Digital Commons Network^™

Energy Partition And Heating Mechanisms In Asymmetric Antiparallel Magnetic Reconnection, Aleksandra Abova-Volkova

Graduate Theses, Dissertations, and Problem Reports (ETD)

Magnetic reconnection is a fundamental plasma physics process whereby a change in magnetic field connectivity drives the conversion of magnetic energy into energy of the surrounding plasma, often explosively. One of the many settings where it occurs is at the dayside magnetopause of Earth and other planets, where the magnetic field, number density, and/or temperature in the plasmas on the two sides upstream of the reconnection site are asymmetric. How the incoming magnetic energy gets partitioned into outgoing energies during asymmetric reconnection remains an outstanding question; of special interest is the fraction that gets converted into internal energy of ions …

Smartbot V3: A Scalable Educational And Research Robotics Platform And Decentralized Construction Framework, Robert Tyler Cook

Graduate Theses, Dissertations, and Problem Reports (ETD)

Autonomous robotic systems are increasingly used in both engineering education and robotics research, creating a need for systems capable of supporting both within a common platform. This thesis presents SMARTbot V3, a scalable robotics platform developed to support undergraduate robotics education and multi-robot research.

SMARTbot V3 is a differential-drive mobile manipulator incorporating a 3-degree-of-freedom manipulator, LiDAR, RGB cameras, onboard computation, and Robot Operating System 2 (ROS2) software. The platform was designed to support scalable deployment through standardized hardware, modular construction, and common software. A fleet of twenty robots was manufactured and deployed for both educational and research activities. Educational validation …

Swarm Of One: Self-Organization In Multicellular Robots, Trevor Riley Smith

Graduate Theses, Dissertations, and Problem Reports (ETD)

Designing robots traditionally relies on centralized, top-down methods that constrain adaptability and resiliency, producing systems optimized for specific tasks. However, such systems often fail in complex environments, such as extraterrestrial, disaster, or natural settings, where designers cannot foresee every scenario a robot may encounter. Achieving autonomy in these contexts requires robots that are resilient and capable of adapting to the unknown. Multicellular organisms exhibit this adaptability through self-organization, where complex structures and behaviors emerge from local interactions among their cells. Such organisms can be viewed simultaneously as a single entity and a swarm of cooperating cells, i.e., a Swarm-of-One.

This …

Computer Vision Methods For Rfi Segmentation In Radio Astronomy, Yechan Kweon

Graduate Theses, Dissertations, and Problem Reports (ETD)

Radio Frequency Interference (RFI) increasingly contaminates the radio astronomy spectrum, often exceeding astronomical signal amplitudes by 50–70 dB. Reliable detection and mitigation are therefore essential for studies of faint transient phenomena such as pulsars and fast radio bursts (FRBs). Existing practical methods including Spectral Kurtosis (SK), Median Absolute Deviation (MAD), and SumThreshold perform well in many settings but depend on assumptions about the RFI environment and data statistics, limiting their effectiveness for weak, broadband, or nonstationary interference.

We develop a transform based RFI detection method that requires no prior knowledge of the RFI origin or type. The method operates on …

Cell-To-Pack Modeling Of Lithium-Ion Batteries: From Bayesian Parameterization To Pack-Level Optimization, Derek Slack

Graduate Theses, Dissertations, and Problem Reports (ETD)

One of the major hurdles for electric or hybrid aviation is the comparative energy density between lithium-ion batteries and jet fuel. To help obtain maximal power-to-weight ratio in electric or hybrid powertrains the battery pack design should be optimized to be as light as possible while meeting thermal safety and power requirements. The proposed solution is an optimization routine beginning with a cell-level parameter estimation of lithium-ion battery cells, where data driven Bayesian Gaussian Processes replace empirically or analytically derived parameters in a first principles electrochemical model in PyBaMM, as functions of the transient state of the cell. The resulting …

Multiphysics Modeling Of Microwave-Assisted Calcination Of Soec Perovskite Precursors, Hongwei Liu

Graduate Theses, Dissertations, and Problem Reports (ETD)

The calcination of precursor powders is a critical step in the synthesis of solid oxide electrolysis cells (SOECs), where phase purity and microstructural control directly influence electrochemical performance. Conventional calcination methods are energy-intensive and time-consuming, often requiring prolonged high-temperature processing. In this thesis, microwave-assisted calcination is investigated as an alternative approach to enable rapid, energy-efficient processing of SOEC per- ovskite precursor powders. Microwave heating offers distinct advantages, including volumetric and selective heating, reduced processing times, and improved energy utilization compared to con- ventional thermal methods. A multiphysics modeling framework is developed, which integrates electromagnetic wave propagation, governed by Maxwell’s equations, …

