SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
12877 results
Search Results
Item Restricted An Exploratory Study of the Challenges of Teaching Cybersecurity in Saudi Arabia's TVTC Colleges(Saudi Digital Library, 2026) Alzahrani, Hameed; Schaeffer, DonnaThe increasing complexity of cyber threats and the global shortage of cybersecurity professionals have made cybersecurity education a strategic priority worldwide. In Saudi Arabia, the Technical and Vocational Training Corporation (TVTC) introduced a Bachelor of Cybersecurity program to support Vision 2030's digital transformation goals. Following a 2023 TVTC administrative decision suspending bachelor-level admissions at its technical colleges, this study was conducted as a post-implementation review, capturing instructor knowledge before it dissipated. This qualitative exploratory study investigated the perspectives of 17 TVTC cybersecurity instructors across six colleges in Saudi Arabia, using semi-structured interviews conducted in Arabic. Thematic saturation was reached after the tenth interview, and data were analyzed using inductive thematic coding in MAXQDA. The study examined three areas: the challenges instructors face delivering cybersecurity education, their perceptions of hands-on and experiential learning, and their views on innovative teaching methods such as Capture-the-Flag competitions, cyber ranges, and cloud-based labs. Findings reveal a significant pedagogical-operational gap between curricular ambitions and the organizational realities of technical colleges, driven by administrative constraints, infrastructure limitations, and student heterogeneity. These insights carry direct relevance for TVTC's newly introduced Cybersecurity Diploma program, informing admission policies, content delivery, and infrastructure planning, and provide a strategic foundation should the bachelor program resume. Ultimately, this work aims to strengthen the preparation of cybersecurity graduates equipped to meet Saudi Arabia's evolving digital security needs under Vision 2030.13 0Item Restricted INVESTIGATING ASPARAGINASE-RELATED HEPATOTOXICITY IN PATIENTS WITH ACUTE LYMPHOBLASTIC LEUKEMIA(Saudi Digital Library, 2026) Alqahtani, Amani; Alachkar, HoudaAcute lymphoblastic leukemia (ALL) is the most common pediatric malignancy and is now associated with excellent long-term survival rates in pediatrics, largely due to the incorporation of asparaginase into multi-agent chemotherapy regimens. However, asparaginase is associated with hepatotoxicity, which may lead to treatment interruption and adversely affect clinical outcomes. Hispanic patients with ALL experience disproportionately higher disease burden and treatment-related toxicities, yet the biological mechanisms underlying these disparities remain incompletely understood. This dissertation investigated the clinical, genetic, metabolic, and predictive determinants of asparaginase-induced hepatotoxicity in pediatric ALL, with a particular focus on Hispanic populations. A retrospective cohort of 250 pediatric ALL patients treated at Children’s Hospital Los Angeles was analyzed to evaluate the incidence and clinical significance of hepatotoxicity using clinically meaningful CTCAE-based criteria. Hepatotoxicity occurred in 45.2% of patients and was significantly more frequent in Hispanic compared with non-Hispanic patients. Hispanic ethnicity remained an independent predictor of hepatotoxicity and was associated with treatment delays, increased minimal residual disease positivity, and inferior relapse-free survival. Pharmacogenomic analyses demonstrated that higher polygenic risk scores and increased Indigenous American ancestry were independently associated with greater susceptibility to hepatotoxicity. Integrated untargeted and targeted metabolomics further revealed profound alterations in lipid metabolism, mitochondrial β-oxidation, oxidative stress, and nucleotide metabolism, with Hispanic patients exhibiting distinct metabolic signatures enriched for oxidative stress and lipid remodeling. Finally, multi-omics predictive models integrating clinical, genetic, and metabolomic variables demonstrated improved discrimination (ability to correctly distinguish patients who developed hepatotoxicity from those who did not) and calibration (closer alignment between predicted and observed risk) compared with clinical-only models. Collectively, these findings provide a comprehensive framework for understanding asparaginase-induced hepatotoxicity and support the development of precision medicine strategies to improve treatment safety and outcomes in high-risk pediatric ALL populations.8 0Item Restricted Advanced Stock Portfolio Optimization Under Multiple Risk Measures: Mixed-Integer Linear Programming Models and a Comparative Analysis(Saudi Digital Library, 2026) Alqarni, Sharifah Dhafer; Subasi, Munevver MinePortfolio optimization is a fundamental problem at the intersection of finance, economics, operations research, and applied mathematics. It aims to allocate capital among financial assets in a way that balances return generation and risk control. Classical portfolio optimization approaches, particularly Markowitz’s mean variance framework and related risk-adjusted performance