Physical Sciences and Mathematics Commons^™

A Nursery Study Of The Interplay Between Soil Texture, Rooting Space, And Nurse Plants On Seedling Growth And Root Development In Woody Species Of Tamaulipan Thornscrub, Lance I. Santos

Theses and Dissertations

Reforestation attempts in semi-arid regions are plagued with seedling mortality. Blanket greenhouse practices may not be optimal, in addition, commensalism interactions between nurse plants are considered. To better understand the effects of soil texture and nurse plant interactions on growth and drought tolerance, two experiments were developed. For soil texture, Border palo verde seedlings were grown in different-sized plant bands with three soil types. To observe the effects of nurse plants, an experiment using guayacan, was developed using Texas Ebony, as a leguminous nurse plant. Plants were also exposed to a drought experiment. The results suggest that some soil textures …

Teachers’ Perceptions On The Impact And Effectiveness Of Certification Programs In Preparing And Retaining Educators, Teresa De Jesus Padilla

Theses and Dissertations

Mathematics is integral to STEM fields, making math critically important for the stability and development of the nation. As a result, mathematics teachers have a crucial role in our society. A role whose importance needs the necessary support to accomplish its numerous responsibilities. However, research indicates that certification routes—traditional and alternative—often fail to adequately prepare math teachers for the challenges they face, leading to high turnover rates. This study explores the impact of these certification routes on teachers’ abilities to support student achievement, address diverse learning needs, and manage additional duties. Surveying secondary math teachers in the Rio Grande Valley, …

Enhancing Muscle Health And Modulating Immune Response With Galectin-1 In Lgmd2b/R2 Models, Stefannia Esparza Pulido

Theses and Dissertations

Limb-Girdle Muscular Dystrophy Type 2B/R2 (LGMD2B/R2) is a type of dysferlinopathy, a progressive muscle-wasting disorder caused by mutations in the dysferlin gene. Hallmarks of the disease include chronic inflammation, impaired muscle regeneration, and eventual muscle degeneration. Currently, no FDA-approved therapies are available for dysferlinopathies. Recombinant human Galectin-1 (rHsGal-1) has shown multiple roles in maintaining skeletal muscle homeostasis, promoting muscle repair, enhancing myogenesis, and modulating inflammation by downregulating the NF-κB pathway. The ability of rHsGal-1 to modulate the immune response is particularly promising for therapeutic applications in patients with LGMD2B/R2. In this study, we evaluated the effects of rHsGal-1 in a …

Improving Data Curation With Spectral Clustering And Shannon Entropy: An Unsupervised Approach Within The Data Washing Machine, Erin Hathorn

Theses and Dissertations

In the ever-expanding landscape of digital technologies, the exponential growth of data presents both challenges and opportunities, demanding innovative approaches to data curation. Effective data curation is pivotal for extracting meaningful insights from vast and complex datasets. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM incorporates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Spectral clustering, known for its ability to handle complex and non-linearly separable …

Ionic Nanomedicines And Biocompatible Hydrogel: Synthesis, Characterization And Application In Cancer And Drug Delivery, Mujeebat Bashiru

Theses and Dissertations

This dissertation is focused on the development of novel class of ionic materials (IMs), and ionic nanomedicines (INMs) for cancer treatment. These IMs are composed of two therapeutics combined via electrostatic attraction. The impact of various counterions on the drug’s cytotoxic and photophysical properties are unveiled. Moreover, the mechanism of internalization, enhanced toxicity of these materials is investigated by determining their cellular uptake, subcellular localization, and cell death mechanism. Furthermore, the in vivo bioimaging capabilities of the drugs is investigated to validate their potential for clinical relevance. The second part of this dissertation is focused on the synthesis of biocompatible …

Advancements Of A Vibration-Based Sound Power Method For Direct And Indirect Applications, Ian Charles Bacon

