Question

我正在努力将文件编织为html，执行以下操作： 1.保留中间降价文件 2.创造＆＃34;数字＆＃34;工作目录中的文件夹，用于保存代码块图 3.在github上查看md文件时，将显示以代码块编写的图。

我可以创建并保存中间降价文件，但是没有创建图形文件夹，即使上传独立的png也不会在github上显示图形。

我想如何设置我的YAML？这是我的git hub帐户及相关的md文件。

https://github.com/ndonawa/RepData_PeerAssessment1/blob/master/PA1_template.md

 ---
    title: "Course5Project1"
    author: "Nevon"
    date: "June 12, 2018"
    output:
     html_document:
            keep_md: yes

    ---

    ```{r setup, include=TRUE}
    knitr::opts_chunk$set(echo = TRUE)
    ```

    1. First we load the walking activity data
    ```{r}
    ##Load Data
    fitness <- read.csv("activity.csv")
            df <- data.frame(fitness)
    ```
    2. Next we will calculate base measure
    ```{r}
    ## Calculations/ Metrics
            stepsbyday <- aggregate(steps ~ date, data = df, sum) ## steps per day
            avgsteps <- mean(stepsbyday$steps)## average steps by day
            median <- median(stepsbyday$steps)## median steps by day
    ```
    3. Following the base measures we plot the average steps by day 
    ```{r}
    ##Plot Histogram & Report Figures
    hist(stepsbyday$steps, xlab = "Number of Steps Per Day", main = "Total Steps Per Day", breaks = 4, col =              "royal blue") 
            ## Add Metrics
            abline(v = median(stepsbyday$steps), col = "red", lwd = 10)
            abline(v = mean(stepsbyday$steps), col = "yellow", lwd = 2)
            legend(x = "topright", c("Median", "Mean"), col =c("red", "yellow"), lwd = c(2, 2, 2 ))
    ```
    4. Afterwards we will look at the steps per intervals 4a.First removing NAs by creating new data set than             plotting the figures
    ```{r}
    ## Calculate Steps by Interval
            library(ggplot2)
            Intervals <- df[!is.na(df$steps), ] ##remove NAs
            intrv <- aggregate(steps ~ interval, data = Intervals, mean)
                    ## Create Plot
                    g <- ggplot(intrv, aes(x=intrv$interval, y = intrv$steps), xlab = "Intervals", ylab = "Avg            Steps")
     g+geom_line() + xlab("Intervals") + ylab("Avg Steps") + ggtitle("Avg number of Stepgs by Intervals")
                            ##Find Max Step Interval
                            max <- max(intrv)
                            print(max)
    ```
    5. Then we calculate the weight of missing values, i.e(how many missing values are there)
    5a. Also we will replace missing values using the value of the average steps per day we calculated before &                create clean data set
    5b. Furthermore we will create a new data set which merges orignal data set with new clean data
    5c. Lastly we will plot the new set & find new measure

    ```{r}
    ## Calculate Weight of Missing Values
                    ALLNAs <- as.numeric(is.na(df)) 
                    Missing_Val <- sum(ALLNAs)
                    print(Missing_Val)
                            ##Substitute NAs with average steps per date
                            library(plyr)
                            dfvalues <- Intervals
                            avgsteps_day <- tapply(Intervals$steps, Intervals$interval, mean, na.rm = TRUE,                                       simplify = T)
                            NAdata <- is.na(dfvalues$steps)
                            dfvalues$steps[NAdata] <- avgsteps_day[as.character(dfvalues$interval[NAdata])]

    newstepstotal <- tapply(dfvalues$steps,dfvalues$date, sum, na.rm = TRUE, simplify = T) ## New data Frame
            newstepstotal <- newstepstotal[!is.na(newstepstotal)]
    ##Plot New Hist & Find New Metrics
                            hist(x = newstepstotal,
                                    col = "royal blue",
                                    breaks = 10,
                                    xlab = "Daily Steps")

                            ##Find Metrics of Newsteptotal
                            summary(newstepstotal)
    ```
    6.The last part of our anlysis will be to explore differences between weekend & weekdays
                    6a. First create new variable for weekends/weekdays
                    6b. Next find value of steps per daytype
                    6c. Lastly we'll plot the data
    ```{r}
    ## Segment Data into Weekdays / Weekends
            wd <- !(weekdays(as.Date(df$date)) %in% c("Saturday", "Sunday"))
            wknd <- c("","")
            for (i in 1:length(wd)) {
                    if(wd[i]) {wknd[i] <- "Weekday"} else {wknd[i] <- "Weekend"}
            }
            df[, "dayType"] <- factor(wknd) ##new daytpe variable

            wk_df <- aggregate(steps~dayType+interval, data = df, mean)##average steps per daytype
            library(lattice)
            xyplot(steps ~ interval | factor(dayType),
                   layout = c(1,2),
                   xlab = "Interval",
                   ylab = "Number of Steps",
                   type = "l",
                   lty=1,
                   data = wk_df)
    ```

Answer 1

md文件确实包含图。 R块形成.Rmd文件，

```{r}
##Plot Histogram & Report Figures
hist(stepsbyday$steps, xlab = "Number of Steps Per Day", main = "Total Steps Per Day", breaks = 4, col = "royal blue") 
        ## Add Metrics
        abline(v = median(stepsbyday$steps), col = "red", lwd = 10)
        abline(v = mean(stepsbyday$steps), col = "yellow", lwd = 2)
        legend(x = "topright", c("Median", "Mean"), col =c("red", "yellow"), lwd = c(2, 2, 2 ))
```

在.md文件中生成以下内容：

```r
##Plot Histogram & Report Figures
hist(stepsbyday$steps, xlab = "Number of Steps Per Day", main = "Total Steps Per Day", breaks = 4, col = "royal blue") 
        ## Add Metrics
        abline(v = median(stepsbyday$steps), col = "red", lwd = 10)
        abline(v = mean(stepsbyday$steps), col = "yellow", lwd = 2)
        legend(x = "topright", c("Median", "Mean"), col =c("red", "yellow"), lwd = c(2, 2, 2 ))
```

![](PA1_template_files/figure-html/unnamed-chunk-3-1.png)<!-- -->

最后一行![](PA1_template_files/figure-html/unnamed-chunk-3-1.png)是将被转换为html的markdown标记：

