Saudi Cultural Missions Theses & Dissertations
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Item 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.39 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.10 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.4 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 choices15 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.10 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.16 0Item Restricted Developing a Strategic Roadmap Toward Hydrogen energy Economy for energy mix integration In Saudi Arabia(Cranfield University, 2024) AlKaheel, Sultan Bin Thiyab; Luo, JerryThe present thesis provides a detailed analysis of the current situation in the landscape of hydrogen energy, underlining an advanced framework of DSS that can work on optimizing all stages of hydrogen production, deployment, and integration into the energy sector. This research, conducted within the larger context of transitioning global energy resources towards sustainability, investigates how DSS might empower leaders to master the complexities associated with transitioning toward a hydrogen-based economy balanced among economic, environmental, and logistical concerns. In this DSS framework, designed on MATLAB Web App Designer, nine strategic scenarios are analysed using MCDM and decision tree approaches, considering the dynamics of demand and supply, production costs, policies, and ecological benefits. A DSS framework is an integrated system wherein data, modelling, analysis, and decision-making are systematically structured. Therefore, the present DSS framework acts as an important tool to arrive at informed, scalable, and flexible hydrogen production strategies. Keywords: Hydrogen economy; Market Penetration Feasibility; Production Capacity, Cost and Efficiency, Strategic Planning, Hydrogen Energy Influencing Factors.20 0Item Restricted comparative study of the framing of COVID-19 by the BBC and Al Arabiya(University of Leicester, 2024) Shbber, Saud; Qian, GongThe COVID-19 pandemic required public service media (PSM) outlets to respond swiftly and adapt their communication strategies. This thesis examines how two major PSM organisations—BBC in the UK and Al Arabiya in Saudi Arabia—framed the COVID-19 crisis, focusing on eight key news frames identified in the study: human-interest, vaccination safety/hesitancy, war, commitment and transparency, uncertainty, economic consequences, government handling; criticism vs. firm state control, and authority-centric frames. This thesis uses corpus linguistic analysis (CLA) to assist the framing analysis process, building two corpora from tweets and the attached full news articles on X (formerly Twitter) from both outlets. This method allows for a detailed comparison of how COVID-19 was communicated in the different political, social, and media contexts of the UK and Saudi Arabia. The findings show that BBC’s coverage often highlighted public engagement, transparency, criticism and economic impact, while Al Arabiya focused more on government authority and firm state control. The thesis also tracks how these frames changed over time, noting both similarities and differences between the two outlets. Early coverage in both media focused on uncertainty and health risks but later shifted to issues such as vaccination, economic recovery, and government handling. However, the degree to which each outlet emphasised these frames varied, reflecting the differences in media landscapes and political environments. This change shows how PSM adapted their messages as COVID-19 progressed. This thesis offers new academic contributions by providing fresh insights into the role of PSM in risk communication and how PSM frames health crises and demonstrates the value of integrating CLA with framing analysis. It provides valuable lessons for policymakers on how to effectively manage public health messaging during future global emergencies.3 0Item Restricted Mechanical and Tribological Properties of Cold Sprayed Ni/CrC-NiCr Metal Matrix Composites(Northeastern University, 2025) Batwa, Sohayb; Muftu, Sinan; Nourian, AhmadCold spray (CS) is a solid-state deposition method involving severe plastic deformation of micron-sized powder particles accelerated to supersonic speeds. These particles are propelled through a compressed gas stream, such as air, nitrogen, or helium, using a de Laval nozzle. Since CS operates at low temperatures, no melting takes place during deposition. This offers a distinct advantage over other thermal spraying techniques resulting in unique and significant advantages. Metal-matrix composites (MMC) combine the high hardness, wear resistance, and thermal stability of ceramics with the ductility, toughness, and thermal conductivity of metals. In the development of coatings through CS technology, the feedstock powder is crucial for achieving the desired engineering properties, and it becomes more significant when cermet MMC powders are used. This dissertation aims to experimentally investigate the mechanical and tribological properties of a cold-sprayed cermet based MMC. Chromium carbide-nickel chromium (CrC-NiCr) was used as the cermet particle where CrC is a ceramic and NiCr is a ductile binder. This cermet particle was used in the MMC by using Ni as the overall binder. Therefore, the MMC is designated as (CrC-NiCr)/Ni. The first part of the study focuses on: (i) investigating the influence of increasing the metallic (NiCr) binder percentage in the cermet particle, and (ii) exploring the effects of varying the matrix-to-cermet ratio, i.e. Ni to (CrC-NiCr) ratio, in the feedstock blend on the microstructure and mechanical properties of the CS deposits. Results indicate that increasing the binder phase percentage in the cermet particles enhances deposition efficiency, cermet area fractions, and interparticle adhesion. This also results in coatings with porosity less than 1%, as well as improved ductility and shear strength. To address inter-splat defects and brittleness of the MMCs, the second part of this study examines the effects of post-spray annealing on the mechanical properties and microstructure. Scanning transmission electron microscopy (STEM) demonstrated improved interparticle bonding between matrix splats, with fractographic analyses indicating a shift from brittle to ductile fracture mechanisms. Mechanical tests reveal that post-process annealing significantly enhances the ultimate tensile strength (UTS), elongation, and adhesion shear strength of the coatings, however, it adversely affects coating hardness. The third part of this study investigates the effects of various post-spray heat treatments on the microstructure and tribological behavior of cold-sprayed Ni/CrC-NiCr metal matrix composite (MMC) coatings. Post spray (PS) laser heat treatment (LHT) and plasma arc heat treatments (PAHT), along with two furnace annealing temperatures were employed. Results shows the wear rate of the MMCs is significantly influenced by the type of heat treatment. PS-LHT and PAHT facilitated the formation of thin oxide tribo-film (mainly Cr22O33) that acted as solid lubricants, reducing metal-to-metal contact and abrasive wear.3 0