Saudi Cultural Missions Theses & Dissertations
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Item Restricted The impact of IT flexibility and IT capability on Business-IT strategic alignment: An empirical study in Saudi Arabia(The University of Manchseter, 2025) Alharbi, Nawal Olayan; Mamman, AminuPublic and private organisations face many challenges to attain the objectives of providing services and products to their clients. Most organisations adopt many enablers customised technological tools to tackle their unique operational issues and achieve their strategic goals. Therefore, IT is considered one of the most enablers for organisations to help overcome their challenges. Higher education institutions have developed strategies and are relying more on their IT systems in delivering most of services to their stakeholders. The roles of IT strategy and business strategy in any organisation are often incorporated. However, the organisations’ objectives are likely not to be attained if the business strategy and IT strategy are not aligned in an effective manner. Business and IT strategic alignment has been considered as IT application in which match the business objectives, goals, and requirements. To improve the efficiency and performance of alignment, IT flexibility and IT capability provide a robust lens to assess their impact on organisational objectives. Although, strategic alignment has been the interest of many researchers, investigation of such issue in Saudi higher education sector is still to be undertaken. To address this issue, the aim of this study was to examine how IT is used in Saudi Arabian higher education to attain their organisational objectives. This aim was pursued by meeting the following objectives: First, to investigate the connection between flexibility and capability of IT dimensions. Second, to investigate the effect of IT flexibility and IT capability on the business strategy and IT strategic alignment. Third, to investigate how business strategy and IT strategic alignment have influenced organisational performance. The research employed the University of Hail as a case study to conduct an extensive examination of IT flexibility, IT capability, and IT strategic alignment with business strategy. A qualitative research approach was utilised to collect data through various methods, including interviews and document analysis. The study participants comprised the Deanship, Strategy department, IT department, Heads of Schools, faculty members and students. This study found a significant shift from viewing IT as merely a support function to recognising it as a strategic enabler for achieving organisational goals. It delved into the interrelated nature of IT flexibility, IT capability, business-IT strategic alignment, and organisational performance. These interconnections allowed the organisation to effectively leverage IT for performance enhancements, as evidenced by improvements in key areas such as student services, research output, administrative efficiency, and university ranking. The study made a significant contribution to addressing gaps in IT research and enhanced the understanding of educational and business organisations regarding the effective implementation of Business-IT strategic alignment.20 0Item Restricted DEEP LEARNING ALGORITHMS FOR BIOMEDICAL IMAGE SEGMENTATION IN LOW-DATA SCENARIOS(University of Delaware, 2025) Alblwi, Abdalrahman Hmod; Barner, KennethAutomatic segmentation via deep learning plays a major role in biomedical imaging, enhancing diagnostics by dividing images into regions of interest. This procedure helps medical experts understand disease characteristics, lesion sizes, and other crucial details. Despite its potential, deep learning-based automatic segmentation often relies on large annotated data to accurately predict lesions and other critical regions. Among imaging modalities, ultrasound, widely used for its accessibility, real-time capabilities, and effectiveness in detecting lesions, remains inadequately investigated due to the inherent challenges in medical imaging, such as data availability and privacy concerns. This work identifies key research gaps in ultrasound imaging segmentation to address these challenges and contributes to advancements in this critical area. This dissertation focuses on three key areas for advancing ultrasound