Physical Sciences and Mathematics Commons^™

The Influence Of Solar Energy Infrastructure On Pollinators And Crops, Deisy Garcia

Theses and Dissertations

As the human population grows, demand for agriculture and renewable energy rises. Landuse competition is caused by the expansion of area-intensive solar photovoltaic (PV) facilities. While solar energy is crucial to mitigate climate change, it presents challenges related to land availability and effects on habitats for essential pollinators. In Chapter II, a spatial analysis was conducted to examine the relationship between solar panel density and pollinator diversity, exploring regions with the least interaction between these two factors. Our findings can inform solar siting and management strategies at landscape levels, in conjunction with field studies, that both mitigate potential interaction and …

Implementing And Evaluating An Ai-Powered Visual Decision Support System To Improve Antibiotic Usage Among Physicians With A Built-In Early Warning System, Akua Sekyiwaa Osei-Nkwantabisa

Theses and Dissertations

The widespread misuse and excessive prescription of antibiotics have played a pivotal role in the emergence and proliferation of antibiotic-resistant bacteria, posing a critical global public health crisis. Addressing this challenge necessitates innovative solutions that enhance antimicrobial stewardship. This study presents the development and implementation of a visual decision support system designed to monitor and optimize antibiotic usage among healthcare providers. The proposed system integrates advanced machine learning algorithms with real-time data analytics to provide a dynamic, evidence-based decision support tool. Specifically, a neural network model was developed after evaluating multiple machine learning approaches, including Random Forest, Logistic Regression and …

Spatio-Temporal Pattern Of Nutrient Dynamics In The South Texas Coastal Watershed, Mohammad Sadman Alam

Theses and Dissertations

Eutrophication threatens aquatic ecosystems by altering productivity through excessive nutrient inputs. In some systems, primary productivity is limited by nitrogen (N), phosphorus (P), or both, based on the N:P ratio defined by the "Redfield" ratio. South Texas, facing rapid urban growth and industrialization, is at risk of higher nutrient loads, but research on nutrient dynamics in this region is limited. This study investigates nutrient variations in the freshwater (FW) abandoned distributaries of the Rio Grande and hypersaline saltwater (SW) systems. Results showed a significant decrease in dissolved inorganic nitrogen (DIN) and an increase in dissolved inorganic phosphorus (DIP), shifting the …

Mitigation Of Droughts And Floods From Conjunctive Water Resource Management Using Managed Aquifer Recharge (Mar): Experiments And Numerical Modeling, Sumiaya Amin Preota

Theses and Dissertations

Water supply of Rio Grande Valley in South Texas has been impacted by intensified climate extremes such as droughts and floods. This research assessed the viability of conjunctive water resource management using Managed Aquifer Recharge (MAR) carbon-modified infiltration basins. Multiple environmental variables in the Arroyo Colorado watershed were investigated, i.e., (i) water availability- quantity of floodwater can be repurposed, (ii) storativity and suitability - capacity within the unsaturated aquifer and ideal locations for building infiltration basins, (iii) water quality- contaminant removal using active carbon/biochar.

Unappropriated flow and high magnitude flow (HMF) analysis indicated that excess floodwater can be a …

Application Of Deep Learning On Gage R&R For Anomaly Detection, Oluwatope Richard Ojo

Electronic Theses and Dissertations

This thesis explores the application of deep learning techniques, specifically autoencoder based models, to enhance anomaly detection within Gage Repeatability and Reproducibility (Gage R&R) studies—an essential component of Measurement System Analysis (MSA) in quality engineering. Traditional Gage R&R methodologies, while effective for linear and low-dimensional data, exhibit limitations in detecting subtle, nonlinear variations in complex measurement systems. To address this challenge, an unsupervised autoencoder was developed and trained on a synthetically generated dataset comprising 2,500 voltage measurements (5V and 33V) derived using Generative Adversarial Networks (GANs) based on real-world manufacturing data measurements.

