Browsing by Author "Alharbi, Abdulrahman"

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DATA ANALYTICS FRAMEWORK FOR IMPROVING THE SAFETY AND CAPACITY OF AIRSPACE
(Cranfield University, 2024-03-21) Alharbi, Abdulrahman; Petrunin, Ivan
Due to their flexibility and general robustness, unmanned aerial vehicles (UAVs), have increasingly been deployed for diverse applications. These include aerial mapping, surveillance, package delivery, and even agriculture. Increased employment, however, has also entailed new demands for smart, nimble and effective UAV traffic-management systems, particularly in urban areas. If numerous, fully automized UAVs are to be flown frequently, and beyond the visual line of sight (BVLoS), then efficient unmanned traffic management (UTM) is essential, not least as UAV traffic will inevitably become denser. In future, indeed, air-traffic management will also be more complex, and airspace more crowded, as the sheer volume of UAVs continues to rise. Consequently, UTM will require swift, efficient decision-making mechanisms. Important challenges also remain in terms of machine-learning algorithm verification, these stemming primarily from a lack of explicability and transparency. Given that traditional safety mechanisms are unequal to the tasks involved, this has been an inhibiting factor in the integration of UAVs into very low-level (VLL) airspace. This thesis aims to develop a data-analytics framework to characterize traffic-flow patterns of UTM airspace by analyzing simulated historical data. The pertinent data analysis supports risk analysis, and it also improves trajectory planning in different airspace regions. It considers all dynamic parameters, such as extreme weather, emergency services, and dynamic airspace structures. Furthermore, and to meet the critical need for accurate congestion prediction in UAS traffic flow management (UTFM), this study uses state-of-the-art machine learning techniques to integrate air traffic-flow prediction with the intrinsic complexity metric. In this study, air-traffic congestion analysis and prediction will be addressed via a deep-learning methodology, within a UTM context, across a timeframe of three minutes. The proposed model is distinct from approaches that would focus on the more conventional issues of conflict detection, conflict resolution and trajectory prediction. In addition, this thesis proposes a tailored solution to the needs of demand-and capacity-management (DCM) services. This solution deploys a transparency based methodology, with a fusion of both black-box and explainable, white-box models. It generates, therefore, an intelligent system that can be both explicable and reasonably comprehensible. The results show that the advisory system will be able to indicate the most appropriate regions for UAV operations, while increasing UTM airspace availability by more than 23%. Keywords:
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Evaluation of Smoking Prevalence, Secondhand Smoke Exposure, and Perceptions of Smoking Cessation among Respiratory Therapy Students in Saudi Arabia
(2023-07-14) Alharbi, Abdulrahman; Gardenhire, Douglas
Background: Tobacco use is a global health concern that results in millions of deaths annually. This study focuses on Saudi Arabian respiratory therapy students in the Western region to assess smoking prevalence, secondhand smoke exposure, smoking cessation education, and perceptions of smoking cessation. Purpose: The purpose of this study is to evaluate the smoking prevalence among Saudi respiratory therapy students in the Western region, their exposure to secondhand smoke, smoking cessation education, and their perceptions of the positive effects of quitting smoking. Methods: Using a descriptive exploratory methodology, this study collected data on smoking prevalence, secondhand smoke exposure, and attempts to quit smoking among students. The Global Health Professional Students Survey (GHPSS) was employed as the survey instrument. Results: The results indicate that the prevalence of cigarette smoking among male respiratory therapy students was 52%, while it was lower among females at 3.7%. The study also revealed high rates of exposure to secondhand smoke in public settings (52%), whereas exposure at home was relatively low (28%). This emphasizes the importance of enforcing smoking bans in public spaces to safeguard individuals from the effects of secondhand smoke. Conclusion: This study sheds light on the smoking prevalence, secondhand smoke exposure, smoking cessation education, and perceptions of smoking cessation among Saudi Arabian respiratory therapy students in the Western region. The findings highlight the need for targeted interventions to address smoking behaviors among students. Additionally, the study reveals the importance of creating smoke-free environments, as the rates of exposure to secondhand smoke in public settings were alarmingly high.
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