Saudi Cultural Missions Theses & Dissertations
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Item Restricted Optimization of a Fully Renewable Hybrid Energy System with Green Hydrogen Production and Utilization(Saudi Digital Library, 2025) Alshoumar, Yousef; Jurado Pontes, NeliaAs the share of renewable energy generation continues to grow, addressing intermittency and developing adaptable storage solutions is required for decarbonizing the power sector. This work explores hydrogen’s potential as an energy vector in functioning as long-duration energy storage (LDES), increasing reliability, while enabling green hydrogen cost reductions that can support decarbonization across multiple sectors. A combined green hydrogen hybrid renewable energy system (HRES) and production plant is investigated to meet the annual energy demand of 58.8 GWh for a rural off-grid community by developing a techno-economic optimization model for system component sizing. The results demonstrate the system’s ability to fully meet the energy demand and deliver continuous clean power while simultaneously producing a 58% surplus hydrogen for additional use. The operation of the system was significantly enhanced by combining batteries with hydrogen storage, lowering the levelized cost of energy (LCOE) to 377 GBP/MWh, with an ability to accommodate seasonal energy shift and a capability of supplying more than 50 hours of energy storage.8 0Item Restricted Lightweight ML-Based Drone Intrusion Detection System Through Model Compression(University of North Texas, 2025) Alruwaili, Fawaz Juhayyim M; Cihan, TuncThe adoption of drones in diverse domains (e.g., surveillance, agriculture, and disaster management), together with their integration of advanced technologies and dependence on wireless communication, has significantly increased the need to secure drone networks against cyber threats. Traditional network-based intrusion detection systems (NIDS) can be insufficient against novel or adaptive cyber threats and exceed the computational limits of drones. Thus, we need lightweight and efficient drone-specific NIDS solutions. This dissertation addresses this concern with the goal of achieving an effective balance between security, efficiency, and model accuracy without significantly compromising detection performance. Hence, two complementary main contributions are proposed: First, a lightweight ML-based NIDS optimized for individual drones, utilizing a quantized deep neural network (DNN) through post-training quantization (PTQ), enabling real-time, on-board intrusion detection. Second, a framework for swarm-based deployments that leverage federated learning and knowledge distillation to enable distributed training and lightweight model deployment while preserving data privacy and minimizing communication overhead. Both contributions were evaluated using real-world drone network datasets. The first contribution achieved 95.03% accuracy with significantly reduced model size and inference latency, making it suit- able for real-time and onboard deployment. The second contribution was deployed using Raspberry Pi 4 devices and demonstrated improved accuracy, convergence, and communication efficiency, achieving up to 76% reduction in communication overhead and 29% lower CPU usage. The results demonstrate the practicality and effectiveness of the proposed solutions in meeting the unique demands of both individual and swarm-based drone deployments, while achieving a robust balance between security and efficiency.20 0Item Restricted ANALYSIS OF CONTAINERIZED GRAIN, DRIED DISTILLERS’ GRAINS, AND OTHER FEED COMMODITIES EXPORTS AND INLAND MOVEMENT(North Dakota State University, 2025-05) Helmi, Wesam; Mattson, Jeremy; Vachal, KimberlyContainerized shipping has become an increasingly important method for transporting bulky products. This research investigates different topic areas associated with containerized grain, Dried Distillated Grain, and other feed (DDG&oF) shipping. It covers both inland movement and exports from the United States. The first part of the research provides a descriptive analysis of U.S. containerized DDG&oF shipping trends, using the PIERS database. This part examines variations by origin, port of departure, commodity type, and destination country. The results are visualized using charts and spatial maps and show the amount of shipped grains, DDG&oF in containers versus bulk shipments. In addition, it shows when the containerized grain started declining, in which commodities, and for which exporting ports. This part of the research aims to provide an understanding of shifts that occurred in recent years. The second part of the research is a prescriptive analysis using a Linear Programming transshipment model to optimize the inland transportation of containerized grain. Using data from the Surface Transportation Board and Freight Analysis Framework, the optimization model minimizes total logistics costs by considering transportation, operational, and freight expenses in both rail and highway modes. It also evaluates potential new inland terminal locations and uses sensitivity analysis to test the robustness of the model under varying conditions. Third, the research explores the mode of