The proposed autoencoder model achieved a 95th percentile-based …

Inferring Feeding Biology In Extinct Shrews (Soricidae: Mammalia): Application Of A Linear Morphometric Approach, Derek J. Den Ouden

Electronic Theses and Dissertations

Linear morphometrics have been used extensively to infer the ecology of fossil organisms, but shrews have not yet been thoroughly explored using this technique. Often considered to have a homogenous insectivorous diet, direct dietary observation suggests shrew dietary diversity is substantial. Diversity of this nature is well established to have influenced skull morphology in other mammal clades, so similar patterns are hypothesized to be detectable in shrews. To test this, I examined the link between known diet and morphological disparity in extant shrews using linear morphometrics and then utilized that framework to infer the dietary ecology of several fossil taxa. …

Revealing Hidden Histories: A Multi-Sensor Aerial Survey For Detecting Unmarked Burials At Sinking Spring Cemetery, Abingdon, Va, Noah Hall

Electronic Theses and Dissertations

The Sinking Spring Cemetery, established in 1773 in Abingdon, Virginia, spans 11 acres and is divided by a road. The 9-acre southern section was reserved for white church members. Enslaved individuals and free people of color were buried in the smaller northern section, where few headstones remain today. This study aimed to map the unmarked graves using thermal, multispectral, and Light Detection and Ranging (LiDAR) sensors deployed on unmanned aerial systems. Graves were characterized by subtle topographic depressions mapped by LiDAR and cooler radiant temperature anomalies in thermal imagery. Thermal data collected at different times of the day and year …

Investigating The Privacy-Utility Trade-O↵ In Synthetic Data Generation Using Correlated Attribute Mode, Kofi Sarfo

Electronic Theses and Dissertations

This thesis explores the privacy-utility trade-off in synthetic data generation using the Correlated Attribute Mode of DataSynthesizer, which employs Bayesian networks to model attribute dependencies. It focuses on integrating differential privacy mechanisms, particularly the Laplace mechanism, to inject controlled noise into synthetic data and enhance privacy protection. As organizations face challenges balancing data-driven decision-making with privacy regulations such as the General Data Protection Regulation and the California Consumer Privacy Act, synthetic data offers a solution by creating artificial datasets that preserve statistical properties while balancing data privacy and utility. This research investigates how different differential privacy parameters epsilon affect data …

Effects Of Ph On Product Yield Of One-Electron Oxidation To Guanine Derivatives, Abdul-Kadir Ibrahim

Electronic Theses and Dissertations

One-electron oxidation plays a major role in oxidative DNA damage in biological cells. Guanine, the most oxidizable DNA base, has been the major focus of studies on oxidative damage to DNA initiated by one-electron oxidation (OEO). The current study aims at understanding the mechanisms of photooxidation of guanine derivatives with OEO in acidic medium. D1A* and D1B*, which are the products of dGuo in acidic medium, were obtained from dGuo upon photolysis at 485 nm of the reaction mixture in the presence of S₂O₈^2- as the oxidant and Ru(II)(bpy)₃²⁺ as a photosensitizer. D1A* and …

Fixdrive: Automatically Repairing Autonomous Vehicle Driving Behaviour For $0.08 Per Violation, Yang Sun, Christopher M. Poskitt, Kun Wang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous Vehicles (AVs) are advancing rapidly, with Level-4 AVs already operating in real-world conditions. Current AVs, however, still lag behind human drivers in adaptability and performance, often exhibiting overly conservative behaviours and occasionally violating traffic laws. Existing solutions, such as runtime enforcement, mitigate this by automatically repairing the AV's planned trajectory at runtime, but such approaches lack transparency and should be a measure of last resort. It would be preferable for AV repairs to generalise beyond specific incidents and to be interpretable for users. In this work, we propose FixDrive, a framework that analyses driving records from near-misses or law …

Specgen: Automated Generation Of Formal Program Specifications Via Large Language Models, Lezhi Ma, Shangqing Liu, Yi Li, Xiaofei Xie, Lei Bu

