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 Optimal Control Problems with Linear and Non-linear Damped Viscous Wave Equations(THE UNIVERSITY OF TEXAS AT ARLINGTON, 2025-05) Abu Qarnayn, Naif; Souvik, RoyIn this thesis, we focus on the analysis and numerical solution of nonsmooth optimal control problems governed by a class of linear and nonlinear Damped viscous wave equations with both linear and nonlinear source mechanisms. These equations play a crucial role in modeling wave propagation in complex media, with significant applications in medical imaging and therapeutic interventions. Using advanced numerical techniques, we explore the complex interplay between damping, viscosity, and control strategies to enhance precision in control problems related to wave-like equations. The work provides valuable frameworks into optimizing wave dynamics, leading to improved methodologies in fields such as photoacoustic imaging, lithotripsy, and tissue elastography.12 0Item Restricted C-Band Air to Ground Communication System for UAV(Florida Institute of Technology, 2025) Alghamdi, Mubark; Kostanic, IvicaC band spectrum 5030-5091 MHz is allocated for command-and-control commu- nication services with unmanned aircraft systems. This document evaluates the possibility of using 3GPP 5G standards for provisioning of such services. Chan- nelization of the spectrum and major system parameters are proposed. The performance of the proposed system in channel fading environment is evaluated using MATLAB based simulations. The evaluations examine SINR, and aver- age throughput of the proposed system at different altitudes of the unmanned aircraft system. MATLAB simulations are used to evaluate system performance in free-space environments. Signal-to-Interference-plus-Noise Ratio (SINR) and Reference Signal Received Power (RSRP) are predicted at different UAS heights. The performance of the system is accessed for different types of antenna arrays on the aircraft. Cases ranging from a single antenna to a 10 by 10 array are con- sidered. The study uses a 20 MHz channel and considers a system load of 50 percent.24 0Item Restricted DISCOVERY, GENOMIC DIVERSITY, AND PATHOGENICITY OF THE BACTERIAL LEAF STREAK CAUSING PANTOEA ANANATIS AND PANTOEA AGGLOMERANS IN WHEAT(South Dakota State University, 2025) alhusays, ahmed; Ameen, GazalaThis study investigates the genomic characteristics, virulence determinants, and detection methodologies for Pantoea ananatis and Pantoea agglomerans isolates recovered from wheat in South Dakota, USA. These bacteria, recently been linked to wheat infections causing bacterial leaf streak (BLS). Using whole-genome sequencing and comparisons between strains, important genes related to disease especially Type III and Type VI secretion systems were found in the virulence strains. One strain, P. agglomerans SD105, was able to infect both wheat and maize, while another, SD119, affected only wheat. In addition, new qPCR primers were designed and tested to help detect these bacteria more easily in plants. This is the first study to provide comprehensive genomic and pathogenicity evidence confirming Pantoea species as wheat pathogens in the United States.. It also helps understand how these bacteria evolve and spread to different crops, and offers tools for better disease detection.12 0Item Restricted GRAPH-BASED APPROACH: BRIDGING INSIGHTS FROM STRUCTURED AND UNSTRUCTURED DATA(Temple University, 2025) Aljurbua, Rafaa; Obradovic, ZoranGraph-based methodologies provide powerful tools for uncovering intricate relationships and patterns in complex data, enabling the integration of structured and unstructured information for insightful decision-making across diverse domains. Our research focuses on constructing graphs from structured and unstructured data, demonstrating their applications in healthcare and power systems. In healthcare, we examine how social networks influence the attitudes of hemodialysis patients toward kidney transplantation. Using a network-based approach, we investigate how social networks within hemodialysis clinics affect patients' attitudes, contributing to a growing understanding of this dynamic. Our findings emphasize that social networks improve the performance of machine learning models, highlighting the importance of social interactions in clinical settings (Aljurbua et al., 2022). We further introduce Node2VecFuseClassifier, a graph-based model that combines patient interactions with patient characteristics. By comparing problem representations that focus on sociodemographics versus social interactions, we demonstrate that incorporating patient-to-patient and patient-to-staff interactions results in more accurate predictions. This multi-modal analysis, which merges patient experiences with staff expertise, underscores the role of social networks in influencing attitudes toward transplantation (Aljurbua et al., 2024b). In power systems, we explore the impact of severe weather events that lead to power outages, specifically focusing on predicting weather-induced outages three hours in advance at the county level in the Pacific Northwest of the United States. By utilizing a multi-model multiplex network that integrates data from multiple sources including weather, transmission lines, lightning, vegetation, and social media posts from two leading platforms (Twitter and Reddit), we show how multiplex networks offer valuable insights for predicting power outages. This integration of diverse data sources and network-based modeling emphasizes the importance of leveraging multiple perspectives to enhance the understanding and prediction of power disruptions (Aljurbua et al., 2023). We further present HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. The multiplex network layers of this framework gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. By incorporating multiplex network layers consisting of data collected over time and across regions, we demonstrate that HMN-RTS significantly improves the accuracy of predicting the duration of weather-related outages. This framework enables grid operators to make more reliable predictions up to 6 hours in advance, supporting early risk assessment and proactive mitigation (Aljurbua et al., 2024a, 2025a). Additionally, we introduce SMN-WVF, a spatiotemporal multiplex network designed to predict the duration of power outages in distribution grids. By integrating network-based approach and multi-modal data across space and time, SMN-WVF offers a novel method for predicting disruption durations in distribution grids, enhancing decision-making and mitigation efforts while highlighting the critical role of network-based approaches in forecasting (Aljurbua et al., 2025b). Overall, our research showcases the potential of graph-based models in tackling complex challenges in both power systems and healthcare. By combining the network-based approach with multi-modal data, we present innovative solutions for predicting power outages and understanding patient attitudes.12 0Item Restricted EXPLORING THE INFLUENCE OF SOCIAL MEDIA COMMUNICATION ON DESTINATION BRANDING: A STUDY OF SAUDI ARABIA(University of South Alabama, 2025) Alshehri, Abdullah; Hair, Joseph FTourists now find social media important in forming their perceptions and decision criteria when traveling. This Research investigates the impact of destination marketing organizations -generated content and tourist-generated content on tourists' inspiration to visit Saudi Arabia, emphasizing the mediating role of destination image dimensions (cognitive, affective, and sensory) and the moderating role of cultural distance. Based on the Elaboration Likelihood Model, Hofstede Cultural Dimension Theory; the research analyzed a dataset of 237 responses using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that destination marketing organizations -generated content and tourist-generated content significantly enhance perceived advertising value, which in turn influences cognitive image and affective image. Furthermore, both cognitive image and affective images significantly shape sensory image, which strongly predicts travel inspiration. Additionally, cultural distance moderates the relationship between sensory image and travel inspiration, indicating that tourists from culturally distant regions may require stronger sensory stimuli to feel inspired to visit. These findings provide theoretical contributions by integrating sensory image as a critical mediating construct and highlighting cultural adaptation in tourism marketing. From a practical standpoint, the research can guide destination marketers and policymakers in Saudi Arabia on how to design social media strategies to maximize engagement and travel inspiration. Future research should explore the role of emerging digital technologies such as augmented reality (AR) technology and artificial intelligence in shaping destination image perceptions. Keywords: Social media marketing, destination image, cultural distance, tourist-generated content, travel inspiration, PLS-SEM.11 0Item Embargo Comprehensive treatment of strut-and-tie approach across concrete deep beams reinforced with different systems(Kansas State University, 2025) Alqarni, Ali H; Hayder A. RasheedThis dissertation investigates the nonlinear behavior of reinforced concrete deep beams, combining experimental and analytical methodologies to assess the influence of material and geometric variables. The study introduces a matrix truss analysis method for predicting load-deflection behavior at critical stages for different reinforcement materials: conventional steel bars and Glass Fiber Reinforced Polymer (GFRP) bars. The method employs the strut and tie model to enhance the accuracy of nodal displacements and load predictions without postulating the nonlinear strain profile. Further, an experimental evaluation explores the effects of concrete strength and steel reinforcement ratios under monotonic loading, highlighting the impact of shear span-to-depth ratios on beam performance. Finally, a parametric analysis employing the strut and tie method, applied to steel-reinforced concrete deep beams, clarifies the relationships among various structural parameters, revealing strong correlations between the characteristics of deep beams and prediction outcomes. This enhances our understanding of deep beam mechanics and contributes to safer, more effective, and accurate structural designs.13 0Item Restricted Between Learning and Space: Shaping an Architecture School(Arizona State University, 2025) Alrajhi, Mohammad Zaid; Neveu, Marc J; Salama, Ashraf; Robinson, Clare; Horton, PhilipWhile architectural education has long been centered around the design studio, there has been little comprehensive scholarship critically examining the relationship between pedagogical foundations, learning styles, and spatial dynamics. Frank Lloyd Wright (1867–1959) is widely regarded as one of the most influential figures in modern American architecture. His contributions have profoundly shaped the architectural landscape of the United States and beyond. Despite this, the majority of scholarly work has primarily focused on Wright’s architectural achievements, with less attention given to his role as an educator. Unlike conventional architectural schools, Wright’s—The Fellowship at Taliesin West—presents a unique case in that it was deliberately conceived as a site for both education and practice, where the design of the campus, drafting room, and student shelters was directly linked to pedagogical objectives. This dissertation explores the relationship between spaces of learning, specifically the studio, through a case study of Taliesin West. Its original contribution lies in its focus on the spatial conditions of Wright’s teaching and the learning styles toward which that teaching was directed. The research relies heavily on archival materials from the Avery Library. The study suggests that Wright’s teaching model—emphasizing the human dimension in learning—anticipated many of today’s educational goals, offering valuable insights for rethinking architectural pedagogy. Limitations of the study include its specific focus on Wright’s pedagogical model and the consistently high regard expressed by his apprentices, which may have constrained opportunities for critical distance.15 0Item Restricted Towards an Intelligent Speculative Software-Defined Networking(University of Central Florida, 2025) Hariri, Ahmad; Yuksel, MuratSoftware-Defined Networking (SDN) separates the control and data planes, allowing better programmability of the control plane to predict, route, and