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 MULTIDIMENSIONAL APPROACHES IN BUG DETECTION FOR PARALLEL PROGRAMMING AND TEXT-TO-CODE SEMANTIC PARSING(University of Central Florida, 2025) Alsofyani, May; Wang LiqiangThis dissertation applies deep learning and large language models to two domains: parallel programming fault detection and text-to-code translation, aiming to enhance software reliability and natural language-driven code generation. Due to their unpredictable nature, concurrency bugs-particularly data race bugs— present significant challenges in fault detection for parallel programming. We investigate deep learning and LLM-based approaches for detecting data race bugs in OpenMP programs. Our proposed methods include a transformer encoder and GPT-4 through prompt engineering and fine-tuning. Experimental results demonstrate that the transformer encoder achieves competitive accuracy compared to LLMs, highlighting its effectiveness in understanding complex OpenMP directives. Expanding this research, we explore the role of LLMs in detecting faults in Pthreads, which requires a deep understanding of thread-based logic and synchronization mechanisms. We analyze ChatGPT's effectiveness in Pthreads fault detection through dialogue-based interactions and advanced prompt engineering techniques, including Zero-Shot, Few-Shot, Chain-of-Thought, and Retrieval-Augmented Generation. Additionally, we introduce three hybrid prompting techniques—Chain-of-Thought with Few-Shot Prompting, Retrieval-Augmented Generation with Few-Shot Prompting, and Prompt Chaining with Few-Shot Prompting—to enhance fault detection performance. In the semantic parsing domain, our research bridges the gap between natural language and executable code, focusing on text-to-SQL translation. To address SQL's limitations in statistical analysis, we introduce SIGMA, a dataset for text-to-code semantic parsing with statistical analysis capabilities. In addition, we address the gap in cross-domain context-dependent text-to-SQL translation for the Arabic language. While prior research has focused on English and Chinese datasets, no efforts have been made to explore Arabic cross-domain conversational querying. We introduce Ar-SParC, the first Arabic cross-domain, context-dependent text-to-SQL dataset. This dissertation contributes to fault detection in parallel programming and semantic parsing with statistical analysis, leveraging cutting-edge deep learning and LLMs techniques. Our findings advance bug detection in high-performance computing and natural language-based code generation, significantly improving software reliability and accessibility.16 0Item Restricted CLIMATE VARIABILITY STUDY FOR ARID REGIONS(South Dakota State University, 2025-05) Almutairi, Faisal; Burckhard, SuzetteWater is the primary source for all living species to thrive, and water scarcity has been a primary concern for biological species and plants in arid regions due to urban planning, population growth, poor water management, and overgrazing. The objective of this research was to study the climate variability in precipitation and temperature for an arid region. This research encompasses two distinct studies. The first study examined the impact of climate variability on precipitation in Phoenix. Precipitation data were acquired from NOAA from 1948 to 2023 and the study was broken down into three time scales: annually, seasonally, and monthly. The accumulated annual precipitation was used to classify specific years as very wet year, wet year, average year, dry year, and very dry year, corresponding to their rainfall depth. Annual precipitation showed stability in the trendline with some fluctuations over the 76-year period of study. In addition, the seasonal analysis showed more precipitation during the summer season more frequently than in other seasons. The monthly analysis showed that months within the non-monsoon season had the driest months as expected. February had the highest precipitation in very dry years and January had the highest precipitation in the very wet years. Also, the prediction of the accumulated yearly precipitation for the years 2030,2040, and 2050 is based on the correlation. The second study was the impact of climate variability on temperature in an arid region. Temperature was obtained from NOAA for the period of 1948 to 2023. The years were classified based on the average annual temperature dataset as hot years, warm years, moderate years, cool years, and cold years. The average annual temperature showed an increase in the trendline. The results showed minor CV for the average annual temperature over the 76-year period. The seasonal temperature showed more variability during the winter season, with a CV of 5%, compared to other seasons. The monthly analysis showed that months within the winter season had more variability at a CV ranging from 4% to 6% than months in the summer season which were around 2%. In addition, the