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 Office Workspace Environments: Understanding the Influence of Financial Workers’ Personality on Their Control of Their Physical Environment and Sense of Satisfaction(Arizona State University, 2024) Naseef, Rawan; Sharp, Nina; Koro, Mirka; Brooks, Kenneth; Mohammad, HassnaaThis study explores financial professionals in Saudi Arabia's banking sector, examining how their unique personalities shape their experiences with workplace lighting, acoustics (speech privacy), and spatial layout, while also considering the degree of control they have over these conditions. Using a qualitative approach, the research delves into employees’ perceptions, behavioral responses, and preferences to build design theories aimed at enhancing job satisfaction in office environments. Guided by a constructivist theoretical framework and employing constructivist grounded theory for data analysis, the study used semi-structured interviews as primary data collection methods and photo elicitation as a supportive method. Findings reveal that employees’ interactions with lighting, speech privacy, and spatial layout vary based on personality traits, leading to actionable insights for workplace design. Three theories emerged from these insights, offering guidance for both design practices and future research. This research was conducted across three different banks in two major regions of Saudi Arabia, with implications suggesting future studies could explore the role of Saudi culture in shaping diverse personality experiences in mixed-gender office environments.8 0Item Restricted ATTITUDES AND MOTIVATION OF SAUDI STUDENTS LEARNING CHINESE AS A FOREIGN LANGUAGE IN SAUDI ARABIA(The University of Mississippi, 2025) Emad, Hamuh; Dyer, DonaldThis study aims to examine the attitudes and motivation of Saudi learners toward learning Chinese as a foreign language (CFL), the effectiveness of Chinese teaching pedagogy, and the learners’ perceptions of Chinese culture and its impact on cross-cultural communication. This study is qualitative in nature, incorporating a qualitative instrument of data collection (in-depth interviews). The target population includes 8 Saudi CFL learners and 4 instructors at two universities in Saudi Arabia, namely King Abdulaziz University (KAU) and the University of Jeddah (UJ). The findings revealed that some Saudi CFL learners exhibited positive attitudes toward CFL learning, primarily influenced by their interest in the language. On the other hand, some learners expressed negative views about learning Chinese, highlighting certain challenges encountered during the first two years of their study. These negative attitudes, however, shifted to a more positive outlook due to the significant enhancements made by the Chinese-language programs. In terms of motivation, the findings indicated that the learners demonstrated both instrumental and integrative motivation, with instrumental motivation being more dominant. The study also identified certain language challenges, including mastering Chinese tones and characters. Despite these challenges, some learners highlighted the impact of CFL learning on their personal and cognitive development. Additionally, the findings highlighted the effectiveness of Chinese teaching pedagogy, indicating that the use of interactive teaching strategies and innovative methods (e.g., the integration of technology and Chinese cultural elements into instruction) were more effective and engaging than the traditional methods of teaching. Nonetheless, pedagogical challenges remained for both CFL learners and instructors, including the lack of qualified teachers, limited learning resources, and issues related to the curriculum and the learning environment. The findings also indicated that all learners held positive attitudes toward Chinese culture and its native speakers. Most importantly, the study emphasized the impact of learners’ exposure to Chinese culture and their interactions with native speakers on their cross-cultural communication. The study concluded with providing valuable insights for policymakers and educators, suggesting effective teaching strategies and curriculum development that help foster learners’ attitudes and motivation to overcome learning challenges and enhance their overall learning experience.16 0Item Restricted Sound-Based Non-Destructive Evaluation to Detect Damage in Lithium-Ion Batteries(Ohio University, 2024) Al Amiri, Essa Salem; Wisner, Brian JIn recent years, lithium-ion batteries (LIBs) have played an essential role in nowadays energy storage system, especially electric vehicles (EVs) and portable electronics because of its high energy density and long cycle life [1, 2]. However, one of the biggest challenges is how to guarantee their dependability and trustworthiness. In the present investigation, Acoustic Emission (AE) and Ultrasound Testing (UT) techniques are systematically employed to verify probable critical defects in the LIBs. Where AE technology is able to record the stress