Digital Commons Network^™

Effects Of Domestic Violence On Third-Grade Students' Academic And Behavioral Performance, Leah N. Gordone

Doctoral Dissertations and Projects

The purpose of this quantitative, causal-comparative study was to investigate the relationship between witnessing domestic violence and third-grade students' academic performance and behavioral conduct, with a focus on gender differences. Additionally, the study explored the connection between exposure to domestic violence and English Language Arts achievement. The research involved 280 third-grade students and parents at the Boys & Girls Clubs of America in southwestern Georgia. Participants were part of social-emotional learning programs. Data collection included parent observation surveys, end-of-grade test scores (using the Georgia Milestones End-of-Grade assessment), and a child survey (the Violence Exposure Scale for Children – Revised). The …

Efficient Fully Bayesian Approach To Brain Activity Mapping With Complex-Valued Fmri Data, Zhengxin Wang, Daniel B. Rowe, Xinyi Li, D. Andrew Brown

Mathematical and Statistical Science Faculty Research and Publications

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be …

Cortical Structure In Nodes Of The Default Mode Network Estimates General Intelligence, Abhinav Yadav, Archana Purushotham

Faculty, Staff and Students Publications

Introduction: A growing number of studies implicate functional brain networks in intelligence, but it is unclear if network nodal structure relates to intelligence.

Methods: Using MRI, we studied the relationship of the general intelligence factor (g) with cortical thickness (CT), local gyrification index (LGI), and voxel-based morphometry in the nodes of the default mode network (DMN) and task-positive network (TPN) in a cohort of 44 young, healthy adults. Employing a novel strategy, we performed repeated analyses with multiple sets of g estimates to remove false positives.

Results: CT and LGI in medial and temporal nodes of the DMN were reliably …

Early Prediction Of Mortality And Morbidities In Vlbw Preterm Neonates Using Machine Learning, Chi-Hung Shu, Rema Zebda, Camilo Espinosa, Jonathan Reiss, Anne Debuyserie, Kristina Reber, Nima Aghaeepour, Mohan Pammi

Faculty, Staff and Students Publications

Background: Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.

Hypothesis: Integrating key maternal and postnatal infant variables in the first 2 weeks of age into machine learning (ML) algorithms will reliably predict survival and specific morbidities in VLBW preterm infants.

Methods: ML algorithms were developed to integrate 47 features for predicting mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). A retrospective cohort (n = 3341) was used to train and validate the models with a repeated 10-fold cross-validation …

Glacial-Interglacial And Millennial-Scale Changes In Nitrous Oxide Emissions Pathways And Source Regions, J. A. Menking, J. E. Lee, E. J. Brook, J. Schmitt, L. Soussaintjean, H. Fischer, J. Kaiser, A. Rice

Physics Faculty Publications and Presentations

During the transition from the Last Glacial Maximum (LGM) to the Holocene, the atmospheric N2O mole fraction increased by 80 nmol mol−1. Using ice core measurements of N2O isotopomer ratios, we show that this increase was driven by increases in both nitrification and denitrification, with the relative partitioning between both production pathways depending on the assumed isotopic end‐member source signatures. Similarly, we also attribute a 35 nmol mol−1 N2O mole fraction increase during the Heinrich Stadial 4/Dansgaard Oeschger 8 (HS4/DO8) millennial‐scale event to increases in both N2O production pathways. In contrast, the 25 nmol mol−1 N2O mole fraction decrease during …

Digital Twins, Synthetic Patient Data, And In-Silico Trials: Can They Empower Paediatric Clinical Trials?, Mohan Pammi, Prakesh S Shah, Liu K Yang, Joseph Hagan, Nima Aghaeepour, Josef Neu

Faculty, Staff and Students Publications

Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data—namely, digital twins, synthetic patient data, and in-silico trials—are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data …

Recommendations For Design, Execution, And Reporting Of Studies On Experimental Thoracic Aortopathy In Preclinical Models, Alan Daugherty, Dianna M Milewicz, David A Dichek, Ketan B Ghaghada, Jay D Humphrey, Scott A Lemaire, Yanming Li, Ziad Mallat, Yvan Saeys, Hisashi Sawada, Ying H Shen, Toru Suzuki, Zhen Zhou

Faculty, Staff and Students Publications

There is a recent dramatic increase in research on thoracic aortic diseases that includes aneurysms, dissections, and rupture. Experimental studies predominantly use mice in which aortopathy is induced by chemical interventions, genetic manipulations, or both. Many parameters should be deliberated in experimental design in concert with multiple considerations when providing dimensional data and characterization of aortic tissues. The purpose of this review is to provide recommendations on guidance in (1) the selection of a mouse model and experimental conditions for the study, (2) parameters for standardizing detection and measurements of aortic diseases, (3) meaningful interpretation of characteristics of diseased aortic …

Brain Morphometry In Infants Later Diagnosed With Autism Is Related To Later Language Skills, Luke E Moraglia, Kelly N Botteron, Natasha Marrus, Et Al.

