Physical Sciences and Mathematics Commons^™

Influence Of The Duchesne Fault Zone On Oil Production In The Uinta Basin, Utah, Alan Matthew Ketring

Theses and Dissertations

The Duchesne Fault Zone (DFZ) is a series of east-west trending en echelon faults that developed synchronously above the neutral surface of the Duchesne Anticline during the Laramide Orogeny. The Duchesne Graben (DG) formed by decrease in displacement downward toward the neutral surface. Lake Canyon, a region that exposes the subsurface geology on the western portion of the DFZ, serves as an analog for fold formation within the saline facies of the lower Green River Formation, while Indian Canyon, an area created from the dip-slip faulting of the DG, provides clear exposures of the faulting within the sandstone-limestone facies of …

Improving Efficiency Of Wind Flow Modeling For Wildfire Applications Using Analytical Parameterization And Neural Networks, Laura Jane Housley

Theses and Dissertations

We model wind velocity fields for applications in wildfire modeling. We improve the accuracy of the mass-conserving WindNinja solver without compromising efficient temporal complexity. We consider two approaches: the parameterization method and the neural network method. In the parameterization method, we analyze eddies in the wake of obstacles by parameterizing cavity zones as inspired by the research by Kaplan & Dinar in 1996. Kaplan & Dinar parameterize cavity zones around rectangular buildings, and we approximate a continuous landscape with rectangular buildings in order to extend this parameterization to complex terrains. In the neural network method, we train a U-net CNN …

Inquiry-Style Planetarium Activities, Emma Rasmussen Groberg

Theses and Dissertations

Introductory astronomy students often face challenges in exploring their own ideas and investigating astronomical phenomena due to the limitations of nighttime observation and weather conditions. Planetariums overcome these barriers by simulating the night sky, making it accessible during class sessions regardless of external conditions. This provides an opportunity to engage students in inquiry-based activities, where they are presented with a scenario and encouraged to use their own reasoning to reach conclusions. In this study, we implemented inquiry-based activities in PHSCS 127 using the planetarium as an investigative tool. We developed four activities designed to help students explore their ideas and …

Modeling The Kepler Systems With Multiple Transiting Planets, Daniel Kempton Jones

Theses and Dissertations

As the study of exoplanets, planets not in our solar system, has developed, new observational constraints have illuminated the formation and evolution of planetary systems. Among these observations, the true distributions of planetary mass, radius, and density are at the forefront. NASA's Kepler Space Telescope has provided an enormous wealth of data on the exoplanet radius distribution as well as a significant fraction of mass measurements through the detection of planet-planet dynamical interactions. The best method for studying these exoplanetary systems is with a photodynamical model which combines an n-body integrator with lightcurve model to generate synthetic lightcurves. To support …

Prime Factors Of The Third Cyclotomic Polynomial Of The Same Form And Inequalities Related To Odd Perfect Numbers, Cody Stenberg Hansen

Theses and Dissertations

This thesis relates some results about odd perfect numbers and factorizations of cyclotomic polynomials evaluated on integers. In Chapter 2 we parameterize solutions to the equality $\Phi_3(x)=\Phi_3(a_1)\Phi_3(a_2)\cdots\Phi_3(a_n)$, where $x,a_i\in \Z_{>0}$ for $1\leq i\leq n$, $\Phi_3$ is the third cyclotomic polynomial, and each $\Phi_3(a_i)$ is prime, in the cases that $n=2,3,4,5$. Let $N$ be an odd perfect number. Let $\omega(N)$ be the number of distinct prime factors of $N$ and let $\Omega(N)$ be the total number (counting multiplicity) of prime factors of $N$. In Chapter 3, we use a result from Chapter 2 to show that $\frac{99}{37}\omega(N) - \frac{187}{37} \leq …

A Novel Approach To Detect Shapes Of Constant Width, Ian D. Monk

Theses and Dissertations

Bodies of constant width have been studied mathematically since Euler and have found various applications in engineering and other areas. In this paper, we seek to develop novel tools to gain deeper insights into these shapes and to better be able to analyze them. The idea which motivates the development in this paper is as follows: a body has constant width if and only if the chord connecting the intersections of the boundary of the body with each pair of parallel hyper planes which lie tangent to a body lies perpendicular to the hyper planes.This is a known fact and …