image segmentation and improving biomedical image analysis. First, it aims to improve supervised learning-based architectures for tumor segmentation, particularly U-Net models, which, despite their success in biomedical segmentation, often lack reliability for clinical use, especially when tested on out-of-dataset samples. Second, it addresses the challenges posed by limited annotated ultrasound data, which restricts the performance of supervised models. Finally, it addresses the scarcity of ultrasound datasets paired with corresponding masks, a significant issue caused by data privacy concerns, the lack of datasets from various countries, and the high costs of expert-level annotations. This dissertation introduces an improved supervised model based on a refined U-Net architecture incorporating ReSidual U-blocks (RSU) and Attention Gates to address segmentation challenges in scenarios with limited data. These enhancements improve the model’s ability to capture critical features and long-range dependencies, improving lesion segmentation performance in ultrasound images. Building on this, we integrate a Denoising Diffusion Probabilistic Model (DDPM) with the RSU architecture to create a deeper network capable of handling the high variability and noise in ultrasound datasets. This combination enhances segmentation mask accuracy and addresses challenges posed by samples with diverse characteristics, such as size and shape variations of regions of interest. Next, we improve data augmentation by enhancing the Mixup technique to address limited data scenarios in image segmentation. Using K-means clustering, ultrasound images are grouped into clusters of similar samples, and Mixup applies within clusters. This approach has the potential to reduce randomness, avoid mixing unrelated regions like tumors and dark backgrounds, and ensure more effective augmentation. It also diversifies the dataset by generating new samples and masks, mitigating data scarcity. Building on this contribution, we extend the application of Cluster Mixup to unsupervised segmentation. The goal is to leverage unlabeled ultrasound images by augmenting healthy samples with Cluster Mixup, followed by unsupervised learning to detect suspected tumors. This approach could show the potential to qualitatively and quantitatively improve the segmentation of regions of interest and enhance diagnostic capabilities. Additionally, we build upon Cluster Mixup by proposing a variant of D-DDPM, a diffusion-based model, to learn the distributions of combined images and masks, enabling the simultaneous and joint generation of synthetic images and annotations. This technique expands the dataset with a large number of image-mask pairs. We involve medical experts in evaluating the synthetic dataset, ensuring the selection of relevant samples, and improving dataset quality. Statistical analysis obtained from medical experts shows the reliability of our approach and its potential application to real-world problems.24 0Item Restricted Healthcare in Crisis: Assessing Medication Adherence, Health Care Access, Telehealth Utilization, and Variations of Depression Treatment in the Era of COVID-19 for US Hypertensive Patients(Howard University, 2025) alharbi, Rehab; La’Marcus, WingateBackground: Coronaviruses (CoVs) are highly contagious viruses that cause respiratory and gastrointestinal illnesses. It disrupted global healthcare systems and affected access to care for chronic conditions like hypertension. It is a major risk factor for cardiovascular disease and a leading cause of death in the U.S. However, the pandemic shifted healthcare focus to COVID-19. Telehealth emerged as a critical tool for maintaining continuity of care. The pandemic also intensified mental health issues, such as depression. This study explores medication adherence, healthcare access, telehealth utilization, and depression treatment patterns among U.S. hypertensive patients during COVID 19. Methods: This cross-sectional analysis used secondary data from the Medical Expenditure Panel Survey (MEPS) and the National Health Interview Survey (NHIS).Outcomes included medication adherence among hypertensive patients, telehealth use, healthcare delay during the pandemic, and depression treatment variations in adults with both hypertension and depression. Descriptive statistics were utilized to