<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3deVxU9f7H8e8MOy6IC6CsAiImpoWKKGqaKCiaoljewMjdUsNfWGYpiUtmmLa53sRwoVTcULHScsnlaqJkpF65bqACKqIgoizz++PcO3cuMIgIzMzx9fyjx5nv9yyf+XLyPWebUahUKgEAAORFqesCAABAzSPgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLggYr16dNHUam5c+fW0qYDAgIUCoW5ubm2GTZs2NCxY0dTU1MjI6N69ep5eXmNGDHi5MmTtVTP04iMjCwzbkqlsmXLlkOGDPnjjz9qdlvTp08vs61mzZp17dp13Lhx586dq9ltAfqPgAdq1/Lly6Ww2bNnT42scO3ata+//vrJkyeLiopKS0sLCgpSU1O///77jh07xsTE1N52a4pKpbp8+fK2bdteeOGFQ4cO1eq2bt26dfTo0VWrVnl7ex88eLBWtwXoG2NdFwDoqXfeeWfYsGHS9M6dO3ft2iWEeP311/38/KTGTp066aSwDz/8UJoYOHDgkCFDlErl8ePHV69eXVhYOH369JdffvmFF17QSWGVGzt27IsvviiEuHPnzubNm5OTk0tLS995553k5OQa31Z4eLiPj49KpcrIyEhMTDxz5kxBQcHf/va3v/76q2HDhjW+OUBPqQA8TlRUlPT/y6pVq8p0FRQUzJw5s0uXLg0aNHBycgoICNizZ4/mDMuWLZOWTUpKklpKS0vj4+O7detmY2Njbm7u7u7+1ltvXb16Vb1Iv379hBBmZmblK7l165a0Nl9f39LSUnX7unXrpPaFCxdq265KpUpNTR02bJirq6uVlVXPnj2XLl2quRJ/f//27duPGDHir7/+GjBgQKNGjdq3b//RRx89evRIs4bdu3f37t3b1tZWujrw/vvvZ2dnaxu6d999Vypj06ZN6sa8vLwmTZoIIRQKxYMHD6pSm7e3txDC3t7+7t27oaGhDRo0uHHjRpltvf/++9K21q5dq2588OCBv7+/1P7ll19W5U8wfPhwaf6ffvpJvZ63335bavzrr7+0vVlArxDwwONpC/ibN296eHiU/9w8ffp0aYaXX35Zs106rPzss8/KL9KqVat79+5JS1US8Pfu3ZPm9/f312zPy8uLioqKioravn27tu3u2rWr/HX94cOHq3PU3t5eCOHk5OTg4KA5T9++fUtKSqR5vvvuu/LFe3h45ObmVjh0FQa8SqXy9fWV2qVYfWxt6oAfOHCg1FvFgFepVIcPH5baAwICpJbK/wRbt26VWt555x31Stzd3YUQ7dq1q/BtAnqIgAceT1vAjx8/XmofNmzYL7/8smHDhubNmwshFApFcnKySqWaOHGio6OjNI+7u/sbb7xRVFRUr149IYSnp+f69et//fXX4OBgaYadO3dKq60k4FUqVdeuXdVxtXr16uvXr5efp/x2CwsLW7ZsKb08cuTIpUuX1MVv27ZNWkoKeCFEs2bNVq5cuXbtWjc3tzLx7OTkJM2wa9euo0ePqvP7s88+q7DaCgP+/v37TZs2ld5jcXFxVWqTAl6pVJqamoaFhU2bNq38RwptAV9aWmpmZiaEcHNzU6lUj/0TPHjwQDqT7+7uLq3h4sWL0gxz5syp8G0CeoiABx6vwoC/f/++UqmUjurUB7jqu8ZGjRoltZQ5VZ6enj506NChQ4eq4/zHH3+UZliyZInUUnnAp6Wllbn8b29vHxwcvGrVqjt37qhnK7PdxMRE6eXGjRulGYqKimxtbYUQPXr0kFrUAb9v3z6p5fz58wqFQgjx8ssvq1Sq/Px8aQZvb+/MzEyVSlVaWhoREfH222/HxcVVWK064MePH79q1apVq1Z99tlnnTt3lhr79+9fxdqkgNf8GFSetoBXqVTSBwgjI6OHDx9W5U/wxhtvSC3nz5/XHMxz585p2zqgb7jJDqimtLS00tJSIURISIiU9EIIPz8/Ozu7zMzMf/7znxUu5eDgsHnz5uLi4mPHji1evDglJUV9l7tKparKdt3c3I4ePfrbb7/t2bPnyJEjp06dunbt2pYtW7Zs2TJz5swtW7aoz35rUtczZ86cRYsWSdP3798XQpR5hKxp06a9evWSpj08PDp06HDq1KkLFy4IISwsLJo0aXL79u2TJ0/a29t37949KChowoQJrVu3fmzZK1asWLFihWZLvXr1pNv+q16bhYVF//79H7ut8qSPKZKq/AlGjBghXYzYvXu3h4eH9Amgffv2VXmngJ4g4IFqSk9Plyak0/JqLVq0yMzMvHLlirYF169fP2XKlJycHOllNe7rNjIy6tmzZ8+ePYUQJSUlBw8ejI+PX7VqVWZm5ptvvlnhM9/Xr1+XJs6cOVOmKzs7Oz8/v379+tJLW1tbzThs3rz5qVOnpAsBSqVyw4YNo0ePzsjIKCkp2b9///79+yMjI7t3775u3Trp7H1VtGjRwsfHZ+HChdKF7arXZm1trVlbFalUKmkTrq6upqamogp/gt69ezdt2vTWrVu7du16++23f/nlFyGE+uY7wCDwHDxQTeo70TIzMzXbpZfq091lnD9//o033sjJyXFzc/viiy9Onz6dlJRU9Y2eOHEiPDw8PDx8586dUouRkVGvXr1WrlwZGBgorV99I16F1Z45c6b8qTx1ggohsrKyNM8lZGVlSYtLydq3b99Lly7t2rXrzTfftLa2luY5dOjQqFGjKq9c8xq8dMpBSvcnqq0a6S6EOH78eGFhoRBCuiOyKn8CExMT6SHJAwcO7Nu3TxrSkJCQamwd0BUCHqgmd3d3KW8SEhLUiXjkyBHpYNHT07PCpQ4cOFBSUiKE+OSTT6ZMmdK+ffsnehDc0tLyu+++++677z799NPi4mJ1e2lpqfQEXb169aQ7yMpQ3y536tQpdeNff/117Nix33//XXPOW7duHThwQJq+cOGCNL/0dk6cOPHRRx99/PHHrVq1Wr16dVZW1o4dO5o1ayaEOHbsWNXfRbVrq4aHDx9+/PHH0rR0CF7FP8GIESOEEEVFRdKl/RdeeKFVq1ZPWQxQlzhFD1RTvXr1Ro0a9e23354+ffr1119/6623