bayesan_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b37f094a-d393-4cf0-9516-16cf9e13525b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pgmpy.factors.discrete import TabularCPD\n",
    "from pgmpy.models import BayesianNetwork\n",
    "from pgmpy.estimators import MaximumLikelihoodEstimator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2ee0da-6067-48f7-baf9-5551ba39cb52",
   "metadata": {},
   "source": [
    "# Bayesan network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5243c070-4362-4ce1-9c6a-7334ea85c9dd",
   "metadata": {},
   "source": [
    "Let's load the dataset first in order to create a bayesan network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "afabc237-6926-4edb-a0fd-60aff143f4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/data.csv\")\n",
    "data.date = pd.to_datetime(data.date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97cf0516-7e4a-40ec-8ab1-0fa3ee4fbfeb",
   "metadata": {},
   "source": [
    "Network structure will be the following:\n",
    "\n",
    "![](./bayesan_network.drawio.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa1c3317-4a88-4fee-82d5-940456573f1f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create CPDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c637d1cd-d3dd-474a-8c10-d4a55e0902dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_names = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e34c4e8-4172-4ff9-94e1-0d3359c8d668",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(Group)$\n",
    "\n",
    "Given $g$ an arbitrary group then the probability of being in a country $c$ is\n",
    "$$P(Group = g) = \\frac{population_g}{\\sum_g population_g}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d99496ab-8613-41e6-9d6b-1ebed3f1b973",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_pop = data.groupby(\"country_id\").first().groupby(\"sdg_group\").sum()[[\"pop_m\", \"pop_f\"]].sum(axis=1)\n",
    "p_in_group = group_pop / group_pop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "032e0577-fac8-4b37-b49b-cda69dc4b3ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Africa (Sub-Saharan))             | 0.135626   |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Asia (Central and Southern))      | 0.262519   |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Asia (Eastern and South-eastern)) | 0.298606   |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Latin America and the Caribbean)  | 0.0833243  |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Northern America and Europe)      | 0.146414   |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Oceania)                          | 0.00545572 |\n",
      "+----------------------------------------------+------------+\n",
      "| Group(SDG: Western Asia and Northern Africa) | 0.0680553  |\n",
      "+----------------------------------------------+------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Group\"] = group_pop.index.tolist()\n",
    "cpd_Group = TabularCPD(variable=\"Group\", \n",
    "                       variable_card=len(data.sdg_group.unique()),\n",
    "                       values=p_in_group.to_numpy()[:, np.newaxis],\n",
    "                       state_names=state_names)\n",
    "print(cpd_Group)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37e3357f-82a9-4282-8112-48d544ae70e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(Country | Group = g)$\n",
    "Given $x$ an arbitrary country, the probability of living on that country and in the group $g$ is $0$ if country is not in group $g$ else\n",
    "$$P(Country = x | Group = g) = \\frac{population_x}{\\sum_{c \\in group} population_g}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6eb09df7-18da-4c9b-bbfd-4c5d40b6f335",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = data.country_id.unique()\n",
    "groups = data.sdg_group.unique()\n",
    "\n",
    "p_in_country = np.zeros((len(countries), len(groups)))\n",
    "\n",
    "for country_idx, country_iso in enumerate(countries):\n",
    "  country = data[data.country_id == country_iso].iloc[0]\n",
    "  country_group_idx = group_pop.index.tolist().index(country.sdg_group)\n",
    "    \n",
    "  p_in_country[country_idx, country_group_idx] = (country.pop_f + country.pop_m) / group_pop[country.sdg_group]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9e6be61-1a76-4bf5-b8b1-11465db9628d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-----+\n",
      "| Group        | ... |\n",
      "+--------------+-----+\n",
      "| Country(ABW) | ... |\n",
      "+--------------+-----+\n",
      "| Country(AFG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(AGO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ALB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ARE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ARG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ARM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ATG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(AUS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(AUT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(AZE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BDI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BEL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BEN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BFA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BGD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BGR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BHR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BHS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BIH) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BLR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BLZ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BOL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BRA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BRB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BRN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BTN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(BWA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CAF) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CAN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CHE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CHL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CHN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CIV) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CMR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(COD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(COG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(COL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(COM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CPV) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CRI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CUB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CYP) | ... |\n",
      "+--------------+-----+\n",
      "| Country(CZE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(DEU) | ... |\n",
      "+--------------+-----+\n",
      "| Country(DJI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(DNK) | ... |\n",
      "+--------------+-----+\n",
      "| Country(DOM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(DZA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ECU) | ... |\n",
      "+--------------+-----+\n",
      "| Country(EGY) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ERI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ESP) | ... |\n",
      "+--------------+-----+\n",
      "| Country(EST) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ETH) | ... |\n",
      "+--------------+-----+\n",
      "| Country(FIN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(FJI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(FRA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(FSM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GAB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GBR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GEO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GHA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GIN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GNB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GNQ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GRC) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GRD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GTM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(GUY) | ... |\n",
      "+--------------+-----+\n",
      "| Country(HND) | ... |\n",
      "+--------------+-----+\n",
      "| Country(HRV) | ... |\n",
      "+--------------+-----+\n",
      "| Country(HUN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(IDN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(IND) | ... |\n",
      "+--------------+-----+\n",
      "| Country(IRL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(IRN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(IRQ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ISL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ISR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ITA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(JAM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(JOR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(JPN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KAZ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KEN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KGZ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KHM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KIR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KOR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(KWT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LAO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LBN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LBR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LBY) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LCA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LKA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LSO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LTU) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LUX) | ... |\n",
      "+--------------+-----+\n",
      "| Country(LVA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MAR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MDA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MDG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MDV) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MEX) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MKD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MLI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MLT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MMR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MNE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MNG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MOZ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MRT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MUS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MWI) | ... |\n",
      "+--------------+-----+\n",
      "| Country(MYS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NAM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NER) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NGA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NIC) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NLD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NOR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NPL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(NZL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(OMN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PAK) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PAN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PER) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PHL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PNG) | ... |\n",
      "+--------------+-----+\n",
      "| Country(POL) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PRK) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PRT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PRY) | ... |\n",
      "+--------------+-----+\n",
      "| Country(PSE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(QAT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ROU) | ... |\n",
      "+--------------+-----+\n",
      "| Country(RUS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(RWA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SAU) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SDN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SEN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SGP) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SLB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SLE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SLV) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SRB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SSD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SUR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SVK) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SVN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SWE) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SWZ) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SYC) | ... |\n",
      "+--------------+-----+\n",
      "| Country(SYR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TCD) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TGO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(THA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TJK) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TKM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TLS) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TON) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TTO) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TUN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TUR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(TZA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(UGA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(UKR) | ... |\n",
      "+--------------+-----+\n",
      "| Country(URY) | ... |\n",
      "+--------------+-----+\n",
      "| Country(USA) | ... |\n",
      "+--------------+-----+\n",
      "| Country(UZB) | ... |\n",
      "+--------------+-----+\n",
      "| Country(VCT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(VEN) | ... |\n",
      "+--------------+-----+\n",
      "| Country(VNM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(VUT) | ... |\n",
      "+--------------+-----+\n",
      "| Country(WSM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(YEM) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ZAF) | ... |\n",
      "+--------------+-----+\n",
      "| Country(ZWE) | ... |\n",
      "+--------------+-----+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Country\"] = countries.tolist()\n",
    "cpd_Country = TabularCPD(variable=\"Country\", \n",
    "                         variable_card=len(data.country_id.unique()),\n",
    "                         values=p_in_country,\n",
    "                         evidence=[\"Group\"],\n",
    "                         evidence_card=[len(data.sdg_group.unique())],\n",
    "                         state_names=state_names)\n",
    "print(cpd_Country)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6333423-5a4f-45c8-add2-34072ab605bf",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(Student | Country)$\n",
    "Given $x$ an arbitrary country, the probability of being a student within that country is\n",
    "$$P(Student = T | Country = x) = \\frac{students_x}{population_x}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c3e69a03-caf7-42ab-9821-30229fc5703b",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_students = data.groupby(\"country_id\").first()[[\"p_m\", \"ls_m\", \"ls_m\", \"p_f\", \"ls_f\", \"us_f\"]].sum(axis=1)\n",
    "country_pop = data.groupby(\"country_id\").first()[[\"pop_m\", \"pop_f\"]].sum(axis=1)\n",
    "p_student = country_students / country_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ef17f166-c1ec-4cb5-a05d-6ef7dda0250f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-----+---------------------+\n",
      "| Country      | ... | Country(ZWE)        |\n",
      "+--------------+-----+---------------------+\n",
      "| Student(yes) | ... | 0.24548398770911575 |\n",
      "+--------------+-----+---------------------+\n",
      "| Student(no)  | ... | 0.7545160122908843  |\n",
      "+--------------+-----+---------------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Student\"] = [\"yes\", \"no\"]\n",
    "cpd_Student = TabularCPD(variable=\"Student\", \n",
    "                         variable_card=2, # yes, no\n",
    "                         values=np.array([p_student, 1 - p_student]),\n",
    "                         evidence=[\"Country\"],\n",
    "                         evidence_card=[len(countries)],\n",
    "                         state_names=state_names)\n",
    "print(cpd_Student)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4920bceb-f95f-4904-b297-664a236b84c8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(Gender | Country)$\n",
    "\n",
    "Computing the probability of being a male or a female, $P(Gender)$, depends on the country the child is living in.\n",
    "We can compute the probability of being male in a country $x$ as\n",
    "$$\n",
    "\\begin{align*}\n",
    "P(G = male | C = x) &= \\frac{P(G = male, C = x)}{P(C = x)} \\\\\n",
    "&= \\frac{Males_x}{\\sum\\limits_{c \\in countries} population_c}\n",
    "\\end{align*}\n",
    "$$\n",
    "and probability of being female as $1 - P(G = male | C = x)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d50b3c3b-fd1a-4ce2-b5fd-7730a4f08d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_male = data.groupby(\"country_id\").first()[\"pop_m\"] / country_pop\n",
    "p_female = 1 - p_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ae559111-9b28-48eb-af96-40330256a8ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------+-----+---------------------+\n",
      "| Country        | ... | Country(ZWE)        |\n",
      "+----------------+-----+---------------------+\n",
      "| Gender(male)   | ... | 0.47681802663024925 |\n",
      "+----------------+-----+---------------------+\n",
      "| Gender(female) | ... | 0.5231819733697507  |\n",
      "+----------------+-----+---------------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Gender\"] = [\"male\", \"female\"]\n",
    "cpd_Gender = TabularCPD(variable=\"Gender\", \n",
