Physical Sciences and Mathematics Commons^™

Wildfire And Fuel Treatments In Utah: A Practical Guide And Outcomes From An Oak-Maple Ecosystem, Annie E. Prescott

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildfire is a natural part of Utah’s landscapes, but as wildfires become more extreme and communities continue to expand into wildland areas, it is increasingly important to understand how wildfires behave and how their negative effects can be mitigated. One way to reduce wildfire risks and protect life, property, and ecosystem services is through fuel treatments — actions that reduce the amount and arrangement of flammable vegetation through mechanical removal, wildland fire, or other methods.

Information on wildfire behavior and fuel treatments is abundant but often scattered, ecosystem-specific and difficult to access. In addition, most research in the Western U.S. …

Toward Optimizing Fruit Production In Utah Soils: Apple Salt Tolerance Screening And Tart Cherry Fertility Management, Kaitlin Dabbs

All Graduate Theses and Dissertations, Fall 2023 to Present

Fruit production in Utah can be challenging due to local climate and soil conditions. Tart cherries and apples are major fruit crops in Utah, and improvements to these cropping systems would be beneficial for farmers and local consumers. Two experiments were conducted to evaluate desired improvements for these crops. The first study looked for salt tolerance in ten new apple rootstocks. Salty soils can be found throughout the state limiting the land suitable for apple production. When grown in salty soils apple trees often have reduced growth and production potential. A specialized drip irrigation system was constructed in a greenhouse …

Fast Computation And Model Order Reduction Of The Friction Stir Welding Process With Pod-Deim, Joshua Kay, Zilong Song

Mathematics and Statistics Student Research and Class Projects

Friction stir welding (FSW) is a solid-state manufacturing process widely used in joining aluminum and other metal workpieces. The FSW process can be modeled by a coupled system of non-Newtonian Navier–Stokes and heat-transfer equations. However, solving this non-linear system with high accuracy requires significant computational power. This work refines the system by introducing corrected coefficients and new treatments for boundary conditions near the tool. Then, model order reduction, including the Proper Orthogonal Decomposition (POD) and Discrete Empirical Interpolation Method (DEIM), is applied to efficiently solve the FSW system in a low-dimensional space. To enhance accuracy and effectiveness, two novel treatments …

Building Wellbeing Through Nature And Adventure For Justice-Involved Youth, Lewis Kogan, Meagan Ricks, Janna Coulter, Miranda Margetts, Lori Butterfield, Ben Ukoh-Eke

Outcomes and Impact Quarterly

Justice-involved youth often experience elevated levels of stress, mental health challenges, and reduced access to positive developmental opportunities. A nature- and adventure-based youth development program designed to strengthen resilience, self-efficacy, nature-connectedness, and well-being among justice-involved youth was piloted in 2025 to address these challenges. Pilot findings indicated improvements in resilience, self-efficacy, hopefulness, and connectedness to nature.

Citizen Science As A Long-Term Environmental Baseline: Assessing Impacts Of A Small Dam Removal In Montana, Usa, Bethany Blakey, Natalie Bursztyn

Watershed Sciences Student Research

As dam removals increase in frequency across Europe and North America, most research has focused on the impact of larger dam removals, despite the removal of small dams being much more common. There are hundreds of small dams in Montana, USA, and this research investigates impacts on stream ecology and morphology using citizen science data collected over eight years spanning before and after a 2020 small dam removal in Rattlesnake Creek. We analyzed pebble count grain size distributions and aquatic macroinvertebrate biotic indices from 2017 to 2024 to assess changes in sediment transport and macroinvertebrate population as well as evaluate …

Is Hanpp A Stressor Of Bird Species Richness?, Kaeli Mueller, Gustavo Ovando-Montejo, Christopher Lant

Environment and Society Student Research

One of the leading drivers of biodiversity loss is land use change due to the ever-increasing demand for food, livestock feed, biofiber, and biofuel. The concept of human appropriation of net primary production (HANPP) was developed to measure the impact that humans have on the environment by accounting for biomass removed from the environment in the form of crops, timber, and grazing. The species–energy hypothesis is an established ecological hypothesis postulating that greater species diversity is correlated with higher levels of energy from plant productivity; this can be measured as net primary production minus harvested HANPP or NPP_ecological. …

