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 BUILDING FUTURE LEADERS: CRITICAL FACTORS FOR SUCCESSFUL IMPLEMENTATION OF SUCCESSION PLANNING IN EASTERN HEALTHCARE CLUSTER ADMINISTRATION IN SAUDI ARABIA(Saudi Digital Library, 2026) Alqahtani, Musaad; William, J. RothwellThis study investigated the critical elements shaping the implementation of succession planning within the Saudi Arabian healthcare sector, with a particular to the Eastern Healthcare Cluster (EHC) of Saudi Arabia. Guided by the Flexible Open Systems Model and the Rothwell Seven-Pointed Star Model, the study’s multi-layered approach mapped the organizational elements internally and the external elements with the aid of the various levels (macro, meso, and micro) of the four variables of the PEST approach (politics, economics, societies, and technology), amongst others. Using the Enhanced Critical Incident Technique (ECIT), a qualitative-driven method, the study captured real experiences, narratives, and practice-based examples from 16 senior administrators directly involved in succession-planning efforts. Participants reported 144 critical incidents, categorized as helping Factors (52 incidents, 36.1%), hindering Factors (50 incidents, 34.7%), and wish list Items (42 incidents, 29.2%). The credibility of the findings was ensured through nine Enhanced Critical Incident Technique (ECIT) validation checks, including interview fidelity, independent extraction, participant cross-checking, expert review, and theoretical agreement. Mapping these critical incident categories into the Flexible Open Systems Model by identifying the internal organizational elements that best represented each category. This process surfaced six core components: leadership, strategy, culture, people, processes, and systems—as the foundational domains influencing succession planning implementation. Interestingly, communication emerged as a distinct and essential internal element, expanding the original components and reflecting its central role in the experiences shared by participants. Synthesizing these insights led to the development of the Resilient and Strategic Succession Planning Model (RSSPM), a contextually grounded model designed to strengthen leadership pipeline resilience, organizational readiness, and strategic alignment within Saudi Arabia’s emerging healthcare transformation landscape, aligning with Vision 2030.12 0Item Restricted Impact of the Estrous Cycle on Intestinal Injury in Exertional Heat Stroke in Mice(Saudi Digital Library, 2026) Aldakkan, Ali; Orlando, LaitanoExertional heat stroke (EHS) is the most lethal manifestation of heat illnesses. EHS is characterized by loss of consciousness (LOC), central nervous system (CNS) dysfunction, and hyperthermia during physical exertion. It leads to multiorgan damage. EHS-induced intestinal injury is one of the severe consequences that can facilitate further systemic damage. Through the process of leaky gut, intestinal injury can lead to systemic inflammatory response syndrome (SIRS). To date, a significant gap remains in our understanding of female-specific physiological responses to extreme exercise-heat stress. One contributing factor to this gap is the inability to perform hypothesis-driven research in humans as EHS can be lethal. Therefore, the use of preclinical models in mammals represents a powerful tool to help fill this gap. In female mice, the variability in EHS susceptibility and its further consequences have not been explained. Mice undergo four phases of the estrous cycle (estrus, metestrus, diestrus, and proestrus). The distinct physiological characteristics of estrous phases (estrus and diestrus) may influence the EHS-induced intestinal damage. The overall objective of this project is to investigate phase-dependent injury severity, in both early recovery (30 min, 3 h, and 24 h) and late recovery stages (14 d and 30 d) post-EHS. Knowing that in our model estrus (E) and diestrus (D) showed no differences in hypothermic depth, led us to hypothesize that the level of intestinal damage would follow the same pattern. A total of 79 adult female (E: n = 42; D: n = 37) C57BL/6J mice, aged 16–17 weeks, underwent an exertional heat stroke protocol. Post-EHS, we performed terminal experiments for early and late recovery timepoints. Following data collection and preparation, we evaluated tissue integrity using Chiu scale for early and late recovery timepoints, and intestinal morphometrics 3 h post-EHS. Our analyses of Chiu injury scores from early and late recovery stages demonstrated no differences. Similarly, the morphometric analyses showed no phase-specific differences. The fact that estrus animals showed similar levels of intestinal damage to diestrus, while running significantly longer, suggests that they were relatively more protected, as they tolerated greater thermal and exercise stress before reaching similar result.25 0Item Restricted THE PERCEIVED VALUE OF AMERICAN ENGLISH IN ENGLISH LANGUAGE CLASSES IN SAUDI ARABIA(Saudi Digital Library, 2026) Albishi, Dhafer; Felice, A ColesThis dissertation investigates the perceptions and preferences of Arabic-speaking English as a Second Language (ESL) learners and faculty members in Saudi Arabia regarding General American English (GAE). Situated within the broader sociolinguistic debate on English varieties, the study explores why GAE is consistently prioritized in instruction, how it is reinforced through media exposure, and what implications this has for pedagogy and curriculum design. The research adopts a qualitative design, combining open-ended questionnaires with 20 student participants and semi-structured interviews with an instructor and program coordinator at a Saudi Arabian community college. Data were analyzed