bty4ERERIYRQKBRvvfWWNJv6oFM6pW9iYiK9XLt2rb29/aVLl9TxUxWenp5t2rQ5e/bsb7/95uvrGxYWZm9vn5OT88MPP5w4cUII0b9/fyMjo/Lb9ff3d3Jyunr1anR0dOvWrVu0aLF///6xY8cWFhaGh4fHxsZqbuW1115bsGCBubn5rFmzpPsMpKfAi4uL582bJ4Q4fvz4Bx980KJFC4VCIW1OHdLV8ES1VcWvv/5aWFgonSrYsWOH9LmhXbt2r7/+uqjyn8DPz8/e3v7atWvSV+pyfh6Gpzbu3ANkRttjcllZWerzzJpmzJihnkfzm2L79et3+fJlS0tLzZnbtm0rTXz88cfSIpXfRX/+/HnpGbPyPD09c3JyKtyuSqVKSEgwNi77md7DwyMjI0NaRLqs0LRpU3UESgYPHiw9j15cXDx48OAKNx0bG1thtdqegy/jsbWpn4OvZCXqu+jLs7S0PHTokDRbVf4EkqlTp6rnSUtLq2TTgB7iFD1QfTY2NikpKTNmzOjUqVP9+vUdHBwCAgJ++ukn6TBX4u/vP3ny5CZNmiiVysLCQmdn58TExM6dO1taWnp5eUVHRycnJ3fq1Klt27a//vrro0ePHrtRDw+PixcvLliwoFu3bvb29qampi4uLi+99NKqVatOnz6tvi5eZrtCiODg4GPHjgUFBTk4OFhaWrZp0+ajjz46fPhwmdsFnJ2dt2zZ0q1btwYNGrRv337OnDmbN2+WzgcYGRnFx8cvW7asS5cudnZ2JiYm9vb2ffv23blzZ3h4+NOMZBVre1KNGzf28fEZM2bMyZMn1d8xXPU/gXSWXgjh7e39NKcoAJ1QqKr2ZA4A2XNwcLh27Zq3t/fTX/mWhwcPHkjH+p9++ul7772n63KAJ8M1eAAo69GjR6WlpbNnzxZCKBQK7p+HISLgAaCsjRs3hoWFSdNDhw6VvggPMCwEPACUZWxsbGRkVL9+/b59+5b5Aj7AUHANHgAAGeIuegAAZIiABwBAhgh4AABkiIAHAECGCHgAAGSIgAcAQIYIeAAAZIiABwBAhgh4AABkiIAHAECGCHgAAGSIgAcAQIYIeAAAZIiABwBAhgh4AABkiIAHAECGCHgAAGSIgAcAQIYIeAAAZIiABwBAhgh4AABkiIAHAECGCHgAAGSIgAcAQIYIeAAAZIiABwBAhgh4AABkyFjXBQCosqwscebMv6dN74r6/xJFjUSe679bbG1Fu3a6Kg0G6uuvv16zZo2uq5AzExOTbdu22dra1v2mCXjAcBw8KIYP//f0K0JsE2K7EIP/0xsSIjZu1FFlMFTJyclBQUEDBw7UdSGyNWLEiKysLAIeAFDXnJ2dvb29dV2FbFlYWOhq01yDBwBAhgh4AABkiIAHAECGCHgAAGSIgAcAQIYIeAAAZIiABwBAhgh4AABkiC+6AfTFiRMnEhISKpmh3blzr2vvPXPmzPrp02u8Kj3n4OAwadIkXVcB6CMCHtAXu3btWrn+cGMHP20z3L+tqCTgM26KzQcUtVGY3ip+lF+U+QkBD1SIgAf0iJVdR6cXJmrrbfqvXeLCNm29lo3cKllWlh7ez7qcuUXXVQB6imvwAACD0aFDB4VCsXTp0jLtt2/fNjY2VigUf/755xOtcPny5U2bNq25AvUIAQ8AMDCbNm0q07J9+/aSkpKnWWd+fr5CoQgPD3+alegVAh4AYEi8vLwOHjyYlZWl2ZiQkNCuXbunWa2RkdHQoUM7duz4dNXpEQIeAGBIfH19mzdvvmXLf2+/uHv37t69e4cNG6Y5W2xsrLe3d7169by8vGJjY9XteXl5Y8eOdXBwcHBwmDhxYmFhodRuYWFx7Nix4uJi9TrHjRvXokULMzMzV1fXOXPmqFQqqcvBwSE+Pn7u3LleXl5WVlbDhg27fft27b7nauEmOwBA1dy5UyurrVdPmJpWfXalUjl06NBNmzZNnPjvu0p37tzZoEGDHj16qOf54osvIiMj33///ejo6KSkpFGjRhUWFk6cOFGlUg0YMCAlJSUqKsrR0XHp0qVr1641Nzcvv5UpU6YkJSVFRkZ6enoeOHBg1qxZrVq1eu2116TeRYsWhYWFnTx58sqVKz169Pjoo4+WLVv2FENQKwh4AEAVvPee+OyzWlmzk5NISxMmJlVfIiQk5Ouvv87KyrK1tRVCJCQkDBkyxNj434mWn58/e/bsmTNnzpo1SwgxYMCAR48ezZ8/f+LEifv27Tt06FBiYmJQUJAQYtCgQR4eHvfv3y+/iXv37sXExIwcOVKabe/evcnJyeqAb9So0TvvvCOE8PDwGDx4cEpKylONQO0g4AEAVeDgIBo3Fv85TV1jlErh4SGMjJ5ooa5du9ra2m7dunXChAn379/fs2eP5hn71NTUO3fu9OnT59atW1JL7969V61aVVhY+Pvvv1tbWw8YMEBqNzMzCwkJWbNmTflNbN26VQihUqkyMjJ++eWXs2fP+vv7q3u7deumnq5fv35paekT1V83CHgAQBVMmSKmTNF1Ef+mPks/YcKEpKQkMzOz3r17Hz9+XOq9fPmy+N8MlqSnp2dmZrZo0UKh+O9XQtnb21e4iVOnTk2fPj05ObmoqMjHx8fa2lqzt8xL/cRNdgAAwxMSErJ///7s7OyEhIRXXnnFVOMqfrNmzYQQ6enpqv/VqlUre3v7GzduqDTOQ2RmZpZfeW5ubteuXa2trXfs2JGTk/Pjjz86OzvXwZuqWQQ8AMDwdOvWzcbG5vvvv9+1a9fQoUM1u7y8vExMTHbt2qVuWbJkyfDhw1UqVadOnXJycpKSkqT24uLiCn8A4sSJE4WFhTExMb6+vkqlMi8v7/z587X6dmoDp+gBAIZHemw9KipKCKF5dVwIYWNjExERERkZmZeX9/zzzx85cmTu3LmffPKJQqHo2bNnjx49/va3v82dO9fR0XH58uUPHz4sv3I3NzelUjl//vzQ0NCcnJx58+aVlJQcPXr03Llznp6edfQOnxpH8AAAgxQSEpKbmxsUFFT+ObcFCxZERUXFxsYOHjz4hx9++PLLLyMjI4UQCoVi586dISEhCxYsmDBhgqOjY3x8vI+PT5nFXV1d16xZ89NPP/n7+8+dO/e9995LTEx8+PDhnj17ypdhaWn54osv1tJ7fBocwQMADMbp06fV0z179tS8mu7n56d+qVQqIyMjpVAvo0GDBqtWrdJsUZ/Mz8jIUDeGhYWFhYVpzvb777+Xn00IER0dXY03Ugc4ggcAQIYIeAAAZIiABwBAhgh4AABkiIAHAECGCHgAAGRIfwM+Nzf3woULJSUlui4EAKAvOnTooFAoli5dWqb99u3bxsbGCoXizz//1ElhekhfAn7MmDH79++Xpm/evDlo0CBra2sPD4/GjRsvXbpUVeO/XwQAMFibNm0q07J9+3YOCMvQl4D/9ttvz507J02PGzfu8OHDixcvTkpKmjJlSkRExObNm3VbHgBAT3h5eR08eDArK0uzMSEhoV27droqST/pS8Cr5eTkbNu2bc2aNREREQEBAXPmzImMjIyJidF1XQAAveDr69u8eXPNH4C/e/fu3r17hw0bpjlbbGyst7d3vXr1vLy8YmNjNWceN25cixYtzMzMXF1d58yZoz5J7ODgEB8fP3fuXC8vLysrq2HDht2+fbtu3lRt0LuAv3TpkhCiV69e6hY/P7/U1FTdVQQA0CPqH4NXt+zcubNBgwY9evRQt3zxxRfjxo0LDAzcuHHjSy+9NGrUqGXLlkldU6ZM2bZtW0RExKZNm4YMGTJr1qwffvhBveCiRYsaNGhw8uTJEydO/Pbbbx999FGdva8ap3ffRe/i4mJsbJyRkaH+xZ4zZ85YWVnptioAeOatFOLT2llzGyEShVBUfYGQkJCvv/46KyvL1tZWCJGQkDBkyBBj438nWn5+/uzZs2fOnDlr1iwhxIABAx49ejR//vyJEycKIe7duxcTEzNy5EghxKBBg/bu3ZucnPzaa69JyzZq1Oidd94RQnh4eAwePDglJaVG32md0qOAj4qK2rJli4eHh6ura0REhPSjPXv37v3qq68CAgJ0XR0APONShbhYO2suEKJICNOqL9C1a1dbW9utW7dOmDDh/v37e/bs0Txjn5qaeufOnT59+ty6dUtq6d2796pVqwoLC83Nzbdu3SqEUKlUGRkZv/zyy9mzZzV/bbZbt27q6fr165eWlj7tm9MdfQn4zZs3p6WlpaWlpaamFhQUHDhwQGoPCgry8vKaP3++bssDgGfeEiEihKjxZ5qUQjR/onQXGmfpJ0yYkJSUZGZm1rt37+PHj0u9ly9fFv8b1ZL09PRWrVqdOnVq+vTpycnJRUVFPj4+1tbWmvOUeWnQ9CXghw4dqvmysLBQmti5c2evXr2MjIyqspJr166tW7dOW++VK1eioqKk8zkAgCekEKKlrmv4r5CQkF69emVnZyckJLzyyiumpv/9iNCsWTMhRHp6uoODQ5mlcnNzu3bt+sorr+zYscPHx0epVHbu3LlO665D+nWT3YMHD65cuVJSUmJubi619OnT5+HDh5mZmVVZvKCg4I5227dv/+OPP2qzfABAHenWrZuNjc3333+/a9euMoeIXl5eJiYm6l95F0IsWbJk+PDhKpXqxIkThYWFMTExvr6+SqUyLy/v/PnzdV57HdGXI/hHjx5FRkYuXbq0pKTE3d09Li7O19dX6tqwYcPYsWOr8l03rVq1WrBggbbeAwcONGjQoMYqBgDojpGR0dChQ6OiooQQmhfRhRA2NjYRERGRkZF5eXnPP//8kSNH5s6d+8knnygUCjc3N6VSOX/+/NDQ0JycnHnz5pWUlBw9evTcuXPqO7tlQ1+O4BcvXrxixYpZs2bFxcU1b968b9++0kUUAAAqFBISkpubGxQUpD7pq7ZgwYKoqKjY2NjBgwf/8MMPX375ZWRkpBDC1dV1zZo1P/30k7+//9y5c997773ExMSHDx9Kt3WXYWlp+eKLL9bFO6kd+nIEHxsb++GHH0qPNAwdOrRHjx5Tp06V7nUEAEBy+vRp9XTPnj01T+76+fmpXyqVysjISCnUywgLCwsLC9Ns+f3336WJjIwMzfbo6OiaKlsn9OUI/sqVKz4+PtK0paXlihUrtm/frr4lEgAAPBF9CXhHR8fk5GT1S29v75EjR44ePfrBgwc6rAoAAAOlL6foR44cOXv27Pz8/MDAQD8/PyHEokWLvLy8goOD5XfjAwAAtU1fAn7atGn5+fmff/55QkKC9LNyTZo02b9/f3BwcIX3PgAAgEroyyl6MzOzBQsW5Ofn//zzz+rG1q1bnzlz5uDBg8uXL9dhbQAAGBx9OYKXGBkZOTo6arYolcru3bt3795dVyUBAGCI9OUIHgAA1CACHgAAGSLgAQCQIQIeAAAZIuABAJAh/bqLHgBQx65cuXLy5EldVyFbOvw+VgIeAJ5d7dq1W79+/c6dO3VdiGxZW1s3a9ZMJ5sm4AHg2TV16tSpU6fqugrUCq7BAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEP6GPDFxcXZ2dlFRUW6LgQAAEOlRwF/+vTpMWPG2NnZmZqa2trampmZ2dnZjRo1KiUlRdelAQBgYIx1XcC/HTp0qG/fvs7OzuHh4S4uLlZWVnfv3s3IyEhMTOzSpcu+ffu6du2q6xoBADAY+hLwM2bMCAwM3Lhxo7Hx/5QUHR09fvz4Dz744MCBA7qqDQAAg6Mvp+hTUlJCQ0PLpLsQQqlUhoeHnz59WidVAQBgoPQl4N3c3I4ePVph15EjR9zd3eu4HgAADJq+nKKfPHny6NGjr127NnToUBcXl4YNG+bl5aWnpycmJq5evTo2NlbXBQIAYEj0JeBHjRplYmKycOHC+Ph4zXYvL6+4uLjQ0FBdFQYAgCHSl4AXQoSFhYWGhqanp1+/fj0nJ6dx48YtWrRwdHRUKBRVXMO9e/eOHz+urffu3bslJSU1VCwAAHpNjwJeCFFYWKhSqTp16mRkZKRuLCgouHfvnp2d3WMXv3DhwsKFC1UqVYW9N27cyM3NrbFaAQDQY/oS8I8ePYqMjFy6dGlJSYm7u3tcXJyvr6/UtWHDhrFjx2qLbU3e3t4//fSTtl5fX98mTZrUWMUAAOgxfbmLfvHixStWrJg1a1ZcXFzz5s379u17+fJlXRcFAICh0peAj42N/fDDD2fNmhUWFrZnz57WrVtPnTpV10UBAGCo9CXgr1y54uPjI01bWlquWLFi+/btldwxBwAAKqEvAe/o6JicnKx+6e3tPXLkyNGjRz948ECHVQEAYKD05Sa7kSNHzp49Oz8/PzAw0M/PTwixaNEiLy+v4OBgT09PXVcHAICB0ZeAnzZtWn5+/ueff56QkHDu3DkhRJMmTfbv3x8cHLxnzx5dVwcAgIHRl1P0ZmZmCxYsyM/P//nnn9WNrVu3PnPmzMGDB5cvX67D2gAAMDj6cgQvMTIycnR01GxRKpXdu3fv3r27rkoCAMAQ6csRPAAAqEEEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIENaA/7TTz+9evVqXZYCAABqitaAnz9/vrOzc48ePVasWHH79u26rAkAADwlrQGflZW1detWe3v7//u//7Ozsxs4cOD3339///79uiwOAABUj9aANzc3Hzx4cHx8fHZ29vr1601MTMLDw21tbcPCwpKSkoqKiuqySgAA8EQef5NdvXr1OnTo0KVLFw8Pj/v372/btq1///4tW7bcuXNnHdQHAACqQWvAq1Sq33///cMPP2zbtm3r1q0XLVrk6+v7448/5uTkXLx4MTAwcNCgQZmZmXVZKwAAqCJjbR2Ojo7Xrl1zdHQMDg5etmxZt27djIyMpK6WLVt+/vnnf//738+dO2dnZ1dXpQIAgKrSGvBhYWHBwcEdO3ZUKBTley0sLC5cuODs7FybtQEAgGrSeor+k08+cXBw+Pbbb/Py8oQQN27c+PTTT69cuSL1Ghsbu7u7m5iY1FGZAADgSWgN+GPHjj333HMREREPHz4UQhQVFS1evLhdu3b/+Mc/6rA8AABQHVoDftq0aZ07d87MzGzatKkQwsnJ6erVqwEBAe+//34dlgcAAKpDa8CfOnVqypQp9evXV7eYmpqOHTs2OTm5TgoDAADVpzXgmzdvXv676C9evGhjY1PLJQEAgKelNeBfffXVGTNmJCQkFBcXCyFKSkoSExNnzJgxePDgOiwPAABUh9bH5D7++OPr16+HhIQolcqmTZvm5OQUFRUNGTJk7ty5dVkfAACoBq0Bb2xsvHr16vfee+8f//hHenq6nZ2dt7f3Cy+8UJfFAQCA6tEa8BJPT09PT8+6KQUAANQUrQF/69atmTNnpqSklJaWara/+OKLS5curf3CAABA9WkN+HHjxh05cmTw4MGaT8oJISwtLWu/KiGEyM3NvXnzpqurq/o78AEAQBVpDfi9e/f+/vvvHrHAZUMAACAASURBVB4edVPHmDFjQkNDX3rpJSHEzZs3R48enZiYKIRo2LDhJ598MnHixAq/Eh8AAFRI62Nyjo6OdflbMt9+++25c+ek6XHjxh0+fHjx4sVJSUlTpkyJiIjYvHlznVUCAIAMaD2Cf+2111auXDl58uS6rEYIkZOTs23bth07dgwcOFAIERAQUFJSEhMTExISUseVAABguLQGvLu7+6JFi7Zv3+7n59eoUSPNroiIiNor6NKlS0KIXr16qVv8/Py+/PLL2tsiAADyozXgp02bJoTIzMxUnzlXq9WAd3FxMTY2zsjIUD+ed+bMGSsrq9rbIgAA8qM14DMyMuqyDiFEVFTUli1bPDw8XF1dIyIi9uzZI4TYu3fvV199FRAQUMfFAABg0B7zRTdCiMLCwoyMDHd3d5VKVXu3sm/evDktLS0tLS01NbWgoODAgQNSe1BQkJeX1/z582tpuwAAyFJlAb9s2bL58+dLh/IqlerVV1/t3Lnzu+++WxsxP3ToUM2XhYWF0sTOnTt79epVxUfhS0pKrly5oq334cOHT1MhAAAGRGvAr169+q233ho7dmzv3r1HjBghhPD19Y2MjKxXr97EiRNruyxzc3Npok+fPlVf6vDhw2+++aa23uvXr2dlZT1tZQAAGAKtAR8TEzNp0qSvvvrq1q1bUsvUqVPv3LnzzTff1EHAV0+PHj3+9a9/aev19fW1tbWty3oAANAVrV90c/ny5cDAwDKN3bp1u3jxYi2XBAAAnpbWI/jWrVsfO3asf//+mo0pKSlubm61UUdkZGTlM8TExNTGdgEAkCWtAT9p0qQJEyYYGxv7+/sLIdLT03fv3h0dHT179uzaqKO4uHjlypUPHjxwcHAwMzMrPwMBDwBA1WkN+FGjRuXl5UVHR0dFRQkhnJycTE1NIyIipk6dWht1LFmyxN/fPygoKCkpycvLqzY2AQDAs0NrwCsUioiIiDFjxqSmpl6+fLlZs2ZeXl42Nja1V0pgYGCd/RYtAADy9pgvuqlfv76Pj4+Pj08dlKJUKteuXWtvb18H2wIAQN60BnxQUFCF7c8999zChQtrqZrg4OBaWjMAAM8UrQHftGlT9bRKpcrOzj569KitrS0ZDACA/tMa8GvWrCnTcvfu3SFDhhQXF9duRQAA4Klp/aKb8qysrGbOnPn111/XXjUAAKBGPEHACyEKCgr4JjsAAPSf1lP069atK9Ny+/btRYsWdezYsZZLAgAAT0trwI8ZM6ZMi1KpbN++/bJly2q5JAAA8LS0Brz6F9kBAIDBebJr8AAAwCBU6Tn4CllbW1+4cKGm6wEAADVAa8DHxMSMGjXK0dExJCTE3t4+MzMzISHh5s2b0dHR0q+9KRSKOqwTAAA8Aa0Bv2fPnl69eiUlJZmamkot8+bNCwoKOnfuHPfZAQCg57Reg//111/feustdboLIYyNjSdMmLBjx446KQwAAFSf1oA3NTVNT08v03j16tXS0tJaLgkAADwtrQE/cODAqKioxMRElUolhFCpVLt27Zo1a5a/v38dlgcAAKqjspvsLl26NGjQIGtr6xYtWly/fv3OnTve3t5Lliypy/oAAEA1aA14S0vLpKSko0ePHjlyJD093cbGpkOHDgEBAUolj84DAKDvtAa8xNfX94UXXsjIyHB3d1epVDwaBwCAQajscHzZsmWOjo4WFhatWrUSQrz66qsxMTHSJXkAAKDPtAb86tWr33rrrcDAwPj4eKnF19f3/fffX758eV3VBgAAqqmym+wmTZr01Vdf3bp1S2qZOnXqnTt3vvnmm4kTJ9ZVeQAAoDq0Bvzly5cDAwPLNHbr1i0mJqaWS4JMPHz4sKCgQNdVGBJ+whFADdIa8K