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Introduction: Coronavirus disease 2019 (COVID-19) has had a profound impact globally, causing the death of millions of people and deeply affecting socio-psychological, human health, and economic systems, with some nations bearing a disproportionate burden. Despite obesity having been established as one of the major risk factors of COVID-19 severity and other degenerative diseases, the effects that dietary pattern intake plays in COVID-19 outcomes remain poorly understood. The goal of this study is to look into the connection between eating habits, the number of non-obese and obese people, and COVID-19 outcomes in countries with populations exhibiting normal Body Mass Index (BMI), which is an indicator of obesity. Methods: The analysis includes data from 170 countries. From the 170 countries, we focused on 53 nations where the average, BMI falls within the normal range (18.5 to 24.9). A subset of 20 nations was selected for a more detailed examination, comprising 10 nations with the lowest BMI values within the normal range (18.5-19.8) and 10 nations with the highest BMI values within the normal range (23.5-24.9). We used Artificial Intelligence (AI) and Machine Learning (ML) applications to evaluate key metrics, including dietary patterns (sugar and vegetable intake), obesity prevalence, incidence rate, mortality rate, and Case Fatality Rate (CFR). Results: The results demonstrate a significant correlation between higher obesity prevalence and increased COVID-19 severity, evidenced by elevated incidence, mortality, and CFRs in countries like North Macedonia and Italy. In contrast, nations such as Iceland and New Zealand with well-established healthcare systems revealed low mortality rate and case fatality rate despite variations in dietary habits. The study also revealed that vegetable consumption appears to provide a slight to significant protective effects, suggesting that dietary patters alone do not consistently predict COVID-19 Outcomes. Conclusion: Data generated from this study showed the crucial role of healthcare infrastructure along with the testing capacity and data reporting in influencing the success of pandemic responses. It also highlights the need of integrating public health strategies, which focus on obesity management and improvement of healthcare preparedness. In addition, AI-driven predictive modeling offers valuable insights that may guide pandemic response efforts in the future, thereby enhancing global health crisis management and mitigating the impact of future health emergencies. Keywords: COVID-19; Dietary patterns; Obesity; Artificial intelligence; Machine learning; Public health; Health care systems 

Abstract The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology. 

The integration of cyber-physical systems (CPS) has been extremely advantageous to society, it merges the attention of cybersecurity for vehicles as a timely concern as a matter of public and individual. The failure of any vehicle system could have a serious impact on vehicle control and cause undesired consequences. With the growing demand for security in CPS, there are few hands-on labs/modules available for training current students, future engineers, or IT professionals to understand cybersecurity in CPS. This study describes the execution of a free security testbed to replicate a vehicle’s network system and the implementation of this testbed via hands-on lab designed to introduce concepts of vehicle control systems. The hands-on lab simulates insider threat scenarios where students had to use can-utils toolkits and SavvyCAN to send, modify, and capture the network packet and exploit the system vulnerability threats such as replay attacks and fuzzing attacks on the vehicle system. We conducted a case study with 21 university-level students, and all students completed the hands-on lab, pretest, posttest, and a satisfaction survey as part of a non-graded class assignment. The experimental results show that most students were not familiar with cyber-physical systems and vehicle control systems and never had the chance to do any hands-on lab in this field before. Furthermore, students reported that the hands-on lab helped them learn about CAN-bus and rated high scores for enjoyment. We discussed the design of an affordable tool to teach about vehicle control systems and proposed directions for future work. 

Traversals are commonly seen in tree data structures, and performance-enhancing transformations between tree traversals are critical for many applications. Existing approaches to reasoning about tree traversals and their transformations are ad hoc, with various limitations on the classes of traversals they can handle, the granularity of dependence analysis, and the types of possible transformations. We propose Retreet, a framework in which one can describe general recursive tree traversals, precisely represent iterations, schedules and dependences, and automatically check data-race-freeness and transformation correctness. The crux of the framework is a stack-based representation for iterations and an encoding to Monadic Second-Order (MSO) logic over trees. Experiments show that Retreet can automatically verify optimizations for complex traversals on real-world data structures, such as CSS and cycletrees, which are not possible before. Our framework is also integrated with other MSO-based analysis techniques to verify even more challenging program transformations.