ4W7uA1rDf4hb3IRTp/dBDG/NxbVZCWVm9P54fURVHcO4/0fgbUa79ovoiy7s93NWtW1lU/XvMWkWlpbdTq5rEm2RBD9ZW8WbDSE+PjWbK892d7r+5e76AHe9WsnUXAd3XVCCxdS1C+GM8kqMVp0LFozl/F72ML77rmo8rji4p72CstWmMW2eg3gM3JstzFygUaM9zjHvWiy6E7Peu0qz/UujOG0503I/juUMlaaVrxXjntvA34Z++0/QCkoAMP72O9S6F9G/fz9abi8X7zEMUhoaSa1vRGka3sJ8UrKzuHVx/98kMoercbRuQFMnV3WJRhSepjhGNmRmm3A4T217NpuNSGS4LZujsAYOABpt7stwp53L19/6HfX5fDz++OMAXPGRK0hJOfO0ASGEECNDl5Lc9evXM2XKlBN/QJYsWcLq1aslyRX9xlAGla2vU5AyHavJlexwuk8lyKq8H3O0rke7B/I+3me9uJbw4OrFVVvXYzx0Lzhd6N/4MdqYCf17PmXwZuNT7G18mizHGBaW3IrTkk1Va4QfrK6iORjnKwsKWDo6vdPjVHgj/HhNNfkpVu5YUtzlBFePxnC0eAnkZKP6cAhtU32MN7aGCPoNSsdYmTTdjsWisfoFH7a8vWwP/JNw3Msk98VMybkMk961YdWDmYqEMX79A2hpQv/qD04kuKqhBrXuRbTFK3qd4Fr9AdIrq7FEIoQy0mktKsCwDMCwVRXH5XkFZ8talG7FML2TsMViChVWZNnB7tTbi6OfLpeNmzAnutnTPAREnePxuy/CsGS881g0yn//+1+i0Sgf/ehHJcEVQghxki4luatXr+byyy8/8f15553HnXfeyTXXXIOpn5f3ECNTU/AAoXgLpemfTHYoPWL3vYE5WkcwYzEJc/eqGbvS3UTog8JLb62LG7PZBkUvrjISqKf/hnr+3zB2Ivrnb0fLyOrXc0YTAV6v+h21/p2MzljC7IJrMelWdtQG+Nm6aswmjR8uL2ViTufFZTyhON9fVYnVpPHd80tIsXX9c8/V3L6mbcDd9cJWnYmEE7yxOUjFsSjOFJ35S12489qTsIrKFppT/0YicxNppiIWln6ZbMeYMxxxaFCJBMbvfwrHD6PffDvauEnvPPfM38FkQvvQFT0+vhZPkFZTi8vTQtxioXnMKCJpA1NV1xyuJK3h35ijDYRSZ+N3fwhlcpBIKN7cHqLiaJScfDOz5jkJ2zq+ueJ2u/EM4rlTfcUwDJ5//nmam5u5+OKLycnJSXZIQgghBpkzJrmtra2kpqYya9Y7y5jk5+cze/Zsjh8/ztix/VsFVYxM5a0bMOs2ClN7XiUzaVQCZ8srxKwF+LPfD91cB9XldkMfXKjava1YwhFaRpX0aS+u2r0VdXBP9/c7fqi9yuziFWhXfg6tn3vHWsPVrK/8FYFoI7MKrmVc5jI0TePZAy08uK2e0vT24jK5KZ3HEYwl+MGqSnzRBD+6cNQp23s9ccKh088D1JRBpCJArT0Lf5MG9K4oUCRscGhvBaFQgrETbZw1xY7Z3P6zrW7bwaaWP2Fk+JiUfQlTci/DpA+PwjlKKdRf7ofdW9GuvhltxjvV1lXFUdTmtWgf+GjPbpoohcPbSlp1LXo8jj/HjS8/D9XFnvpeMWK4PCtxetdhmNPwFlxH1NU+siEcMtj6WoCW5gTjJtmYONWOpg+O0RjJpJRizZo1lJeXc8EFF1BWVpbskIQQQgxCZ0xy09PTueOOUyvb3nTTTf0SkBAJI05V2xaKUmcP8nmEp2f37cAc8+At+FS3E9w+oxSp9Q3E7DZCGZ0Pw+3yIY0E6qm/ol54or1ybXdfm9WKdtVN6Es/0OVd4kYEf7S+m5GCN1zJttqHMes2zi+7gxzXBOKG4sEtdTx/yMs5RSl8dWEBTkvnPbIJQ/GzdTUc90a487xixmadPF+06niUHZuCZ4gmu/2/Q4Fuv47Tycq2Mmeh48SSMdFEgB21f+V463q0WAFT077IlPz+HQI+0NQzf0O9thLtok+gn/f+k54znvoLOFPQ3n95B3t3zBSJkl5Vjd3nJ+pw0DymjLhzYJaMMYfK23tvY02E0s7F7/4ASm9vX82NcbZtCBCPK2YvcFJYMvSHmveVHTt2sHv3bmbPns3UqVOTHY4QQohBqstLCA1FsoTQ0FTt2876il+yuPRrFKbOSHY43aMSZJf/AsPkpKX4Cz3qQe1u21BKsf31IPXV7+olVO29iErTe7ycxruZYwGmv/Fb3M27qSw+n32TPoXS+7cScsJWRaj4jyirp0f766EyHFWfRo9noIBYQmEohVnXMHexRyxmKBKGwmLSMb1nF6XAeKuAr37afF+xLL+JhKGxuiGbPvlBcHKTiqfsJpz/T5TZj7nxQiyN78M8THpvT0gkIBEH3QTvrb6tDIjF2issd2PqjIZiWoaPWVltGMC25nT2tqb0ammnrjLrUWbmr2Nyzlb80XQ2VL6fWn/ZSdskEuBK0TlnkYvU9K6/ruH+d+Xw4cM899xzjBs3jg984ANog6TOwFAw3NuG6B1pH6Ijg71t9MkSQr21c+dOHnroIQzDYNmyZVx22WUnPa+U4qGHHmLHjh3YbDZuvvlmxowZQ1NTE/fffz9erxdN01i+fLksXzTMVXg3YjWlkJ8y9O7S29u2Y4q34Mu5ZMAKPVUei1JTEaOo1ILdoQMKR3MLaBDKyqS3yZW1uZKi536Gxd9E3XmfITj1Qkb1SeQd82qbqDI9igkX+fHr0elej76GmRTzJPQyC/5ogo2VPoLKYEa+i1EZXTvW/qYQ+xpDTMi2Mzb35H2MhKKqPIamKYrLrCeGDL9bNgHGEOZN8hidfnIPsAmDDELdek1vs1mthKJtePR1BLT9WNQE0kPL8AWySC0wcKXGz3yQoaLFgzp2ANIy0cZOAO3dr02hDrwJiQjahFlg6tr9WhMGo2khlSgNuDiAm0iOhVEDMK0zXT/ORPt/cegtVEfP4WhsGbZ8K2Xv2c5s0Rg93orFKsvYvK2uro4XX3yR/Px8VqxYIQmuEEKITg1IkmsYBn/605+48847yc7O5o477mDOnDkUFxef2GbHjh3U1dVx3333cejQIR588EF+9KMfYTKZ+NSnPsWYMWMIhULcfvvtnH322SftK4aPWCJMtW87ZRmL0LUBuwfTN1QcV8urxGwlRJ0DM1w0EjbY+0aYTLeJmfOcaJqGw9NCJh48ZaWEM5y9Or7a8TrGU78Emw39G3dTNG4yRX0U++kYKsEb9Y9R2fw8Oc4JLCj5InZzz4db76oLcO+6GjRN444LipiS27X3Y/WxVh7Z08DS0Wl8ZH7WSRfUSim2bggSjyvmnR0k86m7wdd6yjHciy8hkZFN+rP3k268a81W3UTeBR/BmpXXsxcVfet/Yyrw1o0giwHFg/dOa49lApllb31zmkrlE94u5tW9Ie0JixlPUSnxjPS+KPF2RpoRwdX8As7W14lbsmjJvRGLYwyn+5SIx+McP36co8e6XyW5pqYGn8/X+4AHiXhccfxwhLYWA4Ui0zWfRFDjX3/d3W/n1DQTLntpvx0/ebzJDqDfLL847bRLagkhRrYBySIOHz5Mfn4+eXntF3ULFixgy5YtJyWqW7duZcmSJWiaxllnnUUgEKClpYXMzEwyM9ur0zocDoqKivB4PJLkDlM1vu0kVJRR6QuSHUq32du2YYp78eV+eMB6cffuDBGPK6bPaU9w35mLayec3nll2HC8jb2NzxAzTtOjqBRUHkNVHoMPpaFNOBts66F6fT+9EkgYMRqD+wjHW0m1FuKy5PBG/WM9Pl5NW5QDTSHOLdGZnu/CH9PZVH3m/VpCcXbWBvjAWWamF7jY/J5ZD4GqCQSqppNSuInD+/4KEzml4JFmMuPI3EMs0EZs2cm9wLOci7Ha8tgeWEtrontDsaOWOM0pQVLCNiZX5ZMathOPta+f6kzRSUkdotXulSLx7hsBkQiq/BCYzGhl49qHI59ut/RMjHkXQDeLREWtVpSuQ/BM86l7zx45itv7P0yJVlpdc/Gmno9SltOeu6GhgTVr1tDaeupNEzEwbDYXM2aNT3YYfc7pdBIcgPaeDGaL9OoLIU7V7ST3jTfeoLy8nLy8PM4999wuDRnyeDxkZ2ef+D47O5tDhw6dso37XUtsZGdn4/F4TiS40H4BcOzYMcaNG9fdsMUQUdG6EYc5C7dziF1kGDFcLauI2kcRdQxM7I11MarKY4yfbDsxb8/R4sUcieIpK+000TaUwetVv6UhsB/Hu9aeBNoTXH8bEIVxKeByQvwI9OMoWEPFicR9KAysphQSKkJDcB9xQxFLdL9sQNxQhOMGxek6aTYTvij4ol3bzxtKUJAGmXYzTe+9JvSVwfFpkLYbv/kv+As1SMvgvRN29XgUzaggkWmBrHeemxybyKjYBLaZt7M15+TPwK7Q0Jlc+D5GOZdhOsdMAtjxeoA6V4zlF6WReO/yMkphb9uKzb8bjcFZfiEWj9NQX0/iveu7Tn77xkFDJ3vXwOHuV/oeKGYT5GdFafSbeHJXNpXeCuCRZIc16DltpWS6ppMwwjT7thA3Wlm+fDn5+fn9fm5N08jIGJjiYwPJ7c6mqWlwfgYIIUR/6FaS+9hjj3Ho0CHGjBnDtm3b2Lx5M1/84hfPuN/palu9Nzk+0zbhcJhf/OIXXHfddTidpx9yuHLlSlauXAnAPffcc1LSPNiYzeZBHV8yhGNt1AV2M73ow+Tk5CY7nO6pexU93oo2/tO403s3ua8rbSMeN1j9fCWp6RbmLS7CF41T2RJial0jAbuDow43RDtOciuan6A+sIfxuZ8lP33ZicdN9VVk/P57mBpb8H3kc4Rm9f/c4vq2tRxu+ANWcyaTCr5Gqr19AOn2Ki9/21zZoyQX4Pzxbj4yqQBTF4tMReMGd798EEPBH66YTl7qyT2wAX+c/zxeidURYd7zv8eWmUHmXb/ClPOeC+9oFMv6jRgFeSQmTTzxsOb1Yt62E8OdxbTpX2VaD99Xs9lMPN5+x8HXGqOmwsvk6RkUFr2nzYSb0I7+Ba1tH8qeD2ZXj87XnwwjQZOnAbPJIDszA5SB0dIMiTh6prvfl5kaCFUUUJ0yk7MXmjn7NM97vV7eeOMN/H4/JSUlTJo0CUsPX7eu6xjv7hEfYgwD6ipceJvsuNKiFI1WmC0LKSoqktFbvSTXHKIz0j5ER4Zy2+g0yd2yZQvnnHPOie/37dvHXXfdBbTPG7rxxhu7dJLs7Gyam5tPfN/c3HxSD+3b27y7ete7t4nH4/ziF79g8eLFzJ07t8PzLF++nOXLl5/4fjBXAxvs1cqS4YjnVQyVIMc6fWi9N0aM7Mr/EbWPxhvt/Rq3XWkb+3aF8LXFmLfUhdfr4avPH2OSEWJOUYKvVZhYu63jOWu5Kce5YOy/KG+Zyt93uoFdAMxu3seX9/6DgG7i59NuZE/jaPh3P859I8GsoleYkLOFen8p649fTmRr4EQ8vbXqUBOrDnXvZ2E36/z4wlJMER9NkXfmNhoJxYZVfmLhGOe+/l3M2dmor9xFi2Y+5eedUteA1TBoTkkh/tZzeixGzoHDJCwWGvPzUO/6POyud7ePXVuDaBoUlhrvtBll4GjdhKv5BRQavpzLCKedO2BD6LsqkUjw9NNPU1sb47LLLsOal4vxq+/CkQPoX/ou2qTpyQ6xT1iB0ad5PB6Ps2nTJrZv347L5eLSSy9l1KjelXQbyn9XgoH2dYFbW95eFzgdTX/nZudQfV2DxVBuG6L/SfsQHRnsbaPH1ZW3b9/Oq6++yvXXX09ubi7FxcX84Q9/YOzYsezZs6fLw4bHjh1LbW0tDQ0NZGVlsWHDBm699daTtpkzZw4vvPACCxcu5NChQzidTjIzM1FK8bvf/Y6ioiIuuuiiLp1PDE3lrRtJtRaSYe/v2r19y9G2CVPCR1v+JwYkkWjzJjiyP0JxmYWcPAuNgRjHPWEemKxoNVtZNqeIZR3EETdaOd76X0xaHivGfob3j7ODUuSsfYa8Nx8nnD+K2iu/yscz+rfUbNxoo8b/R0LxQ2TaLuCszI+wpNRELKF4Zr+H3fVBzs5zcsnELCzvXbunHxWlWclxndqLtveNEC3NCWbufoCUXBf6rd9Bc6acegDDwNXUTDg1hbjDfuKxrGMVaIZB89jRKHPfzJsNhwwqj0UpGW19q6o2mKJNpDY8iTV8jIhjPL7cyzHeOxx9EFBK8corr1BdXc2KFSsoKizA+P3P4OAetBu/PmwS3I7U19fz8ssv4/F4mDx5MosXL8ZmG3prgveVxroY2zYGUUpxziIX+UVDvwdfCCFEcnWa5H7uc5/j4MGD3HvvvcycOZOrrrqKdevWcezYMcrKyk7qNe2MyWTihhtu4O6778YwDM4//3xKSkp46aWXAFixYgUzZ85k+/bt3HrrrVitVm6++WYADhw4wNq1ayktLeUb3/gGAFdeeSWzZs3qzesWg0ww5qExeICpOR8eWktDGFFcLWuIOsYSc4zp/fGUQvO2YvX5O3ha8ebmBBYzzBhnYPX5qa7ycUOOQaZm0FxSwIz00yRftM/DXVN+HxDm/NG3k2HPRoVDGA/fC9s2oJ27BNc1X2RKP19sN4eO8lrFvUQTPuYWfZ6yjIVAe8GnH62p4mBzmE9Nz+EjU7IGRVuoLo9y7FCUsooXKMiOoN/8fTSb/bTbOrytmOJxvDnvDO1Jr67FGgziKSt9J/HtA0cPRDAUjJ1oe6v3dgMpzS+hNBNtuR8hnDobNA1VXYHasBKMwTMfb7M/yv5AjHNdFs564zWMl/8Fu7agffzT6OcuSXZ4/ea9vbeXXHIJZWVlyQ4raZRSHN4XYf+bYVJTdeYsShm6xdOEEEIMKpo63WTY9zAMg+eff55169Zx5ZVXMn360LjLXlNTc+aNkmSwd/8PtP1Nz/FG/T/44LifkWrr/+IifcXRspbU5udpKfocMUdZ7w6mFBkVlThbOq6sutfrYl19Fkvzm5mQfnJVpKjTQdP4sR32Jr/Z8CR7Gp/inMLPMCbzPJSvFeMXd0JNJdpHrkVbcVm/J5VHW9ayrfZh7OZ0FpZ8iay33rNjLWF+uLoKXyTBVxYUMr80tV/j6Ko2b5z1L3pJaznCXGMV5hu/1vE8UaVwHzyMZigaJ44HTcPZ7CGjshpfbg6+wr5p1263m5rqBlb+r438QgvnzA6S2vAE1nA5EecEfLkfxnhr2SXVUINxz20QDMAgmd96IDWHlflnMbGtngvqD7ev4qzpaMsuQr/0qmSH128Govd2KP1diUUVOzcHqauOUVhqYfo5ztOuNy36xlBqG2LgSfsQHRnsbaPHw5WVUmzevJn6+npKSkr45je/ycMPP8wrr7zCddddR1ZWVme7C9FlFa0bybSPHlIJrmZEcLWsJeIY3zcJbnklTm8ridGjaDGd2psRCitePxInJ1sja1YeTZpGzDD4waoq5hS5+MCYwg4T3Dr/m+xpfJqy9EWMzmjvKVOPPQh11e1Db6f278iIhBFnZ91fOdzyCrmuySwovgWbuT2R3VTl4/9eq8FlMXHPilGMyeq73s7eiEYNtr5YiymSYJZtJ+ZrvoF2mp/L26yBINZQGG9x+8/BEgiSXlVDODUFX0EP18TtwPHDUYy4wZxRW8iqfAWlWWjN+ziRlBkn2oBqa8H41V2gDPS7fo2W358rHHdNVVUVrz79NMWFhSy79AuYOnk/h4t4PM7mzZvZtm0bTqdzxPfeAvhaE2x5LUDQbzBlhp3RZ9kGxagNIYQQw0enSe5vfvMbGhoamDRpEk8++SSTJ0/mq1/9Kjt37uSee+5h8eLFXHzxxQMVqxim2iK1tISPMyPvk8kOpVscra+jGwEC2V0btt8hpcgsr8ThbaWtIB/72DFET3PXbOeuAAkDps5NIfbWkL7tNX62+DU+WJiFMp/+1zkU8/J61QOk2QqYXXgtmqah9u5EbVqDdtEV/Z7ghmJeNlT9mqbgQc7K/gDT865A10wopXhqr4dHdzYyLtvOt84rJssxIEt3n5GRiPPGv/cQ1IqZa1mD4+ob0fTO12J1NTZhmEyEsjLRYzGyjpeTsJhpGVXSp3O1Y1GD5vIqLp/2HBnBWsKuKfhzLsUwv9P7rcJBjPt+AK0t6F/74aBIcD0eD88++yzp6el88IMf7HWCG4vFqKqqGtTVhBOJBJs3b5a5t+9SXRHljc1BzBaN+eenkJ0zOH7nhRBCDC9nLDz1xz/+EbPZTDQa5f/9v//HlVdeyYwZM5g8eTJPP/30AIUphrOK1o2ARmn6vGSH0mWaEcbZsoaIcwJxe2nPD6QUmccrcLS20VqYTyA3h9P1Y9bXxKipjDFhqv2kOWtbq/1YTRpTck+/rJahErxe9VviRpjzi+/ArNtRsSjG334HuQVoH/xYz2PvgqbgYTZU3kc0EWBe8c2MSp8PQCxh8NvN9bx6tJVFo1K5dV4BNnPnSeRAUfEYR/76CnWOeUwy78V9+ZnniZsiUeytbfhzc1CaRtbxCrR4guazxnZ486FnwSVo2fUUF417GWWy0Zp7JZGUaScl0Soew3jgJ1B5FP0L/w9tzITun0YpGuvjhAJ9k0BGo1G2bn0Dl20sc2bOob5aAyI9Pl5Tcw07d60hEGzrk/j6k93mZN45HyAvt5S6KujN6z4TT0MrPl//Hb+3WlsSlB+JkpltYs5C14mCaUIIIURf6/Tqa9y4cTz++ONMnTqVXbt2MX78+BPPWa1WPv7xj/d7gGJ4U8qgvHUjua6JOCyZZ95hkHB4N6IbIQJZvejFNYz2HtzWNloLCwjknn4dsnhcsXtbkJQ0nXET3+kFUkqxtSbA9HxnhwninoanaAju49yiz5Jub19nUj33b2ioQf/K99As1p7HfwZHWlazvfYRHOZMlo35DplvVc1uDce5Z201extDXDnNzRXTsk+bRGpGBFfzy1jCx/otxlMYirpKCwccVzAq7RBzpmxDq9p2xt1M0RgJRxxLzE7ugfGYw8VEU/aS2rSqT8PT4kHMCS9V4UnYplyOMp1cZEwphXrkN7B3B9q1X0Q7+5wOjtSxRCxO5WMrydzyFGnRjueHd1fJ219serDHx1CAUgncKsEkTUPTTLx8zreIWgfHHO7TsZjTqK+wUF8RGoCzDcQ5eqdsnJUpMxzoA1g1XQghxMjTaZL7la98hZUrV7J582ZKSkq44IILBiouMUJUtG3CH61jWu7lyQ6ly7REGKd3LRHnJOJvJY7dZhhkHq/E0dZGa1EBgXdV40001qHq6058f+CIlVDQyoIZAbTGNt6uFFfri2JqqGZxnhvV3kV0krroQfb6/kOZbTZl4dGouipUYz3q+X/DtDmQlXPa/XoroeLsCPyXo5HN5FnGMS/lE9i8JhRVVPgT/PCNMN6o4utTbSzKDUN9Ne+tfmeNV5IWWYWufERNxZzho6pvKEWgoom1DdeSam1j3pSDKHPaKbGdbj9L3I9hdqLFizCHi4k56ok5w0Ban4YYjKSy7chSCqbPIsd0aiEp9eSjqNdXoV16FfqiC7t1bKUUkS2biT72CCVtVYRzytDn9O4zX6GoqqzC52ujuLiEtLSevx+BoJ/q6kqi0SjZWW7y8vLRdRMXfmgc2E8/kmGkycrKwuPxJDuMDuk62OzSeyuEEKL/dXrl+HaRDCH6g6HivNnwBOm2EkrS5iY7nC5ztK5HN8I9n4trGGQdr8De5sNbVEDwrQRXGQbq8T/R9Mp/T2zamjqKY+fcRUnNKjJWPsS7B4/mAb8B2AzvHVQacmlsuiqFtKBixj9fxYi/evIGu7di7N5KyGQlrvVdAhl2wRvvN+Et0Bm9LcH41/cRVt8lDOxLL+PeSZ/AlojygzcfYfyrlafErVl10s7Pwzkji3hzhObnq4lV7+mz+DpjaCY2zb6DWKaTeRdkEEw/c6XfWFQR3F3LJFMzW0NuZtqbaUzY2NAwF9XQ9z1VLc0JUtOsuPNO/ZkZr/wX9cITaOe9H+1D3Rtlo47sJ/KPh7CU7yPmzKXto18l48IlZ5yHfCbr169ne0uERe//MO4eLvsWi8XYuHEjO3fuJG3MdJYvX05xcQ9vLg1zrhQzobAkkUOCUmhGCM58G61vxOxoicDAnEskkQllGhzFG4VINqn4IJLmWMta/NF6FpV+BU0bGhdmWiKE07uesGsKcVvHZcs79O4Et7iQoDsbAJVIoB79DWrDKzhWXEq4dBxKwZu147HEDSbNz0Fb9LWTDvX4m82E4gbXzsg5+RQYbHI8R1xvYqm6FMt17cPA1aE9sOYFWLgcJk7n3y0u/ulxYdC9ZOzCNIPpzlMvzBxmP4Xph5lOgrqW0fiKsnjzIydv8y2TwWyXjmP6dafsr5vrsDh2gBYiHhlPG2ez45w8Gqc6uhVfTxlKI2KYWDg+QpGvHnydb68MRV11jFJLkKaolUnWFsKGzjpPDhEF/XHx6nDqzFmQjaadvHyUsWV9e7XsGfPQPvm5LleqVbWVGE8+Cjs3YVjTOTzjWoo++SEyM3t/kbR79262b9/OtGnTmDlzZo+OUVNTw8svv0xraytnn302CxYswGrtvyH2YnBRjXWop/+GOro/2aH0MYWe8IMR69HerrluXDO7ubrFMcg581ZiiIs6xuAtujHZYQgxKEiSK5IibkTZ0/g02Y5xFKb07AI4