Digital Commons Network^™

Student Programming Behavior With And Without Phone Notification Suppression, Gavin T. Eddington

All Graduate Theses and Dissertations, Fall 2023 to Present

Many students work on programming assignments while receiving notifications from their phones, such as text messages or social media alerts. These notifications can interrupt focus and make it harder to stay engaged with a task. This study examines whether silencing phone notifications helps students stay more focused while programming. 

We collected data from students in an introductory computer science course while they worked on programming assignments. Students completed some assignments with notifications silenced and others without. We measured their activity using software that records typing behavior and identifies when students take long pauses, which can indicate interruptions or loss of …

Large Language Models For Introductory Computer Science Education: Content Generation, Intelligent Tutoring, And Learner Modeling, Muhammad Fawad Akbar Khan

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation studies how artificial intelligence, especially large language models such as GPT, can help students learn introductory computer programming when the models are used inside a carefully designed learning system. Instead of focusing on AI as a standalone tool, the dissertation follows a connected story: generating learning resources, building a tutoring platform, running a user study, and then analyzing how students behave while they program.

The work first uses prompt engineering to create a large collection of 11,700 Python exercises aligned with introductory computer science topics. Students and instructors then evaluate these exercises to check whether they are clear, …

Assessing Geomorphic Change From Large Wood Additions In An Intensively Monitored Watershed, Alexander Walt

All Graduate Theses and Dissertations, Fall 2023 to Present

Rivers across the American West are under stress. Over time, human activities have decreased riparian vegetation and removed naturally occurring large wood from fallen trees and beaver dams. Overgrazing, wetland drainage, and artificial barriers like levees and berms have further changed these ecosystems, leaving behind simple channels that lack varied habitat for aquatic species.

Efforts to restore rivers have made some progress, but many projects are small in scale and focus more on reshaping the river rather than restoring the natural processes that keep it healthy. This study evaluates a different approach—Low-Tech Process-Based Restoration (LTPBR)—which works with nature to rebuild …

St-Fmformer: An Autoregressive Generation Framework For Scientific Ensemble Data Predictions, Md Robiul Islam

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding how physical systems change over time is important in areas such as weather prediction, fluid dynamics, and environmental science. However, accurately predicting future behavior is difficult because these systems are complex and constantly evolving. 

This research develops a deep learning approach to predict how such systems evolve over time. The model learns patterns from past observations and uses them to generate future states step by step. This provides a faster alternative to traditional simulation methods while maintaining strong predictive performance. 

The proposed method focuses on improving the consistency of predictions over time and is designed to work across different …

Cannabis: A Model Plant To Study Light-Mediated Reproductive Development, Madigan J.H. Eckels

All Graduate Theses and Dissertations, Fall 2023 to Present

Cannabis (Cannabis sativa) is a plant of increasing importance for research, medical, recreational, industrial, and agricultural purposes, but a strict legal landscape means that it was chronically understudied. Now that research institutions can utilize this plant for study, we require updated terminology to describe various cannabis flowering types that occur under ideal and non-ideal growing conditions. We introduce new flowering terminology and describe them in text and with photographs.

Plants use light as a resource to power photosynthesis, and a greater total sum of photons generally increases yield. In controlled environment agriculture where growers utilize light fixtures instead …

A Survey On Digital Reading Materials And Personal Study Of Christian Religious Texts, Teancum Price

All Graduate Theses and Dissertations, Fall 2023 to Present

Religion, including reading from religious texts such as scriptures, are a part of the daily lives of many people. Modern technology has influenced the way that this religious reading takes place, but its effects have not yet been studied. Existing research of the effects of technology on reading focus on topics such as reading comprehension, but the study of religious texts is often focused on achieving a religious experience, so the existing research does not capture the whole scope of these changes. In our study, we surveyed two universities (Utah State University and Abilene Christian University) to ask individuals how …

An Alternative Representation For Temporal Json, Bishal Sarkar

All Graduate Theses and Dissertations, Fall 2023 to Present

JavaScript Object Notation (JSON) is a common format for representing and exchanging data on the web. Most systems only keep the current version of a JSON document, even though, in many situations, it is also important to know how that document changed over time. For example, an application might need to answer questions such as “What did this record look like last week?” or “How has this list grown over the past year?” A simple way to keep this history is to store a full copy of the document every time it changes, but this quickly becomes wasteful, most of …

