text:
\section{Introduction}
Many optimisation problems can be formulated as searching for the best element or solution from all feasible solutions with respect to one or several criteria. While conventional optimisation algorithms like gradient-based methods are rooted in a solid mathematical formulation, they typically fail to provide satisfactory results and often stagnate in local optima~\cite{1-DE_Shift_Mechanism},\cite{2-8845065},\cite{3-10.1504/ijbic.2019.103961}.

Population-based metaheuristic algorithms are able to address this issue~\cite{4-hussain2019metaheuristic} and can be divided into three general categories, namely evolutionary, swarm-based, and physics-based approaches. Evolutionary algorithms, such as the genetic algorithm (GA)~\cite{5-GA_Main_Ref}, are inspired by biological evolutionary processes. Swarm-based algorithms, e.g.\ particle swarm optimization (PSO)~\cite{6-PSO_Main_Paper} and artificial bee colony (ABC)~\cite{7-ABC_Main_Paper}, imitate the social behaviour of animals, while physics-based algorithms, including the gravitational search algorithm (GSA)~\cite{8-rashedi2009gsa} and the mine blast algorithm (MBA)~\cite{9-SADOLLAH20132592}, are based on the laws of physics.

Human Mental Search (HMS)~\cite{11-HMS_Main_Paper} is a relatively recent population-based metaheuristic algorithm that is based on the concept of exploring the search space of online auctions. Here, mental search, a main part of the algorithm, employs Levy flight to explore the vicinity of candidate solutions. HMS has been shown to solve effectively a wide range of optimisation problems, including unimodal, multi-modal, high-dimensional, rotated, shifted, and complex functions~\cite{11-HMS_Main_Paper}, as well as various machine vision applications including multi-level thresholding~\cite{13-mousavirad2020human,13-1-ESMAEILI2021115106}, colour quantisation~\cite{13-2-10.1007/978-3-030-53956-6_12,13-3-9283370}, image segmentation~\cite{13-4-MOUSAVIRAD2020106604}, and image clustering~\cite{12-HMS_Image_Clustering}.

HMS algorithm has three main operators, mental search to explore the vicinity of candidate solutions based on a Levy flight distribution, grouping to cluster the current population to find a promising region, and movement to move candidate solutions towards the promising region. Several improvements to HMS have been recently introduced, including leveraging a random clustering strategy~\cite{14-1-8914636}, and grouping in both search and objective space~\cite{14-2-8914636}.

In the grouping phase of HMS, a clustering algorithm is employed and the cluster with the best mean objective function value is selected as the winner cluster. However, this approach has a tendency of getting stuck in a local optimum. To address this issue, in this paper, we propose a novel HMS algorithm, MCS-HMS, that is based on multi-cluster selection. Here, the best candidate solutions in each cluster have a chance of being selected. We further employ a more efficient one-step $k$-means algorithm for clustering. Extensive experiments on various benchmark functions with different characteristics show that MCS-HMS outperforms HMS as well as other population-based metaheuristic algorithms.

The remainder of the paper is organised as follows. Section~\ref{sec:HMS} describes the standard HMS algorithm. Our proposed MCS-HMS is then introduced in Section~\ref{sec:MCS-HMS}, while experimental results are given in Section~\ref{sec:Exp}. Finally, Section~\ref{sec:Concl} concludes the paper.

\section{Human Mental Search}
\label{sec:HMS}
HMS is a metaheuristic algorithm inspired by the way the human mind searches. Each candidate solution, called a bid in HMS, is initially randomly generated. HMS then proceeds, iteratively, based on three main operators, mental search, grouping, and movement.

During mental search, a number of new bids are obtained as
\begin{equation}
x^i + S ,
\end{equation}
where $x^i$ is a current bid, and
\begin{equation}
S=(2-NFE(\frac{2}{{NFE}_{\max}}))0.01\frac{u}{v^{\frac{1}{\beta}}}(x^i-x^*) ,
\end{equation}
where $NFE$ represents the number of objective function evaluations thus far, ${NFE}_{\max}$ is the maximum number of function evaluations, $\beta$ is a random integer number, and $x^*$ is the best bid found so far. $u$ and $v$ are two random numbers with normal distributions as
\begin{equation}
u{\approx}N(0,\sigma_u^2 ),~~~~~~v{\approx}N(0,\sigma_v^2 ),
\end{equation}
and
\begin{equation}
{\sigma}_{u}=(\frac{\Gamma(1+{\beta})sin(\frac{\pi\beta}{2})}{\Gamma(\frac{1+\beta}{2})\beta2^{\frac{\beta-1}{2}}})^{\frac{1}{\beta}},~~~~~~{\sigma}_{v}=1,
\end{equation}
where $\Gamma$ is a standard gamma function.

The grouping operator uses the $k$-means algorithm to cluster similar bids in the population. Then, for each cluster the mean objective function value is calculated and the cluster with the best value is selected as the winner cluster to represent a promising area in search space.

Bids in the other clusters then towards the identified promising area by
\begin{equation}
x^i_n(t+1) = x^i_n(t) + C(r W_n(t) - X^i_n(t)),
\end{equation}
where $W$ is the best solution in the promising area, $C$ is a constant, $r$ is a random number in $[0;1]$, $t$ indicates the current iteration, and subscript $n$ indicates the $n$-th element of a bid.

\section{Proposed MCS-HMS Algorithm}
\label{sec:MCS-HMS}
One of the drawbacks of the standard HMS algorithm is that selecting the cluster with the best mean objective functions does not necessarily give the best choice for the promising area because the cluster might be placed far away from the global optimum. Therefore, choosing the best cluster and its best bid may limit diversification and may cause the next generations to be misled to a less successful area. Therefore, in this paper, we introduce a novel modification of HMS to tackle this issue and find a better promising area.

Our idea is to select a few promising bids in a memory $M$ and use these. Specifically, instead of selecting the best bid in the cluster with the best mean objective function, we consider the best bid of each cluster as illustrated in Fig~\ref{fig1}. The memory keeps only the bids with the best objective function value of each cluster, and thus the length of $M$ equals the number of clusters.

\begin{figure}[h!]
\centering
\includegraphics[width=.9\columnwidth]{figures/fig1.jpg}
\caption{Multi-cluster selection process after grouping.}
\label{fig1}
\end{figure}

In the next step, we randomly select a bid from $M$ as the target for the movement operator and thus the corresponding cluster as the promising area in search space. In contrast to standard HMS, this mechanism leads to improved exploration since the target is selected from the whole search space, and can also prevent early convergence.

In addition, we address the relative inefficiency of HMS due to its application of $k$-means in the grouping process. Inspired by \cite{14-9185525}, In MCS-HMS we replace $k$-means with a one-step $k$-means algorithm. That is only a single iteration of $k$-means is conducted, leading to quicker execution.

\section{Experimental Results}
\label{sec:Exp}
For evaluation, we use the 30 CEC 2017 benchmark functions~\cite{15-CEC2017} which include unimodal (F1 to F3), multi-modal (F4 to F10), hybrid multi-modal (F11 to F20) and composite (F21 to F30) test functions. Besides comparing MCS-HMS with standard HMS, we also benchmark it against a number of other population-based metaheuristic algorithms, namely particle swarm optimisation (PSO)~\cite{6-PSO_Main_Paper}, the covariance matrix adaption-evolution strategy (CMA-ES)~\cite{18-hansen2001completely}, artificial bee colony (ABC)~\cite{7-ABC_Main_Paper}, the grey wolf optimiser (GWO)~\cite{10-GWO_Main_Paper}, moth flame optimisation (MFO)~\cite{16-MFO_Main_Paper}, and the whale optimisation algorithm (WOA)~\cite{17-WOA_Main_Paper}.

As dimensionality $D$ of the search space we use 30, 50 and 100. The employed parameters of the various algorithms are given in Table~\ref{tab1}.

\begin{table}[b]
\centering
\caption{Parameter settings of experiments.}
\label{tab1}
\begin{tabular}{llc}
\hline
algorithm & parameter & values \\
\hline
PSO & inertia constant & 1 to 0 \\
& cognitive constant & 2 \\
& social constant & 2 \\
\hline
CMA-ES & $\lambda$ & 50 \\
\hline
ABC & limit & $n_e$  × $D$ \\
\hline
GWO & no parameters \\
\hline
MFO & no parameters \\
\hline
WOA & $b$ & 1 \\
\hline
HMS & number of clusters & 5 \\
& minimum mental processes & 2 \\
& maximum mental processes & 5 \\
& $C$ & 1 \\
\hline
MCS-HMS & same settings as HMS \\
\hline
\end{tabular}
\end{table}

\begin{table*}[p]
\centering
\caption{Experimental results for $D=30$.}
\label{tab5}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 7.16E+07 & 2.11E+10 & 2.91E+07 & 1.75E+09 & 8.80E+09 & 8.43E+07 & 4.55E+06 & 1.37E+04 \\
F2 & 8.13E+11 & 3.33E+42 & 3.93E+41 & 4.09E+28 & 1.16E+39 & 1.70E+32 & 4.19E+24 & 6.08E+20 \\
F3 & 3.24E+02 & 2.11E+05 & 3.68E+05 & 3.94E+04 & 1.08E+05 & 1.99E+05 & 1.09E+04 & 1.33E+04 \\
F4 & 7.54E+01 & 3.62E+03 & 1.19E+02 & 1.86E+02 & 6.64E+02 & 2.02E+02 & 1.21E+02 & 9.96E+01 \\
F5 & 2.12E+02 & 3.31E+02 & 2.39E+02 & 9.59E+01 & 1.98E+02 & 2.78E+02 & 1.19E+02 & 9.64E+01 \\
F6 & 5.21E+01 & 6.30E+01 & 5.20E+00 & 7.58E+00 & 3.04E+01 & 6.82E+01 & 1.01E+01 & 3.52E+00 \\
F7 & 2.47E+02 & 1.81E+02 & 2.78E+02 & 1.53E+02 & 3.83E+02 & 5.06E+02 & 1.46E+02 & 1.30E+02 \\
F8 & 1.57E+02 & 2.69E+02 & 2.47E+02 & 8.14E+01 & 1.90E+02 & 2.31E+02 & 1.07E+02 & 9.82E+01 \\
F9 & 4.64E+03 & 1.66E+03 & 3.08E+03 & 9.48E+02 & 5.76E+03 & 7.99E+03 & 1.39E+03 & 7.98E+02 \\
F10 & 4.83E+03 & 7.03E+03 & 8.17E+03 & 3.22E+03 & 4.63E+03 & 5.46E+03 & 3.71E+03 & 3.50E+03 \\
F11 & 1.53E+02 & 1.67E+04 & 9.40E+03 & 8.31E+02 & 3.64E+03 & 1.98E+03 & 2.28E+02 & 1.42E+02 \\
F12 & 1.45E+07 & 4.32E+09 & 4.97E+08 & 6.48E+07 & 2.23E+08 & 8.52E+07 & 7.50E+06 & 2.86E+06 \\
F13 & 1.87E+06 & 3.89E+09 & 2.57E+06 & 1.23E+07 & 4.86E+07 & 2.07E+05 & 4.54E+04 & 3.23E+04 \\
F14 & 1.41E+04 & 5.45E+06 & 2.98E+05 & 2.57E+05 & 1.30E+05 & 2.39E+06 & 4.27E+04 & 3.91E+04 \\
F15 & 1.56E+05 & 4.90E+08 & 1.40E+06 & 1.58E+06 & 4.53E+04 & 1.26E+05 & 7.90E+03 & 5.82E+03 \\
F16 & 1.26E+03 & 3.25E+03 & 2.34E+03 & 9.59E+02 & 1.57E+03 & 2.06E+03 & 9.13E+02 & 1.08E+03 \\
F17 & 5.43E+02 & 1.91E+03 & 1.14E+03 & 2.66E+02 & 7.10E+02 & 9.24E+02 & 4.28E+02 & 3.69E+02 \\
F18 & 2.17E+05 & 3.35E+07 & 1.46E+07 & 1.13E+06 & 3.06E+06 & 4.17E+06 & 5.33E+05 & 4.73E+05 \\
F19 & 5.96E+05 & 4.42E+08 & 8.37E+04 & 8.96E+05 & 9.48E+06 & 4.38E+06 & 1.35E+04 & 1.44E+04 \\
F20 & 5.96E+02 & 8.25E+02 & 9.94E+02 & 3.85E+02 & 6.57E+02 & 8.42E+02 & 3.28E+02 & 3.38E+02 \\
F21 & 4.05E+02 & 5.51E+02 & 4.46E+02 & 2.84E+02 & 3.97E+02 & 4.79E+02 & 3.20E+02 & 3.04E+02 \\
F22 & 3.13E+03 & 7.49E+03 & 8.25E+03 & 2.07E+03 & 4.10E+03 & 5.14E+03 & 3.73E+03 & 2.41E+03 \\
F23 & 8.44E+02 & 7.46E+02 & 6.07E+02 & 4.54E+02 & 5.18E+02 & 7.37E+02 & 5.04E+02 & 4.76E+02 \\
F24 & 8.17E+02 & 7.71E+02 & 6.83E+02 & 5.12E+02 & 5.78E+02 & 8.13E+02 & 5.85E+02 & 5.74E+02 \\
F25 & 3.97E+02 & 1.40E+03 & 4.20E+02 & 4.74E+02 & 8.20E+02 & 4.96E+02 & 3.98E+02 & 3.89E+02 \\
F26 & 2.16E+03 & 5.62E+03 & 3.15E+03 & 2.07E+03 & 3.06E+03 & 5.21E+03 & 2.49E+03 & 2.23E+03 \\
F27 & 5.19E+02 & 6.99E+02 & 5.00E+02 & 5.46E+02 & 5.46E+02 & 7.24E+02 & 5.19E+02 & 5.13E+02 \\
F28 & 4.44E+02 & 3.80E+03 & 5.00E+02 & 5.82E+02 & 1.36E+03 & 5.73E+02 & 5.15E+02 & 4.84E+02 \\
F29 & 1.40E+03 & 2.83E+03 & 2.02E+03 & 9.50E+02 & 1.19E+03 & 2.17E+03 & 9.06E+02 & 8.39E+02 \\
F30 & 1.88E+06 & 4.88E+08 & 4.99E+05 & 6.71E+06 & 1.08E+06 & 2.04E+07 & 3.38E+04 & 1.41E+04 \\
\hline
\end{tabular}
\end{table*}

