Presentation_Preparation.ipynb

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   "source": [
    "## <p style=\"text-align: center;\">INDIAN PARLIAMENT MEMBERS: PARTICIPATION AND SCOPE</p>\n",
    "<img src=\"Raj_main.PNG\" width=\"850px\" style=\"margin:20px auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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    }
   },
   "source": [
    "#jupyter nbconvert Jupyter\\ Slides.ipynb --to slides --post serve\n",
    "- To run the slide slow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "### RAJYA SABHA : \n",
    "\n",
    "- Role of Members:\n",
    "  - Legislative responsibility (Debates and committee reviews)\n",
    "  - Oversight responsibility (Question Hour)\n",
    "  \n",
    "\n",
    "\n",
    "  \n",
    "- How are Members of Parliament (MPs) elected?\n",
    "   - Indirectly elected by state legislatures through propotional representation\n",
    "   - Political party leaders play a major role\n",
    "   - Members of State Legislatures have to show their vote to their party before voting\n",
    "   - Not always, but candidates usually win unopposed\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### POLITICAL PROBLEMS : \n",
    "\n",
    "- Internal conflict within parties\n",
    "- Candidates buying votes of State legislative assembly members\n",
    "\n",
    "### INFLUENCE ON LEGISLATIVE/ POLICY PROCESS:\n",
    "\n",
    "- Absenteeism \n",
    "- Lack of participation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<img src=\"Absenteeism_and_Lack_of_Participation.png\" width=\"850px\" style=\"margin:20px auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <p style=\"text-align: center;\">ABSENTEEISM AND LACK OF PARTICIPATION</p>\n",
    "<img src=\"Absent.PNG\" width=\"850px\" style=\"margin:20px auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "### PROBLEM STATEMENT:\n",
    "\n",
    "- What is the attendance rate of Rajya Sabha MPs?\n",
    "- What is the participation rate RajyaSabha MPs?\n",
    "- Can we predict participation rate based on MPs demographic data?\n",
    "- Can we predict the MPs scope of interests based on demographic data?\n",
    "\n",
    "### HOW DOES THIS ANALYSIS HELP?\n",
    "\n",
    "- Brings public scrutiny on political parties when they select for low performing candidates\n",
    "- Giving an opportunity to political parties to democratize their candidate selection procedure and avoid internal conflicts\n",
    "- Induce competetion among incumbent MPs to perform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### PROBLEM STATEMENT\n",
    "\n",
    "- Composition of parliament based on education status?\n",
    "\n",
    "- What is the participation rate of Members of parliament? \n",
    "(Metric - no of questions/session, each MP will have different metric value for each term, plot (bar plot, sorted by metric, color by party)\n",
    "Unit of Analysis: MP-term-questions/session (filter by year)\n",
    "\n",
    "- How has the topic focus changed over the years? \n",
    "  - topic ranking based on Question count (for government terms) \n",
    "  - Clustering of 'question_title' - top words in each cluster\n",
    "\n",
    "- How does MPs performance compare across political parties?\n",
    "Average number of (questions/session) of MPs by party\n",
    "\n",
    "- Can we predict parliamentary participation of an MP based on their demographic profile? like senirority, previous occupational background, political ideology?\n",
    "Y1 - participation rate, X - party, state, education, seniority, Gender, previous occupation\n",
    "Y2 - topics interested, X -  party, state, education, seniority, Gender, previous occupation\n",
    "\n",
    "### GOAL\n",
    "- Naming and Shaming\n",
    "- induce competition among parties to send good candidates by shedding light on the performance of the incumbent MPs.\n",
    "- Giving an opportunity to parties to democratize their candidate selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   "source": [
    "In India’s bicameral legislature, members for upper house of the parliament ‘Rajya Sabha’ are elected indirectly by members of state legislatures. Political parties at state level decide their candidates and get them elected for Rajya Shaba based on the proportion of their representation in the state legislative assembly . A Rajya Sabha Member of Parliament (MP), holds great responsibility in terms of representing state interests. Given their importance, it is a problem that there is no transparency or scrutiny on whether ideal candidates are being chosen as representatives of their state by respective political parties. As members from multiple parties get elected from each state, there is scope to induce competition among parties to send good candidates by shedding light on the performance of the incumbent MPs. This project aims to answer the following questions: How well are MPs representing their states on comparison? Can we predict parliamentary participation of an MP based on their demographic profile?\n",
    "\n",
    " one third of the members retire every second year, and are replaced by newly elected members"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### METHODS/ APPROACHES CONSIDERED USING\n",
    "\n",
    "Data Collection:\n",
    "- Selenium: To scrape .aspx pages\n",
    "- Beautiful Soup - To scrape html pages and .aspx pages with url hrefs\n",
    "\n",
    "Metrics:\n",
    "- Participation rate - no of questions/session \n",
    "- Clustering of professions \n",
    "- Clustering of 'question_title' - top words in each cluster\n",
    "- Y2 approach - categorical variable prediction (ministry)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <p style=\"text-align: center;\">METHODS & APPROACHES</p>\n",
    "<img src=\"Data Structure.PNG\" width=\"850px\" style=\"margin:20px auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <p style=\"text-align: center;\">OTHER METHODS CONSIDERED USING</p>\n",
    "\n",
    "Data Collection:\n",
    "- Selenium: To scrape .aspx pages\n",
    "- Beautiful Soup - To scrape html pages and .aspx pages with url hrefs\n",
    "\n",
    "Text Analysis:\n",
    "- Tf-IDF: To vectorize question descriptions and perform K-means clustering\n",
    "- Multi-Label Classification models \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### METHODS/ APPROACHES CONSIDERED USING\n",
    "\n",
    "Data Collection:\n",
    "- Selenium: To scrape .aspx pages\n",
    "- Beautiful Soup - To scrape html pages and .aspx pages with url hrefs\n",
    "\n",
    "Metrics:\n",
    "- Participation rate - no of questions/session \n",
    "- Clustering of professions \n",
    "- Clustering of 'question_title' - top words in each cluster\n",
    "- Y2 approach - categorical variable prediction (ministry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <p style=\"text-align: center;\">TOOLS USED TILL DATE/ RATIONALE</p>\n",
    "<img src=\"Accomplished.png\" width=\"850px\" style=\"margin:20px auto;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### TOOLS USED TILL DATE/ RATIONALE FOR THEIR USE\n",
    "\n",
    "Have a table\n",
    "- Selenium: google search and extraction, dynamic website\n",
    "- Vectorization: tdif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "\n",
    "import missingno as msno\n",
    "from plotnine import *\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import math\n",
    "import sqlite3\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# For pre-processing data \n",
    "from sklearn import preprocessing as pp \n",
    "from sklearn.compose import ColumnTransformer \n",
    "\n",
    "# For splits and CV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold # Cross validation \n",
    "from sklearn.model_selection import cross_validate # Cross validation \n",
    "from sklearn.model_selection import GridSearchCV # Cross validation + param. tuning.\n",
    "\n",
    "# Machine learning methods \n",
    "from sklearn.linear_model import LinearRegression as LM\n",
    "from sklearn.neighbors import KNeighborsRegressor as KNN\n",
    "from sklearn.tree import DecisionTreeRegressor as DTree\n",
    "from sklearn import tree # For plotting the decision tree rules\n",
    "from sklearn.ensemble import BaggingRegressor as Bag\n",
    "from sklearn.ensemble import RandomForestRegressor as RF\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# For evaluating our model's performance\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "# Pipeline to combine modeling elements\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
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       "      <th>State Name</th>\n",
       "      <th>Party Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Freedom Fighter</th>\n",
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       "      <td>Shri Jibon Roy</td>\n",
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       "      <td>Communist Party of India (Marxist)</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>Graduate in Commerce Educated at Calcutta Univ...</td>\n",
       "      <td>Industrial Worker</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Several hundred pamphlets on Trade Union movem...</td>\n",
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       "      <td>Shri Kailash Joshi</td>\n",
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       "      <td>kjoshi@sansad.nic.in</td>\n",
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       "      <td>1</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Bharatiya Janata Party</td>\n",
       "      <td>...</td>\n",
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       "      <td>Matriculation Educated at Govt. High School, H...</td>\n",
       "      <td>Agriculturist/Farmer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1955-60 President, Municipal Council, Hatpipal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Set up a trust in the tribal area of District ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Was Member, Bharatiya Jana Sangh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shri Md. Salim</td>\n",
       "      <td>Male</td>\n",
       "      <td>salim@sansad.nic.in</td>\n",
       "      <td>407, V.P. House, New Delhi.</td>\n",
       "      <td>14/1D, Kabitirtha Sarani, P.O. Khidirpore, PS ...</td>\n",
       "      <td>No</td>\n",
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       "      <td>2</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>Communist Party of India (Marxist)</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>M.A. (Philosophy) Educated at St. Barnabas Hig...</td>\n",
       "      <td>Political and Social Worker</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Ex-Member, Editorial Board Chhatra Sangram (a ...</td>\n",
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       "      <td>Reading, music, watching movies, folk culture ...</td>\n",
       "      <td>Mongolia to attend a Round Table Discussion on...</td>\n",
       "      <td>Chairman, (i) West Bengal Minorities Developme...</td>\n",
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       "      <td>Shri R.S. Gavai</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>(i) Kamal Pushp, 44, Congress Nagar, Amravati,...</td>\n",
       "      <td>No</td>\n",
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       "      <td>1</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Republican Party of India</td>\n",
       "      <td>...</td>\n",
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       "      <td>Graduate Educated at Nagpur University (Mahara...</td>\n",
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       "      <td>(i) Five Year Plan in relation to the Schedule...</td>\n",
       "      <td>Recipient of, (i) Kusta-Mitra Award and 'Frien...</td>\n",
       "      <td>Was President, Western Indian Football Associa...</td>\n",
       "      <td>U.S.S.R., U.S.A., U.K., Italy, France, Germany...</td>\n",
       "      <td>Took active part in political and social activ...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shri Gaya Singh</td>\n",
       "      <td>Male</td>\n",
       "      <td>gaya@sansad.nic.in</td>\n",
       "      <td>4, Windsor POlace, New Delhi</td>\n",
       "      <td>(i) V&amp;PO Alanwa, Dist. Nalanda (Bihar Shariff)</td>\n",
       "      <td>No</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Communist Party of India</td>\n",
       "      <td>...</td>\n",
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       "      <td>B.A. Educated at B.N. College, Patna University</td>\n",
       "      <td>Social Worker</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "          Member Name Gender                                     Email ID  \\\n",
       "0      Shri Jibon Roy   Male  jroy@sansad.nic.in; jibonroy@rediffmail.com   \n",
       "1  Shri Kailash Joshi   Male                         kjoshi@sansad.nic.in   \n",
       "2      Shri Md. Salim   Male                          salim@sansad.nic.in   \n",
       "3     Shri R.S. Gavai   Male                        rsgavai@sansad.nic.in   \n",
       "4     Shri Gaya Singh   Male                           gaya@sansad.nic.in   \n",
       "\n",
       "                                       Delhi Address  \\\n",
       "0    B-20, Prendra Mitra Bithi City Centre, Durgapur   \n",
       "1  C-604, Swarna Jayanti Sadan, Dr. B.D. Marg, Ne...   \n",
       "2                        407, V.P. House, New Delhi.   \n",
       "3                                                NaN   \n",
       "4                       4, Windsor POlace, New Delhi   \n",
       "\n",
       "                                   Permanent Address Whether Minister  \\\n",
       "0    35, St. Pauls Road, P.O. Durgapur (West Bengal)               No   \n",
       "1                  B-30, Swami Dayanand Nagar Bhopal               No   \n",
       "2  14/1D, Kabitirtha Sarani, P.O. Khidirpore, PS ...               No   \n",
       "3  (i) Kamal Pushp, 44, Congress Nagar, Amravati,...               No   \n",
       "4     (i) V&PO Alanwa, Dist. Nalanda (Bihar Shariff)               No   \n",
       "\n",
       "                                        Term Details  Number of Terms  \\\n",
       "0  9/08/1993 - 18/08/1999 , 19/08/1999 - 18/08/20...                2   \n",
       "1                           3/04/2000 - 02/04/2006 ,                1   \n",
       "2  3/04/1990 - 02/04/1996 , 03/04/1996 - 02/04/20...                2   \n",
       "3                           3/04/2000 - 02/04/2006 ,                1   \n",
       "4  8/07/1992 - 07/07/1998 , 08/07/1998 - 07/07/20...                2   \n",
       "\n",
       "       State Name                          Party Name  ... Freedom Fighter  \\\n",
       "0     West Bengal  Communist Party of India (Marxist)  ...              No   \n",
       "1  Madhya Pradesh              Bharatiya Janata Party  ...              No   \n",
       "2     West Bengal  Communist Party of India (Marxist)  ...              No   \n",
       "3     Maharashtra           Republican Party of India  ...              No   \n",
       "4           Bihar            Communist Party of India  ...              No   \n",
       "\n",
       "                        Educational Qualification(s)  \\\n",
       "0  Graduate in Commerce Educated at Calcutta Univ...   \n",
       "1  Matriculation Educated at Govt. High School, H...   \n",
       "2  M.A. (Philosophy) Educated at St. Barnabas Hig...   \n",
       "3  Graduate Educated at Nagpur University (Mahara...   \n",
       "4    B.A. Educated at B.N. College, Patna University   \n",
       "\n",
       "           Other Profession(s) Previous Membership  \\\n",
       "0            Industrial Worker                 NaN   \n",
       "1         Agriculturist/Farmer                 NaN   \n",
       "2  Political and Social Worker                 NaN   \n",
       "3                Agriculturist                 NaN   \n",
       "4                Social Worker                 NaN   \n",
       "\n",
       "                                    Position(s) Held  \\\n",
       "0  Aug. 1993 Elected to Rajya Sabha Member, Commi...   \n",
       "1  1955-60 President, Municipal Council, Hatpipal...   \n",
       "2  1977-78 General Secretary, Maulana Azad Colleg...   \n",
       "3  1964-94 Member, Maharashtra Legislative Counci...   \n",
       "4  1982 onwards Vice-President, Trade Union Inter...   \n",
       "\n",
       "                                   Book(s) Published  \\\n",
       "0  Several hundred pamphlets on Trade Union movem...   \n",
       "1                                                NaN   \n",
       "2  Ex-Member, Editorial Board Chhatra Sangram (a ...   \n",
       "3  (i) Five Year Plan in relation to the Schedule...   \n",
       "4                                                NaN   \n",
       "\n",
       "                                         Activity(s)  \\\n",
       "0  Associated with cultural activities with sever...   \n",
       "1  Set up a trust in the tribal area of District ...   \n",
       "2  Engaged in organising students and youth and f...   \n",
       "3  Recipient of, (i) Kusta-Mitra Award and 'Frien...   \n",
       "4                                   Trade Union work   \n",
       "\n",
       "                                             Hobbies  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  Reading, music, watching movies, folk culture ...   \n",
       "3  Was President, Western Indian Football Associa...   \n",
       "4                                           Football   \n",
       "\n",
       "                                  Country(s) Visited  \\\n",
       "0  Erstwhile Soviet Union, China, Philippines, No...   \n",
       "1                                                NaN   \n",
       "2  Mongolia to attend a Round Table Discussion on...   \n",
       "3  U.S.S.R., U.S.A., U.K., Italy, France, Germany...   \n",
       "4  France, Germany, Hungary, Bulgaria, Czechoslov...   \n",
       "\n",
       "                               Essential Information  \n",
       "0  Joined service at the age of sixteen in 1956 i...  \n",
       "1                   Was Member, Bharatiya Jana Sangh  \n",
       "2  Chairman, (i) West Bengal Minorities Developme...  \n",
       "3  Took active part in political and social activ...  \n",
       "4  Associated with (i) All India Students' Federa...  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Member_bio_1952_2016 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/Member_biographical_data_1952_2016.csv') #reading in the data\n",
    "Member_bio_1952_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Member Name', 'Gender', 'Email ID', 'Delhi Address',\n",
       "       'Permanent Address', 'Whether Minister', 'Term Details',\n",
       "       'Number of Terms', 'State Name', 'Party Name', 'Father Name',\n",
       "       'Mother Name', 'Date of Birth', 'Place of Birth', 'Marital Status',\n",
       "       'Date of Marriage', 'Spouse Name', 'No. of Sons', 'No. of Daughters',\n",
       "       'Freedom Fighter', 'Educational Qualification(s)',\n",
       "       'Other Profession(s)', 'Previous Membership', 'Position(s) Held',\n",
       "       'Book(s) Published', 'Activity(s)', 'Hobbies', 'Country(s) Visited',\n",
       "       'Essential Information'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Member_bio_1952_2016.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['First Name', 'Last Name', 'Middle Name', 'Name', 'c_mp_state_code',\n",
       "       'c_mp_party_code', 'Nickname', 'E-mail Address', 'Home Street',\n",
       "       'Home City', 'Home Postal Code', 'Home State', 'Home Country/Region',\n",
       "       'Home Phone', 'Home Fax', 'Mobile Phone', 'Personal Web Page',\n",
       "       'Business Street', 'Business City', 'Business Postal Code',\n",
       "       'Business State', 'Business Country/Region', 'Business Web Page',\n",
       "       'Business Phone', 'Business Fax', 'Pager', 'Company', 'Job Title',\n",
       "       'Department', 'Office Location', 'Notes', 'Unnamed: 31'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Member_bio_2021 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/Member_biographical_data_2021.csv') #reading in the data\n",
    "Member_bio_2021.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "We have biographical information only till 2016, while we have Q&A dataset till 2017. \n",
    "####  Action item: After cross verfying with q&A data, we can scrape wikipeadia and India.gov profiles of the non-exisiting MPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "RS_QA_2009 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2009.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2010 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2010.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2011 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2011.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2012 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2012.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2013 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2013.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2014 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2014.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2015 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2015.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2016 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2016.csv', encoding=\"ISO-8859-1\") #reading in the data\n",
    "RS_QA_2017 = pd.read_csv(r'C:/Users/hp/Untitled Folder/Project/Data/rajyasabha_questions_and_answers_2017.csv', encoding=\"ISO-8859-1\") #reading in the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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></th>\n",
       "      <th>question</th>\n",
       "      <th>answer_date</th>\n",
       "      <th>ministry</th>\n",
       "      <th>question_type</th>\n",
       "      <th>question_no</th>\n",
       "      <th>question_by</th>\n",
       "      <th>question_title</th>\n",
       "      <th>question_description</th>\n",
       "      <th>answer</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2009</th>\n",
       "      <th>0</th>\n",
       "      <td>150000.0</td>\n",
       "      <td>16.12.2009</td>\n",
       "      <td>COMMERCE AND INDUSTRY</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>396</td>\n",
       "      <td>SHRI ISHWAR SINGH</td>\n",
       "      <td>SPURT IN PRICES OF GOLD .</td>\n",
       "      <td>(a) whether Government is aware that there is ...</td>\n",
       "      <td>MINISTER OF COMMERCE AND INDUSTRY (SHRI ANAND ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150001.0</td>\n",
