The COVID-19 pandemic is one of the worst catastrophes that we have faced globally in recent years. It has not only taken its toll on the economic sector but also on the education sector. The social distancing norms that are in place as a direct response to the pandemic have turned conventional classroom teaching into a problematic minefield; as such, students all over the world have been forced into unprecedented situations that have served only to worsen the situation. The current pandemic has given rise to one of the worst crises the 21^st Century has ever seen, resulting in a surge of unemployment. Many companies have taken the route of firing employees or making redundancies, as they cannot afford the monthly reimbursement for staff. While this issue primarily concerns full-time workers, it also carries significant consequences for students – a considerable number of students are required to earn their daily living costs, and, without a job, they cannot pay their educational fees, accommodation costs, or living expenses. This comprehensive study briefly discusses the multitude of problems faced by students in the UK regarding higher education, as a direct result of the coronavirus pandemic. It contains six individual sections: a detailed introduction; the methodological procedures employed; educational disruptions, covering issues from hindrances in field research to examinations and student evaluations; personal problems experienced by students, such as accommodation and loss of income; concerns arising from the global pandemic; and finally, a conclusion and summary of the study’s findings.

Nickel foam substrates are frequently utilised for renewable energy applications as porous 3D-substrates. Preparation of these substrates usually includes an acid washing step, however the degree to which this step affects the final electrochemical performance after spray coating a catalyst ink is unreported. Herein, we report the effect of acid washing through physicochemical and electrochemical characterisation. The electrochemical performance was determined by repeated measurements of catalyst-coated nickel foam substrates both with and without the initial step of acid washing. Acid washing increased current density by 17.9% for the acid treated, MoS2-coated nickel foam electrode. This increment was affiliated with an electrochemically active surface area which increased by 87.1%, where Tafel analysis indicated that the acid treated, MoS2-coated electrodes facilitates the initial water dissociation step of the hydrogen evolution reaction with greater ease. Similar effects were also discovered for acid treated PtIr(1:3)/C-coated nickel foam substrates, albeit with less pronounced effects. Stability was also improved where the degradation rate was reduced by 18.9% for the acid treated, MoS2-coated electrodes. This proves the utility of acid washing nickel foam electrodes.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.

Additive manufacturing has already been established as a highly versatile manufacturing technique with demonstrated potential to completely transform conventional manufacturing in the future. The objective of this paper is to review the latest progress and challenges associated with the fabrication of multi-material parts using additive manufacturing technologies. Various manufacturing processes and materials used to produce functional components were investigated and summarized. The latest applications of multi-material additive manufacturing (MMAM) in automotive, aerospace, biomedical and dentistry field were demonstrated. Investigation on the current challenges were also carried out to predict the future direction of MMAM processes. It is concluded that the further research and development needed in the design of multi-material interfaces, manufacturing processes and material compatibility of MMAM parts are necessary.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

Natural fibers have been widely used as a reinforcing material for the construction of drones. The main goal of this review is to identify a biodegradable natural fiber material that can be built for Unmanned Aerial Vehicles (UAVs). The review shows that natural fibers such as kenaf and PALF can be used as an alternative composite material, particularly for multifunctional applications where their smaller weight and less expensive characteristics make them incredibly realistic. This review also discusses the mechanical properties, advantages and disadvantages, and fabrication of the composites.

During its first month, the recently emerged 2019 Wuhan novel coronavirus (SARS-CoV-2) has already infected many thousands of people in mainland China and worldwide and took hundreds of lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge we leverage the data about the related coronaviruses that is readily available in public databases, and integrate these data into a single computational pipeline. As a result, we provide a comprehensive structural genomics and interactomics road-maps of SARS-CoV-2 and use these information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community at http://korkinlab.org/wuhan .

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.