Engineering Commons^™

Elucidating Differences In Constitutionally Isomeric Diamine- And Bisphenol-Based Polybenzoxazine Networks, Benjamin L. G. Morasch

Dissertations

This dissertation aims to expand the fundamental understanding of diamine-based benzoxazine chemistry by examining how synthesis influences the network properties of 4,4’-ddm and phenol-based (P-ddm) benzoxazine, compared to the constitutionally isomeric bisphenol-F and aniline-based (BF-a) benzoxazine. While bisphenol-based benzoxazines such as BF-a have been studied more extensively, diamine-based benzoxazines are still relatively unexplored due to the competition with the formation of 1,3,5-hexahydrotriazine intermediates and the inconsistent thermal and mechanical properties reported in the literature. These inconsistencies stem from factors including synthetic methods, curing conditions, and degradation during polymerization that have been overlooked.

To address these issues, this dissertation is organized …

Upcycling Commodity Polymers To Advanced Materials For Energy And Environmental Sustainability, Anthony Griffin-Espinoza

Dissertations

Synthetic polymers play an essential role in nearly every aspect of our lives. Extending beyond single-use packaging, polymeric material design has progressed to attain tailorable architectures granting exceptional performance across advanced applications, including carbon-fiber reinforced polymer composites for aerospace, conductive materials for soft electronics, and drug carriers for biomedicine. While highly promising, intricately designed polymers needed to achieve excellent performance often have limited processability, complex synthetic methods, and expensive precursors. Furthermore, due to a lack of recyclability, commodity polymer waste streams result in both environmental impacts and a substantial loss of economic value. This dissertation focuses on developing robust strategies …

Privacy Preserving-Based Artificial Intelligence For Precision Agriculture, Partha P. Sengupta

Dissertations

The research work finds a solution to precision agriculture of cotton cultivation using artificial intelligence (AI) models. Two sets of model performance based on the application are selected namely a low resource and a high resource setting. This is because using drone surveys to capture images identifying the classes of stressed and unstressed cotton plantation requires limited model architecture and CPU based computation. Thus, traditional AI models were selected for low resource settings. Again, for high computation intensive models like transfer learning-convolution neural network (CNN) based architectures were grouped into high resource settings. There was another issue of class imbalance …

Understanding Crystallization Behaviors Of Polyethylene-Derived Vitrimers, Sara Valdez

Dissertations

Polyolefins (POs), particularly polyethylene (PE) and polypropylene (PP), are among the most widely utilized polymers globally, valued for their low production costs and favorable mechanical properties stemming from their semicrystalline microstructure. However, their extensive use and improper disposal have raised serious environmental and human health concerns. In response, PO-derived covalent adaptable networks (CANs) have emerged, integrating dynamic chemistries to enable recyclability and sustainable end-of-life strategies. While existing literature has largely focused on novel CAN systems, structure–property relationships, and network rearrangement in relation to thermal transitions (e.g., topological freezing temperature), the influence of network formation on crystallinity and crystallization kinetics remains …

Development And Validation Of A Thermal Model For An Electric Vehicle Transmission, Ghazal Rajabikhorasani

Dissertations

This research investigates the thermal behavior of a high-speed electric vehicle (EV) helical gearbox with the goal of improving the prediction accuracy of component temperatures and total power losses under a range of operating conditions. The study focuses on developing a physics-based lumped-parameter thermal network model capable of capturing the main heat generation and dissipation mechanisms within the transmission. The model integrates experimentally validated loss correlations for gears, bearings, seals, and churning, as well as convective and radiative heat transfer paths.