Theses and Dissertations

This dissertation advances the Vibration-Based Sound Power (VBSP) method for measuring the sound power of vibrating structures, expanding its applicability to a wider range of geometries and acoustic environments. The research addresses limitations of traditional sound power measurement techniques by developing an alternative method that achieves near Precision (Grade 1) accuracy while maintaining feasibility for in situ testing under uncontrolled acoustic conditions. After reviewing the current VBSP method in Unit 1, Unit 2 introduces stitching techniques for Scanning Laser Doppler Vibrometer (SLDV) measurements, enabling accurate 3D scans and extending the method to complex geometries. Experimental validation is provided for baffled …

Bioluminesence-Induced Photoredox Catalysis And Its Mechanisms, Dominic Jeffrey Bates

Theses and Dissertations

The bioluminescence-induced photoredox reaction (BIPR) is a novel methodology developed to overcome the limitations of photo-induced chemistry by initiating chemical work using bioluminescent Escherichia coli as a photon source. In industry, photochemistry is superseded by thermochemical reaction processes. Despite its massive potential, there are very few applications in industry for photochemistry. Unlike thermochemistry, reaction scale-ups are not as easy as simple dimensional increases in a reaction vessel due to the nature of light. This deficiency is seemingly overcome through the development of flow technologies; however, the overall yield of such methods remains incomparable with those utilizing thermochemistry. This thesis focuses …

Innovative Strategies For Healthcare Data Integration: Enhancing Etl Efficiency Through Containerization And Parallel Computing, Ehsan Soltanmohammadi

Theses and Dissertations

The healthcare industry is rapidly transforming due to technology adoption, resulting in an explosion of data. Extract, Transform, Load (ETL) processes are crucial for integrating and analyzing this data to support decision-making and enhance patient care. However, ETL processes face significant challenges, including data diversity, quality issues, security and compliance, and scalability. Opportunities exist to optimize ETL processes through advanced technologies like big data analytics, containerization, and parallel computing, improving data quality, and enhancing security. This literature review examines current ETL processes in healthcare, highlighting challenges and opportunities for future improvement, ultimately aiming to enhance healthcare outcomes and patient experiences. …

Multi-Scale Ai-Assisted Gene Expression Decoding, Fengyao Yan

Theses and Dissertations

Genes can be treated as a graph that can be mapped. Tremendous information is coded in genes to ensure a complex functioning organism. Decoding this information is critical to understanding our biology and developing treatments for various diseases including cancer. Deep learning, a new branch of computer science, has gained traction over the past decade. It offers more insight into the data that is processed by the deeplearning models. Our study has shown that deep-learning models can be an effective tool in decoding genetic data such as gene tissue-deconvolution, gene graph mapping and genomic imputation. In tasks such as tissue …

Using Machine Learning And Deep Learning Algorithms To Improve Low Birthweight Prediction, Yang Ren

Theses and Dissertations

Low birthweight (LBW) is a major public health issue resulting in increased neonatal mortality and long-term health complications. Traditional LBW analysis methods, focusing on incidence rates and risk factors through statistical models, often struggle with complex unseen data, and thus, their effectiveness is limited in early prevention of LBW, requiring more advanced LBW prediction models. Therefore, this dissertation delves into this important research area by proposing and examining novel machine learning (ML) and deep learning (DL) algorithms, aiming to predict LBW more accurately during the early stage of pregnancy. This dissertation consists of three studies, strategically designed to build upon …

A Low Density Tracker For The Dune Nd Complex., Nibir Talukdar

Theses and Dissertations

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long- baseline neutrino experiment aimed at resolving mass hierarchy and the CP violation puzzle in the neutrino sector. Although neutrinos are the most abundant particles in the universe, their study is challenging due to their extremely low interaction cross sections. DUNE aims to overcome these challenges by sending a beam of muon (anti)neutrinos from Fermilab, located near Chicago, to the Sanford Underground Research Facility (SURF) in South Dakota.

