Physical Sciences and Mathematics Commons^™

The Mechanical Strength Of Sedimentary Rocks At Meridiani Planum, Mars, Tyler J. Seyglinski

Masters Theses

The physical properties of rocks such as their strength, hardness, and density can help inform our understanding of the formation and modification history of rock units. For sedimentary rocks, their strength is inherently linked to factors such as porosity and degree of induration, which are in turn controlled by factors such as burial depth and water-rock interaction. On Earth, rock strength is typically assessed as unconfined compressive strength (UCS) and can be measured directly or inferred via indirect strength tests (e.g., the Schmidt hammer test). On Mars, instruments such as the Rock Abrasion Tool (RAT) onboard the Opportunity rover can …

Using Digitized Building And Weather Records To Improve The Accuracy Of Ground To Roof Snow Load Ratio Estimations, Gideon Parry

All Graduate Theses and Dissertations, Fall 2023 to Present

Reliability target loads refer to the amount of accumulated snow a roof needs to be able to support to ensure that the probability of collapse is sufficiently low. Since ground snow weight, or load, is much easier to measure than roof snow load, models for roof snow loads rely on ground snow load measurements along with a statistical model that estimates roof snow retention as a ratio of the measured ground snow load. This thesis focuses on improving the roof snow retention model using data from Canadian case studies that include information about building geometry and local wind speeds. This …

Leveraging Generative Ai For Sustainable Farm Management Techniques Correspond To Optimization And Agricultural Efficiency Prediction, Samira Samrose

All Graduate Reports and Creative Projects, Fall 2023 to Present

Sustainable farm management practice is a multifaceted challenge. Uncovering the optimal state for production while reduction of environmental negative impacts and guaranteed inter-generational assets supervision needs balanced management. Also, considering lots of different factors (cost, profit, employment etc), the agricultural based management technique requires rigorous concentration. In this project machine learning models are applied to develop, achieve and improve the farm management techniques. This experiment ensures the resultant impacts being environment friendly and necessary resource availability and efficiency. Predicting the type of crop and rotational recommendations will disclose potentiality of productive agricultural based farming. Additionally, this project is designed to …

Gray Seal (Halichoerus Grypus) Diet And Microplastic Ingestion On Great Point, Nantucket, Shannon R. Brown

Graduate Masters Theses

Due to their semi-aquatic lifestyle, pinnipeds are an ideal sentinel group used to study anthropogenic threats to the marine environment. Microplastics, primarily transported to the ocean through river discharge or weathering of larger plastics, are a threat to both pinnipeds and humans. Bioaccumulation of microplastics within the marine food web has been observed, with pinnipeds indirectly ingesting microplastics through their prey. As generalists, gray seals (Halichoerus grypus) are a pinniped species that can provide information on microplastic exposure to many lower trophic level organisms. This thesis explores the relationship between the diet and microplastic ingestion of gray seals on Great …

3d-Magnetometer Arrays In Physics Experiments, Shaun G. Vavra

Masters Theses

This study presents the design and experimental evaluation of a magnetometer array utilizing LIS3MDL chips integrated with an Arduino microcontroller. Magnetometer arrays find crucial applications in various fields, including physics research, geophysics, and navigation systems. The goal of this research is to create an affordable and versatile magnetometer array for scientific investigations and practical applications. The paper begins by outlining the hardware and software components of the array. The LIS3MDL chips, known for their high sensitivity and low power consumption, are employed as the core sensing elements. The Arduino microcontroller is utilized for data acquisition and processing. The integration of …

High Fat Diet & Social Isolation: Interactive Effects On Pain, Cognition, & Neuroinflammation, Ian M. Campuzano