measures, have provided important foundations for portfolio selection. However, these approaches may not fully capture the multiple dimensions of risk faced by risk-averse investors, including downside losses, the frequency of loss occurrences, and the magnitude of extreme losses. These limitations motivate the development of more flexible optimization models that incorporate multiple risk measures, investor preferences, and practical portfolio constraints. This dissertation develops and analyzes six mixed-integer linear programming models for stock portfolio optimization under multiple risk and return objectives, where downside risk, potential loss occurrence, and largest loss magnitude are minimized and expected portfolio return is maximized through a multiple-risk tradeoff framework and through investor-defined objective weights. The utilization and performance of these models are evaluated through computational experiments using real-world stock market data from two distinct financial markets: the Dow Jones Industrial Average (DJIA), representing a mature and developed market, and the Saudi Tadawul market, representing an emerging market. The comparative analysis evaluates each portfolio using downside risk, probability of loss, largest loss magnitude, and portfolio return, allowing the proposed models to be examined across different market structures, risk-return environments, and investor preferences. In addition to the historical average-based optimization framework, this dissertation develops a hybrid forecasting and optimization methodology for portfolio selection. Instead of relying solely on historical average returns, several forecasting methods, including Weighted Moving Average, Linear Regression, Multilayer Perceptron, Gaussian Processes, and Support Vector Machines, are used to forecast future stock returns. These forecasted returns are incorporated into the proposed optimization models and compared with the traditional historical average-based approach through a rolling out-of-sample simulation. This framework provides a practical assessment of how forecast-based return estimates affect portfolio allocation decisions and realized portfolio performance. The computational results demonstrate that the proposed models generate distinct risk-return profiles and provide flexible, investor-oriented portfolio structures that accommodate different risk preferences and return objectives.13 0Item Restricted Reflection-Based MM-Wave Array Pattern (RMAP) Measurement System(Saudi Digital Library, 2026) Aldawsari, Abdulhadi; Adams, JacobThe transition toward millimeter-wave (mmWave) frequencies in 5G and 6G architectures has introduced significant complexities to traditional antenna metrology paradigms. As modern phased arrays scale in complexity, the traditional reliance on mechanical scanning and discrete spatial sampling for pattern measurement has become a prohibitive bottleneck, both in terms of temporal throughput and physical facility overhead. This research pioneers the reflection-based rapid mmWave array pattern (RMAP) system, a deterministic metrology framework that decouples measurement throughput from traditional mechanical constraints. By utilizing a deterministic, reflection-based framework, the RMAP architecture maps the complex radiation profile of an antenna under test (AUT) onto a stationary uniform linear array (ULA) positioned at the focal point of an elliptical reflector. A rigorous analytical investigation into the system's temporal dynamics reveals that while conventional single-probe and multi-probe systems are mathematically bound by mechanical scaling laws, the RMAP architecture achieves a near-constant throughput scaling. This allows for the rapid characterization of high-density beam states in a timeframe governed by electronic acquisition speeds rather than the latencies of physical rotation. Furthermore, this dissertation explores the spatial scaling laws governing anechoic environments. It identifies a fundamental ``mechanical floor'' in traditional facility design, where the footprint of robotic gantries and structural overhead prevents further miniaturization. The RMAP system circumvents this floor by scaling directly with the electromagnetic boundary, enabling a substantial reduction in the total facility footprint required for high-frequency hardware verification. The underlying methodology is rigorously validated through high-fidelity, full-wave electromagnetic simulations within a transient Finite Difference Time Domain (FDTD) framework. This numerical study serves to establish the fundamental viability of the RMAP architecture, proving that the theoretical model holds under realistic electromagnetic conditions. Furthermore, the framework assesses the system's robustness against practical constraints, such as structural scattering and surface roughness, to ensure reconstruction fidelity.10 0Item Restricted Investigating the Efficiency of InGaN p‑n‑p‑n Homojunction Solar Cells(Saudi Digital Library, 2026) Alhejji, Moath; Ware, MorganIndium gallium nitride (InGaN) materials have shown significant potential for photovoltaic and optoelectronic applications due to their excellent optical and electrical properties. The unique direct bandgap, strong