choice between container vessels and bulk carriers for exporting DDG and other feed (DDG&oF), a major U.S. agricultural export. A Beta regression model is applied to PIERS data, focusing on shipments to top destination countries. This analysis identifies the most influential key factors that affect transportation mode decisions, such as shipping prices, exchange rate, Index of Industrial Production, and more. The findings address gaps in current literature regarding DDG&oF modal choice and contribute new insights into optimizing international agricultural logistics. In addition, the study applied multicollinearity analysis to ensure the correlation of independent variables. Together, studies provide a comprehensive analysis of containerized grain export trends, logistics optimization strategies, and shipper decision-making in modal selection. The research offers practical implications for improving cost-efficiency, network design, and policy considerations in the evolving landscape of U.S. agricultural exports.13 0Item Restricted Optical and Mechanical Characterization of Spliced Carbon Fibre Composites.(The University of Sheffield, 2024) Alotaibi,Mohammed S; Fairclough,PatrickThis dissertation investigates the optimization of carbon fibre (CF) strands through pneumatic splicing to improve the performance of composite materials. A novel splicing technique was devised and examined using cuttingedge methods. In this process, carbon fibre tows were joined within a splicing chamber under the influence of highly pressurized air, forming a single joint section. These included high-resolution imaging systems, digital image correlation techniques, and thermal and electrical conductivity measurements. A comprehensive image processing workflow was implemented to understand better the spliced composite microstructure, encompassing surface profiling and flow tracking algorithms. Notably, Electrical resistivity measurements and thermal imaging techniques were employed to investigate the physical characteristics of the spliced carbon fibre tows. The Taguchi design of experiment (DoE) was employed to identify tow overlap length as a critical splicing parameter influencing microstructure, mechanical properties, and inplane permeability. The effect of the spliced fibres on the transverse in-plane permeability has been examined in this research. The extent of overlap significantly impacted permeability, with longer overlaps resulting in reduced values. This substantial effect led to a 20% difference in ply permeability and a 35% difference in tow permeability values. This is attributed to increased fibre count, whisker formation, cross-sectional variations, and altered microstructure within the spliced region. A dual-scale permeability effect was observed, with the flow front advancing while the spliced fibres remained partially saturated. This phenomenon is attributed to the presence of channels within both intra-fibre bundles and inter-bundle scales within the fabric. Fibre volume fraction exhibited lower values in spliced samples compared to unspliced ones due to the effects of the pneumatic splicing process on the joined CF tows. This resulted in unspliced samples exhibiting the highest permeability due to the 4 absence of twisted fibres. Attempts to model permeability using established models (Gebart and Kozeny-Carman) for spliced samples were unsuccessful compared to experimental results, indicating that the geometry of the channel paths between the fibres was altered during splicing. Manufacturing studies examined the impact of splice location (centre or edge) and ply spacing on woven composite properties. Significant variations in fibre volume fraction, thickness, and mechanical properties were observed, with edge-spliced samples exhibiting pronounced reductions in strength and modulus compared to centre-spliced counterparts. Multiple spliced plies demonstrated decreased performance relative to single-spliced samples. Failure analysis revealed predominant modes, including fibre breakage, interply cracks, and delamination, with bending test samples exhibiting increased susceptibility to damage. This comprehensive investigation provides valuable insights into the intricate relationship between splicing parameters, microstructure, and composite performance. The findings contribute to the development of advanced CF composite materials by clarifying the mechanisms underlying the impact of splicing on mechanical properties and structural integrity.38 0Item Restricted Optimal Placement of Fixed and Mobile Primary Healthcare Centers During Hajj(University of Edinburgh, 2024-08) Alomari, Maram; Kalcsics, JorgThis dissertation explores the optimal placement of fixed and mobile primary healthcare centers (PHCCs) to serve pilgrims during the Hajj in Makkah, focusing specifically on the holy sites of Mina, Muzdalifah, and Arafat, and the transportation network connecting these areas. The immense influx of pilgrims places significant demands on the healthcare system, necessitating precise planning and management. To address this, we first define the geographical scope and key locations impacted by the event. We then employ an agent-based modeling approach integrated with Geographic