Research Psychology Theses

Prior research has established a role for both social isolation and exposure to high fat Western diets in altering a range of behaviors from reduced memory performance to increased depression-like behaviors. The present study scrutinizes the interplay among these variables during the peri-adolescent developmental phase, utilizing Long-Evans rats as the experimental model. Our overarching hypothesis is that rats exposed to either social isolation, a high-fat diet, or both will result in heightened pain sensitivity, diminished cognitive flexibility, and increased neuroinflammatory responses within brain regions implicated in sociability, cognition, memory, and pain processing. Behavioral flexibility will be assessed using a maze-based …

An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari

Electronic Theses and Dissertations

Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, …

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …

Enhancing Monthly Streamflow Prediction Using Meteorological Factors And Machine Learning Models In The Upper Colorado River Basin, Saichand Thota

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and predicting streamflow along river basins is vital for planning future developments and ensuring safety, especially with climate change challenges. Our study focused on forecasting streamflow at Lees Ferry, a key location along the Colorado River in the Upper Colorado River Basin. We employed four machine learning models - Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal Auto-Regressive Integrated Moving Average; and combined historical streamflow data with meteorological factors such as snow water equivalent, temperature, and precipitation. Our analysis spanned 30 years of data from 1991 to 2020.

Our findings revealed that the Random Forest Regression …

Md Simulations Of Collision Effects For A Strongly Coupled Plasma, Jawon Jo

All Graduate Theses and Dissertations, Fall 2023 to Present

Studying strongly coupled plasmas can be effectively accomplished using molecular dynamics (MD) simulations. We have developed an advanced MD simulation code that can analyze plasmas with various coupling parameters. This code employs a spherically symmetric cut-off Coulomb force verified through convergence tests by modulating the cut-off and minimum force ranges. Additionally, it incorporates a new algorithm for optimizing the initial positions of particles at a given temperature. This method maintains the temperature constant and the velocity distribution unchanged. As a result, we eliminate the unphysical initial rises and oscillations in temperature that a random distribution of positions causes. The code …

Molecule-Based Quantum Materials Under Extreme Conditions, Avery Leon Blockmon

Doctoral Dissertations

Molecule-based quantum materials are a class of compounds with competition between the spin, orbitals, charge, and lattice. They feature flexible architectures and structural designs that can be easily modified for different functionalities. As a result of their overall low energy scales, they can be easily tuned with external stimuli like magnetic field or pressure to reveal new states and properties. This dissertation presents a high magnetic field investigation of three different molecule-based quantum materials under extreme conditions revealing insights into their structural, electronic, and magnetic properties.

My initial study analyzes decoherence pathways in spin qubit Na₉[Ho(W₅O …

Extending Application Runtime Systems For Effective Data Tiering On Complex Memory Platforms, Brandon Kammerdiener

Doctoral Dissertations

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory tiering, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide …

Enhancing Code Portability, Problem Scale, And Storage Efficiency In Exascale Applications, Nigel Tan

Doctoral Dissertations

The growing diversity of hardware and software stacks adds additional development challenges to high-performance software as we move to exascale systems. Re- engineering software for each new platform is no longer practical due to increasing heterogeneity. Hardware designers are prioritizing AI/ML features like reduced precision that increase performance but sacrifice accuracy. The growing scale of simulations and the associated checkpointing frequency exacerbate the I/O overhead and storage cost challenges already present in petascale systems. Moving forward, the community must address performance portability, precision optimization, and data deduplication challenges to ensure that exascale applications efficiently deliver scientific discovery. In this dissertation, …

Neural-Network-Based Detection Of Radiopharmaceutical Extravasation In Pet/Ct Data, Elijah D. Berberette

Masters Theses

The immediate identification of PET/CT radiopharmaceutical extravasation can eliminate many adverse effects such as misdiagnosis and improper therapy. Radiopharmaceutical extravasation is the leakage of an injected radiotracer from the patient’s intended vein into surrounding tissues. The detection of this phenomenon often requires the use of an external monitoring device; due to a lack of robust visual features that can provide indication that it has occurred. In this thesis, the feasibility of using neural networks trained on PET/CT data to identify extravasation is explored. This approach begins with a novel preprocessing methodology that automatically extracts body weight normalized standard uptake values …

Incorporating Ai-Assisted Sensing Into The Metaverse: Opportunities For Interactions, Esports, And Security Enhancement, Yi Wu

Doctoral Dissertations

With the rapid growth and development of Virtual Reality (VR) and Augmented Reality (AR), extensive research has been carried out in the domain of the Metaverse, including immersive gaming, human-computer interaction, eSports, and the associated security & privacy concerns.

