Digital Commons Network^™

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 …

A Risk Evaluation Framework And Comprehensive Security Compliance Assessment Solution For Space Information Networks (Sin), Bernard Afaare Dawaare

Masters Theses & Doctoral Dissertations

The integration of Internet of Things (IoT) devices into space information networks introduces unprecedented security challenges, necessitating advanced methods for information systems risk assessments. Some proposed approaches in terrestrial networks, predominantly based on Euclidean distance metrics, often fall short of capturing the nuanced and multidimensional nature of risks and vulnerabilities in the unique context of space environments. This paper proposes a novel risk management methodology that leverages both Euclidean distance metrics and probabilistic Monte Carlo-based models for evaluating the likelihood/frequency and impact/severity of vulnerabilities within space IoT systems. Leveraging vulnerability data from established sources such as the NIST and VARIoT …

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.

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Relationship Between Local Fiscal Effort And High School Graduation Rate, Zachary Michael Haney

Educational Leadership & Workforce Development Theses & Dissertations

Education is an investment in human capital, but the cost to provide this public service is significant. Understanding the return on this investment is essential for informed and effective financial decision-making. While previous school finance studies have examined the relationship between funding and various indicators of student achievement and student attainment, most have focused on spending practices rather than the level of investment itself. Although the literature affirms the need for educational resources, further research is required to determine which types of investment most positively affect student outcomes. Therefore, funding must be analyzed prior to budgetary allocation to evaluate its …

Shades Of Families: Exploring Intersectionalities Of Cross-Cultural Parenting Experiences In The U.S., Shu-Han Huang

Dissertations

As multiracial and multicultural families continue to grow across the United States, parents must navigate evolving cultural identities, systemic inequities, and increasing social and emotional pressures. These realities underscore the need for parenting resources that are both culturally responsive and emotionally supportive. This transcendental phenomenological study explored the lived experiences of 11 cross-cultural parents raising children in the U.S., with a focus on how intersecting identities and systemic forces shape parenting practices.

Guided by Critical Multiracial Theory and Intersectionality Theory, this study investigated parenting at the personal, familial, societal, and structural levels. Data were collected through semi-structured interviews and analyzed …

Bridging The Gap: Enhancing Devops Security Through Comprehensive Threat Modeling, Ashutosh Jagdish Sonar

Theses and Dissertations

Today, security is an essential component of software development, especially in DevOps environments where rapid and continuous product release cycles are common. Systems are vulnerable to new attacks because traditional security approaches often cannot keep up with the pace of change. The threat modeling approaches used in DevOps are examined in this thesis, along with their advantages, disadvantages, and suitability for use in current software development processes. Well-known frameworks including STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege), Attack Trees, LINDDUN (Linking, Identifying, Non-Repudiation, Detecting, Data Disclosure, Unawareness, and Non-Compliance.), Practical Threat Analysis (PTA), …

Does The Transit Industry Understand The Risks Of Cybersecurity And Are The Risks Being Appropriately Prioritized?, Scott F. Belcher, Terri Belcher, James Grimes, Lusa Holmstrom, Andy Souders

Mineta Transportation Institute

The intent of this study is to assess the readiness, resourcing, and capabilities of public transit agencies to detect, identify, be protected from, respond to, and recover from cybersecurity vulnerabilities and threats. This study is an update of the 2020 Mineta Transportation Institute (MTI) study, “Is the Transit Industry Prepared for the Cyber Revolution? Policy Recommendations to Enhance Surface Transit Cyber Preparedness.” In the previous study, the authors found that the transit industry was ill-prepared for cybersecurity attacks. Unfortunately, after four years and the development of new, and often free, resources, the situation has not markedly improved. In fact, this …

A Pure Rotational Spectroscopic Study Of Two Nearly-Equivalent Structures Of Hexafluoroacetone Imine, (Cf3)C=Nh, Daniel A. Obenchain, Beppo Hartwig, Daniel J. Frohman, Garry S. Grubbs, B. E. Long, Wallace C. Pringle, Stewart E. Novick, S. A. Cooke

