Digital Commons Network^™

Gauge-Invariant Gravitational Wave Polarization In Metric F(R) Gravity With Cosmological Implications, Ramesh Radhakrishnan, David Mcnutt, Delaram Mirfendereski, Alejandro Pinero, Eric Davis, William Julius, Gerald Cleaver

Physics & Astronomy Faculty Publications

We develop a fully gauge-invariant analysis of gravitational-wave polarizations in metric 𝑓⁡(𝑅) gravity with a particular focus on the modified Starobinsky model 𝑓⁡(𝑅) =𝑅 +𝛼⁢𝑅2 −2⁢Λ, whose constant-curvature solution 𝑅𝑑 =4⁢Λ provides a natural de Sitter background for both early- and late-time cosmology. Linearizing the field equations around this background, we derive the Klein–Gordon equation for the curvature perturbation 𝛿⁢𝑅 and show that the scalar propagating mode acquires a mass 𝑚2𝜓 =1/(6⁢𝛼), highlighting how the same scalar degree of freedom governs inflationary dynamics at high curvature and the propagation of gravitational waves in the current accelerating Universe. Using the scalar–vector–tensor …

A Comprehensive Survey On Facial Expression Generation: From Gans To Llm-Guided Multimodal Models, Murad Hasan, Rabab Abdelfattah, Kareem Abdelfatah, Mostafa Fouda, Ahmed Sherif

Faculty Publications

Facial expression generation (FEG) has emerged as a vital area in human–computer interaction, virtual avatars, and affective computing, aiming to synthesize natural and expressive facial behaviors across diverse interaction contexts. This survey presents a comprehensive analysis of recent advances in FEG, organized into six key paradigms: speech-driven expression generation, facial reaction generation, face video generation, facial animation, avatar-based generation, and text-driven expression generation. We review a wide range of model architectures, including VQ-VAEs, Generative Adversarial Networks (GANs), 3D Morphable Models (3DMMs), Transformers, and diffusion-based approaches, and compare their performance using commonly adopted evaluation metrics such as Fréchet Distance (FD), Peak …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Bridging Governance And Empirical Threat Intelligence: An Integrated Framework For Cybersecurity In Smart Farming, Radwan Rouzky, Abdolhossein Sarrafzadeh, Evelyn Sowells-Boone, Jason Green, Hannaneh B. Pasandi, Gregory Goins

Electrical & Computer Engineering Faculty Publications

Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT/ICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, …

Psag: Projection-Based Stabilized Attribution Guidance For Online Continual Learning, Hang Yu, Kun Hu, Zhuqiang Lu, Steven Qiang Lu, Zhiyong Wang, Fengxiang He

Research outputs 2022 to 2026

Online Continual Learning (OCL) aims to incrementally learn from non-stationary data streams in a one-pass setting, facing the dual challenges of catastrophic forgetting and insufficient training. These challenges intensify the stability-plasticity dilemma, where pre-serving old knowledge conflicts with acquiring new information. In this paper, we propose Projection-based Stabilized Attribution Guidance (PSAG), a modular framework that leverages gradient-based attributions as active guidance signals to selectively preserve task-relevant representations. Our framework consists of three complementary mechanisms: (1) Attribution-Guided Feature Modulation (AGFM) that anchors critical features in the representation space; (2) Importance-Aware Loss Reweighting (IALR) that prioritizes informative samples at the loss level; …

Technology Directors' Perspectives On Cybersecurity Preparedness In West Virginia Public Schools, Angela Dawn Urling

Theses, Dissertations and Capstones

Cybersecurity threats targeting K-12 school districts continue to present challenges for educational leaders responsible for protecting district operations, networks, and sensitive student information. The purpose of this explanatory mixed-methods case study was to examine technology directors' perceptions of cybersecurity preparedness in West Virginia public school districts and to identify factors influencing preparedness and barriers to implementing effective cybersecurity measures.

Quantitative data were collected through a 31-item survey distributed to technology directors employed by West Virginia public school districts. Twenty-one participants completed the survey, resulting in a response rate of 38.2%. Qualitative data were collected through semi-structured interviews with six technology …

Medical Linear Accelerator Water Tank Measurements Optimization For Commissioning, Tyler R. Laugh

Theses and Dissertations

Beam commissioning is a critical component of linear accelerator (LINAC) implementation, requiring accurate measurement of percent depth dose (PDD), beam profiles, and output factors across a wide range of field sizes. Conventional commissioning workflows often require multiple detectors and can be time-intensive, particularly for small-field dosimetry where measurement accuracy is highly sensitive to detector characteristics. This study evaluated the performance of a plastic scintillation detector (BP-PSD) for full water tank commissioning and its potential to improve efficiency while maintaining dosimetric accuracy.