My research explores the potential of incorporating Artificial Intelligence (AI)-assisted sensing technologies to facilitate a more immersive, convenient, authentic, and secure virtual experience. This dissertation mainly focus on the following topics: (1) how to perform facial expression tracking to improve the users' awareness in the Metaverse; (2) fitness tracking for immersive eCycling; (3) running gait analysis for immersive indoor running, and …

Multiscale Modeling Of Morphology And Proton/Ion Transport In Electrolytes, Zhenghao Zhu

Doctoral Dissertations

Understanding structure-function relationships in electrolytes is essential for advancing energy conversion and storage. This dissertation employs multiscale modeling and simulations to study the morphology and proton/ion transport in various electrolytes for electrochemical systems, including anion exchange membranes (AEMs), protic ionic liquids (PILs), pure phosphoric acid (PA) and aqueous acid solutions, ionic liquids (ILs), and polymerized ionic liquids (polyILs).

Mesoscale dissipative particle dynamics (DPD) simulations were employed to study the hydrated morphology of polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (SEBS)-based AEMs. The results indicate that the choice of the functional group moderately affects the water distribution and has little influence on the …

Investigation Of Molecular Transition Metal Complexes: Structures, Magnetic Properties, And Reactivities, Adam T. Hand

Doctoral Dissertations

The dissertation describes the work on transition metal complexes to determine their structures, magnetic properties, and reactivities. Molecular magnetic complexes containing one cobalt(II) or rhenium(IV) ion have been studied to obtain their characteristic zero-field splittings and spin-phonon couplings by magnetometry and advanced spectroscopies. The investigation of spin relaxation and phonon features gives insight into potential magnetic relaxation mechanisms. Studies by ligand field theory, including ab initio ligand-field analysis, show how coordination environments of the metal centers affect the magnetic properties. Through the analyses, the impact of the coordination geometry/symmetry on the zero-field splittings can be explained. Such understanding of magneto-structural …

Codont5: A Multi-Task Codon Language Model For Species-To-Species Translation, Ashley N. Babjac

Doctoral Dissertations

DNA (DeoxyriboNucleic Acid) carries the genetic information for the biological processes and function of all organisms. It is composed of nucleotides, which can be grouped into 3-mer triplets called codons. It is well known that codons encoding the same amino acid, referred to as "synonymous" codons, are selected with differing frequencies between organisms. Prior research has revealed there are codons used with much higher frequency than others, causing to them being "preferred" in highly expressed genes. This has led to the development of multiple computational models that do a good job predicting gene expression in some protein-coding genes; however, their …

Understanding Traits To Support Crowdworkers' Flexibility, Senjuti Dutta

Doctoral Dissertations

Crowdworkers are drawn to the profession in part due to the flexibility it affords. However, the current design of crowdsourcing platforms limits this flexibility. Therefore, it is important to support the overall flexibility of crowdworkers. Incorporating a variety of device types in the workflow plays an important role in supporting the flexibility of crowdworkers, however each device type requires a tailored workflow. The standard workflow of crowdworkers consists of stages of work such as managing and completing tasks. I hypothesize that different devices will have unique traits for task completion and task management. Therefore in this dissertation, I explore what …

Enabling Reproducibility, Scalability, And Orchestration Of Scientific Workflows In Hpc And Cloud-Converged Infrastructure, Paula Fernanda Olaya

Doctoral Dissertations

Scientific communities across different domains increasingly run complex workflows for their scientific discovery. Scientists require that these workflows ensure robustness; where workflows must be reproducible, scale in performance; and exhibit trustworthiness in terms of the computational techniques, infrastructures, and people. However, as scientists leverage advanced techniques (big data analytics, AI, and ML) and infrastructure (HPC and cloud), their workflows grow in complexity, leading to new challenges in scientific computing; hindering robustness.