Ground To Roof Snow Load Ratio (Gr) Data Release, Brennan Bean, Cooper Nelson, Jesse Wheeler, Scout Jarman, Salam Adil Al-Rubaye, Marc Maguire

Browse all Datasets

This data release provides historical ground-to-roof snow load ratio (GR) datasets used for snow load research and model development. The release includes original referenced datasets, cleaned country specific datasets, and a master dataset that combines Canadian and United States datasets into a standardized format for research and engineering applications.

Data Repository For "The Influence Of Hydrologic And Salinity Conditions On Drought-Tolerant Great Salt Lake Wetland Plant Species" By Samantha Kurkowski And Karin Kettenring, Samantha Kurkowski

Browse all Datasets

This greenhouse experiment investigated the effects of salinity (3 levels) and hydrologic condition (5 levels),  on the cover and biomass of a single mix of 11 native drought- and salt-tolerant species.

Criticality In A Heterogeneous Neutron Transport Rod Model, Samuel Kaleb Crowford

All Graduate Reports and Creative Projects, Fall 2023 to Present

This work studies the stochastic behavior of neutron populations in a one-dimensional rod model using Monte Carlo simulation. The first part of this project reproduces the computational results of Dumonteil, Horton, Kyprianou, and Zoia (2025) by independently implementing the Monte Carlo algorithm described in their article, with the asymptotic behavior of the first moment analyzed in relation to the dominant eigenvalue and adjoint eigenfunction of the neutron transport operator. The model is then extended to a heterogeneous setting by introducing a central region where fission is suppressed. A global expectation over initial positions and directions is used to estimate the …

Magnetic Properties And Ultrafast Spin Dynamics In Quantum Materials, Trung Kien Mac

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern electronics mostly work by moving electric charge, which generates heat and hence wastes energy, especially as devices become smaller and faster. An alternative is to use the spin of electrons (a tiny magnetic property) and related “valley” states in certain atomically thin materials to store and process information more efficiently. This thesis explores how magnetism and spin behavior can be created and measured in a family of ultra-thin materials known as van der Waals quantum materials. Three kinds of materials are studied. First, atomically thin semiconductors are shaped into narrow nanoribbons. Because of their narrow size, their edges can …

A Social-Ecological Systems Approach To Integrating Outdoor Recreation And Black Bear Management: A Michigan Case Study, Megan Dorothy Stone

All Graduate Theses and Dissertations, Fall 2023 to Present

Outdoor recreation sits at the intersection of social and ecological systems, representing one of the primary ways in which humans directly interact with their natural environment. As a result, outdoor recreation management must balance providing high-quality outdoor recreation opportunities while simultaneously protecting natural resources in protected areas. Balancing these concurrent management goals has become more challenging in recent years as participation in outdoor recreation increases. This can lead to numerous negative impacts on the environment, such as wildlife disturbance. In this study, I explore the challenges and opportunities for collaboration between managers of outdoor recreation and black bears in the …

Viability Assessment Of Bovine Embryos: A Public Dataset And Deep Learning Baselines, Erfan Khayyati

All Graduate Theses and Dissertations, Fall 2023 to Present

Improving the success rates of cattle breeding is essential for sustainable agriculture, global food security, and high-quality livestock production. Currently, determining whether a lab-grown bovine embryo is healthy enough for a successful pregnancy requires highly trained experts to manually evaluate days of continuous time-lapse video footage. This process is not only incredibly time-consuming but also highly subjective; human reviewers often suffer from visual fatigue when tracking subtle, microscopic cellular changes over a seven-day period, leading to significant disagreement among even top experts on an embryo’s true potential. Furthermore, assessing bovine embryos is notoriously difficult due to their dark, lipid-dense cellular …

Integrating Hydrology And Human Water Footprints: A Case Study Of The Great Salt Lake Basin, Rachel Lynne Seeley

All Graduate Theses and Dissertations, Fall 2023 to Present

Global demand for water is outrunning supply, and how we account for water is part of the problem. Many water management frameworks are based solely on how much physical water moves through rivers, aquifers, and other water bodies, missing the water that is embedded in food and other products a region produces and exports. This study developed a new, generalizable water accounting framework that incorporates this “hidden water,” known as virtual water, into water management and applied the framework in the Great Salt Lake Basin.

