text:
\section{Introduction}
\subsection{Background and Related Work}
\IEEEPARstart{D}{ynamic} growth of maritime time series (e.g., port cargo throughput and vessel traffic flow) sometimes has the predominantly complex, nonlinear and non-stationary properties. Their reliable and accurate predictions play an important role in maritime industry, such as economic investment, transportation planning, port planning and design, etc. In the literature \cite{WangTII2018,LvITS2015,ChandraNNLS2015,LamichTIE2017}, many efforts have been recently devoted to effectively predict different types of time series. However, it is still generally difficult to model and predict such non-stationary time series using traditional mathematical methods, such as fuzzy theory \cite{SunNeuro2015,LiuIEEETFS2018}, Kalman filtering \cite{MetiaIEEE2016}, Bayesian models \cite{HuTIE2016}, hybrid framework \cite{JiangEnergy2017}, autoregressive integrated moving average (ARIMA) \cite{KumarVanajakshi2015} and their extensions.

From a machine learning point of view, several techniques have been introduced to the task of time series prediction. The two most-used approaches are certainly conventional neural network (NN) \cite{ZhangIJF1998,ChenTII2017} and latest deep learning (DL) \cite{SchmidhuberNN2015,KhodayarTII2017}. They are both supervised learning methods that are trained to learn a mapping function between several input features and the output value, which is denoted by the target to be predicted. Recent evidences reveal that DL methods have been promising tools for time series prediction, such as urban traffic flow \cite{LvITS2015,WuPartC2018}, cycle time of wafer lots \cite{WangLSTMTII2018}, wind speed \cite{ZhangTSE2015}, and financial market \cite{CavalcanteESA2016}, etc. However, DL-based prediction performance is highly dependent on the volume and diversity of training datasets. If the training datasets do not include the similar features existed in time series to be predicted, it becomes difficult to generate satisfactory results. The high computational complexity has also limited the deployability of DL-based prediction methods in practical applications. Therefore, to make prediction easier and more flexible, this paper will mainly focus on NN and its extensions for non-stationary time series prediction.

NN has the capacity of learning the potential complex relationships between different variables. Traditional NN methods, e.g., wavelet neural network (WNN) \cite{ChenNeuro2006}, fuzzy neural network (FNN) \cite{TangITS2017}, Elman neural network (ENN) \cite{ChandraNNLS2015}, back propagation neural network (BPNN) \cite{GaxiolaIS2014}, and generalized regression neural network (GRNN) \cite{SpechtTNN1991}, etc., have attracted considerable attention over the past decades. The development of maritime time series is often affected by several complex influences. It is still difficult to generate satisfactory prediction results since NN methods have their own disadvantages. For example, BPNN can fall into the local minimum problem during training process; GRNN often suffers from high computational time. To further improve prediction performance, empirical mode decomposition (EMD) and NN have been combined to contribute a two-step prediction framework \cite{RenTSE2015,WangNN2017}. For example, the combination versions, such as EMD+WNN \cite{SanthoshECM2018}, EMD+FNN \cite{WeiASC2016}, EMD+ENN \cite{WangASC2014}, EMD+BPNN \cite{WangRE2016,WeiChenPartC2012}, EMD+GRNN \cite{ZhouSTE2014} perform well in time series prediction. The reason behind this phenomenon is that EMD can decompose original non-stationary time series into a series of independent and nearly periodic components which admit the well-behaved Hilbert transforms. Each component, which is related to an Intrinsic Mode Function (IMF), can reveal hidden patterns and trends of time series. Especially, the IMF component with the lowest frequency generally denotes the trend or mean of original time series \cite{LangACCESS2018}. This decomposition strategy is thus able to effectively assist in developing prediction methods. In particular, EMD-based high- and low-frequency components are respectively predicted using traditional NN methods (e.g., WNN, FNN, ENN, BPNN and GRNN). The final results are obtained by aggregating the predicted high- and low-frequency components.
\subsection{Motivation and Contributions}
It is generally thought that original time series are commonly composed of several high- and low-frequency components which correspond to different characteristics of time series. The low-frequency components with stable change can be accurately predicted directly using WNN \cite{SanthoshECM2018}, FNN \cite{WeiASC2016}, ENN \cite{WangASC2014}, BPNN \cite{WangRE2016,WeiChenPartC2012}, and GRNN \cite{ZhouSTE2014} in the popular two-step prediction framework \cite{RenTSE2015,WangNN2017}. However, it becomes difficult to predict high-frequency components due to their properties of unobvious mathematical regularity. NN-based prediction results may lead to non-optimal results in practical applications. We find that the self-similarities\footnote{It means that each high-frequency component theoretically contains several small (overlapping) segments which have the similar geometrical shapes.} within high-frequency components can enable robust and accurate predictions. In our proposed three-step prediction framework, the high-frequency components are first divided into several continuous small (overlapping) segments. The geometric similarity between any two different segments can be effectively measured using the dynamic time warping (DTW) method \cite{MeiToC2016}. Compared with standardized euclidean distance (SED)-based similarity measures \cite{LiICONIP2018}, DTW is able to find the points with similar shapes to improve the similarity measures between different segments. The grouped segments with high DTW similarities are then selected to form the training datasets for traditional NN methods. It is able to enhance the predictions of high-frequency components. All the predicted components are combined as the final prediction of our proposed method.

In conclusion, our major contributions, given the current state-of-the-art research work, are mainly summarized by the following three aspects:
\begin{enumerate}
	%
	\item The unified three-step prediction frameworks (called EMD+DTW+NN and EEMD+DTW+EE) are presented for non-stationary time series prediction in maritime industries. The satisfactory prediction performance benefits from the EMD-or EEMD-based property decomposition and DTW-based similarity grouping.
	%
	\item Non-stationary time series are decomposed into high- and low-frequency components. The inherent self-similarities within high-frequency components are taken into account in promoting prediction accuracy. The predictions of low-frequency components are performed directly using traditional NN methods.
	%
	\item Comprehensive experiments on port cargo throughput and vessel traffic flow have illustrated the superior performance of the proposed three-step prediction framework in terms of prediction accuracy and robustness.
	%
\end{enumerate}

The main benefit of our proposed prediction methods is that it takes full advantage of the EMD- or EEMD-based property decomposition and DTW-based similarity grouping. Therefore, the prediction accuracy and robustness can be effectively enhanced among different applications.
\subsection{Organization}
The remainder of this paper is organized as follows. The decomposition-based prediction frameworks are proposed in Section \ref{section2}. Section \ref{section3} is dedicated to several numerical experiments on both port cargo throughput and vessel traffic flow prediction. This paper is concluded by summarizing our main contributions in Section \ref{section4}.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig1_Framework.jpg}
	\caption{Flowchart of the proposed three-step prediction frameworks EMD+DTW+NN and EEMD+DTW+NN. For the sake of simplification, the original time series are decomposed into only two components. In practice, more than two components should be generated to guarantee high-accuracy prediction.}
	\label{Figure1}
\end{figure*}
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig3_DTW.jpg}
	\caption{The visual illustration of differences between DTW and Euclidean distances. From left to right: (a) displays the DTW-based alignments between two time series for measuring distance (similarity), (b) shows the warping paths $P_{\mathrm{DTW}}$ and $P_{\mathrm{Euclid}}$, respectively, generated by calculating DTW and Euclidean distances, and (c) displays the matching between two time series using Euclidean distance. It can be found that DTW performs better in finding the points with similar geometric shapes to enhance the accuracy of distance (similarity) measure \cite{LiDTW2015}.}
	\label{FigureDTWDistance}
\end{figure*}
\section{Similarity Grouping-Guided Neural Network Modeling}
\label{section2}
This section will detailedly illustrate our proposed three-step prediction framework, shown in Fig. \ref{Figure1}. In the first step, EMD or EEMD can be adopted to decompose the original non-stationary time series into high- and low-frequency components. In the second step, the low-frequency components are effectively predicted directly using traditional NN methods. In contrast, the high-frequency components are first divided into several continuous small (overlapping) segments. Several segments with high DTW-based similarities are then grouped to form new training datasets for traditional NN methods to improve prediction accuracy. The predicted high- and low-frequency components are combined to form the final prediction results in the final step.
\subsection{EMD-Based Time Series Decomposition}
\label{sec:EMD}
In the literature \cite{ChenTIE2016}, EMD has been applied to decompose the non-stationary signals (i.e., time series) into a small number of different scaled data sequences. Each sequence can be designated as an Intrinsic Mode Function (IMF), which is independent and nearly periodic from a mathematical point of view. The implementation of DTW must meet the following two conditions
\begin{enumerate}
	%
	\item The number of zero-crossing points and extrema points (local maxima and local minima) in the entire signal should be equal or differ by at most one.
	%
	\item At any point in the signal, the mean values of lower and upper envelopes which are represented by the local minima and local maxima should be equal to zero.
	%
\end{enumerate}

Essentially, EMD highly depends on the local characteristics of original signal, such as local minima, local maxima and zero-crossings. The decomposition of original non-stationary signals into IMFs was commonly performed using an iterative procedure called sifting algorithm. In particular, it calculated the IMFs at each scale from fine to coarse. For more details on EMD, we refer the interested reader to see \cite{FlandrinSPL2004} and references therein. Let $X(t)$ denote the original nonstationary time series, which can be decomposed as a sum of finite number of IMFs as follows
\begin{equation}\label{expressionIMF}
	{X(t) = \sum_{i=1}^{N} C_i(t) + R_{N}(t)},
\end{equation}
with $1 \leq t \leq T$. Here, $N$ is the total number of IMFs, $C_{i} (t)$ is the $i$th IMF, and $R_{N}(t)$ is the residual signal denoting the mean trend of original data $X(t)$. In practice, $R_{N}(t)$ can also be considered as an IMF and denoted as $C_{N+1}(t)$. Therefore, Eq. (\ref{expressionIMF}) can be rewritten as $X(t) = \sum_{i=1}^{N+1} C_i(t)$. In this decomposition, the first IMF is related to the fastest fluctuating part of $X(t)$; meanwhile the last IMF corresponds to the slowest fluctuating part.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig2_NNmethods.jpg}
	\caption{The architectural diagrams of $5$ different NN methods adopted in this paper. \textbf{WNN}: Wavelet Neural Network, \textbf{ENN}: Elman Neural Network, \textbf{FNN}: Fuzzy Neural Network, \textbf{BPNN}: Back Propagation Neural Network, and \textbf{GRNN}: Generalized Regression Neural Network.}
	\label{Fig:NNmethods}
\end{figure*}
\subsection{EEMD-Based Time Series Decomposition}
\label{sec:EEMD}
Traditional EMD, introduced in Section \ref{sec:EMD}, easily suffers from the mode mixing problem. This problem easily causes serious aliasing in the time-frequency distribution leading to degrading the decomposition accuracy. To promote the decomposition performance, Wu and Huang \cite{HuangEEMD2009} proposed to develop a new noise-assisted method named EEMD by performing the same iterative sifting process done in EMD. It repeatedly decomposes the time-series data into different IMFs via traditional EMD method. In particular, the IMF components in EEMD are defined as the ensemble of trails mean. Each trail consists of decomposition results of time-series data added by a uniformly distributed white noise of finite amplitude. Both theoretical and practical results have demonstrated that the added noise could help data analysis in the EMD method  \cite{WangJAG2012}. The ensemble means of the corresponding IMFs calculated from each trial are subsequently regarded as the IMFs of EEMD \cite{HuangEEMD2009}. The detailed procedures of EEMD are given as follows
	%
	%
	\begin{enumerate}
		%
		\item Add a uniformly distributed white noise series to the original time-series data.
		%
		\item Decompose the time-series data with added white noise into different IMFs via EMD shown in Section \ref{sec:EMD}.
		%
		\item Iteratively repeat Steps (1) and (2) with different white noises and yield the corresponding IMF components. The ensemble number is essentially related to the number of repeated procedures.
		%
		\item Calculate the mean of the ensemble IMFs as the final decomposition results.
		%
	\end{enumerate}
	%
	%

For both EMD- and EEMD-based time series decomposition, IMFs can be considered as the combination of high- and low-frequency components in this work. The low-frequency components can be easily predicted directly using traditional NN methods in practice. In contrast, it is difficult to predict the high-frequency components due to their properties of weak mathematical regularity.
\subsection{DTW-Based Similarity Grouping}
\label{sec:DTW}
DTW is an effective and popular technique for measuring distance (or similarity) between two sequences of time series, which is based on the dynamic programming approach. It has been widely applied to voice identification, data clustering, feature extraction, etc. The similarity between two time series sequences is inversely proportional to the geometrical distance. The basic principle of DTW algorithm is to compare two time sequences and measure their similarities by computing a minimum distance between these two time series.