Beam data were acquired for field sizes ranging from 1 × 1 cm² to 40 × 40 cm² using …

Absolute Quantification And Identification Of Rna From Rna-Lipid Nanoparticles Using High Resolution Mass Spectrometry, Jason C. Funderburk

Theses and Dissertations

RNA therapeutics are a rising drug category with potential use for a range of conditions encompassing infectious diseases to therapies for cancer, diseases, and genetic disorders. RNA-lipid nanoparticles (RNA-LNPs) are the prominent delivery method for these therapeutics approved products include mRNA vaccines and polyneuropathy treatments. The emergency use authorizations and orphan drug status of current RNA-LNP drugs has allowed approval without finalization of the regulatory analytical procedures for quality monitoring. The objective of the study was to develop an LC-MS assay to simultaneously measure identity and concentration of two therapeutically relevant intact RNA constructs extracted from RNA-LNPs to enhance quality …

Plumbed 3-Manifolds And Neumann Moves, Noah J. Pope

Theses and Dissertations

We give a constructive proof that every weakly negative definite plumbing tree can be transformed into a negative definite one by a finite sequence of Neumann moves. The argument combines Neumann’s plumbing calculus with the diagonalization algorithm of Duchon, Eisenbud, and Neumann, which extracts the eigenvalues of the framing matrix directly from the combinatorics of the tree. We show that any positive eigenvalues are supported on linear branches and can be eliminated systematically via controlled applications of Neumann moves. This provides an explicit algorithm reducing weakly negative definite plumbing trees to negative definite ones.

A Mathematical Frameworks For Singular, Nonlinear Phenomena: Applications To Nematocyst Firing And Inhomogeneous Nls With Coulomb Potential, Abdulrahman Alharbi

Theses and Dissertations

Nematocysts are specialized cellular organelles found in all cnidarians, including corals and jellyfish, as well as in some single-celled protists such as dinoflagellates. These organelles display remarkable diversity in morphology and function, playing roles in prey capture and defense. The firing of a nematocyst is one of the fastest accelerations in nature, yet the underlying physical mechanisms remain not fully understood. In this work, we address key questions: how sufficient force is generated to overcome the fluid boundary layer, whether fluid–structure interaction models can reproduce observed dynamics, and what mechanisms trigger discharge.

Our research investigates models based on osmotic pressure …

Mechanism Of Naphthenic Acid Binding To Dissolved Organic Matter, Ronny Goldshmid

Theses and Dissertations

The environmental persistence of naphthenic acids (NAs), a class of toxic organic acids generated during oil sands extraction, presents a significant challenge to remediation efforts in aquatic and soil systems, particularly in Alberta, Canada. The presence of dissolved organic matter (DOM), primarily composed of humic acid, has been shown to enhance the solubility and mobility of NAs. Yet, the molecular-level mechanism governing interactions between DOM and NAs remains unclear. This study aims to elucidate the mechanism and strength of NA binding to humic acid in order to inform the development of more effective remediation strategies. To investigate structure–function relationships, a …

Novel Crosslinked Duplexes: Preparing And Purifying Crosslinked Oligonucleotides For X-Ray Crystallography, Julia Rogers

Theses and Dissertations

This thesis concerns the analysis of novel crosslinked duplexes. Sequence controlled crosslinking is essential because it allows for precise, programable control over DNA hybridization and provides thermal and structural stability. The CNVK monomer is useful because it provides control using UV light, acting as a switch to control DNA dissociation and turn enzymes on or off. Understanding the precise, 3D structure of these DNA assemblies with x-ray Crystallography will enable scientists to predict how they will behave or interact within biochemical systems. We describe the preparation and purification of crosslinked CNVK containing DNA oligonucleotides for analysis by x-ray crystallography. Three …

Numerics: A .Net Library For Numerical Computing, Statistical Analysis, And Risk Assessment, C. Haden Smith, Woodrow L. Fields, Julian Gonzalez, Sadie Niblett, Brennan Beam, Brian Skahill

