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 VHealth Suite: A Unified, Secure, and Intelligent Patient-Centered Framework for Legacy System Integration in Virtual Hospital Ecosystems(Saudi Digital Library, 2026) Alsalamah, Sara Abdullah; Chang-Tien, LuA virtual hospital (VH) is a distributed, digitally enabled healthcare ecosystem that extends clinical services beyond physical facilities, facilitating patient-centered (PC) care across geographically dispersed settings through interoperable infrastructures, telemedicine platforms, and hub-and-spoke coordination. However, legacy healthcare information systems remain fragmented, disease-centered, and operationally reactive, which limits secure data sharing, knowledge integration, and system-wide capacity awareness. These challenges are further exacerbated by rising demand, workforce constraints, and the need for predictive operational intelligence to enable efficient and scalable care delivery. To address these limitations, this dissertation proposes VHealth Suite, a unified, secure, and intelligent framework designed to modernize legacy healthcare information systems and seamlessly integrate them into VH ecosystems without requiring system replacement. The framework is implemented as a multi-component architecture that integrates secure data exchange, intelligent knowledge extraction, predictive operational intelligence, and human-in-the-loop interaction. First, the secure data exchange component is realized through VHealth-AC, a novel access control (AC) model that enables fine-grained and secure access to PC data across distributed and autonomous healthcare systems. The model employs a five-tier PC information classification scheme and operates as a neutral collaboration security domain, allowing clinicians to securely access patient data across institutional boundaries at the point of care. Second, intelligent PC knowledge extraction is achieved through VHealth-CNN and VHealth-MFusion. VHealth-CNN leverages a double-layer convolutional neural network (CNN) to extract and classify health-related features from biomedical data, achieving prediction accuracies of 91.3%, 93.5%, and 95% for obesity, hypertension, and diabetes, respectively. VHealth-MFusion introduces a hierarchical multimodal deep learning framework that integrates chest X-ray (CXR) images with structured clinical data, achieving 97.2\% overall classification accuracy, improving robustness under class imbalance, and reducing misclassifications among clinically similar conditions. Third, predictive operational intelligence and clinical routing are addressed through VHealth-Routing, an AI-driven framework that combines clinical decision support with capacity-aware optimization. The framework integrates a clinical routing engine, a spatiotemporal prediction engine, and a constrained re-ranking mechanism to align clinical relevance with operational feasibility. It is evaluated using a large-scale real-world dataset from the Seha VH ecosystem in Saudi Arabia, comprising over 15 million records, with a representative subset of 1,006,111 appointments used for experimentation. Results demonstrate strong routing performance, with XGBoost achieving 73.2% Top-1 accuracy and 97.6% Top-3 accuracy, alongside effective demand forecasting and waiting time estimation, supporting improved workload distribution and reduced system inefficiencies. Finally, the human-in-the-loop component is implemented through VHealth-Bot, an AI-driven conversational platform that integrates natural language processing, diagnostic reasoning, and adaptive learning to support clinician–patient interaction. The system enhances real-time symptom assessment, personalized response generation, and collaborative decision-making, while maintaining clinician oversight to ensure safety and preserve clinical expertise. Evaluation results indicate improvements in diagnostic support, workflow efficiency, clinician–patient communication, and patient satisfaction. Overall, VHealth Suite provides a scalable, privacy-preserving, and intelligent architecture that unifies clinical intelligence with operational optimization. The proposed framework enables proactive, data-driven, and PC care delivery in large-scale VH ecosystems, improving clinical outcomes, enhancing operational efficiency, and fostering more responsive healthcare systems.17 0Item Restricted JOINT NONPARAMETRIC TESTS FOR LOCATION AND SCALE DIFFERENCES IN MULTIPLE POPULATIONS(Saudi Digital Library, 2026) Alrefeadi, Fatimah Abdullah; Rhonda, MagelNonparametric statistical methods are widely used when data do not satisfy the assumptions required for parametric procedures, such as normality and homogeneity of variances. While