Digital Commons Network^™

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

Multimedia Forensics: Identification And Verification Of Source Camera, Vehicle Speed Estimation, And Deepfakes Detection, Jiajun Jiang

Electrical & Computer Engineering Theses & Dissertations

This dissertation advances multimedia forensics by addressing three critical research areas that enhance the authenticity verification and analysis of digital media. Multimedia forensics, which encompasses techniques for examining images, videos, audio, and text, faces increasing challenges due to sophisticated editing tools and massive data volumes. In the first study, a fast source camera identification and verification method based on PRNU analysis is proposed for video forensic investigations. By integrating camera rolling and I-frame analysis, this approach achieves a processing speed improvement of at least 15 times over conventional frame-by-frame methods while reducing false positives. The second study focuses on vehicular …

Advanced Analytical Biosensing For Cancer Detection And Neural Diagnostics Using Tapered Optical Fiber (Tof), Protonic, And High Throughput Microplate –Based Technologie, Bayan Hassan Alharbi

Chemistry & Biochemistry Theses & Dissertations

This dissertation investigates the development and application of advanced biosensing technologies to enhance early disease detection, neurological diagnostics, and bioactive compound evaluation. The research spans four key areas. First, it introduces tapered optical fiber (TOF)-based plasmonic biosensors for the non-invasive detection of prostate cancer, demonstrating high sensitivity and specificity compared to conventional diagnostic methods.

Second, it explores the use of fluorescent biosensors to test the Transmembrane Electrostatically Localized Proton (TELP) theory, shedding light on the role of localized protons in neuronal signaling and energy transfer. Third, the work presents a high-throughput, microplate-based biosensing platform for analyzing mitochondrial function under nanosecond …

Towards Improving Computational Modeling Of The Lumbar Spine - Impact Of Material Properties And Laminotomy On Spinal Biomechanics, Isaac K. Kumi

Mechanical & Aerospace Engineering Theses & Dissertations

Spinal ligaments play a crucial role in maintaining the mechanical stability of the lumbar spine. These dense, collagenous tissues not only resist excessive motion but also distribute loads between spinal components to protect neural structures. Traditionally, the mechanical response of ligaments has been modeled using linear elastic assumptions, which treat the tissue as having a constant stiffness regardless of loading history. While this simplifies analysis, it fails to capture the time-dependent behaviors, such as creep, stress relaxation, and hysteresis, that are characteristic of biological tissues.

Viscoelastic modeling may provide a more physiologically accurate representation by incorporating both elastic and viscous …

Monarch: Old Dominion University Magazine, Victoria Bourne (Editor)

Monarch Magazine

Summer 2025 issue of Monarch, the Old Dominion University Magazine.

Subtitled: GLOBAL IMPACT: Monarchs Make a Difference by Land and by Sea

Electron Beam Irradiation Effects On Bulk Metals And High Entropy Alloys (Crmnv And Crmntiv) And The Synthesis And Characterization Of Irradiation-Induced Damage In Polycrystalline Metal And High Entropy Alloy Thin Films, Najmin Ara Sultana

Mechanical & Aerospace Engineering Theses & Dissertations

Accelerator beam exit windows are designed to withstand intense radiation and mechanical stress while preserving beam transmission and structural integrity. This dissertation explores the structural and mechanical responses of pure metals and high-entropy alloys (HEAs), in both bulk and thin-film forms, under high-dose electron beam (e-beam) irradiation to identify optimal materials for next-generation accelerator exit windows. Monte Carlo simulations using FLUKA, conducted at Jefferson Lab, revealed that amongst Ni, Ti, Cr, and V, Ni exhibits the highest power dissipation, while Ti depicts the lowest power dissipation, with Cr and V demonstrating intermediate characteristics. Bulk polycrystalline (PC) and single-crystalline (SC) samples …

The Relationships Between Expectancy-Value Beliefs, Communal Value Beliefs, Family Identity, And Academic Persistence Among First- And Continuing-Generation College Students, Douglas W. Snuffer

STEMPS Theses & Dissertations

First-generation college students (students for whom neither parent has a 4-year college degree) have lower college graduation rates than their continuing-generation peers (Cahalan et al., 2021; Fry, 2021). First-generation students may experience barriers to higher education, leading to lower levels of academic achievement motivation for college. Two of these barriers may include 1) cultural mismatch (Stephens, Fryberg, et al., 2012) with colleges via communal value (a value set that emphasizes community and family over the self) and 2) a strong sense of family identity, which lends itself to perceived family obligations (Fuligni et al., 2007). To examine the relationships between …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Mission Engineering Competencies Workshop Report: Roles, Responsibilities, And Skills, James D. Moreland, Jr. (Editor), Thomas Irwin, Robert-Allen Baker

Center for Mission Engineering (CME) Publications

This report summarizes the findings of a two-day Old Dominion University Mission Engineering Workshop involving the U.S. Navy, U.S. Marine Corps, U.S. Air Force, National Aeronautics and Space Administration, the University of New South Wales, and Old Dominion University, focused on defining the roles, competencies, and skills essential to Mission Engineering. It presents a structured competency framework and offers recommendations to guide workforce development, training, and cross-agency collaboration in support of complex, mission-driven systems.

Ac Electric Fields Manipulate And Concentrate Dna Molecules On Electrodes, Akila Wijesinghe, Dharmakeerthi Nawarathna

Graduate Student Government Association Research Conference

Point-of-care (POC) electric field-based biosensors have emerged as a promising tool to detect early cancer biomarkers such as circulating tumor DNA (ctDNA), microRNA (miRNA), and proteins. To be effective in screening in clinical settings, these biosensors must be simple and easy to use. In this study, we have studied the manipulation of short DNA molecules suspended in a sessile drop to achieve this goal. Alternative current (AC) electric fields were used to polarize DNA molecules and produce dielectrophoretic (DEP) force on DNA molecules. DEP force is used to manipulate polarized DNA molecules towards the higher electric field gradients (toward the …

Thriving In The Age Of Ai: Navigating Ai Identity Threat Through Ai Job Crafting, Yuming He

Theses and Dissertations in Business Administration

As artificial intelligence (AI) technologies like GenAI tools increasingly reshape the workplace, employees increasingly face threats to their work identity. Grounded in the identity threat response model and job crafting theory, this study investigates how AI identity threat influences employee AI job crafting behaviors and how these behaviors, in turn, affect vitality and learning. Using survey data from 521 full-time employees who actively engage with AI tools, the results indicate that AI identity threat stimulates both AI approach job crafting and AI avoidance job crafting. AI approach crafting enhances both vitality and learning, while AI avoidance crafting only supports vitality. …

Power Asymmetry Dynamics: Assessing Interdependence In France-Cameroon Relations Through Economic And Security Cooperation (2000–2024), Chick Edmond

Political Science & Geography Faculty Publications

This paper examines the dynamics of power asymmetry in the interdependent relationship between Cameroon and France from 2000 – 2024 with a focus on security and economic cooperation. The main research question asks: To what extent has power asymmetry reinforced or mitigated the interdependent relationship between Cameroon and France within the realm of economic and security cooperation? Based on the hypothesis that increased interdependence between these two states during this period has strengthened existing power disparity. The paper examines how such dynamics have restrained Cameroon’s autonomy and its capacity to pursue policy goals.

The theoretical framework combines dependency theory, postcolonial …

Frontlines Of Influence: State Vs. Nonstate Disinformation Campaigns, Lily Wershbale

Cybersecurity Undergraduate Research Showcase

This paper examines the evolution of disinformation campaigns conducted by state and nonstate actors, focusing specifically on Russia and the Islamic State as representative case studies. Through historical examples and qualitative comparative analysis, this research identifies the similarities and differences in disinformation’s role in actors’ core missions, resource allocation, and targeting decisions. This paper further explores the implications of artificial intelligence on disinformation campaigns, investigating how emerging technology will impact the influence operations of state governments and nonstate organizations alike. The findings reveal that while the ultimate intent of both state and non-state actors is to destabilize societies, their approaches …

"Exploring The Training Data Landscape For Ai Based Threathunting For Protecting Intellectual Property", Manzi Siibo, Christopher Kreider

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of the effectiveness that would result in the integration of AI into traditional threat hunting systems. To do so, 10-15 scholarly articles and data sets were evaluated to see the results of AI and machine learning threat hunting versus traditional systems. With so many proven benefits of this integration, this paper also explores how it impacts the protection of Intellectual property which is some of the most important forms of information that threat hunting systems aim to protect.