Information Systems (GIS) to simulate pilgrim movements over the course of the Hajj. Subsequently, we analyze the capacities, costs, and operational parameters of both fixed (FPHCC) and mobile (MPHCC) primary healthcare centers. An integer programming model is formulated to determine the strategic placement and reallocation of these centers across 14 time periods, aiming to minimize setup, operational, and mobility costs associated with each type of center. Constraints include limited numbers of MPHCCs, restricted capacities, maximum allowable distances between demand points and PHCCs, and minimum coverage requirements. We then adjust key parameters such as maximum distance and minimum coverage to examine their impact on the solution and the resulting adjustments in PHCC locations. This analysis offers insights into how strategic healthcare deployment during Hajj can effectively meet the fluctuating demands of pilgrims, ensuring accessible and efficient healthcare coverage. The findings contribute to the broader field of healthcare logistics, particularly in the context of large-scale religious gatherings, providing a framework for improved healthcare readiness and response during similar events globally.35 0Item Restricted The economic viability of blue hydrogen production: Forecasting Saudi production cost of blue hydrogen.(City, University of London, 2023-09-01) Alfaifi, Abdulaziz; Tamvakis, Michael; Alshammari, YousefAs global energy demands are surging and concerns over environmental sustainability intensify, the hydrogen emerges as a promising solution towards clean energy production and storage. This dissertation delves into the economic viability of blue hydrogen production in Saudi Arabia with the focus of the forecasting of production costs. By encompassing the evaluation of various hydrogen types, blue hydrogen applications, and an estimation of production costs according to historical feedstock prices. The literature review scrutinizes different hydrogen types with their economic feasibility in the context of blue hydrogen production. Consideration of cost competitiveness, environmental impact, and scalability lays the groundwork forward to insights. Furthermore, the examination of blue hydrogen's applications elucidates industries poised to benefit from future possible investments in this energy carrier, while also diving into potential challenges and opportunities. Employing a meticulous methodology, forecasting the production cost of Saudi blue hydrogen, placing particular emphasis on feedstock prices. By utilizing ARMA model to leverage forecasted natural gas prices, thereby shedding light on the relationship between feedstock costs and hydrogen production expenses. The results and recommendations have strategic insights and actionable suggestions. By comparing decisions made at the oil and gas industry, this dissertation positions itself at the intersection of industry growth stages. The findings offer suggestion for a transition from the emerging phase to the mature phase in early growth market, with an emphasis on cost-saving strategies and optimizing resource. A pivotal finding emerges in understanding the influence of natural gas prices on production costs of Saudi Aribia. The dissertation implies the significance of efficiently managing feedstock prices and subsidy costs. Implications extend to both national and international contexts as well, particularly in steering the export decision concern of blue hydrogen and blue ammonia. This dissertation holds significant relevance for particularly energy economists in Saudi Arabia, providing them with a nuanced understanding of the economic dynamics that are shaping the production and export of blue hydrogen that is by the insights from the dissertation into the interplay of feedstock prices, industry growth stages, and strategic decision-making, this dissertation contributes to a more informed energy landscape.86 0Item Restricted Computational intelligence approaches applied to various domains(Saudi Digital Library, 2023-03-04) Alibrahim, Hussain; Ludwig, SimoneOver the past decade, machine learning has revolutionized a wide range of fields, from self-driving cars to speech recognition, web search, and even the human genome. However, the success of machine learning algorithms depends on a thorough understanding of the problem, mechanisms, properties, and constraints. This dissertation explores various aspects of machine learning, including hyperparameter optimization, nature-inspired algorithms for semi-supervised learning, image encryption using Particle Swarm Optimization with a logistic map and image originality. In the first chapter, three models - Genetic Algorithm, Grid Search, and Bayesian Optimization - are compared to improve classification accuracy for neural network models. The objective is to build a neural network model with a set of hyperparameters that can improve classification accuracy for data mining tasks, which aim to discover hidden relationships between input and output data to predict accurate outcomes for new data. The second chapter focuses on using nature-inspired algorithms, such as Particle Swarm Optimization (PSO) and Anti Bee Colony (ABC), to correctly cluster unlabelled