SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Delivering Prehabilitation in Cancer Surgery: A Service Evaluation at Nottingham University Hospitals(Saudi Digital Library, 2025) Alharbi, Abdulrahman; O’Connor, DominicAbstract Background: Cancer surgery carries high risks, with complications linked to delayed recovery and poorer outcomes. Prehabilitation aims to optimise patient fitness before surgery, yet evidence from real-world NHS settings remain limited. In 2022, Nottingham University Hospitals NHS Trust introduced a multimodal prehabilitation service, developed in line with Macmillan Cancer Support guidance. Methods: This service evaluation included 1,720 patients triaged to Specialised (n = 329), Targeted (n = 943), or Universal (n = 448) prehabilitation pathways. Outcomes assessed pre- and post-programme included functional capacity (incremental shuttle walk test [ISWT], 60-second sit-to-stand [STS] test, grip strength), psychological health (GAD-7, PHQ-9), and physical activity. Analyses used paired t-tests, ANOVA, and effect size calculations. Results: Significant improvements were observed across outcomes. ISWT increased by 57 m (p < 0.001, d = 0.6), STS by 6 repetitions (p < 0.001, d = 0.9), and grip strength modestly (p < 0.001). Anxiety (Δ –1.9) and depression (Δ –2.0) scores decreased (both p < 0.001, d ≈ –0.5). Weekly physical activity more than doubled (+142 min/week), and strength sessions increased by 2.4 sessions (both p < 0.001, d > 1.0). Between-group differences were limited, although PHQ-9 scores improved more in the Specialised than the Targeted pathway and strength sessions more in the Universal than the Targeted pathway. Conclusion: A multimodal prehabilitation programme delivered within routine cancer care was associated with meaningful functional, psychological, and behavioural gains. However, barriers to engagement highlight the need for flexible delivery models and systematic follow-up to maximise accessibility and sustainability.4 0Item Restricted DATA ANALYTICS FRAMEWORK FOR IMPROVING THE SAFETY AND CAPACITY OF AIRSPACE(Cranfield University, 2024-03-21) Alharbi, Abdulrahman; Petrunin, IvanDue 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:18 0