Research Collection School Of Computing and Information Systems

In the software development process, formal program specifications play a crucial role in various stages, including requirement analysis, software testing, and verification. However, manually crafting formal program specifications is rather difficult, making the job time-consuming and labor-intensive. Moreover, it is even more challenging to write specifications that correctly and comprehensively describe the semantics of complex programs. To reduce the burden on software developers, automated specification generation methods have emerged. However, existing methods usually rely on predefined templates or grammar, making them struggle to accurately describe the behavior and functionality of complex real-world programs. To tackle this challenge, we introduce SpecGen, …

“Ronaldo’S A Poser!”: How The Use Of Generative Ai Shapes Debates In Online Forums, Yuhan Zeng, Yingxuan Shi, Xuehan Huang, Fiona Fui-Hoon Nah, Ray Lc

Research Collection School Of Computing and Information Systems

Online debates can enhance critical thinking but may escalate into hostile attacks. As humans are increasingly reliant on Generative AI (GenAI) in writing tasks, we need to understand how people utilize GenAI in online debates. To examine the patterns of writing behavior while making arguments with GenAI, we created an online forum for soccer fans to engage in turn-based and free debates in a post format with the assistance of ChatGPT, arguing on the topic of "Messi vs Ronaldo". After 13 sessions of two-part study and semi-structured interviews with 39 participants, we conducted content and thematic analyses to integrate insights …

Flexfl: Flexible And Effective Fault Localization With Open-Source Large Language Models, Chuyang Xu, Zhongxin Liu, Xiaoxue Ren, Gehao Zhang, Ming Liang, David Lo

Research Collection School Of Computing and Information Systems

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, …

Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed (Ur) Sites: Ur-35 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Evaluating Free Merchandise Use At Coastal Carolina University, Ally Murphy, Ciara Cassady, Joseph Dumars

Goal 13: Climate Action

No abstract provided.

Training Sensor-Agnostic Deep Learning Models For Remote Sensing: Achieving State-Of-The-Art Cloud And Cloud Shadow Identification With Omnicloudmask, Nicholas J. Wright, John M A Duncan, Nik Callow, Sally E. Thompson, Richard J. George

Natural Resources Research Articles

Deep learning models are widely used to extract features and insights from remotely sensed imagery. However, these models typically perform optimally when applied to the same sensor, resolution and imagery processing level as used during their training, and are rarely used or evaluated on out-of-domain data. This limitation results in duplication of efforts in collecting similar training datasets from different satellites to train sensor-specific models. Here, we introduce a range of techniques to train deep learning models that generalise across various sensors, resolutions, and processing levels. We applied this approach to train OmniCloudMask (OCM), a sensor-agnostic deep learning model that …

Climate Prediction As An Adaptive Force-Multiplier: Reasons For Hope In A Rapidly Changing World, Matthew D. Laplante

All Graduate Theses and Dissertations, Fall 2023 to Present

It's not unreasonable to feel pessimistic about our future under climate change. But "believing the science" doesn't require you to accept that we are headed toward apocalypse. It is true that human-caused climate change is real, it is underway, and our societies face many increased risks because of it. But that's just part of what climate science tells us about our future world. This same field is responsible for the recent and rapid development of tools and techniques that permit the identification of patterns in our oceans and atmosphere that will aid us in making increasingly accurate predictions about climate—the …

Potamogeton Crispus Invasion: Impacts On Native Aquatic Plants And Associated Faunal Habitat, Meghan-Grace Slocombe

All Graduate Theses and Dissertations, Fall 2023 to Present

Aquatic invasive plants are non-native plants that invade a water body and displace native aquatic plants. Invasion depletes the native plant community's ability to provide ecosystem services, including improved water quality, decreased problematic algae, and increased animal habitat. The loss of these ecosystem services reduces the value of fishing, boating, and swimming opportunities in aquatic ecosystems. The potential changes in plant communities and services are particularly important at sites targeted for restoration because project goals typically include increasing ecosystem services, including wildlife habitat. Curly-leaf pondweed (Potamogeton crispus L.) is a problematic invasive species in North America. To better manage …

Contrastive Representation Learning For Highly Imbalanced Multivariate Time Series With Extreme Instance Strategy, Onur Vural

