assignment2.ipynb

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    "%%html\n",
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    "# Assignment 2\n",
    "\n",
    "> Group Members: Julius Kappenberg, Alexandros Gilch, Paul Grass, Tim Mensinger, Philipp Schirmer\n",
    "\n",
    "We suggest reading this notebook [online](https://nbviewer.jupyter.org/github/timmens/macro_problems/blob/main/assignment2.ipynb?flush_cache=True) for the best reading experience.\n",
    "\n",
    "## Question I: Monetary Policy\n",
    "\n",
    "The Stata code which was used to compile the results of question 1 is appended at the end of this document.\n",
    "\n",
    "### i.\n",
    "\n",
    "When correlating the constructed instrument with the short term interest rates, we find a correlation of about 0.317. This correlation is moderately positive and suggests that we can reject the hypothesis of instrument irrelevance.\n",
    "Figure 1 confirms this finding and provides a graphical intuition of the correlation as influential values do not seem to affect the correlation coefficient significantly. Unsurprisingly, there are many values where the instrument is 0, which correspond to floating exchange rate regimes.\n",
    "\n",
    "\n",
    "<figure>\n",
    "\n",
    "<img src=\"figures/figure1.png\" alt=\"Trulli\" style=\"width:100%\">\n",
    "<figcaption style=\"text-align:center; font-size:10pt\">\n",
    "    <i>\n",
    "        Figure 1: Scatterplot of the short term interest rate and the instrument z, with fit.\n",
    "    </i>\n",
    "</figcaption>\n",
    "    \n",
    "</figure>\n",
    "\n",
    "\n",
    "### ii.\n",
    "\n",
    "In order to estimate the effect of a change in the short-term interest rate as instrumented by z, we require some set-up. Working with the Jordà Schularick Taylor (2020) data set, we use the changes in real GDP, consumer price index, credit to GDP ratio and real stock prices over a time horizon of four years (periods) after an interest rate shock in the base country as dependent variables.\n",
    "In the second step, for each time horizon length of one to four periods, we run an instrumental variable regression and include the control variables described in the task. From each of the total of 16 regressions, we save the estimated coefficient of an (instrumented) short term interest rate increase of 100 basis points (i.e., one percentage point) on (i) real GDP per capita, (ii) the consumer price index (CPI), (iii) the credit to GDP ratio, and (iv) real stock prices, respectively.\n",
    "\n",
    "\n",
    "<figure>\n",
    "\n",
    "<img src=\"figures/figure2.png\" alt=\"Trulli\" style=\"width:100%\">\n",
    "<figcaption style=\"text-align:center; font-size:10pt\">\n",
    "    <i>\n",
    "        Figure 2: Response of macroeconomic variables  of an 100 basis point increase of short term interest rate, 1SE and 90% confidence bands.\n",
    "    </i>\n",
    "</figcaption>\n",
    "    \n",
    "</figure>\n",
    "\n",
    "\n",
    "\n",
    "Figure 2 depicts the four response plots for each dependent variable with the confidence bands  (grey shaded areas). The effect of an interest rate increase varies widely, both in magnitude and variability of the estimates. In line with standard economic theory, we find a significant and negative effect of an interest rate shock on real GDP that starts immediately after the shock and amplifies over the subsequent 4 years. On average, GDP drops by -1% one year after the shock to about -2% after four years. The response of the consumer price index is insignificant over the first two years after an interest rate increase in the base country, but turns significantly negative three years after the shock and reaches -3% after four years. Similarly, the credit to GDP ratio remains unaffected in the first two periods, but point estimates indicate a drop by about -1.5% and -2% in the third and fourth year after the shock. However, none of these estimates are statistically significant. While the potential magnitude of the response of real stock prices is strong, it is the only dependent variable that does not exhibit a monotonous response. In the first period after the increase, prices drop by a statistically significant -5%, but estimates for years 2 and 3 after the shock are insignificant. In the fourth period after the shock, stock prices drop significantly, on average by about -10%. The parameter estimates  we obtain are qualitatively in line with Jordà, Schularick and Taylor (2017) and often coincide in quantitative terms.\n",
    "\n",
    "\n",
    "\n",
    "### iii.\n",
    "\n",
    "Part iii) discusses the impact of US monetary policy on macroeconomic variables in other countries in the world. Instead of instrumenting the local short term interest rate, we hence consider how an increase in the US short term interest rate impacts real GDP and international real stock prices globally.\n",
    "\n",
    "If we consider interest rate shocks in the US to be exogenous from macroeconomic factors in the rest of the world, we can estimate the causal effect of the Fed's monetary policy on real economic outcomes in the rest of the world. Since the US has a free floating currency, we cannot use our instrument that exploits changes in base country interest rates for fixed exchange rate regimes. Instead, we run an OLS regression that accounts for the set of control variables and their lags as in ii). Importantly, this simplistic approach cannot be applied to evaluate monetary policy in the US itself, as there might be a strong endogeneity between macroeconomic variables and monetary policy. \n",
    "\n",
    "\n",
    "<figure>\n",
    "\n",
    "<img src=\"figures/figure3.png\" alt=\"Trulli\" style=\"width:100%\">\n",
    "<figcaption style=\"text-align:center; font-size:10pt\">\n",
    "    <i>\n",
    "        Figure 3: Response of international real GDP per capita and stock price to a 100 basis points increase in the US short term interest rate with 1SE (dark grey) and 90% (light grey) confidence bands.\n",
    "    </i>\n",
    "</figcaption>\n",
    "    \n",
    "</figure>\n",
    "\n",
    "\n",
    "Figure 3 depicts the percentage response of a one percentage point (100 basis point) increase in US short term interest rate on i) global real GDP and ii) global real stock prices. \n",
    "Both macroeconomic variables appear to be negatively affected. Global real GDP decreases over the four year horizon by roughly 0.15 percent per year, amounting to a statistically significant decrease of about -0.6% after four years. Similarly, on impact, global real stock prices are negatively affected by an increase in US short term interest rate; in the first two years after the change, stock prices decrease by a statistically insignificant 0.5 percent, but they significantly fall in the third and fourth year for a total of -2%. Hence, if the identifying assumptions hold and we can interpret the estimates causally, the monetary policy of the US has a relatively strong impact on global macroeconomic outcomes. In particular, a monetary tightening would significantly lower GDP growth and stock prices in the rest of the world.\n",
    "\n",
    "However, the importance of macroeconomic interlinkages and spillover effects casts doubt about whether the identifying assumption holds. While US monetary policy is mainly influenced by domestic macroeconomic conditions, other countries, the global macroeconomic environment broadly and the outlook and policies of important trade partners in particular likely affects the Fed’s monetary policy. \n",
    "\n",
    "\n",
    "### iv.\n",
    "\n",
    "Estimating the effect of US monetary policy on domestic economic outcomes makes the endogeneity issue even more salient: arguably, the Fed devises its monetary policy based on the domestic economic outlook, hence causality likely goes in both ways. To account for the endogeneity, we utilize the narratively identified shock measure of Romer and Romer (2004) as plausibly exogenous monetary policy shocks.\n",
    "\n",
    "<figure>\n",
    "\n",
    "<img src=\"figures/figure4.png\" alt=\"Trulli\" style=\"width:100%\">\n",
    "<figcaption style=\"text-align:center; font-size:10pt\">\n",
    "    <i>\n",
    "        Figure 4: Percentage response of US real GDP per capita and consumer price index (CPI) to a 1 percentage point increase in the US short term interest rate. Reported are 1SE (dark grey) and 90% (light grey) confidence bounds\n",
    "    </i>\n",
    "</figcaption>\n",
    "    \n",
    "</figure>\n",
    "\n",
    "\n",
    "Figure 4 depicts our results for iv.1 and iv.2. The panel on the left suggests a very noisy and statistically insignificant effect on the real GDP. Estimates range between +0.2% after two years to -0.5% percent after four years, neither of which are statistically significant. For the consumer price index, however, we find statistically as well as economically significant negative estimates three and four years after the shock. In line with economic theory, consumer prices respond to a 1 percentage point increase in the short term interest rate by a significant drop in prices of about -3 percent and -4 percent after three and four years, respectively. \n",
    "\n",
    "\n",
    "<figure>\n",
    "\n",
    "<img src=\"figures/figure5.png\" alt=\"Trulli\" style=\"width:100%\">\n",
    "<figcaption style=\"text-align:center; font-size:10pt\">\n",
    "    <i>\n",
    "        Figure 5: Percentage response of US real GDP per capita and consumer price index (CPI) to a 1 percentage point increase in the US short term interest rate. Reported are 1SE (dark grey) and 90% (light grey) confidence bounds, split sample pre and post 1985.\n",
    "    </i>\n",
    "</figcaption>\n",
    "    \n",
    "</figure>\n",
    "\n",
    "Figure 5 reports our findings for a split sample pre-1985 and post-1985. We find no statistically significant effect of monetary policy on real GDP in either subsample. Before 1985, point estimates range between -1% after one year to -1.5% after three years, before recovering again entirely in the fourth year after the policy shock. Post-1985, the estimated effect is positive, but very close to zero with a statistically insignificant effect of about +0.5%. However, due to the small number of observations (the Romer and Romer shock measure is only available from 1969 onwards and the specification uses four lags), the pre-1985 estimates need to be treated with particular caution. \n",
    "\n",
    "Again, given the small number of observations, the evidence on price effects of monetary policy in the period before 1985 remain inconclusive. The top-right panel suggests that there is no statistically significant effect in the first two years after the shock, but a very pronounced deflationary effect of more the 5% in the third and fourth year after the shock. This contrasts with the estimated effect in the post-1985 sample, where if anything, we find a marginally significant positive effect of monetary tightening on prices about two years later. The split sample suggests that the negative effect on the prices in the full sample is driven mostly by the earlier period, while there is no pronounced effect in the post 1985 period."
   ]
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   "source": [
    "## Question II: Fiscal Policy\n",
    "\n",
    "This part of the notebook is structured as follows: First we define functions that implement the local projection method, the plotting of the resulting impulse response functions and the data preparation step. This section can be skipped. Aferwards, in the results section, we answer question *i.* Then we apply the local projection method on our data and plot the impulse response functions. At last we summarize our findings and answer question *iii.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code\n",
    "\n",
    "#### Local Projection Method"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _create_formula_with_lags(var_name, n_lags):\n",
    "    \"\"\"Creates formula str of var_name with n_lags.\"\"\"\n",
    "    covariates = [f\"{var_name}.shift({i})\" for i in range(1, n_lags + 1)]\n",
    "    formula = \" + \".join(covariates)\n",
    "    return formula\n",
    "\n",
    "\n",
    "def _create_formula_to_extract_shocks(endog, exog, n_lags):\n",
    "    \"\"\"Creates callable formula str with lagged exog regressors.\"\"\"\n",
    "    exog_formulas = [_create_formula_with_lags(name, n_lags) for name in exog]\n",
    "    formula = f\"{endog} ~ 1 + \" + \" + \".join(exog_formulas)\n",
    "    return formula\n",
    "\n",
    "\n",
    "def local_projection(df, n_response_periods, n_lags, alpha):\n",
    "    \"\"\"Estimate impulse response using local projections.\n",
    "    \n",
    "    This function is not written in a general manner and this is\n",
    "    on purpose. Sets of exogeneous and endogeneous variables are\n",
    "    defined inside to avoid overly abstract code.\n",
    "    \n",
    "    Args:\n",
    "        df (pandas.DataFrame): The data.\n",
    "        n_response_periods (int): Number of response periods.\n",
    "        n_lags (int): Number of lags to use in shock creation.\n",
    "        alpha (float): Confidence level. Must be in [0, 1].\n",
    "        \n",
    "    Returns:\n",
    "        Dict[pandas.DataFrame]: Dictionary containing\n",
    "            impulse_response: The estimated impulse responses.\n",
    "            df: The updated initial data frame.\n",
    "    \n",
    "    \"\"\"\n",
    "    exog = [\"g_pc\", \"gdp_pc\", \"rev_pc\"]\n",
    "    exog_formulas = [_create_formula_with_lags(name, n_lags) for name in exog]\n",
    "    exog_formula = \" + \".join(exog_formulas)\n",
    "\n",
    "    # estimate impulse responses\n",
    "    impulse_response = pd.DataFrame(columns=[\"h\", \"endog\", \"estimate\", \"se\"])\n",
    "    impulse_response = impulse_response.set_index([\"h\", \"endog\"])\n",
    "\n",
    "    for h in range(n_response_periods):\n",
    "        for endog in df.columns:\n",
    "            formula = f\"{endog}.shift({-h-1}) ~ 1 + g_pc + \" + exog_formula\n",
    "            model = sm.ols(formula, df).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": 1})\n",
    "            theta = model.params[\"g_pc\"]\n",
    "            theta_se = model.bse[\"g_pc\"]\n",
    "            impulse_response.loc[(h + 1, endog), \"estimate\"] = theta\n",
    "            impulse_response.loc[(h + 1, endog), \"se\"] = theta_se\n",
    "\n",
    "    # result processing\n",
    "    cf = sp.stats.norm.ppf(1 - alpha / 2)  # confidence factor\n",
    "    impulse_response = impulse_response.reset_index().convert_dtypes()\n",
    "    impulse_response.estimate *= -1\n",
    "    impulse_response = impulse_response.assign(\n",
    "        **{\n",
    "            \"lower_ci\": impulse_response.estimate - cf * impulse_response.se,\n",
    "            \"upper_ci\": impulse_response.estimate + cf * impulse_response.se,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    result = {\"impulse_response\": impulse_response, \"df\": df}\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impulse Response Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_impulse_responses(result, df):\n",
    "    \"\"\"Plots all impulse responses.\"\"\"\n",
    "    fig, axes = plt.subplots(4, 2)\n",
    "    fig.set_size_inches(17, 20)\n",
    "\n",
    "    for ax, col in zip(axes.flatten(), df.columns):\n",
    "\n",
    "        # subset data\n",
    "        _data = (\n",
    "            result[\"impulse_response\"]\n",
    "            .query(\"endog == @col\")\n",
    "            .drop(columns=\"endog\")\n",
    "            .astype(float)\n",
    "        )\n",
    "\n",
    "        # plot estimate\n",
    "        p = sns.lineplot(x=\"h\", y=\"estimate\", data=_data, ax=ax, label=col)\n",
    "\n",
    "        # plot standard errors\n",
    "        ax.plot(_data[\"h\"], _data[\"lower_ci\"], alpha=0.3, color=\"tab:blue\")\n",
    "        ax.plot(_data[\"h\"], _data[\"upper_ci\"], alpha=0.3, color=\"tab:blue\")\n",
    "        ax.fill_between(\n",
    "            _data[\"h\"],\n",
    "            _data[\"lower_ci\"],\n",
    "            _data[\"upper_ci\"],\n",
    "            alpha=0.2,\n",
    "            color=\"tab:blue\",\n",
    "        )\n",
    "\n",
    "        # adjust plot\n",
    "        ax.axhline(0, color=\"black\", alpha=0.85, zorder=-1)\n",
    "        ax.xaxis.set_tick_params(labelsize=14)\n",
    "        ax.yaxis.set_tick_params(labelsize=14)\n",
    "        ax.set(ylabel=\"IR\", xlabel=r\"$h$\")\n",
    "        ax.set_frame_on(False)\n",
    "        ax.xaxis.label.set_size(15)\n",
    "        ax.yaxis.label.set_size(15)\n",
    "        _ = ax.legend(frameon=False, fontsize=14)\n",
    "\n",
    "    # mark relevant variables\n",
    "    rect = plt.Rectangle(\n",
    "        # (lower-left corner), width, height\n",
    "        (0.073, 0.687),\n",
    "        0.415,\n",
    "        0.2,\n",
    "        fill=False,\n",
    "        color=\"tab:red\",\n",
    "        lw=2,\n",
    "        zorder=1000,\n",
    "        transform=fig.transFigure,\n",
    "        figure=fig,\n",
    "    )\n",
    "    fig.patches.extend([rect])\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Reading and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _log_transform(column, ignore=None):\n",
    "    column = column.copy()\n",
    "    ignore = ignore if isinstance(ignore, list) else [ignore]\n",
    "    if column.name not in ignore:\n",
    "        column = 100 * np.log(column)\n",
    "    return column\n",
    "\n",
    "\n",
    "def read_and_clean_data():\n",
    "    \"\"\"Read and prepare data.\"\"\"\n",
    "    df = pd.read_stata(\"data/fiscal_data1930s.dta\")\n",
    "    df = df.set_index(\"date\")\n",
    "    df = df.transform(_log_transform, ignore=\"irate\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    " \n",
    "### i.\n",
    "From the question description we recall\n",
    " \n",
    " > The identifying assumption is that public expenditures are predetermined within a given period (i.e., they do not react to shocks to other variables in the same time period).\n",
    " \n",
    "The below output table shows the time difference between consecutive periods in days. From this table we can be sure that the data contains monthly observations. One may then argue that during the period of a full month, shocks can occur to other variables to which the political process can respond with a change in the same period. However, since the political process is known to be slow, the assumption that such a response takes _at least a month_ does seem reasonable. Even in (economic) crises, bureaucracy may not allow for responses to be significantly faster than a month. To understand the actual severity of this assumption we would have to study the efficiency with which political decisions were taking place during the observed time span."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_and_clean_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>days</th>\n",
       "      <th>31</th>\n",
       "      <th>30</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n_obs</th>\n",
       "      <td>55</td>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "days   31  30  28  29\n",
       "n_obs  55  32   5   2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = np.diff(df.index).astype(\"timedelta64[D]\")\n",
    "diff = pd.Series(diff).dt.days\n",
    "diff.value_counts().to_frame(\"n_obs\").rename_axis(index=\"days\").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ii.\n",
    "\n",
    "#### Application of Local Projection Method\n",
    "\n",
    "Next we call our implemented local projection method with the desired parameter values. We further set the confidence level to 95% using ``alpha=0.05``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = local_projection(df, n_response_periods=24, n_lags=6, alpha=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting\n",
    "\n",
    "The figure below shows the impulse response functions for all columns of the data set. The impulse response function for GDP is highlighted by the red frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hpjoJLkwjiqIwt8TF3BIX/7aqihd39fDsjm5+9OJefvFKE+fOzOGSOQXMzLNN+RMas0FjUZkHXzBGY/cgTzR0UJNvZ1GZG9cJeocLMVpiibeX9oRjSSKJzPdQNMlQNE4imcZm0uEy67GZdFgMmZMBq0GTbBshTtPhRZ4vv/zykfvXr1/PJZdcctTnLFiwgO9+97tEo1GMRuPI4ydCkWdFUbhpeQWhWJI/v9WGxaDjfWed+pgURcGk1zDpzeQ7zCP3xxJJAuFEJugQitPpD5NIpTHpNfLsRmbm2Sn1WMblAkU8maLTH6FlIEj/cIyhaILd3RFim9pZXu2lOnfqz1+EEGIik+DCBDcUiZMag6rODrOeaxcUc838InZ2DfHs9i5e3N3Lszu6qcyxcunsfM6rzcNmnNr/RdxWAyuqcmjzhdjeMciB/hBzi53MKZJ6DNNR/3CU/X1B+oej6FQVnaZg1mtv1+zQFHSqiv5gVoz+YA0PnaagVw/9XhmZ3GaW5WSCB5FY5nY4niQcSzAUTRCNJ0mkIJ5IEU0kSSTTmUIhgFGXCR70DUeJJJIoKBh1KuaDxUptRg2XxYDdpMdi0A5ehdQmVT0VIbJlqhV5VhSFT51XTSiW5KFXm7EYNK6YWzgqr23QaeTaNXLtxpH74okkvUMx2gMhmvqC5NiMVORYqcq1kmszjvo+CcUStA6EafOF8IdihGNJrEYdVV4rpqiJ4VSaZ7d3M6cozNmVXjl+CyFElkztM8dJLp5MsbnVT6c/jKlMwz4GKX+KolBX6KCu0MEnVlXx0u5ent3RxU/+sZ//W9/MOdU5XDInn9mFjgkxgRorJW4LBQ4Te3uHWLe3j309Uo9hOukfjtLUF6RrMEIgFAcyhdOSqTSJVJpUKnPSr6CAkkZTlJHgg6ZmAgqqkvnSVAW9TqGjfZh98S4SyUztj2giSTyZRklDWklj0DT0OgWDpuEw6bEYTJj02rsCFPB2uvJwNMFgOE4kkWQwHGNfb5BUOp0JOug1jPpMpxSnWY/TbMBq1HCZDTgtki4sxOGmYpFnVVH43IUzCceS/OSlfZgNGufX5o3JtvQ6jSK3mSK3mWA0QXN/kDebB2jsGKTQaWZGvpUStwWT/sxO8jNLM8J0+MMMhTMFmz02A2Ue68hrGzSVs8o9tPtCbG7x0x0Is2JGLqWeydMhSwghpgoJLkxgek1leZWXFxq72dDUT7nXSqXXOmYnu1ajjivmFnLF3EL29gzz7PYuXtrdy9pdPZS6zfzLikqWVnrGZNsTgU5TmVXgpNxjk3oM04QvGGNf7/BIUMGgUyjzWHCa9e86uU+l3w44xJMp4ok00WSSeCLTnSSVShFLpUjE06TCaQZCSazRBEadis2Yuepn1msjXUtO5f/T2+nKGjk24xG/iydTBA8GHULxJL1DUVoHQiRSaXSqisWosmpmLmWeyVe8VYixNBWLPOs0lS9fNotvPrGdB/62G7NeY1nV2NaEsBp1zClykkql6PBHaA8E2dc3TJ7NSEWulcocK16r4aQ/85KpNJ2BMK0DYfqGIwxGEigK5NmMeK3GY86Bit0WPFYDm1v9PLm1g4WlbhaWuTHo5AKBEEKMFwkuTHCFLjMfXFLKWy0+3mgaoCsQYU6hA887TjBG24w8GzPyZvDxlZW8sreXxzZ38K0nd3BRXR7/ek4V1im8XOJQPYaBYIydUo9hSvKHDgYVAhEC4Th6VaXUY8H1jqDCIYqioCmgoaDXOKmrcfaEn8pC51gM/wh6TcVlMbzr/2YylSYUTbCjM8DTW7u4en4RRS7zMV5FCDFVGHQqX7uyjtse3869z+zk9qvnML/UNebbVVWVEo+FEo+FoXCcAwNBNuzvZ3tHgCKnmRl5Nord5mMu3YrEk7T5QrQOhPGHYgzHEpj1GmVuCw6zHvUkghNmg45lVV729QZ5dX8/nf4I58z0kmM3jfbbFUIIcRQSzp0E9DqNZVU53Li0nBy7gbdafWzvCBBLpMZ822aDxsWzC3jggwt4/1klrN3Zw7//fhObW/1jvu1s8xysx1DusbK9Y5BHN7XzZvMA4Vgy20MTp8kfirGxxce6vX3s6hpiOJqgxGWhtsCO23LyV9YmA01VsJv1LK7woikKT23tpHswnO1hCSHOwJ7uIZr7giesxWQx6PjG1bMpcZv59lM72Nk5OE4jzLCb9dQXu1g5I4cci5GWgRDPbOtizeYONrX4GAjGRh7rD8XY2hbgpV29vHXAR7s/hE5TqcmzU5vvwGUxnFRg4RBFUZiRZ2NJuZuOQJjHt3TQ0OYnmRr9+lVCCCGOJMGFSSTHbuR9i0q5qC6fgVCMdfv66AqER3ptjyW9pvLPyytYfcN8jDqVWx/fxo9f2kckPvVPtEvcFs6pzsFm1Fi3t4+/bm5nV9cgieTYB3fE6AiE4mxs8bF+bz87OwcJxjJBhboCB+5TSNedjDRV4axyN8lUiqe2dtI/HM32kIQQp8lu1hFJJNnWHjjhsd9u0nPHNfW4LQZuX7Odpr7hcRrl23SqSnmOlbMrvcwtcRJJpHhlbx9PbOngbzu6eL1pgFf29rG13U9/MIrHYmB2oZMKrxWL4cwyJB1mAytn5GAz6nhxVw/Pbe9kMBIfpXcmhBDiaCS4MMmoqsK8EhcfObuMqlwL2zoG2dTiH7er6bUFdr7/oQVcO7+Ip7d28p9/2MT2jsC4bDubDtVjWF6VQySR5Lnt3Ty7vYsBOVGb0ALhOJsOZirs7BxkKBKnxGWmNn/qBxUOp9NUllR4CcdSPNnQiT8UO/GThBATTpHLwntq8/CFYuzqGjrh491WA9++th6zXuO2x7fT7ste9pLLbGBeiYuV1Tm4LHr29w6ztc1POJqg2GlmdqGTAqcZ/SjWlVIVhdmFTuYWudjbG+TRjW3s6R4al4syQoymaCLJQDA2LS7qiclt6i6cn+IcZgNXzStmd9cgL+3pZf2+Pmry7RS7zaeUPng6jDqNf11VxdlVXr7/wm6++shWrltYzEfOLp/yhZMO1WPoH46yrSNAz2CEJZUeZhc6UdXpcaI6GQxG4uzvDdLhz6zdVRWFYpf5lNNrpxK9prK00sNr+/tZ09DJtfOLsJuli4QQk82sAgfRRIqXdvVg0KlU5dqO+/g8h4lvXVvPVx7Zytcf38a9N8wlL4s1CPSaSlWOjaocG/FkalSDCceSazey0uxla8cgz27vot3n5OwqaVkpJqZUKs1wLEEgFCcQjuMPxQiE40QTKdLpNG6rgQKHCa/NiNtimPJzbzG5SHBhkqspcFDqsbBuXz9b2wK0+8PUFzmxmcb+n3ZusZMffGghD65rHqlHcPNFNczMt4/5trPNazNyzoxctncGWLuzhzZfmBUzcnCMQbtQcXLS6TT+UJyWgRDt/jD+UBxVgSKXGfc0DioczqjTWFrhYUNzP09t7eTq+UVYpnBxViGmqnnFTqLxJOv39mPQqZS4j992scRt4ZvXzOFrj27ltse3c/d75+KeAAWKxyOwMLItncaiMjft/hBb2vx0DYZZKS0rxQQQTSQJhOMMhjPBhP7hGOFYkkgiSSSeAtKYdRomo0YknqK5N0hjxxBWY6ZNfY7NSKHLhNtiwGXRj+vflRDvJLPKKcBs0HFRXT61+XbW7uzhtaZ+KrwWKnNsaGN8Nd1i0PGZ82ewvMrLD9bu4Qt/2cIHFpfygcWlU/7DTVMV5hW76B2Ksq3DT/dghOXVOczItUkWwziJxDNpgv3DMXqHowyF4wRjCVRFodBpxGM1SlDhHcwGHUvKPWxo8vHU1k6umleI6QzXNgshxpeiKCwu9xBNpHiz2YdeU8l3HD8boTrXxm1Xz+G2x7fx2T9s4pr5xVxeXzCluz8dTbEr07JyS6ufJxs6WFDmZpG0rBTjJJVKM3SwffThWQmReIpwLEEylUanqViMGl6LEbtZh0mvvWsuE0+m6B+O0R+MsrdniG0dfuxGPXazjlybkQLnoWCDYczPBYQ43PQ6okxxpR4L/7S0lDebfbx+YICuwQizCx14rGPbthJgUbmbH/3TIn7+j/384Y1WXm8e4OYLa6jIsY75trMt127knOocth1Mt2wrdLC0yottmk3YxkM6nSYQjtM3HKN/OJqJ7scThGJJFBRsJo1ilwWnWS8H0+OwGvUsrnDzRvMAT2/r4sp5RTKxFmKSUVWFFdU5RONJtrb7MWge3NbjZyPMLnRw1/Vz+fVrB3jo1Wb+9GYrl9cXcM38Irxj3OJ6IjHrdZxd6WV/X5DX9mVaVl40Ow+7ZB+KU5RKpUmk0iRTaRKpFMmDt5NH3J8mnkyxvSdCS6qbSCxJOJ4kmjiYlaDXsBh15NmtWI26k7o4p9dUCpwmCpyZoGIskaRvOMpAKEZj5yBb2gI4TDocZj15dhN5DiMeiwGnWS8XwMSYkrOfKUav01g+I4fqfBsv7uzhrRYfRU4zNfn2Mc8ksBl13HxxDcuqvfzPi3u5+U+b+fDZ5Vy/sHjKn+jpdRoLy9x0+sNsbQ/QEYiwotpLZY512hQNHCvRRDITnX9HdkI8mcaoU3Ga9ZS4LUeN7Itjc5j0LCpz89aBAZ7d3sll9YVTPttIiKlGUxXOq80jGk+xscXH4goPzhPUUqnJt/Ota+vZ1zvMIxvbeWxzO3/d0sF7anN578KSabNMQFEUqnNt5NmNvHXAx1+3tHP5nEI80yjIMlb6hqM09Q5jMehwWvQ4zXpsRt2knA/1DUfpCkSIJVOkUmniiRSxVIpYIkUimSaZTpNKp0mnIJVOk0pDmkxAIZVKk0imR+7v7Y9SbIxiM2rkWI+dlXA6DDqNIpeFIlfm7zcST9I7FCUQjtEQ8JNOp3GY9dhNOvIdJvLsJjw2A/ZJ+u8iJi4JLkxReXYT7z+rlIY2P+v29bFub5RZBXbyHaYx/xBZXuVldqGD//n7Xh56tZnX9vdz80U1FLvNY7rdiaDQZcZjM7C1zc8z27qoL3aypMIjRaNOQTqdZjCcoC8YpW8oSn8wRjiWyU4AsJl0FDpNOM0GORk+Q26LgYWlbja2+tDv6Oai2fnoZJ8KManoNZULZ+cTS6bY2DLA0grvSS11qM618cVLa/no8nIe39TOc43d/K2xh7MrPbx3UQmzCx3jMPrss5v0LKv08MYBH3/d0sFl9QUUOKf+fGWs9AxF2NzipzMQYTgax6TXsBl1WI2ZdH2vzYDDrMdh0mPST8y5USqVpnMwQkt/iL7hCIFwgkg8iQIoSqYDk0omuKcoCjpVQVUy2UR6TUFFRa+p6DQFvaqg01Q0VaEt6aemxDUu78Gk1yj1WCglE2wIRRP0Dkfxh2OZrjGKgsusx2HWU+g0kWs34rEazrgFrBDyP2gKU1WFBWVuKnOsvLS7l20dg7T7w9QVOsb8w8Np1vOVy2bxjz19/OSlffznHzdx0/IKrppXOOWvLht1GosrvLQOhNjU4qPTH2KFFI06rkg8iS+UyU7oGYoyFIkTiiaIHcxOcJh1FDnNWIy6Kf//Z7x5bUbmF7vY0h5Ap6lcMCtPUiaFmGRMeo1L5uTzxJZO3jgwwPJKL8aTPHErcJj45HnVfGhpGU9t7eSJhg42PNxAXYGdG84qYUmFZ8p/7poMOpZVeXmjeYA1DZ1cMjufMu/UX9Y52noGI2xq9dM/HKPUY8ZjcTEYiTEQjBMIZU5q0+k0FkMm2OA068m1ZzoeOMw6HKbspuzHEina/WFa+oP4QnGGI3FMBo1yjwW7SY+qcEYX6PRa9t6bxaij3KijHCvpdJqhSIK+4Si9Q1Ga+4IYdCoOkx631UChw0SO3Yjbqseom5gBIDFxSXBhGnBaDFyzoJg93UO8tLuXdfv6qM6xUe61julyBUVROK8ml/oiBz96cS8/f3k/z+/o4iPLylla4ZnyaVilHgtem4GGtgBPNnQwv9TFwjL3hI3Uj6dEMoUvFGcgGGMgGB3p3RyKJUkDdqOOAqcJh0laLI2HPIeJ+nSahnY/Bp3COTNyJcAgxCRjNeq5fG4Bf93cwRvNAyyr8p5SJpLTrOfGpWVcv7CYvzV28+imdr79ZCMlbjPvXVjMe2rzpnS2mF5TObvSy8aWAZ7a2skFdfnUTIPuV6OlezCTsdA3HKHYZRmp4eG2GHFb3l5qEk1kCjH7wzHafCF2dw+h1xTsJj0Wg4bXZiDHZsJp1uMw68blSnoolqBlIETbQIhAOE4olsRh1jMz3z4lr+QripLJHjm4hCqVTuMPxegbjtI2EGJ39yAWg4bjYCeKfEdmCYXbIhmj4sSm3l+MOKaZ+XZK3RZeb+pnU6ufzkAmi2GsCz56bUZuu2o2/9jTx283HODbTzZSm2/nI8vKmV/inNJBBotBx9mVHpr7gmzYP0C7L8zKGTkUuqZXymUqlWYwEqc/GMMXzBzAwrEkwWiCVDqdSZs0SXZCNhU6zaRSad464EevqSyr8k7pv00hpiKn2cBl9YWs2dLOGwcySyRO9SKCSa9x1bwiLq8vZN3ePh7e1MYP1u7lN6+1cM2CIi6bM3U7TGiqwlnlHra2B3h+RzeReJJ545TGPpl1BsI0tAYygQW3Be9x5pVGnUah00zhwaUnqXSaoXCCgVCUYDRB92CEeDKAxZBZTuGw6Mm3m/BYDbithlEtlu0PxUbaVw+G4iRSKTxWI+Ve67S6Yq8qCh6rceR8IJFMMXAw2LCnZ5jtHYNYjTocZh15dhP5DhNlHotc/CHTtWNre6Z45ow8CUaCBBemHZNB49zaPGoK7Ly0u5e3WnwUOjIFH8fyQ+JQFsPKai8v7OzhD2+0cuvj25hb7OSjy8qpm8JrOxVFoTLXRq7DREObnycaOlhY5mJ+ydRufTUcTeALxugPxugfijIcTRCMJUgkU+hUFbtZR7nXis2ok3X+E0Sx20I8lWJD0wCaqrK00pPtIQkhTlGu3chl9YU80dDB5lYfC8vcpxWw1VSFc2tyWTUzh82tfh7Z1M4v1x/eYaIYzwm6U0xGqqIwr9hJY9cQf9/VQzSeZPE0yLY8XR3+MA1tfvqDMUrdFtyneMFKVZRM0UfL24VI44kkvnAmu7HDF2Zv9/BI2r7drKPAYcJrNeKy6k+5IGE6naZ3KMqBgRA9gxEC4TgKmQthuTajzEfI1JTIs2eKPsLbnSj6gzG2dwRoaPVTkWPlkjkFU75g+/HEEplCui39IXqHIujnq5TLcioJLkxXBU7zSMHH9fv7Wbevl5o8B0WusS34qNNULp1TwPm1eTyzvYs/v9nKlx5uYHG5m48sK6c61zZm2842m1HH8iove3uGWbe3n3ZfmBXVOeSdoDf5ZHF43YT+4SiDkUxqYTSRQlEySx0KZanDhFfhtZFMwqv7+tBrCgvL3NkekhDiFBW6zFwyu4BntnayrT3A3OLTzxJUlMznwMIyN3t7hnl0UxuPbmrniS2dXDmvkPctKhlJr54qFEVhdqEDo6aybm8/0USa5dWnngUy1bUfDCz4gjHK3BZcltEJNul1Gnl2beTk9lDafv9wlJ7BKE29wcxSCnMmuJDvMJFjy9RusJt0R13Wl0yl6fCHOdAfpD8YYygSx6hpFDnNuK0GyZg8jnd2ougejLClzY/dqHFOTV6WR5cdhwILrQMhdDoFo07j+R3dfGBx6ZT7PDxVElyYxg4VfKzOtfHK3j4auwbp8IepK3KMatrZ0Rh0KtfML+KS2fk80dDBIxvb+dwfN7Oy2suHzy6fssUPFUVhZr6dfIeRhvYAj2/uYE6Rg/mlrkmbZhoIxdnTM0TPYJRwPNPVIZ0Gq1HDYzXgNBkw6VW56jOJVOfZSKTSvLynF4OmMqfYme0hCSFOUUWOlfPr8vjbjm52dQ0xaxQyBGfk2fjipbP4yLIwf3ijlcc3t/PMti6uX1jMtQuKptz69Oo8GwadwhvNA0TiCc6b4nUnTkXrQIit7QH8wRilXgsu89hlsbwzbT/TVSqz1HIgGKPVF8rUETDqsJv05DmMI8EGk16j3R+mdSCEPxRjOJbAZtBR5bVhM0kbxtOR7zAxq8DOmy1+XFYD9cWubA9pXEUTSTYe8NPqC2LQVCq8Nsrdadbv7+O57V1ct7B4WmfATK2jgDgtdrOey+cWMqvAzt939fLq/n4qvRYqc2xjHqU36TXef1Ypl9cXZnptb+7g1f39vKcmjxuXllHgnBpX9d/JYTawojqH/b3DvNE8QHN/kIVlbmbm2SbNB1IolmBfT5BWX+aArakK9oN1E8wGnVzhmeRq8m0kUynW7upBU5VROTERQoyv2gIHkUSKl3b1YNCpVI1SdmCh08zNF9Vww6ISfvPaAX73egtrGjoyx/O5BVNqvXqpx4pBp7G13U80nuLC2fnTvjDzSGAhFKPMa8E5hoGFo1EUBafFgPNgpkQ6nWY4mul+EAjHaPWHUNLgMOuxGHTEEiliySROs4HaPIe0Bx8FZR4rQ5EEL+7qxWnRU+qeHssBookkbx3w0TYQxqDLBBY0VUFTFRaWuHj9gI91e/s4r3Z6ZnSABBfEYSpzbRS7zbzR5OPNlgE6AxFmFzpGKv6OJZtRx0fOLufqeUX85a02ntrayUt7erm4Lp8PLiklZxzGMN5URWFGnp0St4XGrkH+tqObvT1DLC73TOiCj/Fkiqa+IM19QXzBGGmgxGXGZTHIFYApRFEU6godJNoDvLCzh+7BCPNKXLin4BprIaayecVOovEk6/f2Y9CplLhHLzOwzGPhlivq2N09xG9eO8Av1jXx2OZ2blxaxoWz8iZNsPxE8h0m9JqHTS0+ookkl9YXYDVOz9TnA/1BtncMZpZCeMY/sHA0ipLpNmE3vf1vEozG6RuOEYolDra8tEnWySirK3QQOuDj2W3dvO+sklFbFjNRReJJNrZkAgsmvUqZ58iue06Lgdp8G5ta/eQ5TFO6ntzxSHBBHMGg01g5M4eaAhsv7uphY6uPfLuJmnz7uETqnWY9/++cSq5bUMQf32zl+R3dvLCzmyvqC6fsB5dJr7Gw1M1AMEZj5yCdgQ7qCh0sKHUdcaDMtlQqTasvxP7eIP3DUaLJFHk2I7l2k2QpTFGKojC32EnLQIiGNj9N/UFm5TuYU+yYUP83hRDHpigKi8s9RBMp3mz2oddU8ke51k9Nvp07rq1na5ufX712gB+9uJeHN7bx4bPLWTUzZ0qsZ/dYDSyu8LCxZYC/bunkirkFE+LEejw19wXZ0TmIPxSjPAsZC6fCatRP2wDQeFEVhQWlLl7d38dTWzt578JiTFNsadQhkXiSjQd8tPlCmAwaZW7rUee+ZR4r/lCcF3f2kGMzkGufmhnYxyMhPHFUuXYT7z+rlEtnFzAUibNuXx+9Q5Fx277XZuT/e88MfvyRszh3Zi5PNHTwiV+/ya9fO8BQNDlu4xhPHquBFdVeChxmNrX4eHxzB9vaA8STqWwPja5AhPX7+tjY4qfdH8Jq0lFX4KDAaZbAwhSnKArlXivnzMjFbtTzetMAj21qZ1OLj0h8av4tCjHVqKrCiuoc5hY72NruZyAYG5PtzC1xsfqGedx65WyMOpXvPreLz/5hE6839ZNOp0d9e4FwnHB8/I6RTrOeZZVe+oNRHt/cQe9QdNy2nW37e4fZ0TFIIBSjwmOd0IEFMX70mspZZW56BqO8sLOHVGr0/86z7VBgoXUghFl/7MDCIbMLHaiKwrPbu4jEE+M40olhaoaXxKhQFIU5xU4qcqz8fWcPm1v91Bc7R3oTj4cCh4nPXVTDDWeV8LsNLfzpzVb+osBZe6JcMCufpRWeKdV5QFEUKnOsFLtM7Owa4sWd3eztHuKsCg8lbvO4Lzvwh2Ls7h6mezDMYDiBzaRRm++Y9utNpyOdplKTb6fCa2F3zzAv7+lld9cQc0uczMgb21a2YymVSpNIpUmkUiRTaZKpNPFk5nsynSaZTBM/+LvEwfsVBcq8FhySvSEmEU1VOK82j2gixVsHBqjJt1PmsYz6cUVRFJZWelhc4eaVPX38ZsMBvvVkI7MK7PzzsnLmlrhO+TWHowlaBkIc6A/SMhCipT/EgYEQgXAck07hqwYPi8rHp7ON2aBjWYWXNw4M8MSWdi6ZUzCqS00mon29w+zsHCQQjlPhtUrmmjiC1ahnXomLTa0+nCbdlOogEYknebN5gHZfCItBR6nn+IEFyMyXFpa5eHV/Py/u7OWy+oJptWxYggvihKxGHZfNLcRs1Njc6ieVTlPsGt8Daanbwpcvm8U/DYR4ZMNuNnYFeaN5J1ajxqoZuVwwK49ZBfYp88dr0GnMK3ERCMfY0THIk1s7qC1wsKjMjXMcWtyEYgn2dA/T7g/jD8Uw6FQqc2RCITL/N+uLnIRjNnZ2D/LCzm52dg4xr9RFZc6JD7rjLZFMMRRJMBiJMxRJMBSJE02kiCdTxJNpUulMwIB0pt1Z5ivTtiyRSpFKQTKd+a4oCqTTRBIpbCaNhWVuZhU4ZB2vmDT0msolcwqwGXVsbPHTH4xRX+Qck+CgqiicW5PLimovL+zs4fevt3DLY9tYUOrio8vKqcm3v+s5kXgyEzwYCHGgP0TLQCaY0Df8dqaFWa9R5rGwtMJDqcfMMw1tfHPNdj55bjVXzC0c9fdxNEa9xrJKL28eGODJhg4ump1Pde67389UsLdniF1dQwTCcSq9VmwyDxBHkWs3Uptv540Dftw2I3OKJn+XqXAsyZsHBujwh7EZdZR4rCe9xMti0FFf5GRru58Ch4mF4xT8nAgkuCBOiqYqvKcmD01R2NjiJ5nKFHIab6UeC9fUOfn3SytoaPOzdlcPa3f18Mz2LgqdJi6Ylcf5tXmjvp40W5xmA8urc2gdCLKtLUBLf5AFpS7qCsdmMhhLHCzW2B/EF4wCCqVuC06zfsoEbsToMBsytUIGw3F2dQ/y7PYuyjxmFpS6KXaZj9pnfKwdCiQcCiYEwjECoQTRZIpILEEsmSKdzqSIq2Q+11RFOfgdNFVF0xR0iopeU9BUFYOmotNAVVRUNXPClEql2d0zxIs7e2jqDbKkYmIXYRXicHpN5bzaPIrdZtY29rB+fx/zip0jbf5Gm05TuXROAefX5vHUtk7+/GYrn//zFpZXeVlW5aHNFx4JJnQPRjiUVK3XFEo9FuqLnZR7rJR7LZR5LOTajUdM8GfZo/ypMcKPX9pHuz/Mx1dWjkuQU6epLKnw0tDm59lt3bynNkVdoWNKHSv3dA+xq3uIwXCcqhyr1DAQx1XutTIUTfD3nT04zfpJndETiiV464CPdl8Ym0mjxH3ygYVD8h0m/CELL+/tJcdupDQL503ZIMEFcdJUVWHVzFz0msqGpgGSqRSVOaPT1upUaarCwjI3C8vcfPq8BOv39bN2Zw+/3dDCbze0UF/k4MJZ+ayY4Z0SfbdLPVYKnWZ2dQ3x0q5e9vUEOavcTZl3dFJaU+k0zX1B9vUOMxCMEU+myLUbybVJsUZxfA6zniUVXvqGI+zpGabd30FVjo0FpS7yxjDI985AwmA4jj8UPyKQAGDSaZgNGvkOEzajHqNePfPichrMLXbhD8XY1jFIV0MHc4qcLCxzTYnPGzE9zMizk2c38fyObt5s8VHltVGVe+oT6JNl0Klct6CYS2bn8/jmDh7d1M6r+/vRVIVil5kZeTYurMuj3GOhzGOlwHlyxx+TTuXrV87mF6/s569bOugMhPnCJbXj8reoqQrzS13s6BjkhcYeugNRllR5sBkn/+fA7u4hdncPMRSOU51rk882cVLqChy81TLAM9u6xrQQeziW5MBAkNZAjJJIfFQza0OxBG82D9ARiOAw6Sl2m0/7c3Fmvp1AJM5zO7r54OKSaZH5I58U4pSoqsLyai+qovDa/n5SKajOy06A4RCLQcdFdflcVJdPz2CEF3f1sHZnD99fu4cf/2MfK6q8nD8rj/klrkl9oqzTVOYUOykLW9jRNchTWzupybdTdYb7P5VOs7kjjCXiIxhL4rUZyLebJNVbnJIcm4kcm4kOf4i9PcO0DISYkWdj/mm2r0wkUyRSaWLJFIlkmngyRedQnGR74CiBhDSQxqjTsIx2IOE4XBYDK6u9NPcHebN5gAP9QRaXu5mRZ89K5oYQp8ph1nP9wmLeaB7gtaZ++oejzC91jWldHYtBx41Ly7h6fhEDwRiFzjM/3miqwr+dW02xy8zPXt7PVx7Zyq1XzibXPvZtrFVFob7YSYc/zNaOAO3+EEsqPcycpJ8D6XSaJl+MVHyIoUicKgksiFOgqQoLSty8ur+Pp7d1cv3CYkz60fv/k0imaO4P0dQ7TP9wjN0tQfqVDvIcJso8FvIcpjNaPhyMJnjrwADt/ggO45kFFiDz+TC/xMX6ff08u6Ob6xYUT+pzkZMhnxbilCmKwtlVHnSawrq9fSRSaWoLJsZawzyHiQ8uKeMDi0vZ1TXE2l09/GNPL3/f3YvHauD82lzOr82j3GvN9lBPm92s5+xKLx3+MDu6BtnfN4zuJD+ojlXDt28oxqwcqB2nlqNi6ipyWSh0mmnpD7GtPUBTX5BZBXZqCzL9njO1DjL1DhLJ1BHBg1giRSSeJJpIjRRTPFQHIZFK09oeIjfhA9KYdBqmcQwkHEumCKuNIpeZ7e0Bnt3Rzb7eYZZWevHaxv7ERpy6WCzGvffey5o1a4hGoyxbtozbb7+dgoKCYz7nT3/6E4899hh79+4llUpRV1fHZz/7WRYvXjyOIx8bqqpwdpWXIpeZv+3oZt2+PuqLnGO+vNBm1I36Ff4r5xVR4DRz7zM7+cKft3DrVbOZMU4XQIpcZrw2A9vbAzy3o5uW/hBLq7zjUidptMSTKXZ0DNLij+HSJajOsWM2yJxAnBqDLtNBYkPzAC809nB5feEZB9rS6TSdgQh7uofoG44SiWcybGtyjRj1Gk29QXZ2DuK2GsixGSn3WihwmHGYdSed4TscTfBW8wCdgTAOs4FS1+gUUjfqNBaUunijeYBX9/VxzszcM37NiUyCC+K0KIrC4goPOk3lH7t7SKZT1BVMnLWGiqIwq9DBrEIH/3pOFW80D7B2Zw+Pbmrn4Y3tnF+by2fOn4FRN3kPmkUuMwUOI6FTbAd4tH+h1oSfypyJESASk5+iKJTnWCl2m9nfF+SNJh97e4axGDQSqUwgIZnKZM2kU4ACadKoioJOzXxpB7+rqopRU7HpVKJ2HbMKHBh06oSL/Bt1GovKPfQORWjsGqQjEGF+iYv6YqcE7CaYO++8kxdeeIHvfe97uFwu7rnnHj75yU/yyCOPoGlH/7fasGEDV1xxBYsWLcJkMvHLX/6Sf/3Xf+Wxxx6joqJifN/AGCn1WPjQklLW7uo52K7SQk2+fcL9rZ3IWeVuvvO+edyxZgdffqSBz19cw4rqnHHZ9qHPga5AmJ3dg3QEwiwu9zCr0DHh92MgHGdrm5/OQIRoIklVjk0CC+K02Ux65hW72Nzmx2XpY+WM0z+h9odi7OoaomswwlAkjstioMJrw6BTifg0KvPszMiDcCxBuz9M60CIPT1DuCx6cmwmyj0WCpym49YPOzKwoKdklAILh7gtBmrz7bzZ7CPPbqJmglyUHQsSXBBnZEGpC02Bv+/qJZUaZE7RxAkwHGLQqayckcPKGTn4QzGeaMgUlGoZCHHLFXXk2Sdv8UdVVbEZz3z5gnGSthEUE9vh7Ss7ByOQBpumotdUjLrMd1UFTVEyRRZP8NkR9ekm/GQ3127CazOyu3uIV/b20dQXZHGFh4pRqo8izszQ0BAPP/wwd911FytXrgRg9erVnH/++axfv55Vq1Yd9Xn33XffET9/85vf5IUXXuDll1+eMsEFALNRxxVzCynxWHh5dy8DwRjzS12TroZAudfKd98/nzufbOTup3fyLysqeO/C4nH7GyxwmsmxGdnRmemo09wfZFnVxM1mah0I0dg5SN9wFIdJR5nLOOE/a8XEl2s3MjPXxhtNPtwWA7NPsYNEJJ5kT/cwrb4Q/lAMo05jRq7tmIVFzQYdM/IyJ+3hWILOQIR2X4jdXQcDDXbjwUCDGbfl7UDDUCTOW80+OgfDmUKUrrE5Xpd5LPhDcdbu7MZjy2RYTEVT/owiFovxrW99i7PPPpsFCxbwqU99iq6uruM+Z8+ePfznf/4nF154IbW1tfzwhz8cp9FOTnNLXFw4O4/+YJSGtgCp9LGS77PPZTHw0WXl3HrVbDoDEW7+42a2tvmzPSwhpjSDTjtY7d1KkctMrt2Iw6zHbNAw6jR0WnaWNIwVVVGYVeBgWaWXwUicp7d28uLOHgKh+Ki8fiqVJhRLMBxNjMrrTSfbtm0jHo9zzjnnjNxXWFhIdXU1mzZtOunXicfjRKNRHA7HWAwzq5SDa4Q/sLgUu0nHhv39tA6ESE/gY/vRuC0G7ry+nnNm5PDL9c388MW9xA8Weh0POk1lXomLecUumvuDPL65nc0tvnEdw4nEkyka2vxsafPTOxQl32GkzDPxWgqLyasix0q+w8iLO3to84VO6jnJVJq9PcO8vLuXHR0BAuEYJS4zM/OOHVh4J7NBR1WujSUVXpZXeXGZDXQFIqzd2cuTDR08vbWL7e0BOgNh3mz20REI47aMXWABMp+tc4ocpNLw7LYuoqeYeTxZTK5Q9Gk4nfTHcDhMcXExF198MQ888MD4DniSml3oRK+qPLejmy2tfuaXuib0ycKSCg/fe/8C7nxqB19/fBv/75xKrp5XJFcWhRCjxmrUsbTCS7svzPbOAC2+EAvL3MwudBy3gF0skSKSSGbqT8QzdSgi8cx9Q5E44ViSRDJTh+K8mlxyxqFo3VTR19eHpmm43Uf2HPd6vfT19Z3069x///1YLBYuvPDCEz62ubmZcDgMQCQSobGx8dQGnUVzbSk2DoZYt70Xl0mjymNEr02s42QsGqWpuemYv39frQGrYufZHd00d/v4f2d5sRjG99pasS5Nc3+Mv7R2st6hZ16BCa8lu7UYgrEUu/uj9A3HSZOmwG4gmFAJ9p94n4rTM133qzmdxj8Q4Vcv9HFRtR276ejnX+l0mr5QkhZ/DF84QSwJbrOK26zDH1PwH+U5p7JPvYBDS9HrT9DekeS1xjQ2g4pOBYdRwxrT0ewf+883TzrJ1v0Rfu3vYUWZddTOPWLJNLqDLbPPxOHHqbq6ulN+/pQOLpxu+uO8efOYN28eAD/96U/HbbyT3cz8TGXk57Z1sanFx4JS94SOfhe7zXz3/fO5/2+7+fnLTezrCfL/nV89qeswCCEmnmK3mXyHkZ3dg7y0q4fm3mEWlWeK4kbiKcKxJJFEknA0SfBgG81DBS+jiSTxRDpTlyKdRqdlalAcOsGLJSbOVdBsuv/++/nJT35y3Mf86le/OubvTuWq/EMPPcQf//hHfvnLX2KznbhY4OHLJhobG09rspZN8+phV9cQL+7spjOZZn6ha8zay52OpuYmKisqj/uYf6+E2eXd/HDtXn74up/brppNkcs8TiPMqK7kYPvaAI1BlYVeF3NLnFmZc7QOhGjrHARrlAqPjlK3Bd1hAc+T2afi1E3n/VpamuLV/X3sjxu4rv7dHSQCoTi7e4bwx8MotgQVuXoKnaYT/n2czj6tOfg9nkzRFYhg1Knk2o3jeoHRmx9hW0eAmD2PBaWu03qNSDyJPxRnIBjDF4rij8fJt5tYXO45o7Gd6XFqSgcXTpT+eKzggjh91bk2rphXyNPbutjYMsCiMs+EDjBYDDq+enkdf3yjld+93kLLQIivXjFrUtdhEEJMPDpNpb7IRSAcY3vHIE9u7cBlNhBLpDKFLdNpUBQMmopel/luM2rk2jJrn3Wqik47si6FLxjL4juaWG666Sauueaa4z6mqKiIzZs3k0wm8fl8eDxvT8AGBgZYsmTJCbfz0EMP8cADD/Dzn/985CLEdFBbYCfPbuD5xm7eOOBjRm5mmdNEzlB8pwtm5ZNnN3HXU4184c9b+NqVdcw5xTXgZ8plMbCiOoe9PcO8srePlv4Qy6q8FLnHJ9CRSKZo7BziwECQQChGvsNEnt0kWZtizBl0KovK3Lze3M/anT1cNifTQSIST460rz5UV6Eqx4rNNPaZPXpNpdRjGfPtHE2B04Q/HOPl3b3k2AyUuE88jkg8iS8UwxeM4wtF8YXiRONJgrEk6TSY9SqhSPaXS07p4MJopT+eqsmcAjlaas1xXjkQpKOzmzn5plENMIxFWtmyPLAu8fDrTT4++/uNfOwsDzO90yfVeLqm6o0l2aejb6rs0wI1jS+cJBRNYzqYhXCoQwYJMl9A/ODX4DFeZzCSJD2kJ2A9s0nYoePUZLuifjiPx3NEsOBY6uvr0ev1rFu3jquvvhqArq4u9u3bx8KFC4/73AcffJAf/OAH/OxnP5sSLShPldtq5IZFpby2v483m330DUeZU+TEYpg8U8n6Yiffff987lizg68/to3/uGAmF8zKG9cxqIpCTb6dIqeJhg4/f21oZ36Ji4Vl7jHtLDMUibO1LUCnP0IinaIqxzYuJ3BCHGI/1EGi1Y/LrKfAaWZ/7zD9wRipVJoilxmPxTBtgl01+XYC4TjPb+/mA4tLsZqO/Cw9PJgwEIziD8eJxJKE4knS6TQWg4bdqKfQaR75HB6Ojk5tpzMxeY4IhxnP9MfTMdlTIEdDHTCrNsyTDZ10p1IsLfUed43xqRirtLLKClhUE+LbTzbyPxv6+X8rK7lqXuG0+JCbzql6Y0X26eiTfXokXzDGjDzbGV/1nE7HKbvdzg033MDq1avxer24XC7uvvtuamtrWbFixcjjbrrpJubNm8fnP/95AP73f/+XBx54gNWrV1NRUUFvby8AJpMJu33qthR7J01VWDkjlxK3hRcau1m3r4+ZuTbKJlEWQ5HLzHffN5+7n27k/r/tpt0f5sNnl437+G0mPcsrc9jfF2RD0wCtAyEWV3gocJpGPWDT5gvR2DFIXzCKzaij0mUftTmZEKci125iRp6d15t8FLkixJMpcuwG8u3mCZ3pPBbUg8VzX93fz3M7urhkTgGBcPxgQCH2djAhliRNGrNBw2HUU+TKBBPeub+SqYlRdHdSBhfGK/1RnJlCp5mr5xfy1NZONjT1s7TCi2GCtzwscVu47/3z+d7zu/nZy/vZ2zvMZ94zY8KPWwghxMm55ZZb0Ol03HzzzUQiEZYvX87q1auPKPLc2tpKYWHhyM+/+93viMfj3HzzzUe81vXXX88999wzbmOfKMq9Vv5paTmv7e9nc6ufzkCE+mIn9klyJdxm0nH7NXP48Uv7+NObrXQFIvzXxTXjfnKjKArVuTYKHSa2tgd4dlsXOQe76RQ4THisBtxWA1aDdloXOhLJFDu7hmjuDzIYipPnMMoyCJF1FV4LCmnSClQ5rdO61plJrzG/xMmbLT6e2tqJAiPBBItBh82oo9hlxnyUYMJENSmDC+OR/ihGR77DzNXzi3lySwcbmvpZUuEZ07S/0WA16vjalXX84fUWfv9GKy0DIW65vI5cqcguhBCTntFo5NZbb+XWW2895mPWrl173J8FmAwa75mVR02BnbU7u9nQ1E+F10pljm1STIL1msp/nD+DQoeJX712AE2Fz11Uk5UMDItRx9lVXnzBGD3DETr9Yfb1DGPQqdhNOuymTHE7r9WI06LHYdKdMEAwFImztT2zDCKeTFE5TuvYhTgRRVGoyDlxMdzpwmM1Mr/YxUAwhs00+YIJ7zQpgwsn63TTH2OxGPv27QMgGo3S29tLY2MjFouF8vLyrLyXySzHZuSaBcU8saWDdfv6mFvkJM8xsQsmqorCP51dTlWuje89v5v/+tNmvnzZLOqLx7f4kxBCCDGRFbnMfHBxKW+1+HijaYDOQIQ5RQ481okfkFcUhfcvLiWVTvObDS2Y9BqfPq86a1f23QczFQBS6TSBcIz+4Rh9w1Ga+4LoNAWHSY/dqCPXYSTXbsJt0eMw6VEPOxFp94fZ0R6gNxjFZtBRmSPLIISYyPIdJvIn+LnRyZrSwQU4vfTHnp4errvuupGfW1pa+OMf/8jSpUv59a9/PZ7DnzLcVgPvO6uEtTt72NLmp9RjYWaefcJH5ZZVebnv/fO586lGvv74Nj5xTiVXzJ0edRiEEEKIk6HXaSyrymFGrp0XdnbzVouPIqeZmvzJcVL7gcWlhONJHt7YjsWgcdPyiqwf51VFwW0x4rZkgjTpdJqhSIK+4SgDoRit/hCkFRzmTOp0nsNEjs1IJJ7kQH8QfzBOvlOWQQghxteUDy6cTvpjSUkJu3btGuuhTTsWo44r5hZS7Dbzyp4+fKEY80tcE77SdKknU4fhvud38ZN/ZOowfPo8qcMghBBCHC7HbuT9Z5XS0OZn3b4+1u2NUlfomPBX5BRF4ablFYRimQCD2aDjg4tLsz2sIyiKgsOsx2HOLG1Ip9OEYgl6h6IMRRN0tvpJpdJYjTqMOpXKXOukqYEhhJg6JvZZnZhyVFVhYZmbAqeJ53Z0sX5fH/VFLgqcE3viYTXq+PqVs/nd6y388bA6DF7bxE/7FEIIIcaLqiosKHNTmWPl77t6aWjzk2s3MqvAMaFrLimKwqfOqyYST/Kb1w5g1qtcM78428M6JkVRsBr1WI1vBxDCsSTBaAKnRT8pMkaEEFOPfPKIrCh0ZtZo1hU62NYRYHt7gEQyle1hHZeqKHzk7HJuuXwWrQNhvvroVvqHo9kelhBCCDHhOC0GrllQxOVzCwnHkqzb10e7Lzzm7cDPhKoofPbCGpZXefn5y008v6Mr20M6JWaDRo7dKIEFIUTWyKePyBqTXselcwq4aHYe/cEoG5r6GY4msj2sE1pencMd187BH4rztce2MRCMZXtIQgghxISjKAp1hQ4+sqycWQV2GrsGeeuAj1Bs4h7rNVXhi5fWsqjMxY9e3MvLe3qzPSQhhJg0JLggskpRFOYWu3j/4lLsJj0b9vfT5gtle1gnNKvAwe3XzKE/GOVrj23FF5IAgxBCCHE0FqOOy+oLuW5BEQDr9/XT3DdMaoJmMeg1la9eXkddoYP7nt/NG80D2R6SEEJMChJcEBNCnsPE+xeXMLfEyc6uIRra/MQn+DKJ2YUOvnHVHHqHonz9sW0EwvFsD0kIIYSYsCpzbfzT2WUsKnOxvy/Iy3t6ebN5gK3tAXZ3D9HUF6TdF6ZnMIIvGGM4kiCaSGYlCGHSa9x21Wwqc6zc/XQjDW3+cR+DEEJMNlLQUUwYBp3GhXX5FLvNvLizh/X7+lhQ6sJpNmR7aMdUX+zktqtm8801O/j6Y1u587q5I5WchRBCCHEko17jvNo8avLtNLT7GQwlCMYSBMIxEqk0yWQmkJAGFDIFIlUUDDoFo17DqFMx6jT0moJeUwnGxu5ChMWg45tXz+Grj27lW0/u4FvX1jOrwDFm2xNCiMlOggtiwplV4CDfbuTZHd280TzAzDw7ZR7LhO3TPK/Exa1XzuaONdu59fFtfPu6emn/JIQQQhxHoctMocs88nMqlSaRShNLJAnFk4RjSULRBMPRBMPRJEOROKFYklAswUAwRiKVIpmCvt4w3vwwhU7zcbZ2+hxmPd+6tp6vPNLA7U9s5+7r51KZYxuTbQkhxGQnwQUxIbmtRt63qIRX9/Xz1gEf/cEY9UVODLqJuZJnQamLr10xm28/uYPbHt/Ot66rx2aUPy8hhBDiZKiqgkFVMOhUbCcI0CdTaRKpFOFYkkdfDrCtIwAwZgEGj9XAt6+t58uPNHDr49u5571zKXFbxmRbQggxmU3MMzUhAJ2msqoml6sXFBKNJ1m/v4+B4MRt/XhWuZtbrqijuT/IN/66jeAk6HwhhBBCTDaaqmDUabgsBpaUWFlQ4mJbR4DOQHjMtpnnMPHta+eiALc+vo3uwciYbUsIISYrCS6ICa86186HlpZR5DTz5gEfLf4YydTErDC9pMLDly+bxb7eILc/sX1Ct9sSQgghJjtNVTivNm9cAgzFbjN3XFtPOJ7k1selFbUQQryTBBfEpOAw67luYTErq710D8dZv2/iZjEsq/LyxUtq2d09xDef2EE4lsz2kIQQQogpazwDDJU5Vr55dT3+UJyvPy6dooQQ4nASXBCThqYqLKvO4bKZDrw2A2+1+NjeEZiQLStXzsjhC5fUsrNrkG89uYNIXAIMQgghxFgZzwBDbYGdW6+sozsQ4fa/bpdlkEIIcZAEF8Sk47HoeP9ZpVxQm0d/MMq6vX0Tcu3jqpm53HxRDds7Anz7yR1EExJgEEIIIcbKeAYY5pa4+Orls2jqD3LHGrmIIIQQIMEFMUmpqsKCMjcfObuccq+Fre1+NrX4JtzB/T21eXz2wpk0tAW488lGYomJl2UhhBBCTBXjGWBYXOEZyVK886lGWQYphJj2JLggJjWnxcDV84u4cm6mo8S6fX20DoRIpSdOwccLZuXzHxfMYFOrn7ufbpyQyziEEEKIqWI8AwznzMg5eBHBz1cfbcAnRR6FENOYBBfEpKcoCjUFDj6yrJz6Ige7uod4s3mA4Qm0BvLi2QV85j0zePOAj3ue3ikBBiGEEGIMjWeA4YJZ+Xz9ytm0+8N84S9baBkIjdm2xPjqGoywdmc3rzcN0NwXlC5gQpyALtsDEGK0mI06LppdwMx8O3/f1ctr+/upzLFS4bWiqUq2h8dl9QUk02l+8tI+vvPsLr50aS06TeJ7QgghxFg4FGBIA1va/AAUOs1jsq0lFR7uvn4ed6zZzpce3sLXrpjN3GLnmGxrOkil0ySSaQy68Z0npdNp9vUGea2pnw37+2nuf3egyGrUyLObyLMbybUbybMbybObRm47zXoUJfvzTiGyQYILYsop91q5camJN5t9vHFggM5AmDmFDtxWY7aHxpVzC0mm0vz85f189/ndfPGS2gkR+BBCCCGmIk1VeE9tHjD2AYYZeTa++7753P7Edm57fBufvXDmyLano3Q6TSSeYigaJxhNMBxJMBRNMHzw9vDB20OH3T50fzCaIA2UeizMLrBTV+igrtBBodM06ifuiWSKbR2DvLa/nw1NA