    "                   variable_card=2, # male, female\n",
    "                   values=np.array([p_male, p_female]),\n",
    "                   evidence=[\"Country\"],\n",
    "                   evidence_card=[p_male.count()],\n",
    "                   state_names=state_names)\n",
    "print(cpd_Gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7e4674-6c29-467f-87c7-69659930cee3",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(Education | Gender, Country)$\n",
    "\n",
    "Probability $P(Education)$ of being in a particular education depends on gender and country and, of course, wether a it is a student or not.\n",
    "\n",
    "The probability of a male student in primary school in a country $x$ is\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "P(Education = primary\\text{-}school | Country = x, Gender = male, Student = yes) &= \\frac{P(Education = primary\\text{-}school, Country = x, Gender = male, Student = yes)}{P(Country = x, Gender = male, Student = yes)} \\\\\n",
    "&= \\frac{MaleInPrimarySchool_x}{\\sum\\limits_{c \\in countries} MaleInPrimarySchool_c} = C_x\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae28372a-432c-4e4f-9702-5f4adfd4e40e",
   "metadata": {},
   "source": [
    "Conditional probability table (expressed only for males but the symmetrical case happens for females) is defined as\n",
    "\n",
    "| | | |\n",
    "|---|---|---|\n",
    "| | male | female |\n",
    "| primary | $$C_1 \\cdots C_n$$ | $$C_1 \\cdots C_n$$ |\n",
    "| lower-secondary | $$C_1 \\cdots C_n$$ | $$C_1 \\cdots C_n$$ |\n",
    "| upper-secondary | $$C_1 \\cdots C_n$$ | $$C_1 \\cdots C_n$$ |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f63bdf3-9210-4f3d-980a-22e55f914d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_students = data.groupby(\"country_id\").first()[[\"p_m\", \"ls_m\", \"us_m\"]].sum(axis=1)\n",
    "p_education_male = data.groupby(\"country_id\").first()[[\"p_m\", \"ls_m\", \"us_m\"]].div(male_students, axis=0).to_numpy().transpose()\n",
    "\n",
    "female_students = data.groupby(\"country_id\").first()[[\"p_f\", \"ls_f\", \"us_f\"]].sum(axis=1)\n",
    "p_education_female = data.groupby(\"country_id\").first()[[\"p_f\", \"ls_f\", \"us_f\"]].div(female_students, axis=0).to_numpy().transpose()\n",
    "\n",
    "p_education = np.c_[p_education_male, p_education_female]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c0acee1-7b94-443b-9c74-cf5391f625ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "state_names[\"Education\"] = [\"primary\", \"lower_secondary\", \"upper_secondary\"]\n",
    "cpd_Education = TabularCPD(variable=\"Education\", \n",
    "                           variable_card=3, # primary, lower secondary, upper secondary\n",
    "                           values=p_education,\n",
    "                           evidence=[\"Gender\", \"Country\"],\n",
    "                           evidence_card=[\n",
    "                             2, # gender\n",
    "                             p_male.count() # countries\n",
    "                           ],\n",
    "                           state_names=state_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555e441e-2fe1-43b2-8255-7580c533915f",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### $P(AtHome | Country)$\n",
    "\n",
    "Probability of staying at home $P(AtHome)$ because school was closed depends only the country.\n",
    "The probability of living in country x and of being at home is\n",
    "\n",
    "$$P(AtHome = yes | C = x) = \\frac{P(AtHome = yes, C = x)}{P(C = x)} = \\frac{DaysAtHome_x}{SchoolDays_x}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "01720ba7-7f2e-4c7e-8022-9d0919f1c797",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_athome = np.zeros(country_pop.count())\n",
    "\n",
    "for idx, (country, df) in enumerate(data.groupby(\"country_id\")):\n",
    "  tot_days = len(df.status)\n",
    "  at_home = df.status.isin([\"Closed due to COVID-19\", \"Partially open\"]).sum()\n",
    "  p_athome[idx] = at_home / tot_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1795d126-816e-4cfa-99cf-0d66e57e1473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------------+-----+--------------------+\n",
      "| Country     | Country(ABW)        | ... | Country(ZWE)       |\n",
      "+-------------+---------------------+-----+--------------------+\n",
      "| AtHome(yes) | 0.12846715328467154 | ... | 0.4481751824817518 |\n",
      "+-------------+---------------------+-----+--------------------+\n",
      "| AtHome(no)  | 0.8715328467153285  | ... | 0.5518248175182482 |\n",
      "+-------------+---------------------+-----+--------------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"AtHome\"] = [\"yes\", \"no\"]\n",
    "cpd_AtHome = TabularCPD(variable=\"AtHome\", \n",
    "                        variable_card=2, \n",
    "                        values=np.array([p_athome, 1 - p_athome]),\n",
    "                        evidence=[\"Country\"],\n",
    "                        evidence_card=[len(countries)],\n",
    "                        state_names=state_names)\n",
    "print(cpd_AtHome)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9bcdb2-8a90-40de-91fb-f8dc41cbdcf8",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Distant learning measures\n",
    "\n",
    "In order to estimate the probability of a measure being implemented directly from data we will make use of MLE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8751a9a5-1fe5-4994-8b8c-21fc412c3b46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5024/2763478344.py:5: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  values=(country_pop / country_pop.sum())[:, np.newaxis],\n"
     ]
    }
   ],
   "source": [
    "model = BayesianNetwork([(\"Country\", \"Online\"), (\"Country\", \"Radio\"), (\"Country\", \"Tv\")])\n",
    "\n",
    "cpd_tmp_Country = TabularCPD(variable=\"Country\", \n",
    "                             variable_card=len(data.country_id.unique()),\n",
    "                             values=(country_pop / country_pop.sum())[:, np.newaxis],\n",
    "                             state_names=state_names)\n",
    "model.add_cpds(cpd_tmp_Country)\n",
    "\n",
    "data[\"radio\"] = data.radio.str.lower()\n",
    "data[\"tv\"] = data.tv.str.lower()\n",
    "data[\"online\"] = data.online.str.lower()\n",
    "\n",
    "mle = MaximumLikelihoodEstimator(model, data[[\"country_id\", \"online\", \"radio\", \"tv\"]].rename(\n",
    "  columns={\"online\":\"Online\", \"country_id\":\"Country\", \"radio\": \"Radio\", \"tv\": \"Tv\"}))\n",
    "\n",
    "cpd_Online = mle.estimate_cpd(\"Online\")\n",
    "cpd_Radio = mle.estimate_cpd(\"Radio\")\n",
    "cpd_Tv = mle.estimate_cpd(\"Tv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b5cf76-e022-470f-987c-86e6fe37198c",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Media reachability\n",
    "\n",
    "Probability of being reached by a media platform can't be modeled as a unique CPD as values intersects and we cannot disaggregate them.\n",
    "Because of this we compute 3 different probability distributions: $P(InternetAccess)$, $P(RadioAccess)$, $P(TvAccess)$.\n",
    "\n",
    "Each of them, for instance $P(Internet)$, can be computed as\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "P(InternetAccess = yes | Country = x) &= \\frac{P(InternetAccess = yes, Country = x)}{P(Country = x)} \\\\\n",
    "& = ReachedByInternet_x\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "with $x$ being a certain region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "de8e45dc-5234-4375-8858-1e6630b44777",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_internet_reach = data.groupby(\"country_id\").first()[\"internet_perc\"] / 100\n",
    "\n",
    "state_names[\"InternetAccess\"] = [\"yes\", \"no\"]\n",
    "cpd_InternetReach = TabularCPD(variable=\"InternetAccess\", \n",
    "                               variable_card=2, # yes, no\n",
    "                               values=np.array([p_internet_reach, 1 - p_internet_reach]),\n",
    "                               evidence=[\"Country\"],\n",
    "                               evidence_card=[len(countries)],\n",
    "                               state_names=state_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "de94c7a4-882a-42ad-a9af-09bd322b51f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_tv_reach = data.groupby(\"country_id\").first()[\"tv_perc\"] / 100\n",
    "\n",
    "state_names[\"TvAccess\"] = [\"yes\", \"no\"]\n",
    "cpd_TvReach = TabularCPD(variable=\"TvAccess\", \n",
    "                         variable_card=2, # yes, no\n",
    "                         values=np.array([p_tv_reach, 1 - p_tv_reach]),\n",
    "                         evidence=[\"Country\"],\n",
    "                         evidence_card=[len(countries)],\n",
    "                         state_names=state_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "59aede90-4114-4ceb-98e9-a7138decb86b",
   "metadata": {},
   "outputs": [],
   "source": [
    "p_radio_reach = data.groupby(\"country_id\").first()[\"radio_perc\"] / 100\n",
    "\n",
    "state_names[\"RadioAccess\"] = [\"yes\", \"no\"]\n",
    "cpd_RadioReach = TabularCPD(variable=\"RadioAccess\", \n",
    "                            variable_card=2, # yes, no\n",
    "                            values=np.array([p_radio_reach, 1 - p_radio_reach]),\n",
    "                            evidence=[\"Country\"],\n",
    "                            evidence_card=[len(countries)],\n",
    "                            state_names=state_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3709f873-3e3a-4f68-bb3f-d98a5080afbc",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Mental health\n",
    "\n",
    "[Lyubetsky et al](https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/49/e3sconf_interagromash2021_10036.pdf) shows that depressive symptoms went from $29.7\\%$ to $51.3\\%$ while anxiety symptoms went from $24.03\\%$ to $42.86\\%$ when students where experiencing distant learning.\n",
    "We will model the probability of depressive symptoms and anxiety over this values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ddc73e5d-c131-4412-bc3d-5e6ab6134df3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------+-------------+------------+\n",
      "| AtHome       | AtHome(yes) | AtHome(no) |\n",
      "+--------------+-------------+------------+\n",
      "| Anxiety(yes) | 0.4286      | 0.2403     |\n",
      "+--------------+-------------+------------+\n",
      "| Anxiety(no)  | 0.5714      | 0.7597     |\n",
      "+--------------+-------------+------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Anxiety\"] = [\"yes\", \"no\"]\n",
    "cpd_Anxiety = TabularCPD(variable=\"Anxiety\", \n",
    "                            variable_card=2, # yes, no\n",
    "                            values=[[0.4286, 0.2403],\n",
    "                                    [1 - 0.4286, 1 - 0.2403]],\n",
    "                            evidence=[\"AtHome\"],\n",
    "                            evidence_card=[2],\n",
    "                            state_names=state_names)\n",
    "print(cpd_Anxiety)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1bdfd8dd-7a42-400f-bc50-f78142b4222a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+-------------+--------------------+\n",
      "| AtHome          | AtHome(yes) | AtHome(no)         |\n",
      "+-----------------+-------------+--------------------+\n",
      "| Depression(yes) | 0.513       | 0.297              |\n",
      "+-----------------+-------------+--------------------+\n",
      "| Depression(no)  | 0.487       | 0.7030000000000001 |\n",
      "+-----------------+-------------+--------------------+\n"
     ]
    }
   ],
   "source": [
    "state_names[\"Depression\"] = [\"yes\", \"no\"]\n",
    "cpd_Depression = TabularCPD(variable=\"Depression\", \n",
    "                            variable_card=2, # yes, no\n",
    "                            values=[[0.513, 0.297],\n",
    "                                    [1 - 0.513, 1 - 0.297]],\n",
    "                            evidence=[\"AtHome\"],\n",
    "                            evidence_card=[2],\n",
    "                            state_names=state_names)\n",
    "print(cpd_Depression)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a571bc-e1fb-4b6d-b00f-09c23cec2a2f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e056ab80-08e7-49d1-9fb7-8765389970f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pgmpy.models import BayesianNetwork\n",
    "\n",
    "model = BayesianNetwork([(\"Group\", \"Country\"),\n",
    "                         (\"Country\", \"Gender\"), (\"Country\", \"Student\"), (\"Country\", \"AtHome\"),\n",
    "                         (\"Country\", \"Online\"), (\"Country\", \"Radio\"), (\"Country\", \"Tv\"),\n",
    "                         (\"Country\", \"InternetAccess\"), (\"Country\", \"RadioAccess\"), (\"Country\", \"TvAccess\"),\n",
    "                         (\"AtHome\", \"Depression\"), (\"AtHome\", \"Anxiety\"),\n",
    "                         (\"Gender\", \"Education\"), (\"Country\", \"Education\")\n",
    "                        ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "993ad8a6-5b32-48a8-afd8-52f4ecf35e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add_cpds(cpd_Group, cpd_Country, cpd_Student, cpd_Gender, cpd_Education, cpd_AtHome,\n",
    "               cpd_Online, cpd_Radio, cpd_Tv,\n",
    "               cpd_InternetReach, cpd_RadioReach, cpd_TvReach,\n",
    "               cpd_Anxiety, cpd_Depression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "18d34372-defd-4090-b1ac-00a3b693f115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model is formally correct\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "  model.check_model()\n",
    "  print(\"Model is formally correct\")\n",
    "except Exception as e:\n",
    "  print(f\"Error: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e95e19-d729-4691-8b5c-9027b8cd25f3",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Inference\n",
    "\n",
    "We can now perform the inference steps and see if we can discover some interesting insights from the data.\n",
    "We will do that by using the `VariableElimination` inference method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8976873f-abe8-48d5-86a7-cc7dc97616d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pgmpy.inference import VariableElimination\n",
    "infer = VariableElimination(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fae98da-5b67-479c-b456-6c011a86dfb6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Implemented measures\n",
    "\n",
    "We can now try to query which measures have been implemented by each country and which have not been taken into consideration. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354a86ab-a27b-4aca-8c57-0d7401264652",
   "metadata": {},
   "source": [
    "Let's first take a look at how likely it is that a measure has been implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "adada6ba-b679-4be5-b334-e84551241d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------+\n",
      "| Online      |   phi(Online) |\n",
      "+=============+===============+\n",
      "| Online(no)  |        0.0492 |\n",
      "+-------------+---------------+\n",
      "| Online(yes) |        0.9508 |\n",
      "+-------------+---------------+\n",
      "+------------+--------------+\n",
      "| Radio      |   phi(Radio) |\n",
      "+============+==============+\n",
      "| Radio(no)  |       0.6781 |\n",
      "+------------+--------------+\n",
      "| Radio(yes) |       0.3219 |\n",
      "+------------+--------------+\n",
      "+---------+-----------+\n",
      "| Tv      |   phi(Tv) |\n",
      "+=========+===========+\n",
      "| Tv(no)  |    0.0476 |\n",
      "+---------+-----------+\n",
      "| Tv(yes) |    0.9524 |\n",
      "+---------+-----------+\n"
     ]
    }
   ],
   "source": [
    "print(infer.query([\"Online\"], show_progress=False))\n",
    "print(infer.query([\"Radio\"], show_progress=False))\n",
    "print(infer.query([\"Tv\"], show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21293dfb-0718-4d51-8937-58892edb749e",
   "metadata": {},
   "source": [
    "It seems like television and online learning are the most widespread distant learning measure. This makes sense, as trying to teach to students using radio is far more difficult than by using visual media such as computers or televisions.\n",
    "\n",
    "Let's see how the combination of all those measures looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "966cb5fd-fb69-43e9-add1-dc54d576d3bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+---------+-------------+------------------------+\n",
      "| Radio      | Tv      | Online      |   phi(Radio,Tv,Online) |\n",
      "+============+=========+=============+========================+\n",
      "| Radio(no)  | Tv(no)  | Online(no)  |                 0.0155 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(no)  | Tv(no)  | Online(yes) |                 0.0206 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(no)  | Tv(yes) | Online(no)  |                 0.0083 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(no)  | Tv(yes) | Online(yes) |                 0.6338 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(yes) | Tv(no)  | Online(no)  |                 0.0069 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(yes) | Tv(no)  | Online(yes) |                 0.0047 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(yes) | Tv(yes) | Online(no)  |                 0.0186 |\n",
      "+------------+---------+-------------+------------------------+\n",
      "| Radio(yes) | Tv(yes) | Online(yes) |                 0.2917 |\n",
      "+------------+---------+-------------+------------------------+\n"
     ]
    }
   ],
   "source": [
    "print(infer.query([\"Online\", \"Radio\", \"Tv\"], show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07f790e-de0c-412e-a0d6-d19a270f9d36",
   "metadata": {},
   "source": [
    "Of course the most likely combination is the one in which online and tv measures are implemented ($\\approx 63.3\\%$), but we can see that it's fairly probable ($\\approx 29\\%$) that also radio learning is available alongside television and online.\n",
    "\n",
    "On the contrary the chanhes that no distant learning measures are implemented ($\\approx 1.5\\%$) are higher than the probability of only implementing tv ($\\approx 0.8\\%$) or radio ($\\approx 0.69\\%$).\n",
    "\n",