1btz527Fj//v01G1NSUtzc3Gq/KsjB888/f/HyDaHgsYuqKnmU7+w9WddVAJAJrQE/adKkCRMmGBsbS99sk56evnv37ujo6NmzZ9dheTBg+fn5nV/bZ1bPVteFGIxT21/VdQkA5ENrwI8aNSovLy86OjoqKkoI4eTkZGpqGhERMXXq1DosDwAAVEfFAf/w4cMbN26MHz9+zJgxqamply9fbtasmZeXl42NTR3XBwAAqqHigL948eJzzz0XFxcXFhbm4+Pj4+NTx2UBAICnUfENUJ6enr17946Pj+drbQAAMEQVH8ErFIrRo0dHR0d36tSpX79+TZo00fwK+oiIiLoqDwAAVIfWm+zee+89IUR+fv53331XpouABwBAz5UN+L1797Zr187W1jYjI0MnBQEAgKdX9hq8v7//oUOH1C/nzp179uzZui0JAAA8rcd8y9jMmTNTU1PrphQAAFBT+BpRAABkSB8Dvri4ODs7u6ioSNeFAABgqPQo4E+fPj1mzBg7OztTU1NbW1szMzM7O7tRo0alpKToujQAAAyM1sfk6tihQ4f69u3r7OwcHh7u4uJiZWV19+7djIyMxMTELl267Nu3r2vXrrquEQAAg1FBwL/55psTJkzQ9lIIcevWrRqvY8aMGYGBgRs3bjQ2/p+SoqOjx48f/8EHHxw4cKDGNwoAgFyVDfh33nlHJ3WkpKSsWbOmTLoLIZRKZXh4eJmfpQcAAJUrG6hLlizRSR1ubm5Hjx4NDg4u33XkyBF3d/e6LwkAAMOlL9fgJ0+ePHr06GvXrg0dOtTFxaVhw4Z5eXnp6emJiYmrV6+OjY3VdYEAABgSfQn4UaNGmZiYLFy4MD4+XrPdy8srLi4uNDRUV4UBAGCI9CXghRBhYWGhoaHp6enXr1/Pyclp3LhxixYtHB0dFQpFFdewf//+3r17a/uJW6VSmZWVVXP1AgCgv/Qo4IUQCoXCycnJycmpeou/9NJLpaWl2np9fX1tbW2rWxoAAIZEj77oBgAA1BQCHgAAGdKXU/SRkZGVzxATE1M3lQAAIAP6EvDFxcUrV6588OCBg4ODmZlZ+RkIeAAAqk5fAn7JkiX+/v5BQUFJSUleXl66LgcAAMOmR9fgAwMDLS0tdV0FAAByoEcBr1Qq165da29vr+tCAAAwePpyil5S4XfRAwCAJ6VHR/AAAKCmEPAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIkLGuCwCAaiopKsjOznZzc9N1IYbExMQkPj7+hRde0HUhqHUEPABDVVJ0X2liZeP7na4LMSTn979/7do1Av5ZQMADMGAKpbFFQyddV2FIlMYWui4BdYRr8AAAyBABDwCADBHwAADIkP4GfG5u7oULF0pKSnRdCAAAhkdfAn7MmDH79++Xpm/evDlo0CBra2sPD4/GjRsvXbpUpVLptDoAAAyMvgT8t99+e+7cOWl63Lhxhw8fXrx4cVJS0pQpUyIiIjZv3qzb8gAAMCx695hcTk7Otm3bduzYMXDgQCFEQEBASUlJTExMSEiIrksDAMBg6F3AX7p0SQjRq1cvdYufn9+XX35ZlWUPHz78zjvvaOs9f/58dnZ29apKSUkZPXp09ZZ9Zt26dctF1zUAwDNL7wLexcXF2Ng4IyPD09NTajlz5oyVlVVVlu3UqdOKFSu09Y4ePbpZs2bVq+rq1av/um7k3m1W9RZ/NhUlD9J1CQDw7NKjgI+KitqyZYuHh4erq2tERMSePXuEEHv37v3qq68CAgKqsgZTU1Nvb29tvRYWFgqFotrlGZs1bNCsXbUXBwCgLulLwG/evDktLS0tLS01NbWgoODAgQNSe1BQkJeX1/z583VbHgAAhkVfAn7o0KGaLwsLC6WJnTt39urVy8jISBdFAQBgqPQl4MswNzeXJvr06aPbSgAAMET68hw8AACoQQQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMmSs6wIAAHWn8N7VmTNnfvHFF7ouxJAMGjRo8uTJuq7iiRHwAPAMKSq8c9f0FWHxgq4LMRh3s079/PPPBDwAQN/Vb9LG2sFP11UYjJLiQiHO67qK6uAaPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADJEwAMAIEMEPAAAMqSPAV9cXJydnV1UVKTrQgAAMFR6FPCnT58eM2aMnZ2dqampra2tmZmZnZ3dqFGjUlJSdF0aAAAGRl9+D/7QoUN9+/Z1dnYODw93cXGxsrK6e/duRkZGYmJily5d9u3b17VrV13XCACAwdCXgJ8xY0ZgYODGjRuNjf+npOjo6PHjx3/wwQcHDhzQVW0AABgcfQn4lJSUNWvWlEl3IYRSqQwPD+/fv39VVvLHH3/MnTtXW++//vWv3Nzc6pWnUCjybp5J/emt6i3+bFKpVOcPfGBkbK7rQgxGwZ0LRQ9uF9y5oG0Gt/wblSx+Nyv5WdtFix7eLXp491l710+p+FHe1VPLsv65RdeFGIyH97Nadm6h6yqqQ18C3s3N7ejRo8HBweW7jhw54u7uXpWVODg4hISEaOvt1KmTn59f9cp76aWX1sZ+Vb1ln1lpad3c3NwUCoWuCzEYt28HCSGaNGmibQaHo0fF4mRtvS+2c/l66hu1Upm+UqlUaWlprVq10nUhhuTq1V42Njbm5nzyfgKenp66LqE69CXgJ0+ePHr06GvXrg0dOtTFxaVhw4Z5eXnp6emJiYmrV6+OjY2tykoaN25cScA/jfr169fSmoEns3ixtp7KP+ACeNboS8CPGjXKxMRk4cKF8fHxmu1eXl5xcXGhoaG6KgwAAEOkLwEvhAgLCwsNDU1PT79+/XpOTk7jxo1btGjh6OjIOV4AAJ6UHgW8EEKhUDg5OTk5Oem6EAAADJsefdENAACoKQQ8AAAyRMADACBDBDwAADJEwAMAIEP6dRe93rp06dLy5cvd3Nx0XYgh+fPPP9u2bctTjlWXnZ0thLCxsdE2g+vJk320L37x4sW9K1fWQl36S6VSpaamenl56boQQ/Kvf/2refPmlpaWui7EYBQUFFhYWIwfP17XhTwxAr5KDhw48MMPP/Tr10/XhRiSdevWBQcH8+9I1Z08eVII4e3trW2Gh1ev/jfgLwhRKMSZ//Zm5uVJa3h2FBQUbNmyhS/CeiI//vhjmzZteBq56q5evXrz5k1DDHiFSqXSdQ0GIDExcdWqVTt27NB1IYbE3t7+xIkTLVoY5I806MTHH3+s/m/FiotFXt5/XyoKhUrjG8XNzMQz9nHq+vXrnTp1unbtmq4LMSSDBg0aO3bswIEDdV2IwTDcf/85ggcMh7GxsLbWdREADAM32QEAIEMEPAAAMkTAAwAgQwQ8AAAyRMADACBDBDwAADLEY3JVYmJiYmzMWD0ZY2NjBu2JmJiY6LoEA8M+Vg3GxsbsaU/EcP/954tuqqSkpCQvL69Ro0a6LsSQ3L59u0mTJrquwpA8ePBACGFhYaHrQgwJu9mTys3NbdCggZGRka4LMRiG++8/AQ8AgAxxDR4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAh4AABki4AEAkCECHgAAGSLgAQCQIQIeAAAZIuABAJAhAv7xYmNjvb29raysevbs+dtvv+m6HJ1JSEhQ/K8xY8aoeysZpep1GbqIiIjIyMgyjTU+SnIawPIjxi5XoYcPH86bN8/T09PS0vK5555buHDho0eP1L3sYxWqZNDkvJupUKm1a9cKIaZOnZqQkDB48GBzc/NTp07puijdWLBggY2NzTINv/76q9RVyShVr8uglZSU7N6929LS8t1339Vsr/FRks0AahsxdrkKvf/++2ZmZvPmzUtKSoqKijI1NZ08ebLUxT6mTSWDJuPdjICvTGlpabt27V577TXpZVFRUevWrceMGaPbqnRlzJgx/v7+5dsrGaXqdRm0LVu21K9fX/r0rBlXNT5KshlAbSOmYperSHFxsbm5+QcffKBumT17trGxcUFBAfuYNpUMmkrWuxmn6CuTnp5+5syZYcOGSS+NjY0HDx68c+dO3ValK2lpaR4eHjk5OcnJyXfu3FG3VzJK1esyaC+99NLRo0fPnDnj4OCg2V7joySbAdQ2YoJdriLXr193dnbu37+/uqVly5bFxcXZ2dnsY9pUMmhC1rsZAV+Z69evCyGcnZ3VLS4uLllZWcXFxborSmfS0tL27t1rZ2fn7e3duHHj4ODgmzdvikpHqXpddfaOaoO1tbWXl5eXl5eZmZlme42PkmwGUNuICXa5ijg6Op47d87Pz096WVBQsGrVKjc3NwcHB/YxbSoZNCHr3YyAr0xOTo4QwsrKSt1iZWWlUqmk9mdKYWHhrVu3nJ2dU1JS7t69u23btgMHDki3olQyStXrqrM3VZdqfJRkP4Dsco+VnJzco0ePU6dOxcXFGRkZsY9VRZlBk/duZlz3mzQgjRs3FkLk5eWpW+7du6dQKBo1aqS7onTD3Nz8wYMH6pevvPLKzJkzp06dmpubW8koVa+rDt5O3avxUZL9ALLLVeL27duRkZHffffdK6+8snnzZhcXF8E+9jgVDpq8dzOO4CvTokULIUR6erq6JT093cbGxtTUVHdF6YvWrVsLIW7evFnJKFWvq87eQl2q8VF61gZQsMv9xz//+c927dqdOHHi2LFjW7dulYJKsI9VStuglSen3YyAr4yjo2Pbtm0TExOll6WlpYmJiZp3ajw79u7da25uvnfvXnXLkSNHGjZs6OrqWskoVa9Llmp8lGQ/gOxyFSotLR0yZMjzzz9/4sSJzp07a3axj2lTyaDJfDer69v2DU1cXJxCoYiJiTl8+PDYsWPNzc1Pnz6t66J0oLi4+MUXX7S3t//000937949Y8YMExOTb775RuqtZJSq1yUDbm5uZR76qvFRktkAlhkxdrkKHTp0SAgRERGx6n/dv39fxT6mRSWDJu/djIB/vNjY2A4dOjRo0KB79+6HDx/WdTk6c+PGjfDwcDs7u3r16nXq1OmHH34oLS1V91YyStXrMnTlA15VC6MkpwEsP2LscuUtX768wkO1GzduSDOwj5VX+aDJeDdTqFSq2js9AAAAdIJr8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABD2jVsWNHU1PTv/76S7Px1q1bCoVi8+bNNbKJtLQ0hUJx+vTpGlnbY126dKljx44WFhZXr14t05WSkjJkyBAnJ6d69ep5eXl9+OGHubm5dVOVWmRkpEKDpaXliy++uGHDhqdc7aRJkzRX6+zsPGzYsNTU1BqpGdBbxrouANBrRUVFEydO3L9/v0Kh0HUtNWDZsmWZmZk///yznZ2dZntycrKvr6+Pj8/8+fMbNWqUkpKyZMmSvXv3/vbbbyYmJkKI0NDQ/Pz8bdu21XaFFhYWcXFx0vTt27djY2Nff/31+vXrDxo06GlWa2Vl9fe//10IUVxcfP78+Q0bNvj4+GzdutXf378Gigb0EgEPVMbDw+PgwYNr1qx58803dV1LBVQq1fnz51u2bGlmZlaV+e/du+fp6enn51emPTo62t3dfe/evaampkKIoKCgfv36derUadeuXYMHD675urUzNTUdNmyY+mVoaKibm9uKFSuqGPDaBsTc3FxztVOnTu3Xr9/kyZNTU1ONjIxqqnhAr3CKHqiMr69veHj4tGnTbt26VeEM5ubm69atU78MDw8PCgqSpl1cXOLi4t5++20nJydXV9dvvvnm2rVrAwYMaNq0qbOzc3x8vHqpmzdvBgcHN2nSxMPD4+OPPy4uLlZ3xcbGent7S6fNY2Nj1e0ODg4bNmzo06dPmzZtbty4oVlScXFxdHR0mzZtGjRo4OPjs2PHDqk9ICBgxYoV+/btUygUly9f1lzkr7/+at++vZTuko4dO06cONHY2FgI0aVLl/Xr12/fvl2hUEjn7Sus6vLlywqF4s8//xw+fHjTpk09PT3nzZtXWloq9aakpAQEBFhbWzdp0iQ4ODg9Pb0q41+vXr127dpdunTpaQakvIYNG0ZHR58/f37Xrl1Sy927d8eNG9eiRQszMzNXV9c5c+aoVCohxPTp0x0dHaVpIURhYaGVldXChQurUjygYyoAWnh7e7/xxhs3b95s3LhxeHi41Hjz5k0hxKZNm6SXZmZma9euVS/yxhtvDBgwQJp2dnZ2dnbetWtXcXHx7NmzhRCenp7/+Mc/ioqKQkNDzc3NCwoKLly4IIRo0qTJ5MmTt23bNnXqVCFERESEtIYlS5YYGxt/+OGHO3fufPvtt4UQS5culbrs7e1bt24dEBCwZs2agoICzbLHjx9vZmb2ySefJCYmSicetm7dqlKpbt++HRYW5ufnd+PGjeLiYs1FBgwYYGlpuWzZsvv375cfh1u3bgUHB/fr1+/GjRslJSXaqpJi2MPDY+XKldnZ2QkJCQ0bNpw+fbpKpbp//76trW3Pnj3Xr1+/bNkyW1vbvn37lt/Qu+++a2Vlpdly//59Ozu7fv36Pc2AvP3227a2tmW2VVxcbGxsPG/ePOnlyJEjmzVr9umnn27fvv3//u//hBDx8fEqler48eNCiBMnTkizbd26VQhx5cqV8sUD+oaAB7SSAl6lUn377bdCiP3796ueMODVHwsyMjKEEHPnzpVe7tu3Twhx/vx5KeBHjBihXsO0adNMTU1v3ryZl5dnbW09e/ZsddfYsWMdHBykaXt7+xdffLG0tLRMzVeuXDEyMvryyy/VLQMGDGjfvr00PX78+Jdffrn8Oz179qybm5sQokGDBoMGDfrqq68uXLigOcPrr7/+yiuvqFSqSqqSAn7s2LHqrs8//9zCwuLOnTsnTpwQQhw+fFhq37hx46RJk8qX8e6771paWm79j7///e9du3ZVf0Cp3oCotAS8SqVydnYeM2aMND148ODvvvtO3fX8889PmzZNpVKVlpY6OjrOmDFDan/ttde6d+9eflWAHuIUPfB4b775Zvfu3SdMmPDw4cMnWtDT01OasLW11XxpY2MjhFCfvh4+fLh6kZEjRz569Ojs2bOpqal37tzp06fPrf/o3bt3RkZGYWGhNGdAQED5W/9Onz5dUlKiucLhw4f/8ccflVfu6el57ty5n376afz48VevXp08eXKrVq2GDBly9+7dMnM+tqrAwED1zAMGDHjw4MHZs2ednJzMzc2nTJmyadOm3NzckJCQr776qsJKCgoKhvzHuHHj7ty5ExsbK90HUL0BqYTmzFu3bh05cqRKpUpPT//uu+/Onj0r/XUUCkVwcLB0d+H9+/d37Njx+uuvV30TgA5xkx3weAqFYvny5R06dPjss88mTJhQyZyq/1yslZS5gUvb/Vya97Tb29sLIbKzs6Ur8d26dSszc3p6eqtWrYQQTZo0Kb+q69evKxSKZs2aqVuaN2+uUqkyMzOdnZ0r3HpRUdHdu3cbNWrk7+8v3VV+48aNLVu2vP/++++++65087madPG+wqqk++01Ny1NX79+3dfXNykpacaMGcOHD1coFH5+fjNmzAgICChfjJWVlbbH8yrZdCUDok1paem1a9dcXV2ll6dOnZo+fXpycnJRUZGPj4+1tbV6zuDg4C+++OKf//zn6dOnHz16pHmzHqDPOIIHquS5556bNm3a3Llz09LSKpktOzu7GivXvClMOpnv5OQkpWN6enqZ025SmAkhlMoK/v+V4lzzlsCsrCzxn3MGFUpPT2/WrNkvv/yiuZK33347JCTk119/LTPzY6sq/16kjywvvfTSkSNHMjMz161bV1JSEhQUVPlIlle9AdFm//79RUVFbdu2FULk5uZ27drV2tp6x44dOTk5P/74o+aHoW7dutnY2Gzbtu37778PCAh4oo8RgA4R8EBVffTRR/b29pMmTdJsVCqVt2/flqbv3r179OjRaqxZ8476uLg4KyurNm3aeHl5mZiYqG/zFkIsWbJk+PDhZU4SlNG+fXulUqn5PTybNm167rnnLCwstC3i4uLSpEmTZcuWaa65uLg4JSVFfYCr9tiqNm3apO5au3atpaVlmzZtNm/e3K5du3v37tna2v7tb3+Li4srKSk5f/58JW+kvOoNSIXy8/NnzpzZunXrAQMGCCFOnDhRWFgYExPj6+urVCrz8vI0azMyMho8ePD69et3797N+XkYEE7RA1VlYWGxdOnSMieW27dvv2jRIg8PDwsLizlz5lRyoFyJQ4cOvfXWW/379z948GBMTMyCBQvq169fv379iIiIyMjIvLy8559//siRI3Pnzv3kk08qv8zs4uIyevToyMjIBw8etG3bdsuWLTt27EhISKhkEaVSuWzZsuHDh3fp0uXVV19t0aJFdnZ2fHz8n3/+eeDAAWkeExOTCxcu/P777x06dKi8qh07dkyaNCkwMPDQoUMxMTEzZsywsrJq27bt2bNnw8LCwsLCSkpK1q5da2Vl1blz5ycaJRsbm2oMiKSwsFC6ji59sFi3bt2VK1e2bt0qXTRxc3NTKpXz588PDQ3NycmZN29eSUnJ0aNHz507J902MXTo0JUrV9arV2/gwIFPVDOg9h7xfwAAAhNJREFUS7Vw4x4gE+q76DW9+uqrQuMu+r/++qtHjx6WlpZeXl7Lly//9ttvpbuvVSqVs7PzZ599Jk0XFRWJ/9wNrlKpzpw5065du4yMjPT09G7duqWmpgYGBlpZWT333HNLlixR3wpeUlLy2WefScffnp6e33zzjbrL3t5+8eLFFZb96NGjWbNmeXh41KtXr1OnTtu2bVN3abuLXqVSHTx4MDAw0MHBwczMzM3NbcSIEX/88Ye695dffnFxcalfv35ubq62qqS76Lds2dK/f38rK6tWrVpJSSmtYfPmze3bt7ewsGjcuHHfvn2PHz9evobyj8mVUb0BkR6oU3N0dBwyZMiZM2c054mLi3Nzc7O0tPTx8dmyZcsvv/zi7e2tXuHDhw/r168fGhpaSW2AvlGonvzsFgCUd/ny5ZYtW544caJjx466rqWGXbp0ydXVdffu3ZrPCAB6jlP0AKBVcXHxgwcP5s+fb29v36dPH12XAzwBAh4AtLp8+XKrVq0UCsWOHTuk5wABQ8EpegDQqqio6PDhw56enmV+fw/QfwQ8AAAyxHPwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAMEfAAAMgQAQ8AgAwR8AAAyBABDwCADBHwAADIEAEPAIAM/T8JGuFyF16V6gAAAABJRU5ErkJggg==" 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