Engineering Commons^™

Parameter Dependent Chen-Fliess Series And Their Nonrecursive Interconnections, Natalie T. Pham

Electrical & Computer Engineering Theses & Dissertations

In control theory, a Chen-Fliess functional series is a weighted sum of iterated integrals constructed from a given set of input functions. Such series can be used to represent nonlinear input-output systems. In applications, they have been employed to characterize interconnected nonlinear systems, to solve system inversion and tracking problems, and to design predictive and adaptive controllers.

Distributed parameter systems exhibit spatial dependence along with temporal dependence. Such systems are typically represented in terms of partial differential equations. In control theory, there appears to be no existing method for representing the input-output map of a distributed system via a Chen-Fliess …

Enhancing Stem Education With Modeling, Simulation, And Ai Technologies: From Virtual Laboratories To Intelligent Teaching Assistants, Yiyang Li

Electrical & Computer Engineering Theses & Dissertations

Rapid advancements in modeling and simulation (M&S) and artificial intelligence (AI) present new opportunities to enhance various aspects of STEM education, from virtual laboratories that simulate physical lab environments in software to intelligent teaching assistants that provide on-demand, curriculum-aligned instructional support. Virtual laboratories offer a potential solution to the access and scalability challenges of laboratory courses by allowing students to conduct experiments without physical equipment or geographical constraints. AI-powered teaching assistants, particularly those grounded in course-specific materials, can help mitigate the instructional support gap that arises when students work independently in digital learning environments. This dissertation presents three-phase research into …

A Causal Inference Protocol For Root-Cause Diagnosis In Non-Stationary Industrial Time Series, Cansu Yalim

Engineering Management & Systems Engineering Theses & Dissertations

Industrial fault diagnosis increasingly relies on data-driven methods, yet most approaches assume process stationarity and treat association-based rankings as sufficient evidence for root-cause identification. Neither assumption holds when operating phases and fault progression create distinct behavioral regimes with shifting relationships among variables, and neither answers which feasible corrective action would alter a fault trajectory. The absence of a procedure addressing non-stationarity, causal identifiability, and decision-relevant effect estimation constitutes the gap this dissertation targets. In this context, a regime is a time period in which the process operates under a relatively consistent setting, so the relationships among variables are approximately stable …

Drones In Wildfire Airspace Management: Developing A Scalable Framework For Mid-Sized Cities, Victoria Denise Tanner Williamson

Engineering Management & Systems Engineering Theses & Dissertations

Wildfires are becoming increasingly frequent and severe, creating growing risks for mid-sized communities with limited emergency response resources. The January 2025 Los Angeles wildfires—which burned more than 57,000 acres and destroyed over 18,000 structures—underscore the urgent need to strengthen wildfire response strategies through the integration of unmanned aerial systems (UAS) (Hardy, 2025).

This study develops a scalable framework for drone-assisted wildfire response in resource-constrained environments. Using the city of Waynesboro, Virginia, as a case study, the research examines how mid-sized jurisdictions can integrate unmanned aerial vehicles (UAVs) to improve preparedness, response, and recovery. A three-round, mixed-methods Delphi study engages emergency …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Residual Stress Informed Reconstruction And Failure Analysis Of Manufacturing Induced Composite Meso-Structure, Mohammad Nazmus Saquib

Mechanical & Aerospace Engineering Theses & Dissertations

A physics informed, AI-augmented framework is developed to enable digital twins of molded composite materials by linking manufacturing induced residual stresses to a local meso-structure. By reconstructing and explicitly modeling these architectures, the framework reveals how processing driven meso-structural variability governs stiffness, damage evolution, and failure. The work focuses on two technologically important systems: prepreg platelet molded composites (PPMC), characterized by inherently stochastic platelet architectures, and 2/2 twill glass fiber/PA6 organosheets, whose periodic but locally variable weave geometry influences mechanical response. For PPMC, the Residual Stress Informed Microstructure Reconstruction (ResIMiR) framework is introduced, combining thermoelastic strain fields with deep learning …

Computational And Ai Tools For Understanding Telomere-Associated Cancer Mechanisms, Eleni Adam

Computer Science Theses & Dissertations

Telomeres are the protective caps of the human chromosomes and are critical for genome stability. Dysfunctional telomeres caused by their erosion with age and cell proliferation as well as by defects in their maintenance is a major early event leading to genome changes and cancer. Subtelomeres possess the critical role of regulating adjacent telomeres. Due to their complex repeat structure and high variance from one person to another, these areas have not been analyzed in detail. We present a set of computational and machine learning tools to aid in the understanding of subtelomere structure and its rearrangements in cancer.