Scalable Roof Polygon Extraction And Geometric Characterization From Remotely-Sensed Data For Snow Load Assessment, Jashon Newlun

All Graduate Theses and Dissertations, Fall 2023 to Present

Heavy snow accumulation on rooftops is a serious structural risk in cold climates, and understanding how much snow builds up on different types of roofs is essential for safe building design. Currently, most data on roof snow loads comes from small, labor-intensive field surveys that cover only a handful of buildings at a time. This results in far too few measurements of buildings to draw confident conclusions about how snow behaves across communities. This thesis develops and demonstrates a new automated approach for measuring roof snow accumulation and extracting key building characteristics across thousands of buildings at once using airborne …

Gradient Based Optimization Methods For Robust Learning And Biomedical Signal Modeling, Jarrod Mau

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores how modern artificial intelligence techniques can be used to better understand complex biological data. Specifically, it develops new machine learning based methods and applies them to two important biomedical problems: analyzing brain signals and studying protein behavior.

The first part of the work introduces a new machine learning approach designed to improve how computers classify structured data. Traditional neural networks are powerful but can sometimes generalize poorly. This research proposes a method that combines the flexibility of neural networks with the reliability of ensemble techniques, leading to more robust and accurate predictions across different types of datasets. …

Developing Fragility Curves For Steel Special Moment-Resisting Frames In Utah, Olivia P. Murphy

All Graduate Theses and Dissertations, Fall 2023 to Present

Major seismic events along the Wasatch Fault represent a serious threat to Utah's residents and built environment. Engineers currently rely on a statistical approach that calculates earthquake ground shaking based on probabilities of various events occurring over extended time periods. This method informs how strong new buildings must be constructed. However, many structural engineering professionals in Utah worry that this probability-based approach may significantly underestimate the actual ground shaking compared to an alternative method that examines the largest possible earthquake a fault can generate. The substantial differences between the two analytical approaches raise fundamental concerns about whether existing construction standards …

Cultivating Excellence: The Relationship Between Educator Professional Development And Adult Student Success In Arkansas, Marsha Taylor

Graduate Theses and Dissertations

This quantitative study will investigate the relationship between Arkansas adult education teachers' engagement in professional development and their average classroom achievement, as indicated by students' measurable skill gains (MSGs). The study, informed by Dreyfus's (2004) model of skill acquisition, will examine the impact of the quantity of professional development hours, teachers' employment status (full-time or part-time), and the focus of professional development (content versus other domains) on classroom outcomes. Survey data on professional development involvement will be collected from Arkansas adult education teachers using a cross-sectional, regression-based design and subsequently linked to achievement data from the LiteracyPro system. Five hypotheses …

Nanoscale Electrochemical Measurements: Theory, Instrumentation, And Operation, Kamsy Lerae Anderson

Graduate Theses and Dissertations

Electrochemical processes underpin many modern technologies (e.g., batteries, fuel cells, and corrosion protection systems), and because these processes are often controlled by interactions that occur at the nanoscale, gaining insight into nanoscale electrochemical behavior is essential to advancing these technologies. Nanoscale electrochemical imaging probes measure electrochemical activity with nanoscale spatial resolution and have enabled researchers to gain new insights into diverse experimental systems. However, limitations remain, including a lack of methods for direct quantitative interpretation, the influence of atmospheric conditions on experimental reproducibility and stability, and a knowledge barrier resulting from limited accessible resources, all of which continue to restrict …

Age And Characteristics Of Quaternary Fluvial And Dune Deposits Along The Arkansas And Cimarron Rivers In Osage And Pawnee Counties, Oklahoma, Barry A. Duncan

Graduate Theses and Dissertations

Quaternary aeolian and fluvial deposits along the Arkansas and Cimarron Rivers in Osage and Pawnee counties of central Oklahoma, record Late Pleistocene and Holocene fluvial landscape history in the eastern Osage Plains. Sediment supply, climate variability, channel incision, and channel migration control this landscape evolution. Sediment supply of both streams is dominantly sand. Late Pleistocene and Holocene precipitation have varied with periods of drought alternating with increased precipitation and warming. The study area is near the eastern boundary of substantial aeolian activity with drought and aeolian reworking of fluvial sand more severe to the west. This study examines the sediment, …