optical absorption, thermal stability, and high radiation resistance make InGaN alloys particularly promising for high performance solar cells. One of the most important advantages of InGaN is its tunable bandgap, which ranges from 0.7 eV for InN to 3.4 eV for GaN. This wide bandgap range enables absorption across a broad portion of the solar spectrum, extending from infrared to ultraviolet. Despite these advantages, the power conversion efficiency of conventional single p-n junction solar cells remains restricted by the Shockley limit. To overcome this limitation, alternative device structures have been proposed, including the p1-n1-p2-n2 homojunction structure, which introduces multiple junctions and internal electric fields that enhance carrier separation and reduces recombination losses, thereby enabling efficiency improvements beyond the Shockley limit. This work investigated the potential of a InGaN homojunction solar cell based on the p1 n1-p2-n2 structure as a pathway toward achieving power conversion efficiency beyond those attainable with conventional single p-n junction devices. Numerical simulations were performed using nextnano++ to systematically analyze the electrical and optical behavior of the proposed structure. The study examined the effects of varying doping concentrations, from uniform doping to modifying the second or third layers, adjusting the thickness of the second or third layers, applying light doping, modifying the top layer thickness, and observing the effects of incident light intensity on the p1-n1-p2-n2 structure. The impact of these parameters on open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), and overall efficiency (𝜂) were evaluated through current-voltage characteristics and energy band diagram analysis. The results show that, under uniform doping of 6.5E+16 cm-3, the p1-n1-p2-n2 homojunction structure achieves an efficiency of 32.91%, representing an improvement of approximately 7% compared with p-n junction’s 25.31% efficiency [1]. The doping concentration of 6.5E+16 cm-3 was selected as the standard for experiment. A slight increase in efficiency to 33.67% was observed after varying the thickness of the second layer to 750 nm. Further enhancements in efficiency are achieved through structural optimization, where variations in the top layer thickness yield efficiency approaching 40% with a thickness of 30 nm. Most notably, as we increase the third layer thickness to 180 nm, it results in a sharp turn on behavior in the I-V characteristics, near unity fill factor and a maximum demonstrated efficiency of 57%, indicating a transition from conventional photovoltaic operation to a bistable, thyristor like switching device. Under varying illumination levels, the optimum efficiency of 33.85% is achieved at an intensity of 6 suns, with standard uniform doping of 6.5E+16 cm-3 and fixed layer thicknesses. These results show the p1-n1-p2-n2 structure is a promising pathway for improving efficiency beyond that of a single p-n junction solar cell. They also provide a better insight into how internal electric fields across three junctions enhance device performance.14 0Item Restricted INTERACTION OF TIN (II) AND FLUORIDE IONS ON HYDROXYAPATITE CRYSTALS MINERALIZATION ON HUMAN TEETH(Saudi Digital Library, 2026) Alansari, Turki; Fan, Yuwei; Giordano, Russell; Discepolo, Keri; Fan, YuweiObjectives: This exploratory study aims to understand the role of stannous fluoride ions, their mechanical properties, and how these ions react with tooth minerals. In addition, we aim to explore the interaction of added stannous fluoride ions on hydroxyapatite crystal organization, morphology, and crystalline phases in enamel remineralization. Also, to understand the mechanism of stannous fluoride ions penetration in demineralized human enamel. Methods: A calcium phosphate calcification solution was prepared for remineralization. Teeth slices were immersed in a calcification solution with desired different concentrations of stannous fluoride, and sodium fluoride ions in a scintillation vial and incubated at 37C for a desired period. SEM was used to examine the morphology and organization of crystals on the enamel surface. EDS and WDS were employed as the primary techniques to analyze the elemental and chemical composition of the tested samples. to assess the hardness, we utilized a Vicker hardness test. Results: Combined SnF2+NaF showed a significant difference in crystal growth morphology in dense-packed needle-like crystals compared to NaF and SnF2. For the hardness test, a one-way ANOVA was conducted with a significance level of 0.05. The findings revealed a significant difference in the average hardness scores between the SnF2+NaF test group (295.89 kg/mm²) and the NaF control group (257.13 kg/mm²), with a p-value of 0.04. Conclusion: SnF2 showed a promising result in remineralization and could play an important role in protecting teeth from caries in combined with conventional fluoride. Further biomimetic studies suggested to mimic the oral cavity to explore the effect of these combined ions.6 0Item Restricted Enhancing Evidence-Based Practice for Emerging Therapeutic Modalities in Rehabilitation, Integrative, and Preventive Medicine(Saudi Digital Library, 