Digital Commons Network^™

Application Of Machine Learning For Vascular System Analysis, Alireza Bagheri Rajeoni

Theses and Dissertations

The analysis of vascular structures is critical for diagnosing, monitoring, and treating vascular diseases such as aneurysms, stenosis, and vascular calcification. Traditional methods often rely on manual interpretation of imaging data, which is time-consuming, subjective, and not scalable. This work explores the application of advanced machine learning techniques to automate and enhance vascular system analysis. Our contributions include achieving state-of-the-art accuracy in vascular segmentation, developing a machine learning pipeline to automatically quantify vascular calcification in peripheral arterial disease, and designing a multi-stage machine learning system for abdominal aortic aneurysm analysis that identifies aneurysm boundaries and estimates aneurysm volume in a …

Determination Of Tensile Strength Distribution Of Single Carbon Fibers At Microscale Gagelengths, Karan Deepak Shah

Theses and Dissertations

High performance carbon fibers are widely used as reinforcements in composite material systems for aerospace, automotive, and defense applications. The tensile strength of commercial fibers is significantly less than its theoretical limits. The composite systems are often overdesigned, thus any increase in the fiber tensile strength can yield significant cost and weight savings. Modification of fiber surface treatment (sizing) during manufacturing is a potential route to enhance fiber strength. Single fiber tensile testing at millimeter-scale is typically used to characterize the effect of sizing on the fiber strength. However, the longitudinal tensile failure of a composite system is a result …

Downwind-Upwind Flux Path Analysis Method For Model Reduction Performance Evaluations, Christian Robert Smith

Theses and Dissertations

Large, complex chemical kinetic models have long since been limited by the computational cost it takes to evaluate said model. Over time many reduction methods have been developed to combat this computational cost. However, very little has been done towards evaluating the limitations of reductions as a way of understanding the original model’s characteristics. This paper aimed to use a path flux analysis-based reduction algorithm to rapidly develop multiple reduced mechanisms for a 96 species FFCM-2 methane chemical kinetic model as well as an 858 species n-Dodecane chemical kinetic model. The goal of formulating these reduced mechanisms was to identify …

New Approaches On Source Coding For Quantum Stochastic Sources And Implementation Of Quantum Fanout Gate, Rabins Wosti

Theses and Dissertations

The accurate computation of advanced quantum algorithms like Shor’s integer factorization, quantum phase estimation (QPE), and the quantum Fourier transform (QFT) requires quantum circuits of considerable size and depth. It is difficult to achieve reliable computation with deep quantum circuits due to the limited coherence times of the current noisy quantum devices. The quantum fanout gate is known to be a powerful primitive for reducing the depth of many quantum circuits (Høyer and Špalek 2003; Gottesman and Chuang 1999). Shallow or constant-depth quantum circuits are desirable for both near-term and fault-tolerant quantum computations as they reduce noise and allow faster …

Multiphysics Modeling, Analysis, And Design Of Ceramic Hollow Fiber Membranes For Oxygen Separation, Hamed Abdolahimansoorkhani

Theses and Dissertations

Oxygen plays a central role in numerous industrial processes. Several technologies have been reported for producing oxygen from air. Among them, oxygen transport membrane (OTM) technology—based on mixed-conducting, gas-tight ceramic membranes—has attracted significant attention due to its high oxygen selectivity, relatively low capital and operating costs, and versatility for both ex-situ and in-situ applications. Mathematical modeling of OTMs offers a powerful tool to investigate internal multi-physics transport phenomena, providing deeper insight into fundamental mechanisms while serving as a cost-effective approach for optimizing membrane stack designs.

