Digital Commons Network^™

Green Space: Investigation Into The Role Pocket Parks Play In Biodiversity And Human Well-Being, Jacalyn Speicher

Antioch University Dissertations & Theses

Natural communities and the birds that inhabit them are essential to the physical and emotional well-being of human residents in urban settings. The action research undertaken in this study addresses the social and environmental justice issues of equity, accessibility, and diversity afforded avian and human communities in the design of pocket parks within the urban landscape. As urbanization consumes the natural landscape, people and birds must adapt to the novel challenges of a changing environment. I studied 10 pocket parks identified in Center City, Philadelphia, Pennsylvania between January and June 2026. This time frame spanned the seasons from winter to …

Automation And Autonomy In The Ivf Laboratory: Concepts And Implications For Embryologists, Jacques Cohen, Gerardo Mendizabal-Ruiz, Giles Anthony Palmer, Giuseppe Silvestri, Mina Alikani

EVMS School of Health Professions Faculty Publications

Automation is regarded as the next phase in the evolution of laboratory IVF. Despite technological advances, most laboratory procedures remain operator-dependent, contributing to variability in performance and outcomes. While automation has improved reproducibility and efficiency in many areas of medicine, its adoption in IVF has been slow. This review examines how automation may be integrated into IVF laboratories through a set of conceptual distinctions. Automation refers to the execution of procedural steps by machine-controlled systems, whereas autonomy describes the degree to which such systems can operate without human intervention. This review distinguishes between static automation, which maintains or monitors the …

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

Chemistry

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

Hydroclimate Variability And Itcz Migration Over The Last Millennium Inferred From A New Speleothem Record In Central Colombia Using Paleo Data Assimilation, Derick Appiah Kubi

Electronic Theses & Dissertations (2024 - present)

Hydroclimate variability over tropical South America is strongly influenced by meridional shifts in the Intertropical Convergence Zone (ITCZ), yet the spatial expression and dynamical controls of these changes during the last millennium remain poorly understood. This work investigates hydroclimate variability and ITCZ migration over the last millennium (850-1850 CE) using a new isotope-enabled data assimilation (DA) reconstruction made from a suite of speleothem δ¹⁸O records from the neotropics on both sides of the ITCZ. The reconstruction targets the March-August (MAMJJA) season in order to capture the rainfall regimes most relevant to central Colombia and northeastern Brazil. The focus on these …

The Impact Of Downdraft Ventilation On Tropical Cyclone Intensity And Structure Using Aerial Reconnaissance Observations, Nicholas E. Johnson

Electronic Theses & Dissertations (2024 - present)

Tropical cyclone (TC) intensity can be challenging to predict since it is modulated by many physical processes. One of these processes is ventilation: the injection of relatively cool, dry environmental air into the warm core of a TC that acts as “anti-fuel”, inhibiting intensification and increasing uncertainty with intensity forecasts. While ventilation has been examined in numerical modeling studies, there have been a limited number of observational studies investigating ventilation. The two primary objectives of this dissertation are to develop methods for measuring downdraft ventilation from aerial reconnaissance observations and to use those observations to better understand how ventilation affects …

Preliminary Analysis Of Soil Volatile Organic Compounds As Soil Health Indicators, Thorne Taul

Graduate Theses, Dissertations, and Problem Reports (ETD)

Over the past decade, soil health (SH) has gained popularity due to the novelty of sustainable soil use related to human wellbeing. Soil health describes the overall quality of soil and its capacity to function over time. As interest in SH grows, researchers continue to explore new soil health indicators that can improve knowledge on how soil health is assessed and managed. Some indicators directly reflect soil health status, and some indirectly relate to processes and functions occurring within the soil. Volatile organic compounds (VOCs) are naturally occurring compounds that play a role in multiple soil processes. They are associated …

The Maxima Method For Identification Of Principal Periodic Components In Time Series Analysis, Megan Di Maio

Electronic Theses & Dissertations (2024 - present)

This dissertation investigates methods for mean estimation in periodically correlated time series, focusing on the Variable Bandpass Periodic Block Bootstrap (VBPBB) and a novel data-driven maxima method. Time series require specific methods because of the temporal correlation in the data. Traditional methods like the General Seasonal Block Bootstrap (GSBB) account for this correlation but often produce wide confidence intervals because they cannot isolate multiple periodicities, allowing noise and other frequencies to interfere with analysis. The VBPBB method addresses this by applying a Kolmogorov-Zurbenko Fourier Transform (KZFT) filter to the data before bootstrapping, which suppresses interfering frequencies and results in narrower, …