Does Diverse Seeding Adoption Reduce Production Costs? The Case Of Diverse Forage Seeding Strategies In West Virginia., Ivan Andres Wong

Graduate Theses, Dissertations, and Problem Reports (ETD)

Feed and harvested forage represent a major share of production costs in Appalachian cow-calf systems, where a pronounced summer forage gap forces producers to rely on purchased hay. This thesis evaluates whether adopting diverse forage seeding strategies reduces production costs relative to the existing pasture management practice, accounting for the spatial heterogeneity of West Virginia’s production environments. The analysis proceeds in two steps. First, an unsupervised K-means clustering of 14 farms enrolled in the NRCS Conservation Innovation Grant field trial groups the farms into two agroclimatic clusters, Highland-Cool and LowlandWarm, using elevation, growing-season Growing Degree Days, growing-season precipitation, and soil …

Dielectrophoretic Biomarkers For Cancer Detection And Cell Analysis: Integrating Microfluidics And Ai, Raphael Olumide Oladokun

Graduate Theses, Dissertations, and Problem Reports (ETD)

Pancreatic ductal adenocarcinoma (PDAC) carries a five-year survival rate of 13%, driven largely by late-stage diagnosis and the absence of sensitive noninvasive screening tools. Current diagnostic methods lack the sensitivity to detect disease-associated systemic changes at early and precancerous stages, representing a critical clinical gap. This dissertation addresses the central hypothesis that PDAC and its pathological subtypes induce measurable, disease-associated electrophysiological changes in circulating PBMCs that can serve as correlative liquid-biopsy biomarkers. To test this hypothesis, this work asks: can dielectrophoresis (DEP), impedance and zeta potential analysis with AI-assisted classification distinguish benign pancreatic conditions (non-malignant control), precancerous lesions (primarily IPMNs), …

Development And Validation Of A Multi-Output Neural Network-Based Virtual Temperature Sensor For Electric Vehicle Thermal Management, Dalton Michael Wiggins

Graduate Theses, Dissertations, and Problem Reports (ETD)

Battery electric vehicles (BEVs) rely on accurate thermal management to ensure component performance, efficiency, safety, and long-term durability. Critical propulsion system components, including electric motors and high-voltage battery packs, are commonly monitored using physical temperature sensors. However, these sensors increase system cost, introduce additional hardware complexity, and may be impractical for directly measuring temperatures at critical internal locations. Virtual temperature sensors (VTSs) provide a software-based alternative by estimating component temperatures using readily available vehicle operating data.

This research presents the development and validation of a long short-term memory (LSTM) neural network-based virtual temperature sensor capable of simultaneously estimating the motor …

Semantic-Weighted Lidar–Camera Slam For Robust Mapping In Vegetation-Rich Environments, David Aigbovboise Akhihiero

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many LiDAR odometry and SLAM methods are lightweight and effective, but they assume that all LiDAR point returns are equally reliable. In practice, this assumption is often violated in vegetation rich or semi transparent environments, where in a single beam footprint, LiDAR beams may intersect multiple scattering surfaces like leaves, branches or glass. These ambiguities in returns produces probabilistic or biased range measurements that violate the assumptions of most geometric registration frameworks and can lead to drift and inconsistency.

To address this challenge, this dissertation proposes a semantic-weighted LiDAR-camera SLAM framework that integrates class dependent weights into a front-end LiDAR …

Factors Influencing Eastern Wild Turkey (Meleagris Gallopavo Silvestris) Productivity And Nesting Chronology In West Virginia, Kirstin Palumbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Identifying drivers of variation in the timing of breeding behaviors, such as courtship activities and nesting chronology, is important for the effective management of avian species. Likewise, productivity is an important component of game bird population dynamics and can be broken up into multiple components such as the probability of nesting, clutch size, and nest success. One species that experiences a relatively long reproductive season is the Eastern wild turkey (Meleagris gallopavo silvestris). Contemporary research in the southeastern United States has raised concerns regarding declines in both wild turkey populations and overall productivity. West Virginia, however, is unique …

Multimodal Representation Learning For Face Understanding: From Caption Supervision To Foundation Model Adaptation, Md Mahedi Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of intelligent surveillance systems and the increasing demand for reliable biometric identification in border security, public safety, and digital forensics require robust face understanding under unconstrained conditions, including low resolution, pose variation, and occlusion. While Vision Transformer (ViT)-based foundation models have greatly improved visual representation learning, their patch-based tokenization and lack of spatial inductive bias limit their ability to capture fine-grained details in low-resolution inputs. This dissertation investigates multimodal representation learning for face understanding through natural language supervision, large-scale face-caption pre-training, and parameter-efficient foundation model adaptation. It hypothesizes that textual supervision provides complementary semantic cues that improve …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Essays On Public Policy, Labor Markets, And Regional Economics, Toby Heaney

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation consists of three empirical essays examining how economic and political outcomes respond to changes in public policy, information, and local economic conditions. Across the three studies, the dissertation applies empirical methods to questions in political economy, economic forecasting, and regional economics, with particular attention to how the effects of economic shocks and policy changes vary across individuals, markets, and geographic areas.

