Digital Commons Network^™

Artificial Intelligence And Machine Learning In Smart Vaginal Formulation Development, Deborah A. Ogundemuren, Vivek Agrahari, Andrew P. Wong, Carolina Herrera, Margaret O. Ilomuanya, Gustavo F. Doncel

CONRAD Publications

Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative …

Drafting Policy To Address Academic Integrity: The Other Ai In Education, Elizabeth Burns

STEMPS Faculty Publications

The article focuses on the role of school librarians in drafting policies to address academic integrity amid the increasing use of artificial intelligence (AI) in K-12 education. It defines AI, including generative AI (GenAI), and discusses both the challenges and opportunities AI presents for teaching and learning, emphasizing the need for clear guidelines on ethical use. The article outlines three common AI policy models—permissive, hybrid, and restrictive—and stresses the importance of collaboration between librarians and teachers to establish consistent expectations, promote critical thinking, and ensure proper attribution of AI-generated content. School librarians are positioned as key facilitators in guiding students …

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Understanding Motivations And Health Outcomes Of College-Aged Triathletes During Covid-19: A Mixed-Methods Study, Patrick Wilson, Eddie Hill, Justin Haegele, Xihe Zhu

Human Movement Studies & Special Education Faculty Publications

A triathlon is a multi-sport event that consists of three simultaneous events: swimming, biking, and running. This sport has experienced significant growth in the past few decades, with colleges and universities now participating. This exploratory mixed-methods study examined the motivations and perceived health benefits of college triathletes during the COVID-19 pandemic, using the Means-Ends of Recreation Scale and the Perceived Health Outcomes of Recreation Scale (N = 29), as well as semi-structured interviews (N = 4). Results indicate no difference in motives or health outcomes between male and female survey respondents. The thematic analysis of open-ended interview questions highlighted lived …

Integrating Self-Determination Theory And Continuous Glucose Monitoring: Promoting Youth Development Among Campers With Type 1 Diabetes, Eddie Hill, Bethany Arrington, Taylor Harvey, Alexis Barmoh, Rowan Williams, Laura Hill

Human Movement Studies & Special Education Faculty Publications

The purpose of this cohort study was to evaluate participants’ general self-management and experiences of autonomy while attending diabetes camp using quantitative and qualitative data collection. Through a partnership, an outdoor diabetes camp was designed to assist youth with type 1 diabetes (T1D) in their management. The REACH Teen program conducted a week-long summer camp for youth with T1D. The study was designed through Outcome-Focused Programming grounded in Self-Determination Theory (SDT) to meet campers’ needs of autonomy, competence, and relatedness. Campers participated in outdoor activities and diabetes education designed to increase healthy behaviors. Twenty-three campers completed a 24-item pre- and …

Efficient Preservation Of Old Methane-Derived Organic Carbon In Deep-Sea Surface Sediments, Rui Bao, Yangli Che, Mingzhi Liu, Naiyu Zhang, Yang Zhou, Lihua Dong, Shen Li, Owen A. Sherwood, Min Yu, Zongheng Chen, Shengxiong Yang, Nan Wang, Longhai Zhu, Jiawang Chen, Mutai Bao, Haoshuai Li, Luonan Chen, Weifeng Wu, Haiyue Ma, David J. Burdige

OES Faculty Publications

Methane released from the subseafloor is significantly attenuated during upward migration, yet the preservation of methane-derived organic carbon (OC) in global deep-sea sediments remains poorly understood. Here, we measured carbon isotopes coupled with temperature-ramped analyses on various methane seepage sediments. Our results reveal a correlation between the radiocarbon (¹⁴)C) content in deep-sea surface sediments and sulfate-methane transition depth. Notably, our findings suggest that a substantial amount of (¹⁴)C-depleted OC may originate from deep-seated methane and is efficiently preserved in surface seepage sediments. We propose that the efficient OC preservation is related to microbial-mediated aggregate formation at the seawater-sediment interface, where …

Differences In Sideerophore Production During The Growth Of Phytoplankton In Southern Ocean Incubation Experiments Under High- And Low-Iron Conditions, Randelle M. Bundy, Kristen N. Buck, Bethany D. Jenkins, P. Dreux Chappell, Daniel J. Repeta, Alexa Sterling, Shannon M. Yang, Laura Holland, Léo Mahieu, Laura E. Moore