       "      <td>16.12.2009</td>\n",
       "      <td>COMMERCE AND INDUSTRY</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>2927</td>\n",
       "      <td>Dr. T. Subbarami Reddy</td>\n",
       "      <td>OPENING OF TRADE CENTRES IN LATIN AMERICAN COU...</td>\n",
       "      <td>(a) whether in order to capitalize on the lull...</td>\n",
       "      <td>MINISTER OF COMMERCE AND INDUSTRY (SHRI ANAND ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150002.0</td>\n",
       "      <td>16.12.2009</td>\n",
       "      <td>COMMERCE AND INDUSTRY</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>2929</td>\n",
       "      <td>SHRI SHREEGOPAL VYAS</td>\n",
       "      <td>EARLY EXIT OF CHINESE BUSINESSMEN FROM TRADE F...</td>\n",
       "      <td>(a) whether the Chinese businessmen packed of ...</td>\n",
       "      <td>MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150003.0</td>\n",
       "      <td>16.12.2009</td>\n",
       "      <td>COMMERCE AND INDUSTRY</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>2931</td>\n",
       "      <td>Smt. Kusum Rai</td>\n",
       "      <td>DONATION BY STC AND MMTC TO STUDENT WINGS OF P...</td>\n",
       "      <td>to answer to Starred Question 97 given in the ...</td>\n",
       "      <td>MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150004.0</td>\n",
       "      <td>16.12.2009</td>\n",
       "      <td>COMMERCE AND INDUSTRY</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>2932</td>\n",
       "      <td>SHRI GIREESH KUMAR SANGHI</td>\n",
       "      <td>ENVISAGED EXPORT EARNING TARGETS .</td>\n",
       "      <td>(a) the envisaged export earning targets for t...</td>\n",
       "      <td>MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        question answer_date               ministry question_type  \\\n",
       "2009 0  150000.0  16.12.2009  COMMERCE AND INDUSTRY       STARRED   \n",
       "     1  150001.0  16.12.2009  COMMERCE AND INDUSTRY     UNSTARRED   \n",
       "     2  150002.0  16.12.2009  COMMERCE AND INDUSTRY     UNSTARRED   \n",
       "     3  150003.0  16.12.2009  COMMERCE AND INDUSTRY     UNSTARRED   \n",
       "     4  150004.0  16.12.2009  COMMERCE AND INDUSTRY     UNSTARRED   \n",
       "\n",
       "        question_no                question_by  \\\n",
       "2009 0          396          SHRI ISHWAR SINGH   \n",
       "     1         2927     Dr. T. Subbarami Reddy   \n",
       "     2         2929       SHRI SHREEGOPAL VYAS   \n",
       "     3         2931             Smt. Kusum Rai   \n",
       "     4         2932  SHRI GIREESH KUMAR SANGHI   \n",
       "\n",
       "                                           question_title  \\\n",
       "2009 0                          SPURT IN PRICES OF GOLD .   \n",
       "     1  OPENING OF TRADE CENTRES IN LATIN AMERICAN COU...   \n",
       "     2  EARLY EXIT OF CHINESE BUSINESSMEN FROM TRADE F...   \n",
       "     3  DONATION BY STC AND MMTC TO STUDENT WINGS OF P...   \n",
       "     4                 ENVISAGED EXPORT EARNING TARGETS .   \n",
       "\n",
       "                                     question_description  \\\n",
       "2009 0  (a) whether Government is aware that there is ...   \n",
       "     1  (a) whether in order to capitalize on the lull...   \n",
       "     2  (a) whether the Chinese businessmen packed of ...   \n",
       "     3  to answer to Starred Question 97 given in the ...   \n",
       "     4  (a) the envisaged export earning targets for t...   \n",
       "\n",
       "                                                   answer  id  \n",
       "2009 0  MINISTER OF COMMERCE AND INDUSTRY (SHRI ANAND ... NaN  \n",
       "     1  MINISTER OF COMMERCE AND INDUSTRY (SHRI ANAND ... NaN  \n",
       "     2  MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND... NaN  \n",
       "     3  MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND... NaN  \n",
       "     4  MINISTER OF COMMERCE AND INDUSTRY ( SHRI ANAND... NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [RS_QA_2009, RS_QA_2010, RS_QA_2011, RS_QA_2012, RS_QA_2013, RS_QA_2014, RS_QA_2015, RS_QA_2016, RS_QA_2017]\n",
    "RS_QA_2009_2017 = pd.concat(frames, keys=[\"2009\", \"2010\", \"2011\", \"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\"])\n",
    "RS_QA_2009_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['question', 'answer_date', 'ministry', 'question_type', 'question_no',\n",
       "       'question_by', 'question_title', 'question_description', 'answer',\n",
       "       'id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2009_2017.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#We iteratively check the distribution of all predictors to assess which standardizing method could be used\n",
    "fig =( \n",
    "    ggplot(RS_QA_2017,aes(x='ministry')) +\n",
    "    geom_bar(color =\"white\", fill=\"darkblue\") +\n",
    "    theme_minimal() +\n",
    "    theme(figure_size = (10,3))+\n",
    "    theme(axis_text_x  = element_text(angle = 90, hjust = 1))+\n",
    "    labs(x = \"Ministries\",y=\"Number of Questions\")+\n",
    "    ggtitle(\"Question count by Ministry (2017)\")+\n",
    "    theme(axis_title=element_text(size=10,face=\"bold\", color ='black' ))+\n",
    "    theme(plot_title =element_text(size=15, face=\"bold\", color ='black'))+\n",
    "    annotate('text', x=\"CULTURE\",y=450, label='Source:rajyasabha.nic.in', color='black', size=10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kfVWj1aUUf95SXWcsYRcz23ydJDN7EwOL1HvcfUChFb22botNircI1NpiMpxfE56D+ARwg7tfk+G6M2VmJ0TPPRC20rr79Qx+3ce7nw4YjdBSXjcuLvosWhqbFG+ZXxq1etUifm5nsgAHNl9W4UzgP2OT/wC8sUJrdTzXOxjYG+Dd0TDr5QvIx4ug54Bf1Zb68MxsWwZ2q7zR3f861PIxyQMwQ/VoEJEaqNASaWLRCfiHMbCl4nPACjO7OLqA6D2Eo9GVLSPsEkRiWtwvzGwF4ehm+wyz/dsY+ENiH2Clmf3ZwosOX0fYzelPwKFsKfbaCQeeuNfCi+xeFy3/VwYeXX6K8AdrWfycqinArWb2m2jwjfgP56GcSTiARNm7gXvM7A+EJ74vjM27wt1HOmeqLu7+BPCt2KRW4AIzu8vMfm9mt0V5xQfA+DIDhzM/3cxuMbMrCYdwjp9T8z8+8Fo5y2L/bwPcET3Pf2d0T46PP28LonPwfh09b/NTrvNnZna9mV3D4Jbd7wwRU+nI//0MPSx5zdz9WXffyd1nuvteWa13lJwI3GZmj5rZDWYWRPszfv5kP+ElG8qS5zVea2YXRs9lcnCcWpzO4NYyZ2ABVq348/k8M9u+wjLHAR+I3e8nLIzOKg/mE7u9KxEbPyAzjXAfXgzcxcBh07/m7gNajszsf6PX7fXA5xPr/Xx5npldOMRjO5qB50B+b4jlkuLFax+j09ImMv6M9bCHuumm2+jfCLtCPcLgIZWTt18C0yvETyK8flWlmN8RdsOqODw3YcHzhyq27cDcKGZmlct/OLGtVxGej1Jp2YOiZeYnpp+cWMeewMoRtnsDMDMRtzS+TIV9+OBQ+2iE566FsHvZcPmclog5nMFD+ydvZwItibhdCbseVlo+OWx1cr/F5y2t8PobcphqwovrDpXnwir3UzzmymGew4uSjzvxOn8isfznU77n4uuo6vkmu+HdF1f72hxh3joq78P47dREzLxhXkMfr+b1Msz+uTQR96eUz0074WiD5fV8oMIyyf093O3kCvH/ydCfRU44SmJrhbhlVW7zwQqxrYRdgcvLrGKYS3okYs+NxV2aZr/qpptug29q0RIZB9x9GbADYTey3xN2J3w2sdhF7v5ur9AtxsNWj9cSdkVcHcXeAXwcOIiBLWbJ2HWEI5a9k/AaTw9H8c9F//+R8Mj5zh4OSgBhS9V7CQuMvxF2aeuL4h4gLAgXu3v8yDrufgPhaIbXM/AiwVVz978TtvD9N+Gw2E8RthCtJmx5OxrYx8OREUedh+eqfYjwoq5nE7awPBPdyvviwkTMOdFjOJ3wKPp6tuzvCwivu/XvnhiR0N3vJCyKyhdZfhq4hnBgiPhgAJly9wsI9+vfGdhVMq0VwCsIL5b9COFjv4ew++Mhyccdy+MZYhfOJmzFWJpBPkV1BGGLyA2EB1Pi79sAeIe7nxAPcPcVwJsIi92szmsrS7Y4purS6WFL/89jk96dOqOht/E9wusBnke47/oIi/g/Au9290M9Npx9Rt7OwNE0z/Atw+QPKRph823xuIzzEhm3zN3HOgcRGSNmdhgDr6t0hLufPVb5iIw1M1sKvD+6e5m7D3etNMmRmR3JlmH2nyK8kPkzQ0cMu64XER4saiE8kDLXqxuZr+lEXR9/Gd19CNjJo+sHikh91KIlMo65+7kMvFbOmWY2GhewFWlYZnaAmX0mKrLeF5t12hilJBEz283MPmVm3yS8pEPZGWmLLAB3/yfhdcEgPDf043WkWXSfjP3/eRVZItlRi5bIOGdmRtid7J3RpMeAV7n7Q0NHiTSPRCtW2W/c/eAKi0uOEq1YZQ8AL3X3ui72a2azCbuUTiLs5jiv3nUWjZktBq6K7t4KvMz1w1AkMxNGXkREmln0paoflCJhF7KHCLvTjto5aZKKE56reTlwYhYFkYdD/A91DcBxITp/t9YLy4tIldSiJSIiIiIikjGdoyUiIiIiIpIxFVoiIiIiIiIZU6ElIiIiIiKSMRVaIiIiIiIiGVOhJSIiIiIikjEVWiIiIiIiIhlToSUiIiIiIpIxFVp16u7untjd3X1yd3f3RMUVJ8e0cUXIMe+4IuSYd1wRcsw7rgg5po0rQo55xxUhx7zjipBj3nFFyDFtXBFyzDuuCDnWE1eJCq36TQROiv4qLv9t5R1XhBzzjitCjnnHFSHHvOOKkGPauCLkmHdcEXLMO64IOeYdV4Qc08YVIce844qQYz1xg6jQEhERERERyZgKLRERERERkYyp0BIREREREcmYCi0REREREZGMqdASERERERHJmAotERERERGRjKnQEhERERERydiEsU5ARKRBzAS2Tk6cMWPGlJ6eHjo6OuYA6xKz1wOr80hOREREikWFlogIzHT3VWY2qJW/vb2d9vZ2gDuS89y938xmoWJLREREElRoiYjA1mbWsmjROXR1JRutKps9ewrLlx/eQoVWMBEREREVWiIika6udaxc2TvWaYiIiEgT0GAYIiIiIiIiGVOhJSIiIiIikjEVWiIiIiIiIhlToSUiIiIiIpIxFVoiIiIiIiIZU6ElIiIiIiKSMRVaIiIiIiIiGVOhJSIiIiIikjEVWiIiIiIiIhlToSUiIiIiIpIxFVoiIiIiIiIZU6ElIiIiIiKSMRVaIiIiIiIiGVOhJSIiIiIikrEJY52AiIg0nZnA1vEJM2bMmNLT00NHR8ccYF2FmPXA6jySExERyYMKLRERydJMd19lZgN6TLS3t9Pe3g5wR6Ugd+83s1mo2BIRkSahQktERLK0tZm1LFp0Dl1dlRquBps9ewrLlx/eQqIVTEREpMhUaImISOa6utaxcmXvWKchIiIyZlRoVaGvr+8FwAsqzZs+ffrk3t5epk6dumdfX9/T1a6zmeOKkGPauCLkmHdcEXIcKa6lpWW71tbWalc1wKZNm3bv7++fOdo5FiVuPO7LIuSYd1wRcsw7rgg55h1XhBzTxhUhx7zjipBjtXFtbW23VLMuc/dqtztu9fX1nQycNNZ5iMjoaWtrY968M6puhZk7dyorVhxDX1/fKGdWPNqXIiLSzNra2qya5epu0TKzVwKvAW539yvqXV+DOgMIKs3YuHHj5N7e3qunTp2634QJE6qulps5rgg5po0rQo55xxUhx5HiWlpatgMuqXZdidgD+/v7Hx3tHIsSNx73ZRFyzDuuCDnmHVeEHPOOK0KOaeOKkGPecUXIsZ64Smpu0TKznwOHAfsDBlzJlutxHe3uP6knoaLp7u7eBugBOjo7O9cqrhg5po0rQo55xxUhxyri5gEPpWmFAV4IrMghx6LEjbt9WYQc844rQo55xxUhx7zjipBj2rgi5Jh3XBFyrCeukjQXLH4V4TVQrgUOB1qB+wmLro/Wk4yIiIiIiEgzSFNozQYe9LApbCFwp7svAB4AdswyORERERERkSJKU2j1AxOj/3cB/hH9v5awdUtERERERGRcS1No3QcsMLN7gG2Am6LpnUB3VomJiIiIiIgUVZpC6zvR3x2BJ4Gfm9kewLbAjVklJiIiIiIiUlQ1D+/u7meZ2a3ATsC17r7KzFqANxIOiiEiIo1nJrB1fMKMGTOm9PT00NHRMYdwkKO49cDqvJITERFpNqmuo+XutwG3xe4/AjySVVINrNYfKqAfKyIy9mbGDopt1t7eTnt7O8AdyQB37zezWejzS0REJJWaC63oi/oo4PXALMJh3cvc3V+fUW6NpuYfKqAfKyLSELY2s5ZFi86hq6vS8aCBZs+ewvLlh7eQOLAkIiIi1UvTovVt4GPR/+Uiy6P/a7v6cbHU9EMF9GNFRBpLV9e6qi8iLCIiIvVJU2gdSlhUdRNeO2tjphk1OP1QERERERGRkaQZdbAVeBjY0d33dffXxm8Z5yfSEMzMzeygsc6jzMxOjgalGdLHP/5x5s+ff25eOZWZ2fxofy1MGb84ip+WdW4iIiIieUnTovUL4N1AG/BstulIoznuuONmTpw4kXPPPfcOd38+4ZD+fwdOdvflY5xenl5A+NgnjrSgsJJwf6U9L/G6KL4ns4xEREREcpamRWsd4YWKbzWzb5vZF+K3jPOTMXbhhReefeeddzJv3rwPAzsDJWAZMGO0tnn55Ze3jda6466++uqqDzS4+6PurgMLVXD3TdH+StWt2N2fi+Kb+ZxPERERaXJpCq3PELaE7QAcB5yUuEmTMLNpmzZtWnTiiSdy3XXXXePuD7n7De5+qrtfEi0zz8wuMrN1ZrbWzM7/6Ec/um1sHUvN7LeJ9Z5mZsti95eZ2fd32mmnr+6+++4cffTRF0XTdzOzS6L19prZNWa2YyzuKDO7a/bs2Y/tt99+7Lbbbv8+wuM52cxuNbMPmNn9hx566Gp3Z6+99nqDmf3FzJ4ysyfM7OL4dqLYzV0HlyxZwo477viNxPznmdmzZva66P57zeymKO9Ht99++zNXr14dX366mZ1jZo+b2TNmdo+ZHRWb/zUz+9fs2bMfXbRoETvvvPOJZjaoADWzY8xspZmtN7NfVepuZ2afMrNHosf2g/h6RspziP34oJn9t5n9xMx658yZc8fZZ58dnz+o62Cl53LJkiXbD7H+AV0HzezI6Ll5U/R8dx9++OEcc8wxs4ZNVERERGQMpSm0VkS3h2L/x2/SPNYB6/7whz8QBMFWyZlmZsBvCVu39ie8aPWOv/vd75am2Nb7zWzjb3/7WxYuXHicmc0GrgY2AK8DXg78hKi7q5kdDXwFOHHfffd95fHHH89TTz31OTN7fyy/ZWaWzGUn4F3AwXvsscc+AH19fVsTjqb5SsLLFvQDFyaH8i877LDD2LBhwxIzi3cjPJxwgJirovtbAZ8H9gQO2rRp0/xPfOIT8dV8CdgVeDPwYuBYBna16wWO3H///V91yimnsH79+iOBASuIPZa3AQcAC4EfxBfo6+vbF9gReC3wfuDI6FY2Up5D+SRwE/DSKVOmnHnCCSfw9re/fUGlBYd6Lvv6+lqr2VBka+BTwBG77rrrm7u6urj88su/XEO8iIiISK5qPkfL3eePQh7SgNx943bbbXfsBRdc8PMzzzxz5bHHHnsL8GfgF9FFq98AvATY3t1XApjZERs3brzj1ltvZeHCmsZCuPeee+75AnDcRRdddM/s2bOPJzxH5z3u3hct86/Y8p8HPunuv+nu7t4GYMqUKT9Yt27dMcDPomVWMPhC2lsBR7j74+W4W265Jejs7FxbXsDMPgg8RlgI/SOZ6Fve8pbyv28Hzo/+PwpYWu7u5u4/iYXc/6IXvegzV1555VWnn3765FNPPXUtMA/4m7vfFC3zYHwb7v5lgNhjO723t/ddwNdji7UD73f3h6O8PwZcYmaf7OrqWh9Ne8rdP+rum4B/mtklhMXk/1WZ51B+7+7/C3DnnXd+Z//99z/53nvv3Re4ucKy/0GF57L82KrUBnzY3e/r7u7eZunSpXzuc59bXEO8iIiISK7StGgBm7sHHRzd5meXkjSSW265Jbj55puZO3fue4DLgMXALWZ2JGFLzMpykQXg7ncCT91zzz21buqmxP2FwDWxH+abmdm2wFzgx2a2bvbs2d0LFixg3bp1nyZsvSnn8j53PyER/pC7Px6fsGTJku3N7Fwzu9/M1hJetgDCYmiQiRMnMmnSpF8CH4jyWUjYIrQ0luNLoy6VD5lZ7913330JwGWXXTYnWuSHwHuiroxfN7O9E4/xEDP7y5w5c+5ZsGABvb29n6uQz4pykRVZTvie3qU8YcKECf+MiqyyR4Dn15DnUG4r/zNt2jS23XZbNm7cuO0Qyw75XNZgvbvfV74za9Ys3H2o7YmIFNVMws/6zbcZM2bMaW1tZcaMGXOS86LlRaRB1VxomVmLmf0/4F7Co/nnA/ea2Y+irmTSZNrb27n++uuvcvcvuvvehAXFKQx9ker4S6GfLRe2Lqs02MXTifvPDJNS+XV7NLBw0aJFr7n88stZtGjRq4FXDxNXaTtcf/31vwSeF61vr+gGYetXRQsWLPgZ8EYzm0NYcP3J3R8CMLPJwOWEXS/fC7xy7ty5hwM899xzWwG4+6XAC4HTgE7gT2b2zSj+1YSje166yy67vOuyyy5jyrSA1qYAACAASURBVJQp3xwun4gn/gIkixsn2n/V5DmMAes1M9x9qM+T4Z7Lag3aHoNfVyIiRTbT3VcRnpqx+dbe3n7HrFmzaG9vvyM5L1pexZZIg0ozvPsngOSgA0b4I/VfhOe6SHO7Ezgo+jvPzObGug7uCnQsWLD5dJ3Hgd0T8QsZXAAk3UZ43lZbsiXE3VeZWRewg7ufU+6CdsEFF9wf7wJYjTVr1tDf378LcLS7XxM9hteMFHfppZfeOXv27JsIX/eHAR+LzX4R4Rff8eX9su222w5aZ9SythRYambXAN8gPA9pH8Iv0K+UH1tfX9/cCmnMM7NOd++O7i8iLGz/VWHZSqrKMwNDPpciIrLZ1mbWsmjROXR1rRtx4dmzp7B8+eEthOewikgDStN18IOER8W/y5aj/98lLLY+mF1qMtbM7HkvfOELf/frX/+aAw44YDcz297MlhCOPHkRcAXhj+hzzOxlZvYq4KwJEyb8Zc899yyv5krgFWb2PjNbYGanMLjwquT7hJcR+IWZvSKKPcLMyt3iTgZOMLPj3vGOd+x41113scceexxuZv8Vy/8sMzt1uI1MmzYNM1sDfMjMdrJw1MBqDxacCRxPeBHvC2PTVwDPAR8zsx3MrLRmzZrPxgPN7Itm9vZom7sBbwXuimbfS1hEvWfJkiXb//jHP+bZZ599W4XtbwB+ZmZ7mtm+wPeA89390SrzHzHPKNd/mtk7qlxnJRWfy4MOOmgngJe//OVvNbN/1rF+EZGm0dW1jpUre0e8VVOMicjYSlNo7QDc4+6fcPcbo9sngHuInR8jTWHdVlttddP//d//cfvtt19KODDElwgHUvhoNPDDQYQX8r2asPC6/21ve9uR5RW4+2VRzNeBG4GpwFkjbdjdnyAcoW4K4QAcNxO2HvVF888kbFk98oYbbrj+kEMOYe3atYez5fwqCPuvv2C47bS0tDB37tyjCEfC+wfwHeDTI+UXOQ/YCJzr7htiuT9OOLLfEsJWv+NnzZp1YiL2OeBUwkL1amAT8J4o/qIoj+9fd911f7npppvo6Oj4WoXt3wv8Bvg9YRfAfwAfqTL3avOE8JyvjmrXW2E7FZ/LiRMnbgTo6+vbhth5ZSIiIiLNIE3XwQ3ATDOb6u69AGa2DWEXpA3DRkqhuPuz3d3dpwD/Bcyr1C3P3VcQjr63WXI0OXcf9hpr7r54iLjbgDcNE3cucG4U1wO8JZ5jeb2x+ycTtoQNsHz58mWdnZ27JiZvPv8nNox78vDhdMKR/35cIbfzCAsxYMBjuz2a/2VgyOHJ3f0zwGdij+2HnZ2dX4vNjz+WH1Zax2mnncZpp512WGK9H68lz2gZS8TMT27rj3/8I4SFI+7+IInzpyo9l+Vt3Xbbbed2dnb+KLbssni8uy8lNtAIwAEHHEBXV1fq4k9ERERktKUptG4gHB7672b2e8JuhAcC04A/ZpibyJiLDiK8k/Dcp38C9PX18YEPfGAO8Dngene/ZQxTFBEREZEGlKbQ+iLhEN/zCS+yCuHR5z7CLmIizeQUwsEuPuvuD3d3d29z4403cuWVV95BOOjEIWObnoiIiIg0oprP0XL3vwD/BlxD2FVwA+E5Jv/m7tdmm57I2IrORZzl7t8sT9t7773p6urqcPdd3P324eJFREREZHxK06JVPodi/2xTERERERERaQ5VFVpmth+w1t1vjf4fkrtfnUlmIiIiIiIiBVVti9YyYDnhhVSXEQ6AUYnXsE4REREREZGmVEtRZEP8P9QyIiIiIiIi41K1hdb2wLOx/0VERERERGQIVRVa7v5Q7O7+wOPufml8GTObD2ydNpFSqfQ84G7g3iAIXh1N2x04E3gJcD9wbBAE18RiDgG+DswCrgWOCoKgK20OIiIiIiIiWah5eHdgKfD5CtPPA26rI5dvAZuHyi6VSm1AAFwITAf+B7ioVCpNj+a/CPgp8CFgJnAPcG4d2xcREREREclEmkJrKDNIeY5WqVRaDOwInBWbvJiwhewbQRA8GwTB2cADwDuj+UcAlwZBcEUQBM8QFn97l0qlHdOlLyIiIiIiko2qCy0zu9/M7o/uvrR8P7o9CuwEPFFrAqVSaSLwfeAjDBzNcHfg9iAI+mPTbo2ml+f/vTwjCII1wIrYfBERERERkTFRy6iD86O/DkyM3Y/7TYocTgAuC4Lg9lKp9PLY9ClAT2LZp4COEeZPTZHDsLq7uyfOmDFjSnt7e6r4DRs2TFmzZs02icnlPKd2d3fXsroixBUhx7RxRcgx77gi5DhsXLO/v9M+viEe27Dba/Z92QDbKkpcEXLMO65h3qcjaJh9UvC4IuSYd1wRcqwqrrOzc201K6ql0Dol+nsS8DDw49i89cA/gYtrWB+lUmkBYRfAPSvMXgckPzg6gN4q52fphJ6enpPS/njo6em5Y5jZD6dLqRBxRcgxbVwRcsw7rgg5Vozr6emhmd/faR/fCI+t4vaafV820LaKEleEHPOOG/P3aZXGfJ80SVwRcsw7rgg5jhRX1elSVRda7n4KgJm9FrijfL9O+wCzgQdLpRKELWXtpVJpNfBBYI9SqdQS6z64EPhh9P8/iBVo0SAZ86LpWTu1o6PjfGCkD7OKOjo6dluzZk3yyZpK+ATOobbisAhxRcgxbVwRcsw7rgg5DhvX0dExhyZ+f6d9fEM8tmG31+z7sgG2VZS4IuSYd1zDvE/ryTPjuCLkmDauCDnmHVeEHOuJG6SWFi0A3H1x/L6ZvRJ4DXC7u19R4+rOB+IxSwhbuErAKmAD8MlSqfQ94GBgB8JRCAHOBv5aKpVeBywHvggsD4LgvhpzGFFnZ+ezhC1oqbS3t69LNjHGmiJ7q21+LEpcEXJMG1eEHPOOK0KOVcSNxft7JolLYmzYsGFKT08PHR0dHe3t7a2J5dcDqyutqIrtpXp8lR5bFdsbd5+VRcgx77gi5Jh3XIO9T4fUYPuksHFFyDHvuCLkWE9cJTUXWmb2c+BQwlEBDbiSaFANMzva3X9S7bqCIFhP+OMBgFKp9CTwXBAED0f3S4TX0foi4XW0DooGvSAIgrtKpdIHo/nbAX8BDqv18YiIjIGZ7r7KzAYMSNTe3l7uOjToqLa795vZLIYotkRERKSx1FxoAa8Cnia8QPAPgVbgPsLh2T8KVF1oJQVBsJTwOl3l+7cDew2z/K+AX6XdnojIGNnazFoWLTqHrq6RD2LPnj2F5csPb6GOi8KLiIhIvtIUWrOB+9zdzWwhcKe7725m5WJLRESq0NW1jpUrR2P8HhERERlraS5Y3E84aAXALmwZfGItYeuWiIiIiIjIuJam0LoPWGBm9xAOr35TNL0TqGmQehERERERkWaUptD6TvR3R+BJ4OdmtgewLXBjVomJiIiIiIgUVZrh3c8ys1uBnYBrYyNnvZFwZEAREREREZFxLc1gGLj7bcBtZvYCM5vr7iuBR7JNTUREREREpJjSdB3EzN5rZg8RXjX5l2b2NjO70szekm16IiIiIiIixZPmgsUHA2clJt8C7A88Bvw+g7xEREREREQKK02L1n8DDpxWnuDuXYQjDr4yo7xEREREREQKK02htStwt7v/V2L644RDvIuIiIiIiIxraQqtDcA20UiDAJjZRGB7YH1WiYmIiIiIiBRVmkJrOfACtpyLNQe4gvDixcszyktERERERKSw0hRapwAbCa+b5cBsYJ9o2peyS01ERERERKSYai603P2vwOuBq4FnotufgTdE80RERERERMa1tBcs/gvw2oxzERERERERaQpprqO133Dz3f3q9OmIiIiIiIgUX5oWrWWE52ZV4inXKSIiIiIi0jTSFkWWaRYiIiIiIiJNJE2htX3ifgfwLuCzwGF1ZyQiIiIiIlJwNRda7v5Qhcm3mdn+wMeAX9WdlYiIiIiISIHVfT6VmRmwM7ADML3ujERERERERAouzaiDm4aZfXcduYiIiIiIiDSFNC1aQw2EsQ74VB25iIiIiIiINIU0hdZRifsOPAb81d2frD8lERERERGRYkszGMbPRiMRERERERGRZpHmHK2FwHuBXYA+4C7gJ+5+X8a5iYiIiIiIFFJLLQub2fHATcAngLcAbweOB+4ys6Njy30xyyRFRERERESKpOpCy8zeCHw1irHEbQLwQzN7jZn9HDhxFHIVEREREREphFpatP4z+nsj8HrCa2ZNi/6/MVrXn4DDga4McxQRERERESmUWs7RehXwDHBAYnTBq8zsLcAKoB34K/CO7FIUEREREREpllpatKYD91cawt3dnwDuB/qB/dz90YzyExERERERKZxaWrSeBHYws2nu/lR8hpl1ANsDT7h7X5YJNoK+vr4XtLS07N7a2poqftOmTbv39/fPjE+bPn365N7eXqZOnbpnX1/f09WuqwhxRcgxbVwRcsw7rgg5jhTX0tKyXZ7v77Tbq7StRtte3vtyKHoPjG1cEXLMO66R3qf15JllXBFyTBtXhBzzjitCjtXGtbW13VLNuszdq9qomQXAgcANwGeBvxFerPglwKnAa4CL3f3tVa2wQPr6+k4GTmpra2PevDNYubK3qri5c6eyYsUx9PU1Xe0p0nTyfn/Xsr0sPkvy3J4+K0XSyftzQUTSaWtrs2qWq6VF67vAWwnP1bpqiGW+X8P6iuSMlpaWG4BL0gS3tLQc2N/fP6A75caNGyf39vZePXXq1P0mTJhQdZVdhLhqYsxsmpm1x6dt2rRp0tq1ay/YZpttDmltbX0mPs/dNyRbUhv1sY23uCLkOFJcS0vLduT4/k67vUrbarTt5b0vh6L3wNjGFSHHvOMa6X1aT55ZxhUhx7RxRcgx77gi5FhPXCVVt2gBmNlnCYd4r1TFfcHdv1xPMg1uHvBQmqO0wAsJBwvZrLu7exugB+jo7OxcW20SRYirImamu68ys6rPEXT3fjObBazOIse0cUXY/3nHFSHHKuLyfn/XtL3httWA2xt3n5VFyDHvuCLkmHdcg71P68kzs7gi5Jg2rgg55h1XhBzriauklhYt3P1rZvYH4H3Ai6LJ/wLOdveb60lExpWtzaxl0aJz6OpaN+LCs2dPYfnyw1uArUc/NRERERGR+tVUaAG4+9+BT45CLjLOdHWtq/qIt4iIiIhIkdQyvLuIiIiIiIhUQYWWiIiIiIhIxmruOigikqOZJM7NmzFjxpSenh46OjrmAMmT/NZTYcAUERERkbyp0BKRRlVxdMr29nba29sB7kgGDDc6pYiIiEieqiq0zOxK4A53/5iZ/QS4z92/Mrqpicg4p9EpRUREpLCqbdFaDEyK/j8SuB5QoSUio06jU4qIiEgRVVto9QIvMbOvR/fnmNkXKi3o7l/MJDMREREREZGCqrbQuhF4HeH1sxyYDZw0xLIqtEREREREZFyrttA6Bvg2sCuwA/Ac8OhoJSUiIiIiIlJkVRVa7n4f8HYAM+sH/ubue49mYiIiIiIiIkWVZnj37YFns05ERERERESkWbSMvMhA7v4QsMDMrjKz3uh2pZntOwr5iYiIiIiIFE7NLVpmtg9wRRRr0eTFwBVmttjdl2eXnoiIiIiISPHU3KIFfAFoA1YAP4xuD0XThhqJUEREREREZNxIc47WXsATwJ7uvhbAzDqA+4BXZ5ibiIiIiIhIIaVp0WoH1pSLLAB37wHWABOzSkxERERERKSo0rRo3Qe8yMy+BZxHeAHjw4GdgDszzE1ERERERKSQ0rRo/ZRwEIyPA38FbgCOIyy4fppdaiIiIiIiIsWUpkXr28CLgKPYMuqgAz+J5omIiIg0mpnA1vEJM2bMmNLT00NHR8ccYF1i+fXA6rySE5HmU3Oh5e79wL+b2VeAV0STb3b3+zPNTERERCQbM919lZkN6MnT3t5Oe3s7wB3JAHfvN7NZqNgSkZTStGgB4O4PAA9kmIuIiIjIaNjazFoWLTqHrq5kw9Vgs2dPYfnyw1tItICJiNQidaElIiIiUiRdXetYubJ3rNMQkXEizWAYIiIiIiIiMgwVWiIiIiIiIhlT10EpGzQaE2hEJimsWkcXm55bZiIiIjIu1FRomVkbsAF4DOh0dx+VrCRvFUdjAo3IJIU03d0fSDG6WF75SWPQwSURERlVNRVa7t5nZo8AT6nIaio1jcYEGpFJGtqklKOLyfihg0siIjLq0nQd/C7wVTN7s7tfmnVCMnY0GpM0E72eZRg6uCQiIqMuTaH1FmATcLGZ/Qt4FCi3brm7v77aFZVKpYnAD4DXE3bjWAF8NQiCc6L5uwNnAi8B7geODYLgmlj8IcDXgVnAtcBRQRB0pXhMIiIyzqgYFxGR0ZSm0No/9v8u0a2s1u6EE4BuwkLrQWBv4JJSqXQ/cBMQAGdE21wCXFQqlXYMguDJUqn0IuCnwDsIi6xvAucm8hMREREREcldmkLrLGovqCoKguBp4AuxSX8plUrXEhZcUwi7aHwjCIJ+4OxSqfQJ4J3Aj4EjgEuDILgCoFQqfR5YFRVi92WRn4iIiIiISBo1F1rufuQo5AFAqVSaDLyC8Dyw3YHboyKr7NZoOtHfG8ozgiBYUyqVVkTTVWiJiIiIiMiYSX0dLTN7LfBq4EnCLnvTgFXu/mya9ZVKJSPsCngDcDnwKqAnsdhTQEf0/5Qh5k9Ns/3hdHd3T5wxY8aUaCSqmm3YsGHKmjVrtklMLuc5tbu7u5bVZR6X92NLu70htjXi9oaRJq5hnrcGihuVbaV9nfT19U1ua2urOS6tIrwH8txeET4ri5BjE8Q1VI4N8r1ThBzzjitCjmnjipBj3nFFyLGquM7OzrXVrKjmQsvMJhGeO/W6aNJfCa+r9Svgv4Gv1brOqMj6ETAb+LcgCLxUKq0Dkh8cHUD5zOWR5mfphJ6enpPSfjH39PQMGiY45uF0KWUX19PTQ56PLe32RtjWkNurQpq4MX/eGjAu022lfZ089dRTV2y77bYpU6ldEd4DeW4v78+TKhQyxyaKa4gcG+x7J/McW1paGOZagIO25+709/dXWnbYuCoV9nUySnFFyDHvuCLkOFJcVRffTNOi9WXCwSviLgGeAw6kxkIrKrJ+ALwUeEN03hbAP4DPlEqlllj3wYXAD2Pz94ytZzowL5qetVM7OjrOp8J1VarR0dGx25o1a5JP1lTCJ3AOtRWHmcdFF+bM7bGl3d4Q2xpxe8NIE9cwz1sDxY3KttK+TqZNm/YG4Ipa49Iqwnsgz+3l/XkyjELn2ARxDZVjg3zvjEqO06dP36etre2aSteFG4q7969fv37Hnp6eNbXmOYzCv04yjitCjnnHFSHHeuIGSVNovQt4BlhEeM4U7v6smT0E7Jxifd8n7IL4+iAI4s1wy4ANwCdLpdL3gIOBHYALo/lnA38tlUqvA5YDXwSWj8ZAGJ2dnc8StqCl0t7evi7ZxBhriuyttvlxFOPyfmyptldpW1Vur6I0cQ32vDVE3ChuK9XrpK2t7emRl8pOQd4DeW6vCJ+VRcix0HENmOOYf++MVo5bbbVVP1DzRdonT57cP3nyZH2njlJcEXLMO64IOdYTV0maQuv5wJ3ufluimbqP8DytqpVKpRcCHwGeBVaWSqXyrK8GQfDVUjjhTMIi6n7goCAI1gAEQXBXqVT6YDR/O+AvwGEpHo+IiIhIoem6cCKNJ02h9Qiws5ntWJ5gZguBFxNecLhqQRA8xDB9HIMguB3Ya5j5vyI8N0xERERERKRhpCm0LgI+RngulBOeW3UDYcF0UXapiYw7MwmvHbfZjBkzpvT09JT77if7hKwHVueVnIiIiIhUL02h9XlgP7YMRDEx+nsbcFIWSYmMQzPdfVXyZOb29vbyKFSDTpB2934zm4WKLREREZGGk+aCxWvNbC/gUOCVhC1ZNwDnuftzGecnMl5sbWY1n8xMogVMRERERBpDqgsWRwXVz8zs4uj+E5lmJTJO6WRmERERkeaQqtAys48SXpx4VnR/FXCqu5+eYW4iIiIiMnp0brDIKKq50DKzU4DPMXC0wO2A08xsprvrPC0RERGRxjbd3R/QucHSJBryoEGaFq0PR3+vAS6I/n8nsD9wLBoQQ0RERKTRTWryc4Mb8oe3jIqGHVAsTaE1CegCXufumwDM7IfAA8A2GeYmIiIiIqOoSc8Nbtgf3jIqGnZAsbTX0VpMeA2tOAN+W29CIiIiIiJ1aNgf3jJ6GvGgQVWFlpm9L3b3RuAg4Eozu4Cw4FpC2Jp1U+YZioiIiIjUqBF/eMv4Um2L1lIGt2DtG93KDPgOoJEHRURERERkXKul66CNvAgtIy8iIiIiIiIySJpBTBpWVYWWu6uAEmkugz7IQCMyiYiIyJhJO4jJy/JKsFapLlgsUjAqKgaq+EEGGpFJRERExkzaQUwmjX5q6aS5YHEr8AHgtcAsBnYpdHd/fUa5iWRBRcVgNX2QgUZkEhERkXw00yAmaVq0vseWixYnz9tKDpghMtZUVAyhmT7IRESkaTXVOTsyvqQptN4d/b0WuB8VV5KvWj9wp4OKChERkQJqunN2ZHxJU2itBx539/2yTkZkBNPd/YEUH7h55SciIiLZabpzdmR8SVNofQk43czeA1zs7tX1xxKp36SUH7giIiJSUOqVIkWVptC6EPg4cA5AorXA3V0jGcqo0gduIamPvYiIiIwraYqis4AXUd0FjGVspDqPSWSUqI+9iIiIjDtpCq3FhANgnAs8CGzMMB+pX9oftXnlJ+OP+tiLiIjIuJOm0Lob2Mrdj8g6GclE2h+1IqNKXT5FRETGVJpu/M14PdHcpCm0vgIsNbPjgYuBtfGZ7r4ii8SkPvpRKyIyKgZ1tR7hhwrox4qIjL20PZ5mke7zq9aiDprwVJY0hdb5hF0HvxLd4jzlOkWaiQZ+EGlS7v43S/S1Hu6HShRTz48VEZEspO3xtPWICw9Wc1EHzXkqS9qiqLn2gkh2NPCDSBMzM6v2hwrU/WNFRCRTOfV4qqmog+Y9lSVNoXVU5lmINA8N/CCSv1xHWlXXbBGRkemzMkWh5e4/G41ERJqJPlxEcqORVqUR6fwUEam90DKz9w03393PSp+OiIhITTTSqjQanZ8iIkC6roNLCQe9qMQJL2gsIiKSG7UiSwPR+SkiAmgwDBEREZHMqfgXkZqPnrh7S/wGTAM+BDwHHJh1giIiIiIiIkVT9zWv3H0tcKaZHQZ8FfhD3Vk1mL6+vhe0tLTs3tramip+06ZNu/f398+MT5s+ffrk3t5epk6dumdfX9/T1a5rpLiWlpbt0uaZRprHljbHTZs27aLHNnKOI+VZz2ukIPukmV8nNT/feW+vKK+vvOX5PdAIcY2WY1HeA2nk/VmZ9nOokgb8/sjtseUdp/dAxbjUz3dbW9st1WwjzWAY8xKTWoGdgT2B9lrXVxDH9Pf3n5T2RdPf339JctqECROYPn06wNW1rGukuP7+fvJ8cad5bGlz7O/vX6rHNnKOMHye9bxGCrJPmvl1UvPznff2ivL6ylue3wONENdoORblPZBG3p+VaT+H0sQU4Tt1KHoPFOI9UM/zXdVpVGlatB4YZt7fUqyvCM5oaWm5Aaj4hIykpaXlwP7+/kfj0zZu3Di5t7f36qlTp+43YcKEqo9YjBTX0tKyXdo800jz2NLm2NLSciThYCy5KMJjq5TjSHnW8xopyD5JFZdWzvuk5uc77+3V8/qaMGGCWw3Drrm79/f3Hwacl2Z7ecrze6AR4hotx6K8B9LI+7My7edQJQ34/ZHbY0sbZ2bTWltbr8jrs7LJ3wOZPd9DSVNoDfXErgA+UkcuDautre0RoC1tfGtr6z9aW1tXxKc9/vjj2wA8+eSTf+/s7Fxb7bqqiEu2OI6qlI8tVY6tra13p4lLqwiPrVKOMGKeqV8jBdknzfw6SfN857291K8vM7Mah2m31tbWB9NuL085fw+MeVwD5liI90AaeX9W1vE5NEgDfn/k+djSxs0DcvusbPL3QGbP91DSFFqvTdx34DHgHnffVE8yIiIyvmmkNqlCrRcDXp9bZiI50WdlMdRcaLn7n0cjESmsQVeyH+ELr2KMiIhIFWq+GHB0IeCX5ZWgiEhZ1YWWmb2vmuXcXRcsHkfc/W/JfsLDfeFFMf01dC0WkbFT8aDICAdTdCCluAa1FEFVrUWr80guUtPFgGMXAp40+qmJNCV9D9ShlhatpYTdBEeiQmscqeWcChjwpSciDa7SgRSoqvUgj/QkWxVbiqCq53sW+RZb6jYlkhN9D9Sn1q6DI+21agoxaTL6whNpTjqQkqlau1k3dEsRDHi+B7WCiUhz0PdAfWoptF5cYdpOwCnASwmLsFxH+xIRkdGlAynZqLWbtVqKRKRRNPnnQprBdar+XK660HL3zUWUmW0HfAH4IOGw5ysJC66l1a5PqqKRlUREmkCKoevVUiQiMrqmu/sDKQbXqfogWE1dB81sGvBZ4GOEXwCrgVOBH7j7c7WsS0akkZVERJpIkx8VFhEpmkkpB9ep+iBYLaMOngB8GugAeoGTgW+5e11XTJYhaWQlEREREZFRNJoHwWpp0foKWwa7WAUcAByQ6HLu7r5PRrkJOgIqIiIiTaHW0yFAw4RLwdV8wWLCQS92im7JUQg16qCIiIiIxNV8OgRomHApvloKratRISUiMtY0SI6IFE09lw8QKaxaRh1cPIp5NLs0108REUlKO0KSBskRkTGn0yEG0YGzJpem66DUKOX1U/TDSESS0o6QpEFyREQai0aXHgdUaOUg5fVT9MNIRCrSUWERkcLT6NLjgAqtnOiHkYiI5KjWLkka3U1kDOj3YXNToSUiItJc0nZJyis/EZFxQYWWiEh9ah3spmKMSIbSdkmSwSq+V9U6KCLVUKElIlKHWge7iWLUeiCjTl2S6lfp/Q1qHRSR6qjQEhGpQy2D3YBaD0SKRO9vEamHCi0RqYW6yVWglgOR5qX39yDqVij+zwAAIABJREFUTilSJRVaIlI1dZMTyZ0uaCoNRd0pRaqnQktEqqZuNCK5mu7uD+iCptJI9D0gUj0VWiJDUze5CtSNRiQ3k3RBU2lETfw9kKZb5Hpg9ahnJoWkQktkCOomJyKNoAA/atMclNKPU2k4dXSLnIVez1KBCi2RIRSoe0St53A0faubiOSnjoNS+nEqDaWO7/3Z1PY9DPouHhdUaIkMowhHklOew5FXfiLS5Or4cbr1iAuL5CzN9756wMhQVGiJFFvaczhERDJTgINSIqOmQD1gJGcqtESagH7kiIiIjB19D0slqqZFREREREQyVugWrVKpNA34f8CbgbXAV4Ig+N+xzUpERERERMa7QhdawPcJH0MnsBNwRalUuisIgqvGNi0REREZQa3Dwq/PJy0RkWwUttAqlUqTgSXAS4Mg6AX+ViqVlgIfAFRoiYiINLBaR2qLRml7WV75iYjUq7CFFrAzYEEQ3BmbdivwX1lvqLu7e+KMGTOmtLe3M3v2lKrj4stWG1derq+vb3JbW1tucbXkmHecHlv2cbXkmHec9snQMdong2P02LKLqyXHLOLMzA4++CJWrRq5oWrWrK359a/f3tLX1zezmfdJLXHj5XVSS5weW/ZxteSYd9xY7ZMNGzZMiQ4IjcjcvaoFG02pVNoXuDAIgpmxaW8DvhMEwU5Zbqu7u/vklpaWk2bNmkWt1zwo799a4tydxx57jOc///m5xdWaY95xemzZxtWaY95x2ieVY7RPBsfosWUXV2uOecdpn1SO0T4ZHKPHll1crTnmHTcW+2TVqlVst912VQUVuUVrHbBNYloHMBpja57a39//7fXr189obW0dcIHFjRs3Tn766advmDx58qsmTJjwdDJw06ZN6wFqidu0adP6TZs2ral1e/XEVcoxbVyj7ZNmfmzaJ9on2id6bNon2ifaJ3ps2if57ZP+/v41ydyHUuQWrcnAGmBhEAR3RdO+CcwKguCIvPLo7u7eBugBOjo7O9cqrhg5po0rQo55xxUhx7zjipBj3nFFyDFtXBFyzDuuCDnmHVeEHPOOK0KOaeOKkGPecUXIsZ64Sgp7Ha0gCJ4GLgC+VCqVppZKpT2BI4GfjmliIiIiIiIy7hW20Ir8B+DAI8ClwBeCILhybFMSEREREZHxrsjnaBEEwVOEQ7yLiIiIiIg0jKK3aImIiIiIiDQcFVoiIiIiIiIZU6ElIiIiIiKSMRVaIiIiIiIiGVOhJSIiIiIikrHCXrBYRERERESkUalFS0REREREJGMqtERERERERDKmQktERERERCRjKrREREREREQypkJLREREREQkYyq0REREREREMqZCS0REREREJGMqtERERERERDKmQktERERERCRjKrREREREREQypkJLREREREQkYyq0REREREREMqZCS0REREREJGMqtERERERERDKmQktERERERCRjKrREREREREQypkJLREREREQkYyq0REREREREMqZCS0REREREJGMqtEREpGalUmmfsc5B0imVStPGOgcRkfFgwlgn0CxKpdIOQRDcX+c6JgIzgCeDINiQIn4asD3wzyAInqknl/GiVCpNBgiC4OnovgEfBPYArguC4JdjmF6hlEqlvYMguC5F3DuCILgwdn/HIAjui93/cBAEP6oQl9tzl3ZbpVLp5UEQ3By73xYEQV/s/luCIPh9Vnnm7FJgm7FOYiSlUumSIAgOHOs8RkMd74E7S6XSh4IguDifTAcqlUpvIMzx+iAIlg+xTGbvb303FkPsdfG3IAiWVRlzaBAE541qYgVTKpW2GmmZIAieyyOXrKV9vsfyt54KrYRSqXRjEPx/9t48brex7P9/b9mJrXpIkupJlEcSqYQGVIrCR0gUMqRvlCIiKWXKbCtjNCBDZOxoLiElidRTKs0oDYqeiAjt3x/Hufa9rnWd5xqO697b8/xe9+f1ul97r2tda7jWcJ7H8Dk+h61VW/64mb21tnybmf1nZtMbJb3fzE4OHHMt4GjgpXiW8d+SvgPsa2bfK2yzL/AbM7s4Lb8KuASYA9yVDLixbSW9uet8zOzTA8//0fgEtuKQ7aYbkj7V8ZV5ZvaWxmfnAxcDZ6blI4G3At8ATpK0nJl9tHC8//VGtKTcszoCM7ttmg73RUmfAD5gZg8M2O4s4NLa8vV4wKHC0cCYo8UE9y6A6LGuZNQZ+TOjv+18ptFZSfd7npn9Li0vDryfNJkAx5jZvzPbRcaFWROe60bAr8zs15KeChwHPIyPe7dnvr8W8FozOzgt/wxYrPaVrevvYw0vC5zbxWa2VW35QDM7tLb8bTN7aWa7jYF7zOyatLwi8Gmmrv9OZvbnoefTguhzuTtwmqSvA3ua2d+7DiTp1R1fmQfcCfy8MmbSdp8BrjSz09Py3sARwI+AwyS91czOm67fFp0bZ/DIQtK7gcOAn+DPxT65AFsGpwH/v3S0JhhP2oIJs/B39VHTfLqDIGm9ru+Y2dWZj6P3e2HaCyOYcbTGsUpjeSv8ZlRYqrDdK4FPStoaf/hv6XOwZDxcgT84hwB/BJ4MbAt8Q9IrzOz6zKa7AK+vLZ8AnAwcCuwNfBh4VWa73QunMg94Fm4EDnK08Bd3hYHbtCJoMJYMmMcA2+O/relovQDYOR1jNvA23GD7uqR1gU8CpZdvoRrRQdyC39umUTyv9u90jQOr4w7Rf0vaycy+23O75rl1LVeY5N4NRfRY0d8WxSeB04HfpeUT8bHpIvzZXxI4MLNdaFyQ9DRafkOHE38CsGH6/3HAv3AD4TRg08z398F/R4XlgTek/68F7IuPm9OB5tj5bnxsrbB6YbtDgPfUlk8H/gG8CdgVOAbodGoHIPRcmtnnJH0TOB74iaR34gZu/Tu/aGx2Wo/zeRywaHKePps+ewnpmqQo8v7Am83sAkmb4Nc152hF37no3LhAIGkN4ENmtuXCOuYjDUmPB9aoDGVJBzA6z5xoZn9rbPZ24NVmdk0ywk8mH2BrIjSGRoMpCxnR8eTO9HcmHsQcEvhcWChlkOYBjwWWIO8MRufMhWkvjGDG0RrHvMZyyUAdgZl9X9ILcWfg+5KqyEz9O1/LbHoYcLCZHVv77GbgSkk/xyeFXCRxeeCnAJKeAawEHG5m90o6Bnhn4TzXbX4m6b/SeayKv9gRZK/LBBhsMJrZ++rLaVLfBfgQ8EtgZH3Ckmb21/T/56d/L0/7u1bS8i3nGDKiJc01s71ryzua2Vm15XPNbLuW4w7B4pnPqkFmf+DH03QcklO8iaQdgc9JOp/xd+D0zKbNZ6druUL43gUy19FjRX9bFGsAX4P5wYk3Aq8ws+sknQEYGUcrOC7MwR350sTXFTVdzsx+J2lR3PD9T9zZ+mPh+2vj73OFf5vZV9O5Xk3jWavhMV3ZbjPbpW09/Sf3ZwLXpXNaBlgfWMnMbpN0PXBjz/30RfgdMLP/kfQW3OC5uLF67N6Z2TP6nFDKIJ0GVI7Wf9QylGvgBlR1vC8BZxd2Ff1toblxEqR37X3AmvhccxAenJgLbIRn7ZvbvAB4wMxuSstPwA29Kpj4nnpm8P8YqvmtykgcAHw+/X8FnLlzUGObZavMjZldLenJPY/1KEkvoz3gk8uMRIMpCxPR8WR5PFi1E34vLgHONLPrFvgZ94SZjd1fSUvj79FulAP+0fs9ia03EWbEMLrR2xAys4eAY4Fv44bJabW/UmRmHcqRwk/gxkUO9zNlRK8L/NTM7k7LDzFKqclC0lMkfRynat2Gv8AHd223kJAzGLc1s32B16XlIiRthRte7wL2MLMXm9k3M1+9Q9Iz0//Xx2sG5qV9PA6/liVEjehdG8vHN5Y3aznmIJjZA/U/PNL7Qzzyv52ZvaLPfiT9l6QtJD29x9e/BtzE1H2q/qYr21BhknuXy1zX0cxcT3KshYnFajSwFwL3VZOrmf0UWLZrBwPGhXtxo3nxwt8SHYe6W9KTgA2Am8zsH+nzUgBwGTO7r7a8ee3/9wNPLGw3D882t/11oe88MAuo6MNrA7+vsnpmdgcDMtyS/kPSmmn8KyH8XCZH+tvA04Hnmtkitb9BtCJJz5P0UQAz+zrJOEz4W82IeSlwQ5orwZ+TksEU/W0Tz40BnIA/jz/DA6OfBb4F/AJ4hpm9PbPN8cDTasunA89O/66BU5t6QVKJaTMRJG0T3HRLPFha4UEze6OZvRHYBtgis03zOehrny6GBwtKf+f33M90swumA6HxxMweMrPLzOx1wGrAz4FTJf0kvff/qyBpCUnvB34FrAi8yMx2Lnw9er8fsTl8JqM1jtmS3srUS7eYpP9XW1+8ZpJejjtHPwKeOQ1c/Bzlq8KVwFGSzsQjdF+srXs28KeW81waz7z9P3xCWNXMft92IpIOb1m9IJ6jVoNRUtZglBfTHgE8Ac9knVO9TAWcjtcWfQ2nF9YpVOvhE+d0Y2HTyZC0KXA48GjgfWZ2Yct3jwN+aGZnp+U34RHZ/wHmSHpdITuLpJ1xOsPZwKbWr/B8jqQ/1JYfX1ueRdlgn+TeDc1cR4+1hLzessJja8uzyGccJ8HvNFU7+GrckAbmv/dFkZ3AuDDPhtXiNXES7sw9GtgrffYSytfyb6qJDjWilisBpTqjB5rZ7h5YNGVmZhWWS47Ij3Fazzl4cOEb1YpEs/yf3EaK1xWFnktJ78WzDEcBR5nZw4XfU0TKwGyH03FWB75SrUsGdYULgAskXYbf5w/X1q2NG1c5RN+58Nw4AV4LvNDM/ijpJDzT+yoz+0bLNqsC34T5ht6mwJppfvsCng0ayb7JaynvMLOvpOXnA5cBT5H0S2AzM/vlNP6uo+XlELvVsgF9sHyDNjyfJpayMU/NbNM2D1Tb5rIO9xY+H4rpZhdMB0LjSQN/x1kCf8YzOQsi0BBCYjPsjs87NwEbF8a5OqL3+5Gw9YAZRyuH6/AHu8L3GM2eZFOvkk7FswV7mdm5A4/3Vpxi0MSupePh6eCzgauAa3HBgArbA18tnOeBadvLgRfYOA+/hK40/tC6ri4MNhglfQOf8I/Es4T/wh3n+d+xhtKOmR0t6U94jcceZlaPhiyD146UEDWiFxqdLKXYj8Ajp4fg9IEuo+p1wCm15SOBfczsBEkVFXPM0ZL0JZzqsIWZfWvAafbKqjUx4b1rovWaT3CsZk1gnzqXSXAs8GVJ1+H1T6+rrdsI+O/cRsFxYaKAgJkdJelS4GGbUpm8ndGa2Dq+iBvpuWz2oUBJQS9ynnfgE3OFOxvLdxS22x/4gqRT8LqIeo3HNjglLIdQXdEEz+XWwEsq2lpfSFoEeA3uXG0K/AUPaq1tZjcUNjsg/W2AMzvq78DqjF7X+Zjgt4XmxgnxWDP7I8x3JO7tcLIAFq1laF8E/DVlnTGzW9M818Q+jI4pH8N/51G4U3Y0+WxRFM/BA2c/kbRHW4CugUdJWspSHZaZ7VatSL8rF6gIzQMTIBpMWZiIjidIWgd/T7dI3zsd+Hwtm/yIQtIOwMH4GLJdj/dlIkyzvTAIM45WA2a2QXDT5YHVAlmsD+CiF8/Coz5/YKrIewe8Lil3nn9oWffeluMdDNwFPBU4s+6E1LZ/ceazUhq3CAXlvhMiBuPL07/HMDq5QovSjrma2pijaGZnJmpTCVEjela639WAvkhjeTozWt/EjcSz8aj/oc17bmYHNLZ5YmX4Sno2Tjf7eFr3afz65vArYEsb2JqgQOnsu2303g3OXLcdq+X8xmozFiTS7/4NPpkcaanmIeFeyjWYkXHhNdHzTJHMvwJPqmfFOhy8DwLXSvpvPNvzR3ysfB1OoVmnsN0Qp786jxWGbpO2+06i164M/MLM7qmt/jx50QeYoK4o+A5sjGdUSMfsFCuQdBQ+J83BC+w3wbNHt+MU09L5PUThuTOzj8rbmpS2jbxzrXNjevamG80x/eHGcu7Z/pWkVyWq5Wak7BaApOWAexjHf+LUbxId8/nAJmb2l5Sl/Hnu5CQ9tYu1koM5nXd3SRcApyfj+KbGd5rzB3imemvyTvTWwJg66ATzQHS+jAZTphUpeLG9ZdSeo+OJpJtxe+cs/BmpfssiStLvzaDzI4Cz8Gv+PeAt8nrREZjZm8a2ioufnJIovIPGk+nAjKM1AGlCeJuZnZBZ/Xac9/nZ9N3TcNGBCh9IE8AIzOx7ie52NB5NXgT4Nx6BeHWPNGruPNtUjgY7TB3HWgaPEu5sZms0VkflvqMGY69i7RwSDeZvlpQMk4GyP06jmlM4x6gRPQcXPKkPGPUJcjopDJ9O+xvC4b+nFo1cF6cRVhTARShH+k7Gs7AnAUj6Ck4Lq7BbzpBWTKGqvv3ge8fAzLUWgEKV3Jv5kJm9YMh2XUjXcawY2MwsjQ05RMaFV0hqi0LPq1+jxrk8lKKLS9JTESsZk2vhGYuN8SjkX/FM1lwzu6uw3Wv77L8OeV3USrmMj6TVcFn6XFZ9mUSxyhmSP5f0HDyY1kRVV3QfgbqiwDtQOW5DxAr2xZ3xPYALKkMt55T3QZpPdwP2A56SWT+trUjSs78z/t6PUc8nHIf6jOnNcfMg4CJJt+GO9Qa1dZvjzkoTDzNVu7QO/hz+JS3fQ5lm/VMmU8C9C593n4Qbx104ArhU0pK48EkVFNkKD5iM2Sbqbh9QEhRbNfNZJ6LBlAWA2cAZ5FVdo+PJyunfwxml6kJL0FkBtWdJd5tZ5Nk6hJitE7rfuJ2aq5Vc4JhxtDJIxsOa+CD2uRqP9H34gJNztPZhSiEPfDA/Kf1/FVyic+/mRgDmtUfrp4d6KXzCbK1tUUDlKB1r4gi7pEfhnPSd8ajmH4BTM1+Nyn1X51o0GAvfv3XI/gGS4XYxPtHfKRfReB4+QH2NIJ2hzYg2s8EiNFGjw8x2GnosnKL1cXn/m32Z6jsBfm1K0et9GaUyvDh9Bl7cvR/jQiAQU6ia6N4FMtchhSp5LeGxTL2n70zneypOxy31aAv3Y5L0WHyivTVN0pWReRDuoIxRWoPjwrMLn8/Dr9fSjF6jJk4ELpR0BD52zp/AS5mtZOgeSF6iPgtJ/6Z9Qp9nZs25cD+cEveuzPd3xR28wzLrfkPNoJX0MzOrX6dryRu8obqiCd6BLfGxu8KDluqqkrH1ecbfuTXxMX8ucIKki4BOmnzK7Hwqndcv8azYs/DAzB8pzItMQyuSRFOr6sjWwK//HoWvh8YhiI3pZvYFeY3V84Abzey3tdU3UaPL1/A9YG+5WM2ujNIgV8JpWDlEswCPwh2jd+G//QRrr3kGwMyukNfrHs8oA+L3wFssTxPrYoTMw4USmji4w9nP9c8MBVMU69XZB6X7Ex1PokHnSHuQ0LNlZgfFTjF2vx9JzDhaDUjaB6fR/BRYVdKJTEWadisZ+bjjsX5t+SFLBdjJ2LqK8oQCQHKu+natPwHndX81HXtVnE99bjrPYto7DZ7b47VPVVT467hwRJtK1ar4hLUD/sJZOt91ckafxeW+kfTB0nkkZKPlKc1+UOO3XQ4c1JjIKhyHX7Oz02+7CDcG1rbEly9hAiN6Fs7pvzuz7nG4gd2czCYyOhIdaUNq16RwPcCDAh/Br+O30/8rbES5/8X6jPb8eNjMTkvHfzxuJOQQMfpgsnu3dPrelzPrNgaua8ui0X9yOQkPnpyKR3Mvw6PpRwCfaqFvhPqnpHO/EI+w/0suZPJSfJL8JP6sZDF0XLBRwYNqH69Mv+0fjfPP4cT07waNz0vR1miDy5JDuCme9cnd5zdQpkbOBb5M3tFqPhfLdayvEK0rir4Dg8UKzOy/gb3kwh3CpaO/jjsg75J0kpnlHMKPAr/Fn4vt8HfgIdzg/krm+9XxQq1IEhVrY7zubTPcmTsPV1fcsmVujI5DIcjruvexqfrE+WgwOOrYJ53H4biiYT1wtQOZwGRClCVxI3A3sJaZlURLsjCzS4BLJK2CBy3+amZZamP6ftQ5iPTPhFgwJXqsLpTuT2g8iQSdEyLtQSZi4Ehan8acY2ZXtWwSvQeLddmVZhZtb9SKGUdrHLvjD9b3JL0EH7gOMLOjOrZbruFszI/imNkdKmj0d0RbZ+EORY6qFVE5qqLdX8ejX18CfoA7BUfjXOwNbZQHXG13Hc71vQo3Ti4ys39I+mPL+Vf4Gm4Uvo7RyP88CoXQBKLlklbGo/034rVvVfPnbYAbJK2biZSvBrzczB7WlCDAajU6RhuiRvSeuJOc4x+fCnyXKSMUmKz/maS5+GRyO1PX5JRkGL27+f3kAGb7CpWoYAnLmlldBWl+XYmZ/V3lvigRhSqY7N69H1djGnO0cJXLVzKVjcuh7+SyHrCKec+iC/FJYs1ktLYh2j/lMOC9eBbyrXhm+6tp26JqWHRcqG3/Qvy5fy5OVfmYmT1Y+j6EsgChBpdN4y6N60fiWYoD8GxLE0+xQrPldA/GqG61cxmyXO0zWnMbfQciYgXV+TyIZ9EultcSvRnYEX9fchTHtYCnmdn9kr6Fv3crmtktHec4H+l6H4SP5R/Hn+cShe33+LNwIbBRZbSlDEsbouMQGt6XD/z9+omkXczsyo5zq/BLM3umpCdkfv9cXAAqhyUllUSQ2uyMs8wsJ9TVG2Z28yTb99h/pH8mBIIp0WMp1UUV0EYLDo0nEzAiImrPc+RlHkWY2VgmUt40+Hw8uHEdbpusAuwj6cvAG3JzyAT3exZluxIWoOrkjKM1jidaqosy71D+L8rF/3U8LOlJ1cNrZvPl0OV8+dIgV7/xs/AH7kU9jhdROQI3gu7BJ7r5hlMytC5M63MRnhXxKPXNwM021fOmFYrJfUej5YcDZ5j32qrjDHkh95GMc8MXtaTClwyBv/c01CFuRL+ZvJMF7jB9hoajVccQo0PSnriDu57VhEnkndDPlnSrmX2ksc1e9c8kvbxuCEg6ojnYJfxTtaJrMzunts1TKWdro0bfJPduM0YVnOo4DbiGUUcrqlC1ROV8mtcY3dPj+YCO/ikp85nDSmZ2CoCkk/GM685tTlJCaFxIzv7huJNwHPA6W0BNVi3e4LL67ur4ua6Dq7SdaGXhlgckLW+ZutoUMJjWQnJJW5jZpbXlleqZDkm7mVmuF2P0HRgsVpCDeQbraEkn4AGkHBarrrOZ3ZPO8ZY++1egFQle+L8qHv1/kqTFrF+NcNj5JN+Xr66eOVYja2avk/ep+oyki4H9erw735c3uP9hZn9tUt/3AZFa0G9J+pClPnqSfsaoY7C1uTLwCCT9lm667kqNbbpoeVhHY3E5dfZQfOzcw6zIQIJ4MCVyrPvpCKi3HSuAJiPi4/RgRBBrD/IAZcZNGw7EKY7/ZbUMnJyVdHFa35qBGnoPcnblwsCMozWOZir2n9Yo/ivgW3i6MtdvalfccBtDJtr6cPOz0nlquMoRuKOxXtPwShPgO/AMXs7RejJOtdkZuFrSrThl5dGUoypRue/mfvpGy19BmRp1DB7taGIxjfYIW7yxXFJVgrgRvUIpymde4LpCbl3Q6HgbsIM11B/NO6HvhA/AH2lsc0jjs4vxDGKFd5CPGl2OU1vGsmT4oF8KBESNvknu3XJWoBCle9mkaEQVqpoO2azGcqnAO9o/Zb4xaC44cU8PJwsC44KkT+L35zRgV2unWlbbXG5mG6b/X0s5wzOmfNrYzxL4c7YPXt/0IjMr9YxaEX+mN8Mp12+yDG23gctxatFemXX7pvU5PEZSXQlsTmO5FL0+C1fyq3A9o+/c0eSb3kffgcFiBdBav1xRMHPMj9kaVfRsLmcp5Aq2IjGz50l6Hj5XnYSr5V2K1ya2GbSTOJ9D+/JV53qBpMvx9/wXaV6tr2++B2cAV8qbQx9m/aW6/93TrmhiH5yOWqFSRAbPVO5LvhH9bpnPwBkt+5FvRtzMsOxJgX7fhIb3z4RgMCV4rCgtMjqeNBkR69GPERFRe37IzCItE7bDbcPmM3+rvI3M5yg4WsF78IhhxtEaR7Rp3sHAN5OB1py4dmK0fmtazpPhKkfgkbVbCvu8BfiP3Io0oF8GXJYydDvgdJGlgM9KOtGck13Hr4CtSlksSWqLQASi5bNxZaQc7mVUBbLCeYz2CDuf7p5hFaJG9DxJT8xFntOgOMa7jhoduBzwtYV116b1TUQbKn8Q+J68+/pFjL4DL6acqQ0ZfUx27+6WtEIusi6vZ2s6HCv03G8TXQ5aqcA72j+la2IuSeZGxoWd8Uj59sB2ykvCN8fKetQ65zi0QrEGlzfjdLXjcFGJbZvnmjH0D8Sf5WfhGb2q7cbr8Qzj2oVjNRW+moG3UuP36DsXegcsIFag9vrl3VvG8usYVfS8vrFcopCHWpGkz38I7CnpPbiC3074nHmtXOY5R4eLjkM5DDH63oBnWT9BQzq9CTP7iCTDr9cN6R42655zDkJUAn1tRmnk/66MaklXN49dO4cRwzsFDw/F78WJZFhCTZZEyuJ2NhpXoH9mwuBgSvRYTWdiACYZTwYzIiym9hx9tpaj/LzfhCtcjmGC+11sQaF2RfGJMeNojSPaPPUHcnnSo3EJyUqm/bu4THspghCCBVSOEm7D5YNzGbZ1GFVOLB37z3jk41hJL8INro/jvW3q33uXpGclys6vqmyPpM3wSfSpZGR203cGR8uBH6Vtzs6s2wrPEjR/yyRy91Ej+tt4lvOIzLq3kO/7EzU6/o7XouQ41E9P65uI1pn8Rq6Cdgg+CFbFrd/AMw5jRd9pu4hC1aT37gvAkZLemImEHcaU2thEiDpoFu/H1DUxlxAZF16e+awVZnZe7f+DlA4Vb3D5HfyZLZ3vmKFfe5YPZipyeideb/oiKwjJVBSrAKLvXPgdsCmxgv8ivasdWY9Q/bLFe1NO3IokMR8uwmXUl8ODg7viNTjN74bGoYTBfflSQOdT+LXf0DIUvMJv+g2woZyu/n2mno2ibDfObIhgGZtqqgzuKFW4H3hi28Z54D5rAAAgAElEQVTymp4P4tf9LGBlywum5NDXWQ31zyQWTAkdS8H2IG3jibw26WWF1VFGRCUoNKQ9SN85pok/4wJuY3YZXntaYomE7oGZrdaSkW9TFJ8YM45WA9bRNE/Smi3bfhdYL9FaKpn2+0rfT/v7f42PmgN0UZkviNOAT0t6k6Uix3QeL8IHwpPbNk7RvnXxyeEvwHfNbHdJY1GhZBh9En+pl07feTnOFZ+LR/BKiETLD8XlopdnvPnz+8lTHMKYIMtRZT+fghsB9QF+B8ZV2CBudFwCfExeAzI/2ydpDnAKHrkdg7xQdVb6G1suIRmgOww9SRuoUDUN+CBugP+3pEuYugdb4JHvEfERudR968RfyBQVUdGbzGystkXB/ikTGPqDx4WusbIP0ru6Nj6e1DPBuTHvLAINLqOGfnqWO9sq1JHeK6p3LRlDbyEVoZtZSdDjEUN6z+a/a4mx8F4za6rkRuuXo+c1cSuSxv7+hFMbi47hBOPQoL58CT/A36uDcxF4SWua2Q8yny+TtnsRTodtzYIlLKqOFiGW70n2N0krJueuMsIrrEQ+UEfKmLwXl9I3vD9Zq2DCBAjR8lIw5YV4YLBvMCVKAQy1B+nAo3HadM6JjDIiqrrcIe1BrlGHIqzl1WDPBc6StLmlvl3pWE/D7cZcwByC90DS3vi9HqooPjFmHK0ekDeD3B5Poa9CR/PI5FzNd7DU3kC4WZzXHKCztArFVI4q6sHT8Zfj90wZmE8FTjaz40u/S9I78Yj54viA9ARcAOFAM8txqffH5XS/IGlLnNbyMeCNhdRuHZFo+VfSZDKX0SjLbcAulpfy7lRNzDh0E8HMbtRUk+rdmMp+XoNnP8cm1wmMjgNwCsRv5TVz1f1+DXAr7oA2sSSjRa+zasuthbsKtg6oYAMUqia5d4k+8UKcjrkRU5PsZcDxNl5YPi3KWWksqfr6rA6U5K1D/VPUrm4FFKktg8cFSXPrxri8SP+s2vK5ZrZd6TzSmHAObuQ/BzcYV8Pfg5yjFWpwWXNaS+ufY2Y/aXy2cun7FSxP3z0fD16cmZaPxEURvgGcJGm5wljZRlmfRaER7STvgLwOompNcQoeYPgQnv3IOdGh+mW11OLVzjFLAYyMJykT+VorCzi83joYJkPGofT9DYZ8P+FVZjbSlLjL1pD0Brzu7DJcXbJP/SXE24N8EZ/zcyICh+LMgBx+i89ph+JMk2fKKeXzYQ1afWPsagb4qm2mk5aHOXU81yZjUTUEaiY9VgNRul2v/UQZEYq1BwmpweLj+XPw2sTrmJpz1saVcrNKyhPcg7cTUxSfGDOOVgHyXhxVU95N8Wt1HAU5UMUbCA92KBIGqxzVjvluuSLZK5iavK6wlj4ZkrbDswC7AZ81L7RfFKfqfVTSnVZTmUt4iplVA/Gl+Iv3nh5OVjhangbGSxMdpopIttUzhbNcBQPiQdyBOddaCkQt1qR6sNFhLmbwUjzLtCEu2fpX/Fk92/KiItGIUbR1wGCFqoSJMpTJmfogHcpG6bvRTFE1lryGqbHkL/izubaZ3VDYrDmB9u3H1EfdKqueFhgXdmW0N+DxjI5zmxW2q3AosKOZXSjpb2b2/BQoyUZ3Ld7gMuK03oxfq9J1Ll3HF5Cyz8lIfBuuzPZ1udLnJ8kX+Ico6wTfAbkK6/a4U/t6fFxYF8+grmtmP8psFq1fHlyLl84x2nKgS8BhPzLXbYJxqApCXmVmORpUFpWTNdDWOBYXNxqbWyQ9Edg+FxSxeHuQD+J1bf+NsyOqurXX4e/MOoXt7sGv5TsK63O0+ubYlQvwTRstLwd5mcPO+LsBowI11XeG9urMYbqEG4r7Se9GlhHRsr/B7UEsqAabbI8t5XS+qsfnd4BDzOyK0glOcL8Xaka+jhlHqwF5U96d8CjHHDxyJPyBO9bKzQ7DDYSDCKkcVUjG05AGhPvgNKf5Eaxk3H9G0t34C9p0tBapfXeeXIK+tyxyoh+8m3HH4iOZjMMIetI9JqKKkjcgZuMTyFmS9ivQMerH79WkegKjoxrQPkW+X1Du+5MU7kZaB0QUqia6d11Uh7T/UvPP5r6KGetk0O6AjyWX4j1DrsR7mhWLcwnW7BCntgCDx4WogEOFp5nZhY3PzsEFK8baN0SzdZnz6HRaLV4Du2TNIHl++vfytM9rVeinOEFgKfoObAtsYGa/THPeTbiRXqr9g3j9cms2PjkIOUTHk5CAA8FxKOHNwFxJf8drbK8CvmkZGfYKQVtjNaspZqbg2ya4c7AJnh0uslLSNr3bg5grsK6FB1Q2Zmoe/gIw18zuKmy3Qts5FPCI0PIkLcUUy2BNPBP3bvzaNL8b6dUJ8fYgIUyQRY62B6mO21sNtnYeVwBFxyqD6P0OZeSnAzOO1jhuwovi9gPOt1RjJakrAhFqIDyN6BUhUbzWZGXciczha+TTx0tIqnOBH9tYbqOMLIdHWx/AJ9VqQNsaeLOkl1jqI1bbZlpogF30jdq+igaEpMvwGrQxR0vtTarBI6fNdzPa56hZAzgGa9TDSNoR2MTM3tD8rqTzgc+b2bmZXYVaBzSjs+qhUFVC33vHQLpDNGONq1fdhdcqXFA5AsrUHE4H2pzklIHeDM+4NtdFxoWoM1jhz4lK9yfglkTn+CtloyOarRt8npK+aGabFM+8jDskPTM5rOvjNazz0j4fB5TobtPp+Pd5B5Yys1+m/f5U0j87nKxpqcmrneOj8OzNLrgBnzvHaCuSkIDDJOOQma0lr19+Wfp7A95f7F7c8brSGv0KCdgalZMlr9GsMi+Pw5/7N5jZZaVtFWsPgrkQ1YHpb4FhggBfE71oeZIqloFw2tq5eJ3dVfhYnesZFenVCcH2IB02TdvvDGWRCbYHUUANVo02FDlYuT1LHX1pmNGM/MSYcbTGcSLORz4aWEvSOTYqcVlCtIFwFINVjhKitSb34dGsP2bWLUM+K9MsVj9twPGOxCk9b65HHSQdhDsvR+JKRnVMQgMcRBXtgjkPOCedDuXu5Jsy1Y+miajR0dWgL1cDuBvwzsL3j8ZrBHKOVqh1QAUFFaoi9y5Ad4hmrNdM5zUXOEHSReSvXRPR/ilj6EOHITYuNHv5LdJY7poAT8Nl/y/Bo/BX4M9jyaCdKFs3ECU1ry6cDnxR0tfw612vjVkPKEV3o3UOQOwd0KjAzQPqUQ8zKSStxtSzuAxu8JeudXQ8CQk41M4xNA6Z2T+AL6c/5MIou+HZ2U0Z71c42NaQtDt+/Z6Pq9e+H6dJ3ky7wEG0PUiIWRIJ3Mjrg+6proG8/92nSUIywE7misdd6EvL+yLu6G5vZvOpph1BsEivzkmEs0I2TVcWuQWD24MorgbbtxVLF/re7yg9e2LMOFoNmNme8n4hwtP6V8qLwx+Pc5NLBlW0gXAUEZUjgMt7Oo5NfAmP0L3ZxuWwj0zrm/iXmX0mcCxwA+F5zdRuoiC+F8hRMtY1syOHHGQCqmjXfp+OR63G0KQ1pkj+kbgM+wHkKX7R/meRGsBnWqF2yFzIozTRhFoHKKhQNV33rifdIZSxNm9psJekfZkaU76O05DeJemkghEX7Z9S/abedJiEyLjQp5df87zWqwxfMzu2+tzMPi3pKmBOC9XkvWb29oHnCNPotHbBzI6W9Ce8FmgPMzu/tnoZ3PnJbReqc5jgHegjeDMtdCZJ/4HPVbsw5SB8EKeb79lyjtFWJCEBh+g4VNt+Fv771k9/6+KiEOeSl8uO2Bon487BG6zWt7JHhvxgAu1BIsyShEjg5hBGKcMfB/6BPzu74o5MTjkxSsvbB3emz5O0K65yV9WSlxDp1dkJSY/NZY6mM4tcO5ZwqvsLMqsj7UFCarDAgX0yqhmE7veCuJZ9MeNoZWBee1TJvD4Jf7l3BK6X9GUzy41q0QbCwPzJaG2cjnQXcF0pWpTOcYN+v2YMXyajVtYD++MD7k3y/h2VQsxW+KSdowCeBkQdrSXxyFkOfyGvwnUA7rAMQZQqWvG1m5iNR94/QLnXUbX96vhAtg4uOXxiga4AcSfmbjMber8fLenxZjYW+ZX0eFxWNodo64DBClUJ4XuXvjeE7jBRxtq8Tu5i4OJkvFRjyr5kDH0Lim8E6TAQGBcsVsf0heo4ku4ws/l99Cw11GzB9rhy1FBEnNbHSGqtaTSzXQqff5qMY2RmZ7btr0JPx79C9B1YmNnBP+EG8/G44t8tMJ+d0IboeBIVcIiOQ0j6Ii4ocjPuVJ0MvMlqLTVyCNgaW6X150v6NV7TeB7dUf2dO9aXEGGWRMevZ5ICxHIJ+/XwmqHbJF2P10XlEKLlmYuGHC/p+fj1+ShOuXs0fi9zwePBvTrT7xkZ6yR91cw2qn3ldvIqsq/O7a/xO8aeyZSVPZYpqvs7gacAp+KOck6QJ3rfQmqwuMx6xBYN3W+YrO5/Esw4Wg1IOqbBv13GzI4BjpG0Nh55GkPQ6KiOeQDOf56N3/hlcGnNw83ssMI22wGXWEGpTtL+hexOSFbUzP4k6QU4/aD+kF6KF8XmHtJJJEx/kY7zxcy6V5Mv2I8cL0oVhbwy2UO4U3Qu4wYeMJ8ScQheL3MCPiHfnftuDVGjI3JNbsBrDHLZj63T+jFYvHVARKEKJrh3AbpDKGMt6Q913rekD5vZ+9M5H53GlNz5RfsxRegwMNm7OgR3yWXdb8JrOEeuYYVC9j86dkWMh3l4Q81BUKAmsrbt4DoHgu+ABephJJ0SzCh+Fa/D2hZ4SC7736c2KDSeWFDAgfg4BF6A/w98TvgZ8NMuJ0vSc62mUphoca22hk2p6i6Li+zsiDuG/wa2kHSG5SXQo1SyCLOkEjg5Hh+vbgT2Nq/1asMsXLUXPOj8+yr4Yt6OI2uUW5yWV21/I3CjvM/S6/Dr/jm5OmlTWCHaq7PZe2qtxnJpbOsqtyg9kyfhLJhTcQfwMmBZvF/Yp3LPCMQEhyyuBhsdz1eIbDdBdnZizDha43gbHmWu8C08y1RJcpcoeSEko2MvfMC81MwelHPlt8BrOn5mZrmmsmcD+8l7PeToDcXsjrwhXPEhz0WWJW0KbG7eq+uDjXWnSbrMxvtUPUrSyzqOVSrwPg4vMH0ncJGZPSwvoN4KNy7GVMnwFPIOHcf7dGM5ShWdxLm+Ga8TOA6P9m7bNISbxtgETkwk0vRh4LKUvWo2VP4A/mxmYS4RfhLwSmoS4cCvVaONNbZZIXCOE907htMdohnrxzaWK0O6OkZpPIn2Y4rQYYDh44Jixczvwp/7Z6Rj5dRBS9fy0ZJapfjNbEymOmI8AA+Y2fu6tssgUhMZrnOIvgOKSSSHMopmtnkyvHdI5/hhSd/GWQuP6dh2cCuStN1gAYdJDHYze5qkZ+CUwQ2BQ9OY/i08w3V15pxvlKsUXo33LbvKnGrcaWuY0y2PA45LTuXOeGb2UNygHoGkvawmxiHp5WZ2ZW35iMLzHmGWgM/Ry+KOwtZ4jWpXVu3HePb9HPw9mv8OpLFp2jMOaYx8LnBjckYvZMqR2qH5fQv06kzoGn+z680smnleD1jFzP5HzkD6M7Bm9Xy1YLDgkBr9xiStZGa/ri3vZmY5cY7pkrjvi1B2djow42iNY6hEMQAKNhDGFYDeYTWZY3Oa0Wfl6nRvww2uJu7DC1tvkDcK/XzP856D15cM7Q+zN6MSmnWchztfzUFmMTzq03asktzxOXL1rI8DZ0uqMn334/ziXOp+Nu2GwDzylJ4IVTRKywMv7J1HuSlz1hgLGh1zJLXWGZjZio3lK+TKg8fjlMYKt+FSr1fSgjTI/rr+maTFKHexL0LS89Ix9ywcK3TvGEh3mMCp7mrDUEKoH5PF6DAQGxf2x53P7/b9XWZmeP0LckWrpiPahlmUhWSqc8yhnvWf1fheqR4pGm2N1ERCvM4h+g5MJIk9FGb2F9w4nStvFL4zbuD+SNKnzSwn5FNtO7QVySMC8x5KvyUFR+Qy6tvigatlGX/GlsaFQNbD5cGPlvQP3Dn7Jq5UOJIxUqZfl3k/rutTRqausFjHIYyKcVycjl/hHXg9YBMRZgnABriq4V1yAaDrC9+rY3/gC5JOwbMOdUd/GwpiH2qINeSQe3ckHYE/h1cDR0l6ehWoMbM/MDr31fc1tFdnGJJ+bGbPDWy6hCWWUcrw3tPDyYIYpfgsRgWWrmf02TqavArikpIeLuxzFq6+nOubFrrfBLOz04EZR2scQyWKK0QbCD+Pcnf1L1KmhP3bzPaRd9Q+R9IJZlaP3pXO+17cSB+KVfE6jxy+hSuwjR3LJpDLNLOPyuskXkwa0IBrrSw3ep9lGjO2QUGqaELUGNsgsl3adqjR8QCjymd9j1NNJqvgg+ad1rM3WQv6Bi2ewJSQw+rAVwrfC9+7LrqDnMZVX47KfTfV3eapn7pbqB9TbZ9D6DAQGxf2wY36dXHj8tPWQb3QaLPgzgmzgfvNrCtjlMOd6e9M3CB4oMc23wocpxUp2PA2MzshszpU5zDh+FVHn3dzsWBGcQ8zO6n20c/N7B2S3o1nx7PnqHgrkhCm43jJuVq/9rcyHnQam+PTPPal9Ie8hcSL8QDcfniNTdPQLPbrMq+/LFGKuwLIpfsfYZaAG/p3pd95h1z6vhVm9p3E2lgZ+EVjnv885fFiWzzgcxn93u0KbwJebmY/k6thXky5cXPufIfMh4s1GACLN5ZLWfcVBhyjjqZAxKzGcra2y2LtQaLP1n14QHEoovc7mp2dGDOO1jgGy1smRBsIP8YKdVZm9s80ORdhZp+VdBNeZL8W8EZr50LPM7MhD2eFOfiDmOOdL57WTzvSYFvq3zUdmIQqGkp9S1rGCl3W0/rnmNlPGp9FjYCHrNEfZgjMLNoOIIfi+cvlqSshh03xge8JwNpWUEBkAdB85c2Hd8Yn4ToFJyr3HVV3i/ZjusDMtql9tGHKlhfpMAmDx4Va9qy6ZjdK+iFwBokGndlsWUlLmIs2vBG/hwsay+PP1E54Zv4S4Ewr0zYB3iBpjg2vkUPSK/Ai9F+Z2ec0VXv1Prx+bszR6nL8WzBd70CfsSyaUTwcrxmpcCuwdAowXEDZOZjOsacPwsdLAcH18IzAr3AH6BA8+/SHlk2rZ2tN/D3fAHe2biUjJGWxfl0Q7HkXZJbAuKE/u6ehfw/w/cz+bsGf9VyQohII2ZVEt+54tyssZUlkxsxukgtwtELxXp2fYVTO/PzGckk0LEqv6xKMaKs3HIG624NE+yn+Oxi8jd7vaHZ2Ysw4WuOIyFvm0PcFWaQZ4a5hFu3d6AEwbzi5Fp7CvVHSVi1fL0Yu5Tz67S1f73MTXlicozFunNb3PlYXFOtsHjleiCqaMJiWl/Abamo7jSg/OI+4SUmMGgGtv0fSmmb2g8Znv6W7ofJKwfPJncNRuAMwBx/EN8FphrfjdMUSJrl39eMvzVQGbQ38+u8R2VcGUY59tB9Ts2/SuUwZ3kU6DPFxAZuSsH8PLtl9Hj5xXZH5+iXAryTdgkd1s3SgzLsN7c9CEYladxled/hE/F6fmoJYWxYm+88QqJGT10sdjCtqrSrpRNx4Bu+3ZrlzVKyODOLvQEQiOZpRDJ2jBZU325Cc3tMtoxg54fH+hdevXmU9em6lc9kXd67Wwo3Ab+HiBdu3sDaw4f26quPVM+tjyy3Hq5gl6zJFWb8WuFfe7iXXeiBk6AeDFBX7Yhl8rPxYerfPBD5mLUJTjWswqwfbINqr8wMWkzJfQlJuHJ0PMxvrD2UTCoRoeHuQCNrmnEWBzaxW+1Vhgvsdzc5OjBlHq4G2wTZFnkqR7WgD4WbEu45mPUFz3XykwXcrSfvjnONSJmzEEEsP2ib4C/VafMDPGVRzceNkFh6trh7SLfBoZc4wXbX5gVzG/hnAzaVMXsLgzuY2rN6jQpQqCkFaHuMDzHId6ycxAsYCBSlCuT3e12YVxp+V3Qr7Wh2ntGSd/45oX9tkvi8+ie6BS5D/K+2vZRPIHKv3vUsZtI3xa7AZrkB0HvB03PhuigiE5L57UDFOT+fQ3C7Uj4m44R0dF6hlynbEDc59KdRkmNlb5X3jVsQn8N5NzM1stcyxN8SzTN81s2t77Obv+L3+M07JLI2TVR3RoBo5fDx4hXnD8pfgY/EBZlZycCsMLkJPiL4DYYnkAELnqLjyZhsehT+nWWn+KMxs/pgpV9JcBq/dyTawTTgKd8j3B77alfmq7X8WA/p1JfTJrBeRHL+RDFQybs8gX/e8QsfPGEM0SFE75l+Bj0g6FTgIn/tuIB/wgRjbYHCvzoSolPmDlDO+0w7F2oPMkVR/dh9fW55FmZI3xmbokT2bj6H3e4Ls7MSYcbSG4dGUC/qjDYSjEe9zch+a2ZGSvkdN2ayx/tvg9DSmHujH4b9pGzO7rLBdRT06A1c0qz+kH7CalHQN20r6jSXVxBQxvQTPXNwl6bVWli8e3Oy4YOg/iNMwzjWznFEXpYpCBy1PUk78hMw59jJEJM2u6FiSXsqow3ONmY0VlprZEen7i+AGc0XNWxQ31psZEJq/SdIKeKH85njk55jC74pG+9ZM5zUXV9q8CB/guzDJvfs9PgFcCGxkZlcBSCopY4XkvjvQavRZrB9TyKiNjAuS3oif/xq4MbBdMztaONY1uHrmY6whO62pOoAxyOmzV1pS5JTXnx2B97Y5TNJbzSxbxyFpnfS7tsCL6k8HPp+yXTnMsViN3BOrMc3MrpH0L8rvSx3ReSD0DgQj3tmMYi2yvLOZrZH5Sld9Sk6dEuLKmyHIxae6MvlFm0nS5ng26em1z27Fpc1zBuOz8Oz0+sCBcmGAukrhGFtCwX5dLLi+aW1ZiSoIcqP1axYbDVJUx1sHpwdvgWfctqbseELsmkR6dUKc3fNgwW5pRYERVLeFSnZLpD3IWEatD6qxOpo9C9zvYna2LYM8HZhxtIYj+8JYUOCgLeLdsV0xk2JmV1Dw6iXtjj/Qzwe+jTtkF+EDd5bGU9tvU5ziTjyyWHpId8ElwSucgE8Mh+K1Eh9mXP2qQqTZcc7Qnw2sBLxb3n392Mb6SaiibZPMYniUcZDKXsv+3oZPytulj76GR+fBa+T2YsogqW+3Kj4YvRl3cC/Do1VnAcdmMjf1bZfF1SR3TN9fuY0W03MyzW1XUc/2ZUqm+uu4I/kuSScVjjvJvbsDz7guBzxJ0mLWXqMUlfsOQfF+TEs06HiPbdLzcrS84LhwLi7Pfi6eydpa0taNY+UM6Grd/Im0FsncDn+vcobpS0n0jhTV3x+X6r1A0ib4uDLmaEmq+t2dlX5f9cwvokTZy1CEQjVyjI8J/7SGylUO0XmA6aO6d6KeUUwZzypwswlO8y2xELrqU0oIKW9OgFL92ab4s1asfZa0ER78PAL/vVWPnm2BMyXd1zRubUqd9Yy0j6cylaU6XNLDZva0xqEG9+tKx4o+X10oBQXfjdOIf4IHQfaxvMR3HaEghaT34XPUP/F3fDVzlcsuPBu4JwV+kMvzn03KmJIXaYk6TNFaq+jxctd6Ns4kOEvSfpanfA5uDxKd94PZs0nuNxqX818omHG0hmNatf/lPRlakXshmhHBwnY5I+dkPGLxBjO7pLa/rt1V+xwiTrE8njKvBrGVgMPN7F5Jx+DdyksYPMC0vPCXS7oaHzBGHK0JKHnQbdSUfkNXFDpHZ3ozo9frATN7MoBcyvtEMo4WXjt3F075O99chABJbcIUjwPei1P5DFgjF11t2TbUeT1l6y7GhV2ezBQdbV8y12SSe2dmz1OSjsepr6dLuhR3WnPXJlxvGESoHxNOraqjbzQ0Mi58Op3HE3oeYwSBSObjzez29P818IxkVTP6JdwoyGHl9O/hjDsmJYpQtEaujUYD5Ivlo4519B1QBw0Wz96MScynwM3O+Lu5JD4+/BOnVGUzvma2U+QcCSpvSnp1yz5nl1ZYo1YvZVWOxJXfDgDartmBOMW3brz9FjhC3tfsQ7TMm+m3VAIX6+EBoF83v2exfl3h50vttYNtQl1vB16dHKb18PGly9EKBSnwd/rnuPP5IuBFzXGrwGw4hNG6nE/gTuybcKGFY/B5t47BvToTQlLmFJhLFRIrYMwhaXMiJF2G/9YcW2Jwe5CO963a75jwCbHsGQTvt1rk/Bc0ZhytaYKkK+mmHbwy83kpMzUPpxYsTeaFoF9EMIdKseV8Sb/GX+TzWDDN4+7HDdf78FTtT22qUPEh2gfqSZod5777s2S8j0ANeWRlFP9a9nlEx1dK17QrCp1z4FYyl+yucHvt/z+grCB0Im60Hw2sJemcKoLXgt/iRu+hOC3rmZKeWf9CbuDUNHZeT987GlfUWjv3nUnuXTrGD4E95SIOm+MRzDnAtZJOMbO5ta+H5L4nMPpeHjneBFG6weNC1ICORjKBv0la3ryW5aXADTXq3+KUx4rBFCGL18iFaDS4MfNL/L3O/Y6sYz3BO1CiwT4GdyyXpuG0y1uJPB+/T/vjBeX/UIcSmxry7olZ0IeqE80qdgUXWkVVUnb1cGAdvI7qxJZnssLqwGcL6y4CTskcZwemMlgr4ff/m7gD8M1aUGEENrxfFwSfL/rVDuawbDXPmNnVubk3g1CQgmBrBOCZpJIOOf11PXyevU3S9cCNmW1CvTqJS5k/TdKTc/OmpJfj9+xZQ3ZoTs0slTVU3xnSHuQrwJ/wZ6X0bOXsk8HZs4To/Z5Izn8SzDhaDXRMGm1R7fMLnz8Jz0RkCyEt0/dJ3gjvMJzalH0QzKxUS1LtI/si2ZRiy7JMZQwOxQ3rLSSdkaHRRHElHjk4E78GdVnNZ+MvZwnhZsc5yGtPckZ+UR55QSEYhX5sPXplo8IAj6H8fO0pLzKuKHlXpgjr4/Hrl6MO3oNf33cUzqU0cIY6r6eIZwTTcu9SJu0i4KLkLFbSsXVHKyr3PZHRl4Na+jGpIe8u6TVm1mwkPobIuCDpMbjS2lvwe6kAACAASURBVHNxo+SInmNHNJJ5AXBBisjuxWjAYm3K8rzrtNyfIixQI1dl1SUtZe1tNpo4EZfq/g2p31fPaxl6B6xBg03P8y545uWX5JvXrsgUbe1mcwGmPsjKu/fYLpRVNLNQPZKkFfH5djOc5v4ma1Gsa+BhPMOaE3haIq1v4v2403og7ljNn58kzZZ0hWUU5dL63v26EqLPV7S2qzlv92n4Hq31OSiyHX6OVQuKtYHfm9ltaZ93JGe+icG9OhOiUua3AjdJeo+ZVRTTx+LMnNfjTJVBkPcqu7OwLtIe5PN4IP2r9JdaD2XP0nYH9dl/BoPl/KcLM47WOEIF/dYoWEwvw37Au3DD4sDcdo1tnoKrp2yD02dWMrPsC9Gxn876IPPanOOA4+TS8DvjE+KhjPYPmgR741GKq3AD/Ojauu1ppyAObnZcyB7MxieLPck3f46qtJUKTisUJ5ZgFPrnOK3k65l16+HGUe5YTzWXlb0EuETSk3A6xI7A9ZK+bGYjFq7FpWGjndebRvAT8T5aFUpOdfjelWBeC3YU4zLoIbnvqNEHoIDUMS3y7n0wcFz4KJ5Z+iJuxP0HTv3rQjSSeUD62wCfkOtj7urkI/LgWdGtceWyYv+6OpSnZlfF5F+wQm2jpBfidZBPlnQbsLmZ/ajreLWASNXv6wRJlwBnWFkwCKbhHZC3AzkU/317mBXV3Z6czm9n4Gq50MO5uGHUdu9C5zhBVrEIJbqwme2ZWX0zXvd6HB4E3DZDSSo9Y1fgdOuc4bsvHnRs7muVllNdBHegmucf6tcVfb4sXts1ODtlwVqfNsh7/H3IzLbMrP4xnuU4B2d9fKO23dOAVqr7QITmJvPG3hcCn5C0DVN1gN/Ha5OyDBFJK2c+rmyhD1Bu/jy4PYiZba5xqfWzgLOsR6uDgdkz5C06jmcqwLd336CWhsv5TwtmHK0GJn3Z00O2B06tuA5Yz7zYv22bpfHo1v/D6QerWqznQh29X2wzux43ut+NP+jTgvRi5uiSmNl7p+s4NeSyBw/iWYPDq4hQA5PIu3dxzk8tfB6JQp+Oy+tvY2bzGzomw+5kykbHiKyseR3FMcAxiZK3U8dxx9BC/Ql1XrdUa1bb/9+anxUwibz70BqVqNx313lkr6XiUsfT5nz2GBc2xRtK/17SKbix2eloTRDJfIhyhv+jktYsHPI5+DP/k0Rju7DrHMlTs2fj4j3HSdq4ELk9Fmc3fIqpOo+Nehyv+n1Vv69lcMP9O5I2tKSKmcEk78CGuNH2BDyTdY4lal6P81uWqYDNUsBnJZ1otfq+6TjHSFaxCbmkc1ULuDpOdcrhO+ncSrTdEr0OPABwTaIdno9TYpfHAxBr4wJS04HB/boqBJ+vKgNyEKN1t5cDB5nTGHMYnJ1Svo6sCm5cYwWxIkmL48GnNfGA40H4fDoXf/dKdOr9gS+ksesBPGhUYRvyIkDZ8VQuDrMZ7sRvnvlKuDG7mV0laX28ROBVeL3zPh2bVQJA9fN9CLeFzmW8fKFCNCjyV1xx8yPJud0duFXS+mb23Z77eBAvOWjLnoFnZ5fF7b2t8fvcyvBKiMj5TwtmHK0GJM01s71ryztare5B0rlmtl1mu0WYol7cgvfi6azrkHQgnvm5HHiBmf1i8l8BlNWAcpGOOjrlmfuicKwHgd9ZWVa5QpuiXzYqGcwehCXCLV4PM3gwM7OPSVoFuE7S73Aa5PLAU4BTzazk1LXVuF1Hpu2ApDvMbNna8lfNrG4s3k6eqjhdndf7GmKTyLsPrVEJyX1PcC2jUsfRnkWRceGxVUDIzG6R98jrjaGRzBw01RNuZ5yOnBNN+Qewu6QLcNGTHWg0WLeGcJC1ULPT9keTyTjgUdYNzewhSQfgzILeSIG66lo8Dw+i5JrBVwi9A5K+gTsdR+IGy7/wXpD17ZpU0fcCR5vZvJTROxY4NmU+d8FZGDlHKyTvrqBQVNp2ETw6vzMeEPgL7lCubWY3FPa1QdfxWs7j5ynodTBuxD6RKYdkLespJtTjOKUeh70w9PlK48J38MzBB5iqu90GuEHSujmbJRiwzgkAzcbFSP4lbwdzc+Y7J+CiCF/FlTBXxYMr5+JBqWz22cy+k5zIlYFfNAJenyeT9bFGr05Jz2VKLXVxyoI8d8lrlCuFwxXxAMJ8hUMriMlI2hQPRF2FzwOHpPf0/VaoHTSzPlTNHCYJPFML3m6BBzSydYZdKGXPEjbAkxF3yVvBZHs2ZhBml0yKGUdrHLvijk+F4xmNiGR7vOCR5yfgE9fnYdx4KThRB+M0oKfiMrBjX7CMHPMEyEU66phOr750rIfTC7Kbmf19fDOg0ey4EZV8Lj2VD+V1JKsCv7G86l1YHll5quKDwK0dE2u019Fekk7Cs4RVZPEKa2+KGSkaXbyxvFZjufTsLOzO6+F7Z8NrVKKF+dFrGe3HFJJ3JzYuzJI3Z622WaSxXBrzRjAgkgnMN6JzPeFe23Gou4B78brZwZTsGj5DoXkzMLsKIpnZ/cmw7UTKiu4IbIkbXqfhFMWugFT0HaiyNscwSumGcnT39cCWKfg43+CtZT73KhzrPGLy7iGhKElH4c/QHJySuglO3budQE1kH8jFgs7EndcbgRdbB+2ukMGpUBTKiWCC5+twnF64b+PzM9J1PjLts3m8wUp01iIAlIIWx+H3sonXAi80sz+m+fEW4FVm9o3Md5vncA9Ow2t+XqynkqulvokptdTrcLbG6i0ZvqbC4el0Kxwi6WxcXfKdlupZJVna/seS3mItomCSlmSqZ9Rf8HmrrbZy8PwhL3mp6nrvp6fkuuJ965Yws7vSudyRfmMnut7HBYkZR2scXdmGkiFSOVXVxNX8XsmB6ZPyHIOC9UETRDoGI3cseZ3JSvgAfiSFydTMftcSlVwnF5WU86pPwSPb38GNkMvxrM99kjZvDr7WIUyRzreEHFVxNt6X6Xrg9ZbnzU+SRfsVmcyQWuh8Gi4r2+WcZdfbQu68PuG9q3+vT41KVO47dC2JSx2H5N2D48Ic3EGrn2vdQBkctGmLZCrYEy45+x/E62UPAk6wFopcDyyOZ4ByiDbnvQa/difgAjXLArs0MkyD5d1b3oFIdHdtnPJ8bfpNx9avY4na1ZYdbIMFhaLSOd6FU/gvqDJzuSBmY99R5WBw+utt+Ly2A+4UvL7w3QpdLRx6q+r2QOj5wimAJVW7YyjUBtM97pQElUo4Dq+zzuGxlmqVzBUD7+3jZEWQsuICfocHELYxs1/LRdTa+pk1FQ7Xp1vhENxGX81qtfrmgh0bS3orPgZmyw5SwPPD+Hh1J247/VPSgVZu9B2ZP27BbZKzar9jzcazlZN3j/atW1TSq5iad2Y3lrPHk7Qxo33TemcVJ8WMozWOrmxDycgMOTDWQT+TF/7lEKoPktdbnG09i8KnGymC9vMUzSvSFINRyZNxZ+wUnNrwdXwy+ASerTgcNxY6IecZ74xHnLLiIFagKkpaIh13LnlxlcFR6AkoaFFZ2RBsvKl1Z+d1SZ9h9L0aQgHM7a/z3qXv9a5RsQVQmN+BkNRxdDyJjAsLM2iTMLgnXMKNwN04hasvfTULucjRcWTEDRKi2Zur8XegZMi31QeNoesdiER3k6N/lFz18QzgjfJm0PXvDHlPqwL6nc1sjY7vDhGKWhP/7XNx0YeLcBpZF0LKwQkvAp5uZvdJ+jblwMt8tGVwFgCiz9dsyg7EvRQyb6W5cQIsQjnI3cysPxzJrPfEFrgq7+nAeYVAavYcGa5wiJkVnXEz+7ikrKKspO3w4NJuwGfNqcyL4jVNH5V0p5mN9ejqmj8KuB2n3Jfq0LJOdTNrqP596+5g9Fm9s7FccuKbWcWP0yOrOB2YcbTG0UWHmVjZrAspArsp7hxsTL7uIFoftCNwpKQv4Q/yl8yslPFYkPgfvDixhEhU8sXAU8zsAXmD4r8DJycK2yl41qIIuShJRU9cA1dK3KPfz5lCmmz3ZzS6X18fkXePUtAisrJdUfm2JpYVHaNvU2vwrEgdvSmAFYbeOwVqVCxWmB+9ltF+TGPoM54w4biQKHJLA3+z7p5DUUR6woFHWo83V798Fol2ay2UW+XbfMzGf+O3cIN/DBNkbzaIbFfHkHdA0sVmtlVt+UAzO7S2/G0ze2luW9yIeRrwXQpjXMs5Poop2ucmuGhEqb40JBRlLj61l6R9YX5bi6/jxvq7JJ1kGSEJm0w5eLHK8Tezu+WU9f81mOD5+hFunOfYCFvhyn0LFHJa935k6okTpj2z3oLlmaoJPULSN3ElwK79hxQOJW1h3nqjWl7JzOqNrKv6rSb2wQMY8+X+U5D7M5LuxrPCY46WAu1BLK5SXB1jUN+6CY4X6Zs2LZhxtMbR56Udg6TrzWyt2vLHzeytteXbzKy1SZy8idrO+Iu8DD6xvKzw3a7i9WwUx8yel6KdO+IePZLOwfsftBVdTzc2okw7gFhU8tEVfcXM7pV0d2Usmtm/0yQ/gkRP3Bg3QjfDi33PA56OC5pk6Ug9cD+Fhsxdg6ek3cysOXhOFwWtD7qi8p/JbRSl3gQdz0nv3aAalSb9K6FT7hu/VoOvpcX7Mc3HkPEkOi7IhRCOAV6CG7L/lnP697V2WfLBsFhPOMxsrqTNJX0Efzaqc78Vlwa+NLNZLhP9IHBbl6Ffgry1wnutJrY0KSZ4B17VWH43o4GonKzy43Hq2auA3c3scwPOc1X8WdwBD7AZ3nNqnRJdRxMKRZnX/l0MXCzvj1epJO5LYWxOxx2sHIwHaeo1V4s1lseoeWqn/1fbNOthusZYrNx7a1eSJPaAQO2hTNVOXsComuL7KbTDUbeqK2a2S2Ob3PWYDfwn7ojk6rMWdjlEXV3vBfg4dCwegDlW0kfMRX6aiCgcggeJ6uPT9YxSBY8m72itTDnY+TXGW6pUCLUHSeyQ6tnqJYSiyfrWVfuoAmd/6cFWCGUVpwMzjlYDE7y0zZ4YW+F9diosldtIrtT1JnyifD7wbTzlexiwZ8tEGRa1SJPG3initwk++dwg6Ud44WsxwjgEBeO06uWwEX6NsghGJSPZyN/jhawXAhtZkrmVFIpM17ADHg3MITp4RjBYVjYalSdIvZE3k97UMop6kvYDPm+p0WADk9y7odSWkNy3me008DgAKNiPaYLxZPC4kJysK3Bn8WCmFMm2Bb4h6RXmQglt57sirn42kt22gppcisr27gmXjrERU/1nPtM4zzMl3WdmI0ZJX2Oh8Jt2YUpq+hQ8ePch/F0M77eA6Rq/+gRkfoorn61mqRi9DklPtEwBvKTr8GfxKtzgvMjM/lHIGtYxbUJRaa44WtIJFGp9FFQOTriO0Zqr6xvLOWpeZIyvj7GzcCO/S+obSUeQ+p/h9M+nm1mpxm0+zOwrcvXHuYwyDW4DdmnJdjSd5z3pbn+Rux5Ve5bvNRkGjwQknWJmbwcwb7Py/RT82Rx/dr5LhqVgAYXDhKhmwH24A5Lrs7UM+cbaQ/Y/H3La+WHAT4DDJO2TCRTnEO5bJ2lz3OHtGziDhds3bQQzjlYDkp5rZpF0eHPCyIlh5PAnnCd6PC6ecEs6j4PaDjYdUZyU7THAJK2DGyEn0ULlGIiccfog3rj2/daD0jYwKhnJRt6BF1Yvh4tYLGaFou4mNF5bBFOO5Ap4pDmHyOAZpaAt2YysNlEazIZiAurNgcAXCut+l9bnaj/C984G1qi0OZ9qkfuWdBxuYH7L8qqXJUT7MYXGkzoGjAuHAQeb2bG1z27GM00/x2sRi+pjkt6TvnMzbhhUmEeGopk5z7494Q7E6+rqUfzf4tSf3+OG9YijVQgS1TEPrw24wWqqX/La0u1x8YHX433B1sWVzdbt4ywPRPgdaKCPOMg7rdEnS/1oqSviz+TNwM3WrnpWRzjYpXKz76rIPie4ElUODlHzurJKytRTZsbYw5ufFfAm4OVm9rOU6b6YsphI85iXApfKhUiegNNuW7OLNq7qulvzs8w2oXIITQObaAC2B95e/yA5gJVqarEe0wIKhwQ1A/BehEdLerON1x0fSaFX4YD91/F24NXm6rjr4bXyfRytUN+6SOAsIZpVnBgzjtY4bpT0dzzy8028MWAXbSCHvqpWX8UnqG2Bh+R9ujqpKZK+aGbZVHpfpFTpNrjj8kK870FJoncwJsiMlPb3J9ygPToZVs31g53PRJl6Hj6pn4T32bkUr4nquoe5nh4PAp8DvmzlYu3I4Bmi89GtbDU2mPWINGeFGGrbD6XerEvZqLqMggrdJPeuy/lM++/rgLbJff8nTsVbRtKPcafrm8DVuaxADdF+TKHxpI4B48I6lJXVPoE7MG14D/DSrqxX7bz+E3il5RuPr0q5CefqOG0yh4vwrFMTfQQsVgHeK+lMm2rAvi2wgZn9MtHlbgK2N7NSxHoiTPAONJW7mstjbIi6k6UBtFT8Wm6avn91ijyfiweHiuc4geHd1ux7d7Nis++ocvC0oafjGsFSFSvAzG6S16gMQp/AaAs67SHF6wZDbKIFAUvqh/8LsD8e7LlJ0oVMUT63wgPSLylsF2kPsqylWlkzu7rN2WzsZ4M+38tgcOAsHS+aVZwYM47WOJbGJ4z1cGPjaEn/wAugK8crp5Y3Wy63WQ3OTY529lqb2eYpcrUDHpH9sFy1aElcyaWE0qTWCnlh6Ua4ESXgN7iC1JZttKJJIOk1+PVcGqeCfNPMvtKxzaY4XeqtmXWn4Qb4tMDMfgjsmSLsm+P3YQ4uY3yKmc0tbPr9tnOUdFkLtWLoOUaL7CPKVlnefRcmoN4sTVku+0FaJsoJ7t1gB7QFRblvM9saQNKz8Xd2fZyPvryknwBXmlnOiQn1Y4qOJwtgXGijNVf4N8MapB8IlBz2xdP6HFX2YZxel6PLLJHWj6Dv+yZXw7sRqBytpSyJbJjZTyX9c0E5WRWC70CXctfYPVeQlpqe48uAyyQtyxQrYSngs5JObGbK0vH2MrOP1JZfbmZX1paPKGRJQs2+p4MlEsVAxzV6jNn4OzkLp9lXy8C4+E/a5rd0192uNE2nOLhusDqHxnJfNlEEi0n6YNsXrAclcwCWVFl9dhY+fuXO4U/yGrK9cVZB1XfzUlwYqFT3G5F3b17vXu+RpGWsReVW0nPM7CeZVZHAGRDOKk6MGUergXQjvpT+kLQ4rmb3cpwGdSz5qNZ1jNKbvseoMVdSzMGc1z4XmCuvzdgZj2j/SNKnzexd4R80jtvxKNn5wPqW6Uc1XUgD+Rfx6Mn38ajKs/Eaq2uATcypgTnsTVkl8Dx8gh9xYnrQfbB8D5tq3YP4i3pRoinuiA88JWN98DkmtEl3FwfPEjTNRfYWr0+JUm9+j0vQ58QTnk+P7vKBe3d8S2S7N9Qt912d389wyefT5b3GdsIzOu8kny2K9mOKjieRceE6PHKcu8a70jLmJRwOHCTpQ9ZP4fCVlJten4PTiXO4AneE9sus25eOe9cGM7tdDVpmw6B9oI9BOx0Y8g5YTLlrOmipd+Bz6LHyGr9d8IzvmKOFU9s+Ulu+mNFa1ncw3lgc4s2+i0jBi+3NrJS5juxzsOPaHE/p3yR8SVygqcKs2vKY+E8NuxVOf3X8fcoa1ZLqVPZZ6bOh70FU4Xk6HasmZlHu/7Qgjh1qASBpfzM7En+eWh3DOrqyyMq3Bwm1IsGDefPrtiX9zMzq1/Za8nXdgwNnjzRmHK0CUoR3TTwCvQHubN1KWSVsg+k4bjJwbpAXGG5Bue7gMepQ9rGGqk/Cu4HLrMHj14CeJgPwLnxiXNVqNTEpfXshXiB7bGHbVXGaVQ7fAp6T+bxXyroPEk3xKAq0tYTIOUJQuluBInsFlAD7ZBMLmboo9eYc4DRJm9VpbpKeiv/OznqdOnreu3MYHeR/ay29Xwp0yk6577TtE/GM7vrpbxncqPow5aak0X5MIxgwnmTHhQ58ABe9eBbjimQ7UO7ZU+H9+LV4j6QRGmVhYn4i3g8rh3+kfeXwPuA7chnh8xvnuTY+todhoyIhUYN2WtHzHRiKiWmpdSTK6PWSSnT1qBBA8/O+zb5HkGh8mzAlRf9zyhThCCKOa04Eq4+U+VDxHwCsUesiaQU8uLg53m6h5MDez+h4GXkP+jotITZREPdbS2+rBYB1k8M0FAfgAc+J0YPOGm1F0nxPl+tYX2GBBc4WFGYcrQbkilvr432KfoEbUafi0axi09XpRor2XEBZhnMe48o+ffY7f38a72lyO9Ondgfef+Nd1hAeMLNbJVUqRCVHaw4encg1S1w8rR9BlF43AQafI3jGSN5nZWcalEpcTnush4TiRfYRJcBQpm4C6s1ReEbrF5K+x5Qh/KJ0nOk0Fis0B/H/6Ph+SO470QPnkOqygJMKEecRTPez3GM8eXLdyepD00rUrA1xp/pykrw7XlT8auuWdx9KUf0LHk3+aWbdKjg1Zgxm9ouU2TsEz6JVNJrL8SbGvxl4Hm0IGbT/FzABzb1rvyXnPioEEI2wA/NVUCsa3+Nwh+ANZjZtdPWEwY5rdIxtzsFDkSifH8SzpGcBK1umF1kNkfdgcN1gQphN9H8AUYcpmg2cj7501gkYMNH3e4EGzhYEZhytcRyFT+T7A1+1/p2/FzYeKPDTO6FyT5N1rdDTJIhn4XKnOVyHN5Ar4SZ8Ero4s27jtL43pptelxA6R0lL4g7Tsrhk/Q/wgeKDwO6SXppx6kNF9hZTAoxm6kJINRxbypXCXoXTD6/FFe0WVHRqkLrSBJPJ33CntvpbNmXPSpTZTiygZzlE0zKXtF8/UayXwhsW/1PSopI+VciqV9sOvaYX4z31ZKk5LICkOencL2w51i14bdAIJM2WdIUVeg8NxaQG7f92LGSae5OGObZcQJQxsDv+eyoa3/txKubNLABFsgXluJaQmCQHMVqzczlwkJllxXbkwjjvxcWNDFijZ2Di2cA9lkQSJD0Db3r8XPxa7pTZZnDdIEwfm6gnbluIx4K4w7SoXA23uL1lWmhE6Kxpu8fhrIj6s/V14CM2TG23E5MGztJvXJupAPd1032OTcw4WuN4FlM0nwMlPYwbl1fjSmHTGf2cBKEXUPGeJhEsWqJsWKGBcA1zgVMThfNSM3s4fX8LXF1rj9xGEXrdBAidI+7o/AlXXJvvUKXB6vy0vukATVRkr2FKgKFMnTqKhKG9UNjMrsBpAQsDXXSTMdVBSavgamb1LOTVuEOYy7JgZi9N137dtN3BwPPkCoTVmHJ5btuF/CxHaVoAmNk/GeXMPwqPfhcdLQB5wfZ6+GRZr9/I1Z8dhPc++Y2krzAVydwIF185uO1YBSxCRpY/c56b4xL9d6XltzWDGOnzqHLa/zkMoKVG0YeGmTuv6LtxMv5Ov8FGFRaDu+tGh+P6WTMbGZPkEuuvMrOT0vJXGG3tsVsuY55qu76DC7d8gClJ7G3we7huIdP+WzxLfSjeF/KZkkYCpGb2tcx2hzBaT/kJnCb5JqbaVYwEPixWN4ikd+IiZZG2PINgZqst6GM0EHKYcFr72zOfVyi10BhMZ5XXg16DS6ZfyNSztTXwZkkvsbwa42Mk1W2YOY3logBUNHAmV/A9EL8+f8XnnX/J2yQcVjrepJhxtBows18Dv8YVt6o6kaq24nBJD5vZ0x7BU6xQVHPriHhHe5pEMLuR+m+i+PyZWdWN/gzgPEnVS3E/8AEzu6i5TYRep7jyTegcEzYDdmpmrczsbknvw6kZY5kmBYrsFVMCjGYTm0XCWzDamHlBFimPIDk395lZlG4yojooaSU8O3sNHumuy+VeK+kFVuhMn6hRV6U/JC2BK6O9J+1r7BwnoIpGEaVxhCHp7bjB9TXgNbgA0avxyPkYzOxeSS/DMwCvwq/JnTi95pxJsoQ9sC8uZHIGbni+hbwiV1Q5LQRJXwbeMpR5Mcm410QXLVXSqcA+9SxkT4RomM2AScKDeI31NS1Uxa3w4MD5kn6N13Gex0Iat2qO6/twEYojgOZv2ZfR7NqLmRKBWQMP0u2a2f3heNPxpmDMGWmsORLYMrPdPfjvf0fhtOfhNkUTzyRR9uT13+sB/x975x5v21i2/++WbRMKCaXiRb1vyjmnFCodcTkVctpIxVs55pSI5JytTYkUW3ImukOlcgw5VvJG9CLxo4NzKKf9++N+xl5jjvk8Y475zLHmWtu7rs9nffaea8xxWHOO8TzPfd/XfV1Lm9kDkm7GA762sB0eqD6Jr4uuwpWNf9viOYChqzBCfsD0rJmtmXG+nD7MI3EGynblxHoIzr4ftk+N7Hc4nZ9lVcysp7hZBMnEmaRNceGpqXhi/IWwhtoEZ0rcaWaxNc/AmAi0ahAW0e9lRO59MTwIi7036fEiaQfg5w1u2PI+vcQp1s/MeGd5mmSiSgWIbU/CzKbLBT/WwisIjwLXR2h1BXLodbnKN9VrfDdOeet1jQBLpqpJZva7QPGoIrfJPkcJMKtSZ5UmYUmPV383ZNRlAdft81j7AadGkhenSjombI8tcIoAeTVGEjbvDtd2A2mKZhZVNAR8t5ZeTy4HIJI+ZmZRs8pMmtYg2BP4iJldG+6VTSV9CNg6tUP4W04NP0NDqEzOjwfkb8NZAU0wWp9dgT8Cv5e0l5nN6GO/vsc99RbWIZFJfgPwP5J2tD6owOa9vK8BlsaNh5v2SMfGnMm4ifzz4Rno8kC0EWPeRfBgfioeJL8MbCLptFgyKxdyEZlTgRWBP+Hz/VvxMfYR4ibt69BZKXqpqKxKei1x5VZwOuVbE9uOwdcQXcitMuH3fTHurA48aGYPhGP+LXyvHVAPcS88iKnKj2Nmq8rp+MVabXPclucZPPC60ko2AQMiS4VxAOQGTFmwPDrrR4EVq+wlM5spaV8gFfB+zYKFyZDwGeBzZjaLYh7mk/MkvYyvmycCrWEglGmLBdHS+AB0NV4Kv9rMUlLTSljTxAAAIABJREFUuR4v5XP3I05xBP4w9JXxtkxPkxwMMEiXj/E0bpjaBDn0ulzlm+o1xpzIU+g1GMe25zbZ960EOEClroqhVbCGcP51SUvtTiet+ngFvtAoRCKuwiluN/WYZHKpolfSuUD+K529VucQTxz0TdMKQVEKkxtc6yKl6urLkuYws8slpQRciqb8vegWkTnOEv2liQpH7XVK+j5eASgHBofji8dv4WNmkx65UX0GzGx3SecC35W0OV7damKamjPunVPZ/nX8u+h1jRtL2gI4W9KFwD5mFqMldyDcXz/EacxPSdrIGtACrcY7MFCHjsXn19T+fwvvOVYuQb8D/t0fivfVtoXpBKNVPLlwMfAisJOlfSYXsc5+ki+UrvtJpc1iJxOnghN+3+R57Qe/xwPFH+CB7y+LDZLeDMR6YlL94XPjQehCdPs8ARCYOT8JP0Xf5s54ULoBnf2n2bB8FcZhIzvBY/33Yc5HQogIFzBK2dU8LOkH+DjbNkMjhhWBSxLbLsXH9VHBRKDVjQPwxdCBeGDV1Ok71+MlV5zik2RkvMuw/jxNhgpJN9A7e9qlLpNBrxs6ZQqYS539QVV0TXqW2WRv+SpVOZW6UYGk+cfivBUsnKpIm9mDcm+sGC7HqW03WzOvqFnIoYrSfU817bXKCeR7mVn2ahx/QNJS5n2vdwObhaA+WjUIQdateHD1I0bomxsB24RqXmy87FVVjUnsf4TSok7SNLwquTb+Gd5MPNDKVU7LhpndIGklPBD6U6BQlbfHFPb6HvesW1jn8Orvaq7xXEm/wOfCuwOTorw9phR2GD4fn4Ivmr/G4Ca+x+K2Io1gIxL0e+ECQm1iVeDN5obk1wJPAktZ6ItJ4DlJbyrGIjP7QbFB3uYQ8xYC76/6BC5IUcVmeGDUhQZVppSNzH7AJZJOxHt3yn2JWxARF7GKuFdgVBS093uIe6aV37syI0nyNfEg9kzSFhrZUP8qjLnICpjMbP42Tm7N+jDvxinfl0a2fQiv1sbwafzzu1muznsqcJaFPtgUchJnAXOb9xJ3wVzAKdkTNigmAq0KzOy/MnfN8nhRvjjFQOIIVVhvT5O+kUsFCMiRmR8XHjYNUO0Pim0fdaiHCWe5Uidvwl5P0m25QV/NdfzNzBYpvf6ZmX249JaHiFRhJJ1N+hlp+3vOqUJieR4okH8vZyUO6r5TSXPiFOCOBZXV+I41xFF4xfVenDFwAU5hTi1ov4RXr6aWg9bQCzADX4h1jV91FY4azAnMDzwm6es4RfsDYWyei84qYRlZymmDIFzPoXjVdR/6VGQdIjYH1sCFEZpc4zLA8YGCdDyufjco5iBv8foyXtVq02piigUrDzN7WtKTPYIscGW1vfDevyq+SKlyVMGhQMFUqHreHUDaaqGauChsWWphZtcHCvzbgLsribIf471vSUjaLFzzC8DnzdLm8pIuxRk9d+FB1beArZpUTfuF8lUYs9BWwDQorL4P81i81+8L+Pq1aDXYDK/yRQsQ5nYJF4ck5TZ4seHrkn6Ms2l+WqUjBuQkzsDNvTuSlSVMon3a5yxMBFrtIcvjhQHEKTIz3rWw/gxLe2EQKsDpGefLWfhlK9/kwoYrR9sBNTDhlHQs8FszOyO83grP2j2Bfz4bW0RpKpIcaOpfM0/l9aqV16mFUVefRQVJhcMMzFO5L6po+z4ZT35MjRQEq+hViSzur/D/n0haEJirZgz8EG6k3VEZNFcwPZSEiEa4lp1w+sttDceWi3CRkyfxIGvD0nV9hkQFoA26dD+QtAYeZP4ZX/T9peGuQxv35NLep+LJxvWs1EPYA68ys5nglPcwvw1yHZPwQDQ3kdV2v93kSna++rpL+RSvotwkV/67AFd4K0R53o1XXbtgZj+VtB1OCSuLDDwA7GhxA/pYlWnn6u9SCM9+13dtZn+MvL04/no4lfJ1eCXrB8U9UIPlGVlD3Qn8YTSCrIBcFcZXLMzsByFYOgU4o9Jq8JXyOJ/Y/1E8eJ8u9+7aHvge/jkvHnl/TuIMupOXZSRVTNvARKDVHnI9XnLFKcZ99WZAKsBbGhy/Sk3a18zqFHpiOKzyug3lm3EH9WfCuTEurlLgSFw17Hi5AMtXcEpcFf0a0BboNcClqjC1ct6hEtMWqvdJFa3eJwNUDV8tqUzLmb/0ehLdQW02ciuRlWO8Fg/4FwcexJUHU1isqOJXESjU0f4USUcQxlfgKElLWI3NQMBncSr3i7hC2mWS/gK8hHP9PxzbSdLvzWy5HsduEz/Be55O6XO/vsc9VUR08AzxW+lM7sUkwn+DVxkO6TP51ysYxMy6mAEJ2vlk4C14sijZn9UDbS/EbqQzO38zNcqnAGZ2r6TV8D7PIxnxEPolsJq5anIUNiL28bZiP3NPoldJOqjBM1Fc06hA0i/xoOlInJb8PB58znpP7P4xszeHYH4dYD3g0LBP2ZYnRV/rF7kqjK9olFoN1mTknrwBeEbSdhaXoI9hsfDzGpyS2AXlK6aOWfJyItBqDweT4fFi+eIU4ynj3RP9UAEC7scHrZh4Q/Fv9f7dhnop1Bh+YcFU8ZUI5Zlwvr6YsCW9HW8ALxZy3yfR8Gv5/jWtQtIK+N+8FS01r/cK6mquZU7LUFZSvh9TtULcqIcmE7mVSAAkrY33Wt2HV2TeApwYKqaxe6lX4ih1vq2A95nZnSFjeiE9qp3hO5ulICtpRbzHZU5cxjgVCC/Z4xrbxkUZQVbt/RwSYrFeqLvoHpPL1YlUcu+DgZoeO9cKeNY7Ji3eKxhMIUY7fwGv3tyUw/QYDeQyGwJVbdsBzns3nYvYOfHkWZsMgBwUlYpCuKmM2uSxueHyfXh1F0mL44m/w/A5oJWk87Ar1uMNkt4SSXADsyqYl1fePwUfR5OBVmhLmIrf01NwWulalpbnz1KKbrvloR9MBFoNIWlBM3s8td1a8HixPsQpRuOmCVz/u8ystYxMJhUA4pn3yXimeT/i1J0casdP6JF1Hw9QbzPgmeWFeAk5JpxPl+73NXEaYdFEOgeJSUvSBji169ORbScDFycoKlMklRdR81Rez1XdIXL8hXDlrh1wP5kbSBtGDxO3SppaM2mkkOXHlEm5zVUQzKpElvAtYLdytlOu+votIGYMOnflvqgiRXdb0MzuBDCzO+TWGX0hPAt1VhUFhq20+XH6pHQ2wFy4emVVjTS3h+EOSV9lxIrkYJw2Pg1PREbv2dzkBrB6BrOhlwDTqPVv9INIVbELiapiL0TnzrAm6HhPGy0KCQyUPA7B1Tqln7fhdjwppbnW0YsuPTsjBE330X/Qmrq3dsEDrJXxAG13wBqslbOUogN1thZ9VN76wkSgVUH4Mv5mQV5V0sp4xWlxSffgXP0UfaU1jxfrIU4xQMa7DpNoMSObSwUIv+/oFZO0NV4VfBzYOsGDnqtXQBKhR4y2z01bqJoBF5iJL8oXonMhXiDHhPNS4BS52MTehCxhwIqk1eT2TFwD4ZwHEeR3Kzgbp9AWOKfy+uzYAeVmzB/BF5ob4v0KZwFL4MbMrQkPyH02ehlVxsbT04ArJU1nMN+QRvfpAAuxQRUEc7AEfj+WcRZpmd2z6LwvqojeJ9DVzzppFBeLr5ZL+idhca+pXIzW+NV13FDpmt/MukSf5CIBTyeSaMfjvUM/w+1LlgXegVPkd27zOQ3IYTZAbwGmb2ccs23Eqopl5LYMpMa2f1W2jVqLQm7yOFDW1sYDtT/hwmJfBa6yPo28G5xrYLr0bI6c8SZ1b30BX1tsbP0pNuYqRe9Sc7y34muoiUBrSNiLTvrNSfiDexR+YxyNy1x2oNrEGoN1N7Y2qVRAvKSflfFugDYzstlUgAKhSnI4nmXd30pmcxFMIh2QQOJvk/t6JAeQVKl8mLCI8a+kD+DVwn+SVvbJMeH8It5XeDBONyx7kHyYuPIQ+ALqqsS2a/HFVQxftj7MvEt4EPfoOB/4sJldBSA3CG8bqftqA7zCGq12m9k3JBleDbklXNv/VN7TZJHf9LksFmIQv6ejz5zlKQgOWon8EU7vKffdfAJPbHXBzHK/12H2s75A+vkYDeQkl5ogdr/thgdMMcXUbwO/xlXGqvgY8C4ze1jSN3Fa+AfNLKWQNyhyJbGzqsHDRG5VsUcCpu45HVqLwgDJ4+eBL+OB1WhIrJcxEF36FYCYn2Ld/ZMU1TGzZVu5ooawiPFzoC1+DV+7jBp1diLQ6sZbCE7WcinUlYH1zezvcpfrlGJOL8nJrsbWgOoCbhNc8aq8XxOMxwc8e5AONMwjgDfjD8AM6+1D9K9YQNID8+ITf9sZwlGD3ETwCFxF7TDgpF7lduvDhDNkrKN0pAQ9scC8eOATU3yaJ2yP4Q/kZQH/hg+QiwGLSppSrYS2BasoZUlaC6/ULonTg5NV7NBTsZ6k83EVrnIglLq/cv2YbscntxnA9625D2ASNZTiviuR6pTknwLMkHu0PICPvStQox6YuL6FCfLAZrZC5C3D7Gd9wRp6S7WErORSJrYjbUvxVfz7jgVa8xf3oZk9IOmZUQyyIDP4lLQ0TmNcDhdA2X4s+zpaRpNKWBeG/Pfn0qV3Lv4fxFkKoY8o82hADEqXfiWiWvUsI6nml6DqvoD3655pFXPoEgZWTA0004NxT7dTgKXN1Q9HBROBVjdeYoSPvQbwJ3OnbHDFmajLtWVKTlYDA0mPZwQL0PAB79Hj0Or9MOAgfTXe43YGsDQjSkLl439pgOMXeIaEx9l4Q8i+HI6bYx+Ll9z7lrEt0VL3wBUG28IdOJXvwsi2j5D2zsnNQK8YRAp2AL4JfEfSRXhQNyoTnqTl8e9gDbzKfYIFL5yafRbG6XCr4RTHJh5CWX5M4TMpxEBulfQ7nL548QD0uCil2My2zzhWVZL/9sr/G/VTyG0KPsaITcH/I0HtyhmHJG0IXGtmT/S567ATXjnJpZgVQxmpv2FJM4taKpjZHyUtmTqeOtUJX1IztcJc5Aafx+MB/+F49X8aTr0eN2jCgIkFkQNUwqbiiebNI9vOAX5sZmfmHLsBGj9LkjbCmRdLlH73Z2DPwOqYQANk9inmJrJiVN3JuGrj6ZL2SfRMHU7nNTZWig693AfgFh3nActmsmn6wkSg1Y2bgD0lnQLsRDBsDVga98saTTRdIOZmvOt6HKBFjmounbJ0HTNx9cWmyKH4zRytKkibkPQ9nFZ1MrCT1QizNDjWenjW9gYza5PmNA34dujluMhGjAs3wQOhlDhFdlAURCZ2k/RFYCPcg2Ne3APpRDOblnvsMiQthWftN8QXZFvFelUi+22O/+0XA+8sN0qHHrNtiDxzNoC6lZn9Dtg9fCYb4J/JiZLWyxDlKBCjjPwOr+T9oGk2sBA4kEvvbwr8qJ/nT9KyeHC1LU4JNOA5YA0zi/r2ZY5D3wVeJ+l2POlzFS4T3eu5q/acjVfkWDHMlPT6UuJxFkIyIbUwnhcPsPtVKyyO/VG8B2chXNjn6qKHOoGs4BNPhCxhZs9K+hXuyTTe0BYDpil2xlsmYjgaH9tGK9Bqmjz+MP7cHYFXVR/G1zlb4hXzZ2uqI/1iYOGmcY6++xRzE+p1VF1JF+NjcGw9mtXrLOlAvI/8F8AqLSd3ajERaHVjTzyrejgugbpTadu2JFyn1eldE4WZvbuNCwzIzXiPRv9KCrl0yqxsuZl1KZWVgopfm9kNkd2SWTNJrwe2MbPjUu8ZInYAnsUX5ltXq3sQNwMOVK0ri4WkpD3xCel24GuSPm1mdUa8jWFm5we67WnAWeo0LvyymV2Q2HU+SSla6CQ8GK6lbwbq5AXABZIWw3vRdsKDvzZwF/AkXkl8BNgyUmGN3ctfB7ZNTPSTSUjfqh0/pkWA/8TVtx4kTukcBKfhQdzRki7Fg66fNKD4Fga03zWz85qeTNKNOJX7Krwv7gIz+2eP6gz44uEevFE91bdW9StaNAR1a4efbwGLSboDD7yutLgH3RHq4QHYcs9n1rEsz4rhV/gzdURk26fwPszYuXKrKZNxYZ61cMrt/8MDjV0lXYdXWnqq+faBKRY8MM3sKUlzt3jsVpDLgJF0s5mtWnp9ipXUYSU9YGax+3YZM7slcS23hcpkW8hNHh+IW8aUF+734c/ig7jacVuBVlWQp5Fw0+yCXn2KYU1U/V3rwmxmdlPNOPqwpB8Ap5nZ7Yn3xHAInqh5Ex6Ax87b5hp9FiYCrQoCNWIZSa+LZGmnAans6xr4YuwMfEIYVQyS8R4WcumUkGywfKluEZcZVHy0coxX4VSkHXBq0t3AeAi0cj/LtQhCGaHStB/uA3SupPVxDnwrgRZ0GBe+G5f0fxS43uolb58FVmnxGh7BaX1HtXVM3HNsJunvIZU0eGePyleSptX80kYgl+DdBL9/l8eFGbY2s9/02K9vSrGZfQP4RqBTbo9nIGeGSXCGxU0jy7hS0tpmFk1eRbAULvxyF94z9s+G+50AbI77r8zAq609aZRm9ge8f/AkmNXDsymejPs88YXf/fTv/5eNWHKpKUJCYhngDjN7ItAlPwj83uLeXIcAV4f+hgsY8Yr8OJ6EXDfjGuqSWbviVaxly1lzSUvgAji74YmMKqLBZ4NevsmV6ueUajW0hoExVmhawfqvyuvNgLINR4o5Mpek15rZk9UNcrPxNis4WcljfJxLJWwuAE4c/NIcQ05UjwuENdEGeN/2R+jugWpdmC084ymWxKfxROrNkv4HT/CdZWaP9TjsmH13E4FWAgkqzBRconrPyLaV8C9yV1xMYwYNJvRINva1kjoCtUSlYoVAEeoLDbK/0fONAWINljPDZ3MmcHCEcvQe+gwqzOxX4f3vwL+/bXBhhlcBWySy1kNHrwx0WGDHsICZPRT+vwLeY1j0UF2GJwaqx1q7wfV0LY4VHNtDUNWVQVTasf1lq4hNNEEI6Oow08yqBr5ZsHxz0V70wtSzmEsBeiT8nI6PUy9Ap+pYgjKRTSkOWcU9Je2NJym2wxUWbzez1WuO+TBwiaRL8MXxy6Vjxvov34BP+DsA14QejDPxxV7y8zKz3STtxQiN8nhJP8QzojfVXF+xOF+bEW+eRYHrSDAbyPP/y0bueC5pU/yzewpfTB8M7ItXrT4uNyY9sHKc2wJD4GicUjYH/p1dB3yoVzBfOnc5mbU+TiOMBVqfAHatUpPM7M+SdgOmEwm0ysGnunv5HiJNj7qRThbGzZXXSQbGbIDqPZJKBFRxC56kiAXenwjbW8EAyeOX8Hntuci2V4ftE+gTcnP3Yk20MB7MxozMqxjEimQy3vP1ZRIJ4LAmu1jS68K17YB7z/4YZ1n81MxejuzXd7WuLUwEWhFI2pERc8UTcY75V/DJMrrgTfRFHC/pQjwoSGVjcrjy4DSNsjv21Wa2ToP9cs83bMQaLCfjfXIH4t9HdTH22oygYhf8QV0ZX2QcgGfB7sKrGOMaIcDaGdgHWDzylsclvdHcT+Q9wC0lfvM8xAfFat/W6+nsTZyJZ7KryHJsT1xDcYw5ce+6WENztCcHmBsfgBei06ph6EhUZgvUqSPl+jG9NvwcjitSxhZUMXn3gbN95j15f8CfnTUpNaYnMIWR53PRBsd/Ee91u1jSoozYFSwInCfpBCuZctfsuzAeVFwv71u7qvp+Sd/CA6v58LH2WuDblhCDKJ0nx/9vEOSO54fgYjo/k1toXASsYGZ/kPRfwE/xcbYDZnYjsI6kefDP/XEbMTOvRSKZtXlNMuutuGR8DDfi1bjUuVK9fGtaopcvN5kym6JpIucw/Jl5Ld1VzC8TsboZLUiSmcUUSa/An+d9Itv2xs2327qGnmylcZKozoKkBXBl0R0ZWRMdhEug71azji2jXyuS8hz1Ip50OxO/95IIxZDpwPQQFG4PfA9P/sTWQl1oUK1rBROBVgWSjsInguvwweRd+KLhVnyQruWEFhO6pJvwjPIe+MI11TOVw5WH7gVU036Op5pmHscS1SxmCX8Ki7mr6Q60coKKb+G83c3Li7QYf3csEbjwp+KGwffgC4i34tf/MPEqK/i9d668uXR3Ogev1XGDxw6YWUd1I/QB9Kp4QKZjO57A6ECgoxWLMuhs+C6uc//KPpPwAfMr+Ge0f3WfXKhejam4nhi/O0v6lkw/ptx+mBQa0K0KCtEW+ES3Cm5K/Tl6KAgOEtyFxfLX8Uzmavi9cgoQDbTCdU7BVTa3x5+jb5FWgPwU8L949eMa4CbrowFb/fn/ZWOASvcSNtI3eCkuS/+HcMy75Opcded9jngFIXYNucmsOWOZ6XD+l8MiKXa+3F6+cY9cBgxOi/w0I2NwlRaZogdfIVcePI5OKvYD+JjQWhADs+a55XG159+F322IJwbeRMWKJGB/PGmyPN4zVQSDm+NzXJt9Nwvjc+bppJMAszMewenZxwEfN7P7AULFO4Ws3rqW56rFws9r8HaPWgxQrcvCRKDVjS2Bdc3snpAVuwPnkPfsYyn1R2yPV1TOxY0ae/VHzI1/6R3KSnifQ0o6OnfC+LWkO/FF+5kJiuR4x4P451RF30EFzlWfCpwj6X9x9aKzGH8T8nRCgy+wNZ6dfxH4lNUrcH0p/KyLLxrLHj/L04wK0/SzyHJsL54tSQvif9sOeEX5ZTxREaOtdEDSZjg19AW8MbovL6YG6KXGlEKu9O3AfkxhPFoIrzrUStBX9msknS5Xg5sKCA9KZuBVktqsZ4Iy8gLwlybBjKT58OTXwnil9ddmtouk3RPvXzNc56b4wv5k4JIe51ognGMdPJu7vFyB8Jrwc0OskqM8/7/W0aDSPQtmNlNSz/tD0svUjwUzzSy2pshNZk2uLNiqSK1fcnv5ZgfkVjBvpNMD7SY6aZE3pna0EdP7/yT03eZQvXtB0rZ4ReIJYKHwPL8PT95Mw3tAY9d3t9wb8hA8ubEw8A9cXW5Vcx/DtvBGfHE+NfycTkt+heMEP8MrO1sCL0o603rLn+f21g2EcD9OxZPOU/B121qWUNZtqVqXhYlAqxsLWjC6CzSK5xoGWSfjQdb1+KKs10Re7DcfXj1bBPg58Bv8YT4I2EXSeywuJNAri0CCprI4PlBsT7daWDR7OCjCDb46I0Hkjda/P00Za+KmdlX0HVSUJpFFGKEiHYov8jeRdJrl+w+1iVWBN5vZvyRdiyvgLVVknFII92DU8dzMprd+lRkIC/Yd8AV70YO3FZ6RPrcuSAg9I0fgC4Cv4FLjrQfJvfjdNfvlesll+zGFRccxuBDKHMDLclXUvet6kmroVinp9DNxla11zL3ZmiJlnvqSpAuAnS3SfB+u8Qt4AmUefEJ/HfCcXOkqdT9fh/cBHY9P+osAO5YX+lYROQj33JXhp1DAWw1YD0/GLIRTVKsYlv8f4bpyK905MtUpb6oN8MpRSvo+N5lVXcDFtseQ1cs3OyCXAZNLi5S0ipndGo7xR0mTraT0KOljZnZZzrEj2A/Y1MwukfcQnoPP45/sNQeb2X14b+iowsz+gft1fUPu4bg97lf4W2CXAcb7cQEz2yj0Km2L/22Hya0O5iM+3mX31jVgiczEx9JbgOnFnBAq5FPxYOlyPKlu1luBNKda1womAq0IwqQ6Kfz8u/QagMRD/2m8R2VRPIu4T2SCjZWw98FvgI6AStJr8IFmHyJceXpnEWbimb3qNZQHiuXxG/Z7uNDEGXgG9g+R82VB0pfw65+MZ5kWBp6XdLiZfa1mv5j3TdEoORVXnOrAIEFFyGYcCxwbFqo74NmxQ4nTFYaNKUXAYWZPS3qyV5A1Bsh1bL8UD8C3sZIEfK+Mt6Rf4gH0kXhQ/TyeBZ/1nrEOkhP3cQeqi/yALD+mcO9egQdAh9DpKfNLSe+PBUWZdKs3WIYHXYwyIu/FWxp/5o4Edom8Z2s8AbUzcJ65RPyceFP+dEmPmlnsc7sm/A0fSFxSUuQgHH81RsQw1sLngqiUOXn+f4Mgt9Ldt0x1tYohaS38u1oST3BFxWlyk1m5Czjr7MdbBF+AN+rle6VC0o/w7+fSfiiweKKh3Ff7VzrZJOcQ77vNweJmVtCNL8Kfoy/2GsMlLYNX05cDbgOmWrsWClGY2W/lSsfz4cnrRYkngGcrmPvkTQOmSXoXvhZaDrhd0vfNbNfy+yVNB061/sXZmrBEXoP7Y66IMxLAfd1m4OyJR/o4X061rhVMBFrdmA/vqygwqfS66KmIcU5z+w02BLavVq3MfTz2x0vTsabkJTPPVz7G7cBekvbBKULTgb2oMY/sByErtTs+yV1kZi+EoHUTXCjkTjO7MLF7zBvkBZwbvnmMGy7pQ72uKVHlq77nZlw6dE/8IR8PqEoPV1+3Jj0cJo/y4roaMGFmW9GNavNqU8f2vfB75CxJO+HVgGKirUMhtX4MroRWRt2zOkzkesnl+jF9DTjEzMpqbHfhUup/xL+j2HOSQ7f6FV5pBfry5ulCWPz9MdzTKar1XnhfyCWV/c6W9BT+t3cFWjnZfLm55Tq4bcdMnKlwNZ7IuTmVPbUM/78BkVvpzu6PCwm6w/HP5ijghB5V50+a2dljkcwK5yx6+VbFaUO1vXyvUNxHGGcknYkrbjZRwKxWnXu9HgSzEjCBzvpMw0TZdHxdcDgeyB+HV1FHBXKvyII6+By+RnuzRYy8Z3eYe6jdImkPRtpiqlgCuEneO19IrfdsSWnKEpF0Cp4wLPZbtsf754+xwHKqdW1hItDqRlZPRS61CFgylQkws9/J/QRGDYF6MhXP+s2N02vawmeAz1mpETwsUM4LfP/PMqI61gHL8+Dq1dMSrfKpdwNu36IEo4BhSg9XldXqPJbK+IWZXdfvycw9dI6TtDK++JqOZ7vmwsVoUtSU3P6noSHzPoZ8P6Y1cBGfGL6L0ytjyKFb5Xrz1OEJfOKL4W2kjUcvJ/GcKtgOpE6ouO3AWniPx5fpFNUp9nl9bGFVplqF16NJtYKWK92A8tD4AAAgAElEQVSqET+RtBQeaG6IzxNbWW/7AvBxuaNCVkpm7YELlMSupVX7htI5o718r2SYWaGIvD4+398i6fcEc3kzS9E+s/puM/HqQHEuMH/ldYoVtBou7vJsWDjf2eI1dUDSzxjxJ9zSEv1Ar0DMZ2bnEhljzWzjMG4U/dXHyC07TiUhtQ4gaXdzL8bi9fvKCXRJR5jZ/iGJ9N7S7/9mZouUXv/MzD5cOvRDJKqs/Vbr2sJEoFXBIBxbeRP5NnjGuGjI/DneN5Iq1/dSXmlVRQxmqYRtiQ+4q+CLl92AH/dJK+iFFUkrj12K9xGkrrGnEWI122VmfS+8ldmAO2zkZOUHQFbAhKvNZdNIzOw24LZQSSzU4X4UKp9dpoe9nlW5BPjsitHwY4oFbkA23SrXm6cOH8Z7jWJ4Fh9XY43nC5NWwevbdsDMPlI9iJpJAQ+TagUtVLrVUPwET8A8iVelHgG2jNDjY+dKVj0C9TSVyMqyb5B0UOp8ATPpNFR9xaAa2JcRnvEf4WPq6xhZGH9drpy+xRAvNYbqd9lUDGiKmT0Ls5hAo1md+CBOc98C2DxGb7fZW979NTgzYFm8in8CbvWwUqCRb2ARcbeQyCqk1gsD+1ktKWa2b+R0X8XbWApcSOdY+TmCcrCZldVhq3PjqpXXjaqsDat1rWAi0KpA0oVmtlnp9YFmdmjp9a/M7D2R/ebHg6ql8Qz8b/As8dG4qMV6sXImbhZZll2tYnL+X9MNSefhi4V7cZ7rJpbwFWkBc1vCY8XMnlNaehg6F03FZzOz9LqWFib3eVkAeCJ1DQHZDbjDRGZWvti2CE67qqpaHpf47nMDplZoJGGhcD5wfqBpbFv3/rBoeLzInIUAaz+8ojpvG9cUkhMrWDBqlvcelsfPE2JZ4WpGNoZYltby/ZhuxKtK0yLbdqJGXax07ly6VaPASp2iCwWK/ssPk6b9XIYL+Gxn3YInR5KufObaDgB9SwEPk2oFA1S61b/4yfXheKkqbepcrwoZ6bqAq8sA2vLtG1KiHTPxhfJCvEICLbnC7s/x53MePFnSs4puZo9KOg1PXhxAmiJfV2WaRDwhlIUBWEHV5EJVur41Wj0NPtvZHCfiAkMX4snOTfFxdVs8+XYsEPNvnAUbMbC/HE/YfBH3Oasid6zsNc/0leAL67xota4tTARa3fhg5fUedA7KXZn1gMOAp3F+fFnUYn580XgYECtLVmVXY9vbxKO4fH1SfaxFzKGKkEgJk6iv1j0afmbg/TqNmu4lrYv3DawSzjFT0q3AvhYxJiWzAXcMkGUGHIKsW/Hg6keMeIxsBGwTqE7VRdUgindvrtvfIn1FPaqX/8A597FzrYpPCIsDj8ol3lfEqY6X02NC6BOFeluxIPwS8OPw/yXxe/ngyH5r4JWAM/DPvi+ofz+mL+OiF2/FJ46yp8y2pAUhouhBt8ry5qFTdKHAC8BvgQMsLR29H64geIek8xn52zbDA+q1Evv1TX9SvhTwMKlWg6jJ9S1+MkBVfQp+L9YtnGqrAOrDvsHMuvoiJX0AFwz5J77we6XgEvy+3xUXQUn5wgGzgtUP4tXqjfGAdTpp8Z3cKlPfkLQ03u9UiFps35BhNDRaveX7ns4u+CCwTKAhn4vbZ6xiZv8OleJa8Qm5dcdUPCk1Dy66c1ri7UMdK8cSE4FWbzRddG4KrF2tWoUb9nP4Aq0r0GqDEib3E1gIeKxmkVKcr0vNaxRRFRYpo86sFXzi3QAv5+6JZ9NnmFky8JS0El6NOQ3PdpZd7C+TtFak7J3bgDts5Gblv4RXr6Zayc9HLmk6A/+cuhbROQETvti9v2a/VBWyruJYV708Fu8jOgPPzF+ALxxWtxaVMwM2xWlVBV4oFnRy0YofEw+0VgrXtiseSMzAhWF6KWll+TGZ2U1yyfuj8R6jOXB1t+uBD+UmWKoVtoAsbx7LFGIws0ckrYKPB2V69kV4dTbVZ5KDMZMCzoHi3mJ1giateU0F6uGGeF9XrDLyTC6dSgPaN4Q+jCPwxfthwEkpat3sAElvB54ys4cAzOy7kn6Cry8KkYbUfsUCeArO3HhvoGvX4Xkz61KgHCUcT6eoxTQaiFoMk1YvaZqZ7Vl6PbVciZMr2W09rOsZBcxTrGHN7DFJTxVjv5k9L1dh7UBIShWtKIXk+h54K0rts6ZOhe+u1wnkWFOMKSYCrd5oOqgviC8yY7gfp7HVohQwPWpmTdytt8YV1xZl5Eb9K7CPmZ2R2KdOrhlolWOcLVZQ6Rl5Pc4n/3agG26aCCi/iCuuHVn63V3AFZLux6Xyq9nO3AbcYSM3+/MhYKPqIt3MXpZ0KE4XqiI3YHoGX+j1i6zqJfBO4H1m9pJcJW5P4J02OupPb6wEl7NoBmb2gKQ3xXYyF1cpGtGLxMHxki4EDq6pjGT7MYVkxDqBPrsgTqusC2azMMwFTumcT+CVpa4+HElTEgFhju1ArhTw0KhWBZTnLTaw15Sk5cL+WwOvxqXtW4MGsG8Ic+nheAX3WFwK+pk2r2+M8AM8WHoIZtGkf4mLjfwKr7h29RcCtzOyAP5RH8nELiGTUcTQRC0GwE50etMdh1fhCmw43MtpH5VgZ2aD4Odh3LD+dLwVpankehOF7xj6tqYYa0wEWt3oZQSc6gt6AM8oxkQE1gD+kjphCJi+jgdMxe96BUzvw3uJDsUz+eXqzbckPWgRCXTyneX7RsOyfxM8iT/Mf8UzJqnerrWI+GsFnIrTEaoYGjVijLCYBQPuKszsHkkxGlduwDQzsdDthb6rlwFzFgGkuTLRk6MUZIH3mSxYVE3MbOdig6SF6CEjXyQOJN2E/4174MFaKtAa2I8pBFetB1gx9JskavncU3BvrX1wGmkVfdsOWL4U8FDHE2V6i1mm15SkBfEq5g54tfZGPMha3tw0NoZcKnKWfYOk7+F//8nATi1XOscaS5vZnTCLFn4FLtV+VPjeU31OS5hZ39Rl2u8prEOWqEU1MRpDi8nSYfdgDhs5wc+6DebqGHIVvrOtKaoIFfPlcAbADW0dt4qJQKsbvYyAUwujk4HvS9qqfNNJWg0f/KIKewMETLsCe1lnk+e9eLP4Y3jA0bXfMDnGkno6tZtZMgsqaQ18Qt8Epz59h3plxAUsIRhhZv+QK+pUf5/bgDts5JoB9/KRik0MuQFT1iSTWb2E3hSCZNUnAzfji7cY1/8TeB9cFOHvKFSNVsADrHdFaKyzYJl+THLbhFQmcBL+3cYWp9/Gx5Nn+zxf30miXIS+s+/hC/w/4Zn9t+Jj68N0ZppnwcwOyTmfZUgBj8F4kuUtVoY1FD8JPRvCk4ZnAVuY2f8GlkRdtajW96YGuYyIHXCRh22ArauVYJitleEelVuP3IuPI98ysxPDtkXwPq0YTqBEw1NDkS8yhUwykStqMVAfbJ94pfcV5TxzW1CiiTelU/ZKxKuHanCgLK5OSPABNwXGQ+r9ZwNXFveQXN34CLza+zVJnzazs1L7D4KJQKsCy3ej/4bc8+o6SQ8yEjC9CR8Mow39ZAZMeJk9ysfGVbG+Gtsgb67fyErGoqVtJwMXm9lPEsftF6l+sJn4AmkhEnQTSXeF952OV7GKAHcOBfGECP0hSypfebL8w0auGfDc1eCjgliA1kuFbVGLq5J9tG6/hmhavQSnCNRRCNrEEcBF8l6YC8M1FkIMBzHiWt+B8EwViYKTgEua3FPK92MqC6RMwifA1XqdD//c/kfSjonETuwac5NEuZiOU1qPxIPxi/GF5afM7KctnqcL1i0FnBp7h41cb7HbcCGMq4BrS5XaOvGTTXDBp+/g3ktNF7WHxIKdEqJ+WHULsVC92RCIveeVrAy3Py6u9QLO0FhZLiLxMq4YlxIJyRX5GljIpA/kilpk9cFmYlJI+BSfxxyV17N1RSuThTQQnVIZqsFy1d8DcbXaf+DrtuclHW5mX0ucai2CEI5cFGY/YDszO1fS+vjzMBFojXeY2R6SvonzwosF+xVm9qea3bICJmBeS5hFhrL7qxP77Ula2vYsfNHYSqBlZmtWfxcoRoVPQ+pvA19AgAcQ1SAjRRup9kdU9+nqj1C+LP+wkettVeUzVxHjM38U+h8AzexXGddHOHa/1cvsqk8OzOwKSTvgk8gxpU0P4gv9XyZ2/TSeOFkUp7btU110JmgtWX5M1eqfpJdqKoLl/TaWtAVeCbkQr0j16mnpO0nUpModrieWgFkVeHOgiV6LB+VL2QAGvf3CvCH8h7if2XhArrfYqbhE/cnA6+XmtVfjgdc1ZvZYZJ83EsyMgSMkXY1Xy3pVzbP8sGKQe/QUUvvgPZ0dGCZrY9gws/MkFcHUi3il6k48iXgZPj43QdOAIFvIpF9YZs+n5ffB5mBevHpW/vx6jq+zC6oVxBgiVcUsOqUyVYPlNjy74+vmi8zsBXkf2Sb4936nmV0Y2XUBCyIyOLPk1eH84M9OqwyMMiYCrQoqdKwozKxLjl3S781sOTP7X7wxsClyA6Zco+Nl8ck0hmuBd/Q4bhYkLY6rsm2B01KWNrNHa3bJKWE3mqwryJXlHzayvK0y+cz/lvQAw5FNz61eDlL1yULoWfmhpP/CBQf+0SCIyeWTD70XIGT2foEvnu+WCySUt1cDwpwkUbXK/S7cG+wh/H5bALiFeKV7ipn9K1zL0/KevPvTf9GoYQ5gnTE4bwxZ3mJm9k3gmzAr+bV2+JkOLC7pDjNbsbLPP3CD0W/I1R93wCmHC+G0w29YRMXO8v2wivcvyIi57kp49WYPfB75P4fiGQjYRdJewBzWn3LkuKO4aQCvSMjqg+0bZtZr3TW7o8saoYJYVTGXTpmrGvwZ4HNWsjoJ8/55gTr/WUYCqDIel/TGUIl/D3BLKZE7D6NYjZwItLqxJZ6xuJjm6mfgXjo5yA2Y5pWUom5MwqP16H5hWyxbPQ8tGbwWkAsFHIA/HOcBy1oD9a6cEnZmf0SWLP8YYJiUhGHKpkNe9RIyqz6Dwszu6uO9tfdk6EmLYax6ATbHex6+Sw9PHjKSROUqt6RjcB+gI8xVMOfAM/IpAZBqD0f1dZvGpLMLcr3FZsHM/ijp7zgD41E8GRZV0Sztcytwa+hz2AgPnH5ND2ll9eGHJemj+Pij8HediQtxXAWcWwk4/s/CmvVV5op8DXPeyfKKDO/tuw+2bYSqys/MrNVE5DBhZjm021w6Za5q8Ir4vBHDpST0EAimxHKT793pXGusjvf9jgomAq1ubIZnaXfCo+Im6meQv/DJDZhyH+Y7cPnXWMT/EXovrhqj9PD8Aje9a6xGllnCzsHAsvzDgvLMgO+j/t6caWZLV343TNl0yG96H1rVJ/NzTB3rVTjFZUf8mavrQ+sLkeemUUO5pP/A6WQLA+uVK4U1yE0SFdgRWKSgpoZg62g8YN478v6BjEnDwn1tgucgcHXbvV2SPtTrPWZ2eVvns0xvsRDgrwOsG/5dAJcHvxb4oJl1zQOSlsGrzu+k01D2fOB8xRVMi31z/LAuxb+nbczsgtKxeuw2gQhyRb5yhUxyUB2vG3lF5vbBjgLGU6W7dYRg9rNmdnxlUy86Zeo5z1UNntsSdiVm9ly4zhi+FH7Wxe+TsiLs8rRkah3DRKBVgZldhDe9L4zzwE8KX9wM3OwwmsHF+4Ou6HHsWHCUFTANwEOfhiu6TcL5rS+Fhd8mOJXk85nHjeEQfKJ8EzAjNkEm+lPAH4R7cEpRVBmPdh6MbFn+ISPX22rnyO/AB5Z9iC+GswZASaf2eEvfTe+9jtfn60GQ8zl2QNI7GekvWRiv8L438fZcP6Yq9aNqIpx6bn6DZwIPidE0E8hNEhV4Ag98yn1c78F7r7qQ28MRMs2X4tWdW/HqyNuBXSVdB6xvFWPNHomeyTXbfoqbHf+L9Li1VPOr7w2r8RarwV+BP+AmsccFynsvTMfFJw4jYihrZrE+sUH8sPbCk55nSdoJr7BfxDikvY13WL7I1zDnv9zxPLcPdgIRSHo/QdnVzH4kF57ZBaf4PoaPGbMwAJ0yVzV4Do14e1UxicRcHILvqC6AmU2vCdAGxkSglUDBR5fLHh+M05puwX0rYniBhMJTj/NkBUyS1m5w7C7ZVTM7X9IbgdPwCaxQbPkX8OVy5rAFDOJ3cAJOY7qX0VURypLlHwNkeVuZWYcimaQlcerORvhnfExkt9wBMKvpfYjVy2xkfo6FBO1WePVmZbxqcBAuCLObpRu1s/yYMqkfAMdaSe65IQalyBwAXCLpMjzh8RZciKVLEbUOkhayuHhDgV3xe2/ZclAvV4k9Hxft+Hpln169CilJ6x/jiZuf0ZwNkY3ceQCfz94b/r1N0jX433Sjpa0dcg1ls/ywzJV6j5O0Mj6XTMcTcHPhvX3J/ku5x9ReVCqYeFCZGqfGPSStZXmiSOVjzINXMJ9IVQZmM7Tmq/R/HaHf7xA8CbOspBPwChDAznVU3wz0Mh5Ooer1VUbK6ysJ9fZhHBiTZs6cSA7FIFdB2x6v9NyAL7qTKmiSnjKzvvtBcqkmcu+SGGYC8wOvtohfTmn/+YF341SOR4Hrq31Kow1Jr6+rlIRMSqEitCbu63Kamd3U8nUcB3wBV5CryvKnDJCHitz7q7T/IvgCfyp+L3/NEg7ukk7rdTxrILJRaXp/ENg/lliQ1EsCfGasGizpRbxiU2BVnE4GPuC+y8zqqg99o5/PMbz/X8A/cbXCMy2IN4Tnd4WaQGuoGPT+GuC8/4knVN6IP3vnp3rg5D54hWLp9XiA+1M8+/owsKFFxBgk/RrY08y6FEklrQlMN7MmEviNUGJDTMVpoacDp9fdJwOca9B5YAren7AOHpSshC+yrjazAyvv7bhH5GbIr2twjUv0ek+TqnbIYm+MzwcfAu40sy5Z8vCM3ooHVz9iZEzfCJ/vVpldg61BnlNJ6wJHAaswsiC9FdjXzK5q6xpzIel5vB+4wGZ0tjhsZmajVnVoggaV7uPrnrfxDkl/ArYys5skrYUnX75kZkf12O+/8ACtnNi4BmdItNrXnTuehL6xU/Eer3vwqnzZh/EYM+u7WNIEExWtCiTtj0+QzxH46A15o728h1JBRa9MdZRqYmZdkb9ceGJ/PDpPGgGH/Z8m7b8yauinP8U6jWwXBvYFrpdLrl8VOXadR9h38KpYl3S95cnyDxtZ/UZhcbovTgk1fHF/b90+TYKoBudt3PQ+QBUmq+qTg5zPMeBn+H2+JfCi3LyxpxjMGGBM/F/MVRubVtJOxBfKF+IL7k3xqsa2wHZ4VSpWZXsrLtQQw43AMn1cck9YpzrfCjjt5s+S1jGz1HXknmvQeeDfwDWBQvkLYD3gv3Ga5YGVt2cZyg5ADa4e5wVG+sHeiH/vMXwJr15NLSjQAJIOxtkR++PN8LMjcueBlXDl2tPwv7/seXdZqJTVCkeEpMhCwGPWwDIiA1lekZJ2N7NvlF6/z0r+fZKOsIry5QDIrXTPLnh9kcg2s+tC8BtlaxSQ+7j9Gm+/OIBOQZ4b5OrAra2lBhhPpgP34b2iQ/VhnAi0unEY3sh3F06VWC3C9+2Sd8clkTvQJKgws1whgPJ5Xo1Lme6F9zusZmZNaR1DQZ/9KeX9pjCSxVwRzz6kBDvqPMLOJOERJm/UXg74zVhT1GoQNQOWtKAlGt4D7sPlkA/FHdCXkTe1z0KsYpo4V5Gp38HMVki8J6fpve6cU4BnE1nC580s5gM2Gsj6HM1sI7nwwLb4PXxYoF3Nh1MqxwvmlLQt9WIrtYv2fiHptfi4tTL+eZTPFQuYPggsY64Iei7wd7w68W9JB+G9UTHMaUFwowpzAY5RyUBLWp0RVsRP8X7TUUM/80CoDhWVrHVwxgB4pfCbxC1AssRIJF1oZpuVXh9YpqlK+pWZvafX31eGuURzKsv+ITzh9lL5l+G7PpS0qe9sAWWIIuFGrYeY2ZGl390FXCHpfpw2FQ0iJG2NL7YXLc4r6a+4115r3kNmdkjmrl/FExsFLqRTffZz9LAQaIoBkoKzC6r31XOpsbOE/YBTzWzPyu9PlSvL7oeLy401xsyHcSLQ6sZXyWi2NbNZ/jC5QUVp/wVwJba76jjUGmlSPICgJtg2rW4QDNCfUlB6puJZ6+vxakUvNaG+PcIk7RGu53+Ar0nay8xOqv/LxgRLSZqvyLyEvoWLcc+be3Da1D2R/Z7G7+fPJY5b25wfFqEfw+/n9fFs1bcT781teu+F1KLiZOKGy6OB7M8xVLKnAdMkvQv/LJcDbpf0fTMbD/YBk/FKRgoz6VEdycCZuOrnBbjxbi/MU9CbzeyxQKP6d3j9fBgPY5isTinrKlqbB+V+gdviY9e/6I8VkXvOnHngSTybez0elH0FuLlufLVMMRI8QC5jDzoTYl30vwGxWGIsxMzuUY064myAXFGktfBexBhOxVUkuyDpfXhf3KH4c1quhH1L0oPl6tFoIVSGv2Jmm0Y2D9VzUC7Mshxwm+VZyoxnzKdOgaPXVl5j3QbW6zLSh1nFdLy6PB4wZj6ME4FWBWZ2cM5+uUGFpL2Bey04WYcFwQ/xAfUxuelq16QZss+H4Fndrc3slznXPcp4hJH+lI/bSH/KwQ32vQ6vLB6PS88uAuxYWbRXs6c5HmH/DXwolMnXxitm4zHQ2otOqtxJeFB5FN5fdjSeOe+AZapNSVoWDwi2xasNhtNp16jpb8hqem+AVOJjaHS3AT7Hz5ubwxb4o5l9LgT4he/LeMCzVvK4GhLeC7zBmvkAAbMqMZPCz8zK6xSq0tax7W3hftyP5XRGFq8rVcat1uTdB5gH3o8HVi/1fGf7GO3nttcYMyY02ZaQJYoELGAJM2Az+0egRsewK7BXZa69FzfJfgwP3loJtOQiHfvjfYL34CJkC+FJqg/jz1QMQ1OflXQEPi9eAxwlaQkziyrZzabIqdgtnKLDm9mDknr2ccYgac4eifV+MWY+jBOBVh/okVXJDSp2xLNDBY7HF/uH4lS4w+jOCIIPOo/iYgCfkhSTzY5RHIeJQfpTrsEHyA8ktsdoKjkeYYtYUHEys2vGcbbzLcBvAUJ/wsq4LPXfJe1Lp29FI0haEacB7lb5/Y3h+FfhZf8LzOyf8sb7uklrYBpsn3iVpPdST6MZa8784TgVq8CfgYVCZe9cEkqluf2Gw0QLlLB7cPWzpoFWVW1qUul1Um0qN0jOxEM4JfSzie1ty7vnzgNvqPQvLW0liXdJO49iZX+0FbjmVkUltYIxFVQYEDMtrQpZh1zPu9Xw6mwM55GQy87E8eF8P8NZFMviLJQzccW7OgZMR8KlYQImB1vhHpN3BubShbT7GYw11qzQS5tgUD/FDoR19o44lXWRPq+lDgP5MA6CiUCrggGyKrlBxRtxlSfkpqFLA4eb2TOB3/qFxH5ZFMdhYpD+lEyaSo5HWHUgzvWEGG28xMi1rYF7XBR0pKfp7VkEQMgubY1n5ZbHe0eqWApPGtyF01f/2eTYuU2qks4mfS/XZaen4IFKHY2mSnPIQo9rBJIL2lwaS1a/4QDoJeYjsy5Bk0EpYWcBP5J0PBVrgETVZ9iBfN8YclAH+fPA6bgnVYGb6exrOZr2KvtzVqib1dfRZ1zSW/DA4i/h9Tw4PXI5nPJ4TKJ/pCobXcWw6MajgV7P6aIJxkHVl696zJQv37yW8A41s6dCX2Bb+BiuFPuwXJzqftw8u1eVNisBk4kFi75HM7sj9C2/kvAlnPrfD+aRdFbN9p6JDbmAT7EuWQFX+k56uiqtcnhwqi91AOrzwJgItLqRlVUZIKj4Fz7IPYs3JP+hNLC9SOIm7UVxrOlXGCoG7U+RNB/+uSyM02N+nVr4W55HWNV0tQkneSxwE7CnpFPwxtKyYuTS+GcThaQ5cDGNHXBxlr/jYhWrm9ktkV3eEN63A65I9mf8/p+LHpOWXHr1YLwhvVBw/AU+AN6X2C0q511CKmP4zBC/m17XmEIujaXvfsNBYGbzy+Vvl8eD+N8BSNoQn9DeRO/sYr/Z4yKJVP1+U0qruYF8lpH27IBcqjvD7WupUjcfrbxOVSq+F95XmOaegDMcLsBp1PPRrYzYimrqOMZHYVbC7PEi0JS0KM4++Axxinzu/d1qtaIH5rdgem1mD0h6piEVdqgJmEq1bJIq5rmZfcjjBTnPfVUtsoqUWuQceHFiR2BDXGL9LGAJYNOadps6lcNfq2WVwzYwLhbj4wy5WZXcoOJKnOs7A194XFra9nbSSlpRhLLrDniJu82y68AIi/pbmvanSPoC/hDPg0/OrwOeCxSl6YlzTA8Lq7XwTEcvj7BBTVeHhT2BS/BB6246VXy2JSErK+mosH1ePIO9Pn7PPYSbxHbBOmX1F8Gls6fiwgXnSTrBzH4YOdfb8EzzbcCX8YHzDcAW+Pe+ppndHTlfrtrU0DDANeaaP+f0G2ZD0jZ4U/wTwEKSdsf5+qvgY9p3Gxymr8yxtaC42hBZRto5kHQD3Z/DCzhl9EyrGF+3cL65er0nsfAbWl/LAFW+FYDLYVY165PA+83sRrnXnxEJtF7h+LekB3Bj1UflNhor4vPC5STmswFEG6qJyDIm0ZBJ0RCTQrKnWOy/VHlNYv5oxT6gIZpUz2ZbHy0y1GcHmBsfxO+f84EPW7DskdQrUZKlcpgYmztgZu9udun9YSLQ6kZuVqUDfQQVewJn4NnrG+gUEdiGBl5X/ZZdxxq9+lNglqTsQbgXzHlm9mKo0n0CmC43y+yS1A/Hf5o4JS723lpFHLm8+JjD3MR1GUmvM7NHK5unASne/t54Wf3zwLnFoksVy4Ka8/4N9yf6uqRV8ezTKbhgSxWH44bSe1d+f1oI+I7EVSQ7IOkdwAYWMUWUtA9uFB6jAwytqV3SvABm9kx4PQlfnC+HB/Kpe7lKYzqHelpTgZx+w0GwP55FvETSpvh1ngR8siZDm0UJGzas4rWxeXQAACAASURBVKGjTiPte2hJ+jkgRrebjFfoTpe0T3WhMiD+Rb1YzOy88JtiZk+G/78LF2y5EcDM/hCSQF2QdB/1C6qZZrZ0u5c6NByLswvOwOf7C/B7eHVr2Rg2YJiJyHlx5kB5XC/3Ho+He3nc05cHxDDVZ/+GMzcWAxaVNKVh/+G65KkcjpnI2USg1Y2srEoKvYIKcz+QqOCDme2bOm5u2XU2wl64UMMlxS9CpeVsSU/hao4dgVabGYsQYO2M+4ss3t+ljx6qQZZK3lZ4kF3FSmHbNOB4SRfgE3XOuW8Gbg7Vjhjej5vDxnAMviCI4UC8WhfDX8L2WP/TxxL7ACDpM9aeitA5eNAzI7w+Evg08Evgm5IWi1VZB6Ax5fQbDoLFS8/aRfhz9MUeNJhcShgwy0frUNzHaWE6x9hRoYSqDyPtHNRVDiRdjFcG2wy0chd+dZTpVisVPfo3gGR/418CDehWnIr8q9IxF6KzslDGzonfL4+P5+O1D7cJ3omLMbwk6UA8UTtq9gG9EpEtn2vcfy9Drp6NBYamPmtmKyoIcuFz2nckXYQzNurWcVkqh72quqH1Z1QwEWh1Y3bIqkB+2XV2wdtIV/MuJx649p2xCEH0qTj94h6cZvdWXPnxYXwiG1dQt7fVQyT+9tBrs7vcRkB4ZfXn+GJjV0nfNLMOeqqkm81s1dLrU6xT/e4eXAWxisnEqW6E309ObFsz/C0xXEzamPTHcgW+A8uZMElL4f0db6A9FaFVimsMnPzPAp8ws5/LPd++h2fTWkFmv+EgmLXIMbOZoZJf22swACWswLdxbv1BeNJka9xY9aK6nXKglo20c2BmN8kFHto8Zu7Cb5iVii3xOfVi0pX3GL4O/ESugroeblxf4MPA72I7VemZkpbEg+uN8D6vY/q4hvGGOS2oRZobrz45WkEWgNzypBbWkrKrpEvNbP02jjVaUEUOPIYWk3uveJjZb4HdJH0Rfz63x9fgN0g60cymRXZrrW8wrKU2wAsWH2GUFEknAq0KZoesSkBu2XV2wbP4wvLhyLaFcU+nDvTIJs+BV36qmA7chy/CtsYXAy8Cn7JgDjxeoLS31ZqW9rYCwMxewCsyF0pajJG+q73pHlz+q/J6M7x6U2DBxGlux6mdZ0S2bQb8PrHfQri5cQwv1JxvRbxCcHtIMNyAK999BQ88v5LYLwfz2YgPzcrh318AmNkNIShqFRn9hoOgqko2f+V1VzU49M0sbWZdNEa59PGfLBhEJrAe8HYze1TSS2Zmkm7Bq5vHxXZQhtqURs9Iuy/IhWKqtN9Bj5m18BtmpQJ/9qfifRMXAjMKCmAdzGyGpHuBVYEjLdhwBDxDD1ntQC08KJz7dOBt1aTSbIjcns8kwmLzAIv7QaUo0TOB+fFkb1uJ5/e2dJzRxEl4ovEh4tT1UZMIHxLGxGMurE8uAC4I65NivIgFWm2oHL4TX0ttg68nz2MU77+JQKsPhNLiNmYWXQQMEwOUXYeGEu2jeD05PFDF64+Z2WWJ3S/DTRG3i2Sejwzb+8FkvDpQpe2sCrw5ZAevBZ4ElrIhOYY3hfK9rboQFhtH45/v6pG3VI9XHXxT5zsUKCox5zKiBrQ5rg60ZWK/B/GKUZcxN/43PxTbKXxH60naBReR+Rte8fmAxdUUB8HfJC1jrma0Dq5+ORNAbvbZprHiLPTTbzggqmIQJzfYZx+8QhQT+dkJV5z8Ws3+c+DPG8A/5abvfwWWib1Z+WpTo2WkHbvGt0V+PRmn+H0Zp3e3iayF3zArFWZ2EXBRieZ8UqBmzwBOsoR8eOkauq6jjvIZnsd9cXqtASuY2b0D/RHjB7k9n3WYE09KdQVaZtZ17EDb3B+naLZJg81GTgImEyfg89m9+P170TCSNMOCmc3f7z5NqvRmFhXeSrz3EZzFkmKy5KocLoC3IOyIryt+hSdivgbsNprtNhOBVg+EbM/6jNC0/kgi2zpsZJZdh4krgbLj/F/p9Go5p7K9jP3wBdUdks6nc1E1H5CjDhNbiEwpsu5m9nSgYtyfcezRRpa3VS80ySzTMJAzs59K2g7PQpUHuweAHS1tsPsD4GRJG5a515LeBJxIzWQeephegy+U58EnwFgVdFB8B7hU0uX4YnGX0ra1gTYn86ErJPXiryewOUFuOoJpuM9XXaD1W7yx+Re4ZP1JeKUi1QObpTbFcBvY78K/t/JY8yL+DJxJ70VCv8hd+A2zUgFAqAh/Q9K3cQuIw4FbgCti7+9FYZb0gJnFFnn3AS/jiZ/bcRGhjuDd4j5t4x4D9Hz2Qs9Khtwzaw+8f/pKYLWWg5i51cOKwcx2jFzX0OS+zWw3SXvhdLPt8b7nH+IiULFE4f8F3E/3mAcj89dMIrGGXFzrYxZUCyXdSWc16uNmdlt1P6tROQzrgVRl6hF8DXVcOPb9YZ+DU8drCxOBVgJyJbSitFgs5DY3s4uHeA1vAR4qONkpJMqunyJedh0msr1azOwRSavgPVJlT6aLgGlm9kTG9cQWrpMr9Jvq6/HCuc72tsrAZEmfZuT7mVL5TJLjRil7/Z94teMf1ls85ii8onW3pJsYmShXwxfr0cyWpLfji8s58cH1brza+XtJ+5rZKT3O2xhmdrSkR/AK6OfN7JzS5oVxNbAkwrWWs63XWr1K2FAVkiTtbmbfKL1+n5ldWXp9hFXU+3ABjZRFwAOSeonI7MTIPbYrTt+dH6e1xrAuGWpTdX1MciXTDXH59YExbOp57sJvLCoVktYI17gJTvP9BAlbioBcCvPT+Jj4ucT2qE/b7ARJ/4FTb2d5FVrap7AJknNIeEZ2wYOYO4CPjFJQMZO0FUMdchMwWbBO+5OF8erp9ZLWs9Ar/38MMbProo95P9ItA3vh69YCBfsFfJ7dhzQLJoW58CRALEn0M7wPa0vgRUlnWkJUo21MBFoVBBrSDoyUFg/Ab4a7cI+gYeJ+4P6wyGm0aGxQdh0msr1aJG0AbBQymAdVtp0s6eJqhUT1njIp3u6NuD9LgZsrr8cF59oyva0ycSOdKn830fmZNOmv+GOv95Te+yKwqaT3Ax/EA7QbgEPKi/3EdR6N928U1L3dQgX0VEmbm9kHm15Hg+v8PpEFqJnNqNtPbjK9I07vKoLIN0qaYWbRBUBmv+Eg+CrwjdLrC+msPn+Obhn0f0t6o7lyavUa30C67w6YRf0s/v93ei+GstSmYpC0PCOJNBgFAY5hYdCF32hXKiTtj49Vz+G9Uk1V8rIozDa4SMu4hqRpeGLiIUa8Ck+UCxvtkdgnRmktkJw35Z5Kh+Am91tbhtVNH/h3JJnTBOuSJ/edjUB93RhPHKyIi2e1bbkxW8Aq2gBya55DgMfxeyZVPV4dnxcLvGxByEbSNcD/ZF5SNIFvZhuF9p9t8e/tMEm/whlSc2eeqxEmAq1ufAvPOG9eXriqoe9Qy/gPXN2tJ5/+FYg9cepHDGfhwVeVitbEU6YDZrZu5vWNGaw/b6taSFrUKkIauZ9Jk34xq5HtNrMrSNCIEni3RYQYzOxXcuPu1mhakj4CPG2hIV+ubPh9go8WsH31cwzv+xS+CHh3maYZeuPOkLSTmTUxAy4j1W84CHKqz7/As44xuf+9w/Y2MZDalKQFGfEbXAmnl+2BPzutYNiUz9J5+174DbFScRhOub8Lr1KvVp1PLS7vXsXAlXuFvmYz223QY40FJO2Gf89rm9n1pd+viY8nfy5XpkuIUVqb4HRcwOUm4FNhPOtAw++uCXKFGFpLwPRC+Jyn4n6Q1+O9rJeUEn3/ZxGS44fjgfv+ZnZ+j10WNrNnS683Kv3/X0Cu3HpynAgJnmnANEnvwueC5XBBre+bWazfeGBMBFrdKBSSzpH0v3j/yFmMgcBEoLz8Ge9fmB1Rp2Q2iXjJucCyuPBDDNcC74j8PrsXQ9J8uMz4wnj27tdt9UGNJqy3txUAYbJ53MxeDq8Xxcv6n8H7+tpAv2X+gRALskrbnqNdaf6v4tLjBb6D8723wisxxxCnvG0H/He1F87MbpT0eVwgod9AC9pXh8qpPh8I3CS3SCj3UX4cz1bGhFYGQZbalKSP4hOqwjWeiX9vV+Em3nXKiP1i2JTPrIXfkCsVXyVv/symMJcRxr4iwF6e4YjLjBY+C2xbDrJglvLp9njSoCvQGoDSmvvd5SB3ndOa3HcDXIcnDY7HxZcWAXZUp4LpmDNghglJ78Vp32/G75cZvdpdAh6XtJQFoRrrFN9ZmhGhpLau8/Nm9s3Sr/5oZp+TtAdOZ96+zfOVMRFoVVDqMVkELzFOxSsrLwObSDqtYbNxI0j6UINrmi0bd8lTMiswL96QHfNlmodIcFDXi1EHSV/As67z4Nm71wHPSTrQIia0Y4FejeEkvK1CxetC3HT5Ublh64p45uly2vXTWdPMjmzxeLXo9Zko3Syfg2UIlMlAz1oHlzZ/QNLNQFfTbsCyOB0rhivJV6FrffEj9webFH66XldhZveG++sQRjyqHsXvq9UG7BmJIUttClekfAxXjJ3VEzAaLIU6yucoIXfhN7RKhZkdXLc9VNZiyKYwB3ptEWBvgAeUrwNWt/YVSYeJt+C06hhuIO5vWAvVqCkP8N31DTOrNaCvwcBy333gGnzs/UBi+7hoNRgyrsbHkjPwAOnQSMU6ZjlwKT6mfzKy7VDc5qMLPZgzdQnIw3GF7gJ/BhYK6/lzSQsEDYyJQCuBQM86Fjg2LCZ2wL+oQ/HJrC30Cj4aN+4GAYJlgdtyg442MeCi4w68cfHCyLaPkKDFSFoZ/wxuNrM/hiDqg3hD5qHV7HXgEx+EN4CfZ2YvhsnjE8B0SY+a2Q8G+DvaQm5j+LF4Bv8M/B6+AA/KVrd6MYYcfAkXoxgWcj+THEzCPb3AKzUPFkIQZvY3uaR0DHNZydKgDDN7IQQzXcjsNxwE8+F0jQKTSq+jtFuAEEx1VfIkTZG0q5kd39YFWo3aVA/shSfMzpK0E/4sXMQoZupDFa0sfnK1jY4vX+7Cb5iViigCvXcHPJjqmlMHoDAfhSdJ58W/5/XxpMZDuPrj7IwngSVxlckqlqBhFUADqin3+u5yIOll6u/JmWYWW7PmJmD6xuzYajAEfB//3vqdbw/CFbJ/h7c9PIwzIjbGBejWSOyXy5xpLMbWNiYCrQok/drM1ii9/qyZnYzTs/ag051+YJhZFt1N0rHAb83sjPB6KzxL+QQwr6SNx7oSJundwMZmtk9k21G4FPGvE7tPA74tl+u8yMxeCpPDJnhW4vORY+6MU7j+ALxN0vdwxcKf4spiC9CtRLUXztmflT0JtJuzJT2Fy1OPh0Ar19vqncD7wud3IE6na9qM3i8GHrjkJrgLAE8E+l8dcj+THPweX1T8AM/AzaJaSXoz/tzFMEeg1qU+mxStpe9+wwGROw69H+93usfMrNT3sz8eYCQDLUkHJTb9G/gL8FMzeyznusoImfrjQhJmB7xB/iS8l+Bd9O/Jl0QInC/FTaZvxamKbwd2lXQdsH4q8M5B7sKvV6VitCBXNixofCvgVZiusbz03tUtYgsReiZvNLPHI7vujd97n8epoc+HfVr5G8YYP8R9yDYxs1lsD0nz4lYYscQkpfdlqyn3891l4u2J32+A09xj3/UgCZgJtAAz2z5zv7+HIsaeePK8UNC8BFeWjo79lm+23liMrW1MBFrdqPb+HEGoOgV1lVErL/aJjfGBtcCRwF5mdrykHXEDwrGmHO5PutH8GrwCEp39zKwwvj0Nz0T/A38Q/wV8uUwBKmEvYB0zu03SavhE8B+B3nU87rVRDbTehst+xnA54+f7rqLpIDFnwZc2N2V+cpSCLIA5Q+9HnWx/VMBB0rqMyLxPAmZKuhXY15pL5o7mwLkfcImkE/FA4D2lbVuQViSdF29ET30mqWsepvcTuMrqj/tp6pbLih+CJzaWlXQCrgAGsLNZ2lQ2YAU8AfI7PLB6c/jdT/Ds/LclbVR8/wNkvAEw92S5TdKejAhH/EjSnWa2fI9rbYpd8SrWsmVWgaQl8D623XAhmzFFWKhfVHq9tJn9b+n1zmbWSr9ZoPF9BBft2RDPXJ+Ff8ebWtoo9AC8QhPz33sXXsXbO7JtJTwYmIbL3V+AV/VfCfgSLjJzn6TLGOmL/ChOhTogtpMy1ZQH+O76hlWUaiWtha9rlsT/7lqPrWFAYyR2MztCIwblO5jZCrH3hETJgeGn6XFz222mSCpXN+epvE5RHAfGRKDVjV5Z8lahHgZ9+OKhiz8PvL6YGOUePYswEtR8H6/sjDVWJj5JggcxtWpfZjY9fD7vZqT/43ozezqxy6JhMYWZ3STpuRK968EEvetZPICLmdwujEsSjwfkNoYPc3CZDPx3zfaZRJTyJK2E3yen4cF5WVThMklrmdlvYufL/Ez6hpldHxbLbwPurtyDPybRa5XbhD4G1N+TcNPoM3FPmpT3SRm7AO8Pz9pahOSJmTW1lvgnMNXMzi5+IWkL4KPmUrw749TXVcLmrIx3FaGidD5QJHO2bXi9TfAJYNfq92dmf5Yrxk1nHARaOPuhLGl/M51y/kfTnrDHg3i/7fnAh0uBcy/z3Q3pTGiUcTLen9YVaJnZ74DdJe2NJ/K2B36OV493lcugP9L/nzH2MLOnJb0Hv2fXwwPOf+Dj5hk11dJcNeXc7y4bcuuFw3Hq2FHACVXKf+X9AyVg+sRQxW5mNwTW0ccYoaT+P+Dbiff2VNO2ToGMArntNmfjVggFzqm8HjVMBFq9MdrlxZRB39x4NmAhukUlAJ6WtGDICKyJ0wiLoGAO4oZtw8ZrSVOj5sCpC7UIC9pUxakXmoiWXAYcLWk7M6t+10fSIq1oQOQ2hg9zcHnWzNbM2O+LuGdWub/rLuAKSffjEuKxhtmB/b6aQtLCZvYPnA7WgdAL+A58UmkN/fYbDojF8YBlO+AWSXfgGeSza+h7r7cgB25m10l6nv4SPBvjE3IZF+A9RdsD38MX/IRztJ7xNvcAa9Nz8K145TyGG3FRlfGAbDP5DPwNv48XAxaVNMUq3jsJLJaqmATa0WJ1O4eg40LgwvDewntwb0anz3FYmAf4DXB+TdKxilw15dzvrm/ILTO+igfYxwNbmdlTDXZtJQHTBDZ8sZvZApKWxcfybfF+X8OT1GtYxPYkIMUWmokb17+ayDo2t90ml+LYBiYCrW7MK6m8YHpt5XWtF1C/sIpBX+hJKqh/99BtElrgUuAUSWfjE8eM0rYVGR9Nv3/CM5Ix6eD3hO1tYm51qg/NW3kdm1z3wzOjd8iNbotqymY47Wutlq8xCwP0Ymzf7pWMCtbCKVUxnEpC0W/Ijcn3UkoMBLpZeYK/gUjioKYPaRbM7KuR/XL6DbNhnaa3hRz2jrhX24+B0yK9MtXF+HMW7AMa4kG8AlSecDfFRQvAJ+yuRV2/Ge8hY87UZ2BmL4eM73hAtpl8vzCzFRX8q/D+2u9IuggPGOrO85SkJa1kbF1A0n8ATYMMQgXraDyp1rbtwNAQaFM/xBehTwVqbc+eFctUUx7gu8vBXThV9FjgEWDLasXNIgqaswPl8JUMSTfi7KWr8PXUBWb2T/Xw1TSzroRv6APc//+zd97xctRVG/8GDB0ViIiggoC8iNKkhiJKR+HB0CGUBPEFXlF6l94iJfSOAkJoAQJHUJGOQgztVUSJoCIogpSXXiRA3j/Ob3Lnzs7s7M7Ozu7e7PfzyQd2Z2dn7r27M79zznOeg5uTlTknssbeXdK8TSQqWqIfaNVSpt11U8itt4/D3c32yulxOACfl3E0rrmOz87YCK9cdJoL8WBwW/N5T8AMy/ELKV9Ck3QfSroN1bgPmdkLklbCGzI3ZKAhcxJwuqU3W/cMklYys0dij4fH5SWSvmlmZVXtUrPgYXG5Ga7V3jzlJZ8M1aIazOzlDMln1SR/tmQ2PasCkJVtnY5Xp+bHs7hJivQbloKZvYJnlM+SG9pMwBMPySCh1aTU9/HF3/4M9GgthQdbAKsQu661kPGukuGSNiD78zBT3nPN7HfA3pIOwAeTjsETWZMlnWdm41N2uwUYJ2n7FLXB8bhkt8i5lFbp7gAn4P1VF+OL0eOBtRvd2Qq4KRf82xXhAfy6+I2M7XWt07s8ATOUWRyXgU8FplqB+aOS5sKHx++PO4SuamZPZLw2N3A2s11Tnk61d2/2XIswU17069FIdqhsJK3PwByao4ArU24sgwgLjLQPE/jFeMdST7IAZna+pKXwC/I/GagWLYJfBEvVO1tB9yEzew23Gs2tPvQgdzO40vJvBl9crqEBCWcjmNm88ceSlsVv5KPxDOgVGbtWOXCyKIUqAGZWI3mUtB7+fX+LwUOQ4xTpNyyFUFVfH896fxuvPKcNf24pKWVmd0laDJf4LIxX1G6NpIqhoTne1Fwo410xL1J/jk5p5gEtUi9IHoZXTEonJHmuB64Pcr5d8IHfaYv1I/HF9+8l3cjA/WMUvtAvIlPudZYEzjKz6SHhcnDRN7KBYff74QFU3uvT/nbfIf1vV+R8vl5kvx5JwAxlPoNfw8cC90l6Bk/OzUZO1VMDLrWHE0b6RHL0OiSliFHvax59e/eZFUl34tPqx+FVnvfxrOiM16SV9HMYjhsLlFp6LYKZ7SvpXHzhNj9uaHGHmf21bL13COqSTAP+YRluapI2BTa3wcN/o20X4dbyWYYevUCVvRhImg/vmxqLu39NwRdty1n2ANu5JGU5Xw3Dg7SeR9LKeIC1LJ4MuaBO83qS0oakZyFpaXzxtCPeI3o1sLalG5GUkpQKFeOsADxJSxnvNOQX2qPMbKXcFzeAmS1WxvtUQMeUGxFBzvcjMnrkzOfTrYwH+RsxYIh0E642yBqpMJSZNUrCms98TJ3Dl4Z8zMRywF/MDUOQtBnuHPpZmnDYzfvbFSHWB5u1/ctm9seUTb2QgBmyJGTnCzLQCzkfcJ2ksy1mwBIhdyg+Bh8mPtrM0lpM0o6XbLfZI/lcBn1795mYaNFwCrHG70A0L6eIrr+yaD0PM/sLsX4s+TDTvXGTg0VKPNRU/PeV/Nk/lFv87mFmyYGO++GyiTQm4FnVXg60KuvFkHQt7vL1D7zRetsQUD8PvF1n1zSzl26jSP8fMGOQ+Im4HfVp+Gy5er+Plo5XBEkP4r2dt+MSDstL8Giww2PENFyScX9eEkU+f+w4XN8/T3ybmdW4RrWQ8f4U/ntfEe97/T5+3Tkfl4CWNlS5gXOprC+gHnlBsqTSPl+hjzhPobFDxvNDWW1QhLzrQurvMixqf4zP+5tf0j742mMlvCJ1SftOuWEK9cHShgRMFpLGm9l+sce7xA0yJE0ws9FlHKsXCdLUU/He3lXxhOvFeF9hksvxxMmDwHck1awDsq4LCRpdw/Tt3Wdi2jUvp7JoPYuQQfsJvoB7Cm/C/SJuNfs86XKkwliKlXYoTS+BL3TH4WXqOMvgTZxp/JrauWp9shmFN6hfBFxl7uiWi/WGk1PT/X8AwcRia7xavVsTPX+FjtcCN+CV3bQxB1mkOUEOx5vQ3w/9f1Pr7H8VnoE+Ch+zUIgGegDPxbOr5+O9Zjfh/Sgn4Vb2pVULJb1oZgvGHt9mZhvFXvIcJUl120EIsPag3CRYvc9AvXMpZP+ckQBI7terFY6860IWh+Bzr26RtAUuGb8A2L7Mz3+LFOqDLZqAKchuDF63nI4HDBGbVXguXU2QAD4Ygvo0jqXadepV9O3dZ06szrycECRshmeIk9tmq/O23WJdeybwNL6g2RFf4HwAfMfMflnFCYSy9p/DzTdNAjU3Lm1LqzDMGbZ3HElPk58VTpsdkZTlzRt7XLYsb2HCgELgJEn34jbCdSuykj6f98ZRf1KnKNr/h/8u3sF/L6OTkpbw3jWGES0crxBm9iNJH5d0FINNYW4HzkiTaZlZVgYZSYfhVaRv1Tns8sA3smS9eSR6AOciWyr9NWBpM3tN7iz6b2DFSD5VMsnv0yqJx12hNKgyCVbvsxz6AbPMHArZP+MBxFN4UJv2+y6twlE1LVwXFjGzW8L/T8J/Bwd0UZAFJasvGkjAFKFSKX6vEIL3ZfCq4134tX99fBTJ3qS4x5rZ0TnvmRqfJNa+w8Jzw4n97tM+12bWttlvefQDrRSCznR//AY9Pz7o715cF541E6DM4y+HLyAiQ4tJKS97j+wLz7A626pkFeBzZvaepF/jOurFLcWutwJeIyFPCjwObIxn9JNsHLZ3A3vE/n8YPkByqwb2S5bj84b9FSbo688AzpA7OY7BZQTz41KCMyKDhwR/J13yOT32345eq+Rzsja1lGG8kg4CfpbhkpQZjOQcr+l+w1YIze334zfEifhi+zN4NW5n+dDoZqpdp5Ft2R/xMF5t/nPO6+LnWagHMAoUzecvvdmmIAvyr7vdcF2GwUmw0XQgCRaYDTfsSZuXU9T++WxgG1yKdhneZ9tNAUVhJM0NEEmPQ6D6Hbzv8wEzywpOZ6g9gpHG2438Tlrom+oYTSRgilCZFL9XCMm57+L3jz1wF+wReMJjFH6taVhOKWl5/O+3A+lOmMm177DwXPT/TbXbSBpBSBCb2fKN7tcM/UArQQiyHsGDq5sZcDraHNhRbpdderAVFhCjGVhAfIT3SlycsUu7JIdlMrsFe1XzifavdyjIAm+mfirl+fHA+eGGNcnMPgyZsFG4FeheFZ5jJmY2aGizpGnJ5zJ438yubtNpZWJuKf+I3Lo7Gkz7W3xhlSStqjYc2B2XvPyhXefZBEfgdtNp/CNsr9GTN9ALs2LGpiL9hq0wDs9G7myxOVCSjsYXKuPwBudGmYX87O5vgF9IugyXEM4gTdrVQg/gxzTYcn1Y4nHkcDgz0U1JsNwqgJqwfzazvcN1Z1M82XOW3LXwUst3NOt2rsGTgpeFx+PwRe6dwDmSFjKzNAe2esoGAMxsjZT9ivZNFaGVWpZ15wAAIABJREFUPtgiCZgiDAvV4OgzO0vi8cxY0foObpr0dEgQPoEPG38pXLNzA/GQQInWv8vjn6ustVfLa9+wxvtmON638HX++a2+bxb9QKuWw/Dq1S5m9mH0ZFhwXIZn07I0p00jaRP8jy38jz0Bv2DcA1xrGXMgikoOK2Z4Qi+ffFyqVj7Z2BgdE/9iboT3ZwzCzCZKWhh3abxK0st4NuY94Idmdn1Z59chLsTd4zpCyJpeh7sPpeqhk6YJkkbjbkSv4m5E3bAIHol/T9O4iSbct+QDgXfExzMsTcoComC/YStsAqxgiWG7Ift9MPC7Rt8oJC0Owhc79Vgbv0Ylq35Z0q5CPYDUWq6/kng8HZ8FUwZ5Ddf1JN9V0k1JsMwqgIrZPyed0EbgNugPSFrfzO4p5aw7w0qE61CQS+0ObG1mt0saiRtepAVaRZUNRecHFqFoH2zRBEwR5saTYPGfO16Rn+kqWvgczKcBzOzJUC19KTx+JSRJapA0C64a2hVfrz6P//0WxfsJU0dh1Fv75iFpGfz7sxOucDLgXWD1dqrV+oFWLRviTeEfxp80s48kHYf/YcrkVrx6tmN8UZ/Wy5FHg5LDKpnC4Ib5hxKPy9bKpy3kp+GLxMMtMUE+wszOlA/BW4MBC+EHrAvcwUqgsgxbI03o1B84uSl+M50NONTMJpZ1biUwP9kW69Nws4VMwk0lyqBtil97T8MDnIZooN+wFebBe7LSeImUuUqSJlO7sBgOfB6X6tbrz6rb45VBoR5Aq9ZyPa/humNJjwSVJsGKoIL2z7H9Z8er6WPwXrRz6R4peFHmiUn5vhr+eweAmU0OScMarLjhUGVSuRb6z4omYJomLQHWp4ZGpe3/xO8rE4GNogSIpLq9VJJ2Ab5lZtukbLsGl/FPSNk2Bf/O3IMrZa43s7dCQN7WALkfaNWykJmlScwws6eysvItsD8uyblK0m74TJmoWTWXApLDyqjYDailZscQVDUixesIkjZMPJWUQ2XJn2aVtDZ1Ai5Lce4qSKEm9HB+JwGfw52ILksmOrqAf+LZ5LRs+lfxn7mGkEEbg88WmRvPsgt3qjo1K2uXQ1a/YSs8iSeZbk3ZtiGx8Qwx0gaOTwOeBR5M6wGRNNzC7DDVMfTJaGYu2gNYGZ1suG6SypJgOQuZeomgQvbPobKzC7AFbv19IXBLO3obO8CLkpY0H5myDvBbC3O15EPMs+ZFJu8fEEYxmNnf2na21VAoAdOnNIoabr2IG2gsBHxajc9V3QMf0ZHGyXjLR02ghasW3sIrklPN7K0GjlUK/UCrlrwvZ6kVAjM7HThd0lfxC8WZ+AJmNmBl4Odp+xWVHFZJ1Y20kpbAb87LAo8CY1opM3cZSalHo/Kn2XH3rqzP7XT8RlUGRZvQ78V/nitwadxxyYqutWm+RRNcCVwoaTMz+2f0pKTPAueR3XD9OF6xPgi4xszeCfu1kkHL6jdshdOASyV9H8/0Rb2KW+J/1wOSOxTMkr/CQE9HmqFPQ83MiR7AzXH5SWoPYEblLZr3NaHBXschRcVJsO0K7lfU/vl+XM51Fr6YWxDYNX5N6XS1rgUuAm6V9Cs8uIjLh7+G98ekkSYVHI4vcB8CtsqoBFU6z68IVSZgMtoTkufT6XtV1RSSpZrZCpJWwNex5wAXSZqEB2b1vvdLmtnDGe/5aOiZS+MzuJpkLHCfpGfwdfNsOcdrmX6gVcscOV+mtlxYwoXgUUn7MSB3uDk0ny6XsktpksM2UmUjLfiN9VlcfrYTbnRR05fVi5hZ0QbQty3FPrwdtNCE/lP8QldXftdhfoRXtJ6UD/eNTHJWxQdaZ/VonY1XCk4GVpF0pZndn3ewIv2GrWBmV4besYuBKxK9ikeZ2RUZ57kocDS1lvDHZDShx+fStdzUHAL5icDEOmqDtMrbcDwxcbmkg8ysFGcySbmypaq+j91CA4YwqfdUK2j/DNyHX0/Wy9jey/buJ0t6ATcz2cvMroltHoEnTNL2S/2uhf6ZU/B7ZVpAXPU8v5ZoJgFTkEPwishvmTmNL2poQZaKmf0O2FvSAfjfawyu/Jgs6TwzG5+y22ySPmEpZlCSPkHG3zrRt7kgrjLZBV93XCfpbDNLG6zcMsOmT58Ze/eykXRp3muqkocEvfVOlm4pvS/+IVkGn1sQSQ7/gje1F5EklYrcRnne2ONXzWy+rO0lHO8lYFEzeyfIKJ4ws7KGbnYUSX8ws2UL7PeGmXVkQGqsCX1foNeb0AGQtC6wAQO9fL8ys7tz9vkYXnkegzf//hOXS6wRbjRp+6RdhyJZ3sSsfsNWkTQvbvwRBU2Ts3oV5Q5TD+DV46sZsITfFl8IjjSzJ5s49ieADyxYV1eBpFWBSzKSWUXe7338Gnw5vhirIS/wmFlQbDhyM9dpxeyfLTYcuk8xwnf+z0M1ASDpM9bcaIp677UvvkCfA1dt/LSs9x6KyDP/R5nZSk3ssxC+tt3FzJZJ2X4nrg6paY8J7Tfbm1lWkiXteKvgAfk2ZrZAo/s1Q7+ilaCbNPahlJ+aKS8qOayYqmdOzB5Js8zsDUlzlPz+nWSxgvtlZt2isr2Z5c07aoqymtBVwXyLZjGzuyTdj8tSXm1EohsyaTcCN0r6NAOZtIck/cLMasrQnbgOhZvUssCjDWYpT8SrlQcmnr9U0o9wZ8Qt6hzvbOBKM5si6duEAbWStm9XZjGJmT2oBgZmN0HUL7JL+Hc5M/liTCUMR1Zz9s9DFkk7572mQHX2PTKUOio+P7AwoS1i0AxTa2G2W5nfvdi6Kwr2H5X0O9y1eFLUfzozEapDp+IeAU/h/VOL4HbpnyHdBTMTM3sBX/dmqUROwKtSnwCuZ0BdshXwQ9wcpZnjPYTfi0tzE0/SD7Q6jKSPqB9wTDezzL9TAcnhUCbpnDW7usxJqwWKBqWDMkJBHhYtWJalRAOQMprQVfF8i2YIma+TgbXwOVEfhabfA3OkkTMwt5A9BThF0mr49zXtWJX2G0o6iaBdB34kaVEzOzZnt3XxBXMap5DfR7Y13rsGbt+9De4edjYemLadIH18paz3S/SLrID/fR8Ji7E9h1DPaDMUGo6sgvbPSjffiPfktW1oewVkjXSYjn8X56f5Ab07AY9lbCs0P7AIcrv6W4E18Vmm/wK+BPwgJLe+1S2BjPnA832C5O14/HO5Ia4umtk4B5ffnY9L2m/C+yJPAn6S1acd7qfftOA2KekJBgf8W6X11oVk5y7A6QwOxp7Fk7J1FSZZWGNGHIXoB1oJJD1NfuCzRImH/FLG85vieuBXG3mTcAGK9yrkZr4qoOpG2qrt5KtkLkl1L+Jmtm7Kc/8IC5bIPGVT3C55AXx2RGpTaUEKN6GrQ/MtGiXcFO7CJXLH4ou+hXGZ3J2S1g2ZsYYxsylkz5qqut9wB+AbZvaEpK/gQ1HzAq3hZM+peTtsr8ecZvZuqFwuamY3A0j6XBPnnUuQOCaJ+t1+iC+SSsfMfifpavzzvCPwaTo/23AQkj4JrMZA9WCKmb1W8mGKDkcuZP9Meq/RcNxoZ19J85rZqU2cf9dgZiOTz0n6L3yxvwwZ39nwOUwbxfAFXC2xccYhS5sf2AA/wD+Hy8QTEiEZMhHYG6+cdJyorQNPLL4PHIivN2ZGvgYsbWavSZoI/BtYMQSj9dgfr0hFLIwn28CvGQeRYaRjZpOASeGzvwDwSrvk9GXQD7Rq2SPj+eXwP3ypcxSSHw5Ja+KSm8Xw4ck/SduvXkkfvwCUPe+rCJU20lrFdvIVM40grWqGIOHaCW8wnYRXie7G7cifLfMEKdiErg7Ot2iC43GDh/iNfipwl6Q/45/1NAvloqzKQL/hb8h2EyuL+SIJkJk9HoKfPB7Dq1JpRhlbAn/I2f/P8uHUS+IGGpFc9N20F0s6Muf9ppvZcSnPT8U/R3EZ7Qf4538CtdeplgiLsEg6+C5emfychSGe3YKkw/CKxHC8H28E8L6kE83s+BIPVXQ4ciH75zo9cHdIug+/DnbFgr0VJC2CG9Fsi5vYLGFmWdXZqSnPTQNuBn5RZ7+W5gc2ydbAD5JVXzN7RtLeeGW0o383Sdvj3+vl8fvxaDMre6ZhrzFXlJwxs5dC731ekAWe4Nk19vgjCw6w4Xua60jdzcFVnH6glcASVr+SFgOOwx1RzsYlMaUjHzZ8IrA6niU6O6f/o7KSflGs+ADCPrVMKyh5ORDPVO+F2/6/D+1xp2wh0O3YfIsmWB3XgKdxCXBUycervN8wSHeGRf9ijwnnkVxwHYdX0BfGFx2RVn4bXAqYZ+v9PXzx9D4DFsEbkS1nzar+T8cNSuYP5zQIq3DIqKTb8KTctcB2WWYnnUbSFsA++KJxkplNC3/vUbhb6BNmdkNJhys0HNmK2z9nEiq2Zc/CrJTQr3Y48N/AdXgF6J/19sm7F9cJYgvNDyzIF8kwkMEr/0umbZD0kJmtEnt8sZl9N/b4WTMrqw9zAq7amIBft7aWtHX8BTbz2bsnZ3oOU2MzPkdE97jA5rH/fw/4VOln2iH6gVYGocHvSAYampcKTXplH2dxvNy/GS4X2sHM3mhg1ypL+oVQnYGkEVn63T41FLWSXRH/nIzHF1DXkz7Mr60Eg4zdzeyslM0dm29REslqSRlU3W84D35zixgWe5w628rMfhma88czUJ0ehleKdjWzX9Q7YJBarpF4bgIZn08z2z75nKT18F6At0iZ9ZV47TwMOCq+hA97LTuo3wBPbGwLbJOW0LDucHf7b+B7ZjYxeiLIz68LfcO74/LRMigs6bZi9s+ZBCVIzxqTSDoCNw+5A1jJmnD1zHi/Gc6PuIFBkqLzA4vwMTP7KG2DmX0U+nfTWDrxeEvgu7HHZVbdolEkbXGn61FeZPD3t9EZn69KWtzCwGwzuy+2bQlcYlwaklLVYXHMbNe81xShH2glkNuCH4xXAAxY3to7OX0q/oE6DXgB2C55c85YUFVZ0i9KXAIULdaSj/vT2xvjyqwNcgeko8ysxuEt1rR7IAMW47fjEtgfSDqnzASC3P58ReAvZnaz3Np8T+BQfAFaE2hZB+dbNMEU/OadtrDbjexeq1aOV2W/YaGZVjGt/FIES/hGF3+Svlbnfe/L2hb2XRkPsJbFpX8X1GuUlw9iPgGvhryCL5TelXSEmTXlipXDN0p8r3ayAtmKiFtxR8BSKFrpljQKd7X7IPxtrweu14D983dI+T5KSpPwRv1Ie1Piz9YBjsGvo58FLssI5NdIPqfizo9F5wcWYXiyEpIga72aNvS83vbCmNmYst5rqGBmixXc9Vb8mlyTQMOVCVnXp6Ike70jOWrb6QdatTwNfIT/oR8DlpQ0qGSdUQYtygP4hSDrBp21oKqypF+UV8K/y3BdfNtcXWYC9pd0LAMWqkfjwfZ4XG5V1447LFRuAG4I0pmokfdASjIlkQ+JPAb4E7CM3L7762HzHmaW2zdo7iR2KnCqBuZbXExFLnR1+CFuevFFamVyO5HRl6b6rqLD8L6immRD1f2Gyb6IAvs/iS/G1pf0LXz+VpYMKCLZczg//jt5Gf/d1hCan0/Ef9+nAd+2nLlboQ/sSDxzf52ZfRASAFsDZ0p6xcwyExnNUKc/qNuYw8xSe+GCQUnZRkU1hArFZrhT2OYpL7kAr6ZMwMcIPBbOL8/+OU1iHc2gO9HMcmdldjFFxz4Ucn4MSbAtNHh+4GS8X7WQu1sdkpWRtO2N0AsqiD5+TZ4s6ff4/T0ymPo28HFcrp+JpC8xeAzAr83sT1mvN7NDE/vvkXyuXfQDrVrexL+o38vYnlUGLUQLC6oqS/pFWRiXhI3BM2Y3ApeZu621jYqctKrmDPxnug23P18G+DIus9rDsq2OT8NNJn4da1h9HrcpP1luMV4WewLrms8mWhM3xzgsw7AlF6tgvkUT5/KgpPXx39sdBHt3PFGyoWXbu8f7iobhlapV23muVRGczO6OKu7yERMn4Qmq4yV918wyHf3MbFCvTOgROpYMZz5JP8aDowuB3cysIUdW3N1qrJnNyJCGBeTVkt7AjU5KCbR6iFmSPXgxhlGy6VMcScviAcNo3FUw6161CH7/2Bm/DjyOV2WuNrP/y3p/MytUne0FLGe+naSsvpaizo/Rce+izdblLVRGhkv6LgOf5aTMurR1rqR/5b2mS6TBlSFpMvXHKaT23AbjjFXwteHGBEUEXskaX+87LuliPAn7HANJz4UlXWZmuzV46pUF5MOmT+8H/71IyMheh39A00r625jZh507w8GEG8Bo/KY5Oz4HpXTHmCwnLTyTWaaTVqVIeg5Y2cyelw9Y/TuwgZndmbPfRGBt/PfwBzzouhe4r96FrOA5vm5mn4g9fheYO0t336tImhOXNb6aVRWos+//mdn87TmzapH0LDDSzJ6TNAyXZnzfzK4NVa3jzOyrTb7ncODvZlbTLxKqg+8wkAyrIW2RI+kt3FWxRloYjveqmc3TzHn2OkUrrS0cbz7cnGksXpWfgss+lzOzpxvYP5r/twueZPoZXuWq6QOUtJKZPRJ7PDz+t5f0TTP7eYs/UtcQKoOb4gvPjc2sphop6Q0z+3js8atmltteoOJOn5Uh6R5yFs1mVoqkV9L7wF9wBUlqxb6HqtqlIJ9plWQ4XpDYFTjImh+iXe9438FbEUbHk/YhaXwFcLKZXdLA+1R2L+5XtJpAwQHJzPYu8T0LDVesuKRfBq/jpeF/49LG0qUpqtZJq2rmDZUozOxZSW/nBVnhtVvDjDL72sA6eJ/UwpL+iFckyqoYJbPj7w61IAtcWkWs/7Bej9wQ55NmFkmUl8erE9H36+ek277nsTzZ87eKLpbewRMNaSYImXbyQ5zKqj6SrsX7Q/+Bzyzb1sz+Gu59dWWfEeb242fh1/E18Er+lqT3+N6NS48i/o2rGyKuSWzvSeTz7sbiM9pG4InXtTNeXsj5kYJOn0VowKxgupl9J/lkxTLrhfHfdzS+4XLgp9G9eWakXpVV0k24K2+ZCqudgf9JKqPMbIqkvXCZf02gpcHmbMPCc3nOuqXQD7RyiGXSxuK2vZl65oK0NFyxipJ+K0haHf/djcJlVhcRGpzbcLgqnbSqZljoD4ouCh8mHkd9MqmYz0h6ArdIXgCXcx4AfB8PTstg7oS04hNJqUWvyipCFetQCvbIDVFelbSwmf0LWAt4OPa9npMcJ8YUyclcuIPYD9Ne30Km+Oe4THZnM0smtcaF7TMVrfbkNckovAp5EXBV+Lw0RaiYro8vbr+NVxWyDBySn7u8xz1DkMXvgFcKvgr8Bu91OR7YO0tCTkFzHWvR6bNJsgbTz4EHN/MzMAYifj7zA6tlVDc3xlsHGpUZ18XMXsZl/GeExPsY4BFJvwP2rPh71fUEyX1Z1voRy+DJlDTuJnsA/XvUGrLVddYti36glYKkWYBN8ABhU9wKeAH8y/xwmceqs3ioO1xRUu6wX+vwPAdJ0aDQy/GbQnQTmCXKLpScQajMSasDzI07VMYXCXHpZeZFIsg2v4ZXs9bBs5+/wR1/6rq7Ncm6Jb5Xt3EWLstttkfuvxNPNWXTXlW/YUGJ0LXAtSFruQ+DB/+uhi+G63FB4vFbwO/NLHM/uSvsvvhw6EjTfztwRp3fyyHA/cDjQUobyay3xL9Xa+acZ1PInTP3Z3Cj9r3A6WaWtZisFLktf11KlPtEVYCxwEmS7sV74nIXNZKWxoOrHfEF99XA2lZ/SGwymM573Eu8gH9PTge2inqsJB1db6cyqj5q0umzWZLGBCG43hWfUfgUnuhK43BcMZM2TmJl3DjnwLLOM8LMfhf6VOfBP5+fJqO/dGZF0qK4IVqZzJb1uYupmNLoWO9mP9BKIOlHuIvY3HiQ8y08Sn4Ody2qDKs/XLEXhi4uFf57IoMXYdCeDELHnbTahRUcuhrkgXMT+rKAc+pVvlphiGvTv8lAj9w5NNgjR6117YM0aNOe1W8oqR39hkUkQoeFf1/Hg6b4z7EcOfbzeY39SeS23vfj7qUTcSngZ3CDjJ0lrZkm4TGzFySthFdA4gHaJDz4KSXbHc5xQeARPLi6mYGgbnNgx9A/1A3B1p4Zz0/Hbb/npyS5T6IKsBIecJ0ajnGqpDPM7NHkfnI78RXwQHpff6uZfu7ibXhf9nbAB5ImWM6w4lZRAafPEo65JX69mQbsZVbXsXYzvKKexoX4NaO0QEs+oD2SDr6LJ5I/Z2YvlXWMXkI+2iNJNE7hh2RXmIoyS1LNk9ye8fzBZvY/JZ9LQ/QDrVoOxG+SewHXRhd2pcyraDeqM1zRzIravFZJ1RmEjjlpVUkIGOfHm/jfy3n5q3imLfq3oKSny8xEJs6tSMWhFyjaI1eor6jqfsMiEqEgEzw24/1y55PIDX0OxRNbi+DJrCuAcRmfz3F4H+rO8d6/kM3/adie1phN+OwdGf61k8PwpMYuFjMjCud4Gf7zdoOL5sjkc2FBfTxerU39u5Zw3EdwqdV+ePC5K24qkDbc/gZg8wL9L3NJeiD2eN7Y42G4rLUnMbPNg0JhJ1y2doKk3+BVlTnKPp6KO30WPd76+DVnAbySdWWK3DfJQlmKAnNnu4VKPL/b8CTStcB25gO1Z3Yi5VJ83fUBXpiYQG2SfQYFq/9p6p44WZ+XHYF+oNUlrIhn3MbjC5rr8Q9LW9AQHq7YAb3yPAxobpMkByb3HHIr1FNwqdMswEdhAXGgZdiLm9laITAbiV/MjgFWkPQHvMJ1n5ndUdL5Fao49AiFeuSUcPtqgo71GzYqEZI03sz2iz3eJV6lCtn20XUOdRIuNz0QrxB+AZcBfZz0DPQmwApJgxUzmy7pYCBz0RMC12XwQO0uvKKyAe7EuXeoupTBhnhwMMjx1cw+knQckDtLrmokLYL3HG6Lz6xbIphPlPX+S+JZ/68AjwJjwr1hIjCxjmrjyfj1QtISZvbX2OM9zCwpP4XaPp5UQ6leJVROxgPjw3d1LP5dfUzST83sByUebixuJrMjMDot4VxW362kO/EgZhz+N3sfN+2IHyutovmGpMUsxape0hfw/sCy2AAPBrYFtmnn76NXaEFtU6j6X/R4dLA3sx9oJTCz3wP7SDoQd0oag2fkZwF+IOkc84GJZVFouGLSZCCNTn/hU/pTaqjXn1KAITs/JQRZd+E9CscwEMRshw/SXdd87lQNZvYf3Nb9nvBec+HSoQPwhW1Z8s1CFYcWgpEqKdojV/TiXnm/YQGJ0G4MNiQ4ncGmIJvlHHJbYNXY9fT3kqbgjfppgdY8eIU0jZdwM40aJB0FfBdPAuyB9yeOwOWOo3BZ244559ooC5nZU2kbzOypOkFF5QQTgcPxoP46YJk2ydDOxHtXTsArMePx/jhgxly/NC7H5Z0RDzHYPfBkavv88lzQZqG8v3XHCT3jD0vaF/8sjyn5EKXYojd5rFPwv22ceq0GtwDjJG2fUv06Hh8FUPY59mmdqqv/s+X1IptZWyr5/UArg5DBvQG4IWTqd8YXiQdSojW5FR+uOIKceQ5dwAV4E+tzpC84M/tTitCBClqVHI9b98eNUaYCd0v6M76ISauORhamqzJghrEG/veYTAi+SqJoxaHrXcBayKIVraJW2m9YUCLUqrvbbNTae79N9n3pSfwzfmvKtg3JNt/4Dm6g8HToJ3gCD4hekluP/zHnPJshL2nRFZ91SUfgQfIdwErt6tsMrAosambvBJnbEw3u1w73wOHApZRrN90NDAMWwitCpVFx323RtdCRuKPx7yXdyEBlZBSeIKuRyRYl7/chacWyjjUTUKj634BxU1bANIzsXmRoo+KpH2g1QMi4noxbBK/W6fMJ9MI8h7OBbYC/4RmKSe1sZq7YSatqVge2yth2Ca5nr0HSXbgD3Ef4zegevCL2oJVvsV+o4kCPSDrDIv3LwGNxCVMOc0v6W70XmNniKU9X3W9YRCLUqrvbz3HXwoPwiseieMIgzT0MvMp2qaTvA9eb2YfyYa1b4teaLKvpT1oYimtmT4b+upfC41dChbcs5lB9R9huMeQ5BpfsfBa4LOPvvUZJx5rdzN4J7/mGpEZ7idrlHtgVwW4Rglz5xwyMmdgJNy85F1c5ZFneFz1eq/LghqmXKA39nJuR4upnZi8GCeV++KiNBXCnu5vwXp+29gbLx6XsiPcbLk33fMe7naLV/0aMm9ICrffSepGroB9oNYklhqR1CuuBeQ5mtrek/XGL/DF4z9uNwKVZPUUtUpmTVpeRbESN8yu8RP9QMnPUBopWHIoGI5UhaTSeLHgVnw+2vZnd2MCu/yH7c1mPqvsNi0hikn1rSTeovAXt3niA9DBeaXgfHyabOhDezK4Mi5qLgSskRU6M7+EDoxsdkNyOGX4RV1HfEfbqNh67Gao0U0oOx21qxEEb6InETgZn4v2M4/D5njfhn+fvmFnZMz6hdXlwS0hajoGhzDBYSjqDCs1uovOaBXeijUYAfQxPBG1SxfGHCIWq/2nBUp5xU6fpB1pDAOvieQ6hanITcJOkEcDBwAOS1jeze0o+VkectCpiCt5nMj5l225hew1mNq6dJ5WgaMWhaDBSJYcA25rZjZK2xoPXRgKtD8zstqyNyh7mWGm/YUFJTCN9a/WO+SYwRtJY4FPAy0nZaco+Z0r6CS5/XQCvoE4O75VFZS501htusLnW+nJnu7IoNCyX+gPQh5Hdk5fmYBjR69WGVXAr8fck/RqfH7V4mhFESVQ+/FnSfHgQORav3H2EO9lenPH6VMl8HDP7VUnntgyeNN4Zv/7dhPfyXw6catkDo/vU0nL1v1HjpkCl45ni9AOtHkY9Ms8h9JN8G79ArYDLHB5v8zHb6qTVAX6Im158EbeWjXTo2+DykfU6eG5ASxWHusFIl/D5WAXrBuC8BvfLXIiE78XTpGT2uqEinSeJaaFvbRChgb3hBUoIqpr5vAxpF7oAZq0wAAAgAElEQVSyCEmRTfG/98aUFJRY8WG5RQegv0d2kN/r7rOzWxjpYWZvSnq9jUEWVDj8WdImeHAl/P42AdgBl7tfa9mjTPK+z9OBshQRj+OS24OAayJJrKRe/kx1isLV/wLGTQBfTUnCfFiB0qcfaPUq6oF5DpJG4kHgFnh/0IXALW3oDYofsyonrUoxswflM0ZOxhvYZ2Gg72rDNkkxmyZWcRjJwBytvIpDL/RMxG3cPwqL0kaol7Eb9L5xOtVvWKYkRtLywcU1+fxH5CyEzazm9ytpcp39gPS+oiqrN5Kepv45TjezJco6XhlI+goD8qwR+HVz7Y6eFC0ZMQxZ91lqZZjJx2XLMFuVBzfDrXgQs6OZXR89mdY/GKcFQ7EinI1XY08GVpF0pZndX+HxhwxFq/8FjZsgPQEzPVTJJwBHmzs0l04/0OpiwoLkAdxt7vbE5l6Y53A/LiU6C89YLwjsqsFzMUq7KVTspFU5oT9wHUlzAvPhA4tTnek6gSSZ8ybeG9YoecFIN1BPfgZkLvJPynnfrEV5pf2GRSUxkuYFlgKeCX2jSFoeryZvTLosL97MPAyXlq3awGmmzUwqRLuqN7h9fBrL4VnwrhiaLumTeLVgV+CruOX9kbjMeu9ukEAVlYR1QzW4jRSVYRalJXlwk+yPJ2avkrQbPrx8Ut4xJP3BzJYt8TwyifWdR6N/7pb0T+ATuMKk49+bmYCis93SAvLhwBLAEbih2GHlneYA/UCru/kG8Dk8ek8GWr0wz+E+/CKZJWsr+6ZQpZNWxwjBVaEAS9LnGFydKVO3fCU+bDY61tMNZht/JekrZvZ42G8BvOl7WTzRcEADsoB2U6n8rAP9hk1LYiRtjA+dnRt4X9IOwFr47+oSPCCswcziCzUkfZh8LoP3zawlM4l2V2+SElhJiwHH4UM4z8ZnBHUDL+CN46cDW0XyM/kMm26hsCRM0qJ4sL8hA5X1O/Cs9dMlnmOltCDDLHq8yhIDZnY6cLqkr+Lf0TPx5MpswMq4S2kai1VygoGgyLkRuFHSpxkY/fOwpF+YWVsNQoYKLVT/C6196yRg/iLpT/hMr36gNRSRtLiZpTquxaQTV9bZ1rVUfVOgWietSsmRW4FflGq+z5K2ATaJlemfxG9cw/Cy+VZmlurkVICkjOSTDe53Ot7QGvXtXYTfPC/Cm6LHAd8v4fwKkyc/yyJH7tbQIqaifsMikpjjcXOby3Cjlsvx3qkloupWyVxIAde+TlRvJC0YjhGN3ljKyh103yq34ZW87YAP5DbdXSWxLioJk49heAB4FO9tjYa7b4svhkf2qtpBvTHcvSXM7FHgUUn7MdDbfbOkJ8wsbU5Yx/qjzOzfePLkFEmrMoTXIG2gUPW/oHFTHv9k8DD0UukHWp3nUUmHm9m5zeykCudbtIqkeRjo2XkJ+K2ZvVX2cSp20qqarNkRm+KOeFk65d3xRXrEewwEQGvi/WxlBVpFG6WXwbNJSPo4/jOtaGZ/knQLXhntaKDVAnlyt/OzNlTZb1hQErOEmZ0XzvVc4FRgbE4/XisU7QeprHoTPr8HA3vhAzeXz0qkdRIz2zxcD3fC/94nyIcJzwM0Oueqo0RS5ZRNJ+IjRA5MPH+ppB/hiZst2n6C7aHSftYcVzgAzKwtVYDgHjcRmBiMv3bKeOlc8nmR9d6rqLFKKkF18aoFl9RQ2doer251u4NuV1Bm9T/PuKkBRtJGp+5+oJVA0kNmtkrs8cVm9t3Y42fNLMuSuQjrAT+WW0aPacJBqKPzLRpFbvV9At6r8QpuyfyupCPM7MwKjt+uXoxKSZFbrYkvGBbDy90/ydh1abxXLv5e/wnvcS/lzhUbLum7DCwGGp2X87FIqob36rxsZn8Kr38mBBw9SdHgvxP9hnUkMQ8FSUxSiztrfF9JbzYSZCU/EzT+OZlV0trUWWya2X0pT1dZvXkaN6k5DngMWFLSkolzLMVqulXM3WnHA+PlNsljcbnuY5J+amY/6OgJMmNA73LAXyJjFUmb4TLxz+J9v0nWJUO2ii/eUoek9ghVV2/qucJVhpn9C/hRxuZpuClY25G0Cu46uwjwiqQtcQv6E/C+5FIDupmBotV/NWnclHLfAe/R+kI4dur8xjLoB1q1LJ14vCUui4mYr8yDmdkj4SZ3OD5w+Hjgj4nXpN2YK59v0SzyIa9H4iXi68Ji7GN4z9mZkl4xsxpZZEnH7konrVaRD3A8EVgdv/Gcbdm2t+AViVnxxR94oBsxK+V+nqfgEq2IB2msUfsvkjYIhi+bEapbAJIWAtpVIekIDQb/He03TEhiVsOrHknmkHRV7PHciceY2Q7Ukhw42ejnZHZ8QZV1bZuOV98GUXH15s1wHt+rc44dHb6dhpk9jMvq9gVGkf73rhRJOwE/Bl4D5pe0D96fsRIeIF6SsetwIKun8+2wvVepdLh7UWe4iplmZlWNbDgNd6i7Al9fXI8H7qtFycE+jVG0+q/is8xqBh3jQfqzwDZmdnezP0Oj9AOtWpIZo+RNvfSMUghATsX7B47FG3fjx0u7cFY236IF9selRLdET4Ss+dWS3sB7JEoLtDrRi1EVkhbHPxub4S6OO5jZGw3s+iy+MPktuDV5bNtKlDjEr4WevKOB6yU9izsAxd9nc9xZq+dpMvjv+AJH0hy4rPPPZpYmhzkh8bgh90gzK2rk83aGm1Qjx6ykemNmi5XxPp3CzN7Hg9lKKgQ5HAJsYWa3SNoCuAaX4m4fzjOLx/BkXtrcvi2BP5R+ptVR6XB3+UD19czs0pRtY4Hbu6C3r8qk8leAb5jZhzHVwVesy2aX9ghFq/+FZpm1cN9pmX6glU/bAxZJ38Czc48BS4Zsch5VzrcoylJkDxb9FeXfzHvBSasoU4HX8YzaC8B2ySpHhtzqSuDc0M/wXPSkpM8C55C+GKmUsJBaEZdgPJpwBXscD5g7Tgg8xgJfwxtn/w+vvl2WVVUsGvxX3W8od6M8Fw+sHsCDqDtwicw7kjY3szsT53hMmedQFSnVm12qOK6kFfDEU9skKkOMRWJJusjm+4CcIAt84Rb19SSHux+OS0h7laqHux8B1MzCC8wZtu9exoEkbW/FXEXboorJ4GMWBtya2XvygdH9IKsYRav/Lc0yk7QOg91Ibzeze5o476bpB1q15PWZlPo7k3Q+sBWwj5lNaGLXKudbFOUd/MP8fMq2ERS0KK9D1ztptcAD+N80KyuTJbc6GVgFeFLSg/jfYuHw3O1heylIupt8Z8RUq/8gGaiRDZjZ/fJZTR0lGLrcj/eE3A78L/57PBLYU9JaGf1JpQX/be43PBe/6Xwfd2e7HZcOXhKOdyKwWonHK0Jm8qhIEGNm70u6kZIWihnntQDunDkW7zX6ZbuONQSZ4TpmZtMlvd1AkIWZ/VI+8Hs8g6uszwK7mtkvyj/Vyqg6gboecEDGtiuBpOFIKxRyFQVOkvQ5M/sHgHzO5OEMjAc5JaHkaIXZEwYhcyYNQ9plDjLUKFr9L2jchKTheFX8W3ibw/N4q9D+kn6BywenFTmnPPqBVi15fSZTSj7ewsCyjTT/xbEK51u0wM+BkyXtbGbJBfg4sudiFGIoOGllUVSWF6SaoyStB6yPB7iTgePN7I7yzhDwi1gan8YX8Km2xJJeNLMFY49vM7ONYi95LmvfCjkID5oGBVRBZ35N2H5Eyn4tB/8V9RuugVcQ/iPpPrx6em6QyJyHVwk6zTLxB4kgZlmyq+f1mAVYp/VTG3Res+AN2VGT9kt4b+RqoZrWpzEKDQkPz08CJsmt3kfgBjs9aemeoOrh7p8CsiTqb+G/27IoGkT+GE8y/iM8PhsPEK/HZ/rNQ/q1uQhXMdgg5Bq6xDBkKJGXOCtg3AT+GfgC8F8Wm6kln7l3Q9h+ZLk/idMPtBK00GdS9HibV3m8ijkErwI8LmkiAxKOLfGK3JplH7AXnLQ6QZB93Zn7wtaOMaghOVSiDgJ+ANxK9s1uzsTjVRKPu0EGuxnuCjqoamVmb0g6FG/Erfn5igb/Heg3nM2CG6WZvS2f1xNJZD4K1bSOYmb/qBPErN4NQYzcPnwn/Po2Cc+e3o0nC8ocDt4Skk4D7gF+bWavdfh0smh5SHgIrmYEWGFRdrDFRqP0EmZ2UsWHfAkfLZJm9LA0g/vJW6Woq+jyeCtCVM3aHljXzKZIuhQ3Wigl0OoRc5CepGj1v0HjJsJ7j7LE4GJzZ+NdgZvpB1qdR9LswO5mdlaJ75knt0qdASEp9wNhZse2cGotY2YvSFoJbxiNa2InAaebWdbsp7KO33VOWlXTwOdkupmVWq0I35O98EB7CvA1C9bMWeeQ85bdIINdLOtnMLPfh6xYKgWD/6r7Dbu+57NHgpgD8d69vYBrI6lbsp+yC/g8Pvx6hKQ/4EHXvcB9ZvZ/nTyxiLw+xXqEhdOKuCPcefhn5ihcJlp34GmfQdwAnBV6fKMRHEiaGzgDn3NVFoVcRYHZzez18P8rA++Y2RQA81mMaSMASiH0ts44XzPrlutQT1B29T/83bNUZwvhPd9pPI4rb9pCP9BKQdK6+EX6L2Z2s9ySfE/gUPwmWlqgxWC51TB86Of+DeyXHGA7isGDZ7thcUrIlh5JmzIFDZ5DNzlpVU3WoOPpwAa4qUMpgVa4aO6KL2j+jjuG/bqM9+4C8qS6DUl5mwj+q+43bLrnMziS1aXkhUehIEbp81Miyrb6XhFfNIzHF6jX43bQXYWZbQ0g6Uu4DHUd/L62sKQ/Aneb2T4dPEUkzQZ8zsz+Gh6PJja7DZhoZjV9viEg3xFXU2yFL75HAo8AI83ssXaf+xDiaFwJ8TdJv2RAlbIRfo0v0xCnqKvoPyStZGaP4AndGeZJ8hmM9cafNIWkbYBNYpWtJ4HZ8OvmdElbBdlqnxw6kDj7N/Bl0l1Hv0JGb1cZ9AOtBKHJ7hi8VL6MpLMZsJvewyx1En1hUuRWJyafy9hv0EwASa8mn+s0kjYFNrfYwOfYtouAST3emNz1pH0mQr/WSXjFJKvRuQh/wrNR44CfhWMtlTiftD6JvAbj2Uo8x6LMljDJSZK6YJc0wsxq5DVR8C8pNcNWdb9hwZ7Pv+MBWNYIjOmUe48pGsTkXRfT5EiFCFXPfSQdyECz9u14IP4DSec024/bTszsCeAJ4KIg3RmDXxO+D3Q00MIHiH4+nAt4H04UJH0Kv9acnrLfdsDXzewp+cydx4EdzeyqlNf2qUOQEa+NX4c2wIPWV4DDgCvbZR7QJKcCv5A0Be9D/nZs20ZkuyYWYXc8+Ix4D/hk+P81cROOfqDVGFVX/ycAl8sddKN+vqgq+WPa6MDcD7Rq2RPX9z4oaU38JnyYmWVNJe8WuqKClWA/sqslE/AqVz/QagBJe5nZObHH8yb7hRp4j5XxAGtZ3L77gpJvlFFQdQruZpi2AE/r9clrMC7iRFU2SZOctO1p/I2YkYekJ8wsXmWcTIbRRzf1G0paPkU6meytAw84d8dlo6XOKyoaxFgH5qeE79UNwA3yodtRs/aBlOsWWZgQyH8Nr2atg0u7f4NfG0oLPltgW7wyFfG+mY0EkPRfuOtdWqA1n5k9BTOkY+8OpSArSOH2p3bMxOnW2GiYpgif5Z+Ef+2kkDzZzC6TD3FeBRhng62+38bnT5bF0nilNH78/wBIuhf4aYnHGupUXf2/HDdUejIE5VF1djVcQdK2Vpt+oFXLp8zsQZhhLf0+vnDs0zzL4Nr/NH6Nl3H7NMaJ+NyriGfwm2wuYVFyIu7EdBrwbTN7u+wTLFgV6YkG4xZMcpKLh4Vytmcdv+39hsG8ZCngmagKJ2l5PIO7MYnAKlpgxPYfjasBXgVGW/qwyZZJBDGfwbPtXRXExAnB38m4A2unLfIBCPLAuQl9WcA5GdXmTrKomU2NPZ4c/Y+Z/bleX6TcynlY+Pef2ONo/1yb+G4kBFmP4MHVzQwsFjcHdgwSutKCLUkbA29GwYukL+CZ/8g6fUyJx1sm/yXpBJOMmuRA2Qok3EJ8VnzQLnhVNWJWYL6Sjzdk6UD1/2Hgh/gok8iB+QHgWDO7q8Tj1NAPtGpJLnzetfJmMNSQlFZR24SeJbfqBeYG5sKzSknmDNtLJzi2rcZAtm9KFztrNUryc9nQAl3Sj4Gtcceu3dptQFI23eYSJp+nNRK/SL8E/NbM3qqzS7LSnPe4Lu3qNwwLqon4d/J9STsAa+HOb5cAX6yz76Z4ID8bcKiZldkgXxcze54uC2LqETXpdwGv4s3f0b8FJT3dJVKwiDkkzRN9v8zsm9GGkBRIq6iCy2vjfTnDYo+HkV1Z7wUOw4PjXSy4gsIMk5zL8D7yMiWfxzJYXn4JLjnfAdgNT0LvXNKxjsmRjU03s6QTJZKupvY6Og1PRl5rZn8s6fzAe4dWAn4L7sga27YS3WPK0zM0W/2X9BH58zrTYpv1cInga7h1/NOtnnuj9AOtWuaW9K/Y408kHlOwYTOLqdT2OSSb0GtuCpKeZ/CHrd3nWYTH8Uz4DSnbNibbAaYwkg7DrVyH4w6HI/CF44lmdnzZx6uQogv0sfjg6B2B0Wk3srI+J4m+qlQsY5hjL7iESfo+LquaE+9TWAB4V9IRZnZmR0+udY4HDsYXa9/FZRa3AUuk9ZgBhN6Nk4DP4Quyy+KLv7KRtGG73ntmw8zWkruDjsQlaMcAKwQHwvtw98Gy5+w1y2O4I1la4L4J2fePL7TtjDrPhnjf86DvmfkIhuNwK/MyWZIgi5Y0Av+sLGFmz0p6CHi0xGNlVcbmwO9f81Nr+Q++hkoyHD/3yZK2M7OyZnZeCZwrSWb2XPSkpM/iipO29fnMDDRY/c8y+NoUl6ynJpPN7JEgwT8cV4ccD/wx8Zq2qDD6gVYtNVbq7aSo3Apv+O12xgPnSxqGG198KJ/HMwq/KO1V5sEkbYFn83YJx5sWJCOjcA3wE2aWFvT1AkUn0lfZn1JocGMvuIQFWdyRwB7AdWb2gdyNdGvgTEmvmNmVKbvOISneHzJ34nG3SN2WMLPzACSdizeYj83pA7wXDzivAJYAjksG8lmBdUF+idvev0d6RXc6sHiJxxvSBOnnPeEfkubCe5QPwBcjna76nI4vaj8Ebo7dP76NOyRm9SgebGb/U9VJVsxCUf9ZkmD+Ufbw3GF4dQhcJfJPC06iZvaifGB7KZjZofHHYd0Qudg+hVfr0vbLdD6UtBGeRCor0DoZ7wV7UtKDwPO4dHMVXPZ2cknHmenJqv6bWbwQQfBSGAcshld8M3sJw337VHw25bEMngPXtvtHP9BKYGaVZs8l3Wpm32p2v6rPswhmNlHSwsClwFWSogrTe8APzez6kg/538D34tKlUJa+LpSbdye9utYLFJpIX+XnpIVeq15wCdsfDzxuiZ4wn05/taQ38Jt5WqB1QuJxsuqXWwWsiBmL6nAzerMBs5Wf4jenqvoSfoYH4Lfh1bNukeE1RAjMLzKzXTt9LjCjj2lVBsww1sAX1pPJ7q2tDDO7PlQKLscTTdH94z/AUWZ2XcauOwJDNdDKC37Lnnf3B1wmeCXu3jlj6H1wa2uLJF/SlriR1jRgrxZ6rW5n8AidlgjX/FFy596oz2cycHwXVIBnKiQth98/Vwd+BJxtZnWt/CV9A5e/PgYs2Q7zmDT6gVYCpc9cifS+9ycbwEtg7SI7qb51+oXATdYF1ulmdqakn+A38QXwDPgDDSziirACcEvGtlvxJsiepGgQ04jcql3l8iboBZewpfAFfhq/IqNnql62tVkkrY83of/WzCbnvb5J8ipvmNkOicdjSj6Huphb3o/AF9IXBOnb5cDlJTdNt4tZ8Wp7xwMtSXfhFYqP8Ibwe3D54INhMdkVmNkZ4f4R9UW+DEw2szfq7Nbx4dptZI4ciXbZFfJDgFsknYcHuGvFtm2Lf3ZKI1zjTsLXCkfhFvKtOCovANT7rBTCzO6Uj9uYH3g1b4HfpzwkLY5XozbDK9s75FwPov3OxxUz+5hZpbMN+4FWLWkzV4bjZcn3JX0z4YTUKepZp19FF1mnh6Aqa5FaJnNYygDLcA7vhoVZTxNcn6JM2svAHTlNnXkz2bpCbtUDLmHv4L/z51O2jQBSP3dFCQ3ed5vZReHxfvgC5DHgeEnfLTkYzau8NUwsGBprZsu3dFYJQr/YGcAZwRFxT+AZSeuY2W/LPNYQ51e4zOahdvbVlUFYRDVz/5hN0pE579k2K+c2k1Q2JCl1FIaZPRDcHZcCnkwkSH8WzqcUJN0JLIfLwC4E3geGx+XIafcB+WDrJMPxXr0TyU6+Fj3PVXCJ4Fq4Q95Hkh4ADrTgWN0nH0nnFZT4TgVexx2UXwC2S5GsX5Sy38LAsp1IyvUDrQRWZ+ZKMFo4DZ9gXRZzhIxdvXNKy4D2rdNrmSW5QI8xDL8o9iySxuN9Cc/hC/7PAOcFG9R90/Yxs7qN4ZK64XfSCy5hP8cbdHdOybCOI6MHIMchaRjukJT2s61JcPsKvQqHADub2bWSvoUnWUpb5LRaeQu9M9/EzVe+hdtOn1/CqWUdbzXcDngU3rv1XN0d+iS5JMvkBEDSl0t2a6uSYWQ3zEN3zpxsiBbk2a0c8028Xzb5/J9TXt4K0dormsMYp9594D3S/6aRQ+shZZ1gCLLuwgPaYxno0doWuFPSumb2UFnHG+IUlfg+gP+9s9bq0/Hh5oMws80LHKsU+oFWc5yGT6svk+lku+3UoyPW6V1OcsEeJ7pQ9ySS9sabwL9mZg/Enh8JXCHpGTM7I2W/UWaWOqk+LOAvx+cQdZJecAk7BDfreFzSRAbm12yJf9fWzNgvvuAbhjt4rdrA8T4Zc7VaHv+uR/2FP6dkdyvVjpkAl0z/o56ULPTUjcU/Q/PgrmfvAquXrX+XtAgDM7Pewz+7XzEf7NwV5Eh1h1d2IvkUHqTdA7xnZmnKlCFLO6vIFVL0PpC23zTgBSt/NM/xwDFmdmrsuanAXZL+jCsD+u6obcSKz7TsGP1AqzlmoXz993+SbjsNUrl1eg/QCwv2ouwO7BQPsgDMbLKkMcDFuKQqyU8kvZ3swwqVrCvxRXypSPoSbgMczTH7tZn9Kev1ZvZM2edQNmb2gqSVcMnuhgxINycBp1vGfLIUh6QPG8wEvyppYTP7Fy5ReTgW8MxJ+dehtDETAB9Kuh7Yw8xej2+QNAV3b7oHD0SvN7O3VDt6oiz+DvwFD7AiW+kVE9KiTvcb5kl1u2XOTimDtPt0jqqryO2m6H2g4vvH6nifTxqX4H1lfRpj9iISX0kjeq0a3w+0GiRk/w8izJQokbo3NEkfy8goV2qd3gv0woK9BT6PZ5nTmBy2p7E9cI0kmdlvYEaQdRXwFUq2f5d0Md7s/xwDVZ+FJV1mZrvV2W9R4GgGBzF3AEfn9KBVhvnQ6yPDv3ZzLXCtpJvwkQXxHqrV8ICjNCxlzERwyVsC73MYh/dDxVkcH146FZhq9Qc3l8Fz+Eyd3TO2d7zfME+q20WUOki7HUj6PLCemV2asm0scLuZ/TNl124JZttClVXkKpF0g5ltGXt8hJkdF3v8GzNbK2W/tIHFcabjJlwPA1db+4ZypyWq+mRTVOLbc9X4fqCVQNJkav/Aw/GF7GuU258F3k+Vdh7L4wvW7YEFk9uteuv0nqAXFuwFeR03ZPlbyrZFw/YazOyXknYDbg4zRR7F7W6/BHyjTNmVpO/ggdsacevt0E9zhaTdzOySlP2WwnXXjwI/ZKD/bFt8sOBIM3uyrPMsinxmzL4M/mzdDpwRgrAyOSz8+zpwAYM158uRokEvm5Dg+XNwYv3flJd8Bh8SORa4T9IzwARgNtqwUDezxcp+zyoJTftTzazj5jM9whHA7zO2zRm2pwXdu8QfSBoeX1wHQ6uy5ipVSgeqyFWyQeLxvgw2/FouY79GzMk+jifK1yF96HGjTMEHuo9P2bYb5SfihzJFJb49V43vB1q1XJDy3DQ8S/Zg2e5nZvbN6P8lzQ+Mxhcuy+OReWZlqmLr9K6nVxbsBbkRt7QeZWYz+vIkzQ2cR535YGZ2o6R58N6eh/Eq09fN7JWSz3Fn4H8sMd/IzKZI2gv/m9QEWnjF5FIzOzDx/KXyYcbjgC1KPtemkLQQ3qP1H2AiA5+trYGdJa1pZjWOhKodFzF78rk0h6QQ5KQ6o5nZmYV+iOK8hmfOk+fxAXATcJOkBfG//y74XK3rJJ1tZjdWdZKhalt03k4VDMOTJd1ALwzSXo9gCJPClUDyehFxN4Mz2v/GZcwR19BlGe8mqLqK3EkaWjA3auQj6Tjgr7QWaP0QN734Iq46iFQb2+AVxvVaeO8+jdH11fgk/UCrltUKWk4WIsi4NsarV5vhC7ir8CrFFmb2Yr39K7RO7wW6fsHeAofhlbmnJf2cgQv8JviMt8PTdoo157+AZ0E3xJ1+Vop6W0rsa1kGX+SkcTfZLnnrAl/M2HYK8FSL51UG4/DEx87xBmtJR+ODe8eRyKQHkhm7BxPPpTokSRpvZvvFHu9iZpfHHk8ws9EFfo4ibETO3yBcp04FTg3OXLvifYOlBlphgbMc8Bcz+314bjN8BtRnSan+dxndsgjohUHanyJ7BtJbeFU5jeQCPe9xL1FpFbnDlPrzmNlrknZu8T0elM/6Ohm/H8/CwCy6Dfv27k0xpCW+cfqBVi1VT5X/J+4oNhHYyMzugRka9D7N0QsL9kKY2ZuS1sKzZusDK+PStUOBK+rozpPN+a8yeJFVZl/LbFnnYWbTgvV+GsNJd88kPN8Nbm2bACskXazMbLqkg4Hfpe1Ub1xEDrvhxhsRp+MmEBGbFXzfVJQ+BDWaRbMR7q7YEMHe+CFJ+5R0egBI2gn4MV5hmz+8/zeAlXApT1q1tE8Krdr5V8RLuMQ5zUhnafz6l0bPZbwbJVFF/jQDLpwdqSKXzMckbcBAIJx83PKID4wVLeAAACAASURBVDO7tYT3mAKsI2lO/Pf+qmXM7+yTjZl9peCuvVCNH0Q/0Kql6mzXi3glYCHg05JmN7P/VHwOQ4VeWLAXJgQxPwn/Gt2nyub8WULFIes7lDWz6zFcgpdmWb4l8IcSzq1V5iF7YfcSniwpk6qz8mlDUKfhAeThaU6JGcFZksNaPbEYh+BV/lskbYFLwC4Ati9b0l2UnN9J/37bHDcAZwVJ6DvRk0EufQaenJxpCcYXURV5VbzKVXoVuUJeZHB1/5XE47rqnqoJwdWMACv01R9lZr2qmqkUSU+TY2JiZkukPN8L1fhB9C/8tVQ6Vd7MVpC0An6RPAe4SNIkvNm3ZzNvHaIXFuxDmbnx3oGsICDr83wcEJm7JHXvhwPblXyeRXgSl12mZUQ3pGQXQCrOyluxQaiH4H/v31JNgmoRM7sl/P8k/HdwQLcEWYG0gDXOTys5ixxUf5A2+CKn0+uDo4E7gb9J+iUD14WNcKv/rKrcXJLiYzDmjT0eht9be5rQczsSl0++BPzWzPYsu4pcJe0wuwmtGTuaWSnfu1DFOhRYEVfIHI33/43HP5eXZ+7cJ8keGc8vhxuXpCZme6QaP4hOX0i7kTzLydIxs98Be0s6ANgcGIMvWidLOs/M0hxu+tTSCwv2SglmKfWYbmatNAfPIM0ivMH9fhm08+MZnI16FtjVzH5Rxvm1yGl4r9/3caevaJzClsDZZDftF2VYojqYrBZ2Q5/J/rgBxkjgMuCnaYYgJTLj8xUkm293WZBVNGDtBFn3uE3xADp1LlyVmNnbktbG5XEb4HLpV/Aq6ZV15NLJ61nebLOeIlyDTsADxldwI6x3gx161UY53c5w3Jm5rATHWfjA+dvwGWbLAF/G++T2yOup7zOAmQ3yFpC0GL6G2xy/p56Stl9wb8177666LwybPr1fNIkj6Q0zq9SRKNhGL4E3eL8ZnlsI117vbGZfrvJ8ehlJo/AF+6IMzLV4FtjfzDKd+YYqkk7K2DQH3o84v5m1rH0vi+AcOQJ4udscIiXtjd8I5sBlhNE4haPM7PSSjxVVHDKrg93ydwuSmbG4u+fv8IXNpLLn1Uj6ADcTiVgFeCj+GjNbo8xjloWkEfj3bayZlT4kvFUkrYkbuiyGu13+xMw+7OhJ9alB0mhcNrk3cJ2ZfSCfd7c1cCawn5ld2clzLEqizyYVM9uhyfecHXi3aBIw5f2eA1Y2s+flc97+DmxgZneW8f4zI8Gx9kh8vXs5cLyZvVDn9fHr0jAGV+aH0UX3xoh+RasJQjP/bWa2bonvuSGuqZ4LeEPS5mZ2b/ig/Sj869MgZjYJmNTNC/YiSLrVzJqe4WZmhybeZxjuCHcULn04NG2/IuRJbsP5pE1638vMzgnbn5T0fDeOKIiNU4gkOy8Dk9txrmUtDKoguP/tEyryx+PukhsCd5V8qJ6qVISK5zfxIPRbeIX9/I6eVAJJy+FV5NXxe83ZZvZeZ8/KSRmNUEPaaARJS+ALtmXxUR9jbOgMs98fD9YjCW1kkHG1pDfw719PBlq44mQqbvZRZp96mdWEeaOqvZk9G6rq/SCrAKHAcDA+wsiA5c0sbU5oklfCv8twCXnXexr0A61a6llOzoIPvCuTE3BZ28W4ZvV4YO2SjzFTIekL+N9pBPCypGnW28OKoYTPhKQt8YrMNGCvNswcypIjTcelP/OTPhvqRLw/MeIZBs+96TjRjKYQVJVlhz8kCFLdyP3sfXy+0UN1dypA3N6+m5G0DB5c7YSbqBjeNL96MDDoOJIWx7+Lm+FyqB3MLMtKvVPkDTNNHY2A/zzP4teVnXCFQ8OumV3OUmSPc/kVLpnvVbbEryG74UYol1liJmMaOVKysh3okpLuD5MGUEMhsVsRT+PW+Mfh/fVLSloy/oKM0TML4xLnMbgz7400+FnpFP1AK0ELlpNFWRI4K/QcnIVH+H0KImk88APgOQaGyp4n6Rwz27ejJ9chwtyPk3At/1F4f0PpmmFLmfIuab1w7LfI7mPqhTk3VxIbcirp6XY6Ojbi6GdmZTr6IWkOPED4Gh7o/h9wL34Tq6lySNoeXxgtjy/wRpvZ/5Z5TinHnBWX4G3IQFXxdvwz/UE7j90IkqYAX8Vn1h2C9/O9Jel5usvcaCrwOt57+AKwXTRXLyKtWlQlLYxGWBVY1MzekfQb4IkST6vTvIN/7tN6IUcQc8HrNWJqlEhme0GQ/l0GXFAnEfAe2d+tpLSsVdIMn+KOrNMpwYZ+JuFN/Pf1vYztqaNnEiMOPgWMBs4Pn5Ut0hxyO00/0Oo8s0aL3qC37mkL8k4Semi+DXzNzB6IPT8SuELSM2Z2RsdOsDXmyDO2MLNdk89JuhN38RmHS63eB4bHF1XtaByVtDIeYC2LV20vqNOz0wtzbpLB3yfbfLw897pSCS5m9+MDf28H/hfPHB4J7ClprRSJ5AR8kTEB/1xtLWnr+AvKDAYlzRvObQng5+EcP4MPD91T0vpdIDldHE8qTAWmmtlbHT6fLB7Av2dZwUxWtagrCIuqdzJ6MWaP7ODN7I2QQBgq/Bw4WdLOKcmycWF7T2NmLwNnSDofd/U7EXiYbClyZSNMeknS3e2U5DL5Op50+Dee4Oq6GVrQD7RqyNGFtyMIyhu+1nQD6EzM7sBO8SALwMwmSxqDyzN7NdCajl9MmiVaSJ2CL0jjRNm+0jJwkv4LvzGuh2fLv21mWbPNImZPVHDmTFZ0yq7eFKDSYLAD7nUH4ZWNQQFV0NFfE7Yfkdjnp/jvYYGKzvEEPAu6eOIc58VnKp2AV7M7yWdwWctY4D5Jz+CB6Gx0UQLBzL7e6XMogazK9/DEfXz25H2909W6FjgET4g8LmkiA866W+LVljU7eG6lIGl1XBY2CpiMG33cl/X6ev13wShkM1yOXtb5LYU7DT5mZn8t6337DEbSvFmJs/AZGYt/Rh7Ak0I/6wZVQxr9QKuWPF145he+IHnD1/o0zufxC3Mak8P2XuU/SWOLBqks2yfpx/hN8UJgNzNr1CL6KgZXcK6h4opOAwyX9F0GFncdW7xJ+jRwsJntV+LbboabBgy6sYWKwKG4ucARiW1jSjx+I2yBV6uT5/impO/h1+aOBloJWcuCuP39LsB8wHWSzjazXh0o221kBa5TGHwffyjxuKurdfUwsxckrYT3psTls5OA05u45nYd4TqzCy5/vBz4ipm9VPC9lsMX4juGpyaVdI6jcSnjq8AnJG3f/z4XQ9KLZrZg7PFtZrZR7CXPEZPrx143Ff8OX45XsSJL/Vmifr12qHRaoR9oJWhBF170eD03fK2LeR23J05zrlk0bO9VCvUtVey2NRbvIdgRGJ3s+Qjns3DKc70we2gKEK8sP0ibF2+SdmVgMOZ5eMb6KLxye2+ZxwIWC+6BNZjZ7yUtmnJ+aU3oH7bRFnw+3E45jb/TfjlnU5jP1DkVOFXSKrjb58V483afNjFEqnWZmNlruKS3xuVV0uxm1vUubBmcgEuRp+J9dqum9A1mqnskzYf364zFr5sfAfvi37myOATY1sxuDDLpw+h/n4uSHBy+SuJx1ppnqfDfE6ktVJSu0imDfqDVJUhah0SDt5nd09GT6j1uxBtoR8XlapLmxheqvTxH69dFdipqkVyQSpMUVVL14k3Sj/CA9X5gK3xY60jgEWCkmT1W8iHzeg/Stqc1oU+X9C9cLnd0yYu+Z/Hfwf0p21YH/lHisUrFzB4CHpK0T6fPpVeQdDXZVauuWkh1mtCztgcu8V2kw6dTlGMpIK+VtAkeXAmXUk7Ak2L3ANeWPK7g87EK1g34uqJPMfL+1lnbK1PplEU/0EqQ4Q41Ddf4TjCzUme3BPOLa/A5K1Pwxr6lgf0l/QLYpo6JQJ/BHMb/s3feYZKVRfu+F1gJCyKwBPEnkkxIEEmiSJIk6IOAIgICC6LwfSoZBUUBkaSABEVRCZIVBB+CH0mCsEgwIQImkmQJSs77+6NO7/b2dM/Mzp7u06Hu65pr5z3vnDm1093nvPVW1VNwJXCPpEuZlr/+EeL1+2qFts0Utjce46ljlUieYWwPG2WRtGIZ1xkQtgLWtv33Qi78dmBb2yM29Rwjb2hIjWykWX1qswfeeEKs4gAi+lZmbd0PgZ9K2rpeylfSqkQayfdKvNaYGI1aJOX+TfqZu0aYb9Yqoq8ppMR/QkRs/kFsxrydeO8/TKQU9iS2DxzjqZcQCqnb2j6vdrBZRkUJ1Mu4v16ooCYdpBd74qWjNZStmhyrLR72KAr0vlPi9Q4gFizvrH8DFak65xfzIzaCTabWaqxB9E5Zj4gCPE405T29lx1WSa8z/A7QFNtDPs+dToVtRNICxGJgR2IDoStVgbqQ+Wz/HcD2HZJeaKOTBUNTI5vNT8cwD7x/SLqDSG8szamw/d3ivniDpAeYtpHy/4Dv2T6mrGvNBF8hHITf0p1tCnqGTKtvyrFEmuzhRJrchcCrwE62/69Cu2aaQqr7GKY1mt5zlDVnexG1XWdJ+ixwOlGT1Q7xmbkk1YttzdMwxvYH2nDdfmQkEayW/dGK58CBTJ8FdiWRRdF1PVPT0WpgmF35KyVdR3yAy3S0tgE2a1y02L6vqNH4JelojZrCmTq5+OonWjUD/iixuJvhIugi3eTzto+bGcOa/N5ZgI2JdI6PEveZo4jIYjJKimj3uOLrpboxUG7BbxtSIx+gDU2nbe8h6QRC1bL2gP217X+Ufa0xshchgLE6UTT/U9vNeh4lY0DSIsAHgL90Y7+cDrAK8FbbL0r6DVF3vKTte6s1qxSOJ9pL/JAQVTqaeIYMS7HBcoyk9xU/fyzwA2KhvjLlSt7v1DAuNcNpwBhJBOvsZicVqo+TCWf8a0zrl/op4FZJq7vLmkaPmzKlaxRnewJJ/7