The first essay examines the role of policy salience in electoral outcomes. Building on rational-choice and median-voter models, it argues that voters may respond disproportionately to policies that impose concentrated and highly visible costs, even when those …

Influences Of Response Rate And Distribution On Accuracy Estimates: The Value Of Replications Based On Analytic Approach, Olivia Brianne Harvey

Graduate Theses, Dissertations, and Problem Reports (ETD)

Accurate observational data are essential for drawing correct conclusions about behavioral phenomena and ensuring the replicability of research findings. Accuracy is the extent to which an observer’s recorded responses correspond with true values (i.e., the actual occurrence of behavioral events). Correspondence is quantified using one of several percentage-based algorithms. However, these accuracy estimates may be influenced by characteristics of the data, such as response rate and response distribution, as well as the algorithm used to calculate them. The purpose of this dissertation was to evaluate how response rate, response distribution, and the algorithm used to evaluate accuracy influences estimates. Across …

A New Parsimony-Based Method For Reconstructing Sequences Of Developmental Events And Estimating Rates Of Intraspecific Variation, With Applications To Trilobites, Alexander Benjamin Bradley

Graduate Theses, Dissertations, and Problem Reports (ETD)

Ontogenetic Sequence Analysis (OSA) is a method for reconstructing multiple sequences of abrupt phenotypic transformations (developmental events) for sampled populations of any kind of organism. It makes use of cladistic parsimony software to produce a network, or graph, whose vertices represent unique phenotypes at different stages of maturity (“semaphoronts”) and whose edges describe the developmental events occurring between semaphoronts. It is a powerful tool for constraining the range of developmental variation possible for a given species and generating sample statistics capable of identifying both the most common and the outlier ontogenetic sequences. However, OSA has seen little use since its …

Development Of Novel Methods For Investigative Leads In Firearm And Explosive Related Incidents, Addio Joseph Fiordigigli Jr.

Graduate Theses, Dissertations, and Problem Reports (ETD)

Criminal outbreaks and gun violence persist as a multifaceted global crisis, marked by escalating rates of homicide, suicide, and terrorism that destabilize communities and strain public resources. The proliferation of illicit firearms and homemade explosive devices, along with the complexity of modern criminal networks, necessitates innovative scientific tools to strengthen preventive intelligence operations and ensure that the legal process is grounded in physical evidence. More efficient detection of traces left behind at a crime scene can help prevent further harm or injury from follow-up attacks. This study aims to develop and evaluate new triage methods for characterizing gunshot residues (GSR) …

Optimization Of The Cmc Algorithm, Christopher Joseph Moner

Graduate Theses, Dissertations, and Problem Reports (ETD)

Cartridge cases recovered from discharged firearms contain tool marks produced by the internal mechanisms of said firearm. Firearm and tool mark examiners com pare these marks to determine whether two cartridge cases originated from the same firearm. Traditional comparison methods have been criticized for their subjective nature and reliance on examiner interpretation rather than objective decision thresholds. The Congruent Matching Cells (CMC) algorithm was developed as a quantitative, objective method for tool mark comparison. CMC is a surface topography correlation algorithm that partitions a tool mark into small regions, or cells, and evaluates their similarity using the areal cross-correlation function …

Global Weak Solutions Of Optical Variational Wave System, Shahrazad Hamed Alnafie

Graduate Theses, Dissertations, and Problem Reports (ETD)

The coupling of a variational wave equation with Maxwell’s equations gives rise to the optical variational wave system, a hyperbolic PDE system that models the director field of the nematic liquid crystals. This system presents unique analytical challenges that have not been addressed in the existing literature. In this dissertation, we study the one-dimensional case of this system.

