Digital Commons Network^™

Further Evaluation Of Speed Tables On Roadway Curves: Simulation And Expeditionary Deployments, Nathan Reineke

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The road network leading up to a United States military base is known as an Entry Control Facility, or ECF. Entry control facilities are used to shield military installations by monitoring vehicles and refusing access to unauthorized vehicles. Passive control devices, including speed tables, are used to disrupt or delay threat vehicles attempting to navigate ECFs at high speeds. The purpose of this research was to recommend optimized configurations of speed tables in combination with roadway curves which allow safe, efficient traversal for authorized vehicles and disruption or delay for threat vehicles.

First, an analytical model of a Tesla Model …

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 …

Fungicidal Management Of Spring Dead Spot Using Improved Modeling And Application Strategies, Michael Vincent Battaglia

Graduate Theses and Dissertations

Spring dead spot (SDS) is a destructive disease of bermudagrass (Cynodon Rich. spp.) in parts of the world that experience winter dormancy. Fungicides are often necessary to properly manage SDS, and significant research has been conducted to improve fungicidal effectiveness. Several studies were conducted with the objectives of improving fungicide application timing, method of fungicide application, and long-term fungicidal efficiency. In the fungicide application study, a cooling degree-day (CDD) model based on soil temperatures was developed. The CDD model showed that isofetamid was highly effective against SDS during a wide application window in autumn, while tebuconazole was inconsistently effective and …

Emotion Regulation Flexibility And Well-Being In College Athletes In Aesthetic Sports, Kaitlyn D. Chamberlain

Graduate Theses and Dissertations

This study examined emotion regulation flexibility and well-being (including psychological well-being, physical well-being, perceived sport performance and worth of sport participation) in athletes in aesthetic sports. This population experiences high environmental demands and constraints and thus low control over their environment. Collegiate athletes within aesthetic sports and female athletes experience specific social factors and demands that threaten their well-being, compared to those experienced by male athletes and athletes within non-aesthetic sports. Consistent with the transactional theory of stress and coping, individuals with less power to regulate their environment will require greater ability to regulate themselves in accordance with their changing …

Thermal Management Of Power Electronic Converters In Electric Motor Drives Using Active Thermal Control And Active Cooling, S M Imrat Rahman

All Dissertations

The application of power electronic converters (PEC) in electric vehicles (EVs) has significantly increased due to their enhanced controllability and flexibility. Additionally, Permanent Magnet Synchronous Machines (PMSMs) are favored in EVs for their higher torque, efficiency, performance, and heat dissipation capabilities compared to asynchronous machines. The reliable operation of power converters in PMSM drives is crucial for EV operation, and this is driving interest in developing innovative and sustainable technologies that ensure the safe and reliable functioning of PECs. One of the main challenges in achieving reliable PECs is designing effective thermal management systems to control junction temperature and reduce …

Machine Learning-Enabled Safety-Critical Model Predictive Control For Uncertain Dynamical Systems, Hossein Nejatbakhsh Esfahani

All Dissertations

This dissertation explores the interactions between Model Predictive Control (MPC), Safety-critical Control, and Artificial Intelligence (AI)/Machine Learning (ML) methods, with a particular focus on Reinforcement Learning (RL) and Bayesian Optimization (BO). We then leverage AI/ML to address several challenges in the control design and safe operation of uncertain dynamical systems. In many applications, ranging from autonomous vehicles and robotics to energy systems and industrial processes, ensuring safety is as essential as satisfying control objectives. The use of Control Barrier Functions (CBFs) within the MPC framework recently has emerged as a powerful tool to guarantee safety by enforcing constraints in optimal …

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, …

Virtual Coaching: Ushering In A New Era In School Improvement, Tygere Alisha West Charles

All Dissertations

This research study examined virtual coaching as a vehicle for principal professional development. The study focused on coaching a principal in a rural, low-performing, high-poverty South Carolina school district to improve support for teachers and students. This study explored virtual coaching and is rooted in improvement science as measured by plan-do-study-act (PDSA) cycles (Bryk et al., 2017). It explored how virtual coaching supported a rural principal’s professional development needs.

Principals must be transformational leaders who balance their support needs and accountability while meeting the needs of the diverse stakeholders they serve. Ongoing professional development is an essential component in helping …

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 …

An Experimental Evaluation Of A Gamified Learning Approach To Address Bullying Knowledge, Perceptions, And Self-Efficacy Among Elementary Students: Playing Yozgoo In Albania, Sashen Rexhepaj

All Dissertations

This study evaluated the effectiveness of Yozgoo's web-based gamified educational intervention in enhancing elementary school students’ knowledge, attitudes, and self-efficacy related to bullying prevention in Albania. Utilizing a true experimental pre-test and post-test control group design, the study involved 120 students (ages 10-12) equally divided by gender and randomly assigned to either an experimental group (n = 60) or a control group (n = 60). The experimental group participated in Yozgoo gameplay sessions over six to eight weeks, while the control group engaged in standard activities without exposure to the intervention.

Three validated measures were administered to assess outcomes: …

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 …

Atomic-Scale Engineering Of Dilute Pd–Cu Alloys For Selective Electrocatalytic Cox Reduction, Zehua Jin

All Dissertations

The excessive release of carbon dioxide (CO2) into the atmosphere has become a major cause of global warming and ocean acidification, threatening the sustainability of human life on Earth. To address this challenge, scientists are exploring ways to convert CO2 into useful fuels and chemicals instead of simply capturing and storing it. One promising approach is electrocatalysis, which uses electricity—potentially from renewable sources—to drive chemical reactions that transform CO2 under mild, environmentally friendly conditions.