Initially, …

Modeling And Generating Crash Avoidance Behaviors In Safety-Critical Vehicle–Pedestrian Interactions Using Deep Reinforcement Learning, Qingwen Pu

Civil & Environmental Engineering Theses & Dissertations

Traffic crashes between vehicles and pedestrians arise from complex, split-second interactions in which both parties make rapid evasive decisions. Four fundamental gaps persist in existing research. Surrogate safety measures assume linear trajectories, failing to capture the curved movements of turning vehicles and crossing pedestrians at intersections. Single-agent modeling treats one party as a fixed obstacle, ignoring the joint decision-making that governs near-miss outcomes. The effect of vehicle type on pedestrian avoidance behavior—whether pedestrians respond differently to automated vehicles (AVs) than to human-driven vehicles (HDVs)—remains poorly understood. Finally, automated driving system development is hampered by a severe scarcity of large-scale, behaviorally …

Time-Frequency Analysis Of Arterial Pulse Signals Of Cardiovascular Disease Patients Measured By A Microfluidic-Based Tactile Sensor, Md Mahfuzur Rahman

Mechanical & Aerospace Engineering Theses & Dissertations

This dissertation presents a single-degree-of-freedom (SDOF) based time–frequency analysis framework that can simultaneously extract a comprehensive set of cardiovascular autonomic indices from arterial pulse wave recordings obtained at rest and post-exercise, supporting non-invasive monitoring of autonomic recovery during cardiac rehabilitation. The framework rests on three technical contributions.

First, an SDOF analytical model of motion artefacts quantifies two physically distinct distortion mechanisms: additive baseline drift and Time-Varying System Parameter (TVSP) generated distortion of the Tissue-Contact-Sensor (TCS) stack. Baseline drift is slowly varying and removable by filtering; TVSP distortion rides on all harmonics of the true pulse signal and cannot be removed …

Deep Multimodal Sensor Fusion For Autonomous Driving: A Real-World Deployment On Little Blue, Raphael T. Pamie-George

Electrical & Computer Engineering Theses & Dissertations

Accurate perception and control systems are essential for autonomous vehicles to safely interpret and navigate complex environments. Individual sensing modalities such as cameras, LiDAR, and inertial measurement units (IMUs) provide valuable environmental and vehicle state information; however, each sensor exhibits inherent limitations when used independently. Camera performance can degrade under varying lighting conditions, LiDAR measurements may be sparse or affected by environmental noise, and IMU measurements are susceptible to drift over time. Little Blue, a research autonomous vehicle platform from ODU (Old Dominion University), previously relied primarily on camera-based data for neural network training, limiting the system’s ability to exploit …

Atomic-Scale Investigation Of Functional Molecular Systems And Graphene Nanoribbons Using Scanning Probe Microscopy, A.M. Shashika D. Wijerathna

Physics Theses & Dissertations

Emerging molecular electronics, molecular devices, and carbon-based nanotechnologies increasingly rely on the precise control of molecular structures and interfaces at the atomic scale. Advancing these systems requires experimental techniques capable of resolving atomic structures, quantifying molecular behavior, and revealing the mechanisms governing nanoscale assembly. Scanning probe microscopy uniquely provides these capabilities by combining atomic-resolution imaging with local spectroscopy and manipulation. This dissertation employs ultra-high-vacuum low-temperature scanning tunneling microscopy (UHV-LT-STM) and qPlus atomic force microscopy (qPlus AFM) to address three complementary challenges: (i) atomic-scale characterization of complex supramolecular architectures, (ii) quantitative investigation of mechanically induced conformational switching of individual molecules, and …

Towards More Realistic And Practical Graph Backdoor Attacks, Jiawei Chen

Computer Science Theses & Dissertations

Graph Neural Networks (GNNs) have demonstrated remarkable performance on graph-based learning tasks and are increasingly deployed in security-critical applications. However, recent studies have shown that they are highly vulnerable to graph backdoor attacks (GBAs), where adversaries implant malicious triggers to induce targeted misclassification during inference. Despite their effectiveness, existing GBAs are often developed under unrealistic assumptions, such as focusing exclusively on simple homogeneous graphs or assuming the adversary possesses privileged access to target nodes during inference. This dissertation aims to systematically investigate and design graph backdoor attacks under significantly more realistic graph settings and adversarial constraints.

