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This study encapsulates not just the collective resolve but presents a compelling narrative of countries' efforts to mitigate the impact of the COVID-19 pandemic. Spanning from January 2020 to May 2022, this study provides a comprehensive analysis of countries' responses, including a critical examination of the vaccination period. The study introduces a predictive model using linear regression to project future confirmed cases and deaths, providing countries invaluable insights for navigating potential pandemics. The research employs Stepwise Data Envelopment Analysis (DEA) and the Malmquist Index (MI) to unveil efficiency trends in each country's battle against COVID-19 spread. The analysis unfolds in two dimensions: Assessing the efficiency of 29 countries, including those in the Middle East, in reducing the COVID-19 spread rate, and evaluating their efficiency in curbing the mortality rate. Pearson and Spearman Correlation tests shed light on nuanced relationships within the model. The results underscore the critical impact of variables on confirmed cases and deaths, revealing the pivotal role of country-specific characteristics such as the population, population density, and proportion of individuals aged over 65. This study not only contributes to our understanding but offers a strategic roadmap for countries to strengthen their defenses against future pandemics. [ABSTRACT FROM AUTHOR]

Traffic crashes are one of the most severe problems that affect communities worldwide. According to the World Health Organization, the annual number of global fatalities due to traffic crashes is about 1.35 million. Many factors affect the likelihood and the severity of a crash. In this paper, different factors are studied to evaluate their impact on the frequency, the severity, and the type of crashes at intersections, in order to boost road safety. California crash data have been obtained from the Highway Safety and Information System (HSIS) database and have been analyzed statistically. The analysis has included several factors such as severity, type of intersection, and the characteristics of the driver, roadway, environment, and vehicle. A negative binomial regression has been conducted by using the Statistical Analysis System (SAS®) Software for building three models. As a result, different parameters have been considered significant factors in each model. From all the models, the type of intersection and collision, the area of the crash, the type of surface type and its condition, the number of lanes, the age, and the gender have been considered significant factors for crashes at intersections. The result of this study could help reduce the likelihood and severity of crashes. [ABSTRACT FROM AUTHOR]

Early diagnosis of brain disorders can significantly reduce the devastating consequences of these disorders. Physician's diagnostic capabilities and diagnosis time can be improved using computerized diagnosis techniques. Magnetic Resonance (MR) images are used for diagnosing multiple sclerosis, which is a disease that occurs when the immune system eats away at the protective covering of nerves. MR images segmentation is a complicated task due to the variability in the lesion's shape, location and patients' anatomy. This study proposes a new computerized diagnosis technique for detecting brain disorders based on features extracted from MR images. Data of 121 cases were used, including healthy and patients with brain disorders. The cases were classified into normal and abnormal, with abnormal representing brain disorders cases. The abnormal cases were fed into a classifier to identify brain disorders. Classification accuracies in the two stages were 82.7% and 70%, respectively; indicating a significant improvement over methods found in literature. The automated structure of the proposed algorithm is suitable for use in hospitals at low cost. [ABSTRACT FROM AUTHOR]