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

Educational Leadership & Workforce Development Faculty Publications

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

Ai Tips And Traps, Patrick Barry

Books

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

Multimodal Representation Learning For Face Understanding: From Caption Supervision To Foundation Model Adaptation, Md Mahedi Hasan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The rapid advancement of intelligent surveillance systems and the increasing demand for reliable biometric identification in border security, public safety, and digital forensics require robust face understanding under unconstrained conditions, including low resolution, pose variation, and occlusion. While Vision Transformer (ViT)-based foundation models have greatly improved visual representation learning, their patch-based tokenization and lack of spatial inductive bias limit their ability to capture fine-grained details in low-resolution inputs. This dissertation investigates multimodal representation learning for face understanding through natural language supervision, large-scale face-caption pre-training, and parameter-efficient foundation model adaptation. It hypothesizes that textual supervision provides complementary semantic cues that improve …

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

UROP Posters

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

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

UROP Posters

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

Multidimensional Analysis Of Human Outdoor Comfort: Integrating Just-In-Time Adaptive Interventions (Jitais) In Urban Digital Twins, Binyu Lei, Pengyuan Liu, Kunihiko Fujiwara, Mario Frei, Clayton Miller, Xuan Yun Chua, Filip Biljecki

Research Collection College of Integrative Studies

How can bidirectional information exchange be enhanced in urban digital twins, and support human-centric data and processes? Their key characteristic is the nearly real-time exchange of information, allowing adjustments to physical environments based on simulations and analytics within virtual models. Yet, achieving such interaction remains challenging, particularly regarding device deployment and infrastructure development. Embracing the concept of humans as sensors, this work develops a two-way framework based on the emerging concept of just-in-time adaptive interventions (JITAIs), exploring how urban digital twins can play a role in understanding and enhancing human comfort outdoors. Human comfort outdoors is inherently spatio-temporal and personalised, …

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

Graduate Theses, Dissertations, and Problem Reports (ETD)

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

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

Studies On The Depth Formula And On Reducing Dimensions, Brian Mccourt Laverty

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation presents the author’s recent research, conducted under the supervision of Professor Olgur Celikbas, and based on two articles—one published and one in progress. These works develop two closely related research directions in commutative algebra. Together, they contribute to the subject by addressing aspects of existing conjectures, establishing new results, and introducing methods for studying homological invariants.

The first research direction concerns the depth formula, namely the equality \[ \depth_R(M)+\depth_R(N)=\depth(R)+\depth_R(M\otimes_RN) \] where $M$ and $N$ are finitely generated $R$-modules. A classical result of Auslander \cite{Aus} shows that the depth formula holds provided that either $M$ or $N$ has finite …

Data-Driven Methodologies For Mapping Cultural Heritage: The Case Of The National Coal Heritage Area, West Virginia, Usa, Hossain Mohammad Nahyan

Graduate Theses, Dissertations, and Problem Reports (ETD)

The objective of this dissertation was to develop a comprehensive, data-driven spatial framework for characterizing the complex cultural landscape of the National Coal Heritage Area (NCHA) in West Virginia. By transitioning away from traditional, heuristic spatial mapping, this research integrates advanced spatial statistics, machine learning, and GIS-based methodologies to objectively quantify the physical, visual, and cultural dimensions of the post-mining environment. The research is structured around three interconnected empirical studies, each addressing a specific scale of the Landscape Character Assessment (LCA) framework to support heritage conservation and sustainable spatial planning. The first paper focused on landform classification, developing an automated …

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

Open Educational Resources

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

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

Computer Information Systems Faculty Publications

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

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

EVMS School of Health Professions Faculty Publications

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

Crossing The Theory Threshold: The Pedagogical Potential Of Generative Artificial Intelligence In Educational Research, Amanda Burbage, Jennifer L. Styron

EVMS School of Health Professions Faculty Publications

Purpose

This paper presents findings from an educational research graduate course in which generative artificial intelligence (AI) was incorporated to strengthen learners' understanding of threshold concepts related to theoretical frameworks. Medical and health professionals often struggle with the transition from a clinical role into the educational research role.

Methods

The study posits that the use of generative AI will help learners understand and apply theoretical frameworks beyond a superficial level, furthering their understanding, constructing new knowledge, and strengthening their ability to develop sound educational research studies. Journal and AI transcripts were analyzed for 37 participants.