Chemistry Faculty Research & Creative Works

Rotational spectra for hexafluoroacetone imine, the singly substituted 13C isotopologues, and the 15N isotopologue, have been recorded using both cavity and chirped pulse Fourier transform microwave spectrometers. The spectra observed present as being doubled with separations between each pair of transitions being on the order of a few tens of kilohertz which is consistent with a large amplitude motion producing two torsional substates. The observed splitting is most likely due to the combined motions of the CF₃ groups, for which the calculated barrier is small. However, no transitions between states could be observed and, similarly, no Coriolis coupling parameters …

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. …

Quasipseudometric Value Functions With Dense Rewards, Khadichabonu Valieva

Honors Theses

Goal-conditioned reinforcement learning (GCRL) serves as an extension of reinforce- ment learning (RL) that focuses on goals that can be adjusted, making it useful for many applications, especially in complex robotics tasks. Recent research has established that the optimal value function of GCRL, denoted as Q∗(s, a, g), has a quasipseudometric structure. This finding has led to the development of targeted neural architectures that respect such a structure. However, prior analyses have predominantly focused on sparse reward settings, which are known to increase challenges related to sample complexity. In this work, I with the guidance of my advisor show that …

Privacy-Aware Ai-Based Agricultural Monitoring Using Internet Of Drones, Md Benozir Hossain

Honors Theses

Artificial Intelligence (AI) has become a vital tool for agricultural farming. AI-based image processing models utilizing different machine learning (ML) algorithms and deep learning (DL) offer advanced functionalities in disease detection, yield estimation, land use, etc. This thesis examines AI-driven techniques utilizing Convolutional Neural Networks (CNN) with the addition of Federated Learning (FL) to analyze satellite and drone images for agricultural insights, especially in detecting Cotton diseases. The AI models improve agricultural farming in many ways, such as using data to make critical decisions, reducing labor costs, pest infestations, etc. Moreover, these models allow farmers to minimize yield losses by …

Cellular Telemetry For Real-Time River Flow Velocity Data From Fluvial Acoustic Tomography Loggers, Joshua Lee Seymour

Honors Theses

Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents …

Cache-Augmented Generation In Rag Pipelines: Fast And Memory-Efficient Approach To Multi-Agent Knowledge Query Systems, Yaju Gopal Shrestha

Honors Theses

This thesis presents an implementation and evaluation of Cache-Augmented Generation (CAG) for knowledge query systems, building upon the approach introduced by Chan et al. (2024). Traditional Retrieval-Augmented Generation (RAG) systems (Lewis et al., 2020) face challenges including high latency, excessive memory usage, and complex infrastructure requirements. By implementing a cache-augmented architecture that preloads relevant knowledge and eliminates real-time retrieval, our approach significantly improves response time while reducing resource requirements. The research demonstrates the effectiveness of CAG through a comprehensive implementation for The University of Southern Mississippi's chatbot system, achieving a 49.02% improvement in response time compared to traditional RAG approaches. …

The Effect Of The Black Ink Red Ink Testing Method On Anxiety Levels, Test Performance, And Note-Taking Behavior Among College Students, Peyton Murphy

Honors Theses

Anxiety among students has emerged as a pressing concern due to its significant impact on academic performance and mental well-being (Chen, 2004; Davis, DiStefano, & Schutz, 2008) and is particularly evident in student performance within STEM-related courses (Magula, 2019; Vakili & Pourrazavy, 2017). Previous research has explored various factors influencing test anxiety, including testing methods. The present study examined a testing method called "Black Ink Red Ink" (BIRI), proposed by Rodgers and Rodgers (2011). The effects of the BIRI method were examined on (1) test anxiety, (2) test scores, and (3) note taking motivation and behaviors. Thirty-eight students in a …

Tailoring The Morphology Of Block Copolymers Via Solvent-Vapor Annealing, Bayleigh Madelin Loving