    "We could say that students all-over the world has got a pretty decent probability of getting an education even if they were from home but we need to consider the probability of a students actually being able to benefit from such measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7d6324e9-df2a-448e-b37e-5ff94ca3c595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Being able to benefit from tv measures:\n",
      "+---------------+-----------------+\n",
      "| TvAccess      |   phi(TvAccess) |\n",
      "+===============+=================+\n",
      "| TvAccess(yes) |          0.7854 |\n",
      "+---------------+-----------------+\n",
      "| TvAccess(no)  |          0.2146 |\n",
      "+---------------+-----------------+\n",
      "\n",
      "\n",
      "Being able to benefit from radio measures\n",
      "+------------------+--------------------+\n",
      "| RadioAccess      |   phi(RadioAccess) |\n",
      "+==================+====================+\n",
      "| RadioAccess(yes) |             0.9500 |\n",
      "+------------------+--------------------+\n",
      "| RadioAccess(no)  |             0.0500 |\n",
      "+------------------+--------------------+\n",
      "\n",
      "\n",
      "Being able to benefit from online measures\n",
      "+---------------------+-----------------------+\n",
      "| InternetAccess      |   phi(InternetAccess) |\n",
      "+=====================+=======================+\n",
      "| InternetAccess(yes) |                0.5899 |\n",
      "+---------------------+-----------------------+\n",
      "| InternetAccess(no)  |                0.4101 |\n",
      "+---------------------+-----------------------+\n"
     ]
    }
   ],
   "source": [
    "print(\"Being able to benefit from tv measures:\")\n",
    "print(infer.query([\"TvAccess\"], evidence={\"Tv\": \"yes\"}, show_progress=False))\n",
    "\n",
    "print(\"\\n\\nBeing able to benefit from radio measures\")\n",
    "print(infer.query([\"RadioAccess\"], evidence={\"Radio\": \"yes\"}, show_progress=False))\n",
    "\n",
    "print(\"\\n\\nBeing able to benefit from online measures\")\n",
    "print(infer.query([\"InternetAccess\"], evidence={\"Online\": \"yes\"}, show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3da0153-3f5f-40e8-9133-bf07f31c426d",
   "metadata": {},
   "source": [
    "The situation is now much different. Radio measures, being the one less used, ends up not reaching virtually all the population. Tv measures seems the most reasonable to use, as they would reach $\\approx 80\\%$ of the population. Online learning, arguably the most efficient and effective distant learning measure is indeed spread around the world but a lot of students cannot effectively benefit from it: $41\\%$ of the students cannot benefit from online measures even though they have been implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "db3e4bcb-09b4-45c0-94df-ba69520b407e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "groups = data.sdg_group.unique()\n",
    "p_benefit_online = dict()\n",
    "\n",
    "for group in groups:\n",
    "    if group not in p_benefit_online:\n",
    "      p_benefit_online[group] = infer.query([\"InternetAccess\"], evidence={\"Group\": group, \"Online\": \"yes\"}, show_progress=False).values[1]\n",
    "    else:\n",
    "      p_benefit_online[group] *= infer.query([\"InternetAccess\"], evidence={\"Group\": group, \"Online\": \"yes\"}, show_progress=False).values[1]\n",
    "    \n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.barh(list(p_benefit_online.keys()), [1 for _ in p_benefit_online.values()])\n",
    "plt.barh(list(p_benefit_online.keys()), list(p_benefit_online.values()), label=\"Not reached\")\n",
    "plt.axvline(x=.5, ymin=0, ymax=1, color=\"red\", label=\"Not reached = 0.5\")\n",
    "plt.title(\"Probability of being able to benefit from online measures\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01af1a89-358d-4ee4-8114-465968a5f6c7",
   "metadata": {},
   "source": [
    "Indeed from some regions children can't actually benefit from the online learning platforms since they don't have any internet access at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4ff4e077-21f2-44be-ac4f-bd7182742909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7a3110e0aa74acaa6851a1fd6692cc6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "groups = data.wb_group.unique()\n",
    "p_benefit_online = dict()\n",
    "\n",
    "for group in tqdm(groups):\n",
    "  countries_in_group = data[data.wb_group == group].country_id.unique()\n",
    "  \n",
    "  for country in countries_in_group:\n",
    "    p = infer.query([\"InternetAccess\"], evidence={\"Country\": country, \"Online\": \"yes\"}, show_progress=False).values[1]\n",
    "    \n",
    "    if group not in p_benefit_online:\n",
    "      p_benefit_online[group] = p\n",
    "    else:\n",
    "      p_benefit_online[group] += p\n",
    "  \n",
    "  p_benefit_online[group] /= len(countries_in_group)\n",
    "      \n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.barh(list(p_benefit_online.keys()), [1 for _ in p_benefit_online.values()])\n",
    "plt.barh(list(p_benefit_online.keys()), list(p_benefit_online.values()), label=\"Not reached\")\n",
    "plt.axvline(x=.5, ymin=0, ymax=1, color=\"red\", label=\"Not reached = 0.5\")\n",
    "plt.title(\"Probability of being able to benefit from online measures\")\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "plt.savefig(\"online_reach_wb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de74ca7-8d62-4b27-9f82-97b2b42938a1",
   "metadata": {},
   "source": [
    "In fact the gap is even higher if we compare countries by their economical classification. Students from low income areas have an $\\approx 80\\%$ probability of being unable to use the online learning platforms provided by the government."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "399569af-8244-468e-84d4-bb9565fc16b6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### School closed\n",
    "\n",
    "We've seen how, from low income areas, students can't reliably access online lessons.\n",
    "But how many students are actually learning from home?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e83f651-43fc-4b73-b863-f1cc6a5e15ed",
   "metadata": {},
   "source": [
    "Let's first see how likely it is that a child stayed at home because of COVID-19 during the analysed period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b6d9ac10-d9c0-4ad9-a8f0-8cd0e4887d3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+---------------+\n",
      "| AtHome      |   phi(AtHome) |\n",
      "+=============+===============+\n",
      "| AtHome(yes) |        0.5320 |\n",
      "+-------------+---------------+\n",
      "| AtHome(no)  |        0.4680 |\n",
      "+-------------+---------------+\n"
     ]
    }
   ],
   "source": [
    "print(infer.query([\"AtHome\"], show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d6b892-9f51-4612-9f58-665ebca111b9",
   "metadata": {},
   "source": [
    "It seems like, for the majority of the analysed period, students where not able to go to school in presence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d757644f-a5d5-47f9-8050-6ee4c54210e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d9fafd495d84bdaa2c327c5cc458760",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/180 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import geopandas\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "countries_p_athome = list()\n",
    "for country in tqdm(data.country_id.unique()):\n",
    "  p = infer.query([\"AtHome\"], evidence={\"Country\": country}, show_progress=False)\n",
    "  countries_p_athome.append(p.values[0])\n",
    "\n",
    "df = pd.DataFrame(countries_p_athome, index=data.country_id.unique(), columns=[\"athome\"])\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "world = world.join(df, on=\"iso_a3\")\n",
    "world.plot(column=\"athome\", figsize=(15, 5), cmap='OrRd', edgecolor='k', legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a943018-ec20-4f9f-aac0-06d6de8baf8c",
   "metadata": {},
   "source": [
    "In fact, a lot of countries display a low probability of school's being closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "09cf5485-82b7-482b-aa0d-c3b624f80493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = data.sdg_group.unique()\n",
    "p_group = np.zeros_like(groups)\n",
    "for idx, group in enumerate(groups):\n",
    "  p = infer.query([\"AtHome\"], evidence={\"Group\": group}, show_progress=False)\n",
    "  p_group[idx] = p.values[0]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.barh(groups, np.ones_like(p_group), label=\"$P(\\\\neg AtHome)$\")\n",
    "plt.barh(groups, p_group, label=\"$P(AtHome)$\")\n",
    "plt.axvline(x=.5, ymin=0, ymax=1, color=\"red\", label=\"P(AtHome) = 0.5\")\n",
    "plt.title(\"Probability of staying at home by SDG region\")\n",
    "plt.legend()\n",
    "plt.savefig(\"athome_sdg_group\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4313841d-2707-4a5d-9db0-05230569f091",
   "metadata": {},
   "source": [
    "Much like before schools closure are more consistent on asian and Latin America's countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "92c3eafb-bb1c-400e-a5a0-132c5315a794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0ce0fc9adab4104a6e5afdc03910a58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "groups = data.wb_group.unique()\n",
    "p_benefit_online = dict()\n",
    "\n",
    "for group in tqdm(groups):\n",
    "  countries_in_group = data[data.wb_group == group].country_id.unique()\n",
    "  \n",
    "  for country in countries_in_group:\n",
    "    p = infer.query([\"AtHome\"], evidence={\"Country\": country}, show_progress=False).values[1]\n",
    "    \n",
    "    if group not in p_benefit_online:\n",
    "      p_benefit_online[group] = p\n",
    "    else:\n",
    "      p_benefit_online[group] += p\n",
    "  \n",
    "  p_benefit_online[group] /= len(countries_in_group)\n",
    "      \n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.barh(list(p_benefit_online.keys()), [1 for _ in p_benefit_online.values()])\n",
    "plt.barh(list(p_benefit_online.keys()), list(p_benefit_online.values()), label=\"P(AtHome)\")\n",
    "plt.axvline(x=.5, ymin=0, ymax=1, color=\"red\", label=\"P(AtHome) = 0.5\")\n",
    "plt.title(\"Probability of staying at home by income group\")\n",
    "plt.legend()\n",
    "plt.savefig(\"athome_wb_group\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9200cb86-b4f0-4f06-bc23-de656c898f5d",
   "metadata": {},
   "source": [
    "Again, low income areas contain those countries whose school closure have been more consistent. This kind of behaviour is however well spread also among other income areas.\n",
    "We need to take into account, however, that the period of analysis ranges from the initial phase of the pandemic to the end of 2021.\n",
    "At the beginning of the new school year, around september/october 2020 many country started to allow students back into school without any limitation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d4371bc9-4cc6-43a0-ba2c-b91e5c20dcd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = pd.Series(data.date.unique())\n",
    "dates = zip(dates[0::10], dates[10::10])\n",
    "\n",
    "athome = dict()\n",
    "\n",
    "for start, end in dates:\n",
    "  inrange = data[(data.date >= start) & (data.date < end)]\n",
    "  tot_days = len(inrange.status)\n",
    "  at_home = inrange.status.isin([\"Closed due to COVID-19\", \"Partially open\"]).sum()\n",
    "  athome[end] = at_home / tot_days\n",
    "\n",
    "days = list(athome.keys())\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "plt.fill_between(range(len(days)), days, label=\"P($\\\\neg$Closed)\")\n",
    "plt.fill_between(range(len(days)), list(athome.values()), label=\"P(Closed)\")\n",
    "plt.xticks(range(1, len(days), 5), [d.strftime(\"%d/%m/%Y\") for d in days[::5]], rotation=45)\n",
    "plt.yticks(np.linspace(0, 1, 10), [f\"{x:1.1}\" for x in np.linspace(0, 1, 10)])\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"Probability of school being closed\")\n",
    "plt.title(\"Probability of schools being closed worldwide every 10 days.\")\n",
    "plt.axhline(y=.5, xmin=0, xmax=1, color=\"red\", label=\"P(Closed) = 0.5\")\n",
    "plt.ylim(0, 1)\n",
    "plt.xlim(0, len(days) - 1)\n",
    "plt.legend()\n",
    "plt.savefig(\"school_closed_windows\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0389d09a-cca7-4cb1-bd8b-2a4ff458fa85",
   "metadata": {},
   "source": [
    "In fact school closure changes over time: school quickly ramp up to be closed in the middle of the first pandemic round (around mid March 2020) and oscillates between $30\\%$ and $50\\%$ of likelihood of being closed after June 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7fc05e8f-49d6-464a-a1fe-cdb94b51783e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------------------------+------------------+\n",
      "| Education                  |   phi(Education) |\n",
      "+============================+==================+\n",
      "| Education(primary)         |           0.5264 |\n",
      "+----------------------------+------------------+\n",
      "| Education(lower_secondary) |           0.2570 |\n",
      "+----------------------------+------------------+\n",
      "| Education(upper_secondary) |           0.2167 |\n",
      "+----------------------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "print(infer.query([\"Education\"], evidence={\"AtHome\": \"yes\"}, show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2823c20-9901-41d9-92a8-de0f53afc8ea",
   "metadata": {},
   "source": [
    "The probability of staying at home changes between different education degree. That is related to the fact that in many under-developed countries higher education isn't always guaranteed for all the students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b0ad3ead-46f7-400b-af8a-943ef42caa4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = data.sdg_group.unique()\n",
    "p_primary = np.zeros_like(groups)\n",
    "p_ls = np.zeros_like(groups)\n",
    "p_us = np.zeros_like(groups)\n",
    "for idx, group in enumerate(groups):\n",
    "  p = infer.query([\"Education\"], evidence={\"Group\": group}, show_progress=False)\n",
    "  p_primary[idx] = p.values[0]\n",
    "  p_ls[idx] = p.values[1]\n",
    "  p_us[idx] = p.values[2]\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.barh(range(len(p_primary)), p_primary + p_ls + p_us, label=\"Upper secondary\")\n",
    "plt.barh(range(len(p_primary)), p_primary + p_ls, label=\"Lower secondary\")\n",
    "plt.barh(range(len(p_primary)), p_primary, label=\"Primary\")\n",
    "plt.title(\"Probability of being in a specific educational degree by regional grouping\")\n",
    "plt.yticks(range(len(groups)), groups)\n",
    "plt.legend()\n",
    "plt.savefig(\"specific_grade_sdg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2450d01-febe-40bc-8abf-6a3aa0c512ca",
   "metadata": {},
   "source": [
    "Indeed higher educational degrees are only well spread in Northern America, Europe and Asia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "69cbc972-ef4d-4338-a781-38cd4a4431ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of staying at home in SDG: Africa (Sub-Saharan) region:\n",
      "+----------------------------+------------------+\n",
      "| Education                  |   phi(Education) |\n",
      "+============================+==================+\n",
      "| Education(primary)         |           0.7491 |\n",
      "+----------------------------+------------------+\n",
      "| Education(lower_secondary) |           0.1608 |\n",
      "+----------------------------+------------------+\n",
      "| Education(upper_secondary) |           0.0901 |\n",
      "+----------------------------+------------------+\n",
      "\n",
      "\n",
      "Probability of staying at home in SDG: Northern America and Europe region:\n",
      "+----------------------------+------------------+\n",
      "| Education                  |   phi(Education) |\n",
      "+============================+==================+\n",
      "| Education(primary)         |           0.4568 |\n",
      "+----------------------------+------------------+\n",
      "| Education(lower_secondary) |           0.2837 |\n",
      "+----------------------------+------------------+\n",
      "| Education(upper_secondary) |           0.2594 |\n",
      "+----------------------------+------------------+\n"
     ]
    }
   ],
   "source": [
    "print(\"Probability of staying at home in SDG: Africa (Sub-Saharan) region:\")\n",
    "print(infer.query([\"Education\"], evidence={\"AtHome\": \"yes\", \"Group\": \"SDG: Africa (Sub-Saharan)\"}, show_progress=False))\n",
    "\n",
    "print(\"\\n\\nProbability of staying at home in SDG: Northern America and Europe region:\")\n",
    "print(infer.query([\"Education\"], evidence={\"AtHome\": \"yes\", \"Group\": \"SDG: Northern America and Europe\"}, show_progress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db3ac36-668e-49b8-b5a7-257fd1c24e31",
   "metadata": {},
   "source": [
    "If we only take into account Northern America and Europe and compare it to Sub-Saharan Africa the educational gap is even higher."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4a559d-1955-4377-93ec-34cecdee5b60",
   "metadata": {},
   "source": [
    "#### Mental health conditions\n",
    "\n",
    "As we've already mentioned, distant learning plays an important role on students mental health. Many students repoerted higher level of depression and sleep disorder than they did before the lockdown and the distant learning measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d3e978cc-e5a2-4b25-9349-2762de17c6e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Anxiety) = 0.2403 when not in distant learning\n",
      "P(Anxiety) = 0.4286 when in distant learning\n",
      "P(Depression) = 0.297 when not in distant learning\n",
      "P(Depression) = 0.513 when in distant learning\n"
     ]
    }
   ],
   "source": [
    "x, _ = infer.query([\"Anxiety\"], evidence={\"AtHome\": \"no\"}, show_progress=False).values\n",
    "print(f\"P(Anxiety) = {x} when not in distant learning\")\n",
    "x, _ = infer.query([\"Anxiety\"], evidence={\"AtHome\": \"yes\"}, show_progress=False).values\n",
    "print(f\"P(Anxiety) = {x} when in distant learning\")\n",
    "\n",
    "x, _ = infer.query([\"Depression\"], evidence={\"AtHome\": \"no\"}, show_progress=False).values\n",
    "print(f\"P(Depression) = {x} when not in distant learning\")\n",