thematically, following Braun and Clarke’s framework, to capture both learner perspectives and institutional practices. Findings reveal that GAE dominates learner preferences due to its perceived clarity, accessibility, and prestige, as well as its widespread presence in digital media such as Netflix, YouTube, and TikTok. Students described GAE as “normal,” “easy,” and “modern,” illustrating how emotional reactions and social associations reinforce rational judgments of intelligibility. Faculty perspectives aligned with this preference, emphasizing GAE’s pedagogical simplicity and practical utility for academic and professional success. At the same time, results showed that early starters and students with higher media exposure were more open to dialectal diversity, while late starters relied heavily on GAE as an instructional anchor. Although awareness of World Englishes was limited, both faculty members expressed cautious support for introducing dialectal variation at advanced stages, reflecting a pragmatic but forward-looking pedagogy. These results highlight a dynamic interplay between institutional choices, learner experiences, and global linguistic ideologies, with GAE functioning as both a practical learning model and a symbolic marker of modernity. The study contributes to applied linguistics by demonstrating how localized learner attitudes intersect with global language hierarchies. It recommends a tiered pedagogical approach: establishing GAE as a stable foundation at early stages, then gradually incorporating dialectal awareness to prepare learners for multilingual, multicultural communication. Future research should include longitudinal designs, cross-institutional comparisons, and investigations into the role of media literacy in shaping dialectal awareness.6 0Item Restricted Topologically Associating Domains: At the Crossroads of Genome Structure and Function(Saudi Digital Library, 2024) Almansour, Faisal; Misteli, TomUnderstanding the 3D architecture of the genome is crucial for elucidating its role in gene regulation and expression. The complex organization of the genome within the cell nucleus plays a pivotal role in gene expression. Topologically associating domains (TADs) are a prominent and ubiquitous architectural feature of genomes in higher organisms, defined as genomic regions that interact more frequently with each other than with their neighboring regions. It has been suggested that TADs function to facilitate the interaction of regulatory elements with their target genes located in the same TAD. However, the relationship between the structure of TADs and the function of the genes they contain remains unclear. This thesis explores the relationship between chromatin domain architecture and gene expression by application of single-cell and single-allele imaging approaches. I have developed innovative single-allele, high-throughput imaging assays, combining DNA and RNA fluorescence in situ hybridization (FISH) to simultaneously probe the structure of individual TADs and the transcriptional activity of their genes. My analysis revealed that transcriptional activity at the allele level is independent of TAD boundary pairing. Notably, variations in TAD boundary distances between alleles within the same nucleus did not correlate with gene activity. Moreover, my results show that global transcription inhibition does not alter TAD structure, whereas the degradation of cohesin, a key TAD architectural protein complex, leads to reduced transcriptional activity alongside the loss of TAD boundary interactions. These findings challenge prevailing assumptions about the functional roles of TAD structure. They underscore the complexity of genomic regulation and open avenues for further research on the mechanisms governing gene expression and the potential of targeting genome architecture for therapeutic purposes.20 0Item Restricted ADVANCES IN REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION SYSTEM USING DEEP LEARNING TECHNIQUES FOR ENHANCED ACCESSIBILITY(Saudi Digital Library, 2026) Alsharif, Bader; Ilyas, MohammadAdvancements in technology have significantly contributed to the development of innovative tools aimed at improving communication and accessibility for individuals with hearing impairments. This dissertation explores various machine learning and deep learning techniques for recognizing American Sign Language (ASL) gestures, focusing on enhancing accessibility and bridging the communication gap between hearing-impaired and hearing individuals. Traditional machine learning models, such as Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), alongside deep learning architectures like AlexNet, ResNet-50, EfficientNet, ConvNeXt, and VisionTransformer, were investigated for their effectiveness. Experiments conducted on an extensive dataset of 87,000 ASL gesture images revealed exceptional recognition accuracy, with ResNet-50 achieving 99.98% and Random Forest reaching 99.55%, while other models performed within a range of 97% to 98%. Building on these findings, an innovative real-time recognition system was developed, integrating computer vision and deep learning techniques. The project initially utilized MediaPipe for precise hand movement tracking and YOLOv8, a state-of-the-art object detection model, to translate ASL gestures into text in real time. A comprehensive dataset of 29,820 annotated images was created to ensure strong generalization across diverse hand positions and lighting conditions. MediaPipe’s hand landmark annotations significantly enhanced input quality, improving the YOLOv8 models training accuracy. In addition, a more advanced framework was later designed that integrates YOLOv11 with MediaPipe for robust real-time ASL alphabet