Evaluation of Preventive Measures and Forecasting of COVID-19 Infection: Case Study Egypt.

Background COVID-19 is a highly infectious disease caused by SARS-CoV-2. This article assessed the effectiveness of preventive measures of COVID-19 infection, including social distancing (SD) and quarantine (Q) of patients and contacts in Egypt. Methods A simple model was developed to predict the infection rate without preventive measures. The article utilizes fertile meta- heuristic technique and particle swarm optimization (PSO), to predict the growth of the disease. Results A correlation between the predicted and actual infected cases, validated the proposed forecasting algorithm. Preventive measures together with the Egyptian Government stay home order reduced 98% of expected infections. PSO analyses showed that infection and death rates will continue to increase particularly with lifting these restrictive preventive measures. Conclusions The advised PSO model could predict COVID-19 infection and death rates with high degree of accuracy. This prediction model could help health authorities in decision making.

A Symbiotic Organism Search-Based Selective Harmonic Elimination in a Switched Capacitor Multilevel Inverter

Multilevel inverters are increasingly being employed for industrial applications, such as speed control of motors and grid integration of distributed generation systems. The focus is on developing topologies that utilize fewer lower-rating switches and power sources while working efficiently and reliably. This work pertains to developing a three-phase multilevel inverter that employs switching capacitors and a single DC power supply that produces a nine-stage, three-phase voltage output. A recently proposed powerful meta-heuristic technique called symbiotic organism search (SOS) has been applied to identify the optimum switching angles for Selective Harmonic Elimination (SHE) from the output voltage waveform. A thorough converter analysis has also been done in the MATLAB/SIMULINK environment and is validated with the real-time hardware-in-the-loop (HIL) experiment results.

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

Modeling eutrophication risks in Tanes reservoir by using a hybrid WOA optimized SVR-relied technique along with feature selection based on the MARS approximation

Total phosphorus (from now on mentioned as TP) and chlorophyll-a (from now on mentioned as Chl-a) are recognized indicators for phytoplankton large quantity and biomass-thus, actual estimates of the eutrophic state-of water bodies (i.e., reservoirs, lakes and seas). A robust nonparametric method, called support vector regression (SVR) approach, for forecasting the output Chl-a and TP concentrations coming from 268 samples obtained in Tanes reservoir is described in this investigation. Previously, we have carried out a selection of the main features (biological and physico-chemical predictors) employing the multivariate adaptive regression splines approximation to construct reduced models for the purpose of making them easier to interpret for researchers/readers and to reduce the overfitting. As an optimizer, the heuristic technique termed as whale optimization iterative algorithm (WOA), was employed here to optimize the regression parameters with success. Two main results have been obtained. Firstly, the relative relevance of the models variables was stablished. Secondly, the Chl-a and TP can be successfully foretold employing this hybrid WOA/SVR-based approximation. The coincidence between the predicted approximation and the observed data obviously demonstrates the quality of this novel technique.

A Competitive Volleyball Algorithm to Solve Global Optimization with the Engineering Problems

In real life, many engineering problems are nonlineble NP problems, in order to solve some of these problems, we put forward a competitive volleyball algorithm.The algorithm proposed in this paper is a meta-heuristic technique based on swarm optimization. It is inspired by the competition between volleyball teams in a league and the improvement in players’ overall abilities in order to win the Most Valuable Player award. Several specific terms relating to competition, such as pre-match reinforcement, single round robin mechanism, optimal strategy constitute the structure of the algorithm. The sensitivity of several parameters of the algorithm is analyzed and tested for three types of benchmark functions: unimodal, high-dimensional multimodal and low-dimensional multimodal functions. Through the use of these three types of test functions, the performance of this algorithm is compared with nine classical metaheuristic algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Soccer League Competition (SLC), League Championship Algorithm (LCA) and Volleyball Premier League (VPL). CVA has been used to solve three real-world engineering problems. The results show that the performance of the CVA is behaviorally promising and better than the other classical metaheuristic algorithms.

Novel Approach Sizing and Routing of Wireless Sensor Networks for Applications in Smart Cities

Citizens are expected to require the growth of multiple Internet of Things (IoT) -based applications to improve public and private services. According to their concept, smart cities seek to improve the efficiency, reliability, and resilience of these services. Consequently, this paper searches for a new vision for resolving problems related to the quick deployment of a wireless sensor network (WSN) by using a sizing model and considering the capacity and coverage of the concentrators. Additionally, three different routing models of these technology resources are presented as alternatives for each WSN deployment to ensure connectivity between smart meters and hubs required for smart metering. On the other hand, these solutions must reduce costs when this type of wireless communication network is deployed. The present work proposes various optimization models that consider the physical and network layers in order to integrate different wireless communication technologies, thus reducing costs in terms of the minimum number of data aggregation points. Using a heterogeneous wireless network can reduce resource costs and energy consumption in comparison to a single cellular technology, as proposed in previous works. This work proposes a sizing model and three different models for routing wireless networks. In each case, constraints are evaluated and can be associated with different real-world scenarios. This document provides an optimization model that encompasses all of the proposed constraints; due to the combinatorial nature of the problem, this would require a heuristic technique.

Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.

Application of failure mode and effects analysis to reduce microplastic emissions

A complete understanding of the occurrence of microplastics and the methods to eliminate their sources is an urgent necessity to minimize the pollution caused by microplastics. The use of plastics in any form releases microplastics to the environment. Existing policy instruments are insufficient to address microplastics pollution and regulatory measures have focussed only on the microbeads and single-use plastics. Fees on the use of plastic products may possibly reduce their usage, but effective management of plastic products at their end-of-life is lacking. Therefore, in this study, the microplastic–failure mode and effect analysis (MP–FMEA) methodology, which is a semi-qualitative approach capable of identifying the causes and proposing solutions for the issue of microplastics pollution, has been proposed. The innovative feature of MP–FMEA is that it has a pre-defined failure mode, that is, the release of microplastics to air, water and soil (depending on the process) or the occurrence of microplastics in the final product. Moreover, a theoretical recycling plant case study was used to demonstrate the advantages and disadvantages of this method. The results revealed that MP–FMEA is an easy and heuristic technique to understand the failure-effect-causes and solutions for reduction of microplastics and can be applied by researchers working in different domains apart from those relating to microplastics. Future studies can include the evaluation of the use of MP–FMEA methodology along with quantitative methods for effective reduction in the release of microplastics.