    "x, _ = infer.query([\"Depression\"], evidence={\"AtHome\": \"yes\"}, show_progress=False).values\n",
    "print(f\"P(Depression) = {x} when in distant learning\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdcbaf9-a0e7-4c97-a7cd-4f650b352dcb",
   "metadata": {},
   "source": [
    "It's quite clear how the increase in mental health problem due to distant learning is a substiantial problem that needs to be correctly addressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "08b5ca2e-10b1-42fb-a463-6539587d5c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8544dff054a4eaea7aca976d03aa8db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/68 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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atIiLLrqo2++bsJ70VAohhBBCCCGOo7UG4Ouvv+Z///sfSUlJTJ06laKiIlJTUwGYMmUKMTExuLi4cPnll7N27VrAmKN54YUXArB69Wq2bNnC5MmTSUpKYvXq1Rw8eJCYmBgOHjzIHXfcwZdffom/vz8AiYmJLFmyhGXLluHqenwf2IYNG7jiiisAuOqqq5rbBDj//PMxmUyMGjWKvLw8+705ohXpqRRCCCGEEKKP6GqPor0cPHgQFxcXwsLC0Frz7LPPcuaZZ7ba54cffjhuqYqm7z09PZvnUWqtueaaa3j00UePayc5OZmvvvqK559/nnfffZfXXnuNzz//nJ9++olPPvmEv//97+zatavTWFvG4OHh0fx1U1Is7E96KoUQQgghhBDNCgoKuPnmm7n99ttRSnHmmWfy3//+l/r6egD2799PZWUlYAx/PXToEGazmXfeeYdZs2Ydd7x58+axcuVK8vPzASguLiYjI4PCwkLMZjMXXnghf//739m6dStms5msrCxOO+00nnjiCUpKSqioqGh1vBkzZvD2228DsHz58nbbFL1LeiqFEEIIIYQ4yVVXV5OUlER9fT2urq5cddVV/Pa3vwXghhtuID09nQkTJqC1JjQ0tHkO4/Tp07n//vvZsWNHc9GetkaNGsVDDz3E/PnzMZvNuLm58fzzz+Pl5cV1113XXNzn0UcfpbGxkSuvvJLS0lK01vzmN78hMDCw1fGeeeYZrr/+ev7xj38QGhrK66+/btf3RpyYOlm7hSdNmqQ3b97s6DCEEEIIIcRJbs+ePYwcOdLRYXTZDz/8wD//+U8+++wzR4cirNDe50wptUVrPamnx5bhr0IIIYQQQgghuk2GvwohhBBCCCG6bM6cOcyZM8fRYYg+QHoqhRBCCCGEEEJ0mySVQgghhBBCCCG6TZJKIYQQQgghhBDdJkmlEEIIIYQQQohu6/NJpVJqgVJqn1IqTSl1fwf7zFFKbVdK7VJK/djbMQohhBBCCOHsPvzwQ5RS7N27t9vHOOussygpKel0n0ceeaTbxxd9U59OKpVSLsDzwEJgFHC5UmpUm30Cgf8Ai7TWo4GLeztOIYQQQgghnN2KFSuYNWsWb7/9drePsWrVKgIDAzvdR5LK/qdPJ5XAFCBNa31Qa10HvA2c12afK4APtNaZAFrr/F6OUQghhBBCCKdWUVHBunXrePXVV5uTyh9++IE5c+Zw0UUXMWLECJYsWYLWmtLSUhISEti3bx8Al19+OS+//DIA0dHRFBYWArBs2TKmTJlCUlISN910E42Njdx///1UV1eTlJTEkiVLePDBB3n66aeb4/jjH//IM88808uvXvRUX1+nMhzIavF9NjC1zT7DATel1A+AH/C01vp/vROeEEIIIYQQNvTF/ZC7w7bHHDQWFj7W6S4fffQRCxYsYPjw4QQFBbF161YAtm3bxq5duxgyZAgzZ85k3bp1zJo1i+eee45rr72Wu+66i6NHj/LrX/+61fH27NnDO++8w7p163Bzc+PWW29l+fLlPPbYYzz33HNs374dgPT0dC644ALuuusuzGYzb7/9Nps2bbLt6xd219eTStXONt3me1dgIjAP8AI2KKV+1lrvP+5gSt0I3AgwdOhQG4cqhBBCCCGEc1qxYgV33303AJdddhkrVqzg7LPPZsqUKURERACQlJREeno6s2bN4owzzuC9997jtttuIzk5+bjjrV69mi1btjB58mQAqqurCQsLO26/6OhogoOD2bZtG3l5eYwfP57g4GD7vVBhF309qcwGIlt8HwEcaWefQq11JVCplPoJGAccl1RqrV8CXgKYNGlS2+RUCCGEEEIIxzpBj6I9FBUV8d1337Fz506UUjQ2NqKU4qyzzsLDw6N5PxcXFxoaGgAwm83s2bMHLy8viouLmxPPJlprrrnmGh599NETtn/DDTewdOlScnNzuf7662374kSv6OtzKn8B4pVSw5RS7sBlwCdt9vkYmK2UclVKeWMMj93Ty3EKIYQQQgjhlFauXMnVV19NRkYG6enpZGVlMWzYMNauXdvhc5566ilGjhzJihUruP7666mvr2/1+Lx581i5ciX5+Ua5k+LiYjIyMgBwc3Nrtf/ixYv58ssv+eWXXzjzzDPt8AqFvfXppFJr3QDcDnyFkSi+q7XepZS6WSl1s2WfPcCXQAqwCXhFa73TUTELIYQQQgjhTFasWMHixYtbbbvwwgt566232t1///79vPLKK/zrX/9i9uzZnHLKKTz00EOt9hk1ahQPPfQQ8+fPJzExkTPOOIOcnBwAbrzxRhITE1myZAkA7u7unHbaaVxyySW4uLjY4RUKe1Nan5yjQCdNmqQ3b97s6DCEEEIIIcRJbs+ePYwcOdLRYTiM2WxmwoQJvPfee8THxzs6nH6rvc+ZUmqL1npST4/dp3sqhRBCCCGEEP3X7t27iYuLY968eZJQOrG+XqhHCCGEEEII0U+NGjWKgwcPOjoM0UPSUymEEEIIIYQQotskqRRCCCGEEEII0W2SVAohhBBCCCGE6DZJKoUQQgghhBBCdJsklUIIIYQQQgg+/PBDlFLs3bu3R8c566yzKCkp6XSfRx55pEdtdIevr+8J9/nLX/7CP//5z16Ixjo33HADu3fvdnQYJyRJpRBCCCGEEIIVK1Ywa9Ys3n777R4dZ9WqVQQGBna6jyOSyt7U0NBgk+O88sorjBo1yibHsidJKoUQQgghhDjJVVRUsG7dOl599dVWSeUPP/zAnDlzuOiiixgxYgRLlixBa01paSkJCQns27cPgMsvv5yXX34ZgOjoaAoLCwFYtmwZU6ZMISkpiZtuuonGxkbuv/9+qqurSUpKYsmSJTz44IM8/fTTzW3+8Y9/5JlnnjkuxvPPP5+JEycyevRoXnrppebtvr6+/PGPf2TcuHFMmzaNvLw8AA4dOsT06dOZPHkyDz74YIev/eGHHyYhIYHTTz+9+fUAHDhwgAULFjBx4kRmz57d3IN77bXXcvPNNzN79myGDx/OZ599BsDSpUu5+OKLOffcc5k/fz6VlZVcf/31TJ48mfHjx/Pxxx8DsGvXrub3JDExkdTUVCorKzn77LMZN24cY8aM4Z133gFgzpw5bN68GTCS/rFjxzJmzBjuu+++E77+3iTrVAohhBBCCNFHPL7pcfYW92z4aVsjgkZw35T7Ot3no48+YsGCBQwfPpygoCC2bt3KhAkTANi2bRu7du1iyJAhzJw5k3Xr1jFr1iyee+45rr32Wu666y6OHj3Kr3/961bH3LNnD++88w7r1q3Dzc2NW2+9leXLl/PYY4/x3HPPsX37dgDS09O54IILuOuuuzCbzbz99tts2rTpuBhfe+01goKCqK6uZvLkyVx44YUEBwdTWVnJtGnTePjhh7n33nt5+eWX+dOf/sRdd93FLbfcwtVXX83zzz/f7uvesmULb7/9Ntu2baOhoYEJEyYwceJEAG688UZeeOEF4uPj2bhxI7feeivfffddc8w//vgjBw4c4LTTTiMtLQ2ADRs2kJKSQlBQEH/4wx+YO3cur732GiUlJUyZMoXTTz+dF154gbvuuoslS5ZQV1dHY2Mjq1atYsiQIXz++ecAlJaWtorzyJEj3HfffWzZsoUBAwYwf/58PvroI84///wOX39vkp5KIYQQQgghTnIrVqzgsssuA+Cyyy5jxYoVzY9NmTKFiIgITCYTSUlJpKenA3DGGWcwduxYbrvtNl555ZXjjrl69Wq2bNnC5MmTSUpKYvXq1Rw8ePC4/aKjowkODmbbtm18/fXXjB8/nuDg4OP2e+aZZ5p747KyskhNTQXA3d2dc845B4CJEyc2x7du3Touv/xyAK666qp2X/eaNWtYvHgx3t7e+Pv7s2jRIsDouV2/fj0XX3xxcy9rTk5O8/MuueQSTCYT8fHxxMTENPdinnHGGQQFBQHw9ddf89hjj5GUlMScOXOoqakhMzOT6dOn88gjj/D444+TkZGBl5cXY8eO5dtvv+W+++5jzZo1BAQEtIrzl19+Yc6cOYSGhuLq6sqSJUv46aefOn39vUl6KoUQQgghhOgjTtSjaA9FRUV899137Ny5E6UUjY2NKKV44oknAPDw8Gje18XFpXm+oNlsZs+ePXh5eVFcXExERESr42qtueaaa3j00UdPGMMNN9zA0qVLyc3N5frrrz/u8R9++IFvv/2WDRs24O3t3ZykAbi5uaGUOi4+oHl7Z9rbx2w2ExgY2NybeqLnNH3v4+PTvE1rzfvvv09CQkKrfUeOHMnUqVP5/PPPOfPMM3nllVeYO3cuW7ZsYdWqVTzwwAPMnz+fP//5z62O1ZHOXn9vkZ5KIYQQQgghTmIrV67k6quvJiMjg/T0dLKyshg2bBhr167t9HlPPfUUI0eOZMWKFVx//fXU19e3enzevHmsXLmS/Px8AIqLi8nIyACMRKjl/osXL+bLL7/kl19+4cwzzzyurdLSUgYMGIC3tzd79+7l559/PuHrmjlzZvP80OXLl7e7zymnnMKHH35IdXU15eXlfPrppwD4+/szbNgw3nvvPcBI6pKTk5uf995772E2mzlw4AAHDx48LnEEOPPMM3n22WebE8Jt27YBcPDgQWJiYrjzzjtZtGgRKSkpHDlyBG9vb6688kp+//vfs3Xr1lbHmjp1Kj/++COFhYU0NjayYsUKTj311BO+B71FkkohhBBCCCFOYitWrGDx4sWttl144YW89dZbHT5n//79vPLKK/zrX/9i9uzZnHLKKTz00EOt9hk1ahQPPfQQ8+fPJzExkTPOOKN5COmNN95IYmIiS5YsAYwhnKeddhqXXHIJLi4ux7W3YMECGhoaSExM5MEHH2TatGknfF1PP/00zz//PJMnTz5ujmKTCRMmcOmll5KUlMSFF17I7Nmzmx9bvnw5r776KuPGjWP06NHNhXYAEhISOPXUU1m4cCEvvPACnp6exx37wQcfpL6+nsTERMaMGdNcLOidd95hzJgxJCUlsXfvXq6++mp27NjRXLzn4YcfPm5O5ODBg3n00Uc57bTTGDduHBMmTOC888474XvQW1RnXan92aRJk3RTJSUhhBBCCCEcZc+ePYwcOdLRYTiU2WxmwoQJvPfee8THxzs6nE5de+21nHPOOVx00UWODqVL2vucKaW2aK0n9fTY0lMphBBCCCGEcJjdu3cTFxfHvHnz+nxCKdonhXqEEEIIIYQQDjNq1Kh2q8L2VUuXLnV0CH2O9FQKIYQQQgjhYCfrlDTRO+z9+ZKkUgghhBBCCAfy9PSkqKhIEkthF1prioqK2i0mZCsy/FUIIYQQQggHioiIIDs7m4KCAkeHIvopT0/P49YRtSVJKoUQQgghhHAgNzc3hg0b5ugwhOg2Gf4qhBBCCCGEEKLbJKkUQgghhBBCCNFtklQKIYQQQgghhOg2SSqFEEIIIYQQQnSbJJVCCCGEEEIIIbpNkkohhBBCCCGEEN0mSaUQQgghhBBCiG6TpFIIIYQQQgghRLe5OjoAh9m3D+bMcXQUQgghhBBCCOHUpKdSCCGEEEIIIUS3nbw9lQkJ8MMPjo5CCCGEEEIIIRxDKZscRnoqhRBCCCGEEEJ0mySVQgghhBBCCCG6TZJKIYQQQgghhBDdJkmlEEIIIYQQQohuk6RSCCGEEEIIIUS3SVIphBBCCCGEEKLbJKkUQgghhBBCCNFtklQKIYQQQgghhOg2SSqFEEIIIYQQQnSbJJVCCCGEEEIIIbpNkkohhBBCCCGEEN0mSaUQQgghhBBCiG6TpFIIIYQQQgghRLdJUimEEEIIIYQQotskqRRCCCGEEEII0W2SVAohhBBCCCGE6DZJKkWn6hvNaK0dHYYQQgghhBCij5KkUnSopr6R2Y9/z6Nf7HV0KEIIIYQQQog+SpJK0aFPk4+QW1bDy2sOsjm92NHhCCGEEEIIIfogSSpFh5ZtzCQmxIchAV7c+34KNfWNjg5JCCGEEEII0cdIUinatfNwKclZJVw1PYpHLxjLwYJKnlmd6uiwhBBCCCGEEH2MJJWiXct+zsDLzYULJkRwyvBQLpkUwYs/HWTn4VJHhyaEEEIIIYToQySpFMcpq6nn4+1HWDRuCAFebgD88exRBPu4c8/KFOobzQ6OUAghhBBCCNFXSFLppCpqG1i67hCpeeU2P/aHWw9TXd/IkmlDm7cFeLnx0Plj2JNTxgs/HLB5m0IIIYQQQgjn5OroAETX1DWYWbEpk2e/S6Wwoo6x4QF8cvtMlFI2Ob7WmmU/Z5AYEUBiRGCrx+aPHsQ5iYN59rs0zhwziOED/WzSphBCCCGEEMJ5SU+lkzCbNZ8kH+H0J3/k/z7ZRVyYLzefGsuOw6V8tSvXZu1sOlRMan4FV06Navfxvy4aja+nK/euTKHRrG3WrhBCCCGEEMI5SVLpBNakFnDuc2u5c8U2vN1dWHrdZFb8ehq/nz+cmFAf/vX1fpsleMs2ZuLv6cq544a0+3iwrwf/d+4otmeV8Pq6QzZpUwghhBBCCOG8JKnsw3Zkl3LlKxu56tVNlFbX89Sl41h152zmJIShlMLVxcTvzkggNb+Cj7cf7nF7BeW1fLkzhwsnRuDl7tLhfovGDeH0kWH88+t9pBdW9rhdIYQQQgghhPOSpLIPSi+s5Pa3tnLuc2vZdaSUP58zitW/O5XF4yMwmVrPnVw4ZhCjBvvz729TqWvoWVXWdzdnUd+oWdLB0NcmSikeOn8sbiYT972fglmGwQohhBBCCHHSkqSyDykor+XBj3Zy+pM/snpPPnfMjePHe0/j+lnD8HBtv+fQZFLcc2YCmcVVvLs5q9ttN5o1b23MZHpMMHFhvifcf1CAJ386ZyQbDxXz1qbMbrcrhBBCCCGEcG59PqlUSi1QSu1TSqUppe7vZL/JSqlGpdRFvRmfrWzJOMqp//ietzZlctmUSH68Zw6/m5+Av6fbCZ87JyGUiVEDePa7VGrqG7vV/k/7CzhcUs2V0zrvpWzpkkmRzIoL4bEv9nK4pLpb7QohhBBCCCGcW59OKpVSLsDzwEJgFHC5UmpUB/s9DnzVuxHazte7c6lvNPPNb07hofPHEubvafVzlVL8fn4CeWW1vLkho1vtL/s5g1A/D+aPHtildh+9YCyNZs0fPtiB1jIMVgghhBBCiJNNn04qgSlAmtb6oNa6DngbOK+d/e4A3gfyezM4W8osqiJygDcxoSceetqe6bHBzI4P4b8/HqCitqFLz80+WsV3+/K5dFIkbi5d+0hEBnlz74IEftxfwAdbe14sSAghhBBCCOFcTphBKKUuUEqlKqVKlVJlSqlypVRZbwQHhAMtJwpmW7a1jC8cWAy8cKKDKaVuVEptVkptLigosGmgPZVZXMXQYO8eHeN38xMorqzjtbVdW+pjxaZMFHD51KHdavea6dFMihrA3z7bTX55TbeOIYQQQgghhHBO1nRLPQEs0loHaK39tdZ+Wmt/ewdmodrZ1naM5b+B+7TWJ5xMqLV+SWs9SWs9KTQ01Bbx2YTWmsyiKoYG9SypTIoMZP6ogbz800FKquqsek5dg5l3fsli7ogwwgO9utWuyaR4/KJEqusb+fNHu7p1DCGEEEIIIYRzsiapzNNa77F7JO3LBiJbfB8BHGmzzyTgbaVUOnAR8B+l1Pm9Ep2NHK2qp7y2ocdJJRi9lRV1Dbzw40Gr9v9qVy6FFXUs6UKBnvbEhvpy9+nxfLkrl1U7cnp0LCGEEEIIIYTzsCap3KyUekcpdbllKOwFSqkL7B6Z4RcgXik1TCnlDlwGfNJyB631MK11tNY6GlgJ3Kq1/qiX4rOJzOIqAJsklQmD/Dhv3BCWrj9EftmJh6Iu+zmDyCAvTo3vec/tjbNjGBPuz58/3kV1Xfeq0AohhBBCCCGcizVJpT9QBcwHzrXczrFnUE201g3A7RhVXfcA72qtdymlblZK3dwbMfSGjKJKAKKCfWxyvLtPH059o+b579M63S8tv5yNh4q5YkoUJlN7I427xtXFxJ/OHkVhRS2fJrftUBZCCCGEEEL0R64n2kFrfV1vBNJJ+6uAVW22tVuUR2t9bW/EZGtZNuypBIgO8eGSSZG8tSmTG2bHENnBcZf9nIm7i4lLJkXYpF2AqcOCSBjox9L16Vw8KQKlep6sCiGEEEIIIfoua6q/RiilPlRK5Sul8pRS7yulbJeFCDKKqgj188DL3cVmx7xzXhxKKZ5Zndru41V1Dby/NZuFYwcR7Oths3aVUlw9I4rdOWVszTxqs+M6g6q6BlmrUwghhBBCnHSsGf76OsY8xiEYy3l8atkmbCSzuIooG/VSNhkc4MVV06J4f2s2afkVxz3+afIRymsauLKHBXrac35SOH6erryxPsPmx+4rtNZkFVfx/pZs7n8/hXn/+oFRf/6KZT/339cshBBCCCFEe044/BUI1Vq3TCKXKqXutlM8J6XM4iqmxwTb/Li3zIllxaZMnvp2P89fMaHVY8t+ziRhoB+TogbYvF0fD1cunhjJmz+nk18+kjA/T5u30dvMZs2+vHI2pxezKf0ovxwqJtdSCMnP05VJUQMoqapnXVoRV02PdmywQgghhBBC9CJrkspCpdSVwArL95cDRfYL6eRSU99IblkNQ4Nt21MJEOLrwfUzh/Hc92nccmopY8IDAEjOKmHH4VL+dt5ou815vGp6FK+tO8Tbm7K4c168XdqwJ601WzNL2HSomF/Si9mcXkxZTQMAA/09mBwdxJRhQUyONuaQmkyKO1ZsY0t6sYMjF0IIIYQQondZk1ReDzwHPAVoYL1lm7CB7KPVaG27Ij1t/fqUGP63IZ0nv9nPa9dOBoxlRLzdXVg8PtwubQIMC/HhlOGhLN+YwS1zYnFzsWakdd/xzOo0nvp2PwAxoT6cNXZwcyIZMcCr3WR8XEQAnyYfoaC8llA/281TFUIIIYQQoi+zpvprJrCoF2I5KTVVfo2yQ08lQICXGzedGss/vtrHloxi4kL9+DTlCIvHR+Dn6WaXNptcMz2KX72xma935XF24mC7tmVLNfWNvL7+EKcOD+XJS8ZZXcgoMSIQgJTsEuaNHGjHCIUQQgghhOg7Ouw+Ukrda7l/Vin1TNtb74XYvzWtUdnRsh+2cN3MaEJ83fnHV/t4f2s2NfVmrpw21G7tNZmTEEZkkBdvbEi3e1u29Mn2I5RU1XPzqbFdqow7Jtwfk4KU7FI7RieEEEIIIUTf0llP5R7L/ebeCORklVlcjbe7C6E2XNajLW93V247LY6/frqb3UfKGD80kNFDAuzWXhMXk+KqaVE8smove3PLGDHI3+5t9pTWmqXr00kY6Me0mKAuPdfb3ZX4MD9SskvsE5wQQgghhBB9UIc9lVrrTy1fVmmt32h5A6p6J7z+L7O4kqFB3nYrmNPkiqlDGRLgSVlNA1dOtf0yIh25ZFIkHq4m/rfBOZba2JxxlN05ZVwzI7pbP5OxEQGkZJfKepVCCCGEEOKkYU31lAes3Ca6IbO4yq5DX5t4uLrw4DmjmBg1oFfnNwZ6u3Ne0hA+3HqY0ur6Xmu3u5auS8ff05Xzxw/p1vPHRQRQVFnH4ZJqG0cmhBBCCCFE39TZnMqFSqlngfA28ymXAg29FmE/prUms7iKqF5IKgEWjh3M+7fMwNPNpVfaa3L19Giq6xtZuSW7V9vtqpzSar7clctlU4bi7W5NYeTjHSvWI/MqhRBCCCHEyaGznsojGPMpa4AtLW6fAGfaP7T+L7+8lpp6s13WqOxLxoQHMDFqAG9uSMds7rvDQpf/nIlZa66a1v3hwSMG++HmokiWeZVCCCGEEOIk0WF3jNY6GUhWSi3XWkvPpB1kWpYTsdcalX3J1dOjuOvt7axJK+TU4aGODuc4NfWNvLUpk3kjBvZoOLKHqwsjB/uzQ3oqhRBCCCHESaKz4a/vWr7cppRKaXvrpfj6tYyikyepXDhmMCG+HvxvfbqjQ2nXZyk5FFfWce2M6B4fKzEigB3ZpX26V1YIIYQQQghb6Wzi2F2W+3N6I5CTUWZxFUpBxID+n1S6u5q4Ykokz36fRmZRVZ8a8qu15o316cSF+TIzLrjHx0sMD2TZz5kcKqokNtTXBhEKIYQQQgjRd3W2pEiO5T6jvVvvhdh/ZRZVMiTAC3dXa4rwOr8rpkZhUoplG/vWx2drZgk7Dpd2exmRthIjjTVAZb1KIYQQQghxMuhs+Gu5Uqqso1tvBtlfZRZXnRRDX5sMCvBkwehBvPNLFtV1jY4Op9nS9en4ebpywfhwmxwvLtQXLzcXkrNkXqUQQgghhOj/Ouup9NNa+wP/Bu4HwoEI4D7goV6Jrp872ZJKMAr2lFbX82nyEUeHAkBeWQ1f7Mjh4omR+Hh0bxmRtlxdTIwJ95eeSiGEEEIIcVKwZtzlmVrr/2ity7XWZVrr/wIX2juw/q6ytoHCiro+NbewN0wZFkTCQD+Wrk9Ha8cXslm+MZNGrbl6eveXEWlPYkQgu46U0dBotulxhRBCCCGE6GusSSoblVJLlFIuSimTUmoJ0HfGLjqppuVEok6ypFIpxdUzotidU8bWzKMOjaW2oZG3NmZyWkIY0SE+Nj12YkQAtQ1m9udV2PS4QgghhBBC9DXWJJVXAJcAeZbbxZZtogdOpjUq2zo/KRw/T1feWO/Ygj2rduRQWFHLNTZYRqStxIhAQIr1CCGEEEKI/u+ESaXWOl1rfZ7WOkRrHaq1Pl9rnd4LsfVrmZY1KqOCbNtD5gx8PFy5eGIkX+zMIb+8xmFxLF2fQUyID7PjQmx+7Ohgb/w9XUnOlmI9QgghhBCifzthUqmUClVK/UEp9ZJS6rWmW28E159lFlfh7+lKgLebo0NxiKumR1HfqHl7U5ZD2t+WeZTkrBKumRGNydTzZUTaUkqRGBEoPZVCCCGEEKLfs2b468dAAPAt8HmLm+iBjOIqooJPvl7KJsNCfDhleCjLN2ZQ74BiNm+sT8fXw5ULJ0bYrY3EiAD25ZZTUy9TkIUQQgghRP9lTVLprbW+T2v9rtb6/aab3SPr5zKLKk/K+ZQtXTM9iryyWr7elder7eaX1/D5jhwumhiBr42WEWlPYkQgDWbNnhxZ1lUIIYQQQvRf1pxRf6aUOktrvcru0ZwkGs2a7KPVLBw72NGhONSchDAig7x4Y0M6Zyf23nuxYmMW9Y22X0akrXGRAQCkZJcyfugAu7Yleq6h0UxGcRW19WbqGs3UNVhujY3UNZiptXxf27zdjFlrLpkUSYivh6PDF0IIIYRwGGuSyruAPyil6oB6yzattfa3X1j925GSahrM2jl6KmtKwcMflO3nHbqYFFdNi+KRVXvZm1vGiEH2/0jVNZhZvjGDU4eHEhPqa9e2Bvl7EuLrQbLMq+zz9uSU8Zt3trM3t7zLz00vrOSJi8bZISohhBBCCOdwwqRSa+3XG4GcTLKa1qjs60llZRE8NxGiZ8Ml/7NLYnnJpEie/GY///4mlReummjz47dlVJyt5fELo+3ellKKcREBpEgF2D6r0ax56aeDPPnNPgK83Hno/DGE+Lrj7mrC3cXFuHc14e5iwsPNct+0zdXE41/sZfnGTO6YG09kX/99FkIIIYSwE6smlCmlFgGnWL79QWv9mf1C6v8yLEllnz8J/fk/UH0U9nwC656GWXfbvIlAb3fumBvPP77ax+o9ecwbOdDmbbT0xvp0ooO9OXV4qF3baZIYEch3+/KpqG2w6/xN0XWZRVX89t3tbM44ysIxg3h48ViCfNy7dIyb58SyYlMW//3xAI8sHmunSIVwTlszjzI+MhBlhwuSQggh+hZrlhR5DGMI7G7L7S7LNtFNmcVVuLkohgR6OTqUjlUfhY0vwqjzYNT5sPqvcPBHuzT169kxxIf58uePd1FV12CXNgB2ZJeyNbOEq6bbZxmR9iRGBqC10bboG7TWrNiUyYKnf2JfXjlPXTqO/yyZ0OWEEmBwgBcXT4rgvc1ZHCmptkO0QjinLRlHueA/6/lyZ66jQxFCCNELrKn+ehZwhtb6Na31a8ACyzbRTZlFVUQM8MallxKbbtn4ItSVwyn3wHnPQXA8rLweSrNt3pS7q4lHLhjL4ZJqnv421ebHb7J0fTre7i5cPMl+y4i0NS4iEIAdh0t6rU3RsfzyGm54YzMPfLCDpMhAvrr7FBaPj+hRT8otc2LRGl788YANIxXCuW3JKAZgTVqhgyMRQgjRG6xJKgECW3wdYIc4TiqZxVV9e+hrTZkx9DXhbBg0Fjz84NJl0FAL715j3NvY5OggLpscyStrD7H7iO2X4CisqOXT5CNcOCECf083mx+/I0E+7kQM8CJZeiod7sudOZz51E+sTSvkz+eMYtmvptpktEDEAG8unBDBil+yyC+rsUGkQji/pr95Gw4UOTgSIYQQvcGapPJRYJtSaqlS6g1gC/CIfcPq3zKKKntepCfrF/joNkhbbZugWtr0klH19dR7jm0LHQ7n/wcOb4YvH7B9m8D9C0cQ6OXGHz/agdmsbXrstzdlUtdo5poZ9l1GpD2JEQGkSAVYhymrqee372zn5mVbiRjgzed3zuL6WcNsOgT61tNiaTRrXvzpoM2OKYQzS8kuwcWkOFRYSU6pDA0XQoj+7oRJpdZ6BTAN+MBym661ftvegfVXpVX1lNU0dG85EXMj7P4EXjkDXj0dti+DD28y5j/aSm0FbHge4s+EIeNbPzZqEcy8Cza/Ctvfsl2bFoHe7vzx7JFsyyzhrU2ZNjtuUUUt/9uQway4EOLCer+YcWJEIFnF1RRX1vV62ye79WmFLHjqJz5OPsKdc+P44NYZdvkMRAX7cH5SOMs3ZlBYYfuefCGcSVFFLVnF1ZxjWX9YeiuFEKL/s6ZQz2KgSmv9idb6Y6BGKXW+3SPrpzKKKwEYGtyFpLKuEja+BM9OhHevgoo8WPgEXPclVBXDt3+xXYCbX4XqYjj13vYfn/tnY4mRz34DOcm2a9di8fhwZsQG8/iXe8kv7/lQworaBq5b+gul1fX8bv5wG0TYdYkRxohx6a3sXY99sZcrXtmIp5sLK2+ezm/nJ+DmYu2I/6677bRY6hrMvLxGeivFyS3lsDH09dLJkQR6u7FekkohhOj3rDnD+j+tdfOEMK11CfB/douon8u0LCdiVU9lWQ58+1d4chR8cQ/4hMDFb8Cd22DqTRA1HabdAluWQubGngdXVwXrn4XYuRAxqf19XFzhotfBKwjeucpIam1IKcVD54+htt7M3z/b06Nj1TY0cvObW9h1pIznr5jA+KEDbBRl14wND0ApqQDbm7KKq3jhxwOclzSEz++c3Ss/+5hQX84dN4Q3N2RIr7Q4qSVnlaCUMUpjekwwGw4UobVtpzQIIYToW6xJKtvbRxbc66aMIiuSytyd8OEt8O+xsPYpGDYbrv8abvgWRp8PJpdj+855AAIi4bO7obG+Z8FtWQqVBXBKB72UTXxD4ZL/QdkR+OBGMJt71m4bMaG+3HpaLJ8mH+Gn/QXdOkajWfO7d5NZm1bIYxeM5fRR9l3/sjN+nm7EhPhIsZ5elJpfDsCV06Lwcnc5wd62c/tpcVTXN/La2kO91qYQfU1KdinxYb74ergyIzaYwyXVzRdUhRBC9E/WJJWblVJPKqVilVIxSqmnMIr1iG7IKq4ixNcdH482ebnWkPYt/O98eGEm7P4IJl0Hd241Kq8Ondr+AT184ax/QP5uo5exu+prYN3TxtDWqOkn3j9yMix8DNK+gZ+e6H67HbhlTiwxIT786aOd1NQ3dum5Wmv+8skuPkvJ4YGFI7h4UqTN4+uqcRGBMvy1F6XlVwAQF+rbq+3GD/TjrDGDWbo+ndKqHl7kEcIJaa1JyS4h0bKc0vTYYAAZAiuEEP2cNUnlHUAd8A7wLlAN3GbPoPqzjKKq9nspd7wHyy6E/D0w78/wm11GshgUc+KDJiyEkefCj09AcTd7SLa9CRW5Hc+lbM+kX8G4K+CHx2D/191rtwMeri48tHgMmcVVPPddWpee+/TqVN78OYObTonhplNjbRpXdyVGBJBfXktuqSw50RvS8isI8XVngI97r7d9+9w4KmobeG2d9FaKk8/hkmoKK+oYZ5lLHhvqS6ifhxTrEUKIfs6a6q+VWuv7tdaTLLc/aK0reyO4/iizuIqoYJ/jH8hYB14D4O4dMPt34B3UtQMvfAJMrvD574xez65oqDWG2Q6dbvRUWkspOOdJGDQGPrih+wltB2bEhnDBhHBe/OkAqXnlVj3nzQ3p/PvbVC6aGMH9C0fYNJ6eGGu5ap8svZW9IjW/gthe7qVsMnKwP/NHDeT1dYcoq5HeSnFySbEM8x8XGQgY8+RnxAazXuZVCiFEv2a/UojiOHUNZo6UVhPZXk9lwT4IHQGu3exZ8R8C8x6EA6th1wdde+72t6DsMJxyj5EodoWbF1zypvH1O1cZxX5s6I9njcTHw5U/frjzhGtXfpZyhD9/sot5I8J47IKxqK6+FjsaPcQfV5OSIbC9QGtNWn4FcWGOSSoB7pwXT1lNA/9bn+6wGIRwhOTsEtxdTIwY5N+8bUZsMIUVtc3D0oUQQvQ/klT2ouyjVWgNUR0llSE9XPJi8g3G2pJf3A/VJdY9p7Ee1j4J4ZOMqq/dETQMLngF8nbA57/tek9pJ4J9PXhg4Qg2pRezckt2h/utTS3kN+9sZ1LUAJ67YgKudlw6ojs83VwYPtCv+Sq+sJ+C8lrKaxocmlSOCQ9g7ogwXll7iIraBofFIURvS84qYeRgP9xdj/0NnhEbAsi8SiGE6M/61pl3P9e8nEjbNSorC421IUMTetaAyQXO+TdUFcLqv1n3nJR3oCTTmEvZk5694fPh1PsheQVsfq37x2nHxRMjmRIdxCNf7KGonYXlk7NKuPHNzcSG+vLKNZN7tdpnV4yLDGDH4VIZAmZnTb0h8WF+Do3jjrlxlFTVs+znDIfGIURvMZs1Ow+XNQ99bRIZ5E14oJfMqxRCiH7shEmlUipUKfUHpdRLSqnXmm69EVx/05RUHtdTWbDXuO9pUgkwJAmm3mIkdlmbOt+3sQF++icMHgfx83ve9qn3QdwZ8OX9UJLV8+NZmEyKhxePobK2gYdXtV67Mi2/gmtf30SQjzv/u34KAV5uNmvX1hIjAimpqpfS+naWVmCp/OrAnkqA8UMHMDs+hJd/Okh1XdcqGAvhjA4WVlBR29Bc+bWlGbHBbDhYdMJpDEIIIZyTNT2VHwMBwLfA5y1uoosyi6rwdDMR6ufR+oGCfcZ9iA2SSoDT/gD+4fDp3Z2vXblzJRw9ZKxLaYv5hyYTnPOUMfx1zb96frwW4gf6ceMpMXyw9TDrDxQCkFNazTWvbcLFpHjzV1MJ8/e0aZu2lmiphijrVdpXal4Fvh6uDPT3OPHOdnbXvHiKKutYvlF6K0X/tz3LUqTH8reupRlxwZRW17M7p6y3wxJCCNELrEkqvbXW92mt39Vav990s3tk/VBGsbGcyHEFZAr3g7svBETYpqHmtSt3wYbn29/H3Gj0Ug4cAwln2aZdgMBImHA1bFtmDKu1oTvmxjM0yJs/fbiT/LIarn51E6XV9Sy9bgrDQtqpqNvHDB/oh4eriZSsEkeH0q+l5VcQG+bbJwo1TYoOYkZsMC/+dLDL660K4WxSskvw9XAlpp3Ky9NjjHmVMgRWCCH6J2uSys+UUjbMOk5eWcUdrFFZsBdC4m3TW9hkxFkw4hxjDcmj7fSS7PoQilLhlN8bPYy2NPt3xmv56Z82PaynmwsPnT+Gg4WVnP7kj2QUVfHS1RMZE378VXG7MzdC/l5IftsojPTaAng8GlLe7fApbi4mRg3xl2I9dpZWUEG8g4e+tnTH3HgKymt55xfbDQkXoi9KziphTLg/Lqbj/5cNCvAkJtSneaSJEEKI/qXDbEIpVa6UKgPuwkgsq5VSZS22iy7QWpNZXMXQoHZ61Ar2227oa0sLHzeK96z6feuKrGazkfCFjoCR59m+3YBwmHANbF8OR9NteuhThoeyaNwQKmobeObypOaqgnbV2AB5u4ylV1bdC6/Oh0cj4D9T4cObYOsboM3Gvrs+7PRQ4yIC2XmklEaZV2QXpVX1FJTXOnw+ZUvTYoKYEh3Ef384QG2D9FaK/qmuwcyenHLGtTOfssn0mGA2HSqmvtHce4EJIYToFR0mlVprP621v+XepLX2avG9f0fPE+0rrKijqq6RoUFerR+oKYXyI7Yp0tNWQATM/ROkfg27Pzq2fe+nULAHZtuhl7LJ7N+CcrF5byXAPy8ex7e/PZUFYwbb/NjNSrJg1T3wyulGAvnfGfDRLcawXmUyhvie/wLc+jM8kA2/+troGc5YbyTtHUiMCKCqrpEDBbJemz2kFZQDENfO8DtHUUpxx7w4cstqOl0WRwhntje3jLpG83GVX1uaERtCZV0jOw7LaA0hhOhvrKn+utqabaJzmcWVAEQFt+mpLEw17u2RVAJMuREGJ8EX9xkJrNbw4z8gOA7GXGCfNgH8h8DEa43eveJDNj20u6up3Tk7NvXxrbDlDTC5waTrYPFLcNsmeCALrv/S6AVOuhzCRhq9wQDRs6CmxJjL2oGmqojJMq/SLpqWE+lLPZUAs+JCGD80kP98f0B6aUS/1PQ3LbGdIj1NpsUEATKvUggh+qPOhr96KqWCgRCl1AClVJDlFg0M6bUI+4kO16i0deXXtkwucO6/obIAVv8d9n0BeTuMeY8mO6/nOOs34OJml95Kuzq0Bg79BKf/H1z/BSx4FMZdaiT+nb1nUTON+/S1He4SE+KDr4erzKu0k7T8CtxdTUS2N3fZgZRS3Dk3nsMl1Xy49bCjwxHC5pKzSwnxdSc80KvDfYJ9PRgxyE/mVQohRD/UWU/lTcBmYASwFdhiuX0MdFBSVHQko6gKpSBiQJt/uAV7wcUdBkTbr/Eh42HKTfDLK/DlfUZbYy+2X3tN/AfDxOsgeQUUHbB/e7agNXz/CPgOgknXd+25gZEQOLTTpNJkUowJ9yclu6RncYp2peVXEBPi026hEEebkxDK2PAAXvjJSX4XhOiClOwSEiMCT1h1eUZsCJvTj8r8YiGE6Gc6m1P5tNZ6GPB7rfWwFrdxWuvnejHGfiGzqIrB/p54uLbp6SrcbwxFdXG1bwBz/wh+g41lPmb91uhB7A2z7nau3sqD30PmeqMn163jK+4dip59wnmV4yIC2ZNTTl2DDIO0tdT8ij439LWJUoqLJ0VwsKCSjKJKR4cjhM1U1DaQml/R6dDXJtNjg6ltMLMts8T+gQkhhOg1nQ1/vUApdQFwuOnrlrdejLFfyCyuan9IXsE+CBlu/wA8/ODClyHpShh3uf3ba+I3CCb9ClLe7vu9lVrDdw+DfwRMvKZ7x4iaCdXFRg90BxIjAqlrNLMvt7ybgYr2VNc1crikus8mlWDMrQRYkyrD/0T/sfNwKVrTaZGeJlOGBWFSsF7mVQohRL/S2fDXczu5nWP/0PqXjOIqotrOp6yvNpbcCB3RO0FEz4LznwdX995pr8msu8HFA358onfb7arUb+DwZmPtTleP7h0j2jKvMmNdh7s0Xc1PliGwNnWgoAKtIT7Mz9GhdGhYiA9DAjxZK0ml6EeaivR0tpxIkwAvN8aGB7BB5lUKIUS/0uGYS631db0ZSH9WXddIQXktQ9v2VBalARpCe6Gn0pF8w2Dyr+Dn/xgJW0i8oyM6ntbw/cMQGAXjr+z+cQKjjJ7O9DUw5dft7hIxwIsgH3fLvMqo7rclWmlapqUv91QqpZgVH8KXO3NpNOs+OfdTiK5KyS5t/rtmjemxIby69iBVdQ14u9t56ocQQoheYdUihUqps5VS9yql/tx0s3dg/cmxyq9tlhOxd+XXvmTm3eDq2Xd7K/d+Djnb4dR7ezbfVCmjtzJjvZGotruLIjEiQCrA2lhafgUmBdEhfavya1uz4kMpq2mQYk2i30jOLrFq6GuT6bHB1DdqNqcftV9QQgghepU161S+AFwK3AEo4GKke6VLmpPKtj2VBftAmYxCPf2dbyhMvgF2roSC/Y6OpjWz2aj4GhQLiZf1/HjRs4wlXAo7fp2J4QHszyunqq6h5+0JAFLzKogK9jm+GFYfMzM2GECGwIp+oaiiluyj1YyzokhPk8nRA3A1KZlXKYQQ/Yg1PZUztNZXA0e11n8FpgOR9g2rf2mq9BjVNqks3Gcs7+Hm2ftBOcLMu8DVC3583NGRtLbnY8jfBXPut00VXivWq0yMCMSsYdeRsp63JwBIK+i7lV9bCvb1YPQQf9amSVIpnF/TiItEK+ZTNvF2d2X80ECZVymEEP2INUllteW+Sik1BKgHhtkvpP4nq7gKPw9XAr3bDKss2H9yDH1t4hNizDPc+T7kd1wdtVeZG+H7R41iSWMutM0xg2KM5Vs6Syojjav6MgTWNuobzaQXVjpFUgkwKz6ErZlHqayVnmrh3JKzSzApGBtufU8lGPMqdxwupaym3k6RCSGE6E3WJJWfKaUCgX8AW4F04G07xtTvZBRXMTTYu/Wi0I0NRqGe0JMoqQSYcSe4+/Sd3sqd7xs9xnPuB5ONhk0qZfRWZqzrcF5lmJ8ngwM8ZV6djWQUVdFg1sSFOkdSOTsulPpGzaZDxY4ORYgeSc4qIS7MFx+Pro3ymBEbjFnDpoPyOyCEEP3BCZNKrfXftdYlWuv3MeZSjtBaP2j/0AxKqQVKqX1KqTSl1P3tPH6eUipFKbVdKbVZKTWrt2KzVmZ7y4kcPQTm+pMvqfQJhik3wq4PIX+PY2NpbIAfHoOBY2DkebY9dvQsqMjrdG1OKdZjO2n5xpqfztJTOSl6AO6uJlmvUjg1rTUp2aVWLSXS1vihgXi4mmRepRBC9BPWFOrxVko9qJR6WWtdC4QppXplnUqllAvwPLAQGAVcrpQa1Wa31cA4rXUScD3wSm/EZq1Gsya7uJrI9or0wMmXVALMuAPcfY2EzpFS3oHiAzDnATBZVQjZetGWaxsZHQ+BHTMkgEOFlTIE0gbS8o3lRGKdJKn0dHNhSnQQa9MKHB2KEN12uKSaoso6ErtQ+bWJh6sLk6IHsF7mVQohRL9gzZn060AtRoEegGzgIbtF1NoUIE1rfVBrXYcx7LZVl5LWukLr5jGGPkD74w0dJK+shrpGM1FBbZcTscwpDOnna1S2xzsIpt4Euz+CvF2OiaGx3hiCOzgJRpxt++MHx4FPWKfzKuMH+gHH1lcU3ZeWX8GQAE98uzgEz5FmxYewP6+CvLIaR4ciRLckZxkjLbpS+bWlGbEh7M0tp6ii1pZhCSGEcABrkspYrfUTGAV60FpXYywt0hvCgawW32dbtrWilFqslNoLfI7RW9kupdSNliGymwsKeqeHIKOog+VECveDfzh4+PVKHH3O9NvAw99xvZXblkFJBpz2R2MOpK01rVeZ3vG8yuEDjV61/XmSVPZUWkGF0/RSNpkVFwLAOqkCK5xUSnYJ7i4mRgzy79bzp1uW19koc4uFEMLpWZNU1imlvLD0ACqlYjF6LntDe2f7x52ha60/1FqPAM4H/t7RwbTWL2mtJ2mtJ4WGhtouyk5kWdaoPG5OZcHek3PoaxPvIJh6M+z5BHJ39G7bDbXw0z8hfBLEn2G/dqJmQvkRY/5sO4YGeePuYiLVMh9QdI/ZrDmQ7zyVX5uMGuxPsI+7rFcpnNb2rBJGDvHH3bV70wcSwwPw9XCVIbBCCNEPWDNW7P+AL4FIpdRyYCZwrT2DaiGb1mtiRgBHOtpZa/2TUipWKRWite4T/6UyiitxNSkGB7RYi9JshsJUmDDDcYH1BdNvhY0vGr2Vly3vvXa3/g/KsuG8Z+3TS9kkerZxn77OWGakDVcXEzGhPqRJT2WPHC6pprq+sedJZWMDlGZCQ51RRKuxzhgm3VjX5usW25QLjLnAqGjcRSaTYkZcCGvTCtFat64OLUQf12jW7DxcyoUTI7p9DFcXE5OjB0ixHiGE6AdOmFRqrb9RSm0FpmH0HN7ViwnbL0C8UmoYcBi4DLii5Q5KqTjggNZaK6UmAO5An/kPlVFURfgAL1xdWlzJLcuG+ioIPQnnU7bkNQCm3QI/PgY5yTB4nP3brK82eimHzoCY0+zbVmgCeIcYS4tMuKrdXeIH+rE966h94+jn0ixzUuPDejCUPPUb+PIBKErt+nMz1sHiF7rV7Oy4ED5NPsL+vAoSBp2kQ+GFUzpYUEFlXWO3Kr+2NCM2hO/37SG3tIZBLS++CiGEcCrWVrXwBI5a9h+llEJr/ZP9wjJorRuUUrcDXwEuwGta611KqZstj78AXAhcrZSqB6qBS1sU7nG4rOKq4+dTNld+HdH7AfU1026BTS/CJ3fAr74BVw/7trf5NajIhYtetW8vJVjWq5zRebGeMF8+SzlCVV0D3u7OU2SmLzlgqfzarZ7KwlT46g+Q+jUExcLZT4JXILi4W25ux742uR6//ef/wIbnYNL1EDmly83PjDfmVa5JLZCkUjiV7VklAIyL7F6RniZN8yo3HCxk8fju93oKIYRwrBOexSqlHgcuBXYBZstmDdg9qQTQWq8CVrXZ9kKLrx8HHu+NWLojo7iKs8cObr2xKakMOYnnVDbxCoTznoe3r4CvH4SznrBfW3WVsPYpGHbqsSU/7C16ljFv9GgGDIg67uHhA33RGg7kVzK2mxUUT3Zp+RUE+bgT5ONu/ZOqS+DHJ4wLGm7eMP8hmHITuHbhGGAsR7PzfVh1D/z6+y4vTRMe6EVMiA9r0wq5YfbxQ6SF6KtSskvx9XAlJqSDizlVxfDpXbDgUQjoOFkcNdifAC83NhwokqRSCCGcmDVnQOcDCVrrs7XW51pui+wcV79QWl1PSVX98UV6CveBdzD4BDsmsL5mxNkw7VbjBH/Pp/ZrZ9PLUFlgVHztLc3rVa5r9+E4y5BNKdbTfan5FcSFWtlLaW40equfnWD0MiYtgTu2GmundjWhBPDwhTP+DjnbYdubXX8+xtIiGw8WU9dgPvHOQvQRKdkljA0PwGTqYMTH/q+MC2rb3+r0OCaTYnpMsMyrFEIIJ2dNUnkQcLN3IP1RU+XXdoe/ytDX1k7/KwwZDx/fZvTq2VptOax7GuJOh6FTbX/8joSONOaOprefVEYFe+PmokjNl2I93aG1Ji2/griBViSVh9bAi6fCZ78xRgnc9CMsegZ8e1gJeuxFMHQ6rP6r0QPaRbPiQqiub2RrpsytFc6htqGR3TllJHY29LVp2P/ez094vOmxwWQfrW7+nymEEML5dJhUKqWeVUo9A1QB25VSLyqlnmm69V6IziuzOalsURlSayOpDDnJi/S05eoOF71uvD8rrzMqcNrSzy9AdTGc9gfbHvdETCZjaZH0Ne0+7OZiIibEl9Q86ansjsKKOkqr6zvvqTyaAe9eDW+cAzUlxufsulW2KwylFCx8AqqPdmvd1WmxwbiYlCwtIpzG3pxy6ht150V60teAMhm9+KWHOz3eDMu8SllaRAghnFdnPZWbgS3AJxhrP663fN90EyeQUWRJKlsOf60sME5sT+Y1KjsSNAwWPQuHtxi9PraStcnopUw4C8In2u641oqaCSUZUJrd7sNxA32lp7Kb0jor0lNbAav/Ds9NNqq7nvZHuP0XYwkQWxdpGpwIE6+FTS9B3u4uPdXf042kyEDWpMkJtXAOydklAIyLDGx/h5JM42/ehGuM7/d/0enx4sJ8CfH1YIMMgRVCCKfVYVKptX6js1tvBumsMourCPZxx9ejRT2kgr3GvSSV7Rt9Pky+waioue/Lnh8v9Rt4Y5ExxHGhg+o5Rc807jsYAhsf5ktmcRU19Y29GFT/kGaZi3pcUllfDS/OhjX/hFHnwe2b4dR7wc3LfsHMfRA8/ODL+4we9y6YFRfCjuwSSqvq7RScELaTnFVKiK87QzpaAqTpb92k642qyntXtb+fhVKKGbHGvMo+VLxdCCFEF3StVKHoksziSiI7Wk5EKr92bP7DMGgsfHRzh717Vkl+B1ZcZqwHev3XEDjUdjF2xcAx4BkAGe0vLTJ8oB9aH+t1E9ZLy6/Ax92FwW1PbnN3QPFBWPQcXPgyBITbPxjvIJj7Jzj0k1GgpAtmxYdg1jL8TziHlOwSxkUEojrq8U9fC56Bxt++hIXG70RNWafHnBEbTH55LQcKKm0fsBBCCLuTpNKOMour2qn8uh/c/cB/iGOCcgZunnDxG9BYDyt/Zdx31Ybn4cMbjXUir/ms58VYesLkAkNndNpTCZJUdkdaQQVxYb7Hn9zmJBv3MXN6N6CJ1xkn0l/9EeqsLzqSFBmIr4cra2UIrOjjKmobSCuoILGz+ZQZa43K1yaTUd3bXA8HVnd63Ob1KuXCihBCOCVJKu2kvtHMkZKadiq/7jV6zmw9p6u/CY6Fc5+GrJ/h+4etf57W8O1fjAXtR50HS1aCp7/dwrRa9EwoPgBlOcc9FBXsg6tJsV+K9XRZWn4Fse3Np8xJNqrudrI+nl24uBrDrEuzjHm8VnJzMTEtJkiSStHn7cguRWs6rvxakgVH048tpxQ51VhC6wRDYIcGeRMe6MWP+wtsG7AQQohe0Vn110+VUp90dOvNIJ3RkZJqGs26naRyvwx9tdbYi4xCD2ufgrRvT7x/YwN8crux/8TrjCqfrh72j9MaUZZ5le2sV+nuamJYiI8U6+mispp68spq2y/Sk5tiVHd1xMWb6Fkw+gJY9+8uLY8zKy6EjKIqWVZB9GkpTUV6OuqpbPob15RUmlxg+AJI/arTUSdKKc4fP4Rv9+TzecrxF9+EEEL0bZ31VP4T+FcnN9GJ5sqvLZPK6hKoyJUiPV2x8HEIGwUf3NRuL1+z+mpj2Yhty+DU++Ccp4yTmb5iUCJ4+B9bu62N+IG+Mvy1i5rer/gwv9YPNNRB/h7jPXeU+X83llP4+k9WP2VWvDFEe40sLSL6sOTsEiKDvAjycW9/h/Q1xnzKsNHHtiUshJpSyFjf6bHvmjecpMhA7ns/hfRCmVsphBDOpLPqrz92duvNIJ1RhqW3ISq4xRqVhfuNe0kqrefmBRcvhfoqeP8GMLdTIbW6BN68APatgoX/MNai7GvDi11cYei0dnsqwUiMMooqpQJsF3S4nEjBXmiss906lN0REAGzf2sU7Dn4g1VPiQ31YXCAJ2vTZPif6LuSs0o7n0+ZvtYYmWFqcXoROxdcPWFf50uLuLuaeO6K8biYFLcu3yp/D4UQwomccE6lUipeKbVSKbVbKXWw6dYbwTmzrOIqPFxNhPm1GH7ZXPl1uGOCclahCXD2k0bxhx/bLAtSngtLz4bsX+DCV2DqjY6J0RpRM40LC+V5xz0UP9AXs4aDUvnQagfyK3B3MRE5oM0yIbkpxr0jk0qA6XfAgGj44j6rik0ppZgZF8K6tCIazbKsguh7CitqOVxSTVJHSWXb+ZRN3H2Moln7Pj/hcjsRA7x58pJx7M4p42+fdW3NVyGEEI5jTaGe14H/Ag3AacD/gDftGVR/kFFkLCdiMrXoMSvYCy4exomm6JqkyyFpCfz4xLGen+KD8NqZUHwIlrxrzMHsy5pOtNrprWwawpmaL8V6rJWWX8GwEB9cXdr8GctJATcfY308R3LzhDMfNX7vf3nFqqfMjg+htLqeXUdK7RycEF3XNJ8yMaKDIj1t51O2lHAWlGRC3q4TtjNv5EBuOjWGtzZm8vH2w92MVgghRG+yJqn00lqvBpTWOkNr/Rdgrn3Dcn6ZxdVEtS3SU7gfQuL71lw/Z3LWP4xe3vd/DanfwqtnGmufXfupMbyqrxs8zkh22kkqh4X44GJSpObJvEprpeZXEDewg8qvg8a0Hn7nKAkLIXYefP8oVJx4WOvMuBBA5lWKvik5qxSTgjHhHSSVLdenbGv4AkAZ0xSs8Pv5CUyKGsADH+yQ+eZCCOEErDnrqlFKmYBUpdTtSqnFQJid43JqZrNu7qlspWCfDH3tCXcfY35lbTksv9Co7Hr9VxA+0dGRWcfFzZhX2c56le6uJqKDvaWn0ko19Y1kHa0iLrRNUmk2Q95Oxw99baIULHgM6ith9V9PuHuIrwcjB/uzVpJK0QclZ5cQH+aHj4dr+zu0N5+yid9AiJhkdVLp5mLi2SvG4+nmwm3Lt1JdJ/MrhRCiL7Mmqbwb8AbuBCYCVwJX2zEmp3e4pJqqukaGD2xRlbKuyhj6EzrCcYH1BwNHwfnPG70/139lrPnpTKJnQsEeqDw+aYgP85OeSisdLKhE63aK9BQfhLoKx1Z+bSt0OEy7xahMfHjLCXefHR/CloyjchIt+hStNSnZpR0PfS3NhqOH2h/62iThLDiyDcqOWNXm4AAvnrxkHPvzy/m/T3Z2I2ohhBC9xZqkMlprXaG1ztZaX6e1vhAYau/AnFnTIvYJg1oklUWpgHa+JKgvGnMhXPUBBIQ7OpKui+p4XuXwgb6kF1VS2yDJxImkFXRQ+TVnu3HfV3oqm5xyL/iEwqp7jd7UTsyKC6Gu0czGQ0W9FJwQJ5Z9tJriyjoSIwPb3yG9k/mUTUacbdxb2VsJMCchjNvmxPHu5mze35Jt9fOEEEL0LmuSyges3CYs9lmSyuEt53sVWJYTCZHlRE5qQ8aDq1e7Q2DjBvph1nBI1mc7obT8CkzKmIvaSm4KmNz63ogAT384469weDOkvN3prlOGBeHuapIhsKJPSbYU6emw8mv6GvAMgIGj238cjOkfQTGw1/qkEuDu0+OZFhPEnz7a2XzRVgghRN/SYVKplFqolHoWCFdKPdPithSjEqzowP7ccsIDvfDzdDu2sWAvKBcIdnBFSuFYru4wdGqHPZUA+2UI7Aml5ZczNMgbT7c2Ra9yUiBspPE+9zWJl0HEZPjuoU57Kz3dXJgUNYC1aZJUir5jS8ZR3F1MrUfgtNQ8n7KTQnRKGUNgD/1kFFmzkquLiWcuG4+Phyu3Lt9KZa2cggghRF/TWU/lEWAzUANsaXH7BDjT/qE5r315Fa17KQEK90HQMKO4jDi5Rc0yyupXFbfaPCzEB5OCNLkSf0Jp+RXHD33V2qj82teGvjYxmWDKTVB22FhXtROz4kPYm1tOfnlNLwUnRPt2Hi7lV0t/4fV16UyLDcbdtZ3ThtLDJ55P2WTE2WCuhwOruxRHmL8nT1+WxIGCCh78aCf6BOtdCiGE6F0dJpVa62St9RtAnNb6jRa3D7TWR3sxRqfS0GjmQH4Fw9tezS3YL0NfhSF6JqAhY32rzR6uLkQH+5Aq5fM71dBo5lBhJbFtk8qyw1Bd3HeTSoDhZ4KLO+z5pNPdZseFArA+TeZVCsfYn1fOLcu2cM6za/klvZjfzx/Of5ZMaH/nztanbCtiCngFdXkILBhL7tw9bzgfbDvMu5uzuvx8IYQQ9mPNnMopSqlvlFL7lVIHlVKHlFIH7R6Zk0ovqqSu0UxCy8qvjfVQfABCJakUGEuguHq2OwQ2fqCvzBk6gcziKuobNfFhbS7c5KQY932p8mtbnv4QM8dIKjvpaRk9xJ8B3m6yXqXodQcLKrjr7W2c+e+fWJNayJ3z4llz31xunxuPb4dLiTTNp2xnfcq2XFyNNStTvzL+N3bR7XPjmBUXwp8/3sWeHOuH0AohhLAva5LKV4EngVnAZGCS5V60Y1+u0cvUajmR4oNgbpCkUhhcPYy5delrj3soPsyP9KIq6ho6rxB6MmvqyT2+8msyoGCQFSe2jjRykbG8UG5Kh7uYTIoZcSGsTSuQYX6iV2QVV3HPe8mc8dRPfL0rj5tOiWXNvafx2zOGE+Dl1vmTrZlP2dKIs6CmFDI3dDlOF5Pi35clEeDlxm3Lt1Ih8yuFEKJPsCapLNVaf6G1ztdaFzXd7B6Zk9qXV45JtTnhLdhn3EtSKZpEz4LcHVBd0mpz/EBfGs1aKsB2Is2SVMaGtlP5NSQe3H3aeVYfknAWKBPs+bTT3WbHhZBXVtv8eoWwh5zSav744Q7m/usHPk4+wjXTo/np3tO4f+EIBvhYUfCq9LBx4dSaoa9NYueCi0e3hsAChPh68Ozl40kvquT+91MksRRCiD6gg7EsrXyvlPoH8AFQ27RRa73VblE5sf255USH+LSuStmUVIbIGpXCIsoyrzLzZ0hY0Ly5aUhnan55x1UWT3IH8isY5O/ZuroyGD2VQ6c7Jqiu8Ak2fv67P4G5f+pwt1nxIQCsSS0kfqB8FoRt5ZfX8N8fDrB8YyZaay6dHMntp8UzKMCzawdqGsYfNdP657j7GMPA930OCx41qsJ20dSYYH43P4F/fLWPz1JyCPF1Z2iQN9HBPgwNbn0/wNsN1Y02hBBCWM+apHKq5X5Si20amGv7cJzf/rx2koHCfRAQ2fd7UETviZhkFGxJX9MqqYwJNSrApsqyIh1KK2in8mtlkVGopy8X6Wlp1Hmw6vfGBacORjBEDPAmOtibdWmFXD9rWC8HKJyV1pry2gbyy2rIK6slt7SGvPIa8lt8nVdaQ165cY34ogkR3D43jsgg7+41mL4GPAJg0NiuPW/EWca8yrxd3R6yfuucWEYO9mNvbjmZRVWkF1Xy88EiPth2uNV+fh6urRLN+DBfEiMCiAnxxWSSZFMIIWzhhEml1vq03gikP6ipbyS9qJJzxw1p/UAnJ47iJOXmBeGTjivW4+nmQlSwD6n5UqynPVpr0vIruGRSZOsHcpON+8F9uEhPSyPONpLKPZ9A6D0d7jYrPoQPtx6mvtGMm4sxW0FrTWl1PXllteSV1ZBfbrm3JBF55TWE+Xnwl0WjGRzg1VuvqEsqWiQ9+ZaEp+VrKbDcRwX78OJVE7uf8PRRWmvqGzU1DY3U1pupqW+ktqGRGsvXNfXm1t+3+Lq2vpGahqb9Gqm1fF1SVd/8/lXVNR7Xpp+nKwP9PRnk78m02GDCA724cEIE0SE9vNiZvhaiZlg/n7LJ8IXA3bDvi24nlUop5o4YyNwRA1ttr6lvJPtoFemFVWQUV5FRVElGURW7jpTy1a5cGszGPGUfdxfGhAeQGBFAYkQgiREBDA3yll5NIYTohhMmlUqpgcAjwBCt9UKl1Chgutb6VbtH52TS8iswa1r3VJrNUJgK0bMdF5jom6JmwNonoa4K3I+dNMeF+UpPZQeOlBonzO0X6aFvV35tyX+IsbTCnk/hlE6SyrhQlv2cyfVLf6G6rtHoZSqrbbeQk78laQjz92BNaiEL/r2Gxy4Yy8Kxg+35Sk6opr6RL3bm8OG2I2QXV5FXVkNlO0mPp5uJgf6eDPTzZNQQf04ZHsoHW7O54L/ref3ayYwJD3BA9D23dN0h3tiQ0SoBrKlvxNyD+kseriY83VzwdLPcu7rg5+nKqCH+nJYQxqAAD+O9bL554O1uzcCkLio7YsynnPSrrj/Xb6AxYmPf53Bqx78D3eHp5kJcmB9xbStEY1n2q6CSlOwSdhwuJSW7lDc2ZFDXcAiAAC83xjYnmgGMjQhkSICnJJpCCHEC1vyXWQq8DvzR8v1+4B2MqrCihX25Ru9Sq8qvpZnQUA2hMp9StDFkPGgz5O2EyCnNm+PDfPl+b36r3ilhSOuw8msKBAwF7yAHRNVNI8+Fbx6EoxkwIKrdXWbGBZMw0I8jJdWE+XkycegAS+JoJApNSViYv0eredyHCiu5++1t3LJ8K5dOiuTP547Cp6PlIOwks6iK5ZsyePeXLI5W1RMV7M2Y8ADmJIQx0N+DMH+P5tjD/D3x83A97sR9ydShXPv6L1z64gb+c+VETh0e2quvoafMZs1/fzyAt7srs+JC8HAz4enq0ioh9HA14eFm2db0dXPS2Dpx9HAz4eFq6jsJTnoX1qdsT8JCWP03Izn1H3Li/W3A1cVEwiA/Egb5cbFlxENdg5n9eeWWJLOElOxSXvrpYHOPZrCPO8MH+jF8oC/xA/2ID/Nl+EA/6woZCSHEScKas4wQrfW7SqkHALTWDUqp4y8zC/bnlePuaiI6uMVQrYL9xn3oCMcEJfquIUnG/ZHtrZLK4QP9aDBr0gsrpUBLGx0mlbkpzjP0tUlTUrnnU5hxe7u7+Hm68dVvTunyoYeF+LDylhk89c1+/vvjATalF/P0ZUkkRgT2MOjONZo1P+zLZ9nPGfywvwCTUpwxciBXTotiRmxwl+evxQ/044NbZ3Dt679w/dJfeOyCsc2JgDNIzi4hr6yWJy8ZwQUTIhwdju11dz5lk4SzjaRy3yqYfINtY+sCd1cTY8IDGBMewOVThgJGD/uenDJ2HC5lR3Yp+/MrWLklu1Uve4ivhyXBlGRTCCGsSSorlVLBGMV5UEpNA0rtGpWT2pdXTlyoL64te5cK9hr3UvlVtOUfDt4hx4ZuWjQlTKn5FZJUtpGWX8EAbzeCW5601ZZDURqMvcRxgXVH0DDjZLyTpLIn3FxM3LtgBLPjQ/ntu9u54D/r+d38BG46JcbmxUkKK2p5d3MWy3/O5HBJNaF+HtwxN57Lp0T2eF7nQH9P3r1pGrcs28o9K1PIKa3hjrlxfae3rhNf7crD1aSY12bOX7/R3fmUTUITICjGmFfpwKSyPZ5uLowfOoDxQwc0b9Nac6S0htS8clLzKtifV05qfgXvbz3calmTEF93rps5jNtOi3NE6EII4RDWJJW/BT4BYpVS64BQ4CK7RuWk9ueWMzUmuPXGwn3gE+pcw/JE71DK6K3M2d5qc2yoL0oZPd9nOXg+XF+Tll9OXJhv64Qid6dx7yyVX1sauQi+fwTKc8FvkF2amB4bzBd3zeYPH+7g8S/38tP+Ap68dFyPkz2tNVsyjvLmzxms2pFDfaNmekwwfzx7JGeMGmjTodt+nm68du1k7v8ghSe/2U9OaTV/P29M6wt4fYzWmq935TItJpgAb7cTP8HZlB2B4gMw6bruH0MpY93WTS8ZF4c8+vZFNKUU4YFehAd6MSchrHm71pqc0hojycyr4P2t2Sz7OUOSSiHEScWa6q9blVKnAgmAAvZprevtHpmTKaup50hpTev5lGCp/CpDX0UHBo+DA99DfbVRERbwcndhaJA3qbLo/XHS8itYMKZN8pWbYtw72/BXsCSVD8Pez+zaUxPo7c7zV0zgvc3Z/OXTXSz49xoev3AsC8Z07aJFaXU9KdklbM8s4fMdOezNLcfPw5UlU6O4ctrQdguj2Iq7q4l/XTyOIQFePPd9GnlltTx3xXj7FKCxgbT8Cg4WVnLdzGhHh2IfPZ1P2SThLNjwHKR9C6MX9zwuB1BKMSTQiyGWZNNkUvz9s93kl9UQ5t/FdT+FEMJJWVP91QU4C4i27D9fKYXW+kk7x+ZUUvOMIj0Jg1rM9dLamFM5Vjp2RQcGJ4FuhLzdEDGxeXN8mC9pUgG2laKKWo5W1RMb2k7lV59Q8HPCXt3QBAiOh92f2H34n1KKSyZHMnlYEHe9vY2bl23lsslGEZ/2ErOGRjN7c8vZlmUkkduzjnKgoLL58bHhATx2wVgWJQ3ptcROKcXvz0xgcKAnD360k8te+plXr5lMqJ9Hr7TfFV/tygVg/mj79EA7XPoa8PDvecXlyKngFQR7VzltUtnWuAijUnFydilnjJKkUghxcrDmTOBToAbYARxfx14AsLe9yq8VeVBbKmtUio41FevJ2dYqqYwL8+PH/QVSAbaFpiI9x80zzUkxTmydYI7dcZSCUYtg7b+hqrhXhskPC/Fh5c0zeOrb/bzw4wE2HSrm6cvGE+Tr3pw8bs8ylluoqTf+5Af5uDM+MpDzk8JJGhpIYkQgAV6OG9K5ZGoUA/08uX3FVi7873qWXjeZmLYXGxzsq115jB8ayMD+2lOVsa5n8ymbuLjC8AXG0iKN9eDi/EOFRw8JwMWkSM4q4YxR/XQ+rRBCtGFNUhmhtXbCcWW9a39uOT7uLoQHtpin1FSkR5JK0ZGASOMq/ZHtrTYPH+hLfaMmo6jq+EqnJ6nU9iq/NtRCwR6IP91BUdnAyHNhzb+MYiXjl/RKk+6uJu5bMILZ8SH89p1kzn1u7bHHXEyMDvfn8ilDSYoMZHzkACKDvPpcYZzTRw1kxa+n8as3NnPhf9fz6rWTmdCiqIojHS6pZsfhUu5f2E+nPpTlGMWxJl5rm+MlLITktyBzAwzrerXjvsbL3YWEgX4kZ5c4OhQhhOg11iSVXyil5mutv7Z7NE5sX145wwf5tT7xalpOJESSStEBpYx5lW2K9cRb5qY1FaYRRk+lt7sLQwJa9Pzk7wZzg3MW6WkyOMlYY3PPJ72WVDaZERvCl3fP5vV16QzwdmP80AGMHOyPu6tz9I6PHzqAD26ZwTWvb+KKl3/mmcvG94nhpl9bhr6e2QdisYsMG82nbBI7F1w8jCGw/SCpBBgXGcCqHblorfvcBRkhhLAHa84cfgY+VEpVK6XKlFLlSqkyewfmTLTW7MstZ8SgNsPyCvcZc07sVNVR9BNDkiB/j9HrZmFUOIX9Mq+y2YGCCktl3BYnaDmWIj09ndflSEoZvZUHvjMqYPayQG93fnPGcK6dOYxxkYFOk1A2iQ7x4f1bZpAwyJ+bl21hc3qxo0Piq125DB/oy7AQH0eHYh+2mk/ZxMMXYuYYQ2C1ts0xHWxcRCCl1fVkFFU5OhQhhOgV1pw9/AuYDnhrrf211n5aa387x+VUCivqOFpV30Hl1wTnnOsles/gJKO3LW9X8yYvdxciBnhJBdgW0vIriG/ba5ubAu5+MGCYY4KylZHnQmMd7P/K0ZE4pRBfD5bfMBVXF1NzgRxHKaqoZdOh4v7bSwk9X5+yPSPOgpJMY/RBP5AYEQjg1ENg16cV8t3ePEeHIYRwEtYklanATq37yeVDO9jfVPm1vaRShr6KE2kautnOENimqsInu/KaenJKa4htm1TmJBtLiZicq3ftOJFTwXcg7PnU0ZE4LV8PV5IiA9l4yLE9lav35GPW/Xjoa9N8yqiZtj3u8AXG/d5Vtj2ugwwf6Iunm4nkrFJHh9ItWmvufmc71y/dzD3vJVNZ2+DokIQQfZw1Z2I5wA9KqQeUUr9tutk7MGeyr6nya8vhr1XFUJkvRXrEiQ2IBs9AI0FqIX6gLwcLKmlolKLLTUtZtJpfam40enedeehrE5MJRpwNqd8Ya5aKbpkWE8zOw6WU1ThuKeWvduUSHujF6CH9dECPredTNvEbBBGTYcvrUO7Y3mZbcHUxMWZIgNP2VO46UkZ+eS3TY4JZuTWbc59dy87DzpkgCyF6hzVJ5SFgNeAO+LW4CYt9ueUE+7gT4ttirbRCS5EeSSrFiTQV62lTATY+zI+6RjOZxTInJ629yq9FaVBfZfRU9gcjF0F9pTG3UnTLtGFBmDVsST/qkPYrahtYk1bI/NED+29xlvS1tp1P2dLCJ6C6BJZdBDXOn8CMiwxk5+FS6p3wwuB3e/NRCp65fDxv3TCNqrpGFv9nHa+sOYjZLAPXhBDHO2FSqbX+a3u33gjOWezLK29/PiVAyPDeD0g4nyFJxlyihrrmTU3zB6VYD6Rkl+DmoogK8j62saln15krv7YUPcvosZYhsN02fugA3F1M/HywyCHt/7ivgLoGc/8d+gpGUjl0urG+pK2FT4BL3zSWCXp7CdTX2L6NXpQYEUBtg7l5iowzWb03n8SIQEL9PJgeG8wXd83mtIQwHvp8D9cu/YWC8toTH0QIcVJx8olIjmc2a1Lzykk4rvLrfnD1gsChjglMOJfB44xCLS2KVDT1yqXlO98JiS0dKKjg7U1ZnJM4BFeXFn+ycpKNZQj6y4UbFzdIOAv2rWp1cUFYz8vdhXGRAfzsoHmVX+3KJdjHncnRQQ5p3+7Kc6Eo1fZDX1uKmwfn/9eoMPvBr41h7k4qKTIQwOnmVRaU15KSXcK8EWHN2wb4uPPiVRN56PwxbDxYxMKnf+LH/QUOjFII0ddIUtlDh0uqqaxrPD6pLNgLIXG2rY4n+q/BScZ9i3mVPh6uhAd6ndQ9lVpr/u/jXXi4mXjgrDYLyeemwMBRRjLWX4xaZAz7S1/j6EicVtO8yopeLixS12Dm+735nD5yIC6mfjz0FeybVAIkXgLzHzbWbv3iXqddZmRokDeB3m6kONm8yh/25aM1zG2RVAIopbhyWhSf3jGLYB8PrnltEw99tpvaBudN/EXvk7qf/VeH41eUUo9rre9TSl2stX6vN4NyJk3DWo4f/rofIqc4ICLhlIJiwCPAUgH2mubNwwf6ntTLinyWksPatEL+dt5owvw8jz2gtZGAj17suODsIeY0cPc1Tqbj5jk6Gqc0dVgwz36Xxub0YuYkhJ34CTay/kAh5bUNnDlmYK+12evsOZ+yrRm3Q0UerH/GqIx86r32b9PGlFIkRgSyPavE0aF0yff78hno79FhsanhA/34+PaZPLJqD6+sPcSGg0U8e/l4YkJ9291f9F9VdQ2sTS2kpLqeipoGymsaqKitp6K2gbKaBipqGqiobaC8xvJ4bQOVtQ3MGzmQpy5NwtfDDsPohcN09tM8Syn1J+ABQJLKDuxrTipb/DGtKobSTJh0nYOiEk5HKaPgTNtiPQP9WHegiEaz7r+9Hx0or6nn75/tZky4P0umRrV+sCTT6NHrD5VfW3LzhPj5sPdzOPtJGenQDROiAnFzUWw81LtJ5Ve78vBxd2FGbEivtdnr0tfC0Gn2mU/ZntP/ChX58P3D4BsGE6/tnXZtKCkigOd/KKSqrgFv975/Al3XYOan/YWckzi402JTnm4u/O28McyKC+He91M459m1/GXRaC6eGNF/i1SJZg2NZt7dnM2/v91Pfpv5tV5uLvh6uuLn6Yqfhyu+nq6E+vri6+mKr4crZq1ZvjGTS1/cwGvXTmagv2cHrQhn09lfuC+BQsBHKVUGKEA33Wut+2m99K7Zn1tOeKAXfp4thuAd2Wbch09wTFDCOQ0eB5tehsb65iGdcWG+1DUYFWCHhfg4OMDe9dQ3qRRU1PLS1ZOOT6hzU4z7pmHD/cnIc2HXB5C10VhgXnSJt7sriRGBvVqsp9Gs+WZ3HnNGhOHp1k8vBDTNp5xwVe+1aTLBec9BVRF89hvwCTWW3nEiiRGBNJo1u46UOcVc283pxVTUNhw39LUj80cPIjEikN+8s517V6awJrWQf1yU2H9/D05yWmu+3JnLP77ax8HCSiZFDeBfl4wjOtgHf083fDxcWtc+6MBpCWHc9tZWFj+/jtevm3L8FDLhlDr8yWut79FaBwCfa639tdZ+Le97McY+bW9ueeteSoAjW437/njCK+xnyHhorDXm41o0DatOdcLqgT2x60gpS9cf4oopQ5uLXbSSkwzKxZhT2d/EzzcKEO3+xNGROK2pw4LYkV3aawu2b8s8SmFFbf+u+mqv9SlPxMUNLnnD+Pu48nrI2NC77fdQYmQAAMlOMgR29d583F1MzIyzvsd9UIAny26Yyj1nJvBp8hGeWZ1qxwiFo2w4UMT5/1nPLcu34mJSvHz1JN67eTqz40OJDPImwNvNqoQS4LQRYbx703QazJqL/ruedWmFdo5e9AZrlhQ5Tyk1UCl1juUW2huBOYP6RjMHCyoZ3vYKy5HtEBQLXoGOCEs4q6aLEC2GwDZVgD2Z5lWazZoHP9rJAG937j1zRPs75aQYVV/dvHo3uN7g4WvMp9zzqdMWKHG0aTHBNJg1WzJ6Z73KL3fm4u5i4rSEfvrv8WgGbP0fuPvBIAcs4ePuA1e8BwERsOJSyNt94uf0EWF+ngwJ8CQ52zkqwH6/N59pscH4dHGum4tJcdtpcVw0MYIXfzrIriPO8XrFie3JKePa1zdx+cs/k19WwxMXJvLFXbM5Y1TP1uMdEx7Ah7fNZEigF9e8ton3NmfZMGrhCCdMKpVSFwObgIuBS4BNSqmL7B2YM8goqqSu0UxC2yI9h7fK0FfRdUExxklbzvbmTb4ergwJ8Dypeirf25LF1swSHjhrJAHeHVR2zUnuP+tTtmfkuVCWfWzUg+iSiVEDcDEpNh6y/xBYrTVf7c5lRlxw62kQzk5rOPSTsV7kM0lwaA3MuKP35lO25RMMV35gLNW17EIocZ4T0HGRgU7RU3mosJKDhZXM7cHFkT+dPZIB3u7c934KDY1mG0YneltWcRW/fWc7Zz2zhq0ZR3lg4Qi+//0cLpkcaXWP5ImEB3rx3i3TmRYTzD0rU3jym/1SHdaJWfOp+BMwWWt9jdb6amAK8KB9w3IO+3KN3qNWY8HLc6H8CAyRpFJ0kclkFOtpsawIGMV6TpaeyqOVdTz2xV6mRAdx4YTw9neqyIeKXOO96q+GLwCTq9FbKbrMx8OVxIgAfj5o//Uq9+SUk1Vc3X+GvtZVwZal8N8Z8Ma5kLEeZt4Nd6fAnPscG9uAKLjyfairhGUXGEXxnEBiRCCZxVUcrezb689+tzcfgLkjul/BONDbnb8uGs3Ow2W8uvaQrUITvai4so6/fbqbef/6kc935HDTKbGsuXcuN50aa5e5sv6ebrx27WQumhjBM6tT+d17ydQ1yAUJZ2RNUmnSWue3+L7Iyuf1e/vyyjEpiG1ZRvuwpWdBeipFdwxOgtyd0HhsLlh8mC9p+RU0mvv/1bsnvtpLWU0Dfzt/dMfDanIsRXr6W+XXlryDIHq2Ma9Srtp2y9RhwaRkl1BdZ9819L7alYtScPpIJ19K5GgGfP0gPDkSPr3LmLN83vPw291w+v8ZQ0/7gkFj4PK3jHjfusRIMPu4cU3zKvv4epXf7c0jPsyXocHePTrOWWMHccaogTz5zX7SC/v+z0cYzGbNyz8d5NQnvmfp+kMsHh/OD/fM4f6FIzoeNWQj7q4m/nFRIr89YzgfbD3Mta9vorS63q5tCtuzJjn8Uin1lVLqWqXUtcDnwCr7huUc9ueWEx3i0/rKzZGtxj/j/nzCK+xn8DhoqIbCfc2bhg/0o7bBTPbRKgcGZn9bM4+yYlMW18+MZsSgTmqBNQ0PHjS2V+JymFGLoPgA5O9xdCROaVpMEPWNmq2Z9p1X+dWuXCZHBRHq52HXduyi7RDXDc9DzKlw3Rdw8xoYf2XfnLccPQsufAWyN8O71xi9qSWZRuXsPmhseABKQUofnldZXlPPpkPFVld97YxSir+fNwZ3FxMPfLBDhjM6gdLqem58czMPr9rDlGFBfHX3KTx+USKDA3rv918pxZ3z4nnyknH8kl7MxS+s7/fnPf3NCSdHaK3vUUpdAMzCWE7kJa31h3aPzAnsyytnRNsiPYe3QthIcO/ZlT5xkhqSZNznJMPA0QDEWaoLp+ZVEBXcP5cVaWg086cPdzLI35O7Th/e+c65KTAguv8Xwko4Gz77rTEEtj9WubWzSdFBuJgUPx8s6lIly67ILKpib245fzp7pF2Ob1ONDVBdDJUFxq0wFTa/Bvm7wSvIGOI6+Vd9p0fyREYtgrP/BZ//FtK+MbYpE/gOMl5DQLhx7x9h+d5y8w421gXuRX6ebsSG+vbpeZVrUwupb9Q2SSrBqAj7wFkj+cOHO3jnlywumzLUJscVtrcnp4ybl23h8NFq/rpoNFdPj3LoWqMXTIhgkL8nNy3bwuL/rOf1ayczJjzAYfGA0YtbUl1PQXktBeW15JfXUFhRS6C3O6MG+xM/0BcPV1lGx6oZ91rrD4AP7ByLU6mpbyS9qJJF44Yc26i10VPpZOtoiT4kOA7cfIwKsElXAMcqwO7PL+f0UU4+xK4Db/6cwe6cMv6zZAK+J6o6mJNycowE8BsIQ6fDnk86n8vWnCwUGslCVSH4hMGw2b0Xa09oDTUllvgLjfgrC6CyyJhDN/bibiUBvh6ujAkPYKMd51V+tSsXwL7zKbWGxjpoqIEGy33z97XHvq4tt7xvhccSx5ZfVxVjLDXdwsCxxhDXMRf2zR7JE5n8K6NSctEBKM2GssPGfWm28Xdi7ypjmaaWXD1hwDDj4m/zbZRxocpkv5PCcRGB/Li/AK21Q0/YO/Ld3nz8PV2ZGDXAZse8bHIkH28/zMOr9nDaiDBZ5L4P+nBbNg98sIMALzfeuWkaE6P6xlqqM+JCeP+WGVz3+i9c8uIGnro0iXkjwmxWIKilmvpGDhZUklFUSUGFJWksq23+uqC8lsKKWho6mYLkalLEhvoyaog/owb7M3KwP6OG+BPk427zePsyB5Vxc35p+RVo3aZIT0kGVB+VIj2i+0wulmI925s3+Xu6MTjAk7S8/lmsJ7+shn99vZ9ThoeycMwJTs5rSuHoIWNY3slg5Lnw1QOw9t9G4tCcLBQdSyDbSxYA4s+EhY8ZVYUdrTAN9q0yTvqbk9+iY/fmTtaT3P0xnPcceHX9ZHfasCBeX5dOTX2jXQpMfLUrl1GD/YkM6mRkSsYG2P2RkQA21BpJTrtft0gc2+7TVZ4B4BNq3EKGQ9RMy/chx7b7DTI+G30wwemSAdHGrT1aG5+v0iwobUo4s6D4oHEBeFeLa+WunsZ7FTbqWKIZNgICIm3yHo2LDOD9rdkcKa0hPLBvJfBms+b7ffmcmmDbk3aTSfHYhYks+PdP/Pnjnbx41SSbHVv0TF2DmYc/380bGzKYOiyIZ68YT5hf30r6hw/048PbZvCrpZu56c0tuLkoIgd4Ex3iQ3SwD8NCjn09JNALF1PHv6daa4or6zhQUElafgUHCo7dso9WtypdYFIQ4utBqJ9xGzHIr/nrUD8Pwvw8CfXzINjXncLyWnbnlLEnp4zdR8rYcKCID7cdbj7WIH9PRg72Y9QQI9FMDA/s8ZzlvkySym7al2ss8TC85XIiUqRH2MLgccaacObG5ivncWG+/bYC7EOf76Gu0czfFnVSnKdJ7g7jvj8vJ9LSyHPhmz/Dt/9nfO8VdCwxCBsB3iEtkgXL194