SACM - United States of America
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Item Restricted Influence of Participation in the BPCI-A Initiative on 30-Day Heart Failure Unplanned Readmission Rates Among U.S. Hospitals(University of Texas Health Science Center at Houston, 2025) Balhareth, Ibrahim Ali; Linder, StephenBackground: The Bundled Payments for Care Improvement Advanced (BPCI-A) initiative incentivize participating hospitals if they achieved less than the target spending amount for the selected condition and penalizes them if they exceeded the target. The BPCI-A program aims to enhance care quality and reduce spending. One of the quality measures targeted by BPCI-A is hospital readmissions. Heart failure is one of the leading causes of hospital readmission effectiveness of BPCI-A in reducing cardiac-related readmissions, particularly for heart failure, and the influence of hospital characteristics on program outcomes remain uncertain. This series of studies comprehensively evaluated the impact of BPCI-A on heart failure readmission rates. Methods: First, a scoping review was conducted to synthesize the existing literature on hospital characteristics, BPCI-A participation, and associated readmission outcomes, specifically focusing on cardiac care. Subsequently, a second study utilizing a propensity score matching (PSM) with national hospital-level datasets compared the baseline characteristics and readmission outcomes between hospitals participating in the program and a matched group of their counterparts that never participated in the program. Lastly, a retrospective matched-cohort study was conducted to validate the findings from the second study by evaluating whether participation in the BPCI-A program influenced 30-day heart failure readmissions, including subgroup analyses by hospital size, ownership, and teaching status, using weighted regression modeling and interaction analyses. Results: The scoping review revealed limited effectiveness of BPCI-A in reducing cardiac-related readmissions broadly, emphasizing existing disparities among hospitals. Empirical findings from Journal Article 2 demonstrated significant baseline differences: BPCI-A hospitals were larger, urban, teaching-oriented, and for-profit institutions. Post-matching analyses indicated a modest but significant association between BPCI-A participation and reduced heart failure readmissions (4.1 percentage points lower, p<0.001). Confirmatory analyses from Journal Article 3 validated these results, showing a 4.2 percentage-point reduction in readmissions associated with participation, with substantial heterogeneity by hospital characteristics. Small, public, and non-teaching hospitals benefited disproportionately from participation. Conclusion: Participation in BPCI-A is modestly associated with lower heart failure readmission rates, especially among hospitals historically disadvantaged by resource constraints. However, BPCI-A alone appears insufficient to eliminate persistent disparities or achieve substantial reductions universally. Future bundled payment policies must be tailored to hospital contexts, address under-resourced institutions by providing targeted support to enhance equity and effectiveness in reducing heart failure readmissions.21 0Item Restricted AUTOMATED DETECTION OF OFFENSIVE TEXTS BASED ON ENSEMBLE LEARNING AND HYBRID DEEP LEARNING TECHNIQUES(Florida Atlantic University, 2025-05) Alqahtani, Abdulkarim Faraj; Ilyas, MohammadThe impact of communication through social media is currently considered a significant social issue. This issue can lead to inappropriate behavior using social media, which is referred to as cyberbullying. The accessibility and freedom of expression afforded by social media platforms enable individuals to share their emotions and opinions, but it also leads to cyberbullying behavior. Automated systems are capable of efficiently identifying cyberbullying and performing sentiment analysis on social media platforms. In this dissertation, our focus is on enhancing a system to detect cyberbullying in various ways. Therefore, we apply natural language processing techniques utilizing artificial intelligence algorithms to identify offensive texts in various public datasets. The first approach leverages two deep learning models to classify a large-scale dataset, combining two techniques: data augmentation and the GloVe pre-trained word representation method to improve training performance. In addition, we utilized multi-classification algorithms on a cyberbullying dataset to identify six types of cyberbullying tweets. Our approach achieved high accuracy, particularly with TF-IDF (bigram) feature extraction, compared to previous experiments and traditional machine learning algorithms applied to the same dataset. We employed two ensemble machine learning methods with the TF-IDF feature extraction technique, which demonstrated superior classification performance. Moreover, we used four feature extraction methods, BoW, TF-IDF, Word2Vec, and GloVe, to determine which works best with the ensemble technique. Finally, we utilize a multi-channel convolutional neural network (CNN) enhanced with an attention mechanism and optimized using a focal loss function.10 0Item Restricted ADVANCED LARGE LANGUAGE MODEL APPROACHES AND NATURAL LANGUAGE PROCESSING TECHNIQUES TO IMPROVE HATE SPEECH DETECTION USING AI(University of Central Florida, 2025) Almohaimeed, Saad; Boloni, LadislauThe proliferation of hate speech on social networks can create a significant negative social effect, making the development of AI-based classifiers that