Digital Commons Network^™

Musical Self-Efficacy Through Active Music Listening: A Phenomenological Approach, Amy Marie Fraser

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Music is often perceived as a talent reserved for the naturally gifted, rather than a fundamental human ability. This study questioned that perception by examining how active music listening can develop musical self-efficacy and reshape attribution beliefs about musical ability. Using a phenomenological approach, this study explored the experiences of students in a university music appreciation course, investigating how structured listening exercises, peer interactions, and reflections contribute to their perceived musical beliefs and competence. Grounded in Bandura’s self-efficacy theory and Weiner’s attribution theory, the study considered whether musical engagement can be accessible to all and not limited by perceived talent. …

Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation developed disturbance detection models to fulfill the need for remote sensing landcover products describing grassland structure. The use of landcover products derived from remote sensing is increasing over time in pheasant (Phasianus colchicus) research. Such landcover, however, does not provide relevant pheasant structural habitat information (Chapter 1). Pheasants require tall, high-density grassland for nesting, tall grassland with medium density for brood rearing, and tall grassland for wintering. Time since disturbance can serve as a proxy for structure, as it shapes vegetation by removing biomass and resetting succession. Disturbance is easier to detect than structure with current …

Securing Connected And Autonomous Vehicles, Owana Marzia Moushi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. In this dissertation, we focused on detecting various insider attacks in vehicular networks to enhance the security of the network.

Our first contribution in this dissertation is the detection of both binary and multi-class data replay and data replay Sybil attacks in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated …

Effect Of Planting Date, Insecticide Efficacy, And Hail Injury On Soybean Gall Midge (Resseliella Maxima Gagné) Infestation In Soybean, Natasha Hiromi Umezu

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Resseliella maxima Gagné (Diptera: Cecidomyiidae) is a recently described and economically significant pest of soybean (Glycine max (L.) Merr.) in the Midwestern United States. Since its identification in 2019, this species has become widespread and is now associated with substantial yield losses in parts of the region. Prior to its formal description, orange larvae were observed in soybean associated with plants damaged by hail, suggesting that plant injury may influence infestation. Given the pest’s economic importance, understanding the role of hail injury and developing effective, integrated management strategies are critical for mitigating its impact.

Field studies were conducted to …

Real-Time, Co-Regulated Design For Cyber-Physical, Multi-Rotor Uas Swarms, Grant Simon Phillips

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Uncrewed Aerial Systems (UAS) have been integrated into a wide range of research and industrial applications, with growing interest in extending mission duration and spatial coverage through coordinated multi-UAS systems, or swarms. While swarming offers the potential for extended mission endurance and robustness through advanced path-planning, control, and estimation algorithms, significant challenges arise when implementing these methods on decentralized platforms composed of size, weight, and power-constrained (SWaP) vehicles. Limitations in onboard computational capacity and congested communication channels can break critical design-time assumptions, which at best, will degrade application quality of service, and at worst, destabilize the fleet through excessive delays …

Real-Time Estimated Sequential Organ Failure Assessment (Sofa) Score With Intervals: Improved Risk Monitoring With Estimated Uncertainty In Health Condition For Patients In Intensive Care Units, Yan He, Qian Luo, Hai Wang, Zhichao Zheng, Haidong Luo, Oon Cheong Ooi

Research Collection Lee Kong Chian School Of Business

Purpose: Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk-scoring systems to aid monitoring, existing systems do not address uncertainties in risk assessments. We developed an enhanced risk monitoring framework based on commonly used systems like the Sequential Organ Failure Assessment (SOFA) score by incorporating uncertainties to improve the effectiveness of real-time risk monitoring in ICUs.Methods: This study included 5,351 patients admitted to the Cardiothoracic ICU in the National University Hospital in Singapore. We developed machine learning …

When Thermal Risk Indices Work And When They Don't: A Case Study Of Two Maize Insect Pests, Komi Mensah Agboka, Frank Thomas Ndjomatchoua, Luca Rossini, Ritter A. Guimapi, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

The biological life cycle of terrestrial arthropods, using temperature as the primary driving factor has a large interest for insect pests in agriculture, forestry, urban ecosystems, as constitutes the basics for the development of mathematical models for decision making. A recent study proposed a physiologically-based risk index (RI) which finds large applications in the definition of risk maps; however, further case studies are needed to better explore its strengths and limitations. This study aims to extend this knowledge by presenting an application of the RI on two economically significant pests: the fall armyworm Spodoptera frugiperda and the stem borer Busseola …

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Legitimacy, Learning, And Launch: Exploring Relationships, Community, And Connection In An Undergraduate Research Experience, Randi J. Sims

All Dissertations

Undergraduate research experiences (UREs) are critically important methods for integrating undergraduate students into legitimate scientific practices. While widely celebrated for producing positive outcomes such as increased self-efficacy, research skills, and STEM retention, less is known about the processes through which undergraduate mentees become integrated members of their research groups and the broader scientific community. This dissertation presents findings from both a systematic literature review and a quantitative ethnographic study of a biological sciences URE at a southeastern R1 predominantly white institution (PWI). Grounded in communities of practice and legitimate peripheral participation frameworks, this study explores how undergraduate researchers build relationships, …

Contributions To Statistical Modeling And Estimation Of Rainfall Intensity–Duration–Frequency Curves, Jiyun Huang

All Dissertations

Extreme rainfall can cause flooding, damage infrastructure, and create serious risks for communities. Engineers use Intensity-Duration-Frequency (IDF) curves to estimate precipitation extremes over different lengths of time, such as one hour or one day, and the average time one would expect wait for one of these events to occur. Several approaches exist for estimating IDF curves, but no single approach is known to be best in all cases. In this dissertation, I study statistical methods that aim to improve IDF curve estimation. I use the Canadian Regional Climate Model (CanRCM4) Large Ensemble, which contains 35 independent climate simulations. These simulations …

Leveled Homomorphic Encryption Schemes: Noise And Precision Control, Kyle Yates

All Dissertations

Homomorphic encryption allows for computations on encrypted data without exposing the underlying plaintext, enabling secure and private data processing in various applications such as cloud computing and machine learning. In this thesis, we conduct a comprehensive worst-case noise analysis for three prominent leveled homomorphic encryption schemes: Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski-Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS). We propose modifications to these schemes and their residue number system (RNS) variants, ensuring fresh encryption noise falls under a constant bound. For BFV and BGV, we design and prove parameter conditions which guarantee certain homomorphic circuit evaluations return ciphertexts containing noise within a fixed bound. For …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Digital Reflections: Evaluating Body Dissatisfaction In Xr Through Eye- And Body-Tracked Virtual Humans, Deyrel Diaz

All Dissertations

In an era where digital and physical realities increasingly intertwine, the perception of body image is undergoing a significant transformation. Traditional understandings of body dissatisfaction, long studied in relation to psychological distress and eating disorders, are now being reshaped by technologies such as Virtual Reality (VR), Augmented Reality (AR), and Artificially Intelligent (AI)- generated media. These technologies have introduced novel ways of experiencing and interacting with the human form, raising critical questions about their impact on self-perception and internalization of beauty standards.

As virtual representations become more prevalent in entertainment, social media, and interactive platforms, it is becoming more crucial …

Cosmic Duets: A Search For Binary Supermassive Black Holes In Merging Galaxies, Sagar Adhikari

All Dissertations

Galaxies are vast cosmic islands of stars, dust, and gas. They come in various shapes and sizes. They might look static for the timescales we are used to, but they are dynamic and collisional systems that can merge with other galaxies to make bigger galaxies. Most galaxies, including the Milky Way, host a central supermassive black hole (SMBH) with a mass greater than a million (sometimes a billion) times the mass of the Sun. When galaxies merge, their SMBHs form a binary system before ultimately merging. These cosmic duets are of great interest to astronomers and astrophysicists as they are …

On Variations Of Isolation In Graphs, Geoffrey Boyer

All Dissertations

In 2015, Caro and Hansberg introduced a wonderful and natural generalization of the well-studied parameter of domination in graphs. For a graph $G$ and a family of graphs $\FF$, they define $S$ to be an $\FF$-isolating set if $G-N[S]$ contains no member of $\FF$ as a subgraph. The case where $\FF=\{K_1\}$ coincides with domination. The case where $\FF=\{K_2\}$ is now simply referred to as an isolating set. We strengthen known results about the isolation number of a graph, and explore variations of the parameter including the independent and total versions.

