SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted PREDICTORS OF HEALTHCARE PROVIDERS' READINESS FOR HEALTH SYSTEM TRANSFORMATION IN SAUDI ARABIA(Saudi Digital Library, 2026) Alasiri, Ahmed Ali; Qi, ZhangThe healthcare system in Saudi Arabia is undergoing significant health reform to warrant sustainable welfare provision. Privatization or public-private partnerships (PPPs) were introduced in 2021 and outlined the vision for the health system for 2023. The planned initiatives aim to be comprehensive and effectively reach individuals and society, including citizens, non-citizens, and visitors. This dissertation seeks to study and explore privatization, the role of healthcare providers, and their capacity to adapt to the transformation of the healthcare system. To achieve this ambitious goal, several interrelated projects have been undertaken. The first project was a systematic review aimed at studying the transformation of the health system in Saudi Arabia since the launch of Health Vision 2030 and identifying the issues and steps the government has taken toward privatizing healthcare. The second project investigated the validity and reliability of the Arabic version of the readiness to change constructs among healthcare providers. The third project explored the predictor variables and the ability to forecast the readiness level for health system transformation in Saudi Arabia. The systematic review found that the government made significant progress in facilitating and implementing the legislation's roadmap to implement the reform and achieve the health vision of 2023; however, health clusters and the Ministry of Health need to practice causation, as this fundamental change will affect the population directly. The second project found that the Arabic version of the readiness to change framework was valid and reliable for examining the ability of healthcare providers to change in healthcare settings. The third the project found that constructs of the ROC framework significantly predicted the readiness level among healthcare providers.59 0Item Restricted Augmented Reality and consumers’ willingness to pay in mobile e-commerce. Does AR increase consumers’ willingness to pay in an online auction?(Saudi Digital Library, 2024) Aldosari, Mofarj Massfer; Kevin P. ScheibeE-commerce platforms face the challenge of enabling customers to assess the utility of products in their intended environments, particularly in the home furniture sector. Augmented Reality (AR) presents a promising solution, with major companies like IKEA, Wayfair, and Overstock introducing AR applications. Nevertheless, a significant knowledge gap exists, prompting this research to delve into this void and scrutinize the economic value of AR in shaping consumers' Willingness to Pay (WTP) in the context of online auctions. This dissertation comprises three interconnected papers that collectively scrutinize the multifaceted impacts of AR on consumers' WTP in the specific realm of online auctions. The first paper investigates the influence of Augmented Reality (AR) in mobile e-commerce on consumers' perceived risks and WTP in online auctions. Grounded in perceived risk theory, the study addresses Perceived Psychological Risk, Perceived Social Risk, and Perceived Performance Risk. An online experiment involving 61 participants compared AR mobile e-commerce with a 3D mobile e-commerce interface, and data analysis utilized SPSS and Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AR Mobile E-commerce significantly reduces perceived social risk, positively affecting WPT. However, AR in Mobile E-commerce does not substantially mitigate perceived psychological risk, and this risk dimension does not significantly affect WPT. Similarly, AR in Mobile E-commerce positively influences the reduction of perceived performance risk, but this risk dimension does not significantly influence WPT. Mediation analysis suggests that perceived social risk plays a crucial role as a mediator between AR in Mobile E-commerce and consumers' WPT. The second paper explores the impact of AR on consumers' WTP in an online auction context within mobile e-commerce, drawing on the experiential hierarchy model (EHM). The study posits that AR positively influences consumers' WTP compared to 3D product displays, triggering affective (enjoyment) and cognitive (perceived ownership and perceived product quality) responses, which subsequently influence behavioral responses (willingness to pay more). Analysis of 61 valid responses through PLS-SEM and SPSS 29 reveals that AR significantly enhances consumers' perceived enjoyment and perceived product quality, positively impacting their willingness to pay. However, perceived ownership does not directly affect willingness to pay. Demographic factors such as age, gender, purchase frequency, and income do not have a direct influence. Mediation analysis suggests that perceived enjoyment, perceived product