hkPYt/PAoPD8NZt4Fs37T+0PyS7KMk/YdK40hywAe/sbwQ59QCBxqLGjfFL93yLHX4R1i7PfLK/DtX+CF2XDhqzB0apdCmBoTxIs/HWRr5lFmxNp2CGxBeS1bMo9y97xOhmtrDZ/eCUfTwTMQXD3Axd1IYFzdwcXD+NozoMX2Nvu4erZ+zNXD8rw2j7n7gG+Y8d65nlxXxzuk1LHP1JDxxz9eW2HMX8/fawwDzt8D6Wsg5e1j+7j7QWgCBMcaSXhQjNHTGRRjFNSyMuEcFxEIQHJWSZ9LKnccLqWwoo65I2y/zuqwEB/uPn04j3+5ly925LBw7GCbtyG6Jre0hluXb2FrZgm/nj2M+xaMsEsPoC2E+Xnyzk3T+Dwlh4OFlaQXVnKosJINB4qorj9WhM3dxURkkBfDLElmVIgP1XUNHMivJM2SPJZUHZtz7elmIibEl6TIAVw0IZLYMON5gwI8GeDt3mmC2pK/pxsxob6ck3hs1GJxZV1zkrknp4zdOWWsSS1s7ulMjAjggvHhnDtuCMG+TjgXvxMnTCqVUucAq7TWUt+3hf155bi7mohuecXhyFbjH3zYaMcFJpzf4CSof8GY8xQ2AoD4MD9WbMrEbNaYrPxj5wzWpRXySfIR7poXT3SIFfNFT4bKry0FRsJvdhlfewdbv0Zg2AhjSOM3D8JPT0Dy27DgERhxjn17psrzjJ7FnSsha6OxLXwinPkIjDrfmOfWFTNuh6jp8N518PpCmPsnY+6fyboToEnRQZgU/Hyw2OZJ5Te789AazhzTyZD07M1QuB8WPQsTrrZp+8IGPHyNz2f4xNbbq0ugoCnR3AsFe4xiQCnv0mpUgIc/BA07lmQGDTuWePoNbvW7NmKwH24uiuTsEs7qY4nV6r35mBScOtw28ynb+vXsYXyWcoQHP97F9NhgAr3looejbDhQxB0rtlJV18jzV0zg7MS+9Vlsj7e7KxdPimy1TWtNXlkthworSS86lmymF1WyJrWQWsuSJCG+7sSG+nLW2MHEhvoSG+pDXJgvQwK87HYuFeTjzsy4kFZz+WsbGknNq+Dng0ZP5l8+3c1Dn+9hTkIoF0yIYO6IMLuMpult1pyhXAY8rZR6H3hday2lCDGK9MSF+ra+unN4m1GRUq4Si55oLtazvTmpHD7Ql+r6Rg6XVHc+1M6J1DY08uDHO4kK9uaWObHWPSkn2SjE4dc/55a2q7uv1X+wUSFz4rWw6h5450qInQcLn4CQONvFV1VsFBPa+b7Ry6PNxoW1uQ8aiW3QsJ4dP3yiUYn007tg9V+NNha/aPTKnYC/pxujhwSw8WBRz2Jox5e7cokK9iZhoF/HO21fBm7eMHqxzdsXduQVCEOnGbeW6muMKrPFB43b0UPGfW4K7P2s9TDuMRfBRa82f+vh6sKowf59sljP93vzGT90gN3mf7m6mHj8wkTOe34dD3++h39cfJKMNOlDtNa8suYQj325l6hgb96+cRpxYZ387erjlFIMCvBkUIAn02ODWz1mNmvyymvwcnPpMxcwPFxdGBMewJjwAG6YHcO+3HI+2JbNR9sO8+2efPw8XTkncQgXTAhnUtSAPjnv2hrWVH+9UinlD1wOvK6U0sDrwAqtdbm9A+yr9ueWMzWmxQfZ3GgkAeMud1hMop8IGQ6uXkaxnnGXARBvqQC7P6+83ySVr6w5xMGCSl6/brL1V+hyU4w5p8J60bPgpp+MoaTfPwL/nQ7Tb4dTfm8MmeyOykI48J2RSKatBnO90Tsz+/cw5gJjTpoteQbARa/DsFPhy/vhhVlwwUsQM+eET50WE8QbGzJsOq+yrKaeDQcKuW7msI7/+ddVwc4PYNR54OG8J2+iBTdPCB1u3NpqbDg2X3PjC7DvC2OJE5dj6/uNiwzkg62HaTRrq4fX2Vt+WQ07Dpdyz5kJdm1nTHgAN54Sw39/OMB5SeHMirdPRWZxvIraBu5dmcyqHbksHDOIJy5KxM/TvutOOpLJpHp1KZTuSBjkxwMLR3LvmSPYcKCID7YaCeaKTZkMDfLm/PHhXDA+3LoRXH2IVWOItNZlwPvA28BgYDGwVSl1hx1j67NKq+s5UlrTej5lYSrUVch8StFzJhejxzsnuXlT0xXF/jKvMqu4imdWp7JwzCBOS7ByyFV9NRTsO3mGvtqSixtMuwVu32z0Hq59Ep6bArs+gs7WkGuog9ydkPwOfP0neHMx/HM4/CMWPvi1Mcd12s1w4w9wx1aY+0fbJ5RNlIJJ18GvvzOSzP+dD989ZJzMd2LqsGDqGsxst2EP0fd786lv1Jw5upNe5L2fQW0ZJC2xWbuiD3NxNXrl4+YZP/P6SuPCYAuJEYFU1DZwsKDv/B3/fl8+gM2WEunMXfPiGRbiwwMfplBV1/nvrbCNtPxyzntuLV/uzOUPZ43gP0sm9OuE0tm4mBSz4kN48tIkNv/pdJ68ZBxDg7x59rtU5vzzBy74zzo+3m5ciHIG1sypXARcB8QCbwJTtNb5SilvYA/wrD0DVEotAJ4GXIBXtNaPtXl8CdBUb78CuEVrnYwdpeYZHbQJg3yPbTyyzbhvrxiAEF01JAm2vwVmM5hMBHi5MdDfg9R+UAFWa81fPtmFi0nx4DldWH8xbzfoxpOnSI89+A2ExS/AhGuMIbHvXWP09i38h9GblrcL8nZa7ncZRUyahvS5uEPoCGMI7aAxED4JIiZbPb/RZgaONpLYVffCT/+A9HXGMN8O5mtOHhaEUrDxYDHTYoLb3aervtqVS6ifB+MjO6lIu20ZBEYZlVfFyaXpZ56+BiInN29OijTW2kvOLiW+s2HTvWj1nnyGBHgev+a2HXi6ufDYBWO59KWfefLr/fypK3//RZdtyzzKla9sxNPNhWU3TLX5vHJhWz4erlwwIYILJkSQU1rNx9uPsHJLNne9vZ3nvkvj7tOHs3DMoD5dV8OaOZUXAU9prX9quVFrXaWUut4+YRmUUi7A88AZQDbwi1LqE6317ha7HQJO1VofVUotBF4CulYisIv25bVT+fXIVmN9wZATLNwuhDUGJ8Gml6AorXmoVXyYH6n5zj3i3GzWPPjxTlbvzedPZ49kSFeqIOZarhXJ8Neei5puJGabXzN6+56f3Ppx/wgjeRt+pnE/cIyxhqq1hYLszd0Hzn8eYk6Fz35jDIc9/7+QsOC4XQO83Bg12J+fDxZxF/E9brqmvpEf9hWweHx4x//cj2bAoR9hzh96P+kWjucbCqEjIX0tzP5t8+aYEF98PVxJzirhookRDgzQUNvQyNq0QhaPD++1OVxTY4K5YupQXlt3iHPHDWFcZGCvtHuyqW1o5J6VKQR6u7Pylul9fjioaG1wgBc3nxrLjbNj+GJnLk99u5/b3trKiEF+/OaM4cwfNbBPzru05gwhp21CqZR6XGt9n9Z6tZ3iajIFSNNaH7S0+zZwHtCcVGqt17fY/2fA7n+p9+eW4+vh2ros+OGtRu+SHRdPFieRpt64nO3HksqBvrzzS5bTVoBtaDRz78oUPth2mJtPjeVXs7pYwCUnxRj2GBhlnwBPNi6uMPVGo4jMlteN93bgaOPWjTUhHSLxEmNd4JXXwYpLYdptcMbfjkt+p8UEs+znDGobGvFw7dnf6LWphVTVNXLm6E7WVE1eAShIkjn2J63oWcZokxbzKk0mxdjwAJKzSxwbm8XGg8VU1TUyb6T9h762dP/CEazek8d976fwye2zcHeVCy+29vx3aaTlV7D0usmSUDoxk0lxduJgFowZxKfJR3h6dSo3vbmFseEB/PaM4cxJCO1TyaU1v8lntLNtoa0D6UA4kNXi+2zLto78CviioweVUjcqpTYrpTYXFBR0O6h9eeUMH+h77AfZUGfMLZKhr8JWQkcYa8+1mFcZH+ZHVV0jR0qrHRhY99Q1mLnz7W18sO0wvztjOPctSOj6H8KcZGM+ZR/6A9ov+IbCqffC1JuME2FnSSibhMTBDd/ClBvh5+ch+a3jdpk6LIjaBjPJWaU9bu6rXbn4ebp2PJTWbIbty2HYKcZanOLkNGx2+/MqIwPYk1NGbUNj+8/rRf/P3lmGR3V0AfiduBIlWBI0eEJwKFKkaCleF6RQWmihpUZLS929/YC2lJa6ULQULe4eXBI0IUCQCHHZ+X7MJiwe2c0mu/M+zz67e2XumZ3Ze+fMHFlxMAFXJwfa1rqBWWRutgpCaGYquDnzdv9wDp65xDerj5i9fHvnwOkUpqw6wsBm1ehU2JgFmjKNo4Ogf9NqLHumIx8NjiApI5thM7YycOoG1kWfR94sNkIpckOlUgjxhBBiD1BfCLHb5HUM2F1K8l1v9HjdX04I0RmlVL54vf0AUspvpZQtpJQtKlYsXpJfKSWHzlyinqn/QcJ+yMvSQXo05sPRSZkcmgxI8iPA7opNJiM7D0M5cdzOzMlj1M/bWLjnDK/c2YCnuoYVXaHMy1X/M+1PqbkeTq4qVYpvqIq6eRWtCvwqS5ZaJDfPwH8HztK1ftCNV1dOrFNpJ5o+XKJraco5BX6VVxh6ERnsS06e5MBp67oySClZcTCBdnUCcXe5wer9r4Ph83CVe9bMg9ZuDStxZ0QVvloRQ0w5d+soS+TmGXhx1m583J159U7ts2prODk6cHeLEJaP78S7A8I5m5zJQ9M3c++3m9hkgdRZRZbvJvt+Q636vQdMMNl+SUp50aJSXSYOMM14GgzEX32QECIC+A7oJaW06K96PjWbxPSca/0pQZlhaTTmomqkirppDNZTN8gbIWDMbzsKDnF1csDN2RF3Z0fcnNVnN+Nnd+NnDxcn/D2d8fN0IcDTBT8PF/w9XfDzdMHfwwUfd2eLmdOmZeUy8qdtbDx6gXcHhPNA62Ku3Jw/DLmZWqnU3BghIKyHWiXMyQDnyyZfvh4u1K9cgU3HLvBUCfwqZ+84RWJ6zs2T1+/8FVx9oEGfYl9HYwN4BkJQQ6Nf5bMFmyOMPoS745KItKI/4ZFzaZy8mM7IjrWuf8DFo8ov2NUH/noEaneB3h9DQCFzCheC1+9qxLro87w4aw8zR7Utl24dZY0f1h9nd1wy/3ugKX4WyjuqsT4uTg480DqUQc2r8ceWWCavjOG+bzfRrk4Az3WvR9NQ61gc3UyplFLK40KIMVfvEEL4l5JiuRUIE0LUBE4B9wEPXCVLKDAbeFhKedjSAh06Y4z8eoVSuVOZjPnVsPTlNfZElSYqt2DiMQiojY+HMz8Mbcnx82lk5BjIzMkzeRnIyP+cayAzO48LadlkZOeRlpVLYnoOGTnXN2NyEGrQ7efhjL+nUjh7Nq7MXRFVcXIsvq9LckYOw2dsZefJRD69pwkDmpbA3TnfDFinE9HcjLo9Yes0NZAPu9Jzo3VNf/7YepLsXEOxfLiS03N4f/FBWlT3444GN0glkpmiVnWa3HeFUquxU2q0V1GATfwqq/q4EejlSlRsEo+0tZ5oKw6eBW6SSmTPLPX++Bo4tBhWvgNT2kC7cdB+PLiUPF9yRW9XXu3TkOdm7uKXzSd4pG2NEpdpz5y4kMYnyw6pVeCbTXxpbAZXJ0eG3FaDe1uG8MumE3y9+gj3fruJDRO6EOjlWury3Gqlsg+wHWVyajqFJIEbTG+ZDyllrhDiSWAJKqXI91LKfUKIx437vwYmAQHAFKNJXa6UsoWlZCqI/Gpq/npqp/Kn1L5eGnNSJVK9x+8smB3uVC8IipmjOiM7j8T0bC6mqVf+58S0bC6mZ5OYlsPFtGz2xaewZN9ZvvgvmjGd6zCgabUiK5cX07J55PvNHDpzickPNKNXSR5wUqqcf84eEFjy6J0aG6ZGe9VPDi++RqlsUyuAGRuOs+dUEs2r+xe56E+WHSIpPZs3+7W+8YrKvjmQmwFNHyqO9Bpbo0Z7FcU7fieEtAJACEFkiA+740ru31sSVhxMoH5l7ysDDuYjJez5S5nw+tVQuWgbDYBlr6o0Prv/VObm9UoeXmNQs2rMizrFB4sO0rVBpevLo7klUkomzNqDs4MDb/VrXKaCt2gsj5uzIyM61CIyxJfBX29kx4lEut8smJyFuKFSKaXsY3wvYohG8yKlXAgsvGrb1yafRwAjSkuew2cuEeDpcnkGIDtd+XrVfaa0RNDYC0ENVG7A01EQPrjExbm7OOLu4n7LNB4Gg2Tp/rN8uTya5//ezVcrYhjTuTYDmwXjXAjlMiFF2fifuJDOtw+3oHNJk2pH/aqUyi6v6OjKmpvj7Aa1OsPhJcpUz2Rg1aqmUiQ3Hb1YZKVy76lkftl0gofbVKdh1Qo3PjDqVwisB9WaF0t8jY1xRb7KVgWbI4J9WX4wgUuZOVZJRJ+ckcPW44mMupHp65ndyuWgzROXt3lXgoHfQrNH4N9n4ff7lGVArw9KZKUlhODdAeF0/2wNr8zZw/dDW2qFqBj8uTWWjUcv8N7AcCr7uFlbHI2VaFzNBycHQVRsUtlSKoUQN3UQlFLuuNl+W0VFfjVZpTyzRyVk10F6NObG0Vmld7gqeqClcXAQ9GxcmR6NKrH8QAJfrojmxVl7+HJ5DGM612Fw8+Abmg+eSsrgwWmbSLiUxQ/DWpY82fL5aFj4PNTooEyuNJpbUbcHHPpXTfZValSw2d/ThfqVvdl09AJjOtcpdHEGg2TSvL34ebgwvvtNzATOR0PsZpXSRA+KNXBDv8omIb5qMfBUslUS0q+NPkeeQd7E9HUmODhDw/7X7qvRHh5fB5umwqr3YXJrVbfbxqpJnWIQ4u/Bcz3q8daC/czfFU+/yJsF+ddczdmUTN5ZeIC2tQK4r2XIrU8wN+kXITMZstOMr9QbfDZ+z0lXPrpN7tf3SjPj5uxIgyoViIpNssr1b2b++slN9kmgi5llKfMYDJLos5e4u4XJn1YH6dFYkiqRsHe2Mkcq5ZuvEII7Glaia4MgVh06xxfLo3l5zh7+tyKaJzrV5p6WIVfk/Dt+Po0Hv9tMSmYOPz/amubVS+gonpul8g86uakZcr1KqSkMYd3V++HFVyiVoPwqZ26PIyfPUKhVd4BZO+LYcTKJjwZH4ON+k1WlqF9BOELEfcWVXGOLXMevMqKaD6AieVtDqVxxIAFfD+frB/Mw5Cl/yjp3gMcNVvQdnaHdWGg8CJa8rPwtd/0OvT9S5xWDobfVYP6ueN74Zz8dwirir4PMFAopJa/M3Ut2roH3BoaX3iqvwQDRS2DDV3BifSFOEODiBS6eaiyzZ6ayKLnrC3D3tbS0dkVkiC9zdp4izyBxLOXgVzczf+1cmoKUB04lZZCWnXdlOpFTO8C7ClTQTtEaC1A1UiWmTzwG/hZ3Y74uQgg61w+iU72KrI0+zxfLo3l13j4mrzzC47fX4r5WocReTOfB7zaTa5D8PrINjY2DphKxbJKyBLj/T6hQteTlaeyDClXUZMzhJVesDoHyq/xx4wn2nEqmWSGi4yWn5/D+ooM0C/VlULObBJrKy4Wo35Ufp/cNgvho7JMaHa7xq/TzdKF6gAe7rLCakGeQrDp8jk51K15/wHliA1yKh+5v3bown2pwz48QsxwWvQC/DIIGfaH/VHD1KpJcjg6CDwaF0+fLdby1YD+f3RtZpPPtlYV7zrBs/1le7l2fGoGelr9gToaaQNg4BS5Eg08IdH4FfIKVwujieVl5NP3s7H55YtxggA1fwoq34NR2GDQdQltbXnY7ITLEl583nSAmIfVKfaUUuJn5axcp5QohxMDr7ZdSzracWGWT/Mivda+O/Fq1qZUk0tg8BcF6oqymVOYjhKBj3Yp0CAtkw5ELfPFfNK//s5/Jq46Qa1z5+fOxNoRVMsNN7NAi2Pw1tH4C6vUseXka+6JuT1j9AaRdAM+Ags2X/SovFEqp/HTZIRLTs/lxeKubpzs4sgJSz0DkgyUWXWNj5PtVHltzjV/l9uOllZ3tMlGxSVxMy6bLjSIY75kJzp5Qr3fhC63TFZ7YoBSFle/CfCcY/H2RrWvqV67A6E61+XJFDP0iq6rAdJobkpSezWvz9xJezYfh7Swc/iT1nIqsvfU7SL+gxr2DpisTacebGT1eBwcHaP+0mnCZNRx+6AWdXoIO47VFkhmIDPUFICo2sdSVypvZ/9xufL/rOi+7TMBVEPnVmISezGQ1U6NNXzWWIqiB8m05HWVtSQoQQtCuTiB/Pd6WPx5rQ1iQF34eLvw1qq15FMqUeJg7GiqHQ7c3Sl6exv6o2wOQELPsis0BXq6EBXmx+eitB/P74pP5edMJHmpT/dYr71G/gEeAUmY1GlM8AyCokfKrNKFJsA/xyZkkXMosVXFWHkzA0UFwe1jFa3fmZsH+uSrHalFThji5QsfnocursG+2Wp0tBmO61KFOkBcT5+wlNSu3WGXYC28tOEBSeg4fDIooUfqvm3LuEMx/Cj5rBKs/hJDWMHQhjFypAggWVaE0Jbg5jFqrIguvfBt+7AvJp8wnu51SM8CTCm5OVvGrvJn562vG92GlJ07Z5vDZS1Tzdb8crS0/gEo1vVKpsRBOrlCp4eU8jWWMNrUCaFMr4NYHFhZDHsx+TA1uBv+g6q/RFJUqkeBVSflVNrnSx7FNrQBm74gjN89ww4GYCs6zDz8PF57tdoscPukX1cp6i0fBSfuBaa5Djfaw82fIzS7oI5EhvgDsjk3mjoalF61z+cEEmlf3w8fjOv7BMf+pyfLwe4p/gXZPQ+wW5WtZtekVq7OFwdXJkQ8GhTP46418vOQQr/dtdOuT7JDVh88xa0ccT3Wpc/OI1MVBSrWyvvF/EL1UxTWIfADajjF/Wi+3CjDoO7Xa/e9z8HU76DcZ6t9p3uvYEQ4OgshQP3aeTCr9a9/qACFEgBDiSyHEDiHEdiHEF0IIM44iyw+Hzly6vEoJdhGk51TqKdJz0q0thn1TJVJNYEhpbUksz7pPVfj93h/pnJSa4uPgoAL2xKxQAVJMaF3Ln7TsPPbGp9zw9Nk7T7H9RCIv9qx//cG3KXtmQl42NNWmr5obUKO9ingZv7NgU6OqPjg6CHbFJZWaGKeTMzhwOoWuN4v66hEItW6//v7C4OAAA6YqH7u/hiizySLSvLo/j7Spzo8bj7P9RGLxZbFR0rJyeXn2HmpX9OTJLoWPZH1LpIR9c+GbjvBTXxUzpNPL8Mw+uOtzyz2ThVBK66g14BsKfzygor7nlO4qvi0RGeLL4bOXSCvl1f7CrJf/AZwDBgGDjZ//tKRQZZGcPANHz6VR9+ogPX41bhwhrZyzNm4tfeb0YcC8AWyI32BtceyXqpGQmQRJJ6wtiWU5uRlWvgeNB6sHjEZTEur2hKxkOLnpis2ta6o50c1HL1z3tOSMHN5fdICmob4Mbn6T4Dz57PwFKkcoc22N5nqY5qs04u7iSN1K3uyKSy41MVYcTAC4fiqRzBS14t5oQEGU2mLj7gf3/AwZF2HWo8oCpYg837M+VSq48eKs3WTlFv18W+ajJYeIT87gg0ERV0RgLxEp8Sr36MwhkJsJd32plMlOL6rUOKVBYB14dBm0fVKZT0/rAgkHS+faNkbTEF8MxrRFpUlhlEp/KeVbUspjxtfbgK+F5SpznLiQRnaegfqVrw7SY5urlFvPbOWZVc9Q26c2Lo4ujFo2iknrJ5GSfePZfY2FqNJEvZdRE1izkJGoBh++IdDnM527SlNyanUCRxdlAmtCRW9Xalf0ZNMNlMrPlh3mQlo2b/VrfPPgPKCiE5/ZDU0fMpPQGpvkJn6Vu2KTkKVkhbLyYAIh/u7UCbpOZNaD/yplIqIEpq+mVImA3h/DsdUqeE8R8XJ14p2B4cQkpDJ55RHzyGQDbD+RyI8bjzOkbQ1a1DDDgoaUsH2Gyjd6dDV0fwdGb4LmQ4qdd7REOLlCj3fgwb8h9Sx820nJV9qWWlJC1iVIPK4i1EYvg11/wMbJsPxN+Gcc/PkQzBsD6z6HgwvhfIyKBF4GaGI0ry9tE9jCeNiuFELcB/xl/D4Y+NdyIpVNDp1JBUwiv6aeg+RYaD2q1GUxSAMxSTEcuHCAZpWaEeJt3mS3UQlRjFk+hmCvYKZ1n4aHswdTo6byw74fWH9qPa+2fZVOIZ3Mek3NTQhqBA5OygS2YT9rS2N+pIT5Y+HSaRi+VPlYaDQlxdVLRRc8vEQNUkxoUyuA+VHx1/hV7o9P4aeNx3mwdWjh0uLs/FUpruF3m1t6ja1xHb/KJiG+/LE1lhMX0i2eDiIzJ491Mee5t0XI9XMZ7pmpTA+DW5rvos0ehtjNsPZjVW4RI3l3rhdE/8iqTF0Vw53hVUo9kmVZIys3jxdn7aaqjzvP97iFr3dhuHgM/hmr/CdrdFA5IwNql7xccxDWTUUUnjNKKXAHF6r/kG8I+IQq82qvoOJNQEsJaecg8YRSGhOPQ9JxSIpVPvLpF9QrL+v65zs4qcBs7n5qQnznLyb7nFWk/sAw46uuegXUKdV8nP7GtEVRsaVrPn6zlCKXAAkIYDyQ/6s5AKnAaxaXrgxx6EwKDgJqVzTO8OX7RpRCOpH0nHT2nt/LzoSd7Dy3k90Ju7mUoyLRejt789HtH9GuWjuzXOvAhQOM/m80Fd0rMq37NPzcVNj9p5s/Tbfq3Xh1w6s8teIpetfszYRWEwr2ayyIs5uKAluGIsCale0z4MB8uOMNFQ1OozEXdXvCoufhwpErBkutawXw6+aT7D+dQkSwL6CSiE+atxdfDxee616IAVtuNuz+U6VesFEXCI0ZqdkBtnyjxg7GnHwRwWriYldcksWVyt82nyQzx3D9VCKpCXB0JbR/xvxWIr0/UlY2cx5TPnN+NYp0+qS7GrEm+jwvztrNrCduK/Vk7tbkUmYOZ1MyOZOcxenkDNZGnycmIZUZw1ri6VqCqKuGPGVeuvxNEI7KOqjZUOUPW5bwrgQPzVapatZ9BtFLrtzv6KqUS98QlS/TJ8Tkc7BaeU88fpXyaPx8dawQr8rqXN8QqNpEKY0eAcrHuOCzv3p387nyf5KRBBdi4PxhOB99+f3wEjCY+PR7BqmxXFg39dywsALfNMSXjTewyLEUN4v+at9TQldx6OwlagR64uZstF+P3wGIy6aJZiQhPYGdCTuJSohiZ8JODl08RK5US+q1fWrTvUZ3mgY1pXqF6ry96W1GLx/N+ObjeaThI9efgSwkR5KOMGrZKLxcvPiu+3dU9Lgy5HijwEb8eeeffLfnO77d/S2bTm/i5dYv06NGjxLVV1MIqjRRM3VS2pZpaMIBWDwBanWG28ZaWxqNrVG3u1IqDy+BtqMLNrcx5qvcfPRigVI5e8cptp1I5INB4fh6FCKK6+FFymdMm75qCoOpX6VRqaxbyRs3Zwd2xSbTL7KaxS69/cRF3l14gK71g+hQ5zr+cfvmgDSULOrrjXB2h3t+gm9vhz8fVj5zRTCr9Pd0YVKfhjz9ZxQzNhzn0fYWzsdYSuTkGTh89hLxSZmcScnkbHImp5MzOZuSyenkDM6mZF03pcrQ22qULH/nuUMw70mI26KCmfX5TClgZZX8nJbtn1aRiZNilZVgUiwkn4TkOPU5eqkyl70Rzp5qQsOvpnKN8KuhXr7V1Qp9UVPomOLuC8Et1MuUvFylxJ4/fPl1aicsfUW9AutBvV5KwQxuYfYcnZEhvsyNiud0cgZVfNzNWvaNKNRUhxDCDwgDCu4EUso1lhKqLHL4bOqV/pSndkDFeuBqHt07IT2Bz7d/zo6EHZxKVXl6XB1daRzYmKGNh9I0qClNKjbBx/VKk6yfev3EK+tf4eNtHxOdGM2ktpNwcSx6WPuTKScZuXQkTg5OfNf9O6p4Vbnucc6OzjwR+QRdQrswacMknlv9HIuPLWZim4kEupeSM7c9UiVSmVgkx6mZNFsgJwP+Hq7+QwO+KXuzpJryj18NqFhf+VWaKJVBFdyoFaj8Kkd2rEVyRg7vLTpAZIgvdzcv5P9r56/gXQVqd7GM7BrbwsMfKjVWSmXH5wBwdnSgUVUfdlswAmzCpUxG/7qDan7ufHpv5PX9hPfMhErhEFTfMkL411T3+N/vU5M8fb8q0un9IqsyN+oUHy85RPeGlQjxL4ECYCWklBw5l8a66HOsi7nApqMXrlAaHR0EQd6uVPZxo24lbzrWrUjlCm5U9nEreK9Uwe3ywkZRycuB9V/A6g/AxVO1R8S95WuS2s0HKvtA5cbX35+TCSmnLiudzu6XlUePgNKvq6OTWo0MqK2Ux3wST6hn0qGFKm3L+s/Vimjdnuq42p1VG5WQyFBlSRh1Mokq4WVEqRRCjADGAcFAFNAG2AjYzZM0MyeP4xfS6NukqtogpVqprNPNbNf4Ztc3LDq+iE7Bnbi//v00DWpKA/8GON8iCpuHswcf3/4x3+z6him7pnA85Tifd/68SAre6dTTjFg6ghxDDjN6ziC0Qugtz6nnX49fe//Kj/t+ZErUFLbM3cKEVhPoU6tPiVZLNTcg38z6dJTtKJVLJkLCfnholjJz0WgsQd0eKrhCZsoV/rqta/mzYPdp8gyyIDjPD0Nb3To4D8ClMxCzDNqNM/vsssaGqdEetv94pV9lsC+/bTlx07ypxSU3z8BTv+0kOSOHH4a2wsf9OuOJi0chbit0e9Os176Ger2gw7Ow9hMIaV2kFX4hBO8MCKf7p6t5ec4efhreqlyMM86nZrE+5jzros+zLuY8p5NViozqAR70jaxK21oBhPp7UNnHjUAvV8uZ9p7erQLKnNmt4jL0/lj5I9oazm6XlbiyjF91FY+l9ShlOhvzn4q8fOAfiPpF5QWt1Un9Z+r2BO/KxbpMgyreuDg6sDM2iV7h118oMjeFWakcB7QENkkpOwsh6gNvWFasskVMQipSctlJPDlOOflWM0/kV4M0sDJ2JZ2CO/FZ58+KfL6DcOCJyCeo7VubV9a/wn0L7uPLLl/SMKDhLc89l36OEUtHkJqdyvQe06ntW/g/o5ODE4+GP0rn0M68tv41Xl73MouOLWJS20lU9izen0BzAyo1Ur4P8VHQ4C5rS1Ny9s+HbdPhtqegzh3WlkZjy9TtqWboj6yARv0LNrepFcDvW2KZs/MUP208zgOtQgkPLkRwHlBRAKUBIrXpq6YI1GgPm79Wk9KhbQBoEuLD9+sNHD6bavYk9h8uOcTmYxf57N4mNy57zyz13niQWa99XTpPVArsv8+qNDxVIgp9ajVfd17oWZ/X5u9j9o5TDCpMup9SJiM7j63HL7Iu5jxro89z4LSKlu/j7ky7OgE8VaciHcICS2+lNS8HVr2vVsLc/VWal4Z9S+famsLh7gvhg9UrLwdOrFcK5qGFlyOX1+wIrR9Xz7IiTGK6OjnSsGoFokoxAmxhpsUypZSZAEIIVynlQcAMYafKDwfPqKA4BUplQZAe8yiVe87v4VzGObqElmzxt3uN7vzU6ycchANDFg1h8bHFNz0+MTORkUtHci7jHFPumEKDgAbFum4tn1rM6DmDCa0msO3sNgbOH8i+C/uKVZbmBji7KzM+W0grcnS18umo2hS6TLK2NBpbJ7gVuPkqv0oT8vNVvjx7Dz7uzoWPpiglRP2qVlsCzZh4XGP7XCdfZROjT+8uM5vALtxzmm/XHOXhNtUZ0PQGCpiUsOcvJVdp+NU5OMKg71XUzL8eVqs0ReDhNtVpXt2Pt/7dT/TZS5aRsRgYDJKXZu+myZtLeeT7LcxYfxxf4z1l3ph27Hi1G1MebM4DrUNL13R30Qsq8m74PTBms1YoyzqOzmqFstcHMG63in7b6SUVaO6PB+DLSDVBmn6x0EU2DfVlz6lkcvMMFhPblMIolXFCCF9gLrBMCDEPiLekUGWNvaeS8XBxpHr+zSB+hwopXKmRWcpfcXIFTsKJjsEdS1xWff/6/H7n7zQIaMDza57nyx1fYpDXdqaU7BRGLRtFXGock7tOJjIoskTXdXRw5MEGD/L3XX/j7ezNY0sf4+BF205aK6UkMTORQxcPse7UOuYfmU9ipgXDN1eNVOavpZ2vyVxICRv+Bz/3hwpV4O4fC0zANBqL4eikou1FL70iCXtlHzdqBHiQnWfghZ71CxecByBumwq4EPmghQTW2CwFfpWX81VWD/DAx93ZrH6VMQmpPD9zF01DfXm1z00sls7sVn25NFPieFVU9/7kOJj7BBgKP9h1cBB8ODgCJwdB/8nrWbLvjAUFLTwfLDnI71tiGRBZjRnDWhL1Wjd+f6wNYzrXoUmIr3Ui1kb9Dtu+VwHwBkzVEarLG0IoHaPTBKVg3v2jimq7bBJ82lClYTt768WbyBBfMnLyOFRKkzC3NH+VUg4wfnxdCLES8AFuvgRmY+yMTSK8ms9lf4dTO1RjmyExrJSSFSdX0KJyi2uC8BSXAPcAvuv+He9sfodpe6YRkxTDex3ew9NZOf6m56Qz+r/RRCdF82XnL2lZ2Xx5qUIrhPJdj+8YvmQ4I5eOZHqP6dT1q2u28ksLKSXHUo6RkJ7AufRznE0/y7n0c5zLOFew7VzGOXJMw0UDd9a6k/c7vG8ZoapEqhWSlHjwsVykQIuQnQ7zn4K9fyvz3f5TzRbkSqO5JXV7qmAkp3ZAyOX73cBmwew8mci9LYrgpxz1Czi5Q6MBtz5Wo7maq/wqhRBEBPsQFZtsluJTs3J5/JftuDk7MuXBZrg43WTtYM9MlVevtPMfh7aG7m+ryN8bvlCpTApJ7Ype/PNUex7/eTujft7O2C51ePqOuoXzhbYAM7fF8s3qozzUJpS3+jUuG76eZ/bCgmegenvoalfZ/2wTRyflutGoP5zZA5u/Uemsdvyo8ou2ekxFkHW8VqVrGmIM1hObRKOq5tExbkZho782A9qj8laul1JmW1SqMkRWbh4H4lMY1q6G2mAwKL+2xgPNUv7R5KMcTznOgw3MO+vt4ujC621fp65fXT7c+iEPLXyIr7p8RaB7IE+teIq95/fy8e0f0yG4g1mvCxDiHcL07tMZtngYI5eO5Pse3xfJV9Pa5BhyeGH1C/x38r8rtns7e1PRoyIVPSrSvFJzKnpUJMgjiIru6n3B0QXMPDyTMU3GEFLBAsF08tPXbP9BmV47uSqHbic3k88uV353dLF+dLfE4/DHQ3B2L3R5VQVrsLZMGvuidhflkxy95AqlcmzXsKKVk5UKe2erQbibef3fNHbCdfwqI0N8mbLqCBnZebi7FD/wk5SSF//ezdFzqfzyaOubpxEw5Cl/yrBu1lnFav04xG5WuRKrNVd+Y4Wkio87f45qy6tz9/Llihj2xqfw2b2R1w9EZEG2HLvIy3P20L5OIK/d1ahsKJSZycq02M0HBn9/XUVDU46pHA79/qcCa+34CbZ+p9rbJwRaPgrNhlzxfw7xd8ff04WdJ5N4sHV1i4tXmOivk4C7gdnGTT8IIWZKKd+2qGRlhP3xKWTnGYgM8VUbLh6FrGSzBelZcXIFAJ1DOpulPFOEEDzY4EFq+tTkudXPcf+/91PbtzY7zu7g3Q7vckd1ywVICa0QyvQe0xm2ZBiPLnmU73t+Ty2fWha7nrnIMeTw/OrnWX5yOY83eZxWlVsVKI4ezjf3hajmVY050XOYvnc6r9/2uvmFqxwOLt6w5qOinefsoR4wbr7KKfyKz9fZ5u4HgXXNE9XyyEr4e5gKavLgTDWA0WhKGw9/NYA/vBi6vFL8cpZOhKxL0HKE+WTT2BfV2wHCmK9SKZVNQ33JM0jG/xXFy70bFNvvbvq6Y/y75zQTetXntuvlozTlxAa4FA/hVhrKCaFSi5zdp1JLjVqr3CIKiZuzIx8OjiAi2Ic3/tlP/8nr+fbh5oRVKh0LmJMX0hn18zZC/DyY/EAznM0cubdYSAlzR0PSSRiyQEdVt2U8/FXuzrZPqpzJm7+B/15XgZmaD4Me74KDA0IIIkN8iYpNKhWxCjOFcT/Q1CRYz/vADsAulMpdxoaIDPVVG+J3qHczBelZfnI54YHhVPK03J//tqq38fudv/PUiqfYfnY7r7V9jT61+ljsevnU8KmhFMvFwxixZAQ/9PyB6hUsP1NSXPJXKJefXM6EVhOKvHpc0aMiA8IGMCt6Fo83edz8EXBdPODp3SrBb26mMp/KzYTcLPWel//dZFtuNmSnqtnLzCQVGCHlFJzdr7Zl3cDkyicEmg+FZo8UL/S4lLDhK/jvNZXg975fy36Yb41tU7eH8kdJPlU88/ED/8D2GSqNSIj5XAY0dka+X+WxtdDxeQA61Q1iXNcwvllzhOUHEhhyW3We7ByGj0fhV942H73Ae4sO0qNRJUZ1LMQE7p6Z4OIFdXvd+lhL4eqtIpJO6wIzh8LQBSpYSSERQvBw2xrUq1yB0b9up//k9Xx6byQ9Glk2+nxKZg7Df9yKQcL0oS2L1E4WZf0XcHAB9HgPqre1tjSa0sDRSbkUNbhLjevWfAibp0KTewtS0UWG+LLyUAIpmTlUcLNsXy2MUnkccAMyjd9dgSOWEqisERWbRKUKrpfNSOJ3Kn+aiiVPEnwm7Qz7LuxjXLNxJS7rVlSvUJ3f7/ydEyknCpVqxFzU8qnF9O7TGb5kOMOXDGdGjxmWMQ0tITmGHF5c8yL/nfyPF1u+WGxz5OGNhzPr8Cxm7FPRcM2Oh795TZUMeVcqnJnJcOm0Spmw4i0169WwL7R4FKrfVjiz1ew0o//kLGUm2G8KuHqZT2aNpjjU7amUyugl0GJ40c5NiVd9ukokdC7BSqdGA0a/yhlq8s/JFQcHwTPd6nJ/q1A+XXaI79Yd469tcYztGsbDbarf3C8SOJuSyZjfdlLd34OP725yazPM3CzYPxfq91GTldYkqD70/RJmPapWWnq8U+QiWtX0LzU/y9w8A0/+tpPj59P4+dHW1AwseZJ6s3BsLSx/Axr2hzZPWFsajTWo1BB6fgD75sCxNQVKZdNQX6SE3bHJtA8rfA774nDDO5UQ4ishxJdAFrBPCDFDCPEDsBdItahUZYio2KSCkN+ACvRQJcIsdur5pq8lTSVSWDydPUtVocynjl8dpnWfRlZeFsOXDifuUlypy3Az8hXKZSeW8ULLF3ioYfFzz1X1qsqdte5k1uFZXMi4YEYpLYSDo1JS/Wspk+7anSHyARgyH57cBq1GqsS8M3rDlLawZZpKIn8jLh6D6d2V31nX11TEMq1QasoCgXXBr8Y1qUVuiSEPZj+mBuKDpuuIxZqSU6M95Gao8YQJlX3c+HBwE/59qgMRwT68tWA/3T5bzaI9p5E3iPqdk2dgzK87SMvK5euHm+NdmJWImP/UBGJpRn29GeGDVbCRjf+DfXOLVUS+n+XdzYP5ckUMI3/aRkpmzq1PLCJv/3uANYfP8Xb/xrStHWD28otFymnlZuJfW/nblQXfTo118K6kFr2OrSnYFGHUYaJiLZidwMjNpr+2AduBOcDLwEpgFTARWGRxycoAiWnZHL+Qftn0NS9X5Qk0k+nripMrqOlTs1z4GpaUev71mNZtGuk56YxYOoLTqaetLRKgFMoJayaw7MQynm/xPA83fLjEZY4IH0FWXhY/7//ZDBJakcAw6PkejD8Iff+ngv8sfA4+qQ//PK2ikJlyZAV82wmSY+HBv6HDeP1w05QdhFCrlUdXqWjEhWXDl8r/rdeHOi+lxjxUvw3lV7nuursbVq3AT8NbMWNYS9ycHHni1x0M/nojO05eOyh8d+EBtp1I5IPBEdQtrD/hnpngEahy4pUVur8D1VqoHMbnY4pVRL6f5Zv9GrH68Dn6/289MQnmS6Xw86YTzNhwnBHta3Jfq1CzlVsi8nKU6XB2Otz7i46qrlFBr05sVO5PgI+7M7UrepaKX+UNlUop5Y/5L+B3lIK5HfjNuM3myU9GXBCk59xBNbtohiA9yVnJbDu7jS4hpbNKWRZoENCAb7t/S0pWCsOXDOdMmnVzTOUacpmwZgJLTyzluRbP8UijR8xSbg2fGvSo0YM/Dv1B8o18FssTLh7Q7GEYtRpGrlCpFHb9Dl+3V6uSu/+CdZ/BL4OgQjV4bBWEWS4IlEZTbOr2UL7GJsnnb8qp7bDibWXG3bT4FgwazRUU5Ku8cT8UQtCpXhALx3Xg/YHhnLyYzsApGxjz2w5OXlCTIvN3xfPD+uMMa1eDvk2qFu7amSlwaJGKYF+WIoM6ucDdM5RP5V8PKzeKYiCE4JG2Nfh1RGtSMnPoP3mDWfJZro0+x+vz99G1fhAv9W5Q4vLMxrJJELtJmRAHldwtS2MD1OwIOWmXY8AAkSF+7DyZdEOLB3Nxy3BVQohOQDQwGZgCHBZCFD72czkmKjYJIS4vHZszSM/quNXkyTy6hnYtcVnliUYBjfim2zckZSXx6JJHSUhPsIocuYZcJqy9rFAOaTTErOWPCB9BWk4avx38zazlWp1qzaH/ZBh/QEUXSzsPs0cqX5iG/WDEMmVKq9GURaq3U8FJDhci1XJWKswaAV6V4a4v9Kq7xrzUaA+xW5RZ9U1wdBDc1yqUVc91YlzXMFYcSKDrp6uYOGcPL/69mxbV/Xi5KErOwX/VxEpZMX01xTcEBn0HCQdUnsUSDIBb1wpg/pPtqV3Rk1E/b2fCrN3siUsu1qA6JiGV0b/uICzIiy/ub4qjlXJiXsPe2bBpikrPEj7Y2tJoygr5EaZNTGAjQ325kJZNXGKGRS9dmBjInwDdpZS3Syk7Aj2AzywqVRkhKjaJukHeeLkaZ/NO7QBXH7MMmlecXEGQRxCNAhuVuKzyRnjFcKbeMZXzGed5dMmjnM84X6rXzzXk8vLal1lyfAnjm483u0IJyty3U3Anfj3wK+k5RTC1Ky94+EPbMcrv8uG5yuxm8A/gUkaCFmg018PJVZn8HV5y6wHroheVj/DAb1SaHY3GnNzAr/JGeLo68Uy3uqx6vhMDmwbz25aTeLk5MeXBIqaz2DMTfKtDcBmNYFynK3R6SSV33/Z9iYqq6qv8LIe0rc6cnae463/r6P3lOn5Yf4zEtMKlW09My+bRH7fi6uTAd0NaXB4PWptzh1TwsOBW0O0ta0ujKUt4+KvYLyZKZVOjxeVOC5vAFubf4SylPJT/RUp5WAhRRuInWw4pJbtik+jW0CTVR/xOqBoJDiXLR5SRm8H6U+vpV6cfDqIM5DayApFBkUy9YyqP//c4I5aM4Lse3xHobtmoVGBUKNe9zKLji3im+TMMazzMYtcaGTGSBxc+yF+H/mJo46EWu45VcXBQwX3KMbmGXKISolhzag1Hko6QZ8gjT+ZhkIYr3w2Ga7Y7CAeGNx7OXbXvsnY1NIWlbk8Vdv/sXpX79XrsnQ1Rv0CH59TgX6MxN6Z+lUVI/1CpghsfDI5g1O21cHFyIKiCW+GvmZoAR1dC+zLu797xeYjbAosnqDFXtebFLsrN2ZE3+jVmfLd6zN8dz8xtsbzxz37eW3iQbo0qcU+LENrXCbzu6mN2roEnft3O6eRMfh/ZhmA/K0fKzScrFf58GJzclMmwDh6muZqaHVXuypwMcHanfmVv3JwdiDqZVHhT+WJQGI1muxBiuhCik/E1DeVbadOcuJBOYnoOkSHGGercLJWk1xiityRsiN9AZl6m3Zm+Xk2zSs2Y3HUyp1JPMWj+IFaeXGnR6+Uacpm4biKLji3i6WZPM7xxEdMKFJGIihG0qdKGGftmkJmbeesTNKVGclYy/x79lxfWvMDtf97OsCXD+Hn/z5xLP8el7Etk5maSa8jFQTjg4uiCl7MXPm4+VPSoSBWvKlSvUJ3avrVxdnDm5XUvM3XXVIv7KmjMRFh39X4jE9ikWBWIqloL6GSBtEB2Qo4hh4VHF5KRa1lzq3KLhz9Uvrlf5c2oVdGr6ErOvjkgDWXT9NUUBwcYOA28KsFfQyD9YomL9PFw5uE21Zn/ZHsWju3Ag21C2RBzniHfb6HDByv4dOmhAl9VUAsLk+btZdPRi3w4KILm1cuItYKUaoXyQjQM/r54OXc1tk/N21Xu8tjNADg5OhBezcfiEWALs1L5ODAGGAsIYA3Kt9KmuSZIz5m9YMgxS5CeFSdX4O3iTYvKLUpcVnmnZeWW/Hbnb7y87mXGrhzLgDoDeKHlC3i5mDcNRZ4hj4nrJrLw2ELGNRvHo+GPmrX8G/FYxGMMXzKcOTFzuL/+/aVyTc21SCmJSYphTdwa1sStIepcFAZpwN/Nny6hXbg9+HbaVGlT5H6Xk5fDaxteY0rUFOJT45nUdhLODjZvyFG+8a6k/OIPLylIPl9AfvoQmQeDphUpEbvmSmYdnsU7m9/hoQYP8WKrF60tTtmkRgdl4mnMV2lx9sxUq/PlIaCLhz/c8yN831P9Jx/4q8RWYvk0rFqB16o2YkKv+vy3P4G/tsXy1coYvlwRw221A7inRQjxyRn8sTWWp7rUoX/TMqS4bf4G9hlTdtW63drSaMoqoW3AwUmZwBqjPEeG+PLjxhNk5xpumfu2uNxUqRRCOADbpZSNgU8tIkEZZefJJNydHalbyTjINFOQnlxDLqvjVnN78O168GkkzC+M33r/xtRdU5m+dzpbzmzh7XZvm03pPpt2lg+3fsjSE0sZ12wcI8JHmKXcwtCiUgsiK0byw94fGFx3sG7zUiQrL4utZ7ayOnY1a+LWEJ8WD0AD/waMDB/J7cG30yiwUYlM0J0dnXmn/TtU9arKN7u/4WzaWT7t9KnZJ0U0ZqZuT1j1ngo05Wlidr/2Uzi5Afp/rQNOlYCcvBym752Oo3Dk94O/079Of+r517O2WGWPGu1VoJVT243msBbk7D6I2wrd3rTsdcxJteYqrdW/z8Laj+H2F8xavKuTI3dGVOHOiCrEJ2Uwa3scM7fH8fSfUQD0Dq/MM3fUNes1i4WUkHVJBXZaOhHq9YZ2T1tbKk1ZxtVb/X+OrgajUWRkiB/T1h7jwOkUmuQvmJmZmyqVUkqDEGKXECJUSnnSIhKUUaJikwiv5oNTvgP8qR3gWRF8gktU7o6zO0jOSrZ709ercXZ0ZmyzsXQM7sjEdRMZvmQ4jzR8hKeaPYWrY/FmcI8lH2PGvhnMPzIfgzTwdLOnS22FMh8hBCMjRjJm+RgWHFnAgLABpXp9eyU5K5mB8weSkJ6Au5M7rau0ZmTESDpU60Alz0q3LqAICCF4sumTVPOqxhsb32DI4iFM7jqZyp6VzXodjRmp2wNWvQvRyyDSaEEQu1Upmo0HQ5P7rCtfOeefo/9wJu0M73d4n/e3vM+7m99lRs8ZiLLsx2cNQtty2a/SgkplUiz8eo8aw0SUs77d4lGlTK18Vw2S61hm7FTV152nuoYxpnMdNh+7yI6TiQxvVxMHS0Z6zUhSfq5pCcb3cybfz135nu9C41cT+k8126qtxoap2RHWfgKZyeDmQ9NQX0DpN1ZRKo1UAfYJIbYABYmDpJR9LSJRGSArN4/98SkMbVfj8sb4HWqVsoQPxeUnl+Pq6MptVS08K1lOiQyKZOZdM/l0+6f8uP9H1sev593279IgoPAh0/ee38v3e7/nvxP/4eLowqCwQQxpNIQQ7xALSn5jOlTrQAP/BkzfO52+tfvi6OBoFTnsicXHFpOQnsB7Hd6jW/VuxZ6YKAoDwgZQyaMS41eP58GFDzKl6xS9OlNWqdIEvKsov8rI+1XuvlmPqjyrfT4t20FMyji5hlym7Z5Go4BG9K7Zm8zcTF7f+DoLji7QAa2uxtSv0syrcAWknoOf+6uVrqELlPl3eUII6PMZnN6tUvyMWqNSj1gIBwdB29oBtK0dYN6C83JVcLC4rcrPLXYLJJ249jjhqKwnPIPAqyIEhKl3zyDwCoLaXcDd17yyaWyTmh1hzUdwYiPU60kVHzeCvF2Jik3C/DkPFIVRKt+w0LXLLAdPXyI7z3DZnzLrkgrf3Khkq0xSSlbErqBt1bZ4OJeRKGJlEA9nD15p8wqdQjoxaf0kHlj4AKObjGZY42E4OVy/y0op2XR6E9P3Tmfz6c14O3szInwEDzR4oFSiyt6M/NXK8avGs/TEUnrV7GVVeeyBeUfmUdevLnfWvLNUV0duq3YbP/b8kdHLRzNk8RA+7fSpnkAqiwihAvbsmwO52bDweUiOhWGLwM3H2tKVaxYdW0RcahzPt3weIQQDwgYwK3oWn2z7hE4hnfB28ba2iGULS/pVZibDLwMh+RQ8MlelGSiPuHjCvT/Dt51h5lD1Py3rEU/TLigFMm6LUiBPbYf89GLeVSCkFbQYriayTJVGd3+9CqkxD8GtwNFV+VXW64kQgsgQX6IsmFbkhj1XCOEmhHgauBuoD6yXUq7Of1lMojJA/g9eoFSe3g3IEvtT7r+4nzNpZ+gS0qVE5dgL7au1Z06/OXQL7caXO79kyOIhnEi5cmYvz5DH0uNLue/f+3hs2WMcTTrKs82fZengpYxtNtbqCmU+XUO7UsunFtP2TMMgDdYWx6Y5knSEPef30K92P6uY29Xzr8evvX+lqldVxvw3hrkxc0tdBk0hqNsTslJg4bOw+w/o+IIKbqApNnmGPKbtmUaYXxidQjoB4CAcmNh6IhczLzIlyuZj/BWdGu2VaeMpMwfVz06H3+6FhAMqj3B579uBYdDvf3Bqm/IrLGskn4JtP8Dc0fBVc/ioFvx+L6z/ArJTodkjMGg6PL0Xxh+Ae36C9k9DxN0qkEqlhmqFUiuUGnPh7Aahra/IVxkZ6sux82mFztNaVG7We38EWgB7gF7AJxaRoAwSFZtERW9XqvgY8z/FbVXvJUwnsvzEchyEQ8HDVnNrfFx9+PD2D/mw44ccTz7O3f/czZ8H/yQrL4tZh2fRb14/nl39LGk5abze9nUWD1rM0MZDy1ygFAfhwIjwEUQnRrM61qbnZKzOvJh5OAkn7qx1p9VkqOxZmR97/kiLyi14df2rTI3SKUfKHLVuV7O4O36CkNbXRoLVFJn/Tv7HseRjPBbx2BUBsBoFNmJw3cH8fvB3DicetqKEZRDTfJXmIjcb/noETm6Cgd9C2B3mK9uaNOoPbcbAlm9h9ijIsGx6hEJzagdMbQsLnlZRpQPrwR2vw9CFMCEWHlsFvT6A8MHKdFeb12tKi5od4ewetXLO5cWyKGOGC3NzM6WyoZTyISnlN8BgoINFJCiDRMUmERnie3mV4/hadZPwqliiclfGrqR5peb4uZWRfEfliF41ezG772yaBTXj7c1v0+GPDry+8XU8nT35tNOnzOs3j0F1B+HiWHZNYnrV7EU1r2p8u/tbrWBYiFxDLv8c/YcOwR0IcDezT0wR8XbxZsodU+hXux9Tdk3h1fWvkpOXY1WZNCa4eKoVAtcKKieeY2G8QTQ3wiANfLv7W2pUqEG30G7X7B/bdCzeLt68s+kdff8zxd1PpfkoZr7KazDkwZxRELMM7vocGg80T7llhW5vKquCPTNhchs4dIN8s6XFqe3wU39w84UnNsDzMXD/b9D+GajRDly0q5PGitQ0pp0x3l8ign0RAqJOJlnkcjdTKgtGP1LKXItcvQySlJ7NsfNpl01f83KUk2vNkunUJ1JOEJMUo01fS0Alz0pMvWMqr7Z5lc4hnZnWfRp/3PkH3ap3KxfBb5wcnHg0/FH2XtjLxtMbrS2OTbIhfgPnM87Tr04/a4sCgLODM2+1e4vRkaOZd2Qeo5eP5lL2JWuLpcmn3//UKoJfdWtLUu5ZHbuaw4mHeSzisevej33dfBnXbBw7Enaw4OgCK0hYhqnRQfnd5WaVrBwp4d/xKo9htzeh+VCziFemcHSCLhNh5ArwCFAmpnMet86qZdx2+GmACpwz9F+o1EivQmrKFlWbgotXgQmsl6sT9Sp5W8yv8mZKZRMhRIrxdQmIyP8shEixiDRlgF1xyQA0zVcqT+2AnDS1hFwClp9cDkCXUK1UlgQhBPfUu4cPOn5Amyptyl2I+n61+xHkEcS03dOsLYpNMi9mHn6ufnSsVrL/qzkRQvBEkyd4u93bbDuzjZFLR5Jj0CuWZQKvIAiobW0pyj1SSr7Z/Q3BXsE3DUQ2MGwg4YHhfLr9Uz25Ykqt25Vf5U/9lPlkcVdy/3sdts+A9uOh3ThzSlj2qBqpJoQ6Pg+7/4IpbdVvV1rEbVNRdT38YNhCi0ak1WiKjaMzVG93pV+lMViPJSxGbqhUSikdpZQVjC9vKaWTyecKZpekjBB1MgkhIDzYGAHwuLEhqrcvUbkrTq6ggX8DqnpVLaGEmvKMi6MLQxsNZdvZbew4u8Pa4tgUyVnJrIxdyZ217sTZ0dna4lxDvzr9+KDjB+y7sI8f9/1obXE0GrOxIX4D+y7sY0T4iBtG6IbLQXsuZFzQQXtMCesOvT5S+SR/uwem3gZRvytLqcKy9lNY/7nK69h1ksVELVM4uUCXV2DEf8r89Ld7VKCcjCTLXjd2K/w8QK2UDv23xPnLNRqLUrMjXIiGlHhAKZXJGTkcO592ixOLjg4zdRVRsYnUqeiFt5txUHpsDVQKB8/i+2edSz/HrnO79CqlBoBBYYPwc/Xj2z3fWlsUm2LhsYXkGHLKjOnr9eheozt3hN7B17u+JvZSrLXF0WhKTP4qZWXPyvStfev01Tpoz3UQAlo/BuOiYMA3atvcx+GLSNg4BbJSb37+1umw/A1oPBh6f2x/JpjVmsGo1dDhWdj1h1q1jF5mmWtphVJT3si3tDym/CojQ30BLGICq5VKE6SUBUF6AMjJVH4OJTR9XRm7ElBpJTQaD2cPHmn0COtPrWffhX3WFsdmmBczj/r+9anvX9/aotyUCa0m4OTgxNub3tYBSzTlnm1nt7EzYSfDGw8vtIXA2KZj8XLx0kF7rsbRGZrcpwK+PDBT+foueQk+awQr3obUc9ees+dv+PdZCOsBA76235QUTq5qhXbEf+BWAX4dDPPGqFyd5iJ2i1IovSoaFcpq5itbo7EUlRqrgGBGE9iwIG88XRy1UmlpYi9mkJieU6DFE7dV+TmUMEjPipMrCPEOoY5vnZILqbEJ7q13L97O3ny3+ztri2ITRCdGs+/CPvrVLrurlPlU8qzE2KZj2RC/gYXHFlpbHI2mRHyz6xsC3QMZUGdAoc/xdfPl6WZPsyNhB/8e+9eC0pVThIC63ZWv3qP/qVyWaz6GzxvDgvFw8ag67vASFem1+m1wz49KKbV3qjWDUWuUX2nUb2rVMua/kpd7crNRoQzSCqWmfOHgoIKBHVsNUuLoIIgI9tVKpaXZGauihxWsVB5fC8LBmEeqeFzKvsTmM5vpGtq13AWV0VgObxdv7qt/H8tPLic+Nd7a4pR75h+Zj5Nwonet3tYWpVDcW+9ewgPD+XDrhyRnmXEmXaMpRaISoth8ZjNDGw3FzcmtSOcODBtI44DGfLLtE1Kzb2Heac+EtIT7foUnt0L43bDzZ/iqOfz+gMpFWakR3P8HOLtbW9Kyg5Mr3PGaUshdvOCXQfDbvcpPtThRYk9ugl8GglclGLoAKujYGJpyRs2OkBwLiccBZQJ74HQKmTl5Zr2MVipNiIpNws3ZgXqVvNWGY2ugSiS4+RS7zLVxa8k15GrTV801DKo7CFBmm5rik2vI5Z8j/9AxuCP+bv7WFqdQODo48lrb10jOSuaz7Z9ZWxyNplh8s/sb/Fz9uLvu3UU+10E4MLGNMWjPLh2055YEhqkUOON2w21PqUlv31B4aLYy99RcS3BztWp5+4twZo/yU/2ojlpx3PY9XDp76zJObFRKqXdltUKpFUpNeSQ/X6XRBDYyxJecPMm+ePMm89BKpQlRsUmEV/PBydEBstNUyGgzpBIJcAsgomKEmaTU2ArVvKrRukpr5sbMxSAN1han3LL+1HouZF4o0wF6rkc9/3o80vARZkXPYvvZ7dYWR6MpEvsu7GPdqXU80ugRPJyLl+C9cWBjBtUdxG8HfiM6MdrMEtooFaqoHJTPHlIKk2egtSUq2zi7QeeX4Zl9MGIFtH1SrdYseAY+qQff94SNkyHxxLXnnthwWaEcskD99hpNeSQwDLwqKxNYLqdN3HnSvPldtVJpJDvXwL74lMumryc3gSGnRP6UWXlZrDu1js6hnXEQ+qfWXMvAsIHEp8Wz6fQma4tSbpl3ZB7+bv50CC6Z77M1eLzJ41TzqsYbG98gOy/b2uJoNIXm213fKjP+eveVqJxxTcepoD2bddCeIuHioU1ei4IQauWy2xvw1A54YiN0egmyLsGSl+GLCPimo/JdPXcYjq+HXwarlcmh/2qFUlO+EUItkh1bA1ISVMGNqj5uZver1JqOkYNnUsjONRAZ4qc2HFsDDk4Q2rbYZW4+vZn03HS6hOhUIprr0yW0CxVcKjAneo61RSmXJGUmXc5N6VD+glR4OHswsfVEjiUf4/u931tbHI2mUBxOPMyK2BU81OAhvFy8SlSWr5sv45qNY/vZ7TpwlaZ0EAIqNYROL8IT65WS2e1NcHSBFW/B5JbwYx8VjGfov2qlUqMp79TsCGnn4NxBAJqG+mml0lLk/7BNQoz+k8fXQrUW4OJZ7DKXn1yOp7Mnrau0NoOEGlvE1dGVPrX6sPzkcpIyk6wtTrlj4bGF5Bpyy0XU1xvRIbgDPWv0ZNruaRxPPm5tcTSaWzJt9zQ8nDx4sMGDZilvYB0VtOfjbR/roD2a0iegNrQbp9KRjD+gcn1GPqhMXr0rWVs6jcY8FOSrvOxXGZeYwfnULLNdQiuVRqJOJhHo5Uo1X3eV1yh+Z4n8KfMMeayKXUXHah1xcXQxn6Aam2Ng2EByDDk6tH4xmHdkHg38G1DPv561RSkRL7Z6EVdHV97a9JY2AdSUaY4lH2PJ8SXcX/9+fFyLH8TOFEcHx4KgPe9sfof0nHSzlKvRFJkKVaHVSBUUSSuUGlvCrzr4Vr+sVBrTJ0adTDLbJbRSaSQqNonIEF+V9uPERpCGEvlTRp2L4mLmRbqEatNXzc2p51+PBv4NmB09WysUReBw4mH2X9hf7gL0XI9A90Cebv40W85sYf6R+dYWR6O5Id/t+Q5XR1cebviwWcttHNiYEeEjWHB0AX3m9NEBzDQajcbc1OyoLDENeTSu6oOjgyhIp2gOtFIJJKfncPR8Gk2NWjvH1oCjKwS3KnaZK06uwNnBmfbV2ptHSI1NMzBsoFKSLu63tijlhnkx83BycKJ3zfKRm/JWDK47mMiKkXy87WMSM80bkU2jMQexl2L59+i/3F3vbgLcA8xe/thmY/m5189U8azCq+tf5b4F97HtzDazX0ej0Wjskpq3K2vMM7txd3GkfmVvs/pVaqUS2BWXBHA58uvxNRDSSoWiLgYGaWD5yeW0rtK6xEEMNPZB71q9cXV01QF7CkmOIYcFRxdwe/Dt+Ln5WVscs+AgHJjUdhKp2al8vO1ja4uj0VzD9D3TcRAODG001GLXiAyK5OfeP/N+h/dJzEpk2JJhjF81nthLsRa7pkaj0dgF+RaYJn6Vu2OTzVa8VipRpq9CQHiwD6RfVEly8xOFFoMtZ7ZwKvWUzaygaCxPBZcK3FH9DhYeXUhmbqa1xSnzrD+1nouZF+lfp7+1RTErYX5hDGs8jPlH5rP59GZri6PRFHA69TTzjsxjYNhAgjyCLHotB+HAnbXuZH7/+YyJHMO6U+voN7cfn277lEvZlyx6bY1Go7FZvCtDYL0CpbJpqB+XsnLNVrxWKlFKZe2KXlRwc4bj69TGEgTpmXloJhVcKtCtejczSaixBwbWGcilnEssO7HM2qKUeebFqNyU7aq1s7YoZuexiMcI8Q7hrU1vkZVnvqhsGk1RuZR9iY3xG/l619c8veppkDC88fBSu767kzuPN3mcBQMW0Ltmb37Y9wN95vThr0N/kWsw30BIo9Fo7IaaHVXsmNzsyxaaZsLulUopJbuMQXoApb07e0K1ZsUq73zGeVacXEHf2n1xcyqe+azGPmlRuQXBXsHMidEmsDcjMTORVXGr6FOrT7nMTXkr3JzceLXNq5xIOcG03dOsLY7GTjBIAzGJMcyOns3rG15nwLwBtPu9HY8te4zJUZPJys1iQqsJVPWqWuqyBXkE8Xb7t/mjzx/UqFCDtza9xd3/3M2G+A2lLotGo9GUa2p2hJw0iN9BrUBPvN2czFa0+Uoqp8QlZnAhLftKpbJ6W3As3mB1bsxccmUud9e923xCauwCB+HAgLABfLXzK2JTYgmpEGJtkcok+bkp+9bua21RLEbbqm3pU6sP0/dOp1fNXtT2rW1tkTQ2RK4hl+SsZPZf2M+uc7vYfW43e87vITVH5Yis4FKBiIoR9KjRg4iKETQObEwFlwpWlhoaBTRiRs8Z/HfyPz7Z9gmjlo2iXbV2NPRviLeLN14uXng7e1/x2cvFC28Xb9wc3VR0d41Go7FnarQHBBxbg0NoG74f2pJWb5inaLtXKncaox5FhvjCpbNw/hBEPlCssgzSwKzDs2heqTm1fGuZT0iN3dC3dl8mR01mTswcxjYba21xyiTzYmwjN+WteL7l86w9tZY3N77Jl12+JCM344pXek76NdvyX94u3nQM7kg9v3plYiAtpSQxK5FL2Zcuy5ljrEfujesR4BZAn1p9qONXx9pVKDZ5hjziUuPIM+SRK3PJM+RhkIaCz3nS+DJ+zjXkXrvfeK7BcO15+cdn5WVdt1+Y/r75+3IMOQXyOQgH6vrVpXfN3jQJakJEYATVK1QvE/3meggh6Fa9Gx2DO/LrgV/5ef/PbIzfeMv0I07CCS8XL7ycvfB19cXf3Z8AtwD83fzxd/MnwD3ginc/Vz8cHRxLqVYajUZTSnj4Q+VwtYh2+wu0rOFvtqLtXqmMOpmEq5MD9Sp7w36jL1sx/Sk3xW8iLjWOJ5s+aUYJNfZEZc/K3Fb1NuYdmceYyDF6UHMVhy4e4sDFA0xoNcHaolgcfzd/nm3+LJM2TKL9H4VPTeTm6EZWXhZf7fyKKp5V6BTSiU4hnWhZqSXOxbTAKArpOenEJMUQnRjN4cTDRCdFE50YTVJWUqHOd3Zwxt3JHXcndy5kXGD63uk0DmjMgLAB9KzZs0ysmBWWXEMuo5aNYsuZLRa/lunv5uHsUfDZ380fDyePa/Z5OXtRz78ejQIa4eHsYXH5zI2royvDGw9neOPhSClJz03nUvYlUrNTSc1JJSU7peDzpexLBe+Xsi+RnJXMufRzHLxwkIuZF8mV1/pnCgR+bn4FSqePqw++rr74uPrg4+Kj3vNfJt9dHF2s8GtoNBpNEah1O2z+BnIywNndbMVqpTI2kfBqPjg7Oiit3dUHqjQpVlkzD8/E19VXB+jRlIiBYQMZv2o86+PX0zG4+AGjbJF5R1Ruyjtr3mltUUqF/nX64+LowsXMiwVKwfUUB3cndzycPHBzcsNBOHA+4zxr49ayInYFc6Ln8PvB3/Fy9qJ9tfZ0DulM++D2JVbO8gx5nLx08rLymBhNdFI0cZfikEhABVoJ8w2ja2hXavvWxtfV97KC4+x+TZ3cnNyu8JO9kHGBf4/+y9wjc3lr01t8uPVDuoR2oX/t/rSu0rrMT7pM3TWVLWe2MLrJaGr61MTRwRFHYXxd57OTgxMOwqHg8/WOcxJO1z2+rK4slgZCCDydPfF09gTPop0rpSQlO4ULmRe4mHFRvWde5EKGes9/xSTFkJyVTEpWynWV0HzcndzxcfVhcNhgRjUZVcKaaTQajQWoeTts+ApiN0OtTmYr1q6Vypw8A3vjU3ikTXW14dgaqNEOijFQOZd+jpWxK3mowUN6plJTIjoFd8LfzZ850XO0UmlCjiGHf4/+S+eQzvi6+VpbnFJBCMGdtYquQAe6BzIgbAADwgaQkZvB5tObWRm7klWxq1h8fDFOwonmlZvTOaQznUI6Uc2rWsG5OYYcEjMTOZ9xnvMZ57mQcYELmRe4kHHh8rbMC8SnxhdEp3UQDoR6h1Lfvz59a/clzC+Mur51qeZdDQdR/HhwAe4BPNLoER5u+DD7L+5nbvRcFh5byKJji6jsWZm+tfvSv3b/Mul/vOn0JqbtnsaAOgN4IvIJa4ujuQFCiIJVxlo+t3ZbyV8VTcpKIjkrWb2yk0nONL5nJbP3/F6m7JpCz5o9qV6heinUQqPRaIpAaBtwcFJ6jz0plUKInsAXgCPwnZTy/av21wd+AJoBE6WUhc4afvD0JbJzDUSG+kJSLCQeg1aPFUvOOTFzyJN5DK47uFjnazT5ODs606dWH3478BsXMi4Q4B5gbZHKBGvj1nIx8yL9aveztijlCncn9wITWIM0sPvcblbFrmJl7Ere3/I+7295n9o+tRFCcCHjAolZidctx9PZk0D3QALcAqjjW4cO1ToQ5hdGmF8YtX1qWzTatRCCRgGNaBTQiOdaPsfK2JXMjZnLd3u+49vd39K8UnP61+lP9+rdy4Qp5/mM87y09iVq+tS0C1Nte8J0VdR0MsaU8xnn6TmrJ9/t+Y632r1VyhJqNBrNLXD1hmrN4ehq6Gq+Ysu0UimEcAQmA92AOGCrEGK+lHK/yWEXgbFA/6KWHxWrBk9Ngn3h+Fy1sRj+lHmGPGYdnkWryq2o4VOjyOdrNFczMGwgP+3/iQVHFzCk0RBri1MmmBczjwC3AG6rdpu1RSm3OAgHIoMiiQyK5OnmT3Mi5QSrYlex6fQmXBxcaBbUTCmO7gHq5RZQ8N3dyXx+FyXB1dGVnjV60rNGT86mneWfo/8wN2Yur65/lfc2v8fkrpNpUbmF1eQzSAMT103kUvYlvu32bZlQcjWlS6B7IIPCBvHXob94vMnjN1Q+yzqnU0+TlpNGbd/adm1erdHYJDU7wtpPIDPZbEWWaaUSaAXESCmPAggh/gD6AQVKpZQyAUgQQhTZRmxnbBKBXi4E+7nD6rXg7g9BDYss5Ib4DcSnxfNM82eKfK5Gcz1q+9YmomIEs6Nn80jDR+z+gX4x8yJr4tbwYIMHbTI3pbWoXqE6QxoNKbcTF5U8KzEifASPNn6UqHNRvLT2Jd7e9DZ/9/0bJwfrPN6+3/s9G+I38Frb1wjzC7OKDBrrM6zxMGYensn0PdOZ1HaStcUpMkmZSTyw8AHOZ5wn2CuYzqGd6RLShcigSKv9tzQajRmp2RHWfAQnNpqtyOI7u5QO1YBYk+9xxm3FQgjxmBBimxBi27lz59gVm0RkiC8ClF1xzQ7gUPSf5O/Df+Pv5k/XUDOuIWvsnoF1BnI0+Si7zu2ytihWxSANTN8znVyZS7862vRVcy1CCJoGNeX5ls9zJPkIfx36yypyRCVE8b+d/6NnjZ4MChtkFRk0ZYPKnpXpX6c/c2PmcibtjLXFKRJSSt7a9BZJWUmMazaOGj41+OPgHwxbMozOf3XmlXWvsPzkcjJyM6wtqkajKS7BrcDRVek/ZqKsTzddb3lGFrcwKeW3wLcATZs1l0fOpTGgaTW4eBRS4qBm0VcaE9ITWB23mkcaPVIq4fo19kOPGj34YOsHzI2ZS2RQpLXFsQpn0s7wyrpX2HxmM31q9dErP5qb0iWkC62rtGZy1GR61+xdqgGdkrOSeWHNC1TxrMJrbV+ze+sCDTwa/ihzoufww94feKn1S9YWp9AsPLaQpSeWMq7ZOEaEj2BE+AjSctJYd2odK2NXsuLkCuYdmYeroyttq7alS0gXbg+5HX838+W705QPMnIzOJZ8jKy8LHLycsgxmLyM37MN2dfsq+Nbh9uDb9eBLa2JsxuEtrYrpTIOMA3rFwzEm6PgjJw8AJqE+MLxf9XGGkX3p5wdPVsF6AnTAXo05sXLxYvu1buz6NgiXmj5gt35Zi0+vpg3N75JriGXN257gwF1BlhbJE0ZRwjBiy1f5O5/7mZy1GQmtplYKteVUjJp/STOZZzjl16/4OXiVSrX1ZRtqnlVo0/tPsyKnsXIiJEEugdaW6RbcibtDO9sfocmFZswtNHQgu2ezp70qNGDHjV6kGPIYfvZ7aw4uaIgqrSDcCCyYiRdQ7tyd727y4wPtsa8nM84z86EnexM2ElUQhQHLhy4aYqdm+Hj6kOvGr3oV6cfjQIa6Yk4a1CzI6x422zFlXWlcisQJoSoCZwC7gMeMEfB6dl5OAARwb7w7xrwqgyBRVsFyTPkMSt6Fm2qtCG0Qqg5xNJormBg2EDmHZnHkuNLGBBmH0rVpexLvLf5Pf45+g8RgRG81+E9/f/SFJowvzDuqXcPfx76k7vr3U1dv7oWv+ZvB39jRewKXmj5Ao0CG1n8epryw8jwkcw/Mp8Ze2fwXMvnrC3OTcmfHMk15PJu+3dv6Dvp7OBMmyptaFOlDS+1eomDFw+yInYFK0+u5KNtH/HX4b9487Y3aVapWSnXQGNODNLAkaQjBQrkzoSdxKXGASpgWuPAxgxtPJRGAY3wcPbA2cFZvRydL382vlwcXQr2OeDA5jObmR8zn9nRs/nj0B/U9qlN3zp96VOrD0EeQVauuR1R83bAfEqlkLLY1qSlghCiN/A5KqXI91LKd4QQjwNIKb8WQlQGtgEVAAOQCjSUUqbcrNzAmg1kkyensnz87fBxXah1Owz6rkiyrYlbw5jlY/jk9k/oXqN70Sun0dwCKSV95/bFz82Pn3r9ZG1xLM72s9t5ee3LnE0/y2MRj/FYxGM6KISmyCRnJXPnnDup71efad2nWXQGfP+F/Ty08CHaVW3Hl12+1LPtmmuYsHYCK06uYPGgxWXaRPSPg3/wzuZ3eLXNq9xT755ilbH59GZe2/Aa8anxPNjgQcY2G6tXLcsJ+Smntp7ZqhTJc1Fcyr4EgL+bP02DmtI0qCmRQZE09G9oFpevlOwUlhxfwryYeew6twsH4cBtVW+jX+1+dA7tjKuja4mvobkJeTnwQQ3ExPjtUsoSh00v80qlpfCsVlc+/tlMPunkClNaQ9+voNkjRSrjqeVPsef8HpbdvUxHpNRYjO/3fs9n2z9jXv95hUrOXR7Jycth6q6pTN87nWpe1Xi3/bt260eqMQ+/H/yddze/y+edP7dYELW0nDTu+ecesvKy+Puuv0vVh1NTfjiadJT+8/rzaPijjGs2ztriXJcTKScYPH8wzSs1Z+odU0s0OZKek86n2z/lz0N/Euodypvt3qR5peZmlFZjLvIVySXHl7DsxDLOpp8FoLZPbSKDIgsUyRDvEItPmB1LPsY/R/5h/pH5nE0/i7eLNz1r9KRfnX5EBEaU2oSdQRrIyssiIzeDzNxMMnMzycjNUN/zMgu2Z+Rm4Oroip+bH36ufurdzQ8PJ4/yNbl4eAmiXk+tVJYE1yph8rvZy3jYcRksfA7GRoF/zUKffybtDD1m9WB44+Fl9iGhsQ3OZ5znjpl38EjDRxjfYry1xTE7R5OP8tLal9h/YT8DwwbyQssX8HT2tLZYmnJOriGXu/+5m8zcTOb2n2v2GW8pJRPWTmDx8cV83+N7PWjW3JRnVz3L+vj1LBm0BB9XH2uLcwW5hlyGLB7C8eTjzO47m0qelcxS7pbTW5i0YRLxqfE80OABxjYda3exAcoiUkp2n9/N0uNLWXpiKWfSzuDs4Ey7au3oUaMHHap1sGofzTPkseXMFuYfmc9/J/4jMy+TKp5VCPIIwsvFiwrOFfB28b75y9mbXEMuKdkppGSnkJyVrD5nXfXduC05O5nU7NQCxbEkuDi44Ovme1nRNL77uvkS6B5IjQo1qOlTkwC3gDKjfAohzKJU2rVdWWSIH6xfDT6h4FejSOfOjp6NlFKHjddYnED3QDoGd2TekXk81ewpm1kVl1Ly16G/+Hjbx7g5ufF5p8/pWl2n5dGYBycHJ15s9SIjl47k5/0/MyJ8hFnLnxszl4XHFvJU06e0Qqm5JY9FPMbSE0v59cCvjI4cbW1xruCHvT+w+9xuPuz4odkUSoBWVVoxu+9sPt/xOb8e+JU1cWt487Y3aVG5xGNXTRGRUrLn/J4CRfJ02mmlSFZtx9imY+kU0glvF29riwmAo4Mjbau2pW3VtkxsPZFlJ5ax7tQ6UrJTSMpMIjYlltScVFKyUooVJMhROFLBpQIVXCtQwaUCPq4+hFQIwdvZGw9nD9yc3HBzdMPdyb3g5ebkhpuT2pa/z83JjazcLC5mXSQpM4nErEQSMxNJzEpU342f41PjScxKLDAlzsfb2ZuaPjWp4aOUzPxXiHdIuR3n2e1KpVuVMHkp9gDOn9SBer2h/5RCn5tryKXHrB6E+YbxdbevLSilRqNYFbuKp1Y8xRedv6BLaBdri1Nizmec57UNr7Embg3tqrbjrXZvUdGjorXF0tgg41aMY+PpjSwYsMBsASCOJB3hvgX30SSoCd/c8Q2ODo5mKVdj24xdMZZtZ7exdNDSMhMh+MCFAzzw7wPcUf0OPrr9I4tdZ+uZrUxaP4m41DgeqP8A45qN06uWFkZKyb4L+1hyfAlLjy8lPi0eJwcn2lVVK5JlSZEsDlJKMvMyuZR9idTsVFKyU7iUfang5eTgRAXXCvi4+BQokBVcKuDp7GmVFcIcQw4XMi5wLPkYx5KPcTT5KMeTj3Ms+RgJGQkFxzkJJ4K9gwuUzAb+DYioGEEVzyoWk9tcK5V2q1T6Va8vEzf9Bd90gAHfQJP7Cn3uypMrGbtyrF5Z0ZQauYZcuv3djcYBjfmq61fWFqfYnEw5yczDM5kdPZusvCzGNx/P/fXvLzMmIBrbIzYlln7z+tGrZi/eaf9OicvLzM3k/n/v52LmRWb1nVUu0kRoygb7LuzjvgX3MbbpWEZGjLS2OGTlZXHfgvtIzkpmTr85Fjd5TM9J58udX/LrgV8J9grmzXZv0rJyS4te0145l36Otza9xcrYlTg5OHFb1dvoXr07nUM7U8GlgrXF01xFanYqx1OOFyic+a8Tl06Qa1CrsYHugUQERhBRUb3yo+6aA23+WkI8XJwuJ/ys0aFI5848PJOK7hXpGFL0vJYaTXFwcnCib+2+/LjvR2ISYwhwD8DRwREn4YSjgyOOQr3KonKWa8hldexq/jz0JxtPb8RRONIltAtjIsdQ27e2tcXT2DghFUJ4pOEjTN87nfvq3Ud4xfASlffB1g+ISYrhmzu+0Qqlpkg0CmhE+2rt+Wn/TzzY4EGrr9T9b+f/iEmKYeodU0vFh87D2YMJrSZwR+gdTNowieFLhnNfvft4pvkzVv8tbAUpJQuOLuD9Le+TmZvJuGbjuLvu3WXOj1dzJV4uXjQObEzjwMZXbM8x5BCdGM3uc7vZdW4Xu8/tZkXsCkCZ8db1q1ugZEYERlC9QnWrjgPtdqUysllzGfVcHTgfDWN3FPq8+NR4es7qyciIkTzV9CkLSqjRXMnx5OPcNfeumx6Tr1yaKpxODk64Orpe+3K6/jY3Rzdq+tSkcWBjgr2Ci32DOpt2ltnRs/k7+m8S0hOo5FGJwXUHMzBsoM5DpSlV0nLS6DOnD1U9q/Jz759xEA5FLiM1O5V3Nr/DgqMLeLTxozzd/GnzC6qxeaISonh40cM81+I5hjQaYjU5tp3ZxvAlw7m77t282vbVUr9+ek46X+38il8P/EpVr6q8edubtKrSqtTlsCUS0hN4c+ObrI5bTWTFSN5s9yY1fQofgFJTPkjMTGTP+T0FSuae83tIy0kDwMfVh/bV2vPmbW/i4uhS6DK1+WsJadGiudw28CyED4a7Pi/0eV/t/Ippu6exeNBiqnpVtZyAGs11WH9qPadST5En88gz5JEn88g15BZ8z5W512zPMeSQnZdNZm6mes/LJCsvS71yswo+Z+ap/Vl5WQXX83X1pXFgYyICIwpm0fzc/G4on0Ea2HR6EzMPzWRl7EryZB7tqrbjnnr30DG4o845qbEa82Lm8cr6V3i3/bvcVfvmkzNXE5UQxYS1EziddprHIx7nsYjHtB+lptiMWDqCmMQYFg9ajJuTW6lfPy0njUHzB+EgHPj7rr+tukq44+wOXl3/KicvneTeevcyvvl4vWpZRKSUzDsyjw+3fkhOXg5jm43lgfoP6HuUnZBnyONY8jF2n1c5RhccXcDE1hO5r37h3fq0UllCWkQ0lNsGnoLB30PjwkVwzTHk0OPvHtT3r8+UOwof2EejKU/kGHKISYxhz/k97D2/lz3n93Ak6QgSda8I9gomPDCcxoGNCa8YTn3/+mTlZjHvyDxmHp7JiZQT+Lr6MqDOAO6uezchFUKsXCONRk14PPjvgySkJ/DPgH8KNXDNM+Qxbc80vt71NZU9K/N+h/d1/lRNidl6ZivDlwxnQqsJPNjgwVK//usbXmdOzBxm9JxB06CmpX79q8nIzeDLHV8WrFq+cdsbtK7S2tpilQvOpJ3hjY1vsO7UOpoFNePNdm9SvUJ1a4ulsRJSSoYsHsKp1FMsHLiw0Km0tFJZQlrUD5Hb7kuB56LBq3CmeMtPLOfpVU/zZecv6Rza2cISajRlh7ScNPZf2H+Fonkm7QygTG4dhAM5hhyaBjXlnnr30K16N7PnBdRoSkq+6eHI8JGMbTb2psfGp8bz0tqX2JGwgztr3cnE1hPLdaRETdliyKIhxKXGsWjgoiKZqZWU1bGreXLFk2XShHvH2R1M2jCJEykn9KrlLZBSMidmDh9t/Yg8mce4ZuO4v/79xTLt19gWm05vYuTSkbzU6iUeaPBAoc7RSmUJaVHTT257rh6M2VTocx5f9jjRSdEsGbREm/Fp7J5z6ecKFMysvCz61u5LPf961hZLo7kpL619iaXHlzKv/zyCvYOve8yiY4t4a+NbGDAwsfXEIpvLajS3YkP8BkYtG8WrbV7lnnr3lMo1EzMTGTBvAAHuAfx+5++lqswWlozcDP6383/8vP9nvWp5A06nnub1ja+zIX4DLSu35I3b3iDEW1sEaRRSSoYuHkrcpTgWDircaqW5lEr7ndLIToWahY/eGncpjg3xGxgUNkgrlBoNUNGjIp1DOzO22Vieb/m8Vig15YKnmz2No4Mjn27/9Jp9aTlpTFw3kRfWvEBN35rMvGumVig1FqFtlbZEBEbw/d7vyTHkWPx6Ukre2vQWKdkpvNv+3TKpUAK4O7nzfMvn+bHXjzg5ODFi6Qje2vhWQSASe0ZKyV+H/mLA/AHsTNjJxNYT+a77d1qh1FyBEILRkaNJyEjg78N/l+q17Vc7kgaoWfhUIrOiZyGEYGDYQAsKpdFoNBpLUsmzEiPCR/DVzq/YcnpLQcTJ3ed2M2HtBE6lnuLxJo8zKmKUnkDUWAwhBKOajGLM8jEsOLKAAWEDLHatw4mH+XT7p6w/tZ5nmj9TLiYAmwY1ZeZdMwtWLdedWscb7d6gTZU21hatREgpSclO4UzaGRLSE0jLTSMjJ4P03HQycjNIz0knPTed9Bzjd+Pn9Nx0kjKTiE+Lp3Xl1rzR7g2qeVWzdnU0ZZRWlVvRvFJzpu+ZzqCwQaUWEMx+zV+rOsltMQng4X/LY3MMOXSb2Y3wwPBynXheo9FoNJCZm0n/ef3xdPbkjzv/4Id9PzAlagpBHkG83+F9mlVqZm0RNXaAlJJ7F9xLWk4a8/rPM/skxtm0s0yOmsy8I/PwdPZkVMQoHm74cLnzu4tKiOLV9a9yPOU499S9h/EtxuPp7Gltsa5LZm4mZ9LOcDrtNGfSzqhX+pkrtmXkZtzwfEfhiIeTB+7O7ng4eeDh7KG+O7nj7uRO+2rt6V+nf5nMSa0pW+QHBHux5Ys81PChmx6rfSpLSIvq3nLbiUuFOnbx