{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## State space models - Chandrasekhar recursions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-12-14T14:42:28.289908Z", "iopub.status.busy": "2023-12-14T14:42:28.289665Z", "iopub.status.idle": "2023-12-14T14:42:31.701390Z", "shell.execute_reply": "2023-12-14T14:42:31.700638Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "\n", "from pandas_datareader.data import DataReader" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although most operations related to state space models rely on the Kalman filtering recursions, in some special cases one can use a separate method often called \"Chandrasekhar recursions\". These provide an alternative way to iteratively compute the conditional moments of the state vector, and in some cases they can be substantially less computationally intensive than the Kalman filter recursions. For complete details, see the paper \"Using the 'Chandrasekhar Recursions' for Likelihood Evaluation of DSGE Models\" (Herbst, 2015). Here we just sketch the basic idea." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Kalman filter\n", "\n", "Recall that a time-invariant state space model can be written:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= Z \\alpha_t + \\varepsilon_t, \\qquad \\varepsilon_t \\sim N(0, H) \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q) \\\\\n", "\\alpha_1 & \\sim N(a_1, P_1)\n", "\\end{aligned}\n", "$$\n", "\n", "where $y_t$ is a $p \\times 1$ vector and $\\alpha_t$ is an $m \\times 1$ vector.\n", "\n", "Each iteration of the Kalman filter, say at time $t$, can be split into three parts:\n", "\n", "1. **Initialization**: specification of $a_t$ and $P_t$ that define the conditional state distribution, $\\alpha_t \\mid y^{t-1} \\sim N(a_t, P_t)$.\n", "2. **Updating**: computation of $a_{t|t}$ and $P_{t|t}$ that define the conditional state distribution, $\\alpha_t \\mid y^{t} \\sim N(a_{t|t}, P_{t|t})$.\n", "3. **Prediction**: computation of $a_{t+1}$ and $P_{t+1}$ that define the conditional state distribution, $\\alpha_{t+1} \\mid y^{t} \\sim N(a_{t+1}, P_{t+1})$.\n", "\n", "Of course after the first iteration, the prediction part supplies the values required for initialization of the next step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Focusing on the prediction step, the Kalman filter recursions yield:\n", "\n", "$$\n", "\\begin{aligned}\n", "a_{t+1} & = T a_{t|t} \\\\\n", "P_{t+1} & = T P_{t|t} T' + R Q R' \\\\\n", "\\end{aligned}\n", "$$\n", "\n", "where the matrices $T$ and $P_{t|t}$ are each $m \\times m$, where $m$ is the size of the state vector $\\alpha$. In some cases, the state vector can become extremely large, which can imply that the matrix multiplications required to produce $P_{t+1}$ can be become computationally intensive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example: seasonal autoregression\n", "\n", "As an example, notice that an AR(r) model (we use $r$ here since we already used $p$ as the dimension of the observation vector) can be put into state space form as:\n", "\n", "$$\n", "\\begin{aligned}\n", "y_t &= \\alpha_t \\\\\n", "\\alpha_{t+1} & = T \\alpha_t + R \\eta_t, \\qquad \\eta_t \\sim N(0, Q)\n", "\\end{aligned}\n", "$$\n", "\n", "where:\n", "\n", "\n", "$$\n", "\\begin{aligned}\n", "T = \\begin{bmatrix}\n", "\\phi_1 & \\phi_2 & \\dots & \\phi_r \\\\\n", "1 & 0 & & 0 \\\\\n", "\\vdots & \\ddots & & \\vdots \\\\\n", "0 & & 1 & 0 \\\\\n", "\\end{bmatrix} \\qquad\n", "R = \\begin{bmatrix}\n", "1 \\\\\n", "0 \\\\\n", "\\vdots \\\\\n", "0\n", "\\end{bmatrix} \\qquad\n", "Q = \\begin{bmatrix}\n", "\\sigma^2\n", "\\end{bmatrix}\n", "\\end{aligned}\n", "$$\n", "\n", "In an AR model with daily data that exhibits annual seasonality, we might want to fit a model that incorporates lags up to $r=365$, in which case the state vector would be at least $m = 365$. The matrices $T$ and $P_{t|t}$ then each have $365^2 = 133225$ elements, and so most of the time spent computing the likelihood function (via the Kalman filter) can become dominated by the matrix multiplications in the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### State space models and the Chandrasekhar recursions\n", "\n", "The Chandrasekhar recursions replace equation $P_{t+1} = T P_{t|t} T' + R Q R'$ with a different recursion:\n", "\n", "$$\n", "P_{t+1} = P_t + W_t M_t W_t'\n", "$$\n", "\n", "but where $W_t$ is a matrix with dimension $m \\times p$ and $M_t$ is a matrix with dimension $p \\times p$, where $p$ is the dimension of the observed vector $y_t$. These matrices themselves have recursive formulations. For more general details and for the formulas for computing $W_t$ and $M_t$, see Herbst (2015).\n", "\n", "**Important note**: unlike the Kalman filter, the Chandrasekhar recursions can not be used for every state space model. In particular, the latter has the following restrictions (that are not required for the use of the former):\n", "\n", "- The model must be time-invariant, except that time-varying intercepts are permitted.\n", "- Stationary initialization of the state vector must be used (this rules out all models in non-stationary components)\n", "- Missing data is not permitted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand why this formula can imply more efficient computations, consider again the SARIMAX case, above. In this case, $p = 1$, so that $M_t$ is a scalar and we can rewrite the Chandrasekhar recursion as:\n", "\n", "$$\n", "P_{t+1} = P_t + M_t \\times W_t W_t'\n", "$$\n", "\n", "The matrices being multiplied, $W_t$, are then of dimension $m \\times 1$, and in the case $r=365$, they each only have $365$ elements, rather than $365^2$ elements. This implies substantially fewer computations are required to complete the prediction step." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convergence\n", "\n", "A factor that complicates a straightforward discussion of performance implications is the well-known fact that in time-invariant models, the predicted state covariance matrix will converge to a constant matrix. This implies that there exists an $S$ such that, for every $t > S$, $P_t = P_{t+1}$. Once convergence has been achieved, we can eliminate the equation for $P_{t+1}$ from the prediction step altogether.\n", "\n", "In simple time series models, like AR(r) models, convergence is achieved fairly quickly, and this can limit the performance benefit to using the Chandrasekhar recursions. Herbst (2015) focuses instead on DSGE (Dynamic Stochastic General Equilibrium) models instead, which often have a large state vector and often a large number of periods to achieve convergence. In these cases, the performance gains can be quite substantial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Practical example\n", "\n", "As a practical example, we will consider monthly data that has a clear seasonal component. In this case, we look at the inflation rate of apparel, as measured by the consumer price index. A graph of the data indicates strong seasonality." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-12-14T14:42:31.705874Z", "iopub.status.busy": "2023-12-14T14:42:31.705165Z", "iopub.status.idle": "2023-12-14T14:42:32.768629Z", "shell.execute_reply": "2023-12-14T14:42:32.767877Z" } }, "outputs": [ { "data": { "image/png": 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YfYwb+IJR18ncfbI9/OgtH8MzX/1hnN0aLftUMjIyMjIyWkEmxfYxJosgcyche5uRkZGRkZGREQMRWikdJEe5+2TrOHFuG+NpgYe3xss+lYyMjIyMjFaQSbF9DDLY35lMl3wmGRkZGRkZGVciSCmWovYyRvu5IVArKIqCqfMyEZmRkZGRsT+QSbF9DCpl2M7lkxkZGRkZGRmXAGX5pD7WGC9RKTaazPCJu88keaHtVfBu5JkUy8jIuFKxx/sMZjDs1r3MpNg+hvEUy0qxjIyMjIyMjEuAJr5gRMzMCmDWslrsv33kbjzz1R/Bf/vI3a2Ou0zwezTJPm4ZGRlXGAaDAQBga2tryWeSsVsYjeb+l71er9Hn9HfjZDL2JrKnWEZGRkZGRsalRKn2Si+fBIDxbIaVbrOgVwPqzL2fOnSPmZovK8UyMjKuNPR6PRw9ehQnT54EAKyvr6PT6Sz5rDJSMZvN8NBDD2F9fR39fjNaK5Ni+xjGU2yclWIZGRkZGRkZu48m5ZMjS7m00mLUSiTefiqfHHMSMivFMjIyrkAcO3YMAAwxlrG30e128ahHPaoxuZlJsX0MClCXoRT70vFzuO34efyD/+XGzNBnZGRkZGRcoZg0KZ+cLE+5RDHSfiLFJtlTLCMj4wpHp9PBIx7xCFx//fUYj3OX3b2O4XCIbre5I1gmxfYxKD7dWYLR/s/98Wfx2fs28NdvOISvf8Th1sfPyMjIyMjIuPQYNTDLH02Xp1yi8Ub7iBziBGCKsi8jIyNjr6DX6zX2ocq4cpCN9vcxpkYp1n755NmtOTN/+sKo9bEzMjIyMjIy2gGpj1KUR/yYtkkaspjYv0qxXD6ZkZGRkbE/kEmxfQwqadheglKMMsbb2c8sIyMjIyPjisR0VhhyaZzQPbKiXGpdKbb/yifH02y0n5GRkZGx/5BJsX0MY7S/BKXYZJY7X2ZkZGRkZFzJqCi9GpdPtuwptg/LJ6v3KyvFMjIyMjL2BzIpto8xLZZHTBEhl5ViGRkZGRkZVyZ4OV4KyVItn2yXpFlmM6Jlgd+jrBTLyMjIyNgvyKTYPsYyiamsFMvIyMjIyLiyUeke2bB8sm2Sxhjt76M4ZbzExgYZGRkZGRnLQibF9jEoI7jXlGLj6Qzvv/0hbO5Mdvu0MjIyMjIyMnYJTcsnOTHTdjkfKcX2FynGrnfuPpmRkZGRsU+QSbF9jOkS1VpNyhLe/OkH8M9/5+N4+Ttv3+3TysjIyMjIuCQYTWZ45Xu+jM/fv7HsU2kN44bdDLnSrG2SZrxkT7FllC9mpVhGRkZGxn5EJsX2MchTbDorkjK4jcZuoBS7+/QmAODEuZ1dPaeMjIyMjIwQdiZT3HHiPIpCTxh84I6H8NJ33Ib/+PYvXYIzuzzRlNTaWSJJM1li98mXvuNLeMKv/Dm+8tCFVsfl9yh7irWD8XSGv7rn4dbj8IyMjIyMEpkU28eYsgzudstBXxNPsbNbYwA5YMvIyMi4/cR53Htma9mnsW/wgjd/AX/vZe/Hx+46oz724cXadWEflf436WZYFMVSuyFSjLQMUuxjd57B5mjauqpwNOHlqjnGagO//YG78I9+48P4H5+4d9mnkpFxSfCXd5/By995e55TMi5rZFJsH4NPTjstmu3PZgUoyZ6iFDt7MZNiGRkZGRd2JvgH/+8H8QO/+ZFln8q+wVdPzwnIu09tqo+9uFjvmpA7KQo1YL7Wvve2k0lr7nRW4Edv+Rh++U8/rz523KCb4ZTFCgAwXlL55M6k/WZElDBclo8akMsn28L9Z+dzSk5uZFypePHbbsXL33kHPn63PpmUkdEWMim2j8GVYm36ik0ajnt2awQAGOWALSMjYx/j4c0RtsczPLCxnUyWZOhAJMXFBHLp4miuEEtN6Pzr//FX+M5ff18SsfX6j34Vz3rtJ/A7H7pLfexXT2/iA3ecwu8nKFkqSi9l90nby2s/Ge3TmO133Mzlk21jPFleJ/iMjDawNZo/2xe2949KOmPvIZNi+xhTtolqczGukHEpSrFFCUqW4WZkZOxnNE0wZOhBRE0aKdaM6HjPl07izlObuCtBpXb/2YsAgJMJXpz0bO1MZmrytYlH1XhSHavtNX+yRKN9UqeNlURiU1S7T2aivQ3Qe5FJsYwrFTSXZPVpxuWMTIrtYyxPKcbKNlOUYhfnSrGcxczIyNjPqJTAZ1KsFdD6tT3Sb2C3xpPFZ6RtDKh8cCthbNpwp5jdc6WUliDiHlXaDdHOtPo9WyeIZkRiFpi1PLZRirXt99qg3DUjDSNDiuXrnXFlwvgzTjPxm3H5IpNi+xjLUhlUDP4bKMVy+WRGRsZ+xqhCiuVgsw2QeilFKUZEWmoZIB2Xsm5ebDA2jw+0sULVKF97bPVcl0kQta0WM55irfuo5fLJttHkvc7I2AugecxW/2ZkXE7IpNg+xcwysG3TaL8JGTeZznB+UZOeyyczMjL2M/imfSerDFoBqYeSyicXx6SQDUVRmLUzRSlmTP4TFE+ccNU+Z5zU0Y5tk2DtE0TLI8VKT7G2yyeX1+1zv8KUT2a1b8YViukSS9EzMqTIpNg+xdTyBWlzMW6iFNtYdJ4EchYzIyNjf6NpKXqGHrTukD+YBkRmpRBT/Jitkd6s+OKYuhk2K5/UKhKr5ZPK0kvr99smiPj71bbZ/s7SjPbTy10z0jDKnmIZVzho/VpG05KMDCkyKbZPMbWC8r2iFDtbIcVywJaRkbF/wefAXD7ZDkypU8L13m6gFOOqnYspnmINCLndK59Udp+0lWJLMtp3nculRFEUhihpmxSb5PLJ1kHXuc04PCOjTUyN0X6eUzIuX2RSbJ/CDoxb9RSbpivFyE8MyJNrRkbG/kalfDJnYFuBKXVKMdpv4Os1ZqqlJKP9SRNPsXI8LTlULZ9MJ9Tmx+8PpRh/l9suYayQmC2Xq+5XlJ5i+XpnXJnISrGMvYBMiu1TTKfLI8WalPxsLDpPApkUy8jI2N/gRMle8xR7++eP45Xv+TKKYm8pfkmd18RTLIVsqCjFmhjtpyjFxulr9rhB90l7A9V6+eSSPMX4NW7bg4d3+Mxq/HZQeoplpVjGlYmsFMvYC+gv+wQyloOap1iLsu3pLL3k5+HN5uWTO5MpOuhg2M+ccEZGxt7FuIHX07Lx//zp53Hy/A6+5/HH8OjrDi77dMQwnmINiKnxtEBRFOh0OuJjeVlbSvlkE0Ku0uVU+b1HlfLJZt0n2yyf5I0NgHYVDqNlKsXY2HkD2w5GuftkxhUOWndGmWjPuIyRWYF9CjswblMpNq6UTzbxFNOf83RW4O+//AP43ld8ALOWSzEyMjIydhNN/BmXiaIocGZzrvpNKQVcJoikaEJMAXVfzxjGFaP9dD8z7bhAM6VYxaNK+51to/0W1+xlWkxwgrt1TzH2vXP3yXZglGJ7TO2bkSHFNJdPZuwBZFJsj+M9XzqJ93zppPq4mtF+iyqDJkqxja1m5ZPnt8e469Qm7jh5ISnTn5GRkXG5gM+Be4kU25nMzOZ7L6lRiqIwJaspqg5OpGnLGCtKsXFC90mjUtNfb75O643207tP2mO1qRSzCaFlKcXaLmEcL9lo/zXvvxN//oXjrY+7TJSkWI5JM65M7MX1PmP/IZdP7mHsTKb4V6//JADgs7/8XVgd9MTH2gFfmxkqrlLbHs9UZSRNu0/yYHM0meHAivojMjIyMi4LVIz299CG6tx2OY+3bZ7eBNNZAXIeaFI+Ccw3B5o1m693WqVYURRl+WTDdVObyOLlk0Uxv4a9rmy9X6bR/thS0y/LU6xts/tlkmL3nN7Ci952K647tILv+sZjrY69TNA7uTPRxcMZGXsBM7ZuZqVYxuWMrBTbw9iZzDBa/Mc3GRLMistDKQbogk3efXI6K9QlkMs0sM3IyMjYTTRpWrJMXNgulU7jPXTenJRpWj6pJacmDbpPjqYz0KknGe1zUkyZQLO/p4ZoqRvt70elWMvlk+x7t01YUxzL54f9gNEeVfw2xcXRFBsXdXuXjL2HySxdLZyR0SYyKbaHwYMXbRBR88toVSmWrlJ7mJVPAvVsbgyVcqPs35CRkbGHwdVDe2kzdZ6TYntIKcY3r9vqMsJZdXOgXLsq3SeVpBhfY1OURxVSLOF7c2iIltqxLZYS2qWabZJiO0ssnxxNlz/2fktY8ud8P5VQ/sNXfhB/96Xv2VffeT+CCyH227udsbeQSbE9DB7cbu7oFpW6p1h7E1UTPzM7q6Ru8c4m5NE0L8QZGRl7F5OKwmDvzGcXdkpSrE2fqKbgpMxoMlOZ1tvqLu3axTfOWyNdEmy7gUINsD3FdM+ZTf5p7nedUFte8q7N9+tyUYq1PTZ97+msSGoIsVfB1bL7xWy/KArcfuICHt4a46HzO8s+nYxLCD5v5/LJjMsZmRTbw6goxXaUSrGprRRrL+BrolLj5ZOAfkM1apDxzsjIyLicUFGK7aHNVEUptoc63NkkgUbhYP+udu2qlG4q73UTg3+goVJsYpdPyse3x2rzWVlm+SQn4NruAMmf8bYJa9vzdb+Aq2X3i2qKv8tZPXRlY5rLJzP2CDIptmTce2YL/+J1n8BHvnJafSyfaDaVpFjdU6xNpVh1LE0G9qxVPqldTPdr0JWRkXHlYbxHPcXOb/OGKXvnvO1z1Zjt76ZS7KJSKcbPM0WB08RTrInay75GbZI0yzTar8QpLb8f48oGtuXyyX0YnxVFUS2f3EOK3yao+KjtoYROhh6TJc4pGRkaZFJsyXjHF47j3V86if/x8XvUx/KFdFMZJC+zNCC18+VkOsM5yztNXT65D4OujIyMKxOV7pN7aDNVKZ9subteE9jrjcbby/5d7ffm91prtM9JsRQSkm9atc+Z/T01yif7XPePUixNrfXK93wZz37DpxqRh+Mllm5WiJJ9Ym/BO9oC+6d8crxE4jdDjo/eeRr//q1fbKRgrHiK5X1XxmWMTIotGeQFpvUIAaoTjbZ80lZrtbkQp3qKcUJsdTB/dNXlk9O8EGdkZFwZqHqK7Z35bK+WT9rrjWajYKvKmnSfVBvtj5opxZp0xxvVyif13SeHva762Kawx2rz/eIxkeb9uOWDd+F/fvZB/NW9Z5PH5s9Z290n92PS0r6/+6d8cv/d672Il7/zdtzywbvwgTtOJX/GJBvtZ+wRtEaK/eqv/io6nQ6e85znmJ9tb2/j2c9+Nq655hocPHgQz3jGM3DixIm2TumywNZ4vjnQZn6B6mKqLZ+seYolqAy+8tAFNRkHpHefpNLJQyt9rA16APRB8n4MujIyMq5MjPdoBpavG3upfNIO6DXlkzaRpf3efL3fGk9RFHLCoqoUSyifZMerjfZ3ofvk2rCnPrYp7LHafL9Sjfbpd28/cT597CUa7fNnay/NZ01gzyn7hRSr+hTuj++8F0FrNbc80GI63ZtxSsb+Qyuk2Cc+8Qn85m/+Jr7pm76p8vOf+ZmfwVve8hb80R/9Ed73vvfhgQcewD/+x/+4jVO6bLC1UIppgmtCVSmm7D7Z0FPsq6c38Z2//j785Os/qToOSFeKnV10njyyPkB/kTm2s9AxjHJ2KiMj4wrB3lWKlQF220biTWCfq6p80laKKQkePvZ0Vqgy7jzxZKvEJWjiKWaXT6qUYovfXR+mJcGawFYFtqlw2EkkxWaLZ+r24+mkGP/ey+o+CewfRUldfbo/vndWiu0N0Hzf5Lnka8BeSoJl7D9cclLswoUL+Gf/7J/hNa95Da666irz842NDdxyyy34z//5P+NpT3savuVbvgWvfe1r8eEPfxgf/ehHL/VpXTYghZi2HAKoGsFqlWI1Yko54d338EUAwFdPb6mOA9KVYhuLzpNH1wemnELry7Ifg66MjIwrE9Xuk+1m27fHU/zKW76AD39ZX1ZRLZ/cO/NwE6N9+3e139te67ZH8uP52CkkZJPuk/XySfn4tF4TKdYmgWqf59I8xRTkKSU7bz9xIXlsfo3bJqx39qGS337O9otqapzVQ3sC9E7ulqfYXlrvM/YfLjkp9uxnPxvf+73fi6c//emVn3/yk5/EeDyu/Pxxj3scHvWoR+EjH/nIpT6tywYXF+WTTZVi6vJJm5hKLIlIWcBTu08+vCifvGp9iEGvUzkPKcZ7VFmRkZGRYWOZ89mHv3IKr/3Q3fjPf3G7+lhOiqWUxN364Dn8yG9/DJ9u4J2Ugrr/j4KYsnxDtYSDPTZZL4jG5qRYiqfYZBfLJxPKAdeH/fmxLTZlsMdaFik2VoxLp9ykfJLfr8msUJXpNsV+VPLb78d+KZ8cNSDaM9oDPY9NuqJO9qjNQ8b+Q/9Sfvjv//7v41Of+hQ+8YlP1P7t+PHjGA6HOHr0aOXnN9xwA44fP+79zJ2dHezs7Ji/nzt3btfOdxlopBRji6naaH8RYPe7HUxmhb4kYnF8iqQ23VNsUT65NsCDqeWT+zATmZGRcWnxxk/dhy88cA6/+L1fj06n09q4y+w+SWsXlbVrUPEUS5iH3/KZB/DBL5/CX7vuAJ5w01H18amoqbUaeIrpu09Wf1/jQ8qN9lOIpSZKsXr5pF4ptmbKJ/ehp5iCxKRrfXpzhFMXdnDtwRX12GPH/Rr225nT9mN8VvcU23/fe7/c672IUimWfo8q3Sf3kF1Cxv7DJVOK3Xvvvfg3/+bf4L//9/+O1dXVXfvcl7zkJThy5Ij576abbtq1z14GDCnWslKMZPZUlpDaZj1NKWaXbuo8xY6uDzBILJ/cj/L8jIyMS4uXvuM23PLBu3Drg+kKjRTwDWzb2XYi5C5s65utcE8xzaafQOvmZkIyqQmalE9u1conlUox6zql+pmNp3r1TxNPsbGVuNKs2XSNDhij/TY9xSxSrFVPMX6/ZOMWRQH+iKSqxZrcr6aoqIf2SZmV/ZztF6VYReW8T+71XgTNRU3sGapKsf3xfGfsTVwyUuyTn/wkTp48ib/1t/4W+v0++v0+3ve+9+EVr3gF+v0+brjhBoxGI5w9e7Zy3IkTJ3Ds2DHv5z7/+c/HxsaG+e/ee++9VF+hFVxsoBTji6laKbaYpA6uzMWC2iwABas7k5k6wK4FAcLN3MaifPLo2hDDxPLJSnYqL8QZGRm7ACIdNhJUU01QUYq1rDCguVebkAGqRJqmnI5AG8eUdbMJbCJLM/62rRRTkmJNlGI2eaflIaulTsrySYtU0XxvY7S/iFPa9LiqGe0vSSkm/c72PU01268p+5Rq/CbYj0qxevnk/vjelTllnxCBew1FUeySpxg32s9KsYzLF5esfPI7v/M78bnPfa7ysx//8R/H4x73OPz8z/88brrpJgwGA7zrXe/CM57xDADAbbfdhnvuuQdPfvKTvZ+7srKClRW9JPxS4rc/cCf+/Isn8NpnPREHVnSXdGvhMzKZFRhPZ0YBJcGkQfdJOnaNKcWKohCX/lDgVBTzwHWl3xOPnWryz5Viyd0n92HQlZGRcWlBJYDa5ERTTGbpZEXzsRdKsdEEs1mBbldeYtXUU4wC9K1Ru9d7N4329eWTlqeY4rvb5N14OkOvK1+zdyqeYuk+nq6/h2CM9gftd5+0lXltKjErinbhd7bjqtsSzfbtuMgmNS8l9mNJXa18cp8oacY5QX3ZY64qnv+5UfdJtnZlo/2MyxmXjBQ7dOgQHv/4x1d+duDAAVxzzTXm5z/xEz+B5z73ubj66qtx+PBh/PRP/zSe/OQn40lPetKlOq1Lgv/x8XvwlYc28Vf3nMXffuy1qmN5sHpxPNWRYmxy0XefnB9LJN6smG9OyMA+hkrXs4mOFEs1+X/YdJ/cHaP9/RJ0ZWRkXFrQBpqXBbYyrjUPtwlaf4piXhp4UJgQms0KXGCETso8TAG6Ri21G2hS6mSfq758Mt3PzCZMbQIlBK4WAPT3q16OJx+b1muKU1II1FTUlGItbuaqSjHZuDNLsX9HYvmkfY3bVOftx6Sl7am4L8sn94k6bq+BrxtNyFq+3kxmhTqJlnHl476Ht/CFB87hu77hhlZ9eW1c8u6TIbzsZS/D933f9+EZz3gGvuM7vgPHjh3DG9/4xmWeUhIoYEzJWnOfEW0pyKSJp9hiDTowLDcymk1Vk7IdO8iTHl+WT6Z7ijUpA8nIyMhwgQL8tpViy+w+yQ1zNevP5mgCvn9P8Swi1VXbG0ibGEn19QL0CZ26UqyByb+C6Jh3ICz/rlaKLe5vSiKLfpcU7e2WT9pG++09azw2mRUyErOuFDuf1DmyibKvKUYJCrm9jiYdbfcy+PqxX+71sjGdFfjySfm8wJ/FJlYFtaYl+X5nWPi5P/4s/tXvfRKfuufhpZ5Hq6TYe9/7Xrz85S83f19dXcUrX/lKnDlzBpubm3jjG98Y9BO7XEFBQ4pZPg9s9aRYObHoPcUWZQnDUuGl2WA0KdtJVYq5jPa1fhf7MROZkZFx6TBlpMH5BNP5JqgmJ9pWTZXzp+Z722tVCtFRlk8uVymmKp+sEVPNkklNPMU0RKRNgqk9xYjYGuiJLVqjDwyXUT45H4uS1m3GC/Y1l3xvHld1OvN38vi5bdW4RVHUSJo2r/nOHlby33N6C1984Jz6OFsBul/8tZYdi//eR+7Gh798qvVxl4lXv+8rePp/fj/e+Kn7Rb9fVYql3yObsM+kWIaNO07Oy/0fOr+z1PNYqlLsSgEFEVpSazorKouBllSbWKUzmiCbAqhet4Nhv2s+QwoeOGkzW8meYlucFJtHqtrJNRvtZ2Rk7Cb4pnE/KcWqnpby720TaCnGu9tGnb3c7pOata+uFGuz+6Rldq8oQ7Q36Wql2KTqX6oh5EhNsjZcRvnkokP3gHxXl6OYAmTfe8Z+5+ZrDgAAblOa7fPYrLcob2rzmvNYcK95D/3Qaz6K7/+ND+GcsoS+Vj65TyoYqmtXu9/5yycv4Jf+9Av4d2/8XPyXryDc+dAmAODu05ui3+dzXhNVtj2H2M98xv7GaDLDqQtzMqztONZGJsV2AaPEAN0ut9Qeb080mwqzfQp++r0OVogU0yjFGixodN79RdAlCQJms8IEG0fWhmX5pDJwqrSWzxNzRkZGQ1RIsbaVYrNqYiSlXCoV/HtryifrpFiCpxh1bW5ZVVEnxfRKMUpC6Y3205Vitc6XGlLMVopp7RKMKn1ObGnIQLre60tQitE1os6XS1WKCcaesnf/cY84BAC4Q2m2z+8NkYGtdt1cIsnfBEVR4IGNixhNZjhzYaQ6dr+WTy7T3/fhhRVL292ilw3aa0nfLT7XN1EwTu2OtrkDZQbDiXPbptpi2QrhTIrtAkaJ5ZN2plfLxNuB7QWFp9nUKMW6WE3IhNqbMQ3s0k1JkD2ezcxLszLoluWT2mw79zHYQ0FXRkbG5Qk+p7RttL9MM/BJ5XtrSLHqNUrxFKPgvv3ukw3KJxe/e3h14Pws7dhbY0X3Sbt8MqEDJIE6VWuPX0voIGm6Ty7FU6xaurkso31A1gGSlGKdDvA3bjgMYO4rpgEfp1T2tWm0P2V/3jvx2WhaxqdNu7O27ZP4gTsewi/9f59v359xieWTlFDYS8/YboCSI9J7XSmfbNJ90i6f3GfXPSOMBzfKMv9lV3BlUqwhiqIsgdSWT9qZXrWnmPXwaLL1U6bWIqWYZlEcNZDV0gRJ3cokSjMu6+93O+nlk3s06MrIyLg8MVlq+aRVit7inDZO9LS0fzcla3zRBPezStnYpQbda1q7VCWMIyLFFqWAyu9tk4dNTP5TlGKk7KZO1VKMTQmkntgqlWL9yt/bgFGKDZehFNOX2pJSrNfp4GuvWQcA3P/wRdW4XJFW3q/lkIHL3hxpUK1A0MXD9vdsm5z6L++8A7/30a/iQy37ay1TFUhz5156xnYDRikmJLgq5ZO71H0S2H/XPSOMBzfKdWrZ+/JMijUEDw715ZPV399SLob2RKPZmNB5dzusfFKlFEtf0KZWWYJkgubftdvpsPLJBkb7eWLOyMhoCO711LrRfs2keTlKMU1Cxi4xTSqfZGtlmx48dK5EbGmUYrTeH1qbK8X05ZPz603KJQ0pZm+0NesmbfIPrSZ2qraU4RLVE6GmFJsVrZUI070+sLKEMkLbU0zwjlCM1O12sDqYx0faGKfiNdtL+4wmGC2xpG42K/Dhr5zCxpZe7VspM1OeN72LdM/aLp+kfUPrCR3WJKvte31xobKdzgpRZ9crBbRmSInbyjrbxFOs1sk3770ySjxwtlSKLbtsPpNiDdHEKP+iVf5g+37EYGcP05ViCeWTla5naYHXgQUpJvMUK//c63ZY+aRSKbaHuxtlZGRcfuDqiuUrxdojiFIVckQcLoRHad0n2TVv02yfCNBDixJIzUZhe1xViqWWTx5ekGqaJFqNFEsgpug7A3J/Gd7NMKn7pOUpBrRXzmeM9peiFLM9eCTlk/P/9zpl8yR1fLQYd9DroJ+YeGyCZZbUve+Oh/DDr/kYXvCWL6iP5fOuNh4eG/XpYk5p2XSe5oa2N6OjKavaSCBeHzh7Ef/1XXfg9AV9tzq+ZuynfQARrlLitWq0n36dbOJxrzXR2C945xdP4O+89D345FfPtDpuVopdQeAv90Wlv0mtfFKtFGtePtnrdViGSj4+D+i1G7GplfGWBBE8iO91yvJJ7eS6zOxURkbGlYdJYhnhbsCe/9otn0z0FFtco6Prw/nnKOfwWtfmFkkxIgIPr+mVYvS7h0z5pDaZRCq1+eY5pXTTfJZKKVZ6gpF6SPqccQJrPaEczy6fnB/fklJsVlWK7SzTU0zwnen56LOkoTbGofs16HZNjJXi+ZeKZZJiX140JbjnzJb62Cblk7b6tE21L1DOS02M1FMwbpBYB4BbPngXfv0vbscf/uV96mMvJpBi957ZwtN+7b34vY9+VT3e5YKSAJV6iqXb5HDUuk9mUuyyxJs/8wC+enoL773toVbHrXiKZVJsb2PUIGNdK59sqBS7oOg+OWmqFOPlky0oxcgvo9OZlwekGu3zwDaXT2ZkZDTFaJJGDu0GbIKgzQ0VV8jpuk/Oy5OOrlMZoXIOnzRbN5uA1ptDSmKqKIqa0b72e0+MUmy+bkqbDPCxjVpLQXTQ9V4ZdNVWC3zzs0a+YIrvbZdPzo/XPeO/+b6v4Of++DPqskuXUqyt0s0kpVhRlk8OE0kxGmfQ7zJibUlKsZbjs4cWiqOUDsJNyifpexJZ3ranGCmAWleKcSIx4V6f2Zx3kExpbsPXjJ2p7Hp/4u4zuPPUJt76mQfU410u0KoCOVG6M0n377QFHMsmPjLcuP/sXLHV9v2pKMWy0f7eBr+BTbtP6pViFimmWBxokup2OlhZKMU0maJGSrEZycXlmTFeGgDASPtTywPsP2dkZGSkoKIUW7anmHIufuDsRbzxU/clZW45qaMy2l9co6sTlWJ2GUebHSjH1gZWumbvTMrudIdW00zjy7F1hNxoOgPdqoMJJv+0Pg973TJWED5nXJldlk/Kvvd0VpjzrpRPKv3QXvqO2/CHf3kf7j6tUwBNjEqNEXItqdQoNhkqkn90WXvdsnwytRNiv9sxjRVaVYot0d7i5Lm5WiGFZNlp0MBpYhHtbZNiNIe13n2y4b2mREzKsSnlk7TeafdplxPIdkDefXJ3lOi17pOXuSChKAr8+7d+EX/8Sb0KcS+DGrO0TZA/eDYrxa4Y8GClefdJXXBvZ0w3FeNzpdhqkqdYemasVIrNx9UoxbqLQG2YWD6Zu09mZGTsJsaW+rRNXy9afxa5AvVc/B/+5xfx3D/8DN5168mEscuxVOWTi9+96gCRYjqiwd6UtLlJsUkxqTKPr/W0+dV3n1woxVZJKSY0Sx6V52hKNzVqrcV3nivF5JYHQDVGWRvq1N38+Vod9JgHnfwZ/8rJTfNd1WVtVvdJoJ3N3GxWmHEoRpLEOcZon3mKqY32F/dm0OsmNzNqgmoZ4nKUYucTSuB3o3zyoKmcaO97z1gpetvXe9zgmgHl/JeS0OF7LTEptngPNKroyw2l0b5QKWb9XipxWus+eZnvvW4/cQG3fPAu/Ke3f6m1MU+e28aXT15obTwbo8kMJ87Pyak2Y9jt8RSnF6rPtsd2IZNiDdGsfLI6uaqVYrXySflkPWOeYisJnmI8qNZOlDRBHhgquk9OSxIPQHL55DLbQAPAPae3WvXAycjIuLSolbG3qBYjgsDMpco57Y6Fj87DW6PIb7rGTuw+uVNVimm9tez1pl1PMSKm5sTWaDoTnT+t7cN+WYKoLQM03kMLo31pvEBJJ54A05BitD6v9HvJ5ZODXgf97oJkEX5vPsag1y3V4Ypz/9Lxc+bP2o3Y1JRPlkqxNjZzPEYhiwkJMUXlk70uymcsUY046JW+ra12n1xi+eTJc4vyyZ2JukysCZlnl09OZ0Vrnku75RmVgnFTpdhi/zRKIG0rSjHhtaZ5a6/G70VRmLVATopVv2tqE4i6p9jl3fGT1KJtvhM/+JqP4n9/xQdwNiEW2w08uHHRqNnb3BufOLdd+XvuPrnHUTHab1o+OdI9DPZEo9mYVD3F9FL78a4oxUpPsZhXBynFqHwytftkxWi/5aDrrlOb+Du/9h48+w2fanXcjIyMSwd7DmrTbH9sqW41JfBFURgPCS0xBaQ3GDCeYgfSvLXsQLVNT7FyA1t2YpQoO2itXxv0krv62YScdHNGv7c66JXm6Yr7TRujYa/LyvJ05ZNz5RGNrVeKDXodDLr6c7/1wZIUU8cKi+d72C/PvQ1SjMdTpB7SKMXmjYhSPcUWcSEjIdtSihVFUfnuo5YVA6QUKwpdZ1fA8l7Sdp+cVMsngfY24xctz6g20TRBvbXwUE55J/n3VivF9igpxkv4pc+XbVWQ2oFyr3WfpHvcVqdjYG5lMZrMcN/DF+O/fAlwPxu3zbnggbNVUmzZKsJMijUEf3jU5ZOLiYkCrotj3WaKNiXk1aHZmPAAKslov+IppszAzqoZ2KKIZw5MaYBRiiWWTy7Rs+L2E+dRFHNybC/g+MY2fvfDd+9puXhGxqWGvWls02yf5j9KMGjm4rNbY0MopWTb+ZytIsUspZh2Hr4clGLkzSUdn5NiqV39TPfJNV35JG0AVwc99IxPlL77ZMVoX1k+yZVe0mQUPdvDXhedTof5iGqUYufNn7WG8ROmTk81rk8BEY6dDrA2VJRPcqP9fiopVt6vtrtP2ve1zfhsZzLF2a3SS0zrK7Yr5ZNsTkklH7Tgc2nrnmINO8GTUiytfDLFU2x3lGLTWYH3fOkkHt5sVxGU0gyiphRLfEbsGGnZxEcMW4sYpc3ScZpmz13UexruBu47y0ixFhs2cZN9YPnPRibFGoJPyFrDX5pcr154q2gnW3phjyzKKVKUYr1uF6sJRvs8UNJOlDQ2ZUGBuCx3ypRtQIPyyYY+Bk2wsQi69or8+v99zx345Td/AX/yqf1lNpmRoYG9yW+LFJvOCpP5PZhAivGMZLtKsaqnmHbTfTkY7Q97HZOMkqx/REytD3vJXf1qnS/HU1GZl+k8OUxT/xhSrN9VJ9Cq5ZM6pRit1UTOpJA0tz7ISDGtvxYj9EqPrku/dnOTfU2cw2MkIvEms0JVClh+53S1WSrs+9Omkv/UhSpBoS2B326guKJrPuyVVRttEVRNzrspmpZPNvEUSzHap3dwNJ01Ujq989YT+PHXfQIvftutyZ+RgooqUGq0X1OKpXqKLe/dToF5tlpsMkLzwMaySLGKUqy9PeqDG3OlGCXslv1sZFKsIfiEqi2fpGD+6gMrSccTuUSt7Td35MdToNTvpSnFKt0nlawyTZBrw15pEB35jLpSbO91nzx7cR54pdblt42Ni/Pn894zuq5du41P3H0GD53fWeo5ZGT4YG/y2yqf5HNf6Skmn1vuP1u+1ylBfkUppthE0u9eZTzFlEb7ta7N7c3jVK466JXJJMm6zdVaqV39jFKsUroZH3ubl24mjF2SYr3k7pMV43bh2IaAXBAFxpNM+Lw8dH4Hpy6U68ZYrZoqY6TUbo4p4CSkhgjkMRKdL6DbZIzY/So94NrtuOn7+6WEHV+cU5JijTzFJnxOoVi8/fLJ9pVizRLUjbpPcoJI0Q3XHN8gsU3kw/1n2y2TSyFA60b7ae9krfvk5a4UW+zNi6Je+nkpUBRlp+VlkWLLK5+cj/vIq9bmY7cYz7mQSbGG4JuJ8VRnkEkT67UHU5ViVE4xD5I1mzGapLod7imWphTTLmhlWUJXnBmbWZ5i/YTyyaIoKgHirEhTSKTi7B5TihF5aWdR28QXHtjAM1/9ETz3Dz+9tHPIyAih7inWTlDDA00qvdEEmzwzmGJ8a/tpSubS8XRmNmJXHyjN6jWwiSBt1+YmIGKl3+sapZisfHJ+jlwpluopxsusJJszoxRL9DMznmKsSYBYKTaj69VRq7t3jFKsaz5jfrxs7NtY6SSQ0olxMX63m1yOmAKjFOv3VGqtmcNTDNB9b4rr+t0OUvznmuByIsW0iY0KKaYkl3jJ6qppetVW+WQ6mdcU4wax+GQ6M+eboixJ6z7J1rsGMTyReW17k9n+cTEf5/nv7Y7R/l71FAPaOVd+fc4pS7d3CzxJ2uZccHyhFPvaaw4AyEqxPQ97QtWovWrlk6lKsYTySS61N9kpxULMA1vtAm78zLpylRo/BkBZHqAI7l3BeJsv4NlFBmBnMlN3N1oG6NouU6VFKjWS2O4XPLhxEX/4iXuX3p44I44aKdZS+SQP0lPKJ3mmOiXws+deSZDP16ijiUqxevlki55iVF7W7WB14fekUYqtDXtJCR3++yt9plJTkGKrg54xq9dkv0dMuWTWa2GsMmbEVl9JstB6TQTPoKdTLnGT/fnnaZV5TCnWqqdYeb01ai3TjIiVTwK6c+bqPEPIteSrs0xS7OT5anyh9xTTdzMkGFKsXyrFllE+uczuk4DuunGlV1vlk/wd3GyQiDGkWMtevfb9lcQK9u9oCV9Cvfvk5U2KbbF704ZSlo9xOZRPtjn3PrDY2918zXrrY7uQSbGGsCdyTQZh05RPJpJiU7t8MsVTrGNKIjRZAB7Yqo1FK4ScTCk2sUixMmBTlAY4frfNF3CDGbkuu+2sBLRx4iUobYP8hy73RXS38WvvuB0/9yefxTu+cGLZp5IRgR00nW+tfLIclwy5NYkNLpdPCfxSum7S+7zS75pGK9oyQnud1HaKa4IRI2o0nmK0CZuXT6aVpdHvD