quality, and perceived ownership do not significantly mediate the relationship between AR and WTP. The third paper addresses the lack of standardized AR application guidelines for e-commerce. Using sentiment analysis of 1,049 user reviews of the IKEA Place App, this study reveals predominant dissatisfaction with the app, leading to the development of a comprehensive set of AR mobile e-commerce design guidelines. The research also compares AR mobile e-commerce with traditional 3D versions, finding a statistically significant difference in usability, with the AR version considered more usable. However, there was no significant correlation between usability scores and participants' willingness to pay on both platforms. This study sheds light on AR's potential and challenges in e-commerce, offering insights into enhancing user experience and economic outcomes. In conclusion, this dissertation contributes to the understanding of how AR impacts consumers' WTP in the context of online auctions within e-commerce, addressing perceived risks, experiential responses, and design guidelines. These findings offer valuable insights for e-commerce businesses seeking to harness AR's potential to enhance the shopping experience and drive revenue growth. Keywords: Augmented Reality, Willingness to Pay, Mobile E-commerce, Perceived Risks, Experiential Hierarchy Model, Usability.17 0Item Restricted Sensing, Scheduling, and Learning for Resource-Constrained Edge Systems(Saudi Digital Library, 2025) Bukhari, Abdulrahman; Kim, HyoseungRecent advances in Internet of Things (IoT) technologies have sparked significant interest in developing learning-based sensing applications on embedded edge devices. These efforts, however, are challenged by adapting to unforeseen conditions in open-world environments and by the practical limitations of low-cost sensors in the field. This dissertation presents the design, implementation, and evaluation of resource-constrained edge systems that address these challenges through time-series sensing, scheduling, and classification. First, we present OpenSense, an open-world time-series sensing framework for performing inference and incremental classification on an embedded edge device, eliminating reliance on powerful cloud servers. To create time for on-device updates without missing events and to reduce sensing and communication overhead, we introduce two dynamic sensor-scheduling techniques: (i) a class-level period assignment scheduler that selects an appropriate sensing period for each inferred class and (ii) a Q-learning–based scheduler that learns event patterns to choose the sensing interval at each classification moment. Experimental results show that OpenSense incrementally adapts to unforeseen conditions and schedules effectively on a resource-constrained device. Second, to bridge the gap between theoretical potential and field practice for low-cost sensors, we present a comprehensive evaluation of a sensing and classification system for early stress and disease detection in avocado plants. The greenhouse deployment spans 72 plants in four treatment categories over six months. For leaves, spectral reflectance coupled with multivariate analysis and permutation testing yields statistically significant results and reliable inference. For soils, we develop a two-level hierarchical classification approach tailored to treatment characteristics that achieves 75–86\% accuracy across avocado genotypes and outperforms conventional approaches by over 20\%. Embedded evaluations on Raspberry Pi and Jetson report end-to-end latency, computation, memory usage, and power consumption, demonstrating practical feasibility. In summary, the contributions are a generalized framework for dynamic, open-world learning on edge devices and an application-specific system for robust classification in noisy field deployments. These real-world deployments collectively outline a practical framework for designing intelligent, cloud-independent edge systems from sensing to inference.12 0Item Restricted The Role of hnRNPH1 and H2 in Melanoma Immune Signaling(Saudi Digital Library, 2025) Sultan, Maab Khalid; Dmitriy, MinondMelanoma is the most aggressive and deadliest form of skin cancer, responsible for an estimated 58,667 deaths globally, accounting for 0.6% of all cancer-related mortality. Current treatments for melanoma include chemotherapy, immunotherapy, and targeted therapy; however, challenges such as drug resistance, low response rates, and limited options for triple-wild type (TWT) and NRAS mutant melanoma persist. Melanoma is mainly controlled by the Ras/Raf/MEK/ERK pathway, and its tumors typically contain a high number of genetic mutations, making treatment difficult with existing therapies. However, this high mutation burden could also increase melanoma’s responsiveness to immunotherapy. Previous studies have shown that spliceosomal proteins hnRNPH1/H2 support the progression