U9b4m/7zlgHtuvN5mbBXjW9lxlXa+fkfRQvdiCpG/Z7tl0weGQ9EFiV3NxIoXm5FYiBJLWpUg1sf3LQvJ2VyLS96RLatJdpLjtQCw0JxC7rWcQaV3LF+IAyShoEsGsb7w5johgdm3aiqQPACfaXqHE37lCK8GObkPSCsSi71PAH4FTgAt6OareaSQtSyy23klEVPcj0sReIoRPtrN9TnUWdh5JT9uub5z+lO35qrSpLCQ9Aixj+8lCsfMW20NEeEbxe8YT/TR3ICIed9pevlRjk8qQdB5wj+19mswdAbzd9uadt6w1GdGaASS9h+Yd2WeGR4meDH9uMrcs06Qrk5GZp2G8Kz1cl9WMQrb2UKL4/wjg+OGKfSXtBRwE3AEsI+l4YO1iehfbLtG824lc+X2Bc1w0DZU05t2cwuGYDBxk+4pSrOwNOlrwK+lqRk5N/XDDOc2EVsYTtm8P7FaehUAIwtQvMq+1vVbJ1yiFwiHcXdLewCHE7u0GtG66mgzleCKD5GwiHfyXRAuCCyRtCnyT2AUfJBp7hDWOe7lH2Fy2n4RQ7FQ0UZ9his2MnwM/l7Qo8d5J+od1ibrEZnybUOntKtLRakDSBk0O1xYPu1F+wfWZwGmSNrU9VTVL0luJotfTS75eP9O4UOybGglJSxKRq48BxwFb2356FKfuCqxr++YiCnYdsL/tI9pg5vGE+MaRwCqSzrDdTCFuRlgHeCuRSjJIjtaXbf9PB6/XasG6MPBFmvQzobnQyiuEOuCWtq8uybYajZ/nrt2lrlvgbQ+8DOxD9HNKRs97gQ8XogMHAnsTUXKKyPxPqzSuIvq2Rxgwm6T1mfY5H98wxvbljSe1aDNR43Gi7ivpMgp12mFp1g6GWI8/1+Q4xfFmwk2Vko7WUJrl3NYWD4faPqXk6x1MRLT+JukmphV4rwZcxgAqK80MDXUtU9pZ19Jh7iLy8Y8CHgG2atJjpNkDdkHbNxfzN0h6mdj1KR3buxURNBFpG1cXogXzEu/pptHZoulj03zsuprJM8q3uKvZFuiYo9WopippHiIy+SUiXeuAJud0WmhliJR8h68/IpI+TThXKwDnAtvY/kO1VvUss9ZS6m2/LOlZ243ptANFn/cIe4zpncQnGsZTgCWbnNeszUSNruyrlABRY/sPorTgtzNw3m3ExmuzIMQWNM8Oq5R0tBqw3dGUnSLMvXlRR7Me8eabDBxsO9NMZoy5iZtujXF1416/4U4m7G+1uG21k9m4GHmhWT1gWdh+lehn9gtJCxP1WtsTPYR+ZbuZ5u4PaVH4OsBUsogsBFK+QAis3ASs2UV1UY073o3jpjveHeZMounqmUQk65NFY9Op2E5599Ex0uvdq/fypAm2Fx/jqT3XVykBYvN1W2J9sD3hcI1GPOibTEsLPZdpwYktiVKRZsrhlZJiGA1IWoxIVxgSuZI0iWgk/EAH7emZAvCqKSQ/h6UXezDMDJJeI+oAayzcMG4Vni/bjtWI+opdm8w9Y7uxvm6gkfQSQ7veT4ft0qLdhfDOjkTvq3uJ9NJhm2SP0HKgdMEOSfcOcz2K6zXb8e4Ykk5lhEib7RGV1JJRvd4d3xhNOkPRL2wi8O8uUhQFQNIchNDNmoSy6pNEK4tTh6uXTloj6b1EFsyWhHjQrsOt1SRtRqhSLlZ3+H5gb9vnt9HUMZERraEcALRybOYs5j9f5gWLNJ13APfZfrw4tgLRJ2Cj4rrJCPSzEyVpYu290WL+Pbb/0mRq3TaaNWocDWaH9GIqmFXShxgmimP7urYY1r2Mo7WkP5SfNncHsAChZHkRTJXRnUoTydx6+8YRr++qJdtVf/3F2/W7y8L2DlXb0C/0wuudlEshcvJd4G11x+4jempd0OKc7YFNbG/ZZO4c4CLbZ5Zk39zADYQM/RXAH4hoyteBXSWtYfuZMq41SNj+o6SziaykbYkN4ZbrueK9cIGkdxLPrce7TdK9nnS0hvJhoui2GWcQRc2lIWkjQiFnAvCypK2BNYh+DT+mtbpKMljczfSKa3farl/o3kgTwYJh+sJ1E7MTKQCtHK0pxMNskHjRdjOxiXZRc6q+TYiZNL4WQ9JuG/sYSXptQHsbDYuk9YgmrDfanpFahCQZGCRtSKyxDiNSyWv9kbYCTpX0vO3Lmpy6CyHY04wjgROIVN4y2JeokZ7OoZL0RkJQaF+a1LMmzSnS/2qpgy8Q6YNvtf3vYc45CrgGuK5XnjfpaA1lQaCVmtuzRDi7TA4Bvkw0t9yZeKNdBiw1XAQjGTgaF76LjDA/lR5IdXiuE+mLSWtsz1K1DSMhaaQm5FNsNzYU7SjFruzVNWEaSXsSC8fbgEMk7Wz7rCpt7BUk3cjQyO0rxE73mS0W3UnvcgDwBdun1R27BzisEFX6BrE2amRp27c2+4W2f1+kIZbFx4gU+OmiVraflrQfsX5LR2sUSLqMUI49F9jK9h9HeepiwI+AiZL+TDhd1xKO15PtsHVm6fqHawX8m9YpO+8i5ELLZCnb3y96Dn0PmAOYlE5W0sBIimtNU8mKVIebiNSGV4hUh1eK8U1F2mrSfdxftQE1JC0oaY+q7SBqC5t9/ZdiAVSZZdP4IKHSiKRxhKjIdrZXIeoPWmVLJEP5ASGUU/91OvAA0RJluwptS8pneeBnLebOI6LCzXiDpHmbTRTHh5N/n1EWb1UzXxyf4QbLA8z6hBT7p4BLJT3U+NXsJNuftL0I8X44kUjjPA54TNKfJH23U/+B0ZIRraGcDxwnSbWGqwCSJhC5wz8v+XpT03Fsv1oIA2SOb1IWHUt1KD4j2H6uGI8jUmCXAybbPrfFqQMn0zwStpdtPFaXgvZb2ze28/qSZgU2ISKhmxBKesc0/Exjw+LZ29k81fZ+DdcfxzQBj78D+zU7r8O8yfaDxfcrAHMRzxSAS8m+iKOmIbIxHZIuJFLrB7GXVr/yGvF5eaHJ3FzFfDNuJTYxftRk7pPFfFmMFJzI4MXoman2ILbvBO4ETpK0ALHRtjeRRrr7TFtXIuloDeVA4Crgbkn/xzTpyA0JNa6DSr7eHJLqU0kmNIyxvXXJ1xw4JH0duL6HJfNHep/M3uK8TqY6nEMsKk8txocT6bBXASdIWsT2sU3OW6ak6/cNVaWgSXoP4VxtS9T8zUo0H76wyY831pDdTIeap0ragpD5fYVIN3I7rjMGnpK0qO2HiFrbW4uWBxCiRrmpUAJFA/bFRv7JpIf4NVFGsW+TuX2AVg3QvwVcWESvzmPamu0TwNeAzUq08Q2Sdqb157jrmuV2K7X6cUnz2X5qRs6VtCBRCrFW8TURuJ54L3SdcFY6Wg3Yfq5QQPsMEdpcmWictz9wRtH3qkwaJZwPLfn3J8E6wHaSHrX9waqNGQMjvU9avW+GTXUYjST+DLASsUivNY7+PPBJ21dIWh34CdDM0TqosflyA5XX3lTAGhRpZg0paOdK2oRwMkpztCTtSrx27yMeWF8lFi13ET3chlBBw+JaVO8wQmnqG8Q9uZt6lJwLnFtEXHZn+s/takSDzmQmKe5bT1RtR1Iq+wGTJS1PbNrV90daDfhAs5Ns/7pQHjyGEL+YQjhC9xFlGK0ctLFwEzDcxncrZd2kAUkrAxcCb5Z0P7Cp7dtGcd5fCPG4awmn6oRuVhyEdLSaUjhTJxdf7b5W2RGypAm1RaGkt1Rty1iYifdJJ1Md5q6rLXxf8e+VALZvLBSGmvFoi+NzEJGV+YkUxEFi3g6noH2PEEnZ0vYvagdHcIA7iqSriDqOw4l6nZeB8fU22n65Guumsn/xtTbTaoxqLE+bInz9SGN7gYLxRIPar1HiRkNSPbb/Viy+DyY2DicSNfFXAqvYvnuYcxvlvp9ohyKd7bXL/p0DzHcIh/pk4LOE4u2GozjvKUL+vfa1kKR72hAEKY10tBpoUncwhDLrDlo8TF4B/lWXcpKMEUlvIh7Md9l+oW7x2lNIGrGgt8Uis5OpDo9JWrpoMLkWUUs0BabWhDV9P/dI7U2n6XQK2haExO45kv5JyCyfRfn9umaGWgStJkFfzziaSNB3muI1atpIukXabNKau5oce5UQijmTERp6J72H7XuBISInksZL+rXtln0hJS1BpJNNBB6X9LLte9pmbDKzLAesV2gT7E8oTI6I7TUkzQ6sTrzeBwHvLRQIryPUB69sl9FjIR2toYzUu6bsuoO7mBbqruc1SecBu9j+b4nX61sk7QPcXesMLml94BdEmPlJSRvbvrlKG2eC+gLh2qKycdxskdnJVIeTgEskXU5Eonatm1uTKFwdli6uvek0HU1Bq9sRXohIm96eeB1eBzaTdEoXRIuWqPj6SQfphZYDSceYhdi8a4qko4EvAQ8yrf/W9yWdYLs0xVRJVzP85tMU2x8u63p9zvja5qHtFwvnaVTYfomQdb8GQNJcxHpjbyLtvdINt0bS0Wqg03UHzR4mkmYDliLC54cz/YI1ac2ORAFsjeOIlKhvAnsSi9X1K7CrDJ4ovk4FLgBeGs1JnUx1sH2kpEeAVQgn6Zy66YnAUa3O7YHam07TmIJWv7nTthQ0248Rr9NRklYh6rYOJT5DC7XjmjNg231VXj/pPJI+S+x8/344FcJkcJG0G/BxYE3bk+uOrw6cLuk+22VJfp/T4vjChNrdG0u6ziAwu6T62vI5G8bY3r/ZiUUN+KpME8P4ALHhfCOF89VNjJsyZZDXMjNG4XF/3vZxHbreAsAfbKe60iiQ9F9CXnlKkUbwV2BiobA3B3C/7UoXi2OlcL4/SkiYrk5E6k613fPFty1qb6ajC6IpA0tx3/v4MPL8nbKjo2ndSbVIOoxw9K8jIuLft900LTPpb4p70PO2h0QqJN0B7Gz7hiZzawA/st2qN+rM2jUPoZL4JaJ/3gG2/9mOa/Ubkk4Z6WdsT2py3q+JrI7XCaGmawhhjJu7tdwmI1pNkLQusCLwD9u/LBa5uxK1Ik8SkZJO8B9g7g5dqx94kahfeZ5wRu6w/XQx9yqtJdC7nuIGciEhY7sgsA1wYvEA2rwdhb8zSqH8tIntLZvMnQO4hSR519fedJoWTsUrhJLWDUXqREcorlWpk1XQ6bTupFq2BtaxfaekZQkxmHS0+pQRNlKGqyVejIhkNOPGYr5UiufuFwg12JuIaFpTdd+kOc2cqFFyOZHtcYvtVr3Vuop0tBqQtBdRXHcHsIyk44n0HYh6qU7WjGxIiAEko+Nq4AhJpxJh/Evq5t5NNO7tB/5L5KE/Sqj7dYsDuQvxd2/GkcAJNFcKy9qboTRzKsYDiwMvF/WGzcQC+pYq5OTLRNJ2hEBMV0sRdxHzFU1JsX27pIlVG5S0lZE2Ulr1R/ovcV9spkr4tmK+FCTNwjShpnuJTc7flPX7B5Ei+2g96lQmhxMxsX14p2wri3S0hrIrsG7REPGDxId7f9tHtONijTmpBTUJ2w0JNbBkdOxJyF5fQ+xk1UdHtgUuq8Cm0pD0fiKVZjMiZH4ScFEXhcuXtn1rswnbv5f09hZzWXvTwHBORaHQdBSwSecsSkrgIGB+SRfb3qZqY3qBohZjXO2rbgxkSnE/MRMbKb8AfiBpM9vP1Q5KmgB8n2ltMcrgDqKO+HDgouI60ylH50bK6OmUiEnVpKM1lAVrynS2b5D0MpHS1C7e3OTYK8Afga92Q0pYr1BIYTdV/LH95Q6bUyqSauqUpxFRrMeKqVlq0u9dsOh4g6R5m6lkSpoXaCpRL+l821vUjQ+w/c268fW212iLxb3JUcBuVRuRzBi2l6gr4k5GZm4iHbzGuLrxQKYU9zOSJtb1YWw2/x7bf2kytT/Ra+seSZcyrdHxR4hU66+WaGbNqaqluTeqRed7cpR0WMSkUtLRGkrjB+cF26+38XqX2z67jb8/ASStAHzD9uZV2zJGajf4QxnaP6ZbFh23AlsCP2oy98livhmNSpB7ECp3NZafedP6ilkouY9W4QivYPu6Yrw/0z8fjrf9VJnX7HeKOo75gadsvwhQNNUcUrSfNCVTigeLu6lT7ZN0Z4OIxY002xiQqwAAIABJREFUUfWz/UwhevEZIgVtZSIFbT/g9DIb2WbLgVL5PPCZeicLwPaNknYg1hHpaPUpEyQ9VDeet2GM7UVLvN4PgXS0SkDSnMTNdUWitu1AYqFzNJGG2cvywL2w6PgWIdYxL3Ae03YWPwF8jUh5HA1lN+PtG4pmzvtSbv8ziLRbmFYHsT9FagxR/zAL8XlKRqCQxT+SaDQ9C/C6pMnAPj3cx6/jZErxwNF4319khPmpFM7UycVXJRQiVdvaPqYqG3qMjouYVEU6WkNp2Xm8TeSisjyOI9JyLgM2BpYB3gOcSQiZPDbMuV2N7fskvZHor/YP289UbVMjtn9dKA8eA9TXNN4PTLJ99Sh/1cD3nJB0I0P/DuOJh89/KL8+a/OG3/mK7U8XtixGOF0HlnzNvqNwsn5NbJ4dzLS6g62AqySta/uWCk3sC1JYpC9pvN+NNAZA0kbAMzV590Jc4XSi/9pkYAfbj5Zsa+3asxL3zUnFv38lnn/JyHRMxKRq0tFqwPa1Hb7krJI+xPC7Na3UdpLp2RhY2fbDkk4gVIHWt31VtWbNPJI2IIp+5wKelrRpBe/VEbF9AXCBpHcR0cQnRlFnOJuk9Zn2GWgcV50SWQU/aHLsFcJpvbkN9XiL2r6/bjxVzt32/ZL+X8nX61cOAQ6y/Z26Y3cBV0v6KxH13aASy/qLg4AFJF2UwiIDz8HA3nXjHwPPEu0BPkvUU21X5gUlvYdwrrYl0hlnBba0fWGZ1+lzOiliUinpaDVQQa3C7MSippWjNYVIv0pGZh7bD8PUxeFz/eBkFXyLKOr9ESGjfgjwoUotGoYZlB5/jOn7Hz3RMO7ZSORYsd3pNNdZJc1Xu7fZ3qU2IWl+BtPZHQvvJ1Jlm/FjQhY6mUlSWKQvmUNSffuPCQ3jVm1MlqZIpS5aAKwJLFWsAW4Bfl+WgZJ2JRys9wHXE8/k84jNlMnDnJoMpZMiJpWSjtZQOl2r8FzJNV+DzLhCQrzmtL7WMO5l6dWlgeNsT5F0HNB1KoqSHmaEtL9m73Xbi7fLpl5G0kLAXsTCYX6iWfq1wDFtSIW5hRAsadbw95PA70q+3iAyhUwVL4XCybrMdqdT/ZP2cSjTPz8aW980a4UD8ZmqCV6sBjxQi87bfqxIuS+L7xH34S1t/6J2UFKJlxgMOiliUjXpaA0laxV6lwnEzlL9YqY+ba0blPnGyqy2pwDYfrVYaHQbW1VtQL9QOFm/Ix7qv2Tabt+mwLaSVirZ2TqMSPmcm0jZeLi43hbA14n7YjIyNwE7EwI8jXyW8kVMBpVZgLWqNiIplUPG2BPyz0Sa4BlE0+OpWSyS3krUtJbFFsD2wDmS/llc8yyyrnhMtBIxkTS7pNds9+p6bTrS0RpKp2sVWu5wSnovISKQPXNGQZ9Lr46UVoHtrTts03SMtWas8f/R4ndX+n+rgP2J6NX2tl+rHZR0IHAqseu3e1kXK4RMJhGF3PV9Ax8AduqjFNx28zVC9OLtxLOj5iBvSezcNu3zlyQJD0s6AzjF9m0zcN5XgIslfR94iVD7rPEpSkzpq6tBXoj4PG9PtCJ5HdhM0ild0M+yX+ib6H86WkPpdK3CMvUDSQsA2xB5wMsRCnpJ0tg7q1UaRWVIWnOkn2kh7LIVEYm8kHhQJiGYsGm9kwVg+3VJ3wRc9gWLVJhfFEImCwCPZ8P0GcP2zZLWI+Tdr6SQdycWexukvHuStGRnwnG5RdJfiCjHWbafHO4k25MlvY3oNfm3BkXei4iIU6kUCsZHAUcVSqOTiGfyN4GFyr7egNI3UcJ0tIbS0VoF2/+SNAtRADgJ+Cjwb2Kh837brZq8Jg1IGtH5sL1/J2wpG9sHVW3DKDi3xfEpwDyEYmKzjYpaOsZnibS1U20PeorVIrb/3mzC9t8lvbldF55BIZOkgeK9u1bR128+omHxCxWb1XNI+tww092YOp3MBIVi34XFZvO2xHroO5IuAk4B/s/26y3OfYYma7NObBQV7RpukbQH8PF2Xy/pPdLRGkpHaxUkHUGEoCcAFxD1YVcDDxJSzsnoadvis1uQtBYR7ZhIFI5eYfuaSo0qsD3k719EgfcjlBJ/2uK8WjrGROIB+wNJsxMpcj+w/XTbjO5eRoqcl5pWIekeht9BnGJ7qTKv2e8UztVUB0vSCsA3bGe92+j49Ajz2fakD7H9BHAscKykZYEdgJ8QkeG3VGjasNh+idabjUkDks6m9TOnL2qzaqSj1cAwtQoP0p5ahX2IgvcvAOfW8ntTxWbGsT2pahvaRSF+cQ7hiN9EbAC8C9hL0q8IFaSuUemRNBewB6GadzWwqu07hzvH9uPAdyWdSAjOHArcSjSAHTTmGCFC20rqeKzs0uL48sC+RApcMgJFFGs/YEXg78T7eH5CHGNDoNOy/T2L7XWqtiGpnEWKrzcCvaoYnDRnpMyJgztiRQdIR6sJdbUK76SIHNRC0JIWtP3vEi+3IhEiPxo4TtJ5wJkl/v6Bpy5SMsn2ClXbM0YOAJYA3mn7vtrBIjf9/GL+6xXZNhVJswG7Ej0wbgc2Gm1diqT3E7uXmwE3Eqm6g7prfRbDR2jPLvNitqerBZW0OFFvsClwPNNvOiWtOY7o7XQZ0UB9GeA9xD19l6K2IxkFkiYWmy+t5t9j+y+dtClpP8W6a3si02d24l74Qdt/rNSwpFR6pByiFNLRGobCufqrpFklbQrsCGxEibvJtv8E7C5pH0DEQvMKYgf5S5JOsP1IWdcbFCTNSix0JhFRoIeAEys1aubYBtis3skCsH2fpB0JCfBKHS1JnwEOImoMtxlt9FfSfsSD9QVix3/Zkjczeo6qorOFmtbXidfjNOAdef+ZITYGVrb9sKQTgHuB9VO1cUzcTUQyAJB0p+13183fWD+f9DZFM+DtiWbAlxOqqu6mTI2kPCRtVpQN1MZL2f5n3XgX2z+oxrpySUdrGIr84ElENGQi8DPgQ+24VnEzOR84vyh0r0mH7kP5aUJ9i6RliNfsM8DchDrbC4SwSNlNXjvJIkSEqBm3Awt30JZWnAY8AdwM7CRpp8YfaCHT/i2i39ldRDRg1cbU2QGUd+8oRVPPLxMpzAZWsH13tVb1JPPYfhimtgN5Lp2sMdNYh7jICPNJb/NFoi734926uSNpJeAl27cX4wWIerLlCGXRvW0/V6GJvcRphC5BjVuINOsaRwLpaPUjkt5ENL/bkdhZuZ7Y4T0E2K0TqR/Fg/pI4EhJq7X7ev2CpJuI1+waorfGebaflfQwvS8V+iiRgvTnJnPLAt2QknQwY/s7j/W8vqUCcYp7iGLzbwK3AUtLWrr+B2xfXuL1+pVxRQ+tmhPwWsMY21lrMjoa3/8jjZMexvYyw81LmqdBur0KjiEE02qbnicBixf/bgMcTjiMycg0bpSMNO5Z0tEayiPAs8QH6hO274WpjUJLZzS9h5JRsyTx2t0F3GX72YrtKZMzgdMkbWr7X7WDis73PwFOr8yyAtsHdvK8PqfT4hTPEAvX/20xP4X4fCXDM4G4/9QvEuolpqfQZ4paSVIGkh6zvVDd+DLbG9b9yINUnyq6DNFIvpYF8FFgRdt3SLqYqClOR2t0DMxGSjpaQ7mMqMPaCnhV0pm2H2jj9cbaeygZypuJG98k4DpJ9xEOyhvo/Q/twURE629F5O4hou3AasR7tmsUerpZgr5X6LQ4he3Fy/x9g4rtVGcsjzkk1TebndAwzpT6/mLOhvEqDeNuiHDMZvv54vtVCaG0O2BqvfT8rU9NBpV0tBqwvamkBYkanx2Ab0m6nqj3maMN1xtT76FkKLZfBWpNDxcCtiPq3OYDfibp+EJRsucoavg2l7QusB7hxEwGDi5aEixGxX3XxipBL+l1Rk6TG8h7VYpT9BaSxhF1WkN6vxU74M/Y7vVNn07xrYZxY7uDERvUJz3FSJ+Lbvjc/EPS+ravAD5GEd0CkLQIkRmQjI4Jkh6qG89bNx5HBBn6goFcvIxEoXh2NHC0pJWJCMlywG2Sfmr7S+247lh6DyXTkLRCoeJIUUv3HaKz/CpEzd2PgJ50tGrY/jUNfaWK5r73UH3kc6wS9O9ucgwiOvkV4KmS7ex6Oi1OMULzSCAFSUbJbsROd7O/1YnAb4mIZDICgyT/nPQMBwLnSbofWApYu25uU0LQIRkd61ZtQKdIR2sEbN8K3CppD6K/zw5lX2Nmeg8l0/EbppcDvtb2WrZvAW6RtHt1prWdbkirGJMEfa1HXQ1JHySKihcH9gdObpfBXUynxSlGah6ZjI7taO5kQaT3nk06WknSjNkbmrTP2TB+Q6cNasT2xZJWJPqf/t72PXXTtxPiacnoeNr2H6o2ohOkozVKbL9M1FO1qqkaE2PtPZQ0pdHZWL5+YPulDtrSabohrWKmJOglLU+kA70fOAI43vaLpVrYO3RUnCKjB6WxuO2mTqvtvxa1dskoyJTigaOxSfs5DeNSm7SPBUlzAnPZPr/J9H+BfzY5njTnt5LuJDZSz7T9RNUGtYu8SVXPWHsPJUPpW9WaHmFMEvSSliR2+z8GHAds3azGZZDotDiFpAnFdZ8rxuOAnSj6w9gudYOpj5kiacFmDbclTaQ7Is+9QqYUDxBVNWmfQfYh6qOblY98lhB/OqSjFvUubyF61O5AtDK6hHC6fmX79SoNK5t0tKonewiVx2yS1mfaYqZx3LO9gCTdSOv3SbconY1Vgv4uYjfwKKK9wlZNGhaf1BaLkxrnEHV0pxbjw4GdgauAEyQtYvvYimzrJa4nFlyHNZnbiUhvTkZBphQPJkUv09WI5rVPADfb/k+1Vk3lU8BHWswdDfyKdLRGhe3Hge8C3y2yWbYn1glTJJ0OnFpTdOx10tGqmOwhVCqPEY0DazzRMO7lXkAjdUg/sSNWDM9YJegnE6/NOi3mpzD969j3VCBOsRIh+lNTj/w88EnbV0hanXgApqM1MgcB10p6C3Ae0z4DnyCUbNeuzrTeJFOKBwdJ+xOiSeOJ6NBE4GVJh9ruBgfmLbabqvvavr/43CcziO3bCHXifQnV4mMJUbiqBb5KIR2tpG/o515Atk+r2oaRGEmCfpjz1u6MhT1Fp8Up5i52GAHeV/x7JYDtGyUt2mF7ehLbv5e0HnAk0Z5jFkLU5AZgg0Ep/i6DTCkeLCRtDuxORDYusP1KsemzGXCcpDtb1EZ1kpckLWr7ocYJSW8GXq7Apr5A0tuJ1347opXScdVaVB7paCV9Q728e78haXfb360br2P76rrxYbb3q8a66WkmQQ8jvz6S5gZWJxy0fwO/tf1s2wztYioQp3hM0tK2/wGsRfztp8BUqflXO2xPz2L7JmCtonB+PuAp2y9UbFYvkinFg8XngP+1/fPagWLz7meFMMrnifTmKrkS2JdwCBvZp5hPRomkeYGtCAdrJSLzZTfgoqIval+QjlbSTzSVd6/QnjI5mMhnrnE+kcNe43+JJteVImke4B3AfbUIiaQViP4jGwFztjjvi0SD0jmJlM8FgBckHZC1QR3hJOASSZcTBcq71s2tCWQ/v1EgqV6C+jUi/Wm644WCbTIymVI8WLwXuLjF3CXA9zpoSysOAG4uoi8/Z/rU4NWKr2QUSPoZIWxzN1EbvJntRys1qk2ko5X0E42KXstVYkV7aPy/jTTuOJI2Ih4+E4i8+q2BNQgRgB8Db29x3jZEf61dgJ/ZfrXoLfdJ4FhJT9g+oxP/h0HF9pGSHgFWAb5g+5y66YlEVCEZmRdpXVs3rpjri7qDdpMpxQPHHK0iv7ZfkDR7pw1qYsfdklYhajEPIzYEnwAuB1Zt6KuVDM8TwNqD0DM2Ha0uQ9JuwJW2/1KMuyYlrAfoZ/XGkaTru+H/fgjwZWJ3ameidcFlwFJ19T/N2AuYZHvqbmaRNnC2pKeL35uOVpux/VPgp02On9p5a3qWJao2IEl6lFmKmqxmm4bj6BJ13cKZ2q5qO/qAH9v+XW0gaXyRKlobb2z70mpMK5d0tLqPVYAvSrqVSD1ZmS5ICesR+lbeHaaqwY0rvoaMu4ClbH8fQNL3gO8QDtQzI5z3DsIha8bllNwkvBeQtKTtuzt4vUObHH4FuA+42HbTHmjJ9Ni+r2ob+gVJVzNyw+IPd8qepO3MTUSEm1GLBleKpHeM9DO2/9YJW/qAq6kr9SD6cNaXQ5zTMN+zpKNVMZJmIR4YUwBsb1uEyP9ILED7Kf2t3fSzvHvjQ2hc3bgrHkLUpUQV6X/PjMLJAnieSE97uMncRGAQhQR+L+mrtjtVl/DmJsfGA+sDR0naqBB5SIZB0udG+pkUcBg157Q4vjDwRfpkEZZMpReiwXcRz9pWm5uZGjx6ur4coizS0aqe/yN6hFwDIGlW4GzgQSIFa29gx4ps6yn6Wd6d3ngIzSHprLrxhIZxq95PlxKd4berbTjUcXgxP2h8GPiJpE8CO9i+t50Xsz2p1ZykzxBy5f0iLNNOfgD8nbh/N1sopIDDKLH9w/pxIbSzL/AlQhzhgCrsStpDL0SDbXdF+mKf0AvlEKWQjlb1rARcB9M5WbMBGwNvAP5SnWn9g6T3Emlsu1Vtyxh5H90vefqthnGzdLRmfIXoM3S7pHolpy0IYY0PlmZhj2D7d5JWBr4K/E7SITTcCzqYBns2cEyHrtXrHA9syTQlrQtSZXDmKDI8vkDcJ24C1uzXNh5JdyPpEtubVG1H0luko1U9rwFvlfQgUfD/KrB5nfLa3JVa18NIWgDYBpgELE9ED3uVHwA/lHQmcLLtP1dtUBOutH3DjJ5k+xFJKwF7AhsQ6YKPAxcAx9h+qlwze4PiHvAdwsk+mEIqvKCTabBzko04R4Xt3STtRcgW70A0Wv0FcMogqGuVSZFWvyPwDeBe4rn4m0qNSgadD1VtQB8xl6TJdeN56sbjaNEKphdJR6t6fgz8nqhDmQvYvS5qcQDw26oM60WKh/NHCOfqo0Tj2wWA1WzfWqVtM8lbiP/PdsCtkm4HTgbOtv1kpZZN41eMsW7C9n8IifevN85Jmt32SzNpW88haR3i/nAbsHQVPUaKdK2jiMLlZBQU9+8LgQslTSSUOCdLWs/2NZUa11vcQdy7DwcugqFiBCk8kCQ9y04N4x82/ak+IB2tirG9v6QriEjW34DLCwWwV4miyvWrtK+XkHQE8Bki3ewCYBNigfggcH+Fps00DYu3WqRuR+A7ki4idsx/VaWNlFy8WqQM7ULUZbylzN/d7Ug6kWiCubvtMztwvYcZmhM/nlCB+g3wqXbb0E8U792PE1Gt9xLNVm+v0qYepOZUfZuoEWy8v6TwwIBQKOxeZnvdik2ZQ9LJw/2A7aypHwW2T6vahk6RjlYXYHvqbnGRQrUu8dpcb/vpygzrPfYBniTy+c+t1UZIqtSosrH9BHAckZb0AeBMop6p8kWHpLcyjMNle4jDK+ntwE+AFYF/ANsSzY2/RygR7tkWY7ubRYHlbD/Soett1eTYK8D9th/okA09j6TVge2BzYHJxC7txV1eW9mVpPBAUscsdIcYzxRChjwpCUkfAdYkNvWeBK613ctlHkNIR6vLKB7IPdvrqWJWJFIGjyackPMIJ6SvkDQOWI9Y0H2ccE66wRmZQNRSzKj07bHFeYcTkboLiYjuTv12w50BxgGrFcXXbV+k27629n0RjZkfeMp2q742SXNuAP5KbIQ8BiwE7Fi/2ZPy7jNOvieTLuEl29nXtASKKOUlhNjV7wgRrHcDX5J0A7BJfQPjXiYdraRvKJSodpe0DyAibecKYjfsS5JO6GCEoHQkvYtwrrYF5iDU4D5k+w+VGjaN5wghixllFeCttl+U9Bvgv8CS7ZY073L+SSEDXieA0tbUM0mrEClaaxCfmdeL4uR9Ushh1FxHbCi0aqSb8u4zQPGe/DaxGMv3ZFI1fdPbqQv4ErF5sky9tL+ktwE/B3YDvlORbaUybsqUvpGqT5IhSFqEEJDYnhAUmL1ik8aEpJuJWo8rgFMAd5tstKSnbc+wGEbjeZKesj1fudb1HoXq6CbEe3cT4M/Ea39W2UqMxYL214TzfjaRsvlmIqVwa2Bd27eUec0kGY58Tw4WIzT7Hg8cZ7vS9HhJl9reuEob+gVJvwX2tD25ydzqwLG2V+28ZeWTEa2krykiWEcSDXFXq9qemeB8YFPbD1dtyDCMdbdvfMNDtnE8kOlWRcrgL4FfNrQq+I4k2y5ToOIQ4CDb9TuIdwFXS/or0SNtgxKvlyQjke/JweLTI8xf1xErhmdLSRNsPwdT0/h3ApYDJts+t1Lreou301pV+yZg6Q7a0lbS0epSiubFHyOa7G5atT39gO2bqrZhJvhbvZMlaSnb/6wb72L7B9WYNpVNJL2xmYCLpDcCz9huFkK/iekfsrc0jAc+3cr2E5JOAZ4nmhiXfU94P6Fy2IwfE72MkqST5HtygLC9TtU2jIKziU3PU4vx4cDOwFXACZIWsX1sRbb1GrPZfr3ZhO3XizVwX5COVpchaTli13obomHb6dValHQJpxGS9TVuIfKbaxxJNDWukvcRcuxbN5k7kdi9Or5xwvba7TWrdyl2TNdnmvDJ3wnxkDM6aMYUsjYh6S7yPTlAFGIoz1edOgisTKzPamIOnwc+afuKIt3tJ8T9ORmZ8ZLWp/XnuG/8k775j/QykuYjFqeTCOW8m4jmxcvbvqdK25KuofFmNNK4CrajuZMFcDCxGzjE0UqGIundTBM+mR04hxA++X2bLnkTsTN7dJO5zxbzSdJJ8j2Z1NMNz7gJth8vvn9f8e+VALZvlLRoNWb1JI8xfKbKY50ypN2ko1Uxks4lFPL+BZwFfMr2P4sGos9ValyfIWlh273aA6Mx5W6kcRUsbvuuZhO2/ypp8WZzkm5kBPttf2DmzespbiPaPOwB/LJR+ETSgrb/XeL1vgZcVfQ0O5eQ2l0U2JJoAt5KRS9J2kW+J5N6uuEZ95ikpW3/g+jr9dtaOnyRHp/98kaJ7cWrtqFTpKNVPZsBzxCe/Vm2H6rYnp6nEA54qpb/K2lh4CvA54heT0l7mNLKAZA0kdY7klWnPHYjb2u8FxQ56x8FdgQ2IiJdpWD7ZknrESmoV1JIaRNNdzdIKe2k0+R7MulCTgIukXQ5kW2wa93cmsCdlViVdDXpaFXPosQHdhJwmKRrifqLqnORe45CDvh84C3AE5K2ICTRDyWiA+tWaN7MMkFS/cJ73rrxOCLVtGquJ1J6DmsytxPwmxbnXVpydKbnqXeyJC1L3B+2JfqU/Qz4UBuueROwlqQ5gfmIzYoXyr5OkoyWfE8ODpLOpnXUqivWQ7aPlPQI0fvxC7bPqZueCBxVjWVJN5N9tLoISSsRTXY/TQgdnAF8t411GX2FpOuAGwgBkUlEzdDfgc/ZvqNK22YWSWuN9DO2r+2ELa2Q9D7gWkK44zympfp8gkj1WbtZc2VJLwOXAicDl9h+rWNGdymS3kTUu+1I1AJcD5xJSF6/x3bf5K8nSTMkTQBIKe3BQNKIKpK2D+qELUlSJulodSGS3kAojE0CPmz7DRWb1BNIehJY0PZrkuYgatwWyWhJ5yh6lR0JfJBpqT43APu2kteXtAKxwVAT0jgDOMX27W03uEuR9CLwLHAMcKbte4vjDwMrlO1oSXqd1rvJ44ApXaD4lQwQki4Czrd9ajE+gmlS2msDh6SUdtJJRmiqDAxmz8dkeDJ1sAspCt9/BvxM0purtqeHmK0WDbH9oqT/9pOTVTiPk4hc8PmBJ4kI0qm2X6zSthpjSfWx/SdgD0l7EzVI2wG3SvozcApRu/ifNpvebVxG1GFtBbwq6UzbD7Txeu+u+34coei2ahuvlyQjsRIppT0wFBvMw9IoClQBIzVVHviej2VQyPl/3vZxVdtSBhnRqphCDnQN2z8rxj8Extf9yNdSIGN0SHqJ6XOk9yAiAlOxvX9HjSoJSXMTkaGFgCuYlpa3HvBv4j30THUWgqTNbF9QNx5zU2VJ8xMRrr2AhW13Qw1aR5G0IJFyuQOwDJE+uBKROnh/m6/9pO35R/7JJGkPkp62/cbi+9WIzYf56lTe/mP7TVXamJRHRtUHD0nrEi2N/mH7l5JmIwRG9gOetL1spQaWxCxVG5CwNyHeUGNr4NHia75iPhkdZwNvrvs6p2Hcy9HBfYFHgHfY3s72V2xvB7wLeLCYr5rTGsa3NIyPHM0vkTQPsDkRzVmUWGANHLb/bfto28sD7wf+ArwM3CapL3b6kmQYHpO0dPF9Smn3P0sAS7b4qs1ViqSnq7ahX5C0F2DgU8CZkg4jBLO2BnbpFycLMnWwG9iYeIjUeNX2fgCSFgKuAfaswK6ew/YOVdvQRj4G7NAYtbL9tKT9CCfngEosm8aYmyoXhe4bEE16NwX+SaQNbtZP6Z9jxfatRDrlHkRLiB2qtShJ2k5KaQ8Ws9v+W6tJSbtQfSuQbmia3C/sCqxbtHH4IHAdsL/tIyq2q3QyolU9izQ00f127Zui4D07jY+SQrWxfjy+YbxxZy0qlcWLWqYhFMff1mF7mjGmpspFkfu/CFW9J4E1bS9v+5hBdbKKvmNDsP1yobZWaqRb0ufqv4DZmxxLko5h+0jgW8UwpbT7nyslLdZsQtL/AId32J5mZK1NeSxY64Vn+wYiW+Pbw5/Sm2REq3pek7RwzdmyfWhtomi0O/BS1zPA1cAb68aPEqIRNc5pmO8lRtoU6eVNk+WJqO2FXVDs3C3cTd17VdKdtusFK26k3PdyY5H3zQ3Hssg76Ti2fwr8tMnxU4vnY9I//Bj4taQ1bD9SOyjpi8DBRPZP1UyQdPdwP2C78hTHHqExOviC7dcrsaTNpKNVPb8heoMc2mTus4QAQjI6xpy61gO8QdLOtP4/jG9xvJOMqamy7Y+03bLeo/F1XmSE+ZnC9jpl/r4kKQtJCxDqpa8X44WBrwCfAyZUaVsf/r+IAAAfmElEQVRSHrYPLupzr5L0IdtPStoNOBD4iO3fVmshAC8xfQprMnaGWy8AYLsvMrrS0aqeg4BrJS0CnA88TKQLbkHUYYzYqDaZyphS13qEm5jWZ6rVfNWsO5aTJF3N8K/NFNsfHptJPUs/v5eTZEQkrUI8E98CPCFpC+C9xKbk5YzxfpN0L7b3KZytKyT9gkiR3rCWYtYFvGp7IMWZ2sDAfH7T0aoY23+QtAGhyPY/TGvy+ltgA9u/r9K+pDuwvXbVNoyE7WtbzUmaFfgq0ferkXOaHANYGPgivZvumSTJ2DmKqNs8neindR7wd2A123dUaVhSPnV9tHYnxJ32ATYB/lib64LU8mEzCSStaPsPnTKmlxluvdBvpKPVBRQh8TUlzcW0Jq/PV2xWLzKXpMl143nqxuOAOSuwKQlmA75B5NpPh+0f1o+LHc19gS8Bl1C9mmIVzCHprLrxhIbx7J02KEk6zLLAOrZfk3QAUce57KAK5AwALzItUl9zaK6rG08Bqu6jNaTEo0ht3RbYkWi3kvfmUSDpaNt71o23t31a3fhM29tUY125pKPVJUhai5C3ngg8LukK29dUa1XPsVPD+IdNfyqpipF2A2cHvkDUX9xEqA82VVocAL7VMG58wDer6UySfmI2268B2H5R0n/TyeprlqjagJGwfRiApFkIcY5JwEeJtfRRQNYbj57PMn3romOYvhfnxzprTvtIR6tiCgnyc4gQ+U1Ejda7gL0k/QrY0vYrFZrYM9TvhiRdSSt591mI3cBvAPcCm9v+TQft6jpsH1S1DUlSMbNLqt9QmLNhjO39O2xT0iZs31e1DSMhaRmidn47QojlQkCEg/CdoiVPMjr6WbxsOtLRqp4DiJ2cd9bfaCS9jSgEPgD4ekW29RSS3jHSzwzXEDGZOUb4+79hmLk7gAWIPikXNftd+bolycBxNvDmuvE5DeOkj5C0EfBM0VMJSUsS0v7LAZOBHRp6jlbB7USvx32Bc2olHpJSnGjGGRjBp3S0qmcbYLPG3Rzb90naEfgl6WiNlrsYmuNdTzfkePcztb9/q52oVjfOmlP1bUIUpvH8gXvdJL1O67/XOEKJcaD+JslgYXuHqm1IOsrBTN+I/UfAs4Ta7meJ58N2FdhVz/FEf8EjgVUknVFzDJMZZpyktzPteT9LwzgjWklpLELskjTjdkJ5LRkdtxGFqKcCP7X9cLXmDBa2x9Q0eazn9Tn1zYnHEWnFq1ZkS5J0HEkr2f5d3Xh8fRq9pI1tX1qNdUkbWJqiTYmkicCawFK275d0C1C5ArPt3STtRaQL7gBcLekBYF6iLU+mDo6eCcTmbL1D9de67zOilZTGo8B7gD83mVuW/OCOGtvvlbQCUaD6O0l/Ak4BLuwCWdgkGTW26x84SHqt8ViS9DlXM31rh0eB+evG55CtH/qJcUDNkV4NeMD2/QC2H5NU+Wst6f/ZfgD4BfCLonn2dsD2wC2SfmVblRrZIwzSBuvA/Ee7mDOB0yS9tf5gMf4J0UMkGSW2/2R7d2AxQnVwa+AhSe+t1rL+p9h1rB//qGF8f5nnJUnS1wxMsXwCxGbz1sX3nwauqk0U66H/VGFUA9P1b7P9qO1v214WWAN4sBqzeg9JexSRy74nI1rVczAR0fqbpJuAh4gQ9GrAZTTpO5SMioWAdxL1Pw8Az1VrzkDwrobxFsDOdeP5Sj4vSZL+ZWCK5RMg2npcLOn7wEuE41LjU4QgRtW0dO5t30SR+piMiu2BwyVdCpwMXFpr59BvpKNVPW+1vbmkdYH1iD5ak4GDbf+6WtN6i6IP02ZE6uDywLnANtmpvWM0LnyaiVqUeV7fIulzDYdmbzxm+6QOmpQkSdI2bE8u1JbfAfzN9jN10xcBZzU/s6MM3LOoXdSVemxPCJ8g6QzgVNutdAt6knS0quf3kr5q+3tAOlYzxyPF12lEI7xXYHqp8JQJ7yhjfSjlwyxSZ+q5ueHYFCAdraSfmUtSfRRjnrrxOGDOCmxK2kjhXP2uyfFuqU+dW1KrqEuqwc4gtv8E7ClpH6KX7PbArZJuA06xfWKlBpZEOlrV82HgJ5I+SfSJuLdie3qZeYuvQ4FvFcfqoyMDJxPeYcZL2plpf/PGKEyr+81Yz+tbbK9TtQ1JUjE7NYx/WIkVSTKN54GVqjai3yhSBg1Y0vuJHnonAH3haI2bMiU3j6tG0mzAV4EvAYcAf6mft315FXYlyYwg6RpGiEY1cyDGel6SJEmSdApJT9uuXP2w3ygUJT9FRLRWBv6PiGj9slLDSiIdrS5B0gQiB3ld4PG6qSm2l6zGqiRJkiTpPJKWItLAlyN6KO1g+75qrUoGGUnP2J6najv6AUnjgA0J50rA3UQ7njNs91Vbo4FLyelGJK0D/JhouLu07UcrNqknkXQ2QyMjrwD3AT/rtwLLJEmSPuY44H4iFfwzwNGEImmSVMXnqzagj3gQmJ3oh7eW7VsrtqdtpKNVMZJOBD4B7G77zKrt6XHuanJsPNFxfrKkrWxf2mGbkiRJkhlnVeBttp+XdD1wZ9UGJQPPk5I+aPsGAElLAj8loq6TiahrbpSPjj2AC22/VLUh7SYdrepZFFjO9iNVG9Lr2D6o1ZykDYn6t3S0kiRJup/ZbT8PYPtpSXNUbVAy8BwM7F03Pgl4lmi0/Fng28B2FdjVi/wBeJuklj/QLyrR6WhVjO1Nq7ZhQLiCCFEnSZIk3c/4BvXR7CWXVM3SFE2JJU0E1gKWsn2/pFuIWsJkdNxFlHq0agLdNyrR6Wglg8ICwNNVG5EkSZKMipuYvnfcLWQvuaRaxlH05wRWAx6wfT+A7ccK9bxkFNiepWobOkU6WknfIOkNTQ6PB5YgCqov7qxFSZIkyViwvXbVNiRJA38m0gTPIJz+q2oTkt4K/Kciu/oOSSsUDY17nnS0kn7iRZr3Y3oZOBf4SmfNSZIkSZKkT/gKcLGk7wMvAWvUzX2KEMRIRomkeYB3APfZfrw4tgJwILARMGd11pVHOlpJP7FEk2OvAI/Yfr3TxiRJkiRJ0h/YnizpbYRz8Dfbz9RNX0T0Qk1GgaSNgJ8DE4CXJW1NOK47Ee2O3l6heaWSDYu7GEn3ANcDh9j+a9X29CKS3kQ4YHfZfqFqe5IkSZIkSQYZSbcCJwOnAjsTqtCXAbvUolv9wsAUo/UoBwF/BY6t2pBeQNI+kraoG68P/Av4HfAvSatWZlySJEmSJEkCodb4/aKFw/eAOYBJ/eZkQTpaXY3tU4HDbW9UtS09wo5M37T4OOIDPA/hrH6rCqOSJEmSJEmSqUyVbrf9KvBMQypm35A1Wl1KURA4iVC4Wahic3qFRYE7ACQtASwFHGr7OUnfBr5YpXFJkiRJkiQJc0iqr2mb0DDG9tYdtqktpKPVRUiaH9iGcLBWAG4EvlCpUb3Fi4RKzfPA6sAdtmu9s14FZq/KsCRJkiRJkgQYmmF0aCVWdIB0tCpG0iyEjOWOwMeAhwnlmrcBm9t+rELzeo2rgSMknUpEry6pm3s38EgVRiVJkiRJkiSB7YOqtqFTZI1W9TxAOFZPARvaXtz2/kTvp2TG2BNYBrgGeAY4sm5uW0LRJkmSJEmSJEnaTka0qucxwjlYBFhY0uy2X6rYpp7E9kPAh1vMfbnD5iRJkiRJkiQDTEa0Ksb2e4FVgbuBE4DHitS3OYFscpYkSZIkSZIkPUg2LO4iJI0HNgV2ADYE7gO+b/voKu3qFSS9zvDO6RTbGcVNkiRJkiRJ2k4uOrsI268A5wHnSVoE2B7YCUhHa3S8u8XxjwJfIergkiRJkiRJki5E0mLAg7Zfq9qWMsiIVpcg6U3AasD8wBPAzbb/U61VvY2kDwKHA4sDBwMn98sHN0mSJEmSpN8ospPuBQ6z/aOKzZlpMqLVBUjaHzgAGA88DkwEXpZ0qO1DKjWuB5G0PNGT4f3AEcDxtl+s1qokSZIkSZJkBJYAFgPWrNqQMsiIVsVI2hz4AdGY+ALbrxS1WpsBxwH/a/v8Km3sFSQtSUSuPkb87b5d17A4SZIkSZIkSTpGRrSq53OEM/Xz2oGiVutnRfj080A6WqPjLuC/wFFEc+KtJE33A7ZPqsCuJEmSJEmSBJC0EvCS7duL8QLAscBywGRgb9vPVWhiaaSjVT3vBS5uMXcJ8L0O2tLrTCZUB9dpMT8FSEcrSZIkSZKkOo4BDgNuL8YnEfX0JwHbEPX1X6zEspJJR6t65rD9QrMJ2y9Imr3TBvUqtteu2oYkSZIkSZJkWJYBrgWQ9EZCHXpF23dIuhi4jnS0kpKYpajJGtdkbhzZVHqGkTQ3sDohKvJv4Le2n63WqiRJkiRJkgSYzfbzxferAo/bvgPA9n2S5q/OtHLJRXz1zA28CLzQ4mtCdab1HpK+CDwEXEqEpn8FPCRpt0oNS5IkSZIkSQD+IWn94vuPUUS3AIo+ss9UYlUbSEerepYAlmzxVZtLRoGkbYCvA7sAc9peBJiTEBT5qqRtq7QvSZIkSZIk4UDgPEl/BnYGvls3tylwSxVGtYNMHawY2/dVbUMfsRcwyfZUcRHbrwJnS3oaOAQ4oyrjkiRJkiRJBh3bF0taEVgR+L3te+qmbweur8ay8smIVsVIOr9hfEDDuG/ebB3gHcBlLeYuB97eQVuSJEmSJEmSJti+2/b5DU4Wtm8A7q/IrNJJR6t61m8Y79EwXr5ThvQBzxMCGM2YSNS8JUmSJEmSJBUh6bGGceMm+YMdNKetpKPVfTRTH0xGx6XAkZKa/Q0PL+aTJEmSJEmS6pizYbxKw7hv1sJZo9V9TKnagB7mK8ANwO2Sfk6oDy4KbEGoN36wQtuSJEmSJEmSkde6fbMWTkeremYrJC7HtRjPWo1ZvYftRyStBOwJbECkCz4OXAAcY/upKu1LkiRJkiRJBodxU6b0jdPYk0i6lxE8d9tLdMaaJEmSJEmSJGkfkl4Cjqo7tAfR+7TGnrbn6KxV7SEjWhVje/GqbegXJH0U2NT2zk3mTgIusP2rzluWJEmSJEmSFJwNvLlufE7D+OzOmtM+0tGqGEnHAifb/lPVtvQBewLfbDF3JtHMOB2tJEmSJEmSirC9Q9U2dIp0tKrnbcDNku4ATgbOsv1ExTb1KssA17SY+w3wns6ZkiRJkiRJkjQiaTHgw7ZPaTI3CbjC9gOdt6x8Ut69Ymx/HHgLcCowCXhQ0nmSNpaUr8+MMQGYq8XcnMV8kiRJkiRJUh0H0HpNNmcx3xfkQr4LsP247WNtvw9YleiI/RPgAUlHVGtdT3E7sFGLuY2K+SRJkiRJkqQ6Pgyc3mLuDEI5ui/I1MEuw/ZtwJ6SLgdOBPYGvlytVT3D0cCJRcPiC2y/JmlWYDPgBOALlVqXJEmSJEmSLAg83WLuWaI9T1+QjlYXIekdwPbAtkTo9CxgSP5q0hzbP5e0KPE3O0vS48SH9UXga7bPq9TAJEmSJEmS5N/Au4E7msy9i+iB2hdkH62KkfQmYCvCwXofcDnhKFxk+5UqbetVJM0DfABYAHgCmGz7mWqtSpIkSZIkSSR9G1gRkO3n645PAC4A/mh736rsK5N0tCpG0gvAP4HTgNNtP1KxSUmSJEmSJEnSFgqH6ipgceD/gIeARYENgXuB9Ww/V5V9ZZKOVsVIWs32TS3mFga+bHvPDpuVJEmSJEmSJG1B0njgM8D6TMtAuhw4o58yutLR6gIk7UiEUP8OfJ+QvPwG8HngWtsbV2hekiRJkiRJkrQdSSsA37C9edW2lEGKYVRMId++LXAD8AlgZWB14HfA6oUKYZIkSZIkSZL0PJLmBPZjWpDhQGB+Qj16Q6Kcpi9IR6t6tgLWtv13ScsQvZ62tX1WxXYlSZIkSZIkSdkcR/SNvQzYGFgGeA9wJrCL7ccqtK1UMnWwYiQ9bfuNdePnbLfqlp0kSZIkSZIkPYukB4GVbT8saTFCAGN921dVa1n5pKNVMZKeJooAxxVfDwMLF98DYPvlaqxLkiRJkiRJkvJoEmR4xvY8VdrULjJ1sHrmJhrq