Selective Lithium Extraction Using Ion-Sieves From Geothermal Brine, Agyare Asante, Abigail Berardi, Jayla Standin, Elliott Walker, Ujjwal Pokharel, Sandeep Kumar

Graduate Student Government Association Research Conference

Lithium (Li) is a critical component in modern green energy technologies, serving as the cornerstone for advanced batteries used in electric vehicles, renewable energy storage, and portable electronics. To address the increasing global demand for lithium, this study explores the synthesis and performance of ion-sieve novel adsorbents derived from the spinel-type precursor Li₄Mn_4.5Zr_0.5O₁₂ (LMZO). The precursor was produced using an economical solid-phase reaction that involved lithium, manganese, magnesium, and zirconium salts. This was followed by a two-stage calcination process involving 200 K for 2 hours and 450 K for 24 hours. Acid activation …

Carbon Accountability Scores: A Process-Oriented Approach For Carbon Offsetting Using Ai Agents, Joshit Mohanty, Vaishali Vaishali

Graduate Student Government Association Research Conference

Conventional carbon offset programs rely on quantified emissions to determine balancing requirements. While this approach offers a standardized means of measuring carbon output, it often provides industries with a loophole—allowing them to offset their emissions by purchasing equivalent credits for activities such as tree planting rather than tackling inefficiencies at the source. This research proposes a process-based framework called Carbon Accountability Scores (CA scores) to offer a proactive strategy for assessing and reducing carbon footprints. Instead of focusing on the mere balancing of emitted and sequestered carbon, CA scores integrate an organization’s operational processes into the calculation, thereby offering the …

Digital Thread: Bridging Macro–Micro Services In System-Of-Systems, Joshit Mohanty

Graduate Student Government Association Research Conference

Organizations and industries increasingly rely on distributed services in decentralized environments—ranging from large-scale, system-of-system architectures to fine-grained, agent-based microservices. While this distributed paradigm offers flexibility and innovation, it presents critical challenges such as interoperability gaps, inconsistent data formats, and a lack of holistic oversight. Traditional integration approaches, including ad-hoc middleware or enterprise service buses, tend to solve these issues reactively. As a result, technical debt accumulates, stakeholder misalignments persist, and scaling to new demands becomes complex.

This research proposes digital thread (DT) as the unifying framework to create an authoritative source of truth: a continuous flow of information across the …

Meta-Clustering For Specialized Language Models: Enhancing Contextual Adaptation And Mitigating Hallucinations In Diverse Healthcare Environments, Joshit Mohanty, Vaishali Vaishali, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large Language Models (LLMs) have significantly advanced conversational AI by enabling dialogic information-seeking and task execution across diverse domains. However, their extensive parameters and broad domain scope lead to “data hallucinations.” These shortcomings are particularly evident in dynamic and diverse environments like India’s healthcare sector, where myriad languages, regional practices, and cultural nuances demand specialized, localized expertise rather than one-size-fits-all generalist models. This paper introduces a meta-clustering framework that integrates Distilled Language Models (DLMs) and Small/Specialized Language Models (SLMs) with meta-learning principles to address these limitations. By drawing on evidence from works such as MedHalu and Med-HALT, the framework seeks …

Tamos: Task-Aware Multi-Agent Orchestrator System, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Large language models (LLMs) are increasingly at the core of multi-agent systems (MAS). However, the high resource demand, error propagation, and lack of adaptive evaluation mechanisms pose significant challenges in deploying these agentic solutions at scale. To address these concerns, this research proposes a Task-Aware Multi-Agent Orchestrator System designed to refine the agentic framework, categorizing tasks autonomously, assigning specialized evaluation datasets, and balancing token usage against functional effectiveness. This approach underscores robust data management, including AsyncHow, Mosaic AI, and Synthetic Preference Optimization (PO) corpora. Each dataset targets specific dimensions of agent performance, such as dynamic task decomposition and tool integration …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Fids: Accelerating Network Intrusion Detection Through Strategic Feature Selection, James Elmore

Cybersecurity Undergraduate Research Showcase

Network intrusion detection systems (IDS) typically analyze complete network flows to identify malicious traffic, requiring flows to conclude before classification. This approach creates detection delays for attacks like Slowloris that intentionally keep connections open for extended periods of time. This paper introduces a novel approach that classifies network traffic using only features available from the first few packets of a flow, enabling faster detection while maintaining high accuracy. We evaluate three random forest models on the CICIDS2017 dataset using expanding sets of features: the first-packet model trained on on features available from the first backward packet, the few-packet model which …

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

12 - Hydrogen Energy Lab, Xiaoyu Zhang

Undergraduate Research Symposium

Research Lab Showcase

01 - Design Of Ion-Sieve For Selective Adsorption Of Lithium From Geothermal Brine, Agyare Asante, Abigail Berardi, Jayla Standin, Elliott Walker, Alvin Spruill, Ujjwal Pokharel, Sandeep Kumar