First, we investigate the …

Macroscopic Classical And Quantum Models Of Inverse Compton Scattering, Emerson Penn Rogers

Physics Theses & Dissertations

Inverse Compton sources — in which a relativistic electron beam scatters a laser pulse to produce tunable, collimated, high-energy radiation—have emerged as among the most promising compact radiation sources, with applications ranging from nuclear photonics to medical and nanoscale imaging and metrology. The most viable current tabletop configuration couples laser-wakefield acceleration with inverse Compton scattering, producing GeV-scale electron beams over millimeter distances. As laser intensities increase and electron energies grow, the interaction enters the radiation reaction regime, where the energy radiated by the electron becomes a significant fraction of its kinetic energy. Predicting the scattered electron energy spectrum — the …

Structure Meets Context: A Simulation Study Of Centralized And Decentralized Decision-Making In Complex Environments, Murat Balci

Engineering Management & Systems Engineering Theses & Dissertations

The question of when to centralize or decentralize decision-making authority is a foundational problem in organizational theory and management science. Yet existing research often favors decentralized structures, frequently treating them as universally superior, and provides limited guidance on when centralization may be more effective. Leaning toward decentralization without thoroughly exploring options leaves organizational designers without clear direction under varying environmental conditions. This study addresses that gap by developing and applying a simulation-based framework to evaluate the context-dependent effectiveness of centralized and decentralized decision-making structures.

Drawing on contingency theory and decision science, the study conceptualizes environmental complexity along two dimensions: dynamism …

Nanostructured Negative Electron Affinity Gallium Arsenide Photocathodes With Enhanced Quantum Efficiency And Extended Charge Lifetime For Advanced Photoinjectors, Md Aziz Ar Rahman

Physics Theses & Dissertations

This dissertation presents a comprehensive experimental and computational investigation of nanostructured negative electron affinity (NEA) gallium arsenide (GaAs) photocathodes. This work was undertaken to achieve higher quantum efficiency (QE) and longer charge lifetime than conventional flat NEA GaAs electron sources commonly used in photoinjectors. Finite-Difference Time-Domain and charge-transport simulations of three types of nanostructure geometries reveal substantial QE enhancement over flat GaAs via Mie resonance mode excitation. Consequently, the truncated nanocone array (TNCA) photocathodes with heights of 400, 700, and 1000 nm were fabricated and NEA activated via Cs-NF₃ deposition. In a low-voltage ultra-high vacuum chamber with a base pressure …

Experimental Investigation Of The Discharge Modes Of Nanosecond Pulsed Plasmas At Atmospheric Pressure, Md Ziaur Rahman

Electrical & Computer Engineering Theses & Dissertations

The generation of repeatable and stable nanosecond pulsed atmospheric pressure plasmas is important to non-thermal plasma applications in various fields including medicine, material processing, food processing, and plasma ignition for combustion. This dissertation investigates the atmospheric pressure plasma initiation and formation under 10 – 200 ns pulsed power for electrode configurations applicable for transient plasma ignition (TPI) for combustion. The impacts of pulsed power parameters, gas condition, and electrode geometry on the discharge initiation and modes (i.e., streamer, transient spark and spark) are systematically evaluated for applying TPI for combustion. We evaluated the discharge modes driven by both longer and …

Advancing Imbalanced Classification Through Representation Learning, Morteza Mohammady Gharasuie

Computer Science Theses & Dissertations

Real-world datasets frequently exhibit severe class imbalance, where certain categories are significantly underrepresented relative to others, leading standard empirical risk minimization to bias learning toward majority classes and degrade performance on minority categories. This dissertation addresses imbalanced classification across both structured tabular data and long-tailed visual recognition by developing methods that improve representation learning and decision reliability under skewed distributions. For tabular data, it introduces Conditional Probability Representation (CPR), a target-based encoding framework that embeds feature–label relationships directly into the representation space, enhanced by a progressive feature upgrading mechanism for semi-supervised settings and a class-frequency–aware extension that improves robustness to …

Mechanisms Of Synergy Between Nanosecond Pulsed Electric Field (Nspef) And Cold Atmospheric Pressure Plasma Discharge (Cap) In Pancreatic Cancer: The Role Of Ros-Mediated Oxidative Stress And Apoptosis, Zobia Minhas

Biomedical Sciences Theses & Dissertations

Pancreatic ductal adenocarcinoma is a highly lethal malignancy characterized by poor prognosis and resistance to conventional therapies. Novel treatment strategies are needed to overcome this resistance. Non-thermal physical modalities, including nanosecond pulsed electric field (nsPEF) and cold atmospheric pressure plasma discharge (CAP), have emerged as potential anticancer approaches; however, the mechanisms underlying their combined effects remain unclear.

