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With the advent of COVID-19 arose the need for social distancing measures, including the imposition of far-reaching lockdowns in many countries. The lockdown has wreaked havoc on many aspects of daily life, but education has been particularly hard hit by this unprecedented situation. The closure of educational institutions brought along many changes, including the transition to more technology-based education. This is a systematic literature review that seeks to explore the transition, in the context of the pandemic, from traditional education that involves face-to-face interaction in physical classrooms to online distance education. It examines the ways in which this transition has impacted academia and students and looks at the potential long-term consequences it may have caused. It also presents some of the suggestions made by the studies included in the paper, which may help alleviate the negative impact of lockdown on education and promote a smoother transition to online learning.

False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we compare several recent deep learning-based model that proven their high performance and accuracy in detecting the exact location of the attack node, which are convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models. We, then, compare their performance with baseline multi-layer perceptron (MLP)., All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Based on the results, CNN model exhibited consistently high performance in very short training time. LSTM achieved the second highest accuracy; however, it had required an averagely higher training time. The attention-based LSTM model achieved a high accuracy of 94.53 during hyperparameter tuning, while the CNN model achieved a moderately lower accuracy with only one-fourth of the training time. Finally, the performance of each model was quantified on different variants of the dataset—which varied in their l 2 -norm. Based on the results, LSTM, CNN obtained the highest accuracy followed by CNN-LSTM and lastly MLP. [ABSTRACT FROM AUTHOR]

With the rise of the 3Ds--decarbonization, decentralization, and digitalization--the number of electric vehicles is projected to increase, necessitating the implementation of modern technologies to avoid unnecessary energy wastage. Numerous studies have been developed proposing electric vehicle (EV) charging frameworks in networks empowered by renewable energy resources. In addition, more focus has recently been directed on incorporating blockchain technology to assure security and transparency in trading systems. However, fewer studies have delved into developing a practical implementation of their solution due to the complexity of the topic. Therefore, this paper thoroughly investigates integrating blockchain technology in electric vehicle charging systems, analyzing the existing practical implementation and their characteristics. It comprises 48 relevant studies between 2017 and 2023, covering the following main research areas: (i) renewable energy-based electric charging systems, (ii) blockchain frameworks used in energy trading, and (iii) performance metrics of simulated and implemented solutions. Results show that blockchain applications in EVs and energy trading systems are highly current, and researchers are actively exploring ways to improve their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

An editorial is presented on the challenges faced by women in scientific research in the United Arab Emirates (UAE). It reports on the advancements made in education, employment, and leadership roles. It highlights the efforts towards gender equality in science through initiatives like the L'Oréal—the United Nations Educational, Scientific, and Cultural Organization (UNESCO) For Women in Science International Awards.