Let $Y = \left\{ y_{1}, y_{2}, \dots, y_{m} \right\}$ and $Z = \left\{ z_{1}, z_{2}, \dots, z_{n} \right\}$ denote two time sequences. Before the calculation of distance, we first create a $m \times n$ patch matrix where the $\left( i\mathrm{th}, j\mathrm{th} \right)$ element represents the distance $d \left( y_{i}, z_{j} \right)$ between the two points $y_{i}$ and $z_{j}$. To enhance computational robustness, $d \left( y_{i}, z_{j} \right)$ corresponds to the weighted Euclidean distance. Therefore, the DTW-based similarity is accordingly insensitive to the noise or outliers. As shown in Fig. \ref{Figure1}, it is easy to obtain that $m = n$ in our work. The best match between these two sequences $Y$ and $Z$ corresponds to the shortest distance path aligning one sequence to the other. Therefore, the optimal warping patch can be recursively calculated by
\begin{equation}\label{Eq:DTW}
	\mathrm{DTW} \left( Y, Z \right) = \gamma \left( i, j \right),
\end{equation}
where the minimum cumulative distance $\gamma \left( i, j \right)$ is given by
\begin{align}\label{Eq:expression2}
	%
	\gamma \left( i, j \right) &= d \left( y_{i}, z_{j} \right) \\
	&+ \min \left\{ \gamma \left( i-1, j-1 \right), \gamma \left( i-1, j \right), \gamma \left( i, j-1 \right) \right\}. \nonumber
\end{align}

For the sake of better understanding, the visual illustration of differences between DTW and Euclidean distances is shown in Fig. \ref{FigureDTWDistance}. Compared with the widely-used Euclidean distance, DTW performs better in extracting the points with similar geometric shapes leading to enhancing the accuracy of distance (similarity) measure \cite{LiDTW2015}. It is worth noting that high-frequency components (i.e., IMFs) are difficult to predict due to their properties of high levels of volatility. In this work, high-frequency IMFs will be divided into a series of overlapping segments. The segments with high similarity, measured using DTW distance, are merged into the same group. The clustered groups are exploited to form more proper training datasets for traditional NN methods to enhance prediction performance.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig3_oridata.jpg}
	\caption{The growing trends of PCT time series data at 18 major ports in China from 1996 to 2016 (i.e., \textit{Time Point from $1$ to $21$ in the horizontal axis}). It can be found that different ports have different properties of growing trends.}
	\label{Figureoridata}
\end{figure*}
\subsection{Three-Step Prediction Framework}
The proposed three-step framework for non-stationary time series prediction is illustrated in Fig. \ref{Figure1}. The basic theories of EMD, EEMD and DTW have been introduced in Sections \ref{sec:EMD}-\ref{sec:DTW}. The detailed calculation steps for our EMD+DTW+NN and EEMD+DTW+NN are described as follows
\subsubsection{\textbf{Step 1--Decomposition}} EMD or EEMD method first decomposes the original non-stationary time series $X$ into high-frequency IMFs $C^{h}_{1 \leq p \leq P}$ and low-frequency IMFs $C^{l}_{1 \leq q \leq Q}$ with $P + Q = N + 1$. Although $C^{h}_{1 \leq p \leq P}$ can extract the main structures and characteristics in $X$, it is still difficult to predict $C^{h}_{1 \leq p \leq P}$ due to its weak mathematical regularity property. In contrast, it becomes easier to predict $C^{l}_{1 \leq q \leq Q}$ since it indicates the long-period characteristics. Note that the last IMF $C^{l}_{Q}$ generally represents the essential trend of $X$ and its high-accuracy prediction could be guaranteed. For the sake of simplicity, we tend to respectively adopt $C^{h}_{p}$ and $C^{l}_{q}$ instead of $C^{h}_{1 \leq p \leq P}$ and $C^{l}_{1 \leq q \leq Q}$ throughout the rest of this paper.
\subsubsection{\textbf{Step 2--Prediction}} The low-frequency IMFs $C^{l}_{q}$ will be directly predicted using traditional NN methods, e.g., WNN, FNN, ENN, BPNN and GRNN. The mathematical foundations behind these five NN methods have been vastly discussed in current research work. For the sake of maximum conciseness and better understanding, only the corresponding architectural diagrams are introduced in this paper, visually illustrated in Fig. \ref{Fig:NNmethods}. It is more difficult to generate the accurate predictions of high-frequency IMFs $C^{h}_{p}$ because of their properties of high levels of volatility. In order to overcome this shortcoming, we propose to take advantage of the self-similarities within $C^{h}_{p}$. In particular, each $C^{h}_{p}$ is first divided into a series of overlapping segments, i.e., $\mathcal{C}^{h}_{p} = \{ \{ C^{h}_{p} ( 1 ), C^{h}_{p} ( 2 ), \cdots, C^{h}_{p} ( L ) \}, \cdots, \{ C^{h}_{p} ( i ), C^{h}_{p} ( i+1 ), \cdots, C^{h}_{p} ( L+i-1 ) \}, \cdots, \{ C^{h}_{p} ( T - L + 1 ), C^{h}_{p} ( T - L + 2 ), \cdots, C^{h}_{p} ( T ) \} \}$, with $L$ denoting the length of each segment. How to construct the appropriate training dataset adopted for NN methods significantly effects the final prediction performance. Taking the prediction of $C^{h}_{p} ( T + 1 )$ as an example, we consider $C^{h,T-L+1}_{p} = \{ C^{h}_{p} ( T - L + 1 ), C^{h}_{p} ( T - L + 2 ), \cdots, C^{h}_{p} ( T ) \}$ as an input reference segment. The similarities between $C^{h,T-L+1}_{p}$ and other segments in $\mathcal{C}^{h}_{p}$ are then calculated using the DTW method, shown in Fig. \ref{FigureDTWDistance}. DTW has the capacity of generating robust estimation of similarity between different segments.

In this work, we propose to automatically group the segments with higher similarities from $\mathcal{C}^{h}_{p}$ to form more appropriate training dataset for NN-based prediction of high-frequency components $C^{h}_{p} ( T + 1 )$. Analogous to this strategy, the accurate prediction of $C^{h}_{p} \left( T + \cdot \right)$ could be performed accordingly. It is worth noting that some segments in $\mathcal{C}^{h}_{p}$ are inappropriate for promoting prediction if there are low degrees of similarity between these segments and the reference one. Within our framework, only the grouped segments with high degrees of similarity are extracted to enhance prediction accuracy and robustness.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig4_EMD.jpg}
	\caption{EMD- and EEMD-based decompositions of PCT time series for an example of Qinghuangdao Port. From left to right: (a) original PCT time series, (b) IMF$_{1}$, (c) IMF$_{2}$, (d) IMF$_{3}$ and (e) IMF$_{4}$. In particular, IMF$_{1}$ corresponds to the high-frequency component difficult to predict in this work. IMF$_{2}$, IMF$_{3}$ and IMF$_{4}$ are the low-frequency components easy to deal with in practical applications.}
	\label{FigureEMD}
\end{figure*}
\subsubsection{\textbf{Step 3--Combination}} High-frequency IMFs $C^{h}_{1 \leq p \leq P}$ are predicted using our proposed EMD+DTW+NN and EEMD+DTW+NN to enhance prediction accuracy; whereas low-frequency IMFs $C^{l}_{1 \leq q \leq Q}$ are predicted directly using traditional NN methods to shorten computational time. It is worth mentioning that the prediction accuracy of low-frequency IMFs could be further improved via EMD+DTW+NN and EEMD+DTW+NN but at the expense of much higher computational load. In this work, the final prediction result is obtained by integrating the predicted high- and low-frequency IMFs, i.e.,
\begin{equation}\label{Eq:predictionresults}
	{X \left( T + \hat{t} \right) = \sum_{p=1}^{P} C^{h}_{p} \left( T + \hat{t} \right) + \sum_{q=1}^{Q} C^{l}_{q} \left( T + \hat{t} \right)},
\end{equation}
for $1 \leq \hat{t} \leq L_{T}$ with $L_{T}$ being the length of time-series data points needed to be predicted in numerical experiments. Through the proposed two three-step prediction frameworks, it is able to generate more accurate and stable prediction performance.
\section{Experimental Results and Discussion}
\label{section3}
To evaluate the prediction accuracy and robustness, our proposed three-step prediction frameworks will be compared with several popular methods on realistic datasets in maritime industries, e.g., port cargo throughput and vessel traffic flow. The Matlab source codes of our three-step prediction frameworks can be download at \textcolor[rgb]{0,0,1}{\url{http://mipc.whut.edu.cn/}}.
\subsection{Non-Stationary Time Series in Maritime Industry}
\subsubsection{Port Cargo Throughput (PCT)}
In this work, we select the realistic PCT time series from $18$ major ports in China from $1996$ to $2016$. The growing trends of PCT time series are visually illustrated in Fig. \ref{Figureoridata}. It shows that different ports have different properties of growing trends due to several influence factors, such as geographic location, type of goods, port scale, service level, and management level, etc. It means that the proposed prediction method should have the universality and robustness properties to enhance prediction results.