prediction for the average annual temperature for the years 2030,2040, and 2050. In conclusion, these two studies are comprehensive studies on climate variables applying historical data to gain a cognitive comprehension of climate weather behavior. This case study was conducted on arid regions that have high temperatures during summer and relatively low precipitation. These studies hold future awareness of the climate for more practical purposes in engineering. This will be an effective application for future water scarcity, urban planning, agriculture, and food supply.14 0Item Restricted OBESOGENIC MEDICATION USE IN TYPE 2 DIABETES: PREVALENCE AND ASSOCIATIONS WITH SOCIAL DETERMINANTS OF HEALTH AND HEALTHCARE UTILIZATION(Nova Southeastern University, 2025-05) Alharbi, Yasser Abdullah s; Alexandra, Perez RiveraDiabetes affects 38.4 million people in the U.S. with 90-95% having type 2 diabetes (T2DM). Obesogenic medications, which contribute to weight gain, can worsen T2DM and disproportionately impact patients due to health disparities of SDOH. Obesogenic medications tend to be cheaper and older while non-obesogenic alternatives tend to be newer and more expensive. This dissertation explored the prevalence of obesogenic medication use and their relationship with social determinants of health (SDOH) and healthcare utilization outcomes in adults with T2DM through three studies. The first study examined the process on how major healthcare organizations (ACC/AHA, ADA, AAFP) defined and addressed SDOH in clinical practice and research. The ACC/AHA focused on standardizing SDOH cardiovascular care data, the ADA reviewed literature to identify effective interventions for diabetic individuals, and the AAFP provided community screening tools through the Everyone Project. The findings highlighted the complexity of standardizing SDOH approaches across healthcare systems. The second study analyzed the prevalence of obesogenic medication and its association with SDOH using the All of Us database, showing that 42.99% of participants were using at least one obesogenic medication. Although antidiabetic drugs were the most commonly used (28.51%), antipsychotics were the least prevalent (4.60%). Stepwise logistic regression revealed that SDOH factors, such as education, income, and employment, were associated with lower odds of obesogenic medication use (OR = .816, 95% CI: 0.674–0.988, P = .037). The third study examined obesogenic medication use and healthcare utilization using the NHANES database, finding that 64.9% of individuals with T2DM were on at least one obesogenic medication with antidiabetic drugs being the most used (50.2%). Obesogenic medication users had a higher likelihood of hospitalization and increased healthcare utilization. Together, the last two studies showed that over two-thirds of T2DM patients are obese with obesogenic medications prevalent yet tied to poor glycemic control. The studies emphasized the need for access to non-obesogenic medications among those with the highest burden across SDOH, and the impact of these medications on increased healthcare utilization. Reducing reliance on obesogenic medications could improve diabetes management and alleviate healthcare burden. By utilizing two datasets, NHANES (nationally representative) and All of Us (diverse and vulnerable populations), this dissertation provides critical insights into addressing health disparities and optimizing diabetes pharmacological care.12 0Item Restricted Physiological and Molecular Responses of Diverse Rice Genotypes under Drought Stress(University of Arkansas at Fayetteville, 2025) Alshaya, Huda Mohmmed; Andy, PereiraAbstract Climate change-induced drought stress is a significant constraint on global rice (Oryza sativa L.) production, threatening food security. This study evaluated the drought resilience of 15 diverse rice genotypes from the USDA mini-core collection under field, greenhouse, and osmotic stress conditions. Field trials assessed reproductive-stage drought tolerance based on panicle length (PL), number of spikelets per panicle (NSP), and spikelet sterility (SS). Greenhouse experiments examined moisture retention at the vegetative stage. Significant genotypic variation was observed, with genotypes 310724, 310779, 311181, 311603, 311793, and Vandana exhibiting drought tolerance through stable PL and SS. Additionally, genotypes 310100, 310428, 311255, N22, and Bengal demonstrated superior moisture retention. The study emphasizes selecting genotypes with stable performance to enhance drought tolerance, with 310779 and N22 standing out for their low spikelet sterility and strong drought resilience. In contrast, genotypes like 311111, 311140, 311180, and KB showed heightened sensitivity to drought, with reduced panicle length, fewer spikelets, and increased sterility, making them less suitable for drought-prone environments. Under polyethylene glycol (PEG)-induced osmotic stress, Vandana, 301418, and 311140 