text:
\section{Introduction}
\label{sec:sample1}
The Coronavirus Disease 2019, or COVID-19 pandemic, has swept almost all countries worldwide since the first case was found in Wuhan, China, in December 2019. COVID-19 was carried by the Novel Coronavirus (SARS-CoV-2), which was first identified on January 7th, 2020 \cite{Gorbalenya2020}. The virus could be spread through water droplets and direct contacts, such as coughing and sneezing. Since COIVID-19 was first identified, WHO declared it a global pandemic on March 11th, 2020. COVID-19 has spread to 220 countries, with 221,134,742 confirmed cases and 4,574,089 deaths \cite{WHO2021}.
With the increasing number of COVID-19 cases in the world, SARS-CoV-2 has mutated into various variants. Since the first variant, Alpha (B.1.1.7), was discovered in the UK until February 18th, 2022, 12 variants of COVID-19 have been identified by WHO, with the Omicron and Delta variants now spreading rapidly \cite{WHO2022}. Given the increasingly dangerous conditions of the pandemic, it is crucial to know when the pandemic will stop by predicting confirmed cases of COVID-19. The prediction information can also serve as an early warning to all elements of society if the number of cases has reached an alarming level; therefore, many studies have raised COVID-19 as a case study to overcome the ongoing pandemic. The approach initially used traditional Machine Learning models to get better statistical results \cite{Shahid2020}. One uses the Support Vector Regression (SVR) model to predict the total positive cases of COVID-19, daily new cases, death cases, and daily death cases \cite{Parbat2020}. However, the results could be more accurate and produce a high error value compared to the Deep Learning method.

So based on these results, Deep Learning is the right choice, especially for analysis and prediction since COVID-19 has data that consists of values in a specific time (time series data). So the correct prediction is time series prediction, and the suitable model to be used is Long Short Term Memory (LSTM).

LSTM has been widely used, especially for COVID-19 cases that are spreading rapidly today. On a global scale, LSTM has been used to predict the growth of confirmed cases of COVID-19 evaluated using RMSE \cite{Yudistira2020}\cite{Yudistira2021} and also to predict positive cases. Death cases of COVID-19 in 10 major countries with MAE and RMSE evaluations \cite{Shahid2020}. While on a country scale, the use of LSTM to predict time series was carried out in Canada for the spread of COVID-19 \cite{Chimmula2020} and Russia, Peru, and Iran for positive cases of COVID-19 \cite{Wang2020}. Based on the implementation of the LSTM model, which is widely used to predict time series, in this study, researchers will use the LSTM model to predict the time series of the number of confirmed cases of COVID-19 for each identified variant.

\section{Methods}
\subsection{COVID-19 Dataset}
The dataset used in the study belongs to the European Center for Disease Prevention and Control (ECDC), which contains the number of confirmed COVID-19 cases per identified variant from 30 countries in Europe.
\begin{table}[ht]
	\centering
	\caption{Variants Information \cite{WHO2022}}
	\label{table11}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant (Pango Lineage)& WHO Label& Origin\\
    \hline
B.1.1.7& Alpha& United Kingdom\\
B.1.351& Beta& South Africa\\
B.1.427/B.1.429& Epsilon& South Africa\\
B.1.525& Eta& United Kingdom and Nigeria\\
B.1.616& -& France \cite{Fillatre2021}\\
B.1.617.1& Kappa& India\\
B.1.617.2& Delta& India\\
B.1.620& -& Cameroon, Africa \cite{Dudas2021}\\
B.1.621& Mu& South America\\
BA.1& Omicron& South Africa\\
BA.2& Omicron& South Africa\\
BA.2.75& Omicron& India\\
BA.4& Omicron& South Africa\\
BA.5& Omicron& South Africa\\
BQ.1& Omicron& Nigeria\\
C.37& Lambda& Peru, Chile, USA and Germany\\
Other& -&-\\
P.1& Gamma& Brazil \cite{Faria2021}\\
P.3& -&Philippines \cite{Francis2021}\\
UNK& Unkown& Unknown\\
XBB& Omicron& South Asia\\
    \hline
	\end{tabular}
\end{table}
The source of this data comes from the Global Initiative on Sharing Avian Influenza Data (GISAID) and The European Surveillance System (TESSy), recorded on weekly and continuously updated to the latest date. The dataset we use started from 2020 Week 1 to 2022 Week 49 and contains 21 COVID-19 variants. The naming of those variants is based on the nomenclature of the phylogenic framework \cite{Rambaut2020} or commonly known, as Pango Nomenclature. 21 variants we use in this study are shown in Table \ref{table11}. The dataset was filtered using only the data sourced from GISAID because it has earlier records than the TESSy one. Also, all variants in Table \ref{table11} are sourced from GISAID. The dataset snippets for variant B.1.1.7 are shown in Table \ref{table1} for the five first countries and their confirmed case.

\begin{table}[ht]
	\centering
	\caption{5 First Countries for variant B.1.1.7}
	\label{table1}
	\begin{tabular}{@{}cccc@{}}
	\hline Country& Year& Variant& Number Detection \\
    \hline
		Austria & 2021-01 & B.1.1.7 & 56 \\
		Belgium & 2021-01 & B.1.1.7 & 65 \\
		Bulgaria & 2021-01 & B.1.1.7 & 20 \\
		Croatia & 2021-01 & B.1.1.7 & 0 \\
		Cyprus & 2021-01 & B.1.1.7 & 1 \\
    \hline
	\end{tabular}
\end{table}

First, the dataset is divided based on the variant, then divided into training and testing data with a proportion of 80:20, by dividing based on the unit of time, which is week. There are 149 weeks for each variant, so the training data will contain the first 123 weeks, while the testing data will contain the last 26 weeks.

\subsection{LSTM}
LSTM, or Long Short Term Memory, is one type of RNN or Recurrent Neural Network [10], is also a type of strong artificial neural network suitable for solving problems from time series data. The LSTM architecture consists of a cell state and three gates: input, forget, and output.

Cell state functions to channel information from one sequence to another with changes to the value will be passed through 3 gates. Forget gate serves to determine the current input and the previous hidden state that will be stored or discarded with the output in the form of forget gate value (ft). The input gate updates the cell state's value by calculating the candidate cell state (Ct) and input gate (it). Furthermore, the output gate calculates the value of the hidden state \cite{Yu2019}. The architecture of LSTM can be seen in Figure \ref{fig1} below.

\begin{figure}[ht]
	\centering
\includegraphics[width=1.0\textwidth]{LSTM_Architecture.pdf}
	\caption{Architecture of LSTM consists of forget gate, input gate and output gate.}
	\label{fig1}
\end{figure}

The formula for the input gate can be seen in Equation \ref{Eq2}, forget gate in Equation \ref{Eq3}, and output gate in Equation \ref{Eq6}.

\begin{eqnarray}
\ i_{t}=\sigma(w_{f}*[h_{(t-1)},x_{t} ]+ b_{i})\label{Eq2}
 \end{eqnarray}
 \begin{eqnarray}
    \ f_{t}=\sigma(w_{i}*[h_{(t-1)},x_{t} ]+ b_{f})
	\label{Eq3}
 \end{eqnarray}
 \begin{eqnarray}
    \ g_{t}=tanh(w_{c}*[h_{(t-1)},x_{t} ]+ b_{c})
	\label{Eq4}
 \end{eqnarray}
 \begin{eqnarray}
    \ c_{t}=f_{t}*C_{(t-1)}+i_{t}*g_{t})
	\label{Eq5}
 \end{eqnarray}
 \begin{eqnarray}
     \ o_{t}=\sigma(w_{o}*[h_{(t-1)},x_{t} ]+ b_{o})
	\label{Eq6}
 \end{eqnarray}
 \begin{eqnarray}
     \ h_{t}=O_{t}*tanh(C_{t})
	\label{Eq7}
\end{eqnarray}

\subsection{BiLSTM}
Bidirectional LSTM or BiLSTM is a development model of conventional LSTM, consisting of two LSTMs in each layer one is used for forwarding propagation, and the other is  used for backward propagation \cite{Siami2019}.
\begin{figure}[ht]
	\centering
	\includegraphics[width=1.0\textwidth]{BiLSTM_Architecture.pdf}
	\caption{Architecture of BiLSTM.}
	\label{fig2}
\end{figure}
Bi-LSTM can improve the model's ability to learn during the training process and remember long sequences. In addition, BiLSTM is used to obtain minor prediction errors and high accuracy \cite{Shahid2020}. The architecture of BiLSTM can be seen in Figure \ref{fig2}. Each LSTM box is an LSTM cell with architecture according to Figure \ref{fig1}.

\subsection{Robust Scaler}
Before being used, the dataset was normalized using Robust Scaler. It is necessary so that when data is entered into the model, it does not give bad output due to incomplete data or the presence of an outlier. This method was chosen because the dataset used in this research contained outliers. As we know, the nature of the COVID-19 case dataset has many unexpected spikes in numbers. So that causes outliers to exist in the dataset. The scale function is defined in the following as Equation \ref{Eq1}
\begin{eqnarray}
	\chi^{'}=(\chi_{i}-\chi_{median})/(\chi_{75}-\chi_{25})
	\label{Eq1}
\end{eqnarray}

Where $x_i$ is the input data and $x'$ is the normalized output, while $x75$ and $x25$ are the interquartile range (IQR) which is the range between quartile 1 ($25^th$ quantile) and quartile 3 ($75^th$ quantile) of the data. Here we show the applied Robust Scaler to our dataset in Figure \ref{fig3}, how before and after Robust Scaler was applied to the dataset used in this research.

\begin{figure}[ht]
	\centering
\includegraphics[width=0.9\textwidth]{Before_After_Robust_Scaler.png}
	\caption{Comparison using Robust Scaler.}
	\label{fig3}
\end{figure}

Outliers from the data still exist, but the difference in value between the data and the outliers could be more negligible. Thus, the shape of the data is maintained, and the value of each data is more evenly distributed.