This research aims to understand how the arrangement of atoms in metallic catalysts affects the conversion of CO2 and carbon monoxide (CO) into valuable products such …

Integrating Dft And Machine Learning To Predict Structural Properties In High Entropy Alloys, Nathan Linton

All Dissertations

In the past decade, a paradigm shift in the design of metal alloys has been observed. These new alloys are commonly referred to as high entropy alloys (HEAs), multi-principal element alloys (MPEAs), or complex, concentrated alloys (CCAs). In contrast to conventional alloys, which consist of one main element (for example 80%) with other elements in small amounts, HEAs are made of four or more main elements ranging from 5 to 35% each element. Due to the large presence of multiple elements, HEAs have shown substantial material property improvements over conventional alloys such as steel. For example, they have high ductility …

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 …

Navigating Safety: A Mixed-Methods Examination Of Supportive Workplace Practices In Maritime Contexts, Rebecca N. Pool

All Dissertations

Maritime Sailors operate in high-demand and high-risk environments that require sustained adherence to strict safety standards. Using the Conservation of Resources theory (COR), this dissertation examines how supportive workplace practices impact job stress and foster increased safety compliance among U.S. Navy personnel. Using a mixed-methods design, two studies were conducted with data from the Afloat Safety Climate Assessment Survey (ASCAS). Study 1 analyzed quantitative survey responses from active-duty Sailors (N = 15,517) nested in 1,033 departments nested within 122 ships to test a multilevel moderated mediation model linking social support, job stress, psychological safety, and safety compliance. Results indicated …

Interfaces To Enhance Performance In Human-Ai Teams Conducting Safety-Critical Military Operations: A Route Reconnaissance Use Case, Sarvesh Sawant

All Dissertations

Military teams frequently operate in hazardous and dynamic environments where they must recognize threats, act swiftly, and maintain awareness of their surroundings. To assist soldiers in identifying threats, analyzing data, and creating safer routes, Artificial Intelligence (AI) systems are being integrated into these teams. But only when humans comprehend what AI is doing, why it is doing it, and how to collaborate with it can AI be helpful in these high-stakes missions. To enhance human-AI collaboration during military route reconnaissance missions, this dissertation explores ways to improve human-AI communication. In a route reconnaissance mission, teams of soldiers must navigate through …

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 …

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 …

Exploring Glycosomes As Therapeutic Targets For Trypanosoma Brucei, Sabrina Sutton Pizarro

All Dissertations

Trypanosoma brucei is a protozoan parasite which causes human African trypanosomiasis and the livestock wasting disease, nagana. These parasites have highly specialized peroxisomes, called glycosomes, which house a variety of essential metabolic pathways, including glycolysis. Glycolysis is the sole means of energy production in the infective life stage of T. brucei making glycosomes and the glycolytic pathway promising targets for therapeutic strategies. In this work, we have studied the glycosome as a therapeutic target from multiple perspectives. The first portion of this work addresses drug trafficking. The efficacy of small-molecule inhibitors of glycosomal enzymes is restricted by the rate of …

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 …

Carbon Sequestration And Greenhouse Gas Emission Dynamics Of Forage Sorghum Cultivars In Southern Texas, Kusalika Madushani Kularathna

All Theses

Carbon sequestration is one of the major strategies for removing atmospheric carbon dioxide and fixing it in the soil and plants. The research study focused on the carbon sequestration and greenhouse gas dynamics of three different forage sorghum cultivars: Sugar Graze Ultra (annual), PSH20TX01 (perennial seeded), and PSH09TX15 (perennial rhizomes), in Southern Texas, where each cultivar had nine plots. The field study was conducted over two growing seasons, in 2024 and 2025, at the Prairie View A&M University research farm. Three different fertilizer rates, recommended, half-recommended, and zero fertilizer rates, were applied. Soil greenhouse gas emissions were measured using a …

Utilization Of Mushroom Stems As A Feed Resource For Sustainable Poultry Production, Kayla Stamps

All Theses

Soybean meal (SBM) is the primary protein source in poultry diets; however, its escalating cost and the environmental impact of soybean cultivation underscore the need for sustainable alternatives. Mushroom stems are generated at large volumes as agricultural waste, yet contain bioactive compounds and essential nutrients that may benefit poultry health and productivity. Despite these attributes, limited research has evaluated their potential as feed ingredients across different poultry species. This study addressed this gap by investigating whether mushroom stem powder can partially replace SBM in poultry diets while improving performance, health, and environmental outcomes.

Specifically, four experiments were conducted to evaluate …

Modeling And Control Of Spring-Mass Systems With Switched Dynamics Using Reservoir Computing, Osemudiamen Andrew Ihimekpen

All Theses

Hybrid spring-mass systems with dynamic state switching present significant challenges for modeling and control due to their complex nonlinear dynamics and discrete state transitions. In this research, a novel reservoir computing framework based on echo-state networks (ESNs) was proposed to model and control spring-mass systems with two distinct states: F1 (without air friction) and F2 (with air friction). The system switched between states based on velocity thresholds, creating a complex, hybrid dynamic that required sophisticated modeling approaches. The proposed method enabled efficient learning of spatiotemporal patterns in hybrid dynamics, offering accurate system identification and robust control capabilities.

The framework was …