\begin{table*}[p]
\centering
\caption{Experimental results for $D=50$.}
\label{tab6}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 2.57E+08 & 4.64E+10 & 2.81E+09 & 5.59E+09 & 3.75E+10 & 4.96E+08 & 2.99E+08 & 5.03E+05 \\
F2 & 2.66E+25 & 5.07E+79 & 5.48E+80 & 7.96E+54 & 6.94E+76 & 1.25E+70 & 1.42E+53 & 1.22E+45 \\
F3 & 9.48E+03 & 3.75E+05 & 7.71E+05 & 9.42E+04 & 1.67E+05 & 1.85E+05 & 3.48E+04 & 3.71E+04 \\
F4 & 1.62E+02 & 7.61E+03 & 3.24E+03 & 7.14E+02 & 4.06E+03 & 5.30E+02 & 3.04E+02 & 2.20E+02 \\
F5 & 3.94E+02 & 3.08E+02 & 5.49E+02 & 2.07E+02 & 4.62E+02 & 4.70E+02 & 2.38E+02 & 2.27E+02 \\
F6 & 6.95E+01 & 6.80E+01 & 4.93E+01 & 1.76E+01 & 4.78E+01 & 8.30E+01 & 1.87E+01 & 1.16E+01 \\
F7 & 5.01E+02 & 2.20E+02 & 6.50E+02 & 3.59E+02 & 1.13E+03 & 1.06E+03 & 3.00E+02 & 3.31E+02 \\
F8 & 4.15E+02 & 5.17E+02 & 5.51E+02 & 2.19E+02 & 4.79E+02 & 4.49E+02 & 2.54E+02 & 2.38E+02 \\
F9 & 2.47E+04 & 1.35E+04 & 4.27E+04 & 8.16E+03 & 1.71E+04 & 2.76E+04 & 6.46E+03 & 6.91E+03 \\
F10 & 8.82E+03 & 1.32E+04 & 1.50E+04 & 6.50E+03 & 7.47E+03 & 1.01E+04 & 7.53E+03 & 6.73E+03 \\
F11 & 3.59E+02 & 6.45E+04 & 5.59E+04 & 2.97E+03 & 1.28E+04 & 1.17E+03 & 8.25E+02 & 5.58E+02 \\
F12 & 1.07E+08 & 2.24E+10 & 1.05E+10 & 5.44E+08 & 4.63E+09 & 5.79E+08 & 1.07E+08 & 1.94E+07 \\
F13 & 1.47E+07 & 1.29E+10 & 6.17E+07 & 1.05E+08 & 8.80E+08 & 5.55E+06 & 1.17E+05 & 2.51E+04 \\
F14 & 1.17E+05 & 2.17E+07 & 4.29E+06 & 9.45E+05 & 1.08E+06 & 1.97E+06 & 2.85E+05 & 1.68E+05 \\
F15 & 3.05E+06 & 2.51E+09 & 1.28E+07 & 1.56E+07 & 8.65E+06 & 1.08E+06 & 3.24E+04 & 1.43E+04 \\
F16 & 2.15E+03 & 5.37E+03 & 5.11E+03 & 1.46E+03 & 2.73E+03 & 3.81E+03 & 2.10E+03 & 2.05E+03 \\
F17 & 1.68E+03 & 1.05E+03 & 3.12E+03 & 1.06E+03 & 2.30E+03 & 2.55E+03 & 1.51E+03 & 1.39E+03 \\
F18 & 1.50E+06 & 1.27E+08 & 6.83E+07 & 5.10E+06 & 2.96E+06 & 1.41E+07 & 1.78E+06 & 1.29E+06 \\
F19 & 2.78E+06 & 1.10E+09 & 2.55E+04 & 3.16E+06 & 5.93E+07 & 5.13E+06 & 3.12E+04 & 1.44E+04 \\
F20 & 1.30E+03 & 1.62E+03 & 2.50E+03 & 8.97E+02 & 1.40E+03 & 1.70E+03 & 1.10E+03 & 1.08E+03 \\
F21 & 6.61E+02 & 7.99E+02 & 7.52E+02 & 3.94E+02 & 6.41E+02 & 8.43E+02 & 4.91E+02 & 4.71E+02 \\
F22 & 9.09E+03 & 1.43E+04 & 1.51E+04 & 6.78E+03 & 8.28E+03 & 1.01E+04 & 7.85E+03 & 6.99E+03 \\
F23 & 1.55E+03 & 1.14E+03 & 9.78E+02 & 6.64E+02 & 8.41E+02 & 1.35E+03 & 8.14E+02 & 7.58E+02 \\
F24 & 1.21E+03 & 1.14E+03 & 1.09E+03 & 7.24E+02 & 8.16E+02 & 1.30E+03 & 8.78E+02 & 8.30E+02 \\
F25 & 5.12E+02 & 2.72E+03 & 2.22E+03 & 1.06E+03 & 3.08E+03 & 9.11E+02 & 6.34E+02 & 5.65E+02 \\
F26 & 6.09E+03 & 8.76E+03 & 6.33E+03 & 3.95E+03 & 5.82E+03 & 1.05E+04 & 5.47E+03 & 4.60E+03 \\
F27 & 1.07E+03 & 1.12E+03 & 5.00E+02 & 8.99E+02 & 8.75E+02 & 1.69E+03 & 7.22E+02 & 6.95E+02 \\
F28 & 4.97E+02 & 6.77E+03 & 5.00E+02 & 1.48E+03 & 5.11E+03 & 1.20E+03 & 7.09E+02 & 5.87E+02 \\
F29 & 2.55E+03 & 1.04E+04 & 6.25E+03 & 1.62E+03 & 2.31E+03 & 4.85E+03 & 1.67E+03 & 1.31E+03 \\
F30 & 5.58E+07 & 2.43E+09 & 3.38E+08 & 1.09E+08 & 1.29E+08 & 1.35E+08 & 2.31E+06 & 1.51E+06 \\
\hline
\end{tabular}
\end{table*}

\begin{table*}[t!]
\centering
\caption{Experimental results for $D=100$.}
\label{tab7}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 1.07E+09 & 4.20E+10 & 3.61E+11 & 4.25E+10 & 1.24E+11 & 1.98E+07 & 1.51E+10 & 2.89E+07 \\
F2 & 8.45E+71 & 1.04E+166 & 2.96E+182 & 4.30E+133 & 7.99E+159 & 1.15E+148 & 6.53E+134 & 2.10E+109 \\
F3 & 2.36E+05 & 8.50E+05 & 2.22E+06 & 2.45E+05 & 6.55E+05 & 7.55E+05 & 1.14E+05 & 1.51E+05 \\
F4 & 3.87E+02 & 1.71E+04 & 1.53E+05 & 3.36E+03 & 2.38E+04 & 6.69E+02 & 2.01E+03 & 4.34E+02 \\
F5 & 1.09E+03 & 1.07E+03 & 2.03E+03 & 6.41E+02 & 1.20E+03 & 9.25E+02 & 6.78E+02 & 6.48E+02 \\
F6 & 8.60E+01 & 1.44E+01 & 1.39E+02 & 3.65E+01 & 7.32E+01 & 7.93E+01 & 3.32E+01 & 2.24E+01 \\
F7 & 1.25E+03 & 8.65E+02 & 9.19E+03 & 1.25E+03 & 4.04E+03 & 2.57E+03 & 9.67E+02 & 1.13E+03 \\
F8 & 1.20E+03 & 1.27E+03 & 2.08E+03 & 6.33E+02 & 1.27E+03 & 1.12E+03 & 7.14E+02 & 7.20E+02 \\
F9 & 6.32E+04 & 3.25E+04 & 1.93E+05 & 3.27E+04 & 4.21E+04 & 3.54E+04 & 2.22E+04 & 2.05E+04 \\
F10 & 2.20E+04 & 3.04E+04 & 3.26E+04 & 1.57E+04 & 1.68E+04 & 1.95E+04 & 1.79E+04 & 1.60E+04 \\
F11 & 2.44E+03 & 4.51E+05 & 8.88E+05 & 5.33E+04 & 1.32E+05 & 1.17E+04 & 1.35E+04 & 1.08E+04 \\
F12 & 8.27E+08 & 5.27E+10 & 1.19E+11 & 7.40E+09 & 3.48E+10 & 7.23E+08 & 1.52E+09 & 1.71E+08 \\
F13 & 5.08E+07 & 1.16E+10 & 3.13E+09 & 4.15E+08 & 4.43E+09 & 6.20E+04 & 3.78E+07 & 3.73E+04 \\
F14 & 1.57E+06 & 1.15E+08 & 8.61E+07 & 4.95E+06 & 8.14E+06 & 1.45E+06 & 2.07E+06 & 1.58E+06 \\
F15 & 1.52E+07 & 5.73E+09 & 5.70E+08 & 1.02E+08 & 1.81E+09 & 6.21E+04 & 5.00E+05 & 1.24E+04 \\
F16 & 5.85E+03 & 1.20E+04 & 1.52E+04 & 4.42E+03 & 6.36E+03 & 8.43E+03 & 6.06E+03 & 5.17E+03 \\
F17 & 4.32E+03 & 4.22E+04 & 2.97E+04 & 3.54E+03 & 6.44E+03 & 5.47E+03 & 5.25E+03 & 3.98E+03 \\
F18 & 3.18E+06 & 1.39E+08 & 2.86E+08 & 4.83E+06 & 1.63E+07 & 1.88E+06 & 5.67E+06 & 3.21E+06 \\
F19 & 2.67E+07 & 4.44E+09 & 7.29E+06 & 1.15E+08 & 7.69E+08 & 1.33E+07 & 1.54E+06 & 1.44E+04 \\
F20 & 3.77E+03 & 5.14E+03 & 6.57E+03 & 3.28E+03 & 3.78E+03 & 4.23E+03 & 3.65E+03 & 3.42E+03 \\
F21 & 1.69E+03 & 1.44E+03 & 2.56E+03 & 8.69E+02 & 1.55E+03 & 1.79E+03 & 1.10E+03 & 1.05E+03 \\
F22 & 2.44E+04 & 3.11E+04 & 3.34E+04 & 1.74E+04 & 1.84E+04 & 2.17E+04 & 1.99E+04 & 1.73E+04 \\
F23 & 2.91E+03 & 1.84E+03 & 3.16E+03 & 1.23E+03 & 1.49E+03 & 2.44E+03 & 1.41E+03 & 1.12E+03 \\
F24 & 3.04E+03 & 2.44E+03 & 5.16E+03 & 1.75E+03 & 1.94E+03 & 3.55E+03 & 1.95E+03 & 1.67E+03 \\
F25 & 9.45E+02 & 8.27E+03 & 8.55E+04 & 3.42E+03 & 1.12E+04 & 1.13E+03 & 1.59E+03 & 1.01E+03 \\
F26 & 1.71E+04 & 1.93E+04 & 4.96E+04 & 1.22E+04 & 1.53E+04 & 2.82E+04 & 1.58E+04 & 1.28E+04 \\
F27 & 6.43E+02 & 2.00E+03 & 5.00E+02 & 1.33E+03 & 1.28E+03 & 2.27E+03 & 8.75E+02 & 7.95E+02 \\
F28 & 6.38E+02 & 1.91E+04 & 5.00E+02 & 4.73E+03 & 1.62E+04 & 9.13E+02 & 2.37E+03 & 3.18E+03 \\
F29 & 6.94E+03 & 1.34E+04 & 6.10E+04 & 5.28E+03 & 8.22E+03 & 1.11E+04 & 5.04E+03 & 4.30E+03 \\
F30 & 1.07E+08 & 1.02E+10 & 3.27E+09 & 1.03E+09 & 2.33E+09 & 1.83E+08 & 1.02E+07 & 8.14E+04 \\
\hline
\end{tabular}
\end{table*}

Since the employed methods are stochastic, we run all algorithms 25 times and report the mean over these runs in terms of the difference between the optimal value and that returned by an algorithm. The obtained results are given in Tables~\ref{tab5}, \ref{tab6}, and~\ref{tab7} for $D=30$, $D=50$ and $D=100$, respectively, and show impressively that MCS-HMS not only provides superior performance compared to standard HMS but that it also outperforms all the other algorithms.

Its superiority compared to standard HMS is further illustrated in Figs.~\ref{fig2}, \ref{fig3}, and~\ref{fig4} which compared the rankings of HMS and MCS-HMS. From there, we can see that for the vast majority of cases, MCS-HMS is ranked top or second and that in particular, MCS-HMS gives the best result for 24, 25 and 27 functions for $D=30$, $D=50$ and $D=100$, respectively.