\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig5_prediction.jpg}
	\caption{The PCT prediction results for $18$ major ports in China in $2016$. In each subfigure, the adopted prediction methods, from left to right, are NN (i.e., WNN, ENN, FNN, BPNN, GRNN), EMD+NN (i.e., EMD+WNN, EMD+ENN, EMD+FNN, EMD+BPNN, EMD+GRNN), EMD+DTW+NN (i.e., EMD+DTW+WNN, EMD+DTW+ENN, EMD+DTW+FNN, EMD+DTW+BPNN, EMD+DTW+GRNN), and EEMD+DTW+NN (i.e., EEMD+DTW+WNN, EEMD+DTW+ENN, EEMD+DTW+FNN, EEMD+DTW+BPNN, EEMD+DTW+GRNN), respectively. The dashed lines in subfigures indicate the actual PCT data in $2016$.}
	\label{FigurePred2016}
\end{figure*}
\subsubsection{Vessel Traffic Flow (VTF)}
We propose to adopt the original VTF time series at the Wuhan Yangtze Great Bridge to evaluate the medium-term prediction performance. The original VTF data can be generated using the dynamic automatic identification system (AIS) information\footnote{The AIS, an automatic tracking system used on ships, is able to provide real-time vessel position (latitude and longitude), course and speed, etc.}. In particular, the VTF data of $16$ days are extracted at $3$-hour intervals to assist in medium-term prediction. The original VTF data can be found in Fig. \ref{Fig:VTFDecomposition} in Section \ref{sec:VTFprediction}. To the best of our knowledge, previous work mainly focuses on long-term VTF prediction. In contrast, our VTF time series have the property of higher temporal resolution compared with previous research. The medium-short prediction results are beneficial to delicacy management and traffic safety enhancement in maritime industries.
\subsection{Comparison with Other Prediction Methods}
Our proposed three-step frameworks EMD+DTW+NN and EEMD+DTW+NN will be mainly compared with traditional prediction methods (i.e., NN and EMD+NN) as follows
\begin{itemize}
	%
	\item \textbf{NN}: Without loss of generality, widely-used NN methods, such as WNN, FNN, ENN, BPNN and GRNN, are introduced to directly predict non-stationary time series in maritime industries. Both EMD+NN and EMD+DTW+NN frameworks are developed based on these $5$ popular NN methods to further enhance prediction performance.
	%
	\item \textbf{EMD+NN}: EMD+NN is essentially a two-step prediction framework which combines EMD with traditional NN methods. As done in \cite{WangNN2017,RenTSE2015,RenNNLS2016}, the original non-stationary time series are first decomposed into several high- and low-frequency components using the EMD method. The NN methods are then exploited to directly predict both high- and low-frequency components. The final prediction results are obtained by combining the predicted high- and low-frequency components. Theoretically, EMD+NN has the capacity of guaranteeing more robust performance compared with single NN methods.
	%
	\item \textbf{EMD+DTW+NN}: This three-step prediction framework can be considered as an extension of EMD+NN through taking full advantage of self-similarity features within high-frequency components. Under this framework, the prediction of low-frequency components will be directly performed using the popular NN methods. In contrast, the high-frequency components are divided into several small segments which are grouped via DTW-based similarity measure. The grouped segments are selected to form more proper training datasets for NN to enhance prediction accuracy. Both predicted high- and low-frequency components are also combined to generate the final prediction results.
	%
	\item \textbf{EEMD+DTW+NN}: This prediction framework is essentially an extension of EMD+DTW+NN by replacing EMD with EEMD during time series decomposition. EEMD is a noise-assisted data analysis method \cite{HuangEEMD2009}, which generates more robust and stable decomposition results compared with simple EMD. Theoretically, EEMD-based three-step calculation framework has the capacity of yielding superior prediction results.
	%
\end{itemize}

To quantitatively analyze the prediction results, relative error (RE) is selected as a measure of accuracy. The corresponding mathematical formulation is defined as follows
\begin{equation}
	\mathrm{RE} = \frac{|y - \widehat{y}|}{y},
\end{equation}
with $y$ and $\widehat{y}$, respectively, being the actual and predicted time series. To further evaluate the prediction robustness, each prediction method will run $10$ times to generate the mean and standard deviation versions of RE in our numerical experiments.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig6_RE.jpg}
	\caption{Visual illustration of mean RE for different prediction frameworks at 18 major ports in China. To reduce randomness, each prediction method ran $10$ times to obtain the average results. Results demonstrate that our proposed frameworks yield the lowest RE under all conditions.}
	\label{FigureRE}
\end{figure*}
\begin{figure*}[!]
	\centering
	\includegraphics[width=\linewidth]{Fig7_variance.jpg}
	\caption{The display of standard deviation of RE for different prediction methods at 18 major ports in China. This standard deviation is adopted to evaluate the prediction robustness. To reduce randomness, each prediction method ran $10$ times to obtain the standard deviation of RE. Statistical results illustrate that our proposed frameworks generate the most robust prediction under consideration in most of the cases.}
	\label{FigureVarianceRE}
\end{figure*}
\begin{figure*}[!]
	\centering
	\includegraphics[width=\linewidth]{Fig8_ENNPred.jpg}
	\caption{The PCT prediction results for $18$ major ports in China from $2017$ to $2026$ (i.e., \textit{Time Point from $1$ to $10$ in the horizontal axis}). It can be found that the proposed EMD+DTW+ENN and EMD+DTW+ENN frameworks are able to more robustly implement PCT prediction in practical applications.}
	\label{FigurePreResult}
\end{figure*}
\subsection{Experiments on Port Cargo Throughput Prediction}
\subsubsection{Quantitative Performance Evaluation}
To implement the experiments, the PCT time series in Fig. \ref{Figureoridata}, extracted at $18$ major ports from $1996$ to $2015$ in China, are used to train our three-step prediction frameworks. The prediction accuracy is evaluated using the predicted data from all these ports in $2016$. It can be found that the growing trend of PCT time series in Qinghuangdao Port appears more complicated. From a mathematical point of view, owing to the obvious regular changes, it is easier to accurately predict the PCT time series in other ports by directly using traditional techniques, e.g., Kalman filtering \cite{ZuluagaAE2015}, (non-)parametric regression \cite{DonnellyAE2015}, Wavelet filtering \cite{JooESA2015}, NN methods \cite{SanthoshECM2018, WeiASC2016, WangASC2014, WangRE2016, WeiChenPartC2012, ZhouSTE2014}, etc. Therefore, taking Qinghuangdao Port as an example, we propose to decompose the PCT time series into $4$ different components, shown in Fig. \ref{FigureEMD}. The first one is usually related to high-frequency IMF difficult to predict. The other $3$ components correspond to low-frequency IMFs. This similar decomposition manner will also be performed for other ports in this work.

In our numerical experiments, we empirically select $P = 1$ and $Q = 3$ in Eq. (\ref{Eq:predictionresults}) since extensive experimental results have demonstrated that this selection is able to generate satisfactory performance. The quantitative results from our proposed frameworks EMD+DTW+NN, EEMD+DTW+NN and other competing techniques (i.e., NN and EMD+NN) are detailedly depicted in Fig. \ref{FigurePred2016}. It can be observed that the direct utilization of NN generates the lowest prediction accuracy. In contrast, the combination of EMD and NN is able to improve prediction performance. For Qinghuangdao Port with more complicated trend of PCT data, our proposed methods EMD+DTW+NN and EEMD+DTW+NN generate superior prediction results compared with NN and EMD+NN. This is because of the fact that EMD- and EEMD-based PCT decomposition yields relatively stationary components which can be readily modeled using NN methods. It is well known that DTW can robustly measure the similarities between different small segments within decomposed components. The segments with high similarities are selected to group the training dataset for NN methods to enhance prediction performance. Therefore, our proposed two three-step frameworks can significantly outperform other competing methods. The corresponding prediction results, shown in Fig. \ref{FigurePred2016}, are closer to the dashed lines which indicate the actual PCT data. Since EEMD is essentially an improved version of EMD, EMD+DTW+NN produce predicted results which are more similar to the actual data.