waves produced by the growth of the defects, UT uses high-frequency sound waves to penetrate the batteries and provide an indication of the internal voids. The performances of these approaches were systematically tested on as-received, pre-damaged and cold-soaked batteries. Different AE and UT activity patterns were shown in the results under various environmental conditions that influenced battery performance. Combining Acoustic Emission (AE) and Ultrasound Testing (UT) with clustering and outlier analysis machine learning algorithms improved defect detection effectiveness. Such research highlights that AE and UT can be robust noninvasive techniques for on-line health monitoring of LIBs that should aid in maintaining the longevity and operability of LIBs.4 0Item Restricted Learning Based Ethereum Phishing Detection: Evaluation, Robustness, and Improvement(University of Central Florida, 2025) Alghuried, Ahod; Mohaisen, DavidPhishing attacks continue to pose a significant threat to the Ethereum ecosystem, accounting for a major share of Ethereum-related cybercrimes. To enhance the detection of such fraudulent transactions, this dissertation develops a comprehensive framework for machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and the vulnerability of detection models to adversarial attacks. By systematically evaluating these key practices, this work contributes to the development of more effective detection methods. The first part of the dissertation assesses the current state of phishing detection methods, identifying gaps in feature selection, dataset composition, and model optimization. We propose a systematic framework that evaluates these factors, providing a foundation for improving the overall performance and reliability of detection models. The second part explores the vulnerability of machine learning models, including Random Forest, Decision Tree, and K-Nearest Neighbors, to single-feature adversarial attacks. Through extensive experimentation, we analyze the impact of various adversarial strategies on model performance and uncover alarming weaknesses in existing models. However, the varied effects of these attacks across different algorithms present opportunities for mitigation through adversarial training and improved feature selection. Finally, the dissertation investigates how phishing detection models generalize across datasets, focusing on the role of preprocessing techniques such as feature engineering and class balancing. Our findings show that optimizing these techniques enhances model accuracy and robustness, making detection methods more adaptable to evolving threats. Overall, this work presents a comprehensive framework that addresses the critical elements of phishing detection in Ethereum transactions, offering valuable insights for the development of more robust and generalizable machine learning-based security models. The proposed framework has broad implications for improving blockchain security and advancing the field of phishing detection.11 0Item Restricted The Association Between Metabolic Syndrome and Oral Diseases Among US Adults(Harvard University School of Dental Medicine, 2025) Alhassan, Hameedaldeen; Natto, ZuhairThe objective was to assess the association between oral diseases and metabolic syndrome (MetS), examining both the individual and combined effects of MetS five components. Also, to validate the NHANES diagnostic criteria and Overjet artificial intelligent (AI) platform with the gold standard for periodontal disease diagnosis. First, a systematic review and meta-analysis were conducted by searching PubMed, Embase, and Web of Science for studies published between 1990 and 2023 that examined the association between Dental caries and MetS in adults. Two independent authors selected and analyzed articles, assessed risk of bias, and evaluated the overall evidence certainty. Meta-analyses were performed to estimate pooled odds ratios (ORs), or mean differences (MDs), and corresponding 95% confidence intervals (CIs) for decayed teeth (DT) and DMFT (Decayed, Missing, and Filled Teeth). Second, data from NHANES 2011-2018 were used to assess the association between MetS and oral diseases (dental caries and periodontal disease). The sample included adults over 29 years who completed laboratory and clinical assessments for MetS and oral diseases. Logistic regression models (OR and 95% CI) were used to assess associations between MetS and both untreated caries and periodontal disease. Negative binomial regression (mean ratio [MR] and 95% CI) was conducted to examine the association between MetS and DMFT score. Third, clinical and radiographic records of patients aged over 29 years were utilized to validate the NHANES diagnostic criteria (based on clinical measures alone) and the Overjet AI software (using full-mouth radiographs) for detecting periodontal disease. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the accuracy of both clinical measures and the Overjet AI software, using a gold standard defined as a combination of clinical and radiographic analysis conducted by a periodontist. The meta-analysis (nine studies with 59,075 participants) revealed that there was a positive statistically insignificant association between DT and MetS (OR: 1.17, 95% CI: 0.87–1.58; MD: 0.22, 95% CI: -0.08–0.52). However, a significant positive association was found between MetS and DMFT (OR: 1.28, 95% CI: 1.08–1.51). Results from NHANES showed that participants with MetS were more likely to have untreated caries and high DMFT mean score by 34% and 10%, respectively. While there was no association between MetS and periodontal disease. Low HDL was the most significant MetS component associated with dental caries, while insulin resistance was the strongest component linked to periodontal disease. Having all five MetS components was associated with a 17% higher likelihood of a high DMFT score. Results from the validity study showed that detecting periodontal disease using clinical measures achieved 96% sensitivity and 99% specificity. While Overjet AI software achieved 100% sensitivity and 89% specificity. In conclusion, the study revealed a positive association between MetS and dental caries, while there was no association between MetS and periodontal disease. Future prospective cohort studies could provide a better understanding of these associations. The validity study demonstrated that both clinical measures and Overjet AI software achieved high accuracy in detecting periodontal disease.2 0Item Open Access Investor Relations in the Fashion Industry: An Investigation of the Investor Relations in the Fashion Industry: An Investigation of the Role of Sustainability in Investment Using the Situational Theory Role of Sustainability in Investment Using the Situational Theory of Problem-Solving and Organization-Public Relationship of Problem-Solving and Organization-Public Relationship Frameworks Frameworks(Virginia Commonwealth University, 2025-03-03) Banasser, Abrar; Alkazemi, MariamThis study examines the factors affecting individual investors' decision-making in the fashion industry, focusing on the impact of sustainability and investor relations. By employing both the Situational Theory of Problem Solving (STOPS) and the Organization- Public Relationship (OPR) frameworks, this paper investigates the influence of variables such as problem recognition, constraint recognition, involvement recognition, and relationship qualities, including commitment and control mutuality, on investors' situational motivation and communication actions. Weighted regression analysis was conducted to evaluate hypotheses pertaining to these variables. The study also explores the types and sources of investment information that investors most used and preferred. The findings show that most STOPS’ predictors effectively triggers investors' motivation regarding investment, whereas commitment and control mutuality substantially impact communicative behaviors and investment intentions. Among communicative actions, information acquisition significantly influences investors' willingness to invest in fashion companies. In addition, the study emphasizes the importance of financial and sustainability reporting communication, primarily via the company’s annual reports and SEC filings, when considering investment in the fashion industry. The findings highlight the increasing value of incorporating Environmental, Social, and Governance (ESG) standards alongside financial performance reports to appeal to both profit-oriented and socially responsible investors. Practical implications suggest that fashion firms should prioritize the transparent communication of brand identity and accomplishments, enhance investor participation via collaborative platforms, and design sustainable strategies to align with investor expectations. This study advances the knowledge of investors' behaviors in the fashion industry and provides insightful guidance for improving investor relations approaches.23 0Item Restricted SECURE MULTI-ROBOT COMPUTATION FOR HETEROGENEOUS TEAMS : FOUNDATIONS AND APPLICATIONS(Florida International University, 2024) Alsayegh, Murtadha; Bobadilla, LeonardoIn the rapidly evolving field of robotics, significant progress has been made in the planning, control, and coordination of multi-robot systems, embedding robots into various sectors such as household, manufacturing, healthcare, and surveillance. Despite these advancements, challenges arise, particularly concerning privacy due to robots' potential to access and share more information than necessary, risking sensitive data exposure. Addressing this, our research introduces innovative strategies to ensure collaborative computation among robots while safeguarding privacy, thereby preventing unnecessary information sharing and achieving optimal objectives. We propose lightweight communication protocols for data synchronization, reducing the need for extensive data exchange, and a secure multiparty auction-based algorithm for private task allocation without revealing sensitive data. Additionally, we explore the