\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/30.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=30$.}
\label{fig2}
\end{figure*}
\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/50.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=50$.}
\label{fig3}
\end{figure*}

\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/100.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=100$.}
\label{fig4}
\end{figure*}

\begin{table}[b!]
\centering
\caption{Ranking results for $D=30$.}
\label{tab2}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.73         & 1         & 8          & 1.95               \\
CMA-ES  & 7.40         & 4         & 8          & 1.08             \\
ABC     & 5.50         & 1         & 8          & 1.91               \\
GWO     & 3.37         & 1         & 7          & 1.92               \\
MFO     & 5.30         & 3         & 7          & 1.27               \\
WOA     & 6.20         & 3         & 8          & 1.17               \\
HMS     & 2.77         & 1         & 4          & 0.92               \\
MCS-HMS & 1.73         & 1         & 3          & 0.63               \\
\hline
\end{tabular}
\end{table}

Tables~\ref{tab2}, \ref{tab3}, and~\ref{tab4} show, for each algorithm, the average rank, best rank, worst rank and standard deviation over the 30 benchmark functions for $D=30$, $D=50$ and $D=100$, respectively.

\begin{table}[b!]
\centering
\caption{Ranking results for $D=50$.}
\label{tab3}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.83         & 1         & 8          & 2.02               \\
CMA-ES  & 6.70         & 1         & 8          & 1.86             \\
ABC     & 6.23         & 1         & 8          & 1.86               \\
GWO     & 3.27         & 1         & 7          & 1.95               \\
MFO     & 5.30         & 2         & 8          & 1.44               \\
WOA     & 5.93         & 3         & 8          & 1.46               \\
HMS     & 2.87         & 1         & 4          & 0.67               \\
MCS-HMS & 1.87         & 1         & 3          & 0.67               \\
\hline
\end{tabular}
\label{tab3}
\end{table}

\begin{table}[b!]
\centering
\caption{Ranking results for $D=100$.}
\label{tab4}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.90         & 1         & 7          & 1.90               \\
CMA-ES  & 6.23         & 1         & 8          & 1.87               \\
ABC     & 7.13         & 1         & 8          & 1.93           \\
GWO     & 3.47         & 1         & 6          & 1.84               \\
MFO     & 5.60         & 3         & 7          & 1.31               \\
WOA     & 4.43         & 1         & 8          & 1.94               \\
HMS     & 3.30         & 1         & 5          & 0.94               \\
MCS-HMS & 1.93         & 1         & 5          & 0.89               \\
\hline
\end{tabular}
\label{tab4}
\end{table}

As can be seen from there, MCS-HMS clearly yields the best performance for all dimensionalities, and by a significant margin. Table~\ref{tab8} further illustrates this by a pairwise comparison of MCS-HMS with all other methods. It is evident from there that in the large majority of cases, MCS-HMS outperforms the other approaches.

 \begin{table}[t!]
\centering
\caption{Pairwise comparisons between MCS-HMS and the other algorithms.}
\label{tab8}
\begin{tabular}{l|cc|cc|cc}
\hline
& \multicolumn{2}{c|}{$D=30$} & \multicolumn{2}{c|}{$D=50$} & \multicolumn{2}{c}{$D=100$} \\
MCS-HMS vs. & better & worse & better & worse & better & worse \\
\hline
PSO & 23 & 7 & 23 & 7 & 22 & 8 \\
CMA-ES & 30 & 0 & 28 & 2 & 28 & 2 \\
ABC & 29 & 1 & 28 & 2 & 28 & 2 \\
GWO & 20 & 10 & 19 & 11 & 22 & 8 \\
MFO & 30 & 0 & 29 & 1 & 30 & 0 \\
WOA & 30 & 0 & 30 & 0 & 26 & 4 \\
HMS & 26 & 4 & 27 & 3 & 26 & 4 \\
\hline
\end{tabular}
\end{table}

Last not least, we conduct a two-sided Wilcoxon signed rank test to see if the differences between MCS-HMS and its opponents are statistically significant and give the results in Table~\ref{tab9}. With all $p$-value below 0.05, it is apparent that MCS-HMS statistically outperforms all other algorithms.

\begin{table}[t!]
\centering
\caption{Results ($p$-values) of two-sided Wilcoxon signed test.}
\label{tab9}
\begin{tabular}{lccc}
\hline
& $D=30$  & $D=50$ & $D=100$ \\
\hline
MCS-HMS vs.\ PSO & 0.0148 & 0.0034 & 0.0132 \\
MCS-HMS vs.\ CMA-ES & 1.73E-06 & 4.73E-06 & 2.35E-06 \\
MCS-HMS vs.\ ABC & 2.13E-06 & 2.88E-06 & 4.29E-06 \\
MCS-HMS vs.\ GWO & 0.0082 & 0.0057 & 8.31E-04 \\
MCS-HMS vs.\ MFO & 1.73E-06 & 1.92E-06 & 1.73E-06 \\
MCS-HMS vs.\ WOA & 1.73E-06 & 1.73E-06 & 0.003 \\
MCS-HMS vs.\ HMS & 5.29E-04 & 1.15E-04 & 7.51E-05 \\
\hline
\end{tabular}
\end{table}

\section{Conclusions}
\label{sec:Concl}
In this paper, we have introduced MCS-HMS, an enhanced version of the human mental search (HMS) optimisation algorithm. MCS-HMS employs a multi-cluster strategy in HMS's grouping phase which allows for improved exploration ability, while also using a one-step $k$-means algorithm for clustering to speed up the algorithm's execution. Based on a set of experiments carried out on the CEC 2017 test functions, we show that MCS-HMS not only significantly improves the standard HMS algorithm but that it also outperforms a number of other population-based metaheuristics.

\balance

\bibliographystyle{IEEEtran}

\section{Introduction}
Many optimisation problems can be formulated as searching for the best element or solution from all feasible solutions with respect to one or several criteria. While conventional optimisation algorithms like gradient-based methods are rooted in a solid mathematical formulation, they typically fail to provide satisfactory results and often stagnate in local optima~\cite{1-DE_Shift_Mechanism},\cite{2-8845065},\cite{3-10.1504/ijbic.2019.103961}.

Population-based metaheuristic algorithms are able to address this issue~\cite{4-hussain2019metaheuristic} and can be divided into three general categories, namely evolutionary, swarm-based, and physics-based approaches. Evolutionary algorithms, such as the genetic algorithm (GA)~\cite{5-GA_Main_Ref}, are inspired by biological evolutionary processes. Swarm-based algorithms, e.g.\ particle swarm optimization (PSO)~\cite{6-PSO_Main_Paper} and artificial bee colony (ABC)~\cite{7-ABC_Main_Paper}, imitate the social behaviour of animals, while physics-based algorithms, including the gravitational search algorithm (GSA)~\cite{8-rashedi2009gsa} and the mine blast algorithm (MBA)~\cite{9-SADOLLAH20132592}, are based on the laws of physics.

Human Mental Search (HMS)~\cite{11-HMS_Main_Paper} is a relatively recent population-based metaheuristic algorithm that is based on the concept of exploring the search space of online auctions. Here, mental search, a main part of the algorithm, employs Levy flight to explore the vicinity of candidate solutions. HMS has been shown to solve effectively a wide range of optimisation problems, including unimodal, multi-modal, high-dimensional, rotated, shifted, and complex functions~\cite{11-HMS_Main_Paper}, as well as various machine vision applications including multi-level thresholding~\cite{13-mousavirad2020human,13-1-ESMAEILI2021115106}, colour quantisation~\cite{13-2-10.1007/978-3-030-53956-6_12,13-3-9283370}, image segmentation~\cite{13-4-MOUSAVIRAD2020106604}, and image clustering~\cite{12-HMS_Image_Clustering}.

HMS algorithm has three main operators, mental search to explore the vicinity of candidate solutions based on a Levy flight distribution, grouping to cluster the current population to find a promising region, and movement to move candidate solutions towards the promising region. Several improvements to HMS have been recently introduced, including leveraging a random clustering strategy~\cite{14-1-8914636}, and grouping in both search and objective space~\cite{14-2-8914636}.

In the grouping phase of HMS, a clustering algorithm is employed and the cluster with the best mean objective function value is selected as the winner cluster. However, this approach has a tendency of getting stuck in a local optimum. To address this issue, in this paper, we propose a novel HMS algorithm, MCS-HMS, that is based on multi-cluster selection. Here, the best candidate solutions in each cluster have a chance of being selected. We further employ a more efficient one-step $k$-means algorithm for clustering. Extensive experiments on various benchmark functions with different characteristics show that MCS-HMS outperforms HMS as well as other population-based metaheuristic algorithms.

The remainder of the paper is organised as follows. Section~\ref{sec:HMS} describes the standard HMS algorithm. Our proposed MCS-HMS is then introduced in Section~\ref{sec:MCS-HMS}, while experimental results are given in Section~\ref{sec:Exp}. Finally, Section~\ref{sec:Concl} concludes the paper.

\section{Human Mental Search}
\label{sec:HMS}
HMS is a metaheuristic algorithm inspired by the way the human mind searches. Each candidate solution, called a bid in HMS, is initially randomly generated. HMS then proceeds, iteratively, based on three main operators, mental search, grouping, and movement.

During mental search, a number of new bids are obtained as
\begin{equation}
x^i + S ,
\end{equation}
where $x^i$ is a current bid, and
\begin{equation}
S=(2-NFE(\frac{2}{{NFE}_{\max}}))0.01\frac{u}{v^{\frac{1}{\beta}}}(x^i-x^*) ,
\end{equation}
where $NFE$ represents the number of objective function evaluations thus far, ${NFE}_{\max}$ is the maximum number of function evaluations, $\beta$ is a random integer number, and $x^*$ is the best bid found so far. $u$ and $v$ are two random numbers with normal distributions as
\begin{equation}
u{\approx}N(0,\sigma_u^2 ),~~~~~~v{\approx}N(0,\sigma_v^2 ),
\end{equation}
and
\begin{equation}
{\sigma}_{u}=(\frac{\Gamma(1+{\beta})sin(\frac{\pi\beta}{2})}{\Gamma(\frac{1+\beta}{2})\beta2^{\frac{\beta-1}{2}}})^{\frac{1}{\beta}},~~~~~~{\sigma}_{v}=1,
\end{equation}
where $\Gamma$ is a standard gamma function.

The grouping operator uses the $k$-means algorithm to cluster similar bids in the population. Then, for each cluster the mean objective function value is calculated and the cluster with the best value is selected as the winner cluster to represent a promising area in search space.

Bids in the other clusters then towards the identified promising area by
\begin{equation}
x^i_n(t+1) = x^i_n(t) + C(r W_n(t) - X^i_n(t)),
\end{equation}
where $W$ is the best solution in the promising area, $C$ is a constant, $r$ is a random number in $[0;1]$, $t$ indicates the current iteration, and subscript $n$ indicates the $n$-th element of a bid.

\section{Proposed MCS-HMS Algorithm}
\label{sec:MCS-HMS}
One of the drawbacks of the standard HMS algorithm is that selecting the cluster with the best mean objective functions does not necessarily give the best choice for the promising area because the cluster might be placed far away from the global optimum. Therefore, choosing the best cluster and its best bid may limit diversification and may cause the next generations to be misled to a less successful area. Therefore, in this paper, we introduce a novel modification of HMS to tackle this issue and find a better promising area.

Our idea is to select a few promising bids in a memory $M$ and use these. Specifically, instead of selecting the best bid in the cluster with the best mean objective function, we consider the best bid of each cluster as illustrated in Fig~\ref{fig1}. The memory keeps only the bids with the best objective function value of each cluster, and thus the length of $M$ equals the number of clusters.

\begin{figure}[h!]
\centering
\includegraphics[width=.9\columnwidth]{figures/fig1.jpg}
\caption{Multi-cluster selection process after grouping.}
\label{fig1}
\end{figure}

In the next step, we randomly select a bid from $M$ as the target for the movement operator and thus the corresponding cluster as the promising area in search space. In contrast to standard HMS, this mechanism leads to improved exploration since the target is selected from the whole search space, and can also prevent early convergence.

In addition, we address the relative inefficiency of HMS due to its application of $k$-means in the grouping process. Inspired by \cite{14-9185525}, In MCS-HMS we replace $k$-means with a one-step $k$-means algorithm. That is only a single iteration of $k$-means is conducted, leading to quicker execution.

\section{Experimental Results}
\label{sec:Exp}
For evaluation, we use the 30 CEC 2017 benchmark functions~\cite{15-CEC2017} which include unimodal (F1 to F3), multi-modal (F4 to F10), hybrid multi-modal (F11 to F20) and composite (F21 to F30) test functions. Besides comparing MCS-HMS with standard HMS, we also benchmark it against a number of other population-based metaheuristic algorithms, namely particle swarm optimisation (PSO)~\cite{6-PSO_Main_Paper}, the covariance matrix adaption-evolution strategy (CMA-ES)~\cite{18-hansen2001completely}, artificial bee colony (ABC)~\cite{7-ABC_Main_Paper}, the grey wolf optimiser (GWO)~\cite{10-GWO_Main_Paper}, moth flame optimisation (MFO)~\cite{16-MFO_Main_Paper}, and the whale optimisation algorithm (WOA)~\cite{17-WOA_Main_Paper}.