describe patients’ characteristics, while logistic regression identified predictors of outcomes. Results: Medication adherence varied across antihypertensive classes, with ARBs having the lowest (53.4%) and beta-blockers having the highest (60.6%) levels of adherence. Older adults had lower adherence (adjusted odds ratio (AOR) = 0.87, p < 0.05) compared to younger adults (18-34). Whites were more likely to utilize telehealth than Blacks (AOR = 1.13, P = 0.05). Older individuals faced higher odds of delayed care, with those aged 50–64 having an (AOR = 1.19 ,p = 0.02), and v those aged 65 and above having an (AOR=1.29,p < 0.001). Additionally, Older adults aged 50–64 were more likely to receive depression treatment (AOR: 2.81, p < 0.001), and whites had 2 times higher odds of receiving depression treatment compared to blacks, with a p-value of 0.05. Those with poor physical health had the highest odds of receiving treatment (AOR = 5.99, p < 0.001), compared to those with excellent physical health status. Conclusions: The study highlights disparities in medication adherence, healthcare access, telehealth use, and depression treatment among hypertensive patients during COVID-19. Influenced by age, race, physical health, and gender. These findings highlight the need for policy interventions to improve access to chronic disease care.5 0Item Restricted Benefits of Supplementation with LCn-3 PUFA during Diet-Induced Body Mass Loss and Maintenance Phases on Body Composition, Muscle Function, and Appetite(University Of Glasgow, 2025) Alblaji, Mansour Ghazi; Malkova, Dalia; Gray, StuartObesity is a complex medical condition that is associated with a range of comorbidities, including hypertension, type 2 diabetes, dyslipidaemia, gastrointestinal disorders, joint pain, and musculoskeletal complications. Current treatment approaches for obesity primarily involve lifestyle modifications, including diet-induced weight loss and physical exercise. However, evidence from previous research highlights a concern regarding diet-induced body mass loss: approximately 25–30% of the total body mass lost is derived from fat-free mass (FFM). This decline in FFM is associated with diminished muscle mass and function, reduced metabolic rate, and an elevated risk of body mass regain. Attenuating FFM loss during body mass loss is therefore critical for healthy body mass loss. Long-chain n-3 polyunsaturated fatty acids (LCn-3 PUFA) have been proposed as a potential strategy to mitigate these effects by influencing body composition, muscle mass and function, and inflammation during energy balance. Evidence suggests that LCn-3 PUFA can reduce fat mass while enhancing FFM, improving muscle mass, strength, and function, and mitigating inflammation. However, despite these potential benefits, the evidence supporting the efficacy of LCn-3 PUFA supplementation during diet-induced body mass loss on body composition, muscle function, and inflammatory markers remains limited and requires further exploration. The first aim of this thesis was to systematically investigate the effects of supplementation with LCn-3 PUFA during caloric restriction (CR) on body mass, fat mass and FFM loss (Chapter 2). Eleven studies were included in this systematic review and meta-analysis as they met the inclusion criteria of the systematic review, with a total of 637 participants. The participants’ age ranged between 18 and 61 years, with a mean BMI ranging between 27 and 36 kg/m2 . Pooled analyses showed that LCn-3 PUFA supplementation during CR had no additional effect on changes in body mass (SMD = -0.05: 95% CI -0.22 to 0.13; p = 0.62; I2 : 10%), BMI (SMD = -0.06, 95% CI -0.25 to 0.13; p = 0.55; I2 : 18%), fat mass (SMD = - 0.01; 95% CI -0.25 to 0.24; p = 0.96; I2 : 46%), or FFM (SMD = 0.12, 95%CI -0.14 to 0.37, p = 0.36; I2 :35%). The lack of impact of LCn-3 PUFA on body mass and composition observed in this systematic review (Chapter 2) may be attributed to some limitations in the iii included studies. Most of the studies assessed body composition using bioelectrical impedance analysis (BIA), applied low doses of LCn-3 PUFA, and also did not evaluate muscle strength during diet-induced body mass loss. To address the gaps identified in our systematic review, a double-blind, randomised, placebo-controlled trial (RCT) was conducted, including a 4-week preparation