Impaired Tunability Of Brain State And Motor Dysfunction In A Mouse Model Of Rett Syndrome, Victoria Kindler Norman

Graduate Theses and Dissertations

Rett syndrome (RTT) is a rare neurological disorder, caused by disrupted function of the MECP2 gene, resulting in impaired cognitive and motor functions. Previous studies suggest that although MECP2 has important functions throughout the body, the etiological origins of RTT-related dysfunction should be sought within the brain. However, it remains a mystery how neural population dynamics are altered in the RTT brain and how these alterations relate to motor dysfunction. Previous studies using an RTT mouse model point to abnormal correlations among firing rates of neurons. Here we hypothesize that such disrupted neural activity correlation could be caused by abnormal …

Spike-Sorting Algorithm For Neuropixel Probes, Luis David Davila

Open Access Theses & Dissertations

Modern extracellular neural probes like Neuropixels, allow for high-density extracellular neural probes to record terabytes of electrophysiological brain data. Spike sorting is the process of isolating individual neurons on this data through various clustering and filtering techniques. Modern spike sorting algorithms are held back by their need for human intervention to separate high-quality from low quality clusters, due to the volume of data that can be collected a completely automated approach is required. To address this requirement, we have developed a framework to preprocess, cluster, grade, and separate high/low quality clustering results using custom models trained on the grades. To …

Development Of A High-Throughput Framework For Studying Choice-Movement Dynamics In Freely Moving Rats Across Motivational And Pharmacological States, Atanu Giri

Open Access Theses & Dissertations

This thesis develops and applies high-throughput behavioral assays and analysis pipelines to study how external contingencies and internal state shape rodent decision-making. First, it introduces RECORD (Reward-Cost in Rodent Decision-making), a modular platform that combines 3D-printed arenas, microcontroller-based control, and closed-loop trial structure to deliver graded rewards and costs in a foraging-like environment. RECORD supports scalable data collection across multiple arenas and a software stack for parsing, databasing, and extracting spatiotemporal behavioral features and psychometric choice functions. Using this framework, the thesis quantifies how animals integrate sucrose reward magnitude with aversive light cost, revealing structured individual differences and sex-dependent movement …

First-Principles Study Of Uranium Mononitride, Roy Noe Herrera Navarro

Open Access Theses & Dissertations

This thesis presents a first-principles investigation of uranium mononitride (UN), withemphasis on the connection between uranium 5f electron correlation and defect stability. Electronic-structure calculations were performed within density functional theory using the Vienna Ab initio Simulation Package. Exchange-correlation effects were treated within the generalized gradient approximation, and a Hubbard correction was applied to the uranium 5f manifold. The adopted Hubbard parameter was motivated by first-principles linear-response analysis and considered in the broader context of DFT+DMFT descriptions of correlated uranium systems, including recent interpretations of UN as a Hund-metal system. Bulk UN was studied in unit-cell and 2 x 2 x …

Singularity-Enriched Neural Networks For Elliptic Problems In Polygonal Domains, Harshini Reddy Kodiganti

Open Access Theses & Dissertations

This thesis develops and analyzes neural-network-based solvers for the Poisson equation on polygonal domains, where re-entrant corners induce reduced solution regularity and challenge standard numerical methods.

Three neural formulations are investigated: Physics-Informed Neural Networks (PINNs), Physics-Informed Extreme Learning Machines (PIELMs), and Rank-Inspired Neural Networks (RINNs). PINNs rely on gradient-based optimization with automatic differentiation, while PIELMs employ randomly initialized hidden features with least-squares training, achieving significantly lower computational cost. RINNs extend this framework through covariance-driven orthogonalization to improve numerical conditioning and stability.

On convex polygonal domains, all three methods are benchmarked against analytical solutions. Both PIELM and RINN consistently achieve higher …

Metabolomics In The Dryland Critical Zone: The Role Of Soil Metabolites In Phosphorus Acquisition, Kalpana Kukreja

Open Access Theses & Dissertations

Earth's Critical Zone (CZ) plays a key role in sustaining life through carbon, water, and nutrient cycling from the top of the canopy to the groundwater. A key feature of critical zone research is the integration of multiple interdisciplinary techniques. An emerging set of techniques that can add to this approach is the field of metabolomics, the quantification and study of comprehensive metabolite profiles. In this dissertation, I apply metabolomics to address knowledge gaps in dryland critical zone ecosystems, specifically how soil metabolite dynamics influence biogeochemical processes, and with a particular emphasis on phosphorus (P) cycling in the Chihuahuan Desert. …

