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 The Effects of Off-Season Evidence-based Training Block Periodization Program on The Performance of Soccer Athletes in Periodization Program on The Performance of Soccer Athletes in a DI School: An Exploratory Study a DI School: An Exploratory Study(Saudi Digital Library, 2025-08) Almughathawi, Ibrahim F; Ramsey, MichaelThe investigators explored the effects of a two-year evidence-based block periodization strength training program on performance outcomes in NCAA Division I male soccer athletes. The investigators aimed to examine longitudinal changes in body composition, explosive strength, and match-play physical demands, as well as the associations between field-based performance assessment and GPS-derived metrics. Using a retrospective design, data were drawn from a research data repository for athletes who participated in a long-term sport science monitoring program under the same head coach and training conditions. Performance measures included vertical jump assessments (static and counter movement jumps under bodyweight and external load), isometric mid-thigh pull testing, and athlete tracking data collected during competitive matches. Body composition was evaluated using standard anthropometric procedures. Statistical analysis was conducted to determine the significance of pre- and post-program changes and to identify potential relationships between laboratory and match performance indicators. The findings revealed improvements in body composition and vertical jump performance, particularly under load, indicating favorable adaptations to the block periodization model. While no statistically significant changes were observed in isometric force output or GPS-derived variables, positive trends in match-related sprint and running metrics were noted. These outcomes suggest that structured strength training can contribute to enhanced physical readiness and sports-specific performance in soccer athletes. The study also raises consideration of potential type I error due to sample size and the exploratory nature of the analysis. Although, subject number was limited, this research supports the implementation of block periodization in collegiate soccer settings and highlights the importance of long-term monitoring for evaluating training effectiveness. Recommendations for future work include comparison groups, larger samples, and expanded performance testing to further validate these findings and optimize athlete development strategies.14 0Item Restricted Intelligent Workload Prediction and Mobile Task Offloading in Cloud Environments Using Hybrid Deep Learning and Reinforcement Learning(Saudi Digital Library, 2025-08) Alqahtani, Dalal; Tarek, ElGhazawiCloud computing provides users with access to data, data services, and a variety of physical computing resources on demand. Such capabilities can be provided based on explicit requests from individuals or organizations to augment their local organizational compute infrastructure by provisioning requested hardware and software from cloud service provider data centers. They can also be based on implicit requests generated by users’ mobile devices. These devices can then benefit from treating all of these resources as one integrated system, here referred to as the digital continuum. There are many issues that should be considered to guarantee seamless and efficient operation of such an ecosystem, but we limit the scope of this work to only two major ones. As such, in this thesis, I focus on predicting workloads, which is critical for efficient data center operations due to the dynamic nature of workloads. I also consider the issue of offloading mobile device tasks to improve user experience and better address processing needs at the edge. In data centers of cloud service providers (CSPs), inaccurate workload forecasting can lead to poor resource utilization and an unsatisfactory user experience. It can result in suboptimal resource provisioning that wastes energy, breaks service‑level agreements (SLAs), and affects performance. Therefore, it is important to achieve high forecast accuracy for dynamic resource management to optimize reliable operation in data centers despite changing demand. At the same time, mobile devices have become relatively powerful small computers that can run more applications that are sensitive to delay. However, these devices will always be limited in energy and processing resources, and some of their tasks may still need to be carefully balanced between local processing, additional capabilities at the edge, and offloading tasks to the data center when it makes sense, thus optimizing mobile task offloading in Mobile Edge Cloud Collaboration (MECC) environments. Thus, this dissertation first proposes a novel hybrid predictive model to improve workload‑forecasting accuracy. The methodology begins with two stages of signal decomposition via a two-step strategy: First, I apply Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to extract intrinsic structures and remove noise artifacts from