and survival of various cancers; however, their role in melanoma remains unexplored. hnRNPs, RNA-binding proteins, play a critical role in regulating alternative splicing, and modulating this process with small-molecule probes could help overcome resistance to immunotherapy. Two such probes, 2155-14 and 2155-18, were identified to induce apoptotic cell death, autophagy, and immune signaling modulation, with effects mediated through hnRNPH1/H2-dependent mechanisms. RNA sequencing following the downregulation of hnRNPH1/H2 in melanoma cells revealed an enrichment of immune-related signaling pathways. A recent study also linked increased immune-related pathways with improved overall survival in melanoma patients. The present study investigated the effect of genetic and pharmacologic downregulation of hnRNPH1/H2 on melanoma immunogenicity in vitro. Furthermore, it explored how pharmacological modulation of these proteins contributes to the regulation of melanoma immunogenicity using a syngeneic melanoma mouse model. Our results indicate that both genetic and pharmacologic downregulation of hnRNPH1/H2 significantly upregulated pro-inflammatory pathways while downregulating anti-inflammatory pathways. In vivo studies showed that 2155-18 had a stronger effect than 2155-14. Compound 2155-18 increased the population of active CD8+ T cells without affecting immunosuppressive cell populations, including Tregs, MDSCs, and mMDSCs. These findings provide the first insight into the role of hnRNPH1/H2 as critical drivers of melanoma immunogenicity and suggest their potential as novel therapeutic targets for enhancing melanoma treatment outcomes. This study underscores the impact of post-transcriptional regulation on the immune environment in melanoma and in cancer in general.20 0Item Restricted KNOWLEDGE AND ATTITUDES OF FACULTY MEMBERS AT TAIBAH UNIVERSITY TOWARD CHALLENGES FACING DEAF AND HARD-OF HEARING STUDENTS IN HIGHER EDUCATION(Saudi Digital Library, 2025) Almutairi, Hammam A; John L, HospThis dissertation examines the knowledge and attitudes of faculty members at Taibah University toward the challenges faced by Deaf and hard-of-hearing (D/HH) students in higher education. As Saudi Arabia moves toward greater inclusivity in its educational system, understanding how faculty perceive and support D/HH students is critical. Using mixed-methods, research design, the study collected both quantitative survey data and qualitative interview insights from faculty across the Humanities and Science colleges. The research aimed to assess the faculty's awareness of D/HH students' needs, their attitudes toward inclusivity, and the degree of training or experience they possessed in working with students with hearing impairments. The findings of the present study revealed that there was no significant influence of demographic characteristics and D/HH experience on the knowledge and attitude toward the challenges of teaching D/HH students. However, a significant inverse moderate correlation was found between knowledge and attitude (r = -0.647, p < 0.01). Importantly, this correlation demonstrated that higher levels of knowledge about D/HH students were associated with more positive attitudes toward teaching them. This indicates that as faculty members' knowledge about D/HH students increases, their attitudes become more positive, resulting in lower attitude scores on the scale used. While many faculty members expressed positive attitudes toward inclusion, significant knowledge gaps and inconsistent support strategies remain. Issues such as limited awareness of effective communication techniques, inadequate use of assistive technologies, and a lack of specialized training were identified as barriers to full participation for D/HH students. The study emphasizes the urgent need for comprehensive faculty development programs focused on inclusive teaching practices, particularly in relation to D/HH students. It also highlights the necessity for institutional policies that mandate accessible classroom environments and ongoing professional training. Recommendations are provided for improving faculty readiness, enhancing assistive services, and fostering a university-wide culture of inclusion. By focusing on Taibah University as a case study, this research offers valuable insights for other higher education institutions in KSA and the broader Middle Eastern region. It contributes to the growing body of literature on disability inclusion in higher education and aims to inform future educational policy and practice improvements that promote equity, access, and success for D/HH students.34 0Item Restricted Sensing, Scheduling, and Learning for Resource-Constrained Edge Systems(Saudi Digital Library, 2025) Bukhari, Abdulrahman Ismail Ibrahim; Kim, HyoseungRecent advances in Internet of Things (IoT) technologies have sparked significant interest in developing learning-based sensing applications on embedded edge devices. These efforts, however, are challenged by adapting to unforeseen conditions in open-world environments and by the practical limitations of low-cost sensors in the field. This dissertation presents the design, implementation, and evaluation of resource-constrained edge systems that address these challenges through time-series sensing, scheduling, and classification. First, we present OpenSense, an open-world time-series sensing framework for performing inference and incremental classification on an embedded edge device, eliminating reliance on powerful cloud servers. To create time for on-device updates without missing events and to reduce sensing and communication overhead, we introduce two dynamic sensor-scheduling techniques: (i) a class-level period assignment scheduler that selects an appropriate sensing period for each inferred class and (ii) a Q-learning–based scheduler that learns event patterns to choose the sensing interval at each classification moment. Experimental results show that OpenSense incrementally adapts to unforeseen conditions and schedules effectively on a resource-constrained device. Second, to bridge the gap between theoretical potential and field practice for low-cost sensors, we present a comprehensive evaluation of a sensing and classification system for early stress and disease detection in avocado plants. The greenhouse deployment spans 72 plants in four treatment categories over six months. For leaves, spectral reflectance coupled with multivariate analysis and permutation testing yields statistically significant results and reliable inference. For soils, we develop a two-level hierarchical classification approach tailored to treatment characteristics that achieves 75–86\% accuracy across avocado genotypes and outperforms conventional approaches by over 20\%. Embedded evaluations on Raspberry Pi and Jetson report end-to-end latency, computation, memory usage, and power consumption, demonstrating practical feasibility. In summary, the contributions are a generalized framework for dynamic, open-world learning on edge devices and an application-specific system for robust classification in noisy field deployments. These real-world deployments collectively outline a practical framework for designing intelligent, cloud-independent edge systems from sensing to inference.18 0Item Restricted Privacy-Preserving Structure Learning for Geospatial Data Using Information-Theoretic Dependency Measures(Saudi Digital Library, 2025) Mudhish, Ahmed; Pradeep, ChowriappaThis dissertation proposes a privacy-preserving framework for structure learning in Bayesian networks (BNs) that addresses the challenges of distributed geospatial data face. Geospatial datasets often exhibit region-specific patterns such as sparsity and nonlinear dependencies. These patterns undermine the effectiveness of traditional machine learning models. Additionally, learned BN structures may reveal sensitive relationships in the generated graph by BNs. These relationships pose a significant privacy risk if reverse-engineered. To address these issues, three novel algorithms are introduced. First, the Selective Naïve Bayes with HSIC (SNB-HSIC) algorithm applies a kernel-based dependency measure to filter redundant and irrelevant features in sparse datasets, improving structure clarity without compromising classification accuracy. Second, the Controlled K-Dependence Bayesian Network (CKDBN) extends traditional K-dependence models by giving the option to select the optimal number of parents each node can have based on data-driven thresholds. THE CKDBN enables a flexible structure learning algorithm that can handle complex or high-dimensional settings. Third, the BNVeil algorithm introduces a privacy-preserving method that can obfuscate highly connected nodes using Laplace noise to protect the model’s logic from adversarial inference. All the frameworks are validated on both the full and partitioned geospatial datasets via a series of experiments that evaluate the structure quality, the predictive performance, and the robustness of privacy-preserving concerns. The results of the experiments indicate that the proposed methods in this dissertation achieve better accuracy than traditional BN models and significantly enhance interpretability and structural privacy. The three algorithms offer a practical and secure solution for region-based geospatial data.17 0Item Restricted Relationship between Implant Geometry and Primary Stability in Variant Bone Densities: An In Vitro Pilot Study(Saudi Digital Library, 2024) ALSHAYIQI, MOHAMMED SALEH; Li, RuiBackground and Aim: The success of dental implants largely depends on achieving primary stability, especially in varied bone densities. Primary stability is influenced by factors such as implant geometry and bone density, which play crucial roles in osseointegration and the overall implant stability over time. While various implant