can identify and characterize different types of hateful speech in messages highly important for stakeholders. While this is a highly challenging problem, recent advances in language models promise to advance the state of the art such that even subtle and indirect forms of hate speech can be detected. In this dissertation we present a series of contributions that improve different aspects of hate speech classification. We developed THOS, a hate speech dataset consisting of 8.3k tweets. Compared to previous datasets, THOS contains fine-grained labels that identify not only whether a tweet is offensive or hateful, but also the target of the hate. Using this dataset, we studied the degree to which finer grained classification of tweets is feasible. In the follow-up work, we focus on the difficult problem of implicit hate speech, where hate is conveyed through subtle verbal constructs and allusions, without the use of explicitly offensive terms. We evaluate the efficacy of lexicon-based methods, transfer learning, and advanced LLMs such as GPT-4 on this problem. We found that the proposed techniques can boost the detection performance of implicit hate, although even advanced models often struggle with certain interpretations. In our third contribution, we introduce the Closest Positive Cluster (CPC) auxiliary loss, which improves the generalizability of classifiers across a wide range of datasets, resulting in enhanced performance for both explicit and implicit hate speech scenarios. Finally, given the scarcity of implicit hate speech datasets and the abundance of explicit hate datasets, we proposed an approach to generalize explicit hate datasets for the classification of implicit hate speech. Additionally, the proposed approach addresses noisy label correction issues commonly found in crowd-sourced datasets. Our method comprises three key components: influential sample identification, reannotation, and augmentation. We show that the approach improves generalization across datasets and enhances implicit hate classification.11 0Item Restricted Theoretical Studies of Cu (110) Surface with the Different Carbon Coverages(Texas Tech University, 2023) Alsharari, Sami; Sanati, MahdiA Monte Carlo simulation program has been developed to model the secondary electron emission from metal surfaces. This new program is capable of simulating more than 100,000 primary incident electrons in a few minutes. The required input parameters for the Monte Carlo simulations are obtained from first-principles calculations. The calculated dielectric constants, total electron density of states, and work function were used to obtain the inelastic mean free path and stopping power of systems. As a case study, the Cu surface was chosen since it has been thoroughly explored and simulations can be compared with available experimental measurements of secondary electron emission. The goal of this thesis is to investigate the secondary electron emission of both clean Cu (110) surfaces and carbon-coated Cu systems. It was shown that the adsorption of the carbon layer on the Cu (110) surface can reduce the secondary electron emission.78 0Item Restricted Fabrication and Electrical Characterization of Contacts to MoS2 and Oxidation-Based Growth Control(Ohio University, 2025) Aldosari, Norah Abdullah M; Stinaff, EricSince the discovery of two-dimensional (2D) materials, transition metal dichalcogenides (TMDs) such as MoS₂ have garnered significant interest due to their unique electronic and optical properties. Unlike graphene, which lacks a bandgap, monolayer MoS₂ exhibits a direct bandgap, making it a promising candidate for next-generation electronic and optoelectronic applications. However, the controlled synthesis and reliable fabrication of metal contacts on MoS₂ remain key challenges in realizing scalable and reproducible device fabrication. This dissertation explores the fabrication of reliable metal contacts to optimize electrical performance in MoS₂-based devices and investigates the controlled oxidation of molybdenum via Joule heating for localized MoS₂ growth. The first study focuses on the electrical characterization of CVD-grown MoS₂ with both post-growth and naturally grown electrical contacts. By comparing different contact fabrication methods, we evaluate their impact on charge injection and contact resistance. Understanding these interactions is critical for optimizing MoS₂-based field-effect transistors (FETs) and other electronic devices. In the second study, we employ Joule heating to induce localized oxidation of metallic molybdenum, enabling patterned growth of MoS₂ via sulfurization. Our results reveal a power-dependent oxidation process, allowing precise control over the MoOx formation, which subsequently controls the growth characteristics of MoS₂. This method offers a scalable and deterministic approach to patterning 2D materials for large-area device applications. The third study explores the MoOx/MoS₂ interface synthesis and heterostructures through a controlled oxidation-sulfurization sequence. We successfully fabricate layered structures exhibiting distinct electronic behavior by tuning process parameters. Electrical measurements indicate strong interfacial interactions that modulate charge transport, suggesting their potential utility in tunable electronic and optoelectronic applications. This dissertation provides a comprehensive study of the growth and electrical characterization of MoS₂. The findings contribute to advancing 2D material integration in device applications by leveraging Joule heating for controlled oxidation and