{ "cells": [ { "cell_type": "markdown", "id": "3c21aca1", "metadata": {}, "source": [ "\n", "# Filling Non-tidal Quantities Based on a Neighbor\n" ] }, { "cell_type": "markdown", "id": "368f1d62", "metadata": {}, "source": [ "\n", "## 1. Introduction\n", "\n", "### Example Data and Typical Preprocessing\n", "When a physically similar or proximate station is available, it can be advantageous to use that neighbor to impute missing\n", "values in the target series. Our canonical example is **San Joaquin River flow at Vernalis (VNS)**, filled from the\n", "subtidal component of **Mossdale Bridge (MSD)**:\n", "\n", "- **MSD is tidal** → we prefilter with a subtidal filter (*e.g.*, `cosine_lanczos(..., '40h')`). \n", "- **MSD lags VNS** by ~4 hours → we estimate a fixed lag using the `calculate_lag` function and shift the neighbor before modeling. \n", "- **MSD sometimes has its own gaps** → we linearly interpolate *neighbor* briefly before filtering to avoid edge artifacts.\n", "- **VNS − MSD** reflects seasonal channel depletions and episodic Paradise Cut/Weir overflow.\n", "\n", "This notebook shows: (a) how to prepare data, (b) gap-filling methods, (c) how to compare methods on synthetic gaps,\n", "and (d) how to store/reuse Dynamic-Factor (DFM) fits.\n", "\n", "### Bottom Line on Methods (Quick Take)\n", "\n", "- **Residual interpolation** (linear or PCHIP) is the most accurate *and* simple option **when the neighbor is present**. It is fairly easy to explain and it consistently reconstructs Vernalis from Mossdale well. However, it cannot fill when the neighbor is missing. Whether one can live with this limitation depends on the pair of data streams under consideration. In the Vernalis/Mossdale example this would leave about 10% of the data missing.\n", "- **DFM (Trimbur)** is heavier-weight (requires fitting) but, with reasonable joint coverage, **produces complete, robust fills** (handles runs of missing values better than residual methods).\n", "- **Substitution** and **global regression** are useful null baseline models but result in estimates that have high error and have no time connection to the main series. This will cause a lurching between the target series and the substitute at the edges of gaps. \n", "\n", "For VNS/MSD, residual-interp and DFM are the practical choices; pick **Residual-interp** for speed/simplicity or when you have complete data at the neighbor, **DFM** for completeness/robustness.\n" ] }, { "cell_type": "markdown", "id": "f9252ba4", "metadata": {}, "source": [ "\n", "## 2. Setup\n", "\n", "The code assumes your standard environment with `vtools`, `dms_datastore`, and by extension `statsmodels`. The first steps have to \n", "do with imports, acquisition etc.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b8546bcb", "metadata": {}, "outputs": [], "source": [ "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "# Imports (plotting: matplotlib only; no seaborn)\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from vtools.functions.neighbor_fill import fill_from_neighbor\n", "\n", "from vtools import cosine_lanczos, calculate_lag, minutes\n", "from vtools.data.gap import GapSpec, GapStrategy, apply_gaps\n", "\n", "# Matplotlib defaults\n", "plt.rcParams['figure.figsize'] = (11, 6)\n", "plt.rcParams['axes.grid'] = True\n" ] }, { "cell_type": "markdown", "id": "c126a7df", "metadata": {}, "source": [ "\n", "## 3. Data: fetching & preprocessing\n", "\n", "Below is a *template* `provide_example_data()` that illustrates a common setup for VNS/MSD:\n", "\n", "1. Load both series on a common range.\n", "2. Pre-filter **MSD** to subtidal (`cosine_lanczos`, e.g., 40h window).\n", "3. Estimate the lag between the (filtered) neighbor and target (e.g., up to 14h), convert to steps, and shift the neighbor.\n", "4. Return `(target, neighbor, regime)` where `regime` can be `None` or a categorical series (barrier in/out, season, etc.).\n" ] }, { "cell_type": "code", "execution_count": null, "id": "26bf5859", "metadata": {}, "outputs": [], "source": [ "\n", "# These series are provided in the examples. You could replace this \n", "# with a project-specific provider using dms_datastore.\n", "# The function must return (target: Series, neighbor: Series|DataFrame, regime: Series|None),\n", "# aligned (or alignable) by time.\n", "def provide_example_data(start='2017-02-01', end='2022-12-01'):\n", "\n", " sjr = pd.read_csv(\"data/sjr_flow.csv\", index_col=0, parse_dates=True).asfreq('15min')\n", " msd = pd.read_csv(\"data/msd_flow.csv\", index_col=0, parse_dates=True).asfreq('15min')\n", " sjr = sjr.squeeze().interpolate(limit=4)\n", " msd = msd.squeeze().interpolate(limit=4)\n", "\n", " # Subtidal (non-tidal) component for MSD\n", " msd = cosine_lanczos(msd, '40h').squeeze()\n", "\n", " # Estimate and apply lag (non-calendar resolution recommended, e.g., '15min')\n", " lag_steps = int(calculate_lag(msd, sjr, max_lag='14h', res='15min')/minutes(15))\n", " msd = msd.shift(lag_steps)\n", "\n", " # Optional: regime (barrier, season, etc.) — using a placeholder here\n", " regime = None\n", "\n", " # Focus window\n", " return sjr.loc[pd.Timestamp(start):pd.Timestamp(end)], msd.loc[pd.Timestamp(start):pd.Timestamp(end)], regime\n", "\n", "# Prepare example data\n", "y, x, regime = provide_example_data()\n", "\n", "\n", "# --- Stable, colorblind-safe colors and a label->color lookup ----------------\n", "# Okabe–Ito palette (colorblind friendly)\n", "OKABE_ITO = [\n", " \"#E69F00\", # 0 orange (reserved for neighbor)\n", " \"#56B4E9\", # 1 sky blue\n", " \"#009E73\", # 2 bluish green\n", " \"#0072B2\", # 4 blue\n", " \"#D55E00\", # 5 vermillion\n", " \"#CC79A7\", # 6 reddish purple\n", " \"#999999\", # 7 grey\n", " \"#F0E442\", # 3 yellow \n", "]\n", "\n", "# Fixed order for algorithms in the notebook and plots\n", "ALGO_ORDER = [\n", " \"ols\",\n", " \"huber\",\n", " \"rolling\",\n", " \"lagged_reg\",\n", " \"loess\",\n", " \"dfm_trimbur_rw\",\n", " \"dfm_trimbur_ar\",\n", " \"resid_interp_linear\",\n", " \"resid_interp_pchip\",\n", " \"substitute\",\n", "]\n", "\n", "# Build a stable color map for algorithms: skip palette[0] (reserved for neighbor)\n", "_NEIGHBOR_COLOR = OKABE_ITO[0]\n", "_ALGO_COLORS = {algo: OKABE_ITO[(i % (len(OKABE_ITO) - 1)) + 1] # 1..end\n", " for i, algo in enumerate(ALGO_ORDER)}\n", "\n", "def _normalize_label(s: str) -> str:\n", " # normalize method labels like \"dfm trimbur rw\" / \"dfm_trimbur_rw\"\n", " return s.strip().lower().replace(\" \", \"_\")\n", "\n", "def color_for(label: str) -> str:\n", " \"\"\"\n", " Return a consistent color for a plotted series, based on its label.\n", "\n", " Rules:\n", " - \"target (gapped)\" -> black\n", " - \"target (true)\" -> gray\n", " - \"neighbor\" -> first palette color (reserved)\n", " - algorithms -> stable color from palette by name\n", " - unknown labels -> deterministic fallback into palette (excluding neighbor color)\n", " \"\"\"\n", " lab = label.strip().lower()\n", "\n", " # fixed roles\n", " if lab == \"target (gapped)\":\n", " return \"#000000\" # black\n", " if lab == \"target (true)\":\n", " return \"#666666\" # thin gray recommended\n", " if lab == \"neighbor\":\n", " return _NEIGHBOR_COLOR\n", "\n", " # algorithms by name\n", " key = _normalize_label(label)\n", " if key in _ALGO_COLORS:\n", " return _ALGO_COLORS[key]\n", "\n", " # deterministic fallback for anything else (exclude index 0)\n", " idx = (abs(hash(key)) % (len(OKABE_ITO) - 1)) + 1\n", " return OKABE_ITO[idx]\n", "\n" ] }, { "cell_type": "markdown", "id": "5413fd42", "metadata": {}, "source": [ "\n", "## 4. Methods at a glance\n", "\n", "We support a family of neighbor-based approaches (choose via `method=`):\n", "\n", "- **`'substitute'`** — simple substitution: use neighbor only where target is missing.\n", "- **`'ols'` / `'huber'`** — linear regression (ordinary / robust-Huber) on overlap.\n", "- **`'loess2d'`** — nonparametric 2-D smoother in `(time, x)`; captures slow drift vs time.\n", "- **`'resid_interp_linear'` / `'resid_interp_pchip'`** — difference the $\\text{target}-\\text{neighbor}$ series to form residuals. Interpolate the residuals with linear or higher order shape-preserving splines, then add the now-complete residuals back to the neighbor.\n", "- **`'rolling_regression'`** — short-window OLS with time-varying coefficients [poor performer in speed and accuracy].\n", "- **`'lagged_elasticnet'`** — regularized regression on lagged neighbor(s). [not recommended]\n", "- **Dynamic Factor (DFM)** variants:\n", " - **`'dfm_trimbur_ar'` / `'dfm_trimbur_rw'`** — common-trend factor with Trimbur local linear trend; anomaly AR(1) or RW.\n", "\n", "Residual-interp is the simplest and often the best choice (requires neighbor present); DFM is best for complete, robust fills (requires fitting).\n", "\n", "\n", "> The methods are described in the subsections below. All methods are accessed via the single API `fill_from_neighbor(target, neighbor, method=...)`.\n" ] }, { "cell_type": "markdown", "id": "7eec8363", "metadata": {}, "source": [ "\n", "### 4.1 Linear and robust regression\n", "\n", "We fit a global least squares fit where both `y` and `x` are observed. For Ordinary Least Squares (OLS):\n", "\n", "$$\n", "y_t = \\alpha + \\beta x_t + \\varepsilon_t, \\qquad \\hat y_t = \\hat\\alpha + \\hat\\beta x_t.\n", "$$\n", "\n", "Robust (Huber) replaces least-squares with a Huber loss $\\rho_\\delta(\\cdot)$ to reduce outlier influence.\n" ] }, { "cell_type": "markdown", "id": "b41b98f1", "metadata": {}, "source": [ "\n", "### 4.2 Two-dimensional LOESS-like regression (time & neighbor)\n", "\n", "Model the slow drift with time while retaining dependence on the neighbor:\n", "\n", "$$\n", "y_t \\approx f(t, x_t).\n", "$$\n", "\n", "Implemented via distance-weighted KNN in standardized feature space $(t, x)$.\n" ] }, { "cell_type": "markdown", "id": "898f7286", "metadata": {}, "source": [ "\n", "### 4.3 Residual-based filling (linear / PCHIP)\n", "\n", "1. Fit the baseline on overlap: $y_t \\approx a + b x_t$. \n", "2. Compute residuals: $r_t = y_t - (a + b x_t)$. \n", "3. **Interpolate $r_t$** inside *bounded* gaps only (no extrapolation) using **linear** or **PCHIP** in time. \n", "4. Recompose: $\\hat y_t = (a + b x_t) + \\tilde r_t$.\n", "\n", "This preserves local curvature better than interpolating the original series directly.\n" ] }, { "cell_type": "markdown", "id": "e57f8590", "metadata": {}, "source": [ "\n", "### 4.4 Dynamic Factor (DFM) with Trimbur local-trend and anomalies\n", "\n", "In this method, the **structural model** describes how both the target and the neighbor share a slowly evolving\n", "**common trend** $\\mu_t$ that captures basin-scale behavior such as overall inflow, channel depletion, or other broad\n", "hydrologic controls. The companion term $\\beta_t$ acts as a local slope or rate of change, so together $(\\mu_t,\\beta_t)$\n", "form a *local linear trend* that can drift gradually over time. Excellent texts on structural time series modeling include \n", "Harvey’s *Forecasting, Structural Time Series Models and the Kalman Filter* (1990) and \n", "Koopman & Durbin’s *Time Series Analysis by State Space Methods* (2012). \n", "\n", "Dynamic common factor models tend to be hard to fit if you layer in a lot of additional anomalies or include too many factors \n", "without constraints, so the practical choice here is one common trend, which we apply verbatim (with observation noise) to \n", "the neighbor (MSD) since it is smoother. An **AR(1) or random walk anomaly** terms $a^y_t$ and $a^x_t$ represents \n", "short-term departures from that shared trend at Vernalis — mostly due to channel depletions but also encompassing \n", "time correlated measurement noise or operational perturbations. For the AR(1) case the autoregressive parameter $\\phi$ controls their persistence:\n", "- if $|\\phi|$ is small, deviations decay rapidly (almost white noise). Such a model is not sufficient to model the very persistent perturbations \n", " caused by seasonal channel depletions.\n", "- if $|\\phi|$ is near 1, they behave like random walks, allowing slow reversion.\n", "- the choice $\\phi=1$ is the random walk model\n", "\n", "This decomposition lets the Kalman smoother use the correlation structure between $y_t$ and $x_t$ to infer missing\n", "values from the shared state rather than direct regression alone. \n", "The DFM effectively learns how much of the variance is **common** versus **local**, providing reconstructions that are\n", "smooth, stable, and physically interpretable. \n", "For the Vernalis–Mossdale pair, the shared trend corresponds to large-scale San Joaquin flow variability, while the\n", "anomaly terms capture residual tidal or measurement effects.\n", "\n", "\n", "Let a common trend $\\mu_t$ follow a **local linear trend** (Trimbur variant fixes level shock to zero):\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", "\\mu_t &= \\mu_{t-1} + \\beta_{t-1}, \\\\\n", "\\beta_t &= \\beta_{t-1} + \\eta_t, \\qquad \\eta_t \\sim \\mathcal{N}(0,q_\\beta).\n", "\\end{aligned}\n", "$$\n", "\n", "Optional **anomaly** components $a^y_t, a^x_t$ capture idiosyncratic fluctuations in target/neighbor as AR(1) or RW:\n", "\n", "$$\n", "a_t = \\phi a_{t-1} + \\zeta_t \\quad\\text{(AR(1))} \\qquad\\text{or}\\qquad a_t = a_{t-1} + \\zeta_t \\quad\\text{(RW)}.\n", "$$\n", "\n", "**Observations:**\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= \\mu_t + \\mathbf{1}_{\\{a^y\\}}\\, a^y_t + \\varepsilon^y_t,\\qquad \\varepsilon^y_t \\sim \\mathcal{N}(0,r_y), \\\\\n", "x_t &= \\lambda\\,\\mu_t + \\mathbf{1}_{\\{a^x\\}}\\, a^x_t + \\varepsilon^x_t,\\qquad \\varepsilon^x_t \\sim \\mathcal{N}(0,r_x).\n", "\\end{aligned}\n", "$$\n", "\n", "where $\\mathbf{1}_{\\{a^y\\}}$ and $\\mathbf{1}_{\\{a^x\\}}$ are indicator variables (0 or 1) that specify whether the anomaly term is included for the target ($y$) or neighbor ($x$) series, respectively. \n", "\n", "Kalman smoothing provides $\\hat y_t$ and an analytic PI via $\\mathrm{Var}(y_t\\mid\\text{all}) = Z_t P_t Z_t' + H_t$. Note that while the \"Kalman\" name has a lot of name recognition, the Kalman smoother is just a statistical evaluation tool, not a complete description of the assumptions as above. Again Harvey (1990) is an excellent start and includes information on dynamic factor analysis which goes well beyond the simple one-component method described here. \n" ] }, { "cell_type": "markdown", "id": "501fd4a8", "metadata": {}, "source": [ "\n", "## 5. Simple substitution\n", "\n", "This null model replaces **only the missing points** in `y` with the available `x`. It's cheap but not appreciably more\n", "so than other methods. Below we highlight two issues with simple substitution that are shared by a great number of other \n", "models that do not consider time series properties. \n", "- output tends to lurch over to the neighbor and back on entry and exit of the gap. \n", "- there is no recourse when the neighbor is not available.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e7eb4b9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "starting substitution\n", "ending substitution\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y_gap0 = y.copy()\n", "x_gap0 = x.copy()\n", "y_gap0.loc[pd.Timestamp('2020-05-23'):pd.Timestamp('2020-06-04')] = np.nan\n", "x_gap0.loc[pd.Timestamp('2020-05-27'):pd.Timestamp('2020-05-29')] = np.nan\n", "\n", "\n", "# Run the null 'substitute' method\n", "print(\"starting substitution\")\n", "res_sub = fill_from_neighbor(y_gap0, x_gap0, method='substitute')\n", "print(\"ending substitution\")\n", "\n", "# Plot two short windows to show transitions\n", "def plot_window(t0, t1, title):\n", " fig, ax = plt.subplots()\n", " ax.plot(y.loc[t0:t1].index, y.loc[t0:t1].values, label='target (true)',\n", " color=color_for('target (true)'), linewidth=1.5)\n", " ax.plot(x.loc[t0:t1].index, x.loc[t0:t1].values, label='neighbor (true)',\n", " color=color_for('neighbor'), linewidth=1.5)\n", " ax.plot(y_gap0.loc[t0:t1].index, y_gap0.loc[t0:t1].values, label='target (with gaps)',\n", " color=color_for('target (with gaps)'), linewidth=1.5)\n", " ax.plot(res_sub['yhat'].loc[t0:t1].index, res_sub['filled'].loc[t0:t1].values,\n", " label='substitute', linewidth=2, color=color_for('substitute'))\n", "\n", " # Shade times where neighbor is missing\n", " nbr = x_gap0.loc[t0:t1]\n", " miss = nbr.isna()\n", " if miss.any():\n", " starts = np.where(np.diff(miss.astype(int), prepend=0, append=0)==1)[0]\n", " ends = np.where(np.diff(miss.astype(int), prepend=0, append=0)==-1)[0] - 1\n", " for s, e in zip(starts, ends):\n", " ax.axvspan(nbr.index[s], nbr.index[e], alpha=0.15)\n", "\n", " ax.set_title(title)\n", " ax.legend(loc='upper left')\n", " plt.show()\n", "\n", "# Example windows (adjust as needed)\n", "\n", "plot_window(pd.Timestamp('2020-05-01'), pd.Timestamp('2020-06-29'), 'Substitution demo')\n" ] }, { "cell_type": "markdown", "id": "aaed238a", "metadata": {}, "source": [ "\n", "## 6. Residual-based vs direct interpolation (visual)\n", "\n", "In the picture below, we compare interpolating the original `y` versus interpolating **residuals** and then recombining. The direct interpolation doesn't have enough data to track curvature in the original series so it \"cuts the corners\". Residual-based methods\n", "work on the smaller difference between the series, which though wiggly can still be linearly interpolated to much lower error. Note the much narrower y-axis range on the bottom plot below, which basically reduces the linear interpolation to the neighborhood of 100-200cfs instead of 1000s. As shown in the top plot the method of interpolating on residuals and adding back to the original \"neighbor\" to get back to original values works better in non-linear segments. One other thing you can see in the residual plots, because it is more of a close up, is the fuzzy measurement noise at the Vernalis sensor. The DFM methods naturally smooth this over.