designs have been developed, the optimal geometry for different bone densities remains unclear. This study aims to investigate the effect of four implant geometries Bone Level Tapered (BLT), Bone Level (BL), Tissue Level Standard Plus (SP), and Tissue Level Standard (S) on primary stability in medium-dense (D2) and soft (D4) bone densities. Methods: An in vitro pilot study was conducted using 96 osteotomies on porcine ribs as a bone model to replicate human bone conditions. Each implant geometry was placed in D2 and D4 bone densities, and primary stability was measured using resonance frequency analysis (RFA) through the Implant Stability Quotient (ISQ). Descriptive statistics and a two-way ANOVA were applied to assess the main effects of implant geometry and bone density on primary stability, as well as their interaction. Results: The dense bone (D2) group showed significantly higher ISQ values compared to the soft bone (D4) group across all implant geometries, indicating a strong effect of bone density on implant stability (p < .001). However, no statistically significant differences were found among the four implant geometries in terms of primary stability (p = .627). Additionally, there was no significant interaction effect between implant geometry and bone density (p = .506), suggesting that implant geometry did not influence stability differently in the two bone densities. Conclusions: Bone density was identified as a critical determinant of primary stability, with denser bone providing superior stability outcomes. The findings suggest that while implant geometry did not significantly impact stability in this controlled in vitro setup, bone quality assessment should be prioritized in clinical settings. Future research should examine long-term stability, including secondary stability and osseointegration, to validate these results in varied clinical scenarios.29 0Item Restricted A DISEASE BURDEN AND BUDGET IMPACT ANALYSIS OF FINERENONE IN HFPEF IN THE US POPULATION(Saudi Digital Library, 2025) Almasuood, Rawan; Fatimah, SherbenyBackground: Heart failure with preserved or mildly reduced ejection fraction (HFpEF/HFmrEF) presents a growing challenge in cardiovascular care, particularly among older adults. Finerenone, a non-steroidal mineralocorticoid receptor antagonist, has shown clinical benefit in reducing heart failure (HF) hospitalizations and cardiovascular mortality in this population. However, its budgetary impact in the U.S. remains unknown. Objective: To evaluate the 3-year budget impact of incorporating finerenone as an adjunct to standard therapy for HFpEF/HFmrEF from a general U.S. payer perspective, using real-world cost inputs and clinical effectiveness data from the FINEARTS-HF trial. Methods: A static Excel-based cohort model was developed comparing the current standard of care (SOC) with SOC plus finerenone. The analysis included direct medical costs (drug acquisition and hospitalization costs) from the 2023 MEPS database and hospitalization reduction rates from the FINEARTS-HF trial. Uptake assumptions (2%, 6%, and 10% over three years) were modeled, with annual and cumulative cost outcomes evaluated. A one-way sensitivity analysis assessed the impact of key input uncertainties. Results: Cumulative 3-year drug cost for finerenone was $3.21 billion, with avoided hospitalization savings of $176 million, yielding a net budget impact of $3.03 billion. Only ~5.5% of drug costs were offset by hospitalization savings. The model was most sensitive to hospitalization costs and finerenone acquisition costs. Conclusion: Finerenone substantially increases short-term costs when added to SOC for HFpEF/HFmrEF, with limited cost offset from reduced hospitalizations. These findings provide timely insights for U.S. payers in anticipation of FDA approval and market entry.18 0Item Restricted Essays on Gentrification, Income Inequality and Crime Rate(Saudi Digital Library, 2025) Almosa, Rahan; Amirhossein, Amini BehbahanThis dissertation investigates the relationship between gentrifications income inequality, and crime in Washington, D.C. by use of three linked studies, it examines how public safety and socioeconomic situations are affected by urban redevelopment. The first study finds that crime rates in gentrifying neighborhoods increased by 1.3% after the pandemic. According to the second study, gentrification increases income inequality by making economic disparities between affluent newcomers and long-term, lower-income residents. The third study explores how income inequality contributes to rising crime in gentrifying neighbourhoods after the pandemic. These results highlight the need for policies that promote affordable housing, economic stability, and community-based crime prevention. Through stressing the socioeconomic effects of gentrification, this study offers information to legislators and urban designers aiming for more fair and sustainable urban growth.9 0