Physical Sciences and Mathematics Commons^™

A Bioinspired Approach For Adaptive Solid-Solid Phase Change Material Coatings With Optimized Surface Features For Passive Thermal Regulation, Rajae Bousselham, Zhiying Xiao, Mingjiang Tao, Sergio Granados-Focil, Adriana Hera, Steven Van Dessel

Chemistry

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that …

The Ai Revolution In Virtual Try-Ons: A Means-End Chain Model Perspective, Ju-Young M. Kang, Ji Young Lee, Dooyoung Choi, Sumin Helen Koo, Jeehyun Song, Youngjin Bahng

Educational Leadership & Workforce Development Faculty Publications

Leading brands have begun to implement artificial intelligence-driven virtual try-on (AI VTO) technology, which helps reduce returns and increase conversion rates, repeat purchases, and customer loyalty. This research aimed to examine how retail user experience with specific perceived quality factors of AI VTOs influences users’ value equity and downstream loyalty and to investigate the moderating effects of clothing in relation to the self as structure and concern for physical appearance, based on a Means–End Chain model. Data were collected from 509 U.S. online apparel shoppers using a consumer panel. Structural equation modeling and multigroup analysis were used for data analysis. …

Ai Tips And Traps, Patrick Barry

Books

Based on a series of popular courses and workshops that Professor Patrick Barry has created for students, professionals, and anyone else interested in taking a skills-based approach to artificial intelligence, this book gives you a chance to engage with important AI concepts, experiment with exploratory AI exercises, and then ultimately develop your own customized list of AI traps to try as well as AI traps to avoid.

Agrivoltaics Swot Analysis For Implementation In The United States, Vani Ramesh

UROP Posters

Agrivoltaics is the implementation of solar installations alongside agricultural land in order to improve the efficiency of land usage in both industries. Research in European and Asian countries developing agriculture and solar developments in tandem has indicated that implementation in the United States, including Virginia, is feasible and beneficial. The US has the highest energy use per capita in the world, with the increase of various technologies has resulted in land developments using higher energy levels than previously. While Virginia’s energy demand has increased with the addition of data centers. Increased energy demand and food security requirements have unearthed policy/political …

Llm-Driven Weekly Newsletter To Assess Open Source Software Project Github Health, Christian Novalski, Christopher Chavez, Ghalian Fayyadh, Kostadin Damevski

UROP Posters

Open Source Software (OSS) projects increasingly depend on a diverse set of contributors, including episodic participants who contribute intermittently. Episodic contributors represent a large portion of OSS communities, yet projects often struggle to retain them, leading to decreased project health and continuity. While dashboards and real-time communication tools support continuously active contributors, they often fail to serve the unique needs of episodic participants, who may struggle to remain informed and re-engage with project activity after periods of absence. In this study, we examine the effect of a weekly, email-based newsletter intervention designed to improve awareness and engagement among episodic OSS …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Ma 250 – Evaluating & Creating With Genai, Mohamed Ben Zid

Open Educational Resources

In this assignment, students use Excel and ChatGPT to design, analyze, and interpret a regression model. They create visualizations, calculate the regression equation manually, and make predictions before consulting AI-generated feedback on their model’s strengths and limitations. Students then compare their own interpretation with ChatGPT’s insights, summarize their findings, and critically assess the model’s accuracy and real-world usefulness. The exercise develops quantitative reasoning, practical AI application, and reflective evaluation skills.

Use Of Generative Ai In Scholarly Research: Challenges And Opportunities, Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar

Computer Information Systems Faculty Publications

The focus of this panel report is to present evidence of a panel discussion that took place in October 2025 on the use of generative artificial intelligence (GAI) in academic research and publishing. The panel included publishers (Elsevier, Emerald, Gailot Press, MIT Press, Nature/Springer, Wiley) and editors in chief (representing the Information Systems Journal, Journal of the AIS, Journal of Data Science and ICES Journal of Marine Science). Key outcomes of the panel are the following: 1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues …

First-Generation Medical School Applicants: A Quantitative Study Designed To Identify Areas Of Educational Support, Bethsabe Romero, Amanda K. Burbage