3odrC/KZSUxA5GIa8PSaF/jlVIpn+zr1l16RocV43Zd98kVUz6pIxNvPV4lxbRrLo3T73UxTLCYSAUnIYd9OZE4Zer/LlN66UixMjbTkphNYfu1tZmwtBN/Wl/Iiom5tvskzSndDlb7NKe0pRRr7imWWl5WJ1wUpNiOntTiSDPaL79nE6XYhSWRYvZaISLFrGN2q/tk28THb77vK/j1P79N/PucNG1j/psVyyXFprPCKLaAtj3F5nGoUYplUmxvw1YfaSbLi6Z8ckV9LMA9xeakmkbyXfUU03WU4mMDKd0n57/fU/iZzYoqKZaSbXe9bG2+gOQpBugJ0GWAyMtlKsUuNPCN2Mug9vBnlkhIZshgz0FtGe1zE/PVvt6Lpmlm0CY3JAo5ujaHVgeGHBpPC1EpB8EO1NvtPlmWA6qM9kfcaD+NbOC2AzS2tnySkkmajSzFBcN+t/QUU3af7Pc67H7Ljh0xldn8M3Tll19amOwPE/1HK8b1LZZP8nJVumYStZYdI6Wo23gp31CpzCPcdWoTv/Cmz6m9SGskSatKMat8skVPMXf5ZFtKsZnzz1J86fg5POGFf47ffN9X1Mc2UYpxpVZK6X9FKabshmsfrwWRYW15jxLsOVui+rKf5dR9i534akt9Cszv2398+5fwX9/9ZZzdklnBVMsn21aKtftcAMCJc9vVPX1LpPzFUdn192tJKZbLJ/c27IBDM1lu2eWTalJsPrbpPjmaijcXdOzcU6yh0b622w7LRko7X5ZZ0PnfU8on6V71+LhtkmJMKda2VD0FRF6e2Rq16r3GQcHpfiPFyBw7dYHYuDjeEyW6VwLsoKm98knmzZgwn/HySa1aaz5+ilJsPgceWu0bcmg+vvxZtefONkmxsvtk16jzZGqtuq+XNtgeM0KOVHaSzpvbTKVmlGKadZMpttTdJxmxVZKBwu6TpqzM6j4p9K778skLAICvv/Hw/PNSjfa7HawkKNNTQRuSlQH3n5Moxeb/N6RYX08GTpgSsp+gNAOA3/vIV/GGj92DP/zLe1XH0TgU4y3DU4yesSblk+rKCVY+uUJE+xKM9lPUIZ/66lmc357gg18+pT7WnoM095srxbRkd1EUle8tHXe6S+WTFxbnvrkzUSWDmiKpfHJc7plcnyHFMpViW6OpMbGXrlt8XU2JjbSYTjkp1r5SzG760Na+mFRi68OeEQellujuFjIp1hD2hHxxLJ8sa6TYWE5qAeVEQ6TYdFaIsz3ThBJGDv69tRNlE08xypwOEgI+vqEZthjkEjgptheUYhQgFwVwZnM5Zvv7VSlGQXFKluqrpzfxxP/wTvzbP/7sbp9WhgMpZYS7Mi5T4WibpVzYmVSCL+oSqBo/oesm/c6cFCvDD01yg+bOwwvD+TbLJw0RyTrFXRzF5yZa69eGpa+XvvskK90cKkz+eefLHnmKKconF8evDLrq54yfs1GKScsnJ+V6DfDyyfjxd53axGg6w8GVPh597YHFcdrrzVRTbRrtEwnJiETJuRsFvmUxoUt21pV92ueU/Lk03dCB8toeWPgjjqYyM/DdAJFiVMKjLYHfDaXYsNdhnmJLKJ9MGJPmlpRNtP0uaUi5zQSjfIJ9baXH83lnN4z2Z0V79xlwlU9KlGLz36E9ZnL3SSqjT9i3NcVWRVUoJcV4+WSzOWhzZxJVZk+XXD5538NzVe+xw6sA2iufJK/oRxxZNYndrBTb46gZ7QsCZPO742r3yVmRFsAcYt2opBuyFGKKo+opllqWIJeL83bj82MXWUzFC0TnOeyXHiGteopd3GtKsXKifmhJZXwUnErbZl8poKArJdi8/cQFjKYzfOa+s7t8Vhku0Fy4aIzbWvnkhPk1lQbosueF+4kBOo8pM/7iGDL5lyjkaH06uNI3BI12fArMS4V1i90nDVFSlk+qjPYTO0ACjLDodpLLJzXEEmGHkVPq7pOsxFfrUWWUYlb5pISkIZP9v3HsUOmDlqpo7y2nfHJl0GU2EQKjfVKKdapKMU2MxJsb0PHa5/T0olu1duNL53loMZ8URTsm10BJit28IFDb9BQbMcXvqrAb+25hu6FSjObeFHWH/XxoYh1OdGg30ba6Vno8n7ea+IFxQq/NEkqb0JIQXHRPSlKsmVKMFM7SueHf/cln8aO3fKxR1cNmRVUo+5wUIs2FUxd28G0vfhf+r9d/Mvh7fK91fhlKsUU8+Ojr5vNfW0oxmnevP7Rqkl/L9tvOpFhD2BOqpJzB/t2rD6yYn2kmHXqRhr0uDiwmG+lkzTsrabO/gF0+meaFplKKGb+M+d9T2tqPGCmWGiinYjydVRbAvUCK8YD01IUlKcVY+WSbUvNlgzayKQsyHdNWGd9+B21qqAvwhZ12ghqu6tCSFZQZLD9L/27R3HtE0f343DZTinWbKcWuWpBibXafJEWd1v+HSO41ptbSEpG8tG1doxQzKrVuEiFXkjQ9U94l3fhzXzDtms07Ic4/Q15+edepTQDAY68/mNxFsSQhS2W5Njnz3ttO4vP3b6iOGTESks5dpBQrKHE4/3sKkTfhJGZC4hEATm/uqMcFymfqIEvythGfFUVhNme0KdR7iqWTS+aaL737ZMtKsQak2GaD8kl7vZCOy4mLJpUefB+g2S82RZpSzCLFEhVEtJegZI7kvZ7OCvzBX96LD9xxqlbep8HFBNN8/nw1Iea/9OB5XNiZRNcA/mwts3yS5r+29lummc6gXTV2CJkUa4iaUkw4Wc5YqePBlb4JujST7ZiVcpDkXKsU63Y6ZaCrCZ5YQK9dTFNKN6nmureI+FJkuLQID3k5REsKJHui0ygKl4WKUmxJZvsXWm6NfLmA3qmUBYLeCa0vSkYaKNC6atHwRKsy2BpN8Kefvl8dDE2Y4rb0FJOtH3aQmeIZSHPnVQfmAbOk5Ig2mwdW+uh2O8Y/SDOP01px9XqaF2cT8JI6oxTTqLWGPUMGahU4E1Yuq+s+WVeKaconeTfEZuWTuuY4REBSbKQhiOh31oa95GCbd/tM+YzjG9v48dd9Av/q98IqARum2+eAEYmCtW/Gko1AWoMBbvqeqmgkq4VkpdjqoPxZCxukjYtjM/Zfu/YgAOC8MrHBY+B0o/32vW6r552iFJstjk0vn0xJUG81KJ+050xx+SR7B3fDaB9oWylmkWKCfRsdQ6RYqgH71CbFBHPDhe0JiJdpsk9LacrQ5PnioARBbC60SbG2BQDUdOnRi/kPaGdv7Gxm02LZvAuZFGuImqeYcLLkE/P6sGcWQ81kyx8oKmFRK8W6ZaCryU7xQEkrnZ4w7wu1UmyxiUrJYnKlWEpnpibgfmLA3lCK8Wf71JLKJ3nGdhkZhGU1GNhuoBSj67Q5mia3S8+QY2zIIX0XYAD47x+9B//m9z+N33q/roMXV3VoS+BJLn9AWc5QHX/+bBEZKFFXlBuh+bgaooNAgflVzIuzLdD593sdrCqM9i8yTzFNSRyhKApm/F56ikniBZpLVgc9ta8XwEiaftPySRpbduwOI2gAMI+r+LkbNXqnk/SMAVX/uBRS7P6zWyiKeWcvDapKMbnZPU90AmlKsTGLC4c9vffdbFYYUkyrqqPzXBv0SrP9FtZfSvgdXu3jmoPyuYyDE0qpDR2GvfaVYnzuGk8LdbxglGINyidp76K5biklcQR7zpQb7Ze/t9WkfJKdu9Z3rwnsZ0qi+tqt8kmaQ2jdklxzniRsEvtXlGLCuaziKdYghqa5MHb+fIzJrGhV/Q7UyyeBdoj5StVYb/5sFEU7HT99yKRYQ6R2n+S/tzbolZlfxcvAy2dIKSbtisKziinZKR5gJnuK9TpG4RCbbO0saEqgWzHab1mquXGxWn64F4z2LwelGFeftE2KffCOU3j8C96h7qK1G6DnI00pVt63ttt+70eME8ghjq88NO+Sp33HRmb+Z2SFMJN730IpRsbSSeWTte7H8e9NyY2+NY+nlE9es4TySQrihlwppvAUWx/2yjJABdnA5+IB6z4p8VPjKrXSaD/BUyyh+yQvn9R2jB6zceefIfckM8ryBn5g3MMt5TPIcmAyK1TH7TDiWGN2X1pMVJViKlKM7le/JDFHiiYcZy+OTac37fWuJC1bjM+Mr83hVaNSU3uKNTDa5+/IMssnAb1ajI5P2QPQc0LlssvzFJN9Z75Gpq45RVFU1skm3mRapCjFiOw8ur47nmK0Zkr2bbtFiqUpxfQlly6UqtmI0b61HrdZQlkUhakcIE9FIF0VqAH3FycuAFiu2X4mxRbYVnZ+JNS7TwqVYqZNehfdbscw6CmeYr1uBwdW5sdfEGYeJkxlpjVp5scD88VMc+2cYwuVYnYWNNVTbKjMeDfFXlSK8Yl6WUoxHjS0PVF+/K7T2B7P8PG7zrQ67ng6M9c+JRgYscA2k2KXHrQGXK0oI+QgJYm2DN0oWbp6X0jKDN6c2JmPK5c0ZaN2h7y+orsegeZOoxRTblBGkxn+yas+jJ/5g0+rjgOqiag1xQa24imWUD7J19s+I+RkRvvzc17t99QljEBV3act062UTyoVcj6jfZHpvFGWd4ziSd99khntG18y+bN2mvlwap7RHUecIukOO2NxFR0P6NZNc7+6Zbmrhrw9zeIE7fXe4UnLBqbL2tjq5IIUu+7gilEtnWtEiqVVTvR7HWa0306sk2K+zkHPtfaa82cjSSnG3iet/5H9LkrHrRjtJ5JiW6Mp+Km2GZ/VPcUEpFhNKZb2XBpPMaNMj9+vCinWIPbf2tETXHzf0US1ZJRiivJJADjXovXJqQsj7Exm6HSARxxZS/IYTwVXitGcDyzXVyyTYpi3kH7if3gnnpMQJNvmrdLgZ2u88FZZKMRWFUEugQec2vLJSvdJUmtN5MSgHdBrghfuD6btPkmBdRmwFeLOJJVgs6cPGJtgN0ix17z/Tvzz3/l4a4Ta5DIgxS4sUSlGgXHb3VB48JLyfPJFPPuKXXrYZYSjyUwVUBw/N3+3tO8197fS+kKSh8TXXbs+/yxl4MeDuKvWqcGAhBSb/5+SG6acTzG+7Sk2ms5U2dzPP7CBv/zqw3jLZx4QHwNUicBBr6tKZG2Z8smy66auK2D5u32WRNuSeIrx0s1uilJsYbTf1ysSjfKo11Er5Eql2EJVqCBpnF4l6u6TpBRLa8xzZrNcMynek4Bfb0NiCr4zrdd24lCzfnEiMsW39fRmSQQ2U4qldQf/92/9Ip7wwj836lsJSqXYiunkrm2WwssHtQoLXj65sseUYluJSjH++1TloiGc7fJFTYlbqqcYHyO147G9P5Pu1zYujvHHn7yvUTxXJ0DD13synZnv3NRov+Yp1mL5JFcGSp6T6ayoPJ+apIANIsWms3Bpsj3GxlZ7cfvZrfk5HlkbtN6EjpKk/W4H3S5r7pJJseXiM/du4PzOBH9598PqY+nmUeZbWz5Jwe26wp+EwL25DiR6itm1vNLFxQ7SdCb9CUoxK+Ab9MtHV+pRUgZ87bZYBxxG+wlBz2994E68//aH8IUHdN2sUnE5lE9WPMVaVoqR8iXFL6MJtq0sqBb8OuUOlJceNP8cXZA0gO66G6VYohfNQNl9cns8NST31y3KJ7XvFl8n6HtL1p6Z1UU4xbfoouUpBsjIIcIXHzi3GFPnocO/86BbljpJEmHc7L4sI1SsmYw0VHef5Eb7CrUVoeoppiNfK8btSoUctzsAoFKalUm/0qw+9RlPjRd4x2aNd5BR5g1490mBUsxTPqkhtSZMCZlCWHN1XBNSbCVB5QYAH71zru7+wuIdl+ChC6VSjEix7fEsSU0JJBjtu8onWzPat3ymlIQexSqTWaGaz1xKMQ2ZaCu1NPeq5ikmPHayC+WTduJIqjj7nQ/ehZ/9o8/gv33kq0njAi4CNPy9+XU5vCDFUpva0LUznmKCa87VUs3KJ7n/XPxz7PJabcKQgycJQmPb4U+b5ZM80QcgqfFeKjgPAaT5YO42MikG4PjGPGueIhekB/3wKnWDkm2GaHKh4FbTyYpgWrR39d0nudSegk3+mSFMZwVsQZnm2nGVmlYpZgd80nMGeOa5x7Ko7RAeZy/aSjHtBnhmNrKptd7a8uCq0f4o8JuXBjuTaWXxbHuipGxx20ox/mykGKDzY7TeKBl6UFCx0i/JCuk8vDOZmmyiWinGjMA1kvcHFv4R68Merj20Mv8sZTaUP2PUfVJCBE6YShjQlcQRiKQ+vNo3htyadfPWB8sNs2ZO4etMv9cpSTGFp9jqoOzqNysgVjkT8drpzNfANYUHaTk29xSTf29ab1YS1k1u3K4tla0Z7Ss8ycp4IT3Qdr1fGpLmzGbD8sleT+ejRonDbjVxmPKMD1m5q4bsOL2ZXj65G55itJncVlzvk4ukxHWHyvJJQJfY4DHCaDoTv9f0+8D8fknj4d1CvaRON26qqp034Eh5t2q+YIrnJLl8khvtJ5JDNjkuFTEQcfvV05tJ4wJ6VSCPQ0ulWFosTHPTeqLRfpMYfEvpKWbf25QYnMDXgNDzXVOKtUiKTZhaC0Cj0nUtpmaNtdXNy7MXyqQYgOOJni5A+XIfUTLpvJwCgCrAJnCWVd99slSZDXgtr6LdOYfOj6zMRmqVYvTi0v995+MCfbelGO1vNTPaf+j8jiEitV2dAOCTX30YT3zRu/Cnn75ffAxXUZzZHDVaHFJgB6Up9+reM1v46J2nk8Y3SrGWJ+jm5ZPlMW16E+xX8C6QpDSQkpEnz5WbyNQuvpXuk4J5+PjGfL278ehaqSZRmGkDVWKClGISLzVbKdbEU2xtmNag5ouMFNO82/xdHFSM9iVrZt1bC5CrnHkCDCiTaBKF3EVH+aTKi3NaKpfU3SeZcbtpqiAlAhfPJJE7mvLJiUMppl2/6N4MWAmmZqPACSJ7Ex+CISEHunM3ROBCTb+SYLRfNu/oMKVZmlJMbTi/KJ/jnmJqUmxRdqS53kQ4XH94peLXp0ko2fNIEkHU7WC1v9zySbWnGDtecyzvsJoSi9vkko5Qmx9LCXbpc8rnLc3zxVFTikmTZ4tr26Rig+7Puin7D39veqYHvY6x+UmtmqA5ezXVaL+Jp5jSNN++J026Tz7MlWKB52xmCRaWoRSjuMT4hrYwB01Y0gyAOr64FMikGIAHN4gUS1eKESkmL5+cv3jrA6t8UqUUKzdFNGlJjfa58mpQUYrJA04ArHOlbNwZ6zrT13iKWUb7vW4HHWXb7tIwuJsUMDYBKcVSM4G8rXvKOX/4y6dw6sIO3nXrSfEx9mLAsx5twA4gUhbGZ7/hU/jB3/poUoaNguK2FWqVNulKsgKonm822r/04CViB5WKXf5e69WjZUCh2bTT78w7IS42vlqlGFMuHV50bJME+PbGXduREKgqn4y3lnDdnM4KfOnB8+bvOhUNJ8WY0b5gbF7aRsTW/DN1Hp52RjUUaBNoba6Y/AsD/Qlr+jHsddVNeXi3Tu5nJlEt18snFaWETClGgbaG3OFq+H4iScMJIk15L0/eqb6zXT7ZQCnW7+mVfUCVCNSu1zw+K5sE6OJhIuYlRDWBEhPXHVwFgDKxofAVs98H6fvBuzDy8sk2Or8B9TVHuxHm+xVNgsGsmX090Q7USSnNu01+YEcXeza50X7z8kl7jZTGCeTl1aRig9ZN+t6x+8UVwk0VjLZSrM3uk9xofyxY93ZLKTabFXh4i5dPBjzFrH8716ZSzJDyREy1Vz5ZqwBLLJvfTWRSDGXmXFsXD5Q378giW67tPlkrn0xUitGkpTWNnJNLHdaVKj5p8Gt0cEXXlWTKguGewlNsZr08nQ7LPEu7WS2p5TdQGu0fOzwPvNomxSgo1yiHbL+dtn3F7ExtyvcmwvuBs9uR36yDApb2yydZoNnYaL9dUqwoCtz64LlGbayXgdmswJv+6j7cdUpPno7ZRvLgqryUEABOMKWY1sTWVYIi2ZjwuT9l4zv//VK5VJpTC0gxs3G3fKIUpBytNWuDHvPilF3vr57erKyxmne7JCHna+basLsYO37Np45rDijWLmP6rjedr3S+VHq48euzMuiqu0+W510SPIAszuDrNQBVOZ9LKaZZPyqNDXqdJON37iezpfAUI2JirhSTv5/0K002GCUR2VETqEA1cdZ2+STvGKkxQqf7dO2heQx/UKn2BerzyI6QzOPXqFI+2ZI6vaYUU8Y6lblUpRQrlbMpKsyaUkxFqM2PPbJoECP2FJs1J8WWqxSj7z2sfKZ3zAmpu5jXXdPuk8sw2mfPqGTtsO9tqqfY2Ytj8KkzNB/Wu0+2F7fbTezaVGtxf3EAyQrh3UQmxVCSYoB+UWhePtmr/D/JU6ynLxEoPcW65jOkx/OAljYl0iCZv/z9BE+xLiubHHR1Gzqn0X5b3ScXE/yxI0SK6cblz2jKhEHPlSYDQRsn8st7qOUOlLuhFKNrlZLhok4/y+w+KVGC2Fim0f4ff/I+fM9/+QBe/b6vJB1fFDIVyW7jY3edwc/8wWfw83/8WfWxnJw6tKJTGRyvKMW0nmIl4VCSFfH29Lzjz0Bpfl4bu1c2eZE8a2XnYZhz14xfFIV5P1YGZamTdJPCSyeBNHUDBY8aywOukKuU/kvLJ1mn0fk5yBRE/HqtDvRm93ytGfaalk8yMlDwvW2l2KArj1GIfO2zVu+a9cNuqqDduM9mRYUg0pRbcaWYJvFnlGLUjCjhe3MPOOr6qSG3TjUw2i+91LpJvjZnmSpDk1wmEpLmkkPKxMZsVtSusbg7q6U+XV1S90mak5opxRSkGHvGVxII57pSTHGspZiSK8XK39stUkxa2bNjlGI7Kr86DnonjqwtmknElGKmC26vMVlL6x893yPBfHauQoqlvw+8U6lkHt20jfYTu0/y7sNA+P2YLrN80lPC2Gb3yZ5RwKd1Hd5N7HtSrCgKoyYB9N01jFJsMdFIJ0uaoIhUSvEUc5VAShcHu+uDRjHAvWxM+aSynILGlirFbEYZKL1GxKQYz0S2zEiTpxgpxbTP2fFz6WUJQBlEaDIQ9Hw94sgaAOBUy0qx3fAUo4U9JYhZlqdYpftkilKMXacmLbxTcM+ZLQDAbScuqI8dT2f431/xQfzz3/n4bp9WFPc9PD/vLz54Tk3K8eTEQQVBBOxO+eSAdQUsijhRwuf+QcLGl4/dZ36Wo+ks+q6YMnhSPGnN19mznVI+eatFimmuedmN0CozUCrFuDI7tXxSet1G05nJVq9yTzHhxoqu97whj777ZLV8kinFBGXhPIkFcFVh/FgiX7vdTqLhvK0U033Gue1xJQGoI2nmY6wMeipiazKtvlspaquSxNQTqABw+gI32tfNo7zrZsq5841kasUFAHVig78L9KzKu7NWydcySdxOXEoigKPreiN1TrgDOiLPEN79bpK5tt21McVon7wwtdU1QLqnGCnD6DnRKsUms6LWtEsK+oyjazqlGO86nErW0rUjD1DJen9ulzzFOMklSULZql7tPEY4bZW6hr6zvR63arRvrbNl98k2PcVso/1Mii0N5y5Okid2oO4pJl2MjafYYpJYVwb3QPmC93sdtVLM181REiybAL2rD5KnbILhSrHYxsLOggJc3aYrQRn2ehVlRRsolWJzgklrtH+yafmkUilWFIW5rqRuW7pSTPm9i6Iw91cbxIwmM3NsW/4eBJ6NS7nXle6TLXuK0fk+nOA/d8+ZLdz64Dl84I5TrZdfUnnzhZ1JRb0lQbl57zI/mhRSLNFov1uWTwLxYGbKMoP9rm4Otcce9ruVjm12WYuN2aw6j2vXLf4u8vJJ6bX74gO2UkxBGFjdEE1nQAlJ0yAJxX/PVnbHCIvtUfV6qTtALp4lClbpOZsKrSZ42SdXikk2JyNG0AD8O0sSd3WlmIb45fc0RW1me/9o4rqqp5i83HVqNbFISfzxOSWltLrSbU2rFHM1QlKMzckCzfW2yW5TDi5MbPD5lvwVxd1ZF9+v1+2gy5LErXWfpFJC8pnSkLd2yWgK+coaOqiUYjvpSjFDihlvLZ1aF5jH7ymKLSLBrj80j6mlcSl/nk4lxuHGU2xd9r3pGRz2q+WTKUp+Wv/IcqDN8kkuQEhSiiXGog9vyUmx6XR5pBi3/wBa7j7JbA6AdlVqPux7UuzBcxcrf9eyo5TxbFw+qZRNz7gRLPfNkGaePUG65GHkZST0EEvPmwfDPUVXJ5pPGpVPVpRiJOO99C/fbFaYSe7Y4RUA+qCHb9RTjB/puZT6ZPD1ntRtp863a7RvEwvaiXI8Ld8RLQnJFVatl0+yjWzKvebPdNueYnStUpoynGCKXY0x9W6ABzBfPqlTufFuugeVGypeFi0pfeTgXYOqpFhEdTst5/4UwoB/Rr/bRa9bms7Hvnd93dGpUeg9pq7JawOdQtsun0zxmSqVS/Jzn1om6AOt4b3ZuFe7L8fIJYpnup35pp/Gtz1M/MeXagGg7EzF/y0ErjzSKuRqRvuKY3kSjcoAVeWT7B3pdDrqYP20tXG1N/EhmLKlQVfViMImnFPUViOmetXODZPpDA9vpW9ky/isl+YpxjaS0viqKIoaYU0kv1RVT+9Br9vB+oouSWzPKW2WTxZFYRJwpJrSKMXsvY5m72QI0H5XnaCezQoTH5C1h4pQM95a6Ub7RZFWSkjlktcv9gFio32WDEr1Fdu2vnfsGeOK1VXlvG+DYiRar9s02ueqQsm49nOd2n3ytBUDh77DMssnJ8xOA9id7pO3HT+PX/7Tz+Pk+XCC2VaKmXVW0WBlt5FJsY3qTdPKlsvySeo+KZvkjNH+YhFcVXqK8WC43ytLBKQeRDPrYdRk7HlmzXTLkSrFrDIS6bhT68UFGpRPJpRDNMH57YkhZ0pPsXRSLGVRok3jxfFU9J15VmxpSjG7fFK5cedBmrZ8kgcrrZdPsmcj5fkcsdKktj3F6Nm0s2QS8GdcW17cFE1IMUqMDHtdU3qT0n0S0L3bvFNcp6NJMJRzf2lergv8bH+tg0Kz/To5pDN+p3djdfFdNQrr0xd2TGODr7tmHYDWU6zqvdEXnrtr460xjZ+PTdd7oZoSrn0m6DTqtjRPMVKwEFHC/00yfumFJk9kjdjGeX7s4jtL1OzselMCTOPPWCrz0ggmOymgUorRNWcdOyVrn12avKKMj4CqV6CWsOaEGDA/Zw3Jz5OWKd3Bz7LxpeuH7R0HlJ5i0oRS2aVP3511PK2+H6V306WPS+dJmPmfjyYoxexEo0ZRXyrF9FYm25OpOe+rDszJvLTuk4vySfEcXB0jxZKDlGI3LJRiMWU1ga9TKaRYURRm7TTlk5HrzRMitM8D0ghbUkKRCGQyK4JKu6IoKqR0SsMpAk9ISJ4TWymWWj55xlILh54zig8OLK5Pu+WTVWuG3TDaf9V7v4zf/chX8eZPPxD8vem0Ghdlo/3LAMdrpJjSU8w22heXT1aVYkSOSRUStmH9sKGnmAmWBQEnfed+V9f1zDWuaS0fmXicSjFl+aTLx6CNl+/sxfnkeGDYM/J6Lfl6knuKpZBi7LmS+Ezx54tIsdY9xSxPD62ahU/qWpKFB8RaBU9T8DkkyVOsUj7ZrqcYPZtnNkfqa7ZUUmyzvE5qUowR9hqlWFEUle6TQJovy8DaBMc2NnS+pLYC9BvYWsmRkAy0uwgbhbJwDqfsvK2wlnSbu/XB8wDmhNjVB2QbA46JTdIsvsOsQDDA5/9EKh6pUX45tpXEEvo9Ta3kV09JQtrlk31G1IiUYpYSRqOQG0+qz9hQQSSapF+v9M1LMdo3YyvL+U7ZpFhCWdrKoKsipnylySkKuSG7z9JmEKcXxtKVMlnFhrJp98mNhPJJfl17NYJf6ilGhuRddTxsqyFXWXe+VEN1KTiJdURYUsdhX+OUTr4D1rxDeq+JSOp0ylJXldG+KRnVqcym1nuQEqNcWKxTNyyUYmJPMXaOKeWT3FvSlMqKjfa7FZVxit+d3X2SzsmHzdG0sgdptfukRVSmlk/aSrHQXEjf9eqD87hE0xCtKWxrBq0lkgvUxT02D/s8xTIptkTsllLs8GKiGU8L0QRNDwt5ilGQvy1VilW8uVj3SeFCameujXGvMvurlT5PreBeygxPHZ5iJmhLKZ9skxRbZDCPrg+NiaGmnO/CzqSy2UwhSvimUVIWMHGQYktXimk9SjgppiS7zzHisCjSJdQpaKoUq5BiLSvF6NncmcwSfPPK5yu1u1MqzjQpn2SG9wdX5uuA5B07t136WdK0pll/Sk9JXTDDW3DzDay0pA6oK2lMB8rIRrK+7sh9ooByI0LfVWO0Tyb7X/+Iw0mB38j6zn2mmgoRB/y6dhMVcvXuk7T2he9ZPfmlNNpnKhiCZuNvfMGs89b4kZnySQWRSNery9ToKUb7dL7aDLatEtBsoLk6T1PCaN6tXvoGgysSpQlLAn1nihcAXazC73eSp9iWvnySv3/0XlNJnlgpxu6XVmVhEsxW+aTmM1JBCYYea5aiUYrZ11iT0KHyKN59UkokGj/mQS9pHk822rfeA1tRJIHxFFtYkmyOJqJkFL+2KUoxHleYpgpCo316JrUWPxw0N5GyGwjPabZSqhEpxkguyf7YjiWke2obtlo4tLc2pNh6maxry1dwavbzVgljg2t+9+l54yppBZgtkslG+0vE8Y2qp5h2I2cb7Us/4+KYjPatjHeiUsyQYkrTyFr5pMhov8z+riqNQSfWS6Atn+wxpdhQWR7AjVxXErKoqSAD2CNrA/V9BupqxiZG+4AsC8EDgEeQUqxlUsz2FNNOlDzA0xrt24Rcm5M0f5dSPMX4MW2XT/LW2VpfMf6c0/zYFs4yUuwrDymVYkax1VWpDKh08sjawCiFNYFQquy99Esqkynz75HiZ1YtY4yVg0w9ahZpiRYF8kZhPZTPp5wUSyEMeJfR+f87tX9zYVZU12pArxQbW/da2n3SXjd7yo6CtGZyL7EVhWVCvXxS/r1to31puSpQVchpTfL5+VH2XBusk2rqqsUGVKoI4WMM+10x+QkwArRTJfJUCh7WWZyu93RWiDbupI57xOG18vM0pDMr10opo2mqFKNnVNtBmHvAEUmj7WhoyicZ+XypN8VEDq0NSjJP5Slml0+qjPYXisS+ngClNWZ9pZ/kiWmUYuQpJlRJ072iLUij8skFKTYrZGsXv7ZppFjpLWkI0Fj55LhUigG8tFf/venacW+y0DuyYZdiJ8bf4+ms8lyJlGKXyGg/9HzT9Tm8NjDPV1tqsfGsutaVQpe0+Wdja2zm4tg77fMUy6TYElFXiqWVTx5aKR9mSVawZrRvMt6yhZhe1G5n0XZcsTgURWGktLbhsSSI4Rui0pRPKkH2yCWjwX31fIGE7pMNjVwJ2rIw2nAfXR+wltvy5+zkueakGB/vnKB8kjYenU7ZKefs1rjVroAUlNI9b6IU0wYwdpa4iemkFjxImhX6RZlfp9aVYmxsXpIoAS+fbFspxv1wTl0YqbpnmlKQfof5+MTnCCLFjh1eLQ2WFYHIhJH8gNwg1eUpBsjLpPjYA0uVEiVpLN8jjWcSUF4fmkfXFV6cNO9df2hFXeYE1EsB+VoUUl7xf0vtPmkIuVr3SV3QqVWolUqxMsNflulKYoXqNdOMXztW09hgVj5nWsUTPz/7+R4Jn5fTC9XUTVfPves0STC+GdXEdTPLry9JKcZKVvtKwpyaC1x3aMXEwiqlmEPJr9kcbVws52zp9ebvJp1zI08xZeVErZlEr2tUwykJMQ34XGq8gTWeYg2M9kfs3dY+p7RHOjDsqX2F+fFHmZBB8pzSnEDPh11mJwEZ7V97cGjuc8xygPuBAWkVG3T82qDHfKDD5789qc79ZWdU/XM5ZeTvQED015Riie+CHUdKFNJkzF8mYdKUYqcvyLtPztj1oeerLV8xWxW9kpBM4fjqmU3z52gFmFE3WzYFmRRbHkidQIuSlhTjPlVUCinZ0Bmj/Vr3SV2GqWzRLg+yqyozy69DEKxylZlWvlyWc1TPO/YS2AEfjQ/oyydTFmLCa95/J77pBX+Oz953VnwMTW5H1wdlmaxGKbYLpFhVKRYP9vjG+cBKuSFK6Qp475kt/Nb7v6J+tyhYuIpl9DRo4ilmByptZi5490lAb/TJ/ZlG01mrjQL4PTqjNNs/sSRSrCgKQ4LRvPBlhVqMezJoMte09lx/eKXS8lw8rp3hI5WCkCjpdTuGZAG0RuQW2SIkLGpKsb5SKTYio31KJsnXXF66adROmuttqeP4tQuRU5XySfreSsUW73DKzyGm7LbLVXvKQN94ijGCREMo2kbiGqUYvUM0noZIdCnFprNCXCJsK/O0ajNSit101ZwUUxnts5hyoCg3NUSgpcJUGe2z52xYIcXin0HK4GsODhM7X9ZJMc2av5HQfZKTn53FdSOfKlup7oO7fFJog2KVFwPl3JBatiUFL0Uvu8i3oxTjZdVadcgms54ZCvcPHHTeVD4pOb4oCjMnHF6jNSe9fPLQah8HaO2KkGuTWVHxpUxRitF3XmWqwNjaV3aftJRiCXuAicvHNHDN7cR9Kkli3yORtdHiHh0xlkhpY9N8eO3BeJkuj8lo3PZIseoanRIbcXx1UToJKBoBGTslWQx7KZFJscXG5GsX3ag08uHZrGBBX8dsbCQbcKpHX7eUYuIMV62cQuO3wT1O5v+njZXE8Ji/RCvKibKmFBOXT1YDPkBfPlkx2ic2XPHy3XN6Cy99x204vzPBh79yWnwceV0cWRuazdx4WogVQDVSTDlhFEVRea5ESjHWFWTITDa15NJoMsOPv+4TePHbvhTtRGKDiCkyxdZs2mlsgrYs2m5G0Gr5pBVQawMC+31oUy1WVYrJSbHZrMBJFuy1abR/YWdi5sQn3HQUgM5XjKumNP5FXCmmnUf5uLXyyUgwwzv5drsdNVEy/93qZk5qys39zICE7pMeo30J6cADzxSZvq1c6nY7Rl0Suna21QEAY/4uVefVCTkZWTK1iFNtR0Fu+k7QJMJsE1+Vp5jtR6Yw6eeKRCJe+fnEMLHOW+uzQiqBR149LyWUkmI8plzp91RxnZ04XEkglvg15ypSyfNyavGdrz4wTCpZLbuDp3m+ck8xbfkkT7YeNJ5iOqP91UEXQ2X3SaOY6rNkr7IzbSp4KbpUPcRhr9EpTWJS/H2JtDiw0kvaA9C7xC1v4kn58s/ULEsbUwIlKXZgpW/2fjGlmD3PptiY0L1eHfTE5XHcaJ+OnX9WuqcY978OzQ275SlWU4oJ5rGyEcNAfIyNoigMKUalssHuk8wzu21SbGxZKaWo6DnuOcNIsYkuaZeVYkvG+e2xyQZ93bUHAOjkwzygnSvFiNiKb0LLev6F0f5AWT7p8eYSte52KMWMDFkT6PaaKMWqL8GsCBs922QakF4+ucLl+Yr7/atvv9VcX82EVRrtl0oxQE7AnlgQt9SuVzthbI/L1tuArFadL2KdTqfsjqokK1734bsMwWCXKsdA5ZNXretaZxP4pN64fDJhgfi1d9yGV7/vK+rj7GYbO9M09SqhTV8x/mxqPMVObe5U3v82lWJU5rk66OLxNx4BoCPFuBpGo9ilzpM3HF5VezMCPDmh9BTzmK8389eSrR92ckPrrUUqSlp3NGsuJw1SAj+bCAT4+QuVYqZsVKkUM7511fU+tm767rVUMcXXTILGc6Tc/FoKOUmXa4+Hm4Qs4N06ueJJ3BDIY/MgLWenuY+UYpLuqEB1jat4is3ivke7scEwHlfdbiXWkpC3ZxbquGsOriSNzTudNvUUuzieqnyiuOrz8Kqsky6hidG+7V03/7PcR64JePlk20oxXjaqvddcKVbue4TrBzvnAys9cfk+n9+JFIv5Z7pAz9SBYd94e8X8Bu2Y4PTmSE2Y0n5zddA1sUZUKWaVT6ao2Ql8HZIQmbRHIQ1Ecvmk3UlSMI/RHpya50kTdhybo6k552MLUixotL/43V6vJMUk4oXdwMRKuDX19frqaVY+qVWKNSTkdgP7mhSjTP2h1T6uWahRVB1U2EMz6JWkmGRDt2WXTw7LCUfSirneMlxRWsCCBRMsKzL2PJDQbi6mVhkI32CEFkXuEUJQl082kOd/4u4zeNvnjpu/q0ixhdfF0bVBZXMhVcPQ5pn8SbQLhE20ajzF6PnQet4B8/frv7zzDvN323gyBiKsrxHIj13gi75WeVQz+VcGAsc3tvH/vufL+I9v/5I6s2YHm9rg2F58tUqxP/30/fjVP/uS2jsPqC6kmvt9YqOa/UwpTUgFnedV60M85vqDAOSkWFEUrAtkh3mcxK8dKUBvOLLKShMUygpbzSLM9tsbfg1RQeCJEUDe1dBWs2i9tYw3iq2wlijFpvXAPNVvicDNyH1wlf5rCB6Alcr2qtcNkBFydvmkuDkN8+EkaDzFbPKUzluyhtWflTTV1EB4rTjK6131OgEk/qeFKR1/5FVzpdimcP3h15QbzhcR8nM+7vz/RDg36T456M/LCTUkP6njrmFKMU3p0Q4jYFM6oJ1lMVlRyDZ1phEFe0aog/D5bV1nQH7e0njY9oUEWCl6wmZcg22TmO+J1w6OmqeYZu3i5ZOKxh0A8xRb6amU2UB5zlQBIX1H+LqWWj45mc7Mdzy40jcdm2NdLHfMtZork4tC38SIK6xXhIb5O5Z/52qiAftsVpikfKXxSchof/Eu0748laCxr+0oolyaHzP/fodN+aQ+DqZOvKuDLivDjCev+t2Oeb7sZgOXCqUqmpKraeILAi+fjK+V1X3mbnS+bIp9TYodX2zEuNGxRhLLH/JhrysO0Hk5m+0pBgi7Olmyb033ySk7b9sTRpJx4RsibfbAd97zsWXyUoJG3QZUF2JNwDibFfj3b/0igNJvQtMZZIMpxTqdjro2nzbPjyJSTDlh2CStxFOsJF3n11tjaE148dturWwGzipJMVI4UflkM6N9XQBTV4rpxr7/7HxhKIp699AY7OdC+73t9+i8oBOi+d3tMX7ujz+LV7/vK/jc/Ruqce2xNYGbXSLcVjtqoEqK/bXr5ophKSk2ZQGfNuvtMtpXKZcaKsVMKV6CWbHtaSklLGpqFqFhPMGoGxbnrElEEVGS4oUJ1MsM+J8lAW/VD1PnG1SWylaVefzz3ce511upUswuoZn/WX7tbOJWU75J96v0YdMn7nqdObmjVaPUGkkIk3fAfD6hOeGRRikmJMUWquBOhxphyO4zUH2++TlLN3XcN8ncL4Wi8fQmI8VSPMUcRvviBhzjaW0syTV3KbUoxpvOClFcy0uMtfNKuHyyLaXYbnmKaXzz5t+tYmUiVYpR90mmFJOuXUaMMOjN5wXhc8r3TKZ8Uplo5cqyAyt949V7IaI422Z+YNccXAGgN9unipjVfs88o+Np2GPRbrKSomYHrEYzPZlSjEixaxffN5Ukse+RSClmeYqllDGTp+Q1B1ZEVVwzZ/lkO4lhO+GW0uSEo1o+GYlFrfk3k2JLxoMbFwEAx45wTxd9Vo28WdaExNqEb6QWDwEnxSQbeFPKYZNiCpUZUCqvSsNjOSHX76UoxSyVgjTjbXmoAWxDJS0FSZRs33r8HD573wbWBj389NMeA0CrFCs9xQDeVEGqFJtvnm9KJMXs51HilWFvpjSG1gBw50MX8KeffgCdDvBjT/5aANUOf/HxZ+a8rz4wXxg1/m9A9ZlUK8UaGn0+cLYkeWzCJ4aL1hykNfqkc6XFVaMU+/MvnDCL4clzeu+KiqeYRilmXaNWyyeJFDswMEqx+89eFM7DLMHQ03WLo+98Q8VoP72cr1Tw6JRiWsUtUCcNpMon22hfSw4Zo/2BXT4p9xTrdhLLJ02XUZ2qY2Z9Z0DvG1R2n6wnk0KBp02UaJVirvJJTYmDXT45VHzvstR2/ncNoVbzrlMqEu1GEn3mqxlbCygZcHR9YErxtkYy1RHvZMiVWoC8FKVrPSNaawuAd0PUKMXK8kmtgqcoCreSX3g8WVT0mBJFMie4bDnWhz3zzEliJU4e0D5Cet52I4r5ueiTFCnYZuedkpSx16qUvRP3JZZ2duXdJ1eUCR06dnWxbkgJOV4+TN0BpepPwoXF2KRQk5ZP7jA/MCKJtGb7F5nCepX5Q4ae0x1r7td4ZnPYnpoSuwYSHVx3qBkpZivFRJ5iY/IUm9+flIYXtAZcfWBoCG+p0f7hlj3FeGdQoJmn2PZ4WtnvSL3CayX/2Wh/OSAFxyOOpHm62Eaw0qz11NpIAfNAhh5GTYDfs8oQx1O590TFn0sRLHNCTlt/bHeflGZxp1bWGGBeOMoAf8jl+YJjSbF049FVPPra+aZZoxSjwIoCZI26jhuQ37QoxdCXT1pKMQFJYhs0axQZQFny+ehrD+BvP/Y6ADqlGM+qXb3oPqk12q90n9R2vmzoKfbA2YvmzzbhE0NTpRgF2iQ915Bib/5M2QwhxdCVX3ONUmyppNjCU+yq9SGuObhiup3e+dBm6DAA1XdxbrQvm5Mm05kJbOdKsZSkjO0FIVMp+NRDGmXCyBpbSgZOPeWTcqXY/PdIlb2q8DqcMaLE+GKlbOS6LnLLf+1CfpjSa16W6M6P63U7xm8l5PdU8xTrapVi5ZpJ0Kz5tfJJBQk6m7mfFY1vql1KKDbat643IN9A07x5zYGheU5nwnI++m70Llc7nEaUYoZ8XZyvIZZ0pvNAvetmTG02msxMXFFRiomvd5koXun1yi5kwrVvwyQeB8zqQU7a8mRrp9MxhIUkVuJqSnX3SUtJCchL0ZvC+BoPE5VizKcKUCrFTFOFTrpSbKWv9sOkc6Z4VvqcurqxS30Cy/Muyz7n/xd6irHni0giLSnGCVCueg3td01ZsCmfXOxblPEoTxhVuk8KlGLXkVIs1VPMmgNk3Sdto/0UpRglWoei2IiLPlo32rfLJwe695Hjvoe3Kv7Vak+xBC/J3ca+JsUepPKVI2sVTy8peGYLKNU0MSadvxw8UE7xR6GgSeU94fQ4kUvtebtybftW9wYhTsiVAXL5M1OyI6gTBzzyfFFjgTKwP2xMEOUL4thSGEgVhUBpQN7tADceXROfM0fNU0ww2dqbKY2h9fz4kjAmkuGshkhclPyt9LtYXwQPaqN9dn2bG+3rxuZNBbQNBmqkmPJ703tEZacXhIadpy/s4INfPlX+XelbAdjdJ+X3mxIUa4nZyCbg5ZMA8Ojr5sT33afjpBjfSA66cqXY+e2J6Wh11YFhmtG+ecd0wczUKgPUqmiAuvG7lOwwvke1sYVKMVMGQmS9bM0Fqkox7UaMn6NL1REsYQz5YQp9g+xSWYB5wQUJueq9piSaNPttl9DM/ywnFE35pKU8EinFauWTCUoxaiSkVU05rre0rISSAdccXDHPJyBbg+ia0li8O6w2667tPsmfRdsDLjY2zaHdznwzqVWK8d/j8ZlUHU4Jt6NrA5US3xWPAqUaSGK2X/VC08XDxlOsUj7ZklKMlaKnKMVIUXN0Te/7xLtP8udUoqbkSjFNg7H5saWPGo0PxJ9TOt9et2PeabVSjHWe5P+PGfZz9eh1ieWTnMDs98omGqF7VjfaJ+K0iVKsyxIU/nu9sUtKsS3r/Y0RzaPJzDxLVCabUsb8sKOUPEiKsbWubVKsbrSvm8M4uJ8YoOg+uUulm7uBfU2KVZViixc+IdtBE/PaQKb0qmTkuGxb1V6+nKQB23g38iBO64GARibP/U20183lryJZmFxKsbJ8Uhh4TevBi4gUYwRPyoRFY9D11vjXkQH5tSzAVpdP1pRimu6Ti/JJZffJCVNlHF2QDQ8rSBYKIA6t9pPrzPnEujOZidURfHy631ql2P1MKab1FLOfC833ns0Kc+2vVirF3va5ByvXSJuNBKzukwplIEmuqQvwljLwaoKSFJvfa/KUkXnSlPNwt9JZKfysVcouu2VyIUkpZvkxxJ5VW62r7eLLP6NmtC80VrW9LKUbQLonK47ySblCujR3TjEh5+ohCcnjNtrXKcVKM1zX2AFCzlYFduWkFACMpiFPMfn7UZbZyr83vSJdK8aRrPe+sgzpM+5S8Eg30Nxwnnc6lZRj8xiFIC1vNjFSYvdJrsSm+yUhfQHWAGPQU82DBB8pplWKHV4bqEqqbbUtgdYAUfmk8RTTd5+0O6wC5f2+5J5irBQ9RSlGxx9dpxhJLygY9MpYvChkzwvvPqlVgNoNzqTKFK54TfHXBUpFGKkQqZt83Gi/vE/XHprHdKfOJxrtD6oK69AcbvtJpnafrNj1dCAizHeLFKNnRTqH8ntqDPIblk9K1LZ8v1US8s1IsaIocOuD56Lf2bZialI+SaSYNCFjK8VSvF53G/uaFKNN3/WHSk+XmB8Lx9gKYNaHsq4kFeNBFihrfGVsdldqWD8ff/7vXUeQLiufJJVailKsuiniY4fO23555sellU9yo/3JrIh2+xyb46osvrQ7n+39o8m4mA51h1eT660pEDAlARKjfausQBsIcANFCprObU/EmzEqXzy40k+W1NoTq6aEkohD6nypzZqQXyGQXj5JgbkmY8yfDTp3aWt5Kp2kjmlJSjE2/sObI/E7Qtfo0QtSTFua0ATkdXfVgkTUZJ9t6Tk3Mg+RsDwIqjTfUHlcVd9Rk2WLPKu231KKMoF7wvDPkhrtU3JD4xMFsPLJQbX75HRWiEtgut20wK/sFOdQfCmIKUDfddM2wwVkz6mv++SsgKjLtVEpMB8auSKx2oQC0CnkbC82jarQ19BBbLRvNZqpfEbknhnD+cX8q1k7Kf7k5arSEsb69ZofNxNUDwDVhGVHWXZqJzvLTZHs/aJr2lso48r7JTueVOhH1weqkmqfUkxTtUH3bIUr3JTlkzx+b637JCtFL0kS+ZgXjffSghRLaFLGY3FAtuZusTJErSKRYivaq0kTrjwJlNKJHeDlk1WlWCw+294FpZjtxSkhb+nfVge7oxSjWEfiA0cm84YUS1RN0vtblkKG58GtRSXMgN1nrW0LUK4BV7PyydC1LmOTDg4Jn4sQTp7fxo+/7hP4nv/yAbzkbV8K/q7PhiOFiCSTfaq2iKubq+KelAYtu419TYrx7FZK98kRI0sAvpDKSKk+Cz4AXc12qmG961h+vGRzwl8idQvqQHAfCvpsfxEaH5CXT3LJtmYh5t3aqF3udFbIVVNWNrAs1Y0ff/J8acadOmHQM33D4fkCI1GK2WoSrdE+Jz+PLhYkQK6wO09ZtdV+MhloXydpEDObFWZBupZM/hsY7WvKJ2es4xXJt1PULEBZDigp9b3/7EV84u6H0ekAP/qkrwUAnFIqxWwiaDIrzH2MgTzobialWKueYtXySbMRVHgmDU1yQjYP20rfFKP9cmOx2IgKN+32BnaoUOCYz7DmNGlShR4PW7mkVYrR9eINamIb2Kkxne+qFR1A3UcNEBrtOzonSxU4hIlz8xy/b/a91nQzBJinmMNbK/acVWwirOdEVAJplE+wjpUn7upKMR0pxuMN6QaaDOepQcy6Yu20S5YAecmpbYtRiXEkfmZWPAsoVGqz6ruRWj5Jz5Z2zSdLiKNcKSZR+hrvuCopppkfquWTunll7LrmypLyVPBSdGmTFg6KK2nd1HhNEdlZi8UFn0HKqvVhX0wYE0z5pNJTjO/ZDiSXT85/n8gwsdE+U4qVnmLKRKvlxVkqAwNKMUbGAWX3Sa2CqOZrKZgbaI9yXcPuk/SsGFIsEuNQKevaoNeInD6jLZ9ke/KDq7KyWh/ef/tD+Psv/wDee9tDAIA7T4U7qZcq9OYljGQ5Qg2rxGRz15r3r1RS7CUveQme+MQn4tChQ7j++uvx/d///bjtttsqv7O9vY1nP/vZuOaaa3Dw4EE84xnPwIkTJy7laRlwT7AUo+ORFaiWXlERpZgjcwzomHjbIJR3KpKbHdeDe0kQwjuPaSW1ttcHIAs2OZNun7Nk0prNynbjvPskIOg8YxQC3cVkOT8HKcFj3yuNf9BFpvKSbkZ8n3HsyCqAeWCgJU7XhVJvAved6/e6RvUk9RWrKMWSyyer13c7QlYTtsZTo3AwSjFlVyZuMq9RivFnkfzrdAqeMjg0nmICYurdt87n3Cd+3dX4xhuPACjbSkvB7w+9ppKS2e3x1LxLSyHFLKWYlFwC6u92pSugMAgC+JygJ0Bt3yTtu903Qap8Ezb2lEHGyA5bwaPpmgyU7+HasPzONB9LG9z0umkyfRcxRd8jRDDZ33n+GboyRrsb4vzP8Ws+K6rrLT9eoh4yJTSMfBwI52O7CQUgV8gVRaky61mqwhSTfi1Jwy0iCNryyWsXa8eaYu3kvqcEaXmzTx0nOWeAVwA4SN9YObj1fJaxivS9rn5vrWUCdZ/kRvsiT7FpPRaejy+fH7iiRls54VSKKbzzmsAkGFKVYovjrzqQrhQb9uZdXUuPq/hn0Dx/YKWnJk+3DKGm8xTjlQ/Nyyd1RvtOTzFt98lRSYAC5XweVortTvmkbdcTq/DZHk/N/bi2safY/DtQLB2bj4xf3UpfpAL3gSw5jq7LjPa5CIHIUk2DLI7n/uFncGZzJLb6sVXoKQlDwj2L8snHXi9VinkU3ZfYTzGES0qKve9978Ozn/1sfPSjH8Vf/MVfYDwe47u+67uwuVkaGP/Mz/wM3vKWt+CP