many existing nonparametric tests are designed to detect differences in either location or scale parameters separately, practical applications often require the simultaneous assessment of both. When location and scale vary together, procedures that focus on only one parameter may suffer from reduced power or lead to misleading conclusions. This dissertation develops new combined nonparametric tests for jointly assessing differences in location and scale across multiple populations. The proposed methods are constructed by combining the Kruskal–Wallis test for location with scale components derived from the Moses Kruskal–Wallis and Levene tests. Two groups of test statistics are introduced, each incorporating measures of central tendency based on the mean, median, and trimmed mean, to enhance robustness across symmetric, skewed, and heavy-tailed distributions. The performance of the proposed tests is evaluated through extensive simulation studies under a wide range of distributions and sample size configurations for three and four populations. The results demonstrate that the tests generally maintain nominal Type I error rates and achieve improved power when scale differences or joint location–scale differences are present. Among the proposed methods, Moses-based procedures show superior performance in heavy-tailed distributions, whereas Levene-based procedures exhibit more stable behavior across a broad range of distributional settings. Overall, the proposed tests provide reliable and effective tools for joint nonparametric inference on location and scale in multi-sample problems.11 0Item Restricted FROM ACCEPTANCE TO PRACTICE: A MIXED-METHODS STUDY OF GENERATIVE AI INTEGRATION AMONG SAUDI UNIVERSITY EFL TEACHERS(Saudi Digital Library, 2026) Alderaan, Hadir; Thomas, SalsburyDespite growing interest in artificial intelligence in education, there is still limited research on how EFL teachers in Saudi higher education perceive and use Generative Artificial Intelligence (GAI) in their teaching. Much of the existing literature focuses on student use, with less attention given to teachers’ pedagogical decision-making and the realities of integrating these tools into classroom practice. This gap is particularly significant in contexts where clear institutional guidance is lacking, leaving teachers to navigate issues such as academic integrity, ethical use, and the cultural appropriateness of AI-generated content on their own. This study employs a mixed-methods design to examine how 101 Saudi university EFL instructors engage with GAI tools and how teaching experience relates to their instructional practices. The study is guided by the Technology Acceptance Model (TAM), Technological Pedagogical Content Knowledge (TPACK), and Expectancy–Value Theory (EVT). Data were collected through an online survey and followed by semi-structured interviews with seven purposively selected participants. The findings indicate that teachers generally hold positive attitudes toward GAI; however, their use remains selective and primarily focused on preparatory tasks. Participants reported using GAI for lesson planning, generating example texts, and supporting grammar instruction, while avoiding its use in assessment and direct student-facing tasks due to concerns about academic integrity and student overreliance. Interview data further show that teachers actively negotiate how and when to use GAI, setting boundaries based on pedagogical judgment and contextual constraints. The study also highlights the additional effort teachers invest in reviewing, verifying, and adapting AI-generated content to ensure accuracy and cultural relevance. Rather than reducing workload, GAI often shifts teachers’ effort from content creation to evaluation and modification. Overall, the findings suggest that positive attitudes alone do not lead to meaningful integration, emphasizing the need for clearer institutional support and targeted professional development to enable ethical and contextually appropriate use of GAI in EFL classrooms.6 0Item Restricted ABT-263 Exerts Transient Senolytic Activity Against Therapy-Induced Senescence in NSCLC(Saudi Digital Library, 2026) Alshehri, Muruj Ahmed; Gewirtz, DavidTherapy-induced senescence (TIS) can suppress tumor growth but may also facilitate long-term relapse through a subset of cells capable of regaining proliferative potential, while the senescence-associated secretory phenotype (SASP) can further promote tumor progression. Senolytic agents, such as the BCL-XL/BCL-2 inhibitor ABT-263 (navitoclax), have therefore emerged as a strategy to selectively eliminate senescent tumor cells. In this study, we evaluated the senolytic efficacy of