A Complete Guide To Multi-Fidelity No-U-Turn Sampling With Application To An Inverse Wave Problem, Paul Smith

Theses and Dissertations

We employ a Bayesian framework to solve an inverse problem that aims to identify the initial condition distribution of wave propagation, given a limited set of observable data. To optimize sampling efficiency, we introduce a multi-fidelity No-U-Turn Sampler (MF-NUTS) with an adaptive mass matrix. Maximizing sampling efficiency is crucial, as our ultimate goal is to extend this methodology to a large-scale inverse problem to identify the source parameters of historical tsunami-generating earthquakes. The forward model for tsunami propagation (GeoClaw) requires over 20 minutes of supercomputer time on 24 cores to evaluate each sample for a triple-fault rupture, necessitating a strategy …

Optimizing Aircraft Taxi Processes: A Novel Cost-Minimizing Queue Discipline For The Pushback To Runway Process, Kevin James

Theses and Dissertations

Optimization of the aircraft taxi process can significantly decrease operating costs for air carriers, especially at major airports where congestion on the ramp is a primary contributor to taxi times. To improve the efficiency of this process, we propose a novel queue discipline which mitigates pushback delays through a dynamic reordering of clearance deliveries. As an alternative to FIFO, the proposed discipline accounts for aircraft delays at the gate before the aircraft enters the queueing system. Our proposed system implements pushback control to allow for controller-imposed variability in runway queueing. To validate our proposed queue discipline, we provide a computational …

Reservoir Characterization And Basin Modeling Of Mesozoic Siliciclastic Strata For Sedimentary Geothermal Exploration, Iron County, Utah, Theodore L. Morgan

Theses and Dissertations

The transition zone between the Colorado Plateau and Basin and Range provinces of southwestern Utah provides excellent opportunities for future geothermal resource development. A combination of large fault-controlled extensional basins with outcrop analogs along basin margins, an elevated heat flow and thermal regime and the presence of porous and permeable sedimentary formations within extensional sedimentary basins allow for sedimentary geothermal exploration and development. Utilizing a reservoir-centered approach has the potential to dramatically expand the economic limits of geothermal plays in both established and frontier basins. This study characterizes a sedimentary geothermal system in Iron County Utah, centered on the Escalante …

Building And Understanding Cryptographic Authentication Systems, James S. Conners

Theses and Dissertations

The security and usability challenges of password-based authentication have driven ongoing research into alternative cryptographic authentication systems. This dissertation explores the design, implementation, and evaluation of authentication mechanisms that enhance security while maintaining usability. Specifically, we investigate the limitations of existing passwordless authentication systems, such as FIDO2, and propose novel solutions that improve account recovery, user mental models, and adoption rates. We introduce Let's Authenticate, a certificate-based authentication system designed to address usability and security concerns inherent in existing models. Through iterative development and empirical studies, we compare this approach against contemporary methods like passkeys and password managers. Additionally, we …

Tritium As A Model Tracer For Predicting Per- And Polyfluoroalkyl Exposure And Potential Ecosystem And Human Health Impacts, Erika Craig

Theses and Dissertations

For this thesis, historical tritium and per-and polyfluoroalkyl substances (PFAS) concentrations measured in surface waters of South Carolina at or near the Savannah River Site (SRS) were compared. Statistical linear regression analysis was used to assess the ability of tritium as a model tracer for predicting PFAS concentrations, specifically perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) distributions. Nine years of tritium monitoring data, (2014 – 2022), were analyzed and used in linear regressions to assess their association with PFAS levels of PFOA and PFOS measured during 2023, in and around the SRS and the adjoining Savannah River. Initially, a normality …

