SACM - United States of America
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Item Restricted FLUORIDE CONTENT OF INFANT FORMULA COMMERCIALLY AVAILABLE IN CENTRAL INDIANA(Saudi Digital Library, 2025) Altamimi, Ayman; Lippert, FrankBACKGROUND Fluorides have a well-established role in dental caries prevention. Fluoride content in infant formula has raised concerns about whether it is within safe levels for the developing teeth. There is a large number of products on the market with likely varying fluoride concentrations, and these products’ fluoride content will differ depending on whether, for example, fluoridated water was used during manufacturing or reconstitution. Several studies have been published on infant formula containing fluoride and the associated risk of developing enamel fluorosis. However, few recent studies in the US have determined whether liquid or powder infant formula fall within safe/recommended levels. Purpose: This study measured the fluoride content of infant formula sold in grocery stores in central Indiana, prepared using three types of water (Purified, Nursery, and Tap) to determine if they fall within safe levels. Alternative hypotheses: There is a significant difference in the concentration of fluoride between different brands of infant formula. Material & Methods: We analyzed twenty different infant formula products sold in grocery stores in the Indianapolis, Indiana area for their fluoride content. Samples were reconstituted with Nursery water (containing approx. 1.0 ppm fluoride), Tap water (approx. 0.7 ppm fluoride) and Purified water (negligible fluoride content). A sample for the tests was taken from each preparation and the concentrations of fluoride of all samples was determined using the fluoride microdiffusion method. The statistical analysis of results was carried out using two-way ANOVA. Results: When comparing the mean (SD) fluoride concentration among the three types of infant formula reconstitution with water, tap water had significantly higher fluoride concentration mean than both Nursery water and purified water (P <.001 at α=.050 level). Nursery water also had significantly higher fluoride concentration mean than purified water (P <.001 at α=.050 level). When the three types of water were used for reconstitution of the 20 infant formula brands, the overall highest fluoride concentration mean was seen when tap water was used for reconstitution (0.950) followed by nursery water (0.789) while the least fluoride concentration was in purified water (0.102). Conclusion: Within the study's limitations, it can be concluded that apart from one formula none of the tested infant formulas sold in central Indiana grocery stores when reconstituted with purified water were found to decrease the chance of infants exceeding UL levels for both age groups but were found to increases the chance exceeding the AI levels for infants aged 0–6 months. All tested infant formulas reconstituted with nursery and tap water were found to increase the chance of infants exceeding the UL, and the AI levels for both groups resulted in increasing the chance of fluoride concentrations exceeding the recommended/safe levels. Thus, the type of water used for reconstitution rather than the type of formula appears to be the determining factor for the levels of fluoride intake associated with infant formula. Clinical Significance: With the recent increase in the utilization of infant formula, different brands with varying fluoride concentrations and the different modes of reconstitution must be evaluated to determine if their fluoride concentrations will fall within safe/recommended levels and thus increase the risk of enamel fluorosis development.27 0Item Restricted EARNED INCOME TAX CREDIT 2009 EXPANSION: A NATIONAL AND STATE-LEVEL ANALYSIS OF CHILD POVERTY REDUCTION, TAX RETURNS, AND INFLATION-ADJUSTED BENEFITS, 2000-2022(ProQuest Dissertations & Theses, 2025) Aljohani, Mohammed; Gilleylen, JohnnyThis study investigates the impact of the Earned Income Tax Credit (EITC) 2009 on child poverty in the U.S. Census Bureau data shows child poverty has increased by 0.29 points each year, starting at 16.20 percent in 2000 to 19.00 percent in 2008. States with the highest child poverty rates, such as Mississippi (28.08 percent), Louisiana (26.67 percent), New Mexico (25.23 percent), Arkansas (23.86 percent), and West Virginia (23.78 percent), exceed the national average of 17.62 percent for those years. Prior research found that childhood poverty negatively affects the economy, health, and education. Previous research on the impact of the 2009 EITC policy change on child poverty reduction in the U.S.—including metrics such as EITC claims, average credit per household, and inflation-adjusted benefits—has been limited or inconclusive. This study investigates the effects of the EITC provisions introduced through the American Recovery and Reinvestment Act of 2009 on the U.S. child poverty. It also focuses on state-level EITC tax returns and average credit amounts in the five states with the highest and lowest child poverty rates, using a quantitative approach that combines interrupted time series and cross-sectional research designs with data from the U.S. Census Bureau and the IRS. Since 2000, the EITC has helped lift an average of 2.36 million children out of poverty each year. A 2009 policy change boosted that number by 21 percent, with 2.52 million children lifted annually. However, the impact declined during COVID-19, with noticeable drops in EITC claims, credits, and adjusted benefits. The 2009 expansion led to increased EITC use across both high- and low-poverty states, with the greatest gains seen in the most disadvantaged states— contradicting key assumptions of social construction theory. At the end, the study offers policy recommendations to enhance EITC’s equity and effectiveness.13 0Item Restricted How Large Language Models are Reshaping Skills and Job Requirements for Public Health Professionals in Saudi Arabia(Saudi Digital Library, 2025) Alkhinjar, Mulfi; Palmer, PaulaContext: Large Language Models (LLMs) such as ChatGPT, Gemini, and DeepSeek are transforming professional work across sectors by enhancing information processing and decision support. In public health, these technologies offer the potential to improve efficiency, analytical capacity, and data-driven decision-making. Yet, their integration raises concerns about workforce preparedness, evolving skill requirements, and ethical oversight. In Saudi Arabia, where Vision 2030 prioritizes digital transformation in healthcare, understanding how public health professionals adapt to these technologies is vital for workforce and policy planning. Method: This exploratory mixed-methods study examined the professional impact of LLMs and the preparedness of public health professionals for their integration. The validated Shinners Artificial Intelligence Perception (SHAIP) survey, adapted for LLMs and public health, was distributed to employees of the Saudi Public Health Authority, yielding 32 complete responses. Ten semi-structured interviews further explored four constructs: professional impact, preparedness, new essential skills, and obsolete skills. Quantitative data were analyzed descriptively, and qualitative data were coded using thematic analysis. Findings: Survey results indicated that LLMs positively influence efficiency and workflow but revealed gaps in training and ethical guidance. Interview themes reinforced these findings, identifying new essential skills such as prompt engineering, digital literacy, and critical oversight, while traditional tasks like manual data entry and report drafting were viewed as increasingly automated. Conclusion: LLMs are transforming the roles of public health professionals. Successful adoption requires structured training, institutional readiness, and ethical governance. The study offers actionable recommendations to align workforce development and recruitment strategies with Saudi Vision 2030, emphasizing capacity building and responsible AI integration in public health practice.13 0Item Restricted TOPOLOGICAL DATA ANALYSIS (TDA) AS A FEATURE EXTRACTION TOOL FOR EEG SIGNAL ANALYSIS IN SLEEP STAGING(Saudi Digital Library, 2025) Albidah, Hamad; Zhi-Hong, Mao; Dallal, AhmedSleep is a biological process essential for all living organisms. For humans, it plays a fundamental role in regulating emotions, memory consolidation, cognitive function, and overall physical health. Despite its importance, many individuals remain unaware of chronic sleep deficiencies until diagnosed—often after years of suffering. Accurate diagnosis of sleep disorders requires reliable tools and methods, particularly in clinical settings. Electroencephalography (EEG) remains a widely used technique in the study of sleep for capturing brain signals that contain rich physiological information. However, EEG data are inherently high-dimensional and complex, posing challenges for analysis and interpretation. To address this, the goal of this dissertation is to develop an explainable dual hierarchical feature selection and dimensionality reduction framework aimed at improving sleep stage classification. The proposed framework consists of two stages. The first stage is feature construction and selection. Specifically, we integrate Topological Data Analysis (TDA) to explore the intrinsic structure of the data and extract both traditional statistical features and TDA-based features as a supplement to model training. Then, we use Recursive Feature Elimination with Cross-Validation (RFECV) to optimize feature selection. The second stage is to further reduce the dimensionality of the feature space. Four dimensionality reduction techniques are considered: Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Kernel Principal Component Analysis (KPCA). Our results indicate that manifold learning algorithms generally outperform PCA; among them, t-SNE achieves the highest classification accuracy at 78.9%. This improvement arises because the TDA-based features can extract global structural patterns from EEG signals that traditional spectral–temporal metrics cannot capture. Thus, this study demonstrates that a structured, theory-driven approach can enhance both the performance and interpretability of machine learning models in sleep-stage classification. It also provides a practical framework for processing complex biomedical signals, with potential implications for real-world clinical applications.11 0Item Restricted Analysis of No-Confounding Designs in 16 Runs for 10-14 Factors(Saudi Digital Library, 2025) Alqarni, Hanan; Montgomery, DouglasRegular two-level fractional factorial designs are widely used for factor screening experiments, where the objective is to efficiently identify the set of active factors from a larger initial group of factors. The 16-run designs are very popular for screening because they can accommodate a reasonably large number of factors and for 6 – 8 factors they are Resolution IV, while for larger numbers of factors they are Resolution III. Assuming that 3-factor and higher interactions are negligible the Resolution IV designs provide clear estimate of the main effects while aliasing all 2-factor interactions with each other and the Resolution III designs alias main effects and 2-factor interactions. Because of the aliasing, follow-up experiments are often required to obtain complete information about main effects and 2-factor interactions. However, there are many situations where follow-up experimentation isn’t possible. Nonregular fractional designs that do not have complete aliasing involving main effects and 2-factor interactions can be a good alternative for these situations. However, analysis methods for these designs is an ongoing area of research. This work investigate analysis methods for a class of non-regular 2-level fractional factorials for 8 – 14 factors in 16 runs. In these designs there is no complete aliasing between the main effects and the two-factor interactions, so these designs are useful alternatives to the regular Resolution III fractions. The analysis methods are forward stepwise regression, the least absolute shrinkage and selection operator (LASSO) and the Dantzig selector method. The results show that in most cases that for effect sizes of 2 and 3 standard deviations stepwise regression and the LASSO outperform the Dantzig selector in correctly identifying the set of active factors for situations where the number of active factors does not exceed approximately half of the number of degrees of freedom for thedesign. Lastly, additional approaches are explored: the two-stage stepwise regression method, design augmentation and other no-confounding designs with 20 and 24 runs, to examine differences in method performance.9 0Item Restricted A PARSESCIENCING INQUIRY ON FEELING SAD(Saudi Digital Library, 2025) Zain Aldeen, Aisha; Mario, OrtizThe investigation aimed to explore the universal humanuniverse living experience of feeling sad using Parsesciencing, which is a unique mode of inquiry within the humanbecoming paradigm. The historians were 10 English-speaking adults aged 18 and older, willing to share their experiences of feeling sad. The inquiry stance was: What is the discerning extant moment of the universal humanuniverse living experience of feeling sad? The major discovery of this Parsesciencing inquiry was the discerning extant moment: Feeling sad is profound discomfort with disengaging from affiliations surfacing with promising new insights.5 0Item Restricted AN ANALYSIS OF SAUDI WOMEN’S UNEMPLOYMENT AND THE IMPACT OF VISION 2030(Saudi Digital Library, 2025) Alghamdi, Norah; Gilleylen, JohnnyThis study addresses the problem of high female unemployment in the Kingdom of Saudi Arabia. Data from the General Authority for Statistics indicates that the female unemployment rate has increased significantly from 17.6% in 2000 to 33.8% in 2015, an increase of 16.2 points annually. The highest female unemployment rate was recorded in 2012 at 35.7%, while the lowest rate was in 2001 at 17.3%. Previous studies show that female unemployment negatively impacts women's empowerment in Saudi Arabia, whether in terms of education, social and cultural barriers, economic participation, leadership, or gender discrimination. The Saudi government has taken many comprehensive measures and reforms to mitigate female unemployment and increase their participation in the labor market through Vision 2030 and supporting programs such as the Custodian of the Two Holy Mosques Scholarship Program, the Wusool program, the Qara program, the Tamheer program, and the Qiyadat program. The Kingdom's Vision 2030 for national transformation was launched in 2016 by the Saudi Crown Prince. It includes several objectives, including developing human capabilities and reducing unemployment, which is significantly higher among women than men. This study analyzes the role of Vision 2030 reforms in lowering women's unemployment rates. This study uses a quantitative, cross-sectional, and time-series approach to combine trend analyses of the unemployment rate, labor force, economic participation, and unemployment by educational level and age group for Saudi women across the two periods before and after the implementation of the Vision. The study is based on secondary data from the Saudi General Authority for Statistics, covering the period from 2010 to 2023, including the years before and after the implementation of Vision 2030. The results indicated that Vision 2030 expanded the range of economic opportunities available to women, leading to significant progress in reducing the unemployment rate among women and empowering them by increasing their economic participation in the labor market during the post-policy implementation period from 2017 to 2023. At the end, this study provides policy recommendations to enhance the effectiveness of the Kingdom's Vision 2030 initiatives and strategies for continued improvement.7 0Item Restricted THREE ESSAYS ON WATER MANAGEMENT IN AN ARID ENVIRONMENT(Saudi Digital Library, 2025) Alasfour, Abdulelah; Edward, Dekeyser; Robert, Hearne; Ahmed, Harb Rabia; Bakr, Aly AhmedThis dissertation addresses the challenge of water scarcity in arid and semi-arid environments, where limited renewable resources, overexploitation of groundwater, and the impacts of climate change threaten long-term water security. The study first reviews a wide range of global strategies, including desalination, managed aquifer recharge, wastewater reuse, cloud seeding, virtual water trade, irrigation efficiency, crop selection, and economic instruments. While each approach presents technical, environmental, or social challenges, the literature shows that when properly regulated and adapted to local conditions, these strategies can effectively reduce pressure on freshwater resources. Building on this foundation, the research then turns to Saudi Arabia as a case study. An analysis of wheat imports from 2001 to 2023 demonstrates that virtual water trade has conserved 183.71 billion cubic meters of national water resources, including 139.62 billion cubic meters of nonrenewable groundwater, while also contributing to global efficiency by sourcing wheat from rain-fed regions. A second analysis of four key crops, dates, potatoes, tomatoes, and watermelons, shows that adopting modern irrigation techniques could save an average of 823 million cubic meters annually. Economic modeling further indicates that moderate water pricing and targeted tax exemptions can improve the financial feasibility of these systems, supporting wider adoption by farmers. The dissertation shows that addressing water scarcity in arid regions cannot rely on a single solution. A combination of strategies, such as relying on virtual water trade, improving irrigation efficiency, and applying supportive economic policies, is necessary to reduce pressure on groundwater and secure more sustainable water use.5 0Item Restricted USING AI DEEP LEARNING MODELS FOR IMPROVED LONG-SHORT TERM TIME SERIES FORECASTING(Saudi Digital Library, 2025) Alharthi, Musleh; Mahmood, AusifTime series forecasting has long been a challenging area in the field of Artificial Intelligence, with various approaches such as linear neural networks, recurrent neural networks, Convolutional Neural Networks, and transformers being explored. Despite their remarkable success in Natural Language Processing, transformers have faced mixed outcomes in the time series domain, particularly in long-term time series forecasting (LTSF). Recent works have demonstrated that simple linear models, such as LTSF- Linear, often outperform transformer-based architectures, leading to a reexamination of the transformer’s effectiveness in this area. In this paper, we investigate this paradox by comparing linear neural network and transformer-based designs, offering insights into why certain models may excel in specific problem settings. Additionally, we enhance a simple linear neural network architecture using dual pipelines with batch normalization and reversible instance normalization, surpassing all existing models on most popular benchmarks. Furthermore, we introduce an adaptation of the extended LSTM (xLSTM) architecture, named xLSTMTime, which incorporates exponential gating and a revised memory structure to handle multivariate LTSF more effectively. Our empirical evaluations demonstrate that xLSTMTime achieves superior performance compared to various state-of-the-art models, suggesting