The experiment consists of a near detector (ND) complex at Fermilab, which mea- sures the composition of the unoscillated neutrino beam, and a …

Bayesian Nonparametric Models For Pooled Data, Yizeng Li

Theses and Dissertations

This work examines two applications of pooling: group testing and pooled biomonitoring. Group testing, introduced by Dorfman in the early 1940s, was initially developed to screen for syphilis among U.S. inductees during World War II. Since then, the approach has demonstrated cost-saving benefits in diverse fields, including drug discovery, genetics, and infectious disease testing. While various regression methods—parametric, nonparametric, and semiparametric—have been proposed to analyze group testing data, they fall short in addressing age-related disparities in disease presence if such variations exist. In Chapter 2, we address this gap by expanding varying coefficient regression within a Bayesian framework to accommodate …

Engaging Students In The Scientific Process: An Examination Of Active Learning Strategies In Introductory Geoscience Labs, Meryssa Piper

Theses and Dissertations

High-quality instruction of introductory geoscience courses entices students to major in geoscience fields and ensures undergraduate students have opportunities to learn and apply scientific information and practices. Prior studies indicate that active learning strategies in higher education settings can increase student knowledge and interest. This dissertation explores the integration of two active learning strategies in introductory geoscience laboratory courses: inquiry and gamification The first study housed in Chapter 2 investigates the level of inquiry used in introductory geoscience lab activities across the United States. Through a comprehensive analysis of these activities, this chapter assesses: (1) the degree to which instructor …

Nonlinear Optical Studies Of Complex Materials, Govinda Kharal

Theses and Dissertations

This dissertation presents an in-depth exploration of nonlinear optical investigations into complex material systems, employing second harmonic generation (SHG) and complementary bulk measurement techniques. The first project focuses on surface symmetries and phase transitions in IrTe2. Using SHG and electrical resistance measurements, we systematically investigate temperature-induced symmetry changes and reveal significant shifts in transition temperatures due to thermal cycling. Comparison with bulk X-ray diffraction confirms that these shifts are surface-related, providing a new interpretation of inconsistencies in reported transition temperatures. Our findings emphasize the effectiveness of SHG in capturing subtle surface phenomena, with future directions involving straincontrolled studies to manipulate …

The Fifth Element In Three Dimensions: Boron Systematics Along And Across The Aleutian Volcanic Arc, Owen Jensen

Theses and Dissertations

This study uses abundances and isotope ratios of the fluid-mobile element boron (B) to better understand the role of fluids in volcanism at the Aleutian arc in Alaska. In volcanic rocks from the Aleutian volcanic front, patterns in B abundance and isotope ratios (δ11B) point to three endmembers: a sediment melt, a slab interior melt, and a slab-top melt. Most lavas from the eastern Aleutian Islands have δ11B of -2 to +5‰, similar to Aleutian sediment. Hence, sediment is likely to be the dominant B source in the Aleutian Island samples. In lavas from the western Aleutian seafloor, high δ11B …

Topics In Beyond The Standard Model Physics, Sapan Karki

Theses and Dissertations

Standard Model and General relativity are two pillars of modern physics, and their reconciliation would bring a long-sought harmony in physics along with insights into the secrets of the universe. It is then natural to try and modify these theories by searching for the fundamental symmetry describing our world. In this work, I work on the implication of breaking of Lorentz symmetry and the existence of topological objects called solitons. In chapter 3, I show how a cosmologically generated Lorentz- Violating model has instability issues. I then show how the quantum corrections on Lorentz-Violating models can produce ambiguity in β-function …

Measurement Of Single Differential Cross Sections, Polarization Observables, And Beam-Spin Asymmetry In Charged Two-Pion Electroproduction Off The Proton, Christopher Andrew Mclauchlin

Theses and Dissertations

Emergent phenomena are described as such due to functional differences in paradigms based on observation scale, one of the most fundamental being the emergence of hadronic structure from bare quarks and gluons, as dictated by the strong interaction. Measurement of charged two-pion electroproduction off the proton with CLAS in run group E1-6 and the extraction of single differential cross sections, polarization observables, and beam-spin asymmetries was performed as a necessary step in illuminating the length scale currently dark to Quantum Chromodynamics within the structure of the proton. Understanding the scale-dependent nature of this hadronic structure is integral to understanding nature …

Integrating Data-Driven And Mechanism-Driven Modeling In Drug Discovery And Hepatotoxicity Evaluation, Xuelian Jia