As dimensionality $D$ of the search space we use 30, 50 and 100. The employed parameters of the various algorithms are given in Table~\ref{tab1}.

\begin{table}[b]
\centering
\caption{Parameter settings of experiments.}
\label{tab1}
\begin{tabular}{llc}
\hline
algorithm & parameter & values \\
\hline
PSO & inertia constant & 1 to 0 \\
& cognitive constant & 2 \\
& social constant & 2 \\
\hline
CMA-ES & $\lambda$ & 50 \\
\hline
ABC & limit & $n_e$  × $D$ \\
\hline
GWO & no parameters \\
\hline
MFO & no parameters \\
\hline
WOA & $b$ & 1 \\
\hline
HMS & number of clusters & 5 \\
& minimum mental processes & 2 \\
& maximum mental processes & 5 \\
& $C$ & 1 \\
\hline
MCS-HMS & same settings as HMS \\
\hline
\end{tabular}
\end{table}

\begin{table*}[p]
\centering
\caption{Experimental results for $D=30$.}
\label{tab5}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 7.16E+07 & 2.11E+10 & 2.91E+07 & 1.75E+09 & 8.80E+09 & 8.43E+07 & 4.55E+06 & 1.37E+04 \\
F2 & 8.13E+11 & 3.33E+42 & 3.93E+41 & 4.09E+28 & 1.16E+39 & 1.70E+32 & 4.19E+24 & 6.08E+20 \\
F3 & 3.24E+02 & 2.11E+05 & 3.68E+05 & 3.94E+04 & 1.08E+05 & 1.99E+05 & 1.09E+04 & 1.33E+04 \\
F4 & 7.54E+01 & 3.62E+03 & 1.19E+02 & 1.86E+02 & 6.64E+02 & 2.02E+02 & 1.21E+02 & 9.96E+01 \\
F5 & 2.12E+02 & 3.31E+02 & 2.39E+02 & 9.59E+01 & 1.98E+02 & 2.78E+02 & 1.19E+02 & 9.64E+01 \\
F6 & 5.21E+01 & 6.30E+01 & 5.20E+00 & 7.58E+00 & 3.04E+01 & 6.82E+01 & 1.01E+01 & 3.52E+00 \\
F7 & 2.47E+02 & 1.81E+02 & 2.78E+02 & 1.53E+02 & 3.83E+02 & 5.06E+02 & 1.46E+02 & 1.30E+02 \\
F8 & 1.57E+02 & 2.69E+02 & 2.47E+02 & 8.14E+01 & 1.90E+02 & 2.31E+02 & 1.07E+02 & 9.82E+01 \\
F9 & 4.64E+03 & 1.66E+03 & 3.08E+03 & 9.48E+02 & 5.76E+03 & 7.99E+03 & 1.39E+03 & 7.98E+02 \\
F10 & 4.83E+03 & 7.03E+03 & 8.17E+03 & 3.22E+03 & 4.63E+03 & 5.46E+03 & 3.71E+03 & 3.50E+03 \\
F11 & 1.53E+02 & 1.67E+04 & 9.40E+03 & 8.31E+02 & 3.64E+03 & 1.98E+03 & 2.28E+02 & 1.42E+02 \\
F12 & 1.45E+07 & 4.32E+09 & 4.97E+08 & 6.48E+07 & 2.23E+08 & 8.52E+07 & 7.50E+06 & 2.86E+06 \\
F13 & 1.87E+06 & 3.89E+09 & 2.57E+06 & 1.23E+07 & 4.86E+07 & 2.07E+05 & 4.54E+04 & 3.23E+04 \\
F14 & 1.41E+04 & 5.45E+06 & 2.98E+05 & 2.57E+05 & 1.30E+05 & 2.39E+06 & 4.27E+04 & 3.91E+04 \\
F15 & 1.56E+05 & 4.90E+08 & 1.40E+06 & 1.58E+06 & 4.53E+04 & 1.26E+05 & 7.90E+03 & 5.82E+03 \\
F16 & 1.26E+03 & 3.25E+03 & 2.34E+03 & 9.59E+02 & 1.57E+03 & 2.06E+03 & 9.13E+02 & 1.08E+03 \\
F17 & 5.43E+02 & 1.91E+03 & 1.14E+03 & 2.66E+02 & 7.10E+02 & 9.24E+02 & 4.28E+02 & 3.69E+02 \\
F18 & 2.17E+05 & 3.35E+07 & 1.46E+07 & 1.13E+06 & 3.06E+06 & 4.17E+06 & 5.33E+05 & 4.73E+05 \\
F19 & 5.96E+05 & 4.42E+08 & 8.37E+04 & 8.96E+05 & 9.48E+06 & 4.38E+06 & 1.35E+04 & 1.44E+04 \\
F20 & 5.96E+02 & 8.25E+02 & 9.94E+02 & 3.85E+02 & 6.57E+02 & 8.42E+02 & 3.28E+02 & 3.38E+02 \\
F21 & 4.05E+02 & 5.51E+02 & 4.46E+02 & 2.84E+02 & 3.97E+02 & 4.79E+02 & 3.20E+02 & 3.04E+02 \\
F22 & 3.13E+03 & 7.49E+03 & 8.25E+03 & 2.07E+03 & 4.10E+03 & 5.14E+03 & 3.73E+03 & 2.41E+03 \\
F23 & 8.44E+02 & 7.46E+02 & 6.07E+02 & 4.54E+02 & 5.18E+02 & 7.37E+02 & 5.04E+02 & 4.76E+02 \\
F24 & 8.17E+02 & 7.71E+02 & 6.83E+02 & 5.12E+02 & 5.78E+02 & 8.13E+02 & 5.85E+02 & 5.74E+02 \\
F25 & 3.97E+02 & 1.40E+03 & 4.20E+02 & 4.74E+02 & 8.20E+02 & 4.96E+02 & 3.98E+02 & 3.89E+02 \\
F26 & 2.16E+03 & 5.62E+03 & 3.15E+03 & 2.07E+03 & 3.06E+03 & 5.21E+03 & 2.49E+03 & 2.23E+03 \\
F27 & 5.19E+02 & 6.99E+02 & 5.00E+02 & 5.46E+02 & 5.46E+02 & 7.24E+02 & 5.19E+02 & 5.13E+02 \\
F28 & 4.44E+02 & 3.80E+03 & 5.00E+02 & 5.82E+02 & 1.36E+03 & 5.73E+02 & 5.15E+02 & 4.84E+02 \\
F29 & 1.40E+03 & 2.83E+03 & 2.02E+03 & 9.50E+02 & 1.19E+03 & 2.17E+03 & 9.06E+02 & 8.39E+02 \\
F30 & 1.88E+06 & 4.88E+08 & 4.99E+05 & 6.71E+06 & 1.08E+06 & 2.04E+07 & 3.38E+04 & 1.41E+04 \\
\hline
\end{tabular}
\end{table*}

\begin{table*}[p]
\centering
\caption{Experimental results for $D=50$.}
\label{tab6}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 2.57E+08 & 4.64E+10 & 2.81E+09 & 5.59E+09 & 3.75E+10 & 4.96E+08 & 2.99E+08 & 5.03E+05 \\
F2 & 2.66E+25 & 5.07E+79 & 5.48E+80 & 7.96E+54 & 6.94E+76 & 1.25E+70 & 1.42E+53 & 1.22E+45 \\
F3 & 9.48E+03 & 3.75E+05 & 7.71E+05 & 9.42E+04 & 1.67E+05 & 1.85E+05 & 3.48E+04 & 3.71E+04 \\
F4 & 1.62E+02 & 7.61E+03 & 3.24E+03 & 7.14E+02 & 4.06E+03 & 5.30E+02 & 3.04E+02 & 2.20E+02 \\
F5 & 3.94E+02 & 3.08E+02 & 5.49E+02 & 2.07E+02 & 4.62E+02 & 4.70E+02 & 2.38E+02 & 2.27E+02 \\
F6 & 6.95E+01 & 6.80E+01 & 4.93E+01 & 1.76E+01 & 4.78E+01 & 8.30E+01 & 1.87E+01 & 1.16E+01 \\
F7 & 5.01E+02 & 2.20E+02 & 6.50E+02 & 3.59E+02 & 1.13E+03 & 1.06E+03 & 3.00E+02 & 3.31E+02 \\
F8 & 4.15E+02 & 5.17E+02 & 5.51E+02 & 2.19E+02 & 4.79E+02 & 4.49E+02 & 2.54E+02 & 2.38E+02 \\
F9 & 2.47E+04 & 1.35E+04 & 4.27E+04 & 8.16E+03 & 1.71E+04 & 2.76E+04 & 6.46E+03 & 6.91E+03 \\
F10 & 8.82E+03 & 1.32E+04 & 1.50E+04 & 6.50E+03 & 7.47E+03 & 1.01E+04 & 7.53E+03 & 6.73E+03 \\
F11 & 3.59E+02 & 6.45E+04 & 5.59E+04 & 2.97E+03 & 1.28E+04 & 1.17E+03 & 8.25E+02 & 5.58E+02 \\
F12 & 1.07E+08 & 2.24E+10 & 1.05E+10 & 5.44E+08 & 4.63E+09 & 5.79E+08 & 1.07E+08 & 1.94E+07 \\
F13 & 1.47E+07 & 1.29E+10 & 6.17E+07 & 1.05E+08 & 8.80E+08 & 5.55E+06 & 1.17E+05 & 2.51E+04 \\
F14 & 1.17E+05 & 2.17E+07 & 4.29E+06 & 9.45E+05 & 1.08E+06 & 1.97E+06 & 2.85E+05 & 1.68E+05 \\
F15 & 3.05E+06 & 2.51E+09 & 1.28E+07 & 1.56E+07 & 8.65E+06 & 1.08E+06 & 3.24E+04 & 1.43E+04 \\
F16 & 2.15E+03 & 5.37E+03 & 5.11E+03 & 1.46E+03 & 2.73E+03 & 3.81E+03 & 2.10E+03 & 2.05E+03 \\
F17 & 1.68E+03 & 1.05E+03 & 3.12E+03 & 1.06E+03 & 2.30E+03 & 2.55E+03 & 1.51E+03 & 1.39E+03 \\
F18 & 1.50E+06 & 1.27E+08 & 6.83E+07 & 5.10E+06 & 2.96E+06 & 1.41E+07 & 1.78E+06 & 1.29E+06 \\
F19 & 2.78E+06 & 1.10E+09 & 2.55E+04 & 3.16E+06 & 5.93E+07 & 5.13E+06 & 3.12E+04 & 1.44E+04 \\
F20 & 1.30E+03 & 1.62E+03 & 2.50E+03 & 8.97E+02 & 1.40E+03 & 1.70E+03 & 1.10E+03 & 1.08E+03 \\
F21 & 6.61E+02 & 7.99E+02 & 7.52E+02 & 3.94E+02 & 6.41E+02 & 8.43E+02 & 4.91E+02 & 4.71E+02 \\
F22 & 9.09E+03 & 1.43E+04 & 1.51E+04 & 6.78E+03 & 8.28E+03 & 1.01E+04 & 7.85E+03 & 6.99E+03 \\
F23 & 1.55E+03 & 1.14E+03 & 9.78E+02 & 6.64E+02 & 8.41E+02 & 1.35E+03 & 8.14E+02 & 7.58E+02 \\
F24 & 1.21E+03 & 1.14E+03 & 1.09E+03 & 7.24E+02 & 8.16E+02 & 1.30E+03 & 8.78E+02 & 8.30E+02 \\
F25 & 5.12E+02 & 2.72E+03 & 2.22E+03 & 1.06E+03 & 3.08E+03 & 9.11E+02 & 6.34E+02 & 5.65E+02 \\
F26 & 6.09E+03 & 8.76E+03 & 6.33E+03 & 3.95E+03 & 5.82E+03 & 1.05E+04 & 5.47E+03 & 4.60E+03 \\
F27 & 1.07E+03 & 1.12E+03 & 5.00E+02 & 8.99E+02 & 8.75E+02 & 1.69E+03 & 7.22E+02 & 6.95E+02 \\
F28 & 4.97E+02 & 6.77E+03 & 5.00E+02 & 1.48E+03 & 5.11E+03 & 1.20E+03 & 7.09E+02 & 5.87E+02 \\
F29 & 2.55E+03 & 1.04E+04 & 6.25E+03 & 1.62E+03 & 2.31E+03 & 4.85E+03 & 1.67E+03 & 1.31E+03 \\
F30 & 5.58E+07 & 2.43E+09 & 3.38E+08 & 1.09E+08 & 1.29E+08 & 1.35E+08 & 2.31E+06 & 1.51E+06 \\
\hline
\end{tabular}
\end{table*}

\begin{table*}[t!]
\centering
\caption{Experimental results for $D=100$.}
\label{tab7}
\begin{tabular}{c|cccccccc}
\hline
& PSO & CMA-ES & ABC & GWO & MFO & WOA & HMS & MCS-HMS \\
\hline
F1 & 1.07E+09 & 4.20E+10 & 3.61E+11 & 4.25E+10 & 1.24E+11 & 1.98E+07 & 1.51E+10 & 2.89E+07 \\
F2 & 8.45E+71 & 1.04E+166 & 2.96E+182 & 4.30E+133 & 7.99E+159 & 1.15E+148 & 6.53E+134 & 2.10E+109 \\
F3 & 2.36E+05 & 8.50E+05 & 2.22E+06 & 2.45E+05 & 6.55E+05 & 7.55E+05 & 1.14E+05 & 1.51E+05 \\
F4 & 3.87E+02 & 1.71E+04 & 1.53E+05 & 3.36E+03 & 2.38E+04 & 6.69E+02 & 2.01E+03 & 4.34E+02 \\
F5 & 1.09E+03 & 1.07E+03 & 2.03E+03 & 6.41E+02 & 1.20E+03 & 9.25E+02 & 6.78E+02 & 6.48E+02 \\
F6 & 8.60E+01 & 1.44E+01 & 1.39E+02 & 3.65E+01 & 7.32E+01 & 7.93E+01 & 3.32E+01 & 2.24E+01 \\
F7 & 1.25E+03 & 8.65E+02 & 9.19E+03 & 1.25E+03 & 4.04E+03 & 2.57E+03 & 9.67E+02 & 1.13E+03 \\
F8 & 1.20E+03 & 1.27E+03 & 2.08E+03 & 6.33E+02 & 1.27E+03 & 1.12E+03 & 7.14E+02 & 7.20E+02 \\
F9 & 6.32E+04 & 3.25E+04 & 1.93E+05 & 3.27E+04 & 4.21E+04 & 3.54E+04 & 2.22E+04 & 2.05E+04 \\
F10 & 2.20E+04 & 3.04E+04 & 3.26E+04 & 1.57E+04 & 1.68E+04 & 1.95E+04 & 1.79E+04 & 1.60E+04 \\
F11 & 2.44E+03 & 4.51E+05 & 8.88E+05 & 5.33E+04 & 1.32E+05 & 1.17E+04 & 1.35E+04 & 1.08E+04 \\
F12 & 8.27E+08 & 5.27E+10 & 1.19E+11 & 7.40E+09 & 3.48E+10 & 7.23E+08 & 1.52E+09 & 1.71E+08 \\
F13 & 5.08E+07 & 1.16E+10 & 3.13E+09 & 4.15E+08 & 4.43E+09 & 6.20E+04 & 3.78E+07 & 3.73E+04 \\
F14 & 1.57E+06 & 1.15E+08 & 8.61E+07 & 4.95E+06 & 8.14E+06 & 1.45E+06 & 2.07E+06 & 1.58E+06 \\
F15 & 1.52E+07 & 5.73E+09 & 5.70E+08 & 1.02E+08 & 1.81E+09 & 6.21E+04 & 5.00E+05 & 1.24E+04 \\
F16 & 5.85E+03 & 1.20E+04 & 1.52E+04 & 4.42E+03 & 6.36E+03 & 8.43E+03 & 6.06E+03 & 5.17E+03 \\
F17 & 4.32E+03 & 4.22E+04 & 2.97E+04 & 3.54E+03 & 6.44E+03 & 5.47E+03 & 5.25E+03 & 3.98E+03 \\
F18 & 3.18E+06 & 1.39E+08 & 2.86E+08 & 4.83E+06 & 1.63E+07 & 1.88E+06 & 5.67E+06 & 3.21E+06 \\
F19 & 2.67E+07 & 4.44E+09 & 7.29E+06 & 1.15E+08 & 7.69E+08 & 1.33E+07 & 1.54E+06 & 1.44E+04 \\
F20 & 3.77E+03 & 5.14E+03 & 6.57E+03 & 3.28E+03 & 3.78E+03 & 4.23E+03 & 3.65E+03 & 3.42E+03 \\
F21 & 1.69E+03 & 1.44E+03 & 2.56E+03 & 8.69E+02 & 1.55E+03 & 1.79E+03 & 1.10E+03 & 1.05E+03 \\
F22 & 2.44E+04 & 3.11E+04 & 3.34E+04 & 1.74E+04 & 1.84E+04 & 2.17E+04 & 1.99E+04 & 1.73E+04 \\
F23 & 2.91E+03 & 1.84E+03 & 3.16E+03 & 1.23E+03 & 1.49E+03 & 2.44E+03 & 1.41E+03 & 1.12E+03 \\
F24 & 3.04E+03 & 2.44E+03 & 5.16E+03 & 1.75E+03 & 1.94E+03 & 3.55E+03 & 1.95E+03 & 1.67E+03 \\
F25 & 9.45E+02 & 8.27E+03 & 8.55E+04 & 3.42E+03 & 1.12E+04 & 1.13E+03 & 1.59E+03 & 1.01E+03 \\
F26 & 1.71E+04 & 1.93E+04 & 4.96E+04 & 1.22E+04 & 1.53E+04 & 2.82E+04 & 1.58E+04 & 1.28E+04 \\
F27 & 6.43E+02 & 2.00E+03 & 5.00E+02 & 1.33E+03 & 1.28E+03 & 2.27E+03 & 8.75E+02 & 7.95E+02 \\
F28 & 6.38E+02 & 1.91E+04 & 5.00E+02 & 4.73E+03 & 1.62E+04 & 9.13E+02 & 2.37E+03 & 3.18E+03 \\
F29 & 6.94E+03 & 1.34E+04 & 6.10E+04 & 5.28E+03 & 8.22E+03 & 1.11E+04 & 5.04E+03 & 4.30E+03 \\
F30 & 1.07E+08 & 1.02E+10 & 3.27E+09 & 1.03E+09 & 2.33E+09 & 1.83E+08 & 1.02E+07 & 8.14E+04 \\
\hline
\end{tabular}
\end{table*}

Since the employed methods are stochastic, we run all algorithms 25 times and report the mean over these runs in terms of the difference between the optimal value and that returned by an algorithm. The obtained results are given in Tables~\ref{tab5}, \ref{tab6}, and~\ref{tab7} for $D=30$, $D=50$ and $D=100$, respectively, and show impressively that MCS-HMS not only provides superior performance compared to standard HMS but that it also outperforms all the other algorithms.

Its superiority compared to standard HMS is further illustrated in Figs.~\ref{fig2}, \ref{fig3}, and~\ref{fig4} which compared the rankings of HMS and MCS-HMS. From there, we can see that for the vast majority of cases, MCS-HMS is ranked top or second and that in particular, MCS-HMS gives the best result for 24, 25 and 27 functions for $D=30$, $D=50$ and $D=100$, respectively.

\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/30.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=30$.}
\label{fig2}
\end{figure*}
\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/50.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=50$.}
\label{fig3}
\end{figure*}

\begin{figure*}[t!]
\centering
\includegraphics[width=.9\textwidth]{figures/100.jpg}
\caption{Ranking comparison between MCS-HMS and HMS for $D=100$.}
\label{fig4}
\end{figure*}

\begin{table}[b!]
\centering
\caption{Ranking results for $D=30$.}
\label{tab2}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.73         & 1         & 8          & 1.95               \\
CMA-ES  & 7.40         & 4         & 8          & 1.08             \\
ABC     & 5.50         & 1         & 8          & 1.91               \\
GWO     & 3.37         & 1         & 7          & 1.92               \\
MFO     & 5.30         & 3         & 7          & 1.27               \\
WOA     & 6.20         & 3         & 8          & 1.17               \\
HMS     & 2.77         & 1         & 4          & 0.92               \\
MCS-HMS & 1.73         & 1         & 3          & 0.63               \\
\hline
\end{tabular}
\end{table}

Tables~\ref{tab2}, \ref{tab3}, and~\ref{tab4} show, for each algorithm, the average rank, best rank, worst rank and standard deviation over the 30 benchmark functions for $D=30$, $D=50$ and $D=100$, respectively.

\begin{table}[b!]
\centering
\caption{Ranking results for $D=50$.}
\label{tab3}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.83         & 1         & 8          & 2.02               \\
CMA-ES  & 6.70         & 1         & 8          & 1.86             \\
ABC     & 6.23         & 1         & 8          & 1.86               \\
GWO     & 3.27         & 1         & 7          & 1.95               \\
MFO     & 5.30         & 2         & 8          & 1.44               \\
WOA     & 5.93         & 3         & 8          & 1.46               \\
HMS     & 2.87         & 1         & 4          & 0.67               \\
MCS-HMS & 1.87         & 1         & 3          & 0.67               \\
\hline
\end{tabular}
\label{tab3}
\end{table}

\begin{table}[b!]
\centering
\caption{Ranking results for $D=100$.}
\label{tab4}
\begin{tabular}{lcccc}
\hline
& avg.\ rank & best rank & worst rank & std.dev. \\
\hline
PSO     & 3.90         & 1         & 7          & 1.90               \\
CMA-ES  & 6.23         & 1         & 8          & 1.87               \\
ABC     & 7.13         & 1         & 8          & 1.93           \\
GWO     & 3.47         & 1         & 6          & 1.84               \\
MFO     & 5.60         & 3         & 7          & 1.31               \\
WOA     & 4.43         & 1         & 8          & 1.94               \\
HMS     & 3.30         & 1         & 5          & 0.94               \\
MCS-HMS & 1.93         & 1         & 5          & 0.89               \\
\hline
\end{tabular}
\label{tab4}
\end{table}

As can be seen from there, MCS-HMS clearly yields the best performance for all dimensionalities, and by a significant margin. Table~\ref{tab8} further illustrates this by a pairwise comparison of MCS-HMS with all other methods. It is evident from there that in the large majority of cases, MCS-HMS outperforms the other approaches.

 \begin{table}[t!]
\centering
\caption{Pairwise comparisons between MCS-HMS and the other algorithms.}
\label{tab8}
\begin{tabular}{l|cc|cc|cc}
\hline
& \multicolumn{2}{c|}{$D=30$} & \multicolumn{2}{c|}{$D=50$} & \multicolumn{2}{c}{$D=100$} \\
MCS-HMS vs. & better & worse & better & worse & better & worse \\
\hline
PSO & 23 & 7 & 23 & 7 & 22 & 8 \\
CMA-ES & 30 & 0 & 28 & 2 & 28 & 2 \\
ABC & 29 & 1 & 28 & 2 & 28 & 2 \\
GWO & 20 & 10 & 19 & 11 & 22 & 8 \\
MFO & 30 & 0 & 29 & 1 & 30 & 0 \\
WOA & 30 & 0 & 30 & 0 & 26 & 4 \\
HMS & 26 & 4 & 27 & 3 & 26 & 4 \\
\hline
\end{tabular}
\end{table}

Last not least, we conduct a two-sided Wilcoxon signed rank test to see if the differences between MCS-HMS and its opponents are statistically significant and give the results in Table~\ref{tab9}. With all $p$-value below 0.05, it is apparent that MCS-HMS statistically outperforms all other algorithms.

\begin{table}[t!]
\centering
\caption{Results ($p$-values) of two-sided Wilcoxon signed test.}
\label{tab9}
\begin{tabular}{lccc}
\hline
& $D=30$  & $D=50$ & $D=100$ \\
\hline
MCS-HMS vs.\ PSO & 0.0148 & 0.0034 & 0.0132 \\
MCS-HMS vs.\ CMA-ES & 1.73E-06 & 4.73E-06 & 2.35E-06 \\
MCS-HMS vs.\ ABC & 2.13E-06 & 2.88E-06 & 4.29E-06 \\
MCS-HMS vs.\ GWO & 0.0082 & 0.0057 & 8.31E-04 \\
MCS-HMS vs.\ MFO & 1.73E-06 & 1.92E-06 & 1.73E-06 \\
MCS-HMS vs.\ WOA & 1.73E-06 & 1.73E-06 & 0.003 \\
MCS-HMS vs.\ HMS & 5.29E-04 & 1.15E-04 & 7.51E-05 \\
\hline
\end{tabular}
\end{table}

\section{Conclusions}
\label{sec:Concl}
In this paper, we have introduced MCS-HMS, an enhanced version of the human mental search (HMS) optimisation algorithm. MCS-HMS employs a multi-cluster strategy in HMS's grouping phase which allows for improved exploration ability, while also using a one-step $k$-means algorithm for clustering to speed up the algorithm's execution. Based on a set of experiments carried out on the CEC 2017 test functions, we show that MCS-HMS not only significantly improves the standard HMS algorithm but that it also outperforms a number of other population-based metaheuristics.