In this dissertation, we address the needs of diverse scientific communities across different fields to identify three main challenges that hinder the robustness of workflows: (i) lack of traceability, …

General Relativistic Gravity In Core-Collapse Supernova Simulations, James Nicholas Roberts Ii

Doctoral Dissertations

Core-collapse supernovae (CCSNe) are some of the most extreme and complex phenomena in the universe. The toolkit for high-order neutrino-radiation hydrodynamics (thornado) is being developed to simulate CCSNe which will provide insight into the mechanisms underlying these events. The thornado framework is a collection of modules used to calculate the effects of gravity, hydrodynamics, neutrino transport, and nuclear physics through the Weaklib equation of state table. This dissertation will present the development of the Poseidon code, which provides the general relativistic gravity solver for the thornado framework.

The Poseidon code solves for the general relativistic metric using the xCFC formulation …

Evaluating The Effects Of Forage Availability And Landscape Composiiton On Whte-Tailed Deer Morphometrics Across The Eastern U.S., Mark Turner

Doctoral Dissertations

White-tailed deer (Odocoileus virginianus) management often focuses on improving nutrition to increase deer morphometrics, and many landowners use harvest data to track management progress. Better understanding the relationship among deer morphology, nutrition, landscape characteristics, and climate should inform deer management throughout much of the eastern US. I collected deer forage data in 2021–2023 from 43 sites in 25 states across the eastern US and worked with cooperating landowners and managers to collect harvest data from 35 of those sites. Adult female body mass explained 64% of the variation in mature male antler size on sites across the eastern …

Method Development For The Quantification Of Critically Valuable Elements In Permian Basin Produced Waters, Carley Oliver

Open Access Theses & Dissertations

Produced waters (PW) are a major byproduct of the oil and gas industrial hydraulic fracturing processes that create a major waste disposal issue with a need for disposal, treatment, and reuse method developments. The current primary disposal method is subsurface injection, which can lead to high pressures in the subsurface. PWs are often highly saline from mineral leaching due to long residence in subsurface reservoirs. According to the USGS National Minerals Information Center, bromine, calcium chloride, iodine, lithium, magnesium, and sodium chloride have been found in and extracted from these domestic subsurface brines, suggesting that there may be other economically …

Investigation Of Seasonal Trends And Source Apportionment Of Particulate Matter (Pm2.5) In El Paso-Juarez Airshed, Fatema Tuz Zohora

Open Access Theses & Dissertations

Using the data from Texas Commission on Environmental Quality (TCEQ) for the El Paso city throughout the years from 2021 to 2024 Particulate Matter (PM2.5) was measured to examine the seasonal distribution and sources. This study aims to understand the seasonal variability of PM2.5 concentrations, identify the significant impact of wildfire events on this and source apportionment.The analysis begins with a comprehensive examination of PM2.5 data from TCEQ highlighting the seasonal pattern, it was found that fine particulate matter was more prevalent in winter and tends to be down in spring, and in summer it is relatively lower, subsequently in …

Developing Educational Tools For Sustainable Stormwater Management, Lauren Houskeeper

All Graduate Reports and Creative Projects, Fall 2023 to Present

Rapid population growth and development in Western states are exerting strain on the region’s limited water resources. Urbanization exacerbates this issue by increasing impervious surfaces, limiting infiltration of precipitation during storm events and snowmelt, which results in changes to hydrologic conditions with higher runoff volumes and higher peak flows. Stormwater transports pollutants as it flows across impervious surfaces, discharging high volumes of runoff and elevated loads of urban contaminants into receiving waters. The amount of pollution entering waterways continually increases as urban areas expand. Utah is currently experiencing a rapid transition from undeveloped to developed landscapes, necessitating the implementation of …