Characterization Of The Synhalokinetic Megaflap Lithofacies And Stratigraphy At Sinbad Valley, Paradox Basin, Western Colorado, Michael David Laase

Open Access Theses & Dissertations

This study documents the halokinetic evolution of the Sinbad Valley salt wall and flanking megaflap through the characterization of its lithofacies, stratigraphy, and major structures. Sinbad Valley salt wall is in the northern Paradox Basin, in western Colorado, and is the most proximal salt diapir to the Uncompahgre Uplift, the clastic sediment source for the basin. Megaflaps are recently defined structures involving early minibasin strata that are folded up to form steeply dipping "stratal panels" along the sides of steep diapirs. The Sinbad Valley megaflap crops out as a ~7.5km long and 400m wide panel of folded and deformed Pennsylvanian- …

Hinterland To Foreland Regional Transition From Basement-Involved To Thin-Skinned Deformation In The Southern U.S. Cordillera: Sierra Rica, New Mexico, Sarafina Middaugh

Open Access Theses & Dissertations

Late Cretaceous to Paleogene age contractional deformation in the southern Basin and Range province is obscured by widespread, Cenozoic extensional faulting and volcanism. As a result, debate continues about the structural style and tectonic evolution of the region. There are two end-member structural models that have been proposed for deformation: 1) high-angle reverse faulting and basement uplift and 2) thin-skinned faulting and development of a thrust belt. We undertook new geologic mapping, cross-section construction, and structural analysis in Sierra Rica, in the bootheel region of southern New Mexico, to help evaluate these hypotheses. A major thrust system, the Sierra Rica …

Plant Communities Of Arid Ephemeral Stream Streams, Biogeographic Filters, Climatic Sensitivity, Urban Impacts Across Spatial And Temporal Scales, Luis Miranda

Open Access Theses & Dissertations

Ephemeral stream networks, locally known as arroyos, are among the most ecologically significant yet least protected habitats in arid landscapes. Despite supporting a disproportionately high plant biodiversity compared to surrounding uplands, these riparian systems lack regulatory protection in most arid U.S. states and face increasing pressure from urban expansion and intensifying climate change. There is an urgent need to understand how these factors influence riparian plant communities and hinder their ability to recover, enabling effective conservation and management strategies. In this dissertation, I explored the interplay of biogeographic gradients, precipitation variability, and urbanization in shaping arroyo riparian plant communities across …

Three Essays On Workplace Ethics And Ai: Conceptualizing, Scale Development, And Validation Of Self-Value Actualization, And Ai-Enabled High-Performance Work Systems, Mansura Nusrat

Open Access Theses & Dissertations

This dissertation advances understanding of workplace ethics and responsible AI integration across three interconnected essays unified by social cognitive theory. My first essay introduces self-value actualization (SVA), a novel construct defined as the dynamic, integrated psychological process through which individuals continually strive to align and fulfill their core moral values through observable professional behavior. Drawing on social cognitive theory and emotional intelligence theory, I conceptualize SVA as a higher-order self-regulatory orientation comprising three mutually reinforcing processes: value congruence, moral reflection, and adaptive morality. I developed and validated a 9-item SVA scale across four studies using expert panels, exploratory factor analysis, …

Deep Neural Network-Gaussian Process For Housing Price Analysis, Bismark Nyarko

Open Access Theses & Dissertations

Neural networks have demonstrated remarkable predictive performance in complex, high-dimensional settings. However, their highly parameterized structure and nonlinear architecture often make them difficult to interpret, limiting formal statistical inference and principled uncertainty quantification. In contrast, Gaussian processes (GPs) provide a fully probabilistic framework with a relatively small number of hyperparameters, enabling coherent uncertainty quantification and seamless integration into hierarchical and structured statistical models. Despite their theoretical flexibility as nonparametric function approximators, standard GP formulations frequently exhibit weaker predictive performance than modern neural networks in large-scale applications.

In this work, we employ a Gaussian process covariance function designed to approximate the …

Efficient Solvers For Phase Field Models, Raymond Obeng

Open Access Theses & Dissertations

Phase field models provide a versatile framework for describing phase transitions and pattern formation in materials, enabling the study of complex phenomena such as crystallization, grain growth, and defect dynamics. Among these models, the Phase Field Crystal (PFC) equation has gained significant attention due to its ability to capture atomic-scale structures while evolving on diffusive time scales. However, the numerical solution of the PFC equation presents substantial challenges arising from its high-order nature and the large-scale, coupled linear systems generated by discretization.

In particular, standard formulations of the discretized PFC system lead to non-symmetric and often ill-conditioned linear systems, which …

A Penalty-Free Runge-Kutta Discontinuous Galerkin Method For \\[12pt] Time-Dependent Fourth-Order Partial Differential Equations, Jose Armando Perez Becerra

Open Access Theses & Dissertations

Time-dependent fourth-order partial differential equations arise in a wide range of applications in applied mathematics, physics, and engineering, including thin structure models, phase separation, and pattern formation. Their numerical approximation is challenging because the presence of fourth-order spatial derivatives typically requires high-regularity discretizations. A useful alternative is to reformulate the original problem as a coupled second-order system and approximate it by mixed discontinuous Galerkin (DG) methods.

This thesis builds upon the penalty-free mixed DG framework developed by Liu and Yin for time-dependent fourth-order problems. That framework avoids the use of interior penalty parameters, preserves the symmetry of the associated bilinear …

Computational Studies Of Alpha-Lactalbumin Binding To Perfluorodecanoic Acid, Randhal Smith Ramirez Orozco

Open Access Theses & Dissertations

This thesis investigates the structural and thermodynamic interactions between Perfluorodecanoic acid (PFDA) and bovine alpha-lactalbumin (ALAC), a critical milk protein essential for infant nutrition. The primary objectives were to computationally determine the most probable binding sites of PFDA to ALAC and to quantify the strength of these interactions alongside the structural changes they induce. This study addresses a critical gap in understanding how persistent environmental contaminants like PFDA are recruited and transported by nutritional proteins within the milk matrix. The methodology integrated high-throughput molecular docking with long-range, molecular dynamics (MD) simulations to provide a dynamic characterization of the ALAC-PFDA complex. …

Understanding Machine Learning Model Behavior Under Fairness And Privacy Constraints, David Anthony Sanchez

Open Access Theses & Dissertations

Machine learning systems deployed in high-stakes domains are increasingly expected to satisfy demands beyond predictive accuracy-including fairness across demographic groups, protection of sensitive information, and explanations that human stakeholders can inspect and trust. This thesis investigates how those demands can be met through learning frameworks that explicitly govern the relationship between data and models, arguing that trustworthiness is a design problem rather than a post hoc correction. The thesis is organized around three studies, each targeting a distinct point of data-facing control. The first develops CondFairGen, a fairness-aware conditional generator for tabular data that improves subgroup equity by dynamically reweighting …

Eocene To Oligocene Continental Arc Migration In Ecuador And The Origin Of Spatiotemporal Trends In Zircon Lu-Hf Radiogenic Isotopes, Muriel M. Sandoval Vazquez

Open Access Theses & Dissertations

The modern continental arc system in Ecuador is well understood but less is known about arc systems in the past, hindering our understanding of the tectonic evolution of the northern Andes. The Macuchi arc, located in the Western Cordillera of Ecuador, was thought to be the main continental arc system during the Eocene, but new zircon U-Pb LA-ICPMS dates from intrusive arc rocks in central Ecuador indicate a more complex history. During the mid-Eocene, the footprint of arc magmatism broadened and expanded into the Inter-Andean Valley and Eastern Cordillera. During the late Eocene to Oligocene, the arc migrated back toward …

A Numerical Method For The Phase Field Crystal Model, Patrick Ameyaw Tabiri

Open Access Theses & Dissertations

The Phase Field Crystal (PFC) model is a continuum-based framework used to study theevolution of crystalline materials while preserving microscopic structural features over diffusive time scales. It captures complex phenomena such as phase transitions, defect dynamics, and microstructure formation via a nonlinear sixth-order partial differential equation derived from a free-energy functional. In this context, the phrase "sixth-order" indicates that the highest spatial derivative appearing in the equation is of order six. In this work, we study the mathematical formulation and numerical approximation of solutions to the PFC model. Due to the high-order spatial derivatives and nonlinearity in the governing partial …