In March 2020, the World Health Organization declared the COVID-19 outbreak to be a pandemic. Soon af-terwards, people began sharing millions of posts on social media without considering their reliability and truthfulness. While there has been extensive research on COVID-19 in the English lan-guage, there is a lack of research on the subject in Arabic. In this paper, we address the problem of detecting fake news surrounding COVID-19 in Arabic tweets. We collected more than seven million Arabic tweets related to the corona virus pandemic from January 2020 to August 2020 using the trending hashtags during the time of pandemic. We relied on two fact-checkers: the France-Press Agency and the Saudi Anti-Rumors Authority to extract a list of keywords related to the misinformation and fake news topics. A small corpus was extracted from the collected tweets and manually annotated into fake or genuine classes. We used a set of features extracted from tweet contents to train a set of machine learning classifiers. The manually annotated corpus was used as a baseline to build a system for automatically detecting fake news from Arabic text. Classification of the manually annotated dataset achieved an F1-score of 87.8% using Logistic Regression (LR) as a classifier with the n-gram-level Term Frequency-Inverse Document Frequency (TF-IDF) as a feature, and a 93.3% F1-score on the automatically annotated dataset using the same classifier with count vector feature. The introduced system and datasets could help governments, decision-makers, and the public judge the credibility of information published on social media during the COVID-19 pandemic.

Universities face a constant challenge when distributing students and allocating them to their required classes, especially for a large mass of students. Generating feasible timetables is a strenuous task that requires plenty of resources, which makes it impractical to take student preferences into consideration during the process. Timetabling and scheduling problems are proven to be NP-hard due to their complex nature and large search spaces. A genetic algorithm (GA) that assigns students to their classes based on their preferences is proposed as a solution to this problem and is implemented in this article. The GA’s performance is enhanced by applying different metaheuristic concepts and by tailoring the genetic operators to the given problem. The quality of the solutions generated is boosted further with the unique repair and improvement functions that were implemented in conjunction with the genetic algorithm. The success of the GA was evaluated by using different datasets of varying complexity and by assessing the quality of the solutions generated. The results obtained were promising and the algorithm guarantees the feasibility of solutions as well as satisfying more than 90% of student preferences even for the most complex problems.

The need for energy is always increasing as civilization evolves. Renewable energy sources are crucial for meeting energy demands as conventional fuel resources are slowly running out. Researchers are working to extract the most amount of power possible from renewable resources. Numerous resources are in demand, including solar, wind, biomass, tidal, and geothermal resources. Solar energy outperformed all the aforementioned resources in terms of efficiency, cleanliness, and pollution freeness. Intermittency, however, is the resource’s main shortcoming. Maximum power point tracking algorithm (MPPT) integration is required for the system to achieve continuous optimum power by overcoming the feature of intermittency. However, generating electrical energy from solar energy has presented a significant problem in ensuring the output power’s quality within a reasonable range. Total harmonic distortion (THD), a phenomenon, may have an impact on the power quality. Depending on the properties of the load, variables like power factor, voltage sag/swell, frequency, and unbalancing may occur. The quality of power and its criterion exhibits a non-linear connection. The article’s primary objective is to analyze the PV interface grid-linked system’s qualitative and quantitative performance. With respect to varying solar irradiation conditions, partial shading conditions, and solar power quality within the acceptable dimension, a novel intelligent multiple-objective horse herd optimization (HHO)-based adaptive fractional order PID (HHO-AFOPID) controller is used to achieve this goal. Adaptive fractional order PID (AFOPID), conventional FOPID, and PID controllers were used to evaluate the performance of the suggested controller, which was then validated using a commercially available PV panel in MATLAB/Simulink by varying the productivity of non-conventional resources, the inverter’s level of uncertainty, and the potential at the grid’s end. In order to realize the features of the system, sensitivity examination is also carried out for solar energy’s sensitive parameters. The stability analysis of the proposed control topology is also carried out in terms of the integral absolute error (IAE) and integral time absolute error (ITAE). The examination of the sensitivity of variations in solar radiation in kilowatt per square meter per day is based on the total net present cost (TNPC) and levelized cost of energy (LCOE), as optimal dimension and energy cost are both aspects of priority. The suggested control methodology is an approach for the qualitative and quantitative performance analysis of a PV interface grid-oriented system.

In this paper, two new versions of modified active disturbance rejection control (MADRC) are proposed to stabilize a nonlinear quadruple tank system and control the water levels of the lower two tanks in the presence of exogenous disturbances, parameter uncertainties, and parallel varying input set-points. The first proposed scheme is configured from the combination of a modified tracking differentiator (TD), modified super twisting sliding mode (STC-SM), and modified nonlinear extended state observer (NLESO), while the second proposed scheme is obtained by aggregating another modified TD, a modified nonlinear state error feedback (MNLSEF), and a fal-function-based ESO. The MADRC schemes with a nonlinear quadruple tank system are investigated by running simulations in the MATLAB/SIMULINK environment and several comparison experiments are conducted to validate the effectiveness of the proposed control schemes. Furthermore, the genetic algorithm (GA) is used as a tuning algorithm to parametrize the proposed MADRC schemes with the integral time absolute error (ITAE), integral square of the control signal (ISU), and integral absolute of the control signal (IAU) as an output performance index (OPI). Finally, the simulation results show the robustness of the proposed schemes with a noticeable reduction in the OPI.

Smart agriculture modifies traditional farming practices, and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies. In today’s world where technology is everything, these technologies are utilized to streamline regular tasks and procedures in agriculture, one of the largest and most significant industries in every nation. This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems. Divided into three main sections—security analysis, system architecture and design and risk assessment of Cyber-Physical Systems (CPS) applications—the study delves into various elements crucial for smart farming, such as data sources, infrastructure components, communication protocols, and the roles of different stakeholders such as farmers, agricultural scientists and researchers, technology providers, government agencies, consumers and many others. In contrast to earlier research, this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling, penetration testing, and vulnerability assessments. Important discoveries highlight the concerns connected to unsecured communication protocols, possible threats from malevolent actors, and vulnerabilities in IoT devices. Furthermore, the study suggests enhancements for CPS applications, such as strong access controls, intrusion detection systems, and encryption protocols. In addition, risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards, addressing issues like data breaches, system outages, and automated farming process sabotage. The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor. This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards. The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach, which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

In this new era of inter-connectedness, the COVID-19 outbreak has been characterized by a rapid surge of misinformation — or “fake news” — about the disease on social media, exacerbating its negative psychological impacts and hindering public health efforts. By providing easy access to and sharing of information in real time, social media is considered the main channel for the distribution of fake news. In this viewpoint, we try to find the main impacts of fake news related to COVID-19 on social media users and propose strategies for limiting its spread. In this regard, we set out four key objectives: studying and evaluating the impact of fake news on social media audiences; analyzing fake news patterns; and recommending ways to stop its circulation. To achieve these objectives, we follow a review-based research methodology encompassing scholarly articles and reports from sources including JSTOR, Taylor & Francis, MDPI, Google Scholar, and the WHO, covering the period from 2016 onwards. We employ the Uses and Gratification model to identify three key impacts of COVID-19 related misinformation on social media users: (1) an “infodemic” of health-related misinformation; (2) an increase in social media pastime; and (3) an increase in social, political, and religious conflicts. Several actions are proposed to limit the spread of fake news, involving entities such as government and health agencies, as well as social media companies.