The superior performance of our proposed frameworks is further confirmed by the statistical results for prediction accuracy and robustness shown in Figs. \ref{FigureRE} and \ref{FigureVarianceRE}. The mean RE results in Fig. \ref{FigureRE} illustrate that EMD+DTW+NN and EEMD+DTW+NN generate the most accurate predictions under all conditions. In contrast, EMD+NN has constrained the further improvement in prediction performance. Since the PCT time series have the essential property of weak mathematical regularity, single NN methods generate the lowest prediction accuracy under consideration in most of the cases. Fig. \ref{FigureVarianceRE} visually illustrates the standard deviation (std) of RE\footnote{The standard deviation of RE is essentially related to the robustness of prediction performance. In particular, low standard deviation indicates the robust prediction in practice.} adopted to evaluate the prediction robustness. It can be found that both EMD+DTW+NN and EEMD+DTW+NN can generate the most robust prediction. This good performance mainly benefits from the EMD- or EEMD-based property decomposition and DTW-based similarity grouping.
\setlength{\tabcolsep}{2.5pt}
\begin{table*}
	\centering
	\caption{Prediction results (mean$\pm$std) of VTF time series from time points $121$ to $128$ for different methods (i.e., NN, EMD+NN, EMD+DTW+NN and EEMD+DTW+NN) at the Wuhan Yangtze Great Bridge. RE is used to quantitatively evaluate the prediction performance.}
	\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|}
		\hline
		\multicolumn{2}{|l|}{Time Point}         & $121(03:00)$   & $122(06:00)$   & $123(09:00)$   & $124(12:00)$   & $125(15:00)$   & $126(18:00)$   & $127(21:00)$   & $128(24:00)$ & \multirow{2}{*}{RE} \\ \cline{1-10}
		\multicolumn{2}{|l|}{Actual Data}        & $34.00$ & $37.00$ & $36.00$ & $41.00$ & $48.00$ & $39.00$ & $38.00$ & $34.00$ &  \\ \hline \hline
		\multirow{5}{*}{\tabincell{c}{NN}}
		& WNN  & $26.47\pm5.91$ & $27.47\pm6.34$ & $32.51\pm4.37$ & $46.62\pm5.43$ & $44.58\pm7.41$ & $47.49\pm6.62$ & $42.83\pm7.11$ & $30.71\pm3.64$ & $0.15\pm0.07$ \\
		& ENN  & $28.45\pm3.85$ & $29.32\pm4.15$ & $33.76\pm4.26$ & $44.58\pm3.72$ & $44.34\pm5.89$ & $45.57\pm5.86$ & $43.38\pm4.17$ & $30.26\pm3.45$ & $0.13\pm0.05$ \\
		& FNN  & $28.56\pm4.28$ & $31.27\pm4.55$ & $30.28\pm3.97$ & $46.36\pm5.15$ & $42.34\pm6.87$ & $46.14\pm3.49$ & $44.42\pm4.59$ & $30.56\pm3.34$ & $0.15\pm0.03$ \\
		& BPNN & $27.35\pm3.49$ & $28.26\pm3.73$ & $32.21\pm4.54$ & $44.34\pm5.99$ & $44.67\pm3.54$ & $45.23\pm4.05$ & $42.02\pm5.11$ & $31.71\pm3.47$ & $0.13\pm0.06$ \\
		& GRNN & $29.23\pm5.17$ & $30.54\pm4.46$ & $31.42\pm5.62$ & $45.21\pm4.11$ & $43.11\pm4.83$ & $44.04\pm3.19$ & $44.16\pm5.87$ & $28.92\pm4.41$ & $0.14\pm0.03$ \\ \hline \hline
		%
		\multirow{5}{*}{\tabincell{c}{EMD \\ + \\NN}}
		& WNN  & $29.32\pm4.27$ & $31.24\pm4.51$ & $34.16\pm4.53$ & $38.27\pm3.28$ & $45.62\pm4.23$ & $45.43\pm4.74$ & $42.86\pm6.78$ & $31.92\pm2.89$ & $0.10\pm0.05$ \\
		& ENN  & $30.24\pm3.44$ & $32.16\pm3.86$ & $34.15\pm2.24$ & $43.64\pm3.28$ & $45.75\pm3.37$ & $43.24\pm4.19$ & $41.34\pm3.75$ & $32.21\pm2.16$ & $0.08\pm0.03$ \\
		& FNN  & $31.95\pm4.22$ & $34.47\pm3.59$ & $30.71\pm3.81$ & $44.49\pm4.68$ & $44.37\pm4.34$ & $42.61\pm2.54$ & $42.65\pm3.17$ & $31.21\pm1.53$ & $0.09\pm0.03$ \\
		& BPNN & $30.56\pm3.21$ & $33.96\pm3.68$ & $34.88\pm2.39$ & $44.97\pm4.23$ & $46.13\pm3.58$ & $41.41\pm2.13$ & $41.44\pm4.28$ & $32.23\pm2.76$ & $0.07\pm0.03$ \\
		& GRNN & $29.33\pm5.39$ & $34.77\pm3.87$ & $32.65\pm4.61$ & $43.45\pm3.37$ & $45.94\pm4.65$ & $42.98\pm2.62$ & $40.49\pm5.34$ & $36.44\pm3.17$ & $0.08\pm0.03$ \\ \hline \hline
		%
		\multirow{5}{*}{\tabincell{c}{EMD \\ + \\DTW\\ + \\NN}}
		& WNN  & $32.82\pm2.13$ & $35.83\pm2.74$ & $\textcolor[rgb]{0,1,0}{36.81\pm2.13}$ & $42.92\pm1.95$ & $46.37\pm2.08$ & $\textcolor[rgb]{0,1,0}{38.67\pm3.51}$ & $39.23\pm4.22$ & $35.03\pm1.27$ & $\textcolor[rgb]{0,0,1}{0.03\pm0.04}$ \\
		& ENN  & $\textcolor[rgb]{0,1,0}{33.21\pm1.94}$ & $\textcolor[rgb]{0,0,1}{36.12\pm1.51}$ & $37.83\pm2.95$ & $42.75\pm1.47$ & $\textcolor[rgb]{0,1,0}{46.95\pm2.93}$ & $41.34\pm3.79$ & $40.59\pm1.49$ & $34.87\pm1.14$ & $0.04\pm0.02$ \\
		& FNN  & $31.21\pm2.24$ & $38.57\pm1.44$ & $34.23\pm2.89$ & $43.99\pm1.91$ & $50.81\pm2.62$ & $40.43\pm1.47$ & $\textcolor[rgb]{0,1,0}{37.54\pm2.02}$ & $35.26\pm1.36$ & $0.05\pm0.02$ \\
		& BPNN & $35.77\pm1.67$ & $39.08\pm2.35$ & $\textcolor[rgb]{1,0,0}{35.46\pm1.12}$ & $42.88\pm1.47$ & $\textcolor[rgb]{0,0,1}{49.08\pm2.04}$ & $40.19\pm2.55$ & $39.58\pm1.96$ & $35.03\pm0.98$ & $0.04\pm0.01$ \\
		& GRNN & $32.51\pm1,42$ & $38.58\pm2.45$ & $34.46\pm1.43$ & $\textcolor[rgb]{0,0,1}{42.08\pm1.97}$ & $50.48\pm2.67$ & $37.19\pm1.73$ & $38.98\pm3.11$ & $32.32\pm2.49$ & $0.04\pm0.01$ \\ \hline
		%
		\multirow{5}{*}{\tabincell{c}{EEMD \\ + \\DTW\\ + \\NN}}
		& WNN  & $\textcolor[rgb]{1,0,0}{33.25\pm1.74}$ & $\textcolor[rgb]{0,1,0}{36.43\pm2.16}$ & $35.07\pm2.15$ & $39.22\pm2.59$ & $46.12\pm2.67$ & $\textcolor[rgb]{1,0,0}{38.70\pm3.80}$ & $37.11\pm3.98$ & $\textcolor[rgb]{0,0,1}{33.28\pm1.89}$ & $\textcolor[rgb]{1,0,0}{0.02\pm0.01}$ \\
		& ENN  & $\textcolor[rgb]{0,0,1}{33.15\pm1.54}$ & $\textcolor[rgb]{1,0,0}{36.44\pm1.45}$ & $35.02\pm2.41$ & $\textcolor[rgb]{0,1,0}{40.02\pm2.16}$ & $46.55\pm2.32$ & $37.81\pm3.24$ & $36.09\pm0.83$ & $\textcolor[rgb]{1,0,0}{33.62\pm1.60}$ & $\textcolor[rgb]{0,1,0}{0.03\pm0.01}$ \\
		& FNN  & $32.33\pm1.46$ & $35.89\pm1.96$ & $34.94\pm2.71$ & $38.19\pm2.10$ & $44.89\pm2.14$ & $38.08\pm1.68$ & $\textcolor[rgb]{1,0,0}{38.23\pm2.44}$ & $\textcolor[rgb]{0,1,0}{33.38\pm1.80}$ & $0.04\pm0.02$ \\
		& BPNN & $32.62\pm1.23$ & $35.54\pm3.21$ & $\textcolor[rgb]{0,0,1}{35.08\pm1.58}$ & $38.64\pm1.45$ & $\textcolor[rgb]{1,0,0}{48.96\pm2.20}$ & $\textcolor[rgb]{0,0,1}{39.78\pm2.02}$ & $36.50\pm1.88$ & $33.11\pm0.91$ & $\textcolor[rgb]{0,1,0}{0.03\pm0.01}$ \\
		& GRNN & $33.14\pm1.40$ & $35.66\pm1.53$ & $34.34\pm2.20$ & $\textcolor[rgb]{1,0,0}{41.63\pm2.80}$ & $46.00\pm3.26$ & $37.96\pm1.93$ & $\textcolor[rgb]{0,0,1}{38.88\pm2.63}$ & $32.97\pm2.16$ & $\textcolor[rgb]{0,1,0}{0.03\pm0.01}$ \\ \hline
	\end{tabular}
	\label{Tab:VTFPrediction}
\end{table*}
\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig9_VTFdecom.jpg}
	\caption{From left to right: (a) EMD- and (b) EEMD-based decompositions of original VTF time series (the $1$st row). It is difficult to predict the first $3$ IMFs with high frequencies due to their properties of weak mathematical regularity. In contrast, the predictions of last $4$ IMFs can be easily obtained due to the significant mathematical regularities.}
	\label{Fig:VTFDecomposition}
\end{figure}
\subsubsection{Prediction Results and Discussion}
It is obvious that ENN produces the highest prediction accuracy and robustness among all NN methods, demonstrated in Figs. \ref{FigurePred2016}-\ref{FigureVarianceRE}. Therefore, our three-step calculation framework is compared with other competing methods (i.e., ENN and EMD+ENN) on PCT prediction from $2017$ to $2026$ in this subsection. The prediction results are displayed in Fig. \ref{FigurePreResult}, which illustrates the comparisons between different prediction frameworks. It can be easily found that our EMD+DTW+ENN has the capacity of robustly implementing the prediction of PCT time series. The original data in Fig. \ref{Figureoridata} illustrate that the trends of PCT in most ports show steady increases from $1996$ to $2016$. From a theoretical point of view, the predicted results from $2017$ to $2026$ should also grow with a steady increase. Fig. \ref{FigurePreResult} shows that our proposed framework generates the predicted PCT time series which are more similar to the theoretical results. Both ENN and EMD+ENN easily suffer from unstable calculation leading to low-quality prediction performance. By taking full advantages of property decomposition and similarity grouping, our proposed three-step calculation method is able to adequately grasp the changing tendency of PCT time series and enhance the prediction accuracy. In particular, EMD decomposes original PCT time series into a sum of finite number of components. Each component becomes less volatile and thus improves the NN-based prediction accuracy. Experiments have illustrated that our proposed method could accurately and robustly predict PCT time series in practice.
\begin{figure*}[t]
	\centering
	\includegraphics[width=\linewidth]{Fig10_VTFPrediction.jpg}
	\caption{Prediction results of $4$ different frameworks (i.e., NN, EMD+NN, EMD+DTW+NN and EEMD+DTW+NN) on the $17$th and $18$th days (i.e., \textit{Time Point from $129$ to $144$ in the horizontal axis}). Our proposed frameworks have the capacity of adequately grasping the changing tendency of VTF and guaranteeing prediction accuracy.}
	\label{Fig:VTFPred}
\end{figure*}
\subsection{Experiments on Vessel Traffic Flow Prediction}
\label{sec:VTFprediction}
Current research findings \cite{LiuITSC2017,XiaoIEEEITS2017} show that the growth of VTF time series has the predominantly complex, nonlinear and nonstationary properties. The high-quality prediction results are of importance in the fields of maritime management, traffic safety control, and maritime industrial planning, etc. The original VTF data were extracted from the dynamic AIS information at the Wuhan Yangtze Great Bridge. In the case of quantitative evaluation, the VTF time series of $15$ days with an interval of $3$ hours are adopted as the training datasets to optimize the traditional NN models. The VTF data on the $16$th day are considered as the testing datasets to assess the prediction results. Our proposed three-step frameworks EMD+DTW+NN and EEMD+DTW+NN will also be compared with conventional prediction methods NN and EMD+NN. It is well known that the number of IMFs plays a crucial role in decomposition-based prediction performance. As illustrated in Fig. \ref{Fig:VTFDecomposition}, the original VTF data are decomposed into $7$ IMFs to achieve the balance between complexity and performance. Without loss of generality, we observe that the empirical selection of parameters $P = 3$ and $Q = 4$ in Eq. (\ref{Eq:predictionresults}) is able to guarantee high-accuracy prediction.

Table \ref{Tab:VTFPrediction} depicts the quantitative results from our proposed three-step frameworks and other competing prediction methods. It can be observed that NN generates the lowest-accuracy prediction results under almost all conditions. By taking advantage of the characteristics of EMD, EMD+NN significantly promotes traditional NN methods and enhances the prediction accuracy. Furthermore, the DTW-based similarity grouping incorporated into our proposed framework is able to cluster the (time series) segments with high similarities. The grouped segments perform usefully in training the popular NN methods and generating satisfactory prediction results. In Table \ref{Tab:VTFPrediction}, our proposed methods provide the most accurate and robust prediction results. It happens due to the fact that EMD- or EEMD-based property decomposition and DTW-based similarity grouping benefit for predicting non-stationary VTF time series. More detailedly, EEMD+DTW+NN performs better than EMD+DTW+NN since EEMD is able to yield more robust and stable decomposition results. The good performance of our proposed prediction frameworks is further confirmed by the mean and standard deviation values of RE, shown in Table \ref{Tab:VTFPrediction}. Our proposed prediction methods and other competing methods are further adopted to forecast the VTF data on the $17$th and $18$th days. The prediction results are detailedly displayed in Fig. \ref{Fig:VTFPred}, which illustrates the comparisons between different prediction frameworks. It can be observed that both NN and EMD+NN easily suffer from some perturbations leading to prediction quality degradation. In contrast, our proposed methods are able to robustly implement the prediction of VTF time series. This is because that EMD and EEMD can decompose non-stationary time series into high- and low-frequency components to reduce the uncertainties in original data. The self-similarities within high-frequency components are then fully used to enhance the NN-based prediction quality. Experimental results have demonstrated that our proposed framework performs well in non-stationary time series prediction in maritime industry.
\section{Conclusions}
\label{section4}
The reliable prediction of non-stationary time series is of fundamental importance in maritime industries, e.g., economic investment, transportation planning, port planning and design, etc. In this paper, we proposed to develop two three-step calculation frameworks, called EMD+DTW+NN and EEMD+DTW+NN, to guarantee the prediction accuracy and robustness for both port cargo throughput and vessel traffic flow. In particular, the original non-stationary time series were first decomposed into high- and low-frequency components using the EMD and EEMD methods. To take advantage of self-similarities within high-frequency components, these components were further divided into several small (overlapping) segments. The grouped segments with high DTW similarities were selected to form the training dataset for widely-used NN methods to enhance the prediction accuracy of high-frequency components. In contrast, the low-frequency components could be accurately predicted directly using traditional NN methods. The final prediction results were consequently obtained by combining the predicted high- and low-frequency components. Experiments on both port cargo throughput and vessel traffic flow have illustrated the superior performance of our proposed frameworks in terms of prediction accuracy and robustness. Although this work only focuses on predicting non-stationary time series in maritime industry. There is a huge potential to adopt the proposed three-step prediction frameworks to generate satisfactory performance in other industries.