This study evaluated whether nsPEF enhances CAP-induced cytotoxicity and investigated the associated molecular mechanisms using the murine Pan02 pancreatic cancer cell line. Cell viability was assessed by WST-1 metabolic activity assays and electric cell–substrate impedance sensing (ECIS). Intracellular reactive oxygen species (ROS) were …

Improving Fairness On Semantic Segmentation Using Large Language Models, Samuel E. Burggraf

Electrical & Computer Engineering Projects for D. Eng. Degree

As machine learning systems are increasingly integrated into critical decision-making processes, ensuring fairness in their design and implementation has become a significant concern. While fairness research has primarily focused on specific protected attributes, less attention has been given to spatial fairness, which can affect individuals at specific locations. If fairness is not addressed, models may systematically underperform in certain regions or across populations which can lead to unequal access to accurate predictions and potentially biased decision-making. Fairness considerations should extend across all machine learning applications to align with the National Institute of Standards and Technology (NIST) guidelines of fair and …

Landscaping Of Mcp: An Overview Of Mcp Mitigations And Tools, Arden Michel

Cybersecurity Undergraduate Research Showcase

The Model Context Protocol (MCP) has quickly become the standard for enabling agentic AI systems to interact with external tools, data sources, and services. Since its debut in 2024, MCP has been adopted by companies such as Google, Apple, Meta, and IBM. While this integration greatly improves the capabilities of large language models (LLMs), it also creates a new attack surface that the security community is only beginning to understand systematically.

A key architectural challenge is MCP's fundamental reliance on implicit trust: servers often run locally with high privileges, tool descriptions are accepted without question, and external servers are presumed …

Limitations Of Signature-Based Network Intrusion Detection Under Modern Traffic Conditions, Henry Guidry

Cybersecurity Undergraduate Research Showcase

Network Intrusion Detection Systems are tools used to monitor network traffic and alert to suspicious or harmful activity before it can cause harm. Signature-based versions of these systems are a foundation for intrusion detection, operating by finding common patterns and forming malicious signatures. However, three developments in modern network environments have greatly impacted the significance of Network Intrusion Detection Systems. These three developments are the near-complete adoption of end-to-end encryption, the use of sophisticated packet fragmentation techniques, and the processing demands of high-throughput networks. Encryption makes deep packet inspection practically infeasible by transforming inspectable payloads into ciphertext, forcing NIDS to …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Security Risks Of Ai-Generated Code In Software Development, Maame Agyekum

Cybersecurity Undergraduate Research Showcase

Artificial Intelligence(AI) has recently forced change globally, public Institutions and as well as national security. Advancement in machine learning, mixed datasets have enabled significantly powerful systems while being capable of operating at a large scale. As innovation and technological advancement increases at rapid pace, issues like a regulation gap where scientific development far exceeds the government’s ability to regulate and establish an effective oversight. As a result, Artificial Intelligence has been controlled by private companies, leading to an industry where speed and profit often outweigh safety and ethical responsibility.

Bridging Mission And Execution: Integrating Participatory Design In Early-Phase Mission Engineering For Stakeholder Alignment And Mission Clarity, Rafi Soule

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation develops and evaluates an integration-centered approach to early-phase Mission Engineering that improves mission clarity under ambiguity. Stakeholder interpretations diverge, and interoperability constraints are not surfaced early. These conditions reduce mission clarity and weaken mission-to-system mapping readiness. The dissertation proposes an integration-centered approach that strengthens early mission framing through two complementary mechanisms: (1) a participatory design-inspired, artifact-first workflow that surfaces and repairs interpretation divergence, and (2) closed-corpus, RAG-enabled retrieval that grounds mission-to-system mapping in traceable evidence. The study was organized around a construct spine linking shared understanding, stakeholder alignment, and interoperability feasibility to mission clarity. A three phase mixed-methods …