ABT-263 in murine (CMT-167) and human (A549) non–small cell lung cancer (NSCLC) models following clinically relevant radiation regimens: a single high dose (10 Gy) mimicking stereotactic body radiation therapy (SBRT) and a hypofractionated regimen (2.75 Gy × 4). Both radiation regimens induced a senescent phenotype, although senescence was consistently less pronounced following fractionated irradiation. To determine whether enhanced DNA damage could increase senescence depth and senolytic sensitivity, we combined fractionated radiation with the PARP inhibitor Talazoparib. This combination markedly amplified senescence markers and transiently increased sensitivity to ABT-263. In contrast, combining cisplatin with fractionated radiation failed to enhance senescence induction or senolytic sensitivity beyond cisplatin alone. Across all conditions, despite clear evidence of senolytic activity, ABT-263 consistently failed to achieve durable eradication of senescent cells. A substantial population of cells persisted after treatment while retaining senescence markers. Together, these findings indicate that ABT-263 eliminates only a subset of therapy-induced senescent cells, leaving behind a residual senescent population. This highlights the need for improved strategies to achieve more complete and durable targeting of therapy-induced senescence in NSCLC.5 0Item Restricted Experimental and Numerical Study of Pulsating Water and Nanofluid Flow in Photovoltaic-Thermal Solar Collectors(University of Dayton, 2026) Mushabbab, Abdulwahed; Chiasson, AndrewPhotovoltaic thermal (PVT) systems are designed to generate electricity and useful heat from the same surface, but their performance is often limited by high photovoltaic (PV) cell temperature and weak heat transfer under laminar cooling conditions. Most previous work has used steady water flow and geometric modifications to improve PVT cooling, which can increase complexity and cost. Pulsating flow and nanofluids have shown heat transfer benefits in other applications, but their effect on full-scale flat-plate PVT collectors has not been clearly quantified. This work investigates the influence of controlled pulsating flow on the thermal and electrical performance of a flat-plate water-cooled PVT system under laminar conditions. Two experimental campaigns were carried out using the same indoor solar simulator with an average light intensity (I) between 700 and 800 W/m2. In the first part, water was used as the working fluid and the PVT system was tested under uncooled, continuous (steady)-flow and pulsating-flow operation. Flow rates of 1-4 L/min were examined with pulsation frequencies of 0.25, 0.5, 1 and 2 Hz. System performance was evaluated against uncooled and continuous-flow reference cases. Pulsating operation reduced the PVT surface temperature and increased thermal efficiency compared with continuous flow, while electrical efficiency showed a smaller but consistent improvement. The frequency of 0.5 Hz obtained the best performance, with thermal efficiencies above 50% at higher flow rates and electrical efficiency around 9.8% without a measurable increase in average pressure drop. In the second part of the thesis study, a 0.1 vol.% Al2O3/water nanofluid was used at a fixed flow rate of 4 L/min under continuous and pulsating flow. Frequencies from 0.25 to 2 Hz were tested and supported by a three-dimensional (3D) transient Computational Fluid Dynamics (CFD) model of the PVT channel. Pulsating nanofluid cooling further reduced PVT surface temperature, with the 2 Hz case giving the lowest measured value of about 30 degrees C and a maximum thermal efficiency increase of roughly 22% compared with continuous nanofluid flow. Electrical efficiency gains remained modest, on the order of 1%, which confirms that the main benefit lies in improved thermal recovery. The CFD predictions reproduced the experimental trends closely. Overall, the results show that pulsation frequency can be used as a control parameter to enhance the thermal performance of flat-plate PVT systems while maintaining laminar flow and only small changes in electrical efficiency.2 0Item Restricted Efficient Intrusion Detection for IoMT: Integrating Machine Learning, Feature Selection, and Fuzzy Logic(Saudi Digital Library, 2026) Balhareth, Ghaida; Ilyas, MohammadThe internet of medical things (IoMT) has transformed healthcare by enabling real-time patient monitoring, remote diagnoses, and effective data exchange among connected medical devices and clinical systems. The increasing reliance on interconnected medical equipment has also intensified cybersecurity risks, as resource-constrained devices and wireless communication channels are vulnerable