Digital Commons Network^™

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 …

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 …

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 …

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 …

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 …

Antiviral Activity And Mucosal Safety Of Novel Human-Milk-Inspired Antimicrobial Peptides, Nazita Yousefieh, Louise A. Ouattara, Maimoona Bhutta, Ishita M. Shah, Carolina Herrera, Gustavo F. Doncel

CONRAD Publications

Background: Sexually transmitted infections (STIs) remain a major global health concern, with over one million new curable cases reported daily and limited long-term treatment options. Additionally, there are limited treatment options for women with bacterial vaginosis (BV), a dysbiotic condition predisposing them to STIs. Computationally designed human-milk-inspired antimicrobial peptides (AMPs) are promising candidates for novel antiviral and antibacterial therapies. This study evaluated the cervicovaginal mucosal safety and antiviral activity of three human-milk-inspired AMPs (MAT-006, UCD-MAT-001, and UCD-MAT-002) previously shown to exhibit antimicrobial activity against BV-causing pathogens. Methods: AMP cytotoxicity and inflammatory responses were assessed in relevant genital tract cell lines …

Predicting Oral Hiv Pre-Exposure Prophylaxis Efficacy In Cisgender Women Across Pregnancy And Postpartum, Yifan Yu, Kristina M. Brooks, Gustavo F. Doncel, Brookie M. Best, Mark A. Marzinke, Mark Mirochnick, Peter L. Anderson, Landon Myer, Connie Celum, Renee Heffron, Jenell Coleman, Dvora L. Joseph Davey, Lanxin Zhang, Max Von Kleist, Craig W. Hendrix, Jeremiah D. Momper, Robert Bies, Rachel K. Scott

CONRAD Publications

Cisgender women face an increased risk of HIV acquisition during pregnancy and postpartum. Oral pre-exposure prophylaxis (PrEP) with emtricitabine and tenofovir disoproxil fumarate (F/TDF) is recommended in pregnancy despite limited published pharmacokinetic (PK) and efficacy data during pregnancy. Altered PK during pregnancy may reduce prophylactic efficacy and necessitate higher adherence. Tenofovir alafenamide (TAF) is a newer tenofovir prodrug with higher tenofovir diphosphate (TFV-DP) exposure in peripheral blood mononuclear cells (PBMCs), but the prophylactic efficacy of F/TAF across different reproductive stages in women is understudied. We adapted a multiscale modeling framework to predict the prophylactic efficacy of F/TDF and F/TAF across …

Interpersonal Dark Side Of Impostor Thoughts: Predicting Workplace Ostracism Via Surface Acting And Cognitive Dissonance, And The Moderating Role Of Self-Leadership, Asiye Zeytonli

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation investigates the "interpersonal dark side" of workplace impostor thoughts

(WIT), exploring how the internal experience of feeling like a professional fraud translates into

the social exclusion of colleagues through defensive ostracism. While prior literature has

extensively documented the intrapersonal distress associated with the impostor phenomenon,

such as anxiety and burnout this research addresses a critical gap by examining how these

internal states drive behaviors that harm the organizational social fabric. Drawing upon an

integration of Conservation of Resources (COR) theory and Cognitive Dissonance Theory

(CDT), I propose and test a dual-pathway model. The first pathway (COR) suggests that …

Multi-Grade Deep Learning, Yuesheng Xu

Mathematics & Statistics Faculty Publications

Deep learning requires solving a nonconvex optimization problem of a large size to learn a deep neural network (DNN). The current deep learning model is of a single-grade, that is, it trains a DNN end-to-end, by solving a single nonconvex optimization problem. When the layer number of the neural network is large, it is computationally challenging to carry out such a task efficiently. The complexity of the task comes from learning all weight matrices and bias vectors from one single nonconvex optimization problem of a large size. Inspired by the human education process which arranges learning in grades, we …

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

The Mercury 2026

The Mercury

No abstract provided.

The Effects Of Low-Frequency, Low-Intensity Ultrasound On Gene Expression In Macrophage-Endothelial Cell Co-Culture, Evelyn Hergenhan

Honors Theses

Introduction

Low-frequency, low-intensity ultrasound (LFLI US) has been shown to be a promising treatment for noninvasive accelerated chronic wound healing. However, the biological mechanisms of this ultrasound-assisted healing are not yet understood, limiting clinical adoption. The objective of this study is to characterize the effects of LFLI US on gene expression in a simulated chronic wound environment to gain a better understanding of the biological mechanisms underlying this therapeutic.

Methods

A simulated chronic wound environment consisting of endothelial cells and inflammatory M1 macrophages co-cultured in 3D was subject to LFLI US at 50, 100, or 150 mW/cm² Spatial-Peak, Temporal-Peak …

Genre Based Analysis Of Sustainability In Contemporary U.S. Film Production: Inception Or Illusion?, Tanner Ennis

Interdisciplinary Studies | Senior Theses

Anthropogenic activity is actively leading to environmental degradation, highlighting the need for large industries to take responsibility for their environmental impact, particularly the film industry. While sustainable practices in the film industry such as virtual production, recycling of set construction materials, or the use of renewable energy present opportunities to improve sustainability, there is still a substantial amount of progress to be made. For example, research has demonstrated that increases in production budget correlate with higher carbon dioxide emissions, with fuel, utilities, and travel acting as the top contributors (Sustainable Entertainment Alliance, 2022). Conversation among the scholarly community is centered …

Teachers' Perceptions Of The Use Of The Language Essentials For Teachers Of Reading And Spelling (Letrs) Professional Development Training, Colette Burgess

Theses, Dissertations and Capstones

The Language Essentials for Teachers of Reading and Spelling (LETRS) professional development course has been participated in by educators across the United States. The LETRS training focuses on research-based methods and the science of reading. The five components of the science of reading guide the LETRS course. These components include phonemic awareness, phonics, fluency, vocabulary, and comprehension. The purpose of this study was to examine the perspectives and experiences of teachers from West Virginia schools who participated in the LETRS professional development course as a cohort. This study examined the questions: What are the perceptions of K-5 educators in implementing …

Toward Completeness Theorem For Guarded Kleene Algebra With Tests, Hung Pham

Honors Theses

Code refactoring is a fundamental practice in software engineering, in which a program is restructured without changing the actions it performs and the results it produces. To carry out refactoring with confidence, one requires a formal method for verifying that two programs are equivalent. Guarded Kleene Algebra with Tests (GKAT) provides such a framework, an algebraic system designed to reason about a natural class of programs, namely those in which every branch and loop is governed by a Boolean condition, such as if–else and while statements. Central to GKAT is a finite set of algebraic axioms for deriving program equivalences. …

Is It A Failure Of Informed Consent When A Physician Does Not Share A Test Result With A Patient Because The Physician Does Not Believe The Test?, Alan Schwartz

UIC Law Review

No abstract provided.

United States V. Sanders: Dost And The Quest For Clarity, Katie Stonich

UIC Law Review

No abstract provided.

Underdiagnosis Of Stroke In Individuals Aged 0-64, Catherine Wang

Mentorship Program 2025-2026

No abstract provided.

Aura: An Ai-Powered Multimodal Prototype For Adaptive Apraxia Of Speech Therapy And Communication Support, Omotayo Omoyemi, Rachel K. Johnson

Speech-Language Pathology Faculty Publications

Apraxia of Speech (AOS) is a motor speech disorder that significantly limits communication and requires intensive, long-term therapy. Access to consistent treatment is often constrained by shortages of speech-language pathologists, high costs, and limited opportunities for continuous monitoring outside clinical settings. Recent advances in Artificial Intelligence (AI) provide new opportunities to support scalable and personalized speech therapy.

This paper presents AURA (Adaptive Understanding and Relearning Assistant for Apraxia), a multimodal AI framework designed to support speech therapy, progress monitoring, and communication for individuals with AOS. The system integrates speech analysis, machine learning–based error detection, reinforcement learning for adaptive therapy, and …

Temperature-Dependent Optical Properties Characterization Of Thermophotovoltaic Selective Emitter (4h– Sic, Aln Ceramic, And Aln/Al2o3), Po-Chao Huang

Honors Theses

Thermophotovoltaic (TPV) systems convert thermal radiation into electricity, and their efficiency depends strongly on the temperature-dependent optical response of the emitter materials. To support the design of high-temperature selective emitters, we characterized the thermo-optical properties of aluminum nitride ceramic (AlN), silicon carbide (4H– SiC), and aluminum oxide on aluminum nitride (AlN/Al2O3) thin film/substrate pairs, as a promising example of a bilayer emitter. Spectroscopic ellipsometry measurements from 240 nm to 2500 nm were performed while heating samples from room temperature to 600 °C. Using general oscillators modeling, we established accurate temperature dependent optical models consistent with literature values. Increasing temperature revealed …

Leveraging Machine Learning Techniques To Identify Catastrophic Outliers In Photometric Redshift Estimates, Jason Yoo

Honors Theses

Leveraging the unprecedented data volumes from modern astronomical surveys, this work addresses the critical challenge of identifying catastrophic outliers (COs) in photometric redshift (photo-z) estimates. Reliable redshift estimation is essential for precision cosmology, yet COs (galaxies with severely erroneous photo-z) can introduce significant systematic bias. We develop and evaluate two machine learning approaches for robust CO identification: a multilayer perceptron (MLP) utilizing photo-z point estimates and probability distributions, and a convolutional neural network (CNN) leveraging raw 5-band photometric images. Using datasets emulating LSST conditions, we demonstrate that the MLP model achieves high accuracy and stability across redshift bins, correctly identifying …

A Comparison Of Time-Dependent Cloudy Astrophysical Code Simulations With Experimental X-Ray Spectra From Kev Laser-Generated Argon Plasmas, N. Rathee, F. P. Keenan, R. J. R. Williams, Gary Ferland, S. J. Rose, S. White, D. Riley

Physics and Astronomy Faculty Publications

We have generated strongly photoionized Ar plasmas in experiments designed to use primarily X-ray l-shell line emission generated from Ag foils irradiated by the VULCAN high-power laser at the UK Central Laser Facility. The principle of the experiment is that use of line emission rather than the usual sub-keV quasi-blackbody source allows keV radiation to play a more dominant role compared to softer X-rays and thus mimic the effect of a blackbody with a higher effective spectral temperature. Our aim is to reproduce in the laboratory the extreme photoionization conditions found in accretion-powered astrophysical sources. In this paper, we compare …

Setting The Recycling Narrative: A Thematic Analysis Of Recycling Webpages In The United States, Haley Nolde

Dissertations and Theses

Recycling is widely promoted in the United States as a central strategy for waste diversion and sustainable materials management, yet recycling outcomes remain inconsistent and public understanding is often fragmented. While substantial research examines recycling behavior, infrastructure, material science, and markets, less attention has been paid to how recycling itself is framed and communicated to the public. This thesis addresses that gap by examining narratives produced by recycling thought leader organizations (TLOs) in the United States. Using thematic analysis, this study analyzes publicly available recycling webpages from 99 TLOs, including state government agencies, state-level non-governmental organizations (NGOs), national and regional …

Deep Learning-Based Quantification Of Tall Fescue Abundance In Pastures, Rose E. Binkley, Samuel R. Revolinski, Echo Elizabeth Gotsick, Ray Smith, Zeya Wang, Katsutoshi Mizuta

Plant and Soil Sciences Faculty Publications

Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

Scaffolding Minimum Age Thresholds, Eve Rips

UIC Law Open Access Faculty Scholarship

No abstract provided.

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …

Meshless Collocation Methods For Time-Dependent Nonlocal Problems Based On Radial Basis Functions, Qiao Zhuang, Yanzhi Zhang, Zhongqiang Zhang

Mathematics and Statistics Faculty Research & Creative Works

We present radial basis function (RBF) collocation methods for time-dependent space fractional problems on general bounded domains. Building on a recently developed approach for accurately computing the integral fractional Laplacian of any RBF, we design collocation schemes for fractional heat and Stokes equations using extended-domain techniques. In particular, we propose a numerical Leray projection method for fractional Stokes problems, where both the discrete projection operator and the collocation scheme are formulated on extended domains to handle complex domains. Numerical results demonstrate the effectiveness of the proposed methods in solving time-dependent nonlocal problems on complex domains.