In particular for connected graphs of order $n$, a bound …

Closing The Achievement Gap: School Counselors’ Role In Enhancing College And Career Access For First-Generation Students, Conswyla J. Decoteau

All Dissertations

Professional school counselors play a vital role in supporting each student's academic, experiential, and personal growth, especially in preparing them for success in post-secondary education. Due to social, academic, and financial disadvantages, first-generation students face significant obstacles. Given that inequities persist despite efforts to improve access to higher education, this problem is important to address. To close the academic gap that first-generation students face, effective counseling programs that are sensitive to cultural differences are necessary. In order to better understand how school counselors in rural high schools may help first-generation students close the gap in access to college and careers, …

First Principles Calculation Of Electron-Phonon Coupling In Nonequilibrium Quantum Materials, Chendi Xie

All Dissertations

This dissertation combines first-principles computation and light-induced nonequilibrium analysis to understand and control electron–phonon coupling (EPC) and emergent many-body phenomena in quantum materials, in both equilibrium and nonequilibrium states.

Chapters 1 and 2 build the theoretical and computational foundation, beginning with electron and phonon self-energies, linewidths, and perturbation theory for EPC, followed by density functional theory (DFT) and density functional perturbation theory (DFPT), which serve as the first-principles workhorses used throughout. Chapter 3 reviews Bardeen-Cooper-Schrieffer (BCS) and Migdal-Eliashberg theory, including the BCS gap - transition temperature (T_c) relation and the strong-coupling generalization, and delineates the practical validity bound …

Accident Data Analytics And Ai-Based Auditory Enhancement For Highway Construction Safety, Thinh Nguyen

All Dissertations

This research develops data-driven and AI-enhanced approaches to improve worker safety in highway construction environments. The study integrates advanced analytics and auditory signal enhancement to identify high-risk worker behaviors and improve real-time safety warnings. The work consists of three core studies. The first study applies Sequential Pattern Mining and Social Network Analysis to more than 1,000 construction accident reports to identify high-risk worker actions and their hidden sequential relationships with accident types and consequences. These insights reveal sector-specific risk patterns, supporting the creation of targeted, data-informed safety interventions. The second study introduces an AI-based auditory enhancement model built on a …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Enhancing Teacher Planning Practices To Improve Teacher Self-Efficacy Through Professional Learning Communities, Philip L. Price

All Dissertations

This improvement science dissertation investigated the impact of implementing Professional Learning Communities (PLC) on teacher self-efficacy at Parkway Elementary, a Title I school serving a diverse student population with historically lower academic performance. The study addressed the problem of low teacher self-efficacy, a critical factor influencing Tier 1 instructional quality and student outcomes. Utilizing the Plan-Do-Study-Act cycle, this research tested the theory that adopting a four-question framework within weekly collaborative grade-level meetings would measurably increase teacher self-efficacy. Seven teachers participated in the research, with data collected through the Teacher Sense of Efficacy Scale, semi-structured interviews, and PLC observations. Quantitative findings …

Flexible Spatial Priors In Bayesian Neuroimaging: Gmrf, Nngp, And Deep Gmrf, Boyoung Hur

All Dissertations

Structural neuroimaging is essential for understanding neurological disorders such as Alzheimer’s disease, enabling accurate delineation of brain regions through image segmentation. Among various segmentation methods, multi-atlas-based approaches like label fusion have become leading techniques. In statistics, Bayesian hierarchical models for label fusion are increasingly favored for their ability to incorporate uncertainty and prior knowledge. Also, a key challenge in modeling neuroimaging data is spatial dependence among image voxels, making the choice of spatial prior critical—particularly in high-resolution settings where segmentation accuracy and computational efficiency are both essential.

This dissertation proposes fully Bayesian spatial hierarchical models that explore two flex- ible …

Breaking Barriers For Student Success: Leading In Challenging Times, Lisa Marie Dewey

Dissertations

This study explores how Diversity, Equity, and Inclusion (DEI) directors in one Midwestern state understand and lead equity initiatives in K–12 public education. The purpose of the study was to examine how DEI leaders make sense of their roles and the challenges of sustaining equity work within complex and often politicized systems. Using a basic qualitative methodology, the research examined how personal identity, sociopolitical context, and organizational structures shape DEI leadership. Critical Race Theory (CRT) framed the study, centering race, power, and systemic inequity in participants’ experiences.