/QNR1EVqCt08PGVFSwsdRNJJOkditJz6GswQlcgQkNbgPA76n0ZdCq5tkNBh0wAItduxDcQoiPRh6oqaAqoipL5UhXUQz8ffltR0NS3b+s1lXyHUQIXYkKT4IKYkgw6jRUzcqjOs/Lizl7eOOCjxGVmZr4dfZazBa6ZX0QqleYX65pIp9N84ZLarI9JCCGEmKrGM8CQ5zCx+ob53PnUDu57fje9Q1Hed1bJtDohfHFXDw+tb8YfjpNMHbsGlqqA3aTHZtRhM+pwmvUUu8zYjTqsJh0qsKdnmFf29fHsjm4AXBY9dQUO6gozAYfqXNtpzaFCsQRvHfDx2v4B3jowQDCWxKBTWVjq4sNLy1hS6cFp1h/xnFkF736ddDpNMJqkdzhyMOiQCT70DmV+buoL4g/HD3uG75THerhyj4Wr5xdxXk0uJr12Rq8lxFiQ4IKY0vIdZj6wuJSGNj/r9vXRvTfK7EIH+Q5TVsd13cJiUOAXrzRxx5od3HJ5HWaDHCQmgp7BCMFYgjLPxFhOI4QQ4syNZ4DBZtJxx7X1fP+FPfzqtQN0D0X59HnV0+KY8sjGNh5c30xtvp3za/OwmXQjwQP7YbdtJh1mvXZSQZdUOk3rQIgdnYM0dg7S2DnEq/v7ATBoKjPzbdQVOJhd5GBWgR27SX/U1+kfjvJ68wCv7R+goc1PIpXGYdKxvNrL2ZVeFpS6TvmEXVGUzHs02ajMsR31MdFEkv7hGM2trRQWFpNKp0ml0qTSmfd2+M/JkZ8P+10aUqk0g5E4z+3o5kcv7uWh9c1cMqeAK+YWkGfP7pxWiMNJcEFMeYfaVpbnWHlpVy8NbX6KXWbqCh1ZvZJw3YJibAYdP3xxD7c+vo3brpqNw3z0A6IYO+FYkq3tATa1+tjU4qfdnyn6ZdKr1OTbqSvITFZqjzNhEUIIMfGNZ4BBr6n818U15NmN/PmtNvqHo3zp0llT9kJCKp3mwXVNPLa5g3Nm5PBfF9eMWlamqiiUe62Ue61cXl8IwEAwdjDQMEhj1yCPbm7nLxvbgCOXUhS7zDS0B9jQ1M/u7mEACp0mrppXxLIqD7MKHGMe9DHqNIpcZqJ+PZU51jN6rSvnFrK9Y5AnGjp4dFMbj25qY3mVl6vnFzE7y/NaIUCCC2IacVsMXLugiIa2AP/Y3QsMMrsou8WWLpqdj9Wk4zvP7uQrj27lW9fMwWvLfm2IqSyVTtPUF2Rji4/NLX52dA6SSGWKRs0tdnJ5fQFOs55dXUM0dg3y57daOZTVWeo2M6vQQV2BnVkHJy2qHMiFEGLSGM8Ag6oo/PPyCvLsJn780l6++mgDt101B4/VMCbby5Z4MsUPXtjD33f3ctXcQj5xbtWYHxs9VgMrZ+SwckYOAJF4kj3dQ+zoGqKxc5BX9r69lAKgJt/GR5eVc3alhzKPZdKehCuKQn2xk/piJz2DEZ7a1smz27tZt6+fqlwrV88r4tyZueNeCFOIQyS4IKYVRVGYX+pCVeHFnT2oXQqzChxZHdPyKi+3Xz2Hbz/ZyJcebuBb19ZT5Bqbic505QvGRjITNrf6R9Y/VngtXDO/iEVlbuoKHUccjA9NPsOxJHt6hmjsGmJn5yCv7uvn+YMTFrtRR+3BqyOzCuzU5NtlDaQQE1wsFuPee+9lzZo1RKNRli1bxu23305BwVEWVB/FmjVr+PznP8973vMefvrTn47xaMVYGM8AA2S6ReXYDNz77E6+8JctfOOq2ZR7z+wK9kQRjiW5++lGNrX6+eiyct6fpfoSJr3G3BIXc0tcwNtLKdp8YWYV2KfkhZs8h4l/WVHJh5aU8eKuHp5o6OT7L+zhl+ubuay+gMvnFEzJ9y0mNgkuiGlpbrGLVApe2t2DpijMzLdndTzzSlzceV09tz+xnS8/3MA3r5lDVe7R1+6JE4slUjR2DrKxxcfGFt9IKymnWc/CUhcLy1wsKHWf1NUjs0FjXomLeYdNWNr9YXZ2Do4EHN48kCnQpCpQmWOlrsCBS4sQMw1R7DZjM8pHrRATxZ133skLL7zA9773PVwuF/fccw+f/OQneeSRR9C04wcHW1tbWb16NYsXLx6n0Yqx8s4Ag15TyRnDE7HFh7Wq/PLDDdxyRd3IcWWy8odifHPNDvb3DvOfF8zg4tknF6AbD4cvpZjqTHqNy+sLuWxOAVvaAjyxpYM/vdHKX95q45wZOVw9r4jaguzOc8X0ITNeMW3NL3WRTKd5eXcvqqpQneWT+Zn5du65YR63Pb6drz66lduums2cLC/bmKhiiRSBcJxAOM5gJM7gwduBcJz9fUG2tgeIJVLoVIXZhQ7+eXk5i8rcVOZYzzhVU1UUSt0WSt2WkYnUUCR+cBnFEDu7Bvnbzm4i8RS/2ZwJOhyqgF3sMlPsNlPkMlPiMlPgNEmnECHG0dDQEA8//DB33XUXK1euBGD16tWcf/75rF+/nlWrVh3zufF4nP/6r//i5ptvZsOGDfh8Z1b1XWSfpiqcV5NLNJ5iS5uf5VVeLIaxmxqPtKpcs4Nv/HU7/3nhTM6fpK0quwYj3Pb4NvqHY3ztijqWVnqzPaRpT1EUFpS6WFDqosMf5smtnTy/o5uXdvdSm2/nqnmFrJyRI/MOMaYkuCCmtYWlLlKpNOv29h286pzdAEOp28K9N8zltse3c9vj2/nK5bNYUuHJ6pjGy1AkToc/kgkYhOMEIm8HDAYPBhEytxPv6il9iKpkUlsvmZ3PwlI3c4ud41I8y27Ss7jCw+KD/1bJVJo3duxBsXpp94Vp84fp8Id5o3mA5xvfbkmlKpDvMFHsOhhwOCzw4LEaJu2aUCEmqm3bthGPxznnnHNG7issLKS6uppNmzYdN7hw//33U1xczPXXX8+GDRvGY7hiHOg0lfNn5TEUjfPmgQFWVOWgG8OTr0yrynnc9VQj33t+Nz1DUT4wyVpV7u8d5vYnthNPpvn2dfXUFWZ3eal4tyKXmU+squLDZ5fxQmMPaxo6uO/53Ty4rpkLZuVx/qw8yjyWbA9TTEESXBDTmqIonFXuJpFK8+q+fjRVocyT3RS6PLuJe2+Yx+1/3c63n9zBzRfVjKRuTiXxZGbpwuZWP5ta/ezrGead3bANmorDrMdp1uEw6Slymg/+nPk6dNthyvTHthp1E6LAoqYq5Nv0VFZ4ofLI3w1HE3T4w7T7w0cEHhoOZlscYtKrlHuszC12Mr/URV2hHaNO6jkIcSb6+vrQNA23233E/V6vl76+vmM+75VXXuHpp5/mscceO+VtNjc3Ew5nutBEIhEaGxtP+TXEsY3WPq3QJWjyBXn6jT5m55vG/FjyL/Os/J4Yv3ntAPvae/nAXNeEaVUZi0Zpam466u/29EX5+Zv9mHQq/7HMiynaT1Nz/ziPcHI63n4dS/VOmH2Oh8aeKC83D/Pwxjb+srGNEqeeJcUWzio24zBOzvlFtvbpRJRKpwnFUjQmjn0sOxmHf6bW1dWd8vMluCCmPUVROLvSQzoFrzX1o6JQkuVortOs587r67nzyUbue343Q5EEV88vyuqYzlQ6neZAf2gkmLC9I0A0kUJTFWrz7dy4tIzqXBsuy8GggUmPSa9Oqqs5J8Nm1FGTnyn+eLhUOk3/cCwTdPCHafeF2NszPNJeS68p1BU4mFfiZH6Ji5n59gkzERUi2+6//35+8pOfHPcxv/rVr475u3T6naHNtw0MDPCVr3yF++67D6fz1JeqVVRUjNxubGw8rcmaOLbR3KeFZUGe2tpJxGBgTvHYL0u8rSrNbza08Kc3W4kqBr582awxXZZxspqam6isqHzX/ev29vHj1zsodJm545o5Y1qjYio61n4dL9WVcNXZmSLXL+3p5cVdPTy6I8DjjQEWlrk5vzaPsys9k6owdbb36USSTKUZjsapm5l7Rq9zpp+p2f8EE2ICUBSFs6s8JNMp3mj2oSgKxe7sdmywGHR84+o5rH52Jz97eT/D0QQfWlI6qU62+4ejbGnLBBO2tPrxhTJLAkrcZi6enc/CUhf1xc4JMZnKNlVRyLUbybUbWVDqGrk/FEuwo2OQLW1+trQF+M2GFn6zoQWzXqO+2MH8EhfzS1yUeydvay0hztRNN93ENddcc9zHFBUVsXnzZpLJJD6fD4/n7SVnAwMDLFmy5KjP27NnD729vXzsYx8buS+VymQZzZ49mzVr1lBVVTUK70JkW5nXyqqZubywsxtbv27MiwEqisJHl5WTZzfyP3/fy1ce2cpXLps1ITtGPdnQwU//sZ9ZhQ5uvbIOu0mf7SGJ0+S2GrhuQTHXLSjmQH+Qv+/q5e+7e/nuc7sw6zWWV3u5oDaP+mKnXMQQp0xm9EIcpKoKK6pzSKTSbGrxo6pj25rqZBh0Kl+9vI4frt3D715vYTAcH5f+0acrHEuyrSMwkp3QOvB2l4b5JS4WlrqYX+oi1y5XO06WxaA7op5DIBxna3uALa1+trT5eaP57aKRh7Ia5pe4KHCasjlsIcaVx+M5IlhwLPX19ej1etatW8fVV18NQFdXF/v27WPhwoVHfc7cuXN54oknjrjvgQceYHBwkNtuu42SkpIzfwNiwphd5CAQibNh/wBWo25crs5fOqeAHJuR7z63i8/9cTOfOX8G59Wc2dXH0ZJOp/nthhb++GYrSys8fPHS2kl1ZVscX7nXyk0rrHx0eTnb2gO8uKuHdXv7WbuzB6/VwHtqczm/Nm9adN0Qo0OCC0IcRlUVVs3MJZVKs7nNj6oo5Duye5KmqQr/eeFM7CYdj23uYDia4LMXzhzTglOnYjAcZ+3OHl5r6mdX1xCJVBqDpjK7yMFFs/JYUOqiYhS6NIgMp1nPOTNyOGdGDgA9QxEa2gJsafPT0Brg5T2ZtXZ5diPzS12sqPKOBCaEmO7sdjs33HADq1evxuv14nK5uPvuu6mtrWXFihUjj7vpppuYN28en//857FYLNTU1BzxOg6Hg2Qy+a77xeSnKApLKzwMhuJsafOzrNKLdRzaCZ9V7ub7H1rAd5/dxXef20VDm59PrKrK6ol8MpXmf/6+l+d2dHPx7Hw+854ZciV7ilIVZaTt9qfOS/J60wBrd/bw6KZ2Ht7YTlWOlfNr8zi3Jvek2niL6UuCC0K8g6YqnFebRyqdpqHNz4JSF7n27AYYVEXh4ysrcZj0/Oq1AwxHE3z5sllZm3Sk02l2dg3x1LZO1u3tI55MU5Vj5doFRSwodTO70IFBNzGCH1Ndnt3ERXUmLqrLJ51O0+YLZwINbQHW7+vj+R3dfGJVFddM8podQoyWW265BZ1Ox80330wkEmH58uWsXr0aTXv787S1tZXCwsIsjlJkk05Tec+sPAajcd5qGWB51fi078uzm7jr+rn87vUW/vJWGzu7hvjSpbVZuWocTST5zrO72NA0wAcXl/Lhs8tk6d00YdRprJqZy6qZufhDMf6xp48Xd/Xwi3VNPLi+iQWlLj6xqooSt3SbEO8mwQUhjkJTFd5Tm0cyBZvb/CwscZGT5QCDoii8f3EpNpOOH/99H9/463ZuvWo2tnG4onJIKJbg77t6eXpbJ839Icx6jUtmF3DZnAIqciRlLtsURaHUY6HUY+GqeUUkkilWP7uLn7+8H52qcMVcOVkSwmg0cuutt3Lrrbce8zFr16497mvcc889oz0sMcGYDh7fHtvUzlsHBlha6R2XDDydpvLPyyuoL3Zy//O7+a8/b+GT51ZxcV3+uJ3cB2Mpbn18Ozs7B/nUuVVcOU+C09OVy2LgmvlFXDO/iFZfKDMH3NrJnU81cv8HFsgSGfEucmlRiGPQaSoX1OVRl29nU5ufgWAs20MC4PL6Qr54aS27u4e45dGttPvCx610Phr29w7z3y/u5V8efIMfv7QPVVX49/Nn8NDHlvKp86olsDBB6TSVL15ay9IKDz9+aR/Pbu/K9pCEEGLScFkMXFiXRzKdprFjcFy3vajMzQ8+tJC6Ajs/XLuX7z63m1AsMebb3dM9xA9e7WVP9xBfumyWBBbEiFK3hY8uK+fLl8+i3RfmJy/ty/aQxAQ05TMXYrEY9957L2vWrCEajbJs2TJuv/12CgoKjvmcP/3pTzz22GPs3buXVCpFXV0dn/3sZ1m8ePE4jlxMBHpN5cLZBSTTsLFlgLPKPbgt2V9rtmpmLlaDjruebuRTv30Lp1nPzDwbtQV2avLszMy3nXEl52giySt7+nh6Wxe7uocwaCqrZuZwxdxCZubZJD1yktBrKl+5fBZ3PdXIj17ci6YoXDQ7P9vDEkKISaHUY+Xcmbn8rXF8Okgczm018M1r6vnLxjZ+t+EAe3qG+NKls5iRZxvV7cSTKdbv62dNQwc7u4Yw6xW+ec0c5pW4RnU7YmqYX+LiA0tK+eMbrcwvdXF+bV62hyQmkCkfXLjzzjt54YUX+N73vofL5eKee+7hk5/8JI888sgR6ysPt2HDBq644goWLVqEyWTil7/8Jf/6r//KY489dkS/ajE9GHQqF8/JJ5lKZwIMZR5cEyDAsKjczY9uXMTGFh+7u4fY3TPMWwd8HMphKHSaqMm3U5NvoybfTlWO7aTqILT5QjyzrYsXdvYwHE1Q4jbziVWVXFCbj8005T8ypiS9luk68u0nd/CDtXtQVYULZslkQAghTkZdoQN/ONNBwmLQxrUOk6YqfHBxKfVFDr7z7C6++JctfGxlJVfPKzzjIL8vFOOZbV08s62LgVCMQqeJT6yqYoYlzGwJLIjjuHFJGdvaA/zP3/dSk2fPevt2MXFM6TOFoaEhHn74Ye666y5WrlwJwOrVqzn//PNZv349q1atOurz7rvvviN+/uY3v8kLL7zAyy+/LMGFacqo07i0voCnt3by1gEfSyo9OCZAj+cCp4kr5haOrKUPRhPs7R1md/cQe7qH2dYe4KXdvUBmglLptTLzYLChNj9zMFAVhWQqzSt7+3h6WycNbQE0VWF5lZcr6guoL3ZKlsIUYNCpfO3KOu5Ys4Pvv7A7U7h0grQ6E0KIiezwDhIN7QGWVerGpYPE4eYUOfnBhxbywAu7+fnL+2lo8/PZC2eeVpbi7u4hnmjo4JU9fSRSaRaVufmP+TNYVOZGVRSampvG4B2IqURTFb5wSS3/+YdNrH52J99533wp5C2AKR5c2LZtG/F4nHPOOWfkvsLCQqqrq9m0adMxgwvvFI/HiUajOByOk3p8c3Mz4XAYgEgkQmNj46kPXhxTNvdppT5Fe2SYp9/opT7fhMUw8QrZOIDFXljsNcFsE/5wkgP+GAf8MVr8cdbu7ObpbZm190adQqlTT/dQnKFYBx6zxlW1DpaVWnCYNEj4aD7gy+4bmqRi0eiEnKB9tN7CT0Nh7ntuF/19vSwsmjxXGybqPs2WwUiS9JCegPXMAp2HPlPr6upGaWRCTD2HOkgMRxO8eWCAFdXj00HicA6znluvnM1ft3Twy/XN/OcfNvOlS2upKzzx/DSeTLFubx9rGjrZ1T2EWa9xWX0BV80tkqvO4rTk2Ix87sIavvXkDh5c18Qnz6vO9pDEBDClgwt9fX1omobb7T7ifq/XS19f30m/zv3334/FYuHCCy88qccfnt0gE7bRl+19WlOTYE1DBz1DMVaWetGN8+TidCw87HYqnabdFx5ZSrGne4hyt8p7l1SxqMwtPaxHSVNzE5UVldkexlHdVVbB7U9s56FNAxTmz2J5dU62h3RSJvI+zQZfMMaMPBtFZ3hikO3PVCEmC5Ne46LZ+Ty2qZ2NLQMsqRifDhKHUxSFaxcUM7vQwepnd/GVRxr4yNnl3HBWyVHH4gvGeGZ7F09v68QXilPsMvNvq6q4sC4Pi2FKnwaIcbC00sO184t4fEsH80qck2Y+IcbOpPxUuf/++/nJT35y3Mf86le/OubvTqWy/kMPPcQf//hHfvnLX2KzjW4BHTE5WYw6Lp6dz2ObO9jaHmBhmfvET5pA1MPaFV5Ylynslzlp82R5ZGK8mA0a37h6Nrc9vp17n93FVy9XOLvSm+1hCSHEhOeyGLioLp81WzvY3h5gbpZqE8zMt/PABxfwoxf38qvXDrC1PcDNF9eMFJ3e1TXEmoYOXtmbWfqwuNzNVfOKWFjmGveAiJjablpRwfbOQb6/dg/VuTbyHNlt3S6ya8IFF7q6uo7byQHgpptu4pprrjnuY4qKiti8eTPJZBKfz4fH8/aJ08DAAEuWLDnhWB566CEeeOABfv7znzNv3ryTewNiWnBbjayszuH5HV20DoQo9ViyPSQhTonFoOOb18zh1se3cc/TO/naFXUslgCTmIBOZl4gxHgq8Vg4d2YuLzT20NwXzFo7ZqtRx5curWV+iYufv7yfz/5hE9ctKGbdvj52dw9j1mtcMbeQK+cWUuSSpQ9ibOg1lS9dWstn/7CZ7zy3i7uvnzspsnrF2Jgw//K7du3iy1/+MhdddNEJH+vxeKiurj7ul9lspr6+Hr1ez7p160ae29XVxb59+1i4cOFxtgAPPvggDzzwAD/72c+kBaU4qpn5NuaVutjdPcRwJJ7t4QhxyqxGHXdcW0+518JdTzeysUXqa4iJ41TmBUKMt7pCB4sr3OztHaZ3KJK1cSiKwmX1Bdz3/vlYjToeXN9MMJrkU+dW8cuPLeETq6oksCDGXKHTzH9cMIOdXUP8dkNLtocjsmjcMheeeOIJHnvsMbq6uigpKeHf/u3fOOuss9i1axf33XcfL7/8Mg6Hg09/+tOjtk273c4NN9zA6tWr8Xq9uFwu7r77bmpra1mxYsXI42666SbmzZvH5z//eQD+93//lwceeIDVq1dTUVFBb2+m2r7JZMJut4/a+MTkpigKSys99A5F2dTqZ0V1jtQrEJOOzajjW9fW8/XHtnHnk43cdtVs5pe6sj0sMQ1kY14gxGjJzAG8DEYyHSTOrtCwZbGLVEWOlfs/sICuQIQyr0WWPohxt2pmLlta/fxlYxtzS5wsmmTLhsXoGJfMhT//+c988YtfpKuri5qaGgKBAB/72Mf4zW9+ww033EBDQwNf+MIXePHFF/nMZz4zqtu+5ZZbuOSSS7j55pu58cYbsVqt/OQnP0HT3q7y39raOhJAAPjd735HPB7n5ptv5pxzzhn5uvPOO0d1bGLyM+o0zp2Zg1Gvsr0jkO3hCHFa7CY9d1xbT5HLxB1P7mBru/xfFmMrm/MCIUZLpqVvHiUuM2+1+InGk1kdj0mvUZFjlcCCyJp/XVVFmcfC/c/vxheMZXs4IgvGJXPhN7/5Dddddx333HPPyH2//OUvufPOO1m4cCE/+clPTrrN46kyGo3ceuut3Hrrrcd8zNq1a4/7sxDHk2M3sbzayws7emj3hyh2Sf0FMfk4zXq+dW09tzy2jTvWbOf2q+cwp8iZ7WGJKSqb8wIhRpNJr40UeX6jZYBlFZOji5QQY8Gk1/jyZbO4+U+bue/5XXzzmnrJ6p1mxuXTr6WlhWuvvfaI+9773veSTqf51Kc+JRMIMenVFTiYW+xkZ+cQwWgi28MR4rS4LAbuvLYer9XIN5/Ywc7OwWwPSUxRMi8QU4nTYuDy+gIMqsrGFh+pU+hKJsRUU+ax8Mlzq9jSFuAvG9uyPRwxzsYluBAOh7Faj6yke+hnr1fan4nJT1EUllV7KXKZ2Nzql4mFmLTcVgN3XleP26LnG09sZ3f3ULaHJKYgmReIqSbPYeKSOQXEUyka2vyn1PZciKnm4rp8zp2Zy+82HJBlw9PMuBV03LhxIz7f25XIU6kUiqKwceNG+vr6jnjseeedN17DEmLUmPQa59Xk8dctHTR2DkpKuZi0vDYjd14/l68+spXbHt/Gt6+by4w8W7aHJaYYmReIqabUY+Giunye297NDpkHiGlMURQ+c341e3qG+O5zu/j+BxfiMGev4KkYP+MWXDh8XeXh3lkkUVEUGhsbx2NIQoy6fKeJZVUeXtzVg3fQQIFD2j+JySnHZuTO6+v56iNbueXRrbz/rBKunl+ESa+d+MlCnASZF4ipaEaenWg8xQs7uzH2qMzIky5jYnqyGHR88ZJavvRwAz9Yu4evXVGHIsVGp7xxCS688MIL47EZISaEOUVOugejbOsI4DTrMevHLYYnxKjKs5u4+71z+dk/9vOr1w7w5NZOPnx2GRfMypcCTeKMyLxATGVzip2EE0nW7elDr6mUe60nfpIQU9DMfDv/sqKC/32liScaOrhmfvGovG4ylaa5P8hAMEFFOj2uQYtkKk1TX5Ct7X4a2gI0dg6yojqH/7hghgRPGKfgQnHx6PxHEmIyUFWFFTO89A5H2NTiZ1mVV9pCiUkrz27i61fOZntHgAfXNfODtXt5fHMH/7KigrPK3XIgFadF5gViqjurzE00luL15gFMeo18hynbQxIiK66ZX0RDW2YOMbvQedrLLPuHo2xq9bOxxcfmFj9DBwuo21/tZ2aejZl5dmbmZ757rIZRG//hwYSt7QG2dwwSimXazha7zMzMt/N8YzfVeTaunFs4atudrMYluLBs2bJTmoC++uqrYzgaIcaexaDjvJo8ntjSzu6uIWYVSuVzMbnNKXLynffNY/2+fh56tZlvrtnB3GIn/7Kigpp8SfsVp0bmBWKqO1ToORxPsLXdj05147UZsz0sIcadoih89sKZfPaPm1j97E4e+OACLIYTn4LGkyl2dAyyscXHxhYfzf0hANwWPUsqPSwsddHa1YMvYWRPzxB/fquV1ME6ql6rgRl5Nmbm2w8GHmzYTSdX8+FQVsTWtsDBYEKA4GHBhFUzc5lb7KS+yIHXZiSVTvOtNTv435f3U51rZVbB9J7zj0tw4cMf/rBc3RLTTpHLzNJKL//Y3UuO3UiOTCrEJKcoCitn5HB2pYdnt3fx+zda+fyft7BqZg4fXVZOoVNqjIiTI/MCMR1oqsJ5tXlEEyk2t/pZXOHBKUXtxDTkMOv5wiW13PLoVv77xX184ZKadx0D0uk0Hf7ISDBha3uAaCKFTlWYXeTgX1ZUsKjMTYXXMvLcJmOQyopKACLxJPv7guzpHmJPzzB7uofY0DQw8vqFTtNIhsOMPBvVuTbMBo1U+lBmQoBt7QG2dQQIRjPBhCKniXNm5FBf7GRusfOoAUJVUfj8xbV87k+buPeZnTzwwYXT+u98XIIL//Ef/zEemxFiwplX4qJ7MEJDm5+V1TkYpRiemAJ0msqV84o4f1Yej2xq57FN7by6r5/L6wv44JKyaX1QFSdH5gViutBrKhfW5RNLpNjYMsCySi/mk7hqK8RUM6fIyT8tLeM3G1qYX+rkktkFhGIJtrQF2NTi460DPnqGokDmpP7iunwWlrmZW+zEbDjx/Nmk15hd6GD2YdnCw9EE+3qG2d0zxJ7uYXZ0DvGPPZluRKqSyUTwheIMH1xiUeg0sbI6h7nHCSYcjc2k46uX1/GlvzTwnWd38s1r6qdtbSr5dBNiDGmqwjkzc+kfjrG5zc/SCo9crRNThsWg4yNnl3NFfSG/e72FJ7d28sLOHm5YVMI10llCCCGAzEnPJXPy+euWTt44MMDZFV652CCmpfedVUpDe4Cf/mM/a3f2sLNriGQqjVmvMa/EyQ2LSlhU5qbAOTo1SmxGHfNLXcwvdY3c5wvF2Hsws2Ffb5BZhQ7mFTupL3aeUZZxda6NT59XzffX7uG3Gw7wz8srzvwNTEISXBBijNmMOs6tyeXJhg729gwzU9aniynGYzXw7+fP4Nr5RTz0ajO/PthZ4iPSWUIIIQCwGvVcUV/AY5s7eLMlE2DQaWq2hyXEuNLUzBKCrzzSQDie5L0Li1lY5mZWgR39OP09uC0GllR4WFLhGfXXvmh2Pju7BvnzW23U5NtZVuUd9W1MdBJcEGIclHosLK7w8MrePrw2Ax6r1F8QU0+px/KuzhKPHewssThLnSViiRQDoRgDwcxXIBQjmkgRTaSIJVLEkge/J1JEkyniB+/L/D757sckUhh1Kv9+wUzOmZEz7u9HCDF5OS0GLq8vYE1DBxtbfJxV7pHgq5h2PFYDP/vo4mwPY8z827nV7OsNcv/fdnP/BxZQ5Jpe9agkuCDEOFlQmqm/sLXdz/KqHAw6SYkUU9PhnSV+9Wozd6zZQXWulXyHCatBh9WoYTXqsBh02N5526DDatRhNmjHbeEaT6bwHQwYHB486A++fdsXjI20qjoanaqg11SMOhXDoS/t7dtWg2HktvGw+7e2B1j9zE76z6nk2gXSUlEIcfLyHCYunl3Ak1s7aGj3s6DEJcslhZhCDDqVr14+i8/9cTN3P93Id943f1otE5XgghDjRKeprKrJpS8YY0ubnyUV0y9VSkwfR3SW2NHNP3b30uYLE4wmCMYSROKp4z8fsBgOBR4y3xOxKNFXfQwEYwxG3h000FQFt8WA12qg2GWmvtiJx2rAazHgsWa+nBY9Jp2GQaee9hXDaCLJd5/bxf++0kTfcIyPraw4biBETCyxWIx7772XNWvWEI1GWbZsGbfffjsFBQXHfd7w8DAPPPAAzzzzDH6/n8LCQm6++WauuOKKcRq5mCpKPRYuqsvnue3d7OgYZE6xM9tDEkKMojyHiS9cUsvtT2znf/6+l5svend3jKlKggtCjCOHSc+5M3J4elsXTX3DVObYsj0kIcaUTlO5cm4hV84tPOL+RDJFKJYkGEsQjB76fugryXAsQSj69u+GowkiiRQFbit1hY6RYIHnsMCBw6wfl5N8o07jK5fV8fOX9/PY5nYGglE+d1HNuK0XFWfmzjvv5IUXXuB73/seLpeLe+65h09+8pM88sgjaNrRry7F43E+/vGP43A4eOCBBygoKKCrqwuDwTDOoxdTxYw8O9F4irU7e9jTM8TMPKnHJMRUsqjczY1Ly/jd6y3MKnBwxTvmQVOVBBeEGGeVuTYWlrvZsK8fj8WA0yKTUzH96DQVh1nFcQptK5uam0b6WWebpip88twqcmxGHnq1mYFgjK9dORubUQ6rE9nQ0BAPP/wwd911FytXrgRg9erVnH/++axfv55Vq1Yd9XmPPPII/f39/OY3vxkJKJSUlIzbuMXUNKfYSTiRZN2ePgyaSrnXmu0hCSFG0QeXlLK7e4ifv7yf6lwbtQVTP4gol1mEyILF5W4qc61savMTSySzPRwhxGlQFIX3nVXC5y+uYWfXEF9+uIHegz26xcS0bds24vE455xzzsh9hYWFVFdXs2nTpmM+729/+xuLFi3i29/+NitXruSKK67ghz/8IfF4fDyGLaaws8rcLC73sKdnmK5AONvDybpkKk3/cJTd3UO83tRPQ5ufloEQA8EYscTxl9ONhXQ6nZXtiqlBVRT+6+IaPFYD9zzTSCA89Y8ZcolFiCzQayrvqc3lsc0dbG7zs6TcM23WYgkx1bynNg+3xcCdTzXyxb9s4far51CRI1cgJ6K+vj40TcPtdh9xv9frpa+v75jPa21t5bXXXuOqq67ipz/9Ke3t7dxxxx2EQiG+/OUvH3ebzc3NhMOZk8ZIJEJjY+OZvxExYirsU2cqjTsd4u8NvdTlmnCYslv8LRaN0tTcNG7bS6XTBGMp/JEk/nCScCKFCrhMOvpSaRpjSdLpTC0ek17FYVSxGjQsehWzXhmV5XDpdJpYMk00kSaSSBNNpojEU4TiaeKpNHpVwWPRcBo1rIbTq9kz3vt1Opgs+/SmBQ7uX9fLtx7fzKfP9o7JEs5UOk0olqIxcexj2ck4/DO1rq7ulJ8vwQUhssRpNnB+TS5Pbe1kZ9cQdYWObA9JCHGa5pe6uPeGudz+xA6+/EgDX7uijnklrmwPa9q4//77+clPfnLcx/zqV7865u/S6fRxn5tOp/F6vXz7299G0zTq6+vx+/3cfffdfOlLXzpucLiiomLkdmNj42lN1sSxTZV9WjsrxTNbO2nqCzKrOAdjFqvLj9cStOFIgr7hKL5glGESpE1Q4TUwq9BOda4Np1mPoihEYkl6h6N0+MN0+MN0D0YZjCcZTIIureA063FbDNhMOmxG3Qkr80cTSUKxJOFYknA88304miCWSpFIp0hpYDQquPU6Ks063FYDvlCcNl+I3hT44go5NiM5diMus/6kOwFMpKV9U8Vk2aeVQFTv5Adr97KuS+Wfl1eM+jaSqTTD0Th1M3PP6HXO9DNVggtCZFGZ18ry6hz+vrsHp1lHkcuS7SEJIU5TZY6N77xvHrc/sYNv/HU7n7uohvNqzuwgL07OTTfdxDXXXHPcxxQVFbF582aSySQ+nw+PxzPyu4GBAZYsWXLM5+bm5qLT6Y4o+FhdXU04HH7XawlxOvSayvl1+QxuauetFh/Lqsbm6ma2hWIJ+oMx+oeiDEUTxJIpXBY9i8rdzCqw47IY3pUVYDJolHoslHoyc6R0Ok0gHKd7MEK7P0ynP0JT/zDJ1KHsBi1T5Nekx2zQiCVShOMJQrEkw5EE0USKRCpFKg16Tck83qLHazeQ7zCRYzNiNeow6TTUw8YSiydpD4TZ3xOkqX+YrvYIAE6znjybEZfVgN2km5L/buLMXTy7gJ1dQ/z5rTZqC+ycXTk1u8ZJcEGILJtb7GQgGGNLqw+7SY/ddPIF7oQQE0ue3cTq987j20/t4LvP7aJ/OMr1C4tl2dMY83g8J3WCX19fj16vZ926dVx99dUAdHV1sW/fPhYuXHjM5y1atIg1a9aQSqVQ1Uy5qqamJsxm87uWWAhxumxGHRfW5fPXLR1s6wgwr9iV7SGNimgiyUAwRt9wjEAoRjSZwmbUUVtgZ06xA6/VeErddhRFwWUx4LIYqC3IZH3GEkn6hmN0BSK0+0J0DmYyHQ7lJB0KIjgtenLsBvJsJnLsRuwmHUaddlLLHAx6jcocG5U5NtLpPPqGoxzoC7G3d5i9fcOke8GoU8mzG3FbDbgtBukiJI7wyXOr2d8b5P7nd/O9DyygyGXO9pBGnQQXhMgyVVVYMcOLLxRjY4ufFVUe9LrsrrcUQpw+m0nHHdfUc//fdvPg+mb6hqP8v3OqTmuNrhhddrudG264gdWrV+P1enG5XNx9993U1tayYsWKkcfddNNNzJs3j89//vMA3HjjjfzmN7/hzjvv5MMf/jDt7e388Ic/5MYbb5TAkRhVBU4T58zw8sLOHg70BSmfpPVbEskUA8EY/cEYA8EYkXgSs0GjPMfK3CIHeU4TxlGc6xh0GkUuM0UuM4vK3aTTaYLRTJaEUadiN+kx6lR0o3SyrygKuXYTuXYTiys9hKIJ2vxh9vUO09wXpNUXRlXAbTGQazfiMhtOuPxKTH0GncpXLp/FzX/czN1PN/Kd980/6WU1k4UEF4SYAIw6jQtm5fH45g42tfpZUiEFHoWYzAw6lS9eWovXauDxLR30B2P818U1ozaZzhRuSozKa003t9xyCzqdjptvvplIJMLy5ctZvXr1EUseWltbKSx8uyd5YWEh//d//8c999zDddddR05ODjfccAOf/vSns/EWxBRXV+ggEI7z2v4BbCYdXpsx20M6JV2BCM39w4TiSfSqSqHTRH2xkxK3BbNhfE6kFEXBZtJjG6dsUItRR02+nZp8O6lUmu7BCE19Qfb3DrOre4h0GgZ9YaKmQZxmPQ6THotRkyUU01C+w8TnL6nlm09s58d/38fnLpo5peb8ElwQYoJwWQycV5PLM1s72d0zTG3+1O+FK8RUpioK/7qqihy7kV+80oQ/tJ2vX1l3ykuf4skUB/pD7O8bZn9vZrLa1B8kGk/xu389myL31EurHEtGo5Fbb72VW2+99ZiPWbt27bvuW7BgAX/4wx/GcmhCAJkT4yUVHgaCMRraAyyr9GA2TPwpeyqdprkvSLs/jFGvcuGsfCpyrFgN2pQ6eToRVVUodJkpdJlZMSOHwXCc1oEQ6xoC+EOxkeUaRp2Kx2LAfbBWg9Uo9Rqmi7PK3dy4tIzfvd7CrEI7l9cXnvhJk8TE/6QSYhqpyLGytNrDy7v7cJn05DtN2R6SEOIMXbegGK/VwPee382XH27g9qvnkOc4+t92MJrIXO3qG2bfwUBCqy9MMpVJpzXrNSpzrFxUl0+Z20KZR4rACjEV6TSV82flMRTpYGOrj2WVORN6aVU8mWJPzzA9gxEKnCYunZOPw2zI9rAmBIdZz5xiJ+qgjVmzqhgMJ+jwh2nxhWgdCNE1GCENGDRlpFaD3aTHZtRN6H9zcWY+uKSU3d1D/Owf+6nOtVEzRS4qSnBBiAlmQYmbgeE42zoCWI3auKX0CSHGzqqZubgsBu58agdf+MsWbr96Di6Lgf29w+w7mDq7vzdI12Bk5Dlui57KHBtLKjxU5dqoyrFS4DSNXNnyBWPT6mqgENONxaDjgll5PLGlg63tfhaUTszioaFYgl1dQ/hCMeYUOThnZu6UW0c+WhRFwWnR47ToqSvKFKMcisRp94dp7Q/R6guNLKPQqweDDVYDdqMOu0kvwYYpRFUU/uviGj73x83c/fRO7rhmDgadSjKVJplOk0we/J5Kk0q9ffvQ71MjtzNtKOPJFDNys1+jRYILQkwwqqpwzswc/KEYG1t9rKjKGbUCREKI7Jlb7OTe92ZaVX7uj5s5vLRXodNEdZ6Ni2fnU5VrpTrHhtsqV/2EmO7yHCbOrcnluR3d7Osdojp3Yl3d9AVj7O4ZIhxPsnJGDgtKXTJnOUV2k55ZBXpmFbwdbOgKRGgZOBhs6BoCQKcqlLrNlHutso+nCLtJz1cvr+NLD2/h//vdxjN+vcvrC7h6QfEojOz0SXBBiAnIpNe4sC6PxzZ1sLnNz1llbrlCKcQUUO618t33zWNNQyceq4GqXCuVOVYsk2A9tRAiO2bm2/GF4qzf14fDpCfXPjGWTHb4wzT3B0ml01xeX0B1rk3mKqPgUFvymQfT5IPROF2BKPt6h2nsGqQjEKGuwE7OBPl/IM7MjDwb33nffPb1DqMpCpqa+VIPu33ofvUdP2sqI48DsI5TwdTjkdmMEBOU22rkvNpcntnWxd6e4ZGDjBBicvPajNy0oiLbwxBCTCJnlbvxhaIHCzxmiv9lSzKVpqkvSGcgjNWo47I5+eQ7pbDsWLEa9VTn6anOs7Go3MVLu3rZ1BYgxxqmtsAuwekpoDrXRnWu7YxeI5lKMxyNj9KITp/k1AgxgVXl2lha6aFlIETvUOTETxBCCCHElKP9/+zdeXxcdb34/9fsayaZTJZmbdq0TfeNIkup/EBZBJFNEUWt5V4u4HKVr/cqyuK9Kut1uVy5AnqxF6+IXhVREAWFiwoFpNC96ZYmzZ5MMvt+Zs75/ZEmUpqkaTPJzCTv5+PRB8mZOWfe+XBm5nPe5/N5f/Q6zl1UQXWxlbc6fKQzak7iSKVV9vWG6PTHqC62cvVptZJYmEZlTitXra3lfcvnkFQybDk8QOtAZKTorxC5JskFIfLcmno3S6uL2NkVlHXthRBCiFnKajLw3iWV2IxGdnQG0LTpvaCMJNPs7g7SH06wvKaY962swpnDERSzlU6nY0mVi4+f1cDaOjetA1FePTzAYCSZ69CEkOSCEPnOoNfx7uG7Fe0+yU4LIYQQs1Spc2jKZCyV4WB/ZNpedzCSZE9XkHBCYcPCMt69qByLMffzu2czq9nAuU0VfPj0OjxOM291+NnZGSChZHIdmpjFJLkgRAGwmgycv7gSs9HAjk5/rsMRQgghRI7ML3dyZmMpnf4YfcGpnTKpaRod/hj7esNkNJVLV1axus4tSyLmkTnFNj50Wh0XLZ1DJKnwSssA7YNRuRklcmLGJxdSqRRf//rXOeOMM1i9ejU33XQTvb294+7zu9/9jquuuop169axevVqLr/8cn71q19NU8RCjK6syMK5C8uJJDMc9k7f3QohhBBC5JfVtW6WVRezu2doNMFUyKgaB/sjtHqjOC0GPnhaHfPKZEWIfKTT6VhWU8zHz2xgRU0xB/sjvN46iD8qUyXE9JrxyYW77rqL5557jm9/+9s8/vjjRKNRbrzxRjKZsYcMlZSU8KlPfYqf/exn/OY3v+Gqq67itttu409/+tM0Ri7E8RZUFnF6Qymtg1EGZG6dEEIIMSvp9TrOWVhGndvGW+0BUunsDoVPpjPs7QnRFYhRX2rjqtNq8TgtWX0NkX12i5H3LKnkw6fX4bIaeeOIn91dQZJZPj+EGMuMTi6Ew2F++ctf8sUvfpH169ezbNky7r//fvbv38+WLVvG3O+ss87ive99L42NjdTX17Nx40aamprYunXrNEYvxOhOm+tm8ZwidnQGiBd4gUdN04gk03QH4jT3hNjREeBAb4gjg1F6gwl80RTRZDpnVbGFEEKIfGU1GXjPkkqKrEa2d2SvwGM0pbK7K8hAJMnauW4uWl4lyx0WmKoSGx8+vZ73LqkkEE/xyqEBOv0x1GkuAipmnxn9SbF7924UReGcc84Z2VZVVUVjYyPbtm1jw4YNJzyGpmm89tprtLa28vnPf34KoxViYoYKPJYTiCm82e7nrPllBTX3MaFkCMUVgnGFQFwhlsqgZFTsJgM2s4GBaJJkWmU4n6Bj6A6N2aDDbjZiNxuwmgyYjXosxuH/6jEZxs6VapqGqoGqaWRU7W3/5R2/D/3XZNDjcZgxjnNMIYQQItdK7GbOayrn2V097OsNs6TKdcJ90hmVVEZFyWik0ipKZuhfKj20vcWXxO1Jc15TBcuqXegLqI8h/kav17GqroQFFU5ePuhlb0+YDl+Mpjku3HaTTG8RU2JGJxcGBgYwGAy43e5jtns8HgYGBsbdNxwO8+53v5tUKoVer+fOO+/k3HPPndDrtrW1EY/HAUgkEjQ3N5/aHyBGJW06pM6QpmUgwjNeL7UlJkqsBvSn+EWRSiZpbWvNcoRDMqpGNKUSSamEkxliylCHRq8Dl9VAldNEXYkJl9WAxahH0zQUFRJplVhSJaJkCCdVQjGVgUCGqKKRyqiow8kH3dA/o16H1ajHZOBo0mDotTNHkwqqBhqApjE8DkLThv6NtNrbmk+vg3KHEY/diNOsP+kv4als09lK2vRYoUQGLWwi6DBN6jjDn6lLlizJUmRCiOlU73FwVmMZLx3ox2ExUGIzDyULjiYMlMzQ92ZCyZBUMqTVowl1dej7UAcYDDqMeh0mgx6HSc8HVlZT53Hk+k8TWeCwGLloeRXLqov504F+3mz3UWQxMc9jp9xlPeW+oxCjKcjkwne+8x0efvjhcZ/zox/9aMzHJjJszOFw8NRTTxGLxXj11Ve59957qa2t5ayzzjrhvg0NDSM/S4ct+6RN/6Z+fpSX9g8wEE4STeuZW2pnTrH1pJeHam1rZV7DvKzEpGka0WSGYFwhmFAIxlIkdCqaWcPjMrLSbaOxzMmcEht2k+Gk74homoaS0Yim0kQSacLxFL6YQiiuEIgpJNIZDEc7SGaDHqNRj2k48WA0YDLosBj1mI16zEYDJsPQ4yaDHqNRh0mvI5rM0NwTork3TH9aJaYZqS2xUV5kmXDbZrNNxRBp02P5oykWVDipdtsmdRz5TBWi8K2oKcYfS7Gt3Y9ep0M9mjjX6xj6fjPosJoMlBdZKLKacFoNFFtNOK0mHGYjFtPQc0x6Pfv3JyWxMAPVltr5yLvqOTwQY2vbILu6g1j7IzSUOZjjsspoTZEVBZlc2LhxIx/4wAfGfU51dTXbt28nk8ng9/spLS0deczn83H66aePu79er2fu3LkALFmyhJaWFh5++OEJJReEmC51bgcfP9NBhy/Kjo4gLd4ILd4Ic4qt1JTYKbZN7o7mRA1PdQgcne4QS2ZQVBW7eagjM7/cQX2pHZfVNOkvL51Oh9mow2w047abAXt2/oi3cdmG5iu+e2E5h7wRdnUF2dcbZl9vmDkuK1XFVtwOs2T7hRBC5AW9Xsc5C8qYU2RBUTVcNhMOiwGryTgyIqGQplCKqaHX61lQ4WRBhZMOX4y3jvjY1xumpT9CvcdBdcnJ36AS4u0KMrlQWlp6TLJgLMuXL8dkMvHKK69w2WWXAdDb20tLSwtr1qw5qddUVZVUKnVK8Qox1epKHdSVOgjGU+zuCrKrM0h3IIHLZmRuqYMypyWrnQolo47UTQjGFSLJNKm0ismgo8RupqnSyfwKJ267GaupcL+kjEY9i6tcLK5y4Y+maO4Jsbs7SG9HAqtRT63bToXLIoWuhBBC5JzRoGdxdXGuwxAFoq7UTl2pnYFwkjfbfezrCdM6EKGmxEZdqV36NuKUzOizpqioiKuvvpr7778fj8dDSUkJ99xzD01NTZx99tkjz9u4cSMrV67kC1/4AgAPPfQQq1atoq6ujlQqxZ/+9Cd+85vfcPvtt+fqTxFiQoptZtYvKOeMBg8HvRHeOuJnV1cQk15HXamd6hLbKV3spzMq4USaUGIomRCKK6TSKjq9DpfVyIIKJ41lTipLrDjMhhlZJMjtMHP2gjLOnF9Kmy/Gnq4ghwciHPJG8DjM1JTY8GQ5iSOEEEIIMZXKiixctKyKs+aXsb0jwM7OAB3+OFUuK/UeO0XW6RkFK2aGGZ1cAPjKV76C0WjklltuIZFIcNZZZ3H//fdjMPztAqujo4OqqqqR32OxGP/yL/9Cb28vVquV+fPnc9999/H+978/F3+CECfNaNSzpMrFkioX3YE4OzoCHOyPcHggyhyXlRq3jRLb2JWCVU0bSSYMj1BIplU0TcNhMVLnsdNY7qSq2EqR1TSrLqj1ej3zy5zML3MSTaY50BtmR2eAnV1BjHodNW4bc1zWXIcphBBCCDFhLpuJdy8qZ91cN3u6grzVEeD1Vh8eh5kGj4MSWWFCTMCMTy5YLBbuuOMO7rjjjjGf8+KLLx7z+xe+8IWRUQxCFLrqEhvVJTY2JBX2dIXY0RngzSNxnBYTc0vtlBdZ0DSNSDJN6OioBH88RVJRyWgadpOBSpeFhrKhuglFVtO4yz7OJg6LkTVz3ayZ66YrEGdPV5CDfWGODMaIB+PYSxNZn5IihBBCCDFV7BYjp8/3sLrezb7eEG8c8fNmux+nxcj88qGptlJzSoxlxicXhBBDnBYTZ8z3sG6um5aBCNvaA+zpCWHs0xHyJyhO+UeKMLrtJubVOaj32Cmxm6W4zwTUlNioKbHx/zVVcLAvzHNbfezqCmA26KkrdVBVbC3o+hNCCCGEmD1MRj0raktYVu2ixRvljTYfOzuDWI16VtQWU2wz5zpEkYckuSDELGMw6FlU6WJRpQtvKMnOLj87Iz5W1Rczv8xJqcOMzSwXwafKbNSzrKYYfaiY4qp6dnQEONAXpsUbZo5rqEiSy2qUoYVCCCGEyHt6vZ6FlUUsrCyiwxfjlZYBtnUEOKexTJavFMeR5IIQs1i5y8J7XHOoxs+SheW5DmfGGZmSsrCM3V1BdnQFeaPNR5HFyNwyB+UyZUIIIYQQBaKu1M77rFX8alsne3tCrKwtyXVIIs9IckEIIaaY02rizMYy1s0tpcUb4c0OP7uPTpmodZ/6Kh5CCCGEENOp2G7iXfNK+WNzH95wkvIiS65DEnlEkgtCCDFNjEY9TVUumqpc9ATjbG8/dhWPWreN4nFW8RBCCCGEyLWmOS6ODEbZ0xPkHLtMjxB/I2eCEELkQFWxjfetqOL6cxo4u9FDKKmw9Yifv7b66A0lyKharkMUYkZKpVJ8/etf54wzzmD16tXcdNNN9Pb2nnC/xx57jIsvvpiVK1fy7ne/m3/9138lGo1OQ8RCCJFfDHodZzWW4bQY2dsbynU4Io9IckEIIXJoeBWPv18/j0uWz8FhNbK7K8Arh7y0eCNEkulchyjEjHLXXXfx3HPP8e1vf5vHH3+caDTKjTfeSCaTGXOfp59+mn/7t3/jpptu4tlnn+W+++7jT3/6E3fdddc0Ri6EEPmjxG7m9AY3g5EkA+FkrsMReUKmRQghRB4wGP42ZaI3GGfb0SkTrQNRXFYjNSU2PE6L1GYQYhLC4TC//OUvufvuu1m/fj0A999/P+eddx5btmxhw4YNo+63bds2Vq9ezRVXXAFAbW0tV1xxBc8///x0hS6EEHlnSVUxHb4Ye3qCrJfpEQIZuSCEEHlnztEpE3+/YR7vXVKJw2KguTfMy4cG2NHhpz+UIJ1Rcx2mEAVn9+7dKIrCOeecM7KtqqqKxsZGtm3bNuZ+p512Gs3NzWzfvh2A7u5uXnzxRd797ndPdchCCJG3hqdHOCxGmmV6hEBGLgghRN6ym42sqC1mRW0x/miKA71h9vYE2dkVxKjXMcdlpdJlpdhuQi9FIIU4oYGBAQwGA263+5jtHo+HgYGBMfe79NJLCQQCfOxjH0PTNNLpNJdffjn//M//fMLXbGtrIx6PA5BIJGhubp7cHyGOIW2afdKmU2Mmt2uZluSvrVHSoQFKbNM3wjKVTNLa1jptr5fPVE0jllJpTo/9XTYRbz9PlyxZctL7S3JBCCEKgNth5oxGD2c0eugOxNnfG2ZfT4jOQByLUU+N20aF04rTKh/rYvb5zne+w8MPPzzuc370ox+N+ZimjV9A9a9//Svf+973+OpXv8rKlStpb2/nrrvu4j/+4z/43Oc+N+6+DQ0NIz83NzefUmdNjE3aNPukTafGTG7XRaqGYU8Prd4oK+qmb3pEa1sr8xrmTctr5buMqhFJKixZWD6p40z2PJVeqBBCFJjqEhvVJTY2LCij3R9jb0+Iw94oh71D9Rmqiq2UF1mlPoOYNTZu3MgHPvCBcZ9TXV3N9u3byWQy+P1+SktLRx7z+XycfvrpY+777//+71x66aV86EMfAqCpqYlYLMbtt9/Opz/9aYxG6U4JIWYvg17H2Y1l9AUT7OsNsbymJNchiRyRb0MhhChQRqOe+eVO5pc7SaTSHPJG2dsdZH9fhAN9EUodZupK7ZQ5LbkOVYgpVVpaekyyYCzLly/HZDLxyiuvcNlllwHQ29tLS0sLa9asGXO/RCKBwXBsss5gMJxwxIMQQswWJXYz6+aV8uK+fnzRJKUO6XvMRpJcEEKIGcBqNrK8ppjlNcUEYyn294XZ2x1ie0eAeR4HjRXOXIcoRM4VFRVx9dVXc//99+PxeCgpKeGee+6hqamJs88+e+R5GzduZOXKlXzhC18A4LzzzmPz5s0sX758ZFrEAw88wHnnnSejFoQQ4qilVcW0D8bY3RVk/YJyDHqpBzXbyDeiEELMMMV2M++a5+H0hlLeaPXz6uEBVE1jYWVRrkMTIue+8pWvYDQaueWWW0gkEpx11lncf//9x4xM6OjooKqqauT3m2++GZ1OxwMPPEBvby9ut5vzzjuPW265JRd/ghBC5CWDXsdZ8z30hxLs6w2yrLok1yGJaSbJBSGEmKF0Oh2nz3NjMMDLBwfQNFg0RxIMYnazWCzccccd3HHHHWM+58UXXzzmd6PRyGc+8xk+85nPTHV4QghR0EqdFtbOLeWlA/1UyfSIWWd6SnkKIYTICZ1Ox2lzS3n3onI6gzH2yTrUQgghhJhCy6pdNJY52d0dJKNKbZrZRJILQggxC6ypd3Puogp6gnH29YakEJ0QQgghpoTRoOesRg82k4H9fXJTYzaR5IIQQswSq+tK+P+ahhIMzZJgEEIIIcQU8TgtnDa3lN5QAl80metwxDSR5IIQQswiK2tLOK+pgv5Qkr09kmAQQgghxNSQ6RGzjyQXhBBilllRW8J7llTgjSTZ3S0JBiGEEEJkn0yPmH0kuSCEELPQ0upi3rukEl80ye4uSTAIIYQQIvuGp0f0BRP4Y6lchyOmmCQXhBBillpS5eK9Syrxx1Ps6gqiSoJBCCGEEFm2rNrF/HInu7oCMj1ihpPkghBCzGKLq1xcuLSSQFxhZ2dAEgxCCCGEyCqjQc+Z80uxmgwc6AvnOhwxhSS5IIQQs9zCyiIuWlZJOJFmR4ckGIQQQgiRXWVFVk6rd9MbjMv0iBlMkgtCCCFYUFHEhcsqiSTTbJcEgxBCCCGybHlNMfPLnezuCqJkVNIZlYyqoWqa1H6aIYy5DkAIIUR+WFBRhF6n4/ndvWzr8LO61o1Br8t1WEIIIYSYAYanR/SFE/zpgBeTQY8O0L2tq6HX6dDpGNmu0+nQo0OnH9o29LgOHeAdTJKxRzDqdZgMeoyGo/89+rvJoMOgH3q+mB6SXBBCCDFifrmTi1fM4fe7e9ne4Wd1nSQYhBBCCJEdZUVW3re8ihZvmIwKqqqR0TQ0FVSGRjFkVNC0oe3q0Z9V7eh/gYyqogHxtEpvKI6S0VCPFoocHv+g1w0lIgx6HWaDHotJj8Wox2QwYDL8LRnhspqwmgy5ao4ZR5ILQgghjtFQ5uR9K6r43e5e3mr3sba+VBIMQgghhMiK6hIb1SW2SR+nuTlOU1MjiqqipFUSaZVYKkM8lSaWyhBOKMSVDNFkhngqQzCuoGRSpFUVVQO0odERNSU26ksd2MySZJgsSS4IIYQ4zlyPg0tWVPG7XT1sPeJjRU0xdrN8ZQghhBAif+j1Oix6AxajAecEnp9RNZTMUDIikkrT0h9hR2eQDn+cmhIbcz126e9MgrScEEKIUdWX2rl0ZRW/393Dq4cHqXPbafA4MBulFrAQQgghCo9Br8OgN2A1GSiymagqtrG23s3OzgBvdQToCsSZ47LQ4HHitMql8smSHqIQQogx1brtfOyMBt411013MM4rh7y0D0bJqFLVWQghhBCFz24xcmZjGdevn8eGhWWEEmleax1kR0eAUFzJdXgFRdIxQgghxmU1Gzh7YTnLaot5vcXHnp4QR3wxmiqLKC+ySBVmIYQQQhQ8q8nAuoZSVtYUs6cnyJttfv7a5sPjMDOvzEGJ3ZzrEPPejB+5kEql+PrXv84ZZ5zB6tWruemmm+jt7Z3w/s888wxNTU3ceOONUxilEELkv2KbmQuXz+GjZ9Qzp9jCzq4Ab7T68MdSuQ5NCCGEECIrzCYDa+pL2bR+HhcsrSStqWw94mPrER++aApNk9GbY5nxyYW77rqL5557jm9/+9s8/vjjRKNRbrzxRjKZzAn37ejo4P7772fdunXTEKkQQhSGSpeVq9bWceWaWsxmPVuP+NnRESCaTOc6NCGEEEKIrDAY9CyvKWbjmQ28b3kVep2Ot9r9vNHmYzCSlCTDKGZ0ciEcDvPLX/6SL37xi6xfv55ly5Zx//33s3//frZs2TLuvoqi8P/+3//jlltuoa6ubpoiFkKIwtFQ5uC6d83loqWVJJQMrx4eZF9viGT6xMlbIYQQQohCYDDoWVzl4hNnzeXSFVVYTAbe6vDzequP/lACVZIMI2Z0cmH37t0oisI555wzsq2qqorGxka2bds27r7f+c53qKmp4corr5zqMIUQomDp9TqW1RTz8bPmcnajh75QgpcPDdA2ECWdUXMdnhBCCCFEVuh0OhbNKeK6M+q5YnUNRVYDO7uC7OwIyCiGo2Z0QceBgQEMBgNut/uY7R6Ph4GBgTH3e/nll/nd737HU089dUqv29bWRjweByCRSNDc3HxKxxGjkzbNPmnT7JuNbeoCzvJk2NWX4PVmL2/qddSVmPDYjeizUPQxlUzS2tY6+UBniFAigxY2EXSYJnWc4XN1yZIlWYpMCCGEmLl0Oh3zy53ML3fS3BPipX39HOqPsLCyKNeh5VxBJhe+853v8PDDD4/7nB/96EdjPjZeZsnn83HrrbfyrW99i+Li4lOKr6GhYeRn6bBln7Rp9kmbZt9sbtPTgIFIgi2HBmnxRsmoBhZVFlFkNWE2nvqAuda2VuY1zMteoAXOH02xoMJJtds2qePM5nNVCCGEmIwlVS5iqTR/OTBAid1EeZE11yHlVEEmFzZu3MgHPvCBcZ9TXV3N9u3byWQy+P1+SktLRx7z+Xycfvrpo+538OBBvF4vmzZtGtmmqkNDe5cuXcozzzzD/Pnzs/BXCCHEzFXmtPKB1TW0D0Z5+dAA2zr86HU6bGYDJTYzTqsRh9mI3WzAajLkOlwhhBBCiFOyus7NQDjJzq4gZ883YjMX5CV2VhTkX15aWnpMsmAsy5cvx2Qy8corr3DZZZcB0NvbS0tLC2vWrBl1nxUrVvD0008fs+3f//3fCYVC3HnnndTW1k7+DxBCiFmi3uPgI6V2OvxxegJxekMJ+kMJeoJxNA10gNVsoNhmoshqwmE2YDcbsZr06LIwlUIIIYQQYioZ9Do2LCrHH1PY1hHgjHkeDPrZ2YcpyOTCRBUVFXH11Vdz//334/F4KCkp4Z577qGpqYmzzz575HkbN25k5cqVfOELX8But7No0aJjjuNyuchkMsdtF0IIcWI6nY76Ujv1pXZgaGpaPJVhIJrEG0rSE0zQF07QG0ygMZRwMBv1FNtMFNtM2M1GHBbDcdWYT6V4kgYM76Zp2sjvGtrQf4/+DKBqQzuMPHZ0H4NeR5F1cnUORO787Gc/45lnnqG5uZlwOMwLL7wwoRsHzz33HA888ADt7e3U19dzyy23cMEFF0xDxEIIIfKd3WzkvKYKfrOjmz09QVbWlOQ6pJyY0ckFgK985SsYjUZuueUWEokEZ511Fvfffz8Gw9+G4XZ0dFBVVZXDKIUQYvbQ6XTYLUbqLUbqSx0j2+OpNL6ogjecoCeYoD+UpMUbGbrIB/yDcbrT3qzEMJws4G3JhKO/Hn1cO2aD9rbHABrLHcwrc2YlFjG94vE455xzDu95z3u45557JrTPtm3buOWWW/jsZz/LhRdeyPPPP8/nPvc5nnjiCVatWjXFEQshhCgElcVWzlno4Y97++nwxag7elNlNpnxyQWLxcIdd9zBHXfcMeZzXnzxxXGPce+992Y7LCGEEO9gMxupMRupcdtYfXRbKp3BF03hDSd5c2+QqvJjL+gnNOjwHU/S64c2GfV69DodOh3odH/7Xa8fWqdZr9dj0A0tt2nU60ee1xWIs609gNVooKpkcsUUxfT75Cc/CcCuXbsmvM9jjz3GGWecwc033wzAzTffzOuvv85jjz3Gt7/97akIUwghRAFaPMfFQCTFm21+XEdHYM4mMz65IIQQonCZjQbmFNuYU2zDGHayZMmcXIfE/DInmYzGjs7AUIFKuznXIYkptn37dj72sY8ds+2cc87h8ccfz1FEQggh8pFOp+OMeR58kRTbOvycPd+D2Th7CldLckEIIYQ4CfqjhZsiyTRvtfs5c74H+yyuDD0bDAwMUFZWdsy2srIyvN4TT9Npa2sjHo8DkEgkaG5unpIYZytp0+yTNp0a0q7Zl89tWqVLsz8Y5fk3B1haYZ3yItWqphFLqTSnByZ1nLe36aksUy29ISGEEOIkmQx63ru0kqd3dLP1iI+z5pdhMuhzHdas9Z3vfIeHH3543Of86Ec/4owzzsjaa2qaNqHOYkNDw8jPzc3Np9RZE2OTNs0+adOpIe2affnepnPqojy7swfFbqWpsmhKXyujakSSCksWlk/qOJNtU0kuiCn1/ve/n4suuojPfvazuQ5FCCGyym42csGSSn6zo5s32328q8GDXpbPzImNGzfygQ98YNznVFdXn/Lxy8rKGBg49m7Q4ODgcaMZZpNbb70Vv9/PI488kutQhBAiL9V7HLyrsZS/HBig1G6ivMia65CmnCQXhBBCiFNU6rRwwdJKnt7Zza6uICtriqd86KM4XmlpKaWlpVN2/NWrV7Nlyxb+/u//fmTbli1bWLNmzZS9Zr677bbbTmk5WCGEmE1W1boZCCfZ2RXkrPnGGT+NUsZwCiGEEJNQ47Zz/uIKgrEUh7yRXIcjTsDr9dLc3ExbWxsALS0tNDc3EwgERp6zceNGvvWtb438/olPfILXXnuNRx55hJaWFh555BFef/11Nm7cOM3R54+ioiJcLleuwxBCiLxm0OvYsLCCqmIrb7X7yagzOykryQUxYbFYjC9+8YusWbOGs88+m0ceeYQbb7yRW2+9FRgaInrzzTezcuVKzjvvPH7xi18cd4ympiZ+/OMf8w//8A+sWrWK8847j1//+tcTev3Ozk6ampr405/+xEc+8hFWrFjBxRdfzMsvv3zM81paWrjppps47bTTWLNmDR/+8IfZv3//5BtACCHGsKjSxZmNHjp8MTr9sVyHI8bx05/+lCuuuIJ/+qd/AuAf/uEfuOKKK45Zlrqjo+OYYo1r167l29/+Nr/61a+4/PLLeeqpp/jOd77DqlWrpj3+fHHrrbdy4403AvDxj3+cO++8k2984xucfvrpnH766dx3332oqprjKIUQIvdsZgPnNVVg1OvZ3RXIdThTamaPyygQv3yzk//d2jGtr3nNujquPq32pPa59957eeONN3jwwQepqKjge9/7Hlu3buWCCy4Ahjoa3d3dbN68GZvNxj333ENXV9dxx/nud7/LLbfcwpe//GV+//vf86UvfYn58+ezYsWKCcXxox/9iNtvv52mpiZ+8pOf8KlPfYo//OEPVFZW0tfXx0c/+lHWrl3L5s2bKSoqYufOndLBEUJMuTV1bqLJNG8eCWAzGfA4LbkOSYzis5/97AnrAL090TDs4osv5uKLL56qsEYUSp/gnZ5++mmuuuoqfvrTn7J//37uuOMOKioq2LRpU5aiFEKIwlXhsrJhURl/2NtH+2CMeo891yFNCRm5ICYkGo3y5JNP8k//9E+sX7+ehQsXctddd6HXD51Cra2t/PnPf+ZrX/sap512GkuXLuXee+8lkUgcd6wLLriAa6+9lnnz5nHzzTdz5pln8thjj004losvvphLLrmExsZGbrvtNqqqqvjJT34CwE9+8hPsdjsPPPAAK1euZN68eVx++eV5XUlWCDEz6PU6zmosY1GFk+0dASJJJdchCTFtKioquP3222lsbOSSSy7h7/7u79i8eXOuwxJCiLzRVFnEmvoSDnrDBGKpXIczJWTkQh64+rTaSd8xmGodHR0oisLKlStHttntdhYuXAgMTUXQ6/XHPF5TU0NFRcVxx1q9evVxv//pT3+acCxNTU0jPw+/ZktLCwB79+5l7dq1mM3mCR9PCCGyxWTQ856llcSVDFuP+Dl7vgez0ZDrsEQBKYQ+wWhWrVp1TDHTNWvW8MADDxCJRHA6nTmMTAgh8oNOp+OMeR58kRQ7Ov2cNb9sxvURZOSCmJBCqQhdKHEKIWYuq8nABcsqcVlNbD0y84s3CSGEEGJiTAY95zaVU2Qzs70jMOOuXSS5ICakvr4ek8nErl27RrbF43EOHjwIwPz581FV9ZjHu7u76e/vP+5YO3bsOO73+fPnTziWtxdn1DSNnTt30tjYCMCyZct46623SKVm5lAjIURhKLaZuXjZHPR6HTs6/TOu8yDEO+3YseOY83z79u1UVFTIqAUhhHiHYpuZ8xaVo2RUDvTNrFWmJLkgJsThcHDVVVfxzW9+k1dffZVDhw5x++23o6oqOp2O+fPns2HDBr761a+ybds2mpubufXWW7Farccd6/nnn+d///d/aWtr45FHHuHVV189qeW8fv/73/P73/+ew4cPc9ddd9Hd3c1HPvIRAD760Y8Si8X4/Oc/z86dOzly5AjPPPMMzc3NWWsLIYSYiMpiK+c3VRBNZtjXG851OEJMqf7+fu666y4OHz7M73//ex599FE++clP5josIYTIS/UeB2fM99AViNEXOr5GXaGSmgtiwr70pS8Rj8e5+eabsdvtfPKTn2RgYGCkvsG9997L7bffzsaNG3G73XzmM5/B5/Mdd5zPfvazPPfcc3zjG9+gtLSUe+6555haDSfyiU98gv/+7/9mz549VFdX8+CDDzJnzhwAKisr+fGPf8z9998/krBoamria1/7WhZaQAghTk5jhZP1qTJe2t+P3WxgrsdxysdSNY1wIk0ooRCKK/SHktSU2LIYrRCn7rLLLkNVVa655hp0Oh0f/OAHJbkghBDjWFVbwmAkye6uIDazAZfVlOuQJk2SC2LCHA4H//Zv/zbyeyqV4rHHHuPcc88FoKysjIcffviYfT70oQ8dd5yysjIeffTRU46jpqaGn/70p2M+vnDhQn7wgx+c8vGFECKbVtYUE0mk+WurD7vZQHnR8SO6RqNpGtFUhlBcIZRQCMRSJBSVjKZhNxloKLPhcUrxWpEb99577zG/G41G7rzzTu68884cRSSEEIVFr9dxzsJy/FGF1w4P4rQYcdvNuKwmnFYjDrMBo6GwJhpIckFM2N69e2lpaWHlypVEo1F+8IMfEI1GueSSS3IdmhBC5C2dTseZ8z2EEwo7uwKcPteDyzb63Yl4KkMooRCMD/2LpTIoGRW72YDbbmJVrYO5ZXZK7GYsM6zCtBBCCDHbWE0G3r+yin29YTr9MXpCCboDcTTAoNdRZDHitpsoOppwsJkMx6zMk28kuSBOyubNm2ltbcVoNLJ48WJ+/OMfj0xJmKyHH36YRx55ZNTHTjvtNP7lX/4lK68jhBDTzaDX8f81VRBPZdjW4eeMeaVYTUZSafWYZEI0mSaZVrEYdbhsZhZWOplf5qTUYcZmlmSCEEIIMdPYLEbWzHWzZq57aNRiMk1fOElPIE6nP05nII6SiQFgMepx200U28w4LUYcFiNmY/6MbpDkgpiwpUuX8uSTT07qGG9f6eGdrr32Wt73vveN+pjVaqWyspL9+/dLcUYhREGymgy8d2klv97exV9b/RTbjIQTaVIZFb1eh8tqYlGlk8ZyJ+UuKw5zft+dEGLY//zP/+Q6BCGEmBF0Oh1Oqwmn1URj+dBqOxlVYzCSpC+UoMsfpycYpy8cBg10OnCYjZTYzRRZc38TQpILIm+UlJRQUlKS6zCEEGLKFFlNXLR0Dr/b00sirTK3zMH8MgdVJVacFhMGvSQThBBCCPE3Br2OCpeVCpeVFbUlAMRTabzhJD3BBJ3+ON5wArMx90WeJbkghBBCTKNyl5WPnTGXjKZhKrBCTUIIIYTIPZvZSL3HOLSkJaCqGmlVy3VYklwQQgghppter0OPjFIQQgghxOTp9TrMeTD6UW6ZCCGEEEIIIYQQYlIkuSCEEEIIIYQQQohJkeSCEEIIIYQQQgghJkWSC0IIIYQQQgghhJgUSS4IIYQQQgghhBBiUiS5IIQQQgghhBBCiEmR5IIQQgghhBBCCCEmRZILQgghhBBCCCGEmBSdpmlaNg/YvHgJAEv2NWfzsEIIIYTII/J9L4QQQoi3k5ELQgghhBBCCCGEmBRJLgghhBBCCCGEEGJSJLkghBBCCCGEEEKISZHkghBCCCGEEEIIISZFkgtCCCGEEEIIIYSYFEkuCCGEEEIIIYQQYlIkuSCEEEIIIYQQQohJkeSCEEIIIYQQQgghJkWSC1Pk8ccf5/zzz2fFihVcddVVbN26NdchFazvfve7NDU1HfNv/fr1uQ6r4LzxxhvcdNNNbNiwgaamJp588sljHtc0je9+97ucc845rFy5ko9//OMcPHgwR9EWhhO16a233nrcuXvNNdfkKNr898gjj3D11Vezdu1azjzzTG666SYOHDhwzHPkPD15E2lXOVenlvQJskv6BZMnfYLskz5B9km/IPumuk8gyYUp8Oyzz3L33Xdz00038dRTT7FmzRpuuOEGuru7cx1awZo3bx4vv/zyyL+nn3461yEVnFgsxqJFi7jtttuwWq3HPf6DH/yAH/7wh9xxxx384he/oLS0lE2bNhGJRHIQbWE4UZsCnH322cecu9///venOcrC8de//pWPfvSj/PSnP+Wxxx7DYDCwadMmAoHAyHPkPD15E2lXkHN1qkifYGpIv2BypE+QfdInyD7pF2TflPcJtCzb27RY29u0ONuHLSgf/OAHtdtuu+2YbRdccIH2zW9+M0cRFbb/+I//0C699NJchzGjrF69WvvlL3858ruqqtr69eu1733veyPb4vG4tnr1au2JJ57IRYgF551tqmma9qUvfUn7h3/4hxxFVPgikYi2ePFi7YUXXtA0Tc7TbHlnu2raqZ2r8n0/MdInyD7pF2SX9AmyT/oEU0P6BdmXrT7BMBm5kGWpVIo9e/YcNzxv/fr1bNu2LUdRFb6Ojg42bNjA+eefzy233EJHR0euQ5pROjs78Xq9x5y3VquV008/Xc7bSXrzzTc566yzuOiii7j99tsZHBzMdUgFIxqNoqoqLpcLkPM0W97ZrsPkXM0+6RNMHekXTB35rJ068jk7OdIvyL5s9wmMUxHkbOb3+8lkMpSVlR2z3ePxsGXLlhxFVdhWrlzJPffcw/z58/H5fDz00ENce+21PPPMM7jd7lyHNyN4vV6AUc/b/v7+XIQ0I2zYsIELLriA2tpaurq6+Pd//3c2btzIk08+idlsznV4ee+uu+5iyZIlrFmzBpDzNFve2a4g5+pUkT7B1JB+wdSSz9qpIZ+zkyf9guzLdp9AkgtTRKfTTWibOLFzzz33mN9XrVrFe9/7Xp566ik2bdqUo6hmJjlHs+vSSy8d+bmpqYlly5Zx/vnn89JLL3HhhRfmMLL8d8899/Dmm2/yxBNPYDAYjnlMztNTN1a7yrk6taRPkF3SL5geco5ml3zOTo70C7JvKvoEMi0iy9xuNwaDYSSTNmxwcPC4rJo4NQ6HgwULFtDW1pbrUGaM8vJyADlvp1hlZSWVlZVy7p7A3XffzW9/+1see+wx6urqRrbLeTo5Y7XraORczQ7pE0wP6Rdkl3zWTg/5nJ046Rdk31T1CSS5kGVms5lly5YdN9xxy5Ytxww3EacumUzS2to68oEiJq+2tpby8vJjzttkMsnWrVvlvM0in89Hf38/FRUVuQ4lb33jG9/gmWee4bHHHqOxsfGYx+Q8PXXjteto5FzNDukTTA/pF2SXfNZOD/mcnRjpF2TfVPYJZFrEFNi0aRNf/OIXWblyJWvXruWJJ56gv7+fa6+9NtehFaT77ruP8847j6qqKnw+H9/73veIxWJceeWVuQ6toESjUdrb2wFQVZXu7m6am5spLi6murqaT3ziEzz88MPMnz+fhoYGHnroIex2O+9///tzHHn+Gq9Ni4uLefDBB7nwwgspLy+nq6uLb3/725SWlvLe9743x5Hnp3/913/l17/+Nf/5n/+Jy+UauRNht9txOBzodDo5T0/Bido1Go3KuTqFpE+QfdIvmDzpE2Sf9AmyT/oF2TfVfQKdpmlaNgNuXrwEgCX7mrN52ILz+OOP8+ijj9Lf38+iRYv48pe/zOmnn57rsArSLbfcwhtvvEEgEMDtdrN69Wo+97nPsWDBglyHVlBef/11PvGJTxy3/corr+Tee+9F0zQefPBBfvaznxEMBlm1ahV33nknixYtykG0hWG8Nv2Xf/kXPv3pT7N3717C4TDl5eWcccYZfO5zn6OqqioH0ea/pqamUbd/5jOf4bOf/SyAnKen4ETtmkgkTulcle/7iZM+QXZJv2DypE+QfdInyD7pF2TfVPUJhklyQQghhBAnTb7vhRBCCPF2UnNBCCGEEEIIIYQQkyLJBSGEEEIIIYQQQkyKJBeEEEIIIYQQQggxKZJcEEIIIYQQQgghxKRIckEIIYQQQgghhBCTIskFIYQQQgghhBBCTIokF4QQQgghhBBCCDEpklwQQuS93/72tyxfvhxFUXIdihBCCCFyTPoFQuQnSS4IIfLevn37WLRoESaTKdehCCGEECLHpF8gRH6S5IIQIu81NzezZMmSXIchhBBCiDwg/QIh8pMkF4QQeW/fvn00Njby7W9/mw0bNnDaaadx++23o6pqrkMTQgghxDSTfoEQ+UmSC0KIvObz+fB6vfz4xz8mkUhw7733cv311/Pzn/+cF154IdfhCSGEEGIaSb9AiPxlzHUAQggxnubmZgA+/vGPs2nTJgDWr1/PE088QVtbWw4jE0IIIcR0k36BEPlLRi4IIfLavn37cLlcfOxjHxvZpmka4XAYt9udw8iEEEIIMd2kXyBE/pLkghAir+3bt4/TTz/9mIrQ7e3tJBIJFi5cmMPIhBBCCDHdpF8gRP6S5IIQIq/t27ePpqam47bp9XrpRAghhBCzjPQLhMhfklwQQuStVCrF4cOHWbx48THb9+/fT11dHXa7PUeRCSGEEGK6Sb9AiPwmyQUhRN46dOgQ6XT6uDsU+/fvP26bEEIIIWY26RcIkd8kuSCEyFv79u3DZrNRX19/zHbpRAghhBCzj/QLhMhvOk3TtGwesHnxEgCW7GvO5mGFEEIIkUfk+14IIYQQbycjF4QQQgghhBBCCDEpxqk68PAdDSGEEEIIIYQQQsxsMnJBCCGEEEIIIYQQk5L1mgtCCCGEEEIIIYSYXWTkghBCCCGEEEIIISZFkgtCCCGEEEIIIYSYFEkuCCGEEEIIIYQQYlIkuTDF2trach3CjCNtmn3SptknbZp90qZTQ9p1+khbZ5+0afZJm04NadfskzbNvsm2qSQXplg8Hs91CDOOtGn2SZtmn7Rp9kmbTg1p1+kjbZ190qbZJ206NaRds0/aNPsm26aSXBBCCCGEEEIIIcSkSHJBCCGEEEIIIYQQkyLJBSGEEELMKm+88QY33XQTGzZsoKmpiSeffPKE++zfv5+PfexjrFy5kg0bNvDggw+iado0RCuEEEIUBkkuCCGEEGJWicViLFq0iNtuuw2r1XrC50ciEa6//no8Hg+/+MUvuP3223n00UfZvHnzNEQrhBBCFAZjrgMQQgghhJhO5557Lueeey4AX/7yl0/4/N/85jfE43Huu+8+rFYrixYtoqWlhc2bN7Np0yZ0Ot1UhyyEEELkPUkuCCGEEEKMY/v27axbt+6YUQ7nnHMODzzwAJ2dndTV1Y25b1tb20j17UQiQXNz85THO5tIm2aftOnUkHbNPmnT7Ht7my5ZsuSk95fkghBCCCHEOAYGBqisrDxmW1lZ2chj4yUXGhoaRn5ubm4+pc6aGJu0afZJm04NadfskzbNvsm2qdRcEEIIIYQ4gXdOfRgu5ihTIoQQQoghklwQQgghCkxCybC7K0gyncl1KLNCWVkZAwMDx2wbHBwEwOPx5CIkIWa0hJJhZ2cAJaPmOhQhxEmQ5IIQQghRYPb2hNjTHcQbSuY6lFlh9erVbN26lWTyb+29ZcsWKioqqK2tzWFkQsxM/aEke3tCHPZGch2KEOIkSHJBCCGEKCBdgTjdgTiqCnoZkn9KotEozc3NNDc3o6oq3d3dNDc3093dDcC3vvUtNm7cOPL8yy67DJvNxq233sqBAwd4/vnn+f73vy8rRQgxRUIJhXBcoTsQz3UoQoiTIMkFIYQQokAklAz7e0IEYgolNlOuwylYu3fv5oorruCKK64gkUjw3e9+lyuuuIL/+I//AMDr9dLR0THy/KKiIn74wx/S39/P1Vdfzde+9jWuv/56Nm3alKs/QYgZLRhPEUtl6AvK6CwhComsFiGEEEIUiOaeEN5IknKnGZtJvsJP1RlnnMH+/fvHfPzee+89bltTUxOPP/74VIYlxtHZ2cl73vMefvGLX7BixYqR37/5zW9KtfgZRsmoBGIKqgaD0RSqqqHXywghIQqBjFwQQgghCkB3IE7X0SHClS5bjqMRYnpVVVXx8ssvSyJhFgjFFVJpFZfNSCqjEowruQ5JCDFBklwQQggh8lxCybC/N0wwlqLebccgd/HELGMwGCgvL8dolBE7M10okSaaSlNZZAVgICJTI4QoFJJcEBMWi8X44he/yJo1azj77LN55JFHuPHGG7n11ltPuO/555/Pd7/7Xf7pn/6JNWvWsH79eh599NFjnhOJRPjqV7/KOeecw4oVK3jf+97Hs88+O1V/jhBCFIz9vWH6wwk8DgtOq9RaELk32T7Bgw8+yK233sqaNWs499xzefbZZwmFQtxyyy2sWbOGCy+8kJdffnlkn87OTpqamti1a9dJx/r666/T1NTE//3f/3H55ZezYsUKrrrqKnbv3n3M87Zv384nPvEJVq9ezWmnncbGjRvp6+s76dcTkxOKK2RU8DjMGPQ6+kKJXIckhJggSf/mgV++2cn/bu048ROz6Jp1dVx92sktn3Xvvffyxhtv8OCDD1JRUcH3vvc9tm7dygUXXDCh/Tdv3swNN9zApz/9aV5//XW+8Y1vUFdXx4UXXoimafz93/89oVCIu+++m3nz5nH48GFSqdSp/HmiAPUE46ga1JTIcG8h3q43mKDDHwMN5hTL+2Ommy19gh/96Ed87nOf46abbuKnP/0pX/rSlzjzzDO59NJL+fznP88jjzzCP//zP/PSSy9hsVhO5c86zn333cdtt91GZWUlDz74IDfeeCN//OMfsdls7Nu3j0984hNcfvnlfPnLX8ZsNvPGG2+QyWSy8tpi4gKxFGjgsBhxmo30SnJBiIIhyQUxIdFolCeffJL77ruP9evXA3DXXXdx7rnnTvgYq1at4uabbwZg3rx57Nq1i82bN3PhhReyZcsWtm/fzm9/+1saGxsBqKury/4fIvLWYW+EIwMxrn1XvRRuEuKoZDpDc0+IYFxhnsch0yFEXshGn+Ccc87huuuuA+Czn/0smzdvZu7cuVxxxRUAfOpTn+KXv/wlBw4cYMWKFVmJ+1Of+hQbNmwA4J577uHcc8/lmWee4UMf+hA/+MEPWLx4MV//+tdHnj/cHxHTJ6FkCCfT2C16dDodLruRgXAKTdNk2VchCoAkF/LA1afVnvQdg+nW0dGBoiisXLlyZJvdbmfhwoUTPsbq1auP+/0Pf/gDAHv37qW8vFy+yGepVFolGEvjjSYZjKYoL8rOXSohCt3+3jDeSAK3w0SRTIeYFWZLn6CpqWnkZ4fDgc1mY9GiRSPbysrKAPD5fFmIeMiaNWuOec1FixZx6NAhAJqbmyc86kJMnVBCIamo2M1DlyhFFhOdvjjhRBqXLL8rRN6bVTUXHn/8cc4///yRuXZbt26d0H5tbW2sWbPmmC+l2UbTtII+vshvoYRCIp0hlszQE4jlOhwh8kJfKEGHL4aqQrXLnutwhBiRje/sdxZm1Ol0x2wbvkutquqkX2sipB+SH0LxNLFUGpdlKJFgNxvQkKKOQhSKWZNcePbZZ7n77ru56aabeOqpp1izZg033HAD3d3d4+6XSqX4f//v/3H66adPU6T5qb6+HpPJdEwhpXg8zsGDByd8jB07dhz3+/z58wFYtmwZXq+XlpaW7AQsCkowrhBLZTAb9bT7JbkgRCqt0twTwi+rQ4g8lI0+QS5s37595OdYLMbBgwdHRkwuXbqU1157LUeRiWGhhIKqadgtQ4kmh8WIXgf9UndBiIIwa5ILmzdv5sorr+Saa66hsbGRO+64g/Lycp544olx9/vmN79JU1MTF1988TRFmp8cDgdXXXUV3/zmN3n11Vc5dOgQt99+O6qqTngO3Pbt23nkkUdoa2vjf//3f3nqqaf45Cc/CcBZZ53FqlWr+OxnP8tf/vIXOjo6eOWVV/jjH/84hX+VyBfBuEJGValyWen2J+QOkpj1DvSF8YaTuO1mimQosMgz2egT5MJDDz3EK6+8wsGDB/nKV76CyWTi/e9/PwB///d/z969e7njjjvYt28fhw8f5uc///kJb0KJ7PJHU+h0OszGoUsUk0GPw2ykJyjJBSEKwayouZBKpdizZw/XX3/9MdvXr1/Ptm3bxtzvpZde4qWXXuLJJ5/k+eefn+ow896XvvQl4vE4N998M3a7nU9+8pMMDAxgNpsntP+mTZvYv38/Dz/8MDabjX/8x38cSdro9Xp+8IMfcP/99/PP//zPRKNR6urq+MxnPjOVf5LIA5qm4Y+m0Ot0lDrM9IeThOJpiu1yQSVmp/5wgvbBGBlVo1pWhxB5arJ9glz4whe+wL333ktraysLFy7k4Ycfxm4fmnK0ZMkSNm/ezHe+8x2uueYazGYzy5cvP6kilWJyYqmhKREOs+GY7S6bCW8kKUUdhSgAsyK54Pf7yWQyI8WBhnk8HrZs2TLqPv39/dx+++08+OCDOJ3Ok3q9trY24vE4AIlEgubm5lMLPA9df/31I0kaRVF49NFHWbx48Qn/xlQqRSQS4YYbbuCGG24Y2f7O/a677rqR6tFjPWemtWk+yGWbxhWVg50x0qqKMW7C642zZVuchtL87aBOhJyn2Tcb2lTJaOzsjdEbSVNVZKIjNXYxu1AigxY2EXRMLhE33K5LliyZ1HHE7OJwOPi3f/u3kd9TqRSPPfbYhC7GX3zxxeO2vfNmj8ViYf/+/SO/19bWjvr7yXwmrFmzhqeffnrMx9etW8fjjz8+4eOJ7ArF0yQVFYfl2MsTp9VITzBOLJU57jEhRH6ZVe/Q0bKdY2VA//mf/5mPfOQjx61wMBENDQ0jP8+kDtvevXtpaWlh5cqVRKNRNm/eTDKZZNOmTcyZM2fcfc1mMxUVFVlpi5nUpvkil23aHYjjSQ3gtBqpLrbhw4vJXcSSJeOfU/lOztPsmw1tursriCURYkmFkfrS8Ys4+qMpFlQ4qXZPbnTDbGhXkX3v7BP84Ac/IBqNcskll+Q6NFGgQomh+ktV7xix5TQb0TQYjCYluSBEnpsV71C3243BYMDr9R6zfXBw8LjRDMNee+013njjDf7zP/8TGBq6raoqS5cu5atf/Sof/vCHpzzufLR582ZaW1sxGo0sXryYH//4x3R2dvK+971vzH3Gm3oiRDCuEFPS1JbY0Ot0lNjNdAbiuQ5LiGnnDSePTodQqS625jocIU4oX/oEd95555gjEi677DIuvfTSrL+myL5QXEEDbO+YFmEzG9AB/aEk9aWOnMQmhJiYWZFcMJvNLFu2jC1bthzzhbdlyxYuvPDCUfd555fUCy+8wMMPP8zPf/5zKisrpzTefLV06VKefPLJ47YnEgmeeuqpcfcdbQikEDCUXNA0sB1d09ptN3OgL0wslR5Z51qImU7JqDT3BIdWhyi1YzTMmnrLokBNpk+QbZ/73Of4u7/7u1EfczqdeDyeY6ZUiPyjaRq+aAqDXofpHZ9/VpMBq9lArxR1FCLvzZqe+6ZNm/jiF7/IypUrWbt2LU888QT9/f1ce+21AHzrW99i586dPPbYYwAsWrTomP13796NXq8/brsAq9XK3Llzcx2GKEAZVcMfS2Ex6EeW2nNahz6W+oIJ5pWfXL0TIQrVgb4w/aEkJXYTJfbCrjciZrdc9Ak8Hg8ej2daX1NkVySZJq5kjivmOKzYZqIvLMkFIcbS4YthMeqpcOV25OOsSS5ccskl+P1+HnroIfr7+1m0aBHf//73qampAcDr9dLR0ZHjKIWYXUJx5bjiTUUWI3q9jq5AXJILYlYYiCQ5MhhDUVXmF8s5L4SYfUKJoWKO7jFWiiqymugNJYin0iMjHYUQQ4ZGP4ZA07hweVVOY5lV787RViIYdu+9946771VXXcVVV101FWEJMWuFEgrxVJo5b8uyGg16XFYjHT6puyBmvnRGZW93EH8sSZ3bcdxwYCGEmA1CcYWEksFpGb1ArcNsAA18UYUaSS4IcYzeYIJwQhkzOTedpBcjhMiZYFxBUTWc1mM/DN12M95IAiWdyVFkQkyPg/0RvOEUxVYzbpkOIYSYpUJxBVXTxhyVMFyDyStTI4Q4Tk8wQSyVwWwcfVrRdJLkghAiZ/zRFGhgMR77UVRsM5FRoT+SylFkQky9wUiS1oEoSlqlumRyy0kKIUShUlUNXyyF2fi3+kvvZDXpMRv19EhRRyGOEUmm8YYTeTPyMT+iEELMOgklQziZxm42oNMd25lwWo3oddDtj+UoOiGmVjqjsrcnRCCaosZty5tOgRBCTLdwIk1KUXGMM91Bp9NRbDPRH0pOY2RC5L/eYJxwQsHjzI/Rj9KbEULkxFAxx8xx61kDWIwG7GYj7VJ3QcxQh7wR+kMJimxGWR1CCDGrhRLK0EoRlvFrKRTbTAQTKVIyZVIIYGgJ1y5/HEXV8mZqpSQXhBA5EYwrxFIZim2jF5/xOM30hRKoqjbNkQkxtYJxhdaBKKmMSk2JPdfhCCFETgXjCsl0hiLr+MkFu9lIRgVfTKZMCgEwGE0RiCkUWYwY9flxWZ8fUQghZp1gXCGjaSNFmt7JZTWRTKsMRqUTIWaWDl8MfzRFVbFMhxBCiFBcQWNo1OJ4HJahx70yNUIIAHoCCSLJNGVOS65DGSG9GiHEtFNVDV80hVGvH/PiavgORk9A6i6ImSOhZOjyx9GAElt+DGEUQohcUTIqgZiC1WgYs5jjMKvJgNmgp0+SC0KgZFR6gnHQQZE190tQDpPkghBi2kVSaeKpzNC61WOwmQxYTQY6/FJ3Qcwcnf44obhCmdN8wo60EELMdOFEmmRGxW458RJ6ep0Ol81Ib1D6BUL0BhOEEwqldjN6Xf70JyS5IEQOqKrGtnY/B3rDuQ4lJ4IxhUQ6g3Oc+ZU6nQ6Pw0xXIIGmSd0FUfgyqkanL0Yyk6Hcac11OEIIkXOhuEI8lcZlmdid12KbCX9cIZ1RpzgyIfJbTzBBLJXB48ifKREgyQUhcuKQN0LbQJTn9/SyqzOQ63CmXTCukEiruE4wjKvYZiKaTBOKp6cpMiGmTm8ogT+uUGIzS60FIYRgaKUIJaOecKWIYXazkXRGwy9FHcUsFkmm8YYTmI36UVddyyXp3QgxzQYjSQ71R0goGUqdFl7a76W5J5jrsKZVMJ4CTTth8aaRugshGQIpCl/7YIxIQqG8KL/uMgghRK4EYgqgw2Kc2CWJ42gR6IGIJBfE7NUbjBNOKHlVyHGYJBeEmEaptMqe7iD+WJJat53l1S48TgsvNPdzqH92TJFQMirBWHpCxZscFiMmvY5OnxR1FIXNF00xEElgMxnGXCFFCCFmk2Q6QyihYDPp0U1wzrjNbMCo10ndBTFraZpGlz+Oomq47flXGFqSC0JMo+aeEH2hJCU2CyV2MzqdjmXVLoptJp7f00frQCTXIU654fWsJ1q8ye0w0xmQToQobB2+GKF4mgqX1FrIF48//jjnn38+K1as4KqrrmLr1q1jPrezs5Ompqbj/v35z3+exoiFmFlC8TSptHpSCVeDXkeR1UhfUFaMELPTYDRFIKZQZDHm5RTL/ItIiBmqKxCnwx9DVTVqSmwj2/U6HStrS7BbDDy3u4+OGX6XPhRXiKbSuKwTy7a67WYCUYVYSuouiMIUS6XpDsbR6YbqiIjce/bZZ7n77ru56aabeOqpp1izZg033HAD3d3d4+73X//1X7z88ssj/84888xpiliImSeUGPpuP1H9pXcqtpkYjKbIqFLsWZy6hJJhMJIklS6s4qA9gQSRZDovp0SAJBeEmBbRZJrmnhDBqEJ9qf246QB6nY7VtW7MRh3P7uoZWrd2hgrGFTIZcExg5AKA02pEA/qCiakNTIgp0umPE4wNzY3Mp+WiZrPNmzdz5ZVXcs0119DY2Mgdd9xBeXk5TzzxxLj7lZSUUF5ePvLPbM6/IalCFIpQXCGd0SY0kvHtnBYTSkYlFFemKDIx0ySUDP3hBC3eCNs7Ary0v58/Nvfx0n4vz+/pJaFkch3ihCgZdegaQadRdJJJuekiEz+FmGKqqrG7K0h/KEm5y4JzjA8Dg17Hmjo3W4/4eXZnD5etqp6RQ6h90RR6PZgnOJSryGJErxsa+TGv3DnF0QmRXemMSocvhpJRKc/TuwyzTSqVYs+ePVx//fXHbF+/fj3btm0bd9/PfvazJJNJ5s6dyyc/+UkuvvjiqQxViBktEFPQ6XQT7g8Ms5sNaMBAJInbIQk+cayEMlTLI5xIE06kCcRSRJJpkopKXEmTzmjodTpsJgNGg45D/RGUjMr7VlRhNeXXygvv1BtMEE4ouO3mvL1ZIckFIabY4YEIPcEENpOeyhMkC4wGPafNdfPXtkF+u6uby1fXUjqDvjhjqTSxVBq72TDh4k1Gg55im4kO38wdzSFmrp5ggkAsRanDjDEP50bORn6/n0wmQ1lZ2THbPR4PW7ZsGXUfu93Ol770JdauXYvBYODFF1/klltuIZlMcvnll4/7em1tbcTjQ59fiUSC5ubm7PwhApA2nQrT0aaJtMr+jhiptEqbGjipfTOqxuBgjNd3hUj7HFMT4BSQczX7QtE4r7y1i1hKJZpSCSUzxBQNRVVJpjXSGQ2dTofVqMNq0uE063EaDZj0oFOH+qHl+gxv7PPS3dPNOXOdmA35edEOsLsvTps/RYPbTGvw2D6FqmnEUirN6YFJvcbbz9MlS5ac9P6SXBBiCvmjKQ72R4im0jRVFk0oy2gy6HlXg4dXDw/wzI5uLl9dTXEeVoM9FcG4QkJRcVpP7qPHbTdzxBdFSWcwnWD5SiHyhaZptPuixFIZ6ksLpwM8W4yW4Bwr6VlaWnrMSIcVK1bg9/v5r//6rxMmFxoaGkZ+bm5uPqXOmhibtGn2TUeb9oUSlKe8WE2GU/p89Ot8WG1Gliypm4Lopoacq9mjZFTePOJnR+thyioriJMmYwCdAzwmAzaLgWKLCZvFgNlw4tVI6upSvNXu40jaycWLqzBPcGnU6RRJpmlVvMx1KCyuKj7u8YyqEUkqLFlYPqnXmex5mn8tJ8QMoWRUdnUF8EdS1JTYsJzERbHJoOeMeR5CCYWnd/YQScyMeYXBuEJcyVB8kvPEXDYTGRX6ZV1rUUAGIikGIikcFmPeD7WcTdxuNwaDAa/Xe8z2wcHB40YzjGfVqlUcOXIk2+EJMSuE4gqxVOaUi9y67EYGIilUKeo462iaxq6uIJ2+GDElg9mkY67bwbIaF6vrSlhc5WJuqYMShxmLcWIjZd12M2vrSjnkjfDcnh6UTP4VeewNxgklUnjyfIqlJBeEmCL7esL0h5IU20x4HCf/QWAxGnhXQyn+WJLf7uwhliz81RKCMQVV1bCd5IWW82jdhZ7AzF5JQ8ws7b4Y4USaiqL87gjMNmazmWXLlh03BWLLli2sWbNmwsdpbm6mvHxyd4iEmK1CCYWMqp3UMpRvV2QxkVAyRGZA30icnBZvlA5fDA1ocFtoKHWeVCJhLG6HmdPqSznYH+H5Pb15lWDQNI3uQBxF1XDn+WhmSS4IMQW6A3GO+KJkNKh+27KTJ8tmNnL63FJ6Q0me3dVDooCXY8yoGr5YCrNRf9Jzz60mA3azgXapuyAKRCSZpi+UwKDXnfQya2Lqbdq0iV/96lf8/Oc/p6WlhW984xv09/dz7bXXAvCtb32LjRs3jjz/V7/6FU8//TQtLS0cPnyYRx99lJ/85Cd8/OMfz9WfIETB0jQNf1TBoNdhOsVaNG8v6ihmj/5wggN9YaLJNA0ex3Grr01WqcPM2jo3+/vCvLC3j3SeJBgGoyn8UYUii/GU3zPTRWouCJFlsdTQspOBuMJ8j2PSRdwcFhPrGtxsbfPxu929XLKy6qSmWOSLcEIhqainfJfC47TQG0ygqhr6LH+ZCJFt7YMxgjGFSpdlUndSxNS45JJL8Pv9PPTQQ/T397No0SK+//3vU1NTA4DX66Wjo+OYfR566CG6u7vR6/U0NDRw1113nbDeghDieNFUhujR4s7jSSiZMaeUOY6OaOwPJZgvK0nNCtFkmt2dQfyxFPWl9imbbuhxWlhdW8L2jgB6vY7zF1fkvCBzTyBBJJlmrsee0zgmYlYlFx5//HEeffRRvF4vCxcu5Ctf+Qrr1q0b9bmvv/46//3f/82uXbsIh8PU19ezceNGPvjBD05z1KKQqKrGnu4QA5Ek5Q5L1tagdVlNrK1382a7j+f39HLx8qq8z1y+UzCuEE+lT3l5TZfVxJHBGIPRFOUyzFzksVRapcsfI6NplJ7ClCgxPa677jquu+66UR+79957j/n9yiuv5Morr5yOsMQs1T4YozcUp67UzhyXdUYnJUNxhVRapWic4s67uoLc+evd/OdH1446AtRk0OMwG+kJJqYyVJEn0hmVnZ1B+sJJyhwWSmxTOzWgvMjKqtoSdnQG0OvgvMWVWR8lMVFKRqUnGAedlrXriqlUWFcnk/Dss89y9913c9NNN/HUU0+xZs0abrjhBrq7u0d9/rZt21i0aBEPPPAAzzzzDB/5yEe48847efrpp6c5clFIWgejdPvjGA165hSf2kX0WErsZtbUDc0Fy6ehWhMVjCukMuopDxEf7oRI3QWR77oCcYJxBY/TnLPOiBCiMKiqRnNPiJ1dAba1B3h6RzfP7+mlwxebscUKQwmFmJIetz+wvSNAWtXY3R0c8zkumwlvJImmzcx2En+ztydEdyCOzZT9/vVYKlxWVtQUs7MzyEv7+8nk6P3YG0wQTii47eYJrTqXa7MmubB582auvPJKrrnmGhobG7njjjsoLy/niSeeGPX5N910E7fccgunnXYadXV1fPSjH+WCCy7g+eefn+bIRaEIxFIc6htadrLebZ+SD4BSh5k1dW729oZ5ab83Zx90pyIQU9ChG3N5n05/jHbf2IkDm8mA1aSn0y93KUT+UlWNDl+UuJKhPM8rOgshckvJqGzvDHCgN0wkobCypph6t522wRi/3dXN7/f0cmQwWlDf9RMRiqdBY9zizof6wwAc7IuM+Zwiq5FYMkMslcl6jCJ/HBmM0u6LkVFV6ksd03qBPafYxvKaYnZ0Bvjzgf6cJPx6ggliqQxljulJqkzWrJgWkUql2LNnzzFrVAOsX7+ebdu2Tfg4kUiEOXPmZDs8MQMoGZXdXUEGo0mqi21TuuxcmdPCimoXO7sCGPXw7kUVeV+DIKFkCCUUbGb9mF8KD7xwkHgqw4MfXTvq4zqdDo/DQmcgjqZpM3rIqChc3kgSf1TBZTMWZG0UIcT0iKcybO8I0OWPgQ4WVBRhMRpwWk3UuO30BuO0DkY5MhClxm1jWXUx9R57wU2JfCdV1fDHkhgNYxd31jRtJKlw4GiSYTQOs3GoqGM4icMyKy5pZh1fNMW+njDBuMKCcmdOzv+qYhuaBts7guh1OjYsLJ+2fnckmWYgnMRi1GM7QY2SfDEr3ol+v59MJnPc+tUej+e4pajG8n//93+89tprY450eLu2tjbi8aGq9olEgubm5pMPWowpH9v00GCSVn8Ss0GHI2UiNDD1HzpFGYXn3xqgp6uTVVW2SV1sT3Wb+mJpWntiWAw6WhO+4x5PqxqH+sOkVdh94BCOMT5AQ5E0bb4kW3fGcOb5h2w+nqeFrhDadHdfgiP+JDXFJlojA1P6WqFEBi1sIuiY3BzM4XZdsmRJliITQownGFPY3uGnN5TAYtQzt/T44s9zim3MKbbhDSdoHYjy+z291JRYWVJVTEOZvWCTl+FkmkRKHbeYY18oSTiZxm0fqrWUTGdG/XvtFgN6oC+cYG6ZYwqjFrmQUDLs7AgwGE1SU2I75YLg2VBdYkND4632AAa9jrMby6YlwdAbjBNKpCgvKoxRCzBLkgvDRrv4msgF2ZtvvskXvvAFbrvtNlauXHnC5zc0NIz8LB227Mu3Nu0NJtArPirNaRZWFk1bVnUeUO2LcaAvTKOtjHfNKz3lY011mx7qD+NODdJQ5qB4lCI8Ld4IaXWo/knE6GZ5g2fU43jiCmGDD1flHJrmuKYs3mzIt/N0Jsj3Ng3GFQ4rXuodaRbPcU356Bp/NMWCCifV7lNf7hbyv12FmEn6Qwl2dATwRpKU2E3UlIw/jbK8yEp5kRV/LMlhb5Tn9/ZSXWxlSZWL+eXOKR0pORVCcYVEOkOpfeyCfAePjla4cNkcfvZGB63eKIurjv/OtxgNWM0G+qSo44yjqho7OgL0hRK4HSY8eVAcuabEjqrCG21+9DodZzV6pvR7XtM0ugNxFFXDPc77Jd8U9tiqCXK73RgMBrxe7zHbBwcHjxvN8E5bt27lhhtu4B//8R/56Ec/OpVhigIUT2XY2z20LE6de/qHK9aX2plX5uDVlgG2tfun9bVPRjCukFG1MbPOh/qHhj/qgD3doTGP47AYMel1dPrjUxGmEJPS4YsRjCtUzPBK70KIU9M2EOXNdj/94QSVRRbq3BOfP+62Wzhtbilr691EEhleaO7jNzu62NkZIJZKT3Hk2RNKKCQUFZdt7BFXB/oimAw6LlhSOfR7/9h1F0psZvrCyazHKXKruXeogKPJqKe6OH+WX6wrtbOo0snrrT5eP+yb0mKig9EU/qhCkcVYUNOhCifSSTCbzSxbtuy4KRBbtmxhzZo1Y+73xhtvcMMNN/CZz3yGT37yk1McpSg0mqaxpzuIN5ykvMhC0ThflFOpsdxJTYmdvxz0snecqsq5omkavmgKg0E35ofjwb4wTouRxVUu9vaM/TcY9DrcDrMkF0TeSSgZuvxxNJjyJbKEEIVFVTX2dofY3R0kEE1R73FQ4Tq1EUcuq4m1c92sayglqaj8ab+X32zv4q12H+GEkuXIsy8UV9A0bdxpHQf7w8wvc1LpslLqMI+MZBiN02okkkwTL6AEixhfpz9G20CUZDrD3Gku4DgR9aUOFlQ4eK11kL+2TV2CoSeQIJJM58WojZMxK5ILAJs2beJXv/oVP//5z2lpaeEb3/gG/f39XHvttQB861vfYuPGjSPPf/3117nhhhu49tprueyyy/B6vXi9Xny+4+eLi9mpbTBGVyCOQa+jsmhyw5Ina1Glk4oiKy/s6x+psJwvIsk0sVQGxzhz5Q56IyyocLK82sWh/gjxcSo/l9jN+GOpgrpTI2a+Tv/Q8pNlDll+UgjxN0pGZVtHgIN9YaJJhfnlzqwkIJ0WE6vr3Jw530NGg1cODvCbHV28eWRq76ZORjqjEogrWIz6MT8nM6pGizfCwgonAAsrnOOuGOEwG0ADXzT/EyvixIIxhb3dIYIxhfpSx5grjOVag8fJPI+DVw8NsvWIP+vvOSWj0hOMg04bd5RPPpo1NRcuueQS/H4/Dz30EP39/SxatIjvf//71NTUAOD1euno6Bh5/q9+9Svi8Tg//OEP+eEPfziyvaamhhdffHHa4xf5JRhXONAXJppMs7CiKOcXEzqdjqVVLnZ2Bnl+Tx9GvY6GMmdOYxoWjCsklAyljtE7U6m0ypHBGFetqWFptYufvwn7+8KsrisZ9flFFiOaBv2hJA1ls+YjTOQxVdXo9A0VHSsvyo/3nRAi9+KpDNs6/HT74+h0sKDclfWLJavJwMqaEpJKhgP9Yf5yYAC33cz88vz7LAon0iQUddyVHTr9MRKKysLKo8mFyiJeb/URSaZxjrKf3WxEpxuqZVEzyfozIreS6Qw7Ov0MRJJUFlsosub3RfX8cieaBlsODZBRNU6b687a9IW+UIJwQsFtN+fdyI0TmVU98+uuu47rrrtu1Mfuvffe435/5zYhYOjDb3dXkMFIkjnF1rwppqTT6VhRW8y2Dj+/39PH+1fqqXXnfp7aUHJBxTXGl0TrwNAa3gsrnCyZ4zpadyE4ZnLBaTWi10GXP0aDVIcWeaA3lMAfVyi2m6ZtXuSRwShf/+1evvfRtZMu6CiEyL5ALMX29gC9oQR2s4E6t33MpRezwWIysLy6mFfjg2xvD+RlciGUUEik0lS6xq58f/BofYWFFUUALDo6gqGlP8KqUfoFVpMes1FPTyjOGtzZD1pMC1XV2N0VpC+UpNhmptxZGKsjNFY40engtcOD9AUTnL3Ak5WVHXqCCWKpDHXuwuvn5udYEyHylJJR2dYeoMsfp8hipCzP5kHpdTpW17oxGXT8blcPfaHc1yYIxhQ0xp5fOTyNY2FlEQ6LkXnlDvaOU9TRZNBTbDPRLnUXRJ5o98WIJBQqp3GpqJ9t7SAYV0a9kyeEyK3eYII3Wn10B+OU2I3M9Ry/1ORU0Ol0zPXYOeKLMRjJvyKHoXiaVEbFaRn7jvTB/gg2k2FkFMJwkuFA3+hTPnU6HS6rif5Q/v29YuIOeSN0+uLogZqSyS2vPt3mlztZV++mKxDnNzu62dERIJ1RT/l40WQabyiJxajHlufLro9GkgtCTFBG1djeEaDTF8Ns0lFX6sjLDz+DXsfaOjeqpvHsrp6cdjCUjEogpmA1GsacOnKgP0KJ3YTn6LSJZVUu9vWFUcb5YHbbzQxEkijpsWsziPzhDSfZcmiAI4PRSX3h5iN/NMVAJInNZJi2Nbi7A3FeOTTAexZX5v2wUSFmm9aBKG8dXRGioshCTcn0FqSrcFnR62BXZ2DaXnOigvEUABbT2JcfB/vCNJb/rc2cViNVxdaREQ2jKbaZCMYVUtInKEi9wcRQvS0lzdwyR86nGp+KYruZ9QvKcFqMvLS/n9/v7sUfPbX+d08wTiiRwuPMrxuYEyXJBSEmQFU1dnQG6PDF0Otgrju/P/yMBj2nN3iIp1R+u7OHYCyVkzhCcYVkOoPdMnbm9VB/hAXlzpFEzbLqYlJplRbv2B0Jl81EOqPRH8nN3yUmTlU19vWGaO4J8budPfx2Vw/7e8MklOnvBCoZdWgkTRYLL7X7YgRjChXTOGrhl291YtTruXjZnGl7TSHE+IaHde/pDhKIpZjrcVB5iitCTIbJoKemxMae7hDJHHzOjiWVVgnGh242jJVsUTIqrQNRFlYWHbN9UWXRmCMXYKjugqqBLyp9gkITTijs7g7ijyapddvHXUUk3+l1OpZWFbO6roS2wShPbetiT3cQVZ14n0PTNLoDcRRVw20vzJWnJLkgxAlomsbu7iBHBqKomkZDmXNahjdOlsmg513zSgklFJ7Z2UM0Of2VlINxhWgqPWa9hXgqQ6c/xqK3dSSWVrsA2DPO1AinZajuQk8glt2ARda1+2IMRFKUF5lpqnLhiyb5w95enj46dDCSnNpVP1RVoz+cYFdnkP/b188L+/p4fk9fVkZRxFMZuoNDhdqK7dMzgmAgkuTFff1csLSS4gKrIC3ETDW0IoR/aEWIRJrGcifFOVyStqbETiqjjntBPt3CCYVUWh33ZsORwRjpozWY3m5hhZPBaGrM5IHj6DG9YZkaMZ7hmmEDeTJlRsmo7OwM4g0nqHBZc/qeyaZSh4X1jWWYTQZeaO7j+T09BOMT64MPRlP4owpFFuO01XDKtsKMWohpomkae3tCtA5ESWVU5nmcBfVmtxgNvKuhlMFokmd29k77OtDBuEImw5jzwg8PRFA1WPC2joTbbqa62Mqe7uCYx7WaDNjNBjp8UnchnyXTGVq8EWLJNFXFdsqcFtbN9XBavZtkOsOfD3j59fZOXj88mPU7TsGYwr7eEH8+4OXVQ4Ps6AzgjSTQNI0jvijP7uzhmZ3dNPeExl36dDwd/qFRC2VOy7QNe/7Vti404Ko1NdPyekKI8aXSKm8e8dPqjaKqGgsqiqZtitRYnFYjbruFt9oDOY3j7YZvNow3levg22owvd3w7wfHWGrbajJgNujpDSWyFO3ME09leLPNz+6uIL/e3sX+3rFv4EwHTRsa6dMTiOM0G6e1ZtF0MBr0rKwpYXl1CQf6Izy1rZP9vaETjmLoCSSIJNN48qym28mQSlBCjONgf4TD3gjxVJrG8qK8XW93PDazkXc1lPLXVj+/293DpSurp23YWSCmoNODeYyEzPDa1QvecZdiWU0xr7YMomramBdtpQ4LvcEEqqqhz+MpKrNZ60AUXzSFx2k+ZlUVp9XEqlo3CSVD20CUv7b62NcbYq7HweI5LiqKLKf0/zSWStMTTNATiOOPpQjF06iaRrHNRIPHTpHVNHI+DUaStA1G+ePePsqLLCyodLKg3EnJBIchpjMqHb4YSkajbJrmRQbjCs/t6eXcheVUuKz4ZQiwEDmVTGd460iADn8Us0FPg8eZN1Mm60pt7OwM0uGLUlea+4rzoUSatAqOcRIvB/siFFmNVBYd+5k6v8yBXgcH+iKcMc9z3H56nQ6XzUhfUJILowknFLa1++kOJLCZ9CgZPX/c24c/mmJdQ2lORuO2eKN0+GOgg1q3PS9rmGVDeZGFcxrL2N0d4vm9fXT645wx3zPqTTclo9ITjINOw1XAIxMluSDEGA57IxzsCxNJDCUW8mXJyVPhsJhYO7eErUd8PLe7l/etqJryERixVJpIUsFuNoz5pXGwP0KZ03LcvLJlVS7+sLeP9sGxl5t02Ux0+GL4Yqlpu7gTExdNpjkyEEPJqGPWI7CaDCyucrGg0smRwRh7u0Mc7I9Q57axpMpFTYnthJ2eVFqlL5SgN5TAG0oSSigoGRW72UBNiY0Su3nUzr7HacHjtBBNpjk8EOHVQ4Ps7QrRUOZgUWXRCRMcPcEEgVgKt2P6hi4+vaObVFrlg6fVTsvrCSHGlkxnePOIn05fHLMxvxILAB6HBbNRz46OYF4kFwKxFHrAMs5NmoP9YRZWFB3XZ7CaDMz1ODg4zjSPYpuJtsEY6YxaEFNXp0swpvBWu5/eUByXzUxtiQ29TsfhgQivtfrwxxTObSqfttE2yXSG/b1hOnwxIsk0C8uLZvz/L5PRwJp6Nz2B+NBojWCCs+aXMv9t9cYA+kIJwgkFt908rUVgs02SC0KMosMXY19vmGBCYZ7HUZBLwbxTsc3MmrpS3mr38cLePi5YNmdKO0LBuEJSUXGMs1Teof7wcXMrYaioI8CentDYyQWrEQ3oCcQluZCHDvZH8MeSVBZZTnjxbdTraSx3Mr/MQXcgTocvRqs3SlWJlcVzXDSUOY5J7mVUDW84SW8oQV8wQSSpEE1lsBr0eBxmSo92qifCYTGyoqYEJaNyZDDGrq4AB/rC1LptLJ5TRI3bflz8mqbR7osSncY1qGOpNM/s7ObM+R7qSu3T8ppCiNEllAxvtQ8lFqwmPfWl+Vfk2aDXUV9qp8UbIRxXKMrhndCEkiGSTGMzjX2zIaFkaPfFOGP+8SMTABZVOHmlZRBN00Y9hsNsJKNq+GMpymfYEPtTNRhJsq0jQH8oQanDTHXx35Z4nF/mxGU1saMzQDCucP7iCipcU9tuvcEEzT2ho1MUoaHUUdA37k5WVYmNUqeZXZ0BntvTx9LqOO+aVzqS2OkJJohNY79iqszsVJEQp6AnGD9auTZFg9uBcwYt9VbqMLOq1s3e3jAv7e8/qQq2JysUTxNLZcYsOhdJpukOJkZNLlS6LJQ6zOwdp+6CzWTAatLP2LoLgViKFm9kSv8fTRVfNEW3Pw7o8Dgn3lnR6XTUuO2cOb+MJVUu/LEUf2ju4+nt3Wzv8OOLpdnTHeSl/f28eniQXZ0BBqNJ7GYjS+a4WFJdzJxi2ylNXzIZ9CyocLJhQTk1JTY6/TF+t6uXZ3Z0s7c7ROxt9UoGoykGoykcZsO0JR6f3dVLNJXhQzJqQYicSigZ3jrip9MXw2rOz8TCsDkuK6oGu7vG/i6dDqHE0M2G8Yo5tniHajAtGqVPAEN1FyLJoalvoxm+QPPmSbHCXOsLJXjziJ++YILyIgs1JcdPPShzWjh7vodAPMWvt3dzoG9q6jAklAzbOwJsPeKjOxCjyGqkqdI1K5dSthgNrGvwMM/jYHt7gF9v76J9cGgUhzeUxGLUF/wNTRm5IMTb9IcT7OwI4oukqPfYc5rpnyrlRRaWV7vY0RnAZNCxYWH5lLxOMK6goWEbIyvd0j96vQUYushcVu1id3dozLsUOp2OUruFrkB8zOcUqnRGZWdngNaBKD2BOGc3lhVMXQlN0zjQFyYQT1Hrtp9yp3t42kIkodAyEOEvBwZIhqJUKQE0oNhmpLHcidNizOr/e51OR63bTq3bji86VJfhheahugyNFUN1Gdp9MULxNA2e6RlBkExn+PX2LtbUlRxX6EwIMX2GEwsdvhg2s4H6PF+W2moyUF1sZWdXgNMbSjHmqG7U0M2GNJWuse/IHjzaJ1hYMfpn3PCNiIP9EapLjl/i02Y2YNTr6AsmWFpVnIWoC1enP8auriC+SJKqEhvl4yT5bWYjZ84rY1dXgD/s6cMXVVg31521qQrdgTj7e8N4w0k0NOaVOWdlUuGdakvteIrM7OwM8uzObuZVOAklZsaoG0kuCHGUL5piR3uAwWiKGreNkhmyJM5oqoptpDMabx4JYDEaKNKye3c8o2r4oklMBv2YX1AHhqtCj9GRWFbl4i8HB+gLJZlTPPqHbYndRE8wTiQ5fgXqQtM6ED26pJaON9r8pDMaGxaV53UndlhPMEFfKIHFqKckC8m54eKPSSXDW/sC1LntuGymaWmLUoeFUoeFWDJNy0CE1w4P0twdoujo64+1xGq2/bG5n0Bc4UPr6qbl9YQQx0sowzUWYtjNBuryeMTC21WX2OgOJmjxRmiqcuUkhmBcQVU1bKbxizmWOc24HaP3vepL7ZiNeg70hTl30fE3RQx6HUVWI72zvKhj60CU5p4QgViK+lLHhIoUG/Q6Vte5h+owHB7EH01Nug5DQsnQ3BOiKxAnEEtR7rRQ6bIVxHtmuthMQwXXj/ii7OsJUWI3HVeDrBBJckEI/lbwxhtJUumyFPQSMBNVV2onraq82jLIXEuSpVk8dvjoEMjxqkIf6o9QVWzFaR39OSN1F7qDYyYXnJahugvdgThNc2ZGciGSTHN4IEoirbKippiBSJK3OvykVZVzmyryeinUjKpxqD9MOKGMWpRrMiwmA3OKTGN2PKeS/W11GdoHY/hjKRo801PdOp1RefKtTpbMKWJ5dW4uDISY7eKpDG8e8dEViOO0GKgtdRRMwbVim4kii5HtnYGcJBc0TRsq5qjXjTtlbbiY41iMBj2NZY6REQ6jKbaZ6A4kyKjarLuI1TSNg/1DhciDcYX6UjvFJ3mTLFt1GDr9Mfb3hhmIJNHrdDSWOWfUFONs0ul0NHicVLlsJNNqXvfxJqrw/wIhJimcGEos9IcSlDnNVBQdP9xupppX5qSu1M5b3bGRaQrZMDwEcvxijpFRp0QMq/fYcVqM7OkZew6gw2LEpNfR6Z85dRf294YYjKSYc7QQYlWxjZU1JezqCvLHPb2k0mquQxzTkcGhERfFNnPO13mfCiaDnsYKJ+vmllJ2ErUkJuPPB730h5N8aF3djJr6I0ShiKcybD2aWHAUWGIBhi5e6kvtdAcS9Iem/65+XMkQTaWxjzOPPJIYqqUwXp8AhuoutHgjZMaoReS0mFAyKqG4MqmYC42maeztCbG/N0QoPlSI/GQTC8OG6zD4Yyl+s6N73BU63mkoCednW7uf3kCCEruJhRVFkliYAIvJUNDLT76dJBfErBZLpXnryNASPaUOM1XFs68K+8IKJ0VmA3/c24c3nJ2ORzCukMqoYw4bD8YV+sPJUYs5DtPrdCypKmJv99jJBYNeh9thnjHJhd5ggp5gAqMByt42767SZWV1bQnNfWF+v6eHhJLJYZSjSygZDg9EiSsZqsYYaSJOjqpp/PzNTho8dtbNdec6HCFmnVgqPZRY8MdxWozUuQsrsTCsvMiCUa9jR2dg2l87FE+TUtRxE86HvMP1Fk6QXKhwkkqrtPuioz5uNxvQmF1FHTOqxs7OIIf6IsSSGRrLJ38xbzMbOWt+GRajnuf39PJ66yDpzNg3NjRNo8MX45WWAQ71hUkoKgsqnVQXn3rdJVG4JLkgZq3hwkw9oQQum4nq4tkzYuHtdDodC8ssaMDvdvUSTaRPuM+J+GMpQIfFNPpHzMGj9RYWjDMEEoamRnQF4vijqTGfU2I344+lSKTy74L7ZCgZlf29IYIxhdoS+3Ed2LIiK+vmlnK4P8rvdvUQz7O/97A3ii+apMxhwWIs7ErH+eK1w4N0+uNcI6MWhJh2sVSarW1DIxaKLCZq3cd/LhcKo0FPrdvOvt4w8dTkv+NPRiihjLtyFDByd3y8aREAi44WtD3QN/pIS7vZgEGvozc4M244nIiSUdne4aelP0wqrdJY4czaSgPDdRhqS+282jLIH/f2HbNq0rBYKs1b7X62dfjpC8ZxO8wsrCiakaMXxcRIckHMSqm0yrb2wNDdCLOB2lGW6JlNDHodp811E4ineL65d9wM9YkklAyhhILNrB+zI3awL4IOaCwffy3fZUfnh+4dZ2pEkcUIGvRladRFrrQORBmIJIfmx45x18FtN7NubintvhjP7OwiksiPoZ/hhMIRX5RMRqNyitfJni00TePnWzupKrZydmNZrsMRYlaJJocTC4mhxEKprWATC8OqS6ykMxr7eiY+zD0bQnEFTRtauWIsB09Qg2lYVbEVh8UwZt0Fo0GP02KkNzTzRy4k00M3yFq9UTQNGiucU5LYn1/mZFVtCfv6wvxmezfeo1NrNE2jfTDGlkMDHOqPkEqrLKgooqpYijbOdpJcELNOOqOyvSNAp39oKanaAh3mmG1Wk4HVdW5avVFePjiAdoorSIysZ32CYo61btsJM9uNFU7MRj17usdeo9tpNaLTQZc/dkrx5oNwQqHVGyWZVkddYuvtiu0m3jWvlJ5Akmd2dhOKjz2qY7oc6IvgjypUFluztnzVbLetI8Ahb4Sr19ZKR02IaTScWOgOxHHZTNS5Cz+xAGA3GylzmtneEUBVp6d2j6pq+KIpjAbduIXqTlTMcZhOp2NhRdG4dQCKbSYGI0nUMeoyzAQJJcObbX7afTEMBh3zy51TWgjwnXUYmntCbD1ydLRCKInbLqMVxN9IL1DMGvFUhg5fjDeP+OkYjGHQ66gvkKWkpovbbmZJlYttHQF2do59QT+eUFwhlkpTbBn97vtQReOJdSRMBj2LK4vGLepoMuhxWU20+wpzGKSmaezvDeOLDi25OZEOgtNi4ox5pXgjSX6zo5vAONNGppo3nKQnGMdk0M2KVVamy8+3duBxmDl/cUWuQxFi1ogcTSz0BI8mFkpsM2pUY53bTiCu0OabnmR8NJUmnsqMW8zRH00xEEmxsHL8egvDFlY4aRuMkkyPPjXQaTGSUFTCyfwY2ZdtkWSav7YO0uGPYTHqmedxTktSf7gOg9mo58XmPg72hUmnVRYeHa0wExJwIjskuSBmrHRGpT+cYF9viFcOeXlhXx+vtgyyvzcMOo15HkksjKbWbafWbePPB7wcGRy9aNJ4htezdowxvNEXTeGPKSesCj1sabWLtoHoqHP9hpU6zHgjSZQxOhv5rCeYOKWLc7vFyJkNHkLxNL/e0cVADgpYDSeKgrEUc1xW6VxkSXNPiN3dIa5cUzMjlqUSohCEE8rQiIWjiYWZOF3S7TBjNerZ2RGYltcLJdIk0yrOcVaOGq7BdKJijsMWVRahakN1fkYzvErVYCT3o/qyLRhXeKN1aFSN02xi7jT3Y4frMCyocDLHZWVBZVHWajyIydE0jf986RDP7+nNdSiSXBAzh6ZpBOMKrQNR3jzi44/NfbxycICtbX4O9UeJpzIU24w0zSliYUWRDN8ex6LKIhwWI8/t6cV3EhetmjY0BNIwzhDI4bmSE+1ILKsuRtWgeZx5oi6biXRGw1tgnQklo3Kgd2hN6ppRijieiNVs5Ix5HmKpDE9v76ZvmotYdfrjeENJ7GYjJfZTW/ZKHO/nb3ZQZDVy0bI5uQ5lRnv88cc5//zzWbFiBVdddRVbt24d9/n79+/nYx/7GCtXrmTDhg08+OCDpzx9TOSXcELhzTY/PcE4bvvMTCzA0CpM9R47rQMxfNGpT0gHYwpxJTPuEnsH+iPoddBYPvGRC/C3pMQ72c0G9DpysuzmVArGlJFRNSU2M3U5rANSXWKnvEhuKOSTLS2D/GFvX158bsnVlShoCSVDdyDO7q4gL+338tL+fl5rGWBPVwhfNIXJoKfBY2dVbQmL57ioLrFjNxvz4s2Xz/Q6HavrSkhnNH63u5fEBKtLR5JpYqkMjnHm3R082pGYd4JijsOaKovQ6xi/7oLFiF4HPYHCmhrR4o0wEBmar3iqS0eZjXrOmOchlVF5ZmfPtNWeSGdUWvojhBPpE9aJEBPXOhDhjTY/l6+qHrcAmpicZ599lrvvvpubbrqJp556ijVr1nDDDTfQ3d096vMjkQjXX389Ho+HX/ziF9x+++08+uijbN68eZojF9kWSihsbfPTHYpTYjNRXTwzEwvD5rhs6HSwu2vs6YbZEkooaGjjFho82BehvtQ+4c87j9NCqcPMwTFWjDAZ9DjMRnqCMye5EE4ovNXupy+UoMxpocY9s89RcXLiqQw/+Mth5pU58mIqpSQXREFRVY1AIsPBvjCvHR7kxeZ+thwa4K12Px3+GKqqUeGysrTaxYqaEuZ6HBTbzDL94RSYDHrW1pfgjST5Q3MfmQkURwrGh4o5jjcE8lB/mLkex4SrGtvMBhrLnezpHrsjZDUZsJsNtPsLJ7kQSii0DURJZVSqJrkMqsmg510NpWjAb3f20H4K01lOVttglMFoErfdJEWcsugXb3ZiMxm4dEV1rkOZ0TZv3syVV17JNddcQ2NjI3fccQfl5eU88cQToz7/N7/5DfF4nPvuu49FixZx0UUXccMNN7B582YZvVDABiNJ3mgduhtcajdTM0NHLLyd2ainpsTG7q4gSnrqCjtmVA1/LIXFoB+zD3YyNZjeblGlkwPjFHV02Ux4I8kZ8d6MJtO8ecRPTyiOx2GZdH9BzDw/faOdwWiKG989Py+ud6RHKApGQsnw19ZBdnfFcCUHyagaNpOBIuvQ+tM2s0GGaGWZw2JiVW0Jb7X7cdu8nLNo/IxoKJ4moWRw2eyjPq5pGgf7IpzV6DmpOJZVF/PMzm5SaRWzcfScaKnDQm8gjqpq6PPgw3U8w0Uc/bEUVRMs4ngiRoOedXNL2dbh59ldPVywrJLG8pPrsE1UQsnQ6o2SUFQaPBMbyipOrDsQ5+VDA1y5pvaES7KJU5dKpdizZw/XX3/9MdvXr1/Ptm3bRt1n+/btrFu3Dqv1b0utnnPOOTzwwAN0dnZSV1c35ut9/OMfH/n5Ix/5CHffffck/wLxdqfapkm9jbDZjaK3YEonMWtx8vubI3vSOhMhSwXP/6iTonTguMezcZ4qOhN+axWaBvbM6ImApN5GuOzdNL/+Inf86UcTPnaffR49zkV8+av/ilE7fmRlwmAnZizm/36wHyP5U4vpZNs1ozMQNJeR1NsxqUnM6uw5Ryfq4osu4oc//GGuw8iZuMHB/tKzKU1088TDz5HRmXCn+id1zLefp//zP/9z0vvPqt7L448/zqOPPorX62XhwoV85StfYd26dWM+f//+/Xz9619n586dFBcX8+EPf5hPf/rTMz6rna8O9kXoDSXIaBr1bgdOq7Ggi52l0ipxJUOR1ZjXSZEyp4WmyiK2tgdwOy0sqy4e87nBeApVG3sIZF84STiZnnAxx2HLql08tb2Lg/3hMV/fZTPR4Yvhj6XwOPN71YLuo0UczQZDVldYMOh1rK13s6MzwHO7+3jv0qH6Gdl2qD+CL5qivMgyZrJHnLxfvtWJUa/n8tUyamEq+f1+MpkMZWVlx2z3eDxs2bJl1H0GBgaorKw8Ztvw/gMDA+MmF9773vdSXl4OQGlpKR/5yEcmE754h1NpU29cozMCEQXcVnCd2qy0gnYoCEb9ct5dfXz/Ixvn6UBcY39gqG2Lx/iaa/ZDcxu85/RlVNmXTfjYbSH4aQssP/M9NLiOfzyiwOEQvKtyJRW2/OlfnUy7KqpGSxAGE2AzgMcKedxVzBlXcTEXX3RRrsPICU2DnxwCaxw+dnotVmMt8QwscU/uRJns+3/WJBeG51d+9atf5bTTTuMnP/kJN9xwA7/97W+prj6+Izc8v3LdunX84he/oLW1lVtvvRW73X7c3Q4x9bzhJB2+GKoKtcVm3I7CLh43GEnyhZ/vYDCawqDXUWwzUWI34babKbEd/a/9+P86LbmpFzHX4yCcTPPSvn6KbUMjRd5Jyaj4Ywo2k2HMYVmHRoo5ntwF79Kqod7Dnu7QmMmFoqNTMXqC8bxOLqTSKgd7w4TiCgsrirL+/1Ov07GqtoTdXUH+sKeXpKKyuKooa4m4YFyh3RdD1TQqiqwn3kFMyEAkyYv7+rlo2RzcUhxzWoz23hvv/fjOx4aHXJ/oPbxx48aRn5ubmznzzDNPJkxxAifTpkPD8CNofWFMcYW6Ujslttn5flsYSrCzK8iqdbXUvOM7PRvn6e6uINoRP0vmuMZcUeDAK60Y27u5/PyzTuo7alUyzU9bXsNUPpf1645P7CXTGUwHB1i6wMO75p3cSMmpNNF2TaVV3jziJ+mLstBkoN7jyOubULnU2tbKiuUrch1GTry0v5+O7Qf41P/XyAXLq8ioGpGkwjkLyyd13Mm+/2dNcuHt8ysB7rjjDv7yl7/wxBNP8IUvfOG45799fqXVamXRokW0tLSwefNmNm3aJKMXplE6o9LcE8QfS1FfasffX9htn86o3PfcfqKpNJvObiCSTBOIKfhjKQKxoXn4gbgyao0Do15Hid1Eie1o0sFh5r1LKkcuvqfSkjku3mz38fvdvXzwtNrjVgcIxRWSaRWbZbzCTWGMeh1zPaNPmxiLy2airtQ+bt0Fu9mAxaSnfTDO8pqSkzr+dGrxRvBGkpQ6zFNWq0Cv07Gippi9PSH+b38fh/rDLKhw0lDmoOgUC0cOO9gXJhBPUV1iy4u5fTPFr7Z1oQFXranJdSgzntvtxmAw4PV6j9k+ODh43GiGYWVlZQwMDBz3fBga8SDyn6pq7OkO0ToYIZpMM8/jOOVCujOBx2nBpNexszN4XHIhG4LxFKBhMY2dNDjYH2FemeOkk99Oi5HqYuuYK0ZYjAasZgO9BVjUMZ1R2dbup8MfxWI0UFcqiYWpomRUfr+7lyKrkXMWlBXUKnLRZJpHX2llYYWTC5fm18pSsyK5IPMrC1vUWEzY5EavpbFlogU/v6rTuZgB+1zmBrez7anfHfOY8+i/WiCjM5HWm1H0lmP+m45a6NOb6dJbSBpsvLC7kyb/a1gyp75KwETbVEVP0FzBT/8nQUX8CAb+VgwqbnDit1RgTUcxaaMvCXmoZB1mnZGv/etXTzrGWNFStluquP3OO8accxg1FpM02Pheyoc1E8WkJkadjzkdRnvvp3UmApYKFJ0ZezqInqkvNqXozCQMTtIGM0Y1iS0Twa6ET2nuZkpvJWAuR9UZsKeD0z73s9Df+2NJ60zsLXs3xYk+vvvN3094P0Vvxq6EsKqTK2Q6fK6eytzKQmQ2m1m2bBlbtmzhfe9738j2LVu2cOGFF466z+rVq/nmN79JMpnEYrGMPL+iooLa2tppiVucOiWjsrMzQPtgDCWjsqC8aNavxmLQ66grtXOgP8yGZBlOS/YSLUpGJRhLYzWOXQtL1TQO9Uc47xSr2y+qLGJn19irSJXYzPSHp365zWzKqBrbOwJ0+GIYDXrmehySxJ8iHb4Y33x+P4cHhgpg/+i1I1yxupoLl84piM+Gx18/QjCmcOelS/PuHJkVyQWZX1m4YsrQnL2oAtUOMOoLe35Vsx+2t8G6cnjvmtWTPl4wBZv3wUDNBj7RBOPcIBjXybRpXIGDIah2nMHqMkY6Dm0hjbYw1DlHj0PToHknLCuFC9918v//9vjg6SOwZsNFzBnjJktGhd44RFKgAk7T0L8yK7jM4DAxbXcA3vne1zSNQ0Hoi0OJeSie6aSo4I1DMDn0PiqxQpV9qG3MhhO3iappHAxAfxwqrDDOsuVTppDf++P5cw+ovXD12hrKrBMfuRBRYI4dSq25nV9ZiDZt2sQXv/hFVq5cydq1a3niiSfo7+/n2muvBeBb3/oWO3fu5LHHHgPgsssu4z//8z+59dZbufnmm2lra+P73/8+n/nMZ2QkY55LKBm2dwTo8g0l4BvLi6RWzFFVxTYOD0TZ2xXiXfOzNwKnP5wkmc6MO5Kxyx8nrmRYeJI1mIYtrHTy0gEvg5HkqFMhi6xGekJx4qk0tgJY0UhVNXZ0Bmj3xdDpYF6pJBamgqZpPLu7lx++3IrVpOe2S5Zg0Ov45Vud/OAvrfz0rx1curKK96+spjgXHZ0JOOyN8NtdPVy8fA4Lp6Cu1mTl/7sti2R+ZWFRVY3XW32kBiJUl9hGCt8V6vyqdl+Mf//5dpZUObntiuVZG35VOd/Pvz69h23JCj7/noWn1NE92TZdGUqwozNAWWMZZx5d+eH/9vXj8MVYU1cyagyd/hjJ7W+xYeVC1i+tPO7xE1kUTvL0Y29gqpjH+lUnvgCLJBV6gwn8sRSxlErKZsRhN1NdameOy4rHaZ7SgqDvfO93+mOkj/ipzWgsrHDm7IJEVVX6Qkk6AjHiqQyJIgs15Q7mlTnxOMxjxtU+GCPd7mce0FiemxUiCvW9P55YKs13H3uDs+aXcPl7lpzUvv5oigUVTqrdk1uabDZ+T11yySX4/X4eeugh+vv7WbRoEd///vepqRn6bPF6vXR0dIw8v6ioiB/+8Id87Wtf4+qrr6a4uJjrr7+eTZs25epPEBMQSabZ1u6n2x/HYtIzt9RRUEOfp5rNbKDSZWFHZ4B1DW70+sm1TULJcKAvTKcvTjSVoX6c6RbDUxpOOblwtHbTwf7IqMkFu9kAGviiCjV5nlzQNI3d3UGODERRVY355U45T6eAP5biP144yNYjftbWu/n8exaO1HA7vaGU5p4Qv3yrk5++0cGT27q4cEklV6ypodKVP/WlVE3joT+1UGQ18YkzG3Idzqjy+92WJTK/sjAd8cXoCyWwmQ2UFniBs1gqzT2/a8ZqMvCli5qy+qVx2lw3155exxNvdLC0ysVFy6Z+7lWFy0pjuZPXWwdxO0zUlzqIJBUcFsOYF6cHjxZzHGuliBMl78qLLFQUWdjTHeIDE0guOC0mFlQMZZ2TSoa+cJLBaJIjvhh2k54Sh5mqYivVxXbKiyxjFpzKhlRa5WBfhEg8zYLK3CUWAPR6PVUlNqpKbESSCq0DUV4/7GNPd4jqEhuLKpxUl9iPubOnZFRavBGiSYVFlVNf32M2+d3uXqLJDB86TYbWT7frrruO6667btTH7r333uO2NTU18fjjj091WCJLArEU29oD9IXiuKxmqRMzhtoSO2+2+2jxRk/5LqimaXT64xzoCzMQSaJpMLfUflxtprc72BfBatKPWiB6IuaXO9Dr4EBfmDNHGXXhsBjR6aA/lKBmkgnYqaRpR2uBeKOkjk7ZKeSV0PLV1jYfD7xwkGgqzT9smM/7V1Yd1xdbUuXi9kuX0u6L8eRbnfx+Ty/P7u5hw8Jyrl5by7wyR46i/5sXmvvY1xvmc+9ZmLdLVudnVFkm8ysLTyyV5lBfmGgyzaLK7FfUn06apvEfLxykOxDnG1esmJKVDD58ej37esM8/KcWGsudJ73U46mYV+YgkkzzQnM/Zy8oI6moOCxjf6Qc6o9gMeqpKx29I9HijRJX0qwYpxjj0moX29sDaJp2UueExWSgvtROfamdjKrijaTwhhNsaw+wQx/EbTNT4bJQ67ZT7rTgsmV3VY5D/REGwklKi6auiOOpcFpMrKgpIa2qdPritHmjtPRFqCweSh41lDkotploHYgyGEnidpgLYi5ioUimMzy1vYs1dSV5ObRRiELVH06woz2AN5yk1GmmuthW0P2IqVRiN+Ewm9jRGTilz6FQQmFfT5ieYJxIQqHMaaHSdeJEzsH+CI3lzlNO+FiMBho8jpEbF8c/rsds1NMTirMG9ym9xnTY3xfm8ECEhJKhscIpU3ayLKFk2LyljWd39dDgsfONK5Yz1zN+kqC+1M7n37uIj505l19v7+K5PX386YCX0+a6uXptLcurXTn5PAknFP57SxtLqlycf4q1SqZD/vRyp5jMrywcmqbR3BNiMJqi0mUp+IuZX+/o5pWWQTad3cCKmtGXUZwsg17HFy5s4vM/2849v2vm3z+8etKrApyITqdjWXUxf20b5M0jvv+fvfcOj/Ou0r8/03vTqPcuy3KP7fQ4PQ6plF0IsLDsj6W3lx2TnAABAABJREFU0BbyEpZONpTALiyw7C69LQQSEkjvpMeOu2z13mak6fVp7x9jOXYiySozmhl5Ptfly9Jo5pmvRjPPc773Oec+aNVqKh3zZwe6p8I0LhBI+KNJJkNxWstsGLRz/807Khw8fszDmH/5mQiNWk253Uj58TI3fzTJeDBGz2SYw2NBXGYdRRY9tUVmyh0mXGbdij7z/miSgekIkqJQnqOjG7VqNfXFFuqLLcxEEgzNRHmy28OBET+1RWYSokxCkmm0Z6cdYq3ycOcU/qjA3101v4dPgQIFlsbwTJTDYwGmI0nKHQZKbbmbtT6ZiWCcp7pSFbYatQqNWoVWrUKjVqNRp65d2uO3v/Kzk79Xozk+Uap4CUkMlUpFbZGJzolU1cFiHytKMn3eCP2eCNPRBAaNhuZS26IEdEGS6fOGuWbja0fBL4WWMht/6/HMmXBQqVQ4jDqmgrlr6tgzFaZnKkw0nhIW5ot9CiyPPk+Ybz54jGFfjBu3VPKOc+uXVBVSbDXw/y5o5M3ba/nLoXHu2T/GLX86SFuZjTduq+LsRveqTvL4+bODhBMi79/VlNMTRM4YcaHQX5k/jAXijPljaNRQkqObscVyeCzAT57u59xGN6/P8Ig5h0nHZ69ex7/ceYBvP9TFrdeuz/jJR6NWsa3WxbN905i0zNtaIMkKvZ4wu+dp2UiKMjFBwqzX4A0l5h2L1VGVKsk/NBZIW5mj06w/UboZS4qMB+KMB+L0TEVwmLS4LQbq3GbKHUZcZj3qJWRZFEXh6EQIfzQ1ujEfeiiLLAaKLAaSoszATITDYwE0ajWNyxgXVmB+REnmj3tHaC+3saGy0GpSoEA66JkK0zWRGpdb4zThsqS/UjATDE5H+Nzdh/BHhRUfS6dR8eN/2L6kKslSu5GuqTAHhv1c2n56T6SpUJyj40E8oQQJUabcYaTYYli0ED84HUWQFFrL5hesZUXBF0ku+Hu0lFp54PAE44E4lc7XxgR2k44eT5iEIGHIsURVvzfCsYkgwZhAU4k17xNpuYSsKNz18ii/eG4Qu1HHl67vYGvt8qtXrEYtb95ew41bKnm4c4o/vTzC1+47SpXTxBu2VXFJW2nG46OuyRAPHJ7gus2VOdGesRBnjLgAhf7KfCAhSnRNhAjERFpKrTmtzJ2OmUiSf7v/KOV2Ix+7fHlGi0ultczGuy9o4IdP9vH7PSO8eXvmM6IGrYZzG9yEEuK8m+fhmShJUZ7XuCmSFBEkGatBiz8mUDXPNaDaacJh0nFkLJgRbwmTXktjiZVGUoLHWCDGVChOvzeMzajDbdVTW2Sh3GHEbTm90DDiizEZiGPUqfPON0SvVdNaaqO11EYkIWbUk+JM5MluD1OhBO/b1VSohitQYIXIskLnRJB+b4RgTKC+yIItR53eX03PVJjP//kQOrWa7920lQqHCVGWkWTlxD9x3q9l5JNuCyVEvv1QFw93TvLmHbWLXoNOo6bKYaRzPMj5zXN7kUGqxPzYRIgRXxR/VMBh0tFQbF3yxmrWzHGhFs7pcJIjY0HaKqyU2+dOJsyKE12ToTnFBbNei6KkPLxac6j1bHgmSud4SlhocFtyql0y3/GGE9zxcBcHRgKc2+jmQ5c0Y0/TucCg1XDNxgp2d5TzdI+XO/eO8B+P9vCr54d4xzl1XLYIYW45SLLCDx7vxWXW87azF/+5zhaFd3OBnKJrIownHMdtya3e9KUiSjK3P3CUaFLiyzdsWNXf5XUbK+icCPGr5wZpK7OxpcaZ8ec06DQLZgVOF0hEExKKAqV2A1PB5LzHUalUrK+wc3h8/tnW6UKvVVPvtlDvtiBIMuP+GN5wgoHpCFaDliKLgdoiMxVOI26L4TXtHklJoXsyRDgh0FKanf68dLGQl0aBpSMrCr/fM0K928z2utztBS6wNokkRBTAukY+19LsCL/paKpvvcSaN/HD0fEgX7jnMGaDlq/csOHEBlnP8rOgjx6d4oEjk7zprJol+RlUuswM+WIcGQ/y6ppRRVEYnonRNRViOpxABdS5zThMyxPNu6fC2AzaE+2JcxFJiAiyzHQ4Oa+4UFtkQa9V0z0V5uK21/agp/wkNDxxzIM3lGBzjTPr17Mxf4xDYwF8kSR1bjPWDLewnkk83ePle4/1IEgyH760mSvayzISe2nUKi5qLeHClmJeHvbz2xeG+M4j3RwYDfD+XU1pr0J54PAEPZ4wn7yyLS/Obbm/wgJnDJ5QguGZKLKcmr2cz/zs2UEOjwX5xBWtpzWOSTcqlYoPXdJMnzfCNx44ynffsnVJ/ZeZoHsqjFmvmTOzAKnKBaNOQ22RhaGZGElRQj9P7+H6SjvP9k3PO9s6E+g0amrdFmqPCw2TwTiecIKhmQhmvRb3cY+GCqeJYmtKaBjyJ4kZU/2rhax/gVn6vRF+/FQfI74Yn76qLa9FpwL5yb5hH95wkqvWl2PM83NTQpTo9MRRxyMoipJXfesHR/x86S9HcJn1fOXGDZSmqQ30qo5y/u3+o7w87GN7XdGiH2c1aCky69k/4ufskzTPQEygczzIZCBOOCHituopX4Rh40L0TIVpPs1I5khSPOX/udCoVTSVWOmeDM35c51GzTmNbvq9EV4a9NHvjbC1NmWgm402v5moyNBISlioXYE4U+BUokmRHz/Vx8OdU7SUWvnklW3zxpvpRKVKtQZvrnbyuxeH+O2Lw/RMhfnM7nXzmpcvFX80yc+fG2BTlYOLWuavKsolCg20BXICUZLpHA/giyapyvNxUU/3eLlr3yjXbqyYU0lfDYw6DZ+9eh2CpPBv9x9FkOSsrGOW7qkwzSXzt7mEEyKW4/O2rQYNM5H5+047KlL96YfHghlZ6+nQaVKjs7bWuDi3sZhKu4lATODp3mn+emCMew+M8eLADBNhAVlW0hYwFshvgjGBHzzRy8d+9zID3ggfuLiJCxYoPy5QIBP4IkmmI0m6J8M82zud7eWsCFlWODASYCwooFJBY0n+CAt7B3184Z4jlNiM3PaGTWm9TpzdUITTpOP+QxNLfmyt24wvkmQ8JCBIMkcngjzT66XPE0ZSZFpKbVQ5zSuK0eKCxOD0wmMvZUUhGBcwaNXEBRlxgRimpdRKrzcy731UKhWNJVbOa3IjKwoPd05y38FxRn3REyOwV4PpcIJj3tRI7CqnCWdBWEgL/b4kH/vdPh49OsWbt9dw+xs3rYqwcDIatYq3nl3HF6/vIBAT+Pjv9/H4sam0HPunzwyQEOS8aqEsiAsFcoJeTwRPKIHDpFtwLnKuM+KL8t1Humkrs/FPFzRkdS01LjMfuayFoxMhfvJ0f9bWIUgyA94ILfMYNwmSTCQhUmw34LLosRp0TEfj8x6vscSKSafh8Hh2xIWT0WnUVLpMbKlxcX6TmyqXmVBc4IX+aTxhkUpHfpg4Fsgckqzwl4PjvO+Xe7j/0Div21DBj/7hLK7e8NoZ2wUKZJpRf4xwXKTKaWL/iJ/hmWi2l7Rsej1hRn0xNGoVjcvo+88Wz/VN8+W/HKHaZeLrr99IkSW9MY9Oo+ay9jJeHJhhOry0SQlFFj0GrYbDkzGe7fVyeDSVZa90mGgusaWlCq/fG0FWmNeDCSCWlEgKClUuE2admkBs/oRDa5mNpCgzdJr3skGrYUuNi601LiYCce45MMbjXZ4Fj71SFEVhOpzg0GiAvYM+fDGRcoeRojwxGs11/rBnhO8+40GSFb72+o28/Zy6rMZcW2tdfPfNW2gstvKth7r4/mM9JMXlJ/eOjAd55OgUN2ypSlslxGpQaIsokHUCUYE+b5ikKNNQnL+j7mJJia/ddxS9Vs1nrl6XE4HOBc3FdG6u5M/7x2ivsHNhS8mqr2FwOoooKzSXzp2liCYlRFmhwm7EoNVQZNHR753fd0GjVrGu3Mbh0cz7LiwFjVpNhcNEhcOEJMt0EcxroazAyjk44ue/nupjYDrKpmoH77mwcdXbpAoUmEWQZMb8MQDaym2EEyKPHp3irTtr0OVJxn+WqVCc7qkwsaRIuU2XN9WOT3Z5+NZDx2gptfGF6zqwGjMThl/VUcade0d4qHOStyzB2FGtUlFbZObZ8UmKfDGcxuUZNi7ErAfTQuLCrN9CW7mNgyMyM5H52yBnjzM77vp0OM16zm0qZngmwuHRAEPTETZVO1lfaU9b5UsoLjARiDMeiOGLCoQTIooCbouWEmuhmjEdPNc3zc+eHWBLhYnPXLs5614as7itBr72+o384rlB7tw7QtdkiH/ZvW7J1RQpE8ceiq0G3rIjv8ZVZ3/3U+CMRpYVjowH8UWSVDhMObEhXw6KovC9x7oZ9UX51JVtWfc4OJl/PK+e9nIb//5od1ayVKcLJCIJEUlWKHekLrildiOx05RBdlTaGZyJEopnLuOwEjRqNUadppCZPkOZCsa57b5ObrnrENGkxGd2r+MrN2woCAsFssq4P04oLuAy69Fp1GyodDATSfLigC/bS1sScUHi8GgQfzRJdZEZvSY/zrMPH5nkmw8eo73CzpduyJywACnfqi01Th48MokkL630v9plpt6lp6Eo5TOU7risezJMkUW/oGdSJCmi1aiodBixGLSEk9K8961wGLEatPP6LsxHTZGF85uKMeg0PNXt4Z5946mqiiW+XrPMtns81zfNE10e9gzOMDiTar2ocZnZUOmgyJQbG+B8JxAT+P5jPTQWW3jHVlfOCAuzaNQq/vG8ej5/7XqmQglu/r99PN3jXdIx7j0wxsB0lH++sCHvxpTm506uwJphcCbKZDCOSa9Je2nganLvgXGe7Pby9rPr2LwK0xmWgk6j5l92r8Og1fD1+zqJLXCRzgTdU2FsRi2ltrkDiWhSwqBVnxgVVGTRY9armYnOX72wvtIBQGcOtEYUKDBLXJD49fODvP9Xe3lx0Mfbzq7lP9+2jfObiwtCU4GsM+qPEUlKJ8Rvq1FLQ7GFFwdnmAzGsry6xSHLCgdHA0wF4xSZ9XnTt/6XA2N899FuNtc4+cJ1Havi+L67oxxPKMHLQ0sTjzRqFWU2XcZGeXZPhResWgAIx0XMeg1mfWqiRDQpzuuPoFKpaCm10jUVXvJatBo1HRUOdtQVEYgnue/gOI90Ti66nUSUZMYDMfYO+Xjs2BTP983QPRkilpAosxnpqHDQVGLDZdbnTXVNPvCDJ3oJJ0RuvrwVbQ6/rjvqi/jum7dQ4zJz2/1H+a8nexflgTYdTvCr54fYVuvi3Eb3Kqw0vRTEhQJZI5oU6ZkMEU2IVDvNeRt8d44H+Z+n+zm7oYg3nlWd7eXMidtq4FNXtjHqj/G9x3pW1cSoezJEywKu0OGEgFmvwXi8HNFl1mMz6JiOzH9xby2zolWrsmbqWKDAySiKwt96vHzg13v5zYvDnN1YxA/eto237KjNG4O5AmubQFRgOpLArNOckgWrd1swaNU8etSz7IztatLrCTPmi6HVqqlYZdO25fLHvSP88Mk+dtYXces161ctC7lz1tjx8NKNHTNFOCEy6o8taOYoyQqhhEixxYBWo8Zl0QMqIon5p0a0lNkYnI4QF5aXPLEadeysdx8XKUL8ed8oL/TPzJmMOdlH4fFjUzzXO83h0QD+aBKHScu6cjvrKuwU24x5W42byzzV7eHpHi9v3VlLfXHuVwOW2o18/Q0buX5zJfccGOczfzzAZHB+XzGA/316AFGWee9FjXm5N8qtOpICZxSd40GmI0lKbIa8K/mZxRdNctv9Rym1GfjY5a3zTkPIBTbXOHnb2XX84rlB2itsXLupMuPPGRckhmainN0wt/IqyQrhuEiN24z6uPps1GlwWfQLmjMZtBpaSq0FcaFA1un3RvivJ3s5NBakodjCxy9vZUOVI9vLKlDgFEb9MYJR4TV9vxq1io4KBy8OzrB3yMf2+sWPLlxtPKEE3VNhokmRljJbTl9vIbUJ/e2Lw/z6hSEuaC7mE1e0rqrZnE6j5vL2Mv748siqjm5eiN7j1QULVS5EkyJJUabCmWqVdJp02I1aZqICVuPc1RStZVZkJXU+bj8+UWo5lDtMlNgM9ExFeLbXS99UmG11TppKbUSSYspHwR/DHxUIJ1M+Ck6zjhaHDbO+0AqZaXyRJD94vJfWMitv2Jabyby50GnU/POFjXRU2vnuI9187Hf7uPnyFnbOERvvH/HzZLeHt+yoWfWpF+miIKkVyAqj/hhj/hgadUrVy0ckWeEbDxwjnBD57NXrsOZYz9dcvOmsanbUu/ifv/VzdCLzG/OB467QzfMEEtGkiCArVDhOfQ+U2Q1Ek+KCvaIdlQ56POFlZyoKFFgJwZjAfz7ew8d+9zKDM1E+cHETd/z9loKwUCDnECWZMX8UGeY0mXWa9dS4zDzbN83MAhVj2SQuSBw6nh2uLjLnfEWQoij87NkBfv3CEJeuK+WTV7ZlxcX+yo4yZAUe6pxc9eeei65FmTmmTJ5nN1Z2kw6zXkswNn+rZMtxw+iuJfouzIVGraat3Ma5jcXERYkHjkxyz/5RnuzysHfAl/JRQKb2uI9CXZEFi0FbEBYyjKIofP/xHhKizMcub83LNpPzmor5zpu3UGo38OW/dPKTp/tP8RcTJJkfPtFLmd3Am3K0EnoxFMSFAqtOQpTomggRiIvUuMw5n32Yj188N8jB0QAfvLgpb6ZcqFUqPn55G26rnn+7/2hGRzABJ3og5zVzTEqnmDnO4rIYMOk0BBYIJjoq7UiywrE0BBMFCpwOUZKZiSTp84S5Z/8Y7/3lHh44PMHrNlbwo7enRkvmY7BTYO0zEYwTjIu4zPNPVWgusaICHj06taptc4sh33wWZEXhv57s4869o1y9oZyPXtaStXPDrLHjA4eXbuyYCbonw5TbjdjmqUCAVOuEXqPCdVwI06hVuK16Igv4RRVZ9LgterqX4bswHya9hrPqithQaccfFVI+CnYDGyodNJXYcRZ8FFaVR49O8Xz/DP9wbh01rvwZy/hqKhwmvvHGzVy9oZw/vjzKLXcdwnvc4+PufWOM+GK896KmnBdQFyL3U60F1hxdE2E84Thusx6LITOGQZnm2b5p7tw7wu6Oci5dV5bt5SwJq1HLZ3a38+k79/PNB4/xhes6MvZcPVMhiszzu0JHEyI6tQrHq4JFl1mHzajDE07MOw96XYUdFXBkLMjmameaV15graMoCjFBwh8VCMQE/DEBfzRJICYQiAr4YgKB49/7owKhV/X7FkZLFsgXRn0xIgmRKuf8fe7a49Mj9gz5ODQaZGN17lTgnPBZ0Kgod+R2mbAkp7KrDx2Z5MYtlfzT+Q1Zz2jv7ijntvuP8nIOtL10T4Vpr5j/fQgQSQiY9VpMJ7XLltqMHBoNIIjSvGNTW8tsaalceDXFViPFhfGRWcUbTvDjp/roqLRz/ebMt/RmGr1WzQcubqaj0sH3H+vho799mXed38BvXxzi7IYiduRwe9piKIgLBVYVTyjB8EwUWU6pd/lGXJD4/Z4R/rh3hJZSK++5qDHbS1oWzaVW3ntRE997rIffvjjEeRnSR7qnwvO2REBqDKXJoDkliAAw67U4zDrGfPM7mFsNWuqLLRweC6RtvQXWLrKi8PixKf56cIKZaJJAVCA5j2uzxaDBadLjNOuoKTKzsVqP06TDYdLhNOsosRpoXsCktECBXCEYF/CEExh1mtNOKHBbDVQ6TDzV46HObcKeAxUCJ3wWBJGWUltOZ4pFSeY7j3TzRJeHN++o4W07a3PiHHF2QxFOc8rYMZvigi+axBtO0FJaMe99REkmFBepLnrFhwnAYdJh0mnwxQRKbXOLCy2lVp7tmyYcFzM65rPA6qIoCv/+SDeirPDRy1ryttp5Lna1ltBUYuHf7j/Kdx/pRq9V854L83NfcTKFT1+BVUOUZI5OBPFFk9QWmXM6SHg1iqLwTO80//N0P55QgovbSnj3BY157QR85foyOseD/PbFYZw73TTUp/f40aTIqC/GrtaSOX8uKylH6HK7cc73QpnNSM9kGFlR5r2YdFTYeahzElGSs9LPWiA/6JoM8V9P9nFsMkS928zGSgcOsw7ncbHAYdLjMOlwmXXYTbq8/lwXKHAyY/4YwZhwwhzvdLSW2Xi618sTXR6u3VSZ1c3xrM+CL5Kk1p3bPguCJPONB47xbN807zi3jr87qybbSzqBVqPm8nXZN3bsOdEmOX/lQiQpIcgKVa96vzrNOqwGLTORJKW2ud/LsxMouqdCbK11pWnVBbLN/YcneHnYz/t2NeVlUvJ0VLvMfONNm/nNC0M0FFvy1ofuZAriQoFVo9cTYSoYP579y35GZLGM+KL86Mk+9g37qXeb+cQbNtJRmTslo8tFpVLxvl1N9HrC/Hq/j4s3i1jSaErZOxVGYSEzR4mkKFNmnzvQKbLo0WvVBGJJXOa577O+0s69B8fp80ZoXWC0VYHsEooL7Bv2s6O+aFUnw/iiSX7x7CAPd07iMOu4+fIWLm4rXVOZjwIF5kOSFUZ9MWRZwWVa3IZSr1WzvsLOgVE/XZMh2sqX77y/EmRZ4dBxnwW3Jbd9FsJxka/f38mBkQD/fGFjTpZtX9VRzh/2jvDgkUlu2lmblTV0T4ZQq6CpZOFqRukkM8dZjDoN9tNUM87GGl1T4YK4sEaYCMb536f72VLj5OoN5dleTsYw6jS86/yGbC8jbeScuDAxMUF5+dp9A2WDaFLEpMvuiJzxQIw+b5ikKOeN+WEsKfG7l4a5e98oem1qjMw1G9eWaZtRp+Ejl7bwid/v55fPDfLeXU1pO3b3abIU0YSYcoR2za1Euyx67EYd0+H5xYVZkefQaKAgLuQokqzw1b92cngsiNWg5eoN5VyzsSKj2TNRkrn34Di/eWGIpCjz+q1VvHlHzWnLwgvkJoW4YHlMBuMEY8KSjefK7EZKAgaeOOahxmXGnIVJSH3eMKO+GFp1bvssTATifPHew0wE4tx8eUvOejCVO4xsqXHy4JFJ/n57TVbimO6pMNUuMyb9/AJzJCFi0Kqxm17rx1VqNaSSFooyZzxrNWipcproLpg8rwlkReG7D3ehVqn48KXNhaRAHpEztZ/Hjh3jX/7lX7j88suzvZQ1RSAq8ESXh+f7ppGz5BQ8EYhzYDjATDhJtcuc8yXHiqLwtx4vH/j1Hu7cO8JFrSX88O1ncf3myjUlLMzSUmbjwnoLfzk4nlYzpB5PmFKbAcccQQKkyh816lccoV+NRa/BZtISjItz/hxS1Q0VDiNHxjM/VrPA8vjZswMcHgvy1p21bKxy8Ic9I7z75y9xx0Nd9HvT5+w9y94hHx/+7cv8z9/6aa+w872btvGu8xsKwkIeUogLVsaYP0Y4KVJiW7qQt67cTkyQeKrHm4GVLYw3nKBrMkwkKVLrtuTsdbdzPMgnfr+PQFTgyzdsyFlhYZbdHeV4wwn2DvlW/bkVRaF7KrzgCEqAUELEatBinKMFpshiQKNWEU7MHxO0lFnTOjGiQPa498A4h8aCvPuChnlbYQrkJqsWbd1zzz3cddddTExMUF1dzXve8x7OOussjh07xre+9S2eeuop7HY773//+1drSWcEI/4oM+EkR8aCCLLC+U3Fp5jkZJqpYJz9w368kQQ1LlPOt0MMz0T50ZO97B8J0Fhs4dNXraO9IjtloavJNW12DnkEvvdYD3f8/Za0BHPdkwubOUaTImb9a80cZ1GpVJTbjfR7IvNmKiA1kvL5vpkFvRkKZIdner386eVRXrex4kQp7nggxj37x3ioc5JHj02xudrBjVuq2FbnWtHfbyIQ57//1sfz/TNUOIx8/tr1ee+4vNYpxAWZI5wQmQomMGjUyxLWjDoNrWU2jowFaS210rBAKXs6iQsSh0aO+ywUmVe1jWopPNHl4buPdFFsNfCF6zpeU8afi8waOz5weGLVz42eUIJATDjhizAXgiQTTYjUl1jmjFMdJh12ow5vODHvKMuWUhuPH/Nk1VuiwMoZ9cX42bMDbK9zcXl7bot2BV7LqogLv//977n11ltpamqitbWV8fFx3vWud/HpT3+a2267DavVyic/+UluuukmzOb8nV2aawiSzLg/BipYV27jpQEfkqRwYWvJqmQCpkJxXh724w3HqXKZcc0zUjAXiCZFfvfiMHfvH8OoU/O+ixrZfQbNrTfp1Lz3oka+ft9R/rx/lNdvrV7R8UJxgYlgnKs65i5llhWFYFzAbdEvaMTothjQalSE4uKcZZIAHRUOHu6cYngmuqbGAj7fP82LAz7ev6spL9+HI74o33m4m7YyG+++4JVewgqHifdc1MRbd9bxwJEJ7tk/xhfvPUKNy8QNW6q4uK1kScZtsaTE7/cMc9e+UTRqFe88t54btlTmfIVUOhDluadd5AOFuCCzjPljhOLCsqoWZqlympgIxHns2BSVThOGDG/0FeW4z0Iojtuam95MiqLwu5eG+dXzQ3RU2rnl6vZ5r025hlaj5or2Mu7cO4I3nKB4FTffr7RJLuy3kJRkquZpg7EZtZj1mteMBT6Z1pN8F84tiAt5iSQrfOeRLvQaNR+6pDknJq4UWBqrIi788pe/5MYbb+S22247cdtPf/pTvvrVr7J161Z++MMfYrev/ezwajMRiBOICbjMOmpcFrRqNS8P+5EUmV2tpRl11/eGE+wb8jMdTlLlNOPOUWFBURSe6vbyP0/3MxNJckV7Ge84ty4ng5pMc26jm531Rfzq+SHObypekWPt6QKJuCCRFBTKHQs/h9OSylR4Qol5A7j1lalzx5Hx4JoRFxKixPcf68EXFah3m7l2U+4ZhC1EXJD4+n1H0WlU/MvudXNu9K1GLW/cVs31myt5usfLn/aN8r3HevjFc4O8bkM5r9tYseDnUFEUnujy8NNnBpiOJLmkrYR3nlt/xmSrQnGBg6MBalz5ufEuxAWZQ5YVxnwxkpKyomuZSqVifaWdp3u9PNs7zcXrStO4ytfS60n5LGhUKsrtufe+FiSZ/3i0m8eOebikrYQPX9qSdyLmlevL+f2eER5aZWPH7qkQWrWKhuL5r9GRhISsQPk8k03UahUlNiOTQf+8x2goSbXRdE+GOLfRvdJlF8gCd+0b5ehEiE9c0XrGXM/XGqtyVhwaGuKGG2445bY3vOENKIrC+973vkIAkSFG/TEiSYliS+pEXek0saHSzv7hAI8dnUKYZ8b7SpkOJ3h50Ic3nKDMrs/Zk8PgdITP3XWIbzx4DJdZxzfetImPXNZyRgoLkAok37urEZUKfvhkL4qyfI+O2ZFTTQtMihBkmYrTiAs2gxabUUsoIcx7nwqHkSKznsNja8d34b5DE/iiAlVOEz9/dpDpcCLbS1o0iqLwvcd6GPFF+dRV606bOdVp1FzcVsp3/n4LX7txA61lVn7z4jD/9LMX+Y9Huxmaib7mMb2eMJ/540G+9VAXLrOe29+4iY9f0Zaz55pM4AklUKnAacnP81UhLsgcnnACf0zAbtauePNr1mtpKbGyb9jPqO+1n8V04Q0n6J7KXZ+FYEzg1rsP8dgxD28/u5abL2/NO2EBUsaOW48bO0qr6MPVPRmmvtiy4GsWOW4+bp+n5QGg2KpHkBUSojTnzw1aDXVuc8F3IU8ZnI7wy+cGObfRPe8Y8wK5z6pULsRiMSyWU9XK2e/d7oKymAkCMQFvOIFJpznFmbfcYUKlUnFgxI8kK1y2viytF8iZSJK9gz484QSldgOlttzrQ4wmRX7zwhD3HBjHpNPw/l1NXNVRnnPBTDYotRl5685a/vfpAZ7tm+a8puJlHadnKkylw4h1HpfxSEJEo0oZNC2ESqWizG5kaCY6r+/CbHbt8FhgQW+GfCEuSNy5Z4QtNU7ev6uJD/1mLz/+Wz+f2b0u20tbFH85OM4TXR7+4Zw6ttQ4F/04lUrFxmonG6udjPii3L1vjEePTvHgkUnOqnNx45Yq1IlURccDhyewm3R86JJmLm8vO+M+u5Ks4AkncFsMFOWpuFCICzLHiC9GOCHSuECWeCnUui2MB+I8dnSKN++oQbeEtqXFcLLPQo0r93wWxvwxvnjPYTzhBJ+8si3vNz1XdZRz2/1H2TvkWxXvBVlR6PGET/u6BWMCFoMGg3b+mNRh1mHVa5mJJKmYp32ipdTG33o8BR+mPEOUZO54uAuLQcsHLm7K+1juTGbVDB337t2Lz/eKQ60sy6hUKvbu3YvXe6ob8a5du1ZrWWuWUV+MYFSgao4xf2X2lHL98ogfSVG4Yn05+gVO5ovFH02ydyglLJTZclNY6JkK89W/HmE6nOTK9WX8w7n1804zOFO5fnMVjx3z8KMn+9hS41yWGVj3VIj1FY55fx5NShj0mkUdu9hqQKVKOUTPZ+LUUWnnbz1epkIJylbQzpEL/OXgOP6YwFt31lLpNPHm7TX88vkhXhqcYXtdbhsUHh0P8j9/62dnfRFvOmv5vh3VLjMfvKSZt59Tx32HxvnLgXFuvfsQsxrCdZsruWln7bzi1VpnJpIkmpQ4q9aV18JKIS5IP9GkyGQwjkatStvnQ61SsaHKwfP907w04OPc5uWJznMx67MwGYrjsuSez8Kh0QBf+2snKhV85caNrF8DBs+zxo73H1odY8dRf4xoUlrQbyEpysQEieYi64KbSodJh8WgxR+bX1xoLbPywOEJxv3xOWPgArnJ7/eM0OuJ8Nmr1+XceaDA0li1yOzkvsqT+epXv3rK9yqVis7OzrQ9bzKZ5N/+7d+49957SSQSnHPOOXzhC19YcGb2//3f/3HXXXfR09ODLMu0t7fz0Y9+lO3bt6dtXZlElGTG/FEUwGGa+wNabDNyVm0Re4ZmkBSFqzrKl2Si9moCUYE9gz6mgnFKbAZK7bl3Qn+m18u3HurCYdLxjTdtpq18ftfifEGSFVQq0qrOa9QqPnRJM5/8/X5+8dwg772oaUmP90WSeMNJWsrmDiQURUnNXjfpFiVqOc06HEbtgg7RHcd9Fw6PBfNaXIgmRe7cO8K2WteJKSVv2FbN410efvB4L99/qyPnsnqz+KNJbrv/KMVWAzdf3pqW96TDpOMtO2p5w9ZqnuzysKd3jJvOb6O2KPf6sVeT6XACrVpF8zyfsXwhW3HBWmbMHycYS5nlpjPzZzPqqHdbeGFwhsZSC2Vpusb3eiKM+mJoVSoqcsxn4dGjk/zHoz2UH58+M99mNt9YbWPH7slZD6b5Y65IQkSQZKpPM3XDoNXgNOsYnqNdbpbZ5+meChXEhTyh1xPmdy8Ns6u1ZNkVswVyh1URFx555JHVeJo5+epXv8ojjzzCt7/9bZxOJ7fddhvvfe97+eMf/4hGM3eQ/vzzz/O6172Obdu2YTQa+elPf8q73/1u7rrrLurr61f3F1gGE8E4wbiI06xbMKtVZNGzva6IvUM+7js4we4N5cvauARiAnsGZ5gMxim2GijPMWFBURR+v2eEXzw3SFuZjf/vmnZca0AVDcUFjk2G0GlUbK52pfXYrWU2rtlYwV8OjHNJWymtC4yPejWnM3NMiDIJUabZvriAxm7UYTVqCcbmd4iuLbJg0Ws4Mhbg0gybjmWSew+ME4qLvO3sV4y2dBo1H7i4mVv+dJDfvTjMO8+rz94C50GSFb7x4DFCcZFvvGkTVmN6Ly16rZrL15fRZI6e8cJCQpSYjiSpWKDtKB/IZlywVpFlhVFfFEGSM7JhbCi2MhGM89hRD3+/vWZZY60VRSEhykSTEsGYQPdUiHBSpLXUljNVOIqi8KsXhvjdi8Nsqnbw2d3taT+nZZsrO1bP2LFnKoRBq6ZmgXN3OJm6vpfaTp8cKLUZODYRmrftobbIjEGrpnsqzMVt+RsPnCkIkswdD3XhMOp470WN2V5OgTSwKmfLqqqq1Xia1xAKhbjzzjv52te+xvnnnw/A7bffziWXXMIzzzzDhRdeOOfjvvWtb53y/Re/+EUeeeQRnnrqqbwQF0Z9MSIJkSrn6TeELrOes2pd7Bn08dcD4+zeUI55CQFrMC6wd9DHRDCO22LIOWX/ZHfnXa0lfOTSlrS0gGSb6XCCnuPmVwBDMxFqi9I7KeHt59TxTN8033+sh2///ZZFB349UyHUKmgsnltciCRERFmm0rG4TaJanfJdGPX75r2PRq2ivcLO4fH8NXWMJET+9PIoO+pdrxFzNlY5uGxdKX/aN8rFbSU5NxXjV88PcmAkwEcva6GxJL+z6bnOdDhJTJDYXO3M657UbMUFaxlvJIE/KmA36TJiNqhRq+iosPPioI99w3621c0tap8sIESTIrGkdOLrQEwgKckIokJSlBBkhQa3JWcqspKizHcf6ebJbg9XtJfx/oub8tK48XSU22eNHSf4++01GRV2uqfCNJVYF3yOSELEqFMvSsRxmvUYdGoCsSQu82tFNI1aRVOJla7J0IrWXWB1+M0LQwzORPn8tevnrU4tkF+sirhwzjnnLCkIevbZZ9PyvIcOHUIQBC644IITt1VUVNDU1MTLL788r7jwagRBIJFILNq9emBggFgsBkA8Hl/Vcs5IUmbfaJS4KDMp+Rf9uFKNxN4uD8Ojo1xYZznFBHKh5zoyFcMbFXEYNMSTWvr9mQ92k4kE/QP9p71fKCHx3y/N0O9Lck2bjSubdYyODGZ8fZnGExEZDQioVArn1FiYDIs8e8RDtNy0oBHSQsz3mt7QZuUne2f46eMHubRxcdUL+we9lFm1TIwNzfnziZCAJ5BkZjxBp29xp6BAIMnkZIQjchCTbu7fscIk8dJgjAPHerAZsh+oLvZ9Ost9XUHCCZGLa7RzPu6yWjXP9ar49v2H+ch5xTljVHVwIsbv98xwXq2ZJlN0Sb/zUlnqa7oWOeaJk5RkQlMCndPp2fTMXqfa29vTcrzFkK24AJbXLvnHP/6Rz372s6+5/cCBAxgMuTGlZNQXI5QQacyg+OiyGKgpMvNMr5cqlwm9Rk1UkIglRSKJlIAQiovERQlRUkiKMnFRAgUUFAxaDXqtGoNWjdNkxGLQYDHkxoYiEBP46l+O0DkR4p3n1vPGbVV5LeCdjt0byvn6fUfZM+hjZ0NmvBdESabPE+HqDfN/tgBC8ZSn0mJEJqdZh9WgZSYizCkuQKpy8r5DE4iSnNGx6/lGvzfCE11TCJKCXqM+8Vk0aGe/Tn0+X7n9lc/rybelS4w6NhHizr0jXNFetir+HwVWh1URF972trdl5QTt9XrRaDS4XKeq6263+zVmUQtxxx13YDabueyyyxZ1/5OrG1Y7YOscD2JP+Gh1GE+MoFwsDXUCL/b76E4aeF1bxYIKYjgh8tLADDpHjPXleiqdplX7G/cP9NNQ37DgfQanI3z3iSP4oyL/snsdF6TRgCpbKIrC4HSUpBCltkrD1RvKKXOYiCZF7np5lJAgsa5+eS7r872m9XUKB6ePcH93gOt2tJ62ZFFRFMYemWJbXdG8f6PERBDFnGTrxvpF+3yURZJMqcawmgzzlsVfaAhyz9EDhLVONtVn/++9mPfpLOG4yJMPvsi5jW4u2tw27/3eLdr47qPd9ETNXNWxcLC2Goz5Y/z6wX00l1j5xOs2ZbwqaCmv6VokFBcYEfxsLbexub0sbcdd7esUZC8ugOW1SwKYTCYeeuihU27LFWEhLkhMBuOoVWS8hL+5xMpUMM79h8axGXUkj7e6KYqCgoJeo8GgS21EHEYDZr0GvVaDTqPK2c36sC/Kl+45wkwkyWd2r+P8NRAznI6d9UW4zDoeODyRMXFhaCZKUpJpWaC1Mi5IxAWJBvfiqhmtBi1WgxZ/NDnvfVrLbNy9f4zBmShNZ3g1nSDJPNM7zV8OjtM5HkSrVmHQqkmIMuIyx5Fq1SrsRh1Oi44isx6XWY/Losdl1p34usisx2meXzBKiBJ3PNxFkcXA/7vgzL2ur0VWRVz48Ic/nNbj3XHHHfzwhz9c8D4///nP5/2Zoiz+w/Szn/2M3/3ud/z0pz/Fas3tE1TKyDGGLCu4TEsPeKwGHTsaXLw44OPe/WNcs6kC+xyGkNGkyJ6BGcYCMYrMOiodqycsLIaXBma4/YFjmHQavv6GjUvyC8hVJFmh1xNm3B+nxK5n94byE2adZr2WsxuKuP/wJGP+GJWnMURaCiqVivftauKDv97Lj57o43PXtC/4t/aGk/hjAq0LuEKH4yJWg3ZJBqIOkw6bQUcglgTmDkCaS63oNWoOjwXzzhDorn2jRJLSaXtfL2sv5eGjk/z0mYHjjt/Z8w6JCxJfv68TtUrFZ65etybajXIdbziBIMlsrMp/x/p0xwWLZbntkpA6H5aU5OYYwjF/jGBMpNiaXiPHudBp1GyrdTHqj6HVqLAZ9Vj0qXN6LgsI83FkPMiX7j2MTqNeMzHDYtBq1Fx+3NjRE0pQYku/UHY6DyZ4xcyxcpHmiyqVihKbgWFfbN77zBpKd0+Gz1hxYSoU54HDkzx4eAJ/TKDCYeSfzq/n8vayE8lDSVaOi4NS6n9JJiHIJCX51NuP/0se/z4uyATiAr5IEl80SZ83gj+aZC6twqzXpAQHs+64AJH61+8NM+qP8eUbNmDJY/+gAq8lL/+a73znO7n++usXvE9lZSX79u1DkiR8Ph9FRa+osjMzM+zYseO0z/Ozn/2M73znO/z4xz9m06ZNK153ppkMJQjEBOynMXJcCKtBxzkNRbwwMM2f949x7abKUzYw0WSqYmHMH8Np1lHpMOdMIKEoCnfvH+MnT/dTX2zh1mvWZ9wFeTVIijJdkyE84ThNJRYuXVf+mraVxhIr68tjHBwL4LbqVzT549WU2Y28dWctP3lmgOf6pjl3gY1791Sqx7F5HlfohCgREyRqFpmhmEWjVlFsMzARjM97H51GzbpyG3/eP8ZzfdPUFpmpc5upLTJTW2Sh2mXKmb7ekwnGBP68f4zzm4tpOM1cepVKxQcvaeYjv3mZ/326n49fMX+VQyZRFIUfPNHL4HSUf72uI68ndOQLkqzgCSUpsuhxW/L/vJYtVtIuGY/HueSSS5Ak6cQUqfXr16/GshdEURRG/TESooTbsjobKZtRx7ry3GhnWAmBmMBt93XiMOr48g0bKD3DzmVXdpTzhz0jPNyZGWPH7skQVoOWCsf8r2skKQGLM3Ocpej4OTCWlOZs4y23G7EZtHRPhdhN9qv8VgtZUdg/7OcvB8d5cWAGRYEd9UVcs7GCLbXO17RTatQqTHrNolqhT4ckK4TiAr5oEl9EYCaaPP51El80dXvvVBhfVCAmpP7m12+uZEuNc8XPXSC3yEtxoaio6BSxYD42bNiATqfj6aef5rrrrgNgYmKC3t5etm7duuBjf/KTn/Dv//7v/Nd//VfejKCcNXJsXYSR40KY9FrOrnfzwoCPP+8f47pNlbgsemJJiT2DPsYDcewmPdWr2ApxOkRJ5odP9PLAkUnObXTz8Stac3IjuVSiSZFjEyF80SRbapyc21Q8Z4ZYpVKxo7GIUX+UQ6MBzqpLb4nj9ZsrebzLw4+e7GNzjROzfu5TR89UGI1aNe8mOZqQUhmKBQKN+Si1GZEkP3FBmvdv+4GLm/lbr5eh6ShDMxH2DftPlP2pgHKH8bjYYD4hPlQ5zVnNuv/p5VHigsRNO2oWdf8al5k3bqvmdy8Nc1l7GZurnZld4Bw8cHiSR49OcdOOGs6ax9StQHrxR5NEkiKba9yFHuIVsNx2yYaGBr72ta+xbt06IpEIP//5z7npppu4++67T2v0nGkfJn9M4tBEFElWGF2C19JaYbleLIqi8OOXZgjGBd6z3UVkZpz+mQwsMMdpKzHw1wOj7CgWTySm0uVvc2hkhiqbhoHBgXnv0zeTIJyUGOoXmdAsLqb0xyRCM2H2x2Yos84tclXZNRwanqZ/IHe2OpnyDYomZZ4fifK3wTCeiIRVr+ayJivn1Vpwm7Wg+Bkc9Kf9eefDBbhMgAlwqwHD8X8pEqJMVFBwmVQrfj0KXkyvICsK0aRMp7j41v+5OPk6tZyWydz5xGUAm83GG9/4Rm6//XbcbjdOp5Ovf/3rtLW1cd5555243zvf+U42bdrEJz7xCQD++7//m+985zvcfvvt1NfX4/F4ADAajdhsuVkuF4oLeEJxDFr1vBu/pWDUa9nZ4DouMIxyeXs5/d4wY/4YNoOOKlfuCAuhuMBt9x3lwGiAvzurmrefU5czZncrIRAT6JoMEUmKXNBSzJYa14IVKVaDlp2Nbh44PMF4IJbWyR1ajZoPXtzMp/6wn18+N8h7Lmqa837dU2Hq3PNv1iNJEUVZWoZiFqc5NZLSE0rMO9KqymXizdtf2aRLssJYIHZcbIgyOJP6/6VBH9Jx0UGtggqH6RTBoaPSQZEl8y0H/miSew+OcVHr0iZA/N32ap7s9vCDx3v5j5u2rqqbeddkiB892cu2WhdvyfAIswKv4Akn0KhfKfctcCqZbpfcunXrKUmJrVu3cuONN/LLX/6Sz33ucws+NtM+TAdG/NiTAWqLzFltlcoWy/Viue/QOIcm4/y/Cxq4YNOZO73k9ZKNr993lBmVk53HTfXS4W+TECXGQ6O8cVs1DfMIcIqiMCXPUGnSsnnD4q8ngiQzwRgJUaah0jHnfTZPqPn9nmEqqmpzJtmUbt+gnqkwfz00zhNdHpKizLpyG+84r4Lzm4vX5JSTuTjTvZhORpIVwgmB9paVte+t9Dq1psUFgFtuuQWtVsvNN99MPB7n3HPP5fbbbz/FtGl4eJiKiooT3//6179GEARuvvnmU471+te/nttuu23V1r4URo/3W5Y70lcua9RpOafBzQsD0zxweByLXoPFoKXKZcqZzfuoL8aX7j3MVCjBzZe3cOm69JmcZRNPKEHPVAhBlrmyvZTWcvuixJzmEiuD5TY6J4IUW/To0tge0VZu4+qNFfzl4DiXtJW+xqBJURS6p0Jc0Dz/SS2aTFUdLMdwzGlKiQuBeJKaeXwXXo1GraLGZabGZeb8k24XjvuTnBAcjosPz/dPIyspoeabb9pM1SJ7QJfLnXtHSYoyb1lk1cIsBq2G9+9q4vN/Pswf9oxkfE75LMGYwG33H6XIoucTV7TmzHlgrZMUZXyRJOV2E/bCqK45Wa12yVk0Gg0bNmxgYGBguUtOCwlRYtwfR6UCu6nw3lgsw74o//23frbWOLl+c2W2l5MWZEVhOpwkLkpLqiydNXa8//B4Wo0d+z0RZGVhv4WYIBEXZVqXmAzRadQUWfT0esLz3qelzIqsQJ83wvqK/PepmSUpyvytx8tfD45zbDKEQavmktYSrt5Yccb6SxTILda8uGAwGLj11lu59dZb573Po48+uuD3uY4kK4z6YkiyjHMOA8aVoNeqObvBzeGxABaDjpoic85sKPYP+/n6/Z1oVCq+cuMGOuZRr/ONEV+UwekIarWKGzZVUr2EjLZarWJng5sxf4wj4yE2p7mX7R3n1PFsr5fvP97Dt/5uyymVFBPBOJGEtGAgEU6IWAyaZY3M1GrUlFgNdI4Hl7X2k9Fp1NS5LdS5LZzcYZ0UZXo9Yb7ylyN88d7DfPNNmzMWsM9Ekvz14DgXt5ZS7VqaBwXA1loXF7WU8H8vDXNRS0nGhRBJVvjWQ8fwRZLc/sZNhY3MKuINJ4gmJS6ucuRMxViusRrtkiejKArHjh2jrS07viezjPvjhOICRRZ9zlybcx1Bkvnmg8cwaNV87PL8F0kFSWYqmGAiGCMYFxFlBZ1aRfkiN+yZMnbsmjVzXMAgM5KQECWZKufSqxmLbQYOjQXmHTfZctz7qWsytCbEBVGS+e2Lw9x3aJxgXKTKaeKfL2zg0nVlWAuGiAVyiDOjZmaNMxmME4wJOC36jPTi6jRqttS4qHNbcuYifN+hcT7/50O4LQa+9fdb1oSwICupiRD93ghmvZY3nVW9JGFhFodJx456NzPRBFOh+Q0Ql4PFoOU9FzXR64lw74GxU37WPZkKJJrnERcESSaSECmxGZa9QSq1GxEkhYQoLevxp0OvVdNeYedz16zHG07wtfs6ESQ5I891594RRFnmzUusWjiZd1/QgEGr5j+f6FnSFJzl8LsXh9g75Od9u5oWDBYLpB9vOIHFoKF2iUaoBV7Lye2SzzzzDEeOHOFTn/rUnO2S3/rWt058/73vfY+nnnqK4eFhOjs7ueWWWzh27Bg33XRTNn6NE4z6o8QEqWDyuQR++dwgfZ4IH7m0ZVXa3zJFNCnS7w2zd9DHsckgkqKws8FFvdvMqH/+SQpzcWVHOYoCDx2ZSNv6uqdCuMw63Au8xtGkiFoN7mWYbzvNekw6DYGYMOfPiyx6iq36E7FJvvM/f+vndy8Ns77Szpdv2MAP3raN6zdXFYSFLCArqTg0nBDxRZJMheLEhczEpflI4R25Bhj1xwgnV27kmA9IssKdh/080R/hrDoXn76qLS0eE9lGlGR6psJMBONUOo1ctaFiRReMtnIbg9NhjowHKTKnV3Q6v8nN9joXv3x+kPOaik9kObqnwug0Kurm8UOIJlNmjhUrcON2mnVYDVpmwkkq0jhy89W0V9j56GWtfPPBY/z7o918/PLWtGaMp8MJ7js0zmXrylY0OtRl0fPO8+r5z8d7eeyYh0vXlaZtjSfz0uAMv31xmMvWlXLl+rXRepQvhBMigZhAS6k1Z/qG853ltEsGg0E+//nP4/F4sNlsrF+/nl/+8pdZnSTliySZjiQx6zWF98Yi2T/s548vj7K7o5xzGt3ZXs6yCMQExgMxpsMJEqJMic3A+c1uGkpS54jOsSAPd04ST4oYFxkflduNbK118lDnJG/ekZ42u56pMC2ltgWvneGEiEmnWdYoQqdJh82oYyaSmFecaCm1nZhilc88cHiCew+Oc+OWKv7fBQV/gUygKAqCpCBI8vF/r/06IUrHR3LKyEpKZJBlBYVUG+6mKseyhLK1Rv7vys5wwgkRTzCBXpMeI8dcRpIVvvKXI7w0GOH6zZX80/kNyx65mUskRImuiRDeSJK2ciu7WktXHChq1CrObSpmIpigcyLIxipnehZLajLF+3Y18YFf7+VHT/byuWtSo9i6p0I0FlvnFTIiCRFZgbJlTIqYxWnSYzVqmY5mVlwA2NVawnggxq+eH6LSYUqrr8Hv94wgK6yoamGWqzrKeaRziv99up8d9a4T86vTxeB0hG892EV9sYX3X9xUKMtfZbyhBElJpqMy/8t6c4XltEvecsst3HLLLZle2pIY9ccIx0WqnUuraHn06BSiLHPl+jNnRB+kPGPueLiLKqcp7zZokqwwHUkwGYjjiwooKFS7TGyvc1HuMJ1y3a0qMuG26hn2x060BiyG3R3lfO2+o+wZnKFkhaf5aFJk1BdjV+v8HkyyohA83tKzHPNB83EfMG84Me99WsqsPNs3jTecyNvR5IfHAvzwiV621jj5x/Pqs72cNYUkK/R5wgRiAnFBQlZAOkkwUAFqNejUarQaFXqtGpdZj1mvwWbUYjVosRm1aDVq9g/52TvsY12ZfV7T8TOFtb0bPQMY9cUIxgTK7Pl50lwKf9w7wkuDPt7Y4eAfL2zM9nLSQjgh0jURJBAX2Vbn5OwGd9ocfp1mPdvrXTx2dIqZSOLEXOh0UGY38tadtfz0mQGe7ZtmZ30RfZ4Ily2QOY8mJQwa9Yp69fVaNcVWw6qVOb55ew3j/ji/fmGICoeRi9tWXhkwFYrzwOEJrmgvoywNM9XVKhUfvKSZj/3uZX76zAAfvrRlxceEVCn+b18Y4qHOSSx6LZ+9eh2GNBqEFjg9sqLgDScoMuspScN7pcDaYdaUFpZm5ChIMv/9VB8xQWJTlZPyFYi9+YSiKHzvsR4CMYHPXbM+byo9BElmMhhnIhgnFBfRaVSsq7CxpcZJkUU/p9hrN+qodJo4Oh5akriw44Sx4wT/sGHpmyNJVhjzxxiYjrB/JIACCz5/LCmRFBSqlpkoUKlUlNuNDE5HUBRlztdia40rNeHqFy9xfnMxuzvKWV+xOIPsXGAqFOe2+45SajPw6avWrYmEWi7R740w6o9RZNFTajdgMWixGbRYDFqsRi1mnRaDTo1GrUKnUS/4+le7zDzT4+GlQT/huEhbhS1nWslXm4K4kMdIssKoP4YgK2t+/NSAN8KvXxji/CY3F9WvDSHFF03SNRkiJkhc3FrMhion6jRfONrL7Qx6oxwaC3B+U0laL0w3bK7k8WNT/NeTvbgtemKCNK/fAkA4IWA2ajCucINaYtNzcFQiKUroM7zZValUfOjSZiZDcb77SDclNsOK/T3+76URIDVOMl00FFu4YUsVf3p5lEvXla5ojaG4wB/2jHDvgXFkReHaTZX83VnVa/4ck4v4owLhhMjOBtcZM1aswOKYNXJ0mfVLOq/vG/YTSogA/OK5QT51VXYNKVeLhzonebZvmnedV7/gdSpXiCZFJgJxpkIpo2S7Scu5jUW0VzoW1TLZVGLl6ERoSYkFrUbNFevL+cOeYa5t0rNQbUcgJjDgjdA/HWHAG2FgOsLQTBRBemXEc0uplfaKhcwcRQR5Za2SruMCSyQhYp2jaq+51Mp33ryV+w9P8NjRKR4/5qG2yMzujnIuWVea034FcUHia3/tJCHKfO31G5c1ZavA/Iz5Y4wHYlS6TFy7qWLFyRONWsWFraU4zXoe7/IQHhTZUuNctWu3P5qkzxvBqMt+rFB4p+YxU6E4gZiA06zNiJFjriBKMt95pAuLQcv7L25mZnIk20taMcGYwLGJIKKs8LoNFTSWWDKipGs1as5pLGIqFKdrMkR7Gh2TtRo1H7y4mU/feYBvPngMmN/MUZIVwnGRGrd5xQJKkcWA1aDBFxEoc2Q++6TTqLnl6nY+9Yf9fPWvnXzzTZuX7ZMwEYzzcOckuzvKKbWlN2P41p21/K3Hy38+3st33rxlyRe0uCDx5/1j/HHvCNGkxCXrSnnrztq0VFcUWB7ecAKVCtrKCi0RBU5l1B8jkpSoLVqa6e8TXR5sBi1XdpRx595RbtxSueYNWkd9Mf7ryT42VTu4cWtVtpczL4qiEIgJTATieMOpdqhSm4GLWkqoK7YsqdqizG6k1GZgaCa6pKrFK9eX8fuXhnl2KMq29lTlxPBMlIHpCP3e1CSrgekIvugrJoous456t4VrN1VS7zZT77ZQU2Q+7TUokhTRqFW4VzCdwmHSYTdqmYkKc4oLkBLf37+riXedV8+T3R7uPzTBfz3Vx0+fHeDC5mJ2byinrWxhb4jVRlEU/uPRbvo8EW69dv0ZX2afbnzRJAPTEcx6DVeuL0trVebGaid2k44HD0/wXJ+XbbUuLIbMTdcSJJnB6SgTgRiyorC5ujhjz7VYCuJCHjPqixFOCLSUrO3A4M69I/R6Inxm9zocJh0z2V7QCokLEl2TIZKSzPWbKqlZxkSIpVBsM7Kt1sWTXR4qHMa0ZqDXVdjZvaGc+w5NYNSp5x2pmMpQKFSkoQTXYdJhNeiYiSZW5N+wFOwmHf96XQef/P1+vnTvEb75ps3LyiL834vDqFXwd2elr2phFqNOw/suauTLf+nkrn2j/N1Zi/NzECWZB49M8tsXh/BFBc5uKOIfzqmjLsPvywILI0gy0+EE5XYjDnNh7GeBVwhEBaYjCSy6pRk5xgWJ5/qmubitlL/fXsPDnVP85JkBvnrjhpzaWKUTQZL55kPH0GnUfDyHx04qikKfN8KYP4asyNQWWdhe56LUblxW8kivVdNQbOHpHi+iLKNVL+4YZXYjW2tdPDHg58iv9zLiiyIfH0Sk06ioLTKzrdZFfbGFBreFOrd52TFFOCFh1mswraBFxW7UYtZrCcaTwMIbcKNOw5Xry7lyfTk9U2HuPzzBk10eHjk6Rb3bzO4NFVzcWrIsc8l084e9IzzZ7eUd59axo/70I3YLLJ5oUqR7MoykKOzeUJ52nyqAOreFv9tew18PjvNc/wybq50Z8fyYCsUZmo4SjAvUuEzsaivNCUPJ7H+CCiyLSEJkKpjAoFFj1udH7+By6PeG+e2Lw1zUUsz5zdlX41aKIMkcmwwRSgpcvq4s48LCLB1VDganoxwaDXBec3FaA6x3nFvPc33T1LjM85bnRpMSkqxQsci52wth1GlwWXQMeCIrPtZSqHSa+P+uaedzdx3ia/d18sXrO5ZUHTDmj/HI0Umu3VSZsZP/zgY35za6+e2Lw1zYUkL5AlUHsqLwVLeXXz0/yHggTkelnc9e3Z7W6pYCy2c6nCQmSGyoyp/+4AKrw6g/RjAqLLmC6oX+GRKizK7WEsx6LTftqOGHT/axZ9DH9jW6gfnNC0P0TIX5zO51ORF0z8fgdJRRX4wyh4FL15XiMs/tp7AUaovMHDBqGfXFliQWv2lbFd99OESZ3cDZDUU0FFuod1uodJrS1lopyQqhuED5MsWTWbQaNW6rnq6J+U0d56K51MqHSpv5p/PreaLLw/2HJ/jhE7385Ol+Lmot4eqO8qxV9LzQP8Mvnh3kopZi3rQt/YmIMxlRkumeChNJCuxeX055GmLS+XCa9bxhaxUPdU6yd8jHunLbkivN5iMuSPR7I3jDCbRquLy9jLZyW85UsRfEhTxl1B8jFBcothnWbOApSDLfebgbq1HLey9qyvZyVoysKHRPhZmOJDinoWhVN3E6jZpzm9z8ef8YXRMh1qXxua0GLd940+YFg45IUkSvUeFYgZnjyZTZjXSOhxAleVVPph2VDj5yWQvffqiL7z/Ww0cva1n05++3Lw6h1agzHiy856JGPvCrvfzwiV7+9dr1r1mfoijsGfLx82cH6fdGaCi28K/XreesWteaPZfkI55wAqNOQ22hgqTASYiSzJg/igxLzhg/0eXBbdGfmDxyVUc5f94/xk+eGWBrrWvNmcUdHA3whz0jXLG+LKeTE8O+KMO+KMVWPa/bWJG2yV9FFj1ldhMjvuiSxIWN1U7+v4vLaKjP3ESNWFIiKcppMRQttRk5OBJAECV0SyxvN+u1XL2hgt0d5XSfVM3w0JFJmkos7O6o4KLW4lWbxjY8E+WbDx6jscTChy9dfHxR4PQoikLPVJjpcIJzGt20lGdePDLqtbxuYyUuk4eXhvyE4iLtFfZlJ/hkRWEiEGd4JkpUkGgptXJesxuHKbc8sXJD4iiwJGRZYcwXIykpuNawydrvXxqmzxvhgxc3r2jCQK7Q740wFYzTXm7jrLqiVb9olNqNbKlxMhaIEYwl03rsMrtxwZKvSELEpNemzaG7yKLHrFfji6b391gMl7SVctOOGh45OsXv9yzO/2PYF+WJLg/XbKzAZcnsZ7bYauDt59SyZ9DH073Tp/zs6HiQz/7pIF+85wixpMQnrmjlO2/ewvYsvB8LzE80KeKPJqlzm9f8iOECS2MiGCcYF3GZdUsSA0Jxgb1DPi5sKTkR2Go1at5xbj1DM1EePTqZqSVnhXBc5NsPdVHhMPLPF+TudKkxf4xBbwS7Ucs1myvT+nlXqVQ0l1hICBKR4yaeuUI4KSLKClWulWeOHSYdZr0GX0w4/Z3nQaVS0Vpm4yOXtvCzd+3kfbuakGSF7z/ewz/+5EX+8/EephcYeZkOwnGRr/zlCAatmlte1543E03yhaGZKJPBBG3lNs6qW71kikat4oLWUi5vL8MTTrBn0IcgyUs+Tjgucng0QNdkCK1WzTWbyrmqozznhAUoVC7kJVOhBP6YgN2sXbMO4j1TYf5vzwgXt5VwTqM728tZMaO+GGP+lCvtRa2lWStd2lTtZHgmyoHRAOc3Fa/KyVWSFUIJkQq7MW2ZMZdZj82gYzqSpCTNxoiL4aadtYwF4vziuUEqHEYubJl/ljfAb18YRq9V88ZVKnG8ZmMljx6d4sdP9rG1xok3nOAXzw3yfP8MTrOO9+1q4sr1ZWv2/JHveEIJkqLMpqqVTSYpsPaYDieJJESqnEvLuj3TO40oK+xqPfVcdV6Tm7YyG798fogLW0rWxIZGUVKbQl80ye1v3IQpR1tHp4Jx+r0RTHoN126uzMjkgkqXCbfNwMB0ZMWTjtJJJCGiU6vS4gGV8mHSMhNJpsUo2WLQcs3GCl63oZxjkyHuPzTBw52TPNHl4V3nNXBlR1navTskWeEbDx5lKpTgKzduSLvh85nOVCiV7S+26diVpRh8Q5UDh1nLA4cmebbPy1mLNHqUZIURXzQ1HVCS2VTjYGe9O2fPa1CoXMhLRv1RwnGB0iU4AOcTgiTz3Ue6cBh1vOfC3M04LJbpcIKB6QhWg5arOsqzGrzptan2CI1aRe/U6ngWxAQJIU3lj7MYdRqcZn3WsjEqlYqPXNpCe4WdOx7u4uh4cN77Dk5HeKrbw7UbK9PWFnI6NGoVH7y4GX8syaf+sJ8P/+ZlDo4GePs5dfz4H7ZzzcaKgrCwTGYiqRGycUHKyPFlRcETTuC06CgpTOoo8CqaS6yU2Y1LznA/0eWhymmiqeTU8niVSsW7zq9nJpLkz/vH0rnUrPHo0Sn+1uPlbTtrac3RSRjT4QQ9njA6rYrrt1RmbNSvWa+ltsjMdCSJoigZeY7lEI4LmA3aFZk5zmLSa7CbdUST6Y0HVCoV68rtfOzyVr530zaaS618//EebvnTQUZ80bQ+10+fGWDvkJ/37WrKKRFoLRCKC/R5wug0anZ3VGR1U17jsvCms6pxmfU81zeD9zTVML5Ikv0jfno9YexGLW/YVsVFLSU5LSxAQVzIO6JJkclgAo1GnROOtpngty8OMzAd5YOXNGfExXU1CcUFeqbCqFVwzaaKnJipXO4wsbnGyaAvQngVNufRhJgyc3Smd6NUZjecMIrMBnqtmv/vde0UWw185a+dTATjc97vNy8OY9RpeP0qj0BrKbNx/eZKJoJxbtxaxY//YTtv3l6zJjKT2SAuSBybCHF4LMDgTISXBmcQl1HaeDoCUYFwXKStzFYQgAq8BpNeg167tPfFdDjBodEAu1pL5qxW66h0cE5jEX/YM0JgBaXlucB4IMaPnuxjQ6WdN+SoGZ4vmhIoVcC1GyuWNCpyOTQUWzBo1UyF5r5GrTaSrBBOiJRYDWmrZiy1puKBTAkolU4TX7lhAx+5tJmB6Qgf+e3L/O6l4WWVt7+aR49Octe+Ua7ZWMFVHeVpWG2BWRLiK9PZruoow5nhttTFMGv02FxqZe+Qj8Hp1yb6kqJM92Qq3ogmRc5rdvOGs6qpdJrzooW1ELnkGaO+GMGYQIl15U7CuUjXZIg/7BnmsnWl7GzIb/fquCBxbDJEQpLY3VGeU07Vm6udVLtMHBzxZzybEUlKqNWqtPuDFFkNGHRqAmn2j1gKDpOOz1+7HklW+NI9h18j1vR7wzzd4+X6LZVZ8Q35p/Mb+PW7z+Gfzm9YE74l2UBWFMb8MfaP+Bn1R6l2mXj91iqMWg0vDc4gp/nz4w0nUKmgbRXMpgqcGTzV7UUBLlqgfesd59aTECV+++LQ6i0szYiSzLce7EKtho9f0ZaTBpXBmEDXRAhJUbhmUwVlGXSrn6XUZqTUZmDUnxviQiQhkpQUKl3pSzgUWVJCRSYTJiqViivWl/ODt57F2Q1ufvncIB//v310TYaWfcyuyRDfe6yHjVUO3n1B5gw0z0QkWaFrMkwwJnJRc8mqTWdbDEa9lqs3VnB2vYvuqTCHxgJIsoKiKEwF4xwY8TPsi1LuMPL322vYUe/GsESz0mxSEBfyCFlWGPXFEGQ540p3NkiKMt95pBuXWc+787wdQpwdORkT2dVWSm1x7pzUINVWcE6DGxmFAW9m2yOiSXHFs6znwmXWYTfq8GTYZOl0VLvM3HL1OsYDcW67r/OUbPavXxjCotdw4+bVrVqYRaVSFSoVVkAwJnDouIGSRq3imk0VXLOpkoZiK1d2lCEpCgdHA2kT6ARJxhtJUGIz4MxBk6YC+ckTXR6aS6wLmufVuMxcub6c+w5NMOaPreLq0sdvXxrm2GSID17cTIkt92KkcELk6EQqi3r1hgqqXOZVeV6NWkVjiZVQXCAppr/aaqlEkqlqxqWOUl0Ih0mHzaA9bZl5OnBZ9PzL7nV87pp2QnGRT/1hPz9+qo9YcmmtctPhBF/9Sycuc+p4uTJGcC2gKAp9njDT4Tibaxx05KB/0azR45UdZcyEE+wZmqFzIsTRiRCirHDF+jKu25y50eWZpPBOziM84ZSRo8OoW5Plsr9+YYjhmSgfvrQlJ9oHlsuJkZPhBDsbXKwvX72Rk0uhusjMpmon/dMR4mLm+seDcYEisy7tF06zXovdqCUcz74L9sZqJx+6pJn9IwF+8EQviqIw5E/yXN8MN2ypwmrM3/fzmYggyfR5whwcDeCPJtlc6+AtO2ppLrWdyIZWucxctq6MQEzg2MTyM1cnMx1JEktKbKx0oM7BrGuB/GPUF6PHE36NkeNcvHVnLTqNil88N7gKK0svh8cC/P6lYS5dV3pag91sEE2KHB0PEhdErlhfSv0qJxxqXGYcJh1DM6vjtbQQkYSEQatOqweRzajFYtCuSqvnLGc3uPnPt21j94YK/rx/jA/9Zi97Bn2LemxSlPn6fUeJCiKfu2b9qvkxnSmM+eNMBOPUui2c21Sc09fTjkoHN2ytQlFg3B+jqczCW3bWsL7SkbeCUyHizSNG/THCCZHGHMuCp4OjE0H+9PIIV64v46w6V7aXsyIGvBEmg3HWVdjYXl+U0ye1bbUuRmai9AxO096c/uPHBYmkoFCexgzFyZQ7TPR6IsiKknb35qVyWXsZY4E4//fSMJVOEy/2hLAatFy/uTKr6yqweJTjZopD01ECcYFKh4mLWospsxvnbENrKbMRTYo83uXBpNcsaZb8XEyHExh0aurW4Dm+QHZ4stuDCriwpfi093VZ9Lx+SxW/eXGYGydCedOaExVkvv1EF6U2I++9KPeqHuOCxNGJEOGkyGXrSmkuXf3X1WHWUeUy0T0Zzsrzn0woLmA2aDCmscxbrVZRYjMyGfSn7ZiLwazX8v5dTVzUUsz3HuvhC/cc5uK2Et59QeO8gsHsNJNjkyE+e/U6Ggrn+7QyE0meMFG/vL1syR412aDaZeatO2sJJ0SKrYac3jcshtx/xQsAKdV7IhBHo1LldVZ/LhKixHce7qbIYuD/5XnP2Zg/xqg/RoXTmLVxN0vBqNOwra6IhKQwE0l/OWEkISHIMhX2zJR1FVn06DTZ9V04mbedXcuFLcX89JkBDk/FecPWqjVrvLrWiCbFVEnieAhBkrh0XSmv31ZFucO0oL/Npmon2+uK6J4KMzmPqedin98XSVJTZC68ZwqkBUVReKLLw4Yqx6JLa2/cWoXTrOMnz/Tn1HSBhfj9QT/ecIJPXNm65CkamSYpyhwdDxKIClzY4qa9wp41v6ymEiuKrOCLZu96KUgykYRImS39G6hiqx5BVkhkqBJzIToqHfz7W7bylh01/K3bywd+tYfHj03N+Rm6e/8Yjx6d4qYdNZzXdHrRr8DiiSZFuqdCoIKrN5bn1bXUbNBSajfmvbAABXEhbxjzp4wc3WvQyPGXzw0x6o/x0ctaci4wWAqzIyctOTBycinUu82UWbX0esJpP3Y0KaJWgStDHiFOsw67Sct0ODfEBbVKxccua6W93IbdoOaaTRXZXlKB0yDJCsMzUfYP+5kMzZYk1rKp2rmo9jOVSsU5jW42VtlPtFEsB284SUKS2VQYQ1YgTfR6Ioz6Y4tqiZjFrNfy1p21HB4L8uLATAZXlx4e6Zxkz1iMm3bWsi7HWhAFSeboRBBfLMm5zW42VbmyGr+VO4yU2AxpH6O4FKJJCUFWqMiAkaXDrMOi1zITyU48oNOoedvZdXznzVuocJj41kNdfOGew6eIzp2eOD95up9zG928ZWdtVta5VhEkmWMTIWJJiSvayyixFUY5Z4uCuJAHzBo5irJM8TKMPTIxLi1dHBkPcve+Ua7eUM6WGme2l7NswnGRnqkwoPC6jeV5NUJTq1HT7DYQE+S0Vy9EkhImvSZjopHVoMVm1BFaxT7L06HXqvn6GzZxy66yvBbLzgR80SQHRvz0eEKYjxtvXtlejn2JZooatYqLWktpKrGwd8hHZInvR0VR8IYTOIzaVXGPL3Bm8ESXB61axXlN7iU97or2MqqcJn76zEDWRv0uhj2DPv7jsR6a3Xr+7qyabC/nFCRZ4dhECG8kwY66IrbVurKekTRoNdQVW5iJJBHl7MSFkROjqTMgLph0WA1afFmuZKxzW/i3N27iPRc2cmQ8yAd/vZe7940y4ovy070z1LjM3Hx5a9ZbOdcSs15nvmiSc5vcNJYUWk2ySUFcyAO84QT+qIDVsHQjx6GZKG/7n+e5c+9Ihla3fOKCxHce7qLEZuAfz6vP9nKWTWrkZJCEKHFlR3leqqWVNh3VThPdU+mrXlAUhVBMwGnSZaznTaVSUeEwEolnbr71ctCoVZj1hdNrrjI7+/rwaIBwQuCcRjdv2l5Drduy7A2AXqvm8vVllDuMvDQ4Q3IJpbmBmEAoJtJSZs+L/tACuY+sKDzV7WFbrWvJYrdWo+ad59Uz7IvxcOdkhla4Mo6MB/nafZ3UFZn55+3unBo7mRqBF8ITirOl2smOhqKcWV+tO9V2NZGlsZThhIhRp8aegQSMQavBadYRS6x+W8Sr0ahVXLe5ku+/dRsbqxz899/6+fBvXkYFfO6a9Zj0+VHZmi8MTkeYCsZZX2lnS41zzVV45xuFKCYPGPHHCCUESpdRtXDXvlGiSYmfPjPAw0dyK0j4xXODjAfied0OIUoyXZMhgnGRi1pKaCi2ZntJy0KjVrGhykFClJlO0yinhCiTEGXKHJkVW9wWAxoNhHJgakSB3EZRFCYCcfYN+xnxRalwmnjzjlp2NrjT0sZk1mvZ3ZGqXHpp0LforK83nERGob0iPwz0CuQ+h8eCTEeSS2qJOJlzGopor7Dzq+cHiQvZ36ydTL83zJfuOUyxRc8Xru/ApMudUFZWFHqO+6+sq7BxXnNxTnkvFVsMlNmNjAezM240nBCxGrQYM/Q3K7UZiCYl5BxJNpTajHz+2vV88so26t0W/uksN+UZjonONKajIqO+GOUOIxe2lOTU5+1MZc3/BZLJJF/+8pc5++yz2bJlC+973/uYmJhY9OPvvfde2traeO9735vBVc5PLCmljBzVqiWPs/NHkzx+bIor2svYUuPkPx7r5oX+3OihPDga4M/7x7h2YwWbqp3ZXs6ymA0ivOEEZ9W5cnKO7lKoc5updproSZP3QiQhIsoylfbMlnk7zDrsRh2eUObnWxfIX0RJ5thkiGOTqbGRuzvKuXZTRdpnSDvMenZ3lKNWq9g34jttRY0oyXjDcUqsBlzmpbVjFCgwH090eTDq1OxsKFrW41UqFf90Xj2+qMBd+0bTvLrlM+aP8fk/H8ao0/DlGzbk1GdGURT6PBHGAzGaS6zsaivNubHharWKpmIL0aRENLm6gnxSTJk5ltrmnr6TDpxmPXqtmmBMyMjxl4NKpWJXawl3vHkLrcWZ8Z86UwnEBEYCAnqdmis7yvLG62ytk1tnvQzw1a9+lQceeIBvf/vb/OpXvyISifDe974XSTq9Ej88PMztt9/O9u3bV2GlczPqjxGKC7gthiWfjO87NIEgKbxhWxWfvXodjcVW/u2BoxwdD2ZotYsjlpT490e6KbcbeWcet0MMTkeYCMZpK7exsyG3R04uBq1GzYYqB0lRxpuG6oVoMvUZK7Zl9mJqN2qxGbWEE7kTTBTILaJJkcPjQcb8MZqKLdy0s5a2CnvGMhxlDiOXrysjmpA4Mrbw+XYmkiQqSGyosuf9OaRAbiBIMk/3eDl7hRU56yrsnNfk5o97R7M6YWCW6XCCW+8+hCQrfPmGDZTacysDPDCdMtCscZu5pL0UQxpHLaaT6iIzboueoZnVNXaMJEQESabalbmEg9Osw2bUMp0lU8cCq4cgyXRPhpAUhas3lONYoldSgcyxpsWFUCjEnXfeyac//WnOP/98Ojo6uP322zl27BjPPPPMgo8VBIGPf/zj3HzzzdTUZMcoSFEUxvxRkuLSjRyTosxfD46zvc5FtcuMWa/lX69bj9ui50v3HmF4lS8qJ/OzZweYDMb52OUteaUyKkpqxFEwJjA0E2XkeBnWxTmYnVgudW4z1S5TWiZHRJIiRp0GsyGzf2OVSkWpzUAoIeaU70KB3MAXSXJoNMhMJMk5jW6u6ChblfFUDSUWLmotYSocp2+Bz5MnnECvVtNQkp8tVQVyj5eH/IQT4rJbIk7mHefUk5RkfvvicBpWtnyCMYFb/3yYUFzki9d1UFNkzup6TkaSFXo9YUZ8McrsBq5an9vToiwGLTVFFryrXO0XPl4pUZZBUchq0GIxaIkUkg1rnhFfjGBcYGu5iUpn7pwPCqxxceHQoUMIgsAFF1xw4raKigqampp4+eWXF3zsHXfcQVVVFa9//eszvcx5SUoygaiIw6xd8ub1ia4p/DGBG7dUnbjNadbzpes3oNGo+PyfD6clO71UDoz4+cvBca7bXElHjo1cUxSFpCgTigtMhxOM+WP0e8McHQ+yb9jHs33TPN83w0uDM3RNhrAatFy1xsqw0lm9EI6L2IzaVcneFB8vswzn0NSIAtlFUVJTdo6MB0lKEldvKGNnQ9Gq9mN2VNo5u8FNnzfCuP+1Pc5xQcIXSVLjMmPNo3ncBXKbJ7o82IxatqZhAlOVy8TujnIeODzBqC87ffqxpMQX7z3MRCDG565pp6Usd7xJwgmRQ2MBhmYilNkNXL2xIi/M+hpKLGg0KqZCq2fsGDlu5rjUFt+lMJtsCOeAqWOBzBFOiIwHUmJeQ1Gh1STXWNPRjNfrRaPR4HK5Trnd7Xbj9Xrnfdzf/vY37rvvPu66665lPe/AwACxWOoiHI/H6ezsXNZxAFySQN+MQH9o/vW+GkVR+P2LU1TatNjEGfoHfKf8/J/PcvLvz3q55c6X+ei5Javmah8XZb71xBQlZg0XVSr0D/Qv6zjJRGLZj4WUaBMTFJJS6p8gKSREmbioIMoKkqKgHJ/SpNWo0KlV6LUqrHoVdr0Gm1GD3aDGaRQYHegld7pRl8/J71NJVlBFwzxzyMPG8uWVLyYlhYGJGA0uPZ2dma+SCSUkov4IByLTVNpzozRupe/TAq9lsa+pJCuMBAW8ERGrXs15tWbEmVGOZsFyxqoouJUoTxzwsK7UgOOkwHoyLDDqS9JittLZGVj9xR1n9vPf3t6etTUUSA9xQeL5/mkuaStNm5D2lh01PHp0ip8/N8Bnr17d94ggyXz1r0fomQrz2avbc8ajSVEUxgNxBqcjJCWZnfVFbKsryptpL2U2A6V2I2P+GKWrNOEqHBexGnQYMvwaFVkMKKREqXwQegosDUVRUp87UeailhICE9mrxC4wN3kpLtxxxx388Ic/XPA+P//5z+f92UKl0zMzM3zmM5/hW9/6Fg7H8jLr9fX1J75eacDmCSUQxwK4LYtX5l4e8jEeGuOjl7XQ2FD2mp83AA53GV/482F+fjDCl27oyHh2WVEU/vPxXnwxidveuIm2Cvuyj9U/0E9DfcOyHhtNihwcCRBWRFCDRqvCoFVj16lxGHU4LTocRj0uS8ok0KTXoNOo10zbw3y8+n1qLg1z/6EJbMX2JbfkQKoUvSjpY2dHGe2rUKEiywoTjBGKizTkSPC5kvdpgblZzGsaFyS6J0MoxgRnVVu4rH112iAWorVN5sFDE3RPhWmuKMJq0KEoCv5hP/VWmQvPqstqf3ZBWFg7PN8/Q0KU09ISMYvTrOcN26r41fNDHB0Psm4F1++lIMkK33jgGPtHAnzsshbOaXSvyvOejqQo0+cJMxVKYDZouHpDOdVF5rwaf6fVqGl0W3iy20NSlDMuisQFiZggUe/O/OvkNOuw6DVMRxJU6wvl8msNbzjJdDhJa7mVCqeJwOI9+gusEnkpLrzzne/k+uuvX/A+lZWV7Nu3D0mS8Pl8FBW94pg8MzPDjh075nxcd3c3Ho+Hd73rXSduk+VUGnv9+vXce++9NDY2puG3yBx37x/DadYtGFxsrnby8Sta+cYDx/jGA8f47NXtGZvDPB1O8IMnenm+f4Ybt1SxfpUCk1cjyQq9U2GigsRl7aWU2Y1YDNrj4oEqrwKDTFNbdNx7YSq8LHEhkhRBxaplRNRqFaV2I+MB/6o832owGYzjDSVYX2nP6/emoiiM+mNMhRIUW/VUOEwZE+sCMYHuyRChuMi2ehc76905kUnUadRctr6MmCDx0qCPcxvcxEWZYFxkU7U9Z43fCuQfT3RNUWzVs74yvdfZG7dU8deD4/zvMwP82xs2ZvycpCgK33+sh2f7pvnnCxu4rP21iZJs4Ism6fWECcYEWkttXNRWknXxcrnUFJlxmHSM+KM0ZniM9qyZY2UGzRxncZh0WA1aAjGBatfp718gfxAlmeGZCBo1nNPozuvYaC2Tl2fEoqKiU8SC+diwYQM6nY6nn36a6667DoCJiQl6e3vZunXrnI/ZuHEj99xzzym3fec73yEYDPL5z3+e6urqlf8CGWR4JsqeQR9vO7v2tAH8hS0lBGICP3qyj/98vIcPXdKc1g+qoig8eGSSnzzdjyApvOu8em44yQNitRnxRZmOJNlW52RDlaNwUlqAWe+F+30TeMOJJQsM0aSEQavJaG/lqymxpUohowkRc54Ge7PIisKIL8pUKIHDrKPalZ/ZF1GS6fWkpqroNNA1maRnKkyNy0yVy4RZn76/03ggxoA3gigrXL6+lHXluTV9wajTcGVHGX/eN8aLgzOU2ozIisL6HPOeKZC/BGMCe4f8XL+5EnWar29GnYa3nV3H9x7r4bn+Gc7NYBWBoij879MDPNQ5yZt31HD95uzFDbNIcuqcPOKLIaNw6boy1lfaM5aUWQ1cFj3lDiMD3kjmxYXj06NWI+Gg06hxmfX0edMzVrtA7jDqj+GPCexqLSlMh8hh8jsCPw02m403vvGN3H777bjdbpxOJ1//+tdpa2vjvPPOO3G/d77znWzatIlPfOITmM1mWltbTzmO3W5HkqTX3J6L3L1/DL1GzdUbKhZ1/2s3VeKLCvzfS8O4LHrefnZdWtYxEYjzH491c2AkwIZKOx++tIVKZ+YV6/nwRZOM+GKU2PRsrysqCAuLYCXVC6G4iMWgyXhv5cm4zHpsRg3eSJLaPBcXpsNJgnGRIoueYV80L8WFaFKkayLETDRJa6mNXetKiAsS+4f9HB4LMjgTpcRqoNZtxmnSLfszKckKA9MRxvwxTHoN13aU5axztN2k58qOcu45MMbQTJQyuwGXuRAgFUgPz/ROI8lKWlsiTuby9jLu3jfKz54ZYEedK2PmqH/YM8Jd+0a5ZmMFb9tZm5HnWArRpEjvVJjpSJISm57L15cvq6IvF2kqsdI9GSYYS2LP4GYtZeaY+elRs5TYDRweDyLJSl4LQAVeIZoUGfPHKLYZaE9zZVaB9JLfEfgiuOWWW9Bqtdx8883E43HOPfdcbr/9djSaV05ww8PDVFQsbjOeywRiAo8dneKSthIcJt2iH/f2s2vxRZP87sVhXGY912xc/mshyQr3HBjjF88NolGp+MDFTVzVUZ72LMpSEKRUf6SMwmXta2u6QybRatRsrHJwn28CTyhOySIzDoIkE02KNJVYV1XEcZh0WPU6grEkkJuby8UyEYyjU6s4t8nNU8c8TIcTuPMomJ0OJ+jxhIkJEuc0utlW50KnUWPWa7lkXRnnNLo5Mhbk5WEfewZnsBp01LnNlNqMSwoEE6JE92QYbzhBpcvElevLsBkXf+7LBqV2I1esL+OBQxN0VDoKgW+BtPFE1xRVThONxZaMHF+jVvGP59Xz5b908lDn5KKTGEvhvkPj/Py5QXa1lvCeixqzngiYDMYZ8EaICiJba53sqHevqRii0mmi2Gpg2BelI0PigqIoBGMCTrNu1VrAnGY9Jp0afzSZV9fOAvMzOB0lLkrsbi4vtBLmOGteXDAYDNx6663ceuut897n0UcfXfAYt912W7qXlRHuPzROUpK5fomtByqVig9e3EwgKvCjJ3pxmnSc31y85OcfnI7w74920zUZZke9iw9c3JwT6n6fJ0IgJnBJW8miN8gFUtS6LanqBU9k0a9dNCmleiudq/taa9Qqim0GXuiPEIh5UatSt2nUKjQqFepXfa0i5dWgVoFalfLcmP3abtStakvHyQRiAv5okqZSK61lNronQwzORPIiQJIVheGZKCO+KGqVitdtqKCxxPKaDYJJr+Ws+iK21Djp9YbZO+jn8FiAbk2IWreFCofxtMFDKJ7yV/DHBLbUODmnyZ03AUdtkYV3nlefVdH1TOZ3v/sd9957L52dnYRCIR555JFFtTw+8MADfPe732VoaIja2lpuvvlmrrjiilVY8enxhhMcHgty087ajG7Id9QX0VFp59fPD7GrtSStrU1PdXv4weO9bK9z8bHLWrKelOj3RpgMxtFr1Vy7qZKG4teey/Ido05DfbGZFwZmkGUZtTr91ShxQSYhyrQ4Vi8mcJp02Iw6ZiL5JcwXmJvpcAJvOEFLiS0vKznPNLLvdFUgLQiSzL0Hx9lW66K2aOkfPI1axaeuamNduY1vPniMgyP+JT33r58f5GO/28dEIM6nrmzj1mvW54SwMBGMMxWK01RqKfQ2LwONWsXGKgeCJONZ5DzsSEJEVlIZ2tVmY7WDrTVOqlwmyhwGnCY9Zr0WtUaFJCtEkyL+WBJvOMFkKM5YIMbQTJR+b4ReT4hjEyGOjAd5YWCauJCdOdmTwTiionDW8Wx/W7mdYEwgmhCzsp7FIkgyRydC9HsjOM063rS9mqbShatXNBo1rWV23rKzljfvqKWhxEKfJ8zTPV6OTgQJx+f+naeCcQ6PBQgnRC5tK+WClpK8ERZm0Ws1GSsrL7AwsViMCy64gA996EOLfszLL7/MzTffzHXXXcfdd9/Nddddx0c/+lH279+fwZUunqe6PSiQsZaIWVQqFf90fgP+mMCPnujj4Ih/3s/pUtgz6ONbD3WxvtLOZ65el9XPRiAmcHA0cLwlzcTfb6+mcZUr8VaTOrcFs07DRDCRkeNHkikzx+pVbI016zVYDFrCyexcxwukD0lWGJyJolLBuc3unPJSKjA3a75y4UzhyS4P/qjAjVsql30Mo07Drdeu51/+eJCv/LWT296wkYbTmPwcmwjx7492MzQT5eLWEt59YeOSWjIySTQpMuiNYNCqubitrBDIL5PZ6oU+7+KqFyIJMTXeMwvl6XajjovXlZ72frKsoJDKtMuKgqKAoqS+jyREHjoySfdUiI1Vzoyv+WTigoQnlKDKYaToeC9+ndtMic1Irze86utZLOG4SNdUCH80SXuFjYtaS5dcOlzpNFHpNBFsFtg/4ufQaIARXwy3RU9tkRmXRY+sKPR7I4z6oui0am7YXEm1OzMl4AXWLv/4j/8IwMGDBxf9mJ/97GecffbZvP/97wfg/e9/P88//zw/+9nP+Pa3v52JZS6JJ7o8NJdaV8XbqLXMxu6Ocu4/PMGjx6YAKLUZaCyx0FhspaHYQmOxhRKbYVEb8iPjQb52Xyd1bjO3XrM+a0KhrCiM+mIM+6KIssyu1mI2VDnX/CjqEquBUpuRsUAsI++fSEJErWJVKwhUKhWlNgNDMxEURVmzwtCZwFggRiCW5NwmN86CR1FeUBAX1gCKonD3/jHqisxsqXGu6Fg2o44vXtfBp+/cz7/++TC3v2kz5XNkoOOCxC+fG+TP+8dwW/V8/tr17Kg//QSP1UJWUmMnY4LENZsqsOa5wV82ma1euO/Q4rwXIkkR8yqbOS6VWeVbw2sDDotBS3uljae7vcQFaVX7ayeDceKixPa6ohNrNOu1NJVaea5vGkGScy7QnQrG6fOGSYoyF7UWs6natSIfAbtJx4UtJeysL+LoRIi9Qz5eHvZj1mkI+xPokxFK7Qau6igvBBoFVo19+/bx9re//ZTbLrjgAn71q19laUWvMOKL0uuJ8P8uaFi15/zgJc289exa+r0R+jwR+r1h+rwRnu+bQTl+H6tBS2OxJSU2HBceql2mU4T+fm+YL91zmBKrgS9e15G1sY5xQaLXE8YTSuAy67h8fSXljuyZUK8marWKphIr/d4I8aSIMY2tLgDhhIjpeCXBauK2GkBREUmIWHPci6fA3MQFiVFfFKdZl7PJlQKvpbDjWgMcGA3Q743w4UvTM0qyxGbgi9dv4F/uPMC/3n2I29+0+ZRqhP0jfr73aA8TwThXbyjnH8+rT2vfZToYnkmNndxc46AhQ+ZWZxK1bgu1RSZ6T1O9IEoy4bhIjduc16VrbWV2jo6H6JkKsWGVLmiiJDMRjFNk0b9mFnhziZXOsSBD01GaSjM7MmyxyIrC4HSEUV8MnVbN9ZsrqS4ypy1DZNBp2FzjZGOVnYHpKC8P+Rkel7ioxbqsyogCBVaC1+uluPhUL6Li4mI8Hs9pHzswMEAsFgMgHo/T2dm57HXIisLwaIyg6ZX3/1+PBVEB9cYY/QP9yz72cnABZ7nhLLcB2gwkRJmxkMhoUGA0kGQkGOWv4wEEOXV/jRoqbDqq7ToqbDoe7g2hU6v45212fFOj+JaxhmQisaLfO5SQGPQniQkyTUUGOqwmfGMD+MaWfci8I5KUEUIhnjs8Q53LsOLXdBZZUeiZjGMzqOnpSqZhpYsnEJeI+MMciE1TYcsNITpdr+uZwoAviScscFG9lf6erjnvs9JzaoHXcvJr2t7evuTH59aOsMCyuHvfKA6TjotbT18Ovlhqi8x8/tr1fO7uQ3zxnsN89caNSIrCT57u58Ejk1Q6jHzt9RvZWJV7Pgb+42Mn3VY9OxvchXK4NKBRq+iodDA8s3D1QjQpIcgKFato3JQJLAYt6ypWt3rBE04QSYrsbCh9TXWCy6Kn1m3myFgwJ8SFhCjRM5XK8pU7jFzZUZaxmdNqtZrGEiuNJVaa9H42tpcXJiwUeA133HEHP/zhDxe8z89//nPOPvvstD3nYsut6+vrT3zd2dm5rGBtFllW8Gg8uC2GE2s48NQeNlY52LKuednHTSfrXvW9JCuM+WP0eSP0eVIVDkc8YZ4bjmI3avnaGzdRswKTtv6Bfhrql1e1ISsKB4b9OFQCb2ovo7nMltfC+EqImqboHA/RUF+8otf0lGMmRUaSPrbVO2lvWrpR+EoQJZkJxokJIg2VzlV97vlI1+uaTaJJkaHpKEUWfUa9tXzRJDohwI4aE5duqpz3ur/Sc2qB17LS17QgLuQ5I74oLw74uGlHDfo0l6G3V9j5l6va+OpfO/nXPx9iMpjAH0vyxm3V3LSzJicN1E6MnVRkLm8vZDfTSa3bQk2RecHqhWhSQpIVKtZAOWlbmZ3O8eCqVC8oisJEII5Zp6Gl1Db3esptdE+FGfNnpi92sQRiAt1TIYIxgU3VTs5tWr3RbAatpiAsFJiTd77znVx//fUL3qeycvmeRMXFxXi93lNum56efk01w2rTMxVmLBDnDdtOP+1iln5vBEGSaS2b+1yTbjRqFTVFZmqKzCcMJxVFYSaSxKTXZLXy0RNK4I8JnFXnpLXCnrV15AINxVaOjIeYDqfP2DGcEBFkmcosGDxrNWrcVj1dE5kxqjwTmQjGGfRGCCVEhn1R6ostNGfA7FRWFAa9ERQULmgpLlz384yCuJDn/Hn/GDqNiqs3pn/eNMDOBjcfuqSZf3+0h4ZiC7deu57mHMiczke/N4I/JrCrtYRSe/5vcHMJjVrFxmoHwwei81YvRJIieo0qZ0w9V4LFoKW9ws7fujJfvTATSRKMiWyudWDSz/08ZTYj1U4TwzPRrIkL44EYA94Igixz6boyOirtZ2yWr0BuUVRURFFR5nx/tmzZwjPPPMO73/3uE7c988wzbN26NWPPuRie7PagVas4f5FZYUlWmAzGmIkkKbbqKbJkZ6qTSqXK+ohASVYY9ccwaNVsrnFldS25QLnDSKnNwIg/SrpklkhCQqNW4bZl529dYjNwYCSAIErocjAhli+cPJrVoFXz+i1VeMJxnumbJpaU6Kh0pFUAGPfH8cUEdtS7snaOKrB8CuJCHhOMCTxydIqLW0txZdDY7Ir15XRUOii1GXJ64sJkMM5kME5TiYUNOdiusRaodpkXrF6IJERMeu2aqRhpPV690OsJ05HBUaaToQQqFQu2GanVKtrKbSkBLZpcVTNDSU5NaRgPxDDpNVzbUUGlszBrukB+4vF48Hq9DAwMANDb20soFKKiogKn0wmkKiE2bdrEJz7xCQDe8Y538Pa3v50f/ehHXH755Tz88MM8//zz/PrXv87Sb5H6XD7Z7eWsOhdW4+LCuUBMICbIuCx6+r2RMzpwnwrFCcQEzmksypqRZC6h06hpLLbwVI8Xs1ZOyzEjCRGzXoMpSzGB06THotfw8rCfMrsRq0GL1ajNycrbXCUQE+j1hPHHkjS6LVzSXobVoKW+xILDrOPhI1O8ODDNttqitFRQJ0SJEV+qZWpLQfTLS3J3p1jgtDxweIKkKHPDCsZPLpZKpymnhYVoUmTAG0GvVbOrrTSn15rPzFYviKKMJxQ/5WeSrBBKiBRbDWumhM1q0LKu3M5UKE5cyMy87HBCZCacoL7YfNqKj2qXmTK7gf7pSEbWMhdxQeLIWIBhX4RKl4m/215TEBYK5DW//e1vufHGG/nkJz8JwHve8x5uvPFGHn300RP3GR4ePsWscdu2bXz729/mT3/6EzfccAN33XUXd9xxB5s3b1719c9yeCzATCR5otVgMcxEkqhUsK3WRSghEooLGVxh7iLJqbGTZp2m4EJ/EjVFZuxGHZ6wuOJjyYpCMC5QbNFnLSYrsRnYVO1Aq1HT4wmzb9jP0z1enu3zcnQ8yJg/RjAmIMnK6Q92hiErCsO+KIfHAoQTAhe1FHP1xlOnr7WW2blxaxUatZpn+7yE03A+GZqOEkmKnN/knreSs0BuU5Bq8xRBkrn3wDhbapzUneFz3mVFodcTISqIXLupElth5FBGma96IZaUEESZcsfayoS1lds5OpG56oWJQJykLLOtznnavkW9Vk1buY3HjnpWxWhSURR6PWG8kSTb6lzsrHen3dulQIHV5sMf/jAf/vCHF7zPyULDLLt372b37t2ZWtaSebLLg1GnXvQYaFlR8EWTFJl1dFTaGfBG6PGE2XoGZgfHAzGCCYFdLSWFDcxJFFn0lDuMvDS2cjE9mpBIijIVWfQI0qhVbKsrYltdEfGkhDecYCIYY8QfZyIQZ9QfQwG0ahV2kxaX2YDFoMFq0GLSac5YQ/CljGatdJp4w7Yq7js0zvMDM2ypdi675SkQE5gMxqkpMtNQkrst2AUWpiAu5Cl/6/EyE03ykctasr2UrDM8E2U6nGBztbMwdnIVmM97IZIUEWWF8jyfFPFqrAYtbWV2nun10ixYMaRxQ588XgFSbjdSaltcANZQbOWANTV+tj3DBmTjgTjeUJL1FTbOaywu+CsUKJAjCJLM073TnNOweEPVUFwkkhBpr3Bh0qc8ZR49OkU0IWI+g9oCBElmzB/HbtSx7gw3cXw1KpWKphILzxxSeGFgmlKrAYdJj82oXXL1Qfh4TJBNA+KTMeo1VBeZqS4ys52UeB6MiUyF4oz6YowFYgxMR5BkBRVg0KkpMhuwmbTYjbo14SW1GKbDCfq8EcIJgY5KB+c1FZ9WgHOa9bx+SxUPdU6xZ8hHe4V9yRNgZsdbyyhc1FpSqEDOY86cq8kaQlEU7to3So3LxLZaZ7aXk1UCMeHE2MmzGwtjJ1eL2eqFHk/4hLgQTYpo1KqM+n9ki7ZyG0cngvSkuXphMhgnKkhcUlu66FYSi0FLU4mVFwdmaC2zolFn5gIcTYoMzUSwGDWc11wQFgoUyCX2DvkIJ8Qlt0TIikLz8Yk0jSVWDo4G6PGE2VTtzNBKc4/xQJxQXOCKjsJEqbloLLZyVqWJqEFLnzeCrETQqKDIYqDIqsdu1GEzalGfJt6KJkR0GhUuS27GBCqVCodZh8Oso+X45BRBlJiOJJkMxhnxxRgPxJkIxlAUaCmzUlu0dhNYkqwwNBNh1BdDpVZxxfoy2soWb9ps1Gt53cYKbEYtLw/5iSZEWspsp32fzDIZjDMTSbK51oE7R98zBRZHQVzIQw6NBenzRPjQJc1n9GZakGR6p0JIisxl6wpBwmpyonph5pXqhUhCyqpxUyaxGXW0ldt5No3VCynX9jhOk45a99IU/uZSK50TQYZmojQUp790UFYU+jwRYoLEDevLszoqrkCBAq/lyS4PNqOWLTXORd1fOd4S4TDrcJlTGViTXsO6chtPdWd+Ik6ukBRlxv0xiqy6EyJLgVNRq1W0FBtpb68lIUiMB2IMzkQZ9EY5NhECQK9R47bocVn0OEw6zPrXthCEEyJmvTavYgKdVkO5w0S5w3Rigkg4LvB8/wyHR4OU2Y1r0gwymhTpmQozHUlQ4TBxWXvpssxeNWoVu1pLcJp1PNnlIZqU2FjlOG0VQlKUGZ6JYdZr2F5XdEbvbdYChYgxD7l73yh2o5aL2xafsViL9Hsj+OMpk5myeXrBCmSOapeZumILPZ4wbquBUCK7xk2ZZl25jaPjQXq9YdZXrLx6YTqcIJQQOb/ZveRgxW01UOsyc2wylBFxYdwfxxtOsKnKuWTho0CBApklLkg81z/DZesWb14cSUiE4yIdVfZTHtNcZuPweIC+NJ3Xcp1Rf4xIUuSi1vI1uUlMNwadhvpiK/XFVmiFSEJg1BdjcDrK4HSU8WDK2Nmo01Bi1eMy67GbdOg0akJxgSqnOe8Nnq1GHec0upkMxjk8FmRb7dryKJkIxhn0RoiJEmfVudheX7Siz4ZKpWJLjQu7UceDhyd5YWCGbbWuBcXLYV+UcELgivVlhWTGGqDwF8wzxvwxXuif4e+315zRF8ap42MnG9yWgtNzltCoVWyotDPkjTA8EyUpKJTnSG9lJrAd7899tneapmJpRdULiqIwcXxe9Lry5fX8tlXY6faEmQzGKLOn73WPJFLtEHajttBqVKBADrJ3yE9SlJfUEuGLJhFkmXXlp2brrQYtraV2nu2bpqVURrdGxWFIiTITgRilNkPBLG6ZWAw6Wst1tJbbURSFQFRgxB+lfzrCyEyMYV8MFamqmKSkUOFcGx5MFoOWbXUuHjo8yXQ4sWzDwpWiKErarsmCJNPvjTB5PBa5blMldW5z2o7fWGLl9du0KaPH/mm21biwzeFbEYoLTATiVDpNJ9pTCuQ3BXEhz7hn/xgatYprNlZkeylZIyHKTE2nxk5e3FYwfckmVS4ztcUWuiZCKECFfW1Ning1s9ULfd4w7SvI8gViAv6oQHulbdnz1cvtRqqcJgZnomkTF1LtEGESkszVG8oLLuoFCuQgz/dNU2zVL8nQdTqSwGrQzlnq3HrcU6bfG6a1bO0aHI76Y0QFicvWl65pEWW1UKlUOC16nBY9G6qcyLKCN5JgeDolNsxEklS71k7CoaXURs9UmM7xAOc1lyzaSyBdRBIinRNBEoKEQavBoNNg0KrRa9ToNGp0GlXqf+0rX8+3xkBMoNcTxhdN0lhs4ZJ1pRmZtFZmN/LGrdXcf2ic5wem2VztPGXKmKIoDExHEWWZC1uKC5/LNUJBXMgjwnGRhzonuai1JGcNcjKNrCgMBQR0NpHXbajAbjozX4dc4eTqhYggUZQlNX+1sBl1rCtPZfmaSiT0y6wemgjGUVDYvAITNY1aRVupjQFvhFBcSEtgMOqP4Y0k2VLjoLqo0A5RoECu4YsmOTQa5IYtlYve3ESTIqG4SHOpZc5Rsg6TjqYSK3uHfDSVZM4kNptEkyKTwTgVDiN1a9iUL5uo1SpKbUZKbUbOqi9CkhXyvCPiFDRqFTvri5gIxOnzhFfVs0OSU2OhwwmRereFeFIikhQJxJKIsoIkKahUICugIvW3UKtU6DUqDDoNRp0avUaDTqNCVlKjWEVZZldrMRurnBlN0tlMOm7YWs0jnZPsG/bTUmqj/vhkt6lQgplwkvVVdsrsa6PKpUBBXMgrHjgyQUKUuXFLZbaXkjVGfDH8MZHL1zlpzECveYGlU328emE8EDsjeuXaKlJZvl7P8qoXokmR6XCCapeJohWKhDVuM+V2I33eMJurV9YHGo6LjMxEKTLr2NlQaIcoUCAXue/gBJKiLKklwh8VSIivbYk4mbZyG11TIQamozStwZaBEV+MhChxXmN5odpxlch3r4W5KLUb2VBl57m+Gaqc5lWr7huaiTIdSbC9rohzm165PsuygigrJEWJqCARTYhEkxLhhEgkIRGMC0QTIqG4QFJMIkoykgLFVj2XtVdQ6VydJIJeq+aqjnLsJi0v9vuIJEVaSm0Mz0Qx6NSFmGONsfZ3AmsEUZK5Z/8Ym6odGTFwywdSYyejOI0azm50F0bj5QhqtYpdrcX4osIZUdJmX2H1wmQwTlyU2V7nWvHF1KjT0Fxm5akub6pUcpk+EJKs0OdNtUNc215xRrjGFyiQjzzZ7aHCYaShePHZ95lIEotBQ+kC7VNuq4GGYiuHRgM0uM2o11D1Qjgh4gnFqXaaqHQVKrIKrIzN1S4GvVEOjwfYXleU8efzRZKM+aOU2Y2cVX9q3KBWq9CrVei1aqxGHSxQTCHJCoIkkxRl9Fr1ql/n1WoV5zeX4DDpeezYFDORJIIkc+m6UqzLbA8tkJusnavHGufp3mmmI0lu3FKV7aVkBUGS6fWEEWWFs6vNhc1PjmEx6Kg+g4K2tgobLrOOPk9kSY8TJJmpYIJSm4HyNE04aSyx4rLoGJhe2lpOZsQXZSaS5KxaFxWFySsFCuQsH7qkeUljqBOihD+apMJhPO11s73ChlGnZsQXT8dSc4YRX5SkpHBOk3tNZtMLrC4mvYaz6lxEE6lWm0ySFFOxr6zA5e1lKzJy16hVGHUa7CZdVmPoDVUOrt9ciVajosJhom2ZptYFcpeCuJAHKIrCXftGqXKaOKtubY3AWSz93gi+aJLzmopwW9JvOlOgwFKwG3W0ldsYD8YRJHnRj/OEEkSSIltr0tfjaDfqaCy2MhmKI8uLX8ssobjAqD+G26pnWxqqKQoUKJA5NlQ5qFqCSZ7veEtE2wItEbOUWA3UFlkY8i1fqMw1gjEBbyhBndtMeRqn6hQ4s2kssdJUauPYZAhZUTLyHMpxg+VgXGBXW0nWJlRkgjq3hXecU8e1myvm9IEpkN8U/qJ5QPdUmJ6p8JIMnNYSU6HU2Mn6YgsbCmMnC+QI6yrsuMw6ej3hRd1fVpT/n737jpO7rhb///pM72Vne8smm+xuCumEEoo0QRAFsWFD+F4U2/XyU0FR9F6l6sVyQfFa8KIidqQIYgEUCAQSEtI2m7abbK/Te/n8/tgkJKTtZmd2yp7n4xF2d+rZN7Mz78/5nPd5M+CPYjXosr4N2twqGxa9dtJnHNMZld1DIZLpDG9prZCKICFKjC+cwKjTUDeBtdWKorCw1oFWo9Dvj05DdLnX44uSVlXOkKWUIos0GoVTm9xY9Bp2DAZz8hz9/hhDwTgtVfaT3rK6kJkMuhnRp2smkuRCEXh6ywB2o47zWivzHcq0iyXTdI6E0Ws1nNdaMSPW9IvicLB6wT+x6oWxcIJALMWiekfWD+IrbEbqyiz0+CKTul/3WISxaIJTm2Q5hBClJpnO4I0kqHSYsEyw8Vy1w0RjmYW9oxNLmhYyXyTBaChOc6WNCnvpnPUVhcFjM7K4wUWvL0o4nsrqY4fiKfaOhrEYtZzTUiHLeURRKfkjtUQiwTe+8Q1OO+00li5dyg033MDAwMAJ7xcKhbjttts466yzWLRoERdddBFPPvnkNER8uB5vhNf2+bhkUfWMO6uYUVV2DYWIJFKc1zbeBEaIQtJa7cBlnlj1woA/hlYDC2onv8PEiSiKQluVHRWVkVB8QvfxR8eXQ1TZjSxtlOUQQpQaXyRJNJmmtco24b9vjUZhQY2DdBqGc7yePJdUVaXbG0VVYVVTmby/iZxYWOukzmVma78/a495YNvJRDrDhW2VWKXZoSgyJZ9cuP3223n66af59re/zUMPPUQ4HObjH/846XT6mPdJJpNcd911dHV18d3vfpe//OUv3HnnndTX109j5ON+/Wo3Go3CZafUTPtz51uvN8pIKM4pdbLtpChMTvN49cLACXovBKJJvJEEcyptOEy56RlS6zJT6zKzdwKNHdOZ8bWcaTXDea2VU2oSJYQoTN5IAq1GoXESO0sA1Lkt1JVZ2DNWvL0XxsIJvOEELdW2KW/5K8SxmPRaVjaVEU9m6PNlZynRvrHw/m0n3dSXzZxG2aJ0lHRyIRgM8oc//IGbbrqJ1atXs3DhQr75zW/S0dHBmjVrjnm/P/7xj4yOjvKDH/yAlStXUl9fz8qVK1m8ePE0Rj9eFvX4632sml120o1c4qk0m3t9jE7wbGahCESTdHsjuC0GzmiWtZKicLXVOHCa9OwZOXb1wkAgRiqtsrwhdw1ZdVoNLZV2gvEUoXjyuLfdNxbGF0lwalMZVbIcQoiSk86ojIXjeKxG7JM886ndX72QSGbwRopr7gDjVQu9vigaReVUqVoQOdbksdBaZWfHUIDUJBo8H81oKE6vN0qt08wyabAsilRJJxe2bNlCMpnkrLPOOnhZTU0Nzc3NbNiw4Zj3+/vf/87y5cu57bbbWL16NZdeein33nsvyeTxJ+y5sLTBxTuWnHzVQp8vxmAgzsZuH6EsrwnLlVQ6w67hEKlMhgvmV8645SCiuDjNelqq7PT5jl69EEumGQnFqXGbKM9xt+emciuVdiOdI8c+4+iNJOjzRal2mlmaw2SHECJ/AtEk0WSGluqJL4k4VKPHQq3LNOntdgvBSCiBN5xkQa0Tl0WqFkRuKYrCiiY3dpOejik0d4ynxnuMKRqFC+ZLRaEoXiW9kGdkZAStVovbffgE2uPxMDIycsz7dXd38/LLL/P2t7+d//3f/6W3t5evf/3rRCIRbr755hM+b1dXF9HoeHlULBajvb39pH+HT6+wsmt0hM6Ed9L3TaRVtg/H0GsUTDqFv6wbYWmNueAbw+zzJRgIJllWa8bX34Wv//Drpzqm4kgyplOjiaeJBUK8uGmMWe7xBEIiHqezq5P+YJJuX4I5c2x0dOS+SZo+EmNrfxRjbPSIBqjpjMqOkRixlMriuXZ27yyupm3yOs2NA+M6f/78fIcismQskgA46SWFeq2G+TVO/rZtgGAsiT1Hy7myLaOq9Hgj6HUKy2fo1t1i+rksBpbWu/jXjmEa3Rbs5sn9vaiqyu7hMIFYkosWVFFmlQakongVZXLhO9/5Dj/84Q+Pe5uf//znx7xOPcGetKqq4vF4uO2229BqtSxatAifz8edd97JTTfddMKzAE1NTQe/n+qEbTgYJ2H14zmJN5rOkTC2ZIjLF9dQZjXwp419+IAVBdy8bTgYR5MMsLLWxGVLao+6O4RMgrNPxnTq0vZRXu0ao76hHL1WQ2dXJ42NTQzv9TLPCeeumDUt+znXzkoSfr0XxaBjdtXhe9vvGgphSoR5a0s5yxrLch5LtsnrNDdkXEtLRlUZCycos+hxTPIg51BN5RaqHSZ2DYdYViRVTkPBOP5okpWz3EWTEBGlYUGtkz0jYbb0+zljTvmk7tvvjzEcjDO/xk5rCW47KWaWokwuXHPNNbzjHe847m1qa2vZuHEj6XQar9dLWdkbE+mxsTFOPfXUY963oqICnU6HVvtGSVJzczPRaPSIxypU8VSaAf94J/hGjxW9VsNbWit4avMAO4dCtLzpoKMQHNh2UqdVeEtbpWw7KYrKghoHOweDdI6EaKkanxwMB+OEEkneMq9yWhILAE6LnlkeK5t7fTRXWNFoxp/XG04w4I9S5zazqM41LbEIIaZfMJYiEk8zv8k1pUpFo07L/BoHz2wfIhJPYSnwrvXpjEqfN4JRr2Fxgyvf4YgZxqDTcGpTGX/e3Ef3WJiGsok1Ug3FxredtBm1nDVPtp0Uxa8oj97Kyspobm4+7j+z2cyiRYvQ6/W8+OKLB+87MDDA7t27WbZs2TEff/ny5ezbt49M5o31052dnZjN5iOWWBSqPl+MSDLNqjllBw/SZ5fbOL25jB5vhEF/YW0xlVHHt94JJ1Kc11Ih6yRF0XFa9MyrstO7v/eCqqoM+KOYdVrmVU/vbiet1XZMOi19+//Ok+nMeMNJBc6XxJ0QJc0bTpBWM8ytnPpJhDkVNirsRnZNYLvdfBsMxPDHkixrdMn2fSIv6t1m2qod7BoKkUwde1e6A9IZlV1DwYPbTloM8roVxa+kZ5h2u52rrrqKb37zm6xZs4Zt27bxhS98gdbWVs4888yDt7vmmmu45557Dv589dVX4/P5uP3229mzZw/PP/889957L1dffXXBLic41GFVC2/KnC6td7Ow1smWfj+h2PQ3qDyWPl+U4WCchbUOmrMwIRIiH+bX2HGadXSOhAjGM/ijKebXOqZ9wlBpN1LnNtPriwDQNRLGH0ty5hyPrOUUooSpqspYJIHDrMNlmfqyALNBS1u1nbFwnFjyxAdL+ZJKj28FaDHoWFTrync4YoZSlPFeH26rgfaBEzd37BoN440kOLXJTZ1sOylKREknFwBuueUW3vrWt3LjjTdy9dVXY7Va+eEPf3jYkofu7m6Gh4cP/lxTU8MDDzzA1q1bueKKK/ja177GVVddxY033piPX2HS+nzRI6oWDtBoFM6aV069y8xr3b4pb5uTDcFYku6xCC6LnjOby2XbSVG0XBYDLVUO+nwxhsJJUFROqXVOexyKotBWbSeZUtk9FGIwEKOxzMKCuumPRQgxfcKJNKFYitnltqxVKM2tslNmMxx3u9186w/ECMSSrGwqw2yQLvsifxwmPcsa3YyG4/j2N1Y9mtFQnH5flFq3mWWNsmWqKB0lX39jNBq59dZbufXWW495m2eeeeaIy5YuXcqvf/3rXIaWE7FkmgF/7KhVCweY9FoumF/Joxv7eG2fN6/7QKfSGXYNhUikM7x9fo1MCkTRm19jZ8dggO2DaVY3WXBm4ezhyahzW6h2mtgzGsJi0HFeiyyHEKLUeSMJEukMrVnsq2Qz6mipdPDSnlHmVWYK7n0kmVYZ8kVxmvW0VUvlo8i/1mo7e4ZDbO0PcOYczxFz7FgyzZ6RMBoNXNhWNW09mYSYDvJqLjH9/mNXLRzKbTVyXlslyVSGHYP5OxvRNRrBF0mwanYZtS4pCRPFz2UxMK/SjlZRWJ7HnVn0Wg1tNQ6MOg2rm8txWaWPiRClbiwUx2bSUWbL7t97S5Udl1lPVwFWL4xEUoTjKVbNLsOklxMUIv/02vHmjhplfJ57KFVV2TMSJhhLck5LhXw2i5IjyYUSMpGqhUPN8lg5vbmcXl+EgTw0eBwNxQ92r19aJNtcCTERK5vKuKDZRpXTnNc42qrtfGDVLBbUytZWQpS6aCJNIJaiwW3GqMvuQbbTomdupY0+f4x0Jv/LKQ+Ip9IMh1N4rIasNLAUIltqXGYW1DrpGgkTP6S5Y68vynBg/7aTVfLZLEqPJBdKyESrFg61uN7J4noXW/t8BKexweOhJWEXSEmYKDEGnYZquz7vW0opioLbash7HEKI3PNGEiRSmZwtDWittmMz6dj7pjOx+ZJKZ+gcDhNLZTh9jkfmEaLgLGtwUW43sLUvAEA4kaF7LILDPL7tpPQYE6VI3olLxGSrFg7QaBTOnOuh0WPltX0+EhPYOmeqDpSEheJJ3tJSKSVhQgghxBR5IwnMBi1VjtxUTHlsRpo8Vnp80cO26s6HaCLN1v4Aff4oTS4Ds8onPu8RYrpYjTqWNboJRJMMBWJ0+8d7oly4oEq2nRQlS5ILJeJkqhYOMOq0XNBWid2kY0O3D1VVcxTluD5fjOFAnLZqO/Oy2HRKCCGEmIkSqQy+SIIapzGnjZEX1Dgw6TX0eKd/KeUB3kiCzb1+xsJxTp9TxukN1oJrMinEAS1VdmZXWNnU6yMQS0uPMVHy5N24BBxatTBrElULh3JaDJzXWkEqnaFjAnvznqxQLMW+sTB2k5SECSGEENngjSSIpzK0Vud2DXfF/urIfd5wTp/nWPp8Udr7AyTSaS5ZUM2q2R5Z9iUKmlajsKqpDLtJT7VNLz3GRMmT5EIJOLRqQTeF7H2jx8oZzeX0BaL0+bK/pjKdUdk9PL7t5AVtlVISJoQQYtr95je/4cMf/jArV66ktbWVnp6eE97nj3/8I62trUf8i8fj0xDxiXkjCfRaDfXu3J4RVRSFhbUOtBqFfn80p891qHRGZddQiN3DIUx6Le9aVkdLjSNvu/EIMRmVDhPvWVHP6llW6Q0iSp4c3RW5bFQtHOqUOifeSIIN+7zYjHocZn0WooRkOsPe0QhjkTinzS6jrkxKwoQQQky/aDTKWWedxQUXXMCdd9454fuZzWb+9re/HXaZ0WjMdniTlkpn8IYTVNlNWHK4JOKAaoeJBreFHYNBDFoNZVZDTg/y46k0OwdDDIdiNLotXLiwGptRpq+iuDgtBkx6SSyI0ifvzkXuQNXCBQsqp1S1cIBGo3BGs2c8wdDt5Yw5HgwnsaVVRlUJxlIEokl80ST+/V2s68vMLGssk7MNQggh8uKjH/0oAJs3b57U/RRFoaKiIgcRTY0vmiSaTNNSbZuWz1aNRuH0OWX4owle6/biMhuYW2nDbcl+c+ZgLMnOwSC+aJKlDS5Ob/ZkfZtNIYQQ2SPJhSJ2sGrBkZ2qhQOMOi3nt1Xyp419bOz2sbKpDM0EJiyRRIpANIUvmsAfGZ/spFUVq1FHvdvC3CobszwWKQkTQghRdGKxGOeddx7pdJr58+fz2c9+lgULFpzwfl1dXUSj0YOP0d7eftIxZFSV7t4oAfMbB9j7fAnGQkliIwnag/0n/diTtdim4koneL3fy659fTjNWhqdeqxZqp4Yi6bo8SdJpFRW1JnxpFLs2Tl6xO2mOqbiSDKmuSHjmn0yptl36JjOnz9/0veX5EIRO1C1cOGCqqxULRzKaTZwfmsFf97cT8dAgPk1ziNuk0xnCEST+PdXJ4TjKRKpDEadhjKbgcUNTprKrbjMBkkoCCGEKFqzZ8/mjjvuoK2tjXA4zM9//nOuvvpqHn30UZqamo5730Ovb29vP6nJ2gGZjMqwdhiPdXw5RjqjMqSO0VahY8XihmlvkrwAuCidoWMwyEt7RhmIpqjUGZlTYTvppQuqqrJvLEI0GaG6UsPFC6to8Bz7BMpUx1QcScY0N2Rcs0/GNPumOqaSXChSB6oWqh1GGnPUv6ChzMrq5nKe7RjCYdJT4zIfttQhEB3vTq3RKDhMetpq7DSXW6lwmDDrtbL0QQghxLT4zne+ww9/+MPj3ubnP/85p5122kk9/rJly1i2bNlhP19xxRX88pe/5Ctf+cpJPWY2BKJJIok0Sxucedt9SavVsKDWSWuVna39AdbuGeXlPaPUOE3MLrdOqnlzMp1h91CIwUCccrueSxbW4LJmf7mFEEKI3JDkQpHq8+WuauFQi+qceMNJ1u8bY99YhHgqM77UwaClzmWmudJGrcuM3aSX7aCEEELkxTXXXMM73vGO496mtrY2a8+n1WpZtGgRXV1dWXvMk+GNJACYU2HLaxwwnmRYXO+ircrOpl4/6/Z6WbN7lHq3mSaPFZP++MslIokUOwdDjITjtFXbObelEvM0NKgUQgiRPZJcKEKxZJrBQIxqZ+6qFg5QFIXTmz0kMhnGQnGaPFaaKqw4zXppqiSEEKIglJWVUVZWNm3Pp6oqHR0dtLa2TttzHi2GsXACt0WP01w4Z/cNei0rm8pYVOdkw14vr3X76PFGaSyzMMtjOercwRtOsHMoRDiR4ow5HlbMcuf0xIkQQojckORCETpQtXBRU26rFg4w6DRcOL8q588jhBBC5Nrw8DAjIyMHqw52795NMBikpqYGl8sFjFdCLF68mM997nMA3HfffSxZsoSmpiZCoRA///nP6ejo4D//8z/z80sAwViKcDzNslmugqwcNOm1nDG3nMUNTl7b62Njt49ub4TZHisNZRb0++cvfb4oXaNhMqrK2xZVMbfSLssqhRCiSElyocgcWrXQkOOqBSGEEKLU/PrXv+a+++47+PPHPvYxAO68807e9a53AdDd3U1NTc3B2wQCAb761a8yPDyM3W5nwYIF/PKXv2Tx4sXTG/whvJEEKTXDvMr8L4k4HqtRz9ktFSxtdLGuy8vmHh9do2GaK2wk0yr9/igWg5a3LaqmymnOd7hCCCGmQJILRWa6qxaEEEKIUvKZz3yGz3zmM8e9zTPPPHPYz7fccgu33HJLLsOatLFwAodJh7tIGh7aTXrOa6tkWaOLVzrH2NYXIK2qNFfauHB+1UnvLiGEEKJwyDt5EZGqBSGEEEKE4ylC8RSt1faDywuKhcti4K0Lq1k+y81IMM7sCqv0cBJCiBIhyYUicqBq4a1N1VK1IIQQQsxQY5EEiXSGtmp7vkM5aeU2I+U2Y77DEEIIkUVyhFokDq1aqC+TNYlCCCHETOULJ7AYtJTb5eBcCCFE4ZDkQpHo80WJJtOc3lQuVQtCCCHEDBVPpvFHkzS4j76toxBCCJEvcpRaBOL7qxaqpGpBCCGEmNH80SSxVHEviRBCCFGaSj65kEgk+MY3vsFpp53G0qVLueGGGxgYGDjh/R588EEuueQSFi9ezDnnnMN//dd/EQ6HpyHiIw0EYkSkakEIIYSY8XzRJBa9lmrZtlEIIUSBKfkj1dtvv52nn36ab3/72zz00EOEw2E+/vGPk06nj3mfxx9/nG9961vccMMNPPnkk9x9993885//5Pbbb5/GyMdFE2lGQglqnCapWhBCCCFmsHgyTTiepsZlxGyQJRFCCCEKS0knF4LBIH/4wx+46aabWL16NQsXLuSb3/wmHR0drFmz5pj327BhA0uXLuWKK66gvr6eM844gyuuuIJNmzZNY/Tj9o2FSaUznNbkkaoFIYQQYgYbCSUAaK1y5DkSIYQQ4kglfbS6ZcsWkskkZ5111sHLampqaG5uZsOGDce834oVK2hvb2fjxo0A9PX18cwzz3DOOefkOuTDJNMZhoJxmsqtUrUghBBCzHCj4TgOs466Mku+QxFCCCGOoMt3ALk0MjKCVqvF7XYfdrnH42FkZOSY97vsssvw+Xx86EMfQlVVUqkU73znO/nCF74woeft6uoiGo0CEIvFaG9vP6n4M6qKMZqg3KBh54789HsoRFMZU3F0MqbZJ2OafTKmuXFgXOfPn5/vUMQJNJRZ0GoUrLIkQgghRAEqyuTCd77zHX74wx8e9zY///nPj3mdqqrHve8rr7zCD37wA772ta+xePFi9u3bx+23387//M//8NnPfvaE8TU1NR38fqoTtoUnfc/SJZPg7JMxzT4Z0+yTMc0NGdfi4bEZ8diM+Q5DCCGEOKqiTC5cc801vOMd7zjubWpra9m4cSPpdBqv10tZWdnB68bGxjj11FOPed/vfve7XHbZZbznPe8BoLW1lUgkwle+8hU+9alPodMV5bAJIYQQQgghhBA5UZRHyWVlZYclC45l0aJF6PV6XnzxRS6//HIABgYG2L17N8uWLTvm/WKxGFrt4SWHWq32hBUPQgghhBBCCCHETFSUyYWJstvtXHXVVXzzm9/E4/Hgcrm48847aW1t5cwzzzx4u2uuuYbFixfzuc99DoDzzjuPn/3sZyxatOjgsojvfe97nHfeeVK1IIQQQgghhBBCvEnJHynfcsst6HQ6brzxRmKxGGeccQbf/OY3D6tM6O7upqam5uDPn/jEJ1AUhe9973sMDAzgdrs577zzuPHGG/PxKwghhBBCCCGEEAWt5JMLRqORW2+9lVtvvfWYt3nmmWcO+1mn0/HpT3+aT3/607kOTwghhBBCCCGEKHqafAcghBBCCCGEEEKI4ibJBSGEEEIIIYQQQkyJJBeEEEIIIYQQQggxJZJcEEIIIYQQQgghxJQoqqqq+Q5CCCGEEEIIIYQQxUsqF4QQQgghhBBCCDElklwQQgghhBBCCCHElEhyQQghhBBCCCGEEFMiyQUhhBBCCCGEEEJMiSQXhBBCCCGEEEIIMSWSXBBCCCGEEEIIIcSUSHJBCCGEEEIIIYQQUyLJhRx56KGHOP/88znllFN417vexbp16/IdUtG69957aW1tPezf6tWr8x1W0Xn11Ve54YYbOPvss2ltbeWPf/zjYderqsq9997LWWedxeLFi/nwhz/Mzp078xRtcTjRmH7xi1884rX73ve+N0/RFr7//d//5aqrrmL58uWcfvrp3HDDDezYseOw28jrdPImMq7yWs0tmRNkl8wLpk7mBNknc4Lsk3lB9uV6TiDJhRx48sknueOOO7jhhhv405/+xLJly7j++uvp6+vLd2hFa/bs2bzwwgsH/z3++OP5DqnoRCIRWlpa+PKXv4zJZDri+h//+Mc88MAD3Hrrrfz+97+nrKyMa6+9llAolIdoi8OJxhTgzDPPPOy1+6Mf/Wiaoywer7zyCh/4wAf49a9/zYMPPohWq+Xaa6/F5/MdvI28TidvIuMK8lrNFZkT5IbMC6ZG5gTZJ3OC7JN5QfblfE6giqx797vfrX75y18+7LKLLrpI/e///u88RVTc/ud//ke97LLL8h1GSVm6dKn6hz/84eDPmUxGXb16tfqDH/zg4GXRaFRdunSp+vDDD+cjxKLz5jFVVVW9+eab1Y997GN5iqj4hUIhta2tTf3HP/6hqqq8TrPlzeOqqvJazSWZE2SfzAuyS+YE2SdzgtyQeUH2ZXtOIJULWZZIJNi6desR5XmrV69mw4YNeYqq+HV3d3P22Wdz/vnnc+ONN9Ld3Z3vkEpKT08Pw8PDh71uTSYTp556qrxup2j9+vWcccYZXHzxxXzlK19hdHQ03yEVjXA4TCaTweFwAPI6zZY3j+sB8lrNPpkT5I7MC3JH3mtzR95np0bmBdmX7TmBLhdBzmRer5d0Ok15eflhl3s8HtasWZOnqIrb4sWLufPOO5kzZw5jY2Pcf//9vP/97+eJJ57A7XbnO7ySMDw8DHDU1+3Q0FA+QioJZ599NhdddBH19fX09vby3e9+l2uuuYY//vGPGAyGfIdX8G6//Xbmz5/PsmXLAHmdZsubxxXktZorMifIDZkX5Ja81+aGvM9OncwLsi/bcwJJLuSIoigTukyc2LnnnnvYz0uWLOHCCy/kT3/6E9dee22eoipN8hrNrssuu+zg962trSxcuJDzzz+f5557jre+9a15jKzw3Xnnnaxfv56HH34YrVZ72HXyOj15xxpXea3mlswJskvmBdNDXqPZJe+zUyPzguzLxZxAlkVkmdvtRqvVHsykHTA6OnpEVk2cHKvVyty5c+nq6sp3KCWjoqICQF63OVZVVUVVVZW8dk/gjjvu4M9//jMPPvggDQ0NBy+X1+nUHGtcj0Zeq9khc4LpIfOC7JL32ukh77MTJ/OC7MvVnECSC1lmMBhYuHDhEeWOa9asOazcRJy8eDxOZ2fnwTcUMXX19fVUVFQc9rqNx+OsW7dOXrdZNDY2xtDQEJWVlfkOpWDddtttPPHEEzz44IM0Nzcfdp28Tk/e8cb1aOS1mh0yJ5geMi/ILnmvnR7yPjsxMi/IvlzOCWRZRA5ce+213HTTTSxevJjly5fz8MMPMzQ0xPvf//58h1aU7r77bs477zxqamoYGxvjBz/4AZFIhCuvvDLfoRWVcDjMvn37AMhkMvT19dHe3o7T6aS2tpaPfOQj/PCHP2TOnDk0NTVx//33Y7FYePvb357nyAvX8cbU6XRy33338da3vpWKigp6e3v59re/TVlZGRdeeGGeIy9M//Vf/8Wjjz7K97//fRwOx8EzERaLBavViqIo8jo9CSca13A4LK/VHJI5QfbJvGDqZE6QfTInyD6ZF2RfrucEiqqqaq5/iZnooYce4qc//SlDQ0O0tLTwpS99iVNPPTXfYRWlG2+8kVdffRWfz4fb7Wbp0qV89rOfZe7cufkOraisXbuWj3zkI0dcfuWVV3LXXXehqir33Xcfv/nNb/D7/SxZsoSvfvWrtLS05CHa4nC8Mf3P//xPPvWpT7Ft2zaCwSAVFRWcdtppfPazn6WmpiYP0Ra+1tbWo17+6U9/ms985jMA8jo9CSca11gsJq/VHJM5QXbJvGDqZE6QfTInyD6ZF2RfrucEklwQQgghhBBCCCHElEjPBSGEEEIIIYQQQkyJJBeEEEIIIYQQQggxJZJcEEIIIYQQQgghxJRIckEIIYQQQgghhBBTIskFIYQQQgghhBBCTIkkF4QQQgghhBBCCDElklwQQgghhBBCCCHElEhyQQhR8P785z+zaNEikslkvkMRQgghRJ7JvECIwiTJBSFEwdu+fTstLS3o9fp8hyKEEEKIPJN5gRCFSZILQoiC197ezvz58/MdhhBCCCEKgMwLhChMklwQQhS87du309zczLe//W3OPvtsVqxYwVe+8hUymUy+QxNCCCHENJN5gRCFSZILQoiCNjY2xvDwML/85S+JxWLcddddXHfddfzud7/jH//4R77DE0IIIcQ0knmBEIVLl+8AhBDieNrb2wH48Ic/zLXXXgvA6tWrefjhh+nq6spjZEIIIYSYbjIvEKJwSeWCEKKgbd++HYfDwYc+9KGDl6mqSjAYxO125zEyIYQQQkw3mRcIUbgkuSCEKGjbt2/n1FNPPawj9L59+4jFYsybNy+PkQkhhBBiusm8QIjCJckFIURB2759O62trUdcptFoZBIhhBBCzDAyLxCicElyQQhRsBKJBHv27KGtre2wyzs6OmhoaMBiseQpMiGEEEJMN5kXCFHYJLkghChYu3btIpVKHXGGoqOj44jLhBBCCFHaZF4gRGGT5IIQomBt374ds9lMY2PjYZfLJEIIIYSYeWReIERhU1RVVfMdhBBCCCGEEEIIIYqXVC4IIYQQQgghhBBiSiS5IIQQQgghhBBCiCmR5IIQQgghhBBCCCGmRJILQgghhBBCCCGEmBJJLgghhBBCCCGEEGJKJLkghBBCCCGEEEKIKZHkghBCCCGEEEIIIaZEkgtCCCGEEEIIIYSYEkku5FhXV1e+Qyg5MqbZJ2OafTKm2SdjmhsyrtNHxjr7ZEyzT8Y0N2Rcs0/GNPumOqaSXMixaDSa7xBKjoxp9smYZp+MafbJmOaGjOv0kbHOPhnT7JMxzQ0Z1+yTMc2+qY6pJBeEEEIIIYQQQggxJTMiufDqq69yww03cPbZZ9Pa2sof//jH496+p6eH1tbWI/7961//mqaIhRBCCCGEEEKI4qHLdwDTIRKJ0NLSwhVXXMHNN9884fv95Cc/oa2t7eDPTqczF+EJIYQQQgghhBBFbUYkF84991zOPfdcAL70pS9N+H4ul4uKiopchSWEEEIIIYQQQpSEGZFcOFmf+cxniMfjzJo1i49+9KNccsklE7pfV1fXwWYYsViM9vb2XIY548iYZp+MafbJmGafjGluHBjX+fPn5zsUIYQQQhQxSS4chcVi4eabb2b58uVotVqeeeYZbrzxRuLxOO985ztPeP+mpqaD38uELftkTLNPxjT7ZEyzT8Y0N2RchRBCCJENklw4irKyMq677rqDP59yyil4vV5+8pOfTCi5IIQQQgghhBBCzCQzYreIbFiyZAl79+7NdxhCiCKQyaikM2q+wxBClJhMRiWWTOc7DCGEEOKopHJhgtrb26W5oxBiQnYOhXi5O0ztrDhlNmO+wxFClIht/QF2D4XQ6xTcFgMOsx6rQYfFqMVm1GHWa1EUJd9hCiGEmKFmRHIhHA6zb98+ADKZDH19fbS3t+N0OqmtreWee+5h06ZNPPjggwA88sgj6HQ6FixYgKIoPPvss/zqV7/i85//fD5/DSFEkRjwR9nnS/Loxj7edko11U5zvkMSQpSAOeVWtg8ECITS9PliJFMZdFoNZoMGs16LQafFYdbhMuuxmd5IPFgNOrQaSToIIYTIrRmRXNiyZQsf+chHDv587733cu+993LllVdy1113MTw8THd392H3uf/+++nr60Oj0dDU1MTtt98u/RaEECcUjCUJxJJ4zBqS6QyPb+rnrfOrmFVuzXdoQohDPPTQQ/z0pz9leHiYefPmccstt7By5cqj3nbt2rX83//9H5s3byYYDNLY2Mg111zDu9/97mmN2aTXUm4z4rGOV0SlMyqRRIpgLEUoniQYSzIcjJFIqSjK+O0tBi0GrQa7SY/Losdm0mHSadFqFXQaBY0y/vXAz1qNgk6jkWSEEEKISZsRyYXTTjuNjo6OY15/1113HfbzlVdeyZVXXpnrsIQQJWgklCAcT+My61gy28OGbi9Pbenn/PlVtFTZ8x2eEAJ48sknueOOO/ja177GihUr+NWvfsX111/Pn//8Z2pra4+4/YYNG2hpaeHf/u3fqKys5Pnnn+erX/0qRqORyy+/PA+/wTitRsFu0mM36YE3KqQyqko8lSYUTRNMJIkl0vgiCXYOZVBVFaNeg1YZTyAc+KdRQKMoKIqCooBGAb1Wg16rQafRoNcqGHQadFoNOs3492a9FqtRh8WgxaTX5m0chBBCHG7t2rV85CMf4aWXXqKsrGzanndGJBeEEGK6jIbiJFIZrMbxifvyRjebe/z8bdsgsWSaxfWufIcoxIz3s5/9jCuvvJL3vve9ANx66608//zzPPzww3zuc5874vY33HDDYT9/4AMfYO3atfz1r3/Na3LhWDSKglmvw6zXUcEbfV9UVSWZzhBLpUmlIJHOkM5kSGVUUhmVjKqSTqtk1AxpVSWTgYwK6cyBn1VUQGH8cr1WwazXYtRrMek1OE0GnBY9FsMbSQeLLMkQQohpt2zZMl544QXcbve0Pq8kF8RJSSQSGAyGfIchREFJpjOMhOLjZ/W045vxaBSFxfVO2geCPNcxRDyZZmVTmTRdEyJPEokEW7duPWzLaYDVq1ezYcOGCT9OKBSiuro62+HllKIoGHTjvRlOVkZVUfcnHCKJNIFokkgyzVg4Md4HIp1Bo1Ew67SY9y/JsJl0OM3jFRbjCYfx5INUOwghRG4YDIa8bEYgyQUxIR/+8IeZM2cOFouFRx55hLq6Ou6++26++c1v8uqrr2IymTjjjDP40pe+REVFBc8//zyf+MQneP755w/LmH3729/mueee47HHHjvu8/3xj3/kG9/4Bvfccw933303fX19LF26lDvuuOOw2z333HN8//vfp6OjA7PZzLJly/je976H0Sgd+sX0GwsniCTSOCx6MoE3LlcUhQU1DoxaDS/uGiWRUjm92SNn84TIA6/XSzqdpry8/LDLPR4Pa9asmdBjPPvss7z88ss8/PDDJ7xtV1cX0WgUgFgsRnt7++SD3i+jqnT3RgmYC+ug3Lj/n0OBjFYlkc4QDqkMJzPEUyqJlEo8raKiYtAqGHUaDFoFi17DXI+BcovupBOuUx1TcSQZ09yQcc2+UhrT9evX861vfYuHHnoIrVZLX18fn/zkJ7nkkksOVs/98pe/ZOfOnbz73e/m1ltv5ctf/jIPPfQQvb29NDQ08MlPfpK5c+cCsHnzZm699VZ+/vOf43A4JhzHoWM6f/78Sf8eklwQE/bYY4/xvve9j4ceeohAIMAHP/hB3v3ud3PzzTeTTCb57ne/yyc+8Ql++9vfcuaZZ+JyufjLX/7C1VdfDYyXYz7xxBN84AMfmNDzJRIJ7rvvPu644w7MZjO33347n/rUpw72yPjXv/7FJz/5Sa6//nruvPNOUqkUL774IplMJmdjIMTxjITihOMpqu02RgJHXt9cacOgU3ila4xoMsW5rZXo91c4CCGm19EOZidygLt+/Xo+97nP8eUvf5nFixef8PZNTU0Hv29vbz+pydoBmYzKsHb4YEPHYpNMZwjHUwRjScKJFCOhBB0RDWm7jaX1LtzWyVdETnVMxZFkTHNDxjX7SmlMGxsbDx7PLFq0iM2bN+N2u+no6Dj4O+7evZvzzjuPWbNmAfCrX/2Kr3zlK1RVVXHfffdx11138fe//x2z2UwgMD4RnTdv3qR6Lkx1TCW5UAD+sL6H367rPvENs+i9Kxu4akX9pO5TX1/PF7/4RQC+973v0dbWxhe+8IWD1999992sWrWKLVu2sHjxYi699FIef/zxg8mF9evX09/fP+H1qalUii9/+cusWLECgG9+85tceOGFbNq0iQULFvCDH/yAiy++mBtvvPHgfdra2ib1OwmRLaqqMhKMo6pgMR77rbWhzIpBp2Vzr49EKsP586ukNFiIaeR2u9FqtQwPDx92+ejo6BHVDG+2bt06Pvaxj/Hv//7vE06UizfotRpcFgMuy3gSQVVV9o1F2NLrp2skzIIaBwvrnNiO8x4qhBCTVQzHWlarlQULFrB27VqWLl3K2rVr+dCHPsSPfvQjhoaGsNvtbNmyhS984QukUikAPvnJT3L22WcDcOedd3LuuefyxBNP8J73vCcnv9NEyCkzMWGLFi06+P3WrVtZt24dy5YtO/jvLW95CwD79u0D4B3veAevvfYavb29ADz++OOsWrWKqqqqCT2fRqM57KxQXV0dlZWVB7cNbW9v54wzzsjGrybElAXjKfyxJFaj9oTLHaocJpY1uOkYDPLU5n4iidQ0RSmEMBgMLFy48IglEGvWrGHZsmXHvN+rr77K9ddfz6c//Wk++tGP5jjKmUFRFGZ5rJzVXI7dpGdt5yiPbuhlU4+PWDKd7/CEEGJarVq1ildeeQUY/8w555xzWLx4Ma+88gqvvfYaOp3usGOjQz+zrFYrLS0t7Nq1a9rjPpSkhgvAVSvqJ11FkA9m8yHbXGUynHvuudx8881H3M7j8QDjyYg5c+bwxBNPcN111/GXv/yFm266adriFWI6jYYSROJpKu0TK1f22Iyc2uThtX1jPP56H29bVI3DLE1ShZgO1157LTfddBOLFy9m+fLlPPzwwwwNDfH+978fgHvuuYdNmzbx4IMPAuNben384x/n6quv5vLLLz9Y9aDVaqd1i69SpdNqaK2yM6vMwo7BAM91DNExEGRpg4vZ5VZ0snxMCDEFxXKstWrVKn71q1+xa9cuwuEwCxcuZNWqVaxdu5aysjKWLVuGXq/Pd5jHJckFcVIWLlzIU089RW1t7XFf5JdffjmPP/448+bNIxqNcvHFF0/4OTKZDJs3b2b58uUA9PX1MTQ0RH39+JvD/Pnzeemllw5uJSbyIxxPsWsohEWvZV61Pd/h5M2BLSgd5om/6TvNelY1eXh17xiPbuzj0lNq8NiKcy21EMXk0ksvxev1cv/99zM0NERLSws/+tGPqKurA2B4ePhglRzAI488QjQa5YEHHuCBBx44eHldXR3PPPPMtMdfqkx6LYvr3YRiSdoHgjy9dYBZHgtLG9zUu82yy44QoqStXLmSRCLBT37yE1asWIFWq2XVqlV89atfpaysjHPPPfew22/cuJGGhgYAIpEIO3fu5IorrshD5G+Q5II4KR/4wAf47W9/y4033sj1119PWVkZ3d3dPPXUU9x8883YbDZgfGnE9773Pb73ve9x/vnnH7x8InQ6HXfccQdf/vKXMZlM3HHHHcydO5clS5YA8IlPfIIbbriBxsZGLr/8clRV5cUXX+R973vfYVUWIjeS6QydI2E6R8IMB2P4IykqHMaDa2lnkmQ6w3Aojk6rYJzkFm9Wo44zZnt4pWuMxzb2ccmiampc8voVItc++MEP8sEPfvCo1x1oHHzoz2++TOSOzaTn1KYyRsNxdgwG6fX10VxuZ2mji4oJVocJIUSxOdB34bHHHuNzn/scML70ob+/n56ensN63QHcf//9lJWVUVlZyfe//330ej1vf/vb8xH6QVJnJk5KVVUVDz/8MBqNhn/7t3/jsssu47/+678wGAwYDG8cXNbV1bFixQq2b9/OO97xjkk9h8Fg4IYbbuDmm2/mPe95D6qqct999x08c3Huuedy33338fzzz3PFFVfwoQ99iJdffhmNRl7WuaSqKr2+KC/uHGFzj4+RUByP1UgyneG1vd58h5cXY+EE0UQa5ySqFg5l1Gs5fY6HZCbD45v66BoJZzlCIYQoPh6rkTPmlDPbY2XHUIDHXu/lhZ3D+KPJfIcmhBA5cdppp5FOp1m1ahUARqORpUuXYjAYjtih6HOf+xx33XUXV155JXv37uWHP/whFoslH2EfJJULYkJ+8YtfHHFZU1MT//M//3PC+z700EMn/bwXXnghF1544WGXHbqf7QUXXMAFF1xw0o8vJscXSdAxEGQgECMYS+K2GJhdbkOv1ZBIZdjc62fFrDKclsJeD5Ztw8H9W1A6Jl6Z82Z6rYZVTR42dHv5y+Z+zptfSWv1xPclFkKIUlXrslDtNNM1Eua1fT72DIdZWOdgQY0z36EJIURWff7zn+fzn//8YZcd7TgMxqsaHn/88aNed9ppp9HR0ZH1+E5ETvEKIU4olkyzpdfPmt2j7B4OkU6rzKu001hmRb+/0Va9ezxT+tq+mVW9oKoqo6E4qGAxTC1fq9UoLG90YzPp+fu2QTb3+rITpBBCFDmNojCnwsZZc8sx6jW8uHOExzb20umNk8mo+Q5PCCEEUrkg8uTf/u3fWL9+/VGv+/jHP05lZeU0RySOJp0Z34N893CI0VAcVVWpd5lxWQxHNNYy6DTMKrOwpdfPyiY3dtPMqF44uAWlSXfCLSgnQqMoLK530t4f4NntQ3isRmqlB4MQQgDjVV4LapzM9tjYPhBg674wetcQq+d6ppzgFUIIMTXyLizy4vbbbycWix31OqfTicvl4l3vetc0RyUONRSIsWMwOF7yn0hRYTdSZTcf9wC6vsxC11iEDfu8nNMyMxJEI8E44ViKSrspa4+pKAptNQ580STPbR/i/asa0WQhcSGEEKXCbNCyrNFNKjTCtj4/I6EY58yrpM4tyVghRGnL15KHiZDkgsiLqqqqfIcgjiEYS7JjMES/P4o/ksRm1NJa5cCkP/EuCEadlka3hde7/axodGOdAdULo+EEyXQm630mNIrCwlonr3SN8nqPj2WN7qw+vhBClIJyi45FdR427vPx+KZeVjV5WNLgykolmRBCiMmRngtCCGB8O8XtAwHW7BqlYyBINJ5mtsfKnAr7hBILBzSUWUirKhv2+XIXbIFIpDIMB+PodZpJb0E5EU6znlqnmTW7RwhEE1l/fCGEKAVmvY7T53iosJp4fucwT2/pJxiTHSWEEGK6SXJBiBlOVVUGQ0le2DnClh4/Y+E4VXYDLdV27CextaJJr6XBbWFDj49wvLQndwe2oLQbc1ehMa/STjqj8vzOkZw9hyhOaWliJ8RBiqLQUm1nSb2L3SMh/rShl66RUL7DEkKIGUWSC0LMYJmMymv7vGwbitHrjWIxammtdlDpMKNRTr6ktLHMQiqt8nq3P4vRFp6RUJxIMkWZ1ZCz5zDoNLRUOtgxGKJrJJyz5xHFxR9N8vf2QUbD8XyHIkRB8diMnDmnnGQ6w5ObB3hpzyjJdCbfYQkhxIwgyQUhZrD2gQD7RiMkMyrzqmw0uN/YWnIqxqsXzGzo9hFNpLMQaeFRVZWRYBwyU9+C8kRqXSYcJj3/3DFMMlWa4ykmp98fZSQYJxJP5TsUIQqOQadl5awy6t1m1u4e5cnN/XglESeEEDknyQUhZqjusQidI2HiqTT1DkPWD5Aby6wkUhk2dnuz+riFIhAb34LSlqUtKI9HURQW1DgYCydYt7c0x7PQxZJphoIxdg2F2NrnJ5XHM6GZjMqAL0ZaVdEq8jEuxNEoisLschsrZ7npGYvyp4197BgIoqqynEgIIXJFdosQYgYaCyfY1hfAH0kyp9zK8ED2D1jNBi31+6sXljW6J9UUshiMhsbPGlc5srcF5fHYTDpmeSys2ztGa5WdMptxWp53Jool0wRjKQKxJIFoEn80SSieIp5ME0mkCcdTpNMZFjfkZwePsUgCfzSJXivd8IU4EafFwOq55Wzu8fHXbQMMBFysml1Wcp9JQghRCCS5IMQME02keb3bx2g4Tp3LjM2kZzhHz9VYZqHHG2VTt5dVc8pz9Cz5cWALSsdJNL08WbM9Vvp8Uf65Y5grltWhTKEvhhgXT+1PJESTBGMpfJEEwXiKeDJDNJkilVZRFAWLQYvFoKXKbmL7QJDNfYG8JRcG/DFCiRR2o3yECzERWo3C0kY33WMRXts3xlAwxjnzKqicpuSwEELMFDIzEWIGSaUzbOz2MRiIUWYx4Mnx2W+LQUet08z6fT4WN5RO9cKBLSgNOm1OtqA8Fp1WQ1u1g009PnYMBmmtdkzbc5cCVVXxRsYrEYKxJL5IkkAsSSKVIZJIkUqDRgGzQYPZoKPSZsVs1GLQag5L5DR6LGztCzAaiuf8b+jN0hmVwUAMVQWHafoSW6IwfOdvO/jNum7q3WZqnGZqnCZqXeNfqxymrPTMKWUNZRbcVgMbu7089nofp8/xsKDGgSbHS9uEEGKmkOSCEDOEqqps7QvQ54ti0muocZmn5Xmbyi307Y6yucfPqbPLpuU5c+3gFpSm6X8LrXKY8NiM/GvnMLPKrJgMpZGwybUDr/+u0TD+aIJUGhTApNdgMejwuK1Yj5JIOBqP1YhGgfb+AGfNq5ieX2C/0VCcQCyFw6yb0o4uojitmOVmbdcYY6EE2weGiBzSMFejQLnNeDDZUOs0U+MyUeM0U+0wYdBJ4gHAZtRxxpxy2gcCPLN9kMFAjDOaPTlvzCuEEDOBvJMKMUN0joTZNxYmlckwu9w+bQcmFoOOGqeJ9Xu9LKl3YiiB6oWRUJxIIkWN05aX52+rtrNmzwivdI5yTmtlXmIoJgcSC7uHQsRSaepcFqwGHUbdiRMJR2PQaai0m2jvD3JmsweNZvoO2gYCMcKxJHPKbaQy0phupjmnpQKU8QSXqqoEYin6fVH6/DH6/VH69399fucIoUN2ElGAcruRGud4sqHWaaLObabWNZ54mGkVD1qNwqJaJwN+A1t6Awz4Y7RU2akvM1NhM6KbYeMhhBDZIskFIWaAoWCM7QNBgrEUcyvs0z6RbPJYeck/wqZePyubirt6QVVVhoNxVMCapzXvFoOO5nIbG7p9tFTbqXZOTxVKMcpkxhMLe4ZDxNMZ5pTbsrI8p8ZpYkO3jx5flMYyaxYiPbFkOsOAP4aiAatJhz+SnJbnFYVJURScZj1Os562miOXSAVjSfr9Mfp840mHPn+Ufl+MNbtHCMbeSDxolPGKqDrXeLKhzmWmzj3+tcxqKOkKmWqnGadFz+6hEK90jbKpV4vHZqSpzEKt20y51ShLJoQQYhJmRHLh1Vdf5ac//Slbt25laGiIO++8k3e9613HvU9HRwff+MY32LRpE06nk/e973186lOfkgZqougEY0k29fjxRhI0llkw56GM3mrUUeUYr15YXFfc1QuB6PguAnZjfsvSG8veaO74nhUNMgE+ikxGZUufn87hMPF0mjnltqz1yHBZDBi0Grb1BaYtuTAcjBOKp3CZS/uAT2SH3aTHbtLTUmU/4rpQLEWvL0qvL0rfIV839/qJp97YZtWo01C7P+lQf2jywWXGlodlYblg1utYVOcik8kwHEzQH4jywq4RrEYdFTYjTeVWal1m3Ba9zAGFEOIESuOT4QQikQgtLS1cccUV3HzzzSe8fSgU4rrrrmPlypX8/ve/p7Ozky9+8YtYLBauu+66aYhYiOxIpDJs6vEzHIxRYTPiMhvyFstsj42XO0fZ2u9nWWPxVi+MhKd3C8pj0WoU5lc7WN/tZWufn1PqXXmNp9BkMiqbe/10jYSJpzLMqcheYgHGx7/GZWL3UIhEKjMt69kHAjHC8RR1riMPFoWYDJtJR2u1ndbqw19LqqoyFk7QcyDp4B1PPHQOh3hp9wiHrsSpcZo4r7WS89sq8/5+mA0ajYYqp4kqp4lUJsOgP85gMMre0TAOs54Ku4kmj4UalxnnNO4SJIQQxWRGJBfOPfdczj33XAC+9KUvnfD2jz32GNFolLvvvhuTyURLSwu7d+/mZz/7Gddee61krkVRUNXxg6t+XxSbQUd1nid/NpOOSruRV7u8LKp1oS/S5mKjofEtKJ15TNQcUGYzUu0w8eKuEeZU2PK2TKPQZDIqm/YnFpKpDM0Vtpwc/FfZTewdjbB7KMj8WmfWH/9Q8VSaoUAMrUbBXMSVP6KwKYqCx2bEYzOy5E0Jy1Q6w2AgTq8vQo83yvp9Xn71yj5+9co+FtU6uKCtijPnlkZjRJ1GM740xG0mkcrQ54vS54uwayiI26qnym6iqdxKjdMs77tCCHGI4pzd59jGjRtZuXIlJtMbB2NnnXUWQ0ND9PT05DEyISZu51CInrEIKNBQZi2IpNjscivheJpt/f58h3JSEqkMI6HxLSgLpfP6vEo7sVSGF3cN5zuUgpDJqLze46NzOEQqM16xkKv/V3aTDptRx9b+QE4e/1BDgTihWAqXVUqzRX7otOMH3Ktme3jX8npuv+IUfvqRlXzotEZGwwm+98xOPvLAK9zztw5e7/aRUUuj4ahBp6Gp3MqpTR5OayrDrNfRNRLm6a2DPP56H//sGGLvaJhYMn3iBxNCiBIn6dajGBkZoaqq6rDLysvLD17X0NBw3Pt3dXURjUYBiMVitLe35ybQGUrG9MSGwym2D8cIJdI0ugzsi44e9/aJeJzOrs5piS0VivHE2lE0fkfRdeQeDqfYPRDBqFHoTIwd97bTOaaWZJJ/bhpGHxmm2la65bon+ttPZ1R2jMbpCyTIqFDnMNB7gtf+VKnhBBt6k8zS+bHlsJ/JlsEYnd4Ys91GOn3jfzeBWBo1qMdvndr/8wPjOn/+/GyEKmaQSoeJ953ayHtXNrB9IMg/tg/xws5hnusYptxm5LzWCs5vq6Tebcl3qFlhMuiYV2mHSjvheIoeb4TtA0Ha+wOU24wsa3Qxp8ImCUAhxIwlyYVjePMHg7o/Az+RD4ympqaD38uELftkTI/PH0nS0zmK1R1nkceKfQJrQzu7OpndNHsaogNPVZJXusbAVcX8+tyWkmdbutePO+mlucKGzXT8cZ3OMZ2lqrBnlN60lrNbGosuaTNRx/vbT2dUNnb7IBai2gpzym3TsitKTTJNeNcIODzMn+3JyXNEE2k6U0PMsiZpq3njb8YbTjC30kate2q7hczU99SHHnqIn/70pwwPDzNv3jxuueUWVq5cedTbxuNxvva1r7F161b27NnD8uXL+cUvfjHNERcuRVGYX+Ngfo2D68+ezSudY/xj+xB/eK2H363vobXKzgXzKzl7bkXJNIK0GnW0Vo/v0uGLJNgxFOQvWwdZWBtl1eyyklgeIoQQk1WaM9ApKi8vZ2Rk5LDLRkfHz355PLmZPAqRDbFkmtd7vAyH4lQ5jBNKLEw3h1mPx2rglc5R0unMie9QIFRVZSgYJwNYCmyNrUZRWFDrZCgQZ+M+b77DmXbjiQUve0fDZJi+xAKASa/FYzWwtS93SyMGArHxJRGWwvt7LlZPPvkkd9xxBzfccAN/+tOfWLZsGddffz19fX1HvX06ncZgMPChD33oYA8ncXRGnZaz51Xwn5cv5GcfXcW1ZzYRS6b5wXO7+cjP1nLXX7bzatcY6UxpLJuA8d1jVjV5aC638nq3jz9t6KVzOHTwxJQQQswUklw4iqVLl7Ju3Tri8fjBy9asWUNlZSX19fV5jEyIY8tkVDb1+On3x3BaDFQ5pnY2M5fmVNgIxJJsHwjmO5QJC0RTBGNJ7Kb8bkF5LE6znvoyC2u7xvBFEvkOZ9qk0hk27PPSNRJBzajTmlg4oNZlxhtJMuCP5uTxBwMxYskUHqsxJ48/E/3sZz/jyiuv5L3vfS/Nzc3ceuutVFRU8PDDDx/19haLha9//eu8733vo7q6epqjLV5lVgPvWl7PvVcv4zvvXcolC6vZ3OPj609s46P/9woPrd1LIlU8SeYTqXNbOLO5nGgyzVNbBnh+5wjRhPRiEELMHIV1+i1HwuEw+/btAyCTydDX10d7eztOp5Pa2lruueceNm3axIMPPgjA5Zdfzve//32++MUv8olPfIKuri5+9KMf8elPf1rW0YmC1T4QoNcbQa/VUOcs3MQCjB8Ie6xG1naN0lZtR1sEZfwHtqCsdhbulmtzK2wM+mM8v3OYy5fU5TucnEulM2zs9rF3NAKozK6Y/sQCgMdqQKdRaO8PUp3lv71QPMVIKI5Jr8vqVpozWSKRYOvWrUdsLb169Wo2bNiQ9efLZh+mjKrS3RslYC6+14IWuKhRw3n1lWwbivFyd4Rfv9rNc+39fGipm0bXye3AM539bSaqAhiIJfnr+kHWt2tZUm2mxpH/HYYmSnpb5YaMa/bJmGbfoWN6MksmZ0RyYcuWLXzkIx85+PO9997Lvffey5VXXsldd93F8PAw3d3dB6+32+088MADfP3rX+eqq67C6XRy3XXXce211+YjfCFOaN9ohM6RMIlUhnlVdrSawk+CzS63sm7vGB2DQRbkeBu/bBgNJUikVRymwp0g6rUaWqvtbO0LsHs4RHOFLd8h5Uxyf2Jh30gERVFpykPFwgE6rYZqh4nt/QHOnluOLou7Uwz4Y4RiSSrshZvUKjZer5d0On2wUfMBHo+HNWvWZP35stmHKZNRGdYOF30Vy7w58M7TYf1eL/c+s5NvvzjMe1Y08L5TGyb9dzyd/W0mYzawJJFic6+fHZEMhjInK5vKMBXBVrIztQ9Lrsm4Zp+MafZNdUxnRHLhtNNOo6Oj45jX33XXXUdc1traykMPPZTLsCYkFE9h0WvRFMHBosiP0VCc9v4AgWiSOeXWvB1gTZbLYsBtMfBK1xitVYVdvRBPpcfPHus0BbMF5bFUO0z0eqP8q2OYBrcZQwme7U6mM2zY56N7NIxGo6HJY817E8sqh4keX5S9Y2GaK+1Ze9wBf5RkSsVtKdykVrE6WiWiVCdOrxWz3Nz3geX8+Pk9/GZdN2s7R7nxwhbmlEhi1GLQcdpsD3tHw6zb66XPF+WM5nIaykpj9wwhhHizwp4lz3CJVIbndw7z9NYBukZCpIqo+Z2YHpFEik09fkbDcWpdZqzG4mr4NqfChjecZOdQKN+hHNdYOEE0kcJuLvx87IGu7d5oknVdpdfcMZnO8NpeL/tGwygahSaPJe+JBQCnRY9Jr6G9P3uNHf2RJN5IAotRWzRJw2LgdrvRarUMDw8fdvno6OgR1Qwi92xGHTde2MJXLpuPP5rk//vd6/z61X0lNeeZ5bFyxmwPvmiSP2/qY83uEWJJ6cUghCg9MlspYAadhrkVNjpHwzy1eYA/b+pn50CQeEo+kGa6WDLNrqEQr3aOMRiIUWY1FGWZrNtiwGXWs7ZzrKC7ao8EE0QSadzm4jh7bDXqmO2xsG7vGCOhWL7DyZpkWmX9Xi/dYxF0WoU55baCSCzA+I4ddS4zu0fCRBKprDzmQCBGMJaizFocr7tiYTAYWLhw4RFLINasWcOyZcvyFJU4bbaH+65ezllzy3lo7T6+8PtN7B0N5zusrLEYdZw+20Ot08wrnWM88Xofvd5IvsMSQoisKoxZmTgml8XAgv17R49G4vxl2wCPb+xja69fOhDPMONbIcbYsM/Lsx1DrNs7xr6xCHazjpoCb+B4PHMqbIyFE+wcLMzqBVVVGQ6N7xxTaFtQHk+Tx4pOo+GfHcMnvnERiCXTtA/HxhMLOoUmj63geotU2E1kMrBzcOq7oKiqyoA/Siqt4iqSpFYxufbaa3nkkUf43e9+x+7du7ntttsYGhri/e9/PwD33HMP11xzzWH32bVrF+3t7Xi9XsLhMO3t7dJILMscZj2ff2srX7ykjeFQnP/4zUZ+t767ZLatVBSF2RU2Tp/tYSyc4PFNfby8Z1ROGgkhSkbxzJRnOI/NiMdmJBBLsmsoxDPbh9jc66e12s7cSht2U3GVw4uJiyXT9Hij9PkieMNJgvEUGg2UW42U24xFXy7ttuhxmHSs7RxlXpWt4NY8+6NJgtEktgLdgvJYdFoNcyttbO3zMxyMFW1DQFVV6fFG2TEYpD+QoMmt0FRWeIkFGC/vdph1bOsLsKTBPaXHGgsn8EWSOMzagqnOKCWXXnopXq+X+++/n6GhIVpaWvjRj35EXd34LitvbvQM8LGPfYze3t6DP19xxRUAx+3pJE7O6rnlLKpzcv9zu/j5S3tZu2eMz144jwZ3afQqsBp1nD7Hw56RMC/vHqXXG+HM5nJqXMV7okAIIUCSC0XHYdKzvNFNJJ5i10iIF3YOs6XXz7wqO61VdtxSPlsSDpwt7/VGGQjE8EeTJFMZ7CYdczxWbCZdwR2EnyxFUWiusPFat5ddwyHmZbEZXjaMhBKEEylqCngLymNxWwwoKOwdiRRlciEUT9HeH6DfHyUUTWE3ags2sXBAvcvCtv4AY+E4ZVNYqjQYiBOKp0rmYKoQffCDH+SDH/zgUa87WqPnZ555JtchiUM4zXpuvqSNF3aNcP9zu/nsrzfw4dNn8Y4ldQX9HjBRBz77qhwmNvV4eXxTH8saXSxtcBf9SQMhxMwlyYUiZTHqWFznIp5M0zkS5tXOMToGAswut9FWY6fCZiyZg8+ZpNSrFI6lzGrAYdLz8p5RmsttBbU7ymgoTjKt4izC0nSTXovDpKdzNMTK2WX5DmfC0hmVzpEwu4dDjIUT6DUKzZU2BlPegj+oKLcZUYDt/UHOnHtyyYVMRmUgEENVVRxmqUoTM5eiKJw9r4JFtU6+/9wuHnixi5f2jPEfF8yjtkTO8tuMOs6YU86uoRBrdo0y6ItxblulVKQKIYqSJBeKnFGvpa3GQXOljX2jETb3+Ng5FKSxzMqCWgc1DlNBHaiJI82kKoVjURSFtmoHr3SNsbHbx/JZUyspz5Z4Ks1oOIFRpynaxE653UDnSJh4Mo2xCPZXHwsn2N4fYMAfJZrKUG03Um43Fc2SFINOQ5XDxNb+AKfPKUOjmfzrZiQcJxBN4rDoCz6ZIsR0cFsNfPnS+TzbMcyPnt/NZ369gY+e0cRli2vyHVpWKIrCvCo75TYDr/f48G7o5dyWCho91nyHJoQQkyLJhRKh12porrQxu9xCjzfGrqEge0ZCNLotzK+x01hmlSRDgVBVlUQ6QyKVYSgYp9c7s6oUjsVp1lPvMvHSnhGaK6w4LfmvFBgNjW9B6Szis8cus4GMGqbPH2V2eeHuHZ9IZdg5FGTvaARfJIHVqKXVY8eoK/yEyJtVO01s7PbR64ud1H72g/7xJRFz5MBCiIMUReH8tkqW1Du599ld/Oj5PazZPcJVbWZm5zu4LHFbjZzZXM7Gbh9Pbu7ntDkeltS7ZP4mhCgaklwoMRqNhkaPhUaPhQF/lK7RCF0jYZY0uDij2VPyZ8Cnm6qqpDIqyf3JgkQ6QzKtkkxlxi878HM6QyyZJpZMk0hlSKsqmQzEUukZV6VwPHMr7QwG4vxzxzDvWFqX73AYDY1vQVnnKt517w6zHp1GoWskXLDJhX5/lI7+IMOhOOlMhga3BVcBJJdOlttiQK/V0N4fmHRyIZXO0O+PAipWk3xEC/FmHpuRr719AX9vH+THz3fyzaEgX7ZXsKTBle/QssKg03JqUxk7BkP8a8cww8EYZ8+rxGwovkSrEGLmkZlLCat2mql2mukaCbO2cwyjXsOKWcWz7rpQqarK5l4/I6E4sWSGdCZDJgNpVSW9P9GQSo9/P7551vh/NYqCXqug02jQahS0WgWPxUCZ1YhBN7OqFI5Fr9Uwv8bB5l4fOwYCtFQ78hbLG1tQKkU9qdNqFDw2A50jEc7LdzBvEkmk2D4QpM8XxRdJUGYzUGO3Ff3uCFqNQq3TxM7BIG9prZzU3/dwaLxqwWUxFM1SECGmm6IoXLSgmsX1Lm59ZCNfe3wrn3pLMxctqM53aFmhKAqt1XbcFj2b+/x4I0ne0lJJVRE2FhZCzCySXJgBmsqtJNMZXtg1gsWgZX6NM98hFbVeX5TO4TC+aAKDVoNWq6DXaNBpFcx6LUadFqNWg06njCcRlPGvM7kiYTIq7UbKrEb+uXOYhjILZkN+3qYObEHpMGmL/iDPYzWyrT+AP5LEacn/Eo9MRmXvWIRdg0FGw3G0iobmchu2EmpgVukwsXcsQudIiNZJJMkG/DEi8TQ1DjmIEOJEqhwm/uPMCh7eFuV/ntlFvz/Gh06fVfTv2QdUOkycadKxodvHY6/3sXquh/k1DplPCCEKliQXZoi5lTbiqQzPbB/GrNfRVC5reU9GMp1h12CIYCxFW7UDS54OfEvZeHNHOy/tGeXlPWOc11aZlziKeQvKNxvvGaGybyzMKRZXXmPxR5K0DwQY9McIJVJU2IxU2k0l17jQYdJhNerY0heYcHIhkcowFIij0YDVKO8t4kiDgRh7RyPEEhkMOg1Gnebg15l6wGnWa/ja2xfww3/u5nfrexgIxPiPC1pKpiLQbNBx+hwPW/v8/KN9iJFggtOay4qyH40QovTJ7GWGUBSFBbUONuzz8petA1yxrJZqR2ls4zSdukbCjIbjuK16SSzkkMWgo7ncyuvdPlqr7XnZcmwkFCdVpFtQvpnFoMWi19E5EuaUeldeYkilM+waDtE5EsYbTmDSa2mptGMqgh0sToaiKNS7TOwYChGMJSe0rdxgIEYonsRtMczYA0VxfPFUmngqzfaBADC+6E6jgE6jwaTXYDXoMBm0hyQd3vi+VM7mH41Oq+FT582l1mXmZ2u6GAnG+fJlC4q6Ge+hNIrCKXUuerwR1u8bYzQc5y2tFbitJ7fdrRBC5IocHc0gGkVhSb2LV7vGeGrzAFcuqyvqpmnTLZJI0TkSJpbKFGxjvFLS6LHS54/x3PYh3reqcVrPbMeSacZCxb0F5aEURaHCYWTfWIR0OoN2mn+nUDzFa3vHGA7Giacy1LrMlM2AA+hym4mOwRA7B4Isbzpxv5vBQIxwIk2DWyrLxNE1ldu4brWFeDJDIJYkFE/hiyTwRZL4Y0kC0SSjkfHEqKq+kXzQHpp80GuxGLQ4THosxuJf9nWAoii8a3k9VQ4T3/7bDj7/u9f52uULqHcXb0PeN6t3W3Ca9Wzo9vGnjX2c01JBc4XMR4QQhUOSCzOMTjve1PHlzhGe2NTPlctqsRpLI7OfazsHQ3jDCSpn4DaR+aBRFBbWOnm1a5QNe8dYOdszbc89Gk4QTaZwlMhZLxjfwWDvaISBYGzad7/YOxqmxxvFadIzu9w2Y/5+zAYtHquBzX3+EyYXYsk0Q8E4Bq2mqBuIitzTaTXotJpj7iaSSmeIJzMEEymC0ST+/f980fHkw1gkTjI13nDYqNNQZjHgthqw71/KU+zJhtVzyym3Gbntz9v4wu83ccvb2vJWsZULdpOeM+d4eL3Hx9NbBlg+y83KWe6ib4QrhCgNklyYgQw6DSsby3i5c5Sntgxw+ZJaWbt3AmPhBL3eKCpQYS/+NfjFwmnWU+e28FLnGM1VdtzTVGkzGooTSaRL6oyX06xHo8Dekci0JhcyGZXBQJyMqlLntpRcb4UTqXGZ2dLrZ9AfO26n98FAjFAshbsAGm6K4nZo8qH6KI1B0xmVUCxFny/KPm+E7rEIA4EYKuPzA08JJBtaq+186z1L+PrjW/nqY1v5zPlzOb+tKt9hZc2BE0V7RkK8vGeU0WCcc9sqsUmvFiFEnsm70AxlMepYOauMV7tG+fu2QS5eWC1Z72NQVZUdg0G8kQQNZTPv4GgiQvEUQE4mNnMrbAwFYvyzY5h3Lq3NeSl9JqMyEkqgoJRUPwC9VoPTrGfPSJgz55ZP2/OOhhMEokkcJv2M/NvxWA3oNArb+wPHTS4M+GPEUmlmW2VJhMgtrUbBadHjtOiZX+tAVVUC0RT9gSh7RyP0eCMM9L+RbCizGHBbDDjMxZVsqHaY+Oa7l3DnU+185+876ffH+MCqxpJajjWn3IbLrOf1Hh/eDQne0lpZUklxIUTxkeTCDOYw61nS4GbDPi9mvZa3tFaimYGT/xPp88cYDMQwGTS4SqhMPls29/i446nttFbb+c/LF2b98fVaDW3VDjb1+OgYDNI2iW39TsaBLShtJbAF5ZtV2k10DAUJx5PTthzqQJPCJs/MPGjWazVUOoy0DwY4a175UftdhOMpRkJxTHpNSSW0RHFQlDeSDQfeXwPRJH3+KPtGI3R7IwwMxAAwaBXKrEbcFkNRNDa2GXX85+UL+cFzu/j1q930+2N89oJ5JbU0q8xqZHVzOa/t8/LEpj5On13OwjpHSf2OQojiUdifCiLnym1GFtY6eb3Hj8U4vt2ReEMynWHnYJBgLMm8SntJnfHIhr+3D/L9Z3eRUVU29/hJpjM5mdBUOUxU2I083zHMLLcFcw5LP/v9McKJ1LT3JZgODrMeVOjxRmmtzn1yYXxJRAxQsM3g3i41DjN9vih7xyLMOUrztYFAjGAsRblNOr+LwuAw63GY30g2BGNJen3jyYYeb4TBgfHKhsYyC7PLrQV9IKvXavj38+dR4zTzi5f3MhKKc8vb5pdUTx2DTstpsz3sGAjxzx1D7B0Ls6zBRUOZReYtQohpVbifBmLa1LrMzKmw8vLuUTb1+HLyHP5Ikh2DQSKJVE4eP1f2joYZDcVxWwwFf4ZmOmVUlZ+/1MX3/rGTRXVOPnP+PBLpDDuHQjl7zrZqB+Fkmhf3jObk8dMZlS29fvaMjP8O9mM0SytmdpMOg05D12hkWp5vJBwnEEvhMOtm5JKIA5wWPUadlm39gaNeP+CPkkhnpq2niBCTZTeNJxreurCa686aw3Vnzeb02W76/BFe2DVCvz+Kqqr5DvOYFEXhvSsbuOniVnYMBvnC71+nzxfNd1hZpSgKrTV2lje6GQzEeHJTP/9oH2I0FM93aHkTSaTo8UaIp9L5DkWIGaP0Zs/ipMwptxFLpvlnxzAWvZa5VfYpP2YmozIUjNPtjTAUiDEWTrDXaeKiBdVZiDj3IokUe4Zl68k3i6fSfOfvO3lx1wgXL6jihnObCSfGP7i39PpZUJObZQsmvZZ5lXa29Pppq7JTX5a9yoJoIs3rPT56vRHSKjRXlOaOBhpFodxqYO9oGFVVc35GaygQJxxPMXuGLok4QKMo1LnN7BkOE02kMB+SqPRHk4yFE1j0Ggy60nvNidLkMOs5Y24FbbVOnt8xzNa+AN1jUdqq7QVdEXD2vIqDO0l8/nev8+XL5rOw1pnvsLLKZTFwxpxyen0Rtg8E6B4LM7/WwSl1LqwzpOGjP5Jk71iYXl8UXziBXqtlaaOL2eXWGZ3oFmI6yExGHDS/2oHLbOCv2wbp8Z78mc14Ks2e4RAv7Bpm7Z5Rtg8ECMdTKIpCe3+QcLw4qhd2DcnWk2/mjST48iNbWLNrhOtWN/Gp8+ai298osLHMwtY+f06fv95txqLX8mzHEKl0JiuPORZOsHbPKHtHw2g0CnMrbCVdpVJmMxKOpxkNJXL6POmMyoB//MygrQSrQCar0mYinVHZPRw+7PLxXSLSlMmSCFGE3BYD71hax5XL6tBpFV7pGqW9308ilZ3351yYX+Pgv9+zBIdZz1f+tIXnOobyHVJO1LksnDW3HIfZwNo9Yzy6sZctvYX9/2YqVFVlKBhjXdcYL+waZkuvH284gcOsJxhP8vSWfp7a3EevN1LQVTZCFDs5YhIHKYrCKfVOjDoNT23uZyQYm9T9A7EkW/v8/LNjmPVdXvaORlAUmFtuo63awbxKG+mMyvb+3B6AZoM3nKBnLEoG2XrygL2jYT7/u9fpGg3zpUvnc+Wy+sPOfC+sddDeHySdyd2HtkZRWFTnZCQUZ/3esSk/3r7RCK92jtHnj+I065hTXpoVC4c60JR032j4BLecmtFQnGA8jdNcPN3lc8lm0uEw6djS6zt4maqOJ2CSmQwusyyJEMWrqdzKB09r5Ox55QyH4rywa5jusQiZAj2Iq3Ga+da7F9NWbeeev+3g5y91Fd2yzYnQajS0VNk5s7mcdEbl2e2D/HlTH10jYTI5/KyeTumMSo83wprdI7y0e5SOwSCReJo6p5mFtU4ay6ysnFXGgloHvb4YT2zu55ntQ4yFc5tgF2KmKu1ZtJg0rUZhWaObtKry580DBKLJ495eVVWGAvszxTtG2NTjYzgYx27WsaDGyexyGzaTHkVRsBh0lNsMvN7jJ5Mp3Mz5ga0nfdEE9S6zlNABr+31ctMfNpFKq9z1rsWccZTGn4tqnUST41UruWQ36Wl0W3mla4yxk1xLeqC/wqZeH6PhOLUuM3Uu64w4CDbptdhNOvaM5Da5MLh/SYTHImfkD6hzmen3x/Hun9T6Ikm8kSR2k67kk1qi9Om0GlY2efjI6U20VdvpGAzy8p5RvJHCPIizm/R8/Z2LuKCtkt+t7+G6/3uVn7/UVbDxToVJr2Vpg5tljW5Gw3Ge2tzP39sHGQ4Wbz+GeCrN7uEQz+8Y5tXOMXYPjydMZpdZaa22U2YzHvaZXm4zccYcD41uC9v6Azy6oZeXd48e3EpbCJEdMpsRR9BrNaxoLCMYS/LnzX3EjpLNT6QydI2EeWHXG5niYDxJtdPEwlondS7LUdcP17st+KMpur2F20ipzx9jIBDDqBsv95/pntzcz389sZUqh4l73ruEuZVH7z+xsHa818LWvqM3rcumORVWNIqG5zqGJ13eGEumWdc1xo6BAOFYijnlVjzWmXUAXGEz0uePkkjmpslVOqMyGIiiAFZZEnFQuX38dbZ9YPxvpN8fIxSTBIwoLXaznosX1fDelQ04TDpe7Rpjc6+PWI7eb6ZCr9XwHxe2cM97lrC0wcXv1/fw/x58lR88t4t+f+HOU06W02zgtNnlNFfY2DkU4rHXe1mza4Rg7PgnkgpJJJGivT/Av/ZXyfb4Iuh1GtqqHMyttGM364/ZT0hRFOrdFs6eW47DrOeVzjEe3dDLph6fNH0UIktk1ieOyqTXsnKWm5f3jPGXLQNctqQWvVZDKJ5i32iEPl8EfzRJJJHGbtIz22PFZtSdsEFcmdWAQadhc6+fWQXY5C2VzrBrMEgglqRlhm89mc6o/OzFTh59vY+Vs9x84eLW4/Yi8NiM1DhNbOnzc8WyupzGptNqWFBtZ0OPn239gQk35PKGE7ze7WMwEMOg1zBvBiyDOBqXxcCekTB9/ihNOWhWOhKKE4ylcJr1M6IaZKKMOi1VdhPb+gKsaipjIBAjo6oF3QBPiJNV5zbzvlMb2dLrZ83uEV7cPcKcciuNZYXXVK+lys4X3zafXm+URzb08Ldtgzy9dYAzm8u5ann9MZPqxarGZabKaaJzJMS6vV72jIRZXO+ktdqOUafNd3hH5Ysk2DsaodcXJRhNklJVyiwGKu22STfDPbBcZJbHQsdAgOc6htg5EGKJNH0UYsokuSCOyWrUs2KWm1f3jvGPbYNUOEwMBmL4I0nSqorHaqCxzIpJP/EPIo2i0Oi2sHsoRCiWxGYqrEl112iEkVAct3lmbz0ZTaT577928ErXGO9YUst1q2dP6MN2Ya2DtXvGyKhqzg8qy+0mKm0xXtw5zGyPFcsJumB3j0XY1h8Y31rUqqfWaZmxB74Okw6tAntHIzlJLgwGYoTjKeZUlNaEPBuqnSZe7/GypS9AMJrEYdbLRDZPHnroIX76058yPDzMvHnzuOWWW1i5cuUxb9/R0cE3vvENNm3ahNPp5H3vex+f+tSnZnQS+kQ0GoXFDS7mVtp4uXOUzT1+enxR5lfZKS/AfkZ1bjOfPn8eHzhtFo+93sdTW/p5YdcISxtcvHt5PYvrnSXz/1ujKDRX2GlwW+gYDB48wF42y3XCXhmqqpJRD/mKiqoy/o83rtMoChpFQVE47KtGYULjqKoqw8E4e8ciDAZiBKJJFGW8+s5jNaKb4skBo07L4no3oViS9oEAT2/pZ5bHwrJGN7Uuc8n8vxZiOs2oo6fJTCR6enq44IILjrj8xz/+Meecc06uQy0YLouBxbVONvX6qQjF0Ws1VDuMuK3Gk54QVzlM7BoOjZ+9O8ra/XyJJtJ0DoeIJTM0eWbuQdFIKM43nthG12iYG86Zw2WLayd830W1Tv7ePkT3WGRaKlNaq+28uHuEF3aN8NaFR9/iNJNRaR8I0DkSxh9JUucy45nhnfl1Wg0em5E9I2HObc3uY6fSGQb8MRQNM2bbs8kosxrQaTRs6vGhKNBUgBVcM8GTTz7JHXfcwde+9jVWrFjBr371K66//nr+/Oc/U1t75HteKBTiuuuuY+XKlfz+97+ns7OTL37xi1gsFq677ro8/AbFxWLUcX5bFQtqHDy/c4QNPX7KrVFaq+0Fmcgvsxr46JlNvGdFPU9tGeCx13v5yqNbmFth46oV9Zwxx1MySUGDTsspdS4CsSQdAwH+smUATTTMkGaIjKqSUccTBxkVMmqGA30gVVVFZTyhwIHLDny7/zoFQFFQDn4PijL+vaIoaBUFreaNpINWM/5Po4BW0ZBIZ/CGEwTjSYxaDfUuC05L9ivibCY9pzZ5GAnF2DEYotffz9wKG8sa3ZRZpdmuEJNReO/oOTLZicQBP/nJT2hrazv4s9NZWvshT0SV08wZJh2qyoSWPpyI2aClwm5kc6+flU1uNJrCKEvfNRRiLJKgwm6csfvN7x4O8fUnthFNpPnq2xeyYpZ7UvdfWDf+97GlLzAtyQWTXktrlZ2tfQFaq+1HPGcsmWZTj5+esTDJtMqccmvBVcvki8dqZPtAgMD+s+fZMhJKEI6ncJoNM7Yy5Hi0GoVal5leX4QyqxG7vB7z4mc/+xlXXnkl733vewG49dZbef7553n44Yf53Oc+d8TtH3vsMaLRKHfffTcmk4mWlhZ2797Nz372M6699lo5wzlB1U4z715Rz/aBIC/uGuGlPSMsrHFR7Sy8KgYYT5C+e0U971hSy7MdQ/zxtR7u/st2apwm3rWsnvPbKktmvuDYf4A9GIixfvsw+8Yi4xUGjB/8H0gKcOhljL+noYKiAVRl/Ksy3tRNVSGTyaCgjCcpeCNRcWhyYrx3kkJazey//o3KCZfFwJxyW1bmnydSbjPhsRrp9UXZ1h9g31iEBTUOFtY5sZVAsjyWTNM9FqHGZS6J30cUphnzyprsROIAl8tFRUXFdIVZsGzG7E6A611mNnT72DsWZXZ5/s/c+SIJur0RVBUqC7BUczqs7RzlW0934DDr+eZVi2k6if8vVXYj5TYDW/v8XHZKTQ6iPFKty0yfL8pzHcN8YJUJ/f71or7IeH+FAX8MvU7DvKqZ2V/hWJwWPSrjy0UOJIWyYTAQIxRPMdcxc6t/TqTaYaLbG8GdgzNw4sQSiQRbt249ouJg9erVbNiw4aj32bhxIytXrsRkeuPz4ayzzuJ73/sePT09NDQ0HPP5PvzhDx/8/uqrr+aOO+6Y4m9QGtJo8Bpr+IPBjTkZwJwOcjJ/DZdcfDEPPPBA1uM7mgrAYKxiMDmb7z8X43//sZWK6F7Ko91o1dLZdeCSiy/mkZ88nO8w8ioDRLUO4jor+kwMe8KLNeVHw8nvdpavv38VhajORlRrJ6E1oagq5bFudCXwmpX31Ow7dEx/8YtfTPr+MyK5cDITiQM+85nPEI/HmTVrFh/96Ee55JJLchnqjOG2GjDpNGzu8eU9uaCqKh0DQbzhBA3umbf1pKqqPLsnxJ+29TK30satly3AfZJlgIqisLDWyaYeH6qqTsvZPI2isKDGycudo6zb6+WM5vI3+isE47j291eYaf9fT8Rq0GLSa9kzGspaciGVzjAYiKFRFFkScRwOs57VzeXo5DWZF16vl3Q6TXl5+WGXezwe1qxZc9T7jIyMUFVVddhlB+4/MjJy3OTChRdeePAkRVlZGVdfffVUwi8pqqrSGYDtXjDrYJYdJlsI4HA6ueTii3MT4DGoKuwNwdpBI53aFkYdLSwrh1WVYC2BYqR8jGmhSqahLwLBBJSZYLYDKsycVGI4H3///rhKXxj8CUhkwKKDoSg4DaexsgKMuuL+HJL31Oyb6pjOiNnfyUwkLBYLN998M8uXL0er1fLMM89w4403Eo/Heec733nc5+vq6iIaHd/CKBaL0d7efvKxR1PsG00QMBdm996pUKIJXhlIUqt4sRom/vtNdUzfbDicYutQlFQabEk9vhl2JvFP2/w8syfEkmoTH15mxzfci2/45B+v2pjkn5Ekr2zdRaVt+t5i9PEET706TF93N6ORFMFEmnKLjlRKxz7/tIVxUCIep7Orc/qfeBJSoTivDaaZo/Nn5Qz6cDjFjoEoBg10pbxZiPBwxTCm0ykQS6MG9fineDRz4D11/vz5WYqsOBwt+Xm8hOibrzuwDe6JkqjXXHPNwe/b29s5/fTTJxPmjLB3NMzTWwdIpDIsbnBjn8RSrc6uTk5ZdEoOozu6s4APMr6c8A+v9fDirhE2jmm49JQa3rWsDpeleNfq52tMC9mBnhSRZAZthZVlDS6qneZJPcZ0/v0HY0l2DAbJ+GNUR5K0mHTUuMwYdVrC8SRrO72kXSbOX1yDqQD7nkyUvKdm31THtHhfTSdhMhOJsrKywyodTjnlFLxeLz/5yU9OmFxoamo6+P1UJ2zDwTgJqx+PtfQa0NUk00R3jZCyeZjfPPHGjtmcBKfSGYZ2jWBPRZhXWZiNpXJpc4+PZ/b0srrRwk2XL8vKAabOEeE3m1/Dr7FzWtPRmyzmQkM6w0t7RvHpNDjKtZxSlt/+Cp1dncxump23558IWzDO6z0+ymobqHFNbpJ0NPFuH66kn+aK3Ix9MYzpdPKGE8yttFHrntr/u5mWWHC73Wi1WoaHD8+ijo6OHnES4oDy8nJGRkaOuD2Mn6gQUzPLY+W9Kxt4ems/r3SNsrC2cPswvFlzhY2bLm6jZ1WE36zr5tGNvTy5ub8kkgziDQd6UgwFYuwYCtIzFqWlysbSBjdOS+GUqyTTGfYMh+kaCTMaTqDXKMz2WA9L2FmNelY2uXm1a4yntgxw6eKagt2CVBSfGbEA+WQmEkezZMkS9u7dm+3wZiyTXku1w8TmXj+ZzMmvYZuKA1tPuiwzb+vJZDrDD/65myqHkSsXOrO29rvebcZh0rG1N5CVx5sonVbD8kYXlTYT8yod0rhxApxmPZr9W1JOVfLAkogS3yViOBjnV2v3Ekum8x2KOEkGg4GFCxceUbm4Zs0ali1bdtT7LF26lHXr1hGPxw+7fWVlJfX19TmNd6ZwWQxcsbSehbVOtvT56RgInHBLxEJS77bwuYta+f4HlnNGs4dHN/by/36+jp++0Ik3ksh3eCJLKh0mVjeXj28r3O3jTxt7WL93LO+fCaqq0j0W4cWdI2zu8TEWjlPtNNJSbT9qJZDDpGdFo5u9Y2Ge3jJeNSRENsyI5MLJTCSOpr29XZo7Zlmty0wonqJrZOoHN5MVS+7fejKVocYx9bO2xeaRDb30eKPccE4zhiw2OjzQd2FL3/SvRbAa9TSVW0ume3euGXQanGY9e0bCU36s4WCcUCyF06wv2c750USab/x5Gw+/2s0/d0xh7ZDIu2uvvZZHHnmE3/3ud+zevZvbbruNoaEh3v/+9wNwzz33HLac4fLLL8dsNvPFL36RHTt28Ne//pUf/ehHslNElhn1Wi5cUMVbWivo80dZv9dbdAc9B5IMP/jAClY3e3js9V7+7efr+OkLe/CGJclQChRFoclj5ex5FZj0Wl7YOcKjG3rpGAiQSk//69UbTrC2c4zX9nrp8UaxGLW0VjuosJmOe+LIZTGwvKGMXcMh/rZtkGQeYhelp3RPL73Jtddey0033cTixYtZvnw5Dz/88BETiU2bNvHggw8C8Mgjj6DT6ViwYAGKovDss8/yq1/9is9//vP5/DVKjsuix6zXsKnXz5zK6e0uv3Nw/9aTtpm39WS/P8pvXu1mdbOHlU1ldHZlNxGwqM7BS3tGGQrGZuzuG8Wi3Gpk10iIaCKNeRK9T95sMBAjkkhR77ZnMbrCkVFVvvP3HewdDeM063m2Y4iLF07fsh+RXZdeeiler5f777+foaEhWlpa+NGPfkRdXR0Aw8PDdHd3H7y93W7ngQce4Otf/zpXXXUVTqeT6667jmuvvTZfv0LJUhSFZY1uyqwG/rp1gJf2jLCswYXDXFzLC+rcZv6/i1p538pGfruum8de7+PJLQO8bWE1Vy2vP+nGyaJw6LQaFtQ4afJY2T4Q4K9bB5nlCbJ8lps6lznnicdYMs3OwRDd3gi+SAKTfnxnrMlU4rqtBpY3uHmt24tOAxcuqJYG2GJKZkxyYbITCYD777+fvr4+NBoNTU1N3H777SfstyAmR6MoNJZZ2DEUwh9N4JymycOBrSczqjrjDn5VVeWH/9yDVqNw/dlzcvIci2rHdx/Y1hegsnVmjW+xcVkNMAy9vihzTzLBl0hlGArE0WoUzPrSXLf58Cv7eGnPKP9v9WyS6Qw/f3kvA/5Y0awLF0f64Ac/yAc/+MGjXnfXXXcdcVlraysPPfRQrsMS+83yWHnPygae3jrAK11jLKhxUOuy5DusSatzm7nxohbed2oDv1nXzeOb+nhqywBvWyRJhlJhMehY3liGN5ygfSDAE5v6mFdhZ2mjC48t+z3T0hmVvaNh9gyHGA0nUFWVBrflpCsHPTYjS+pcvN7jQ6vRcF5bpSQYxEmbMckFmNxE4sorr+TKK6+cjrBmvEqHiR2DIbb2Bjhz7sR7YJwsVVXZMRjCF0lQ75p5W0++uHuU1/Z5uf7sOTn50IPxSaHVoGVLX4C3tFbm5DlEdtiMOnQahc6R0EknF4ZDcULxZMkuiXhh1wi/frWbC9oqeefSWoZDcX7x8l6e7Rji6lWN+Q5PiJLlshh41/J6/tkxxObeAIFYipYqe9Z6BE2nWpeZGy9s4X0rD08yXLI/yVAmSYai57YaOLO5nF5fhPaBAPvGwrTVOFhc78paL6KhYIwdA0GGg3HCiRSVdhOVdtOU57KVDhOL6pxs6vWh08A5LZVoZtj8WGTHjEouiMJk1GmpdprY0uvntNllaLO4/v9oBgIxBgJRDDrtjOviHEmk+PG/9tBcYeWyU2py9jxajcL8Ggdb89B3QUyOVqNQbjPSORJGVdWTSg4MBmJEkmnq3cV3VvFEdg+H+M7fdzC/2s6nzpuLoihU2k2cUu/k2Y4h3n9qQ0kmVIQoFHqthgvmV1FuN/H8jmGCsRRL6l1Fu5zx0CTDb9d188SmPv4iSYaSUueyUO0wsXs4zCudY+wZDnNKvZORYBLzSBiV8RNd418BVFSVgz9n1AM/qxzoaaqqEEulGQzE8EeS2E1a2qodWd3locZpJqOqbOj2o9NqWD23XD7fxKRJckEUhFqnmX5/lM7RMHMrc7dmO5HKsHMwSCCaorncOuPeNH/x8l68kQRfvmx+zis2FtU5WbfXizeSwD3DkjjFxmMz0h+I4Y0kKJvktrfjSyJiaJXSWxLhjSS47c/tOEx6vnTpfPSHJD7Pb63ku//YSftAkAU1jjxGKUTpUxSFpQ0uyqx6/rp1sGj7MByq1mXmPy4cXy5xaJLhssU1vHt5PY6jdPgXxUOr0dBSZaexzELHQIDnOoaI+MPsSw4dTBqM5w0UDmYQlP2XqaAAyv6rVEBBBUXBYdIfsbVkNtW5LKQz8GqXF51Gw2lzymbcXFlMjSQXREFwWfRY9Do2dftyllxQVZWtfX4GA3FcFv2M26pw52CQJzf3c9kpNbRU5b7p3sLa8QOubX0BVk/DcpdCEkum8UZTzM53IBPkMI9/FHSPRiadXBgKxgjHU7gthpKagCTTGe54sp1gLMndVy0+IkF2ZnM59/9zN89sH5LkghDTpLFsfx+GLf2s7RxjYQn87dU4zXz2ghbeu7KBX7/SzZ829PL01gGuXFbHO5bUzrhtskuNSa9lSYObWDLNjj1BZlfaUTQHkgcKCuOfmwc+Pg9+ReHQj9QDt9co5PyztrHMQiaT4aU9o2i1Cqc2leX0+URpKc6aMlFyFEWh0WNh71g0Z1s17R2N0O0d3/KyvgibQk1FOqPyg+d24zTr+dDps6blOZsrbBh1mrxsSZlPqXSGr/xpC7c/N8S+senfYvVkWAw6bEYtu4ZDk77vYCBOJJEuqVJeVVX5/rO72D4Q5MYLW2iuOLIXhdmg5cxmDy/sHC66rfKEKGZOs54rl9ezpN7FtoEgnWNx1ANnfotYjXO88eO9Vy9jcb2Th9bu42O/WM+jG3vlPaYEmPRa7EYtdrMem1GP1ajHYtBhNmgxG7SY9OP/jLrxfwadBr32jX86rQatRpm2JH5TuY3ZHitrdo2wYZ93Wp5TlAZJLoiCUWk3oSjkZJ2+N5ygYyBIOJZiVpl1xjVxfHJzP7uGQ1x/9pysNRU6Eb1WQ1u1na19gWl5vkLx8KvddAwGUYC7nmonmkjnO6QJqbCb6PPFJrVHdzyVZigYQ6ctrSURj27s4x/bh7j61IbjVt2c31ZFOJHmla6xaYxOCKHXajh/fiXnt1UyGkmxpS9QEgkGGG+I/OVLF3DPe5bQ5LHwkxc6+fgv1/PXbQOkM6XxO4ri0Fxpo95l4V87h9nc48t3OKJISHJBFAyDTkON08TWPj+pLGbp46k0m3t9jIbi1DjNmA2lcxA0EaP7O9sva3Bx1jQvT1hY66RrJEwolprW582XzT0+freum4vmV3H9qR56fVHue3ZnUUx6XWY9qYxKny824fsMBeKEYylcJbQkYt3eMX62ppMzmz28/wQ7QZxS58RjNfDM9sFpik4IcYCiKCxpcHF6oxVvOM7mXj+ZInivnaiWKju3XXEKt71zEWVWPfc+s4tP/eo1Xtg1UlK/pyhsLdV2ahwmnu0YYlv/zKpEFSdHkguioNQ6zUQSGXaPTL48+2jG+ywEGAjEcFoMOdt6sZD95IVOUpkMn3hL87QfAC6qdaAC2/pLv3ohGEtyz992UOsyc/3Zc2gpN/Kh02bxr50jPLGpP9/hnZDTrEerwN7R8ITvMxSMEU2m8VhK4++q2xvhW093MMtj5cYLW0643Z1Wo/CW1krW7/Xii+RmOZcQ4vhmu41ctLAaXzTJ5h5fyR14L2lw8d/vXsItl443Yr77L9u58bcbWb/XWxSJa1H82qodVNhM/KN9iB0DwXyHIwqcJBdEQXGa9ViNOjb3Zic72jkSpnssggLUucxZecxisn6vlxd2jfC+lQ3UOKf/92+ptqPTKCW/JaWqqtz7zC780SSff2vrweqYq1bUs6qpjAde7GT7QGEnWHRaDWVWI50TTOzFkmkGA3F0Gk1JVAOFYilue2Ibeq2Gr1w6H9MEl3mc11pBRoV/7RzOcYRCiGNpqbLz1gVVBGIpNpVggkFRFM6Y4+F/3r+MGy9sIRRL8Z+Pb+VLj2yeEcl7kV+KorCw1oHbbOBv7YPsPon+TGLmkOSCKCiKotDoNtM9FmUsHJ/SY42G4nQMBgknUjR5bDOuz0I8leaH/9xNncvMu5bX5yUGo05LS1Xp9114eusgL+0Z5SNnzGJu5RvN/zSKwo0XtuCxGbj7L9vxR5N5jPLEPDYDY5EkwdiJ4xwOxonEU7itxb/rSjqjcvfT2xkKxrnl0vlUOkwTvu8sj5W5FTae2T6UwwiFECcyr8rOWxdWEYyleL279BIMMF4tdX5bJT/80ApuOLeZPl+Um/+wif96fOuEE8NCnAxFUTil3ondqOPpLQN0DATISA8QcRSSXBAFp8JuQqPAlt6TPyCNJdNs6fXjDSeod5onfBaylPx2XQ8DgRifekszem3+/tQX1jrYNRwqmsaGk7VvLMKPX9jD0gYX71xad8T1NpOOL71tPv5okv/+a0dBN+RymvWoKvR4oye87WBg/5KISW5dWYh++sIeNnb7+ORbmk9qW8nz2irZPRye1JISIUT2za20c/GiakLxFBtLNMEA4w0tLzulhh99eCUfOWMW7QMB/v3XG/nW09vp8534/VuIk6HZ3+fEYdbzt22DPNsxNKGTEWJmkeSCKDjjjR3NJ93YcbzPgp+BQAyXRY+7BA5+Jqt7LMIfX+vh/NZKTql35TWWRbVO0hm14JcFnIxEKsN//7UDk05z3DX6zRU2bji3mY3dPh5+dd80RzlxNqMOk07LnhOUPMaSaYaCcfRaTdEn7p7eOsDjm/p5x5JaLlpQfVKPcW5LBVqNItULQhSA5gobbzulmmg8xYZub0EndKfKpNfynhUN/OQjp/KeFfWs7Rzjk796jQde7CSSmBmNlMX00igKS+pdtFTa2drn508beqWKQRxGkguiINW6zMSSGXadxLqu3cNhesaiaBWFWqclB9EVNlVV+cFzuzDptVy7uinf4dBWY0ejUJJLIx58qYvOkTD/cWELZVbDcW/71gXVXDi/kt+82s26vYW5daGiKFTYDewdixx3ojAUiBOKpSizFPeSiK19fn74z90sb3Rx3erZJ/04TrOelbPcPNcxXNIHMkIUi9nlNi45pZpYPM3GEk8wwHhi+CNnNPHjD6/k/LZKHtnQyyceeo1/7RiWpo8iJ2pcZlY3l5PJqPx12yD/2D5IQKoYBJJcEAXKYdJhN+p4fZL76o6E4uwcChJKpJjlsRZ0n4Wdg8GclC8+s32ILX0BPnpmEy7L8Q94p4PFoGNOhY0tJdbUcd3eMR57vY+3L67h1KayCd3nhnObmV1u5dt/3cFgYOJbPk4nt9VAPJlhKHjs+AaDMWKpdFFXBQ0GYtzxZDtVDhNfuLhtyu8V57VWMhZJTPo9SwiRG03lNt62uIZYKsNr+8ZKPsEA4+/f/37+PL717sW4LXq+9dcOvvKnLbJkS+SEQadl+awyWqvstA8E+NOGHtr7Z1YVQyajMhZOEEuW5tLfkyHJBVGQFEWh3m2mzxdjJDixxo6xZJrNPX7GQgka3ZaCLtd+ec8on//963ziofV86+ntdI1k54M/EE3ywIudzK+2c9GCqqw8ZjYsqnWwYzBI4iSWuRQibyTB9/6+kyaPhWvPnPgZb6NOyxcvaSOtqtz1l+0k07kbj3RG5ffre/iff+yc1Jkrp1mPwngviaOJJdMMB+MYdcW7JCKaSHPbn7eRzqh85bL52Iy6KT/mqtllWI1anpWlEUIUjFkeK5eeUkMyrbJ+hiQYYHzrwHves5RPvqWZPSNhPvubjfz0hT2yVELkRI3TzFlzylFQ+Pu2Qf7WPog/UrpVDKqq4g0naO8P8NyOYZ7fMcyTm/sZKtCTRtNNkguiYFU6TGg1Clt6fSe8bSajsrnXz1AghsdqKIgz9sfyerePu/+ynbmVNq5cVserXV4+8+sN3PbnbewYnNr+wQ++1EUonuKTb5l7zPX/+bCw1kkyrbJzqPj3R86oKt/9+w4iiTRfuLgNg25yb6O1LjM3XtjCrqEQP35+T05i7PWOdxB/8KUu/tY+yN7RoycKjsao0+I069kzfPSE12AgRiiWwmUuziURGVXl27kUFdMAAQAASURBVH/vYN9YhJsuaaPenZ2lU3qthnPmVbBmz6hM4IUoII1lFi49pYbUDEswaDUKb1tUww8/tIIL2yp5dGMfn/jlazzXMSRLJUTW6XValja4WVDroGMgwJ829rCtz19Sf2/BWJKdg0Ge3znMv3YO89peL73eCBlVZTAQ49GNfeyc4jy+FEhyQRQsvVZDrdPE1v4Tn/HePRyizxtFp9NQ7TRPU4ST1zEQ5LYnt1HnMvOfly/ko2fO5qfXrOQDqxrZ2hfgc797nVsf3cLmXv+kP/y39vn567ZBrlhaR1O5NUe/wck50IF/Swn0XXjs9T5e2+fj386eTWPZyR2Ynj7Hw1XL63hqy0BWmwBmVJXHX+/j33+zgV5flOvPngPAq5Ps8VBhMzIQiB21zG8wECeWTFNWpEsifrV2Hy/vGeO61bNZ3ujO6mOf31pJIpVhze7RrD6uEGJqGsosvH1xDemMyrquMVI5rBorNE6znk+fP4//fs8SPDYD9/xtB196ZHPWKiaFOFSl3cTZcyvQaTT8vX2Qv27txxdJ5DuskxZJpOgcCbNm9wj/3DHMuq4x9o6O96VqcFs4pc7F3Eo7Z8wpx6TX8NetA7zSOXOSmEcjyQVR0GqcZhKpzHEzgUPBGDuHQkQSKWaVWQq2z0LXSJj/fHwrLrOBr79zEXbT+Jlfu0nP1asa+ek1K7n2zCa6RsLc8shmbv7jZtbtHZtQkiGVzvCD53ZTYTdy9arGXP8qk+Yw65lVZmFrb3H3Xdg9HOLBNV2cPqeMSxae3M4CB3z49CYW1Tr4/nO7sjLJGwrGuPXRLfzo+T2cUufkvquX8Y4ltcwpt7J+r3dSj+W0jG9J2fumLSmjiTQjwTimIl0S8eTmfn6zrpuL5lfxjiW1WX/81mo7NU6TLI0QogDVuS28fXEtKLBur3dGJRgAWqrs/Pd7lvDp8+aybyzCZ3+zgR8/v4dwvHQrrWLJNDsGgzP6QC8fdFoNi+tdnFLrYvdwmEc29LK511c0/x/iqTTdYxFe7Rrjue3DvNI5yq7B8S3Vqx0mFtY6mVtpx201HDzm0GoUlja4qXdbWLN7hL9tG5ixfRgkuSAKmsOsx2HWsekYB6XRRJqtvQG8kQR1bgtGXWEe8PT5onz1sS0YdBq+ccWio+4sYDHoeNfyen5yzUo+fs4choNx/uvxbdz4242s2T1y3P26H329j31jEW44Z07BHvQtqnPSPhAomg+XN4sl03zr6Q4cZj2fOW8eyhSXnWg1Cjdd3IbVoOXOp9pPupReVVX+3j7IZx7ewM7BEJ8+by5fe/sCPLbxyoKVTWW09wcIxSb++HaTHp1GoXPk8N1aBgMxQvEUTmtxLYlQVZWHX9nH/f/czcpZbj7xluYp//87GkVROL+tkk37l2gJIQpLrcvMZYtrUBR4ZYZVMMD4NoIXL6zmhx9cwcULq3n89T5ueGg9z2wvraUSA4EYD7zYybX/9yqf+93rfO2xLXjDxXv2vFiV242sbi7HqNPw7PYh/rKln9HQxPqoTbdUOkOfL8pr+7w8u32ItXtGae8PEIwlcVsMLKh10FbtoNxuQq899uHznAobi+ucbB8I8tjG3oL9fXNp6l2sRM4EYklu+OV63raoGs/s4ixBzoZGt5UtfeOT9UqH6eDlB/ssBGOU2wy4C7TPwkgozq2PbiGVUbnrXadQfcjvcDRGnZa3L67l4oXVPNsxxO/X93DnU9tpKLPw3hX1nD2v4rDqjMFAjF+9so/T55SxarYn17/OSVtY6+DPm/vZPRyipcqe73Am7cfP76HPF+UbVyzCkaV+A26rgZsvaeOWRzbzvX/s5IuXtE3qoNcbSfD9Z3extnOMhbUO/uOCFqqdh7++Vs5y89t13Wzo9nL2vIoJPa5Wo1BuM7J3NIKqqgdjGgzEiCfTlFkKa9nN8WRUlR//aw9PbO7n/NZKPnP+XHTHmRhM1XmtlTy0dh/P7hjmfSsbcvY8QoiTU+M0c/niGp7Y1M/arlFWNXmOe7BQihxmPZ98y1wuml/F//5rD9/5+w7+snWAT5w7h9nltnyHd1JUVWVjt48nNvXzatcYigJnzPHQXGHj1+u6+fdfb+D/u6iFZVleDieOT6fVcEqdi7FwnC37E+/LZ5WxsNZx1M9iVVVJZVRSaZVkJkMqrZJKZ0hm9n9Nq6T2X55MZ+gajpGw+VAUDvYa0yjKwZ/HvwIoaJTxkwAaBRTGr4PxudSAf/zkSTieQqtRcFsNzPJYT+qEXYXdxBlzdKzfN8ZjG/t4S1tFTv+uVFVlMBBn72gYt8VAS3V+59iSXChgRp2GAX+M+5/bzcIaJzbTzPzfVW4zoNMobOn1c/4hB+Y7h0L0eiMYtBqqHYXZZ8EfTXLro1sIxlLcfsWiSa3R12s1vHVBNRe0VfHC/8/encfXVZeJH//cfV+z70mTJk3TpntLF0DWIggiiAOKVpgZwBkdh0EdRXFwYR1ZZnQUnZ8LDIiIKCKorKJCoVBoKW3StE2TNm327e77Pb8/0kRKtyz35t4kz/tFXk1Ozjn3ew8n537Pc77f59nXzy+3dnDP83t4ZMtBPrqilLMX5KNVq/jhX1pRq+C606vT+E6mrqHYAYzkhphpwYVX9/XzXFMPH11eypJSZ0r33VDsYNPaSn66uZ3fvtPJpUtLxt2m77+8j1AswbXrK7lkSclxpwTVFtiwGbRsbR9/cAEgx6qnuzPMcDCKy2IgGI3T749g0M+cKRGxRJL7X9jDX/b2c+nSEq5ZX5n2RKcFdiOLiu38aXcvH1tRmpYREkKIqSlwmPjQkiKefqeLN9sHWDUHAwwA8wts3P3RRl5s7uFnm9v518e2c+HiIjYUnXgbRVFIKn/7N6koKO/7N6koaDXqlFTiOZVgNM5Lu3t5ekcXh4dDOEw6rlhZxgcXFZJ7ZATf6io3dz3bwn88tYuPrijlE2sqsnYK7WzlthhYX5PH7m4PL+/ppWMwSJnbRDw58lkdjSeIxJNE48mRcyj5t3MpnlRIHAk4JJIKCiMPPZKKQldvmH4GABWggEqFShn5F+XIYhQURYVaPfKjaiy4oELFSPBBpQanSU+J04ZJp5nyZ7dZr2XdvDy2dwzxh3e7Oa3azdJSF+oUnneKotDtDdPWH6DPG8EXiVPiNEpwQZyYQavhtksX8fcPbuW7f5r4U83ZQnsksWNzl5f1NbnAyBPU1j4/kXiS+fm2rKqMMCoQifMfT+2k1xvh1ksamD/JG2qNWsWZtXmcPj+XLW2D/HJrB9/70z5+8eZBVlW6ebN9iL9fX0WeLbtHt7gteoodRnYe9vKRZZluzfj1+sJ89097mZ9v5RNr0pPP4iPLStjd7eOnr7YxP986Fog5Hn84zg//0srLe/qozrPwb+fVnTRopVGrWF7h4q2DQyQVZdx/K44jozMODgRxWQz0eCP4I3HcWTpC6P1C0QR3/KGZbR3DfHpdJZctK5m26+fZC/L575f2safHT12GP+SFEMdXYB8JMPz+3S627B9gcalz7Lo3l6hVKs5bWMjaebk8vOUAv3+3iz/sBP1LvccEDBRl5H5tvIodRhYW21lYZGdhkYNipzFl1+FDQ0Ge2dHFi7t7CcUS1BZYufHcWk6fn3tMoKgix8K9Vyzhf/+6n8ffOsTOwx6+sLGOfNvJR5KK1NKoVTQUOykORmnu8tDtCaNW/+13WrUKjVqFWq1Cq1KhUavRadRY1Wp0WjV6jRqdVoVadWQ9lYp2xUNlxcioXUVRxs7PkXN15Jwd/TmpJFFgJHCBgnIkgKHXqrEYtCm/l9CoVSwvd7G3189f9/Qz6I+xYX7ulB/QJJOjQQU//b4ovnAMs0FDVY55Qn+f6SLBhSzXUOLgsuUlPP7WIZ5r6mHjFJPIzVRFThMHh0Ls7fUTiyXZedjDUDBKhds84VKA0yEcS/CtZ5poHwjytQvrWVxy4pvF8VKrVKydl8NpVW62dQzzy60d/GFnN5U5Zi5OQ3K6dGgocYzlj8jGgND7JZIK9z6/h2QSvrixLm3D6VUqFZ8/Zz43/jLA3X9s4f4rlx53ms/bB4f47xf3MhSMctWqMj62smxcbVpZ4eLPe/rY1zv+KSlmvRarQUPbQJAl5S56vGGi8SSu4+QLyTaeUIxvPr2Lfb1+/uXsGs5bOL3XzfU1uTzw5/281NIrwYUsFI1Gueuuu3j66aeJRCKcdtpp3HrrrRQWnvg82bt3L9/97nfZtWsXhw4d4rOf/Syf+9znprHVIh0K7CYuWVLCs7u6ebN9gAK7kfn5thkzOiuVrEYtN5xZzXkLC3jqzX3YbHZUKhUa9d+GkKvVKtSM/KtSved7Rvoo6iPrqlUQjCVo6faxpW2QF5pHktw6TLojgQY7C4vtzMu1TOhzNZFUeOvAIL/b0cX2jmG0ahWnz8/lQ43Fp/xsM+o0fO7s+TSWOvmfP+3j87/YzufPmc9p87J3Ouls5TLrWVedRzyRHDmnptAfHA00jDjVfqb/71qlUlFbYMNh1LGz04M3HOPsBfk4J/GgJplU6PSEaO8PMOCP4o3EsOp1zC+wYdZrSSQV/JFYGt7FxEhwYQb44OJC9vb6+dFf91NfZJ90+buZzGbU4TBp2X5wCF04gmKJkGsx4DBl341OLJHkzj/upqnTyxc31rGy0p3S/atUI5HQ5eUu9vb4yLEaZszwvkXFdp5v6uHAQJCqLCuXeTyPv9XBrk4vN55bS1GaS5xaDFq+8sF6vvCrd/jOsy1888OLxv6/hqIJfrq5jT/s7KbMZeKrFy6Z0EiY5eUuVMCb7YMTmpKSazVwaCiIJxRjwB/BoFNnbdLUUX2+CF9/aic93jBf+WB9RjqOZr2W0+bl8Nc9ffzDhqq0DLdOJBV2dXqYl5f9f0fZ5rbbbuPFF1/k3nvvxel0cuedd3L99dfz61//Go3m+Od3KBSipKSE8847j/vvv396GyzSKsdq4MpV5ew47OG11n5ebe1nXq6Fcrdlxny2plJ1npVLFzqoqqxKyf4UReHQcIimTi9NXV6aOr28tn+kXK9Bq6au0DYWcKgrHLlJej9fOMbzTT38fmcXPd4IORY9V59WwcaFBRO+STuzNo/5+VbufnY3t/2+mYsbi7hmfXqu0+Lk0pn/KNsUOIyYDRrePjjMb7d3clZdHuU54/v8TiYVDg+HaB8YCSr4IjHsBi21+cf/e8m07GuROIZapeLGc2v53KNv85/P7uaeK5Zm5dP6dCtzW9h5eBiCUSqd6mMS12WDRFLhvhf28NaBIT57Vs2E5rhPxmSnWmTKe/MuZHtwobnLy6NvHOTM2jzOqkvv/8dRVbkW/vkD1dz3wl4efv0Am9ZV0tTl5f4X9tDtCXPp0mKuPq1iwjf4dpOOukIbWw8M8Yk1FePezmXW0z4Q5N3DHnzhODlZPmqhYzDI15/aSTCa4BuXLErJiKHJOntBPn/ZO1ITe211bkr3rSgK3395H8819bC0zEnpHAw4T5bP5+OJJ57g9ttvZ/369QDcfffdnHXWWWzevJnTTz/9uNs1NjbS2NgIwA9/+MNpa6+YHmq1iqVlTmrzrWxpG2DHIQ+HhkLUFdjIsxnm5JTUVFGpVJS5zJS5zGOjbwf8EZq7fTR1emjq8vLLrR0klZG571W5liMjGxzkWPS80NzDy3v6iMaTNBTbuWZdFWuq3FO6MS12mvjPjy7hZ5vbeeqdTpq6vHxp4wKKndmZv0vMDjajjrXzRkYf//7dLtZW57K4xHHCPAyJpMLhoRDtA34GAjEC4Rg2U/YGFUZlb8vEUdwWPTeeW8s3nm7ip5vbuP6M7E7elw65Fj1GnYYgUOG2ZN2wekVR+MHL+/jr3n6uWVc5Z6ewnEy+zUCu1cDOTu9IvfEsFYjE+c5zLeTZDHzmzPSULTyRsxcU0NTl41dvH6LHF+aVvf3k2Qzc9pHFU7pZXlnp5uHXDzAUjI67sordpEOtgo6BANGEgtuSvXk99vT4uPV3u9CoVdzxkcXMy8tsxvOlZU5cZh0vtfSmPLjwizc7eK6ph0sai6kY55MPMWLnzp3EYjE2bNgwtqyoqIjq6mq2bdt2wuCCmBvMBi1nLShgYbGDv+7tY8dhDy6zjrpC+7QkJ5wrcqwGNtQY2HAkj1YwGqel2zcysqHLy3NNPfxuRxcAeq2as2rzuKixOKUPJXQaNf94+jwWlzj4rxf38q+Pbeefz6rhzNrpeZgg5ia9VsPqSje7u328vKeX4WCUtdW5Rz00TiQVDg0Fae8PMBiIEojEsZt01BbYMemze/QoZGFwobu7+6TzHueylZVuLllSzFPvdLKszJnVZQfTQatRs25eDvvafFk3fE1RFH66uZ1nm3r42MoyLltemukmTasBfwS1WnXKm1aVSsWiYjvbDw0fVeIwm4w8FW6l3x/hrssbsWSgQ3nd6fPY1+vjr3v72biwgGs3VE05Sr2ywsXDrx/grQNDnFtfMK5tdBo1LouewUCUfJsxa0dMvX1wiDv+0IzTpOebH25I+xSW8RhJxJrP0zs68YRiKUsU9+yubn7+xkHOWZDP5cvHV1lkpktlv6C/vx+NRoPLdXQ5upycHPr7+1PyGu/X3t5OKBQCIBwO09zcnJbXmavSdUwXmhRs1hhvHx5gz4FOCm06Suw6dJrs+9xKtWgkQlt727S+phNYVwDrCqwkkhYOeWL0B+PU5xkx69Xg76XNn/rXLVDDFzbk8NDbQ3znuRZeae7go4sc6NPQz8zEcZ3tZuoxNQKWWJzn3u5j59521pSaMerU9PjjHPZG8YQThOMKNr2GHIsGEmq6T3H+JxWFYDRJc3xqn2XvvabW19dPePusCS60tLTwk5/8hGeeeYadO3dmujlZ69PrKtl52MP9L+7lu1daybFm75PEdFCr1Vn5wf74W4f4zbbDXLS4iKvTVFEgW/X7I7R0+0goSc6Yn3/K9ReVOHh5Tx+dw2FKXJm/CXy/P7X08Ze9fVx9WgULCu0ZaYNeq+ZbH15Etyecsqkv83ItuM16tk4guACQbzUy4I/gtmbnlIi/7Onjvhf2UOY2c+vFDbizaOrG2QvyeXL7Yf66ty8lI3XeaBvk+y/vY3m5i8+eVYMvHE9BK7PXRPoF9913Hw888MBJ13nooYdO+DtFSV+O7crKyrHvm5ubJ9VZEyeWzmO6EDg7nuDtg8O82T7I4bjCfLeNYqcp60ZPplJbe1vKci5MVs00vlYVsLRO4ZEtB/jVW4c4HIB/31iX8pFh2XBcZ5uZfEyrgAWhGNs6BmkO6Sk2mBjWRlHbElTl6ymwGyeUXHY0oWP9FKdkT/WaOm3Bhd/97nc8+eSTdHd3U1paynXXXceKFStoaWnhnnvu4a9//St2u53PfOYzaWvDI488wo9//GP6+vqYP38+N998MytXrjzh+i0tLXzrW99ix44dOBwO/u7v/o5//ud/zujTVp1GzRc31vGvj23n3uf3HJX0TWTGMzs6+b/XD/CBujyuO2NeVj6NTxdPKMbeHh/BWByDVkM0nkB/inwADcUjN+w7Oz1ZF1wIxxL8bHMbdQU2Pprh0Sc2ow6bMXVl0VQqFSsqXby6r594Ijnu+apFTiNGnXpSmY3T7ZkdnfzwL/tZWGznaxctzLphy1W5FqpyLby0u3fKwYXd3V7uenY38/KsfPmCBbMiEVYq+wWbNm3ikksuOek6xcXFbN++nUQiwdDQEG7335LtDg4OsmrVqim/JzH76LQa1szLYUGRjc37+tnd7ePgYJD6Ivu4p5iJ7KdRq/jU2koWlzi49/k9/Nvj73Dd6fM4f2HBnOrXienlMOlYNy+X7YeGOTAQoMBupNxtmdEVa6ald/L444/zxS9+ke7ubmpra/F4PFxzzTU8/PDDXH755ezYsYMvfOEL/OlPf+Kf//mf09KG3//+99x+++3ccMMNPPnkkyxbtox//Md/pLOz87jr+/1+rr32WnJycvjVr37F1772NX784x/z05/+NC3tm4hSl5kbzqhmx2EPT7x9KNPNmdP+1NLLA3/Zz5oqN/96Tu2sfpLxfoHIyBzJeFLh9JpczDo1wWjilNuVOE04TTp2dXqmoZUT84edXQwFY3x6XeWsDNqtrHARjCZo7vaNexu1SpV1FUkUZeQJ0wN/2c/qKjffuKQh6wILo85ekM/eXj8dg8FJ7+PwUIhvPt1EjkXPf3xo4YyYc3kqqe4XuN1uqqurT/plMplYtGgROp2OV199dWzb7u5uWltbWbZsWTrfspjhHCY9H1xczBUrS7EZtbzZPsiOQ8OExvG5J2aOZeUu/vvKZdQX2vjen/bxnedaCEZn9ygxkVkjeRhyWF7unvGBBQCVks6xgEd8+MMfpr6+njvvvHNs2c9+9jPuuusuli1bxgMPPIDdnt7hx1dccQV1dXV8+9vfHlt2/vnns3HjRm666aZj1v/5z3/Od77zHTZv3ozROFKV4Pvf/z6PPvoof/nLX04axfzkJz859v1VV13Fo48+Oul2R9UG/DonumT0qOUKcMDeyLChgPlDb2CJZ9+NWrpcsHEjf3z22Uw3A48+jzbHUqyxIeYNv42aZKabNGkTPaZJ1Pj1LuIqHbmhQ+iVCAPGEtSJGKZk4JTbt9mXENQ5aBj4y1SanVIJlYamnDMwx71UD7815f1ly3n6XgmVhp25Z5MXPEBxYE+mmzNhF2zcyB+efZZD1noGzOW4Q4co8zWhIu0fY5MWU+vZlXMm+cF2igN7J7X9XtcakioN84fewJAIHvU7c8yLMRmaUhtHP6f+7//+b0r7mYhM9gv+4z/+g5deeom77roLp9PJHXfcgdfrPaoU5aZNm2hsbBzrH0SjUVpbWwH4l3/5F9auXctVV12F2WymomL8FVhkWkTqZeKYJpMKTV0eNu8bIBBNHCldaZ4VI4pgZg81T5VEUuGJtw/xyJYDFNiN3PzBeiqnmFBSjmvqyTH9m9FpERvmwrSIgwcP8uUvf/moZZdddhl33nknN9xwQ9oDC9FolF27dnHttdcetXz9+vVs27btuNts376dlStXjgUWADZs2MB//dd/cejQIcrKyk74eueeey55eSP/Y91uN1ddddWk2+6NKnT4wXac0dHhBPx0N/QUnsa1dWDMzgd3KWd3OLhg48aMtqHNC7/aD8Um+LuaHAya8zLanqmayDFNJKEjAP4oNLih3LaEhAJv9Y38rnQcSfq39sELh2DtWRtxZMmo0s3dkOiCjyzKpdgy9fMrG87T4/HuhYCpigs2zLwPY7PNTqR2IwPDsCYfPlBcikqV/clTw63Qq5/HxvXzmMjgpkgCfr4XlAh8ogaKLEdXMvDHoNAMbuPURpVM9XNqMjLZL7j55pvRarXceOONhMNh1q5dy9133z0WWADo6OigqKho7Ofe3l4uvfTSo9r/2GOPsXr16mkNyojsoFarWFTipDrPyhttg2zvGKZjMEiR00Su1YDDpMuq0V5i4jRqFR9bWUZDsZ27/9jCl57YwRc31rGq0n3qjYWYw6bldjQUCmGxHB3tG/05Jyf9FQ+GhoZIJBLk5h5dDiwnJ4fNmzcfd5v+/n4KCo5Oeja6fX9//0mDC5s2bRr7vrm5mdNOO22yTafPF2FXp4ecE5SAK6n18u9P7OCtcC5fPL9uTswLa2tvY/GixdP6mklFYU+PjzfaBtnSNsjBwSAVbjN3XLY4pfPiM2W8xzSRVNjd7SXuC7Om0s3qqpyx+ryGXd30eMMsK3OdYi9Q3O/nhV9sx1hUy/oFp04CmW7+SJzvPvQmqysdXHH+wpTsMxPn6Xj0mQ/z41fbmL94Jfl246k3yJCRCHwcXziGLzzy7yNb9tMyDNesq5xRFVmS+X3c/WwLtspFLCl1jmubWCLJt55uoi88zC0XLWTlcTq0Q4EoNflWiqeYu2Sqn1OTkcl+gcFg4JZbbuGWW2454TovvfTSUT+XlpbS0tKS1naJmcek13JmXT4NJXbeOjBMa+9IPgadRk2hfaT0stOsl0DDDNZQ7OCejy3hW8808e1nmrhmfRUfXlI8J/rbQkzGtD3rfvvttxkaGhr7OZlMolKpePvtt48p/3TmmWempQ3HuxCc7OLw/t+NziDJpgvKgkI7V6+p4KHXD7CszMl5C6WMZ6qEYwm2dQzzRtsAW9uHGA7FUKtGPmj+fkMV5yzInxWBhfFSFIXWPj+9vjCLih2srHSPBRZgJCnNwYFTT4kAKHdbsBg07Or0cHYWBBee3H6YQCTBJ+ZApY8VlS5+/GobWw8MceHiolNvkAKKotDnizAc+lugwBeO44/E8Y4FD94TSIjECESOncesVsG/njOfcyZQ7SIbrK5yY9ZreGl377iCC4qi8N2X9rKtY5jPnz3/uIGF2SAb+gVCpEKu1cjGhkISC/LpGAqxp8dHa1+AjqEQWrWKfJuBXJsBl1mfdaW0xanlWg3cdVkj9z6/hx+/0sahoRA3nDFv1kyDESKVpi248N55le912223HfWzSqVKeb1il8uFRqOhr6/vqOUDAwPHjGYYlZube0znZmBgAJie0RYTcdnyUrYfGuaHf9nPgiI7ZS5zpps0Yw34I7zRPsgbbYO8c2iYWELBotewosLFqko3KyvcWOfK/JP3aR8I0jUcZl6uhdPn5x3zoeow6YgnFBJJ5ZRPaTRqFQuL7Ozq9KazyePiCcV4ansn62tymZc3jjkdM1yp00SB3cDWA4PTFlx45t0ufviX/cf9ncWgwW7UYTVosRl1FDtN2Iza9yzTHqmcoSU03MOSupkVWAAwaDVsqMnlL3v7+MyZ1adM1vTQawf4U0sfV68p59yFM+/9jlcm+wVCpINGo6Yy10JlroVEIslhT5h9vT729vrp9ITRqCHPaiTvSKBBr5Wb05nCqNPw5Q8u4OHXD/D4W4fo8oT48gUL5tRDJiHGY1rukl588cXpeJkT0uv1NDQ0sHnzZj74wQ+OLd+8eTPnn3/+cbdZunQp3/nOd4hEIhgMhrH18/PzKS3NruG4GrWKfzu3ls/9Yhv/+WwL3/noEvnAGidFUdjfH+CNtpGAwr4+PwAFdgMfXFTE6io3DUX2OR+d7hwOcWgoSJ5dzzn1hcc9vyx6LXqdGn8khsN06kQKi4odvNk+xFAgisuSucQLv377EOFYgo+vnv2jFmDkRm1VhZvnmnuIxpNpv1Ykkgq/2XaYmnwrV60qPxIsGAkYWA3aCQ0Xbgv1n3qlLHX2gnyea+phc+vASUfrPL2jk1+9fYgLGgr52MoTT7+b6TLdLxAi3TQaNeVuM+VuM2fV5dPlCdHa62dPj593D4dRqyDXYiDfbsRp1s34DPFzgVo1Uq6y1GXiuy/t4wuPv8PXP9SQdWW1hcikaQkulJSUTMfLnNQ111zDl770JRobG1m+fDmPPvoovb29XHnllQDcc8897NixgwcffBCAiy++mP/5n//hy1/+Mp/5zGdob2/nRz/6EZ/97GezalrEqByrgX89p5ZvPdPEg6+184+nz8t0k7JWNJ5kx+Fh3mgb5M32Qfr9UVRAXaGNT62tYHWlm3K3OSv/P2dCny9CW38Aq0HDhYuLTlgGz2LQYNJp8IXj4wsulDgA2NXlZUPN8UcQpdtgIMrT73ZxZl0e5e65M+JnRaWLp9/t4t3DHlZUnDpHxlRsaRug1xfhHzZUsbpqdg7vH4/6IjsFdgN/auk9YXDh1X39/OhIadsbzqye1degbOgXCDFdVCoVxU4zxU4zG+bn0eeLsLfXz94eHzuPlGV2m/UUOow4TDrMes2s/vuf6c5eUECB3cjtv2/mC796hy9/cMG48+kIMdtNS3DhtNNOm9BF8rXXXkt5Gy688EKGhob4wQ9+QG9vL7W1tfzoRz8a6+D09fXR0dExtr7NZuMnP/kJ3/zmN7n88stxOBxce+21XHPNNSlvW6qsrnLzocYinnqnkyWlzjndkT+RaDzJZx99my5PGINWzfJyF59Y7WZlpQunOUvKFmSR4WCUfb0+tGoVFzUWn3T4n8WgxaDVEBhnPeh5uRaMOjW7DnsyFlx4/K0O4okkV62aG6MWRi0ucaDXqtl6YDDtwYWn3ukk32ZgdVV2TSebbmqVirPr8vnFmx30+yPkWo9O0rur08M9z7dQV2jjC+fXzfoEcNnQLxAiE1QqFfl2I/l2I+trcun3h9nfG6Clx0dT18hUQbNeQ/6RqRN2k07yNGShhmIH91yxlG8+08R/PLWLz5xZzcYGyXsmxLQEFz7xiU9kRQT2E5/4BJ/4xCeO+7vjzf2sq6vjkUceSXezUuqadVXsPOzhv17cw39fuYwc6/GrTMxVr+0foMsT5p8+UM05Cwpk+shJ+CNxWnp8xBWFDzcWnfJcMuo0GHVqhoLRce1fq1GzoNA+9tRmuvX6wvxxZzfn1hdQ7JxbQxoNWg2NJQ62tg9x3elK2q7P+3r97Or08vcbqmb9zfJ4nLUgn0ff7ODllj4+uuJv0+sODAT41jNN5NuM3HLRwjkxPDpb+gVCZFqu1Uiu1cjqeTl4QzEODARo6w9wcDBI+0AQtQpcFj15VgNOkx6LQUY1ZItCh5H/vLyRu5/dzff+tI+OwSDXrJfPOzG3TUtw4XOf+9x0vIwA9Fo1X9q4gH/95Xbue2EP3/zwItTyITTmuaZu8m0GNjYUynE5iXAswZ5uL+FYgg8uKqJknElCnWY9XZ7wuF9nUbGdR7YcxBeOTXtSpF9uPQTA362avfPaT2ZVpZutB1o5PByiNE1JYJ965zAmnYbzZlh1h3QpcpioL7LzUksvly8vQaVS0e+PcOvvdqHXqPnGJQ3YTXMjOZj0C4Q4lt2kY3Gpk8WlTuKJJF3eMAf7A+zvHxnZoChg1GrIsxlwW/Q4TDp5SJJhFoOWr3+ogf/3yn5++04nh4dDfHFjHWb93Ez+LYRckWahMreZ606fxzuHPPz67cOZbk7W6PKE2HHIw/kLCySwcBKxRJKWHh+ecJwz5+cxL89y6o2OcJh0xBJJEkllXOs3FDtQYGwo6HTp8oR4obmHCxoKybcZp/W1s8XodIit7UOnWHNyBgNR/rq3n3Pr87EYpJM16uy6fDoGg7T2BfBH4tz61C4CkQS3XtxAgX1unotCiGNpNWrKXGbWz8/jk2sr+fsNVVywqJDyHBO9/jDvHBrmlX19vHVgkI7BIJ5QjKQyvs9ekVoatYrrz6jmhjOrefvgEP/+xA56veN/0CLEbCLBhVnq/IUFrK/J5eEtB2jp9mW6OVnh+aYe1Co4R56inlAiqbCnx8egP8KaKjcNJY4JDb+0HMn+H44lxrV+bYENrVrFzsPTG1z4xRsdaFQqrpjF2fhPpcBupMxtZuuBwbTs//c7u0gkFT7UWJyW/c9UG2py0WlUPLurm9ueaeLwcIibL6yfE2VQhRCTZzPqqC+yc1FjMTecUc2Vq8pYU5WDXqthT6+PN9sHeWVfPy3dvnF/BovUumhxEbde3ECfL8JNj7/D7ml+cCJENpDgwiylUqn47Fk15Fj0/Odzu+me4xHURFLhxeZelpe7jkmkJkYoisK+Xj+9vjCLS52sqHChnuC8wdGKEd5QbFzr67Vq6gpt7JrGvAsdg0Fe3tPLRY1FuDNYAjMbrKpwsavTS3CcSTjHKxpP8od3u1hd5Z5z+SxOxWrUsroqhz/u6mZnp5fPnzOfpWXOTDdLCDGDqNUqipwmTqvO4eNryrnu9HlctLiQmnwrPd4wr7b20zEYlJEMGbCs3MV/XrEEk17DzU++y8stvZlukhDTSoILs5jVoOUL59cxGIhy3UNb+Y+ndvHa/oFxD1mfTd46MMhgMMr5ksn3hNoHAnR7wtTkWVlfk4t2EtmprQYtZr0GX2R8wQUYmRrR2udP+Q3uifz8jYPotWouX1566pVnuZUVLuJJhXc6hlO63z/v6cUbjnPJEhm1cDwbF46MnrpmXSUfqDt+WUohhBgvs0FLbaGdjQ2FXLO+kkXFDvb0+nijbRBfePyfxyI1ylxmvvPRJdQW2Ljn+T08vOWABHrEnCHBhVmuvsjOjz65kitXlXFgIMDtv2/m2gff5OEtB+j1zZ3RDM819eA061iV5rJ7M1WPP8ahoRAFDgNn10++ioZJp0Gv1RCNJ8e9TUOxnaQCu6dh+k5bv59X9vVzyZISHHMkcd7J1BfZMes1bD2QurwLiqLw1DudVOaYWVziSNl+Z5Nl5S4e/vs1XCYBLiFEipkNWs5dWMDly0sx6NRsaRtgb6+PeGL8n8ti6uwmHd/68CLOrc/nsTc7uPvZFrxhma4iZj/JsjUH5FoNfHxNBX+3qpw32wf5465ufvlmB49v7WBFhYsLGopYUeGataVzBvwR3mwf5CPLSif1NH626/WF6fTGqS7T8sHFRVMqg6dSqbCbtPRNIHC1oNCGWgW7Or0sL09v8OeRLQex6DV8ZGlJWl9nptBq1Cwrc7L1wBCKkpqSlDsOe2gfCPL5s+dLubSTkOCWECKdytxmrlpVxtYDQ2xtH6TLE2ZhkY1cqySOnS46jZp/OXs+ZS4zP9vczmutsKo1yvkLC2d1v1vMbRJcmEM0ahWnzcvhtHk59HjDPNfUw/NN3XyrvYk8m4HzFxZwXn0BObMsJ8FLu3tJKiNJLsXRYokk7f1BtGqFCxuLsKYgq7/TpCMaV0gqyriqcpj1WmryrWnPu7Cnx8eWtkGuXlOO1SiXvlErK9y82jpAW38gJUkFn9reicOk44zavBS0TgghxGTptBrWVudSW2DjTy29vH1wmEK7kdoC25QeJIjxU6lUXLa8lNVVbn712h7e6hrpi7gtes5ZkM+59QVpz03kDcXYemCILW0D7O72sXFhAVeuLpfKaSItpIc9RxXYjXzytAquWlXGlraR0QyPbDnIo28cZE1VDhc0FLK03DnjLzxJReH55h4WFdslsdxxdHvD+CMxlhSacFtSE1SyGnWoVBCJJzDpxneJaSh28Lt3OonGk2mr2f3w6wewGbVcLHkAjjJWkvLA0JSDC53DId5sH+TvVpXN2trr8USSHm+EIqdxxl8fhRBzQ47VwOXLS2nq9PLXvX282tpPXYGNEqdJRphNk1KXmQ8vdPDZjRW82T7Ic009PPH2IR5/6xCLSxycv7CAtdU5GLSpCfp0eUJs2T/IlrYBmrq8JBVwm/UUO408+mYHB4dC/Os58yXIJFJOggtznFajZn1NLutrcukcDvFcUzcvNPfy2v4BCuwGNjYUcm59AS7z+LLqxxNJwvEk4ViCUDRBKJYgfOQrllBYXu7CpJ++C9nOwx66PGGuWl0+ba85U8QSSbqGwzhNOsocqRutYtFrMR6pGDHe4MKiYju/2XaYPT0+FqVhnv6uTg/bOoa5Zl0lZr1c9t7LZdFTk2dla/sgH5tiac7f7ehEo1Zx4aKiFLUu+3R5wuzrG5m/XJFryXRzhBBiXFQqFQ0lDipzLbyyt4+mLh+dwyEWFjtSMmpRjI9Wo2ZtdS5rq3MZ8Ed4YXcvLzT1cM/ze7D8RcMHavM5b2EB1RMM9ieVkVLib7QN8nrbIB2DQQAqc8xcsaKM1VVuavKtqIDfbDvMzza30+MJ87WL6mfdiGWRWXI1EWOKnSY+va6KT6yp4LXWAf64q5uHXjvAz7ccZFWlG6dZ976AQXLs+9B7Aggnc+HiIj5zZvU0vaORRI4WvYZ11TnT9pozxeiohXMX5qP2pC65p8WgwazX4I/EGe9ElIVFDlTAzk5PyoMLiqLwf68fwGXWceHi2XvTOxUrK138cmsH3lAM+yRzAfgjcV5o7uGM+Xm4ZmmJz0RSodsbJpEc+fuR4IIQYqaxGLRsXFTEgiI7L7f08fr+ASpzzFTlWiUHwDTLsRr4u5VlXLGilHcPe3i+qYfnmrp55t0uqvMsnLewkDNr804Y/InEE7zT4WFL2wBvtA8yHIyhVsGiEgcXNFSxuiqHQvuxOTYuW15KqcvEd57bw789/g63XLSQmvypT4sUAiS4II5Dp1FzRm0eZ9TmcWgoyLO7uvnLnn4SioJJp8GoU2PSjdxAui36kWV6DSadBpNOjVGnwajTHFn3b8ueebeLZ3d1c+nSYooc6Z+i4A/H2dzaz/kLC1M2zGy2GB214DDpqMm3sd/TlbJ9m/Va9Bo1odj4S0tajVoqcsw839RDY6mThUX2lLXnnUMednV6uf6MeTL87wRWVrj5xZsdvH1waNKlEV9o6iEcS87qaSd9vgj+SIz5+RYODobwhWPYjJKYUQgx81TkWLhqtZG32od448BIwseGIjtueYo97dQqFUtKnSwpdeIPV/Pynl6ea+rhgT+38pNX2lhXk8P59QUsKnHgCcXY2j7ElvYBth0cJhJPYtZrWFHhYnWlm5UV7nHllVpdlcNdlzfyrWea+Pdf7+Dfzq1lfU3uNLxbMdtJcEGcVKnLzN9vmMffb5g35X19ymrgr/v6efj1g3xxY10KWndyL+/pJZZQJJHjcbx31EKqAy8atQqbUcdQMDqh7a47o5p7n9/Dvz+xg/MWFvDptZWTfoo+SlEUHn79ALnWkSk+4vjmF1hxmHS8dWBywYVEUuF3OzppKLbP2qcfiqLQ7QlhPJIgzR/t4eBgkIZiKbcphJiZ9FoNa2tyqS6w8nJLL1s7hii2m9AlFBTl5CNRT0VyOUyO1ajlQ43FXLS4iNa+AM81dfPnPX283NKHy6xjOBhDYaQS3Ln1BaypcrOoxIFuEtXQqnIt3HPFEm7/fTN3/nE3V68p52Mry+T/nZgSCS6IaeO26PnwkmIef+sQly8