\balance

\bibliographystyle{IEEEtran}

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CONGRATULATIONS you made it from now on this is your point of return. Return to the refreshing Spring.
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Home | Patient Info | Knee | Conditions | Knee Ligament Injuries
The knee is a complex joint that consists of bone, cartilage, ligaments, and tendons that help in your joint's movements.
The knee is a hinge joint made up of two bones, the thighbone (femur) and shinbone (tibia). Ligaments are tough bands of tissue that connect one bone to another bone. The ligaments of the knee stabilize the knee joint. There are two important groups of ligaments that hold the bones of the knee joint together, collateral and cruciate ligaments - medial collateral ligament (MCL) and lateral collateral ligament (LCL), and anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL).
Knee problems may arise if any of these structures get injured by overuse or suddenly during sports activities. Pain, swelling, and stiffness are the common symptoms of any damage or injury to the knee. The common causes of knee injury include:
Fracture of the femur (thighbone) or tibia and fibula (leg bones)
Torn ligament: either anterior or posterior cruciate ligament (ACL or PCL)
Rupture of blood vessels following a trauma that leads to the accumulation of extra fluid or blood in the joint
Dislocation of the kneecap (patella)
Torn quadriceps or hamstring muscles
Patellar tendon tear
Types of Ligament Injuries
The most common types of ligament injuries include:
An ACL injury is a sports-related injury that occurs when the knee is forcefully twisted or hyperextended. An ACL tear usually occurs with an abrupt directional change with the foot fixed on the ground or when the deceleration force crosses the knee. Changing direction rapidly, stopping suddenly, slowing down while running, landing from a jump incorrectly, and direct contact or collision, such as a football tackle, can also cause injury to the ACL.
MCL tear
The MCL is the ligament that is located on the inner part of the knee joint. It runs from the femur (thighbone) to the top of the tibia (shinbone) and helps in stabilizing the knee. MCL injuries can result in a stretch, partial tear or complete tear of the ligament. Injuries to the MCL commonly occur because of pressure or stress on the outside section of the knee.
PCL tear
PCL injuries are very rare and are more difficult to detect when compared to the other knee ligament injuries. Cartilage injuries, bone bruises, and ligament injuries often occur in combination with PCL injuries.
Injuries to the PCL can be graded as I, II or III depending on the severity of the injury. In grade I, the ligament is mildly damaged and slightly stretched, but the knee joint is stable. In grade II, there is a partial tear of the ligament. In grade III, there is a complete tear of the ligament and the ligament is divided into two halves, making the joint unstable.
The PCL is usually injured by a direct impact, such as in an automobile accident when the bent knee forcefully strikes the dashboard. In sports, it can occur when you fall to the ground with a bent knee. Twisting injury or overextending the knee can cause the PCL to tear.
Treatments for Ligament Injuries
Immediately following a knee injury, before being evaluated by a doctor, you can initiate the R.I.C.E. method of treatment:
Rest: Rest the knee, as more damage could result from pressure on the injury.
Ice: Ice packs can be applied to the injured area to reduce swelling and pain. Never place ice directly over the skin. Ice should be wrapped in a towel and applied to the affected area for 15-20 minutes, four times a day for several days.
Compression: Wrapping the knee with an elastic bandage or compression stocking can help minimize the swelling and support your knee.
Elevation: Elevating the knee above the heart level will also help reduce swelling and pain.
It is important to seek your doctor's advice if you hear a popping noise or feel as if your knee has given way at the time of injury and if you are unable to move your knee because of severe pain.
Other Knee Conditions

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While striving for perfection in every facet of life isn't realistic, there are times when you need to be exceptional. Case in point: e-discovery. High stakes and tight deadlines make good process critical, and troves of data can make it expensive. But, as the Relativity community often proves, the right approach can yield impressive results.
Scott Carlson, a partner at Seyfarth Shaw, knew this to be true. In 2004, he embarked on a journey and established the firm's e-discovery group. When he saw electronic discovery blooming on the horizon, he and his team buckled down and established the kind of strategy that would ultimately make waves for their firm.
Many attorneys are all-stars in crafting, and subsequently winning, their cases. Few are well versed in technology and the e-discovery process.
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As their workload increased, Seyfarth's two-person e-discovery team needed to bring in more members. The new recruits came from myriad career paths. Some were newly minted attorneys, fresh out of law school. Others had spent time working for e-discovery vendors. A handful were more seasoned attorneys with technology skills who also joined the group.
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Jay joined the e-discovery team in 2008 after graduating from Chicago Kent College of Law. Like other team members, Jay had an extensive technology background. He was in the technology industry for several years before attending law school.
At Chicago Kent College of Law, Jay helped establish the Center for Access to Justice & Technology (CAJT). Through CAJT, technology makes the court system more accessible to people with limited resources.
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Additionally, the team explains technology in terms litigators understand. For example, to describe active learning, Jay likens the tool to Netflix.
"The more movies you watch, the more Netflix's suggestions improve. It gets better at knowing your preferences," Jay noted.
In the same way, Relativity Assisted Review learns from reviewers' coding decisions to mark documents as relevant or not relevant.
"It makes a difference explaining technology in this way," Jay added.
Seyfarth's e-discovery group continues evolving alongside the e-discovery field. In 2011, the group rebranded to include information governance (IG) services. Much like e-discovery once appeared on the horizon of everyday legal work, IG was becoming key to getting growing data volumes under some control—an important tactic for streamlining work downstream.
By expanding into IG, the group helped clients assess the risk around keeping information longer than its value may last. Then, they could decide the next course of action.
Information governance encompasses many layers—discovery, privacy, records management, and information security.
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Today, clients in this space are becoming savvier. They are not just asking for efficiency in the e-discovery process—they are demanding it. According to Scott, clients appreciate having an open dialogue about their options. When working with clients, he first asks them about their tolerance for risk. Based on their response, he outlines their options based on their comfort level.
"Clients are more cost conscious and want law firms to do e-discovery in a smarter way," Jay said. "Having an e-discovery group like Seyfarth's is a key differentiator for these clients."
Though e-discovery has been a staple in the legal world for years, its impact is still newly felt by some professionals who don't see it as their bread and butter. Satisfying savvy clients while simplifying the learning curve for those who are less familiar with the demands of this work is what Seyfarth's dedicated e-discovery team was born to do.
As technology advances, Seyfarth's e-discovery team will continue gaining traction—and members. The team has 12 attorneys and counting. Scott and Jay expect the group to continue growing as new technological advancements take hold.
Mary Rechtoris is a member of the marketing team at Relativity, where she specializes in customer advocacy.
Going Cloud: Time to Take that Leap?


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ST∆ER/Rose Winston/Timo Lee/Repelican
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Aug 8, 2019 @ 7:00 pm — Aug 9, 2019 @ 12:00am
ST∆ER is the solo project of Philadelphia-based electronic musician Ian R. Staer. Inspired equally by the classic "Berlin School" material of early Tangerine Dream and by contemporaries such as Chuck Van Zyl, Erik Wøllo, and Jason Sloan, ST∆ER performances take the audience on a meditative, hypnotic journey.
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Brooklyn DJ Timo Lee, a former resident of DJ Harvey's thirtyninehotel in Hawaii. Timo now brings the sunshine disco vibes to NYC. Timo hosted a weekly three-hour radio show for 8 years on KTUH Honolulu, and played with numerous talented DJs including DJ Harvey, Bonobo, James Murphy, Thomas Bullock, DJ Garth, Tiger and Woods, Daniele Baldelli, Tim Sweeney and more at thirtyninehotel.
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Aug 8, 2019 @ 7:00 pm
Aug 9, 2019 @ 12:00am
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Karumba Library
Karumba Library is located at 154 Walker Street Karumba. Our little library is part of Rural Libraries Queensland, which gives you access to over 300 000 books available through our catalogue. Being part of 27 other shires gives our library access to so much more. Once you are a member there is free talking books and e-books, search your family tree, Trove and many many more. To find out more either drop into the centre or go online to http://plconnect.slq.qld.gov.au/services/rural-libraries-queensland
Email: kbalib@carpentaria.qld.gov.au
9:00am to 1:00pm Monday to Friday
9:00am to 12:30pm Tuesday and Thursday
Karumba Visitor Information Centre
Karumba Visitor Information Centre is located at the Les Wilson Barramundi Discovery Centre, Yappar Street Karumba. We have local brochures, and information that covers Townsville to Cooktown, Townsville to Mt Isa, the lovely Atherton Tablelands and along the Savannah way to Karumba and Karumba to Winton.
Email: info@barracentre.com.au
9:00am to 5pm Monday to Friday
9:00am to 4:00pm Saturday and Sunday
Normanton Library and Visitor Information Centre
Come into the Normanton Visitor Information Centre in the historic Burns Philp Building, located on the corner of Landsborough & Caroline Streets, where you can enjoy a cup of tea or coffee, a cool drink and talk to the friendly staff. You might like to purchase a small souvenir and browse Normanton's attractions and history. The Visitor Information Centre houses the Heritage Centre as well and has displays featuring flood photos of Normanton, pioneers and explorers, wildlife displays and historical artefacts.
Email: tourism@carpentaria.qld.gov.au
1st April to 30th September
10:00am to 3:00pm Saturday and Sunday
1st October to 31st March
9.00am to 12.00pm Saturday


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You can download it straight to your mobile. On your mobile device go to get.madringtones.org and enter ID 1245719.


text:
The Guthridge Nunataks () are a scattered group of sharp peaked nunataks and small mountains, about  long and  wide, midway between the Rathbone Hills and the Blanchard Nunataks in the Gutenko Mountains of central Palmer Land, Antarctica. They were mapped by the United States Geological Survey in 1974, and named by the Advisory Committee on Antarctic Names (US-ACAN) after Guy G. Guthridge, Director of the Polar Information Service, Division of Polar Programs, National Science Foundation; Editor, US-ACAN, from 1989 (Chairman from 1994).

References

Nunataks of Palmer Land

redpajama_set_name: RedPajamaWikipedia


Children Social Care provides a wide range of services for different people. If you are unhappy with the service or a particular member of staff, you can talk to your social worker or their manager about the problem you have.
If you are unhappy with the outcome there are stages you can go through to resolve the matter.
You can talk to the Complaints Manager in private, who will try to deal with your problem.
You can make contact by telephone, letter, fax, email or in person.
If you need help making a complaint, compliment, or suggestion let them know and arrange can be made for someone to help you - this person is called an advocate. Alternatively, you can ask someone in your family, or a friend to write or speak on your behalf.
You will be sent a letter within two working days to let you know your complaint has been received by the Complaints Unit. You should then receive a full response within ten working days. If the complaint is detailed or if an advocate is required, a further ten working days can be added.
If you are still not happy, you can meet with the manager who sent the letter.
The Children & Young People's Service will consider mediation and conflict resolution at this stage and at all other stages.
Write to the Complaints Manager saying which part of your complaint has not been sorted out.
The Complaints Manager will arrange for someone independent of the service to investigate your complaint and write a report. The Complaints Manager will also appoint an independent person to oversee the investigation.
This person will not work for Hackney Council. They will write a report.
If you are unhappy with the investigation or the response to your complaint, you can ask for it to go before a Review Panel.
You must ask for this within 20 working days of receiving the letter at Stage 2.
The Review Panel will normally meet within 30 working days of your request, and will be made up of three independent panel members, one of whom will chair the meeting.
You can also ask your advocate, someone in your family, or a friend to speak on your behalf.
The Review Panel will send their recommendations to you and the Director of Children's Services within five working days of the meeting.
The Director of Children's Services will send you a letter with the final decision within 15 working days of receiving the Review Panel's recommendations.


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Le Fiumorbo (en Corse u Fium'Orbu) est un fleuve côtier français qui coule en région Corse, et se jette dans la mer Tyrrhénienne.

Géographie
Le Fiumorbo prend sa source en Corse-du-Sud dans le massif du Renoso au Foce d'Astra, à , et à moins d'un kilomètre à l'ouest du Monte Grosso (), sur la commune de Palneca. Dans sa partie haute, entre sa source, juste sous la Punta Orlandino, et la maison forestière de Marmano, il s'appelle ruisseau de Marmano. Il draine également les Pozzi d'Ese en rive gauche.

Passant rapidement en Haute-Corse, il traverse sur toute sa longueur la commune de Ghisoni, se dirigeant d'abord vers le nord, puis vers l'est par le défilé des Strette (« étroits ») et celui de lInzecca («entaille»). S'orientant ensuite au sud-est, il longe la plaine orientale, entre Ghisonaccia et les communes du pays Fiumorbu. Il se jette dans la mer Tyrrhénienne au lieu-dit Calzarello, sur une étroite zone de la commune de Prunelli-di-Fiumorbo entre les communes de Serra-di-Fiumorbo et Ghisonaccia, après un parcours d'environ .

Les fleuves côtiers voisins sont au nord le Tavignano et au sud l'Abatesco.

 Communes et cantons traversés
Dans les deux départements de Corse-du-Sud et Haute-Corse le Fiumorbo traverse huit communes et quatre cantons :
 dans le sens amont vers aval : Bastelica, Palneca (sources), Ghisoni, Lugo-di-Nazza, Ghisonaccia, Poggio-di-Nazza, Prunelli-di-Fiumorbo, Serra-di-Fiumorbo.

Soit en termes de cantons, le Fiumorbo prend source entre les deux canton de Bastelica et canton de Zicavo, traverse le canton de Ghisoni et a son embouchure dans le canton de Prunelli-di-Fiumorbo, le tout dans les deux arrondissement d'Ajaccio et arrondissement de Corte.

 Bassin versant
Son bassin versant est de  avec deux zones hydrographiques référencées  (Y942) et le Fium Orbu de sa source au ruisseau de Saltaruccio (Y941). Ce bassin versant est constitué à 84,97 % de , à 12,90 % de , à 1,67 % de , à 0,61 % de , à 0,12 % de .

 Organisme de bassin ou organisme gestionnaire
L'organisme gestionnaire est le Comité de bassin de Corse, depuis la loi corse du .

 Étymologie
 Fiume, du latin flumen, fleuve (existe à l'identique en italien).
 Orbo ou orbu, du latin orbus, « privé de.. », d'où aveugle, ou sombre (cf. Vallorbe).

 Affluents

Le Fiumorbo a trente-huit affluents référencés :
 le ruisseau Guado Alla Macchia (rg),  sur les communes de Bastelica, Palneca et Ghisoni et avec trois fontaines sur son bassin versant : la Fontaine de Buona Cena, le Fontaine d'Isolo et les fontaines de Cozzanese.
 Géoportail signale aussi : le ruisseau de Faeto Rossa (rg), environ  prenant source à la Punta Cappella () à la limite des deux communes de Bastelica et Ghisoni.
 ----- le ruisseau de Radicello (rd),  prenant source près du col de Verde () sur la seule commune de  Ghisoni avec un affluent :
 ----- le ruisseau de Lattugona (rd),  sur la seule commune de Ghisoni.
 le ruisseau d'Ariola (rg),  sur la seule commune de Ghisoni.
 le ruisseau de Cannareccia (rg),  sur la seule commune de Ghisoni et traversant le chemin de grande randonnée GR 20, et prenant source près du Monte Renoso () et du lac de Nielluccio, avec un affluent :
 ----- le ruisseau de Lischetto (rd),  sur la seule commune de Ghisoni.
 ----- le ruisseau de Rivusecco (rd),  sur les deux communes de Ghisoni et Isolaccio-di-Fiumorbo.
 le ruisseau de Paglione (rg),  sur la seule commune de Ghisoni et prenant source à la fontaine de Forli.
 le ruisseau de Casso (rg),  sur la seule commune de Ghisoni avec deux sources les lac de Bastani () et lac de Nielluccio () et avec deux affluents :
 le ruisseau de Tomba (rg),  sur la seule commune de Ghisoni.
 ----- le ruisseau de Forno (rd),  sur la seule commune de Ghisoni.
 ----- le ruisseau de l'Arresto (rd),  sur la seule commune de Ghisoni.
 le ruisseau de Chigheri ou ruisseau de Lattineta (rg),  sur les deux communes de Ghisoni et Vivario avec trois affluents :
 le ruisseau de Pruno (rg),  sur les deux communes de Ghisoni et Vivario.
 ----- le ruisseau de Giargaloezo (rd),  sur la seule commune de Ghisoni avec un affluent :
 ----- le ruisseau de Scarpaceja (rd),  sur la seule commune de Ghisoni.
 le ruisseau de Rodi Mela (rg),  sur la seule commune de Ghisoni.
 ----- le ruisseau de Vaglie (rd),  sur la seule communes de Ghisoni et Géoportail lui signale un affluent :
 ----- le ruisseau de Renoso (rd),  environ sur la seule commune de Ghisoni.
 le ruisseau de Ciuntrone (rg),  sur la seule commune de Ghisoni.
...
 Le ruisseau Regolo (rg),  qui arrose Ghisoni sur la seule commune de Ghisoni avec trois affluents :
 ----- le ruisseau d'Alzitone (rd),  sur la seule commune de Ghisoni prenant source au lac d'Alzeta ().
 ----- le ruisseau d'Assatojo (rd),  sur la seule commune de Ghisoni.
 ----- le ruisseau de Vadina ou ruisseau de Forno (en partie haute) (rd),  sur la seule commune de Ghisoni.
...
 ----- le ruisseau de Grotta del Prete (rd),  sur la seule commune de Ghisoni.
 ----- le ruisseau de Paganello (rd),  sur la seule commune de Ghisoniavec un affluent :
 ----- le ruisseau de Finosa (rd),  sur les deux communes de Ghisoni et Poggio-di-Nazza.
 le ruisseau de Ruello ou ruisseau de Galgaccio ou ruisseau de Ficuccia ou ruisseau de Lorceta (rg),  prenant source à la Punta Chiova () sur la seule commune de Ghisoni avec quatre affluents :
 le ruisseau de Zuccarello (rg),  sur la seule commune de Ghisoni et prenant source à environ  au sud entre le col de Sorba () et la Punta Muro ().
 le ruisseau de Galgaccio ou  ruisseau de Bruscheto (rg),  sur la seule commune de Ghisoni et prenant source près de la Punta Martinellu () et du col de Rospa () avec un affluent :
 le ruisseau de Nebbia (rg),  sur la seule commune de Ghisoni.
 le ruisseau de Susinelle ou ruisseau de Cannocchio (rg),  sur la seule commune de Ghisoni prenant source à la Punta Chiaragiolo ().
 le ruisseau de Tiglio ou ruisseau de la Posta (rg),  sur la seule commune de Ghisoni prenant source à la Pointe de Prunicciosa ().

 ----- le ruisseau d'Olmiccia (rd),  sur la seule commune de Ghisoni.
 le ruisseau d'Occhio-Griggio (rg),  sur la seule commune de Ghisoni.
 le ruisseau de Rosse (rg),  sur la seule commune de Ghisoni prenant source près du col de San Pietro ().
 le ruisseau de Zoppi (rg),  sur la seule commune de Ghisoni prenant source sous la Punta Fioravanti ().
 ----- le ruisseau de Monte Grosso ou ruisseau de Fungaya (rd),  sur la seule commune de Ghisoni prenant source près de la Punta Kyrie Éleïson () avec un affluent :
 le ruisseau de Sampolo (rg),  sur la seule commune de Ghisoni.

 ----- le ruisseau de Sampolo ou ruisseau de Tassi (rd),  sur les deux communes de Ghisoni et Lugo-di-Nazza.
 ----- le ruisseau de Roncatojo (rd),  sur les deux communes de Ghisoni et Lugo-di-Nazza confluent avant la retenue de Sampolo avec un affluent :
 le ruisseau de Campo Piano (rg),  sur la seule commune de Ghisoni.

 ----- le ruisseau de Giargolo (rd),  sur les deux communes de Ghisoni et Lugo-di-Nazza et confluant après la retenue de Trevadine.
 le ruisseau de Trevadine ou ruisseau de Cardo (rg),  sur les quatre communes de Lugo-di-Nazza, Vezzani, Pietroso, Ghisoni avec un affluent :
 le ruisseau de Tavone (rg),  sur les trois communes de Vezzani, Pietroso et Ghisoni.
 ----- le ruisseau de Tramonte (rd),  sur les deux communes de Ghisoni et Lugo-di-Nazza.
 le ruisseau de Scarrone (rg),  sur les quatre communes de Aghione, Lugo-di-Nazza, Pietroso, et Ghisonaccia.
 ----- le ruisseau de Tibiani Bujosa (rd),  sur les quatre communes de Aghione, Lugo-di-Nazza, Ghisonaccia, et Ghisoni avec un affluent :
 ----- le ruisseau de Casetta (rd),  sur les quatre communes de Aghione, Lugo-di-Nazza, Ghisonaccia, et Ghisoni.
 ----- le ruisseau de Puralbello (rd),  sur les trois communes de Aghione, Lugo-di-Nazza, Ghisonaccia.
 ----- le ruisseau de Casillilli (rd),  sur les quatre communes de Aghione, Lugo-di-Nazza, Poggio-di-Nazza, Ghisonaccia avec un affluent :
 ----- le ruisseau de Camelle (rd),  sur les trois communes de  Lugo-di-Nazza, Ghisonaccia et Ghisoni.
...
 ----- Le ruisseau de Saltaruccio ou ruisseau de Chiaraggiolo (rd), , avec huit affluents et qui traverse la commune de Poggio-di-Nazza et conflue à la hauteur de Ghisonaccia-gare.
 ----- Le ruisseau de Varagno ou ruisseau de Muro Cinto, ruisseau de Poggio  et ruisseau d'Albarelli (rd), , avec dix affluents (et sous-affluents) à la limite entre les communes de Poggio et Prunelli-di-Fiumorbo.
...
 ----- le ruisseau de Forno (rd),  sur les quatre communes de Aghione, Ghisoni, Isolaccio-di-Fiumorbo, Prunelli-di-Fiumorbo.
 ----- le ruisseau de Pedivaldo (rd),  sur les deux communes de Prunelli-di-Fiumorbo, Ghisonaccia.
 ----- le ruisseau de Casa Valda (rd),  sur les deux communes de Prunelli-di-Fiumorbo, Ghisonaccia avec un affluent :
 le ruisseau de Petrapola (rg),  sur les deux communes de Prunelli-di-Fiumorbo, Ghisonaccia avec un affluent :
 ----- le ruisseau de Volpaja (rd),  sur les deux communes de Prunelli-di-Fiumorbo, Ghisonaccia
 ----- le ruisseau de Capanella (rd),  sur les deux communes de Ghisonaccia, Prunelli-di-Fiumorbo.
 ----- le ruisseau d'Alzitana (rd),  sur les deux communes de Ghisonaccia, Prunelli-di-Fiumorbo.
 le ruisseau d'Alzeta (rg),  sur les deux communes de Ghisonaccia, Prunelli-di-Fiumorbo avec un affluent :
 ----- le ruisseau de Cortina (rd),  sur les deux communes de Ghisonaccia, Prunelli-di-Fiumorbo.
 ----- le ruisseau de Serracinella''''' (rd),  sur les trois communes de Serra-di-Fiumorbo, Ghisonaccia, Prunelli-di-Fiumorbo traversant l'etang de Gradugine et les marais de Canna, juste derrière la Marina di Calzarellu.

Rang de Strahler
Donc le rang de Strahler est de cinq.

Hydrologie

Le Fiumorbo à Ghisoni (Sampolo)

Le débit moyen annuel est de  à Ghisoni à la station de Sampolo, à  et pour un bassin versant de .

Étiage ou basses eaux
À l'étiage, c'est-à-dire aux basses eaux, le VCN3, ou débit minimal du cours d'eau enregistré pendant trois jours consécutifs sur un mois, en cas de quinquennale sèche s'établit à , ce qui reste très confortable.

Crues

Sur cette période d'observation, le débit journalier maximal a été observé le  pour . Le débit instantané maximal a été observé le  avec  en même temps que la hauteur maximale instantanée de  soit .

Le QIX 2 est de , le QIX 5 est , le QIX 10 est de , le QIX 20 est de  et le QIX 50 est de .

Lame d'eau et débit spécifique
La lame d'eau écoulée dans cette partie du bassin versant de la rivière est de , ce qui est plus de trois fois au-dessus de la moyenne en France, à . Le débit spécifique (Qsp) atteint  de bassin.

Aménagements

Deux barrages et une centrale hydroélectrique de 43 MW ont été aménagés sur son cours et mis en service en 1992, l'un au niveau du hameau de Sampolo (entre les Strette et l'Inzecca), et le second, à Trevadine, dans la partie inférieure des gorges de l'Inzecca.

Les ponts sont nombreux sur son cours : le pont de Marmano, le pont de Cannareccia (sur son affluent le ruisseau de Cannareccia), le Ponte di Mela (pont génois), le pont de Sorbo (au confluent avec le ruisseau de Rosse), le pont de Pinzalone, le pont du Fium'Orbu avec la RT 10 (ex-RN 198). Sur les affluents, on peut signaler le pont de Casaccie et le pont de Casso sur le ruisseau de Casso, le pont de Chigheri sur le ruisseau de Chigheri.

Écologie
La partie amont du Fiumorbo est incluse dans le parc naturel régional de Corse, porteur d'une démarche globale de gestion des milieux.

L'embouchure du fleuve est incluse dans une zone humide de 162 hectares classée Zone naturelle d'intérêt écologique, faunistique et floristique (ZNIEFF) depuis 1985, sur les trois communes de Ghisonaccia (lieudits Erba Rossa, u Caprone), Prunelli-di-Fiumorbo (lieudit Calzarello), Serra-di-Fiumorbo (marais de Canna, étang de Gradugine).

Voir aussi

 la Liste des cours d'eau de Corse
 le Fiumorbu
 la Plaine orientale
 le Parc naturel régional de Corse
 les Zones humides de Corse
 l'Abatesco

Notes et références

Notes

Références

Cours d'eau en Haute-Corse
Fleuve côtier en Corse

ZNIEFF de type I en Haute-Corse
Cours d'eau en Corse-du-Sud
Source SANDRE inexacte
Cours d'eau à crue exceptionnelle enregistrée de plus de trois mètres de hauteur


Kelsey Merritt: 'I work this hard to make the Philippines proud. 🙏🏽❤ Love you all!!!!"
Filipino-American model Kelsey Merritt is on cloud nine after bagging a stint for Victoria's Secret. In a Twitter post, Kelsey shared, "I'm shooting with Victoria's Secret next week!!!!! I cannot contain my happiness 🏽  All for You!!!!"
The model added, "If only you guys know I work this hard to make the Philippines proud. 🏽  Love you all!!!!"


via my cottage yard farm.
This entry was posted in How to and tagged composting, how to build a compost fence by Julia. Bookmark the permalink.
OK. I've never heard of a compost fence. Seems like the idea would not be to harvest the material – ever…. so what is the purpose?
I have seen gardeners plant zucchini or cucumber plants in these structures. The vines grow out to cover the wire mesh. As more leaf/grass material is added, more soil is produced for the crops. It's ingenious !
The woody material breaks down slowly over time, releasing a fine dust of composted material at the bottom of the fence that slowly leaches into the garden.
Would there be a rotting odour?
I have never heard of a compost fence…..seems like a lot of work with little rewards….I do see how beautiful it could look if it were planted with flowers or vegetables. Wire is not cheap. Why not just make a compost bed OR a fence?
Hmmmm. Seems interesting idea. I could use it for the rear of the garden boxes. It could be double up as the deer barrier to keep the deer from sneaking up in rear. And I like someone's comment about putting zucchini and cucumber on the inside side. Hmmmmm!
I would be interested in hearing from the Boy Scouts Troup 348. How did the fence work. If it is working properly you should need to replenish the material.
of course like your web-site but you need to test the spelling on several of your posts. Several of them are rife with spelling problems and I in finding it very troublesome to tell the truth on the other hand I will definitely come again again.
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We at PHKY are starting to gear up for AAU season and we begin our program previews with Kentucky Premier 2021. Coached by Bryan Mabry, the team is built with Central Kentucky players and have a good forward prospect from the mountains.
Gage Mabry, Spencer County – 2022 Wing averaged 5 points and 3 rebounds per game for the Bears.
Scott Hamm, Russell County – Sophomore big man averaged 14 points and 7 rebounds per game for the Lakers.
Zach Martin, Taylor County – Athletic 2020 post player averaged 4 points and 4 rebounds per game.
Isaiah May, Johnson Central – Big bodied forward averaged 16 points and 6 rebounds for the Region Champs.
Carson Watson, Taylor County – Averaged 1.5 points in 4 games for Taylor County.
Brice Starks, Adair County – Averaged 3.6 points per game while playing in nearly every game for Adair.
Collin Powell, Bullitt East – Averaged a point per game in limited action for the 6th region Runners-Up.
Jacob Dawson, Male – Did not play varsity for the Bulldogs.


