Digital Commons Network^™

An Informative Analysis Of Applying Feature Reduction Methods To Supervised Machine Learning Algorithms, Mustafa S. Abd

Baghdad Science Journal

Feature reduction techniques are fundamental to enhancing machine learning (ML) algorithms by reducing the number of features in a dataset. The study here explores the impact of Principle Component Analysis (PCA) on ML algorithms within an unbalanced classification framework, in partnership with feature selection techniques like Cluster Variation Attribute Evaluator (CVAE) and Correlation Attribute Evaluator (CAE). In addition, the research introspects a comparison analysis evaluating the effectiveness of several ML methods, including Multilayer Perceptron (MLP), Decision Tree J48, k-Nearest Neighbor (k-NN) and Sequential Minimal Optimization (SMO). The informative analysis of results signifies that the MLP technique with PCA minimized the …

Mandibular Landmark Determination Based On Statistical Features Of Panoramic Radiograph Images Using Multi-Output Neural Network, Nur Nafiiyah, Agus Harjoko, Kang-Hyun Jo, Rini Widyaningrum, Eha Renwi Astuti, Alhidayati Asymal

Baghdad Science Journal

The mandible is crucial in orthodontic treatment, forensic identification, and clinical diagnosis. However, manually identifying mandibular landmarks is time-consuming and highly dependent on expert skill, necessitating a more reliable automated prediction method. Previous research has used linear and nonlinear regression methods, in which each model predicts a single landmark point, resulting in inefficiency. In addition, these methods only use the centroid of the binary image of the mandible as input. This research proposes a multi-output neural network model that is able to predict multiple mandibular landmark points simultaneously. The proposed neural network architecture in predicting mandibular landmark points is 11 …

April 23, 2026, The Daily Mississippian

Daily Mississippian (all digitized issues)

No abstract provided.

Family-Based Gwas Of Cognitive Endophenotypes Reveals Genetic Architecture Of Memory And Executive Function In Alzheimer’S Disease, Kesheng Wang, Xueying Yang, Gayenell Magwood, Chun Xu, R. Osvaldo Navia, Jean Neils-Strunjas, Xiaoming Li

Health & Biomedical Sciences Faculty Publications

Alzheimer’s disease (AD), the most common cause of dementia, is characterized by progressive memory and cognitive decline. Conventional genome-wide association studies (GWAS) comparing AD cases and controls may miss genetic influences that act along a continuum of cognitive function. Using data from 3007 participants in the National Institute on Aging Late-Onset Alzheimer’s Disease Family Study (NIA-LOAD GWAS), we conducted a family-based GWAS of eight quantitative cognitive phenotypes encompassing episodic memory (Logical Memory IA and IIA), working memory (Digit Span Forward, Backward, and Ordering), and semantic fluency (Animal, Fruit and Vegetable, and Vegetable Fluency). Family-based association testing in PLINK v1.9 identified …

What Happens After Oil And Gas Decommissioning? A Global Systematic Review Of Marine Environmental Effects, Anaelle Lemasson Lemasson, Antony M. Knights

School of Biological and Marine Sciences

The thousands of oil and gas (OG) platforms placed at sea for fossil fuel extraction have introduced new hard substrate to the marine environment. Over time, these structures can become colonized by a diversity of marine life, fostering novel ecosystems. However, an increasing number of OG platforms are reaching decommissioning age and decisions regarding their fate must be made. Some view these artificial structures as litter that ought to be removed; others view them as valuable contributors to marine biodiversity worth preserving. Evidence of the environmental effects of these structures following different decommissioning strategies is needed to identify the potential …

April 23, 2026, James Madison University

The Breeze, 2020-

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia.

Creating Sustainable School And Home Gardens: Gardening With Native Plants, Kathy Cabe Trundle, Lawrence Krissek

All Current Publications

Native plants form the foundation for healthy local and regional ecosystems. By adding native plants to your gardening and landscape plans, you will contribute to nature’s best hope for the soil health, habitat resilience, and biodiversity we need for a brighter, more sustainable future.

Exploring The Impact Of A Brief Mindfulness-Based Intervention On Autonomic Functioning: A Multisystem Approach, Li Shen Chong, Rachel C. Bucher, Edward Merritt, Elana B. Gordis

Faculty Research, Scholarly, and Creative Activity

Previous work on brief mindfulness-based interventions (bMBI) reveals that they consistently alleviate stress, but their impact on biological stress response systems, such as the autonomic nervous system (ANS), varies. Nevertheless, most of this work has focused on only a single physiological response system or a specific biomarker, limiting insight into how multiple systems work together. The present study tested whether bMBI alters ANS markers and coordination across ANS subsystems. Participants were young adults (bMBI: n = 44, M age = 18.64 years; control: n = 47, M age = 19.00 years) who completed two sessions of either a brief mindfulness …

Dual Membrane-Spanning Anti-Sigma 2 Controls Omv Biogenesis And Colonization Fitness In Bacteroides Thetaiotaomicron, Evan J Pardue, Tengfei Zhong, Nichollas E Scott, Biswanath Jana, Wandy Beatty, Juan C Ortiz-Marquez, Mohammed Kaplan, Clay Jackson-Litteken, Mario F Feldman

2020-Current year OA Pubs

UNLABELLED:

IMPORTANCE: Dual membrane-spanning anti-sigma factors (Dma) are a novel class of regulatory proteins found solely among Bacteroidota. Previous studies demonstrated the importance of Dma1 in vesiculation, but the overall role of the Dma family in Bacteroides physiology remains poorly understood. Here, we show that Dma2 modulates vesiculation and the expression of select polysaccharide utilization loci (PULs) that target host-associated glycans

Identification Of Lipid Quantitative Trait Loci Linked With Cardiometabolic Disease In Asian Indians And Europeans: A Genome-Wide Association Study And Mendelian Randomization, Madhusmita Rout, Christopher E. Aston, Ravindranath Duggirala, Harald H. H. Goring, Oliver Fiehn, Dharambir K. Sanghera

School of Medicine Publications

Background: Genetic mechanisms that predispose people to type 2 diabetes (T2D) and cardiovascular disease (CVD) remain poorly understood, partly because of a lack of sufficient data on non-European ethnic groups. Extending these evaluations to diverse cohorts is essential for gaining insights into the molecular pathways involved in disease development among human populations. In this study, we aimed to evaluate the genetic connection between the human lipidome and cardiometabolic disorders. We conducted a metabolite genome-wide association study (mGWAS) in a Punjabi population from India, along with multi-layer replication studies using the UK Biobank and other independent European and non-European cohorts.

Methods …

Project Evaluation Of A Workplace Violence Prevention Program At A Rural Hospital, Stacy Ochoa-Sofoifa, Yolanda L. Rodriquez, Melissa Bowe, Teresa Mendoza

Publications 2026-present

No abstract provided.

Reemergence Of Street Planning In Yafo (Jaffa): The Late Ottoman Ha-Ẓorfim Compound, Yoav Arbel

'Atiqot

In the second half of the nineteenth century, Yafo (Jaffa) was a rapidly changing town, reemerging from centuries of stagnation. While the mound retained its unappealingly medieval character, new neighborhoods sprouted up around it, replacing the orchards and fortifications. Urban development demanded access and connections, which were ensured with the construction of broad new throughways, with stone paving and drainage systems not seen in Yafo since antiquity. All this development naturally lured traders, officials and diplomats to build their mansions and businesses along these thoroughfares. One of these was the presently named Ha-Ẓorfim (“The Jewelers”) Street, on the eastern outskirts …

Global Trends In Dietary Exposure To Polychlorinated Naphthalenes (Pcns): A Review Of Contamination Levels And Health Implications, Vhodaho Nevondo, Okechukwu Jonathan Okonkwo

Engineering and Technology Journal

Polychlorinated naphthalenes (PCNs) belong to a class of persistent organic pollutants that are structurally related to chlorinated dibenzo-p-dioxins, dibenzofurans, and other chlorinated biphenyls as a result of the presence of benzene rings in their respective structures. Additionally, they display high levels of persistence in the environment, bioaccumulative, and dioxin-like toxicities characteristics. Although the production and use of PCNs is either banned or greatly regulated in several countries around the globe, unintentional production in industrial thermal processes and legacy usage have resulted in the ubiquity of these compounds in the environment and the food chain. Several studies have shown various exposure …

Exploring The Dual Advising System For Students’ Success: A Developmental Advising Model, Louisa Godwyll, Eugene Kwarteng-Nantwi, Pious Jojo Adu-Akoh

Mid-Western Educational Researcher

This qualitative study explored the dual advising system for student success at a U.S. higher education institution. Research has indicated that approximately 40–60% of students enrolled in doctoral programs do not complete their doctoral degrees. High attrition rates have led some institutions to adopt a dual advising system, a form of developmental advising, to support doctoral students’ success. Creamer and Creamer’s (1994) developmental advising model served as the conceptual framework of the study. The model defines the first stage of advising as defining task, where advisors engage in teaching and utilize strategies to help students attain educational, career, and individual …

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

Grp-08-168 Diagnosing Faults In Electrical Power Systems Of Satellites, Jared Lasley, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Satellite systems cost hundreds of millions of dollars or more to launch. To be resistant to catastrophic failures (and total loss of investment), satellite systems are designed with redundant sub-systems and are further equipped with numerous sensors and other health-monitoring sub-systems. In this poster, we consider an approach to fault diagnosis based on probabilistic logic programming. In particular, we propose to use ProbLog to model and reason with the electrical power system (EPS) of a satellite. Once we model a system using (probabilistic) first-order logic, we can take the system state and any (unexpected) sensor readings, and through automated reasoning, …