phase, an 8-week alternate-day fasting (ADF) phase, and an 8-week body mass maintenance phase, with participants taking 4 capsules/day of krill oil as a source of LCn-3 PUFA throughout (Chapter 4). Body composition was evaluated via the deuterium water (D2O) dilution method, and parameters of muscle function, and fasting blood samples were measured at the pre- and post body mass loss phase. Forty-one healthy adults completed this RCT. The two-way ANOVA revealed significant time and time*group interaction effects on FFM, handgrip strength, chair rising test, TNF-α, CRP, and systolic blood pressure (all p < 0.05). Post-intervention, there was a small, non-significant reduction in FFM (- 0.2 ± 0.9 kg, p > 0.05) and handgrip strength (-0.2 ± 0.5 kg, p > 0.05) in the krill oil group, whereas the placebo group experienced significant reductions in FFM (- 1.2 ± 2.0 kg, p < 0.05) and handgrip strength (-0.9 ± 0.7 kg, p < 0.05). The time to conduct the chair rising test decreased significantly in the krill oil group (-1.8 ± 0.9 s, p < 0.05), whereas the reduction in the placebo group was not significant (- 0.3 ± 1.2 s, p > 0.05). TNF-α levels decreased significantly in both groups (all p < 0.05), with a greater reduction in the krill oil group (-1.4 ± 0.2 pg/ml) compared to the placebo group (-0.9 ± 0.5 pg/ml). Similarly, CRP levels were significantly reduced in both groups (all p < 0.05), with a greater reduction in the krill oil group (-51.4 ± 25 ng/ml) than in the placebo group (-33.5 ± 12.6 ng/ml). Systolic blood pressure decreased significantly in both groups (all p < 0.05), with a greater reduction observed in the krill oil group (-9 ± 6 mmHg) compared to the placebo group (-4 ± 4 mmHg). No significant difference was observed in changes between groups in body mass, body fat, insulin, glucose HOMA-IR, TAG, or diastolic blood pressure (all p > 0.05). Therefore, from this RCT, it was concluded that supplementation with krill oil during diet-induced body mass loss via ADF helps to attenuate the associated decline of FFM and muscle function, improve functional capacity, and reduce TNF-α and CRP levels. Supplementation with LCn-3 PUFA, in the absence of CR, has been associated with appetite reduction and enhanced sensations of fullness and satiety in individuals iv living with overweight or obesity. However, the effects of LCn-3 PUFA supplementation during diet-induced body mass loss on appetite and gastrointestinal appetite hormones remain underexplored. In Chapter 5, the impact of LCn-3 PUFA during diet-induced body mass loss on changes in appetite and gastrointestinal appetite hormones was examined in a subset of the participants of the RCT (Chapter 4). This exploratory study included 28 adults (mean age: 39.4 ± 11.7 years; BMI: 27.9 ± 3.2 kg/m²) who participated in the RCT (Chapter 4). Body mass, body fat, and FFM were measured at baseline (week 4), at the end of the body mass loss phase (week 12), and at the end of the body mass maintenance phase (week 20). Fasting and postprandial subjective appetite scores, along with plasma concentrations of acylated ghrelin, Glucagon-Like Peptide-1 (GLP-1), and Peptide YY (PYY), were assessed before and after the body mass loss phase. The ANOVA revealed a significant time (p<0.05), but not group (p>0.05) or time*group interaction (p>0.05) effects for body mass, fat mass or FFM during the body mass loss phase. During the maintenance phase, no significant (p>0.05) time, group, or time*group interaction effects were found for body mass and FFM, but for fat mass, a significant time*group interaction effect was observed (p<0.05). During the maintenance phase, in the krill oil group, fat mass remained unchanged (p>0.05) but increased significantly (p< 0.05) in the placebo group. This coincided with the body mass loss-induced significant reduction (p<0.05) in the composite appetite score (CAS) in the krill oil but not the placebo group (p> 0.05). There was no significant (p>0.05) time, group, or time*group interaction effects for acylated ghrelin, GLP-1, and PYY during the body mass loss phase. Changes in body mass during the body mass loss and body mass maintenance phases were not correlated with acylated ghrelin, PYY, or GLP-1 (all p > 0.05). Body mass changes during the body mass loss phase showed a tendency toward a significant positive correlation with changes in