growth. Future work will focus on refining synthesis techniques, optimizing doping strategies, and exploring novel heterostructures to enhance device performance and functionality.21 0Item Restricted Carbon Fiber Microelectrodes for Sensitive and Selective Voltametric Detection of Neurochemicals and Neuropeptides(The Catholic University of America, 2025) Alyamni, Nadiah; Abot, Jandro L; Zestos, Alexander GScientific research has established carbon fiber microelectrodes (CFMEs) as powerful instruments that enable high-sensitivity real-time detection of neurochemicals along with neuropeptides. This dissertation investigates the development and optimization of voltammetric methods with CFMEs for improved detection of neurotransmitters including dopamine, serotonin as well as neuropeptide Y (NPY) and glutamate. Due to the challenges encountered in measurement of NPY using conventional waveforms, under in vitro conditions and in vivo conditions, this study employed modified sawhorse waveform (MSW) in combination with fastscan cyclic voltammetry (FSCV) techniques to enhance selectivity and improve signal resolution levels. This technique enabled co-detection of NPY with other catechols such as dopamine, and serotonin. Additionally, glutamate is not electroactive hence making it difficult to measure using conventional electrodes. As a result, we employed enzyme-modified CFME that incorporated glutamate oxidase coated with chitosan. The production of hydrogen peroxide allowed effective measurement of glutamate as well as selective detection among other neurotransmitters such as dopamine and other neurotransmitters. Further, glutamate was detected among other neurotransmitters including dopamine and norepinephrine establishing high selectivity of this technique. The practical aspects of the methods employed were tested in vitro using biological samples. Here we established that NPY could be detected in urine with a sensitivity of 5.8 ± 0.94 nA/μM (n = 5) while glutamate could be detected in both urine and food samples with high selectivity. This study presents combined detection techniques that distinguish between chemically similar neuropeptides and monoamine neurotransmitters which enable distinguishing them in complicated biological settings such as urine. These clinical applications extend to neurological condition diagnosis solutions and therapeutic tracking procedures with specific benefits for Parkinson’s disease and epilepsy as well as depression assessment. Neurotransmitter observation methods that operate at less than one second intervals provide researchers with new opportunities to explore the links between brain operation and actions. The work provides foundational knowledge to develop electrochemical sensors in future through nanomaterial and natural intelligence analysis strategies despite present issues with electrode fouling and interference in signal detection by background noise. The present dissertation promotes CFMEbased sensing technology advancement while supporting its capacity to improve neurochemical analysis applications and enable personalized medicine practices.6 0Item Restricted Oil Price Volatility Vs. Sustainble Investmnet Impact on Global Dividends(University of New Orleans, 2024) AlQayidi, Abeer; Kabir, HassanSustainability has become a central concern for businesses and investors worldwide, yet obstacles arise when investors' perceptions change, and regulatory policies hinder businesses from committing to Environmental, Social, and Governance (ESG). This study examines the impact of (ESG) performance on dividend policy across six major sectors—Financial, Industrial, Technology, Healthcare, Basic Materials, and Utilities—in fourteen countries across the Americas, Europe, and Asia (USA, Canada, Brazil, Mexico, Chile, Turkey, India, Japan, China, UK, Germany, Italy, France, and South Korea) from 2010 to 2022. We explore the relationship between ESG scores and dividend policy utilizing a comprehensive dataset from publicly traded companies. We focus on three key dividend measures: dividend per share, dividend payout ratio, and dividend growth. We assess the differential impact of overall ESG performance and individual ESG pillars (Environmental, Social, and Governance) on firms of varying sizes, small, medium, and large— within each sector. Robust econometric techniques such as Two-Stage Least Squares (2SLS), Generalized Method of Moments (GMM), and Difference-in-Differences (DID) models are employed to address potential endogeneity issues and validate findings during the economic shock of COVID-19. Our results consistently show that ESG performance positively influences dividend policies; however, the effects vary by sector and firm size. Generally, medium and large firms benefit the most. This study offers detailed information about how the ESG score affects dividend policy across diverse sectors globally. It provides insightful analyses for managers, investors, and legislators who want to comprehend how sustainable investments affect business financial choices22 0Item Restricted Mechanics of 3D Printed Multi-material Metamaterials with Cooperative Components(Northeastern University, 2024) Batwa, Ammar; Li, YaningConventional mechanical metamaterials often exhibit unique mechanical properties arising from their geometric arrangement. Metamaterials with cooperative components are deliberately designed with a focus on the interaction between individual elements, where the properties of these elements are engineered to work together synergistically to achieve targeted behaviors and properties. This dissertation