\subsection{Adam Optimizer}
Optimization is widely used to obtain the highest possible accuracy with minimum training time. However, general optimization methods such as stochastic gradient-based optimization still need to be more effective in high-dimensional parameter spaces. Thus, it is necessary to use a more efficient optimization, namely Adam Optimizer. Adam optimizer has several advantages, including small memory usage and an adaptive learning rate for each parameter \cite{Kingma2015}.
The algorithm of Adam Optimizer can be seen in Equation \ref{Eq10} to Equation \ref{Eq16}.

\begin{eqnarray}
\label{Eq10}
\ g_{t}\leftarrow \nabla_{\theta}f_{t}(\theta_{(t-1)})
\end{eqnarray}
\begin{eqnarray}
\label{Eq11}
\ m_{t}\leftarrow \beta_{1}\cdot m_{(t-1)}+(1-\beta_{1})
\end{eqnarray}
\begin{eqnarray}
\label{Eq12}
\ v_{t}\leftarrow \beta_{2}\cdot v_{(t-1)}+(1-\beta_{2})
\end{eqnarray}
\begin{eqnarray}
\label{Eq13}
\ \hat{m}_{t}\leftarrow m_{t}⁄(1-\beta_{1}^{t})
\end{eqnarray}
\begin{eqnarray}
\label{Eq14}
\ v_{t}\leftarrow v_{t}⁄(1-\beta_{2}^{t})
\end{eqnarray}
\begin{eqnarray}
\label{Eq15}
\ \theta_{t}\leftarrow \theta_{(t-1)}-\alpha\cdot \hat{m}_{t}⁄(\sqrt{(\hat{v}_{t}+\epsilon)})
\label{Eq16}
\end{eqnarray}

\subsection{MSE and RMSE}
In this study, each model's performance is measured using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Both methods are differentiable functions, meaning it is easier to perform mathematical computations than non-differentiable functions like MAE. MSE is defined as the average square of the difference between the estimated and actual value, and RMSE is the root of MSE \cite{Chai2014}. The formula for MSE is shown in Equation \ref{Eq8} and RMSE in Equation \ref{Eq9}.
\begin{eqnarray}
	\ MSE = 1/N * \Sigma^{N}_{i=1}(y_{i}-y^{'}_{i})
	\label{Eq8}
 \end{eqnarray}

\begin{eqnarray}
	\ RMSE = \sqrt{1/N * \Sigma^{N}_{i=1}(y_{i}-y^{'}_{i})}
	\label{Eq9}
 \end{eqnarray}

\section{Result and Discussion}
This paper aims to find the optimal model configuration for predicting COVID-19 confirmed cases based on its variant from 30 countries across Europe. LSTM and BiLSTM were implemented as a model for prediction along with RNN for comparison. The test was done in 2 types, Univariate and Multivariate. Both were going through 2 test stages, hidden size test and layer size test. Similar research on testing the hidden and layer size on LSTM has been done before \cite{Yudistira2020}, but we take a different approach.

Our first test was conducted by testing the number of hidden sizes implemented, combined with layer size 1 for each test, to avoid bias. Hidden sizes tested are 25, 50, 75, and 100. The optimal hidden size was then obtained by examining which hidden size gave the largest frequency of minimum loss value from each COVID-19 variant. Then the second test was carried out by testing the number of layer sizes implemented starting from 2, 3, 4, to 5, combined with the optimal hidden size value obtained from the previous test. The result of the layer size test was determined in the same approach as the hidden size one. The final result is the optimal model configuration of both hidden and layer sizes to be implemented.

The training process of each model is performed for 1000 epochs using a sequence length of 10 and an optimizer value of 0.01. The trained model is then used in testing. Each Univariate and Multivariate test is performed individually and not continuously from one another, and the same applies to hidden size and layer size tests. Loss is then calculated to determine the performance of each configuration and model.

\subsection{Univariate Test}
Our model successfully predicted confirmed COVID-19 cases on each COVID-19 variant for one week ahead. On the Univariate hidden size test, our result indicates that most minimum loss values were obtained using hidden size 25, as shown in Figure \ref{fig4} and Figure \ref{fig5} for the LSTM model. The other models show the same result, with hidden size 25 giving the most minimum loss value from all variants compared to other hidden sizes.

\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Univariate_MSE_Hidden_Size_Test_English.png}
	\caption{MSE value of Univariate Hidden Size Testing}
	\label{fig4}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Univariate_RMSE_Hidden_Size_Test_English.png}
	\caption{RMSE value of Univariate Hidden Size Testing}
	\label{fig5}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Univariate_MSE_Layer_Size_Test_English.png}
	\caption{MSE value of Univariate Layer Size Testing}
	\label{fig6}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Univariate_RMSE_Layer_Size_Test_English.png}
	\caption{RMSE value of Univariate Layer Size Testing}
	\label{fig7}
\end{figure}

We use an area chart to represent the loss result, MSE, and RMSE, from all testing for each COVID-19 variant. Here the hidden size 25 provides a smaller area in Figure \ref{fig4} and Figure \ref{fig5} from all variants compared to other hidden size areas, meaning a smaller loss value is obtained mainly from using hidden size 25. There is an upward trend in the loss value if we look at the figure. The larger the hidden size used, the greater the loss value resulted.
\begin{table}[ht]
	\centering
	\caption{Minimum MSE Univariate Hidden Size Testing.}
	\label{table2}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& MSE LSTM& MSE BiLSTM& MSE RNN\\
    \hline
B.1.1.7& \textbf{0.062374894}& 0.15336436& 77.83555603\\
B.1.351& \textbf{6.31998E-05}& 8.71519E-05& 0.000206085\\
B.1.427/B.1.429& 9.12305E-05& 9.58116E-05& \textbf{8.00817E-05}\\
B.1.525& 0.000100649& \textbf{7.64034E-05}& 0.002185906\\
B.1616& \textbf{0}&	\textbf{0}&	\textbf{0}\\
B.1.617.1& 0.000100018&	9.44639E-05& \textbf{7.12258E-05}\\
B.1.617.2& \textbf{0.132738456}&	0.337521702&	23.38211823\\
B.1.620& \textbf{1.23469E-05}& 0.000103341& 0.000701719\\
B.1.621& 0.000109367& \textbf{7.1065E-05}&	0.000339199\\
BA.1& 2202.093994&	3290.208496& \textbf{2112.996582}\\
BA.2& \textbf{269726.9063}&	575534.8125& 42769.47266\\
BA.2.75& 443.3800964&	440.393219&	\textbf{400.5347595}\\
BA.4& 1436.005859& 1619.013306& \textbf{1277.752441}\\
BA.5& 721738.0625& 714504.375& \textbf{673358.12}\\
BQ.1& 17984.62109& 17956.88867&	\textbf{17573.03125}\\
C.37& 9.94712E-05&	\textbf{9.42242E-05}& 0.000351999\\
Other& 29.14533615&	\textbf{3.766394138}& 80.57791901\\
P.1& 0.002713137& \textbf{0.001758261}& 0.003104303\\
P.3& 0.000377027& 0.00037388&	\textbf{0.000313186}\\
UNK& 0.024996253& \textbf{0.023208618}&	0.084605187\\
XBB& \textbf{555.9832153}& 561.4128418&	561.8463135\\
    \hline
	\end{tabular}
\end{table}

From all hidden size tests, 25 to 100, we take the minimum loss value obtained for each COVID-19 variant. The result is shown in Table \ref{table2} and Table \ref{table3}. The bolded values on the tables indicate that these are the minimum values of the three models tested for each COVID-19 variant. When we compare all models, RNN gave the most minimum loss values than the other two from all variants tested.
\begin{table}[ht]
	\centering
	\caption{Minimum RMSE Univariate Hidden Size Testing.}
	\label{table3}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& RMSE LSTM& RMSE BiLSTM& RMSE RNN \\
    \hline
B.1.1.7& \textbf{0.24974966}& 0.391617626& 8.822445869\\
B.1.351& \textbf{0.007949829}& 0.009335518& 0.014355659\\
B.1.427/B.1.429& 0.009551466& 0.009788342& \textbf{0.00894884}\\
B.1.525& 0.010032415& \textbf{0.008740902}& 0.046753675\\
B.1616& \textbf{0}&	\textbf{0}&	\textbf{0}\\
B.1.617.1& 0.010000885&	0.009719253& \textbf{0.00843954}\\
B.1.617.2& \textbf{0.364332885}&	0.580966175& 4.835505962\\
B.1.620& 0.003513813& \textbf{0.010165691}& 0.026489971\\
B.1.621& 0.010457873&	0.008430004&	0.018417358\\
BA.1& 46.92647552& 57.36034012& \textbf{45.96734238}\\
BA.2& 519.352417& 758.6401367& \textbf{206.8078156}\\
BA.2.75& 21.05659294& 20.98554802& \textbf{20.01336479}\\
BA.4& 37.89466858& 40.23696518&	\textbf{35.74566269}\\
BA.5& 849.5516968&	845.2836304& \textbf{820.5839844}\\
BQ.1& 134.1067505&	134.0033112& \textbf{132.5633087}\\
C.37& 0.009973525&	0.009706914& \textbf{0.018761635}\\
Other& 5.398642063&	\textbf{1.940719962}& 8.976520538\\
P.1& 0.052087784& \textbf{0.041931629}& 0.055716276\\
P.3& 0.019417183& 0.019335989& \textbf{0.017697062}\\
UNK& 0.158102036& \textbf{0.15234375}& 0.290869713\\
XBB& \textbf{23.57929611}& 23.69415283& 23.70329666\\
    \hline
	\end{tabular}
\end{table}

As we can see from both Table \ref{table2} and Table \ref{table3}, for variant B.1616, the loss value is 0, meaning the three models predict the cases accurately. This perfect score happened because most of its data contained 0 cases and no higher than 1 case.

Unlike the hidden size test, adding more layers resulting in LSTM gave the most minimum loss value obtained here on the layer size test. Using the configuration of hidden size 25 and layer size 3, LSTM topped the other two models. Figure \ref{fig6} and Figure \ref{fig7} show different results of LSTM using layer sizes 2 to 5. The same result also goes for BiLSTM and RNN.

From the figure, the overall area of layer size 3 is smaller than other layer sizes, which means that using layer size three results in a smaller or minimum loss value. Increasing the number of layers does not help reduce the loss value. Instead, it increases its value, as shown from the area of layer size 5. BiLSTM and RNN also gave the same result as LSTM, with layer size three as the optimal configuration.