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "616da888", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAJOCAYAAAAK+M50AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3XVcVff/wPHXTeDSDQICimJ3d8zOmbPbtdNZc35tN2uhc1MXOp1z5szNGbM7MGYgxsAiFKTj5vn9wbg/r4CiooB+no8HD7jnnnifew9w3+fz+bw/MkmSJARBEARBEARBEIRXgrygAxAEQRAEQRAEQRDyj0jyBEEQBEEQBEEQXiEiyRMEQRAEQRAEQXiFiCRPEARBEARBEAThFSKSPEEQBEEQBEEQhFeISPIEQRAEQRAEQRBeISLJEwRBEARBEARBeIWIJE8QBEEQBEEQBOEVIpI8QRAEQRAEQRCEV4hI8gRBEPLB8uXLkclk5i+lUom3tzdvvfUW165de2HHnTp1KjKZLE/rBgQEMHDgwBcWy9PGUxCaNGlCkyZNzI8jIiKQyWQsX778pceyfft2pk6dmuf1Bw4cSEBAwDMd6+jRo0ydOpWEhIRn2r4gPM/1+vnnn7N58+Zsy/fv349MJmP//v3PFZsgCEJhJ5I8QRCEfPTzzz9z7Ngx/v77bz744AO2bt1KgwYNiI+PfyHHGzp0KMeOHXsh+34deHt7c+zYMdq1a/fSj719+3amTZuW5/UnTZrEpk2bnulYR48eZdq0aUUqyXseuSV51apV49ixY1SrVu3lByUIgvASKQs6AEEQhFdJhQoVqFGjBpDZamQ0GpkyZQqbN29m0KBB+X48X19ffH19832/rwsrKyvq1KnzxPXS0tLQaDQvIaLclSxZskCPn5PC8Lo8DQcHhzy934IgCEWdaMkTBEF4gbISvpiYGIvlp0+fpmPHjri4uGBtbU3VqlVZt26dxTppaWmMGTOGwMBArK2tcXFxoUaNGqxevdq8Tk7dI/V6PePGjcPLywuNRkODBg04efJktthy61qZ1fU0IiLCvGzt2rW0bNkSb29vbGxsKFu2LJ988gmpqalP/ZrMnz8fmUzG9evXsz03fvx41Go1sbGxAJw9e5b27dvj4eGBlZUVxYoVo127dty5c+exx5Akiblz5+Lv74+1tTXVqlXjr7/+yrZeTt01s16XM2fO0K1bN5ydnc0JliRJLFq0iCpVqmBjY4OzszPdunXj33//zbbvHTt20Lx5cxwdHdFoNJQtW5ZZs2YBmV0vv/vuOwCLbr4Pv+aPyqm7pkwm44MPPmDlypWULVsWjUZD5cqV+eOPPyzOZ+zYsQAEBgaaj/Vwl8W1a9dSt25dbG1tsbOzo1WrVpw9ezbb8e3s7Lhw4QItW7bE3t6e5s2bA5k3NCpUqMChQ4eoU6cONjY2+Pj4MGnSJIxGo8V+Hjx4wHvvvYePjw9qtZoSJUowceJEtFptrucOkJGRwejRo6lSpQqOjo64uLhQt25dtmzZku01SU1NZcWKFeZzzeqim1t3za1bt1K3bl00Gg329va0aNEiWwt51nVx6dIlevXqhaOjI56engwePJjExMTHxi4IgvCyiSRPEAThBQoPDwegdOnS5mX79u2jfv36JCQksGTJErZs2UKVKlXo2bOnRbLx8ccfs3jxYkaMGMGOHTtYuXIl3bt3Jy4u7rHHHDZsGF988QX9+/dny5YtdO3alS5dujxXl9Fr167Rtm1bli5dyo4dOxg5ciTr1q2jQ4cOT72vvn37olars42DMxqN/Prrr3To0AE3NzdSU1Np0aIFMTExfPfdd+zevZv58+dTvHhxkpOTH3uMadOmMX78eFq0aMHmzZt59913GTZsGGFhYXmOs0uXLgQFBbF+/XqWLFkCwNtvv83IkSN544032Lx5M4sWLeLSpUvUq1fPIpFfunQpbdu2xWQysWTJErZt28aIESPMyemkSZPo1q0bAMeOHTN/eXt75zm+LH/++Sfffvst06dP5/fff8fFxYU333zTnHgOHTqUDz/8EICNGzeaj5XVZfHzzz+nV69elCtXjnXr1rFy5UqSk5Np2LAhly9ftjiWTqejY8eONGvWjC1btlh0N42Ojuatt96iT58+bNmyhW7dujFz5kw++ugj8zoZGRk0bdqUX375hY8//pg///yTvn37MnfuXLp06fLY89RqtTx48IAxY8awefNmVq9eTYMGDejSpQu//PKLeb1jx45hY2ND27Ztzee6aNGiXPf722+/0alTJxwcHFi9ejVLly4lPj6eJk2acPjw4Wzrd+3aldKlS/P777/zySef8NtvvzFq1KjHxi4IgvDSSYIgCMJz+/nnnyVAOn78uKTX66Xk5GRpx44dkpeXl9SoUSNJr9eb1y1TpoxUtWpVi2WSJEnt27eXvL29JaPRKEmSJFWoUEHq3LnzY487ZcoU6eE/5aGhoRIgjRo1ymK9VatWSYA0YMCAXLd99FzCw8NzPKbJZJL0er104MABCZDOnz//xH0+qkuXLpKvr6/5XCVJkrZv3y4B0rZt2yRJkqTTp09LgLR58+Yn7u9h8fHxkrW1tfTmm29aLD9y5IgESI0bNzYvCw8PlwDp559/znYOkydPttj+2LFjEiB9+eWXFstv374t2djYSOPGjZMkSZKSk5MlBwcHqUGDBpLJZMo1zvfffz9Pr1WWAQMGSP7+/hbLAMnT01NKSkoyL4uOjpbkcrk0a9Ys87J58+bl+J7eunVLUiqV0ocffmixPDk5WfLy8pJ69OhhcXxAWrZsWbbYGjduLAHSli1bLJYPGzZMksvl0s2bNyVJkqQlS5ZIgLRu3TqL9ebMmSMB0q5du8zL/P39La7XRxkMBkmv10tDhgyRqlatavGcra1tjtvu27dPAqR9+/ZJkiRJRqNRKlasmFSxYkWLazE5OVny8PCQ6tWrZ16WdV3MnTvXYp/vvfeeZG1t/dj3WhAE4WUTLXmCIAj5qE6dOqhUKuzt7WndujXOzs5s2bIFpTJzCPT169e5cuUKffr0AcBgMJi/2rZtS1RUlLm1qVatWvz111988skn7N+/n/T09Ccef9++fQDm/Wfp0aOHOYZn8e+//9K7d2+8vLxQKBSoVCoaN24MQGho6FPvb9CgQdy5c4e///7bvOznn3/Gy8uLNm3aABAUFISzszPjx49nyZIl2VqVcnPs2DEyMjKyvQb16tXD398/zzF27drV4vEff/yBTCajb9++Fu+bl5cXlStXNncBPHr0KElJSbz33nsvpdJo06ZNsbe3Nz/29PTEw8ODmzdvPnHbnTt3YjAY6N+/v8U5WVtb07hx4xyrUD76umSxt7enY8eOFst69+6NyWTi4MGDAOzduxdbW1tzK2aWrCqae/bseWy869evp379+tjZ2aFUKlGpVCxduvSZrkGAsLAwIiMj6devH3L5/38ksrOzo2vXrhw/fpy0tDSLbR49x0qVKpGRkcG9e/eeKQZBEIQXQSR5giAI+eiXX37h1KlT7N27l7fffpvQ0FB69eplfj6rS9+YMWNQqVQWX++99x6AeTzaN998w/jx49m8eTNNmzbFxcWFzp07P3ZKhqyunF5eXhbLlUolrq6uz3ROKSkpNGzYkBMnTjBz5kz279/PqVOn2LhxI0Ceks9HtWnTBm9vb37++WcA4uPj2bp1K/3790ehUADg6OjIgQMHqFKlCp9++inly5enWLFiTJkyBb1en+u+c3sNcluWm0e7TsbExCBJEp6entneu+PHj5vft/v37wO8tII4Ob2vVlZWeXpfsq7HmjVrZjuntWvXms8pi0ajwcHBIcd9eXp6ZluW9XpnvSdxcXF4eXllS349PDxQKpWP7Yq8ceNGevTogY+PD7/++ivHjh3j1KlTDB48mIyMjCeea06yjpdTN9lixYphMpmydXN+9PW2srICnu33QBAE4UUR1TUFQRDyUdmyZc3FVpo2bYrRaOSnn35iw4YNdOvWDTc3NwAmTJiQ6xik4OBgAGxtbZk2bRrTpk0jJibG3KrXoUMHrly5kuO2WR9Ao6Oj8fHxMS83GAzZPkBbW1sDmWOdsj6oAtk+2O/du5fIyEj2799vbr0Dnqscv0KhoF+/fnzzzTckJCTw22+/odVqs1UgrVixImvWrEGSJP755x+WL1/O9OnTsbGx4ZNPPslx3w+/Bo+Kjo7O81xzjyYibm5uyGQyDh06ZPF6Zcla5u7uDvDE4jCFQdb1uGHDhjy1cj6uZfLR4kLw/+9B1nvi6urKiRMnkCTJYl/37t3DYDCY48nJr7/+SmBgIGvXrrXY9kkFWx4nK66oqKhsz0VGRiKXy3F2dn7m/QuCIBQU0ZInCILwAs2dOxdnZ2cmT56MyWQiODiYUqVKcf78eWrUqJHj18Nd77J4enoycOBAevXqRVhYWLYuZFmyqgiuWrXKYvm6deswGAwWy7KSnX/++cdi+bZt2yweZ32gfjSx+f777x9/8k8waNAgMjIyWL16NcuXL6du3bqUKVMmx3VlMhmVK1fm66+/xsnJiTNnzuS63zp16mBtbZ3tNTh69GieujDmpn379kiSxN27d3N83ypWrAhkdgt1dHRkyZIlSJKU6/5eZgtQbsdq1aoVSqWSGzdu5Ho95lVycjJbt261WPbbb78hl8tp1KgRAM2bNyclJSXbHHZZhVOyqnXmRCaToVarLRK86OjobNU1s843L69rcHAwPj4+/PbbbxbvVWpqKr///ru54qYgCEJRI1ryBEEQXiBnZ2cmTJjAuHHj+O233+jbty/ff/89bdq0oVWrVgwcOBAfHx8ePHhAaGgoZ86cYf369QDUrl2b9u3bU6lSJZydnQkNDWXlypWP/eBZtmxZ+vbty/z581GpVLzxxhtcvHiRL774Ils3u7Zt2+Li4sKQIUOYPn06SqWS5cuXc/v2bYv16tWrh7OzM++88w5TpkxBpVKxatUqzp8//1yvTZkyZahbty6zZs3i9u3b/PDDDxbP//HHHyxatIjOnTtTokQJJEli48aNJCQk0KJFi1z36+zszJgxY5g5cyZDhw6le/fu3L59m6lTpz5Vd81H1a9fn+HDhzNo0CBOnz5No0aNsLW1JSoqisOHD1OxYkXeffdd7Ozs+PLLLxk6dChvvPEGw4YNw9PTk+vXr3P+/Hm+/fZbAHNSOGfOHNq0aYNCoaBSpUqo1epnjjE3WcdasGABAwYMQKVSERwcTEBAANOnT2fixIn8+++/5nGkMTExnDx50tyanBeurq68++673Lp1i9KlS7N9+3Z+/PFH3n33XYoXLw5A//79+e677xgwYAARERFUrFiRw4cP8/nnn9O2bVveeOONXPffvn17Nm7cyHvvvUe3bt24ffs2M2bMwNvbO1sX5ooVK7J//362bduGt7c39vb25hbyh8nlcubOnUufPn1o3749b7/9Nlqtlnnz5pGQkMDs2bPz+hILgiAULgVY9EUQBOGVkVWR8tSpU9meS09Pl4oXLy6VKlVKMhgMkiRJ0vnz56UePXpIHh4ekkqlkry8vKRmzZpJS5YsMW/3ySefSDVq1JCcnZ0lKysrqUSJEtKoUaOk2NhY8zo5VbPUarXS6NGjJQ8PD8na2lqqU6eOdOzYsRyrFZ48eVKqV6+eZGtrK/n4+EhTpkyRfvrpp2yVGI8ePSrVrVtX0mg0kru7uzR06FDpzJkzuVamzKsffvhBAiQbGxspMTHR4rkrV65IvXr1kkqWLCnZ2NhIjo6OUq1ataTly5c/cb8mk0maNWuW5OfnJ6nVaqlSpUrStm3bpMaNG+e5uub9+/dz3PeyZcuk2rVrS7a2tpKNjY1UsmRJqX///tLp06ct1tu+fbvUuHFjydbWVtJoNFK5cuWkOXPmmJ/XarXS0KFDJXd3d0kmkz22oqkk5V5d8/3338+2bk7v9YQJE6RixYpJcrncosKkJEnS5s2bpaZNm0oODg6SlZWV5O/vL3Xr1k36+++/LY5va2ubY2yNGzeWypcvL+3fv1+qUaOGZGVlJXl7e0uffvpptiqycXFx0jvvvCN5e3tLSqVS8vf3lyZMmCBlZGQ88Rxmz54tBQQESFZWVlLZsmWlH3/8Mcdr7ty5c1L9+vUljUZjUVH10eqaD59/7dq1JWtra8nW1lZq3ry5dOTIEYt1crsunlSNVhAEoSDIJOkxfUkEQRAEQRCeoEmTJsTGxnLx4sWCDkUQBEFAjMkTBEEQBEEQBEF4pYgkTxAEQRAEQRAE4RUiumsKgiAIgiAIgiC8QkRLniAIgiAIgiAIwitEJHmCIAiCIAiCIAivEJHkCYIgCIIgCIIgvEJe2cnQTSYTkZGR2NvbI5PJCjocQRAEQRAEQRCE5yJJEsnJyRQrVgy5PPf2ulc2yYuMjMTPz6+gwxAEQRAEQRAEQchXt2/fxtfXN9fnX9kkz97eHsh8ARwcHAo4GqGg6fV6du3aRcuWLVGpVAUdjlDEietJyE/iehLyi7iWhPwkrqfCKSkpCT8/P3Ouk5tXNsnL6qLp4OAgkjwBvV6PRqPBwcFB/KESnpu4noT8JK4nIb+Ia0nIT+J6KtyeNBxNFF4RBEEQBEEQBEF4hYgkTxAEQRAEQRAE4RUikjxBEARBEARBEIRXyCs7Ji+vjEYjer2+oMMQXjC9Xo9SqSQjIwOj0VjQ4RRZarX6seV6BUEQBEEQhIL32iZ5kiQRHR1NQkJCQYcivASSJOHl5cXt27fFvInPQS6XExgYiFqtLuhQBEEQBEEQhFy8tkleVoLn4eGBRqMRH/xfcSaTiZSUFOzs7ERL1DMymUxERkYSFRVF8eLFxe+MIAiCIAhCIfVaJnlGo9Gc4Lm6uhZ0OMJLYDKZ0Ol0WFtbiyTvObi7uxMZGYnBYBDllAVBEARBEAqp1/LTbtYYPI1GU8CRCELRktVNU4xrFAojnU5HVFRUQYchCIIgCAXutWzJyyK6mwnC0xG/M0JBS0pKwsHBwfzYaDRy+/ZtJEnijz/+4PLly7z99ts4Ojpm29bd3R07O7uXGa4gCIIgFIjXOskTBEEQio6zZ8+yZMkSevbsSfXq1QFYtWoV58+fBzJvQjg7O7N48eIct7e3t2f06NE4OjqKnhyCIAjCK00keS9RVHQUyWlpyGUyvNw9Xss7ymFhYTRu3Jhr165hb29fIDHUrFmTCRMm0KVLlwI5viAITy8xMZFff/0Va2tr1q5dy9q1a83P9e/fH39/fzQaDfb29sTExGTbXq/Xs2jRIqZOnYpcLmfKlCl4eXm9zFMQBEEQhJdGJHkvUXJaGkkOapAg7e5tygSWfOpS9E2aNKFKlSrMnz//xQT5DJ4mpokTJ/L++++bE7zly5czcuTIlzqVxaRJkxgzZgydO3cWRVgEoQjYt28fa9aswd7enmnTphEZGWkeF+rg4IC/v7/F+r6+vjnu59NPP+X27dssW7aMAwcOcO3aNVq1akXNmjVf+DkIgiAIwsskkryXSCaBOjkdvZ0NBmdbLoZeJjioFLa2ti89Fp1O99LnOrtz5w5bt259pgQ1P+Nt164dw4YNY+fOnbRp0yZf9ikIQv7QarV89dVXJCcnm5fFx8dTqVIlunbtipOTE05OTs+0b2dnZ5ydnalatSp79+4F4KeffuLUqVO8/fbbKBSK/DgFQRAEQShwohnjJQr096eUlx/KuCRMchkmH1eu371FVFQUkiQ9cfuBAwdy4MABFixYgEwmQyaTERERgdFoZMiQIQQGBmJjY0NwcDALFizItm3nzp2ZNWsWxYoVo3Tp0gAcPXqUKlWqYG1tTY0aNdi8eTMymYxz586Zt718+TJt27bFzs4OT09P+vXrR2xs7GNjysm6deuoXLmy+S77/v37GTRoEImJieZtp06dCkBAQAAzZ85k4MCBODo6MmzYMPbv349MJrNo9Tt37ly2Yx49epRGjRphY2ODn58fI0aMIDU11fy8QqGgbdu2rF69+omvuSAIL8/x48eZNWsWERERVK9enVq1alGrVi26du3K8OHD8617Zbt27WjXrh1NmjQB4Pz583z22WfMnz+fnTt3snHjxjz9TRYEQRCEwkq05L1ESqUSpVJJgLcvEfci0TvZoXex415GGvFhVygdVAqlMve3ZMGCBVy9epUKFSowffp0ILNanMlkwtfXl3Xr1uHm5sbRo0cZPnw43t7e9OjRw7z9nj17cHBwYPfu3UiSRHJyMh06dKBt27b89ttv3Lx5k5EjR1ocMyoqisaNGzNs2DC++uor0tPTGT9+PD169GDv3r25xpSTgwcPUqNGDfPjevXqMX/+fCZPnkxYWBiAxTjFefPmMWnSJP73v/8BmS2BT3LhwgVatWrFjBkzWLp0Kffv3+eDDz7gww8/tGhBrFWrFnPnzn3i/gRBePHS09PZsGEDhw8fBsDW1pauXbu+sOO5ubnRsWNH9Ho9Tk5ObN68mbt373L37l1CQ0MB8PDwoEGDBi8shoKQlpbGli1bSE1NRSaT0aRJE0qWLMnhw4dxcXGhXLlySJLEjh07qFChAn5+fgUdsiAIgvCMRJL3EJ1OR3R09Es5lrVJzr0LV5Gc7HDwcsforOHi1SsEFQ/ItSCLo6MjarUajUZjcUdboVAwbdo08+PAwECOHj3KunXrLJI8W1tbfvrpJ3O3xyVLliCTyfjxxx+xtramXLly3L17l2HDhpm3Wbx4MdWqVePzzz83L1u2bBl+fn5cvXqV0qVL5xhTTrLuzmdRq9U4Ojoik8ly3LZZs2aMGTPG/DgvSd68efPo3bu3OVktVaoU33zzDY0bN2b27Nnm0us+Pj7cunULk8kkxuUJQgGJjY3l0KFDhIeHExYWRr169XB2dqZUqVIv5fgqlYo2bdrg7e3N/fv3qVChAn///Tc6nY5169YRHR1tnjbE2tqa5s2bY21t/VJiy4t79+4RERFBlSpV2Lt3LxqNhvr167N3716SkpKyrR8REcHNmzcJCAggPj6exYsXU716dfbv34+1tTUNGzYkMTGRkydPcujQIapXr07ZsmUpV65cAZxd7o4dO0bp0qVJTEzk3LlzVKxYkdDQUIoXL06VKlWIiYnh1q1bYqylIAivNZHkPSQ6OprPPvvspR+3++B+OJX0x+ThyI17d3FNtMXXJ+fCAblZsmQJP/30Ezdv3iQ9PR2dTkeVKlUs1qlYsaLFuLawsDAqVapk8aGlVq1aFtuEhISwb9++HBPPGzdumLt95kV6evpTfUB6uNUvr0JCQrh+/TqrVq0yL5MkCZPJxM2bN/Hw8ADAxsYGk8mEVqvFxsbmqY8jCMLz+eeff1i9ejXp6ek4OjoyfPhwi5tAL9PDfyv79etHRkYGKSkp5qkZAB48eMCVK1fo1q0bSUlJBAcHExUVhZWVFffu3cPV1RWAuLg4ypUrZ+6VER8fz7lz5yhbtiz37t2jQoUKAJw6dcrcjdxoNBIREcH+/fupWrUqbm5uREZGIpPJ8Pb2Nseg1Wo5deoUOp0OgAMHDhAdHY2bm5u5C/3OnTuJi4vLsUeFQqFg8ODBVKlShcTERH744QcuX75MnTp1SExMNJ9vkyZNuHnzJidPnmTv3r106tTJopeJnZ0dNWrUQC6Xk5qayq1btyhbtqz5dUpMTCQwMPCp34fLly+TmJhIrVq1uHHjBvfv3ycoKIjLly9TqVIlVCoVf/zxBwcOHMDBwYGMjAx0Oh07d+4EMqfQGDlyJL/99hsxMTGo1WpkMhnly5dHoVCQkZHB9evXKV++vJjzUxCEV55I8h7i5eXFxIkTX/pxlUolMdGxGD2d0TtoiNXqiQ+9RHDJUnkqNrJu3TpGjRrFl19+Sd26dbG3t2fevHmcOHHCYr1HC7xIkpTtH92j41BMJhMdOnRgzpw52Y778IePvHBzcyM+Pj7P6z8ab1aL28Mx6vV6i3VMJhNvv/02I0aMyLb84WINDx48QKPRiARPEF6i8PBwEhMTuX//Phs2bMDW1pbRo0cXum6B1tbWfPTRRxbLTp48ydKlS5k9ezYmk4nixYsTFRWF0Wi06BFgMpmoX78+lSpVAmDLli1ERkYil8sxmUw0b94cSZLYu3cvKpXKvH+j0ciVK1fYt28fXbt2ZdWqVcjlcnr37m3e94kTJzh79qw54bKzs6NixYpcvXqVPn36EB0dzaFDh+jcuTOtW7d+7Dk6OjoyduzYx66j1+tZsGABW7duzbY8MTGRcuXK8csvvxAREUH37t1xc3Nj06ZNxMbGMnHiRFxdXbly5QqBgYFER0eTlpYGZPbi8Pf359q1a+Z9PnjwwDwtxsGDBwkPD0eSJPPrtmPHDpRKJbGxsVSuXJmIiAjc3d3p168fK1asoHv37mzbto0FCxagUCgoUaIES5YswWQy0ahRI8qXL8/Ro0c5f/48LVu2pFWrVsTHx+Pr6ysSPkEQXkkiyXuIWq2mePHiBXJsFxcXrl6/jsnFFoOVEqObPZf+vYavmwfubv9/R1atVptLh2c5dOgQ9erV47333jMvu3HjxhOPWaZMGVatWoVWq8XKygqA06dPW6xTrVo1fv/9dwICAnIdL5hTTDmpWrUqly9ffqZt4f/H+kVFReHs7AxgUSAmK95Lly4RFBRksdxkMll0X7p48SLVqlXL03EFQXh2kiRx9+5dIiIiWLlypXl59erVGTZsWJH5gF2rVi0qVqzI7Nmz0Wg0hIeH4+HhQVpaGh4eHuYbWM7Ozhw5coQjR44AmQnje++9x8qVK/Hw8GDPnj0AtG/fng4dOgCZSdP27dupVasW8+bN44cffsDFxQVJkvj+++/NMcjlcgYNGkTt2rVzjfPhLvrPS6VSWXSZz7Ju3To2b97Mxo0bMZlM+Pv7s379eiCzl4SLiwvff/891tbWREREYGVlhVartdhHTssqVqxIqVKl2LhxIyVLlqR27dps2bKFAQMGsGLFCtLT0/nkk0+ytRJmFexyc3Nj7ty5dOzYkcqVKzNr1ixcXV05ePAgBw8eRC6XExgYyK5duzhw4ABarZbBgwc/9vUUBEEoqkSSV0hYW1tTsXx57ty9S1x8MgZnO4xuDkQlPkBjozG3agUEBHDixAkiIiKws7PDxcWFoKAgfvnlF3bu3ElgYCArV67k1KlTT+wu07t3byZOnMjw4cP55JNPuHXrFl988QWA+YPX+++/z48//kivXr0YO3Ysbm5uXL9+nTVr1vDjjz+iUChyjCmncW6tWrVi6NChGI1Gc6nygIAAUlJS2LNnD5UrV0aj0aDRaHKMNygoCD8/P6ZOncrMmTO5du0aX375pcU648ePp06dOrz//vsMGzYMW1tbQkND2bVrFzNnzjSvd+jQIVq2bJnHd0cQhKeRnp5ubmXfvXs3u3btAqBy5cr0798fyGypf1kJXrpBT2xGKrHaVPP3eG0aKQYdqQYdqfrM7ykGLal6HRlGA0bJhFGS/vv+0M/VnZHLZMjLlUOlUKCSKVDK5chlzihlclRyBbJq7qhkcqwUShQqK1bpbmPTqzlGlZqSxmpYK1Q8sNGwLvwcNgoVSmRc1ifiJdPSf8LHKE0SLhp7NEoVMoMRhSzz76lSqSwUYwLffPNNQkNDUSgUjBo1Co1GY+56qlariY6OZvbs2RiNRt588022b99O48aN6dixI5DZzfTPP//k448/xsfHx7zfrGuiYcOGWFtbI5fLadiwIXK5nDlz5iBJ0mN7t3h6ejJv3jzz/59Zs2Yhl8tJS0vDZDKhVCqxsrIiLCyMb775BmdnZ1avXo2Pj0+ucysKgiAUVSLJK0RkMhl+vr44JSdzLeJfZB5O6Bw0hN38F183Tzw8PBgzZgwDBgygXLlypKenEx4ezjvvvMO5c+fo2bMnMpmMXr168d577/HXX3899ngODg5s27aNd999lypVqlCxYkUmT55M7969zR8kihUrxpEjRxg/fjytWrVCq9Xi7+9P69atzf9Ic4opICAg2/Hatm2LSqXi77//plWrVkBmhc133nmHnj17EhcXx5QpU8x3ZR+lUqlYvXo17777LpUrV6ZmzZrMnDmT7t27m9epVKkSBw4cYOLEiTRs2BBJkihZsqTF3e27d+9y9OhRfv3116d5ewRByIPz58/z/fffW7TQt2nThurVq+Pj45PvhY70JiP/JscRnvyAW6kJ3E5N4FZKfOb31ASi0pNIM+ifvKP/yGUyrBVKFDL5f18yy5/lciRJwiCZMJhMD303YjD9fzL4TP76J8fFVgolGoUKW6UajVKNRqkyfzcvUzy6TGWx3PaR7SyWKdQonuJ9UalUTJgwAZlMZu5y+vC47eLFizNr1iyMRiMuLi40bNgQjUZjTuqzpq/IbY7Yh2/0ZV0vD3dtfZyHr6+snx+9cVimTBnmzZsHwPTp05kxYwadOnWibdu2eTqGIAhCUSCTXtHJgJKSknB0dCQxMdFcUTFLRkYG4eHhBAYGFoq7ojkxmUyEXrtKhostkgxUqRmo0wy4uLjg7u7+wipCrlq1yjx33YsYr7Zo0SK2bNliHij/smR113RwcGD8+PHmogPC0ykKvzsvQ1b3uqwbF0Lm79iSJUs4f/485cqVM89BZ21tTenSpZ+71S7doOdCfBSXE2K4knjP/HUjKQ5DDkmVo9qa4rbOeNvY425th5u1Le7WtrhZ2eJmbYuz2gY7lRW2SjV2KjW2ysyfrRXK545VkiR0JiPpBj0ZRj3pWV8Gw/8/NmR+zzAaSNFmEHLxHwJLl0InGUk36kkz6Ek16Egz6Egz6DO/G/Wk6jO/Zy1PNeiePal8iFquyJ4MZkscs5JCyyTRXmWNh40dntZ2eNrY425ti1JeNCaWT0xMZOPGjRw/fhyFQoFMJqNjx47mG5FFjfjbJOQncT0VTo/LcR4mWvIKKblcToni/oSFX0dycUBva43eFnTaNO5dvkypoKB8+ZD9yy+/UKJECXx8fDh//rx5DrwXVZBk+PDhxMfHk5ycjL29/Qs5xpNktYgKgvD8kpOTWbJkCbGxsSQkJNCuXTtatGjxXH9D0gw6QmLvcCbuLmfiMr+HJt6zSGYUMjlBDq609S1LGUd3Sti7UtzOieK2zvjZOuGgLribEDKZDCuFEiuFEnjy66DX6/G5HkfbCk2e6YOU3mT8L/l7KCF8NEl86LnURxLHzHW1/y37bx29jvsZKeZ19aa8jZ0GkCHD1UqDl409xTQOBNi5EGDvTKCdC4H2LgTaueBubVcoxmM6OjrSv39/KlSoQFpaGuHh4WzcuJGzZ88yevRo8cFWEIQiSyR5hZiNjQ2VypTn+o0bZKhAUinQW6mQeTpwNeJfypQIylP1zceJjo5m8uTJREdH4+3tTffu3V/oNBJKpbJAKpg+7EkV5QRBeLw1a9aY561MSEggPT2dBg0a4O/v/0wFje6lJ3PkXgSHY8I5HBPOmbi7Fq1zAXbOdCpenqouPlRw9qKMowcl7F1QK8S/MACVXIGTlQ1OeUgon5X+v5ZJiyTxv8QxUZfBvYwUYtKTM78yUoj+7+fD98LZFXk12/7slFaUc/KgvLMXFZy8zN+LaRxeevKnUCjMc+o1aNAAOzs7du/ezcWLF6latepLjUUQBCG/iP+QhZxcLqdUUBDp6enExMSQrEtHZ2+DwcWOsOvXCCzuj42NjbmQydMaN24c48aNy+eoBUF41aSkpLB161Z0Oh3Hjh2jfPny2NnZ4erqSsOGDbNVtH3svvRaDkTfYNfdq+yOvEpo4j3zcy5WGtr4lqGeRwA13Hyp6uKDq3XOY7eEl0clV6BSK566hVSSJGK1qYQnP8j8Ssn8fj05lksJMZyMvW2xvoe1HXXc/antXpza7sWp6eb3UltlFQoF3bp1IzQ0lM2bN3PmzBk0Gg3dunUTrXqCIBQpT5XkLV68mMWLFxMREQFA+fLlmTx5Mm3atAFg4MCBrFixwmKb2rVrc/z4cfNjrVbLmDFjzJPgNm/enEWLFllUtoqPj2fEiBHmuXk6duzIwoULLeY5e53IZDI0Gg2BgYEkJCQQEXUHg7sjRmdb/n0QDSlaSgWWwMrK6oWN1RME4fV1+fJlNmzYwN27d83L3nnnnTz3JJAkidDEGLbcusSuu1c5ci/C3P3PR+NI35LVaOhZggaeAZRx9EAuE3/HXhUymQx3azvcre2o5Z59iqLYjFQuJURzKT6aC/HRnIq9zfY7oWy9fSlze2RUdPaiebFSvOFdikZeJbBTWb3wuNu1a8e+ffuIj4/n5MmTlClTRrTqCYJQpDxVkufr68vs2bPNd2xXrFhBp06dOHv2LOXLlwegdevW/Pzzz+ZtHv0QMHLkSLZt28aaNWtwdXVl9OjRtG/fnpCQEHNrVO/evblz5w47duwAMsdx9evXj23btj37mb4inJycCJTJuBkXg87BBlQK5FYqrkXfBq2eoOKBuU5BIAiCkJvo6GhMJhMuLi6cOnUKg8EAgMFgYPPmzajVaj788ENu3ryJQqF4YoJnkkycuH+LTTcvsvnWRa4lxQJgo1DxhncpWvqUpqVPaco6ehaKsVlCwXCztqWxV0kae5U0L0s36DkTd4fj929y4v4tDsaE8/Wlg3x96SBKmZy6Hv609S1Lp+LlKePo8UKun2rVqpm7Hs+YMYNTp06JJE8QhCLlqZK8rIlbs3z22WcsXryY48ePm5M8KysrvLy8ctw+MTGRpUuXsnLlSt544w0Afv31V/z8/Mxl9UNDQ9mxYwfHjx83T1D6448/UrduXcLCwggODn7qk3zVODo6UlySePDgAUn6DAwu9ujsbcDehmtRt3FWayju51fQYQqCUEiZTCbCwsJwcXFBr9dz//59fvvtNwyGzAq+d+/etegCHhAQwMiRI1GpVFSoUCHX/UqSxIn7t/j1Rgi/37xAdHoyAN42Drxbpi6di1egsVfJ/wqSCELObJQq6nsGUt8zc65XSZK4lBDNnsjr/B11lf1R/3IoJpwJIdsp7eBOp+LledO/AnXc/V9IwlevXj02bNhgriwsCIJQFDzzf1qj0cj69etJTU2lbt265uX79+/Hw8MDJycnGjduzGeffYaHhwcAISEh6PV6i0moixUrRoUKFTh69CitWrXi2LFjODo6mhM8gDp16uDo6MjRo0dFkvcfJycnnJycSE5O5l7sfRJTUzB5OqN31BCfpsU+Ph5nZ+eCDlMQhEIgNTUVGxsbTCYTkZGRHDt2jL1796JSqTAYDEiShJOTE9bW1kRGRvL2228/VatFWOI9Vt04w2//nuVGchwApR3cGRhUk87+5anp5ie6YArPTCaTUcHZmwrO3nxUviFao4H90TfYcusiW25dYt7F/cy7uJ8AO2f6lqxOnxLVKOPkkW/Hb9KkCSdPnuTnn3/mf//733MXPBMEQXgZnjrJu3DhAnXr1iUjIwM7Ozs2bdpEuXLlgMwJb7t3746/vz/h4eFMmjSJZs2aERISgpWVFdHR0ajV6mzJh6enJ9HR0UBml6GspPBhHh4e5nVyotVq0Wq15sdJSUlAZmlqvd5yIly9Xo8kSZhMJkym559fqCDZ2toSaGuLTqfj2o3rGJ1s0WusuHk/msj7MXg4u+Lq6lrQYRa4rOkgs9534dmYTCYkSUKv1z9zsZ9XQdbflEf/thQ2GRkZxMfH89VXX1GmTBmSkpK4ceMGkPnB9eLFizg4ODB06FDzlAcGgwEbG5snnluyXsu6iPMsvX6K03GZlTa9bewZWbYBvQKqUMWlmLlVxWgwYiTvJfhfN0Xleios5EAzjxI08yjB19XbcybuLhtuXWRtxDlmnv+bmef/prqLD/1KVqNPYDUc86FwS9++fZkzZw6//fYbPXr0KLRFWMS1JOQncT0VTnl9P546yQsODubcuXMkJCTw+++/M2DAAA4cOEC5cuXo2bOneb0KFSpQo0YN/P39+fPPP+nSpUuu+5QkyaKLRU7dLR5d51GzZs1i2rRp2Zbv2rUr2xg1pVKJl5cXKSkp6HS6x55vUeJbzIeoezGYVAoMLnYYgKjkePOHNiFzTi/h2el0OtLT0zl48KB5zNbrbPfu3QUdQq7u3btHSEgIkiShUCg4e/YsAFWqVMHBwQGNRkO1atWQyWQcPnw4z/u9YUhhlzaKA9r7ZGDEGjnN1J40tnKngtIJRYyMqJjzRHH+RZ3aK6swX0+FXSOgvlUlLikSOaC9x9EH0Yx8sI3xp/6kkZUHray8CFI+39yspUqV4tixY4SEhNCgQYN8mav2RRHXkpCfxPVUuKSlpeVpvadO8tRqtbnwSo0aNTh16hQLFizg+++/z7aut7c3/v7+XLt2DQAvLy90Oh3xj3QlvHfvHvXq1TOvExMTk21f9+/fx9PTM9e4JkyYwMcff2x+nJSUhJ+fHy1btsw2G3xGRga3b9/Gzs6uUP+Rfha2trZcC7+BUQ4mW2t09jZEx8Ti4+6Jm5tbQYdHWFgYTZs2JSws7KVOhi5JknkC9vweszFo0CASEhLYtGkTAN27d6devXqMGjUqX49TGGRkZGBjY0OjRo1eud+dp6HX69m9ezctWrQoFHf0N2/ezO3bt4mLi8NoNOLl5UVoaCjBwcE0btwYf39/4uLiUKlU+D3DeF2d0cDam//w3ZWjnEnKrLBZ1aUYw0rVpmdAZexfQrXDV1lhu56Ksg7AJ0CaQcf6m//ww9UT7I67w25tNDVcffmwTH26+VdEJX/6ngiSJHH58mVWrlxJZGQk77//fqErGiSuJSE/ieupcMrqrfgkzz36XZIki26SD4uLi+P27dt4e3sDUL16dVQqFbt376ZHjx4AREVFcfHiRebOnQtA3bp1SUxM5OTJk9SqVQuAEydOkJiYaE4Ec2JlZYWVVfYPGiqVKtuFaTQakclkyOXyIjflQJMmTahSpQrz58/P8Xm1Wk354LJIksSV61cxqhQYPZ2ISksi4XoCgYGB+f6L+qSYHjZp0iTef/99HB0d8zWGJ8nqopn1vucnmUxmsd8pU6bQtGlThg0blu0GQ1Enl8uRyWQ5/l69jl7m63Ds2DEuX75M69atWb9+PT179mTLli3cvn2b2NjMypUajQaZTEZoaCht2rShZcuW5p4Mz3KTJy4jlSVhx/g29AjR6cnYKtUML12H4cF1qO7m++QdCE9F/F7lH0eViqFl6jK0TF1CYu/wfdgxVv17hgFH1jLp3E5GlW/EkNK1sFc93c2qKlWqoFKp+Oabb5g0aRKjRo3KtdhcQRLXkpCfxPVUuOT1vXiqJO/TTz+lTZs2+Pn5kZyczJo1a9i/fz87duwgJSWFqVOn0rVrV7y9vYmIiODTTz/Fzc2NN998E8isCjlkyBBGjx6Nq6srLi4ujBkzhooVK5qrbZYtW5bWrVszbNgwc+vg8OHDad++vSi68hRkMhkligdw5d/rmJzt0GusMFmZuBgWSnFvH6ysrLCzs3upMd25c4etW7fmKRksyipVqkRAQACrVq3i3XffLehwhCJOq9WycuVKTp06BcDJkycBmDp1KnK5nGbNmpmLXbm5uaFQKIiMjDSXf38W1xLv89Wlg6y4fpp0ox4/Wyfm1WjP0NK1cbISXb+FoqW6my8/uHVnVvW2LL5yjIWhhxl1civTzu3mvTL1+Lh8I1ytbfO8v/LlyzNkyBCWLl3K4cOH6dat2wuMXhAE4dk8VZNGTEwM/fr1Izg4mObNm3PixAl27NhBixYtUCgUXLhwgU6dOlG6dGkGDBhA6dKlOXbsmEW3vK+//prOnTvTo0cP6tevj0ajYdu2bRZFHFatWkXFihVp2bIlLVu2pFKlSqxcuTL/zrqIGjhwIAcOHGDBggXm1qOIiAiMRiNDhgwhMDAQGxsbgoODWbBgAVZWVpQvFYxNYgbT3x3JqEHDWbp6FWXLlyM4OBitVsvRo0epUqUK1tbW1KhRg82bNyOTyTh37pz5uJcvX6Zt27bY2dnh6elJv379zC0HucWUk3Xr1lG5cmWLie8hc4oMPz8/NBoNb775Jl999ZXFxPc3btygU6dOeHp6YmdnR82aNfn7778t9hEQEMCMGTPo3bs3dnZ2FCtWjIULF1qs4+zszOLFi2nTpg02NjYEBgayfv16i3Xu3r1Lz549cXZ2xtXVlU6dOlmcj9Fo5OOPP8bJyQlXV1fGjRtnLurysI4dO7J69erc3kpByJMrV64wZ84cTp8+TZMmTejXrx9169Zl2LBh1KlTh8GDB9O9e3caN25M5cqV8fHxwcvL65kTvMsJ0fQ5sIoym+ayJOwYFZy9WN24Dze6TWBMxSYiwROKNFdrW/5X5Q1udp/I9/W64WFtx+f/7CFg/edMOrODeG3exrkA1KpVi8aNG3P69GkOHjxIWFjYC4xcEAThGUivqMTERAmQEhMTsz2Xnp4uXb58WUpPTy+AyJ5dQkKCVLduXWnYsGFSVFSUFBUVJRkMBkmn00mTJ0+WTp48Kf3777/Sr7/+Kmk0Gmnt2rWSJEmSyWSSevToIWk0Gqldty7S2oO7pTUHdknHT5+UXFxcpL59+0qXLl2Stm/fLpUuXVoCpLNnz0qSJEmRkZGSm5ubNGHCBCk0NFQ6c+aM1KJFC6lp06aPjSknnTp1kt555x2LZYcPH5bkcrk0b948KSwsTPruu+8kFxcXydHR0bzOuXPnpCVLlkj//POPdPXqVWnixImStbW1dPPmTfM6/v7+kr29vTRr1iwpLCxM+uabbySFQiHt2rVLkiRJMhqNEiC5urpKP/74oxQWFib973//kxQKhXT58mVJkiQpNTVVKlWqlDR48GDpn3/+kS5fviz17t1bCg4OlrRarSRJkjRnzhzJ0dFR2rBhg3T58mVpyJAhkr29vdSpUyeL89q+fbtkZWUlZWRkPNubXUgV1d+d/KbT6aTNmzdLOp0u3/d97do1aePGjdLGjRuljz76SBo7dqwUEhKS78d52Pm4u1L3vSsk2bIxEstGS612/iAdjLohmUymF3pcIdOLvJ6E3BmMRmn1jTNS8O+zJZaNlhxWTpSmnNkhxWek5Wn7W7duSe+88440fPhwaeTIkVJ8fPyLDTgPxLUk5CdxPRVOj8txHiZmpH1IjRo1HjtNw4vi5eXF6dOnn7ieo6MjarUajUZjMQZAoVBYVBYNDAzk6NGjrFu3jh49eiCTybCxscHOzo5v531JfGoSRk9nNi77BZPJxNdff42bmxvlypXj7t27DBs2zLyvxYsXU61aNT7//HPzsmXLluHn58fVq1cpXbp0jjHlJCIigurVq1ssW7hwIW3atGHMmDEAlC5dmqNHj/LHH3+Y16lcuTKVK1c2P545cyabNm1i69atfPDBB+bl9evX55NPPjHv58iRI3z99de0aNHCvE63bt0YOnQoADNmzGD37t0sXLiQRYsWsWbNGuRyOT/99JN5MP3PP/+Mk5MT+/fvp2XLlsyfP58JEybQtWtXAJYsWcLOnTuznauPjw9arZbo6Gj8/f0f+7oIwsNWrVpFQkICGo0GPz8/3n333WwVgvPL5YRo/heyg023LgLQzrcsk6q8QW13cc0Krz6FXM5bJarSPaAya8LPMe3cLqad282Cy4eZUKkZI8o2wFqZ+9gXPz8/unfvzs2bNzl37hyHDh2iQ4cOL/EMBEEQcieSvIdER0dz9+7dgg7jmSxZsoSffvqJmzdvkp6ejk6no0qVKhbrZI0Vc01OJvx+JBH/hhNUoRx3E+O4G3ePYi7u5mI3WUJCQti3b1+O4/du3LhB6dKl8xxjenp6toqMYWFh5jGbWWrVqmWR5KWmpjJt2jT++OMPIiMjMRgMpKenc+vWLYvt6tatm+3xo+P/6tSpk22drK6pISEhXL9+PVvVz4yMDG7cuEFiYiJRUVEWx1EqldSoUSNbl82sKSvyWuZWeL0lJCRw69YtkpOTiYyM5L333rO4sZHf7qQmMPXsLn6+fgqTJNHRrzyTq7QQxVSE15JCLqdPyWr0DKzMb/+eZerZXYw//SffhR5hVvW2vFWiCnJZzqNbmjVrBsDy5cvZtWsXtWrVemwlcEEQhJdFJHkPKagKWc973HXr1jFq1Ci+/PJL6tati729PfPmzePEiRMW69naZg4st7e3x1fvDinpyAC9Y2YrQWRaYrbxdCaTiQ4dOjBnzpxsx82qmppXbm5uxMfHWyyTcpj/8NGEaezYsezcuZMvvviCoKAgbGxs6NatW57mOMxLeeusdUwmE9WrV2fVqlXZ1nF3d3/ifh724MGDZ9pOeL1IksS///7LkiVLzCWRHRwcKFeu3As5XoI2ndkX9rLg8iEyjAYae5VgTo12ouVOEAClXEH/oBr0DKzCd6FHmHH+b/oc/I2vLx/ki5odaOxVMtdt69aty7Fjx5g+fTqTJ08WiZ4gCAVOJHkPyUuXyYKmVqsxGo0Wyw4dOkS9evV47733zMtu3Ljx2P24uLhQr05ddmz/C8OtGKy93dBrrAi5cQWAazdu4OTiQtWqVdm4cSMBAQEolTlfLjnFlJOqVaty+fJli2VlypQxVwvM8uj7cOjQIQYOHGhu8UtJScmxuMvx48ezPS5TpozFshMnTjBw4ECLdapWrQpAtWrVWLt2LR4eHrlOfeDt7c3x48dp1KgRAAaDgZCQkGyFLi5evIivr2+hmJtQKLzOnz/P4sWLsbW1ZerUqdja2mJtbZ3vpaoNJiOLrhxl6tldxOvSqejszZwabWntU6bQzfMlCAXNSqHk4wqNGViqJjPP/823oUdo8tdi3ixega9qdSTA3iXbNsHBwcyePZsvvviCn376iY8//tjco0MQBKEgFK1J4gQCAgI4ceIEERERxMbGYjKZCAoK4vTp0+zcuZOrV68yadIkc7n1x+nfvz8ymYxF878h+p8wTv25m5WLfwRA72pHgspEz549efDgAb169eLkyZP8+++/7Nq1i8GDB5sTu5xiykmrVq04duyYRUL44Ycfsn37dr766iuuXbvG999/z19//WXxwTMoKIiNGzdy7tw5zp8/T+/evXM8xpEjR5g7dy5Xr17lu+++Y/369Xz00UcW62zYsIFly5Zx9epVpkyZwsmTJ83j+vr06YObmxudOnXi0KFDhIeHc+DAAT766CPu3LkDwEcffcTs2bPZtGkTV65c4b333iMhISFbLIcOHaJly5ZPfA+E19uJEydwcnJi+vTpeHt74+DggFqtztdj7Iu6TpUtX/HRiS3Yqaz4pWEvznYcRRvfsiLBE4THcLHS8FWtjoS+OZau/hXZdOsiZTfNZca53WQY9NnWd3Z2ZujQody+fZuRI0cSGhpaAFELgiBkEkleETNmzBgUCgXlypXD3d2dW7du8c4779ClSxd69uxJ7dq1iYuLs2jVy42DgwPbtm3j/PnzdOrYke+//obhbw8HQG1thUGlQKZWsmHDBlJSUmjZsiUVKlTgo48+wtHR0Tz5d04x5aRt27aoVCqL6Q/q16/PkiVL+Oqrr6hcuTI7duxg1KhRFmP3vv76a5ydnalXrx4dOnSgVatWOZaIHz16NCEhIVStWpUZM2bw5Zdf0qpVK4t1pk6dypo1a6hUqRIrVqxg1apV5q5xGo2GgwcPUrx4cbp06ULZsmUZPHgw6enp5pa90aNH079/fwYOHGjuGvvomMKMjAw2bdpkUcBGEB5mNBpZsGABZ86coVmzZi9kzspbKfH02PcLzXYs4XpyHJOrtOBKl3H0C6qOQi7+9AtCXpV0cGNDswHsbjWcADsXJp/dSYXNX/Dn7cvZ1g0MDGTixIkUL16c5cuXk5qaWgARC4IggEx6dADUKyIpKQlHR0cSExOzdb3LyMggPDycwMDAbIVAXncrV65k8JDBHLpwBqWrU+bC/64QdUo6xZ09LOawe1qLFi1iy5YtOVakzDJs2DCuXLnCoUOH8rzfgIAARo4cyciRI3N83mQyoVAo+P333+nSpcvThv1UvvvuO7Zs2cKuXbte6HEKgvjdyaTX69m+fbv5xsXT2rp1K9u3b6ddu3a0aNEiX19LndHAvIv7+ez8HtKNejoXr8BXtToQaO+ab8cQ8tfzXk/Cy6MzGph/+RDTz+0m1aCjo1955tfumO3368GDB0yfPh2TyUTfvn2zFTV7UcS1JOQncT0VTo/LcR4mxuS95n755RdKlCiBj48P58+fZ8KECfTs0ZMSLp5EJyWAKnOSepNSgc7ehoioOwSQOZ3Ds3T1Gj58OPHx8SQnJ5urWH7xxRe0aNECW1tb/vrrL1asWMGiRYvy7yRfMpVKlW0idkHIEh4ezl9//UW7du3yvdz6sXsRDDuynksJMQQ7uvNN7c609AnO12MIwutMrVAyrmJTepeoyphTf7A2/By7I68ytWpLPi7fCKU883+mi4sLH374IevXr2fp0qX8888/DBo0CIVCUcBnIAjC60Ikea+56OhoJk+eTHR0NN7e3nTv3p3PPvsMjUaDg70Den3muIOomGhSHOQY3B25lRSLTVwcgf7+uRZjyY1SqWTixIkWy06ePMncuXNJTk6mRIkSfPPNN+a57Iqi4cOHF3QIQiEkSRJ79uzhzz//pHjx4rRt2zbf9p2sz+DTkL/4LvQoKrmcaVVbMr5iM6wU4k+8ILwIvrZOrGnSl+Gla/POsd8Zf/pP1vx7jh/rdzdPRVKyZElGjRrF8uXLOXXqFPXr16ds2bIFHLkgCK8L8QngNTdu3DjGjRuX43PW1tbmbmTW1taE3riGwdkWncYKg1HiyvWrlC0V/Nx3JtetW/dc2wM5Vtt8VHx8/GObtQXhRTp16hTr168nMDAwX+/o/3H7Mu8e/Z07aYk08Azkh3rdKOskyrcLwsvQrFgpzncazYzzu5l3YT+1/ljAqHKNmFa1JbYqK6ysrBg+fDiTJk3i5MmTIskTBOGlEaPvhTxRqVSUDiiBh16BOi4ZSQ56Z1tu3r5d0KEJQqFmNBo5fPgwv/76KzVr1uSTTz7Jlzm0ErTpDDi4mg5/LyNJr2Vx3S4caPOuSPAE4SWzUar4vHpbQjqOpLqrL19eOkCFzV+w624YkDkXa4MGDTh+/Dg3b94s4GgFQXhdiCRPyDNra2t8fX0p7V8CVUIqRoWcZKWR81dDuXr9Wq5TJwjC62z79u2sXLkSFxcXevXqlS/7/DvyKhU3f8EvN0Jo61uGy2+O5Z0y9ZDLxJ90QSgolVyKcazdh8yv1Yn7Gam02vUj/Q7+RmxGKm+88Qa+vr58//33T5zHVhAEIT+ITwTCU7O2tsbbyQ1Vhg69tRq9ix2p9mrOXbpAcnJyQYcnCIVGeHg427dvp3379ubJzp9HmkHHh8c30WLnDyToMvixfnf+eGMIPraO+RSxIAjPQyGX81H5hlx6cwxtfcvw640zlNs0l023LzFo0CBSUlKYO3cuu3fvRqvVFnS4giC8wkSSJzwTNzc33OQ2WMWmoEpKx6iQY/J24Ub0HW7fvs0rOjOHIOSZTqfj559/xs/PL1+KrJy8f4uqW77m29AjNPQM5J/OHzO0dG0xobkgFEL+di788cYQfmvcB5Mk0WP/Sj4K+5sx0ydTqVIlNmzYwPLlyws6TEEQXmGi8IrwTGQyGT7FiuFTrBixsbHcjo3B5GKPwdmOOJ2BhMuXKFUy6LWeS014vf3+++88ePCA//3vf89VZMUkmfjy4gE+DfkLuUzGFzXbM7JcIzGhuSAUcjKZjF4lqtLMO4j3j21kQ8Q/7Iu6zjetOlHf3p4TJ06QlpaGRqMp6FAFQXgFiU8JwnNzc3OjQsnSqONSUGr1GNRKtJ6OhN8SA8yF14vRaOSbb75hxIgR7N+/ny5duuDl5fXM+4tJT6bt7qWMO/0npRzcONXhI0ZXaCISPEEoQjxt7NnQbADrmvRDhow+h1azziODJJmRjz/+mFu3bhV0iIIgvILEJ4VXQJMmTRg5cqT5cUBAAPPnz3+pMahUKiqWLYcbalRpmeMM9EoZ48aNo0qVKi81lvwSFhaGl5fXE8cZvozXW6vVUrx4cUJCQl7ocYTns337dkJDQ2ndujVDhgyhSZMmz7yvvyOvUnnLV+y8G8aQUrU41eEjKrkUy79gBUF4qboHVuZyl7H0DKzCn1FX2VrLhUh/R5YuW4pOpyvo8ARBeMWIJO8VdOrUqRc+Iffy5ctxcnLKttzXxxdnmRUKowmdgw1d+r7FunXrSEtLy/M4vYJIUnMyceJE3n//fezt7YHcz/llvN5WVlaMGTOG8ePHv9DjCM/u9u3bbN++nTZt2tC2bVtq1aqF/Bla3PQmI5+GbKflzh9JM+hY3bgPPzXoga3K6gVELQjCy+RubceaJn35vekArFVqtnkZ+cUxiWUbn3++WEEQhIeJJO8V5O7u/tg+/nq9/oUe38vTE3lCKjJJQlnMnTQrGdfu3yUyKvKlTrPwPHdG79y5w9atWxk0aNAT133S651f+vTpw6FDhwgNDX3hxxKe3qFDh3B0dKRdu3bPvI/ItESa/rWYWf/spYabL2c7juKtElXzMUpBEAqDLgEVufzmWHqXqEqEk4KPks8yZNlXJCYmFnRogiC8IkSSV8SkpqbSv39/7Ozs8Pb25ssvv8y2zqMtYTKZjCVLltCpUydsbW2ZOXMmANu2baN69epYW1tTokQJpk2bhsFgMG+XkJDA8OHD8fT0xNramgoVKvDHH3+wf/9+Bg0aRGJiIjKZDJlMxtSpU83bqdVqgv1LoIxO4Ic5X9GjXWf0ttbck7S82bUrnTp14osvvsDb2xtXV1fef/99c+LZpEkTbt68yahRo8z7znL06FEaNWqEjY0Nfn5+jBgxgtTUVIvznjlzJgMHDsTR0ZFhw4YRERGBTCZjzZo1tGzZEo1GQ/ny5dm/f/9jX+d169ZRuXJlfH19AR57zjm93t9//z3t27dHo9FQtmxZjh07xvXr12nSpAm2trbUrVs321xJT3o/XF1dqVevHqtXr35s7MLLFxISwoEDB6hRo8YzF1k5EH2Dalvnc+ReBKPKN+Jw2/cp6eCWz5EKglBYuFrbsqpxHzY2HYBGqWKZLJLaq+ewbNN6IiMjCzo8QRCKOJHkFTFjx45l3759bNq0iV27drF///48jdOaMmUKnTp14sKFCwwePJidO3fSt29fRowYweXLl/n+++9Zvnw5n332GQAmk4k2bdpw9OhRfv31Vy5fvszs2bNRKBTUq1eP+fPn4+DgQFRUFFFRUYwZM8bieNbW1pQrUxZF+v+3phmtVJjUCvbt28eNGzfYt28fK1asYPny5eZS0hs3bsTX15fp06eb9w1w4cIFWrVqRZcuXfjnn39Yu3Ythw8f5oMPPrA47rx586hQoQIhISFMmjTJvHz8+PF88MEHhISEUK9ePTp27EhcXFyur9fBgwepUaOG+XFezvlhM2bMoH///pw7d44yZcrQu3dv3n77bSZMmMDp06cBLGJ/0vuRpVatWhw6dCjX4wov3+XLl/nhhx+wt7enfv36T729JEl8eXE/zXd8T6pex/qm/fiqVkfUClH8WBBeB28GVORG78l0dCtJmJWOd+OOM2jZV5w+fVpMRyQIwjMTnyIeYkhPJyk8/KUf1yEwEKWNzRPXS0lJYenSpfzyyy+0aNECgBUrVphbmx6nd+/eDB482Py4X79+fPLJJwwYMACAEiVKMGPGDMaNG8eUKVP4+++/OXnyJKGhoZQuXdq8ThZHR0dkMtljKweqVCpcHJ2QG4wotQYMVkokmQx7Bwe+/fZbFAoFZcqUoV27duzZs4dhw4bh4uKCQqHA3t7eYt/z5s2jd+/e5gIzpUqV4ptvvqFx48YsXrzYPFVDs2bNLJKviIgIAN5//306duyIg4MDixcvZseOHSxdupRx48blGHtERATVq1c3P1ar1Xk65yyDBg2iR48eQGaCWbduXSZNmkSrVq0A+Oijjyy6gn722WePfT+y+Pj4mM9JKHhpaWmsWLGCsmXLMmLEiKceg5esz2Dw4XVsiPiHMo4ebGw2gLJOni8oWkEQCisXKw1bOrzLH7cvM+zwOnb5pHD50K98euMK/du/ia2tbUGHKAhCESOSvIckhYezo3v3l37c1uvX41Ku3BPXu3HjBjqdjrp165qXubi4EBwc/MRtH26VgszuZadOnbJoKTIajWRkZJCWlsa5c+fw9fU1J3jPSqVSYW1tjbvMinv3HwBQIrgUoVfDKF0yCLVajbe3NxcuXHjsfkJCQrh+/TqrVq0yL5MkCZPJRHh4OGXLls3xPLPUqVPH/LNSqaRGjRqPHduWnp7+XHP8VapUyfyzp2fmh/aKFStaLMvIyCApKQkHB4cnvh9ZY/5sbGxIS0t75riE/LV27Vp0Oh0DBgx46gQvNCGGLntXcCXxHt0CKrGsQQ/sVWJeSUF4nbX3K8flLuP4+ORWll8/zUfJZ9n23SWW9v8I7+eYjkUQhNePSPIe4hAYSOv16wvkuHnxPN02Hr0LaDKZmDZtGl26dMm2rrW1NTZ5aFl8Gj7FiqFWqZAbTSis1Ghd7fj3ZgTBQaWQyWRPLMhiMpl4++23GTFiRLbnihcvbv75ae52Pjze71Fubm7Ex8fneV+PUqlU2Y6T07Ks837S+5HlwYMHuLu7P3NcQv558OABISEhDB48GGdn56fa9veIfxh4aC3pRj1f1uzAqPKNHns9CoLw+nC20vBzw7foEVCZIQfX8Jd7KtXXzmZB1fZ0bdDsmar2CoLw+hFJ3kOUNjZ5alErKEFBQahUKo4fP25ObOLj47l69SqNGzd+qn1Vq1aNsLAwgoKCcny+UqVK3Llzh6tXr+bYmqdWqzEajU91TDc3N9TIkemNSHIZWo2KmHsxedp3tWrVuHTpUq7xPsmJEyfM8/UZDAZCQkKyjed7WNWqVbl8+fIT48ovT3o/sly8eJGqVUW1xcIgMjISZ2dnatasmedtJElixvndTDm7C08be/5sMZhGXiVfYJSCIBRVbfzKcqX7BEYd3ciy8DO8FfYn3iu+pZbJHheNHf8bO56AgICCDlMQhEJKJHlFiJ2dHUOGDGHs2LG4urri6enJxIkTn+mu3uTJk2nfvj1+fn50794duVzOP//8w4ULF5g5cyaNGzemUaNGdO3ala+++oqgoCCuXLmCTCajdevWBAQEkJKSwp49e6hcuTIajeaJ0wjIZDLsbG3JSE9HnZKOzs6GmIQEEhMTLapIBgQEcPDgQd566y2srKxwc3Nj/Pjx1KlTh/fff59hw4Zha2tLaGgou3fvZuHChU8830WLFuHj40O1atVYsGAB8fHxFmMUH9WqVSuGDh2K0Wg0V0t8lnPOqye9H1kOHTrEjBkz8uWYwrORJInly5dz69YtmjdvnuffvzSDjkGH1rIu4jxVXXzY0nwQfnZOLzZYQRCKDJPBQMaDB2TExpIeG0vGf1/vxsZSOdXADDcddxsEsQmwztCxtlk93u3Wl7feeovg4GCWLl1qMZZcEITXm0jyiph58+aRkpJCx44dsbe3Z/To0c80r06rVq34448/mD59OnPnzkWlUlGmTBmGDh1qXuf3339nzJgx9OrVi9TUVIKCgpg9ezaQWW3ynXfeoWfPnsTFxTFlyhSLaRQeR6lU4qSw5oHBiN7Jlgy1HK1Bz82bN82VNd9++21KliyJVqtFkiQqVarEgQMHmDhxIg0bNkSSJEqWLEnPnj3zdMzPP/+c+fPn888//1CyZEm2bNmCm1vu5enbtm2LSqXi77//NhdLeZ5zfpK8vB/Hjh0jMTGRbt265csxhWdz4MABTp8+TUBAgLkA0pPcTU2k056fCYm7Q1f/iqxo+JaY3FwQXgOSJKFLTDQnbQ8nb+lxcf//c2ws2vh4eGRYhpWzM9ZublRwc2PMiX9Z7QKJTrbcrlUK7cRefKPT8svHg3lw4V8cx/RA/dlvVMYFg8GAwWCgWbNmoou/ILymZNIrWp83KSkJR0dHEhMTcXBwsHguIyOD8PBwAgMDn6u4hvDsTCYTodfCMNqoMFqpMCrkKHUGeJBM6RJB+dZCFhERQWBgICEhIZQoUQIHB4c8t7wsWrSILVu2sHPnznyJ5Xl1796dqlWr8umnnxZYDK/7705MTAwzZsygdu3aODg4mG8GPM6p+7fotGc5UelJTK7SgilVWiCXiTE1wv/T6/Vs3749T9eTUDgY0tKyJW7ZErn/kjjTQz1VAJS2tti4uWHt5pbtu8XPzs7IH7oe7t+/T1JSEhcvXuSdLyajqFOWxOJuZFirUOsMpGkybxw5PUhBGZuIWiYnY8tRFkyYgbOzM/Xq1Xvq8cPC6038bSqcHpfjPEy05AkFQi6XUyaoNKmpqYRH38HoYo9BrQQvZ67dvYmnvTOenp4FWoxi+PDhxMfHk5ycjL29fYHFAaDVaqlcuTKjRo0q0DheZ0ajkWXLluHs7Mybb77Jnj17nrjNmn/PMujw2syfG/elZ4kqLzhKQRCelVGnQ/vgQe5J20Otb4ZHqhzLVar/T9BcXXEuWzbnRM7VFeUz3sR0d3fH3d2dkiVLUqdOHU6dOsWQCSOx61IPo1qF/cWbyF0dSHO1J7a0DwDKkZ0ZG7YTucGINPFjVi/4wfyh0NnZWYzpE4RXmEjyhAKjUChwcHDAMd6WBJ0BCTCqleid7YhJTyXh2lVKBpZAqVQWSLKnVCqZOHHiSz9uTqysrPjf//5X0GG81nbs2MHNmzcZN24cVlaP72ppkkxMObuLmef/ppjGgS3NB1HDze8lRSoIQhbJZEKbkPD/ydp/SVpOCZw2IcFyY5kMaxcXrF1dsXZzw87PD7eqVXNM3lQODi/1/5Snpyft27fnUp067NmzB71ej18DPyIjIxk9cQKaXvXJUCkwONuR5OlEho0KzeAW9Nsw//9fm5h4BpZtxIgPPkSj0WBjY4NMJhOVfgXhFSGSPKHAFffzw+7BA2QyGTfv3Ebm5YzeRo2kVBB6+1/kOiPBJUs9U1eBgIAA83x6SUlJLyB64XVw8+ZN/vjjD9q0aUOJEiXQ6/W5rpuq19L/0Bo23rxATTc/NjcfSDGN40uMVhBebZIkWXSXfFKXSemRqsgqe3uL7pFOQUE5dpm0cnZGrizcH5Pc3Nwsxqbr9XqsrKxITk5GJpMRHR3NzZs3+f3iXuL6NuV2Nctqvj//c5kVI3thiEtCn5iKh9qOfeu34eHh8bJPRRAKnYyMDI4dO0bTpk0LOpRnUrj/egmvBblcbi6C4ujoSEREBKkKE3p7G1BpkJkkLt24iq+b52OLpQjCi6DT6Vi2bBm+vr60b9/+seveTkmg455lnHsQyVuBVVjWoCc2SjGOQRDywqjTZe8e+WiRkrg40mNjMaanW2wrV6stWtZcK1TIecybqyvKV3w8sUKhoHfv3hY3RgecPMkvq1ahM2gBWL/xd1Tvt+VupQCLbWOBfu8NY8f6zaJFT3jtjZv4KRvunKXt+rUsmPflU83FXBiIJE8oVJRKJUFBQcTFxXE//gEZRj0GV3sMbg7cTX5AZNw9XOwc8SlWTPwDEl6KI0eOcO/ePSZNmmSeTiMnx+5F8ObeFcSkJzOjamsmVm4urlHhtWcyGtHGxz+2m2TWz7pHelvI5HKsXFzMCZpDQAAeNWrkmLyp7OzE79tj1KpVi1q1apkff/v1fCZPncKpnWEcOnIYh/c6EuflBMCZKi74Vgxm7+/bCA4OLqCIBaFgHTp0iE0PQolqXZ0/NxxhVEQE5cuXL+iwnopI8oRCydXVFVdXV5KTk7ka8S8yD6fMlj0gLj0DmwcPcHV1LeAohVed0Wjk6NGjlC9fnmLFiuW63srrIQw9sg6lXM6Gpv3pGlDpJUYpCC+XJEnoU1KemLSlx8aiffAAyWSy2F7t4GCRoDmVKYPNf+PeHl5u5eyM/DE3VoRnp1armf35LCCzsNeIMR/z61fLcO7RiLs1gpCPbE+t3h0I9g3Ew9qOBZ/PpWTJkk/YqyC8Ov735WzudCqP5837fNysa5FL8EAkeUIhZ29vT9XyFbkR/i+pBi0yW2v0Nmpux90jPT0dHx8fcfdWeCHS0tKYNWsW9+7dY8iQITmuY5JMfHL6T+Zc2IevxpGtbwymqqvPS45UEPKHISMj5xa3R5ZlxMZi1GottlVYW1skaG6VK+c6NYBCrS6gMxRyYmVlxfcLv+P7hd8xaeoUfty4i3ud65H0QUdOASq9gUrtmtK5fnPeGzyU+vXrF3TIgvBCLfx2IVftdQBoDl/h453LCjiiZyOSPKHQk8vllCoZBMCN8H9JVJkwuNgRq9UTf+kSpYKCXss524QXa82aNSQmJjJgwABq1KiR7fl0yUC3A7/yx51QarsXZ3OzgXhpcp+vRhAKgsloRPvgQc7FSR6ZjFufnGyxrUyhMFeWtHFzw7FkSbxq1842xs3GzQ2lra244fYKmDF1Gm9d6kH7YX0wejsiKeVEtqiKfnwP1hhN7Jn0Ae806vTE7uuCUFRdvHiRuSe2Ed2sMpo0LV1rNCmy17pI8oQixd+vOFeuXcXgaIPBSonB05GrEf9SLqg0ykJeBU0oOkJCQjhx4gSDBg2iTp062Z6PSInnk6Tz3IxPo2/JavxYrzvWosCK8JJIkoQ+KSlvk3E/eACSZLG9lZPT/7eueXjgUq5cjuPcrJyckMnlBXSWQkEpX748/x45i1ar5cKFC3QZORR5OT/iK/gT068531+/xtqqVfhk9Bjc3Nzw8PCgZs2aBR22IDy3jIwM2nXswIPRmUXWNMt38/76HQUc1bMTn4oFBg4cSEJCAps3b851nSZNmlClShXmz5+fb8edOnUqmzdv5ty5czk+v3//fpo2bUp8fDxOTk5AZmGW8mXKcuv2bRK16ejsbTA42xEadoXivn7Y2dkVijsuOp2OcuXKsWLFiiLTtUWr1VKqVCk2bdpE9erVCzqcApOYmMiqVauoVq0atWvXzvb84ZhwuuxZTqwxjZlVWvFplTdEC4aQLwzp6Wijox/bVTLru+mRaTwUNjbYPJSg2fv75zifm5WLi+guKTyRTCbD2tqamjVrErb7GKGhoXQe1g9V93pEB3mTOsSRT8L3YgzVoQ6PplfxGsyYMUP0qhGKLEmSeLPfWyS815I0jRW+f5zi6Mbd+PkV3TluRZInsGDBAqRH7vQWBvXq1SMqKgpHR8s5xmQyGf7Fi5OYmEh41B0Mbg4YXO2JSI5l0oD+SEYT27ZtK6CoM/3www/4+/tbJHgPJwJ2dnYEBwfz6aef0qVLF/PypKQk5syZw++//05ERAROTk5UqFCB9957jzfffBOZTJZrwr18+XJGjhxJwn8T+ub0eNCgQeb1vby8aNiwIXPmzCEwMBArKyvGjBnD+PHj+fvvv/P/RSkCzp49y8aNG1EoFPTp0ydb8rb82imGH92AWq7gE7tyjKvQRCR4wmOZ9Hoy4uMfW5wk/f59UmNi2PTppxbbypTKzATtvy6TTqVL41WvnsUyc3XJIlbaWyg6NBoN1atX5/KBEwwZPoxjETFkBHgS7e+euUKwDyv/jWJbo7oc+nMX7u7uBRuwIDwlvV7PhE8ncL6EDUmu9rhGxVPVyrNIJ3ggkrwiT6fToX7Ou7KPJlGFhVqtxsvLK9fnHR0dKW40cicxFoOtNXorNZK1mpTYB9y/f/+5/tE87+u6cOFCpk6dmm35zz//TOvWrUlISGDevHl0796dw4cPU7duXRISEmjQoAGJiYnMnDmTmjVrolQqOXDgAOPGjaNZs2bmFs1n5eDgQFhYGJIkceXKFd5++206duzIuXPnzInN2LFjCQ0NpWzZss91rKLm1q1b/PDDDzg7OzN06FDs7OzMzxlMRsad/pOvLx3E386Z3xv3486xswUYrVCQJElCl5j4xMqSGXFxaOPjs3eXdHExj2WzLVYM5/Llibh/nyoNGmDn5WVO3tQODqK7pFBo2Nvbs/a31ezdu5cNGzaw7dQJUmsFY1IqiCnhRXJ/Zyp2ac7yiXNp3bp1QYcrCHmi1Wqp364Fd2r7ERPkg8/p63TCh7k/flPQoT03keQVMU2aNKFChQqo1Wp++eUXypcvz4EDB7h8+TJjxozh4MGD2Nra0rJlS77++mvz5OEbNmxg2rRpXL9+HY1GQ9WqVdmyZQu2trbZumumpqby7rvvsnHjRuzt7RkzZky2OGQyGZs2baJz587mZU5OTsyfP5+BAwcCMH78eDZt2sSdO3fw8vKiT58+TJ482WKC1sd5tLtmVsvU2rVrGTlyJLdv36ZBgwYsWLAAB5mJ2V/O44+1GwDw8PAAYM+ePTRr1oy7d+/y0UcfsW/fPuRyuXm7gIAA4P+7rNauXZuFCxeiVquJiIggICCAIUOGEBoaytatW3FwcGDChAl8+OGHucZ95swZrl+/Trt27bI95+TkhJeXF15eXixZsoQ1a9awdetW6taty6effkpERARXr161KNdfunRpevXqlS/dYGQymTlx9vb2ZsqUKfTt25fr168THByMq6sr9erVY/Xq1UyfPv25j1eU7NqVeQd68uTJFuM7E7TpvHXgV3beDaOBZyC/N+2Ps9KaO4gk71WjT019YjfJrOUmg8FiW6WtrUW3SIcSJXKsLGnt7Iz8kb+Ber2eu9u349+2bZ7/PgpCQZDJZDRv3pzmzZvT/9gxLl68yNGjR9mdfoa7raqRNrglQ9ctoMn6taz8aZno6SAUepOnTyWiRTBxHo64RifQVOfKdz9+V9Bh5QuR5BVBK1as4N133+XIkSNIkkRUVBSNGzdm2LBhfPXVV6SnpzN+/Hh69OjB3r17iYqKolevXsydO5c333yT5ORkDh06lGsXzbFjx7Jv3z42bdqEl5cXn376KSEhIVSpUuWp4rS3t2f58uUUK1aMCxcuMGzYMOzt7Rk3btwzn3taWhpffPEFK1euRC6X07dvX6ZPn86qVauYOnES/169Rqpex6SFX6IwmbAzKoiJiaF58+bUrl2b/fv3o1armTlzJq1bt+aff/4xt9jt2bMHBwcHdu/ebfHazJs3j08//ZSpU6eyc+dORo0aRZkyZWjRokWOMR48eJDSpUvj4PD4SosqlQqlUoler8dkMrFmzRr69OmT43xsD7cq5Scbm8y5B/UPje+pVasWhw4deiHHK6y0Wi3nz5+nbdu2Fgne1cT7dNyzjLDE+wwpVYtFdbugVigtXi+hcDPqdGgfPMi9OMlDPxvS0y22latUFgmaS7ly2bpJZnWdVGo0BXSGglAw6tatS926dRk2bBhbt26lx/tDkX36FncblmfvjWh8WtdGiYy2wTVZvGChSPiEQken07Hm9F7i3mpIsZ1naOdYim8WLijosPKNSPIe0vHvZdxIjnupxyxp78rWNwY/1TZBQUHMnTvX/Hjy5MlUq1aNzz//3Lxs2bJl+Pn5cfXqVVJSUjAYDHTp0gV/f38AKlasmOO+U1JSWLp0Kb/88os5iVmxYgW+vr5Pe2r873//M/8cEBDA6NGjWbt27XMleXq9niVLlpgnZf3ggw/MLU5eXl54e3kTGRmJt609ervMBGbJjz+iNxiYMXMmxf38kMvl/Pzzzzg5ObF//35atmwJgK2tLT/99FO2bpr169fnk08+ATJb1Y4cOcLXX3+da5IXERHx2ImzITOpmDdvHklJSTRv3pzY2Fji4+MpU6ZMnl6HRYsW8dNPP1ksMxgMT9Xad+fOHebNm4evry+lS5c2L/fx8SEiIiLP+3kV7NmzB71eb1EhbufdMHruX0myXsuC2p34sGwD8SGlkJBMJrQJCU+uLBkbi/a/MalmMhnW/3WXtHZzw87PD7eqVXMsUqJycBDvuSDkQceOHQmveZ4O3btyu44vUeWLQ8nMXiPbwu6yoVY53Fxc6FStIbM/nyV+r4RCYffu3RiqlEBuMlFL5cUP339f0CHlK5HkFUGPztkVEhLCvn37cmztuXHjBi1btqR58+ZUrFiRVq1a0bJlS7p164azs3OO6+t0OurWrWte5uLiQnBw8FPHuWHDBubPn8/169fNieaTWreeRKPRmBM8yOxyeO/ePfNjmUyGra0tPnYu3LoXheTuyMVr17h96xblypVDxv8XQMnIyODGjRvmbStWrJjjOLyHX4usx4+rMpqenp5rstWrVy8UCgXp6ek4OjryxRdf0KZNG2JiYszx50WfPn2YOHGixbKNGzdaJPo5SUxMxM7ODkmSSEtLo1q1amzcuNHivG1sbEhLS8tTHK+CW7dusW3bNlq1aoWbmxuSJDH/8iHGnNqGg8qaHS2G0cKn9JN3JDwXSZIwpKY+tqvkwwmcZDRabK+yt7foHulUqlTO0wI4OyMX060IQr7z9vbm9OGjzJk3l5Vr/yA2Pg5lvbLcrRQAwT7EAStDI/i5UjAlgkpiLVMwasAwOnXqVNChC6+pn9f9RnRDb7yuRTG4R++CDiffif90D3naFrWCYvtIFTWTyUSHDh2YM2dOtnW9vb1RKBTs3r2bo0ePsmvXLhYuXMjEiRM5ceIEgYGBFuvntcqmTCbLtu7DXdiOHz/OW2+9xbRp02jVqhWOjo6sWbOGL7/8Mq+nmaNHx6vkFAeAm5sbDg4OXI8IR5aupWylCkxfshC5yYR0LwEnB0dUKpVFcZFHX9fHeVwy5ubmxoULF3J87uuvv+aNN97AwcHBPG4QwN3dHWdnZ0JDQ/N0fEdHR4KCgiyWPby/3Njb23PmzBnkcjmenp45nvODBw9eq+pov/32Gz4+PnTo0AGt0cC7x37n52unCHZ0Z2vzwZR2fH1eixfBqNPlXpzkkWTOmJFhsa1crcbG3d1cpMS1QoUcEzdrV1eUonS7IBQK48eOY/zYzB47n076H7/9tpsMow5F9dJElvODsr7c/2/dsF+/Yusff/DdwoVi+gXhpVq9ejWH4sMxKbxRnAun5bSWBR1SvnuqJG/x4sUsXrzY3JWrfPnyTJ48mTZt2gCZCcK0adP44YcfiI+Pp3bt2nz33XeUL1/evA+tVsuYMWNYvXo16enpNG/enEWLFll0B4yPj2fEiBFs3boVyOwGsHDhwueuLPiqqlatGr///jsBAQG5Tgguk8moX78+9evXZ/Lkyfj7+7Np0yY+/vhji/WCgoJQqVQcP36c4sWLA5nvx9WrV2ncuLF5PXd3d6KiosyPr127ZtH6c+TIEfz9/S1am27evJkv5/s4arUa4393+NVqNeVKB9O8SVN2/bUDD1s7rDzd4L/cSGEwcfP2LXQ6HWlpaeh0OiRJypbAHT9+PNvjx3WrrFq1KosXL85xX15eXtmSMwC5XE7Pnj1ZuXIlU6ZMydbdMzU1FSsrq+ee8F0ul+d4/IddvHiRqlWrPtdxioqYmBjCw8N5++23uadLpdveXzh2/yZtfMqwukkfHNU2BR1ioWQyGtHmNi3AI4mbLinJYluZXI6Vi4s5QXMICMCjRo0ckzeVnZ3o1iUIRdjnM2byOTMBGDH6YzZtOILMxgqAlDrBRLetyZaYBLaVDmL8R6NISEigSpUqdO3atSDDFl5hqampjBw5kr+Sb3DvzXooDUYa+5XHysqqoEPLd0/1idHX15fZs2ebPySuWLGCTp06cfbsWcqXL8/cuXP56quvWL58OaVLl2bmzJm0aNGCsLAw7O3tARg5ciTbtm1jzZo1uLq6Mnr0aNq3b09ISIh5EuvevXtz584dduzInGV++PDh9OvXr8DnPius3n//fX788Ud69erF2LFjcXNz4/r166xZs4Yff/yR06dPs2fPHlq2bImHhwcnTpzg/v37OZbIt7OzY8iQIYwdOxZXV1c8PT2ZOHEi8kfKeDdr1oxvv/2WOnXqYDKZGD9+vEUrW1BQELdu3WLNmjXUrFmTP//8k02bNr3w1yIgIICdO3cSFhaGq6srjo6O9OvXjy+//JKxw95n2Afv4hJcgug7kez78y/6ffAOkpRBulwi3ajn6tWruLi4mPdnMpk4fPgwkydPpnPnzuzZs4f169c/9lyaNm1Kamoqly5dokKFCnmO/fPPP2f//v3Url2bzz77jBo1aqBSqTh06BCzZs3i1KlTL+VGx6FDh5gxY8YLP05hcPToUaytrXngrqHt1vnEpCcztkITZlVvi+I1K10vSRL6lJQnFidJj41F++ABkslksb3awcEiQXMqU8aipc2iu+R/f+sFQXh9fPPlV8zTajEajWRkZNC0c1vUDUoSU8obpr3FtHuhaH1s8di1nPP/nKd2rdq0adMm2+cPQXgeYyZOYENpOQlu1bBNycBuwyHGfftrQYf1QjxVktehQweLx5999hmLFy/m+PHjlCtXjvnz5zNx4kTz5M4rVqzA09OT3377jbfffpvExESWLl3KypUreeONNwD49ddf8fPz4++//6ZVq1aEhoayY8cOjh8/Tu3atQH48ccfqVu3LmFhYc80NuxVV6xYMY4cOcL48eNp1aoVWq0Wf39/WrdujVwux8HBgYMHDzJ//nySkpLw9/fnyy+/NLfAPmrevHmkpKTQsWNH7O3tGT16NImJiRbrfPnllwwaNIhGjRpRrFgxFixYQEhIiPn5Tp06MWrUKD744AO0Wi3t2rVj0qRJOc4dl5+GDRvG/v37qVGjBikpKezbt48mTZqwf/9+Ro8ezbiPPiYpKQkPL09q1a2Drb0dJpUCCZAUctIcrUk3ppr3Z5BM9HlnKCfOnWHeF19ga2fLx5MmULxUCWJjY3F1dc3W0uDq6kqXLl1YtWoVs2bNynPszs7OHD9+nNmzZzNz5kxu3ryJs7MzFStWZN68eS9lPsNjx46RmJhIt27dXvixCpJOp2P58uWcDglB37QSrfcsw0ahYn3TfnQLqFzQ4eUrQ0ZG7tMBPLLMpNNZbKuwtrZI3NwqV7bsJvnQz4rnnK9TEIRXX1ZriUaj4cy+I0ybPo1lOw5xt3V1kj2cALhTJ5hFV6+xbO8VvD6fyZ8bN+Pp6YnJZCIkJIQyZcqYGw4E4WnodDq2h54goXJD7ONT8V1/iuM7Dz13vYjCSibldRDWI4xGI+vXr2fAgAGcPXsWa2trSpYsyZkzZyy6enXq1AknJydWrFjB3r17ad68OQ8ePLAo+lG5cmU6d+7MtGnTWLZsGR9//DEJj1REc3Jy4uuvv2bQoEE5xqPVatFqtebHSUlJ+Pn5ERsbm+3Ny8jI4Pbt2wQEBIg+4K8JSZJITk7G3t4eSZLM18q1iH+RO2jQx6egcrbDpLRsYWhbqwF9hg2m57vD4JFeY+pULTZ6KObtbV4mk8mwsrLi4sWLtGzZkqtXrxapf0Y9evSgatWqTJgwIcfnMzIyiIiIwM/Pr0j/7oSEhPDDip+5WNOHU7IkSju4s75xX8o6PnlcI2SOP929ezctWrQokHnNTAZDZnfJ/5K3rGIk2ri4/1/233d9SorFtjKFInMy7v9a2B73XanRiO6SL0FBX0/Cq6MoXkvHjx+ny/uDsG5ZjfvrDyAf14N0TWYyaJecjmblXr6fOIsvFy/kVoAG06kb7Fr1Ow4ODjg5OZkLh4WHhyOXy7G2tubevXvY2tri4OBATEwMpUqVyrGwmvB4RfF6epx169Yx7uKfRAd54TV3K2Eh54pkN82kpCTc3NxITEx8bIL61AN8Lly4QN26dcnIyMDOzo5NmzZRrlw5jh49CoCnp6fF+p6enuaxWNHR0ajV6mxVHT09PYmOjjavk1MBCQ8PD/M6OZk1axbTpk3LtnzXrl1oHpm/SKlU4uXlRUpKCrpH7lwLr7bk5GSLx35excjIyMDG15n09HSMesuKfQpk2BhAeS8BXOzJuiNiUsjR2VohaQ3ciI16aAsJUrX4+voydepULl68aDEmtTDTarWUKVOGwYMHk/TIOKosOp2O9PR0Dh48iOGRyaCLkp3/nGJrZXviZEnUVbnyoSKI8COnCX/K/ezevTvfYpIkCTIykJKTM79SUnL8bkpOhrQ0ePT+nEaDzM4Omb195pevL7IyZVDb2yPPWm5nl7meXI4e0AMWvxEGA0RHZ34JL11+Xk/C662oXUtfjZ1CWFgYpWe053+LvyCjfQ20chlJXs7oh7Tio13LyWhQnHu+rlAnmOafDEWhkKO8Gc/Ed0ey8c9tXHTRQ2oGhtQMVH4ekJSKNjYRK39PpJPXmfLBGJydnTEYDMhkMpH0PYWidj3lJCEhgYnLFhA5sBk+F24yqEcv9uzZU9BhPZO8VkB/6iQvODiYc+fOkZCQwO+//86AAQM4cOCA+flH7/rmVHziUY+uk9P6T9rPhAkTLIqIZLXktWzZMteWPDs7uyLdGiHk3cMtebldRzmNd1MoFDg7O1M2qDQJCQnI5XIkSSIuLg6dnRq9jRqsLH+N5DZW3L4fzVtvvfVSuljmp6w5B3OTkZGBjY0NjRo1KnK/OyaTiYsXL7L25j/85J2OUSFjVtU2fFyu4VO3Vj3N3U1Denq21rWcvmvj4jA9Msm6wtoa6/+qS1r7++fe8ubigvwVuMv6unrV7pYLBedVuJYaN27Mli1bSE9PZ/bX35P+Xgdu1bQsGBbVOXNqI6XewPS/f0Mb4MK9SqVy3adjGV+m7V5FUugt7PzckXQGJnUbypBBRaOqekF5Fa4nyPwM2KFXd+J71EeTpqVseAaT1k4qsj1VcrsR/6inTvLUarW58EqNGjU4deoUCxYsYPz48UBmS5z3Q93X7t27Z27d8/LyQqfTER8fb9Gad+/ePerVq2deJ2vOsIfdv38/Wyvhw6ysrHJsclWpVNkuTKPRiEwmQy6XiwG9rwnTf0Uist73vHp4UvCHrz93d3fu379Paur/303R6/WkSwb0znaY3By4FX8P2b0o3BycLbp0FmVyuRyZTJbj71VhlpKSwppNvzMv6gxX3VU4yJRsbDmE5s84/51Jr8eUmEjKtWvo/5uUOyMuLsd53QypqRbbypTKzHFs/yVoLmXKZK8s+d9zqqeY1kMo+ora75VQeBXla8nf358RI0YA0LlzZxYuXkRqRuaHWmuZghUb16Ee34NEZ1sMKiW3W1Uzb+sS+QCDxoqUGavwHNKGRH8PbE9cIbZpJRJbVYNW1ciqMDBz66/s3PM35xMi+erdsXTu3Pkln2nRUZSvJ4BlPy/jYkkb0jRWFF91kF9XbSvSLbl5fS+ee568rPFNgYGBeHl5sXv3bvOYPJ1Ox4EDB8zzt1WvXh2VSsXu3bvp0aMHAFFRUVy8eJG5c+cCmRNNJyYmcvLkSWrVqgXAiRMnSExMNCeCglDQZDJZjt2KExMTuX4zHJmXC3qHzG7C9zPSiL8SSumSQUX6j2RRZDQaiY6OZtJPC9noriPRXUUbnzIsb9gTDxvLsZKSyYQuKemJxUkyYmPRxscD8PdD25unBXB1xbZYMdwqVcpWnMTGzQ21gwMycXNJEAThiYKDg/l2/gKLZfPmzePTyZMI2XQcK3dnToVfIWVEJ3xPX6dihJ4SpYL4POIOP/z0I1cuX6N528Es27iW49cvkfZ+BxzuJ2GyVhHdriZbZCDJ/fl44Sw6duwobvy/giIiIpiy/kciu9fHJ+QGCz749LGNRq+Sp0ryPv30U9q0aYOfnx/JycmsWbOG/fv3s2PHDmQyGSNHjuTzzz+nVKlSlCpVis8//xyNRkPv3pmzyDs6OjJkyBBGjx6Nq6srLi4ujBkzhooVK5qrbZYtW5bWrVszbNgwvv/+eyBzCoX27duLyppCoefo6EjlchUIv3mTlIw05E626K3VGNQq/gkLJbCYr8UUDcKLNXfeXD47+ifpHWshVyiY5FCKXvesifphGeGPtLxp4+IwPTLOUGlra5GgOZQogY2bGypnZy6Eh9OwdWvsvLywdnYW3SUFQRBeAjs7O7756mvz42vXrvHOxLHUqVCFz76bal4+5uPR5p979epFWFgY747/mPIBQew+d5yr/RogyTO762mrFKdWx5ao1Wo8bBxY8Pkc/P39X9o5CS/GqlWrGDrrf5g+7IRDYhqN0h1fqxbbp0ryYmJi6NevH1FRUTg6OlKpUiV27NhBixYtABg3bhzp6em899575snQd+3aZVFd8Ouvv0apVNKjRw/zZOjLly83z5EHmW/KiBEjaNkyc/b5jh078u233+bH+QrCC6dUKilVsiSSJBFx8yZJ2nT0Djbg5cyd+PtYWVlhK7rhvXDnzp3ju9M7SH2zDp7xqXz4xzkCY7ZzXqWyaF1zKVcuWzfJrJ+VjxRtyqLX6wndvh3nsmVF66wgCEIBKlWqFHvWbX7iesHBwezd/CeQWUSwxwdDeGDKwNi6JpEV/ImskJnUWWkNlG1ej06NWyCXK5ABzes1YODAgUV2DNfrJiUlhSHDh3FI9YCM0V0B8F19hCVbi2ahlWf1zFMoFHZJSUk4OjrmWF40IyOD8PBwAgMDi1zxCOHZmEwmkpKScHBweOndMZKSkvj37i1MLg6Y5DKsH6RQrlRwkewWUlR+dzIyMqjetilXetfFJTaZt8Mlxo6bkNkK5+Dw3P+o9Xo927dvp23btiLJE56buJ6E/CKupaeTnp5Oo86tiXGQgQwkpZLI5pUwPfL/2efUNYqfj6XFf73OchIQEMCAAQOK5P/23BTF6+ncuXM0a9USwyddSXayxT4xDfXKv9m6cPkrM+zrcTnOw557TJ4gCI/n4OBAJbtyXL4ahtbVDr2jhvCICBzs7VGpVDg6Ooq7g/koMjKSPn37EtOiPDLAc9clJu/YX6QHWQuCIAj5z8bGhlM7D5CamookSZw5c4Zen36IosL/d9XUFnPhbs1S3K8cSIiUkOu+3EJ3sqDmQt5/510AqlSpQs2aNV/0KQgPiYuLo/244cTP7g+AY1wypbdf5vCxi6/lZwCR5AnCSyCXyynpH8DVuzfRO9mSZAvJhlRkGUY8MjLw9vIq6BBfCTExMdR7qz33utcj3c4av4OX+PWrRa/lH3dBEAQhb7KGUDRq1Igrfx3mzp075ufe/fgjrKLjMTrmPsxCr7Eisqwvqd7OfPLgJAA2s35hSMVmTJw4UfwPekk2bNhA7Jt1zY+dVx3heMg/r1Tr6tMQSd5rZurUqWzevJlz587leZsmTZpQpUoV5s+fn+s6MpmMTZs2vVYDWp+WjY0NXvbOxKQkYbJSYZLLMNmoiY2Lx8vTU7TmPQej0cjVq1f5cMJYbg5qZl5u/W8cVapUKbjABEEQhCLF3t6esmXLmh/v+eMvTpw4QUpKSq7bzF/4DRei40n3cwdAa2vFgw61+fHqNTY3bcDqH5dTunRplErxsftF0Ov1hIWFMfP7b9B+2AaAYvsusHjWF69tggciyXvtjBkzhg8//LCgw3hteXp6YpNkg06nIyYulnRXWyRrlbl/tfD0dDodNds0JaGUK8nV3ABQp2bgfCuW0X2GFHB0giAIQlGmUCieOJarRYsW7Nixg6ioKCRJYtxnU3F+pw1RpYuhd9DQ5ptPsL0SQ8hf+7GxsXlJkb8eUlNTqd62KellvUjvmfk+2c9ey+ZfN7723WVFkveasbOzw87OrqDDyBOdTvdKdnF4eJDsHV0yOlsrIuKicUpMxL948QKMrGjauXMnsTWLExnsY14WvOwIE8d/Yp6PUxAEQRBeFJlMRps2bcyPe/TowTvvvMNBeQR3mlUCLye83B3Ztm2b+L+UzzZu3EhCs7LEFM9sRfXddZaV36987RM8gNe3DbOIatKkCSNGjGDcuHG4uLjg5eXF1KlTzc8nJiYyfPhwPDw8cHBwoFmzZpw/f978/NSpUy26rxkMBkaMGIGTkxOurq6MHz+eAQMGZOt2aTKZcj1mlqioKNq0aYONjQ2BgYGsX7/e4vkLFy7QrFkzbGxscHV1Zfjw4RbdHwYOHEjnzp2ZNWsWxYoVo3Tp0s/1WhV2Li4uqJK1yCQJvYOGRHQ8ePCgoMMqMm7evEmVpvUY/u10okt5Y5uQilpnoNjRUH5a8j09e/YUXWAFQRCEl87e3p5Vq1Yxv/PbBM7dhs/FW0SX8OTtbz/jzz//LOjwXhlbtmzho+VfE1PcHZ/zEQTP38m3b42gSZMmBR1aoSCSvCJoxYoV2NracuLECebOncv06dPZvXs3kiTRrl07oqOj2b59OyEhIVSrVo3mzZvnmjzMmTOHVatW8fPPP3PkyBGSkpLYvHlzno/5sEmTJtG1a1fOnz9P37596dWrF6GhoQCkpaXRunVrnJ2dOXXqFOvXr+fvv//mgw8+sNjHnj17CA0NZffu3fzxxx/584IVUnK5nKCAQKzuJ6PU6tFprLidcJ/wiAhe0ZlN8o3RaKTH24MIfasW0W81xiSXY7PxCD1CUphYowO1atUq6BAFQRCE11zXrl25dvEyynPhACQMasGQ1fMZ/P47mEymAo6uaLt58yYfLJlFfN/Mcfjqf25y+ex5OnXqVMCRFR6iu+ZDJEMahsQrL/24SscyyJQ5T7qck0qVKjFlyhQgcxLQb7/9lj179qBQKLhw4QL37t3DysoKgC+++ILNmzezYcMGhg8fnm1fCxcuZMKECbz55psAfPvtt2zfvj3Px2zRooV5ne7duzN06FAAZsyYwe7du1m4cCGLFi1i1apVpKen88svv5irWH377bd06NCBOXPm4OnpCWRWuPrpp59eyW6aObGysqJ82XJcD/+XZOV/LXo6A1HRURTzLlbQ4RVKFy9epHmXDhgGNkOnVuK+ah+eanumjZpKly5dCjo8QRAEQTBTKBR89/FU3po0EuPQNsQ0r8yuy7fZvn077du3L+jwiqQ1a9YwcPp4pI/eRJOqRfbjdhbN+e61LrKSE5HkPcSQeIXYbdVf+nHdOoSgcq2W5/UrVapk8djb25t79+4REhJCSkoKrq6uFs+np6dz48aNbPtJTEwkJibGotVDoVBQvXr1bHeYcjvmw+rWrZvtcVYVz9DQUCpXrmxO8ADq16+PyWQiLCzMnORVrFjxtUnwsshkMgKL+3P1+jX0dmr01mruJyaTfO0qzk5OuLu5i26HwMGDB1m3fj1rD+8k7pMuSDIZvvsv8NuU+TRs2LCgwxMEQRCEHLVr145b9erhX7EMVp/2JCrYhw4DezPpw4+ZMG68KMbyFI4ePcrYLT+iHdsdAN/Vhzlz8IwoXpcDkeQ9ROlYBrcOIQVy3KehUqksHstkMkwmEyaTCW9vb/bv359tGycnp1z392gCkVNXwdyO+SRZ+5YkKddE5eHlDyeBrxOlUkm5MmW5c/cOsXoDekcNeiAjLZnY0Fjz+6dUKpHJZLi6ur42d6x0Oh0TJkxgTdhR7nWsg6FaR2QmCe9tJxhWo5VI8ARBEIRCz9nZmdh/bxPUoSG332oI84bzTVwUptmzmDltekGHV+idOnWKjRs3snjLapLGdMU+KQ31yj38smCZSPByIZK8h8iUmqdqUStsqlWrRnR0NEqlkoCAgCeu7+joiKenJydPnjR/UDYajZw9e/aZ5hY7fvw4/fv3t3hctWpVAMqVK8eKFStITU01J3JHjhxBLpe/8gVWnkYx72IkX7+GXC1HUivRa9QYrFWkY0AGyIw6ZJLE3UtR+Hh5m7eTyWSo1Wp0Ol2exvOpVCocHR0LdQthcnIye/bsYcW61Rwtbc29rvXNz3msO8jeBSspU+bpbpAIgiAIQkFRq9UMatCOn/cdRhfoRUyAB9+tWk3Z0sF06NDBovq2kOnQoUNs27aNX8/uJbFzXbQfd0FpMBKw8SynjvxjHp4kZCeSvFfIG2+8Qd26dencuTNz5swhODiYyMhItm/fTufOnalRo0a2bT788ENmzZpFUFAQZcqUYeHChcTHxz/Th//169dTo0YNGjRowKpVqzh58iRLly4FoE+fPkyZMoUBAwYwdepU7t+/z4cffki/fv3MXTWFzGIsZUqVRqvVcuvObWR6A5JCAYBRpcSoyvxZ7uHEXUPq/28oAakGUOftV1qebkQdE4NPsWKoVCpkMlmh+kN56dIlmvfoiLJdTdLKaoj3dcXtThyKCxEoZfB5nw9FgicIgiAUOdMmTWbYnTvUafsGjGqPfkgrxoTtYMxnUziwebu48f2QX3/9lQk7fyG9vD8JPRuhSdPieSMa+ZFQNi5bX6g+txRGIsl7hchkMrZv387EiRMZPHgw9+/fx8vLi0aNGuWaSI0fP57o6Gj69++PQqFg+PDhtGrVCsV/icXTmDZtGmvWrOG9997Dy8uLVatWUa5cOQA0Gg07d+7ko48+ombNmmg0Grp27cpXX331XOf8KpLJZFhbW1OqZBCpqakYjUYkSeLmndsonGyRTCZQWf7qGtQKUCuQmSQUxsd3o5VkMnQaNSaVgvD4e0h6A0gSnnZOODo6olKpUCoL5k+DyWTi22+/Zc7uNdwb3Rnpv5sNxa7coVWsLRNn/YTJZKJUqVIFEp8gCIIgPC9fX1++mfo5I/5aRnqQNzH+Hig/ak/Tj/oxtctQhgwZ8toMycjNsWPHGPvz10T3bYpdcjpudx9gWnuARbPmU/uj2nh5eRV0iIWeTHpFa7UnJSXh6OhIYmJitubvjIwMwsPDCQwMxNrauoAiLJxMJhNly5alR48ezJgxo6DDyTcmk4mkpCQcHByK7B9Oo9FIUlISCoUCrVZrXm4wGIiJjwVne0yxifh6ej+2JTYuLg6tRoXBWoUk///11GlakCQkrR5fV0+cnZ2zvVb5+bsjSZLFPIlGo5H2vboRXsaJyEoBADhExWNlNFH13AN2bNhcaLqX6vV6tm/fTtu2bbONVxWEpyWuJyG/iGupaNm/fz/r169ny+2z3H2zHgA+Z25Q5paOjStXo1Qq0WjyXn09v73s60mr1aLT6Zgx6zNWh5/iTsuq2Cel83/s3Xd4FNX6wPHvbE3bbHoBQkInEJogEKRLl46ioiiKqD9RRORargVQ77VXFPWKAgqIqIA0adKlByIt9A4J6b1sm98fkZUQWiBkU97P8+RJdubMmXd2TzZ595w5x/uLZbz92kT69OlTbHLBquhqOc7FpCevijt58iQrVqygU6dOFBQU8Pnnn3P8+HGGDRvm6tDEJbRaLb6+vpfd5+fnR3p6On4Nq1/zjTggIIDU1FTS0tLIUexYvT1AAYvH38MePN04m5XKmZREHFYbeq2WerXqlOqwiJMnT9Jt2CAKql/yAUz72iRV88M7PQfbuz/z9utvoNVqeXjWw+UmwRNCCCFKQ+fOnencuTP3bdhA97sHoH/lfs7eVgdbYDJRj/VHLbDRxjuMn7+fXen/Br7z/ntM/uNXtN7uOGoEcrZHCxRVJXD+VnZul9kzb4QkeVWcRqNh+vTpjB8/HlVViYqKYtWqVURGRro6NFECRqPxuu9tvDA7p7+/PxkZGWRkZOBQVRyqSmp6GppQPyxe//TS2YG4k0ex5xVgNLih2mwlju+1iRP4fuPv8Pc9hRa7jbThHSkwFn8LqrZ+H7dnujPj4FF5UxdCCFHpdejQgXNxRxg2/EF219AS37YBhAUAYDueSLVurahr9GPhrLlX/LC3okpJSaHb4H4ktKlJwr3/zJYdcDoZ95kbWfzbIvlf4AZJklfFhYWF8eeff7o6DOEiZrO5yJtnTVsNTp85Q1pWGlo/E9a/kzCbnwkwkW+1kRp3jJfefZMXH3+ajh07kpqayqgxozEaDHz96eecP3+eR8aOJsWeB0BaWhraNvU5+0DHIudWVBWfb5ZRP7CG8x5QvaJh5ID7i8zSKoQQQlR2fn5+LFuylG+mfsMPi3/j1NkzWFvU5Fzr+lAriJysPGp1bU21kFDq+4Qw46tvKnTys2fPHka9MJaD509hfaQnOV5uBJ5JIWfGSpo1bUbnRs15Y/8Bl80RUBnIMyeEcNLpdNSKiCBCVQuTvYR0bHY7mmBf7LrC+/McJnf29WnM4Eljqe0RQOze3RjG3Y3WZsevcR0MZi8sYwZg0xWdvCf0cDz5y7YTFBQEgJeq4+vJM2jZsmWZX6cQQghRHo16bBSjHhsFwEeffMyXP/xEtq+BhL6tYcwAMoBT2fn4R9VjydQf6Nmzp2sDvgE5OTkMfmYkx4a3x6EpXGoreOUuenlGMPXACUnsSkmVfhYr6ZwzQtw0RVGoGRZGzbAwVFXl+MmTZGSn4XCoaHLyMeZZOPlgV1IABjfFeuHASQ9hAzR2B6HzNqG3F/6OqXYHPevexv9iDlb6+wqEEEKI0jBu7HOMG/scixYt4pkPJmL30OMIMHGuazN44yFGfPI6Lb6aQo1q1Xno/mG0b9/e1SFfVXx8PO99+CFffz8N40v3oLWrBP+6EWOOla/+/d8KmbCWZ1UyybswMUVubi7u7u4ujkaI8k1RFGpHRACQkZGBUaPF/PYmwnz/SdbyzySh0WkxhPgVHnMqhUVf/UDTpk1dEbIQQghRafTr149+/foBMG/ePP7vszfIuLcTCUM78DugtTtYNHEsD7boQu3atenQoQNRUVGuDZrC2TmnT59OQkICVquVD6Z/jeaFe8h79xHygBrzN7P60x9kWaRbpEomeVqtFh8fHxITE4HCNdykd6FyczgcWCwW8vPzK+wSCq7mcDhISkrCZDKx5fc/yMzMdO7z8PBAVVXy8grvwzOZTDe01qIQQgghrmzw4MF06dKF6D53kte0OqpWS0b9aiQ80Jn/JWWgS4/B8PTXjGzfl7p16xIeHs6pU6eIiIigU6dOxepTVZVVq1ZRr149Iv7+QPdmZWRksHTpUl7/z5tkD7qN9NDCD4Btrw3DoVEIizmK43QSEweNlATvFqqSSR7gXETxQqInKrcLCYi7u7sk9DdBo9FQs2ZNNBoNPj4+xfaX5jILQgghhCjO19eXPeu3cvjwYTIzM7nn/x5BN/QOUkN8Cgs83I2vExJQzsWj2ToPtXoAhrVz6PLddzw0fHiRuib8501ONQ3Asv0gkx56mrp16zr3BQQEcOLECbKyssjOzsZms3Hu3Dny8/MBqF27NqdOncJ20azbVquVR59/GmVIO3JGdiLTx5Og08loCwpv7FA37eeNh5+lx797UK1atVv7RFVxVTbJUxSF0NBQgoKCsFqt1z5AVGhWq5X169fTsWNHWSD2JhgMBukJFUIIIVxMr9fTqFEjAPat28qoJ55g0/HzWDyN4GfCZtST5eOBNfTvJReq+7HiRCKrV31bpJ6C7nVJDvWF5rV46+A6OLHOuc9xLgWdTsvrn7+PV93qOLw90Af54vA0ggq2w2fQ1wpB1RdNJ3If70mWyR3flCxq/BlHu2wTD/89a3bjxxsTHh5+C58ZcUGVTfIu0Gq1MqysCtBqtdhsNtzc3CTJE0IIIUSl4e3tzU8//si6desoKCjg3LlzpKWlMfHDj/B+pBeO+Rsx9Lqd1Gp+lz3eMzULjU5LYq1/1tt1aBTsDaoXPujQmMyLyustNmw6LWrD6s7HF9M4VEIXbGZQ+G0MvO85unXrVqrXK65PlU/yhBBCCCGEqOguvefu/vvv58cff6T/7JdYvnw58WfiL3uct3d1LNkW8pPzndu+mPo/NGP6kRLig8buwKEtHMVTffVu6pwq4K+kk1hG98Pnty10D21EzZo1nccqikLPl97njjvuuAVXKa6XJHlCCCGEEEJUMiEhITz33HMAPPXUUyU69tVXX+W9D95n3ex1VIuoiRYFN4ORnkOeYuDAgZw4cYLJX3zOkImf0q5du1sRvrhJkuQJIYQQQgghnIxGIy+98CJNo5rQp0+fYre6RERE8OH7H7goOnE9ZAYFIYQQQgghhKhEJMkTQgghhBBCiEpEkjwhhBBCCCGEqETknjwhhBBCXJaqqqi2HFRLOg5LuvN74c9pOCwZ4LCiOqyg2v7++e/vqh1FowONHkUp/I5Gh6IxoOi80BjMKHrvf767BaJxD0Wj93T1ZQshRIUnSZ4QQghRSamqCva8ixKzS5K1grRi2/5J5Ar3odovX7nGgMZgBo3xn2ROowdF9/d3LapqA2fSdyEJtKBas1CtWZetVtF7o/GohtajGlqP6mi966Ez1UPnXQ+tdz00Bu9b94QJIUQlIUmeEEIIUY6p9oIiyVjxhOxq+9LBYbl8xYoOjdEXxeCDxuBT+N3oi2Kqhcbgg8Zwyb6LvmsMPig695u7LtVRmOxZMnBYM3DkJ+HIjceedw5H7jnsueewZR6h4MzvOAqSncdpPKqj92uB3r8Fer8W6PxvQ+tZE0VRbioeIYSoTCTJE0IIIW4hZ29aQSqOgpS/v6eiWlJxFKRhy08hPGc3mRtno9gyiyVr2PMvX7GiLZp0XfjuGfb3Y9/i+y76rug8XJoYKYoGxWAGgxntNco6CtKxZR3GnnEIa/pebCm7yD34NY78RAA07qEYgjtgCG6PIagDOt8mKJpr1SqEEJWXJHlCCCFECagOe2Gyln8eR9557HkJOPLOFz7+O4lTL0nocBRcpiYFxWBGMfjiZVdQ82uiGH3RmUMLEzGjb/HesyJJmleV6b3SGH0wGG+HgNu5uP/QnhuPNSUGy/mNWBI3krl9PDgsKAZfjNW6Y6zeG2P1Xmg9QlwWuxBCuIIkeUIIIcTfVIcdR1489uyT2HNO/fOVfQp7zunCRC4/EVRHkeMUnSca92A0xkA0Rj80nmHo/JqhMfoXPjb4oRj9/nls9EPRm1E0WqxWK1uWLqVPt+ILDour03qEovXoi1tYXwBUWx6W5O1Y4v+g4OzvZPz5KKCi82uOW9gA3CLuQe/b2LVBCyFEGZAkTwghRJWiqo7CpC3zELbMg9gyDmHLPIQ98xD2nNNFJhpRDD5oPWui9ayJISgajXsIGrdgtO4hhUmdezAat2CZEbKcUHTuGEM6YgzpiKnFJOz5SVjOriD/7FJy9n9M9l+T0JkjcYu4G7eIoeh9o1wdshBC3BKS5AkhhKi07PlJ2FL/wpoaW/g9bTe2zEP/3OemMfw9a2N93CKGovWqhdarJlrPcLSeYTKTYwWndQvEvc4DuNd5ANVeQMG5leSf+Jmc/Z+R/deb6Pxa4FHvEdxrDUPj5u/qcIUQotRIkieEEKJScBSkY03ehiVpM9akrVhTY3HkxQOFwyl1vk0wBEbjUW8kWu/66MwNCmdllAk6qgRFa8QtrHBop2q3UHB2GblHppG5bRyZ28fjFtYf93qPYKzWU9qEEKLCkySvjFmSd6D3a164ppAQQogboqoq9qyjWBLW/p3UbcGWHgeoKEY/DAFt8Kj3KDq/Zuj9mqM11UFRNK4OW5QTitaAW83+uNXsjz0vkbxjs8g7Mo20VXeh9ayJR8On8Kj3mPTuCSEqLMk0ypCjIJ2U3zui9QrHdNt/cKs5qMrMjCaEEDfLnnOWgoTVWOJXUxD/B46c06Bo0Pk0wRDcEc+oFzAERqP1rifvreK6ad2D8Gr8HJ6NxmJN2UFu3Bdk7ZpAVuxE3Gs/gGfkM+j9mrk6TCGEKBFJ8sqQxuhDQO8NZO78N+lrhqAPuB3TbW9jrHanq0MTQohyR3VYsZzfQMHpReSf+R175kEAdL5NcQ+/G0NoVwzBHeW+OVEqFEXBEHA7hg7TMd3+PnmHviHnwJfkHf628EOEJi9irN5bPkAQQlQIkuSVMX1AS/x7LKcgfg1ZMS+TuqIbhtA7MbV8G0PA7a4OTwghXMpRkErBmd/JP72IgrPLUK0ZaDyqYaxxF8YWb2AI6YzWPcjVYYpKTusWiFfTf+MZ9QL5pxaQs/cD0lbdhc63KV5NXsIt4h657UIIUa7JO5SLGEO7YLhrMwWnfiNr1yukLG6NW8Q9mFq+i85Uy9XhCSFEmXEUpJJ/ch55x+dgSVgLqh29f0s8Gz+HW1g/dH4tpPdEuISi0eEecTdu4UOwJKwjZ887pK8fhnbnK3hG/QuPuiNQdO7XrkgIIcpYie5Cf/vtt7n99tsxmUwEBQUxcOBADh48WKTMiBEjUBSlyFfbtm2LlCkoKOCZZ54hICAAT09P+vfvz5kzZ4qUSUtLY/jw4ZjNZsxmM8OHDyc9Pf3GrrKcUhQFt/CBBPTfjfmOaVgS/yRpfkMyd7yEw5Lp6vCEEOKWcVizyD06k9RVfTk/J5iMzU8A4N32C4LuOUNAvx2Ymk9A73+bJHjC5RRFwRjaGb8eywjotxN9QGsytz5N4i+1yN73Maotz9UhCiFEESVK8tatW8fo0aPZsmULK1euxGaz0aNHD3JycoqU69WrF/Hx8c6vpUuXFtk/duxY5s+fz5w5c9i4cSPZ2dn07dsXu/2fBWiHDRtGbGwsy5YtY9myZcTGxjJ8+PCbuNTyS9Fo8ag3gsBBh/Bq8jI5cZ+RNK8uOQe/RnXYXB2eEEKUCtVhp+DsctLW3sv5OUFkbBiOw5KOd+uPCbrnLP49V+HZ4Am0ntVdHaoQV6T3b4Fv5zkEDjqIsUYfsnb8i8Rf65Cz/zNUW76rwxNCCKCEwzWXLVtW5PG0adMICgoiJiaGjh07OrcbjUZCQkIuW0dGRgbffvstP/zwA926dQNg5syZhIWFsWrVKnr27ElcXBzLli1jy5YttGnTBoBvvvmG6OhoDh48SIMGDUp0kRWFRu+JqcVEPOqPImvnK2RufpLcuMl43/4Rxuo9XB2eEELcEFvWcfKOTCP3yHQcOafR+TTG1OIN3CPuRetV09XhCXFDdN518Wn/HV5N/032X2+Ruf05sve+h1fTf+NRbySK1ujqEIUQVdhNLRqUkZEBgJ+fX5Hta9euJSgoiPr16zNq1CgSExOd+2JiYrBarfTo8U/SUq1aNaKioti0aRMAmzdvxmw2OxM8gLZt22I2m51lKjOtZ3V8OkwnoO8ONEY/Ulf2JHVVP2xZx1wdmhBCXBfVYSPv5DxSlncj6dfa5Oz/BLfqvfG/aysBA/bgFfUvSfBEpaDzrotPh+kEDozDENKZzC1Pkziv3t+jcayuDk8IUUXd8MQrqqoybtw42rdvT1RUlHN77969ueeeewgPD+f48eO89tprdO3alZiYGIxGIwkJCRgMBnx9fYvUFxwcTEJCAgAJCQkEBRWfPS0oKMhZ5lIFBQUUFBQ4H2dmFt7TZrVasVor6JusuSmmO1dhOT2PnJgXSJrfCPfG4/Fo9ILc6F1CF9pAhW0LolyR9nRljvxk8o98S/7h/+HIPY0usB1e0d9irDkERecBgM0mw9AvJu2pkvCohVf0NNwavUDunv+Qufn/yN77AZ7NJmGoOQRFuanP1a+LtCVRmqQ9lU/X+3rccJL39NNPs3v3bjZu3Fhk+7333uv8OSoqilatWhEeHs6SJUsYPHjwFetTVbXIzfWXu9H+0jIXe/vtt5k0aVKx7StWrMDDw+Oa11O+uaMxvE+o41dC97xL+r5vOOn+KOn6NiATEpTIypUrXR2CqESkPf3D3XackIJF+Fs2AJBi6Mh503Pk2mrDAeDAWpfGVxFIe6pMhuFuiiYsbyaOjQ+Qo32d0+7DydQ3L5OzS1sSpUnaU/mSm5t7XeVuKMl75plnWLhwIevXr6dGjRpXLRsaGkp4eDiHDx8GICQkBIvFQlpaWpHevMTERNq1a+csc/78+WJ1JSUlERwcfNnzvPzyy4wbN875ODMzk7CwMHr06IG3d2VZKHcw9szDZMc8T/1z76AP7YlXq4/QetdzdWDlntVqZeXKlXTv3h29Xu/qcEQFJ+2pkKqqWBPXk7fvA6zxy9F4hOHWaAJudR4lyC2ASFcHWEFIe6rMRmM9vwFd7Ct4Jk9EH9IVj+ZvofdvdUvOJm1JlCZpT+XThdGK11KiJE9VVZ555hnmz5/P2rVrqVXr2uu5paSkcPr0aUJDQwFo2bIler2elStXMnToUADi4+PZu3cv7733HgDR0dFkZGSwbds2WrduDcDWrVvJyMhwJoKXMhqNGI3Fb3LW6/WVqmHq/Rth7L6UgtMLydw2lrQlLfCMGo9Xk3+j0Xu6Orxyr7K1B+FaVbU9qaqDglO/kb3nXazJW9H5NsGnw0zcag1F0VS956O0VNX2VNnpa3TFvfomCk4vJGvnv8lY1g638Lsx3fYWOvOtmUhO2pIoTdKeypfrfS1KNEB89OjRzJw5k9mzZ2MymUhISCAhIYG8vML1YbKzsxk/fjybN2/mxIkTrF27ln79+hEQEMCgQYMAMJvNjBw5kueff54//viDXbt28eCDD9KkSRPnbJuRkZH06tWLUaNGsWXLFrZs2cKoUaPo27dvpZ1ZsyQURcGt5gACB+4vXHJh74ckLYgk/9RCV4cmhKjEVNVB3vGfSF4QRdqawShaI77dlhLQ/y/c6zwgCZ4QV3Dh77ZzXdzkrSQtaEz6psex55x1dXhCiEqoREnel19+SUZGBp07dyY0NNT59dNPPwGg1WrZs2cPAwYMoH79+jz88MPUr1+fzZs3YzKZnPV8/PHHDBw4kKFDh3LHHXfg4eHBokWL0Gq1zjKzZs2iSZMm9OjRgx49etC0aVN++OGHUrrsykHRuWNqMZHAQfvR+0SRtnoAqasHYc857erQhBCViKo6yDvxK8m/NSN93X1ovSLw77MJ/97rcKvRWxYrF+I6XVgXN2jQIUyt3if/5DwS59Ulc8dLOArSXB2eEKISKfFwzatxd3dn+fLl16zHzc2NyZMnM3ny5CuW8fPzY+bMmSUJr8rSmWrj220J+Sd/IXPrGJLmN8LU4k08Ip9G0dzw3DpCiCpOVVUKTi8iK3YCttRYDNW649/ufxiCol0dmhAVmqJzw6vxc3jUe5ScvR+Qs/9jcg9+hVeTF/GIHCO3Xwghbtqtn89XlAlFUXCPuIfAQQdwr/swmdvHkbykDZbkHa4OTQhRAVkSN5Pye3vSVg9AY/DBv/d6/HuskARPiFKkMZgx3fYmgUOO4l73IbJiJ5A0ry45B6ag2i2uDk8IUYFJklfJaAxmzG0/x/+uzeCwk7KkDRlbn8Vhub6ZeIQQVZst8yhpa4eSsrQdqjUHvx4r8O+1BkNwB1eHJkSlpXUPxtzmMwIHHcRYrQeZW54maUEkecdmo6oOV4cnhKiAJMmrpAyBbQjotwNTy/fIOzyVpAWNyDs575pDboUQVZOjIJXMbc+RtCASS+ImzO2nE9AvBmO17q4OTYgqQ2eqhU+HGQQM2I3OJ4r09Q+QvLAF+aeXyN9vIUSJSJJXiSkaHV5RzxMwcD96vxakrxlC2uoBMjGLEMJJddjJOfg1ifPqkXtoKqbmEwkafAiPug+jaLTXrkAIUer0vlH43fkb/n02oTH4kPZHX1J+74jl/EZXhyaEqCAkyasCdF7h+N65EJ8uv2JN3kHSgsaF4/1lCIgQVZolaRspS9qSuflJ3Gr0I3DIEbya/htF5+Hq0IQQgCEoGr9ea/Ht9juqLYeU3zuQuqov1tTdrg5NCFHOSZJXRSiKgnv4YAIH7se91v1kbhlNyu8dsaUfcHVoQogy5shPJn3T46QsaYuq2vDvvRGfDtPRuge7OjQhxCUURcGtRi8C+u3Ap9McbBkHSV7YnLR1w+RvuBDiiiTJq2I0Rh/M7b7Gr9daHPmJJC1sRtZfb8ksXkJUAaqqknvkexLnNyD/+Fy823xGQN/tGILvcHVoQohrUBQN7rXuJXDQfryjv8RyfgNJCxqRtv4BSfaEEMVIkldFGUM6Edj/LzwbP0927ESSF7XEkrTV1WEJIW4RW9YJUlf2ImPjwxir9yZw8CE8ZS1NISocRaPHs8ETBA05gnfbKf8ke+uGYcuIc3V4QohyQpK8KkzRuePd8r8E9N0BWgMpS6LJ3PYcDmu2q0MTQpQS1WEnZ/9nJP8WhS0jDt9uS/DtOBOte5CrQxNC3ARFa8Sz4ZMEDT5cmOwlbiR9cXPqZH8oyZ4QQpI8AXr/5gTctRVTq/fIOfg1yQuiKDi73NVhCSFuki39ACm/dyBz27O41x1B4MB9uNXo4+qwhBCl6OJkz7P153jZ4khf3Jy0NfdgSd7h6vCEEC4iSZ4ALiy3MJ7AAXvQetchdWUv0jc8hCM/xdWhCSFKSFUd5MRNJmlRCxwFKfj33oC57edo9CZXhyaEuEUUrRH3eqPYbf4Sr9ZfYE3dRcri20lZ3o2CcytlnT0hqhhJ8kQROu86+PVYhfmOb8k/vYikBZHkHftR/jgIUUHYc86SurIXmVvH4FF/FIH9d2EIbu/qsIQQZURV9LjVe4zAQQfx6TwX1ZJO6ooeJC9qSd7xn1AdNleHKIQoA5LkiWIURcGj3qMEDozDENKZ9PXDSPujnyyiLkQ5l3fsR5J+i8KWvg+/7ssxt/lM1rwToopSNFrcI+7Bv+92/HqsQmP0J33dfSTNb0BO3GQc1ixXhyiEuIUkyRNXpPUIwbfzXHy7LsCauouk+Y3IiftCFlEXopxxWDJJWzeM9PXDMFbrSeCAPRir93B1WEKIckBRFIzV7sS/50oC+u5A7387mdueI3FuDTK3PYct86irQxRC3AKS5Ilrcqs5oHAR9doPkLn1aVJ+74A1XWbuEqI8sKbsJHlRSwrOLMan4yx8O89BY/RzdVhCiHJIH9AS385zCLr7BB4NR5N79AeS5tUj9Y/+FJz7Q27NEKISkSRPXBeNwYy53Vd/L6KeRPLC5mT99aYsoi6Ei6iqSk7c5yQviUYxeBPQbyfutYe5OiwhRAWg9ayBd8v/EnzPaczt/oc96zipK7qR/FsTcuK+wGHJcHWIQoibJEmeKJF/FlEfR3bsJJIXt8KStM3VYQlRpTgK0klfezeZW5/Bo8ETBPTZhM67rqvDEkJUMIrOHY/6jxEwYDd+PVej9a5H5rZnOf9TKOkbH8WStFV694SooCTJEyVWuIj62wT03Q4aPSlLo8ncNg6HNcfVoQlR6VmStpG8qAUF8avx7TKvcHIVrdHVYQkhKjBFUTCGdsGv63yC7jmFV9NXsMSvJmVJW5IXNpfePSEqIEnyxA3T+7coXES95TvkHPyS5N+aUHBupavDEqJSUlWVnANfkfJ7ezRuQQT024Vb+CBXhyWEqGS0HtUwNXuFwCFH8e32O1pT7Yt69x6hIGG9TMAmRAUgSZ64KYWLqP+rcBF1rwhSV/QgfcMIHAWprg5NiEpDteWTsekxMrf8Hx71H8e/9wZ0pghXhyWEqMQUjRa3Gr2K9u4lrCN1WSeSfq1L1q6J2LKOuTpMIcQVSJInSoXOuy5+Pf/A3O4b8k8tIGl+JHnH58pYfiFukj3nNCnLOpJ3dBbm9tMxt/0cRWtwdVhCiCrkn969I/j1WochtAs5+z4k6dc6pPzeidzD38m6e0KUM5LkiVKjKAoe9R8jcFAchuD2pK+7l7TVA7HnnHV1aEJUSAXxa0le1BJH3nkC+vyJR92HXR2SEKIKUxQNxpCO+NzxLUH3JmDu8ANoDGT8+RiJP4WQvn44BedWoTrsrg5ViCpPkjxR6rQeofh2+RWfLr9iTd5G0oJG5Bz8WsbwC3GdVFUlZ98npK7ohs63Cf79dqAPaOnqsIQQwkmj98SjzoP491xJ0D0nC4dzJm8jdUV3En8JJ3PHS1jT9rk6TCGqLEnyxC3jHj6YwIH7cYsYSubmJ0ld1gVbxiFXhyVEuabaLYX3321/Ds9GY/HrvhytW6CrwxJCiCvSeobh1fTfBA46gH+fzbjVHEDu4W9I/i2KpIW3kbPvE+x5510dphBViiR54pbSGH3xueMb/Hr+gT33DEm/NSV799uoDqurQxOi3HHkp5C6ogd5R2dibj8D79s/QNHoXB2WEEJcF0VRMAS1xdz2C4KHxuPbZT46Uy0yY14gcW51UlfdRd6xOai2PFeHKkSlJ/89iDJhDO1K4IA9ZMVOJGvXq+SdmItPu6kyBE2Iv9nSD5D6R19USwb+Pf/AENze1SEJIcQNU7QG3MIH4hY+EEdBKnnH55J37AfS19+PojfhFnEP7rWHYwjpiKJIn4MQpU1+q0SZUXQeeLd6j4C7toGqkrykNZk7XkC15bo6NCFcquDcKpKXtEXRGvHvu00SPCFEpaIx+uHZ8EkC+vxJ4ODDeDYahyVhLanLu5D0Sy0yd76CLf2Aq8MUolKRJE+UOX1ASwL6bcfU4i1y9n9G0m9NKYhf7eqwhHCJnANfkrqyF4agaPz7bEJnquXqkIQQ4pbRedfF1GIigYOP4N97I8bqvck9MIWkBZEkL25NTtxk7PlJrg5TiApPkjzhEopGj1fTlwkcsButR3VSl99J+p+P4ShIc3VoQpQJ1WEnY+uzZG55Co+Go/G9cxEag9nVYQkhRJlQFAVD8B2Y231F8NB4fDr/gsY9lMxt40j8qRqpf/Qn78QvqLZ8V4cqRIUk9+QJl9KZ6+PXaw25h74ha8cLFJxZgnfbL3APH+zq0IS4ZVRbHukbHiT/1G94t52CZ8P/c3VIQgjhMorODfeIIbhHDMGRn0ze8Z/IO/o96WvvQdGbcas1FI86D6EPugNFUVwdrhAVgvTkCZdTFA2eDZ4gcOB+9AGtSV8zhLQ1Q7Dnxrs6NCFKnaMgjZQVPcg/8zu+XRdIgieEEBfRuAXgGTmagL5bCRx0AM/IZ7CcXUHK7x1I+rUOWbtex5Z5xNVhClHuSZInyg2tZ3V8uy7Ap/NcLOc3kjQ/ktxDU1FV1dWhCVEq7DmnSfm9PbaMOPx7rsYtrK+rQxJCiHJLZ26A6bY3Cbz7GH691mEI7UrO/k9JmlePlOXdyTvxqyzJJMQVSJInyhVFUXCPuIfAQXG4hQ8iY9MoUpffKZ/aiQrPmraX5CXRqNYcAvr8iSGoratDEkKICkFRNBhDOuJzx1SC703A3OEHVHse6WvvJvHnmmTtfA179ilXhylEuSJJniiXNEY/fNpPw6/HCuzZx0n6rQnZu9+RT+xEhVSQsJ6U3zugMQbgf9dmdOYGrg5JCCEqJEXnjkedBwnos5GAAbtxCx9CTtynJP5ai9RVfck/vRjVYXd1mEK4nCR5olwzVutOwIC9eDYcTdauV0he1BJL4hZXhyXEdcs7OY/UFT3Q+9+Gf+91aD1CXR2SEEJUCnrfJpjbfk7Q0HOYo7/GnhdP2h/9SJpXj5x9n+CwZLo6RCFcRpI8Ue5p9J543/4BAX13oGgMpCxtR8aW0TgsGa4OTYiryon7gvQ1d+MWPgi/bktliQQhhLgFNHovPOo/RmC/GPz7bsMQdAeZO/5F4s81yNg6FlvmUVeHKESZkyRPVBh6/xb437UV79Yfk3dkBkkLGpF3cp5MzCLKHVVVydz5Cplbn8az0Vh8Os5C0RpdHZYQQlR6hoDb8en4A0H3nMQzcgx5x2aSNK8eqasHUZCwTv5nEFWGJHmiQlE0WjwbPUvgoDj0/q0Kl1tYPRB7zmlXhyYEAKrDSsafI8nZ/V9MrT7Au/VHKIq81QohRFnSelTDdNtbBN9zCnO7r7FnHiJ1WWdSFrf++wNih6tDFOKWkv88RIWk9QwrXG6hy69YU3aQNL8ROfs/lZuthUs5rDmk/TGAvKM/4NNhJl5Rz7s6JCGEqNIUnQce9UcRMGAvft2Xoei9SF8zhKQFjck9PB3VbnF1iELcEpLkiQpLURTcwwcTOHA/7nUfJnPbc6QsaYs1ZZerQxNVkD0/idTlXbGc34Bf96W413nA1SEJIYT4m6IoGKv3xL/XGvz7bEbnXZ+MPx8hcV5dcvZ/isOa4+oQhShVkuSJCk9jMGNu+zn+fTah2vNJXnw7mdvHyxu2KDO2rGOkLL0De/ZJ/Huvw1itu6tDEkIIcQWGoLb43fkbAQP2YAzuROb250n6JYLs3e/gsGa7OjwhSoUkeaLSMAS1JaD/Tkwt3iLnwBckL2hM/pnfXR2WqOSsKTtJWdIOVBX/uzah97/N1SEJIYS4DnrfKHw6/kDg4MO4RdxNVuzrJP1Si+y978sHxaLCkyRPVCqKRo9X05cIHLAXrbk+aav6kLb2Phx5Ca4OTVRClvhVpPzeCa1XTfzv2oTOVNvVIQkhhCghnakW5ugvCRp8BLfwwWTF/JukX2uTve8jVFuuq8MT4oZIkicqJZ13Hfy6L8enw0wsCatJW9yUwILlMpuWKDX+BevIXNMfQ3AH/HquRusW6OqQhBBC3AStV03M7b4mcPBhjGH9yNrxAom/1imc2M2W5+rwhCgRSfJEpaUoCu51HiBwYByGsIHUyv2SjJVdsabvd3VoooLLjfuYOrkfY6w1DN87f0Oj93J1SEIIIUqJzhSBzx1TCRx0EGP1XmRuf57EeXXJPfg/VIfN1eEJcV1KlOS9/fbb3H777ZhMJoKCghg4cCAHDx4sUkZVVSZOnEi1atVwd3enc+fO7Nu3r0iZgoICnnnmGQICAvD09KR///6cOXOmSJm0tDSGDx+O2WzGbDYzfPhw0tPTb+wqRZWmcfPH1PZ/xHm9iZqfRPLC5mTtfA3Vlu/q0EQFo6oOMreNI3fni5xzuxuvtt+gaPSuDksIIcQtoPOug0/7aQQOOoAhpDMZm58gaUFj8k78IouqVwGOglQyt4+vsEN2S5TkrVu3jtGjR7NlyxZWrlyJzWajR48e5OT8c3Pqe++9x0cffcTnn3/O9u3bCQkJoXv37mRlZTnLjB07lvnz5zNnzhw2btxIdnY2ffv2xW7/Z42zYcOGERsby7Jly1i2bBmxsbEMHz68FC5ZVFVZ+ib43BWDV5OXyd77LkkLm1IQv8bVYYkKQrUXkL7+AXL2f4Jnq0854/4giqK4OiwhhBC3mM67Lr4dZxHQbxc6U23S195DypI2FMSvdnVo4hYpOLeSpN+aknv4W6zp+659QDlUoiRv2bJljBgxgsaNG9OsWTOmTZvGqVOniImJAQp78T755BNeeeUVBg8eTFRUFDNmzCA3N5fZs2cDkJGRwbfffsuHH35It27daNGiBTNnzmTPnj2sWrUKgLi4OJYtW8bUqVOJjo4mOjqab775hsWLFxfrORSiJBStG6YWkwjo/xda9xBSl3clfeMjOPJTXB2aKMcclgxSV/Uh/+R8fDrPxb3B/7k6JCGEEGVM798cv+6/49dzDaCQuvxOUlb0lPV5KxGHNYeMLaNJXdEDnTmSwAF7MATc7uqwbojuZg7OyMgAwM/PD4Djx4+TkJBAjx49nGWMRiOdOnVi06ZNPPHEE8TExGC1WouUqVatGlFRUWzatImePXuyefNmzGYzbdq0cZZp27YtZrOZTZs20aBBg2KxFBQUUFBQ4HycmZkJgNVqxWq13sxlikrgQhtwtgXPupjuXEnB0enk7HqJ/NOL8bztXYy1pHdGFGXPPUvmmv44ck9j7roUXXCH4u1JiJsg7UmUFmlLZUMTcAfePTZgOb2A3NhXSV50G8bwe/FoNhGtqY6rwys1Va09WZM2k7V5JI7cs3i2+hS3+k/gUDQ4ytn1X+/rccNJnqqqjBs3jvbt2xMVFQVAQkLhNPXBwcFFygYHB3Py5ElnGYPBgK+vb7EyF45PSEggKCio2DmDgoKcZS719ttvM2nSpGLbV6xYgYeHRwmvTlRWK1euvGRLCDr3TwjP/RZ180jObf+EEx5Pkq+t7pL4RPniZj9Ng6xJgMoh0xvkxWQBS537i7cnIW6ctCdRWqQtlRUjaN8h0OMPqp+aQ97JX0gy9uSs21BsGh9XB1dqKnt7UlQr1fN+JLRgATnaehzz/ID8o9Xh6DJXh3ZZubnXd4/gDSd5Tz/9NLt372bjxo3F9l3aE6Kq6jV7Ry4tc7nyV6vn5ZdfZty4cc7HmZmZhIWF0aNHD7y9va96blH5Wa1WVq5cSffu3dHrLzdRxjAs8SvRbBtD0+zncG/8Lzwav4iidSvzWEX5YE3cSOa619CYa+DdZRHVPf5J/K/dnoS4ftKeRGmRtuQq/VBt/yXv4Odo971PSM563BuNw73hWJQKPPtyVWhPttRYsjY/it16EI/mbxAQ+TwRGq2rw7qqC6MVr+WGkrxnnnmGhQsXsn79emrUqOHcHhISAhT2xIWGhjq3JyYmOnv3QkJCsFgspKWlFenNS0xMpF27ds4y58+fL3bepKSkYr2EFxiNRoxGY7Hter2+0jZMUXJXaw/6mn3wqNaF7N3/JXvvu1hO/IQ5egrGat3LOErhanknfiFj/YMYgtrh23U+GoP5suXk/UWUJmlPorRIW3IBvRlD81cwRf4f2bv/S87ed8g/9BVezSfgUX9UhZ6JuTK2J9VhI3vPu2T/NQmdOZKAvjvQ+zV1dVjX5XpfixJNvKKqKk8//TTz5s1j9erV1KpVq8j+WrVqERISUqRb12KxsG7dOmcC17JlS/R6fZEy8fHx7N2711kmOjqajIwMtm3b5iyzdetWMjIynGWEuBUUnTum294ksP9utJ41SF3Rg7R1w7DnXn6YsKh8cvZ/SvraobiFD8av++9XTPCEEEKIS2mMfnjf/gGBgw8VrrG35WmS5jci78TPsuxCOWFN30/K0vZkx76OZ9S/COi7vcIkeCVRoiRv9OjRzJw5k9mzZ2MymUhISCAhIYG8vDygcIjl2LFj+e9//8v8+fPZu3cvI0aMwMPDg2HDhgFgNpsZOXIkzz//PH/88Qe7du3iwQcfpEmTJnTr1g2AyMhIevXqxahRo9iyZQtbtmxh1KhR9O3b97KTrghR2nQ+DfHruRpz+xlYzq0kaX5Dcg58hao6XB2auEVU1UHm9n+RuW0snlHj8ek4E0VbfHSAEEIIcS06r3B8OswgoH8sOnN90tcO/XvZhbWuDq3KUu0Wsv56k+SFLXBY0vDvvRHv2/6DojW4OrRbokTDNb/88ksAOnfuXGT7tGnTGDFiBAAvvPACeXl5PPXUU6SlpdGmTRtWrFiByWRylv/444/R6XQMHTqUvLw87rzzTqZPn45W+88Y2FmzZjFmzBjnLJz9+/fn888/v5FrFOKGKIqCR92HcKtxF5kxL5G55f/IOzIdc7uv0fs1c3V4ohSptjzSN44g/8TPeLf+BM9Gz7o6JCGEEJWA3q8pft2WUBC/lqyYF0hd3gVj9d6YWr5TKXuPyitL8nYy/hyJLX0/nlEvYGr2Ooqucs+7UKIk73q6mRVFYeLEiUycOPGKZdzc3Jg8eTKTJ0++Yhk/Pz9mzpxZkvCEuCU0bv743PENHnUfJmPzkyQvaolno2fxaj4JTQW+oVoUsucmkLZ6ANa0Pfh0+QX38MGuDkkIIUQlYwztjOGureSf/JWsnf8meWFz3OsMx6vFG+i8wl0dXqWl2nLJ2jWBnP0fofNtVjg007+Fq8MqEyUarilEVWYIbk9Av52YbvsPOQe+JGl+JPknF7g6LHETrKm7SVnSBnvOafx7r5cETwghxC2jKAruEXcTOHAf3m2/oODscpLm1Sdz+3gc+SmuDq/SKTi3kqTfmpITNxnTbf8loO/WKpPggSR5QpSIojXg1eRFAgfuQ+/XlLQ1g0j9YwD27FOuDk2UUP6ZpaQsvQPF6EdA320YAlq5OiQhhBBVgKLR49nw/wgccgSvpq+Qe/BrEn+tQ/aed1Ftea4Or8Kz55whbe1QUlf0QOtRncABf+HV5MUKPcPpjZAkT4gboDPVwvfOxfh0/hlryg6SFkSSvfcDVIfV1aGJa1BVlZz9n5H2Rz8MoV3x770BrWeNax8ohBBClCKN3gtT89cJHHIU9zrDydr5Konz6pF76FtUh83V4VU4qsNK9t73SZrfEEvCeswdfsCv11p05qo5aaMkeULcoH+GXcThXu8xsmJeJHlRKyyJW1wdmrgC1W4hc/P/kbntWTwbPYdvl3lyX6UQQgiX0roHYW47mcBBBzAEtSdj02MkL2xG3sl5Mqv3dSqIX03ywuZkxbyEe72RBA4+iEedB1EUxdWhuYwkeULcJI3BG3ObTwm4axuKRk/K0nZkbHpCxteXM/bcc6Qs70LukWmY232D9+0foGi01z5QCCGEKAM67zr4dp6Df9/taNyrkb5mCMm/NSPv+FxUh93V4ZVL1vQ4Ulf1I3X5nSh6MwF9YzC3+VTWuEWSPCFKjT6gJf53bcW7zafkHZ9D4vz6hWvryRuzy1nO/0nyopbYs0/g32sdHvUfc3VIQgghxGUZAlrh33Ml/r03ovWoTvq6e0n+rQl5R2fJMM6/2fPOk7H5/0j+rQm29H34dPoJ/z5/ovdv7urQyg1J8oQoRYpGi2fkMwQOPoRbWH8yt/wfyYtvx5K4ydWhVUmqqpJz4EtSlndB512PgL4xGILaujosIYQQ4poMwXfg12MZ/ndtRWuqQ/qGB0la0Ijcw9+h2gtcHZ5LOApSydr5Gkm/1iXv+E+YWr1H4KA43GsNrdJDMy9HkjwhbgGtezA+7afh32cziqIhZekdpG94GHtugqtDqzJUWx4Zmx4jc8tTeDR4Er+ef6D1CHF1WEIIIUSJGAJb49dtEQF9d6DzaUzGn4+R+HM4WX+9iT0/ydXhlQlHfkrhxDS/RJCz/yM8Gv4fQUOO4NV4HIrW6OrwyqUSLYYuhCgZQ1Bb/O/aSt7hb8nc+W/yT83Hq/lEPCOfqXJT+ZYla/p+0tfeiy3rKOb20/Go+7CrQxJCCCFuij6gJX5d52PLOETO/k/I3v022bv/i0edh/BoNBa9T6SrQyx19tx4cuImk3vgc1DteDQcjWfj8Wjdg1wdWrknPXlC3GKKRotHg8cJGnyocIrkHf8i+bdmFJz7w9WhVTqqqpJ7+DuSF7UCHAT03S4JnhBCiEpFZ66POXoKwfecxtTsNfJPLyR5QSNSfu9E7tGZlWKtPWvKTtLXDyfxl3By4ybj0eBJAu8+gXer9yTBu06S5AlRRjRGP8xtvyCgbwyK0Y/UFd1I/aM/1vQ4V4dWKTisWaRveJCMP0fiXvsBAvpuR+/b2NVhCSGEELeExs0fr6b/JujuE/h0nA2KlowNwzk/txoZW57BmhyDqqquDvO6OazZ5B6eRvLS9iQvaonl/AZMLd8haOiZwuTOLdDVIVYoMlxTiDKm92+Of+8N5B//iaydL5P8WxM86j+OV/OJ8unUDSqIX0vGn4/gKEjBp+Ns3Gvf7+qQhBBCiDKhaI24174f99r3Y8s8Qu7hb8k7PI3cA5+jNdXBLWIo7hFD0fk1K3eTk6gOG5bzG8g7NpP84z+h2nIxVOuGT+dfcKs5AEUjqcqNkmdOCBdQFAX32vfhFj6InLjPyd79FnnHZuLV5CU8G41F0Xm4OsQKQbXlkhnzMrlxn2EI7oBfj1XovOu4OiwhhBDCJXTedfFu+TamFm9iSVhD3vG55B76mpw9b6P1rodbjbswVOuJMaQTis7dJTGqtjwK4leTf2oe+ad+Qy1IQetVC88mL+JR52G0XjVdEldlI0meEC6kaI14RT2PR90RZO9+i6zYieQc+BLTbW/hXvtBWaz7KiyJm0jfOAJ7zmlMt3+EZ6NnURQZgS6EEEIoGh3Gat0xVuuOGj0FS/xq8k78Qt6JX8jZ/wlojBhCOmIIugNDYFv0Aa3RGH1vSSwOazbWlJ1YEtZgiV+NJWkLOCxovevjUf9x3MIHo/dvWe56GSs6SfKEKAc0bv54t/4Yj4ajyYp5mYyNI8jZ8y5ezSfiFnG3JC8XcVgyyNr1GrkHvkDvfzt+dy5CZ27g6rCEEEKIcknR6DFW74mxek9UVcWWEUfB2eVY4leRE/cZ2bETAdB610NnjkRnbojO3BDcw3Gzn8VhyUDV+V81CVMdNhy557DnnMKecwpb1nFsabuxpsZizzwMqCgGXwwhnfBu9T6Gat3QmSMlsbuFJMkTohzRedfFt8vPWJK2kb3rddLX3Ytud1NMzSdhrDmgSr8ZqqpK/vE5ZG4fh2rNwtTyvcLeOxmvL4QQQlwXRVHQ+zRC79MIGj+HqqrYs45gSdqCNTkGe8YB8o//hD3nJABNgdSfR4PGiMZgBo0BRWsARQ8OC6otG4c1G+xFZ/RUDL7ofZtgrN4LfZOX0Pvfhs4nSkYolSH570iIcsgQ2Bq/HsuwnP+TrF2vkbZmEHr/lng1ew1jWL8q17NnTd1N5vZxWOL/wC18MN6tP0HrGebqsIQQQogKTVEUdN710HnXgzrDndsd1hwKMo6yee1CWjerhWJNxmHNAocV1WEBh6Uw4dN5odGbUPReaNxD0XqFo/UMQ6M3ufCqBEiSJ0S5Zgi+A/9eqymIX0PWrtdJWz0QrbkhXlH/wr32Ayhao6tDvKXsOWfJ2vUaeUemozXVwbfbEtxq9HF1WEIIIUSlptF7ojNHkqU/jjGiD3q93tUhiRKSJE+ICsAY2gVj6AYs5/8ke+97ZPw5kqxdr+HZaCwe9Z9AY/B2dYilypGfTPa+j8jZ/wkanSfebSbj0eBxFI38kRFCCCGEuBZJ8oSoQAzBd+AX/BvW9Dhy9n1A1s5XyI59A/c6D+LR4P/Q+zV1dYg3xZ57jpx9H5J78CsAPBs/h1fUi5UuiRVCCCGEuJUkyROiAtL7ROJzx7eYmr9B7qH/kXvoG3IPfoU+MBqPBk/iHnF3hVprz5oSS87BKeQdmYGidcez0Tg8Gz2Lxi3A1aEJIYQQQlQ4kuQJUYFpPatjajEJr2avkn9qIbkHvyRj48NkbhmNW/hg3Gs/gCG0a7mcgVK15ZN38hdyD0zBmrQZjUc1TM0n4tHwqcIZvIQQQgghxA0pf//5CSFKTNHocY8YgnvEEGyZR8g7Npu8Y7PIO/o9GrdgjGH9cAvrh7FaN5f28Kn2AgrOrST/+E/kn/4N1ZqFIbQbPl1+xS2sn9xzJ4QQQghRCiTJE6KS0XnXxdT8dbyavYYtZSd5x+eQf3oheYengtYNY0gXDKFdMAR3RO9/2y1PrGzZJ7GcW0nBuZUUnF2Oas1AZ47Es/HzuNe6H525/i09vxBCCCFEVSNJnhCVlKIo6ANaog9oifft72PLOET+6UUUnP2d7NiJqLZcFJ0H+oDW6Hybovdtgs63CTpz5A1NdKKqDuw5p7FnHsKa+hfW5G1Yk7YVLqiqaND7t8Kz0bO4RdyD3jfqFlyxEEIIIYQASfKEqDJ05vp4mZ/HK+p5VIcVa8pOLOc3YE3aQsHZZeQe+BxUBwCK3oTWowYazxpojP4oWncUnQeK1h1woNoLCr8sadjzzuPIT8Secwrs+YXH6zzQ+7fCLeJu9IHRGEO7ojH6uvDqhRBCCCGqDknyhKiCFI0eQ2AbDIFtnNtUWx629P3YMg9hzz2LPecMjtzTOPITUW25qLY8VHsuoEHRuqFojSgGH3ReEWgC26D1DEPr3QCduT5az3AUjdZ1FyiEEEIIUYVJkieEAEDRuTuHdwohhBBCiIpL4+oAhBBCCCGEEEKUHknyhBBCCCGEEKISkSRPCCGEEEIIISoRSfKEEEIIIYQQohKRJE8IIYQQQgghKhFJ8oQQQgghhBCiEpEkTwghhBBCCCEqEUnyhBBCCCGEEKISqbSLoauqCkBmZqaLIxHlgdVqJTc3l8zMTPR6vavDERWctCdRmqQ9idIibUmUJmlP5dOF3OZCrnMllTbJy8rKAiAsLMzFkQghhBBCCCFE6cnKysJsNl9xv6JeKw2soBwOB+fOncNkMqEoiqvDES6WmZlJWFgYp0+fxtvb29XhiApO2pMoTdKeRGmRtiRKk7Sn8klVVbKysqhWrRoazZXvvKu0PXkajYYaNWq4OgxRznh7e8sblSg10p5EaZL2JEqLtCVRmqQ9lT9X68G7QCZeEUIIIYQQQohKRJI8IYQQQgghhKhEJMkTVYLRaGTChAkYjUZXhyIqAWlPojRJexKlRdqSKE3Sniq2SjvxihBCCCGEEEJURdKTJ4QQQgghhBCViCR5QgghhBBCCFGJSJInhBBCCCGEEJWIJHlCCCGEEEIIUYlIkieEEEIIIYQQlYgkeUIIIYQQQghRiUiSJ4QQQgghhBCViCR5QgghhBBCCFGJSJInhBBCCCGEEJWIJHlCCCGEEEIIUYlIkieEEEIIIYQQlYgkeUIIIYQQQghRiUiSJ4QQQgghhBCViCR5QghxHaZPn46iKM4vnU5HaGgo9913H4cPH75l5504cSKKolxX2YiICEaMGHHLYilpPDciNzeXiRMnsnbt2lt2DlExKYrCxIkTr1lu165ddOrUCbPZjKIofPLJJ6xduxZFUYq0q8u15c6dO9O5c+dSjbssfi+FEOJSOlcHIIQQFcm0adNo2LAh+fn5/Pnnn/znP/9hzZo1HDhwAF9f31I/32OPPUavXr1Kvd7yKjc3l0mTJgGU+j/bomp49NFHycnJYc6cOfj6+hIREYGHhwebN2+mUaNGrg5PCCHKhCR5QghRAlFRUbRq1QooTELsdjsTJkxgwYIFPPLII6V+vho1alCjRo1Sr7eyyM3NxcPDw9VhiHJk7969jBo1it69exfZ3rZtWxdFJIQQZU+GawohxE24kPCdP3++yPYdO3bQv39//Pz8cHNzo0WLFsydO7dImdzcXMaPH0+tWrVwc3PDz8+PVq1a8eOPPzrLXG5ImdVq5YUXXiAkJAQPDw/at2/Ptm3bisV2paGVF4aenjhxwrntp59+okePHoSGhuLu7k5kZCQvvfQSOTk513wOVq9eTefOnfH398fd3Z2aNWsyZMgQcnNzr3nsxU6cOEFgYCAAkyZNcg6NvTDU7cL17Ny5k7vvvhtfX1/q1KkDXHmY3YgRI4iIiCiyzWKx8NZbb9GwYUOMRiOBgYE88sgjJCUllSjei+3bt48ePXrg4eFBYGAgo0ePZsmSJcWGCK5cuZIBAwZQo0YN3NzcqFu3Lk888QTJyclF6rtwrbt27WLw4MF4e3tjNpt58MEHi8UZERFB3759mT9/Pk2bNsXNzY3atWvz2WefOctkZ2fj4+PDE088USz2EydOoNVqef/990t83SW9nn379nH//fdjNpsJDg7m0UcfJSMjo0jZzMxMRo0ahb+/P15eXvTq1YtDhw5dM5YL7dpms/Hll1862w9w2eGa1+t628v1/l4KIURZkJ48IYS4CcePHwegfv36zm1r1qyhV69etGnThq+++gqz2cycOXO49957yc3NdSYt48aN44cffuCtt96iRYsW5OTksHfvXlJSUq56zlGjRvH9998zfvx4unfvzt69exk8eDBZWVk3fB2HDx+mT58+jB07Fk9PTw4cOMC7777Ltm3bWL169RWPO3HiBHfddRcdOnTgu+++w8fHh7Nnz7Js2TIsFouzl23EiBHMmDGD48ePF0u6LggNDWXZsmX06tWLkSNH8thjjwE4E78LBg8ezH333ceTTz55XUnoxRwOBwMGDGDDhg288MILtGvXjpMnTzJhwgQ6d+7Mjh07cHd3L1Gd8fHxdOrUCU9PT7788kuCgoL48ccfefrpp4uVPXr0KNHR0Tz22GOYzWZOnDjBRx99RPv27dmzZw96vb5I+UGDBjF06FCefPJJ9u3bx2uvvcb+/fvZunVrkbKxsbGMHTuWiRMnEhISwqxZs3j22WexWCyMHz8eLy8vHn30Uf73v//x3nvvYTabncdOmTIFg8HAo48+WqLrvpHrGTJkCPfeey8jR45kz549vPzyywB89913AKiqysCBA9m0aROvv/46t99+O3/++WexXrnLueuuu9i8eTPR0dHcfffdPP/88yW+nkuVpL3cit9LIYS4YaoQQohrmjZtmgqoW7ZsUa1Wq5qVlaUuW7ZMDQkJUTt27KharVZn2YYNG6otWrQosk1VVbVv375qaGioarfbVVVV1aioKHXgwIFXPe+ECRPUi9+q4+LiVEB97rnnipSbNWuWCqgPP/zwFY+99FqOHz9+2XM6HA7VarWq69atUwH1r7/+umKdv/zyiwqosbGxV72ORx99VNVqteqJEyeuWi4pKUkF1AkTJhTbd+Hcr7/+erF9nTp1Ujt16lRs+8MPP6yGh4c7H//4448qoP76669Fym3fvl0F1ClTplw1vsv517/+pSqKou7bt6/I9p49e6qAumbNmssed+F5PnnypAqov/32m3PfhWu90us8c+ZM57bw8HBVUZRir0H37t1Vb29vNScnR1VVVT169Kiq0WjUjz/+2FkmLy9P9ff3Vx955JESX/eNXM97771X5JinnnpKdXNzUx0Oh6qqqvr777+rgPrpp58WKfef//zniu3iUoA6evToItvWrFlT7LW43O/Hpe3oettLSX4vhRCiLMhwTSGEKIG2bdui1+sxmUz06tULX19ffvvtN3S6woERR44c4cCBAzzwwAMA2Gw251efPn2Ij4/n4MGDALRu3Zrff/+dl156ibVr15KXl3fN869ZswbAWf8FQ4cOdcZwI44dO8awYcMICQlBq9Wi1+vp1KkTAHFxcVc8rnnz5hgMBh5//HFmzJjBsWPHLlvu22+/xWazER4efsMxXjBkyJAbPnbx4sX4+PjQr1+/Iq9N8+bNCQkJuaHhfOvWrSMqKqrYpB73339/sbKJiYk8+eSThIWFodPp0Ov1zufkcs/zlV7nC+3ggsaNG9OsWbMi24YNG0ZmZiY7d+4EoHbt2vTt25cpU6agqioAs2fPJiUl5bK9jtejpNfTv3//Io+bNm1Kfn4+iYmJwJXb97Bhw24ovpt1ve3lVv1eCiHEjZIkTwghSuD7779n+/btrF69mieeeIK4uLgi/8xfuDdv/Pjx6PX6Il9PPfUUgPN+pc8++4wXX3yRBQsW0KVLF/z8/Bg4cOBVl2S4MJQzJCSkyHadToe/v/8NXVN2djYdOnRg69atvPXWW6xdu5bt27czb948gKsmn3Xq1GHVqlUEBQUxevRo6tSpQ506dfj0009vKJbrERoaesPHnj9/nvT0dAwGQ7HXJyEhodi9ZNcjJSWF4ODgYtsv3eZwOOjRowfz5s3jhRde4I8//mDbtm1s2bIFuPzzfKXX+dIhvZeWu3jbxWWfffZZDh8+zMqVKwH44osviI6O5rbbbrueS73p67m0jRqNxiJlU1JSLtuWL3d9ZeF628ut+L0UQoibIR8vCSFECURGRjonW+nSpQt2u52pU6fyyy+/cPfddxMQEADAyy+/zODBgy9bR4MGDQDw9PRk0qRJTJo0ifPnzzt79fr168eBAwcue+yFfxgTEhKoXr26c7vNZiv2j7+bmxsABQUFzn+mgWKJzOrVqzl37hxr16519t4BpKenX/P5AOjQoQMdOnTAbrezY8cOJk+ezNixYwkODua+++67rjpK4nKTybi5uRWbwAOKX2tAQAD+/v4sW7bssnWbTKYSx+Pv719s4h0ofI0utnfvXv766y+mT5/Oww8/7Nx+5MiRK9Z9pdf50sTh0nNdvO3isl27diUqKorPP/8cLy8vdu7cycyZM69xhZd3I9dzLf7+/pe9xstdX1m43vZSkt9LIYQoC9KTJ4QQN+G9997D19eX119/HYfDQYMGDahXrx5//fUXrVq1uuzX5RKJ4OBgRowYwf3338/BgwevODPlhRkkZ82aVWT73LlzsdlsRbZdmOBk9+7dRbYvWrSoyOMLSdPFiSDA119/ffWLv4RWq6VNmzZ88cUXAM5hgiVxac/O9YqIiODQoUMUFBQ4t6WkpLBp06Yi5fr27UtKSgp2u/2yr82FBLwkOnXqxN69e9m/f3+R7XPmzCny+Eae5yu9zpfOJLpv3z7++uuvIttmz56NyWQq1ks3ZswYlixZwssvv0xwcDD33HPPlS/uKkqr3VysS5cuQPHrnj179g3XeTOut72U5PdSCCHKgvTkCSHETfD19eXll1/mhRdeYPbs2Tz44IN8/fXX9O7dm549ezJixAiqV69OamoqcXFx7Ny5k59//hmANm3a0LdvX5o2bYqvry9xcXH88MMPREdHX3Htt8jISB588EE++eQT9Ho93bp1Y+/evXzwwQd4e3sXKdunTx/8/PwYOXIkb7zxBjqdjunTp3P69Oki5dq1a4evry9PPvkkEyZMQK/XM2vWrGJJw+V89dVXrF69mrvuuouaNWuSn5/vnCmxW7duznIjR45kxowZHD169Kr35ZlMJsLDw/ntt9+488478fPzIyAg4Iozcl4wfPhwvv76ax588EFGjRpFSkoK7733XrHn5L777mPWrFn06dOHZ599ltatW6PX6zlz5gxr1qxhwIABDBo0CCickv+RRx5h2rRpzhlRL2fs2LF899139O7dmzfeeIPg4GBmz57t7I3VaAo/T23YsCF16tThpZdeQlVV/Pz8WLRokXPo5OXMmzcPnU5H9+7dnbNrNmvWjKFDhxYpV61aNfr378/EiRMJDQ1l5syZrFy5knfffbdYW3rwwQd5+eWXWb9+Pa+++ioGg6HYeS883xcvs3GpG7mea+nRowcdO3bkhRdeICcnh1atWvHnn3/yww8/3HCdN+N620tJfi+FEKJMuHjiFyGEqBAuzEi5ffv2Yvvy8vLUmjVrqvXq1VNtNpuqqqr6119/qUOHDlWDgoJUvV6vhoSEqF27dlW/+uor53EvvfSS2qpVK9XX11c1Go1q7dq11eeee05NTk52lrncDIAFBQXq888/rwYFBalubm5q27Zt1c2bN6vh4eHFZvHbtm2b2q5dO9XT01OtXr26OmHCBHXq1KnFZtfctGmTGh0drXp4eKiBgYHqY489pu7cuVMF1GnTpl0xns2bN6uDBg1Sw8PDVaPRqPr7+6udOnVSFy5cWCSOhx9++Kozel5s1apVaosWLVSj0VhkZsIL505KSrrscTNmzFAjIyNVNzc3tVGjRupPP/1UbHZNVVVVq9WqfvDBB2qzZs1UNzc31cvLS23YsKH6xBNPqIcPH3aWmzx5sgqoy5Ytu2bMe/fuVbt166a6ubmpfn5+6siRI9UZM2YUm510//79avfu3VWTyaT6+vqq99xzj3rq1KliM0deuNaYmBi1X79+qpeXl2oymdT7779fPX/+fJFzh4eHq3fddZf6yy+/qI0bN1YNBoMaERGhfvTRR1eMd8SIEapOp1PPnDlz2f0BAQFq27Ztr3ndJb2eS1+7y830mp6erj766KOqj4+P6uHhoXbv3l09cOCAS2bXVNXrby8l+b0UQohbTVHVv6fYEkIIIYTT0KFDOX78ONu3b7+h4x9//HF+/PFHUlJSLttbdjUTJ05k0qRJJCUlOe/zvJKIiAiioqJYvHjxddVtsViIiIigffv2zJ07t9j+/fv307hxYxYvXsxdd91VoriFEEKUDzJcUwghhLiEqqqsXbv2uicleeONN6hWrRq1a9cmOzubxYsXM3Xq1CsOh3SFpKQkDh48yLRp0zh//jwvvfTSZcutWbOG6OhoSfCEEKICkyRPCCGEuISiKM61266HXq/n/fff58yZM9hsNurVq8dHH33Es88+ewujLJklS5bwyCOPEBoaypQpU664bMLo0aMZPXp0GUcnhBCiNMlwTSGEEEIIIYSoRGQJBSGEEEIIIYSoRCTJE0IIIYQQQohKRJI8IYQQQgghhKhEKu3EKw6Hg3PnzmEymVAUxdXhCCGEEEIIIcRNUVWVrKwsqlWrhkZz5f66SpvknTt3jrCwMFeHIYQQQgghhBCl6vTp09SoUeOK+yttkmcymYDCJ8Db29vF0QhXs1qtrFixgh49eqDX610djqjgpD2J0iTtSZQWaUuiNEl7Kp8yMzMJCwtz5jpXUmmTvAtDNL29vSXJE1itVjw8PPD29pY3KnHTpD2J0iTtSZQWaUuiNEl7Kt+udTuaTLwihBBCCCGEEJWIJHlCCCGEEEIIUYlIkieEEEIIIYQQlUilvSfvetntdqxWq6vDELeY1WpFp9ORn5+P3W6/pefS6/Votdpbeg4hhBBCCCGupMomeaqqkpCQQHp6uqtDEWVAVVVCQkI4ffp0mayb6OPjQ0hIiKzRKIQQQgghylyVTfIuJHhBQUF4eHjIP+OVnMPhIDs7Gy8vr6suHHmzVFUlNzeXxMREAEJDQ2/ZuYQQQgghhLicKpnk2e12Z4Ln7+/v6nBEGXA4HFgsFtzc3G5pkgfg7u4OQGJiIkFBQTJ0UwghhBBClKkqOfHKhXvwPDw8XByJqKwutC2531NcjaqqnEzJcXUYQgghhKhkqmSSd4EM0RS3irQtcT0W/nWOTu+vZcPhJFJzLK4ORwghhBCVRJVO8oQQwpUSMvIBGP7tNvp+tsHF0QghhBCispAkT5QLnTt3ZuzYsVctExERwSeffFKq5x0xYgQDBw4s1TqFuF65ln+W8ziXkY/DobL1WArrDyW5MCohhBBCVHRVcuKViqxz5840b9681JMdV5s3bx56vd7VYQhRphKz8os8PpCQxZfrjnIqNZfVz3d2TVBCCCGEqPCkJ68SUlUVm81WZuezWG7+XiI/Pz9MJlMpRCNExTB61k5+3HaadnX8WflcRwxaDVuOpXA+s4BjSTmcz8y/diVCCCGEEJchSV4FMmLECNatW8enn36KoigoisKJEydYu3YtiqKwfPlyWrVqhdFoZMOGDZcdijh27Fg6d+7sfKyqKu+99x61a9fG3d2dZs2a8csvv1w1joiICN566y1GjBiB2Wxm1KhRAGzatImOHTvi7u5OWFgYY8aMISfnn5kDp0yZQr169XBzcyM4OJi7777bue/S4ZqJiYn069cPd3d3atWqxaxZs4rEcOLECRRFITY21rktPT0dRVFYu3YtULhUxsiRI6lVqxaenp7cfvvtfPbZZ9fxTAtxax1LymbJnngAagd6Ui/YRPOaPmw9nkLi38ndlmMprgxRCCGEEBWYJHkVyKeffkp0dDSjRo0iPj6e+Ph4wsLCnPtfeOEF3n77beLi4mjatOl11fnqq68ybdo0vvzyS/bt28dzzz3Hgw8+yLp166563Pvvv09UVBQxMTG89tpr7Nmzh549ezJ48GB2797NTz/9xMaNG3n66acB2LFjB2PGjOGNN97g4MGDLFu2jI4dO16x/hEjRnDixAlWr17NL7/8wpQpU5wLjF8vh8NBjRo1mDt3Lnv37uVf//oXr7zyCnPnzi1RPUKUtgW7zjp/NrsXDlNuW8uPzUdTSPl7ls3NRyXJE0IIIcSNkXvyLpJnsXM0KbvMz1sn0At3w7UXzDabzRgMBjw8PAgJCSm2/4033qB79+7Xfd6cnBw++ugjVq9eTXR0NAC1a9dm48aNfP3113Tq1OmKx3bt2pXx48c7Hz/00EMMGzbM2RtXr149PvvsMzp16sSXX37JqVOn8PT0pG/fvphMJsLDw2nRosVl6z506BC///47W7ZsoU2bNgB8++23REZGXve1Aej1eiZNmgQUJnxDhw4lNjaWuXPnMnTo0BLVJcTNUlWVcXP/ok+TUObHnqVpDTO7z2SQZ3EA0La2P5+tPgJAhL8Hm6UnTwghhBA3qMRJ3vr163n//feJiYkhPj6e+fPnO4cEWq1WXn31VZYuXcqxY8cwm81069aNd955h2rVqjnrKCgoYPz48fz444/k5eVx5513MmXKFGrUqOEsk5aWxpgxY1i4cCEA/fv3Z/Lkyfj4+NzcFV/F0aRs+k7eeMvqv5LFz7Qnqrr5putp1apVicrv37+f/Pz8YomhxWK5YgJ2pXPFxMRw5MiRIsMqVVXF4XBw/PhxunfvTnh4OLVr16ZXr1706tWLQYMGXXZB+ri4OHQ6XZFzNGzY8IZe+6+++oqpU6dy8uRJ8vLysFgsNG/evMT1CHGjjiRmMfzbbUzo15j5u86y42Qqp1PzeG9IM2JOpnLv7TUBaFHTF71WwWpXGdC8Op/+cZhz6XlU83F38RUIIYQQoqIpcZKXk5NDs2bNeOSRRxgyZEiRfbm5uezcuZPXXnuNZs2akZaWxtixY+nfvz87duxwlhs7diyLFi1izpw5+Pv78/zzz9O3b19iYmLQagt7tIYNG8aZM2dYtmwZAI8//jjDhw9n0aJFN3O9V1Un0IvFz7S/ZfVf7bylwdPTs8hjjUaDqqpFtlmtVufPDkdhD8KSJUuoXr16kXJGo7FE53I4HDzxxBOMGTOmWNmaNWtiMBjYuXMna9euZcWKFbz++utMnDiR7du3F0veLsR8tQXFNRpNkbKXXhvA3Llzee655/jwww9p06YNiqLw1VdfsW3btqtemxA3Y8W+BGr4etComjcAs7aeIj4jn9d+2wvA6dQ8qvu406aWH9F1/J3HuRu0NA/zYfuJNPo3r8anfxzm2Tm7aFbDh3tahVE3yAut5sq/E0IIIYQQF5Q4yevduze9e/e+7D6z2czKlSuLbJs8eTKtW7fm1KlT1KxZk4yMDL799lt++OEHunXrBsDMmTMJCwtj1apV9OzZk7i4OJYtW1ZkuN4333xDdHQ0Bw8epEGDBiUN+7q4G7Sl0qN2KxkMBux2+7ULAoGBgezdu7fIttjYWOdSBY0aNcJoNHLq1KmrDs28Hrfddhv79u2jbt26Vyyj0+no1q0b3bp1Y8KECfj4+LB69WoGDx5cpFxkZCQ2m40dO3bQunVrAA4ePEh6enqRawOIj4939jpePAkLwIYNG2jXrh1PPfUUDoeDzMxMjh07dlPXKcTVZOVbeXZOLF0aBjLlgZZYbA7n/XdJWQU0DDFxICGLgS2qoblMwhZd2589ZzOoHeBJwxAT20+kEXs6ne/+PE6PRiHU8HXn1b6NyvqyhBBCCFHB3PKJVzIyMlAUxdlbExMTg9VqpUePHs4y1apVIyoqik2bNgGwefNmzGazM8EDaNu2LWaz2VmmqoqIiGDr1q2cOHGC5ORkZ2/c5XTt2pUdO3bw/fffc/jwYSZMmFAk6TOZTIwfP57nnnuOGTNmcPToUXbt2sUXX3zBjBkzShTXiy++yObNmxk9ejSxsbEcPnyYhQsX8swzzwCwePFiPvvsM2JjYzl58iTff/89Dofjsgl7gwYN6NWrF6NGjWLr1q3ExMTw2GOP4e7+z7A1d3d32rZtyzvvvMP+/ftZv349r776apF66taty44dO1i+fDmHDh3iP//5D9u3by/RdQlREot3x5NntbPjRBqqqvJH3HnScq3cUbewx+6/g5twV5NQhrUJv+zxj3WszcyRhb3OgabC3nSrXcWhwrJ9CUzdeLzMrkUIIYQQFdctnXglPz+fl156iWHDhuHtXTh0KSEhAYPBgK+vb5GywcHBJCQkOMsEBQUVqy8oKMhZ5lIFBQUUFBQ4H2dmZgKFQ/guHcZntVqd94tdLUkqj8aNG8cjjzxCo0aNyMvL4+jRo85ruPR6unfvzquvvsoLL7xAfn4+jzzyCMOHD2fv3r3OcpMmTSIwMJC3336bY8eO4ePjQ4sWLXj55Zev+txceP4uiIqKYs2aNbz66qt06NABVVWpU6cOQ4cOxeFw4O3tzbx585g4cSL5+fnUq1ePWbNmERkZ6azn4jq//fZbRo0aRadOnQgODuaNN97g9OnTRcpMnTqVxx57jFatWtGgQQPeeecdevXq5XweHn/8cXbt2sW9996LoigMHjyYJ598kuXLlxc556XXUhocDgeqqmK1Wp1DkEXlceE95dL3ljnbTuHjricxq4BjiZn8tP0UTat78+9e9VmxP5EmoV58MrTJZY8FcNdCs+omrFYrY7vWwe5wEBefRVruP2Wz8wow6mRi5MrkSu1JiJKStiRKk7Sn8ul6Xw9FvfSmrRJQFKXIxCuXBnDPPfdw6tQp1q5d60zyZs+ezSOPPFIkIYPChKROnTp89dVX/Pe//2XGjBkcPHiwSJl69eoxcuRIXnrppWLnmzhxonMmxYvNnj272OQeOp2OkJAQwsLCMBgMJb1sIa7JYrFw+vRpEhISynRheuE68bnwzl867q1t56djWvrXtLPolIa7azloH3LDb7MczlDIsMAPRwo/LJhwmw2/q98yK4QQQohKKjc3l2HDhpGRkeHMry7nlvTkWa1Whg4dyvHjx1m9enWRAEJCQrBYLKSlpRXpzUtMTKRdu3bOMufPny9Wb1JSEsHBwZc958svv8y4ceOcjzMzMwkLC6NHjx7FnoD8/HxOnz6Nl5cXbm5uN3WtomJQVZWsrCxMJtNVJ3QpLfn5+bi7u9OxY0dpY5WQ1Wpl5cqVdO/e3XmP64/bT6Pdc4DXh/dkx5TN/JGQj0Gn8tL9XfH+ey28m3FPfBb9p2ymye130KxG+b53WJTM5dqTEDdC2pIoTdKeyqcLoxWvpdSTvAsJ3uHDh1mzZg3+/v5F9rds2RK9Xs/KlSuda5XFx8ezd+9e3nvvPQCio6PJyMhg27Ztzok3tm7dSkZGhjMRvJTRaLzsjJB6vb5Yw7Tb7SiKgkajcc7SKCq3C8MxL7zut5pGo0FRlMu2P1F5XPz6JmdbCfQy4ulupHUtP37cdpr+zarh7118mZAbEepbOKNtWp5d2lQlJe8XorRIWxKlSdpT+XK9r0WJk7zs7GyOHDnifHz8+HFiY2Px8/OjWrVq3H333ezcuZPFixdjt9ud99D5+flhMBgwm82MHDmS559/Hn9/f/z8/Bg/fjxNmjRxzrYZGRnpnHjj66+/BgqXUOjbt+8tm1lTCCFuRkJmPsHmwl7bluGFSd49rWpc46jr5+dpQKMUztIphBBCCHE1JU7yduzYQZcuXZyPLwyRfPjhh5k4caJz8fJLF5xes2YNnTt3BuDjjz9Gp9MxdOhQ52Lo06dPLzJBxaxZsxgzZoxzFs7+/fvz+eeflzRcIYS45eZsO8WGw8k0/XsYZd+moQDcUSeg1M6h1Sj4exklyRNCCCHENZU4yevcuXOxBbYvdj3zuLi5uTF58mQmT558xTJ+fn7MnDmzpOEJIUSZOpuex8vz96Cq0KNR4T3Dbnotd7csvV68CwK9jCRm5XM0KZs6gV6lXr8QQgghKge5IU0IIW7CzztOc+GzrQvDNW+V2oGezNp6ijs/XMfesxm39FxCCCGEqLgkyRNCiBvkcKj8vOMMXsbCQRHu+lu7JmL7uv8M/0zKlmGbQgghhLg8SfKEEOIGbTqWytn0PCb0awRA/WDTLT1f+3r/JHnJcm+eEEIIIa5AkjwhhLhBP8ecoV6QF3e3rMH+N3pyR93Sm2jlcmr4evBy74aA9OQJIYQQ4sokyRMVXufOnRk7duxVy9SuXZsvv/yyVM87YsQIBg4cWKp1ioojxwor4xK59/YwFEXBw1Dqy45e1hOd6lAvyIvETEnyhBBCCHF5ZfNfiRC30Lx582SRTlHmzuYqWO0qXRsGlfm5g7yN0pMnhBBCiCuSJE+4lMViwWAw3FQdfn5+pRSNENcvw1L4vZqPe5mfO9DLyLmM/DI/rxBCCCEqBhmuWcFkZWXxwAMP4OnpSWhoKB9//HGx4YozZ86kVatWmEwmQkJCGDZsGImJic79a9euRVEUlixZQrNmzXBzc6NNmzbs2bPHWWb69On4+PiwYMEC6tevj5ubG927d+f06dMAnDhxAo1Gw44dO4rEN3nyZMLDw6+4XmJERARvvfUWI0aMwGw2M2rUKAA2bdpEx44dcXd3JywsjDFjxpCTk+M8bsqUKdSrVw83NzeCg4O5++67nfsuvf7ExET69euHu7s7tWrVYtasWUViOHHiBIqiEBsb69yWnp6OoiisXbsWALvdzsiRI6lVqxbu7u40aNCATz/99CqvjKhq0i1gdtfhdotn1LycQJNRJl4RQgghxBVJklfBjBs3jj///JOFCxeycuVKNmzYwM6dO4uUsVgsvPnmm/z1118sWLCA48ePM2LEiGJ1/etf/+KDDz5g+/btBAUF0b9/f6xWq3N/bm4u//nPf5gxYwZ//vknmZmZ3HfffUBhstatWzemTZtWpM5p06YxYsQIFEW54jW8//77REVFERMTw2uvvcaePXvo2bMngwcPZvfu3fz0009s3LiRp59+GoAdO3YwZswY3njjDQ4ePMiyZcvo2LHjFesfMWIEJ06cYPXq1fzyyy9MmTKlSJJ7PRwOBzVq1GDu3Lns37+f119/nX//+9/MnTu3RPWIyumXnWfZmKAh2HRr18W7kmBvN+Iz8hn+7VZ+iz3rkhiEEEIIUX7JcM2Lfd0JskuWDJQKryB4Yt01i2VlZTFjxgxmz57NnXfeCRQmVdWqVStS7tFHH3X+XLt2bT777DNat25NdnY2Xl5ezn0TJkyge/fuAMyYMYMaNWowf/58hg4dCoDVauXzzz+nTZs2zjKRkZFs27aN1q1b89hjj/Hkk0/y0UcfYTQa+euvv4iNjWXevHlXvY6uXbsyfvx45+OHHnqIYcOGOXvj6tWrx2effUanTp348ssvOXXqFJ6envTt2xeTyUR4eDgtWrS4bN2HDh3i999/Z8uWLc64v/32WyIjI6/5/F5Mr9czadIk5+NatWqxadMm5s6d63x+RNWUa7Hx1tID5FgUmngbXRLD7RF+5FntbDicDMCA5tVdEocQQgghyidJ8i6WnQhZ51wdxRUdO3YMq9VK69atndvMZjMNGjQoUm7Xrl1MnDiR2NhYUlNTcTgcAJw6dYpGjRo5y0VHRzt/9vPzo0GDBsTFxTm36XQ6WrVq5XzcsGFDfHx8iIuLo3Xr1gwcOJCnn36a+fPnc9999/Hdd9/RpUsXIiIirnodF9cJEBMTw5EjR4oMq1RVFYfDwfHjx+nevTvh4eHUrl2bXr160atXLwYNGoSHh0exuuPi4q4Yd0l99dVXTJ06lZMnT5KXl4fFYqF58+YlrkdULkv3JJBTYAfA2901E/5EVTfj46EnPdfK1uOpZORZMeo0Lhk6KoQQQojyR5K8i3mV/Sx5JTnvhfvcLh0KefH9bzk5OfTo0YMePXowc+ZMAgMDOXXqFD179sRisVzzHJfWfblhlxe2GQwGhg8fzrRp0xg8eDCzZ8/mk08+ueY5PD09izx2OBw88cQTjBkzpljZmjVrYjAY2LlzJ2vXrmXFihW8/vrrTJw4ke3btxdL3q70HF1Mo9EUKQsUGaYKMHfuXJ577jk+/PBDoqOjMZlMvP/++2zduvWa1ycqt7k7ThNqLhwumeSi++K0GoV+TasRF5/JjpNpNJu0Ak+Dln1v9HJJPEIIIYQoXyTJu9h1DJl0pTp16qDX69m2bRthYWEAZGZmcvjwYTp16gTAgQMHSE5O5p133nGWuXRylAu2bNlCzZo1AUhLS+PQoUM0bNjQud9ms7Fjxw5nz+HBgwdJT08vUuaxxx4jKiqKKVOmYLVaGTx4cImv67bbbmPfvn3UrVv3imV0Oh3dunWjW7duTJgwAR8fH1avXl3sfJGRkVeM+4LAwEAA4uPjncM+L56EBWDDhg20a9eOp556yrnt6NGjJb42UbkcT85h2/FU3h8Sxb9+3cuAZqEui+XNgVGoqkr7d9dwNj2PHIvdZbEIIYQQonyRJK8CMZlMPPzww/zrX//Cz8+PoKAgJkyYgEajcfZcXej5mjx5Mk8++SR79+7lzTffvGx9b7zxBv7+/gQHB/PKK68QEBBQZHFvvV7PM888w2effYZer+fpp5+mbdu2RYaLRkZG0rZtW1588UUeffRR3N1LPp38iy++SNu2bRk9ejSjRo3C09OTuLg4Vq5cyeTJk1m8eDHHjh2jY8eO+Pr6snTpUhwOR7FhqgANGjSgV69ejBo1iv/973/odDrGjh1bJC53d3fatm3LO++8Q0REBMnJybz66qtF6qlbty7ff/89y5cvp1atWvzwww9s376dWrVqlfj6ROXx847TmNx09GocjOFcLH1a1XBpPIqi0KlBILO3ngLA4VDRaK7ciy2EEEKIqkFm16xgPvroI6Kjo+nbty/dunXjjjvuIDIyEje3wln+AgMDmT59Oj///DONGjXinXfe4YMPPrhsXe+88w7PPvssLVu2JD4+noULFxZZs87Dw4MXX3yRYcOGER0djbu7O3PmzClWz8iRI7FYLEUmfCmJpk2bsm7dOg4fPkyHDh1o0aIFr732GqGhhb0kPj4+zJs3j65duxIZGclXX33Fjz/+SOPGjS9b37Rp0wgLC6NTp04MHjyYxx9/nKCgokNiv/vuO6xWK61ateLZZ5/lrbfeKrL/ySefZPDgwdx77720adOGlJSUIr16ouqx2R38uvMMA5tXL1f3vt3d8p9EMz3PepWSQgghhKgqFPVKC5pVcJmZmZjNZjIyMvD29i6yLz8/n+PHj1OrVi1nclRR5eTkUL16dT788ENGjhx5XcesXbuWLl26kJaWdsUJSaZPn87YsWOLDHO8kv/85z/MmTOnyDp75Y3D4SAzMxNvb2/nPXm3UmVqY6LQ6gPneXT6DhY93Z6GwR4sXbqUPn36oNe7ZvKVi8WcTGPIl5tYNrYDDUO8r32AKFesVmu5ak+i4pK2JEqTtKfy6Wo5zsWkJ6+C2bVrFz/++CNHjx5l586dPPDAAwAMGDCgzGPJzs5m+/btTJ48+bKTpghRmczdfoaGISaiqpe/JCrIVLiUQ69PNrD/XKaLoxFCCCGEq0mSVwF98MEHNGvWjG7dupGTk8OGDRsICAgo8ziefvpp2rdvT6dOnW54qKYQFUFydgGr4s5z7+1hV5251VUCTf+s1/dLzBkXRiKEEEKI8kCSvAqmRYsWxMTEkJ2dTWpqKitXrqRJkyYlqqNz586oqnrVteNGjBhxzaGa06dPp6CggJ9++gmttvzcoyREaVuw6ywaRWFgOV10/OJ7BNccTHRhJEIIIYQoDyTJE0KIq1BVlZ+2n6Z742B8PQ3XPsDFjifncD4z39VhCCGEEMKFJMkTQoir+OtMBocTsxnaKszVoVzVzte68+v/RQOQkCFJnhBCCFGVyTp5QghxFXvOpKPTKLSvW/b3vZaEn6cBm8MDgMSsAhdHI4QQQghXkp48IYS4ioTMfIJMRrQVYJFxf08jGgUSs6QnTwghhKjKJMkTQoirSMgoINhcMdY61GoUAryMnEvPw2JzsPrAeSrpUqhCCCGEuApJ8oQQ4gq+33yCNQcTCfGuGEkeFC6n8MWao/T8ZD2PTt/BhsPJrg5JCCGEEGVMkrwKRlVVHn/8cfz8/FAUhdjYWDp37szYsWOdZSIiIvjkk0+cjxVFYcGCBTd13hEjRjBw4MCbqqOk1q5di6IoV13KYfr06VddCuJGnDhxwvnciqrrXHoeExbuIzXHQnAFSvI8DIXLKRxPzgFgxf4E9p3LICPP6sqwhBBCCFGGZOKVCmbZsmVMnz6dtWvXUrt2bQICApg3bx56vd7VoZW6du3aER8fj9lsdnUoogqav+ssF0Y6VqQk70hitvNnrUZhxb7zzNxyim6RwUx9uJULIxNCCCFEWZGevArm6NGjhIaG0q5dO0JCQtDpdPj5+WEymVwdWhF2ux2Hw3FTdRgMBkJCQlCU8j/hhahcVFXl15gzuP+9yLheW3Ha4PDoCBQFBrWozr96NnDOtClr5wkhhBBVhyR5FciIESN45plnOHXqFIqiEBERAVBsuOa1nD17lnvvvRdfX1/8/f0ZMGAAJ06ccO632+2MGzcOHx8f/P39eeGFF645ecOFYZOLFy+mUaNGGI1GTp48icVi4YUXXqB69ep4enrSpk0b1q5d6zzu5MmT9OvXD19fXzw9PWncuDFLly4FLj9cc/r06dSsWRMPDw8GDRpESkpKsefo0mGlY8eOpWvXrs7Hy5Yto3379s7r69u3L0ePHr3u509UfjtPpXMsOYdJ/RsD0KKmr4sjun7jutfn+Nt38fG9zXmsfS18PAp7+f29yv9C7kIIIYQoHZLkVSCffvopb7zxBjVq1CA+Pp7t27eXuI7c3Fy6dOmCl5cX69evZ+PGjXh5edGrVy8sFgsAH374Id999x3ffvstGzduJDU1lfnz519X3W+//TZTp05l3759BAUF8cgjj/Dnn38yZ84cdu/ezT333EOvXr04fPgwAKNHj6agoID169ezZ88e3n33Xby8vC5b/9atW3n00Ud56qmniI2NpUuXLrz11lslfg5ycnIYN24c27dv548//kCj0TBo0KCb7nkUFd/K/efZdDSZX3eeoZrZjbtb1uDEO3fRMrziJHkX02k1PBQdAUBytqydJ4QQQlQVck/eRZ754xlOZ50u03OGmcKYfOfk6yprNpsxmUxotVpCQkJu6Hxz5sxBo9EwdepU5zDIadOm4ePjw9q1a+nRoweffPIJL7/8MkOGDAHgq6++Yvny5des22q1MmXKFJo1awYUDi398ccfOXPmDNWqVQNg/PjxLFu2jGnTpvHf//6XU6dOMWTIEJo0aQJA7dq1r1j/p59+Ss+ePXnppZcAqF+/Pps2bWLZsmUleg4uXNcF3377LUFBQezfv5+oqKgS1SUqD6vdwajvdwBgctPxUHQ4mgqwNt61jOteH40Cs7aecnUoQgghhCgj0pNXxcTExHDkyBFMJhNeXl54eXnh5+dHfn4+R48eJSMjg/j4eKKjo53H6HQ6WrW69oQNBoOBpk2bOh/v3LkTVVWpX7++81xeXl6sW7fOOTxyzJgxvPXWW9xxxx1MmDCB3bt3X7H+uLi4InEBxR5fj6NHjzJs2DBq166Nt7c3tWrVAuDUKfknuCpbfyjJ+XNWvo3Bt9VwYTSlK8jkRkp2ATa79FYLIYQQVYH05F3kenvUKjKHw0HLli2ZNWtWsX2BgYE3Vbe7u3uRSVIcDgdarZaYmBi0Wm2RsheGZD722GP07NmTJUuWsGLFCt5++20+/PBDnnnmmWL1X8+izhqNplg5q7Xo1PH9+vUjLCyMb775hmrVquFwOIiKinIOVxVV09bjqc6fb6vpQ53Ayw8broiCvY04VDiTloePhx4fD7k/TwghhKjMpCevirnttts4fPgwQUFB1K1bt8iX2WzGbDYTGhrKli1bnMfYbDZiYmJKfK4WLVpgt9tJTEwsdq6Lh5uGhYXx5JNPMm/ePJ5//nm++eaby9bXqFGjInEBxR4HBgYSHx9fZNvF692lpKQQFxfHq6++yp133klkZCRpaWklvjZR+ZzPzOf2CF9qB3jycLsIV4dTqoJMhUtAjJ69k/v+t+UapYUQQghBfgZU4PkaJMmrYh544AECAgIYMGAAGzZs4Pjx46xbt45nn32WM2fOAPDss8/yzjvvMH/+fA4cOMBTTz111QXJr6R+/fo88MADPPTQQ8ybN4/jx4+zfft23n33XecMmmPHjmX58uUcP36cnTt3snr1aiIjIy9b35gxY1i2bBnvvfcehw4d4vPPPy92P17Xrl3ZsWMH33//PYcPH2bChAns3bvXuf/CjKL/+9//OHLkCKtXr2bcuHElvjZR+SRk5BNqdmf1+M4MaF7d1eGUqogAD/RahX3nMjmQkMXWYyl8tPKQDN8UQgghriB90RiWftaQjF8fhZyUax9QzkiSV8V4eHiwfv16atasyeDBg4mMjOTRRx8lLy8Pb29vAJ5//nkeeughRowYQXR0NCaTiUGDBt3Q+aZNm8ZDDz3E888/T4MGDejfvz9bt24lLCwMKFyuYfTo0URGRtKrVy8aNGjAlClTLltX27ZtmTp1KpMnT6Z58+asWLGCV199tUiZnj178tprr/HCCy9w++23k5WVxUMPPeTcr9FomDNnDjExMURFRfHcc8/x/vvv39C1icolMauAYG+jq8O4JUxueu6oG+B8/MDUrXz2x2HWH066ylFCCCFEFeVwsOHsJl70NbLq1Bpw83Z1RCWmqNdzo1MFlJmZidlsJiMjw5m8XJCfn8/x48epVasWbm5uLopQlCWHw0FmZibe3t5oNLf+sw1pYxWLqqo0nrCccd3r81iHK8/weoHVamXp0qX06dMHvV5fBhHevAW7zvLqgr1E1/Fn5f7zANzdsgYf3NPMxZGJitieRPkkbUmUpirdns7u5JX5Q1ho8mKFviGhw352dUROV8txLiYTrwghqrzsAhu5FjvB3pU3IR/QvBp3Rgax5ViqM8n780iyi6MSQgghyh/1yCo2ubtT12IhtFEvV4dzQ2S4phCiyjufmQ9QqZM8RVEwuenpWD+Ano2DGdEugsSsAhyOSjmYQwghhLhh8fE7SNZpaZVfALU6uTqcGyJJnhCiyjudmgdASCVO8i4w6rR8PbwV7er4Y3eopORUnKVD8q123lq8n8x867ULCyGEEDfofHbhiJdQmw18wlwczY2RJE8IUeX9vjeemn4ehPm5uzqUMnOh1/JCL2ZFsOtUOlM3HmfbsdRrFxZCCCFuUFJe4d8ZX8UNdBVzUjZJ8oSoarIS4PS2Cr32S2nKt9r5fU8CA5pXQ1EUV4dTZi4keX0nb2TdoYoxy+bBhEwAzqTlujgSIYQQlZaqkmYr/HvjZ6h4s2peUKUnXnHIP7niFim3bev8Pn6b3ZdtengzehKalg8VLvZpt4Gnv6ujc4k1BxLJKrBVurXxriXAy+D8ecamEyRk5GFzqDzQJtyFUV3dgYQsAM6m5112v8Ohkm+z42Go0n/ahBBC3IyDS0lRCu9XD3YPuEbh8qvEfwnXr1/P+++/T0xMDPHx8cyfP5+BAwc698+bN4+vv/6amJgYUlJS2LVrF82bNy9SR0FBAePHj+fHH38kLy+PO++8kylTplCjRg1nmbS0NMaMGcPChQsB6N+/P5MnT8bHx+eGLvRiBoMBjUbDuXPnCAwMxGAwVKlP8Ksih8OBxWIhPz//li6hoKoqFouFpKQkNBoNBoPh2geVlYJsdv38IBN9PbEpCk/EzaNmvW6s/CaaHNXOwAd/h5Amro6yzP0We44m1c3UDfJydShlSqf95/dg/aEkVh9IBKjQSd6bS/Yz7c8TnHjnrrIMSwghRGWRncispY/zpb8fAEGmai4O6MaVOMnLycmhWbNmPPLIIwwZMuSy+++44w7uueceRo0addk6xo4dy6JFi5gzZw7+/v48//zz9O3bl5iYGLRaLQDDhg3jzJkzLFu2DIDHH3+c4cOHs2jRopKGXIxGo6FWrVrEx8dz7ty5m65PlH+qqpKXl4e7u3uZJPQeHh7UrFmzTNbkuy6qStrCp3nRPR+bUvhrf+TcFgI+bsS4iMIbigcsGYcycqUro3SJffEZ3NWk4r6Jl4aKML+mw6Fy6HwWGgXOpv2T5NkdKnaHikGn4YfNJwHIyrdicqtiazoJIYS4efF/8c7fCR6Aj1fF/f+gxEle79696d279xX3Dx8+HIATJ05cdn9GRgbffvstP/zwA926dQNg5syZhIWFsWrVKnr27ElcXBzLli1jy5YttGnTBoBvvvmG6OhoDh48SIMGDUoadjEGg4GaNWtis9mw2+03XZ8o36xWK+vXr6djx463fEFPrVaLTqcrV73DauxsXk/aQLynB/dkZvGzt4kpPmbGBv0zDCE+cR/Vzu+DnKTC6YLLUfy3iqqqnM8sIMS7Yt5UfbOmPXI7JqOOz9ccYe3BwvvyHA4Vjca1r/3JlBxq+nkU+R06nZZLrsXO7RG+HE/OcW7/at1Rpm86wdwnorH9vRzEmbQ8IkMlyRNCCFFCeem4ORzk//0hvcaS7eKAblyZ37gQExOD1WqlR48ezm3VqlUjKiqKTZs20bNnTzZv3ozZbHYmeABt27bFbDazadOmyyZ5BQUFFBQUOB9nZhbeMGm1WrFarz7d9oXeQ1F5ORwObDYbWq22TF5vm812y89x3TLPsXDtq6z18+Ku7ByeV334GTsHjUWHksZpHcz+qQ8WFF7o+C5q48EuCrjspOVasNgc+Hvqr/k+cbELZUtyTHnUvrYvAI/dEc5fp9NJy7WSmJGDv5frkt6MPCtdP1zH/3WsxbN31nVu33cmHYD+TUN5beF+ftp2gsEtqrPtWApJWQUMn7rFWfZkUhZ1AyrOTKmVpT0J15O2JEpTVWxPmsxzeP+d5IXabNhqdUYtZ9d/va9HmSd5CQkJGAwGfH19i2wPDg4mISHBWSYoKKjYsUFBQc4yl3r77beZNGlSse0rVqzAw8OjFCIXlcHKlVVsOKKqEnH8fT7w8SDIZmO4vT6HPRtQz7KMVI2WFN0/Ce9Lgf7OT64Grp/B4ZOVf824czkAOo7ujWHpyZIfX5na0yO14aO9On79/Q9qeLoujoRcsDt0TFl7FH3yISJMsD1JYeaRwrZqStxN2yAN/56/l3MH/+Kvk1paBqgczcwDCnv+VmyKoeB4RRiEWlRlak/CtaQtidJUldpTw7ObSPXQclt+Pu+kaVh6REU9ttTVYRWRm3t9M0yXmynIVFUtMjTnckPdLi1zsZdffplx48Y5H2dmZhIWFkaPHj3w9q6405+K0mG1Wlm5ciXdu3e/5cM1y5VdP/BM/HmyNe78N8NO/Ud/AJ2RLxanYTywiE7h/0x2lK/RoFVV7IpCvls2ffr0AYe9cNimUk7uLSxlGw4nw+6dDOrZhWo+19/zUxnbU0JmPh/tXU9g3ebkozK4RdnPNpqVb2PjkWT4azehPu7MO6fht6faMn/ubiCZQC8Dd93Vg+42ByNmxPD98WzSLVaGdW5KZKiJ7/48yYYjyXiHBtOnT8Myj/9GVcb2JFxD2pIoTVWtPSmHl7P08AZsnv7UtlgJeHgpvQPquTqsYi6MVryWMk/yQkJCsFgspKWlFenNS0xMpF27ds4y58+fL3ZsUlISwcHBl63XaDRiNBYfYqTX66tEwxTXp0q1h+wkFvz5H/7082BAVjZd+nwN3oEAhA79Hg6vhE2FH4x0yclloEWLw5LJc8GBnM48wm07/sf2TR+g05to8dha8PC7yskqpuTcwmG1ob5e6HUlT2QrU3sK9dGiKPD52mOcSs2lcXVfoqqbyzSGfh9ucM6c+fmw2xj2zVbeXXGE5BwLrcJ9ef+eZn8/5/D18FYM+GIjablWGlX3ITLUmw+G+vLItG1sPpZGnh28K9jkK5WpPQnXkrYkSlNVaU/blo3hlcDC5aQC7Q70vtWhHF739b4WZf7xfMuWLdHr9UW6fuPj49m7d68zyYuOjiYjI4Nt27Y5y2zdupWMjAxnGSHE1aWv+Dcfm4342u08H9IZ6vcoWqB6S55JTQfgTb/WdH16L+HBtwEwy9vEo399wqMBXjxkVmH9+2Ub/C2WnF1AxEtLmL/zLP6eBgw3kOBVNjqthuo+7pxKLRwGMnfH6TKP4UKCp9UoNKvhw2t9G/HjtlPsP5dJnyah1Ar4Zxypn6eBaSNuZ3jbcOpdtPzFc93rE5+Rx4NTt5KeaynzaxBCCFEBZSexXfPPfAr+djsYK/ZIwBL/Z5OdnU1sbCyxsbEAHD9+nNjYWE6dOgVAamoqsbGx7N+/H4CDBw8SGxvrvJfObDYzcuRInn/+ef744w927drFgw8+SJMmTZyzbUZGRtKrVy9GjRrFli1b2LJlC6NGjaJv376lMrOmSx1eRfzsIXB8vasjEZXZqS18cXYlqVotYzPz8e31bvEyHn483ncae6rfjbnvZ6AzEBEYhU5ViTMa2O7+zz15trM7yjD4W+9CIrP5WAoh5sp/7+H16h0VAoCHQcuCXWfJt5bdzMObjiY7f/bQa9FoFO5vHcadDYNwqNAw1FTsmLpBJt4cGFVkzb+mNXyYPaotp1Nzuf+braRkFxQ7TgghhLggb83b7JzcmE0X/d+jQIWfZbzESd6OHTto0aIFLVq0AGDcuHG0aNGC119/HYCFCxfSokUL7rqrcDHa++67jxYtWvDVV1856/j4448ZOHAgQ4cO5Y477sDDw4NFixYVmfVw1qxZNGnShB49etCjRw+aNm3KDz/8cFMX63KWXOYuGkkP6yHWLX7a1dGI0ma3wYk/ITe18PHBZRz8thvq3vllHseeJWOYa/KiRX4+A6NfAq/iExkBUK8bdJsAXoXDOPWNB/FKSgaPpBcd733aUbn+UU7K+ud6ujS4wnNTBQ1qUXiP5mt9G5GZb1s7p/EAAH8NSURBVGP5vstPdFXa4uIzGfbNVufjApsDKLw3+927m/J4x9rcVtP3SocXE1XdzJzHo0nKKmDo15s5k3Z9N6kLIYSoYrLO886+//FwtWB2uxmpa7HQLSeXHrX6uDqym1biJK9z586oqlrsa/r06QCMGDHisvsnTpzorMPNzY3JkyeTkpJCbm4uixYtIiwsrMh5/Pz8mDlzJpmZmWRmZjJz5kx8fHxu5lpdb988PvAtHFa0lkzIlIXYK5PcPybx8pLhxH7aALZP5Y/fRnC37jzfr3iGo990gZzka1dSCtRdM/lAkwbAS2ogmttHXv/B4dHc/UQMT9XoXmTzyfSjcC4WTm25/HHl3Oytp1j01zkycgunHU7MzHfu69+84i50WtoaVfNm76Se3N+6Jq1r+fHT9n+GbBbY7Pyw5SRWu4O9ZzPIzL/5KaW3n0jlpV93s/vv5REusNgdzp8DvIz8u08kbvqSLX3SIMTEz09GY7WrDJqyib1nM246XiGEEJXMsTXM9/rnVoD7M7P5uMfX+AyY4sKgSofciFKG9m//mry/p6g/ZtDD6W3XOEJUGHYrq/+aymIvT4ZXC+HRmHf51VSY0H/g78tAQzLrP29M3pq3b20clhxW/fkOO93cGJyVTaPeH4KmhOsCegXhFtq8yKbnAn35fG5/Pps/FI6tK714y4DN7uD95QcY//NfNHtjBbGn0zmfWYDZXc//hrekfnDxYYBVmZexcD6ue1uFseloCqdSCnvBlu87z2sL9jJn2yke/m4bby3ef9njrXYHP20/hfWiRO1KFsaeY87203yy6jBhfu78/myH0rsQoFaAJ7/+XztCzW7c+/VmNhxOKtX6hRD/395dx1dZtgEc/z0nt511M2J0d4PS3aUgCC8oWFgotr6K+qqAigEmoiKpdMcIUaS7m9HrzlPP+8cZG5OGbWdxfT8fPuw8cT/Xs92Mc537fq5biKIt/uRq1GumZbZQjVC5I2gLX8GVuyVJXkGxWalSsQOTIqMJsFo5bDBgubD99ueJouHMJtaYctZj3Onqwt9uuUvyPxscyNcHv4eM/BtRsG6ZzNdu4Gq387RfEwhtcW8NufnlbldR+MHHi6neXthXvZYHkRacXefiiU+zZE8B/OdUDJFJGVTwN9G5VrCToyu8utUJxt2oY97uC7z8xz5emLMXgD92XSQ21czifZeJucHzbpuOR/P6goPM23Xxun0RiRmoqmP9Ortd5fDlRDQKXEnMoG5Whcy8FuBhZO6TzalTxov/Lj6U5+0LIYQoug5FHQSgstnMwKRkyna+QQ2DIkqSvIKi1aHvOI5Oz+zjoeRUMjUalh+aTvyyF0Eteov2itwSD/7OZrfbr7O21dUl/0ZwU6KYv/cHwg16hielENT543tvq3oPvopLY3BiMj9eyb2cSWRG3H0GWrDWHo7EyzXnE7ntZ+OITM4kyPP6JVdEDjeDjl71Qpi/+yIL91zK3n4wa9qj2Wpn1rbzuc45E53C2iOO5/i+2XgKs9WOza4SnZxJbEomrSZu4LetjimfFd9ayZ7zCTzZuhJ+JgONQh3P3H36UF0WP/tAvtzLhfh0rHcwwiiEEKJkiDU7/k97Pj6R//aYDnUecnJEeUeSvILmEUwtnePNzLsBfrx2aQUsfT5fR3dEPrNmsv78eqyKQv/kFAKsjhK8OlVl4cUrvBGbkxRF6HRYw/8GVSV9w0dcnjUA4sPzJIyUDR/ynZcrflYbIyr2hftZwNPFk/ajtvBW//k0qfdYrl3hRei3hrrufWrt/5AxFS/yQc9qDG5ajl3hcVyKTyPIU6pq3s6gJmW5kpjz/GKlgJznFuqW8WLGtnPEp5pp8+lG/jkVQ/vPN/HHrotUCjBxOTGdebsvsGTfJR6YsIGVB69gsal8se4Ee88nZLfzYGV/Nr3WjhEtywPwcOOy1C/rnef3UtbHDZtdzXU/QgghSjCrmTjV8X+Cj081qNimyFfUvFYRertWfNT3DM3+epurKw9HhpGw5FmwyJuPIun0BtYaNehUlZeM5dng1oCn4xN5MiGJKsOW8+gjy5kaGUf3lFRSNRqOnNsEF7bzyZGf6G8+RvrWb+4/hpQoZp9ZQpxWyzNJ6ZjavX3/bXoEQZnG6P6VLF6wp4G94Err3zO7jW0HZjCl1H4OpYwnJOQE/RsGkma2cTo6lVA/0+3bKOHqlfGiWpAHWo3CwtEtmfhQvex9r3SuRkxKJh+tPMq52DQmrj6Wva9+WR961g3hmw2n2Ho6FrPVzkcrj+Ju1GG22nl1/n4AhjYvR+PyPrgbdWg0+fsfa1lfx3TqC3FSaVMIIQSQGk1MVmX/wJtVIS/CJMlzAq/aD+Fry3mTfMxo4LX4bez4tgGkJzgvMHFP4g7+znZXF5qnZ+Dd+nUYPJtnByzgmYcXQbnmENKA5q9epAeO5432JoeTsvNHVpncSNVoiI4/ff9BRB3loEGPTlXpX7X/zZdMuBc+5XO9XGVyY8mksqhHluXdNfLDhR0sMtqJ0WlZ62bkpb9f5/ktfXAJ+R2t+1Ha1/B1doSFnqIojOlYhaHNytGwnA/l/XKeO21awZe21QKYv9vx7N3+i47ZCF1rBfNCh8q80L4yV5IyWLj3Et5uejIsduqW8eKp1pU4F5tGeT83/te3zl1XzbxXId4uKApckOUUhBBCAKREZid5/u7Fr9K2JHnOUGcg893q51qLbKurKyO9dez6qiocX+3E4MRdsaSz7sImrIpCp0w7VGrv2F62CZRulHOcotCwTEu0qsoeo44NZ1eTkVVpNSr1Piv+WdJh8xdE6HQEWW3o/e5jmuaNBFSnZ0pq9stdri68E+DHgS2f5RxzaTdEHMzb694ny7HlbHZ1pU5GJmE1nuOVxq9Q0asieq+9uJWdzqNru/HuP++y5dIWrHars8MttLrVKcX7fWoD4GsyYNBq8DDqcNFrefyBCoAj4asYYMKg0zB5SANC/UxUCfKgZ90QbHaV59pVpkqgO80q+PFE6woEe7pQt4x3gd6HUaclyMOFC3HpBXpdIYQQhVRKFLFaLR42O0aP4pfk6ZwdQImkdyHgkdmMXPIsSedXssDTPXvXY6WC+GrJCFo1eR5927eK1dzgYulkGGGuWvSqSsfQjqAz3PRQ9/JtqBG5jj0uRlI1OZ+vxGTG31cIMUue5dW0wxxzdaFhRgZ4lb6v9q7jVYb3G73Ci0cW46E3sTh6B+P9fAlPuUw9gHNb+W7RQFztMGLIKgiunbfXv0fbT60g2V1Dm8QMgms9xHCTH8NrDedozFnWn1/LpkvrWHRqEYtOLcLXxZeO5TrStUJXGgY2RHu3y06UEIqiEORlRJv1e6lVFX8alvOmffVAaoV4sv9CAnptTt9+sUMVNp+MplWVAIa3LI9Oo6AoCvOeblFgI3jXqlPGi+lbwmkY6k376kEFfn0hhBCFSEoEMVotATZb3s6AKiRkJM+JvHpNZlyL//JQUnKu7S8GBfD8sV/g5y55VpRD5JGMJNgyBcI3AxB98Hd2uLjwQFo6nnUG3vrc0JY0ysgkQatlu6sLPllTdhOtyfdeYTX6BDMurWOXq6OISJDVBp55nOQBhuajCX58LaZhi6jnVR2Ay2RARiLq6tf41sebz/284e/P8/zad+TYSuxr3obUGNSjy0md1om/bAkAtPasAqacJSFq+FfguYZPMa/XPJb2Xcro+qPxMfrwx4k/eHzN43Sc35GPt3/M7sjd2FWpxPhvwZ4u+Lk7KpMqisLC0Q/wdJtKtKoSwHPtc48iVw50Z89/O1Et2AO9VoOSlRyW9XUjwKPgq5t+PrAezSr6Mmr6LiKkAIsQQpRsKVHEaDX422zgXvw++JMkz5k0Gmj8OO+1fJ/pl3OXqf/HzZXWykWOzh/hnNjEDZk3f07fw5P5adFgLk5pxIaLf2JXFDqbFajQ5tYne5elvpIzajsoKQWAOMUO5pR7iidu08fM9cxZzDs4n5K8a5XPmg56Xq+HuLNEpERk77NkJIHVDPtm514qIvY0bP8R0u9v1PKG0uP5ddUz1ItYSswfjzJlzWia6yL4w9Od0hYr1av3uempFbwq8Ey9Z1jcdzELey/kybpPYtKbmHNsDiNWj6DjvI58sv0T9kTukYQvS+eawXSoceefeCqFaDaCp4ueV7pUw67CpQSZtimEECWZOfkKSVotfpLkiXzTaDgNH57Dr/9K9OK1WuZnhENa0VqXrDi7uH0Kpw0GvvL1ppuHmRleHhjtdtpV6AJa/W3PbxLSDEVV8bTZ6JviSOxitVpIibr7YOLOMPviRtKumfrpabeDKeDu27oL7n5V8bXZOK/TwcF5nCXnebZLaib2HVMZvv09Jix6GPuuX0BV2TF3IN0OTmL/z53yvops3Bk+93UUtfkz7iA/+ngBYFMUamdmopRrfkfNVPGpwvMNnmdZ32XM7zWfJ+o8gavOldnHZjN89XA6zevE+B3j2Ru1t0QnfE+0rsjotpWdHcY983Z1TKlOSrc4ORIhhBDOFJPiWAPW32ZzVBQvZiTJKywqd6DR2LPMTNbS7ZoiF+f0eriw3fHiyn4486csnu5EsdrczxGd0+tplZ6B++2mambxqjOIUYlJvJiQSlBoWxRVJU6rhXsovpL6z5fM8TRRzmKhdZpjVMLNbneMEOcn34qEWiwcNRp46+QcVl0z6+588mUunlrNHhcXZnp58uHW91H//px/rNFc1Ot515BCxoYPHX14/++we/q99+erbez6mcpmMwBrTG65Dgmw2cAj+K6aVRSFar7VeKHhCyzvt5x5veYxqs4ojDojs47O4j+r/kP3xd1ZkbaC/dH7S3TCVxR5uTo+jElINzs5EiGEEM4UleIYXPG32fL9A3JnkMIrhYnBRL1ndlFz/fsYT8xkqbuJA0YDlt3T0bv68uuCh7mk0/B24IPQ5WPwLpdzrqo6/uT3G/wSLsJw/dpqXVLToPyDd9ZA1S684L4EjJ5wZDHeJ48Rq9VA8pU7O99qdhyrd2P+6aUk+XjwQmI6XUq1YN6Vv3mo0fN3cTf3qPyDDFtj43vFwjKP3N+Py+ZYLIkW8ARvm435nh747P2aEwbH6MkZg54px2byiqsPH+yfwiWdju8s6WiaP333cRxZwuub38TDbgcXR6a5zdU11yH3O89eURSq+1anum91XmjwAsfijrEmfA1rwtewNX0rW8O2EuQWROfynekc2pm6AXXRKPJvsDBz0Wsw6DQkpslInhBClGTR6XGgBx/FBXQF/5x4fpMkr7DR6tB3fJ8Py7UgePXTfO/jxeikXQyf05vvA/1J1WjYlrSLd37rTrPn9oNGC8kR7P61E6rNSuMek6FiW9DKjzZPqSoRcwez1sXxBr6y2Uy0VotZUWhVurXj53CnQho4/vYqR2mrleMGA/NWP0uvyEO4tP/vzc+z2zg6tRXrM6/wpFnHTD9XfG02+lQfiEu3CTyRkQguXvdxk3fI5E+nJ7fT6fgKzh+ax/yEQ5zV6/jT5MZqVwN1M5MATyZGxfC1rzdTvR0xVTGbcbWr/OblQft/JrI6KIBkrYa1f79P1waPgtHj+mulxuYUTYk97RjVrtELjB7EHV3MSncTOlXFcJPRwACbPc9+cSuKQg2/GtTwq8HoOqOZtmwaaeXSCDsfxowjM5hxZAbBpmA6h3amc/nO1PWvW6ieRxMOiqLg5aonQaZrCiFEyWVOI8EcC/jha/B0djT5Qj5yLow0GqjenXp6x5vbba6uPBMcmF12P9yg53+uVji1Dtvqt7D+0IaXTTae9NYybvUTXJ79UN7HVNKniF7ZzzMpe/kzazrg5EQrP6S5MCVJxdR1/L21WbYp9TMySdVo+MDfj58PTHWM1N1M7CmeNSTxg48X77upROh0PJqUikvLrNG7gkjwrnIPgEYjKDd8BS//ZxNfl+lJ7+QUdru68Iu345dlzSo9+Mq9Hu52x3TGEKuND9N16FV4NdCP5KxS+994e2Hd+s11l0hZ8TLfTm1A6oqX4PBitk5twaPbx5G05m2w29l2cQsAVkXJ9Vzitfyttvy4exRFIUQXwgv1X2BV/1XM7TmXx2o/hlbR8tuR3xi6cihdFnThs52fcSD6AGpJ//dTyHi76kmUJE8IIUqeqGOc/bkzez4vzwf+jvfZga7+Tg4qf0iSV4g18Ai96b4UjYbo3x+h1eUlvOxmJU6rxaIoLPB058ukA3DmT6J/H4z90IL7DyTiEP98VZ3TP3UEa+b9t1cUpURyypCzBl5gtZ7UenY3TV84DL4V7q1N73I0ysj5fh436B3PXd5McgTxWc8ELvVwx81uZ2DZjuBV5t6un1d8yqP0/or/tfmUD6NjAQiyWvEq1ZDAh6bzU4Y7QVYr3TM1VBw0h5GJyUTpHCPNFc0Wwg16Fu39MafAkM2KuuFjJpxdxHc+Xvx0agEvbxrLk6WCOOBiZO/R31n1eWnW62+/gHmALX+SvGspikItv1q83OhlVvVfxZwec3is1mMoKEw/Mp1HVz5K1wVd+XzX5xyKOSQJXyHglZXklX9jBT9sOu3scIQQQhSQxE0f01t7heEhOY9yBLqXcmJE+UeSvELM1GgED6TllPlunp5Os3RHZcIYnZZfvDxJ1mrY+K9iE5d0Oo7NfYhO6QeZF/YyZN5leX6blbSVr5K2YixYzcT8NYFnvfV8ZD0HJ1bf930VSSmRmOw5BTYM6QmOhervZzqeotCgYtfsl0eNBji/9cbHpsaQ+fvQXEnLQ8kpeD849t6vn8eUOg/R9/F/WEI5viEE6j8KBjdqDV/Fuhqj6T7wDyhVj8erPkwZi2MU5d2YOLxtNn7wNJJxdY29g/MYeeIXFns4lps4ajAQdk0f32By47VAf9a6myhtsVIr05EoX23zWgWR5F1LURRq+9fm5cYvs3rAamZ3n82IWiNQUfn18K8MXjGYbgu7MWnXJA7HHJaEz0m83fSci00DYM6O806ORgghREGJOLnyum0+mak3OLLokySvMKvRm29rPcOsyxFoVJWH0qz8VH8sn0TFADDnmvXRyl3zBveo0cACD3dsisImFz1c2Ja73du9sdz9C60jV9Iyeg1s/46NlzZjUxT2uRhJP70hz26vyLBmwq5f0GR92/SqCpU75EnTfu3fZW2Sjj7JKVzR6biyYRyc337dcdHLX6RDkAdXskbAdKrKo6oXBNXMkzjyjG8FKg5fQbWR68HN17HNzRdaPg8h9QFw6TCOCVYfno5PpMGDb/JYUiqROh2Lj8zGvOEjNqx9iZ1Zi7sD7HHJ/UzdBrec4ipNMzJol/VBSHXz9Umel3/1PL7BO6coCnUC6jC28VjWDFjDrO6zGF5zODbVxi+Hf+GRFY84Er7dkzgcKwlfQfJ01bP7nGPNRn/34vewvRBCiBuLMrhft00TXNsJkeQ/SfIKM40GTeux1B0exmavlnTpOx2aP02j0o5KjlZFcSQcQL/kVMLSPRkdn4BFUZifNQqyx8WI9dplF85tYeOXlTn/Wx+w32SUY/8cMjUabIoCYe+y3sUxRdCiKOw9Mhd2THUUwSghUta8wxP2CyRrNTRLz2BtihHucMmE2/KtSKlnd9PY4vin2LlcaWYseBgSrhldSLrCost/kZg1VdPHZmN8VAwhA2fkTQwFzehO3VF/8uyT+9C0eolBlfriZbPxq4cLvxz4nheDcpcxTv/X83YJ1yxjUTvTzIDS7RmUlMxDyTkj1jMvR/BpbDJKv+/z917ukKIo1A2oyytNXmHNgDXM7D6TYTWHYbVb+eXQLzyy/BG6L+zOF7u/4EjsEUn48tnVtfIAfEyGWxwphBCiOInW5byH+CoympkpemjyhBMjyj+S5BUFIfXx6PcjVGwDQKmyLXJNdxuSmMyA0G4EP/0PLdzKA44E0NVuJ1WjYdahXzkypT4cWUr4/P/wgq8bX6YcgrN/OdpX1VyLcVvUnOQvTqNhu6sLIRbH809PBQfS7cDnfDqrPYkLRhb/gixWMxuOzsouzV8j04z/f5aD3uU2J94FjYaGfrWyX0709YKTa3NC2P0L8zxzPnnqlpJGl1FboFS9vIuhoOmM2SN9pgdfZkhSKpf0Or71zike80RCItqs/lXRbGH32ZzEt05GJq/HxtO32av495/GO4+spdI1I3n1OnxM1+eOFMrvkUbRUC+gHq81eY21D61lRrcZDK0xFIvdws+HfmbQ8kH0WNSDL3d/ydHYo5Lw5QNvN3321xmWgp3SK4QQwkmsmcSrjkc8Zl+KoP3zR6n37B7wKu3kwPKHJHlFUfWe9EhJo4rZTI8GT/Hmo+vx6esYsagdWDf72bGnExIB+MzPh0Gedl788yXmZBWr2OFixB6+GYDLM/vzzU+NMX9aBfbPJfqaJG+RhwmrojAyMQkPm6Pdi3o9v3l58u2l9XB0GcemtnEkfOe23nx0sKg682euBbbza8HMskF1c71ODP8b89q3sf31GRsP/EaELmdJjAStBrxvXpSnyPEJZXDZ9rjZ7diznnEMslp5WvWiVqaj2miDjEwMtfrjnfWMXfu0dIY+uQfDg2Mcy4UEVMWv7hAAuqakQoXWYHC74eUKE42ioX5gfV5v+nquhC/Tlsm0Q9MYuHwgPRf15Os9X3Ms7pgkfHmkX4PSGHWO//4SZL08IYQoGVKiiMmaDeRXsR24+jg5oPwlSV5RFFCV57p8y8LqT6Jv/Tr4VcpeBF1XfwhdUtOolZnJkKTcBVc2mNyY7eV4ji9Rq+X4zm9h23f8krCX7328aBTowjt/v8mMzAvZ58zz8ECjqrTX+aLH8QbzkaRkgq1Wtri6ELlgBIP1sQyL+4e3lj1KxEcBpK39L8SHF8z3Ip8lHprHlmueD7MoSr6sQag0GsEH0bH4ZiUx+8+sZsC5Bbxz8FvmG6xoVZXxWc9i9lH87m5dviLAp9VrDMzqrz1TUlmrq4Kh6wQCs74fDTMzoVTd7EIqTTIywD0wVxv6Th+wJ7A7E1q8DwHVCvYG8sC1CV/YQ2H81u03Hq3xKOnWdKYenMrDyx6m1+JefL3na47HHZeE7z6U9XXj2Iddeap1RY5HJPPT32ecHZIQQoj8lhJFbFaS5+9ePEfvriUrZhdVNXoCPa/fXv4B3m8xDlKjoVlZdHs/xPqvCpClLVYu6XWMDvCh3c4JbLqmkMUSj9wPpF7S62icnoF/2//xxoa3eNPHlUHpNtKVDJZ4uDPb0x2ronDWoOesQc8yDxPlz82jzbHfeKHOkxjavZ0PN19ArGbWn1+P1cfE0MQkjhoM9AxumT/X8q9Mv5FbqfxzW4b4uzPFx5twg54InZYMjYZOqWn0eOBtuideQqn/SP7E4EyBNRga1JLdibvpatGjeXQ+JF+hnMWCRlVpqOohuA4V9luI0WqpWb7T9W24+aLvNqHgY88HGkVDg8AGNAhswGtNXmNf1D7WhK8h7FwYUw9OZerBqZT3LE/n8p3pXqE7lbwrOTvkIkdRFHxNBsw2O/9bcZShzUNx0RevD0+EEEJcIyWSWK0WT5sNg0fxXDbhWpLkFUeNhjv+tmQwd9tkdqWeZbyfb/buMfEJvBroT4xOy7xrKnTeTPt0M1TvQTevMnQ5uhxNr1E0mdGFJcAML8/rjg836Ak36Gm4czLti3KSd+ZP1hs1aFWVJ7zq4tv4caiUN1U1b8gnlOq1B+N6eYljOQUgI2uEdqDdHZqPRrnJot/FQVC/H5l9aCGEPgA6A/iE8ljVR2gXvokyQ36DUvV5J6QLKcmX0Pf+2tnhFhiNoqFhUEMaBjXk9aavszdqb3bC9+OBH/nxwI/U9KtJ70q96VahG74uvrdvVACO9fKuSky3SJInhBDFWdIlorRa/Gz2fHn0prCRJK8407tQ7cm/qRZ9nM6rxrIjag8r3E20dysLpOc6tG1qGn+abvwMU4fAxmB0h/IPoCn/AADNghqD+TAWRcHDZidZe33ykXE/a8gVAomH/mCLqwvN0jPwbfc0VO+R79fUl2tJ7bPz2Onqkv19rWi20Kzhc9lTcosto0fOBxRZvLtNpP41r336fUfxnkF/axpFQ6OgRjQKasTrTV5nT9Qelp9ZzprwNYzfMZ7Pdn7Gg2UepHel3rQp0waDVipH3sq1BVgS0y0EeeZhQSUhhBCFSkz4X4Qb9HRLSYWgWrc/oYiTJK+4UxQIrE7A8BX0iD5Bj6gjUK45L0xrxtc+jlG4EIuVr1U/1BSYbr7MKpMpeySpTkYmIc0euq7Z4ModqbhvH2cMet6Mi6OqamSn1srvHu6EGxxvnGK0WkchFkUD5hTHm/iiwmpm/fkNWH1MdMyw5+8I3rXKNadRRiY7XV14Iy6en708eDLFgtLg0YK5vigytBotTYKb0CS4CW80fYON5zey9MxS/rr4F39e+BNPgyddy3elV6Ve1Auoh1LEP3TJD54uOUmeFGARQohiTFXZfmU7eBtpYlYhpKGzI8p3kuSVJAFVHX+AkR0+45Glz/ODh5HqVg3KyDAUrzI8lhbHsCOLSV/xMt/7eNHC7grVu1/fVqUOdPrnQ37XamhdqRde/X6kWsRBhu6fy9xDv/KRv68jyUuJ4vicgRxOPk3/gEbQ6pXspSAKtWumanYo2zpvl0y4FZM/w6oNpuqJxXRs8z962y1QrkWxrwAl7o+rzpXuFbvTvWJ3otOiWXl2JUtOL+GPE3/wx4k/KOdRjl6VetGrUi9Kl4CHze/UtYlvYrokeUIIUWxFHWGXxgwYaRZQL1+K6BU2xf8OxQ1p6jyMR+2HeOXyHse8ZK8yjh1uvugaP45H48d5NeIgeISAi9f1DfhWYHTbCTxxcTvGtm85tgXXgeA69M6I46OEzcRqNfD3ZzzkkgAufrxHOC8uGcqoJ/aAe+GeC51rqmbzPFr4/A55dptAp2JSQEQUvAC3AIbXGs7wWsM5HnecJaeXsOLMCr7Z9w3f7PuGZsHN6FelHx1DO2LUGp0drlM1reDLm92q88mqY5yMSqZFJT/cjfLfohBCFDtnNrHd1YUyFgvlqt6geFsxVMwf8hG3pChQuhF4l7vx/uA6YPK76ema+o9g7PnFdQmbm0dpXO12lnq4M//YnFz7vvL1hnP/3G/k+evqVE1FoVOGDSq1d3ZEQtyTar7VeK3Ja6x/eD3fdPiGTqGd2B21mzf+foN2f7Tjo20fcTT2qLPDdBqtRuGpNpUwGbRMXH2c2u+t4WJ8mrPDEkIIkccung7jgl5Ps4xMqFAEZpTlAfnIUuQ9UyD2rC/f979Bkqjar99WmJzZyLqsqZrty7YpuKmaQuQTnUZH6zKtaV2mNXEZcSw/vZxFpxYx9/hc5h6fSw3fGvSv0p/uFbvjabi+Ym5xZ9BpSDU71mD83/KjVA1y5+XORW+tRSGEEDdgs7Itej/4mmhs1UFgTWdHVCBkJE/kPaM7mbeqBJkWW3Cx3IPEQ/PY6upC8/QMfGsX7FRNIfKbr4sv/6n1Hxb2Xsis7rMYUGUA55PP89H2j2j/R3ve+PsNdlzZgb2wfxiTh+KvKbqy+nAE07eec2I0Qggh8tTlvezMKjbdLKRZ8a9WnkVG8kTeC6hGo/QMdrveZAQsJapg47kb1sx/VdWUqZqieFIUhboBdakbUJfXmrzG2nNrWXRyESvOrGDFmRWUcS/DgKoD6Fu5L/6u/s4Ot0DUKOXJ0StJJKZbSEyz4HXNEgtCCCGKJvXMRna4uFDFbCagdkdnh1NgSkYqKwpWSEO+LtubRRevoFXV63anJV92QlB36MyfrDNq0KkqHWWqpigh3PRu9K3cl+ndprO071Ieq/0Y6dZ0vtrzFZ3md+KVTa+w48oO1Bv8ey5OaofkTFW9IM/mCSFEsXDyzDpidFqapWeUmOfxQJI8kR8UBc8en1O517fUyjRftzs2pfAmeYkH/2BrVlVN7zoyVVOUPBW8KvByo5cJeziMSW0n0TioMWvC1zBy7Uh6L+7Nb4d/IzEz0dlh5qknW1ekZSU/yvm6ZW87HydJnhBCFHmWdHYkHAegkeIOPuWdG08BkiRP5J8KrWmWkXHd5sjUaCcEcwesmaw/vxGroshUTVHi6TV6OoV2YmrnqSzvt5wRtUaQkJnAp7s+pf0f7Xl789vsi9pXLEb33upeg9lPNKecnyPJ0yiS5AkhRJFnt8HhReww6tCqKs3KPOioLF9CSJIn8o9HMH28atEhNfebpejE02Sueh0s1yeATmGzYl7/PpafOhDmkjVVs1wb0JXsNcSEuCrUM5Sxjcey7uF1fNLqE2r712bp6aUMWzWMh5Y9xO/HfifFnOLsMO9bl1rBfD+0ETVKeUqSJ4QQRZXNCkeWcuGziny56Q22urpQK9OMR6WS8zweSOEVkc9CB//Bl+H/cO6fLzgae4BXA/15LdCfoMtLCdtZFqXlc84OEfbNZOCZ2ShaCNdnTdVsIVM1hfg3o9ZIz4o96VmxJyfjTzLvxDyWnV7G/7b/j893f06Pij0YXH0wVX2qOjvUe+Ki19K1djBL9l3igiR5QghR9Jz9C3XBE4x1sxIWlPOcdbOMDKjQ2omBFTwZyRP5y+gB1boS+vgaOtYbmb05UqcjfPMEJwaW40zYW5w2GDhlMGQtgC5TNYW4nSo+VXir2Vusf3g977d8nwpeFZh/Yj4Dlg7gsdWPEXYuDKvd6uww70k5XzcZyRNCiKImI5Hpi4ZQN8BAmMkt165m6RngEeSkwJxDRvJEgdFVbIvm8mLsWfOht+uggtUMOsOdNWBJh+OrIKQB+FbIm6DS4ghz0ebEqKp0kKmaQtwxN70b/av0p3+V/hyIPsDsY7NZE76GXZG7CDYFM6jaIAZUGYCPi4+zQ71jZX3duBSfjtVmR6eVz0KFEKJIiD7OZ343/r+mfo2HCjgY55P/vUTBKdeSLxIyqZpVcXOHiwEu7oA9M+DI0uuPV1U4vQGiHVWRkte8xRObX2PTb10dCV9eiDnJcUNOktksPQPvGv3ypm0hSpi6AXUZ32o8YQ+FMbr+aGx2G1/t+YqO8zry9ua3ORx72Nkh3pFyvm5Y7SpXEgvJc8NCCCFu7wbPhn8QHcv8DC+Mbd50QkDOJSN5ouAY3Gg/dBXtV77CoIwj7HQxYl/6PC/okwm1WGi1dBTNmo9FqfMQ+FVC3fMb/906jkYZmbTyrMyWpFNsC/DD1Z5GmwvboWLb+48p+QoxWg2+Nhtj4hKo41UVqna9/3aFKMH8Xf15pt4zjKozivXn1jP72GyWnl7K0tNLqRdQjyHVh9AptBN6beFcbPzqUgoX4tIo6+t2m6OFEEIUCim5q7d/FhlNlyEroEwjJwXkXJLkiYLlXxn6TKHZrw/wi7eRlZkRbPLyB1z5zcuT0qd+Zdier+nvXpkzMYdZUroUSzzcgXhCvR0P0O5yccF2ZhPa+0nyLuyAhPOQGk2UVkeg1Ua/Nh9C48dLVHldIfKTXqOna4WudK3QlaOxR5l9bDYrz6zk9b9f59NdnzKw6kAeqvoQAW4Bzg41lxBvVzxcdLy56CCvdqlGjzqlUOT3ghBCFG6pUbleersFldgED+5huuZff/1Fr169CAkJQVEUFi9enGu/qqqMGzeOkJAQXF1dadu2LYcP556ik5mZyfPPP4+/vz8mk4nevXtz8eLFXMfEx8czbNgwvLy88PLyYtiwYSQkJNz1DYpCyKsMjbTeAHzh651r1yW9jvF+vjQ1xvGBv2+ufef0jk/9k7Uadu36huSlL0LmPZRtT7jAzHkDePHv14kKe5tInZYAmw18K0qCJ0Q+qeFXgw8f+JB1D69jTMMx6DV6vt3/LZ0XdObtzW9zLO6Ys0PMZtBpmP90Syr6m3hu9l76fvMPW0/HOjssIYQQt2BPjsj1OtBud1IkhcNdJ3mpqanUq1ePKVOm3HD/xIkTmTRpElOmTGHnzp0EBwfTqVMnkpOTs48ZM2YMixYtYu7cuWzevJmUlBR69uyJzWbLPmbIkCHs27eP1atXs3r1avbt28ewYcPu4RZFYdQktA06VSVKd/PB5CPG64ufdMpac29UqSB6R4cx65tqpK58/e4ufnoDE/y82WByo0O50lgVxZHkeZS6u3aEEHfNx8WHkXVGsrL/Sr5s+yX1A+qz9PRSHl72MCPXjOTPC39iV53/H3O1YA9+eawpc55oDsDgqds4FpHk5KiEEEJcJyMJYk+TlHIl12Z/5/9X4lR3PV2zW7dudOvW7Yb7VFXlyy+/5O2336Z///4ATJ8+naCgIGbPns1TTz1FYmIi06ZNY8aMGXTs6FiUcObMmZQtW5Z169bRpUsXjh49yurVq9m2bRvNmjUDYOrUqbRo0YLjx49TrVq1e71fUUi4VWxH3cur2OPicsvjeqakkqTRsNdoJE2jMDYuPrssboxOy3g/Xy6emsvrFwaQsP0bvGoNRKnePfeInM0KkYdR1KwPEWzm664TYLWVuNK6QjiTTqOjQ2gHOoR24EjsEWYcmcHqs6vZEbGD8p7lebTGo/Su1Bs3vXOfiWtRyY85Tzan5rtrOHolierBnrc/SQghRMGwpLP9+2bsJJWuqWlQJucDe3e77RYnFn95Wl3z7NmzRERE0Llz5+xtRqORNm3asGXLFgB2796NxWLJdUxISAi1a9fOPmbr1q14eXllJ3gAzZs3x8vLK/sYUcSVb02L9EwAGmTkVLBbduEyD6SlY8waYu+fnMI37b5iZror3yRYKN38xeuaOmowcGxGd9ql7Obrtc/w9+flYOu3kBoDQOzip/hocT+aHxyFZvfPqImXrmvD3W4HF+98uFEhxO3U9KvJJ60+YfWA1YyqM4q4jDg+2v4RneZ34svdXxKZGunU+NwMOvzdjZyLlbXzijK7XSUySSqmClGsXN7HH/pMfvDxYoOba65dSpNRTgqqcMjTwisREY65sEFBuUdEgoKCOHfuXPYxBoMBHx+f6465en5ERASBgYHXtR8YGJh9zL9lZmaSmZmZ/TopyTGtxmKxYLFY7vGORL4xePJojceoenwecZZU9maN6JXp+gVT9C5E/fkR26zx1O86GUulzpSt1JmyqoolM5mvfprJbFfY7uo455jRwBJ3E1ZF4SdvLwBa75vE43+Oo27lXiy6uJYFvt4s8HDnwb2fMiohCUJy91Evux2LtWgu3CwK3tXfKfK7JW/5GnwZXWc0j9V4jOVnlzPr2CymHZrG9MPT6RTaiUerP0pN35pOia2sjwvh0Sn58jOX/lQwpm89x/9WHmfHm21JybTy2oJD/Di0IR4uxacGnfQlkZeKQn9SkqOI1TrWO57h5QE4qmo+UHMIlsZPQCGO/V7d6c8jX36z/bsKmaqqt61M9u9jbnT8rdr55JNPeP/996/bvnbtWtzcpAR24dQQQhvid/yp7C3bj10ixqMmlP8AnWpl5TkdnFuZ6yyXsm/zcuzfxEQsJ8zkxmIPd+Z5uOc65i83V/5yc8UneSs+7qbs7ZvdXNmc9UnPqIRE+tlC2amep7z3AFauzH0dIW4nLCzM2SEUWyZMjNKM4oTpBP9k/sOq8FWsCl9FeW15WhpbUl1fHY1ScEu9atM17D+TwMqVF/LtGtKf8tfqkxpAw29L1pNohl3ntPy0MIxq3qqzQ8tz0pdEXirM/Sk0ZkN2kpeQ9Xdpl7qst7WBtRucGVq+SUu7s1kleZrkBQcHA46RuFKlcubERkVFZY/uBQcHYzabiY+PzzWaFxUVRcuWLbOPiYy8fnpOdHT0daOEV7355pu8/PLL2a+TkpIoW7YsnTt3xtNTnqEozLYey/kPtmnbrhBU+w7OepQqqop2agsWYyZTc+M3e/FaLfFZ/+j/LcBqo9Tz6+iNAorCnVxVCHB8ihYWFkanTp3Q6wvnWm/Fycu8zPH448w6NovV51YzO202Zd3LMqzGMHpW6ImL7tbP9uaFk+tPMWfnRTp0bo1Rl7fJpfSngrFlyWF2xVwiwaM8Bq0GTp3DVLY63VtVcHZoeUb6kshLRaE/af4+TGx4zvu8KmYzNZo9TLWG3Z0YVf66OlvxdvI0yatQoQLBwcGEhYXRoEEDAMxmM5s2bWLChAkANGrUCL1eT1hYGAMHDgTgypUrHDp0iIkTJwLQokULEhMT2bFjB02bNgVg+/btJCYmZieC/2Y0GjHeoBqjXq8vtB1TONSv+QhBUWt5PikTfVB1uIufV2P/mhjS9mLWKHwbEUW0Vssnfj5k/Cvp65mSyp9urqRcs93XbkdvuL7PCHGn5PdLwakdWJtPAj/h5bSXmXNsDr8f/52Pd37M9we/59EajzKo2iC8jF75dv2WVQL4ZtMZXl90mNFtK1Ej2BONJm+XXJH+lDdWHrzC0n2X+X5Y7vWxopIdRbdmbs8ZjT0WmVosv+fSl0ReKsz9yZwWTbJWQ/2MTI4a9DyYloG2cnu0hTTevHCnP4u7/jgyJSWFffv2sW/fPsBRbGXfvn2cP38eRVEYM2YMH3/8MYsWLeLQoUOMGDECNzc3hgwZAoCXlxcjR45k7NixrF+/nr179zJ06FDq1KmTXW2zRo0adO3alSeeeIJt27axbds2nnjiCXr27CmVNYshz47jWFfrRfoMWgB619ufcA1jg6G0Tk+nstnMgy3foP9zR9ng04qZlyOomfWMpofNzvsWd5bGejImLh5TVlEXf83dXUsI4XwBbgG80PAFwh4K4/Umr2PUGpm8dzKd5ndiwo4JXPlXCe280rKSP0+3qcTGY1H0nLyZdUcjiUzKICHt+mq9wrk2n4phzZEI0sy5n7O+nHB90ZXDlxMLKiwhRF6LPs6pAzMAqJVpZlmmJ6Mbvgh+lZwcWOFw1yN5u3btol27dtmvr06RHD58OL/++iuvvfYa6enpjB49mvj4eJo1a8batWvx8PDIPueLL75Ap9MxcOBA0tPT6dChA7/++ivaa6bVzZo1ixdeeCG7Cmfv3r1vujafKOJcfaD50/d2bpXOTLzyJPb0eJQWo0FnxKPv99TrMI7fd//Kpu1fYNeZUIatYcvWI/zngTo8unsau04up3Gv7/L2PoQQBcZN78bQmkMZVH0Qa8LX8MuhX5h5dCZzjs2hW4VujKg1gmq+efuhYAV/E2lmR0nu09GpfL/pNKW8XQn1dWNMx6oY8ngap7g3l+LTUVU4dCmJradjeapNRVz0Wi4npDO6bSVsdpUf/jqDi17D2ZhUUjOtmIzFp/iKECXFudWvMai04/EwP5uNUiM3gj7/p+8XFXf9W61t27ao6s0fUlYUhXHjxjFu3LibHuPi4sLkyZOZPHnyTY/x9fVl5syZdxueKGkUBX2bGyyG7hEMbd+gTcsXQKPFomqAI+BVFpcuH/Fgl48KPFQhRN7Ta/T0rNiTHhV6sOXyFn459AvLzyxn+ZnlPFD6AUbWHknjoMa3Lf51J0J9c4p4nYtN5VhEMnvOJwDw98kYGoX60LyiH51qBqHN46mc4tYS0y2kZFop7e3KpYR0ACZvOMnfJ2PwdTfQvXYwyZlWapTyJMTbhR/+OkMZHzdORaUQdiSSbnWCMeq0fPfnaZpX9KVBOZ/bXFEI4VR2GzuidoK/HwC+NpskeP8iHzuK4s3gBjp57k6I4k5RFB4o/QA/dfmJuT3m0qV8F7Ze3srjax5nyIohrA1fi+0+F8Yt55eT5O0Ij8se1QM4eCmR2TvO8/TM3Ww7E3tf1xF3Jz7VTL3319Lu0z9RVZWL8Y7KcwcvOaZiRidlMGH1MUwGLU3K+1Ita0H7xx4oD8CY3/fxzcbT7AyPY8LqY8zeft4p9yGEuAuRhzlwTS0Ot1sMQJVUkuQJIYQoVmr51+KzNp+xvO9yBlUbxMmEk4zdNJZei3vxx/E/yLDe24LYQR4u2VMyz0SnXrffbHU873s2Jmff+qORPDVjF8cjku/pmnnNbi9+b4TCjjiqcZttdi4nZpBhseOq15KQ5lhLatG+S/yx6yLv9qpJsJcL7kYdxz7sypCm5bLbOHI5iQmrjgGw+3w8O87GFfyNCCHu3Ll/2Gc04mOz8XpsPB1bvuHsiAodSfKEEEIUS2U9y/JO83dYM2ANT9V9isTMRD7c9iFdF3Tl50M/k2JOuav2NBqFxqE+VA1yv+Vx5+Ny1jBavO8yaw5HMub3fTT/eD3RyZn3dC954VJCOjXeXU2/b//hp7/PXFeYpKiJTclk6E/bmfr3mextm45HA9Cqin/2tgtx6XSoHsjAxmWzt7notSiKwvynW1C/rDd/n4xm17l4utQK4kx0KgN/2EpyRvFbRFmI4iBm/ih+2vox4VnVNIc+tAD9Ay86O6xCR5I8IYQQxZqfqx/PNXguuyKnTqPji91f0HlBZ6bsnUJ8RvwdtzX7ieb8+lhTPF10lL9m+ua1zsXmjOSdjXEkkkevJBGRlMGxiCQikzKoO24ND3+/hal/neFc7J0tbPtv/5yK4e1FBwGw3cEI3cGLiWRa7Xi66Bm/6hjfbjwNwJXEdBLTil5CM2H1MTafiuFkVArVgx3F3TYcc4zqdagRmH1cKS8XPhlQ54bPZTYu78ujzcqRabXTrIIvz7arnL0vJkUqpwpR6CRdZnzkBr7y9QagntkKpRs6N6ZCSpI8IYQQJcLVipyr+q/i/Zbv42P04YcDP9BlQRcm7pxIZGrkHbUT4u3Kvnc7s35sW4Y1D6V3vRBc9TnVoa8mbaqqcjY6FR83fa59By8mkpRhxUWv5bO1x+n45WY+2adl0rqTXM4qGnInVh26wpwd5zkXm0qVt1cybNp2VFW9aXG0szGpeBh1/PpYE1pU8uNEpGMK6dMzdjNhzbHs4yw2+y0LrDmbNSu+k1E5I7HVgz0o7e3K5lMxmAxamlVwFGMY9WAFtrzRnkCPmxdkaFrBF5NByxvdqlM7xIshzRzTOGNTnDfqKoS4iQs7OGY0ZL9sYLGDtviuiXc/pGawEEKIEkWv1dO/Sn/6VOpD2Lkwph6cyowjM5h7bC59Kvfh8VqPU9az7C3buLoQ+od9a5OYbmFUqwr8b/lRLHY7hy8nMW3zWZpV8CXVbKNL7WAW7rkEOKZyppttuBm0/PZ4U9ItNjYejeDXdXuZvvU8kclmJg2sz5noFDSKQnl/001jOBuTil2FNYcjsKuOCp/hsWn0nrKZCv4metQpRfc6pSibVRX0THQKFQNMKIpCqJ8bu8LjsdtVjkcmY9Q5klRVVWn32Z+M6ViVhxqVyYtvd55SVZXKb6/ipY5VuRCXjrebnoQ0C6W8XakebGX9sXSqBrlTzteNQA8jNUp53rayaqifiYPjumT/TMd2qsrs7eezR/JUVc2T6qxCiLtkTiNu2bMoGhd0BnfOnv+LGgZPIrKWXAuxWKnc/AUnB1l4SZInhBCiRNJqtHSt0JUu5bvw96W/+fHAj8w/MZ+FJxfStXxXRtUZRRWfKrdtx8tVT90y3vzxdAtiUzJ5a9FBxq86isXmGA1rWy0wO8kLj0klOcNCBX9HsuVm0NG5ZhDWcDsb0wIJzyra8t8lh9BrNfz6WNObXvdq8ZeNx6Kzt4UdiSA5w4qHi44v1p3gk1XHqFfWm551SnHochLVsp4nDPU1sXDPJS4nppNhsXMuztFWTIqZi/HpHLiYUCiTvLhUR+L1w1+nSTPb6FUvhGX7L6OqUDXYg/XHoijj44ZGo7Dp1XYY73DtQs01S154uxlQFMe1tp6OZfSs3Wx8pS3eboZbtCCEyHP75/Bk7BZsCrRMz+A3L0/+G3ORTH9fnkhI5Jly3dE0f8bZURZakuQJIYQo0RRFoXWZ1rQq3Ypdkbv46eBPrDy7kpVnV9KubDtG1RlF3YC6d9SWn7uRH4Y1JiHNzMqDEYTHptKmagAARp2Gk1EpRCbpqBhwffGWcn5ubD7tWH7hZGQKrgZtrv0X49OYuPo4TSv40qqKP1cSHVVCt56JpVKAidPRqWw8Fo2iwLThTbDZVdYfi2LFgct8uvY4ZqudHnWCs6+VZraxM9xRRTIyKZN0s40z0Y4pkPf6nGB+uxDvmM56dfmKLrWCWLb/Mu2qBWR/P0p7uwJc9/27U1qNgq+bgdiUTBbvvUR8moWL8emS5AlRwBKOLOJ41tTMSzpHyvLF1WfxtD7o+33vrNCKBEnyhBBCCBzJXpPgJjQJbsLhmMNMPTiV9efXs/HCRpqVasYTdZ6gaXDTO5q65+1myH62C+CXEU2ISMrgrUUHUVXH6N6/lfN1IybFTGRSBlHJmeg0ClabHZ3WMRq18Xg0yw5cZsXBK9mFVhQFVBXqlvEmMd3C1jOxlPFxxSXrGcHe9ULoXS+ElEwrW0/H0rS8LwDl/RzTQKdvOZd9/fNxaZzJGkm8tkJoYXLhX3E1KOdD+PgegGMZBIAyPq73fR0/dwNrjkRw6JKjzdhUKcIiREE7kHIJsj4PS9c4fg+maDQoqkr90jef5SAcJMkTQggh/qWWfy2+bPclpxNOM+3gNFaeXcn2K9up61+XUXVG0aZsGzTKndcua1fdkdR1rBHE5lPRtKoScN0xob6O5OTqMgBWu8rlhIzsRdjPRKdQwd/Egqdbsv5YFCcik7mckM7yA1eoFGDiXGwqMSnmG44Suht1dKoZlP26UoCJhxqVYdn+y5T2duVSQjq/bjlLcoZjWYULcWm5EszC4mJ87sI0wZ45BVUqBZqoGuROw1Cf+76Or8nAtjNxVA5051RUihRhEaKgZSRy1BwDeFPRbOGMQU9ls5lTBgOVLBa8JMm7LUnyhBBCiJuo5F2Jj1t9zOj6o/n18K8sOrmIFza+QBWfKjxZ90k6leuEVnPn0wIDPIz0a3DjZ93KZRVImbb5bPa28NjUa5K8VCr6u+NjMmQ/L5duttG3fmmalPclJsXMnvMJVAq4ebGWq3RaDZ89XI/3e9fCpqqMX3WMZfsvk5xhxd2oIyXTytL9l+lWu9Q9T3vMDxfi0/B3N+LjpueFDlXQXvMsnVGnZe1LbfLkOn7uRgBe7FCFNxceJFaWUxAifyVcgL0zoFo3CGkAl/dxyGhAp6pMjI7hf36+vBMTx38D/OiYlg6VOzg74kJPkjwhhBDiNsp4lOGd5u/wVN2n+O3Ib/x+/Hde3fQqFbwq8ESdJ+hWoRs6zf39l+rjpmd020os2nuJiv4mzsel8f2m0yRnWGlTLYAzMSl0q10q1zmuBi0ds0bo3uhWnQcr+1O3rNcdX9NkdMT8cb86jOtVi13hcZiMOl76Yx8v/7Gf5Qeu8POIJvd1X3nldHQK645E0qKSH5MHN8jXa1XwM1EtyIPudUrx2drjxKTKSJ4Q+enc0mcZaj3F23t/omu7D1gb9gpbAvypYrZQre04ZrgHQfkH+ePwQvCrAr4VnR1yoSdJnhBCCHGHAtwCGNt4LI/XfpwZR2Yw+9hs3tr8Ft/t/44n6jxBz4o90d/jmk2KovBa1+q82qUadhVmbT/HnB0XeHb2HgxaDWabnYq3WFLBRZ+T8N0Lg05Dy8r+AGwY25aPVx5l2f7L99xeXvt95wXsqspb3avn+7Ve6lSV59pXRqtR8DMZZCRPiPxkt7MrahcJAX5M8jISvPolxoY4ikTVysyEyh0hMOvffYtnnRho0VK4JtsLIYQQRYCPiw8vNHyBNQPWMLreaBIyE3h3y7v0WNSD34/9jtl270mBoihoNQr/aVGeVS+24u/X2vF6t+p0qx1Mq6rXP8uXXyoHunMlMYMMi63ArvlvlxPSGfnrTv7YdYELcWnUKOVJKa/7L6xyO1qNkl28xs/dmL10gxAij1kzCf+2CUs9HB9gXdHp+J+fb/buyhYL+N9+KRtxPUnyhBBCiHvkZfTimfrPsHbAWl5s+CIZ1gz+t/1/dFvQjZlHZpJuTb99I7dR1teNkQ9W4LuhjbKXBygIVytw/ruiZUFIyrCwYPdFvlx3gvXHopi/+yLn49Io4+NW4LH4uxuk8IoQeU1VIfo47PmNx13T2OPiKKIUaLVmL5tQJyOTzmXaw1089yxySJInhBBC3Cd3gzuj6oxi9YDVvNL4FWyqjQk7J9B1QVd+OfQLaZbCuSTBrYRmFXxxxpp5j/ywjbHz9jN/90XAMaJ3IS6Nsr4Fl+Re5WcyEpkkSZ4Qecm+Zwb/m9uFvza+Q3TWGni+NhvPJCQCjmRvtrEqAf2mOjPMIk2SPCGEECKPuOndGF5rOKsHrObNpm+i1+iZtHsSXRZ04ccDP5JsTnZ2iHcs0MOIi17D8gOXOXQpMXttvvzy9fqTtP/8Ty7EpXE80vF9sqvgZzJwMT6dpAxrdgXSglQ12IOIpAwZzRMiD53c8S2/e3rwUqBjCrqHzc5XCWb61nuSBhkZdE1Nhy4fgd7lNi2Jm5HCK0IIIUQec9G5MKTGEB6q+hBLTi9h2sFpTN47mV8P/cqQGkMYVnMYXsY7r4LpDIqi0K9BGRbsucjifZdxN+poGOpDq8r+jHywAhrN7ReFv1OZVhuTwk4A5FrsHRxrDF4d0SvrhOma9ct4A3DgYmL2eodCiPugqpxKOQdu/pizfo+Mi4ml/qtXQFX5za8yuPlDqXpODrRok5E8IYQQIp8YtAYervowy/ot48MHPsTX1ZcfDvxA5/md+WL3F8Smxzo7xFv6pH8dDo7rzLynW/BM20qYrTY+Wnk0e6Tt0KVE3lx4MPv4qOQMjkUk3fV1LsTlPLu48VgUAC0r+QHQPiuxctVrqRbscc/3cq/K+rri46Zn/8WEAr+2EMVScgTHDY7n7qpmmlFUlRohLUBRQKOBBkOhWlcnB1n0SZInhBBC5DO9Rk/fyn1Z0mcJ41uNp5SpFD8f+pmuC7ry+e7PSbYX3mmcRp2WJuV9ebZdZb4Z0hCAc7GpAKw9HMGcHedJSHNUn/xq3Umembnnrq9xPs7RXoiXC9vPxqHVKLTOqiRaK8STsJdas+e/nbIrXhYkRVGoV9ab/RcSCvzaQhRLkYc5YdBjtNuZfiWSXyx+lO34gbOjKnZkuqYQQghRQLQaLT0q9qBbhW6sO7eOHw/8yKzjs9Ch48zOMzxR9wlKuZe6fUNO4msy4GHUEZ5VjOVs1t/hsWnUdzNwOjqFc7GpmK12DLo7/xz5XGwaBq2GFpX8WbDnIiHeLnSqGcShS4mU9nZFp3XuZ9L1yngzY9s5VFVFUfJumqoQJVLkQU4Y9FSyWHDv+wON6g1ydkTFkozkCSGEEAVMo2joXL4z83rN44vWXxCkDWLeyXl0X9SdcVvGcSH5grNDvCFFUQj1d8uuuHk2JgXIGdkLj0nDrsLF+LuryHkuNo0yvq5UDXIHoEFZHyoFuDNlSEOnJ3gA9ct6E5dq5mL8/S+JIUSJdWk3+6a2IWLjh0TrdFQzWyColrOjKrZkJE8IIYRwEkVRaFOmDSnuKfg08GHakWksOLmAxacW06NiD0bVGUUFrwrODjOXUD8T52JTUVWVs9GO5O5sTCppZisRSRmAI2mrGOB+y3asNjv7Lyay4Vgky/ZfpkE5bx5pWo5yvm6FrsBJ3TKOIjn7LiRQ1gkVPoUoklJjiVvzBh7+VdA/OJYz695lmCGO0qWCAKhisYF/VScHWXxJkieEEEI4maIotAxpSetyrdkZsZMfDvzA0tNLWXZ6GV3Ld+XJuk9S2aeys8MEoLyfG/vOJ/DWooOkmm0oCuw+F0/YkcjsY87GpNIOsqc3RidnciY6hQvx6cSlZuLhoufjlUdJzrDi5aqna61gRrerhJernm51Ct90VT93I2V9Xdl/IYFe9UKcHY4QRYJl62T6JPxDSMwmfog5wbGoPRDozyW9I/2ooriBzuDkKIsvSfKEEEKIQkJRFJqWakrTUk3ZG7WXHw78wKrwVawKX0Wn0E48VfcpqvlWc2qMob4mLiemM2eHY0ppv/qlWbj3En+fjEGrUfB3NxAem8r83Rd5Zd5+OtYIYsOxSK5dZs+o0xDq58Zvj9elbhlvtHm4HEN+qVfGm582nyUh3cJnD0tpdyFuJ/zSDhK0WhK0Wp6N/otaRiMA/lYbCVoN1cs94OQIizdJ8oQQQohCqEFgA77v+D0How/y44EfCTsXRti5MNqVbcdT9Z6ilp9znmUJ9XNDzUrYFjzTgoblfHirRw0iEjPQKApfrDtBeGwaGRYbAOuOOkb4Fo5uSc1SnrQcv4G4VDONQn1oUM7HKfdwL+qV8Wb5gSvM332RsZ2rUsrL1dkhCVE4WTPh4k5Oxx0HXzdqZJo54GLkgIsjyfv9cgTnA6vj3ep1JwdavEmSJ4QQQhRidQLqMLnDZI7EHuHHAz+y/vx6Nl7YSOsyrXmq7lPUDahboPGU9zdlf13B3x1FUfB3N+LvbszaZmL1oQgyzLbs49yNOhqU9UZRFCr4m4hLNRPqZ7qu7cKsTpmcxev3nU+gVB1J8sRNRBwEgwl8Kzo7Eqe4vGAEn8TuAMevBP6bYmdlRhIzvTwBCOzxJYEN/+PECEsG55esEkIIIcRt1fSryZftvmR+r/l0Du3M3xf/5tGVj/J02NPsi9pXYHEEehhx0WvwctXj46a/bn95PxMX49M4GZVMtSDH4uXl/d2ylx6okJUklvcrWgVMmlXwZdlzD1LKy4V9smaeuJmTYXwztwfzf20DMacg4TxXZvYn/c/xzo4s71jNkJly4312O39f3sqfJjf+NDn+jVeq3IVXm/+XkQlJfBybDJU7FWCwJZeM5AkhhBBFSDXfanze9nNOxZ/ix4M/siZ8Df9c/odmpZrxVN2naBLcJF+vrygK5f1MGPXaG64ZV97PDbsK8WkWBjYpy/HIZMr7XTv65/i6qI3kKYpCnTJe1C/rzV5J8sRNnFv4ON8He+Nts6H82poWGRn0KV2Kwft383L9IeBdztkh3j3VDlumYD34O6vSI4hUM7Ao0D41nTT/KlQNboCpWg+o0hmSIzindcznDrZa8bXZcCvTFBr+hzGV24PBA9wDnHxDJYMkeUIIIUQRVNmnMhNbT+SZes/w08GfWHFmBduvbKdRUCOervc0zYKb5dvC3T3qlMKov/FkoGunc7atGsgPm87kSvIahfrgZzLk2laU1C/rzZfrThJ2JJK21QLQF4J1/EQhkXCeJS6Of3MJWi3jAvwobbGSodEQrtdBfHjhS/Ku7GffoicoV6Y5vr2/zr0v+jgVz36BOnc6gzIOccTFCC4ugAsA3/p4Ayno4v+i2ca19F2toZN3Fc7o9RjsKisvXEZtNRbqPuJor4ROX3UW+c0khBBCFGEVvCrw0YMfsbTvUvpV7sf+qP08sfYJ/rPqP/xz6R9UVb19I3fp+Q5VeLJ1pRvuC/Z0YVDjsnSvE0yDct683rU6fernLDvQvKIfu//bCVeDNs/jKgj1y3qTbrHxxG+7WH0owtnhiELEtncWS9xzf3hxdbmAWK0W0mKdEdYtxSx6kmGmdEZGroFLux0br+zn9M+dWT+rE0N8Yvk0cQ9HsipjAnwcFYP+mt8rzdIz2O7qwqs+Rh6ynOIfN1fKWyzo+3yLocO7skyCk8hInhBCCFEMlPMsxwcPfMCTdZ9k2qFpLD61mKfXPU0d/zo8Xe9pWpVulW8je9fSaBQmPJRTDOaZtjdOBouqawuwpF9TXEaUYAf+gNRoNh/4jShvHX2SU1ji4Z7rkFitFlKinRTgzV1JOAOmYE4ZDBB5BOvZv5i6a5JjlC7QH4C5nh65znlQ40Hz9Az+dnPFw2bne315oox+zI7axm9ejmPLWq0Q4NzlXko6GckTQgghipEyHmV4r8V7rOy3kkeqPcKxuGM8u/5ZBi0fxPrz67GrdmeHWKS5GXQ8286RuMammp0cjXC6U+sZ99frjNr/Jct0mQA8aXNnQ6yFLimpAJSyWonTaiA1ypmR3tDVkUaA2HXvMurQt1nTMG/Ou2IHQi0WAFxVOzQfTeDg3xkzeA3LNZUYkZDEY1YTBNfJz9DFbUiSJ4QQQhRDpdxL8Xbzt1nVfxVDawzlTOIZxmwcw8PLHmZt+FpJ9u7Dq12qUynARGxKprNDEU4WufZNFrub2O7qwnqTG03TMyjX4X0CxhzhRc96TI6IpnF6JukaDWmX9zk73NwsGVzS5UybHuTnwm5XFwYnJvPfmLibnqZUaI2P7ZrfH35Zo/WB1QkZtpixwzZS7+ltoDPeuAFRICTJE0IIIYqxIFMQrzd9ndUDVjO85nAuJF9g7KaxDFg6gFVnV2Gzy5TDe+HnbiQmJZPkDAuZVvkelkgp0SzLjMCWNQ3aqij0SrdDte6g0VB22CLaPrMHP5ujf3yYuAfb9h+cGXFuiRe5rMsZyYvWankzPoW3unxPNfMtRqndAwnIuqcqZgv4XjMlW1EcBVYMRWuJlOJIkjwhhBCiBPB39eeVJq+wqv8qHq/9OJdSLvHaX6/Rd0lflp1ehtVudXaIRYq/u4HYVDN1xq3lkR+3OTsc4QTq/jks9nDDxe4Y1TLZ7XQOaQl6l5yDTAHZSd5ydxPzNn9A9LwRYC8EI+kJ57iUleTVz8hkUoKFIQMXQrWulM+ajnmtQUnJTEuwQugD9G75JmPiEvif/4NgdL/uWOF8kuQJIYQQJYifqx8vNXqJtQPW8mTdJ4lJj+GtzW/Re3FvFp1chMV+/Zs7cT0/k5GYFMdox97zCc4NRhQ8VWXHvumc0+t5ODmFpukZ/CcxBbc2r+c+TqvP9Wb7I39fnovfApd2FWi417Fmcm7TeA4aDVQ2m5lhqkOHJ7dCSAMAvNq8BUC1TDOfRMWwRleNd7r+RNOntoHeBW3L5xn5zGH8H/7ViTchbkWqawohhBAlkLeLN883eJ7/1PwPs4/OZsbRGby75V1+OPADo+qMok+lPui1emeHWWj5uRu4FJ+W/Tol04q7Ud5WlRgXd7FUjQPc6eNanmoNHwOf8hBc+7pDm3tWA3KKrhwxGh1r5pVtWlDR5qaqxC96kufsF0jW6xiXaIEhP4Kbb84xLZ5jW3Ikpy/FU+PF79EbbrAMgotnwcUs7pqM5AkhhBAlmJfRi2fqP8OaAWt4vsHzpFhSeH/r+3Rf1J3fj/2O2SYVJG/E391IUkbOFNcDFxKcF4wocMm7fybM5EbdjEyqNXkKGo2Aim1veGzVvj9wIB58s6ZtBlqtkJ5QYLH+m23rN7wZ8w/hBj2vxqfQ6eG5YPLLfZDBDUOX8ZwN7uN4zk4UOZLkCSGEEAIPgwdP1n2SNQPWMKbhGMw2M//b/j+6LezGrKOzyLBmODvEQqW0j2uu1+fj0m5ypCh2MpNZcXY16RoNfdOsULPPrY/3r4zy3G7+iEyktMVKikYD0ccg9nT+x5qewLlZAzg6rSOkxcG5LXyz81P+cXNlQHIKw7p8DWUa5X8cosBJkieEEEKIbCa9iZF1RrKq/ypeafwKNruN8TvG021hN6Yfnk6aRZIZgPplvLO/dtFriJHlFEqOQwtYZNLjZrfTvVJ3MJhuf47OQNDwFTTIzCRNo2H10TlM/a0tHF+Vr6EmLxlNT+sJBuoiUf8cz4ZFI5jq7UmtzEzeqPoo1Oydr9cXziNJnhBCCCGu46Z3Y3it4awesJo3mr6BBg2f7fqMbgu78fOhn0t8sudjynlGqZyvW3YRFlH8Hdr1E0eMRrqlpmFqPPLOT3QPzK60+Z6/L1/7emPeOiWfogTSE/j9yt/ZLw/v+4X3PHV42Wx8aqiES4dx+Xdt4XT5kuQlJyczZswYQkNDcXV1pWXLluzcuTN7v6qqjBs3jpCQEFxdXWnbti2HDx/O1UZmZibPP/88/v7+mEwmevfuzcWLF/MjXCGEEELchIvOhUdrPMrKASt5u9nbGLQGvtj9BV0WdGHqgamkmFOcHaLTeLg4Cq34mYzEpkqSVyJEHGSx2fF+tK8uKLsa5R1x889+Li9N43gLHmtOzvMQs0Ue5rQhp3jSi0H+JGi1/DfJStmHfgOtFAoqzvIlyRs1ahRhYWHMmDGDgwcP0rlzZzp27MilS5cAmDhxIpMmTWLKlCns3LmT4OBgOnXqRHJyTkcfM2YMixYtYu7cuWzevJmUlBR69uyJzSYLjgohhBAFzag18kj1R1jZbyXvtXgPk97E13u/pvOCzny37zuSzEnODrHAbXylLevHtsHfw0hMskzXLAlSd01jhbuJqplm6jW8i1E8AJ0BP1vu9fGitNq7uHgMx39szYkfW0F6vGOb3e54ts9mhbN/s21KfZJXv4l6aCEzFg7iL9ecNfuidDr6JKfQpdtX4B5wd7GLIifPk7z09HQWLFjAxIkTad26NZUrV2bcuHFUqFCB7777DlVV+fLLL3n77bfp378/tWvXZvr06aSlpTF79mwAEhMTmTZtGp9//jkdO3akQYMGzJw5k4MHD7Ju3bq8DlkIIYQQd0iv1fNQ1YdY1m8ZH7T8AG+jN9/u/5Yu87swee9kEjISnB1igfF3N1IpwB0/k4HYVEnyij1zGmtOLSVFo6F/aiZK3YF33cTV6ZqKqgKQGHMENn3qWFLhZtLiIDOZjFWv86QuhrFKNOxzvGdOXfM2E2e159xvPTmxaCRPeNh4MXw+q1c9y0Q/H5K0WkItFnSqSojFymtlu0G1bncdtyh68jzJs1qt2Gw2XFxccm13dXVl8+bNnD17loiICDp37py9z2g00qZNG7Zs2QLA7t27sVgsuY4JCQmhdu3a2ccIIYQQwnn0Gj39qvRjad+lfPzgx/i7+vPjgR/psqALX+7+kriMOGeHWGD83Q2ciExhwupjzg5F5KcjS1jkqsNot9MztBO4eN11E9UqdCbUYmFgsmOa8xqTG21O/8LxuYMhM2fqs7r/D9Z8WYXUX7oy//t6/PVVdVaFryJOqyXcoOfnbZ+Qvuhp5hybyQwvTxYlHSHKkgjATlcXvvTxzm6rgtnC9xFRfG+ojGfXiff3PRBFRp5PxvXw8KBFixZ8+OGH1KhRg6CgIObMmcP27dupUqUKERERAAQFBeU6LygoiHPnzgEQERGBwWDAx8fnumOunv9vmZmZZGbmfIqWlOSYNmKxWLBYLHl2f6JoutoHpC+IvCD9SeSl4tCfupbrSqcynQg7H8bUQ1OZdmgas47O4uGqD/Of6v/Bz9Xv9o0UYT6ujrdT3/15mqceDMXkpEXRi0NfKsxO7/qBfS5GeiWnYmo74p6+z95dPmXJofkc2vo5vwNLPdwBeF8Ty6ivKtNqxF/gWZoNa17glaAABiQdZ6GvF+UtVvRZo38AX/j6kHx+JQu9PABYa3Ij5pqpn5f1OX3Q026n4eP/gE95LAB3GLf0p8LpTn8e+fJbaMaMGTz++OOULl0arVZLw4YNGTJkCHv27Mk+RvnXwoqqql637d9udcwnn3zC+++/f932tWvX4ubmdg93IYqjsLAwZ4cgihHpTyIvFZf+9JjyGEfcjrAxYyMzjs5gztE5NDU2pbWxNe4ad2eHly+ORiuA4w32TwvDqOKl3vqEfFZc+lJh4p5xiV1pZ8HLky4ZRlbsj4YDK++xtbKU05cDcka7D7oYedElgM3fNyPOty3zPB3J2wJPx7+Zs1kFVBpkZLA3a7bcDE8PMjUaFFXlgl7PBX1OkZWyFguhFiub3VxJ0mhY/c9+7Joj9xSt9KfCJS3tziob50uSV6lSJTZt2kRqaipJSUmUKlWKQYMGUaFCBYKDgwHHaF2pUqWyz4mKisoe3QsODsZsNhMfH59rNC8qKoqWLVve8JpvvvkmL7/8cvbrpKQkypYtS+fOnfH09MyP2xRFiMViISwsjE6dOqG/5pegEPdC+pPIS8WxP/WkJ6+or7Dp4iZ+PPQjW+K3sMe6h8HVBvOfGv/By3j309wKsw4WGzUPRPDRymMYQirTvU1Fp8RRHPtSYWENe4v3Uk2UN1t4oNkzqM173F978+aA5fopzQ+GlmFs7E62e/hct09RVcbFxPG5rw9/ubmSqdHga7NRxWxhu2vux6ReSrbipWrY7AYDrG480PM2C7bfgPSnwunqbMXbydf5BCaTCZPJRHx8PGvWrGHixInZiV5YWBgNGjjKzprNZjZt2sSECRMAaNSoEXq9nrCwMAYOdDzUeuXKFQ4dOsTEiTeeS2w0GjEajddt1+v10jFFNukPIi9JfxJ5qTj2p84VO9OxQkc2nN/AN/u+4ZcjvzDv5DyG1RzGsJrD8DB4ODvEPKHX6xnSvDyrDkey/2KS03+OxbEvOYWqwql14ObHmhOLSfRxYWRSKrqGQ+E+v796jyA8og+iR2VEYhKTfHOSus/9HF/XyszksNFIt5RUVrmbaJOWTsV+P/PNhR28emoOq91NDEtM5gG7kZEGO8laR6mNv85dxOeJTaBo2Ld7OtqOj9xXvNKfCpc7/VnkyxIKa9asYfXq1Zw9e5awsDDatWtHtWrVeOyxx1AUhTFjxvDxxx+zaNEiDh06xIgRI3Bzc2PIkCEAeHl5MXLkSMaOHcv69evZu3cvQ4cOpU6dOnTs2DE/QhZCCCFEPtAoGjqGdmR+r/lMaDUBP1c/vtv/HV0XdOWngz8Vq0XVG5TzYf2xKJ6ZuZuENFk3r6iz7/yJN9c+xfy5PVnsAjpVpVdIKzDlwTOmjR/nrfhkPkyw8Fjb8YSdv5Rrd3mzhfFRsTySlMy7GTrejE3gVbdqUL0XtP8vvS16WqelM6jyAGo8u4dlFy9nn+vTeBSUqgvBtdH2+BRKN7r/eEWRky8jeYmJibz55ptcvHgRX19fBgwYwEcffZSdeb722mukp6czevRo4uPjadasGWvXrsXDI+cTvS+++AKdTsfAgQNJT0+nQ4cO/Prrr2jvZj0RIYQQQhQKWo2W7hW707l8Z5afWc73+7/nqz1fMePIDB6v/TiDqg3CRedy+4YKsYblvAFYdSiCjjWCGNCojHMDEvfObmf7hrdZXiqIbS4uxOi0dE5Jxb/1E3nTfql69By9H3SuoHch2K8KP8zpzTfeXhxwMTIwOYXyLx7m7bQ4CKjOEFsm6F0d52pcaDViHa2u7IOqXUGrx6/TRwQemUy9TDN0GJQ3MYoiLV+SvIEDB2ZPs7wRRVEYN24c48aNu+kxLi4uTJ48mcmTJ+dDhEIIIYRwBp1GR9/KfelRoQeLTi3ihwM/8Nmuz5h+eDqj6ozioaoPYdAanB3mPWlQLmfK3cFLiZLkFWVnNvBHVvGTGJ1jgKGv3R3Kt8q7a7he89xduWa0fOksqd/U5Tcy6VWxF3gEO/4AaFxzn+td1vHnqqZPsN6aDm5+UKZx3sUoiqx8ma4phBBCCHEreq2egdUGsrL/St5o+gZ21c4nOz6hx6IezD8xH4u96JVt93LV8/XgBjxY2Z895+OdHY64Hbsd4s/dcNfl7d+z0c0VXdayBaUtVh6o9x/Q5ONbZ4MbnYauYEbTd/Hu/vndnavVQ6ux0GhEvoQmih5J8oQQQgjhNEatkUdrPMqqAat4udHLZFgzeH/r+/Rd3JfVZ1djV+3ODvGu9K4XQtfawRy5nES62ebscMQtXJ7Vn0m/tca88ePcOxIvsjh6BzZF4Zn4RBRVZVByMpoGQ/M/qIBq0GAoGGT5L3F/JMkTQgghhNO56lx5rPZjrB6wmmfrP0tsRiyv/vUqjyx/hC2XtqCqzl177m40CvXBaldp9vE6Vhy44uxwxLXO/Im67n2IPc3Y9CP84u3Jst1THPvCN8PWb7FumcICDxPBVisjA5qxXFuR4W0n5kydFKIIyNclFIQQQggh7oZJb+Lpek8zqNogfjr4E3OOzeGpdU/RLLgZYxqNobZ/bWeHeFtVgxzPciVlWHlx7l60Guhau9RtzhL5LvESM5cM53svE0vObOCizvE2OEOjQNwZZiwawmKTKyMTk4gK9OfZ+CS0D39NOc8QJwcuxN2TkTwhhBBCFDo+Lj682uRVVvRbQZ9KfdgZuZPBKwbz8p8vczbxrLPDuyWtRkGrUQCw2lWenrmHxPSi94xhcWPeNoUJvp4karXsSjiOVXH8jOwoZGz+kqle7pwwGvjU1wedqtI/qDlIgieKKEnyhBBCCFFolXIvxf8e/B8Lei2gXdl2hJ0Lo9+SfozbMo7I1Ehnh3dTYS+15pXOVbNfx6RkOjEaAbDl5Krsrw8bjVxNu+O1GladWkJ81jJdMTot7VPTCGz6tBOiFCJvSJInhBBCiEKvsk9lvm7/NTO6zaB+YH0WnFxAj0U9mLR7EomZic4O7zoVA9wZ3rI8fibHchAxyZLkOZWqEmHJqXh60GjAkjWSF6fVMsfdBVe7HU3Ws5+DklOgQhunhCpEXpAkTwghhBBFRv3A+vzS5Re+7fAt5T3L88uhX+i2sBvTD0/HbDM7O7xcPFz0bBjbFoDY1MIVW4lwZhPbvq5D/PKXYNt3RGkcCZyvzcZeFyP2rCTvb1cXjhoN9E5JpUFGJjUyzTR58O38XS5BiHwmhVeEEEIIUaQoikKrMq14oPQDrDy7ksl7JvPZrs+Yc2wOYxqNoUtoF5SsN/DO5umqQ69VZLpmQbPb+GfeQJ4ODqT7uSVYz8Faby8AOqWm8XvWQucAUVkFWB52KUe5Ms1RUVBajHZK2ELkFfmIQgghhBBFkkbR0LNiT5b2W8rLjV4mKTOJVze9ytBVQ9kXtc/Z4QGOhNTPZJTpmgXt+Cp+y0rkVrqbWOtuAsBgV2mennHd4c3SM6jW4kVcu03ErdsEx+LiQhRhkuQJIYQQokgzao08VvsxVvRfwZDqQzgSc4Rhq4bx8p8vcyHpgrPDw9/DQIxM1yxQ6qU97HUxXrfdrFGok5nzs6iX4Ui+B2VqoUbvAotPiPwmSZ4QQgghigUfFx/ebPYmi/osokO5DoSdC6P3kt5M2DHBqcVZZCSvgKkqSdGHSb/mmTp/qy3766CqPRiQlMK7MbH0SUmhfWoa7Rs/I6N3oliRZ/KEEEIIUayU9yrPl+2+ZFfELj7b9Rkzj85kyeklPFX3KQZXH4xBayjQePzdjZyJSSnQa5ZYqkrE7IFMStgN7ibapKVzSq/n3dg4ngoOpGFGBnR5nXFpo8AjGCxpPJxwAWr0cnbkQuQpGckTQgghRLHUOLgxs3vMZnyr8bjr3fls12f0WdyHteFrUbNK5ReEigEmTkamYLMX3DVLrNPr+TxhN6uynsFrl5rG6pqjafnQXOab/fm6bF8Irg0V20BANQhpADV7QyEp1CNEXpGRPCGEEEIUWxpFQ4+KPegY2pFZR2cx9cBUxm4aS5PgJrze5HWq+VbL9xgah/qQkmnleEQyNUM88/16JZaqcvLPj1ljcsveFKg1QcvnAahWqZ2zIhOiwMlInhBCCCGKPaPWyOO1H2d5v+U8VPUhdkXsYuDygXy49UPiMuLy9dp1y3ij0yjsPh9/+4OdIeky4b92J3bRM2C3Ozuaexe+mZ8zw1GvGZUrlVZIv+dC5DNJ8oQQQghRYvi5+vFei/f4vefvNAhswB8n/qDnop7MPDITi92SL9d0NWipVdqL3eH5m0zeK3XjJwy3h/PhlbVweKGzw7lntuOrWG1yczx3lyVY4+LEiIRwHknyhBBCCFHi1PCrwS9dfuGzNp/hrndnws4JDFg6gH8u/ZMv12tUzofF+y5T/o0VxBayhdHjD8whTqtlj4sR9fhqZ4dzz6ITz2BVFKpnWvgkKoaBScm4d/vU2WEJ4RSS5AkhhBCiRFIUhS7lu7Ck7xJG1x/NlZQrPL3uaZ5b/xznks7l6bUahfpkf/3e0sP8dSI6T9u/V+q+ORwxaAGI12q5lHrJyRHdm7Q177Dm0mYAgm1Weg7fyH97zYR6jzg5MiGcQ5I8IYQQQpRorjpXnqn3DMv6LaNbhW5suriJvkv68vmuz0k2J+fJNRqXz0nylh+4wn9+3pEn7d4L9cA8Mle8DGf/4rs/X+eZ4MDsfYcSzzotrnt29i+eCv+Dz/wc32N/jQkCq0P5B6VqpiixJMkTQgghhACCTcFMbD2R6V2nU8W7Cr8e/pWei3qy8ORC7Or9FSQJ8nRhwTMt8XFzLLh99e8CF3GIyRvH0j5yNZdm9eU3L49cu4+paZAc6XgRexqOrQRb/jyrmFfO//0Z+1yM2a+DjL5OjEaIwkGSPCGEEEKIazQMasjcnnP5oOUHALy35T2GrhzK4ZjD99Vuo1AfvhvaCACT0TmrWMX9+QkzPT1I0mp529+PVE3ut4K7XVyImFwPLu1m5W+deH7D81jWjYNDCyClcEwx/bejCadzvQ6x25wUiRCFhyR5QgghhBD/olE09KvSj+X9ljOs5jCOxB5h8IrBfLD1AxIyEu653eYV/fhvz5rEpGQW6ILsACScZ27EZtKzErvdri6Utlizd1fLNLPPxUin0gHET+vIBC8X/jS5sf7Azwz/5w0Oz+yT05bdDntnwZGlBXsPN5BsS8/1OiglxkmRCFF4SJInhBBCCHETHgYPXmvyGvN6zaNhUEPmnZhHz8U9+eP4H9juccTI391AhsVOmrlgR5zStnzNXE8TpaxWtFkJ5qjERIKsjkQvxJqT8I3z9yVO6yjI8r6/L3tcXNiQcRFSsxKoY8t5Zct/+XD9C3B+e4HeRy52O6mqGQC9quJrs6Fv/47z4hGikJAkTwghhBDiNqr4VOGXLr8wodUEDBoDH277kEdXPsqB6AN33Za/u+P5sZgCWEpBPbSIsC+rkrhgFIuPzydeq2VEQhJ1Ms2EWKz0rvYIi0P68KdbQ4bmLC/HBpMb/lZHEpqSNfK3weTKiS+rQ2os6saPWONu4g9PD9QNH+T7fdxUZiLJWfH9fimCjaFDoN5g58UjRCEhSZ4QQgghxB1QFIXuFbuzrN8yRtQawfG44zy68lHe2/IecRl3vtD51SSvzad/cjkh/TZH3wNrBpxaBynRbFj5DC/7GJl+cS2/ebjibbPRp3I/vvJ/kJleTTF0eh/3buPxe3g6TYevZeP5i9nNvBSfQIDVipI1QnbKYGBAmVLYpvfgYNKZ7OMWxuzFfmBe3t/HncjISfK8KndB0+Ed0Ls6JxYhChFJ8oQQQggh7oJJb2Js47HM7z2fpsFNWXhyIb0W9WLusbl3NIXT392Q/fX6Y1F5Hl/i8ud5LuxpznxVPbt65ixPDy7pdQxOSsXU+lV8B/xMwMPTwXhNdU2/Svh3/B/lzRYqm830KNeJMQkpvBMbT2VzToXNd+2RPBoSnP16XIAfKzc6aYrkNUmep6tU1RTiKknyhBBCCCHuQSXvSvzU+Sc+bfMpLjoXPtr+EYNXDGZf1L5bnuftlpPkHbyYkOdxLbu4jk1urvQpE8IeFxcA0jQaXOx2BpVuAz6hNz+5wVB+9mzEtID2aPv9QO8ndzFw1A5eUoLxsDmWkVjq4X7daceUDLi4O8/vJVtaHOyYCtHH4fw2dn7TiKRVr0NGIklaDXpVxejic/t2hCghJMkTQgghhLhHiqLQtXxXlvVdxuO1H+dkwkmGrRrGO5vfITY99obnaDUKs0Y1o0/9EHafi8/bgFQ7mn8V7Wye7pgS2j85Fb8HX7n1+S5eBAyahW/f70DvAh5B4FWa2o+tYeaVyOsOr5/heK5wupcnM//oAxGH8uQ2/i125Sv02/8Zf83uR/iikTzubua58wvZP3cAm11d8LDbUVy98+XaQhRFkuQJIYQQQtwnN70bLzV6iQW9F9C8VHOWnF5Cr0W9mHV0Fla79brjH6jsT7tqgZyOTiU+1Xx/F084T+rip7HvnUGNM59xwpCz0Hrz9HTGxCXQJD2D4X6NoVTde7uGwY3QFi/hb7XRND2nQsvH0bEY7I6scoKfD/Z9s2/f1sVdXJr7COqJsDu+/N/hqzllMPCcl4bIdMd6fXtdXHg6OBCbouBps4MkeUJkkyRPCCGEECKPVPSqyI+dfmRS20mYDCbG7xjP4BWD2R+9/7pjG4U6phfe72he3Lp3aZ74DxO3f8DrHjG5plMOS0ym1rP7+fnBCYQ89Mt9XUf7wAv84fMAkwnMnroZ5FmOAckp2ceE7/4BLu+F1BjsGz6GCzvBkuGYanlpD6TFsXLeI3TNPMzqlaMd6+1ddWHnjUcCVRWLkvWlojD/mvu7WvkzU6OAi9d93Z8QxYkkeUIIIYQQeUhRFDqFdmJJnyU8XvtxTsWfYujKoby35T3iM3ISujI+rgR5Gtl1n0nehZMrAPjD04NThpzn/b6JiKJV75/BqzTU7p+7yMq9MHoQ0H8qbk/+zSL3xsxMM2EY/DtjGrzAM/GJAPQpE8Km+cM4MqM3D4bPYuWCR7ky5xF67f+cxfMGkrr6dT73dIw07tVYIP4sAOq+uXy4+GGmzekBUcfAnJpz3fR4IrS67Jer3U3ZXytZ6/1d0ekkyRPiGpLkCSGEEELkg2uncDYLbuaowrm4F/NOzMOu2lEUhUahPuw+l7P8wsZjUZyITL6r68RkLVr+b627fIFSo8d93cMNaTQEDZxOvWe2QUBV3Fq9TAdNToK1VpPKRKJJ1mrYorPwdfIBwg16NhpUpl1YQ5TOkbDN83Tnr59aQuQRNqwdwx+eHnzp60n4jw+wYHINOL7K0WDSJSJ0N77HUYlJOS8UeVsrxFXyr0EIIYQQIh9V9K7I1M5Tmdh6IgaNgQ+2fsDQlUM5HHuYRqG+7AyPp+OkTRy8mMizs/fw5boTJGdYbt8wgN1O5DVJXqjlmvMM11fBzC+VGj+Z/azeBpMbu10dVT3/dHNledbI2x4XI9M9PaliNlPFbMaqKDwbHIh18dMsvWZ0rlfZEMb5e3Fm/lDHhsSLRGQlhg0zcp4H/OfcBZ5yr4a/1cYrcUkQfI/PGwpRDEmSJ4QQQgiRzxRFoVuFbiztu5RhNYdxJPYIg5cP5lDGL6BJ41RUCk/N2EWa2cbKgxHUGbeWK4l3sFB6WgyR14xyfRAdR6P0DN6JTYRyLfLxjnLTNXuGaf6taJCRQYpGg4fNTv2MTBK1WnSqSv2MTBK0WswahddSoGZmTrGZszFHHNMt/+WkXg8XdmQleVoqm818HRmdvd+z7qMYhy5iY41nGN5nOpj8C+RehSgKJMkTQgghhCgg7gZ3XmvyGr/3/J0GgQ1Yd2kRpkqfo/PazeXEdDxdcpKdF+bsZcm+S7duMOly9vTH7eEXSKjwHj81eotBD88H94D8vJXctDpo/w41Mh0jiU8npdA8a2RvWGIyHdMcCWuXlFSad/uCF8v1onOK47m7/mVKcdRoQKPmXvvhlaAAZv/Rj+iDv3NFpyPEasOrWk/KWCy0T02DBkPB6A4tn4eKbQvuXoUoAq7/2EQIIYQQQuSrar7V+LXrryw9vZTx2z8jJWQeVu+dPFbzFb5a5ahWuTM8np3h8VTwN1G3jHfuBmxWuLQLEs4TqdXiYbNjbPUaycllUOt3B73++ovmN58KDPFtRHD0TgY/+C5Rpzdgj9jM47VHkhJ/lrMRm3g66AGo1o2A6t159rvtrCUt+/TWaelscnNFVZTsbZ/4+3I26RQWTw96ZirQ51tW7ZrmKLJSrnnB36MQRYQkeUIIIYQQTqAoCn0q96Ft2bZ8sHkSay8uYvq5FxnecyAbt9XnfIxjZKv3lH+Y+2Rzmlf0I2n5S+w8vZpmGlc+VhIYlpTE5cAAgmxWVI9ScHc1W/L6hggduoDHLOmgd6V045E8nx4Pbr6YbBbGxZ8Dv0qQlcSVr/kQQad+JjJrJLKSxUK4RU/4Nev86VWV+R7u1MvIpGuLV8HFEx58ySm3J0RRItM1hRBCCCGcyMvoxecd3mduzznU9KvJwtNzoPREOjS+DDgSvdGz9jB78TK+PLuIMd46PlHiWeZh4j1/Py7pdTTOyEQt3di5N3KV3tXxt6KAm6/ja60e/CtnJ3gAmpbPsSzNNft1oNVGDbPjWb3n4xIYmpiERVGwKgpjLCaUxo8V2C0IUdRJkieEEEIIUQjU8qvFzO4zebfFu9iwsCP1a9xDp6ExRBGXaiZz90+syKpCeXXB86NGx7p4D7lWhIDqTov9nuhdcf3PUkpZrYDjTWn1rCQv2GbLLs7SKTWNxh3/50gUhRB3RJI8IYQQQohCQqNoeLjqwyzvt5z+VfqjuJ3CvdJXuAcsw+qznzTN9W/dmqRnUK3ZaCdEmwc8S/Ol2ZvWael0rdibzn6N6JiaRouy7Whf7wlGJCTxim9TqNbd2ZEKUaTIM3lCCCGEEIWMj4sP77d8n36V+/H+lg85xT98jQdudjt+NhsX9HoeSUpmrqcHjyQlQ43eV2d2Fi2KQs3hK/jm0m4o3wpv1cYXl/ZA2aagNTC25XNgCsg1zVMIcXuS5AkhhBBCFFL1A+szr9dcfv+5Jd9qU3k4OYX6GWbOG/QM8m9C26QztOzxPegMYLnDBdQLGzdfqNIp53WFVjlfuwcWfDxCFAN5Pl3TarXyzjvvUKFCBVxdXalYsSIffPABdrs9+xhVVRk3bhwhISG4urrStm1bDh8+nKudzMxMnn/+efz9/TGZTPTu3ZuLFy/mdbhCCCGEEIWaTqPj0W7fs9m3LS961aHtC0f5z8jtGIct5IFn96HU6uvsEIUQhUyeJ3kTJkzg+++/Z8qUKRw9epSJEyfy6aefMnny5OxjJk6cyKRJk5gyZQo7d+4kODiYTp06kZycU/d3zJgxLFq0iLlz57J582ZSUlLo2bMnNpstr0MWQgghhCi8FAXKNELp+y3K8OXg6gOeIc6OSghRiOX5dM2tW7fSp08fevToAUD58uWZM2cOu3btAhyjeF9++SVvv/02/fv3B2D69OkEBQUxe/ZsnnrqKRITE5k2bRozZsygY8eOAMycOZOyZcuybt06unTpktdhCyGEEEIUfvJsmhDiDuR5kvfggw/y/fffc+LECapWrcr+/fvZvHkzX375JQBnz54lIiKCzp07Z59jNBpp06YNW7Zs4amnnmL37t1YLJZcx4SEhFC7dm22bNlywyQvMzOTzMzM7NdJSUkAWCwWLEV1jrrIM1f7gPQFkRekP4m8JP1J5BXpSyIvSX8qnO7055HnSd7rr79OYmIi1atXR6vVYrPZ+Oijjxg8eDAAERERAAQFBeU6LygoiHPnzmUfYzAY8PHxue6Yq+f/2yeffML7779/3fa1a9fi5uZ23/clioewsDBnhyCKEelPIi9JfxJ5RfqSyEvSnwqXtLS0Ozouz5O833//nZkzZzJ79mxq1arFvn37GDNmDCEhIQwfPjz7OOVf0w1UVb1u27/d6pg333yTl19+Oft1UlISZcuWpXPnznh6et7HHYniwGKxEBYWRqdOndDrZTFVcX+kP4m8JP1J5BXpSyIvSX8qnK7OVrydPE/yXn31Vd544w0eeeQRAOrUqcO5c+f45JNPGD58OMHBwYBjtK5UqVLZ50VFRWWP7gUHB2M2m4mPj881mhcVFUXLli1veF2j0YjRaLxuu16vl44pskl/EHlJ+pPIS9KfRF6RviTykvSnwuVOfxZ5Xl0zLS0NjSZ3s1qtNnsJhQoVKhAcHJxr6NdsNrNp06bsBK5Ro0bo9fpcx1y5coVDhw7dNMkTQgghhBBCCJEPI3m9evXio48+oly5ctSqVYu9e/cyadIkHn/8ccAxTXPMmDF8/PHHVKlShSpVqvDxxx/j5ubGkCFDAPDy8mLkyJGMHTsWPz8/fH19eeWVV6hTp052tU0hhBBCCCGEENfL8yRv8uTJ/Pe//2X06NFERUUREhLCU089xbvvvpt9zGuvvUZ6ejqjR48mPj6eZs2asXbtWjw8PLKP+eKLL9DpdAwcOJD09HQ6dOjAr7/+ilarvaM4VFUF7nzeqijeLBYLaWlpJCUlyZQDcd+kP4m8JP1J5BXpSyIvSX8qnK7mNldznZtR1NsdUURdvHiRsmXLOjsMIYQQQgghhMhTFy5coEyZMjfdX2yTPLvdzuXLl/Hw8Lht1U5R/F2ttnrhwgWptirum/QnkZekP4m8In1J5CXpT4WTqqokJycTEhJyXR2Ua+X5dM3CQqPR3DK7FSWTp6en/KISeUb6k8hL0p9EXpG+JPKS9KfCx8vL67bH5Hl1TSGEEEIIIYQQziNJnhBCCCGEEEIUI5LkiRLBaDTy3nvvYTQanR2KKAakP4m8JP1J5BXpSyIvSX8q2opt4RUhhBBCCCGEKIlkJE8IIYQQQgghihFJ8oQQQgghhBCiGJEkTwghhBBCCCGKEUnyhBBCCCGEEKIYkSRP5KlPPvmEJk2a4OHhQWBgIH379uX48eO5jlFVlXHjxhESEoKrqytt27bl8OHDuY758ccfadu2LZ6eniiKQkJCwnXX2rNnD506dcLb2xs/Pz+efPJJUlJSbhvjwYMHadOmDa6urpQuXZoPPviAa+sPjRgxAkVRrvtTq1atW7b7119/0atXL0JCQlAUhcWLF193zI3abt68+W1jLqmkP926P92oXUVR+PTTT28bd0kjfenWfSkyMpIRI0YQEhKCm5sbXbt25eTJk7eNuaQqyf3pTu594cKFdOnSBX9/fxRFYd++fbeNtyST/nTrex83bhzVq1fHZDLh4+NDx44d2b59+21jLukkyRN5atOmTTz77LNs27aNsLAwrFYrnTt3JjU1NfuYiRMnMmnSJKZMmcLOnTsJDg6mU6dOJCcnZx+TlpZG165deeutt254ncuXL9OxY0cqV67M9u3bWb16NYcPH2bEiBG3jC8pKYlOnToREhLCzp07mTx5Mp999hmTJk3KPuarr77iypUr2X8uXLiAr68vDz/88C3bTk1NpV69ekyZMuWWx3Xt2jVX+ytXrrzl8SWZ9Kdb96dr271y5Qo///wziqIwYMCAW7ZdEklfunlfUlWVvn37cubMGZYsWcLevXsJDQ2lY8eOub4/IkdJ7k93cu+pqak88MADjB8//pZtCQfpT7e+96pVqzJlyhQOHjzI5s2bKV++PJ07dyY6OvqWbZd4qhD5KCoqSgXUTZs2qaqqqna7XQ0ODlbHjx+ffUxGRobq5eWlfv/999edv3HjRhVQ4+Pjc23/4Ycf1MDAQNVms2Vv27t3rwqoJ0+evGk83377rerl5aVmZGRkb/vkk0/UkJAQ1W633/CcRYsWqYqiqOHh4Xd0z6qqqoC6aNGi67YPHz5c7dOnzx23I3KT/nRrffr0Udu3b3/H7ZZk0pdyHD9+XAXUQ4cOZW+zWq2qr6+vOnXq1DtuuyQrqf1JVa+/92udPXtWBdS9e/feVZslnfSnG/enqxITE1VAXbdu3V21XdLISJ7IV4mJiQD4+voCcPbsWSIiIujcuXP2MUajkTZt2rBly5Y7bjczMxODwYBGk9OFXV1dAdi8efNNz9u6dStt2rTJtbBnly5duHz5MuHh4Tc8Z9q0aXTs2JHQ0NA7ju9W/vzzTwIDA6latSpPPPEEUVFRedJuSSD96eYiIyNZsWIFI0eOzNN2iyvpS7ljBnBxccneptVqMRgMt4xZ5CjJ/enf9y7un/Snm/cns9nMjz/+iJeXF/Xq1burtksaSfJEvlFVlZdffpkHH3yQ2rVrAxAREQFAUFBQrmODgoKy992J9u3bExERwaefforZbCY+Pj57esKVK1duel5ERMQNr31tbNe6cuUKq1atYtSoUXcc261069aNWbNmsWHDBj7//HN27txJ+/bts99kiZuT/nRr06dPx8PDg/79++d528WN9KXcqlevTmhoKG+++Sbx8fGYzWbGjx9PRETELWMWDiW5P93o3sX9kf504/60fPly3N3dcXFx4YsvviAsLAx/f/+7ar+kkSRP5JvnnnuOAwcOMGfOnOv2KYqS67Wqqtdtu5VatWoxffp0Pv/8c9zc3AgODqZixYoEBQWh1Wqzj3F3d8fd3Z1u3brd8to32g7w66+/4u3tTd++fbO3/f3339nturu7M2vWrDuOe9CgQfTo0YPatWvTq1cvVq1axYkTJ1ixYsUdt1FSSX+6tZ9//plHH30012iMuDHpS7np9XoWLFjAiRMn8PX1xc3NjT///JNu3bplxyxuriT3p1vdu7g30p9ufO/t2rVj3759bNmyha5duzJw4ECZCXUbOmcHIIqn559/nqVLl/LXX39RpkyZ7O3BwcGA45OfUqVKZW+Pioq67lOi2xkyZAhDhgwhMjISk8mEoihMmjSJChUqALBy5UosFguQMx0hODj4uk+drv6S+Pf1VVXl559/ZtiwYRgMhuztjRs3zlUp7G7jvlapUqUIDQ2VKna3If3p1v7++2+OHz/O77//ftfnljTSl26sUaNG7Nu3j8TERMxmMwEBATRr1ozGjRvf+Y2XQCW5P93s3sW9k/508/5kMpmoXLkylStXpnnz5lSpUoVp06bx5ptv3tX9lygF+QCgKP7sdrv67LPPqiEhIeqJEyduuD84OFidMGFC9rbMzMy7fnj4RqZNm6a6ubnd8thvv/1W9fb2VjMzM7O3jR8//oYPD1+99sGDB2977X/jDgtlxMTEqEajUZ0+ffpdX6MkkP7kcLv+NHz4cLVRo0Z33W5JIn3J4U5/N504cULVaDTqmjVr7voaJUFJ7k+3u/drSeGVOyP96c7607UqVaqkvvfee3d8fEkkSZ7IU88884zq5eWl/vnnn+qVK1ey/6SlpWUfM378eNXLy0tduHChevDgQXXw4MFqqVKl1KSkpOxjrly5ou7du1edOnWqCqh//fWXunfvXjU2Njb7mMmTJ6u7d+9Wjx8/rk6ZMkV1dXVVv/rqq1vGl5CQoAYFBamDBw9WDx48qC5cuFD19PRUP/vss+uOHTp0qNqsWbM7vvfk5GR179692ZWqJk2apO7du1c9d+5c9v6xY8eqW7ZsUc+ePatu3LhRbdGihVq6dOlc9y5ySH+6eX+6KjExUXVzc1O/++67O267JJK+dOu+9Mcff6gbN25UT58+rS5evFgNDQ1V+/fvf8fXKGlKcn+6k3uPjY1V9+7dq65YsUIF1Llz56p79+5Vr1y5csfXKUmkP9383lNSUtQ333xT3bp1qxoeHq7u3r1bHTlypGo0GnNVBBbXkyRP5Cnghn9++eWX7GPsdrv63nvvqcHBwarRaFRbt2593Sc+77333m3bGTZsmOrr66saDAa1bt266m+//XZHMR44cEBt1aqVajQa1eDgYHXcuHHXfRKVkJCgurq6qj/++OMd3/vVT6/+/Wf48OGqqqpqWlqa2rlzZzUgIEDV6/VquXLl1OHDh6vnz5+/42uUNNKfbt6frvrhhx9UV1dXNSEh4Y7bLomkL926L3311VdqmTJlsn83vfPOO7k+tRe5leT+dCf3/ssvv9zwGBl5uTHpTzePOT09Xe3Xr58aEhKiGgwGtVSpUmrv3r3VHTt23PE1SipFVa9Zrl4IIYQQQgghRJEm1TWFEEIIIYQQohiRJE8IIYQQQgghihFJ8oQQQgghhBCiGJEkTwghhBBCCCGKEUnyhBBCCCGEEKIYkSRPCCGEEEIIIYoRSfKEEEIIIYQQohiRJE8IIYQQQgghihFJ8oQQQgghhBCiGJEkTwghhBBCCCGKEUnyhBBCCCGEEKIYkSRPCCGEEEIIIYqR/wP3PDkPmLaJTQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Choose