recognition. This system was trained on a large-scale dataset of 130,000 annotated images with custom keypoint-based annotations, enabling the model to capture subtle variations in hand and finger positions. Experimental evaluation demonstrated outstanding performance, achieving a mean Average Precision (mAP@0.5) of 98.2% with minimal latency, confirming its suitability for real-time applications in education, healthcare, and professional environments. Overall, the findings of this dissertation underscore the transformative potential of AI-driven solutions for ASL recognition. By bridging communication gaps through both traditional classification models and real-time deep learning frameworks, this work contributes to fostering inclusivity, accessibility, and independence for individuals with hearing impairments.6 0Item Restricted PATHWAYS TO FUNCTION VIA THE EMERGENCE OF A MECHANICAL SWITCH IN EVOLVABLE MATTER(Saudi Digital Library, 2025) Alqatari, Samar; Sidney, NagelThe underlying principles of how sharp switches occur in rugged fitness landscapes, while integral for understanding evolution of function and adaptation in biological systems, re- main elusive. Here I use elastic mechanical networks as a platform for probing the physical principles governing single-mutation transitions between two highly-fit, incompatible func- tions. The function used is an allosteric coupling of two pairs of source and target nodes that respond to an input strain in-phase or out-phase with each other. I study the complete fitness landscapes for ensembles of networks, and find that high-fitness pathways between these functions nearly always exist. At the largest fitness threshold for viable evolution, the functional transitions occur via a “jumper” mutation: a single bond addition or deletion that connects distinct fitness peaks with opposite functions. These mutations can be viewed as a mechanical switch, which I find can switch between incompatible functions with minimal perturbation to the system. In some cases, the mere presence of a bond, regardless of stiff- ness, constrains the deformation mode and determines function. However, bond formation or breaking is not always necessary: subtle geometric deformations that conserve connectivity can be sufficient to induce sharp functional transitions. The study of this physical system suggests that the single mutation function switches often found in biological systems may be fundamentally mechanical in origin.7 0Item Restricted EXAMINING PATIENT SAFETY CULTURE TRENDS IN U.S. HEALTHCARE THROUGH A MULTI-YEAR ANALYSIS(Saudi Digital Library, 2026) Alabdullah, Hassan; Karwowski, WaldemarPatient Safety Culture (PSC) is recognized as a cornerstone of healthcare quality and a key determinant of patient outcomes. Despite the Institute of Medicine’s early calls to establish safety-oriented systems, evidence on the long-term stability of PSC in U.S. hospitals has remained limited. This dissertation addresses this gap through a multi-year evaluation of PSC using the Hospital Survey on Patient Safety Culture (HSOPSC v1.0) and advanced statistical methods. Drawing on one of the largest national datasets—comprising over 993,000 healthcare providers from 1,601 U.S. hospitals across three survey cycles (2013–2020)—the study employed a longitudinal repeated cross-sectional design. Analyses combined descriptive statistics, second-order factor modeling, and Partial Least Squares Structural Equation Modeling (PLS-SEM) with multi-group analysis to capture temporal trends, determinants, and outcomes of PSC. Findings showed that overall PSC scores averaged 65% across years, with strengths in “Supervisor/Manager Expectations” and “Teamwork within Units,” and persistent weaknesses in “Nonpunitive Response to Error” and “Handoffs and Transitions.” PSC declined slightly over time, with regional and institutional variations: smaller, non-teaching, and Southern/Central hospitals reported higher PSC. Hospital size and region exerted inconsistent effects, while workforce factors—such as staff role, tenure, and patient contact—were stronger and more stable predictors of PSC outcomes. Importantly, PSC demonstrated robust predictive power, explaining 56.7% of the variance in overall safety perceptions and 23.2% in error reporting frequency. The dissertation provides rare longitudinal evidence confirming PSC as a dynamic, multidimensional construct. While PSC’s influence on safety outcomes has strengthened over time, sustaining improvements remains challenging, particularly in fostering blame-free reporting, ensuring adequate staffing, and improving care transitions. Practical implications highlight leadership commitment, nonpunitive systems, and workforce-centered strategies, alongside interprofessional education to embed safety in daily practice. Collectively, the findings offer actionable insights for policy, leadership, and training, while advancing methodological rigor in PSC research.12 0Item Restricted Saudi and American Students’ Motivation and Anxiety in Online Collaborative Learning in The United States(Saudi Digital Library, 2024) Alqarni, Nawal; Boston, MelissaThis research study aims to explore motivation and anxiety Saudi and American students in online collaborative learning environments through online courses in U.S. universities and how they relate to their academic performance. The study utilized a quantitative approach for data collection through an online survey of 99 Saudi students and 39 American students who were enrolled in U.S. universities. The results showed that there was no significant difference in motivation scores between undergraduate Saudi students and their American counterparts. Nor was a significant difference observed in motivation scores between