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text:
Joint Committee of Orthodox and Catholic Bishops Holds Eighteenth Meeting in Milwaukee
October 17, 2001 By Public Affairs Office
WASHINGTON (October 17, 2001) -- The Joint Committee of Orthodox and Catholic Bishops held its eighteenth meeting in Milwaukee, Wisconsin, from October 2 to 4, 2001. It was hosted by Archbishop Rembert Weakland, the Catholic Co-Chairman of the Committee.
The members considered a number of topics of mutual interest during the meeting. Metropolitan Nicholas of Amissos presented a paper, "Vinum non Habent: The Ever-Virgin as Intercessor and Paradigm," based on the proceedings of the 1999 Orientale Lumen III Conference whose theme was "Mary the Mother of God and the Church." Bishop Dale Melczek of Gary presented a response to the paper from a Catholic perspective. In addition, Bishop Seraphim of Ottawa and All Canada gave a paper, "The Local Church in the Understanding of the Orthodox" as part of an ongoing discussion of the nature of primacies in our churches and the relationship between the local church and the universal church.
The Committee invited Professor Michael Fahey, SJ, of Marquette University to make a presentation on the theological significance of the work of the North American Orthodox-Catholic Theological Consultation. Fr Fahey served for many years as Catholic Co-Secretary of the Consultation and offered a number of perspectives on the Consultation's work in his paper, which was entitled "Forth-One Years of Orthodox-Catholic Dialogue in the USA: Kind Words, Tough Love, Clearer Heads." The bishops noted with satisfaction that tremendous progress has been made in our relationship in recent decades due in large part to the work of our dialogues.
Archbishop Vsevolod of Scopelos presented a paper, "Finding the Road Home: A Reflection on the Situation of the Byzantine Catholics." Bishop Nicholas Samra of the Eparchy of Newton of the Melkites responded from an Eastern Catholic perspective. The bishops also discussed a number of recent developments in the lives of the Catholic and Orthodox Churches, including responses by our churches to the terrorist attacks on New York and Washington on September 11, the appointment of Cardinal Walter Kasper as President of the Pontifical Council for Promoting Christian Unity, the SCOBA encyclical "And the Word Became Flesh and Dwelt Among Us" and the meeting of SCOBA bishops in Washington in early May, the Apostolic Letter Novo Millennio Ineunte, the agreement on marriage reached between the Greek Orthodox Patriarchate of Alexandria and the Coptic Orthodox Church, the visits of Pope John Paul II to Greece and Ukraine, the delegation of the Ecumenical Patriarchate in Rome for the feast of Sts. Peter and Paul in June 2001, the process of revision of the Charter of the Greek Orthodox Archdiocese, and the visit of the Ecumenical Patriarch to Calabria and Sicily in March 2001.
The members also attended two liturgical services during the meeting. On Tuesday evening October 2, they attended a Mass presided over by Archbishop Weakland at Saint Josaphat's Catholic Church. On Wednesday evening October 3 they attended a Vespers service at St. Sava's Serbian Orthodox Church presided over by Metropolitan Christopher. Since this is the last meeting that Archbishop Weakland will attend before submitting his resignation to Pope John Paul II on his 75th birthday on April 2, 2002, the Orthodox members presented him with an icon of Christ and the Four Evangelists to thank him for his years of service as Co-Chairman of the Joint Committee.
The Joint Committee of Orthodox and Catholic Bishops was established in 1981 and is currently under the joint chairmanship of Metropolitan Maximos of Pittsburgh and Archbishop Rembert Weakland of Milwaukee. Other Catholic members of the Committee include William Cardinal Keeler, Archbishop of Baltimore, Archbishop Alexander Brunett of Seattle, Archbishop Oscar Lipscomb of Mobile, Bishop Robert Mulvee of Providence, Bishop Dale Melczek of Gary, Bishop Edward Kmiec of Nashville, Bishop Nicholas Samra, Auxiliary of the Melkite Greek Catholic Eparchy of Newton, and Rev. Ronald G. Roberson, CSP (staff).
The other Orthodox members include Archbishop Peter of New York (Orthodox Church in America), Archbishop Vsevolod of Scopelos (Ukrainian Orthodox Church of the USA), Metropolitan Isaiah of Denver (Greek Orthodox Archdiocese), Metropolitan Christopher (Serbian Orthodox Church), Metropolitan Nicholas of Amissos (Carpatho-Russian Orthodox Diocese), Bishop Seraphim of Ottawa and All Canada (Orthodox Church in America), and Bishop Dimitrios of Xanthos (Greek Orthodox Archdiocese, staff).
In addition to this Joint Committee of Orthodox and Catholic Bishops, a North American Orthodox-Catholic Theological Consultation has been meeting continuously since 1965. At its last meeting in October 2001, the Joint Committee of Bishops issued a common statement entitled "On the Catholic-Orthodox Dialogue At the Dawn of a New Millennium."

redpajama_set_name: RedPajamaCommonCrawl


text:
OAU as a Predecessor to the African Union
The aim of this section is not to give the detailed account of the OAU as an African organization. Rather the OAU is highlighted to show in way that it can be considered as a predecessor to the African Union.
The OAU, placed in a longer term of historical current, is a manifestation of the Pan- African movement which originated in the USA during the late 19th century. In the USA, thousands of blacks, with African origins found it intolerable to bear the agonizing experience of racial discrimination and alienation. Some of their prominent leaders, namely WEB Du Bois (1868-1963) and Marcus Garvey (1885-1940) raised a flag of revolt against the then prevailing injustice and chose to speak for the entire black race which was leading a dehumanized existence. They subordinated the immediate problems of American blacks to a grand and enlarged vision of Pan-Africanism, which, in essence, stood for the unity and dignity of the black race.
As it has been reflected by Harshe in an article in the USA, the Pan-African movement has grown, substantially, acquiring different forms with charging times. The period prior to the birth of OAU had its own historical contribution to fully appreciate the on going Pan-African process.
Prior to the birth of the OAU, there was an inter-state politics in Africa which was characterized by growing rivalry between the Casablanca and Monrovia group of states. This rivalry, at least for a while, hindered the realization of the OAU.
The Casablanca group was principally led by Kwame Nkrumah of Ghana, Sekou Toure of Guinea, and Madibo Keita of Mali. The group vehemently opposed colonialism, racism and neocolonialism. Among other things, it opposed the Katanga secessionist movement, gave an extended support to Patrice Lumumba's efforts to oust the Belgians from Congo, demanded French withdrawal from Algeria and was sympathetic towards the Soviet Union due to concrete Soviet support to their activities. This group had a more radical approach involving the creation of the federation of African states with joint institutions with a joint military command.
The Monrovia group, on its part, was constituted by the Brazzaville group including most of the moderate Francophone states such as Ivory Coast, Gabon, Niger, Senegal, Monrovia, etc. In addition, it had members like Ethiopia, Liberia, Nigeria and Somalia, which were neutral towards the rivalry between Casablanca and Brazzaville groups. It stood for the protection of national sovereignty, territorial integrity and independence of its members. It defended the principle of mutual non-interference in inter-sate relations and welcomed interstate technical and economic cooperation. Instead of snapping the ties with the west, the Monrovia group sought western cooperation in the process of promoting development.
The rivalry between the Casablanca and Monrovia groups was not, however, an unbridgeable gulf that could prevent the birth of the OAU. article Harshe stated three basic justifications for this historical scenario. To begin with, like the Monrovia states, the Casablanca states were also getting absorbed in the world capitalist economy despite their sporadic tirade against neo-colonialism and imperialism. The penetration of western finance capital in the extractive sectors of Guinean economy and Ghana's membership of British Commonwealth amply illustrated this position. When succinctly expressed, both groups were eventually moving in the same direction. Secondly, despite their theoretical differences, both groups were keen to regulate and promote inter-state cooperation in Africa. Thirdly, though on their own ways, both groups aimed at liquidating colonialism and racism.
Harshe concludes that both groups had a lot in common. These commonalities were backed by the mediatory efforts of uncommitted (i.e. not strictly a proponent of either group) states like Ethiopia gave birth to the Organization of African Unity. Having passed all these ups and downs, the OAU was formally established in Addis Ababa, Ethiopia in May, 1963. The OAU Charter presented both views but using the vision of the Monrovia group as its core.
Wolfer states the tension in early days of the OAU and the compromise adopted as follows. "The main contention that surrounded the founding of the OAU is well known: whether the institution should lead to a union of states or merely to an association of the independent units." Nweke, also states "The OAU was the product of a compromise between African statesmen who wanted political union of all independent African states and those who preferred functional cooperation as a building block towards the construction of an African socio-psychological community".
The above statements can create a historical link between the OAU and the African Union. In the contention that surrounded the founding of the OAU, the latter statement views the OAU as an association of the independent units prevailed over the creation of a union of states. The latter view had to wait for another favorable historical ground to be a reality. Wolfer state that the former position which failed to be operational has left its foot prints in the naming of the organization. He states "the agreed name [for the organization] was proposed in French by President Hubert Maga of Dahomey (possibly at the instigation of President Kwame Nkrumah of Ghana); and President William Tubman of Liberia insisted that the English translation be organization of (emphasis added) African Unity, rather than organization for African unity".
The Assembly of the African Union has passed the Durban Declaration in tribute to the OAU and the launching of the African Union. The Durban Declaration describes the OAU in the following manner:-
The establishment of the OAU was a statement of determination to define Africa, not as individual countries but as collective bound together by geography, history and destiny. It was a self-empowering decision to find a framework for cooperation and forum for advocacy for African's causes and for joint action. This determination found concrete expression in the objectives the founding fathers set for the OAU in its Charter of promoting unity and solidarity among African states, of coordinating and intensifying cooperation for development, of defense for sovereignty, territorial integrity and independence of African state, of eradicating colonialism and of promoting international cooperation within the framework of the United Nations. The founding fathers saw a common future for Africa not contained by borders, linguistic differences, color, religion or other divisive legacies of colonialism. They saw one Africa, united in its diversity, speaking one language of freedom, unity and development under the Organization of African Unity.
Article II of the OAU Charter specifies the OAU's purposes and indicates areas of intra-African cooperation. The following are the purposes of the OAU: –
– To promote the unity and solidarity of the African states,
– To coordinate and intensify their collaboration and efforts to achieve a better life for the peoples of Africa;
– To defend their sovereignty, their territorial integrity, and their independence;
– To eradicate all forms of colonialism in Africa, and
– To promote international cooperation, having due regard for the Charter of the United Nations and the Universal Declaration of Human Rights,
Article III of the OAU Charter lists down the principles of the OAU, which are derived from the postulated purposes of the same. The principles of the OAU: include
– The sovereign equality of all member states;
– Non interference in the internal affairs of member states;
– Respect for the sovereignty and territorial integrity of each state and for its inalienable right to independent existences;
– Peaceful settlement of disputes by negotiation, mediation, conciliation, or arbitration;
– Unreserved condemnation, in all its forms, of political assassination as well as of subversive activities on the part of neighboring states or any other state;
– Absolute dedication to the total emancipation of the African territories that are still dependent; and
– Affirmation of a policy of non-alignment with regard to all blocs.
Many of the purposes and principles of the OAU were keen in those days. Most of them are, however, not pertinent to the contemporary situation of Africa. A clear exemplification of such a holding may be the last that state the principle of the OAU i.e., affirmation of a policy of non-alignment with regard to all blocs. By now, the mentioned blocs, the capitalist and socialist blocs are no more in rivalry. That is way a need was felt to bring a timely organization for Africa.
It can be argued that the African Union was conceived in the womb of the OAU. Stated other wise, though the objectives and principles of the African Union and the OAU are different, as is evident from the surrounding historical conditions, the idea of establishing the African Union was consolidated inside the OAU. Baimu shares this opinion as "It was noted that in the period between 1966 and 1999 efforts were made to realize African unity through the means of economic integration. This was expressed theoretically in a number of OAU declarations, resolutions and plans of actions that were adopted between 1968 and 1980, and in concrete terms in the formation of several sub-regional blocs."
The conception of the African Union inside the OAU is highly reflected in a number of resolutions, decisions and declarations adopted by the OAU Assembly of the Heads of States and Government with a desire to realize African economic integration. The Monrovia declaration of commitment on the guidelines and measures for national and collective self-relations in economic and social development for establishment of a new international order called for the creation of the African Economic Market as a prelude to an African Economic Community, and the Lagos Plan of Action (LPA) which was adopted by the second extra-ordinary summit of the OAU in April 1980 and envisaged the creation of an African Economic Community by the year 2000.
The idea of continental economic integration was concretized in the 1991 Abuja Treaty Establishing the African Economic Community, which was adopted under the auspices of the OAU on 3 June 1991 and entered in to force on 12 May 1994 after the requisite number of ratifications was attained. Article 6 of the Treaty envisages the establishment of the African Economic Community, AEC, as an integral part of the OAU upon passing six consecutive stages over a transitional period not exceeding thirty-four years. The Abuja Treaty envisions the establishment of the AEC as a goal that should be achieved through encouraging the formation of sub-regional economic bodies which would eventually amalgamate to create the AEC.
Article 4 of the Abuja Treaty enumerates four basic objectives of the AEC. These are:
To promote economic, social and cultural development and the integration of African economies in order to increase economic self-reliance and promote an endogenous and self-sustained development;
To establish, on a continental scale, a framework for the development, mobilization and utilization of the human and material resources of Africa in order to achieve a self-reliant development.
To promote cooperation in all fields of human Endeavour in order to raise the standard of living of African peoples, and maintain and enhance economic stability, foster close and peaceful relations among Member States and contribute to the progress, development and the economic integration of the continent, and
To coordinate and harmonize policies among existing and future economic communities in order to foster the gradual establishment of the community.
With the coming into force of the Abuja Treaty, the OAU committed itself with the realization of the aforementioned objectives and therefore it has to coexist with the AEC. The gradual operation of the OAU based on both its Charter and the Abuja Treaty made it clear that there is an emerging need to come up with an institution that would combine OAU's political nature and the AEC's economic character. At the same time, the end of the millennium led to a sense of urgency among African leadership to reposition the OAU in order to set the African continent as a whole on a firm path to development and peace in the new millennium. It was in this context that the forty four African leaders met in Libya from 8 to 9 September 1999 at an extraordinary summit of the OAU called by the Libyan leader Muammar Gaddafi, to discuss the formation of a 'United States of Africa'. The summit basically aimed at 'strengthening OAU's capacity to enable it to meet the challenges of the new millennium.' It was there that the African leaders adopted the Sirte Declaration which called for the establishment of The African Union.
Having been instructed to model it on the European Union and taking into account the Charter of the OAU and the Abuja Treaty Establishing the African Economic Community, the OAU legal unit drafted the Constitutive Act of the African Union. The resulting draft Constitutive Act was debated on a meeting of legal experts and parliamentarians and later at a ministerial conference held in Tripoli from 31 May to 2 June 2000.
The Constitutive Act of the African Union was adopted by the OAU assembly of Heads of States and Governments in Lome in July 2000. By March 2001, all members of the OAU had signed the Constitutive Act and hence the OAU Assembly at its 5th extraordinary summit held in Sirte, Libya, from 1 to 2 March 2001 declared the establishment of the African Union. However, to fulfill the legal requirements for the African Union, the Constitutive Act had to wait for ratification by two thirds of the member states of the OAU. It was on 26 April 2001 that this requirement was met. On 26 May 2001, the Constitute Act entered in to force and thereby making the African Union a legal and political reality.
All what has been stated above might be taken as evidencing the assertion that 'the African Union was conceived and be made realty in the womb of the OAU.' It is this fact that capitalizes the importance of studying the development within the OAU to give birth to the African Union.
As has been stated earlier, the theme is not an in-depth analysis of the activities of the OAU. Rather it is a bird's eye view of the same targeting on the historical tracks that led to the emergence of the African Union. The forthcoming subsections dictions ., 1.1.2 and 1.1.3 deal with issues similar to that dell under section 1.1.1. However, as they are worth being treated in depth, they are elaborated with a subsection of their own.
What were the major tenets of the Casablanca and Monrovia groups?
Why do you think that the OAU was mainly based on the ideas of the Monrovia group?
What decisive steps did the OAU take to smooth the ground for the emergence of the African Union?
Categories: AFRICAN UNION LAW, OVERVIEW