GZzedehGhEDWsu7vbBhkHSvH7vOfnODGYhgP/gK2b0C7+EpSr7+FaHMzRw9GaC0PMWueE9uoRScdKhBN8PiRQ1w6KQt9Zu5Jz71Z/y+amuqYW/R5MjIWtp/D34b65x9hzAQSV93MbzY3sMbTxsLSVCbndi2JNCuDhbFWJiSCRNHe0wsbR6EBZ6FhZ3KKBinvTfI0LCYTGicPLVVEUJZtYD4CRjqEz+Noyyg21aeRUBpj0kKYtIHp7ch1RplgDkFL59spIB5TlFjBZAKrXcccidE0fizznP2blLvdTpqa3kly1f5dqD//H4ydiH7j19D0M1ctVp4m1H//gfHaKxhmG4fHfITowouYuiCrT9YqPX78OKtXr6asrIzzzjuv28vDnNR7m5bG5ZdfLr23I4gK+lHP/gv16n9B19GmzwXTMLlcMSJYQ0fQDCsJawmqByNpokWTMNzjurWPK8VFwC89ucNd4q010oUQkuSKJDnieYVQvIV5xTcNmfURtUQAh/c1winTSNgKurfzuxPckiKC2e134VUkjPHbH7cXCrri0+jLL0XTNEJBg/27Q+TkmyksPXXu5c66AAkFc4pOLjxU69vFvqb/MDrjPMreSnAB1BOPQNBP4GOf44evVnGgKczV0918dMrpiz69lzkcJvN4BeZEBH+Om0iKCzQwjASHPa9QF9hNlqOMidkXYTZ1fYkUc7gSp3cbmhEmYn8fjfoMtpVDkwdysmDWVI1UV8qZD9SHzpDfogw4tDdMiyfBmLNslDhCuJo9tJQWE+vnBPeUWKqOYfz2R5BTgH7LnWjWzt97FfChnv836tVnUYZB9ZgVHCi+iHHn5jFpvLVPfhcbGxt5/vnnyc7O5v3vfz96N4c819TUsHLlSrxer/TejjAqHketeQH1v39AwI82/wK0y65Gy8xOdmh9whI6RnrtX4ESWvOvwnCO7dFxYm/96w6X203oNEvTCSHEcNWtJLepqQmPx8NZZ53VX/GIESCWCLG36T/kuaaS65qU7HC6zNmyDk3FCGR2sxf3rQTX5vPTUlJE6O0EN+jH+PUP4MgBtGu/eFKhoN3bgygFZ892nDbx2FodIMWqM9H9TlIVjHl4vfoB0m0lzCq45sTj6tBe1PqXaVtyMd/YDa2RCLctLmRBadeKADlavKRXVqN0jZZRxaRU1ZDS+M7FUg4TgAngB/x1HR6nY3NJKDhYncq2ZoVJUyzJ8zIxPYBW34PD9ZDSgii9a0V75qYCqbSvBhOBOpOiqqYCavozwnYeu4NwOAThMOqN12F6KdqMuXBsbcc7JRKobeXkbF+HHonQNGYWewo/QdiVjbuoCV9oP7t29U18W7duxWq1cskll3QrOY3H42zcuJEdO3aQlpbGhz/8YUpKSs68oxjylFLtQ+b//TA01MDEs9E/dgNa6Zhkh9Zn7G3bSG14ioQlk9aCa0lYT19NXwghRN/oUpLb1NTEvffey/HjxwH4y1/+wuuvv87OnTv5/Oc/35/xiWHoQPPzRBN+puX17xqtfUlL+HG0biSScjYJW17X9zMMso4ex+oP4C0pJpT91vzYNi/Gr74LNZXon/sG2ux3el3Lj/qpr44z6Ww7zpRTh54aSrGtxs/MAhcmXXvrsQQbq+7HUDEWlHwRs96eXKh4DOOvvyWSns2XTPMwK7hnxSjGZnVhzqVhkF5di6vZQ8TlpKW0hMyKSrRYnL+Wb4FRR9B0g8CxQqLerlfNHZ0dYcnoADazYnu1gx1VBWi2WWh6Bka8hnDsDV7xh3mly0c8Wbq/hTG1RzEn4l3a3mTTyJ9uJ3eKDb2HQ3Xdb/0bMPa3/p339kf41k43VwlF3ca9HE/NZte4S4hlzSIab8Zbu4ojNdE+Dc1isbBo0SI8Hk+XK/1GIhE2btyI1+tl2rRpLFy4UHpvRwh1/BDGvx6Cg29CQQn6F78N0+YMmRE+Z6QMXM0v4vKuJeoYR2v+J1GmgR3xIYQQI1GXktw//OEPzJw5k+9973t8+tOfBuDss8/m0Ucf7dfgxPATifs40Pw8xalzyHYMnbv0J3pxs7q+pEpzbZT4sUaaIhrBzGJizU5ojkDQj1r1HJgmol35RUgtgYORE/sdO+gjLV1nzITTDz094gnTGk6cGKocN2KsLf8pTcGD5Dgnsr/pfye2zVt3iOKaCv67dArnjl3J9HwnzSGd5uozvN6EjXPbzsIVd3HQUcM+RyVTj5eSHyrg6cAaGsdvw2Vxc07eTaSe1bUCXHoiQHbr87jC5UQs+VSlXkxLwI3JY2CxwoRpZnILyoCyLh3vFI11mJ99DNOujSiHC5VxhsIsJkiZaCV1qg3NCsEjMQIHY2Ccfv5vNKKIxxQWq4bFqqGUIhwKEYt1LZlOppDZwrrRs6lJyQQ84F3Zb+eKxWKsWtX99YFTU1Ol93YEUc2NqKceRW1aA6npaFfdhLZ4BZrpzHPKhwrNiJBW/xi2wD6C6fPwuy8Cbfi8PiGEGMy6lOQePnyY22+//aS5VU6nk2Aw2MleQpxqX9P/iBsRpuZ+NCnntwSPYo52d0itwtm6kXDqDBLW3DNvbSj27w5zeH8EcLX/awAIvbWFCQovbv+yDqgLnbS/2aIxa74LXT99T8a26gAaMKvARWPgIOsqfkHMCGLSrPijDfijDQA4vDGmr6mmfLSd+DQfpbYgLeF3jjNeMzFds/DeSy4zZuzKDtZyfLYwOcQpahuDK1pAyLyXJXlVnK8XYzOnonmfOOP78TYtEURTCfxZK6iILuCNjRGCfoPSMVYmTbdjtfaswrZqbUH97zHUuhfBZEb70MfRV3wYzenqYAcDu287Ls9KTPFWIs4J+LPfT2J8Prz/9Lvs3x3i0N4Io8dbmTLTweEjR1i1ahURS4RzzjlnwBIzlUhgfvafRPfvRr/0KrQJ07q0X5s3gXl3iKK4YuwkO7n5p87zTracnBwslsEXl+hbKhRsnxe+8j8AaB/4aPs/x/Ba61iPeUmvfQRztB6f+2JCGQuSHZIQQowoXUpy09PTqauro7DwnR6bqqoq3G6ZUyK6LhjzcNjzMmXpC0m3Fw34+TUjQkbtw2iq+8s2KM1CMPPMvbjRiMH214M01sWZlO5nwlQboZy3qijXVGA8cA8YBvpNt6MVl532GLl5brzejod5bq3xM8Ft5pDn7xxueRmAsvRFnFv02RND/NpCMar++m1iqoFdS3/AldPOQn/rOT3mJbXxSWzBQ8RsxcTfTtyVwhoKYQ5HMMxmIi4XmHSsMRvOwCji1gDHQ7WEW9IZ67J1u/CJUul4W4p4c3celdEQTj3IXNcB3J4WWHXqWr9nPF4sCgf3tP8zEjD6LJg8HexO1LoXT7OAj8LmbCUluxKLNUw07KK1eQLRcBqwocPzHAmXcig8jhJrDaNr9/DCG14O+ULk2CxcWuLG7a0G7xm6xk96H+BYpITaWO5bFam7IRIG/QJY/Am0eAZ0YQlhpdqT3LSUdOYsdJGWIT1Jg5mKRmDvTujicPt3C6emonxnWPsqiVRzI+qFJ8DXijZvKdpln0LLHn4L25jDFaTX/gVNxWgtvI6oU+qYCCHEQOtSknvxxRfzk5/8hMsuuwzDMFi/fj1PPfUUl112WT+HJ4aTvY3PoDCYkvvhpJzfGtiPpmJ4C64lZu9ez5vSLKB3PkewtSXB1tcChEMGC0r9TE7zUV9YiFXXUUf2Y9z3PbDa0b/2A7SCjpdDMZs77tH0huI0BY/zgbOe4nCLB10zM6/oJkrSzz2xTUVrhOf+/j9urNvL0Quv5rIFE956EQp722ZSmp4HVHvvQvo80HRM0SiZxyuwBkP43dm0FeaDrqPH47gPHCZhgboxE/j7n7YxviWKxdP9ilB+ZwH7z5pL1JLKmIr/Mf7o05iMaN+tJnv0ABw9cNrjWYocpJ2Xh7XARbw5QsvaesIHfcDmTg9ZXryM/RMvoKBuI86KJ/h74QQiJgvnNh5jVsNxTKhuxR8z2dk15bPU544nvfUI1pi/O68QAFNBMYY7s1v7lI6xMulsO5Ye9paL/qcMA7V5Leqpv4CnsUfHaO3jmPrFWVPQb/0OWtn4ZEfSL2y+naQ1PIFhSqOl6MYujf4RQgjR97qU5F5wwQWkpKTwyiuvkJ2dzdq1a7niiis499xzz7yzEIAvUs/RljWMzTqflCT90bf7d5EwpbbfVdf69mK/ujzKzi1BrFaNJXNNTPC04M0rBF1H7XsD4/67IS0D/as/QHN3vXDVuyWMOGvL/8n7z3oRNHBZ3Cwtu+Ok93N7jZ/frDrCz/Y8RbiwjHEf+QgAesxDWsOTWENHiDrG0pZ7OYalfc6qra2NzPIqUApPWSnhjLfW2VOKzOMVmOJxmsaPpXLfXmyWUUTGXM5r43tWOCUtXePcmRbSMz4KdG/IujIM2Loe9exj0NwIE6ahXXpVpxVYTbFGUlpfxR7aT0JPoS39PELFM2GGiTO1gKqKBHu2x8hyR2lMMdignY07O5vLzl+KO7v7S5r42gy2bo4RDCgmTzEzeuzk7hfX0TRyCotokqVAhhV18E2Mx/8M5YehdCz61TdBVvd6OBv8MVoSZgKBwbsWqjKZCWflg6ZBeVuX9mlubqa6uusjJZJplKuBElcDrdGz2NdaSnz3QeBgssMCwGKxEov1bZG5oSzVlCDfIu/H25YtW9bt5d6EGOy6lOQahsG5554rSa3osTcbn0DXTEx2X5qU82tGGGvwIKG0c/s0wTUMxb5dYY4eiJDlNjF7vpPiquPELRYiKS5sO7egXnwKps1Fu+gTaGY7eDvvb9EMhb3t5AvA5mgF61v+QihRSyqpTLTOZ6L7A5iCFgi2ooCt1X7WHm3lG9UvkB714fjo1zC1+bCEt2MPrAZNI+R6HzH7DKwBDWjFGgiS0thEzG7HM7qUhO2dYldpNXXY/AFaSovxeoPs3amTk76ItLQ446bau52gmUyQnWvucK5xR5RSsGc76sm/QNXx9iTgmi+gTZrR4T56wofL8wr2tq0ozYo/60KCGYtAt3ZpgHBNZZSdO8KY7NXsPbqRSCTM3LlzmTNnDqYeFMapqYiyc0sYs1lj/tIUsnNliXIBqq4a44lHYOfrkJGNdv2X24fxduNiUynFi4e9/HF7A3EjDqfMsh9MFFDbg/2GyNSoNjcwOdlRiC4oSTQyO1GV7DAGjWAwSErKwK5JL0R/05RSZxxt9+lPf5r58+ezaNEiJk6cOBBx9YmamgFYtLKH3G73iOmN8YYrePHInUx0f4jpeVckJQabbwfp9Y/jKfo8cceoPjlmJGKwfUOQpoY4ZeOsTJnhwBHwk330OE25uaTU1WPvZkL3XnHibNI3skV/HSculidWMEaN6zim+ioa/v07UqadS8Z555OwbwJTA1o8Hz0yF029U4zJiIQIHtlDxG4llJF+UvJvDQZxeFoIu1xUh2zU18QxUGTnVzJtdhiLdfAXnVOYCKXPJZB1PsrU9T/e9bUxXl/roS28hZbWo7jdbi688EJycro/d9AwFPveCHP0YITMbBOzF7hwOHt3k2UkfXZ0xho4gN64me7P5k4+IxAlvPEo4Z2VYNZxzB2NffYoNGv3EtRwQue3R0azpjGHmelePj2+GosWOvOOQ0RbWyvBUAh3djZm8+AtSqYl2jDF2ghmLCacNqu9p3qQyczIpMXbkuwwBg2XRSfbOXjb1ECTvyuiI4O9bby7XtR7dak74c477+S1117j3nvvRdd1Fi5cyKJFiygtLe2zIMXwtbvhCSy6g4nZH0paDHbfbhLmdOLdnIvbEa8nztbXAkTCihnnOigZbQOlSK2rpwEz1UfrmOTUufuQj1BWbrd6j81mM/F4HJO5mpT0pzGbGrAaTkxGOr+sTsWs23Gd5mJ4YpaVpa//EdIz0D86h3joeRQmgukXE3XOOHHhpWJREmtfJPHyUxA8/dDGyFv/ALKB/EIHqUvzsJW4iHsi+Pa1DlxuYbO1D00eNRb0riYBJsKpZ58Ykt1VTQ1x1ryyn6bW1zFUhHPPPZdzzjmnR723kbDBtg0BmhsTJ26C6KbBd/E75ChFrHor23ebqG67ONnR9FwqsPhd3+/o2WFKgKvNQCCPivIMlk1e3evQBoNYLIbVaMSV5SI97d2fSIOQZiGYcSkprsHbCeDOdpKqBv/NSSGE6CtdSnJHjx7N6NGjufrqq9m7dy/r16/n+9//PhkZGfz85z/v7xjFENYUPEyNbzvTcj+KzZycoTBaItQ+VDljfp8MVa48HmXX1iBWm8bCZSlkZLX/Gtl8Pso9IZ5pMvh6sc6O8gquu+xCNEvnBaveKzMrjTX7HmR/07OYdCtxA1zOXGpar2JrK/zlo2NwWk5NuoyVz6AHa8j4zDlYg6uJOCfgy/0whrl9jq0yEqiNq1DP/B1ammDKLLxLPkAsNeOdgyQSpFbVcrTVxrFWB1k2D4vyV+MeZeAPa1SFZhDInAYLBm7ujrI7wWym2+WcIxG6MzSyzRtnw/r/z95dh9dxnYkf/87MZdQVs2TLtmRmiDF2wA6nDbRpmxS23ZS7hS3slnbblHb7a7dNmlKSYtI0DbNjx05MMbNlWxYzXOZ7Z+b8/pADjiGSzOl8nsePLd2ZM2fmjuR57znnfXcTTTaRm5vH8uU3jmj0FiA4oLJtY5xMRjB9roPy6uHdA4YTy6SzNG87SENHDSZFY9wEExbbRTAaIwSirRGxazPEo1BaiTxtHrxTLeeTOOJP8WprGJMksazGS7lncJlBUfE0Qu7aM9nz80IIwaOPPkogYOHDH/4wwnrimuEGg8FgMJzMsBeGlZaWUl5eTmNjIz09w603avhnIoRgb9/fsSoexuYuP2/9sMYPIKGRcg2tpujJ6LrgwK4kzQ0Z8goGp55abUeDPSHYdaCHu1vgT2MkwrEohVdfiTTMup/+ZBMvbb+fQKIVhzmPRNZPlXcBs0o/xhee7WBioemEAa7w9+HoeBb3x8YizGnCBbeSdk0DSRpc07p7M/pjf4budhg1jtRtd/JSazdt6zefsB9uq8aysTFmlCfIahKrDrnY2OIko/UwWNz33UpixozZXHLJnBGN3gohaD2SYd+uJHa7zMLLXHh9F/IayYuDrgvajsQ4vDdBRi2jpqyf0TNrsNov/LXN4kg9+j/uH8z+XV6NfMtHkSZMH1FbWU3ngR19PNsYYnyBnX9bWHLMlMv8fCcDAxf/dOXDhw/T1dXFsmXLsBoBrsFgMBhGYEhPCPF4nM2bN7N+/XoaGhqYMmUKN9xwA7NmzTrb/TNcxHrj++mL1zO9+EOYFdt564c1tgfNlINqHflU5XRKZ9vGOIF+jVHjrEyYansjgZIQgqd3dHH/EZW7S1M4ZQv9M6YPK8DV9Az7+x/nYP+z2FIwc0Oa/M4WrCYPFnk3qvg8X41mybEpaGuPDZpMOTI582xYFuaRMtUQK38fusk92LfD+9Ef+yM0HoSiMqRPfp16q4d169cjhGDhwoXk5eWhadB1IEJ/WGNOxX6mFB9AEhp9PR46ym7g1eY1VFVVMWXKlBFfwwtVKqGzf1cSSYZ5i4soKRl+5mQAVRXs3Z6goyVLYYmJ6fMcWIySPaetvzfLgR0xIhGJYlcvk6fK2MonIep3oz31IKjDqyebtGok7SOba++NmFD0YUw5V1XoaAZvLtJHPo90yVKkIU+5P1ZfLMtP1nfS4E9x4/hcbp9WgOk01/xfiDKZDOvXr6ewsJAJE4wkTgaDwWAYmSEFuXfeeSe1tbUsXLiQr3zlKzgcjrPdL8NFbnAU9xEc5jxqfMvOWz8kLY4lcWQws64k0XgoRdCvDbud4IB6wqmnWU1w7+YuVjdH+bTUypzccsIlRWiOoZfY8Sca2dL5WyKZLqr3Zxi1P0vKY8ZSPQnr0anG/ZEMLSLFrDIn0usjuZLAWZXCVZ1EqBKh1EwyE987OHrb2Yr+2J9gz9bBB+zbP0N8ylxefuUVWlu3UVZWxuWXX47X66W3K8v+3WGq3Ye5buxGzEqK5P4QsVAl0u1fQ7S3o+s6kydPpqrqzCTtulAk4hobVsdw2mHBMhcuz8gCkHhssEZyJKRTO8nG2AnW4ZcHMhwjHtM4sCtFT2cWlzXC0jEb8I5fgOaoQrQ0DJblcnmgZGgfXmVMGvWjAhypCCNG+NnD8g2VuJPDm3ouzZyPdMUNSNaRf9C3vTPGzzZ2oQn4+qIyLql0j7itC922bduIx+NcffXVRkkTg8FgMIzYkILcX/7yl/h8vrPdF8O7SGd0O4FkE7NLP44in781c4NTlXXSrin0dGY5sCuF3Skz3JmodqfMnEV2vL43f2QiKZUfvdrB/v4Ud3S9wh3L5pBx2IkXDm0dp6Zn2Nf/OIcGnsWWUlj4fBzd42bbbUVcOuY7OMxvjijev6adrmiGJdfXAGBKd+Hu/QfmTJCkczIHk9fiD5gwr22jYNsj5Bxeh26xMzDnNgYmLac70MqRvz6EEIKxo+ZTWjye1gaJeDSCV93LdWNexWmJEM+W0Pl4E2l7HdIH7kQK6zQd6cVmycfjKiEcHN6o2YUskxbs2pJAzQqmzHKgaWJE5xeP6uzZngQBcxY5KSq9CNaIXsCyWUHDgRTNh9PIks7M0vWMr2glWvYhNLMP0deF/ov/BrcX+es/QfKe+v8mTc/SEHiJA/1PoupJRuVcSqlnOtKQikkdyzV+PIp87malaLrgoT0DPLLfzyifla8tKqPE/e5d3x0KhdixYwd1dXWUlJSc7+4YDAaD4SJ20iD3wIEDb0wV6uzsPGkx9kmTJp2dnhkuWrrQ2dv3D9yWEqpzFp7Xvtiie1DNuaTkEvZuj+L2yiy+0j3sWq1v1xZO8/017QRiaf6t/u/csnwZssmMv7J8SOUjBhJH2NL5O6KZLkY1mpmyKkjwiktYP2oPK+o+g0N6M8BNqzp7exMsH5MDQsUZWIMjuBahOOj23sHGXeXEe0LUtDxNVfsqkCSaq1bQWH0dSVliYPcakplObOYi8j3zURNu2poylHmaWVj2CnmOPnQ1l5eP3EJzeDSMOnrgl1NH/zGOEt84Nqy++Nf6ncyO104v66gnR2HWAgdOl7H+dqSEELQ3Zzi4N0U6JRhV3MPcgkcx5ZQTKf5XhGxFRILoP/8uCIH8he+eMsAVQtAe2cye3r8Tz/ZT4prClKL3k3OGMqyfbaGkyk83dLGnN8HlNV7+dVYRVtO7e2Rz3bp1KIrC/Pnzz3dXDAaDwXCRO2mQe9999/HTn/4UgHvvvfeE20iSxN133312ema4aLWFNxJJdzK//LPI0vl76Je0GOZkEwnfYg7uT5NKCmbOd552gLujK8b/rOvEko7zvT0PsGDpFVg8uUSKC1Ftb47yRMMaZouEzf7mg6mqZ9jf9yiH/M9jF04WPqdS3C1I3/llNkgPUOqawej8+fj9/jf22dOTIKMJlhVHyG1/CFOml6RzGnvCV3Fgmw5qikU77sIR7yY19VJiS27F4cnD03aI5r2b0HWdaVMWUDN6IpIkYdc7KVVX4RbN6LobKTWfJqbh2/o3fKZXkN97BzgHp0PG4wnWrl3DxIkTqa6uPq3rdqHQVDi0L0UyqTNughW39/TuUVmWyC80oZiGfl9l0jrBgAbvWKX8eMlYnEhkuKmmL2yaKjhyME04qOHLlVhau4Yy0xYSOYsI560ASUakEui/+B6Eg8hf/j5ScRnoGaQT1LLqTzSwq/cR/KlmcqzlXFrxRYpdR9d36qnjtj+bBDLIwxt93d+X4H/WdxHPaHxuXjGX1+Scnc5dQFpaWmhubmbBggW4XOcnE7/BYDAY3j1OGuS+HuAC3HPPPeekM4aLn6ar7Ot7jBxbFeWe2ee1L9bYfiR0etSpNDekqaqxkJs/8mysQgiePRzkvm19VKYH+Mae+xl75Q1YS0eRsduIvWWacmBAZcu6OL48hbmLBx/YBhINR0dvuxkdrWTyX/ZhLhqF9J/fYFv8z0gJiRnFdxy3lnNXV4jPVu5gbmo/uuKizfUxtu4tJhQYXFs81f88zlgX8ue+hWvKbIjFePnllbS0tFBaWsrll19OTk4OSmYAZ2AltthedNmBrs3DnKginJeH89ffxxmPIX/tx0hvSby0e3c9iXQ7EyYvw+sd+TTJVY0hHtjRRzJ7rorrnpwQg7GlJIG0e/B7blnwwXydKfbhR50C6G8e2rYS4EDGJSnk6jIvdI6kRNGJaxtf7OJCY5uI0tSfgP5aoI7BK3YYk67ytT1/YHKoiR9P/jD9O8N81n8PC30dx7QR0rNs1CI06kmcKFxu8lErBHLfI9B3Ps4K1gSq+NaRS4e8vRCDJajzHSa+tKCEco+V9vDQasTG5ATBIW57IdE1nWfXbUXOKSZv1Pghn+/FTpJ4o/yTwWAwGM6sIT3x/+QnP+GrX/3qcd//3//9X77yla+c8U4ZLl5NobXEs/0sLvkK0hmoSXs6bLE9ZEwF7NjlwGoVjJ8y8rV0qi74/bZenm8IMSdyhC/U/w3f7Z/GbXYjgNBbpin3dWfZtiGOzS4zeYYdVc+wr+8fHPK/gMOUy6K9VRSt3oM0ZwnSHZ+lI7WL7thuphV9AKcl/5jjmpKt3O74G5W5IWLOWewauJLDWzWEGAxwp1QGKFv7JNLsRTB5FvX19bzyyivous7ixYuZOnUqshbD2f8k9vAWkEyoptlYQtUIk51gaR7x3/0YQn7kL30fqaT8mOO3tLSQk5OD1+sd0XVLqzq/2drL6qYwkwrt1BWcv6R1Qgi62rIkEjolZWZcHgUzOjNJMJc4VgRdmFFHsFZzKCQVlKwEAoQsyFoE0crhrwG22aykUu/CIEAJcEliH5cAadckNNPRqchC55JV9zEqeJhtV9zOhydpTDc/iYbClsxsEsKBSoo+9uHnMDIKRUwlnzp6VRO95/WkIGTP4T0ThpaxO57R2NkdpyeWZSCh8qNXu4Z5tCF+2nIhkgdLvL30fNt57si54zDLPHTruPPdDYPBYHhXGlKQu3///mF93/DPSdXTHOh/ggJHLcWu81tqRlajmJPN7Ii8n0hIZ+Z8B+YRlnOJpTV+vL6TPT0J3tP7Gh9sXYnpC9/GlxKQVYkVFaDaB7Mpd7Zm2Lk5gdurMG+Jk6h+hLWNvyOa6aHGPo9JD9ZjbtuHdMtHka64kayeZEfPn/HZqhmbdyUAfr+f/r5uHOHXsEZ3EslYeSR8A4mBYuLRfiQJTGaYOMVO0RO/QVdMBJdex2tPP33M6K3PY8cRWI09tA5JqGRsU7GGx2LLmojn5RIuzEf79Y+gtRH5099Aqqk75ryz2SwdHR1MnnxsfWFJi6Nk/byTgXiWP+/qpyeW5fNTPFxRY0eWh7euV1ajmFMtw9rnRIQOPV1ZEnk6hRPNuL0ylrgLa8SLrCtkbUlinhAuy5mfBhyO29nVMIpuvw+3I8H0sS2U5gdxA58YQXt2m4Nk6vTWEF9oZC2JLboTrcBDvOBqNJMNGLxXxMvPIOK7sX18KdcUHEDSU2QcdSS8cxklmTkY3sTB0FpUPctYz2wm+y7Dbrqwprsu4dT3vaYLXm2JsKo9RIki8cFpnmPq3w6Vw+kgEb+47o1MJsOuXbvweNzU1Y0/o23H8JGRLtxqEO/GElAGg8FwoThlkPvwww8DoKrqG/9+XW9vLwUFI5lqZ3i3agquJaWGmV/+ufNePsUa20cs7WZvUwWFJSZKykeW8bYrkuF7azvoi2X4XPNTLA3uQ/73u3DYvZjaO1HNZqJFhQA0N6TZtyNJboHCjAVmDgQe4nDgRRzmXJbwHgp++Q8A5H/7JtL4aYDGvp6/kVUjLK74N2QEO7ZvZf2GTW/pwesju1uOP8f6bkZ11PNKaS37nl+JyWQaHL2dMhFHZCvO1peRtThpx0Tk1GScAzJZm5WBqjLSTgfigf+DfduRbv8M0rS5x7Xf2dmJpmlvrsUVGvbwazj9LyGLdx5JzAW+99aKQ8MdlDrDxrgAJ0jZauSuyUjCjZD7UO27kJQB7AngDMYHadXKzu6FHOibilnJMrd8NeMLdqDIOoROr23nGenhyYX0LNu1KPETrHc9q7J90P6LY783ChjlAQ7A67ddeD2E1+PXs8TQqJZtLDAXkJvugp4/n9s+nyE3WeCmt8Z4w690BpEz1ZtzSIHlM4/+W9t6RpuOFN5CyjPjjLZpMBgMhovDKYPc15Pf6Lp+TCIcgPz8fG699daz1zPDRacltB6fbRQFztrz3RUs0T2s7bwGkJg804EkSYhsFvHoHxBdQ5sOt9dcxP/kLEJG8F/1f2W8HkT+2o+QCkrw7j2AAILVlQhJ4vC+FIf3pygqNVE1rYs1LfcQVQNMlp3Ml6xYrFvhM69HfI9C46MArACwlkHXfbQGzGzcnEddYZppVTJdLGDvETtmXcZsgWwGCktMVIyyYMokKfvtD0mXVFL1oX+lSpYpyM+nQG7F2f5zTNkAGdsoVPt8XH0SkhBEiguJFeaDLCMe/zNi08tI192GvHj5Cc+/paUFk8lEaWkppmQr7v4nMWe6STvGkvReAhw/Mq6JwfW3r7XHKPWYuXViPl7byBI72aI7scV2E/NdjmrOf+cdTkjQ1pRhoE+ltsxGmWRCyUioFkE8Vydrzwfp8hG2fWK6Ds3tLg405JDJyoyqiDFxbAirdQxRxpx2+x6Pm0gkegZ6eryUlmBv5DUOxXahSApeU+5ZOc5xJDjufkolkLUYkkVGoCAUB0I+dv2iW7ZziXc+xfZq4LQ/OzinElmdVY0hdnYn8FoVrhqXQ23+0Otrn4jH6yUSDp+hHp59gUCAdevWMWbsWCYereZwJqnW4jPepsFgMBguDqcMcj/96U8DMG7cOC6//Mw+CBreXSLpboKpFqYVfeB8dwVZDdPZY6UjWMmEaTYcThmRTqH/6gdwYBeMrgX51FOXVzrH8bucSyhVw/zHwCqKynKQb/sqUm4B7q5uZF0n6fWQcdjZtyNJy5EMZVUKVD7G2raX8KBwvW00uY0mUu3NpMoqkWYsQDINjigLNBoCq9D0DOPyVpDJSPxtbQMep8TVy5ew9VANHa0aJrOG3SRjQmLuJQ7KqweTP+l/vgeRSmD+8vepqRyNOdmCa+DPmNOdqJZionkfwNlvw5FIkXY5CFWUoVkHAwR9zbOI5x5BWnQl0nXvP+H5CyFoaWlhTHUxvsCT2CPb0BQP4eIPkHZOOmGZpIFElv9Z38XBAS/X1FbzgemFmBWJzAjeQyXrxxrfT8o1lUTeZSNoYfAc9u9KkWoPs7gySk42hWqxEKgqIpXjHVKpp+Hq782yf2eSaFgnr9DExGl2vD4fUDGi63BC+flkGDhTrQGDNZsPB1ZS3/80qp5ktG8pEwveg92cc0aPMxSSnsbR8DAO836E2U0ibymJvKVwinrbZ+zangNCCNY2R3hgRx/RjI/r62p4/+R87Gb59M8jJ5+MembvjbNFCMEzTz9MPJnHZVOvIWN599b/NRgMBsO5N6Q1ua8HuMlkkmg0ihBvZiAtKio6Oz0zXFTawpsAiUrvvPPdFaTgATa3X4bXqzNqrBWRiKH/4r+h8RCUlMM7jORmNMFCvYkl0otYTNIbaYj073wWgLCmEwY0k5ndYz9Cd+EcioIv0VX9NPGgYLLsZOo+SK07QCyVQbrxQ0jLbz5mCnd9/zPsSXewsOKLpNzTefrpp4mnNK65+iaeX+cindIwecEekrGaJS5Z7MbrGxwRFUfqEa++iHTFDUiVozEnm8jpegBdcREpuBlzrIScVj+6kiVYWU7Sl/NGQCe2b0Q89FuYOgfpg5866bTyUDDIOG8P14xtxhRRiecsIpF72XEjaa/b1R3npxu6yGiCrywoZVG1Z+hv2Am4+p9BIBPLv3rEbXTuDVMX76eyMoVmMhEqLiWRl3tWgtt4TOPArhQ9nVnsTplZCxwUl5lPen0PHz5MY2PjiI5ltVpJp89M4imBQHW0ksrZhTAlMCVLcQSX0t/mZS2b3rmBM0pQ4+5lZm4TdotK5FCKVcwn2qwBq85xX86OkGZmXTyPTtVOkZLiCref/N5m1p6h7Fhn8t4428LhMH19fRQVFbF69erz3Z13vYvh3qisrGTixInnuxsGg+FdYkhBbkdHB7/4xS9obW097rW3r9U1/PMRQtAW3kShsw672Xe+u8OBAyaSWScz53iQYmH0n38HutqRrns/4umHYOZ8JN/x68mzus72zji98Syjc61MKnAcF6TYQiGUrErC6WG7Yyn9FONxr6F5wlN4ZIXr9QJczdWkdRvSApCmzEIaP/WYNmKZPvb3P06ZexZlnhns2LGDlpYWlixZQl+HG13TKSg20det0itl+PDyAmy2wZFnoarof/kV5OYjXf8BTKlOvF1/QlV89Jtuo7gtjEXzE3R46c0pRBMmOFpqSLQ1IR5+DOouRbr5k0ghARyf3deqdlHgf5jayWGiUjkD7hvJSEUQ5LjtdSF4ujHIE0cClLosfGZ2MaUuC0H/sdtlM4Le7iz6ENYZmrUBPJk8EubZ5AUTeOXAO+/0NiZVZY4lSdoh0yjn0SFy0Htk6DnzD3nplE57cwZJhrrJNkbXWlGUEwe36XSatWvXcujQIZxOJ5YRjF4pioKmjWTB5rEkVwCl7DCyM4KecKM1zSQTyyNBFkY4UmyWNUrdyWF/jmCWdWaXDVDiSpHqTtG+1s/TJQtIWjMj7suFREPioFTCIUpQ0JkuWhit90PozJ7dmbo3zjYhBOFwGEVRyGQyDAxc/O/xhe5iuDfy8oaWhdxgMBiGYkhB7u9//3smTpzId77zHT772c9yzz338OCDDzJunJH63gDBVAvRTA+1eSMfdTtTQj0hDveMYWxlP1409J98G4L9yJ/9Jvr6leB0I//Ll5DMxwYXPdEM33+lg67SDHfOLmbq2Jzj2rZEouQ3tRDHxEsD5QT9KlLFk/R4VzFFcTE17xoyecvRJptPsFp1kBCC7V1/QJJkZpTcTnd3Nxs2bKCmpobykvFsPJhgoi/GOHOcZJWGZIbitjfXX8Y3v0yssxXvjR/D1rYfSXoWMJEKLKHSPEAoY+LFngK6kjYg9bajF8LMbw7+85U0b2bxGWSW08wsW8fYgh0khMLje0oIZD/A4ILJ2HHnkhI6a/UQHSLDGMnGgqSHptcyNJ32pEs3Ne7pzM6P4DX1k1TlYac/0iWJHX43uwMeMrrM2ZzQKklQVmVm/BQ7NvvJp8F3dXWxcuVKotEo8+bNY9asWcjvMG3+RPLz808rKIiku9jd+zBd0R3YTblMKbqTKu98pNmnUfJL6NiiO3D6V6JoI1svrMkuQmvjJHf3Y/nqj7m5rHLk/bmA7OmJc++WHrqiWRZXe/jYjEJ89kln5Vine28MldA1SMSRXCObsfHqq6+ya9cubrnlFgoLC89w7wwncq7uDYPBYLhQDCnIbW1t5Zvf/CYmkwkhBA6Hgw996EN8+ctfZvHixWe7j4YLXFt4E7KkUO6ZfV77oeuCPdtTOM0ZxpZk0X/8dUjGkb/435BfBDtfG5zi+7YAd39fgh+92okuBN9dVsGU4uNz10qahq+1nbgq81RPEZFEhnTFAzi9+7jBPg5X6UfJ2MqP2+/t2iKv0RPfy/Ti25E1B88//wRut5srFy1m56th7IpMrSeOcCh0hLOMclnRlcFpylrIT2zji1jGTsZSW4PEkyBgILgCTfWQ8LjoL8ilvFzmrT0R0TDikftBCKRbP4bkzjm2U0KQo+2hOPMiJuIMyDP53bpOKqqnMmfKiUuxNIVT/GZ/P9GMxodqC1hU4j5u1DsUUGlrypBKCrw+hcrRFmz2UwzxCUFl8jXykhKS7iOhWDniLCJicQ57irGiQEGBiSvPUYmOU2UT13WdLVu2sHXrVtxuNzfffDMlJSXnpF9vlVLD7O97nMbgGhTZwpTCWxmbtxyTfHprIQfXgz+DOd1J1lpBtPC96MrwyraITIbMvb+G1i7kL/430jsEuEIIVjeFWdMcQdfFKbc9n7K6oMGfosRt5r+WVTCt5GznxT77wg0b0P/8S1JKhnU3Df9nUwAiV1C0TGJt/zbof+d93NZSrhrzo5F12GAwGAz/lIYU5JrNZjRNw2Qy4Xa7GRgYwOl0EosdP7pj+OcihE5b+DWKXVOxnufalI2H0oRjNhYXvwy/eB4A+St3IVXWoD/1EOg60uIVx+yzujHEr7b0UOi08K1Lyyn1nPiB393VQzQp8WRHLiktS6rqt0zydjE171Yy+VeiSu/8o5RWY+zs/gu59tHU+C7juWefI5FIcMdllyPv7aE3XkCFJ428cDb3b2/kH+0qf5ozGqwKQgj0X/4ZFAXtY3dC/B8IVWVlywfoCReQHK3REchA4Ngs6CKrwr7t4KqESTOQAlkIvPlUWaAEuN61lgpLJx3ZQp6KXc3BoJMOcw59pjIOth7/BKpqsKEtQr7TzI+XVjAmz3bM69GIxoFdSfq6VZxumTmLHBSWmE4ZCFpicTxdHVgSuehKmlBVBckcLw5J4sKtcvnOwuEwL774Ij09PdTV1bFkyRKsViuarhJKtTL4yD88amSAcCI0pG2FpiF2baY3sIND1QE0WTC608v4Jh+27A5gx4gq1QDINoGnLou9TEdLQvCgmVRXHzCCJSyBfujuQP7U15HGnjrLbns4zb1betjfl6TSayHHNqT/xs4Lkyxx2+R83jMhF6vpNEbKLwDZeAj/3+8ib9MhsnaZ8DXzGJ9fM6wgVyA4dOgQiXiCyVMmYzYNrbSb1eQeabcNBoPB8E9qSE8HdXV1bNq0iUsvvZR58+bxgx/8ALPZbCQIMNCfOERSDTLNe9s5Pa45Hsfb0Y10NEjQNJgY0ZhWncIey4drPwKFJUhJHXHgIP1rnsVcXUduMAbBBoSAQFJlTkplyViZQqeG0t0K3Sc4mICBUIpnO/JQyWIa9VuuzbPhLv0WGWvpkPu8p+9hMlqMJSVfZfeu3TQ3N3N17XhqU1ke7s3DpMD4ZQW4cyxs74pRl2/HbT1afmfHJti7DfnWD+NLPoWiBlnT/j5aQvkkCg9yg/IqNQWhEx/4jS7uPuHLMc3CvZ3zWRmsRUcmmUqRteSiRkGKnbhw7PxKN5+cXYzL+mZ5oExG5/D+NC0NaRQTTJhmY9QYK/JJ1qcCmJJJPN292CJRdDmLZt1D/+gb0K05p76YF4GDBw+yZs0aJEli+fLl1NYOltZS9TRrW36EP3nk3HQkd/BPabeFyftduGMmTqcgsKSAs07BVTcYtEX368TrNYR2GlPCrTakj34BafrJE9elVZ1H9vl5vN6PzSTzmbnFXF7jRT7PNbn/GQxsfBDbPx6hIKoxMLMa3wf+k2rP8JNONjY20r2rmyVLrmNq2dR33sFgMBgMhhEaUpD7pS996Y1/33bbbVRUVJBKpYypygZaw5swyVZK3dPP6XEd/iCmdIq0a3D0OBjRyKqCvEAzmiJDzXg4mtQn3bgfPRbBuuJ9qGYzuqbTGEwTTGkUOc0UeK0gcdIRrX3hPra3V6ErCUaN/iOzKhaTyV2GKg29/mt//BBNwbXU5l1NOmBi44YNTMzLZ0ZuKU+25xHOmpk8047VKjMQz9AYSHP7tMHkWCKZQP/bb6FqFL7xfkypbjb23ES938WEqsdY6mskKrzEfEuRjq4GFkJHbN8APZ1IM+YjlZ54+qeQzSTdM7mp1sVNDE4DfeCBBygqKuKaa64Z0rnp+mAt2oN7U2QzgqoaC7WTbFhtJx+5UtJp3N29OEJhdEUmnqdgTT1MLP9ydOv5T152Ot6aXKq0tJQrr7wSj2dw7aIuNDa1340/2cj04g/htgy/jqfH4yESiZz0ddHXjb5uJXS2gC8PecEV2MbNIXdiNZxOJTghsMZ24fK/iKKGSbmmEMtbgV7nQ77hNNodgh1dMX6ztZeeWJZLR3n46IxCSMV4bdMmdH24K7bfvex2O8lk8oy1Jye6qdr2JMXNEcJ5ZuqvuYJ0/hTY0wA0DLu9w4cPk5eXx+TJk89YHw0Gg8FgOJFhz/OSZdkIbg0AaLpKR2Qrpe4ZmGTbO+9wBlljMRJOF+2l5fS2ZznUlGZ048OUp7YiPvtjJI/3jW1tj/4O2ZdP98IrMB9q4dtNgsY0fLlYYomShVj2hMfIkOGFeBNdnUuQLEGW1L5A3uhPk7EObwRD01W2dd+Pw5zPJBbz4LPP4rVYmVg0kye7CxGKgsMJlaMHg/Ln9vcAMKt0cP2eePKvEAniu3MullQzWweupS/dy4cnPoFV1mizLsJWdjkcXVsphEA8+BvE2p1I7/s48uTrh9zXQCBALBZjzpw5Q9p+oDfLvuPqwp48+JezWdw9fTj8AYQsES0sIFbgw9f1S1SLj0TOwiH39ULU1dXFiy++SCwWOy65lBCCbV330xXbxRjLrcRaC4iNYLJwxJkhHj9+P5GIwb4diNYjYLEhTbwJRtciaTLUB2hh+Fmqi4uLKS0txZRqxz3wDOZUG1lrGZGi95O1Vw+7veEKJlXu297LutYopW4L37tscM18Z2cnzz77LOl0GkUZ+odN73aSJB1T4m/E7QiNaZn9TG/oQhKwfXwxO2yT0LuArj0jbtdisbB06dIRJVwzGAwGg2E4Thrk/vKXvzzlGrrXffaznz2jHTJcPHrje8loMaq888/pcZV0BlMmy72tGk++1sjNSj5RNO6rWoJgCbzUz+vZTMrivfzy8F7+MmoFjz3RAoBTkbhrppdZ+VZCJzlGV+Yw65sPoHfcgM3exTUTdpEd/Rk0efjr/w76n0FPRbnFfCdPr36FSDrN+KLLaFLKGD3JzJ6tSabMsbOjO84TB/zs7UsyOd1FZZ+KvrcTsfoZnFfVIrXX0xivo1B+gSn2MG2tBViLr8AqfJh278JkMqMUlSPqd0MoDO/7JNKU2dA3hMwuRwU7OlhUVsE0bw7WU+yXSQt6u7LEwxrjbDJFo014vApkk9B34n2UTBaHP4AkBIn8XKJFhehmM47AGkxZP6HSj8IQ1jZfiIaSXGpf3z9o6NqM0rGYDe3NQPNZ6IkMJUez3vf4oWfjabU2d9ZUapX12KM70RQ3kcKbSLlngHR2gxRNF7x4JMRfdvWT0QS3Tcnnpgm5mBWZ+vp6Vq9ejdfr5dZbbyUnJ+es9uViciYy6IYPrUX/8714epMEanw47vgqc0onMrSPvQwGg8FguDCc9ImyuHj40+gM/1xaw5uwKC6KnGenHMbJZP1hAAJWO7fbgKyEI7iaL05IkSi48pjMrnUvv4QuK5SuuIbPZwW2eJyaGaMpzrGfcFViVkuyu/dvtDYILD3vJd/dyvK6tcSq7oQRBLjRVDfmnjY+on2C11q7qPcPkOeaRcm4UdROtvLyi1FarEle2DNAR0wlNxPljvZXuaJrM2LTmyWA4s8fJA44aBk8d8BFC7AVncFiQFLVOHKv/TB4i2DB0XJOXT3D6q9XNjGxZgz4g++4bYEVeL36R4J3XOYpgKTPS6SoCM06OOosZ4LYA2tIOiaQto+FMzAKda6Fw2FWrlx5XHKpt9rb8Ryvbd5NqmsKipJl9uzZTJ06FZNp6PeUpKexRnfiskAimRgcsWtvRjQcgGwaqaQCxk5CcpyZVF2SnsYZexlzVCOes4RE7lKEbH3nHU9TUyDFr7b00OBPMaXYwadmF1PqsSCE4LXXXmPLli2Ul5dz9dVXY7Od2xkk54KkqtiDYSQx/GnYcjyBMx4f0XHVRITgC/fj3LEXzWFFv+kWqqYvH/ywexgflL2TlNeDZj3795HBYDAY/rmd9AnrlltuOZf9MFxkVD1FZ2Q71TkLUUYQ/J2Ogc4AFuDKcCuH1emMTu5gwfJuJIudYHnZG9uJdAq9fiPSrIVcMb2Sov0HyRS6CObYT9huT2wfWzt/T7ZjPpaBK6nIa2dZ1eNEyv4VoQz/YfoP61q43dnCpfJSXu6LsLKxEZu1nD3OUp453Iar3kRUy5KUoCrWxRfaXyEnG+Ol4jlM+OACxrXvwtazHsdUH6quIEkaG0LVeErmUuO04PAHsSST6CYTCV8O6ViIjt9/D8ZNRr7z35GGmLn0del0hj/88Q9MnTKFefOOTQAkhKCzLcuhfSkyKUE01UwovgtNf3st3pPThUA94frJfCAI/HJY/b2QWCwWVqxYcVzt8HQ6zZqNT3N4fzuIAiZPmsScOXNxOodRSkbo2KK7cPpfRNEiCCSsQgACfMAcF0huIAX6thOVNB4hibR7PP78q9HNuWeq0ZNKZnUe2tPP04eCuK0KX5xfwpJqD5Ikoaoqq1at4vDhw0yYMIGlS5e++6YpC4E9FMbT2YWijjTnNXjfeZO3HVaQbNpPfN0zWOMxXJPn4Z17BbLVBt29I+7HyWhWqxHkGgwGg+GsG1J0sm/fvpO+NmnSuR3FM1wYOqM70USGSu/Js6GeDXu6Y8yTMnSkVDqDRdjtUcbfXIel9yWirmuP2VZseRWScaSlV2GJJ1BUlWTO8Y+AWS3Jrt6HaAqsxdnzUcz+6dSU9bO46CFihTeSsRTijx9CF+qQ+xnwp/k3p4SQbTwRSrLt8EEUxU5yzEySKZ10SCdBlumBg1zXsR5nfi77F15LY34ltV4XY3OTOA78Hu/1FQghOBgr5MGexdx5eR0VQT/2cASt0EWsqIB4fh6i9Qj6n/4PisuR//XLYLUNuzhNW1cnqWyW8upqxFsCiMCAyr4dScJBDbMtTbd4jNy6EGXmoQfRprRKUePAMQ/vklCRtRhCtqGf4zXdZ5IsdLKjC4lJq9jRsAoAXRN0twnaGjW0rISjKE7tOA9O10EOdR0cctte0ownhEfKEBZm6jUf4ZZ29IFecDiRasZD4dmsuStg4Nmz2P6gnliGfb0JUqrghvFWxhfYMSsSO3tAVVWam5tJEGfcklJyC7vZ3ffgiI4jqTp52xqxBC6s8ncmXSY/4wbdQr+cxW+JkZWGH+gqJhOaOvTfUwBK3wA5zUGihXakj38e0/hLGfpHV8MnjPW4BoPBYDgHhhTk3nvvvcd8HYlEUFWVvLw87r777rPSMcOFrS28CbvJR4Gj9pwdU9UFL+3pZkURNPRkiDtHMWeRA1d2HQKJtOvND1yEEIg1z0J5NdSMx97ZhS5JpD3H1lvsie1la9d9JNMR8vr+g6S/mNqxCea7HyDjmUrIMY6NLT+iP3FoyP00Cwu3qR8C2cGT/RINTZ34ZTPZ0ZewpyeBInQu7dnOVd1bKZ4yA9ft30Dy5VF3dP/8HCex39yJe1k+2Qz8pH0+sjyN/5og42xpRsgy0aJCYoX5CEVB9HSi/+K/wetD/sK3kWwjm67a0tKCxWJ5Yy1pMqFTvztJZ1sWm12iYnyAfZGf4vBFUGUbGu/8sGpK64zeGWfM9jjm9MU3FXmodik9tDotCAGJvnwibWVoGSuW3Aje0e3YrQn6deg/eVLkYzglhbk2L+MsTuK6xsvJEIezR+eDFwJlLjCZgUYINZ6t0zrrBIOlgTQdyjxgNcnIEnRGj74uBNlsFuERuPPMJOQYLaHDIzpWbkeGqavDePwqWas0ggrFZ4eCggkTOhBGRUPDBpyrj310k8zA1fPJv+5LKCbLBXNdDAaDwWA4HUMKcu+5555jvtZ1nUcffRS7/cTTPg3vbmk1Rk9sD2NzlyOd5QQ0b/XsoSDjjo6mHhKjmFgRpqg0B1vbHrK2KnTTW0Zpmw5BezPShz6NBNhCERIuN+vWJgj5NYScJFP0BGruRqRkBbbmr5HU7ZjlNDPs9xNO5fLU7kvJahrwKRxDPk/BFSUBfK4kT7XlsSnYRb21mLB5PK7eGDd1buSqVAN9eQvpv/zbjFmRf0yCNyXTT/aZr+KeZiYWgs+23sKXynOYYY9ACOL5ecSKCtHNgz+6IhxE//l3QJKQv/BdJM/Iyu8IIWhtbaWyshJdlzhSn6KhfnA8p2a8iVTuCxwMPoXiUpiafwe1hZed8r0XqSRizbOIFx+HeAymzkG+5n1QMJiZ2hHaiDO4mnDR+8k4xo6oz+ebaD6M+NUPoaSCaWNvJMcfZ1NbJ8FEinyXFXnCIRy5CS5L34QD1xBb1XBaG3FaGwFBLDWaRHoMEzExEUCSyJt/KYHUadSkvQBouuCZQ0Ee3NOPLuC2yflcPz4Xk/zmz0JbWxvPPfccJpOJa6+9dsR5IkQ8ivjHHxDrX4LcAuTPfhLb1Nln6lRGzJRMktPeiSWRJOV2Ea4oQz5a9mykRpp4ahiT5w0Gg8FguCiMaDGlLMu8973v5ZOf/CTXXnvtO+9geFfpiGxFFxqV3kvO2TEDSZV1eyL8qFwhlNKZFHiGwls+hpLuxZTpI5p/bJkcsfZ5sNmR5i7BEoujqCo7Al7U5mZqnVsIsQGtO4Gjaxb9vJ8ENqpoYJr8JHp9kKbkVAqS9yNLZnIdozHLQ/tAJ1/EKeyI83TUwf22UXSbXRQl/dza/DiXZDrI1kxgwLqM1IBEQXwzrc+/NVAUjHVtw1IgkQpKvBT5AA/UyJilOEmfj2hxIdpbHoJFMoH+i/+CaBj5Kz9AKiod8fUdGBggHo+Tl1PHmucjpBKC0goz+WNa2Ru6j3i4j1RvHvNHf5xxRSdfoiDSacTa5xAvPAqxCEyehXz9bVA1hr6+PtKBELIWx923FdVaRzxVAKnQiPt93gT60R/6PRRVkb7hDnYdPERvby9er5cly2bSZn6QrB5nXtnXUCylpN+pPSFwJPeTE16FSQuTsE8g6L0czXT8hxZpxUwqNcQh4QtQYyDFfdt7aQ1lmFbi4CPTCylwmlEzaV6faFtfX8+6devw+XysWLECt9tNKjW8SbRCCMS2DYgn/wLxGNIVNyKtuAnJYoVhtnVG6Tqu/gFcfQNkTAr+kmKSXg/o+jH9slgsRqkdg8FgMBhGaMQZg/bs2WP8B/xPqi28CbelBJ+t6pwcTwjB02uCLJW8lNo7iNbvpODaywCwxfYcP1U5GkFsW4e0aDmSzY69v5OBjJm2+hYWbf0u8tHkR0lrHltmXEPKZmXm3p9RNLCLLJAFSmnnzZDx1WH1d4+jgF9P+ThxYeJLB/7KJX17UF6fBDjQTsHrGx54cx/bODfuxUWYcu1oAzUopjncWiiR9LgJlRahvi2LrFCz6Pf+EDpakD/7LaRRpzca2nComxLfCvrac/HkyEycpdGq/ZHX+jdhk/MJ7Kiltmw+lSUTOLArSST8tvWCQofAAPT3gJoL074MhaXgcJI6mGRgwyE07a37vPfo39HT6vephOP7SGXPfOKcN5RNGPz7lTfvj3A0wN7I7zC5EgR31vK38DPv2EypJ8vVE8Lk52bpCpt4rj6XlkAQeOQsdfz8qz76hyA8fuDk2wWDQR566KHTO1jljMG/e2Lwhz+eXlvn0Pvf/34KCwvfeUODwWAwGAzHGVKQ+6lPfeqYrzOZDJlMho9//ONnpVOGC1ciG6AvcZCJBTcOqY7y6cpmdF55JUpu1EyuL4ZJBs0mI5VVgRBYY3vJ2kehm95cays2vASqinTpVSAEllCYxyMDjG++G9Wk0/aJqyn2XseW3Q5UFeZOzVK88Co8/XFaUVib7qTGt5Ta/GuQhzhNWclkyG1u5dWYzLf63NjVNN/d/VvE8htoqf6XN7aLdOqE2jSKJpmwuiU8dFEtb8ZDP1q2FiU+DpPdRZ9qI1pTiuY+fiKh0HXEA7+A+t1IH/0C0uSZI76+qaTOwb0pBjorsJgzTJllI5Ozgdf6/o4mMtTlXs/2F8K4FS+5rjmsfiaCEODJUZAkBkv+RMOIUAA0FRw+pJw8sNlRVZVw9wBZNQso2Kx2TIqErCcRkgUhn97UzHfiKxiFYsk/s42qWcTurZBOIE2ZA67B7L9erxdZgQ7lCRJSgjLteuqmjjllUxYpyRjLXspM3WSElf2pWXQqo6icJFN5iv2cTifxEZaJOR+EEDQF07zWHiWl6kwocDCzzIlFOfZnS9M0Dh48iN/vp6SkhDFjxgz7d4zQNKjfhTiwCxQT0tTZUDP+nPyuOiVNxxaJYEkk0BUTqRzPcR9cvZ3LNdQp7gaDwWAwGN5uSEHu5z73uWO+tlqtlJSU4DhD9RgNF4/28GZAUHUOpipHQhpbN8SJx3T2KHG+L3Yj9HIyiwZHcU2ZHkzZfiI5C97YR+ga4pUXoHYyUmklhPt4LP0C9tBmCjtTJG65maKKD7Dp1TiyDPMvc+JzxfC0v0jYo7Aq62fmmM9T6Z075H5Kmk5eQyN/C8ncHfTizca4a+e9FN7yAcyLr3xju2xGZ/X+KLk1CtMmx3D5X8AaP4QQ45Cyy7BmZbJShLV9RdQtLUAxnfjBXDz6R8SWV5Deczvy/MtGdG01TdB8OE3DgRSaBpH4AcrHh2mQtuLvOUKhczwzSz7KxtWHsUmj8Nhq6GhVqai2MGa8FYdNR2xYjXj27xAcgLETkG/4IFLteKLRKGvXrqW5uRkAt9vN8uXLKS0pIrf9F0h6Fn/lF0E+dXZmSU/jCL6CKdU2onM843Qd2pthZhwqRoHTD/iBwUBuTeIgiWwfi+3jqLOGgG2naExgTnUiCZVEziLiOUvJV2wMJSTPy8vD7/ef/vmcAz2xDL/Z2svO7gQ1hUV8fk4xY3KPD+7i8TjPPPMMfr+fRYsWMW3atOEHuAf3oP/lN9DbiTR7EdL7Po7kHdka9TNGCGzhCN6OLmSfmXhtLdHiIiPDsMFgMBgMZ9mQgtwJEyac7X4YLhKt4U34bKNwW89m2RLoas+wa0sCDcGzaoAP1ym4uiGTjr9RMsUa24NAJu2a+OaO+3fCQC/yTR+mK7qLbR2/QyXCVa/oiFFjSU1+P1vXxrBaZeZd6sTpBEvrr9G1JGuExtLR3yXHVjH0jgqBpbGJ7+5o5iW9nJxshJ9u+wW+WbOR3xLgAjQeSmMhzOLq13C37wK9HNRbMWdMqBYJzfYK6zomkz+hFMV04jIg+ktPIlY+jrT0aqSrbh7mVR0Mxnq7VPbvSpKI6RSVmrB6O+jreIJeTx/WjJO5ZXfiE/PYsXaATGQKboegeoyVmjobNouOeO1l9GceBn8f1NQhf+TzMH4q6XSaTWvWsG/fPoQQmEwmFi5cyOTJk5EkCXtwHaZMH6GS208d4AodW3QHzoEXUPQ4HSEz2onK655jOekEdi1D0O4kFQpCKPjGa/vNARotYeoyOeTEs/TwzoF5KKnwckMu/sQR4MhZ7Pm5pyPRZq+m2T4aSQjGJRso97fzfMPJ9zGbzVx77bWMHj16WMcS0TDikfsRm9ZAQfFgArZJM064rZLO4OnqxpQ+N4m7JKFjSmfI2G0ERleTdRjJGg0Gg8FgOBeGFOQmEgmee+45Wlpajkv+8c1vfvOsdMxw4YmmewimmplW9IGzdgyhCw7uTXHkYBq3T+ZPwT4qii3M2/J3LDOvIJZ7NIOyEFhje8jaRyOUN6f16WueI1OQw56ifbS0bcCSKWb6C0WYMztI3vx5tm5I4HDJzFviwmzViLX+PwrVMJtMbuZW/BtW0/CmCLZt38WvdndTbxuNW4tx145f483LQfrAJ4/ZLpuIkRt9iQWTt6Mk80B7D6a0DdVsJlhRhDnzPHJ0gKTuZaH5AdSO5PEHi4YQSgf86xSkEgk6f/dm+0IjoZ/6wT2SyGFfy2wGIqW47SHm1W3B5DnC+vgRnFVZ6sxFjFVncmirj72BGLpQEPp+bpl1BIclRWZNF4lXDiOCCfQSN/r7JyFG+1C1Vex96WV2NChoGshmlfHVKnPHyphNK0m3rgRAzfYRsJYSxwSJE5eBMaV7sIfWo2UHaE1YWd9kJ6dkAibTiNMHnD4hKNq3hUhLI30TZhGomXjMyxm9kYClmZzYWPTwLA4x9BHIiaeal3wSdruNZPI8Jk56B10pmVf9NhKqzCVSHwvy0jgVG3DydeOSJFFZWUGO04bo7RryscThfYhH/wipJNLVtyBdc+tgYqnjNhQ4+wdwd/eCJJF2uRjG23Ra4nl5xAvy4HxPmTYYDAaD4Z/IkJ4c/9//+3/ous6cOXOwnGaJA8PFqy28CZCoGMZU3qGwxA+hZAdIZxRe21NOr9/F6PIAa7MRwpqVH3kOYOvrQ5JlsPZgD3Uia0lM2QCRnEvfaEf099AV382O9/lIRzZRJa0gu3UUla0/Qr/ivexozkVWdOYudqGbQhxu/hlLhEq7OZ9RlV8c8vpbgGQyyaoXV/HCgJUO+2iKrDqf2P03CrJRlDu/i2Q7OmKjZ3GEN2IdWENpnhWRvQpTxoNmUgiXFRLPy0XW47ha9tMUrMCsPcsvn3Wg6cqxBxQ6qDJI1YP1Ufe8GdBmhEpKZE9R31JCUp2D023ZhFASEEmyvxfAhMx4zLqVzbqDDXoKic0IMqhaBq8d7n9JQ09mQHMgiqeTqgbNLEET6IdlRNYESCBryFYVSRYc6pA41CEYrIQqGJursqjKwmP1SUKpJ97h6pqAN8vF9KcO4Jt6iiHAc6BnLjDXBRw8+udYgf5KttbPgSHUDj59g2ucL2xpXEBfBB6PDPGaHOkEOod/qKmfhLwisFjg5e7jXpZ0HVMmg6Tr6IoZzWJBSOeyGmzs6J+zz2zqIKueeBbIqdw5u4ixecYos8FgMBjePYYU5DY0NHDfffed39EUw3klhKA1vIlCRx0Oc+4Za9cRfAWX/wX8iQJebnwv8ayNhVXPk7F2s/LA1dxespeSFx/DPmopAhV77GmID85d1WUbadfgVPqMFmdHw09pvd6B15TLvOI72fuSm7kHvwO+fA6NvolIo8bshU5iooG9TXfzHtlB3JyLtfILcIoAV8n0Y43tAwazAwcDQRoaW3gtO50OezlXlgR5T9OLFAUPo7xnGQ7HEQgcQRIatsgOlKxKMrYYm1SAUGQixfnEC/IRytFApW8TaxocbGxOkVJdjKquwmJ9c92iiEcRB3aD1Yo0YRqSaXCqb1ZL4E82oaoRHLIXh3J8HdFM0kwmYUEoEhZnFosjg/SWckhqBmIhE5Jsx2yGXI9CRg0z0B+l3GnDHuiHVBzdZiWUD3GnTjLjRaQ9KKkUki5A0pFMOprNiWZ66yiaoM4X47JyP+WuFIGUGYc7hz7eXMsvS4JcJYpPGQwC/FknPUkLAhmLxYIkSQjFTDS4cOg31TAIIJTMEkhqSIDbphwbpmYyiGQcyWwBx5tJwCypBM6YH1nXSNhyCGRGg/fcrLOUJQldnMsgbXgUCdxWBekcDJVKJgVcHk48LCswpTMo2QxCllAddvR3+f9hFouZTGb498Zb6xMbDAaDwfBuMKT/8evq6ujs7KSqauQlY3bt2sUDDzyArutcdtll3Hjjjce8LoTggQceYOfOnVitVj796U+/sTbrV7/6FTt27MDr9fLTn/50xH0wjFwo1Uo0001t3lVnrE1HYA2uwEoOJa5k0+HpmC2wYIEJj+96vvxSD3l2navzKsm0xsldMYOMy01g1H++sb+QzCCb6YzuYFvn/aTdIca3FjBxxQ/YuzVNaePjOKKdDHz4BzQ3alSNsRC1rWV3y1+51VKMSbYRLfsESCf+MZDVCI7Ay9jDW5GEnTdG6VQTT6TnsS9TyOcq9nBTdgeBLQewjc8npyaONLDlaAdlENORU2XYgZAvl3R50RsP2qlUil07trFn1wFSqpvqAhvzll3PuNo6BgYGBpsY6EX/6X+CpCB/5S6k3Hw0PUP9wHMc9j+LS7IyN+9mGtZPgOzxieDsJsDzlm9ovB6rD5LA7okTSdYTTx0h0adSGezh8r4m8lIxAlYnW4tH0Wj3IiVktLiMjIQkBqdTS5LEzJlzmD17Nmbz0XW2QmCJH8AZfBlzugvNlEsk9xpU93TeM+loYC90rLHduAZeQNEipFxTWNdWRNNr+xhTVsA111yD7R2yz56u/X0J7t3SQ3s4wyUVbj4xq5A8x5trhcXe7eh3fw9qJyN//ttIJvPg+/Hgb2DvNigfhXzHZ5BGjTur/Xy7/Pz8N+4Pw4lZojFy2jsxZTLEc31ESksQpgt99Pv0GfeGwWAwGAyDhhTkfvrTn+aHP/whY8aMIScn55jXbr75nZPf6LrOfffdxze/+U3y8vL4xje+waxZsygvL39jm507d9LT08MvfvELGhoa+P3vf88PfvADAC699FJWrFjBPffcM4xTM5xJreFNyJJCuWf2GWnPEViNK7CKoGUOG3ZOJ8enMGuBE5td5rnDQZqCWb6yoATrX+5FLq3CbLIScXsRypuBXFqNsbPrPlrDG/BqPhb8LUbeh77CQL9goL6LJc2Pk52yiF0DFbi9kCx4iPqetVxlryFfTxMqvhX9BKPSkp7CEXwVR2g9UjYPSbsRJTsYcA1k4fNtJppS8F9lKstNFfQ8+iQmbz55Cz+NnDx2PaAADoad9DrzGVc9GG2mUil27tzJ7t27yGSyTCjKMDq/mNJLPkhz62HuuuuuY2vKVhxNovO3v7+tp9MBCNtSFHgdBGM7yajhYb0PQmgkM90gNKqjA8zpbaIgFSVotfPyxCraqp1oqowSzKAlbZisaRwVPYBEXnYhS5YsefN3gtCxxg/gCLyMOdONas4lUngzKfc0kN4MMEypdtz9T2NOt5O1lhEsuJXnNxzh0KF9jB8/nmXLlqEoZy8giaQ1/rizj1WNYQqdJr51aTmzyo5diy2aD6P/+kdQVoX8qW8AEvqLjyGeGqzZKt3yMaTLrkM6i/00DJ+kqni7enAEgqhWCwM1o8i4jVI8BoPBYDD8sxlSkPvQQw/h9/spKCggmXwzIc5QSzwcOXKE4uJiioqKAJg/fz5bt249Jsjdtm0bixcvRpIkxo0bRzweJxgM4vP5mDBhAn19fcM5L8MZJIROW/g1il1Thp2Y6QSN4Qyswhl8maR7Bvv7VyD0NNPmOrDZZcIplb/s7mdKkYMFA/sQrUewffyrAKTf8rDaGdnGtq77SGsJJudcyoQXD6KUViAqnPRsqWd20x+RZYkd4z6AGtHIq/kjqdh+lnsuYUy6nXjOIjLOt2UNFyr28GacgTXIWStoV2HKuEgiWN3YQKNm4xmtmjTwo/FWplR66bn/QbKpJJsvu5Mrx9Qcd7q7j0g09MGya1wkk8mjwe1ustksE0qyLK0J43MobIi9B3+wh9WrV1NeXk55SQli3YsQCiAtvBI110NXdBehVCsWxUWZZxZuSxGZTJbelgpULYQv30o4kI8kQUGRCV++6ZS5brLZLFu3bGGGXWZKSz3O/i7CVhs7Lq2ieXIQSU5RlZ2CS53My3TiHrOXgoJmQFBonsGl464f/B0gdKzx/TgDL2PK9KCa84kU3kLKPfWY4FZWwzj9L2KP7kRT3EQKbyag1PHsc8/R3d3NJZdcwqxZs85aTVMhBGuaIzywo49YRuO9E3J53+R8bKZjpxmL3i70X/w3eHKQv/Bd6GpD/8uvoKMFps5Bvu1OpLyCs9JHwwgJgS0UxtvZhaxqRIsKiBYVglGqx2AwGAyGf0pDCnI3btzI//3f/+HzjazmYCAQIC8v742v8/LyaGhoOG6b/Pz8Y7YJBALDOuaqVatYtWoVAD/60Y+Oae9CYzKZLuj+vVVXaC9JNcDC8k+cXp+FQGp/Ain4MqJgAeaq22nf2kZZpYPqUYMfgPx+VQMpVfDvl41B/u5PoGIUOVXjEH4/3opyUmqUV4/cS0PfWvIlMzea8ylIHoHFJsAFvfczPxkm1N1B57Jb6As5mV/5IuPtfUABpNs5EC/ka3vGotEIgIRgSU4jtxdtx22WiMTnkysVENbg0QGVI4c30mopo9k9Gqcs+NVUF+MWTiP+6J/QG/bx23HvZcWypTirj71Xw8EMh1e1MabOwsFDu9m8eTPZbJaJY4q5tGQ/RblOpEyWnd1zqJnh4K8P/o3c3Fzu+NAHif/022QaduD5yvdpqk6yqel+NFeKJRW3MLPq/ZjkwQRwD/35FUyKC+QEpCYwe5aHGXNzsTtO/aOt6zpP/+E+3tu0jZJEGN2Xx4ZpJfQuTIM5xtSym5lZ+T4iqV7+uvV+RtftRJbMTMxfwAwtgyfVD03fAXQQOhJiMMWUpKCoYdwDT+IeePJtB82CpCBKr0Iqu5pkIMqjf/kL0WiUW2+9lUmTJo383noHrYEE/7OmkZ0dYSaVuPnqsjHU5DuP204L+gn+8r+RZJmcr/6A1KonSL74BHJuPu6v/RDr3MVnLQgfqovpd8c5kUphqj+E7A+ge9yo4+uwul2cIMfyu55xbxhOxrg3DKdi3B+Gk7mY740hBblFRUWnNX1QnCBJytsfFIeyzTu5/PLLufzyy9/4+kJem3QxrZ3a0/UCimTBw5iR91kInP4XcYZeIemZTdRzNV17e0nENSZOlxgYGODwQJJn9vdyw/hcXOueRutqQ/7MfyL8AVIOB3ubX2R79x9Iq1HmKh7G516J6ppMYOXjiPpdJD70NfbvSDDp5f8jVTGNPco1mN17acnbjqfgJrZ0WHipMUxu3mhmlQ8GiaNNrVzu2ECJHCeVno0cL8WJzMuanQ1pE/GWVfhtRTQ56yg0C342RsY+rpT+Ta+iP/g7dpXPoL52EZ92qsdcGzUr2LS2j0B0Hy+vO4iqaYwtL2PxmAyjHDvIyCXEsnk4RZD+7Fhe/vsfEZrKVbOmkfz9z8hs20Dkjvez1vQ4A4cPU+CoY1bpR/FYSwkFIgDs3dNKLJiDIEZhvptJ02zk5AricT/x+Knfjm0rX2DCyodwSNB7w2I2lOwHW4Iy1xwmF91EUg3x7O7v0RPfi4QFMwt4X46N3OghNNlFWinDonYjiyy6ZCdtKkVTck9ZJkVICinbZLRsDl0bX2P16tXIksxNV1xBod1Kf+OZz6Cc0QT/OBLnsSNxrCaJT012c0WlHTncRf/bZ3arWfRf/QACA0hX30Lwrq9AJIx02XVwwweI2RzE/P4z3sfhGvbvDl3HlE6fvQ6dR9ZYfLAsEBAuKyGenwfp1OCff0IX0/8rhnPLuDcMp2LcH4aTudDvjdLS0pO+NqQgd9GiRfzkJz9hxYoVx63JHcroS15eHv63PBz6/f7jRmjz8vKOuYgn2sZw7ulCpT2yhTLPTEzyCBMBCYHL/zyO0DoSnrnECq4HSablSAa7Q6KoxISmC36ztZccu4lba92I7z4ENXWY6qaSPbiblfrzNLXvwGct5UbZiddeS9K2FCmegvoBqL2KtsYCKg4+hEhn2Tz+X5DkONWjOphY+O8EEwobG3uZmZ/DzZN8yNlubLE1mDPdSNnpyKlROJFIeT0kfV6myDIta9fyKl4OOicxyga/qNIR40eRTcTRf/e/ZHIL+Z/qG/lwXS7yW4K71qYw617ZSjB6CCE0xoa7meVvYfRoH3aHl8SeIOFV9eR/ZiIt8TE0bHmUsM3B9S078Gx/npgJDt4xjUO5KzGn7cwp/QTVOYve+NAnEdfYtyPOocNt5LgmU1ubZk7eX7EE2yE4tLfk6hrg3wanV1fgZxrFCCFoSTWyvelbdIsMdmTmKR6mWFxYpVY4ulJB0WMoeoxsf4rwxn5SByPDviWKgQ++/sWul9CH3cI72+Uby+/GvoduRz5Lerbz4cZnyXlpMIvzSY8nSVAxGvHkX6FqDPLnvo1Udfw09IuFJRbH296J+V0a5AKk3C7CFWVoRnk7g8FgMBgMRw0pyH3xxReBwbW5byVJEnffffc77l9TU0N3dzd9fX3k5uayceNGPv/5zx+zzaxZs3jhhRdYsGABDQ0NOBwOI8i9APTE9pHRYlR5LxlZA0LgGngGR3gjCe8lxPKvA0kiEtLw96mMn2JDkiVWNYQ4Ekjxxfkl2Nc9hwgFkD/xFboHNvKE6WFSqTST8m9gfrIFc8aLHL4Ex0Dr4DGu+gAAOX076D2yhgMLvk5K93BdWT+lqUnQ0kMR8LNKEFIPeueLCFMncmYSUnYuDOYLBsAeiWCPRNjU1cFLvYIjrknMcuj8pEIjW1NJ0mJB/82PIRbhb8v/HSXrYOloLwDBQJTVL22hu/cgAo3qZJIFs6aQ552Az7UNkxImkqgjUTEa6ye6MVl2snqHnW6Hi2U1VZQvmEW33MZux2tE9WaqcxYxreg2rCY3MDg6fORgisaDaYKxfbjtY/B5I8zNfQJTpp+4b+lgxulTCPf2YNm6BrOu0TLRTNzppN9fRMomE7G3kJbCmIWDIn0CvqSd8d4erFKWPjWXxmwVmc4eSoIdSMEsoXYB5MMwprGoahZV1VAU+Wg25rMz9TcjJIQu8Zkjj1FhyuJWdMj3Aaf4nSIEDPRAXxfS+/8VaelVSPLFmVhK0jQ8XT04/QFUs5lgRdmbJaveRXSTiYzTccoZBAaDwWAwGP75DCnIPd2sxoqi8LGPfYy77roLXddZunQpFRUVrFy5EoArr7yS6dOns2PHDj7/+c9jsVj49Kc//cb+P//5zzlw4ADRaJRPfvKT3HrrrSxbtuy0+mQYmrbwJiyKkyLn5OHvLASugadwhF8j4V1ALP+aNx5GW46kkWWoGG0hktb4864+JhbaWVwgI37+D9IzprHLvpb28GYKpGIWjfoMVcEt2GO1yGo1WauCv7oU9f7/R8pZyK7i9zBh46/pq5xPi3081eUDmKeW0w+82hLh6UMB/nWyzizpJZTsKOTETchCIe1yEs/LPWYUqLu/n59ul2l11rKo1M5XZuaTsJrRrBbE84/Cvu3Ebv4ET/lzuHF8DmoqwbNrt9LUfAAhdEokB0sa15F438fw29KMcm5AklS2xy+hTy0FCWaZWni5IYfWYD+lpaXEinJ4zvwycVMDVj2P0vRNKN3l7O0+hBCQjLmJ+vPQNROypQtdz+K0Ci6regg5HeGV+ET6woFTvh3ZaC8VuzZgyajsn+Nmb2I8oUA+tQXbcVlDRFL5HOq7Dlu6iE9X7mBaXjsNcR/3HJnN7mgxM3r38pEjW3GnwzS6y9FPUVv4hLcDYjDdtGxCIKFmz8b47SAJqLAL8uxmJGnowZ00exHSe+5A8uW988YXqDcSMGVVYgV5RIuL3pUBrsFgMBgMBsPJSOJEi2HfJbq6us53F07qQp7jLoRgc0eMvniSPb2PkGsfRaV33nAbwRrbhyXVQsZeQ9o54Y1BO6EB+yXwglQl2NWdYHtXjJ9dVU3lmr/T3vAEO68rJCtSzNUXMNF9BbIygMMvAWaixUXECgsQh/ai/b9vsfXaX+I6spHqlpd4ZeFd5Np15l1TgqzIdEczfP7ZZq4sz/AfBTsxpeqQhJ20y0WktIis49jasvFkiq/9Yxvtcj4ratzcObf0janI4sgB9P/5D6Tpl/DH2R/hhQPd3FEQoKmhHiF0cr01LI42UrpnNVuv+hDxbCM3Tw2RyCj8eZuP3ujgKGuxO8uVdVH+tHVwVNFW2o97bAeSpBNvKSXeWjxYYxewmvPJdc3GZikgnR1gILIZVYswpngFV497mLi1j2eDKTRPdMTvdzbsJN5Sgjni5vLaGDPKk4RUG8815HGoRcORTXNj03ZyMkkSioUXqqbQ7cwZ9nFkWWb+/PlMnz79vCdvupid7HeHnM3i7ejCHo6QtdkIVZYdd38b3t0u5P9XDOeXcW8YTsW4Pwwnc6HfG6e9JvdTn/rUSV+79957h98jwwUrqwnu3dLD6qbXs/JcdvTvkZRwKjr659j9J0gO5isenugfYKBfBeCWiXkU6b1sUp6n41onPmshC/JuZ2xLCjURw5SxopmjDIyegGa3A6CvfZ7OqmXEQhpTGx9l89xvYpItLKpLoSkyuhDcvbmbG31JvuxJIienk7WZCZeVn7B2ZiKj8dWn6umQ87lhtJWPzi19IxgTsQj6b/8X8goJXH8HDc9uYkGqg8agwOOoYeasWUzY9TDqntW8tPB6yiyHuGFyjLSlnFT1B7lp/JvHC+35O3/bpODxuime10pYbaXQMYFpRbfjnlBMXl4enR39HNqXoatNxWqTqJ1kobSyioaGDPu3tTO97gHWKl10a2ksPhd1vvdS7Jx6zPkINDoi22jvWc3UlwLYooIHaq+mxTGKivgRJhU7mTv7EvLGluGu3YQr9CpCz/JQ90Tivku5dkUBy9a+AI//CUnNIkaNw/7F/+Y95pGtfZQk6azWv/2nJQQOfxBPVzeSEERKBj8EMqbwGgwGg8Fg+Gc1pCD3c5/73DFfB4NBnnvuORYsWHBWOmU4PyIplR++2smB/iTvn5xHrvNhwqlWltf8AGmoU1OFjqv/Geyx3cRzFpLwXXrMw7YQgm2rEyhm+NmSUQDIQCC1jReaf406SmGycwV1FbdS0NyOQKBkM2Tt9QzU3IgwDQa4IugntX8/+xf8kCkHfklz9Q3EbNVcVjSAUlKMJgT7DvXwNWeYOruCQCdU5iKRX33Ch/9QUuUbzx+mO2vl+pIUH7uk7i2npKPf/3NEJMTOK25l06OPUiwEbvsYRldNY/aCImwvPUj8tbWsnH0F86vamVqWoleeTzeXQ5/C66mO0skwL2/1o0sK9qoQqWARdTk3UWCqI+mHpNAIth6h8WAAIWDqBAuVoy2YTBLxeJI9jY+TO6OFtVIKp+xmlm85NZ7Zb5QUev0ad8QPsNP/HOlIH4sfSeMMCf4w6xZsVhuXJQ4wZ9Z0Ro8ejSnrx9V1N4oaoFcZy+d2TqKquIwvuxOIn3wDWo8MNjppBsrnvnXRrlEdikTWTyDZfL678Y5MjslwtECOkkqT096JNR4n7XISqihDs/4zFs8xGAwGg8FgeNOQgtwJEyYc972JEydy1113cfXVV5/xThnOvbZwmu+v7SCQUPnyglLmVSg8eWgb4/KuxGs7dTKj1/njDRzq/DWyFkEz5aFldkLvzmO2yaQFwTwNj1dhV89gsJnREviTR/D5NWaHZlF05Q3kHG7ClM6gK/1otg0EKz6CMA2OhorAAOl7vs3BMe8nz38QSxqaJl1LTUGGUb4MAUnCfbiJFakEWXsK1baXYOWlZB2j6OjooL+//5g+BTISf2oxE8nCfNHIdF85O3ce7beuU7r+efL3bWN9WR17uwZw2WpwuSYy/5JiyqrMiFeex7/qWV6evpAbp/dRkZOhTbmBl7fXoWlvZrUVQqM7uIpMVqYkdznW3sGETW2t0EaCfEc38ypWUeTqYmLtm/1TuwQH9Tjrk3Gk0VnAzEJKmaJIKNEdEN3xxrY9eoYNaogukaEgLXPF42nkcJbcmyv59qi9bznrbug42r6liCM5d/DJlxUKHAqfaX8B8aenwe4ASYYx45E//R/v2gA3nhmgfuBpmkOvoAvtfHfnHQnzv1BhXYyrbwB3bx9ClghVlJHI9RmjtwaDwWAwGAwMMcg94Y4mE319I5nCarjQ7OiK8T/ru7AqEnddUUltvp3G4Fp0oVE5jKzKR3r+RHe2nxxzHrpiBS123DbxpIZkBsmikH5LPDG5pZjal3vJ/dQNOBubUS0WNKUNYV9PLP8aVFslAHrDAbK/+i+6cmbQWziLBTu/x+apX8Xpklnk60fICgVHmojqEi0cpsy9i1DpbWQdNfT29vL4448fU5M5orjZ5ZmBQGNGdCc2Ncz69S0AKLrGFe37yI/0s6ugio7qZZRbJtEiKdTMclBebUHs2ETbk4+wc+ZM7pjtx22T6PLcwaubSjE5ItRNjyLLkFZjbN21Bj2jUlgep7amFbctA4CsJ/CmtuDIHiEt2WjOuYQMFlQ9S0eygdbEQdJ6klyThVlyIX2hG3CMTXDkLddVFxpHYntoSR/AKjsoTM6k9m/bERGVDTMvIRWQGeOrYdy4ccjyW0blZTMhUzXfeLEDWU/x9U33YO1vgRnzYe92KK1A/ux/Io1wivKFLJENcKD/KZpDawEYlXMpo3yLkYeRqOp8KLcV4Dh4BHMqTTLHS7isBN08tA+iDAaDwWAwGP4ZDCnIffjhh4/5Op1Os3PnTqZPn35WOmU4N4QQPHMoyP07+qjKsfKfS8opcA4+LLeFN+G2FOOzVQ+pLV2N0JVqo8ZayrQxPzrhNom4zuptEWprrYyvsb/Zj9Yj2Or/RM4HvogcSxAtKkA1BXGHNpC1jCPpHZwWr659Bu1v97NvwvvpKrqSKR1/42DVe8la3FxZHsIqdHRVsMfioT+2kiW5TUQKbyHjnICqqrz00kuYFDvFvquRJROdIstOPYpZzzI1soXR7rnYLIUAmLMxZu2+h9zIAPvHvo+OyhUUOU3sNsU5kIrzuZpiRMMB9j7yF3rn1PHRGUFks5O+gg+zbr2brNJFqvpnbA0k3jhPczXkHr2cB2J/gOM/AxjU237Ml7KAZXohNWY7Kxtvpa30AUTv8UkAFMlCnu1qnt9dy8fX30dezM9TVdOJmAq5csGVlJeXo779fROCn61qoiuc5ju7f0ehSyDd+TXEg78Glxv5C99Fchy/fvlilsgGqB94mqbgWkAwKmcJ4/Ovw2kZeimk02WJxrBFogymmx46WdWwBxvRzWb8o6pIez1np4MGg8FgMBgMF7EhBbl+v/+Yr61WK9deey2LFy8+K50ynH2qLvjt1l5ePBJibrmLL84vxW4eHOELpzroi9czqeA9Q86CG+17nAw6+ftM6Bt+ecJtWkzTQJlExe6H0HfFAVDMNmwBP5jMBDY+T8btQsgCW3QvYQEpdxG6dA96w15En5++gsnkBjqpUv9ANGOjr3wG8wpClIgouiTRMGo0rQf+xlX5TYTzriHtmQHA5s2bCQQCLK9ZRm1xivUxnT91qpSYdT7v7qeyZjpOpxOII6IBsk/9GqIDKMs/zPSx05kpRcAKpT1xPlVqJf/gQQaam5hy3XUUu1V01U7WXI2oTzPXHQbPYYS6AllSyKQ01LgdmwWKXVlUeyWynkTJBkBkEYoTzZyLkMyksmGimR4EArOwYdcd5ClpZEmnJ1jFzFxYIG6C1LHXVgDRhEw0oXON1IztkgUErQ7e63KTk5ODPNAPA8dO0wZoi+n0hhx8tPl5ply+GOYuRvzPf4KqIn/lrou6lM7bJbNB6geepjG4FiF0RvkWMSH/hnMa3EqqirezG0cwhJAkxHCnF0ugl5fR5/MaZYEMBoPBYDAYTmJIQe5ba9YaLn7RtMZP1nWypzfBTRNy+dC0gjfK5ADs7XsUs2xjTO7lQ2pP0uK0d6xHtgoK1jYgzJ3HbaNJCu1TbqQwtA974zqELOMePwvhHyC2ZxOSzYE4WqtW0pOkhQZY0eVtiEQUdAnV7CAv2IhiVogUL6e+fAUVjiRjRimIECTzfLQ2PctV+Q10Oxaj+BYC0N3dzY4dO5heOZmlZYK/hjL8vBOmOgX/kRfFZ/XicruALNn+boKP/Q5JzZBz0yewVowBsgAE4ypjbYJiOUsyEKSgOBebSUfHipA9aKEsLjRcrgBCz0EAklBQs2YUu4LPlkYXVuRUCklXEVIuusmFkCygQjodRkelUM3DHhfIgOIxA1ZiSTtORcGR7ENOvW3dqCyj2JwU2hR0PYaw5CDbnJSajk5h1QDt+Jq0uiRT7bDwf2UJ8ideA4oCO3bCouugtBJiGWhoHNI9cCGLiyg7tfXs17ejo1EnT2OmaTGeuA/iYSD8jm2cNgGKpqKkB6eoq2YTmtnyRlmt4TBHY+QGg2e4g/9c0h43saLC890Ng8FgMBgMZ8kpg9yDBw+ybds2PvShDx332l//+ldmz57NuHHjzlrnDGdeZyTD99e20xdX+cIlJSwb7T3mdX+ikc7oNiYV3ITV5B5Sm6YNf6TFlKAwbMbyX79E8vqO26a7JUNmc4JRN87D7pqBt6OTxPoXiO7ZhHzp1fCBO5ElCUdgDa7ASqTUbMKpUoIP/ZyO8ss4OPYD2BwyVaPtdDWniMbBY9GYuNBHWqRwhkI0JXazzLWDvepUikpWAKCqKqtWrcLpdLG4sIS7ewV/HIC5ZQ5K2l7hr/4Mt912G0mHA3FoL/o/7gWrDfkr3ydSXg1AT08Pu/cd4Hd9pSwrNVGy9TGWz05Q7c0S9i0jkbuAQ/vTHDoSQR//M9LyYEanCttSDqzVMSkmVtR58Tq3IABVdhDLu5KUZxZICtlwiK2rfkB7XTe+NsiTPowz184s7xpkkeQv2yuRTLfickfx5WaPua7lCoyVJQ4HTJhW/w67v4mOGR8kPWrMKcv1aAJCWYEiQY7dTGOvgGgEskXg9iD5L/4MvVkpSo9zDX32TQh08lIzKY1fhlXP4+C57IgQyJqGJCwIyYmuKKeVIEpWZHTtwl4jPbMsgTLEhOznw7BH0A0Gg8FgMFxUThnkPv744yxfvvyEr02cOJHHHnuMr3/962elY4Yzb3dPnB+v60SRJL53WQUTCh3HbbO37xGsiptxeSd+399ObHievudWEr/dTe2oW04Y4AK0HEnjcwvGZnpxHgkS3r+FyOZVSJcshdv+FUmSMCeacAZeImudgL4tQvDVv7Fn4kfoy5+P0y2hZuHQvhR51gyLq9J4ZpYgmU3Ym3uJmpoYL7/GxkgN1VNveSOI2LRpE8FgkBvmrWB/MsMfB0xcXuOlJribxnCI9773vTgcDvSt6xD3/wwKSpC/8F2EL4+mxka2bdtGb28vAAvZhzog6JLs/GGrHSHJwGGEOIwQOt4ph7FIISL7asgM5NOnx7GYFT64pIRKsR4hIJmzgHjuZQjFjhCC9uefo7/nL7TPUbD3+pg+778YX5GD2PcTRDbLbzf68BVcQyohccnScmz2wQRcpmQKd3snLZ2w+nAH1Yf+gT3axs4JH6evYCFy4uQP8UJAStURcgolfxPtcho0DZw6yArIF3B0MkTCFCXr2QySijkyG6t/BdlsAa3nuB+SLgYvOIAkIeTTD64kSUaI40fmLyTVi4pQTEYgaTAYDAaD4fw4ZZDb0tLCtGnTTvja5MmTuffee89GnwxnwQsNQX6ztZdyj4VvXlpOkev4kaDe2H564/uZVvxBzIr9BK0cS1/zHOLBX9N3+WBiorL8E2diDvmz5GaiLC4NYQ5o+LsbSax9EqbOQfrw55FkGVmN4un9G5opj8jKboK7D7N57ndI2UqRZYhHBYX5EjMLBij26fjHjiYuJPREhqL4fhTrZl4LlZEufx9JFVA1enu62blzJ3UTJuHN6nyjS6HYnMFx8CWOREI4XS7WbdjI2NZ6ph3eRn9OIevGzSP69LMk43E0bXBasNOisWRMHK9NxWvTAIloxoKqy0gogA3Z3YNsk9AS5ehlZigL47ZqlHjSWGkHCXrjdoLBemiuR8lmcYf9RD1BjlQqFKdzmOgqR274PYmeKCZJ54EtuQRTRViiTjSaefqZI5gkiTm+fNymcrYe7qb88JNMDR0mZnbwQuVkalaMYfb4E3/QAKDpgv9e28HB/gAfmvIYCbXpHd/ni5GEQpV3HhMLbsRtLT7nxzclk+S0d2JJJEl53ITLS9EsZ2b0NT8/n4GB4xOPGQwGg8FgMBgGnTLITSaTqKqK5QQPZ5qmkUwmz1rHDGeGpgvu39HHM4eCzCx18pWFpTjMx09jFUKwp+8RHOY8xviWnbJNIQTi+X8gHv8z1jEeuqfmkmP20N7Uz6uvPoquvznKlGO1cnXlaC4v9dEZi/HatnXM2r+BHnceLyq5aL//PRKCD07vxetN0/BYA+H0VOrn/wgdBQTEUq1YaWW5t5ykqvJ/m/YRWPMyGU2nLj9F5cwBDsTyuXvPKMo2/fn1TmJiMEg1+5P82Kyj6TAmsIm4lkKWQE+FGd+0nUm97RzKLeHV8joygQCDCyUFIFHkyvCxeQGsikCRQReQUC14HOrgNsKGUPpBUUFzIlmtyNY0NpOKSRZouoSqS8iSwG7WsJtiKNksZjnFq2Ux6k1ZavEw15qLZBvMxJzSLDy1P4/OUJaKolmABuYOahweRtmqaW0cwHzod0wJ1hM3O9hYNZmBcVMYU1tHXe04hH7yWq9/3jXAvt4At019lKTayiWhBZT94TmkRVci3f6ZIScaM5yEruPu6cPV149uMhGoqiCV4zXq1xoMBoPBYDCcQ6cMcsvKyti9ezezZ88+7rXdu3dTVlZ21jpmODMe2e/nmUNBrq/z8ZHphSgnmS7ZFd1JINnI7NJ/QZFPPuIkhEA8+kfEi49hnjYG62Vm+rRuxroX8NoLq5lTpZPrG1znWyIXUCaXIITgcLyduL+e2Qc3ki3IQbpmEcstg4mRci1hqh0pDr7iZHfxV0k4SgCBxRbE6uql3KJyubMKHdiQiaPlldHWm2ROeYoP1e6lLeXlu4cXM9vnR8mtASAV7CMTDeIqrKDdVc2BkMwifT/5pgTCBJ+e14e6uoOukMqrU6ZxgDw0AV6bRjhlwmnRec/kMLWF6Tfik0TQSXzmv5MWKjvbn6Jnx1w07zayxU8z2nMpswvfhyu4Gnt4M0K2Es29nN0NFSzIuZeIZylt7bnw13vJTfSz8j1lxCqSjM+/nsmFN5N+SxCUSqVoevm3TJowj7i/iNo6E6XadJp2t5M++EcmB/aRNDvYUD6eghtvY4GWRDz+J3jpr5xqEuu6wmk8Pelm3lP+O3QtxNznEpQdeW5wRP2DnzIC3NNkicbIae/ElMmQyPURLi1GmEZcitxgMBgMBoPBMEKnfAK75ppr+O1vf4uu68yePRtZltF1na1bt3Lfffdxxx13nKt+GkZoQ2uESYV2/mVm0Um3EUJnb98juCzFVOcsOvl2uob4628Qr76AvPgyci/pZ585FxHrwt8E75vSQ6UvC1oKJT0HSfehKx0I6zbK434CK1tQcswUv7+QKkfDG+12hEfx5M7b8LsH15vmWdPMWuTCkjcaJVNOXkMTktDxjxkNfpXHX+ngqvFpPlX2JJGUgy8cvIJ/XzaeuoLBKdYdHR089tghpk6dii9/LN/aFWGmOYalpxPZJri2QOXQUzF2yKNoG5ePAhQ7JfoSgmjaxKxihcVVEjm+LEJXCIeL6Ux6SY9eQv+RVzg8sAbar0JY9pG1rSU/PZ3qaCnxrp+T0LOkXVNJumeR8ltJ+9fTlFBoeGorBW0HGXB42XjbBDLOTqrtV5GvLaC7u/uY67x+/XqcTid2pY60opHcVU+0/kkm+veQsThYXzyO/vHTuXLyeJwvPIw4vB9KKpCuuw1O8iFGk2bn14kqrq75KxZHhLl9M6mYWAKznEgLr0QyytGMmKSqeLp6cAaCqBYLAzWjyLjfXbWFDQaDwWAwGC4mpwxyFy5cSCgU4p577iGbzeLxeIhEIlgsFm655RYWLlx4rvppGIHeWIa2cIaPzTh1qYzW8CbC6Q4uKf8MsnTiYEeoKuKBnyO2vIp01c24FnqRIn00oWORPVQkD1NZrKPK78MaU9BNCpEyH6u25OLJFDFhzf+CKxe+8m0CObmDbeo6PU89zxZ1OSBA0pldlWGKI0BfbiFyNkvekWZkTWNgzGjq44KfrOtkdkGGj+c8x4YGC3/pqmWuN0iydT87Wwen0e/YuRO71YIkm/nT5j1MRcfbf4Qid5qS/ggvxDwMeMdiN5mYMG4cPT09dAYCVFRUsHjxYopcaXI6f4+Q3OzMXs6a+v2Ew0HY/cTRq1EC7Br8Z3sdQaCBBsDz+pUHnn3L1SsEM0jjZuCd0oDV2UnkYBWbOwfYzD9OeL3nTVlOb69OWdd6Jh34HZrNxY7qyWx15jN34ngWtuyDXzyMcHvZc9OXeNoymq5oFk4wU1kX4E9ksJpivNx6AzaTl9WybfDFGPBCxynvj7eShEBWVaTXkym9yxSa4d6a4Z2bpOsgBNHCAqLFhe+KxF0Gg8FgMBgMF7N3nEt37bXXsmzZMg4fPkwsFsPlcjFu3DgcjuMz8xouLNs64wDMLjv5qJIuVPb1PUaOrYoKz5wTbiMyafRf/xj2bkN674cxXX4Zjtb/Je6eRndgJY5oDotGJ9C0a7DGFRL5uURKiunp1Un0tjJ77y9BMSF/+fuI/FIEIOIxIr/+ITsLPwNmCcmiUzXNhtXfyz7ZQaI/wdT+DiQ1y+6CMloDKv+3sYsaupnavoW7650AFNBOKgrrThCn7dq5jcrXv5ChN26l11GALx1nRrHAlVeGFoqQ67biHOPF4h5A7X0IT6CblCbxl51u2vo34rKYmOCswoyFjDmHrMWKMA+gAB7FBBpocQ2RfTM4GlzRqyPpAtVkQvM5iVQ+SdYSwzdwLWX2STDmxO+J1R8g1pjBYg4xvvkROmct5fmUhMNu54NWFddjv0OVFTZccSdPOupo8Wfx2TNMLnIcV3ZVCMHu3hgFzhZs5jj5jrE4zSdPTHVyAlMqjTmZBjOoFgsjKvJ6gcsxSyR8tuHtJEHC50N1vHOytpESQrC1M8aT9QESWhuaevJ11xerJaM83DQx73x3w2AwGAwGw7vAkBaMORyOk2ZZNly4tnTGKHVbKPWcfI1tU/BV4tk+FpV8GUk6fgRKpBLod98Fh/chffBTyJdehaPvCQCarWWoeoq5LpWYOgpvyks8P49IeSkAHXt7mLfrJygihfzvP0AqHPy+3tlC4pffYWvtf5K1uOgXGV5MBkltGgwNHXKSX1U1Y7YJPtemsG1vN6WpLubFDiJknU5VZnK4gx5zCUtKXfgKBkdQI5EI9QcPkpeXy0um0WzwTuLjiX3UZXoYCPYwNdeNw+YlZ8IsLPlHM+6+ZRa30PxkbS+TSCv8ZlMOquZkXP5kMnItWRmQBRJgARBVACTUozuf4jMfXYkSqbgXzdKLq/OjyNEppE6wnSPWSXXjkyjpOFtmfp1x7Gbl/Kto6+1lqZKlrn4byWSaJ+d9iGfcE/GndCqR+Py8YhZXezCfoDDpb7e2MbPsLxS7W5lX9kmqcqaevKMnYU4kyGnrxJxKkfR6CJeXopvNw27nYhE53x14CyEEu3sS