Seven DEI directors participated in semi-structured interviews. Three key themes emerged. First, participants’ …

Exploring 2d Geometric Shape Classification Using Ai-Driven Feature Tables In Mathematics, Yasemin Gunpinar, Woonhee Sung

Education Faculty Publications and Presentations

This study explored the effectiveness of an AI-integrated instructional task designed to enhance preservice teachers' understanding of the features and hierarchical relationships of 2D geometric shapes. Originally developed and tested in online K-12 professional development settings, this intervention was adapted for in-person preservice teacher education context in this study. Data were collected from 17 preservice teachers through demographic surveys, pre- and posttests using the Van Hiele geometry framework, hierarchical diagram tasks, feature table creation during the intervention, and postintervention reflections. Findings indicated a statistically significant improvement in the accuracy and complexity of postintervention hierarchical diagrams, along with a descriptively higher …

Elena Asins Y Las Afueras Del Canon: Animación Computacional Y Resistencia, Maria Mar Garrido Román

GDI. Revista de investigación de Género, Diseño e Innovación

Este artículo examina las animaciones generadas por ordenador producidas por Elena Asins, analizando cómo su práctica artística moviliza el pensamiento lógico-matemático, la programación y la experimentación visual. A través de una revisión de su metodología, contexto de producción y resultados formales, el estudio explora la incorporación de procesos algorítmicos en su obra, que generan imágenes con extensión temporal y la consolidan como una figura clave en la historia del arte digital y la animación experimental en España.

La investigación se basa en el análisis de fuentes primarias y, con el objetivo de situar su producción en un marco internacional, establece …

A Comparative Framework Of Hybridization Of Arima And Sarima Models For Forecasting Gold Price Movements In Iraq (2015–2025), Hiba Dhahir Alwan, Suhail Najm Abdulla

Journal of Economics and Administrative Sciences

Predicting gold prices is crucial for policymakers, investors, and financial planners, especially in commodity-dependent economies like Iraq. This study examines the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models using daily gold price data in Iraq from (April 30, 2015, to April 30, 2025). After preprocessing and testing for stationary, both models were estimated using Maximum Likelihood Estimation (MLE) and further refined with modern machine learning post-processing, Maximum Likelihood Estimation (MLE) method demonstrated greater stability and interpretability compared to the machine learning-based approach. However, the application of Gradient Boosted Trees (GBDT) to the residuals of …

Modeling And Analyzing Supply Chain Reliability Under Uncertainty: A Simulation-Based Study Using Real-World Data, Mostafa Abduljabbar Dawood

Journal of Economics and Administrative Sciences

This paper has presented a simulation-based framework for analyzing supply chain reliability under uncertainty, supported by a comprehensive literature review, theoretical grounding, and a real-world case study. The main contribution lies in demonstrating how simulation modeling — particularly hybrid approaches — can capture the complex dynamics of modern supply chains and provide decision-makers with practical tools for stress-testing, scenario planning, and reliability enhancement. This study aims to evaluate supply chain reliability under uncertainty by integrating simulation and probabilistic modeling. The purpose is to investigate how disruptions in supply, demand, and logistics affect performance indicators such as service level, recovery time, …

The Effect Of Momentum And Liquidity Factors On Stock Returns In The Iraqi Stock Exchange: An Analysis Utilizing The Six-Factor Fama-French Model And Random Forest Methodology, Aseel Riyadh Joodi, Shatha Abdul-Hussein Jabr

Journal of Economics and Administrative Sciences

This research investigates the influence of momentum and liquidity factors on stock returns in the Iraq Stock Exchange, employing the six-factor Fama-French model and enhancing the analysis with sophisticated machine learning techniques, including Random Forests. This research seeks to address deficiencies in prior studies by implementing an integrated model within the Iraqi market context, which is under-researched in the context of multi-factor asset pricing models. A quantitative analytical approach was applied to a sample of 10 companies listed on the market from 2014 to 2023. The findings indicate that the random forest model markedly outperforms conventional regression models, elucidating 72% …

Genetic And Environmental Determinants Of Streaming And Aggregation In Myxococcus Xanthus, Trosporsha T. Khan, Patrick Murphy, Jiangguo Zhang, Oleg A. Igoshin, Roy D. Welch

Faculty Research, Scholarly, and Creative Activity

Under starvation conditions, a spot of a few million Myxococcus xanthus cells on agar will migrate inward to form aggregates that mature into dome-shaped fruiting bodies. This migration is thought to occur within structures called ‘streams,’ which are considered crucial for initiating aggregation. The prevailing traffic jam model hypothesizes that intersections of streams cause cell crowding and ‘jamming,’ thereby initiating the process of aggregate formation. However, this hypothesis has not been rigorously tested, in part due to the lack of a standardized, quantifiable definition of streams. To address this gap, we captured time-lapse movies and conducted fluorescent cell tracking experiments …

Illinois State University, One Hundred And Sixty-Sixth Annual Commencement, December 2025, Illinois State University

Commencement Programs

Illinois State University

One Hundred and Sixty-Sixth Annual Commencement

December 2025