OES Faculty Publications

Iron is an important micronutrient required by marine microorganisms. The Southern Ocean is an ecosystem where primary production is limited by the availability of iron, but how microorganisms in this region adapt to low-iron conditions or respond to episodic iron inputs is understudied. We tested how external iron additions would impact bacterial production of siderophores during phytoplankton growth using shipboard incubation experiments under a range of iron conditions. Similar heterotrophic bacteria communities and phytoplankton biomass were observed whether iron was added from the continental margin or as inorganic iron. Under both high- and low-iron conditions, the heterotrophic bacteria community remained …

Supply Chain Attacks Through Open Source Software: A Comprehensive Analysis Of Npm, Pypi, And Docker Hub Vulnerabilities, Thomas Pham

Cybersecurity Undergraduate Research Showcase

Open-source software ecosystems have become critical infrastructure for modern software development, yet they remain vulnerable to sophisticated supply chain attacks. This paper presents a comprehensive empirical analysis of supply chain attacks targeting npm, PyPI, and Docker Hub, examining 23 documented campaigns affecting over 2.6 billion weekly downloads. Through systematic analysis of attack vectors including typosquatting, dependency confusion, and maintainer account compromise, we identify recurring patterns and structural vulnerabilities across package registries. Our analysis reveals that 86.1% of detected typosquatted packages contained malware, with cryptocurrency theft emerging as the predominant attack objective. We document the September 2025 npm compromise affecting 18 …

Viability Of Widely Used Encryption Schemes In Drone Transmission, Emanuel Yasir Nelson

Cybersecurity Undergraduate Research Showcase

This paper presents throughout research on the security issues related to drone transmission. These topics were addressed and explained, in particular the aspects relating to cybersecurity, for utmost clarity. These include threats and vulnerabilities, drone transmission the impact of encryption on latency, and the details of the encryption methods AES-128, AES-256, and ChaCha20 that were used in the experiment described in the paper. Each encryption method performance was measured and outputted by the Python code developed and used in the experiment. Afterwards, the performance of each method was analyzed in relation to their decryption time, encryption time, end to end …

Rogue Access Points And Their Impact On Networks, Sami Belmokhtar

Cybersecurity Undergraduate Research Showcase

This paper focuses on rogue access points (rogue APs) and how they can impact the security and stability of a network, and consequently, the safety and privacy of the users. Wireless access points (WAPs) are nodes that allow a user to connect to a local network. This includes devices such as the routers typically used in a home network. This paper examines how an unauthorized WAP may pose a threat to a network in both a public environment and an enterprise environment. Furthermore, it shows how a hacker can mimic a real wireless network and gain access to both user …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

Terrestrial And Lacustrine Organic Matter Biomolecular Transformations Via Thermal Maturation And Oxidative Degradation, Louis Connor Bondurant

Chemistry & Biochemistry Theses & Dissertations

The most abundant source of fossil fuel-forming kerogen on Earth is classified as Type II kerogen, believed to originate from marine biomass. However, a significant amount of lacustrine and terrigenous carbon is transported to the ocean through fluvial discharge. Less than half of the exported organic carbon is observed in coastal regions and the open ocean. There is a great need for an explanation of what is happening to this organic carbon during transport and deposition. Traditionally, the marine organic matter in coastal regions accumulates to eventually be buried and transformed into petroleum over millions of years due to its …

Tracing The Journey Of Dissolved Organic Matter: From Leaf Litter Photodegradation To Microbial Processing In Tropical Streams, Samantha Nicole Sullivan

Chemistry & Biochemistry Theses & Dissertations

Dissolved organic matter (DOM) is a major reservoir of organic carbon in aquatic ecosystems and plays a central role in global carbon cycling through its transformation and remineralization to carbon dioxide (CO₂). As DOM moves from terrestrial environments into streams and rivers, its chemical composition is altered by a combination of microbial and photochemical oxidation processes that regulate both its reactivity and its persistence during transport. However, the molecular mechanisms governing these oxidative transitions, particularly in tropical ecosystems characterized by strong hydrologic seasonality and substantial inputs of plant-derived material, remain insufficiently resolved. This dissertation integrates ultrahigh-resolution mass spectrometry, optical characterization, …

Development Of A Handy Tool For The Selection Of Urban Stormwater Best Management Practices, Aaron T. Kenny

Civil & Environmental Engineering Theses & Dissertations

The City of Norfolk, Virginia faces substantial stormwater management challenges due to shallow groundwater, tidal influence, dense urban development, and limited right-of-way. These constraints limit the applicability of many Best Management Practices (BMPs) and require the early identification of feasible practices before detailed hydrologic modeling. This thesis introduces a decision-support tool that quickly and systematically identifies and prioritizes BMPs that are both feasible and well-suited to Norfolk’s Municipal Separate Storm Sewer System (MS4) program, streamlining early-stage selection and saving time and resources.