1hhXNx4HTOH/t3fnwZaU9RnHvy/DODBoRR2WAoksgoQdogkEZZEyERPqJxYEWYKKIhZGU0kMCZJYLBpQBDFgTKVMkCQiS0gsf8GkQCPDgMiAokQgbGGTwAjKpmwDzJs/usc5uTPDcIc+3ZfD91N165y3+5zbz6X4Y556334bZvUdSpIkSRqDEhFbsmxS4dkpYzLzlkGSdcyiNbzNhg4gSZIk9WQd4CZGihVw88j7iZlkcOmgJEmSJHXMGa2BRcRJKzj8NHAXcNEk7bwiSZIkvVQ4ozWwiPjSCg7PBjYHtgb2ycyF/aaSJEmSuhcR59IsDxy1dJLhgsy8vv9U42HRmsEi4jDgiMzcc+gskiRJ0gsVEcet4PDSSYZ9gYMy89/7TTUeLh2c2c4FTh86hCRJktSFzDxhZeci4m3AJ4GJKFprDB1Az2ltwGdoSZIk6aXgG8AWQ4foikVrhoqIVwCnAZcOnUWSJEnqwTzg0aFDdMWlgwOLiPtY/obA2cCrgcuBd/UeSpIkSRqDiHjZCg7Ppnm27EnARf0mGh+L1vAOWsGxp4G7M/OevsNIkiRJY/Qky08yQHO7zPnAMf3GGR+L1vB2BU7JTLd/lCRJ0qTbbAXHngYWZeaSvsOMk9u7DywirgGWAO/JzJuGziNJkiT1ISK2BNYFfpKZtw6dp2sWrYFFxBrA0TTTpCcBpzq7JUmSpEkVEe8APgdsMnL4LuCPM/Orw6TqnkVrhoiIrYAvAWsB/29mKzMPGSSUJEmS1KH2WVkXAifTPDP2PmBDmn0LjgEOzMyLh0vYHe/Rmjk2B34ZuAq4eeAskiRJ0jh8HPhwZv7DyLE7gJMj4h7gOMCipRcuIn4JOAP4TeCozPzawJEkSZKkcdkBuGAl5y4EvtBjlrHygcXDu5Gm8G63opIVEev1H0mSJEkai2eBuSs5N7c9PxGc0RreRzLzX0cPRMQsYF/gfcA+wJwhgkmSJEkd+xbwZ8CfruDc0cCl/cYZH4vWwEZLVkRsBxwO/B7NVpcXALsPFE2SJEnq2seAKyNiB+A84F5gI+BAYBdgtwGzdcpdBwcWEa8EDqGZvfpV4ArgHOCTwLaZef+A8SRJkqRORcRmwAnAW2mfowV8Ezg+M28fMluXnNEa3iLg58DpwAGZeSdARBw/YCZJkiRpLDLzDuDdQ+cYN4vW8C6muQ/rIOCZiDgnM+8ZOJMkSZLUuYh4LVAz80fteG3gz4HtgSuBz2TmkgEjdsZdBweWme8ANqZ5WPGhwJ0RMR94Oc3DiyVJkqRJ8ffAriPjM2n+DXwL8H6aJYUTwaI1A2TmA5n52czcgeZ/vBuAxcB/RcQZw6aTJEmSOrMjcAn8YjbrYOCgzDwa2K8dTwSL1gyTmd/NzN8HNgQ+CGw5cCRJkiSpK3My85H2/RuBxzNzIUBm3gisP1iyjnmP1gyVmYuB89sfSZIkaRL8KCLekJnfA36LZsdtACLi1cCTgyXrmEVLkiRJUl9OBf4jIhbSbO++38i5twHXDZJqDFw6KEmSJKkXmXk2cAAwH3hrZl48cvox4MQBYo2FDyyWJEmSpI45oyVJkiSpFxFxzZTxF6eM7+430fhYtCRJkiT15VemjPefMn5VX0HGzaIlSZIkqS9T71sqqzj/omXRkiRJkjSUiSlWU7m9uyRJkqS+zI6ID7BsJmtORBw5cn5i+snE/CGSJEmSZryFwCEj46uBg6ecnwhu7y5JkiRJHfMeLUmSJEmDi4g5EfEHQ+foiksHJUmSJPUmIvYGdgZuy8yvRcSawFHAx4AHgTOGzNcVlw5KkiRJ6kVEfBQ4AbgR2AY4E9irPX1yZuZA0TrnjJYkSZKkvhwF7J2ZV0fEm4AFwLGZ+emBc3XOe7QkSZIk9WW9zLwaIDO/DSwGPjNspPGwaEmSJEnqS5kyfiIzlwySZMy8R0uSJElSLyLiWeDHI4c2mDImMzfqNdSYeI+WJEmSpL7sPXSAvjijJUmSJEkd8x4tSZIkSeqYRUuSJEmSOmbRkiRJkqSOWbQkSZIkzQgRscHQGbriroOSJEmSehUR84CHlj5Dqy1YxwBHAusMma0rFi1JkiRJvYiIXwP+BXgN8NOI2B/YCTgJuIQJ2v7doiVJkiSpL6cB5wD/BBwOXAjcCuySmTcOGaxr3qMlSZIkqS/bAX/RlqqPA+sC75y0kgUWLUmSJEn9WTMznwXIzCeBRzLzgYEzjUWptQ6dQZIkSdJLQEQ8RbN8cKk/Ak4f/UxmHttrqDHxHi1JkiRJfTkX2HBkfN6U8cRwRkuSJEmSOuY9WpIkSZJ6ERFvmDKePWX82/0mGh+LliRJkqS+XDpl/OMp4/P6CjJuFi1JkiRJfSnTHL9oWbQkSZIk9WXqBhGrGr9oWbQkSZIkqWNu7y5JkiSpL3Mj4sqR8StGxgVYe4BMY2HRkiRJktSX908Z/+0gKXpg0ZIkSZLUl8WZee7QIfrgPVqSJEmS+jKxM1hTWbQkSZIk9WVitm9fFZcOSpIkSerLrIjYnecoXJm5oMc8Y2PRkiRJktSXOcD5rLxoVWCj/uKMj0VLkiRJUl8ey8yJKFKr4j1akiRJktQxi5YkSZKkvqz03qyI2Cki/qrPMOPk0kFJkiRJfdlmdBAR84BDgcOB7YGLhwg1DqXWOnQGSZIkSS8REbEG8HaacrUv8AAwD9gjM787ZLYuuXRQkiRJUi8i4tPAPcBXgJ8DvwNsAjwC3D1gtM65dFCSJElSX44GHgQ+DJyfmYsBImLQUONg0ZIkSZLUl51plgx+FjgjIi4Ezhk20nh4j5YkSZKkXkXEbCCA9wL70NzSdDLw+cxcNGC0zli0JEmSJA0mIjYEDgPeA2yRmXMGjtQJi5YkSZKkXkTEacB84PLMfHgF53fJzIW9BxsDi5YkSZKkXkTEPwO7A+sCP6QpXZcBCzLzwQGjdc6iJUmSJKlXEbE1TeHas33dCLgBuDQz/3DIbF2xaEmSJEkaTETMo9kU40+A9TNz1rCJuuH27pIkSZJ6ExHrAXvQzGbtSbOM8ArgL4EFA0brlDNakiRJknoRETcA69Del0WzKcYtw6YajzWGDiBJkiTpJeMh4OXABu3P+u0ztSaOM1qSJEmSehMRc4DfYNnywZ1odiBcQLP74DcHjNcZi5YkSZKkwUTEXOAo3AxDkiRJklZPu1Tw11m2GcZuQAG+Q/NcrYngjJYkSZKkXkTEt4BdgCXAlSx7YPHVmfnMgNE654yWJEmSpL5cAhwLXJOZzw4dZpyc0ZIkSZKkjrm9uyRJkiR1zKIlSZIkSR2zaEmSJElSxyxakiRJktQxi5YkacYppWxaSqntz17T/O7Z7ffmjyfdL66z10jGTcd5LUnSi49FS5I0dqWU+SOl5Lop5+aVUp4YOf8p4ClgYfvz6DQv9z/t926cRr7j22vfOY3rPMqyjE9NK6EkaeL5HC1JUt92KKXsUWtd0I6PANYa/UCt9T5g19X55bXWTwCfeGERn1sp5WW11mtZzYySpMnnjJYkqU9Pt68fASilzAI+NHKc9vhySwdHZ51KKQeWUm4qpTxWSllQStlq5LvLLR0spby7lPKDUsrP2u/cUkr5cntuPnBc+9FNRq773ik5jiil/Gcp5Ung2JUtHSylvL2Ucll7rSdKKZeXUt4y5e/7aJv/8fZzN5RSTu3gv68kaYawaEmS+vQD4HZgv1LKxkAArwUunMbveA3wZaACawO7A2et7MOllB2Bs4EdgUXt9TcEDm0/ciPwv+37xSxbDvjAlF/118BOwK3Asyu51ruArwN7AD8F7gPeDHxjadkqpQRwKrAVcAdwD7AZcMCq/3RJ0ouFRUuS1KclNIVlTeAo2pkt4Mxp/I41gf1rrVsDn2uP7VZKWXsln98CKDQFa6ta6/bAK4E9AWqtHwL+rv3sfbXWXdufr0/5PQuBjdvvn7SSa32qvdZZNOXpdcBXgVnAie1ntmxf59dat23/jlcBh6z6T5ckvVhYtCRJfTsLeIymZL0F+F6t9TvT+P4jtdZ/a9+Pbnix/ko+/23gIWBz4MFSykLgC9OLDMDf1FqfAKi1LjejVUpZD9i0Hb6PplQuAd7ZHtulfb2YZuZsr1LKA6WUK4BTgMdXI5MkaYZyMwxJUq9qrQ+390d9sD00ndksgIdH3j8z8r6s5HqLSinbAocBbwS2A44Ejiil7FZrXfg8r7toFedHr387yy89XLqJxvVtnkOAnWmWNL4J+EApZeta613PM48kaQazaEmShvB5mqL1E+C8cV6olLIRsG6t9ZSRY7fRLOt7M82SwKWzSXNLKaXWWlfwq1Z0bNnJWu8vpdwFbAJcCxxca32mvd7rgU1qrYtLKVsCS2qtJ7bn1qK5n2suTRG0aEnSBHDpoCSpd7XW64F5wOtqreN+BtU2wHWllPvbnQdvpylZAD9sX29qX9cDbi6lXFVK2Xw1rnVs+3oAcG8p5fullEXAzSzbfGNP4LZSyr2llGtpnvs1l2aDjef97C9J0sxm0ZIkDaLW+mCtdboPI14dt9PMmj0KvJ6mTF0HHFlrvaT9zEXAF2lmlrakuZ9q7nQvVGv9CrAvcBnNjohbAT8D/pFlG258n2aDjMU0JXAd4Crgd2ut/z39P0+SNBOVFa+OkCRJkiStLme0JEmSJKljFi1JkiRJ6phFS5IkSZI6ZtGSJEmSpI5ZtCRJkiSpYxYtSZIkSeqYRUuSJEmSOmbRkiRJkqSOWbQkSZIkqWMWLUmSJEnqmEVLkiRJkjr2f+19roWEraUjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#(Metric - no of questions/session, each MP will have different metric value for each term, plot (bar plot, sorted by metric, color by party)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "connection = sqlite3.connect(\"MP_attendance.sqlite\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "session_200=pd.read_sql(\"select * from session_200\",connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>DIV NO</th>\n",
       "      <th>NAME OF THE MEMBER</th>\n",
       "      <th>No. of days attended</th>\n",
       "      <th>session_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>Brahma, Shri  Urkhao Gwra</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>218</td>\n",
       "      <td>Gnanadesikan, Shri  B.S.</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>Singh,  Shri Rao Man</td>\n",
       "      <td>19</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>Agarwalla, Shri  Parmeshwar Kumar</td>\n",
       "      <td>17</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>Rao, Shri K. Rama Mohana</td>\n",
       "      <td>07</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DIV NO                 NAME OF THE MEMBER No. of days attended session_no\n",
       "67      68          Brahma, Shri  Urkhao Gwra                   10        200\n",
       "217    218           Gnanadesikan, Shri  B.S.                   10        200\n",
       "64      65               Singh,  Shri Rao Man                   19        200\n",
       "39      40  Agarwalla, Shri  Parmeshwar Kumar                   17        200\n",
       "90      91           Rao, Shri K. Rama Mohana                   07        200"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_200.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#PM, Minister, LOP and HDC are not required to record their signature.\n",
    "Exception = ['M','HDC','LOP','None']\n",
    "session_200_filtered = session_200[~session_200[\"No. of days attended\"].isin(Exception)]\n",
    "session_200_filtered = session_200_filtered.dropna(subset=['No. of days attended'])\n",
    "fig2 =(\n",
    "    ggplot(session_200_filtered,aes(x=\"No. of days attended\")) +\n",
    "    geom_bar(color =\"black\", fill=\"darkgreen\")+\n",
    "    theme_bw()+\n",
    "    theme(figure_size=(15,7))+\n",
    "    #geom_text(aes(label='active'), size=10) +\n",
    "    labs(x = \"Attendance\",y=\"Number of MPs\")+\n",
    "    ggtitle(\"Rajya Shaba: Absenteeism (session 200)\")+\n",
    "    theme(axis_title=element_text(size=15,face=\"bold\",style ='italic', color ='black' ))+\n",
    "    theme(plot_title =element_text(size=20, face=\"bold\", color ='black') )+\n",
    "    annotate('text', x=3,y=30, label='Source:rajyasabha.nic.in', color='black', size=15)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(fig2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No. of days attended\n",
       "00     2\n",
       "02     2\n",
       "03     4\n",
       "04     5\n",
       "05     5\n",
       "06     3\n",
       "07     8\n",
       "08     3\n",
       "09     6\n",
       "10    12\n",
       "11     7\n",
       "12     7\n",
       "13    12\n",
       "14    10\n",
       "15    18\n",
       "16    20\n",
       "17    25\n",
       "18    22\n",
       "19    27\n",
       "20    30\n",
       "21     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  #geom_text(aes(label = car_count), vjust = -0.2, size = 5,\n",
    "  #          position = position_dodge(0.9)) +\n",
    "\n",
    "#data=pd.pivot_table(CC_subset, index=['primary_type', 'month'], columns='day', values=None, aggfunc=len,fill_value=0).round(2)\n",
    "#data=pd.DataFrame(data.to_records())\n",
    "\n",
    "\n",
    "session_200_filtered.groupby([\"No. of days attended\"]).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>id</th>\n",
       "      <th>answer_date</th>\n",
       "      <th>ministry</th>\n",
       "      <th>question_type</th>\n",
       "      <th>question_no</th>\n",
       "      <th>question_by</th>\n",
       "      <th>question_title</th>\n",
       "      <th>question_description</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>231247</td>\n",
       "      <td>02.02.2017</td>\n",
       "      <td>ATOMIC ENERGY</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>13</td>\n",
       "      <td>Shri A. K. Selvaraj</td>\n",
       "      <td>Status of second reactor of KNPP</td>\n",
       "      <td>(a)whether it is a fact that the second reacto...</td>\n",
       "      <td>THE MINISTER OF STATE FOR PERSONNEL, PUBLIC GR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>231248</td>\n",
       "      <td>02.02.2017</td>\n",
       "      <td>EXTERNAL AFFAIRS</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>8</td>\n",
       "      <td>Shri P.L. Punia</td>\n",
       "      <td>Sale of items with images of Indian tricolour</td>\n",
       "      <td>Will the Minister of EXTERNAL AFFAIRS be pleas...</td>\n",
       "      <td>THE MINISTER FOR EXTERNAL AFFAIRS (SHRIMATI SU...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>231249</td>\n",
       "      <td>02.02.2017</td>\n",
       "      <td>HOUSING AND URBAN POVERTY ALLEVIATION</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>4</td>\n",
       "      <td>Dr. V. Maitreyan</td>\n",
       "      <td>Assistance to Tamil Nadu for construction of h...</td>\n",
       "      <td>Will the Minister of HOUSING AND URBAN POVERTY...</td>\n",
       "      <td>THE MINISTER OF HOUSING &amp; URBAN POVERTY ALLEVI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>231250</td>\n",
       "      <td>02.02.2017</td>\n",
       "      <td>HOUSING AND URBAN POVERTY ALLEVIATION</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>11</td>\n",
       "      <td>Dr. Vikas Mahatme</td>\n",
       "      <td>Prefabricated toilets for the poor</td>\n",
       "      <td>Will the Minister of URBAN DEVELOPMENT be plea...</td>\n",
       "      <td>THE MINISTER OF URBAN DEVELOPMENT(SHRI M. VENK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>231251</td>\n",
       "      <td>02.02.2017</td>\n",
       "      <td>HUMAN RESOURCE DEVELOPMENT</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>2</td>\n",
       "      <td>Smt. Chhaya Verma</td>\n",
       "      <td>UGC norms on ad hoc teachers working in D.U.</td>\n",
       "      <td>(a) the details of vacancies in various univer...</td>\n",
       "      <td>MINISTER OF HUMAN RESOURCE DEVELOPMENT(SHRI PR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id answer_date                               ministry question_type  \\\n",
       "0  231247  02.02.2017                          ATOMIC ENERGY    STARRED      \n",
       "1  231248  02.02.2017                       EXTERNAL AFFAIRS    STARRED      \n",
       "2  231249  02.02.2017  HOUSING AND URBAN POVERTY ALLEVIATION    STARRED      \n",
       "3  231250  02.02.2017  HOUSING AND URBAN POVERTY ALLEVIATION    STARRED      \n",
       "4  231251  02.02.2017             HUMAN RESOURCE DEVELOPMENT    STARRED      \n",
       "\n",
       "   question_no          question_by  \\\n",
       "0           13  Shri A. K. Selvaraj   \n",
       "1            8      Shri P.L. Punia   \n",
       "2            4     Dr. V. Maitreyan   \n",
       "3           11    Dr. Vikas Mahatme   \n",
       "4            2    Smt. Chhaya Verma   \n",
       "\n",
       "                                      question_title  \\\n",
       "0                   Status of second reactor of KNPP   \n",
       "1      Sale of items with images of Indian tricolour   \n",
       "2  Assistance to Tamil Nadu for construction of h...   \n",
       "3                 Prefabricated toilets for the poor   \n",
       "4       UGC norms on ad hoc teachers working in D.U.   \n",
       "\n",
       "                                question_description  \\\n",
       "0  (a)whether it is a fact that the second reacto...   \n",
       "1  Will the Minister of EXTERNAL AFFAIRS be pleas...   \n",
       "2  Will the Minister of HOUSING AND URBAN POVERTY...   \n",
       "3  Will the Minister of URBAN DEVELOPMENT be plea...   \n",
       "4  (a) the details of vacancies in various univer...   \n",
       "\n",
       "                                              answer  \n",
       "0  THE MINISTER OF STATE FOR PERSONNEL, PUBLIC GR...  \n",
       "1  THE MINISTER FOR EXTERNAL AFFAIRS (SHRIMATI SU...  \n",
       "2  THE MINISTER OF HOUSING & URBAN POVERTY ALLEVI...  \n",
       "3  THE MINISTER OF URBAN DEVELOPMENT(SHRI M. VENK...  \n",
       "4  MINISTER OF HUMAN RESOURCE DEVELOPMENT(SHRI PR...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       int64\n",
       "answer_date             object\n",
       "ministry                object\n",
       "question_type           object\n",
       "question_no              int64\n",
       "question_by             object\n",
       "question_title          object\n",
       "question_description    object\n",
       "answer                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2017.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1973-01-25\n"
     ]
    }
   ],
   "source": [
    "#datetimeobject = [(date_input.strftime('%m/%d/%Y')) for date_input in RS_QA_2017['answer_date']]\n",
    "#RS_QA_2017['answer_date'] = RS_QA_2017.to_datetime(RS_QA_2017['answer_date'])\n",
    "d = datetime.datetime.strptime(\"25-01-1973\", \"%d-%m-%Y\")\n",
    "d = d.date()\n",
    "x=print(d.isoformat())\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'02.02.2017'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "RS_QA_2017['answer_date'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "for index, value in RS_QA_2017['answer_date'].iteritems():\n",
    "    d = datetime.datetime.strptime(value, \"%d.%m.%Y\")\n",
    "    d = d.date()\n",
    "    #d=print(d.isoformat())\n",
    "    RS_QA_2017['answer_date'][index] =d\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>id</th>\n",
       "      <th>answer_date</th>\n",
       "      <th>ministry</th>\n",
       "      <th>question_type</th>\n",
       "      <th>question_no</th>\n",
       "      <th>question_by</th>\n",
       "      <th>question_title</th>\n",
       "      <th>question_description</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>231658</td>\n",
       "      <td>2017-02-06</td>\n",
       "      <td>ENVIRONMENT, FOREST  AND CLIMATE CHANGE</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>366</td>\n",
       "      <td>Shri Vivek Gupta</td>\n",
       "      <td>Transfer of ad hoc CAMPA funds to States</td>\n",
       "      <td>(a) whether Government has received annual pla...</td>\n",
       "      <td>MINISTER OF STATE (INDEPENDENT CHARGE) FOR ENV...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4253</th>\n",
       "      <td>235518</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>HUMAN RESOURCE DEVELOPMENT</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>3888</td>\n",
       "      <td>Smt. Jaya Bachchan</td>\n",
       "      <td>National Literacy Mission in Gujarat</td>\n",
       "      <td>(a) the targets achieved out of the targets se...</td>\n",
       "      <td>MINISTER OF STATE IN THE MINISTRY OF HUMAN RES...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6613</th>\n",
       "      <td>237882</td>\n",
       "      <td>2017-07-27</td>\n",
       "      <td>SKILL DEVELOPMENT AND ENTREPRENEURSHIP</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>1415</td>\n",
       "      <td>Shri Harivansh</td>\n",
       "      <td>Agreement with private companies for skilled l...</td>\n",
       "      <td>(a) whether it is a fact that the National Ski...</td>\n",
       "      <td>(a) to (c) National Skill Development Corporat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>233714</td>\n",
       "      <td>2017-03-22</td>\n",
       "      <td>TOURISM</td>\n",
       "      <td>STARRED</td>\n",
       "      <td>218</td>\n",
       "      <td>Shri Dharmapuri Srinivas</td>\n",
       "      <td>Tourism development in Telangana</td>\n",
       "      <td>(a) whether Government proposes to develop the...</td>\n",
       "      <td>MINISTER OF STATE FOR TOURISM (INDEPENDENT CHA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7404</th>\n",
       "      <td>238673</td>\n",
       "      <td>2017-08-03</td>\n",
       "      <td>HUMAN RESOURCE DEVELOPMENT</td>\n",
       "      <td>UNSTARRED</td>\n",
       "      <td>2132</td>\n",
       "      <td>Smt. Sarojini Hembram</td>\n",
       "      <td>Installation of jammers in school premises</td>\n",
       "      <td>(a) whether Government has directed schools, b...</td>\n",
       "      <td>MINISTER OF STATE IN THE MINISTRY OF HUMAN RES...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id answer_date                                 ministry  \\\n",
       "410   231658  2017-02-06  ENVIRONMENT, FOREST  AND CLIMATE CHANGE   \n",
       "4253  235518  2017-04-06               HUMAN RESOURCE DEVELOPMENT   \n",
       "6613  237882  2017-07-27   SKILL DEVELOPMENT AND ENTREPRENEURSHIP   \n",
       "2458  233714  2017-03-22                                  TOURISM   \n",
       "7404  238673  2017-08-03               HUMAN RESOURCE DEVELOPMENT   \n",
       "\n",
       "     question_type  question_no               question_by  \\\n",
       "410     UNSTARRED           366          Shri Vivek Gupta   \n",
       "4253    UNSTARRED          3888        Smt. Jaya Bachchan   \n",
       "6613    UNSTARRED          1415            Shri Harivansh   \n",
       "2458    STARRED             218  Shri Dharmapuri Srinivas   \n",
       "7404    UNSTARRED          2132     Smt. Sarojini Hembram   \n",
       "\n",
       "                                         question_title  \\\n",
       "410            Transfer of ad hoc CAMPA funds to States   \n",
       "4253               National Literacy Mission in Gujarat   \n",
       "6613  Agreement with private companies for skilled l...   \n",
       "2458                   Tourism development in Telangana   \n",
       "7404         Installation of jammers in school premises   \n",
       "\n",
       "                                   question_description  \\\n",
       "410   (a) whether Government has received annual pla...   \n",
       "4253  (a) the targets achieved out of the targets se...   \n",
       "6613  (a) whether it is a fact that the National Ski...   \n",
       "2458  (a) whether Government proposes to develop the...   \n",
       "7404  (a) whether Government has directed schools, b...   \n",
       "\n",
       "                                                 answer  \n",
       "410   MINISTER OF STATE (INDEPENDENT CHARGE) FOR ENV...  \n",
       "4253  MINISTER OF STATE IN THE MINISTRY OF HUMAN RES...  \n",
       "6613  (a) to (c) National Skill Development Corporat...  \n",
       "2458  MINISTER OF STATE FOR TOURISM (INDEPENDENT CHA...  \n",
       "7404  MINISTER OF STATE IN THE MINISTRY OF HUMAN RES...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2017.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>session_no</th>\n",
       "      <th>session_name</th>\n",
       "      <th>session_end_date</th>\n",
       "      <th>session_start_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>255</td>\n",
       "      <td>Winter</td>\n",
       "      <td>23-12-2021</td>\n",
       "      <td>29-11-2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>254</td>\n",
       "      <td>Monsoon</td>\n",
       "      <td>11-08-2021</td>\n",
       "      <td>19-07-2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>253</td>\n",
       "      <td>Budget</td>\n",
       "      <td>25-03-2021</td>\n",
       "      <td>29-01-2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>252</td>\n",
       "      <td>Monsoon</td>\n",
       "      <td>24-09-2020</td>\n",
       "      <td>14-09-2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>251</td>\n",
       "      <td>Budget</td>\n",
       "      <td>03-04-2020</td>\n",
       "      <td>31-01-2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 session_no session_name session_end_date session_start_date\n",
       "0           0        255       Winter       23-12-2021         29-11-2021\n",
       "1           1        254      Monsoon       11-08-2021         19-07-2021\n",
       "2           2        253       Budget       25-03-2021         29-01-2021\n",
       "3           3        252      Monsoon       24-09-2020         14-09-2020\n",
       "4           4        251       Budget       03-04-2020         31-01-2020"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sessions_meta = pd.read_csv('Sessions_meta_data')\n",
    "Sessions_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             int64\n",
       "session_no            object\n",
       "session_name          object\n",
       "session_end_date      object\n",
       "session_start_date    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sessions_meta.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 2, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2017['answer_date'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "for index, value in Sessions_meta['session_end_date'].iteritems():\n",
    "    d = datetime.datetime.strptime(value, \"%d-%m-%Y\")\n",
    "    d = d.date()\n",
    "    Sessions_meta['session_end_date'][index] =d\n",
    "    \n",
    "for index, value in Sessions_meta['session_start_date'].iteritems():\n",
    "    d = datetime.datetime.strptime(value, \"%d-%m-%Y\")\n",
    "    d = d.date()\n",
    "    Sessions_meta['session_start_date'][index] =d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RS_QA_2017['answer_date'][0] > RS_QA_2017['answer_date'][7404]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### LESSONS LEARNT THUS FAR\n",
    "\n",
    "- Selenium and Chrome: Version control is crucial and so is a virtual environment\n",
    "- Always search for alternative data sources before venturing into scraping\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}