Honors Theses

Triblock polystyrene-polyisobutylene-polystyrene (PS-PIB-PS) copolymers have widespread applications from thermoplastic elastomers to protective coatings for ballistic applications. The incompatibility between the soft PIB and hard PS phases leads to phase separation and subsequent formation of morphological domains like lamellae and cylinders. The structures are stabilized by physical crosslinks, which can greatly improve the toughness of the triblock systems by preventing chain pullout during tensile deformation. We tailored the morphologies (achieving different morphologies with the same block copolymer) by changing the annealing conditions of the block copolymers. Solvent vapor annealing was used to selectively activate the mobility of a particular block, causing …

A Spectroscopic Survey Of Atmospheres Of Super-Earths, Luke Brust

Honors Theses

Recent research has made it possible to use spectroscopy to analyze the composition of the atmospheres of exoplanets, planets that orbit other stars. Most projects thus far have focused on the atmospheres of gas giants, as it is less challenging to observe them with available equipment. This project studies the atmospheres of four Super- Earths, planets that have a mass greater than the Earth but smaller than Neptune. This is accomplished by processing the raw spectroscopic data from the Hubble Space Telescope and modeling the atmosphere using the program 𝝉-Rex3. This survey found clear results from two of the selected …

Globally Adaptive Exponential Integrators For Stiff Systems Of Odes, Anzhelika Vasilyeva

Honors Theses

This thesis introduces a novel method for solving systems of Ordinary Differential Equations (ODEs) resulting from the spatial discretization of Partial Differential Equations (PDEs). The proposed approach builds upon an existing technique that employs Krylov projection, which requires evaluating a matrix function at each timestep. The innovation of the new method lies in its reuse strategy, which shifts the perspective from direct matrix function evaluation to polynomial interpolation. Numerical experiments conducted on constant and variable coefficient heat equations, with both smooth and discontinuous initial data, demonstrate the computational time advantage of the new approach. The results indicate that this method …

The Numerical Method For Finding All The Zeros Of A Function F (X) On An Interval [A,B], Franissa Simon

Honors Theses

In this study, we develop a simple mathematical method for finding all the roots of a function on a specified interval. Existing classical numerical methods, including the Bisection Method, Secant Method, and Newton Method, cannot find all the zeros of a function f (x) on an interval [a,b]. Popular numerical root solvers like Matlab’s ‘fzero’, Maple’s ‘fsolve’, and SageMath’s ‘find_root’ typically yield only a single zero. This thesis explores a proposed interval computation bisection method, a systematic approach based on the traditional Bisection Method and interval computation. Unlike traditional bisection, which relies on the intermediate value theorem, this approach uses …

Knighted Pawn's Tour, Morgan Wilson

Honors Theses

The Knighted Pawn’s Tour is a variant of the traditional Knight’s Tour problem, whichitself is an instance of the Hamiltonian cycle problem. The deviation from the Knight’s Tour problem is that a pawn begins on any field on the second rank (row), and advances with legal moves that can include captures to the opposite end of the board to become a knight. These fields, used by the pawn on the path to knighthood, are subsequently forbidden for the knight’s return to the starting field. The knight must traverse the rest of the chessboard, visiting each remaining field exactly once …

Chemistry Education At The University Of Southern Mississippi Through Computation And Active Learning Methods, Sadie Pitre

Honors Theses

Education is one of the pillars of society. It evolves alongside scientific progress, shaping how students learn and engage with complex topics. The incorporation of new educational standards at The University of Southern Mississippi (USM) was implemented in two parts: through computational chemistry laboratory exercises for teaching laboratories and through the synthesis of organic molecules for research projects in an upper-level spectroscopy class. The goal of these projects is to improve learning of abstract and/or complex topics beyond the teaching methods traditionally utilized in the undergraduate laboratory and in the classroom.