The thermal network model was coded in MATLAB and validated through a series of controlled experiments performed on an …

Rehabilitation Of Reinforced Concrete Columns Pre-Damaged By Corrosion Using Advanced Composite Materials, Feras Farid Abu Obaida

Dissertations

Reinforced concrete (RC) columns exposed to aggressive environments are highly susceptible to corrosion-induced deterioration, resulting in significant reductions in load-carrying capacity. This research investigates the structural performance of RC circular short columns with varying levels of corrosion damage and evaluates the effectiveness of two composite-based repair techniques, namely carbon fabric-reinforced cementitious matrix (C-FRCM) and carbon fiber-reinforced polymer (C-FRP) composites, combined with concrete cover replacement. The study aims to establish these methods as practical solutions for rehabilitating corrosion-damaged columns under concentric and eccentric loading conditions. The experimental program included 30 RC column specimens tested in two phases. Phase I involved thirteen …

Development And Characterization Of Sustainable Aluminum Metal Matrix Composites With Date Palm Agro-Residues As Reinforcement, Ansar Kareem

Dissertations

Aluminum Matrix Composites (AMCs) are extensively used in various industrial applications owing to their exceptional mechanical, material, and tribological properties. This led to the development of AMCs with every possible aluminum alloy as matrix, incorporated with various reinforcement materials to achieve desired material properties. There has been an increasing trend in the utilization of agricultural and industrial waste products as reinforcement material in AMCs. Date palm trees produce huge quantity of agricultural waste in different forms. Usually, these wastes are burned or disposed of in landfills which cause environmental pollution. Date palm agro-wastes can be incinerated to produce date palm …

Jet Impingement And Vortex/Swirl Cooling Of Different Inlet And Outlet Geometrical Configurations For Turbine Blade Leading Edge Cooling, Irfan Ahmad Sheikh

Dissertations

Gas turbine blades operate in extreme environments, exposed directly to high-temperature combustion gases that cause severe thermal stresses, weaken material integrity, and may lead to structural failure. Proper cooling is crucial to lower blade temperatures, reduce thermal stresses, prevent failure, and improve overall engine efficiency. This work presents a detailed numerical study of various cooling configurations by applying two advanced leading-edge cooling methods, jet impingement and swirl cooling, across different inlet mass flow rates and jet Reynolds numbers (Rej) ranging from 1,000 to 20,000 to evaluate their cooling performance.

Several advanced leading-edge cooling configurations are proposed and compared with the …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Joint Kinematics And Kinetics During Vertical Jump Landings With Progressively Increasing External Loads, Darcie L. Yount

Dissertations

This study examined sagittal and frontal plane kinematics and kinetics during countermovement jump (CMJ) landings performed with either a traditional barbell (BB) or a hexagonal bar (HEX) across increasing submaximal loads in recreationally active males. Generally, hip and knee flexion angles and joint mean angular velocities decreased as loads increased. Hip extension and knee flexion moments increased as load increased up to 20 kg, at which point these values decreased. Performing the CMJ with the HEX exhibited increased frontal plane stability compared with the BB group. Additionally, the BB group exhibited stiffer landing strategies, with decreased sagittal plane joint angles …

Understanding Structure-Property Relationships Within Polyolefin-Derived Vitrimer Systems, Mikaela Sadri

Dissertations

Polyolefins are extremely ubiquitous materials due to their satisfactory material properties, ease of synthesis, and low cost. Unfortunately, their extensive use has led to a global plastic waste mismanagement problem. As such, notable efforts have recently focused on converting these commodity polymers into vitrimers, or dynamic networks, to extend their use-life and tailor their properties. However, the fundamental polymer physics of these emerging materials, remain largely underexplored, hindering their widespread implementation. To address this challenge and enable a more sustainable future, the overarching goal of my dissertation research is to understand the fundamental structure-property relationships within complex polyolefin-derived vitrimer systems. …

Reinforcement Learning And Virtual Human Animation: A Novel Approach To Data-Driven Animation, Portraying Dynamic, Flexible Human-Like Behaviours, Vihanga Gamage

Dissertations

Virtual characters require animation capable of portraying dynamic, context-sensitive human-like behaviours. Several approaches to generating such animation have been developed, but each carries limitations. Motion capture can produce high-fidelity animation but is expensive and ill-suited to systems that must respond in real time. Physics-based reinforcement learning (RL) enables flexible, dynamic behaviour portrayal, yet relies on simulation feedback signals that are unavailable for social gestures. Supervised approaches can learn social behaviours from motion capture data but yield agents with limited flexibility and generalisation.