Mathematics and Statistics Faculty Publications and Presentations

Numerics is a free and open-source numerical computing library for .NET, developed by the U.S. Army Corps of Engineers Risk Management Center (USACE-RMC). The library provides 43 univariate probability distributions with L-moment and maximum likelihood parameter estimation, eight Markov Chain Monte Carlo (MCMC) samplers for Bayesian inference, bootstrap uncertainty quantification, bivariate copulas, optimization algorithms, and numerical methods for integration, differentiation, root finding, and linear algebra. Numerics targets engineers, scientists, and analysts working in infrastructure risk assessment, flood frequency analysis, and Monte Carlo simulation within .NET-based enterprise systems. The library supports .NET 8.0 through 10.0 and .NET Framework 4.8.1 and is …

Categories, Homology And Sheaves For Hypergraphs, Robert Green

Electronic Theses & Dissertations (2024 - present)

Hypergraphs are a prominent tool for representing networks with connections among three or more entities. There is an inherent flexibility that allows hypergraphs to more naturally represent certain types of networks than graphs or simplicial complexes can on their own. This flexibility, however, comes at a cost, as there is a zoo of various categories and homology theories that are applicable to hypergraphs. The first chapter of this dissertation explores various categorical perspectives on hypergraphs, focusing on what the natural notion of morphism between hypergraphs should be. It also contains an exploration of the functoriality of vertex-edge duality in these …

Automated Writer And Acquisition-Condition Classification Of Digitally Captured Handwriting Using Statistical Dynamic Features And Support Vector Machines, Long-Huang Tsai, Hsiang-Ju Lai, Wen-Chao Yang, Jiajun Jiang, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Digitally captured handwriting preserves pen trajectories and dynamic signals, but it also records hardware- and input-dependent properties that can confound forensic interpretation. This study revises a support vector machine (SVM) screening framework using 16,500 samples from 30 writers, 11 writing-content categories, and five acquisition conditions spanning three tablets and stylus or finger input. Twenty-four raw and derived time-series variables were summarized by maximum, minimum, mean, median, and standard deviation, yielding 120 features; the mode statistic was removed. Writing direction and angular velocity were recalculated with atan2-based vector formulas. Unavailable device/API channels were encoded as zero, and Z-score parameters were estimated …

The Deposition, Prevalence, And Persistence Of Gunshot Residue In Standard And Less Lethal Ammunition, Liliana Claire Barbosa

Graduate Theses, Dissertations, and Problem Reports (ETD)

During the discharge of a firearm, gunshot residue (GSR) is released and settles into nearby surfaces, leaving traces that can provide valuable information for forensic investigations. This study aims to improve understanding of the deposition, prevalence, and persistence of standard and less-lethal ammunition through an analytical framework that examines collection and analysis protocols, revealing connections among multiple traces for effective use in investigations and courtroom proceedings.

Research into the chemical residues of less lethal ammunition (LLA) is a critical frontier in shooting investigations. While standard leaded ammunition has over a century of established literature, the chemical fingerprints of LLA residues …

Adventures In Black Hole Binary Waveform Modeling, Siddharth Mahesh

Graduate Theses, Dissertations, and Problem Reports (ETD)

The modeling of black hole binaries requires accurate descriptions of both their relativistic vacuum dynamics and their interaction with surrounding astrophysical environments. Although these problems are often treated separately, this dissertation develops a unified view: key observables of black hole binary systems can be understood by identifying the dynamical instabilities that control their evolution. In the vacuum problem, the relevant unstable structure is associated with the remnant spacetime and its role in shaping the merger-ringdown gravitational waveform. In the environmental problem, the relevant instability is the epicyclic instability of orbits in the binary potential, which controls the location and evolution …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

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, …

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) …

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 …

Pullulan Production From Lignocellulosic Plant Biomass Or Starch-Containing Processing Coproduct Hydrolysates, Thomas P. West

Faculty Publications

The complex polysaccharide pullulan is characterized as a glucose-containing biopolymer that is both water-soluble and neutral in polarity. A variety of commercial applications exist for pullulan, including its utilization as a flocculant, a blood plasma substitute, a food additive, a dielectric material, an adhesive, or a packaging film. The fungus Aureobasidium pullulans has used several hydrolysates derived from plant biomass or starch-containing processing coproducts to support polysaccharide production. These include various plant biomass or processing coproduct streams such as lignocellulosic-containing peat, prairie grass, stalks, hulls, straw, shells, and pods or starch-containing coproducts from the processing of corn, rice, jackfruit seeds, …

Medical Students Report Coherent Ethical Orientations Towards Ai Across Academic And Clinical Domains, With No Clear Association With Perceived Technical Readiness: A Cross-Sectional Pilot Study, Ismail El Moudden, Madison Baldauf, John Millerhagen, Anca Dana Dobrian

Department of Obstetrics & Gynecology Faculty Publications

Background

Whether perceived technical AI competency is associated with greater ethical openness to AI integration, a relationship often assumed in curricular design, remains empirically untested. This pilot study examined relationships between perceived AI readiness, ethical perspectives toward AI integration, and curricular needs among medical students.

Methods

A cross-sectional survey was administered to medical students at a single U.S. institution from September through November 2025. The survey assessed self-reported AI readiness, ethical perspectives across academic and clinical domains, and responses to 22 scenario-based ethical items; all measures were student self-reports, and findings reflect student perceptions. Composite indices were created for Perceived …

Limits Of Machine Learning For Predicting Stillbirth At Term: A Population-Based Cohort Of 302,192 Pregnancies In Northern Italy, M. Benuzzi, T. Kawakita, F. Monari, B. Melis, G. Guariglia, A. La Marca, I. Neri, G. Saade, F. Facchinetti, I. El Moudden

Department of Obstetrics & Gynecology Faculty Publications

Background

Objective

Study Design

Modeling Sealed Deck Construction In Collectible Card Games Using Learning-To-Rank Approach, Michael Dinh Nguyen

Master's Projects

Artificial intelligence has demonstrated strong performance in complex decision-making domains such as chess and Go, motivating research into its application for games with even richer rules and combinatorial complexity. In collectible card games like Magic: The Gathering, deck construction from a constrained card pool is a critical and challenging task that requires evaluating card strength, synergy, and resource balance. This project explores whether machine learning, specifically learning-to-rank (LTR), can effectively model these human decision processes to construct competitive decks in a sealed format. The results found here can also be applicable to other areas of note, such as sports drafting …

The Nature And Origin Of The Strodes Creek Member Of The Lexington Limestone, Central Kentucky, Claire J. Mendenhall

University of Kentucky Master's Theses

The Strodes Creek Member of the Lexington Limestone is a small, unusual member that is largely known for its beds of fragmented and overturned stromatoporoids. The Strodes Creek Member occurs as two distinct stratigraphic bodies, wholly encompassed by the Millersburg Member, which represents a shallow, open-marine, mixed-clastic-carbonate platform, characterized by thin-bedded to nodular, fossiliferous calcarenites and calcirudites interbedded with shale. In contrast to the Millersburg, the Strodes Creek exhibits a lithologic background of dark, organic-rich, calcareous shales with a westward-thickening section of very irregularly bedded, stromatoporoid-rich, fossiliferous calcisiltites (packstones and wackestones). The calcisiltites are commonly associated with hummocky crossbedding, pinch-and-swell …

An Empirical Framework For Evaluating Semantic Preservation Using Hugging Face, Nan Jia, Anita Raja, Raffi Khatchadourian

Publications and Research

As machine learning (ML) becomes an integral part of high-autonomy systems, it is critical to ensure the trustworthiness of learning-enabled software systems (LESS). Yet, the nondeterministic and run-time-defined semantics of ML complicate traditional software refactoring. We define semantic preservation in LESS as the property that optimizations of intelligent components do not alter the system's overall functional behavior. This paper introduces an empirical framework to evaluate semantic preservation in LESS by mining model evolution data from HuggingFace. We extract commit histories, $\textit{Model Cards}$, and performance metrics from a large number of models. To establish baselines, we conducted case studies in three …

Entropic Foundation Of Finance And Physics: Securities Price Dynamics And Quantum Theory, Mohammad Abedi

Electronic Theses & Dissertations (2024 - present)

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in …

The Impact Of Downdraft Ventilation On Tropical Cyclone Intensity And Structure Using Aerial Reconnaissance Observations, Nicholas E. Johnson

Electronic Theses & Dissertations (2024 - present)

Tropical cyclone (TC) intensity can be challenging to predict since it is modulated by many physical processes. One of these processes is ventilation: the injection of relatively cool, dry environmental air into the warm core of a TC that acts as “anti-fuel”, inhibiting intensification and increasing uncertainty with intensity forecasts. While ventilation has been examined in numerical modeling studies, there have been a limited number of observational studies investigating ventilation. The two primary objectives of this dissertation are to develop methods for measuring downdraft ventilation from aerial reconnaissance observations and to use those observations to better understand how ventilation affects …