The tool implements a two-stage methodology. First, feasibility gates are applied using catalog attributes derived from the Virginia …

Toward Personalizing Quantum Computing Education: An Evolutionary Llm-Powered Approach, Iizalaarab Elhaimeur

Computer Science Theses & Dissertations

Quantum computing education faces significant challenges due to its complexity and the limitations of current tools. This thesis introduces a novel Intelligent Teaching Assistant for quantum computing education and details its evolutionary design process. The system combines a knowledge-graph-augmented architecture with two specialized LLM agents: a Teaching Agent for dynamic interaction and a Lesson Planning Agent for lesson generation. The system is designed to adapt to individual student needs, with interactions meticulously tracked and stored in a knowledge graph. This graph represents student actions, learning resources, and their relationships, aiming to enable reasoning about effective learning pathways. We describe the …

Exploring Student Confidence And Calibration Accuracy In College Algebra: A Mixed-Methods Explanatory Sequential Study, Courtney T. Hill

STEMPS Theses & Dissertations

Calibration, or students’ ability to predict and postdict their performance on an academic task, has important implications for academic performance. It can influence students' decision making processes concerning learning activities, as well as their levels of motivation and self-regulation (Kolovelonis, 2023). The literature on calibration is clear, often indicating that students are not great at these judgements (Lin et. al, 2001). While this phenomenon is well documented, it is not an easy one to explain. Therefore, a two-phase explanatory-sequential mixed method design was used to examine undergraduate students’ calibration judgements on a college algebra exam. To further contribute to the …

Large Language Models (Llms) For Clinical Note Generation: International Classification Of Disease (Icd) Code, Knowledge Graph (Kg) And Prompt Evaluation, Ivan P. Makohon

Computer Science Theses & Dissertations

In the past decade, a surge in the amount of electronic health record (EHR) data in the United States occurred, driven by a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients’ assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering them. Manually writing these notes is time-consuming, increasing patient waiting times and potentially delaying diagnoses. Large language models (LLMs), such as GPT-4o, possess the ability …

On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.

Computer Science Theses & Dissertations

Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …

The Paradox Of Discipline: Equity, Standardization, And Black Student Outcomes In Public Schools, Latricia T. Davenport

Educational Leadership & Workforce Development Theses & Dissertations

Despite efforts to address profound discipline disparities impacting Black students, the implementation of alternative disciplinary approaches like Positive Behavioral Interventions and Supports (PBIS) and Restorative Justice (RJ) has failed to eliminate significant disproportionality in school discipline. This study investigates how teachers navigate the challenges of disciplining Black students within a paradoxical school environment that juxtaposes equitable, human-centered methods with a historically rooted managerial paradigm emphasizing uniformity and zero tolerance. Drawing upon systemic racism and the concept of 'apprenticeship of observation,' this study explores how teachers' implicit biases about Black students are formed by long-held institutional norms and personal experiences. It …

Evaluation Of Ocean Lidar Enhancement Through Miniaturization And Gated Pmt-Based Range Extension, Chandler Austin Slater

OES Theses and Dissertations

Oceanographic lidar systems remotely characterize the vertical structure of the upper ocean by recording the light backscattered from a laser pulse as it propagates through the water column. Historically these systems have been constrained by limited detection ranges, often capturing only a portion of the illuminated water column, typically fading to noise around the 10% isolume. A primary constraint has been the limited dynamic range of digitizers, which inhibits the ability to resolve both intense near-field and faint far-field backscatter signals within a single acquisition. To address this, we developed a novel optical lidar system incorporating gated photomultiplier tubes (PMTs) …

Aluminum As A Tracer Of Dust Deposition To The Ocean: A Case Study From The Bermuda Region, Tara Elizabeth Williams

OES Theses and Dissertations

Aluminum (Al), a major component of mineral aerosol (dust), partially dissolves in seawater and is widely used as a tracer for estimating time‐averaged dust fluxes to the ocean. Such estimates rely on dissolved Al (DAl) inventories in the surface mixed layer (SML), an assumed SML residence time of DAl (_TDAl), the fractional solubility of Al in dust (Al_S), and the mass fraction of Al in dust. In this study, dust flux estimated from seasonal, water-column DAl data from the Bermuda Atlantic Time-series Study (BATS) region are compared with direct dust flux estimated from contemporaneous measurements of …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Relativistic Three Particle Scattering, Md Habib E Islam

Physics Theses & Dissertations

In this dissertation, we develop and apply a relativistic framework for studying three-body scattering amplitudes, their analytic structure, and the emergence of universal phenomena such as the Efimov effect. The work integrates three complementary components.

First, we introduce a systematically improvable numerical method for solving relativistic three-body integral equations in momentum space. By discretizing the continuum problem into finite matrix equations and extrapolating to the continuum limit, we obtain stable partial-wave amplitudes in the presence of a two-body bound state. Two complementary treatments of the pole contribution are implemented and shown to reproduce previous finite volume results, with controlled estimates …

A Kinematic, Data-Parameterized Approach To Constrain Subducted Seafloor During The Emplacement Of Shatsky Rise, Alexzandrya Katrina Shotorban

OES Theses and Dissertations

This study investigates ridge spreading rates and plate boundary geometry in interactions between large igneous provinces and mid ocean ridges, and their role in the kinematics of Shatsky Rise, an undersea volcanic plateau in the western Pacific Ocean that is roughly the size of the state of California. Shatsky Rise formed at a ridge-ridge-ridge (RRR) triple junction (TJ) (Hilde et al., 1976; Sager et al., 1988), at the intersection of the Pacific-Izanagi, Pacific-Farallon, and Izanagi-Farallon ridges, beginning at approximately 147 Ma. Investigations into Shatsky Rise’s formation are limited by the fact that two-thirds of the seafloor data has subducted with …

Investigation Of Laser Based Flow Diagnostics With Metastable Argon, Sterling S. Gordon

Physics Theses & Dissertations

The overarching motivation of this work is the development of seedless, non-intrusive, laser-based diagnostics for supersonic airflow in wind tunnels. To advance this goal, this dissertation fo-cuses on three-photon excitation in a research-grade argon beam within a tabletop vacuum system, which offers a controlled environment for testing and refining the approach. The chosen excitation scheme drives argon atoms to the 3d[5/2]₃ state using a pulsed Ti:Sapphire laser system, enabling time-of-flight (ToF) measurements on atoms that subsequently undergo a multi-step decay to the metastable 4s[3/2]₂ state. Although full realization of this excitation scheme was hindered by technical …

The Presentation Of Self In Everyday Digital Life: A Study Of Self- Disclosure And Work Environments, Mackenzie Michelle Skiff

Communication & Theatre Arts Theses

The digital age impacts individuals’ lives in many ways. One impact is how and where work is completed across many careers. The work-from-home strategy enables individuals to complete work that is not within a shared space, such as an office. With the absence of this shared space, communication practices within workplaces could be changing. Specifically, self-disclosure while working from home may differ from self-disclosure within the office or hybrid (both in office and remote) work environments. This thesis investigates whether there are differences in self-disclosure practices across three different types of contemporary work environments and offers a digital update to …

Making Explanations Make Sense: Xai For Smishing Detection, Eleni Alexandra Katsarakes

Psychology Theses & Dissertations

Explainable Artificial Intelligence (XAI) is a key component of effective human-AI collaboration, particularly in high-stakes domains such as cybersecurity. While AI tools hold promise for mitigating threats such as SMS-based phishing (SMiShing), their real-world effectiveness may hinge not just on detection accuracy, but on whether users can make sense of the system’s outputs. As SMiShing attacks grow in both frequency and sophistication, so does the urgency of designing human-centered AI systems that support user decision-making under uncertainty. This study examined how four distinct AI explanation types - Normative (rule-based), Attributive (feature-based), Exemplar (case-based), and Recommendation-Only - influence user performance, confidence, …

Component Model Development Of Heat Exchangers, Expanders, And Control Valves For Autonomous Cryogenics Plant Cool-Down, William Harris Buhrig Iv

Mechanical & Aerospace Engineering Theses & Dissertations

The traditional method of cryogenic plant cool-down involves having continuous on-call staff to head into the office at any time to modify the existing multi-layered PID control systems if the on-call staff member detects a significant deviation from the cool-down plan. This thesis aims to outline an effective method for modeling the structure of systems with performance characteristics that deviate from design requirements and from ideal inlet-outlet correspondence, enabling the adjustment and modification of existing control structures across all Thomas Jefferson National Accelerator Facility (JLab) cryogenic refrigeration plants. Analytical Modeling and Gaussian Process Regression (GPR) are applied to model the …