to attacks such as man-in-the-middle, spoofing, data injection, and ransomware. Intrusion Detection Systems (IDSs) play a critical role in mitigating these threats; however, traditional IDS approaches often struggle with high-dimensional IoMT data, class imbalance, and uncertainty in traffic patterns, which can increase false alarms and reduce reliability in safety-critical environments. This dissertation investigates efficient and deployable IDS designs for IoMT networks by integrating machine learning, feature selection, and fuzzy logic to improve detection reliability while reducing model complexity. First, the dissertation provide an extensive examination of IDS approaches proposed for IoMT, classifying them into machine learning, deep learning, fuzzy logic , and hybrid categories, while analyzing IoMT architectures and security vulnerabilities across layers. Next, it develops an efficient IDS model based on machine learning classifiers combined with feature selection techniques to enhanced detection accuracy and reduce computational cost in edge and gateway settings. Building on this direction, the dissertation proposed a multi-level feature selection pipeline that combines complementary ranking methods and consensus selection to identify consistently informative features, followed by a fuzzy inference system that supports uncertainty-aware intrusion classification using interpretable rule-based reasoning. The suggested IDS systems exhibit robust detection capabilities with reduced false-alarm rates, utilizing small feature sets appropriate for gateway and edge deployment throughout benchmark tests. The dissertation presents a cohesive security system that prioritizes efficiency, interpretability, and practical implementation for the protection of IoMT communications and the safeguarding of sensitive healthcare information. Future works will expand these IDS designs to include other IoMT datasets and real network traffic, while further investigating robustness in the context of concept drift and increasing adversarial strategies, all while maintaining low complexity and transparency.6 0Item Restricted Analyzing The Impact of Social Media Buy Recommendations on Saudi Stock Market Reactions: A Bert-Based Sentiment and Network Analysis Approach(Saudi Digital Library, 2026) Alharbi, Fayez Dakhel; Wallace, ChipidzaSocial media has become part of the daily rhythm of financial markets, its actual influence on prices remains debated. This dissertation examines whether buy recommendations posted by Saudi-focused social media analysts on Twitter/X are associated with short-term stock market reactions in the Tadawul exchange. The central question is straightforward: when influential online analysts issue concentrated buy signals, do prices and trading activity respond in measurable ways? To address this, I combine multiple empirical strategies in a triangulated research design. Fixed-effects panel regressions estimate the association between sentiment measures, analyst exposure, abnormal returns and volume while accounting for firm-level and time-specific factors. Propensity score matching is used to compare exposed and non-exposed stock-days with similar observable characteristics, helping reduce selection concerns and strengthening causal interpretation. An event-study framework aligns returns around recommendation bursts to examine dynamic price adjustments within clearly defined exposure windows, helping isolate the timing of market responses. Each method approaches the question from a slightly different angle. The patterns suggest that analyst activity is strongly linked to short-lived increases in trading volume and modest return movements. Effects appear concentrated around exposure shocks and tend to dissipate within days, which consistent with attention-driven responses. Pre-event drift hints that analysts often post during periods of existing market stress, complicating clean causal interpretation. To complement the quantitative analysis, qualitative interviews with retail investors explore how credibility, repetition, and network visibility are perceived to influence decision- making. Investors describe using informal mental checklists before acting on recommendations. Their accounts help contextualize the statistical findings and suggest that social media effects operate through attention and trust. Overall, the findings suggest that social media recommendations can redirect attention and temporarily intensify trading, especially in retail-driven markets. The influence appears situational, shaped by credibility, timing, and existing sentiment. These patterns are consistent with a short-term causal influence that is conditioned by market