Neural Network-Based Low-Level 3d Point Cloud Processing, Pingping Cai

Theses and Dissertations

3D computer vision is a promising research field with the potential to revolutionize future lifestyles. Among various 3D representation formats, point clouds stand out for their efficiency in depicting 3D objects using a set of coordinates, enabling advancements in fields such as autonomous driving, virtual reality, and robotics. Due to the limitations of sensor fields of view and scanning trajectories, the collected point clouds are usually sparse, noisy, and incomplete, impeding the performance of many downstream applications. Thus, the tasks of low-level point cloud processing are proposed to refine and generate dense, clean, and complete point clouds. To accomplish these …

Augmenting Deep Learning For Efficient Nextg Wireless Communication And Sensing Systems, Hem Kanta Regmi

Theses and Dissertations

Wireless networks have become an integral aspect of our daily lives. Over the years, earlier generations of wireless networks have enabled some innovative applications, such as wireless gaming, fast internet browsing, and home automation, which were previously unattainable. However, to support emerging technologies like autonomous driving, virtual reality, telemedicine, and intelligent manufacturing, which require high data throughput and low latency, there is a need for advanced wireless networks. Legacy networks like WiFi/LTE, which operate below 6 GHz, have limited bandwidth and are insufficient to fulfill the high data throughput demands of several applications. Millimeter-wave (mmWave) networks, operating between 30 GHz …

Building Trustable Methods For Group Recommendations: Advancing Fairness And Robustness Across Domains, Siva Likitha Valluru

Theses and Dissertations

On the internet, where the number of available choices for information is exponentially growing, there is a need to prioritize and deliver relevant results to users efficiently, on demand. Recommendation systems (RSs) address that need by searching through and filtering large amounts of dynamically generated information and providing users with recommendations tailored to them. These systems have primarily focused on (1) single-user models, where recommendations are tailored towards a specific individual, or (2) single-item models, where items are recommended based on a broader appeal to users and similarities in item metadata, in the past. In many real-world scenarios, however, recommendation …

Hallucinations In Large Foundation Models: Characterization, Quantification, Detection, Avoidance, And Mitigation, Vipula Rawte

Theses and Dissertations

Deception is an inherent aspect of social interactions, with research indicating that most people engage in deceptive behavior at least once or twice daily . In parallel, advances in artificial intelligence have led to machines exhibiting deceptive tendencies. These deceptions can be categorized into two types: unintended and intentional. Unintended deceptions - often referred to as hallucinations - occur when generative AI systems produce plausible and convincing narratives yet are factually inaccurate. This phenomenon primarily results from the systems' architectural design, extensive parametric memory, and reliance on statistical assumptions. In this thesis, we provide a comprehensive discussion on the characterization, …

Enhanced Vapour Detection Through Electrospun Metal-Oxide Nanofibers, Sri Varshini K Ms

Theses and Dissertations

The objective of this work is to fabricate metal oxide-based nanofibers, which helps in monitoring indoor air quality, which is deteriorating due to VOCs like Formaldehyde, Toluene, and Ammonia, commonly found in paints, plywood, and adhesives. Prolonged exposure to these VOCs can affect the respiratory and nervous system. They are classified as carcinogens by the IARC.

Based on the analysis, this work was framed to deposit Fe2O3, NiO, and ZnO/Fe2O3 as well ZnO/NiO and Fe2O3/NiO nanofibers on the glass substrate through horizontal electrospinning method. The obtained nanofibers are vacuum annealed at 50 ℃ for 30 mins to remove the water …

Impact Of Stochastic Travel Times On The Military Port Selection Problem: A Stochastic Programming Approach, William M. Titus

Theses and Dissertations

This research models and analyzes the impact of stochastic travel times on port selection during a large-scale mobilization of equipment from continental United States installations to deployment locations using sealift ships. A stochastic mixed-integer programming model is developed to minimize the average arrival time of equipment into theater. The model is solved using Sample Average Approximation. In the first stage, the model selects ports to open and assigns installations, equipment, and ships to open ports. In the second stage, travel times are realized, and equipment is assigned to specific ships that are scheduled to depart. Results show that the marginal …

Advancing Defense Software Cost Estimation Through Regression, Probabilistic, And Machine Learning Models, Stephen D. Chatterton

Theses and Dissertations

Accurately estimating software costs is critical for effective project management within the Department of Defense (DoD), where early decisions shape resource allocation and risk management. This work evaluates regression-based Cost Estimating Relationships (CERs), probabilistic models, and machine learning techniques to address limitations of traditional estimation methods. Using records from two DoD repositories, the analysis applied Ordinary Least Squares (OLS) regression, Multinomial Logistic Regression (MLR), Random Forest, and neural networks to model and classify software costs, with key predictors including Source Lines of Code (SLOC), Equivalent Source Lines of Code (ESLOC), and programming hours. The findings highlight strengths and trade-offs of …

Analyzing U.S. Army Recruiter Productivity Through Cohort Differentiation And Behavioral Tendency Composition, Mary M. Bates

Theses and Dissertations

This research analyzes differences among aggregate achievements of U.S. Army recruiting cohorts, determines which behavioral tendencies are indicative of performance level, and investigates aggregate behavioral composition with cohort achievement. Analyses require implementation of OLS regression, ANOVA, Tukey’s Test, Mann-Whitney U test, Holm-Bonferroni adjustment, XGBoost decision tree, logistic regression, and the Kolmogorov-Smirnov test. The results show insignificant achievement differences among cohorts and weak yet prevalent abilities of select measures of behavioral tendencies to indicate recruiter performance.

Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente

Theses and Dissertations

Cyber competition and conflict remain an enduring concern for the Department of Defense (DoD). Positive control of cyberspace is crucial across the vast diversity of military operations and supporting activities. Military members play an important role in cyber prevention, detection, and remediation, but most receive relatively little training outside of the annual Cyber Awareness Challenge. Particular career fields within the DoD may benefit from specialized training in cybersecurity, in particular the civil engineering (CE) community supporting critical infrastructure protection. Prior research has suggested that game-based learning (GBL) can be beneficial for teaching cyber concepts.

Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii

Theses and Dissertations

The goal of this research is to gain insight into how players of a game learn their strategy during the course of repeated play. The study employs the Experience Weighted Attraction (EWA) model, developed by Dr. Colin F. Camerer and Dr. Teck-Hua Ho, as the foundational behavioral framework. Using historic observed strategy decisions, the parameter values that define an opponent’s learning process are updated using various inference methods.

Evaluating Learning Outcomes In A Serious Game: A Practical And Model Checking Approach, Matthew D. Douglas

Theses and Dissertations

This research introduces a novel computational framework to evaluate and predict the educational impact of serious games during development. By using finite state machines (FSM) and model-checking techniques, this study evaluates two serious games. Traditional evaluation approaches, often reliant on resource-intensive human trials, lack scalability and fail to provide early insight into the alignment of game mechanics with learning objectives. This study addresses these challenges of traditional evaluation methods.

Atmospheric Water Generation: Experimental Observations And Assembly Of An Artificial Neural Network, Houston Hoss Anderson

Theses and Dissertations

This research investigated the performance of a Tsunami T50 Vapor Compression Cycle styled Atmospheric Water Generation (AWG) machine operated under ambient conditions in Dayton, Ohio. Water yield from the device was measured volumetrically and these values are paired with respective weather data, collected from a local monitoring station, to build an Artificial Neural Network in MATLAB and JMP software. Water yield varied over the course of this study but averaged 1.2L and maxed at 5L for 4-hour operating periods. This work is part of a 3-year project; future data is needed to enhance both training and validation of the model …

Investigation Of Node Refinement Methods In Local Adaptive Kernel Based Approximation, Shelby W. Woodrum

Theses and Dissertations

This thesis explores computational efficiency and accuracy of six node refinement methods for local adaptive kernel-based approximations of solutions to the two-dimensional Poisson equation. Using an adaptive kernel-based approximation algorithm, this research investigates performance of Delaunay triangulation-based methods (shifted barycenters and edge midpoints), refinement via approximate Fekete and discrete Leja points, and a meshless predefined shift refinement method across two domains with varying complexities. Computational experiments reveal that Delaunay triangulation-based methods achieve a practical balance between accuracy and efficiency, particularly in square domains. Refinement via approximate Fekete and discrete Leja points produce accurate results but incur greater computational costs, making …

