Digital Commons Network^™

Teaming With Technology: Adaptive Automation In Joint Cognitive Systems For Industry 5.0, Jessica Johnson

Virginia Digital Maritime Center (VDMC) Faculty Publications

Adaptive automation enables dynamic reallocation of functions between people and autonomous agents to improve performance in complex work. This paper presents a meta-analysis of experimental and quasi-experimental studies (2000-2025) on joint cognitive systems in industrially relevant contexts, quantifying effects on task performance, safety/failure management, workload, trust, and learning. Across studies, adaptive automation reliably reduces operator workload and shows moderate gains in task performance and safety, with healthier trust dynamics when adaptations are triggered by human-state or event cues, made transparent to the user, and remain rapidly overridable. Risks emerge when performance-triggered switching is opaque or poorly timed, which can erode …

Contributors, Karin Dejonge-Kannan, Travis N. Thurston, Amanda Deliman, Amanda Morris, Anna Kuthy, Anne Cook, Beth Buyserie, Bethany B. Stone, Caroline Wienhold, Casandra E. Harper, Chris Kilgore, Cynthia Korpan, Elisabeth Schussler, Ferlin Mcgaskey, Gabrielle Stecher Woodward, Hannah Mae Lewis, James Agutter, Janette R. Hill, Jeff Spears, Jim Lamuth, Jordan Shipley, Kate Tregloan, Kim Hales, Letizia Guglielmo, Melissa E. Ko, Michael Morrone, Miranda Yaggi Rodak, Peggy Brickman, Raoul A. Mulder, Shannon M. Sipes, Sharon Lyman, Shihua Brazill, Stephen A. Klien, T. Adam Halstrom, Terri A. Dunbar, Victoria Mondelli, Vijay R. Kannan, Virginia Stormer

Documenting Teaching Excellence

No abstract provided.

Credit Card Fraud Detection Via Model Retraining And Fine-Tuning, Anamol Khadka

Computer Science and Engineering Student Research - Archive

Credit card fraud detection is a critical task in financial systems, especially given the rarity and evolving nature of the fraudulent behavior. The highly imbalanced class levels of the fraudulent and non-fraudulent transactions make it a challenging classification problem to solve. This study investigates the effectiveness of machine learning models: Logistic Regression, XGBoost, and Multi-Layer Perceptron (Neural Network), evaluated under temporal retraining and fine-tuning scenarios using a publicly available, highly imbalanced dataset of European credit card transactions. The dataset includes 284,807 transactions, of which only 492 (0.172%) are labeled as fraudulent, making it a well-known example of an imbalanced classification …

Comparative Evaluation Of Traditional Machine Learning And Deep Cnn Models For Static Hand Gesture Recognition, Anamol Khadka, Prit Desai

Computer Science and Engineering Student Research - Archive

Hand gesture recognition plays a vital role in facilitating natural and intuitive human-computer interaction, with applications ranging from sign language translation to touchless control systems. This study presents a comparative evaluation of traditional machine learning models and a deep convolutional neural network (CNN) for static hand gesture classification. The experimental dataset comprises 24,000 training images and 6,000 testing images, spanning 20 gesture classes. Traditional models, including k-Nearest Neighbors (KNN) and Support Vector Machines (SVM), utilize handcrafted features such as convex hull, convexity defects, and Hu moments. In contrast, the deep learning approach fine-tunes a ResNet18 architecture to learn features directly …

A Random Walk Simulation Of Positron Annihilation Depth As A Function Of Positron Beam Energy, Saniya Anjum Aqeel Ahmed Yadgeer

Physics Theses - Archive

Depth resolved defect and chemical characterization studies using positron annihilation spectroscopy depend on the ability to estimate the fraction of positrons annihilating at surface, in the bulk, at defects or as Positronium. The depth resolution is limited by the implantation profile of the energetic positrons and the diffusion of the thermalized positrons. Here we implement a random walk simulation as described in S. Eichler et al. [1] to track the monoenergetic positrons implanted into multilayer graphene on Cu. For our simulations the implantation depths were sampled from a Makhovian distribution that has been shown to accurately represent the implantation profile …

Impact Of Climate Change On North Central Texas, Diana K. T. Hansen

Earth & Environmental Sciences Dissertations - Archive

ABSTRACT

IMPACT OF CLIMATE CHANGE ON

NORTH CENTRAL TEXAS

Diana Kay Thomas Hansen, Ph.D.

The University of Texas at Arlington, 2025

Supervising Professor: Arne M.E. Winguth

The North Central Texas region is a rapidly expanding metropolitan area. One of the largest climate hazards that will affect human health in this region are extended heat waves and heavy precipitation. This study will examine how these hazards will change in response to climate change and the impact to human health due to weather extremes in a changing climate.

Coupled Model Intercomparison Project (Phase 6) simulations were used to evaluate future projected temperature …

Kinetic Analysis Of A Carbon-Rich Sico Model System, Takhya Holley

Chemistry & Biochemistry Theses - Archive

Kinetic Analysis of a Silicon Oxycarbide (polymethylhydrosiloxane/divinylbenzene) material was done for meaning of kinetic analysis methodology development. The material was synthesized using the standard procedure from, Blum 2005. A series of TGA’s was done using non-isothermal experiments. The analysis involved an initial isoconversional analysis using Friedman method, then decomposition of processes into individual processes using the Fraser-Suzuki function. The validation agreed within 1%, which is permissible by the ICTAC recommendations. The activation energies of the individual processes were determined using the Friedman method.The pre-dominant process #2 was found to have an activation energy of 220 +- 20 kj/mol and first-order …

Estimating Vapor Pressures Of Pheromone Compounds Using Gas Chromatographic Retention, Bipasha Sarker

Chemistry & Biochemistry Theses - Archive

Accurate determination of the vapor pressures of organic materials is important for predicting the sample’s environmental impact. It is difficult to directly measure accurate and precise vapor pressures of chemical compounds with direct experimental methods. Thus, indirect or predictive models for estimating vapor pressures are used. In this study, we follow a gas−liquid chromatography (GLC)-based approach, where retention data is correlated with vapor pressure using multiple reference standards. The method (GLC-RTyS) requires temperature-dependent retention factors (k-values) for test compounds and reference standards with known vapor pressures at matching temperatures. It has been observed that reference standards selected according to functional …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

The Evolution Of The Stress Response In The Trinidadian Killifish: Ecological Drivers And Maternal Mediation, Mikaela E. Feder

Biology Dissertations - Archive

Ecological variables can induce the stress response in organisms. Yet, few studies have tested for evolutionary shifts in stress responses across ecological gradients. In these studies, we used Trinidadian killifish found in sites with predators (high predation, HP) and sites where they are the only fish present (killifish only, KO). HP and KO populations differ in a suite of behavioral and life-history traits, making the Trinidadian killifish an excellent model to examine evolutionary physiology. My dissertation addresses the knowledge gap by examining: (1) how baseline cortisol differs in wild-caught Trinidadian killifish found in HP and KO sites, (2) the influence …

Understanding Misinformation On Social Media Through Truthfulness Stance, Zhengyuan Zhu

Computer Science and Engineering Dissertations - Archive

Misinformation on social media has become a pervasive issue that profoundly influences public opinion and decision-making. As false or misleading claims circulate widely online, there is a critical need for analytical tools to understand how people react to such claims. This dissertation introduces the concept of truthfulness stance as a key lens for social sensing. In essence, truthfulness stance assesses whether a textual utterance believes a factual claim to be true, false, or expresses a neutral stance or no stance toward the claim. Leveraging stance in this manner fills an important gap in misinformation research: it enables us to gauge …

The First Thing We Do Is Kill All The Lawsuits, Kenneth S. Klein

Faculty Scholarship

As insurance premiums spike across the Nation, insurers are (yet again) pointing the finger at lawyers and lawsuits as an explanation. This Article offers new and important data on whether, in fact, currently there is a crisis of litigation in the United States. Neither the assertion of a litigation crisis, nor legislatures adopting systemic reform in response to the perception of one, is anything new. For almost two hundred years, there have been recurring cycles of complaints about lawyers, lawsuits, and their impact on society. Yet each time independent researchers have looked at the assertion, they have found the data …

Office Of Grants And Research Services Awards 2024-2025, Office Of Grants And Research Services, Seton Hall University

Office of Grants and Research Annual Reports

No abstract provided.

Office Of Grants And Research Services Submissions 2024-2025, Office Of Grants And Research Services, Seton Hall University

Office of Grants and Research Annual Reports

No abstract provided.

Investigating Woody Vegetation Recruitment Post Riparian Enhancement Extraction Along The Baduwa't, Rize Martins De Oliveira, Wiley Hughes, Owen Lenox

Forestry, Fire, & Rangeland Management Senior Capstones

The Baduwa’t River in Humboldt County, California, faces a legacy of channelization, poor land management, and severe flooding that has left high, dry floodplain terraces disconnected from the river and dominated by invasive vegetation. In response to sediment deficits during drought years, Riparian Enhancement Extractions (REEs) were introduced as an alternative gravel mining method that simultaneously presented opportunities for habitat restoration. This study evaluates five REEs along the Baduwa’t to identify the construction features and site conditions that promote successful riparian vegetation establishment. Findings show that REEs can create seasonal wetlands conducive to native riparian habitat reestablishment, when excavation is …

Assessing Understory Along The Elk River (Hikshari') In Headwaters Forest Reserve, Humboldt County, California, Julie R. Meyers, Aliyah M. Ingram, Caleb N. Larkin, Joshua L. Carpenter

Environmental Science & Management Senior Capstones

Northern California’s extensive logging history and lack of forest management has altered old-growth coast redwood (Sequoia sempervirens) forests, leaving dense, homogenous second and third-growth forest stands. This has led to the use of modern restoration techniques such as variable density thinning (VDT) which aims to rehabilitate old-growth forest conditions by improving stand heterogeneity and light availability for understory species. Our study was done in Headwaters Forest Reserve (HFR) along the Elk River (Hikshari’) Trail in Humboldt County, California. Initially, the project was supervised by the Bureau of Land Management (BLM) Arcata Field Office. However, the 2025 U.S. government …

Restoration Plan For A Seasonal Wetland In Fortuna, Ca, Samuel Webb, Jack Mccann, Cody Swabash, Bryan Randolph

Environmental Science & Management Senior Capstones

This paper outlines the initial phase of planning for a seasonal wetland restoration on private property in Fortuna CA. It includes a site description, general background information, the methods we used to create our 30% plan, and a discussion of challenges and possible next steps of the project. Our plan consists of a revegetation plan that has the goals of the landowner and environment in mind, and includes our methods for planning revegetation in a seasonal wetland, our design, as well as suggestions for additional restoration design elements.

Ecological Assessment And Restoration Strategy For The Riparian Wetlands Of Woody Ryno Farms, Bee Krstich, Ethan Michael Gilmore, Kai Jeremy Hensley, Nicolas Augusto Yanez, Vanessa Luis

Environmental Science & Management Senior Capstones

Woody Ryno Farms is situated in McKinleyville, Humboldt County, California. The surrounding area is a mosaic of farmland, homes, and wetlands. This project aims to gather information about the health and species composition of the wetlands on Woody Ryno Farms. A step-point method was used to collect data on species composition along 16 m transects at 2 m intervals; all plants within a 1 m radius were identified and documented. Following data collection, we used Excel to analyze data based on several factors, including transect number, plants identified to the lowest taxonomic level, and native versus invasive taxa. Statistical analysis …

Restoring Riparian Health And Cultural Resources Along School Creek, A Baduwa’T Tributary, At Blue Lake Rancheria, Humboldt County, Ca, Karlie G. Hall, Fern W. Mcbride, Natalie R. Christenson, Olivia C. Brock, Emily A. King

Environmental Science & Management Senior Capstones

Riparian corridors are essential components of the landscape, supporting healthy streams and surrounding habitats by stabilizing banks, retaining water, and regulating stream temperatures. Indigenous peoples have stewarded these environments since time immemorial, cultivating deep relationships with the land and its culturally significant plants. Their continued ability to practice, transmit, and sustain this knowledge is vital not only for maintaining Traditional Ecological Knowledge (TEK) but also for fostering resilient and healthy ecosystems. The purpose of this project was to revegetate a riparian area of School Creek with culturally significant plants. School Creek is a tributary in the Baduwa’t watershed at Blue …

Becoming Reacquainted: Baseline Habitat Assessment Of Streams On Returned Tribal Lands Of Blue Lake Rancheria, California, Haley Davis, Sophia Enriquez, Cecilia Olnes, Grace Poitras

Environmental Science & Management Senior Capstones

This project presents baseline habitat data for three tributaries of the Baduwa’t (Mad River) – Powers Creek, School Creek, and Kelly Creek – located on or adjacent to reacquired Blue Lake Rancheria (BLR) lands. These small coastal streams hold ecological and cultural significance and have the potential to support salmonids and other aquatic species. Powers Creek and School Creek were dry during the survey period and were assessed using qualitative, waypoint-based observations of vegetation, substrate, instream cover, channel morphology, and potential fish passage barriers. Kelly Creek, a perennial tributary, was surveyed using the California Department of Fish and Wildlife’s (CDFW) …

Assessing Wood Jam Formation In The South Fork Elk River In Headwaters Forest Reserve, California (Year 2), Blaine Follrath, Nico Hutton, Diego Rodriguez, Torrey Stapp

Environmental Science & Management Senior Capstones

Large woody debris (LWD) contribute to the formation of complex habitat structures by stabilizing channels, creating refugia, and enhancing spawning conditions. A lack of LWD in the South Fork Elk River in Headwaters Forest Reserve has been identified by the BLM as a limiting factor in salmonid resurgence. Salmonids provide vital ecological functions to their watersheds; however, their populations face the pressure of habitat loss. This study expands upon last year’s assessment of wood jam formation on the South Fork Elk River and examines how log jam structures have changed over time since the BLM’s 2022 accelerated recruitment project. This …

2025 F1rst Look, Nova Southeastern University

First Look

No abstract provided.

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

"Discovering The Mysterious Ocean: A New Approach To Igniting Children’S Interest In Marine Sciences", Laura Goretti

Theses & ETDs

This thesis presents the development of a series of interactive musical books designed to ignite children's interest in marine sciences. The project addresses the lack of ocean literacy and school curricula in children’s books by integrating ocean principles and the New College of Florida Marine Biology curriculum with art and hands-on activities. The books target kindergarten and early elementary-aged children as the importance of early exposure to scientific concepts and children's innate scientific inquiry are discussed. Current challenges in science education are examined, highlighting the possible tools that should be utilized to enhance students’ engagement, interest, and excitement towards marine …

Analyzing The Evolution Of Major League Baseball Hitting Strategies (1950–2010): A Machine Learning And Interactive Dashboard Approach, John Mcintosh

Theses & ETDs

This thesis examines the evolution of Major League Baseball (MLB) hitting strategies from 1950 to 2010 using statistical analysis and machine learning techniques. The study investigates changes in player performance metrics to determine how hitting styles have evolved, specifically focusing on distinguishing "power hitters" from "contact hitters." Principal Component Analysis “(PCA)”, t-distributed Stochastic Neighbor Embedding, and clustering methods, such as K Means Clustering, were applied to historical MLB data from Baseball Reference to reveal underlying trends and shifts in player roles over time. An interactive dashboard was developed utilizing Streamlit to visualize these trends dynamically. This was done by incorporating …

1-Extra 3-Component Edge Connectivity Of Modified Bubble-Sort Networks, Guozhen Zhang, Zhimin Yue, Dajin Wang

School of Computing Faculty Scholarship and Creative Works

The classic edge connectivity of a network G is the minimum number of edges whose removal disconnects G into multiple components. It can be used as a measure of the reliability of the network. The extra edge connectivity (resp. the component edge connectivity) imposes conditions on the size (resp. the number) of components, and both are the generalizations of the classic edge connectivity. The extra component edge connectivity combines both conditional edge connectivities to measure the network’s reliability in a more particular and targeted manner. The modified bubble-sort graph MBn is a topological model proposed for the interconnection network of …

Robo-Csk-Organizer: Commonsense Knowledge To Organize Detected Objects For Multipurpose Robots, Rafael Hidalgo, Jesse Parron, Aparna S. Varde, Weitian Wang

School of Computing Faculty Scholarship and Creative Works

In the rapidly evolving field of robotics, integration of commonsense knowledge (CSK) in AI systems is becoming highly crucial to enhance the decision-making capabilities of robots, especially in next-generation multipurpose environments. This paper presents Robo-CSK-Organizer, a pioneering system that employs CSK, via a classical knowledge base, to facilitate sophisticated task-based object organization helpful in multipurpose robots. Unlike systems relying solely on deep learning tools such as ChatGPT, our Robo-CSK-Organizer system stands out in various crucial aspects. This includes (1) its ability to resolve ambiguities and maintain consistency in object placement; (2) its adaptability to diverse task-based classifications; and moreover, (3) …

Artificial Intelligence In Radiology, Olivia Sweeney

Theses, Dissertations and Capstones

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview …

Cartography Between Ptolemaic Accuracy And Scope For Communication: The ‘Genoese World Map’ Dated 1457, Gerda Brunnlechner

Peregrinations: Journal of Medieval Art and Architecture

No abstract provided.