Our prediction chart contains 30 countries in Europe corresponding to the dataset we used. The country started from Austria at the bottom left of the last row, then continued to the right and started again from the left, repeated until the upper rows. We show the prediction chart from our optimal configuration, using hidden size 25 and layer size 3, divided into three figures based on the model implemented, Figure \ref{fig8} for LSTM, Figure \ref{fig9} for BiLSTM and Figure \ref{fig10} for RNN. All of them are predictions for the "Other" variant only. LSTM gave the closest prediction to the real data compared to the other two models. On the other hand, BiLSTM and RNN predict way too far from their original data, resulting in a larger loss value than LSTM.
\begin{figure}[p]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_LSTM_Univariate_Hidden_25_Layer_3.png}
	\caption{Univariate LSTM using hidden 25 layer 3 on "Other" variant}
	\label{fig8}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_BiLSTM_Univariate_Hidden_25_Layer_3.png}
	\caption{Univariate BiLSTM using hidden 25 layer 3 on "Other" variant}
	\label{fig9}
\end{figure}
\begin{figure}[ht]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_RNN_Univariate_Hidden_25_Layer_3.png}
	\caption{Univariate RNN using hidden 25 layer 3 on "Other" variant}
	\label{fig10}
\end{figure}

Table \ref{table4} and \ref{table5} contain the minimum loss value of the overall Univariate layer size test. Most loss values on the tables come from the LSTM model, followed by BiLSTM and RNN. These results further proved that RNN is unsuitable for long-term patterns, which is the fundamental reason LSTM developed to overcome this issue \cite{Li2018}. RNN may work well on single-layer architecture, but multilayered RNNs still cannot beat LSTM on the same configuration. Thus we concluded on the Univariate test that the optimal configuration is using the LSTM model with hidden size 25 and layer size 3.
\begin{table}[ht]
	\centering
	\caption{Minimum Univariate MSE Layer Size Testing.}
	\label{table4}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& MSE LSTM& MSE BiLSTM& MSE RNN \\
    \hline
B.1.1.7& \textbf{0.285582066}& 0.048046913&	0.697912753\\
B.1.351& \textbf{0.000278501}& 0.000596882& 0.050010335\\
B.1.427/B.1.429& \textbf{8.72087E-05}& 9.60345E-05&	0.000313051\\
B.1.525& \textbf{9.04E-06}&	0.000110109& 0.000420311\\
B.1616& \textbf{0}&	\textbf{0}&	\textbf{0}\\
B.1.617.1& 9.93619E-05&	\textbf{9.77854E-05}& 0.000324554\\
B.1.617.2& 0.272779733&	\textbf{0.12485259}&	37.21570206\\
B.1.620& 0.000102891& \textbf{9.17507E-05}& 0.000704678\\
B.1.621& \textbf{2.74123E-05}& 0.000104615&	0.001252985\\
BA.1& 51.86838531& \textbf{21.66649628}&	135.2298737\\
BA.2& \textbf{1069615.5}& 1938490.125&	604102.8125\\
BA.2.75& 445.1516724& 445.1611938& \textbf{431.5044556}\\
BA.4& 2279.553711&	1890.885254& \textbf{1747.677124}\\
BA.5& 680154.625&	693546.5625& \textbf{643830.4375}\\
BQ.1& \textbf{18005.15234}&	18011.95898& 18012.54688\\
C.37& \textbf{2.77505E-05}&	8.96806E-05& 0.001253018\\
Other& \textbf{4.598902225}& 275.0593262& 1948.237183\\
P.1& 0.002071609& 0.002713932&	\textbf{0.013807666}\\
P.3& 0.000377038& 0.000377015& \textbf{0.000313259}\\
UNK& \textbf{0.003363617}&	0.01490442&	0.085280031\\
XBB& 561.8049927& 561.9882813& \textbf{557.6511841}\\
    \hline
	\end{tabular}
\end{table}
\begin{table}[ht]
	\centering
	\caption{Minimum Univariate RMSE Layer Size Testing.}
	\label{table5}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& RMSE LSTM& RMSE BiLSTM& RMSE RNN \\
    \hline
B.1.1.7& 0.534398794& \textbf{0.219196066}& 0.835411727\\
B.1.351& \textbf{0.016688347}& 0.024431167& 0.223629907\\
B.1.427/B.1.429& \textbf{0.009338558}& 0.009799719& 0.01769325\\
B.1.525& \textbf{0.003005981}& 0.010493278& 0.020501493\\
B.1616&	\textbf{0}&	\textbf{0}&	\textbf{0}\\
B.1.617.1& 0.009968042&	\textbf{0.009888649}& 0.018015385\\
B.1.617.2& 0.522283196&	\textbf{0.353344858}& 6.100467205\\
B.1.620& 0.010143518& \textbf{0.009578658}& 0.026545763\\
B.1.621& \textbf{0.005235672}& 0.010228172& 0.03539753\\
BA.1& 7.201971054& \textbf{4.654728413}& 11.62883759\\
BA.2& 1034.222168&	1392.296753& \textbf{777.2405396}\\
BA.2.75& 21.09861755& 21.09884262& \textbf{20.772686}\\
BA.4& 47.74467087& 43.48431015& \textbf{41.80522919}\\
BA.5& 824.7149048&	832.7944336& \textbf{802.3904419}\\
BQ.1& \textbf{134.1832733}&	134.2086334& 134.2108307\\
C.37& 0.005267873&	0.009469985& \textbf{0.035397992}\\
Other& \textbf{2.144505024}& 16.58491325& 44.13883972\\
P.1& \textbf{0.045514934}& 0.052095413& 0.11750602\\
P.3& 0.019417465& 0.019416876& \textbf{0.017699111}\\
UNK& \textbf{0.057996694}& 0.122083656& 0.292027444\\
XBB& 23.702425&	23.7062912&	\textbf{23.61463928}\\
    \hline
	\end{tabular}
\end{table}

By comparing the loss value of the hidden size test with the layer size test, most loss values for each variant decrease in the layer size test. Although not all variants, for example, the B.1.427/B.1.429 variant has increased its loss value. However, the B.1616 variant still provides a 0 loss value even in the layer size test.

\subsection{Multivariate Test}
For the Multivariate hidden size test, our result is similar to the Univariate, with hidden size 25 giving the most minimum loss value obtained from all variants. Here in Figure \ref{fig11} and Figure \ref{fig12} for LSTM, we can see that from all COVID-19 variants, most minimum loss values were obtained by using hidden size 25, proved by its smaller area on the chart. Interestingly though, among all the tested hidden sizes, they all have a slight difference in the frequency of minimum loss values obtained between each other. Nevertheless, it resulted in a quite balanced distribution on the area charts. The upward trend remains in the Multivariate hidden size test, just like the Univariate. The difference is not too much. Thus, it is not too noticeable.

\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Multivariate_MSE_Hidden_Size_Test_English.png}
	\caption{MSE value of Multivariate Hidden Size Testing}
	\label{fig11}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Multivariate_RMSE_Hidden_Size_Test_English.png}
	\caption{RMSE value of Multivariate Hidden Size Testing}
	\label{fig12}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Multivariate_MSE_Layer_Size_Test_English.png}
	\caption{MSE value of Multivariate Layer Size Testing}
	\label{fig13}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=0.9\textwidth]{Grafik/Multivariate_RMSE_Layer_Size_Test_English.png}
	\caption{RMSE value of Multivariate Layer Size Testing}
	\label{fig14}
\end{figure}

The minimum loss value from all hidden size tests is shown in Table \ref{table6} and Table \ref{table7}. Judging by looking at the bold value on the tables, here on Multivariate, the LSTM performs better. Different from Univariate cases where RNN outperforms LSTM. The frequency of the minimum loss value obtained by LSTM for each COVID-19 variant exceeds the RNN. Another example is that RNN works well only on Univariate single-layer cases.
\begin{table}[ht]
	\centering
	\caption{Minimum Multivariate MSE Hidden Size Testing.}
	\label{table6}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& MSE LSTM& MSE BiLSTM& MSE RNN \\
    \hline
B.1.1.7& \textbf{0.04227148}& 0.060507733&	3.309589386\\ B.1.351& 0.002829222& \textbf{0.002783381}&	0.003932106\\  B.1.427/B.1.429& \textbf{8.19067E-06}& 9.41505E-06& 8.33682E-06\\
B.1.525& \textbf{2.63134E-05}& 2.73274E-05& 0.000232415\\
B.1616& 4.02546E-06& \textbf{3.81788E-06}& 4.01232E-06\\
B.1.617.1& 3.59397E-05&	\textbf{2.58086E-05}& 2.88424E-05\\
B.1.617.2& 51.76334& 9.645352364& \textbf{9.133572578}\\
B.1.620& \textbf{2.18086E-05}& 2.54787E-05& 4.9359E-05\\
B.1.621& 8.94553E-06& \textbf{1.20327E-05}& 7.92159E-05\\
BA.1& 6295.939941& 11615.52539& \textbf{4038.050537}\\
BA.2& \textbf{371409.6875}& 375215.8438& 373718.8438\\
BA.2.75& 1332.728638&	1333.700928& \textbf{1330.381226}\\
BA.4& \textbf{6960.57959}& 7049.269043&	7252.370117\\
BA.5& \textbf{1516468}& 1522720.75&	1532071.125\\
BQ.1& 20797.63477&	20828.27734& \textbf{20750.55469}\\
C.37& \textbf{2.18528E-05}&	2.7185E-05&	2.97164E-05\\
Other& \textbf{67.05672455}& 74.06816101& 88.95769501\\
P.1& \textbf{0.00020777}& 0.000266735& 0.000521\\
P.3& 1.87686E-05& 2.0501E-05&	\textbf{1.81369E-05}\\
UNK& \textbf{80732.13281}& 83808.39844&	81823.41406\\
XBB& 93.5736084& 93.7795105& \textbf{92.58743286}\\
    \hline
	\end{tabular}
\end{table}
\begin{table}[ht]
	\centering
	\caption{Minimum Multivariate RMSE Hidden Size Testing.}
	\label{table7}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& RMSE LSTM& RMSE BiLSTM& RMSE RNN \\
    \hline
B.1.1.7& \textbf{0.205600291}& 0.245983198& 1.819227695\\
B.1.351& 0.053190429& \textbf{0.052757762}& 0.062706508\\
B.1.427/B.1.429& \textbf{0.002861935}& 0.003068395&	0.002887355\\
B.1.525& \textbf{0.00512966}& 0.005227563&	0.015245159\\
B.1616& 0.002006355& \textbf{0.001953939}&	0.002003078\\
B.1.617.1& 0.005994972&	\textbf{0.005080214}& 0.005370509\\
B.1.617.2& 7.194674492&	3.105696678& \textbf{3.02218008}\\
B.1.620& \textbf{0.004669973}& 0.005047639& 0.007025594\\
B.1.621& \textbf{0.002990909}& 0.003468824&	0.008900332\\
BA.1& 79.34696198&	107.7753448& \textbf{63.54565811}\\
BA.2& \textbf{609.4338989}&	612.548645&	611.3255005\\
BA.2.75& 36.50655746&	36.51987076& \textbf{36.47439194}\\
BA.4& \textbf{83.43008423}&	83.95992279&	85.16085052\\
BA.5& \textbf{1231.449585}&	1233.985718&	1237.768555\\
BQ.1& 144.2138519&	144.3200531& \textbf{144.0505219}\\
C.37& \textbf{0.004674699}&	0.005213923& 0.005451273\\
Other& \textbf{8.188817024}& 8.606286049& 9.431738853\\
P.1& \textbf{0.014414218}& 0.016332025& 0.022845136\\
P.3& 0.004332275&	0.004527803& \textbf{0.004258746}\\
UNK& \textbf{284.1340027}& 289.4967957&	286.0479126\\
XBB& 9.673345566&	9.683981895& \textbf{9.622236252}\\
    \hline
	\end{tabular}
\end{table}

Another interesting thing to observe is that variant B.1616 did not give a 0 loss value like the previous result on the Univariate test. The three models failed to predict accurately. Our assumption with the simultaneous inclusion of varied data into the model caused this result, unlike Univariate, which enters the data one by one, allowing for a better training process. However, the overall result proves to be better than the Univariate one.

Next, on the layer size test, more layers added to the model did decrease the loss value. However, our optimal result was on a smaller layer size. Instead, it is on layer size 4. This result is represented in Figure \ref{fig13} and Figure \ref{fig14} for LSTM. Using a layer size of 5 makes it the worst configuration, with the many COVID-19 variant resulting in the highest loss value compared to the others. The other two models brought the same result, with layer size four as the optimal configuration.

Identical to Univariate, here we show our prediction chart for each model shown in Figure \ref{fig15} for LSTM, Figure \ref{fig16} for BiLSTM, and Figure \ref{fig17} for RNN using our optimal configuration for Multivariate, which is hidden size 25 and layer size 4. All of them are "Other" variant predictions. The prediction results in this test are rather flatter, different from Univariate, which still shows a tendency to follow the shape of the data. However, the predicted value is larger than the original data, unlike in Univariate, which tends to be smaller than the original data.

\begin{figure}[p]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_LSTM_Multivariate_Hidden_25_Layer_4.png}
	\caption{Multivariate LSTM using hidden 25 layer 4 on "Other" variant}
	\label{fig15}
\end{figure}
\begin{figure}[p]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_BiLSTM_Multivariate_Hidden_25_Layer_4.png}
	\caption{Multivariate BiLSTM using hidden 25 layer 4 on "Other" variant}
	\label{fig16}
\end{figure}
\begin{figure}[ht]
	\centering	\includegraphics[width=1.0\textwidth]{Grafik/Other_RNN_Multivariate_Hidden_25_Layer_4.png}
	\caption{Multivariate RNN using hidden 25 layer 4 on "Other" variant}
	\label{fig17}
\end{figure}

Our prediction result for all variants, both Univariate and Multivariate tests, gave unfavourable results, with most predicting far beyond or even below the original data. From this problem, we acknowledge these results due to the dataset containing many zero values, thus influencing our prediction result. TThis problem can be solved by increasing the amount of data used or trying other normalization methods.

The minimum loss value for each variant from the entire Multivariate layer size test is shown in Table \ref{table8} and Table \ref{table9}. The loss value in our Multivariate layer size test provides a loss value with an upward trend compared to the hidden size test. Although this loss value is already the minimum value of the entire layer size test, this result is inversely proportional to the Univariate test, which has a downward trend with the addition of layer size.

When we calculate the frequency of minimum loss value for each COVID-19 variant, we found that both LSTM and BiLSTM have the same frequency of minimum loss value. Meaning that both LSTM and BiLSTM perform approximately equally well. In reality, BiLSTM should outperform the LSTM and provides better result and performance \cite{Siami2019}; in our case, the frequency of minimum loss value should be larger.
\begin{table}[ht]
	\centering
	\caption{Minimum Multivariate MSE Layer Size Testing.}
	\label{table8}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& MSE LSTM& MSE BiLSTM& MSE RNN \\
    \hline
B.1.1.7& \textbf{0.030201122}& 0.087667666&	0.125225887\\
B.1.351& \textbf{0.000407222}& 0.004255008& 0.006206059\\
B.1.427/B.1.429&  7.28716E-06& \textbf{6.82E-06}& 9.39745E-06\\
B.1.525& \textbf{4.07541E-05}& 0.000141382& 0.001054534\\
B.1616&	\textbf{0}&	\textbf{0}&	\textbf{0}\\
B.1.617.1& 3.01427E-05&	2.76918E-05& \textbf{2.18035E-05}\\
B.1.617.2& 0.702166438& \textbf{0.168214917}& 296.4614868\\
B.1.620& 2.63529E-05& \textbf{2.30556E-05}& 2.60507E-05\\
B.1.621& \textbf{1.16705E-05}& 5.12911E-05&	5.84191E-05\\
BA.1& 1181.40564& \textbf{601.9147949}&	13268.86914\\
BA.2& \textbf{374334.625}&	374705.1563&	374586.1563\\
BA.2.75& 444.7804871&	445.1540833& \textbf{431.7670898}\\
BA.4& \textbf{2412.172363}&	3070.185791& 4485.07959\\
BA.5& \textbf{650916.25}& 726488.125& 813796.4375\\
BQ.1& 18016.07813& \textbf{18001.36328}& 18012.54688\\
C.37& \textbf{2.55481E-05}&	2.98774E-05&	3.05288E-05\\
Other& 	54.60609055& \textbf{28.08243752}&	46.32154465\\
P.1& \textbf{0.000335828}&	0.000404327&	0.025433218\\
P.3& 1.98145E-05& \textbf{1.66194E-05}&	1.98912E-05\\
UNK& 0.082096986& \textbf{0.000518148}&	0.08528506\\
XBB& 93.92437744& \textbf{93.88194275}&	93.96388245\\
    \hline
	\end{tabular}
\end{table}
\begin{table}[ht]
	\centering
	\caption{Minimum Multivariate Layer Size RMSE Testing.}
	\label{table9}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& RMSE LSTM& RMSE BiLSTM& RMSE RNN \\
    \hline
B.1.1.7& \textbf{0.173784703}& 0.296087265&	0.353872687\\
B.1.351& \textbf{0.020179749}& 0.065230429&	0.07877855\\
B.1.427/B.1.429& 0.002699475& \textbf{0.002611829}& 0.003065527\\
B.1.525& \textbf{0.006383896}& 0.011890434&	0.032473594\\
B.1616&	\textbf{0}& \textbf{0}&	\textbf{0}\\
B.1.617.1& 0.005490233&	0.005262296& \textbf{0.004669424}\\
B.1.617.2& 0.837953746&	\textbf{0.410140127}& 17.21805763\\
B.1.620&0.005133511& \textbf{0.004801628}&	0.005103985\\
B.1.621&\textbf{0.003416212}& 0.007161778&	0.007643239\\
BA.1& 34.37158203& \textbf{24.53395271}&	115.1905746\\
BA.2& \textbf{611.8289185}&	612.1316528&	612.0344238\\
BA.2.75& 21.08981895& 21.09867477&	\textbf{20.77900505}\\
BA.4& \textbf{49.11387253}& 55.40925598&	66.97073364\\
BA.5& \textbf{806.7938232}& 852.3427124&	902.1066895\\
BQ.1& 134.2239838&	\textbf{134.1691589}&	134.2108307\\
C.37& \textbf{0.005054518}&	0.005466022&	0.005525291\\
Other& 7.389593601&	\textbf{5.299286366}&	6.80599308\\
P.1& 0.018325612&	0.020107888&	\textbf{0.15947794}9\\
P.3& 0.004451346&	\textbf{0.004076692}&	0.004459959\\
UNK& 0.286525726&	\textbf{0.022762863}&	0.292036057\\
XBB& 9.691458702&	\textbf{9.689269066}&	9.693496704\\
    \hline
	\end{tabular}
\end{table}

However, when we delve deeper onto each loss value for each variant, the loss value of BiLSTM tends to be smaller than the LSTM one. Therefore, we conclude that BiLSTM outperforms LSTM despite having the same frequency of minimum loss values. Therefore, the optimal configuration from the Multivariate test uses BiLSTM with hidden size 25 and layer size 4.

Multivariate test results in our study show better results than the Univariate. Table \ref{table10} shows the MSE loss value of Univariate compared to Multivariate on LSTM. The majority of COVID-19 variants produce smaller loss values in Multivariate. In total, there are 13 variants with minimum loss values. This result is consistent with similar studies implementing Multivariate \cite{Yudistira2021} \cite{Said2021} \cite{Nick2020}. Multivariate can provide better results because more data is processed at one time, contrary to Univariate, in which computing one data at a time allows the model to train better and use data with more variety.
\begin{table}[p]
	\centering
	\caption{Univariate vs Multivariate.}
	\label{table10}
	\begin{tabular}{@{}cccc@{}}
	\hline Variant& MSE Univariate& MSE Multivariate\\
    \hline
B.1.1.7& 0.285582066&  \textbf{0.030201122}\\
B.1.351& \textbf{0.000278501}& 0.000407222\\
B.1.427/B.1.429& 8.72087E-05& \textbf{7.28716E-06}\\
B.1.525& 9.04E-06& \textbf{4.07541E-05}\\
B.1616&	\textbf{0}& \textbf{0}\\
B.1.617.1& 9.93619E-05& \textbf{3.01427E-05}\\
B.1.617.2& \textbf{0.272779733}& 0.702166438\\
B.1.620& 0.000102891& \textbf{2.63529E-05}\\
B.1.621& 2.74123E-05& \textbf{1.16705E-05}\\
BA.1& \textbf{51.86838531}& 1181.40564\\
BA.2& 1069615.5& \textbf{374334}.625\\
BA.2.75& 445.1516724& \textbf{444.7804871}\\
BA.4& \textbf{2279.553711}& 2412.172363\\
BA.5& 680154.625& \textbf{650916.25}\\
BQ.1& \textbf{18005.15234}& 18016.07813\\
C.37& 2.77505E-05& \textbf{2.55481E-05}\\
Other&\textbf{4.598902225}& 54.60609055\\
P.1& 0.002071609& \textbf{0.000335828}\\
P.3& 0.000377038& \textbf{1.98145E-05}\\
UNK& \textbf{0.003363617}& 0.082096986\\
XBB& 561.8049927& \textbf{93.92437744}\\
    \hline
	\end{tabular}
\end{table}