Undergraduate Research Symposium

The growing demand for lithium-ion batteries necessitates efficient and sustainable lithium extraction technologies. Geothermal brines provide an alternative lithium source; however, their complex ionic composition challenges selective lithium recovery. Hydrogen manganese oxides (H₄Mn_4.5O₁₂) are widely studied as lithium-ion sieves due to their high selectivity for lithium ions. Still, its structural instability and manganese dissolution during adsorption-desorption cycles limit long-term performance. This study introduced zirconium (Zr) doping into the spinel-type Li₄Mn₅O₁₂ precursor through a one-step calcination process (450 °C for 24 hours at 10 °C min^-1), ensuring an …

Why Do Different Llms Give Different Answers To The Same Question? Model Uncertainty And Variability In Llm-Based Intrusion Detection Systems Ranking, Charlise Calloway

Cybersecurity Undergraduate Research Showcase

Large Language Models (LLMs) are increasingly applied across business, education, and cybersecurity domains. However, LLMs can yield varied outputs for the same query due to differences in architecture, training data, and response generation mechanisms. This paper examines model variability and uncertainty by comparing the responses of three LLMs—ChatGPT-4o, Gemini 2.0 Flash, and DeepSeek-V3--to a query on ranking practical intrusion detection systems (IDS). The analysis highlights key similarities and differences in the models’ outputs, offering insight into their respective reasoning and consistency.

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

82 - Characterization Of Manganese Ferrites For Combined Mph/Mpi Treatments, Alana Canty, Robert Ivkov, Hayden Carlton

Undergraduate Research Symposium

Magnetic particle hyperthermia (MPH) is a form of cancer treatment in which cancer cells are made more susceptible to treatments, primarily radiation, through heating. The heat is generated from exciting magnetic nanoparticles (MNPs) within an alternating magnetic field (AMF)—nanoparticles which have been injected into a tumor—and both the heating quality and imaging quality of these MNPs are determined by their magnetic properties, with the primary one being anisotropy. Magnetic nanoparticles have been introduced as both a treatment option as well as a diagnostic tool through magnetic particle imaging (MPI). When used as a diagnostic tool, MNPs act as tracers to …

65 - Evaluating Drone-Based Stem Camps For Enhanced Engagement And Sel Development, David Morgan

Undergraduate Research Symposium

STEM camps are programs designed to promote exposure to learning associated with science, technology, engineering, and math educational outcomes. However, there is a significant gap in participation for Black and Brown individuals in STEM careers. This study seeks to understand how STEM programs during out-of-school time may support students from underrepresented communities and foster future interest in STEM-related careers. This study employs a retrospective qualitative research design investigating program participation in a week-long STEM camp in Hampton, Virginia. Thirty middle and high school students participated in the curriculum, technical drone skills, emphasizing emotional intelligence, self-regulation, and social awareness. These findings …

48 - Improving Worship Experience: A Secure Application For Generating Worship Questions Using Ai, Hind Aldabagh, Rhys Ferris

Undergraduate Research Symposium

Improving worship Experience: A Secure application for generating worship questions using AI

Rhys Ferris

Under the direction of Hind Aldabagh, School of Cybersecurity

Generating discussion questions for family worship time during the week based on sermon notes presents an innovative and helpful tool to assist pastors in developing worship questions. Instead of using ChatGPT, hosting a small AI model with Mistral provides an alternative solution that better suits our project.

Using prompt engineering, a custom prompt was created within the statement of faith to guide the AI in reading the text file of the sermon notes. The front end consists …

40 - Design, Fabrication, And Installation Of Morphing Control Surfaces For Small-Scale Uas, Kyle Purser

Undergraduate Research Symposium

Morphing control surface technology, inspired by organic structures and compliant mechanisms, offers significant potential for enhancing the aerodynamic efficiency and performance of unmanned aerial systems (UAS). Despite these benefits, its adoption has been hindered by complexities in design, manufacturing, and installation, particularly when compared to traditional flap systems. This research explores the design, fabrication, and integration of morphing control surfaces for small-scale UAS using additive manufacturing techniques. To reduce costs and streamline the design process, fused deposition modeling (FDM) additive manufacturing was employed for component fabrication. An off-the-shelf Horizon Sport Cub S2 served as the testing platform, modified with a …