2020-Current year OA Pubs

Autism spectrum disorder (ASD) presents early in life with distinct social and language differences. This study explores the association between infant brain morphometry and language abilities using an infant-sibling design. Participants included infants who had an older sibling with autism (high likelihood, HL) who were later diagnosed with autism (HL-ASD; n = 31) and two non-autistic control groups: HL-Neg (HL infants not diagnosed with autism; n = 126) and LL-Neg (typically developing infants who did not have an older sibling with autism; n = 77). Using a whole-brain approach, we measured cortical thickness and surface area at 6 and 12 …

High-Dimensional Mediation Analysis For Longitudinal Mediators And Survival Outcomes, Lili Liu, Haixiang Zhang, Yinan Zheng, Tao Gao, Cheng Zheng, Kai Zhang, Lifang Hou, Lei Liu

2020-Current year OA Pubs

Mediation analysis with high-dimensional mediators is crucial for identifying epigenetic pathways linking environmental exposures to health outcomes. However, high-dimensional mediation analysis methods for longitudinal mediators and a survival outcome remain underdeveloped. This study fills that gap by introducing a method that captures mediation effects over time using multivariate, longitudinally measured time-varying mediators. Our approach uses a longitudinal mixed effects model to examine the relationship between the exposure and the mediating process. We connect the mediating process to the survival outcome using a Cox proportional hazards model with time-varying mediators. To handle high-dimensional data, we first employ a mediation-based sure independence …

Estimation Of Breach Hydrograph Resulting From Dam Embankment Failure Due To Internal Erosion Or Overtopping: Comparison Of Simplified Methods With Real-Case Failure Data And Recommendations, Laurent Del Gatto, Jean-Robert Courivaud

5th International Seminar on Dam Protections Against Overtopping

The opening of a breach in an embankment dam, whether due to overtopping or internal erosion, is a critical failure mode for these structures. Estimating the resulting breach hydrograph is essential for assessing downstream risks. While no universal physical model covers all dam types, empirical formulas are commonly used. EDF analyzed 14 such formulas, comparing them with rupture data from 32 embankment dam failures. The best formulas - Froehlich 1995, Xu & Zhang 2009, and CLF 2020 - yield a “best estimate” for peak flow. However, due to variability, results should be interpreted cautiously. Consider estimating breach characteristics like width …

Sabrina Vs Steph: The Battle Between The Wnba And Nba, Naysha Mcgriff

Symposium of Student Scholars

The average salary of a Women’s National Basketball Association (WNBA) player is 110 times less than a National Basketball Association (NBA) player’s. Despite growing WNBA viewership, gender inequality in sports remains high, with critics claiming female athletes are less skilled. Gender bias in sports is severely understudied, making direct comparisons to men’s leagues unfair due to long-term lack of investment in women’s sports. This study investigates whether the perceived disparity in skill levels between WNBA and NBA players' is genuine or influenced more by external factors by developing an unbiased measure of player efficiency to compare athletic performance. This dataset …

Zeros Of L-Functions And Arithmetic, Micah Milinovich

Showcase of Research and Scholarly Activity

The PI currently holds two awards: NSF DMS 2401461 (Zeros of L-functions and Arithmetic) and NSF DMS 2101912 (The Distribution of the Zeros of L-functions and Related Questions). These awards concern research in number theory, a very active area of mathematics. L-functions have played a pivotal role in the modern development of number theory and they can be used to study a wide variety of problems. The tools used to study L-functions draw from many branches of mathematics including analysis, algebra, algebraic geometry, representation theory, and mathematical physics while number theory has important applications outside of mathematics to fields such …

2025 Nsf Career: Portable Wind Tunnel, Wen Wu

Showcase of Research and Scholarly Activity

As part of his NSF CAREER grant, Dr. Wu will collaborate with the University of Mississippi Museum to host the Art of Fluid exhibition. The exhibit will showcase artistic visualizations of fluid dynamics research and include live wind tunnel demonstrations. Dr. Wu will also launch a Mobile Fluid Exposition Program to engage high school students throughout north Mississippi, aiming to inspire interest in STEM through hands-on activities. The Center for Mathematics and Science Education (CMSE) will support this outreach effort.

Structure Disorder And Magnetic Behavior Of An Olivine-Type Cathode Material, Hamida Hassan, Madalynn Marshall

Symposium of Student Scholars

Our research investigates the structure disorder and magnetic behavior of Li(Mn,Fe)PO₄, an olivine-type material relevant for lithium-ion battery applications. Using single-crystal neutron diffraction at Oak Ridge National Laboratory, we precisely determined Mn and Fe occupancy, revealing a 56% Fe and 44% Mn distribution at the atomic 4c site within the Pnma space group. Additionally, we observed significant lithium site vacancies (78% occupied), influencing the material's electrochemical and magnetic properties. The varying Mn/Fe ratio affected spin reorientation transitions, shifting from antiferromagnetic alignment along the a-axis to the b-axis. Our findings provide crucial insights for optimizing olivine-based cathodes, enhancing energy …

Campus Navigator: A Mobile App For Seamless University Navigation, Hafsa Mohammed, Ali Rahimzadehfard, Rachab Wilson, Mathias Rossi, Turaj Ashuri, Amir Ali Amiri Moghadam

Symposium of Student Scholars

Navigation services play a big role in everyone’s daily lives, from directing them on new roads to guiding them through buildings. Outdoor navigation services have evolved from physical maps to digital ones like Google Maps for ease of use and accessibility. Upon conducting literature research, the team found that many navigation apps lack clear and accurate instructions for how to navigate university campuses such as Marietta campus. Due to the size of KSU’s Marietta campus and its buildings, effortless navigation has been a common challenge for students, faculty, and visitors alike. Additionally, all buildings are referred to by letters, numbers, …

Computational Study Of The Proton Transfer In The H7o3+ Cluster, Anna James, Martina Kaledin

Symposium of Student Scholars

Proton transfer (PT) from one molecule to another is among the most studied phenomena in chemistry. PT requires the bond cleavage and the formation of a new one, AH⁺ + B -> A+ BH⁺. In protonated water clusters, such a process consists of the interconversion of hydrogen bonds. Experimentally, such a process can be observed as a significant increase of a dipole moment. However, other vibrational transitions often occur with small changes in the dipole moment while large changes in polarizability. In this work, we study the PT process in a protonated water cluster, H₇O …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa Shuqfa

Thesis/ Dissertation Defenses

Brain Computer Interface (BCI), Also known as brain-machine interface (BMI) is a mean of controlling machines without the need to activate peripheral nerves or muscles. It has received the attention of research for decades. Motor imagery-based BCI is a paradigm that is characterized by its user friendliness where users can generate control commands at their freewill, without waiting for a que from the BCI module. Motor imagery brain–computer interface (MI–BCI) has considerable potential in increasing the quality of the lives for people with mobility impairment and the healthy ones as well. Though, its diffusion in application still has many pitfalls …

Deep Learning Algorithms For Traffic Flow Predictions, Adegoke Ojeniyi, Prince Pal Singh, Ankita Vashisht, Swati Kumari, Karan Karan

AUIQ Technical Engineering Science

Given the growing complexity of urban transportation systems, precise traffic flow forecasting is essential for reducing not only issues of congestion but also, for boosting road safety and enhancing mobility management. This study integrates Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) to present a hybrid deep learning framework for traffic prediction. Of these, the CNN-LSTM model is a reliable option for real-time traffic forecasting since it successfully captures both spatial and temporal dependencies, resulting in superior predictive performance. The dataset used to assess the framework includes 48,120 records from a traffic monitoring …

Investigating The Impact Of Waterhead, Time And Temperature On Dam Displacement: Application Of Computer Aided Models, Maaz Abdullah

AUIQ Technical Engineering Science

Dam displacement is a crucial indicator for assessing the safety of a concrete dam through structural health monitoring. Since the displacement data exhibits a non-linear and complex relationship with influencing factors like waterhead, time and temperature, machine learning models are deployed to accurately predict dam displacement. Furthermore, the limited availability of monitored data in the majority of the dams renders the studies conducted with a large number of observations valueless. In order to address the aforementioned issues, this study proposes a feature selection approach to predict dam displacement by examining the ability of four ensemble machine learning models on different …

Investigating The Differential Effects Of Smote Variants On Class Imbalance And Exploring Their Applicability To A Thalassemia Prediction Model, Hussam Mezher Merdas, Ayad Hameed Mousa

AUIQ Technical Engineering Science

Researchers work around the clock on many datasets provided by various institutions. These researchers strive to come up with highly efficient Artificial Intelligence models. Often, researchers face the problem of imbalance in the distribution of classes in a particular feature in the selected dataset, which creates an Artificial Intelligence model biased towards one class at the expense of another class that is no less important than the first. On the other hand, thalassemia is a disease that affects people of different ages. The degree of disease varies according to the thalassemia class. This study proposes an improved Machine Learning model …

Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

The increasing use of large language models (LLMs) in mental health support necessitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, and dataset-enhanced Gemma 2 and GPT-3.5-Turbo—in recommending mental health applications. We constructed a structured dataset of 55 mental health apps using RoBERTa-based sentiment analysis and keyword similarity scoring, focusing on depression, anxiety, ADHD, and insomnia. Standard LLMs demonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve up to 55% higher accuracy than baseline models while recommending apps with …

Properties Of Eigenvalues Of The Fractal Laplacian, Eric Stachura, Andrew Chincea

Symposium of Student Scholars

We investigate the properties of the eigenvalues of the fractal Laplacian. We begin by defining the fractal Laplacian operator in one dimension and formulate the corresponding Dirichlet eigenvalue problem. Analytical solutions are obtained for specific fractal parameters, and computational results illustrate the structure of eigenvalues and their associated eigenfunctions. We extend our analysis to two dimensions using separation of variables. Our findings contribute to a deeper understanding of how fractal geometry affects the spectral characteristics of differential operators.

Older Groundwater Reservoirs In Nebraska, Marvin P. Carlson, Steven S. Sibray

Conservation and Survey Division: Faculty and Staff Publications

Sedimentary rocks below Nebraska's regional water table are saturated down to the crystalline igneous and metamorphic rocks of Precambrian age. The total thickness of these sedimentary rocks ranges from about 600 feet to over 10,000 feet. Nearly all current wells in Nebraska produce water from the near-surface, unconsolidated or partly consolidated rocks. Most studies to date -- interpretive reports, test drilling, inventory of wells, monitoring water levels, and water quality determinations -- have concentrated on these near-surface sources of groundwater.

Opioid Epidemic In Maine: An Analysis Of Increasing Overdose-Related Deaths Following The Coronavirus, Aysel S. Hamlin

Thinking Matters Symposium

The rate of drug overdose resulting in death doubled in Maine following the COVID-19 pandemic from the onset of the COVID-19 pandemic in late 2019 through 2022. The correlation between increased isolation during the pandemic and overdose death rates sheds a concerning light on the insufficient resources for people struggling with Opioid Use Disorder (OUD) throughout Maine. The increasing trade and access to fentanyl following the pandemic accounted for the majority of drug-related deaths in Maine in 2021 and 2022. This study examines the need for long-term access to drug treatment in rural and urban Maine, both environments with varying …

Enhancing The Accuracy And Performance Of Eye Tracking In Head-Mounted Displays, Jason Spencer

Defensive Publications Series

Eye tracking on head-mounted displays (HMDs) is computationally intensive and requires multiple illuminators. The wide gaze angles at which eye-tracking cameras are positioned on HMDs make it difficult to obtain optimally good views of the eye. Further, it is difficult to determine the optical axis of the eye with high accuracy since the human physique does not always conform perfectly with the geometric shapes used to model it. This disclosure describes techniques that can establish the optical axis of the eye by leveraging the refracted view of the pupil under general illumination to infer the shape and position of the …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

Analytic Hierarchy Process Based Interpretable Decision-Making Structure Of Gpt In Unified Developer Efficiency Modeling, Fuming Guo, Biju Abraham

Defensive Publications Series

The present disclosure relates to a method and a system for Analytic Hierarchy Process (AHP) based interpretable decision-making structure of Generative Pre-trained Transformer (GPT) in unified developer efficiency modeling. The method includes receiving and preprocessing a plurality of metrics from one or more data sources. The method further generates pairwise comparison scores for each metric of the plurality of metrics. Additionally, an AHP comparison matrix is constructed based on the generated comparison scores. The method further includes performing a consistency check on the comparison matrix to confirm the reliability of the pairwise comparisons. When inconsistencies are identified, the method includes …

Team Software Design Analysis, Jaden Juyoung Yu, Elisabeth Jean Elgin, Vasilia Cecile Douglas, Alexander Nicholas Wood, Jason Alexander Lake

Honors Capstone Projects and Theses

No abstract provided.

The Role Of Individual Values In Bryant University's Sustainability Efforts, John Boccuzzi Iii

Honors Projects in Data Science

This research examines student, faculty, and staff perspectives on sustainability at Bryant University, with the goal of understanding how individual values align with the university's environmental initiatives. The objective is to assess perceptions of Bryant's current sustainability practices, explore how effectively these efforts are communicated across campus, and identify potential gaps between institutional action and community awareness. To achieve this, the study gathers qualitative data through an open-ended survey and applies sentiment analysis to interpret student attitudes toward sustainability. By analyzing these responses alongside Bryant's sustainability marketing efforts, this research will identify gaps between student engagement and institutional messaging The …

Promoting Sustainable Drinking Water Consumption At Bryant University, Matthew Gerdenich

Honors Projects in Biological and Biomedical Sciences

Many higher education institutions have either banned or restricted the sale of disposable plastic water bottles to address environmental pollution and climate change. This project seeks to gather knowledge and data related to disposable water bottle consumption at Bryant University that can be used to provide recommendations for promoting reusable water bottle use on campus. Additionally, the project seeks to find out why community members choose single-use water bottles over reusables ones and how to make reusable water bottle use more attractive and accessible. Primary research included a survey distributed to undergraduate students on campus as well as water samples …