After a comprehensive introduction, in the first part of this dissertation (chapter 2), a comprehensive …

Implicit Neural Representation For Image Reconstruction, Canyu Zhang

Theses and Dissertations

Image reconstruction seeks to restore corrupted images and recover visual content that has been lost or degraded. Such degradation may result from low resolution, occlusion, masking, or shadow interference. This problem has become an increasingly significant research topic, as visual information plays a central role in almost every aspect of modern life. Neural network based approaches have recently emerged as highly effective solutions for this task. In particular, convolutional neural networks and transformer based architectures have demonstrated remarkable success in producing visually convincing reconstructions. However, these models remain constrained in several important ways, one of the most critical being that …

Recognizing The Unexpected: Deep Learning Across Complex Environments, Ge Song

Theses and Dissertations

Ensuring the security, trustworthiness, and operational integrity of modern autonomous and cyber-physical systems presents a critical challenge. While widely utilized in various engineering applications, such as intelligent transportation and industrial manufacturing, these systems require robust monitoring frameworks to identify unexpected anomalies in real-time, thereby maintaining operational safety and efficiency. This dissertation develops advanced deep learning methodologies for anomaly detection and health monitoring, with a particular emphasis on semisupervised reconstruction-based approaches that identify anomalies in complex environments using models trained only with normal operational patterns.

Building on this theme, the first study focuses on analyzing pedestrian behavior and detecting anomalies at …

Rate-Dependent Interlaminar Shear Characterization In Solid-State Extruded Ultrahigh Molecular Weight Polyethylene Film Composites, Frank David Thomas Jr.

Theses and Dissertations

Ultrahigh Molecular Weight Polyethylene (UHMWPE) cross-ply [0o/90o] thin film composites are emerging as an effective material for impact applications due to their high axial strength-to-weight ratio and favorable delamination properties. Mode-II interlaminar shear (ILS) fracture-driven delamination has been shown to be a significant energy absorption mechanism. Therefore, accurately characterizing ILS behavior as a function of strain rate in these composites is essential to inform computational models. Since these composites are significantly thinner and weaker in both interlaminar shear and transverse tension than traditional carbon fiber epoxy composites, standard ILS Mode II test methods/specimens such as end-notched flexure (ENF) are expected …

Calculating Surface Current Velocities From Video Generated From A Near Forward-Facing Camera, Colby James Weeks

Theses and Dissertations

This work presents a camera system for estimating surface currents. A combined camera and IMU system provide high resolution images and high precision orientation information, respectively. This eliminates the need for leveling and enables forward looking operation. The hardware stack includes a Lucid Vision Triton TRT162S monochrome camera, an SBG Ellipse D with dual antenna GNSS for precise pose, and an Nvidia Jetson Orin for acquisition and processing. The Ellipse D provides a hardware trigger at 10 Hz to time stamp each frame and to synchronize images with inertial measurements.

A camera calibration step estimates the intrinsic camera parameters and …

Computational Analogies In The Era Of Large Language Models, Amarakoon Mudiyanselage Thilini Wijesiriwardene

Theses and Dissertations

Analogical reasoning is an important part of human cognition requiring the integration of abstract reasoning, pattern recognition, and background knowledge. Despite significant advances in language modeling, the capacity of current methods to accurately identify, model, and evaluate analogies remains fundamentally underexplored.

Analogies enable individuals to perceive deep similarities between superficially different situations. Effective analogy-making requires integrating knowledge about the external world with abstract reasoning and pattern recognition capabilities. While current language models (LMs), trained on massive textual corpora using autoregressive or masked objectives, achieve impressive performance across Natural Language Processing (NLP) tasks such as text generation, summarization, and classification, their …

Multi-Layer Decision Making For Long-Term Autonomous Mission Based On Dual Process Theory, Shruti Jadhav

Theses and Dissertations

Unmanned aerial vehicles (UAVs) are increasingly used in precision agriculture, where extended autonomous operation is required for monitoring, intervention, and field management. However, achieving long-term autonomy remains challenging due to battery constraints, environmental uncertainty, and the need to balance exploration with event-driven tasks. To address these challenges, a multi-layer decision-making framework inspired by Dual Process Theory (DPT) is developed. The framework combines reactive return-tobase strategies, exploratory navigation, and directional bias from prior missions, with a conflict-monitoring mechanism that adapts system behavior based on real-time conditions. The approach is implemented in a simulated agricultural grid environment, demonstrating improved adaptability and coverage …

Reduced Order Simulator Of A Nuclear Thermal Propulsion System, Jackson Taylor Zazzaro