The Great Salt Lake is shrinking, and an often-overlooked driver of the lake’s decline is …

Mining Time Series Shapelets And Association Rules For Solar Flare Prediction, Drew Watson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are the largest explosions in the solar system; they are caused by changes in the Sun’s magnetic field. Strong solar flares can disrupt power systems, damage satellites, and interfere with radio communication, so improving flare prediction is important. This thesis develops a way to predict severe solar flares while also helping researchers understand why those predictions are made. The approach looks for short patterns in solar magnetic field data that are linked to future flare activity. It then studies how these patterns appear together and in what order they happen over time. By doing this, the research not …

Spatial Prediction Under Uncertainty: Methodological And Computational Advances In Bayesian Maximum Entropy, Kinspride K. Duah

All Graduate Theses and Dissertations, Fall 2023 to Present

Environmental decisions such as infrastructure design, water management, and snow load estimation depend on spatial data that are often incomplete or uncertain. In many cases, measurements are not exact values but ranges, reflecting limitations in data collection methods. Traditional mapping techniques typically simplify these uncertain measurements, which can lead to less accurate predictions. This dissertation introduces improved statistical tools for making spatial predictions when data are uncertain or partially known. By utilizing a framework called Bayesian Maximum Entropy (BME), this research demonstrates how exact measurements and range-based data can be combined in a mathematically consistent way. The work demonstrates that …

Learning Latent Structure In High-Dimensional Data Via Geometry And Graphs, Haozhe Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern datasets often contain many measured variables for each observation, such as gene-expression levels, brain activity signals, or features in tabular data. These data are also often noisy, meaning that useful patterns are mixed with measurement error or irrelevant variation. Although such datasets can appear complex, they are frequently represented by simpler hidden structures, such as trajectories, clusters, or relationships between observations. This dissertation develops methods for uncovering these hidden structures by learning geometric and graph-based representations directly from data. The first part introduces Functional Information Geometry, which represents local patterns in high-dimensional data using functional features and constructs a …

Unifying And Expanding Global And Local Variable Importance Methods For Explainable Machine Learning, Kelvyn K. Bladen

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning methods are powerful analytical tools used across all scientific disciplines and many other fields of investigation for prediction and inference from diverse data sources. Despite their broad applicability, machine learning methods are often highly complex and difficult to interpret. Developing a greater understanding of which variables most influence a response is essential for increasing the interpretability of these models and supporting informed decision-making. This research focuses on improving how we evaluate the importance of these variables.

One common approach is to shuffle the values of a variable and see how much the model accuracy gets worse. Another approach …

Hidden Symmetries And Higher Dimensional Rotating Black Holes, Luis Fernando Temoche Hurtado

All Graduate Theses and Dissertations, Fall 2023 to Present

A standard approach to probing the dynamics of a physical system is to expose it to an external perturbation—such as an incident wave—and analyze its response after the interaction. Mathematically, this procedure is formulated in terms of differential equations governing the evolution of the perturbation.

In the context of black hole physics, an analogous strategy can be employed by studying the response of the event horizon to external perturbations. The differential equations describing these interactions are often invariant under nontrivial transformations, revealing hidden symmetries that help explain distinctive features of higher-dimensional black holes.