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Q: How can I declare and fill at the same time an array of arrays (matrix) using the JavaScript Array fill() Method? I tried to make an array and then to map over it and declare new arrays but failed miserably.
let matrix = (new Array(5).fill(0)).map(new Array(5).fill(0));

A:

function matrix(size) {
  return new Array(size).fill(0).map(el => Array(size).fill(0));
}

console.log(matrix(4));

A: I like the following solution
Array.apply(0, {length: 5}).map(function() {return Array(5).fill(0)})

redpajama_set_name: RedPajamaStackExchange


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PHOTO COURTESY OF MAURA TRABOLD
On a pilgrimage to St. Patrick's Cathedral, New York City, are, from left, missionaries Peter Longoria and Maddi Dorrain with Slippery Rock students Kelsey Kuzniewski, Zach Probst, Maura Trabold and Tyler Green.
Maura Trabold, 19, always had a desire to grow in her relationship with Jesus but needed a supportive faith community. With the help of young missionaries, she found one at Slippery Rock University.
Now in their third year on campus, missionaries from the Fellowship of Catholic University Students, or FOCUS, have helped start 11 Bible studies that draw more than 60 students. They also provide one-on-one mentoring, helping young people grow in virtue and encouraging them to evangelize others.
"They make everyone feel so comfortable, approaching you as a friend," said Trabold, a sophomore from Johnstown who is studying early childhood and special education. "Everyone is so welcoming and inviting."
FOCUS encourages college students to pursue truth and meaning in their lives by participating in the sacraments, going deeper in prayer, and building relationships with Jesus and their fellow students through Bible studies, mission trips, national conferences and further outreach.
Tyler Green, a sophomore from McCandless Township studying safety management, went to a FOCUS conference in Indianapolis last January.
"It was so powerful," he said. "I started by going to Bible study every Thursday night. The FOCUS missionaries inspired me to grow closer in my relationship with Christ."
The evangelization effort, partnering with Slippery Rock's active campus ministry program, is funded through sacrificial gifts to Our Campaign for The Church Alive!
"The first two years have been key, building the foundation," said Peter Longoria, 26, who leads a team of four other FOCUS missionaries at Slippery Rock. "We're meeting more students and investing in them, forming authentic friendships and helping them encounter Christ in a new way."
Already this school year the missionaries and students have gone hiking at McConnells Mill State Park, enjoyed an evening bonfire, and are planning mission trips to Colorado, New Mexico, and Trinidad and Tobago.
"We have a balance of new and returning students," said Diane Magliocca, longtime campus minister at Slippery Rock. "The missionaries' emphasis now is to challenge those they've been forming as disciples, to build them up and send them out."
Trabold said she is ready to disciple others.
"I continue to grow closer in my relationship with God so I can lead others in that direction," she said.
Despite being a member of a large parish in Johnstown, Trabold said there were few opportunities for young people to be involved in parish life beyond religious education programs.
"I do think that community is so important, especially for youth," she said.
Father Adam Verona, director of Slippery Rock's Newman Center, said the missionaries put students at ease.
"This is their spiritual home on campus," he said. "The students can relate to them as peers, both in their ages and in their spiritual journeys."
"The FOCUS missionaries are wonderful," Green said. "If it wasn't for them, my faith life would be more secondary."
Founded in 1998, FOCUS now serves 164 university and college campuses nationwide, including the University of Pittsburgh, Carnegie Mellon University and Edinboro University, which is one of 14 new colleges to welcome the FOCUS missionaries.


"Today it is impossible to find a company or an organization without computers. It's safe to say that digital economy has "captured" all important areas of our life", stated Stats-secretary, Deputy Head of FAS Andrey Tsarikovskiy as the session on "Competition and competition policy аunder the conditions of digital transformation", at Gaidar Economic Forum.
"In my view, it is possible to move away form the term "digital since everything ahs become a digital economy. It's about time for us to analyze all events that have occurred in the past several years", added Deputy Head of FAS.
"The theories maintaining that with time a particular information field will form, where information does not disappear and is registered, have come true. The time is ripe and we have been able to take to the challenge and respond to all questions raised", continued Andrey Tsarikovskiy.
He gave cartel investigations as an example, pointing out that one area – inspections - has transformed most of all.
"Nowadays, in the course of inspections designed to expose anticompetitive agreements, we aim at simply confirming cartels because the evidence is already known to us", concluded Andrey Tsarikovskiy.


text:
UNI WITCH HUNT
Snowflake students who ban anyone they don't like from speaking are dragging Britain back to the Middle Ages, top historian warns
Presenter and author argues there has been a 'revolution by stealth' which is stifling debate, created a 'quasi-religious fervour' for Jeremy Corbyn, and suggesting Britain could become a 'military dictatorship' as a result
Brad Harper
THE RISE of no-platforming at universities has put Britain back in the Middle Ages, according to the historian David Starkey
The TV presenter and author argues there has been a "revolution by stealth" which is stifling debate, created a "quasi-religious fervour" for Jeremy Corbyn, and suggested Britain could become a "military dictatorship" as a result.
David Starkey argues there has been a 'revolution by stealth' which is stifling debateCredit: BBC
He suggested that no-platforming guest speakers at academic institutions – which prevents them from appearing - is a "heresy trial without a stake".
It has been used by student unions across the country to ban people with specific views from speaking to students or engaging in debate.
The No Platform Policy was first introduced by the National Union of Students in 1974. The purpose of it was to prevent controversial or offensive speakers from speaking at universities. Many student unions have used the policy to repress opinions they disagree with.
The likes of Eric Pickles, Germaine Greer, George Galloway and Douglas Carswell have all been no-platformed recently.
Earlier this year, a violent hard-left mob interrupted a talk by Tory MP Jacob Rees Mogg at the University of the West of England.
Earlier this year a violent hard-left mob interrupted a talk by Tory MP Jacob Rees Mogg at the University of the West of EnglandCredit: PA
Speaking on BBC's This Week, Mr Starkey raised concerns of a "stealth" revolution which has destroyed patriotism and free-speech.
He described the support among the young for the Labour leader as having a "pseudo-religious intensity", which could lead to the abolition of parliament.
Mr Starkey spoke out against the handling of historical sex abuse cases, saying that the process of proving someone guilty had turned into a "witch craze".
The 73-year-old said: "For in the last 20 years we have had a revolution by stealth.
MOST READ IN POLITICS:
"Not in our streets but in our values as a generation brought up with no rules and no religion has lurched with quasi-religious fervour into a puritanical group-think where debate is stifled and difference of opinion cannot be tolerated.
"Everywhere is back to the Middle Ages. In the Universities, no platforming is a heresy trial without the stake.
"In Law the uncovering of historic sex abuse has turned from due-process into a witch craze. Accusation proves guilt, every victim must be believed. This is Salem."
Moment Conservative MP Jacob Rees-Mogg was caught up in a scuffle after protesters disrupted a speech at University in Bristol


text:
Home Enter The Podcaster Matrix The Perspective Review of: The Terminator (1984)
The Perspective Review of: The Terminator (1984)
http://podcastermatrixvault.com/downloads/shows/2guystalking/terminator/2gt-terminator-podcast-t1-review.mp3
It's August 29th again – and Judgment Day arrives once more on everyone's calendars. It's the movie that keeps on giving, and will not stop – UNTIL YOU ARE DEAD! It launched a franchise, careers and a movement inside of feature film fandom that cannot be denied. The movie that started it all wasn't planned as a mega hit but, James Cameron's story, characters, storytelling and film making skill set all bonded together to create a bold, endearing statement that left deep impact on moviegoers forever. What kind of Hype was there? What kind of money did it make? What was great about the project and more importantly – where did it miss the mark? The details of the franchise, all of these bits and a rating are all housed inside The Perspective Review of 1984's The TERMINATOR assure you that you should "Listen to This Podcast If You Want to Live!"
Click Here to Review the Detailed Shownotes for this Program!
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Stories You Didn't Know About Xbox One Cheats
English: The Xbox console with the S controller, made by Microsoft. (Photo credit: Wikipedia)
After an eight year long wait, Xbox 360 lovers finally got to lay hands on Xbox One, an efficient, multi-faceted and powerful console. Microsoft has revolutionized gaming with this launch and taken gaming experience to new heights. According to Microsoft, content, capabilities and the features are meant to be intuitive and improving. This means, updates will be done regularly without hampering gameplay.
Xbox One is very advanced and comes with a state-of-the-art gaming OS with a ruthless Windows OS. You get the best off both, without compromising on comforts or performance. It's a great balance of performance and power. It looks and feels great, power is used better and gaming experience is super-fast. About 40 new innovations are added, to make the experience better. In other words, it is built by gamers for gamers!
With so many features, there's no way users can stay away from it or swap it for other consoles. For Xbox lovers out there, Xbox One cheats are already out! There are many different games and cheats make playing quite easy. Basically, cheats are commands that are either entered using buttons or could be directional commands and adds something new to the game. It could offer different possibilities, depending on the cheats used. Cheats can unlock a character, create special effects or add certain powers, gives immunity in a gameplay, etc. There are many things Xbox One cheats can do and give you unlimited power in a game play. This makes playing interesting and exhilarating.
There are many games for Xbox One, with different gameplays. Besides, Xbox One is quite new and yet, Xbox One cheats are already out. Basically, the cheats are for individual games and can vary with each game. Besides these, Microsoft also released an Xbox One cheat sheet to make operations easy. This list includes useful commands to operate Xbox One like "Xbox Help" to bring up help options in any window you are using, "Xbox Invite" as a shortcut for inviting friends, etc. Though not really Xbox One Cheats for games, these are more like command cheats for making operations easy.
Those who love gaming should know about cheats and how they can alter the way a game is played, or progresses. While some think adding cheats give gamers undue advantage over the others, it purely depends on an individual. Not many are unaware about Xbox One cheats and many users use them quite often. In those lines, Xbox One cheats can add to the excitement by giving gamers advantage or edge over their friends on multiplayer games. Besides, playing games with by adding cheat codes are a real fun. Don't you think so?
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The Monmouthshire Meadows Spring 2016 Newsletter can be read at http://www.monmouthshiremeadows.org.uk/?page_id=883. It has the latest news about Kingcoed Meadow and much else about the group's activities and events.
The latest newsletter from the Monmouthshire Countryside Service can be read at http://us3.campaign-archive1.com/?u=1b937f9c254d11056df6b1977&id=fdbb63c036&e=c62f13cb48. It includes news of Pathcare groups, the Chepstow Walking Festival (www.walksinchepstow.co.uk) and Woodland Trust volunteering opportunities at Cefn Ila.
Monmouthshire CC is inviting comments on its Draft Charging Schedule for the Community Infrastructure Levy (CIL) by Thursday, 5 May. For example, there will be a charge of £120 per sq m for most new developments of 3 or more dwellings, but none if less than three. Details at http://www.monmouthshire.gov.uk/planning-policy/current-consultations.
Uncategorized Comments Off on Unsure About Fracking?
Abergavenny and Crickhowell Friends of the Earth have arranged a showing of a film about fracking and an opportunity to hear and question several expert speakers at the St Michael's Centre, Pen-y-Pound on Tuesday, 12 April at 7.00pm.
The University of South Wales is consulting on the future use of its Caerleon campus – the old Caerleon Training College. The consultation is at http://www.caerleon.site/ and it will close on Friday, 15 April. The future of significant heritage and modern buildings and facilities is uncertain, and the early expression of well-argued views is important.
A community project, long in the making, is under threat. In August 2015, Resilient Energy Severndale, a 500kW community wind project, was granted planning permission by the Forest of Dean Council. But, late in the day a single individual representing a small group of local objectors, is taking the case to Judicial Review with two key Community Energy principles challenged. The situation is grave enough to threaten the legality of any energy projects (sustainable or otherwise) that are community-based and set up for the benefit of a community. Resilient Energy have launched a crowd funding appeal to help contribute to their legal costs. Assuming the case is won and the project proceeds, the intention is to repay any contributions made by supporters. Please go to www.crowdfunder.co.uk/high-court-challenge-to-aims-of-community-energy for more details, and, if possible, make a pledge. The case will be heard in the High Court on 21 April. Pledges of support are needed by Tuesday, 22 March.
Despite the continuing controversy, an issue at the coming Assembly election, Welsh Government has published the draft orders for the new £1.1 billion M4 across the Levels. Details, including a programme of exhibitions starting on 17 March, are at http://gov.wales/topics/transport/roads/schemes/m4-road/m4/?lang=en and responses must be no later than 4 May.
In the run-up to the Assembly elections in May, Oxfam is organising a series of Candidate Cafes in Oxfam shops to give you the chance to chat to local political candidates about the issues that matter. There is to be one in Oxfam's Frogmore Street shop in Abergavenny at 5.30pm on Wednesday, 30 March. Space is limited so book your place with Hayley at HRichards@oxfam.org.uk.
Monmouth Friends of the Earth are planning a hustings meeting at the Bridges Centre on 17 or 18 April – more information to come.