Theses and Dissertations

This thesis presents the development of a reduced-order simulator for a nuclear thermal propulsion (NTP) system, designed to capture the key thermo-fluid and performance characteristics of such systems while maintaining computational efficiency. The primary objective of this work is to develop and demonstrate a modular simulation framework capable of modeling the coupled physics of an NTP reactor, nozzle, and orbital transfer system. The simulator integrates a reduced-order thermal-fluid model of the reactor, a simplified nozzle model, and an orbital transfer model, providing a comprehensive yet computationally efficient representation of the propulsion process. By employing reduced-order modeling techniques, the simulator achieves …

Multi-Perspective Feature Learning For Facial Expression Recognition In The Wild, Xiangyu Hu

Theses and Dissertations

With the rapid progress of deep learning, Facial Expression Recognition (FER) has seen substantial improvements in performance, particularly “in the wild” meaning real world conditions. Despite these advances, most existing methods extract features from facial images as the sole emotional cues, which limits the model’s ability to capture the full complexity of human emotional expressions.

In reality, facial expressions are composed of diverse and multi-perspective information, including appearance-based cues and geometric structural deformations due to activations of facial muscles. Depending exclusively on one type of representation may fail to exploit the complementary nature of these cues, an issue that becomes …

Deep Learning-Based Change Detection In High-Resolution Remote Sensing Imagery, Hazem Badawy

Theses and Dissertations

Remote sensing has become a key tool for monitoring Earth’s surface over time, offering valuable insights into both natural and human-driven changes. Among its many applications, change detection focuses on analyzing multi-temporal imagery to reveal how specific areas evolve across different time periods. It plays a pivotal role in Earth observation applications, including urban development monitoring, environmental degradation assessment, and disaster response. However, existing approaches often struggle with limited contextual awareness, high sensitivity to noise, and imprecise localization of change boundaries, especially with high-resolution imagery. This thesis investigates the complex problem of change detection in remote sensing imagery by proposing …

The Effectiveness Of Public-Private Partnerships In Addressing Residential Urban Development Challenges In Greater Cairo, Sara Nagy Abdelmessih

Theses and Dissertations

This research explores the effectiveness of Public-Private Partnerships (PPPs) in tackling the residential urban development challenges facing Cairo today. In the past two decades, PPPs have emerged as a dominant mode of housing production, positioned as collaborative ventures between state agencies and private developers. They have become integral to the expansion of Greater Cairo, producing vast new urban extensions under the promise of mobilizing private capital, technical expertise, and delivery capacity. Yet, questions remain about whether these arrangements genuinely address Egypt’s most urgent housing needs, or whether they reproduce market-driven logics that leave large segments of the population excluded. The …

Adapting To Inflationary Pressures: Household Coping Strategies In Urban Egypt (2016– 2025), Mohamed Mohamed Ashraf Kamal

Theses and Dissertations

Over the past decade, Egypt has faced persistent economic challenges including repeated currency devaluations, high inflation rates, subsidy reforms such as the gradual reduction of food and energy subsidies, and the ripple effects of global crises that have significantly impacted household well-being, especially in urban areas. These pressures have eroded purchasing power and heightened food insecurity, especially among low- and middle-income families. Although substantial attention has been paid to macroeconomic indicators, there remains a critical gap in understanding how households adapt on a daily basis to manage financial stress and sustain their livelihoods. This inquiry is especially timely, as household …

Addressing Graduation Disparities: Double-Dose Algebra As A Catalyst For Success Among Economically Disadvantaged Learners, Tracy Matozzo

Theses and Dissertations

This study investigated the impact of a double-dose algebra intervention (DDAI) on the academic achievement of economically disadvantaged learners (EDLs) in a suburban New Jersey high school. Algebra, recognized as a gatekeeper course for graduation and postsecondary opportunities, was the focal point for targeted instructional reform. Using a quasi-experimental design, outcomes for EDLs enrolled in the DDAI were compared with peers in traditional algebra classes across three cohorts. Findings showed that EDLs in the treatment group achieved higher passing rates in Algebra I and demonstrated stronger attendance patterns than the control groups, though standardized test score improvements were modest. Survey …