that refined recurrent architectures may present a competitive alternative to transformer-based designs for LTSF tasks. More recently, TimeLLM demonstrated even better results by reprogramming i.e., repurposing a Large Language Model (LLM) for the TSF domain. Again, a follow up paper challenged this by demonstrating that removing the LLM component or replacing it with a basic attention layer in fact yields better performance. One of the challenges in forecasting is the fact that TSF data favors the more recent past, and is sometimes subject to unpredictable events. Based upon these recent insights in TSF, we propose a Mixture of Experts (MoE) framework. Our method combines the state-of-the-art (SOTA) models including xLSTM, enhanced Linear, PatchTST, minGRU among others. This set of complimentary and diverse models for TSF are integrated in a Transformer MoE model. Our results on standard TSF benchmarks demonstrate better results surpassing all current TSF models, including those based on recent MoE frameworks.8 0Item Restricted AI-BASED DESIGN OPTIMIZATION AND GRID IMPACT MITIGATION OF DYNAMIC WIRELESS POWER TRANSFER SYSTEMS FOR ELECTRICAL VEHICLE CHARGING APPLICATIONS(Saudi Digital Library, 2025) Almazmomi, Rakan; Arkadan, Abd AThere is growing interest in electrifying the transportation sector to reduce fossil fuel use. Electric vehicles (EVs) offer a promising alternative but face challenges like limited driving range, range anxiety, and long charging times. Dynamic Wireless Power Transfer (DWPT) systems, which charge EVs while in motion, have emerged to address these issues. The widespread adoption of DWPT systems could be delayed by several challenges, including high costs, system efficiency, safety concerns, ensuring maximum power delivery, and accommodating tolerable misalignment offset, among others. To address these challenges, this thesis develops a design optimization environment for the DWPT charging systems composed of two main modules: a Characterization Module (identifier) and a Design Optimization Module. Designing and optimizing DWPT systems could be challenging due to their complex electromagnetic interactions, magnetic materials' nonlinearities, the switching effects of power electronics converters employed in these systems, and their dynamic operational conditions. To manage these challenges, an electromagnetic Finite Element–State Space (FE-SS) based characterization module is first developed to accurately predict DWPT system performance in terms of magnetic coupling, output power, system efficiency, and electromagnetic field (EMF) leakage under various misalignment conditions. However, directly using FE-SS models as an identifier in the design optimization requires numerous iterations and extensive computational time. To overcome this limitation, this work integrates the FE-SS characterization environment with an AI-based predictive model and Taguchi algorithm to reduce the computational requirements for the characterization module. The Optimization Module utilizes Particle Swarm Optimization (PSO) in conjunction with the AI-based identifier (Characterization Module) to rapidly predict DWPT performance indicators. The developed approach is applied to different classes of EVs, including passenger cars and heavy-duty trucks, and demonstrated through two case studies: stationary and dynamic EV wireless power transfer systems. These case studies adopt the proposed multi-objective design optimization environment to optimize system efficiency, output power, transmitted energy, charging system material cost, and EMF leakage under a range of misalignment conditions. Beyond the device design stage, large-scale deployment of DWPT might introduce new challenges for electrical grids. The highly variable, high-power charging demand of EV-DWPT can cause voltage deviations and increased power losses. To mitigate these impacts, this thesis develops a planning framework that utilizes distributed energy resources (DERs). A Mixed-Integer Nonlinear Programming (MINLP) multi-objective optimization model is developed to determine the optimal placement and sizing of DERs across several EV-DWPT load-penetration scenarios. Two solution approaches are investigated: a cost-based aggregate method (CB) and a Chebyshev goal programming (GP) approach to balance trade-offs across conflicting objectives, including cost, power losses, voltage deviation, and load curtailment. Finally, this thesis contributes to the advancement of EV-DWPT research by (i) developing an AI-driven design optimization environment suitable for DWPT systems, and (ii) proposing a DER planning optimization framework to mitigate EV-DWPT charging load grid impacts on a distribution grid. These contributions enable more efficient DWPT system design, optimization, and integration into distribution networks, supporting the deployment of roadway electrification.9 0