8cU8v/p5JnedTMfgwpvMajQajaZssuzEMsavGk81r2qcSj1Frxq9eKXtK1RwqWBt0TR2RH4AwPc6vEefWn3MUmZqdirf7/2en/f/TJ7M44H6DzAyYiQ+rj5mKd8aZOZmMjlqMj/u+5FKnpUYGDaQXjV6UcOnhrVFY8+5PXyz+xt2n9tNYlbiNfsD3QOp4lmFyp6V1ctDvQd5BOHl7FWgOHo4e+Ds4KwVRo3ZGLZ4GMdTjrNo4KKbrlZqpbKEtKhbRW47fPqWxxmkgXv+uYfMvEzm9Zun8/5oNBqNDSClZOSykew5t4eJbSZyV6279GBOU+oYpIFB8wdhkAZ+u/O3Eq3A5Rhy+Pvw33y962suZl6kV81ejG069oYBqcojUQlRfLHjC7af3Y5E0sC/AT1q9KBnzZ6lbg564MIBJkdNZnXcavxc/ehavStVPateVh49K1PJo1KZ9V/V2D75q5UvtHyBhxs+fMPjtFJZQlo0ayq37dh5y+PyZxGLkzBbo9FoNGWXjNwMsvOyy/UKjqb8s/T4Up5d/SwOwoH6/vVpFtSMZpWa0TSoKYHugbc8X0rJ8pPL+XzH55xIOUGLSi14rsVzNApsVArSW4ezaWdZemIpi48tZvf53QBEVIygV41edK/RnSCPwqWKKw6HEw8zJWoKy08up4JLBYY1HsYD9R/Q6U80ZZJHlzzKkaQjLBq0CHcn9+seo5XKEtKiRQu5bdu2mx5juko5t99cHbRBo9FoNBqN2dl2ZhubTm9iR8IO9pzbQ2ZeJgA1KtQoUDCbBzUn2Dv4ihX1qIQoPt3+KTsTdlLLpxbjm4+nY3BHu1p1j7sUx+Lji1lyfAkHLx5EIGheqTm9avbijup34O9269gZheFo8lGmRk1lyfEleDp78kjDR3io4UM6f62mTLPtzDaGLRnGcy2eY0ijIdc9RiuVJaQwSuXyk8t5eqVepdRoNBqNRlM65OTlsP/ifnac3aFeCTtIyU4BoKJ7xQIlc/vZ7Sw7sYxA90DGRI6hf53+dj/5fTT5KEuOLWHR8UUcSz6Go3CkdZXW3Fb1Nmr71qa2T20qe1YuktJ9MuUkU3dNZeGxhbg5uvFggwcZ0miItnDQlBtGLBlBdFI0iwctvu5qpVYqS8itlEopJfcsuIeM3Ay9SqnRaDQajcYqGKSBI0lH2HF2B9sTtrPj7A7Opp/F3cmdYY2GMaTREG16eRVSSg4nHmbx8cUsPraYuNS4gn0eTh7U9q1NLZ9aStE0vqp4VrkiMm7cpTi+3f0t84/Mx9nBmfsb3M+wRsPwc/OzRpU0mmKz4+wOhiwecsPVSq1UlpBbKZX5q5TvtH+HvrX7lqJkGo1Go9FoNNdHSsmZtDN4OHvo1bJCkpiZyNHkoxxJOqJeyUc4mnSUcxnnCo5xd3Knpk9NavvURgjBwqMLcRAO3FPvHh4Nf7RQ/q0aTVll5NKRHE48zKKBi66ZhNJKZQm5mVKZv0qZnpNukdxRGo1Go9FoNBrrkpyVzLHkY8QkxXAk6UiB4pmYmcjAsIGMjBhp0aA/Gk1pEZUQxcOLHmZ88/EMazzsin3mUiq1tnQdVsSu4ODFg7zT/h2tUGo0Go1Go9HYID6uPkQGRRIZFHnFdimlXQU70tg+kUGR3Fb1Nn7Y+wP31rvXIibzDrc+xL6QUvL1rq8J9Q6ld83e1hZHo9FoNBqNRlOKaIVSY4s80eQJErMS+ePQHxYpXyuVV7EydiUHLx5kVJNRepVSo9FoNBqNRqPRlHsigyJpV7UdM/bOID0n3ezla6XSBCklU3dN1auUGo1Go9FoNBqNxqYYHTmaxKxEfjv4m9nL1kqlCfmrlI9FPKZXKTUajUaj0Wg0Go3NEFExgvbV2vPjvh9Jy0kza9laqTSS70sZ4h3CnbXutLY4Go1Go9FoNBqNRmNWRjcZTVJWEr8f/N2s5Wql0siq2FUcuHiAURHal1Kj0Wg0Go1Go9HYHuEVw+lQrQMz9s0gNTvVbOVqpZLLvpR6lVKj0Wg0Go1Go9HYMqMjR5OclWxW30qtVKJXKTUajUaj0Wg0Go190DiwMbcH386P+340W5l2r1TqVUqNRqPRaDQajUZjTzwR+QQp2SlmK8/ulcr8VUod8VWj0Wg0Go1Go9HYA40CGvFggwfNVp5dK5Wmq5R9avWxtjgajUaj0Wg0Go1GUypMaDXBbGXZtVK5Om61XqXUaDQajUaj0Wg0mhJg10rllKgpepVSo9FoNBqNRqPRaEqA3SqVl7Iv6VVKjUaj0Wg0Go1GoykhdqtUnss4p1cpNRqNRqPRaDQajaaE2K1SmZGbwcjwkXqVUqPRaDQajUaj0WhKgN0qlS4OLvSprVcpNRqNRqPRaDQajaYk2K1SWdGjIs4OztYWQ6PRaDQajUaj0WjKNXarVPq6+lpbBI1Go9FoNBqNRqMp99itUqnRaDQajUaj0Wg0mpKjlUqNRqPRaDQajUaj0RQbrVRqNBqNRqPRaDQajabYaKVSo9FoNBqNRqPRaDTFRiuVGo1Go9FoNBqNRqMpNlqp1Gg0Go1Go9FoNBpNsdFKpUaj0Wg0Go1Go9Foio1WKjUajUaj0Wg0Go1GU2y0UqnRaDQajUaj0Wg0mmKjlUqNRqPRaDQajUaj0RQbrVRqNBqNRqPRaDQajabYaKVSo9FoNBqNRqPRaDTFRiuVGo1Go9FoNBqNRqMpNlqp1Gg0Go1Go9FoNBpNsdFKpUaj0Wg0Go1Go9Foio1WKjUajUaj0Wg0Go1GU2y0UqnRaDQajUaj0Wg0mmKjlUqNRqPRaDQajUaj0RQbrVRqNBqNRqPRaDQajabYaKVSo9FoNBqNRqPRaDTFRiuVGo1Go9FoNBqNRqMpNhZVKoUQPYUQh4QQMUKICWYob4gQItr4GmKyvaYQYrNx+59CCJeSXkuj0Wg0Go1Go9FoNLfGYkqlEMIRmAz0AhoC9wshGhby3FVCiBpXbfMHXgNaA62A14QQfsbdHwCfSSnDgETgUbNUQqPRaDQajUaj0Wg0N8WSK5WtgBgp5VEpZTbwB9BPCFFbCLFYCLFdCLFWCFG/kOX1AJZJKS9KKROBZUBPIYQAugB/G4/7Eehv3qpoNBqNRqPRaDQajeZ6OFmw7GpArMn3ONQq47fA41LKaCFEa2AKSiksTnnVgAAgSUqZe9X2axBCPAY8ZvyaKoQ4VMi6AAQC54twvC1hr3W313qD/dbdXusN9lt3e6032G/ddb3tD3utu73WG+y37sWpd3VzXNiSSqW4zjZP4DZgplpgBMAVQAgxDBhn3FYHWCiEyAaOSSkH3KA8eZPt126U8luUUltkhBDbpJQtinNuecde626v9Qb7rbu91hvst+72Wm+w37rretsf9lp3e6032G/drVlvSyqVcUCIyfdg4DhqVTHy6oOllD8AP4DyqQSGSimPX1Vep6vKW4XSxn2FEE7G1cpgIN48VdBoNBqNRqPRaDQazc2wpE/lViDMGJnVBbgP5fd4TAhxN4BQNClkeUuA7kIIP2OAnu7AEimlBFYCg43HDQHmmbMiGo1Go9FoNBqNRqO5PhZTKo2rhk+ilMEDwF9Syn3Ag8CjQohdwD6gXyHLuwi8hVJWtwJvGrcBvAiMF0LEoHwsp5uzLkaKZTZrI9hr3e213mC/dbfXeoP91t1e6w32W3ddb/vDXutur/UG+6271eot1EKfRqPRaDQajUaj0Wg0RceS5q8ajUaj0Wg0Go1Go7FxtFKp0Wg0Go1Go9FoNJpio5VKjUaj0Wg0Go1Go9EUG61UljJCCLv6zYVJQlJ7xd7a3BR7q7vu7/bX5vnYa701Go1GowEdqMfiCCFeReXNzJBS/mbc5iClNFhXMstjHGQJKWWetWUpTey8ze257rq/21Gb22u97RkhRB3giLTDgZM9191e0W1uX5ijvfXMqgURQnwLtAM8gHuFEHMApJQGW5/VFkJMBX4BPhJC9La2PKWFnbe5Pddd93c7anN7rTeAEGKIEKKSteUobYQQ84AVQFtbb+Orsde622tfB93m1pajtDFXe9tNRylthBDuQEVgtJTyKyllP8BLCLEQCgYfNmkqJ4R4CwgF3gN2AtOEEA9ZVyrLY+dtbs911/3djtrcXusNIIT4HvgBGGdPAy8hRBcgDZiByottNwNte627vfZ10G2OnbW5Odvb5juJFTGgTKMa52+QUnZDuV39z/jdVk0KLgCzpZR7pJQ/A/cCXwgh7reyXJbGntvcnuuu+7sRO2lzu6y3EKIGcARoCdQEnjMdeNmqIm3kIPCalHISsA6YiBp4uZgeZKO/gd3V3c77Oug2t7c2N1t7a6XSzAghQoUQblLKLGAN8KUQor7JIWMADyGEr1UELB3SgDvyv0gp1wGDgPFCiMY3PKucYs9tbs91N0H3dztoc3utdz5SyuPAL1LK7cCTQG3gWSFENeN+W1Sk89s8XkoZDSCl/AhlJvYKUN943EPGfTbzG9hz3e2xr4Nuc+yszS3R3lqpNCPGpfPJwH9CiG5Syj+BqcAqk8FlLFAXCLSSmBZBCHGHcQkdKeU0oLIQ4o/8/VLKVaiBmKd1JLQMdt7m9lx33d/tqM3ttd5Q0Ne7CiFcpJQnAKSUF4CRqPqOEkL4CyG+vUrJLteYtPkyIUQ34zYXACnlx8B84F0hxHZMJpVsAXutu732ddBtbm9tbqn21tFfzYQQ4mugopRykBDiOaAncKeUMksIMQ54AKX9twFOSimHWFFcsyKE+AnwB4KBFVLK8cbtW4Bo4Fkp5RkhxBrgOynlT9aT1nzYeZvbc911f7ejNrfXesM1fX0p8I6UMlkI4SSlzBVCOAO/Ax2B5VJKmzD5vk6bdwP6GtvcURojPAshLgAL8ttcCCHK+4qGvdbdXvs66DbHztrcku2tVyrNgBCiIpANjIUCLT8N6GD8/gUwDvgP+NO0gawisBkRQrwH+Esp+wBdgaZCiKcApJStAIma7dgAHLOhAbY9t7k91133d+ynze213nDdvt4SuB9ASplrfM8BqmAy4Crvdb9Bm2cCtxm/5wkhHIQQfYE5Jm3uUJ4H2GC/dbfXvg66ze2tzS3d3k4Wk9yOkFKeE0K8DyQYl9CzUcEcqpoctt3YQZdDQQPZQj6zrcBvoEwGhBDfAHXyd0opHxJCBADBUspdYBt1t+c2t+e6o/u7XbW5vdbbyE37OoAQohZKmf7S+L3c1/0mbR5icpgAlkkp54Nt1Bvsuu522ddBtznYV5tbur21UlkChBAvAClAnJRywVW7jxj3IYT4AvgV2JK/s7x3TChYCp8thHA12ewANMzfD4RIKU+iImTmn1Nu627PbW7PdQfd3+2pze213vncqq8bjwmRUh4FbGLAVdQ2l1LaTJvbed3trq+DbnN7a/PSam9t/lpMhBBTUL412cDnQojxQgg/k0POAPWEEDOAQNMGKu8IIWYIZZM9WwgRLFU0xHzSgCTj55koW+0Cyrm5hD23uT3XXfd3O2pze603FL6vCyH+BnqYnlvOB1z23OZ2WXd77eug2xw7a/PSbG+tVBYDIUQgUAt4WEr5PdAP6AKMEJfzuriikqEnSCkfNJ5Xrm2xAYQQL6GiG04EjgK/CCE6CSEcjYccBPyMf8pUKeV0K4lqVuy8ze257rq/21Gb22u9oVh9/TsriWpW7LzN7bLu9trXQbc5dtbmpd3eWqksBlLK88A+4E4hhKeUch/wPDAAGGE8bAUwWUr5ApR/p2YTPIG1UsoLUspngVnAs0CEcb8XKvl7gpRyKKi6W0NQc2LPbW7PdUf3d7tqc3uttxHd1+2sze247nbZ10G3ub21eam3t5RSvwr5Qjmyuhk/3wNMA8IBJ+O21sB2oHL+NuN2B2vLbsbfoAfwOVDdZNuLxnp7Gb8/YLJPWFtm3ea67iX4DXR/t4M2t9d6X/Ub6L5uJ21uz3U31sOu+rpuc/trc2u1d7nXwksLIcT/gBnAFCHEECnlXyj76/FAM6Hy2mwG4lGuVLn558pybIsNIIToK4SIFEKEogJSVAb6CxWaGCnlB8BuoJ3x+2/G88r17Jadt7k91133dztqc3utN+i+jn22uV3W3V77Oug2t7c2t2Z7i3L8u5UaQoipQADwBHAXKp/LaKmSo76NstP2Ry2vZ0opB1lNWDMjhPgBNeORDJwGJhm/v4PKzbZdSrlWCLEK+ExKOc9aspoTO29ze6677u921Ob2Wm/QfR37bHO7rLu99nXQbY6dtbnV27sky5z28AJCgZ+BSsbvHsBG4E6TY+oDA4HhJtvK9dK5sQ7vAnONn+sCPwB9jN+boP6cc4HVwO/Wlle3ua57Ceuu+7sdtbm91ttYB93X7a/N7bLu9trXdZvbX5uXhfbWeSpvghDiSSBJSvmwEMJDCOEqpUwXQkQBbvnHSSkPoiJH5Z9XrvPZAAghgoA9wGIAKeVhIcQBoLsQ4l8p5S4hxGHAGagvjSGIy3vd7bzN7bnuur/bUZvba71B93U7bXO7rLu99nXQbY6dtXlZaW/tU3lzVgCjhRAdpJTp8nJOmwzAD0AI8akQorPpSeW5YwIY6/MnME9KuUaIgtDCp1EOzfk208FSyhSTP2W5TvRuxC7b3Ihd1l33d/trc+y03rqv21+bG7G7utt5Xwfd5vbW5mWivbVSeROklPuBD1GOrW5CiPyV3bNAkBDiW8BHSrnSakJaAGN9VgCvCSFcTP6Ip4EsACHEr8AdV51X7h107bXNwX7rbq/9XQjhaMdtbq/1tte+7mCvbQ722d/tta/no9vcvtq8rLS3ViqvQghRTwhR3WRTHNAICJCXIySdA94GkqWUjxrPK9eJYa/Dj8AloJrJtmSggRBiJpArpZxqFcnMjBDiIyHE/SabTmAnbS6E+FMI8ZPJJrup+1X8hP30dxcAKWWecdNx7KDN9b29AHvq64OEys2WPxtvN/c3IcQoIURDk03HsYO6CyGamHy1m3EMgBCiqxCip8mmWOygza/Cnu5vZe6ZppVKE4QQU4CpwGdCiC8BpJTbgMPA10IIT+OhccB8KeXzxvPKdfhhACHEV0KI201mN06jksE+YnJYJtAROC6lHGI8r1zfjIQQvwBhwL/526SUO4EY4Btbb3OgOuAghIiAgrofwvb7+wjjA7i1cdMZwBvb7++fAQ8JIfzyt0kpo7DxNrfze/sQIURvIUQr46bT2EdfvxOYCdyb39+N97dobP/ePhXoZly9AAr+50ew4f4uhPge2CqEiDRusotxDIAQYhoqqf0rQog/AaSU27H9Nh8lhOglhGhj3HQGO2jzMvtMk2UgYlFZeAFfALNQD9t6wF9Ae5P9E1EJRB2uOq/cJ4YFKgIXgF9RCVFdjNu9gIVcmRB2vK3UHagEzDb53ghoYfL9JeBeG23z/6F8D3yB74FxV+235f7+NbAIGAmcBEYat3ujnPvvNznWlvr760C6sb3vAXztoc3t/N7+LbAceB+Vp629cbuX8T9gk33dWIdmqAHWMuBZwMNk38s23OafA1Em3ysB/ibfX7fFuqMG2POMff1hk+320NfHAkuMnz2AT4BAk/2TbLTNpwFrjffwWGCocbutP8vL7DNNR38FhBBVUIlBX5dSXhJCHEc5t1Y1Oewg0Ehe5dR69fdyykXUg9cB9cB5BZXDJ1UI8Q7QXgjhK6VMklJ+CuU/UpaRLKCqEKIu0A14AMgRQqQAz6GS4ja3tTYXQtQHLkgpnzR+/w34TgixVkq5w3jYQVRkNFurewtUiPGuUkophEgHfhRCZEopfxZCvAvcZmv9XQjhilIqGgBtUfmrHIQQi6WUScZZ24NAY1tqc6EiASZhh/d2IcR4oLKUsqvx+3MoRWudyb3dFvu6kIodxtn8TcAbQIpxxXIWttvmfqjn+WYhRG3U6sxgIFcIkSOlHIz6PVraUt2NfTlAStlPCHEfMEEI8YeUMsekr7eztb5uQhywxuja8DRqla6hEMIADAA2A61trM1roRSqHlJFOV2FGsc4Symn2fCzvEw/07T5KyClPI2a5coSQjhJFTVpD1faZM9BddCnrSCixRAqWEce6kHzNmrG41UhxBwhxEPAKZR5qKlvRrm+GeUjpUxCrcT2BFoBvaWUnVB1fgtYCtwuhHjGWjJaAinlQSnla6CUDSnlf8BvQKTJYf8CXW2t7kAqkAC4G79vAeYDHxl9UfYBdbCx/m68p62RUp6QUv6BmkTqDdxpYg4zG2hjS/c4KWUC8B2QFK64lgAAFdFJREFUabzX2c29HTiKmiTM5yJq8iyfeNSgrJHpSTbQ11XiNSHcgc4o/6oRqNWa51D1ng90srX7m5QyEbV6swd1T38eZXHzGFDJONBeDnS2sbpvl1LeA2C8vx1EmYHmmzjGY4P3dRMSgHaofIyjgEgpZS9AovrDMmyvv8cBe4HmRkVyPfAo8KYQoj+wExts87L+TLNrpVII8ZIQ4l3jYNJFSpmO+hOCyutS0XjcX0AH4CFUY5V7hBAV4YpgHWkoU4EfgCBUdKyTUsrjqIfQ60IIZxuwQ39JCPGOEKKn8Tf4F+gD1Obyn3IcgJQyB2X+OtsqwpoZ0/4uhKhp3JxtfD8CPC6EcAOQUmaizCdspe75/f0g6j/+k/EB+yvKXOwJoLOU8gKwBtvp761Nvqblf5BS/gSsBDoBA4UQW4D+wMPYwD3OtN5SylgpZYbJvc7W7+2VAKSUc4GdQoj85/w2lG9RPpdQCcBfs5G+bnpvryWlzEAlAg8AhqF8rfYAY6QKYmGT93bAgDJx/xnoL6XcLaU8C7yJClKSA9yNDdTdpK/PNn7Pnyz8C6gM5AclOwqswkbu63BFm/cAdqES2r8HzJJSnjIe9jhqnsWAbT3PnaSU2aiJsiGgrC6llBtQSvUQKeUlYDU20ubl5ZkmjJN6docQYgYQiLrRVAGCgQ+lcmxGCDEYaIExv4uUcpRVBLUARpOg2qgH7H4p5ffGm/OLQA6q3stQytZIKeUBIYSPlDLZakKbgavavBrKXOBFVBu/jVIm9qFuUslSyhFWEdQC3Kq/G4/5FTWj+yKU/xm9fEz6+37Uat0cIcRQ1I04zWj2ejtwt4lJcAUpZYrVhDYDQgVruBt4Vkr5mXGbgCtWc+5H+ZgukFI+aC1Zzcmt6i2EGAS0xLbv7XuBPVLKGSb7GgAfSSn7CCFmA4ellBNs8N6ef397B+VPtw7YLKVsK4QIAwZLKd+zkqhm56q6V0U9296WUu656rjZwEEp5culLaMlMOnru1HjmB9M9vmgJsP/klJ+aLq9vPd1uOFY5iPU+GWW8fNB4DMgUUr5hFUENTNCiNeAr42TJPnb5qKiu46XUl4QQlRARf192Gj6bPPP8rL0TLNnn8pcYLiUMkGokLx9UTO2k6SKkpYEvABMMRloivzBWHlFCPECyq/qfpTS2EoIUV9K+YJQEdP8pZSRxmP3SSkPANjCjZhr27w/8CUwBhXIoQXQAzgkpXwFbKPNjdyov78qpdxlPGYJ4GQryiRct7/fKYRont++JjyHGogDYAMPobaoVej2wGxjP/7U+AByMH43oEy/V+UrlKL8+5vcrN6OQB72c29vI4SIkFKONx6SC4QIIeYDp6WUE4zby3VfN3L1/a0faqJwDMrkdy2AlDIatZpjS1yv7u/k39uNVhpfoSZKbUWhvLqvtxZChOf3dSllstESZaJxQH5SKmxhHAPXf55PQq1M/mL8nAnE5iuU5f0eZ5xEeBwYLoRoI5XbGlLK/kLFhfhQCHEa5caTLKVMNe4v1/e3cvdMk2UgklFpvoDHjO+/AF+abA9CRdB6F7WC0Q74xGR/uY4WZVKPJ4BRxs+eQDjKDv8FwOsG5whry23BNn/G2OZO1zmv3Ld5Ifu7h3Gbq620uUk9rtffpwPvmGx7FfjDVuqOMv0BqGd8DwfOc2X0u3wrldYm28p1fy9MvY3bmwKf2Uq9Tepxvb7+nUlfd0Mlwv7i6n5QXl+FvLe7G7ddc48vz69C3ttdgcbAkyb7y31/v0lff9vkmHr59wRbeRWmvxu/VwEq2Uqbo2IgvIsyZ34dFbW9ylXH9Eb5Dk8w2Vbe729DjO/l5plm9R/NCo00GxiPsj9ehkkqBZQiOQ/wueqccv2HNNZhEjAIFZDmCBBh3O6EWjb/FhXt0xYfvsVp83J9M7L3uheiv39tcqOuZXKeLfzX5wCvXbWtkfFh9Ljx+0jAz07r7Wxj9S5KX3/Uxupe5PubrbzscSxTyL7e4DrnlftnWiHb/B9be54Dr6F8gwEcje/voxTLkJucV677urEO5e6ZZo+BekYD/qj8Lm8BdwghXgKQKnqUD1Df9ARZjs3BTPgPFWY6DmVv/64QopFUAQt2oOrdAMC4zZYoTJvXNT1BGv+dNoC91v1W/d0PNYOPVEEc8s1Fyu1/XQiR787wBOBo9B9DqMh4+1DpJN4SQpwBWkkVKRIo3/e4Ita7tVSBSoDyXW8TCtPXIwCklNPBJsyc88cuRb6/lXeKWHdbG8sUpq83uPqk8v5MM7nH3arN8/MWFlDe645SnocKIRpIY2AaqUz3/wBWCyEChRDvCSE6mp5kA30dyuEzzS58KoUQdwNLpbKnTwUE0FZK+asQ4hLwrTGIQXWUr8lmK4prEaSUG4QQi4HmqKhwvsA0IcRzxn1eqJuVTSCEaIQKSpBH4dp8qxXFNSv2XHcTNqKSH9+sv/uanmADD9/8SHAZgCMqH2V0/gNHSnlSqJxWB6SUI6H8+9kYKUq9R4DN1Ds/WENh+noF0/OsPfAoKSby2939rYh1t5mxTBH6up/1pLQMJhP9GaisDTdr8y3WktNCbAIWodKDHBBCuEgps6WKA5KDSqfyt5RyjVWlNBNCiInAz1LKk6iI7eXqmWbzSqUQ4hdUCoHlAFJFg/odmCGEOC2lXGGc4eiA8in7x3hemWigkmCcwdoqVR5CUPkXHwZWSCnfF0IkAv8TQsQBWfmz2OUdIcT3wBlpDEpgbPPfUEnubb3N7bnuNYEMKeUZKaU0Ou3bQ39/G+VrckkI8YOUMs7Y5jOEEKeklMuNxw1CJb5/xvi9vK9W2WW9AYQQIaiojqnG7wnYR1/vDqyXUqZBwf3tV1SKIFu/v9ll3e21rwMIIT5CBdzJAP6UUh4RKvDQdBtv8xZAnpRyp5TSYFyRe0QIsdLY7/Pv4aGoCL/3Gc8r7/X+ChV8KkUI8T8p5SXjf/zH8vJMs+mUIkKIe4ChUsrexu8C9cfLFEL0QUX7nCylPHfVeWWmgYqLcXDlgap/ksn2d1GhqJ+UUmYLISoDBqkSqpb7ugshpqN8CgabbBNGJaMvymzAVtvcnus+GaiF8jXYJqX8wrj9bVQQA1vt7zNQ//N/UE78majALFlCiF4o36MvpJRJpnXV9S6/GP/ndYBoIElK+Zxx+3uonIy22tf/QOXaGw38Yhxc5t/feqN86qbY6P3NLutur30dQAjxDcp67DPUStVo4C4p5X5bfp4LIb5FPctzge1SyonG7fnP8jFSyhyhot72kVJONu4v7/WeimrvpUAPKeU9JvvKzTPN1n0qXVDOvAghxgFTgCVCiFbAZpTPgZdxf8FvUZYaqDgIZf5YQUrZ39gBQ4UQtY27X0flIowwfj9nciMu7z5lnVA32l+M3wcJIcYDLxtXsVahzMBssc07oSKA2WPd/wdUAh4EVqIiHubzJirpuS329z6Ar5TyHinlz6g2ri+lzDIecgTV3vnmYMJ4ni3U28fe6g0ghHgCNeDqDXwCtBAq/yDAK9huX2+IsrQZiEqZMUQI4WWyKpHf5rZ4f7PLuttrX4eCNnQGnpBSbkAFUrwALBJC1EXlXvXG9tr8FaCqlPIOVICeNCGEs3H3a6g2DweQUp6wIYVyNBAopbzXuNIeIoR43eSQcvNMs0mlUggRaPy4B0gSQnRF5ST8CjWz/RsqvPo+4HshhFtZa5gSkgy4CiHchcrV9BPKJGwmqjNeQOVjRBodn42fy+2ytRDCF2V7/yNQXwjxF/C8cXcAyqkb1J/TptpcKD+Srai6NxbKPMZe6h6GGlAMllJeRAWmeFgI8bYQ4jUpZTaqv3cDm+rvPsAK1Cx2PsuBACFELQAp5WHgEOq/7yYvBzkoz/X2RCmRn5tstvl6m3AEowmklPKAlLITUEkI8YuxnonYXl8PAA6gVuLmoiaKBqGUq/xk34eMx8ywsfubD3CYy3V/HTupO3bY16GgvzsCNYDhxs3eqCBFfwJvGp91J1FmsLbU5klcHq/0AkYBvwghfjW28XlUbuUrKM/1Nz7TZkgp7zbZ/BpQ2eQ/fhgVnOr7sv5MszmfSqESpFYWQqSgVm3CgSbAHCnlfmC/8U/bU0o5TajEwL6oGRBbIR11Qx6AinB5N3AR5ez8CSok9TohxAkp5S9Wk9JMCCG+A6qi6rwDZd6bAwyUUsYbj5mBSh0xWQjhjY20ubG/VwNOo27GLVEPoH5SyrPGY2Zgg3UHlcxcKL8ThBAdUKu1o1EO7p8K5WfzHbBKCBFrY/39JPCfEMLVuErnisrnlWY87nFU3W3iHmfS188BU43bHFHPMZut91XkAF2EELWkMWox0BFYaDSRmgastLG+XgV1f1sshEiQUm4UKpjFu0C6UKahLwIfAJWxkTY39vcqqEnirwCklJuFEBNQdbXZuhuxq74OBW1eFTgKfAN8ZLQ8C0dNGv8ATDbe878QQrhjW22eCjwnhOgG3AF0Rf33/zOu3H2Aivh60hba3ORZniCEmAuslioq+xHUWK4n8DuAsb09KOPtbVMrlULZYldCze5kAd2Bh1CDrbbisgloI4zLx8D7Usoy20CFRQjxgxDiZwDjLNZ2YCLKxDfHOLPxKCqfTTYwDGUqWK4RQjyJMpEZCMQCIahkyB/nK5RGQlB9A+ADG2nz/P4+xLhpkJTyI1T+orMmh9pi3X8QKggXUsp04+adwD1SyrVSyh0oiwQvqQJbDMX2+nsM0Bp4VQjhL6VMQa1YGIQQPwLhUspcKeV75b3Nr+rr2cCTULBCkYGyOrG5esM19/aVwBLUJEmIcVseKiJmVamCmQzFtvr6IOAgyqdoghDCT0q5EdUHHgT2o3ITZkgp37WRNs/v78NQk8RP5u+TKrrnaGyw7vba1+GKNh+KMnUMR41VPwBGSSm/MN7jA1GrmGADz/OrnuUzUPWfAXwlpTwoVdaGpwEPKWUGtjl23QvcBjwrhPCVUh5BTZqNFkLUMDmtzOsrNqNUGs3gPIGRUgWmGYMyjXBFDURSUQ30H5AgpfwWyubycVERQkwCGgLuQkWKQko5DWUiVhPoJoSIBD5Fzf4hpdwnpTxlFYHNixPwn5QyE2UKuA4VtKOXEMJJCOEmVLTfo1LKJWAzbX51f38SuE0o/5tY4zHuxplsW6t7fn93yx+AGEmXJrkXgTtRZu622t+/AP5F+Y4/JpTviR8QhYqCOAYKApSVW67T18eizLzzfWsMqIHWLmyo3nDNvf03ACnlGyiXhhVCiDuEENVQE6hVjfttta8vRPX10eJyvraqqCiI94LNtPn1+nsjIUR4fv2klAdQq/Y2U3d77uvXafPRKF/SECnlfinlVuNYZjFq/HoIyv/z/KpneX6bR6HGcANNFKpnMOorttLmXP/+5gqMMa5Ar0alS6mSf0J5aG+bif5qvKFWB04ALlJFAVwFvC3l/9u7v1DLqjqA499f4wyUNkMP9VKKDIxFik0x9CBBIM0IPphkIeUUCTFQaDU9GFQ65ku95N/R7GnMYSYUlBnxQSnQECl60FGQhEB7qxGGmFJGbOzXw1qbux3uvefO8d575qz1/cBw5569zrB/s35nnfM7a+298g+1k84HLqqzGHN/ce+gzsB+hDJL8TDwTmZ+vR67jrL58yeBk5n5g/r4XN96eRARn6VcxH5LZj5T8+AqytLfn1M2Sr4oM79X27cS91L5fntmPluP30q5+Pv7w3Maif3MfD+VmbvrsaDMzP4KeDMzb5zZia6BJfJ9F2U253ZgP/DvzPx2bT/3Y9ykXK9tnqRspTPs2TX3ccPEsf27wHbKB+wTQ5+3YsLYvo8yq3NdZt5W27fS5yvJ989TLuG5o/4+97F3nusreT//KnBZZu4bnjPv7+eL9PnbmXlDPfYLymv9deB4g32+3Ph2a2a+ERE7M/P3Mz3Rs9RMUQkLL7LRz4eAX2e5DuE24FCdVm7iBTk2vKlEuV70N5Q9foZvMLcCf89z9BbE71dE7KEsjTqQmc/Xx45SrkF4Msty3xbjXi7ffwQ8l3Xj7wZjPzPf38nMb9Rj3wEuzsyfjdvO8HRX1RL5/gTlDerZXNjPrZm4J+T6TcCLo/+LZuKGiWP7pcDrWZeANxj7UmP7I5l5eNSutbiXy/efAEfrTG1TsXee68v1+Y+BpzLzpdq2mdgn9PnVwKtZr6ltKW5Ydnw7kpkHRu3mpl5pZvkrLDo1fBK4pL44tw0F5RJt59rwQsvME5Slv6cj4mD9sLlzVFCec7cgXgWPUu70e3NEDHs0bgA+PCoom4t7Qr7vGBWULcZ+Zr5nRDxcB+S3Wi0oq8Xy/QOUWemhoGyqzyfk+hWjN+Sm4oaJY/sXRh+ym4udpcf299xksLW4J+T7p4eCsrZtJvaec31Cn18+FJS1bTOxL9Hnh2qff3xUUDbX5yw9vp0eN5qneqWpmcrB6JuPe4A9lAt+b6nH5qbin8boW67NlLskHhm+9WlZlG01dgG/pNy8gMy8dpbntF7M9/fk+9Gsmwa3HHuv+W6uO7ZDH7kO/eZ7r7kO9nlvfd7a+NZkUTmIiN3Arsz8Vv29xVmLRUXEg8DmXFgS2EXsUfar3JQLGyF3ETeY75jvPcVtrpvrXcQN/eZ7r7kO9nlvfd7K+NZ6UXleZp6uf5/LDppWRFyYmcNdQLuKfdDyt3qLMd/N917y3Vw313vJdeg333vOdfu8r7jH5nl8a7qoHMxzB71fPcfeq577vOfYe9Rzf/cce6967fNe44Z+Y+817nnXRVEpSZIkSVobTd39VZIkSZK0viwqJUmSJElTs6iUJEmSJE3tvMlNJEnSSkTEu5QNrTdSNrH+LXB3j3cxlCT1w6JSkqTVcyoztwNExMeAw8AWYN8sT0qSpLXk8ldJktZA3ch6D3BTFBdHxHMR8UL9cwVARByMiC8Pz4uIQxFxTURcGhF/iYhjEfFyRGybVSySJC3HLUUkSVolEfFmZl5wxmP/Aj4F/Af4X2a+XQvE32Xmjoj4IrA3M6+NiC3AMWAbcBfw58w8FBGbgA2ZeWpdA5IkaQVc/ipJ0tqK+nMjsD8itgPvApcAZOYfI+L+ulz2K8BjmXk6Iv4E/DQiPgE8npl/m8G5S5I0kctfJUlaIxGxlVJAvgHsBY4DnwF2AJtGTQ8CNwA3AgcAMvMwcA1wCng6Iq5cvzOXJGnlLColSVoDEfFR4EFgf5ZrTbYA/6h3gv0msGHU/CHghwCZ+Up9/lbgtcy8F3gCuHzdTl6SpLPg8ldJklbPByPiGAtbihwE7qzHHgAei4ivAc8Abw1PyszjEfFX4Mjo37oe2B0R/wX+Cdyx5mcvSdIUvFGPJEkzFhEfouxv+bnMPDnr85Ek6Wy4/FWSpBmKiC8BrwL3WVBKkuaRM5WSJEmSpKk5UylJkiRJmppFpSRJkiRpahaVkiRJkqSpWVRKkiRJkqZmUSlJkiRJmtr/AcH5t70G1eoCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dates = pd.Series(data.date.unique())\n",
    "dates = list(zip(dates[0::10], dates[10::10]))\n",
    "\n",
    "depression = list()\n",
    "anxiety = list()\n",
    "anxiety_and_depression = list()\n",
    "\n",
    "for start, end in tqdm(dates):\n",
    "  inrange = data[(data.date >= start) & (data.date < end)]\n",
    "  \n",
    "  p_athome = np.zeros(country_pop.count())\n",
    "  for idx, (country, df) in enumerate(inrange.groupby(\"country_id\")):\n",
    "    tot_days = len(df.status)\n",
    "    at_home = df.status.isin([\"Closed due to COVID-19\", \"Partially open\"]).sum()\n",
    "    p_athome[idx] = at_home / tot_days\n",
    "  \n",
    "  cpd_AtHome_tmp = TabularCPD(variable=\"AtHome\", \n",
    "                              variable_card=2, \n",
    "                              values=np.array([p_athome, 1 - p_athome]),\n",
    "                              evidence=[\"Country\"],\n",
    "                              evidence_card=[len(countries)],\n",
    "                              state_names=state_names)\n",
    "  model_tmp = model.copy()\n",
    "  model_tmp.add_cpds(cpd_AtHome_tmp)\n",
    "  infer_tmp = VariableElimination(model_tmp)\n",
    "  \n",
    "  depression.append(infer_tmp.query([\"Depression\", \"AtHome\"], show_progress=False).values[0, 0])\n",
    "  anxiety.append(infer_tmp.query([\"Anxiety\", \"AtHome\"], show_progress=False).values[0, 0])\n",
    "  anxiety_and_depression.append(infer_tmp.query([\"Anxiety\", \"Depression\", \"AtHome\"], show_progress=False).values[0, 0, 0])\n",
    "\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "plt.plot(depression, label=\"Depression\")\n",
    "plt.plot(anxiety, label=\"Anxiety\")\n",
    "plt.plot(anxiety_and_depression, label=\"Anxiety and depression\")\n",
    "plt.xticks(range(1, len(days), 5), [d.strftime(\"%d/%m/%Y\") for d in days[::5]], rotation=45)\n",
    "plt.yticks(np.linspace(0, 1, 10), [f\"{x:1.1}\" for x in np.linspace(0, 1, 10)])\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"Probability of mental health condition\")\n",
    "plt.title(\"Probability of incurring into mental health conditions.\")\n",
    "plt.axhline(y=.5, xmin=0, xmax=1, color=\"red\")\n",
    "plt.ylim(0, 0.6)\n",
    "plt.xlim(0, len(days) - 1)\n",
    "plt.legend()\n",
    "plt.savefig(\"meantal_health_window\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd24d97-d13d-40ef-a074-2a66eab53cb6",
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   "source": [
    "Of course the probability of incurring into mental health conditions follows a similar pattern. Depression and anxiety are not indipendent event, they are indeed strictly correlated and one might be the causation of the other. \n",
    "However with this kind of analysis it's easy to see how high the values can ramp up. Nearly half of the students, in the middle of the lockdown, are predicted to have suffered some kind of depressive symptom."
   ]
  }
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