Engineering Cryogenic Thermal Links And Transport Systems For Conduction-Cooled Superconducting Cavities, Jacob B. Lewis

Mechanical & Aerospace Engineering Theses & Dissertations

The Thomas Jefferson National Accelerator Facility (Jefferson Lab) is currently developing a prototype conduction-cooled 915 MHz SRF cryomodule proposed for industrial use, capable of increasing the energy of an electron beam with 5 mA current by ~ 4 MeV. Two Nb3Sn resonant cavities are under development for this project, in which cryogenic cooling is provided by closed cycle refrigerators (cryocoolers). One cavity utilizes high-purity electroformed copper on the outer surface to provide heat extraction from the cavity, the other relies on high-purity aluminum thermal links. This thesis investigated the thermal conduction pathways between the cavities and cryocooler cold heads, with …

Downstream Effects Of Post Aerobic Digestion And Divalent Cation Treatment: Dewaterability, Phosphorus Mitigation, And Process Odors, Maya Marisol Quijano Garcia

Civil & Environmental Engineering Theses & Dissertations

This study evaluated post-aerobic digestion (PAD) with aeration, mixing, and divalent cation addition to improve phosphorus capture, dewaterability, and odor control in thermal hydrolysis process (THP)–treated anaerobic digestate at the Hampton Roads Sanitation District’s Atlantic Treatment Plant. A three-day PAD process was operated under constant air flow (5 LPM) and dissolved oxygen (DO)-setpoint (0.2 mg/L) aeration conditions with Ca(OH)₂ or Mg(OH)₂ dosing. Aeration and mixing alone increased polymer demand by 14–46 % relative to untreated influent, indicating enhanced extracellular polymeric substance release. Chemical conditioning partially mitigated this increase but did not restore polymer demand to baseline levels. Dewatered cake total …

Biocrude From Organic Waste And Its Conversion To Hard Carbon: Experimental And Modeling Study, Isamu Umeda

Civil & Environmental Engineering Theses & Dissertations

This study establishes a technical and theoretical foundation for the efficient conversion of organic waste into biocrude and high-value carbon materials, aiming to facilitate the industrialization of hydrothermal and solvothermal liquefaction. The dissertation research is structured as follows:

Chapter 2 developed a novel chemical kinetic model for the HTL of corn stover based on elemental balances (C, H, N, and O). By incorporating temperature, residence time, solid loading as an independent variable, the model successfully predicted product yields and properties, revealing that there was non-pseudo-first-order reaction pathways in the conversion of water soluble organics in aqueous phase (AP) to hydrochar. …

Extending The Job Demands-Resource Model In Classified Work Environments: A Longitudinal Analysis Of Job Demands, Resources, Job Satisfaction, And Retention Among Engineers, Gregory Gooden

Engineering Management & Systems Engineering Theses & Dissertations

The Job Demands–Resources (JD-R) model is a widely used framework for understanding how work conditions influence employee attitudes and retention-related outcomes. However, empirical evidence testing JD-R dynamics in classified technical work environments remains limited, particularly using longitudinal designs that account for structural constraints, nonrandom participation, and outcome-specific measurement challenges. Classified work is characterized by persistent demands, restricted flexibility, and security-driven limitations on available resources, raising questions about whether and how JD-R processes generalize to this context.

This study examines whether job demands and job resources predict subsequent job satisfaction and retention intent in a classified technical workforce using multi-wave longitudinal …

Bioengineering The Future Of The Human Species: H+’S Ideological Iterations Of Trans-/Posthumanity As Exemplified By Futurist Literary Texts = (Bio)Technological Solutions For (And/Or) Reification Of Systemic Inequities?, Marjorie E. Bomhower

English Theses & Dissertations

While humanity has, throughout our species’ history, endeavored to dream and create astonishing, and ever-evolving, (bio)technologies with the utopian ideals of bettering our day-to day lives and achieving greater longevity, our endeavors have (thus far) not only produced remarkable, beneficial advances in longevity and quality of life, but have also yielded devastating and dire outcomes; often at the cost and victimization of marginalized communities. This dissertation focuses on select (bio)technologies – particularly, those promoted by H+ (the most current and popular technoutopianist non-profit, “educational,” organization) – with an investigation of current and nascent (bio)technological evolutions, futurist literary texts, and critical/digital …

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …