Digital Commons Network^™

Design And Assessment Of A Single-Blade Rotary Wing For Use In Vertical Takeoff And Landing (Vtol) Aircraft, William C. Mcmasters

Mechanical & Aerospace Engineering Theses & Dissertations

A single-bladed propeller was designed to determine its efficacy in increasing efficiency of quadplane aircraft in the cruise configuration while still providing sufficient vertical lift for the vertical takeoff portion of flight. Aerodynamic theory predicts higher efficiency for single-blade propellers, resulting in a lower power requirement. A counter-weighted single-blade was modeled after a 10x5 model aircraft propeller used in small unmanned vehicles with a steel counterweight to balance centrifugal forces. The propeller was tested in the ODU wind tunnel to determine performance at various Advance Ratios (J). The research suggests that single-blade propellers show comparable performance compared to two-bladed propellers …

Sciteuq: Toward Uncertainty-Aware Complex Scientific Table Data Extraction And Understanding, Kehinde Ajayi

Computer Science Theses & Dissertations

Scientific tables report critical research insights, data, and findings for scientific progress. Because Portable Document Format (PDF) is the de facto standard format for scientific paper publishing, there has been an emerging need for an automatic method to extract data from PDF files. A significant fraction of scientific tables exhibit complex structure and content, making it challenging for machine learning tools to accurately extract the content directly from PDF files. Despite the advancements in Table Structure Recognition (TSR), automated extraction of data from complex scientific tables remains a challenge due to variations in table structures and contents. In this dissertation, …

Comparative Analysis Of Emergent Behaviors Of Three Drone Swarm System Models For Targeting Using Agent-Based Modeling And Simulation, Arsenio T. Gumahad Ii

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation introduces a novel computational simulation framework for evaluating the emergent behaviors of three swarm drone models using Agent-Based Modeling and Simulation (ABMS). The three swarm models are a Leader-Follower swarm model based on Bruckstein's antline theory, a Flocking model based on a simplified Reynolds 'Boids’ model, and a Stigmergic model with pheromone-based coordination. The primary objective of the simulation is to evaluate the performance of these models in delivering a user-defined number of drones of each type to a target area of interest in four separate scenarios, resulting in 50,000 separate simulation trials. Each scenario was structured to …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

Margalefidinium Polykrikoides Group Iii Va, Usa Strain Growth And Sac-Like Pellicle Cyst Dynamics, Eduardo Pérez Vega

OES Theses and Dissertations

Margalefidinium polykrikoides is a harmful cosmopolitan dinoflagellate that blooms in coastal waters. The effect of temperature and salinity on the growth of M. polykrikoides VA strain was examined using microscopy and growth models. M. polykrikoides Group III VA strain grew better at warmer temperatures and lower salinities than M. polykrikoides Group III NY strain, Group I Korea strain, and Japan strain (unknown group). Modelers need to use the temperature and salinity growth responses from M. polykrikoides Group III VA strain to better simulate and predict M. polykrikoides blooms in the Chesapeake Bay.

Dinoflagellates produce cysts as a strategy to withstand …

Examining The Perceptions Of Economics And Personal Finance Teachers' Preparedness To Teach Financial Literacy, Tasha L. Wearren

Educational Leadership & Workforce Development Theses & Dissertations

Financial literacy has become an important topic in our society. Despite an increased focus in financial literacy education, there is limited research assessing teachers’ ability to teach financial literacy by examining their endorsement area. According to Soroko (2021), research is limited regarding teachers’ beliefs and practices as it relates to financial literacy. As O’Neill and Hensley (2016) indicated, “Teachers cannot teach personal finance well if they do not understand it themselves and/or cannot engage their students” (p. 639). This qualitative multiple case study examined how Economics and Personal Finance teachers perceive their preparedness to teach financial literacy with different endorsements. …

Insider Threat: A Case Study Of The Maroochy Water Services Attack, Samuel Rector

Cybersecurity Undergraduate Research Showcase

In 2000, a former employee at Hunter Watertech went rogue and caused a spill of 800,000 liters of sewage. He leveraged his insider knowledge and access to stolen equipment in order to seek retribution for the company that wronged him. After three months of torment police arrested him and an investigation and many studies were done on the incident. A consensus remains that much of this chaos was preventable with simple cybersecurity implementations.

Behavioral Detection Methods For Automated Mcp Server Vulnerability Assessment, Christian Coleman

Cybersecurity Undergraduate Research Showcase

The Model Context Protocol (MCP) has emerged as a critical standard for connecting AI agents to external data sources and tools. Still, its adoption has introduced significant security vulnerabilities across multiple attack surfaces. While recent research has catalogued extensive vulnerability taxonomies and attack implementations, automated detection methodologies remain limited. Current detection tools primarily employ static code analysis, which fails to identify behavioral vulnerabilities that only manifest during runtime server interactions. This study explores behavioral detection approaches for identifying MCP server vulnerabilities through systematic query-based testing, with particular emphasis on context manipulation techniques. Preliminary analysis of existing vulnerability research reveals 48 …

Game Hacking & Anti-Cheat Analysis, Quang Hoang

Cybersecurity Undergraduate Research Showcase

Reverse engineering and analyzing game hacking and anti-cheat mechanisms is a complex and evolving field. This research document explores the history of game hacking, various techniques used in game hacking, and the countermeasures implemented by anti-cheat systems. Through case studies, we illustrate the strategies involved in creating cheats. Specifically, we demonstrate how to hack the open-source game AssaultCube using memory editing and code injection techniques available in Cheat Engine in a step-by-step manner so that the reader can theoretically reproduce the results shown in this paper. We also dissect the game’s anti-cheat mechanisms, identifying their strengths and weaknesses. This document …

A Scalable Cybersecurity Model For Academic Makerspaces, William Faircloth

Cybersecurity Undergraduate Research Showcase

Academic makerspaces have become integral hubs of innovation on university campuses, providing students with access to industrial-grade operational technology (OT) such as 3D printers and CNC machines. However, the security posture for these spaces has overwhelmingly focused on physical safety, creating a significant cybersecurity gap. This oversight leaves networked OT vulnerable to cyberattacks, which threaten student intellectual property, expensive equipment, and the integrity of the broader institutional network. This research addresses this critical vulnerability by developing and implementing a secure and scalable cybersecurity model at the Old Dominion University Computer Science Makerspace, founded on two core principles: robust network segmentation …

Deconstructing Tycoon 2fa: A Static Analysis Approach To Threat Intelligence And Automated Defense, Daniel A. Austin Jr

Cybersecurity Undergraduate Research Showcase

It's gotten much easier to be a cybercriminal. We're seeing a boom in "Phishing-as-a-Service" (PaaS) platforms, which sell advanced phishing attacks as a ready-to-use product. This means almost anyone can now get the tools to launch sophisticated attacks, even if they don't have a lot of technical skill.

This research dives into one of the most prominent threats, the Tycoon 2FA phishing kit. This kit is dangerous because it's designed to bypass Multi-Factor Authentication (MFA) using what is known as an Adversary-in-the-Middle (AiTM) attack.

This paper covers how I built and tested a set of Python-based tools to perform "static …

A Systematic Review Of Poisoning Attacks Against Large Language Models (Llm), Patrick Mcguffin

Cybersecurity Undergraduate Research Showcase

This paper provides a comprehensive review of poisoning attacks against large language models (LLMs), drawing primarily from Fendley et al. (2025) and complementary studies from 2022–2025. It categorizes poisoning research into two key dimensions, Metrics and Specifications, to evaluate how attack success is measured and how attacks are implemented. This paper synthesizes quantitative results, experimental findings, and defense strategies across data, model, and multi-modal poisoning contexts. Finally, it highlights emerging challenges posed by self-adaptive and synthetic-data-driven LLMs, and proposes future research directions to strengthen model security and reliability.

Detecting Generative-Ai-Enabled Polymorphic Malware: A Semantic-Behavior Approach, Allyson M. Morris

Cybersecurity Undergraduate Research Showcase

AI drastically reduces the effort required to produce malware that mutates both its code and behavior, thereby creating polymorphic and nondeterministic variants in which traditional signatures and many heuristic defenses fail. In this paper, I survey recent developments in AI-assisted malware generation, explain why conventional defenses are insufficient, and propose a layered detection architecture emphasizing semantic behavior, streaming anomaly detection, and defensive generative augmentation. I also outline why this approach generalizes to previously unseen AI-mutated samples, provide an evaluation plan with meaningful metrics, and describe a feasible MVP roadmap for practical deployment. Recent disclosures and threat intelligence further highlight the …

A Comprehensive Evaluation Of Next-Generation Firewall Effectiveness Against Encrypted And Evasive Threats In Enterprise Networks, John Costanzo, Favour Anene

Cybersecurity Undergraduate Research Showcase

Encrypted traffic is becoming a pillar of security and privacy in enterprise networks. According to Google Transparency Report, over 95 percent of internet traffic is encrypted with the use of Hypertext Transfer Protocol secure (HTTPS), Transport Layer Security (TLS) 1.3 and Quick UDP Internet Connections (QUIC). Although encryption safeguards confidentiality and integrity, it has also introduced new blind spots to the conventional security solutions. Encrypted channels are used to hide command-and-control (C2) traffic, issue malware and extract sensitive data without their notice.

To make the issue even harder, the opponents have sophisticated avoidance methods including traffic fragmentation, tunneling, and polymorphic …

Investigating The Security Vulnerabilities Of Ip Cameras: Classifications And Trends From Public Cve Data, Sam Oliver

Cybersecurity Undergraduate Research Showcase

Internet of Things (IoT) devices are increasingly targeted by cyber attacks due to weak authentication, insecure communication protocols, outdated firmware, and many other vulnerabilities. Internet Protocol (IP) cameras, a subset of these IoT devices, are particularly vulnerable and often transmit sensitive information. This paper analyzes vulnerability data from the National Vulnerability Database (NVD) to classify security vulnerabilities affecting IP cameras. Using this dataset, the paper examines the types and frequencies of these vulnerabilities, including authentication bypass, web interface exploits, and default and weak credentials. We additionally examine trends over time and across categories. This research aims to identify the primary …

Review Of How To Win Environmental Victories, By Richard S. Groover, Phd, Shalini Upadhyaya

Virginia Journal of Science

Book Review:

Groover, Richard S. 2025. How to Win Environmental Victories. Nighthawk Publication, 116 pp.

Sociohydrodynamics: Data-Driven Modeling Of Social Behavior, Daniel S. Seara, Jonathan Colen, Michel Fruchart, Yael Avni, David G. Martin, Vincenzo Vitelli

Data Science Faculty Publications

Living systems display complex behaviors driven by physical forces as well as decision-making. Hydrodynamic theories hold promise for simplified universal descriptions of socially generated collective behaviors. However, the construction of such theories is often divorced from the data they should describe. Here, we develop and apply a data-driven pipeline that links micromotives to macrobehavior by augmenting hydrodynamics with individual preferences that guide motion. We illustrate this pipeline on a case study of residential dynamics in the United States, for which census and sociological data are available. Guided by Census data, sociological surveys, and neural network analysis, we systematically assess standard …

A Modern Analytical Method To Forecast Cerebrovascular Diseases (Cd), And Heart Diseases (Hd) Using Multivariate Time Series Model Utilizing The Cdc Provisional Mortality Data, Aditya Chakraborty, Mohan Pant

Cardiovascular Research Symposium

Background: In this study, a new analytical approach was introduced to answer specific questions related to mortalities due to cerebrovascular diseases, heart diseases, and the association of these mortalities with twelve other causes of death (COD).

Methods: A multivariate time series forecasting model was developed utilizing each of the CODs by taking the weekly and yearly seasonality into account, and the mortality counts were forecasted using the most recent CDC weekly mortality count data. A new COD data matrix was structured for all CODs as a function of weeks by combining the observed and predicted values of the mortality counts. …

Empathetic Differentiated Instruction In Deaf Education: Examining Influencing Factors And Experienced Educator Practices, Laura A. Blackburn

STEMPS Master's Level Projects and Papers

This mixed-methods study investigated how experienced K–12 deaf educators implement empathetic, differentiated instruction (EDI) to support the academic and linguistic needs of deaf students who use American Sign Language (ASL) as their language of learning. Guided by Tracey and Baaki’s instructional design empathy model and grounded in constructivism, the study explored how teachers navigated the tension between standardized, English (sound-based) curriculum demands and the need to make instructional decisions aligned with signed language modalities and responsive to students’ real-time learning needs.

A custom electronic survey was distributed to a national sample of experienced educators, with 19 respondents completing Likert-scale items, …

Ai-Enhanced Structured Literacy Intervention For Secondary Students: A Case Study Of Science Of Reading, Jennifer Bird

Teaching & Learning Faculty Publications

This study examines the effectiveness of Lexia PowerUp, an AI-powered literacy program, for sixth-grade students requiring Tier 3 reading intervention. Seven sixth-grade students (six boys, one girl; five African American, two Caucasian; all qualifying for free/reduced lunch) participated in a six-month intervention combining 50 minutes of daily small-group instruction with individualized Lexia PowerUp usage. Researchers measured progress through Achieve 3000 Lexile assessments and Lexia PowerUp performance data across three skill strands: Word Study, Grammar, and Comprehension. All participants demonstrated Lexile level improvements from beginning-of-year to mid-year assessments, though students remained below sixth-grade benchmarks (925-1070L). Analysis of Lexia PowerUp progression showed …

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L²-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

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 …

Examining How Self-Regulated Learning Professional Development Affects Teacher Srl Knowledge, Beliefs, And Practices, Stephanie Greenquist-Marlett