Data Driven Acceleration Of Coupled-Cluster Calculations Using Machine Learning, Multitask Learning And Physics Imposed Learning, Perera Don Varuna Sanjaya Pathirage

Doctoral Dissertations

Data-driven coupled-cluster singles and doubles (DDCCSD) method developed by Townsend and Vogiatzis aims at predicting the coupled-cluster t₂ amplitudes using MP2-level electronic structure data with machine learning. In this work we address limitations of the DDCCSD method to expand the applicability and increase the accuracy. First, we implement localized molecular orbitals (LMO) to the DDCCSD method. There is a ten-fold increase in accuracy when the LMO implementation is used compared to the canonical molecular orbital implementation. Next, we introduced five data selection techniques to select data for testing and training. Here we were able to achieve accuracies less than …

A Uniformly Most Powerful Test For The Mean Of A Beta Distribution, Richard Ntiamoah Kyei

Electronic Theses and Dissertations

The beta distribution is used in numerous real-world applications, including areas such as manufacturing (quality control) and analyzing patient outcomes in health care. It also plays a key role in statistical theory, including multivariate analysis of variance (MANOVA) and Bayesian statistics. It is a flexible distribution that can account for many different characteristics of real data. To our surprise, there has been very little work or discussion on performing statistical hypothesis testing for the mean when it is reasonable to assume that the population is beta distributed. Many analysts conduct traditional analyses using a t-test or nonparametric approach, try transformations, …

Advancing Telehealth Through Artificial Intelligence: Incorporating Emotional Intelligence And Addressing Cybersecurity Challenges, Mahima Rajendra Pulgaonkar

Electronic Theses, Projects, and Dissertations

This culminating experience project explores the integration of Emotional Artificial Intelligence (Emotional AI) into telehealth systems, addressing the dual challenges of enhancing patient care and mitigating cybersecurity risks. The research questions are: (Q1) How can Emotionally Intelligent AI improve telehealth systems' ability to recognize and respond to mental health symptoms? and (Q2) What are the specific cybersecurity challenges associated with AI in telehealth and how can they be mitigated? The findings for each question are: Q1: Emotionally Intelligent AI can significantly enhance telehealth by providing personalized, empathetic interactions that improve patient engagement, adherence to treatment plans, and early detection of …

Exploring Overbank Sediment Deposition Variation In Heavily Modified Floodplains Of The Lower Mississippi River: A Sedimentological And Geophysical Analysis, Seth Fradella

Master's Theses

Since the 1930s, the Lower Mississippi River (LMR) has experienced large-scale modifications to the channel profile and surrounding floodplains through dams, dikes, revetments, dredging, and channel cutoffs. Although these changes have improved navigation and reduced flood risk, unanticipated changes to the major flood return period, individual flood severity and duration, and sediment regime have become increasingly apparent and sometimes problematic, such as the 2011 and 2018-2020 floods. Flood control levees along the LMR have reduced the natural floodplain area by 70-90%, resulting in heavily restricted overbank storage capacity of water and sediment. For the same flood events in recent history, …

Gps Accuracy Of Smartphones For Crowdsourcing Research, Derron L. Dike

Master of Science in Environmental Sciences and Management Projects

ABSTRACT GPS Accuracy of Smartphones for Crowdsourcing Research Derron Dike Utilizing crowdsourcing with smartphones as a method for field data collection can contribute an array of data for scientific studies and land management applications. This study used crowdsourcing to examine smartphone built-in GPS performance for providing location-specific information with potential forestry applications. The usability of smartphones for GPS data collection in forestry studies or other practical applications is dependent on the level of accuracy required for those applications. A Smartphone Accuracy Trial was designed to test the GPS accuracy performance of current smartphones. Participants used the ArcGIS Field Maps application …