By exploiting these hidden symmetries in the …

The Role Of Heterogeneity In Earthquake Rupture Dynamics: Insights From Friction Experiments On A 1-Meter Laboratory Fault, Alejandro Aguilar

All Graduate Theses and Dissertations, Fall 2023 to Present

Tectonic faults are made of many kinds of rocks and minerals, creating natural weak and strong zones along faults where earthquakes occur. These differences can affect how faults move, including whether slip occurs slowly and quietly or suddenly and destructively during an earthquake. However, it is not fully understood how these variations influence when earthquakes start, how fast they spread, and whether they stop or grow into large ruptures. This research studies how different fault materials interact during earthquake slip. Using a large laboratory machine that simulates fault movement under realistic conditions, we recreate earthquake processes and closely measure how …

Counterfactual Explanations For Time Series Classification: From Localized Perturbations To Realistic Generation, Peiyu Li

All Graduate Theses and Dissertations, Fall 2023 to Present

Machine learning models are often used to classify signals collected over time, such as heart rhythms, movement recordings, industrial sensor measurements, and scientific observations. These models can be accurate, but they are often difficult to understand. Users may need to know not only what a model predicted, but also what would have needed to change for the model to reach a different decision.

This dissertation studies counterfactual explanations for time series data. A counterfactual explanation answers a “what-if” question. For example, if a model classifies a signal as one activity instead of another, the explanation shows how the signal would …

Investigation Of Yy Production Methods For Invasive Channel Catfish Ictalurus Punctatus Population Management, Andrew M. Wisniewski

All Graduate Theses and Dissertations, Fall 2023 to Present

Aquatic invasive species are one of the leading causes of native fish declines in the Southwestern United States. Channel Catfish Ictalurus punctatus were introduced into the Colorado River Basin, including the San Juan River, New Mexico, Colorado, and Utah for fishery enhancement during the 20th century. This introduction has contributed to the decline of native species and despite intensive mechanical removal efforts, the Channel Catfish population has persisted in the San Juan River. The Trojan Sex Chromosome approach is a promising eradication strategy that proposes the production and release of Trojan sex chromosome carriers (YY) to skew a targeted population’s …

Mapler: An R Package For Estimating The Impact Of Climate Change On Maple Syrup Production, Matthew T. White

All Graduate Theses and Dissertations, Fall 2023 to Present

Successful maple sap tapping depends on the freeze/thaw cycle (i.e., temperatures fluctuating above/below freezing) during the winter and spring. Climate change threatens to alter the timing and duration of the tapping season. This necessitates research into how maple sap tapping will be impacted by climate change in order to help maple syrup producers prepare for the future. We define a sap day as a day where the freeze/thaw cycle occurred. Using information climate scientists use to predict future temperatures, we calculate how many sap days could occur each year. We develop software to analyze these sap day calculations to determine …

Fostering Safe Space For Children In Online Navigation And Parent-Child Interactions, Rizu Paudel

All Graduate Theses and Dissertations, Fall 2023 to Present

As children and teenagers spend increasingly more time online, digital devices have become a major source of family friction. Disagreements frequently arise over privacy boundaries, and online activities. When these conflicts are unresolved, they often lead to broken trust and secretive behavior, leaving children vulnerable to digital harms like cyberbullying, toxic content, or account hacking. Therefore, it is important to create a safe and open environment for children where in order for them to share their feelings with parents. This dissertation investigates the human and technological dynamics of parent-child interactions, developing new ways to support collaborative conflict resolution and online …

A Data-Driven Nutrient Density Scoring Framework For Beef Using Principal Component Analysis, Teja Vuppala

All Graduate Theses and Dissertations, Fall 2023 to Present

Beef is one of the most nutrient-rich foods in the human diet, providing high-quality protein, iron, omega-3 fatty acids, B vitamins, and a wide range of other compounds important to health. However, current nutrition scoring systems used on food labels were designed to compare different foods to one another — for example, beef versus broccoli — and do not work well for judging the nutritional quality of different beef samples relative to each other. A grass-fed steak and a conventionally-finished steak can carry nearly identical Nutrition Facts panels while differing substantially in their content of omega-3 fatty acids, vitamins, and …

Causal Discovery In Photospheric Magnetic Field Time Series For Interpretable Solar Flare Prediction, Nathan W. Nelson

All Graduate Theses and Dissertations, Fall 2023 to Present

Solar flares are capable of damaging many valuable resources, including satellites, power grids, and even human lives. Being able to predict solar flares can allow for proactive measures to be taken that can prevent that damage. Many new deep learning methods for predicting solar flares have shown promise in this task, but the decisions they make are harder to explain to humans. This makes understanding why these models make mistakes difficult, which in turn makes fixing and maintaining them more challenging. We test a recent deep learning method that helps discover relationships between different measurements of the Sun as they …

Understanding And Predicting Precipitation Characteristics In The United States Through Machine Learning, Numerical Modeling And Measurements, Cody Luther Ratterman

All Graduate Theses and Dissertations, Fall 2023 to Present

Precipitation is one of the most important yet uncertain variables in the climate system. It varies dramatically in terms of timing, accumulation, rate, and phase, depending on location, season, circulation patterns, and atmospheric conditions. Precipitation forecasts, especially snowfall, are essential to supporting drought mitigation and water management in the Intermountain West. Because rainfall and snowfall lead to opposite effects on snowpack, accurately partitioning rain and snow is important to estimate snowpack levels, winter recreation, mountain ecosystems and runoff. The research findings in this dissertation have advanced the understanding and prediction of precipitation and snowpack in the U.S. by addressing the …

Dbssnet: Dual-Branch Spectral-Spatial Network With Data-Driven And Knowledge-Guided Band Selection For Uav Hyperspectral Wheat Rust Detection, Subin Kim

All Graduate Theses and Dissertations, Fall 2023 to Present

Wheat rust is a serious plant disease that can reduce crop yield and quality. In practice, the disease is often noticed only after visible symptoms appear, when some damage may already be difficult to reverse. This thesis studies whether drone-based imaging can help detect wheat rust earlier and more reliably in field environments. 

Unlike an ordinary color photograph, a hyperspectral image records reflected light at many narrow wavelengths. These measurements can reveal useful information about plant condition, but they are also high dimensional, noisy, and difficult to analyze when only a limited number of labeled field samples are available. To …

Visualizing The Phase Space Of Two Interacting Spherical Magnets Via Lagrangian Descriptors, Matthew Pontius

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis studies the motion of one spherical magnet sliding on another fixed spherical magnet. Although the setup is simple, the resulting motion can range from regular and predictable to chaotic as the energy increases. To understand this behavior, mathematical tools are used to visualize how all possible motions are organized in phase space. These methods reveal patterns such as stable regions, repeating motions, and chaotic trajectories. The results show how order and chaos coexist in this system and provide insight into how small changes in the initial conditions can lead to very different outcomes.

Pesticide Dissipation In Agroecosystems – Exploring Pesticide Fate And Transport Mechanisms To Protect Alternative Pollinators, Calvin Luu

All Graduate Theses and Dissertations, Fall 2023 to Present

Honey bees are the most well-known pollinators, but there exists thousands of other bee species that contribute to pollination; one such group of bees are the solitary bees. True to their namesake, solitary bees do not live in hives like honey bees. Some solitary bees, like alfalfa leafcutting bees (ALCB) and blue orchard bees, can pollinate crops more efficiently than honey bees. However, since they live solitary lives, pesticide exposure is much more harmful to their overall population. If one honey bee dies from pesticide exposure, the hive can still survive, and the queen bee will continue producing offspring. If …

Applications Of Machine Learning To Gas Plume Analysis In Longwave Infrared Hyperspectral Images, Scout C. Jarman

All Graduate Theses and Dissertations, Fall 2023 to Present

Each pixel from a hyperspectral camera measures the intensity of light over a continuous range of wavelengths, which is in contrast to traditional color cameras, which just measure the intensity of red, green, and blue wavelengths of light. Longwave infrared hyperspectral images can be used to detect gases from a distance by measuring how different materials emit and absorb heat. This makes them useful for applications such as monitoring industrial emissions or locating hazardous gas leaks. In practice, however, gas signatures in these hyperspectral images are often weak and easily obscured by variations in the background scene, making reliable identification …