STEMPS Theses & Dissertations

Self-regulated learning (SRL) strategies promote valuable student learning outcomes including academic achievement and lifelong learning (EU Council, 2002). Teachers have an opportunity to cultivate student SRL development within their classrooms (Azevedo et al., 2008). While teachers believe in the benefits associated with SRL promotion (Spruce & Bol, 2014), their positive attitudes are not often supported by adequate knowledge of SRL strategies. This leads to a lack of self-efficacy for developing SRL instruction in classrooms (Dignath & Büttner, 2018; Zimmerman et al., 1996). SRL-based professional development (PD) interventions have demonstrated their effectiveness for increasing teacher SRL instruction and implementation (Cleary et …

Enhancing Non-Visual Interaction With Online User-Generated Content, Mohan Krishna Sunkara

Computer Science Theses & Dissertations

The Web has become the dominant medium for our everyday activities, including communication, business, e-commerce, news, and entertainment. Consequently, the online world is experiencing an explosion of User-Generated Content (UGC), particularly on social media platforms and online review systems. To facilitate convenient interaction with UGC, web platforms have adopted various presentation strategies that enable users to efficiently browse and contribute to the UGC. However, these user interfaces are primarily designed for sighted individuals, so they do little to assist blind users who rely predominantly on audio-based screen reader assistive technology. The extant efforts to improve web interaction for blind users …

School Counselor Burnout And Self-Efficacy And Their Impact On The Degree Of Implementation Of A Comprehensive School Counseling Program, Rebecca Pierre-Louis

Counseling & Human Services Theses & Dissertations

School Counselors are educators who are vital in supporting students’ educational development. School Counselors do this by delivering a comprehensive, developmentally appropriate, and prevention-oriented school counseling programs that provide direct, indirect, and responsive services to support students’ career, academic, and social/emotional development. School Counselors do this while often experiencing an expanded definition of their role within a school building. While research supports the benefits of comprehensive school counseling programs to schools and students, school counselors often face large student caseloads. As well, they are tasked with many non-school counseling duties, which hampers the delivery of a comprehensive school counseling program. …

Enhancing Data Usability For People With Visual Impairments, Yash Prakash

Computer Science Theses & Dissertations

Human-Data Interaction (HDI) focuses on how individuals engage with, analyze, and extract insights from data. For blind and visually impaired (BVI) users, interacting with data, whether searching for relevant information from structured data (e.g., web data items) or interpreting visualizations to draw insights (e.g., data charts), presents significant challenges. These challenges arise from the complexity and sheer volume of data which cannot be effectively handled by assistive technologies like screen readers and screen magnifiers. Despite its importance, data usability, the ease, efficiency, and satisfaction with which BVI individuals can interact with the data, has received less attention compared to data …

Flux Trapping Sensitivity On The Metallurgical State Of Superconducting Radio Frequency Niobium, Bashu Dev Khanal

Physics Theses & Dissertations

Trapped magnetic flux in superconducting radio-frequency (SRF) niobium cavities due to the incomplete Meissner effect is a significant source of rf losses. The relationship between flux expulsion and flux trapping sensitivity to the metallurgical state of 1.3 GHz and 3.0 GHz SRF niobium cavities fabricated from standard and cold-worked Nb sheets and subjected to various heat treatments has been studied. Flux expulsion increased with higher heat treatment temperature in all cavities. Nb cavities made from cold-worked sheets showed better flux expulsion after 800 ◦C/3 h compared to the standard SRF-grade Nb cavities. Similar flux expulsion was observed after annealing at …

Computational Investigation Of Energetic Materials: Influence Of Electronic And Steric Properties On Sensitivity And Decomposition Mechanisms, Elizabeth Ruth Zengel

Chemistry & Biochemistry Theses & Dissertations

Developing novel high energy density materials (HEDMs) requires knowledge of the causes and mechanisms of detonation. These chemical events are almost instantaneous and involve the release of a large amount of energy, which limits the experimental studies that can be performed on them. Computational methods including density functional theory (DFT) and molecular dynamics (MD) simulations have been used to investigate trigger bonds, those which break to initiate detonation. These bonds are commonly found within explosophores, substituents that increase the explosive potential of a molecule.

The Wiberg bond index (WBI) is an estimation of orbital overlap and bond strength between two …

Analysis Of Multi Grade Deep Learning, Ronglong Fang

Mathematics & Statistics Theses & Dissertations

Multi-Grade Deep Learning (MGDL) is a training framework that incrementally builds deep neural networks. It does this by dividing the training process into multiple “grades,” where each grade sequentially trains a shallow neural network to learn the residue from the previous one, using the outputs of prior grades as input. This approach progresses from shallow to deep architectures. This dissertation offers a comprehensive theoretical and numerical analysis of the MGDL methodology.

We first demonstrate that MGDL can effectively learn target functions within the sum-composition learning format. In this context, MGDL approximates high-frequency components by composing multiple low-frequency functions. This unique …

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 …