This thesis presents RLAnimate, a model-based, data-driven RL framework for character animation that enables a single agent to …

Enhanced Point Cloud Generation From A Novel 360° Underwater Lidar, Olagoke E. Daramola

Dissertations

This dissertation presents novel algorithms to improve the mapping capabilities of a 360-degree underwater Pulsed Laser Line Scanner LiDAR (PLLS-360°). Due to its 360° field-of-view (FOV), the PLLS-360° is a compact full-waveform omnidirectional imager suitable for seafloor mapping, underwater asset inspection, object detection, ice-sheet mapping, and construction progress monitoring. The proposed methodology includes an improved waveform fitting technique for saturated waveform recovery, detection array response correction, radiometric corrections, and fusion of LiDAR and sonar bathymetric datasets. The first part of this dissertation assesses the LiDAR’s performance and describes how the data for this unique 360° FOV architecture is processed. The …

Enhancing Credit Path Planning With Llm-Based Multi-Agent Systems, Sahar Yarmohammadtoosky

Dissertations

This work explores applying Multi-Agent (MA) Large Language Models (LLMs) to enhance credit card management, an underexplored area for their multi-step reasoning capabilities. Focusing on Equifax’s Optimal Path™ model [1]—a personalized solution for credit score optimization—the study addresses two key challenges: first, designing a natural language interface for financial credit models to improve accessibility and aid customer decision-making, and second, enhancing the reliability and real-world applicability of complex financial models prone to generating invalid or unfeasible recommendations caused by a lack of practical interpretability and susceptibility to edge cases. To tackle these, we propose and evaluate various MA designs, including …

A Parallel Fuzzy Logic Framework For Surgical Skill Evaluation Via Instance Segmentation And Deepsort Tracking, Mohsen M. Mohaidat

Dissertations

Manual evaluation of suturing skills during laparoscopic training is often subjective and labor-intensive, resulting in the lack of scalable and consistent feedback for trainees. This study proposes an automated framework that not only significantly reduces the need for in-person assessment by experts but also ensures scalability, thereby addressing the objectivity and cost-effectiveness limitations. While low-cost laparoscopic box trainers have become increasingly popular for residency training, performance assessment still depends on expert supervision. The proposed system aims to alleviate these limitations.

This study introduces a novel automated framework incorporating an optimized DeepSORT algorithm for classifying, localizing, and tracking surgical tools using …

Developing Machine Learning Models And Graphene-Based Flexible Humidity And Temperature Sensors For Machine-Learning-Assisted Sensing, Seth Hajian

Dissertations

Flexible sensor technology has recently gained tremendous momentum in both academic research and industrial applications, transitioning from conceptual frameworks to practical implementations across diverse fields. This remarkable advancement can be attributed to several converging factors, including the maturation of nanomaterial science, the advancements of machine learning algorithms, and the critical demand for intelligent sensing solutions in healthcare, environmental monitoring, and industrial automation. The growing emphasis on personalized medicine and real-time health monitoring, accelerated by global health challenges, has further highlighted the necessity for accurate, cost-effective, and adaptable sensing platforms. This dissertation presents the fulfillment of three interconnected research projects focused …

Experimental And Computational Study Of Slow Crack Growth Of High Density Polyethylene, Abdulla Fawzi Almomani

Dissertations

This dissertation investigates the slow crack growth (SCG) behavior of High-Density Polyethylene (HDPE) under various mechanical and environmental conditions. The study combines experimental analysis and computational modeling to enhance the understanding of SCG mechanisms in HDPE, particularly in pressurized pipes and under the exposure to hydrocarbons. A novel Crack Layer (CL) theory-based SCG model is developed and validated through experimental data, offering a predictive framework for HDPE failure assessment. The main objective of this dissertation is to quantify and model the viscoelastic-viscoplastic behavior of HDPE under monotonic and cyclic loading conditions while addressing SCG kinetics in structural applications. The study …