a curvy slice with a bounded gap\n", "spec1 = GapSpec(n_gaps=1, min_len=400, max_len=400, seed=9, strategy=GapStrategy.TARGET_ONLY)\n", "y1_gap, x1_gap, gaps2 = apply_gaps(y, x, spec1)\n", "\n", "# (A) Direct time interpolation of y (inside-only)\n", "y_direct = y1_gap.interpolate(method='time', limit_area='inside')\n", "\n", "# (B) Residual-based interpolation via the API\n", "res_resid = fill_from_neighbor(y1_gap, x1_gap, method='resid_interp_linear')\n", "\n", "# Plot\n", "t0 = list(gaps2['target'])[0][0] - pd.Timedelta('3D')\n", "t1 = list(gaps2['target'])[0][1] + pd.Timedelta('3D')\n", "\n", "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 6), sharex=True)\n", "\n", "# Top: main series and fills\n", "ax1.plot(y.loc[t0:t1], label='target (true)', color=color_for('target (true)'), linewidth=1.0)\n", "ax1.plot(y1_gap.loc[t0:t1], label='target (gapped)', color=color_for('target (gapped)'), linewidth=2.0)\n", "ax1.plot(y_direct.loc[t0:t1], label='direct interp (time)', color=\"brown\", linewidth=1.0)\n", "ax1.plot(res_resid['yhat'].loc[t0:t1], label='residual interp (PCHIP)', color=color_for('resid_interp_pchip'), linewidth=1.2)\n", "ax1.plot(x1_gap.loc[t0:t1], label='neighbor', color=color_for('neighbor'), linewidth=1.0)\n", "ax1.set_title('Residual vs direct interpolation')\n", "ax1.legend(loc='upper left')\n", "\n", "# Bottom: residuals\n", "true_resid = y - x\n", "gappy_resid = y1_gap - x1_gap\n", "filled_resid = res_resid['yhat'] - x1_gap\n", "\n", "ax2.plot(true_resid.loc[t0:t1], label='true residual', linewidth=1.0)\n", "ax2.plot(gappy_resid.loc[t0:t1], label='gappy residual', linewidth=2.0)\n", "ax2.plot(filled_resid.loc[t0:t1], label='filled residual', linewidth=1.2)\n", "ax2.set_title('Residuals: true, gappy, and filled')\n", "ax2.legend(loc='upper left')\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "d0e55ed1", "metadata": {}, "source": [ "\n", "## 7. Method catalog — sample calls\n", "\n", "Below are concise examples for several methods. All return a dict with keys:\n", "`filled`, `yhat`, optional `pi_lower`/`pi_upper`, `model_info`, and `metrics`. `filled` uses the original values where available, but imputes missing values as the algorithm is capable. `yhat` is estimates for the target time series which may include predictions at non-missing times. In the case of regression, the estimates may not be great and there may be a 'lurching' effect between original series and fill values. In the case of `dfm`, the `yhat` estimate may low-pass a lot of jitter in the instrument and conceivably is preferable to the origin data. PI stands for prediction interval, a measure of confidence in the estimate that is available with dfm and characterizes the growth of uncertainty with gap size. \n" ] }, { "cell_type": "code", "execution_count": null, "id": "85929d59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Z(y): [1. 0. 1.] Z(x): [1.0403 0. 0. ]\n", "diag(T): [1. 1. 1.]\n", "diag(Q): [0.00e+00 0.00e+00 1.09e-06] diag(H): [1.e-05 1.e-05]\n", "modes: factor=trimbur anom_mode=rw anom_var=target\n", "active params: ['log_q_beta', 'log_q_ay', 'log_r_y', 'log_r_x', 'load']\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spec2 = GapSpec(n_gaps=100, min_len=400, max_len=1000, seed=9, strategy=GapStrategy.TARGET_ONLY)\n", "y2_gap, x2_gap, gaps2 = apply_gaps(y, x, spec2)\n", "\n", "# Common inputs\n", "lags_0_4 = range(0, 5)\n", "\n", "examples = {\n", " \"substitute\": dict(method=\"substitute\"),\n", " \"ols\": dict(method=\"ols\"),\n", " \"huber\": dict(method=\"huber\"),\n", " \"loess2d\": dict(method=\"loess2d\"),\n", " \"resid_linear\": dict(method=\"resid_interp_linear\"),\n", " \"resid_pchip\": dict(method=\"resid_interp_pchip\"),\n", " \"rolling_reg\": dict(method=\"rolling_regression\", window=72),\n", " \"lagged_enet\": dict(method=\"lagged_elasticnet\", lags=lags_0_4),\n", " \"dfm_trimbur_ar\": dict(method=\"dfm_trimbur_ar\"),\n", " \"dfm_trimbur_rw\": dict(method=\"dfm_trimbur_rw\"),\n", "}\n", "\n", "# Run a quick fit for selected methods\n", "to_run = [\"ols\", \"resid_pchip\", \"dfm_trimbur_rw\"]\n", "results = {name: fill_from_neighbor(y2_gap, x2_gap, **examples[name]) for name in to_run}\n", "\n", "# Simple overlay plot\n", "fig, ax = plt.subplots()\n", "ax.plot(y, label='target (true)', color=color_for('target (true)'), linewidth=1.5)\n", "ax.plot(y2_gap, label='target (gapped)', color=color_for('target (gapped)'), linewidth=2.0)\n", "ax.plot(x2_gap, label='neighbor', color=color_for('neighbor'), linewidth=1.5)\n", "#\n", "for name, r in results.items():\n", " # draw the method line and keep the handle to fetch its color\n", " # draw the method line and keep the handle to fetch its color\n", " line, = ax.plot(r['yhat'].index,r['yhat'].values, label=name, linewidth=1.5)\n", "\n", " # add CI only for DFM-style methods, if available\n", " if name.startswith('dfm') and r.get('pi_lower') is not None and r.get('pi_upper') is not None:\n", " # ensure we have aligned Series for fill_between\n", " yhat = pd.Series(r['yhat'])\n", " pil = pd.Series(r['pi_lower']).reindex(yhat.index)\n", " piu = pd.Series(r['pi_upper']).reindex(yhat.index)\n", " color = line.get_color()\n", " ax.fill_between(pil.index, pil.values, piu.values,\n", " alpha=0.15, color=color, label=f\"{name} 95% CI\")\n", "ax.set_xlim(pd.Timestamp(2017, 7, 15), pd.Timestamp(2018, 2, 1))\n", "ax.set_ylim(0,7500.0)\n", "ax.set_title('Overlay of selected methods')\n", "ax.legend(ncol=2, loc='upper left')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "01d28073", "metadata": {}, "source": [ "\n", "## 8. Comparisons on synthetic gaps\n", "\n", "We evaluate each method on *known* artificial gaps. Following the analysis harness, we tabulate **coverage** and **RMSE**\n", "segmented by neighbor availability:\n", "\n", "- **RMSE_all** — over all overlapping timestamps. \n", "- **RMSE_all_with_neighbor** — where the neighbor is non-NaN. \n", "- **RMSE_in_gaps** — only where the target was synthetically gapped. \n", "- **RMSE_gaps_with_neighbor** — gap points *and* neighbor present.\n", "\n", "These are often the most decision-relevant numbers when choosing a method. \"All\" in the RMSE table refers to all missing values in the target series. No RMSE is reported for \"all\" for methods such as OLS and residual interpolation which require the neighbor also be non-missing. A separate column for that case provides apples-to-apples comparison for times with supporting neighbor data. DFM is nearly as good as resdidual interpolation for cases where the neighbor is present and can survive periods when both sources of data are missing which is 10% of the data!! Timing data is output, which shows the main downside of DFM and that is the fitting time. Reapplication of DFM on a larger/later time set with pre-fit parameters on a few years is much faster and that workflow is the focus of the next section. \n", "\n", "In this particular comparison DFM comes out on top not only on coverage but on accuracy. This is NOT a generalizable result -- as the period and length of gap are varied the RMSE estimates between methods will change by an amount that is greater than the difference between DFM and residual interpolation and. The latter are often a smidge more accurate. The main point is that they are about the same, so it is really about time versus coverage. " ] }, { "cell_type": "code", "execution_count": null, "id": "06ee9218", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting method: substitute ... done in 0.02 s\n", "Fitting method: ols ... done in 0.08 s\n", "Fitting method: resid_interp_linear ... done in 0.11 s\n", "Fitting method: resid_interp_pchip ... done in 0.15 s\n", "Fitting method: dfm_trimbur_rw ... Z(y): [1. 0. 1.] Z(x): [1.0403 0. 0. ]\n", "diag(T): [1. 1. 1.]\n", "diag(Q): [0.00e+00 0.00e+00 1.09e-06] diag(H): [1.e-05 1.e-05]\n", "modes: factor=trimbur anom_mode=rw anom_var=target\n", "active params: ['log_q_beta', 'log_q_ay', 'log_r_y', 'log_r_x', 'load']\n", "done in 74.68 s\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RMSE_allRMSE_all_with_neighborRMSE_in_gapsRMSE_gaps_with_neighbor
method
dfm_trimbur_rw57.12710555.10007296.50494193.092671
olsNaN549.685366NaN507.951475
resid_interp_linearNaN55.535812NaN94.703307
resid_interp_pchipNaN65.007505NaN110.855058
substitute320.128587325.340769554.792402554.792402
\n", "
" ], "text/plain": [ " RMSE_all RMSE_all_with_neighbor RMSE_in_gaps \\\n", "method \n", "dfm_trimbur_rw 57.127105 55.100072 96.504941 \n", "ols NaN 549.685366 NaN \n", "resid_interp_linear NaN 55.535812 NaN \n", "resid_interp_pchip NaN 65.007505 NaN \n", "substitute 320.128587 325.340769 554.792402 \n", "\n", " RMSE_gaps_with_neighbor \n", "method \n", "dfm_trimbur_rw 93.092671 \n", "ols 507.951475 \n", "resid_interp_linear 94.703307 \n", "resid_interp_pchip 110.855058 \n", "substitute 554.792402 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import time\n", "\n", "def _avail_mask(obj):\n", " return (obj.notna().any(axis=1) if isinstance(obj, pd.DataFrame) else obj.notna())\n", "\n", "def rmse_by_mask(y_true, y_pred, mask):\n", " common = y_true.index.intersection(y_pred.index)\n", " if len(common) == 0:\n", " return np.nan\n", " m = mask.reindex(common).fillna(False)\n", " if not m.any():\n", " return np.nan\n", " resid = (y_true.reindex(common) - y_pred.reindex(common)).to_numpy()\n", " return float(np.sqrt(np.nanmean((resid[m.to_numpy()])**2)))\n", "\n", "def _is_neighbor_required(method):\n", " # Add here any method that requires neighbor to be present for prediction\n", " return method in ['ols', 'huber', 'loess2d', 'resid_interp_linear', 'resid_interp_pchip', 'rolling_regression', 'lagged_elasticnet']\n", "\n", "def _mask_exclude_leading_trailing(mask):\n", " # Only keep True inside the first and last True in the mask (i.e., exclude leading/trailing gaps) \n", " idx = np.flatnonzero(mask)\n", " if len(idx) == 0:\n", " return mask\n", " mask2 = pd.Series(False, index=mask.index)\n", " mask2.iloc[idx[0]:idx[-1]+1] = mask.iloc[idx[0]:idx[-1]+1]\n", " return mask2\n", "\n", "methods_for_table = [\"substitute\", \"ols\", \"resid_interp_linear\", \"resid_interp_pchip\", \"dfm_trimbur_rw\"]\n", "fits = {}\n", "for m in methods_for_table:\n", " print(f\"Fitting method: {m} ...\", end=\" \", flush=True)\n", " t0 = time.time()\n", " fits[m] = fill_from_neighbor(y2_gap, x2_gap, method=m)\n", " t1 = time.time()\n", " print(f\"done in {t1 - t0:.2f} s\")\n", "\n", "gap_mask = y2_gap.isna() \n", "nbr_mask = _avail_mask(x2_gap)\n", "\n", "rows = []\n", "for m, r in fits.items():\n", " yhat = r['yhat']\n", " # For methods that require neighbor, mask out where neighbor is missing (except leading/trailing)\n", " if _is_neighbor_required(m):\n", " valid_mask = nbr_mask\n", " gap_mask_valid = gap_mask & nbr_mask\n", " # Exclude leading/trailing gaps from this restriction\n", " gap_mask_valid = _mask_exclude_leading_trailing(gap_mask_valid)\n", " all_mask_valid = valid_mask\n", " all_mask_valid = _mask_exclude_leading_trailing(all_mask_valid)\n", " # If there are any False in all_mask_valid (i.e., neighbor missing inside), set RMSE to nan\n", " if not all_mask_valid.all():\n", " RMSE_all = np.nan\n", " else:\n", " RMSE_all = rmse_by_mask(y, yhat, mask=pd.Series(True, index=y.index))\n", " # For \"in gaps\", do not output for neighbor-required methods\n", " RMSE_in_gaps = np.nan\n", " RMSE_gaps_with_neighbor = rmse_by_mask(y, yhat, mask=(gap_mask & nbr_mask))\n", " else:\n", " RMSE_all = rmse_by_mask(y, yhat, mask=pd.Series(True, index=y.index))\n", " RMSE_in_gaps = rmse_by_mask(y, yhat, mask=gap_mask)\n", " RMSE_gaps_with_neighbor = rmse_by_mask(y, yhat, mask=(gap_mask & nbr_mask))\n", " rows.append({\n", " \"method\": m,\n", " \"RMSE_all\": RMSE_all,\n", " \"RMSE_all_with_neighbor\": rmse_by_mask(y, yhat, mask=nbr_mask),\n", " \"RMSE_in_gaps\": RMSE_in_gaps,\n", " \"RMSE_gaps_with_neighbor\": RMSE_gaps_with_neighbor,\n", " })\n", "\n", "table = pd.DataFrame(rows).set_index(\"method\").sort_index()\n", "table\n" ] }, { "cell_type": "markdown", "id": "7cbc8785", "metadata": {}, "source": [ "\n", "## 9. Storing & reusing DFM fits\n", "\n", "You can save fitted DFM parameters and reload them to avoid re-estimation (useful for reproducible operations).\n", "The exact helper names may vary by version; the example below shows a common pattern.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5e46f183", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Z(y): [1. 0. 1.] Z(x): [1.0403 0. 0. ]\n", "diag(T): [1. 1. 1.]\n", "diag(Q): [0.00e+00 0.00e+00 1.09e-06] diag(H): [1.e-05 1.e-05]\n", "modes: factor=trimbur anom_mode=rw anom_var=target\n", "active params: ['log_q_beta', 'log_q_ay', 'log_r_y', 'log_r_x', 'load']\n", "has fitted_params? True \n", "DFM fit completed in 76.14 seconds\n", "Parameters saved to dfm_trimbur_rw_vns_msd.yaml\n", "Z(y): [1. 0. 1.] Z(x): [1.0403 0. 0. ]\n", "diag(T): [1. 1. 1.]\n", "diag(Q): [0.00e+00 0.00e+00 1.09e-06] diag(H): [1.e-05 1.e-05]\n", "modes: factor=trimbur anom_mode=rw anom_var=target\n", "active params: ['log_q_beta', 'log_q_ay', 'log_r_y', 'log_r_x', 'load']\n", "DFM reuse completed in 8.838 seconds\n", "\n", "Speed-up from parameter reuse: 8.6× faster\n" ] } ], "source": [ "\n", "import time\n", "from vtools.functions.neighbor_fill import (\n", " fill_from_neighbor,\n", " dfm_pack_params, save_dfm_params, load_dfm_params,\n", ")\n", "\n", "# --- 1. Fit once (expensive) -----------------------------------------------\n", "t0 = time.perf_counter()\n", "\n", "res_fit = fill_from_neighbor(\n", " y2_gap, x2_gap,\n", " method=\"dfm_trimbur_rw\"\n", ")\n", "\n", "\n", "mi = res_fit[\"model_info\"]\n", "print(\"has fitted_params?\", \"fitted_params\" in mi, type(mi.get(\"fitted_params\")))\n", "\n", "blob = mi.get(\"fitted_params\")\n", "assert isinstance(blob, dict) and \"param_names\" in blob and \"transformed\" in blob, \"No fitted params!\"\n", "\n", "t1 = time.perf_counter()\n", "fit_time = t1 - t0\n", "\n", "print(f\"DFM fit completed in {fit_time:.2f} seconds\")\n", "blob = dfm_pack_params(res_fit[\"model_info\"])\n", "\n", "# --- 2. Save parameters (YAML) --------------------------------------------\n", "save_dfm_params(blob, \"dfm_trimbur_rw_vns_msd.yaml\")\n", "print(\"Parameters saved to dfm_trimbur_rw_vns_msd.yaml\")\n", "\n", "# --- 3. Load and reuse (cheap) --------------------------------------------\n", "t2 = time.perf_counter()\n", "\n", "blob2 = load_dfm_params(\"dfm_trimbur_rw_vns_msd.yaml\")\n", "res_reuse = fill_from_neighbor(\n", " y2_gap, x2_gap,\n", " method=\"dfm_trimbur_rw\",\n", " params=blob2 # skip fitting, just run the smoother\n", ")\n", "\n", "t3 = time.perf_counter()\n", "reuse_time = t3 - t2\n", "\n", "print(f\"DFM reuse completed in {reuse_time:.3f} seconds\")\n", "\n", "# --- 4. Compare timings ----------------------------------------------------\n", "print(f\"\\nSpeed-up from parameter reuse: {fit_time / reuse_time:,.1f}× faster\")\n" ] }, { "cell_type": "markdown", "id": "56e685b7", "metadata": {}, "source": [ "\n", "## 10. Appendix — API summary\n", "\n", "```python\n", "fill_from_neighbor(\n", " target, # pd.Series\n", " neighbor, # pd.Series | pd.DataFrame\n", " method: str, # 'substitute' | 'ols' | 'huber' | 'loess2d' | 'resid_interp_linear' | 'resid_interp_pchip' |\n", " # 'rolling_regression' | 'lagged_elasticnet' | 'state_space' | 'dfm_trimbur_ar' | 'dfm_trimbur_rw'\n", " lags=None, # Iterable[int] for lagged methods (e.g., range(0,5))\n", " bounds=(None, None), # optional min/max clipping after prediction\n", " window=None, # for rolling regression\n", " **kwargs # method-specific args (e.g., params=... for DFM reuse)\n", ") -> dict # with keys: filled, yhat, (pi_lower, pi_upper), model_info, metrics\n", "\n", "API CONTRACT (neighbor_fill.fill_from_neighbor)\n", "\n", "Signature\n", "---------\n", "fill_from_neighbor(\n", " target: pd.Series,\n", " neighbor: Union[pd.Series, pd.DataFrame],\n", " method: str = \"substitute\",\n", " regime: Optional[pd.Series] = None,\n", " bounds: Tuple[Optional[float], Optional[float]] = (None, None),\n", " *,\n", " params: Optional[dict] = None,\n", " **kwargs,\n", ") -> Dict[str, Any]\n", "\n", "Inputs\n", "------\n", "- target: Regular-grid pandas Series (DatetimeIndex), may contain NaNs (gaps).\n", "- neighbor: Series or DataFrame on the *same* grid (same step & phase) as target.\n", "- method: One of\n", " {'substitute','ols','huber','rolling','lagged_reg','loess',\n", " 'dfm_trimbur_rw','dfm_trimbur_ar','resid_interp_linear','resid_interp_pchip'}\n", "- regime (optional): Categorical Series aligned to target for per-regime fitting.\n", "- bounds: (lo, hi) applied as a final clip to yhat.\n", "- params: Packed fitted params for reuse (currently used by DFM backends).\n", "- **kwargs: Method-specific options (see below).\n", "\n", "Grid requirement\n", "----------------\n", "No internal resampling is performed. target and neighbor **must** be equally spaced\n", "with identical step and phase. Regularize upstream as needed.\n", "\n", "Method-specific **kwargs**\n", "--------------------------\n", "Common\n", " - lags: int | Sequence[int], optional\n", " - seed: int, optional\n", "\n", "'ols'\n", " - lags: int | Sequence[int], optional\n", " - add_const / fit_intercept: bool (default True)\n", "\n", "'huber'\n", " - lags: int | Sequence[int], optional\n", " - huber_t: float (default 1.35), maxiter: int (200), tol: float (1e-6)\n", "\n", "'rolling'\n", " - window: int (REQUIRED; samples, not time offset)\n", " - min_periods: int (default=window)\n", " - center: bool (default False)\n", " - lags: int | Sequence[int], optional\n", "\n", "'lagged_reg'\n", " - lags: int | Sequence[int] (recommended)\n", " - alpha: float (L2), l1_ratio: float (ENet), standardize: bool=True\n", "\n", "'loess'\n", " - frac: float (default 0.25), it: int (default 0), degree: int (default 1)\n", "\n", "'dfm_trimbur_rw' / 'dfm_trimbur_ar'\n", " - rx_scale: float (default 1.0)\n", " - maxiter: int (default 80), disp: int (default 0)\n", " - anom_var: {'target','neighbor'} (variant default; may override)\n", " - ar_order: int (AR anomaly order; usually 1 for *_ar)\n", " - param_names: list[str], optional\n", " - params: (top-level) packed params blob to skip refitting\n", "\n", "'resid_interp_linear' / 'resid_interp_pchip'\n", " - min_overlap: int (default 3)\n", " - clip_residuals_sigma: float, optional\n", " - enforce_monotone: bool (PCHIP only; default False)\n", "\n", "Returns\n", "-------\n", "dict with keys:\n", " - yhat: pd.Series # filled target on target.index\n", " - pi_lower, pi_upper: pd.Series | None # uncertainty bands if available\n", " - model_info: dict # diagnostics, see below\n", "\n", "model_info (typical fields)\n", "---------------------------\n", " - method: str\n", " - scaling: {'y_mu','y_sd','x_mu','x_sd'}\n", " - param_names: list[str] (if applicable)\n", " - fitted_params: dict # portable blob for reuse (DFM)\n", " - llf, aic, bic: float (if applicable)\n", " - regime_info: dict[...] # present when regime is provided\n", "\n", "\n", "\n", "```\n" ] } ], "metadata": { "kernelspec": { "display_name": "schism", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 5 }