2026) Alanazi, Abdulaziz M; FACSM, FAHA; Navin, KaushalWithin emerging therapeutic modalities, cupping therapy (CT) has gained increasing visibility across rehabilitation, integrative, and preventive medicine, particularly following the 2016 Rio Olympics, when elite athletes displayed visible cupping marks. In the United States, however, the rapid rise in public and clinical interest has outpaced the development of standardized safety guidelines, regulatory oversight, and competency-based training. This dissertation addresses these gaps through six chapters and advances a framework for strengthening evidence-based practice, patient safety, and workforce preparedness. Chapter I establishes the clinical and conceptual foundation of cupping therapy modalities (CTM), outlining their historical development, proposed mechanisms of action, and relevance across rehabilitation, integrative, and preventive healthcare. It argues that CTM should be evaluated not merely as traditional practices, but as emerging therapeutic modalities requiring the same standards of safety, evidence, and professional accountability expected in modern healthcare. Chapter II presents a systematic review of literature published between 2016 and 2023 and develops a novel safety taxonomy for adverse events, providing a standardized framework for categorizing clinical risks and highlighting the need for clearer terminology, stronger safety reporting, and greater procedural consistency. Chapter III examines the regulatory landscape of CT in the United States. Findings reveal substantial fragmentation in terminology, oversight, and scope of practice across states, underscoring the need for greater policy alignment to improve patient safety, administrative clarity, and consistent professional standards. Chapter IV investigates health science students’ perceptions and knowledge of CTM using the TPB. Subjective norms were the strongest predictor of intention, while limited academic exposure contributed to insufficient formal preparation and skill development among future providers. Chapter V evaluates a theory-based educational intervention grounded in the Theory of Planned Behavior and the Health Belief Model. Among 84 participants, knowledge increased from 9.67 to 16.30 with a large effect size (d = 1.22), and the integrated model explained 69.4% of the variance in behavioral intention. Subjective norms, attitude, and perceived barriers emerged as the most influential determinants of readiness for future clinical integration. Chapter VI synthesizes these findings and proposes a strategic framework to advance the safe, sustainable, and evidence-informed integration of CT into U.S. healthcare.10 0Item Restricted Intimate Interfaces: Rethinking Data Relationships in Menstrual and Reproductive Health Technologies(Saudi Digital Library, 2026) Alsebayel, Ghada Mohammed; Harteveld, CasperFemTech, short for female technology, is an emerging sector of technological innovation that aims to support women's health across diverse life stages. It has already produced accessible solutions that support self-care in a wide range of areas such as menstruation, fertility, pregnancy, menopause, and more. The field continues to expand with increased investment, growing adoption, and rapid technological advances, creating exciting opportunities to improve access to care and deepen understanding of women's health. At the same time, these technologies handle highly sensitive data, raising critical concerns about privacy and data misuse. In this dissertation, I examine FemTech as a site of promise and risk. I focus specifically on health applications available on mobile devices (FemTech apps) to study how users engage with these systems. Building on third-wave HCI, I emphasize the socio-cultural, ethical, and experiential dimensions of user interaction with these systems through a mixed-method research program. In the first phase of this research, I combine large-scale computational analysis of user reviews with qualitative thematic analysis to identify users' unmet needs, pain points, and concerns with commercial FemTech systems. In the second phase, I more directly interrogate how users understand and negotiate the sociotechnical tensions embedded within these technologies, including questions of privacy, commercialization, autonomy, trust, and data use. Together, these studies reveal the limitations of utility-driven approaches to the design and governance of FemTech systems. Instead, I argue that users engage with these technologies through diverse value configurations that extend beyond utility alone. As such, FemTech should be developed in ways that recognize and accommodate this plurality of values. I introduce "Intimate Interfaces" as an organizing concept for understanding how FemTech systems mediate users' relationships with their data, bodies, experiences, memories, and broader social and institutional contexts. Building on this concept, I propose the Triple-E Framework as a design framework for creating intimate interfaces that acknowledge and accommodate the diverse value configurations through which users engage with these systems. The framework operationalizes three design dimensions: empathic attunement to users' lived and situational