SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
2 results
Search Results
Item Restricted Advancing Emergency Department Efficiency, Infectious Disease Management at Mass Gatherings, and Self-Efficacy Through Data Science and Dynamic Modeling(Virginia Polytechnic Institute and State University, 2024-02-27) Ba-Aoum, Mohammed; Hosseinichimeh, Niyousha; Triantis, KonstantinosThis dissertation employs management systems engineering principles, data science, and industrial systems engineering techniques to address pressing challenges in emergency department (ED) efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is structured into three essays, each contributing to a distinct domain of research, and utilizes industrial and systems engineering approaches to provide data-driven insights and recommend solutions. The first essay used data analytics and regression analysis to understand how patient length of stay (LOS) in EDs could be influenced by multi-level variables integrating patient, service, and organizational factors. The findings suggested that specific demographic variables, the complexity of service provided, and staff-related variables significantly impacted LOS, offering guidance for operational improvements and better resource allocation. The second essay utilized system dynamics simulations to develop a modified SEIR model for modeling infectious diseases during mass gatherings and assessing the effectiveness of commonly implemented policies. The results demonstrated the significant collective impact of interventions such as visitor limits, vaccination mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying levels of self-efficacy and developing tailored strategies to enhance their academic and personal success. Collectively, these essays underscore the value of data-driven and evidence-based decision- making. The dissertation’s broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical applications across the healthcare, public health, and education sectors, fostering advancements that could significantly benefit society.27 0Item Restricted Assessing Transit Oriented Development using Satellite Imagery: Riyadh vs. Phoenix(Saudi Digital Library, 2023-08-23) Almazroa, Noor; de Weck, OlivierAs urbanization becomes the way of the future, the demands on the cities are becoming more urgent, with an increased awareness of the need for sustainability and resilience, making the utilization of today’s Technology and data critical in decision-making and planning. In the first part of this thesis, I combine a few of these techniques and datasets to explore their ability to provide a helpful assessment of Transit-Oriented Development (TOD). This research assesses the transit-oriented characteristics in two cities, Riyadh, Saudi Arabia, and Pheonix City, Arizona, US. Both share many similarities in urban design and climate. I use high-resolution satellite imagery with Computer Vision methods to detect the built area around public transit stations to measure the building density and, combined with land use data, measure the residential and nonresidential density. Both of these measurements are important indicators of the success of a public transportation system. I found that out of the two different building detection methods, the one based on deep learning techniques was more precise, with better generalization abilities. While the method based on classical image processing techniques is more sensitive to threshold choices, with considerable variability when tested on different years. Both methods, however, were able to give a useful prediction of buildings. And from their results, I found that Phoenix City has a building density of less than 50%, even around the busiest stations downtown stations. Riyadh, on the other hand, is more compact and with at least more than 50% of the land being developed. In the second part, I formulate a System Dynamics that is validated by Phoenix’s actual ridership for the 2010-2020 period and predicts transit ridership in Riyadh. The model closely approximated Phoenix’s ridership up until 2016. The Riyadh model estimated that the ridership would start with six million riders, surpassing the predictions of the Royal Commission for Riyadh City (RCRC) of 1.6 million initially. The results of both parts indicate that given that Riyadh is more densely built with a smaller area and has a more extensive transportation system and bigger population, this should serve as an incentive to promote a more transit-oriented built environment by increasing walkability and dense mixed-use developments throughout the city.13 0