high-dimensional workload traces. Then I apply Variational Mode Decomposition (VMD) to filter out high‑frequency signals. The decomposed signals are then passed to a deep‑learning model comprising parallel 1D Convolutional Neural Networks (CNN1D) and Bidirectional Long Short‑Term Memory (Bi‑LSTM) networks, which will allow for learning short‑, medium‑, and long‑term temporal patterns. This yields adaptive and accurate prediction models for intelligent resource provisioning within data centers. Addressing the second challenge, the dissertation explores dynamic multi‑objective task offloading within the MECC framework. It does that by formulating the problem as a Markov Decision Process (MDP) designed to jointly minimize delay, reduce energy consumption, and control computational cost. This formulation takes into consideration changing network conditions, different task requirements, and real‑time availability of resources across mobile devices, edge nodes, and the cloud. A deep reinforcement learning (DRL) algorithm is also proposed, which combines the sequential modeling of Gated Recurrent Units (GRUs) with the stable optimization technique of Proximal Policy Optimization (PPO). The outcome is an offloading policy that prioritizes executing tasks locally and, on the edge, and only sends them to the cloud if it is strictly necessary. To maintain secure task execution, we integrate a lightweight Hash‑based Message Authentication Code (HMAC) that allows data integrity to be verified with minimal overhead. The proposed CVCBM system was shown to improve the accuracy of workload prediction by 73.8% in related work. Moreover, the proposed HPPO-GRU system was shown to enable safe and efficient task offloading, while reducing total computing cost by approximately 7% to related works. Collectively, these advances improve the reliability of distributed computing across mobile, edge, and cloud layers, enabling next‑generation intelligent infrastructure to respond dynamically to modern digital demands.4 0Item Restricted Rampant Caries Patients: Characteristics and Treatment Patterns at the University of Iowa College of Dentistry and Dental Clinics: A Retrospective Analysis(Saudi Digital Library, 2025) AlZahrani, Manal; Kolker, JustineObjectives: Rampant caries is a particularly severe and aggressive form of caries characterized by rapid and widespread decay across multiple teeth. The aim of this study was to evaluate treatment patterns and identify factors associated with care completion among adult patients diagnosed with rampant caries at the University of Iowa College of Dentistry and Dental Clinics (UICOD) between 2010 and 2024. Methods: This retrospective exploratory study included patients aged 16 years or older who initially presented with at least eight cavitated teeth, including one or more anterior teeth. Patients were classified into six treatment groups: complete dentures (CUCL), one-arch complete denture (OACD), removable partial dentures (RPD), extractions only (EO), restorative care only (REST) and less than needed treatment (LTN) Data were extracted from the AxiUm electronic health record system. Bivariate comparisons were performed using Fisher’s exact and Kruskal-Wallis tests with Holm correction. Logistic regression and a Random Forest model were used to identify predictors of treatment completion. Results: Out of 7,227 patients, 56% received less than needed treatment. Incomplete care was significantly associated with younger age (16–49 years), Black race, Medicaid or self-pay insurance, fewer teeth remaining at the end of care, and longer time to final treatment. The Random Forest model identified number of teeth at the end of care as the most influential predictor, followed by treatment duration, number of medications, and distance to the clinic. Conclusions: This study highlights critical disparities and care gaps in managing rampant caries. A high rate of incomplete care was observed among patients with rampant caries, particularly among socially and medically vulnerable groups. These findings underscore the importance of targeted strategies to improve treatment completion, including patient education, financial assistance, culturally competent care and system-level policy reform. Addressing these disparities is essential to improving oral health outcomes in high-risk populations.27 0Item Restricted Assessment of Tooth Movement Using Three Different Thermoplastic Materials: In-Vitro Study(Saudi Digital Library, 2024) AlThenyan, Turki; Karakousoglou, Maria(1) Background: To assess tooth movement using three different commercially available thermoplastic materials.; (2) Methods: A fully aligned typodont was digitally modified to displace a maxillary right central incisor 1.20-mm palatally. Four sub-setup models were created for each thermoplastic material using an Ortho software analyzer to produce an aligner sequence in which the max-illary right central incisor discrepancy will be corrected by 0.30-mm in each stage. A total of twelve aligners were divided randomly into three groups based on the thermoplastic