CAS (r=0.36, p = 0.06). Therefore, krill oil supplementation during body mass maintenance may induce favourable changes in subjective appetite and prevent short-term fat mass regain. Overall, the current thesis demonstrates that supplementing with LCn-3 PUFA during diet-induced body mass loss is a promising strategy to attenuate the loss of FFM and muscle function. Beyond these benefits, LCn-3 PUFA supplementation also reduces inflammation and lowers blood pressure, underscoring its potential to enhance body composition, preserve muscle mass, and promote overall well- v being during body mass loss. Furthermore, LCn-3 PUFA supplementation may reduce subjective appetite and might help to prevent fat mass regain during the body mass maintenance phase, further supporting its role in long-term body mass management.9 0Item Restricted Nonlocal Boundary Value Problems for Linear Hyperbolic Systems(Florida Institute of Technology, 2025-05) Almutairi, Afrah; Kiguradze, TarielBoundary value problems in a characteristic rectangle Ω = [0, ω1]×[0, ω2] for second order linear hyperbolic systems are considered. For initial–boundary value problems there are established: (i) Necessary and sufficient conditions of well–posedness; (ii) Necessary conditions of solvability; (iii) Effective sufficient conditions of solvability of two–point initial–boundary value problems; (iv) Effective sufficient conditions of solvability of initial–periodic problems; (v) Necessary and sufficient conditions of solvability of ill–posed initial–boundary value problems; (vi) Necessary and sufficient conditions of solvability of ill–posed initial–periodic problems. For nonlocal boundary value problems there are established: (i) Necessary and sufficient conditions of well–posedness; (ii) Necessary conditions of solvability; (iii) Effective sufficient conditions of solvability of problems with Nicoletti type boundary conditions; (iv) Effective sufficient conditions of solvability of problems with boundary conditions of periodic type ; (iv) Effective sufficient conditions of solvability of doubly–periodic problems; (v) Necessary and sufficient conditions of solvability of ill–posed doubly–periodic problems.17 0Item Restricted Tenofovir Disoproxil to Tenofovir Alafenamide Switching Impact, Real-life Experience(University College London, 2024) Alabdulaal, Zahra; Awosusi, Funmi; Wei, LiAbstract Background: The NHS policy limits Tenofovir Alafenamide (TAF) to renally or bone damaged patients due to its favourable effects on bone and kidneys compared to Tenofovir Disoproxil (TDF). This study aimed to assess healthcare providers' adherence (HCPs) to the NHS policy and the impact of switching to TAF on various health indicators, including viral load, renal functions, weight, and lipid profile. Methods: This retrospective study included HIV patients aged ≥ 18 years who were on TAF (Descovy) between January and December 2022 and switched from TDF to TAF. HCP adherence was assessed by reviewing hospital documentation. The health indicators were the biochemistry tests obtained from hospital laboratory results. ANOVA and Non-parametric tests were used to compare the results at baseline, 6 months and 12 months. Results: The study included 79 patients. The HCP's adherence to the NHS policy was 62% (49/79). 12-month post-switch TAF impact was only significant for five variables. The mean serum creatinine (sCr) decreased from 101 to 93 μmol/L. The mean rank eGFR decreased from 2.38 to 1.81 in patients with stage G3a baseline. The mean rank urine albumin creatinine ratio (UACR) decreased from 2.25 to 1.84. The median weight increased from 78 to 79 kg. The mean total cholesterol (TC) increased from 4.75 to 5.22 mmol/L. Conclusion: The HCPs' low adherence rate highlights the need to increase their awareness of the NHS policy. TAF improves renal function by improving patients’ sCr, UACR, and eGFR stage G3a. However, it increases weight and TC.12 0Item Restricted تقييم دور البرامج التوعوية في تحسين مستوى جودة الخدمات الصحية: دراسة تطبيقية(جامعة حلوان, 2025) الرشيد, رشيد; مهدي, ممدوحتهدف هذه الدراسة إلى تقييم مدى فعالية البرامج التوعوية في تحسين مستوى جودة الخدمات الصحية، وسيتم تحقيق ذلك من خلال دراسة تطبيقية بمستشفى الدوادمي العام بمدينة الرياض بالمملكة العربية السعودية، حيث سيتم تحليل أثر تنفيذ برامج توعوية موجهة على رضا المرضى وجود الخدمات المقدمة، كما سيتم جمع