Theses and Dissertations

The pharmacological activities and hepatotoxicity significantly influence the success or failure of new drugs during drug discovery and development. Traditional experimental methods, such as animal models, are costly and time-consuming for chemical testing. There is a great need to develop in vitro and computational modeling to help identify the pharmacological activities and hepatotoxicity potential in the early stage of drug discovery and safety evaluation. In this dissertation, new computational models and associated modeling frameworks were described for predicting the biological profile and hepatotoxicity of chemicals. Firstly, a novel data mining and Quantitative Structure-Activity Relationship (QSAR) workflow was developed to construct …

Computational And Experimental Advances In Nuclear Magnetic Resonance For High Resolution Structures, Ryan Toomey

Theses and Dissertations

Since its inception, nuclear magnetic resonance (NMR) has been a valuable tool for determining chemical structure. In recent years, the field of NMR has been advanced forward by the ability to calculate theoretical parameters with increasing accuracy and efficiency. These calculations are compared to experimental data to produce high resolution structures. The progression of these applications has been made possible by improved instrumentation, data processing methods, probe and experiment design, better quality functionals and basis sets, as well as increased computational power. This research is especially relevant with the emergence of artificial intelligence, which has great potential to expedite steps …

Locating Diversity In Reservoir Computing Using Bayesian Hyperparameter Optimization, Whitney Lunceford

Theses and Dissertations

Reservoir computers rely on an internal network to predict the future state(s) of dynamical processes. To understand how a reservoir's accuracy depends on this network, we study how varying the networ's topology and scaling affects the reservoir's ability to predict the chaotic dynamics on the Lorenz attractor. We define a metric for diversity, the property describing the variety of the responses of the nodes that make up reservoir's internal network. We use Bayesian hyperparameter optimization to find optimal hyperparameters and explore the relationships between diversity, accuracy of model predictions, and model hyperparameters. The content regarding the VPT metric, the effects …

Improving Ligand Pharmacokinetics Via Bioisosteric Substitution Of Amides With 1, 2, 3-Triazoles While Maintaining Dopamine D4 Receptor Selectivity And Potency, Mohammad Alkhatib

Theses and Dissertations

Several neurophysiological functions, such as attention, cognition, and emotional regulation, are regulated by the binding of dopamine on G-Protein coupled receptors. D4Rs are mainly located in the prefrontal cortex and the hippocampus and are known to have a key role in decision-making, memory formation, cognition, and attention. Administration of D4Rs- selective ligands during the preclinical studies enhance the outcomes and the results in animal models suffering from neuropsychiatric disorders, including ADHD, substance use disorders (SUD), and Alzheimer's disease. Recently, we reported a group of D4R-selective ligands with different efficacies depending on their phenylpiperazine ring scaffold to illustrate D4R function during …

Estimating Dis Performance Using Mininet, Ryan D. Winz

Theses and Dissertations

Real time distributed simulation is an exceptionally useful tool for training and wargaming used by the military and industry alike. This research aims to provide scenarios and structures to evaluate the effect of distributing simulations among different compute nodes. Specific scenarios involve the analysis of performance as a function of latency and the degree network protocols and reliability affect simulation performance. Various standards exist for administering geographically separated simulations. The focus of this thesis will be on the Distributed Interactive Simulation standard, a peer-to-peer open standard for simulation messages to adhere to, but lessons can be extended to other standards.

Investigating Temperature And Emissivity Separation Techniques Through The Use Of A Spectral Emissivity Database, Mitchell A. Manzardo

Theses and Dissertations

This study evaluates two temperature and emissivity separation techniques using data from an ABB MR304 FTIR and a Telops Mid-Wave Hyper-Cam on carbon-based samples heated by a high-energy laser. The established Linear Spectral Emissivity Constraint (LSEC) and Gray Body Emissivity (GBE) methods, as well as a new Robust Ratio Linear Fitting (RRF) method are assessed for their applicability in controlled lab environments. A spectral emissivity database aids in analyzing method performance. The RRF method shows promise but has limitations, particularly with low-temperature data and complex Hyper-Cam data. Despite these challenges, the RRF method offers potential for further refinement.