graduate Saudi students and graduate American students. The data also indicated no significant relationship between motivation scores and anticipated self-reported academic performance among undergraduate Saudi and American students. For graduate students, the correlation between motivation scores and anticipated academic performance was weak and non-significant for both Saudi and American students. Additionally, a significant difference in anxiety scores was found between Saudi and American students in both the undergraduate and graduate groups, with American students exhibiting higher anxiety levels than their Saudi counterparts. However, there was no significant relationship between anxiety scores and anticipated self-reported academic performance for either Saudi or American students across both undergraduate and graduate levels. The results also revealed that the relationship between motivation and anxiety among both Saudi and American students was weak and not statistically significant. Finally, the support structures in online courses survey results showed that both Saudi and American students identified interaction and collaborative learning as the most valuable support structures. Keywords: motivation, anxiety, academic performance, online collaborative learning, support structures, Saudi students, American students20 0Item Restricted The Influence of Artificial Intelligence on EAP Learners’ Oral Fluency(Saudi Digital Library, 2026) Alwadaeen, Norah Bakheet; Abbuhl, RebekhaThere is ongoing debate on how AI speaking tools can support the development of oral fluency in second language (L2) instruction. Despite the widespread usage of these tools, such as AI chatbots and Automated Speech Recognition (ASR), questions persist about how well they will work to improve oral fluency, reduce speaking anxiety, and foster learner autonomy. This study investigates how an AI-mediated speaking partner influences English for Academic Purposes (EAP) learners’ oral fluency, speaking anxiety, and autonomy over a short, intensive practice cycle. Five upper-intermediate ESL students at a California community college completed nine EAP Talk chatbot sessions across 3 weeks, framed by pre- and post-intervention IELTS-style monologic speaking tasks. Acoustic analyses of the pre/post tasks in PRAAT targeted three utterance-fluency indices (speaking ratio, repair phenomena, and pause placement). Session-by-session Likert questionnaires captured perceived fluency gains, anxiety, and autonomy, and post-intervention semi-structured interviews explored learners’ experiences with the AI-mediated practice. Oral fluency findings indicated that the speaking ratio increased, whereas pause and repair indices generally shifted in favorable directions. Anxiety, which was scaled so higher scores indicated less anxiety, exhibited clear gains. Autonomy trajectories were positive at the group level. Furthermore, the study highlights both the promise and limitations of AI chatbots for EAP speaking. It emphasizes the value of multi-indicator fluency assessment, explicit autonomy supports, and longer comparative designs in future work.22 0Item Restricted Resilience enhancement of post-disaster power distribution systems using Deep Reinforcement Learning(Saudi Digital Library, 2026) Alotaibi, Raed; Zohdy, Mohamed; Kaur, Amanpreet; Alghamdi, Ali; Edwards, William; Al-Salman, ZeinaWeather-driven extreme events are placing growing stress on aging distribution infrastructure and increasingly threaten continuity of service for critical loads during prolonged outages. Microgrids can enhance resilience by transitioning to islanded operations and supplying prioritized loads with local distributed energy resources (DERs); however, post-disaster restoration remains challenging because operators must make coupled discrete–continuous decisions under tight resource and operating constraints. This dissertation addressed this challenge by developing a parameterized deep reinforcement learning controller, PDQN-CLR, that targets priority-weighted restoration while enforcing operational feasibility under scarcity. The PDQN-CLR modeled restoration as a hybrid action in which a discrete operational category was selected and paired with a continuous parameter vector specifying the DER real and reactive power setpoints. The approach was evaluated in a closed-loop OpenDSS environment using the IEEE 123-node feeder configured as an islanded microgrid with five grid-forming battery energy storage systems and 17 prioritized critical loads over a 72-step (36-hour) horizon with a 30-minute decision interval. Snapshot power-flow evaluation was performed at each step. Uncertainty was represented through capacity-factor derating under sufficient and scarce regimes, and each episode randomized the fault scenario, derating level, and initial state of charge. This dissertation also introduced (i) an uncertainty-aware evaluation protocol based on capacity-factor derating; (ii) a three-tier priority-weighted reward to encode the critical-load hierarchy; and (iii) a DER-aware load service rule that reduced voltage-only overstatement in islanded operation. With sufficient resources, PDQN-CLR achieved a mean PCS of 0.94 versus 0.80 for a Greedy baseline, while maintaining a low constraint violation score (CVS) of approximately 0.009 in both sufficient and scarce regimes. The baseline produced substantially larger violation magnitudes (CVS ≈ 0.388–0.667), indicating more severe and/or more frequent exceedances of the constraints. These results indicate that parameterized deep reinforcement learning can improve priority-weighted restoration when capacity is available and preserve feasibility as a primary outcome when scarcity limits achievable restoration.20 0