exhibited strong tolerance, while 310428, 310724, 311111, 311180, 310779, and 311181 were sensitive. Drought-resistant genotypes exhibited increased root traits, including root length (RL), root-to-shoot ratio (RSR), total root number (TRN), and dry root weight (DRN). Further, drought-resistant genotypes Vandana, N22, 311255 and 311181 displayed an ABA-sensitive phenotype at early growth stages, with ABA-mediated signaling influencing osmotic stress tolerance. RT-qPCR analysis revealed increased ZIP gene expression in drought- tolerant genotypes following ABA application. These findings underscore the importance of stress-specific evaluations in identifying drought-tolerant genotypes. However, genotypes such as Vandana, N22, and 311255 emerged as promising candidates for breeding programs aimed at improving drought resilience in rice. The study provides valuable insights for developing climate-resilient rice varieties, integrating physiological, morphological, and genetic approaches to enhance adaptation to water-limited conditions.7 0Item Restricted The Relationship Between Health Literacy and Diabetes Self-Care Management among Adults in Southern Riyadh(Barry University, 2025) Almutairy, Bader; Beason, FerronaThis dissertation is dedicated to my parents, whose love and sacrifice have shaped who I am today. To my wife and children, for being my constant source of motivation and joy. This work is also dedicated to my brothers and sisters, whose support has been a pillar of strength throughout this journey. May this accomplishment bring pride to my family and serve as a reminder that with faith and determination, all things are possible. This dissertation is dedicated first and foremost to my parents, whose unconditional love, tireless support, and endless sacrifices have been the cornerstone of my life and success. Their wisdom, patience, and unwavering belief in me have given me the strength to persevere through every challenge. I owe everything to them, and this work is a testament to their dedication and the values they instilled in me. To my beloved wife, this journey would not have been possible without your love, patience, and understanding. Your unwavering faith in my abilities, even when the path seemed difficult, has been my greatest source of motivation. Your support in both the good and challenging times has made this accomplishment as much yours as it is mine. To my dear children, your laughter, joy, and innocence have been my inspiration. You remind me daily of the beauty in life and the importance of perseverance. I hope this achievement serves as an example that dreams can be realized through hard work and dedication. To my brothers and sisters, your support has been a pillar of strength throughout this journey. You have always been there to lift me up, encourage me, and remind me of my goals. I am truly blessed to have you all in my life, and I dedicate this success to our shared bond of family.7 0Item Restricted Electromagnetic Modeling and Retrieval of Soil Properties Using Signals of Opportunity Reflectometry(University of Southern California, 2025-05) Melebari, Amer; Moghaddam, MahtaSoil is the foundation of life on Earth, supporting plant growth, animals, and microbes that form the base of most food chains. Additionally, soil is essential in controlling the interactions between vegetation and the atmosphere, as well as their dynamics. Soil moisture has a first-order impact in controlling the global water cycle balance and a high impact on determining weather patterns. Healthy soil is essential for sustainable agriculture and food security, as well as mitigating climate change. Soil properties include soil moisture, texture, and carbon content. Additionally, properties such as surface roughness and aboveground biomass are intimately connected to the properties of the soil itself. Monitoring the dynamic properties of soil and what overlies it assists in preserving soil health. Multiple spaceborne and airborne remote sensing missions exist to monitor various soil properties. Furthermore, numerous analytical and computational algorithms have been developed to estimate soil properties using remote sensing systems, including radars, radiometers, and global navigation satellite system (GNSS)-reflectometry (GNSS-R). Although existing methods have shown great success in retrieving soil properties, such as soil moisture, there remains a need for new systems and algorithms to estimate these properties more accurately and with higher spatial and temporal resolutions. This dissertation presents the development of a suite of electromagnetic signal models and their use in the retrieval of soil properties. More specifically, this includes the development of a next-generation GNSS-R delay-Doppler map (DDM) model with the purpose of retrieving soil moisture. The model, called improved geometric optics with topography (IGOT), is applicable to surfaces with topographic relief. Additionally, the model is extended to forested areas by improving accounting for vegetation attenuation and adding the vegetation volume scattering effects. Moreover, the analytical sensitivity of the model to land surface parameters is investigated. The model is validated against National Aeronautics and Space Administration (NASA) Cyclone GNSS (CYGNSS) mission observations over multiple areas with good performance. The effects of volume scattering of vegetation were found by the model to be insignificant and negligible in most cases. Multiple physics-based algorithms are developed in this work to retrieve soil properties from GNSS-R DDMs as well as from multiple monostatic radars. Specifically, an algorithm for retrieving soil moisture and surface roughness from DDMs is developed. This algorithm uses a hybrid local/global optimizer and an electromagnetic forward scattering model that is applicable to vegetated surfaces without topography. The algorithm is validated using retrievals from CYGNSS observations compared with in situ soil moisture measurements. An unbiased root mean square error (ubRMSE) better than 0.1 m3m−3 is achieved. The same optimizer is used with a backscattering version of the forward model to estimate soil moisture from multiple radars with various frequencies and polarization. Retrievals from simulated measurements showed high retrieval accuracy. The last algorithm is for retrieving both soil moisture and vegetation water content (VWC) from multiple GNSS-R observations. It uses a local optimizer with the IGOT model. A surface roughness map, which is derived using multiple years of CYGNSS data, is used in the retrievals. Such a roughness map is needed because the small-scale of roughness (at the electromagnetic wavelength scale) is not captured by the digital elevation model (DEM). Using CYGNSS DDMs over the Jornada Experimental Range (JER) area, NM, USA, the algorithm is validated against the NASA flagship Soil Moisture Active Passive (SMAP) mission products. The validations showed that the algorithm can retrieve soil moisture with an ubRMSE of 0.075 m3m−3 over the validation site. Additionally, the results showed that the observations, and therefore the retrieval algorithm, are not sensitive enough to VWC for retrievals from CYGNSS observations. Two innovative radar observation architectures are explored for next-generation agile Earth observation systems. The first architecture employs a nonuniform sampling (NUS) receiver for signals-of-opportunity (SoOp) beyond GNSS-R. Both simulated and measured data are used in the study. The analysis demonstrates that while a NUS introduces a minor negative impact compared to the conventional uniform signal sampling schemes, its overall performance remains promising for future applications. The second architecture is tasking agile satellite constellations to reduce the uncertainty of retrievals of geophysical parameters. This is achieved by optimizing satellite measurement schedules and configurations. In this investigation, calculating the retrieval performance across ranges of radar frequencies and configurations and vegetation landcover types is performed using Monte Carlo simulations and the hybrid optimizer retrieval method mentioned earlier.7 0Item Restricted Teacher Perceptions of Reasons for Transfer in the k-12 Public Schools in Saudi Arabia(irginia Tech University, 2025) Alhomoud, Nouf; Alexander, Michael DA significant body of research globally has examined teacher turnover and mobility, highlighting their critical impact on educational systems, teacher retention, and student outcomes. However, this study was the first of its kind in Saudi Arabia to investigate the factors influencing teacher transfers, providing a foundational understanding of this critical issue within the Saudi educational context. This quantitative, non-experimental study examined the reasons prompting teacher transfers between school districts and within schools in the same district. This study focused on all public-school teachers in Al-Jouf district who transferred within the past 5 years in Saudi Arabia. A total of 245 responses were collected across various educational levels in the Al-Jouf district. Data were collected using a validated survey instrument, the Reasons for Teacher Transfer in Public Schools Questionnaire, which measured five key factors: social conditions, working conditions, insufficient organizational support, leadership style, and student characteristics. Responses were gathered via an online distribution process and analyzed using SPSS (V29) to conduct descriptive and inferential statistical analyses. Descriptive and inferential statistical analyses were conducted to examine the factors influencing teacher transfers between different school districts in the Kingdom, transfers within the same district which is Al-Jouf district, and gender-based differences in transfer motivations. Descriptive analyses revealed that social conditions, particularly