/HV3P4f9KYpdZmqLPGQy6fPdtTPOazOmzBsMBoPBYDgzjKwo71KJrMa+3gTX1p581E7VMxzof4J8xzhKXMcHPyIWQf+//4K2RqR/+RLy3CXI2SD2yDaSnlm0xeqRhUS5bMdqvgKRyRArGpwaHe+PMPqFH2JLB5G//N9I5aMAyGxbS+ovf+S16d8kbSugS0/TU5jhS7Wl2MMRXIEAdTkWcjWVDXYfk60N5LbuRZXAm40zI92Nb8pM/JU3MzsVxTe5hqzTQTqd5rkHHyRTPo5uzcku7zSu8erkuJehOA5wTb4PDZWVHY3sajhMWTLN/Pnz8XoHk2QlB+opDq8inoI/7yzB7ZuJrI5FqCnGdz+Lc/kVxPp30lHwFGk0rsxZhLngeoTy5ih5SyjFX3d0sNi+jRuKDiNkO325y1g58AioAZaU/RsldZOPv849nejP/R2xbR3CYmXzpT/BJKms7T9Eeaibfwm2EY1n+NPkG3nJO4mEBpO9Nj4zL5cZpc6TJox66UgfSe13FLlamVt2J1U584d+AwGSpuPu6cHZ7x/MFFxdOZgp2HBO1Pcl+Mvufvb1Jcl3mPjM3GKWjfZSXFhglBAyGAwGg8FgOAUjyH2X2t2dQNUFs8qcJ93mSOAlkmqQS8qPLx0jQn70n30H+rqRP/0fSFMHpzI7g2sAiPsupfvQV6hUrERs11EZThEtKkQ3mxDpNPovv4cr3ol25zcxjxmP0HVSj/+W6OZmtsz+PrrJQUBk8U2U+WyVF2E1405nsXokwvEIj/V2sqe1jQxQEg8y3RxhxgyV1Ox/wZwqxdvVQ7LYS/DoFNGVK1cSjUbJSBYa8iZTZRbc7jNRYPPjVvI5MNBMU/IgMyrjlBbU0pK6lP17IJv1oyVe4YbancSyMo/tX4DdMhNZs2DJhHDFexAz6igRf2ZtUT0BoXFZ2Z0oOfPfyGSczOo8tKePRP9OvlW1lVxzkqRnDgfVarZ0/R6NFJXcwkC7iQHq37jG5pCf/C0v4z24C6GY6Jh1PS2lVxCLe0gNvMz1rVuJZ2R+PfZa1ufUISSJBeVubhyfx5i8U0+pre8Pc2Dgbopcrcwp/QTVOQuGdf9YI1G8HZ2YMlnieblESosRRnKqc+KIP8WDe/rZ3hUnx6bwiVmFXDkmB8sJRuoNBoPBYDAYDMczgtx3qa2dMRxmmdrNT6OtewHelihICEGFFqFKUrAoPzg+X1EqCUjIX/gOUt0UAORsAFtkO0nvXPx960hKaWyp0RRaitGUJLHCfISaRfvVD7F3H6bl0s8zZuZMRDJB/NffpjtWQ/2Mr6EjoaOzfKbOZD2CpTlFPNeHv6uTVw7Xsz8WRyAYHRkgKdxceUU+YwpSRAre92aAm+MlWFWBqmm8+OKLNDc3YzbL9BXVkklZuKtCZbRNI5TtZ29iG6PKQ4wzCQYShST10WSzaXoG9mLVdnL7rH6SqpXnDt+GpBSiSGCO+ymQWpg85whFnmaeyoTo17PM75+Nt7UTnUcA2Jxx87zu4l+qdjB7TDeppIP2nir2mLbR4nkMSZPIW+MlG3yR0NFLa9WylMWCFKaiCCQaCyfRUnUtGe94iKTJ6VuNvWsTv66+kV3eGmwmiatrcriuzkeR6+RTz1/XF0uwuvlnFLmamVr0cUbnnjxj9tvJqoqnsxtHMETWamVgzGgyrpN/UGI4c9pCaR7c08+m9hgui8wd0wq4ptaHzWQEtwaDwWAwGAzDYQS570K6EGzrjDEj24Py0oMwcTpS3rEZloPJZoLJBGWeWUgm9/GNyPJgaZmqmje+5Qy8DJJM2jWZUMtPARhXdAe2vhjh0mJ0CcR9P0c6sIM94z9GxRVL0Lrbid/zX9QXvY++cbOJiSwzvRnmlkVxxLOoVgtNdisvvfAMzakMZk1lctyPu2wMP/Uu5K5ZDYyx7SGWuxxTqhpPdw+JHC+hqgq6e3pYuXIl4XAYm1mipDaXF7pL+HpJhhpnGwP6PnJ8YcZqcFhN09b+UYKZUnKKWzm4/1EK7RE+PCdIIuthZdMHKBuTTyKuo+xZx0L3Yzhm53FQzfCLtlKOJObj6dc5GBv8sCAjm+hwFuH2CMqsUR7qGs/Dhwvw5u/HW7wRgEhvJf7mOurNLjhBgmun2UuxswqPNQ9Vz9Ifa6Y/0UG3I5/2qR/HZ5W5vS6PFWNzcFmHNoqayqZ44uD/ku9opMb3EcYXLBnSfgiBPRjC09mNrOtEiwqJFhUYmYLPge5ohof2DPBqS+T/t3fn8VXU9/7HXzNn33JyluwJCVlYw74oICCCuFEVW5davVVsva12+WkXbW9/trebta0/WytWW6u2Wm2tV637hqyyQ8IaSAKEkH05J8nJ2c+Z+f2BRhGC0noN4Of5ePCAc+Y7M98580nCOzPz/WI1qlw1zsfFo7w4zHLlXAghhBDiXyEh9zRU1xmhN55myu5lKAsXo3zuuiNuR46l+lhZ9y3ynHOpKPr6R9qmIdGFNVRFNGM6luanadCi2NJZFPXrpE0m+n1e9L8+gL5pNQ3jP09v+TmMbV5H7xNPsHnst0nZs8hydXO1P47LlCZpsRLIzmXv/lqeX7MOeyrO9P4u+owWdnvyifRH+V7FG0y39rHuYAZvvrEbRduNroD+TvBKJBKYTEYUYMYYO/d3VHBn2RbO9tajqzH0iJFX99hpKdqHJfwFOpvjRFL/JLKvn3FFGVw2ppdIMpOdiWs589ws6nZHyep5kykz3+aAyccDDcPZ0jOFWMqJ35oikesggU5PLE0iESXb3I9J1Ygak+R6q/EMr0HXDTT3TGTPoXGYbUVYCiyQTkFvEPoPD2nkceVSYs4mUzOTUDT2mSI0m+Ok3ZlAJj6bmUuHOZg7yGBSg0mlEzy1+9e4bXV4LV9gWsH8j3Zu4wncTc1YQ/0k7Ha6iwpI2U5whGFxwjrDSZ7a2cWb+3oxqgqLx3hZPMZHxkf8hYYQQgghhDg2CbmnGT2ZZOMLb6BaRjDlzPEoiy476nnbmq4XSWtxKrM/+5G3aw++BYoB0mF0rZNOPcHZGedh7onSU1SA/s/H0Ve9RnzuZew2ns+0jifoWNPBrql3MNIbZ6ynFbdRpyNhIVZSSNLtIrKrmmUrVuACFuWUUV20iJjJgqk/wcL8PZxTfIi64Aj29c6kzKehG1TS7xvVNxpro6ltE9OH+7DZ+3hq4rOo6AQj+bzdOIGGoBuTrZNobSW9nS0kUzU4HX6mlAxn0cjlJHDTN+xLVLjctOzYyyTjS3QV93Nr4wS29o4npZkZ4Q7x+Ul5TFRDNASivF7TwMKMrUzOaKXRZKPKrtCmHcKEhTLrHLyhClauX8uM0gAzppShr30TfeWr6LpG51nXUe+bTU+fitWiUDbKyrBSM0bjkefH7/ef8MBCaS3Jc3v/HxbjXtLpyzm3/Pzjtk+mdd7c18PyvV2kE4eHiNaMVjSjCnWtJ7RvceJ0oLkvAcAFIzxcPtaHxybfjoUQQgghPg6Krn/gYc3TSEtLy1B3YVD/SpD5MHosinb/z7nVMQ+7x8OdV0w6qk0k2c1Ldd+h2D2D6QVf/kjbNSQ68Db+hoStAku0lmdaUrR42/mKehsGxUR7ww70Z/6MYdZM1Ik2el6rotV8Ppnjz2R0Zj9Wg05rTGVzp4epI1fgtvcR2t7Js1sTNLp8fGbYSEK2HHRFJ62nyXM1Mi5nCx39eezsmI6uq+9MyXo4DOpqlLhtK1mJXkZlxcmw6gSTFsKJPHIZwUu8SkpJgp5G05Lo6CgoqKoJl2pkntVECJ0XUgliaBjTUQJRD1s7zuBgzyhApyRzD5W5OxnT2szItQEyOw8HEh1oLTWyZ5qFQJ4RS1ijoipB2fY4psTRn11aUdg1ZQ5NWRdCMg/dFCSdvQbdsw3UY891qqgquqYdc9lgUlocnRDtocV87YzFqIOMuJzWdFY29PG3bZ20R1KMsupk2QwkbdaBK+Tik+F3mFg82kuW48SmY/rf+N4hTg9SG2IwUhvieKQ+xGBO9trIz88fdJlcOjhN6OF+tN/9mK7mdhrOzOeLlVnHbLer8zlAZ2zW4uNuL53WSacO//7D1bGaeNqGObqfAwELrRaNCcp0rAmN1uZd6M/9GW3KbBzjDXS/Uo866RvMzjajKiF2J+KkkwnWHxzDmeWbsVmS1L+apLG+jYb8kZyVl8vUyrdRlKMDX7azlXOc/zxuP7sSNu6on8oYW4xr3BWkzVu4xKwClkHXaUuqPBi0k2XuI5nMZHXHFJr7h2FU45R6t+G1NVPR2sms1YfI6+4n6LLy+vThhPPTaDlhdFsK4gYMjQ60bhu1doXaMz+4FxVFHYtmPguT5iWuB+nMfIte2z7QNQgc+/z8O2LJ0Xx39kXHDLiarrO2McST27to6kswyqpz23AYMzqPeKYbBgnFQgghhBBCnGok5J4G9L7g4el+2prY8rn/gjaYVuA8ql0o3saB4CrKvQtwmP2Dbq/lUIKq9RHevZhoVGdzyej9pNMar9WdhVL5d6bEL6e1ZhfJFU/S6RvPXtsFTNrfzsjzz0UHVvca6SnwUeH1sGFZP7kFRoKWmfQ+8TiOlvWsLZ9ORWYm5407hK6rbD1QhtEcpTL/ENGElT2tw9DTKroCmiFFr72JrnAMd8zB/PIIBhWWHyzix51zKXdE+V6Bmbim81SdiUjIiw6oWWGy1fkY9Qxyx5iI6O00xTaSUALkOdppi9uIK2HG5C5nutpHpgYWqwtToAPVE2XXOUZWWIvxZGok1X7SSgKrIZ9sx/l4rFNRhr335dN0qIm169YyetQYiryjCRxMkYqBza7gKzHgzM5BUS74SOfT7XbT29t7IiUAQHGm5aiReHVdZ3NzmL9u7+RAMM5wK9xVlGJ6sZtQQT5xozz/KYQQQgghTi8Sck9xencn2v/7v9DTjfr1/8vmdj+5zgSFGUdPNbOz439QFSNjsi4+9rZ0nV3VUQ7UHr7vVlN0jKSYXfwKGZYgL+1dDO4o45UxmA4dJLHy7wQzK6iqvJGp2TFGZ2dT02fh3k6F2dk62d29bNiuoigKnc0Rsl97hOyO9fxjzBxcFguLK40oxi6ers7EbFQ5v7iF/rSDVYfGQn+auA0CmQfo7g4S2ZvFpDwL51f20puw8FZHJa+TJNNq4o6pPlxdHTxdu5fdbUEy7BUoxctwWUfS1lJJ6eQm6tIv0da/HXQTgUgWKd2JUU3iMXfh7+/HohlBNUBPgLTBSJvDT5/ZyUhXL2YSYBtOqe9C8l0TUZQjg2Q4HGblplWM9o4hIzSCjs4UmV4DFVOt5OQbj3om+sP4/Rl0Heve5xO0rS3MX7d1srcrRr5V4b8LUszPNhIqKqHPdfQvQYQQQgghhDgdSMg9heltTWj33AHRKOotPyZeMpLt2+o4vyLzqGAVjB2ksW89o/0XYzW6j9pWMqGzflWInm4NFFiWCuLN7OS+Ec8C8HK9nzUHt2KdmODa5kI6X3mCFmcO7VYrV6ZfwZU9kxeDCv8TTPLrYRo2BV5uyiaZUlFTCWbsugdX526WT5xPX1ph8fBsHM7N7GqzMjJzBKOLd6CaXFiMi7kgr5dVxu3s2n+A8DYfpLOZW5lkwbBemoy5bLHaaOElxpFiYjAH7yEH7ZpOpzGDQv9MurVnceWEMbSNIVXxG3Yl9qPHrOxqn0NN5zR8jhbGZ2xgbLScmctWoPRHIJ0i6fLwbMkFPO2ZzBklHv6raAOeyCb6si8nljH5mOcgkdB489W9ZLsuwqBYcbpUJp1hxZ9z4uH241LTGeGv27rY0R7Bb1W5vVDj4ow0sZwsunOzZVogIYQQQghxWpOQe4rSG/ej/eaHAKjf/hnKsFK2NYVIajpTj3Gr8s72pzGpdkb5LzxqWV9PirXLwyQTOharwnZHP909Kf40vho9BsF0Lutq4cJz5+Bre53Qi0+hZnootJoZN7Ic5+gpbAonsDk28mBREwo69V1j6I4vYoJvFcUrHifVFaNhxoXU9CeYXxFmYukmAEbnxFCVjUSTRp7blUe35Qn6YkmiLX70dBYGK1w5pQ+3N8gT/Qm6jE1ouoGGwDiUZj9zjd1kjcjioM+DJzyWPi2IbVg1imYlWvAIqZSL6qaFHOgeR0FmHQuHP0ZJVz7+loWM33A/SiSE7nCxpvJC7jOPx5Nh53vTcpjt3ImrcxPhzDnHDLiJuMaBujh1NRFUrRSrK8rkM5z4sobuS2pfIMZft3WypSVMpsXAN0qMXGGLojhtBIsKSNlsQ9Y3IYQQQgghPikSck9Ben0N2r0/BpsN9ZYfo+QWArC5uR+bUWVstv2I9p2RWlr6qxmffQVmg+OIZU0Ncao2RkEHX7aRYRNN3P9yK98cH8UWO0BaMfPoOgu5eT4KLX04n12Pbjahx2L4pi/AXjqWXpoZl7UKTTGyNziOYL+bvZ3T8Fi7yX/tKVL9SdILruT1jiB+p8b4UguKAvv6MulJ2Aj0BjlgSNOlNRDZ5wdNJW1SsdnNXDS1jh2GAB3JJLpiZm/7TGq7pnCW1sGURAsXjppAAo1IUyPx8HaiJRsx2pJEog6qWi6mPVhGpama6xz3Uxa1E6k+m2HNW/C23QVAx6jp/FfhYnrSBi4d7eXKcX6ciQacLS8Qt48k7DvviM8rHtPYtzdOQ32cdAoi8Rasrg4WXHTOEVdu9wdi/GNXN62hE7/t2Gg8RCqVOqF1NA0O9sZxmlVuqLBzrTmETYVQbh7hLJ8MLCWEEEIIIT41JOSeYvTdVWhLfw6ZPtRbf4LiOzxKr6brbGoOMynfgcnwXqBREkF2NT+EVbUxzuDE1LPmnfawZVc+B5q9gM6Y0g7GlneyvinEVXkRPmvfha7Dqo6JBPoOcfWscdiW/hR0nbTDRvasi7HlDSdtrsZs3MMrvVk0EyRpfRVL6y0YSDBm9U9JJvpxX3wNjzYH0IwJvJP2kKl6qU1Hec3SRBojkUgekeYs0FRSniy2KsNZWFGN37WetXoKl8FLgfV8Htw0DINi5sfOOkre/Buu8nGYVQNbdj/KhvI24iNU+kJZVO2fS7Qzm0VNazm35R840rH3fYL70c1WADYNn8GduYsZ7bHxw+m5FGdaUJMB3G1/JW3y05d7Fbzz/G00orFvT4yD+xNoGuQVGtl3cDm9sVYWffYLAwG3qTfOE9u7eLsxhMOsMibLfsL50mw2k0iceCg9K9/GtdYQvkQvMZeTzsIC0pajn80WQgghhBDidCYh9xSib12H9sdfQW4h6i3/jZLhGVi2PxAnGE0dNapyX8tDtCdamWvMxBt4HYB4ysrLtVcRiHoxKEnOLfsfCtwHoRsW2mDhMECHgFLG8i3NTKwow/Pob1CicaKL5lLsGYnRlkPaspFXY41s7ddwOraipFz4264lEi1m4s77sOlh4p89n2fbu+mKxCgY0ctok41dySibu62EAgVocTPoCu7SFLWqD797H5dmvEGaFHbFRlHmbBr6KvnNWhceNcJ/Bn6PGm6leUYFk0ecy0GlgVUTOujsK2Vb/SysMQdT7OspKHoKd5aJ+PxLcPonoKd1tqzvZ6SxCcfaJ4lkF+OdeSFP+Y3kOEDpaYdgGnN0H2hT0ezlZDZ2AFDXYWJDgxUdKPUnGZeXIBHuoMRvJ2/cWTg7u2k51Mmjh5K83pHGosJ/FBq5ssCEy3hi890CmM0aicQJrqfpWPuCaGkDweIiojItkBBCCCGE+JSSkHuK0Nua0P7wSyguR/3GD1EcR4bZTc0hFGBK/nu3Ixsj+9kcqcFpcJJd9nM6FSM9gTRrV6VIpcDugFlnOzDbvkgn8HJtkC31e/jN6GUk7RX8Y0MGdrWFGetfga52fNd8BnNGCaruo8tczV+Tm9DNYZyKC3vHBSiKRiiaSU74aeK+Kl65xEp/ZDvhaAGukV2kCrtYD6ADXnB5jzzG8e/8PVy1ka3Y2ZQO8XZ9kE2HnHjtLZxV+hSNhiRubQ7jtWmYMPJM/35ebb+eYQaNc3LWozj3Yki6KF+RpGR3CIW/o/N3AKa8uyNHBu4LrqHEa0VVNIjFATCkelA0K2lTLoa4DsTY3WVjfYuNAmecmYUhXGaNVCpFXyLBMI+XSNrAPXsjPN8FBgWuzIZrchQ8Jg2ScUie+LlWk0mMqaPnDf4wEa+HUH4umlG+rIUQQgghxKeX/G/4FKE99zgYzag3/9dRARdgU3OYEX4bbut7p7Sj/W906EmmZ1+LanRxoD7Oji2HA11uoYkpZ9pRDQo6kNZ03qgP8NvydehGN9si02k5X/JSAQAAKFRJREFU+BpfbK/B6grhufkMTJSB5uZty1o2aWuJa3ZsbZ/DFi0nkbGRpGcDZL1GYxk0cvi5YIsthMW350OPL60ZKdLdLLCZeLljFPe0jCSetpNI2zEoCcyajfzuL7PYbSbDoFAVSfHn7gQWQwVfKF5OwNIAmoqxYxFTw9kUjg7A6MPbjiQ0dtYHOSczSVNSpf2shYweW0b3+/bv6H4NR3AFIf9niGZWArBvT4zdLTFyCoxMmOEmZsihNx7nySefJI4RZcx8Xt/dhw4srMjk8kofPruJFND5L5zjd/n9frq6uv6NLQghhBBCCPHpJSH3FKAfqIMta1E+cxVKRuZRy7sjSfYFYlw7IWvgPUO4js3ROtxGD0XuOWxdF6a58fBlxbGTrJSOsB6xjY2HQnw1dwWZpigtrs+x4rk1fDGyg4L5DizDCtGjZ5HWHTxveIYWOujsmEVez1ngX0k47zlQUvibrFQwDl9RJWoyyVO1e4gmEjgdNs4Z3obFqPHMDg89ruE0qz6iKR2Aed4Ovuirw23U2RsooLiui0m9Dlb5JzKzv55bKwwUZuZhUA10tNVRF6olWZrkktIAu/Tuw2E1bcZa/x38mQUULXKiKAqarvNaXQ8vbuvgj7MTpAwGMior8JpNRxy7JVSNI7iCaMY0ou4ZANTuirF3Z4z8IhOTzrSjqodv/X19xWqqUtm0OktJ1vdy9nA3V43zkeOUZ1+FEEIIIYQ4GUjIPQVoz/4FnBko5156zOVbWsIATC1451ZlXael/UmCeorp7mvZ/VaAVFQj3wljJ9nIyEhDf/iIbbjb1zLLHadDOYe1L63nKxn7MRfkEYoaSNWUY6CZneouCg3l6F0F+G370ewrUHpUPDUqw4yleEeejb0/gjNZyB9q99DbZgNsTPb3MdFp4hfbx/KyNhm9V6XCZ6U12snXK15ikStKEp3VtW34V9Tyj4IrWJ81noszWvnemEIUBfYou9ls2EigqPuIfmeEStCSBaiJHNRUDpWTbSiKwoFgjPs3tLE/EOXhEg23QSEwejjGDwRcY6yJjI7/IWEtIZR1MTqwZ3uU+po4hSUmJk6zo6gKkWSaP6/dx5sdOaRsJs4qdPH5cX4K3ZaP4xQLIYQQQgghPiYSck9y+u5qqNmGcuWXUGz2Y7bZ1NxPtsNIcebhwKVG9rIlup/M+ERy6zzMymp7r3HHO38+YIG5AKIF5AOX5yVp/8czhPXDV1qTVANQAEA1ecfsRTXJddX0Ao9NWcihhAa6znBXgjllYV7sKOelyCQW2EPMNrbTeGgtM8qDjHJ5iPSm6H05SEFrjLumf52d5hxuyU1zlddL16EqOvasRJ2Wx9wCM5BF3DyCqGUsRks2Gw/Y0HLvx9x6OcOKDZidKo9s7eD5PQFcZgMPjrIwWg2zz5FHUwSe3tREZ/idB2V1DUOiA7iItDkLth+iLG5jWMJKsynO8u4gj712uGl7KEl/UqPAEOHWheMo9x/7XAghhBBCCCGGloTck5iu62jP/AW8WShzLzhmm3hKo7o1zIIy9+FpbHSdxrYniXTOoaL/fMoKu2lIu3CVeY492G46hrHln8QP9JHYFcBi0Ii27AMUfBd8Ac1oYk2oiQ7vTnRTH6aomcLaGME8jbhNZdKKKNYI9Bss7PHmcdDloy2WwqjrNFvL+NK03XQnbDSuDvHHgz/FnQyDQWHm+fnYi7xEd/fS90oz4fHn8N9jzuVgQuEnhWlm9NbQ+tzTmMbnUn7ZCIz0EbePpt9/EWZzNi6gvSVJ2PUwprZLMJAmkm3iay/upyuSYmG5m5tLzOS3tvB6t4Pne2Ns2NiFy6xS7jGjoGNJtKCaEsTMReiKgaKwjayEhQ5rnHZHFPf7PjBdCzK2v46vXnERHo8EXCGEEEIIIU5WEnJPZlvehoP1KNd/E8VkOmaTHe0REml9YOogvW8XVXsX4Ogfy+zhbUQ0AyWGEDSEjrOjGVAEFEFo+zqitbvxnnsF6ZJSnjY8RY+xFVenkwktZZTlnI0634yKgk11oZeZiCXi2JMpfJrGob01mCIRrhw3jWKnglPz0x818OXzzSS1qaBEMFurMBp70EMjsJVMpXOJym0HVHqT8LOsICOevZ+IUyPzyxOw2PtImSz0+L9IwjHqiF7XNxxCM/diDo+myR7mD+sCFLstfPvcfMa5FCLb9/GtVhOrwwlsphTnFxrQa1eTag3x2fG9TCqJ8tctHmrad+PPOBOXrYKe8E7C7VvxfOAT8gDz5s3D4/ngEiGEEEIIIcTJRELuSUpPpdCefRzyh6Gcefag7TY392M1KlTm2IlFUqx4ywWJQs7IDWA1pDGqsD9koy364c+OGiJBcte9QTyvjE0jAtQaHsberHBRbyXmrPOJD4/Rbm/AohjJ1u00Riz09XWh6zpN8SQ729vpi4YYN2w0OmEc5hDdURM9Fp0kfZiNaXLN9ahKkuSBYlqyZ/CPLiMvt6ewqDr/3fQs5Ws34bpkLPYSDV1NEPJedHgwKMVwRF9jUY1W/XnMbYvp0+MsC/fzHxOzuGS0l55QnAdWNvBytwEFuHhkJnm9e6jbth2/38+Fs+yMMLdRn6gka2QlaoaPSJ+TDF8PRSNdKMrcoz4bp9NJaWnpiZ5GIYQQQgghxCdMQu5JSl+7DDpaDk8ZpBqO3UbX2dTcz4RcB72daTauDqFpbnIymxjr1kmkoS9upH/9Kww3Hx5oSotHSLc3o/X3gtGI0ZuF0ekBg4F0835SWoIVCzswR0N8pm06roLp/E/MTV9qB0nX01goIrPWwc5mJ4qyH83hIRYOYdXiJBQz9rTKnqbdLBjeSTc6f6UTQypJcXA45xQlCCcMvP2GieedZexq11FJMCncwA07n6JgopWMz1RiMqTZEqtkZWwG0W4bH5yQx5hQcIXrUM2ZqMks2m0Bfjd/OGaDysNb2nm9rgfQmWyyMqfQwMGaN6gLBpk0aRJnT/Di7fgrMec4nP6rSG2IEulLMmq8lYrRJf+r51QIIYQQQgjxv09C7klIT8TRX3gSykbBhOmDtmvoidMVSfE5v5P1K8OAQtL/OufljSHcr2Az6dRuq2fyubNJamnCq18mVbcNxWzBM2smzjHTUVUXiqrSs/4NQv1Bdp5lJdMznEuSl5Ic6eHv9Wkirk2kcl8g3pNDZ3UWzZpCyupGj/VjDHWhqzaK+wJ4Y71U5RWxaFQfPkeaf3T3Ubw7yghXHiMnRtnd4+G+6rFU5ZRhT0U5r2Ut81o346vIIX/JcDyuGLv7snny0Eyao753jjI2cLwqUKSC370LzbEfU8vlJBIt3HDJCJ6rCfDi3iApTefiTI2ZGS627j/E9q1bsVqtLL74QkY567F3vErKkkfQ91m2rIvQ3pJi7EQrpSOtx/qIhRBCCCGEEKcYCbknIf2tF6EngPrlbx8eTGoQmw71c4HqIdUCiqKRKHqQsz0l0A9Os071oTRjChU692xCW/kKoNA/6zyKRufgsJSBrtDQ2Yxl2TNEI51E/CrJM6ayOL6QxrTCW3ui9DmXoeZuJtrupW/3MBKqFaMeR432EjVl4O9pYWK4gT2+XNrKdcrMYc4sjrOpC/ZsL+GSMTFG5kZ4saOMXzbMwGaMMCK8h7xYM95sM0VnVzLc20RfLJM36y/gYG8FJSiUDHbYOtAzC3pmEUvH6PUk+M9/7ieW0phXYONmWx9Ot4PfrVhPNNHK8OElfGZWEb6+pzH09BJ1TaEn83w2rU3S2ZZi3BQbJeUyDZAQQgghhBCnCwm5Jxk93I/+ytMwbirKiMpB20XCadQ9KgWqEatNIav0t7TRxajYInRFpyVswt+1lb7GLejNjZjmX0y6eCyjLFZQdMLd9fy9vpkFW5dxaLiJvIBO8LLJTOmZw1/21dDc14ZzRCNKThfRfYX0NhWhaCmMegLFYsFjCJOtdkOOhWYKcAFqNINLxgSJ6HYyyv+DGz1P41ej/PbgVN6u9/J1bwPTFs9Hj1egHFpOkWUzad3AgepM4r2ZFCjbKWD7EceZwki9YxithdvRbAdxhN1UNGazITWcV41F9PdamVHk4NrRbqa0NRLVVX69YhmxZILz5kxlenYtlsA6UuZcggVXETUWs3FNmO6OFBOm2RhWKgFXCCGEEEKI04mE3JOM/tozEAmjLr520DatzQm2vB3BoquknBpzZtTwfMt+Pq9fRzSmYzapdO2swtu1HTMGXDf9CBsG0qkEunkP/b2N/KPRwrxty9gy387UNyL0jc+mqXsML7dspMwfY/JZ+9neZiO9YTzhsAW3Nc6MsjBTh0WwmfRB+7Y/4uatwCwuSz2KWUnz7b3zGVa9m98Wt8Hi/yS6fwP52jIs1igtHYVoz67DdvmXcXzu6MGeWpojbNr/LLHMv6AqFsZlXUl9cAI/6wjQE0szJd/BFyZkUZZpxl9bTzKZZOnmDYCFa2ZnMcL1CnpUJeS/iEjGmXS06ezZ0U+oN83kM+0UFJs/jlMmhBBCCCGEOIlIyD2J6D0B9GXPo0yfi1I0/Ojlus7u6ij7axMAbEj3sWSGn5rOfzBCH4W1PwuXTWHr3iAlajuOKedgyR1GNJmks2Y17omtpFUHNaE85lT9k+VXOpn+aoS0w8jTqTHEW1rJ9aWoVTOoWTkBs5YkZHDS5Cymw5zD850KdIL+7q3E7/6tg4KCoiic56vntuGvEkg7uWnXOUyqq+IqTx/RuZ/Fu/d35Fk66EoW0bHXiunF5zB87nrUM44MuPGYxsbtW2k1PoHu7STbPIOItphfrw3TFemkMsfO7bP9jM62g65jravHHIvzyO6djCj2MLtgP27rHmKO8YR8F9DSbqd2Q5S+njQ2u8LUWQ5yC449JZMQQgghhBDi1CYh9ySiv/g3SKdRLrn6qGWplMa65WF6AmkMBqjLjNDSlyBX38COZD/XJr6I3apwIKhSauogc9p8kqkkG/ZuJ3v1c2RfVQR2Jw1NOWS88U/evNpF8Z4EmV0abwwfS8DoRLdYSHWHMJAgbHER9NtQbCPxdvUzvLOaHruNZFYCozOCqhiwKwW0NyTwe33MK1eYZN5GqfEAXWoJ11edQXFnI5dnBOCsAkoTfyasujlovgJz7T5ML/4FZcElKAsvfe/4dZ39Dd1sa3uCZMYmTHoOqnIbf6+x0RrqZYTPyjdm5DE+x46iKOi6TnfVNsarBta3H+KCMyP42Uk47aU79wYOdg2j7s0Y/X0RHE6VCdNsFJaYUdXBn3MWQgghhBBCnNok5J4k9PYW9NWvo8w9HyU774hlob40a97soyfUgNEYp7jUzOs7u5jstfLm5mXMdF4ERgMRVUVtrsGe52bP3mpW7d9Hfm8n3gsrsOTqbN5hxLbpRVZf5cIUVRizLk5tRi6bMkeQkepFj8UJZ1iw+yI47QfJ7Y9SsnE3uqrTON5A0fBWVIOOWxuHuaeSAzW7WFyaZnpxFLsaJq5Z2Rmt5Du7xuGL9vKtikaKh0fQ9P3s7JlCq1qCtqcefcMKlElno4yYDNu2AZBMqrSF9xPxvozuTBDoupitnWPojBvItca5eliKEc4YelsP29oOfy6RlhYuzS0gmA4yuWw9uq6zsWUOyaw5NK5ME+mP4HKrTJ5hJ7/QhCLhVgghhBBCiNOehNwh0B1J0tsVJhh8b3oc/fnn0TMKUWZfhvK+99tbUuzaFiAQqiKR6gagbiNkA8m4gXEl8yjUfDjMCjt2NJB7cD3/rNFZmT8WMoqwj8wnqFhoXL4Hd2sVKy53kUwbGf88pFBZl1eOPR0m6TfhK9vJiI4STN15mNrtpFqbaBypYa1oxWlLoPblYu0Yj8+oMsq/m8vndWNQdVpDGWzoHMGh3lxeTBVwhbeFK8dvxm4O0xCooKGjlFTajEICg1oAM75w+OAaowCo5jiWrIOUebrZ030ur7WV0abZ8RLnUkMzo9N9KG2Q+MDneH5JMYohhsu+jJCpnOe3LCCp2Um3JXF7DEydZSe3wHTcEaqFEEIIIYQQpxcJuUPg7zu6ea1+35Fv2s+GiWfDqgAQOHol58iBf/qNOl/waSz2aGjxNC67gb0tSTJX/4EX8qbxwOjPYlDg8lQL7ck8onEL+ZHlbFvsIhG1433Ly/CenSzLH0skv4yRY14naoySER6HITMXa0Ycq9JMy5QWHOY41pSLgtBESs0qBSP24zLFSGkqrVEfzRE/kbQV7FBk7+Ne115yHe0EI7mkInMpN2dSXvhhn4iLLeEsfn9IZVtEJd+k88O8FOe7VYxKHpB3zLV0EkSs1VRHl1Cz3U06BRmZBkaPt5KVa5RwK4QQQgghxKeQouv64EPlnuJaWlqGugvHtC8QI6JYCYX6ANBe/Dt0tqFc/RUUi4V0Wmf3tjCd3bXEkm3kGVUq+ztwh/sYNWo0xSXDURSFYG8Ih9NNJKUSeOLXhLQkda4iLJk+GgpnYDOXo2kmGvVmDmbuJplQSXc5KQ92kjaZSBWXELQ2kEKnCCt2xUACnXY9Tj9pyjP2cbWnh1KDBVXRURTQdUjrBnRd4d2Rp95fQLGkjb5kCZm5WQO3B+uRfvTNa0A1okw7C8VipTXRyub+rRzozeBQ2yICER8uk8bZ2VEm+eIYPkI+DUZc1DZlk0yAokCm18Cs+c5TPtz6/X66urqGuhviJCX1IQYjtSEGI7UhjkfqQwzmZK+N/Pz8QZfJldwhUOa1vlM0oO/Zjlb1Asrl16NWZNETTLH81QbSgdUURDu5MNFNXqiXjDPmYyueg64ovNEepU+vZp59GkYDtC57BYMexW9Js3fMdNpsE7BqXtrTEVZrIfrVNGpPBemUCZsxyVafH81iJhlPoscr0FNGDqGgKWkUgwaACRMLnXYqTBvQdY24Usy6GpXq3Z2cddZs8guGsbfRxoFWC1azzrjSCDneJLgzMWd4iLxzrHpfD9pvvwvRMOptdxHzOalqe5ztbQfoaLmK/GgJUxQzGAANYm2wru2jf5Y5+UbsDpUDdQnGTLSd8gFXCCGEEEII8e+RkDuEdF1He+Yv4PGjnH0hB+oirFm9hXDvJmZ0NzHVDBmTZ2MrLEdTVfr9Pv4eUPlDVzf/yB+NP8NITW0H1oMbCE6cygrvQkiXEEunWa/10GFqZmz227gTSYblfAF9+wouXPMMiYnDeGN6iLSuYtxfRtAQx1TYiEtVmGYawWiLjjHdT9roQdOsJC15vNIwli0HtjBrwQXYCsazcnOUaFijuMzM6Ak2TCbv0ccXi6Ld+2PoDcAtP6beVMOaHW/SfOhiCqJXMkkxoZl1Ro21kp194lP6mMwKVqvCspf68GUZ8PqlnIUQQgghhPi0k1QwlKrWw4FauPZrrH07yO6a5ZS3beUchxXvWQuw5BaRVlX6crII+33oBgOrdhzgG74g+e5M2vrAvPpPrD7rS0QME1HSZnbpEfYaDjEi+23KNAOLxn6RYn8O962s42s7XiaVYeLNWb2gm0g3FREt2E+5TWUCBZSYAfpJWEbQ7z4TdI3Mtseo7S1ky5YtjB0zATU5ig0rwzhcKjPPceLLOnYJ6akU2gO/gEP76b3pRpbHX6J+zRnkRb/NBMVE2qwzapyVsjLLvzWlT+P+OLGozoTp1n95G0IIIYQQQojTh4TcIaKnU2jPPU4iv4JXmrOx7P8DSxwGcs8+H5M3m5Sq0JOfR8TrAVUFoC+WojsQ5sJhTtK6wv41b7Fpxk+wGnx06Amq1f3kZa5hpsHF1TNuwmWzAdAVSfKFmodJB2Ksv8SBYjSTChsZV9JBpZKFx6CiqXYiGVOJuqejvXNV1t36GElsPP1WPcWFU0n1j6U5kKRijIWKMVYMgzw4q+s6+p9/R6q2inVfPIfNB13kRa5njGIkZdEYNd5C+XDrvz2lj67p1NfEcXsMZOVIKQshhBBCCCE+wZBbXV3NI488gqZpzJ8/n0svvfSI5bqu88gjj1BVVYXFYuGmm26itLT0I617KoqteJX2qI3qwnEs6ltB4Zx5GF2ZJNEIDCsk5sk8PJrS+2xu7uF3njA2i5NH6o0oZdei6RprlWbMrlXMyMjj82fejNF45Gk9sGkNk6p30TTChGm4mxkGM+VeOyZFIWEdRq/7TOKOSlDfu2VYTfViDtewYl8u+f7zUJM52L0qE862k5FpOO6x6c/8hd3dO3jrvO+S3TiKkYqRuCXNqAnvhNuP6bnZ1qYk4X6NKTPt8iyuEEIIIYQQAviEQq6mafzpT3/iBz/4AT6fj+9973tMnTqVwsL35papqqqira2Ne++9l7q6Oh566CF+/vOff6R1TzV6MsHGZdspGzeG6ytGYrA5iCdj9BQXkch0HxVu31VQtZmdzhG8vM+NQVfZqwTpt69mVlEZiyZ99ZjrxOMJxi1/EM2oUDJ/GNMsVpK6QiJjMqHMmaQsxx6VTOlcR03HRPYF52E2mBg13kppheVDr762vPEsL6VKcA9bxPCYgbAlwciJFipKPr5wC4d/KVJXE8PpUskrPPHneYUQQgghhBCnp08k5NbX15Obm0tOTg4AM2fOZNOmTUcE1c2bNzNnzhwURWHEiBGEw2GCwSCdnZ0fuu6p5uGHnyVudbKyH6ja/96CTXXHXU9RFGw9+9DtY/F2vsjN3cuxkobNoD177zHXMaZ16IjjPi+PqDmHNxvHs6t7FAnNwuHJf5oH2ds4QAElCVqCNdVh1lR/+LE59DnkKga61TBmu4VCq42OfSk69vV/+MonQNOgr0dj4nQZUVkIIYQQQgjxnk8k5AYCAXw+38Brn89HXV3dUW38fv8RbQKBwEda911vvvkmb775JgC/+MUvjtjeyUXjyNllPxpd19HjTVzR9ihWJQ0W0FE/fG9TfLxgv4oDu0t5d25blPQJ7/+j6FF7yHKbGefM/l/Z/vtl5RiZMDkb9aNMqnsKMRqNJ3HtiqEm9SEGI7UhBiO1IY5H6kMM5lSujU8k5Or60YHug1ffBmvzUdZ914IFC1iwYMHA65N18uIlN16F1+slGAz+i1u4/oRaG4B57/w53QSC3UPdhY/dyT7xthhaUh9iMFIbYjBSG+J4pD7EYE722sjPP/Zjl/AJhVyfz0d393thpLu7G4/Hc1Sb93+I77ZJpVIfuu6pRlEUDIbjD94khBBCCCGEEOLEffi9rh+DsrIyWltb6ejoIJVKsXbtWqZOnXpEm6lTp7Jq1Sp0Xae2tha73Y7H4/lI6wohhBBCCCGEEPAJXck1GAwsWbKEn/3sZ2iaxrx58ygqKuL1118HYOHChUyaNImtW7fyjW98A7PZzE033XTcdYUQQgghhBBCiA9S9GM99HqaaGlpGeouDOpkv8ddDB2pDXE8Uh9iMFIbYjBSG+J4pD7EYE722jjeM7mfyO3KQgghhBBCCCHEJ0FCrhBCCCGEEEKI04aEXCGEEEIIIYQQpw0JuUIIIYQQQgghThsScoUQQgghhBBCnDYk5AohhBBCCCGEOG1IyBVCCCGEEEIIcdqQkCuEEEIIIYQQ4rQhIVcIIYQQQgghxGlD0XVdH+pOCCGEEEIIIYQQHwe5kjtEbr/99qHugjhJSW2I45H6EIOR2hCDkdoQxyP1IQZzKteGhFwhhBBCCCGEEKcNCblCCCGEEEIIIU4bEnKHyIIFC4a6C+IkJbUhjkfqQwxGakMMRmpDHI/UhxjMqVwbMvCUEEIIIYQQQojThlzJFUIIIYQQQghx2jAOdQc+jaqrq3nkkUfQNI358+dz6aWXDnWXxBC5//772bp1K263m7vvvhuA/v5+7rnnHjo7O8nKyuKWW27B6XQOcU/FJ62rq4ulS5fS09ODoigsWLCACy+8UOpDkEgk+OEPf0gqlSKdTnPmmWdyxRVXSG2IAZqmcfvtt+P1ern99tulNsSAm2++GavViqqqGAwGfvGLX0h9CADC4TAPPPAAhw4dQlEUvvrVr5Kfn3/K1obcrvwJ0zSNb37zm/zgBz/A5/Pxve99j29+85sUFhYOddfEENi9ezdWq5WlS5cOhNzHH38cp9PJpZdeynPPPUd/fz/XXHPNEPdUfNKCwSDBYJDS0lKi0Si333473/nOd1ixYoXUx6ecruvE43GsViupVIo77riD6667jo0bN0ptCABefPFF9u3bN/C9Q36uiHfdfPPN3HnnnWRkZAy8J/UhAO677z5Gjx7N/PnzSaVSxONxnn322VO2NuR25U9YfX09ubm55OTkYDQamTlzJps2bRrqbokhMmbMmKN+I7Zp0ybmzp0LwNy5c6U+PqU8Hg+lpaUA2Gw2CgoKCAQCUh8CRVGwWq0ApNNp0uk0iqJIbQgAuru72bp1K/Pnzx94T2pDHI/Uh4hEItTU1HDOOecAYDQacTgcp3RtyO3Kn7BAIIDP5xt47fP5qKurG8IeiZNNb28vHo8HOBx0+vr6hrhHYqh1dHRw4MABysvLpT4EcPiuoNtuu422tjbOO+88KioqpDYEAI8++ijXXHMN0Wh04D2pDfF+P/vZzwA499xzWbBggdSHoKOjg4yMDO6//34OHjxIaWkp11133SldGxJyP2HHujtcUZQh6IkQ4lQQi8W4++67ue6667Db7UPdHXGSUFWVX/3qV4TDYX7961/T2Ng41F0SJ4EtW7bgdrspLS1l165dQ90dcRL6yU9+gtfrpbe3l5/+9Kfk5+cPdZfESSCdTnPgwAGWLFlCRUUFjzzyCM8999xQd+vfIiH3E+bz+eju7h543d3dPfAbEiEA3G43wWAQj8dDMBg84rkZ8emSSqW4++67mT17NmeccQYg9SGO5HA4GDNmDNXV1VIbgr1797J582aqqqpIJBJEo1HuvfdeqQ0xwOv1Aod/lkybNo36+nqpD4HP58Pn81FRUQHAmWeeyXPPPXdK14Y8k/sJKysro7W1lY6ODlKpFGvXrmXq1KlD3S1xEpk6dSorV64EYOXKlUybNm2IeySGgq7rPPDAAxQUFLBo0aKB96U+RF9fH+FwGDg80vKOHTsoKCiQ2hBcffXVPPDAAyxdupT/83/+D5WVlXzjG9+Q2hDA4TuD3r2NPRaLsX37doYNGyb1IcjMzMTn89HS0gLAjh07KCwsPKVrQ0ZXHgJbt27lz3/+M5qmMW/ePC677LKh7pIYIr/5zW/YvXs3oVAIt9vNFVdcwbRp07jnnnvo6urC7/dz6623njLDtYuPz549e7jjjjsYNmzYwCMNn//856moqJD6+JQ7ePAgS5cuRdM0dF1nxowZfO5znyMUCkltiAG7du3ihRde4Pbbb5faEAC0t7fz61//Gjh8e+pZZ53FZZddJvUhAGhoaOCBBx4glUqRnZ3NTTfdhK7rp2xtSMgVQgghhBBCCHHakNuVhRBCCCGEEEKcNiTkCiGEEEIIIYQ4bUjIFUIIIYQQQghx2pCQK4QQQgghhBDitCEhVwghhBBCCCHEaUNCrhBCCDEErrjiCtra2oa6G5+IpUuX8re//W2ouyGEEOJTwjjUHRBCCCGG0s0330wgEODBBx8kIyNj4P3vfOc7HDx4kPvuu4/s7Ox/ax8/+tGPmD17NvPnz/93u/u/ZsWKFfz+97/HbDYD4Ha7ufjii1m4cOEQ90wIIYQ4MXIlVwghxKdednY2b7/99sDrxsZGEonEEPZoaIwYMYLHHnuMxx57jFtvvZXHH3+cAwcODHW3hBBCiBMiV3KFEEJ86s2ZM4dVq1ZxwQUXAIevas6dO/eIW2wjkQgPP/wwVVVVWCwW5s+fz+LFi1FVlRUrVrBs2TIqKipYvnw5drudL33pS0yaNIknn3ySmpoa6urqePTRRzn77LO54YYbANi+fTs///nPCYVCzJo1ixtuuAFFUWhra+P3v/89DQ0NGI1GKisrueWWW47Z982bN/PEE08QCAQoKSnhS1/6EoWFhcDhq9TnnXceq1atorOzk4kTJ3LzzTcPXK09ntLSUgoKCmhubmb48OEfuq+mpiYeeughGhoa8Hq9XH311UydOvWo7UajUX75y19SVFTE9ddfT1VVFY899hjd3d3YbDYuuugiLr744hM4e0IIIcSR5EquEEKIT72KigoikQhNTU1omsa6deuYPXv2EW0efvhhIpEI9913Hz/60Y9YtWoVK1asGFheX19Pfn4+f/rTn7jkkkt44IEH0HWdz3/+84wePZolS5bw2GOPDQRcgK1bt3LnnXfyq1/9inXr1rFt2zYA/va3vzFhwgQeeeQRfv/73w+E7w9qaWnht7/9Lddddx0PPfQQkyZN4q677iKVSg20WbduHd///vdZunQpjY2NR/T5eOrr62ltbaW0tPRD95VKpbjrrrsYP348Dz30EEuWLOHee++lpaXliG2GQiF+/OMfM3LkSJYsWYKiKDzwwAPceOON/OUvf+Huu++msrLyI/VPCCGEGIyEXCGEEIL3ruZu376d/Px8vF7vwDJN01i7di1XX301NpuN7OxsFi1axKpVqwba+P1+FixYgKqqzJ07l2AwSG9v73H3eemll+JwOPD7/YwdO5aGhgYAjEYjnZ2dBINBzGYzo0aNOub6a9euZdKkSYwfPx6j0chnPvMZEokEe/fuHWhzwQUX4PV6cTqdTJkyZWAfx1JXV8d1113Htddey/e//33mzJlDXl7eh+6rrq6OWCzGpZdeOnDlefLkyaxZs2Zg24FAgB/96EfMmDGDq666auB9g8FAU1MTkUgEp9M5EKqFEEKIf5WEXCGEEILDIXfNmjUDtyq/X19fH6lUCr/fP/BeVlYWgUBg4HVmZubAvy0WCwCxWOy4+/zgOu+2v+aaa9B1ne9///vceuutvPXWW8dcPxgMkpWVNfBaVVX8fv+g/TKbzcftU0VFBY8++iiPPfYYf/jDHzh06BBPPvnkh+4rGAzi9/tR1ff+W/HBz6eqqopEIsG55557xD6/9a1vUVVVxc0338wPf/hDamtrB+2fEEII8VFIyBVCCCE4HMqys7Opqqpi+vTpRyzLyMjAYDDQ1dU18F5XV9cRV3uPR1GUE+pLZmYmX/nKV3jwwQe58cYb+dOf/nTM6YY8Hg+dnZ0Dr3VdP6F+fVgfzjjjDLZs2fKh+/J4PHR1daFp2sDyD/Zj/vz5TJgwgTvvvPOIoF1eXs53v/td/vjHPzJt2jTuueeef7vvQgghPt0k5AohhBDv+MpXvsIdd9yB1Wo94n1VVZkxYwZPPvkk0WiUzs5OXnzxxaOe2x2M2+2mvb39I/dj3bp1dHd3A+BwOAb68EEzZ86kqqqKHTt2kEqleOGFFzCZTIwcOfIj72swoVCITZs2DQwsdbx9VVRUYLVaef7550mlUuzatYstW7Ywa9asI7Z5ww03kJ+fz1133UUikSCVSrF69WoikQhGoxG73X7M4xRCCCFOhIyuLIQQQrwjNzd30GVLlizh4Ycf5mtf+xpms5n58+czb968j7TdCy+8kKVLl/LGG28we/ZslixZctz2+/bt49FHHyUSiZCZmcn1119/zLl68/Pz+frXv87DDz88MOLxbbfdhtH4r/14r62t5dprrwUO3z5dWVnJ9ddf/5H29d3vfpeHHnqIZ599Fq/Xy9e+9jUKCgqO2L6iKNx4443cf//9/PKXv+TWW29l1apVPPzww2iaNrAPIYQQ4t+h6LquD3UnhBBCCCGEEEKIj4PcEySEEEIIIYQQ4rQhIVcIIYQQQgghxGlDQq4QQgghhBBCiNOGhFwhhBBCCCGEEKcNCblCCCGEEEIIIU4bEnKFEEIIIYQQQpw2JOQKIYQQQgghhDhtSMgVQgghhBBCCHHakJArhBBCCCGEEOK08f8BoyvAaZVSEI0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot vintage line chart\n",