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data refers to a sequence of data points collected or recorded at regular time intervals. In many fields including space weather, healthcare, and finance, predicting events from such data is crucial because these predictions can help protect infrastructures, improve healthcare outcomes, and forecast financial trends. However, one challenge in working with time series data is the presence of rare events, which are often underrepresented in the data. This imbalance makes it difficult for traditional prediction methods to provide accurate results, as they tend to focus more on the more frequent events and overlook the rare ones. To tackle …

Describing Functionality In Natural Language May Improve Decomposition Behaviors, Matthew R. Burns

All Graduate Theses and Dissertations, Fall 2023 to Present

Problem decomposition—the ability to break complex problems into simpler parts—is a critical skill for computer programming that many beginning students struggle to develop. This research examines how using natural language to describe program functionality can help students develop better problem-solving approaches.

We created a tool called ”Natural Language Functions” (NLFs) that allows students to write descriptions of what they want their code to do in plain English, which then generates working Python functions. We studied how students used this tool compared to students who solved programming problems in traditional ways.

Our findings show that students who used the NLFs tool …

Managing For Disturbance: Trade-Offs Associated With Population Resilience And Ecosystem Size, Ellie Wallace

All Graduate Theses and Dissertations, Fall 2023 to Present

Populations can recover from and withstand disturbance through characteristics such as reproduction and maintaining large abundances. While these traits are beneficial to native species conservation, they can simultaneously challenge effective invasive species management. Effective invasive species control requires sufficient efforts to overcome a population's ability to recover from disturbance, such as harvest. My dissertation aimed to address how the size of an invasive population and its ability to recover from disturbance impact a suite of invasive species control methods, including those that (1) target mainly larger and older individuals (i.e., mechanical removal), (2) target all sizes of individuals (i.e., targeted …

Advancing Multi-Agent Robotics Simulations Through Heterogeneous Reinforcement Learning In Isaaclab, Jacob R. Haight

All Graduate Theses and Dissertations, Fall 2023 to Present

Robots increasingly operate in collaborative teams across domains such as search-and- rescue, warehouse automation, and autonomous driving—scenarios that demand advanced coordination strategies enabled by multi-agent reinforcement learning (MARL). However, existing simulation frameworks often struggle to balance realism, speed, and scalability, especially when supporting diverse, heterogeneous robot teams. This research extends Isaac Lab, a high-performance robotics simulator, by integrating heterogeneous-agent reinforcement learning (HARL) capabilities. The result is a flexible and GPU-accelerated platform for training both homogeneous and heterogeneous robot teams in complex, physics-based environments. These enhancements significantly narrow the gap between simulation and real-world deployment for multi-robot systems.

Development Of Saddlepoint Methodologies For Sparse Sample Multiple Parameter Generalized Linear Models And Correlated Data Scenarios, Christopher Johnson

All Graduate Theses and Dissertations, Fall 2023 to Present

Parameter estimation using maximum likelihood techniques may be biased when sample sizes are small, event rates are low, or otherwise sparse counts exist in a parametric model. This in turn may lead researchers to draw invalid statistical conclusions when conventional methods are utilized. The saddlepoint approximation has potential to lessen the degree of bias in sparse data conditions through its use of moments beyond the mean and variance, which allows for more accurate approximations using a smaller number of observations. We propose two novel saddlepoint methods for use in practical analysis scenarios, as an alternative to maximum likelihood estimation. First, …

Determining The Structure And Function Of Type Iv-A Anti-Crisprs, Olivine Redman

All Graduate Theses and Dissertations, Fall 2023 to Present

Bacteria face a constant existential threat in the form of infection by viruses along with other forms of mobile genetic elements, such as bacteriophage and transposable elements. To survive, bacteria and other prokaryotes have evolved various immune systems to evade these would-be invaders. One such immune system is the CRISPR-Cas system, an adaptive immune system able to record the genetic signature of invading viruses in order to recognize and destroy them should they be encountered again in the future. In this thesis I present data that sheds light on the mechanism of one particular subtype of CRISPR-Cas systems: the type …

Conservation Laws For Asymptotically Perfect Fluid Spacetimes, Lyle Arnett Jr.