text:
Q: How to apply the Bloch-Floquet theorem for a square lattice in a magnetic field? Generalizing the question here, if I have a square lattice in the homogeneous magnetic field $B$ as the given picture, how can we apply the Bloch-Floquet theorem in this periodic structures (with the unit cell which contains $N$ vertices)? As we know, from the Bloch-Floquet theorem, the free ends of a unit cell are related by a phase factor; in this case, this rule applies to which free ends of the cell?

A: The Hamiltonian of your system can be written as
$$H = t\left[\sum_{n,m} |n+1,m\rangle\langle n,m| + e^{inaB}|n,m+1\rangle\langle n,m| + h.c.\right]$$
Defining the unitary lattice translation operators $T_x = \sum_n |n+1,m\rangle\langle n,m|$ and $T_y = \sum_m |n,m+1\rangle\langle n,m|$, it's easy to show that $T_y HT_y^\dagger= H \iff [T_y,H]=0$.  On the other hand,
$$T_xHT_x^\dagger = t \left[\sum_{n,m}|n+1,m\rangle\langle n,m| + e^{i(n-1)aB}|n,m+1\rangle\langle n,m|  + h.c.\right]$$
which is generally not equal to $H$ - unless $aB= 2\pi k$ for some $k\in \mathbb Z$. More generally, given any nonzero integer $q$,
$$T_x^q H T_x^{q\dagger} = H \iff aB = 2\pi p/q, p\in \mathbb Z$$
where $T_x^{q(\dagger)}=\underbrace{T_x^{(\dagger)}T_x^{(\dagger)}\ldots T_x^{(\dagger)}}_{q\text{ times}}$.

To summarize, your system is generically not periodic unless $\frac{aB}{2\pi} = \frac{p}{q}\in \mathbb Q$ is a rational number (which we take to be in fully-reduced form, so $p$ and $q$ are relatively prime).  In that case, the system is periodic with primitive lattice vectors $\mathbf v_x = qa\hat x$ and $\mathbf v_y = a \hat y$.
To apply Bloch's theorem, we should first rewrite our Hamiltonian to explicitly encode our unit cell.  In the above, $|n,m\rangle$ refers to the lattice site at position $(na,ma)$.  We now define $|N,M\rangle\otimes|\ell\rangle$ to refer to the lattice site at position $\ell$ in the unit cell whose left-most site is at position $(Nqa,Ma)$.  In other words, $|N,M\rangle\otimes|\ell\rangle$ refers to the lattice site at position $\big((Nq+\ell)a,Ma\big)$.
From here, our Hamiltonian becomes
$$H = t\sum_{N,M} \left[\sum_{\ell=0}^{q-2} |N,M\rangle\langle N,M|\otimes |\ell+1\rangle\langle \ell|\right.\tag{1}$$
$$ + |N+1,M\rangle\langle N,M| \otimes |0\rangle\langle q-1|\tag{2}$$
$$+ \sum_{\ell=1}^{q-1}\underbrace{e^{i(Nq+\ell)aB}}_{=e^{i\ell aB}\text{ because }qaB\in2\pi\mathbb Z}|N,M+1\rangle\langle N,M| \otimes |\ell\rangle\langle \ell| + h.c.\bigg]\tag{3}$$
The sum in $(1)$ refers to the left-right hopping within a given unit cell.  The second term $(2)$ refers to the left-right hopping from one unit cell to the adjacent cell.  Finally, the term in $(3)$ refers to vertical hopping between adjacent unit cells.
Having rewritten it this way, we may perform a pseudo-Fourier transform over the unit cell degrees of freedom $|N,M\rangle$ by defining
$$|N,M\rangle = \frac{1}{2\pi}\int \mathrm dk_x \mathrm dk_y  \ e^{iNqak_x} e^{iMa k_y} |k_x,k_y\rangle $$
$$|k_x,k_y\rangle = \frac{1}{2\pi} \sum_{N,M} e^{-iNqak_x} e^{-iMak_y} |N,M\rangle$$
where $(k_x,k_y)\in [-\pi/qa,\pi/qa]\times [-\pi/a,\pi/a]$, where the opposite edges of this rectangular region are identified to form a torus.  To see why this is necessary, simply observe that $|-\pi/qa,k_y\rangle=|\pi/qa,k_y\rangle$ and $|k_x,-\pi/a\rangle = |k_x, \pi/a\rangle$.
Substituting this into the Hamiltonian, we obtain after some algebra
$$ H = t \int \mathrm dk_x \mathrm dk_y \ |k_x,k_y\rangle\langle k_x,k_y| \otimes h_\mathbf k$$
$$ \mathbf h_k := \left(\sum_{\ell=0}^{q-2}|\ell+1\rangle\langle \ell| + |\ell\rangle\langle\ell+1|\right)\tag{4}$$
$$+e^{iqak_x}|0\rangle\langle q-1| + e^{-iqak_x} |q-1\rangle\langle 0|\tag{5}$$
$$+ \sum_{\ell=0}^{q-1}\underbrace{e^{i\ell aB}e^{iak_y} |\ell\rangle\langle \ell| + e^{-i\ell aB} e^{-iak_y} |\ell\rangle\langle \ell|}_{= 2\cos(\ell a B + a k_y)|\ell\rangle\langle \ell|}\tag{6}$$
This appears to be a horrifying mess, but in fact it's not so bad.  For concreteness, let $q=3$ so $h_{\mathbf k}$ is a $3\times 3$ matrix.  The terms in $h_\mathbf k$ are respectively:
$$h_{\mathbf k} = \underbrace{\pmatrix{0 & 1 & 0\\1 &0 & 1 \\ 0 & 1 & 0}}_{(4)} + \underbrace{\pmatrix{0 &0 & e^{3iak_x}\\0&0&0\\e^{-3iak_x}&0&0}}_{(5)}+\underbrace{\pmatrix{2\cos(a k_y)&0&0\\0&2\cos(aB + a k_y)&0\\0&0&2\cos(2aB + a k_y)}}_{(6)}$$
$$=\pmatrix{2\cos(a k_y)&1&e^{3iak_x}\\1&2\cos( aB + a k_y)&1\\e^{-3ia k_x}&1&2\cos(2aB + a k_y)}$$
In words, the Hamiltonian has cosines on the diagonal, $1$'s on the adjacent diagonals, and $e^{\pm iqak_x}$ in the upper right and lower left corners, respectively.  Further recalling that $aB = 2\pi p/q$, we may write
$$=\pmatrix{2\cos(a k_y)&1&e^{3iak_x}\\1&2\cos( 2\pi p/3+ a k_y)&1\\e^{-3ia k_x}&1&2\cos(4\pi p/3 + a k_y)}$$
This matrix can subsequently be diagonalized to yield the spectrum of the Hamiltonian.