aims to design and explore the mechanics of mechanical metamaterials with cooperative components. These components are designed to have specific shapes, sizes, and material properties to enable unusual mechanical properties including negative Poisson's ratio, programmable deformation, and significantly enhanced toughness. In Chapter 2, the fundamental mechanics of two-phase laminae fabricated via multi-material polymer jetting is investigated. The influences of printing direction, layer thickness, and material mixing at dissimilar material interfaces on the overall mechanical properties of 3D-printed laminae are systematically analyzed. In Chapter 3, bio-inspired two-phase auxetic chevron composites are designed. By cooperatively tuning two levels of laminae with different principal directions, the effective Poisson’s ratio is shown to be tunable across a wide range, from positive to negative. Unlike cellular auxetic materials, these new designs eliminate voids and pores, achieving auxetic behavior without sacrificing stiffness. Furthermore, the designs demonstrate significant potential for resisting impact, enhancing mechanical stability, and efficiently reducing thermal stresses. In Chapter 4, the static and dynamic mechanical responses of 3D auxetic laminates are investigated. Using Classical Laminate Theory (CLT) and finite element simulations, the interplay of fiber orientations, phase stiffness, and impact dynamics is explored. Experiments validate the auxetic laminates’ ability to dissipate energy efficiently and reduce damage under impact. In Chapter 5, a novel class of three-dimensional (3D) auxetic chevron-patterned composites is introduced, designed to exhibit negative Poisson’s ratios in two orthogonal planes under uniaxial compression. Comparative mechanical testing demonstrates that the auxetic designs significantly outperform non-auxetic and unidirectional counterparts in energy absorption, achieving a 4–6-fold improvement due to effective load redistribution and bending-dominated deformation mechanisms. In Chapter 6, the role of fiber waviness in enhancing the toughness of polymer composites is investigated through sacrificial bonding and hidden length mechanisms inspired by biological materials. Utilizing multi-material 3D printing, composites with varying waviness levels are fabricated and tested, demonstrating improved energy absorption, strain hardening, and resilience to strain-rate effects while preserving stiffness.11 0Item Restricted A PROPOSED METHOD FOR THE SITE SELECTION OF SPACEPORTS(Purdue University, 2025) Alkhaleefah, Ali; Marais, KarenSpaceport site selection often overlooks broad geographic regions, which can lead to locations that increase risks or costs. To address this issue, this research proposes a comprehensive, three-step method for identifying spaceport sites that balances safety, environmental concerns, cost, and operational needs. First, the Factor Selection System (FSS) recommends the essential location criteria (factors and constraints), such as low population density, proximity to the workforce, environmental constraints, and proximity to transportation. It divides them into “factors” (which vary in importance) and “constraints” (which must be avoided, for instance, legally protected zones). Second, the Analytic Hierarchy Process (AHP) compares these factors, determining whether items like utility access or workforce availability carry greater weight. This pairwise comparison helps stakeholders clarify trade-offs and assign weights based on each mission’s goals. Third, Geographic Information Systems (GIS) overlay the weighted factors on large-scale maps, excluding areas flagged by constraints (e.g., restricted airspace or no-build zones). By scanning entire regions, this method can reveal new, sometimes better, alternatives that conventional, preselected approaches might miss. Three case studies illustrate the method. The first confirms that Spaceport America in New Mexico meets the criteria, has a sparse population, suitable flight paths, and adequate safety buffers, and identifies other more suitable areas. The second compares Launch Site One (for suborbital) and the third Starbase (for orbital) in Texas, showing how varying factor weights can shift the most suitable regions for different mission profiles. Then, we apply the method in Saudi Arabia to identify potential orbital and suborbital sites across multiple parts of the country. A scenario-based sensitivity analysis then adjusts factor weights, workforce availability, infrastructure, or cost priorities by fixed increments to see how suitability scores change. Although these adjustments alter some site rankings, workforce availability, transportation infrastructure, and utility access consistently emerge as major drivers of feasibility. This step-by-step method helps commercial firms, government agencies, and research institutions align spatial requirements with legal mandates, environmental protections, and evolving mission needs. While additional high-resolution data, such as detailed environmental or demographic layers, can refine results, the framework remains robust and adaptable for diverse applications. Looking ahead, future work can integrate reusable launch vehicles, point-to-point travel, and new launch trajectories, further improving site selection for the growing commercial space industry.7 0Item Restricted OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES(Florida Atlantic University, 2025-03-11) Al Hanif, Abdulelah; Ilyas, MohammadThe rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic.23 0