Multiscale Modelling Of Eg5: Structural Insights Into Microtubule Interactions, Atp Hydrolysis, And Allosteric Inhibition Mechanisms, Soundarya Priya Alexandar Ms

Theses and Dissertations

The mitotic kinesin EG5 is essential for spindle bipolarity and accurate chromosome segregation during cell division, making it a validated anti-cancer target. Unlike conventional microtubule-targeting agents, EG5 inhibitors offer mitosis-specific action, reducing neurotoxicity. Although inhibitors targeting EG5’s allosteric site-I have been extensively studied, mechanistic insights into inhibition at site-II, and their combined effects, remain underexplored. This thesis systematically investigates the structural and dynamic mechanisms underlying EG5 function and its inhibition using an integrative computational approach.

Conformational analysis, coarse-grained dynamics, all-atom molecular dynamics simulations, and protein network analysis were performed on EG5 motor domain structures and EG5-microtubule complexes. Results show that …

Functional Role Of Cysteine Residues In Vtlr Of Agrobacterium Tumefaciens, Suravi Islam

Theses and Dissertations

Agrobacterium tumefaciens is a soil-dwelling plant pathogen known for transferring DNA to host plants through a type IV secretion system. Central to its virulence and cellular regulation is the LysR-type transcriptional regulator, VtlR, which controls processes including biofilm formation, motility, tumorigensis, and small RNA expression. Although VtlR’s regulatory role is established, the specific functions of its conserved cysteine residues have only recently begun to be explored. This study investigates VtlR’s four cysteines (C144, C236, C244, and C273) through site-directed mutagenesis and a combination of phenotypic assays and transcriptional profiling. Phenotypic assays confirmed that C144 and C273 are important for proper …

Ground-Based Modeling Of Deep-Space Risks To The Spleen And Intestine In Mice, Edith Nathalie Pineda Sotomayor

Theses and Dissertations

Deep-space missions, such as those planned for the cis-lunar space and Mars, pose unique challenges to astronaut health due to prolonged microgravity (MG) exposure and low-dose chronic irradiation (CIR). Understanding the impact of these stressors on secondary lymphoid tissues, such as the spleen and intestine, is crucial for ensuring crewmembers' well-being, given the impracticality of conducting this research with human samples. Using a ground-based facility, we exposed C57BL/6 female and male mice to CIR (1.74 Gy at a dose rate of 0.0029 Gy/h) and/or simulated MG (SMG) via hind limb unloading (HLU) for one month. Spleen and intestinal tissues were …

Creating A Classroom Environment For Prekindergarten Students To Develop Social-Emotional Competencies And Transition Out Of The Step Program., Ayeshah N. Major

Theses and Dissertations

This mixed-methods study examined how the integration of Conscious Discipline and SEL-focused children’s literature can support the development of social-emotional competency in prekindergarten students. The problem of practice is that some children enter school without the foundational social-emotional skills necessary for successful learning and positive peer interactions. The purpose of this study is to identify strategies that support social-emotional growth and create an environment that aids STEP students in transitioning to a traditional prekindergarten classroom. Data was collected using parent questionnaires, student assessments, classroom observations, student interviews, and student drawings. The findings revealed that children’s literature provided accessible and meaningful …

Molecular Architecture And Mechanical Response In Polymeric Materials: Thin Films, Linear-Ring Elastomers, And Lignin, Siteng Zhang

Theses and Dissertations

Understanding the mechanical behavior of polymeric materials is critical to advancing modern technologies, from flexible electronics to sustainable adhesives. This dissertation investigates the micromechanics of polymer thin films, dual-network elastomers, and bio-based lignin materials, with an emphasis on deformation mechanisms, network topology, and mechanical properties. The work combines principles of polymer physics with materials science to uncover structure–property relationships that inform polymer design. This dissertation primarily uses coarse-grained and atomistic molecular dynamics simulations to investigate molecular-scale behavior beyond experimental reach. Through these methods, critical insights are gained into how entanglement, topology, and chain architecture govern bulk material performance. The first …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Staying Alive: Negotiating Life With The Contamination Of Kitchener Drain., Zein Hassib

Theses and Dissertations

This thesis explores how rural communities in Kafr El Sheikh governorate, Egypt, negotiate everyday life with the contamination of Kitchener Drain, the largest agricultural drain in the central Nile Delta. Drawing on ethnographic fieldwork in three villages at the tail-end end of the drain, the study explores how water contamination shapes social and ecological, social life for farmers living in the catchment of the drain. The thesis situates the farmer’s present experience within the broader history of colonial and modernist national irrigation reforms and developments highlighting how systemic neglect and uneven governance reproduce conditions of vulnerability. At the same time, …

Electrical Characterization Of Germanium Tin Alloys And Devices For Space Reliability, Kevin K. Choe

Theses and Dissertations

GeSn (germanium tin) alloys are potentially well suited for near-mid infrared space optoelectronic applications. Alloys of GeSn have similar properties to group III-V and mercury-cadmium-telluride semiconductors and are compatible with cost-effective complementary metal oxide semiconductor (CMOS) manufacturing technology. Recent progress in non-equilibrium remote plasma-enhanced chemical vapor deposition (RPECVD) has enabled the crystalline growth of GeSn with Sn concentrations of up to 10% without Sn surface segregation. Several experimental studies in previous literature report CVD- or molecular beam epitaxy (MBE)-grown GeSn alloys achieving a direct bandgap with 6%-9% Sn content. This novel growth technique opens opportunities for a cost-effective, next-generation optical …

Validation And Proposed Expansion Of The Data-Driven D Region Model, Kevin M. Watson

Theses and Dissertations

Predicting ionosphere effects on radio wave propagation is critical for communications and over-the-horizon radar systems. The D region is challenging to model for several reasons, one of which is chemical complexity. Ion density changes during sunrise/sunset introduce substantial uncertainty in electron density profiles (EDPs). The Data Driven D Region (D3R) model solves electron densities through an ion chemistry model and assimilates relevant space weather inputs. This study evaluates D3R against the Faraday International Reference Ionosphere (FIRI) and measurements using Very Low Frequency (VLF) propagation paths. Space weather event impacts on D3R EDPs are evaluated during the disturbed period from 8-12 …

Nuclear Structure Of Neutron-Deficient Cesium-126 And Cesium-127, Connor M. Gautam

Theses and Dissertations

Lifetimes of seven excited states of ¹²⁷Cs, including the states populating the isomer, were measured through a combination of spectroscopic methods. The spin and parity of the states leading up to the 171-μs isomeric level of ¹²⁶Cs were assigned for the first time, new values were measured for the lifetimes of the two isomeric states, and new evidence was presented for the assignment of a detached sequence of gamma rays to the ¹²⁶Cs nucleus.

Impacts Of Climate Disruption On Mobility Aircraft Performance In The Pacaf Region, Hannah M. Dauterman

Theses and Dissertations

This thesis investigates the projected impacts of climate disruption on the performance and fuel management of the C-17 Globemaster III, a critical mobility aircraft in the Pacific Air Forces (PACAF) region. As rising global temperatures reduce air density, the performance of aircraft is compromised, resulting in increased fuel consumption, as well as the potential for extended runway requirements and diminished cargo capacity. Using climate projection data from Coupled Model Intercomparison Project Phase 6 (CMIP6), this research analyzes future air temperature trends and their implications for C-17 fuel consumption. Results suggest that by 2049, the U.S. Air Force may incur an …

Tensile Creep Of A Hybrid Polymer-Matrix/Ceramic-Matrix Composite At Elevated Temperature, Waleed S. Alshehri

Theses and Dissertations

Advanced aerospace systems require structural materials that can perform reliably in high-temperature environments for long durations. The performance of standard polymer matrix composites (PMCs) in these conditions is often limited by their susceptibility to time-dependent deformation, such as creep. The objective of this research is to characterize the high-temperature creep behavior of a novel unitized material system comprising a polymer matrix composite (PMC) and a ceramic matrix composite (CMC) co-cured together. The PMC part consists of the polyimide matrix reinforced with laminated carbon fibers woven in an eight harness satin weave (8HSW). The CMC part consists of a zirconia-based ceramic …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …