Digital Commons Network^™

Grm-0216 Towards Analyzing The Bridge Dataset With Quantum Machine Learning, Gayathri Kolavennu

C-Day Computing Showcase

This research presents a comparative evaluation of classical and quantum machine learning models applied to the Bridge dataset. Classical algorithms like Support Vector Machine, Random Forest, and Neural Networks are benchmarked against Quantum SVM, Quantum Random Forest, and Quantum Neural Networks using identical preprocessing and training conditions. Results indicate a consistent quantum advantage, with quantum models achieving higher accuracy, stronger nonlinear feature separation, and improved minority-class detection. QSVM and QNN exhibit the most significant performance gains. Although quantum models require greater computational resources, the findings underscore the emerging effectiveness of quantum-enhanced learning for structural classification tasks in the NISQ era.

Grm-1145 Autumn Lite Llm, Michael Knighten

C-Day Computing Showcase

Autumn Lite is an inspectable, small-footprint language modeling pipeline for reproducible experimentation and practical integration into video-game non-player character (NPC) systems. It comprises four components: (1) a regex-aware tokenizer/normalizer for vocabulary construction and mixed prose–code handling; (2) a classical evaluation track that reports perplexity to quantify predictive quality; (3) a compact neural language model (decoder-only Transformer) targeted at low latency and controllable outputs; and (4) a lightweight sentiment classifier (logistic regression) that assigns positive/neutral/negative tags to steer text-to-speech (TTS) prosody during NPC dialogue. By combining transparent preprocessing with baseline metrics and a small, deployable decoder, Autumn Lite aims to deliver …

Grm-1245 A Synthetic Data Engine For Explainable Injection-Area Perception, Yukang Shen

C-Day Computing Showcase

Vision-Language-Action (VLA) systems are beginning to support everyday clinical workflows. Deltoid intramuscular injection is a representative task, but progress is limited by data scarcity, privacy constraints, and the cost of expert annotation. Recent text-to-image (T2I) models make large-scale data synthesis possible, yet ensuring anatomical correctness, diversity, and label quality remains difficult. To address this gap, we propose a Synthetic Data Engine tailored for medical perception, integrating cold-start filtering, controlled T2I generation, CLIP-based quality checks, and iterative segmentation training. We further introduce an anthropometry-grounded formulation of injection safety that produces interpretable safe-zone guidance. Experiments show that synthetic data can effectively bootstrap …

Grm-20169 Proxy Recognition And Inclusive Scoring Method (Prism): Evaluating Context-Dependent Bias In Large Language Models For Resume Screening, Crystal Tubbs, Destiny Raburnel

C-Day Computing Showcase

AI-driven hiring tools are reshaping recruitment but often mirror biases in their training data. PRISM examines how large language models express or reduce demographic bias during resume evaluation and how linguistic context within prompts shapes these outcomes. Using a controlled dataset of 324 synthetic resumes with racially neutral surnames, differing only by first name as the demographic proxy, we compared GPT 3.5 turbo with a Sentence BERT similarity model. Under neutral prompts, no stable bias was observed across demographic groups, yet contextual shifts in the prompt changed how the model responded to proxy cues. These findings show that LLM bias …

Grm-20188 Intelligent Book Recommendation And Rating Prediction System, Destiny Raburnel

C-Day Computing Showcase

When selecting a book, readers often rely on surface level information such as the title, author, synopsis, and keywords to determine whether a story matches their interests. These features contain important cues related to genre, tone, and narrative elements that help set expectations before reading. The Intelligent Book Recommendation and Rating Prediction System works to automate this process by using natural language processing and machine learning techniques. It takes in readers’ personalized reading data such as book titles, author, subjects, synopsis, and personal ratings to learn semantic patterns using TF-IDF vectorization. A supervised Linear Regression model was then trained to …

Mobile Computer Vision Application For Agricultural Disease Detection Of Pepper Diseases Using Two-Stage Deep Learning System, Carlos Jose Estevez, Mai Dang, Ryan Bass

SMU Data Science Review

Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Predictive Analytics In Public Health: Developing Ai Strategies To Combat High-Temperature Impacts On Violence And Overdoses In Las Vegas, David Camacho, Stephanie Duarte, Kosi Okeke, Chris Papesh, Jacquelyn Cheun-Jensen

SMU Data Science Review

Public violence and overdoses are key issues that Nevada agencies aim to address in the Las Vegas Metropolitan Area. A proven and effective strategy is via the implementation of a Cardiff Violence Prevention Model. This model provides a way for communities to gain a clearer picture about where violence is occurring by combining and mapping both hospital and police data on violence. In an effort to support this model, the exploration and implementation of AI models to predict the impacts of high temperatures on key public data and outcomes based upon historical data will be applied.

Ai-Powered Compliance: Accelerating Efficiency And Decision-Making For Compliance Related Inquiries., Amberly R. Rodriguez

SMU Data Science Review

This research examines the potential of an AI-powered chatbot to streamline compliance workflows by reducing the time and effort required to locate and interpret complex compliance documents. The prototype integrates a centralized MySQL-based document repository, a contextual document querying engine, and a Streamlit web interface, enabling employees to retrieve accurate, document-backed answers within seconds. The system supports both stored and user-uploaded documents, with features such as automated summarization and source citations to enhance transparency and trust. Manual evaluation demonstrated notable gains in efficiency and accuracy compared to traditional search methods, with strong potential to improve adherence to compliance policies. Future …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Analyzing The Global Happiness Index, Victoria Hernandez, Christy W. Wachira

SMU Data Science Review

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and …

Development Of Composite Membranes And Nanofluid Absorbents Incorporating Surface-Functionalized Nanoparticles For Enhanced Co₂ Absorption In Gas–Liquid Membrane Contactors, Riya Ahammed Labeeb

Thesis/ Dissertation Defenses

Natural gas sweetening demands the efficient removal of CO₂ to enhance heating value and prevent equipment corrosion. Gas–liquid membrane contactors (GLMCs) have garnered attention as promising solutions to traditional separation methods due to their modularity, high interfacial area, and energy efficiency. Nonetheless, they suffer from some serious challenges such as membrane wetting, while absorbents either exhibit low absorption capacity or high energy demands and corrosion. This work addresses these limitations through two complementary strategies. In the first, ZnO nanoparticles were hydrophobically functionalised and incorporated into polymer matrices to fabricate composite membranes with superior hydrophobicity and reduced pore wetting. In the …

Addressing Junk Science In Abortion Legislation, Kirby Tyrrell

University of Memphis Law Review

Junk science has been a longstanding feature of antiabortion legislative and litigation strategy. But after the Supreme Court’s opinion in Whole Woman’s Health required courts to independently evaluate scientific evidence, rather than defer to a legislature’s factual findings, antiabortion experts took on greater importance in litigation. This Article examines three theories of antiabortion junk science, which, despite overwhelming scientific evidence establishing their falsity, have been repeatedly litigated in federal and state courts. The current tool for addressing dubious evidence in litigation is the standard created by the Supreme Court in Daubert and adopted by Federal Rule of Evidence 702. However, …

Bio-Corrosion Studies On Novel Zr‑Co‑Ti Based Metallic Glass Alloys For Biomedical Implant Applications, Shubhra Shitole

Thesis/ Dissertation Defenses

The relentless pursuit of advanced biomaterials that synergistically combine high mechanical strength, exceptional corrosion resistance, and inherent biocompatibility is critical for next-generation medical implants. This research addresses this challenge through the development and multi-faceted evaluation of a novel library of Zr-Co-Ti-based metallic glasses (MGs); Zr₆₀Co₃₀Ti₁₀, Zr₅₅Co₃₅Ti₁₀, and Zr₅₀Co₄₀Ti₁₀, fabricated via melt-spinning. The alloys were confirmed to be fully amorphous by X-ray diffraction (XRD), a structure underpinned by exceptional thermal stability, with the Zr60 composition exhibiting a larger supercooled liquid region (Δ𝑇𝑥) of …

"Lofty, Where All Is Shrunk And Changed": The Duality Of Susan Pringle Frost's Measures To Retain Power In Progressive Era Charleston, Emily Anne Harris

Furman Humanities Review

2025 Meta E. Gilpatrick Prize Essay

Furman Humanities Review. Volume 36, August 2025, Gretchen Braun Editor

Furman Humanities Review

No abstract provided.

Durability Of Basalt Fiber-Reinforced Polymer Bars In Moist Concrete Under Sustained Load, Osama Ahmed

Thesis/ Dissertation Defenses

This thesis is concerned with the long-term durability of basalt fiber-reinforced polymer (BFRP) bars embedded in moist concrete. The study focuses on the combined effects of exposure duration, elevated temperature, and sustained tensile stress, which together influence the mechanical and chemical stability of BFRP reinforcement in aggressive environments. The main objective is to evaluate the degradation mechanisms of BFRP bars subjected to coupled hygrothermal and mechanical conditions and to develop a predictive durability model capable of estimating service life under realistic climate scenarios. Concrete-encased BFRP bars were conditioned at three temperatures (20, 40, and 60°C), three durations (3, 6, and …

Cloud-Managed System For Distributing Audio Content To Point-Of-Sale Devices, Ramprasad S, Gulmohar Khan, Ankul Batra, Gajen Agrawal, Malay Singh, Saurabh Mimani, Ajay Prasad, Venu Annamraju, Sailendra Bathi