context and investor attention.3 0Item Restricted Essays in Applied Empirical Economics(Saudi Digital Library, 2026) Alhunayshil, Hanouf; Bai, JinhuiThis dissertation presents three empirical studies on how structural constraints and market frictions shape economic outcomes. The first chapter uses a 52-country panel from the World Bank Global Findex database and two-stage least squares to show that female financial technology adoption raises divorce rates, while formal savings has a stabilizing effect, consistent with a household bargaining framework. The second chapter examines credit gap dynamics in Saudi Arabia (2010–2025) using Bry–Boschan cycle dating and a vector autoregression, finding that non-oil GDP is the dominant predictor of the credit gap in oil-exporting economies. The third chapter uses System GMM on weekly U.S. data to show that bulk truck rates are highly persistent and significantly driven by fuel prices.9 0Item Restricted Textbooks in Saudi Arabia from TEIL Perspective: Analysis and Pedagogical Implications(Saudi Digital Library, 2026) Alzubaidi, Noor; Matsuda, AyaTextbook evaluation is a significant assessment tool for selecting appropriate materials in English Language Teaching (ELT). Within the field of ‘Teaching English as an International Language’ (TEIL), textbook evaluation is one of the major areas of research, but it has received relatively limited attention in Saudi Arabia compared to other contexts. To address this gap, the present study evaluates English textbooks used in Saudi Arabia from the TEIL perspective. The study adopts a content analysis approach, examining representations of English varieties, users, uses, locations, and cultural themes in the Super Goal textbook series. The findings reveal a clear imbalance in the representation of Global Englishes across the analyzed dimensions. While Saudi and Islamic identity are well represented in cultural themes, phonological and lexical features remain strongly influenced by a monolithic model of American English. Furthermore, English users, patterns of use, and locations are left unspecified. These patterns highlight a gap between standardized teaching approaches and the complex socio-linguistic reality of English use in the real world. This study contributes to the limited body of TEIL research in the Saudi context by providing a systematic evaluation of textbook representations of Global Englishes. The findings suggest that education policy in Saudi Arabia, particularly under Saudi Vision 2030, CE, could place greater emphasis on integrating Global Englishes into the curriculum for Saudi learners, so that it can prepare them to become competent users of English in the global marketplace.24 0Item Restricted Central Bank Uncertainty, Investor Sentiment, and Crash Risk in Digital Assets(Saudi Digital Library, 2026) Alahmadi, Marwan; Hassan, M. KabirThis dissertation contains two essays. The first essay examines the connectedness among central bank digital currency uncertainty (CBDC), investor sentiment, and digital assets using the Quantile Vector Autoregressive connectedness model, following Ando et al. (2022). This analysis uses data from June 2018 to November 2024. The findings show that the connectedness is substantial and varies across market conditions. Spillovers become stronger during extreme states than during normal periods. The results also suggest that the pattern of shock transmission changes across quantiles. Overall, the evidence shows that CBDC and investor sentiment are linked to digital asset market dynamics and play an important role in the propagation of shocks. The second essay examines the interdependencies among cryptocurrencies and their crash-risk dynamic. The cryptocurrency market is highly volatile, raising concerns about sudden downside risk and crash transmission across digital assets. This study examines crash risk among eight major cryptocurrencies using crash risk measures constructed from 5-minute high-frequency data and the connectedness frameworks of Diebold and Yılmaz (2014) and Baruník and Křehlík (2018). The results show substantial crash risk connectedness across the sample, although the direction and intensity of spillovers differ across assets. The frequency-domain results further show that crash risk spillovers are driven predominantly by short-run dynamics, with much weaker long-run connectedness. Additionally, the COVID-19 period slightly strengthens the long-run component of connectedness and changes the distribution of transmitter and receiver roles across cryptocurrencies. Overall, the findings provide useful insights for investors and policymakers concerned with downside contagion in the cryptocurrency market.15 0