data/notebooks/flag-24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expense Processing Time Analysis (Flag 24)\n",
    "\n",
    "### Dataset Description\n",
    "The dataset consists of 500 entries simulating ServiceNow fm_expense_line table, which records various attributes of financial expenses. Key fields include 'number', 'opened_at', 'amount', 'state', 'short_description', 'ci', 'user', 'department', 'category', 'process_date', 'source_id', and 'type'. This table documents the flow of financial transactions by detailing the amount, departmental allocation, and the nature of each expense. Provides a clear view of organizational expenditures across different categories, highlighting both the timing and the approval state of each financial entry.\n",
    "\n",
    "### Your Task\n",
    "**Goal**: To understand the factors contributing to faster expense processing times in a specific department and evaluate whether these practices can be extended to improve efficiency across all departments.\n",
    "\n",
    "**Role**: Operational Efficiency Analyst\n",
    "\n",
    "**Difficulty**: 3 out of 5. This analysis requires  data manipulation and interpretation skills to extract required insights in finance expenses.\n",
    "\n",
    "**Category**: Finance Management\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Necessary Libraries\n",
    "This cell imports all necessary libraries required for the analysis. This includes libraries for data manipulation, data visualization, and any specific utilities needed for the tasks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:50.746694Z",
     "iopub.status.busy": "2024-10-29T18:39:50.746020Z",
     "iopub.status.idle": "2024-10-29T18:39:52.521198Z",
     "shell.execute_reply": "2024-10-29T18:39:52.520462Z"
    }
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import pandas as pd\n",
    "import json\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from pandas import date_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Dataset\n",
    "This cell loads the expense dataset to be analyzed. The data is orginally saved in the from a CSV file, and is here imported into a DataFrame. The steps involve specifying the path to the dataset, using pandas to read the file, and confirming its successful load by inspecting the first few table entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:52.527099Z",
     "iopub.status.busy": "2024-10-29T18:39:52.526674Z",
     "iopub.status.idle": "2024-10-29T18:39:52.550664Z",
     "shell.execute_reply": "2024-10-29T18:39:52.550052Z"
    }
   },
   "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>department</th>\n",
       "      <th>amount</th>\n",
       "      <th>user</th>\n",
       "      <th>category</th>\n",
       "      <th>opened_at</th>\n",
       "      <th>type</th>\n",
       "      <th>number</th>\n",
       "      <th>processed_date</th>\n",
       "      <th>source_id</th>\n",
       "      <th>state</th>\n",
       "      <th>short_description</th>\n",
       "      <th>ci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sales</td>\n",
       "      <td>3022</td>\n",
       "      <td>Carolina Kinlaw</td>\n",
       "      <td>Miscellaneous</td>\n",
       "      <td>2023-06-24 10:54:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EXP00000000</td>\n",
       "      <td>2023-07-07 03:51:35.598841559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Processed</td>\n",
       "      <td>Auto-generated asset entry for miscellaneous item</td>\n",
       "      <td>Wireless Bluetooth Speaker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HR</td>\n",
       "      <td>2558</td>\n",
       "      <td>Rosalie Krigger</td>\n",
       "      <td>Travel</td>\n",
       "      <td>2023-12-28 01:05:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EXP00000001</td>\n",
       "      <td>2024-01-08 10:21:28.095225833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Processed</td>\n",
       "      <td>Travel equipment asset record</td>\n",
       "      <td>Travel Mate Backpack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Customer Support</td>\n",
       "      <td>966</td>\n",
       "      <td>Valerie Pou</td>\n",
       "      <td>Services</td>\n",
       "      <td>2023-03-11 08:02:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EXP00000002</td>\n",
       "      <td>2023-03-13 08:02:00.000000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Processed</td>\n",
       "      <td>Provisioning of new cloud service</td>\n",
       "      <td>AWS EC2 Instance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales</td>\n",
       "      <td>1002</td>\n",
       "      <td>Melody Saddat</td>\n",
       "      <td>Services</td>\n",
       "      <td>2023-04-02 15:10:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EXP00000003</td>\n",
       "      <td>2023-04-15 03:49:51.831926525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Processed</td>\n",
       "      <td>Service configuration for asset management</td>\n",
       "      <td>Enterprise Cloud Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Customer Support</td>\n",
       "      <td>4187</td>\n",
       "      <td>Marianne Earman</td>\n",
       "      <td>Travel</td>\n",
       "      <td>2023-07-17 14:23:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EXP00000004</td>\n",
       "      <td>2023-07-22 16:45:08.994376662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Processed</td>\n",
       "      <td>Travel kit including laptop and accessories</td>\n",
       "      <td>Dell Latitude E7470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         department  amount             user       category  \\\n",
       "0             Sales    3022  Carolina Kinlaw  Miscellaneous   \n",
       "1                HR    2558  Rosalie Krigger         Travel   \n",
       "2  Customer Support     966      Valerie Pou       Services   \n",
       "3             Sales    1002    Melody Saddat       Services   \n",
       "4  Customer Support    4187  Marianne Earman         Travel   \n",
       "\n",
       "             opened_at  type       number                 processed_date  \\\n",
       "0  2023-06-24 10:54:00   NaN  EXP00000000  2023-07-07 03:51:35.598841559   \n",
       "1  2023-12-28 01:05:00   NaN  EXP00000001  2024-01-08 10:21:28.095225833   \n",
       "2  2023-03-11 08:02:00   NaN  EXP00000002  2023-03-13 08:02:00.000000000   \n",
       "3  2023-04-02 15:10:00   NaN  EXP00000003  2023-04-15 03:49:51.831926525   \n",
       "4  2023-07-17 14:23:00   NaN  EXP00000004  2023-07-22 16:45:08.994376662   \n",
       "\n",
       "   source_id      state                                  short_description  \\\n",
       "0        NaN  Processed  Auto-generated asset entry for miscellaneous item   \n",
       "1        NaN  Processed                      Travel equipment asset record   \n",
       "2        NaN  Processed                  Provisioning of new cloud service   \n",
       "3        NaN  Processed         Service configuration for asset management   \n",
       "4        NaN  Processed        Travel kit including laptop and accessories   \n",
       "\n",
       "                           ci  \n",
       "0  Wireless Bluetooth Speaker  \n",
       "1        Travel Mate Backpack  \n",
       "2            AWS EC2 Instance  \n",
       "3    Enterprise Cloud Service  \n",
       "4         Dell Latitude E7470  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = \"csvs/flag-24.csv\"\n",
    "flag_data = pd.read_csv(dataset_path)\n",
    "df = pd.read_csv(dataset_path)\n",
    "flag_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Question 1: Which departments have higher proportions of expense rejections compared to the organizational average?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot processing period by department\n",
    "\n",
    "This box plot visualizes the distribution of processing periods for expenses by department, highlighting median, quartiles, and potential outliers within each group. By examining the spread and central tendency, this plot aids in identifying departments with notably quicker or slower processing times, compared to the organizational average.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:52.556759Z",
     "iopub.status.busy": "2024-10-29T18:39:52.556520Z",
     "iopub.status.idle": "2024-10-29T18:39:52.866921Z",
     "shell.execute_reply": "2024-10-29T18:39:52.866269Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14876/287017439.py:15: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  box_plot = sns.boxplot(x='department', y='processing_period', data=valid_data, palette=palette)\n"
     ]
    },
    {
     "data": {
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337/X277/9H24cjern14E2kqBgYG68MIL9be//U1LlizR2rVrVVpa2utOaAdq586dqq+vdwy7erbrjTfeqEmTJvX7GFeHeHV1dRo3bpwiIyN15513KicnR6GhoVq/fr1+//vf9zlD60DvOra3Xva3/Mdd7e7uVmJiopYvX97v4xMSEpx6HXf3HwCAA8XwBwAAE2VmZurdd99VY2NjrzM0ej7mkpmZKUnKyclRd3e3Nm7cuNdBRk5OjiQpMTFxrwORn65/ww036IYbblBBQYGOPPJIPfjgg73u6nTsscfq2GOP1cKFC/Xcc89pxowZ+te//qXf/OY3B/qWHe+ru7tbhYWFvc4K2bJly8963h/r2R6RkZFObQ9n9WQvKCjodYHeXbt2qa6uzvFn1vPfgoICDR482LFeZWWlS2eA9JzB9GP5+fmOi3f3mDlzph588EG99tprWrVqlRISEvY6nHHFP//5T0lyPFfPewkODnZ6uxYUFOikk05yfN3U1KSysjJNmTJFkvTBBx+ourpaL730kk488UTHej++a5szPDWQy8nJ0bvvvquxY8e67Xb3B9PwEADgf7jmDwAAJpoyZYq6urr05z//udfyhx56SDabTZMnT5a057ovAQEBuvPOO/ucBdFzNsGkSZMUGRmpe+65p9/r9FRWVkqSWlpa1Nra2ut7OTk5ioiIcNyqvLa2ts9ZCj1Dp5/ezvxA9AwSlixZ0mv5Y4899rOfu8eoUaOUk5OjBx54QE1NTX2+37M9XNUzsHj44Yd7LV+8eLEkOe5YNnHiRAUHB+uxxx7rtS1/+rj9eeWVV3pdQ2fdunX6/PPPHd3oMWLECI0YMUJPPvmkXnzxRZ1//vm9zvY6EKtXr9Zdd92l7OxszZgxQ9Ke4eL48eP117/+VWVlZX0e0992feKJJ3p1cunSpers7HS8h54zZX68ndrb2/v0Y38GDhyouro6lx7jjPPOO09dXV266667+nyvs7PzgF5z4MCBkuSRvAAA7A9n/gAAYKIzzzxTJ510kubPn6/t27friCOO0Ntvv62VK1fquuuuc5y9kpubq/nz5+uuu+7SCSecoOnTp8tut+uLL75QSkqKFi1apMjISC1dulQXXXSRfvGLX+j8889XQkKCiouL9Z///Edjx47Vn//8Z+Xn5+vkk0/Weeedp+HDhysoKEgvv/yydu3apfPPP1+S9PTTT2vJkiU6++yzlZOTo8bGRv3tb39TZGSkY/jxc4waNUrnnHOOHn74YVVXVztu9Z6fny/JPWdFBAQE6Mknn9TkyZN16KGH6uKLL1ZqaqpKSkr0/vvvKzIyUq+99prLz3vEEUdo1qxZeuKJJxwfV1q3bp2efvppTZs2zXGGS0JCgm688UYtWrRIZ5xxhqZMmaINGzZo1apVio+Pd/r1cnNzdfzxx+vKK69UW1ubHn74YcXFxenmm2/us+7MmTN14403SpLLH/latWqVNm3apM7OTu3atUurV6/WO++8o8zMTL366qsKDQ11rPv444/r+OOP1+GHH67LLrtMgwcP1q5du/Tpp59q586d+u9//9vrudvb2x2d27x5s5YsWaLjjz9eU6dOlSQdd9xxiomJ0axZs3TttdfKZrPpn//8p8sfkxo1apSWLl2qu+++W7m5uUpMTNzrNbBcMW7cOF1xxRVatGiRvv76a5166qkKDg5WQUGBXnjhBT3yyCM699xzXXrOI488UoGBgbr33ntVX18vu92uCRMm7PW6QgAAuJVFdxkDAMArOXObbMPYc6v3gQMH9vu9xsZG4/rrrzdSUlKM4OBgIy8vz7j//vt73ZK6xz/+8Q9j5MiRht1uN2JiYoxx48YZ77zzTq913n//fWPSpElGVFSUERoaauTk5BizZ882vvzyS8MwDKOqqsqYM2eOMWzYMGPgwIFGVFSUccwxxxjPP/+84znWr19vXHDBBUZGRoZht9uNxMRE44wzznA8Rw/t5VbvlZWV/W6nwsJCx7Lm5mZjzpw5RmxsrBEeHm5MmzbN2Lx5syHJ+NOf/rTP7dlzG/R93a6+x4YNG4zp06cbcXFxht1uNzIzM43zzjvPeO+99/ab+8ff+7GOjg5jwYIFRnZ2thEcHGykp6cbt9xyS69bkhuGYXR1dRkLFiwwkpOTjQEDBhjjx483vvvuOyMzM9PpW73ff//9xoMPPmikp6cbdrvdOOGEE4z//ve//T6mrKzMCAwMNIYMGbLf7dKj58+m51dISIiRlJRknHLKKcYjjzxiNDQ09Pu4rVu3GjNnzjSSkpKM4OBgIzU11TjjjDOMFStW9HnuNWvWGJdffrkRExNjhIeHGzNmzDCqq6t7Pd/atWuNY4891hgwYICRkpJi3HzzzcZbb73V53bo48aNMw499NB+M5WXlxunn366ERERYUhy3K59b39P9/bnvre/r0888YQxatQoY8CAAUZERIRx+OGHGzfffLNRWlrqWCczM9M4/fTT+zx23LhxfW5D/7e//c0YPHiwERgYyG3fAQCmshkGV6IDAADW+PrrrzVy5Eg9++yzjo8ZwXlVVVVKTk7W7bffvtc7cZnpqaee0sUXX6wvvvhivxdEBwAA5uGaPwAAwBS7d+/us+zhhx9WQEBAr4v+wnlPPfWUurq6dNFFF1kdBQAAHMS45g8AADDFfffdp6+++konnXSSgoKCtGrVKq1atUqXX3650tPTrY7nVVavXq2NGzdq4cKFmjZtWp87gQEAAPwYwx8AAGCK4447Tu+8847uuusuNTU1KSMjQ3fccYfmz59vdTSvc+edd+qTTz7R2LFj3XrHNAAA4Ju45g8AAAAAAIAP45o/AAAAAAAAPozhDwAAAAAAgA/z+Wv+dHd3q7S0VBEREbLZbFbHAQAAAAAAcAvDMNTY2KiUlBQFBOz9/B6fH/6UlpZyBxEAAAAAAOCzduzYobS0tL1+3+eHPxEREZL2bIjIyEiL03iP7du3c9tYmIKuwSx0DWahazALXYNZ6BrMQtdc19DQoPT0dMfsY298/m5fDQ0NioqKUn19PcMfF3R3d+/zlDHAXegazELXYBa6BrPQNZiFrsEsdM11zs482KroV0FBgdUR4CfoGsxC12AWugaz0DWYha7BLHTNcxj+AAAAAAAA+DCGP+hXbGys1RHgJ+gazELXYBa6BrPQNZiFrsEsdM1zGP6gX3a73eoI8BN0DWahazALXYNZ6BrMQtdgFrrmOQx/0K+ysjKrI8BP0DWYha7BLHQNZqFrMAtdg1nomucw/AEAAAAAAPBhDH/Qr4yMDKsjwE/QNZiFrsEsdA1moWswC12DWeia5zD8Qb9qamqsjgA/QddgFroGs9A1mIWuwSx0DWaha57D8Af9ampqsjoC/ARdg1noGsxC12AWugaz0DWYha55DsMf9CsoKMjqCPATdA1moWswC12DWegazELXYBa65jk2wzAMq0N4UkNDg6KiolRfX6/IyEir4wAAAAAAALiFszMPzvxBvzZv3mx1BPgJugaz0DWYha7BLHQNZqFrMAtd8xyGPwAAAAAAAD6M4Q/6FR0dbXUE+Am6BrPQNZiFrsEsdA1moWswC13zHIY/6FdYWJjVEeAn6BrMQtdgFroGs9A1mIWuwSx0zXMY/qBfpaWlVkeAn6BrMAtdg1noGsxC12AWugaz0DXPYfgDAAAAAADgwxj+oF/p6elWR4CfoGswC12DWegazELXYBa6BrPQNc9h+IN+1dXVWR0BfoKuwSx0DWahazALXYNZ6BrMQtc8h+EP+tXY2Gh1BPgJugaz0DWYha7BLHQNZqFrMAtd85wgqwPg4BQQwFwQ5qBrMAtdg1noGsxC12AWugZP6+7u1qZNm7Rp0yZ1dXVp2LBh9M7NbIZhGFaH8KSGhgZFRUWpvr5ekZGRVscBAAAAAAD/s27dOi1fvlyVlZWOZQkJCZoxY4ZGjx5tYTLv4OzMg1Ea+pWfn291BPgJugaz0DWYha7BLHQNZqFr8JR169bpkUceUXp6uhYsWKBbbrlFCxYsUHp6uh555BGtW7fO6og+g+EP+uXjJ4ThIELXYBa6BrPQNZiFrsEsdA2e0N3dreXLl2vkyJGaO3eu8vLyFBISory8PM2dO1cjR47U8uXL1d3dbXVUn8DwB/2KioqyOgL8BF2DWegazELXYBa6BrPQNXjCpk2bVFlZqbPOOstxfZ+ergUEBGjq1KmqrKzUpk2brIzpMxj+oF8RERFWR4CfoGswC12DWegazELXYBa6Bk/oua17enq6Y9mPu9aznNu/uwfDH/Rr586dVkeAn6BrMAtdg1noGsxC12AWugZPiI6OliTt2LHDsezHXetZ3rMefh6GPwAAAAAAwFTDhg1TQkKCVq5c2ee6Pt3d3Xr11VeVkJCgYcOGWZTQtzD8Qb9SU1OtjgA/QddgFroGs9A1mIWuwSx0DZ4QEBCgGTNmaMOGDVq8eLHy8/MVFxen/Px8LV68WBs2bNCMGTMc1wPCzxNkdQAcnJqamhQeHm51DPgBugaz0DWYha7BLHQNZqFr8JTRo0frd7/7nZYvX6477rjDsTwhIUG/+93vNHr0aOvC+RiGP+hXfX29kpKSrI4BP0DXYBa6BrPQNZiFrsEsdA2eNHr0aB111FHatGmTNm7cqOHDh2vYsGGc8eNmDH/QL5vNZnUE+Am6BrPQNZiFrsEsdA1moWvwtICAAA0fPlxBQUEaMmSI1XF8ks0wDMPqEJ7U0NCgqKgo1dfXKzIy0uo4AAAAAAAAbuHszIPzqNCvLVu2WB0BfoKuwSx0DWahazALXYNZ6BrMQtc8h+EP+tXV1WV1BPgJugaz0DWYha7BLHQNZqFrMAtd8xyGP+hXRESE1RHgJ+gazELXYBa6BrPQNZiFrsEsdM1zGP6gXzExMVZHgJ+gazALXYNZ6BrMQtdgFroGs9A1z2H4g34VFxdbHQF+gq7BLHQNZqFrMAtdg1noGsxC1zyH4Q8AAAAAAIAPs3T4s3TpUo0YMUKRkZGKjIzUmDFjtGrVKsf3W1tbNWfOHMXFxSk8PFznnHOOdu3aZWFi/5GcnGx1BPgJugaz0DWYha7BLHQNZqFrMAtd8xxLhz9paWn605/+pK+++kpffvmlJkyYoLPOOkvff/+9JOn666/Xa6+9phdeeEFr1qxRaWmppk+fbmVkv9Ha2mp1BPgJugaz0DWYha7BLHQNZqFrMAtd8xxLhz9nnnmmpkyZory8PA0ZMkQLFy5UeHi4PvvsM9XX1+vvf/+7Fi9erAkTJmjUqFFatmyZPvnkE3322WdWxvYLtbW1VkeAn6BrMAtdg1noGsxC12AWugaz0DXPOWiu+dPV1aV//etfam5u1pgxY/TVV1+po6NDEydOdKwzbNgwZWRk6NNPP93r87S1tamhoaHXLwAAAAAAAH8VZHWAb7/9VmPGjFFra6vCw8P18ssva/jw4fr6668VEhKi6OjoXusPGjRI5eXle32+RYsWacGCBX2WFxQUKDw8XLm5udqxY4fa2to0YMAAJSUlqbCwUJKUmJgowzBUWVkpSRo8eLBKS0vV2tqq0NBQpaSkaNu2bZKk+Ph4BQQEqKKiQpKUlZWliooKtbS0KCQkRBkZGdqyZYskKTY2ViEhIY7cmZmZqq6uVlNTk4KCgjR48GDl5+dL2nNru9DQUJWVlUmSMjIyVFtbq8bGRgUGBio3N1f5+fkyDENRUVEKDw9XSUmJpD0fo2tsbFR9fb1sNpuGDBmigoICdXd3KyIiQtHR0dqxY4ckKSUlRS0tLaqrq5MkDR06VFu3blVnZ6fCw8OVkZGhzZs3S9rzucu2tjbV1NRIkvLy8lRUVKT29naFhYUpMTFR27dvd/z5dHV1qaqqSpKUk5OjnTt3OrZ3cnKyYxsmJCRIUq/tXVZWpt27d8tutystLU1bt251bO/AwEDHNZ9+ur0zMzNVUFDg2N52u73XNqypqXFs75ycHMd7i46OVlhYmEpLSyVJ6enpqqurU2NjowICApSXl9dre0dERGjnzp2SpNTUVDU1NfXa3lu2bFFXV5ciIiIUExPjuFp9cnKyWltbHZPsIUOGaNu2bY7tHRcXp6KiIklSUlKS2tvbHds7NzdXxcXF/W7vxMREdXd3O7b3vjqbkJAgm83m6Gx2drbKy8sd2zs9Pd3R2bi4OAUFBfXa3pWVlWpublZwcLCysrIc23tfnd3f9k5LS1NERIQ2b97s2N49ne25FljP9t5XZ/e3vfPy8rR9+3Z1dHRo4MCBSkhI6NXZzs5OVVdXO7Y3+4j97yNiY2N7bW9v2EcYhqHNmzezj/CyfUTPP+R40z6ip2vsI7xrH+GNP0cYhqGSkhL2EV62j/DGnyMMw1BzczP7CHnXPsJbf47o7u5mH7Gf7f3jfURTU5OcYTMMw3BqTQ9pb29XcXGx6uvrtWLFCj355JNas2aNvv76a1188cVqa2vrtf7o0aN10kkn6d577+33+dra2no9pqGhQenp6aqvr1dkZKRH34sv2bp1q3JycqyOAT9A12AWugaz0DWYha7BLHQNZqFrrmtoaFBUVNR+Zx6Wn/kTEhKi3NxcSdKoUaP0xRdf6JFHHtGvfvUrtbe3q66urtfZP7t27VJSUtJen89ut8tut3s6ts/r7Oy0OgL8BF2DWegazELXYBa6BrPQNZiFrnnOQXPNnx7d3d1qa2vTqFGjFBwcrPfee8/xvc2bN6u4uFhjxoyxMKF/CA8PtzoC/ARdg1noGsxC12AWugaz0DWYha55jqVn/txyyy2aPHmyMjIy1NjYqOeee04ffPCB3nrrLUVFRenSSy/V3LlzFRsbq8jISF1zzTUaM2aMjj32WCtj+4W4uDirI8BP0DWYha7BLHQNZqFrMAtdg1nomudYeuZPRUWFZs6cqaFDh+rkk0/WF198obfeekunnHKKJOmhhx7SGWecoXPOOUcnnniikpKS9NJLL1kZ2W/0XBAM8DS6BrPQNZiFrsEsdA1moWswC13zHEvP/Pn73/++z++Hhobq8ccf1+OPP25SIgAAAAAAAN9y0F3zBweHfV1UG3Anugaz0DWYha7BLHQNZqFrMAtd8xyGP+hXe3u71RHgJ+gazELXYBa6BrPQNZiFrsEsdM1zGP6gXzU1NVZHgJ+gazALXYNZ6BrMQtdgFroGs9A1z2H4AwAAAAAA4MNshmEYVofwpIaGBkVFRam+vl6RkZFWx/EaXV1dCgwMtDoG/ABdg1noGsxC12AWugaz0DWYha65ztmZB2f+oF/FxcVWR4CfoGswC12DWegazELXYBa6BrPQNc+x9FbvOPh0d3dr06ZN2rhxo3bv3q1hw4YpIIAZITyHi7rBLHQNZqFrMAtdg1noGsxC1zyH4Q8c1q1bp+XLl6uyslKS9NJLLykhIUEzZszQ6NGjLU4HXxUWFmZ1BPgJugaz0DWYha7BLHQNZqFrnsM1fyBpz+DnkUce0ciRI3XWWWcpMTFRFRUVWrlypTZs2KDf/e53DIDgEW1tbbLb7VbHgB+gazALXYNZ6BrMQtdgFrrmOq75A6d1d3dr+fLlGjlypObOnau8vDyVl5crLy9Pc+fO1ciRI7V8+XJ1d3dbHRU+aPv27VZHgJ+gazALXYNZ6BrMQtdgFrrmOQx/oE2bNqmyslJnnXVWn+v7BAQEaOrUqaqsrNSmTZssSggAAAAAAA4Uwx+orq5OkpSenu5YlpiY6Ph9z/Ke9QB3+nHXAE+iazALXYNZ6BrMQtdgFrrmOQx/oOjoaEnSjh07HMt+/BGvnuU96wHuxMcJYRa6BrPQNZiFrsEsdA1moWuew/AHGjZsmBISErRy5UrHX7aqqipJe/7yvfrqq0pISNCwYcOsjAkf1dM1wNPoGsxC12AWugaz0DWYha55DsMfKCAgQDNmzNCGDRu0ePFi5efnq62tTfn5+Vq8eLE2bNigGTNm9LkeEAAAAAAAOPhxq3c4rFu3TsuXL1dlZaVjWUJCgmbMmMFt3uExHR0dCg4OtjoG/ABdg1noGsxC12AWugaz0DXXOTvzCDIxEw5yo0eP1lFHHaVNmzapoKBAeXl5GjZsGGf8wKNKS0uVmZlpdQz4AboGs9A1mIWuwSx0DWaha57D8Ae9BAQEaPjw4QoMDNTQoUOtjgM/0NraanUE+Am6BrPQNZiFrsEsdA1moWuewykd6FdoaKjVEeAn6BrMQtdgFroGs9A1mIWuwSx0zXMY/qBfKSkpVkeAn6BrMAtdg1noGsxC12AWugaz0DXPYfiDfm3bts3qCPATdA1moWswC12DWegazELXYBa65jkMfwAAAAAAAHwYwx/0KyEhweoI8BN0DWahazALXYNZ6BrMQtdgFrrmOQx/0C+bzWZ1BPgJugaz0DWYha7BLHQNZqFrMAtd8xyGP+hXRUWF1RHgJ+gazELXYBa6BrPQNZiFrsEsdM1zGP4AAAAAAAD4MJthGIbVITypoaFBUVFRqq+vV2RkpNVxvEZ7e7tCQkKsjgE/QNdgFroGs9A1mIWuwSx0DWaha65zdubBmT/oV3l5udUR4CfoGsxC12AWugaz0DWYha7BLHTNc4KsDoCD0+7du62OAD9B1/xHW1ubSktLLXv9oqIidXV1Wfb6KSkpstvtlr0+zMN+DWahazALXYNZ6JrnMPxBv/gfFJiFrvmP0tJSzZ8/3+oYllm4cKGys7OtjgETsF+DWegazELXYBa65jlc8wf96urqUmBgoNUx4Afomv+w8syfkpISLVmyRFdddZVSU1MtycCZP/6D/RrMQtdgFroGs9A11zk78+DMH/Rry5YtGjp0qNUx4Afomv+w2+2Wn/mSmppqeQb4PvZrMAtdg1noGsxC1zyHCz4DAAAAAAD4MIY/6FdcXJzVEeAn6BoAX8N+DWahazALXYNZ6JrnMPxBv4KC+EQgzEHXAPga9mswC12DWegazELXPIfhD/q1a9cuqyPAT9A1AL6G/RrMQtdgFroGs9A1z2H4AwAAAAAA4MMY/qBfWVlZVkeAn6BrAHwN+zWYha7BLHQNZqFrnsPwB/2qrKy0OgL8BF0D4GvYr8EsdA1moWswC13zHIY/6Fdzc7PVEeAn6BoAX8N+DWahazALXYNZ6JrnMPxBv4KDg62OAD9B1wD4GvZrMAtdg1noGsxC1zyH4Q/6xWctYRa6BsDXsF+DWegazELXYBa65jkMf9CvgoICqyPAT9A1AL6G/RrMQtdgFroGs9A1z2H4AwAAAAAA4MMY/qBfMTExVkeAn6BrAHwN+zWYha7BLHQNZqFrnsPwB/0KDQ21OgL8BF0D4GvYr8EsdA1moWswC13zHIY/6FdZWZnVEeAn6BoAX8N+DWahazALXYNZ6JrnMPwBAAAAAADwYQx/0K+MjAyrI8BP0DUAvob9GsxC12AWugaz0DXPYfiDftXW1lodAX6CrgHwNezXYBa6BrPQNZiFrnkOwx/0q7Gx0eoI8BN0DYCvYb8Gs9A1mIWuwSx0zXMY/qBfQUFBVkeAn6BrAHwN+zWYha7BLHQNZqFrnsPwB/3KycmxOgL8BF0D4GvYr8EsdA1moWswC13zHIY/6NfmzZutjgA/QdcA+Br2azALXYNZ6BrMQtc8h+EPAAAAAACAD2P4g35FR0dbHQF+gq4B8DXs12AWugaz0DWYha55DsMf9CssLMzqCPATdA2Ar2G/BrPQNZiFrsEsdM1zGP6gX6WlpVZHgJ+gawB8Dfs1mIWuwSx0DWaha57D8AcAAAAAAMCHMfxBv9LS0qyOAD9B1wD4GvZrMAtdg1noGsxC1zyH4Q/61dDQYHUE+Am6BsDXsF+DWegaPK27u1sbN27UBx98oI0bN6q7u9vqSPBx7Nc8J8jqADg4NTQ0KDk52eoY8AN0DYCvYb8Gs9A1eNK6deu0fPlyVVZWOpYlJCRoxowZGj16tIXJ4MvYr3kOwx/0KyCAk8JgDroGwNewX4NZ6Bo8Zd26dXrkkUc0cuRIXX311Wpra5PdbtfKlSv1yCOP6He/+x0DIHgE+zXPYcuiX3l5eVZHgJ+gawB8Dfs1mIWuwRO6u7u1fPlyjRw5UnPnzlVeXp4OO+ww5eXlae7cuRo5cqSWL1/OR8DgEezXPIczf9CvgoIC/uL5iba2NktvqVhcXKyMjAzLXj8lJUV2u92y1wfgeziGwix0DZ6wadMmVVZW6uqrr3achdHTtYCAAE2dOlV33HGHNm3apOHDh1ucFr6G/ZrnMPxBv5jk+4/S0lLNnz/f6hiWWbhwobKzs62OAcCHcAyFWegaPKGurk6SlJ6e7lj24671LO9ZD3An9muew/AH/YqMjLQ6AkySkpKihQsXWvLaJSUlWrJkia666iqlpqZakiElJcWS1wXguziGwix0DZ4QHR0tSdqxY4fjDIwfd23Hjh291gPcif2a5zD8Qb/4S+c/7Ha75We+pKamWp4BANyFYyjMQtfgCcOGDVNCQoJWrlypuXPnKiAgwNG17u5uvfrqq0pISNCwYcMsTgpfxH7Nc7jgM/q1c+dOqyMAAOCVOIbCLHQNnhAQEKAZM2Zow4YNWrx4sfLz87V161bl5+dr8eLF2rBhg2bMmMFdmeAR7Nc8hzN/AAAAAAAOo0eP1u9+9zstX75cd9xxh2N5QkICt3kHvBTDH/SL66AAAHBgOIbCLHQNnjR69GgdddRR2rRpk8rKypScnKxhw4Zxxg88iv2a5/A3F/1qaWmxOgIAAF6JYyjMQtfgaQEBARo+fLgOO+wwDR8+nMEPPI79mufwtxf94taNAAAcGI6hMAtdg1noGsxC1zyH4Q8AAAAAAIAPY/iDfg0dOtTqCAAAeCWOoTALXYNZ6BrMQtc8h+EP+rV161arIwAA4JU4hsIsdA1moWswC13zHIY/6FdnZ6fVEQAA8EocQ2EWugaz0DWYha55DsMf9CsiIsLqCAAAeCWOoTALXYNZ6BrMQtc8h+EP+hUTE2N1BAAAvBLHUJiFrsEsdA1moWueY+nwZ9GiRTr66KMVERGhxMRETZs2TZs3b+61zvjx42Wz2Xr9+u1vf2tRYv9RXFxsdQQAALwSx1CYha7BLHQNZqFrnmPp8GfNmjWaM2eOPvvsM73zzjvq6OjQqaeequbm5l7rXXbZZSorK3P8uu+++yxKDAAAAAAA4F2CrHzxN998s9fXTz31lBITE/XVV1/pxBNPdCwPCwtTUlKS2fH8WnJystURAADwShxDYRa6BrPQNZiFrnnOQXXNn/r6eklSbGxsr+XLly9XfHy8DjvsMN1yyy1qaWnZ63O0tbWpoaGh1y+4rrW11eoIAAB4JY6hMAtdg1noGsxC1zzH0jN/fqy7u1vXXXedxo4dq8MOO8yx/MILL1RmZqZSUlL0zTff6Pe//702b96sl156qd/nWbRokRYsWNBneUFBgcLDw5Wbm6sdO3aora1NAwYMUFJSkgoLCyVJiYmJMgxDlZWVkqTBgwertLRUra2tCg0NVUpKirZt2yZJio+PV0BAgCoqKiRJWVlZqqioUEtLi0JCQpSRkaEtW7ZI2jPMCgkJUXl5uSQpMzNT1dXVampqUlBQkAYPHqz8/HxJey5wFRoaqrKyMklSRkaGamtr1djYqMDAQOXm5io/P1+GYSgqKkrh4eEqKSmRJKWlpamxsVH19fWy2WwaMmSICgoK1N3drYiICEVHR2vHjh2SpJSUFLW0tKiurk6SNHToUG3dulWdnZ0KDw9XbW2tamtrJe2Zvra1tammpkaSlJeXp6KiIrW3tyssLEyJiYnavn27JGnQoEHq6upSVVWVJCknJ0c7d+50bO/k5GTHNkxISJCkXtu7rKxMu3fvlt1uV1pamrZu3erY3oGBgdq1a1e/2zszM1MFBQWO7W2323ttw5qaGsf2zsnJcVxbKjo6WmFhYSotLZUkpaenq66uTo2NjQoICFBeXl6v7R0REaGdO3dKklJTU9XU1NRre2/ZskVdXV2KiIhQTEyM4zOrycnJam1tdWzTIUOGaNu2bY7tHRcXp6KiIklSUlKS2tvbHds7NzdXxcXF/W7vxMREdXd3O7b3vjqbkJAgm83m6Gx2drbKy8sd2zs9Pd3R2bi4OAUFBfXa3pWVlWpublZwcLCysrIc23tfnd3f9u7q6pIkFRUVqbOzU3l5eY7ORkZGKjIy0rG999XZ/W3vvLw8bd++XR0dHRo4cKASEhJ6dbazs1PV1dWO7c0+Yv/7iNjY2F7b+2DfR3R3dzu6Fh4ezj7CS/YRaWlpjn/I6dne3rCPKCgoUG1tLfsIL9pHeOvPEeXl5ero6GAf4WX7CG/8OaKqqkoDBw5kHyHv2kd4488RtbW1io+PZx+xn+39431EU1OTnGEzDMNwak0Pu/LKK7Vq1Sp9/PHHSktL2+t6q1ev1sknn6wtW7YoJyenz/fb2trU1tbm+LqhoUHp6emqr69XZGSkR7L7os2bN2vo0KFWx4CPKyws1Pz587Vw4UJlZ2dbHQc+jK7BTBxDYRa6BrPQNZiFrrmuoaFBUVFR+515HBRn/lx99dV6/fXX9eGHH+5z8CNJxxxzjCTtdfhjt9tlt9s9ktOf5OXlWR0BAACvxDEUZqFrMAtdg1nomudYes0fwzB09dVX6+WXX9bq1aud+tfYr7/+WhIXgvK0ntPUAACAaziGwix0DWahazALXfMcS8/8mTNnjp577jmtXLlSERERjs+pRkVFacCAAdq6dauee+45TZkyRXFxcfrmm290/fXX68QTT9SIESOsjO7zOjo6rI4AAIBX4hgKs9A1mIWuwSx0zXMsHf4sXbpUkjR+/Phey5ctW6bZs2crJCRE7777rh5++GE1NzcrPT1d55xzjm699VYL0vqXgQMHWh0BAACvxDEUZqFrMAtdg1nomudYOvzZ37Wm09PTtWbNGpPS4Md6ro4PAABcwzEUZqFrMAtdg1nomudYes0fHLz4rCUAAAeGYyjMQtdgFroGs9A1z2H4AwAAAAAA4MMY/qBfgwYNsjoCAABeiWMozELXYBa6BrPQNc9h+IN+dXZ2Wh0BAACvxDEUZqFrMAtdg1nomucw/EG/qqurrY4AAIBX4hgKs9A1mIWuwSx0zXMY/gAAAAAAAPgwhj/oV25urtURAADwShxDYRa6BrPQNZiFrnkOwx/0a8eOHVZHAADAK3EMhVnoGsxC12AWuuY5DH/Qr7a2NqsjAADglTiGwix0DWahazALXfMchj/o14ABA6yOAACAV+IYCrPQNZiFrsEsdM1zgqwOgP61tbWptLTUstfv6OhQYWGhZa+fkpIiu91u2esDAHCgkpKSrI4AP0HXYBa6BrPQNc9h+HOQKi0t1fz5862OYZmFCxcqOzvb6hgAALissLBQQ4cOtToG/ABdg1noGsxC1zyH4c9BKiUlRQsXLrTktUtKSrRkyRJdddVVSk1NtSRDSkqKJa8LAAAAAICvYfhzkLLb7Zaf+ZKammp5BgAAvE1iYqLVEeAn6BrMQtdgFrrmOVzwGQAAwI0Mw7A6AvwEXYNZ6BrMQtc8h+EPAACAG1VWVlodAX6CrsEsdA1moWuew/AHAAAAAADAhzH8AQAAcKPBgwdbHQF+gq7BLHQNZqFrnsPwBwAAwI1KS0utjgA/QddgFroGs9A1z2H4AwAA4Eatra1WR4CfoGswC12DWeia5zD8AQAAcKPQ0FCrI8BP0DWYha7BLHTNcxj+AAAAuFFKSorVEeAn6BrMQtdgFrrmOQx/AAAA3Gjbtm1WR4CfoGswC12DWeia5zD8AQAAAAAA8GEMfwAAANwoPj7e6gjwE3QNZqFrMAtd8xyGPwAAAG4UEMCPVzAHXYNZ6BrMQtc8hy0LAADgRhUVFVZHgJ+gazALXYNZ6JrnMPwBAAAAAADwYUFWBwAAAPAlWVlZVkeAn6Br/qOtrU2lpaWWZigsLLTstVNSUmS32y17fZiH/ZrnMPwBAABwo4qKCqWnp1sdA36ArvmP0tJSzZ8/3+oYllm4cKGys7OtjgETsF/zHIY/AAAAbtTS0mJ1BPgJuuY/UlJStHDhQkteu6SkREuWLNFVV12l1NRUSzKkpKRY8rowH/s1z2H4AwAA4EYhISFWR4CfoGv+w263W37mS2pqquUZ4PvYr3kOF3wGAABwo4yMDKsjwE/QNQC+hv2a5zD8AQAAcKMtW7ZYHQF+gq4B8DXs1zyH4Q8AAAAAAIAPY/gDAADgRrGxsVZHgJ+gawB8Dfs1z2H4AwAA4EZcrBJmoWsAfA37Nc9h+AMAAOBG5eXlVkeAn6BrAHwN+zXPYfgDAAAAAADgwxj+AAAAuFFmZqbVEeAn6BoAX8N+zXMY/gAAALhRdXW11RHgJ+gaAF/Dfs1zGP4AAAC4UVNTk9UR4CfoGgBfw37Ncxj+AAAAuFFQUJDVEeAn6BoAX8N+zXMY/gAAALjR4MGDrY4AP0HXAPga9muew/AHAADAjfLz862OAD9B1wD4GvZrnsPwBwAAAAAAwIcx/AEAAHCjmJgYqyPAT9A1AL6G/ZrnMPwBAABwo9DQUKsjwE/QNQC+hv2a5zD8AQAAcKOysjKrI8BP0DUAvob9mucw/AEAAAAAAPBhDH8AAADcKCMjw+oI8BN0DYCvYb/mOQx/AAAA3Ki2ttbqCPATdA2Ar2G/5jkMfwAAANyosbHR6gjwE3QNgK9hv+Y5DH8AAADcKDAw0OoI8BN0DYCvYb/mOQx/AAAA3Cg3N9fqCPATdA2Ar2G/5jkMfwAAANwoPz/f6gjwE3QNgK9hv+Y5DH8AAADcyDAMqyPAT9A1AL6G/ZrnMPwBAABwo6ioKKsjwE/QNQC+hv2a5zD8AQAAcKPw8HCrI8BP0DUAvob9mucw/AEAAHCjkpISqyPAT9A1AL6G/ZrnMPwBAAAAAADwYQx/AAAA3CgtLc3qCPATdA2Ar2G/5jkMfwAAANyosbHR6gjwE3QNgK9hv+Y5DH8AAADcqL6+3uoI8BN0DYCvYb/mOUFWBwAAAPAlNpvN6gjwE3QNgLu1tbWptLTUstcvKytTSEiIZa+fkpIiu91u2et7EsMfAAAANxoyZIjVEeAn6BoAdystLdX8+fOtjmGZhQsXKjs72+oYHsHwBwAAwI0KCgqUl5dndQz4AboGwN1SUlK0cOFCS167pKRES5Ys0VVXXaXU1FRLMqSkpFjyumZg+AMAAOBG3d3dVkeAn6BrANzNbrdbfuZLamqq5Rl8ERd8BgAAcKOIiAirI8BP0DUAgLMY/gAAALhRdHS01RHgJ+gaAMBZDH8AAADcaMeOHVZHgJ+gawAAZzH8AQAAAAAA8GEMfwAAANzIl+8UgoMLXQMAOIvhDwAAgBu1tLRYHQF+gq4BAJzF8AcAAMCN6urqrI4AP0HXAADOYvgDAAAAAADgwxj+AAAAuNHQoUOtjgA/QdcAAM5i+AMAAOBGW7dutToC/ARdAwA4i+EPAACAG3V2dlodAX6CrgEAnMXwBwAAwI3Cw8OtjgA/QdcAAM6ydPizaNEiHX300YqIiFBiYqKmTZumzZs391qntbVVc+bMUVxcnMLDw3XOOedo165dFiUGAADYt9jYWKsjwE/QNQCAsywd/qxZs0Zz5szRZ599pnfeeUcdHR069dRT1dzc7Fjn+uuv12uvvaYXXnhBa9asUWlpqaZPn25hagAAgL0rLi62OgL8BF0DADgryJWVu7u7tWbNGn300UcqKipSS0uLEhISNHLkSE2cOFHp6ekuvfibb77Z6+unnnpKiYmJ+uqrr3TiiSeqvr5ef//73/Xcc89pwoQJkqRly5bpkEMO0WeffaZjjz22z3O2tbWpra3N8XVDQ4NLmQAAAAAAAHyJU8Of3bt368EHH9TSpUtVU1OjI488UikpKRowYIC2bNmiV155RZdddplOPfVU3X777f0OZZxRX18v6f+fwvrVV1+po6NDEydOdKwzbNgwZWRk6NNPP+33dRYtWqQFCxb0WV5QUKDw8HDl5uZqx44damtr04ABA5SUlKTCwkJJUmJiogzDUGVlpSRp8ODBKi0tVWtrq0JDQ5WSkqJt27ZJkuLj4xUQEKCKigpJUlZWlioqKtTS0qKQkBBlZGRoy5YtjvcTEhKi8vJySVJmZqaqq6vV1NSkoKAgDR48WPn5+ZKkmJgYhYaGqqysTJKUkZGh2tpaNTY2KjAwULm5ucrPz5dhGIqKilJ4eLhKSkokSWlpaWpsbFR9fb1sNpuGDBmigoICdXd3KyIiQtHR0dqxY4ckKSUlRS0tLaqrq5O051ahW7duVWdnp2NZUVGR2tvblZycrLa2NtXU1EiS8vLyHN8LCwtTYmKitm/fLkkaNGiQurq6VFVVJUnKycnRzp07Hds7OTnZsQ0TEhIkqdf2Lisr0+7du2W325WWlua4i0V8fLwCAwMdH/n76fbOzMxUQUGBY3vb7fZe27CmpsaxvXNychwfL4yOjlZYWJhKS0slSenp6aqrq1NjY6MCAgKUl5fXa3tHRERo586dkqTU1FQ1NTX12t5btmxRV1eXIiIiFBMT4/gXueTkZLW2tqq2tlaSNGTIEG3btk2dnZ0KDw9XXFycioqKJElJSUlqb293bO/c3FwVFxf3u70TExPV3d3t2N776mxCQoJsNpujs9nZ2SovL3ds7/T0dEdn4+LiFBQU1Gt7V1ZWqrm5WcHBwcrKynJs7311dn/bu6ury9G1zs5O5eXlOTobGRmpyMhIx/beV2f3t73z8vK0fft2dXR0aODAgUpISOjV2c7OTlVXVzu2N/uIfW/v8PBwxcbG9treB/s+oru729G18PBw9hFeso9IS0tTQ0ODGhoaHNvbG/YRbW1t2rx5M/sIL9pHeOvPEW1tbSopKWEf4WX7CG/7OaKnW7t371Z5eTn7CC/aR3jbzxE9Gbu7ux3bkH3E/vcRTU1NcobNMAxjfyulp6drzJgxmj17tk455RQFBwf3WaeoqEjPPfec/vrXv2r+/Pm67LLLnArQo7u7W1OnTlVdXZ0+/vhjSdJzzz2niy++uNeZPJI0evRonXTSSbr33nv7PE9/Z/6kp6ervr5ekZGRLmXyV4WFhZo/f74WLlyo7Oxsq+PAh9E1mIWuwUyVlZWO/+kAPImuwQwcQ2EWunZgGhoaFBUVtd+Zh1Nn/rz99ts65JBD9rlOZmambrnlFt14440H9PnjOXPm6LvvvnMMfg6U3W6X3W7/Wc8BAABwoGpqavgfcpiCrgEAnOXUBZ/3N/j5seDgYOXk5LgU4uqrr9brr7+u999/X2lpaY7lPaek9Zx61WPXrl1KSkpy6TUAAAAAAAD8kct3+3rzzTd7nZ3z+OOP68gjj9SFF17o+MybswzD0NVXX62XX35Zq1ev7nNq16hRoxQcHKz33nvPsWzz5s0qLi7WmDFjXI0OAADgcXl5eVZHgJ+gawAAZ7k8/Lnpppscd9D69ttvdcMNN2jKlCkqLCzU3LlzXXquOXPm6Nlnn9Vzzz2niIgIlZeXOy4KJUlRUVG69NJLNXfuXL3//vv66quvdPHFF2vMmDEHfFFpAAAAT+q5YCXgaXQNAOAsl271Lu25CNPw4cMlSS+++KLOOOMM3XPPPVq/fr2mTJni0nMtXbpUkjR+/Phey5ctW6bZs2dLkh566CEFBATonHPOUVtbmyZNmqQlS5a4GhsAAMAU7e3tVkeAn6BrAABnuTz8CQkJUUtLiyTp3Xff1cyZMyXtueVdzxlBznLiRmMKDQ3V448/rscff9zVqAAAAKYLCwuzOgL8BF0DADjL5eHP8ccfr7lz52rs2LFat26d/v3vf0uS8vPze12sGQAAwB8lJiZaHQF+gq4BAJzl8jV//vznPysoKEgrVqzQ0qVLlZqaKklatWqVTjvtNLcHBAAA8Cbbt2+3OgL8BF0DADjL5TN/MjIy9Prrr/dZ/tBDD7klEAAAAAAAANzH5TN/xo0bp2eeecZxRy4AAAD8f4MGDbI6AvwEXQMAOMvl4c/IkSN14403KikpSZdddpk+++wzT+QCAADwSl1dXVZHgJ+gawAAZ7n8sa+HH35YDzzwgF599VU9/fTTOvHEE5Wbm6tLLrlEF110Ef8CARygqqoqNTY2Wh3DVCUlJb3+608iIiIUHx9vdQwAHlBVVaW4uDirY8AP0DUAgLNcHv5IUlBQkKZPn67p06eroqJCTzzxhG677TbNmzdPU6ZM0bXXXqsJEya4Oyvgs6qqqnTDjTeqo73d6iiWWLJkidURTBccEqIHH3iAARAAAAAAjzug4U+PdevWadmyZfrXv/6lxMREzZ49WyUlJTrjjDN01VVX6YEHHnBXTsCnNTY2qqO9XcPHXqyBkUlWx4GHNTeUa+PaZWpsbGT4A/ignJwcqyPAT9A1AICzXB7+VFRU6J///KeWLVumgoICnXnmmfq///s/TZo0STabTZI0e/ZsnXbaaQx/ABcNjExSRFyG1TEAAD/Dzp07lZWVZXUM+AG6BgBwlsvDn7S0NOXk5OiSSy7R7NmzlZCQ0GedESNG6Oijj3ZLQAAAAG/S1tZmdQT4CboGAHCWy8Of9957TyeccMI+14mMjNT7779/wKEAAAC81YABA6yOAD9B1wAAznL5Vu/7G/wAAAD4s+TkZKsjwE/QNQCAsw7ogs8rVqzQ888/r+LiYrX/5O5E69evd0swAAAAb7Rt2zYNHTrU6hjwA3QNAOAsl8/8efTRR3XxxRdr0KBB2rBhg0aPHq24uDht27ZNkydP9kRGAAAAAAAAHCCXhz9LlizRE088occee0whISG6+eab9c477+jaa69VfX29JzICAAB4jf5uhgF4Al0DADjL5eFPcXGxjjvuOEl7LjLX2NgoSbrooov0f//3f+5NBwAAAAAAgJ/F5eFPUlKSampqJEkZGRn67LPPJEmFhYUyDMO96QAAALxMZWWl1RHgJ+gaAMBZLg9/JkyYoFdffVWSdPHFF+v666/XKaecol/96lc6++yz3R4QAAAAAAAAB87lu3098cQT6u7uliTNmTNHcXFx+uSTTzR16lRdccUVbg8IAADgTQYPHmx1BPgJugYAcJbLw5+AgAAFBPz/E4bOP/98nX/++W4NBQAA4K3KysqUkZFhdQz4AboGAHCWU8Ofb775xuknHDFixAGHAQAA8Ha7d++2OgL8BF0DADjLqeHPkUceKZvNJsMwZLPZ9rluV1eXW4IBAAB4I7vdbnUE+Am6BgBwllMXfC4sLNS2bdtUWFioF198UdnZ2VqyZIk2bNigDRs2aMmSJcrJydGLL77o6bwAAAAHtbS0NKsjwE/QNQCAs5w68yczM9Px+1/+8pd69NFHNWXKFMeyESNGKD09XbfddpumTZvm9pAAAADeYuvWrRo6dKjVMeAH6BoAwFku3+r922+/VXZ2dp/l2dnZ2rhxo1tCAQAAAAAAwD1cHv4ccsghWrRokdrb2x3L2tvbtWjRIh1yyCFuDQcAAOBt4uPjrY4AP0HXAADOcvlW73/5y1905plnKi0tzXFnr2+++UY2m02vvfaa2wMCAAB4k8DAQKsjwE/QNQCAs1we/owePVrbtm3T8uXLtWnTJknSr371K1144YUaOHCg2wMCAAB4k127dik6OtrqGPADdA0A4CyXhz+SNHDgQF1++eXuzgIAAAAAAAA3c+qaP5999pnTT9jS0qLvv//+gAMBAAB4s6ysLKsjwE/QNQCAs5wa/lx00UWaNGmSXnjhBTU3N/e7zsaNGzVv3jzl5OToq6++cmtIAAAAb1FRUWF1BPgJugYAcJZTH/vauHGjli5dqltvvVUXXnihhgwZopSUFIWGhqq2tlabNm1SU1OTzj77bL399ts6/PDDPZ0bAADgoNTS0mJ1BPgJugYAcJZTw5/g4GBde+21uvbaa/Xll1/q448/VlFRkXbv3q0jjjhC119/vU466STFxsZ6Oi8AAMBBLSQkxOoI8BN0DQDgLJcv+HzUUUfpqKOO8kQWAAAAr5eZmWl1BPgJugYAcJZT1/wBAACAcwoKCqyOAD9B1wAAzmL4AwAAAAAA4MMY/gAAALgR10CEWegaAMBZDH8AAADcyG63Wx0BfoKuAQCcxfAHAADAjcrKyqyOAD9B1wAAznLqbl+PPvqo00947bXXHnAYAAAAAAAAuJdTw5+HHnqo19eVlZVqaWlRdHS0JKmurk5hYWFKTExk+AMAAPxaRkaG1RHgJ+gaAMBZTn3sq7Cw0PFr4cKFOvLII/XDDz+opqZGNTU1+uGHH/SLX/xCd911l6fzAgAAHNRqamqsjgA/QdcAAM5y+Zo/t912mx577DENHTrUsWzo0KF66KGHdOutt7o1HAAAgLdpamqyOgL8BF0DADjL5eFPWVmZOjs7+yzv6urSrl273BIKAADAWwUFOfWpeuBno2sAAGe5PPw5+eSTdcUVV2j9+vWOZV999ZWuvPJKTZw40a3hAAAAvE1OTo7VEeAn6BoAwFku/3PBP/7xD82aNUtHHXWUgoODJUmdnZ2aNGmSnnzySbcHBAAAcEVbW5tKS0ste/2ioiJlZmZa9vopKSmy2+2WvT7Ms3nz5l6XYgAAYG9cHv4kJCTojTfeUH5+vn744QfZbDYNGzZMQ4YM8UQ+AAAAl5SWlmr+/PlWx7DMwoULlZ2dbXUMAABwEDngDwoPGTJEeXl5kiSbzea2QAAAAD9HSkqKFi5caMlrl5SUaMmSJbrqqquUmppqSYaUlBRLXhfmi46OtjoCAMBLHNDw55lnntH999+vgoICSXsGQTfddJMuuugit4YDAABwld1ut/zMl9TUVMszwPeFhYVZHQEA4CVcHv4sXrxYt912m66++mqNHTtWkvTxxx/rt7/9raqqqnT99de7PSQAAACA3kpLS7nmDwDAKS4Pfx577DEtXbpUM2fOdCybOnWqDj30UN1xxx0MfwAAAAAAAA4iLg9/ysrKdNxxx/VZftxxx6msrMwtoQB4l/zv1mrVCw9re/56NTVUS5JmzFms8VMucaxz/x9OV/63a/s8Nnf4sfr9/W/u8/krygr16rOLlP/dWjXWVSo0LFwpGcN0yrQ5OnLM6e59MwAAeIn09HSrIwAAvESAqw/Izc3V888/32f5v//9b8cFoAH4l+It/9UPG97XwIiY/a6bkJSl7KFHOX6lZAzb5/qGYeih+dP0+QfPq7G+SimZw9Td1aX87z7RkoW/1o5t37rrbQAA4FXq6uqsjgAA8BIun/mzYMEC/epXv9KHH37ouObP2rVr9d577/U7FALg+46dcL5OnHyxGuoqdMslR+xz3dPPv0ljT5nh9HPXVpeqaleRJGnqjFs0+ZfXadN/P9SD86bKMAzVVJUoffDhPys/AADeqLGx0eoIAAAv4fLw55xzztHnn3+uhx56SK+88ook6ZBDDtG6des0cuRId+cD4AXCI2OdXvf5v83Ts3++XtFxyTrkyPGadtF8RcYk7nX96JgkJaYMVkXpNr26fJG+/OglVZUXKTAwSMdOOF+HjzrFHW8BAACvExDg8kn8AAA/dUC3eh81apSeffZZd2cB4ONC7AMUHZ+ilqZ6Ve0q0kdvPa0fvv5Adyz5RPbQgf0+JiAwUDcsek1L7pqhoi1fq3jrN5KkiKh4ZeYeoYDAQDPfAgAABw0uuQAAcJZT/1zQ0NDQ6/f7+gUA/fnVZffo4X9v14Iln+q+p7/X5PPmSpKqdhVpwyev7/Vx3d3devbPc1W05WudfNZv9ecXS/TbW55WY32Vnlt6kzZ8uvfHAgDgy/Lz862OAADwEk4Nf2JiYlRRUSFJio6OVkxMTJ9fPcsBoD8ZOUcoONguSbLZbDpm3LmO71VX7tzr4zb9d42+/eItSdJxJ18ge+hAjTr+LA0Ii5Qk/fD1Gg+mBgDg4GUYhtURAABewqmPfa1evVqxsXuu6fH+++97NBAA39NQV6nP3v+3Tpw0S6FhEZKkLz56yfH9+EEZkqTaqlItnn+WJOnsWbfrF8edqd3N//+Mwu0FXysj5wiVl2xR6+49F7kMsYeZ9TYAADioREVFWR0BAOAlnBr+jBs3TpLU2dmpNWvW6JJLLlFaWppHgwHwHuvXvqoVy/6o7q5Ox7JXn71Hb7/0mLKHHqWzZ96qF568VS8tu0MJyYPV3taimv+d7ZOcPlS/OO5MSVJXV4fKdxZIkmPoM3TECQoLj1ZLU52effx6rX71r6raVSTDMBQYFKzR484x+d0CAHBwiIiIsDoCAMBLuHSLgKCgIN1///3q7Ozc/8oA/MbulkZVlhWqumKHY1ljfZUqywpVV12qiKh4TfnVjUrPGaHG+ko1NVQrKW2IJv/yev3+/rcUHBK61+cOj4zVH+5/S8eMP08xcSnaVbpVoQPCdfhRp+qmP/1HGTkjzHiLAAAcdHbu3PvHpgEA+DGX7/Y1YcIErVmzRllZWR6IA8AbjT1lhsaeMmOf65w981adPfPWfa4TPyhTf/tPXZ/lyRlD9Zubnvg5EQEAAADAb7k8/Jk8ebL+8Ic/6Ntvv9WoUaM0cGDv2zNPnTrVbeEAAAAA9C81NdXqCAAAL+Hy8Oeqq66SJC1evLjP92w2m7q6un5+KgAAAAD71NTUpPDwcKtjAAC8gEvX/JGk7u7uvf5i8AMAAACYo76+3uoIAAAv4fLw58daW1vdlQMAAACAC2w2m9URAABewuXhT1dXl+666y6lpqYqPDxc27ZtkyTddttt+vvf/+72gAAAAAD6GjJkiNURAABewuXhz8KFC/XUU0/pvvvuU0hIiGP5YYcdpieffNKt4QAAAAD0b8uWLVZHAAB4CZeHP88884yeeOIJzZgxQ4GBgY7lRxxxhDZt2uTWcAAAAAD6x/U2AQDOcnn4U1JSotzc3D7Lu7u71dHR4ZZQAAAAAPYtIiLC6ggAAC/h8vBn+PDh+uijj/osX7FihUaOHOmWUAAAAAD2LSYmxuoIAAAvEeTqA26//XbNmjVLJSUl6u7u1ksvvaTNmzfrmWee0euvv+6JjAAAAAB+ori4WEOHDrU6BgDAC7h85s9ZZ52l1157Te+++64GDhyo22+/XT/88INee+01nXLKKZ7ICAAAAAAAgAPk0pk/hmFoy5Ytio2N1apVqxQU5PKJQwAAAADcIDk52eoIAAAv4fSZP4WFhRoxYoSGDRumESNGKCcnR19++aUnswEAAADYi9bWVqsjAAC8hNPDn5tuukmdnZ169tlntWLFCqWlpenyyy/3ZDYAAAAAe1FbW2t1BACAl3D6c1sff/yxVqxYoeOPP16SdOyxxyotLU3Nzc0aOHCgxwICAAAAAADgwDl95k9FRYXy8vIcXycnJ2vAgAGqqKjwSDAAAAAAezdkyBCrIwAAvITTZ/7YbDY1NTVpwIABjmUBAQFqbGxUQ0ODY1lkZKR7EwIAAADoY9u2bcrJybE6BgDACzh95o9hGBoyZIhiYmIcv5qamjRy5EjFxMQoOjpaMTExLr34hx9+qDPPPFMpKSmy2Wx65ZVXen1/9uzZstlsvX6ddtppLr0GAAAA4Is6OzutjgAA8BJOn/nz/vvvu/3Fm5ubdcQRR+iSSy7R9OnT+13ntNNO07Jlyxxf2+12t+cAAAAAvE14eLjVEQAAXsLp4c+4cePc/uKTJ0/W5MmT97mO3W5XUlKS218bAAAA8GZxcXFWRwAAeAmnhz9W+eCDD5SYmKiYmBhNmDBBd9999z4PdG1tbWpra3N8/ePrEQEApKqqKjU2Nlodw1QlJSW9/utPIiIiFB8fb3UMAB5QVFSkoUOHWh0DAOAFDurhz2mnnabp06crOztbW7du1bx58zR58mR9+umnCgwM7PcxixYt0oIFC/osLygoUHh4uHJzc7Vjxw61tbVpwIABSkpKUmFhoSQpMTFRhmGosrJSkjR48GCVlpaqtbVVoaGhSklJ0bZt2yRJ8fHxCggIcNztLCsrSxUVFWppaVFISIgyMjK0ZcsWSVJsbKxCQkJUXl4uScrMzFR1dbWampoUFBSkwYMHKz8/X5IUExOj0NBQlZWVSZIyMjJUW1urxsZGBQYGKjc3V/n5+TIMQ1FRUQoPD3f8z0xaWpoaGxtVX18vm82mIUOGqKCgQN3d3YqIiFB0dLR27NghSUpJSVFLS4vq6uokSUOHDtXWrVvV2dnpWFZUVKT29nYlJyerra1NNTU1kqS8vDzH98LCwpSYmKjt27dLkgYNGqSuri5VVVVJknJycrRz507H9k5OTnZsw4SEBEnqtb3Lysq0e/du2e12paWlaevWrY7tHRgYqF27dvW7vTMzM1VQUODY3na7vdc2rKmpcWzvnJwcbd68WZIUHR2tsLAwlZaWSpLS09NVV1enxsZGBQQEKC8vr9f2joiI0M6dOyVJqampampq6rW9t2zZoq6uLkVERCgmJkbFxcWS9twdr7W1VbW1tZL23J1j27Zt6uzsVHh4uNrb2/vtM3xbz98jM/cRxcXF+tOf/qSOjg5r3rTFlixZYnUE0wUHh2jOnKsUFRW1z+NaQkKCbDab47iWnZ2t8vJyxz45PT3dcVyLi4tTUFBQr31yZWWlmpubFRwcrKysLMc+eV+d3d8+OS0tTQ0NDWpoaHDsk3uOa5GRkYqMjHTsk/d1XNvfPjkvL0/bt29XR0eHBg4cqISEhF7Htc7OTlVXV0vSfn+O6PlHp6KiIqWlpfnlzxHh4eGKjY3ttb35OcIzP0dUVVUpPDxccXFxKioqkiQlJSWpvb3dsb1zc3NVXFzc7/ZOTExUd3e3Y3uzj/D8PsIb/1+jp1u7d+9WeXk5+wgv2kf0bG9v2Uf0ZOzu7nZsQ/YR+99HNDU1yRk2wzAMp9b0MJvNppdfflnTpk3b6zo9dzR49913dfLJJ/e7Tn9n/qSnp6u+vp47kTmpsLBQ8+fP18KFC5WdnW11HL/Qs82PnnyLIuIyrI4DD2usLtYXqxZZ8nesp2sTzrxYMfHJpr42zFdbVabVry1jf24ijqEwU319vaKioqyOAR/Hfg1moWsHpqGhQVFRUfudeRzUZ/781ODBgxUfH68tW7bsdfhjt9u5KDQA7EdMfLISkhg0AoA348xhAICznL7V+8Fg586dqq6uVnIy/1oNAAAA/9bzsQ0AAPbH5TN/zj77bNlstj7LbTabQkNDlZubqwsvvNCpi881NTU5Pvcn7TnN6+uvv1ZsbKxiY2O1YMECnXPOOUpKStLWrVt18803Kzc3V5MmTXI1NgAAAAAAgF9y+cyfqKgorV69WuvXr5fNZpPNZtOGDRu0evVqdXZ26t///reOOOIIrV27dr/P9eWXX2rkyJEaOXKkJGnu3LkaOXKkbr/9dgUGBuqbb77R1KlTNWTIEF166aUaNWqUPvroIz7WBQAAAL+Xm5trdQQAgJdwefiTlJSkCy+8UNu2bdOLL76oF198UVu3btWvf/1r5eTk6IcfftCsWbP0+9//fr/PNX78eBmG0efXU089pQEDBuitt95SRUWF2tvbtX37dj3xxBMaNGjQAb1RAAAAwJf03HkGAA4mH374oaZMmeK4A5jNZtNf/vKXXutccsklysvLU3h4uAYOHKicnBwtWLCg182b9vcap512muNuYFlZWfrd737nibfjM1we/vz973/Xddddp4CA///QgIAAXXPNNXriiSdks9l09dVX67vvvnNrUAAAAAD/Hxd8BnAwWr9+vd555x3FxsbudZ2VK1eqq6tLw4YNU3x8vLZt26ann35an3zyyX6f//nnn9eECRP01ltvKTAwUMOHD5fNZtMbb7zhzrfhc1we/nR2dmrTpk19lm/atEldXV2SpNDQ0H6vCwQAAADAPcLCwqyOAAB9XHTRRWpoaNBbb72113VKSkq0bds2ffnllyoqKtLxxx8vSaqqqtrnczc3N+vKK69UV1eXbr75ZpWXl2v9+vUqLCzU+vXr3fo+fI3LF3y+6KKLdOmll2revHk6+uijJUlffPGF7rnnHs2cOVOStGbNGh166KHuTQoAAADAITEx0eoIANBHXFzcftcJDQ3Vbbfdprffflu7du1SUVGRJCkhIWGfj3v33XcddzrctWuX0tLS1NHRobFjx2rx4sWKiIj4+W/AR7k8/HnooYc0aNAg3Xfffdq1a5ckadCgQbr++usd1/k59dRTddppp7k3KQAAAACH7du3O3WHXQA4GBUUFGjdunWOr8eOHavk5OR9Pmbz5s2O3z/zzDMaPny4tm3bptdee03r16/X999/r6ioKI9l9mYuf+wrMDBQ8+fPV1lZmerq6lRXV6eysjLNmzdPgYGBkqSMjAylpaW5PSwAAAAAAPB+//rXv9Te3q4NGzbosMMO09q1a/Xll1/u8zGdnZ2O399555367rvvHB8vKykp0csvv+zRzN7M5TN/fiwyMtJdOQAAAAC4gI99Ab6rqqpKjY2NVsf4WXbu3On4fVVVlQoLC/tdLyoqStOnT9d3332n7du369NPP93rc4aEhDh+n56ersLCwl4fFduwYYPGjRvnhvTmioiIUHx8vEdfw+Xhz65du3TjjTfqvffeU0VFhQzD6PX9nos+AwAAAPCc7u5uqyMA8ICqqirdeMMNau/osDrKz9LU1OT4/cqVK7Vx40ZJUnV1tTo7OzVo0CBJe2YIa9eudaz7zDPP6PXXX1d1dbVjEDRmzBjFxcWpublZNptNhmFo8eLFeuutt1RZWel47BdffKH58+eb8fbcKiQ4WA88+KBHB0AuD39mz56t4uJi3XbbbUpOTuauXgAAAIAFqqqqnLqwKgDv0tjYqPaODk0OD1RsoPf9//Yn24q17LP16vrRgLrgu29Vkr9ZQxLjNDItRQ9/8InC7SFKCB+oqqZmNba1S5IGx8VoTlaCAmw2fdNk6O3/nf003m5oRFSQFBUl49Cheu27Tfr2m29Uv7NY5Q171smIidLNh2Ur+H+Xo/EWNV2GVjV1qLGx8eAa/nz88cf66KOPdOSRR3ogDgAAAAAAiA20aVCQ9w1/gro6VNbQ+yNr9a1tqm9tU3J4mEYmRGtMRooKqmq1o7ZegTabsmOiNDYrVRePOlyRwXsuTfzjwdePt8Wt445WZkSYVm4sUGldgxLCw3R8ZqquOOZIRdp/1pVtfJrLWyY9Pb3PR70AAAAAmGvw4MFWRwCAPs48JFdnHpK7z3UenTpxv88zKi1JX1w9s8/yAJtNs0YdplmjDjvgjP7I5eHPww8/rD/84Q/661//qqysLA9EAvxXc3251RFgAv6cAQDuUFpaqszMTKtjAAC8gMvDn1/96ldqaWlRTk6OwsLCFBwc3Ov7NTU1bgsH+JuNnyyzOgIAAPASra2tVkcAAHiJAzrzB4BnDD/uYg2MSrI6Bjysub6cQR8A4GcLDQ21OgIAwEu4PPyZNWuWJ3IAkDQwKkkRcRlWxwAAAF4gJSXF6ggAAC/h1PCnoaFBkZGRjt/vS896AAAAADxn27ZtGjp0qNUxAABewKnhT0xMjMrKypSYmKjo6GjZbH1vN2cYhmw2m7q6utweEgAAAAAAAAfGqeHP6tWrFRsbK0l6//33PRoIAAAAwP4lJCRYHQEA4CWcGv6MGzeu398DAAAAsEZ/Z+MDANCfAFcf8Oabb+rjjz92fP3444/ryCOP1IUXXqja2lq3hgMAAADQv4qKCqsjAAC8hMvDn5tuuslx0edvv/1Wc+fO1ZQpU1RYWKi5c+e6PSAAAAAAAAAOnMu3ei8sLNTw4cMlSS+++KLOPPNM3XPPPVq/fr2mTJni9oAAAAAA+srOzrY6AgDAS7h85k9ISIhaWlokSe+++65OPfVUSVJsbOx+bwMPAAAAwD3Ky8utjgAA8BIun/lz/PHHa+7cuRo7dqzWrVunf//735Kk/Px8paWluT0gAAAAgL52795tdQQAgJdw+cyfP//5zwoKCtKKFSu0dOlSpaamSpJWrVql0047ze0BAQAAAPRlt9utjgAA8BIun/mTkZGh119/vc/yhx56yC2BAAAAAOxfenq61REAAF7C5TN/1q9fr2+//dbx9cqVKzVt2jTNmzdP7e3tbg0HAAAAoH9btmyxOgIAwEu4PPy54oorlJ+fL0natm2bzj//fIWFhemFF17QzTff7PaAAAAAAAAAOHAuD3/y8/N15JFHSpJeeOEFnXjiiXruuef01FNP6cUXX3R3PgAAAAD9iIuLszoCAMBLuHzNH8Mw1N3dLWnPrd7POOMMSXs+c1xVVeXedAeBqqoqNTY2Wh3DVCUlJb3+608iIiIUHx9vdQwAAID9Cgpy+Ud5AICfcvmIcdRRR+nuu+/WxIkTtWbNGi1dulSSVFhYqEGDBrk9oJWqqqp0ww03qqPDP69ltGTJEqsjmC44OEQPPvgAAyAAAHDQ27Vrl6Kjo62OAQDwAi4Pfx5++GHNmDFDr7zyiubPn6/c3FxJ0ooVK3Tccce5PaCVGhsb1dHRruis6QoKZRjg6zpbq1S3/SU1NjYy/AEAAAAA+AyXhz8jRozodbevHvfff78CAwPdEupgExQar5CwFKtjAAAAAA5ZWVlWRwAAeAmXL/gsSXV1dXryySd1yy23qKamRpK0ceNGVVRUuDUcAAAAgP5VVlZaHQEA4CVcPvPnm2++0cknn6zo6Ght375dl112mWJjY/XSSy+puLhYzzzzjCdyAgAAAPiR5uZmqyMAALyEy2f+zJ07VxdffLEKCgoUGhrqWD5lyhR9+OGHbg0HAAAAoH/BwcFWRwAAeAmXhz9ffPGFrrjiij7LU1NTVV5e7pZQAAAAAPaNa/4AAJzl8vDHbreroaGhz/L8/HwlJCS4JRQAAACAfSsoKLA6AgDAS7g8/Jk6daruvPNOdXR0SJJsNpuKi4v1+9//Xuecc47bAwIAAAAAAODAuTz8efDBB9XU1KTExETt3r1b48aNU25uriIiIrRw4UJPZAQAAADwEzExMVZHAAB4CZfv9hUVFaV33nlHa9eu1X//+181NTXpF7/4hSZOnOiJfAAAAAD68eObrwAAsC8uD396jB07VmPHjnVnFgAAAABOKisrU2RkpNUxAABewOWPfV177bV69NFH+yz/85//rOuuu84dmQAAAAAAAOAmLg9/XnzxxX7P+DnuuOO0YsUKt4QCAAAAsG8ZGRlWRwAAeAmXhz/V1dWKiorqszwyMlJVVVVuCQUAAABg32pra62OAADwEi4Pf3Jzc/Xmm2/2Wb5q1SoNHjzYLaEAAAAA7FtjY6PVEQAAXsLlCz7PnTtXV199tSorKzVhwgRJ0nvvvacHH3xQDz/8sLvzAQAAAOhHUNAB37sFAOBnXD5iXHLJJWpra9PChQt11113SZKysrK0dOlSzZw50+0BAQCAd6qqqvK7MxNKSkp6/defREREKD4+3uoYfiUnJ8fqCH6H/Zp/Yb8GX3JA/1xw5ZVX6sorr1RlZaUGDBig8PBwd+cCAABerKqqSjfeeKPa29utjmKJJUuWWB3BdCEhIXrggQf4HyUTbd68WUOHDrU6ht/Ys1+7Qe3tHVZHsYR/7teC9cADD7Jfg0844HNFKysrtXnzZknSsGHD+AsBAAAcGhsb1d7ernPPPVeJCQlWx4GHVVRWasWKFWpsbORnQvisPfu1Dl14eIYSw0OtjgMPq2hq1XPfFrNfg89wefjT3Nysa665Rs8884y6u7slSYGBgZo5c6Yee+wxhYWFuT0kAADwTokJCUpJSbE6BuCToqOjrY7glxLDQ5UWyf/zAPAuLt/ta+7cuVqzZo1ee+011dXVqa6uTitXrtSaNWt0ww03eCIjAAAAgJ/gH10BAM5yefjz4osv6u9//7smT56syMhIRUZGasqUKfrb3/6mFStWeCIjAAAAgJ8oLS21OgIAwEu4/LGvlpYWDRo0qM/yxMREtbS0uCUUAAD9+e+Xa7X8Hw9p0/frVV9bLUmae+tinXXepY51Ojs69OyTD+rNV59T5a5SxcQmaPyp03TJ1fMVFrb3GxQsW7JIT/3lT3v9/r9WfaPk1Ez3vRkAAADAJC6f+TNmzBj98Y9/VGtrq2PZ7t27tWDBAo0ZM8at4QAA+LH8H/6rLz97X5GRMXtd509/nKNlSxdpV9kOpaRlqbamUi88u0S3XP0rx7Xq+pMwKEXDDz+q16/IqD2vExJiV0RktLvfDgD8LGlpaVZHAAB4CZfP/Hn44Yd12mmnKS0tTUcccYQk6b///a9CQ0P11ltvuT0gAAA9Tj3zfE395cWqqa7Q+ZNH9Pl+/sav9c7r/5YkXfP7ezX9gsu19oNVmnft+fr6y4/18erXdeLEqf0+9xnnzNIZ58xyfN3WulvnTTrM8brhEVEeeEcAcOAaGho0cOBAq2MAALyAy8Ofww8/XAUFBVq+fLk2bdokSbrgggs0Y8YMDRgwwO0BAQDoERUdu8/vf/7xO47fj/vfkGfMiZMUYg9Ve1urPl/77l6HPz/15qvPqa62SjabTb+aec2BhwYAD2loaFBycrLVMQAAXsCl4U9HR4eGDRum119/XZdddpmnMgEAcEAqdpU4fh8dmyBJCggIUFR0nCp3laiibKdTz9Pd3a3n//m4JOm4cZOVkZ3n/rAA8DMFBLh8BQcAgJ9y6YgRHBzc61o/AAB4BcNwafWP3/+PdhZtlSSdP/taTyQCgJ8tL4/BNADAOS7/c8GcOXN07733qrOz0xN5AAA4YImDUh2/r6uplLTnLJ76+po930927uKo/376MUnS8BFHa8QvuJkBgINTQUGB1REAAF7C5eHPF198oZdeekkZGRmaNGmSpk+f3usXAABWGX38RMfv17z7qiTp0w/fUnvbnrNWjxm75/s/fPuVLpp6lC6aepR++ParXs/x3def67uvP5cknT+La/0AOHjt6w6GAAD8mMsXfI6OjtY555zjiSwAAOzTh+++qr88dLu6urocy/7x+D3699OP6ZDDj9Jtf3pSJ08+V++tWqHH7v29Xv7X31S6o1CSNOIXx+n4CWdIklpbW1S8vcDx+x/719OPSpJSMwbrhJPPNONtAcABiYyMtDoCAMBLuDz8WbZsmSdyAACwX83NjSr53zCnR11tlepqq5Twv498zbv7L0rLyNFbr/2fSncUKjo2XuMmnqXfXHPrfi+OurN4q9a+/4Yk6Ze/voqLqQI4qDH8AQA4y+nhT3d3t+6//369+uqram9v18knn6w//vGP3N4dAGCayWfN0OSzZuxznaDgYF0yZ54umTNvr+uMPPoErfmmvs/ytIwcvf917c/OCQBm2Llzp4YOHWp1DACAF3D6nzQXLlyoefPmKTw8XKmpqXrkkUc0Z84cT2YDAAAAAADAz+T0mT/PPPOMlixZoiuuuEKS9O677+r000/Xk08+yWnxAOBlaqvKrY4AE/DnDPi2lJQUqyMAALyE08Of4uJiTZkyxfH1xIkTZbPZVFpaqrQ0526dC3Pt3PaF1r3/hMp3fKvdzXs+xjBx+h06YswFjnW6ujr0+Xt/1cavXlZj/S6FhcdqyIjTNHbS7xRiH7jP529vbdLatx9V/jdvqqWpRhFRSRo+apqOPfm3Cgh0+XJSAEy0+rV/WB0BAPAztbS0KCIiwuoYAAAv4PT/oXd2dio0NLTXsuDgYHV0dLg9FNxjV8n3Ksr/RFFxaY7hz0+99fw8/bD+VdlsAYqOz1R9zU6t/+hpVZT8oPOueFq2vZzVZXR36+VlV2rntnUKCAxWVGya6qqK9Ok7j6m+uliTL7jPk28NwM804cxLFBOfZHUMeFhtVTmDPsCH1dXVadCgQVbHAAB4AaeHP4ZhaPbs2bLb7Y5lra2t+u1vf6uBA///GSIvvfSSexPigA3/xVkacez5amms0pOLTu7z/V07v9cP61+VJJ101nyNHPtrbd24Wq/8b6iz5ft3lXf4qf0+95bv39XObeskSVNnPqac4Sdp/cf/1Psr79bG9Sv1ixNmaVDaoZ57cwB+lpj4JCUkZVgdAwAAAIAJnL5Yz6xZs5SYmKioqCjHr1//+tdKSUnptQwHjwEDYxQcHLrX7xdu/tDx+54hz+Bh4xUUZP/f9z/a+2M37XlsUHCoBg8bJ0ka8qNB0fZ9PBYAAAA/H3f6AgA4y+kzf5YtW+bJHLBAY12Z4/dh4XGSJFtAgEIHxqipvlyNtaX7fWxoWLTjo2FhEfGO7zfU7f2xAAAA+Pm2bt2qnJwcq2MAALwAt+lCP4wDfNgBPg4AAAAu6+zstDoCAMBLMPzxYxHRyY7ftzRVS9pzIefW5ro934/Z++1Dex7b2lwro7u713NIUmQ0tx4FAADwJO70BQBwFsMfP5Y99ATH7wu+fVuStG3TB+rsbOv1/bLib/SP+07TP+47TWXF30iSsv73vc7ONm3btEaSlP+/5/jx9wEAAOAZMTExVkcAAHgJp6/5A+9T8O3b+vA/96u7u8uxbO1bj+rLNf9QUsYInX7hgxp25Bna9PXren/lQn39yXLVVe+QJKVmH6XcQydKkjo7dqu2stDxe0nKPWyiUrNHqaTwK736zDWKjktXbeV2SdKwkWdwp68D1NxQbnUEmIA/ZwCAOxQXF3PRZ8CH1XRxWQ1/YNafM8MfH9bW2qS66uJey3Y312h3c43Co5IkSaed/ydFx2dq41evqK56h8IGxihvxCQdP+k6x4Wc+xMQEKizL3lCa996WAXfvKW66h2KiE7W8FHTdOzEKz36vnxRRESEgkNCtHEtF1b3F8EhIZyuDwAAgL1a1dS1/5UAJzH88WGHHT1dhx09fZ/rBAYGa+ykazV20rV7XSc95xjdcP/mPsvtoeGacNatmnDWrT87q7+Lj4/Xgw88oMbGRqujmKqkpERLlizRVVddpdTUVKvjmCoiIkLx8fH7XxEAgL1ITk7e/0oAvNbk8EDFBtqsjgEPq+kyTBn0MfwBDhLx8fF+OwxITU1Vdna21TEAAPAqra2tioyMtDoGAA+JDbRpUBDDH7gHF3wGAAAAvFBtba3VEQAAXoLhDwAAAAAAgA+zdPjz4Ycf6swzz1RKSopsNpteeeWVXt83DEO33367kpOTNWDAAE2cOFEFBQXWhAUAAAAOInl5eVZHAAB4CUuHP83NzTriiCP0+OOP9/v9++67T48++qj+8pe/6PPPP9fAgQM1adIktba2mpwUAAAAOLhs377d6ggAAC9h6QWfJ0+erMmTJ/f7PcMw9PDDD+vWW2/VWWedJUl65plnNGjQIL3yyis6//zzzYwKAAAAHFQ6OjqsjgAA8BIH7d2+CgsLVV5erokTJzqWRUVF6ZhjjtGnn3661+FPW1ub2traHF83NDT87CydrVU/+zlw8OPPGQAAeJOBAwdaHQEA4CUO2uFPeXm5JGnQoEG9lg8aNMjxvf4sWrRICxYs6LO8oKBA4eHhys3N1Y4dO9TW1qYBAwYoKSlJhYWFkqTExEQZhqHKykpJUkDAnk/F1W1/yS3vCd6hsrJS7e3tCggIUF5envLz82UYhqKiohQREaGdO3dK2nN78qamJtXX18tms2nIkCHasmWLurq6FBERoZiYGBUXF0uSkpOT1dra6rgrx5AhQ7Rt2zZ1dnYqPDxccXFxKioqkiQlJSWpvb1dNTU1kqTc3FwVFxervb1dYWFhSkxMdJzmnZiYqO7ublVV7RlcDR48WKWlpWptbVVoaKhSUlK0bds2SVJCQoJsNpsqKiokSdnZ2SovL9fu3btlt9uVnp6uLVu2SJLi4uIUFBSkXbt2SZKysrJUWVmp5uZmBQcHKysry3H9rZiYGIWGhqqsrEySlJGRodraWjU2NiooKEg5OTnavHmzJCk6OlphYWEqLS2VJHV1dUmSioqK1NnZqby8PBUUFKi7u1uRkZGKjIx0bO+UlBS1tLSorq5OkjR06FBt3bpVnZ2d+93eeXl52r59uzo6OjRw4EAlJCQ4tuGgQYPU2dmp6upqx/Z2dh+xr+0dHx+vgIAAx/bOyspSRUWFWlpaFBISooyMDMf2jo2NVUhIiGPflpmZqerqajU1NSkoKEiDBw9Wfn7+frd3YGCgcnNze3U2PDxcJSUlkqS0tDTH+4R/KSoqUnt7u6n7iJ59GvxLUVGRQkJCFBsb22uf3NbW5jiu5eXlOTr50+PaoEGD1NXV5Tiu5eTkaOfOnY59cnJycq/OSuq1Ty4rK3N0Ni0tTVu3bpW0Z58cGBjYq7M/3idnZmY6jmuxsbGy2+299rM1NTWOffK+jmvp6emqq6tTY2OjKT9HtLa2ymaz+e3PEWlpaWpoaFBDQ4Nje3vy54ie78O/lJSUmL6P6Hlu+JeGhgbV1NS4/P8aTU1NTj2/zTAMw2PpXWCz2fTyyy9r2rRpkqRPPvlEY8eOVWlpqZKTkx3rnXfeebLZbPr3v//d7/P0d+ZPenq66uvrFRkZ6VKmwsJCzZ8/X9FZ0xUUGu/6m4JX6WytUt32l7Rw4UJlZ2dbHccv9PwdY5ubp2ebn3PxPCUkZVgdBx5WWV6sF5fdY8nfsZ6uXXXllUpJSTH1tT2lpaVFjz72mN5++23t2rVLQUFBSk1N1bRp03TZb34jm83W7+Oam5v1t7/9Tf954w2VlJQoKipKEydO1I033KCoqCiT34VnlJaWasnSpezPTbZ582YNHTrU6hh+o2e/dt2YIUqLDLM6DjxsZ0OLHv4039Jj6IyoIA0K6v/YAt+xq9PQ8vrOA+5aQ0ODoqKi9jvzOGjP/ElKSpIk7dq1q9fwZ9euXTryyCP3+ji73S673e7WLEGh8QoJ840fXAEAwIH54x136KWX9pwNnJeXp8bGRm3evFn33nuv7Ha7Zs2c2e/jLrv8cn3++ecKDAxUXl6edu7Yoeeee07ffvutVrzwgoKCDtofxwAAgI+w9G5f+5Kdna2kpCS99957jmUNDQ36/PPPNWbMGAuTAQAAf/Tll19Kkk488US9uWqV3nv3Xcc/OPV8vPKnCgoK9Pnnn0uSbrv1Vv3n9df1ysqVkqRvv/1Wb7zxhgnJ4at+enkEAAD2xtJ/ampqanJ8Nljac3rb119/rdjYWGVkZOi6667T3Xffrby8PGVnZ+u2225TSkqK46NhAAAAZjn66KNVXFysDz/8UKdNnqzGxka1tbXp6KOP1m8uvbTfx3T/6NP1PdcSDPjRx8PWfvKJpk6d6tng8FmdnZ1WRwAAeAlLhz9ffvmlTjrpJMfXc+fOlSTNmjVLTz31lG6++WY1Nzfr8ssvV11dnY4//ni9+eabCg0NtSoyAADwU3fdeae6u7v18ssvOy78GRIcrGFDh+712j25OTkaMmSI8vPzteDOO/V///qXduzY4fj+rn3cxALYn+rqasXHc11KAMD+WTr8GT9+vPZ1vWmbzaY777xTd955p4mpAAAA+lq2bJleeeUVjRo1SkuXLFFNTY0uuPBC/fPZZxUYFKTbbr21z2MCAwP1j7//Xffff7/WfvKJdhQX6+jRo7V161YVFxcrKDjYgncCAHu0tLVr8SvvatX671Re26DgwAClxcXonON+od9OPrHfC9nvqKzRsTfdu9fnnHvWRN1w9imejA3gAHCFQQAAgP3YvXu3Hnr4YRmGodMmTVJcXJzi4uI06he/0Lvvvae1a9fu9bHJyclavHix4+u2tjYdc+yxkqTB3BkLP0Nubq7VEeDl5j3zil5Y+5UkaWjqIDXsbtUPO8t19/NvyB4cpEtOGdvnMSHBQRo5OL3XsoaWVm0t33N76sToCM8HB+Ayhj8AAAD7sXv3bsf1Vb777jtJe4Y4+f/7+FdY2J7bPv/6oou0a9cunXrKKbrpppsc62dlZSk8PFxdXV1a9Kc/qbGxUZJ0+umnm/1W4EN27NihrKwsq2PAi31RsF2SdNLhQ/TsDZdqd3uHDptzh1o7OrWzurbfxwyKjtTrt1/da9n8f76ireWVih44QNPHjPR0bAAHgOEPAADAfsTGxmr00Udr3RdfaOWrr+rr//5Xzc3NqqqqkiRNP/tsSVJxcbFKSkpUUVnpeOyKFSv0/AsvKDMzU1WVlaqp3fM/VBfPnq0jjjjC/DcDn9HW1mZ1BHi50UOytL2iWu9/m68J8xerYXerWjs6dcyQbF1x2olOPUdNU7P+/dGeuyFedNKxGhhq92RkAAeI4Q8AAIAT/vKXv+gvf/2r3nnnHZWXlyskJERHHnGEZs6cqbPOOmuvjxtxxBH67LPPtGPHDhmGocMOO0wzLrxQ5513nonp4YsGDBhgdQR4uT/Nmq5uw9CKteu1uWSXJCkkKFCHpCcpKsy5fj393qfa3d4he1CQLpnY92NiAA4ODH8AAACcEBUVpd/ffLN+f/PNe13nwzVr+iybfvbZjjODAHdKSkqyOgK83N/e+kgvfrJBR+dl6e/XXKTqxmZNX/QXPfXepwoMCNCdM6bu8/FtHZ16+r1PJUnTjxvJ9X6Ag1iA1QEAAAAAuK6wsNDqCPBiu9vadf/Lb8swDE056jDFRYZrSOogHZ2XJUn6eOOW/T7HirVfqbKhSTabTVecdoKHEwP4OTjzBwAAAAD8zO72DnV2dUuSvt1eIklqbe9Q/v8+/hVmD5EknXfvEyqvbdDkUYfqll9OdjzeMAz99c2PJEknjximvJRBZsYH4CKGPwAAAIAXSkxMtDoCvFhsxEAdOzRbn20u1EufbtD6bcVq3t2myoYmSdK5Y0dJkooqarSzula76hp7Pf6dr39w3N79yinOXRwagHUY/gAAAABeyDAMqyPAy/392pl6/D8f6M3136uspl724CCNHJyuSyaO1fTj9n3L9r+s+lCSNHJwuo4dOtiMuAB+BoY/AAAAgBeqrKxUbGys1THgxaIHhmn+eVM0/7wpe13n8wf/0O/yl+b91lOxAHgAwx/Az7W1tam0tNSS1y4pKen1XyukpKTIbrdb9vqAr6usrLQ6AkzAnzMAAAc3hj+AnystLdX8+fMtzbBkyRLLXnvhwoXKzs627PUBX/fCihVWRwB81uDBfNQGAOAchj+An0tJSdHChQste/2ysjIlJydb9vopKSmWvTbgD3557rlKSEiwOgY8rLKykkGfBUpLS5WZmWl1DACAF2D4A/g5u91u6Zkv7e3tnHkD+LCEhASGrICHtLa2Wh0BAOAlGP4AsFRoaKjVEQAA8EocQwHfVtPFHf38gVl/zgx/AFiKMwIAADgwHEMB3xQREaGQ4GCtauqwOgpMEhIcrIiICI++BsMfAJbatm2bhg4danUMAAC8DsdQwDfFx8frgQcfVGNjo9VRTFVSUqIlS5boqquuUmpqqtVxTBUREaH4+HiPvgbDHwAAAAAADiLx8fEeHwYcrFJTU7kmqAcEWB0AgH/z14MaAAA/F8dQAICzGP4AsFRAALshAAAOBMdQAICzOGIAsFRFRYXVEQAA8EocQwEAzmL4AwAAAAAA4MMY/gCwVFZWltURAADwShxDAQDOYvgDwFKcsg4AwIHhGAoAcBbDHwCWamlpsToCAABeiWMoAMBZDH8AWCokJMTqCAAAeCWOoQAAZzH8AWCpjIwMqyMAAOCVOIYCAJzF8AeApbZs2WJ1BAAAvBLHUACAs4KsDgAAAAAA3qKiqdXqCDABf87wNQx/AFgqNjbW6ggAAHgljqHWeO7bYqsjAIDLGP4AsBQXqwQA4MBwDLXGhYdnKDE81OoY8LCKplYGffApDH8AWKq8vFxRUVFWxwAAwOtwDLVGYnio0iLDrI4BAC7hgs8AAAAAAAA+jOEPAEtlZmZaHQEAAK/EMRQA4CyGPwAsVV1dbXUEAAC8EsdQAICzGP4AsFRTU5PVEQAA8EocQwEAzmL4A8BSQUFcdx4AgAPBMRQA4CyGPwAsNXjwYKsjAADglTiGAgCcxfAHgKXy8/OtjgAAgFfiGAoAcBbDHwAAAAAAAB/G8AeApWJiYqyOAACAV+IYCgBwFsMfAJYKDQ21OgIAAF6JYygAwFkMfwBYqqyszOoIAAB4JY6hAABnMfwBAAAAAADwYQx/AFgqIyPD6ggAAHgljqEAAGcx/AFgqdraWqsjAADglTiGAgCcFWR1AAD+rbGx0eoIAAB4JY6hANytra1NpaWllrx2SUlJr/9aISUlRXa73bLX9ySGPwAsFRgYaHUEAAC8EsdQAO5WWlqq+fPnW5phyZIllr32woULlZ2dbdnrexLDHwCWys3NtToCAABeiWMoAHdLSUnRwoULrY5hmZSUFKsjeAzDHwCWys/P15AhQ6yOAQCA1+EYCsDd7Ha7pWe+sF/zHC74DMBShmFYHQEAAK/EMRSAr2G/5jkMfwBYKioqyuoIAAB4JY6hAHwN+zXPYfgDwFLh4eFWRwAAwCtxDAXga9iveQ7DHwCWsvJWjgAAeDOOoQB8Dfs1z2H4AwAAAAAA4MMY/gCwVFpamtURAADwShxDAfga9muew/AHgKUaGxutjgAAgFfiGArA17Bf8xyGPwAsVV9fb3UEAAC8EsdQAL6G/ZrnMPwBYCmbzWZ1BAAAvBLHUAC+hv2a5zD8AWCpIUOGWB0BAACvxDEUgK9hv+Y5DH8AWKqgoMDqCAAAeCWOoQB8Dfs1z2H4A8BS3d3dVkcAAMArcQwF4GvYr3kOwx8AloqIiLA6AgAAXoljKABfw37Ncxj+ALBUdHS01REAAPBKHEMB+Br2a54TZHUAAP5tx44dGjp0qNUx/E5tVZnVEWAC/pwB38YxFICvYb/mOQx/AMCPREREKCQkRKtfW2Z1FJgkJCSEU6gBAAD8HMMfAJZKSUmxOoJfiY+P1wMPPKDGxkaro5iqpKRES5Ys0VVXXaXU1FSr45gqIiJC8fHxVscA4AEcQwH4GvZrnsPwB4ClWlpaOCvBZPHx8X47DEhNTVV2drbVMQDALTiGAvA17Nc8hws+A7BUXV2d1REAAPBKHEMB+Br2a57DmT8AAMBjKiorrY4AE/DnDADAwY3hDwBLcTV/wDf1XFx8xYoVVkeBSbi4uPk4hgLwNezXPIfhDwBLbd26VTk5OVbHAOBmXFyci4vD8ziGAvA17Nc8h+EPAEt1dnZaHQGAh3BxcS4uDs/iGArA17Bf8xwu+AzAUuHh4VZHAADAK3EMBeBr2K95DsMfAJaKjY21OgIAAF6JYygAX8N+zXMY/gCwVHFxsdURAADwShxDAfga9muew/AHAAAAAADAhzH8AWCp5ORkqyMAAOCVOIYC8DXs1zyH4Q8AS7W1tVkdAQAAr8QxFICvYb/mOQx/AFiqpqbG6ggAAHgljqEAfA37Nc85qIc/d9xxh2w2W69fw4YNszoWAAAAAACA1wiyOsD+HHrooXr33XcdXwcFHfSRAbggLy/P6ggAAHgljqEAfA37Nc856CcpQUFBSkpKcnr9tra2Xp8TbGho+NkZOlurfvZz4ODHn7M1ioqKlJ2dbXUMAAC8DsdQAL6G/ZrnHPTDn4KCAqWkpCg0NFRjxozRokWLlJGRsdf1Fy1apAULFvT7POHh4crNzdWOHTvU1tamAQP+X3v3HVBl3f9//HmYioCKogxRFEXL3FauMrfZcGSOypXmSq203ObOrYnmTs1RaubKXTlSU3NrDtwLRFFQQJR5/f7wx/lKWXfdd5wLDq/HP+FZvM/Fq891nfe5rs8nJz4+Ply6dAmAAgUKYBgGkZGRAOTOnRsnJ2fuXl6VMW9OMh1nZxfi4+MJDQ3FwcGBEiVKcPbsWQzDIHfu3Hh4eHD9+nUA/P39iYuL4969e1gsFoKDgzl//jwpKSl4eHiQN29erl69Cjyatf7hw4dER0cDEBwczMWLF0lOTsbd3Z18+fJx5coVAHx8fEhMTLRe71q8eHGuXr1KYmIibm5uFChQgMuXLwOPMpuamsrt248aV8WKFSM8PJyHDx+SI0cO/Pz8uHjxIgDe3t5YLBZu3boFQNGiRYmIiODBgwe4uroSEBDA+fPnAciXLx9OTk7cvHkTgMDAQCIjI7l//z7Ozs4EBgZy7tw5APLmzUuOHDm4ceMGAIULFyY6OprY2FicnJwICgoiNDQUgDx58uDm5kZ4eDgAhQoV4s6dOyQmJlq397lz50hNTcXT0xNPT0/r9vbz8yM+Pp67d+8CULJkSS5cuEBycvJ/3N4lSpTg8uXLJCUlkStXLry9va3bsGDBgiQnJ3Pnzh3r9v67Y8Rfbe/8+fPj4OBg3d6BgYHcunWL+Ph4XFxcKFy4sHV7e3l54eLiQkREBABFihThzp07xMXF4eTkRLFixTh79ux/3N6Ojo4UL148XWbd3d0JCwuzbu/Y2Nh0mU3b3h4eHuTJk4dr1679x+3t7u6Ol5dXuu2dkJBgzWyJEiW4cuXKEzNbsGBBUlJSrJkNCgri+vXr1u3t6+ubLrNAuu1948YNa2YLFSrEhQsXrNvb0dExXWYf396pqanAowMKd3d3XF1d023DqKgo6/b+q8wGBARw9+5dYmNjNUbYaIyIiYkhJiYmy4wRaV86XblyhUKFCmmMIGuMEUWKFLFm1svLK8uMEREREbi4uGiMsNEYkXa/ZC9hYWFZdozIiscR0dHR6bZ3VhojzPqsERcX97eybDEMw/hbjzTBpk2biIuLo2TJkty4cYPhw4cTFhbGb7/9hoeHxxOf86QzfwICArh37x6enp7/uIbbt28TGxv7X7+HrCgsLIwZM2bQvXt3/P39zS7Hpjw8PMifP7/ZZWQr165dIyAgwOwyxM5dunSJQYMGMXr0aH2bJBlKWRNb0j7UttL+//6wajCFPN3MLkcy2PWYeD7fe1bjuY1pXPvnYmJiyJ0793/seWTqM39efvll689ly5bl+eefp0iRIqxYsYKOHTs+8Tmurq64urr+azXkz58/2zYD/P39NdBJhitQoIDZJYiIiGRJ2oeKiL3RuJZxMvVqX7+XJ08e6+luImIf0k6JFBERkX9G+1ARsTca1zJOlmr+xMXFceHCBXx9fc0uRUREREREREQkS8jUzZ+PP/6YnTt3cvnyZX755ReaNm2Ko6MjrVu3Nrs0EfmXFCxY0OwSREREsiTtQ0XE3mhcyziZes6f69ev07p1a+7cuYO3tzc1atRg37591tUdRCTrS0lJMbsEERGRLEn7UBGxNxrXMk6mbv4sW7bM7BJEJIPdvn2bfPnymV2GiIhIlqN9qIjYG41rGSdTX/YlIiIiIiIiIiL/GzV/RMRUQUFBZpcgIiKSJWkfKiL2RuNaxlHzR0RMdf36dbNLEBERyZK0DxURe6NxLeOo+SMipkpISDC7BBERkSxJ+1ARsTca1zKOmj8iYqqcOXOaXYKIiEiWpH2oiNgbjWsZR80fETGVr6+v2SWIiIhkSdqHioi90biWcdT8ERFTXbx40ewSREREsiTtQ0XE3mhcyzhq/oiIiIiIiIiI2DE1f0TEVN7e3maXICIikiVpHyoi9kbjWsZR80dERERERERExI6p+SMipoqMjDS7BBERkSxJ+1ARsTca1zKOmj8iIiIiIiIiInZMzR8RMVWxYsXMLkFERCRL0j5UROyNxrWMo+aPiJjqxo0bZpcgIiKSJWkfKiL2RuNaxlHzR0RM9eDBA7NLEBERyZK0DxURe6NxLeOo+SMipnJ1dTW7BBERkSxJ+1ARsTca1zKOmj8iYqpChQqZXYKIiEiWpH2oiNgbjWsZR80fETHVhQsXzC5BREQkS9I+VETsjca1jKPmj4iIiIiIiIiIHVPzR0RMlT9/frNLEBERyZK0DxURe6NxLeOo+SMipnJ0dDS7BBERkSxJ+1ARsTca1zKOmj8iYqqbN2+aXYKIiEiWpH2oiNgbjWsZR80fERERERERERE7puaPiJgqMDDQ7BJERESyJO1DRcTeaFzLOGr+iIipbt26ZXYJIiIiWZL2oSJibzSuZRw1f0TEVPHx8WaXICIikiVpHyoi9kbjWsZR80dETOXi4mJ2CSIiIlmS9qEiYm80rmUcJ7MLEJHsrUiRImaXICIikiVpH2qOW3EPzS5BbEB/Z3NoXMs4av6IiKnOnTtHyZIlzS5DREQky9E+1LY8PDxwcXHm6xNXzS5FbMTFxRkPDw+zy8hWNK5lHDV/RERERERE/oP8+fMzceIkYmNjzS7FpsLCwpgxYwbdu3fH39/f7HJsysPDg/z585tdhsi/Qs0fETGVl5eX2SWIiIhkSdqH2l7+/PmzbTPA39+fokWLml2G2DmNaxlHEz6LiKlcXV3NLkFERCRL0j5UROyNxrWMo+aPiJjqxo0bZpcgIiKSJWkfKiL2RuNaxlHzR0RERERERETEjqn5IyKmKly4sNkliIiIZEnah4qIvdG4lnHU/BERU0VFRZldgoiISJakfaiI2BuNaxlHzR8RMVVcXJzZJYiIiGRJ2oeKiL3RuJZx1PwREVM5OTmZXYKIiEiWpH2oiNgbjWsZR80fETFVUFCQ2SWIiIhkSdqHioi90biWcdT8ERFThYaGml2CiIhIlqR9qIjYG41rGUfNHxERERERERERO6bmj4iYKk+ePGaXICIikiVpHyoi9kbjWsZR80dETOXm5mZ2CSIiIlmS9qEiYm80rmUcNX9ExFTh4eFmlyAiIpIlaR8qIvZG41rGUfNHRERERERERMSOOZldgDxZQkKCaV3PsLCwdP81g5+fH66urqb9frGdgIAAs0sQG9G4pnHNVpQ1Zc1WzMwaQGpqKpcuXTLt9ytrtqNxTVnLLvTZIONYDMMwzC4iI8XExJA7d27u3buHp6en2eX8bZcuXWLQoEFml2Ga0aNHU7RoUbPLEBsIDw/Hz8/P7DLEBjSuaVyzFWVNWbMVZU1ZsxVlTVnLLvTZ4J/7uz0PNX8yKbO/Sbpy5QpFihQx7feru599hIaGUrJkSbPLEBvQuKZxzVaUNWXNVsw+G2PGjBl0794df39/U2pQ1mxH45qyll3os8E/93d7HrrsK5NydXU1tbudnJys7rrYhIODph7LLjSuia0oa2IrZmcNwN/f3/QaJOOZnTWNa2Ir+myQcbRl5YlKlChhdgmSTShrYivKmtiKsib/hmXLllGxYkVy5syJl5cXzZs358KFC3/5nFu3btGtWzcCAwPJkSMHefPm5bnnnmP+/Pk2qlrslcY1sRVlLePozB95orNnzxIcHGx2GZINKGtiK8qa2IqyZnu3b98mNjbW7DL+NcuXL2fAgAHAo8lPo6Oj+e6779ixYwcbN27E29v7iZPwtm7dmv379+Po6EhwcDC3bt3iwIEDHDhwgNTUVOrUqWPK+/m3eXh4kD9/frPLyFY0romtKGsZR80feSI7nwpKMhFlTWxFWRNbUdZs6/bt23z88cckJiaaXcq/IiUlhbVr1wKPGj81atQgPj6eDRs2cOfOHdq0aUOlSpWsj58xYwbwKHcHDhwAoGjRopQvX564uDi+//57AGbNmsW2bdts/G4yhouLCxMnTlQDyIY0romtKGsZR80feaLcuXObXYJkE8qa2IqyJrairNlWbGwsiYmJtK1fHR+vrLO4x585evosK1YkANCtxau8/GJVAG5eucC+o7/xMO4efVu9/MTnnv/tGAd/O82lixdxSEnkdtRdLBYLLz5bns96d8EtZw6bvY+MEhEVw6Kte4iNjVXzx4Y0romtKGsZR80feSIPDw+zS5BsQlkTW1HWxFaUNXP4eHkSUCCf2WX8zw4c+78zmEoVKWR9TwE+3uwDbt6O+tP3uXzyMN4bMo5t+w9z9tJVANzdcvJcmacoEeCriVTlv6ZxTWxFWcs42gPIE12/ft3sEiSbUNbEVpQ1sRVlTTLC37kUYuTMr9i2/zCv16rOpR+/Zf2s8SQmJTHhy6+Zs2KdDaoUe6VxTWxFWcs4av6IiIiIiGQS/gW8rT9HRt+1/nw7+t6j+wt6//4pAFy4FsbC1RsBeKP+S3jkcqNKudKUKBIAwM4DRzOmYBERyRLU/JEn8vf3N7sEySaUNbEVZU1sRVmT/0WFp0vglfvR3EXrd/wCwI3IOxz87QwAdao8muy5SssuVGnZhXnfPprQOSYu3voaR8+cAyDqXgxXb9wEsIv5fsQ8GtfEVpS1jKPmjzxRXFyc2SVINqGsia0oa2Irypr8L1ycnRnUtS0A32/fQ6U33qVa667ExT8gXx5PerV5E4DzV69z/up17tyLAeCZEkUp6u8LwJSvVlCtdVeee/M9Yu8/agq1fLm2Ce9G7IXGNbEVZS3jqPkjT3Tv3j2zS5BsQlkTW1HWxFaUNflftWvyMjOHfUyZEsWIuB2FxWLh1ZeqsXHORHy9nzzZs7OTE2tnjKV900YU8SvI1fCbODk5Ur1iGZZNHk796s/Z+F2IPdG4JrairGUcrfYlT2SxWMwuQbIJZU1sRVkTW1HW5N/wZoNavNmg1p/ef3vvhj/c5lcgPxP7vp+RZUk2pXFNbEVZyzg680eeKDg42OwSJJtQ1sRWlDWxFWVNROyNxjWxFWUt46j5I090/vx5s0uQbEJZE1tR1sRWlDURsTca18RWlLWMo+aPPFFKSorZJUg2oayJrShrYivKmojYG41rYivKWsbRnD/yRB4eHmaXINmEsia2oqyJrShr5oiI0iSh2YH+zubQuCa2oqxlHDV/5Iny5s1rdgmSTShrYivKmtiKsmaORVt/MbsEEbulcU1sRVnLOGr+yBNdvXqVkiVLml2GZAPKmtiKsia2oqyZo239avh45Ta7DMlgEVH31OgzgcY1sRVlLeOo+SMiIiIiWZ6PV24CCuQzuwwREZFMSRM+yxP5+vqaXYJkE8qa2IqyJrairImIvdG4JrairGUcNX/kiR4+fGh2CZJNKGtiK8qa2IqyJiL2RuOa2IqylnHU/JEnio6ONrsEySaUNbEVZU1sRVkTEXujcU1sRVnLOGr+iIiIiIiIiIjYMTV/5ImCg4PNLkGyCWVNbEVZE1tR1kTE3mhcE1tR1jKOmj/yRBcvXjS7BMkmlDWxFWVNbEVZExF7o3FNbEVZyzhq/sgTJScnm12CZBPKmtiKsia2oqyJiL3RuCa2oqxlHDV/5Inc3d3NLkGyCWVNbEVZE1tR1kTE3mhcE1tR1jKOmj/yRPny5TO7BMkmlDWxFWVNbEVZExF7o3FNbEVZyzhq/sgTXblyxewSJJtQ1sRWlDWxFWVNROyNxjWxFWUt46j5IyIiIiIiIiJix9T8kSfy8fExuwTJJpQ1sRVlTWxFWRMRe6NxTWxFWcs4WaL588UXXxAYGEiOHDl4/vnn+fXXX80uye4lJiaaXYJkE8qa2IqyJrairImIvdG4JrairGWcTN/8Wb58Ob1792bo0KEcPnyYcuXK0aBBA27dumV2aXYtKirK7BIkm1DWxFaUNbEVZU1E7I3GNbEVZS3jZPrmz+TJk3nvvffo0KEDTz/9NLNmzcLNzY358+ebXZqIiIiIiIiISKbnZHYBfyUxMZFDhw4xYMAA620ODg7UrVuXvXv3PvE5CQkJJCQkWP8dExOT4XXao+LFi5tdgmQTyprYirImtqKsmSMiyvbHfEnJKdyJibP5780s8nm64+zkaNPfacbfWTSuie0oaxknUzd/bt++TUpKCgULFkx3e8GCBTlz5swTnzNmzBiGDx/+h9vPnTuHu7s7xYsX59q1ayQkJJAzZ058fHy4dOkSAAUKFMAwDCIjIwEoVqwY4eHhPHz4kBw5cuDn58fFixcByJ8/Pw4ODtbLzwIDA7l16xbx8fG4uLhQuHBhzp8/D4CXlxcuLi5EREQAUKRIEe7cuUNcXBxOTk4UK1aMs2fPApA3b15y5MjBjRs3AChcuDDR0dHExsbi6OhI8eLFOXv2LIZhkDt3btzd3QkLCwOgUKFCxMbGcu/ePSwWC8HBwZw7d47U1FQ8PDzIkycP165dA8DPz4/4+Hju3r0LQMmSJblw4QLJycm4u7tz//59DMMAwNfXl4SEBOspeCVKlODKlSskJibi5uZGgQIFuHz5svVvk5KSwu3btwEICgri+vXr1u3t6+tr3Ybe3t4A6bb3jRs3ePDgAa6urhQqVIgLFy5Yt7ejoyM3b9584vYuUqQI586ds25vV1fXdNswKirKur2DgoIIDQ0FIE+ePLi5uREeHg5AQEAAd+/eJTY2FgcHB0qUKJFue3t4eHD9+nUA/P39iYuLS7e9z58/T0pKCh4eHuTNm5erV69at+HDhw+Jjo4GIDg4mIsXL1q3d758+azLGvr4+JCYmGjd3sWLF+fq1atP3N4FChQgNTXVur3/KrPe3t5YLBZrZosWLUpERIR1ewcEBFgzmy9fPpycnNJt78jISO7fv4+zszOBgYHW7f1Xmf1P27tQoUKcO3eOnDlzWrd3WmY9PT3x9PS0bu+/yux/2t4lSpTg8uXLJCUlkStXLry9vdNlNjk5mTt37li3t8aI/zxGeHl5pdveWWGMOHDgAHnz5tUYkcXGiJiYGGJiYrLUGHH27Fny5s2rMcKGY4SzszOLtu5BsgdnZ2ciIyMpUKBAlhwjsuJxRHR0NGXKlMmyY0Ta9tZnjcx/HBETE0OlSpWy9XHEPx0j4uL+3pcQFiPtE34mFB4ejr+/P7/88gtVq1a13t63b1927tzJ/v37//CcJ535ExAQwL179/D09LRJ3fYgNDSUkiVLml2GZAPKmtiKsia2oqzZ3u3bt4mNjbX5701MTLQepJshIiLC1JVxvL29cXFxsfnv9fDwIH/+/Db/vdmZxjWxFWXtn4uJiSF37tz/seeRqc/8+X0HNs3Nmzf/dEfn6uqKq6urLcqza25ubmaXINmEsia2oqyJrShrtpc/f37TmgFmfki5du0aAQEBpv1+yT40romtKGsZJ1NP