material.; (3) Results: The displaced maxillary right central incisor in group A, group B, and group C moved labially by 1.08-, 1.05-, and 1.20-mm, respectively. The incisal edge in group A, group B, and group C moved apically by 0.29-, 0.45-, and 0.66-mm, respectively.; (4) Conclusions: Many factors contribute to the clinical performance of clear aligners; mainly, the material construction and thickness. The average correction per aligner for group A, group B, and group C was 0.27-, 0.26- and 0.30-mm, respectively.6 0Item Restricted THE BURDEN OF CARE AMONG CAREGIVERS OF CHILDREN WITH TYPE 1 DIABETES MELLITUS IN SAUDI ARABIA(Saudi Digital Library, 2025) Alruwaili, Manar; Upvall, MicheleBackground: Caregiver burden is a significant psychological issue affecting individuals who care for children with chronic diseases such as type 1 diabetes mellitus (T1DM). While the global literature addresses this burden, limited research has focused on caregivers of children with T1DM in Saudi Arabia. Purpose: The purpose of this quantitative study was to evaluate the burden of care among caregivers of children with type 1 diabetes mellitus in Saudi Arabia. Theoretical Framework: The Pearlin Caregiver Stress Process Model provided theoretical guidance for this study. Methods: A descriptive cross-sectional design was employed. Participants completed a 13-item demographic questionnaire and the Zarit Burden Interview (ZBI). Statistical analyses included independent t-tests, one-way ANOVA, and Pearson correlation analyses. Results: Statistical analysis showed that hypotheses one and two were supported, while hypothesis three was not supported. In hypothesis one, significant differences in burden were found based on gender, relationship to the child, marital status, employment status, and income. Females reported higher burden scores (M = 47.9, SD = 16.60) than males (M = 20.38, SD = 12.39; t = -10.86, p < 0.001). Mothers had significantly higher burden scores than fathers and other relatives (F = 55.96, p < 0.001). Unemployed caregivers reported greater burden (t = -4.06, p < 0.001), and lower-income caregivers showed higher burden levels (F = 10.80, p < 0.001). In hypothesis two, positive correlations were found between burden and gender (r = 0.564, p < 0.001), while negative correlations existed between burden and both employment status (r = -0.254, p < 0.01) and monthly income (r = -0.360, p < 0.001). In hypothesis three, no significant relationship was found between the age of the child and caregiver burden (r = -0.11, p > 0.05). Conclusions: This study enhances understanding of caregiver burden in the context of pediatric T1DM in Saudi Arabia. Findings highlight the need for increased awareness among nurses and healthcare providers of the emotional, social, and financial strain caregivers face. Culturally appropriate, family-centered care plans should include caregiver assessments to improve holistic diabetes management.4 0Item Restricted Assessing Dental Patients' Acceptance and Trust of Dentists in COVID-19-related Services: A Cross-Sectional Study(Saudi Digital Library, 2025) Alzunaydi, Ayoub; Aguilar, Maria L; Psoter, Walter JBackground: Dentists have traditionally been underutilized in public health crises, despite their clinical expertise and trusted role within communities. The COVID-19 pandemic highlighted the need for an expanded healthcare workforce, yet the extent to which dental patients trust and accept dentists providing pandemic-related services remains unclear. Objective: This study aimed to assess dental patients’ acceptance and trust in dentists conducting COVID-19 testing, vaccination, and public health communication while examining the influence of demographic and insurance-related factors. Materials and Methods: A cross-sectional, self-administered, structured questionnaire-based study was conducted at multiple clinics within the University of Florida College of Dentistry. A total of 150 adult dental patients participated, providing self-reported responses on their trust in dentists discussing COVID-19 topics and their acceptance of dentists administering COVID-19 services. Data were analyzed using descriptive statistics and logistic regression to identify associations between demographic variables and trust levels. Results: Trust in dentists discussing COVID-19 topics was high, particularly for face masking (85.4%) and COVID-19 disease (76.4%), but was lower for discussing vaccination (70.2%). Patient acceptance of dentists administering COVID-19 vaccines was divided, with 51% expressing comfort and 49% expressing hesitancy. Private insurance holders were significantly less trusting of dentists in administering COVID-19-related services, while Medicare recipients exhibited the highest levels of trust. Age trends suggested older individuals were more trusting, while demographic variables such as sex, race, and ethnicity were not statistically significant predictors of trust. Conclusion: These findings highlight the potential role of dentists in public health crises beyond traditional oral healthcare. While