Defensive Publications Series

The utility for merchants of point-of-sale (POS) audio devices can be expanded beyond announcing transaction details. A cloud-managed system can facilitate the creation and distribution of custom audio content to these devices. For example, a user can input text through a client application, which can be sent to a cloud backend. The backend can use services, such as generative artificial intelligence for content refinement and a text-to-speech engine, to generate an audio file. The backend can then orchestrate the distribution of the audio file and an associated playback schedule to one or more endpoint audio devices. This system can expand …

Dissecting The Efficacy And Immunogenicity Of Tlr7 Agonist–Antibody Conjugates Through The Lens Of Fc Effector Function, Conjugation Strategies, And Linker Cleavability, Siteng Fang, Samantha R. Benjamin, Lina Wu, Mohammad Asikur Rahman, Kayla K. Elder, Victor T. Ojo, Tao Zhang, Tracy A. Brooks, L. Nathan Tumey

Pharmacy Faculty Scholarship

The advent of immuno-oncology therapeutics has given rise to antibody-drug conjugates (ADC) designed to selectively activate pattern recognition receptors such as Toll-Like Receptors (TLRs). In contrast to classical ADC technology, these Immune Stimulating Antibody Conjugates (ISACs) are widely reported to rely on Fcγ-mediated uptake and noncleavable linkers. Herein, we systematically study the impact of Fc effector function, linker cleavability, and conjugation strategy on the efficacy and immunogenicity of a series of TLR7 agonist-based ISACs. We demonstrate that the combination of FcγR-ablation and incorporation of a cleavable linker results in increased stimulation of myeloid cells, enhanced efficacy, and improved tolerability as …

Innovation Curse: The Wastefulness Of Technologies Believed To Mitigate Climate Change, Minh Hoang Nguyen, Valoree Gagnon, Thanh Tu Tran, Thi Mai Anh Tran

Michigan Tech Publications

Technological innovations are increasingly promoted as solutions to climate change. However, many innovations, including Carbon Capture and Storage, bioplastics, and glacier geo-engineering, suffer significant limitations from high costs, speculative efficacy, and adverse ecological consequences. Using Granular Interaction Thinking Theory (GITT), a transdisciplinary framework grounded in information theory, quantum mechanics, and mindsponge theory, in this study, we explain how such technological innovations become systemically favored despite their flaws. We introduce the concept of the “innovation curse,” which arises when institutional and cognitive filtering systems, operating under high informational entropy, default to familiar but ineffective techno-solutions while marginalizing Indigenous and Local Knowledges …

A Detailed Investigation Into Word-Order In The Modification Of Pronouns, Kelli Hesseltine, Joseph C. M. Davis

Publications and Research

This study builds upon Hesseltine and Davis ([2020]. “The Communicative Function of Adjective-Noun Order in English.” WORD 66 (3): 166–193. https://doi.org/10.1080/00437956.2020.1793499), which examines word order in modification in English and proposes the Assertion of Characterization hypothesis. To communicate an assertion of an entity’s traits, speakers employ two different meanings: weaker, signaled by the order AB as in long (A) hair (B), and stronger, signaled by BA as in hair (B) long (A). The attribution of the characterization is thus made less or more assertively. This work focuses on examples where the characterized entity, the B, is a form traditionally …

Mark Munsell Named Distinguished Fellow Of Artificial Intelligence, Lindenwood University

Archived Lindenwood Press Releases

No abstract provided.

Deconstructing The Black Box: An Explainability Analysis Of Deep Learning Architectures In Cytopathology, Chase A. Garrett

Master's Theses or Doctor of Nursing Practice

Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology remains limited. Cytopathology could benefit from deep learning applications by improving diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box” that keeps the models from being transparent and trustworthy for widespread clinical adoption. We conducted a comprehensive and comparative analysis of several deep learning architectures for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from peripheral blood smear images. The models in this research include a Vision Transformer (ViT) and a diverse selection of Convolutional Neural Network (CNN) models. The …

Characterization Of Antiretroviral Therapy (Art) Adherence Phenotypes And Psychosocial Symptom Clusters Among Black/African American (Aa) And Hispanic/Latine Adolescents And Young Adults (Ayas) With Hiv In The Adherence Connection For Counseling, Education, And Support (Access-Ii) Trial, Ann-Margaret Navarra, Taehoon Ha, Eva Liang, Maurade Gormley, David R. Garcia, Jason Fletcher, Lloyd A. Goldsamt, Michael G. Rosenberg, Karin Hasegawa, Jie Yang

School of Nursing Faculty Publications

Antiretroviral therapy (ART) adherence behavior is heterogeneous among adolescents and young adults (AYAs) with HIV and influenced by individual and interpersonal psychosocial factors. The primary objective of this study is to characterize ART adherence phenotypes and psychosocial symptom clusters, as related to ART adherence and HIV viral load suppression. This analysis included 60 AYAs with HIV enrolled in an ART adherence support clinical trial. Self-reported ART adherence at baseline, 12-weeks, and 24-weeks was used to define four ART adherence phenotypes: consistently high adherence (YY), early-only adherence (YN), late-only adherence (NY), and consistently low adherence (NN). Symptom clusters were empirically derived …

Flow Control Tactics For Photovoltaic Modules: Coherent Structure And Heat Shedding Amplification, Sarah Elizabeth Smith

Dissertations and Theses

Solar photovoltaic systems have become the fastest growing renewable technology in the last several years. However, large-scale systems are at the mercy of their external environments, where extreme heating and wind-flung debris impact their efficiency and useable life. The work in this dissertation presents studies aimed to both understand and mitigate for these effects by identifying the underlying physics which govern them. Exploring flow control as a tactic to combat environmental heating, collective work discusses the role of turbulent fluid dynamics within large-scale systems as pertains to convective heat transfer, turbulent structure formation, and particle-laden environments. Wind tunnel experiments on …

Advanced Deep Neural Network Architectures For 3d Gait Assessment From Video In The Early Detection Of Alzheimers Disease, Abhishek Singh Kushwaha

Electronic Theses and Dissertations 2020 - Present

This thesis presents an advanced deep learning framework for markerless 3D gait assessment using monocular video, aimed at the early detection of Alzheimer’s disease (AD). Recognizing gait abnormalities as potential preclinical biomarkers, the study leverages video-based human pose estimation to derive detailed spatiotemporal gait parameters without the need for specialized laboratory equipment. The proposed system adapts the MeTRAbs (Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation) architecture to reconstruct metric-scale 3D human joint coordinates from RGB video recordings. A complete processing pipeline is developed to extract stride length, cadence, stance and swing phases, and gait variability, validated against the …

Node-Attributed Spatial Graph Partitioning: A Kullback-Leibler Divergence Based Approach, Valentin Nechita

Electronic Theses and Dissertations 2020 - Present

This thesis presents a novel approach to node-attributed spatial graph partitioning (NSGP) that addresses limitations in existing homogeneity measures. We introduce a Kullback-Leibler (KL) divergence-based formulation we name KLD that measures attribute similarity by comparing each node’s distribution to its subgraph centroid, providing a computationally efficient method of measuring homogeneity. Integrated within a multilevel graph partitioning framework and Fiduccia-Mattheyses refinement, our approach achieves approximately 90% runtime reduction compared to the baseline Clustering and Local Refinement (CLR) algorithm while maintaining comparable partition quality. Applied to COVID-19 infection data combined with American Community Survey demographics across U.S. FIPS codes, KLD successfully identifies …

Archaeology As Alternative Prison Education, Jayne Price Dr, Caroline Pudney Dr

Journal of Prison Education Research

This paper presents the educational focused outcomes of a set of workshops entitled 'Introduction to Archaeology’ that were delivered within a prison. The workshops were delivered as part of a project that seeks to explore the pro-social benefits and social capital (i.e. social and structural aspects that support positive identity formation) that could be developed through engagement with archaeology, specifically for those within the criminal justice system. The workshops offered a bespoke, alternative, educational offering within an adult male prison in Wales. It was designed and delivered by an archaeologist who has an established background in the field, especially in …

Healthlitpro™: Development Of An Ai Driven Audit & Feedback Tool For Organizational Health Literacy, Carol J. Howe, Yunhe Feng, Song Fu, Donger Chen, Lindsey Patton

2025

RESEARCH

Purpose: Organizational Health Literacy (OHL) refers to the ability of healthcare institutions to support patients and families to access, understand, and use health information. Nurses play a key role in OHL using evidence-based health literacy practices (i.e. plain language, teach-back, open-ended questions, chunking information, and three to five key points) when educating patient families. Yet, implementation is challenging. Audit and Feedback (A&F) is an implementation strategy for promoting fidelity to health literacy practices. Conventionally requiring expert human evaluations that are costly, labor-intensive, and difficult to scale.

Methods: HealthLit was trained using a dataset of simulated nurse-patient education …

Development And Applications Of A Health Equity Summary Score Using The Medical Expenditure Panel Survey, Ronald R. Sanchez

Dissertations and Theses

Despite sustained national efforts, disparities in healthcare delivery persist among racial and ethnic minorities, low-income individuals, and socially at-risk populations. Monitoring modifiable aspects of care delivery, such as the structures and processes that health systems can directly improve, is a key strategy for addressing these gaps. However, most measures evaluate modifiable aspects of care delivery separately, limiting understanding of how improvements in one domain reinforce the other and how their combined effect influences equitable care.

This dissertation addresses this gap by combining patient experience and clinical quality, two modifiable aspects of care that captures how patients interact with the health …