\clearpage
\section{Conclusion}
The COVID-19 pandemic has become a serious global threat, with many variants emerging due to virus mutations. Since the first variant of COVID-19 was discovered in the UK until now, and the prediction of COVID-19 variants that have been identified is needed. This research aims to help governments and health organizations implement the right policies to deal with the pandemic. We are focusing on predicting confirmed cases of COVID-19 for each variant that has been identified. By utilizing ECDC's COVID-19 case dataset, we try to determine the optimal model configuration to be implemented by testing the number of hidden and layer sizes with Univariate and Multivariate forms. From the testing in our research, we concluded that in the Univariate test, the optimal configuration is using LSTM with the hidden size 25 and layer size 3. Multivariate uses LSTM with hidden size 25 and layer size 4. Both are proven by the frequency of the minimum loss value obtained from each COVID-19 variant.
\clearpage
 \bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


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Four field guides were also released. They give more information about the Clans that are not mentioned in the series. Firestar's Quest, a "Super Edition", was released on August 21, 2005. After it, two other super editions were released; Bluestar's Prophecy and SkyClan's Destiny. Several three volume manga series have been printed as well. The title of the first volume was The Lost Warrior, and followed Graystripe after he was taken away by twolegs (humans). The second volume, Warrior's Refuge, was released on December 26, 2007, and the second book of Power of Three, Dark River, was also released December 26, 2007.
The series focuses on a group of cats called ThunderClan. Each mini-series focuses on different characters.
The original series and the first two books of Warriors: The New Prophecy are set in a forest that the authors have said was based on the New Forest area of England. The first six books center around Rusty, a kittypet (housecat) who joins ThunderClan, and finds out that there is a prophecy about him. The second series finds the forest in turmoil. "Twolegs" (humans) are destroying the forest, and a cat from each Clan (there are four) must go on a journey. Then, all the cats must find a new home. This involves mostly Leafpool and Squirrelflight, daughters of Rusty, now called Firestar. The third series, The Power of Three, involves Squirrelflight's "kits", Jaypaw, Hollypaw, and Lionpaw, and the special powers that they have. The next series, Omen of the Stars, came out a while ago. Squirrelflight's"children" discover their true identity and save all the clans.
This mini-series focuses on the young kitten Rusty. In the first book, Into the Wild, he discovers a kitten named Graypaw in the woods. After a cat named Bluestar asks him to join ThunderClan, Rusty decides to leave his home in the town and join Bluestar's Clan - who she turns out to lead. There, he is given the name Firepaw. Some of his friends include Graypaw, Sandpaw, Dustpaw, and Ravenpaw - they become Graystripe, Sandstorm, and Dustpelt, but Ravenpaw is chased off due to his mentor's evil intentions. Firepaw himself is later named a warrior, Fireheart.
Fireheart finds out that a fellow warrior, Tigerclaw, has killed a cat in ThunderClan because he wanted to take this cat's position. Fireheart reveals this to his Clan in Forest of Secrets. Tigerclaw is then forced away from ThunderClan and lives as a lone cat in the forest. However, he is always watching for a chance to take revenge on Fireheart.
However, Tigerclaw then becomes leader of a rival clan, ShadowClan. He tries to get the four Clans to join and form one single Clan called TigerClan. When they refuse, he asks a group of city cats called BloodClan to fight the other Clans. However, this plan fails and Tigerclaw is killed by the leader of BloodClan.
The next mini-series focuses on three cats: Brambleclaw, Squirrelpaw, and Leafpaw of ThunderClan. One night, in a dream, Brambleclaw speaks to the old ThunderClan leader, Bluestar, who tells him to meet with three other cats and to listen to what midnight tells them. He meets Tawnypelt of ShadowClan, Feathertail of RiverClan, and Crowpaw of WindClan, and together with their friends Stormfur and Squirrelpaw, they travel to the "sun-drown-place" (the ocean). There they learn that twolegs (humans) will destroy the forest because they want to build more thunderpaths (roads). The six cats hurry back, but on the way, they meet some cats in the mountains known as the Tribe of Rushing Water. The tribe is being hunted by a large mountain cat, named Sharptooth. (mountain lion) . And they think that Stormfur is the cat that is foretold to save them. In the end, it is Feathertail, Stormfur's sister, who kills the mountain cat, but she dies on the way. After leaving the tribe, the five remaining cats go home but find that the clans are worse than they had thought. The cats are all starving because the humans are scaring away all the prey and destroying the land. Firestar finally manages to convince the other clan leaders, (Leopardstar of Riverclan, Blackstar of ShadowClan, and Tallstar of Windclan, though he was willing) to come with him, and the four clans travel to a new territory by a lake.
But even though the cats are now safe, the trouble is not over. Squirrelpaw, given her warrior name Squirrelflight, does not trust Brambleclaw because he is friends with his half-brother Hawkfrost, son of Tigerstar and a rogue called Sasha. And Leafpaw falls in love with Crowpaw, now known as Crowfeather. As the cats fight and fall in love, lots of badgers attack ThunderClan, making Leafpaw make the choice to give up Crowfeather and letting Brambleclaw and Squirrelflight realize how much they mean to each other. In Sunset, Hawkfrost wants to kill Firestar so Brambleclaw can become leader. Brambleclaw realizes that he is more loyal to Firestar than to his half-brother, and he kills Hawkfrost to save his leader.
This series is about three cats - Jaypaw, Hollypaw, and Lionpaw. They are the kits Squirrelflight and Brambleclaw and they are also grandchildren of Firestar.
These three kittens are about to be ThunderClan apprentices. Hollypaw wants to be a medicine cat, and Jaypaw and Lionpaw will train to be warriors. However, Jaypaw is blind, and training to be a warrior will be hard for him. Eventually Hollypaw and Jaypaw switch roles after StarClan, the warrior ancestors, tell Jaypaw he needs to become a medicine cat. A few moons later, Lionpaw begins secretly meeting a WindClan apprentice, Heatherpaw while being trained by Tigerstar. Heatherpaw finds a set of secret tunnels under the ground and she and Lionpaw meet in them. Hollypaw finds out about this, and she and her friend Cinderpaw try to stop him from going to the tunnels. He eventually realizes that it is wrong to meet a cat from another clan, and breaks off the friendship. Then, three WindClan kittens get lost in the tunnels and Heatherpaw, Lionpaw, Hollypaw, Jaypaw, and Breezepaw have to save them.
In Outcast, the Tribe of Rushing Water is in danger from another band of cats in the mountains. Firestar sends the original New Prophecy cats, plus some extras, to the mountains to try to help them. Jaypaw meets the Tribe of Endless Hunting, the tribe's ancestors, and realizes that the tribe is not all that different. In Eclipse, WindClan is not happy about ThunderClan taking in non-clan cats, so all four clans battle(Thunder and Shadow VS. Wind and River) and the fighting is only stopped by an eclipse. A strange cat named Sol had predicted it, and Jaypaw thinks Sol is very powerful. After the battle, Hollypaw and Lionpaw get their warrior names, Hollyleaf and Lionblaze.
Sol tries to become powerful within the clans by convincing ShadowClan to stop believing in the Warrior code. Jaypaw, Hollyleaf, and Lionblaze fake a sign from StarClan with TawnyPelt's kits Help to make Blackstar believe in his warrior ancestors again. Sol is driven out, and Jaypaw, after saving ThunderClan from a greencough, is given his medicine cat name, Jayfeather. When the clan is in the camp, a fire starts, and the three are trapped on a cliff. Squirrelflight tries to save them, but a warrior called Ashfur blocks the way. It turns out he was mad that Squirrelflight chose Brambleclaw to be with her and not him. In order to save the three, Squirrelflight has to tell Ashfur an important secret - Jayfeather, Lionblaze, and Hollyleaf are not her kittens. Ashfur wants to tell all the clans, so right before the gathering, he is found dead in a river. The cats think Sol did it, so some of them go on a trip to a Twolegplace, find him, and take him back to ThunderClan. Meanwhile, Jayfeather is trying to find out who his mother is. After finding some clues, he realizes that she is Leafpool, and his father is Crowfeather. Hollyleaf is very mad upon hearing this, and she herself tells the cats at the gathering about it. She also tells her brothers that she killed Ashfur to protect them. Then to escape what she did, she runs into one of the tunnels and it collapses.
The authors have said that a fourth six-book series called Omen of the Stars is going to be written. Book 1 is called the "Fourth Apprentice" It is based on Dovekit and Ivykit, the daughters of Whitewing and Birchfall and the granddaughters of Cloudtail and Brightheart. It is out in stores now. The second book is called Fading Echoes. This book is out now. It is available for people to read. And the third book, Night Whispers, is now also available at book stores. The fourth book, Sign Of the Moon, is now out and available for purchase and the fifth book, The Forgotten Warrior, is now able for purchase.The sixth book, The Last Hope, is out.
Firestar goes on a quest with Sandstorm to save SkyClan.
Tallstar sets out on a quest for revenge outside the borders of the warrior code.
SkyClan's life after Firestar leaves.
Cloudstar leads his Clan from the forest after Twolegs take their territory.
Mistystar discovers a shocking secret about RiverClan after she becomes leader.
Hollyleaf's life in the tunnels.
Secrets of the Clans has a list of herbs, all four camp tours, maps, stories, mythology, clan histories, prophecies, and a catspeak:humanspeak glossary.
Cats of the Clans has biographies and drawings of most important Warriors characters.
Code of the Clans explains the reasons behind all the rules of the warrior code using 15 short stories.
Battles of the Clans explains all of the clans' battle moves.
Enter the Clans is Secrets of the Clans and Code of the Clans in one book.
The rest of the books in this arc have not yet been released.
There is a three book manga series based on what happened to Graystripe between the time that he was taken by "twolegs" in Dawn until he returned to ThunderClan in The Sight.
This series follows the life of Sasha, a kittypet who is forced to leave her home and who falls in love with a dangerous clan cat, Tigerstar.
This manga series features Ravenpaw, a former apprentice of Thunderclan, and his adventures to take back his home from former Bloodclan cats.
This graphic novel follows SkyClan four seasons after SkyClan's Destiny, and features Leafstar's experience having kits while leading her Clan through Sol's regime in SkyClan.
A small kitten named Tiny eventually becomes the feared leader of BloodClan.
Written by Vicky Holmes for a tour, a play entitled After Sunset: We Need to Talk was first premiered on April 28, 2007 at the Secret Garden bookstore in Seattle, Washington. It shows a meeting between Leafpool of ThunderClan and Crowfeather of WindClan after the events of Sunset. The script was released to the public at www.warriorcats.com, the official site for the Warriors series. In 2009, another short play called Brightspirit's Mercy was released. This play was about the ThunderClan cat Jayfeather helping the other clans even though it is against the warrior code. This play was also released at www.warriorcats.com.
↑ "Author Interview: Erin Hunter". Writers Unboxed.
↑ "Second Erin Hunter Chat". Wands and Worlds.
↑ "Comic Book Bin Tokyopop News Release". www.comicbookbin.com.
↑ "Transcript Of Erin Hunter Chat". Wands and Worlds. http://www.wandsandworlds.com/community/node/52. Retrieved on 2008-03-02.
The Official Warriors Site The official site of Warriors.
The Guttersnipe Author Kate Cary's personal site.
Warriors Wiki A comprehensive Warriors Encyclopedia.
This page was last changed on 25 April 2018, at 18:39.