Improving Smartphone Gnss Jammer Localization With Cloud-Based Environmental Occlusion Modeling, Glenn H. Jones

Theses and Dissertations

The advancement of Global Navigation Satellite System (GNSS) technology in modern smartphones has made these devices pervasive in both civilian and military applications. Although smartphone GNSS chipsets are more susceptible to jamming and spoofing than military grade hardware, smartphone networks offer an underutilized opportunity to detect and mitigate threats to position, navigation, and timing (PNT) services essential to the Department of Defense (DoD) and civilian first responders. Traditional methods for geolocating ground-based jamming sources using smartphone GNSS often fail in environments with dense vegetation or significant occlusions, resulting in substantial localization errors.

Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson

Theses and Dissertations

The Department of Defense is committed to developing and maintaining a highly skilled workforce capable of defending the United States and associated interests abroad. Digital badging systems, a form of micro-credentialing, offer a way to record service member competencies. By providing decision-makers with granular data, this technology could augment the military’s development of a highly skilled workforce, especially in technical career fields including cyber operations. Mixed-method data from thirty-six participants suggest that establishing a digital badging program could increase deterrence and operational effectiveness.

A Multi-Objective Reinforcement Learning Framework For Title Autonomous On-Orbit Inspections, Austin C. Reynolds

Theses and Dissertations

The rapidly evolving landscape of space operations necessitates dynamic and autonomous systems to address complex challenges such as Resident Space Object (RSO) inspections. This research explores the application of a Multi-Objective Reinforcement Learning (MORL) framework to rendezvous and proximity operations (RPO), enabling agents to balance conflicting objectives like time efficiency, fuel conservation, and information gain. Unlike traditional reinforcement learning, MORL allows dynamic reweighting of objectives without retraining, offering adaptability and efficiency in multi-objective environments. The study demonstrates MORL's capabilities through custom 2D and 3D simulations of Hill-Clohessy-Wiltshire (HCW) environments and comparing its performance to traditional RL in RPO scenarios. Tasks …

Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty

Theses and Dissertations

This thesis addresses the challenge of generating optimized UAV waypoints for complete coverage of complex 3D environments, utilizing graph-based computational techniques. The proposed framework replaces computationally intensive steps—triangulation and three-coloring—within the Vantage Waypoint Set Generation Algorithm (VWSGA) pipeline with Graph Neural Networks (GNNs). By learning structural patterns, the GNN achieves scalable and robust triangulation and node classification, enabling enhanced coverage planning in irregular geometries. A novel penalty mechanism ensures alignment with graph structure during adjacency prediction. Experimental results demonstrate the effectiveness of GNNs in balancing accuracy, computational efficiency, and adaptability, advancing UAV coverage optimization.

Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros

Theses and Dissertations

Classification “flickering,” where the classification of an object changes inconsistently between consecutive video frames, remains a persistent issue in modern object classification algorithms. This problem undermines the reliability of autonomous vision systems and poses significant risks in high-stakes applications such as autonomous vehicles. This thesis explores the use of response surface methodology, a statistical design of experiments technique, to optimize hyperparameters across three object classification pipelines. The first pipeline combines YOLOv8 with SORT to establish a benchmark. The second integrates a Bayesian back-end, while the third employs an exponential smoothing back-end. Hyperparameter tuning was conducted using a two-step process: an …

Tracking News Narratives: Topic Modeling, Sentiment, And Media Coverage Patterns, Alexandria G. Lai

Theses and Dissertations

This study introduces a novel content-driven influence measurement framework, built around a custom influence formula that integrates Non-negative Matrix Factorization (NMF) topic modeling, sentiment analysis, and influence metrics to analyze media narratives over time. Applied to news coverage of the 2020 U.S. presidential election and the COVID-19 pandemic, the framework identifies key topics, sentiment patterns, and influential sources. Results demonstrate its ability to distinguish between transient political controversies and sustained public health discourse while capturing shifts in media influence. While effective, refinements in topic separation, sentiment analysis, and temporal weighting could enhance adaptability. This study highlights the novel influence formula …