/uiP8L73vQ8PPPAA/vE//seX8rQMKt0IEzL1dgAj7dBXSvOrl1+jFgh1tIopp+wAhh8rUopxbxZt90kHISjqzuH4vkNFCQp/yYb9biUzFzfZLCeNToIR4thSk6wqFlVuVJxKDtHzeP2hVfOz2ITrNdoXK8WqmzHKJko7UF4wAcRARdhy2M/klrBJAPmI9LsdVhogH9tWhp08vyP2M+PvPnUrTTGw7bPWzhJfFHqWH33tAeNbQZs7KfhzSRlNSQdKbrJ/w+H5M6qV6DdBqRSbXy/NBto2Xx9WSLFASZ31fpkOXiqjfWts4Vxcz9ymK8WMgbqwLM4OgtTdJ426oSRpVoUb2CkLPFN8Cm1/LEBW/ulSHWnVIK5uiKbVeyjYnrqvNyArYSzXLr3Vgt2EApAr5Pg/l0b78mel1tVK2w3RNDJi8YawJJDIL9rcqJRLjhJdLdltv9cAsCMoYzTPd4WQUyrFEstg7Fha60l29uJ8Dj+yPlSVT9p+OgRNgrpR90nHXkDjndcEpB5a7fcqHkDSWKUsn5zHC0lzaV//vBCJtD7sq8lu03HTlBHKSsV4hYs2QUy4YHuKCcvkyuerVIqdUsZmZt20lGKhuNaUbTYsn7RV8cPFNffNKRSLdjvlvkHahMEGedMeFnaSNJ1Nh82UYnTt1oc9M5dLRB+9bvl8pHiKzWaF6X78on/0eADxxmp2AmgloZssMP8O9z48J8UeY0gxZTKlgZ/ZbqEf/5V0vP3tb6/8/XWvex2uv/56fPKTn8R3fMd3YGNjA7fccgve8IY34GlPexoA4LWvfS2+/uu/Hh/96EfxpCc96VKeXmUh1nSsIdgbIqkPwdQj2dacw9giHcrzmKo3JvNj5QQTV0foyyerCgNAFrC6jPY1CgebFOt05mawvNuID3Y27/DqAKcujERdHAG+oamqQi4KiBZeppRqOH+RBekHhj1sjqY4d3FslEQu2N5vmjbnQF1Bc9X6APeckXckJKXYodUGSjHr+krP/TwbOyWD+sDCZP/maw/gnjNbmC4WKiJ8QqBAc9grO0Jpvjf3zSNS7YKorXyZjbxmUfZzZmuEyXRWMyH2fsZi49XpzEmxE+d2TMYshNI3b0X9nO0G7O6T5XwkKSWvzg3dRdZ7MitEJL9dPpmmXNJtLJqa3bvGLn3JhGoWy/dIqoqg92PVVi7tKEiDjn7dAjxrroCocRntl+RQOkkj6z5pqQLZuU8F19wuowdYMiqy7tpNKAAWZwgJTIAb7c/PgfzQuhaRUTm+cBNEYtN5BwmpNeWmY6XJUqBeTsLPPfas2H59PMaRdRWvx2bS95quF3UR05IV9B4OreSC3Gi/LJPSGO27CGtApxTbZh1a1R0/rWZd/FxaK59k9jHA/JrTOhwC7XNSjPZ3rO897HexNZqK7rdRig376s7JRJKY8knye4oS/OW7oel4zGF7ipFiLDYvlErELq5dKMU2Lo6xM5lWyqxD4AQoAJEIhMeE0mNcqJdWh+di3jSDe1mOpjPx9yWQ/9xRoacYVXmtD/uiRjY+cKJcI/rgSrHt8Qzj6awmngmBzzu034jZp9DYpdE+kZZzOxR7bvThwY2LGE8LDHodPOrqdQCaRGVVpXbFlk/a2NjYAABcffXVAIBPfvKTGI/HePrTn25+53GPexwe9ahH4SMf+YjzM3Z2dnDu3LnKf6kw5ZNsA5pSvkKbkbJ8MjxpuEohANaVUGQQWifWxBlcJykmz8zxMpJVpVzcPbaGSdd/X/uzaTOxIg5yqxvAwwqlWFEU7H4TeSrPuPDMcVOj/fVhz5x7zFdsbCkM1hXPJlDfjB2hDpRCpdh5llVLN9qvnqv03IkUO8g7XyquOZFij7xqzcjdpR0oy02/rHW1jRF7VqmLjUSGzRMEV60P0OnMu0FRaaFobOYLQ346ZwQk6AnWTELb5bQpLo6mZuGuGe2LSo7883Do+FLBQ6UJ+iysbZCqVfA06T5pE0TGl0xYuk97fpofpO8Xrc98EyedH/gaIjWL5zAb2H55r5uWT8q7IVbv9fzP8WtO50UE0txEffGZGrN7FilK/bncyjpSFMaUYuW/p6jcvGW6WqUYN9oXKjHr1gPyTbTdBGP+Z1nyz0W+atZOO8k7/7OOkLO9YaTXe2RtwLWJMFKgH10blHG4QMljeyMSNKR5WWJcdlLUlk+6lIFS9WwqjKfysEy48p/HUHqKDVTHAVVLEkBXsmWUYqx8UutJqS6fZMROauKu3n1SarRfKsWOrg+MNcVZRWxmFHK2UkxRPrmWWD7pa8Lhu2ckNjiyNlCXgNuge1R2kpQrxWgu0yQLCTtMZSdJsnJlM5Gm8/PRqRH5uV6ziGdj4o260X7aNSfBwzUHysaFUi7CLt3cF6TYbDbDc57zHDzlKU/B4x8/l/UdP34cw+EQR48erfzuDTfcgOPHjzs/5yUveQmOHDli/rvpppuSz4lvBFM2JbY5qDQj6PLVAnRG+y5iTZrBLX1GXBlB3dicNJB0s3IFfZKA1R3wyWXmPEDXtiy322Zryif5ZomeE5UikE1Y0s52Nnh27LCQKDHZA/NsyzqrmvOulU/Ox5Uu5KRuOrCSRkwBjvJJMSk2P8dDK4MkA1oy2b/xyBpuWPi4HRf6im2ze6XtwAVUifqDii423OC53+vi6gWJeUrR5YjPp1cv7rfEU4xIsWNHVlmpUTvdJ88sNlODXse0SNcE2rRRdW0kVZ5iZu7XlE9WlR1ypZitHtJl24GSIKp3sJQpxYwPmukCLCXFStKYICkj5GP3uh315nV+jpRRtdXZ4fO31XGAbr3lnz9wka/B58yh/ln8WbKG2Ooj6bgAI1mcHlXhYzkpRqfOPyeUvS+KonEGulSGMKWY8DNsvz2NpxhX1xH6XR0xxe+Vhlyyk3/zP2uJ9sVcpFTmcX/fynkLy6bOObpPNlKKKTyGueeTlmx3Ne8oS9FbUoot1vzS10s3F1P5ZIrNw8C637HPKIqiohTTJky3mEk/H1fuKdbBAaPw0sUo5z3dJ+NG+6VSrNPpqBp3lJ9RXTdLZXpcKVZ2n9Q3owNcTeHC13xjV0mx+bWlBmnR7pPMU6xMCKQoxco5obQ1Cuxv2Tw0ZGXYWl8xHn9dw5qEhfaadmyRSorxOXwoXDfs+OSKN9rnePazn43Pf/7z+P3f//1Gn/P85z8fGxsb5r9777036XOKoqgqxRTm5wR7IZd26HMFPoBOLcDlloS+MFh1+Xpp/LnKl6hbMeCVmfQ7yjEEm1B3W3tZBnX+O1UCk48rVYpRgHzEqK3kmcj5+VobYIVSrNa6OsEQda4Umz+j0TpzK1jUZsem9Iyb8smFp9hFqafYgphi5ZPa1sh2gCVdzIlEOrTaFxmS2nhwY64Uu/HoGo4d1inFeElDilJszEgaam4g6TZqGx1TgwGNrxi9/yv9rlFdnZGUT27Mibdjh0tSbN7s4NJuCoBq50nqBKzaSAbKwcOeYtU1YCXJaL9KyEnJ47H1bhtiSvGclX5mVWJNunGnaVhrKr1tlb/Mz0E2P/CSupTyyZGDNJB4gwWbxEg9xVxG+6l+Zoprbpch8nMXK8UcpvGxjUalfNIY7ZfnEG4uUP7ZLtOVkpC2+TAgNwG2E3/rCvWr0ztVSNLTJdlNpZjUbNrnKdZUKQbI4sqzbCNdJlbSiEBA12mPJ5TUdiIuo/2WlGJ2KbqkI2Hl+Fr5ZLqgQPqcjqYzM3esr/QYQS+bR7csTzE10d3rYH0wj6vG00IVh9vlkyUpFr5uRilmk1MKUqzupRYmfYuiqL2T6Z5ibtWu755xUqzbLRvIpZivb1pKsdg7xatqJOurDyNH+WTQ+9O6RhS7SxLaHLwE/tBq36jCQ+SaXcnU73VNIkplL8FJMeH8b9szpFZDjacz/P7H78FJRVMzH1ohxX7qp34Kb33rW/Ge97wHj3zkI83Pjx07htFohLNnz1Z+/8SJEzh27Jjzs1ZWVnD48OHKfymYzMruRnOlmFylRbBbWK8KF0R7Q0FQZbicWT0ZseUq55Bm+udjl+fPWWVJ1t1tmB9fmFxt7VPKJ3mwJTXOLV92Kp+cT1gSpRiX69qkmPY+p2ZNKPuxNuwbpViMKLEZfI1PB8BVFfPjKXCSluNxU9J0o327fFJqtM/8zMhzQnG971+UTz7i6CoecWQNgFwpRoE873Kq+d6cqD/IFtaYitOWypN3RZJSjCnNJEqxhxZjXHeo9BQrirTuN1rYfmKATuVgl9ADOuVrOSfoA05f50uxpxgdl6IUszZz0iBoZqlZpN0ICaZ8knmLDIVr15QlVlIykq6OhBKCydkUR0giEsaOsSWf4Rq7r8iAmwx2R7de83OukIjCZ41/JVM+yb5DqAyGXw9KymjbzLvICvEG2tpk0OZXsol1eYpJialZgFCLxTjzz3c8Y8KNoV1Grr3eNaN9ZayzwZRi0i7w8/MOK8UkMa3baF/3XruI9kvdfdIuRdf4WhZFwcon52sneRBJYBp4kF+VkITcYgTSOk8cKtVt65anWOz9mLJ3midkNMQUkV9l98lSQRpKABrFltXQTfJ8E7atLsIx8pY/AzYZp43LfGqgmNE+Wb2k2qcA5f2hfU9MfVkhxRLiIoIhygc9s28UVUIt1llNlQcH9xTudjs4tBJPjE+sOBJI687NY0JuDxHae9SSKSyWk1SeEd7xheP4d2/8HH76f/yV+BgfLikpVhQFfuqnfgpvetOb8O53vxs333xz5d+/5Vu+BYPBAO9617vMz2677Tbcc889ePKTn3wpT63ygvHyyWnEIJmj3BBZk0ZkIXWpnoCSxZcsxGFfsBg7W1UpaI6d/061pI8+R1L6EzrvULbHnW2XbyxsY0+AmWxGrrcd5BqlmIQUc3iZlaSYgMxj2VttNymCmegHzFPsoq77pFYpZmcA9N0nKYDoV5Q3momS7itdN3X55OogqRMLdZ/8mqNrxuzyRAOlWLrR/vxeF0VZQhs7rlSK6UkxHggYpZiAFNth6h8qDQDa8RUjkpbaygM6lYOrjF2UGbTWAI161P6MWrZd2PGn7rekUIrVuk8KiamiOpdqvbW2LXXD/DOkSpryXqUEffb1ps8CysDfOa5j7dIkoQB3Ik2jUus5SBaJEqX0gGPPd192z+x1c37OMpKlWj45H6/T6bBzDyjF2NeqN5PQXe+ei2BSNrLQrJ3mfjXoPumKkSRqeoqjmnW+pPdal8iqlU8qSLHZrKhspKUNrwA30Q3ISZr579C6l+IpViWHADaXKrrevfWzD+BT9zws/n2gXoquUYrtTGZGkUkJz/nPZevXyIrHpe8WlSCv9LsVSxHpc7bl8daKe4qV7zTvXi/taA6UBAd5iRHxURThuWHH6gKpadxBoHdh1d6rer43f35tTzFt2agv3oga7S/i1yYldfS8GE+xyHPi6myqLZ8siqKsvOh1MezFG2bZ681BRZMsDjtJe1iwV7U7NQNpHSi5PyJXh4cTWNXvra08I1BFy8fuOoM7H7ogPs6FS0qKPfvZz8brX/96vOENb8ChQ4dw/PhxHD9+HBcvzpUUR44cwU/8xE/guc99Lt7znvfgk5/8JH78x38cT37yk1vrPAlUjfYB+cbEVoqtCLP9vPyQQ6MWcBmEirP1Dk8xDcFklwxJ2vsSXB4nkmyPM9tuymbik5ZL0WGymWLDYD0pxl96ClZ1nmIli0/dpIAG5ZPCkrpaCYiy446tZDRKMXH3yfnvHWTlk4BuoqSFiTwvpEEELUQHmZ+ZNMgtisIY7T/iyCqOHZmTSw8mGO2nKOT4s7rSL31CzkfuNy8BAUqTztMCUotQNdqXN1bgXk1cxSMJvrbHU/zS//d5/Ke3fwlfeGBDXXLJyycJdN1j8wJQnxvmf16QBor5LKXRi0+dofYUU5aW8bEH5jNkREtp/D7/u6ZUaMxKZ9bYei2V3HPTeXqvJ7NCXKY0mlavN/9zkJgyWeDyZwPh9SK4n7P4fXMFvH1FsO9Sisk9xeprrlQhN3V4igEyZSH/7G5CSTTA4iu+UUgw5QZYkxrBBtrVAVKq1nJ511HCVrKGEHHmauYQL5X1lE8KrzffRAJQlU2d35mYqo8jjBTTdfu0qzYUSjFH98mdiaz838wprm6jQgL3Cw9s4Kfe8Ff410qVhF2KrlEC8diV4mFAHifZpejS0n/7nCUqnMq4iQ0dSiFANR7WEFO20f7aoGfmtpCv2Lal4k8pn6S4wiYDfXsQIkO6nfLdMFUmkYS6DdtHNGa0T9eUFHVa1Wnls3bs8kmZUuzASk+tYifwZ3hl0BXNY7Z6P1kp5tmrhqqaXE18YuW1zrEZ0c2TGpKkna0UA3T3m7+/f/CJNEstwiUlxV71qldhY2MDf/fv/l084hGPMP/9wR/8gfmdl73sZfi+7/s+POMZz8B3fMd34NixY3jjG994KU8LQPnwdDvzh2Gl3zX1t9IHwe6gsirMPvsk200NQksGXqcU4MdKCCabkNP4KISVYoFJw5WxVpAGwfJJZRkILQ6i8klHkKsjP6vP2IrZCMk3/xdZIHFoVUbo2Vn+Mjslm6Tt+3xU2X3ygqP7JKAsJVzcVzL5lwYR51j5pFZRcu7ixCysN3KlmLR8ku5VQmkAUM3WdDqd0psgknGy34/rDi2UYufTjPaJZJIoxUrvoWqpruT9+Kt7zuL3PvpV/MZ7v4LvfcUH8Z2//j78+p/fhtuOnxedMz2PR3n5pOK6u5ITElWKraRcVWbmXF1ty/lMVr5vfByUBA1QD6SkprQza14oVRHx+Yw/D7xVu6R7MR+73+3UWr1LELrXshLGulpLaqZtK/MqYysV1hK1lTk+0NEwrfukbP0qy2xhvP4A5kEnKMkAXGUZwnttNiksXkj0bDVG+xHvIH7uzu7cQiUk79/U1B9RqrCzz1t6rQjO+ExYNrWxUPuuDXpY6ffEXeABd7kqoCsfqpZPzo+bFTLSOVw+Kbt2H7zjFADg5Dn5Wg3US9GHEaKEg67toNfB6qAsNdOWjdrkVIxUs8lTUuFIY6R612TZc1pvPLWIhwXvNMH2FOt0OkY1tpmgFNMY3tdVgRGlGEuS0vx7hOLo8VSlIKp7ioXXa1t12sRnio45LFSK8UYMWr9TQqX0tN81qilJ46WuKZ+cn6/aU2xCanaLyAzE/0bAUEkAyZMphGpVE4uvPPfZ1RCHz4MpJv8A8Cefuq+RUf8lJcWKonD+96xnPcv8zurqKl75ylfizJkz2NzcxBvf+Eavn9huwl6EO52OSj7s+gxpCYwrwNUcP/+M+mKqbZ/tJMUUnRzrxvHxY91GshJlxfz/1Yy1XN02ssgl/mdpliil+6TLvDal+6RWZs5RKsX6pdF+tPtkNUAupdNSpVj1vImYklwzoJrRq2QdFN+bFm4iaKRBROkpNlARvkDpJ3b1gSFWB72Kp5gkc7zDysO0Gwugrig5JFgUgbqnWIpSjJv1l0qx+P22Syk0xtTbrKvSSr+LO09t4r+++8v47pe/H89+w6eix1M31Kt5+aTiurs2NRLFbk0ppjTa5xt/Glu7gbWVYlKzYoCrnWUlEWZsyztDo5iyg02C3DagDDwrGUllEsztkSUhpsqflWWEOpJm4CC3JOUJLk8xyVpPp1ctn5SSYnXCQXq/6fG2E4eS7D1/NwxJo9xY2cqQymcolZgaVYmrw6m45NShFFsRvpeAzx9RqgCtxgvaOKWMpR0K0BgpxvzEAF1SxRWPArpEb8Von5PtiqSKu3xSNh9/5M7T8/Gm4S5zNmyjfY1SzJTjCT2qbIytNV/cJMaKK6Wdhwm2f5JUhWQ3R1tTJokBVj65UlpErJOvmEIpllI+uc2SrUBcCWl3qwSAQyulcbs0jgfqVScxMYNPPaQlOvj1MUqxyDu1yT3FFPtLDn5Nh0w1FRKc2N9Zmsy2YZRipnwy3litvD91wYimO7d5p/vzio9eN7xWV5NXJQ+TQoLyZ+PUhRHedesJ8bE2LikpdjnDzjgAel+XkUcxFSvJmDoCXD6+xGuqmadY/ViNVNQO3NKUYnVyKhzoVhclQKdus2XTgF4pRteo7D4pKJ90bKRWFZkee+wUUox33DksVIrZzwgtxDuTmchM1T7+KqVSjAc/3W7pJZNSPknBsrh8cqcs3aQgV3q9y86Tc4XYsYVSbGs0NS25Q6goxRKCgZEVZEtl2DbBn2S0z4hn8ug6uzWKPi82kacqf1l838cdO4xP/tLfw3/5wSfgqX/jOgDAX3wxvjCecZRPat4xl/TclF8KPCSIbNCuPTy469sbC6VSOaXbWa1jkUDBM2PNbVL8zHgyp+Mwfg8a2VrqoT7zwhQTJZZaACjn5dAzPrN81OafoSuRcj5nAkWjrczj5yGZx0NG+7F1t9z4uoi8CHG7uGb8PgOyUt8y614eL1UTEsrsuWOjoIwXyFBbUj7p8hQTt7d3qfpURvsOAlVI0NgbK60yz5m0FF5v6mhNcdmqIqli3yuCJtFrjNAH1QSehFyyy53mf47PKYTxdIZP3HXG/F0Sk9Jx9Pn1joRypRjFhdou3fb3Lr3YpE2vupXj1WXRyiSzrfY9kFA+SU3cuFH/AUF8tuNpiCAduygKM3ZJYlJjOfdnbLOSYEK321FZxxDseCOWNLTVzaYEXEmKEXHb73bMczqdFcHk9BbzfePWCBpbDp5g5iSPxOaArhGtGZI9A4cdS0saq41n9TkwpTs3r1KZn0N4zeWxIl/vKJETmwtcY9P1+/0GJZT7lhRzZaa02Xp7gua+ZKHFYTytBy+ATkFUZuYc5FIkWHUF6dJjgXogoZGa21kDQObhE1S3KconefAhzU7ZC+nhBKUY38zQcyYp5/N1eZMEuYSy+yQz2o95ink8FABd10xDJC6Iqe2xLJu5G2QgPY9GKabsPnk4oXzygYV3GCnE1piP23GBrxh1n1wd9hplTEql2HxsuafYQil2cKEUu5DgKcbKJ2eFnIAdWqSY5P3gisaDK338wyd8DX7tmf+LOZ9YYwZ390n5vFKaU9c3NRKSv/QUkyVUCPyZqKkzoqUg1bGlXYs56p0v44oS7hNlyicTkjH2ull+b/+95gEYBdtav0AiedzdJwMB79RVwqhTg9j+cQD354p/7yrRIb/mrpI8qW2ByztVqpBzkXEA+84SD7dKyaeuHGTsiFOkJSX2BlqjsjbrZkLCMlR6qYnrtL51QP05a9p9kv856ilm1ut5jKEpn2yqFJswcmml30O3W6pQJRtK1zXXeDx+7v6NSumd1O/JVYqeohQzyiMFocZL/01jBeG95r6l/P9SO5FxoqJxbD0nlGzUkBauMjVJrOFTikkTaLwrqCHFokqxWeX3CESKnRV2kQdc8Ub4mttq8lSj/U1TCtmrrNmhZ8WoJ4e9SkJE2lUVqMfSEuLWnrtN+WSi0b69Vw3NC6V/Zn1vnNp9Eog/2y6bA6B85lL2Pd/1DTcAAN5/x0NGoKDF/iXFrJp2gHk9JXZQ4Z8VmrDs+nR7fAlDGiqL0Hpe8M+RlFTY5Z8pDQIqgZvJeIey7agfpygDsbsbASzIVXqKaconXWUva0M5+Wp3HSsDLvmEwbN6UrNMewO6Oig99ySScfv5PLTSN3+WqMVsv74kUmzxvfVKMYfRvpQUW5RPfs3RNfOzY0fmajEJKWbKAfs9dRYUqC9MFLyF5Pl8DFsp9tCFHXGWjH/GoNc17aDPRO73yJANCwJ2IM/CusqcpMkJgJFiju6TkutunzugM0C3u08CZUY5BE4KmFISk9gQbmB7VZWXlKDh49O8JPGKmjqIKU2Hp5lnAyv53rybYa1BjHC9t0tGAV6KKCBp2GlrfYNcgavsmi8yqGxweuZSlWJS1ZVbzV5m30NwETxAGSuESlrcMQaRQ9LrXSdApSUlNmFsyicF/kPhCoBIstNxrLSZEODzzJM9p9oOczbs8n1AXjY1sjbwdL23VetHNRYvu6+Fx+ZzNR2jKT1yKdUkxC/hI185Xfl7LPlFoJiw0ymveYpSrCRZ5ITa2LF2ie+1KQ/TJYII9t5F7hNYvU/a7oCzWWH2MFpfSFspVpZjCwlQtr7RXm01svZR7MzfR0C39yHUOgz2w/OZnZBfEd4jGzTfzjtJlu+XzP+zU3knNR0obQ84mdF+9RqZ8skd+XUG6motkVLMEUOndOe2K+di72ZFKeYgilP2PY+5/iD++g0HURTAHSfSulDuX1LMkZnSlrDYZpE8SxTa2NhSP3t8mRLHUU4hldm7Ai+hTwhQJ3p0SrF6JrRcmOJEYtfRBUtTPplktG+RmMS+b49n0Q1VaSLuIF8F99kmXrXB5ng6M5Pe+rDHPMVi6h26T/PxOp1OmYEVeaPUjydySpJl8gUvmknadJ9kBqESVD3FZLJ+wokF8UUG+wBwjPmKxWAysMOuuJssR6leJRk2kWLh8691n1woxUaTmdjskwze6bxp7Nhm0F8+KSFfq88ZYBFMkftG5DDvoFUqYeLzisuoWVYOXiV4pAkVM+7is7udellCbANcMzdVEgZA/Z5J1GYuYqrPCJqYqm/iIGj4OYSCTlcApg38XP6QktJT2zi4epxQ4eDaPGvIV8e6mWy0L5yXXCSLIWCjnmLzY61bLfJwCyrh1SSkK06RKsXmx5r5TFE+mUJMGVWfq/ukwlPMSfpGSUy3AkfbFbAan8nO3VYPrQ3n/98SKcXq8SjAq0Yi6yb7d9MMSZFIM55iLjWlINn70TurpFjMO5Sww0z2qcQ4SSlmdTOUEIH8/THXTEhCjq17Te/JdFaISH7bzkRbPmkU+Cs60oIn7t3di/3j24SxtnySyOFuh1/vcGLejgcJKaSYrZyVKsVSOwcTjGn+Sq+yFsgaIHUqc7AmBrfvl8ZjtlSKJXaftOZRiaeYK9m3ohDoEOoxYXh/XvH+7NTjixSj/WGva66dxl+RI5NivKxN4SPAP0P7MJUTrJsUSzasV0rdK6SYotOGnV1bUXROc41ddnuLqwzcnZHkC7HTaD9yvF02ww0nY4orl0+Hrky2uqBoJwy+cK4xpdj5SPDkUiJqDD5d90vTgdInc5dvambmO5Tlk1JSbL6AHFrtq8tAKCtHngAAcOzwXHUlUYrRO5TsKWaRqHQeMYLJ9txbH/bN/ZaWUBr1bY8+Qza2TTZouitNHFmueUAz/3tM9Vtm1lwGzwKlgQlC6vNSiFSzgyBto5eyvMutrAip+ybWnCTp6Ff7DE+gKynlA8qAlycLYopfl98SIG3U4iDFlB0/XZ2E6bMk3RC5GEWjzOaf72ysoyBfpedsxnWorrRxRoXI68vijJmDjANkhF7ZUKH8mdpoPxCnxOYF+900nmIKo/0UmwhXslOzhoT8EbUxZWr3yRTPV9ufam2hpJHZU9SfUUBO0nBv4lpZtmBecXmKSYnI0WSGT9x9BkDZGEfq9WR3I+TnrVGK2X5kopJRdk1N+aRWKWapUQBZbGh3n5T6cNqWN6UthbAbO1fHOboQhwi90t+rGh9JN/3bTGlGBGjsfrmUm0Ba+aRXReq5XzNrvUwnxRaxuKUUk3iP9pmPMf8OEtjlk/Rszwr/Wm2v06Y8V220X42lJSRmeX/qc4GqyZcVC8fuM5Fx3U7VWqKJVc6g3y3tVzIppoOr0w4tENKL6SJaJMSWK+DSjh/OHEeyekU9UDZqAYVSzPYUSyXzZB48oVIMASkWUIrFAp/SN2g+XrfbMZmiWMbElS3nnhex0jRb+aMN7ikw7C1UjKRyu7AzCW+maKJkz5fGAN0VYF+lUIp5PSeURq58XGlmjRailPJJV8e16w7JTet5V6eSCJQvyGMr87wuaPkNuIMgrdm+/Y6VCgkpMWUdp9hE8usNyP0hXZsiqWcSUA+yAZkBuot01iRFQv4k/PNd8LXB1gRAdsZd4lM1cxBTfIMQCzzNeXuUYuKOhJSBVqpPnZ5LglInF8GjVYpNHPOKpOzV1dhG0/EzdO6xecnlj1X6oMUInvn/7Xst6Zzp7KZlNt2y6z11+KEld59UlIM7PcWUZvdVIk8R1wU8xaTvZi2JJbzerlhaWjZVW3eY4immHnKpOAFu/C4jxfi6qSslrCfWywR1+PhP33sW2+MZrjkwxDc/6ioAcqN9m9QCdEoxu5thTHnEQd/ZqXIWWpmURvvldZOsXz6lWHQPYB13sAEpVlGKCd7tspEDlU8qlWKO2C52r31k8VFlF3kgMDdEyuronTTrtLJ8cpN5KXc6ZTdESUKFd08E5MkroK6yqyrO3GPb39mU52qN9q2k+GFB93lXsm8onP9cn2Nb3sQ8xezYXStEmI9R7ns0ohMX9i0ptuMgSdTdJxk7STDZlkDGxOVXAegURE5yidoTx4I2R/mmpguY7W+i8hRzBCGSSc9VGqA5Z/uFBTSZyPrLSyV5sSDEtZGiAGJWyAM+bWaLYCTEiywRZbiA8ITrVIoN5BlY1/N5ZE2mFJsxKXytLbNwYeSTOSnUJGTzaDIzc8Ph1UGymoQHa8bjROnVkeIpZs9JByiAUnqKAWUJ5SmpUsz6jHWhYb4dgEmPA9zPKcCD9IhRsks6rtjQuc2pNSR/PSkja0Th2Pizexd6ZmwFj9T8PDR+KjFV8e1I9Jkq54Y4EdhhWUmpGsQ+P6dxvMjjyqUG0RFyfaVSbGbd6/l5yJViTtsCYWLGRRhLfNAAXj5pkWICRXvIakGsFHP6rMQ/oyjqaxcn+cUlwgl+aK7vrVOK1edCqaLR9inUJu9c8bi2O7hRiinK512er4DcX9gkkxyKK5Gn2MQ1p9A1Dz8r5Cf2pEdfYxQhUpJm2/Kpqp63IE7xlU8K4qSdSX3NlJay0/Uy5ZO8LE5RrlracshKdO3kmzFCF5IWlfJJhw/0NPB+2f5emqQh4BaAxO61T6m7G55iMU9K24OzuVJsfr0kiTu7w/WwF19vbJj7ZXkMAgF1nLne878fEnoB2/AZ7Z8P3C9XyX6K0b6tUovtX1wq9PnYCX5mi3ln0O+qy4tt7FtSbOQIAFaV/kGuDZGEWPN1vDHsvSjbsntSd6BcXEQSZCtAX2tY9ilRVoS6VjZVikUXRIcvGDHwscXBtZmpBm2yeyXt6GFjywpeBoxJD5V+ujbtGq8n1/eWKsV4AEHXPFUpRl0JpefNibO1Yc9M0ONp3PNo/nv+AETyfvBgNaX7pE3+rq9IlWLV7BYAXHNApxTbscZeE7Ytt+dRIhFTupwSpASTS4Gjue52BhmQvaMhpZisDN0xrpAUq/n/KJIL9vh2RylJV0BOTPHvL/Vq8pmvi4JdRhhI1SD2+Wk9l2Yuo33FegvU1wH+Z6lHin2cxLPIrRQrNzUhpfPU8W6aBgPSUlkrQk3x6wO4cklKgFaTUXxsCeHMjyWSH4iTLE5PMWF5M/2zs+QzVfUqrT6wrrk2odMkPrOP5SWBsTXEbMzs8kmpUmxcXzdL8lRe8ZHSofWv7n0YAPCkR19tkp3S8smLZuOephTbqpVP6tVxKQnqHet6dZlVgig57us+Gd0zVYUE0q7e5nhmWcCJfolHoq0U0yphXNULsfvlSuQAwNFFYlunFKuuA7E53G4YkkyK7ZCnWL8yrkZFXzbEUZBilnqUr0NedZx1vbWNHOzPJ4VwzEO6KAqnzYG2CRFQT8gPI4k/Hw+iFUDMx5h/1grb3+bySSXKhZQvCvLNK8A8dBwlmKHFwTVJzY8tW37HZd/VyR2QvfTzYwPZSFH5JAVuncp5izaxAd8LSaDbdXTBmsyKaBmiM+jqyV58E7hUVE/UxTFmWF9V1dF500fFFjZv+aTU44p1niSYACqwoNubXv4Zqa3Or1r4XpyNKMVc/gtaf60d5sOgyazRs9DtzO/TiiDLw+EMQBRBDC9rkHZ5q45f3bgfEBCZ01m5MPL347pD8/sl9RSja26UYqYxQ8xTzCLTBnLy1fbHIkhLEV1NTzQbupD5elC55BhXWvIJlBlz/pz1uuW8Iut8uQjalKoloK6a6gvmYhcx1el02Dyu25gQRC3PHeSOuvuk0zieMv2SQLu6BgByHzdXiYPkvrnmYfqzyGg/sF4DMg+5avmkLPNeEok2AarL9pvjlIG2y8NNsva6yqR4Eiy2Bu2Gp5iLDJTMZS6vW2mprT2PpnqKOeMz4di0CdM0BXLFZ0B1bgjFls7ySQXZ7i6flL2fpAq77tCqUYRIyyfL8kdX8i4+H27bSjGFMbdtjcH/LPX35QlqzXptz+HixLj1bhxSlre5ksTzv8/H960fs1lR22dqyydDVkG+faqrYzLQVClWEpl8jNjvJ5NitPdZzAWSjs92SZ/W+xOoJ5i54syrjvN5immN9q3n25RPXpw45zG+fg8UpKkLXqP9WJlszSNWf79LQq5j5iRJ92EXMimmVHlxuKTPkm51U0eWH9DJvseugFO4OLg8xcrMdZxgMsbzXVKEaMo+/QqH0ILoajfOVXKxLJGtYgHKhVxqssnvs5QUK7tPVjeC0myPrWTR1luXSrGybFJCbrkMzDWLsUvJSH4ED0eUYq6Nhb4co8yEaoxJeeaXm5/zfwvBFeSuKu6ZCVZZ90lNxsQ2ojWeYoEOkPz5599XqxQrgzed94X9jmieM7tshyAtf3EpcFTqCkewK2kK4dr8qsonZ/V5hY8detZ8qg4NKTayvnfFG8wT4JvEhkVqSUriAN5V0M4sCogSl2G8MvhyqjoEiq/yXpc/K9UgMlLMRcjpTOfL790TKo8AT0me0NzatQZICVg6NftZkfjAuZv56IjfUHlyeOzy32j8bldB0iSSnwCzmKgQmPLNjaucJZWQS1V2N1EP8edSarbselaAqsVFWMUzrY2tKct2xZamxDjyfnKz/MNKjys6dqUiClB0n/QY7W8LjrWbAQH8Xofvl+s5kRLeLlWM1FeypuRZ0Sl5fORrmaBwj8/PyyjFhAp8giuhshLprurabwFlOV4ssc1hzw0mkeR5r2pG++b+6kgO6npOXdAlpfv2Wq31/gTcTUNilhq2PRD3FIvtx11j0zWj+zWazpzPuM/rriS59aTY0NqzeYlXn1IshRQzY/eyUiwVo4D5YGwjZT7DMUFL1GZ2JxMCP5fYDS2N0PUB59Sa4AF59hdghJytFFOQJa729MHuk47MMT/n2Hd2+b8ZZYbQs4JPGvLyybAqMHafa912lBlYQ4qxjKCkO5NrwtIsxlOHqoOk17EFlX+31CCbZ3DJC208LaLPiTluQAqYrvkOorI2R5CrIdtNoNvvld5WKeWT5Ckm6D7JrykP7q9deIqd3mxotB94XoqiqG0M1jSeYg4SEpAlJyr+Pw51RKw8DPB5itF8plPwaMyKbV9HgmR+8Hl8aAI/e3xOzvnLIub/t4Mg6bpFY9pEiWRTFFaKxa+3r8xAcu1cGwxNkxiAKXcdY4dVU/7jJKSYqySvYhwc7BjtUMEIx3aRcYBsHXB35pZ5BxFCjSyC7xZXObPrRET/ZkT96lLXSX1tzHNW8X+TP2cudbhE9crP2zbH1hLOSZ5ijuS0loSsrx8sGRZY93cc3lzSCgTA/b2l6jyuSCkVITIFj0uttaJIynDvU/5/ySbaxLS8Y7NQHRfq9iy1jZmPZ6uQdM/JIWEXd/t4n1LMR9TwZ8h4iiWWT1Z9jSOEhcdTLMVo34436P8+pVg5tn2P5HEwUDXaByBq8mKrulNU9DwhTxCbztPztfCsKwqdN1aZYF7E/8Oeud6uuaHidef0z1SUT3qVYj5FYH2PCHAyLaF0s9dRVa65sH9JMaeklBZSYXYrQLSEbqjLNwKYB/tSCbPLIFTvKVb+TGN4TBO8Xe6UWlan2sg5AmzJObt8DKRkBX02P/aIcHHwBV3S8i5ftxzpAnFxvKirZ0oxIshCk62rIYK0HI4fz7/3VVKlGNtMkf+CtvyFZ3DXWOlobIFxdWHUbJ6DPoOKDOzqsKHRvkIpxktG+Xt5tek+2dBoP6RIdASqZfmkRDHlDt4kZexe6Xiv5/wd52dM68+5SLnkCDo15KkvyE4hDVKUYhNLqcZJ/2gQZBMdUt8in1KsHz/eRdJrSgQqzwp7tyWqq7CvV5yY4sSxtvukq5Sw7MIV/96uklepD5wr+adVBfpKZUVWCy4fNGX5pEspJvUU46cuLeE319uR7Iz7HtH3Ln+mIafC31nqhVaNU8Skb8DbMbY5oo2bSykmL1etxuJVUsz/HdzdJ2XxwozZFriqNmLvCF2X1UGXeQfpyvkqcYpiPqQ9UorRvisWl6rjnI2rhM+aSxUjaZ4BcLJ6/vva8jaX8nT+eQuCyPO9aX/Q63bMsesKb1+A7SFc+1RPrOHz70wpn6wrxbqVn/vHnv9dS7ATjBdbn8onJXGC+1ylNgd83KpSLEJCWkmg1UHXrB+aDpT2u9XpdIyK1HXPKu+Ea+5VKcWqvEZsrfZ1n9Q2k+Nj8P2elMexsX9JMWf55IKQUnaacxMtgWDRw5BKjwdi3lyyLKzLUBUQLBDWBL+W1H2SZzPjL4ErC9oTBuf8s90G6LLyLr6gleWTEb8kz4svVcPYE42WFLON9gFuZB432q8qxRRlbQ5FIHWBjHWfdGV+1Uox5ik27HfN94hdb5fhvCZz4SyfVPhtmGCTGe3rlGJVAleiFOOdv7gJ7MEVuWILcCnF4g0O+Hcryyc1XU5J3VB9v1Yj5QGAXzouNawHyvdb7ylWVx1Rma1k/fHNKzF/Rq6OK81kZesG/wybpKkmKDxKMUd513x8ocLZox6SKfMWYyV6ilWeU6VxvMsfS9IJi8A3D1q/J2djG0VJSFmSV/6Me6SEy0br76b+Xld/nqwUSyyVdc0LYVKsntABEjrxOmwiJGpEwG4mIf/eTUqM7dJLrgiUNKhxxYXSZFRIKRZPeLqTC9w6IfQZLlWI1FPM1VCIn0vMx2ibWT0cNsolqVKsrrjSKMVsr1pNgsFVPilWxznLJ0tldwguVYz2+TZKHq3RvqfLacyzyhCfCYQvweWJbO61kKQhcKWYtKzPnhvo1kVtFjrWPVLEwUA9HpWsPX4VvUIpxvYehJhqyl6zOp2yQZhUjQi4mwGF/Abpe3U66QlDgu33FyOrvaXrDTzFhr2uWklpI5NirvJJ4cUcuRYWwULq2hCV5yBbmNweI7KMokulINnUEOwOLprrFirnkCgrUoLz+b/X1V5SyffYUQYSYt8rxzrUhPOxtYRcp3L+YqXYqG60L1FEuqStOq+n+kJ81YHFgirsPlnZwCq/t01uSTtnuhY0TfdIV0ZQ837ssLKEWHbJBXtRNEqxwD1zEYH8vKUyZOMpVsto+o/n5VcD+7gAaUtoYrTvk47z9zxeulMnzCXdEN2eYpryyfqcBMSDfE6w2ISWlHx1kTSdTsd8F3/gN/+/HQRJs7GuUldAqjSe/xsP8jW+Gfw7uTdz8exzN5GYqnQ0dJXpip6z8jijFJOUT3oCV8lG1KUWliozZoV7UyZqyuOMb3QJBmd5s+DcfW3m14XWAy5Fo0Z5WjtW44/oat4kVtJUj60kFkSlm/VnRbpuuhpeaZVi9jzKPy+0/m4H4oUY2c7ffRfJE1WKmQ6S3XLjG0nSElzElGYjTHEU3aMkpVjfMSdFyycX98tltK9QitkNnGZFeN9jvxtEim2PZ6I5xasUW/zd569V2nmUsZl202+brwPl/ZrOCuf3jinFxtNCTMrZTeFoLfJ9Z1u0ofVRJvCSOgCiJi/2/KtNGM7Ps+7XFyeI6vM+leiqlGKOBAE326/9vsfrLqX7pO0pFosRdrf7ZDmnZE+xRLjMOY0htlAyOHYtLAKzSpdkmiDOcDleIrMZixntO8bvdDqi9sBAnZCTmpryY10ZwVCLXor5bGWENIBwZX6lykDnRCOUEfsyRNL7bKsRtVmTLQcpJpFfu8rCpNluwL3pv2qhFDsbyTK5CF+tasoug5ROlGXmNy1YdKpHFV0FuYGtlPCtjG+RsAdoMxZYWF0JAjqH+XmnKcUkz4vLP07lKeYo+QFkWW+fdLzf64q6OM7HrwYCgK7Ei4+rMtr3BNmxkltOhPRM8CIjpVyf0Xds5mJyeR/BIlUPpXQkJP6zSjYsfKZEm5r6czr/s+Ze6xRm0bETykEAXfbb5eUJCIlfh1o4ZvBMcN0vQKoUq8dG2uyzq1xV4kvmM9SWljy5SBpNmSzgJl81isTKdRMmo3wd4wAhKTat3zNpOZ/tZQPI13vXM0pYESRNm9gtVNWnbP0RzClAqfJZHfREHcWrY9eTxCmeYnSdNQkdZ/mkecZl18zVkCG+b6HESPmOSJ/TqRVnkHk7AGwKSAuvp5hRirnPvfSYLc+T5hKJR65vbE7YuNRivrWad0WXllDWul3HPMV2qXzSNrw3cYLIU6xTOVfJOk2wfYmBuJrdlTDUNnMA3OXFZWl1/X75BDoayxiCz1PM9175EkiNuk/2ulhV7B9cuGJIsY2LY7z988fl3W52VSmm29iU3UDql1/uNVUPnsSeYhHD4tDxLsNhjdF+qJzDyyjzLKgVnPcjLx4h5PUUI0HLkojy2CPCFthjz6QjUeFMZ4UxOqbvqV0gSqN97ikmKC1zKAxMOZyC/HRlLKazIqhcct2rZKP9gZygqRznyPLIsqD1+y0mX6czc91XB92kxcEOGtepfHI89ZaxuAL7+Tk0I8Uk3mCuciMV+drAs88nHeffIb4hc22e9TJ9fs6ytvbu7x0jzSuEluUppi0t458BsExszCvEV/4o9HXx+UxJytoqG36FUoyvtx1HGaTPH4X/W9dJpsW7PfvM2wcCYs211kuVYkVRJqN8XSDDqqn6M8r9lkLf2+7GZY+7Exw3pBSTEr/1c1cRctZ7KVVZ20bPgCzZOSvq7zU/Z41vXsq6a2+uOCkoKt10jC1NErhieYmfZWVcp5VJfN12xgsDWVOHkWf9KQlz/7NaFEVl00+x1dZoKiK7bUNuQOkptpgz7e6TovLJQJVLaB4F3Pdaun6NLWLL/pzg+mEp0gesTEtS3ubtPhlJUDiVYgqPXMBNJPJ5zaXY8iVDOp0OjiwaZklJMVvhHVt/7Lk/1Wjf3p9LvPp8fquaJkTGz7hSPTD/PN+6ZavpAN6BUu7f5no/Qk04XOpgQJYQqH2Wdb3LdStGfqbF3hw0pwz73dL7er8rxX7jvV/G//X6T+JPPnWf6PddnmI02Yi7TwYkyKHNmE82CMhLllykhelSFC1Dcb8ImpprADVj7IuSzYUr6It48FSzoNV/0xKBLq+n2P22ZbiAXCnmIngA1o0qkGVy+dholWJkis+VYpLSAtczWpIV8iCguunvmmc0dN1cqr6BYDPEscP8NgB558wmxrn83J1lCZH3gwf/q0wpNiviwSLBDgRIKVYU/uc8phSTZlxsvw+JN5hLhVmWTzYw2u/H53JfoMrPJz6v+J/VkaAzX99BikkaMriIekBQPsnm2NRsqI+kiQX4vo5WEs8koCzl0/qoAW7j9pSSH3tsiUG0a4NR2ZTEzKVnns0zI9a8Y4eMvCP3m5+Xn8gMKH4DxG9RhNewmcMuAYgH2vzYSqdq5cbK9k3lnxHyyfJ1m5auP65ycE3XNKBKJKqM9h1qLbHnknWvu92Oqix77BhbbG8RUopFYpWQv69k3TZxRkVZLosXXF0B+d9D5Bb/7JV+1yjFABlJ4yJKVJ5illftisI31dWgTFqm5koGSRX1ro6y/W4H9LpIkip8nT+4Kvd8csW0ND4QMtqvJyyHvbIjukZVXm0QU56Ha17xlc0DwJGF8uhsxAal/Hy3+ipmtF/zfWvoKSZRSJtmJYuHQtrkhMM1J8SUYq610ijFAk2ybLjea0OKOZ5Tl3iBH68rYax+Vuy9dL1TQJqf2cjRWG3fe4qd2NgGANx+4rzo993G60pjaVeplNlAh5Qw9YDLPl5aPunejEWCXc/mRGLU7zKnpodQsiCGTHC95ZMev4z5OS8mrQRDbGkphyuDKu3C4pt0ji6yLaGFxSWv3w2jfUm5a2NPMUcQ0Ol0SjIx+L3r0n61UszyGZGe+44jCKHnRLOx8JGvIXUE3Y9Op2wQQBCreCySaXXQNUGfrwOlz1OsTBKEVR32OdrlkyFvMGfpi3ADCZQkj/1+SeZR2zyXQ+p95M7CxoP0slMcLxeSb0pchtz8PPxKsfLnPSsTK1XR+EiamFw+Vj4ZI2lc3fUAXsoXX7dSzWS9m5pEs3u+9os7nHqsA7T+WiUJGkmesXe+phRTlE+6OtwB4cShq7EOwJ9vicrZ/WxK5rKSBK3HKfQ5LvgSngeECSWnLUYkacjHtY/VrJvBBgXR0mbH9VIQciEPtxSlWFlCIyN+7fWDjx+K5cPlkzKTf6/5euB+c6JuddBDv9c1662khNJlyK0pMTblkwlG+841sxufR/l5u8snZSQkn3u5L3HQ8sax3h4ypIXC+9TjKea71yXBUs6bnU5H16HbE4vTV3GRU65yPgI1zFIrxRhhTj93zcW20X6K8Tr/fSp7l5Sh017TVrzqlGL1eDq2t3aRkEYpJiyJBnxG+4vySZdSzBPXrAh4DN9nSa1+XKroynEJpZvDXlfthWzjiiHFqBzrgbMXRb8/tjbNAN9ICTegTLJXfoZEKVZ/cO3jpV4ILq8QqTdLilLMZU5Nga7IU8zx8seCp4kn4ANYNjHBEFt6v5112qbbzyTYXcknTz26MJ1/OEAOuRQZ2gmD7knFU0ygAHJ2n1QsxL52u6H6doKLUNOWjdrkVllOETHaD5ZPSgI+B/kqVEdsLwL41X4PnU6n8hnSjI1NMnU6ndJXzLMh8ynF6N2azgoRYcKzNYBMkeh6t9ZZ+YlUReObFyRG+y7FrrgkwxHsSrrpOjeBinJVX5Ad2yDwrn4UnGo6Ic5/z0fShANIHylG44eUdQALWD1KseD1dqiHUt7roXW9Zea9dQKU33epytkOXCWlHS7/Urkyr/yz1wcucO1c79egV27GQkk01zUD2PMdVGHWrxef2yRzmbOUkP3Zpx6yrSUI0riuJGn4/ZITr4ClkFNk/F3+jBLCoHIsv9eKWCXUoCZawm42vyyBJ7zeYwdZbo8fWkOC3aqlcWXfM48GkgS0We12ymvOY9IYXAptndG+7SmmSehQvOGYk4Tlk1Wj/Y7ovO1mEAQJ8evq9qzpQBnrPjn13GtX5QIgbxwFlPO8vXaFGty4fBkJpSAg3EXejG+b17PPdN1uO05I9hSzyycVSrHSaF+WsONwEeXlM+Z+P1z7Yg3pSnDNKSGrn4lnn7YinMM47Mq5mNG+z1u9iUqtYrS/3z3FLhpSbFv0+24/MHn55HRWtravSJApuxVSigWkqWtGMi5TL/UdZYhSo/wUTzFXB5e14fz/ElLMlVGMjetrNw6U90+qrKu0JRYoxYqigMsn6sAKN6oUlIdZi5oxnd/yLyx0PbodxwIhLp+sk2ISpZgrSJWUw5njPdJYicLOFYDojfarwaqk42blOFc5ROQZK4rCqXxaZZ8VCrDpOaL7o+mCSHB1dipLdWNKMZsUK/8uebftOVXiDWZ3rAFsz4xwQOAj+FcF5Rw+FSegyT7XExyibnGB0jJR90lPYkWaYHCvG7JnzGsaHAkgOSHHIdkEzsdNX7dc17tMMKRl2/nnhc69JPPqax4QJ6d85K0kieXa3Eh80IDqd7LHlpSzu655p9MRWUzQqfmVYvFxXWWEQPw5L4rCSazN/eTmf97xbW4cCjNA7l8XssWQdp/sOp5xjdG+UykWIxyIXHIQapJYxTW2tkM3X/PWhMo8Vzd0QrpSTJio9ZTalpv3gFKMxTfkcRhShNTHrscpGkWOMYA3nmJyQs1lOyOZy3znrbVQ8ZVrSbwCXeWTEtLC5y0cUy/Z15mg8V0dRdYup1LM4+kIyKtkzGdZ73asdLNGiu1a+WQ4GcQbuvVM+aRsT83hiqfL8knP2A5FN5VPnleRYvWYNmT144vlkkoYrViYFHraxkuauIw+hyt+NY3/XLhiSDHaPEmVYi5PMamHAWCVtjmItdBnTD1Zfn58bGPkDtpki0OsFWooW+Pq4KIx2ncqxSJeHb7SAD621BDbabQfKA+reKh5ykBERuLWeV+1TkqxACkWMAbVlk/yRTXVU2xNUA5njveUd4lIMUc2UCuh9hntx0iWUJYn9oxNK89KdUNmNlOBifqilX3l0n5pQOAiaahTklcpNnUb7Q97ZRdGSQaY5rzSU0yiFKs/4yv98nrF5hQfsSVJcLh8bAhihYTTNFhO0qRsAuef7VFNxTzFAqVl0hIBnxdOrOzHFfhpxp86yKX58YuMpJKg0SnF6psxPnZ4zayTeT3mZRPruOZ7xjVJLJeRdyxO4KdVM7wXmL/738144nDm2ZRJ1j9XBlpTij52JP4Aaz5WeMMA8k2GqwJAouxr0q2T4JoP+YY02DHapRRTKDxcdiArwiT1joNk0Vdc1GNxyfzg8g+i51s6tp3YkHSfLMkSrlySNX8CfPYxcRLQHG8RkTqj/freS1qmRse64rPQXATwkmi3Kib0nLrWW0NaiIz23euH2Gjf5/eqasrjXq/dSrH5/8NKMaGnmNXhlV/DECFn2zskK8V6NG44Tqg2IVoQacKyXg5XyWssRnF6iq2md590eopdrH+Oz4ZDMxfYY9O+jZoLqJViSqN9Pk9mpRgDbbxOb45kGzjnQirflPCHW+sp5pPxzo+Xyr7rnyHZHADcZ8Q9QYdLIuqb2DVGLoVKCQEfmccUMY5z5wqDjhUkiz3YHIw4Dyh8L6DL7B6Yb3AkL5/PP47q8kPlk2MHcaslh7ZM+WRpxLouMCJ0TdKa7JRL7QWEO6GUY9eJxJhfkQ27A4y4+6Rl0M//LM388vMF5s/sqkAdQe88D7ClAR/BRfYbpZjnu5ffuXqvuHeFZk6kz1mTGO07SLxOp2PKX2Kluj5iS3K9XSVSBHn5pD9zHfIAcilCyuYA8g2sr6TON7Z781oGxxK/JZ9KLUbS+DwkpCRNLLOoJSE1HZZ8z4qEsPB18hqYzW9EKea913FCznXNpEqxacDLU6JK9523KHHo848TrAMuf5ZetyzbjHc5Lf/d7iIZ26D5iECxz5TTU0xA+vp8YhM8xVwm5kURe87q573Slz3fgHteKj3FZHOwa82Lrh9BpVh83ad/40lSyboXGlvyXrvKNg+vkjWF3Pjd1V11VoRJgHn1RPWaq5qWOKonpGVqrrJPaeJw4ni+AZkSybV/OKQpV/WQDzF/R59STJLYLs/dPSfR3Ob2FPPvUYkUkxrt1z3F2Lk5xrbn7+TySa9SzP05Fe/qHinFFrFJZK3kcCfW/aoprlBzka6a8kkXrxGyrPGRtauK5mIEejeNp1g0HnTH7qkCCGD+btOeZTIromu9C1cMKcZl0g9uxEsoXZkSDfs+sm4EQVKS52NIATnJ4/JsGgg2B6HxJQGnyxyUlzvFsnp21oCP6zt3X8AHKMzyA0oxwB+g+4gOQBZ4TRybZkBWPhlspiAun3R0nxQQDq5FUWO07yI/AVbfHiDFRtP6sWqjfSuDuzZYqKUiz4mz+6Qw4HP57REk6ghSch1gBKaU6Dbn4HjOjaeYZ3F1lZITNFLkkRWoG2+w6cwbiPiymGZjkeDBA8g6aYVKZ6Tm866NTbpSTOMpVt8I8vP2K8UWgUjFs4jPv4INrEddIfcUq/5cXPZPx3tK6iRdGLuOTbdsI+cmAiV+bF4vNYHHCeD3/dC1mOcbUNn1rnY0rP6bqKTPW9ocVwD5VIUapZj3WOH1dp17rAGHi9Tix0nVxgPlfBJtTCBSa9VjM6nCLqTOkzWoqT+npZI/YrTv8NGRJnN8ivb5+It7FvgMswFmiSyT0JHGpJ6OtuHyybpS7LAgtiK4bB44wRY0nWebeEOKKRIMpEyvNqcJq4xD5y1dP0qS3lozBe+I670sSQuBp1jEaN93r31KMVWC2kGAAuHkCE01tlUBABxdVyrFbJ8urhRzfG/797XKIcLIunYxstk175f3Rz62u3zSr5qqJJ86jucrQSlWMdoPCBF8HphU+qghIu1YPvZeulTRgJ4UqyjFel2sDsvrnlJCecWQYlwFISmhDHqKjcNd4oDqw8fVSxKGNdT1TK4UcxAHkbplAk1E9oQnWYydhqjCjlKAx7eCfZZz0nB4hJixhYy2a/PdZ9ljX4DOXzj75ZWYXfoWpLJ8MkAOubJiieWT1e6T8Uyma2PBCRLp+5FSPukiEqXlbAQ7kypWijmCXKkfDFdz2YGuhPAgzy/uV6e9366gcX1FqhTr1f5N05HXTjRUvME839vlBwNoMv1u9ZHkevuyt4B8M+kysSUDYUn3yaqnWHwTVjt3rZLF5dfH/a0EhrKxToxRpZitmBKqBMzG36eO0zY20JRP+oipQLbdHtvXYCDuAepeQyQJklD3SZ+5M6EsYawrtCVkve+aSboKxsongwRRpExX+k7zY8z4sfJJz+ZXShq4iCnJJsHf2XXxjAs2da5NSiw2s8fvO8iKUKdQM7aDTJSofYG6QgGQJ3N8zyggU4ptO9ZOqZ+Zz8+HEs6hsmrXuIeSlGJ6ArRqHTM/V0mFTHl8nWAqCf7wvXaJGbS+d16jfUH5JH8vDynK23zq6phql+bIZuWTHqVYwA9z5lE3Aw08xRbfnV/+qWMfUVOKpXqKWbFlLBnECTpad8rySY1SrP5uhpJIFXsgh2edzlOs/n4YstzxnPoSnCsJSrGap1jkvkW7Twrvd+lR2DEWB2Zfn1BCecWQYnzhvF9CijkmV1qEZ4UgY+EoU+KfEVaKEWHgUGaIPcXqpIPYsNjrKSbZzNUn9263Y65j1EfBEbx1u53gBsEX8AFydYVL0dHpdKLHc98He2Mg8kzyXGsqnzy3PfYuiK6yAK2U2Gm0L1hQXUEulWAWhYb8rD7jpZTXP9G7VH16o/1qMLEmJMVcwabYD4Y9pzbhLNmAb+64lGK6BcK1KEa7T3pUR4B8czGbFeb7c58Rugy+6+4L2KQkpq+Tr8SbcTeN9p1KMaVxr9QfEfCvITEFj9uzqPxzLKEyH9t93WLlgD7Fb19YRuhTig0EQZTLOFinZPFtapqTNNGSIQ/RYshEgadY30GKxUpCQuuuzM/MvelfEbybRqlQUz75M+7luO5gW1rOR+fd6eiVar7YSlw+6bhfkkQpjWvfqhU2j8USWa7YrM82GMEKgqBSTDCnOJX8cfKUn5er6iOWVPEp2gGeDNMZ7UtL2/xE+/zvReEnS1zJO4k1hRnbkczpsTg8tAbxNaIsn1QoxUJG+5E5yZVklsZIvlIxiRLJKMXY8112n9SUT7qTKr551JTn1sonKabTNImxxl6ci1sp5p/71Z5iFuHd6XSCZKC9VmuVQwRfN0TfeskJOlspFvP95HC9myXR41fG8XGBRKWYI0HAq3PsNcAX19A1n84KsUrOpxTTd5/UqdTsfTK3fdnXSjEOkVKM2FwuH+Zd4mKlUg7JNv8MyWYsVbJd+QyHBDn2MPmC9FgnlOq5ezIXwk2sL1sTYtLd5ZMyRts1WQDxjajPnBMoSSJR+aT1nJAEuSj8i4urfFLLotO5rQ/qnmJBpZjjGV1jC3PMsN6nhpQsqHbGAUgon7QkzGvCkgZ3NynZ2DxjYUOkFBuRUqy8V7vqKebrPunwMiOsCRUOFY/Ffrk4xd4Rn6JQ2nLcF2zKugC7N+38O0hNudWeYtNq4AfoPC19Js3y7FydJAFkwZ/vnpFqINZ9MrWMcOYJogZsY+Hb+Ls2v5oOS1FiSrBmpjYY8HmQivzMPEQH4C5dcR3rUmgPBO+Hr+xzVaIUa1AC6er2CSh8AgP2FnFSzH2vpHFKSKG9PfZ7tsbKTWOeYIC/nFxUsurwdpR6PRVF4fzeUnWdy3tVmgTzzWUAL/P1j+8qlZImdLxEO09SeK4d7S94lQYpQmQkTZ1I5H8P3WvqvMpJ46ZNS2J+T/axldI0UmYL1+p6qVj8OR07lIwHVxbXW6Dk8asCKTESIT9tJb3Zbynutbdzsp+oce25tOWTLpV2NzC2vVanlPLx3zdKscj+lubuTqc8v5JIkyvF7LLN+efE97dAda0l0nVTcI8JLl6CyPLJrKiRRH41t6yUujp2NSk+iBC+JjbxWJ9IxzX7LkdiIpNiC6SWT/KuZ7GNyY5jIZ5/RtwHwWVWTxB3zXFMNFpvlvqmJr4B97X3lXo4+CZbI/N3jO0L+ABu2iszsK15PUUCdF/5IyDraGg2zQ7/nUML8sPXgbJp+eSMTYK8lG11ED9vVzlDr9sxE30sQ+W73pryyUrXTaViyixMA7t8MtZ9sr6gSd5pwO+Pxc8jRJYbpRgvn0xUilGgOP+8sFJsZ+qeywC5TJ+/t67NiW/sWPlkvINX/TkFZGbePkUJUL7vsbIjc70dHolq43dhudD8s91riLQ7nq2YjQUwkrFjii8fySLxp+LnXlNhLoLmkLLCNbbGU6wJMeUrRZE2GPApGkvFV5yYcpGg0XLVkJdnA6WYRBHJSzc5JMTWxPOcSRUlUw/RDjBVSSTQT21x71K+cpW37/hYEwpAU17mnlMkSppKPCqMVXxdm1cZARpq4LTj2AhKmglVz9uxZtO9DinFHJ3mKAEZbxLjIdrZuUTJEpdSTNF90q+aitsODHpdUz2xIrxX8+Praz49N7MCweNd5y2ZiwC/h5uM9K3fK1PeJrjeRl1tjR0ipgBGfnqM9puUT4bUWr6kBKA32neVhJuxQ5VBVvdJjWl6URT17pMxpZhDiS4t6+VwlU+G1i1+/fsO0lWjFHPZp6wOumZetfddvjWav5sScmo6K8z3kBrtR1XVQkLLVemyKhTpuHCFkmJpRvudTofdEL1CAJCV7YQMnqXlgCET9ngXL/dGctiLB8u+AF06SfuCkDCTPv+/q75dks2bf64nax25X777DMgygj7/HQA4eoAWFzcp5tqIxYx+OfjvcFJsfSh5Rt3BvYSsmM0K0Dyf0n2yJBJ3USkmLGlwBbnSzEWom6FEHbHpMNrXBATzrlD1c4grxeg7u8i8+c9ii4uv8UjsHXF1kwJYYwRh+Uu9fDIe3LuahhBMtyDpRrJXf0eDpJiDcJAYkJtxHR1agfjGP+Y9JMmI+j2uwkGnV6EsNFn2qYw5Aew1dXUEYJoOS775UEIwRa+5cBPpC1zDDQbq10xrtO9adyUJOF+3N4ki0lXuCsiSBD6Df7H3UEhBGiHl/HGGjIANdaUF/MkFH+HM59X49/YRa3GlhutYLdltH19phOQZ29UJESgTQTHVVMjfV6J+CpVPXhxPgwSPj2jn18A3l7o6RhtPMVH5pHsOl3xnV5WN5F4RXCpn/r6ESP6RI77Rlk+mKCFdcYbxFBMoxWI+nN777FGKabpP+sonDSHnIqY88y8AHFkrrV9iBCjgno9pbJenmN0Uh+7PZFaIxgPclQvy7th8raTEk1wp5no3Q9ZEFaUYJ8UaeIrZ3dzLfVf1s3xVa71umSiV+QSy+H/xvcVNn+wqEWXpY9OmiTauUFIszVMM0Bjd1x98/vegp1iwfFKnFHO17o5NHr5yDlqYwmU/7oVcTuZ5JJOBjaSvoxMgIxv45/q6tcWVYg4Vjah80q8KpA6UD2+6gxhXRk0jJebE7qpjwgh1BfSx+KKSUZ75sO7zYYFSbDyrX3N990kKJkgpJiNZnO2UBZlyfm7J5ZOLxW+dl08qPOR8nVKjSjGjeq0b7a8JiRquvOXeezFvF1epLKDwZfHMZRKTZl/nHUC2+ebZSHc3LB3pLFk7CL55KbW8i/4uIdt9iYKUoHM+NgW9sWQOnMdLNv6uzPeKYLNP8HrCCBQOPrJB0rmSH1/rUscy/bGyUbfRfjjQN2otl6fYgohM8faSzIWx8knJu2Wftzxp6I/PpIF+PfstNNp3xIZdptD2xYW+ktF+t2OqH6Tf20eUSK55tZthilKsfjwQ8Hyd1TshAsC1B1cAAGe2RmHPPc/9AmTPaah8Egivm74EM7/+PtLZGLAzMkpXPrkY21M+GfQUcyjz+PcXdwt1eIoB4XnJFROXisTwfOaLxct3Op7c5vPRIYXnk1/tG06M7HiUYusKJQw9477yyZCnmOu9IKVYUcieNdd+M9TsxR67onZVVooA5bMZa2zjVLsKy3oJRVEEPcVCpJh9rclTbDSZiYgp+l2gvs895FE1lhYi9f2pZu/B7wvNXzEbEd8eUyIs4nD5I64JlcIuXFGkGF2UBzYuRk1FfQ+PtAzQJz/mpQG+c/B5bVTHDwcg9NGVsh02eYSyLV7PDUH55NgR/ABlJlTqo+DzhXG9gPRVnGUcUjLOkxGMBT4hpde6gMD01fMDpdm+r3zS1R1PQw7RRNrrdiqTHleN+c596tnQSLye+OY2rftkPXjRNhiwg9UD4u6TdJxDKSYuaXYoxUgdETj/rYWS6yArnwyVFdvgC64rSPd1nyxLTV3Er+y6ubLHfGwfueUqlQXknhmuzRgg29D4jHcBlB0kBdnj+WfUkxPjqZ+scBEGPBiId3eNBfgxYsp9nEgpFikljKm16qSWTinmI5cAf9DsVLIofHD8/j/xNdevkJNdc7/KgI0dDfTrG9A4SQLneQMygsmnZlwVlBLGiC2Zl1maUszVaa78jDCR6vMtkqqNfcRxLMj3XS/qxiUZ25d8lPi4hZK00rI2+/h+r2vuoaQ7ON88X7U+RLcz37if2XTHVxVFu8vyQKQUq8cLVe9VfRVBp9MJdgasjlue92HTfVKuFPPZv4TeEVfyr9/rmnOWdoLn17wvIAL52K7mU/Hkgns+WunJn28eKxzUGO1718xmSjGJEobIwprdQaAyyDefAPP3jJ5xia9YSCnm9BSzVGr8Xmt9pvjxZr31vFMhq4FYbMI/g76Sq3zSdf6+efcgS477qjxsxCrY7PF93rQA83SU7D14pchi3YwmSX0NcYhLGE+jcTAQUYqNZM8LxxVFin3dtesA5puKhyP1zjuOjAMgZyldnmTz4+MvsK+Olx8v2cwBVcXVUBAkz493P4yDyELMz72m7BAQRDwI8bZLdzHpASmvJHApisKpPgLiZIXpqOdUilFZWsBTLECUXLUers0Ptc2WSIldgRP9nS6lt6zNs4jLSkaZUqzWfXJgzi1GRHIiMdS9xYUda6I096qRp5gs2LPnBEDmfUcy/HVn+aR8cQAspRip5DzPKQVeIU8xaZKg7g02H/vi2Ocp5n6/pEGf7zml93oy83fPcZHOBJH5rkeZV0lOKLKSkrWD4JuHY935fISBpItiObZH4RBZP4z6x1PiFSVpPOQS3/j7PsNFTGk6LPkIb34dfeSW32g/vt7y433+cUDct4PHCVKlWMhoX7IR9Z33iiDGoVOzx5aoj7wqMzEZmD4v+OwhxN0nPSVesSA/5P8mbc4T8xSTGO27EnhSMs4+Hoh7Q/rK9nvdDq5ZqMUeurDjGddNxhEk676rSU232xEliX3q0/nPiCzxKIgcZNwhRfdJr6eYYN3z7X201jM8vqv4qAXX3Pp5S73rvEb7iY0k6HrLjPbdcyE9s/4uo+6EZUr5pH2vaf8X6gDp2KIC0Jnt+/yJAffzbYsg+Hst70hYzuGmDDNimh9qShNbowl8rnAb7ce/L6HX7Zj9ltRXzPdu+p7xUCJf+j4DnusdURj7YlGeVEht3JGN9hc4uj7EdYfmC2GohLLiQ5BQ/gj4S6UqXRs8D5PvYQCECge+GXMYPAMyhYO9B5b4hPgUCpLNM68f95ldusb2KQQA2fWqKOt8pr8+gmbiJzDJEF3SfdJ1/FVRpZifFAPiQa4vy9TpdKIqN5+HjqTVuc84EphLzmmf48toup6xUiEgm+Ts704lhFGlmCPIlW5oQk0ZynI+/2fQNeUZImnHND5+t1N9V9ZXwoRgSCkmLef2kWKx4M1XPknXIJYhiylAgQDhHShtllx3rgzqe5MTvjWgPqdVfFliG4uIGiaenbNVwjLCgP+Ozx/FSwQ2VExNPKQaIAjAHFlJTYclsykKdIrzlcB4jfaFWWifwqEydizQd5gHx3xSSqKl/m8axZavCUaYFHNvyjReZimKp/nx/jU7Rsr53g1JE42QF2csyDfPdyBxKPUUSyINnEox3bi9bqdSeg/wpGV4/eh26vM4lVA+dN5HirkJNYKEvKV5o1beZpJBEqWyXolJ58QTKYfXSo+rWMLUt+5KVPFjD6Em9cR0xbXdbsc01fDOo7OyS2nVUkSqSKR3U0YYVI51lLBrytt8SaxeNzyfmfvct58vfflkTeUcUmvRfOKa/MHM9i+69y6Vz3LMKzS/zRxKIFuJ3ul0xHM3wRWPxpTsLhX9IEDeucDfm4oFS6AKK2QPRM/Y+Z04+ch5DR/ZbT+nof2pphGRc68amf99qmhNHDw/v/q9zp5iCxwY9nDj0TUAwP0BUoz7EKxYPjpSbyzfwzfolZO7b3HwZeQAWfmkL8PVYwtLsCOVR7YoKavwlc6sCiZpTpbYm4twdw44xwQ4iSm7XvaCaMgKX5DrKc8CWOAjkMi7VDiUbfGpGkspLA80yz/HNnLbxvDR4RUVK2vzTFiSxXjCAlVbft3tdowXgy+j6dpYaBoMAHXDfHqvYkqxsp0yK58UZrxDmV9J+UypFKuPLSErXF4dAFOKee6Zr5MuIFfOGrVZTSkWMdr3nDMF+LGsd0yZMT933zPu35RIgjCXZHz+eXFSzOdfQe9bbGMRDfCV2blBJDh3f4a99kUIOV/5pEChDDAFkHMTGV67XCSLpsNSuSlyXzfAr3DwlaKsCIhyoFTH+kz+Afd6XxSFcx6n6+fyc3Gdd4iElPiP1hJRgvnUd81kptjuGEVK0jTzFHM/45INRki5VCajPEb7If+3ht9bovhyzcPSJjGhJHGMTPQpngDg2oPzpOOpC75GRs2UYnP/IIoX3EnicPmkPylTlrbJlWJkpj0r4nFObPMsUUjXSy9lyhLf2P3I+uHyLQI0ZLdnDo94H/Fj+RzMk5cxJY+vYiS29nmVYoM46WrG9jQzCnqKeRI5BIkNCsFVmeQrnyyKMjHA57IV4RxGGE3r8WisKY5r/pM2pSFwgoYT/CFldWi9OShsGEKf4/JXBPz7D59fKSCvkAHcJHss0eqNRVnZvKg82DG24VH2e/fJ9ZU+vuboKoCwUqxSb+wpf4x1M/QpIzqdTlTyHTJ4XmX1tD7w4NsX+IVeYq+nmKIkot4tLkwuARY5VSttiE8aroCvrJX2Xy/fYgrEM2OlUsxPYG5Jmio4jielmK/7ZIh9B+ILhG9BBcKk2IxNrvYiXhrWhzzF/OQQABwx0mv3Z0wcRGLT7pOkFNsez4JlQ87yycj7TDAZ1ED5ZFgpNr8eVaXY/HnVmIHbC+J6pMy3VIr5jfbFSjGlp5iXFFuVBV0uA1yg2knYX6brTg4AenUEn5t4ckLjcQXwMimZQq5ePhkOZLxm9xFvk+rYbrIj1sHS6xMlNUoOkDQx7z1fYG4aDIhLb6rPikTh4At6ywYYaQRop9MJkoH8dCrkazd8n8x508YoQckC+NcBiQ+or3ySb4D9zQUWx3pijNjm2de8Q/IZvk0G35TEmiIA/pIS3zUzJTgJKkqCby6VXLeQZ19UKebxmQXiai2z5jnWXKoa8SrFPBUX9ti+2JJfD5sUW5d4rwaU5bFydjonrhRbHfTMvToXI2k8100S67gIB0CjqHfvIWKqnEqHO2cVQdocrimftH2xyKs21oHSp8gJeWsBfqWYqnzSYx0TUoqVJejuzzRKsYhNERDpPmmNzf/K57LU+H/oeE68NgcBX0Rt+aQ9H5jGNAF7INd6c9RY7MQVeRU7j757Drfjo5BntuaaOxtgRJVii/UmsTGZGdtwMeU7sprLJ+c4MOzhxiNzpVg6KSZjGEMKILMZ8ynFGna8Kb0+/IFfqHwyVl4QCpZ9L5HEQ2HKPlfTVcpXfjIfV6CsCwQ/sbI2X+cWgKtgQp5i/kmnVIp5MpmO4EHin0PwlU8CwPqASKL69w5mrIcaIjCcZfIqxVxZ58V3nhWyjbv93bn6KjRRujrHSFVqvjIngHdJ9X8GlQpWuk8qFHK+kvDY5jvkKWaSBLGyU8/YlNH0G+27yR3zjERMg31G+/NzD88NZlMSUFeE3rFQt9FYciJmqC3tPlxXa8Wyc+75SNrWfv477s2FKQeMZGJ9ZYTx7pP+jKr8eld/Lt3I+dRH858t1k0fKeZR8VD5fciTEmDvSGhsp+2AW5kd24jZx7tJyPBzxj/f39zGf6xvza+qMH332qMUEzQSmh/vT+oY1VSsjNFTPlkU/vOeeMqxgfi8EFKKSZvU+OZSmTqvPo9LScjyOXHM4RG1gi8ZAwDXLconT3k9xebHdhxxNB/b95zyn9tqfEmDmpBKw2zGvWV1daUYUCqs7S5zNnwlkCJPMY/ySFIiDPhtJmL+TS7zdH4e0vLJeiKJvrOPcPY3ZJCa7fvinJh1gC+xLdl7EHx7tpKY8hM1Lj9JoPQGljR1cKqVPQSo1y9bSYq5yyfDa1aoU7O4fNKz5woRRD7PUaAk9k96iH0On6fw/HzcDTR8Ipf5MbLYCHCrEYeRmExiI6VRivE9ukSp68MVRYqtD/umfPKBs9ve3+M+BL7ysFCGB/ArxYA4sRVSD/EuET5fgHEgiJCUQHrVApQ1T8j+SjzFKhNeLdid/93dncM/aUg84LhJph38lI0NPAtxqNRVkK2he+0unySlWMxo35MtlyrFHOWTq4FzD3ltiMonAzXyQFwFVCqu6osiIJMyl8HE/HxX+l2j6PAZzgOs7NJZPikrc3IuMIL3Y9MoxcqxNcGAr5QkqhSbugMvQE7SjDwZsljw5iv5lMrzQ5muWNOSkGJX4sMT2tTEkhO+OfiAcP3xkVux58VfOi9TD81/x00mmlIDj+IrplCOKpccai9C1EvNc6y0w5LPpJn/zF8+ufg9j1Is5psXapseWu99yqNB5Hzt430dyHzjmvP2KIAkzYT8JaflNfCa3Xsy75Iuc4D/3QIYKRYhnWsd7iqlumEfT6Aeb5hNgs9oX1Dy6TtnwF9qy4/XGpGLuwIGGp7EnpVQcxupUsw1fwNxpRj9vNOpx0my8kn/cxbqDDgf273eHjZm+5H9SxNPMc+xRPLHVFO+OCXWhdhl5g3ImxH5ShhLv6fw5h2o3ytjtp9YPhlreuKKRwHdpp++l70HCZnd+zyFCVTNsCXoiugsS1z82fYU44+7UynmIS5tuMjyQUR9GS6fFCrFPPcrNBeGRB/XH5pXvp08JyDFAhVkvhL4cWCvJu2YzMd2+V/HmtK44jlJjEBwrQHSBmEuXFGk2IGVHm6k8smNuFLMtZBeG+lYQ3CZuxF87U8JEqVY6PhpIIiQZPy93i6i8kn3gigpswop3EJj+7pJAeX1CraQDsjzY+WX5vs67jN5NW0mBj7x7pMRuXdMKTZ2B04Agkb7IqWYJAMaMen0ZZlcZAP/rBhBVBRFjaTpdDpRby3AHWyW6gBZJtJN0MTLizcd3SelWVCALUx9O1gtn1NX6Y55ToJqq0tjtO/3FJORYpJsk//d9icXJPNoaG4YCAMCX3lylCjxdYoTeh7Vy9fjyRSC7/0eRjZyEw/RIUnk8HN3TStyErL6c6kPTrlmO4ipbngzZ8oErKCXCNCY/0+obbohFB2bqkoSyhHox5Rivm6hfNxQyatPebQS8fEE/Cb/kmZCPoWbxFqCH5/iKebr7F0lxdzH8vOyh44la0MEZvmMpzXHKb9zfM13eYpJu08G103Pue94CBagjOV9SjFfHEyIzQ08vrIbBMi8V/1xUszjsTTar26+D63KvDi9jcZESjE61lapydRDXk+xSFm375ylSkhffBYvifaTDUQOScsn60qx8Nq37SiTBXTd9XwdukOEXKjzMFA+37HvDbiT5D61Mm/G5iLYJQQNwNRDDqWYb92j8+Rz6CByjA1/+aT/GXN1iCZcb5RifpEPgb8f9nzkS/KGqnqMp5io+2T9+ab3ezornM+YrOFgmhhAotT14YoixapKMT8pFjKWvuHwnFQ7viEjxVyEQ0y9FHoQV9nnebNjAVJNEvj5gr6YZJt/bs2wXiEXD5UluALdsKdYnFEOSkQjL5952R1ji8onA0qzlO6TgDwIcJUCEtYC587LXGub9kg5HBCWAwNMBeQhA0eO4KXSYCCSLfJ5fcS6MM5mRama4p5iQsPJUEl17DkdTWbmWTngKp+U1PVHlGLTWeH8DkGlmLr7pLtLkq/c1mQxrXlUqhSTlN54yycD5a7ldffPhSGyPeYF51NllB1t0zxKYuoM37jSjoT8s+vdJ8Nrj49kkRK/ocxiyLODH+tryBDvHiYhpsJj18sniQCNqQL9a0joOauUT1a6T8bXeSDsdSLymfKsA5I1e+bZlPW6HfN53k2sp4RRStKEPMViClLf5pd3UIv5/fW79S6Mscz31NFIwhw7TG/eBMiIEtfGTtx9MtSgJkJMNVGKhRKWgJyQcynxJd6roTiezsmrIPIpxRbrZqhb3ZSVA3oNuQPPShnnVM+7VKnJ1uxh314/FgRRRB1XV5jJ1g9vMifyTo8D8fChVWG5qmffFEpqAH7lkab7pNlHWM9KqIw+RhiXlhwCA3jH+10ScjPn79pj70b5pLQRUFXRJk9KA/6GU2XSLqDKc8x/1x8Oz2Ecof3H0BPnmMRVaO5VdJ/k8X+1uscRnwTWWY3Sa+T43prySxtXFCl2YNjDIxaeYifP7/i7mHg2cEBJip04F2ZmQ2qzENHCZerOrHOvawIqnyfZNLARlJQ1GEbcszkJBz/ujaTEaD800YaCbFF3osC4Prk3IJHn+wk1Wfmkf6IiT7Gdycy5uHk9EITBfShoCzHpfJG0L/mqQF4fIhuAuAqo/N7lNau0ZRZ+b6D63dcjSrEKmcYysLtRPhnzruMb4wPM/0zqywIw89waKVaSbK7vToHXsFd/TsrFJTy+a2Gajx0O3koTWCvAXjwj2+Nwu/OQSbP03XbOo4pNvzPTnxCAAXKlWKxTXMzcNNVvCfB/b2kZYb3zsCwbG1KKRU1dPR4pYjPwACEXKkPh510rnxzKSLFpQEkT8jPzER2xjLl93i61gOR58flpxnyigLCnTbTVe8SPTEq+huIrLdkNxBVbEsWU11MsMK5GxQ808xTrOzayUrIiVAngNbvfBaWYa+0A4l2nQ56tOu9Vxz4gZrQ/dic8JeWTPsN6QLbu+YjI0s8sUkoYIbdiSjFf2WW8gYZn7Yt855CKkkixqFLMs+6Wa4d7r7jtSWxTcnoyK8Sksx2b0TMWUor53o2DwtJ/Okeg+n53PWtmRSnWwGjfWT4ZEX04PcUEQhEOu+s9ITQX8goqG6Z8UkGKufa5Pk+xUGIgNv+5PmfoETJo9/Wa8kmzBjjKJ/c9Kba+0sfVB+bqm6Lwb7pdahDCMSkpZh4Cx0YysIkOTbDm+EgHMt/kDrC6fEkAozR9nI/tPlbSNS30EoQUbmGj/fjLE9r8xki10KRBG9jQdw550Rxc6Ztr4VKL+UgWvaeYXwHkCtpCGeuyDDB+n11yYEBQPulRZUjbMtPCZHt9xDoh7lSMc5lSTNA5bH7e8U2Nb4Eh9dpKv1t5TqW+LPx37GC11+2Y98S1AZcoxUIkKOD3FFuLZMx9SspDK33Qo+cL8CsGuCHCPGK075yPBCVDoSAkbjI6PzZVKZZaWu3bgMZaw1fG9gT4sWfVV0YolemXyqUQCRkhpjwG6NESr8C9jmX7/Ub7tKGSNlXwr/euTRW3Hei4lGKRez0LKsUWcYbnXoc6GK8K5hRDoIZUmFGvklQC1D8vREutBLFGXCnmJ1l8nmIhAjMWTwJhpVjsuhVF4bzmTcva+LnHlPwhpdjDW2PncxqKC4Hq/XLaDnhM0AFp+WRoTglvxiletbsSSsonq93YbaVYfD70KbYMIRdRTfl8evsBgggo77UdY8i7nEbWzIj602X7QuSQ3GjfIvQCSY3xtJxD7cT26rD8nNjGP2607yDFIkb768LSf/75fHyfp5iv+YUmOcx/r1I+GVHDu4jAmArchrd8MnD+oXlfZ7TvT9L6nvFQYlcqvuCfW+kKy76PuwLMHQMDOqWXSyG3NpSTajauKFLswLCPXreDw4uFwefVFFJ5HTsyfwiPR0ixkhF2qEICC3nItFFyPCDswhXIAjfxlfEtLLKumYuXIGRgGyyfrH9mjGwAwsqlqBl3wCg/RrIA/mw5MFc/HQ2UUPoWM+kCYbKJLk8xKqkIGO2HzcsFChqPgW1MKeYzX5caqvJuinwzaO6XJ6tHx3U71XdD0jlsfl71ybn8jLBKgDJuvHQSKMkZycIU8lcJ+amFnhNaXOTlk3ZGM0LwO7I8wDwgOrS4Fl5FYWUudVzzBkb7MVKL/1vIkyyqFKs1spASJe45Ld4G2z33l0FbPCPqWwPK7pNhcqjmUygg2oEwORQn5NxBvlQF6jNKBuIKOYqna55iQgK0LDPwP2dBrxJPqWy8scH8/y7bgtjzzUugfJ2qQ7FCYTZl9X8berLe5Xmnxzf8+KTySUH2O+YplqL2ChGYkveLP7s+sttn1F9RmTk8xZp4uElsB+bnWD/26NrAXI/TF+rxVUwNw32cXPfMV9oGyKoIwnNKeDPuU4qZcr6AcolvUL2dGIOVF+6N9CGhyX9JZHqS8p71w9ftWdzQwbNmStWfrnX+4IrQaH/qJgBoL+T6zvx9tT3Fhr2u+axYCaXvfoU8xUI+zgBXikk8xeqf5Svd9K2VWqWYy/M7tk67PE9jfqH1cd2xdMjmIKSUJU+x0xd2vKXUBN++CYgb7YeSyjKj/foz1u12gtdvtz3F+DWXiHR8uKJIMfIMIqJh46LbqynUxvn6hVLs/PYkGKxSF4zQBti1kFc2ch7SgCZAXwAk2owFSSJ3kB3zhJkf615YJMyuTCnmyHgHAj7KlE1nhT9ADxhiR1t+B7KY3OzS1Sl0yrLlrucECJvt+0ovd6P7ZKh7TcjjQ0J+xrtPUjbT/X6lZvQIvmxNrHySXy9Opkk6hwFhVWBskqeM24GV6r2SkDPl+P7Mc8hPzZVVs887trjEyidjRvuuTc2R9VhDBv9Gjp+7bzMYMk+XdJ/0lXMAcY8r31wo7j7pe0diShbv3B8OGKtju5+zUmns2zzP/18jpmi9izxjRA6FuiHGPcXcY8cCv2ng3Y4Fzl5VoLB8MrRuhtTdvu9cbkiE5JBjGo+RqPx87ORITMHJx3be6wi5FVOKNemGKC1PTimfDJE0Mf/SYPlkIAHmOt5Wh0tJA6Ca8JSu1yFyqFTyhy0PXGtXt9vBtQfnewFXCWUoYcnHBjykmMcEHZB6r/rHj80pI09sV3ZDjCvFBr36vZYkCWLlk+lG++F5ybfplzbQ8KqrI/NJaD4oyydlPmr2mmkU2o6xuXLajqs6nY5JOMZV5bRnq35G0FPMo24mrAtVzkDYq8smenzjSuIxDnf5ZDgZ5Orgq1WK+d7LYPfJwNx9zcEVdDvArABOb4bVYiHVbNxoP5DIl3iKeZLbkrLRnsuaSKUUqxNy2VNsAQoyj0a6+hlCy/HwHFrpmwDkRKANakhtFlIvVUzMPYtxrGQp2B1JkDWJd2eKZ4nsl0jS7SFkwE6bSBeZVx7nV4MAAu+gICOt644HlM8b4H75+HX0SfRDZvve8klhZsyXTQTC3WvCvigCUixSlhAzUfc1Nyg3YuGJzlfXH1Nm+BoTcFIslSQxGW/PAkMbY/5MAbIyPsn4RilmBTGzWWHeDxd5Kl1cfN14YxnzUHaLyjFkSjE9ESkxT5d4MwaP922eieCxu5YJvTp872gsePSraGRzCuDfxMZKfii54S29jzxjFDAHbQNiSrHa9RI20QiZYge8WeY/n/+/Xiob714MhJW7pR+bPAMd2xwQQsmo2PsRKseLzYWA3xcM0Ph6uTfP8W6I/vUrWp4cmFNiz9oyPcVCa76GFHNtKH0Ks/Lc/d97JaJMD3mKAaybvKP8KFS2NP/Mjinhd20Mw0b78jgppXlH2X3SrRQ7F1AujQVlVqF3xCcqEBvtRzwpfeSW8UxNLp/0KM0i73Ro7jekWLR80p2AozlmVqCWWOeqI5u8BOQdKEtRgJ3EIqWYf8/l9xSTEXKAOynk9RTzjBu7RzZcBFGpZI+tGw5STKwUo72HjxzyK6Zcc2+v28E1iznsZICPALhXXyAhY81jIasASdMNM7YnuR1ac3fLU8y198ieYgvQQkRKsZTyyU6nY3zFjm/4SyhDfk2hGzqOqBvmx0cUDqEMk8B00uf5FNtYhMaWSPRDQUiow1Gos5Ks1XmcrPCa34bM0wddEzS5Nv0VUsxzr4nAfTigFKuVT+6ip5iLxJSVM8RJV993jpJiM/c1lzcYcEuY1wbhTej22H29JJ3DgNizEjPad5dPrvT8i6kNn+cG4PeA4O+baz4sCfrwNfeWT5LvXrQTb31s4z3nbcgQVt2St2OS0b7gHQt5OEQ9rgr3XChXirkDoAELsp1tsD2Kp9iGhIOCL/szYobFEw8RKJXK+5I5lbGVZW3SVu9BU+xI17SZh8wjkj5utB9QigUUJT7VElcIhDwSQz5VUh8eoP6MrgrmFDqtlLHLzHv151JFia8ZBVB6yPqJrXgCz3tsQKFdBvnhZ8ztuyogxQKb4GhJdqRjXKx5RzBpaYjE8BzuU+IbUsylFAt4SgLzdX814HcYjK8E87ikJNv3rPoIOVKKhUiaUCJKYq7tU6QfNl6x/rGLovArviJl3T4ClK87ofls7F0DZOXYrusl9RTzVRHw98VWbIWsWwCZEKEoyqSnff4hpVhoPgHknZMB977PV7rpS4ZI/QkJrni0rIQK32eX0b62+6TXUyxgD+SrrLk+0kWXEFLNrnhIxVB1i6Z80t8Ew3/NQ99b4jsaGluijvbhiiLF6EU9ukZEg6d8MjLZUBvUkNm+z1h6/jP/DQ3J1AllyZI+8JIEfi6ZKD82bLRPKh6bcIg/hCFGPFg+GTiu0+kEy1WBMFkRK+UIlaR1Op0guVQpIYkoxc5u+j3FLkX55HpQKebeTAEyo/1Yq/PDUaN9DxkoXBh9wWqpFIuXT9pYEWyeZV1O3cdTcLE+rI6t8VIIdb/0tdD2NRcg0PM9ms6CpXW+VtTrkc1BsHwyRooxU9Zw57KIMiOgPJJ4Fbr9GGRKGjsxYbpPxnxCPBsqfv3dAZj7nKWd+fi517pZxcoTjPKo+nNp+21J9+KQ8bvr2JVIUoQQUgXGvrcxavYpxWLlkwJPMdf39hGB/DuEfErCRvvC59sR56wwpZhvExsqn4ySr574qJxL01SY1c8IzymhbH3M4zClBDLk/xYz6efnHZrL/NfbnfyTep+GkqUxwpzupcsaAyiNqkNKMd9mFOBEZv26030MNjJKNNqP+f7RumQrxUqSxq/WCiWJY359gH/zTbYYobE5AVMvv/erXvl51+Jh9vdQR90yrrQ37gshQSSp7npOTLmqsPukr3Mm/53y7+4YgSB6xjx+f0BY4RyafwFe+h8nHUKeYlNr/veNqzF9B9z7c3n3yfoxYk8xj89giLiNzUOl2X7Y59xHfgLs+lnz6MSzn59/B//cZ8Nf1eQnFUNKMY3Sy0WAhva3MVxZpJhRikWMvANKMUDWgTJcPul/gWPZqfnx4U2CqGzHE4gURREtqwi2YzalBe6SCEnb77BXh05eCvBgM7yoBY3jI5lIXymg2fSP64siKQe6nk07ABw94FeK+TNbi+crVj4ZaBm+GlhQQ5kxSZZfqhQ7vz1xLsgxMjButO9emIynWMRo30l0C7y1whnY8DtNpNFB22hfuLEAwqUkRilmBTE7i1LUTsezODGSztehlY9t3zOzmRvPnL57oQA9Wj4ZaeggVYGG5qNwKbl/DZC2efeph3zPqH28rxQEiJm6ujN6kjIB33wa62DpCjoBeRlIKHiMJYNi5aZSpZhz7IAvDADTIbVmtM8I0GBXW4EXZ8hTzKcU45/tQvB69/3BLv/cEFldFIH3o0H5pI98lXdDlMQpYbVXyHog3n0yQGx5yycXG3dXmZWoCZI/zon7qLkJUDJSj3qKBeKzGJFoSv8jSjGXp1goWVofP6QUCyQdRZ3JXetHeC4tCbnq2CUxFVCoBRJRkvnQF2ccjiSx+Niu42Oexr49V2zNI/jKVeUl0fX7dFBcPul+t0NzcSyOlmz8K4n5vntsp6dYZGxK6FwcT8OJlRnvQswUWJ6xvfYOWk8xxx5C3KHa4X0m8VoF/An5EHEbSkgApVIsWj4p2H/Yz3iw2ZTimvvGDnuK+cnmFWH1AP9sPp9lT7EF1i2lWEr5JADccGRRPhkgxUILYojUitVqA2XZT6zkyJ3JDAer/H20A6hYpmY+tvvhlxnt+1+C0CYylLHmY8ck9illbaGJBgh7JsUINYApxVzdJyPmhVHFVKBDaqisLdQFS1LrHSoLAEqyA3BnFZsa7ZsskfW9yxLCiFLMcb0kneJCJVZcteTaAJNh6brlKbYbCxPAu0+6lWI+3wq+uAcJQW/5ZDk/up61kLrtSCS5EZoHAfm7HTIZDSYIgiVH/nm4KAov4VD6TKV1s+LnsuPw3vN1vZSUzhO83SeFJaP2uiNplsLPPUXB4ysHkZYIGKNkx5wW2lzMf+6+VzSnTGdFRJHo/96h8g6fAmcQCNBdx7tKGKPlqh5fSKC834A/6C08xBYf20uAepR1q0LyNegpJlSppWTeQ55ipSokwWi/qaeYsDS51thgUZbWpNtnVCkWic9CSrFYspWP7+4+GfdsDXafDCTIQ3PpZDoz525vvkuj/Tgp5lLXSdSUvjiDd750JcCA0s/MdXwsucAbBLjOGYgo+SPqau3zDZR7zRPntiOlmx6VGjsXO7ERa1glKZ/k81TNz8xTwgjEjfZ5Q6hQrOJrwkGfO7XutVcp1nOTOj644tEY0ey6z6U1glAp5vEl5udhz4ex/e31h+Z8BJWAf/a+s3jBm79Q2zOGGgj6lWL+tU5VPmmut1to4/QUE9kFpCnF1ti64ZuLfLiiSDG6EEeIaPAqxfxG+0BzpVgouxQKuAhrEZPOUOAV91Ypf27Lck1JhEOtZY73ZFtiihAglnmmsf3BvSs4BwSG2gFlXaxNOh3ryqoBZZchd/mkf2NAuGrdX+rru8+70n1y2PWed9BTTGC0HzLOBebnT++qqwOlr+NnuSGJmb67FV+xDl6hFutEKl0ItToPZGBj7d1LpZgtu5Zl2wH/wgSw7pOWUszXNZLQ6XRERCh9jn3N+QbY3enUv6kpyyc9XUojz1lsXvCprfj5BLv4Bgi90PE8CK13n4x3LQuNzf3vNKauks7D5We457Wo2b0hK6o/Xx0y4jVRaSw1fvcqxYRmxW6z+0U22ecptvixPTYnwEMllFORMlx+r/nfp4H7LSuf9Gx+IyoYY2AeMY4PEfW+dcDXkOGAYBM5H9tPVjTZQK9EVNahMtm12Fy2uA2uTeyq4HuL1HERJU2quiPm2wr4iURfgxdCqPtkqHyREJofQp5iMS9NwF9SB4QJIh4/0BpHOCQoYaRqDNeaL/MUcxNylOwsCuCCzy6BzZG+8kkfAeFTZve6pYVKOGnpnsOj5cGBuf9vHDuEYb+L05sj3HVqMzC2O9bodjug16ZWPhkgDYDSI1dCvLrGNskcxxxu1izPnmvY65r33W7exOGLdXxKMV/ySqsUo+eXv1uxkmSX2rZUYmuVYu44HvB3gPSSYoerSrH/9Pbb8LoP343/+bkHK7/nI43n57N4r2tKMf8eVaqiB/zVPaH4JJwMie85CK7ECK9wkZbcEq4YUmx10DUX9yrTfdLnKbZ4cD0bwRskRvuBzWRIuRSTSgLcXFQfeJUBuvvF5/OufQ6x7hzzf3NP1GuD+EPoM3kGuKG4fxMZK5/0BdihsrJYWRsFEN7yyYBZslGoeQI2oGwK4SyfNBm9tAUiVA5IC2q6p1iagoYQMtv3GYxKsq+An9yK+TWFrtdBQVfA0Eaw0t79/2fvX6M2u67yQPTZ7/W71f0qqUqyJNuyLF9ky9jYxAZjg024JGlC6CadjoljknRy6BE7aXDo5tKdc0iAAYcwckLIaDNIBj3gRzJORs4IaYiTc0LAAWIw5mIbG3yRJZWkUpWqvvpu7/X82O9ca+6115przv1W7U9V9c4xPGSVar9rX9ea65nP88zIxobAto1hnCmm6goozElZpli/DgRSaOQ3KY/FTkf23ZN82EgKkus+mXrPXHEixSAVGI2abkeiN4sCrABiQInOfF0CBF3TEgGQW8ZTLPWe5ySYqerzoOsblojd2kiOLsj58ubrwf1WMvOkNShnij1NgEtd9m1I84rsKZaufqfOmT97aa2nV6FJ90kJwOQG5mnAOj12jrmUKqKtZ5jCFHL3ZR1DTpIC3gr5ZMonFtAyxdLnnfPzSeULrqCTmVM0UtsUkJhj8i/LFJPYEhr5pGi0L+TDXrVRn1P4uYQAEYFiO6O0rE32FMvnlql8eq3fdcenJJR8vQ8B714GgJDyeO+Zmp9Hk2bgOaP9hOfd4xePAwB+8/NXkmOPBYArBdbk3k+LfDJ6vx1TzL7nKorC3XOpUFwhYbDfov8/m+uuubF8smK0nynaRTzcHHhn9hSrA7d0+8P5MNWIh+Is8xSbzeb43SdfBAA8G8gpNd91uEcW9yxKv1VA8BQTyC4aTzEVKBYpjFSL8flmEDzuGFBskyGDxzd08snUQnrOMcXSGl6JOi3pYSVgiCJHi5USr9wEn5qkAMbWEpKYVHWNV6zyRrKR5L6XTqByE3S2W6ew+eUtv2P0Z6maB8gLk6fHpz8zST6Zek/1jCkCPCKgmPCOaRB8SeqU8g7iIYJiiQT95OJeXbkRB7spUtVjonynviup8qvpFCctSv2urwjG3lOqtoWeYlqz4vLvpCvuKUAwxxQDdNJoiTkr+u4J53w016VUkD/y80434FDMo9N5WgoiAHqSHLxaPQ3fUSVTTJjT+kICmdzAZvzAYr9Rq/RniiqpjTtvWLIvmIFTXhrvCphjisWveyvBoKwdL7ynUgVaksoCOrmslDzS2HGZVXzcoii80bEon6xXzinoO5/M4t9HrtlKjgEkdT/z31b83FP3iwoDOabYUqwpKdfIyifTuaFUWAAY4Lysp1isQKAEIRszxRRM/lRul7NCOeM8xWJMfDm34+PHnplYdFQU8CZCfih1yqNx+92i9m0fYdYUKZ8rST6pkUxJklVii6XY3aRCia5bGQBCKvppTOfHCUJC7pseZ/Yfb3nwJAAZFJMbtcSvO2V1QOHnhHxOGrvflJ/HgFcNYKzplJ3KdXKeYilG9zLdJ3OEkdi64fJARV4EpOeEoiiShVIaN6WE8kb7B/iTyzuuqUPIfk3Z7QDpfYRYXG3SfTLhpSbu66Pzvt5TLDYfdRh71OordseAYutMinBsneSTCaZYzmh/4SlGyGz0N4QJWmIoSEmTOz5DHZSSZKnbA1CdpFJtbyX5ZApZ7nb8R58zg7W2/ZZMdwFuqC1Xb+NJl8xwkza+gExhTlGmeXj5ZAwcir8r2gVCkgPK3SfTi7EK/FTIEo6ul/ct1oEyBQaeXEghXoh06uSR7oQob0Clyq+lKpbqVCqBNCQ3SHWfzLW1B9h9E0C90MBdAvcpctIdIN2RCpA3CFKCfSzTpdRXcDNgeUauJFXWgHRCJErEBG/HZZli04R5rTt3Beu2xhQTGp2Ekap651qeS8mfhs0ylZhiGaAkxTxyc0KGmZfaUPE/i1XcJQ9PQAe2N+7aLFSgNbJsubGB/7NYwpvLc3LfJp17bMnP+selWIGOKZZjYaa/a9fkpgHQnjfaT79jnCkWK975Z1X/XWeVIPmAij618v1OdvtkGyKxkYTAhMwx+SXLAsBvKK/tjWvAlsbfV+pOLnu2KiSri/sWszuQACI6l7VInjLodVx+uH0gF5Ni90wDZEoNZiivS8k3JYlXLwNA0DsaG1fToCa13nOwIrbfy+Wzb16AYr8hgWJCruA9KROARcZTTCOflMC4WGEk53MFaHNi/9v8p1xRJni/U+NaisNA3F+Le6jFnrObQ9mikwIsefzx8zfw65+7DIATESLfZmKPmwM/nafY9gE+sWCJAcALISgmNB1JSfc1BSANKJbas2ny4KhNj0E+mZqPcjZUqbhjQLENA1PsQJjUAU9XHE/nuJKSYIrdJ9OU75zfEuAXu5zRfmyC7WU2B9KGTGW0L0zUuQ6UGjPXaHKflU/KTDGJQbOWkbVJTBaA0+QjTDFFJdJ1cRGkZSnzwnwXRkk+ma7yTBOb3vC30h5u+QVVZorF6eqnNrWgWDxpdM8qwQqRACIvn0wnAAQopJ63tBGkZG5zie6TElPtVjPFHDuvKwCwhncckN+R8th0pak8b3lhlYAOfj+yZuKSp5gwnwFpo/1doSMh/02r3Cm1Ae0lEnNp/JTnX04+KRtqS6BY9VwrY2dYzqlOR1tK+aTIFBPkk3zDEQPzNjOSbn7ucUYjJe3ppDN2vzXXLflU8fsgdZbK+v0l1mxdp1Hb+rOhZGGqPMUayEaHmeKdBEytV4pRthxpLcMyy5239ppTBujzucxIlHxtcp6vuQL3sfW+O68XAraYpFygkNh9onxyUSydzObJ9UOU1AkAUcrMm+JIpgNlzG+JIsdmBPgGuP68HFMsw1KLd2yWAQjpvHPWGHzsWpOZTAEs5wP9xvtPoNsp8NSLe/jy1V15bIPlTU5xoZFPSvsXKk5J3SdT7CWAsZwVnmK9TlW+mWpOk2I3DxTvJY+YnQd/frHnHMtPeoxNl8rHvuuf/1f8xf/jN/DklV1RbZJSYuXuNQH7B5MZy9WKxQABAABJREFUfvWzz7s/D9mvUoE5BSpKTEJPvjAAU7UGTgLZRWHTYzLaD+123LqnA1IpXhKg2D/+x/8YL3vZy7C2toa3vOUt+M3f/E3zb3DTWuoIsr0/iSaLuYW03+04g86U2b7YfVJgimn8lrhpfSw0jKtk90mWPIUa85xJPyBvDnKyT9HYWki8ciDLGpNAxs9Z3lTQz8YmXGkxA2TzdmL3SAAoBz3CSTeVNGkRfNloP90YQQI+i8LTUtNMMRkcAnzyJHmKhfft1OKbvLIjtybOdUKMyfgAnXGuJulKgmLCfaPEYvMmdJ+MVYBT3TNVnmKaLkfCnLoumMdL70pOPil5fQCsKUQDj8NBZdOfk3NIIH9amgBUq6eAf0cns3lWogXYfWFyUidL98kwCcl5a0m+R7mCSnnuiyRKkNQlQbHFKYXH+mp3c58pyWhfaqpQjq9giombZwmQS8/jBIqlJFYAyxUiy1c/833k8pzcGiKa/CuZYuHYG4s5bjSZicbJYvGONbGIy0bTG2jvFSvnR7HnzBnaEru7KQtTltE0AyH5WiAVdWQPt4zna2bNLYoCp52Espo3TIR81I0vgHL0HNcEewpAKm6n77nEUJEUAEC+A6UElGhUCJKMkQC5lKeYToaeAhHTz1rVHTzxfeUKYDkj9M1hD6+57xgA4Le+EGeL+Xctdt3xZ53zTM1JqgH5nondJzNjA7Z7Xve1LM8n9BRLyebPLczmv3A5DjqGEVMu8O889l1NI/lg5ZgEsP/89gHmc+Bzz90QiQgpdnbuXq/1u85f9z9++jn35+F85pUiUkEmYMsKc6DFaD81D2vyYGm90zHF4gxSSwdLHocOiv3iL/4iPvjBD+IHfuAH8Nu//dt4/etfj/e85z147rnn8gez4IsQMQyAeMVCal1KcS7TgVLsPikwQqTEh0IC1QA58crJJ8XkXtCY+7HTC3nOu0KiyUvSzSxTzDHzZIZa7HlXZW3pBTEFoEoUZqlLKEW1VW8IiiUmGmv3SYHeD9TfsxwISdec80aRmGIpwGM+nycn2JOb5cIYVnzDSHefXIAzKaaYACJuqWRO6QoqIE/0xNjYDLpP2jzF0nOSl4nFmWKppiPlecsbWCDjKSawEn2n0fpxNI/fSLR4p+Q55zWYmxdic3GnU7gkJfWdSQxUuUIWr54C1eJO6j3lkgMrkyaXpGq6TyYr7hmmcardOqBliklMY/lZpZhLzhcl19hAWPdSVW/AV59T522RoERN/t2abdvMbRGbRJLpJhoj0G/S70bfs0xhRMqRAN8QSOw+mfFNDc97g82tuwpzaok1Bcg+KSJTLCNDTL1jNHYsyZdARI2MRM7NPJgYPzYOvvYzYAPFVAISM+9JrsANwIFiodl+bv0A5G7bUr4w6PnufElvYAEo6QsA0b7AaAfyHShVhtwKo/24fFK2PJBATKm5AADWXVuf3/BIySCzoFgmrwOArxR8xbivZBQATbC0pxkwziSfjAAl4rql8hTLr13TxFyaGjuVI7x+0czgM89uZz0hAdlTDIiDYrHCQi9zDD/uy1d3RcA6VXjUMFbPLvAIjmeE+yBN46eap5iiOZjOUyw+l3lbDpt3tcayhSKF51jYZjwOHRT78R//cXzgAx/Ad37nd+LVr341fvqnfxobGxv4yEc+YvodbrTf63bcwnBVMDCXFtLzrgNlnJUiIcIEasU2YzkDWoAlAhnGVROmWEw37Y4V9L8UOnRXHtsqf5FMZIE8I4Sed+qeSx5wbtOemLA2BRaMtCBRVBkp4YQlL+I5oMTJAaPvKNsgBOee07jnOodpPMU2Eok6r1qFx2vlk6lOiJLUFZC/6U2FfHKckU868Dbynu4k5JPE6Ngfz7IsHqkCnDJFzUkxAGX3SaF6nJJPzudztpCnpRjzebzq7bpHJu+3DOZJQAeQB58lw2JJ9ip1d+12CgdCpqRtnPovyp0kQC4BaKmYYok1QNvFK7b2mMzAxcYIGUAuwRTLd/uUANA0qyMHYDq5rAIUk9dN28aG5pUUm6RyfGLdlQzYpXcckFmzgOwjmntXk2zIru9QngKcy3OXio4yKCZJsvPdJ9PHAjIzRLPBGE/TzXF8Ac/OzEsD7ZyFLzCPJCAx857k2NkAnOojZFZMhW/ajS9IXlOd5igkiwp+7lGfQnrHI4CFBMYBefmkZFlAvyl2n5TY3TmjfWG9zxmh+xwjMo+aun1Wz5sXwOSGJen3RPIV478ZK5B7L86QKSbPB5qmPC4nFFjG0e6TQkGkNr44l8bXgVSjl9Tcff7oGs4eGWI6m+P3n76WHI/CvysMF2C/GZNPxjw4NZ2a6f348tU9WT6Z8RQTpiHXMATw7/qNg0llXpQYnDSPjSZVRZL0jlm6T6ZM/kWmmFB8sjDFUkC7RuESi0MFxUajET7+8Y/j3e9+t/uzTqeDd7/73fjYxz5m+i3OFANkXzHHjhBAsXPHdEyxKOAgbH41tFRX2UsxxST5ZMYwWdvJKqWflgCPXNtwjQQl6oOTZYplNr/ufsWft/QB5gCedWExljwjKCqGxcG1p0COgYC+85CSpw7bfIeTRi4JyDGHNEyx1Ca2kkAE103yyas7I9G4NwV6O/+2cbxduWScq2F0SN8lIN83kmWG8smjjPWakhFSjFxXqggwlfB/0LBml+0+mapocsP42PiDXsdtLGLXPs0x83LgbWZTlPNzcwl+dB5eJNkN1gAJaAeqyUSUSSOcN51zrftkxg+sOn78vnsfnPhvSJ5iubUDkCVieaP9+D3XfNfl2GmQR5L9VJhiMaN9Z/7ezFPMb+Zs6yZJrFTyycR76uQgAlMs1xk2BZbMhI1Cjs2S2tAVRcEKIxIYmH7WPN+zgM7lsTJzVfIUA2RvRglE1DTHEVmYGVAspX4oisIzh4S1S3q/+XsSW/M1BW7HMN8JmRX5PEViS+QKSjkmj8Sm9PNxbNzyz2KyTQA4MiT5ZIKtJXhz8a6yKR84CYiUGijljpWYp7ljU/kNDwkAkN5xyfOO4k0PnERRAH/y/E6dkciLWMIcHt7vXB6tmcsk31PH1oqsmV46L4FiHpxJhZ8Pq+Onxk4xxYqiwOMLttjvMrP5VMTmhaLw4KfEFOP7nhy7DPB5fwmKpQvrKYAo5x0HAGePelDsKx486a6LA/2iLHoBDs7mVdBZmoMs8sm0p1g6R9AxxZqxAq2/weNQQbHLly9jOp3i3LlzlT8/d+4cLl26FD3m4OAA169fr/wPqG8mjy86UF6LdKDULKTnjqRBscl05rpKxeWT6c2vJH10x/dkhDOmfaaQ/E0AuQ0q35gmKcwKz4xc90lr9VfyouHjpj5eqWsNIDPcljHa1zCmet2Oq6bWAaJ4smiWT2YqmeHzWrqrXwaEBHhXxep7NhYSiJMLpthkNk9WIgFu+h5nigHxd1SWT+aNqSXmUPm7xEiUmGLVsbudwvkJpBqH+PEX77mJKZYGAik02nwpGUgZwkoAKIXUgTK3qXEgZFKGLoOYaqaYwCixghWAl3ml2EuS/w8gM36T3ScNMt3UGuA7pmXWHtE7KD3+LLH5BvJdWlPJtvdFSTc2AFjyKPl6CTKU2Njl+HmmmuwplgYzfeW7ftwRBwYKYEWGLSCxu3MbyVxhRWKHZ1mBQgU6xxYuzz19fFEUMkNO0aQmxWjPWWtI87AEYA57HdfFM12wTM8pDoRsYMlB8/eLAigmMwb8vYh3B0+veRRkfRDm07miCiDn8jlvr5wRunTPpc27k08m8gzn67WEpxgg2AZI8sk1GZCT1kyp4ybA8roYkz/jF8t/V/ThnKZtUCR1z7GNPl51/iiAuq8Yz3Nk/7g4gyi998gzxUaJPQTg37HQ1wvQFbZVa5fAIo2N7cH9+m+RhPJ3GoJiAPfg1BU0Su/t8v9Hi16ssPvlq7ueLBPJp1PrpYYoQ83/AODxi8dxepPYrx7fkKTJ/Hz4dy0p13jxScqNpLHlHCH9XXGP2dzYKYbcbSuftMYP//AP49ixY+5/Fy9eBACsB5tJiSkm0Qwpzh8rX8JLEVCML87R7pOCD0FOVgAwqmOqYiJtDhpKC4CqzC+1MEkyKcdwS8rT0oua8wmJTjzlP1PJebbLXGbzK3XcydHzxc56Cno+/+9h0pd6T6V7xSNXyaRFNTz3ceYdHWYQ+JxBKJDXuAP1Deiw13WbucuC2X5qYRr2PAAZq65p5JOSIXfKgJwixUiczeYusQnlkwBwfCMN8POQ5LqpireFKZb6riu/I/h9hPdcAkAppA6UWaN9pcdh6vvMyZRdt1GBKi/J2lKFET1TTD5vmXUbTxYlA3KKlASGrmc2l817RaaYAqyIXbZWPpliik1mc1niJRSiljEsdt0nFdV2yUtN8vWSPMV0Rvsppli8sAHkN1W5b5NuZQwUyzKXBEZi7tsC5PuWG1/K73JylNzmW0rypRypKApfNU904tL4vYbSGz92GlyiplfXhIKOJFflLLeYhFFT4E6xiG4aUyzpNyu/a7ruk/X7nZdP5oz280yxchw5n47lDJ4ZmAPkYuCQXNCXwLz1jF8sP2+JTBBT2OTWW4pXntsCADx1dS843l9PLFehb07biZHCIhmNM8Xi40rdsavjazonx9c/7ymWAojq5/uGBSj2iS+9mByPIrVPlXyz6X6Haw4dE/sW+bpblU/Wv83UPk/j33Z2QdIBSnDw9BHyV2ZMMWEu5PeBjy/5XvNryHnNpuYUTffJuHTTM9tyYyeZYrejfPL06dPodrt49tlnK3/+7LPP4vz589FjPvzhD+PatWvuf08++SQAYLMfMMUWG8kYKCaBMxTnnKdYHRTjDzi2MGjkk2J1SniRyt9IJ17S5A4wE9pIssknotRGUAJacowSyQhc8qNJdSShyLEMtFKOOFNMZmXInfXoWPkzS6HpSflk5v0AyveMjk8lTwQm2j3FZAZO7n4DaVotl3fFEvyTrgNlGiA6SCyIRVH4TVEkeZKYdZoucaMM+LqWkM9w0+eQ8QrIwFBlfAdwpU0+wzkpl2ADXM6dft8kcC0lI+HfegrYIjlG7NpTUkAKX+WPb+ZyBYpcR0ZvwJuezyRpWU4WkZIl5JhijbpPCiBeffw4+MvXNKkSG1t7cnMKIDOfyB8ntW6lWE/8e2vaRKMnAHI5tpWrtmuYS+LYNkDOeYopjPbT8sn8O570V8xIMyTmUw6s1oCvEuM3x9AYCEUpWY4iM9qlvI6fe9xTbHFsIkfK5WYSO1xqBgTI81mug3B5fHrd5L5kkuerVHhMgQeapldSwTS3dkqdyQFZSdAXihQEJKfkkw7wTrBAaZ6K5Tn8fue9NCPP27HU7EyxXPdiadO/qfiuJQBAmlM0PtBA2ifXy/7j60CqQU2uoOLmMkVzgVhelirm5CT/FMswxeh3U55isU/ytReOoSiAp17cq0lUw/BASVi4S39XMU+x3DEcXHthZ+TwBqn7ZNpoP32vz3Cm2IXjzl+ZyydHwnySahzl5m6h+ySQBsgpUmosnWIijSUAMtNrNpsnO2hu3I5MscFggCeeeAIf/ehH3Z/NZjN89KMfxVvf+tboMcPhEEePHq38DwDWB9VLoQrVi02N9heeYs9FPr4R24zFuzZ4NDr8kMZCRY4i55kxUVRbGjHFMpsaQN785qq/0n2XmBVZ095MBTaldw6PjzP7MhRmMlMVmipIXgTlecWvPbUYU6L5h09fx6eeuR79TT7xpSqZxOCpLeJZT7HmDRUoiF0TgqA5Vh9JKKUOlN4brJ6sSpsi6TgPpjWXT64xkIYH/WaniCe6Eus1Or6wwE0DrxBNgcAxFBQ0fan7ZI2RyGQzKdDgmOBJ48FXmVkByNKbVIEiBz5L84okB88ZmHsDXbnantrMyd0n43OpJCuojZ/wpKn4b0SqqqmkE8izjPlvysWgTGEkUrFOFQd4TIU5UZL95NhWGrBd9jOLb6jKP0sDchqmmGMfJZliAktNyXrKeVzFzj0PVqffs80EO7p6fAYsl+STGRkjkLd5yBntx+5ZDnzNSUk05w2kGoek5yOVfFIYm3cHb8oUSwHPOQ9QQM51ctYDOamu1ChG6gacA+OOZoz2pc1zURTZ/UfKnqIcW+4+ORbyBKnjJj/vaDMfjem7kC9IHW014CmQ/sZ8Hh8/PmXynwNLuEduKiT5ZM7XC4iDJW58gz9jbd1NNKcRJf9rfbz8TMnGy/mKpQrEPamQk8qNEs+nPP/q7+w5aXOaKRauW1LnYIqLJ9cBAA+d3sSJzYHrqMvlk9K3VZ5THeAfCzYHVVBMzgtT7FFNI6AUFkF/LBn98+eYYordVp5iAPDBD34Q/+yf/TP83M/9HD71qU/hb/yNv4GdnR1853d+p+l3NkJPsY30YixNrhTU7eHKzqj24tPinJNJAXV2xTTjtQHkO8BICGuuM5Mz2Yx8ODkjQn58bPNOwGRqkpYWU159DVkddCk5KUY22UxWrdMfj98AJlgd5EkT2dRIkhseKWZHaqJ584Mn8eYHT2JnNMX7fvY38eWru7Xf5BNfvjtSgim2rFxVuO5UBSHnEXLKmeYK8knh+3aJRCRR1cknFZ4VRjkf/ebmoBc1TncbiwwoNhJAOb5g8PcsJwEB8iyDGWMlWoz2NZ3DJKZBjtHBO6zKTU8S4FKGfTQSuo1quunmmGKpCnCObdyEKZbr/MUjlWjz30z5bwDxjbvGt05qUZ+TT0ps460lm2hIsp+clFwlQRHmFfE9E875iKL7pE/W4/99sPi+4tJNGXDIGu07T7HIuFkmfZqR6NkVCnPoDIM0Kk9WFCZSVfec4bJk3O5ypBRTLCMlET3FuvG1wx+b/i4pD5eZYpl5nNZN0d5C2rzHC1rTTF4IZJhiGW+vVH5F4d6V6JySBrtza3a2+2SuS3bWoze9bjuj/ZR8cpL+tiRvxsq4EYaahikmNihTAN0SeAqkAVSfF8aPp/yjzpqS8+gNxVwmfVtJphgHxRRMMU1OnAKapsFez0kYE/fKme1/+cXkmEC6SEtzWbRTdGIek3y6U6zGGFA+TKzVOeUAALzx/hP4oW95DD/+7Y8DAE45UIwxxTLfdZgXcj+02BwYA8j/rz+4hJ/4lT+q5Xep5h1NG+hxyb8G9I2NrSnmx+LQQbFv//Zvx4/92I/h+7//+/H444/jE5/4BP7dv/t3NfP9XHATbUDeSGqqS8eErm9kxpiqEFUQ1oS5tAgY5FpgC4mARFcE/AK9HmHDlL8pH3+QQOCBvNG+ZELIX+hwosl1wVq2bbc35I5MekuYXUqdX3jEZCjT2dz5qtQMrbsd/LO/9Ca88twWnr1+gL/8kd+sLUz0nFJsRiDd8S2XBGir/KKnWEKCMskAakQbviIwxagLYxSg6VPy1NRoP238KCVtQDq5l/zEABngr4w/SQOKKa8QzVzITS9jIVVrgPQ3otnQUOU5Kp/MMDpy1aYceKtliknyyZjfUi7Z3Ux4sIXj5uZDywY2V0yJjR++Z/w3xW5DsY6ZGfYp4BNoqXtYKkmVEjCL2b21i5cEBJZja5hi6XOXGCXScV5ilQeHkkb7JIEX1s1kx+fMmi2BmPpOo7HiRj7RznlNyZ5iAuNKYDzxY5OdrhVG+0mbB6W/YuyepaQ3FNJ85qT/EcWGGzvnPye8K5pO8q5xSTCfasAOid2nl0/GQMx0fgdw+WT9Hac5ci2Rwx9Zwuwe0DSYSRfAckwxSe7qm4bIBf3Y3iNnOp8DAKTiglY+mfo+PeCcYIol1t6cqoiu+WAyU3QKFZhioWyT5bcSOW5L0fEzNRd3U0CgYLQPeLP9T2iZYgmj/RjAlQLFesK3mPK/i8sn49+VVLyiKIoCf/ltL3Og4OmtutF+7rsOwe5cV1R+HZQ//2//5g/xkx/9LP4/n3ym8vdS7FMJT8iTL/I5IVcahfmoBlSLxaGDYgDwt/7W38IXv/hFHBwc4Dd+4zfwlre8xfwbdabYwlMsxhRTmEv3uh1HQ74aAGsS4wkokwj6GOtMsTwVNyuf1MhIEgkjLRohiEiRk9FIm+icBMaBYoLkNDa22rQ35XGV6TLk6fn2SqTUYSjXuZIiloSMKwh4JNnc6OPn/sqbcfbIEH/8/A7+46efq/x33xlJwQAKk8VMEqDt9il7isVBg9yx5CkWtlfnofEGi/l8aI6bCobcOVmbN1quHk8b041h/Jt0nXSFjQWQSTgTXiEqTzHh++DjAglpg/tGAqN9xfehMtpPvKdcehNbWHPS6JwkT+zcs4ynmANKZKlT7ry1HZb4MSmGMD+e8thwfN6ZLwoQUcIbuV+aBEaSA+aKQVJhZSPD1prP55k1lxLt+jVLklHA5sti6VwGyEnnkeFi47wEY0p8zwi4TSXbN0E+OcoxrqLvmX4j16QBh5Tf5eSTWU8xjXxySU+xJp14pe6RGj/MSWZTKAHmKSk3j1RzhZw3IyAXAOk5pry9JL9Z/s3Em3ekv61lmWK5QpjEjuP2C5LR/vb+JO7jKbDZc90nJdlnrriQu9/SnktT5AXSCoqcTQN97ynGVmpcvodLFtCEuYzuQzhuxWhfYIptGHzcUkyxcM3MsWUfZ6DYLLLeUiRBMUkKmbjf0reYeldNRvuZuTsWZ2JG+5m9aviOS03NKMI16+piD/KRX/t85ftOWcekiq3z+TwJQlLkyBdA1cc5zBWccu12Y4rdrAhBnhMb6QqV5H/DI9X1TePDk2IvaaoOUqUfkKvWKa8mCtqYhiAihWS4PGWmdrEFOedbofEU43+PIme0v5YAGyhyYIUE8uSo/Z4iH5FPKioA/Lf5xMEXi9TY9xxbxxvuPw6gnnRqZHEpQC8PQspVfk3lI2XSnOsMS0wxCRTTdEKMbYqcHCKS5HJD7hS7InfurittjSlW/t5WgimmNdqn5xFjgaa8QjRMsRSjkIIWnW6niL5vqflMI590nmKRBD/3XQNpwJwvyrmNYApokZ53XzhW25EwxxRLnjclX0ISF94z+vdcR9sKWB8zahaqqpLEKzenAIwN08RTTGJNZTZU08pcbNvA5uQg3u9IYqmlwRIJkJM2GN5TLD2n5NZdqQDnTa2bVYHpchp1nxQYiVJRhELbgCMcn4Onkm9RSj4pgUuALIH0oG/0UKxl5JM51q1ndseBkvLY+uCuoKPwFGvSHVyTy6cal+Tud3VsCZzKMMUEr1ogviGVvmvvfZoCxbTdJ+0AaG7+J6bYdDZPgIFpQE3quFk5bwMbnSJ3v2Wj/XyRF0iD1rnulamClOa7oO816Vs3SbPUkl0vOSgmfBuagk6qCEfr4TS45mlmHnvk/BEMex1s70/wJ5d3on9nPp+nu082kk+mv8VUbhibj9w+NUmU0YNiZCNzOQKKpfYf4TzK70GaKeatnPg3/ckvX8PHv3jVj51QqaQkozynSu7LMwqVyriCKui2ZIrdjAhBMZIchSwvQLcRBDywdnUnkE8KjBKKlDmopv2q5K8F+A2epcMFhZNPJphiUvW30nVTeAlTia6UwHQZkyX1AaU2FsMMUyyXBEgbMu+fE3/WvuPMrFa5cIBa5j2LMsV4Zz5VFTQun5QYQGsJj5IcmzGH4I8Vk3wKcMhVb0+57pNpTzEJtN4QzEFHwj3rdIqsh0NOPjlMGO3fOJDZm8eU8slcV6pBpFIlseMoctp8uh8b/W7UE22YSAZGmfsFyJ5iOd8iIC294aB/ktmX2XyPpOKEwoQ8J4tIMZdya0ij7pO9NOOIR47B6iqxUXlCeuOdk3cBjCkWldQt5F0NmGI5bxSeDFulTjlgaVMA6Sl8Z8B0McnqhaPxUcutu1IBLu/jKYOgkp9ZrlOqBID6RisayY/t+6om+rF5UGZY5+YFqTiRZ4rJG4ycv1Zs7aCQ1vujGqP9TI4lAagpLxsemwkTdg9gCsVtIY/33b3lgmkMgOXzbNwEvVP7exS0qV1L5HZHMh0gcx07JU+xUWb+X+t33HsQG19ie0kdNwG/AY4pTQjsTsonMwCANJfl7A4okkb7mePTrCn5uKIoXBOjbE4qrNUpplinQDSfo0h9VzysTLGc6Xy/28HLz5Zm+198IQ6KSXYe3rcunRvV5ZNC0SsClPW7RaJhSfy7kpjNqTh9pN5wLIdrhOsWvwf5gsSsVrj7yK993v3/VIE7lZ9UgNeGzXgAX6SJzWU5kk4q7hxQLJAeHVtUqJp2nwQ8U+xq8Bv+eAFwcD5V8YU4RVcMzyuW5O87Rkt9fCdfSVRb9paQT+bM23OMkpxsNeUz5Su/0cNYgm1n1gGylCMHqHEfqFTXmXz3yfo9pwmrKGQANSW1clXMBEACpFvW5phiOf8fZ4gtyicXm9gQKMmYRZ4ko33RUywNbkkVxRxAJG2e53OfJOcYieF7RgbAKabYcaXR/p4DxeLfNj03fs8l/zV33pnnnQPaXTIQHK+RvkgsuVyHO+ncNX4KOX9FSQqSY9wCkqRukeCnQJrMfCYDJXFgSjJE5cGvx278nt6459aOWQZwyD4rBSiWut8V6U3k+FTFPTduObbBU0xgqUlMsRiodUTVfVIG9GLrlj9WBqw9g1Nme1mZYhXpT+xZZxglQN5TzIHl0/i6CcSBljV23rFiZ04OLhm3596zrKfYEvJJSSqrMdpfprGPpmlWSuZlKqoEeXylu3dSPpl+XtWiTOS7dt9WA6YYy1Ni79kynmKVwnjCnPuo6xgdY3enAVBpLgPkRkJZdnUGAFAxxbLySZkplrrfKZ+r3DcNsEZfGYZc7Fl1U2CcohsiwNauUfw9A4Tuk4t/nyWM9lOFAQA4u5AOPrcdL4xXvs2afDLPFKvLJ9NFr9jalyIieEVTYr9lkE8SU+zKrm8EKBVpy/Oq5nicKZsCP726ZFrLUf7d719yTd5ynmIhmzxXQAJ0hdKRxBTLsKNTceeAYv1498nr+5MaEq7xFOO/UZem5dkVqY6GYyGBoODnFduk0AYvJpPKyUhynmLSZo5+s1Mkkr6MBObAbcBlllqqApsCWXwnK9nzKN2lLg6qcXNOSXNN80mYdGlp1zEwUOtHlmSKaTzFEpNGrkKVe87Ow01iiiU2sTl/EWe0r5BPxq59Q2AK5IBE1+EoUhnjiUWuK22dKSZLmr2MO72xmM/nfl7IfNv8PdPMZbnFiYCMVKOAlPxFUyAgUGw7yhTLJ1ApwLu6gc0lBInKtVj1zjN40t0nZaZYtkGARroZHMvBYsmrg5v8x8AWKemkn41Ly2TgNVdZ9DT9+LlLFejNDHsol8A1Me/1Y3uz5BRDQgIsJHaFhim2M5omTZqzRvuiObUMWKc2COHY1k6jOemPxBQOx856ignVb4kpBqS6caWZlIA8D+eaEeXa02fZpw0ZiU7+LqxdS3UHF0zfKRzoHTTJ0Rjtp95xfi6p9d69a1H5pF87YhvSvgOIYsXp8s9yRvvzeXxOy3X4liSjfM1LbaSPCkw1SeIl+SMCMiuQA5Cx9SsHAMjNaSgnlXNxL9cK5oUM+JpqlqLxn86B/HQ9cd+6OFNM0w0R8GvIbJ4uZjVmiglDnz2yBgB4PgGK8ftYl0+mCzkppq7UfZLuFS9op3LpLFPMIJ88uTlAUZTfOKnhrGB3rmETUGXKUgHt+EYfX/XyU5jNgX/+sS+KY7v5U2KKKS0W/uDpa/jj529U/o5UFNGAarG4c0CxYEPJu0eGCzJ5reTlkzmmmMSuiC8sXgohvIicKRYFxdIyqZxRPk1e6/34BlZamHKSPE8XT4Fi8gY8BchNc55iuQ1VZhOZ2jjn/BOAKoU5vG5z98mIfDIH3KY20CoD9QQolu0+6byx7KwMin7iWeeqal4+OUpWpw4E9tOGZLRPXZ0S90zy/9Ho8/2cEHqKyaCS6z4pGO3zeSbVWTbsJAMoPcUyNGSqIKXHjScDOvlkeU/iRvsyixNIA7gak1GpegzImwvZ7F5ORrwURPa4ypmjyoBc9dgtgfHKw29MEgm+yB5SMMUUwJTUkZAbQfOQ5rScfJLPUaKvlySfzHiKAfENbM6QVvL1koAS8hQD0tedYwzI7dbljWRuDaGpXWKKxcblzAOpqYLEFMutX6nxK5K4qHxGLnbmPK4kXzDp2wLyUpKc/2mKxc/Hjt0v3gU+zSbRMcViubRk+k5B4FTYJMdt/kVGO23qqudOv9PrpLt7S90ncz5TkjejU4ok1mwuYYx1oMzaPAj+ceMMkx8AY4qlC1mifDLjKRZ71tzzNd70St4DSGBJjsFJ4TbxYZftjEzXg1N2pth6ggXpfyO9B0l1n8xJ/t3Y/a4nBCQklOnuk/FrzhViAODsUWKK7Uf/+4h9m+HvSP6fKT8zibVJ9+7U1sAxNKXvEqjnok1AsW6nwMkN6kB5sDg/yuVTc3g1D881bAK8VPlgMnN5wuagh7/wposAgN/4kxcWY8eLEynv6Fw+B1SZl9v7Y/w3/69fx7f99Mcq6+xYwHI03StjcceAYiE7ot/tuJeU+xnM53ODfDIuW6LFQvThcZKhuJeNxG7gXbyioBhViiKb91wXrpx8MvUSA/kGA8sY7fM/D889ZyI7zDKX5EXNM82q4+ZkMxSpLkO5cSlim29NZyQgLbVSGe2THCMBGKTe0ZzHVA7YAtIbC8lzAigrJEB5b2P0fEBmim0KLBwHJKaYYoKxaNVrI8cUq4694xab+Dd5nEkIUywe/ixSFeTYxkblPZfxo8nNKSlPMat8MtxUjRMJV+XcE5tvWlg7hcYzKX7Ppco1JSbx5Kv8Z5Yplkg0x8pNu9wdr3rsWr/jPB01Ur6cdFM2fq+fd0qm5I7NgB28aGEF5LYy8skcq0NjtC89K5rrYve9YvIfec+lIpYEBA57XfeepEAxt+5mjPYtnbwopI7PgOyRJXZ2zbC1dEwxJUBkrH7zDsAxVnvWU0zIr3JdTn3BMp4XZj3FFEB77P2kHHoyixuvA3nAIrVuaoqWQLVgzs9BxWhP5KSa/GpdkPSpQRqBKZZas4uiEDtQ5nzYfPGsPjbtfaS901HB6F/ukJ2eT/ixsU3/Wj+t2ADyrHQJaNfs2QDf7a5mmZPJaVOgi0beS/lssoGGkIvT/U6Z3UvAFFC+Z94TUy4ohfOhA+SMzQUAJp+8Lssnow3dFn8mG+0HwI7Afif5Xq9T4L4T6wDitkZAnilmMdoHgNNbVSuZXD4dFks1XnlDVsinPOHIWg8XFtd6ZXckFidSeTDNvVL+zfcdT7+4j4PJDFd2RvgC85KTVH+Sv60UdwwoFvsAjjmzfc+w4BPPsJveCAJpLx+NNC1lqq1hNwDyJH3gmGJp+WRqYaEFOiWxoqQoNgHkEoEcXTHXKShVec4li6mmBm7czGSRa6UsHQvwRDvwnXCLWoYpFqGY5ry1/NgZppjgKeYkFVamWILx5I+XmTBAmkkzySTnw17Xgd0vJMz2pffMPatgEZ/NfMeaVLK5JYBikwooJt+3cE6gJC7FFKPK62wO3Ehs5mijNOh2kvc9xiCyNA1JfddOkp2TTyZbledBsclsXve+y2wsgDRgnqveAnzzbd/MyYCBnOxuZjbuuev2zNM0WBG+I5UEV/RbkhmsbjNnMPkH0nORO1bJ1gJyYGBsTiCmmAzyp76rlAwF0FXdJbA9JxuVQchZ8jiAeQ8lfMXolidBY0nGmJvHM4UsieUmA845ppjswcPPPSclrDPF/PsdA0+Lokj6K/JzT60fEvMot5HNsX2znmJCkVYyil7vd931pMz2s0yxxGaS/7vEFOt2Crf28m/MwmgPr9vnV+k9REpBwMdO+kwJG/FcQx2Ad6C0SRgBuaiiyUuJ3S3JJ+MgTRrgB+RNP1+/YsbvuTVTBH0zoBZFqlic8ySjObImYzR4iqVAKYnZTUuh1eyeB/cVi0XqXaV7EXqK5WTgAHAm5ykmmK8TAC5ZHdQ8xQR7BM7Eu3BiA4CdKZbb36aCVDPEFMt5ivl3vBxf072d7xlc8X7Y8yq6nbFYnMhZIklj88Z9XCr7qWe23f+X1CZSgwQp7hhQLBbOE4yBWvwGZeWTmwn5pEJ+mUpCcl4bFJIEZk9YFPvCggYwU+wUm0SgEeeYXt63Qgan0qBYfGypExXADGyn9Q6QAE/40kALILNJxIUpJUNUyMMAT1GNMcVyJv0pqZUHbiX5ZJzhlpN35UASiSJPkWI05hI2ADi5WAxeiPiKzWbe8D72Gyn5DL/3eaP9tE+HZFrpk/uQKUbyyZRBZ9d969cSZvvSnEARA9p1FW//Xce+L3r3NowVMnrHpWfNN1WhhFJTVUwxdjVVWM8SsMs5JMZtji6/MZSZYjkmpsSkkTagmhbrkkdJ+buLexYDaQT2T27TXmGKCR48gAzIxZaArczGIscycMmXkR1HIYGROT8zlXddYj4iCeWNg/ic4pli8fOWcoXcRjJneTBz70ps3PJYCaApzzsGiuk7pllZU5pi0FAoKGnN8uPdJ8t/pp51zlMsd+4SUCLNpUVReLZvYu3KMX6ThQ1FIYoi5r2kYbSnutpqCuMSADvOrD+i0b6C3e07UMZYaovrznkfRb5NTX52ZJiWT3r5ZXoTmzPaT+0fUg0VyvOWv2mNT2EOJEoa7TtGTgoAjYOB08w8BORBfkk+6ZhiCTBOw1zKdU+mexHuN1PNaXJddAHgTMZTTFI0aaSQ6e6TkWPYfp7YU1ZPsZwSKhXEFHOgWKZpVspoX5o7+TkT83Nr2HOKnRsHk0reFP5W0jta4VvHlWfP3/BS2U89c939f4kp5poOJvL3VNzZoBh1oNyrty0F8qDYsQRTTGPUn9pgpCiaYUiVi32RKeYT1ZiHg1Y+GUvwc6wSrXwy1lIZ8Jrn8APK+WXkDGxzHSRTCH7OoJ8i3d1I3kxRxFgl3vdByRRLdZ8UJb6L604YoOe7QcU3NJrKR0qWkJMVAF5CGetAWQG3It9HqnU3TwBT90zaPOdMLvn51JhiBzJTDGBzWWJjIc0JbvxefTNp8RQD4t9XrnkH3c9J4PeUA1iAhWdfSp6s6AqVelc1HaUkdgT/jWhFkiUD4Tyc+76yTLEcqyOoCFaOFcxVqeqcktMB+fdcMn4X5ZMZ/wdKWIsE3b7TKdjalwanYhtvySsQyLMMJAmjChQT5hWe0KUkeYCt8k1BzNeYzElz7iJQkjk21Z3bjU2bo6hUVgDjMu+J69Y2zhvtW/21NCCL1Ck7B9RL+VWOYZGzPMix1CTlQu5ZSx2Ey7FlRmMKbOB5eKoQRbERYbRoQMzUps5STIp2C1V2EI4b7efHluSTWaaYUFTREAI8U8yWJ0l+lLy7d+qe8YYKYeSKzDJTLA9OAVVmS+z4pHwyUUjSgFM5j0TJAy7ZfTKz3+KRK6TRfBMqk+h0QkBOIyUk+eTz2wfRPa4oqXNASf0506dW68wtMBg9uN1hoJhsI3KzmGIeFFvIJ5UNNJzRvkY+Gek+uTXs4eha3xWsnmUy1nCvmyLpaJ4zb8bDAdBPX/JMMd18sgLFXByLeIKN2KYgh/wTRTA0uNZUarYS5r1aBpBPvOqTHU26sQ0wfRDzeVzOsZuYpCgcKh6TbboNdPxYrdF+0lOMqnJJ+WSq6ux/L1YJzSUgaTZJPskF/MIUXvdIAfAA8YRT2wY6tYHWdBVMbURzFSpf5ZerU9J18w0sZx/lWH0Aa0ccYYrlJBX0joYbYEo0u4JxruSfoAHz/IYoYIqRfDLRfRJg/oZ7cbN9V5FLfNdAPPHTeYr5/xbbkHn5pJwMhGNrO6ym5pUco4Ofe8rctInvHYXUIp7Pw6mkM5UEbQ5lGWPu+5KkmxI45Ly1BL+lXOIlUdZTRrZAdcMfS3QpYZXmQ7G5gQNZ6sdtKH1R8kbJ6fst+bPI8kl/LRJTLFYNzSXbqRzFnbsATPGxY5572TU3Y3ngNiiCp1i4duSOA7h8XmKKKVlTxjwFSLMFqsfH75nUjMJ5tDQA1ABeuc8AJUZZNMBBsfjalSseriXm4Vyxk0dMWqditC/GngX5tGbddHmh0H0yzbiljXj926J8TZJubg3T8smsp5jQGdZLGNP3jDzFokwxYd2VvJsqHQUzTLE4M0+ew2NqDQrPFNYxxUbTWeVd4cBJLOgdqAFECjZNroA2nqWfV2rdSoFDsZDYeYAveoR71W7iWWvWS5JPjqazKNAukTf6CSCw/LMUU2yxVxFUSP1ugTfcfxwA8MCpjeh5J5liGT/IVITyyVzBMhxfQ9rwc4E32t8a9tDpFA4feXbR8CDW2GCQyBHcWifMI5wZfZkRIKJMsdizFmSvUtzRoNiJGCim7OpXHk/yycBTTMGu2Er4dfiONxmgpOtfxjAkTwH+QcQ+fDJVD7t1+nHT1ZocqyS3sRllEonUpmaWSbp6Xd9tR2KK5YxcmyZdqYqgFlSLXbeGeQSkpVaapC3l9ZRj4DjGU8pTTAHoVYyxZ/XrTnVQAYBTjilWp08TuFUU8edGG9BwY6EBETeETWQOLAAko32ST6ZBsVy1nQyUU7Jofm5cuqNhinU7vvFHDAh18snknOJ/m4+tkWIAaemPxlNsLfGuakBMCWTh40c7abH3t24ymmOKlfdxNJlFx855Qai6T0bOmRLclLcWwK9ZZorFALmZsOnna5nUzVDj/xOTJ9ByJIGB+Q5amUq/0eyewssn00b73YQsW2JXuHUz5SkmsEn48Tmj/WiXuoxv3pAxxaIgqGS0z7tzJwx8k90+Nd0nMxYXMcYtH1uWo9TnYIpp5nlJnq05MHBpT7EGHW0pji/y6NTalQMTU93BtQ2zgPjmXcPQ4PMc/8YOJukcPBwzBmLmip7LMsWOrsX3H0C+mZHLExozxRagWMxTjEA1QdoWu2b+nafyBQ98pj1f84WkZmsPUM29+Luaa7jlDMGNeQLgLVCSTXmEXCHVfVJ7vYDsswuk89KUp9hU4cG51u+69zvmKya9o5IUMu0pliaKcMDziQdO4j/+na/B//3PvTZx3gmm2DQPBMbijDPaP8BsNs++ZyHDWVINuGNYTnUj2KeQvdRz1/cX46bxiJT6S3q3+bzPmWLPXNt3RCVpDyH5MkpxR4NiJDnijBINoEVBTLO98bTyImsW462EJCPnT0LhENrgZZrPfVtpyWgfiFc99twGNsEUEzxhcsABZ6nENja5BdV/tPGJUqLzplg4QH7znTPaz9GmUx2tNEky4DfQPOGkDzn3niaZYtS2W0jach4IWelLRj4psb0GlWRzXvv/0rGSp1hOUpFqYa1JNLcS0svyvDUADW0Eq/dtN/NNAmkpN4X3FBNA0EgXSA0YyM9dYoqlALle15v/V9mQuu8jxXLQJI0pXzDVOypsBKezOWiKlDzFgHqVbErJSGrzy96DuPRGftdy5w3E71muCyMgG5sC8mZOSnjXEhuK8LylZDmZgGX8yHJmwTmZga9IpsEd2Wif5JN2n6mBkOTnjk0V7ihyTDH3bUWYYjnpJT3v+Vz2vot9nimgAsgzCh3bajxNdvLNe5AmmGIKZkeqEy+Q95mSmDDTjDm264yXA8WUG6rK2JnNWK6gk/OfS0lO6R7mCocAl9bV5ZOawggQdG1WeLbydSt813IFUy/JTufhstF+Xj6ZZ4qlC+Mao31ZuhljvaYLKhyQSI3tJbLpebRJ90ltcZvnUDxXmTDgJBYpxlbO2xfw+f9eQg6uASHDpdpmtC+DcnQf6p5i8TVzqiSNnD2a9hXTsIdkNnl1bMmc370bi7EePL0peHrFyRdNu096ptioQixIewVWSRCaZlNDxn4k71HyIj1JTLGFfDI2fyetBhTvdspoH/Bm+9Kzpvc75a+eijsaFLv/ZElj/OPnb7g/s1SXjq713EPjC7pmA72Z8OvQmjam6Or8g5I8xYA4sp2TT8Y6IVJQRTjpKZaRMeaN9uPMJU1HkhQjBMh7AgwTpr9aJkuqIjhSADyAp27ziXqUSZDd2AmplUU+aZ2kc0b7uYo3EIIGNoYcMcUk+WTqHUu1sN5XJLmbIlMsf96OXj+pGtZ7OXT6WNc0JLGx0HSkinmFaOdDSbpDf5ZqFADE27zr5ZOd6NgaYMsZJSeaaMjNIOLHlucuJ+n826knBHlWBj2rmCwid3wKHJrP5+L647sw5uWTuQRfAmlSQAc9i6iRuEqaJlclgXiynfNFyckMpI7NErjjxhfkmznWrcSuyM3jrkNdSj6ZZYqlNxi5jeSwkitEWG7Cxoyvxym2VgrI4xL1ZFOH3PeZkFpp5iNN98msL1iEia8+NukplmH2CUA7Pf+cp1iqoJP3n4vnG55VbmCKReSTsiSbzeOxYpKCKQZEmMpZ+SQVF+a1Z033QTbal7pPygVX18hCYCmLoJggn5Q6sntmh/yOJRvUCFLCXNFSZkLmwVOgnHNixflcc7UUe2msAEuyTXlcM6P6b6SYYnT7pUIOheSHCaS9bmnsGhCoKCIB3lfsue392n8TPcWEdyw1H4hG+0o7JCBNvtCQPmJxmjHFKvJipVegJgfmQBq9Y0QOIG/nZxdMMQmEbNJ9kq93JBGlQt6nL10vz0sA6b3R/goUc/HI+SMAgM8wY7aREugASpPn45EFXcUUW4snutr2vqnKBU9q1iLjF0Uhbkzog0wa7QuoeK7jTq/rN3OxhDMvv4wzaTRAouRRkgOYUqa/2sVwvb+oQjaQdwHLySepUhRKrXReUfFJWpukpvxgchVv+m36/UrXTcX3ccoxxdJVotR1x5JjQJfkSlTxXIt1oJoY8O9aY5J/POFvSJGqyPEYBMDUbDZ39z7PFCMAVWCKCZ5oMdmQT1SVgFyD7ysFEGlYoKlmEED1nY2NXxQFY/EEbBZF4rchsYcy555aO3KdDCWjYopcoUCSocwyiZDUkVADiqVAmlxHwly1Oycz0Jnd5wtokqdYemyfsKcaOqSS7S1BYgXkGQNSh9XsJrTbAZ1WzLtIAuQ6nXSOky/o+HGTjRUaeopNFfmCJJ/MgWoSEz/XoTsrn1R6iklegXlPsRRTTF47U8U7ixWKl83W5ZPSt1kURXRe8cBU+li+FqebxKTmUX8vOZuGK0WkXEVkiqk9xdLftSjdpD1TzFNM8OGUvJv85ldYMwUpYW7NFLv4Kv19gbjVg9QBsjwnYgUG84kiR5GAQEAuzNOfzeaoFGk1vl4U9F3dSIxP+9W1mtF+fM3UECAABootWEpPXtnFL/zmlzCazERFktRILpVj0DsX8wzNAZ480l3YmzHFTlPDgRsHlfct2ZU8yP814/KOld5TrPy+T2yGTLE0MJWSBotMMcZuJqbYWx8+BcD7ikkAP3034fudizsaFHvluSMoilJm5duW6qtLgJdQXt2td7BUeYoFie44k3xQpHwrCLjpCYbg0sbEmWL34xtYyaRZ4ycwTLA65vN5FpD0YEvcFFsjS4ga7c/kyli6u5GOyeI6GtYA0MW4meNjm1gtZTsltVJ1n3TsuvgmMt2YwG+GJHPpHEMuVkXQLDInN6lCkpZPpq57IyGfUbE/JaN9RdLGQWy+KdKAYrlqu8Zo380pi++w4tORY4o5pkF9TqHN5Yai82VMPhmrYsbGToG3GglkfVHOA7eSZKgi58hsbJokBCJ7KNdJK8NkSY296QBjRffJnHwyxpoSzO4BnzjH2CxeHpZ+1n5jEweHyuPr571FHm7TmeiZlDTaF6ROJqP92HVn5mK+ttQbOsggjc9RUl0B5fdUAo1zQElRFL7BTeSe06WkugomgakMkFcUhZunUqypvKdYPNfQNMdJATxAnmHN14/w3HMd41K+jBS30lPsmACSVI/PFC0T91vFFIsUGTSMdiDeUEJTdOx0Cm87MArPXc4X+FzD55XRdOb8EaV8gZhisQ6QWk+xaPdJhXzSMfkj+ZnPietj+7UjDcZJ40qm89ruk1EPT2U+C/jCDs+TcvNCSj6p6z6ZllQDrMgc+Ub498YtBixyvg1nuWCTTzpPseB2a5VUZxxTrNzX/9C/+UN877/6Pfxff3BJVIuoumMHcyhnbYbhC7OKdyPFFMt0303FuSNDdDsFxtM5vnx1r/wNgUnpAC7HFMuDrjxvp+LZpmOKlXMMsfWioFiiUYmq+2SPPG4nuLLAX97xitMAfAdKjXwSqPpW5+KOBsXWB108sJBQfia8iYqXGIh3oLR1n4x/ANqOhDVQbIHySowQBzZE5DN7mc2zZNyr8VFIVSQrXQGT8st44qZhVjgJZMRkOfcBDplHFq+2azbOAKtQhYnPTDdhxoDIXOJCMWBNBngy4J6VUE2kSXI6m1eed84bpVqxFkxkbxFDzhntN5FPDuPyGc03LcknR4rz5g0hOBtG6iZLoZZPCucfzin8O5PmE35uMaaBSj7pvHSYN6OSDZmS/miYYinPpZyHDj+vuGTIzynJ7nwpOZ/i+5C6OuVAGmpSITGmYgn+ZsIHk0dOPpNaP7jZfWoel3zrdFKn+GZulgHFeNdUcUOVkTpJPiXSVCiBkblNAn+HUp28UscSmyQll811IZMKaJqNZGqTUI6tA+RCk3+Nr5dkTs0lxklGY9Z2IL/mxr2L5HxDYuLnnnWuM7jWU0wE8zJMsZicrjxevu61RG5Hz17lKRZhimkY7fz3K55iiiIa4HPDELTIdiRk58Q3dPz+i/6njimWZmslZVaJBkyALi+l7uDbB5Ok5FWyHIgxO2iO0TRCin3XuZx0KBTALKCFA59ZnpMDH1I+zjpPMblTtbvfkd/g71gFsDDI+bYy8sm8p1iC8ZqVT3pPsfl8jt/50lUAwJNXd2XzdcG3jq47fEe81DNdENDIJ4fJdWMxjlE+2et2cO/x8j78ycIiSu6kW1X4qBrDsLyd8kLKG1z3SWe0X/8dvp+qsCcV6zSts0+/uIf5HOgUwFsfLkGxz1zaxmQ6E581L1ZbzPbvaFAM8BJKhywuFlItU6ypfHIzUYXNdYig8ElI6H1EBurS5jcubytBn/L/J+WTAstMIz1NVST5YpP0JEsYuHsvGukDSlQTFQaEHIwYRQCa3LviDdhDVqAVANUvpBRFUUQlgTr5ZGLCUiap5XHpJCKXbHoPIA5E5hlypKW/sjOqJU85ptiwF5fP5KTBgNylTvu8QnBpyiSMMTk0BTUNSVXbqTIZ0tR5hHMKnUO/m2adUlBSEwMNTPJJ7ilGTEwtSy0xLzQBSnLyFUCumGvA2xxzSUoIpApsbvPsPGESNP3U2JLhO0XuulNV1YrZfWbjHtuMacx/U36Y/Fxih/e7HfdtSJ1l0x5ulOCnmWISw01iimk9xYB6NTQHVmwlfE/d8ZnNUcr3tDzv/NrnGdoSmCgDcuG35dvbS/eb5pP6dfNHmLVbSGz4VfJJgdEuA3pxZkjO/40XK2PdPnMbcMlnKgeo5Qo6Wc/Xfvye0bPXeYrVvzEtoz3GItIUHYH0upljI1a6yLP8iMYtCjkPT8knKw1iMobc0ro3EPLKo+s99+2Evq8joahSYccl8joNUyz2XeckjBr5ZK45GhAv4GWLKk6el8hvFMWzVHMc6X7zb53fb62EEcgX0ryCoTp+0lNMKd08e9R7il26vu8K5Je3RzJ7yOUn6eccrtXOHiFW/FHm/QBT2ASewtr9UizIN/1Pnt8BIH8f4XetaWrmuk+OGVNsMZeSpxhJG2NzArcr2I3MvRpQjOSZJzeHeOj0Jtb7XRxMZvjCC7uZTqMsNzL4it35oNg58hW7DsAunyQvn6scFCNdvWJRCv06ct4NFMPEJK0x1D626P7y4l51QeILc4qVkurWBug6Gq4nEl1+HTn5ZI0pZqBa1vTa7DpSi1rK9FdrBJ7yMlDLJyMVd63JPxDvrKSpZPL/VpFuZiasTsf7JUXlqsqFIpaI+ORHqEQuPMWms3kNJMqB3kVRRM323f2SjHOFqphWChi+4xz01sgnr2W6T0qMr9As2fk9CAkuxYYgv9F0z4zJJ7UmpWlPsfx7lkp2VUb7CnNp2ag/wRTT+EwJTLFc1TvtecSZYjFQTDacL8eWr9ux1ISxs2yWhoBBrGEJUAXUUnK8LcFPLfe8Up20+LHSVCiZFef8rfialgIDU7lGDhTL3XPJU0wjbZaa49AzSxXPcxJhKb2i9yze7dP/XooZMkyct6rNvCAZ1cwLKSl5rlMoFUtmc9lAPTW23NFW3tRpjfZzjX1qnmJKNj0Qt7jQMtpj87im6Aik5W05kKacqxZ/N+FllprLgLJJGBCzb2FFYqX3EA9NflYUhcvRQlBMAo6rPmqpZy3kZwIDNFfY8PNJfQ7XgFMUMcA835wmvufSFFQ2Mj6gknyywhSLFKZVoJhwzwGWYybkk+GaqfXX4vLJ33/quvvzyzcORDWYl0+m1+qU0b4kn9TMQcME+UIDEKXi4okFKHa5ZIpJ+8WQAaoy2u/7ddZ5ihFTbAGKOZA94a0bKwxo/LpDjOPMkSE6ncIRnT71zHVxPuLPccUUY/HI+aMAgM88W740B8IHE4sTG7Sgc/lkfgOdMu/VbgTdyxga7Sv8hwjBvbpTTUJo4hz2OlnT4Fjyc6AAalJSJ/7yphK3ZLKZSfjKY+MgDf8YrKa/Wnp9alOjPT6WcGpZZkDcPF5jxloU8W45Gq8NjYdbE1mCZ6nJQAdVn8krkIISOekdbcqs22JVsbDarvWdoIoZfccchNXIJ0Ogm2K/ASjmZJsCmOXOTfCucD6FIihWZakB+mYSaU+x/PeV8vXSPC/JXFrjZ5P2M1MwxRKym/J4+RvhzCVekaTjiiI+l3opRhoUy1XsU1KQmYIpNpSYQ4p7Rj5nKaBEvt/evyKM3FwseeEsa7Sf8xTrMA+RuqeYPA+T71BaPikDenL3ST1rKi6fxGJsGfhtBDgLG8lKvpCT89W6VedBei9HsTOugHSBIFc45OvCfsQXMrfmywUCefOeNdrPbJBSthpjV+DWFHViTDFdfhZ7z7XyydS3rfGf60fmUi0Yl+o+OVKAYqH3aPW8dfsn8n0N8zNR2sYB/mATq1PnCJ5iyuY0IutVAVrECjs58MEzxewAkeSjBsjyySpTrBlII1mKAJJ8srzf0+A5W432n79+gN976pr78+e3D2SmmCD5TzGjU55vgNVvjpMvbKypVFxchimmKTQy1Zk32l8wxRaEIYrU2LHCgAbwDS2eTi+A9kfvKTGdT1+6ztRr9fmQN0mJMQNTcReAYiWq+NlntzGbzd3LQ5vMXLjNaEw+KbyA9OKMprPKRlCLwqcqoQfOfyg99gnHbqtuoGmCkjavMdZROLa0MKV8YTT3LCWfdAmyUBlzTDFBtqkx/eVjTxQbX0DwFLMypirSTR1LDUgwxRT+b0BcwqKRlknSF23Xzliy6RsMyMeShPLydjXpcixORav0KrOO7ld+MzWbRzZEBBYopYD7DhTzEkZpTtAa7UvzQugLs6c4hmKjnwbF9hwoJsgnI5vBkVI+mQLaNRuLJDCl8SNTMcUk8DYO0mg2YxJ7yAMl8gY2HDsHkuS6MAJ6T7EQIKpKGO1MMV33yXjC6xJtYf0QO8tmPWHK34154aiM9gUwUpMv+E6M4Tsuj51is9fOPWN2HwXFFDYRIgiakcumpJv+nJPDih3b+Hua9hSLg3mqhkCSfFKx+U7NhbnCYZ/5WUpMzCZG+1lPsQ0yfR9Hu4A1Ndr3GyIDU4yzFYz5GV+7NPkCUC2k8Zgo1o/YXKoF47h8khfwKg1iMv5aMb9YrdKGNrBhMySpgVSV2RECzssxxXISxpiFiR9b954AcbVLDuxOz9/lv2vk1ClPMXe/I8+rKHzOyQEfra8XkAflnAd2sOd0QFNQXKalO+dndmbhKbZ9MMF//cIV9+eXbxyouk/GvePiz6mbeD6ALo+k6HU9GcWizJHiwol1AMDnLy9AsUQDJKA+h2uaBNBcsDueuNzfgWKbOlAsJvnXAL6hgoXYgY/eQ0yxbbbvSn1bVLBcMcVcvOzUBga9DnZHU3zpyi7+3594CgDw9Y+dUx1/PAIwqaoWbBLgk7S2q2DK2FRjqO2ZYqF8Mr95lTbfDnBoYLSv6VyZqgjmJCSAT5zq7W79YijRzWMV2JEiSQXSFaqJS9p0z5onnJqkiWIjsjBqkyeRKabxcBNay2eB38Xz5ouN1nydkq7nGzHF6kwBJw2WjFxZtSslS7DKJzWdJwEPzh9MZlFmhYZBGjLFDhTsMgrXtTPSVXanoXxSzRRLsCNyLBqAsb1CXy+DcbvUiVdj1J8y+W/KFMsxM1KgWA7s1nSfzM1LjjUVMsUy0k1ANtrPeSbxsVNeajIImd9QSQboFKGv10xjtC+MrZmLUx0/tZ5iSaaYEpgKvy1+LlIleE3YfNO5J+WTGYmwVNCROrblJMZAuiCkMi4W5JMWRmOdKVb+U/o+RHlyZgM+FDaTufOmnHI+j3tc0b44bW+RYubpABogDphYGe18HveeYvLaSblhzUZFARp7sMSPq2nKA3im2GQ2j+a0Uj4c5gk8JI8qHq4DZc1TLL1+dDqFA7PD9UPzrCWwOydhlNZ6SzfGmH1MtqlCZv6W3hEqqIwmsyhTObeHibGMfSEnOawfP1NISykY0uxmHch0dK3n3tPfCkExgYCxlvAnBNJzYD/B5AP0OawbP7Lfsni4hUFMMVpPZKZYde3xXnn59YqrzuiZnwhBsVRzs4hdjaaAFAKpHhRbMMWe4UwxuUi76j7Jotft4OVntgAAv/BbT+KLL+xiY9DFex47rzredZ9k1G8NhbnX7biJgC+IWsDAUZhDUGxCxoXpsellvbIb9xQTj3XdNiOgmAIMTBnt61hm8eTHdedQfLx1ir1uEafK2nVGN9cCmKmFYZyoPIQRY0xpPIvc+LSBPogxn3RMsYqsTQNCJu63JsmlIACpCgbqqnKOKRZUIg8UwG0MxNS2WE8BB1oata/0h2wt+TltDXvuecRkKLTgSN+279hmG7v83ThIczCZOU8BnXySV8h0QGJqMzfObOSAdGVQ0xlW6rg2ViQhLtGOdMTNnbfkKZaTY/Dvjn9bWqZYyp8EyIO//cg3zccGmhnta5KonIebxNbaEADB3OY51cWLHyv6wghj697xeCU7x7baYr5DMQZPDohMdVcFdDYRqfWeM1vSRvspFqYFcE57uKUkxkCa7aUBAiUWjsVTrMYU0xgXJwob/Picz5ToKZZkHnXdeYdrl8bDbY11QOPvhsZ8nSK2bqsZ7ZEGHtqi49aQJMp2FYEvMNjH3Rz4e/48Y9Nr8mFx3VOCAE4+uZOQT6aYHYk53AMd+e863rAk946m32/tewLEu3Tn1A8peZ5q7eCdkwV/37TdQX1sbQMKQDban8/ngnwyfs05djJFURTObJ8DmVd3xy5/ie01U51sAe+NFa45dP9jnmIatQKPWAfKZZhi5ClGofEUc0yxWX4OomNeWHzH/a633NkcdCvjpb7N9Ugu24gpttjzkfrv6Wv7zOQ/U6RdMcWq8arFTfzZX/s8AOC9j50X2VI8jkc8xbQU4pjeWku3TDPF8pt38kELmWIEYEibV8m7SEMZT5nlkwG63A0xnjBqOo+lpJdjpW9ErG24NgGItfuuHq971hXDeYN80rWirjDFqJKZA2kowY9snhWdw0IzVk2SSxHrdKplXHlQLJBPKr7NWOKivV8pDwWrfDL0FMtJGIuiEFmcGqN90t3TM9NWnQHOUKheN99gifLJyEaS5JPazpf7iY2gyNbKeA/JrI70BtZ5ijXYXCzbfXKcSSg6nSIKTrlxE9fsWA0CU2yU2cylujtx5k+KobCu2LRr5K5pn6n0sSmZE6DYWFS6HMWTfOkVTxmJ8+OlbyTlk5JLPOmaAeBG5Lo1krzUeWsS7pwMEch7iqXAVw3bSvIUkwo6qQYBKqP9yHrrxjasuzXWbKZTKLBcIwvZcym/iXZr115cTgcITLHFec/m1Xdc0w2dIgb4a5pBlL9fB519vpArZsUBb43JtZdPMobaRLdmc9CAurcBOiZ+qiDP/yy393FG+0HRkvKkJGOrU79m7XnHmilQOGZerpFEgyYUPKJG+5kCnGMEGrsHA+W7T99sNFfIMOxkppiiIC82n5q73wq/Ew6KcaDbwsojkAQAHjqz6X7zmWv7ABKgWCKXBNKKJN99UgJMmzPFLCBkGKe3BpWcX/ouw3xWZbTvzrf8u5vDnsvfiqLAiU1vQ5XzFNuL+DnKRZzq7xFT7Oha38lGyU8utQZIvqepuCtAMUIWaUH5c2+8T30sgUS8++SBcmE4EukAo16IExsyjXfQiUjHTMAnBNLGOXa9FKS3F5liAROGQnPPUoCaRlsfYzyVx+rApZgZrBbU4kl2xdhaK5WNgEMWrbrT9XOmmEIOCMQTfJX0RdHYIMsUc4mIP0br30ATZOgpdqB4R7l5ZHic2iMkTHK1nRSDjQk9J00HyOPrdYCe4kABcLlFsQFTLLWRJBBhIDTvoP8ONJNPriUkQ5q5NMVc0iS6Kdar9txT85km8ZOYYmRO2zVubPRMMU33STkJqUlBFD4lju0rsGikTX+OPST6egkykKzRPnuHwsRZwwqUDOc1FVW3kaxtquR1c9jruHsW8xXLnbtstK+57sT3wTZJOUCuWROLvHxSk2vwTR+gK8CJ8kmFXUMKOFbJURLzET8+153vIPKsNWOnzPYnCgCU57p8bhgpC1EA27wv5jbOaFc3QoqBYg0K44COWe67ErJxFYoLinML36Vnr+/7cRXrloYplitaknzyhVr3yUVOnDh/3+kvvl7L8sk0U0zbLCVkdQP+Pcs9ayBltC9/WzR2mimWvtdFUYiyUa18csru91Qh+aegez6ezpNN4YD6npOfD79sCyB3dvF+A8DrLxx379xTL+4BiL8r1OhKyjHCd8SB0zE/RIUEkUeMKeYZ3aqfqERRFA4gApRG+4uxNXNQSBLghTTAYw3S2Ov9SEFC4wfMGuABnggBAK9aNFAkOX6u8VPs2aXirgLFgLJrxdsePq0+ljzFru2OHaJtNtlsQNlOGcmauk/W5JMaplj9eiksTLGbarSfoLXyCBFtCu3G++haDBTTATSbbKLgFGa/ECvBoQhgoKmCxpIBtXwy4teh8xSLb2A13T4pYomIpqMg4D3FUkwx6R2Nmb5rGxNsDONghbYxggNaFs+K7p8GmDrmWJwNmWK17pN0jCLZS2wkaXO2KcwpQM5TzAYkhseL7KHU5llx7Drb/Na6VyrO3VUFw29EJYtIM8U0AGxsI5fzZuQJbowdV/43eVOUkqtaPCTCgkp57gpwKOFxZQIhhcYGqXOXvHA0GxvOFAvXXF3XzdQ7Lh9bFIXYgTLHPpK6s2qq0CkfUH4LUmOn5E7Lyyc1ax8DaaxdmwX2qepdWRyf8ldsKp/MyXTFpiOKe0ZrVw0UUzRC4jnQQcSryZIj0XzKQRd1nlLx9lLKGFNFNJUnZX0zri3eAXBMsee4fFIBasmeYrp8+tRiA/sCy8/m87kDjpPSfwe0B0wxhVSWgM/Y+50rUA+CgiEPi29UjInkvQZltlaKZZz1NJY6VSvlkzGmmJSfUPC9ZHjf6RvpMuY6RbLzpWKvR0HvNwA8du9RB5o8TaBYzFMsUYgpzyM+h4rdJw1NGICEh7OChCAF+YoB8nobgt26DtHVvD4ExbjZfuodi9nVaN4x3gAP8EQIAHj1PUcqfzctn0yz/FJxV4BihCoCwJ95/F6TdpekiKPpzE06GsYU4F+G7Yp8Mk/PB/zGveYppuk+mTC51HSJi10vhcpTLCWf1DDFBJ8qIFN1Toyr2TgDwFEnn/TPSrsYDnucwhw5XmnkWmFMmXT9kkeW7j2zdMspfzfuB1NJNtXXHUl0s92N4p5iTd8zDcMMYJ3iah4huvcsBI218knAM8WinmIKsDxkn6ZMUGORMtrXNO8A4ptB7aYm56MjzaU5s3uNTAqIzSt5EDTtkZhPdqmpQ4wppknEHLMjCnYnkheW4MbAOD52Wkq4qMzVOkCW/9QwWSSjfamC7OcTu7RMai2veVdSFUlNJy/+7YcAkTdpFmRWmU2VdM8pyQ0N0IH8M/Ny1UgF3QIGSvLJTOfLZp5ixKyOvWcLcEgEu+PzgoZ9GivIhOeuYfGEQKSGkbiuaGSR9xRrds9S0n/eLTT1bRdFEV0/tFI+gBmST2cLU3JWvMvkWLEGHtqiY7L7pOJZx/y1tHkdAJw7WjJpnosxxYTjJQDUKp/kTDHOzksaYye6xWlyhY3gGVeOzxRbOQmBFyZms7l7Vpr3zLPLeU4r59Ppjs0yUE0hMV9zexgnn2T3WyP5p+h3Pdt4N2Gtsd7v1iwT+DVV/cx0eTRQElwoXnvfMZxe/Dvlx7FvJNXxeMbezZSnWHyd0xV2w/EreZmh22csLqqZYlX1gGYOCu+hBIqlfAJjewdtkxPukczlsq+652jl7+XzUXGYStwVoNi5o0Pcc2wNRQF86xMXTMeu97tuMiSGhtbLgEw2dyLyyWxnvkTFXSO3Orkhd5+UNs7rfW+eFzJSNIlAit6v6T45TLCPNBsiz1yK066byCc11W6gSmEmttZsNncMN22nUZ5w5lrN8oh1VnIVxQzYEmPwaNhaSfkke7+lbp+An0SrnmI6MDDlKaaR6UpMsRw4lZJPqjspNuw+CVRZnGFoZNVhBdgytqdAV697R9G8A4h76ejZdfF3bZxhwgDpzbNjRmQq5vQKp1hqshw8/40kx04URQClHxodP60nIqlxe92Oe0dSvmK5TZVnNyQSfAU4FDXanyvo9gmQRpNoS9JRDeCd3tjkK6J8PQ29plRMsUz3MmkulTpQZo32EzkKYEu4Q4CoKp+MH5vqNKrZYGwIDSU060+nU7gciecbU4t8UvAU08xnyaYKt8hTTHzWCilMSj7pmxHJa0CswKDxdaRYDxgtHLzWqjaq4JQuv/Jge6KIpgC7+XdtAQLPOU8xD4qNFEb7Q7Zmhg04tHmOk0+yoiWfl3P+jKG/lqX7JFDff+QaOPF3iL8b/H3XAB9xo30qjstAYE0+qcyFU+qF+XzO5uHEmh8ZW+PhzIPmlZqKQMgvq0yxOiCXM9oHqvLJV9971KlHKOKeYvHCAF9zwvtN7+R0lp77zN0nJ5G8TAmshcGZYhqP28mslP1rmviEoNhmA6YY7R24kmmqvGa6X72O91QGfAfK1HlSpPz6pLgrQLGiKPBzf+XN+IUPfGWFNaY9lhgaV3dGmM/nalPurUg7Zi3Q4lgdQfLk2B2KDpI7o2mlsra3mDQl+WRRFN5XLGynbDHaD4AtL0/TbCLjCZ8GpKl3hNLd76Pr5YfLkzaL2b1nEJX3eGyg5/uky0/MGnaCGztgipUTXnl8vvtkfeOuofOm29LrF9SYRFgrqaOq0As3RtGOVNJ1xyjMWhlj0iNE4XlR/n51UbaY3afMisvfyc8LYQX4ZniK3Qz5pJVdRzFVVOmS3kMKL4iiKJjJf/X4nFcHP+86E0ZPW5d8XTSdGCveFQbD+VQHytxmrpeoqrpEWyFXjTNZ9Ndcl9SV/5QAAy+ftEtQgDRbyzN4koei3/Xyy5TUVtXl1Nh9EmAdKCNMMW+0nx83lH1qNv2pd3zO/jUn3awzxfLflsTC1EqWYgzrHCMESDcTAnSeYul3XH5WgOyRmBtbktRpgNsUy3mqANT4+Py+0Xujyc8GvY67dzujiUq2SRHzztPkswDfA4Sy0TwYGO8+qWOoAZ4pxo32NYwrnsPUi0k6IJLkk3vjqctL+W/lvL1CgN8/a3mtp3OvMfMyeWXlmisNr2bRv5OKWD6dU16kgECNYgOoS4Mp+FqUes9Eo30lc4nGD4FIZ+kxqI/NgScOvGoY5RT3HC/f74fObOLIWr/CJAISnmIZn1egnqPEJKYU2hyWIsoUMzQXiEVVPqljfXG2rMaGg4LyBQruKZaaEzxTzNZ9EvCS/9Nbwwop5v6TG6oGAx4IXIFitXjluSN4y0OnGh1LD/7F3TEmjGY57Gao04LRfu6jd1THYFHSbKKPrPXc73O6usZTDPBm+2ECo/EzSEmd6DqadK7UTNLepDmkTeuqLa775H6dKaaZ8DaCLiwTRVWMIsZmGSnBIaDuKcYXdbXRPpukLb4qqaYIGuPJWPVZW3mhSuRoOqtIXi3ALd+Mue8qk2ymDLk1m0CgvihbgKkjwgaWU9VTETIzmnWfjMsns0yxWyCf1Jh5O8A50QFS3flyEn/eKvlkOLbKhDzOoqmcu1GmpRl3I9OBMlexT5mva4CplAS+crzGU6yBDEUjn5SOp/co2WI+A76muvnqmGLlfwtzBQ2D54i77jr7dJZ5z+jbms/rGwYNSy3ZfZIBbEmWGjGmav5xi+PEZy14Dynzs1iuovFJSQGBnFmue8cToLMCpJeuO8mkUYBi0lzomGK7IVNMt27G5jOt1QLFBise8u8qx2iP5Sl6+SSpRUKwO58nObCkIVOMPHie3bbJJyvM1eB5a8ffHHTdGkZsMT4vp1lTcaaxvhN83UoEyO8DUqAYzxs0BepYYSfnLew8qxp6iqWM9vn9TstVy9+esTnXCtKkCqb7Qk7Kf5qvHVrJKAC89aFT+L997cvx9//sawBUPacA322dR26dBWLyyTpjMzx3zbsB1Jli8/ncDEKGcfEEY4opZNFAmYdrOmfWPMUGAlMsMbafd/laqXvOtB87faTKAux2iopXfM4zLyZ9TcVdA4otE8c3PEODT5h5T7EIKKbc9Kcqc/sKYKrTKZw3GPcV21WwSQAv0wqN+lWeYoM4PVXl9RSZsHgVQU744gm2lyHK9ztutK/btAN1s0u+IGm9taYzP0Fqz7scmzxSyveMAw95UCzCFFMZ/sbBAgtTLLaxsbCHCCR6nkkonTeYomJSYYqRca6S/Vk32tclbaG82EkYVR204lKM+XyuMtoPPQU0x4TnHSY+u459qvUU45uaxVyYufaYTwcAZtorVNt7KcDAyFIb2eeVWPttwCegMosmzygRTVUjjBLNdx0yXsPIyW9SlX5KwCRwSMMUE4326VnXAIfynxpvrdh1a94Vn3wlwFctyFJrXKJgDy1hOk+Fu5inWK4rFpf2p5iYTdiQfIOSel88UyzOwhRlhEKXOq2nTYzVbmlxL0lGRa/A1PEZqSvACzJpYEvjuRTGWLHBSRntTzNgHMVaRHbqzdd1m0nuBzo25CmDSJ6iNbzfTBQZaB4VmWIRfy2L0b73FLMZ7fP7GT5vrdF+URS1DpQcpE991ylPSi0YF7MSKX9P/q67Hc/WTRWnNR0RY2B5jpHj2NU1+aQOOEgZ7VcL8+nrDv+upQMkwHPD6jtOa1ksvyyKgnW+ZGNTYUABEPW6HXzo6x9xjfNOB0yx2DXT8xlNZ5VxpS64qe6gwPJMMf6TzZliOk+xHn/HGVMs1+yDP4oaU0whnyRm9m6MPan0FAtZgADwKDPbT80Lrli5AsVubjg54e64kozkJmhXhWUJpyZR5b8dbor2FL5ggGe3cQnknpYpljBF1fg1pczyVd0nGWOK7pOmZTcA10Xreq3lt4655JhiHBRrYHZPYAlt+Isi/6z55E2TrKUCsTGsJvn0nLqdIptsDiPglqZDaqriYtHYx7w6LJ1+zkR8xRwjUQC34kwxSjabySf9dStBlsV4Bwam2GaC1TGeepbBUPidkJGo8SGjSFHktexTOq+4VFbH1hpNZxXZi6bbG2dWcImX9vtKeVBoZCQp5tNEURmM0ezD4zUVvqhXYENvLf4bWU+YmhF4+U+d51H6mqV7luw0qti0h3NobGxNQ4cmRvtAHKjnv9eMIacAxQSj/VwFm78DIWNrrJEIJ6SEM9cYIXlosmiouebNiJzDnbdSzudAmpg3jMpTLC3fUXVYZc96Pvfzf1PQOcciTXkzAjoJpAOkEsUktVw1wjTWAEQAmO/rxJ2zhdEeM7xXe5DWjPbzgJ7zKWSsKZunWAmK3TiYOLBfIzlNNTYA9PYWQL0DpWa9T3lSagvU/Bnz0ORn0S7wCg82HjHgOeebl+qQp2HbAulr5t9q6ruOA1O6NSscvyafHNE3Es8NY9JNTcOQVISgmOQpBsSJAEB9LvLnGclLjN0nQ6YY/82mnmJH1voOo0iZ3QPld82LKpq8hs8FQMRTrCKfjI/t5b0MB1F23KTnFbIAgaqvWK6b7Uo+eZPDySd3PFOs1ynyCD5tYtlkpUkWgXT1d38iTzThOV9hbK9dZ4otszq8XDTlKZYeey0hs9IY7fNrosWY03ql+x1Wpfy4+Y0zkADFDGb3IcPCg3EdheE8o24v7pOnuWvGrlZqtH4XAPdiW1QumIyjifTFQn+OmQbnjEF5xMz26To0TLGofDJnnJtg0mgrqOHGf1+ZXAMeZA+roPxbk1hfA1bxmkxnKh8yCi6f5OCSGhSLMsV0G6JKF8jF8fO5983TAAbleHZG4npiPsu1lufnnew+2YBRAuhkcbH1QyPvSrERKXKbolQXRo2/VcqDDWBSPiUAWjlW460VyN95aOa0lNG+tuoeA+r58Spj7Nqx+e+Likkx2Whug9Lr+CryAWNs8U5e4hqSMJ3XPK/ks1ZsqtbdJjLGFNNtRGNFIVVzgUT3ybFiAwvE10wtoCZ6imXyUt7EIjRfVwGRiWKS2sNNZIopQTEC/A+mKnYbRQwQ9DmW1m4hDtKIRvuRd9ziKbY17Lnc8LltAqZ0IE9q/2EB5cIOlBpLkJTcSevZyp9x5XjFdx3zKSQmqlaiKxntSyw1oL5mag3YU6AUfdODbnoPEgNep8o9E8V6gqmWswZx0s0IY6sRKHZEYbSf6BwssZMlTzEt6YIibDjFsZqm3ScBL6HMK9B8nqHNgflcc2QYMsW8+X3SaD+iMlEz6Ek+GWGKcX/4tNF+muGcihUopojjDGBy8izFJLkVMMW0ySIgyCeVkid6Wa8ythdtxDcyxzq5aMAUU8knExtBFcuMb34Xx2vaswNe20zNECgmSrDiaKz7ZBO21mIxHk/0Cwv/fQLiPGCgZ/HQ2BaKfVjx1jLzUv5vFqYYJUcVoMSQ6NIi+Px2nSkmdp+MsBkPjEb7ySRXKZ9s0n0ytbGg3+h2ClVHQqC8T1ofNX7e09m8kjTq5ZN1wEOb6Fa6QFJ3V5afSN8n/wYq4KuSEeKN9kP5ZP49T/vu5cEpST6p8eGJbWo0iUjKk8WNnbnuWEdZQMeQTknv+bmLTLEEkKjxUkt91/x46dtKbWz0m/7mTLEUi0dzzyWfwtw9L4oi3ixF2WQmxv7h593oWRskwst5itWfl8UrUDJ6lo32y/9WAbvn6Q1d9Zzjm2dNIUwyX7d0Oa13bdatm7EGBZq1nscmZ4opAEyKfnDPy2ZbOrsFmlfG0zlifpqyfLIOtFtAKYCb7e9XxrVsnnlYLEVOBh0oNWOn5JPapgpu/WrQcCu2Zmq6dfKQ5oVUnpLvHpwBxVI+t4o9COUgMaaY2mg/Io8D8vYct5opFtv/dDqeLbUfKRjG7nXqnQT08xdFaDtTYYo1lE8CXkKpBbsPJlP1nkVkimnkk5HCslYxR79/gfmmUbyKySezdh4RQDMVK1BMEdTq9QpjimkAhzARMHUkTCS6Xm4lj89BIgpN90mAe4rF5ZNNjPa911N6bL6pp0VlUkHw0+dM1zuZzSvG61qQhphiO6OpW0AtZvfhZlLjd0TRYdftmWL6xTjsPmmpJoZsFm1yPkyAnyZPsZh80iBZjcknNde+FqnWa03nY80zAJ1XB1A32reBYnF/Eu5LJrESeSI7msy8FFvBFOOJDf+2l2KKKRPdShfIcd2zT6qkVpli9Y17fjOWYIppjPYTzT80ye4wwRwCdABsDFSzgEMpo/0cIzLVfVKTBMXA6vB4TWe+mnRTIQdJybvK38vf75j/Dz/vHFMsNZ9qrrsfAaaqx+bBiu2Y0b6CsRUFX7mXjQb4DZlii3/VMMXS15w+lsuNwq6Zek+xtO2AxBpIgQ3aYlRszdSyDVLyyamCjV9ZO1K+ecI9o3Uz3Li3yhRjjBZNB1+K8J5PGIiYZYqxdZFfu4YR77sScqaYvuAJAGePLsz2A1BMklnx3w+/L60PKOBBiis7C5aaAmBa1mg/JtXi5y369UW+rZEBBATibMycOigmYZzP5+pcOlXI0qgXYiwoKzAV6y4IMKP9RG7or7sO8DcxnT+xMaicc2qvGfN6ldjgvcjzCY/Te4oJTLElQLHH7j0GADi/AMFTwYtYWpYbB+Cl7pNJo/0Ik1DLFPvud70C/8s3Poo/+4Z7a//t6Fof/80b7sMb7z+O+46vR4728+vUIJ+Uy/urAOBpwJdvHKgYTxThBlqbLAL+5Q0TRu3m3cknudG+slOc7z7pj53P56rKXIqi78DEDJi31utiPJ2442cVkEZmmW0OutgZTfHCzoEzdtUCW0fZx359f4KTmwNGe9YAU1VPGq3HFEW/28F4OnWJg6ayRcEnndlMX8UE6pUtbeUi1MZTWDp2RuWThut28slt/55qvk+ZKSbfsyMJDx5t0hZW6/eU3zOQrrb71tfyb/S6HXSKkmV1MJm5Z6cx+e93O+h3C4ync+yOpji+KNxofQpjzA4L6Lze72J3NHXXypM4aS4lA93ZvLqZ027GUhtJjbdKkk2pkO7wTcl8Pq+AnVPFhirOFMszzHzTjobyyYyMUCNrizF4NNLPlA+nRd61Py596/icr7lvfgNrB2mAdFMGzeY9Jvspzzsvv9GYr+eAzG1MAu86HcCT7MSo2BjF1g5+zhIIScyK+WIe5HOv1lPMM77q161hgE4WEvawa2kpSdWwAuvMjnJsxfeVYK7S+NFxg4IKD807vpUo6GjzBbF7sZYpxiTSNqZY9V3j32gOnOp1O1jrd7A/nmHnYOKKtxolgGeo1IFANSh2pGq2rwUSU/J9jTcwRY0pRptwAZCLSUbLf9c9a+/tG19zrexq6zsWIwXk9gKxtWOqzG+AtHwx5/8JyJ5iWmAq1YRJK5/k64XGWiIV3U6Bk5sDpxxJPbO1fhfX9yeIySdj7wfdo3BtB/IswNrYNWUO228tIZ98/596EK+7cAxvfvCk+PdoTziazNwakgPj+FxD8zjFWr+LjUGZm6c9xSin5N7qi7wm85wvntzAX337Q8n//uPf/rh4fGo+kWLFFFPEaWcYOTKBYmHVXZssAowpECR9+8rNu2OK7daN9rPSy4269JIvjtKCTB99svtkbjEOmUtcGpCZM065ypS/Zi2w1et23IaQJJQaM22KsAOkRf4I8GR3uvinnSkGlPfd4ikWAkSVxViUhsU3/JZ2zoPIhGVhyJ0+EjHaVySNIlMsU/k9kujWZpWB7NWYYnrmabix0DbfAKodKPeUIDkFzRs8+dlxv6HtPlmvFmm+rxBM5N1kcglUjFWiZZCmgBpNFdb79dm/Ef7+piQsVsBB1X0ywxTLyWeWYWuJAE2mEyIgdPZTXTdjdNQ2F/nnlZJYaDcYy3iKZbtPqmSI1WuumLdLDSGiTDH//zVy7nDsqcJoP3XNmmfF85+wwKD1FIs10dAAPKGEnULLXPJzWcIPR/V9xTfP0rlX2OwN5iMuT441PMkZLse+bSuLp8oU07NhwjxFmwtTbA3LfJrnDKrOb04+yZliNlDsXI0pplv3kp5ihkZI5PN7eZGPa5jhMckoP48cEz/FFNOs93H5pO0d8wzSmXvPJ5m9AAEq87n/lit7xcw1p5hinpknrFsRYErL4qFIGu0r5ZPTCCjWhCkGVCWUEigGxD3FYsUUzzaqAysjJahPETLF/PXqu33GYq3fxdtfcSbLXHVkm8kMzy3mhJiJfeWc2W/SXMaDCDg5+WST7pPLhptPVkb7NzdObfrNt8ZsnuJICIqxiT73MnCaI08iaOLJjR+TQHqpk7yBPbZeN9rnCZzoC8Z8Yfh5a70Q1gKfkRmbNHKG9ScjZvsuCVB8fKHZvqXd7kZgUG0xygd4shsyxRSAQa/rPJd2RhNY5JOh8W9FriqcespE3CJ/jIEVlnseM9ofKTz/Yv5WrvtkjimWMKbWdmVKeoopjXOB+sYil3zw4Iwt30FLB4rFOlASZX4zJ58MjKmnszmrCubflZDlUJGiK+fSRkb7CbBCJZ9cQmLMv92U1Mpqvq7yFMt1n8zIZ1KVOQ2DJ9UuHdB1K0p17NSAYoNux92X8No1hRUvsbCPDdz87pNa+U1KJsVvv9XPjI8rrdm+IBOMrXpei+p9rctp/thup3B5Rp1doXtea5Fz13xf/Lvmx2qLSbEGAxXf1QaeYlXLBAE0WEKmS/PKZDaPd8TNerhFZGmLXGkZTzELoz3s2jzI2BVQbAWdyYGqCXoq3FzasPsk4D3FvNG+lSkWZ67m5JdARD6pKDLHJKOW8/bPOH7emkYtVaN9Wx5P7+l05hsBjTPvGge96L3Q2pgAgqeYiSnmr9kqn0wa7buCaSJXiPmZLeEpBnirIyD9jcSKbzJTLF7w4n+mVQSFsntNA6KbGdyW40tXdgEAD5zazBzj7+PmsJ7n077bYrSvYf7fjPDd0FdMsZsaZOh9ZWfkF0TFRs77KJSbWAsqnDI21XoQnSSj/Z1Y98kMU2yzbrTPNxrStdOEQ9KE8BpyFa4w+ZkoEi4KqkxxppjF2ys029cuxEC9WmMx6QfqEhhL6+tOp/BmlwdTm9F+eL+ZfERK+FIm4lpPFiCehFiMXE87WbNRPhkCgVPfnljPFBsHDR2UzKNgY3JgkE/SxmI2rzIxLb/BmSFa5ijFRqQiqZVkh5V+vpnVyicBf91T5cYbyHQ5VXaZCzeSmg1ZCPBTaDaR/S7r7JeSKBvBClX3SdpUJOSTo8zYKU8YP3Zy6Mp7mAaH0senNnKaRLsoimwTjSZd09TyySWYtzFgi+8prZIhPi4gM81ivmBaYCn1vDQMtZTnqnbsmM8JYPEUq69/Gq8/7psaY81qTY9jXWUBuZCV7KQ71R0fm0e14OsmK8TygpKGMQXITLEm3Sc1cyhFWLxzxSS1bLPOvtUwy91c2rD7JACcTRjt5xhX2e6TgjcwRcpoX3rWMckooFdOxHIUQLePuDlMMf/3nJl65j3lOQg9a4uqKGV0T8w88X5H3jG69Vrm0kZCPplrCkeXHZVPNgSJznCmWOJ+R+duITdK5TRAngUYRrheanxeb2YMF/fk6Rf3sDuaolMg6cfljmHz3JEIU+y9rzmPc0eHePzi8ejxNPePJjN3v1pjiglNElKxAsUUcXJjgKIok7VLi8XFIp8sO8/MWMUgfyx/EfkkTabNWWAr4ilGyVDWaJ+YYnt+48/9xGSwhDEcIqaoWqZYjV6qeFNPRkAxbYcNwINi1/fHlWMtvl5UobIwnoB6wmmREQKsWsSZYipPsWrVRON3VD0unmCr5JPBNVe6YKlAsXIBfP7GgX9PNfLJgCnGE+0csESgGH3TFNoEnYM78/lcLYcGyu+WPj2eYFuYYnxTZRkbYBUf9sx3lOzTcANcBcUUTLGgC2QTo+QmjMSk0b4D2/OskBQzQ5rTiqJIGpFrEnx/rI2yzueRWOQKBSnmkQaQ499sOK+Q/FJzzenuePKz3grYvhRjxbn7LkfxsXMbjJikG1B6imWAEkl+k+riOOO2BcJ7Gsr+AbYJbHjNmntG9zt13rlNSmrzrF2/Ykb7y5jG25liHADVFQhSTZC4R6HGz+ygAfja7fhmKRx01m6OYo1HrKwpzhSzFFv7ASvR0hwHiHe21Rh0x/wZzUyxhTSKmGLajp257pMa5hR5Mb9wY1TxJNbJJwOmmPK6HQC5HwfFRB9OoUirzcMHC99WwOcquU7XfG6n89QWJQBggxptJZoLSIBetPukkr3pxndge9zrNu0pFhl7CaN9wFuqAOl3JdbURira0XcYk0/mWIBh1JhihnnoZgTtCf/o2W0AwL3H17Pf1IAB8DGm2N9858vxXz78Llw8We8QCVSxCto7TFsCA1eg2C2KXrfjQKZnXtwDoGPh8OrYzsHEvQgWvyXAL4Sz2dz9/1yVKvQUG09nbpLMd58swaHpbI7txURLm6thZnEgQ24gYLMoKy5rgceVxouG4uRWtTIF6CtjgJdPElNsXyHFo3CyI+cpZgO1QmmCFVTbZNUa7ymWT9pCw3yrAXm6W5o92bR0ZwW8Fn40mbn3VMNIHDoGTzXJzR0HlN80vYoEnvJryAGo1OlxNi/fEUuCXRQFtgZ1qryliyTfQFu8yIC4d4RZPuneb79IqSTCAcvBG5wqQLFeXWqlrdCljfbz33ee/dNsY6JhV8QS/CkBLFKXuARbisIl2onvJMk8cvN4cmh0Oh4IrJmBK7xGBgkQUZtoxwxhAb9JUHWKC5Iv7foVk3SXY+fnY8+4rVfcc8dyj8HYuIDS5L8COGtZT3G5q+aepbzrNO8JkGY3aGUdYfGOn4uaZRYxjc/NRyIDtOE1awthcamsfs2OdW7WglOxRhQ0L2qlSxWmmIHRHhbvtM2uKGJzqsag23XyZd+itfvkOcYUm8/nqg6QQHwen8/npryWbGdG0xluHExUbPqYZBTQg1Pnj5XX+9Rin0ahkcDHmJBWEJJ3yg6tHpJMMS6fdEya8p8aVdFmgvWqUclEze6tRvsRr1mAMcUy3Sc5YGGVbobBmWKpbyRW6JRystg9orDY3PBzOgg8s1siirl187PP3gAAPHAqDmTxqBjtr8WL37JNggeKae/QFlPMMfjndZZfKlagmDJIqvXUi3qmWLdTuETkxsHEt+ZVvAgVY9NplbYN5Bdk8hTbHU2xP55WJqwcy2yt33WJ27WFhNKBDRr2UcRsX919MkDSXZKruGennKeY95jSJuhA2eIV8KDYM4tnff6oTC8FPIIe+sdpxgX8xj2UTzYxkbUkTqEPj1bGkZKGWcC8OhA4r/03+Ry6LuG8vKiE0nVI9P6QKbbPEp/ce9bpFG7MmHGulikGlN9HrpoWRqwS6pli+XtG93W/gadYzDvCKp8kj8QJ2wRqEqD1oEGB9j0F4kwxbfcx8sPYr8kn89/3WgC+UljlZTVja8WGLjR05eNKQOJGRj6ZM0tOAXlar5BU9+KZoiKbGzs3J6WaDHj2UH5zEVaT1R5VmXdFs5mLdRrNjZ1iilUb3NiOVzOm2BrCpejcZiI3btLfSukVmJLp5ruF1o9vhym2yBMi9ztXh0o1apko3m8gDoBq/cj4+Hxu0cpVY36BdO9zLGWKClPMUKB27LwFoHTg1mwlQy3CQNV5XNWZYpZGXwBwdmG0vzualnsQ5SbeG3KzHF7pK0yxPui69eSFGyNVTutMzZPdJ+XnRWyVJxd+SRSa5x0H+G15OBBRX2SKWEXhcyBntK/8JgGB9aqQT8bN7qv/LT9+wlNscf0pBQM9C85K1q6XqSCrI0DwuBLlk/VxNZ5iWnBnmGKKGd6vZcIxxZ4rmWL3n5T9xACfh/a7hVq2zaMoito7MlUWYpaNVAMkKVagmDKo6vG0gSkGVBMR6wfgFqZxndGS28geXeu5F+7F3bE7ttspVBP8iY0q08yDDQZWx6gOiuW8CLze27PjAN2kc3JT6j6pZ4pd35tg52DiDPvvO5EHxcKP3mLwD9QXZIvsE/Cg3O4BN9rXAEvV5Mea2KdYMCZZG/koGBpRUIS+YhrwNvQU82b3untN4CkHxbTJZp8Zeu+Pp6zqbHvOfGNjYZvRorzNWG5qTzEnv6l7imnlk0D5jHLeVGGE0h+tQTOQMNpXHp9miuUZqM44fjJz8xhgBw34xmQ2m4NySIll4NYOo6fYVk4+mWFN0XOezOZBoq0EhxLm6xbT+JSnWK7ynbp2zRrSc4lzMzDQdZ8M51MFozHKClTKb3g3Kh78fdXdcztjitaQ+bz6bdImSSpQpJhi2medBNUUbEogYfyuZYAKm7Lcei/KJ3PvN2Nq8ec7UQKJMYYFf265d5zWrp2Yt5bRf24+n5tsA4BqjmaTT1a/L287oGWKpWWjMvu0vm5ZGn0B5TVTw69nrx+omU9hHh6ehxYkchLKnRGTT+bn0dBoX+spdv8CFHvm+n5lHdCs96KnmDI3BKrfCffck941+nboui2NILzlQRzg1xjtcxaUla2V6j65n1Ei0By9zNhhkKWKVGyNzd1edl8/RvIUs3hWAxGmmHK9ullB3y35heuYYuX9ovyoSawHwG17nmL1wkIuVqCYMkir/PQ1AsVs1Okb+75KY0WVaTGhJKDfzbMriqKo+Iq5zWu/q+qYQwDRizWmWP66YxvJg6lucQlbnVu6c5zalOST+VedyyeJfn10ref+XAoHSo1IPmmbLMNkV5NA8OCeZhYz1nATqpVx8GpYE8N5gNHVJ9Vr1oxPwTtQTljXOul58437ZDpjnSd13zQ326fQsHcouHm71Z9kawHI7URAMZWn2OK+EBvSMnZMfkPve06SHXZT1Phd8AgZRFqDZiC+kdS+pylPMY0/SsVfMcLiyTPF6swnLiuRNu4xZoXKUywiz+WRk89UGsRUvIdsTLHwfmskYhzsjjGPsl28Eiw5zfFZ+aRhPuVh2cyNJ/VnnRub7tkoASQCGVBN7HKaWevZu1IBfhX3LMsUU0oBk51dtcy+CtvLBgZW5gQjOBTrPpljOfNNzW4DhlvMG8vSJY9kXjcix+fkqqH8nneozbGU/fl7cEoLBAKCfFK5B4hdt4YJ1I9sxq1MMcCzxZ7b3lfbPBxxhT+W47D3VZvXUqH6hRsHDCwXGLeJTaw2nz61OcDGoIv5HHjqqpdQaorMMRm6FozjwY3c+Tws3XPvpVZVbGjyYGJAciNzQGfpEeuabGVrhYAHRQ609l5dbA412OXEgvYDUl65Jsy/sTlU8hSz7HcAyVOsJVAsmDceSPiA8aAcYXMJUGwzAE7dvH+LGXKxbs25WIFiyiDA5RmDfBJgHShHOk09j5A95BktusXYdaDcHak7T1KETDFLF5b1SNVbXaEKQBoLkk5VqWr3SX1F8Oh6+ayu743x5asl/frCifykAfjEhzZUFs8KoJ7ka6o8lfEZKOf830xG+9PK+HmwgG9omi2oIVgxYUCJBrgFqqCYlt4fghVWtlZMPum6ESpln0CZNFg6R5ZjewkIhUWCSfeFQLFBt9Mg+fELG927HChW6aY4njHDXlsyUZNPKr7rmBeOls2ynqiCapJ0zjyMyqwadFzjNHANUyzqPdTQU2zK2F85o/3yvOvXnJvH6fgmDFTOPIp1tNKCBuG1ayT4KaN9rXedu+4GnTNjz5pX3EXz9MixgN+cFEWGsRXxJNODQ/z74MeX/9SAcU1luikvNakLWeX4iNG+9lnHu5/pgHJJCq55x2jzxeX32rGj3liLY3PvCT++Cg7pQJpwXtgf+eu3MsXKXFwv30kZ7WvyKyDupWbrxOjnMosKgIJ8xZ67fqAq5gAsF2bvyYjdM3XRctMzxcaKPYSfR6ubWC0oVhSFY4t9iUkoNe/ZzWKK8cIOf3a5rrQAN9rXEyhiRuaAzvc0yhQzAlMpplguL415ik0UOYoUrzi7hXe88gz+2zdfTP6d2PwrgVOOxTedV4ptFRag8nxr3SeXZMZZIyRK3G/wFFuOKVZlM7bmKbZiit26cKbeRo05gSXb+xPzB+ATv0UiQBtoI7B1ZWfkJiwtKEZm+7R5dj5VGqAlUu2nSnTWaD+YsCxIOu8+SZOXRldPcYx1n/zyosp0QSGdBDyKvjeeYjqzGZICVTnIwWTqrlvP4vGgXBP5JLGm6Hnn2HGpLqOTjLyKxyDwUbOwfyjIQ+Dy9kElmZGunb+D+2PvwaYFm2NMMQsISj5Vu6OJu3Z1e/dBHZCzGP8OA1BMm9gDvMvQtPLP8r/JC2alm+Jkqq5YU3j5JL0r+vsdbmoAPajmvIdqXbjy33ePS2X55lktUaYEqr7xLn9fk+DbxiVwneYxHtWOofHf6LHuWzH2kJoplpCCaKR8QByob8KEKY9fvKvC8bHNBWAw2o8w+/jvSe+ZxNbKAiUJdh1he/kGAXVwSiuJC+cENzYD5FKR8n/TXndMXgzo842YBMfKkIv5sGVBsZjRviD94VEUBQNo2Nql9C+KeWNZ8rN4F0YbaEz3jNaebqdQryHeYmKq+q4o6sVpPRMfSIGJmvWjzj4dGZo/UXCzfS24RBYR1xmj3BdM9fkZL1SrJKOd+loNQAWoUcR8xTTF8dg8alV8ANVO2ZXmUcI7HnbJ04LzQHlP6Ld3I751mnds2oB5SuEUBEEhK7fnjHmKuWJOQ6ZYr9vBP/8rb8YPfPNjyb/DlRoUUmGAFx/58l5hASq7R4brhsVu5mZEjSl2Ku8pNrgJoJgHTss5sK3rpucSa5KQihUopgxiilE0qRJZNnJAvRrqvQx0YxMo9uKul09qq2rH3bEL+aSJKVb+nWj3ycxivp6gl+qM9qvdbgD/MWjOmxvtEyiWajMbBmfK7I4mrkGBlnLq2SzzShVXOxFtMgqzRT4Zytq0oFifMYzi1fL8/fYGtlX5pCUBcT5yuyN33Z1Crsp1mK/ewWRmZmsdiXiKaRl2gH/H6duyjB1LsC3eKvSeUbKrnQ8AX+2hZILYp0Whm5O4bEhjXl4Z2zHF7J4Ecfmkbi5eYwkuD63xb0wWp5YMxWj+yiQ7vvHOsyP4fFUz7q2AYulzj5231htyGNn08+M1jCmgCiRqfUo2HAszAOQUDCDvXVFNvtRG+ymmmMZTTOhImL3f3QS7TuHrVRk7wmbUzIWx91TzvI6webAqldUxgAYRwJmfe9ZTLMbivAmAmtYAfTz1IKaFTe+lfDE/sxzTK+KNZZjHo90nle9KOKc42b7SDgRgbH6m2rAx2stjvOWBspAVWbNdsVaaU9yGri6ftDDFSD556fq+2rbgKCsQU1gJAYDPzziTX9N9MpxHnYwxs94CaM4Uc6zZusLFcr+5RK7C7NbIRmdBgVjxfpZG5nUFgabwGCvmWCV9sQZMQN7WIzZ2G8ypUMIIyJJ9vhbwb1FboOQRrndtyyf5e3xqc6DaXzpPsUTnSU2E1itWK6mmsWKK3cI4zVq9AvqFgW9irR9A2H3MKp884ZhTY2aIbWOKOaN9ZfdIII7EaxeXtaBirvVkAcqKBE3AJKG0dEM8tuGrY14+qWOKDXseJNodTfHMtVJme++iRXQuuCyCwJbNQVdvdsk6O9G5H1FMYlUJy9SBJUSflyLWJl3DqqDgQGDlWEMl8iS9pztjE9WdMySsSS7d1+sVo309+Erv6NUmoNhaPcHedxU5RVUxYIppxwXqC9ue0afQb0RnZjl3yCCybMbiXaVsG9i60b5uM2el6lfOW9h4Z2VxoteTDKbxeYyHNsEPG1kA+jbvsc58/Nyl+TAEuy3HAsDWIMUUy8snY+8YoAfkQi/N2tgKlkETpljSA05ZsY9tJLVyPCDOkNO8KwQ0zObVb1PbOS0FvmrXr2EE7NYC7V4qW99gaRluHMScKa8Z8GsXL7xpq/abke/DUpyQGFNWyamTZinXD8C/M/vjGZ7bLvMzTa6xrNF+jNWoMeh2TZAW79h8PvfAlAGkefmZLQDAb33hCrMtkK/bM8XqIItlbGqE9MKNkbOYkCwTYj5qfGwN0B4HxRRzeMSvz8poB/j+x3t8FYX8jveCDodWJg0x9W9EclIRjFuMO4t4cKqZYqyhEGeX54q1YedLbXOXZSNWqJTmXz638RyowgJUvh9uf7tgZh8mU0wjnQT8erWMp1i4d2jrul2zkkiThFSsQDFlEA2YQu0pxo32DT44fAxK8K2MFu4pRgwLbfvqEySf3CX5pIUpVk/wnVG/uutNdfOr/XhOMg8DwLaYcqP9J6+QfFI3cVSqNQcTPLNoyHCPERQbM5YbMZI0QUyx57b38Z8+exkA8NWPnMke1+kU7j3bn8wc0KNpLrDlZIT2JBfgm7lF9WBi+z4ADvyOGjHk9sdTc5IbM6G1yCdpnKuLd9Ti60UL03ZDplgon7Qwxbx8shyb/PPWlXMKl0rRvHJc8Z4BMU8xi3yyCr4CflHOzQu57pN5qVSaFZJLOmPSMm2FLcbWmipYnHweC6VpNHYnk+B7YKoOBKoZPCG4pJSIRZlHSgljUpKn2BilQC0t48oXGOIgjdV03sqYApoBiaFHCqDbgFK49d7oP7cx6Dp5ZdW4Xfd9DCLnDehlS44pFgW7tYU/fqztvAH/PWoBZ0D29dLKi2P3W+MBFGWpqT3FqvMZfWfaIi9Q5rSvue8oAOD/+NXPl+et8qSsSvrsHqSUF9YZ9dL6Fa55padR+d+00k0A+NpXnUWnAH7/qev4wgs7APLr3rEIU8yBWgamGJdPulxckqEnGpZYGmZ5UMwb7asalkSKC00aG3AmKO35cvK6GlPMWCB++dkS+PzXn3ja/ZkmR4kyxYyeYuuBSgaododdSxRruyEQyIC5pvJJTVgLlfz+VVhtSn9XHqHFQ9ugGJ83NCb7APCmB05gY9DFVz18uvG4vvNvqPS4tRDUIMHgl2IFiikjZIppFyXuw6A1oKUIJ+k9I6Ml1n1S7Sm2XjXatwAO6xEkXt8KOtBcGyfoU1vVDpSenp8/3lXH9id40sgUA3zCuTua4ukFU+ye47rj+caGEhELXZUmnf/w6ecwmszw4OlNPHLuiOpYzvjSyicBLs+N+JMYvDooAXLVUwVFnoIks1d3R24Trklg1ipMMf27DXBPMZ8AWDzk6Bukb8vi6xWrtlu6Z9aZYvqx6bt2TLEFOEZeLbnggMWLi/GJkaode48MSg3yAqpMV7vz6eZi1/WysXyyDnaopW0xFo1WZtWQKQaweSzowqiVN0eZYmbvILunGB87zpqSz9s3KwmetSKBi8npAP17yivIPDTPm0vq3LjKZ93tFO7vxMzbc59XjHFlYQ/FmIG0N5KedVEUjtlXYT0tjtUyEmsNBpRSwpgEUstKj3Y/03qKsd+m52XZWMWALe0GRWJ66TzFIvJLs/cdSfdtdiBA+c58/zeVXkNUVNIw2kObhwOjYiNk2FUMuoV7Hnbi5e+qRc53amuIN73sJAC4Yq/aaJ97ik3tfmZcPqkpUDvJaMPuk0DVU4wkxhqZbr9bn8ObeYqVf7c02tfNCaHpvJUM8F3veAgA8H/+5hdx+cbB4jfyOYrvPulBA89uVg2NYc/7iBKTfzSduXk82X2Sxg5k4EBzo31NxOZuqbDA57ZpBRTzRUItqy70Yrawqm9GVJlieT8xAHjLQ6fwez/4HnzHW+5vPG6sSRfQAlOsQ0yxFSh206Mmn1QuDEeYsalWskMxZLRUgHsZaJliJWhx6fq+lzoZ5ZO0ebVI0+JG+7rjQ1mCRRoAcLP9cmEYKRkdgAeCprO5A3ruM4BinGFxycgUG7gK2cwl+Rr5I0W4ofuG15xXe23wRYIYPBpQjP4OgTuALUkOW527xMXEFCvP4YWdkcn/gXezsnaTOhoY7VsNNymZJvmkRcK4xWSyFCZPsYWHUBP5ZLiwWTcmvNpP3xd5F2rHJnDKg1oW8DUmn2zIFFN3aa36V1go85KJuVoe1gCYSjPFdBLhWGc/a1fAGlPMfHzMU0w8NMkU0wAlKaaY2mg/53ElMsUWoFas06hFxh5j1zXxFDN0TouBoOpuoWv1uVBbeIydN6DfjPrvugnYnWaZ5eaTUjZd/n963haLCefrxRhAWiAwZrRv2bxHWWozXX4WdiWnfNayfgHAmx88iW