text:
Max Air joins Nigerian domestic market
The owners of Max Air Limited have concluded plans to venture into the Nigerian domestic air transport market in addition to its usual Hajj and Umrah operations.
Alhaji Bashir Mangal, Vice Chairman, Max Air, in an interview with the News Agency of Nigeria (NAN), on Wednesday in Abuja, said the airline would commence full operation on July 2.
Mangal said the demo flight would be carried out on Wednesday afternoon to mark its readiness commence domestic operation.
He disclosed that Max Air would begin operation from Abuja, Lagos and Kano routes, while other routes like Sokoto, Maiduguri, Port Harcourt and Yola would follow subsequently.
"By the grace of God we are going to start our domestic operations in Nigeria soon and today, one of our aircraft is coming to Abuja for test flight.
"We plan to start our operation on Monday, July 2 with three destinations: Abuja, Lagos and Kano.
"Within one week, we are going to deploy the other aircraft to start doing Sokoto, Maiduguri, Port Harcourt and Yola and the rest will equally follow," he said.
The vice chairman told NAN that the airline was ready to exploit the opportunities that abound in the nation's aviation industry and contribute its quota to the development of the sector.
He said that Max Air got its Air Operation Certificate (AOC) from the Nigerian Civil Aviation Authority (NCAA) in 2017 and had been operating in Nigeria mainly in Hajj and Umrah operations since then.
According to him, the airline has in its fleet a number of Boeing 737-300, 737-400 and 737-500 as well as wide body B747-600 and 747-700 to operate with.
"We are not new in Nigerian aviation market, we have been here for about 15 years now and we got our Air Operation Certificate (AOC) since over 10 years ago doing Hajj and Umrah and worldwide charter.
"By the grace of God, we are going to do our best to contribute to the development of Nigerian aviation industry and we will try to cover all the destinations in Nigeria within the shortest possible time.
"Now, we have three aircraft on ground in Kano which is our operational base and we are going to commence operation with two while more would be deployed subsequently.
"We have in our fleet Boeing 737-300, 737-400 and 737-500. We also have Boeing 747-600 and we 747-700 which are the ones on ground in Kano at present.
"We are aware of the challenges in the industry and we are ready to face them.
"We have followed and fulfilled all the requirements and today, the Nigerian Civil Aviation Authority will finish its job before we carry out the demo," he said. (NAN)
Oscars 2018: Nigeria's inclusion, boost for Nollywood -NTTF
Saraki, Dogara meet Buhari over Plateau killings
By Elizabeth Vincent World Bank has embarked on recruitment in its bid to reduce youths restiveness...
By Uruemu Favour The Ovie of Ughelli Kingdom, His Royal Majesty, Wilson Ojakovo, Oharisi III, has...
By Politicsgovernance.com ONE of the wives of the Ovie of Ughelli kingdom, HRM Wilson Ojakovo,...

redpajama_set_name: RedPajamaCommonCrawl


At IPC APEX EXPO 2019, Guest Editor Judy Warner sits down with Elmatica CEO Didrik Bech to discuss the company's business model and CircuitData, their award-winning open-source PCB design data language that is designed to facilitate existing design data formats, not replace them. As Bech says, the future is all about data.
To watch this video at our Real Time with... IPC APEX EXPO 2019 site, click here.


Anthem-Cigna still undecided by despite our 19 January report that the merger would be denied by Judge Amy Berman Jackson of the Federal District Court for the District of Columbia. Reading the SEC 8-K filed in July 2015, the extension to 30 April is automatic if the merger is not consummated or is non-appealable by 31 January. Likely this is to Cigna's chagrin, as multiple sources over the two years this has been going on have detailed the growing disagreements between the two companies. As we noted in January, Anthem is also running up against 'the Blues rule' where it does business as a Blue Cross Blue Shield plan. The arguments that this internal competition is beneficial are pretzel-like indeed.
A labor union investor, Westchester Putnam Counties Heavy & Highway Laborers Local 60 Benefits Funds, is suing Aetna for shareholder losses in the Federal District Court in the District of Connecticut (complaint here). The demand is for a jury trial and details what they believe to be false and misleading statements by management and not disclosing adverse facts.
Healthcare Dive is recommended for their two deeper dives: 1 Feb on Anthem-Cigna and the outcomes of both mergers, 30 Jan on the labor union lawsuit. The likelihood of either happening becomes more remote as time goes by, but there could be a surprise.
Henry Purcell of the BJC was kind enough to post us with information on the first-ever BJC Digital Healthcare Forum. Organized by the BJC in association with the NHS, the Digital Health and Care Alliance (DHACA), and the Telehealth Quality Group, it is a novel 'hands on' meeting to assess if digital medicine can fill gaps in healthcare provision throughout the NHS. It is also in response to the massive pressures which winter has wrought on NHS health and social services. The Forum was designed by clinicians and leaders in healthcare informatics for UK commissioners, doctors and other HCPs involved in the management of long-term conditions (cardiovascular, obstructive pulmonary disease, diabetes etc.), as well as those engaged in health informatics, IT, and Trust CEOs. Speakers include Dr Malcolm Fisk of De Montfort University, our own Charles Lowe of DHACA, Professor Tony Young, National Clinical Director for Innovation (NHS England) and many more experts in digital health and care. For the latest information and to register, see the event website or the attached PDF.
research2guidance has published a short article on how migraine sufferers constitute an underserved market, and how present apps do not meet their needs. Here is an opportunity for app developers and companies to address a common and often near-intractable pain that affects everyday life. The author is David Ireland, Research Analyst, r2g Berlin.
Migraine is the third most prevalent illness in the world affecting 1 billion people worldwide^. However, this demographic of migraine sufferers is heavily underserved by mobile health applications, with only 0.2% of migraine sufferers using a migraine app*. But why? A major opportunity exists for an application with the right strategy, and the right balance of features and functionalities to lead the market, while having a positive impact on the health of migraine sufferers.
Far from being just a bad headache, migraine attacks can cause a diverse range of disabling symptoms such as severe pain, nausea and visual disturbance. While medications exist to help treat symptoms, migraine sufferers need to carefully manage their medication intake, while monitoring risk factors such as food, sleep and triggers to treat, and prevent the condition from becoming chronic. Tracking medication is critical, as most chronic migraine sufferers are a result of medication overuse^.
[grow_thumb image="http://telecareaware.com/wp-content/uploads/2017/02/ripplenetwork_5890862790fc7.jpg" thumb_width="300″ /] A startup company in the US, Ripple Network Technologies, announced on 31 January its Kickstarter campaign for what appears to be a small, stylish personal safety device styled like a small metal lock or locket. The release states that "Ripple users can discreetly signal their monitoring team for help with the click of a penny-sized Bluetooth wearable, designed by the creators of 360fly and Misfit Shine" that connects to a smartphone. One of the features is that there's a partnership with Tunstall Americas to provide that 24/7 monitoring, with an extensive quote from CEO/president Casey Pittock.
It's clearly aimed at a younger market than typical for PERS, concerned with stylish safety in 'lone' situations and not with 'falling and I can't get up'. The stated Kickstarter price is also appealing: $129 for the standard sensor, $199 for a specially designed sterling locket style, both inclusive of one year of monitoring service. Release is scheduled for April.
Despite this announcement, the Kickstarter site is not up yet. The Ripple website has a flashy animation homepage without detail, found only through their LinkedIn company page which also is bereft of details. Other than the release pickup on ReadItQuik.com, this Editor cannot locate the release on the standard PR release sites such as Business Wire or PRWeb. (The Tunstall Americas website has not been updated for news since last August.) A curious start indeed.
Update: Ripple's communications director in the Comments has supplied the Kickstarter link which went live on 1 Feb, and is here. The website now has an Order Now button which links to the Kickstarter page. Key features: click once to receive a call from the monitoring center, or click 3 times or more to summon emergency help or a call per your profile setup. A potential drawback: no two-way communication except via phone and the BTE connection, which if you are separated from it (for instance, your purse is stolen), the system won't work.
The requested raise is $50,000 by 3 March. PRNewswire release.
NHS Wales Informatics Service–Velindre NHS Trust. The NHS in Cardiff, Wales is looking to appoint partners to develop solutions to engage citizens digitally in the proactive management of their health. It is anticipated that this could encompass a wide range of services from existing applications (Apps) to innovative joint developments. Contract notice will be published 4 April, but the notice as published does not have a deadline. (Tenders Electronic Daily) See the Sell2Wales website for documentation.


Fished the outgoing from 9am till 1pm and only a small spot to show. Cut him in half and managed a skate from each piece.
Wind was wrong from the West and SW and some scattered grass in the mix as well.
To top it off (no pun intended) my tip on my CTS heaver came off, but luckily I noticed it before I casted it. Couple drops of Krazy Glue (emergency stash) and it was ready to go again!
Looks like I'll be paying a visit to my rod builder this week for some follow up repairs/business!
THX 4 da fishin' report.
Ronnie the rod builder of yours stops by the tackle shop every Tuesday.....so should you both Beotches !
Thanx for the heads up Geo - That little pier is no more, its rotted out so bad that security has blocked it off. The little beach you speak of I know oh so well, I have launched there 34,872 times with my Jon boat too. I have walked all the way across the lake several times as well and seen the whole place bone dry more times than I can remember.....first time I ever fished there was 1981 - yea I been there a few times !
PS> I fshed it when there was NO Ocean Lakes Housing or even a Dam Neck road !
Thats not ALL I will show you .......Every one already knows I was born in 1941 ok teeny bopper.
Geo I wont be hunting any deer this year, theres is no room in my freezer.Along with the packs of striper,catfish,flounder and spade fish, I just added 8 more packs of trigger fish and spanish mac's last week- I have so much ground venision and deer sausage I dont know what to do with it all. I wish Dixie would remind me to give him some when he stops by next time.
HA HA, good on, you still make him bait yur hook????


text:
package org.apache.camel.processor;

import org.apache.camel.ContextTestSupport;
import org.apache.camel.builder.RouteBuilder;
import org.junit.Test;

/**
 * @version
 */
public class LoopNoCopyTest extends ContextTestSupport {

    @Test
    public void testLoopNoCopy() throws Exception {
        getMockEndpoint("mock:loop").expectedBodiesReceived("AB", "ABB", "ABBB");
        getMockEndpoint("mock:result").expectedBodiesReceived("ABBB");

        template.sendBody("direct:start", "A");

        assertMockEndpointsSatisfied();
    }

    @Override
    protected RouteBuilder createRouteBuilder() throws Exception {
        return new RouteBuilder() {
            @Override
            public void configure() throws Exception {
                // START SNIPPET: e1
                from("direct:start")
                    // by default loop will keep using the same exchange so on the 2nd and 3rd iteration its
                    // the same exchange that was previous used that are being looped all over
                    .loop(3)
                        .transform(body().append("B"))
                        .to("mock:loop")
                    .end()
                    .to("mock:result");
                // END SNIPPET: e1
            }
        };
    }
}

redpajama_set_name: RedPajamaGithub


text:
Q: Storyboard error unarchiving document objects - *** -[__NSArrayM insertObject:atIndex:]: object cannot be nil My team has a project and we are updating our codes by pushing/pulling it on bitbucket.org. We encountered a problem lately, we cannot open the storyboard of the pulled updated project code.
This is what the error said:
The document "MainStoryboard.storyboard" could not be opened. The operation couldn't be completed. (com.apple.InterfaceBuilder error -1.)
and this is the screen shot

Do you have any idea why this happened? What possible wrong move did we do? and do you have any idea how to fix it..?
EDIT
I have already upgraded to Xcode 4.6.3 but I still get this error.

A: I recently found the answer for this, what we did is

*

*We open the Console Log.

*While the console log is open, we tried to open the broken storyboard.

*We found the error in console log, in this case we found this :
Exception raised while unarchiving document objects - *** -[__NSArrayM insertObject:atIndex:]: object cannot be nil
We found out that we used a font style in that storyboard in the other computer, and that font style was not installed in the other Macbook that's why we had that error stated above.
4 . We installed missing font style in the other Macbook.
This question helped us a lot - xcode storyboard - ibtoold unarchiving exception

A: Try doing git status in terminal to make sure you have a clean copy of the file, not one with local modifications and/or merge conflicts.
Open the file with a text editor and see if it looks the same as on other machines.
Have you looked at the console tab in XCode to see what it says the problem is?

A: I had this problem. Turned out it was an attributed string causing the issue. I had to manually edit the storyboard file to replace it with a regular string (i.e. replace the <state>...</state> section). Look for hasAttributed...=YES in your storyboard file and go from there.
Ex. Replace stuff like:
<state key="normal">
  <attributedString key="attributedTitle">
    <fragment content="Sign up">
      ...
    </fragment>
  </attributedString>
</state>

With:
<state key="normal" title="Sign up">
  ...
</state>

redpajama_set_name: RedPajamaStackExchange


If you had a successful dental treatment at Premier Dental Spa, please share your experience by clicking on your favorite review site below. If for any reason you were not completely satisfied, please call us directly at (732) 257-7675. Thank you for your business, and we appreciate you taking the time to help us improve and grow.


text:
Book star Raza earns his stripes
A TEENAGE celebrity, who moved from Denmark to Rochdale because he considered our education system to be the best in Europe, is celebrating after achieving 10 GCSEs.
RAZA Muneer with his GCSE results and the book which made him famous in Denmark
Seventeen-year-old Raza Muneer, of Ashfield Road, Deeplish, couldn't walk the streets in Copenhagen without being recognised.
His face is well known from a series of 'Raza' books widely sold in Denmark, which chronicle his time growing up in the capital's Asian community. But two years ago he left his beloved Denmark behind and moved to Rochdale.
Raza, who speaks English, Danish and Punjabi, asked his parents, who are originally from Pakistan, if they could move to England so that he could pursue his education over here.
So Raza said goodbye to his Copenhagen private school and his family closed up their clothes shop. And now, after scooping his stunning set of results, he considers the move to be the best thing he and his family ever did.
"The English education system is really, really good. I am living proof of that. Even if you are well educated in Denmark, you can't get a job, but here people are more open minded and there are better opportunities.
Raza, who works as a volunteer for Oxfam and Cancer Research shops, got an A* in Urdu, As in English language, maths, religious studies and geography, two As in science, a B in literature and French and a C in ICT.
He will now be staying on at Oulder Hill to study A level maths, business, English language, physics, Urdu and Danish.
Raza will be the only pupil to study A level Danish and will get special tuition from London. Raza, who wants to go to university in this country to pursue a career in business administration, hopes his story will be an inspiration to other young people.
"Rochdale should be proud of the education system and I want to thank all my teachers for their help, support and learning."
Education director Terry Piggott said this is marvellous news for Rochdale LEA. "It just shows what can be achieved and what good and flexible provision we have. If people want to learn, then the opportunities are there. With qualifications like that Raza can go a lot further in life. I'm sure he will be a millionaire by the time he is 30."


text:
Explore IYF's 28-year history through an interactive timeline.
Read and find out more about "A Future Youth Want."
Your tax-deductible gift helps ensure that young people around the world, including some of the most vulnerable and underserved, are able to realize a brighter future.
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Learn about Zangar, an initiative launched in partnership with Chevron and local government to teach STEM and life skills.
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For scale and sustainability, we've always emphasized building local partner's capacity to serve youth long-term.
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3 Ways to Increase Youth Apprenticeships in the United States
Laura Rosen - November 3, 2016 - Posted In Employment Education, Livelihoods Comments
For many of us, the word 'apprenticeship' conjures up images of soot-streaked blacksmiths silhouetted before a blazing furnace. In some countries, systems of apprenticeships do have roots in medieval guilds, but today many employers, educational institutions, and government widely support them as part of modern business. However, in the United States, apprenticeship offerings remain limited, and the public vision of the system ranges from largely unknown to downright Dickensian.
Not to be relegated as a thing of the past, apprenticeships represent a critical opportunity for the United States to fill its middle skills jobs gap. Requiring more training than a high school diploma but less than a four-year degree, middle skills positions constitute a full 54 percent of U.S. jobs. Despite apprenticeships' potential for aligning the skills youth possess with those employers struggle to find, benefiting youth as well as businesses, fewer than five percent of U.S. youth train as apprentices. Earlier this month, an array of decision-makers—from Hilton, Adecco, and IBM to the International Labour Organization and the U.S. Department of Labor—came together to discuss these challenges at the 2016 International Apprenticeships Meeting, hosted in Washington, D.C., by the Global Apprenticeships Network (GAN).
During the GAN meeting, three themes emerged for how to reduce barriers and increase the uptake of youth-friendly apprenticeship programs in the United States:
Eliminate the stigma of apprenticeships and change mindsets not only among students, but also with teachers, parents, guidance counselors, and employers. In countries like Germany and Switzerland, young workers view an apprenticeship, where trainees split their days between classroom instruction and time on the job, as a highly respected career move. In fact, roughly 60 percent of young Germans hold apprenticeships. In the United States, the recent study From Medieval to Millennial, by nonprofit organization Young Invincibles, found that three key misconceptions dissuade youth from pursuing this track: these young people believe apprenticeship programs don't currently exist in their communities, are just unpaid internships, and preclude receiving a college degree or viable credential. The rigid emphasis in the United States on tertiary education, coupled with the prevailing impression of apprenticeships as a "second-class path" or something from the feudal era, keeps young people and the adults around them from valuing the training, experience, income, and qualification that apprenticeships can provide.
Remove limits on apprenticeships and diversify skills training in apprenticeship programs. In Switzerland and Germany, the average age of apprentices is 17 or 18. In the United States, it's 28, a difference that means the system is neglecting the vital population of 16- to 24-year-olds. Those are the youth most in need of support systems to start down pathways to productive livelihoods. Apprenticeships in the United States also must be expanded to different skill sets and more occupations. Programs in skilled trades such as construction and manufacturing have eroded, and fields such as information and communications technology (ICT) and hospitality offer potential for long-term careers, growth, and decent wages.
Show companies the value of hiring apprentices as an innovative way to bring on talent. Many European companies see apprenticeships as a business investment, not corporate social responsibility. In contrast, many U.S. companies see only a heavy upfront cost and ignore the payoffs in employee productivity, engagement, and loyalty. While a number of federal and state-level apprenticeship initiatives have successfully taken root, expansion remains slow. Improved metrics stemming from apprenticeships, such as the business impact of greater employee retention, must be used to sell corporate profit and loss managers on the value of such programs.
In light of the complex distinctions between markets, no one expects the German or Swiss model to function perfectly in the American context. However, given the prohibitive costs of U.S. higher education, the acute challenges of youth unemployment, and the growing need for a workforce with market-relevant competencies and experience, apprenticeships represent a solution for employers and workers alike to bridge the gap in the U.S. labor market.
Laura Rosen is Director, Corporate Partnerships.
Tags apprenticeship, on the job training, vocational training
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text:
DuPage County Business
Elmhurst's Holly Trolley returns for the holidays
Submitted by City of Elmhurst
Nov 20, 2022 11:40 AM - All aboard the Holly Trolley! Explore Elmhurst Express Trolley is back for the holidays.
Neighbors in Need returns: How you can help fellow suburbanites this holiday season
By Barbara Vitello
Nov 7, 2022 11:11 AM - For the second year, the Daily Herald is partnering with the Robert R. McCormick Foundation to raise money for charitable organizations that address hunger, homelessness and health care disparities in the suburbs.
State 24th District Senate candidates differ on how to improve the business climate in Illinois
By Dave Oberhelman
Nov 2, 2022 2:15 PM - The candidates for Illinois' 24th Senate District seat have differing views on how to improve the business and economic climate in the state.
Leaders & Legacies: Eudora Hull Gaylord Spalding, a pioneer for health care
By Mindy Saban
Sep 30, 2022 1:00 AM - Eudora Hull Gaylord Spalding is featured in the next "Leaders and Legacies: Stories of Local Impact," an ongoing series brought to you in partnership with DuPage Foundation and the Daily Herald.
Downtown Naperville Alliance to host 'Progress in Progress' celebration
Submitted by Downtown Naperville Alliance
Sep 15, 2022 10:18 PM - The Downtown Naperville Alliance invites you to celebrate construction wrapping up at the free "Progress in Progress Celebration" on Saturday, Sept. 24
Will DuPage Airport's new $1.3 million customs facility lure more international travelers?
By Katlyn Smith
Sep 1, 2022 5:30 AM - DuPage Airport has opened a $1.3 million U.S. Customs and Border Protection facility intended to set it apart from other flight hubs in the suburbs and bring more convenience to international business travelers.
Twin brothers get 4 years in prison for thefts, ordered to repay $933K to Home Depot
By Susan Sarkauskas
Aug 29, 2022 6:31 PM - Two brothers were sentenced Monday to four years in prison for stealing from Home Depot stores throughout the suburbs, in many cases by making fraudulent returns of items, and they were ordered to repay nearly $934,000 after being suspected of the crime in 24 states overall.
'A legacy project': DuPage Care Center to undergo $31.5 million renovation
Aug 26, 2022 5:15 AM - A major overhaul is slated to transform a county-owned nursing home into a more modern facility with a new entrance, more inviting communal areas and remodeled living spaces for the 300 residents of the DuPage Care Center.
Nominations due Aug. 26 for Best of Bloomingdale
Submitted by Bloomingdale Chamber of Commerce
Aug 22, 2022 12:07 PM - The Bloomingdale Chamber of Commerce is looking for businesses and general public to nominate their favorite business for the Best of Bloomingdale.
Northwestern Medicine hospitals rank among the top 20 in the state
By Alice Fabbre
Jul 26, 2022 4:27 PM - Northwestern Memorial Hospital is ranked No. 1 in Illinois and the Chicago area according to U.S. News & World Reports "America's Best Hospitals." Its affilated hospitals in the suburbs also claimed spots in the top 20 hospitals around Chicago and the state.
Show 11-20 of 894 « previous next »
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Discussion in 'General Discussion' started by denisdekat09, Nov 1, 2004.
Cannot fetch rpm updates list at /scripts/cPanelRPM.pm line 160, line 254.
Benn waiting all day for Cpanel, no reply as of yet, had a exim issue and I somehow sorted it out. But I was a bit freaked as I could not uscp nor anything, and Cpanel must be having a busy monday morning.
Anyways, if anyone knows what it could be, let me know I would really appreciate any tips.


Yves Rocher is one of the largest beauty care firms in the world. When the French firm decided to upgrade the manufacturing areas of the plant at Kilbarry Business Park on Dublin Hill in Cork, Yves Rocher appointed Storage Systems Ltd to fitout the new changing room facilities building.
Both the male and female changing rooms is fitted out with Dexion two-person storage lockers. Two person lockers save space for 2 users by having a vertical hanging compartment as well as a personal possession compartment at the top of each locker. Dexion eight door lockers were installed in one area for the storage of staff mobile phones, handbags and wallets.
The installation was complete with PPE wardrobe cupboards and First aid cupboards along with single sided island bench seating 1800mm high x 1500mm long with overhooks. All lockers have camlocks which are in a master key series and are finished in a grey carcass and light blue door finish specified by the client.


text:
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text:
The Lying Life of Adults
Ann Goldstein, Elena Ferrante
Review by Lauren Bufferd
The Lying Life of Adults is the first novel from Elena Ferrante since the final installment of the Neapolitan quartet, the series that made her an international literary star, was published in 2016. Set in an upscale neighborhood in 1990s Naples, her new novel is a powerful coming-of-age story like no other.
Published in Italy in November 2019 (fans lined up outside bookstores to purchase their copies at the stroke of midnight), The Lying Life of Adults is the first novel from Elena Ferrante since the final installment of the Neapolitan quartet, the series that made her an international literary star, was published in 2016. Set in an upscale neighborhood in 1990s Naples, her new novel is a powerful coming-of-age story like no other.
Dutiful, bookish and sweet, Giovanna is on the cusp of puberty when she overhears her father comparing her to his ugly sister. Used to receiving compliments, Giovanna is alarmed but curious, and despite her parents' concerns, she initiates a relationship with her tempestuous Aunt Vittoria. As Giovanna learns more about her father's background, she begins to see how her parents' lies and treachery have impacted their lives as well as hers.
Giovanna travels between areas of Naples so different, they might as well be opposing planets: from the comfortable, progressive household where she was raised with a secular education, including access to sex education, to her aunt's working-class neighborhood, which is mired in violence, religion and superstitions, all expressed in the dialect that Giovanna's parents forbade her to speak at home.
Ferrante's ability to draw in her reader remains unparalleled, and the emotional story is well served by Ann Goldstein's smooth and engaging translation. The novel simmers with overt rage toward parental deception, teachers' expectations and society's impossible ideals of beauty and behavior. For readers who are familiar with Ferrante's work, there will be much that is recognizable: the belief that poverty can be transcended through education, the power of a talismanic object (in this case, a bracelet that may or may not have belonged to Giovanna's paternal grandmother) and the absurd linkage of physical beauty with purity and goodness. There is even an unattainable man who holds the promise of escape.
But The Lying Life of Adults is very much its own story. Giovanna's self-reliance and her efforts to become the kind of adult she has yet to meet will resonate with thoughtful readers.
By Elena Ferrante, By Ann Goldstein


India is a member of the HCCH (Hague Convention) of October 5, 1961, abolishing the requirement of attestation of foreign public document. Kolhapur issued all Degree certificate apostille attestation is acceptable in approx 101 member-countries of the Convention. Apostille is done for All Degree Certificate such as MTech, BTech, BCA, MCA, MBA, BBA, MCOm, BCom, BA, MA, MBBS, BDS, MS, MD, MDS, Nursing, BHMS, DHMS, BPT etc.
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Guu, although known around the town for some time now, was a pioneer that introduced small Japanese tapas in true Japanese bar-like Izakaya to Toronto scene. This restaurant is a Japanese equivalent of an old-fashioned sports bar. The patrons and staff are often yelling across the room, chanting hymns and songs or just talking, sometimes very loudly, with their table-mates. This restaurant might not be for everyone when it comes to a loud and thrilling type of dining, but for those who enjoy a bit of mayhem, Guu is a place to try. Most items on the menu are fairly inexpensive, since they are served tapas-style, so you are able to try a variety of flavours for fraction of a price. And the flavours! They serve almost anything from seafood to vegetarian options, grilled or deep-fried and seasoned with the best combinations. You can pair your meal with delicious Sake (rice wine), Umeshu (plum wine) or Shochu (Japanese vodka). And if you are not much into experimenting with your alcohol, they have cocktails and beer on tap!
190 University Avenue is 5-in-1 experience. Upon entering you will find Momofuku Noodle Bar on the ground floor, accompanied with Momofuku Nikai, Daishō, Shōtō, and Milk Bar all on subsequent floors. All these restaurants are Asian-inspired eateries, together creating a true experience for any adventurous foodie with a taste for comforting ramen or steamy buns. The menu is ever-changing and always sourced locally, so if you have the same meal twice, it may taste totally different, or it's possible, it will be long gone from the menu. Better head there now, to make sure you don't miss out on something!
Pai still has some of their patrons in a twist. Presenting itself as a traditional Asian restaurant, they serve very specifically selected Northern Thai food in a setting, that could, at first glance, seem very traditional. If you look closely, though, you will notice the irony and humour in the decor; for instance, two drawings that are made to look like the classic image of the Buddha, are actually Brooklyn-esque hipsters. There are many moments where you find similar decorations. On the other hand, Pai serves impressive portions that feel authentic despite the confusing setting you find yourself in. If you can stomach the strange atmosphere and decor, you will be rewarded with delicious food (and lots of it).
There's one thing sure in Toronto and that is the fact, Chef Susur Lee knows his Asian cuisine. From perfect Asian-inspired appetizers to main courses that embrace the strangeness of Asian-french inspired flavours typical for Lee, there's not one bad dish on the menu. But the food itself might not be the most memorable part for a visitor, considering the stunning decor that compliments the whole vibe of this restaurant. Channeling part expensive nightclub/casino vibe with glowing neon signs and red reflective glass wall, another part upscale hip eatery with exposed bricks and beams, Lee brings his schizophreniac conjunction even in decor. But the truth is, it works. Pairing traditional Asian flavours with French-cousin techniques and bold red decor with modest and elegant seating gives this place a truly unique vibe that compels you to come back for another bite.
Address: 326 Adelaide Street W.
Kiin is a very different and luxurious experience. Inspired by the old Royal Thai cuisine, their menu consists of almost forgotten recipes that were originally only served in the Royal Palace. Chef Nuit and Jeff Regular decided to bring back tradition and tastes of old Thailand. Inspired by Chef Nuit's childhood and with a bit of an architectural flair, Kiin serves intricate dishes that are fascinating to both see and taste. Not only the serving of the dishes but also the decor screams royal. Upon entering, you might feel overwhelmed by the amount of gold and shimmer, that is even more prominent in the daylight provided from french windows decorating one whole wall. Even though the restaurant is decorated in gold, the whole place has a very elegant flair thanks to the beautiful wooden bar and deep sea-green seating. You truly might feel like a royal upon entering.


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For Light Night Nottingham 2017, Sarah created an upcycled light installation made from hundreds of waste plastic bottles.
Each bottle was collected from local homes and businesses, cleaned and then sandblasted. The sandblasting turns the bottles surface from transparent to opaque and enables the bottles to diffuse the light beautifully.
Hundreds of the plastic bottle bases were then made into the 1.5 metre wide chandelier, each joined one by one together using metal eyelets.
The small lights placed on the floor were cut by hand, sculpted into the flower forms and then illuminated with LEDs.
The floor pieces were arranged so there was a pathway through the centre. Therefore people could walk through the installation to the centre and look up at the chandelier whilst being surrounded by the LED lights on the ground.
The installation was exhibited at Nottingham Trent University in a fantastic space, the atrium of the Newton Building. Alongside the installation, Sarah also ran workshops. Around 250 people made their own plastic bottle LED light.
This piece is available for sale or exhibition, please contact us for all enquiries.