dentists are trusted for public health communication, hesitancy in accepting them as vaccine providers suggests a need for further public education and policy reinforcement. Recognizing dentists as part of the frontline healthcare workforce could enhance healthcare system resilience in future crises.18 0Item Restricted RESPONSE RATES AND RESPONSE QUALITY OF ONLINE COURSE SURVEY FOR DEAF COLLEGE STUDENTS: A MIXED-METHOD STUDY(Saudi Digital Library, 2025) Alqahtani, Abdulaziz Abdullah M; Merchant, WilliamThe study design investigated how data are collected from deaf and hard of hearing (DHH) college students and how different delivery formats of the online course survey evaluation of total effectiveness affect the response rate results of course evaluation, its psychometric properties, and students’ perceptions of the survey-taking experience. A sequential explanatory mixed-methods design was used. In the first phase, the quantitative data is gathered and analyzed. Ninety deaf college students were surveyed (experiment group = 48; control group = 42). The results found that the overall response rate is very low (12.5%). The results also indicated that the experimental group attained a marginally higher response rate (RR = 13.41%) in comparison to the control group (RR = 11.73%). The chi-square test indicates this difference in response rate between the experimental group and control group of deaf college students was not statistically significant (p = 0.573). This result is a relatively low response rate, which is not uncommon for surveys. Next a confirmatory factor analysis (CFA) was performed with the data for only the evaluation course subscale (four items). The model fit results indicated that the data aligned very well with the course survey evaluation (CSE) model. I also tested a one-factor model across groups for configural invariance, metric invariance, and scalar invariance. All groups demonstrated configural, metric invariance, and scalar invariance, which confirms that the psychometric properties of the online course survey are not vary based on the delivery formats of online survey used to evaluate overall effectiveness. The internal consistency reliability (Cronbach’s alpha) and McDonald Omega reliability were conducted on the CFA data for the CSE instrument, and the results met the acceptable standards. In the second phase, qualitative data and its analysis were implemented to clarify and explain those statistical results by delving more deeply into the participants' perspectives on their course survey evaluation and their response rates. Eight deaf college students participated. Relevant data were collected by in-depth semi-structured interviews. From the semi-interviews, several themes were identified as follows: (a) more support needs; (b) being authentic in communication; (c) problems with online surveys; and (d) visual quality; (e) recommendations for increasing response rate. Participants believed these themes to be worthy of note in order to increase deaf college students response rates. Finally, several limitations, future research, and impications were reported.14 0Item Restricted VOCATION, EDUCATION, AND MARRIAGE IN NOVELS BY GEORGE ELIOT, CHARLOTTE BRONTË, AND ANNE BRONTË(Indiana University of Pennsylvania, 2025) Alazmi, Miad; Michael T. WilliamsonThis dissertation intends to pose a question that links an important nineteenth-century theory of education with literary works: how John Ruskin’s educational theories offer new insights into exploring the intellectual growth of Victorian women as shown in George Eliot's The Mill on the Floss (1860), Anne Brontë's Agnes Grey (1847), and Charlotte Brontë's Jane Eyre (1847), Shirley (1849), and Villette (1853). The study compares the novelists’ educational views with Ruskin’s theory and the ideologies that form its base to develop a counter-narrative to traditional feminist critiques of these texts. Drawing on Clare Carlisle’s analysis of vocation and marriage and Ruskin’s view on education, the study explores how the themes of vocation, education, and marriage intersect to shape each female protagonist’s journey toward a fulfilling intellectual life. While feminist critics such as Elaine Showalter, Sandra Gilbert, Susan Gubar, and Sally Shuttleworth have focused on gender constraints and patriarchal repression, this dissertation revisits the selected novels through the lens of vocation and education as intellectual and moral callings. Carlisle’s readings offer a more nuanced framework in which marriage and vocation are not only sites of conflict but also potential spaces for growth. Ruskin’s educational philosophy particularly in Sesame and Lilies and The Ethics of the Dust emphasizes a curriculum that develops both intellect and character, proposing an educational vision that, while gendered, urges moral and mental refinement for women. Through examining how the selected heroines capitalize on education to discover and pursue their vocations and redefine marriage as a site of mutual development, the study argues that these women novelists