963T3u3y3fR91TrJl8spIvCPc8VFzw98Xi6wUAX//qc5V/z+XhvEBMMWrCFOPdJ50vWPp411yA3aNq0TH/vKiQfeNggqu7Y3WXwDAfBRp2n+RMMeWeLzQE17JOKd7+itN4/cXj2B/P8M9+9U/Kc7d0n4x1gFSOXbLLq75ivDtsrvvkNLhm4NYyxWKkDWkO7HQKB/pxsNYVywzfQ4/91sFkapq7b0bwvZGWKQYsf34ksXX2Jy15qcUaSeRiBYopY33QdRI1QD9JbjpJ3ZR9eLpjw3bpli5zAPD6i8cBAL/9xat4+sWSuaQHxaosIAsLJ9xITmd+Ucp2n+z5KgvgkyaNNADwzCGST04UC7Ebu9+pnN+x9b5LDjRByc+1vTGe2y5BuXuO2ZlixECydPsIZbHf8Jp7En+zHpzZcc15ilmYYgwUM3UF9InAbDZvVJUjEPQqk0+qPMXY5puO03efrBrtVwzIFSw3AnjovlnYWrGNO7GnVPLJxVjkFdLEU4wWNtqMaucUvhmka9fKJ8M5xQa+VuUvgF62Q+NO2PsJ6BJOoC6V4omfXgoY8exT+hYdNOh66Rmvcflkbi6N+XrpuzDSO2KXEQI5H7YcUyzuKaaRrMYATEAP8MSq1/x4WfaTBiE1myoJmGp0v5c02vdVe/lYLyWvM5X15x0yEvMSLyAuwRkrWdIxQE0LdhdFUWNY0zVr9heuQcGIn7fu24oxvSzvmcxSswGJtA5Y5JMUH/7Tj7r3TpOLc0+x+Xzu8wWjB6kDxSoG3QqmGGPflOdTqJkpFO957Hzl37VG+7xBWJP8jMgEZX42zR7vjfbr3wagzeO7OLdoLvClK7vqoqXUfdICQvK53AJ2A/57sBrOF0WB7/7alwMA/sXHvliRq0rfpmeKxQoiqqEB+Gv2KoKp+/3UtVMu4IBAJp+8laq62NydaxDWC0BLgBVGDd9iURQVTzMrI3DZ4HujB5SeYjcjNoZV0NQiu18mwq6umliBYoY4xdhiWvryFm2gDybmjgth9dhaoXr4zBZedf4IJrM5/s0nS635et/mKUabVwsLhyYdt5izRcYqn3ReMsqPx3kYkHzSYPxeFEXFYP7iST1LDPAJ2ucv72A+LxfSsGtpKnji5T3F7EwxoKyUkXeGJtaY75FJPrlJ70ik6qySJTApyHRmaorgzmEBzE1mcyeZ1SQwfGFsarTvQTEOdOiSNsDfNy0YB3h/Er6x2DdUzenekETX1H2yXwVLvHzS6inGmWINPcUUniwUMU+xsTLp5H4Ye+P6RlLrSUYFDZ5UNWFNaRsMiD5TmXvmTHsT4FAWkIswYZbxUSvP3cq4irGmxEOdzCnVYEA2xW4OavHjQ1Bt3NBTzLKpigFb2o6GMVagZezYfZsrAdQYa9bKzKsxxbSeYuy8wy6QarC7gacYULfV8H57emBqO2K0n2M5u/s9ioBaink41iRGa7cQgqd7DeSTFPcdX8d3v+sVAICHTuclRPy7H0/njZliu6NpWfxTdhAOC0EHRpsHHhdPbuDRe3xOmFu3eO5J70oT1hTliNX8LH3Nzmg/YnZfHqsb+34modR2CaTz4nNCk8YGPFfR5rS+e3FVEm3Jhb/2VWfx2L1HsTua4iP/+fO+8CcUarsRsMfSzZYi9JvVqBe6jsWzuGZjHt00HKubF4EynqUhqw2wqRUq47Mc5zC7T2qN9m9GhN1RLfYny4Szhlh1n7w1cZqZ7VuN9ivdJ7VMscCvwxmCGybob379vQCA5xfMJW1ljToxXtsbYzabm1g4Ie2bLzK5BT2sotK7rJ2gTzLjdUDP6KDgDKkLx22TBj3rP37uBgDg3LGhuqJXZYqVgMGRoZ6lxpliFukkUO34SWCJhil2zPnOMTNWg6dYNdmcmY6lWOt33Tt96XrJhtR4a3HAwcrAJKYYgQYVppgiifHdJ4kppk9yY2wWmhcs3SepKGdhqYVMsaXkk3vltR8zyifr9GtDpb8CLulArUHX+2XE/IOyzNegKlllimU2ghHWlHYTGgXUlAnYZgRsAPSS05jP1EwNGNRBFmA5T7KpEujYjADOgI6llmSKKY32uZy7crziedOcs7R8kj8vo3zyIPptWeST/NzLf2blk455xBueNJcmA35jpvUFm80ZY0sp3Yx2P7M0qAlkXpZuccvcsyjTS8ky42PHmWZ60Lhq4m0HiADgb77z5fjoh74a3/lVL8v+3UGQpzgPUm3HaFY02hlN1F18nWVA4ClmAWh4cAll7tvsdztuvaec0K95+txy2Os6gI3yMym/893i4qCYNo+/yDpQarsESvJJCzuuwgRS5inuut1cYmcQFUWBv/XOki32C7/1pHtPJbDbycsqYI9NugmkO5NL36f3FMPin4wpdguxklCKDeSBQCfrjTAYLZ1Jger7YWmScjOC1r2NQRdnAkuoWxnh3qEtTzFaK8NutlLcElDsC1/4At7//vfjwQcfxPr6Oh5++GH8wA/8AEajUeXvffKTn8Tb3/52rK2t4eLFi/iRH/mRW3E6Ny1ON2GKse6TVnQ0rGZaN+8A8I2vrcrorEb7s3lZKfJMsfzxYfX3gLVrz00gawHLwPrxhN0nLXI+ABW5pMVkH/DA1OeeL0ExrXQSqHafJBNYi9H+JgPF3muQTgLVjbvNaL/KJgSaVbyBcoGZNEi6yvNYeOctfNyGBqbYwVJMsWqy2O8WKjCSwCsCxSzV7pgvi8VfJZy3tPMBwBKfxf3atcon2Xz2orH7ZAi0azumAdVvCygBGsoBc8dX6O4jvvEnWXaONVU970qHpSbySeV1D1wy4D3MrOBQSj6Z25QNIubpWqAjBVYsAwZqm7XENu3asZNMMaPR/n543QogMsoUM8gyYmb3WrZAaPEA6BoT1MZe4nndOIgBoMsyxXRsSMAXJdRSQAG41Wy+Q6bY1CB3irHrtKyUZUAtIN59UsukCbu20eZ7owFTjOLhM1vK4p0/t/F0Zi5OD3u+u/TOgTdf7xTyexY2tvFMsYag2GMcFMv/hvMVW1gtaButhEFqiUuL5lOy2X1dzjdS3i8eKaaYSj4ZsUmwyCdjRvu5fU/dX8umKqJ416PncHyjj8s3DvBrn7u8GDt97rHuk1ajfaDuN6tiigXSzZlbc2Aq6lsjVDIB+bk71pBAy9oPI8oUu8UyQgoq6D94evOW3uMwwrnM6pnXNOh7588tF7fkjD796U9jNpvhn/7Tf4o/+IM/wE/8xE/gp3/6p/H3/t7fc3/n+vXr+Pqv/3o88MAD+PjHP44f/dEfxQ/+4A/iZ37mZ27FKd2UaCaf9JtYTVtgHmEV1rp5B4CXnd6sSOm0G/BBr+OSoCu7IxN1utY1hx2b+xBr3SeNoNipgClm9UHgYJAVFKPN5OeeLUGxe5Um+0A1SW9itH/hxDrW+1284uwW3rDwktOP7enEFlAsbMYA2DZEnY7vfDaezkzH8iDJ7KXrJRvS4im2P5m5zYneU2zRmWk/pALrztszxcr71qT7ZNVTTM/YCuctixQjZbS/oZVPumq/f8/s3SfLMceGZGIQVPl4AqjZFIVjz+dzdQOPtV4V7KhKBOwAj5ZNyZ8zzb/aokzKcN43F7ADW066mQNZEvLJmbKiGus+qWX/cLPgGQMSCceUNlRZplh23fPJG/fTUXmKsc2cVcoHxIEtLVMsahpP7CHFtxkWwQAvn8xNp940vg7SNGkQAOibBAx7HWd474F6JZMywhTTeooBHogfTaubSY059VYg/S/H1rG9YgxSiyn3MqAaX6dKdjexo5uDYtoImxtY5ZNFUThv4XIfoJvD+bpTrjn27o88Xn3PUbzq/BH0uwXOK3JT14Fy395siwftm55fdEWUip6uW1xFPmlTewAeFPvSlV11l0DyZuRz+Ih80CxG+6wpkHa9dvLJxfyjZWWHMeh18N6Ff9yfXN5ZjC3d7zrY04S9FDKB9jWgWOAp1oSh1iTo+VSKEpk1KyYzHSuZwWFUmGLGpgbLxpsfPIn/6V2vwA9+y2OtjEdBuVWo9Ljl3ScjDL/sMbfiRN773vfive99r/v3hx56CJ/5zGfwT/7JP8GP/diPAQB+/ud/HqPRCB/5yEcwGAzw2GOP4ROf+AR+/Md/HN/1Xd91K05r6TjTSD65oCDvjU0+OEC9ImhdjCm+8bX34vefug7AZkx6emuAGwcTXL5x4CYQDRjoF/PyvB3LTMPgCait2qoxRSiftJj+AiEoZpNP0odPTK/zjZhi80ZG+yc2B/iVD74DG4Oe2YSVNia7o6kbW9NggJox8O6T2g0oRb/bwWQ2xWgy85tuY9JFTLFnF5VIVffJClNsVvmzXFC1ZbQA1EbKaiAFfb/0XSwrnzwwMEjDecvGFCvHHk3KxXx34R+hrdZHPcXWdfJJ8vaiDcLUMQwUzIqwyymrQmueWc3PjCVGVvkkP7YJa0q7aR8EoNj6oKs2qz8yrIMNgJ4p4AH+umdHU1mb2kvNmfzXx84l+Xy+3R1PsTXsVZKpRkyxxeHaboZACaBuORlR/pnRNc/n5eam1y3U9xtI+c/pzjsGqNE7rvHxjDLFlCy1mBRQW4GOyT75uWsYpMNeB/vjmZt/td9XnClmZ1jTermsfFJ7z5zR/miC+XyOoihsTLGhn0enszm6ncIEGhRF+Y4fTKbeU6wFUKwoSrPw0WS28BSzr9tbwx6u70+wczBxa062mcPi2sprni3NFCuKAr/4XW/F9sG4onpJBeXC1/eIEW8HpwBfqNawIWmOrRjtExjXEBTTgpD9yAZa23GZB+84rV2vHegyDYoaDRhE3/z6e/ELv/Wk+3fp3GWmmH5s7zdrkE8GnQH9PKYetlFE5ZOLy0/tM73nW50pZv0e+PzvwKGWSFv9bgd/++te2c5gLDbY2gHYinbLhP+mD5kpFotr167h5MmT7t8/9rGP4R3veAcGA78pes973oPPfOYzuHr1avJ3Dg4OcP369cr/2grOFKOqQi4unNjAWr+D7YMJPvnlawD0SDjvCgg0B8V4C2pLEnHmyKLCs31gNNr3lRLAn78GSHSbyEXCaJ2gfffJg7KVsxHN50b7F4xG+5vBvb33uJ4pxpN0YmhYOl8C5bt2Umnsz4Mq15e3Pbilk09WO5QCMLMhuR+OpSkCD7rmZ66X8knVe1bpPmn7rvjmeZvJorWLYziOKbleVPrH03m5qE5nbkOqY4oFYxuYYhxQ3x1NGssnD8beU8zKFKMNgqX7pPcUK4+xNkYI/cws3UZD/yBLMiAZ1ufetR5jN5B8XTu2Z0KOK3+ufc/jxu3lP/PMo7qUrzxeJ+kI10x+bF7C6P3jCHTWesClu08qQUx2YRxY02yOKgCo85nSz8OSnC93eLxjm24TCiSYZso1X5ICZt+TlHcdMbZUEvxQ0q37Ppb2FAv8h7TNBYD4PbN2pZ3P/ebXYrTPG7LccN+X7j0tisKv2eOZk/A3MdpvEsOuf8+bKDZ4MUv7ffBr2x9PcbCkpxhQegVri71OPlnzFGvG5KfQyCcrjBxl12MeBIo9/eKeW7dzeWVsLvPsOH1O6opoo6l6TnANBmZVNnsTJs1bHjxZ8b+WCn+x7pNNxvaeYqHRvh6Qs8xjy0SsgJWbh7rB8wFs3pk8hofIFDus2OhXc2htAWnZcF1DX2pG+5/73OfwUz/1U/hrf+2vuT+7dOkSzp07V/l79O+XLl1K/tYP//AP49ixY+5/Fy9evDUnHYmKp5hyQVzrd/GOV5wBAPzyH5bXpZZPUqt1MtpvUKECStPJN7+sBCRpsdAEB8UODKBYKDey0K5riaahCgoAJxeLwf649J0YT2yVnmWYYmEXvvNH9aBYxVNskYRYPMWWCUo2n9sumVbr/a7qWR2PeorZFlTyHxpPZybwlIdnihnkkxGmmBYg6nYKt0HY3p+YJbphIm9pnMG943YOphUPIg3gXWeK6cfmkqG90dS9p9rukzT2tb2xu+dWUAwo5waLvCD0FOMLpGYuDv3MaE7hv52KOlNMnwxI3SdzxxOTBQBjsuiOJSbk9n7CUyzLFKuft1qOl+o+Odclj6I/luKe0fdFoJi2c5lvVhI3ys+N3ekUCdP6/HvOn0colW3KFNN2NIwDoE0AufrYOaZYrJOiVs7nGIVBsmwB2znjYD6fNzD55/IdPZDo5bL2HInbeVBMMtIhivV+NwIa2541/T06XtsFGOCA99QVXNsCxXhzA3pXmxSztg8mvvinMLunv7M3ni7VfbJJHF0PPMWayic3q6w0Wc5XBXwBmJn4QLl3GfY6mM2BL75AMkLdXFYF+GnN099zDnprPfN6ARjY1FOs/K0O/jTzkpauO2SolWM3B8X2DJ5i9Kxpvm9LUkc52WQ2r1lqpMaOyUyb2r3wpjraIs7tHhtBZ++2vNQoP+cgey5Mj+J7v/d7URSF+L9Pf/rTlWOeeuopvPe978W3fdu34QMf+IBluGh8+MMfxrVr19z/nnzyyfxBNyl4xcNSLXnPQuNNyZtaPhlsappUqCj+yX//RvyfH3gL3nD/CfUx1J2CM8WaAFu0qFlkbZRoaruWUWwOum6cKzsjdUcoCqqOndjom+SLQN1b6d7jeqZZtfukXT65TND79OzCk0vDEgM8oEEdSgE7pdj5PU3mjqZvZcidXLT9Nr1nbDNG76nF24ub7VOCrz0+BK8syXW3U7hE48b+pGIWqrnucN6yjF0Uhav47I6mePJqycy7T+m9R8n8swvwlYOLueh1O+7cS/mNPmnsB8mu20RmPEYoaiC/AVTzjEQbWwuQjfY1G4RQ2qZNRIgtG4JiI2WCHzMxp3kh6wmWZFxpGUBp1pNO4lVl0lQ6lwnHp4zyKRfTVL/XWLJModmghJ5H2uMoYs9LC+bFGFdjg4SedxWkoHuWlU+u1aXkM6XpfIyhBujlyUCV1c43S/lmQhFPsSYecJNAamWQT8aAxByThoPGvvOy/j0riqL2zCxgoN9MztxcbLEDWSYco53nCwZwiPupWTxIOeto1JCp1TSOLp4V+X82AaeAOlNMOv+ofLKBTK0oCjx6z1EAwP/3M88vjtexdePdJy1SQr//8d9WjtldBaeWBYi+6XX3uv8vyyfLf/L5qwljaz1owkTzqlSopfndMcVaA8WqBVY+dmrNCruDAs2ZYnzNa8tw/rBjnTEJ53MvQbdKT63Rj8wnuTDtuj/0oQ/hfe97n/h3HnroIff/n376abzzne/E2972tpqB/vnz5/Hss89W/oz+/fz588nfHw6HGA7bayXKowlTDADe9ehZdDvMf0ELGATdUCjhtkieKE5tDfE2YwvWGFNsWaP9XPAJ62Bib1lbFAVObQ7w9LV9vLDjGwRoJx0ChKwsMaAun7zHYLTPzcC3GxjtLxPDgCnGJaRShB1Kj230zbTYPqt6kwzzmJI9RHEikIxagdsmsuQjaz08c628buqqdJ8SBK0xxYwg9+awh73xFDcOJm4zvNbPN7EA6vOWlXW6PuhhZzTF7miKL76wCwB44OSm6ljaRDy3aM1+fL1v6oCz1u9gNJ1hr2Jgq9lE+m8L4JIfJdt3UAX5OWNK3zjELouIe4rpk6hhvwvsTzxTzG1g5WOPDIkpVpVPajcnMWDKM4/sIAug75y5DMADeN+jGwFTrASe0sd7Vl4cZNExYcrnxUExjTytKAoMuh0cTGY14LcpI1Hb0TAKgM70a+5QBALlY7eCZwXo3/GYFxofWzOvuGYtbPNbjt2AKWZ4T/rBuWtZmIBsdq+R8GwOe9g+mDjQ2Lqp2xz08OLu2H9fJhDSFxiIcWBdv5oGZxs3yRc4A1XLHgLKXGF7f7JgitmLd8uEY4rVumzbxg8tPWT5ZJ3Z0VS2+d7XnMcnnnwR//b3ngGgZ71W5JMNJKu8iGZlio3DAlZDsORND5zA+aNruHR9Xzz3mIH8MvLJkCkmfSPOU4zYcYZ5bJkgxcN8XuZlR9Z4odTAFDMA+uH4QLlu0K/d6ms+7OB+ks9vH+DGwQSdQr9nahq9yHySPcYywJkzZ3DmzBnV333qqafwzne+E0888QR+9md/Fp3g437rW9+K7/u+78N4PEa/X06+v/Irv4JHHnkEJ07o2UxtxumGTLHjGwN85UMn8WufewGAnvUUGhbvt5wIOFDsBvcUy48dyo1MoBjvMlTRXOvP++TWAhS7ceA+Bu3zevODJ3F6a1ihH2tjg0nbBr2Oyd+LA6CUMLYFihEo85yRKTbodbA56GJnNMXV3VEJirmNsxIUY1VvS+dLHic3wkqkxluLM8XscggvMRvji1cW4NApHZAajmOVgBxZ6+HyjQPsjLxpr7qrbPAdWMem5OfyjQNcXnSTul953cOQkWgEP9cHXVxfbBA0pr0UYXHBVfiU8zBvsV4er3/HQ/mkpTAS89eyMF+TTDG1p1hcPqkHxRowjyKeYJZzj0oQGzDFyBtFu2lPMsUW/6pJetcYS5rC0liBg2Ja+SMdCwTySWVHw1i3UMv9XkZqu7UAb29EmGLq9yTwrtOCagDvnDmrJN3LeIpZmK/eaL/8c03hkECxvXHpR9nrdozfRwAaGzfQoaeZ7V3x93tPwUS5mTFgTBGr3QLAO/pOTcyndVaQaZ8pVjXap6LlESOTPzT1l7zBeDdyCt/12Hbd3/Ca8/gHv/RpvLBoupVbM8M8AWgmGeVr/kQJnHQD0MUif49Fp1PgB7751fiF33oS73zkbPLvxcCeJowtL49beIqN8jm18+ma3pxr1gZvklLLyyyeYg0lrpwpRsfe6aDYRt97If/+09cAAC87vXnLsQy6v6PDNtp/6qmn8DVf8zW4//778WM/9mN4/vnncenSpYpX2Hd8x3dgMBjg/e9/P/7gD/4Av/iLv4if/MmfxAc/+MFbcUo3JY6t990m0iptIwkloEf/wwR/3xmCt7MoVplii3bQmoU8MNVzLDPFsf1u4XwruCzBUjG5uGB5ffrStrnj50NntvBb3/cu/I2veVg9HgUljEDJErOwYDw4xEExW/LRNGhiopbZFlAq7EBpadEOVCnrBIodtYJiAfioqaRWmGL0XTWQQ1zfn+BLC8+Ki0q/vjCRHxoXBrcxWQBEgAEUC67RuigRKPZHz24DKCW02vfFyScZU8wSHGzXdOWjCD3FrBW+tWA+8zJCPbAVmnHbmGIecPBeanZZnDaJ44Avj4mrmueArTrba6IEWcjQuuYppuxIKIIsivk4lIdp5eB0r6ezeYWqb2LCBFJbgMnLlMyncYMNRow1pZdPRphiTYz2OatwMXZu/QwBGiBf8ffnXfVrpbA1wiAgdOqeE6DpNBphilk8xYL5zLKJ5f6PDpgy5Egh08zqfZRiYlqYedzyoD35pH/Pm+ThxGos5ZP6e0Zr3u6IeYq1VBQnxQAVR565VtolWBpIATGj/fR1hzJCgMk2DWb3APDAqU28eiGhLMeVn1e/W91vAc1YarxbtbZ5VD9gbFltX2LxDa+9Bz/3V94sFh9jYI9T5xiAGv6eAlpPsfL3aX1uy2gfqFv85NiyjnF0E7tPcobxnQ6K8T3PJ54sQbFHzh255ePSs+Ggby5uCbryK7/yK/jc5z6Hj370o7hw4QLuuece9z+KY8eO4Zd/+Zfx+c9/Hk888QQ+9KEP4fu///vxXd/1XbfilG5KFEWBf/jnX4f/5RsfxVmDiToAfN2rfVMB7UQXJpz7xg3wsnFmq7zGitG+CnDwzIr5fO4BNQXoUBRFZcKySF8ovmLRVOA3P39F7YMTnkOT4AmnRToJ+EX3+t7YXXPbnmI0rgWUcmb7C0BLayJLwQ1sCRSzgiUhKKZJYDibpUmy6T3FJvjSFZuMsC6fNIJibOO+Z2SPhkxP61xCi9unnilBsQdO6a65HLuadJ7YsHVK9eDUzLSZCru1Wd/RmtG+QUYSskK0TBYgIZ9sYMhd8zNTeoqFTLGRMgkcRDYXOc8OiixTTOlnFmMuWUADz2TRrR8Vn5LFuc/nc++PZTESb9CVMGR7LdvlVLtBibErLF4r3KyegvYdubFJ5huTAqpZIcF7ZumeXDHUVnrPAfFmEBbGFIHSjgFqsJgY9Dru2m+M7MCW+z5GVaaYNr/yjKmQiWlg5h2CfJLuWemJU/6ZJV/g1+384wzrx+F4ilWZYk+/WBaz7j1mkzxZ5JPecH45TzGKb3gNJyNk5gTBU8zCFKP7tj+eOYsJtdF+4CnWxGjfEnGmWPlPCzgVyic1e1XvKVZdsyx7vaaxHuRlOUVSz4GHdVmvFbjkTDFLPng7x4A1WfnEky8CAF7RCihWf79zcUtm1/e9732Yz+fR//F43eteh1/91V/F/v4+vvzlL+N7vud7bsXp3NT4ltffi7/69ofMx91zbB2vv3AMQBP5JIFi7VaKiCl2+caBm+Q0CzJ58Mzm5YLmFxbdefNk0yfn+vN+84MlKPZfv3DF/VkbiQTvDHiPMXEgcIio3p2ivSpomFhajO4J2KAOlFYKNPk9jSazxvLJ0FNMA9zyqvNBg8ovZ9N8ySyfDNlatneTV+udz2BjpphtbHonP33pOgDgAUM329CYuIl8EkBFPmkxpvbdJ23sU1755b8zUG36F1XBSVUu1NjryQDo1ZliOhYnvdvcVBpgSWDO1yvGhFFu3GOAAT/37PERYMnGpPGGsICemcffbQKXeB6mM9qvgq9AA5CnQUfCWCdG7fGcoeaarTTsKEihfV6cMUuhZxTKjERVk4BoFzHZew6oGvRTWLznBt3qd221mDhCAM1+KIHM/0AN1DLOpSHTbGJivvq8kDbf3LLiVgbNt7xBgWXt3KwY7evn8A225vniXTug2LHAU+zpF4kpZgTFNiyeYiSfZEyxhvJJoGRLUeRAyBjrtQkgd2yjj7c9fAoA8Au/9aRqbA+6LL5pg4x7mehGwB6rLzDAjdQXTDHqDivsYUJAzpLTLRtrjOUL5OfAUN7Kj1mm+6RrQHSHg2KAn8t+dwGKtcEUs/q9AbcIFFtFPP6Ht74Mw14HTzxwQvX3a0b7S3SfbBJEe57M5s4HSAPI8erAHvNC0HbrWYvQSy2TxqP3HMWRYWkITtHk47AGB7GaMsVoA7w17DVmrFmjBlY0YIpd3SGmmG2h4O94U1AsZJapmGLM96GJRwh1Znr2+j4u3ygBQbV8MmSKGRtnUAcvzhTTequEz9pstL/wBvjsszcA6IFAoD53UKMG/dgRA1vFe9YPNpF0rDbpc2DcqHq85I1CETLFLIbaMQbP2JAsh6CaNunkDFW+CRy74oZdUjdTbtyzYIWWhRORMFqYYjecPEw3n3U6hZt39h0I6c+h27ATo1YmzL0Zy/PWs39irCkHtCiZYgD37NNvJLlPFAUVwnLLn5sHFx2t+HlrQczRZFYp1to8xZic2zCn0Ds6ns5rG0INUBIyX81m9zW2VnP5ZFNPsfrYFnb3tHXVBN1zAoiKwlZo3WIMO4sEnrOUCfBujSlGoNhe2fyDCrZW+WSv23F5IqCVTy4nYaR4+dktvOLsVnbc8r+Xvz+bl+PP5/NGRvtAudcDgCuLe5aTT4bdDVtjii3uyYyBPXTrG3mKjfVG+92AHdcmUyxs8JJbN/oRBuPN6T7ZzJfsdgx6R2if98j5rVs+pvXZACtQrNX41icu4A//t/fiXY+ey/9leMNwShibdL1ZJvpdbxa/Z2CK9bueKrk/npopyDH5pGXS6HYKPPGyKvDY5OOwRkU+aaymhfemLT8xoP4+NQHFvHzS9rx4gt8UFOt1O5VjNOBrzJ/E2n0SAP7w6ZIxZfHWWgsALLN8km0srJuDpUGxQdWL5/4lmGLHrUwxmhdGU+MmMpAbGY7l4/puuvpNv0uAlvIUqwM8miYBIdihZVt1O4Vjk3BfMa1sVDK7b+IJBqDCxJGPjzHFoDoWqG/6LRKJsAMly59VTLEwUZ/N5k6qpfUUc8DUkoxErdk9l2PT8RYJotj5MnPP6FnN556hoH1P6B2dzeMsCZ180r/nbk4xHFceG84LivwqIZXVbiZrwJQBpPGeYHS/rZ5ioSeZfmy+maTn3RYoRu8LFQnWel1T0XLLzacTJsnWqy72Ru0zxZx8cn+MZxYdtjcGXXN+BgCnNnVNyrx8kjHFGoIPFMQWywLlle9yVmGrWVlq7370LO5lhXEtU2waSAlvtddUrPukpZsthZdPLoz2DZ5iNN9r5/2bEfVipTyHdiNed8t2nzwYT913ZVHn3K7BSSODbsdkvdI0mnRvXYFiLYdloqkzxWxSqZsRZ4LOMdoFmZvtu86VysnDG9jOGnVCAbyvGEX/FtOQgcBo3+g5Fy66bXWeBOo+U5akJ5RPUodUDYsG8Ne9N5q4RLdJ0sV9KzQdUmljsnMwcQlBE/nkpy4tvLUM4FDdU2wJ+aSRPRqCr9buXRvBuTfxFKOwgmJrTEpiYUzRNTc12l/OU8xWkeTBAQNis/hz14NitPH2kqP8M+eeeRRjBybmZChpoEO7MamBYspEPd4NUc/ioXtDLGNTd7ww0WYMJF33yep7xjcqVo8sk7w4yhQr/5kDWvhGtS7n07MhuWRUu+av97uuKY+VucTXiFhDCJunmM0wuQIkjqsgZhM5uNViwjHs9pfwFFu6+2Rz0HlvNG29+yTNeVQksK7ZVfmkvijjCzIz7+vbGlOMOgpO8cVFM6F7j683UjCcYvsIWT5ZB2ks620svv0rLuLhM5vZjvIl0Fn+/13m4QboVS4UvW4Hf/ErH3D/nnvWvUA2epieYhMjkx6oyyddsXaQvm+Uu4Vs2TZM57nvNZC/37H75N5L4/lycP93FlLCxy8eN/3G7Rh8rn7ozGbj79kSK6bYHRac4j+deSqvpUveskG+YhRaCvOQsSvsTLGIfNK4EL/lQQ+KdYp2KLkDxpC7x0gxD+9NWyb7QD3BsxntEyhWNgj4wiJ50oJE9D49v5AgWsenOMEAFs17RhsTYqcBzZhi9G5rpZNAlUkJ2KvdvtI/xRcWRq5h2/NU1DzFjHNJuAkxySeXAF+BKmPLYpbv2IgTSjht7AYC4zxYYfAUYwB/eaxB/si+y5DlZpEb0TtK0p8tBeB+ZK3qJwN4+aS2E2PMaF/L9Aq7OJrlkxVArvynpgKd9DyyAKDU2MDQkRCoA4I8AbeaROe6aVWOleSumdMuiqIGRNrkkzHfPP/bubEJbNg2dkOsyD4jslGVpxgHxQzX3O0Ubt5q4jUYgvxNmWIOmDK8K1uDgOllLDBssrWLn7vl++BzUnugWHl+jilmXLNprbu6OzZJ/9cjeXRbnsI8B/30ovhn9ROj4Ewx6dvi4IMrBNG603Dfc9/xdXz0Q1+T9YLudArWRXHixgWaAXLf/hUX3byce9Z1gGgBTN1ihUvMU2xmWC8pQqP9PQWTky5tcgigWFjozNla0HvJG6pYm4xQUJ7w3PY+PvdcaUPyhvuPm37jdgxejH3k/K33EwPKHMEKLK9AsZdw8GSTV77bSgSAOiimZooNPBJ/YAXFmOFw0+4cr71wzG2w2kCkgfID/NpXncUrz23h5Wdteulwc98mU2wp+aRL9kb48tVdHExmGPQ6apCI3onnt0vPuiPDXqNF8eSmf09VoNjiPeagmKUaGMpbLeAQUE0WrEkul09+7I8vA/DNJXIRgtpmphj7+2v9Ds4e0YFxQP25HDd2n+TsU1u3tuom0tpKm4DDUD6pAkp6YfJlYYpV5Rzludu9oohpRnIpzdwSZYopZSxu3DFna+k27jGPKkC/8Y8BcpYNRp0JY5EC0nVHmGIao/2AacYTcGv3SRPDLcKu0zLz+PGhZ59JcsqN9t3Y2cOdzDeU4+Xek27HJ8ux7q4qTzEnd52ZzanDhhKWBhr0dxx4apQdbYVAooF9mvQjUwNyCyZmcLwFQL26y0Cxlj3FmoJilB88eWXXrQUqphgryLgu7i3ls71ux70rnyFQzOiVS0H+xEBOPun/G7Gm6J9tXPcGYzyNGGOqSU56emuIb3pdyU7L5dR9B05V2ey3WuESY0A1Aac2+p5VCHiwX/YUWwCB0/ZBsdDWglKNJFMs0sXQr3NW+WQ5NnVhfNmpjQqT8k4N3hTllS2Y7FNYQcv2dt6rMAff1PCW5VZj7mWiBop1dWNXDUIXFS4jU+xgPDNXQd159rp4/OJx/Mbnr7QGigHAP/1LTwDIV7nDCO/rVosa82WM9k9sLjzFdsfOfP3hM1vqhY0Sw8s3SlDM2pGQ4uSmzVOMviFaxAe9jumZhcDCAydt+vi1QddtSuzyyfLcL13bw+89dQ0A8NZFt6Nc9LoddDuF+66scwkHxe4/uWG6Z+FzOdGw+2TJzCCgI3/vUkb72sVyPaiCasEhoMp6nc/nNkYIu7aD8QxY86wODcuAA0Q7o6nzpzoyzN93YmtyT7GREkykpI+DWjPl5jm8ZsJN1UyxSHMCZxyseFc9KGZnPdU7WpUDF0qmsvMUizDNsp5iS/hMSUb7Gob2sNfBNiJsRqMvF8XMAPJsrfWAa1wKaAPLJ8zeATCCoA2ZYkB53TcO2LtikYN3yah/IZ+0ShgD+aQF2Kob7dvAwDqoZmhQsHhXyKph0Ou0soGmsQDPUrPK6c4fXcPGoIvd0RR/crlk02velYoNCeXRLXmKAWVDoRsHE3zqmesAlmGKaeWT/nlOZjMM0FnaU8wS5cZ9hN3RFFvD5cf9gW9+DA+f3cK3vemC+PdCo/32PMWqYBzAOkU3kE/ujaeYzeYqpljNU+wQQDFtA6SYp5ib883yyeqzfuP9J0zH367B9w5tgmJWYHnFFHsJB6/AUnVp0O202r419BTTsr0qoFhTo/3JdCnzRZJQtrGYUhRFviV7LPpBR7fbhSl2bNFB8MW9ET67oAK/wsCSowSJmGJN/MQA4AQ3cjUwxSisMsLw+Vjkk0AVCGtqtP/bX3oRsznw4OlN3HNMn6wS8DDo2eeSdVbtsRplhuCftfvkGpOSWIytQ6N9K7thGU8x2jjP5uX4lk17URQ1Jo2lSQAHO2gD3OsUKhBWZoop5ZPjSKKdmRt7TFocazCgNeqvsswMnknOU6zqeWRqjDAmsKL8c+3alfIUKwpN98nm77jLM6YRYMoA3h44hpsFuK1eM2AD9GrdQpXvGRD3n2skn5zMzN5aIVPM8rwoV6C8StPpjQfvhAjYOuLWQGMzUyxu8q/ZuDim2KLTdVssMcC/49cbMsU6nQIPni7Xyz96tmRdaRgm3KuJ3pW2mGKAL4788fNlbtcYFGNMMUkGyecMzxRbzlPMElwG6DpPLjHusY0+/uY7X46zR2SGXcjYas9TrMrW4udgGZsDHvuTqcr/uhtcs6UYsmykPMVSc2DI5ANYQ5mGTDGKu0E6CVRVKY+8hJliK1DsJRzcaJ8+3jarREDEU8wIbO2NvEHoQMkyq3SfpApqA2DrzQ+W7BmN8fphR7jwHmnVU2wZo/0FU2xnjM8+VyZ7hwGKndzQ0fMpwmu2Jrlht5hl5JPWsWljQcmEliVGQXNIE29CnvxYmgsAEU+xht0n90bcaF+/afdG+3pPl8q4ASime8/839lnzFf95rnKpLGBNN6vaZv5iWlAewLFrjN5sbYFeaz7pMk7KCapI3mDWrppl14CEZDFAEJ6A90FgGmsuK8F/lo21tMCDJzY2UNRELMJ08zJkw3yScYUIw8hUqhogK0QZLGAWvHurnaWG2euahPwsMFAEwYpzQW7hgYaQLUTIqBvoAHw7pNV6aU2P6szzezvKXW61l7vzYhljfYB4KEzZV70mUslwKQp6EQVF60yxco1mtbbxvLJClMsfd0VpliwXrcBiq2zoohb51vwcPZG+wFz6RA8xSwsYQqez+6OpthdAO6SPQeBX5MACGyXKbbo9Jyx6YndJys7mCL8ft9wlzHF1vtdXDjRDFxvElbQciWffAkHSermc+BLV0pD7RNGH55lg4NiPYO2nrM6mhvtz5Ziir314VP4S1/5AB6796j52Laj2ylQFPASp1aZYv659Ls6JgkFvY/bBxN86pkFKHZOD4pRouvkkzeBKabx6ArlD1Zgij+fQbeD88ZuoxVQzJh0hU0Y3mYExWhT1cSbsAKKGYFAfs87hR34XWfVPW+0r5AbLcadzcuEz7JxBqpdLwG9jBAo7zV91wfsvNWgWL8L7E88o8RgbM2ZYtsGPzHAb4auV5hi88rvJs/ZjVtn4OhkUl3sjKZRplhuHYh7itnlYbsLJkwjuSpJMqb6cYEYUKJ/V+ibbsIUC0EtoFmX1LqfmZ79M5+X79egV5hYasuALNHrNsgYOcvNyq6oA6B6UM17JJZj0ru6qZxPa8CvocAQssysjJaaZ59Jil7es2sL+WSbTLHQU6xJofWhBVOMch3Ns+YFGc8Ua++6w6ZHN4UpJnxbRVE4ewd6t7Trzs2IzQUTfo/Jqttg5h0aUyzwypox0McCTnUWDPT98QxPXtl1vn/nBIacH7taBDoM+STlVakikDvXinyymbyWzx0bgy5e1ZLp/GEHfVuvPLfVqtrN+v2uQLGXcPBF4Df+5AoA4PUtt27loJjFR6HSNcdYceHJptUvg0e3U+B//7OvMR93GFEUBQbdjkuSW+0+ySbpY+t9k/zz6Hrfbfo/c6n0nXj5Wf0kT8kmXXdbTLE6KNYcmLpwct08ya8twRQLNz9f+ZARFFtcu3VcoLoRud8onxwG75n1npEZ6W994YoDITUbCw5ejaczc4VvnbFeAVs3rKIosNbrYm88bcQUc9K0wKdKwzLwQMnUbea2FH5igG8kweWTIyXDbtivbviBhkyxRcI6n899Ryy10X7dNF7zvhHoG27abXK6KlNMLZ8MrtsBoKoOkgumQQCyaJgGtMkeNfT1qpn8NzDaB8pnNuh1TEwFbho/m81dUUl13oHsE2CSGIsf2nim/jYoagCo4VmHa+buOO/fwyNsTmDZgIeg1tQAgPLjQ6N+y7xAG+4m61fTIMnqckyx6nqpatTC5HwHRm/emxFH16u5xvnGTLEyP+t2iuw83FuAYsSaonmlDRsULld1DLVWmGJVoNvSPXiZCBlQk4agGFD6se2PR/i1z5XNn155bktUAyTlk22AYr1grc7MgTS/jdn9sRTMKmOzueN1F46ZmUy3a9C78Og97ZJUVkb7d1BUQLHPvwAAeEPboNiWrasfBS0uB+OpMz02e4qNZ44q32YCdFhRAcXaNNpnk3RYGcxFt1Pg6Fof1/bGmM3LxMXCIAqBibY8xZaVT/a6HWeca5URAlWWVlP5JFBq808bO9dQUt2k0s47yJjlk+w9s3aeBICve/U5XDixji9f3cOz1xfVdpWnmB93NJ2ZNr5A3VNsYugUB5RJ0N54iv2JrWsmUJWXATam2JBt2slTTMsUc/JJZrSv7j7JNu3z+RxFUZhAFu9xVU1YgYaAnIGxxZkwvDFCo+6TxiYxofzS0vI9xRRT3e+I2T2RpyzyydD3ztphdX88w5E1Lt/JHl4BaXi3TwtLLcqQ04CgPf+8vrAwT79HCRqETLGl5JMLgEktn6wZ7eu9cWqeYA09xcrGH3NTgYLeFWLstimfpHuu8UpKxcNnqgx6i3xyr0EefTOC20Sc3ho0zsPvP7WBh85s4j4F06y/yIMnh+gptjuaMNuXNplizXxPlx+3CkwBdnCK3tX/9NkSFHvigZOmsScGxuqysZaUr8efdTd4PoCt+MODF4fvFpN9APgLb7qI3YMpvv0rLrY6rvUbujsgyts0eNvwT375GgDgjQ+0+xEd3+i7TZCFMu49xZp3n9yfTPHxL14FALz2vmPqsW/X4MlOq/LJgMFjDd5F8MHTm6bkJWwwYAXlKE5x+aTiPet1isqmq0n1lZ6R1XAe8AnEoGvvoMVBMaufGOD9hzQy0zAIzOt2Ctxn9AXgyWWT92yt38Xffc8jlT/TMSv8/R1NGjDFBlVQzNouveKRaKwAp6VpNqCEGA5ayao32q+DYln5JAM/6V5NDABR6AvGwY7cbePNacijymK+TiDLbL6Q6RpAyNCnxCqpG/arG29rJ0Wg3n3S9J40ZIrVfe/09yzWTGJuYAwcYQAPB081n5d/zzyr0AJO8eLdHy6682mtGlKeYjqj/QAUI08xo3xyu8b20n8f++MZJtMZ6wKslU923ZhW5mwIyDSR/zeNEBhp0gGejPYpVEb7S3RxvxnB87Gm0kmgXMf+/d/+avzzv/Lm7N8NOyK2C4ot5PPMt7SNcQlYcYythoCLNTpF9V7zOdRqWUOA4m8v9mxPZPaqncBTrE2j/TCny3mK9TrVcwW4p5hVJeLfp7sJFDu9NcTfec8j5qZky4b1+12BYi/xoMRtOptj0Ovg1S1TD4uicGwxE1NssZhvH0xcsq0Hxcpjv/jCDr58dQ+don0w8DCCf7xtGu13OoVL+kIDeU0cY6yfVxi7ioTJ5nGj+TrFCSMoVm7GmrO1AC8xazLJ0/fRxDR3c2lQjJhi9rHvP7mBfrfA6y4csy827D070fA5f/Pr7sXrL3iAXLMZK4rCJS7jqe/kq56PFu/JZCHpcHKOni4Z4ptnM1MsYbSvMzEntu7MySDVnmLrdfnkeKLbJISSOMB7lVg8qkLD+fJ4eWy+RtEm0sL+4ezJnYOpiRVYk09amWJBJ0aLp1g/xRRTSS9jTDE7iOmBW73EF6i/401AzBsHU9N7wsele8bll5rjefGOQLFXK0ExL99p4ClGnUZJPrnw99qwdp8MOkDquk+y72Nk91LbZEzjGwcT1sHNxioE2pZPhmM3W7e596hqPmJ+ls5TrFWmmH9e9xo6XMei09F1ZvdrdeAp1ipTzO6FvEw4eV64ZrXsKWYpQIVB947Wnjdl9my1sQ3s5GXDr7VaT7FO5e8BzeWTXG1xt3SePMxYdZ+8w4InAq+971irCyIF+YpZKlRktv4bn79iNqwkb5Xf+kJZcXjs3mOtemwdVlSZYu3JJwEPzizLFLN0ngTqCUdT+eTRtR4ePL2Js0eGamkeT2ybGOdS5fTRBkaZBFg0SewHvQ7uP7mBI2s9s58Y4L/jJmOfOTLE/+/vvhP/4v1vMR/Lx24inwTKpOXv/elH3b9r2VoEGowncycJ1LIS1wZ+jL3x1Fy5pmsuu+namBUhi8Ykn+RMscUGeEtttF/tUAd4v6XcdfN5vgZ0NOg+OTEwgKqAXGDgq9iQdToFNpmv2NhkvB6XT6rN1wPpp81TrApMzQxAYNSHzYFq+fPmclnA3kVsjYG35djln9u6T44rGzqT0T4BzkwaY/GQu7E/wR8tOgo+dq+O0e6lzXZPMW+0HzDFlMwpx647COSTSsCaQIudg4kJfAWq39f2/tgEQobM5sPoPknRFJDjvmKatWs9qrg4HKP9ZZhilqB3gb6JkVK2fzOCyyctXaaXjZTRftueYtxI3gr2cObm6a1B1kalGzznqdHWYpkImbp5T7Hq8wGaG+2f3Bzgr73jIfydr3+l88ldxa0La/H+zkcabvPgoEHbfmIUBIpZALl3veosAOB3n3zRtXG2eopRsvqml935LDGgOrlqN683K9b6XWzvTxqBUsfXOShmA4hulqdYURT4t9/9dkxmM2NDh+bGuf+PP/ca/N6XrzVia607UKxZ0vOv/+ZXYTSdNbpfy3iKAUvKKPodbB80f84A8JaHTuEvv/UB/MfPPK+WVQ96nbL6O53h+l65GTyq/MYG3Q46RSmp22cGvGqQn8sn3SZQK6mrAgaUvKqM9hnY4eSTSrD9iOs+WZdP5gCDoigw6HUwmsyYnK/8b008rmYWphgH5MYzYM0O0mwOe9gZTXHjYOISdc39HoZMMQPjCaiCp0DDTopNPKqCYwEbkDgM1msLcFse7xlXAJdP5o/1rKep68JYFEYPuCgjUQOKlce/sFN2Qzwy7KlbzS/jKVYz2m8on6z5gimf1+awhxd3x6WPWwNGC31f1/b83GIx2qdos/vkIJjzmlgPACUo9ut/XPoDm5hioynoUzwsT7F7jzcz2beGlxLaG9ssG1w+2SpTLGG0f+s9xcpx5/Nyna0wxYxD8+/xiQdOZFmBdG0kXbQyq5eJmtVBRrIfSnoBm01AGB9mhd1V3NrQFs4pVqDYSzwqoNgh6Y+bMMXOHl3D6y8cw+9++RqevrYPwA6KUbz5ZSfV497OMWAVwDY9xQD/bBuBYhX5pJEpdpNAMYASSIvvXYf9f3uSe+HEBi6caKaPX1+wj5p4kwBVuag16J4fRvMKqnI3lclS/NCfeQ1+yPD3HVNsOjMzxYqiwHq/i53RdMEUs3mNeJnVzHcFbCgts0jquL/WDdd9UssU8/JJMssn+aS2u+toMmOAg+W840AgkE/UyaPqYDKrSTctoBi2Dyq+MibGVcAUUz/rhPzSYrTfRH7jQLGpb4xgYfaFJv9Ozqdk8oTP27I54t0nP7XofvzQ6U0lIzHu1wfY3lOKR+89qu7cHDLFlnnW1vtN92y0+EaaSCBf3B3jxsGEmfTrN7InNwd4bvsAT7245/7MIk+maNNTrM4UawaUPHTa50ea9YMb7dM9Pqzuk20xxQYBQHQYRvucmdcGQy2UErZlOs9/fzKbu7WyU8DUhR6oygLflDHZL8cIWGqL6bcNTzHneR3kGKmx6R3g8kkLw3YVhxcr+eQdFnwD8sYHjh/KOTTxFAOAdz96rvLvemZF9e+96W4BxThTrGW5KC0SYQtuTZzY8O22X2Y0nQ+rf8uAYtaoeoq1OxVSsttmYk9BG7LDAMVoDjne4nMGqhvJ6wuGgsU/j3u7jIxJOjdLbu4pVpVZacbmDCCSQWrZcQTKT2dz1+3NsjkJz9sCEIXHclBLk6gPl2BNAd43aedgwjYn+Wv2nmJN5ZNVUM2SdHNgqzxWz1KjeXA+9xvRmYEpFjZGuG70r6u9KwbDZcd62h/jD58uQTGthLHWNdMoHQrnT4vfa/iuWJ61N9ovz9cqn+S5xc7B1LwB5+y8p18sC56WdfuVC9/RP1g8L+3Yh8kUq4FiDYtZXD6pedZ0jZNFYwKgXVDs2CHIJ4cBi6dNTzFa53dGk5Y9xbznKdAmU8z//nTGuy3br5nnshoP6LqnWHsg07pj4FN+IttaOKln1Gh/BaO8lMPK5Fs9zZd4UMJ6/uga7lnS6LJpeKaYLRF4VwCKaSnn/O89eHrTjX+nBy2+g26ndcBibQlPMWL9vOzUhjmBCGUJrYJinCnWok8H4DdFbY8LcKZY+9P/sp5iTaPPzKlp027pdMq76Xo5h9Uzaeo23nqmWCizshhT+2NJKqWVT24Muu4cCVCzVM5DFg7lkjZQrBmoNQju2czQzRDwZuAV+aSJKdbQaD/YDFoAubCD5GgB9GgkR3yTHTYnMDESp9OyS97iHNSefYGXGil4NIAeAW87B1P8wdPXABg6QCY8xYpC2w2xem+14/Kx6V0hwEMFdrNnPZ3N3X3jTA0pup3CbQp3GrC9CDS+fOMAn1o0GLCoGB45XwfFNBKXQwXFbpLJ/8NnPFNM1dF2UP87rXqKcfnksXbkk9QAiIox1iLUMrFZ6T7Z3rg1P0n3Td7asfk8N53P2Zpl/y0C5Qe9Dl5zX34uDCWJrRrth91/pzIIGfMUGyntJFZxuKFt+EOxAsVe4kEgw2GxxADgna86i9dfPI5vfeKC6bhH7zmC+1h1SW+07xf9r7hL/MQAv/i27ScGeLbX2aP2xIc2A028tcKEo80GA/w9a9IFcpmgqlrb4wK8+2T7gNy5xft1WG2ZR5wpZmBFerbXjPl62eTg/Fi9pC5gD2WSNx5Vplh5zVoGalEU7u/S/WrCUguTfA3QER5r8bcClgfViH20O5qYOkyF7B9ri3nnKdaAaebf7/IYCzOQr8vOA65BYwTOSCwKYEsJ0oReahYwkftjEVNM2wEy9FJr2hiBQjsuP3Z/XEpWX9wtfclOKmTx3GifpJOAzXh+izXSsLJD6J7/+h9fxmQ2x7mjQxNg8siCKfaHCxCzKHTP+jDlk2Hu2rSgdN/xdfe+a4D2QbdTm7fa9BQ7c2SIY+t93HNsDadbMgWn57pfYyjfevChIp9skSlG+QV9z1ZGedOoMMWmc/OaxYOe2+svHFMBt85TbAGGLTO2NaxrNQFf9C4C3mi/CatuFe3Fymj/DgtaQN9w8fDAoQsnNvCv/+ZXmY8rigLvevQs/vnHvgjA4inm/95X3CXSScDfn8PotPm/ftOr8eufu4y3v/y0+dg3vewk/suH39WI0cffiaNrvVvuocDjMJlij188jo1Bt1H3yGXjgYXE1Sp1vRnxD7/1dfjUpet4Y8utqP1GknWfbCCf3B/bvUZ4VXJq3ICG0jILSFMBhw5skjagBA2v7Y0ds66RfHJMZvlYnLeN4QbYwQru1zSbzR3zSJtse6BlauowFbJ/mnZhHE1mmHEpi2bz7ACe6eLc9c+70ynQ7xYYT+d1yaoBxCxZmB581Vb8a4b3boOSP3aLgWJ0zWr5ZNg108ji7C8Ai+lsjn63MDWZ4d/1NutyqgHFOMC/t5BOdgqbrG5r2MPz2wcLNmQz+eSvfvYyAOCN9+dNtXkQU+zyjRII1BYXakyxNkGxgBXclCnW6RR48PQmPn1pWzWHk58lvdvlubS3CV/rd/HLf/sd6HWKVhg8QLXjJsDWnTbAKSafbLP7pOt6eRCyhNv0FJstZXZP8vGve/W5zN8sw3uKBfNvC+An5fvapjbR7pNUHF0xxV7SYWXyrUCxl3h86xsvYGc0wTe9/p7DPpVG8e5HzzlQTJu08YTjbgLFKNlt22QfKH0+yOujSZxvSK3nG+xjS5qvW6PqKdYuKPbYvcfwuz/w9YfiR/DX3vEQ3vnIWbzqfPPn3TTOH1tr/K4sE85of+K7T1qkupRE7Y2n5m5Y9J7tT6a+C2NT+aSBss8ZVwTkWVioR4Z9AHuOZWaTT3pJHbCcp9jEmKjze8a7aWmByC3mKTY2bE5q1eeGoBhQ3muLx0rIetp2jRV07/ig28F4Om3EFOPgkmeo6b+t8B2fGZ53uFbec2xNBSwB9S6nVsAaKN/V3dEULz97xARWDBlT7MoCHNoYdFXrEJeC7zg/sZ4JmPK+YBPGALWBxs8sGig9ofAP4nHhxDq2hj0H9FgbUVAcqqfYEgzvNz94Ep++tI2XndaxpdcYKNbrFK0WDgHP7m4rap5ihgYvywbJJ9tmijl28niKOZMx3mqmWFEUDtin/zUd9888fh++8qFTOKssjtc8xdpkig28RLdyvw2eYst0n1xFe7Fiit1h8Re+4iL+wldcPOzTaBxveegkTm0OsH0wUXecu+fYGgbdDi6cXMcDp9qVWR1m0KJ/GEyxw4oKKNay+XqFKXYIMsbDMujsdTsmqc+dEPRt7YwmzqvEsnFfY7IKq9cIl0pRrtnUaH/qqpN6CeNo4rsoWq6ZAIcmTLHQfN2BLBrj90AyStIG+z2bVSq7WqyDvJl2RhOWLGvkk3GmmJUxBVSbMmg2wZwJCcABmdoCy6DXwQ7bCDrw1vC8RhMvTbYUdsL7NrO8Kz3P1gJsvl6DbnO/Poq1fhe7o6lpXABYY9/1Czt66SQQl09aWVO8a6eloy0/lsLaFb0oCrzy3BZ++0svAtDf77UAnLDIRZeNm2W0D5Ss/A+8/SG1hcA68xVr02T/sMJ33Fx4/bXo7UXf0e5o6qTobY5LHoHWb3KZoPlzMpt7YKrhuBYAldZzWuesRaRlgnKy+ZwKULJ/Z891n6zLJ62eVatoN6wA792z+17FocSw18W/+h/fhr3xVO0XdXxjgH//wa/G5rBrbgt8Owclu236ah12DA4TFGMJprYJxCpuzyBT/BcWrAzAxpri5r/WblhcPkmsmKaeYrRBsDCudkceYLEA7mSUTgCLu26VeftCChh20zI2CADsUhLOmuKgmNUzaYdJyzSJ77KyTy7H2x/PmH+cAoQMjPbtHSC7AMY1tpaKkUhMsenNYYpZ2JTkfXdtAca9WimdBKpgHtDMx4eAGkvnyXJsD5RftYJii/s9m3tG4KYRIPJdOydmhhwZ7dO5aEy1w3jk/FEPiinvd69bBUDbZHeHwMgyXqD9bsfkqckZcW1KJw8rPCgWGu3fuZ5iG+wZ742mpvVy2eh1CoyAClNMU5BYNgiAojGtdgPLBAe198cMFEt5igUAHv//bch6V9E8Vt0nV/GSiwdObeJV522J0/2nNnCqJWPPl0rQon8Y8snDCp5wtA2K8aS6bfnkKtoN2tRcvnEAADgytPnXrTNJh7UbFmeKWduO17tP6qVO9G3xRM4kn2Rm3DOWMDfxFLMk2yE7zto9kh/P5ZNappiXT3oAVONzEna0amIczBlEjTzFpjMcTPyG7ohWPpnw9dI1RqDuqt5TzNLEYphkiumO50CvjSnm7xnQzMfn9EIq9LjRI5E/5ysNmWIAHBi4rmxqQHHuaHnef/L8DZNEGPCAGgC85r6jjbohcum+ZdPCC1nabps3I8ICSJsdIDko1ua4hxVpo/32vL1G0xn2xpPWxu11O+673hlNGEB068fuMsCnLdkm4NdFGtO6zi8T/W7h1pcDBSub/pwX2cZTWy63isOJgRFYXoFiq1jFSyQ8U+zuAcX45v5QmWKras8dHZTYElPsqPFdo43glZ2RuRsWbYD3J1Nzslvz1zKwh8IN1Fq/Y0rwie2zvT/GeOZlAzY/s8C43djNELBXkCueYlM7U4wahjx5ZZcxxfT3u2nXS6DKIDJ5ijGm2I19b8qtBUFDTzL/vAzHTn2XUxtTrArIWeSTQBUUszC2vMS3Kk22eIr96J9/Pf7Rf/cGvNEoIax4ihk6TwLVTfq13fJ+W5lib1k0ePm1P36Bgd32+229bopHOChm+T7YOt2qp9hNMtpvElwaezcwxdZCTzEDQ3nZ4EArAc5t5YabjKXWJjjlTeSXM9q3BhWaaJ1sk6VWFIV7z/YWDXmAdG5Dc+6E5ROWbtyrOLxYMcVWsYrbNE5ulElx28amhxl8QbECFcvGYRrtr6LdoIT6hZ0FU8wIPD+62Gx/8svXzMkQvVsHrPukdp0OAQOL71GYzFtl2c5TbM8bzgM62Wi6o6CBeeTkdDZ/Fc48qjDFlLn2a+87DgD4/aevuY2Z5n7XmGINQDEHoBo9xXhHQi6pa8KuA4wecMy7jppY2DzFqptgazdEkvMdW+/jwol19bhDBuYBzTzFHjl/BN/y+nvVf58iyhTb0IFifKP84l55rNVT7G0Pl6DYp5657nzJ1PebAQdWk32KKlPM5uFGwb22bnWEc16bHqRVptidv2Wje+u6T07aY4oNeh33fb24AJzb6i7ovSynzMS9PRnjZDZvla0VdnRsEwgEOPt/5v0/E+udZ9MxT7EG68Uq2o9V98lVrOI2je/8qgdx/tg6vvF1t2en0SbBk7z25ZN+7NDEdxV3VgycfLIZU4w2f7/zpau493i58W4inzQzxfpemgbYmGLhRu6IsYFHhSk28cmgTj6ZYE0ZwLwmHlPh8Rxg0fpTPnR6E0eGPWwfTPCHz1wHoKs2pjzFLFV3B6BOjJ5ijOlFneosUtmQKWYBprj00jHFDN9XiimmNn5fvKevvueoyYM0bAbRRD7ZNDhTjNirJ7d0oFhRFBj0OhhNZm7jbjWdP701xKvOH8GnL22DFEFWzz0AeGNDUOz4xgDnjg7x7PUDc7dPCqtkdJmod59cMcVuVRympxhQ3u/t/Yn7ttroegn4b3h3NDHbLCwTzi9rOjc1WFk2uCSRd4Bsg6UG+G+40lAnMXYI4AGs+2QLEtdVNA8Ny5/H6mmuYhUvkTixOcB3vOX+1sGhwwyebB5f120KblasmGJ3T1BCTUwxi7wLAF557gi2hj3sjKb4/OWdym/mgsDXF3ZG+PgXrwKomlVLETJ4nI+FYuxOp6ico5Udxz3FaNxOYQdKAMaaMnWfpC6OM/WxADP5n+QNdGPR6RR47YXSsP2LL+wC0G1O6DlPZ3OMWUcry8Zm0JApRu/JeOp9vSzMwNBfywJMcUDNavAP1MFEKsZrAS7ygLN2gEw2g2il+5n/rq/u2phigH9eL+6RfNIOEL3t4dOVf9dW1E8suohfOLG+FKv9ledKtphNPsmYYi2u2SEY1Wa+sHaXMcXWa/LJheF9S+AUfUskn2zLSN2BYgd2m4VlgoNTbXa95N/9bM4KZy01VyM/yMvbB9nrpqLYuCKfbBesXUWzsDLF7vwZdhWrWMVLNvovFabYChS7oyP0FLO+a91OgccvHgdg7zpErJBPPXMdn7+8g3NHh/jG1+rYoHXWlN7jCqhuJCzMIcCDKtf3xxgbTPaBui+Yk+MpDk+BJMsyxSzx+sWzptBs3PmG/WAyM0kQKbiUcGqQZ/SZp9h2E2AqYCQ2aYwwmjT0FOtXgV+L1BYAvvZV53Bqc4A/bWRYp73rbn1aPGTNCV4wGu0DfiNGnmJW+SQAfNXLT1X+XQtOPfHACXz3174c//BbX2cekwdJKE3yYrZmt+opFjLFWgSn7rrukwPPopzO5o7J2JZ3E4FTLy7A6vaYYuV8vTtu11OMG+1b19plgjPCJrOZqePxzYhzC1Ds2ev77rpTY0eZYu587/xv8nYO67yxkk+uYhWrOLQ4XKN9zhRbLWx3ctDCuLvwKbF0x6N44wMn8J8/d9n9uzZZ5u2/+90C/+S/f0LdWTcEiMbGxHHY72Jncc3aToQUFaaY0deFAx2z2Rxzg0RrGR+16vHT5qDYheOVf7d03AQI1LL5xwHMl2zipbYqo33WafT6guGwZZDLhkwxy33j3lzeU0z/rtH3sT+2m/wDwJ9/4gK+9Y33maSTQL0ZBL1nbVT+/XOe4moDUIzOnTzFNo3SaAB484Mn0e0U5m+k1+3gg1//iHm8MB5ZdCS3bFoOa83m70SvU7S6Eb7buk/SNe6Np44lBrTH2CJQjtbNtoBIzxSbNGrS0jQ44NOkkLPsuIcxNgCcP1ayXJ+9fpBlo0c9xYgptuo++ZIO63q+2gmuYhWrOLTgwMJhdp9cMcXu7AgTW6t8EgDeeP/xyr9rN3Pc7+cHv+UxU8e2kEUzMbYBX4YpRr5QXD6pTTAGXdYBkpndq+STYedKI3OIg2pNNxePB0wxzfGdTlGRPzbpZugZRFPmcaX3FAPgjNubdICseYppGiN0PevpuvMUs7DU4kwxC8hlBcSACFOsTU+xxXMeT+e4fKOUdNuYYuW5k8SrCWvqyFofr1vIhPlvthXvetVZvPH+4/i2N11QH0Pvynq/2+iZNw1+b9rOFe46TzHWhXHEQbGWGEShFLk1ptgC2N5tufskB3ws7OSbNS6wAMVaNton6fel6/uOjZia++ndo3xixhiMK6bYSzusnm8rptgqVrGKQ4uiKDDsdXAwmeH4RtvyybvLq+NujjCxbdLp9A0BmKVNHF9971G897HzePSeo/iON99vGtMBPONZo0SMb6Kaeopd3x8zs2MbU4z7egFK+WSiuYC++6Q/vmk3rfPH1nD2yBDPbZeAhXZDNlwYoHPpps1ovxlTjJ8fgWI2X68QmCr/XHPu7llPuXSzgdE+McVog3KLQY/Q9+4wPMUAz149taljjwKMKdbQaJ/iqx4+jd/50osA2gEDeZzYHOBf/Y9fZTqGwMSm19s0BhVQrN1cgYNid0Oewj3FKg1eWjI0D6XIrck2yfj9gBm/tzA29xQjDLIdppi/Ng6KtWW0f3Yhn3zm2l70nHi4Dp2LhXHMGGOr7pMv7VgxxVaxilXcVvF33/MIPvD2B3HhxHqr466YYndPhIntUSNABJRMxlee23L/rq0g97sd/PRfegL/07tfYWY3cPnkhIFLVikhYAMqyr9f3qMbBxMHHFg9xWrnbZBPNpHyhccvw/7hvmLaamPVE8wOsrjuk0ZPMf4uUodVk3wyYE1ZJIxOejlhTLEmfma1sW/tZoM3ZADQqmQplMF1O4UJxKR7Tp5iGw3kkwDwNuYr1pbB9TJBAGzb63WnU7jvuG0J413nKcZBsamfQ9sCS0LAtTX55KJhyPaiezDQknySsaCa2g00CT7EhMkn28KYiCn21FUPiqWW+dBTjBvutwXWrqJZWIHlFVNsFatYxaHGX337Q4cy7oopdvdEv1fNtJowxQDgjfefwB89e6P8zRaquJzBw/0s1PJJDooZN84kv5vPfYMC7QbBsXDG3jNp2OuoWBb8WIAZrxuv+WAyNUsveTx+8Th+5Q+fBdDEz2zmQD0LKObZeRMTU6woCgy6HYymM1xZdFg1dZ9MyCc1jIGKZHRxv5swxajbHI19qzGa8Jq9p1g77Ix+t3CbqxMbA9Omn86RNtAbDUGiJx44gUfvOYoTG/3WQIdlgt6VJo0Flo1+t4PJbNo+U+wu8xSj+8s9xdqU9m4E8sm2Df7JExJoSz5ZXt90Ol9qvbRGURTO03A6m2NKBayW7jeBYk9f23d/lip+eeCQ2OsrptjtEtbvd7UTXMUqVnFXBlWdh71Oq/4kq2g/avLJBp5iQGm2T9GGx4n3W5pVqpNa5tJwCfnkWr+Ll53aAAD8+h9fXoyrBYcWLJzpzEkQzxwZqr6zVMdN6zUfjJt7igFVs/0mTLHnF9dNrd81cXKj9JW6snPgNwnKsel9XE4+We0Wqrlv3OSf7ONsYwdMsYaSV2uE8sk2PcWAagOOk5u2+SgEp5vKCYe9Lv7td/8p/PxffUuj49uOw5JPAv77OkxPsbuheEf3d288ZbL99vKzQ2OKDbxdAUWbRvuT2bxVtiwfp8oUa8lofwGKjZhEN3Xd/B4BCHKx1d7hpRwr+eQqVrGKVSiCEsyVdPLOj5p8skH3SaBkVlC04fdBm8D5vDQeplAb3rOE3mq0DwBf/cozAIB//6lnF+PagannGSimO9aDJPO53WuEH7+MHOS1zIS8CVPsue2yAn3OAIqdWJitX90Zm5higH/WTj5pkeMFAJGTMBpATP5bljl12cYKTYPLbPl71tYmZ8gYRxaTfaD+/TeVTwIlY+N2KQqtHZJ8EvDfSNtj322MdgIB98cz5zM4aJEhVwPFWmaKkS8j0K7R/mw+b026TkHXN5vNzV2Hl42j673a95S635xNB7CO2J3bZ+68W8NqtH/nz7CrWMUqVhGJiyc2sNbv4BVnt/J/eRW3ddQ9xZoxxR46vYn3PHYO7370bCNfMmvwpG1nVCbLXUMixuU2Vk8xAPjqR0pQ7Mkrpe9G3yqfnEzx/AIcOrOlBMUYWDCa2gzngSrIsgzQcWy9j8fuPQpAD+hxT7Bnr5dgIMk0NEHgyJWdkU+8jQCo7z5p8ajyzD7AM8U0QOSynV3XAk+xtgyX+Xkv06m0aQwrTDErKHZzmGK3W9A9a9Jtc9mge942MHW3eooBpZ8lAAxaZYoF3SdbuufU9ZLLJ9tmirVtdk+FjypLrZ37XRRFbW1OXTfdo/GsyipeSSdf+mF9RitPsVWsYhV3ZZzYHOA/f8/XmgypV3F7Rm3j3tBTrCgK/NO/9KabcUqq4FXqnQMPiqmP50yxBu/5Vz50ynlVledjN7snptjZo1qmWBWsmBn8rYCq5JS8P5om+T/zP7wJX3phFw+f0QHnnCn27PUSDDzbEBR72ayUrmoBPdq07y28uUy+XnTPxlWmmGbsujTZ9p6FnmLUl+FWd0Dj5102ZSAQsp1N2TJMsRCYuXtAsfK6D0c+eThMsY27VD4JANsLKaG2GHMzIny32pJurgdMMUvxa5nw3SdnrbNlu10/tpfNtzI0AODc0SG+dGUXgHzNPXeeJJ9s3+tuFc1iJZ9cxSpWsQplnN4aruSTd0HwhbEo7KbzhxWdTuE271Q17zcExZow2zYGPbz5wZPu3/XySWJMzfD8jYV8cksHDnGwgvuCqWWExHqazPDk1TLhveeYHpjicd/xdbz14VP5v7gI54czmuI5xxQzyCfJU2x3xDyubOw8ClP3ya4HMQEbU6woimpDByPgzEHM+Xze2uaIAwyjif09WzYqnmIbyzLFbo/5bNmgd/owCllePtnutmntLmOKddmaR/5abYIPYROH1phii+6TdM1tzUOOKTb1vl63uiBRG5uz1FqUI3KmmFRs5N5n/J8rUOylHyuj/VWsYhWrWMUqWHCgZWvYuy06rVHQ5n33oGTSWJgsfOPfxFMM8L5ilrEHEaaYVoJYFEWFaWb1BeMgC3UKfcXZI6pjlw3aMF+6vu8AJu11A8CpLfIUG7lqtB4MrD4bi9m978RIHSDLP9f6eg27zcFX7ps3nra3OaKOncDy/nNNYhmm2M0y2r/d4ptffy/+26+4iO/8qgdbH9sxxVruAFk12r87njPNo9f3FoWgFsEHAqco2vIUW++TfLK85tbYWuSXxYCptm63Z6nNWVGivWetBcX6i3Oaz8tzta7Nqzi8WHmKrWIVq1jFKlbBgifVxxpKJw8raPNMnmIWOni1+2Sz6yZfMcAunzwYV7tPasN5ko2ndlCMHftHz24DAF55rh3fQNq0kiTj5ObAtJElpthkNsfV3ZIxoL3uECgxySdTRvsNxrZ7ivlj98ZTs1x2mXDg62EzxZR+exR3q6fY+WNr+Aff+jq8euH112bQ3Ddsu/vkXcYUAzwQSP5abXqKEThF0TZTjIopbZvdT2bz1jr/UlDRZXoIRvtAlcUtMsXY+zeZzRyLe8UUe+nHSj65ilWsYhWrWAWL/hKb9sMOAlVuNPAUo2OLAthouJl7xdktJz/UJoFrjq01NTPFgHgHSb3Rvj/2s8QUO9cuU+xLL5Sg2FnDNZfHdx3AQfdN3Wn0pjDFAvmkEpiqgGLGzq6Dbsc9273R1HmKtbEx4w0hvHnyIXiKreSTL/nwnmKHZ7R/N3iKAf6atw/aZ4rVPcXa7T5J0dY81GV+WVbJ/s0aezJrX7oJVJlioqcY+28lq83WBGcVhxfW7+jumGFXsYpVrGIVd21wwMC6aT/soI0QgSQWOjht+peRjBZF4SSUavnkwtdrfzzD5YWnmAUg4swli78VP/ba3hiXFmb3rzgkppil8yQFSenIi62Jp1i/W5g20MMAFLMyxZZhJBZF4TaEO6NJq5sjDgZOZ+1KYtaW6D7J7/eg12mN2XE3x2EZ7XP55N3CFKN7TEyxQ5VPtnTPQ2D7UJliLU0nvYh0s02g6ewRnXyS51zj6Rzjabus4lU0D+szujtm2FWsYhWrWMVdG4OeXxhvN6YYbRD+n//+swBsdHACA5dtLPDfvfl+nD+6hne96qzq7xMLZm88dQkk+WVZjufySbW31mITQ3K4e46ttfbM6bxJMmox2acggOSFGwSC2rpPAiUwZelcxtl1gGeKafeiN6OhA1D65jn5ZAvZaUw+eTt4ivE5YPMukU4edhDbuG1PsWGvA/qU7x5PsarpfJvdJ0P5ZFvAR40p1pqnWL37ZFueq9xT7DCM9s8f04JiAVNsJZ+8bcIKaq+e6CpWsYpVrOKOjn6FKXZ7gWIfeMeDePD0ptsYPXxGz3qiTX9TPzGK1188jv/y996FP/uG+0zjUhzf6Js2dDH5pJUpRtGWdLIcu3qNTZhi5CtmlRHy5M/anS+UT1LS30Q+2eRd21iwM3ZHE8ZWaFM+aZfpLhscXDmxabtnfD5bSSfbicEhySeLonBywruFKbbumGKlfLJNT7GNgJlnKS4sE2HXy8NgirXtq8g9xdpudAJU2esSA7/TKVz+NZnNMF7JJ2+bsL7Lq9V0FatYxSpWcUdHBRS7zZhif+4NF/Dn3nABe6MpvvDCDh48vak+duBAsXaX+nDzdsZoJM7lk9ZEPTTCfuXZdqSTQH3DfLYBKHYqYA1pE+8qMNUQFFuYPFsNlzkY2ESevElMsdG0VbYCnfdoMvOSmJaq//SubA17ZgYQf9Z3i8n+YcfWArg9jKLKer+L3dH07vEUW7zT2/vtyyc3mHyyrc6TNFavU7j1rq1rdt0nF/Nf+WftMsUmhwSKbQ57ODLsYftgkh233+lgNC1N9p3/ZIudMlfRLKzf0QoUW8UqVrGKVdzR0b+NPcUo1gddPHqPresabaK22gbFgkTEYrIPVGVt5PWkZS2FG8dXHiZTzHjdAHAiBMW0nmLd5qBY6ClmlbJUxh7aQQPaBO9yo/1WmWLT1j3FCLy1SieBkCm2AsXaiL/xNS/H+WPr+NOvvaf1sY+u9/HCzsjMAL1dgwDj6/uHYbTv73GbzLyiKLA+6GJ7395QZ5mgWzuZzR0bqi0JY8+Z/Hvv0DbmfR5njw6x/XweFOt2CmBK8slyrbB2NlxF+2Fl890dM+wqVrGKVaziro3BbcwUWyZee+E4+t0CX/Gyk62O2wuq3tYujFWwYvGbyk1Cr1OgU3j5YVsm+0CdKbaM0T6FWj7J3vEtIzDF7zdgZ4pVu0/av69NbrTforcM3bODQ/AUW1vcsyagGAd+Q9nVKm5NPHL+CL73G151KGN//ze9Gr/75Rfx2L22osjtGodptM+7fbYNemwOeg4Uawuc52b3NGTbTLHpzN7c5WbFuaNr+OPnd7Lj9roFMC7Bw/FsxRS7XcL6jFag2CpWsYpVrOKOjmU37bdrPPHACfzeD76n9Y5pQHnPJ6MSZLEzxbinWImKdZUblKIoMOx1sTcux27TUyw04eZGvtoIQRJrgwHAbnZfM9pfqvvkMkb73lOsFaP9Pmcktuunc7OYYpsrT7E7Pt75qrN4p7LJyZ0Q64HRPm+Uc6uj2ymw1u9gfzxr3cONsz4PQ8LYNijW63imWNtFCYrzi8JVbt533mvTGcaTlafY7RJWCfQK5lzFKlaxilXc0cErvk26493OcRiAGFAFSsygWKX7ZPlnFlkFbWbuPbbWquSIdxTsFHV/ME2Q0T6FNvHuLyGfDI32zfLJJUFn2gzujqdmltoywZlibXuKXTixDgB4eQPPO/6sV0yxVdxpQaDY+JC6/BFI3/q4zM+sPabY4UkYaX2ZzOaHxhQj38/cWkfea2VDApJPriCUl3qs5JOrWMUqVrGKVbDo36VMscOMkn1UVvqbeopxpphlk0DHt8kSA6pMsdNbw0YAS50ppvQU490nraAYA4fm8/lSnS+bMMU2h8QUm7YqnyS21qjhe7ZMfNPr7sU9x9bx2vuOmY9dGe2v4k6OsJDTNvjgun22DYr1/dx5GEyxXvBntzq8p5jvfNmWnxnFuaPDyrmkos/O1RVQWgbwVmGPFSi2ilWsYhWrWAULntweW4FirQTfuJ/ZsskIK/LJub0bITG2XtminxgfF2jmJwYs4SlWAaZs7zg/7/GUdQIzNjcoCmCrgZwvZrR/p3uKdTsF3vxgM68/znzdWMknV3GHRch+bJ8ptgDF2pZPVphi7YztmWKH0X1ywb6azh1DuG1J4r3HS8ZujlFP92Q8nTGj/RVT7KUefeN3dMuf6MHBAR5//HEURYFPfOITlf/2yU9+Em9/+9uxtraGixcv4kd+5Edu9emsYhWrWMUq7rKodp9cgWJtxFLyyYrRvr0qS6Ba60wxllhTBdoaNaaYcpMwYH/PLJ9k38doymQ0yrHpfm8NeybwkoKM9m8cjN2ftSKfZLLR/TH5xLz0NzqDVffJVdzBEQIUg5aBko0Fc7V1phj7ltsCh7rMaL9tCaMD5Ob2QszNiq955Aze/6cexN9+9yvFv8fBQyqgrDzFXvrRWTReUv/9W3cqZfzP//P/jHvvvbf259evX8fXf/3X44EHHsDHP/5x/OiP/ih+8Ad/ED/zMz9zq09pFatYxSpWcRdFt1Pgax45g9ddOIZzRoBmFc2Cs4+s3Se5AfpkSom6Pl154v4T2Bx08baHT5nGXTY4EHi2IVPs2HoffF/QhClm9VHjm7+D8dRvjoyeYk07uxLb6cbBxP1ZG/syel6j6RSfeuY6AODh05u3fuAlgz/rzRY981axijZi/ZDlkxuL8Q/Lyww4HAmjY2W37ClWkU+2LEkc9rr4X7/p1XhrJlegYsmEySdXTLHbIyyFrlu6mv7SL/0SfvmXfxn/8l/+S/zSL/1S5b/9/M//PEajET7ykY9gMBjgsccewyc+8Qn8+I//OL7ru77rVp7WKlaxilWs4i6Ln33fVwAouxOu4tYHAS39bmGWrA6ZrM2xlgz55z/41tfih/7MY603GagwxY40A8W6nQInNga4sjMCoKf/c2DLCk51OgUG3Q5G01mFKabFIWnsJn5igGdIbO8zUKyFzRGBYk9d3cNTL+6hKIDXXrB7fLUdFaP9Q2qksYpV3KpY61cnnv4hdYE8zO6TrXXBXVzj7mjqAPa2mWKjycwVYl6qPl2+++TcNaRZgWK3R/QNjL5b9kSfffZZfOADH8C/+Bf/AhsbG7X//rGPfQzveMc7MBh4qcB73vMefOYzn8HVq1dv1WmtYhWrWMUq7sIoimIFiLUYJKk7vTU0AxxkgH4w9l0BLdXroigOpesmZ4o1lU8CwIkND2ppNyj9Jc3uaQN4MJ5hTkb7Rk+xptJkkitxUKwNGQ2d929+/goA4BVnt8x+bIcRK6P9VdzJcehMseEhdZ+sMMXaGZsYzc9e33fNRtoCxagD7+eeu9E6S80aviHBzMn8mxaBVtFuWIDWW/LVzedzvO9978Nf/+t/HW9605uif+fSpUs4d+5c5c/o3y9dupT87YODA1y/fr3yv1WsYhWrWMUqVvHSCdq4W/3EAA9W7E+m+K9fKAGLl5166cvaqp5izZhiQNVXTO8p1rz7JOCf19546v7MKt082nCT4D3FGCjWoqfY09f2AQCPXzx+y8e8GcE36xsr+eQq7rBYGxyyp9hiHh/eBUyx84t16tL1fSz841sDxd5w/wkAwO88edV7h75EfbpIgjedzd06dWQ1994WYWlaYfriv/d7v9dV21P/+/SnP42f+qmfwvb2Nj784Q+bTz4XP/zDP4xjx465/128ePGmj7GKVaxiFatYxSqaB20ozmw1B8X+6xeu4plr+9gYdPGnXnH6pp7frYi1nt/UnF2CKcZBsTa6TwL+nnNQTMvwe+hM2eXzkfPNGhusO/mkN9pvgzAQyqMev3ji1g96E6JitL+ST67iDovDZorRfGSRXd2M4KCYtsnJsnHPsRIUe+YaY4q1xNZ6w/3HAQCfembbMcLbNtrXBoGU4+ncMZqbFJ9W0X5YgFbTE/3Qhz6E973vfeLfeeihh/Af/sN/wMc+9jEMh9Wk8E1vehP+4l/8i/i5n/s5nD9/Hs8++2zlv9O/nz9/Pvn7H/7wh/HBD37Q/fv169dXwNgqVrGKVaxiFS+hIAlkE3CIpJdPvbgHAHjnq84eihzSGsN+B0UBzOe+At8kOCjWxFNsGfnk3ogxxZQblK979Tn8p7/7Tty3kMNYY5OM9luXT1bfqduFKVaRTw5f+t/FKlZhiXCubxsU2xwelqeYn7f7LbG1aH0eTWa4fKP0sWyLKXbv8XWcP7qGS9f33Z+1bbSvjS7rPulAsRVT7LYIy/xheqJnzpzBmTNnsn/vH/2jf4S///f/vvv3p59+Gu95z3vwi7/4i3jLW94CAHjrW9+K7/u+78N4PEa/X1Y1f+VXfgWPPPIITpxIV+uGw2ENbFvFKlaxilWsYhUvnSDm0ekmTLHAaPkbXpMulL2UYq3fxYe+7pUYT+c41eC6KU5sMKaYVj7JOxIO7Mk6sTOu7Xm2lmVzdP+punesNoghsTOySzeXCX7P1vtdvPLc1i0f82YEZ7BsNHjWq1jFSzlqTLGWwalXnC0Zry9ruRPtJgO42/IUG/a6OL01wOUbI1eEagsUA0q22C/9vrdMeqka7dOcW3qKLeSTt4H/5CpuIVNMG/fff3/l37e2ykTj4YcfxoULFwAA3/Ed34Ef+qEfwvvf/358z/d8D37/938fP/mTP4mf+ImfuBWntIpVrGIVq1jFKlqKb3n9vfjss9v4xtfdYz6We7kMeh18zSNnb+ap3dL4W1/7iqV/o+IpZpRPbg17jTY1F06s49OXtvH5yzvuz9oyPY75YrXRFIO/Z6+9cMzUuv0wo+IptjLaX8UdFiEo1ran2J95/F48fvE47j/ZHOj//7d378FR1ff/x19nL9lkc1lIyAUMARTlWqEEa9Cf1sp9wHLRLxTKbWpV2tBSUVR+Y0WdcaxV7JRi+XbUBLAXMBa0+FNaNJRajVUsoYFaboJUSbhJuEoSkvP7I9llN0GFkJxzkvN8zGTG7J6cz/nsvv2Qfef9eZ/miJ63lcmhrFB8TFLMymqtxkkx51aK1a+5Z2vNSEUzjfbbhguttpdaKSl2IUKhkP7yl78oPz9fubm56tSpkx566CHdeeeddl0SAABoATdela4br/rqyvLzia7gufHKdNdtU2hOT7FwoqS5v6h3a7iRwe5DJy967EuV2CixY9W40XH29TaydVKKTeaRFEN7Ex8X+yHW6u2ThmFYXiUmSYlR/85Z1VNMkrJSErT10+OqPmttTzHpXLP9MKf3FKvfPllfTe2230vaqov5f8mSd7R79+4yw/f4jnL11VfrrbfesuISAABAGxDd62lUG9k62ZI6NqOnWK/MZHVKCujGK5uXiAx/CNx9KLpSrFmnumgJjZNiFn0wiu7D1lb6iUmNK8X4YIb2xe5G+3ZJsOHuk9K5Zvt2jN2/S0g+j6GzDXeftHLr5sUIvyZn60ydqKJSrC25mLtP8o4CAADHCFfC+DyGhvVpO1snW0pa4sX3FOuYGKd//N+hzf5Q0aOhUuyjg/WVYh7Dmi2MUn1yKvqDkVXFAoGoD98DG+6E1hYEA17F+z0yZFCtgHbH7kb7donuBWllciirUVLMyi2MCXFe9emcorJPj0lycFLMG7775LmeYtx9sm24mO3XvKMAAMAxvpYd0vU905TbLVUdoprOu0V0o/2L+av9pXyg6NbQKD/8V3ArP5wYhqFgnFfHz1g7dlJDY+vOoXh1DjXvzpl2CPi8Kpz1DRmG9XfIA1qbv1GSPM7nzERJSws6pFLM6hzk13M6nEuKOXT7ZLin2PHPaxTe+JY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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cpi_apparel = DataReader('CPIAPPNS', 'fred', start='1986')\n", "cpi_apparel.index = pd.DatetimeIndex(cpi_apparel.index, freq='MS')\n", "inf_apparel = np.log(cpi_apparel).diff().iloc[1:] * 1200\n", "inf_apparel.plot(figsize=(15, 5));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will construct two model instances. The first will be set to use the Kalman filter recursions, while the second will be set to use the Chandrasekhar recursions. This setting is controlled by the `ssm.filter_chandrasekhar` property, as shown below.\n", "\n", "The model we have in mind is a seasonal autoregression, where we include the first 6 months as lags as well as the given month in each of the previous 15 years as lags. This implies that the state vector has dimension $m = 186$, which is large enough that we might expect to see some substantial performance gains by using the Chandrasekhar recursions.\n", "\n", "**Remark**: We set `tolerance=0` in each model - this has the effect of preventing the filter from ever recognizing that the prediction covariance matrix has converged. *This is not recommended in practice*. We do this here to highlight the superior performance of the Chandrasekhar recursions when they are used in every period instead of the typical Kalman filter recursions. Later, we will show the performance in a more realistic setting that we do allow for convergence." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-12-14T14:42:32.773219Z", "iopub.status.busy": "2023-12-14T14:42:32.772707Z", "iopub.status.idle": "2023-12-14T14:42:32.789922Z", "shell.execute_reply": "2023-12-14T14:42:32.789106Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12), tolerance=0)\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "171 ms ± 19.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "85 ms ± 4.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The implication is that in this experiment, the Chandrasekhar recursions improved performance by about a factor of 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we mentioned above, in the previous experiment we disabled convergence of the predicted covariance matrices, so the results there are an upper bound. Now we allow for convergence, as usual, by removing the `tolerance=0` argument:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-12-14T14:42:32.793843Z", "iopub.status.busy": "2023-12-14T14:42:32.793331Z", "iopub.status.idle": "2023-12-14T14:42:32.814406Z", "shell.execute_reply": "2023-12-14T14:42:32.813719Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "186\n" ] } ], "source": [ "# Model that will apply Kalman filter recursions\n", "mod_kf = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "print(mod_kf.k_states)\n", "\n", "# Model that will apply Chandrasekhar recursions\n", "mod_ch = sm.tsa.SARIMAX(inf_apparel, order=(6, 0, 0), seasonal_order=(15, 0, 0, 12))\n", "mod_ch.ssm.filter_chandrasekhar = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, we time computation of the log-likelihood function, using the following code:\n", "\n", "```python\n", "%timeit mod_kf.loglike(mod_kf.start_params)\n", "%timeit mod_ch.loglike(mod_ch.start_params)\n", "```\n", "\n", "This results in:\n", "\n", "```\n", "114 ms ± 7.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "70.5 ms ± 2.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "The Chandrasekhar recursions still improve performance, but now only by about 33%. The reason for this is that after convergence, we no longer need to compute the predicted covariance matrices, so that for those post-convergence periods, there will be no difference in computation time between the two approaches. Below we check the period in which convergence was achieved:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-12-14T14:42:32.818057Z", "iopub.status.busy": "2023-12-14T14:42:32.817601Z", "iopub.status.idle": "2023-12-14T14:43:17.150852Z", "shell.execute_reply": "2023-12-14T14:43:17.149982Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Convergence at t=186, of T=454 total observations\n" ] } ], "source": [ "res_kf = mod_kf.filter(mod_kf.start_params)\n", "print('Convergence at t=%d, of T=%d total observations' %\n", " (res_kf.filter_results.period_converged, res_kf.nobs))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since convergence happened relatively early, we are already avoiding the expensive matrix multiplications in more than half of the periods.\n", "\n", "However, as mentioned above, larger DSGE models may not achieve convergence for most or all of the periods in the sample, and so we could perhaps expect to achieve performance gains more similar to the first example. In their 2019 paper \"Euro area real-time density forecasting with financial or labor market frictions\", McAdam and Warne note that in their applications, \"Compared with the standard Kalman filter, it is our experience that these recursions speed up\n", "the calculation of the log-likelihood for the three models by roughly 50 percent\". This is about the same result as we found in our first example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aside on multithreaded matrix algebra routines\n", "\n", "The timings above are based on the Numpy installation installed via Anaconda, which uses Intel's MKL BLAS and LAPACK libraries. These implement multithreaded processing to speed up matrix algebra, which can be particularly helpful for operations on the larger matrices we're working with here. To get a sense of how this affects results, we could turn off multithreading by putting the following in the first cell of this notebook.\n", "\n", "```python\n", "import os\n", "os.environ[\"MKL_NUM_THREADS\"] = \"1\"\n", "```\n", "\n", "When we do this, the timings of the first example change to:\n", "\n", "```\n", "307 ms ± 3.08 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "97.5 ms ± 1.64 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "and the timings of the second example change to:\n", "\n", "```\n", "178 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", "78.9 ms ± 950 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", "```\n", "\n", "Both are slower, but the typical Kalman filter is affected much more.\n", "\n", "This is not unexpected; the performance differential between single and multithreaded linear algebra is much greater in the typical Kalman filter case, because the whole point of the Chandrasekhar recursions is to reduce the size of the matrix operations. It means that if multithreaded linear algebra is unavailable, the Chandrasekhar recursions offer even greater performance gains." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Chandrasekhar recursions and the univariate filtering approach\n", "\n", "It is also possible to combine the Chandrasekhar recursions with the univariate filtering approach of Koopman and Durbin (2000), by making use of the results of Aknouche and Hamdi in their 2007 paper \"Periodic Chandrasekhar recursions\". An initial implementation of this combination is included in Statsmodels. However, experiments suggest that this tends to degrade performance compared to even the usual Kalman filter. This accords with the computational savings reported for the univariate filtering method, which suggest that savings are highest when the state vector is small relative to the observation vector." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Bibliography\n", "\n", "Aknouche, Abdelhakim, and Fayçal Hamdi. \"Periodic Chandrasekhar recursions.\" arXiv preprint arXiv:0711.3857 (2007).\n", "\n", "Herbst, Edward. \"Using the “Chandrasekhar Recursions” for likelihood evaluation of DSGE models.\" Computational Economics 45, no. 4 (2015): 693-705.\n", "\n", "Koopman, Siem J., and James Durbin. \"Fast filtering and smoothing for multivariate state space models.\" Journal of Time Series Analysis 21, no. 3 (2000): 281-296.\n", "\n", "McAdam, Peter, and Anders Warne. \"Euro area real-time density forecasting with financial or labor market frictions.\" International Journal of Forecasting 35, no. 2 (2019): 580-600." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 }