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Effects Of Environmentally Relevant Concentrations Of Roundup On Oxidative-Nitrative Stress, Cellular Apoptosis, Prooxidant-Antioxidant Homeostasis, Renin And Cyp1a Expressions In Goldfish: Molecular Mechanisms Underlying Kidney Damage During Roundup Exposure, Md Imran Noor, Md Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Roundup is one of the most widely used glyphosate-based harmful herbicides in the United States as well as globally, which poses a severe risk for terrestrial and aquatic organisms. In order to identify the detrimental effects of Roundup exposure in aquatic organisms, we investigated the environmentally relevant concentrations of Roundup exposure (low dose: 0.5 μg/L and high dose: 5.0 μg/L for 2 weeks) on renin expression, oxidative-nitrative stress biomarkers (e.g., 2,4-dinitrophenol, DNP; and 3-nitrotyrosine protein, NTP), prooxidant-antioxidant enzymes expressions (e.g., superoxide dismutase, SOD; and catalase, CAT), cellular apoptosis, and cytochrome P450 1A (CYP1A) mRNA levels in the kidneys of goldfish …

Optimized Filtrations For Stormwater Quality Improvement By Porous Media–Biochar Applications: Column Experiments And Inverse Modeling, Sumiaya Amin Preota, Chu-Lin Cheng, Myung Hwangbo, Jongsun Kim

School of Earth, Environmental, & Marine Sciences Faculty Publications

Stormwater reuse plays a critical role under changing climates and increasing water demands. This study investigates the removal efficacy of lead (Pb2+) and ammonia (NH3) using sand and rice husk (RH) biochar for potential stormwater quality improvements and treatments. Column experiments combined with HYDRUS inverse modeling were conducted to optimize adsorption isotherms from breakthrough curves. Among linear and non-linear models, the Langmuir and Freundlich models performed better for sand and biochar, respectively. RH biochar showed much higher adsorption capacity of both Pb2+ (4.813 mg/g) and NH3 (6.188 mg/g). In contrast, sand showed a relatively limited adsorption capacity for Pb2+ (0.118 …

Comprehensive Global Data Set Of Uniformly Processed Shear-Wave Splitting Measurements, Jonathan Wolf, Thorsten W. Becker, Edward Garnero, Kelly H. Liu, John D. West

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Seismic anisotropy can inform us about convective flow in the mantle. Shear waves traveling through azimuthally anisotropic regions split into fast and slow pulses, and measuring the resulting shear-wave splitting provides some of the most direct insights into Earth's interior dynamics. Shear-wave splitting is a constraint for path-averaged azimuthal anisotropy and is often studied regionally. Global compilations of these measurements also exist. Such compilations include measurements obtained using different data processing methodologies (e.g., filtering), which do not necessarily yield identical results, and reproducing a number of studies can be challenging given that not all provide the required information, for example, …

Unraveling The Causal Influences Of Drought And Crop Production On Groundwater Levels Across The Contiguous United States, Nitin K. Singh, Sheila M. Saia, Ruchi Bhattacharya, Hoori Ajami, David M. Borrok

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Groundwater depletion in agricultural-dominated regions is attributed to climate and irrigation withdrawals that support crop production. However, despite decades of effort, knowledge gaps remain in understanding the relative influence of drought and crop production on groundwater levels at the continental scale. Here, utilizing empirical observations, we simultaneously track how long-term trajectories of groundwater levels, crop production of seven crops, and drought have evolved over time, and then integrate these observations with a causality-based attribution framework to unravel the relative impact of drought and crop production on groundwater levels across the contiguous United States (CONUS). We find a dominant pattern of …

Greening Intelligence: Why Ai Infrastructure And Governance Must Evolve Together, Heng Wang, Poh Seng Lee

Research Collection Yong Pung How School Of Law

AI infrastructure is evolving faster than the regulation and governance needed to ensure it serves public and planetary interests.

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 …

Organizational Commitment Of Korean American Nurses: A Cross-Sectional Study, Myunghee Jun, Hye-Kyung Kim, Sungsu Kim, Michael Wreen, Yeon Hee Kim, Christine L. Vandenhouten, Soo Ii Shin

Philosophy Faculty Research and Publications

Background:

Studies on reducing the turnover of foreign-educated nurses (FENs) can help address the nursing shortage in the United States. Organizational commitment and associated factors have been explored in relation to lower turnover. Nursing researchers have identified job satisfaction, psychological contract breach, person-organization fit, and person-work setting fit as major factors in enhancing nurses’ organizational commitment. Research on the organizational commitment of Korean American (KA) nurses would provide insights into reducing turnover among FENs.