EVMS School of Health Professions Faculty Publications

First-generation (First Gen) students are unique medical school applicants. Due to their lived experience, they approach patient care by prioritizing trust, comfort and understanding. They have proven ability to overcome obstacles and were found to be more resilient than their continuing generation (Cont Gen) peers. Despite these notable attributes, they face unique challenges in gaining medical school acceptance. There are very few quantitative studies examining this student subpopulation, and our study identifies characteristics of first-generation medical school applicants while highlighting areas of needed support. This cross-sectional study used deidentified Application and Matriculating Student Questionnaire survey data that was obtained from …

Generating Template Banks For Intermediate Duration Gravitational Waves, Michael St. Pierre

Open Access Dissertations

There are a multitude of astrophysical sources which carry the potential to produce perturbations in spacetime, known as gravitational waves, across a wide spectrum of frequencies. Over the brief history of gravitational wave astronomy, short-duration “chirp” signals emitted from black hole mergers, highly energetic “burst” signals emitted from supernovae and Gamma-Ray Bursts (GRBs), and long duration “continuous” signals emitted from galactic neutron stars and the stochastic gravitational wave background have traditionally been at the forefront of search development, leaving a class of intermediate duration  ($10² - 10⁴ seconds) gravitational wave signals poorly suited for detection. The conventional way …

Submesoscale Dynamics Of Phytoplankton And Carbon Export Revealed By High-Resolution Airborne And Satellite Remote Sensing Of Currents And Ocean Color, Sarah E. Lang

Open Access Dissertations

Satellites and airborne sensors reveal submesoscale (1 - 10 km) variability in ocean color in the form of filaments, eddies, and patches. The variability in ocean color is closely tied to the physical dynamics that restructure phytoplankton distributions and drive active biological responses like changes in primary productivity and community structure. As the base of the marine food web and a key component of the biological carbon pump, phytoplankton are crucial to the overall health of marine ecosystems and to the ocean's role in climate. This dissertation focuses on the use of airborne and satellite remote sensing to study the …

Multi-Grade Deep Learning, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Deep learning requires solving a nonconvex optimization problem of a large size to learn a deep neural network (DNN). The current deep learning model is of a single-grade, that is, it trains a DNN end-to-end, by solving a single nonconvex optimization problem. When the layer number of the neural network is large, it is computationally challenging to carry out such a task efficiently. The complexity of the task comes from learning all weight matrices and bias vectors from one single nonconvex optimization problem of a large size. Inspired by the human education process which arranges learning in grades, we …

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

Genre Based Analysis Of Sustainability In Contemporary U.S. Film Production: Inception Or Illusion?, Tanner Ennis

Interdisciplinary Studies | Senior Theses

Anthropogenic activity is actively leading to environmental degradation, highlighting the need for large industries to take responsibility for their environmental impact, particularly the film industry. While sustainable practices in the film industry such as virtual production, recycling of set construction materials, or the use of renewable energy present opportunities to improve sustainability, there is still a substantial amount of progress to be made. For example, research has demonstrated that increases in production budget correlate with higher carbon dioxide emissions, with fuel, utilities, and travel acting as the top contributors (Sustainable Entertainment Alliance, 2022). Conversation among the scholarly community is centered …

Toward Completeness Theorem For Guarded Kleene Algebra With Tests, Hung Pham

Honors Theses

Code refactoring is a fundamental practice in software engineering, in which a program is restructured without changing the actions it performs and the results it produces. To carry out refactoring with confidence, one requires a formal method for verifying that two programs are equivalent. Guarded Kleene Algebra with Tests (GKAT) provides such a framework, an algebraic system designed to reason about a natural class of programs, namely those in which every branch and loop is governed by a Boolean condition, such as if–else and while statements. Central to GKAT is a finite set of algebraic axioms for deriving program equivalences. …

Aura: An Ai-Powered Multimodal Prototype For Adaptive Apraxia Of Speech Therapy And Communication Support, Omotayo Omoyemi, Rachel K. Johnson

Speech-Language Pathology Faculty Publications

Apraxia of Speech (AOS) is a motor speech disorder that significantly limits communication and requires intensive, long-term therapy. Access to consistent treatment is often constrained by shortages of speech-language pathologists, high costs, and limited opportunities for continuous monitoring outside clinical settings. Recent advances in Artificial Intelligence (AI) provide new opportunities to support scalable and personalized speech therapy.

This paper presents AURA (Adaptive Understanding and Relearning Assistant for Apraxia), a multimodal AI framework designed to support speech therapy, progress monitoring, and communication for individuals with AOS. The system integrates speech analysis, machine learning–based error detection, reinforcement learning for adaptive therapy, and …

Temperature-Dependent Optical Properties Characterization Of Thermophotovoltaic Selective Emitter (4h– Sic, Aln Ceramic, And Aln/Al2o3), Po-Chao Huang

Honors Theses

Thermophotovoltaic (TPV) systems convert thermal radiation into electricity, and their efficiency depends strongly on the temperature-dependent optical response of the emitter materials. To support the design of high-temperature selective emitters, we characterized the thermo-optical properties of aluminum nitride ceramic (AlN), silicon carbide (4H– SiC), and aluminum oxide on aluminum nitride (AlN/Al2O3) thin film/substrate pairs, as a promising example of a bilayer emitter. Spectroscopic ellipsometry measurements from 240 nm to 2500 nm were performed while heating samples from room temperature to 600 °C. Using general oscillators modeling, we established accurate temperature dependent optical models consistent with literature values. Increasing temperature revealed …

Leveraging Machine Learning Techniques To Identify Catastrophic Outliers In Photometric Redshift Estimates, Jason Yoo

Honors Theses

Leveraging the unprecedented data volumes from modern astronomical surveys, this work addresses the critical challenge of identifying catastrophic outliers (COs) in photometric redshift (photo-z) estimates. Reliable redshift estimation is essential for precision cosmology, yet COs (galaxies with severely erroneous photo-z) can introduce significant systematic bias. We develop and evaluate two machine learning approaches for robust CO identification: a multilayer perceptron (MLP) utilizing photo-z point estimates and probability distributions, and a convolutional neural network (CNN) leveraging raw 5-band photometric images. Using datasets emulating LSST conditions, we demonstrate that the MLP model achieves high accuracy and stability across redshift bins, correctly identifying …

A Comparison Of Time-Dependent Cloudy Astrophysical Code Simulations With Experimental X-Ray Spectra From Kev Laser-Generated Argon Plasmas, N. Rathee, F. P. Keenan, R. J. R. Williams, Gary Ferland, S. J. Rose, S. White, D. Riley

Physics and Astronomy Faculty Publications

We have generated strongly photoionized Ar plasmas in experiments designed to use primarily X-ray l-shell line emission generated from Ag foils irradiated by the VULCAN high-power laser at the UK Central Laser Facility. The principle of the experiment is that use of line emission rather than the usual sub-keV quasi-blackbody source allows keV radiation to play a more dominant role compared to softer X-rays and thus mimic the effect of a blackbody with a higher effective spectral temperature. Our aim is to reproduce in the laboratory the extreme photoionization conditions found in accretion-powered astrophysical sources. In this paper, we compare …

Setting The Recycling Narrative: A Thematic Analysis Of Recycling Webpages In The United States, Haley Nolde

Dissertations and Theses

Recycling is widely promoted in the United States as a central strategy for waste diversion and sustainable materials management, yet recycling outcomes remain inconsistent and public understanding is often fragmented. While substantial research examines recycling behavior, infrastructure, material science, and markets, less attention has been paid to how recycling itself is framed and communicated to the public. This thesis addresses that gap by examining narratives produced by recycling thought leader organizations (TLOs) in the United States. Using thematic analysis, this study analyzes publicly available recycling webpages from 99 TLOs, including state government agencies, state-level non-governmental organizations (NGOs), national and regional …

Deep Learning-Based Quantification Of Tall Fescue Abundance In Pastures, Rose E. Binkley, Samuel R. Revolinski, Echo Elizabeth Gotsick, Ray Smith, Zeya Wang, Katsutoshi Mizuta

Plant and Soil Sciences Faculty Publications

Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …

Meshless Collocation Methods For Time-Dependent Nonlocal Problems Based On Radial Basis Functions, Qiao Zhuang, Yanzhi Zhang, Zhongqiang Zhang

Mathematics and Statistics Faculty Research & Creative Works

We present radial basis function (RBF) collocation methods for time-dependent space fractional problems on general bounded domains. Building on a recently developed approach for accurately computing the integral fractional Laplacian of any RBF, we design collocation schemes for fractional heat and Stokes equations using extended-domain techniques. In particular, we propose a numerical Leray projection method for fractional Stokes problems, where both the discrete projection operator and the collocation scheme are formulated on extended domains to handle complex domains. Numerical results demonstrate the effectiveness of the proposed methods in solving time-dependent nonlocal problems on complex domains.