All Graduate Theses and Dissertations, Fall 2023 to Present

In physics, conservation laws, like those for energy or momentum, are powerful tools that help us understand how systems evolve and interact. In general relativity, where spacetime itself is curved by matter and energy, defining such conservation laws becomes especially subtle and complex. Traditional methods rely on idealized conditions like empty space or special symmetries, which limit their usefulness in more realistic, dynamic settings such as an expanding universe.

This dissertation explores a modern mathematical framework, developed by Iyer and Wald, that allows conservation laws to be derived directly from the equations governing spacetime. Using this approach, I examine not …

Zero Waste Initiatives Across Three Institutions: Local Government, An Academic Institution, And A Retail Business, Soren G. Gray

Graduate Student Portfolios, Professional Papers, and Capstone Projects

No abstract provided.

Ml Playground: Data Modification/Preprocessing And Model Simulation Tool, Marco D. Cerrato

Electronic Theses, Projects, and Dissertations

There is a heavy reliance on programming when it comes to learning machine learning (ML). This often creates barriers for students and newcomers unfamiliar with coding. While the lessons you learn in the classroom provide essential foundational understanding, some technical or practical aspects of ML—such as data preprocessing, feature engineering, and model tuning—are best learned through hands-on interaction. ML Playground was developed to act as a proof-of-concept application to address this gap by offering a browser-based, graphical user interface that lets users engage with core ML workflows without writing code. Designed with educational accessibility in mind, the application allows users …

Sediment And Debris Flows Resulting From The 2020 El Dorado Wildfire, San Bernardino Mountains, California, Andrew Suarez

Electronic Theses, Projects, and Dissertations

The Yucaipa Ridge is a section of the San Bernardino Mountains within the Transverse Ranges of Southern California. During the summer and early fall of 2020, Yucaipa Ridge experienced severe vegetation damage from both the Apple and El Dorado wildfires. Burned slopes exhibit a higher susceptibility to geomorphic change within several years of burning from the introduction of meteoric water onto the slope. Yucaipa, Oak Glen, and other nearby communities are currently at risk of damage by hyperconcentrated flows and debris flows during the seasonal rainy season.

Debris flows and hyperconcentrated flows (a.k.a. mudflows; are grouped and referred to in …

Geochemistry Of The Inyo Volcanic Chain, And Evaluation Of The Portable Niton Xrf Instrument, Dylan Terry

Electronic Theses, Projects, and Dissertations

The Inyo Volcanic Chain (IVC) is a series of rhyolitic lava domes straddling the northwest rim of the Long Valley Caldera (LVC), most recently erupting ~650 years ago, producing the South Deadman, Obsidian, and Glass Creek Domes. For this study, samples were analyzed for geochemistry and petrography at six of the IVC domes. To measure geochemistry, a portable x-ray fluorescence machine (PXRF) was used, in part to test how well it performed on felsic rocks. The PXRF performed poorly with the factory calibration in detecting most elements, though detection of some elements improved with a calibration curve applied. The three …

A Mineralogical And Geochemical Investigation Of The Influence Of Tectonic Setting On Accessory Mineral Assemblages In Serpentinites Along The Western North American Margins, Bryan H.T. Seymour

Electronic Theses, Projects, and Dissertations

Serpentinites are metamorphic rocks typically produced by hydrating mantle peridotites to form assemblages containing one or more serpentine minerals. They occur in various tectonic and geologic settings, such as submarine hydrothermal systems, ophiolite sequences, and the forearc mantle. Serpentinites are associated with highly reduced conditions, as indicated by low oxygen fugacity (ƒO₂) values. Previous investigations suggest that oxygen fugacity varies with tectonic setting due to differences in environmental conditions. To test this hypothesis, we analyzed serpentinites from a wide variety of tectonic settings in western North America: The New Idria forearc diapir (CA), Canyon Mountain Island arc complex (OR), Josephine …