vSUlm+hNRFIVnd3VTk2dN6+vMRO8ftZAOLrOOPT3JCZU3XFzi4PsfX84v3jzIb9/p5PX9A1yzrpJz6gsmnTjvzfYhWnp8fPasmkl98M4VapWK5eUjJSkTSWXCQ2Nf3z9Ary/CP2yoSlMLM28wEMUTirOk3EGO1UC5y8w7h4Yz3SwhhJiyfJuRjy4vY2enh1f39dPRGaQ32T+lfapVUOgwUeI0ZXTUYDyRnJFlyFUqFTX5Vmrya7h2fRWbWwd4o22AihwLq6vczMu1pCQI4DLrue3SxXz3T3t5eMtBDg6G+JdzamTEr5g0CS6IaXXZ8lL+sLObB187wDcuaUjb6+zr9dM+EOSfPjB9+R1minSOWhhlNWpRqSAaT2KYQKfCpNdwzfoqzl6Qz/dfbuW/X9rH8829/NOZ1VROcH57UlF4ZMsBCu1GzlkwuaH+c8nKCjd/auljb4+PBROclvLUO53k2wysrpq9uU26vWFU6pEgGEBFroVdnR76fGHybFI3Xggxs6nVKhpLnVTlWHj6NQ85U3wwE4wn6BgI0tYfoNhhpNRlnvJoxPFKJBUG/BF6fRGGg1EqciyUuc3T8trpYNRpOHtBPmenqS+j16r5t3NrKXeZeej1A/R4w3z1wvpZmz9JpJcEF8S0shq0XLGilJ9ubufdQ8MsLnWm5XWea+pBr1Vzxvy8tOx/poq/Z9RCdV56Ri3ASMIog1aNLxKfUHBhVEWOhTsuW8yLzT38dHM7n39sG5cuLeHKVeXjrjbyWusA+/sD3Hhu7Yx8ajHdlpe7UKtGSlJOJLiwt8dHU5eXv99QNWuTgfnCMQYDUapyzTiOdI7zbQZyrQYOD4ckuCCEmDVsJh1Li8zU1099KqE/EmPnIQ/bO4bp9IRxmnVU5VhwWfRpKeXrD8fp9YXp80cIRuPoNGocZh0tPT4i8UTaRmvOBiqViitWllHiMnHv86OJHutl9K+YMOlxi2l3UWMRORY9D752YMpz+o4nHEvw5z19bKjJxSLllY7SdWTUwspKV1qHKVr0Wkx6Db5QbNL7UKtUnLewkAc+sYJz6gv49bbD/NPP3+a1/QOnPG8SyZFRC6UuE2fWSoBpPKxGLQsK7bx5YHBC2z21oxOTTsN59bM3t0mXJ0w8obCywj02DFWnUVOVa2E4FCOeTGa4hWIiotEo3/rWt1izZg1Lly7lhhtuoLu7+6Tb/PKXv+TjH/84q1evZuXKlXzyk59k69at09RiIWYmq0HHadW5/P2GeZzfUIBarWJbxxCvtw5weChIPDH1a2c8kaTbE+bdQ8Ns6xiifSCASafhzPn5fGptJVetLmfD/FwODgZp6vKkpd85m6yrzuWuyxsBhS89sYPX9g9kuklihpHggph2Bq2Gq1aX09Lj4/W2id3IjMcr+/oJxRJZn8hxKBAlGB1/RYWpik9DroVRZoMGvVZDKJ6Y8r7sJh3/cvZ87rq8EatBw+2/b+ZbzzTR4z1x+cy/7u2jYyjEJ9ZUzNqn6emwstLF/r4AA/7IuNYf8Ed4ZW8/5y0smLWBvHAsQb8/QrHLSO77sqiXus3YjVo6h0IZap2YjNtuu41nn32We++9l0ceeYRAIMD1119PInHi69WWLVu48MIL+dnPfsYvf/lLqqqq+Id/+Afa29unr+FCzFA6rZqGYgefOq2CS5eVkGcz0Nzt45V9/ezv8xOOTbyv4AnF2NfrZ+uBQZq6PPgicebnW/m71WVcubqcJeVOLAYtKpWKVZVuzlqQT683wo7DHpISYDip6jwr91yxlIocM3f8vplfvXVIgjJi3CS4IDLi3PoCSpwm/u+1dhLJ1F6wnmvqocRpSmk5w1QLxxLs7vaypW2AaApuwMdjNNfCior0jlqAkae6NoNmUh2GE1lYZOe+jy3l2vWVvHvYwz/9/G0e39pB7H1PPuKJJD9/4yBVuRbWVc/eHADpsLLCDcBbB4fGtf4fdnaTSCp8qLEonc3KqG5PmFAswYpyF+r3BapyLHrybUZ6fCcOdIns4vP5eOKJJ/jSl77E+vXraWho4O6776alpYXNmzefcLt77rmHq6++moULFzJv3jy+8Y1vYLFY+Otf/zqNrRdiZlOpVFTlWrlsRSmfPK2CBUU2DgwGeWVf/0iAIHzy0Y6xRJLO4RDbO4Z4p2OYjsEgVoOWc+sL+OTaCs5rKKTQbjruQ4XGUicbGwrxhmJsOziU8r7nbOO26Ln9I4vZMD+XB19r5/4X9h7T3xLieCS4IDJCo1bxydMq6BgK8afdvSnbb8dgkOYuL+cvLMjqUjqdwyGC0QQmnYbtHcNpjwjHE0k6h8PYTTrmF0zPnEOHWU84ltoPIq1GzUeWlfL9j69gRbmLh14/wOd/sY13D3vG1nmppZcuT5ir15SnZU7nbFaZYybXqmdr+6mDC5F4gj/s7GJ1lZsih2kaWjf9YokkPb4wuVY9pcdJBjbSUbYQjCamdRSSmLydO3cSi8XYsGHD2LKioiKqq6vZtm3buPcTi8WIRCLY7dkbxBYim+XaDJy3sJBr11eypsrNUCDKlrZB3j4wxIA/Mja6QFEUhoNR9vb42No+SHOXl3AsQX2xjavWlPOxVeU0lDgw6089eq620MZFjUWE40m2tg/KzfIpGLQavnh+HR9fXc5LLb189cmdDE+wzLiYe2bnOFYxI6yrzmF+vpVH3jjIGbV56LVTj3U919SDRq3irCyuDhCOJejxhimwG1g9L4fnd3azvz9AdRqT5oyOWjh7Qf60lYSyG3UkFYVYIpnyMpB5NgM3X1jPm+2DPPDnVm7+zbucXZfP1adV8Is3O6gtsLKq0p3S15wLVCoVKyrc/GVP3yn/v/15Tx/ecJwPLymexhZOrz5fhEAkzmnz8k94LEpcJtwWPYeHQtMWuBOT19/fj0ajweVyHbU8JyeH/v7xl9677777MJvNnHPOOadct729nVBoZOpMOBymubl5Yo0WJyXHNPWm+5i6gHW5SdqHYzT1DNByIIlJpybXrMEXTeKPJlEUyDVrqM01UGzTYVCi9B8aZDIFMxda4vy13c/vurtpKDCh10zPg4hoJEJbe9u0vFYqnZYP+uUuHtk+xOcffYvrVuVQbJ+eyh+nMlOPaTokFYVgNElzfGplZN/7919fXz/h7SW4IDJGpVKxaV0lX3tyJ7/f2cWlS0umtL9YIslLu3tYXenGZc7e8jldnjDBWIJzFuZTnWelv8LFa/sHyLHocaah3ZkYtQBg1mswaNUEIvG0vC+AVZVuFpc4+OXWDn6z7TB/2dtHPKnw2bNqsnrkSjZbWeHi2V3dNHV5WXKCai6KovDU9k6qci0sOlKacbZJKgrdnhAWvfakOUpsRh3FThMt3T4JLmTQfffdxwMPPHDSdR566KET/m4io8cefPBBHnvsMX72s59htZ46KFxZWTn2fXNz86Q6a+LE5JimXqaOaSNwsaLQPhDg7QPDHBwM4rZrOa3ASkOxA5dZf8z0tMmoB+rrwvx+RxfdkTirSt2YpyFvUFt7G1WVVWl/nXSoqoTF1T5ue6aZ/3ptgOvPmMfZC/Iz3teaycc01RJJBX8kRv0UK+VN9e9fggsio5aUOlla5uSXWzs4r35qSeHeaBvEG45zfkP2JnKMxpP0eEPk2QyUuy0ALK9w0eMN886hYdbNy0GnTe3IgtFRC2fVTd+oBRgpO2rSafCGYmkLLsBI/edPra3krLp8/vev+zHrNSwrc6bt9Wa7JaVOtGoVW9uHThhc2HHIw4HBIJ8/Z37GOxbpMuCP4g3HWVV16hwlVbkWdnd5GQpGszqwOZtt2rSJSy655KTrFBcXs337dhKJBENDQ7jdfxvdNDg4yKpVq075Og8++CD3338///u//0tjY+OU2y2EONpoXoaqXCvBSBy1WpWWvkuezcily0p4+t0utrQPsKLCjd2YHU/js1VtgY17P7aEu/64m/tf3MtzTT3ccGY1VbmWTDdNZBHJuSAybtPaSnzhOL/ZfnhK+3muqZtcq55lZa5Tr5whXZ6RXAurK91jw6x1GjVn1OZhNWh5t9Ob0td776iF2sLpfapqNmgw6jT4p2kuepnbzDc/vIgvf7B+1t7wTgeTXsOiEgdbT1KS8rfvHMZp0nHGFKPj2azbE0KrUdFQfOqRGQV2IzlWA4eGgtPQMnE8breb6urqk36ZTCYWLVqETqfj1VdfHdu2u7ub1tZWli1bdtLX+OlPf8r999/Pj370I1auXJnutyTEnGc2aNP6UMRh1nPp0mIKHSbebB9kKCD5BE4lx2rgzssb+Zezazg0FORfH9vG//51P4GI5B0SIyS4IDKuJt/Khppcfrv9MEOTTBTT6w2z7eAw59YXZG3pwVgiSbc3jNuipyLn6Civ06xnfU0u/nCMjsHU3aCMVYgoT3+FiPczaDWY9BqicUmYlCmDgSgt3d4JJwxdVeni0FCIbs+xVRA6h0O82T7EBxcVpiRPSjYaDkYZDsWoybNiG8eTLKNOQ0WOmcFglGRSzvdsZrPZuPzyy7n77rvZvHkzTU1NfPGLX6Suro5169aNrbdp0ybuueeesZ//3//7f9xzzz3cdtttVFZW0tfXR19fHz6fLxNvQwiRIhaDjksai6jKMbP14CB9vvGVYp7L1CoV5y0s5IGrV7CxoZDfvdPJDY+8xZ9aeqVkpZDggsgOV6+pIBpP8ss3Oya1/QvNPcBIicts1e0JE4jEWVXhPu5NWU2+lcYyJ3t6ffhPUY5pPOKJJF2ezIxaGOU061NajlJMTLc3TFt/gH7/xDpLYyUpjzN64XfvdKJVq/jgollcftIbJqkoLK8Y/yiocrcFk05Dj3RMs97NN9/M+eefz4033shVV12FxWLhgQceQKP5WwC2o6ODvr6+sZ9//vOfE4vFuPHGG9mwYcPY12233ZaJtyCESCGjXsuFjcUsLLSz/dAwncOhTDdpRrAZdfzTB2q454ol5FkN3Pv8Hr7ym3c5MBDIdNNEBs36nAvRaJS77rqLp59+mkgkwmmnncatt95KYWHhCbf59a9/zVe+8pVjlu/YsQODwZDO5s5ZJS4T5y8s5I+7uvnw0hIKHcZxb5tIKjzf3MvSMicF9vFvN51iR270XWYdVfnHn5umUqlYXeWm1xthW8cQ66rzpjQKo9sbxhee/lwL72U36ognFRJJJWtHlMxWkXiCoUAEo05DtydMnm38fxvFThNFDiNvHhjiosa/VYPwR+K8sLuHM2rzcFlmZ26BYDTOgD9C6ZEqEOOVZzOQbzPS5QnP2tKcs4XBYOCWW27hlltuOeE6L7300kl/FkLMLjqNmvMaCtHr1LzT4SGeUCjPObYEsTjW/AIb37liCc839fDg5nb+5RfbuLixmI+vKR9XiVAxu8z6kQu33XYbzz77LPfeey+PPPIIgUCA66+/nkTi5E9TTSYTr7zyylFfElhIrytXlaFWq3hky4EJbbe9Y5h+f4TzG04cMMq0ntHpCZUuDCdJ2GjQajizNheDTkNz1+TzL4yOWrAZtRkbtQAjSR21ajVBmYs37YYCMcKxJEVOI97IxEfCrKp08+4hz1EjT57b1U04luSSWVx+sssTJhJPsrrSPaHcHRq1iqpcC75wTKYCCSHEDKRRq/hAbT6rq9zs6fWxr1emPY2XWqViY8PIVInzFhby1DudfObht/nznj6ZKjHHzOrggs/n44knnuBLX/oS69evp6GhgbvvvpuWlhY2b9580m1VKhV5eXlHfYn0yrEauKSxmD/v6aOt3z/u7Z5r6sZu1LKmyn3qlTPgvdMTqvNOfaOfazOydl4Off4w3Z7JDc3r8UbwhWOsrHBnbNQCjCR1tBg0eCW4MO0Gg1GMejWLSxyACs8E85msqHARTSR597AHGBkh9PS7XSwqtlOdd+ryezNRNJ6kzxcm32YgfxKjoEqcJhwmHYdlSK0QQsxIarWKddU5nF6by8HBIE1dHrk5ngC7Scdnz6rhO1cswW3V853nWvjqkzs5mMJ8YiK7zergws6dO4nFYmzYsGFsWVFREdXV1Wzbtu2k24bDYc466yzOOOMMrr/+epqamtLdXAFcvrwUs0HDQ6+Nb/TCcDDKlrZBzl6QP1Z9Idv0+kZu9JdPIKnigkI7i0scNHX5CE2w2sJIMCOU8VELABa9FoNWg38ST87F5I1OiSh2GClzm3Gb9XR6J3bDu7jEgUGrZuuBIQBe3z9Any/CJUtL0tHkrDAywijBigo32klcT5xmHQUOI73eYxNhCiGEmBlUKhUrK9ycU19ArzfCjsMekhJgmJDaAhvf+egS/ukD1bT1B/iXX2zjJ6+2EZymCmLpoigKA/4IW9oGeGTLAb7xu11c87M3uf+FPZJj7IhZPRGmv78fjUaDy3V0Uq6cnBz6+/tPuF1VVRW33347CxYsIBAI8NBDD3HVVVfx29/+lsrKylO+bnt7O6HQSEc+HA7T3Nw86fcwFIpzcCCK15S5p8/T7ewqC7/bPcTzb+2mJufYqSjRSIS29jYAXmr1kUgq1DviY8uySSKpsLs/DAoowzGa/V3j3tYZT5IM+HjurT4WFZpQj3OIdq8/RvtQlJUlZtr27RnXNlM9T09EURQGegN4Iwn0oYGU7z+bvfc8nW79gTiHBiNU6S0caPURGQ5w0BPDGD5xecnjmZ+j5/V9vWwsV/HLLf3kmDXkqzy0tae2ZOp4pfOYJpIKu/vCqFUqQv1xmocmmSPEE+FQVxBrfBizPr0BT284geLT4bFMrTb76N9/fX19ilomhBAz36IjQfbnm3rYedjD4hKHlLqeAM2R5M/rqnN58LV2frPtMH/Z08ffb6hiQ01u1h9LRVEYCETZ1+tnX5+f1iP/DgdHHpipVVDqMlOdZ+Gl3b0cGAjy1YvqybXO7Wn0MzK4cN999/HAAw+cdJ2HHnrohL871fCmZcuWHVXvetmyZVx66aU8/PDDfO1rXztl+94bgJhqh63PFyFq8ZBjmTsn6qdKErza8RbPt0U4d3ndMReftvY2qiqrUBSFra++TX2hjXWLazPU2pPr8oSwxLxsqMllacXEp20UloX43Y5OYkY9C4rsp1w/nkgy0DHMPKvCBWsqxj1SIp03FoO6PnZ3+6iqzEnL/rPV6HmaCaEuL6XGCKevrMSs16Jz+/Du6qGw2IVpAsmVzvAb+f7LrbRHLOwf6uQfNlRRXZW5kQvpPKbd3jDmqIfT5+eyZBJ/q6MqonEGNZ2oVVBV5EhhC481FIhSk2+l2DW1BJISWBBCiOObX2AjqSi80NxLS7dvXH0xcTSHSce/nD2f8xcW8IM/t3L3sy08u6ubJWXOkYdvjHyhKIzeoY3eqilHlo3duY2uf2SFoN9LiacTs16D2aAd+VenwWLQYtJrMOs16DXqUwYyFEWh3x89KojQ2utnOPS3QEKZy8zychc1eVZq8q1U5VrG+tlvtA3ynedauPGX2/nqB+vn9HkyI4MLmzZt4pJLLjnpOsXFxWzfvp1EIsHQ0BBu9986i4ODg6xatWrcr6fRaFi0aBHt7e2TbbKYAKNOw1Wryvmfl/fxRvsga6qOf1Pa3O3j0FCIz589f0L7TyrKuEcBTEUiqdA5HMKs01I/yZuMIqeJVZVu/rKnj1yb4ZTR0B5vBG84xpkZrBDxfk6znngiKRUjpsnYlAincSxLc57NiMuio8sTZt4E8iWMlKRs5Qd/bsWk03Dewuwt9ToViqLQPRzCpNNMuUNg1mspdZl599BwahonhBAio+oK7YRjCV7e04dJr6Ei5/hVv8TJLSi0c+8VS3l2Vzf/9/oB3jnkmdD2oz3I93bhkwrQcvLEm1q1CpNeg0U/Enx4//c93gitfX487wkklLvNrKhwUZNvpSbPSuV7AgnHs7rKzXeuWMK3n2niK795l8+eVcM59bOzz3QqMzK44Ha7jwoWnMiiRYvQ6XS8+uqrXHzxxQB0d3fT2tp61MiEU1EUhZaWFurq6ibdZjEx5y0s4Mnth3notQOsrHAf96b0uV3dmHQa1tfkjnu/SUVhe8cwFr2GusL0RhX7/BG84Thr57kx6Sd/o99Y6qTHG2bHoWE21OSiP0G1idFcC1ajlgUZzrXwXhaDFrVaRTiWwGKYkZecGWW0SsR7A1p2kxaXRU+PZ2K5APJsBipzzLQPBLlkSfGsLSk1FIzhCcVpLLOn5D1W5ppp6vIw4I+QM8eHRwohxGzQWOokFEvyWusABq2aQik5PCkatYoLFxexsaGQRFI5KlCggqNGGKhUxy57v73795NfVEYomiAYjROIJghF4wSjCQJHloXe/30kTn8gQmgoQTCawGXWsapyZERC9ZERCSer7HYi5W4z91yxhLv+uJv7X9xL+0CQT6+rnHMP1mZnT/EIm83G5Zdfzt13301OTg5Op5M77riDuro61q1bN7bepk2baGxs5KabbgLge9/7HkuWLKGyshK/389DDz1ES0sLt956a4beydyjUau4+rQK7vrjbl5u6T0m+heIxHllXz8fqMuf0I17IBLHG4rR74+gUauoyU/PTXhSGRm1YNSpaSiZ2tBojVrFhvl59PsibO8YZtUJSuRl46gFAIteg0mnwRuKzYjgQjAaJxxL4rboM92USRmtElHynqHyKpWKcpeZ/b1+YonkhJKfrq7K4eBgkA81FqWjuVmhyxMCFTSWOFOyvwK7kVyrgcPDIQkuCCHELKBSqVhd6SYYifPOoWH0GjVuub5PmkatSslNt1atwmHS4TBNLfdQqtiMOm69uIEfv9rGk9sPc3AwyBc31mGdAf3fVMnO9PopdPPNN3P++edz4403ctVVV2GxWHjggQfQaP5289XR0UFfX9/Yz16vl69//et88IMf5Nprr6W3t5eHH36YxsbGTLyFOWtddQ41eVYeeeMgscTRdeP/srePSDzJ+RMcpu0JxYgnFRaXODgwEKQrTSXj+n0RvKEYjaWOlDwJtRq0nD4/j1g8yf6+wDG/f2+FiGwatQAjIxfMeg3+GZIhuK0/wNsHB9lxaPiY8y7bvbdKxPvPu3y7EatRO+FKBlesKOW//m4ZRbP0KY0vHGMoEKUq14zDnJrOiU6jpjLHwmAwSjw5s84hIYQQx6dWqzi9No/afBvbOobxhaUSljiWVqPm+jOq+exZNew4NMwXHn+Hw0Nzp0T1rA+jGAwGbrnlFm655ZYTrvPSSy8d9fPNN9/MzTffnO6mnZKiKPR4wySTc7P8jVqlYtO6Sm757U7+sLOLS5b8LZHcc009VOaYmZ8//vnjAN5wHLNec2R6gZptB4cwaFMbfR4dtaDXqliUoiehABW5FpZVuHht/wA5Vj1O89+erPf6IngjMc6sza5RCwAmnQadRkNgBpSjjMaTDAejuC0GvOEYm/f3s6TEedSxzmbHmxIxym3R4zLr6Q9EKHGZx71Po05DZe7snV/a7QkTSyqsqHSlNHN1qduE9ZCW7uEwpe7xH28hhBDZS6dRc87CAkKxBG8fHGJNpRvjLJ0yKKZmY0MhpS4Tt/++mZse386XNi5geYXr1BtOQiKpsKvTg8WQ+XuAWT9yYSaLJRRaun30+OZuzfSlZU6WlDp47M2Osdq4hzwjZWHOX1g4oZuBRFLBE4ySazVgNmhZX5M7En0+NIw/hTe+A/4ow6EYi0ocKR8GtbzCxbxcy8hT9fhIPd3RxJE2Q/aNWoCRSL/dpJ0R9X8Hg1HCsSSnzXNz5aoyCmwG3mgfZF+vf0bUuD7elIhRGrWKEpcJbyh2yoo5c0U4lqDPH6HIYSTfakzpvnMtBgpshjl9/RZCiNnIqNOwcVEhLouOrQeHZtwoRzF9Good3PuxpeTZDHzj6V08uf1wSvtgncMh/u/1A/zDQ1v56pM7eb6pJ2X7niwJLmQxvXbkJmHAHyUxh4fWfmptJd5wnCe3HQbg9Y4gOo2KD9TlTWg/vnCMcDxJZc7IU8TR6HOx08jWA0NE41O/+VUUhU5PCK1GRWOpc8r7ez+dRs0ZtXlYDFre7fQC0OMN443EWF7hzrpRC6NcZh2ReDLrb9CHAlEMWjWlLjMui4HLlpexvjqHg0MB3mwfHAtwZaOTTYkYVegwoddqGAhEprl12anHGyYUS7CywoU6xQmX1GoVVblWfOE44Sw+b4QQQkyczajjgw1FGLUa3jowSGKOjjIWp1ZgN3L35UtYU5XDj19p479f2julgFQwGuf5pm7+/YkdXP/wW/zqrQ4qcsx84fw6PrqiNIUtnxwJLmS5mnwrFr2WQ3Nors771RbYWF+dw5PbO+n1hnnzcJC183KxGSc2P9oTipFUlKNKCBl1GjY2FGI36th6YGjKHw6DgSjDgRgLi+wTbt94Oc16NszPxR+O0dYfyOpRC6MsBh0qlYpICgI46RJLJBkKRil0GMcST2rUKk6rzuWjy0vRqlVsbu2nM015OqbqZFMiRuVa9bjMOnq8ElyIJ5J0e8O4Lfq0TVsodplwW3UcytJzRgghxOS5rQY2NhSgAO8cGpJRgeKETHoNX/7gAq5aVcYLzb3c/Jt3GQpEx719UlF499Aw9z2/h0/95A3++6V9eEIxNq2t5CebVnHrxQ1sqMmdUMLudMl8C8RJ5Vj0lOWYOTwczHRTMurq0yqIxBN8/aldhGIK5zdMvHasNxzHatBif19GWYdJzwUNhWjUKrZP4cNBOZJrQaNWWFrunNQ+xqs6z0pjmZPWPj/DoewetQAjCSmNOg3+UPYGF4YCUcKxxHGDNCUuM1euLmNhsZ2mLi/vHMq+YZAnmxIxyqDVUOQwjdVynst6fRECkTjLy51p+zB2mHQUOUz0+SSYI4QQs1Gxy8w59fkEIgmajowoFeJ41CoVH19TwZcvWEBbf4B/e3w7+3r9J92mxxvm0TcOct3/beXmJ3fyetsAH6jL5z8/2sgPPrGcj64ozbqqVBJcyHIqlYq6AhsK0DfBLO+zSanLzHn1BSOl3cwaFk+wvGMskcQTjFJgM6DXHnvaFziMnL0gn2AkQXOXb1JtHArGGArGqCu04zClNwGgSqVidZWbyhwzTlN2j1oAMBtGylH6otl7UzsYjKLTqCnLOX7yQqNOy8aGIi5YVIA/HGdzaz9DWTK9YDxTIkYVO00oioJ3DgcYkopCtzeMRa9NWznaUfNyrSP5XkLjf0IhhBBi5qjOs3FmbR69/jCtp7hZFGJ9TS53X94IqPj3X+/gr3v7jvp9OJbgpd29fPU37/IPD23l0TcOUuQwcdN5tTx4zWo+e1YNCwrtKU1CnUqS3nQGKHaaKHaaODgUJM+e2qRjM8lVq8v5675+Tq+0oJ7gH5Q3FCOaSDIv78RZ7+flWTl9fh5/aunB3D/xDPmdwyFQwfLy9GSCfT+DVsOHGosIRhNZPWoBwKLXYtCqCUezc+RCPJFkKBAl32bAoj/5sawvclDkMPFicw9bDw5RlWOhKteaknrNkzWeKRGj8mwGnBY93d7wMaN45ooBfxRvKMaqKlfa/3YKHUZyrAYODQXTHnQUQgiRGQ0lDoLROK/uG8CgG8ndJMSJzMuzcu/HlnDHH3Zz97MtHBgIsrzCxQvNPbyyt59QLEGRw8jVa8o5a0E++baZc/8nwYUZQKNWUZdv4/mBHvyRGFbD3LwhyLEa+NmnV9N1+MCEt/WGY6hVKkqcJ7/YLyqxE4jGea11AJNeQ8E4gzlDwShDwSi1+Vaclum7gdBpNTi02R1YgJFz2GbUMRTMzqe3Q8EYoViCukLbuCLBTrOejywrZeuBQV7fP0C/P0pjqeOUowbSZTxTIkZZDFryrAbaBwLT0LLs1O0No1WraCie2AioyTDqNJTnmHmrfWTKVbY+aRBCCDE1KyrchGNJth4YKXOeN4NuCMX0c5n13HbpIn7w51Ye29rBY1s7MOrUbKjJ5dz6AhYWZe/ohJOR4MIMUZFrIc9moK0/wOISZ6abkzEmvWZSf2ieYAyrUYPVePJTXqVSsbrSjT8c493Dwxi0bpzmUwcLOodDKIrCisrpGbUwEznNOvb2JrPyBmsoGEWjVlExgdEqarWK1VU5lDhNvLC7h82tA9QX2sd1g59KY1MinKeeEjGqzGVid7eXcCz7R72kmicUYzgYZX6+NW1JV9+vIsfMzsMeer0RChzS2RRCiNlIrVaxtjqHQDTOjsPDrCzPwWGemw8ExfjoNGo+d1YNK8pdROJJ1s7LwXSKEbTZTnIuzBBGnYb5+TYG/FGi8exKJJftwrEEvkicEqcJ7TgSt6nVKs6ozac6z8q2g0OnLD/oCcUYDESZl2/FNY5AxFxlM2pRFCXrEiEmkgqDgQh5NgM2w8TjrSUuM1euLGdRsYPm7ulP9jiRKRGj8mxG7EYd3XMwj0uXJ0QiqbC8YvoCgXlWA3k2A51eqRohhBCzmVaj5qwF+VTmWHn7YHaXsBbZQaVSsb4ml7MX5M/4wAJIcGFGmZdnwWnWcWAOD2eeDG84RjSeZF6uddzb6LVqzl1YQIHDyNYDgye9WewcDpFUFFZXurPuiXw2MRu0GHQavOHs+qAdDkYJRhPU5o9vSsTxGHQazmso4MLFRfjDcV5t7cczTe9zIlMiRjnNOnIsBgazJCHldPGFYwz4I5S5TbincfqSVqOmMsc8kvtFgsNCCDGrGbQazm8oIP9IHzKaxWW4hUg1CS7MIE6znoocC93ekNTSnQBvKI5Wo5rwcGSzXsv5Cwux6LW8dWCQRPLYYz56s1KVY5nWm5WZyKLXYNZr8GdZcGEwMDIlouokyT7Hq67QxlWryyl2mNjdGyEYSe97nUiViPdSqVSUuk34w3HiWTaSJF0GA1GaurzEEpkJBJa5LdiMupHEr0IIIWY1s17LBQsLsRp0bD0wdNw+pBCzkQQXZpjaAhs6rYYuz9wbzjwZiqIwHIziMOmwTCLZnsui5/yFBSjAu4eHjwnqHB4OEVcUVlXJqIVTsRi06LUagrHsCS4kkgqDwSg5FgP2FM2/d5j1XNRYRJ5Vy84uT0r2eSKTmRIxqtBuxGzQ0ueb/aMXOodD7O72Ek8qXNRYRJFzevNiALjMOgrsRnp8cu0WQoi5wGnRc8GiQjRqFdsODpGUB4NiDpDgwgyTbzNQ6jTRMRTMdFNmhFAsQTCaoNxtRj3JUoHFLjNn1eXjDcfZ0+MbW+6PxOn3RSh3m8m1GlLV5FlLp1Fj0WuIxLLnSbknFBuZElFonfT5cTxGnYaFeUbCsSQ9acxrMJkpEaPcFj0us54+/+wNLiQVhf19flr7/Bh1Gj6ytJh5edaMBAJVKhXVeRYisYTMwc2waDTKt771LdasWcPSpUu54YYb6O7uPuk2f/jDH7jssstYuXIlS5cu5cMf/jC/+c1vpqnFQoiZqsBu5PyFBUTiCZp6wzI1Tsx6ElyYYdRqFXWFNqLxJMNZWtYvm3hDcWKJJFW5U6s3PL/AxvqaHA57QhwcHMl50TkcGhliXeVK6Y3pbOY06whn0dzDwUAUlQqqJ5CPY7xKHDrm51tp6fGl5WnFZKdEjNJq1JQ4TXhCsVk5zSqWSNLS7ePgYJB8u4HLlpdQ4Jj+EQvvVeQcyfXQMSjB4Uy67bbbePbZZ7n33nt55JFHCAQCXH/99SQSJ742OZ1O/umf/onHHnuMp556issuu4yvfvWr/PnPf57GlgshZqLyHAsfXFyEosBr+/vxhqT/LmYvCS7MQKUuMwX2uV2nfrw8oRgGrZrcFNQaXlLqZEWFmz09fg4MBOjzhSnLMVFgy+wNy0xiN+lIJLOjYkRSURgMRHGb9dhNqS8VpVapWFHhwqRTs7fHn/L9T2VKxKgipxGdRsVgYHZ1dELRBE1dXjo9IeoKrXyosXjayk6ejNWgpcRlntWjRbKdz+fjiSee4Etf+hLr16+noaGBu+++m5aWFjZv3nzC7dauXcu5555LdXU15eXlbNq0ibq6OrZu3TqNrRdCzFQVORbOq7FRYDPwRvsgh2UEspilJLgwA+m1amoLbAwHY4Rj2fMUONskFYXhUBSnRY9JN/XSLiqVirXzcmgottHS4yMcT7Km0i2jFibAotdi0KoJpDnR4XiMTImIU1OQ2ikR75VjNbC4xMnh4WDKh8JPZUrEqFyrAZdZT2+a8wBE40m6PKFpCSp5QjF2dXoYDERYO8/N2QsKMKbg7z9VqnItqFSqOVepI1vs3LmTWCzGhg0bxpYVFRVRXV3Ntm3bxrUPRVF47bXXaGtrY+XKlelqqhBilrEZNFy6vJTGEifN3T6aOr2S6FHMOhMfSyuyQlWelXcPe2jrD1BfZM90c7JSIBInFE3QUGxP2RxrjVrFmXX5ROIKKhUZH2Y901gMWkw6Dd5QDKc5s9U1hgJRkijU5KV+SsR7LS51sL/fT1OXl5UV7pTsc2xKhHNyUyJGGXUa8u1Gdnd5U9KuE2nt89PlCaEAVTkWSl1m9NrUx7Z7fWFae/3EkknOW1jAgsLU/e2nSoHdSJ7VwKHhEG6L5GqZbv39/Wg0Glwu11HLc3Jy6O/vP+m2Pp+PM844g2g0ilqt5utf/zpnnnnmKV+zvb2dUGikSkg4HKa5uXnyb0AcQ45p6skxTY9wOMy+PS0UoRAwRtm6v5+9B1QsyDeg18jz3smIRiK0tbdluhlZIakoBKNJmuMn/yw7lff+/dfX1094ewkuzFBWg5Z5eVa2tg9SW2BFo5aL0vt5Q3HiSYWq3KmXGHwvg1bDxUuKiSWSaGTUwoSY9RoMOg3BDI+4SSoKA0emRKQ7yGHUaVhR7uKPu7rp8YYpsE99ik4qpkSMKnWZ2HXYQyASx2JI/UdCny9Cvz9CZY4Fi0HDrk4v7QMByt1mSl3mlIwqUBSFjqEQBwcC6DRqLl1SQol7anlW0kWvVVORY+b1/QMkk0nUcu1Oifvuu48HHnjgpOs89NBDJ/zdePKOWCwWnnzySYLBIK+99hp33nknpaWlrF279qTbVVZWjn3f3Nw8qc6aODE5pqknxzQ93ntcFwIrh4M8u7OHzkicpUVOXBl+6DITtbW3UVVZlelmZIVEUsEfiVE/P29K+5nq378EF2awmnwrzZ1eDg2FqMhJ7Q30bOAJxzDrNWm7edRJlHnCjDoNZr0m4+UPfeE4gUicFZWuaQkQzcuzUpNvY0+PlzybAfUUn6anYkrEqDybAZdFT5cnRE2+bcr7e69YIsmBwQAaNXxgQR4Ok55VlTls6xji3UMeDgwGKXOZKXOZMeknF2RIJJUjIyPCuC06LlxUhNOS3R208hwz7x4a5u2DwywqcWTVtI2ZatOmTVxyySUnXae4uJjt27eTSCQYGhrC7f7bSKLBwUFWrVp10u3VajUVFRXAyNOc1tZWHnjggVMGF4QQ4niKnWauWFnKc009vNk+SF2BTfrzYsaT4MIMlmPRU+o20drnl4vR+ySSCp5glFyrQTruWcZh0mU8W/5gIEpSUZif4pvpE1GrVSwvH8m90NrrZ37B5F83VVMiRtmMOtwWPYfSkFzq0FAQbyjGWQvycZhGbvgdZh0fqMtnZYWL7QeH2X54mIODQUpdJsrd5gm9p2g8SUuPj35/mKocC+csLEjJMUm3fJuRDfPz+NPuHl5t7Wd+vo1Sl2nKQae5zO12HxUsOJFFixah0+l49dVXufjiiwHo7u6mtbWVZcuWTeg1k8kk0ejsSoYqhJheVqOODy8tYfO+Pt46OIw3HGNhkUNGxooZSx69zmAqlYoFhSP5FjL9JDjb+MIxwvEklTnZOTR6LnOY9MSTSsaSGCmKwmAggtOsx2WevgoC+XYjDcV2OoaChKKTnxaSyikRo8rdZkKxJJEUTlfxhmJ0DocpcpiOmxfGatSxoTaPa9ZVclqVmz5/hM2tAzR1esaV8NMfibOz00OfL8ySUicbFxXNiMDCqPkFNq4+rZJFJQ729PjYsn8AbyiW6WbNejabjcsvv5y7776bzZs309TUxBe/+EXq6upYt27d2HqbNm3innvuGfv5Bz/4AZs3b6ajo4PW1lZ+8pOf8NRTT51ytIQQQpyKRq3i9Np8NjYUMhyMsaVtYEr9BCEyaeb0xMRxFTmMFDtNHBwMkGeT5GCjPKEYSUWRER1ZyGLQoFWrCUbjGSkP6AvH8UcSLCm1o53mqS2NpU7a+4I0d3lYPsnkjqmcEjEqz2bAbtTS7Q2n5G8mqSi0DwSIJ5OcUZt70ilEFoOOtTW5LKtw8e6hYbYeGKLTE6LAbqQyx3Lcc2QwEGVvr49QLMGZdbksLpme6S2pZjZoObe+gAUFNl7e08uWtgHK3Gaq86wy7SqNbr75ZrRaLTfeeCPhcJi1a9dy9913o9H8bZRbR0cHRUVFYz8Hg0FuvfVWuru7MRqNzJs3j7vuuosPfehDmXgLQohZqL7Ijsus57ld3bzW1k9jiZNcq/TtxcwiwYUZTqtRU5tv4/mBHvyRGFZD5mu5ZwNvOI7VoMVukuORbcx6LSa9Gm84M8GFoWCUeDJJbeH0TIl4L7Ney/IKJ8829dDvD5NrnVhyx1RPiRjlMutxmfUMBqMpCS50e8IMBqIsLXeMO4GlUadhVVUOS0qdvHvYw9YDQ2xpGyTPaqAy14LjyN9y53CI9oEACnDR4qKx0o4zWanbzJWrytl+cIjX2gbp8oRZWGQn32aY8e8tGxkMBm655RZuueWWE67z0ksvHfXzTTfdxE033ZTupgkh5rhCh5ErVpbyfFMPbx8coibPOis+58TcIY9GZoHKXAv5NgPt/YFMNyUrxBJJPMEoBTZDWsrdiamxGrSYdFoCkekfAq4cqRJhN2ozVgawOt/GvFwLu7t948pQ/17pmBIBIzkhSt1mfOE4iWRySvsKxxJ0DAaxGbWsrMiZcIdIr9OwotLNteurOHtBHuF4nDfbB9l2cIiDw1Fa+/wYdRo+srSYeXnWWdPh0mrUrKzK4erTKih3m3n38DDbOoYJRk89RUQIIcTsYdJr+VBjMeuqc2gfCPDOoWHiial9NgsxXeTOaxYw6jRU51vp90eJycUHbyhGNJFkXp5MichGRp0ao05NODb952ogksAfjjMvg8PONWoVqyrdaNVq2iYYEEzHlIhRhXYjZp2aPv/UEtS1DwQIxuKcXps76QoQADqtmiVlLq5ZV8V5CwuIK0k6vTHy7QYuW15CgSP1xyAbuMx6Pry0mIsWFxFNJHhtfz/t/f6M5SgRQggx/dRqFWurc7mwsYhgJMFr+wdO+FAmqYzksYolkkTjScKxBMHoSFUsXziGJxRjOBhlKDDyJZ8nIp1kWsQsUZNnpemwl4MDQarzrZluTkZ5wzHUKhUlTknmmI1UKhV2k45ub3jaX3swGCWWTFI3hWoNqTCa3HHrgUFKXCYM2lPfhKdrSsSoHKsep0VPny9M4TinMrzfgD9Cny9CdZ6FqpzUXIc0GjWLShzUF9p41eBndWPxrK8Ao1KpqC20U+628Nr+AXYcGqbzyFSJdJXWFUIIkX3m59twmfU8u7Ob1/YPjk0RTCSTJJIjgQUARQEFxkZEjn1/ZPnoMhVg0KlZWGjHLfkcRBpIcGGWcJr1VOZa2NXpYV7e3J6b5QnGsBo1WI1yemcrp0lHNJ4kqSjTWn5v0B/BZtSSkwUfqEvKnLQPBGjq8rKszHXK9dM1JWKUTqOmyGFk28FhFEWZ8DUknkhyYCCAWq1ifU0e6hQnWNRo1ORZdLM+sPBeRr2Gsxbks6DQxp9aennzwBDFDiPz820y5UsIIeaIXKuBy1eUsKVtkJ7hMGqNCo1KhVajQq1SodOo0ahHptdpVSq0GjUatQqNauSzXTf6s1pNPJlkW8cQWzuGKLabqC2QzxORWrP+7uuxxx7j6aefprm5GZ/Px4svvkhpaekpt3v22Wf5r//6Lw4ePEh5eTk33ngj55133jS0ePJqC2y09Pjo8oQpds7OIcOnEo4l8EXi1ORbpr0SgBg/i0GLWq0iFE1gMUzPZSgQieOLxKkrzI4PUotBy7JyFy809TAYiOK2nPyJdDqnRIwqcZrZccjDcDCG6xTteb+OoRDDoRhn1ubJ0/UUK3KauHJVOe8cGmZzaz+9rWEWFNoxyDVOCCHmBKNOy5m1+SnZV3WelZ2dHl7Z18+rrX3UFdgpchjn9INJkTqzvmcSCoXYsGEDn/3sZ8e9zbZt27jxxhu5+OKL+e1vf8vFF1/M5z//ed555500tnTq8m0GSp0mOgaDmW5KxnjDMaLxJPNy5/bUkGxnNWgx6zT4wtOX1HEwGCWaSLIgA1UiTmR+vpXKXDPNXd6TJnccmxLhSM+UiFG5VgNOk55uX2hC2/nCMbo8IQocBhYWp2dkxVynVqtYVu7ik6dVMC/Pwq5OLzs7vTJ3VgghxISo1SoaS51cvaac6jwrTV1e3jo4JAmERUrM+uDCpz/9aa6//npWrFgx7m0efPBB1qxZw2c+8xmqq6v5zGc+w+rVq3nwwQfT2NKpU6tV1BXaiCaSDAenlpRtpvKG4mg1Kgock5szLqaH2aDFpNPgi0zfB9mgP4LVoCXXlvkpEaO0GjWrKt1o1CraB04cFEz3lIhRJr2GArsBT3D8QZ+kotDeHyCaSHLG/LysGBUym9lNej7UWMIlS4txW3QoSBJfIYQQE2c36bmosZhLlhajKAqvtvbTJgmExRRJL/A4tm/fzvr1649atmHDBrZt25ahFo1fqctMgd0w4Sz0s4GiKAyHojhMOixpfLorps6s06DXaojEEtPyesFoHG84Ttk4kydOp0KHifpiO+39ASLx4x+P6ZgSMarEZSaWUMb9BKPbE2YgEKWxxEHRLK3gkI2q86xcubqcMrdUxRFCCDF51XlWPr66ghXlLtr6A2xpG5izDynF1Mkd2HH09/eTm5t71LLc3Fz6+vrGtX17ezuh0Miw4nA4THNzc8rbeDK6YJh9h0OYooMYZuFTxGgkQlt72zHLQ7Ek7b1hatx6WlomNqx7rsvEeerpC3DIG8cSG0r7a/X6YxweirLAbKW5eTjtrwcTO6amWJKoz8fL2waozTt61E00obC7J0y+RcOB1vRPI/FHE4Q9frY2D1LmOHnuhGgiSUtfBLUK7HlRdu8eSGvbMnGezgWjx7W+vj7TTRFCCJEBRr2GM+vyqS208afdvbx5YJBSl5maDJbuFjPTjAwu3HfffTzwwAMnXeehhx5izZo1KXvNiWRPr6ysHPs+Ex22skgc3/ZDqDQaqgrt0/ra06GtvY2qyqpjlnd7wrjiHs5cXkx5isrgzRWZOE8jliECbQNUVuSmPYmQ/7CHSnOMdcsrp63awESPqTHHw4u7e3Dlu45KiNjtCeOIeji3sYj501BCU1EUBrU9dA6HqKpwn3Tdlm4f1liQCxcVMn8arjVyA5weclyFEEIAFDlGEghv7xjmtf39dB8pg1wwyRLVYu6ZkcGFTZs2cckll5x0neLi4knvPzc3l/7+/qOWDQwMHDOaIVtZDVqq82xsbR+kJt+KRj35iGMwGmcoEMMXjmHSacixGrK2xKMnFEOvVZNrkwvgTGA16lChIhxPYNKl75wKxxJ4glHKcy1ZXcawrtBGa5+fXV1e1s3LGQu4TOeUCACVSkWp08S+Hh+xRPKETywGA1F6fWGqci3My8+eJJlCCCGEmDy1WsXyChdVeRb+3NLHjkPD5NkMLCi0Z3U/SmSH7LxLPAW3243bffInalOxdOlSNm/ezD/8wz+MLdu8eTPLli1L22umWnWeleZOL4eGQlTkjH9OrqIo+MJxhoJRhgJRfJE4kXgSo05NJJ5kb58fq0FLkcM4EmiYpjKCp5I8km/BZdFjkgvfjGAxaDDqNPhD6Q0uDAaihONJFk7DU/+p0GrUrKx08bsdXRwcDFKRY/lblQhneqtEvF+Bw4jVqKPbE6bMbT7m9/FEkvaBACoVnDE/D41aylcJIYQQs4nLrOfDS4tp6fHx15Y+Xt3XT22BjRKXCbWUrRQnkB13hmnU19dHf38/7e3tALS2tuLz+SgqKsLpdAIjIyEaGxu56aabAPjUpz7F1VdfzQ9/+EPOPfdcXnjhBbZs2cLPf/7zDL2Licu16ilxm9jf5z9lcCGRVPCEYgwFowwGogQjCeJKErtRS12hjdp8K3l2I4mkQlt/gOYuL/v6/Ozt9WMzaimyjwQaLBkMNAQicULRBA3FdqnTO0NYjlSM8Edj5JG+Cg5DwShmvYZCZ/YnGyx2mqkvtLG9Y5gih3HaqkS8n9usx2XWMRiIHDe4cHg4xHAwyoaaXJyWk+dlEEIIIcTMpFKpWFBop8Jl5tX9A7x7yMPh4RD1RXYcJl2mmyey0KwPLvziF7/ge9/73tjP1113HQB33HEHl112GQAdHR0UFRWNrbN8+XLuvfde7r//fr773e9SVlbGfffdx5IlS6a38VOgUqmoL7Sxv89Pny9C3vvK78USSYaCUYaDMQYDEUKxkXJmLrOOZRVOavItuC2GY4ZELypxsKjEQSASZ/+RQMPePj97ev3YjX8b0TCdT1lhpARlPKlQlSuZ02cKi16LXqsmFE1fxYiRJ/9RSt0mTPqZMaJlWbmLAwNBWnp8aNTqaZ0SMUqtVlHqMvHaUJCkohz1hMIfiXN4OES+zcCi0ukNegghhBBi+pkMWs6tL2BBgY0/tfTyRtsABXYjNfnWae/zi+w268+Gz33uc3zuc5876TovvfTSMcsuuOACLrjggnQ1a1oUOUwUO00cHAyQZzMQiiZGAgqhGEOBKJF4Ap1aTY5Nz4oKG/NyLdhMunENcbYYtCwucbC4xIEvHKOtL8Dubi8tvX7omf5Agyccw6zXHJUIT2Q3jVqFzahNa7mjoUCMSDxJ/QxKbGoz6lha5uJPLT1oVGrK3NM7JWJUocOEUadhwB8h70geE0UZGb0USyQ5sy4v68p6CiGEECJ9St1mPr66nJ2dXl7fP8Dm1n7KXGYqcy3SJxDAHAguzGVajZrafBvPD/Tw9sEhAkfyJ5h1GoocBuoK7ZS4TFgN2ilNJbAZdTSWOWksc+ILx9jf52d3t489vX6UHj8Ok44ihxG3RZ+Wm6REUsETjJJrNUiimRnGZdazr9c/oWosEzGaDLF4mp/8T9WCIhutvT529/ioLyrISBtyrQacJj09vvBYcKHHG2HQH6WhxEGRY2YdUyGEEEJMnUajZkmZkwWFNt4+OMRbB4Y4NByiOtdKqcuEVkpXzmkSXJjlKnMtlLpM9PuiVOdZqCu0UWBP3xBxm1HHkjIXS8pceEOxkRuk7pEvgJp8C5UpLhPpC8cIx5NU5hw7N1xkN6tRi6IoxBJJ9CmOeEfjSYYCUQrsmXnyPxU6jZq11bkYdRpKXZk5r/VaNUUOI+8cGgZGppgcGAxg0qtZXeWW3CZCCCHEHGbQaVhbncviEgdb2gbZedjDgcEANflWihyS9HGumlk9bjFhRp2GDy8pIZ5U0GunN5JoN+lYVuFmWYUbTzDK2weH2N7hwWrQkWtNXQI/TyhGUlEmVBVDZAeLQYtBp8YfieNOcXBhKDgy9Wdh0cyZEvFeBQ4jFywuOvWKaVTiMrHjsAdPKEqPN0IgEueChsKMJm8VQgghRPawGnWcU1/A0jInr+4boLnLR3t/kNoCK7lWgzyMmGNk3MocoFarpj2w8H4Os54zavOpL7LxzqFhQtF4yvbtDcexGrTYJWvtjGPRazDptHjDqTsfRg0Foug0akqOU+1AjE+ezYDLrGNPj49eb5iKHAs1WV7SUwghhBDTL8dq4JKlxVyxshSXRc/2Qx7eOjCEJxTLdNPENJLggpg2GrWKM2rzKLQb2dYxTCKpTHmfsUQSbyhKgc2Q8QCKmDizXotBp0lpsAn+Vg2lwG7EMkOqRGQjs15Lns2AJxRHUcEZtbnjSvgqhBBCiLmp1GXmYytLuXhJEWoVvNk+wI5DwwRT3NcT2UnuxsS0Muu1nFWXj1qlYleXZ8r784ZGqgHMy5MpETORXqvGotcQiSdTut+hYJRQLMGCQpsMx5uiMpcZvUbNqkoXbkvqpjMJIYQQYnZSqVTMz7dx9WkVnFNfQCASZ3NrPy3dXiLx9JUgF5knE2fFtCtwGFk/P4cXm3rpGAxSNoVh695wDJUKSpwy9H2mcpp0tA8EUrrPoUAMnVpNuST5nLKqXAuXLC2mwG7MdFOEEEIIMYNoNGoaS53UFdjY3jHMm+2DHB4OMy/XgsuiR6NWoVGp0GpUqFUqGR05C0hwQWREfaGdAX+Ut9qHsJt0OCaZL8ETjGEzarEa5VSeqWwmHYmkQjyRTEn5ongiyWAwQp5dj1USD06ZVqOeUgBQiGwTjUa56667ePrpp4lEIpx22mnceuutFBYWjmv7p59+mptuuokPfOAD/PCHP0xza4UQYuYz6DSsmZdDQ7GdN9oGefewh2SfggoVKhWoVSpUKhUaNWjVavQaNVqNCp1GhUatRqNWoVWrUB/5dzQo4Y8mCEUT6DQqKYGZJaTnLTJCpVKxpiqHoUCUbR2DrJuXO+FShOFYAl8kTk2+RS4oM5jVoEWvHakY4TTrp7y/4VCMUDTBafOkXKIQ4li33XYbL774Ivfeey9Op5M777yT66+/nl//+tdoNCf/HOro6ODuu+9m5cqV09RaIYSYPaxGHWfXF7Ci0kWfb6SqVziaIBxPEIkpIz/HEkTjSSKJJKFwnHhSIZFUUBSIJxRQgUoBRQW9vWH6kv2oj4x+0GvUGLUaDDo1eq0arUaNTqNGp1ah04ws02nUpxwhoSgjr5lUOPKvctS/CWXkd8kj3ztNOmxGSSwPElwQGaTXqjmzLo8nt8fY3jHMqsqJ3Qx6wzGi8SRVOdY0tlKkm1mvwazT4AvHUhJcGAxE0ahVUppUCHEMn8/HE088we2338769esBuPvuuznrrLPYvHkzp59++gm3jcVi/Nu//Rs33ngjW7ZsYWhoaLqaLYQQs4rDpMdhGl+fb/QGPpFUSCSTRONJonGFaCLJrpYghSV5eMMxApEEwWiCYDTOQCBCLPG3AMEo9ZFRElqNCoNWg1GnQQ3EkgrJZJJ4UiGWGAkiKAojX4z8zMh/jO5u9I5FAVQqqC+0USzTtCW4IDLLYdJzVm0ev3+3iz3dfuqKxl/mzhuKo9WoKHTKXPCZzGIYqRgRiE49wU8iqTAYiJBjMWCXCLIQ4n127txJLBZjw4YNY8uKioqorq5m27ZtJw0u3HfffZSUlPCRj3yELVu2jPs129vbCYVCAITDYZqbmyf/BsQx5JimnhzT9JDjmnoFRgVDoJs8IE/LyJ2t+cjIA2WkelgkDuF4knA8STCaJBRPEgop+GJJ+mIKSRQ0KhUajQq9GnQaFXq1Cp1KhVbDyMgHjQqdBvRqNVq1Cq2akX81KhSgqSfEn3f0UWTXUu7QZ2TkbFJRCEaTNMf7p7Sf956n9fX1E95eggsi48pzLJw2L5c/7+nFadZR4Dh1sEBRFIZDURwmHRa9nMYzmVGnwahTM+CPTnlfw8EooViSFRVSJUIIcaz+/n40Gg0ul+uo5Tk5OfT3n7hD9sorr/CHP/yBJ598csKvWVlZOfZ9c3PzpDpr4sTkmKaeHNP0kOOaetlyTFcrCm8fHObVfX14NHoWlzimfcp2Iqngj8Son583pf1M9ZjKXZnICo2lDvr9YXYe9mAxak+ZiC8USxCMJGgotqOWzLIznsOkp3M4POntw7EEff4Ifb4IKhTm5cqUCCHmkvvuu48HHnjgpOs89NBDJ/ydoign/N3g4CBf/vKXueeee3A4HJNuoxBCiNlJpVKxosKF06Tj+aYetrQPsqLchVE3sXxys4EEF0RWUKtVbJifx1AgdiTBY95Jk614Q3FiiSTz8mRu02zgNOuIJZIkksq4yxDFEkkGA1H6/RGGAlEiiSRWg5ZVlTnYJ1l9RAgxM23atIlLLrnkpOsUFxezfft2EokEQ0NDuN3usd8NDg6yatWq4263d+9e+vr6uOaaa8aWJZNJABYuXMjTTz/NvHnzUvAuhBBCzGTV+VZsJi1/eLeb11oHWFbhxDnO3BKzhQQXRNYw6jScXZ/Pb7cf5p1DQywvd59wXU8ohl6rJtcm+RZmA4tei1atJhiNnzTbblJRGA7G6PdHGAhECEUT6LUjpRIXldgpcpowTLDqiBBi5nO73UcFC05k0aJF6HQ6Xn31VS6++GIAuru7aW1tZdmyZcfdZvHixfzud787atn999+P1+vl61//OqWlpVN/A0IIIWaFfJuRy5eX8Oyubt5sH2RRkYMipynTzZo2ElwQWSXHauCM+Xk829TD/n4/83KPrQSRPJJvwWXRY5qDw41mI4tBi0mvxhs+fnDBH47T54/Q748QiMRRqRTy7UY2VOdSkWvBcoppNEIIAWCz2bj88su5++67ycnJwel0cscdd1BXV8e6devG1tu0aRONjY3cdNNNmM1mamtrj9qP3W4nkUgcs1wIIYSwGnVcsrSEP+/u5d1OL/5InJp865zIByY9cpF1agps9AeivL5/AKdJh9tiOOr3oZhCSEmwsNg+J/5I5wKLQYNJpyEQiQEj0d1wLMGAP0qfP4wvPDINxmXWs7rKRV2BHbtJJ/k2hBATdvPNN6PVarnxxhsJh8OsXbuWu+++G43mb8Hqjo4OioqKMthKIYQQM5lOo+achQW4LAZeae0jEI2zuMQ57um/M5UEF0RWWlnhYsAf4Z1Dw6ybl4vhPSMUAtEEcY0k7ZtNTDoNBp2GwUCUXl+Yfn+UoUCEcCyJxaClttDGohI7uRbDtGffFULMLgaDgVtuuYVbbrnlhOu89NJLJ93HnXfemepmCSGEmGVUKhUrKl04zDpeaOpmS9sAy2d5okcJLoispNWo+UBdPp5gjLc7hlhTlYP6yCgFfzSJxaHBaZ5bCVJmM5VKhcOkY2+Pn6FgFJ1aTanLyOJiJ0Uu06y+CAshhBBCiNmrJt+KzVjKH3Z28dr+AZaVpybRo6IohGIJvKE4w8EoBl3mH8BJcEFkLYtBy1kL8vndjk6aOr0sKnGQSCr4IknKrAa54Zxl5udb8QRjVOSYqcqznrIcqRBCCCGEEDNBgd3IR5eX8sed3WxtH6Rhkokew7EE3nBsLKAQjCaIJZIYtGoWFNnS0PKJkd67yGpFThPrqnN5aXcPh4Z0WA1aookklTlSgnK2KXSY+NCSuZNNVwghhBBCzB1Wo44PL3tPosdonJq8kyd6jCWSeEMxvOEYnmAMXyRONJ5EqxkZ9dtQbGdenplcmzErEt1LcEFkvYZiO4P+KG8fHKTYaSapQEWO5FsQQgghhBBCzByjiR6dFj2vtvYTiCRYXOIYS/SYSCr4wjG84TieUAxvKEoknkStUmE1aqjJtzAv10qBw4hFr8265OYSXBBZT6VSsabazUAgQlt/ELNOjd10bLlCIYQQQgghhMhmKpWKlZVuHCYdLzT38EbbAGVu85GAQpRwLElSUbAYtJQ4TczLs1DiNGM1arM+sbkEF8SMYNBqOGtBPt7tnUTiWvTa7P7DEkIIIYQQQogTmV9gw2bS8sed3TR3ebEatORYDVTmmKnIsWA36WbcPY8EF8SM4TTr+eiKElp2+zPdFCGEEEIIIYSYkkK7iY+vKscTjmEz6mZ8wnoJLogZxWLQYdLP7D86IYQQQgghhADQ6zTkzfCgwqhZH1x47LHHePrpp2lubsbn8/Hiiy9SWlp60m1+/etf85WvfOWY5Tt27MBgMKSrqUIIIYQQQgghxIw064MLoVCIDRs2cM4553DHHXeMezuTycTzzz9/1DIJLAghhBBCCCGEEMea9cGFT3/60wC8++67E9pOpVKRl5eXhhYJIYQQQgghhBCzy6wPLkxWOBzmrLPOIpFIUF9fz+c//3kWLlyY6WYJIYQQQgghhBBZR4ILx1FVVcXtt9/OggULCAQCPPTQQ1x11VX89re/pbKy8pTbt7e3EwqFgJEgRXNzc5pbPLfIMU09OaapJ8c09eSYpsfoca2vr890U4QQQggxg83I4MJ9993HAw88cNJ1HnroIdasWTOp/S9btoxly5Yd9fOll17Kww8/zNe+9rVTbv/eAIR02FJPjmnqyTFNPTmmqSfHND3kuAohhBAiFWZkcGHTpk1ccsklJ12nuLg4Za+n0WhYtGgR7e3tKdunEEIIIYQQQggxW8zI4ILb7cbtdk/b6ymKQktLC3V1ddP2mkIIIYQQQgghxEyhUhRFyXQj0qmvr4/+/n727dvHF77wBX70ox+Rn59PUVERTqcTGBkJ0djYyE033QTA9773PZYsWUJlZSV+v5+HHnqIp556ikcffZTGxsYMvhshhBBCCCGEECL7zMiRCxPxi1/8gu9973tjP1933XUA3HHHHVx22WUAdHR0UFRUNLaO1+vl61//On19fdhsNhYuXMjDDz8sgQUhhBBCCCGEEOI4Zv3IBSGEEEIIIYQQQqSXOtMNEEIIIYQQQgghxMwmwQUhhBBCCCGEEEJMiQQXhBBCCCGEEEIIMSUSXBBCCCGEEEIIIcSUSHBBCCGEEEIIIYQQUyLBBSGEEEIIIYQQQkyJBBfS5JFHHuHss89m8eLFXHbZZWzdujXTTZqxvvvd71JXV3fU1/r16zPdrBnnzTff5IYbbuD000+nrq6OX//610f9XlEUvvvd77JhwwYaGxv55Cc/yd69ezPU2pnhVMf0y1/+8jHn7sc+9rEMtTb7/fCHP+Tyyy9n+fLlnHbaadxwww3s2bPnqHXkPJ248RxXOVfTS/oEqSX9gqmTPkHqSZ8g9aRfkHrp7hNIcCENfv/733P77bdzww038OSTT7Js2TL+8R//kc7Ozkw3bcaqqqrilVdeGfv63e9+l+kmzTjBYJDa2lq++tWvYjQaj/n9//7v//KTn/yEW265hV/96le43W6uueYa/H5/Blo7M5zqmAKsW7fuqHP3Rz/60TS3cuZ44403+PjHP84vfvELHnzwQTQaDddccw3Dw8Nj68h5OnHjOa4g52q6SJ8gPaRfMDXSJ0g96ROknvQLUi/tfQJFpNxHP/pR5atf/epRy8477zzlO9/5ToZaNLP993//t3LRRRdluhmzytKlS5Unnnhi7OdkMqmsX79e+f73vz+2LBQKKUuXLlUeffTRTDRxxnn/MVUURfn3f/935brrrstQi2Y+v9+vLFiwQHnxxRcVRZHzNFXef1wVRc7VdJI+QepJvyC1pE+QetInSA/pF6ReqvsEMnIhxaLRKLt27TpmeN769evZtm1bhlo183V0dHD66adz9tlnc+ONN9LR0ZHpJs0qhw4doq+v76jz1mg0smrVKjlvp+itt95i7dq1bNy4ka997WsMDAxkukkzRiAQIJlMYrfbATlPU+X9x3WUnKupJ32C9JF+QfrItTZ95Do7NdIvSL1U9wm06WjkXDY0NEQikSA3N/eo5Tk5OWzevDlDrZrZGhsbueOOO5g3bx6Dg4P84Ac/4Morr+Tpp5/G5XJlunmzQl9fH8Bxz9ve3t5MNGlWOP300znvvPMoLS3l8OHD3H///WzatIlf//rX6PX6TDcv6912223U19ezbNkyQM7TVHn/cQU5V9NF+gTpIf2C9JJrbXrIdXbqpF+QeqnuE0hwIU1UKtW4lolTO/PMM4/6ecmSJZx77rk8+eSTXHPNNRlq1ewk52hqXXTRRWPf19XV0dDQwNlnn83LL7/M+eefn8GWZb877riDt956i0cffRSNRnPU7+Q8nbwTHVc5V9NL+gSpJf2C6SHnaGrJdXZqpF+QeunoE8i0iBRzuVxoNJqxSNqogYGBY6JqYnIsFgs1NTW0t7dnuimzRl5eHoCct2lWUFBAQUGBnLuncPvtt/PMM8/w4IMPUlZWNrZcztOpOdFxPR45V1ND+gTTQ/oFqSXX2ukh19nxk35B6qWrTyDBhRTT6/U0NDQcM9xx8+bNRw03EZMXiURoa2sbu6CIqSstLSUvL++o8zYSibB161Y5b1NocHCQ3t5e8vPzM92UrPXtb3+bp59+mgcffJDq6uqjfifn6eSd7Lgej5yrqSF9gukh/YLUkmvt9JDr7PhIvyD10tknkGkRaXDNNdfwpS99icbGRpYvX86jjz5Kb28vV155ZaabNiPdddddnHXWWRQVFTE4OMj3v/99gsEgH/nIRzLdtBklEAhw8OBBAJLJJJ2dnTQ3N+NwOCguLuZTn/oUDzzwAPPmzaOyspIf/OAHmM1mPvShD2W45dnrZMfU4XDwve99j/PPP5+8vDwOHz7Mvffei9vt5txzz81wy7PTN77xDX7729/yP//zP9jt9rEnEWazGYvFgkqlkvN0Ek51XAOBgJyraSR9gtSTfsHUSZ8g9aRPkHrSL0i9dPcJVIqiKOl+E3PRI488wo9//GN6e3upra3lK1/5CqtWrcp0s2akG2+8kTfffJPh4WFcLhdLly7l85//PDU1NZlu2oyyZcsWPvWpTx2z/CMf+Qh33nkniqLwve99j8ceewyPx8OSJUv4+te/Tm1tbQZaOzOc7Jjeeuut/PM//zNNTU34fD7y8vJYs2YNn//85ykqKspAa7NfXV3dcZd/9rOf5XOf+xyAnKeTcKrjGg6H5VxNM+kTpJb0C6ZO+gSpJ32C1JN+Qeqlu08gwQUhhBBCCCGEEEJMieRcEEIIIYQQQgghxJRIcEEIIYQQQgghhBBTIsEFIYQQQgghhBBCTIkEF4QQQgghhBBCCDElElwQQgghhBBCCCHElEhwQQghhBBCCCGEEFMiwQUhhBBCCCGEEEJMiQQXhBBZ75lnnmHRokXEYrFMN0UIIYQQGSb9AiGykwQXhBBZb/fu3dTW1qLT6TLdFCGEEEJkmPQLhMhOElwQQmS95uZm6uvrM90MIYQQQmQB6RcIkZ0kuCCEyHq7d++murqae++9l9NPP50VK1bwta99jWQymemmCSGEEGKaSb9AiOwkwQUhRFYbHBykr6+Phx9+mHA4zJ133sm1117L448/zosvvpjp5gkhhBBiGkm/QIjspc10A4QQ4mSam5sB+OQnP8k111wDwPr163n00Udpb2/PYMuEEEIIMd2kXyBE9pKRC0KIrLZ7927sdjtXX3312DJFUfD5fLhcrgy2TAghhBDTTfoFQmQvCS4IIbLa7t27WbVq1VEZoQ8ePEg4HGb+/PkZbJkQQgghppv0C4TIXhJcEEJktd27d1NXV3fMMrVaLZ0IIYQQYo6RfoEQ2UuCC0KIrBWNRtm/fz8LFiw4anlLSwtlZWWYzeYMtUwIIYQQ0036BUJkNwkuCCGy1r59+4jH48c8oWhpaTlmmRBCCCFmN+kXCJHdJLgghMhau3fvxmQyUV5eftRy6UQIIYQQc4/0C4TIbipFUZRMN0IIIYQQQgghhBAzl4xcEEIIIYQQQgghxJRIcEEIIYQQQgghhBBTIsEFIYQQQgghhBBCTIkEF4QQQgghhBBCCDElMyK48Mgjj3D22WezePFiLrvsMrZu3XrCdSORCF/+8pe5+OKLaWho4JOf/ORx13vjjTe47LLLWLx4Meeccw6PPvroMes8++yzXHjhhSxatIgLL7yQ559/PmXvSQghhBBCCCGEmC2yPrjw+9//nttvv50bbriBJ598kmXLlvGP//iPdHZ2Hnf9RCKBXq/n6quv5swzzzzuOh0dHVx33XUsW7aMJ598kuuvv55vf/vbPPvss2PrbNu2jRtvvJGLL76Y3/72t1x88cV8/vOf55133knL+xRCCCGEEEIIIWaqrC9FecUVV1BXV8e3v/3tsWXnn38+Gzdu5Kabbjrptt/85jfZu3cv//d//3fU8v/8z//k+eef57nnnhtb9tWvfpV9+/bx2GOPAfCv//qveDwefvrTn46t8+lPfxq32829996bircmhBBCCCGEEELMClk9ciEajbJr1y7Wr19/1PL169ezbdu2Se93+/btx+xzw4YN7Ny5k1gsdtJ1Jvq67e3tk26nOD45pqknxzT15JimnhzT9JDjOn3kWKeeHNPUk2OaHnJcU0+OaepN9ZhqU9OM9BgaGiKRSJCbm3vU8pycHDZv3jzp/fb397N27dqjluXm5hKPxxkaGiI/P5/+/v5jXjc3N5e+vr5T7r+9vZ1QKARAOBwe+16khhzT1JNjmnpyTFNPjml6jB7X+vr6TDdl1pPzN/XkmKaeHNP0kOOaenJMU2+qxzSrgwujVCrVuJZNZZ+js0NOtl9FUcb1upWVlWPfNzc3S4ctxeSYpp4c09STY5p6ckzTQ46rEEIIIVIhq6dFuFwuNBrNMaMFBgYGjhlVMBG5ubn09/cfs0+tVovT6TzpOlN5XSGEEEIIIYQQYjbK6uCCXq+noaHhmCkQmzdvZtmyZZPe79KlS4+7z0WLFqHT6U66zlReVwghhBBCCCGEmI2yOrgAcM011/Cb3/yGxx9/nNbWVr797W/T29vLlVdeCcA999zDpk2bjtpm3759NDc3MzQ0RCAQoLm5mebm5rHfX3nllfT09HDbbbfR2trK448/zm9+8xuuvfbasXU+9alP8frrr/PDH/6Q1tZWfvjDH7Jly5ZjXksIIYQQQgghhJjrsj7nwoUXXsjQ0BA/+MEP6O3tpba2lh/96EeUlJQA0NfXR0dHx1HbXHfddRw+fHjs50svvRSAlpYWAMrKyvjRj37EHXfcwaOPPkp+fj5f/epX2bhx49g2y5cv59577+X+++/nu9/9LmVlZdx3330sWbIkze9YCCGEEEIIIcT/Z+++4+Qq68WPf870Xrb3kk02vYcECL1GijRFEEXJFa8oXOSqgCiiqBiuoiL4U7gCojSFK80gNUgLJED6ZrNJtvednZ3eZ875/THJkiU9W2Z293m/XpvdOXPOmWcmM2ee8z3P8/0K40vWBxcArrrqKq666qoD3rdq1ar9lq1Zs+aw+1y6dCnPPvvsIddZsWIFK1asOLJGCoIgCIIgCIIgCMIklfXTIgRBEARBEARBEARByG4iuCAIgiAIgiAIgiAIwrCI4EIWk2WF9oEw8aSc6aYIgiAIgiAIgiAII0RRFPzRBK3uEH3+KIqiZLpJwzYuci5MVilFYUuHF7VK4qyZhWjUIhYkCIIgCIIgCIIwHsWSKQZCcdzBOO5gDF80QTiWIppMUZFjYkG5kxyzLtPNPGYiuJDFtGoVNfkWXtzchU6t4rTpBahUUqabJQiCIAiCIAiCIByGLCv4IgncoXQwwR2KE4knCcdToIDZoCHfoicUT1LX5afVHWZGkZXZpXZsBm2mm3/URHAhy+Va9FTkmNjc4cOk13D8lNxMN0kQBEEQBEEQBEE4gEg8RX8wxkAoTn8gRjCWJBRPkkgp6DUqrAYNJXYjRp0G9T4XjityTDS5QnzY7GF3X5BZJTZmFdsx6tQZfDZHRwQXxoFCuwGjTs0HjW6MWhXzy52ZbpIgCIIgCIIgCMKkl5IVBkLxdDAhGMUTThCJpwjHU6hUYNVrKXEYsRm0aA8xzV2tUjGt0EplrondfUHe29XP7t4gc8rsTC2woNdkf5BBBBfGieo8C7GEzFs7XZh0GqYVWjPdJEEQBEEQBEEQhEklmkjhjybwRxL4Ign6g3HC8RShWJKUrGDWqbEaNFTkmDBq1UjS0U1r12nUzCqxU51noaHXz5r6XnZ0BZhfYacq15zVefhEcGEcmV5kJZaSeW17L3qtioocc6abJAiCMGbiqfGfRVkQBEEQhPEjJSv4Iwn80XQgwROKE4gliSVkIvF0MEGtUWEzaKjMMWHRa0bs5N+oU7Og3Ik/mmBnb4CXt/VQkWNifpmD8hxTVubiE8GFcUSSJOaW2vm41cPL23q4aEEphTZDppslCIIw6rq8ET7sCFFdE8dmHL9ZlAVhIvjyl7/MtGnT+NGPfjS47NZbb8Xj8fDAAw/w5S9/mZqaGmw2G3/7299QqVRcfPHFfO9730OlSne64/E49957Ly+++CJ+v5+amhq+/e1vc/LJJwOwbt06rr76ah588EHuvfdedu/ezZw5c/j1r39NW1sbP/vZz2hvb2fWrFncf//9OJ3OIe2YP38+jz32GJFIhBUrVnDHHXdgMIg+kyCMZ7KsDJ7k+yNJmt0x1L0BtGoVeo0K3d6fPbePdsSAoiiE4qnBEQm+SAJPOD4YSIinZCQkjDoVJp2GQpsZk05zyKkOI8Fm0LKkMgd3KMbO3gBdW7upyjczv8xBkd1w1M9zNIngwjijkiQWljtY1+zmpa3dXLKwFIdJdLQFQZjYun1ReoNJtnT4OGlafqabIwij5v8+7uDvH7WP6WNevqScyxaXjeg+X3zxRa6++mqeeuop6uvr+e53v8vs2bO54IILAPj+979Pe3s799xzD0VFRbz11ltcd911PPPMM8yYMWNwP/fddx+33XYbFouF7373u3z7299Gr9fz05/+FJVKxTe/+U3uv/9+br/99sFt1q9fj16v589//jO9vb3cdttt/OpXv+KHP/zhiD5HQRBGVzSRGjzJ94YTeMIxIvFPTvTbuyP0yH2oJAmdWoVWLaHTqFCpVKhVYNCoMenSPzqNer8AhE6jIpJI4Y8k048RihNK7BmVkEghKwp6tQqzXkOhzYDVoE3vP0Mn87lmPSdM0dPji7LbFaB9IEJNQTrIkGvRZ6RNnyaCC+OQRq3iuKpc3m/qZ/XWbi5ZUIpJL/4rBUGYmOJJGVcgSighU9fl58Sa3MGrn4IgZKepU6dy4403AlBdXc3TTz/N+++/zwUXXEBbWxurV69mzZo1lJSUAPClL32JtWvX8tRTT/HjH/94cD833ngjS5YsAeCKK67gpz/9Kf/4xz+YPXs2AKeffjrr1q0b8thqtZpf/OIXmM1mamtr+e53v8sPfvAD/vu//xuTyTQGz14QhKMlywqBaHIwmDAQiu2ZfpBOjJiSFXQaFWadhgKbAZtBiy3ppbIyh3hCJppMEU2kiCVlErJMLCHjCydIyjKJlIKigARo1BJatWrPjwRIRBJJUikFlUrCpFNjM2goc+xfzSFbFNkNFNr0tA2E0+Ur+0PMKLYxv9yBSZfZc0JxRjpO6TQqllQ6Wdc8wEvbuvns/FJ0GtHZFgRh4nEFY4RiSfKMasLxJC3uMFPyLZluliCMissWl434KIJMmD59+pDbBQUFuN1uAOrq6lAUhfPPP3/IOvF4nOOPP/6g+8nNTZfjrq2tHVzmcDgG97vvNmbzJ3mpFi5cSCKRoK2tbcioCEEQMiea+GT6gXdPMCEaT48YiCVTSKRP9I06NUU2I0ader/pB2rVJ4EC82FOa1OyQkpWiO0JQkQSKeIpGa1aRYHFjEmvGVfnUpIkUZlrpsxppMkV4qNWD5FEijNmFGa0XSK4MI6Z9VoWVzr5qGWA17b3sGJOcVZG1wRBEIajzx8lHE9RbNPildTUdflEcEEQMkiSJBRlaILVRCIx5LZGM7SLue82iqIgSRLPPPPMfut9Oi/CvvfvnVes1WqHLJNl+RifiSAIY0lRFHr9MZpcQQb25DIIx5PISnpEgVmnJs+ix2bQoteO7PQDtUpCrUpPm7AatIffYJzYW76yOs9MKJ7MdHNEcGG8sxt1zC93srHNg1Gr5rTpBVmZOVQQBOFYJFIyfYFYeqiiVoXFaqLRFSQYTWIxiK8wQciEnJwcXC7XkGUNDQ2UlpYe0fYzZ85EURRcLtd+IxVGws6dOwmHw4NTIDZt2oRWq6WiomLEH0sQhCPjDsbY3RekNxAlEEmiVUt7chmMTVLEiS5bkjqK/8UJIM+iZ1aJjc0dPta3DGS6OYIgZIk+f5SNrZ79rjCOJ/17pkQ4jFokSaLApkdRYFdvINNNE4RJ6/jjj+ftt9/mjTfeoKmpiV/84hd0d3cf8fbV1dVceOGFfP/73+fll1+mvb2drVu38tBDD/Hqq68Ou33JZJLbbruNXbt28d5773HPPfdw+eWXi3wLgpAB/miCj1s9fNDkptEVJJFUmJJnZmaxnYocM3ajTgQWJhBx2WeCKHWYiCdlPmh0Y9SqmV/uyHSTBEHIsC5flE0dXorsBoodxkw355j0+dPBhWKbBZcfTDoNuWYdWzq9LKx0Zrp5gjApXXbZZTQ0NHDbbbcB8MUvfpGzzz4bj8dzxPv4xS9+wR//+Ed++ctf0tvbi91uZ+7cuSxbtmzY7Vu6dClTp07l6quvJhqNcs455/C9731v2PsVBOHIheNJGvtCtHvCeMNxNGqJcqcJ+56LBcLEJIILE0h1noVYQuatnX2YdGqmFVoz3SRBEDJEURTcwRjecILm/tC4DC4kUzK9/igqFZj1GvYOwi5xGNna6aPHF6HIPv6elyCMd1qtljvuuIM77rjjgPf/9a9/3W/ZqlWr9tvHDTfcwA033HDAfSxbtoyGhoYhy1asWHHAZTfddNN+219//fVcf/31h3wegiCMvFgyRXN/iFZ3GG8ojqwoFDsM5Jj0GSvhKIwdEVyYYKYXWYmlZF7b3oteq6Iix3z4jQRBmHCCsSTBWJJkSqapP8SJU/My3aSj5g7FCcWS2A26IVc5cs06NCqJui6fCC4IgiAIQhZIpmRaB8I094fwBOMkZZk8q558i0EknJ9ExASXCUaSJOaU2DFo1by8rYc+fzTTTRIEIQO84QTRRIpCuwFXIEYwljj8Rlmmzx8jFE+RY9YNWa5Rqyi2G9jREyCeFFniBUEQBCFTZFmhfSDMu7v62dTmodcXxWbUML3IRpHNKAILk4wILkxAapXEwnIHsqywems3vnA8000SBGGMecJxYokUZXuu7HcMRDLcoqMjy8pgcNSs33+QXZHdSDyp0OQKjnXTBEHIYqtWreKBBx7IdDMEYcJTFIUeX5S1jf183OqhwxPBoFEzvchKicMkkjROUuPif/3xxx/njDPOYO7cuVx66aV89NFHh1y/oaGBL33pS8ybN4+TTz6Z+++/f0i29FtvvZXp06fv97NgwYLBdf7xj38ccJ1YLDZaT3NEadQqllTlEIolWb21m0gW1D0VBGHsuINxQCLHosOgUdHUP75OwvtDMQKxJDaj5oBzNG0GDRa9hm1dvgy0ThAEQRAmL3cwxvrmAda3uGl1h5FQqC20UpFrRq9RZ7p5QgZlfc6Fl156ibvuuos77riDxYsX88QTT3DttdeyevVqSkpK9ls/GAyycuVKlixZwjPPPENzczO33norJpOJlStXAvCDH/yA73znO0O2u/LKKznuuOOGLDMajbz22mtDlun1+hF+hqNHr1GzpNLJumYPr9T1cMG8EjQiiigIE14wliQQS2DWq1GrVORb9bS6w8iygmqcDE/s86enclTlHjhvjCRJlDmN7OgJ4A3HcZh0B1xPEARBEISRkUjJbOv00eWN4A0n0GvVVOeZsRq0mW6akCWy/kzzkUce4ZJLLuHyyy+npqaG22+/nfz8fJ588skDrv/CCy8QiUS4++67qa2t5dxzz+Xaa6/lkUceGRy9YLVayc/PH/xpa2ujvb2dz3/+80P2JUnSkPXy8/NH/fmONLNey8IKB039Id7d1T+u690LgnBkvOE40biMdc90AqdZRzQh0xcYHzlYZFmhL5AeJWbRH7zDkm9NB3t3dAfGpF2CIAiCMFnFkzIbWj009gUJxpKU55ioLbCIwIIwRFYHF+LxOHV1dSxfvnzI8uXLl7Nx48YDbrNp0yaWLFmCwWAYXHbSSSfR19dHR0fHAbd5+umnmTZtGosWLRqyPBqNcvrpp3PKKafwn//5n2zfvn2YzygznCYdM4psbGz3sqVTDCEWhInOG04QSaSw77mabzdqUUnQ6g5nuGVHZiAcJxBJYDVoD5kISq9RU2QzsLXLhyyPTWJHbzjOu7v6cQfHxxQ5QRAEQRiuWDLFhjYPbQNh1GqJ6YU2nKahlZwEAbJ8WoTH4yGVSpGXN7SEWm5uLmvXrj3gNv39/RQWFg5Ztnf7/v5+ysvLh9wXCAR4+eWX96uRXF1dzV133cWMGTMIhUL85S9/4corr+T555+nqqrqkO1uaWkhEkknT4tGo9TX1x/2uR6MJ5KkzR3Hbxz+/CUpEuP/3nXhnmKh2Dp+hxAP9zUV9ide05GXydd0U2eYXn8Ce8ozmK8g4o/w3tYBbLG+jLTpaDQNxNjlilJs1dAc/ORrKh6L0dzSPGTdRCRFsyvKvz8KUmwd3asniqKwtTdK80CMd7fCadUW7Ias/ho9InvfqzNnzsx0UwRBEIQsE02kAwsdAxF0GomqXIuoACEc1LjoFR0oKnaoSNmn79s7FeBA27zwwgukUikuuuiiIcsXLlzIwoULh9y++OKLeeyxx/jhD394yPbuG3wYbofNFYgRN/vINQ8/10NlpZIezhSVWTi7DOcI7DMTRCd45InXdORl6jUNx5M0JvowOFLUFNkGl6ttYXb1BaicMgXTAaovZAtFUehtcFGoCjO71DGkA9Pc0kx1VfWQ9VOyQkDTT8RgYubM/fPwjKT2gTCGmIcqSwp3IM6uqIbPTi8ZHCEyXonPvyAIgnAg0USKDa0eOjxhDFo1FTlmEVgQDimrp0U4nU7UajUul2vIcrfbvd9ohr3y8vLo7+/fb31Ij3j4tL///e+cc845OByOQ7ZFrVYzZ84cWlpajvwJZBmVJDG/3EEiJfOvbT1EE6KChCBMNJ5wgkg8heVTV9TtRi2KAp3e7C5J6Qkn8EUSWA2aI+rAqFUSpQ4jja7QqFbFiSdldvcFCUaTTMm1sLQ6B18kweqt3QSjiVF7XEEQBEHIhEg8xUctA7QPhDGKwIJwhLI6uKDT6Zg9e/Z+UyDWrl07ZFTBvhYsWMBHH300pGTk2rVrKSgooKysbMi6W7ZsYceOHVx++eWHbYuiKDQ0NIzLpI770qpVLKpw0heI8UZ9H7IsEjwKwkTiCcWJxFM4jEOvplsNGnQaFU39oQy17Mj0BaKEYklyTEc+sqrIZiAlK+zsHb3Ejk39QdzBGE6LFqNOjUGr5rjKHPqDMVZv6SYcE8FaQRAEYWIIx5N81DpApzeCWa+mIlcEFoQjk9XBBYBrrrmGZ599lqeffprGxkZ+9rOf0dfXxxVXXAHAPffcw1e+8pXB9S+88EKMRiO33norO3fu5NVXX+XBBx/kmmuu2W9axN/+9jeqqqpYunTpfo97//33884779De3k59fT233XYbDQ0NXHnllaP7hMeAWa9lXqmDht4g7zf1H34DQRDGDW84joKCQTs0T4tKksi36GnuD2Vt1RhFUej1RUnKCtajyGVg1mtwGrVs7fSPSrsC0QSt/WHiKZkim3FwuUmvYWlVDj3+GC9t6yaaSI3K4wuCIAjCWAnFknzUkg4sWPVaynPMg/mbBOFwsnfi7R7nnXceHo+HP/zhD/T19VFbW8uDDz5IaWkpAC6Xi/b29sH1rVYrDz/8MHfeeSeXXXYZdrudlStXcs011wzZbzAY5KWXXuKb3/zmAXMx+P1+fvSjH+FyubBarcyaNYvHHnuMefPmje4THiP5Vj3T8i182Owhx6xnZrHt8BsJgpDVookU3kgCk+7AUwpyzDo6vRFcwRgFVsMB9pBZvkgCXzQ9JUKjPrrYd6nTxLYuL33+KAW2kX1uO3uDuENximwGtJ9ql1mvZUmVkw+bB3h5WzfnzS1Bp8n6uL0gCIIg7CcYS/JxywDdvghWo45Sh1EEFoSjkvXBBYCrrrqKq6666oD3rVq1ar9l06dP5/HHHz/kPi0Wy0HLWQLcdttt3HbbbUfX0HGmMtdEIJZgzY4+HEYtxQ7j4TcSBCFrecJxovEUloMkbLQbtUhAW384K4MLfYEYwWiSEvvRH4tyLTo0KhX13f4RDS70+aN0+yJoVRK5lgNP1bAZtCyqdPJx6wCvbu/h3NlF+wUhBEEQBCGb+aMJNrR46PZHsBu1lDmMotSkcNRE72cSkySJWcV2dGqJf23rIRARSckEYTzzhBKEEynsxgOXZDRo1dgMWhr7g2PcsiPT609PiThY+w9Fq1ZRaDNQ1+UnmZRHpD178zj4wonDXr1xmnQsLHeyszfAG9t7SaZGpg2CIAiCMNp8kQQft3jo8kdwGnWUOUwisCAcExFcmOTUKomFFU5CsSQvb+smITrEgjBuecIxFAVMuoMPSsu36ujxRbMuP4AvksAbjmPWqY96SsRexXYDsaRMy8DIJK1sGwjT549iNaixHkHAI9eiZ36pg+09Af7d4BIJc7PYhx9+yDe+8Q1OPvlkpk+fzj/+8Y9Drt/R0cH06dP3+3n77bfHqMWCIAijwxuOD06FyDXrKBEjFoRhGBfTIoTRpdeoWVThYH3LAG/u6OOsmYWoREZYQRhXookU3nACo0Z1yIzODpMOWQnR6YlQU2AZwxYemisQJRhNUmQ/9ikNdqMWk1bN1g4fUwusw2pPNJGi0RUknEgxPefIc9IU2AzMURS2dHrRqOCU2gJxPM1C4XCY2tpaLr74Ym655ZYj3u5Pf/oTM2bMGLxtt9tHo3mCIAhjwhOK83Gbhz5/lDyzniKbQQQWhGERwQUBAJtRx5wSB1u7vOSYdSypysl0kwRBOAq+SIJoIoX5MFUWrAYtWpVEc38wq4ILvf4oyZSC/VMlNI+GJEmU5xjZ2RfEH4ljG8a+dvelS0/mW/X7Vd44nGK7EVlR2NjuQ6dRc0JNruisZZlTTz2VU089FYDvf//7R7ydw+EY9yWpBUEQANzBGBvb04mQ8yx6io8h35EgfJqYFiEMKrIbqM41815jP7v7snNOtiAIB+YJx4nEUzgPc0KtVknkWfQ0u7OnJGUgmsATTmDUq4edCDHfakBRYEdP4Jj34Q3HaRsII8sKhdZj62yVOkzUFlhZ1zzA+paBrHmtheG54YYbOOGEE7jiiit4+eWXM90cQRCEY9IfjLGh1UNfIEqBVQQWhJEjRi4IQ9TkWwjGkry+vRebUZOVGeUFQdifJxQnqSgYdYe/yp5r0dPdFcUdjJNnPXAFhLG0t0pE/gi0xaBVk2/Rsa3Tx3FVOUc9YkBRFBp6AnjDccocxkNOMTmcilwTSVnm/d1utCqJRZViRNh4ZTKZuOWWW1i0aBFqtZo1a9Zw0003EYvFuOiiiw65bUtLC5FIBIBoNEp9ff1YNHnSEK/pyErKCn2+MMm67WjElK4RlS3v1YFIkgZXDE8kSY5RQziuoXkg0606NvFYjOaW5kw3IyvIikI4LlOf7B/WfvZ9n86cOfOotxfBBWEISZKYU2JnXcsAL2/t4bJFZYcdZi0IQmbFkzKeUAKjRnNEyRD3VmNo94SyI7jgjxFLyjiGMY1hXyUOE5s7PHR6IpTlmI5q2y5flB5/FL1GjcM0/PZMybeQUhTe2d2PWq1ifplj2PsUxl5OTg4rV64cvD137lw8Hg9/+tOfDhtcqKqqGvy7vr7+mDprwsGJ13TkJFIyG1o9bG/ejd9goybfQkWuaUSOhUJ2vFd7/VHa2r3oHTEWVhrIt4zvi4jNLc1UV1VnuhlZISUrBGMJZk4b3tS94b5PxbQIYT8atYrFFU68kTiv1veIkmqCkOW8kTjRZAqT/shyAxh1aqwGDY19I1NVYTjC8SQDoRgmnRqdZmS+knLMOnRqNdu7/Ue1XSIls6snQCCaoMw5ctmypxVYKbYZeKuhj+3dvhHZp5B58+fPp7W1NdPNEIQRkUjJbGzz0jEQJpWCYCzJ27tcvLi5izfqe2l1h4iPUJlfITO6fRE2tXnpD0QptRvHfWBByE4iuCAckEGrZkG5k2ZXiHd39Yv5woKQxbzhBKFYEqfp8OUS9yqw6unyRYhnuCRlrz9GMJbEcQSlHo+UWiVR4jDS0BsgGk8e8XbN/SHcoRhOk+6Q5TyPxYwiG/kWA2vq+9jVe+z5IITsUV9fL5I7ChNCMiWzqd1LmzuEpJKodOpYUO7khCl5WA1advcF+deWbl7c3MmGVg/uYEz0C8eZ9oEwm9q99AdjlDpN5FoyP2pRmJjEeHfhoJwmHTOLbWxs9+I065hf7sh0kwRBOABPKI6sKEd1Quww6kjJITp9EarzMlc1os8fJZ6QcZpHdthtoU1PiztEoyvE7NLDlwsMxZI094eIJuVReT0kSWJ2iY0tHT5e296LWiUxJT97qnVMNqFQiLa2NgBkWaarq4v6+nrsdjslJSXcc889bNmyhUcffRSAZ599Fo1Gw6xZs5AkiTfffJMnnniC7373u5l8GoIwbIOBhf4wapWKqlwT7eH0nG2dRkVNvoWafAueUJx2T5j3GvvZ2qmhyG5kar6ZUqfpqCvqCGOrpT/E9m4/3nA8Pc1lhKYgCsKBiOCCcEhlTlN6aNxOF6FYuga906TDbtSK2u2CkAUSKRl3KI5eozqqSgs2oxaNSqLJFcpYcCEST+EOptuu14xs59Rq0GIzatja6T2i4MLO3gCeUIwCq37YFSsORpIk5pbZ2dzu5dVtPVQXWLAZNFgNWkw6NSadBqNOjUk3/KoZwqFt27aNq6++evD2fffdx3333ccll1zCqlWrcLlctLe3D9nmD3/4A11dXahUKqqqqvj5z39+2HwLgpDNUrLCpnYvre4wkqRQlWs+aN4ep1mH06wjKct0DkRoHwjT2Bck36KnMs9EVZ6ZPLNe9A2zzO6+IA09fvyRBBU5pmGVexaEIyGCC8Jh1RZaAdjS6WNLhxebUYtFr6HAZiDfqh8MNgwnq7ogCMfGG04QS6Qw64/ucL63JGWLO3N5F/oCUYKxJHbzyE2J2Fep3Uh9TwB3MHbIIaCuQIwubwRJkkZ9DqpKkphX5qC5P0ifP0r7gEwyJSOpJIwaNQatGq1GhVGrwmbUYjNoBwMOJm06+DBSuSkms2XLltHQ0HDQ+1etWjXk9iWXXMIll1wy2s0ShDGTDix4aHWHAYWqPMsRJQTWqFRU5pmpzDMTiCRoHQixvnmAuk4/hXYDU/MtlOUY9xtJpygKiZRCUpZJJBUSskwypZBIySTl9O9EKr0sKcvEEjIKMLfUhk2cEB81RVHY2RtkV18AfyRBda4Zi2F0vmsFYV8iuCAclkqSmFFkAyAUS+AOJfBF4nS3e1EUBYtBg82gJc+qp8BqwGnSYjdqj+hLShCE4fFF4oTiSSqd5qPeNteio6crijcUxzHC0xKORF8gRjSZotp09G0/EvlWAw29Aeq7/Zx0kOzJsqywqzeALxKn3GkekyCpWiUxtcA6eDslK0QTKUKxJMFYklgyRTCaoMMTGQw8GDRqjPsEHqx6LU6zjmmFFjHKQRCEo7LviAUUher8YzuOWI1a5pQ60lOLvFG6fRGaXUFyLXpKnUY0KolYUiaelImnZGRFQZbTJfNkRUkHF2SZVEpBARQZkEBRZCRJIhxP0eoOcdGCEsx6cWJ8pBRFYXu3nyZXkGAsSU2+ZcTzCAnCwYh3mnBUzHrtngN8urxbJJ7CFYzhj8Tp9kWRZS+WPcN88616Cm16HCYdDhFsEIRR4QknSMkcU8lYu1GLBLS6Q2MeXIgmUrgCMXRq1ajN19VpVBTZDNR1+TmxJheVav9jULsnTF8ghkmnzVi5NbVKwqzXYNZrKPjUfSlZIZZMEY6lCMaSRJLJwcADgN2opvQYAkuCIExOsqywucNLa38QmXS53OEGKFUqFWU5JspyTIRiSVrdIXZ0B1Cr0sc3jUqFRiWh3vOTvg0agwqdWoVWo0KrUqFSgVqSUKkkVJJEOJZkXYubl7b2cOG8YgziBPmwZFlhW5ePZleIaCLF1HyryIkhjCnxKRWGxahTU5FjYm+wYe8JgzcSZ3tXlM0dCla9BotBQ75Fz8xim8hQKwgjJJmScQdjaNXSMXUOTToNZr2axv4Q8yuco9DCg3MFYoRjSRxHUeHiWBTbjXT5IrS4w/slUIwlU+zuCxKKJQenf2UbtUrCpNNg0mnIsw49dnpCcSRR9EkQhCP0SWAhlA4s5I38yCezXsOsksPnuTkSJr2GxZU5fNji5pW6Hs6bVyJGah1CSlbY0pEekRJPpZiSbxGBBWHMiU+oMKIMWjXlOSbmljo4fkoex1XmkG/RE02k2Nrp47lNnTT2BTPdTEGYEHyRBJGEjOUYRi3slW810OVND78fS32BKOFEihzT6AYb7SYtBq2abV2+/e7b3RfEHYqRZ9GJDpggCBOaLCts6fTR0h8iJSujElgYDTaDlkXlOTT2h3hjey8pWZTAPJB01Q8Pza4gyZRMTZ4YsSBkRvYfVYRxzaBVU+o0MafEwQlT8tCoJF6p62FjmwdZfEEIwrB4wgnCsSR2w7EP53eadCRSCl3e6Ai27NDiSRlXIIZGJWHUjW7nRyVJlDlMNLtCBKPJweW+SIJWd5hUSqHAOrpJHAVBEDJJlhW2dvrSJ56yQvU4CSzs5TTrWFDmZHtPgLd29on+46fEkzIb2ry0uMIoSnqqi0j8K2SKeOcJY0atklhY7qTQZuDtnS7e2eUiMcZXSwVhIvGG46RkGctRVorYl82gQS1Bc//YjShyBWOEYkmcY5TjoMCmR1ZgV28A2JtFO4A3EqfYYRT5YARBmLD2zsFv6Q+RSKVHLIzHE898q57ZxTY2t/t4v8mNoogAA6Sn933c6qHVHUJSjUwODUEYDvHuE8aUJEnUFlqZUWRlY5uHf23tJhRLHn5DQRCGkGUFdyiOSq0aVkdRo1aRa9HT1D92JSl7/XumRIxREkmTTkOuWceWTi8APf50VnOdSkVOhpI4CoIgjDZFUajr8qeT+yVT1IzzK9olDiPTCiysbx7go1ZPppuTcdFEio9bPLQPhNGqJaYcYTlRQRhN4h0oZESJw8Tiyhxa+kM8v6kTl3/shmQLwkTgiySIxFPYhjFqYa88ix5vOIE/khiBlh1aIiXj8sdQSxLGMZwPWuowMhBK0DEQZldvgEAkSanDiCSNfulJQRCEsbY3sNDkChKbAIGFvSpzzVTlmli7u58tHd5MNydjwvEkH7YM0O4JY9CqqMq1jEkpZUE4nHFxlHn88cc544wzmDt3LpdeeikfffTRIddvaGjgS1/6EvPmzePkk0/m/vvvHzJ8at26dUyfPn2/n8bGxiH7eeWVVzjvvPOYM2cO5513Hq+99tqoPL/JymHScUJNLv5oghe2dIlEj4JwFDzhOOF4EuswkjnuZTemKza0ukd/9EJ/MEYwlkyXwRzDE/scsw6NSuK9xn5cgRh2oxaLQdRNFwRh4tkbWGh0BYmlZKbkW9BrJk5yv6kFVorsBt5qcLGzJ5Dp5oy5QDTBh80DdHgiWPVaKnPNIrAgZI2sDy689NJL3HXXXXzjG9/gueeeY+HChVx77bV0dXUdcP1gMMjKlSvJzc3lmWee4Yc//CEPPfQQjzzyyH7rrl69mnfffXfwp6qqavC+jRs3ctNNN3HhhRfy/PPPc+GFF3LjjTeyefPm0Xqqk5JBq+H46k8SPW5qF4keBeFIeMIJEikZ6wicIJt0akw6NU2u0Q8u9PljROIpcs1jW5JWo1ZR6jDS648STcgU241j+viCIAhjQVEUtnenAwuReIrqXPOECizsNbPIhtOs4/X6XlrGcFpfpvnCCT5q8dDpjWA3aihzGlGJEXhCFsn64MIjjzzCJZdcwuWXX05NTQ233347+fn5PPnkkwdc/4UXXiASiXD33XdTW1vLueeey7XXXssjjzyyX/KXnJwc8vPzB3/U6k8Ovo8++ijLli3juuuuo6amhuuuu46lS5fy6KOPjurznYwGEz1a01FokehREA5NlhUGQjHUKmlEOo2SJFFg1dPuCY9qScpkSqbXH0WS0gGNsVbiMGLQqiiw6ifE8GBBEIR9KYpCfXeARleQaDw9FWKiliOUJIm5pXZMOjUv1/XQ6YlkukmjzhOK81HrAN2+CDlmHWUOk5jaJ2SdrO5dxeNx6urqWL58+ZDly5cvZ+PGjQfcZtOmTSxZsgSD4ZPSYieddBJ9fX10dHQMWfdzn/scJ510El/5ylf44IMP9tvPpx/3pJNOOujjCsMjSRK1RVamF1nZ0ObhZZHoURAOKhBNEo6lsI5AvoW99pak7B7F/Cf9wTihWBK7aWynROxl1qdHShWJUQuCIExAu/uCNLoChGNJpkzgwMJeKkliQbkTjUriX9u6cQUmbv6uvkCUj1oG6PVHybPoKRWBBSFLjVzPdBR4PB5SqRR5eXlDlufm5rJ27doDbtPf309hYeGQZXu37+/vp7y8nPz8fH784x8zd+5cEokEzz//PF/96lf561//ynHHHTe47qcfNy8vD5fLddh2t7S0EImkI6jRaJT6+voje8IH4IkkaXPH8Rsn9hfEvvKlFOvr+9nV0sHxFSacxqFv0+G+psL+xGs68kbzNe3yJ2jsiZBjUtMc6h+RfSZlBbc7zDsbAiwsMY3IPj9tZ3+M3f1Ryuxamv1Hf0yLx2I0tzSPQsvGJ380hRLQ4jMPb2rM3vfqzJkzR6hlgiCMtU5vhF29QYLRJDX51gkfWNhLrZJYVOFkfYubl7Z0c9HCUhwTrApQhydMXaef/lCMIpueAqsIkAvZK6uDC3sdKDJ3qGjdp+/bOx1i7/IpU6YwZcqUwfsXLlxIZ2cnDz300GBw4UAURTmiKOG+uRuG22FzBWLEzb4xn5+cadOnJPmozUN9WMXp5QVMybcM3ic6wSNPvKYjbzRf02ibh5yUj9nFthHtQIa1XpKKzMyZ1SO2z71SskKn0kuxNsasUvsxzRFtbmmmumrk2zZeeUJxphZYKHEOr6MpPv+CML4NhOJs6/ThDcepyjVjzMC0s0zSqlUcV5nL+039/HNLN5csLMGsnxgJextdQRp6AnhDMcqdJpyT7HxAGH+yelqE0+lErVbvN1rA7XbvN6pgr7y8PPr7+/dbH9IjHg5m/vz5tLa2HnY/B3tcYWQZdBpOmJKHWpJ4eVs60eOnc2YIwmSkKAoDwThqCfQjnDcg16JjYJRKUrpDMQKxFDajRiSfEgRBGCHheJLN7V7cwRhFDgNW48Q4qT5aOo2KpVU5DIRj/HNLD5H4+J5aqygK27v81Hf78YTjVOZaRGBBGBeyOrig0+mYPXv2flMg1q5dy8KFCw+4zYIFC/joo4+IxWJD1i8oKKCsrOygj1VfX09+fv6Q/RzN446GcDzJDU9u4H/fbuKVuh46POFJdYK9d6hbvlXPWztdvLurXyR6FCa9QCxJMJ7EoteM+HxLh1GHokD7QHhE9wvpKhGhWIJck+gcCYIgjIRESmZTm5cef5Qcs458i+HwG01gRp2G4ypy6PFFeHlbD/Hk+OwzpmSFzR0+dvUGCEYTTM23YJukQSNh/Mn6aRHXXHMNN998M/PmzWPRokU8+eST9PX1ccUVVwBwzz33sGXLlsEqDhdeeCG///3vufXWW7nuuutoaWnhwQcf5Prrrx/siP/5z3+mrKyMqVOnkkgkeOGFF3j99de57777Bh/36quv5ktf+hIPPPAAZ511Fq+//jrr1q3jiSeeGLPnrpIkcsw63tnZz3uN6dEXdqOWWcW29E+JjZp8y4SubStJEjOKbFgNGj5u9eANxylTj88vC0EYCd5Qgkg8Rd4oXMEw69UYtGqa3EFml9pHbL+yrNC7J1GkeQSTUAqCIExWsqywpcNHlzeCWaemRCSqBcBi0LKo0slHLQO8tr2Xc2cXolFn9bXUIeJJmc0dXtrcIWSZSZU/Q5gYsr6Xd9555+HxePjDH/5AX18ftbW1PPjgg5SWlgLgcrlob28fXN9qtfLwww9z5513ctlll2G321m5ciXXXHPN4DqJRIK7776b3t5eDAYDU6dO5cEHH+TUU08dXGfRokX8+te/5re//S333Xcf5eXl/OY3v2H+/Plj9twNWjU/+ewctnV6iSRktnf50z/dft5vcu9ZR8X0QiuzS+zMKrExvXBiHoRKHSZMWjUb2720xULMn62gmsBBFUE4GE84TjwlYzeN/FWMwZKU7giplIx6hDpkfYEYgWgSm0E7oYOhgiAIY2VHT4COgTCSBBU5ZlE5YB8Oo44F5U42tnnQa1ScOj0f7TgIMEQTKTa0eej0RFCrJGoKzOOi3YKwr6wPLgBcddVVXHXVVQe8b9WqVfstmz59Oo8//vhB93fttddy7bXXHvZxV6xYwYoVK468oaNEkiTKnSbKnSbOnV0EgDsYY3t3OthQ1+3nyfVtKKSnEtTkm/eMbLAzq9iGfYIMpXKa9Swod/DKxy4+bBlg2ZSD59AQhIlIURTcoTgS0ojnW9grx6yjbSBMTyBKqWP4VSM6PGHquvwEY0mqckenCoUgCMJk0uoO0dQfJJJIMrXAKoK2B5Bn0TOn1M6WTi/+aIKF5Q4qc81Ze2EqEE2wsc1Dty+KUaemwmkW/6+TRDSR4sOWAYpsBqYVWjPdnGEbF8EFYX+5Fj0nT8vn5GnpPBGhWJL6nk9GNqze2s1zm7oAKM8x8bWTqllU4cxkk0dEjllPqU3LB81uShxGynPEyYoweQRjSYKxBCadatSuUtmNWlQStPaHhxVckGWFnX0BmvpCeMMxSh1G7MaJVR5MEARhrLkC6YtLvkiCmjwzes3EG606UortRkw6NTt6/PxrWw/V+WYWlDkoshuyaqTHQCjOpjYPPf4odpOOModRJD6e4BRFodEV4tXtPby100U4nkIlwWWLyrhyacW4HrEiggsThFmvYUllDksqc4B0kp9dfUHquny82eDiJy/W8Z+n1HDe3OIMt3T4Su1a3Gh4bXsvVxxXjknM4RYmCW84nW8hxzx6J+latQqnSUdTf4gTpx5bdZxESmZLh4+OgTCRRIqqPAtWw8QYQSUIgpApgWiCzR1eBkJxyh3GCVNucTTZjTqWVefR64+yqy9Ax0CEqYUWFpQ5cI7id+mR6vVH2dLuxRWMkWvWU5xlgQ9hZAVjSd5q6OPV7b009YfQaVScVJPHGTMKeGuXi6c/7mBjm5f/PqeWcuf4vIAqzsomKK1aNZj48fy5xfzq1Qb+8FYjHZ4w/3HSlHE91EolScwrdbC2sZ81O/o4b25x1g5zE4SR5A0niCZkbKN8op5n1dPQEyAYS2A5ys5rKJYui9bljYAEUwss4sqaIAjCMEUTKTa1e+nzR8m36EVZwqNUaDNQYNXTNhBmW6ePlv4Qs4ptzC61Y8nQRar2gTDbunwMBGMU2Q0UWEVSzolIURTquvy8ur2H93a7iadkpuSbue7UGk6pzR98/80vd3BcpZP73tzNt/+2if9YXs1n5hSNu2CTCC5MAiadhh+cN4s/r23muU1ddPmi3HzudEy68fvfb9CqmVNiZ3OHl1KnkYUTYMqHIBzOQCiGJDHqSVsde/K0dAxEmFF85MGF/mCMLe1e+gJRTDoNFTlizqggCMJwpfZUhuj2RnEYdRTZJnfJyWMlSRKVuWZKHUYa+0Osa3azuy/InFI704vGNiH67r4AO3uCeMJxKnLMOEyZH0UhjCxPOM6aHX28WtdDly+KSafmzJkFnDOriKkFlgNuc0JNHtOLbNz7xk7+8FYjH7YM8F9nTMuKUTZHavyeXQpHRa2S+I+TplDqMPHHtxu5+Zkt3H7BLArH8RdUgc1AeY6Jd3f1U2Q3UCzKMAkTWCiWxB9NYNapR30uplmvwaBR0dQfZEax7Yi2aXOHqe/20x+MkWcRQzsFQRBGgqIobO/y0zEQQqOWKHEYxbF1mDTqdKW1yhwTO3v9vLWzj529ARaUO6jOM49q6UpZVtje7ae5P0gwlqQ6zyymDU4gKVlhY5uHV7f3sr5lgJSsMLvExheOK+fEmrwjCmDlmHX8+MLZrN7azSPvtXD9kxu44YxpHD9OEtmL4MIks2JOEcV2A7/4Vz3ffXozPzh/JjOKjuzkIRtNK7DiCcd5dXsPX1hSjkEr3tLCxOSNpKdE2A2j/x5XSRL5Vj2t7jCyfOiyr7KsUN/jp7k/hD+SoNRpJFcM1xUEQRgRTf0hWtxBkrLC1AKLGA02ggxaNfPKnASjCXb0BHilroeKHBMLK5yUOUc+iJOSFTZ1eGl3h4kmUtTkWTHqxLTBiaDXH+W1+l7eqO+lPxjHbtRy0fwSzppVeEy5EyRJ4oJ5Jcwrc3DPqw38/KV6zp1VyH+cNCXr3zPiTGwSml/u4Jefn89P/7md257dyrfPrOWU2vxMN+uYqFUS88scvN/k5t8NLs6dPf7mJgnCkfCE4kQSKSrGqEKK06yj3ROhLxCl6CCjgmLJFFs7fLQPhImnUkzJM4sEY4IgCCOkxxeloSdAIJpkar51XGeQz2YWg5YlVTm4QzF29gZYvbWLmjwL8yscFFhHZoRvLJlie18UdTSEoijUiHxE41I4nqTLG6XbF6HLF6XLG6HTE2FnbwCAhRVOvnbSFJZW54zI57Uix8SvPj+fx9e18Y8NHWzp9PGds6czvSh7S1aK4MIkVe408cvPzecX/6rnl6820OmNcMVx5ePyxNyk0zC72M62Lh+lTiNzSx2ZbpIgjDh3MAbK6Odb2Mth1KVLUrrDBwwuBKIJNrV76fZFUEsSU/Nt6DSi4ysIgjASvOE4Wzu86Tn5TlPWX62cCHLNek6YoqfLG2ZnX4A2T5jphVbmlNoxaNWkZIWUoiDLyj5/Q0pRSKX23N57v5JeZ+/9gWiC7kCCCgdU5lhEoCiLfTqA0O395Lc3khiybo5ZR4ndwBXHlXPWrMIRC0btS6tW8dUTq1hc6eQ3r+/k5v/bzBXHVXD5kvKsHMkkgguTmN2o5acXzeH+N3fzxPo2OjwRbjxz2rg8QSiyG3CHYrzd4KLQaqBgHOeSEIRPi8RTBGJJTGOQb2EvnUaF3ail0RVi2afm+fUFomzp8NHnj2IzaChzisSNgiAIIyUST1eGcAVjFFr1ItnfGCtxmCiyG2npD7GhzUujK4jdqEMhnQNDURQU2BM8+CTIIO8JLigKIIEEe7YBlQR6jUR1rpjacqxSskKLO8TWDh9bO33UdfuIJ1Lotb3oNCr0e350GhU6tQq9Vp3+ve9yjXrIba1ahS+SoGtvAMEXwRveP4BQbDdwXHUOJXYjxXYDJY7077FMAjq31M59Vyzkj2838sT6Nj5u9fCdc2qzLuecCC5Mclq1im+fOY0yp5G/vN9KXyDKbefNxDkOv8hmFNn4oMnNa9t7+fySMnRiuJkwQXjCcaLxFOYxyLewr3yrgV19AcKxJKY9pZKa+0Ps6PEzEIqTb9FTZBOJGwVBEEZKMiWzqd1Lrz+K06yl0JZdJw6ThUqSmJJvoTzHRNtACFmWkVQSapWESpJQSyrUe26rJQmNWhq8rZIkJCm9D4n0/HmVBC2tfhFYOAqyotDSH2JrZzqYsK3LRyiWAqDEbmB5TR6pWAij2UosKRPf8xNLpoglZXyRxJBl6d8ySVnZ77FyTDqKHQaOq8yh2GGgxG6kxJFOFj+WAYTDMes1fOfs6RxXmcP/e2s3Nz61iWtPruasmYWZbtogEVwQkCSJzy8up8Ru5Nev7+S7T2/mRxfMojLXnOmmHRW1SmJ+uYN1TW7e2dXPmVn0QROE4fCE44TjKUodY9vJtBu1KAp0eiNMybdQ3+2nxZ1O3FjhNImraYIgCCNIURS2dvro8kTQa1SU2Mcmx45wcFq1ipr87J3fPpHIikKrO8zWTm86mNDpJxhLAlC8J5gwt9TO3FI7uZZ04ujmlmaqq6qP6nFSsjIk4GA1aMfdtKNTavOZWWzjt6/v5HdrdvNhi4frTq1BlQWDz0VwQRi0fGoeBVY9P1tdz/ee2cItK2awuNKZ6WYdFYtew/QiK1s60vkXxnMlDEHYyxuOo6Bg1I3tIdtq0KDTqGjoCeAJJ+gYCJFUoCbfgmmM2yIIgjDR7ewN0uYOo6BQmWMZs2lwgpAJsqLQ5g5/MjKh00dgTzChyGbghJpc5pbamVNiJ986clWo1CoJo0497gIKn5Zv1fPTi+fw3MZO/vpBKzt6/Nx45jROmpbZJP2idygMMa3Qyj2XpytJ3PnPOq49eQoXzCvJdLOOSpnTxEAozr939FFo1eMUZfGEcSyaSOEJJzDp1GM+nFIlSeRb9DT1hwjHk2jUKqbmmUUiKkEQhBGUSMk0uUI09gUJJ9KVITTiOCtMUP3BGH95v4WPWz34o+lgQqFNz7IpOcwtdTCn1DYqiREnIpUkcemiMhZWOLhvzW62dPgy3SQRXBD2l2fRs+rSedzzWgMPvN1EhyfCtSdPGVfzxGYW21jb2M+r23u5bFGZ+JIWxi1vOEEskcKsz8zhuthuYCAcw6zXUuowjqvjgCAIQjZTFIVOb4TdvUFcgRhJWaYy15xVc7wFYaQoisIrdb08sraZpKxw0tQ85u2Z5iASsQ9PdZ6FX35uPsFY4vArjzIRXBAOyKhT8/3PzOQv77fwj42ddPsi/NcZ0wbnOGU7rVrFwnIH61sGWNvYzym1BZlukiAcE084Tiieylg2YIdJx/HVeRkPKqRkhQ5fnKObWSkIgpCdPKE4Db0Ben1RAtEkuRYdhTaDGBkmTEjdvgj3r9nNlk4f88rs3HD6NIrsIqAwEYnggnBQapXENcurKXEY+cNbjax89ENOqMnjgrnFzC6xZX2GeJtRx7QCKxtavZQ6jSIhjzAueUJxZJmM5jjIdGAB4K8ftPB/G1y0Rw1cubQi648/giAIBxKJp9jVF6DDE8EbjmPQqphWKPLYCBNTSlZ4cXMXf13XikYlcf3pUzlnVqH4Dp/AxJFMOKxzZxcxv9zBS1u7eW17L+/t7qcq18T5c0s4bXp+Vg/fq8gx4Q7FeWN7H/lLDdiM2kw3SRCOWCyZwhtOYNKpsuIEP1N6fFGe39SFVa/iyQ/biadkvnJCVUY6J/GkTH23n6kFloxNVREEYfxJyQot7hBNfUEGwnEUBcqdJuxGrTjREiakVneI+9bspqE3wHFVTr552lTyxskIaOHYiZ6RcESKbAZWLq/mi0sreHuXi9Vbuvn9v3fz57XNnDWzkPPmFlMyxmXyjoQkScwpsbO2qZ/Xtvdy8cLSSX2SJowvvnCCSAbzLWSLR9Y2o1FLfO+kAt7vhf/b0Ek8KXPtyVPGtFPuiyT4+ert1PcE0GlUnFSTx9mzCsfFSC5BEDKnxxdlZ68fVyBGNCmTb9FTYDWI/ogwISVTMs9s6OBvH7Zj1Kn5ztm1nFqbL74nJ4nJ3WMVjppBq+acWUWcPbOQ+p4Aq7d08c+t3Ty/uYvFlU4umFvMokpnVpVP0mlUzCu183Gbh/VN/ZwwNbMlWgThSHn2BBeKJnGio62dPtY2uvnSsgocxhTXnVqFXqPiuU1dxFMK3zytZkyON+0DYX7yzzo8oQRfP3kKbQNh3t7lYk1DHyV2A2fNKuTMGYXkmHWj3hZBEMYHXyTBrt4A3b4IvkgCu1FHVa4FnUbkVRAmpt19Qe59Yyct7jCnTMvj2pOn4DCJ78XJRAQXhGMiSRKzim3MKrbxH6E4r9T18K9t3fzkn9spths4b04xZ80sxGLIjrdYjlnPlFwL61s8lDhNVOaaD7quLCvEUzKJlEwipZBIycSTn7qdknGadFTkmMSVB2HUeMJxUrKMaZzXYj5WKVnhT+82kWfRc/HCUro62pAkiZXLq9Fp1Pz9o3YSSZn/OnPaqH4ON7d7+cW/6tFqVPzi0rnUFqbzt/zHSdWDVWn+8n4rj33QypLKHM6ZXciSyhxxbBCESSqWTLG7L0jbQBhPOI5WkqjJs2AxiKmZwsQUS6Z4cn07z27swGHU8cPzZ7KsOjfTzRIyIDvO/IRxLces48qlFXxucRkfNLn555ZuHnqvmb+ua+X02nzOn1dMdZ4l082kOt/MQDjOa9t7OX16AbKyN4iQDhhEEymiiRSxpExKVpAVkGWZpJy+P5lSUBRASp/0xJMy5TlGFpQ7KHWYUIkTCWEEJVIyA6E4eo160pZSfXNHH02uEN89Zzp6zScBFkmS+PLxlejUEo+tayOekvnO2bWj8jq9UtfDH95qpMxh5EcXzBpSLsugVXPGjELOmFFIlzfCa9t7eWNHL+tbBnCatJwxo5CzZxZS6sy+KWOCIIw8WVZo94TZ3RdkIBQnmZIpsRvJMevEkHBhwqrr8nHfmt10eiOcPauQlcursUzy6ZyT2bj4n3/88cd56KGHcLlcTJs2jdtuu40lS5YcdP2GhgZ++tOfsmXLFux2O1/4whf41re+NXhgf/XVV3nqqafYvn07sViMqVOn8o1vfIMzzzxzcB//+Mc/+P73v7/fvrds2YJeL5KRHIhWreLkafmcPC2fJleQ1Vu7eXOni1e29zK7xMb5c4s5sSZzJe1UksTcUjvrmt38e2cfKiQUgD3/qiQJrVpCo0onz1OrJXQaFRa1Gp1Glf5Rp+9TSRLeSJydvQE6B7qpyjczv8xBkd0gOhDCiPCE40QTqUn7BR2OJ/nLBy1ML7RyyrS8A67zheMq0GlUPPxeC4mUzC0rZoxYGTdZUXh0bboU76IKJ7esmH7IbO4lDiNfObGKLx1fyUetA7y2vZdnN3bwfxs6mF1i4+yZhSyfmpfVCXAFQTh2rkCMXb0B+gIxgrEk+RYdBVbDpA0OCxNfOJ7kr++3snprN/lWPT+9aA4Lyh2ZbpaQYVnfa33ppZe46667uOOOO1i8eDFPPPEE1157LatXr6akpGS/9YPBICtXrmTJkiU888wzNDc3c+utt2IymVi5ciUA69ev5/jjj+fb3/42drudF198keuvv56//vWvQ4IWRqOR1157bcj+RWDhyEzJt3DDGdP46olVvF7fy+qt3fzPKw0sruzj5nMP3UkfTQatmuVT8wjFUmjVUjqIIKV/H21QINes54Qpenp8UXa7ArQNhKnJtzC/3CGy4QpHTVEU/NEk3nAcTziBOxAjEEsy5RBTeCay/9vQiSec4AfnzTrkZ/OShWXo1Cr++HYTP1tdz23nzRgyyuFYRBMpfv3aTt5vcnPe3GK+fvKUIw6KqlUSy6pzWVady0Aozpodfby2vYffvrGLB95u4pTafM6ZVci0AosIRGbQhx9+yEMPPURdXR19fX384he/4NJLLz3kNoe7cCFMTr5Igt19Qbp96dKSFoOG6YVWEUgUJrQNbR7uf3M3/YEYF8wr5svHV2GcpFM4haGyPrjwyCOPcMkll3D55ZcDcPvtt/POO+/w5JNP8p3vfGe/9V944QUikQh33303BoOB2tpaGhsbeeSRR7jmmmuQJIkf/vCHQ7a5/vrr+fe//83rr78+JLggSRL5+SL533BYDVouWVjGZ+eX8nJdDw++3cj3n93Kj86fRW6GTsA1KhV248hdSSiyGyi06enwRNjR46fFHaK20MrcUrtIYiMclKIoBGJJPKFPggnBeJJIPEU8JSNJ4DBpsU7CObp9/ijPbuzgtNp8phdZD7v++fNK0GpU3L9mN3f+czu3nz/rmDv2A6E4P129nca+INeeXM2F80qO+eQxx6zjc4vLuGxRKXVdfl7b3subDX28UtdDVa6JSxaWcWptvsjNkAHhcJja2louvvhibrnllsOufyQXLoTJJRxPsrsvSIcnHVTQqFRU5ZqxG8X3vjBxJVMy/++tRl7b3kupw8iqy+Yxq9iW6WYJWSSrgwvxeJy6urr9vriXL1/Oxo0bD7jNpk2bWLJkCQbDJ/NiTzrpJO699146OjooLy8/4HahUAibbeiHIxqNcvrpp5NKpZg5cyY33ngjs2bNGuazmpzUKonz5xZTaNVz9ys7+O4zW/jxhbMOmVhxPJEkifIcEyUOA63uMBvbPDS5gswosjGrxDYpTxCFoQ4VTIglZVQqsOg05Jn12E1a9BrVpL0i+uj7LUiSxNUnVB3xNufMKkKrVvHb13fyoxfq+PGFs456hFRzf5A7/7mdYCzJD8+fydIRSkYlSRJzSu3MKbXz9VOm8PYuFy9t7eY3r+/kqQ/b+PziMk6fXiCGT4+hU089lVNPPRXggFMgP+1ILlwIk0M0kaK5P0SbO8xAOAakp0U5TbqsqpQlCCNNURTue3M3a3b0cdmiMr64tEJUPhH2k9XBBY/HQyqVIi9v6Hzb3Nxc1q5de8Bt+vv7KSwsHLJs7/b9/f0HDC48/vjj9PT0cNFFFw0uq66u5q677mLGjBmEQiH+8pe/cOWVV/L8889TVVV1yHa3tLQQiUSAdICivr7+sM/1YDyRJG3uOH7jxBhqlAvccHwuD6x3872nN/EfS3KpzTu6EQzxWIzmlubRaeAIkIByrUyHO8Hqti7e1quZmqunyqnDkKUH4eG+T4X9RSIRPtpchz+Wwh9N4YumCCdlogmFREoBCUwaCYtOjVmvQqeWkJIS4TCEPZlufeY0DcR4e1c/K6ZZCbq7CLo/ue9wn/0qPXxlYQ6Pbhzg5r9/zHVL8zDpjuwzV9cb5c8bBjBoJf7r+FzyJT/NLf7hPp0DmmGB2uMdbOs18PKuAL9bs5vH3m/mrKlWlpWZ0KqP7ATFH02hBLT4zMMLXu79/M+cOXNY+5nIjvXChTBxJFIyre4wLf1BBsJxkimFfKuefItBjD4SJoXH17WxZkcfX1xawZVLKzLdHCFLZXVwYa8DXRE41FWCT9+nKMpBt3nllVf4n//5H379619TWlo6uHzhwoUsXLhwyO2LL76Yxx57bL9pFZ+2b/BhuB02VyBG3Owj1zxx5vBXAzOqo/z4n9v543o3/3XmNE6fXnDE2ze3NFNdVT16DRwhU4F4MsXOviDtgSjxiIE5ZXamFliGPSd8pIkTi0+E40ncwTiKkk7ql/5JH0fkfZbtvV9RILXnt6IopOT0z872FpwFDiKkiGlkVFYo0muwGbTYjJN7ZMLByIrCfes3k2PW8R9nzNlvasORfParq6Cs2M0v/rWDBzf4ufOiOdiNhz75fnFzF3/6qJPqPDO3j+GUrZpq+OwyhY9bPTz1YTt/3+rljaYwly0q45zZhYc9TnhCcaYWWCgZZjUK8fk/vGO5cLHXSF5wEPY32q+prCj0BJJ0+OP4oiniKQWnQU2OSUMkIdE2MGoPnTHZfhFnvBrPr+u7rSH+vtXLCeUmluUns+Z5jOfXdKTJikI4LlOf7B/WfvY9ph5L3yCrgwtOpxO1Wo3L5Rqy3O127zeaYa+8vDz6+/v3Wx/SIx729corr3DzzTdz9913D6kUcSBqtZo5c+bQ0tJylM9COJACm4H/uWwed71Uz69f20mfP8rlS8on3MmWTqNmTomdSNxCQ6+fNfW91Hf5mV/uoCrPPGKZ7YWR4Ysk2NDqoccXIZ5SUJT0j4wESnq0AYoEKOk/Ufb8BemqIxKSlF6tP5jEnqeQb9GLYMIRemuni119QW46a9qwkqEtrc7l9vNn8fOX6vn+s1v5+UVzcJr3nwedkhX+9E4T/9zazbLqHL57zvQxT8ImSRJLqnJYXOlkc4ePpz5s48F3mvj7x+1curCUz8wpFonhssTRXLjY10hecBD2N1qvqaIodPmiNPYF8GvjqK1JphbpKLQaJ+xQcEVReGlbD89+3ItRr8OoVaPXqvf8VqV/a9QYtSoMWvU+P5/cNmpV6DVqTDo1NqNWTBXZx3i5OPZpHzS5eWZbJ0sqndxy/qysGqkzXl/T0ZCSFYKxBDOnDS9f4HCPqVkdXNDpdMyePZu1a9fymc98ZnD52rVrOeeccw64zYIFC/jVr35FLBYbrOywdu1aCgoKKCsrG1zvpZde4tZbb2XVqlWsWLHisG1RFIWGhgamT58+zGcl7GXRa/jJZ2fzuzW7eGxdG72BGN88tWZCzjs26tQsKHcSiCbY2Rvg5boeKnJMLChzUJFrEiedWcAXTvBx6wA9/igOkw6rQYNaklBJ6ZOHvR2kdPDgkyCCJA39e69m/FQXHD4ZoZAWTaR4dG0LUwssnHYUI5kOZlGlkx9fOIs7V2/n+89u5WcXzxlSxSUcT/LLVxr4qNXDxQtK+eqJVRntMEmSxIJyBwvKHWzt9PG3D9t4+L0Wnvm4g4sXlnL+3OKMVdkRju7ChTD+9QWi7O4L4vLH8EcTOIxaphfaJnSgLxhN8rs1u3i/yU2VQ0uhzUA0kSIST+EJxYkmU0QTMtFEOk/QkdCqJfIs+j3TR9K/B2/vWTaRX9OJYEe3n1++2kBNvoVbVszIqsCCkJ2yvqdyzTXXcPPNNzNv3jwWLVrEk08+SV9fH1dccQUA99xzD1u2bOHRRx8F4MILL+T3v/89t956K9dddx0tLS08+OCDXH/99YMd/9WrV3PzzTdz8803c9xxxw2OjNBqtTgcDgDuv/9+5s+fT1VVFcFgkL/85S80NDTw4x//eMxfg4lMq1bx32fVUmg18LeP2nEHY9yyYsaE7URbDVoWV+bgCcXZ2Rfgpa3d1BZaWVKVg90kkj5miicUZ0Obh15/lFyznmK7YUIEfN7Z5eLlbT388PxZWV8i6tmNnbhDcb537vQRu9I1t8zBnZ+dw49frOPWf2zh5xfPpdBmoC8Q5af/3E7bQJhvnTaVFXOKRuTxRsrcUjtzS+dS3+3nqQ/b+cv7rfxjQyefnV/ChfNLsOgn5vExmx3phQthfPOE4ux2Ben1R/GG45h0amoLrRO2T7LX3hNIdyjOyuVVzHPEqamectD1ZUUhtifQEEmkiCVTRPbcTv/IhGJJ3KEYrkD6Z3OHl4FQHFkZui+rQTMk+JC/T/Ch2G487LQ2YfR0eMLcuXo7uWYdP7rg2KswCZNL1h8tzzvvPDweD3/4wx/o6+ujtraWBx98cDA/gsvlor29fXB9q9XKww8/zJ133slll12G3W5n5cqVXHPNNYPrPPXUUySTSe666y7uuuuuweVLly7lr3/9KwB+v58f/ehHuFwurFYrs2bN4rHHHmPevHlj9MwnD0mS+NLxlRTY9Pz+zd3c+o+t3HFB5kpVjgWnWcey6lw6PRG29/ho94ZZUOZgVol9wg63zFYDewILff4oeZZ0Z2aiWL21m7ouP39+v4XrTq3JdHMOqj8Y45kNHSyfmsfsEvuI7ntmsY2fXTSHH72QDjCsXF7N/77TRCwp8+MLZ7OwwjmijzeSZhbb+MlnZ7OzN8DfP2rnifVtPLepkwvmlXDR/JJMN29cC4VCtLW1ASDLMl1dXdTX12O32ykpKTmmCxfC+BWIJmh0hejyRPBG4ujUElNyLVgn+ImtrCg8u7GTv7zfQr5Vz/9cNo/aQuth57CrJAmjTo1Rp+ZojqDJlMxAKI4r+EnQYe/fvf4o2zp9hOKpwfXVKolbV8zg+ClidNBY84Ti3PFCHSpJ4iefnS1KqwtHTFL2ThoURsVIJHSs65pYCR0PZUOrh1Uv78CsV3PHBbOpytu/VOVEm1+VTMk09Abo80cpyzFxXFUOZU7jmHZYJ+s8YHcwxoZWD65gjHyrniLbyAUWMv0+9YbjfOWR9diNWjzhBHd+NntPpH/9WgPv7u7n/121mCKb4aDrDec1be4P8sPntuGPJimw6rnjwtlU5JiOtckZ0eQK8reP2lnb6MaoVXPmjAJu/cwMyob5PCbj53/dunVcffXV+y2/5JJLWLVqFbfeeivr169nzZo1g/c1NDRw5513smXLFux2O1dccQXf+ta3jupYPRlf69E2nNdUlhV29QVp6g/iCSVQSVBiN+IwaSd80MgbjvOb13eyoc3L8ql5XH/61MFRUZn8/grFkvTvCTg8sb6NtoEwqy6dx9QCS0baM5Iy3S84UuF4ktue3UqHJ8Jdl8yltjB7p3iOl9d0LOzNuXCSyLkgCJ9YVOlk1aVz+ck/t3PLP7Zw22dmMr/ckelmjSqNWsXsEjsVThPbun2s3tLFjGIbiyqd2AwT+6pJJrkCMTa1eegLxCiy6SkYwcBCNviwZQBZgds+M5PfrdnFvW/s4v4vLsq6IfU7ewO82eDic4vKDhlYGK7qPAurLp3HK3U9XLa4DOc4vAozJd/C9z8zk1Z3iL9/1MGr23v5yolVww4uTEbLli2joaHhoPevWrVqv2XTp0/n8ccfH81mCWMoFEuytdNHlydMNClTZDeQa9ZPigSEmzu83PNqA8FYkm+eVsOK2UVZE0wx6zWY9Roqc83U5Fv4zjOb+enq7fz68/Mn9IjWbJFMyaz61w6a+0Pcfv6srA4sCNlJjL8Wss6UfAu/+tx88i167nixjjfqezPdpDFhNWo5YUoeFTkmtnb4eG5jB1s6vCRSR5Y4SThyff5oeipEMEaRfeIFFgDWNQ+Qb9UzvcjKTWfV4gnH+d+3mzLdrCEUJV2twWHS8vkloz9vvTzHxNdOnjIuAwv7qsw1871zp/Onq5dQ6ph4711BGG2d3gjvN7lp7g+hUklML7SRbzFM+MBCSlZ4bF0rtz+3DbNewz2fX8Bn5hRnTWDh05xmHT86fxaReIo7V28nss+UCWHkKYrCfWt2s7Hdy/WnT2VJVU6mmySMQyK4IGSlfKueuy+bx5wSG799YxdPrm9jsszgKc8xs7wmF41Kxb8b+nhpSxdd3nCmmzVh9PqjbGz30h+MUWw3UGCdeCdn0USKjW1ellXnIEkS0wqtXL6knDUNfbzf5M508wa9u7uf+p4AX1pWOeETpgmCkHmJlMy2Th+b2jz0+qLkmnVU51kmRa6j/mCMHzy3lb992M4ZMwr4zeULqD7A1NNsU5Vn5uYV02npD3HPaw2kPp0RUhgxj61rY01DH19cWsHZs7Ir0bEwfkz8o6kwbpn1Gu64cDZnzCjgifVt3PvGLpKT5Cq+VqNmbqmDheVOevwxXtzSzTs7XQRjyUw3bVzr9kXY1JYOLJTajeRbRm8YfiZtbPMQT8lDkmB9YUk5U/LN/P7N3fgiiQy2Li2WTPHI2haq88ycNbMw080RBGGC80USrGty09AdIBhLUZ1npthunPCjFQDWNw/wX09tpNEV5Kazavn2WbXjKvP/ksocrj15CuuaB/jz2pZMN2dC+te2bv7+UTvnzirkiuPKM90cYRwTwQUhq2nVKr595jSuPK6cN3b08ZN/bieSmBwBBgCHSccJU3IptRvZ2ObhuY2dbO/yTZogy0jq9EbYtGfEQrnDOKHnbn7QPIBFr2F2sW1wmWZP2ddQLMnv39yd8ZFAz2/qwhWI8bWTqkXdbEEQRo2iKLS6Q3zQ5KZtIIxWIzGtwIJ1EuQ0SqRkHnq3iZ+u3k6eRc9vLl/AGTMKMt2sY3LBvBIumFfMc5s6eXlbT6abM6F80OTmj281clyVk+tOm5q102SE8UGMQxWyniRJfHFZJQVWA/f/ezfJeJS7piqT5uAnSRKVuWaK7Abqu/28Xt9LoyvIcVU5FE2gsomjqX0gzLYuH55gnIoc04QuqZSSFT5sHmBJlRONemj8uDLXzJePr+SRtS38e6eL06dnppM5EIrz9MftHD8lh3lljoy0QRCEiS+WTFHX5afDEyYQTVJsN5Bn1k+K/kOPL8r/vLKDXX1Bzp9bzMrl1eN++sfXTppCjy/KH97aTZHdwIIJnvB7LOzo9vPLVxqYVmDl5nNniGC/MGzj+ygjTCpnzSpk5fJqtvVGeX5TV6abM+b0GjULyp0sKHfS6Yny4uZu1u7uJySmShxSmzvM1k4fnlCc8tyJHVgA2N7lIxBLcnz1geuCX7SglJnFNh54q5H+YGyMW5f22AetJFMK15woykcJgjA63MEYHzS62d0XIJaQmZZvJd9imBSBhXd2ubjxbxvp8kX4/mdm8I1Ta8Z9YAFArZL43rnTqcgxsepf9bQNiHxUw9HhCXPnP7eTa9Fx+wWzxtVUGSF7jf8jjTCpXDivmHlFBv78fgsNPYFMNycjnCYdJ9bkUmDV81HrAM9t7GRjm0dkUT6AVneIbV0+vOE45U4TDuPEDixAekqEVi2xqMJ5wPvVKolvnzmNpKxw35pdYz49YndfkNfre7lgXgklotKBIAgjTJYVdvcFWN8yQIc3jFmnZVqBFaNu4p84xZMyv39zN//zSgPlThP3fmEhJ9bkZbpZI8qk03D7BbPQalTc+c+6rMghNB55QnHueKEOtUriJ5+djd048acJCWNDBBeEcUWSJL4430muWcf/vLKDQHRyfqlIksSUfAsn1uQhSemrFM9vSpeujCZEkAGguT9EXacfXyQx4adC7KUoCh80uZlf5jhkR7rEYeSa5dVsaPPyct3YzV1VFIU/vduE1aDhCyJhlCAIIywST/Fxm4e6Tj8DoTjlDhMVOaZJMdRbURR+t2YXL9f1cNmiMlZdOpdC28RMWlxgNXD7+bPwhBP8fPV24kmRh+pohONJfvzPOvzRBD+6YBbFYoqtMIJEcEEYd0xaFbesmMFAKM69b4z9lddsot9TVeL46jxSisJbDS6e39RJXZdvUgcZGl1Btnf58EbiVDpN2CfBiAWAFneIvkBsSJWIgzlvThELyh08/F4z3b7IGLQO3m9yU9fl56pllVj0IuWPIAgjp9cf5YMmN419AZKKQm2BFad54ibu/bSX63p4a6eLLx1fyVdPrNov585EU1to5b/PqqW+JzDp+4JHI5mSWfWvHbT0h7h1xUymFVoz3SRhgpnYR55JTlEUevxRNrZ52Nnjn1DD5msLrVyzvIp1zQO8sHny5V/4NKNOzbxSJ8dVOYklZdbU9/LPzV3Ud/smXUR/d1+A+u70iIWqXDPWSTTU74OmASRgaXXOYdeVJIn/OmMaaknit6/vGvXa4YmUzCPvtVCRY+Lc2aJ+tiAIIyMlK2zv8vNxi4cuXwSnWcfUfMukmj/e6Aryv+80sajCwecXl2W6OWNm+dQ8rj6hkrd3uXhyfVumm5P1EimZ363ZxcZ2LzecMY3FlQeePikIwyEuHU1Q4XiS5v4Q/cE4WrWEP5qgzROhxG6kMteEeQJcNbxwXglbO338eW0LM4tt1IroK2a9loXlTgLRBLv6Ary+vY96R4A5pXaq881oJ+CVjJSsEIgm8IYTuENxun0RAtEEU/LMmPWTJ7AA8EGzmxlFVpxHOAUk36rn66dM4Tev7+KFzZ1csnD0OqUvbu6ixx/lJ5+dPSmGKAuCMPqCsSR1fVE0UR+JlEJlzuQZqbZXKJbk7pd3YDNo+e+zp6OaBAkr9/W5RWV0eSM8+WE7JQ4jp2WoClK229kb4L41u2hxh/nS8ZWcNbMw000SJqgRPcPs6emhqEhckcokWVHo9EboGAgTTaaYWWxLz8sHtnR42djupcsXpsBqoDrPPK7rPEuSxI1n1HLj3zZy98s7uPcLC7EYxn/QZCRYDVoWVeTgjybY1Rvgle09VDhNzCm1U5lrGtfDJWPJFL5IAl84gTeSYCAUIxKXCceTJGQFnVpiSp4Fk25yvRf6/FGaXCGuObHqqLY7fXoB7ze5+esHrSyqcFKZax7xtg2E4jz1YTtLKp0HTTQpCCNF9EUmh/aBMDu6/XT6E0xxSFTnWSZkAP1Q9uZZ6PVH+cWl8yZlUj5JkvjmaVPp9ce4941dFNgMzCq2ZbpZWSOaSPH4ulZe2NyF06Tj9vNnsvQg1aQEYSSMyFG4oaGBW265hbPOOmskdiccI38kwdZOH7v7ghi0Ki5eUMqZMwox6zWY9BqOr8lj5fJqTpmWTyieZF3zABvbPHjD8Uw3/ZhZDBpuPncG7lCce9fsFHPuPsVm0LK4MocFZQ7coRgv1/Wwems3za7gqA+DHwmKohCMJen0Rqjr8vHebhevb+/lnZ39fNDkZnuXn/5A+v1bbDMwq8jG7GL7pAssAKxrHgBg2VF2GiRJ4lunTcWoVfOb13eSTI3cNJpESub5TZ1864kNJFIyK08SpSeF0SP6IpNDLJliY5uHTe0e+gIxnEY1UyZhYAHgxS3drG1085UTqib1CbVWreL7n5lBgVXPz1dvp8cXzXSTssLmdi/XP7mB5zZ1ce7sIn7/xUUisCCMuiPqgb/44os899xz9PT0UFZWxte//nUWL15MQ0MD99xzD++88w42m43rrrtutNsrHEAyJdPuidDljZCUZRZXOlhcmXPA+YZ6rZrFVTnMK3NQ3+3no5YBPmodwGHSMSXPgtOkHXc1oKcXWfnqiVU89G4zL27p4rPzSzPdpKzjMOk4riqXgVCMXb1B/uXpoSLHxLwyO2VOU6abNyglK/gj6REJ3nCcgVCcUDxJNC4TSaQABaNGjcmgodBmxqTTTMoO5YF80Oym3Gmk1Hn0WZ8dJh3fOn0qv/jXDp7+uIMrl1YMqy2KovBeo5tH17bQ44+yoNzByuVVlGfRe00Yf0RfRHAFYtR1+egLRJGQmFpgoTfpGXf9lpGwszfAI+81s7Qqh0sWin6P1aDljgtn892nN/OTf9bxy8/Nn7SJg4PRJA+/18xr9b2U2A3cdclc5pbaM90sYZI47Kfu6aef5vbbb6empoba2lq6u7u55ppruPnmm1m1ahUWi4Xvfve7XHnllZhMouM41gZCcVrcIbzhOEV2A6fW5lNoMxz2i1arUTGv3MHsEhs7e4Osbxng47YBbAYtU/Is5Fp042re3kXzS9jW6eOR91qYUSTyLxxMjlnPsil6XIEou11BOgbCVOaZkQIxbN4IJq0ao06NXqMa1c5aSlYIxZOEY6nB38FYOqgQS6SnOMRTCioJzDo1DqOGylwTeo1azNc/gGA0ybZOH5ctOvacCSfW5HFabT5/+6id46pymFpgOab9bO/28/C7zTT0BqjMMfHjC2ezqMIxKTv/wsgRfZHJLSUr7OoL0OQK4QnFcZi1lNonR4nJAwlEE9z98g5yzDpuOqtWHF/3KHEYue28mdz+/DZW/aueH184e1xPAz0W7+3u54G3G/FFEnxuURlXLC1Hr5k8yU2FzDtscOGxxx7j4osvZtWqVYPL/vznP/Pzn/+chQsX8sc//hGbbfIOxcqUeFJOl53zR0GCU2rzmF3iQKc5uoOoWq1iZomNGcVWdruCfNg8wKYOL2atmin5ZvKthnHx5S1JEjeeOY0b/7YpnX/hioWTNmJ9JPKtBvKtBnp9UZr6A3T3hOlRejBo1eg0KnQaFVaDBptBi1mvwbgn6GDSqTFo1KiO8D0RTaQIx1OEYsn073iSYDRJIJogkVJIpGQi8RQyCooCeo0Ks15Dsc2AxaAd9SDHRPFh6wCycvRTIj7tP0+pYUunj1+/vpPfXr7gqI4nXd4If17bwvtNbnJMOm44YypnzigcF8cPIfuJvsjkFYimp3x2eyMkkgrlOaYjTlo7ESmKwm9f38VAKM7dl80TuaY+ZU6pnetPn8pv39jFH99q5FunT50U/YiBUJw/vtXI+01uavLN3HHhbGryj+0igSAMx2GPSG1tbdx6661Dll166aWsWrWKb3zjG+LLfIwpioIrEKPFHSYQS5faO6U2j5xh1nKWJIlpBVamFVhp6Q/yYYuHbV1+DJog1XlmiuzGrD9JsBq03HzudG79x1Z+98Yuvv+ZGZPiC2U4Cu0GCu0GGhQ/eXlmgtEk0T0JE13+KPGUjKKATqPCoE2PaNCq1XsCD+lcHiZdOvCgKAwGEkKxJP5okmgiSSKlEEukiCdlZEAlgUGjxqBVYdVrKLYb0GvSQY3xNFomm3yw54R+WuHwOhIWg4b/OmMaP36xjsfWtbJy+eFzJPgiCZ76sI1/betBq5b44tIKLllYOqnKwAmjT/RFJh9FUWgbCNPQE8AdimHUaajOM036qXDPbuxkfcsAXz95ihileRBnziykyxfl7x+1U+o0jmolpExTFIVXt/fyyHvNJFIKXz2xiosXlGZ9n12YuA4bXIhEIpjNQ7OH772dmyuSgoylSDxFsztEfyCGTqPinFmF1BZaR3zIV1Wehao8C52eMB+2DFDfE6CxP0hNvoViuzGrTwBnFNn4ygmVPPxeC//c0s2F80sy3aRxQadRkWvWk/upIJWiKCRlJR0wiKcIxRKEYgk8oRjRZDrwoNVI6DUqdGo10USKlKIgARq1hH5PEMFuMGAxpPMjTPaO4UiLJ2U2tHk4fXrBiHw2F1c6WTG7iOc2drKsOofZJQeepxlPyry4pYu/f9RONJHi7FlFXLW0Aqd58l5RFEaP6ItMLtFEirouP53eMIFIkkKbngLr4ad8TnTbu/08+n4Ly2tyuWBecaabk9WuWlZBpzfCI++14A7G+dLxlRMu6N3ti3D/m7vZ0uFjTomNG86YRonj6PMuCcJIOqKxVBs2bMDj8QzelmUZSZLYsGED/f39Q9Y99dRTR7aFQrq8pCdChydMOJGitsjC8pq8US8jWeo0Ueo00eePsq55gPruAB0DEWYU27K63NHFC0rZ1unn4feamVFkZZqI7B8zSZLQqiUcJh0OE8DQL61kKp1oMRhNoihQYjeg16rRqlUiaj5GNnd4iSbkYU+J2NfK5dVsbPfw29d38bsrFmLUfdIhkxWFt3e6+MsHrbgCMZZUOvnqiVWjUsJSEPYl+iKTQ18gSl2nn75AFPWeUZX7HoMmK18kwf+8vINCm4Ebzpg26QMth6OSJG46axpWvYbnN3fxfpObb542lcWV478cckpWeH5TJ4+vb0OjSld8Omd2YVZf/BMmjyMKLuw7x3FfP//5z4fcliSJ+vr64bfqUx5//HEeeughXC4X06ZN47bbbmPJkiUHXb+hoYGf/vSnbNmyBbvdzhe+8AW+9a1vDTkQr1+/nlWrVrFr1y4KCgr42te+xpVXXjlkP6+88gr33nsvbW1tVFRUcNNNN3H22WeP+PM7FH8kQUNPgGRKwWrQcOHMEipzTUc8530kFNgMXDi/hLaBEG81uPiwxU2Jw8jUfOtR53gYC5Ik8e2zpvFfT23i7ld28NsviPwLo0WjVmFVq0Y90CUc3AdNboxaNfPKRi4TtFGn5qazavn+P7byyNpmvnnaVAC2dHh5+L1mGl0hpuSbufHMacwvc4zY4wrCoWS6LyKMrmRKZmdvkBZ3CE84jtOkpWQSJ23cl6wo/Pq1BvzRBL/83HzMok9zRPQaNd86fSqnTc/n/jd38+MX6zi1Np+vnVSNY5zm7WjuD/K7NbvZ3RdkWXUO151aQ65leFOjBWEkHfbo9MYbb4xFOw7qpZde4q677uKOO+5g8eLFPPHEE1x77bWsXr2akpL9h7wHg0FWrlzJkiVLeOaZZ2hububWW2/FZDKxcuVKANrb2/n617/OZZddxi9/+Us+/vhjfvKTn5CTk8O5554LwMaNG7npppu44YYbOOecc3j11Ve58cYbefLJJ5k/f/6YPPdESmZzh5dIIsXiSidLq3IzGr2vyDHzxaVGNrV7+aDZTY8vSm2hlRJH9k2VsBq03HLudG59div3rdnFrStE/gVh4knJCuubB1hS5Rzx6SazS+xctKCU5zZ1UpVr5qPWAT5s8ZBn0fPfZ9dyam1+1n3uhYkr030RYXT5IgnqOn10+yLEUzIVTtO4PfkbDU9/3MGGNi/fPK1GJOk7BrNL7PzuioU8/VF7+rVs9fAfJ1VzxoyCcdM3jCZSPP1xB/+3oQOrXsMtK2awvCZ33LRfmDwOG1woLc1s7dxHHnmESy65hMsvvxyA22+/nXfeeYcnn3yS73znO/ut/8ILLxCJRLj77rsxGAzU1tbS2NjII488wjXXXIMkSTz11FMUFBRw++23A1BTU8PmzZt5+OGHB4MLjz76KMuWLRusl33dddexbt06Hn30UX7961+PyXPXqlUsrc7hhJocimzGrDiAqNUqFlflUFto5b3d/dT3BGgfCDOrxJ51UyVmFNu4+vhKHlnbwktbuzl/3vDzL8SSKbZ0+NjY5mH51LyDzkcXhLGwszeAN5IY0SkR+/ry8ZV83DrAH95qxKRT85UTqrhwfrEoayWMuUz3RYTRoSgKLe4wO3sDuIMxLHoN1XkWkZtnH1s7vDyxrpVTa/NZMbso080Zt7RqFV9cVslJ09KjGH77xi7ebOjjW6dPpdievXkKkimZ1+p7eXJ9G55wgjNmFPAfy6uxZVmfWxD2Omxw4fjjjz+qk9r3339/WA3aVzwep66ubnDEwV7Lly9n48aNB9xm06ZNLFmyBIPBMLjspJNO4t5776Wjo4Py8nI2bdrE8uXLh2x30kkn8dxzz5FIJNBqtWzatIkvfelL+63z+OOPH7bdX/7ylwf/vvLKK7nrrrsOu814FVGb8OiLSKhN6JIhTEk/KuRRfcwV557Lww8/fETrKoDNvpAH3pJZ89xjmJKBo368hEqHX1eAT59PQJeLIqVPrF7e2MQM93uoSR31PrPN0bymwpEZi9e0y1yLZKrklcf/H68ryVF5DLPaTJG+kLxIO5s7Emx+flQe5oiI9+lQCZUOU8KPQY4Maz97v6f++te/jlDLRl4m+yLC6EgnbfTR4YkQiiYpchjIM+uz4kJKtvCE4vzy1QZKHEa+ddrkKKk42ipyTKy6dC6v1PXw57UtXP/ERq5cWsHFC0pGPEH6cCiKwtpGN3/9oJVOb4RZxTZu+8xMZhSLyjhCdjtscOGqq67K2MHM4/GQSqXIy8sbsjw3N5e1a9cecJv+/n4KCwuHLNu7fX9/P+Xl5fT393PCCSfst04ymcTj8VBQUEB/f/9+j5uXl4fL5Tpsu8866yzy8/MByMnJ2S+Xw0QjKwqtAWjwQAooNkGuHkbrbWOz21mxZ4TJkYgk4eEd0Fd0Il+dAYbDXHRVFOiNwG4f7PZDT3jP4+pgoQ2m2dPP7andRixzz+LUCVCQ4mhfU+HwRvs1VRR4sB6qdHD+ojNH7XE+MW0MHuPQxPt0qGACikyQYxjewXY8fE9lsi8ijLxuX4T6Lj+uQAy1SmKqSNq4n5Ss8KtXGwjFU/z0ojni9RlBKkniM3OKWVqVwwNvN/Ho+y28vcvFDadPzYok4Ns6ffx5bQsNvQHKc0zcfv5MjqvKEcdAYVw4bHDhhhtuGIt2HNKBPkyH+oB9+j5FUfZbfiTrfJqiKEf0wf7KV74y+Hd9fT3HH3/8YbeZCILRBGsb+6nrCqDSqZlVbBuVOZPNLc3MnTP3qLYprfVz6z+28GE4j1vOnb7f/2M8KbOlw8v6lgE+bBmgPxhHAmoLrZw9L4elVTlU5pqGbNf3+k7e3uni6rMWUuY0jcRTy5hjeU2FQxvt17R9IIxn0wa+cHwNy+dOjpJk4n06lCcUZ2qBhRLn8Ib0jofvqWzoiwjDF0/K1Hf7afeE8YXj5Fn1FFqNImnjATz1YRtbOn3ceOY0UY1nlORa9Nx23kzeb+znj2818d1nNnPhvBKuWlaZkWBOqzvEo++38GGLh1yzjv86YypnzCgUnw9hXMnqdLNOpxO1Wr3faAG3273fqIK98vLy9itJ5Xa7gU9qYR9sHY1Gg8PhOOQ6B3tcASwGLefMLmZ2iYO3dvbxYauHYpuBqQWWjNcWnlls4+oTqvjz2hZeKrVz/txiPKH4YDBhU7uXWFLGoFWxsNzJVctyWFLpPGRw5KsnVrGuyc0Dbzdx52dni4iyMKY+aEof15ZV52S4JYIgCIfXF4iyvdNPXzCKIkN1nkVUGjqIDW0e/vZhO2fNLOCsmYWH30AYlhNq8phX5uDR91sGy1Zed1oNSyrH5vvVFYjxxPpW1uzow6hV89UTq7hgnshvJIxPWR1c0Ol0zJ49m7Vr1/KZz3xmcPnatWs555xzDrjNggUL+NWvfkUsFkOv1w+uX1BQQFlZ2eA6r7/++pDt1q5dy5w5c9BqtYPrrF27lq997WtD1lm4cOGIPseJqNRp5IrjKtja6WNtYz/v7e5nWqGVMmdmq0pcsrCUbZ0+/vROE2/U97KrLwhAvlXPWTMLWVqVw9wy+xEnknKadHzp+EoeeLuJtY1ulk8VgSdh7KxrHmBagYU8UYJKEIQslkjJ7OwN0NofxhuJYzdpKRUlJg/KHYxxz6sNVOSY+M9TajLdnEnDrNfwzdOmctr0Au5fs4ufvLidU6blc+3Jo1e2MhhN8syGdl7c3I2CwkULSvn84jIRdJuE/JEEOo0q4xdjR0L2ZC45iGuuuYZnn32Wp59+msbGRn72s5/R19fHFVdcAcA999wzZBrChRdeiNFo5NZbb2Xnzp28+uqrPPjgg4OVIgCuuOIKent7+fnPf05jYyNPP/00zz777JDEkVdffTUffPABDzzwAI2NjTzwwAOsW7duyGMJB6dSScwvd/CVE6qYU2pnZ2+A95vcBCKJzLVJkvj2WbWDpTO/dHwlv7tiIQ9dvYRvnFrDosqjL+f3mTnFTMkz86d3m4jEx39iR2F8cAdjNPQGWDZldKpECIIgjISBUJwPmtzUd/nxR+OUO01UOM0isHAQKVnhf15pIJ6SueUzMybEicZ4M6vYxr1XLOSLSytY29jPdY9v4JW6HlyBGClZGZHHiCdl/rGhg6/99UP+saGTk6bm8cerFrNyebUILEwyvkiCui4fmzu8vN/Uz0AonukmDVtWj1wAOO+88/B4PPzhD3+gr6+P2tpaHnzwwcGyVC6Xi/b29sH1rVYrDz/8MHfeeSeXXXYZdrudlStXcs011wyuU15ezoMPPsgvfvELnnzySQoKCvjBD34wWIYSYNGiRfz617/mt7/9Lffddx/l5eX85je/Yf78+WP35CcAk17DWbMKmV1i482GPta3uFlQ7iQ3Q1db7UYtv//iohHbn1ol8Y1Ta7j5/7bwt4/a+eqJVSO2b0E4mPUtAwAcL6ZECIKQhVKywu6+II2uIN5QHLtRS7FDlJg8nL9+0Mr2bj/fObuW8nGey2k806pVXLm0gpOm5nH/m7u5/83dAKikdJ6GfIuefOsnvwuse25b9Zh0Bz+1khWFN+p7eWxdG/3BGIsrnXzlhCqq80ROjcnGF0nQ6Y3gDsZQUKjKMxOLp/i4dYA5JXaKHdlbHvVwsj64AOks0VddddUB71u1atV+y6ZPn37YkpFLly7l2WefPeQ6K1asYMWKFUfeUOGgih1GLltcxqt1vWxo9zC3xE5RFtcVPhozi22cNbOA5zZ1cuaMAspzRIdAGF0fNA1QbDdQId5rgiBkmb1X4rq9EWJJmfIc06gNK58ofJEEL23t5v82dLBidhGnTS/IdJMEoDzHxC8uncvWTh89vih9gRiuQBRXIMaOHj/vBuP7jWYw69TkW/XkWT4JOORb0iVWn3i/j65AkmkFFm46axrzyhyZeWJCxuwbVJAVmSn5FpZV55Jj1pGSFf7d4GJrp49IIsWUfEumm3tMxkVwQZgY9Bo1n5lThHGHmi1dXuIpZcKcHH3lhCreb3LzwNuN/PSiOSK5ozBqwvEkWzq8XDCvRLzPBEHIGrKs0OwOsbs3iDsUw6xXU5VrQ6cRoxUOpqU/xAtbunirwUU8JbOsOodrT56S6WYJ+1BJEvPLHMwv2/++lKzgDcdxBWO4Ant+gp/8bugNEIgmB9fPN6m5ZcUMltfkiu/vSWZoUEFhSp6ZpVNyyTXrBt8LGrXEGTMKsBm0vN/UTziRYlaxLaP56o6FCC4IY0qjVnHGzAL0WhUftXqIJ1NMLch8TeHhcph0fHlZJX98u4l3d/dz8rT8TDdJmKA+bvWQlBWOnyKmRAiCkB2CsSR1nT66fVFC8STFdgN5Zr04gTqAlKzwUesAL2zuYkuHD51GxRkzCrhwfsmEueAyWahVErkWPbkWPTOKDrxONJHCFYzhjyTQR91MnSKSf08m/n2CCilFoTrXzLKaoUGFfalUEkun5GAxqHlzRx8b2jwsKHOgGUdTykRwQRhzKpXESdPyMOjUrN3dTzwlM7PINu47ISvmFPNqfS8PvdvMksqcjNRIFia+D5oGsBu1zCiyZbopgiBMcoqi0DYQZmdPgP5gDINWTW2BVSQiPIBwPMnr9b28uLmbHn+UPIuOr5xQxbmzC0USvwnMoFWn82c4oXlPviRh4gtEE3R6IvQHYyTl9EiFQwUVPm1WiR2zXsMrdT2sa3azuDJn3BxXRXBByAhJkjiuKgejNh2ZSyR9zCm1j+sM0mqVxHWn1PC9/9vC3z5q46snVme6ScIEk0jJfNQ6wPKavHH9WREEYfyLxFPUdfno9EYIRpMU2vUUWAzj/kLBSOvyRvjnli5er+8jkkgxs8jKV06s4oQpueI4LggTzJCggqJQnWtiaXUu+dajH8lVmWvm0oWlrN7aw/tNbpZUOLEasz8QKYILQkbNKbVj0KrSiR7bPCwsH19Dfz5tRrGNs2cW8tymLs6cUSiSOwojamunj3A8JaZEjGOyohCMJokmUuRa9OLkQhhXUrKCL5JgIBSn1R3CFYiiVauYVmAVo/X2oSgKWzp8vLC5iw9bBlDvGbH52XklTCsc/1NBBUEYKhBNT3/oDww/qLCvPKuBSxeW8q+6HtbtqbiXl6GKe0dKBBeEjJtaYEWnUfHyth7Wt7hZUpk7rhNAfeXEKtY29fPHtxv5mUjuKIygdc0D6DUq5pc7Mt0U4ShEEyl8kQS+SAJvOEE4niQpK5j1ahaWO8fNUEdh8kmmZHyRBJ5wAm84jjsUIxKXCceTpBSFQquBQpth3CUcGy2xZIp/N7h4cXMXrQNh7EYtlx9Xznlziskxi4oZgjBeKIpCUlZIyQrJlEJClgf/Tsrynt/pvxPJ9HEyIStU5ZpYVp1DgW3kRnFZjVouXlDKa/U9bGz3MqPImtWlakVwQcgKFTlmPrughH9t7WZds5sllU6Mh6gVnM3sRi1XH1/FH95qFMkdhREjKwrrmtwsqnCi14iT0WyWkhX8kQTeSAJfOE4gliSWlNFrVDhMOmaVWHGadXzQ5OaDZjeLyh3YjOLEQ8i8RErGG07gi8TxhBP0B2NEEzKhWBJZltGoVZj1GkocRqx67bi+EDCS+gJRXt7Ww8t1PQSiSarzzNx45jROmZYvXiNBGAWKohAWhyF1AABXQ0lEQVSOpwjEUnjCcRQlvUyB/f9G2bMMZBRQ0n0qAFlJf2enAwYy8ZRCIimTSMnIe9ZL/6QfVwIUQKOS0KgkVHt+lztNLKl2UmA1oBqFEYk6jYoVs4ux6lx83OYlEk8xrcCSlRcwx+fZmzAhFdmMXLygjNVbu/mgxc1xFTlYxmmSo3NnF/Hq9h7+9G4ziyudmMZpoETIHrv7grhDcTElIgspikIonhoSUIgmZUDBatAwtcBMTZ6VQocBs0492BkotRt5eVs361sGmFvqoNBmyOwTESadRErGE47jDSfwhOIMhONE4ylC8SQpGbRqCYtBQ7nThEWvESfK+0ikZD5ocvPa9l42tXuRJFhWncuF80uYUzL+k1QLQraJ7xkhkB5NFSccT9HTF6VP9uy37qc/fcre5VL6vvRvCUmSUEvpZPM6tYROq8Zq0KHXqDDp1Bi1akw6DUadGoNWjUGrQqdRoVWrUKsk1JKEWiWNyeddrZI4uTYfq1HLO7tchOMp5mZhvjpxxiNkFadZx8ULSli9tYd1LQMsKnfiHIdDCdUqietOncp3n9nMUx+2s3K5SO4oDM+65gFUEiypFMGF4UjtufzwSQfj2L6U9+3keMNxQvEUiZSMSasm36ZnSr6ZihwTNoP2oHlkrEYtFy8qY019H1s6vdTELFTnmcVJiTCqfOEEPf4onnCcgWCcaDKVHpmgKOg16ZEJlU4zZoMG7TjOgTRamvuDvLa9l383uAjEkuRb9VxxXDlnziwUAUJBGEGyohCIJge/a33hOPGUjFol4TBqmVZuZ6oxyLSpxagkFSoJ1JKEpNoTMJAkVJKEpEovV0mgUqk+FVxI/62WpHGR802SJBZWOLHqNbxW38uHLelKEtl0rBbBBSHrWAxaLppfzCt1PXzUNsCCMif51uxOXnIg04usnDOrkBc2d3HmjAIqc82ZbpIwjn3Q5GZ2iR3bOMgUnI1SskLbQIhuX5Rk6pMAA6Q7IHuDDZC+gqEi3SGRSHdAVFL6S12SPknKuG8nZ165jan5VnLMuqPKoaBVqzh7ViEOk5YPmtwEY0lml2TflQhhYmjpD9Gwp2xkUpbRa9RYDBoKrGZMOhFMOJhgLMnbO128tr2X3a4gGpXECTW5nD2zkHllDvF5FYQREk2k9owATAfuI4kUKUXBrNdQnmtiar6FUqcRs06DSiVRn+ynJn/yJUmdWmjFpNfwr63dvN/Uz+IKJwZtdpzWZ0crBOFTDDoN580r4Y36Xja1e5hVbKM0i5OXHMzVJ1SxttHNA2838fOLRXJH4dh0eSO0DYS59mQxAuZY+CMJmlxBPOE4+TY9hVYDKUVBkdPzL2VFISWnpzekFAV5z9/ynnmbMpCSZRTS64FCRa6ZmnwzJU4jlj2dnGOlUkksm5KLw6Tljfo+1reI3BrCyEqkZLZ3+WkbCBGIJilxGHEYDz6qRkgHEbd1+nhtey9rG93EUzLVeWa+fvIUTq3NF4FeQRgBB8tRZNCocJh1zC2zU51vxmHUiWlZn1LiMHLZ4jL+uaWbdc0DLChzoFZn/jxDBBeErJW+oleEQaNiY7tvzxe7JdPNOip2o5arT6jk//27kbd39XNqrUjuKBy9dc1uID2fVzhyKVmhwxOm0xshKcscX5PLgnJn1nZQphfZsBm0/GtbD+83ulk8Tmpaj1ePP/44Dz30EC6Xi2nTpnHbbbexZMmSA67b0dHBmWeeud/y//3f/+WUU04Z7aYOSyCaYGuHjy5vhJSiUJNvEXmADqE/GOON+l5er++jxx/FrFNz1qxCzp5ZSE2+mLZ0OIqiEIwlicRTWA1aUaJU2I+iKPQGYnhCcbx7cxQpClZjOkfR1HwrhXYDpn1yFAkH5jDpuGxRKS9v6+Hjdg/Ts6DUrfh2EbKaWiVxSm0BBp2GDxrdxJMyWkU5/IZZ5JxZRby6vZeH323muCqR3DGb9fii/HtnH+5gnOo8M1PyzVTlmjNeKvCDpgGq88xiPu9RCMaSNLmCDITi5Fp0nDGjgCK7MdPNOqziPVciXt7WxfoWN3NLHRSI//cR99JLL3HXXXdxxx13sHjxYp544gmuvfZaVq9eTUlJyUG3+9Of/sSMGTMGb9vt9rFo7jHr8kao6/TRH4ph1qUTM4rRCvtLpGTWNw/wWn0vG9s8yArMK7Vz1bIKTqjJFaOIDiMlK4P5ZwbCcUKxdA4aCbAaNBRaDTjMOqwGjShbOsklUjK7+4L0+WOo1VBgNTAl30xl7qFzFAkHZ9RpuGB+Ce/tchFJyJlujgguCNlPpZI4fkouBo2Kt3e5UMJxpmS6UUchndyxhu8+vZkn17fxHyeNp9ZPfMFoknd2u3izwUV9tx8JMOnVvFzXA4BKglKHkSn5FqbkmanJtzAl34x1jCqZeMNxdvT4+cKS8jF5vPFOVhQ6vRE6BsLEUzKLKh0srszJeIDoaNiNWi5eUMYbO3rZ3OFlaoGVqlyTuIIzgh555BEuueQSLr/8cgBuv/123nnnHZ588km+853vHHQ7h8NBfn72j0CTZYWG3gDNrhCecJxCm54C68jVXZ8oEimZZzd28vymTvzRJHkWHZ9fXM5ZMwspsoug3qHEkim84QTecIKBcLpkqQQ4TFrmldmoyjMzEIyxszfI7v4gigsMGhX5VgN5Fh12MS1n0vFHEuzuC+CNJJhRZOWkafmY9eJUdCRo1SpOm1GILGf+Aqz4HxXGjQUVTiQJnn7PRY8vMi6uQu5VW2jlnNlFvLC5i7NmFg47uWMwmmR9ywDrm90srHBy7uyiEWrp5JBIyXzc6uHNhj7WNw+QlBXKnUauPqGS02oLyLPocAVjNLlCNLqCNLlC1HX5eGuna3Af+Vb9kGDDlDwLeRbdiHfeP2wZQFZg2RQxJeJwwvEkTa4Q/cEYdqOW8+YUUZozPk/K9Vo1K2YX4zTqWNc8QCiWYGaxSPQ4EuLxOHV1daxcuXLI8uXLl7Nx48ZDbnvDDTcQi8WorKzkq1/9KitWrBjNph6TSDzFlg4vXd4I8aRMda5ZTK85gA2tHh54u5EuX5SlVTmcN7eYBeUiOePB7C25u7d0qS8cJ5aU0WlU5Fl0TK20Up1nxmbUDr6GFTlmFlTkEE2kaBsIs7svQGt/mHZPGJWU/h7Ns+hxGHVi+sQEpigK3b4ore4QCVnmlNo85pU5xWdtFAwn/9NIEcEFYVyZW+pgY46eui4/NqN2XE0xuPr4Stbu7ucPbzXyi0vmHvUJjycU54NmN+83utnS6SMlK+jUKt5vclOVa2Z6UebnWWUzRVHY2RtkTUMf7+xyEYgmcRi1nDe3mNOnF+w3l7bAaqDAauD4fU7qfXsSAzb1h2hyBWl0hVjfPDBYP9lm0AyOcHCqIhSVpIbdYfqgaWAwkCEc2N6OS/tAmHAiybxSB8um5I77zqpKJXHC1DwcZh1rdvTx8Z7qOdmaM2K88Hg8pFIp8vLyhizPzc1l7dq1B9zGZDJxyy23sGjRItRqNWvWrOGmm24iFotx0UUXHfLxWlpaiEQiAESjUerr60fmiRyAN5JipzuKJ5JCI0GxTUd/fID+UXvEzIvHYjS3NB/x+gORJM/W+djcEyXfrOa6ZbnMzDeA4qWtzTt6DR1H9r6msqIQjMkE4jK+aJJIQiEp///27jy+qvpO/P/r7ntyl9zsCVkgAQIJIMjuQgFxnVGn1daFoV9t60wdBzvjqK0dO60b04KOti4d7Q87SmsVbYtOa9XaaYniUlCRTSAxG1nuzXb39fz+iEQjO7nJvUnez8cjD27OPefezz2cnPs57/P5vN8KJp2KXIuWaXY9bosOozaKKhigvQnaj/O65VoozU3iDSZo6YvycUuMnZGBYdw2gxqHSUO2UYNZpx6TQeETOdVjdTxIJBVa+mJ4gnFMOhULS8wYA3H27e1IyeuP9Dl1IvrsPp02bdopbz92rsyEYKCzXVdgYl/YyPamXhZWusbM/L0sk47Vi8p46I/7+dO+Ls6pzj3hNu39Yd484KX+oJc9h/pRgMJsI387q4hFlS4Ks0380y+388OX9/LAlbPGVLBltLT3h3l9byev7+2itTeEXqNmfoWTc6tzmV1iP6VhmdkmHbNLHcwudQwuC0UTNHo/CTZ8EnT4zXttxJMKT+54k7piO/PLXZxZ7sRp0Z9S28OxBDuaezmvJm9cdrRSIRxLcNAToMsXwWLQcMn0IiaNsykE0wqysBm1/G5n+2DJKesoTcsZz452jBzruHE6nUNGOsycOZOenh7++7//+4TBhbKyssHHu3fvPq3O2okoisJBT4DeDh9aW5Sp+XoK7KYx8/04HA2NDZSXnbiSTiyR5IUdrfzy7UMowDULJnHp7CIpv/k58USS9/YewGbKoTsQIaRKgkGhwK6n3GVmct6pl9w9nu5AhEZPgP2dftr6woQSYFRpyM0aGNVwqt+bmexkj9XxIhCJ81GHj6QpytwSC+dOy8Oa4mkQI3VOnciGu0/lSkSMOUatmnOqcvnNe23sautnRlFmJ9T6rBXT83h5VztPbG1gXpnziLlmiqLQ3BPijQMe6g96OdgVAKAix8JX5peysMJF6eeGef/Lympu2/w+D//pAN9aUT2qnydT+SNxtu738Me9nXzY1g/AzKJsLp9TxKLKnJTO8TPpNUwryGJaQdbgslgiyR937OPjoJ5tDV7e+biHH78O1Xk25pc7WVDhothhOuEF8PamHqKJpEyJOIaO/jCN3gDBSIKpBVYWTx6/8zeLHWb+7oxi/nfnIbY1dlNbZEebAcMfxyKHw4FGo6Grq2vIcq/Xe8RohuOpq6tj8+bNqW7eKYvGk+xs66O5O4g/EqfUacZuHj8XZKmwo7mXR/50gNbeEAsrXFy3pFwSpX5ONJ6kvT9Me1+Yhu4IhdowTque2aUWKtzWEUu257QYcFoMzJnkJBiJ09Qd5GCXnwZPgKbuIJVuK+Uycm/M6ewP0+AJEI4nWFjpYnapQwJ5E8T47IWJcS8v28iiShev7umgtVdHkd2c7iadFLVKxTfOquRbv3qPp99q4vqlFSiKwv5OP28c9FJ/wEtr78Dw2Wn5Nr66uIyFFTnHTSw1vSCLK+eV8vRbTcwpdXDuSYyIGOsSyYFSV/5wHH8kji8SG3y8s62ftxq8xBIKxQ4T1yyYxDlV7lHtSOo0aqa4DKw8o5zrlpTzsTfItgYvbzZ08+SbH/Pkmx9TmG1kfoWL+eVOpuZnHXXu4ZsHu7EatNR8JnAhBhKJNXwyWkGvUXP+zDwq3baMmGs4kuxmPZfOLuLV3Z3saOkl32Zgcu7YKs+bCfR6PTU1NdTX13P++ecPLq+vr2flypUn/Tq7d+9Oe3LHvmCM91t7ae8LowKqcm1jKnnpSPP4Izz+lwb+st9DQbaRf794OnMnOdPdrIwSiiY41Bei0xcmEE3gNOuYW2hm+fwyLKNcCtBs0DK1IIupBVkkEknebOjm7cZusoxaXFbDqLVDnL5EUqHRG6Dtk5GiF9UWjrvRhOL4JLggxqzphVl0+SLsaO4h26RP+VCrkTIlz8aqGflseb+NSHwgsaDHH0GtgtpiO5fUFTK/3HlKX6RfmlvC9uZeHn79ANPys8ZkluvO/jD7Ov34wwPBgkAkju+TgMHhwIH/k2WhWOKYr5Nl1HJeTT7LqnOZnGtN+xeaSqWiLMdCWY6FK+aV4vFH2NbQzbaDXn77XhvPb28l26RjXpmD+eUuZpXYMeo0JJIKbzd2M6/MIRm1P8PjHxhC2x+OUZVrY0lVzqhV7sgERp2W82cUkG3s4q2PewlFj/23II5tzZo13HLLLdTW1jJnzhw2bdpEZ2cnV155JQA/+tGPeP/999m4cSMAzz//PFqtlunTp6NSqfjjH//I008/zb/8y7+k7TM0dwfZfah/MIFpkd0sCdI+EUsk+e17bWx6u4lkEq6aX8pls4slX8ln+MIxDvWF6fJFCMcT5NkMnFOdyySXhQMfhdPep9Jo1Mwvd9IXjPJ+ax8LKpyYdGOjnzdRhaIJ9nf68AQiFNlNLJ+eR7ZJRlFNNPJXKsYslUrFgkoXHn+E7U3dLKp0j5mO1TULJvHGAS+v7elgTqmDq+eXcma587QvkjRqFf+yoop/+sV2/vPlPdx3We2YuSCNxBM8+24Lz77bQvwzJXS0ahU2oxarUYfVoCXHaqDMZcFq1GI1DPzYDj82Hv59YN1MPg5yrAYunFnAhTMLCETi/LWphzcPdvPGAS+v7O5Er1Uzu8ROicOMLxJnfrlMiYCB3Aof90bRxPrRqAemGFUfY8THeKdWq1hSlUtVftaECqyk0gUXXEBPTw8PP/wwnZ2dVFVV8dhjj1FUVARAV1cXzc3NQ7Z5+OGHaWtrQ61WU1ZWxl133XXCfAsjIZFU2H2on0ZvgP5QjLwsA26rlJk87P2WgSkQzT0h5pc7uW5pBfkyBQIYmHrZG4xxqD9Mtz9CLKFQ5DByZpmTArsp4/oNWo2as6tz6Q21sr2plwUVYyfP1kTj9UfY3+UnGIszZ5KDeWVODFoZRTURZXRwIRqNct9997FlyxYikQgLFizgzjvvJD//+GX3fv/73/PAAw/Q1NREaWkpa9euZcWKFYPPP/roo7z88ss0NDSg1+uZNWsWN998M1VVVYPr3HrrrTz//PNDXreuro5nnnkmtR9SDItRp+Gcaje/fq+Nna291JU4TrxRBrAZdfzkqjlo1eqUZbTPzTLyj+dOZt3v9/L0W01cu7AsJa87kt5p7OaR/ztAR3+Es6a4uWxOEXaTDotBi0E7PrNFf5bFoGXpFDdLp7iJJZLsbO0bGNXQ4GVbQzd6rZo5pWPjmB4p/aEY7f1hPP4I7b4Y84pNnFPlljnlIHPGh+mqq67iqquuOupz995775DfL730Ui699NLRaNZxBaNx3mvuo603RCwxUGZSknsO8PojPLG1kf/7qIu8LAN3XDidM8tlCgRAUlHw+qMc6gvRG4yhoFDusjCv3EmO1ZDRU8osBi3Lpubxm/fa+LCtj5lF9nQ3SXxGUlFo8gZp7Q2iUsH5NfkTYpqiOLaMDi7cddddvPrqq6xfvx673c69997L17/+dTZv3oxGc/QLsu3bt7N27VpuvPFGVq5cycsvv8xNN93Epk2bqKurA+Ctt97iK1/5CjNnzkRRFP7rv/6LNWvW8OKLL2K32wdfa9GiRaxbt27wd51OvsAzUY7NyOLJOfxhVwfN3UFKnGMj/8JI3HFcOsXN9qZenn23hVkldmqL7Sl/j1To9IX56Z8P8ubBboodJn7wtzOoy9C2jhadRj1YieLrZ1VwoCuAoihjvpzi6TjcEW7vD9MbjBJXFAqzjVRUWDl3ZkHG3V0TYrQc7ArwsTeAUaemPMcmCdIYGMnxwvZWnn6riXgyyZfnlXD5GcVy15SBfdPpC3OoL0x/KIZWo2JaoY3ZJXbsZv2YCeDnZxtZMtnFK7vHVj9vvIvEE+zv9NPli+C26VlZk4/TIrkxJrqMDS74fD6ee+457r77bhYvXgzAunXrOPfcc6mvr2fp0qVH3W7jxo3Mnz+fG264AYAbbriBbdu2sXHjRtavXw/A448/PmSbdevWMXfuXP7617+ybNmyweV6vT7tyZrEyanOs9Hli/BuYw92s25CDxW+fmkFuw71s/4P+/ivK2eTZcqcffFpKbBmFODahZP421lSCuzzVCrVhEzUF0sk6egP09Efpj8cR6dWUZlrZU6pA5dFz969fgksiAltSq6Vjv7wwN3mMXJhOJI+bOvjgT93csgXZ+4kB187q4KCbFO6m5V20fjAubS9P4wvEsOs0zKv3EFNYfaY7R9NK8jCG4jybmMPWSYd2RnUt5mIeoNR9nf66Q/HmFlsZ1GlS5LJCiCDgws7d+4kFouxZMmSwWUFBQVUVlayffv2YwYXduzYwdVXXz1k2ZIlS3jqqaeO+V6BQIBkMklW1tCM7O+++y4LFy4kKyuLefPmsXbtWlyuE89/bmxsJBQayPgfDofZvXv3CbcRJ+9Y+zQroaAN+/n9Ox5mFZom5Fzsw74y08r6v3Rx35b3+H9znSe8OxGNRGhobBjRNu3zRPjVzl46/HFq84xcVpON0xynpfnjEX3fdBmNfTpehGJJPME4PaEE4VgSs07NFJeecqcRizqGp6UHD3I+HSmH96vUCs98Oo0ajVo14QMLfaEYP9vawKt7OnGaNHz7gmnMLz/xd914F0skaekJ0tE/UPnBbtJxzpRcqvJtY34UnEqlYn65i25/lO3NPSyqcKGX0Slp0dITpKk7SEJRWD49j2n5WTINQgzK2OCCx+NBo9HgcAydb+xyufB4PMfd7vN1qnNyco6oZ/1Zd911F9OmTWP27NmDy5YuXcqKFSsoLi6mtbWV+++/n9WrV7N582b0+uPP9S0rKxt8LB221DvePi0tj/DCjjZ8KhWzJ/Bc9XLAm7TwxNZG9gZMnD+j4LjrNzQ2UF5WPiJt6Q5EeWJrA3/a5yEvy8B3L6piXtn4nwc7kvt0PDicWKy9P0x3LELMkKQyx8CcUgeTcixHvQMi59ORIftVjBVJReEPuzr4/+obCcUS/N2cYhbkJaiukMS3iqJwoMtPe1+YHKues6a4KXNbxtX0EL1WzTlT3bywo40dLb3MmyQBpdHW3h+mwRPAatByXk0eeTJSSHzOqAcXNmzYwCOPPHLcdZ588sljPqcoyjGfO942xzr53HPPPbz77rts2rRpSB6HCy+8cPBxdXU1NTU1LFu2jNdff/2U6mCL0eWwGFgyOYeXd3XQ6PFTlpP6oeWJpEKXP0K2UZfRdwL+ZlYR25t6+e8/NzC9IItJLsuovn8iqfDiB4d4atvHRONJrphXwhdlHuyEF08k8fijtPeF6AvFUalgksvEnFIHedkTe8SREOLYGjwBfvL6fva0+6gpzOIfzplMqdMsI8Q+0emL0OWLMNltZXlN3ridbpht0nNulZuXPjjEvnY/1QW2dDdpwvCFYzR0+THo1PzN7EIpMymOatSDC6tXr+aSSy457jqFhYXs2LGDRCJBT08PTuendzm7u7uZN2/eMbfNyck5YmSD1+s9YjQDwN13381LL73Exo0bKSkpOW6b8vLyyMvLo7Gx8bjrifSbnGulyxfhrYZuHGY92SnKKn84MVJrb4j+cAyrQcuZZZl7t0StUrF2eRU3/mI7P3x5Lz/64qxRq/G9p72fh18/wEFPgNkldr5xdiWFdoluT2ThWOKTfAoR/NEYJp2GutJsZhZmk23Wyd0nIcRRhaIJnn6rid+814rVoOWfvzCFZVNz5ZzxGeFYgkZvAINOzdJq97gNLBxW6rKwsDKH1/d2YjfryMuWyjkjLRpP8lGHj1gyyQUzJLAgjm3UgwtOp3NIsOBYZsyYgU6nY+vWrVx88cUAtLe3c+DAgSHTFz5v1qxZ1NfXc9111w0uq6+vP2KbH/zgB7z00kv8/Oc/p7Ky8oTt6e7uprOzk9zc3BOuK9JLpVIxr9xJlz/C9pZeFle40A3jbnlSUejyRWjtDdEXimHWaZjsttLoCRCMxjHrM3Z2EQ6Lnn/+whS+t2UXP6tv4OtnnfhYH47+UIyNbzTy8q4OXBY9t66ayqJKl3QCJyBFUfBH4vSFYvSFYvQGo4TjSZwWPeeU5TIlz5rRfztCiPRSFIU3D3p57M8H8fijnDc9j9WLysZsQsKRcng6RDCS4ILafKyGiXFenVmUjdcf4f3WPixG7YT53OmQVBT2d/npDcU4uzqH4lEeCSvGloz9S7TZbFx++eWsW7cOl8uF3W7nnnvuobq6mkWLFg2ut3r1ampra/nWt74FwLXXXsvVV1/No48+yvLly3nllVfYtm0bTz/99OA23/ve9/j1r3/Nj3/8Y7KysgbzMZjNZiwWC4FAgIceeoiVK1fidrtpbW1l/fr1OJ1Oli9fPro7QpwWnUbNOVWfzsubexrz8hRFwRuI0tIdpDcUw6BVsaDCyYyibJIK9IdaaekJUZWX2UPy5pY5uaSukN+818bsEseI1P0+PA92Y30jgWicS2cXceW8Erl4nGAi8cRAMCEYozcUIxhJEFeSWPQaCu1GaoscFDlN4/6umhBieNr7wzz6pwO883EPZS4zt5w3lWkFWSfecAI61BfG44syvdBGxQhMBc1UarWKRZNz6A5E+WtTN4sr3TKtboQ0dwfp7A8zrcDGzKKJm89MnJyM7vnffvvtaLVa1q5dSzgcZuHChaxbt25IboTm5mYKCj5NVjdnzhzWr1/P/fffz4MPPkhJSQkbNmygrq5ucJ3DgYa///u/H/J+3/zmN7nxxhvRaDTs27ePF154AZ/Ph9vtZv78+dx///1YrRPnxD3WZZv1LJ2Sw+92ttPgCVDhPvn/u+5AlNaeIN3BGBo1nDHJTm2JY0hkvMBuYm+7L+ODCwCrF5bxQWsfD7y6jwe/PAenJXVTRbbu9/Dc9hYOdgWoKczihrMrRz2/g0iPRFLBF/50ZIIvHCeaSKJRq3CY9VTn26h0W7Cb9VKiSghxQrFEkhe2t/KLd5pRq+D/LS7n4rpCuWg8hmA0TlN3AKtRw6LJORMuY79Rp2HZ1Fx+vaON95p7mDNp/CeLHm1ef4SWniBum56zqnLlb1GcUEYHFwwGA3fccQd33HHHMdd57bXXjli2atUqVq1adcxt9u7de9z3NRqNPP744yffUJGxKtxWzihz8MYBL3azDqfFcNz1+0IxmruD9ASjKCjUFGYxZ5LjqHPLynMs7DnUT28wij1FeR1Gil6r5l9WVrP2mR1seGUf37ukZlilzELRBH/Y3cGvd7TS6YtQZDdx84oqzqlyyxSIcS4YjdMfitMbitIbjBKKJUkqClaDlrIcC5VuCwV2Exa9dsJ1dIUQp++D1j4efn0/zT0hFla4uH5pBW7b8b+zJ7KkonCg008oluS8mvwJO1LQaTWwtMrNyx+2c7DLf0o3ksTxBaNx9nf5UalgZU2+3CQQJ2VinonEhHJGqQNPf4QPWntZWJFz1LrIvnCM5p4QXn+EpKJQlWfjzDIn9uPc4c/LMuK0GGjtDWZ8cAGg1GnmuiXl/OT1A7ywvZXL5hSf8mv0BKNsef8QL31wCH8kzvSCLL52VgXzypwTvu76eJVIKvQGo/SHY/QEYwQicSLxJCadGqdFz+xSC5NcVrJNulFLGCqEGD96g1F+trWR1/Z2kmsz8N2Lpk+IcsXD1doTwhOIUleSTYnTnO7mpNXkXCvdASdvHPCQZdKRY5Wg1HDFE0k+6vQTjCa4YGb+CW/OCXGYBBfEuKfVqDmr2k339ig7mnuZV/Zp/oVAJE5LT4guX5i4olCZY2F+hQunRX/CO/BGnYYSp4m/NvUet9xpJllVk8/2pl6efPNjZhZlM+Ukp3S09oR4fkcrr+3pIJ5QWFDh4rLZRUyVObDjWk8wyseeAL2hGAA2k5bqfBuT3RbcWUZMOs2YOO6FEJknqSi8/GEHG99oJBxL8MUzivnS3BK5O3oSBm6IBHGa9cwvl6TJAGdMcgwkeGzpZWGFC9MEHcmRCoqicNATwOuPsKDCSaWMBhGnQP7yxIRgM+o4u8rNS+8fYn+nn2KHmZaeIJ2+CNFEgklOCwsqnORmGU/pS3qSy8LOtn66/BFybZlfCkmlUnHjssncuGmgPOX9V8zGpD92R273oX42b29h28FutBoVX5iax9/OKqLIIWUlx7NwLEHTJwmckorCGZPsTC/IxmbSyXxLIcSw7WjuZWN9I/u7/NQUZvEP50ymdILffT9ZieTAhV8skWTZNLcEYz6hUas4u9pNTyjK9uZe5pe75PvqNB3qC9PeF6Yix8Kc00iILiY2CS6ICaPUZWFuhZO/7PPQ3h8mnkxSZDexsMJFfrbptOaHu20G3FYDh/pCYyK4AAOBlm+tqOLbL+zksT8f4KYvVA15PqkobGvo5vm/trC73YfNoOVL80q4aGbBmJj+IU5fUlFo7wvT3BMkEIkzyWVh6ZQcXDLEVAiRAh91+HjyzY/Z0dyL22Zg7fIqzq2WXD2noqk7iDcQYX6Zk4JsCfR/llmv5QvVefzmvTY+PNRHbZE93U0ac/pCMRq9ASwGDcum5Ul1J3HKJLggJpTZJQ784Tid/WEWVLgocpiHFdnWadRMcpnZut9DPJlEqx4bJ+GZxXa+OLeEZ95pZk6pg2IdRONJXtvTyQs7WmntDZFrM/C1pRWsmJ4nd0YmgMMdiu5AFJtRywUz8qnItcmdHyHEsLX2hPj5to/Zut+DzajluiXlnD+jQPK0nKLeYJS23hD5WUZmT3JIUOYo8rKNLJni4pVdnTR3Byd8PopTEY4l+KjDRyKpsGpGPhaDXCaKUydHjZhQNGoV51TnpjRHQpHDhMWgpb0vTLFj7HyJfXleCe819/LQH/ezdJKZba+9TW8wxmS3lVvOq2ZRZY5cWE4A0XiSpu4g7X0hkijMKc3mjEmu406XEUKIk+H1R9j0djN/2NWOXqvmynklXDq7aMJWNhiOeCLJQU+AhJJk2dRcDEdJTi0GTM3PwhuI8k5jDzajVkZdnoREUmF/px9fJM4XpuWSL6NixGmSs7uYkFIZ7c+xDEyN6OgfW8EFrWagPOU//WI7v//Ix5xSB5fPKWJmUbbcDZkAkopCR3+Y5u4QvkiMUoeJs6pzJcu2EGLY/OE4z/61hd++10ZSUbhgZgFfmluCQy7yTlujN0hvMMpZVTm4x8g0zHRRqVTML3fR7Y/yXksP8ya5MMtd+OP62BvA449QV5zNtHxJ1i1On/ylCTFMarWK8hwLjd4A0XhyTA3zzM828sMv1tHS2sKimVUn3kCMC/2hGB97A3gDUcx6DefX5FOZa0UrcyuFEMMQjiXY8v4hnv1rM8FIgrOr3Vx15iTys+VieDi8/gjtfSFKHCZmSB6Bk6LTqDmn2s2W9+O80eChpsAux+ExdPaHB6bbZBtZUOk6rRxkQhwmwQUhUqDIYSLbpKOlN0hFztgq2VPqNJPo16W7GWIUxBKHp0AMJDStLc5mXrlThigLIYYlnkjyyu5ONr3dRHcgytxJDq5dWEZ5jiXdTRvzovEkDZ4AKrWKZVMlwd6pyDLpuXRWEa/v62JnWx8ef4Sp+TYJpH+GPxKnwRNAp1GzYnqeTLcRwyY9SiFSwG7Wk5dtpMkz9oILYvxTFIUOX4SW7iD94RiFdhNLp+SQd4qlV4UQ4rMURWHrAS//8+bHtPaGmJZv45bzqqkpzE5308aNRm+AvnCML0zNw26RaSWnymTQsmpGPiVOM3/a18UbB73UFWeTZZJ9GUsk2dfhIxxP8Dd1hZKbQqSEBBeESJEKl4WP2v0Eo3G5Eywyhj8cp9EbwOuPYtQP3JmYkmeTu19CiGHZ0dzLxvpG9nf5KXWa+c6F0zizzCkByxTq9IXp6A8z2W1hWoEt3c0Zs1QqFTOKsinINvLyrg62NXQzJdfGJJd5wh6viqJwoNNPbzDKkik5FEtVDZEicgUkRIoU2E24rHqau4NUSzIckQG6fBE+6vQRSySZXpTF/HKXlJYSQhxXIqnQG4zSE4zRHYjSExz4GXwciOENRPH4I7htBv75C1M4pzpXqgulWDiWoNETRKdRs7TKLUP5U8BlNfDFM4p544CXdz/uweuPMqM4a0JOBWjpCdHRH6E630ZdsX3CBllE6kkvU4gUsRi0FNqN7D7Un+6mCIGiKLT2BFEUhcvmFFGQbZLOgxACrz/Cnnbf0IBBMEZPIEp3MEpfMIZylO1sBi0Oix6nRc9MexbVeTZW1uTLKKgRoCgKBz0BAtEYq6bnky1D+FNG+0mwpthp4pVdHdTv9zKjOAu3deIke+wORGnqDuKw6Di7KlcCVyKlJLggRAqVuazsauunJxiVklsirXpDMfrCcWYU2ii0y3BHISY6RVF4eVcH//2Xg4RjSQDUKrCb9DgsOpwWPZNzrQMBBLMeh1n36WOLXoIIo6i9P0yXb+CucmWeTIcYCeU5Vr58ppFX97Szo7mPEkeUKbm2cT8CJxRNsL/TDypYWZOHST/xRm2IkSXBBSFSKDfLQI7VQGtvUIILIq06+sNAktoSe7qbIoRIs55glAdf+4i3G3uoLcrm2oVl5NoMZJl04/5iaqwJRuM0eYOY9GqWTHbL/88Ishq1XFxbxHstvfzlIw89gSi1xfZxOX0wkVToCUZp7Q0RiA2MiHHbJs5oDTF6xt9fjxBpZNRpKHGZeaexB0VRZBi6SItgNI7XH6HYYZYglxAT3BsHPDz0x/2EYgmuW1LOxXWFqOW7KSMlFYWDXQGCsTgX1RaOy4vcTKNWq5hd6iA/28iruzp446CXqfk2iuxjfyrh4fwp3YEo3kCEYDSBRq1iXqmTKTIiRowQOWsJkWIlDjMftPTR5Y+QK1FhkQad/RHCsSRzJznGfOdICHF6ApE4j/35IK/t6aTSbeHmFdWUSkb4jNbWG8Ljj1BbZKfMZUl3cyaUgmwTX5xbwp8/8vBBax/eQITpBdljbjpQUlHoC8Xw+j8NKKhV4LIYOLPcSbnbis2glb6BGDESXBAixXJtBnJsBtp6QxJcEKMunkjS4QuTY9OTl21Kd3OEEGnwQUsvG179CK8/whVzS7hiXsmYu0iaSCLxBD3BGM3dQbJNWuZXuFDLdIhRZ9BpWD49jxKnmT/u6aD+gIe6Yjv2DB8BqCgK/eE43YEIHn+UQCSOAuRY9cwutVPptpJl1MkxJUaFBBeESDGtRk2Z08xf9nuIJ5No1dKhE6PH448SiMaZX5ErFxNCTDDReJKfv9nIr3e0kZ9t5L7LaplaIKWRM42iKPgjcXqDsYHku8Eo0UQSvVbNsupcSbKXZtX5NvKyDLyyq4O3GrupyLFS4jRlVMnKw8eQ1x/FE4gQCCdIKEmcFj1nVjiZkmvFbtJLQEGMOgkuCDECihxmbEYt7b1himUYqhgliqLQ3hfCpNVQ6bamuzlCiFF0oMvP+j/so6k7yPkz8vnq4nKMusy5GJroYokkfaEYvcEYPcEowUiCuJLErNdQ6rIwJddCocOMVfIsZAS7Wc+lc4p5u8HLtsZuPvYGyDLpcFv1ZJv1ZBnTkwx1IKAQwRuI4g/HiSeTZJt1zJ5kpzrfhsOslySgIq0y+gwWjUa577772LJlC5FIhAULFnDnnXeSn59/3O1+//vf88ADD9DU1ERpaSlr165lxYoVg88/+OCDPPTQQ0O2ycnJYevWrYO/K4rCQw89xC9/+Uv6+/upq6vju9/9LlOmTEnthxTjksuix2010N4/ssGFRFJBpUKScwlgaPlJsz6jT+9CiBRJJBU2/7WFp99qwmbU8u8XT2fuJGe6mzXhKYpCIJqgLxijNxSlNxglEk+iUauwm3VU59updFtwWPQZdUdcfEqjVrGgMocpeTYOdPlp8AQ46AmQVAJo1SpcVj0uy0DVFYtek/I8BtF4kkA0TiiaIBhN4AvH8EfiRBNJsoxaaoqymFZgw2UxoJWRiiJDZHTv86677uLVV19l/fr12O127r33Xr7+9a+zefNmNJqjn4i3b9/O2rVrufHGG1m5ciUvv/wyN910E5s2baKurm5wvfLycn7+858P/v751/vpT3/KE088wb333kt5eTk//vGPWbNmDb/73e+wWuWOoDg+tVpFWY6FA10BIrEEhhG4exSJJ/iwtQ+9TsOMwuyUv74Ye6T8pBATy6G+EBv+sI/d7T4WT87hH86uJMukS3ezJqz4J6MT+kIDoxMCkQTRRBKLQUNBtpEpeTaKHCZsBpn/Ppa4rAZcVgNnlruIxBK09YVo9ARo9AbZdagfGKgW5rYacFh0ZJt0pxQwSioK4ViCQCRB8JNggi8SJxxLEEskSSpg0KoxGzRU59uYlm8jx2ZEr5WAgsg8GRtc8Pl8PPfcc9x9990sXrwYgHXr1nHuuedSX1/P0qVLj7rdxo0bmT9/PjfccAMAN9xwA9u2bWPjxo2sX79+cD2tVovb7T7qayiKwpNPPsnXvvY1zjvvPADuu+8+Fi5cyJYtW7jyyitT+VHFOFVkN2M362jpCVKZm9qSP0lFYX+nn+5gDIgyyWnGZpQO5UQm5SeFmDgUReH3H3bw+NaDaFQqvrWiirOr3JIBfpQlkgPz3v3hOP3hGL3BKKFYApVKhd2kY2ZRFpPzbDgtepmiMk4YdBrKc6yU5wzcaOwPDSTiPOj10+wN0dwTRK2CLKOOnKNMoYglkgSjCULRBIFonGAkPjAaIa4QSyZRqwYCFTajllKXmYIsI+4sAzaDDoNWLUEpkfEyNriwc+dOYrEYS5YsGVxWUFBAZWUl27dvP2ZwYceOHVx99dVDli1ZsoSnnnpqyLLm5maWLl2KTqejrq6Om2++mZKSEgBaWlro6uoaDGoAGI1G5s2bx/bt2yW4IE5KtllHfraRjz1BKnNT+9qNngBdvgjTC7PoCUQ56PFTV+xI7ZuMso7+MP2hGDqNGr1WjVajGnisUaPTDPwu0z+OTcpPCjEx9AZjPPTaft75uIfa4mz++QtVuG2GdDdrQoglkoOBBH8kTl8oRiSeJJFUMOnUuLOMTHFbKXWasZnSMydfjK4sk46aomxqirJJJhU6fWGauoODUygUJYBGrcJh1tPaGaIl5iWeVEgkFfQaFUa9htwsA7lZRgqyDDgsBsx6rYxKEGNWxgYXPB4PGo0Gh2PoBZPL5cLj8Rx3u5ycnCHLcnJy6OrqGvy9traWe+65h4qKCrq7u3n44Ye58sor2bJlCw6HY3Ddz7+Oy+Wis7PzhG1vbGwkFAoBEA6H2b179wm3ESdvLO1TpS9Cy6Eg1ngvZn1qvii8wThNvVGcJi35SoxQJMJfW0OYIt2nXR0gGonQ0NiQkvadjq5AnJa+KImkAkBC+eQJBVCBWgUqlQq9WoVeq8KgUaHVqNCqVejUA/9qNaBVq9BnSBBiNPdpIqmwqzOMXqui51AcX0f6P/9IGEt/+2PJ4f06bdq0dDdFnMDW/R6+88JOovEk1y8t56Lawow4341X4VgCXziOPzIw1cEfHpjvrgBmvYZcm4Fip4kShxm7WUYnTHRqtYr8bBP52abBKRStvQNTKJp7QqhUKircFvKzjORmGck26TDqNBKEEuPKqAcXNmzYwCOPPHLcdZ588sljPqcoyjGfO942n72Td/bZZw95vq6ujuXLl/PCCy+wZs2aweWne/evrKxs8LF02FJvLO3T0kicbk0bGjWU5w8/L0J/KEZ7Wx/FBSr+bm4x2SY95eEYPm0ril5Ded7plRxraGygvKx82O07HZ39YdrifiYVqfnbWUWYDBqisSTBWIJgJE4wGscXTuCPxAhEEvg/WRZLKIQTCkFFQQUoCVAnQZMcuEPgMOuxGbXY0pTReTT3aXtfGGusj2VTc5lZZB+V90yHsfS3P5bIfh07fv9hO/nZRm5eXkWJVCJKKUVRPkmaF8cXjtEfjhGMJojGk6jVKqwGDaU5ZiY5zBQ6TFgNOrm7LI7LoNNQ4bZS8Un1pl27gkyfXpDmVgkxskY9uLB69WouueSS465TWFjIjh07SCQS9PT04HR+mvW4u7ubefPmHXPbnJycI0Y2eL3eI0YhfJbFYmHy5Mk0NjYCDOZi6OrqoqDg05PAiV5HiM+zGLQU2U3sausf9mtF4gk+6vQRSya5sLaIbNPAvPoso45Kt5XtzT1McVtRq8dOZ8frj7C/y49Wo+LiukLsloHPZNBqsJ0gKVk8kSSeVAjFEoSicQKRBIFIHG8gSmtPiH0dPmAg23O2SYfTMjDv0WbUjqusyp8tPznZndrcHkKIzPIffzOD//uoC5dFpkGkQlJR8PgjdAei9AVjhONJYokkeq2KLIOO6nwbpQ4zudkGrAaZ5iCGR6Ysiolg1IMLTqdzSLDgWGbMmIFOp2Pr1q1cfPHFALS3t3PgwAFmz559zO1mzZpFfX0911133eCy+vr6424TiURoaGhg/vz5ABQXF+N2u6mvr6e2tnZwnXfeeYdbbrnlpD6nEIdNcln48FA/PYEoDsvpJdpLJBU+6vDTF4qxbGoeRY6hd6wm51nZ095PS0+YUtfYuJvVE4yyr8OHCri4tgCX9dQ6y1qNGq1mIPHR0RIYBqNx2vsG5j62dAfZ3+UHBVQqsJt1uCwGbJ8EG053Okkm+Gz5SZNehuQKIcSJJJIKXb4IbX0h+kOxgTnxFj1THSZKnWZcVgMmnUaS5wkhxCnK2JwLNpuNyy+/nHXr1uFyubDb7dxzzz1UV1ezaNGiwfVWr15NbW0t3/rWtwC49tprufrqq3n00UdZvnw5r7zyCtu2bePpp58e3Oa+++7j3HPPpaCggO7ubn7yk58QDAa59NJLgYHI4rXXXssjjzxCRUUFZWVlPPzww5jNZi666KLR3RFizMvLNuC26GnpDZx2cKHRG8DjjzCrxE5N4ZFTH9xWA8VOMw1dgTERXOgLxdjX7iOhKFxSV0hetinl72HWa4cMRwxHExzqC9HSE6KpO8iBLj/KJ8GGz45syDLpxlSwQcpPCiHEyYklknT0h2nvC+OLxDDpNMwtczCjKFsqLgkhRApkbHAB4Pbbb0er1bJ27VrC4TALFy5k3bp1aDSf3p1rbm4eMnVhzpw5rF+/nvvvv58HH3yQkpISNmzYQF1d3eA67e3t3HzzzfT29uJwOJg1axbPPPMMRUVFg+tcf/31RCIR/uM//oO+vj7q6up44oknsFqto/Phxbhh0Goodpl5u7GbZDJ5ytMW2vvCtPWGKLQbWVDpOuqdFJVKxdQ8Gwe7/HT5IhmdOdwfjrOnvZ9IIsFFMwuPGIUxUox6DeVuK+WfBBsisQTt/WFaeoI0eUM0eAIklYHkkXaTnmKHCZfVkNHDYKX8pBBCnFg0nuRQX4j2/jCBSBybUcuSyTlMLcjCrM/orrAQQowpGX1GNRgM3HHHHdxxxx3HXOe11147YtmqVatYtWrVMbfZsGHDCd9bpVJx4403cuONN55cY4U4jlKnmZ0tfXT5ouRlG096u75QjAaPH4NOzYrpeRi0xx72Xmg3UWg30dQdyNjgQjD6SWAhlmBlTR6Tcixpa4tBp2GSy8Ikl4XFkyEaS9DRH6GlN8hHnX7eb+1Dp1ZR7DBTYDdmZAdUyk8KIcSxhWMDI9Y6+sMEowmcFj0LK3OpdNuksoMQQoyAsTP2V4gxzG01kGMzcKg/dNLbhGOHEzgqrKrJJ8t0/DvTWo2aqXlZ+CNx/OHYcJuccuFYgj2H+vFH4iyblsuU06xsMVL0Og0lLjMLK3O4dmEZV8wrYUqejeaeIPUHvGxv6sHjjwyWy0y3eCJJhy9Mjk0/ItNKhBjvnnrqKZYtW8bMmTO57LLLeOedd467/t69e7n66qupra1l6dKlPPTQQ6dVwUqMvEAkzv5OP39t6uGgJ4DVoOWCmQVcMa+UmkK7BBaEEGKEZN6tOCHGIa1GzSSnmb/s9xBLJE84pz+RVPio048vHOfcqbknPXWg1GXGbTNw0BOgttiegpanRiSeYPehfvoiMc6tymVqfmYFFo7m8EiQs6py2HPIx3stvexo7kGv1VDqMJOfbUxrB9XjjxKIxplfkTumckQIkQleeukl7r77bv793/+dM844g6effprrr7+eF198kcLCwiPW9/v9fPWrX2Xu3Lk8++yzNDQ0cOutt2I2m/nqV7+ahk8gjqY/FKOtL4TXHyGaSFKQbeLMMidFDtO4qhIkhBCZSoILQoySYqcZm1HLod4Qpa7jTwdo8ATw+MPUFdupKTj5C3GjTkNVno2/fNRFNJ7MiBrcsUSSPe0+eoMxFk/JYUZx9pgawm/Sa5k9ycHsSQ6avAHeb+3jQKef/V1+cm0Gih1m7GYd6lH8TFJ+Uojh+dnPfsall17Kl770JQDuuOMO/vznP7Np06bBBNGf9Zvf/IZQKMR9992H0WikqqqKAwcO8LOf/Yw1a9aMqXPaeKMoCj3BKG29IboDURKKQoljIKiQl23K6Lw5Qggx3khwQYhR4rLocVuNdPjCxw0utPeFOdQXosRhPmYCx+OpcFvZ2dpHo9dPVZqnHsQ/CSx4/REWVDiZXTK2cwOUuiyUuiz4wzF2tfXzXksff23qwaRTU+o0k5tlPG5ejFSR8pNCnL5oNMqHH354xIiDxYsXs3379qNus2PHDubOnYvR+GnOnCVLlvDAAw/Q0tJCSUnJMd/vmmuuGXz85S9/mbvvvvu0264APfp8dErktF9jvFCAuNpA7YJzqH/6fwEwx/qxxTzokxGeS2/zxrThHqfi6GS/pp7s09T77D79+c9/fsrbS3BBiFGiUqkoz7FwoMtPOBrHeJQEgYcTOJr0GpafIIHjsWSbdJTnWHivuY/J7lOvTpEqiaTC3g4fXb4wZ5Q5mFt26oGSTGU16jizwsXcMgeN3iDvt/Sxt8PPvk4/BVlGCu2mEZ2LLeUnhTh9PT09JBIJcnJyhix3uVzU19cfdRuPx0NeXt6QZYe393g8xw0uLF++HLfbDYDT6eTLX/7yabc9qSjs7gHbBK+a6I9Bdxj8cUBv4vyFdZRngVXHmA5gZ4rhHqfi6GS/pp7s09Qb7j6V4IIQo6jQYcJp1dHcG2JK7tDh7OFYgo86fMSSSS6qKRhWze2qPBv7Ony09oYpcY5OqcfPSioKH3X46PSFqS22s6DcNS6HpqrVaircVircVvpCUXa29rGztY+2vjDB/jCFxYmU52WQ8pNCpMbRLkKPd2H6+ecOBxBPdDG7evXqwce7d+9mwYIFp9LMIZJJBetHXbgsmVkRaKT1hWK09ARJBqKYgGn5NqyRTs6cNTPdTRtXhnuciqOT/Zp6sk9Tb7j7NP0TsoWYQLJNOvJsRrz+oUNaBxM4RuKcU51LoX14AQG3zUCx3UxLT3BYr3M6korC/k4/7f1hqvNsLJ6cMyESaWWb9Cye7Ob/La7ggpn5qIFtDV58Ka7cIeUnhRgeh8OBRqOhq6tryHKv13vEaIbDcnJy8Hg8R6wPAyMexMjyhWPsPtTPBy29ePwRKtwWvnxmCcum5WEzyH0yIYTIFOO/xy9EhinPsRBLKPjD8cFlnyZwzGZaCiopqFQqqgtsJJPKEYGMkaQoCge7ArT1hih3mzlnam5GJJUcTVqtmur8LFZMtpFrM/BWoxdPiv4PpPykEMOn1+upqak5YgpEfX09s2fPPuo2s2bN4p133iESiQxZPzc3l+Li4hFt70QWiMTZ2+7jvebegXxFTjNXnlnKeTX5OCfo6A0hhMhkE6vXL0QGKLCbcFr1NPcEADjUF+JQX4jS00zgeCxFdhP5diMNXn9KXu9kNHqDtPaGKHGYWD4tf1SSG2Yqi17DJbMKmey28demnpSMIjlcfrKuxC7lJ4UYhjVr1vD888/zq1/9igMHDvCDH/yAzs5OrrzySgB+9KMfDZnOcPHFF2Mymbj11lvZt28fL7/8Mo899phUihghoejANMEdzb209QUptJv44hnFXFBbQI7VIPtcCCEylIwlE2KUmfVaiu1mPmjtpS8Uo9ETwKzXsLwmtRfjWs3AHfRXdnfgj8SwGkY2A1hTd5CWniB5WQZWzshPea6Bscio03L+zAKsRi3bm3oJRRNMzrWeVsdYyk8KkToXXHABPT09PPzww3R2dlJVVcVjjz1GUVERAF1dXTQ3Nw+ub7PZeOKJJ/iP//gPLr/8crKzs/nqV7/KmjVr0vURxqVwLEFrb4jO/jDheIIiu4mFFS7ys03jJiGwEEKMZxJcECINJrnM7GzrY2drL0kFLqrJwzoC80bLcyzk2gw0egLMKLKn/PUPa+sN0eQNYDfruGBmAeajVMKYqDRqFWdXubEatWzd7yEUS1BTmH3KCS6l/KQQqXXVVVdx1VVXHfW5e++994hl1dXVPPXUUyPdrAkpGk/S2huivS9EMJagINvIqop8ihzmcZkMWAghxiu5AhAiDXKzDLitBho8fi6YWTDsBI7HYtRpmJJrY+t+D7FEckSG0rf3hWnw+LEYNFxYW4BFkmsdQaVSMXeSE5tBy6u7O3n3425mlzpO6f9Dyk8KIcabWCJJW2+I9r4wgWicXJuBL0zPZZLTMiESAQshxHgjVwFCpIFBq2F+hZPqfBtTU5DA8Xgq3VY+bOujwROgKi+1w+k7fWEOdPnRazVcUldEtklKIx5PdX4WFoOW3+1s582DXuZOcp7UKAQpPymEGE+C0Thdvgjt/WECkTgui56zqvIpd1sln4wQQoxhElwQIk2KHWaKHSP/PtlmHZNcFj5o7WWy24JanZqOm9cfYX+nH61GxSV1BdgtctF7MoodZi6fU8yLHxzijYMe5kxyYD9BUEbKTwohxrp4Iok3EMXjj9ATiBJJJHGadSyansfkXNuEqywkhBDjkQQXxEm59dZb6enp4dFHH013U8RpqM638VGHj7a+MMWO4U/B6AlG2dfhQwVcXFtAjs04/EZOIA6LnstmF/G/Ow/xTmM3tUV2crOOvg+l/KQQYqxSFIX+8MAoBY8/TDCWQK9RU5pjZlaxHbfNKEEFIYQYRyS4IE7Kt7/9bRRFGdZrbN68me9///ts3749Ra0SJyvXZqDYYebj7sCwgwt9oRj72n0kFIVL6grlgvc0mQ1aLplVxGu7O3mvpZcpuTbKcixHrHe4/OT8ilwZLiyEGBPCsQQef4QuX4T+cJykouC26llQ6aIixyq5eYQQYpySs7s4KTbbsefqR6NR9HoZEp/JVCoV1flWDnb58fojuKyG03odXzjGnvZ+ookkF80soCgFoyAmMp1GzYrpeVgMGt75uIdQLEF1vg31J1MfpPykEGKsSCQVeoJRunwRugMDU7msRi0zi7KYUZSNw6yXcpJCCDHOSXBBnJTPTou45pprqKiowGw28/zzz1NUVMRzzz3Hz372MzZv3kxzczM2m42zzjqLf/u3fyMrK4tt27Zx2223AQPlvAC++c1vcuONNxKNRnnggQf47W9/S39/P5WVlfzzP/8zS5cuTedHHneKHGYK7EY+7g6cVnAhEImzt91HJJ5gVU0BpUe5yy5OnVqtYskUNzajjv/b10UoFqeu2IFGrZLyk0KIlEsqComkglqlSkmZR384Tpc/TJdvYJSVVq2i0G6krshOodOEQSvnLiGEmCgkuJABnnu3hWfeaR7V9/zS3BIuP6P4tLf/zW9+wxVXXDGk5rdKpeL222+npKSEtrY2vv/97/P973+f//zP/2T27NncfvvtbNiwgT/84Q8AmM0Dd71vu+02mpub+dGPfkR+fj5/+tOfuOGGG3j22WeZOnXq8D6oGKTTqKnOz+KVXe0EI3HMpzAsNRiNs7u9H380zsppuVTmWkewpRNTXYkdm1HLyx928FaDlzMmOaX8pBAipboDURq9AXzhOABqFWjVKrQa9ZB/NYd/VCo0moF/1Yd//+S5QCSOxx+hLxQjlkjisuqZVeqiKs+G1aCV5LNCCDEBSXBBnJbi4mJuvfXWIcv+/u//fsjz//qv/8o//MM/cN9996HX67HZbKhUKtxu9+B6TU1NvPjii7z22msUFhYCcPXVV1NfX88vfvEL7rzzztH4OBNGmcuC22bkgMfPzCL7SW0TjiXY0+7DF46zbFouVSNcOnMiq3Bb+ds5Gv73g3bqD3jQqlVSflIIMWzhWIKPvUE6fWFQwdR8K2q1mmgsQSSRIBaHSCJBNJ4gkEgSTyokFYVkcmCkQ1KBw6GCz2Zfsho0VOVZmVGcTY7FgFbywgghxIQmwYUMcPkZxcMaRZAOM2bMOGLZG2+8wWOPPcaBAwfw+Xwkk0lisRhdXV3k5eUd9XU+/PBDFEXhwgsvHLI8Go2yYMGCEWn7RGbSa5icZ6V+v5dYInnCBIHReJLdh/rpDUY5uzqHmoIsuRs1wvKzTFz6SSWJ1p6wlJ8UQpy2pKJwqDdMS0+QQDROeY6FpVNycFiOPzUu8UlwIZFUUBSIJZJEE8mBf+MDj/VqNblZRpmyJYQQYlBGBxei0Sj33XcfW7ZsIRKJsGDBAu68807y8/OPu93vf/97HnjgAZqamigtLWXt2rWsWLFi8Plly5bR2tp6xHZnn302jz32GDCQY+D5558f8nxdXR3PPPNMCj7Z2GcyDa0Q0Nrayte//nW+9KUv8U//9E/Y7XZ27drFzTffTCwWO+brKIqCSqXi2WefRasdejgajVLecCRMdlvZ1dpPkzd43OkNsUSSPe399ASjLJqcQ22RXOSOFrtZz2WziznUFyZfqnEIIU5DXyhGozdAdyBKllHLhbUFlOdYTyrPgkatQoMK3SdxAxMSQBBCCHFiGR1cuOuuu3j11VdZv349drude++9l69//ets3rwZjeboX3Tbt29n7dq13HjjjaxcuZKXX36Zm266iU2bNlFXVwfAs88+SyKRGNymq6uLyy67jPPPP3/Iay1atIh169YN/q7T6UbgU44PO3fuJBaLcdtttw3+37z++utD1tHpdEP2O8C0adNQFIWuri4ZqTBK7GY9ZTkWdrb2UuG2HDVgEE8k2dPuw+OPML/cyRmlDsnyPcoMOs1RS1MKIcTxRONJmrqDdPSHSCgKZ0yyM6fUKSMMhBBCjLiMnRzn8/l47rnnuOWWW1i8eDE1NTWsW7eOvXv3Ul9ff8ztNm7cyPz587nhhhuorKzkhhtu4Mwzz2Tjxo2D6zidTtxu9+DPn/70J6xWK6tWrRryWnq9fsh6drt9pD7umDdp0iSSySQbN26kubmZLVu2DNnnAEVFRUQiEbZu3Up3dzehUIjy8nIuvvhibrvtNn73u9/R3NzMBx98wOOPP87LL7+cpk8z/lXl2dBpNbT1ho54LpFU2Nvho8sX5owyB/PKXRJYEEKIDKcoCu39YXY099LUHcBtM3DFvFIWT3ZLYEEIIcSoyNiRC4fvhC9ZsmRwWUFBAZWVlWzfvv2YZQp37NjB1VdfPWTZkiVLhlQ1+CxFUXj22We55JJLjhjq/+6777Jw4UKysrKYN28ea9euxeVynbDtjY2NhEIDF23hcJjdu3efcJtM19vbi9/vZ/fu3QQCAXp6eo74XNdddx0//elP2bBhA9XV1Vx11VX88Ic/ZP/+/fh8PkwmE6tWreKmm27C5/NxxRVX8OUvf5nVq1djNpu5++678Xq9WK1WpkyZwhVXXHHUfTde9mk6JRUFVSDAOx0xovlmopEIDY0NJBWFxp4o3kCcyTkG7JE4+/Z60t3cMUmO09STfToyDu/XadOmpbsp4jT5w3EavQG8/ihGnZoV0/OoyrNJgkUhhBCjKmODCx6PB41Gg8PhGLLc5XLh8Rz7Ysfj8ZCTkzNkWU5ODl1dXUddf+vWrbS0tPDFL35xyPKlS5eyYsUKiouLaW1t5f7772f16tVs3rwZvf74mdvLysoGH4+XDtsjjzwy+Hjz5s1HXWfatGn867/+65Bl119//ZDfH3jggaNuO3PmzJNuy3jZp+lmdvv5353tZLuz6OtqY9KkMj7q8KOJhlhaaeWc6jz0WumYni45TlNP9unIkP06dsUTSZp7QhzqCxFLJKkpyubMcieWUyg1LIQQQqTKqH/7bNiwYciF6tE8+eSTx3xOUZRjPne8bY6ViO6ZZ55h5syZR3SsPlu9oLq6mpqaGpYtW8brr7/OypUrT7kNQmSaYqeZgmwjjd4gdkXhYFeAQ30hKt0Wzq7OlcCCEEJksC5fhI+7A/SHYuRlGTiryk1BtkkS7wohhEibUQ8urF69mksuueS46xQWFrJjxw4SiQQ9PT04nc7B57q7u5k3b94xt83JyTliZIPX6z1iNMPh5a+99hrf/e53T9juvLw88vLyaGxsPOG6QowFOo2aqjwbr+3ppC8YRR0NUeoys2xaHgatzM8VQohMFIol2HWoH68/gloFZ1e5mV6YLQFhIYQQaTfqwQWn0zkkWHAsM2bMQKfTsXXrVi6++GIA2tvbOXDgALNnzz7mdrNmzaK+vp7rrrtucFl9ff1Rt9m8eTM6nY4LLrjghO3p7u6ms7OT3NzcE64rxFhR7raQ06Jnx6E4ZxYaWDk9H6NOAgtCCJGJWntD7G33oVbBlDwbiypdZJuOP1VTCCGEGC0ZOynPZrNx+eWXs27dOlwuF3a7nXvuuYfq6moWLVo0uN7q1aupra3lW9/6FgDXXnstV199NY8++ijLly/nlVdeYdu2bTz99NNDXv9wIscLL7wQq9U65LlAIMBDDz3EypUrcbvdtLa2sn79epxOJ8uXLx/5Dy/EKDHrtdSV2On3drJqRoFkFBdCiAymKAqTXGbOKHVQ7DBLJR8hhBAZJWODCwC33347Wq2WtWvXEg6HWbhwIevWrUOj+fQCqLm5mYKCgsHf58yZw/r167n//vt58MEHKSkpYcOGDdTV1Q157W3bttHY2Mh//ud/HvG+Go2Gffv28cILL+Dz+XC73cyfP5/777//iECEEGPd9MJsVH1WSQAmhBAZrtRlodRlSXczhBBCiKPK6KsJg8HAHXfcwR133HHMdV577bUjlq1atYpVq1Yd97UXLFjA3r17j/qc0Wjk8ccfP7XGCiGEEEIIIYQQE5Rk/xFCCCGEEEIIIcSwSHBBCCGEEEIIIYQQwyLBBSGEEEIIIYQQQgyLBBeEEEIIIYQQQggxLBJcEEIIIYQQQgghxLBIcEEIIYQQQgghhBDDIsEFIYQQQgghhBBCDIsEF4QQQgghhBBCCDEsKkVRlHQ3QgghhBBCCCGEEGOXjFwQQgghhBBCCCHEsEhwQQghhBBCCCGEEMMiwQUhhBBCCCGEEEIMiwQXhBBCCCGEEEIIMSwSXBBCCCGEEEIIIcSwSHBBCCGEEEIIIYQQwyLBBSGEEEIIIYQQQgyLBBdGyFNPPcWyZcuYOXMml112Ge+88066mzRmPfjgg1RXVw/5Wbx4cbqbNea8/fbbfOMb32Dp0qVUV1ezefPmIc8risKDDz7IkiVLqK2t5ZprruGjjz5KU2vHhhPt01tvvfWIY/dLX/pSmlqb+R599FEuv/xy5syZw4IFC/jGN77Bvn37hqwjx+mpO5n9KsfqyJI+QWpJv2D4pE+QetInSD3pF6TeSPcJJLgwAl566SXuvvtuvvGNb/DCCy8we/Zsrr/+etra2tLdtDGrvLycv/zlL4M/v/3tb9PdpDEnGAxSVVXFt7/9bYxG4xHP//SnP+WJJ57gjjvu4Nlnn8XpdLJmzRr8fn8aWjs2nGifAixatGjIsfvYY4+NcivHjrfeeouvfOUr/OIXv2Djxo1oNBrWrFlDb2/v4DpynJ66k9mvIMfqSJE+wciQfsHwSJ8g9aRPkHrSL0i9Ee8TKCLl/u7v/k759re/PWTZihUrlB/+8IdpatHY9l//9V/KhRdemO5mjCuzZs1SnnvuucHfk8mksnjxYuUnP/nJ4LJQKKTMmjVL2bRpUzqaOOZ8fp8qiqL827/9m/K1r30tTS0a+/x+vzJ16lTl1VdfVRRFjtNU+fx+VRQ5VkeS9AlST/oFqSV9gtSTPsHIkH5B6qW6TyAjF1IsGo3y4YcfHjE8b/HixWzfvj1NrRr7mpubWbp0KcuWLWPt2rU0Nzenu0njSktLC11dXUOOW6PRyLx58+S4HaZ3332XhQsXct555/Gd73wHr9eb7iaNGYFAgGQySVZWFiDHaap8fr8eJsdq6kmfYORIv2DkyLl25Mh5dnikX5B6qe4TaEeikRNZT08PiUSCnJycIctdLhf19fVpatXYVltbyz333ENFRQXd3d08/PDDXHnllWzZsgWHw5Hu5o0LXV1dAEc9bjs7O9PRpHFh6dKlrFixguLiYlpbW7n//vtZvXo1mzdvRq/Xp7t5Ge+uu+5i2rRpzJ49G5DjNFU+v19BjtWRIn2CkSH9gpEl59qRIefZ4ZN+Qeqluk8gwYURolKpTmqZOLGzzz57yO91dXUsX76cF154gTVr1qSpVeOTHKOpdeGFFw4+rq6upqamhmXLlvH666+zcuXKNLYs891zzz28++67bNq0CY1GM+Q5OU5P37H2qxyrI0v6BKkl/YLRIcdoasl5dnikX5B6I9EnkGkRKeZwONBoNIORtMO8Xu8RUTVxeiwWC5MnT6axsTHdTRk33G43gBy3IywvL4+8vDw5dk/g7rvv5sUXX2Tjxo2UlJQMLpfjdHiOtV+PRo7V1JA+weiQfkFqybl2dMh59uRJvyD1RqpPIMGFFNPr9dTU1Bwx3LG+vn7IcBNx+iKRCA0NDYMnFDF8xcXFuN3uIcdtJBLhnXfekeM2hbq7u+ns7CQ3NzfdTclYP/jBD9iyZQsbN26ksrJyyHNynJ6+4+3Xo5FjNTWkTzA6pF+QWnKuHR1ynj050i9IvZHsE8i0iBGwZs0abrnlFmpra5kzZw6bNm2is7OTK6+8Mt1NG5Puu+8+zj33XAoKCuju7uYnP/kJwWCQSy+9NN1NG1MCgQBNTU0AJJNJ2tra2L17N9nZ2RQWFnLttdfyyCOPUFFRQVlZGQ8//DBms5mLLroozS3PXMfbp9nZ2Tz00EOsXLkSt9tNa2sr69evx+l0snz58jS3PDN973vf49e//jU//vGPycrKGrwTYTabsVgsqFQqOU5Pw4n2ayAQkGN1BEmfIPWkXzB80idIPekTpJ70C1JvpPsEKkVRlJH+EBPRU089xeOPP05nZydVVVXcdtttzJs3L93NGpPWrl3L22+/TW9vLw6Hg1mzZnHTTTcxefLkdDdtTNm2bRvXXnvtEcsvvfRS7r33XhRF4aGHHuKXv/wlfX191NXV8d3vfpeqqqo0tHZsON4+vfPOO/nHf/xHdu3ahc/nw+12M3/+fG666SYKCgrS0NrMV11dfdTl3/zmN7nxxhsB5Dg9DSfar+FwWI7VESZ9gtSSfsHwSZ8g9aRPkHrSL0i9ke4TSHBBCCGEEEIIIYQQwyI5F4QQQgghhBBCCDEsElwQQgghhBBCCCHEsEhwQQghhBBCCCGEEMMiwQUhhBBCCCGEEEIMiwQXhBBCCCGEEEIIMSwSXBBCCCGEEEIIIcSwSHBBCCGEEEIIIYQQwyLBBSFExnvxxReZMWMGsVgs3U0RQgghRJpJv0CIzCTBBSFExtuzZw9VVVXodLp0N0UIIYQQaSb9AiEykwQXhBAZb/fu3UybNi3dzRBCCCFEBpB+gRCZSYILQoiMt2fPHiorK1m/fj1Lly7ljDPO4Dvf+Q7JZDLdTRNCCCHEKJN+gRCZSYILQoiM1t3dTVdXF//zP/9DOBzm3nvv5atf/Sq/+tWvePXVV9PdPCGEEEKMIukXCJG5tOlugBBCHM/u3bsBuOaaa1izZg0AixcvZtOmTTQ2NqaxZUIIIYQYbdIvECJzycgFIURG27NnD1lZWVx99dWDyxRFwefz4XA40tgyIYQQQow26RcIkbkkuCCEyGh79uxh3rx5QzJCNzU1EQ6HmTJlShpbJoQQQojRJv0CITKXBBeEEBltz549VFdXH7FMrVZLJ0IIIYSYYKRfIETmkuCCECJjRaNRDh48yNSpU4cs37t3LyUlJZjN5jS1TAghhBCjTfoFQmQ2CS4IITLW/v37icfjR9yh2Lt37xHLhBBCCDG+Sb9AiMwmwQUhRMbas2cPJpOJ0tLSIculEyGEEEJMPNIvECKzqRRFUdLdCCGEEEIIIYQQQoxdMnJBCCGEEEIIIYQQwyLBBSGEEEIIIYQQQgyLBBeEEEIIIYQQQggxLBJcEEIIIYQQQgghxLBIcEEIIYQQQgghhBDDIsEFIYQQQgghhBBCDIsEF4QQQgghhBBCCDEsElwQQgghhBBCCCHEsEhwQQghhBBCCCGEEMPy/wPgR4ACajeHpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1224x1440 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_impulse_responses(result, df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iii.\n",
    "\n",
    "Our estimates show a negative effect of a decrease in government spendings on GDP. The effect manifests fully after 10 months and amounts to -0,2 to -0,25 percentage points. This supports the argument that Brüning’s austerity policy was detrimental for the German economy at the time. However, the estimate for the loss of GDP after a budget spending shock is too small to advocate for this policy to be a main reason for the output slump. Brüning’s policy contributed to the negative effect but could not have avoided it by increasing government spending."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix\n",
    "\n",
    "### Stata Code for Question 1\n",
    "\n",
    "```Stata\n",
    "* Assignment 2 *********\n",
    "\n",
    "* Exercise 1: The effects of monetary policy\n",
    "clear\n",
    "set more off\n",
    "\n",
    "* change graph schemes\n",
    "set scheme s1color\n",
    "graph set window fontface \"Palatino Linotype\"\n",
    "graph drop _all\n",
    "\n",
    "* Set working directory\n",
    "* local path \"/Users/paulgrass/OneDrive/Documents/BGSE Semester 2/Macroeconomics II/Stata\"\n",
    "\n",
    "local path \"/Users/Philipp/OneDrive/Master Economic Research/Macro II/PS2 Schularick/Assignment2_2021\"\n",
    "\n",
    "\n",
    "cd \"`path'\"\n",
    "\n",
    "use \"JSTdataset.dta\"\n",
    "\n",
    "*******\n",
    "* sort out panel structure, drop 2014 - 2015 observations\n",
    "drop if mi(country)\n",
    "xtset, clear\n",
    "xtset ifs year\n",
    "\n",
    "* gen variables and transformations of them used *****************\n",
    "* real inv per capita , real house / stock prices :\n",
    "gen riypc = iy * rgdppc\n",
    "gen hpreal = hpnom / cpi\n",
    "gen rstocks = stocks / cpi\n",
    "\n",
    "* first diff of log:\n",
    "\n",
    "foreach var of varlist rgdppc rconpc riypc cpi hpreal rstocks {\n",
    "\tgen l_`var' = log(`var')\n",
    "\tgen dl_`var' = d.l_`var'\n",
    "}\n",
    "\n",
    "* first diff of credit to GDP , short / long term rate\n",
    "gen credittogdp = tloans / gdp\n",
    "gen d_credit = d.credittogdp\n",
    "gen d_stir = d.stir\n",
    "gen d_ltrate = d.ltrate\n",
    "\n",
    "* world GDP growth\n",
    "gen rgdp = rgdppc *pop\n",
    "sort year\n",
    "by year: egen rgdp_wld = total(rgdp)\n",
    "xtset ifs year\n",
    "gen gr_rgdp_wld = d.rgdp_wld / l.rgdp_wld\n",
    "gen d_gr_rgdp_wld = d.gr_rgdp_wld\n",
    "\n",
    "**********************************************\n",
    "\n",
    "rename openquinn kaopen\n",
    "replace kaopen = kaopen / 100\n",
    "\n",
    "* create instrument\n",
    "\n",
    "gen z = dibaseF * peg * l.peg * kaopen\n",
    "\n",
    "* remove world wars\n",
    "drop if year > 1938 & year < 1948\n",
    "drop if year > 1913 & year < 1920\n",
    "\n",
    "\n",
    "* (i) *******************************************************\n",
    "\n",
    "* scatter with correlation\n",
    "* calculate here\n",
    "\n",
    "corr d_stir z\n",
    "local corr : di %5.3g r(rho)\n",
    "\n",
    "* use as subtitle in scatter\n",
    "twoway scatter d_stir z || lfit d_stir z , subtitle(correlation `corr')\n",
    "xtreg d_stir z, fe\n",
    "graph export \"i.pdf\", as(pdf) replace\n",
    "graph export \"i.png\", as(png) replace\n",
    "\n",
    "\n",
    "* (ii) ******************************************************\n",
    "\n",
    "* prelims\n",
    "\n",
    "local L = 2\n",
    "local H = 4\n",
    "\n",
    "forvalues h = 0/`H' {\n",
    "\tgen fgdp`h' = f`h'.rgdppc / l.rgdppc - 1\n",
    "\tgen fcpi`h' = f`h'.cpi / l.cpi - 1\n",
    "\tgen fcredit`h' = f`h'.credittogdp / l.credittogdp - 1\n",
    "\tgen fstocks`h' = f`h'.rstocks / l.rstocks - 1\n",
    "}\n",
    "\n",
    "matrix irf_gdp = J(`H'+1,1,0)\n",
    "matrix irf_gdp_lb = irf_gdp\n",
    "matrix irf_gdp_2lb = irf_gdp\n",
    "matrix irf_gdp_ub = irf_gdp\n",
    "matrix irf_gdp_2ub = irf_gdp\n",
    "\n",
    "matrix irf_cpi = irf_gdp\n",
    "matrix irf_cpi_lb = irf_gdp\n",
    "matrix irf_cpi_2lb = irf_gdp\n",
    "matrix irf_cpi_ub = irf_gdp\n",
    "matrix irf_cpi_2ub = irf_gdp\n",
    "\n",
    "matrix irf_credit = J(`H'+1,1,0)\n",
    "matrix irf_credit_lb = irf_gdp\n",
    "matrix irf_credit_2lb = irf_gdp\n",
    "matrix irf_credit_ub = irf_gdp\n",
    "matrix irf_credit_2ub = irf_gdp\n",
    "\n",
    "\n",
    "matrix irf_stocks = irf_gdp\n",
    "matrix irf_stocks_lb = irf_gdp\n",
    "matrix irf_stocks_2lb = irf_gdp\n",
    "matrix irf_stocks_ub = irf_gdp\n",
    "matrix irf_stocks_2ub = irf_gdp\n",
    "\n",
    "\n",
    "\n",
    "forvalues h = 0 / `H' {\n",
    "\t\n",
    "\t* GDP\n",
    "\n",
    "\txtivreg fgdp`h' l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_ltrate dl_hpreal dl_rstocks d_credit d_gr_ ///\n",
    "\t\trgdp_wld ) l(1/`L').d_stir (d_stir = z), fe vce(r)\n",
    "\t\n",
    "\tmatrix irf_gdp[`h'+1,1] = _b[d_stir]\n",
    "\tmatrix irf_gdp_lb[`h'+1,1] = _b[d_stir] - _se[d_stir]\n",
    "\tmatrix irf_gdp_ub[`h'+1,1] = _b[d_stir] + _se[d_stir]\n",
    "\tmatrix irf_gdp_2lb[`h'+1,1] = _b[d_stir] - 1.64 * _se[d_stir]\n",
    "\tmatrix irf_gdp_2ub[`h'+1,1] = _b[d_stir] + 1.64 * _se[d_stir]\n",
    "\n",
    "\n",
    "\t* prices\n",
    "\n",
    "\txtivreg fcpi`h' l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_ltrate dl_hpreal dl_rstocks d_credit d_gr_ ///\n",
    "\t\trgdp_wld ) l(1/`L').d_stir (d_stir = z), fe vce(r)\n",
    "\t\t\n",
    "\tmatrix irf_cpi[`h'+1,1] = _b[d_stir]\n",
    "\tmatrix irf_cpi_lb[`h'+1,1] = _b[d_stir] - _se[d_stir]\n",
    "\tmatrix irf_cpi_ub[`h'+1,1] = _b[d_stir] + _se[d_stir]\n",
    "\tmatrix irf_cpi_2lb[`h'+1,1] = _b[d_stir] - 1.64 * _se[d_stir]\n",
    "\tmatrix irf_cpi_2ub[`h'+1,1] = _b[d_stir] + 1.64 * _se[d_stir]\n",
    "\t\n",
    "\t* credit\n",
    "\txtivreg fcredit`h' l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_ltrate dl_hpreal dl_rstocks d_credit d_gr_rgdp_wld) ///\n",
    "\t\tl(1/`L').d_stir (d_stir = z), fe vce(r)\n",
    "\tmatrix irf_credit[`h'+1,1] = _b[d_stir]\n",
    "\tmatrix irf_credit_lb[`h'+1,1] = _b[d_stir] - _se[d_stir]\n",
    "\tmatrix irf_credit_ub[`h'+1,1] = _b[d_stir] + _se[d_stir]\n",
    "\tmatrix irf_credit_2lb[`h'+1,1] = _b[d_stir] - 1.64 * _se[d_stir]\n",
    "\tmatrix irf_credit_2ub[`h'+1,1] = _b[d_stir] + 1.64 * _se[d_stir]\n",
    "\t\n",
    "\t* stock prices\n",
    "\txtivreg fstocks`h' l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_ltrate dl_hpreal dl_rstocks d_credit d_gr_rgdp_wld) ///\n",
    "\t\tl(1/`L').d_stir (d_stir = z), fe vce(r)\n",
    "\tmatrix irf_stocks[`h'+1,1] = _b[d_stir]\n",
    "\tmatrix irf_stocks_lb[`h'+1,1] = _b[d_stir] - _se[d_stir]\n",
    "\tmatrix irf_stocks_ub[`h'+1,1] = _b[d_stir] + _se[d_stir]\n",
    "\tmatrix irf_stocks_2lb[`h'+1,1] = _b[d_stir] - 1.64 * _se[d_stir]\n",
    "\tmatrix irf_stocks_2ub[`h'+1,1] = _b[d_stir] + 1.64 * _se[d_stir]\n",
    "}\n",
    "\n",
    "svmat irf_gdp, names(irf_gdp)\n",
    "svmat irf_gdp_lb, names(irf_gdp_lb)\n",
    "svmat irf_gdp_ub, names(irf_gdp_ub)\n",
    "svmat irf_gdp_2lb, names(irf_gdp_2lb)\n",
    "svmat irf_gdp_2ub, names(irf_gdp_2ub)\n",
    "svmat irf_cpi, names(irf_cpi)\n",
    "svmat irf_cpi_lb, names(irf_cpi_lb)\n",
    "svmat irf_cpi_ub, names(irf_cpi_ub)\n",
    "svmat irf_cpi_2lb, names(irf_cpi_2lb)\n",
    "svmat irf_cpi_2ub, names(irf_cpi_2ub)\n",
    "svmat irf_credit, names(irf_credit)\n",
    "svmat irf_credit_lb, names(irf_credit_lb)\n",
    "svmat irf_credit_ub, names(irf_credit_ub)\n",
    "svmat irf_credit_2lb, names(irf_credit_2lb)\n",
    "svmat irf_credit_2ub, names(irf_credit_2ub)\n",
    "svmat irf_stocks , names(irf_stocks)\n",
    "svmat irf_stocks_lb , names(irf_stocks_lb)\n",
    "svmat irf_stocks_ub , names(irf_stocks_ub)\n",
    "svmat irf_stocks_2lb , names(irf_stocks_2lb)\n",
    "svmat irf_stocks_2ub , names(irf_stocks_2ub)\n",
    "\n",
    "* plots\n",
    "\n",
    "gen horizon = _n-1 if _n <= `H'+1\n",
    "gen zeros = 0 if horizon !=.\n",
    "\n",
    "foreach var of varlist irf_gdp1 - irf_stocks_2ub1 {\n",
    "\treplace `var' = 100 * `var'\n",
    "}\n",
    "\n",
    "* IRF of gdp, cpi, credit, stocks to local interest rate change\n",
    "\t\n",
    "twoway (rarea irf_gdp_2lb1 irf_gdp_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_gdp_lb1 irf_gdp_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_gdp1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real GDP per capita\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(ii_gdp, replace) nodraw\t\n",
    "\t\n",
    "twoway (rarea irf_cpi_2lb1 irf_cpi_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_cpi_lb1 irf_cpi_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_cpi1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"CPI\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(ii_cpi, replace) nodraw\n",
    "\t\n",
    "twoway (rarea irf_credit_2lb1 irf_credit_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_credit_lb1 irf_credit_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_credit1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"Credit to GDP\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(ii_credit, replace) nodraw\n",
    "\t\n",
    "twoway (rarea irf_stocks_2lb1 irf_stocks_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_stocks_lb1 irf_stocks_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_stocks1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real stock prices\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(ii_stocks, replace) nodraw\n",
    "\t\n",
    "graph combine ii_gdp ii_cpi ii_credit ii_stocks, ///\n",
    "\tname(combined_ii, replace)\n",
    "\t\n",
    "graph export \"ii.pdf\", as(pdf) replace\n",
    "graph export \"ii.png\", as(png) replace\n",
    "\n",
    "* (iii) ****************************************************************************\n",
    "* use OLS, IV doesn't make sense since there is no base country for US\n",
    "\n",
    "* generate US interest rate for all observations\n",
    "\n",
    "local H=4\n",
    "\n",
    "gen usa_i = stir if country == \"USA\"\n",
    "bysort year(usa_i) : replace usa_i = usa_i[1]\n",
    "xtset ifs year\n",
    "gen d_usa_i = d.usa_i\n",
    "\n",
    "matrix irf_gdpus = J(`H'+1,1,0)\n",
    "matrix irf_gdpus_lb = irf_gdpus\n",
    "matrix irf_gdpus_ub = irf_gdpus\n",
    "matrix irf_gdpus_2lb = irf_gdpus\n",
    "matrix irf_gdpus_2ub = irf_gdpus\n",
    "matrix irf_stocksus = irf_gdpus\n",
    "matrix irf_stocksus_lb = irf_gdpus\n",
    "matrix irf_stocksus_ub = irf_gdpus\n",
    "matrix irf_stocksus_2lb = irf_gdpus\n",
    "matrix irf_stocksus_2ub = irf_gdpus\n",
    "\n",
    "forvalues h= 0/`H'{\n",
    "\t\n",
    "\t* GDP\n",
    "\txtreg fgdp`h' d_usa_i l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_ltrate dl_hpreal dl_rstocks d_credit ///\n",
    "\t\td_gr_rgdp_wld) l(1/`L').d_stir, fe\n",
    "\n",
    "\tmatrix irf_gdpus[`h'+1,1] = _b[d_usa_i]\n",
    "\tmatrix irf_gdpus_lb[`h'+1,1] = _b[d_usa_i] - _se[d_usa_i]\n",
    "\tmatrix irf_gdpus_ub[`h'+1,1] = _b[d_usa_i] + _se[d_usa_i]\n",
    "\tmatrix irf_gdpus_2lb[`h'+1,1] = _b[d_usa_i] - 1.64 * _se[d_usa_i]\n",
    "\tmatrix irf_gdpus_2ub[`h'+1,1] = _b[d_usa_i] + 1.64 * _se[d_usa_i]\n",
    "\t\n",
    "\t* stock prices\n",
    "\txtreg fstocks`h' d_usa_i l(0/`L').(dl_rgdppc dl_rconpc dl_riypc ///\n",
    "\t\tdl_cpi d_stir d_ltrate dl_hpreal dl_rstocks ///\n",
    "\t\td_credit d_gr_rgdp_wld ) l(1/`L').d_stir, fe\n",
    "\tmatrix irf_stocksus[`h'+1,1] = _b[d_usa _i ]\n",
    "\tmatrix irf_stocksus_lb[`h'+1,1] = _b[d_usa_i] - _se[d_usa_i]\n",
    "\tmatrix irf_stocksus_ub[`h'+1,1] = _b[d_usa_i] + _se[d_usa_i]\n",
    "\tmatrix irf_stocksus_2lb[`h'+1,1] = _b[d_usa_i] - 1.64 * _se[d_usa_i]\n",
    "\tmatrix irf_stocksus_2ub[`h'+1,1] = _b[d_usa_i] + 1.64 * _se[d_usa_i]\n",
    "}\n",
    "\n",
    "svmat irf_gdpus, names(irf_gdpus)\n",
    "svmat irf_gdpus_lb, names(irf_gdpus_lb)\n",
    "svmat irf_gdpus_ub, names(irf_gdpus_ub)\n",
    "svmat irf_gdpus_2lb, names(irf_gdpus_2lb)\n",
    "svmat irf_gdpus_2ub, names(irf_gdpus_2ub)\n",
    "svmat irf_stocksus, names(irf_stocksus)\n",
    "svmat irf_stocksus_lb, names(irf_stocksus_lb)\n",
    "svmat irf_stocksus_ub, names(irf_stocksus_ub)\n",
    "svmat irf_stocksus_2lb, names(irf_stocksus_2lb)\n",
    "svmat irf_stocksus_2ub, names(irf_stocksus_2ub)\n",
    "\n",
    "foreach var of varlist irf_gdpus1 - irf_stocksus_2ub1 {\n",
    "\treplace `var' = 100 * `var'\n",
    "}\n",
    "\n",
    "twoway (rarea irf_gdpus_2lb1 irf_gdpus_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_gdpus_lb1 irf_gdpus_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_gdpus1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real GDP\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iii_gdp, replace) nodraw\n",
    "\t\n",
    "twoway (rarea irf_stocksus_2lb1 irf_stocksus_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_stocksus_lb1 irf_stocksus_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_stocksus1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real stock prices\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iii_stocks, replace) nodraw\n",
    "\n",
    "graph combine iii_gdp iii_stocks, name(combined_iii, replace )\n",
    "graph export \"iii.pdf\", as(pdf) replace\n",
    "graph export \"iii.png\", as(png) replace\n",
    "\n",
    "* (iv) ***********************************************************************************\n",
    "\n",
    "keep if country == \"USA\" & year >= 1969 & year <= 2007\n",
    "* tsset year\n",
    "\n",
    "\n",
    "local L = 4\n",
    "local H = 4\n",
    "\n",
    "\n",
    "matrix irf_gdprr = J(`H'+1,1,0)\n",
    "matrix irf_gdprr_lb = irf_gdprr\n",
    "matrix irf_gdprr_ub = irf_gdprr\n",
    "matrix irf_gdprr_2lb = irf_gdprr\n",
    "matrix irf_gdprr_2ub = irf_gdprr\n",
    "\n",
    "matrix irf_cpirr = irf_gdprr\n",
    "matrix irf_cpirr_lb = irf_gdprr\n",
    "matrix irf_cpirr_ub = irf_gdprr\n",
    "matrix irf_cpirr_2lb = irf_gdprr\n",
    "matrix irf_cpirr_2ub = irf_gdprr\n",
    "\n",
    "\n",
    "\n",
    "forvalues h = 0/`H'{\n",
    "\t\n",
    "\t*GDP\n",
    "\tnewey fgdp`h' annualized_RRextended_shock dl_rgdppc l(1/`L').(annualized_RRextended_shock dl_rgdppc), lag(`h')\n",
    "\n",
    "\tnewey fgdp`h' annualized_RRextended_shock dl_rgdppc l(1/`L').(annualized_RRextended_shock dl_rgdppc), lag(`h')\n",
    "\tmatrix irf_gdprr[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdprr_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdprr_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdprr_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdprr_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    "\n",
    "\t* prices\n",
    "\tnewey fcpi`h' annualized_RRextended_shock dl_cpi l(1/`L').(annualized_RRextended_shock dl_cpi), lag(`h')\n",
    "\tmatrix irf_cpirr[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpirr_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpirr_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpirr_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpirr_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    "} \n",
    "\n",
    "svmat irf_gdprr, names(irf_gdprr1)\n",
    "svmat irf_gdprr_lb, names(irf_gdprr_lb)\n",
    "svmat irf_gdprr_ub, names(irf_gdprr_ub)\n",
    "svmat irf_gdprr_2lb, names(irf_gdprr_2lb)\n",
    "svmat irf_gdprr_2ub, names(irf_gdprr_2ub)\n",
    "svmat irf_cpirr, names(irf_cpirr)\n",
    "svmat irf_cpirr_lb, names(irf_cpirr_lb)\n",
    "svmat irf_cpirr_ub, names(irf_cpirr_ub)\n",
    "svmat irf_cpirr_2lb, names(irf_cpirr_2lb)\n",
    "svmat irf_cpirr_2ub, names(irf_cpirr_2ub)\n",
    "\n",
    "foreach var of varlist irf_gdprr1 - irf_cpirr_2ub1 {\n",
    "\treplace `var' = 100 * `var'\n",
    "}\n",
    "\n",
    "* IRF of gdp , cpi , to RR shock\n",
    "\n",
    "\n",
    "/*\n",
    "gen horizon_trick=year-1969\n",
    "replace horizon_trick=. if horizon_trick>=5\n",
    "*/\n",
    "\n",
    "local H=4\n",
    "\n",
    "drop horizon\n",
    "gen horizon = _n-1 if _n <= `H'+1\n",
    "drop zeros\n",
    "gen zeros = 0 if horizon !=.\n",
    "\n",
    "\n",
    "twoway (rarea irf_gdprr_2lb1 irf_gdprr_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_gdprr_lb1 irf_gdprr_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_gdprr1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real GDP per-capita\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_gdp, replace) nodraw\n",
    "\n",
    "twoway (rarea irf_cpirr_2lb1 irf_cpirr_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_cpirr_lb1 irf_cpirr_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_cpirr1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"CPI \") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_cpi, replace) nodraw\n",
    "\t\n",
    "\t\n",
    "graph combine iv_gdp iv_cpi, name(combined_iv, replace)\n",
    "graph export \"iv_12.pdf\", as(pdf) replace\n",
    "graph export \"iv_12.png\", as(png) replace\n",
    "\n",
    "* (iv.3) Split sample **\n",
    "matrix irf_gdppre = J(`H'+1,1,0)\n",
    "matrix irf_gdppre_lb = irf_gdppre\n",
    "matrix irf_gdppre_ub = irf_gdppre\n",
    "matrix irf_gdppre_2lb = irf_gdppre\n",
    "matrix irf_gdppre_2ub = irf_gdppre\n",
    "\n",
    "matrix irf_cpipre = irf_gdppre\n",
    "matrix irf_cpipre_lb = irf_gdppre\n",
    "matrix irf_cpipre_ub = irf_gdppre\n",
    "matrix irf_cpipre_2lb = irf_gdppre\n",
    "matrix irf_cpipre_2ub = irf_gdppre\n",
    "\n",
    "matrix irf_gdppost = irf_gdppre\n",
    "matrix irf_gdppost_lb = irf_gdppre\n",
    "matrix irf_gdppost_ub = irf_gdppre\n",
    "matrix irf_gdppost_2lb = irf_gdppre\n",
    "matrix irf_gdppost_2ub = irf_gdppre\n",
    "\n",
    "matrix irf_cpipost = irf_gdppre\n",
    "matrix irf_cpipost_lb = irf_gdppre\n",
    "matrix irf_cpipost_ub = irf_gdppre\n",
    "matrix irf_cpipost_2lb = irf_gdppre\n",
    "matrix irf_cpipost_2ub = irf_gdppre\n",
    "\n",
    "\n",
    "local H=4\n",
    "local L=4\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "forvalues h = 0/`H'{\n",
    "\t\n",
    "\t* GDP pre\n",
    "\n",
    "*\tnewey fgdp`h' annualized_RRextended_shock l(1/`L').annualized_RRextended_shock if country == \"USA\" & year < 1985, lag(`h')\n",
    "\n",
    "\tnewey fgdp`h' annualized_RRextended_shock dl_rgdppc l(1/`L').(annualized_RRextended_shock dl_rgdppc) if year <1985, lag(`h')\n",
    "\tmatrix irf_gdppre[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppre_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppre_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppre_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppre_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    " \t\n",
    "\t* prices pre\n",
    "\tnewey fcpi`h' annualized_RRextended_shock dl_cpi l(1/`L').(annualized_RRextended_shock dl_cpi) if year <1985, lag(`h')\n",
    "\tmatrix irf_cpipre[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipre_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipre_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipre_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipre_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    "\n",
    "\t* GDP post\n",
    "\tnewey fgdp`h' annualized_RRextended_shock dl_rgdppc l(1/`L').(annualized_RRextended_shock dl_rgdppc) if year >= 1985, lag(`h')\n",
    "\tmatrix irf_gdppost[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppost_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppost_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppost_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_gdppost_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    "\t\n",
    "\t* prices post\n",
    "\tnewey fcpi`h' annualized_RRextended_shock dl_cpi l(1/`L').(annualized_RRextended_shock dl_cpi) if country == \"USA\" & year >= 1985, lag(`h')\n",
    "\tmatrix irf_cpipost[`h'+1,1] = _b[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipost_lb[`h'+1,1] = _b[annualized_RRextended_shock] - _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipost_ub[`h'+1,1] = _b[annualized_RRextended_shock] + _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipost_2lb[`h'+1,1] = _b[annualized_RRextended_shock] - 1.64 * _se[annualized_RRextended_shock]\n",
    "\tmatrix irf_cpipost_2ub[`h'+1,1] = _b[annualized_RRextended_shock] + 1.64 * _se[annualized_RRextended_shock]\n",
    "\t}\n",
    "\n",
    "svmat irf_gdppre, names(irf_gdppre)\n",
    "svmat irf_gdppre_lb, names(irf_gdppre_lb)\n",
    "svmat irf_gdppre_ub, names(irf_gdppre_ub)\n",
    "svmat irf_gdppre_2lb, names(irf_gdppre_2lb)\n",
    "svmat irf_gdppre_2ub, names(irf_gdppre_2ub)\n",
    "svmat irf_cpipre, names(irf_cpipre)\n",
    "svmat irf_cpipre_lb, names(irf_cpipre_lb)\n",
    "svmat irf_cpipre_ub, names(irf_cpipre_ub)\n",
    "svmat irf_cpipre_2lb, names(irf_cpipre_2lb)\n",
    "svmat irf_cpipre_2ub, names(irf_cpipre_2ub)\n",
    "svmat irf_gdppost, names(irf_gdppost)\n",
    "svmat irf_gdppost_lb, names(irf_gdppost_lb)\n",
    "svmat irf_gdppost_ub, names(irf_gdppost_ub)\n",
    "svmat irf_gdppost_2lb, names(irf_gdppost_2lb)\n",
    "svmat irf_gdppost_2ub, names(irf_gdppost_2ub)\n",
    "svmat irf_cpipost, names(irf_cpipost)\n",
    "svmat irf_cpipost_lb, names(irf_cpipost_lb)\n",
    "svmat irf_cpipost_ub, names(irf_cpipost_ub)\n",
    "svmat irf_cpipost_2lb, names(irf_cpipost_2lb)\n",
    "svmat irf_cpipost_2ub, names(irf_cpipost_2ub)\n",
    "\n",
    "foreach var of varlist irf_gdppre1 - irf_cpipost_2ub1 {\n",
    "replace `var' = 100 * `var'\n",
    "\n",
    "}\n",
    "\n",
    "* IRF of gdp , cpi , to RR shock , split sample\n",
    "\n",
    "\n",
    "twoway (rarea irf_gdppre_2lb1 irf_gdppre_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_gdppre_lb1 irf_gdppre_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_gdppre1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real GDP per-capita, pre 1985\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_gdp_pre, replace) nodraw\n",
    "\n",
    "twoway (rarea irf_cpipre_2lb1 irf_cpipre_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_cpipre_lb1 irf_cpipre_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_cpipre1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color (black)), ///\n",
    "\tlegend (off) title(\"CPI, pre 1985\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_cpi_pre, replace) nodraw\n",
    "\n",
    "twoway (rarea irf_gdppost_2lb1 irf_gdppost_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_gdppost_lb1 irf_gdppost_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_gdppost1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color(black)), ///\n",
    "\tlegend(off) title(\"real GDP per-capita, post 1985\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_gdp_post, replace) nodraw\n",
    "\n",
    "twoway (rarea irf_cpipost_2lb1 irf_cpipost_2ub1 horizon, color(gs15)) ///\n",
    "\t(rarea irf_cpipost_lb1 irf_cpipost_ub1 horizon, color(gs13)) ///\n",
    "\t(line irf_cpipost1 horizon, color(blue)) ///\n",
    "\t(line zeros horizon, color (black)), ///\n",
    "\tlegend(off) title(\"CPI, post 1985\") xtitle(\"year\") ytitle(\"response in percent\") ///\n",
    "\tname(iv_cpi_post, replace) nodraw\n",
    "\n",
    "graph combine iv_gdp_pre iv_cpi_pre iv_gdp_post iv_cpi_post, ///\n",
    "\tname(combined_iv_split_sample, replace)\n",
    "graph export \"iv_3.pdf\", as(pdf) replace\n",
    "graph export \"iv_3.png\", as(png) replace\n",
    "\n",
    "```"
   ]
  }
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