In higher education institutions (HEI), particularly in biology and medical education, the use of 3D animation, virtual reality, and simulation offers great potential in terms of enhancing learning and engaging students. Higher education researchers are still investigating virtual reality’s possibilities and outcomes in various fields. This study focuses on the effects of 3D gamification using an Artificial Intelligence integrated Internet of Medical Things (AIoMT) implemented with virtual reality application for biology and medical students to learn about the human brain. Nowadays, both theoretical and practical education frequently incorporate virtual reality and augmented reality. Virtual tours of the human body’s systems are offered to biology students so that they may comprehend such systems’ functions. This study focuses on the use of 3D animation, virtual reality, and simulation in medical education, with a specific focus on the effects of a 3D gamification app using the Internet of Medical Things (AIoMT) on medical professionals’ passion for learning. This study uses the ARCS model and SEM analysis to examine the impact of virtual reality on students’ motivation and learning. The results show that virtual reality positively impacts motivation and the understanding of the concept-to-execution process through practice and simulation-based training. To assess how well students are learning, what they are analyzing, and how well they can understand the objects of analysis, a 3D-simulation-based and user-feedback-based design has been developed using the proposed research methodology. According to this article’s findings, a smartphone app that uses virtual reality can help medical professionals better understand the concept-to-execution process through practice. VR simulation-based training, as well as Biology teachers or medical colleges, can offer high-definition 3D VR models rather than organs in jars to understand the human anatomy and its functions more experientially and effectively.

Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R₀ is 1.3. Vaccination of infants and kids will be considered as future work.

The entire world is suffering from the post-COVID-19 crisis, and governments are facing problems concerning the provision of satisfactory food and services to their citizens through food supply chain systems. During pandemics, it is difficult to handle the demands of consumers, to overcome food production problems due to lockdowns, work with minimum manpower, follow import and export trade policies, and avoid transportation disruptions. This study aims to analyze the behavior of food imports in Saudi Arabia and how this pandemic and its resulting precautionary measures have affected the food supply chain. We performed a statistical analysis and extracted descriptive measures prior to applying hybrid statistical hypothesis tests to study the behavior of the food chain. The paired samples t-test was used to study differences while the independent samples t-test was used to study differences in means at the level of each item and country, followed by the comparison of means test in order to determine the difference and whether it is increasing or decreasing. According to the results, Saudi Arabia experienced significant effects on the number of items shipped and the countries that supplied these items. The paired samples t-test showed a change in the behavior of importing activities by—47% for items and countries. The independent t-test revealed that 24 item groups and 86 countries reflected significant differences in the mean between the two periods. However, the impact on 41 other countries was almost negligible. In addition, the comparison of means test found that 68% of item groups were significantly reduced and 24% were increased, while only 4% of the items remained the same. From a country perspective, 65% of countries showed a noticeable decrease and 16% a significant increase, while 19% remained the same.

Connected vehicles in vehicular networks will lead to a smart and autonomous transportation system. These vehicles have a large number of applications that require wireless connectivity by using cellular vehicle-to-everything (C-V2X). The infrastructure of C-V2X comprises multiple roadside units (RSUs) that provide direct connectivity with the on-road vehicles. Vehicular traffic applications are mainly categorized into three major groups such as emergency response traffic, traffic management and infotainment traffic. Vehicles have limited processing capabilities and are unable to process all tasks simultaneously. To process these offloaded tasks in a short time, fog servers are placed near the RSUs. However, it is sometimes not possible for the fog computing server to process all offloaded tasks. In this work, a utility function for the RSU to process these offloaded tasks is designed. In addition, a knapsack-based task scheduling algorithm is proposed to optimally process the offloaded tasks. The results show that the proposed scheme helps fog nodes to optimally scrutinize the high-priority offloaded tasks for task execution resulting in more than 98% of emergency tasks beingprocessed by fog computing nodes.

The new COVID-19 variants of concern are causing more infections and spreading much faster than their predecessors. Recent cases show that even vaccinated people are highly affected by these new variants. The proactive nucleotide sequence prediction of possible new variants of COVID-19 and developing better healthcare plans to address their spread require a unified framework for variant classification and early prediction. This paper attempts to answer the following research questions: can a convolutional neural network with self-attention by extracting discriminative features from nucleotide sequences be used to classify COVID-19 variants? Second, is it possible to employ uncertainty calculation in the predicted probability distribution to predict new variants? Finally, can synthetic approaches such as variational autoencoder-decoder networks be employed to generate a synthetic new variant from random noise? Experimental results show that the generated sequence is significantly similar to the original coronavirus and its variants, proving that our neural network can learn the mutation patterns from the old variants. Moreover, to our knowledge, we are the first to collect data for all COVID-19 variants for computational analysis. The proposed framework is extensively evaluated for classification, new variant prediction, and new variant generation tasks and achieves better performance for all tasks. Our code, data, and trained models are available on GitHub (https://github.com/Aminullah6264/COVID19, accessed on 16 September 2022).