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This paper explores the student learning experience using technology as an e-learning tool during the COVID-19 pandemic. This article utilized qualitative methods to examine the quality of student learning using deep and surface approaches to understand what influences student engagement with technology. Interviews were conducted with 21 students from various academic majors using deductive content analysis to evaluate their responses. The findings show that technology increased student engagement with class discussion, and students became more informed about lecture material. It is noteworthy that there were some variations in the students' interpretation of the learning experience with technology, indicating a gap in the quality of learning. Notably, there was an improvement in grades compared to the last online session and the face-to-face learning experience prior to the COVID-19 pandemic, and there were fewer missing quizzes and late assignments. These outcomes may be used to enhance teaching strategies and problem solving within teaching and learning to develop a new mode of delivery. In addition, these findings are important for the future of education in a post-pandemic world. [ABSTRACT FROM AUTHOR]

The present paper reports the impact of the Covid-19 pandemic on the electricity peak load and power generation in the State of Kuwait during the partial and full curfews imposed in March, April and May 2020 using historic data measured data and the predictions provided by a statistical genetic algorithm model. A quantitative assessment is made of the economic and environmental impacts caused by partial and full lockdowns. Comparison of measured peak demand for 2019 and 2020 with predicted peak demand for 2020 has: (i) enabled an accurate evaluation of residential energy consumption in the state of Kuwait at nearly 18 MWh yearly the highest energy consumption per capita in the world, (ii) shown that the imposition of the curfews to reduce the spread of COVID-19 caused a fall in the demand for electrical power of 17.6% compared with the expected demand and (iii) quantified the reduction in CO2, NOx and CO pollutant emissions produced by power plants due to less fuel being consumed. A mathematical model has been developed to predict the peak electric load in the national grid according to climatic data supplied by the Meteorological Department of Civil Aviation of Kuwait and National Control Center (NCC). [ABSTRACT FROM AUTHOR]

Revisiting the classical model by Ross and Kermack-McKendrick, the Susceptible–Infectious–Recovered (SIR) model used to formalize the COVID-19 epidemic, requires improvements which will be the subject of this article. The heterogeneity in the age of the populations concerned leads to considering models in age groups with specific susceptibilities, which makes the prediction problem more difficult. Basically, there are three age groups of interest which are, respectively, 0–19 years, 20–64 years, and >64 years, but in this article, we only consider two (20–64 years and >64 years) age groups because the group 0–19 years is widely seen as being less infected by the virus since this age group had a low infection rate throughout the pandemic era of this study, especially the countries under consideration. In this article, we proposed a new mathematical age-dependent (Susceptible–Infectious–Goneanewsusceptible–Recovered (SIGR)) model for the COVID-19 outbreak and performed some mathematical analyses by showing the positivity, boundedness, stability, existence, and uniqueness of the solution. We performed numerical simulations of the model with parameters from Kuwait, France, and Cameroon. We discuss the role of these different parameters used in the model; namely, vaccination on the epidemic dynamics. We open a new perspective of improving an age-dependent model and its application to observed data and parameters. [ABSTRACT FROM AUTHOR]

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman's correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries. [ABSTRACT FROM AUTHOR]