+Ozi4kKlSpX46aefrLelpqby008/pTsTSP59BQoUMLsEySaUNbEVZU1sRVkTW1HWxFaUNbEVZS3jZOrmD0Dv3r2ZO3cuX331FadPn6Zbt27cv3+fDh06mF2aXUu7RlEkoylrYivKmtiKsia2oqyJrShrYivKWsbJ1Jd9AbRs2ZLIyEg+/fRTIiIiKF++PJs3b/7DJNAiIiIiIiIiIvJHmXrC53/D3538SNKLjo4mb968Zpch2YCyJrairImtKGtiK8qa2IqyJrairP1zf7fnkekv+xJzpKamml2CZBPKmtiKsia2oqyJrShrYivKmtiKspZx1PyRJ7p9+7bZJUg2oayJrShrYivKmtiKsia2oqyJrShrGUfNHxERERERERERO6Y5f+SJkpKScHZ2NrsMyQaUNbEVZU1sRVkTW1HWxFaUNbEVZe2f05w/8j8JDw83uwTJJpQ1sRVlTWxFWRNbUdbEVpQ1sRVlLeOo+SNP9PDhQ7NLkGxCWRNbUdbEVpQ1sRVlTWxFWRNbUdYyjpo/8kQ5cuQwuwTJJpQ1sRVlTWxFWRNbUdbEVpQ1sRVlLeOo+SNP5OfnZ3YJkk0oa2IryprYirImtqKsia0oa2IrylrGUfNHnujixYtmlyDZhLImtqKsia0oa2IryprYirImtqKsZRw1f0RERERERERE7JiaP/JE3t7eZpcg2YSyJrairImtKGtiK8qa2IqyJrairGUcNX/kiSwWi9klSDahrImtKGtiK8qa2IqyJrairImtKGsZR80feaJbt26ZXYJkE8qa2IqyJrairImtKGtiK8qa2IqylnHU/BERERERERERsWMWwzAMs4vISDExMeTOnZt79+7h6elpdjlZRmJiIi4uLmaXIdmAsia2oqyJrShrYivKmtiKsia2oqz9c3+356Ezf+SJIiIizC5BsgllTWxFWRNbUdbEVpQ1sRVlTWxFWcs4av7IEz148MDsEiSbUNbEVpQ1sRVlTWxFWRNbUdbEVpS1jKPmjzyRq6ur2SVINqGsia0oa2IryprYirImtqKsia0oaxlHc/7IE6WkpODo6Gh2GZINKGtiK8qa2IqyJrairImtKGtiK8raP6c5f+R/cv78ebNLkGxCWRNbUdbEVpQ1sRVlTWxFWRNbUdYyjpPZBWS0tBObYmJiTK4ka4mLi9M2E5tQ1sRWlDWxFWVNbEVZE1tR1sRWlLV/Lm17/aeLuuy++RMbGwtAQECAyZWIiIiIiIiIiPz7YmNjyZ0795/eb/dz/qSmphIeHo6HhwcWi8XscrKEmJgYAgICuHbtmuZJkgylrImtKGtiK8qa2IqyJrairImtKGv/HcMwiI2Nxc/PDweHP5/Zx+7P/HFwcKBQoUJml5EleXp66n86sQllTWxFWRNbUdbEVpQ1sRVlTWxFWfvn/uqMnzSa8FlERERERERExI6p+SMiIiIiIiIiYsfU/JE/cHV1ZejQobi6uppditg5ZU1sRVkTW1HWxFaUNbEVZU1sRVnLWHY/4bOIiIiIiIiISHamM39EREREREREROyYmj8iIiIiIiIiInZMzR8RERERERERETum5o+IiIiIiIiIiB1T80dERERERET+ktYJEvlzhmGQnJxsdhl/Sc0fERGRTCSzHziIiEj2cvv2beLi4rBYLGaXIpJpxcfH4+TkBMCWLVu4efOmyRX9kZo/8o+ldf1TU1PT/Vvkf6UsSXa0d+9e63g6fvx4Nm7caHJFIiIijxw5coQ6depw4sQJs0sRybS2bdtG6dKlMQyDjz/+mA8++CBTNkudzC5AshbDMLBYLOzcuZPdu3fTq1cvPDw8zC5LsrB79+5x//59APz8/EyuRsS2Lly4QJcuXShdujTe3t7MmDFDB9jZUNq+VSSz+vXXX8mZMydlypShd+/eVKtWjebNm5tdlmSwo0ePUr16dbp3707VqlXNLkck03Jzc6NIkSL4+fmRkJDAsWPHKFCggNll/YHO/JG/Le3g9LvvvqNJkybExcVx/vx56306a0P+qZMnT9KoUSPq1q1LyZIl+fLLL80uScSmChUqRN++fdmyZQvz589n3759PPXUUyQlJZldmthI2r51+/bt9O/fn5YtW7Jw4UIiIiLMLk0EgMuXL9OxY0emTp1Khw4dCAkJITg42OyyJIOFhoZSs2ZNhg8fzsSJE61nqIrIH1WpUoVy5cpx8+ZN8uTJQ968eQFISUkxubL01PyRv81isbB37146duzI+PHjGTNmDBUqVAAgKSlJ31rKP3L06FGef/55qlatyuDBg2nZsiWdO3dm27ZtZpcmkuHSDqJdXV0JDAzEy8uLwoULM3XqVB4+fIizs3OmO2CQjGGxWFi9ejVNmzYlPDycYsWK8d577/HJJ58QHh5udnkiBAYGMnz4cDZs2MDXX3/NsmXLKFu2rL70s2PHjx/n+eefJzY2Fi8vLwAcHBzUABJ5zONToRiGQePGjfn6668pVqwYlStXJiIiAkdHRxITE02u9P+o+SP/yO7du6levTrvvfced+/e5fvvv6dly5ZUr16dtWvXApq3Rf6z06dP89xzzzFo0CAmTpzIW2+9Rbt27XB1dWXTpk3Wx6UdZOhgQ+yJYRg4ODza/Xbv3p0vvviCdevW0b9/f06fPk379u1JSEjA0dHR+pzMdOAg/64rV64wcOBAxo4dy6JFixgzZgw5cuTA399fl8KK6dL2vwUKFMDLy4ugoCB++OEHjhw5Yv3ST8d99uXYsWNUqVKFNm3aMGbMGD766COmTZsGqAEkkiY1NdU6BsbGxhITE0OdOnVo1aoVI0eOpGDBgrz00ktERkbi4uICwNdff01kZKSZZav5I//Z4zt1b29vfvnlF7744gtat27N7NmzMQyD8uXL07x5c8LDw3UGkPwlwzBYvHgxycnJNGjQwHr7tm3bePjwIRcuXGDJkiXs2rWL6OhoAOsHZRF7kDZGhoWFcfDgQbp168bTTz9Nq1at6Nq1K+fPn+fdd9+1XvrVq1cvtm/fbmbJkoGSk5Px9PSkS5cunD9/Hn9/f1q3bs3YsWOBR9/Ai9ha2gf8tP1vlSpV+O233xg6dCgHDx4kJCSEo0ePAui4z46cOXOGChUqWBs+7dq1o3fv3gwaNEgNIJHHpI2NQ4cOpX79+jz77LOMHTsWwzCoWrUq48ePx8fHh+eee46tW7dSr149vvjiC/Lly2dq3RZD7Xr5E2nzEMTHx+Pm5kZSUhIxMTGMGDGCVatW0bBhQ9q1a0f16tW5cuUKb7zxBsuWLaNEiRJmly6Z1OXLl8mbNy/Ozs506dKF1atXc+TIETZv3syQIUPo378/ycnJXLlyhW+//ZayZcvi7e1N3759ef75580uX+RfM2bMGPbu3YuHhwfz5s0jZ86cADx8+JClS5cyc+ZMkpOTKVCgACdPnuTKlSvW5UPFvhw+fJjXX3+dRYsW0blzZ2rXrs3MmTNxdHTk0KFDjBw5ks8++4ynn37a7FIlm0hNTbV+sFm/fj3x8fF4e3tTq1YtABYuXMj06dOpUKEC3bt3p0KFCrz++ut07tyZV1991czS5X9w48YNBg4ciKurK7NmzbLeHhERwaxZs5g8eTKjR4+mZ8+eQPqciGQXj+d++vTpfPbZZ/Tp04fbt28zefJk2rVrx6RJk/Dw8ODYsWMMHjyY3377jeLFi7Nx40acnZ1NXeRBR5LyRGmh3Lx5MwsXLuTmzZsUKlSIgQMHMnXqVAYPHoy3t7f18bNmzSIpKcl6XbDI7yUlJdGhQwfOnj3LyZMnmTdvHklJSZQsWRJXV1d27dpF5cqVrY/v0qULx44dY8aMGaZ3yUX+Tampqbi7u/PTTz8RGBhoPQBITk4mR44cvPPOOwQEBPD999+TkpLCxo0bcXJyIiUlJd2lYJL1pO1b9+/fz/nz53nzzTepWLEitWrVokGDBjRu3Jg5c+ZYH//dd99x69Yt7VvFZh6/LLVPnz4sWbIER0dH8uXLx4svvsgXX3xB+/btsVgszJw5k86dO2MYBrdu3Up3Nq9kPXFxcVy6dAl3d3d2795NjRo1APDx8aFbt24ADBo0CICePXvi4OCglQol20kbHw8dOkRCQgIzZsygSZMmANSuXZvXXnsNwzCYPHky5cqV4/vvv+f8+fMUK1YMBwcHkpOTzf0yzxD5E2vWrDFy5MhhjBgxwpg1a5bRqFEjw2KxGFeuXLE+5ueffza6dOlieHl5GUeOHDGvWMkSTpw4YVSuXNl45plnjKioKOP+/fvG+++/bzg7OxsHDhwwDMMwkpOTjZSUFOtzHv9ZJCt6UoZjY2ON+fPnG87OzsbAgQOttycnJz/xNZKSkjKsPrGN1NRUwzAMY+XKlUb+/PmNAQMGGMePHzcMwzDWr19vvPjii0aNGjWMnTt3Gps3bzZ69+5teHp6GseOHTOzbMmmzp07Z7zwwgvG8ePHjbNnzxohISFGcHCw0bZtW+tj1q9fb4wfP97o27evdYzSWJW1nTt3zmjYsKHRoEEDY9euXenui4iIMIYOHWp4eXkZ48ePN6lCEfMdPXrUsFgshpOTk7F48WLDMP5vH//DDz8YOXLkMDp37mxERkame15m+Eyj5o+kkxbce/fuGbVr1zYmTZpkGIZhXL9+3ShcuLDx3nvvWR978+ZNY9iwYUajRo2MEydOmFKvZA1puUpJSTFOnz5tVK1a1ahcubIRHR1txMfHG61atTJy5cpl7Nmzx/o4EXvweJYvXrxoHD9+3EhOTrb+PzFjxgzDwcHBGD58uPVxqamp1vvFvuzbt8/IkyePMXv27D80+r799lvjjTfeMFxcXIwyZcoYL7zwgnH06FGTKpXs7MsvvzQaNGhgtG3b1trMuXfvnjFnzhyjRIkSRrt27Z74vD9rXkvWcvbsWWsDaPfu3enui4iIMD755BMjICDAiIqK0r5KsoUn5XzJkiVGjhw5jF69ehkJCQnpHvfjjz8aFovFGDdunE3r/DvU/BFjwoQJxtSpU9PdFhERYRQuXNg4ffq0ERERYfj7+xudO3e23v/NN98Yd+/eNaKiooy7d+/aumTJIh48eGD9OTEx0fpznz59DIvFYlSoUMGIiooyHj58aLRu3drIkyePsWPHDjNKFfnXPX6wMHDgQKNkyZJG3rx5jaCgIGPYsGFGWFiYYRiPGkBOTk7GyJEjzSpVMlhaFsaPH280atQoXTYeHxsNwzBOnz5tREdHa98qpoiLizP69etnFC5c2KhWrVq6+2JiYoy5c+capUqVMho3bmxOgWITf9UAunnz5h/OaBCxV49/iffgwQMjNTXV2uieO3eu4eDgYIwYMcJ6W9r+/cCBA5nyTEjN0pXN3b9/n7CwMPr168fcuXOtt+fJk4fKlSuzdetWnnvuOV599VW++OILAMLDw/n+++/Zvn07efPmJXfu3GaVL5lYWFgYbdu2ta5S5OzsDMD48eNZuHAhc+fOxcHBgRdffJH4+HgWLFhA9erVefvtt3nw4IGZpYv8K9LmQZg4cSKzZ89m7Nix7Ny5k2bNmrF582ZGjBjBrVu36NatG1988QWffvopCxYsMLlqyQhpWYiMjOTu3bvpVspJGxv37dtHYmIipUqVIk+ePNq3ik38ftWmXLly0bNnTzp37szJkycZMGCA9T4PDw9atmxJ165dyZEjh1Z8smMlSpQgJCQEi8XCyJEj+eWXX6z3FShQgPz585tYnYhtPD658+eff0779u2pU6cOAwcOJCwsjE6dOjFnzhyGDRvGZ599lm7598qVK+Pk5ERycrKZb+GPzO4+ifnCwsKMIUOGGB4eHsasWbOst3fs2NGwWCxGkyZN0p3K269fP+OZZ54xrl27Zka5kkVcuHDBqFq1qtGoUSPrt0ZjxowxvLy8jB9++MEwDMM4deqUUaFCBaN8+fLGnTt3jISEBOP69etmli3yr0lNTTXu379v1K1b1xgzZky6+6ZNm2aULl3aeq14fHy8sWbNmkz5LZH8eyZPnmzkzp3bOHPmTLrbHz58aPTs2dP47rvvTKpMsqPHv9E+cuSI8fPPPxvnzp0zDOPRGUAjRowwnnrqKWPw4MHpnnf//v10l3OL/Tp79qzx6quvGlWqVDH27t1rdjkipujXr5+RP39+Y8aMGcakSZOM4OBgo3LlykZ8fLxhGIaxYMECw8XFxejTp0+mHxO11LsAj5Z3/OKLLwgJCWHcuHHWWf1r1arFtWvXaN26NQULFuT48eOsWLGCnTt3Uq5cOZOrlszu3Llz9OrVC1dXVwoUKMCaNWtYsmQJ9evXtz7mzJkzvPzyy/j5+bFr1y4tGyp2JTU1ldq1a1O1alXGjBmTbpWHxo0bExsby7Zt29I9x/SVIOR/Zvz/FXBCQ0NJSEjAxcWFUqVKAVCtWjWioqL47rvvCAwMxNHRkREjRrB48WJ27dpFYGCgucVLtmA8tkrToEGDWL58Oc7OzsTGxvLaa6/Rr18/PDw8mDFjBsuWLaN58+YMHz78T19D7NeZM2cYMmQIkyZNonDhwmaXI2JTx44d45133mH27NlUq1aN9evX89ZbbzFx4kTraocWi4Vp06axYsUKfv7558w9LprZeRLzPd6dvH79ujFo0CDDw8PDmDZtmvX2Ll26GLVq1TLKlStntG7dWpM7yz8SGhpq1KtXz8iZM6cxceJE6+2PZy80NNS4ePGiGeWJ/Gv+7Nue1q1bG88884wRGxub7nGjRo0yXnnllUz/LZH8d7799lvDz8/PKFiwoFG5cmVj9OjRhmEYRnh4uFGzZk0jT548RunSpY0XXnjBKFCggHH48GGTK5bsaMqUKUbBggWNnTt3GoZhGN26dTNy585tXekpIiLCGDVqlOHl5WXMmTPHzFLFRGkT2opkNzt27DACAwMNwzCM1atXG+7u7sbMmTMNw3h0huTSpUuNuLg4wzD+b76fzDwRus78yaaM/9+l/P03zFevXmXOnDmEhITw2Wef0aNHDwAePHiAYRg4Oztb5ycQ+bsuXLhA9+7dcXR0ZODAgdSoUQNIfy2tSFb2eJaPHTuGm5sb8GjehOjoaCpVqkSxYsVYsmQJHh4euLi4ULduXYKCgpg/f76Zpcu/KG3fevPmTerWrUvv3r0JCAhgx44dLF26lLfeeovRo0cDsHDhQqKjo8mZMycNGjSgaNGiJlcv2Una4X/Lli159tln+eSTT1i7di3t2rVj7NixdO3alYcPH5IjRw4iIiLYvHkzbdq0wdHR0eTKRUQyhvHY2YxpPx87dox+/frRpEkT+vbty4QJE+jSpQsAe/bsYfbs2fTr14/SpUv/4TUyIzV/sqG0UG7bto3FixeTmJhIQEAAY8eOBeDatWvMnj2bkJCQdAEX+V+kXQJmGAZDhgyhevXqZpck8q94fEf/ySefsHz5ch4+fIi7uztt2rRh+PDhHD9+nGbNmpGSkoKXlxeOjo7Ex8dz5MgRnJ2dM/3Bgvx9e/fuZdmyZTx48IDp06fj4uJCZGQkX331FdOmTaNly5aMHz/e7DIlm0tNTSU5OZm6desyfvx4UlNTadCgARMnTqRLly4kJiYya9YsypUrR82aNa3PS0lJUQNIROzOn30h/eDBA6pVq8axY8cYO3Ysffv2BeDhw4c0a9aMnDlz8u2332aZL7M1qUA2k/YBY/Xq1bRv356WLVtSsGBBli9fzunTp1m1ahUBAQF07doVR0dHunXrhqurK+3btze7dMni0laO6N27Nx9//DFTpkyhSpUqZpcl8j95vGmzefNmli1bxldffUVSUhLnzp3j448/Jjo6mpCQEEJDQ5k2bRrx8fHkzJmTnj17WleC0Bw/9uHBgwesXLmSpUuXUqJECVxcXADw9vamXbt2AMyaNYuHDx8SEhJiZqmSzfz+g42DgwMuLi4EBwfTvHlzoqOjmTVrFm3atAEgJiaG1atX4+DgkK75o8aPiNijtPFx8uTJ7N+/Hx8fH5o0aUKtWrXYuHEjVatWZd26dTg5OeHu7s7y5cu5desWR44cwcHBIctczaAzf+xcWhB/f0lCixYt+PDDD+nWrRuXL1+mWrVqREREUL16dbZv346TkxNXrlxhyZIlNG/enJIlS5r8TsReaOJAsUdr1qxh3bp1+Pn5MWrUKOvt33//PU2bNuXzzz+3Xkb7OH2Lbh8ebwKePXuWefPmMW3aNMaMGcOHH35ofdzt27eZMWMGK1eu5KeffsLb29ukiiU7efwY8Pjx4zg6OhIUFESOHDk4ffo0Xbp04fbt2xw7dgwnJyeioqJ45513iImJ4eeff9YYJSLZwrhx45g8eTINGzbkypUrnD9/nsmTJ9OiRQuuXr1K9+7duXHjBp6engQFBTFz5kycnZ2z1Jd4av7YsbSd/eXLl9m6dSsVKlTg2WefZdOmTfzwww9MnjyZa9eu8dJLL1GnTh1atWpF48aNqVu3LitWrMhyYZasIzEx0fqNuEhWd/78ed59911OnDhBmzZtrGd0pDV2unXrRnh4uHU1HX2Qsh9/Nn/ehQsXmDFjBhs3bqR79+707NnTet+dO3ewWCx4eXmZUbJkY3379uXbb78lPDycN998k3fffZfatWvz3XffMXLkSMLDwwkKCiIlJYXU1FT27t2Ls7OzmtQiYpd+f7bOkCFDqFOnDi+99BJnz55l2rRpLFq0iLlz59KiRQuSk5O5f/8+Li4u5MyZE8h6K7RmnUrlH0kL84kTJ2jevDmlS5fG398fwLqstmEY9OrVi6pVqzJ79mzi4+MpVaoUa9eu5dVXX2XLli1ZKsySdajxI1lZ2gf+tP8WL16c/v37M378eFauXEmTJk2oXbu29cOSl5cXJ06cUOPHzjw+f97y5cut+9C+ffsSFBRE586dsVgsfPHFFzg4OPD+++8DkC9fPpMrl+zi8TPSduzYwYYNG5g/fz7R0dFMnDiRsWPHkpiYyBtvvMELL7zA4sWLMQwDHx8fWrdujaOjY5b7YCMi8nc83vjZtm0bjo6O7Nixg1q1agEQHBxMnz59AOjcuTMODg40b96c3LlzW1/DMIwsNz5mrWrlb3NwcODMmTPUrFmTLl260LNnT/z8/Kz3lytXjnv37nHp0iWGDRuGxWLBycmJ8uXLM3z4cJ566ikTqxcRyZweP1i4f/8+Tk5O5MiRg0aNGuHs7Mz48eMZOXIkhmFQp04d7t69yy+//EKhQoWyxLXg8velzZ/Xrl073n77bdzc3Fi7di179+5l1apVlCxZkvfeew9HR0eGDx+Os7MznTt3NrtsySZ+/412rly5aNiwofWDTYkSJejduzeTJk0iMTGR119/3fpBJ01KSkqW+2AjIvJ3pI2P/fr1IyQkhCJFinDlyhVOnTpF7dq1AQgMDOTjjz/G0dGRFi1asH379nRzoGXFhTp02ZedevjwIW3btqVAgQJMnz7dentSUhK3bt0iPj4ef39/XnzxRQICApgyZQpffPEF69atY+fOnfj4+JhYvYhI5jZq1CjWr1+Pk5MTxYoVY8qUKeTLl4+tW7cyZswY9u7dS7ly5ShatCgXL15k165duLq6alUvO3LkyBFatmzJxx9/TOfOnbl06RLVqlUjNjaWsmXLsn37dlxdXTl16hTLli2jXbt2BAUFmV22ZDOTJk1i9+7dREVFUaJECebNm2e977fffqN37944OTnx9ttv8/bbb5tYqYiIbZ04cYK2bdsyZ84cEhISWLt2LZMnT2bp0qW0atXK+rgLFy6wbt0660IdWZm+hrRTTk5OREREUKpUKettW7ZsoW/fvjz99NPUq1ePpk2bMmjQIE6cOEGNGjVYvnw5y5YtU+NHROR3UlNTrT9PmzaNCRMm0KRJE+rVq8f+/fupWrUqhw8fpn79+gwaNIhq1aqRnJzMiy++yK+//oqrqyuJiYlq/NiR8PBwXnzxRTp37szVq1epV68ejRo1YtGiRZw5c4amTZuSkJDA008/zZAhQ9T4EZt4fKyaOHEiQ4cOxcfHh/DwcNavX8/cuXOt9z/zzDNMnjyZGzducOjQITPKFRExxdixY5k2bRrVqlXj2WefpUaNGgwZMoS+ffvy9ttvs2zZMutjg4KC+Oijj6wrtGZlWbt1JX8qPj6eyMhIjh8/TmhoKKtWreKrr77imWeeYeTIkbi7uzNx4kR+/vlnfvnlF86dO0dQUJAaPyIiT/D4deGRkZEsXLiQpk2bAjBgwABq165NmzZtOHHiBHXr1iUxMZFZs2axbt06KlasSJUqVTTXlZ155ZVXKFy4MIZh8MEHH1C1alW+/PJLHj58SLFixdi8eTONGjXip59+yvLfFErWkTZW7d69mwcPHrB27Vrq1KnDuXPnGDFiBIsWLcLR0ZF3330XeNQA+u677wgMDDSxahER23r48CHz5s2jSpUqxMXF4e7ujqenJwMHDsRisdC2bVvi4+OtY2WarL4/12Vfdmzbtm00aNAAf39/oqKimDBhAnXq1KF48eIkJiby6quv4uvry1dffWV2qSIimd6uXbt4++23uXv3LsuWLaNRo0bWleuio6MpU6YMvXr1om/fvgBs2rSJGTNmcPv2bSZPnkzVqlVNfgfy30q7XO/evXtYLBY8PT2t9924cYMGDRowatQoXn/9de7fv8/7779PgwYNqFGjBgEBASZWLtnR9u3beeeddzAMgw0bNlChQgUATp8+zZgxY7h48SIdO3akQ4cO6Z73+3mCRETswZ+NbZ9//jm9e/cmJCSEHj16WG+PjY2lf//+HD9+nF27dtmy1AynEd6O1a5dm4sXL/Ldd99x8eJFunTpQvHixYFHXcvcuXNbv7VUD1BEJL3fj4t+fn60a9cOBwcH1q1bBzxauS45OZkcOXIQGBhIfHy89fEvv/wynTp1IiAgwLraomRNFouFtWvX0qRJE5577jnGjBnD6dOnAcidOzeGYbB06VKuXLnCyJEjOXz4MLVq1VLjR0zh7+9P69atiY2NZcOGDdbbn3rqKQYOHEiJEiUYM2YM69evT/c8NX5ExN483vg5cuQI27Zt4/DhwyQmJvLhhx8yfPhwPvjgA2bNmmV9joeHBxMmTODnn382q+wMk7XPW5L/KCAg4A8Hn4mJiYwcOZJffvmFzz77THNQiIg8QdrYGBISQvPmzQkKCqJLly44ODiwYMECBg0axOjRo3FycsLR0ZHY2Fjrc1NSUnB0dKRx48bUrVuXXLlymfU25F+wf/9+OnToQJcuXUhMTGTatGmcOnWKHj168Pzzz9OvXz+GDRtG1apVcXJyYs2aNbqMWmziSd9oBwcH07NnTwzDYMGCBbi7u/Phhx8CUKpUKXr37k1QUBAvv/yyCRWLiNiGYRjW8bF///6sX7+eu3fvEhwcDMD69esZMmQIzs7O9OzZEwcHB+uqnG5ubtbXsKfPyrrsK5tZsmQJBw4cYPny5WzatMl6KrCIiPzR7du3qV27NhERERw/fhwfHx+uXr3Kl19+SUhICFWrViUwMJDIyEiOHDnCmTNnrNeD29sBQ3Z15coVli1bhmEY9O/fH4AffviBPn368MwzzzBgwADKlClDeHg4Z8+eJTg4GD8/P5Orluzg8TFmwYIFXL16lfv379OpUyeCg4O5ceMGkydPZt26dXTv3p0PPvjgD6+R1qgWEbFXn3/+OaNHj2bt2rVUq1aNAQMGMG7cOLZs2UK9evUAGDNmDIMGDWLVqlU0adLE3IIzkJo/2UhoaChdu3Ylb968jB49mqeeesrskkREMpUnfYt+8uRJevTowdmzZzl06BA+Pj5cu3aNefPmMW/ePHx9fRk9ejQNGjQAIDk5OctPCJhdhYSEkDdvXtq0aUNKSgoRERE899xzxMfH0717d0aPHm197JYtW+jTpw8VKlSgW7duVKtWzcTKJbt5fKzq06cP8+fPp3z58kRGRnLt2jXGjh1Lp06duHnzJiEhIWzYsIHWrVszePBgkysXEbGdxMREOnTowAsvvEDXrl1Zv349rVu3ZsqUKXTq1In4+HhcXFxwcnJi0aJFvPXWW3Z9DKeLe7ORkiVLsnz5chYsWKDGj4jIE6R9mHp8Kc/SpUvzxRdfEBQURKVKlYiIiCAgIICOHTvy3nvvkZyczL59+/7wGpJ1pP29t23bxgsvvACAo6Mj/v7+jB07FhcXF44cOUJoaKj1OQ0aNGDKlCls27aNhQsX8uDBA1Nql+wpbZy5desWV69eZdu2bfz444/89ttvvPfeewwdOpQVK1ZQqFAhOnfuTI0aNTh58qTmeBQRu5aampru387Ozty+fZs8efJYm+ATJkygU6dOJCcns2jRIlatWgVA27Zt7WI597+iI9RspkCBAuTOndvsMkREMq358+fz1FNPpZu8+emnn2bmzJn4+vpSvXp1bt++TeHChenQoQNNmjRh5cqV9OvXD1DzJ6tJTU3FycmJS5cuceTIEQoXLsy+ffuYM2cOAG3atGHy5MkcO3aMmTNncu7cOetz69Wrx+LFi+nfvz85c+Y06y1INjV37lwqVqzI1atXKVCggPUSsIkTJ9K8eXM++eQT4uLiKF68OMOGDePrr7/GYrGoASQidivtGCwiIgKApKQkChUqxOeff84777zD+PHj6dq1K/Coeb527Vru3LmT7jV05o+IiIid2rdvHzt37iQpKQl4tFKOm5sbderUsTaADMOgdOnSdOjQgUuXLhEQEMCdO3coUqQIXbp0oWHDhuzYsYPbt2+b+VbkH0q7dObo0aOULVuWxo0b4+DgwPjx45k5cybz5s0D4O233+azzz5j5cqVTJ8+nfPnz1tfo3bt2hQrVsystyDZVGpqKvny5cPf35/z58/j4uKCg4ODdczq168fSUlJ7N+/HwBfX19r40dzkYmIvXn8jJ9NmzZRo0YNTpw4gYuLCwMGDODq1asULlyYxo0b8/DhQ27dukWnTp2IiYmxTvKcHaj5IyIi2dbixYtp27Yts2bN4sqVKwDUrVuXqVOnkpiYyEsvvcT9+/etH5YCAgLo0KEDvXr1sp5F6evrS58+fdiwYQP58+c37b3IP5PW+Dl+/DjVq1enV69ehISEALB06VKKFCnC6tWrrWcAtWvXjtGjR7Nu3TrGjh3LxYsXzSxfspnfX8rg4ODAyy+/zLBhw/Dw8KBx48bA/61Q8/DhQ1xdXXFxcUn3PDV+RMTePD4H2tq1a1m/fj2XL1+ma9euHDlyhOLFi7Ny5UquXr1Ko0aNKFu2LM2aNePmzZvs2LEDR0dHUlJSTH4XtqEJn0VEJFtatGgRXbt2Zc6cOVSvXp2iRYta70tJSeHnn3+mT58+WCwWvv32W5ycnOjduzfFixdn7NixgCZ3zuquXbtGxYoVqV27NsuXL7fevnjxYrZs2UJsbCxRUVG0bduW9957D4DZs2cTEhLCtm3bKFiwoFmlSzby+AebQ4cO8eDBA/LmzUvp0qWBR5OPd+nSBW9vb4YNG4ajoyPTpk0jIiKCX3/9Vat5iUi20KdPH9atW0ebNm24ePEie/fuxdPTk9mzZ1OxYkWuX7/O1q1biYqKolixYjRu3BhHR8dsdSyn5o+IiGQ7J0+epFmzZgwbNozWrVunu+/mzZvWD/UHDx6kZ8+e7N+/n2LFiuHm5sbhw4ezzUGCvbt8+TItWrTA19eXvn37Ur16dcaMGcOoUaPYu3cv/v7+dOnShVu3btGuXTs6duwIwL179zR/ntjE45dp9evXzzpvz40bN3j33Xfp1q0b5cuXZ8uWLfTq1Ytz587x3nvvUaBAAQYMGICbm5uWcxcRu3fo0CGaNm3KV199Ra1atQBYt24dM2bMICoqirlz51KuXLk/XPqa3cZHXfYlIiLZTlRUFG5ubtStW9d628qVK+nSpQvBwcHUr1+fefPmUblyZfbu3cuaNWuYPn06R44cwcnJKducHmzvAgMDWbp0KYmJiYwfP57OnTvz+eefs3r1asqWLUu+fPmYPn06vr6+TJ06lUWLFgHg6elpcuWSHaSmplo/pMyYMYMFCxawdOlS9uzZw8qVK9m3bx/jxo0jNDSUevXqMWnSJJ599llCQ0MZOXIkbm5uPHjwIFt9sBGR7CkpKYno6Ghy5cplve3111+nQ4cOnD17li5dunD8+PE/THqf3cZHNX9ERCTbefDgAceOHePs2bMkJyfTtWtXJk6cyLVr1/j0009xdXXlyy+/5OjRo8CjA4iGDRtarwvPbgcL9qxEiRJMnTqVBw8esGTJEvr27Uv9+vWBR5f1+fj4MGXKFCpUqEDNmjUBzZsiGeunn34CHs3rk9Zo3rdvH02aNOHFF18kICCAxo0bM3nyZPbs2cOyZctwcHCgbt26fPrpp9y8eZOGDRsCaBU6EbFraY0cb29vgoODOXz4MAkJCdb7W7RoQcmSJUlISOCjjz7i0qVL2XofruaPiIhkOzVr1qRNmza88MILBAYGWi+ZmDt3Ln369GHw4MEcOnSI8PDwPzxXjR/7ExwczMyZM3nhhRf46aef2L17N/BoudekpCT8/PyYP38+RYoUMblSsXcTJ07k/fff58svvwSwzkcRGxtrXZEwKSmJ1NRU6tSpQ/fu3Zk7dy737t0jR44c1K9fnylTpnD06FGaNGli4jsREfn3/X7y+7RGTlBQEKVLl2bSpEls27aN5ORkAO7cuUPhwoVp3749UVFR7Nq1y+Y1Zyaa80dERLKlmJgY9u3bR3R0NM2aNcPZ2dl638mTJ3n33Xf5/PPPqVq1qolVii2dO3eOXr16YRgGQ4YMoXr16maXJNnMjRs3+OCDD7h58yZt27a1zjM1ZcoU+vbty6FDhyhbtqx1Eug5c+awaNEitm3bZl3ZKzExkZ07d1KsWDGCgoLMfDsiIv+axye/X7x4MYcOHcLd3Z1KlSrRtGlTABo2bMjFixepU6cOTz31FKtWrcLR0ZGffvqJChUqUKlSJebNm2fm2zCVmj8iIpKtPH7w8CT379+ndevWPHz4kM2bN//lY8X+nDt3jt69e3P79m2mTJlClSpVzC5Jsom0S0pv3brF+++/T0REhHWlOcMweOONN9i1axdr167lqaeewtnZmTfeeAN3d3dWrlyZrS9lEJHso1+/fixdupQaNWrg4ODArl27GDBgAN27dwdg8ODBHDt2jGvXrlG8eHEWL15Mzpw5adiwIXXr1uXjjz82+R2YR80fERGxW483eiIiIvDx8fnTx969e5eff/6Z2bNnc/36dQ4ePIizs/N/bBaJ/Tlz5gxDhgxh0qRJFC5c2OxyJBtJawDdvHnT2gDq0KEDHTt2JCwsjH79+vHtt98SGBiIk5MTTk5O1rHq96vYiIjYm3nz5jF69GiWLVvG888/z6JFi+jYsSNOTk4MGzaMfv36AY/OgExKSiJXrlykpKQwbNgwZs+ezZ49eyhRooTJ78I8av6IiIjd++STT0hMTGTo0KF4eXn94f6UlBTmzp3L8uXL8fX1ZdGiRTg5OZGcnKxl3bOpxMRE62U0Ihnp9w3mtHEnIiKCnj17EhYWRufOnWnfvj0AGzZs4M6dOzg5OdGyZUvrvEAaq0TEnqUdx3l7e9O7d2++//572rRpw8CBA4mMjOTzzz9n+vTpdOnSxfqcy5cv89FHH3Ho0CHWrVtH+fLlzXsDmYCaPyIiYnce/wb84MGDtGrViqVLl/L888//6XPCw8O5ffs2zzzzjHWVHU3uLCIZ6fHGz5dffsmJEyeIiIigRYsWNGvWjDt37tCtWzfCw8Np3749nTp1+sNraKwSEXv0pLMZIyMjuXfvHk5OTrz88st07tyZjz76iG3btvHyyy+TlJTEokWLeOedd6zP2bFjB4ULF6ZYsWK2fguZjs5jFxERu5N2sDBx4kRWrFjBK6+88peNHwA/Pz/Kli2Lg4MDqamp+jAlIhkurfHTt29fhg0bxsOHDwkMDKR58+aMHj2afPnyMX36dPz8/FiyZAlTp079w2torBIRe5Oammo9louLi7Pe7u3tTfHixTlx4gQ5cuSgbdu2ALi7u9OiRQuWL19O69at073WSy+9pMbP/6fmj4iI2K3Q0FAmTpzI0aNHiY+P/9vP0xw/ImIrP/zwA8uXL2fVqlXMmjWL119/HYAiRYoAUKBAAaZNm4aDgwOhoaHopH0RsWeGYViPw8aNG8dbb71FgwYN+PHHH4mJiQHA1dWV06dPs2XLFu7cucOIESNwdnamefPm1kth5Y90dCsiInYhNTX1D7fNnTuXfv36sWfPHtasWWP7okREfuf3zZs7d+5QtmxZnn32WVasWEGDBg2YMWMG77zzDnfv3uX06dMULFiQlStXMn36dCwWixpAImKXHj/jZ8qUKYwZM4YKFSpw9+5dunfvzty5c4mKiqJGjRp06NCBDh068Nxzz3Ht2jVmz55tHR81B9qTaauIiEiW9/i8GadOnSIuLg5PT09KlSrFmDFjuHfvHh07dsTNzY0mTZqYW6yIZGtpH2zSxq3k5GQiIiJYunQp3bt3Z/z48XTt2hWAzZs3s3z5cmbPnk2BAgXSPU9ExN6kjW2nT5/mzJkzrFy5krp16zJ8+HA++OADFi5ciGEYvP/++0yaNIl33nmHyMhIXnvtNU1+/zdozyEiIlna46cHDxw4kLfffpvXXnuNHj160KpVKwBmzJjBe++9x9tvv83atWvNLFdEhPnz59OoUSNSUlKoVq0a7u7udOjQgQEDBtCtWzcAHjx4wDfffEOePHnw9va2PleNHxGxZytXrqR27dr88MMPuLm5WW+fOnUqtWvXZuHChcycOZP4+HiqV69OkyZNcHR0JCUlRY2f/0B7DxERydLSvkUfP348c+bMISQkhPPnz/PMM8+wYsUKdu3aBUBISAgdO3akadOm/Pzzz2aWLCLZWGpqKgkJCURHRxMREUGxYsVo3LgxwcHBnDp1ip9//pnvv/+eZs2acfnyZebOnatLvUQk22jevDn16tUjLCyM3bt3p5uzcerUqdSvX5+xY8eydevWdM/T5Pf/mZZ6FxGRLC8+Pp42bdrQpEkT2rRpw8aNG2nVqhWTJ0+mU6dOPHjwgJw5cwKPriHv2bOnvh0SEZt40nLFUVFRlCtXjjfffJPJkycDMHnyZLZu3cq2bduoXLkyBQsWZMWKFTg7O2s5dxGxS391GWvLli05ceIEAwcO5I033rAex8GjJlCPHj00Lv5Dav6IiEiWl5SUxIsvvsiQIUMwDINWrVoxYcIEunbtSlJSEnPnzqVIkSK88sor1ufounARMdM333zD6NGjmTdvHlWqVAEefRA6f/48Pj4+eHh4YLFYNFaJiF16vPGzZ88ebt26RbFixShcuDB58+YF4I033iA0NJT+/fv/oQEEqDH+D+myLxERyVKetKpXSkoKRYoUYerUqbRp0ybdhKkRERGsX7+eyMjIdM/RhykRsZXRo0fTtm1bNm3aZL2tQoUKODk5cfjwYeD/PggFBwfj6emJxWIhNTVVY5WI2J3H52scMGAArVq1YsCAATRs2JARI0Zw5MgRAL777jtKlSrFhAkTWLx4MQkJCeleR42ff0bNHxERyTJ+v6pXeHg49+7dI0eOHPTo0YNdu3bxzDPP0KpVK1JTU7lz5w5du3YlNjaWNm3amFy9iGQXvz+xvnTp0oSGhjJ48GBeeuklfv31V0qVKkWPHj0YPHgwYWFhT7z0QZM7i4g9SrsUdty4cSxevJilS5dy5swZ3nrrLb788ks+//xzDh48CDyaANrLy4tdu3bh6upqZtlZni77EhGRLGfgwIEsXLgQDw8Pihcvzueff06JEiVYtWoVrVu3pnLlyjx8+BA3Nzfi4uL49ddfNW+GiNjE7+ewiI+Px83Njfv373Ps2DFGjBjB5cuXKVy4MM2bN2fRokU0adKEjz76SOOTiGQLKSkpRERE0KNHD5o3b87bb7/NunXraNu2LU2aNOGnn37ihRdeoE+fPlSqVAn46/mB5O9R80dERLKUH3/8kW7dujFjxgzOnDnD5s2bOXXqFFu2bCE4OJijR4+yY8cOoqKiCA4OpnXr1jg6OmreDBHJcI9/OJk0aRIHDx7kyJEjdOzYkXr16lG+fHkA1qxZw44dO5g5cyZJSUl07NiRuXPnmli5iEjG+vrrrzly5AgTJkwA4P79++zbt4+KFSty4cIFmjZtSt++fenZsyf9+/dnzpw5vPDCC3z22WeULl0aUAPof6Xmj4iIZGq/39Fv3bqVX3/9lcGDBwNw6NAhBg8ezKlTp9i6dSslS5b8wxk+OuNHRGxpwIABLFiwgH79+pErVy769etH3bp1mTp1Kn5+ftbHHThwgB9++IG+ffuqOS0iduvs2bOUKlUKgA8++IApU6YAEBcXh7u7O4MHD+b06dN88803uLi4MHr0aDZu3EiFChUICQlRw+dfor2MiIhkWo9PCDhlyhQuXLjAmTNneOqpp6yPqVSpEqNHj2bw4ME0atSIDRs2WA8w0qjxIyK2cvDgQVatWsWaNWuoUqUKBw8eJDY2ltdffx0/Pz9rQ9swDJ599lmeffZZQCsQioj9cnFxoXr16uTPn5+dO3fSuXNn5syZg7u7OwCxsbHExMRw8+ZNAgICOHToED179qRly5bWye/VAPrfaQuKiEimlJqaap0QcMSIEYwaNYorV64QHR3N4sWLOX78uPWxFStWZPTo0eTLl49BgwaZVbKICCkpKXh6elKlShVWrFhBrVq1mDZtGm3atCEuLo7t27cTHx9vHd/SqPEjIvYm7SKjwMBAqlatyuHDh2nfvj27d++mW7du1seVLVuWS5cu8eabb/LMM89w+vRpmjdvjsViSfdFoPxvtJcREZFMKW1Hf+3aNW7fvs2GDRuoUqUKly5dolevXtStW5cdO3bw9NNPA4+WTV6yZAnFixc3s2wRyUYiIiKIjIzk2LFjlC9fHn9/fzw9PQkLC2POnDn07duXcePGWT/k7N+/ny+++IKAgACCg4NNrl5EJGM9ftbOJ598woULF8ibNy89evRg/PjxdO3alVmzZtGxY0ecnJy4evUqCQkJDBs2DCcnJ122/y/TnD8iIpJprVq1iubNm1O0aFG+/vprnn/+eQCuX79Ot27d2L9/Pzt37kx3GRhoQkARyXirVq3iyy+/5PDhw8THx5OUlES9evUYNGgQy5Yt4/PPP2fo0KEMHToUgISEBJo3b46rqysrVqzQGCUidmv58uVs376dvn37kidPHry8vIiLi6Njx44ULFiQkJAQZs6cyYQJE6hfvz6zZs36w2voUth/n5o/IiKSaSUmJvL+++/z5Zdfsnz5ct58803rfWFhYbz//vusW7eOixcvEhgYaF6hIpKtzJ07l379+jFo0CDKly9PpUqVmDZtGl9//TWGYdCuXTt+++03fvnlF4YPH050dDQbN24kLCyMI0eO4OzsrCa1iNilkydPUrZsWQzDoHnz5qSkpNCxY0caNWrExYsXqVOnDgsXLqRy5cosWrSIKVOmUKlSJb755huzS7d7aqWJiEim8KQPQi4uLsyePZuYmBi6du2Kj48PL7zwAgD+/v6EhIQQHBxMQECAGSWLSDY0d+5cevTowTfffEOzZs2stw8ZMoTg4GAmTJjA5s2b6datG3ny5GHw4MEUL16cYsWKsWHDBpycnPSNtojYLQ8PDwYOHMjChQtxcHCgXr16tG/fnnr16lGqVCkaNmzIwYMHqVmzJq1btyYuLo4DBw6oIW4DOvNHRERM9/gOf+3atVy7dg0PDw/Kly9PuXLlAHjjjTfYuXMnq1evtjaAHqcPUyKS0Xbs2EHt2rUZNmwYn376qXUy05SUFOv4ExISwqeffsr8+fNp1qwZkZGReHt7W19DY5WI2LurV68yd+5cQkJCWLx4MVWqVGH58uUsWLCAo0ePUqpUKQ4dOkTOnDmJi4sjV65cWtXLBtT8ERGRTOOTTz5h4cKFlCpVilOnThEUFESzZs3o378/AG+++SY///wzS5cupW7duiZXKyLZzblz5+jYsSNeXl706dMnXSP68Q8tZcqU4YUXXmDGjBkkJSXh7OwMPFr55verfImI2KOwsDCmTZvG9OnTmTNnDm+99RZJSUksWrSIatWq8dRTT6UbEzU+Zjy11UREJFNYtWoVS5Ys4fvvv2fXrl0cPXqUl156iZUrVzJ16lQAli1bRtmyZZk8ebLJ1YpIdlSiRAm+/PJLEhISGD16NLt377bel/ahJSYmhocPH+Lr6wtgbfw8/hgREXvn7+9Pr1696NmzJ126dGHWrFk4OzvTsWPHPzR+QOOjLaj5IyIipkhNTU3339DQUIoUKcJzzz0HQEBAAD179qRcuXJs2rSJhIQEHB0d2bx5M+vXrzetbhHJ3kqUKEFISAgWi4VRo0axZ8+edPdfvHiRQoUKUaVKFQB0kr2I2CPDMEhJSfnLx/j5+dGzZ0969uxJv379mD9/vvU+NXtsT80fERGxuccvj7hw4QIAXl5eJCQkEBERATw6qAgICKBNmzZs3bqV0NBQABwdHXFwcLA2jUREbO3xBtDIkSPZtWsXFouF5ORkBg0ahLu7O3Xq1AH0AUdE7FNERASOjo7Ao6XdT548+cTH+fn50aNHD3r06EGnTp30BZ6J1PwRERGb+vbbb1m4cCEAffr04b333iM5OZkKFSoQGhrK/PnzSUhIsH5gyp07N2XLliVHjhzpXkcTAoqImR5vAI0dO5Y9e/bQsmVLLl++zKpVq9SkFhG79euvv1KsWDEOHDhA//79+fDDD/H09PzTx/v5+dG1a1dmzpxJw4YNbVipPE4TPouIiE0NGTKE0aNH07BhQ3bv3s3u3bspW7YsAPPnz6dTp0707duXOnXqEBAQwIcffkhcXBw///yzGj4ikumcO3eOjz76iK1bt1KsWDFOnDiBs7OzVvUSEbt14cIFJkyYwNdff42joyOnTp3C19f3b6/WpfHRHGr+iIiIzZUuXZqzZ8/y6aefMmTIkHQHC19//TVDhw4lJiYGLy8v8uXLx/bt23F2dtYSoCKSKZ05c4YZM2YwefJknJyc9MFGROzS45M0T5gwgX79+uHh4cHGjRupXr26dY4zXe6aOan5IyIiGe73TZv27dtjsVhYvHgxc+bM4d133033uOvXr3Pv3j3u379P5cqVcXBw0IcpEckSNFaJiD16/FguIiKCmzdvEh8fz6JFi1i2bBkrVqygXr16GgMzMf1VREQkQz1+sLBy5Ur8/f2tc/4ULlyYzp07A/Duu+9aH3f37l2eeeaZdK+hAwkRyQo0VomIvXn8WG7kyJFcvXqVd955h5o1a5IvXz4SExNp2bIlK1eupHbt2gBMnz6d+vXrExwcbGbp8hjtnUREJMMYhmE9WOjXrx/Lly/n008/pWjRovj4+DB8+HBSU1Pp2rUriYmJvPzyy3z44YcArF692np6sS71EhERETFH2nHYgAEDmDdvHjNmzLA2dYKDgxkwYAAWi4XXXnuNkSNHsmnTJiIiIujWrZuZZcvvqPkjIiIZJu2a7/Hjx/PVV1+xatUqqlatmu5a8JEjR+Lk5ET37t15+umnsVgsHD58ON3zRURERMQ8e/fu5dtvv2X16tXUqFED+L85gIoXL87QoUPJnz8/CxYsoESJEmzcuBFHR0fN15iJaM4fERHJMIZhEBcXR7NmzWjUqBEfffQRV65c4dSpUyxdupRcuXIREhKCq6srv/zyC3FxcdSpUwdHR0ddMy4iIiKSSWzdupUuXbqwc+dOChcunO6+lJQUABwdHYmKiiJv3rxYLBYdy2Uy+kuIiMi/6vFveCwWCzlz5iRnzpwcO3aMBQsWsHr1auLi4siZMyeHDh2iZcuWrFq1imrVqllfIyUlRQcLIiIiIiZ40tk6d+7c4ebNm+TMmROApKQknJ2dAdixYweGYVCrVi28vLysr6FjucxF51+JiMi/Ku1gYfHixZw7dw4nJyfq1atHWFgYPXv2pEKFCowePZoNGzbQtGlTcufO/YcDDEdHRzNKFxEREcn2Hl+oY/PmzQA0bdqUp556itatWxMfH29t/MTHxzN+/HgOHjyY7vhNl3plPrrsS0RE/nVxcXH4+fnx1FNPsWrVKvz9/YmMjCQ+Pp4iRYpYH1evXj1KlCjBjBkzTKxWRERERB4XERFBtWrVePrpp+nTpw+1atVi9erVjB49GsMwGDVqFJGRkXzzzTeEh4dz6NAhnemTyan5IyIi/7O0Cf8eFxYWxksvvUSBAgVYsmQJRYsWBSAmJoZTp04xbNgwwsPDOXz4ME5OTk98DRERERHJeE86Djtw4AA9evSgYMGCDBgwgKpVq7J3715GjRrF4cOH8fHxoWjRoixfvhxnZ2dSUlJ09nYmpuaPiIj8axITE3FxcbEeQISFhVGjRg0CAgJYsGABQUFBbNmyhZkzZwLw7bff6mBBRERExESPT8wcGRmJt7e39b6DBw/StWtXfH19GTBggHWOxsuXL+Pl5YWHh4cmd84i1PwREZF/xaRJk/jhhx9YsWIFnp6e1gbQ9evXracNz5o1i8DAQE6cOEHp0qVxcHDQwYKIiIiICRYsWECLFi3IlSsXANOnT2f79u0MHTqUsmXLWh934MABWrRoQVBQEAMHDqR27drpXkfLuWcN+guJiMh/5dixY6xdu5Y9e/YAUKVKFX755Re6dOlCTEwMFouF1NRUChUqxKeffsrWrVt54403iIiIoEyZMjg4OGglCBERERETfPvtt4waNYohQ4bw4MEDAPz9/dm3bx9Tp07l+PHj1sc+++yzjBgxgv379zNq1CgOHjyY7rXU+Mka9FcSEZF/bOnSpbRv35758+ezYcMGAKpXr86PP/7I1q1b6dSpE/fu3bMeDLi5udG1a1eCgoLSnUqsgwURERER23vllVdo3749e/fupV+/fsTFxdG0aVPmzZvHjz/+yJQpU9I1gCwWC7Vr1yYoKIiKFSuaWLn8t3TZl4iI/COLFi2ia9euzJ8/n4YNG5InT5509+/fv59XXnmFmjVrMnjwYPz8/OjSpQt169alR48eAJrjR0RERMQkaXM0AgwfPpyffvqJ5557jhEjRuDm5saGDRt4//33qVWrFm+88QY1atSgffv2NGrUiM6dOwO61CsrUvNHRET+tpMnT9KyZUs+/PBDOnXqZL399ytEnDhxgkaNGpGUlISzszP58+fn119/xdnZ2YyyRURERIT0x2zz589n3759bNiwgZiYGLp06cKwYcNwd3dn8+bNjBgxgkuXLuHq6kqePHk4cOAAzs7OWqE1i9JECyIi8reFhYURHx/Piy++mG7Hn/bftNvKlCnD0aNH2blzJxaLhddffx1HR0dN7iwiIiJiorRjthEjRjBlyhRmzJhBkyZNWLRoET/++CNJSUl89tlnNGzYkKJFixIWFkZUVBRNmzbVsVwWp7+aiIj8bYcOHSI2Npbg4GDgj2f8WCwWTp8+TUREBLVq1aJZs2bW+1JSUnSwICIiImIiwzCIiopi48aNjBo1itatWwNQq1YtRo0axTfffEOOHDkYOnQoJUuWpGTJktbn6lgua9NFeiIi8rcVL16c+/fvs3XrVoAnnvK7aNEivvnmG35/VbHm+BERERExl8Visc7XGB4eDjxqCOXMmZPRo0fj6+vLggUL6NGjBw8fPkz3XB3LZW1q/oiIyN9WqVIlXFxcmDNnDlevXrXentboiYmJ4dy5c5QpU0bXgouIiIiYLDU19Q+3JScnExgYyL59+7h582a6+ypVqkSRIkXImzevdVJosQ+a8FlERP6RZcuW0b59e9544w0+/vhjKlSoADz69qhTp07ExMSwY8cOnRYsIiIiYqLHV+Q6d+4crq6uABQuXJgrV65QqVIlatasyYQJEwgICMBisdCqVStefvll3n33XSwWi1b1siNq/oiIyD+SkpLCggUL6N69OwULFuSZZ54hNTWVe/fukZqayp49e3B2dtZy7iIiIiImeXxexsGDB/Pdd99x//59UlNT+fjjj/nwww85efIkdevWxdfXF1dXV5KSkoiNjeXUqVM4Ojqq8WNn1PwREZH/ytGjR5k/fz6hoaEEBARQoUIFunbtqpUgRERERDKJcePGMWHCBJYsWUJqaiq//fYb/fv355NPPmHcuHHcunWLr7/+mvDwcHLmzMmQIUNwcnLSl3h2SM0fERH5V+lgQURERMQcaWfrGIZBUlISr732GjVr1mTgwIHWx6xYsYJWrVqxdOlS62pfj9OXePZJ53CJiMh/7UnfH6jxIyIiImJ7hmFYL9M6ffo0Li4unDt3znpbSkoKycnJtGjRgvbt2/P111+TkJBAcnJyutdR48c+qfkjIiL/Na3oJSIiImK+x+f4+fjjj3n11VdJTU3l9ddfZ9GiRZw9exZHR0frYzw9PbFYLLi6uqrZk02o+SMiIiIiIiKSRT3e+Dl69CgXLlxg8eLFODg40LRpUwoVKkS/fv24cOECjo6OJCQkcPLkSXx9fU2uXGxJLT4RERERERGRLCqt8bN06VLmzJmDk5MT5cuXB6BmzZrcuHGD+fPnU6lSJSpWrEhUVBTJycls3LgRSN88Evul5o+IiIiIiIhIFnP27Fmio6NxdHSkcuXK3Lt3jxs3bhATE8ODBw/IlSsXAK1ataJSpUrs3LmTCxcuUKBAAXr27ImTk5Mmd85GtNqXiIiIiIiISBby1VdfMW7cOMLCwnB3d6dFixZMmTKFb775hk8//ZSKFSvy+eef/+WlXVqhNXtRi09EREREREQki5g9ezYffPABU6dOJSgoiDVr1vDNN99QuHBhPvroI27fvs3y5csZOHAgY8aMwcfH54mNHjV+shed+SMiIiIiIiKSBaxZs4ZmzZqxdu1aXnvtNQBiYmKoWbMmRYoUYc2aNQBMmzaNFStWEBwczIgRI/D39zexaskMtNqXiIiIiIiISCaXkJDAli1bKFasGFeuXLHe7unpSZkyZbBYLMTHxwPQs2dPWrZsye7du1m4cKFJFUtmosu+RERERERERDI5V1dXPv30U1xdXVm8eDGxsbEMGDCATZs2sWTJEn788Ufc3Nysl3j16NEDHx8fmjZtanbpkgnosi8RERERERGRLCIiIoLRo0dz5MgRihQpwvfff8+0adNo164dqampODg4WP+bRpM7i5o/IiIiIiIiIlnIjRs3GDNmDCtWrKBKlSrWuX7U5JE/ozl/RERERERERLIQX19fBg0aRIsWLbh58ybjxo0DHq3gpfM75El05o+IiIiIiIhIFhQREcFnn33GoUOHqFWrFqNGjTK7JMmkdOaPiIiIiIiISBbk4+PDwIEDCQoK4tatWzrrR/6UzvwRERERERERycKioqLIkycPDg4OGIaBxWIxuyTJZNT8EREREREREbEDv1/lSySNmj8iIiIiIiIiInZMLUERERERERERETum5o+IiIiIiIiIiB1T80dERERERERExI6p+SMiIiIiIiIiYsfU/BERERERERERsWNq/oiIiIiIiIiI2DE1f0RERERERERE7JiaPyIiIpKltG/fHovFgsViwdnZmYIFC1KvXj3mz59Pamqq2eUBsGPHDiwWC3fv3jW7FCuLxcKaNWvMLkNERERMoOaPiIiIZDkNGzbkxo0bXL58mU2bNlGrVi0++OADXn31VZKTk02tLSkpydTfLyIiIvJ7av6IiIhIluPq6oqPjw/+/v5UrFiRgQMHsnbtWjZt2sTChQsBuHv3Lp06dcLb2xtPT09q167NsWPHrK8xbNgwypcvz+zZswkICMDNzY0WLVpw794962MOHDhAvXr1yJ8/P7lz56ZmzZocPnw4XS0Wi4WZM2fy+uuvkytXLt577z1q1aoFQN68ebFYLLRv3x6Al156iZ49e/Lhhx+SN29eChYsyNy5c7l//z4dOnTAw8OD4sWLs2nTpnS/47fffuPll1/G3d2dggUL0qZNG27fvm29/6WXXqJXr1707dsXLy8vfHx8GDZsmPX+wMBAAJo2bYrFYrH+W0RERLIHNX9ERETELtSuXZty5cqxatUqAN58801u3brFpk2bOHToEBUrVqROnTpERUVZn3P+/HlWrFjB999/z+bNmzly5Ajdu3e33h8bG0u7du3YvXs3+/bto0SJEjRq1IjY2Nh0v3vYsGE0bdqUEydOMHz4cL777jsAQkNDuXHjBlOnTrU+9quvviJ//vz8+uuv9OzZk27duvHmm29SrVo1Dh8+TP369WnTpg3x8fHAoyZW7dq1qVChAgcPHmTz5s3cvHmTFi1apKvhq6++IleuXOzfv5/x48czYsQIfvjhB+BREwtgwYIF3Lhxw/pvERERyR4shmEYZhchIiIi8ne1b9+eu3fvPnH+mlatWnH8+HHmzJnDK6+8wq1bt3B1dbXeX7x4cfr27Uvnzp0ZNmwYo0aN4sqVK/j7+wOwefNmXnnlFcLCwvDx8fnD66emppInTx6+/vprXn31VeDRmT8ffvghU6ZMsT5ux44d1KpVi+joaPLkyWO9/aWXXiIlJYVdu3YBkJKSQu7cuWnWrBmLFi0CICIiAl9fX/bu3UuVKlUYNWoUu3btYsuWLdbXuX79OgEBAYSGhhIcHPyH1wV47rnnqF27NmPHjrXWuXr1apo0afIPt7iIiIhkdU5mFyAiIiLybzEMA4vFwrFjx4iLiyNfvnzp7n/w4AEXLlyw/rtw4cLWxg9A1apVSU1NJTQ0FB8fH27evMngwYPZsWMHt27dIiUlhfj4eK5evZrudStXrvy3ayxbtqz1Z0dHR/Lly0eZMmWstxUsWBCAW7duAXDs2DG2b9+Ou7v7H17rwoULBAcH/+F1AXx9fa2vISIiItmbmj8iIiJiN06fPk3RokWJi4vD19eXHTt2/OExj5+J85+0a9eOO3fuMHXqVIoUKYKrqytVq1YlMTEx3eNy5cr1t1/T2dk53b/TVi17/N+AdeWyuLg4XnvtNcaNG/eH1/L19f3L180sq5+JiIiIudT8EREREbuwbds2Tpw4wUcffUShQoWIiIjAycnpLyc3vnr1KuHh4fj5+QGwb98+HBwcKFmyJAB79uxhxowZNGrUCIBr166lm2j5z7i4uACPLuv6X1WsWJHvvvuOwMBAnJz++0M3Z2fnf6UeERERyXo04bOIiIhkOQkJCURERBAWFsbhw4f57LPPaNy4Ma+++ipt27albt26VK1alSZNmrB161YuX77ML7/8wqBBgzh48KD1dXLkyEG7du04duwYu3btolevXrRo0cI630+JEiVYvHgxp0+fZv/+/bz99tvkzJnzP9ZXpEgRLBYL69evJzIykri4uP/6vb7//vtERUXRunVrDhw4wIULF9iyZQsdOnT4R82cwMBAfvrpJyIiIoiOjv6v6xEREZGsR80fERERyXI2b96Mr68vgYGBNGzYkO3btxMSEsLatWtxdHTEYrGwceNGXnzxRTp06EBwcDCtWrXiypUr1jl14NEE0M2aNaNRo0bUr1+fsmXLMmPGDOv9X375JdHR0VSsWJE2bdrQq1cvChQo8B/r8/f3Z/jw4fTv35+CBQvSo0eP//q9+vn5sWfPHlJSUqhfvz5lypThww8/JE+ePDg4/P1DuUmTJvHDDz8QEBBAhQoV/ut6REREJOvRal8iIiKSLQ0bNow1a9Zw9OhRs0sRERERyVA680dERERERERExI6p+SMiIiIiIiIiYsd02ZeIiIiIiIiIiB3TmT8iIiIiIiIiInZMzR8RERERERERETum5o+IiIiIiIiIiB1T80dERERERERExI6p+SMiIiIiIiIiYsfU/BERERERERERsWNq/oiIiIiIiIiI2DE1f0RERERERERE7Nj/A3OYjZXU15CwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Assuming 'flag_data' contains 'department', 'processed_date', and 'opened_at'\n",