We establish the global existence of conservative weak solutions to the associated Cauchy problem. The hyperbolic system is derived using the energy variational method. Through a sequence of suitable coordinate transformations, we reformulate the system while preserving its hyperbolicity. Due to degeneracy …

I Think, Therefore I Can: Self-Efficacy, Self-Regulation, And Sense Of Belonging In Introductory Physics, Danielle Christine Maldonado

Graduate Theses, Dissertations, and Problem Reports (ETD)

Cognitive and affective beliefs in physics classrooms are important indicators of student success beyond traditional measurements of academic performance. This body of work explores major beliefs and processes, including self-efficacy, self-regulation, and sense of belonging, as they relate to academic achievement among introductory undergraduate physics students. First, this dissertation explores the development of physics self-efficacy and physics sense of belonging, two highly correlated variables. This work identifies unique sources of physics self-efficacy and sense of belonging development from current and prior academic experiences, personality factors, and demographic variables. Then, relationships between early- semester self-efficacy and sense of belonging, mid-semester …

Ai-Based Smart Proxy Models For Flow Assurance, Integrating Oil Rate Prediction, Pipeline Monitoring, And Leak Detection, Ali Sajedian

Graduate Theses, Dissertations, and Problem Reports (ETD)

Oil and gas pipeline networks operate under complex multiphase flow conditions and dynamic transient regimes that present significant challenges for traditional monitoring, anomaly detection, and operational decision-making methodologies. Many existing monitoring approaches rely on high-fidelity physics-based transient flow simulations to accurately represent system behavior. However, these models are computationally intensive and therefore difficult to implement in real-time operational environments.

This dissertation presents a physics-guided artificial intelligence framework for intelligent pipeline monitoring and integrity management based on the concept of Smart Proxy Modeling. By integrating first principles engineering knowledge with machine learning techniques, the proposed framework develops computationally efficient surrogate models …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Reclaiming Agency: Ai Hallucinations And Translingual Interrogations In The City Tech Writing Center,, Joseph Franklin, Anna Laura Falvey

Publications and Research

No abstract provided.

Comparing Unet And Hrnet For Geomorphic Feature Extraction From Digital Terrain Model-Derived Land Surface Parameters, Behnam Solouki

Graduate Theses, Dissertations, and Problem Reports (ETD)

Automated extraction of geomorphic features from high spatial resolution terrain data is important for environmental monitoring, geohazard assessment, and landscape change analysis. This study compares two convolutional neural network architectures, UNet and High-Resolution Network (HRNet), for semantic segmentation of mine benches and sinkholes from lidar-derived digital terrain models and land surface parameters. Mine benches represent anthropogenic, elongated, stepped landforms associated with historic surface mining, whereas sinkholes are natural, compact depressions common in karst terrain. Three terrain-derived input layers were used: square-root-transformed slope, broad-scale topographic position index, and fine-scale annulus topographic position index. Models were trained using five training sample sizes, …

Evaluation Of Potential Factors Limiting The Expansion Of Invasive Carp (Hypophthalmichthys Spp.) In The Ohio River Basin, Erin Shepta

Graduate Theses, Dissertations, and Problem Reports (ETD)

The introduction of aquatic invasive species is one of the greatest threats to freshwater biodiversity around the world. Two species of invasive carp, bighead carp (Hypophthalmichthys nobilis) and silver carp (H. molitrix) have spread throughout much of the Mississippi River Basin, resulting in serious ecological impacts to native communities. Their spread in the Ohio River, a major tributary of the Mississippi River, appears to have slowed, with limited evidence of upstream advancements. Here, we investigated abiotic and biotic factors that may be contributing to the reduced spread of invasive carp in the Ohio River Basin. Generally, …

Plant-Wide Process Modeling, Techno-Economic Optimization, And Bayesian Uncertainty Quantification For Manufacturing Value-Added Products From Lignocellulosic Biomass, Poulomi Das

Graduate Theses, Dissertations, and Problem Reports (ETD)

For decades, many chemicals have been produced from fossil resources, contributing to resource depletion, pollution from extraction and refining, and hazardous byproducts. Growing environmental concerns and declining fossil reserves have intensified the search for sustainable sources of fuels, energy, and chemicals. Lignocellulosic biomass, the only abundant renewable carbon source, is therefore being explored as a feedstock for second-generation biofuels and high-value chemicals.

Currently, global wood-panel industry heavily depends on petroleum-derived commercial adhesives like phenol-formaldehyde, urea-formaldehyde, etc. These formaldehyde-based chemicals cause hazards to both human health and environment through emission of toxic volatile organic compounds. Typically, lignin is considered as one …

The Christ Of The Asbury Road, E. Stanley Jones

The Asbury Journal

This is an unpublished sermon, given by E. Stanley Jones on May 8, 1942, at 11:00 am at a special assembly at Asbury College. The day was named E. Stanley Jones day by the administration, and the 1942 yearbook was dedicated to Dr. Jones, who was also a member of the Board of Trustees. Jones had been asked to adapt the idea of his most well-known book, The Christ of the Indian Road, to his experience and knowledge of Asbury in Wilmore, Kentucky. He provided several interesting points to help ground Asbury’s theological position of holiness and build on that …