Objectives:

This study identifies some of the key factors that significantly enhance the organizational commitment of KA nurses.

Methods:

A cross-sectional study conducted with a survey …

Bioavailability Of Lead In Terre Haute Soils, Terre Haute, Indiana, Zach Kemp

All-Inclusive List of Electronic Theses and Dissertations

Contamination of soils from heavy metal pollution has been a global concern for decades. Heavy metals are released into the soil from natural and anthropogenic releases. The largest producers of heavy metal pollution include mining, industrial production, and the combustion of fossil fuels. Vigo County formed in 1818, and Terre Haute was established in 1832. After the discovery of coal in Clay County in 1867, Terre Haute became the third largest coal producer and fifth largest iron manufacturer in the state during that time. Lead, in particular, is a heavy metal of concern because of the harmful effects to humans …

Air And Missile Defense Threat Scenario Variation To Reduce Pretest Sensitization, Video Games As A Case Study, Julie Renee Szekerczes

All-Inclusive List of Electronic Theses and Dissertations

This study uses fixed and variable video game types to measure pretest sensitization as a proxy for repeated and varied threat test scenarios in system performance testing of air and missile defense systems. The pretest sensitization phenomenon exists when repeated exposure to a test condition influences the participant's response. Research shows air and missile defense development correlates with video games, resulting in similar interfaces and computer operating environments. Department of Defense acquisition test and evaluation results must reflect system performance without prior knowledge of the threat scenarios confounding the results. System performance results inform acquisition decisions, such as further funding …

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. …

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, …

Energy Forecasting Inaccuracies And Their Direct Impact On Grid Performance, Rashed Khalid Alboom

Theses

Accurate energy forecasting is critical for the stability, efficiency, and cost-effectiveness of modern power grids, particularly as renewable energy sources like solar and wind become more prominent. The variability of these sources presents challenges for traditional forecasting models, which struggle with non-linearity, external dependencies, and evolving grid conditions. These limitations lead to operational inefficiencies, grid instability, and increased financial risks. This study investigates the effectiveness of traditional statistical models, advanced machine learning techniques, and hybrid forecasting approaches to enhance prediction accuracy and grid performance. Using real-world datasets from Kaggle, including historical energy generation, consumption, pricing, and weather variables, this research …

Uvm Based Verification Of Ee621 Risc Processor Module, Collin Neidel

Theses

In the process of digital design, the majority of efforts are focused on verification. This is because a proper verification environment can help engineers discover bugs that otherwise would have flown under the radar. On the other hand, a poorly designed verification environment could fail to fully confirm the device’s functionality and leave the customer with a buggy mess. For obvious reasons, this should be avoided at all costs, hence the emphasis on functional verification and validation efforts. Throughout time, the complexity of the average design under test (DUT) has increased dramatically, making verification a significant challenge for today’s verification …

Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey

Theses

This report presents a novel influence function for gradient-boosted decision trees (GBDTs), a widely-used class of predictive models. Influence estimation aims to quantify how individual training samples affect a model’s predictions, offering valuable insights for model debugging, data quality analysis, and in- terpretability. Existing influence functions for GBDTs—such as LeafInfluence, LeafInfluenceSP, and BoostIn—have shown varying degrees of success, with BoostIn currently recognized as the state-of-the-art in terms of estimation quality and computational efficiency. In this work, we propose BoostInLCA, a new influence function that extends BoostIn by incorporating information from non-leaf nodes via the Lowest Common Ancestor (LCA) path, thereby …

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …

Sales Opportunities Lead Qualification In B2b Market, Hajar Hussain Ahli

Theses

This thesis addresses the challenge of inefficient lead qualification in the business-to-business (B2B) market by applying machine learning techniques to predict the likelihood of winning a sales opportunity. Using a real-world dataset of over 78,000 records and 17 variables, the study aims to improve how sales teams identify and prioritize high-conversion leads. A thorough data preparation process was conducted, including handling of missing values, outlier detection, and under-sampling to resolve class imbalance between won and lost opportunities. After thorough data cleaning, preprocessing, and under-sampling to address class imbalance, five machine learning models were developed: Logistic Regression, Random Forest, Neural Network, …

Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov

Theses

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related …

Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko

Theses

In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on …

Predicting The Probability Of Crime Related Danger In Los Angeles, Joshua Oghenetega Okpako

Theses

This thesis examines the use of advanced machine learning to predict crime danger in Los Angeles, where 2023 violent crime rates (503 per 100,000) surpass the national average (363.8 per 100,000). Rooted in theories like social disorganization and victim vulnerability, it addresses the lack of combined victim-centric modeling by focusing on three questions: (1) Do blended ensemble models outperform individual models in predicting crime danger? (2) Can unsupervised learning techniques enhance supervised models’ accuracy through label generation or augmentation? (3) How can we interpret accurate machine learning models' decision-making processes? Using historical crime data from Los Angeles (2020–2025) and various …

Methods Of Quantum Circuit Simulation: A Comprehensive Comparative Analysis, Anthony Peter Bacchetta

Theses

The advent of quantum computing has ushered in a new era of computational capabilities, promising to solve problems that were previously intractable for classical computers. Quantum circuit simulation is an essential aspect of harnessing the potential of quantum algorithms, and understanding quantum systems. This thesis presents an exploration and comparative analysis of various methods used in the simulation of quantum circuits, aimed at providing a comprehensive understanding of their strengths, weaknesses, and practical applications. This starts with a dive into the foundational concepts of quantum circuits, quantum gates, and the fundamental principles underlying quantum computation. Quantum circuit simulation emphasizes the …

Modelling And Load Frequency Control (Lfc) Design Of Microgrid Frame-Worked As Technological Norm To Multi Energy System (Mes), Mekonnen Shewarega Worku

Theses

In today's world, the trend of using renewable energy sources as an alternative energy source is becoming more and more common due to various driving factors, such as energy scarcity (fossil fuel depletion, etc.) and environmental issues (carbon footprint). This trend is leading to a steady increase in the penetration of renewable energy sources (RESs) through microgrid (MG), which is formed from combination of various distribution energy sources (DERs). The inherent intermittent nature of RESs coupled with abrupt load changes can instigate sustained frequency fluctuations and can keep the frequency deviation out of the allowable range that leads to an …

Managing Software Dependency Risks In Web Applications, Christopher Alan Scott

Electronic Theses and Dissertations

Web applications commonly rely on third-party software dependencies to reduce development time. This thesis examines how vulnerabilities in a dependency chain propagate to compromise an application. It analyzes two vulnerable Markdown libraries from the npm and Composer dependency ecosystems, both of which are used for managing packages in applications developed with JavaScript and PHP. The analysis demonstrates how each library’s sanitizing functions—intended for removing unsafe user input when transforming Markdown text to HTML—are defeated to achieve a cross-site scripting exploit and take control of the application. The paper discusses potential business impacts of a compromise, underscoring the need for security …

The Ties That Bind: Co-Twin Influences On Psychopathology In A Sample Of Twins, Anjoli E. R. Cero

Doctoral Dissertations and Projects

Research has begun to better describe twinship and twin identity development, the potential advantages and disadvantages of twinship, and factors related to twins’ shared psychological symptomatology. Research also supports the concept of emotional contagion, in which one person’s emotional state can be transmitted to another person. This phenomenon may be more common among twins due to factors such as closeness, dependence, and general similarity. The current study proposed a series of regression models to investigate how predictive pathways toward anxiety among a sample of twins could be impacted by the psychological traits and functioning of their co-twins. The current sample …

A Phenomenological Study On Teachers' Lived Experiences With Digital Learning And Students With Disabilities During And Post-Covid-19 Pandemic, Angie R. Tyson

Doctoral Dissertations and Projects

The purpose of this hermeneutic phenomenological study was to describe the experiences of educators with digital learning and students with disabilities during and post-pandemic in various areas in the Satellite Central Regional Area of eastern Georgia. The central research question of the study, "How do teachers describe their experiences with digital learning and students with disabilities during and post-pandemic?" was addressed by considering Bandura's perspectives on the theoretical framework of self-efficacy. Participants were 11 educators with lived experiences of self-efficacy working with students with disabilities and achievement gaps during and post-pandemic. The participants described their experiences in detail through focus …

Aiming High: A Narrative Inquiry Of Christian Music Educators’ Higher Purpose, William E. Rhoden Iii

Doctoral Dissertations and Projects

Educators in the United States may question music education’s higher purpose, considering high-stakes testing and an emphasis on non-musical subjects. This qualitative narrative study examines three American protestant Christian music educators’ perceptions of music education’s higher purpose and compares them to Martin Luther’s (1483-1546) writings. The researcher conducted interviews with three participants to record the story of how they developed their higher purpose for music education. The data was tabulated and coded for thematic analysis. This study supports a Christian defense of continued music education in America’s schools, distinguishes modern Christian perspectives, and offers a fresh understanding of the historical …

A Descriptive, Comparative Study Of Preexisting Leadership Qualities And Leadership Effectiveness Of Theologically Conservative United States Army Battalion-Level Chaplains, Aaron Jonathan Fabian

Doctoral Dissertations and Projects

This immediate demand for spiritual and professional leadership can be overwhelming for a new battalion-level chaplain (Blackaby 2001; Howell 2003). They perform or provide religious service for Soldiers who have volunteered to serve their country. By going through the same military training in conjunction with their religious education and experience, chaplains are able to integrate with Soldiers where traditional clergy cannot. There is additional pressure to perform because chaplains, as commissioned officers, are evaluated immediately through an annual Officer Evaluation Report (OER hereafter, DA Form 67-9). With no time to acquire new skills, battalion-level chaplains are forced to rely on …

Resources And Readiness: Exploring The Lived Experiences Of Secondary Teachers In Rural Texas With Being Prepared For Students Diagnosed With Attention-Deficit Hyperactivity Disorder In Their Classrooms, Barbara L. Chase

Doctoral Dissertations and Projects

This dissertation examines how prepared secondary teachers in Texas rural public schools are to work with students diagnosed with attention deficit hyperactivity disorder (ADHD) who are placed in their classrooms. I investigated the professional development and higher educational training experiences of secondary teachers regarding contributions to both their knowledge of the etiology of ADHD and interventions and behavior management strategies. This dissertation was motivated by a curiosity to understand how secondary teachers in rural schools describe the knowledge they obtained regarding ADHD etiology and interventions through their higher education programs and professional development opportunities. As well as a desire to …

Dunning-Kruger And The Reforming Mind: A Causal-Comparative Study Determining The Role That Confidence When Learning Mathematics Plays In Sixth-Grade Students' Mathematics Performance, Rita Renata Davison-Yragui

Doctoral Dissertations and Projects

The purpose of this quantitative non-experimental causal-comparative study was to determine the relationship between sixth-grade students’ confidence level (high, middle, or low) when learning mathematics and their mathematical performance. This study adds to the current body of knowledge on student learning and contributes to a better understanding of the impact of confidence on student performance in mathematics. The sample comprised 132 sixth-grade public school math students in western Washington who completed the i-Ready diagnostic to determine their mathematical performance. The confidence when learning mathematics portion of the Attitudes to Technology in Mathematics Learning Questionnaire was used to measure student confidence …

Investigating The Influence Of Modern And Traditional Instructional Practices On Creating Lifelong Habits Of Music Engagement In The Prospect Mountain Schools System, Thomas D. Stevens Jr.

Doctoral Dissertations and Projects

The purpose of this study was to investigate the impact of modern and traditional pedagogical approaches on student development of music engagement and learning in the Prospect Mountain Schools system during and after high school. Student retention is critical to maintaining music programs; therefore, educators must discover ways to motivate students to continue participating in general music and ensemble courses. Combining modern and traditional pedagogical approaches creates learning experiences that inspire students to continue playing music throughout their lives, both during and after high school. One of these pedagogical practices is incorporating popular music into the classroom, which can enhance …

A Phenomenological Study Of String Teacher Perceptions Of Professional Development At Three Career Stages, Michael James Hanf

Doctoral Dissertations and Projects

This hermeneutic phenomenological study aims to understand the perceptions of elementary and secondary string teachers’ experiences with quality professional development from early-, middle-, and late-career stages. This study analyzes perceptions of quality professional development for music teachers, including content-related, evidence-based, applicable, relevant, collaborative, providing feedback, actively involving teachers in learning, longitudinal, coherent, and allowing for reflection. It follows a hermeneutic phenomenological research design guided by the theory of human motivation and aims to understand how professional development affects teacher needs. Participants include elementary and secondary string teachers across the United States from early-, middle-, and late-career stages. String teachers sampled …