    "# Calculate processing period in days\n",
    "flag_data['processing_period'] = (pd.to_datetime(flag_data['processed_date']) - pd.to_datetime(flag_data['opened_at'])).dt.days\n",
    "\n",
    "# Filtering out None values for processing_period for valid plotting\n",
    "valid_data = flag_data.dropna(subset=['processing_period'])\n",
    "\n",
    "# Creating the box plot with a color palette to differentiate departments\n",
    "plt.figure(figsize=(14, 8))\n",
    "palette = sns.color_palette(\"coolwarm\", n_colors=len(valid_data['department'].unique()))  # Create a color palette\n",
    "box_plot = sns.boxplot(x='department', y='processing_period', data=valid_data, palette=palette)\n",
    "\n",
    "plt.title('Processing Period by Department')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Processing Period (days)')\n",
    "plt.xticks(rotation=45)  # Rotate labels for better readability\n",
    "\n",
    "# Add grid for easier analysis\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "# Calculate means and ensure they're aligned with the x-axis labels\n",
    "means = valid_data.groupby(['department'])['processing_period'].mean()\n",
    "labels = [tick.get_text() for tick in box_plot.get_xticklabels()]\n",
    "vertical_offset = valid_data['processing_period'].mean() * 0.05  # Offset from mean for annotation\n",
    "\n",
    "# Annotate mean values\n",
    "for label in labels:\n",
    "    mean_value = means[label]\n",
    "    x_position = labels.index(label)\n",
    "    box_plot.text(x_position, mean_value + vertical_offset, f'{mean_value:.1f}', \n",
    "                  horizontalalignment='center', size='medium', color='black', weight='semibold')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate JSON Description for the Insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:52.875081Z",
     "iopub.status.busy": "2024-10-29T18:39:52.874833Z",
     "iopub.status.idle": "2024-10-29T18:39:52.886259Z",
     "shell.execute_reply": "2024-10-29T18:39:52.885229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_type': 'analytical',\n",
       " 'insight': 'There is variability in expense processing times across departments',\n",
       " 'insight_value': {'description': 'There is considerable variability in the average processing times for expense reports across departments. The HR department experiences the longest average processing time, significantly higher than other departments.'},\n",
       " 'plot': {'plot_type': 'bar',\n",
       "  'title': 'Average Processing Time by Department',\n",
       "  'x_axis': {'name': 'Department',\n",
       "   'value': ['Development',\n",
       "    'Sales',\n",
       "    'HR',\n",
       "    'Customer Support',\n",
       "    'Finance',\n",
       "    'IT',\n",
       "    'Product Management'],\n",
       "   'description': 'This axis lists the departments within the organization, showcasing the diversity in their operational speeds for processing expenses.'},\n",
       "  'y_axis': {'name': 'Average Processing Time (days)',\n",
       "   'value': {'Development': '0.8 days',\n",
       "    'Sales': '10.0 days',\n",
       "    'HR': '15.8 days',\n",
       "    'Customer Support': '10.7 days',\n",
       "    'Finance': '8.9 days',\n",
       "    'IT': '8.7 days',\n",
       "    'Product Management': '13.6 days'},\n",
       "   'description': 'This axis displays the mean processing times for expenses in each department, highlighting significant differences that suggest varying levels of efficiency or complexity in expense management.'},\n",
       "  'description': 'The bar chart illustrates a significant range in processing times, with HR showing the longest average at 15.8 days, which may indicate more complex or less efficient processing systems in place. In contrast, the Development department shows an exceptionally low average of 0.8 days, suggesting highly efficient operational processes.'},\n",
       " 'question': 'Which department has faster expense processing times, and how significant is the difference compared to others?',\n",
       " 'actionable_insight': {'description': 'To address the disparities in processing times, it is recommended that the organization conducts a detailed review of the expense management workflows in departments with longer processing times, particularly HR. Best practices from departments like Development, which exhibits exceptionally fast processing times, should be analyzed and potentially adopted by other departments to streamline operations. Additionally, training and resource allocation should be considered to enhance efficiency across all departments, aiming to reduce bottlenecks and improve overall processing speeds.'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"data_type\": \"analytical\",\n",
    "    \"insight\": \"There is variability in expense processing times across departments\",\n",
    "    \"insight_value\": {\n",
    "        \"description\": \"There is considerable variability in the average processing times for expense reports across departments. The HR department experiences the longest average processing time, significantly higher than other departments.\"\n",
    "    },\n",
    "    \"plot\": {\n",
    "        \"plot_type\": \"bar\",\n",
    "        \"title\": \"Average Processing Time by Department\",\n",
    "        \"x_axis\": {\n",
    "            \"name\": \"Department\",\n",
    "            \"value\": [\"Development\", \"Sales\", \"HR\", \"Customer Support\", \"Finance\", \"IT\", \"Product Management\"],\n",
    "            \"description\": \"This axis lists the departments within the organization, showcasing the diversity in their operational speeds for processing expenses.\"\n",
    "        },\n",
    "        \"y_axis\": {\n",
    "            \"name\": \"Average Processing Time (days)\",\n",
    "            \"value\": {\n",
    "                \"Development\": \"0.8 days\",\n",
    "                \"Sales\": \"10.0 days\",\n",
    "                \"HR\": \"15.8 days\",\n",
    "                \"Customer Support\": \"10.7 days\",\n",
    "                \"Finance\": \"8.9 days\",\n",
    "                \"IT\": \"8.7 days\",\n",
    "                \"Product Management\": \"13.6 days\"\n",
    "            },\n",
    "            \"description\": \"This axis displays the mean processing times for expenses in each department, highlighting significant differences that suggest varying levels of efficiency or complexity in expense management.\"\n",
    "        },\n",
    "        \"description\": \"The bar chart illustrates a significant range in processing times, with HR showing the longest average at 15.8 days, which may indicate more complex or less efficient processing systems in place. In contrast, the Development department shows an exceptionally low average of 0.8 days, suggesting highly efficient operational processes.\"\n",
    "    },\n",
    "    \"question\": \"Which department has faster expense processing times, and how significant is the difference compared to others?\",\n",
    "    \"actionable_insight\": {\n",
    "        \"description\": \"To address the disparities in processing times, it is recommended that the organization conducts a detailed review of the expense management workflows in departments with longer processing times, particularly HR. Best practices from departments like Development, which exhibits exceptionally fast processing times, should be analyzed and potentially adopted by other departments to streamline operations. Additionally, training and resource allocation should be considered to enhance efficiency across all departments, aiming to reduce bottlenecks and improve overall processing speeds.\"\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Question 2:  Are there differences in the categories of expenses submitted by this department that could explain the faster processing?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the distribution of expense categories by department with processing times\n",
    "\n",
    "This stacked bar plot presents a comprehensive view of the distribution of expense categories across departments, with the counts of expenses shown for each category within a department. This visualization aids in identifying whether certain categories within departments are processed more quickly or slowly, potentially explaining variations in processing efficiency.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:52.892805Z",
     "iopub.status.busy": "2024-10-29T18:39:52.892276Z",
     "iopub.status.idle": "2024-10-29T18:39:53.308190Z",
     "shell.execute_reply": "2024-10-29T18:39:53.307539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'flag_data' contains 'department', 'category', and 'processing_period' columns\n",
    "# Calculate processing period in days if not already calculated\n",
    "flag_data['processed_date'] = pd.to_datetime(flag_data['processed_date'])\n",
    "flag_data['opened_at'] = pd.to_datetime(flag_data['opened_at'])\n",
    "flag_data['processing_period'] = (flag_data['processed_date'] - flag_data['opened_at']).dt.days\n",
    "\n",
    "# Group data by department and category to count frequencies and calculate average processing time\n",
    "category_counts = flag_data.groupby(['department', 'category']).size().reset_index(name='count')\n",
    "category_processing_times = flag_data.groupby(['department', 'category'])['processing_period'].mean().reset_index()\n",
    "\n",
    "# Merging counts with processing times for richer insights\n",
    "category_data = pd.merge(category_counts, category_processing_times, on=['department', 'category'])\n",
    "\n",
    "# Pivoting data for better visualization in stacked bar plot\n",
    "pivot_data = category_data.pivot(index='department', columns='category', values='count').fillna(0)\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(14, 8))\n",
    "pivot_data.plot(kind='bar', stacked=True, colormap='viridis', alpha=0.7)\n",
    "plt.title('Distribution of Expense Categories by Department with Processing Times')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Count of Expenses')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='Expense Categories')\n",
    "\n",
    "for n, x in enumerate([*pivot_data.index.values]):\n",
    "    for (category, count), y in zip(pivot_data.loc[x].items(), pivot_data.loc[x].cumsum()):\n",
    "        # Filter the DataFrame based on the conditions\n",
    "        matching_values = category_processing_times.loc[\n",
    "            (category_processing_times[\"department\"] == x) & (category_processing_times[\"category\"] == category),\n",
    "            \"processing_period\"\n",
    "        ].values\n",
    "        \n",
    "        # Check if matching_values has any elements before accessing values[0]\n",
    "        if matching_values.size > 0:\n",
    "            plt.text(\n",
    "                n, y - (count / 2), f'{matching_values[0]:.1f} days',\n",
    "                ha='center', va='center', color='black', fontweight='bold', fontsize=9\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate JSON Description for the Insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:53.316150Z",
     "iopub.status.busy": "2024-10-29T18:39:53.315772Z",
     "iopub.status.idle": "2024-10-29T18:39:53.323860Z",
     "shell.execute_reply": "2024-10-29T18:39:53.322929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_type': 'diagnostic',\n",
       " 'insight': 'Processing Times are uniform across expense categories in departments',\n",
       " 'insight_value': {'description': 'The analysis reveals no significant differences in the processing times of various expense categories across departments, suggesting that the speed of processing is not influenced by the nature of the expenses themselves but may be attributed to other factors.'},\n",
       " 'plot': {'plot_type': 'bar',\n",
       "  'title': 'Distribution of Expense Categories by Department with Processing Times',\n",
       "  'x_axis': {'name': 'Department',\n",
       "   'value': 'All departments analyzed',\n",
       "   'description': 'This axis categorizes expenses into different departments to illustrate variations in expense submission patterns.'},\n",
       "  'y_axis': {'name': 'Count of Expenses',\n",
       "   'value': 'Number of expenses segmented by category',\n",
       "   'description': 'This axis displays the count of expenses, categorized by types within each department, along with annotations showing average processing times.'},\n",
       "  'description': 'The stacked bar chart displays the distribution of expenses across categories within departments, annotated with average processing times. The uniformity in processing times across different categories suggests that departmental efficiencies or specific operational practices may not be tied to the type of expenses processed.'},\n",
       " 'question': 'Are there differences in the categories of expenses submitted by this department that could explain the faster processing?',\n",
       " 'actionable_insight': {'description': 'Given the uniform processing times across expense categories, it is advisable for the organization to look beyond the nature of expenses to understand departmental processing speed disparities. Factors such as departmental staffing, the efficiency of workflow systems, or even the use of automated tools could play a significant role. A further analysis of these operational aspects could provide more definitive answers and help in implementing strategies to enhance processing efficiency across all departments.'}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"data_type\": \"diagnostic\",\n",
    "    \"insight\": \"Processing Times are uniform across expense categories in departments\",\n",
    "    \"insight_value\": {\n",
    "        \"description\": \"The analysis reveals no significant differences in the processing times of various expense categories across departments, suggesting that the speed of processing is not influenced by the nature of the expenses themselves but may be attributed to other factors.\"\n",
    "    },\n",
    "    \"plot\": {\n",
    "        \"plot_type\": \"bar\",\n",
    "        \"title\": \"Distribution of Expense Categories by Department with Processing Times\",\n",
    "        \"x_axis\": {\n",
    "            \"name\": \"Department\",\n",
    "            \"value\": \"All departments analyzed\",\n",
    "            \"description\": \"This axis categorizes expenses into different departments to illustrate variations in expense submission patterns.\"\n",
    "        },\n",
    "        \"y_axis\": {\n",
    "            \"name\": \"Count of Expenses\",\n",
    "            \"value\": \"Number of expenses segmented by category\",\n",
    "            \"description\": \"This axis displays the count of expenses, categorized by types within each department, along with annotations showing average processing times.\"\n",
    "        },\n",
    "        \"description\": \"The stacked bar chart displays the distribution of expenses across categories within departments, annotated with average processing times. The uniformity in processing times across different categories suggests that departmental efficiencies or specific operational practices may not be tied to the type of expenses processed.\"\n",
    "    },\n",
    "    \"question\": \"Are there differences in the categories of expenses submitted by this department that could explain the faster processing?\",\n",
    "    \"actionable_insight\": {\n",
    "        \"description\": \"Given the uniform processing times across expense categories, it is advisable for the organization to look beyond the nature of expenses to understand departmental processing speed disparities. Factors such as departmental staffing, the efficiency of workflow systems, or even the use of automated tools could play a significant role. A further analysis of these operational aspects could provide more definitive answers and help in implementing strategies to enhance processing efficiency across all departments.\"\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Question 3:  Are there any specific brackets of amounts these expenses from the Development department fall into that could explain the faster processing?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Processing Period by Expense Amount Brackets in Development Department\n",
    "\n",
    "This visualization showcases how processing times vary across different expense amount-brackets within the Development department. The boxplot shows spread and median processing periods for each bracket, while the line graph overlays the proportion of total expenses falling within these brackets (for easy visualization). This dual-axis plot helps to understand if smaller or larger expense amounts correlate with quicker processing times and highlights distribution of expense magnitudes within the department.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:53.330896Z",
     "iopub.status.busy": "2024-10-29T18:39:53.330488Z",
     "iopub.status.idle": "2024-10-29T18:39:53.764639Z",
     "shell.execute_reply": "2024-10-29T18:39:53.763979Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14876/1162767483.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dev_expenses['amount_bracket'] = pd.cut(dev_expenses['amount'], bins=bins, labels=labels)\n",
      "/tmp/ipykernel_14876/1162767483.py:21: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x='amount_bracket', y='processing_period', data=dev_expenses, palette='coolwarm', ax=ax1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# Assuming 'flag_data' contains 'department', 'amount', and 'processing_period' columns\n",
    "# and is already loaded with the data\n",
    "\n",
    "# Filter data to only include the Development department\n",
    "dev_expenses = flag_data[flag_data['department'] == 'Development']\n",
    "\n",
    "# Define the amount brackets\n",
    "bins = [0, 100, 500, 1000, 5000, 10000, np.inf]\n",
    "labels = ['< $100', '$100 - $500', '$500 - $1000', '$1000 - $5000', '$5000 - $10000', '> $10000']\n",
    "dev_expenses['amount_bracket'] = pd.cut(dev_expenses['amount'], bins=bins, labels=labels)\n",
    "\n",
    "# Calculate the proportion of expenses in each bracket\n",
    "bracket_counts = dev_expenses['amount_bracket'].value_counts(normalize=True) * 100\n",
    "\n",
    "# Create the box plot to visualize processing periods by amount brackets\n",
    "fig, ax1 = plt.subplots(figsize=(14, 8))\n",
    "sns.boxplot(x='amount_bracket', y='processing_period', data=dev_expenses, palette='coolwarm', ax=ax1)\n",
    "ax1.set_title('Processing Period by Expense Amount Brackets in Development Department')\n",
    "ax1.set_xlabel('Expense Amount Brackets')\n",
    "ax1.set_ylabel('Processing Period (days)')\n",
    "ax1.tick_params(axis='x', rotation=45)  # Rotate labels for better readability\n",
    "\n",
    "# Create a twin axis to show the proportion of expenses on the same plot\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(bracket_counts.index, bracket_counts.values, color='k', marker='o', linestyle='-', linewidth=2, markersize=8)\n",
    "ax2.set_ylabel('Proportion of Expenses (%)')\n",
    "ax2.set_ylim(0, 100)  # Limit y-axis for proportion to 100%\n",
    "ax2.grid(False)  # Turn off grid for the secondary axis to avoid visual clutter\n",
    "\n",
    "# Adding annotations for proportions\n",
    "for i, val in enumerate(bracket_counts.values):\n",
    "    ax2.text(i, val + 3, f'{val:.1f}%', color='black', ha='center', va='bottom', fontweight='bold')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate JSON Description for the Insight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T18:39:53.772250Z",
     "iopub.status.busy": "2024-10-29T18:39:53.772043Z",
     "iopub.status.idle": "2024-10-29T18:39:53.779076Z",
     "shell.execute_reply": "2024-10-29T18:39:53.778462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_type': 'diagnostic',\n",
       " 'insight': 'Lower expense brackets has faster processing',\n",
       " 'insight_value': {'description': \"Expenses under $100, which constitute 71.4% of the submissions from the Development department, are processed almost immediately (0 days), contributing significantly to the department's overall faster processing times. In contrast, expenses between $100 and $500, while constituting 19% of submissions, take considerably longer (2 days).\"},\n",
       " 'plot': {'plot_type': 'histogram',\n",
       "  'title': 'Expense Processing Times by Amount Brackets in Development Department',\n",
       "  'x_axis': {'name': 'Expense Amount Brackets',\n",
       "   'value': ['< $100', '$100-$500', '$500-$1000', '$1000-$5000'],\n",
       "   'description': 'This axis categorizes expenses into distinct brackets to illustrate how processing times vary with the amount of the expense.'},\n",
       "  'y_axis': {'name': 'Processing Time (days)',\n",
       "   'value': 'Variable processing times',\n",
       "   'description': 'This axis displays the processing time required for each expense bracket, highlighting the trend of quicker processing for lower amounts.'},\n",
       "  'description': \"The analysis reveals a clear trend: lower expense amounts are processed more rapidly, contributing to the Development department's overall efficiency. The immediate processing of the smallest expense bracket, which makes up the majority of submissions, significantly lowers the average processing time for the department.\"},\n",
       " 'question': 'Are there any specific brackets of amounts these expenses from the Development department fall into that could explain the faster processing?',\n",
       " 'actionable_insight': {'description': 'Understanding that lower expense amounts are processed more quickly suggests that the Development department may be benefiting from streamlined approval processes for smaller amounts. To leverage this efficiency, other departments might consider adopting similar streamlined processes for lower-cost expenses. Additionally, investigating why expenses in the $100-$500 bracket take longer to process could help in identifying bottlenecks and implementing solutions to enhance processing times across all brackets.'}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    \"data_type\": \"diagnostic\",\n",
    "    \"insight\": \"Lower expense brackets has faster processing\",\n",
    "    \"insight_value\": {\n",
    "        \"description\": \"Expenses under $100, which constitute 71.4% of the submissions from the Development department, are processed almost immediately (0 days), contributing significantly to the department's overall faster processing times. In contrast, expenses between $100 and $500, while constituting 19% of submissions, take considerably longer (2 days).\"\n",
    "    },\n",
    "    \"plot\": {\n",
    "        \"plot_type\": \"histogram\",\n",
    "        \"title\": \"Expense Processing Times by Amount Brackets in Development Department\",\n",
    "        \"x_axis\": {\n",
    "            \"name\": \"Expense Amount Brackets\",\n",
    "            \"value\": [\"< $100\", \"$100-$500\", \"$500-$1000\", \"$1000-$5000\"],\n",
    "            \"description\": \"This axis categorizes expenses into distinct brackets to illustrate how processing times vary with the amount of the expense.\"\n",
    "        },\n",
    "        \"y_axis\": {\n",
    "            \"name\": \"Processing Time (days)\",\n",
    "            \"value\": \"Variable processing times\",\n",
    "            \"description\": \"This axis displays the processing time required for each expense bracket, highlighting the trend of quicker processing for lower amounts.\"\n",
    "        },\n",
    "        \"description\": \"The analysis reveals a clear trend: lower expense amounts are processed more rapidly, contributing to the Development department's overall efficiency. The immediate processing of the smallest expense bracket, which makes up the majority of submissions, significantly lowers the average processing time for the department.\"\n",
    "    },\n",
    "    \"question\": \"Are there any specific brackets of amounts these expenses from the Development department fall into that could explain the faster processing?\",\n",
    "    \"actionable_insight\": {\n",
    "        \"description\": \"Understanding that lower expense amounts are processed more quickly suggests that the Development department may be benefiting from streamlined approval processes for smaller amounts. To leverage this efficiency, other departments might consider adopting similar streamlined processes for lower-cost expenses. Additionally, investigating why expenses in the $100-$500 bracket take longer to process could help in identifying bottlenecks and implementing solutions to enhance processing times across all brackets.\"\n",
    "    }\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4: Can the efficient practices from this department be implemented in other departments to standardize processing times?**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Development department demonstrates a highly efficient process for handling low-cost expenses, which are processed almost instantaneously. This approach could serve as a model for other departments experiencing slower processing times.\n",
    "\n",
    "To improve overall expense processing efficiency across the organization, it is recommended to analyze and potentially adopt the streamlined procedures used by the Development department for managing low-cost expenses. This could involve simplifying approval protocols for small amounts, enhancing the use of automation tools, or providing additional training to ensure all departments handle expenses under a certain threshold with similar speed and efficiency. Conducting pilot implementations in departments with the longest processing times could provide valuable data on the impact of these changes and help refine the approach before a wider rollout. Additionally, regular review sessions to assess the effectiveness of these new practices should be established to ensure they meet the organization’s financial control standards and operational goals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Findings (Flag 24):\n",
    "\n",
    "1. **Departmental Analysis**: This dataset delves into the expense processing times across different departments, highlighting significant variances in efficiency. The focus is on understanding why certain departments, like Development, process expenses faster than others such as HR, which experiences notably longer processing times.\n",
    "2. \n",
    "3. **Expense Categories and Processing Times**: The dataset evaluates how different expense categories impact processing times within departments. For instance, expenses under $100 in the Development department are processed almost immediately, which sharply contrasts with longer processing times for slightly higher expense brackets.\n",
    "4. \n",
    "5. **Operational Insights**: Through analyzing expense submissions and their processing times, this dataset provides insights into operational practices that could potentially be optimized or adjusted to enhance overall efficiency in processing times across the board.\n"
   ]
  }
 ],
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