schedule traffic at the data plane. As a more flexible approach, Reactive SDN installs the right flow rule dynamically when a new flow arrives. This helps respond to application dynamics, making Reactive SDN a strong candidate for low-latency applications. Low-latency applications like online gaming and AR/VR have become very popular these days. However, they require millisecond-level response times for an acceptable quality of experience. A limitation of Reactive SDN is that it necessitates a miss upon the arrival of a new flow, causing a Packet-in message to be sent from the switch to the SDN controller, increasing overall delay. To meet millisecond-level delays, reducing the miss rate by predicting the arrival of flows and pre-installing the necessary flow rules dynamically is necessary. Reinforcement Learning (RL) is an approach where agents interact with an environment by making actions to receive rewards to accomplish tasks like forecasting. RL has potential for predicting flow arrivals, where agents can predict the arrival of flows that may not have been seen before. We show that the RL agents can learn and speculatively install the unseen flow rules to avoid latency from reactive installations. We propose an SDN design called ‘speculative’ to overcome Reactive SDN limitations for applications requiring fast responses. The contributions of our work are: 1) We present a Speculative SDN framework that incorporates RL to predict and install never-seen-before flows, reducing control latency. 2) We design a reward function to improve the RL agents’ prediction accuracy for the best set of flows to install. 3) We develop a priority policy when selecting a flow for removal. 4) We use spatial locality information to assist agents when speculating unseen flows. 5) We evaluate the framework using real traffic traces across various metrics.13 0Item Restricted International Law Institutions and the Control of Multinational Corporate Economic Violations: the Role of Fianancial and Arbitral Mechanism.(Indiana University Bloomington, 2025) Almrshed, Ziad; Ochoa, Christianan; Hughes, Sarah JaneAbstract Some investors, including multinational corporations (MNCs), engage in economic crimes and illegal activities without facing accountability, often due to inadequate oversight. These investors wield significant power and operate across multiple jurisdictions, requiring collective efforts from international institutions, states, and private actors to address these challenges. While oversight and control mechanisms exist, some remain underutilized. This study examines the role of international law in addressing investors’ economic misconduct through financial and dispute settlement mechanisms. It explores whether international financial institutions (IFIs), such as the International Finance Corporation (IFC), and dispute resolution centers, particularly the International Center for Settlement of Investment Disputes (ICSID), can effectively regulate and respond to such activities. The study considers the strengths of IFIs, including insurance and guarantee organizations, in curbing illegal financial practices through preventive measures, such as contractual power, and assesses the role of arbitration institutions and arbitrators in resolving disputes and deterring future violations. Oversight remains a key focus at the financing stage, whereas arbitration primarily involves greater control at a later stage, emphasizing the complementary roles of these mechanisms. By examining the potential of the IFC and ICSID, this research highlights their respective capacities as leading institutions within the World Bank framework. While the study focuses on these institutions due to their prominence, its analysis may be applicable to other international financial and arbitration mechanisms addressing similar challenges.14 0Item Restricted ADVANCING TEMPORAL SAFETY PERFORMANCE FUNCTIONS: A COMPREHENSIVE EVALUATION OF EXPRESS LANES, RAMPS, AND RAMP METERING EFFECTS ON FREEWAY SAFETY(The University of Central Florida, 2025) Faden, Abdulrahman; Abdel-Aty, MohamedFreeway safety remains a critical concern, especially in high-risk areas such as ramps, merges, and managed lane segments, where complex traffic interactions significantly elevate crash risks. This dissertation advances crash frequency prediction by developing short-term Safety Performance Functions (SPFs) that address the limitations of traditional long-term SPFs models and real-time safety analysis. By leveraging high-resolution microscopic traffic detector data from multiple states, the dissertation introduces innovative methodologies and delivers actionable insights into freeway safety dynamics. The dissertation pioneers the application of Multivariate Poisson-Lognormal (MVPLN) models to identify interdependencies between crashes at ramp and merge segments. To address challenges like data skewness and excessive zeros, advanced Bayesian frameworks, including the Negative Binomial Lindley (NB-L) and Poisson-Lognormal Lindley (PLN-L) models, are proposed. Additionally, a novel copula-based framework uncovers intricate safety relationships between Express Lanes (ELs) and General-Purpose Lanes (GPLs), offering new perspectives on inter-lane safety, particularly at critical access points. The key findings of the dissertation emphasize the significant role of traffic exposure, geometric configuration, and operational strategies in shaping crash risks. For instance, managed lanes, such as the I-4 Ultimate Express Lanes, exhibit unique safety patterns, with elevated crash risk associated with higher rightmost lane occupancy near merge areas. Ramp metering (RM) is demonstrated to effectively reduce crashes, particularly in weaving areas, with safety impacts varying based on control strategies and segment types. This dissertation delivers a robust framework for crash prediction and safety assessment, blending methodological advances with practical insights. Its contributions lay the groundwork for safer freeway designs, optimized Active Traffic Management (ATM) strategies, and enhanced safety practices. By bridging the gap between theoretical research and real-world applications, this dissertation equips policymakers, engineers, and researchers with the tools needed to improve freeway safety.5 0