the desire to be closer to family and home region, was the most influential factor in both district and intra-district transfers. Working conditions, such as overcrowded classrooms and excessive teaching hours, ranked second, while student characteristics had the least impact on both transfers. To explore gender-based differences, independent samples t-tests were utilized. Results indicated statistically significant differences for external transfers between different districts, with male teachers rating excessive weekly teaching hours as more significant reasons compared to female teachers. Additionally, for internal transfers, the desire to be closer to family and home region showed a significant gender difference, with male teachers rating it higher than female teachers. These findings align with international research emphasizing the significant role of social and working conditions in influencing teachers' decisions to transfer, while highlighting the minimal impact of student characteristics.21 0Item Restricted STAKEHOLDERS' PERCEPTIONS AND IDEOLOGIES TOWARDS LEARNING SAUDI DIALECT IN MODERN STANDARD ARABIC CLASSROOMS(The university of Memphis, 2025) Alshehri, Mughram; Thrush, EmilyArabic is a diglossic language with two varieties: high -- Modern Standard Arabic (MSA) and low -- spoken Arabic. As a result, there has been an ongoing discussion among specialists over which variety should be taught. The integrated approach, which teaches two varieties (typically MSA plus a local dialect), aims to enhance learners’ communicative competence. Recent studies have investigated learners’ and teachers’ perceptions and ideologies toward such integration, although as yet no studies have investigated the views of policymakers or university professors. Some studies have found that learners and teachers believe an integrated approach would enhance learners' communication needs, motivation, and cultural comprehension, while others found that students and teachers held ideological beliefs against this approach, or that they had expressed concerns such as confusion and decreased motivation. To bridge this gap, this mixed-method study explored 262 stakeholders (male, n=157; female, n=105), including learners, language teachers, policymakers, and university professors, to investigate their perceptions and ideologies regarding learning Saudi dialect in the oral skills classroom as support for learning MSA in an Arabic language program at a Saudi public university. Quantitative data was collected via questionnaires with all the stakeholders, and was followed up by three focus groups involving learners and by individual interviews with five policymakers and ten teachers. The quantitative data was analyzed using descriptive and inferential statistics test such as repeated measures analysis, and the qualitative data was analyzed using thematic analysis. The study found that stakeholders had positive attitudes toward dialect instruction / the integrated approach, and that the learners were the most enthusiastic group because their hope to better understand local culture and achieve academic success. In contrast, although the stakeholders perceived dialect instruction positively, they were concerned about some difficulties that might accompany dialect teaching in MSA classrooms, with teachers as the less concerned group. Regarding ideologies that work against the teaching of dialect, half of the learners held such ideological beliefs, while in general the teachers, policymakers, and professors did not hold such strong ideological beliefs, , with teachers as the less ideological group. Age was the most pertinent variable affecting stakeholders’ responses, and the older learners and younger policymaker and professors had more positive attitudes. Other variables such as gender played a lesser role in stakeholders’ responses. Younger female policymakers and professors held more positive attitudes toward integrated instruction, and policymakers who had not studied abroad tended to have negative attitudes toward integration, and to hold ideologies against dialect teaching. The finding of this study highlights strong agreement on the need for dialect instruction for academic and cultural reasons, and also the need to take into consideration the concerns expressed by those who such instruction would affect, including providing a well-planned curriculum and engaging with the concerns of those stakeholders who are skeptical of integrated instruction.23 0Item Restricted Assessing the initial Primary Stability of Dental Implants via various Osteotomy Preparation Techniques: An Ex Vivo Comparative Study Utilizing Two Distinct Non-invasive Methods(University at Buffalo, 2023) Fadhl Almawla, Sawsan; Sebastiano, AndreanaBackground and aim: Stable connection between dental implants and bone is crucial in implant dentistry. Osteotomy preparation methods, such as Conventional drilling (C), Piezoelectric Bone Surgery (P), and Osseodensification (Densah bur) (D), play a major role in this process. The primary stability of implants, which influences prognosis and loading protocols, is affected by several factors, including cortical bone thickness. . Two innovative noninvasive devices like Periotest® (Siemens AG, Bensheim, Germany) and resonance frequency analysis (RFA) (Osstell Mentor) (Integration Diagnostics AB, Göteborg, Sweden) have been introduced to measure implant primary stability. The aim of this study is to assess and compare the primary stability of dental implants inserted through (C), (P), and (D), utilizing Resonance Frequency Analysis and Periotest, and to explore the effect of cortical bone thickness on the primary stability. Methods: Ten porcine ribs were utilized, with three osteotomies per rib employing (C), (P), and (D) techniques. Each technique involved ten implant preparation sites for 4.1 x 10 mm implants. Primary stability was assessed using Periotest and RFA. Bone thickness was measured using a calibrated dental ruler after bone sectioning at osteotomy sites. Statistical analysis involved one-way repeated measures ANOVA and multiple comparison tests, with significance set at p < 0.05 for the first objective. The second objective utilized the Pearson Correlation Coefficient (r) test. v Results: The mean ISQ for RFA was reported as 69.85, 68.25, and 73.05 for (C), (P), and (D), respectively. The PTV (Periotest Value) was recorded as -5.2300, -3.2250, and -3.5000 for (C), (P), and (D), respectively. The (D) technique exhibited a higher mean RFA score compared to (C) and (P) techniques, while Periotest scores were lower for (C) than (P) and (O). Interestingly, there was no consistent ranking between the RFA and Periotest results. Furthermore, no significant correlation was found between cortical bone thickness and either RFA or Periotest scores. Conclusions: While statistically significant differences were observed, all three techniques demonstrated results falling within the range of good clinical primary stability. The study suggests that there is no significant clinical distinction between (RFA) and Periotest. Additionally, cortical bone thickness did not show a significant correlation with either RFA or Periotest results.9 0Item Restricted Detecting and Analyzing Frequency Events in Power Systems Using Tunable Parameters-Based Algorithms: Development, Optimization, and Analysis(Portland State University, 2025) Alghamdi, Hussain A; Bass, Robert BThe rapid growth in the integration of renewable energy sources into power grids has driven a transition from conventional thermal-based generation to inverter-based resources. As a result, power system inertia has decreased, and the rate of change of frequency has increased. This presents a challenge for frequency stability in modern power systems. Power systems disturbances, such as significant faults or major disruptions in generation or load, cause imbalances between power supply and demand, which may result in severe frequency fluctuations known as frequency events. Following such events, fast frequency response is needed to provide frequency support and prevent system collapse. Therefore, monitoring and detecting frequency events promptly and accurately is critical to stabilizing power systems. This dissertation addresses the challenge of detecting frequency events in diverse power systems by enhancing existing frequency event detection methods through detection process modifications and developing unique tunable parameters. Since system characteristics differ across regions, frequency event detection algorithms must be customized by domain experts for each balancing area using tunable parameters. By optimizing these parameters for specific power system, the algorithms can accurately detect frequency events and can also be used for further analysis to determine trends in frequency events over time, ensuring system stability. This dissertation focuses on the enhancement and optimization of frequency event detection algorithms. These detection algorithms are compared with other state-of-the-art frequency detection methods. The study examines the impact of signal denoising techniques on detection accuracy, analyzes frequency performance over time, reviews global frequency performance standards, and conducts comprehensive sensitivity analyses. The five primary contributions of this dissertation are: the development of frequency event detection algorithms with tunable parameters for specific balancing areas; optimization of the developed algorithms parameters to enhance results and adaptability, conducting a comprehensive analysis of signal denoising methods and their impact on frequency event detection; the proposal of criteria-based tunable parameters to assess frequency events trends and severity; presentation of an enhanced understanding of global frequency performance standards; and deeper insights into frequency specifications across diverse power systems.8 0