context, eudaimonic consideration of how data accrues meaning over time, and ethical governance of consent, control, and accountability across the data lifecycle. This dissertation contributes to HCI research on privacy and women's health in three ways. First, it provides an empirical account of how users experience, interpret, and negotiate contemporary commercial FemTech systems beyond traditional measures of usability and health outcomes. Second, it advances methodological approaches for studying user perspectives at scale by combining computational analysis of user-generated content, qualitative inquiry, survey-based design provocations, and Q-methodology. Third, it contributes a conceptual and design-oriented account of intimate interfaces, alongside the Triple-E Framework, to support the design and governance of technologies that engage with intimate data and reproductive experiences.3 0Item Restricted The Influence of Business Short-Form Video Features on Consumer Engagement Intention: The Mediating Role of Perceived Value(Saudi Digital Library, 2026) Almohammadi, Bayan; Wallace, ChipidzaShort-form video (SFV) has become an important way for businesses to reach consumers on social media, yet research has focused mainly on paid advertising, in-app shopping, and purchase-related outcomes. Less is known about business-posted SFVs and how their features are associated with consumer engagement. Guided by the Stimulus-Organism-Response framework, this study examined how diagnosticity, vividness, entertainment, interactivity, and personalization are associated with engagement intention through utilitarian, hedonic, and social value. An explanatory sequential mixed-methods design was used. Survey data from 405 TikTok users in Saudi Arabia were analyzed using partial least squares structural equation modeling, followed by thematic analysis of 14 semi-structured interviews. All five features were significantly associated with their hypothesized value dimensions, with personalization showing the strongest associations and relating positively to all three value dimensions. Significant indirect associations with engagement were found through hedonic value for entertainment, vividness, and personalization, and through social value for personalization and interactivity. No significant indirect associations were found through utilitarian value. The interviews supported several quantitative patterns but also revealed important differences. Participants described vividness mainly in terms of product evaluation rather than enjoyment and interactivity mainly in terms of trust rather than social connection. Theoretically, the study highlights the importance of examining perceived value as distinct utilitarian, hedonic, and social dimensions and shows how qualitative findings can reveal mechanisms not fully captured by the quantitative model. Practically, the findings provide insights into how businesses may combine short-form video content and platform features when developing strategies to enhance consumer engagement.4 0Item Restricted DECISION SUPPORT SYSTEM FOR RESILIENCE-SUSTAINABLE SUPPLIER SELECTION USING MACHINE LEARNING(Saudi Digital Library, 2026) ALBARIQI, IBRAHIM; Zohdy, Mohamed AGlobal supply chains have become increasingly interconnected and dataintensive, but recent disruptions have shown that traditional supplier selection approaches, often focused on cost, quality, and delivery, are insufficient for ensuring continuity and responsible sourcing. Small and medium-sized enterprises (SMEs) are especially vulnerable because they typically lack the analytical capacity to evaluate suppliers using both resilience and sustainability criteria. This study develops and evaluates a machine learning based decision support system, the Resilient-Sustainable Supplier Selection System (RSSSS), to support data-driven supplier classification and risk-aware sourcing decisions. The proposed RSSSS integrates operational performance indicators with resilience attributes (e.g., reliability, agility, and geographic diversification) and sustainability-related indicators within a unified evaluation framework. A historical supplier dataset containing 74,339 records and 13 predictive features was preprocessed through data cleaning, categorical encoding, and handling of missing values. Seven supervised machine learning models—Decision Tree, Random Forest, AdaBoost, Naive Bayes, k-Nearest Neighbors, Linear Discriminant Analysis, and XGBoost—were trained and evaluated using accuracy and computational training time. Results show that XGBoost achieved the highest classification accuracy (82.23%), outperforming all alternative models, while requiring 5.96 seconds of training time. The findings confirm that tree-based ensemble learning can effectively capture complex, non-linear relationships among resilience and sustainability indicators, enabling more accurate identification of resilient-sustainable suppliers than traditional rule-based or static scoring methods. Overall, the RSSSS demonstrates a practical and lightweight approach for SMEs to improve supplier selection speed, transparency, and comprehensiveness, supporting more resilient, sustainable, and risk-aware sourcing decisions under uncertainty.23 0