Computational chemistry, which uses theoretical calculations to model molecular …

Evaluating Accuracy In Legal Research: A Comparative Analysis Of Traditional And Artificial Intelligence-Driven Methods, Lily Collins

Honors Theses

Legal professionals are challenged with performing accurate legal research to meet professional standards. The use of artificial intelligence (AI) presents a potential for enhanced efficiency. However, knowledge and process gaps exist, questioning the accuracy of results generated by AI. The uncertainty of accurate results poses a risk for poor outcomes for all stakeholders. This thesis aimed to quantitatively measure the accuracy of AI legal research in comparison to traditional legal research. Accuracy and relevance were measured by analyzing case precedent results using three legal research platforms, Westlaw, Nexis Uni, and Chat GPT. The three platforms represented traditional and AI-driven research, …

Smoking And Oral Health In The Tipu Maya: A Study Of Nicotine In Dental Calculus, Emily Byrd

Honors Theses

Tobacco smoking was a deeply rooted tradition in Maya society, often associated with ritual and daily life, and may have contributed to patterns of dental health. However, despite its cultural significance, direct biological evidence of tobacco use in ancient populations is rare. This study addresses that gap by analyzing nicotine residues preserved in dental calculus from the Tipu Maya, offering new insight into both cultural practices and potential health impacts.

Mass spectrometry identified molecular peaks corresponding to nicotine and its degradation products within the calculus samples.  Analysis revealed that over two-thirds of the individuals tested positive for nicotine, supporting the …

The Relationship Between Weather Variablity And Agricultural Land Cover Change In North Dakota (1997-2023) And Its Implication For Future Change, Sabrina Karim

Theses and Dissertations

Climate variability and weather patterns play a critical role in shaping agricultural land use and land cover (LULC), particularly in states like North Dakota, where agriculture dominates the landscape. This study explores the impacts of climatic factors—specifically rainfall, temperature, and Growing Degree Days (GDD)—on agricultural land cover (ALC) changes in North Dakota from 1997 to 2023, with projections for 2033. By combining historical analysis and predictive modeling, the research examines how climate variability has influenced crop dynamics and provides insights into future scenarios. The research uses advanced geospatial tools, including Google Earth Engine, and machine learning techniques such as Random …

Achieving Reward-Based Crowdfunding Project Success: An Examination Of Value Congruence, Bright Frimpong, Emmanuel Wusuhon Yanibo Ayaburi, Francis K. Andoh-Baidoo

Information Systems Faculty Publications

Crowdfunding founders increasingly rely on reward-based platforms to raise capital for their projects. However, the low success rate of reward-based project campaigns remains a concern for many founders and platform managers. Prior studies investigating participants’ motivations have focused on either the founder or backer perspective, with little research examining the joint effects of founder and backer motivations. Drawing on the theory of basic human values, we assert that founders and backers are strategic about their motivations for engaging in crowdfunding campaigns. We identify four basic human values—self-transcendence, self-enhancement, openness to change, and conservation—that may reveal their intrinsic motivations and investigate …

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 …

Investigating Students’ Proficiency Across Statistical Software And Preferences Of Statistical Software Design, Sabrina White

Honors College Theses

This paper investigated students’ perceptions of their proficiency with statistical software applications and their preferences regarding software features. Results indicated that students’ statistical and coding experience, as well as the specific application used, did not significantly influence their self-perceived proficiency. This suggests that it may be more effective to focus on building student skills within a chosen application, rather than tailoring the application to match existing student capabilities. While students showed clear preferences for certain features, favoring clarity over depth, flexibility over safeguards, and built-in checks over unrestricted freedom, these preferences generally leaned toward balanced design rather than extremes. This …

Simulations Of Magnetar Spin-Down Lifetimes Using Magnetic Braking Models, Sophie Wessel

Honors College Theses

The beginnings of magnetar lifetimes are not very well understood. It is important to improve our understanding of magnetar origins in order to investigate possible evolutionary connections to other classes of NS, such as X-ray dim isolated NSs (XDINSs). Here we follow in the steps of another paper1 in modeling the spin evolution of magnetars, utilizing two avenues of evolution: one involving exponential B-field decay, and the other involving suband super-exponential B-field decay. We replicate the results of the paper, utilizing Monte Carlo methods to generate and evolve synthetic populations of magnetars, which are then compared to the current known …

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 …