As we know, from the Bloch-Floquet theorem, the free ends of a unit cell are related by a phase factor; in this case, this rule applies to which free ends of the cell?

If I understand what you're saying, the application of the translation operators $T_x^q$ and $T_y$ to an energy eigenfunction $|\psi\rangle = |k_x,k_y\rangle\otimes u_\mathbf k$ (where $u_\mathbf k$ is an eigenfunction of $h_\mathbf k$) yields
$$T_x^q|\psi\rangle = e^{iqak_x}|\psi\rangle\qquad T_y|\psi\rangle = e^{iak_y}|\psi\rangle$$

redpajama_set_name: RedPajamaStackExchange


text:
'e-commerce will co-exist with brick and mortar retail'
October 7, 2014 / 8 min read
Rahul Narvekar, CEO, IndiaRoots, shares his views on E-commerce trends.
Nibedita Roy
IndianRoots had achieved Rs 1.58 cr till October 2013 (within 4 months of its launch). Where is this figure now?
Rs 15 crore.
There was a time when you compared IndianRoots with Flipkart. Do you still hold the ground?
We are a curated niche market place with a ASP of Rs 10,000. So, we are at the premium end. No comparison with flipkart
Do you still have 2/3 revenue coming from outside India?
Why you have not introduced cash-on-delivery system till now?
We are now introducing cash of delivery and pre paid cash for Made-to-order high value designer garments.
Any plans to raise funds?
We have raised 2 rounds of funding and we have just closed a Series B with Astro Malaysia.
You are quite an expert of retail industry? Where do you see 'brick n mortar' form of retail in coming days?
Brick and Mortar will continue to dominate organised retail and e-commerce will co-exist with brick and mortar retail. India is a huge country and offline retail cannot reach everywhere. With the current consumption boom, I expect that offline and online will both continue to grow. For online, the biggest growth will come from mobile.
Where exactly do you see e-commerce down the line after two years?
The mainline will be dominated by a few players such as Amazon, Flipkart, Snapdeal and other niche players. But the growth will continue for everyone for the next 2 years.
Do you see any distinct pattern appearing in this sector?
Niche players will proliferate with few clear leaders across categories. The big 3 (Amazon, Flipkart, Snapdeal) will continue to fight for market dominance and small new categories will certainly emerge in the space.
What happened to 'Fashion and You' Venture?
It is still on and has raised another round of funding. It continues to be the leader in the flash sales category.
Your both ventures having a pattern that is tough to die down. Are you sort of anticipating extremely niche marketing in e-commerce?
Yup absolutely!
Rahul Narvekar


text:
Iran: 'We Haven't Lost a Drone'
Wine-and-Condoms Offer for Secretary Day is Off
Trump: US Warship Destroyed Iranian Drone
Search Is On for US Sailor Who Went Overboard
Woman Enters Car Dealership With $16K in Home-Made Bills
Alleged Japan Arsonist: 'They Ripped Me Off'
Man's Shock: Amputated Leg on Cigarette Packs Is His Own
Louvre Covers Sackler Family Name With Tape
World /
Historians: Hitler's Manhood Was Deformed
The Nazi leader supposedly suffered from hypospadias
Posted Feb 23, 2016 7:47 AM CST
Updated Feb 28, 2016 3:15 PM CST
This Oct. 24, 1940 file photo shows German Chancellor Adolf Hitler, right, shaking hands with Head of State of Vichy France Marshall Philippe Petain, in occupied France. (AP Photo, File)
(Newser) – A seedy British song written in 1939 poked fun at Nazi leaders' testicles, and kicked off with the line "Hitler has only got one ball." According to historians Jonathan Mayo and Emma Craigie, that's only, well, half the story. Their 2015 book Hitler's Last Day: Minute by Minute takes a look at Adolf Hitler's manhood via medical records they say they've accessed, and the two assert that Hitler suffered from "two forms of genital abnormality," per the Telegraph. The first, an undescended testicle, was said to have been confirmed by another historian in December. The second is hypospadias, "in which the urethra opens on the under side of the penis." Though hypospadias can be accompanied by other conditions, Craigie clarifies in an email to the Independent that it would be a "leap" to say the condition led to an abnormally small penis in Hitler's case, as has been reported.
The CDC—which says half-a-percent of male babies born in the US have the condition—reports that untreated hypospadias can snowball into future problems, "such as difficulty performing sexual intercourse or difficulty urinating while standing." Hitler's sex life has long been a subject of debate, with some arguing he was "repelled" by sex (ostensibly due to a fear of STDs) and others asserting he and Eva Braun enjoyed a typical sex life. In an April 2015 discussion about the book with the Gloucestershire Echo, Mayo doesn't address Hitler's penis, but he does recount other unusual facts he says he uncovered, like this: "At the same time that Hitler's body was being burned, Winston Churchill accidentally set fire to his clothing and had to be put out by a member of staff." (Another claim: Hitler suffered from "uncontrollable flatulence.")
Next on Newser: Reformists Make Big Gains in Iran Elections
smokey347
Feb 28, 2019 8:34 AM CST
still more balls than obozo.
leon redgrave
eric trump also has 1 ball that why he act alot like a woman in private
nicky karunarathna
He didn't need both testicles like the Catholic priest who were abusing, raping and rampant pedophilia of children and women. And even now he doesn't need both as he's in heaven by reading the medieval middle eastern desert donkey rode tribal primitive nomadic ultra-illiterate hallucinating Ethiopian slaves found brutal storytelling Christian Bible.


Title: Gracious Empowerment? Evaluating the impact the GRACE-network project had on the empowerment of female university students in their stance against Gender-Based Violence.
This minor thesis (click here for the complete thesis) is part of the Research Master International Development Studies at the Wageningen University and Research Centre. The objective of this minor thesis was to evaluate the GRACE-network project. This project was conducted in Irbid, Jordan. The aim of the GRACE-network project was to investigate how ICTs could be used to empower female university students in their stance against Gender-based Violence (GbV). The project was evaluated during four months of field work in Irbid, Jordan. During these four months the women participating in the project were interviewed and a focus group discussion was organized. The women, participating in the project as participants and as researchers, were asked during these interviews what empowerment meant for them, how they had experienced the GRACE-network project, if they felt empowered because of their participation in the GRACE-network project and how their stance against GbV was influenced.
To investigate if the GRACE-network project empowered the female university students in their stance against GbV the theory of De Vries was used. De Vries stated that the development of autonomy is needed for someone to become empowered enough to say "no" to the GbV directed towards them. The process described by the women involved in the project as participants, the process described by the researchers and the theory of De Vries were compared in this thesis.
The women aligned empowerment with three concepts, Self-love, Breaking Boundaries and Freedom of choice. Freedom of choice was the description used by the majority of the women, seven out of ten. The changes the women described were that they improved their Self-confidence, improved their Self-love and their Awareness of GbV, (self-)abuse and their own dreams and wishes. The researchers involved in the GRACE-network project reported an increase in Awareness and Self-love as project outcomes, not an increase in Self-confidence. All the women participating in the project stated that their level of empowerment increased because of their participation.
In their stance to GbV most of the women did not increase their empowerment enough to say 'no' to the GbV directed towards them. Two women indicated that they addressed the people who were treating them in an abusive way in a direct manner, making it clear they did not accept the violence anymore. The other six women indicated that they had become more aware of GbV and were discussing the topic with people in their environment. They were however not directly saying 'no'.
This thesis argues that a reason for this outcome could be that the researchers did not directly and consciously address the area of autonomy in the project outcomes reported. This, together with the theory used by the researchers, indicated that there was also a lack of focus on this area during the process of empowerment. This however cannot be stated with certainty and more research would be needed to investigate which role was given to autonomy in the GRACE-network project in Jordan. The outcome of this thesis is the recommendation that if the outcome of more research would be that autonomy was not given direct attention in the GRACE-network project, future GRACE-network projects should give autonomy more direct attention during the process of empowerment. By doing that the researchers may increase the already positive influence of the GRACE-network project on the lives of the women participating.
This entry was posted in Policy, Regional Development, RSO-student and tagged Autonomy, GRACE-network project by RSO-Students. Bookmark the permalink.


Three sculptures outside Hjältarnas Hus, a temporary home where sick children and their families have the opportunity to have a functioning living, 300 meters from the children's department.
"Stjärnpekare", Dog, Sculpture molded in aluminium.
"Stjärnpekare", Glove on a stick, Sculpture molded in aluminium.
"Stjärnpekare", Plastic bag, Sculpture molded in aluminium.
"Stjärnpekare", Dog sitting on an inflatable bathing ring, Sculpture molded in aluminium.


text:
package imperial.modaclouds.monitoring.datacollectors.basic;

public class ConfigurationException extends Exception {

    public ConfigurationException(String message) {
        super(message);
    }

    public ConfigurationException(String message, Exception e) {
        super(message, e);
    }

}

redpajama_set_name: RedPajamaGithub


text:
Despite brawls, no arrests at clashing protests at Ohio Statehouse
The Ohio Statehouse.
By Jake Zuckerman
Ohio Capital Journal
https://ohiocapitaljournal.com/
Clashing protesters brawl for the second time Wednesday on the Ohio Statehouse campus.
A pugilistic day of political demonstrations that included two brawls between far-right demonstrators insisting the presidential election was stolen and Black Lives Matter activists ended with no arrests.
At least two rounds of fisticuffed rumbles with an array of participants broke out at different points through the afternoon along with some more minor spats.
The Proud Boys, a radical conservative group with white nationalist ties known for violent confrontations with liberal demonstrators, tussled with BLM activists after hours of vitriolic name calling and antagonizing between the two.
Among the Proud Boys and those alongside them, firearms were everywhere. People were carrying assault style rifles, shotguns and pistols. None were seen used or brandished. One man carried a telescoping baton.
At least two people standing with BLM activists carried pistols, and one man carried a baseball bat, none of which were seen in use during the fights.
Members of the Ohio State Highway Patrol, which protects the Capitol building and campus, intervened at times to break up the physical altercations and try to keep the two groups separate or at least in peace.
OSHP Staff Lt. Craig Cvetan said Thursday the primary function of the officers on the scene were to make sure everybody stays safe, not necessarily make arrests.
"As you make arrests and try to start apprehending individuals, that takes away those trooper's ability to separate those groups," he said.
He said the officers are well aware of the arsenal of weapons around the protest.
"Ohio is an open-carry state," he said. "Certainly, we prefer for people not to have weapons on the Statehouse grounds. It makes it easier for us."
The first rumble broke out around 12:40 p.m. It was captured in video via a Statehouse News Bureau reporter.
The Columbus Police Department assisted in breaking up the fights. Repeated calls to the department seeking information on arrests or lack thereof were sent to a Public Information Officer who did not answer calls and could not accept voicemails.
A separate fight with at least about 10 people engaged broke out on the Capitol lawn at roughly 3 p.m. OSHP officers broke things up and separated the two groups.
Shortly thereafter, the Proud Boys groups left on buses, after which the situation quickly calmed down.
Gov. Mike DeWine briefly mentioned the protests at the Ohio Capitol speaking to reporters Thursday. He acknowledged there's no guarantee that Ohio's Statehouse is safe from a similar invasion. He made no mention of either the fights or the firearms that were present through the event.
Newest ▼ Oldest ▲
Win A, Answer is politically correctness has continued to blur once reasonable people. This comment has been hidden due to low approval.
Pray tell, why are conservatives called far right and antifa and blm only activists? This comment has been hidden due to low approval.
Win A
Mikey, This is our house. We expect you to keep it safe for everyone. We need a guarantee our house is safe. Second point, is, if a weapon is visible, why is it not brandishing? Brandishing a firearm or weapon is a crime which is prosecuted under penal code 417. However, merely taking out a weapon to display or show off is not a crime so long as you did not do so in an angry or threatening way. Seems to be some angry people here since a "rumble" broke out and there were two reported brawls. . Third point, is why have a Public Information Officer at expense to the City of Columbus taxpayers if they are not available? This comment has been hidden due to low approval.
2020-21 FAC/SHAC High School Basketball Results (1022 views)


text:
Q: What is the Reason why using does not work with uint8_t? I tried to modernise C++ code to C++11, converting typedef statements to using statements:
class Configuration {
    typedef uint8_t Protocols;