Results

Open-ended codes were grouped …

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

Open Access Dissertations

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

Linking Phytoplankton Physiology And Ecology To Global Biogeochemical Cycles: A Modeling Approach, Margaret Bernish

Open Access Dissertations

Phytoplankton, or microscopic primary producers, contribute to carbon export through the biological transformation of carbon dioxide into organic molecules. Phytoplankton are responsible for approximately half of global net primary production, while only contributing minimally to total photosynthetic biomass. Despite their important role in the global cycling of nutrients, phytoplankton physiology and ecology are often highly simplified in global biogeochemical models. This dissertation elucidates the role of phytoplankton physiology and ecology within biogeochemical cycles, allowing for better characterization of marine microbial distribution and better representation of nutrient cycling for interpretive and predictive models. The development of formulations that link physiological responses …

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

Open Access Dissertations

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

Performance Assessment And Design Improvements For An Urban Coastal Detention Basin Under Intensifying Rainfall Extremes, Imiya Mudiyanselage Chathuranika, Agyare Asante, Faeghe Borhani, Xixi Wang, Mujde Erten-Unal, Dalya Ismael

Civil & Environmental Engineering Faculty Publications

Coastal urban areas are increasingly exposed to flooding driven by more frequent and intense rainfall events, rising sea levels, and expanding impervious surfaces. Norfolk, Virginia, a low-lying coastal city with aging stormwater infrastructure, faces heightened vulnerability to these hydrologic pressures. This study evaluates the hydraulic performance of an existing urban detention basin within the Edgewater–Larchmont catchment under 10-, 50-, and 100-year, 2-h design storms using the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). Simulations were conducted for both pre- and post-development conditions to assess changes in peak discharge, storage capacity, and water level dynamics. Results show that urbanization, …

Geospatial Analysis Of Sinkhole Density And Characteristics In West Virginia, Marissa Shannon Loftus

Graduate Theses, Dissertations, and Problem Reports (ETD)

Creation of a sinkhole map of West Virginia provides useful data to help identify areas susceptible to sinkhole related hazards, as well as allowing for detailed analysis of factors controlling karst development. Newly available, statewide LiDAR-derived digital elevation data have made mapping sinkholes over larger spatial extents feasible. For this study, sinkholes were mapped for all areas of West Virginia that are underlain by carbonate rocks; then the distribution and characteristics were evaluated overall and in four focus areas (Berkeley and Jefferson counties, Pendleton County, Greenbrier County, and Monroe County). These areas correspond to broader physiographic regions, with Berkeley, Jefferson, …

Comparison Of Push-Pull Air Curtain To Overhead Downdraft Systems On Aerosol Containment And Distribution In Controlled Environments, Manal Mutairi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

Comparison of Push-Pull Air Curtain to Overhead Downdraft Systems on Aerosol Containment and Distribution in Controlled Environments.

This dissertation looks at the ongoing issue of tiny airborne particles created during medical and dental procedures and evaluates ways to reduce the risk of exposure for healthcare workers and patients to these particles. The first study focuses on the size, how long particles stay in the air, and how far they travel from common medical procedures that create aerosols, showing that tiny particles (less than 5 µm) can stay in the air long enough to escape basic droplet controls. As a …

I Think, Therefore I Can: Self-Efficacy, Self-Regulation, And Sense Of Belonging In Introductory Physics, Danielle Christine Maldonado

Graduate Theses, Dissertations, and Problem Reports (ETD)

Cognitive and affective beliefs in physics classrooms are important indicators of student success beyond traditional measurements of academic performance. This body of work explores major beliefs and processes, including self-efficacy, self-regulation, and sense of belonging, as they relate to academic achievement among introductory undergraduate physics students. First, this dissertation explores the development of physics self-efficacy and physics sense of belonging, two highly correlated variables. This work identifies unique sources of physics self-efficacy and sense of belonging development from current and prior academic experiences, personality factors, and demographic variables. Then, relationships between early- semester self-efficacy and sense of belonging, mid-semester …

Factors Influencing Eastern Wild Turkey (Meleagris Gallopavo Silvestris) Productivity And Nesting Chronology In West Virginia, Kirstin Palumbo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Identifying drivers of variation in the timing of breeding behaviors, such as courtship activities and nesting chronology, is important for the effective management of avian species. Likewise, productivity is an important component of game bird population dynamics and can be broken up into multiple components such as the probability of nesting, clutch size, and nest success. One species that experiences a relatively long reproductive season is the Eastern wild turkey (Meleagris gallopavo silvestris). Contemporary research in the southeastern United States has raised concerns regarding declines in both wild turkey populations and overall productivity. West Virginia, however, is unique …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …