SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
Browse
12513 results
Search Results
Item Restricted The Effectiveness of The Hague Convention in Addressing International Child Abduction(Saudi Digital Library, 2025) Althubiti, Areej Dakelallah A; Grossman, Joanna LParental child abduction is a growing concern fueled by immigration, cross-cultural marriages, evolving family dynamics, and gaps in the implementation and enforcement of developed international frameworks. This study examines the effectiveness of and challenges to the enforcement of the Hague Convention on the Civil Aspects of International Child Abduction (HCCA), which was developed to address this problem. Specifically, the goal of this research is to assess the effect the HCCA has had on parental kidnapping, analyze its effectiveness in resolving international custody disputes, and identify the legal and practical obstacles that inhibit implementation. While the prevalence of parental kidnapping continues to increase, this study’s significance emanates from its illumination of inconsistencies in the HCCA’s enforcement and its recommendations for reforms. The study employs a qualitative research approach that analyzes cases from 2007-2016, legal texts, and scholarly literature to evaluate how cross-border parental kidnapping cases are handled across jurisdictions. This analysis allowed the researcher to explore Shariah law perspectives on parental abduction to understand why most Islamic states are reluctant to ratify the Convention. The findings reveal that while the HCCA has streamlined the legal process for returning abducted children, enforcement challenges remain due to non-compliance by member states, conflicts between international and domestic laws, and cultural and religious barriers. Other challenges that member states must address to make the treaty more effective include discrepancies in legal definitions, procedural delays, and lack of enforcement mechanisms in non-member states. The research also highlights the social, financial, and psychological impact on children and left-behind parents, showing the need for reforms to make the convention more effective. Thus, this study contributes to international legal discourse by highlighting practical, theoretical, and social implications that must be addressed to improve the HCCA and mitigate vi parental kidnapping. While practically it advocates for stronger international enforcement mechanisms and legal reforms to enhance the Hague Convention’s effectiveness, theoretically, it contributes to discussions on the intersection of international law, human rights, and child welfare. Socially, the study illuminates the adverse psychological and emotional consequences and advocates for policy changes that prioritize the well-being of children across national borders. Thus, this study is important because it illuminates challenges hindering the effective implementation of international frameworks aimed at curbing parental kidnapping and advocates for stronger legal reforms to enhance their effectiveness.14 0Item Restricted INVESTIGATING NOVEL ANALYSIS APPROACHES FOR STRUCTURAL CONDITION ASSESSMENT USING ULTRASOUND AND INFRARED DATA(Saudi Digital Library, 2025) Alqurashi, Inad; Catbas, NecatiAging civil infrastructure, particularly reinforced concrete bridges, is experiencing progressive deterioration that threatens safety, serviceability, and long-term performance. Traditional inspection methods such as visual examination and hammer sounding are limited in their ability to detect subsurface defects and are prone to subjectivity. This dissertation develops and validates an integrated, multi-modal structural condition assessment framework that combines rapid Infrared Thermography (IRT), high-resolution Ultrasound Tomography (UT), Artificial Intelligence (AI)-driven anomaly detection, and immersive Digital Twin (DT) visualization to overcome these limitations. The research advances three main areas: (1) a dual-mode IR–UT workflow exploiting the complementary strengths of each modality, enabling rapid surface screening with IRT and in-depth defect characterization with UT; (2) optimized deep learning (DL) models tailored to each modality, with a transformer-based Grounding DINO model applied to raw Infrared (IR) imagery for automated detection of thermal anomalies, and a lightweight You Only Look Once (YOLO)-v8n model applied to UT volumetric slices for detecting internal delaminations, voids, ducts, and rebar, both trained on large, segmentation-assisted, color-standardized datasets to ensure robust performance under diverse field conditions; and (3) integration of Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR), photogrammetry, and multi-modal non-destructive testing (NDT) data into a geo-referenced Virtual Reality (VR) environment to support real-time, collaborative decision-making. Laboratory testing on engineered specimens with embedded defects and field deployment on multiple in-service bridges, including the NASA Causeway Bridge, achieved high detection accuracy (mAP@0.5 up to 0.93 for UT using YOLOv8n and 0.80 for IRT using Grounding DINO), strong localization (Average IoU ≈ 0.80–0.90), and significant efficiency gains through targeted UT scanning. The VR-based DT enabled inspectors to seamlessly review thermal anomalies, volumetric UT slices, and 3D geometry in a single immersive scene, reducing defect confirmation time from several minutes to approximately one minute per location. By fusing complementary NDT modalities with AI models purpose-built for each data type and immersive visualization, this research delivers a scalable, repeatable, and field-validated methodology for rapid, objective, and data-rich condition assessment of reinforced concrete structures, with potential for broader application to other infrastructure types to enable proactive maintenance strategies and improved lifecycle management.8 0Item Restricted Overcoming Platinum Resistance in Ovarian Cancer Cells by Epigenetic Reprogramming Strategies(Saudi Digital Library, 2025) Alssamani, Fatimah; Rathinavelu, AppuOvarian cancer causes over 200,000 deaths annually, with platinum resistance limiting five-year survival to less than 30%. Chemoresistance emerges through complex molecular mechanisms, including aberrant epigenetic regulation via DNA methylation and histone modifications. Elevated epigenetic regulators (DNMT1, EZH2, etc.) in resistant cells silence pro-apoptotic genes and enhance DNA repair capacity. This dissertation research investigated the use of RG-7388, an MDM2 inhibitor, and CM-272, a dual G9a/DNMT inhibitor, as a strategy to overcome platinum resistance through comprehensive epigenetic reprogramming. Cisplatin-resistant cell lines (A2780Cis, SKOV3) and sensitive A2780 cells were characterized for epigenetic profiles and CM-272 sensitivity using cytotoxicity assays, Western blotting, and RT² Profiler PCR arrays. Pathway enrichment analysis elucidated mechanisms of action. CM-272 demonstrated superior efficacy over Cisplatin and RG-7388 in resistant cells, with baseline DNMT1 expression inversely correlating with therapeutic sensitivity, establishing it as a predictive biomarker. In addition, CM-272 induced comprehensive transcriptional reprogramming affecting multiple resistance mechanisms. Pathway analysis revealed significant enrichment in DNA replication, cell cycle regulation, p53 signaling, and platinum resistance pathways.11 0Item Restricted ESSAYS ON ENERGY AND ENVIRONMENTAL ECONOMICS(Saudi Digital Library, 2025) Albijadi, Abdullah; Alberini, AnnaThis dissertation examines the design and impacts of clean-energy and transportation pricing policies, exploring how rebates, taxes, and exemptions influence technology adoption, economic efficiency, and environmental outcomes. Across detailed empirical analyses in both the U.S. and Norway, the research identifies key behavioral responses to policy interventions, revealing circumstances under which incentives significantly accelerate adoption but may also generate large unintended fiscal and environmental costs. Chapter 1 assesses the impact of policy incentives on the adoption of residential energy storage systems, focusing on California's Self-Generation Incentive Program (SGIP) from 2017 to 2022. I find that upfront rebates significantly influence the installation of residential energy storage systems (ESS). Using variation in rebate rates over time between electric utility companies and controlling for other factors that affect adoption, I find that a $0.05/Wh decrease in rebate rates-the typical step change in this policy-decreases the adoption of ESS by 15.2%. In general, I estimate that installations would have been 42 percent lower without the incentives. Based on my model's estimates, from the total of $177 million in incentives distributed, $74 million was allocated to people who likely would have installed them regardless of the program's existence. Chapter 2 studies how Norway's one-time registration tax on new vehicles affects car sales and vehicle type choice between 2013 and 2022. Using detailed vehicle registration micro-data combined with changes in tax schedules across time and models, I estimate an elasticity of approximately −0.6 for new car sales with respect to the registration tax burden. Counterfactual simulations show that removing existing electric vehicle (EV) exemptions in the import tax would reduce EV sales by about 15%, while eliminating import taxes for all vehicles would lower EV adoption by roughly 21%. By contrast, a Pigouvian tax system that targets each vehicle type's external damages reduces EV adoption by only around 10%. Decomposing the tax schedule reveals that CO2 and engine-power components have strong and precisely estimated effects on purchase decisions, whereas weight and NOx taxes play a more limited role. Tax responsiveness also varies across emission bands, vehicle classes, body types, and fuel types, with lower-emission and smaller vehicles reacting more strongly to tax changes. An externality-based tax scheme that more directly prices environmental damages generates greater government revenue while preserving much of the EV uptake. Overall, the results highlight the quantitative trade-offs policymakers face between fiscal and environmental objectives and indicate that well-targeted fiscal instruments can effectively steer the vehicle fleet toward cleaner technologies. Chapter 3 examines how Norway's vehicle registration tax shapes the used-car market between 2013 and 2022. I focus on the domestic resales of used vehicles rather than imported used vehicles since they constitute 95% of the used car market in the study period. I estimate the effect of this tax on used-car sales, taking advantage of variation in the tax between models, ages, and years. Across baseline specifications, a one thousand NOK increase in the transfer fee is associated with a 10-14% reduction in sales, implying semi-elasticities of about −0.4 to −0.6 with respect to the tax. Heterogeneity analyses show that responsiveness is strongest for mid-range vehicles in the 71-160 g/km emission bands whereas sales in the very low and very high tax segments are less sensitive. A counterfactual experiment that halves the registration tax suggests that actual sales are only 80-90% of the levels predicted under the lower fee, so that current taxes discourage roughly 10-20% of potential trades. The same exercise implies that government revenue would fall by about 35%, from 18.9 to 12.3 billion NOK over 2013-2022, because the mechanical revenue loss on infra-marginal trades dominates the modest revenue gain from additional transactions. Taken together, the results show that this tax is an effective but costly instrument for influencing used-car market activity and highlight a quantitative trade-off between fiscal revenue, market liquidity, and mobility when reforming vehicle registration taxes.14 0Item Restricted Applicability of Jarzynski’s Equality in a Two-Dimensional Lattice Gas System: Numerical Simulation and Thermodynamic Analysis(Saudi Digital Library, 2026) Alqarni, Eidah Awdah; Pérez-Cárdenas, Fernando CThis thesis investigates the Jarzynski Equality in microscopic systems driven far from equilibrium states, based on key idea in stochastic thermodynamics. The study explores how fluctuations influence energy transfer, change in free energy, and irreversibility during finite-time transformations. To do this, we introduced a two dimensional piston lattice gas model, where interacting particles in thermal contact are confined by immobile walls and manipulated via a movable piston. The movement of the piston drives the system out of equilibrium, and simulations combining stochastic particle dynamics with deterministic piston behaviour under Markovian assumptions, establish a comprehensive framework for probing both equilibrium and nonequilibrium phenomena. Simulations were carried out across a wide range of system sizes (N = 5, 10, 20 & 30) and piston velocities (v = 0.05 to 1). For each scenario, ensemble averages of energy, work, and heat were computed, and the Jarzynski Equality was applied to obtain equilibrium free-energy differences from the nonequilibrium measurements. At slow piston speeds, the system remains close to equilibrium, and the Jarzynski Equality provides a description of the reversible free energy changes. At high speeds, higher the average work exceeds the free energy difference, indicating strong dissipation and irreversibility. Analysis of work distributions shows systematic broadening and asymmetry with increasing piston velocity and system size. These distributions reveal that, though average trajectories yield works values well above the free energy difference, rare low-work events are required to satisfy the exponential average stipulated by Jarzynski's relation. The limited occurrence of these trajectories under higher drive rates is responsible for the deviation of observed results from the equality of strongly nonequilibrium regimes. It is demonstrated that Jarzynski's Equality remains applicable for finite systems with appropriate sampling of the full statistical ensemble, including the complete set of rare fluctuations. The study also shows how driving speed, system size, and relaxation dynamics control entropy production and the crossover of dynamics from reversible to irreversible behaviour. In summary, this work connects microscopic stochastic dynamics with the macroscopic laws of thermodynamics. The piston lattice gas model developed here a computational framework to study nonequilibrium thermodynamics, fluctuation theorems, and the statistical basis of the second law. The findings confirm of recovering equilibrium properties from ensemble averaged microscopic behaviours even under nonequilibrium conditions and highlight the broad significance of Jarzynski's Equality in modern statistical physics.5 0Item Restricted Investigating the Factors that Affect the Adoption of Cybersecurity Data Visualization Applications Within Organizational Context: An Application of the T-O-E Framework(Saudi Digital Library, 2025) Aljasir, Afnan; Chinazunwa, UwaomaCybersecurity visualization (VizSec) tools have emerged as critical enablers for organizations to detect, interpret, and respond to increasingly complex cyber threats. Despite their potential, the adoption and effective use of these tools remain inconsistent across industries. This dissertation examines the determinants of VizSec adoption through the application of the Technology-Organization-Environment (TOE) framework; and the effect of its adoption on organizational performance thereafter. Mixed-method approach was used in this study to provide an in-depth understanding of quantitative and qualitative results. During the quantitative step, a survey of 230 cybersecurity professionals and decision-makers in various industries was used to gather data and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The qualitative stage was based on 14 semi-structured interviews, conducted with the help of the six-phase thematic analysis of Braun and Clarke, in order to render the lived experiences and the practical knowledge of the participants. The results show that the most powerful drivers of adoption are technological determinants, especially ease of use, lesser complexity, and compatibility with the already existing infrastructure. Influencing factors include organizational aspects, comprising of top management support, financial and human resources, as well as the organizational ability to learn, without which the value of VizSec is constrained due to the lack of skilled professionals. Environmental factors were considered key determinants, whereas competitive pressure had a small influence. Notably, the research proved the mediating effects of Security Data Visualization (SDV) between factors of the TOE and organizational performance. Adoption of VizSec was found to have a high level of customer satisfaction, financial performance, innovation and agility within the organization. Theoretically, this research contributes by generalizing the use of the TOE framework in the space of cybersecurity visualization and introduces SDV as a mediating construct to redefine organizational and environmental variables in this sense. In practice, the study provides a roadmap on how organisations can get the best out of VizSec through strategic investments, enhancing compliance, developing skilled human capital, and establishing vendor relationships.7 0Item Restricted DYNAMIC REFINEMENT OF SOCKPUPPET DETECTION MODELS WITH HUMAN-IN-THE-LOOP PROCESSES GUIDED BY MACHINE LEARNING ENGINEERING RULES(Saudi Digital Library, 2025) Baamer, Rafeef Abdullah B; Boicu, MihaiIn recent years, people have increasingly relied on Online Social Networks (OSNs) for various aspects of their daily lives, including communication, information sharing, and entertainment. Although these platforms provide many benefits, their massive and continuous use has also caused negative behaviors and malicious activities. One of the most critical challenges is the growing presence of malicious accounts that undermine the trustworthiness and integrity of online interactions and communication. Such accounts include personal spammers, impersonators, and cyborgs. However, one of the most harmful and complex types is the sockpuppet account. Sockpuppet accounts refer to accounts created by an individual or a coordinated group for deceptive or manipulative purposes, such as spreading misinformation or promoting specific agendas. The term encompasses several subtypes, which are impersonation sockpuppets, fake-profile sockpuppets, promotional or antagonistic sockpuppets, misinformation sockpuppets, troll sockpuppets, and spam sockpuppets. These accounts negatively affect OSNs in multiple ways: they reduce the authenticity and integrity of online communication, degrade information quality by disseminating false or biased content, manipulate public opinion by supporting certain agendas or campaigns, and contribute to community disruption and toxicity through hate speech or coordinated harassment. While prior studies have achieved promising results in sockpuppet account detection, several limitations and research gaps remain. First, most existing approaches focus on identifying a specific type of sockpuppet account—such as spammers or fake reviewers—which limits the generalizability of their models. Second, only a few studies have explored or implemented hybrid detection techniques, as most rely on a single methodological approach. Third, many models are tested on a single platform or dataset, which restricts their scalability and cross-platform applicability. Moreover, no prior research has proposed a detection model specifically designed for Arabic sockpuppet accounts. Finally, there has been limited involvement of human expertise and underutilization of Human-in-the-Loop (HITL) analysis in refining and validating detection outcomes. To address these limitations, this dissertation presents three major experiments conducted across Wikipedia, Reddit, and X/Twitter platforms, targeting different categories of sockpuppets—general, troll, and spammer accounts. In these three experiments, various detection approaches were employed, including individual machine learning classifiers, ensemble voting, deep learning, and transformer-based (AraBERT) models, to detect and classify sockpuppet accounts across multiple platforms. These models were subsequently integrated into a Human-in-the-Loop analysis framework to enhance their performance through multiple refinement cycles, identifying and applying machine learning engineering rules (MLE), e.g., mixed-initiative feature optimization, data improvement, and hyperparameter tuning of classifiers. The process involved iterative model tuning and evaluation, resulting in the formulation of MLE rules derived from both model insights and human feedback. This research yields several contributions: it developed generalizable hybrid detection techniques that increased the performance in sockpuppet accounts detection (as measured by accuracy, precision, recall, and F-Score); second, it introduced a validation process for sockpuppet datasets combining transformer-based model for posts labeling and Human-in-the-Loop analysis and review which also resulted in the first Arabic labeled sockpuppet accounts dataset, addressing a major linguistic and cultural gap in existing research; third, it established a systematic approach for identifying borderline cases that require human review and translating these insights into model-refinement and MLE rules to enhance overall detection performance and generalizability; finally, it developed a Human-in-the-Loop process for analysts for model development and dynamic refinement that was tested across multiple datasets representing diverse online platforms and different types of sockpuppet accounts.12 0Item Restricted Nonlinear Pattern Formation in Stratified Kolmogorov Flow(Saudi Digital Library, 2026) Alqahtani, Khalid Falah; Gregory, P. Chini; Baole, WenThis thesis explores stratified Kolmogorov flow (SKF), a fundamental model in geophysical fluid dynamics that combines a vertically-varying sinusoidal shear flow with vertical density stratification. This setup gives rise to linear instabilities, exact coherent structures (ECS), chaotic dynamics, and turbulent transport, all of which are crucial for understanding mixing processes in layered environments like oceans and atmospheres. A central theme through out this work is the examination of linear stability, ECS, and chaotic dynamics in stratified shear flows, drawing on tools from advance dynamical systems theory to uncover underlying patterns and behaviors. Chapter 1 outlines the two complementary scalings employed in this thesis: the anisotropic scaling in strongly stratified Kolmogorov flow (SSKF) at order one Prandtl number (Pr) and the isotropic scaling in standard stratified Kolmogorov flow at Pr≪1. These two flow regimes are highlighted as prime examples of forced-dissipative pattern forming systems, viewed through a dynamical systems tools, with direct relevance to oceanic and atmospheric flows. We also provide an overview quasilinear (QL) approximations for both systems, com- paring their performance against the full nonlinear (NL) model to assess how well simplified models capture complex behaviors. The chapter concludes with a comprehensive outline of the thesis structure, laying the groundwork for the subsequent analyses. Chapter 2 discusses the limit of strong stratification, in which a highly anisotropic, layer-like horizontal flow develops with emergent vertical shear sufficiently strong to trigger spatially localized instability regions. We employ tools from modern dynamical systems theory to better understand the spatial structure of these instabilities, their nonlinear saturation, and the diabatic mixing they drive. Specifically, we develop a Newton-Krylov iterative solver to compute ECS, i.e., fully nonlinear, invariant solutions, in the idealized and well-controlled setting of two-dimensional strongly stratified Kolmogorov flow. Unlike prior related studies, We investigate the physically relevant regime of small Froude number (Fr; strong stratification) and large buoyancy Reynolds number (Reb). We explore the dependence of the numerically computed ECS on these parameters, and compare our results with DNS. Chapter 3 examines SKF under low-P´eclet-number (LPN) conditions, where thermal diffusion dominates convective heat transport, a scenario pertinent in astrophysical settings including stellar interiors. This regime influences instability thresholds, pattern emergence, and chaotic dynamics by damping buoyancy effects. We adapt the Newton-Krylov solver described in Chapter 2 to search for ECS, comparing results between the standard governing equations and the LPN-reduced equations. Unlike prior related studies, we focus on practical parameter spaces with large Reynolds numbers (Re), small Richardson numbers (Ri), and small P´eclet numbers (Pe), exploring ECS dependencies on these factors and cross-checking with the DNS to highlight diffusion’s role in stability and structure formation. Chapter 4 examines the QL approximation applied to both the strongly stratified Kolmogorov flow at order one Pr and the stratified Kolmogorov flow at Pr ≪1 using the standard equations. In the first part, we compare the QL model against the full NL system for SSKF to evaluate its accuracy in capturing essential dynamics. This involves analyzing the structure of the flow fields, mean velocity and buoyancy profiles, and energy spectra, highlighting where QL successfully approximates self-organized patterns and transport in extreme stratification limits. In the second part, we perform similar comparisons of the SKF using the standard equations, assessing how well QL reproduces field structures, profiles, and spectra under dominant diffusion effects. These analyses reveal the strengths and limitations of QL as a simplified reduced model for exploring instability and mixing in stratified shear flows, particularly in parameter spaces challenging for full NL computations. In Chapter 5, we synthesize insights across stratification strengths and diffusion regimes, blending linear theory, ECS computations, and DNS to illuminate how structure arises in stratified shear flows. The QL method emerges as a powerful means of accessing challenging parameter regimes, with implications for improving geophysical models. Future directions for both systems could include three-dimensional extensions and integrations with observational data to further bridge theory and real-world applications.38 0Item Restricted Evaluation of Powder Spreading Effects on Powder Bed Quality(Saudi Digital Library, 2025) Alshammery, Omer; Taheri Andani, MohsenThis thesis explores the effect of powder spreading on the quality of the powder bed in electron beam powder bed fusion (EB-PBF) additive manufacturing for Ti-6Al-4V alloy. The motivation stems from the pivotal role of deposited layer thickness in the integrity of produced parts, productivity, geometric accuracy, and effective defect avoidance. A build in the Freemelt One system with in-situ electron optical imaging (ELO) in backscattered electron (BSE) detectors were conducted to image pre-and post-melting surface conditions. Data analysis involved STSA contrast correction, calculation of gradients through normalized differences, and global least squares integration towards surface reconstruction. Height map results indicate increasing levels of topographical irregularity with the number of layers, thickness gradient asymmetry resulting from recoater kinematics, and a reduction in average layer thickness from about 108 μm to 50 μm in 18 layers due to cumulative heat impact and solidification-induced sinking ending with consistent layer thickness. Thickness profiles of layers confirm spatial and time-dependent variations with implications for melt pool stability and quality of fusions. The results emphasize the imperative of even spreading of the powder for the attainment of the desired parts quality and indicate ELO to be an effective tool for in-process control.11 0Item Restricted Toward Implementation of the Berry Phase Method of Polarization within the OLCAO Method: Recursive Analytic Integrals of Plane Waves and Atomic Orbitals(Saudi Digital Library, 2025) Alotaibi, Ala; Rulis, PaulTraditional definitions of polarization are not applicable to solids with periodic boundary conditions. Instead, the so-called modern theory of polarization, which is based on the Berry phase of the wave function in reciprocal space, is required. In this dissertation we will discuss the implementation of the Berry phase calculation within the density functional theory (DFT) based orthogonalized linear combination of atomic orbitals (OLCAO) method using a recursive technique for atomic orbital and plane wave integration. Although polarization calculations can be performed by many other electronic structure packages, the OLCAO implementation will be particularly useful because of its efficiency and inherent ability to permit decomposition of the contributions to the polarization from different elements or sub-units of the material. By combining theory and computer modeling, we aim to speed the calculation of the integrals of plane waves and atomic orbitals while reducing the cost.39 0