Uc-099-189 Spectre, Alexander Tobal, Chris Higgins Jr, Jaylin Reeves, Logan Leichter

C-Day Computing Showcase

Spectre consists of four levels, where players complete various objectives and fight off ghosts while doing so. Our tutorial level introduces players to the mechanics, such as shooting, rear view mirror shooting, walking and jumping. With the rest of the levels focusing on completing objectives in order to progress. The final level culminates in a boss fight, ending the journey. While players explore and complete objectives, enemies drop a currency that players can spend to obtain upgrades. Getting hit by enemies not only reduces the players’ health but also applies debuffs to them making players more cautious of their surroundings. …

Uc-122-135 Mutatio Mentis, Isaac Alderman, Braden Mizell, Collin Sutton

C-Day Computing Showcase

Mutatio Mentis is a first person, narrative heavy, puzzle-lite RPG that follows the story of a renaissance era plague doctor and their attempt to alter the minds of three subjects; a gardener, a street urchin, and a priest. The narrative is set in 1637 Florence, Italy, in the wake of the Great Plague of Milan, and draws heavily from renaissance culture. Each of the three subjects have progressively more complex personal conflicts, which present through the gameplay aesthetics of each act, as the gameplay changes to reflect the problems of each subject. Our focus is on tackling mental and emotional …

Uc-151-197 Nest: An Ai-Powered Transition Navigator For Aging-Out Foster Youth In Georgia, Stephen Sookra, Tylin Delaney, Brenden Bryant

C-Day Computing Showcase

Each year, approximately 600–700 young people age out of the Georgia foster care system with no permanent family, no housing plan, and no clear guide beyond a 250‑page state transition PDF. The outcomes are severe: high rates of homelessness, low college completion, and unstable employment. Nest is an AI‑powered, mobile‑first web application that turns this overwhelming bureaucracy into a personalized 90‑day transition plan generated in under 60 seconds. Through a short conversational intake, the system collects a youth’s age, county, housing status, and education or work goals, then uses a deterministic rules engine to determine likely eligibility for key programs …

Gc-172-139 Detection Of Sms Spam Using Transformer Bert Model, Nathan Bonner, Zachary Kandell, Michael Hayes, David Quintanilla, Leon Greenberg

C-Day Computing Showcase

This project evaluates automated SMS spam classification by comparing traditional machine learning against modern transformer architectures. We built a Bidirectional LSTM (BiLSTM) baseline using TF-IDF feature extraction and NearMiss-1 undersampling to handle severe class imbalances. We then compared this against a fine-tuned Hugging Face Sentence-BERT model. Preliminary results show Sentence-BERT significantly outperformed the BiLSTM baseline (99.01% vs. 95.65% accuracy). These findings demonstrate that transformer-based embeddings offer a highly accurate, scalable solution for spam mitigation without relying on aggressive data undersampling.

Grp-093-174 Transforming Everyday Smartwatch Data Into Clinical Early Warnings, Nursat Jahan

C-Day Computing Showcase

Cardiovascular Disease (CVD) related most machine learning (ML) models trained on clinical data offer high accuracy but are not practical for continuous monitoring. Smartwatch-based wearables provide continuous real-time physiological data but lack clinical validation for robust risk prediction outside the clinical setting. To bridge this gap, we proposed a novel teacher-student knowledge distillation framework that transfers knowledge of complex and large EHR datasets to a small Fitbit smartwatch dataset-based prediction model. The student model achieves promising accuracy, identifying all types of derived CVD risk profile groups. Our study introduces a non-invasive continuous health monitoring framework, demonstrating that passively collected daily …

Uc-115-161 Wayward Stray: Selix, Arly Tinoco, Tyler Ercole, Ivy Stansel, Austin Lothman, Jeremi Charland-Martin

C-Day Computing Showcase

Wayward Stray:Selix is a 3rd person platformer which places importance on exploration and discovery. Players will take the role of Selix as they explore an arid desert, fighting off enemies and discovering items hidden around the map, which reveal more about the game world and its characters. Selix, a young dragon, is exiled from the only home he’s known, forced into a strange land in search of a new place to call his own. Along the way, he finds a companion, a small dove that aids and guides his way. Exploring these uncharted areas, Selix discovers there’s more to the …

Uc-162-194 Smart Soil Analyzer, Samuel Florez Garcia, Edward Johnson, Aaron Gamino, Tassha Burton, Wyatt Kinney

C-Day Computing Showcase

The Smart Soil Analyzer is a machine learning-based application designed to maximize agricultural efficiency and sustainability. Our team developed a predictive system using a K-Nearest Neighbors (KNN) classifier trained on a comprehensive crop recommendation dataset. The tool allows users to input key environmental and soil metrics, including Nitrogen (N), Phosphorus (P), Potassium (K), temperature, humidity, pH levels, and rainfall. By processing these variables, the model accurately predicts the most suitable crop for the specific land conditions. This solution provides farmers with data-driven insights to optimize yields, reduce fertilizer waste, and combat soil degradation through precise crop matching.

Ur-133-165 Quantum Machine Learning For Science And Engineering, Barclay Barnes, Anna Zharikov, Meriem Hamzi

C-Day Computing Showcase

Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks, including PennyLane, TensorFlow Quantum, and Qiskit, to implement and compare several QML models against their classical counterparts. The proposed system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, …

Grm-153-198 Safecircle: Ai And Micro-Radar-Based Remote Monitoring For Patients With Ad/Adrd, Awan-Ur- Rahman, Soarov Borty, Gowtham Ankolu

C-Day Computing Showcase

Alzheimer's disease and related dementias (AD/ADRD) are irreversible and degenerative neurological conditions that severely impacts neurons, resulting in cognitive decline and memory loss. This study explores a mHealth system, including a SafeCircle iOS prototype, a novel solution that combines artificial intelligence with cutting-edge micro-radar technology. The platform offers a variety of features, including management of patient and caregiver profiles, real-time alerts in case of emergencies, emergency contact lists, one-touch SOS support, sharing of live locations, and recording of unusual events in video. It is a responsive and reliable care assistant that optimizes patient safety while reducing caregiver burden.

Treating Depression With L-Methylfolate: A Review Of Evidence, Marianna G. Pettit

Epsilon Sigma at-Large Research Conference

Abstract

Introduction & Background: Depressive disorders have many different causes. Folate deficiency can sometimes be behind these disorders, due to this vitamin’s role in neurotransmitter synthesis. Some research suggests that supplementing with an active form of folate improves depressive symptoms with and without antidepressant use. Purpose Statement Question: For individuals with depressive disorders, does supplementation with L-methylfolate (LMF) reduce symptoms of depressive disorders with or without use of an antidepressant? Literature Review: A manual literature search for sources published within the past 7 years revealed 557 documents from four different databases. Key search words included, “depression,” “treatment,” …

A Historical Analysis Of The Development Of Prenatal Care In The U. S., Kristen Montgomery Phd, Cnm, Aprn, Shelby Greene Bsn, Rn, Lauren Griffin Bsn, Rn, Destiny Lewis Bsn, Rn, Lauryn Lewis Bsn, Rn, Bryleigh Newberry Bsn, Rn, Brittany Scott Bsn, Rn

Epsilon Sigma at-Large Research Conference

Purpose: This paper examines the historical development of prenatal care in the United States, tracing its evolution from informal, home—based management to structured, evidence-based systems that are central to maternal and fetal health.

Aims: The specific aim of this project was to determine how prenatal care evolved in the United States.

Methods: A historical methods approach was used. Historical documents were reviewed using PubMed, CINAHL, and Google Scholar to find relevant resources. Relevant articles were reviewed by the research team and analyzed for components regarding the development of prenatal care. All members of the research team reviewed the articles and …

Nurse-To-Patient Ratios In Adult Medical-Surgical Units, Kayleigh A. Runion, Emily G. Barnette, Maddison K. Wood, Jeremy Walker

Epsilon Sigma at-Large Research Conference

Nurse-to-patient ratios in adult medical-surgical floors vary across healthcare settings and have been linked to patient safety and quality outcomes. Nurse-to-patient ratios of 1 to 6 or greater are most common in practice settings, despite the growing evidence that higher nurse workloads are associated with increased mortality, longer length of stay, higher readmission rates, mistakes, and miscommunication in nursing care. The purpose of this evidence-based practice project is to examine whether applying safer nurse-to-patient ratios, 1 to 4 instead of 1 to 6 or higher, improves patient safety outcomes. Using the Johns Hopkins Evidenced-Based Practice Model, a project was put …

Does Time Matter? Comparing 8- And 12-Hour Registered Nurse Shifts., Ava P. King, Abby L. Goodall, Cheryl L. Goodall, Makayla B. Ramsey, Imari K. Golden

Epsilon Sigma at-Large Research Conference

This project analyzes the effect that registered nurse's shift length has on patient outcomes and quality of care. The research specifically explores a well-debated healthcare issue; are 12-hour shifts associated with poor patient care due to fatigue? By comparing errors made in 12-hour and 8-hour shift lengths of registered nurses, it can be determined if a trend can be seen in declining patient care. Not only will this research shed light on nurses' fatigue and burnout, but it will also showcase the patient dangers associated with overworking. Overall, the goal is to bring awareness using statistical data that explains why …