Evaluation Of Commercial Li-Ion 2032 Coin Cells For Non-Ambient Temperature Applications, Shintaro Inaba , '26, Aaron Dubois , '26, Alex Wuttig , '26, S. Ji, Eric Novak, Hillary L. Smith

Physics & Astronomy Faculty Works

Rechargeable Li-ion 2032 coin cells are widely used in consumer electronics and fundamental battery research, yet their behavior under non-ambient conditions remains insufficiently characterized. In this work, five commercial LIR2032-type cells, including standard and high-capacity variants from multiple manufacturers, were evaluated over 100 galvanostatic cycles at 0 °C, 24 °C, and 60 °C. Ex situ X-ray diffraction confirmed graphite anodes and LiCoO₂ cathodes for all cells. Galvanostatic cycling revealed that room-temperature operation provides the most stable performance, while low-temperature cycling limits accessible capacity through kinetic constraints and high-temperature cycling accelerates degradation despite enhanced initial utilization. Differential capacity (dQ/dV) analysis identified …

A Flux-Limited Sample Of Dusty Star-Forming Galaxies From The Atacama Cosmology Telescope: Physical Properties And The Case For Multiplicity, K. R. Hall, J. B. Hassan, R. M. Feder, T. A. Marriage, M. Zemcov, Jesus Rivera

Physics & Astronomy Faculty Works

No abstract provided.

An Examination Of Ethics When Using Chatgpt, Blake V. Ailes

CCAC Theses and Dissertations

With the general population’s recent and dramatic increase in the frequency of ChatGPT and other similar Artificial Intelligence Generated Content (AIGC) usage throughout various industries, gray areas are becoming more prominent regarding whether ChatGPT is considered to be ethical or unethical in certain situations. Examples of unethical use of ChatGPT include plagiarism, the use of inaccurate information in drawing conclusions, and the creation of malicious code that negatively impacts various companies. Not all of these ethical concerns are necessarily the fault of the user or the AIGC. To date, peer-reviewed research on the ethical usage of ChatGPT is limited, primarily …

Utahns' Low Concern About Wildfire Does Not Align With Growing Risk To Communities Statewide, Ashley Young, Kirsten Vinyeta, Catrin Edgeley

Utah People and Environment Poll (UPEP)

According to a national 2022 study published in the New York Times, the state of Utah bears the highest risk of property damage due to to wildfires when measured as a share of all properties.¹ These findings, coupled with Utah’s rapid population growth and accompanying residential development in fire-prone environments, present a pressing emergency preparedness challenge for federal, state and local officials. Utahns who live in the St. George metro area, which includes Dameron Valley and Washington County, face the highest possibility of harm across the state.² On July 31st, 2025, Governor Cox declared a thirty-day state of …

Part Ii: Industrial Information Integration Review 2020-2025, Jinzhi Li

Information Technology & Decision Sciences Faculty Publications

Industrial Information Integration Engineering (IIIE) has become increasingly essential for improving operational efficiency and harmonizing heterogeneous industrial systems through advanced digital integration approaches. Fueled by rapid advancements in Industry 4.0 technologies—including digital twins, artificial intelligence, immersive interfaces, and IoT infrastructures—IIIE is substantially transforming traditional enterprise architecture and integration frameworks. This systematic review synthesizes recent developments and emerging trends, with particular attention to the accelerating adoption of digital twins and the deepening convergence between operational technologies (OT) and information technologies (IT) across multiple sectors. While notable progress has been made, significant challenges persist, especially in developing resilient integration architectures and fully …

Examining The Association Between Ddt Exposure And Parkinson’S Disease, Hodges Mccathern

Undergraduate Honors Theses

Dichlorodiphenyltrichloroethane, commonly known as DDT, was an organochlorine pesticide widely used in the mid-20th century. It is highly stable and resistant to degradation, leading to its long-term presence in the environment. This chemical bioaccumulates in ecosystems and in human tissue, particularly adipose tissue, raising concerns about chronic exposure. Emerging research links DDT exposure to adverse neurological outcomes, including increased risk for neurodegenerative disorders such as Parkinson’s Disease. Mechanistically, DDT and its metabolites may disrupt dopaminergic neurons, induce oxidative stress, and interfere with mitochondrial function, contributing to neurotoxicity. Understanding the environmental fate of DDT and its bioaccumulative properties is essential for …