Design And Development Of Hip Implants For Longevity Through Integrating Advanced Groove Structures And Additive Manufacturing, Asarudheen Abdudeen

Dissertations

This doctoral research concerns the design and optimization of hip implants (HIs) to enhance performance, durability, and improve patient outcomes by addressing the key issues of wear, deformation, and stress distribution. Innovative surface groove designs have been introduced to both a solid and a hollow femoral head with the aim of reducing friction and wear. The addition of grooves, with both a hemispherical and rectangular cross-section, onto the femoral head reduced friction, as debris produced by the inner liner was trapped within the grooves, significantly reducing adhesive wear. A numerical simulation study compared the effects of surface modifications both grooves …

Colloidal Quantum Dots: A Path Toward Making Mid-Wave Infrared Sensing A Ubiquitous Technology, Mohammad Mostafa Al Mahfuz

Dissertations

Reducing the size, weight, power consumption, and cost (SWaP-C) of infrared detectors could make infrared sensing more widely accessible. In the critical mid-wavelength infrared (MWIR) spectral range of 3-5 gm, commercially available detectors are limited by the high costs associated with epitaxial growth and hybridization, as well as the need for cryogenic cooling. These factors restrict their use to defense and space applications.

Colloidal quantum dots present a promising material for overcoming these challenges, with wafer-scale monolithic integration and Auger suppression being the key material capabilities to minimize the sensor's SWaP-C. Infrared sensors based on colloidal quantum dots have been …

Childhood Neuroanatomical Markers Of Familial And Nonfamilial Attention-Deficit/Hyperactivity Disorder, Rahman Baboli

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the …

Machine Learning And Optimization For Intelligent Decision-Making, Elson Cibaku

Dissertations

This dissertation presents a series of innovative machine learning and optimization model designs that address complex operational challenges across logistics and power systems. By integrating advanced neural architectures with robust optimization techniques, the work delivers scalable solutions designed to improve efficiency, reliability, and decision-making in dynamic and real-world environments. The first study introduces a two-stage approach to effective vaccine distribution. This framework tackles the capacitated vehicle routing problem by combining adaptive clustering techniques with reinforcement learning and a simulated annealing pickup policy. Through extensive computational experiments, the approach demonstrates substantial improvements in routing efficiency, reducing both computational time and logistical …

Model-Based Reinforcement Learning And Deep Learning For Power Converter Circuit Design Automation, Shaoze Fan

Dissertations

This dissertation presents a comprehensive automated framework for power converter design, leveraging reinforcement learning (RL) and graph-transformer networks (GTN) to address critical inefficiencies in traditional manual topology optimization. Motivated by the combinatorial increase of circuit design spaces and the computational cost of iterative simulations, this work develops a robust framework for generating energy-efficient topologies requiring rapid and reliable circuit design.

The framework integrates three key components: (1) an upper-confidence-bound-tree-based (UCT-based) RL model for circuit topology space exploration, (2) parallelized UCT algorithms to accelerate exploration processes, (3) a Graph-Transformer-based Network enabling fast circuit performance evaluation. Experimental validation demonstrates the whole framework …

The Interplay Of Accommodation And Oculomotor Vergence Within Young Adults With Binocularly Normal Vision And Typically-Occurring Convergence Insufficiency, Sebastian Fine

Dissertations

Concerted binocular coordination evoking oculomotor and refractive responses to visual stimuli are essential to daily function. Oculomotor dysfunctions can inhibit binocular responses to visually-near stimuli and have high comorbidities to accommodative dysfunctions. Three visual cues for inward (convergent) and outward (divergent) oculomotor movements, when presented concertedly create natural-viewing conditions: disparity- the binocular difference in light cast onto the fovea due to differing ocular perspectives, blur- the acuity of a visual target which stimulates accommodation, and proximal- the perceived distance of a visual stimuli based on size.