Generation And Implementation Of Mixed Radiation From Laser Interactions On A Liquid Target, Benjamin M. Knight

Theses and Dissertations

The simultaneous generation of different forms of radiation from the same source is highly desirable but technically challenging. We present the generation of D-D fusion neutrons and >MeV-energy x-rays from extreme intensity (∼ 10¹⁹ W/cm² ), high repetition-rate (1 kHz) laser pulses on a thin liquid target. Total neutron fluxes of ∼ 105 neutrons/s and x-ray exposure rates up to ∼ 200 R/hr at 74 cm are measured. Evidence of D-D fusion is verified with a measurement suite including three independent neutron detection systems: an EJ-309 organic scintillator, a ³He proportional counter, and a set of 36 …

Collisional And Spatial Characterization Of Electron Broadening In Rubidium Vapor, Jordan C. Mindrup

Theses and Dissertations

Stark broadening of the 4²D → 12²F transition of rubidium is examined in a rubidium vapor cell with modulated double pump-probe spectroscopy at helium pressures of 0.001 Torr, 3.0 Torr, and 30.0 Torr. A pressure dependent critical rubidium number density was observed after which the presence of electrons becomes dominant. It is observed that after the rubidium density threshold, a linear relationship is seen between rubidium number density and the Stark broadened width of the line shapes, with a slope of 0.079 ± 0.002, 0.18 ± 0.01, and 0.9 ± 0.1 for helium pressures of 0.001 …

Advancing Robust Autonomous System Localization: Labeling Optimizations For Convolutional Neural Networks, Jeffrey L. Choate

Theses and Dissertations

AAR is increasingly critical as aircraft autonomy advances, particularly for the Global Strike mission of the USAF, enhancing operational range and endurance. Traditional methods relying on GPS and custom communication links are limited in GPS-denied environments. This dissertation advances a single camera method to estimate object pose across three interconnected studies. The system trains a CNN on synthetic imagery to predict bboxes for object components, Solve-PnP algorithm finds the 6DoF pose, then employs novel pseudo-labeling on real-world images. These findings are pivotal for the AAR community and contribute to robotics, computer vision, and CNN research. By enabling robust GPS-free autonomous …

Autonomous Experimentation For Accelerated Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano

Theses and Dissertations

Additive Manufacturing (AM), also known as 3D printing, has emerged as a key component of Industry 4.0, enabling reduced cost, quick production, greater sustainability, and increased design complexity compared to its traditional manufacturing counterpart. Currently, Fused Deposition Modeling (FDM) technology dominates the AM market with respect to the number of 3D printers in use. However, the FDM process is sensitive to changes in system configuration, especially the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters, acting as a barrier to the technology.

To enable greater accessibility …

Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii

Theses and Dissertations

This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …

Machine Visual Perception For Autonomous Docking Maneuvers, Derek B. Worth

Theses and Dissertations

This dissertation presents a novel approach to autonomous docking using machine learning for visual perception, particularly during probe and drogue aerial refueling. Autonomous vehicles have become pervasive in both civilian and defense sectors, and their ability to interact with their surroundings and each other autonomously is critical for future operations. Traditional methods relying on signals or inertial sensors face significant limitations such as interference, jamming, and drift. This research focuses on developing a computer vision-based solution to overcome these limitations. A novel pipeline, termed relative vectoring, is introduced, which utilizes dual object detection and machine learning to estimate relative positions …

Analysis Of Retrial Queueing Systems With Working Vacations And Breakdown Services, Sundararaman M

Theses and Dissertations

The Chapter One titled “Introduction and Preliminaries” deals with the basic definition of queues; characteristics of the queueing theory. It also presents the motivation for this thesis. The literature survey in the area of retrial queue with different phases of service, priority arrivals, feedback, breakdowns, repairs, working vacations and working breakdown service models together with their applications are analyzed.

In Chapter Two “Analysis of an M/G/1 Retrial Queueing System with Priority Customers Under J number of Working Vacations” elaborates on a single server retrial queueing system with priority arrivals under J number of working vacations. If an arriving priority customer …