Digital Commons Network^™

Revolutionizing Higher Ed: Ai’S Impact On Faculty And Pedagogy, George Antoniou, David G. Wolf, Ellen Ramsey, Matteo Peroni, Saidi Porta, Philip L. Fazio, Kristen Migliano

Faculty and Staff Publications & Presentations

No abstract provided.

Global Sporadic-E Prediction And Climatology Using Deep Learning, J. A. Ellis, Daniel J. Emmons, M. B. Cohen

Faculty Publications

Sporadic-E (E_s) is an ionospheric phenomenon defined by strong layers of plasma which may interfere with radio wave propagation. In this work, we develop deep learning models to improve the understanding of E_s, including the presence, intensity and height of the layers. We developed three separate models. The first, building off earlier work in (J. A. Ellis et al., 2024, link in AFIT Scholar, 10.1029/2023sw003669), includes only the main features from radio occultation (RO) measurements. The second adds to that time, date, location, geomagnetic and solar indices, solar winds, x-ray flux, weather and lightning. A …

Relaxation Maximum-Based Iteration Method For Solving The Generalized Absolute Value Equation, Ximing Fang, Zhidong Wang, Zhijun Qiao

School of Mathematical & Statistical Sciences Faculty Publications

The generalized absolute value equation (GAVE) has wide applications in scientific computing. Establishing a high performance computing method to solve the GAVE is a hot research topic in recent years. In this paper, with the aid of the maximum function, the GAVE is decomposed of two equations, and then we present the relaxation maximum-based (RM) iteration method. To see the feasibility of the method, we discuss the necessary and sufficient conditions for the GAVE to have a unique solution. Next, the convergence analysis of the RM iteration is discussed under some convergence conditions. Moreover, some numerical examples of low and …

Finding Bert Errors By Clustering Activation Vectors, William B. Andreopoulos, Dominic Lopez, Carlos Rojas, Vedashree P. Bhandare

Faculty Research, Scholarly, and Creative Activity

The non-linear nature of deep neural networks makes it difficult to interpret the reason behind their output, thus reducing verifiability of the system where these models are applied. Understanding the patterns between activation vectors and predictions could give insight as to erroneous classifications and how to identify them. This paper explains a systematic approach to identifying the clusters with the most misclassifications or false label annotations. For this research, we extracted the activation vectors from a deep learning model, DNABERT, and visualized them using t-SNE to decode the reason behind the results that are produced. We applied K-means in a …

Aircraft Routing And Crew Pairing Solutions: Robust Integrated Model Based On Multi-Agent Reinforcement Learning, Chengjin Ding, Yuzhen Guo, Jianlin Jiang, Wenbin Wei, Weiwei Wu

Faculty Research, Scholarly, and Creative Activity

Every year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, …

Temporal Reversibility Of Reactive Systems Out Of Equilibrium: Molecular Dynamics Simulation, O. Politano, Alejandro L. Garcia, F. Baras, M. Malek Mansour

Faculty Research, Scholarly, and Creative Activity

The second law of thermodynamics states that entropy production in macroscopic systems is non-negative, reaching zero only at thermodynamic equilibrium. As a corollary, this implies that the state trajectory of macroscopic systems is inherently time-irreversible under out-of-equilibrium conditions. However, over the past half-century, various studies have shown that this principle does not universally apply to the composition sample paths of certain isothermal reactive systems. Theoretical frameworks leading to this surprising observation primarily focus on perfectly homogeneous systems (often referred to as zero-dimensional systems), which inherently exclude the effects of local fluctuations. This oversimplification may account for the paradoxical theoretical predictions. …

Predicting Cardiac Resynchronization Therapy Response: Development And Validation Of A Single Photon Emission Computed Tomography-Based Nomogram, Zhongwei Jiang, Zhongqiang Zhao, Zhuo He, Qiushi Chen, Ju Bu, Chunxiang Li, Dianfu Li, Chang Cui, Weihua Zhou, Huiyuan Qin, Cheng Wang

Michigan Tech Publications

Background: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with drug-refractory heart failure. However, more than thirty percent of patients do not benefit from CRT. This study aimed to develop and validate a novel model based on single photon emission computed tomography (SPECT) phase analysis features to predict CRT response. Methods: We identified 163 CRT patients who received gated resting SPECT myocardial perfusion imaging (MPI) between 2010 and 2020 at The First Affiliated Hospital of Nanjing Medical University. All variables were first processed by univariate logistic regression, and those with a P value < 0.05 were retained. The selected variables were subsequently used in the least absolute shrinkage and selection operator (LASSO) regression to construct a predictive model, which was then represented as a nomogram. Nomogram performance was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs). Internal validation was performed by bootstrapping with 1,000 replicates. Results: Of the 163 patients, 93 (57.1%) responded to CRT during follow-up. Responders had a wider QRS complex duration (QRSd) (164.80 vs. 154.51 ms, P=0.003), fewer premature ventricular contractions (PVCs) (1,392.98 vs. 2,283.60, P=0.003), lower prevalence of non-sustained ventricular tachycardia (NS-VT) (45.2% vs. 77.1%, P< 0.001), and better cardiac function [based on N-terminal pro-B-type natriuretic peptide (NT-proBNP), New York Heart Association (NYHA), and left ventricle (LV) parameters] compared to non-responders. Univariate logistic regression revealed 14 variables significantly associated with CRT response (all P< 0.05). The area under the ROC curve (AUC) value for the nomogram was 0.845 [95% confidence interval (CI): 0.785–0.906; sensitivity: 0.771; specificity: 0.849]. Internal validation yielded a mean AUC of 0.814 (95% CI: 0.777–0.836). The calibration curve demonstrated strong consistency between the predicted and observed outcomes. DCA revealed that the nomogram consistently provides a net benefit over the baseline, demonstrating its high practical value in clinical decision-making. A web-based dynamic nomogram (https:// jzw20000624.shinyapps.io/CRTpredictionmodel/) was developed for clinical application. Conclusions: We developed and validated a SPECT-based prediction model for predicting CRT response, which can assist clinicians in optimizing CRT candidacy preoperatively. Pacing at the latest contraction and relaxation segments, while avoiding scarred regions and optimizing preoperative status, is anticipated to improve CRT response.

Trust And Success: Franchisee Perspective On Relational Trust With The Franchisor And Impact On Unit Growth, Giovannie M. Hoff

Electronic Theses and Dissertations

Abstract

The problem addressed in this study was that it is unknown how franchisees perceive trust and its influence on unit growth and contractual control within the franchisor and franchisee relationship. The purpose of this case study was to explore the franchisee’s perspective of trust within the franchisor and franchisee relationship and its impact on growth and contractual control. A qualitative case study approach was used, with data collected from 13 participants from one franchise organization in the United States through semistructured interviews and questionnaire. Data were transcribed, coded, and analyzed thematically, with triangulation and member-checking ensuring credibility. Two key …

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 …

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 …

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 …

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 …