data in semi-supervised learning problems. Two hybrid versions of K-means clustering, one with PSO and the other with ABC, are developed. The third chapter uses PSO to develop an image encryption algorithm using the logistic map to aid in the encryption process. The optimization problem is formulated by converting the image encryption problem into an optimization problem. In the final chapter, a new algorithm is developed using different techniques such as classification, optimization, and image analysis to detect whether an image is original or has been edited and modified. Overall, this dissertation investigates a variety of machine learning techniques and their practical applications across numerous fields. The techniques have the potential to be applied in diverse areas, such as biology, meteorology, healthcare, and finance.25 0Item Restricted Timing and Marketing Mix Decisions Under New Product Diffusion With Dual-Market Structure and Repeat Purchases(2022) Alenzy, Muhammad; Erkoc, MuratOver the last three decades, the success rate for new products in the marketplace has been one in ten. Although this rate has been increasing slightly in recent years, it is still below 30% as of the end of 2021. The lack of a significant market to adopt the product is considered the leading cause of the failure. It has been established that newly introduced products in the marketplace encounter early adopters before they are accepted by main adopters, the larger market. The time-to-market to introduce the new product to the main adopters emerges as a pivotal decision to achieve higher demand and traction levels, as they represent a larger population with respect to the early market size. Previous research reports that the transition from the early market to the main market is challenging due to the heterogeneity in the adoption attributes of the two segments, which could lead to sales slowdowns if not foreseen and planned previously. Although product managers leverage pricing and advertising to help their products cross the slowdown and successfully diffuse to the main market, price declines and market penetration could negatively influence this transitional process. Consequently, several trade-offs arise when planning a new product introduction in a dual-market structure, given the two markets' heterogeneity. For instance, delaying the time to enter the main market could enable the firm to increase prices when selling the product solely to the early market, given their lower price sensitivity; however, it would decrease the early market size. And decreasing the size of the early market could decrease their word-of-mouth influence on the main market to adopt the new product. Hence, the advertisement spending level in the main market increases, and accordingly, profit margins decrease. Further complications arise with the existence of repeat purchases, for example, service-based products, as most of the literature body on new product diffusion, especially the dual-market diffusion, assumes a single purchase transaction from both markets’ adopters. Under such settings, it is commonly accepted that customers who subscribe to the service every period will end up repeating the subscription or canceling, which is known as the churn rate. Churn rates would also be affected by the heterogeneity in the dual-market structure where early market churn rate would vary from the main market’s. Customer churn has attracted significant attention from researchers and managers in recent years after the rise of service-based firms, as they form about 80% of the US gross domestic product and demonstrate the relationship between a service firm’s customer churn rate and its long-term profits. We investigate the underlying trade-offs when planning a new product introduction with a dual-market structure and repeat purchases which, to the best of our knowledge, no previous work in either the marketing or the operations management literature has analyzed the profitability of a product under the combined effect of such conditions and underlying trade-offs. We contribute to the current literature by jointly optimizing the time-to-main-market, pricing, and advertisement spending across the life cycle of newly introduced products under these conditions. We introduce an integer nonlinear programming model that optimizes these critical decisions simultaneously to maximize total profit across the product's life cycle. The model optimizes the decisions in two outcomes: (1) when the product is introduced solely to the early market and (2) when both markets coexist and are introduced to the product at the same time. The demand mapping is built by extending the Bass Diffusion framework to the dual-market structure and repeat purchases. We conduct extensive comparative computations with multiple periods and multi-level parametric combinations and reveal that delaying the time to enter the main market is a persistent optimal timing strategy that maximizes the profit function in various parametric settings. Additionally, the communication level between the two markets notably impacted different performance metrics when investigated independently and under the interplay effect with other model parameters.21 0