Into:
class Configuration {
    using Protocols = uint8_t;

Where I get this error message from Visual Studio 2012:
error C2873: 'Protocols' : symbol cannot be used in a using-declaration

What is the reason why I can not use using there?


text:
Die Islote Trumao ist eine kleine Insel vor der Loubet-Küste des Grahamlands auf der Antarktischen Halbinsel. Sie gehört zu den McConnel-Inseln in der Darbel Bay.

Die Benennung der Insel erfolgte wahrscheinlich bei der 1. Chilenischen Antarktisexpedition (1946–1947). Namensgeber ist Trumao, eine Bodenart der Andosolgruppe bestehend aus Vulkanasche, wie sie auf der Insel zu finden ist.

Weblinks
 Trumao, Islote im Composite Gazetteer of Antarctica (englisch)

Insel (Antarktika)
Insel (Südlicher Ozean)

redpajama_set_name: RedPajamaWikipedia


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Seigneur lui en tient compte. En andelsboligforening blev stiftet i forbindelse med erhvervelse af en udlejningsejendom i henhold til en frivillig aftale med udlejer. Lalu sy benar2 gak menginginkan cc ini boro2 sy mau pake. Un certificat mdical dlivr par un mdecin agr par lambassade belge. To sensory deprivation she can arrange without a flotation tank and. Penny blonde Sara brunette and Michelle redhead . Bob Graham announced a citizens petition drive Thursday for a constitutional amendment to dedicate state funding annually for the acquisition and management of critical environmental land. Ns ficamos em um hostel terrvel que no recomendo bem afastado do centro histrico . Los hay adems quienes hacen bien las cosas a pesar de la confianza de sus padres o maestros. Regular Show for being picked up as a full series but the pilot lingered online for years. Theyre like that stupid little ankle-biter dog that everyone knew in the neighborhood when they were kids. Durch die ergiebige Quelle des Westerbaches ausgeglichen werden. Windows 7 je izdanje Microsoft-ovog operativnog sistema u seriji operativnih sistema proizvedenih za koritenje na personalnim raunarima kao i na laptop-ovima netbook-ovima tablet PC-evima itd. Ive gotten real comfortable with my weaknesses. I am basically ready to sell the lot perhaps keeping one for remembrance. Akan mengangkut ke tempat pelarutan. Anglo-Saxon Freemasonry as exemplified by the grand lodges of England. Den udfordring skal lses mener han. Sonhei que ela estava do lado de fora do quarto enquanto eu dormia. Entiendo por tanto que las oyes y que lo que fundamentalmente oyes son golpes al cerrar la puerta del microondas cuando funciona golpes de cacharros en el fregadero conexin de los enchufes. 23 people lost their lives that day and in hospitals within days of the accident. Has a two-tier qualifying system FHA sets. In anderen Kulturkreisen haben Schuhe und das Tragen von Fussbekleidung eine andere Bedeutung als in den westlichen Zivilisationen. Team om ons heen onze helpers zijn bij ons gedetacheerd om ons te ondersteunen bij deze missie. They jubilantly greeted the Fhrer as he walked through a forest of banners on the rostrum accompanied with cheering of their approval to the words of Dr. We heard footsteps squeaking and sloshing in the mud outside. Bellylandded at Chelveston UK after collision. Barrels have been re-blacked recently and stock refinished. Chevrolet has a Roadside Assistance program to aid owners during an emergency and with other services.


On a winter afternoon, I entered Arbol in Nishigawa. It was cold outside, so I ordered a hot coffee and sat down at the middle table. The mochis on top of the heating stove were expanding, and the steam from the kettle fogged up the front window. Ms. Ota brought me water and a snack, and asked me, "D'ya know what this is?" I looked at the label and replied, "Oh, is that saichu?" Ms. Nakahira gave a big laugh as she made the coffee in the kitchen. Standing at the counter, Ms. Kobayashi said to me, "No, no. 'mo-na-ka', it's monaka." One of the regulars chimed in, and had a fun back and forth with the Arbol three. Soon my coffee arrived, and I slowly sipped it while enjoying the conversation around me.
While helping out at Yamaiki-ichi these three years, Arbol gradually became something I enjoyed every week. Coming to shop at our market, Ms. Kobayashi always quietly and kindly gave us the business advice of an experienced merchant. Ms. Ota would make everyone laugh with one word and liven up the market. Ms. Nakahira's friendliness would spread to those around her, even eliciting a smile from those she met for the first time. Around midday, one of the three would call out from their doorway, "Oi~! It's ready!" Thanks to them, not only were the other Yamaiki-ichi members and I able to eat warm lunches, we were also able to spend Saturdays filled with laughter.
In late March, Arbol commemorated their five years by serving zenzai to its customers. They originally planned on running their shop for only a year or two, but while serving up anything from meharizushi to dam curry, it turned into five years before they realized it. In April they will enter their sixth year, and while closing the shop on Tuesdays and Wednesdays, they'll continue their business as strong as ever. I too look forward to the things they will teach me from now on. The "flavorful Japanese" I have learned from them in these three years may not appear on the JLPT, but it is a precious tool that I do not want to forget.


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About Energy Forecast Magazine
Ed's Note
Ed's Note
FUTURE WATCH
IPP signatures are costing jobs
Eskom delays on IPP signatures are costing jobs
Tags: renewable energy, south africa, projects, eskom
The credibility of the renewable energy independent power producers (IPP) program which has received international acclaim is being threatened by ongoing delays within government departments. The delays are directly affecting the viability of projects in an advanced stage of development and are turning away foreign investors and causing the shutdown of local manufacturing plants resulting in the loss of hundreds of jobs and potentially thousands more.
This comes at a time when employment is a vital factor in lifting South Africa's economic growth rate which is projected to be marginally above 0% this year.
The three areas of the IPP programme being affected are
(i) Round 4 projects in preferred bidder status,
(ii) the Expedited Round where projects are awaiting announcement and
(iii) projects in the Small Projects IPP awaiting financial close.
In all these cases the private sector has outlaid significant capital to bring the projects to advanced stages of permitting, licensing and design but the government has missed several key deadlines to execute the required agreements or make announcements. The dates were originally communicated to the industry by the IPP office on behalf of the Department of Energy. Although the IPP program adjusts the tariff by inflation annually, delays impose costs for projects and other industry players for which there is no compensation. Project developers, manufacturers and financiers are all affected.
For projects and developers, the added burden is made up of
(i) salaries for the development staff
(ii) increased legal fees
(iii) interest on the capital already spent to date and
(iv) upward movements in project construction and supply costs where quotations have expired.
These costs create problems especially for smaller projects where salaries and legal fees make up a larger portion of the total project costs.
The delays make planning very difficult for our members and we are concerned that this may result in projects being abandoned.
Projects in preferred bidder status have spent up to R15m on securing land, legal fees, bidding and design. Abandoning a project means this money is wasted and thousands of jobs may never materialize.
There are currently 49 projects in preferred bidder status, equivalent to 2,254MW. At the current average (based on operational projects) of 11.63 jobs per MW if Eskom were not to execute the Power Purchase Agreements over 26,000 jobs would not materialize.
Delays are also causing factories to shut down. Equipment manufacturers who have already invested hundreds of millions of Rands setting up factories, have had to incur operational costs with no revenue and no foresight as to when revenue and orders may be generated. Two leading international inverter manufacturers, SMA and AEG that invested in factories in South Africa have closed their doors, one of which cited the "lack of the South African government adhering to communicated timelines" as a reason.
At least one solar module manufacturer has also shut up operations within the last three years.
Lenders who provide up to 75% of the funds to build projects as debt are also suffering. Delays cause costs in commitment fees and having to ring fence money that is not deployed builds interest.
The reasons for the delays are not clear, but it appears from sources within the energy sector that the issue lies in Eskom refusing to execute further PPAs even for projects in preferred bidder status. According to one source Eskom is refusing to spend any money on purchasing independent power despite NERSA's instruction that a certain proportion of the increases granted to Eskom must be ring-fenced and spent on IPPs.
Eskom was granted a multi-year price determination increase (MYPD3) of 8% per year from 2013-2018; this included provisions of 1.1% on average per year for IPPs. Eskom is not financially burdened by executing power purchase agreements with IPPs because all expenses are seen as a pass through cost. On the contrary, estimations are that in 2015/16 Eskom are collecting R1.6bn more from consumers than what they are paying for Renewable IPP power.
NERSA approval was granted for the Rounds 1-3 of the REIPPP program. Round 4 onwards is to be covered by MYPD4 which is yet to be approved, however an inter-governmental agreement ensures that the pass through of costs will not leave Eskom in a position of risk.
Money from energy sales continues to be collected by Eskom meaning delays in executing PPAs leaves more revenue in Eskom hands.
Despite Eskom's media statements, renewable energy has already brought huge benefits to South Africa. Through the competitive nature of the IPP program and the learning within industry, renewable energy prices are now cheaper than Eskom's average selling price. The IPP office quoted the average price of all renewable in bid window 4 as being "77c per kWh". Eskom have stated their average selling price is 83c per kWh, and this is likely to increase by more than inflation.
Eskom are correct in stating that the currently contracted IPP projects from Round 1 and 2 are expensive, however this was necessary to attract major energy participants and to create a competitive and stable environment. It would be nonsensical to discontinue the program when South Africa is about to reap the rewards of having invested in the first expensive rounds. Renewable energy IPP projects have already created 24,965 full time equivalent jobs and attracted a total foreign investment of R194.1bn.
It appears as if Eskom is holding up a strategic government initiative to protect their own financial vulnerability and that Eskom's actions may result in a lost opportunity for thousands of workers.
The Presidency and the Department of Energy have recently confirmed their support for the IPP program. However the ongoing delays, coupled with the recent comments from Eskom's Chief Executive indicating they would not enter into future power purchase agreements with IPPs, creates uncertainty and casts a shadow over what up to now has been one of government's most successful public/private sector initiatives.
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