    "vantage0 = vantage_wide.replace(0,np.nan)\n",
    "lst = [i for i in range(0,61)]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [16, 6]\n",
    "\n",
    "vantage_wide[lst].T.plot(legend = False)\n",
    "plt.xlabel('Months on Books')\n",
    "plt.ylabel('Cumulative % > 60 Days Past Due')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c554eff",
   "metadata": {},
   "source": [
    "# Observe Window Analysis\n",
    "Because of two reasons, account cancellation and observe over, our observe on accounts will be truncated. Observe window is a significant parameter to be considered. If observe window is too short, users' behavior will not fully show off, which will bring unnecessary noise to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4e8f08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_observe(credit, command):\n",
    "    '''calculate observe window\n",
    "    '''\n",
    "    id_sum = len(set(pivot_tb['ID']))\n",
    "    credit['status'] = 0\n",
    "    exec(command)\n",
    "    #credit.loc[(credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\n",
    "    credit['month_on_book'] = credit['MONTHS_BALANCE'] - credit['open_month']\n",
    "    minagg = credit[credit['status'] == 1].groupby('ID')['month_on_book'].min()\n",
    "    minagg = pd.DataFrame(minagg)\n",
    "    minagg['ID'] = minagg.index\n",
    "    obslst = pd.DataFrame({'month_on_book':range(0,61), 'rate': None})\n",
    "    lst = []\n",
    "    for i in range(0,61):\n",
    "        due = list(minagg[minagg['month_on_book']  == i]['ID'])\n",
    "        lst.extend(due)\n",
    "        obslst.loc[obslst['month_on_book'] == i, 'rate'] = len(set(lst)) / id_sum \n",
    "    return obslst['rate']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "edf844a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"credit.loc[(credit['STATUS'] == '0') | (credit['STATUS'] == '1') | (credit['STATUS'] == '2') | (credit['STATUS'] == '3' )| (credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\"   \n",
    "morethan1 = calculate_observe(credit, command)\n",
    "command = \"credit.loc[(credit['STATUS'] == '1') | (credit['STATUS'] == '2') | (credit['STATUS'] == '3' )| (credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\"   \n",
    "morethan30 = calculate_observe(credit, command)\n",
    "command = \"credit.loc[(credit['STATUS'] == '2') | (credit['STATUS'] == '3' )| (credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan60 = calculate_observe(credit, command)\n",
    "command = \"credit.loc[(credit['STATUS'] == '3' )| (credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan90 = calculate_observe(credit, command)\n",
    "command = \"credit.loc[(credit['STATUS'] == '4' )| (credit['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan120 = calculate_observe(credit, command)\n",
    "command = \"credit.loc[(credit['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan150 = calculate_observe(credit, command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4976e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obslst = pd.DataFrame({'past due more than 30 days': morethan30,\n",
    "                       'past due more than 60 days': morethan60,\n",
    "                       'past due more than 90 days': morethan90,\n",
    "                       'past due more than 120 days': morethan120,\n",
    "                       'past due more than 150 days': morethan150\n",
    "                        })\n",
    "\n",
    "\n",
    "\n",
    "obslst.plot(grid = True)\n",
    "plt.xlabel('Months on Books')\n",
    "plt.ylabel('Cumulative % of Bad Customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca131cde",
   "metadata": {},
   "source": [
    "This plot could be seen as a average (across open-month) version of vintage plot. For longer past due date, it needs longer observe window. For example, more than 150 days past due needs at least 5 months until first bad customer appears. For most situation, a 20-months observe window could cover most bad customer. However, For 30 days past due, we could see that after 30 months on books, there still are new bad customer join in the list. So a 20 MOB observe window will be appropriate. Those who exists shorter than the observe window should be excluded from our analysis, thus you could see I deleted users whose observe window less than 20 on last section (the window could be changed)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e38d94",
   "metadata": {},
   "source": [
    "# Overall Past-due Ratio\n",
    "Calculating overall past-due rate. Respectively, we analyze 1 day past due, 20 days past due, 60 days past due, 90 days past due, 120 days past due, 150 days past due. This analysis could help us to define who are bad customers. We could see that almost 87% users have past due more than 1 day, which is too common, thus it's inappropriate to be a standard. What about 150 days overdue? Only 0.4% of accounts appear to past due that long. If we use that, we will left many bad customers in our scrutiny. A table like that will help you to determine what will be the most suitable standard of bad customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e433638",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_rate(pivot_tb, command): \n",
    "    '''calculate bad customer rate\n",
    "    '''\n",
    "    credit0['status'] = None\n",
    "    exec(command) # excuate input code\n",
    "    sumagg = credit0.groupby('ID')['status'].agg(sum)\n",
    "    pivot_tb = pd.merge(pivot_tb, sumagg, on = 'ID', how = 'left')\n",
    "    pivot_tb.loc[pivot_tb['status'] > 1, 'status'] = 1\n",
    "    rate = pivot_tb['status'].sum() / len(pivot_tb)\n",
    "    return round(rate, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7870e0a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "command = \"credit0.loc[(credit0['STATUS'] == '0') | (credit0['STATUS'] == '1') | (credit0['STATUS'] == '2') | (credit0['STATUS'] == '3' )| (credit0['STATUS'] == '4' )| (credit0['STATUS'] == '5'), 'status'] = 1\"   \n",
    "morethan1 = calculate_rate(pivot_tb, command)\n",
    "command = \"credit0.loc[(credit0['STATUS'] == '1') | (credit0['STATUS'] == '2') | (credit0['STATUS'] == '3' )| (credit0['STATUS'] == '4' )| (credit0['STATUS'] == '5'), 'status'] = 1\"   \n",
    "morethan30 = calculate_rate(pivot_tb, command)\n",
    "command = \"credit0.loc[(credit0['STATUS'] == '2') | (credit0['STATUS'] == '3' )| (credit0['STATUS'] == '4' )| (credit0['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan60 = calculate_rate(pivot_tb, command)\n",
    "command = \"credit0.loc[(credit0['STATUS'] == '3' )| (credit0['STATUS'] == '4' )| (credit0['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan90 = calculate_rate(pivot_tb, command)\n",
    "command = \"credit0.loc[(credit0['STATUS'] == '4' )| (credit0['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan120 = calculate_rate(pivot_tb, command)\n",
    "command = \"credit0.loc[(credit0['STATUS'] == '5'), 'status'] = 1\"\n",
    "morethan150 = calculate_rate(pivot_tb, command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a4847a9",
   "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>situation</th>\n",
       "      <th>bad customer ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>past due more than 1 day</td>\n",
       "      <td>0.87054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>past due more than 30 days</td>\n",
       "      <td>0.11634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>past due more than 60 days</td>\n",
       "      <td>0.01450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>past due more than 90 days</td>\n",
       "      <td>0.00720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>past due more than 120 days</td>\n",
       "      <td>0.00528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>past due more than 150 days</td>\n",
       "      <td>0.00424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     situation  bad customer ratio\n",
       "0     past due more than 1 day             0.87054\n",
       "1   past due more than 30 days             0.11634\n",
       "2   past due more than 60 days             0.01450\n",
       "3   past due more than 90 days             0.00720\n",
       "4  past due more than 120 days             0.00528\n",
       "5  past due more than 150 days             0.00424"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_dt = pd.DataFrame({'situation':['past due more than 1 day',\n",
    "                               'past due more than 30 days',\n",
    "                               'past due more than 60 days',\n",
    "                               'past due more than 90 days',\n",
    "                               'past due more than 120 days',\n",
    "                               'past due more than 150 days'],\n",
    "                      'bad customer ratio':[morethan1,\n",
    "                               morethan30,\n",
    "                               morethan60,\n",
    "                               morethan90, \n",
    "                               morethan120,\n",
    "                               morethan150, \n",
    "                      ]})\n",
    "summary_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86555545",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "087c95c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a10bf3ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9ea1ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e982dff0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ec78549",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4da2027e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75661254",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b891fe0a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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