not only portray the limitations of their time but also envision education as a pathway toward intellectual agency. Thus, the dissertation highlights how Eliot, Anne Brontë, and Charlotte Brontë challenge traditional gender norms and participate in a broader philosophical discourse on women's intellectual lives during the Victorian period.6 0Item Restricted Lightweight ML-Based Drone Intrusion Detection System Through Model Compression(University of North Texas, 2025) Alruwaili, Fawaz Juhayyim M; Cihan, TuncThe adoption of drones in diverse domains (e.g., surveillance, agriculture, and disaster management), together with their integration of advanced technologies and dependence on wireless communication, has significantly increased the need to secure drone networks against cyber threats. Traditional network-based intrusion detection systems (NIDS) can be insufficient against novel or adaptive cyber threats and exceed the computational limits of drones. Thus, we need lightweight and efficient drone-specific NIDS solutions. This dissertation addresses this concern with the goal of achieving an effective balance between security, efficiency, and model accuracy without significantly compromising detection performance. Hence, two complementary main contributions are proposed: First, a lightweight ML-based NIDS optimized for individual drones, utilizing a quantized deep neural network (DNN) through post-training quantization (PTQ), enabling real-time, on-board intrusion detection. Second, a framework for swarm-based deployments that leverage federated learning and knowledge distillation to enable distributed training and lightweight model deployment while preserving data privacy and minimizing communication overhead. Both contributions were evaluated using real-world drone network datasets. The first contribution achieved 95.03% accuracy with significantly reduced model size and inference latency, making it suit- able for real-time and onboard deployment. The second contribution was deployed using Raspberry Pi 4 devices and demonstrated improved accuracy, convergence, and communication efficiency, achieving up to 76% reduction in communication overhead and 29% lower CPU usage. The results demonstrate the practicality and effectiveness of the proposed solutions in meeting the unique demands of both individual and swarm-based drone deployments, while achieving a robust balance between security and efficiency.14 0Item Restricted Artificial Intelligence Law in Saudi Arabia(ST.Thomas Uinversity, 2025) Alharbi, Ibrahim; Wiessner, SiegfriedThis dissertation examines the legal and regulatory frameworks governing artificial intelligence in Saudi Arabia, analyzing the intersection of modern technological advancement with traditional Islamic principles and international standards. Through the lens of the New Haven approach, this research investigates how Saudi Arabia balances innovation with cultural values while developing comprehensive AI regulations. The study focuses particularly on the kingdom's efforts to establish effective legal mechanisms for AI governance while maintaining alignment with Sharia principles and meeting global technological standards. The research employs a comparative methodology, analyzing Saudi Arabia's regulatory approach alongside the United States' frameworks, identifying potential areas for enhancement while recognizing the unique cultural and legal context of the Kingdom. This analysis reveals significant developments in Saudi Arabia's AI regulatory landscape, particularly through initiatives such as Vision 2030 and the establishment of the Saudi Data and Artificial Intelligence Authority (SDAIA), while also identifying areas requiring further development. Key findings demonstrate that Saudi Arabia's approach to AI regulation reflects a sophisticated understanding of the need to balance technological advancement with ethical considerations and cultural preservation. The research identifies crucial areas for regulatory development, including the need for specialized legal frameworks addressing AI-specific challenges, enhanced institutional capacity for implementation, and mechanisms for ensuring compliance with both international standards and Islamic principles. The study makes several original contributions to the field. First, it provides a comprehensive analysis of Saudi Arabia's emerging AI regulatory framework from both legal and ethical perspectives. Second, it demonstrates how Islamic legal principles can effectively guide modern technological regulation. Third, it offers practical recommendations for developing regulatory frameworks that serve both technological advancement and social welfare. This research has significant implications for policymakers, legal practitioners, and technology developers in Saudi Arabia and beyond. It suggests the need for a carefully balanced approach that promotes innovation while protecting social values and individual 8 rights, potentially serving as a model for other nations seeking to harmonize technological advancement with cultural preservation.11 0