البيانات من خلال استبيانات موجهة للمرضى والعاملين، إلى جانب مراجعة السجلات والبيانات الإدارية للمؤسسة الصحية.2 0Item Restricted دور إدارة المخزون في تحسين اداء المستشفيات الحكومية السعودية: دراسة تطبيقية(جامعة حلوان, 2025) اليامي, نواف; سعد, بهاء الديندور إدارة المخزون في تحسين اداء المستشفيات الحكومية السعودية: دراسة تطبيقية2 0Item Restricted The requirements of digital product passport (DPP) for the defence sector(Cranfield university, 2024) Alnijaidi, abdullah; Matopoulos, ArisThe adoption of Digital Product Passports (DPPs) is emerging as a strategic solution to enhance supply chain transparency, regulatory compliance, and sustainability within the defence sector. This thesis explores the potential of DPPs to address key challenges, including complex global supply chains, stringent regulatory demands, and the growing pressure for environmental accountability. Through a mixed-methods approach involving literature review, stakeholder interviews, and surveys, this study identifies the specific requirements, benefits, and challenges of implementing DPPs in the defence industry. Key findings demonstrate that DPPs offer significant advantages, including improved traceability, predictive maintenance, cost savings, and support for circular economy goals. By providing detailed, secure product data across a component’s lifecycle, DPPs enhance both operational efficiency and compliance with defence regulations. The thesis also highlights the role of enabling technologies such as blockchain, Internet of Things (IoT), and artificial intelligence (AI) in driving DPP adoption. However, high initial costs, cybersecurity concerns, and integration challenges remain barriers to implementation. Despite these challenges, DPPs can provide defence companies with a competitive edge by improving trust with government clients, mitigating supply chain risks, and fostering long-term sustainability. In conclusion, the research positions DPPs as a vital tool for the defence sector’s future, enabling organizations to meet evolving geopolitical, technological, and environmental demand3 0Item Restricted The Impact of Artificial Intelligence on Project Management: A Case Study of (Saudi Aramco)(University of Portsmouth, 2025) ALAMRI, FAIZ SGEIR; Coulter, ClaireWith the field of project management (PM) becoming increasingly focused on AIdriven software, companies can gain ample advantages – but only if the risks are suitably addressed (Kerzner, 2022). Embracing AI within PM means mechanising simple managerial tasks to perform complicated tasks like modelling and resource allotment. Considering AI’s varied benefits, businesses are discovering how to execute AI within their PM activities (Zhang, Wu & Shen, 2020). Adopting AI in PM has radically transformed the manner organizations strategize, implement and manage their project initiatives. Through mechanizing repeated tasks, interpreting large datasets, and improving decision-making skills, AI is shaping the future of PM (Rodrigues & Penedo, 2022). Integrating AI into PM is suitable for large companies such as Saudi Aramco, a global leader in energy that is well-recognized for its complicated and capital-intensive projects. Headquartered in Dhahran, Saudi Arabia, it is the world’s leading integrated energy and chemical organization and is known for its extensive infrastructure projects, including oil refineries, petrochemical plants, and sustainable energy projects (Saudi Aramco, n.d.). As the level and complication involved in such projects demand creative PM tools, the use of AI technologies is a sensible decision. The company is committed to adopting high-tech techniques, making it a perfect choice for this case study research proposal. The motivation for this topic comes from the progressive nature of AI in optimizing project deliverables and the opportunity to discover its relevance in a high-stakes, realworld landscape. Since worldwide energy organizations are increasingly scrutinized regarding cost-effectiveness and sustainability, PM innovations such as AI are both beneficial and important (PMI, 2021). This proposal seeks to assess how Saudi Aramco uses AI to deal with PM challenges like resource allotment, risk evaluation and performance supervision. This case study can generate insightful knowledge on the broad impacts of AI in PM for the energy industry and elsewhere.5 0