This study aims to quantitatively investigate oculomotor vergence and accommodation performances between individuals with …

Modulation Of Cerebellar Cells By Transcranial Ac Stimulation In Anesthetized Rats, Qi Kang

Dissertations

Noninvasive brain stimulation (NIBS) is increasingly utilized in clinical trials for the treatment of neurological disorders. Each NIBS technique offers distinct advantages. Transcranial electrical stimulation (tES) is easy to apply and requires only simple equipment, while transcranial magnetic stimulation (TMS) can penetrate deeper than tES into the brain and it is more focal. Transcranial focused ultrasound stimulation (FUS) is superior to both in terms of penetration and focal stimulation. This study focuses on the modulation of the cerebellum, traditionally believed to be associated with motor coordination but increasingly recognized for its role in cognition and emotion as well. While tES …

Adversarial Robustness In Advanced Machine Learning Models Integrating Graph Neural Networks And Large Language Models, Mahmoud Nazzal

Dissertations

Artificial intelligence (AI) has achieved remarkable performances across various domains. In most real-world applications, data often takes relational forms, such as graphs and networks, or sequential forms, such as text and time series. As AI evolves, specialized models have emerged to handle these structures; Graph Neural Networks (GNNs) for relational mining and Large Language Models (LLMs) for sequential understanding. Despite their success, these models face challenges in security, robustness, and interpretability. GNNs excel in relational reasoning but are vulnerable to adversarial manipulation and lack interpretability, while LLMs are strong in linguistic reasoning and generalization yet struggle with relational data and …

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Next-Generation Extended Reality Systems With Real-Time Edge Artificial Intelligence And Mobile Computing, Pedro H. Regalado

Dissertations

Mixed reality (MR) and augmented reality (AR) systems are reshaping digital experiences by seamlessly integrating physical and virtual environments. This dissertation presents a comprehensive framework for next-generation immersive systems, combining advances in real-time data processing, multi-user synchronization, and secure communication. The core contributions are structured around three interconnected systems: MediVerse, TeleAvatar, and MultiAvatarLink, each addressing critical challenges in mobile MR.

MediVerse is a secure and scalable framework for real-time health and performance monitoring, integrating intelligent IoT sensors, wearable technologies, and MR interfaces. It supports multi-camera fusion, adaptive compression, and real-time three-dimensional (3D) point cloud generation, enhancing data accuracy and responsiveness …

Utilization Of Imidazolium Ionic Liquids For Enhanced Oil Recovery In Harsh Emirati Tight Carbonate Oil Reservoirs, Noran Hussein Mousa

Dissertations

In response to the increasing global demand for energy and the limitations of traditional chemical oil recovery methods, this study explores the potential of imidazolium-based ionic liquids (ILs) as enhanced oil recovery (EOR) agents for Emirati tight oil reservoirs. Four ILs—C10mimCl, C12mimCl, C12mimBF4, and C16mimBr—were evaluated for their effects on the interfacial tension (IFT), wettability, emulsification, and rheological properties under reservoir conditions. Experiments conducted at varying salinities, temperatures (up to 110°C), and IL concentrations (500–3000 ppm) demonstrated that longer alkyl chain ILs, particularly C16mimBr, effectively reduced IFT by >99% and improved wettability by lowering contact angles down to 15.37°. Statistical …

Balancergnn: Balancer Graph Neural Network For Imbalanced Datasets, Mallika Boyapati

Dissertations

Addressing imbalanced datasets is challenging due to machine learning models' inclination to learn the majority class. Graph construction plays a major role in determining how Graph Neural Networks (GNNs) perform on imbalanced datasets. In this research, we introduce the BalancerGNN framework to tackle highly imbalanced datasets, demonstrating its effectiveness in fraud detection as one of the case studies. This framework is designed to work for any binary node classification dataset with significant class imbalances. This research addresses the following questions: i) How effective are feature engineering techniques in the case of imbalanced datasets? ii) How do graph representation learning and …