use of secure multiparty computation with Markov Decision Processes (MDP) for planning, ensuring privacy in multi-agent cooperation. Building on this foundation, we delve into decentralized multi-robot information gathering (DMRIG), presenting the Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) and Distributed and Decentralized Robotic Information Gathering (DDRIG) algorithms to improve environmental monitoring data collection efficiency, balancing communication complexity, and privacy. Through practical experimentation, these algorithms' real-world efficacy is demonstrated, emphasizing their role in enhancing environmental monitoring via sophisticated information sharing and task allocation among robots. This dissertation provides a comprehensive approach to addressing privacy and efficiency in heterogeneous robot systems, showcasing the potential of these technologies to advance robotics applications securely and effectively. Together, these components form a comprehensive approach to addressing privacy concerns in heterogeneous robot systems. By interlinking efficient data sharing protocols, secure task allocation, private planning strategies, and optimized multi-robot information gathering, the dissertation lays the groundwork for a new paradigm in robotic collaboration. This synergy ensures that robots can work together effectively, achieving optimal objectives without compromising sensitive information, marking a significant advancement in the field of robotics.11 0Item Restricted Human Activity Monitoring for Telemedicine Using an Intelligent Millimeter-Wave System(University of Dayton, 2025) Alhazmi, Abdullah; Chodavarapu, VamsyThe growing aging population requires innovative solutions in the healthcare industry. Telemedicine is one such innovation that can improve healthcare access and delivery to diverse and aging populations. It uses various sensors to facilitate remote monitoring of physiological measures of people, such as heart rate, oxygen saturation, blood glucose, and blood pressure. Similarly, it is capable of monitoring critical events, such as falls. The key challenges in telemonitoring are ensuring accurate remote monitoring of physical activity or falls by preserving privacy and avoiding excessive reliance on expensive and/or obtrusive devices. Our approach initially addressed the need for secure, portable, and low-cost solutions specifically for fall detection. Our proposed system integrates a low-power millimeter-wave (mmWave) sensor with a NVIDIA Jetson Nano system and uses machine learning to accurately and remotely detect falls. Our initial work focused on processing the mmWave sensor's output by using neural network models, mainly employing Doppler signatures and a Long Short-Term Memory (LSTM) architecture. The proposed system achieved 79% accuracy in detecting three classes of human activities. In addition to reasonable accuracy, the system protected privacy by not recording camera images, ensuring real-time fall detection and Human Activity Recognition (HAR) for both single and multiple individuals at the same time. Building on this foundation, we developed an advanced system to enhance accuracy and robustness in continuous monitoring of human activities. This enhanced system also utilized a mmWave radar sensor (IWR6843ISK-ODS) connected to a NVIDIA Jetson Nano board, and focused on improving the accuracy and robustness of the monitoring process. This integration facilitated effective data processing and inference at the edge, making it suitable for telemedicine systems in both residential and institutional settings. By developing a PointNet neural network for real-time human activity monitoring, we achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Furthermore, the proposed system provided activity data reports, tracking maps, and fall alerts and significantly enhanced telemedicine applcations by enabling more timely and targeted interventions based on objective data. The final proposed system facilitates the ability to detect falls and monitor physical activity at both home and institutional settings, demonstrating the potential of Artificial Intelligence (AI) algorithms and mmWave sensors for HAR. In conclusion, our system enhances therapeutic adherence and optimizes healthcare resources by enabling patients to receive physical therapy services remotely. Furthermore, it could reduce the need for hospital visits and improve in-home nursing care, thus saving time and money and improving patient outcomes.8 0Item Restricted ADAPTIVE SELF-LEARNING AND MULTI-STAGE MODELING FOR EFFICIENT MEDICAL AND DENTAL IMAGE SEGMENTATION(University of Missouir - Kansas City, 2025) Alqarni, Saeed; Yugyung, LeeMedical imaging has revolutionized healthcare by enabling non-invasive visualization of anatomical structures and pathologies, significantly improving diagnostic accuracy, treatment planning, and patient monitoring. Modalities like computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound provide critical insights into the human body, yet precise medical image segmentation remains a challenging task. This difficulty arises from factors such as image variability, noise, artifacts, and the limited availability of annotated data necessary to train robust segmentation models. Overcoming these hurdles is essential to unlock the full potential of medical imaging in diverse clinical applications. This dissertation presents a novel framework for efficient and accurate medical image segmentation, incorporating multi-stage transfer learning, uncertainty-driven data selection, and weakly supervised learning. By combining human-guided refinement with adaptive data selection, this research addresses fundamental barriers such as data scarcity, computational resource limitations, and the high cost of annotation. The framework is structured around three key objectives: 1. Adaptive Uncertainty Sampling with SAM (AUSAM), which introduces a flexible, real-time data selection and segmentation approach, reducing reliance on large annotated datasets through dynamic thresholds and DBSCAN clustering. 2. AUSAM-SL - Active Self-Learning with SAM, which integrates entropy-based active learning with iterative self-labeling, supported by SAM for initial training, refining the selection criteria, and enhancing model predictions. 3. AUSAM-3D- 3D Modeling for Domain-Aware Segmentation and Aggregation, which builds upon AUSAM by incorporating a spatial and volumetric dimension, improving segmentation accuracy for organs and tumors, and enabling more clinically relevant outcomes. Preliminary results on medical and dental imaging datasets (MRI, CT, X-ray) validate the effectiveness of the proposed framework in improving segmentation accuracy while maintaining computational efficiency. The research offers scalable solutions suitable for resource-constrained environments by integrating human feedback with semisupervised and weakly supervised learning techniques. This work advances the field of medical and dental image segmentation and provides practical methods for leveraging multi-stage learning in real-world applications where data and computational resources are limited.10 0Item Restricted The Patient Reported Indicators Survey (PaRIS) in Saudi Arabia: Measuring Patient Reported Outcomes and Experiences in Adults Aged 18-44(New York University, 2025) Almunif, Sara; AlShaya, AhmedChronic diseases are a leading cause of morbidity and mortality worldwide, placing a significant burden on healthcare systems. While chronic disease management has traditionally focused on older populations, younger adults are increasingly affected by chronic conditions due to demographic shifts and lifestyle effects, yet their healthcare needs remain underexplored. This dissertation expands the implementation of the OECD’s Patient Reported Indicators Survey (PaRIS) in Saudi Arabia to assess patient reported outcomes (PROMs) and patient reported experiences (PREMs) among adults aged 18-44 with chronic conditions. The study aims to fill a critical gap in understanding how young adults experience chronic disease management within primary care and how provider characteristics and sociodemographic factors influence their outcomes and experiences. A cross-sectional survey of 7,500 patients was conducted using a validated, standardized methodology aligned with the OECD PaRIS framework. Analysis was performed to examine the relationship between key provider characteristics, and sociodemographic factors and patient reported measures, in addition to comparing PROMs and PREMs between patients with and without chronic conditions, as well as between younger and older patients. Findings indicate that having chronic diseases was the main contributing factor to worse outcomes and experiences. Health outcomes were particularly impacted, highlighting the need for tailored interventions to support younger patients. Socioeconomic and demographic disparities were evident, with women and lower-income individuals reporting poorer experiences and outcomes. Additionally, provider sector significantly influenced patient experiences, with private sector patients reporting better care coordination and person-centered care, while public sector patients demonstrated stronger mental health outcomes. Insufficiencies were found in Electronic Medical Records and virtual consultations. Analysis showed that younger adults had generally better outcomes but worse experiences of care. These findings underscore the importance of strengthening primary care models to be more responsive to the needs of younger adults with chronic conditions. Policy recommendations include expanding digital health solutions, integrating person-centered care approaches, improving care coordination, and addressing equity gaps in healthcare access. By incorporating patient-reported measures into routine healthcare evaluation, this study contributes to Saudi Arabia’s transition towards a value-based healthcare system and provides actionable insights for optimizing primary care delivery for younger adults managing chronic diseases.16 0