Physical Sciences and Mathematics Commons^™

Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An

Civil Engineering Faculty Publications

Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based …

Collective Agency And Community Resilience Among Farmworkers In The Lower Rio Grande Valley, Angel Velazquez

Theses and Dissertations

This research assesses how farmworkers in the Lower Rio Grande Valley of Texas use everyday practices to build resilience and adapt to changes in their lives. I apply two interconnected theories, White’s (2017, 2019) framework of collective agency and community resilience and Castro and Sen’s (2022) framework of everyday adaptation actions, to study these practices. I conducted 19 semi-structured interviews with Latino farmworkers and professionals who work with local farmworker organizations in the Rio Grande Valley (RGV) of Texas. Interview transcripts were analyzed to determine if and how Latino farmworkers in the RGV can work towards community resilience and implement …

Determination Of An Effective Natural Boxelder Bug (Boisea Trivittata) Repellent, Reyna Chavez

Theses and Dissertations

This study investigates the use of ecofriendly repellant alternatives to pest management to control boxelder bugs (Boisea trivittata). This bug gathers by the thousands on warm surfaces of exterior suburban homes during early fall and late spring. I determined bug repellency to turmeric oil (Curcuma longa), catnip (Nepeta cataria), citronella (Cymbopogon nardus), and lavender (Lavandula angustifolia). The treatments consisted of essential oils containing 20% methanol. All treatments demonstrated repellency of Boxelder bugs. Citronella was the most effective and had the highest level of repellency range of 90-99%. Catnip was the least effective with a repellency range of 63-82%.

Clustering 24-Hour Ambulatory Blood Pressure Time Series With Dynamic Time Warping And Time Warp Edit Distance, John Knight

Theses and Dissertations

Ambulatory blood pressure monitoring (ABPM) captures dynamic circadian changes in blood pressure (BP) that are not reflected in static clinic readings. This study applied time-series clustering with two elastic distance measures—Dynamic Time Warping (DTW) and Time Warp Edit Distance (TWED)—to identify distinct phenotypes in 2,155 24-hour ABPM time series from participants in the Maracaibo Aging Study. DTW and TWED both yielded three clusters corresponding to non-dipping, moderate-dipping, and strong-dipping patterns. The non-dipping group showed elevated nighttime BP, associated with greater cardiovascular and cognitive risk, while the strong-dipping group was associated with higher education and younger age in baseline clinic measurements. …

A Picture Tells A Thousand Words—, But Ecg Signals Have More To Say, Ashley N. Gomez

Theses and Dissertations

With the increasing adoption of deep learning classification models in the medical domain, a critical challenge remains: achieving high predictive accuracy while maintaining clinical Inter-pretability. This study examines how model architecture, dataset origin, and the use of full versus subset data affect both classification performance and Interpretability in Electrocardiogram (ECG) signal analysis. ResNet18 is evaluated using an open-source ECG Image Dataset, thus a custom dataset derived from digitized ECG images. Post-hoc explainability methods, such as Integrated Gradients, are applied to determine which time steps have the most significant influence on model decisions. The findings demonstrate that model architecture and dataset …

Efficient Self-Supervised Representation Learning For Large-Scale Time Series Classification, Kevin Garcia

Theses and Dissertations

Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical …

Interpretable Alignment Of Textual Weather Reports With Local Sensor Time Series For Extreme Weather Event Visualization, Juan Luis Garza

Theses and Dissertations

This thesis presents an automated and interpretable pipeline that links natural-language weather narratives with local meteorological sensor time series. Using large language models, NOAA-style event reports are transformed into structured records capturing event type, timing, descriptive context, and uncertainty. Each extracted event is aligned with harmonized temperature, precipitation, and wind measurements from nearby weather stations, enabling systematic comparisons between narrative evidence and observed atmospheric conditions.

Across roughly fifty stations and more than two thousand events, the analyses show that discrepancies between narrative descriptions and sensor behavior arise primarily from spatial separation rather than from temporal offsets, sensor preprocessing artifacts, or …

Fractals, Reachability, And Computation In Models Of Dna Self-Assembly And Chemical Reaction Networks, Ryan Arlie Knobel

Theses and Dissertations

This thesis serves as the bridge between results compiled across varying models of tile self-assembly, molecular computation, and game complexity. As such, this thesis is broken into three chapters. In the first part, we show how to generate any Discrete Self-Similar Fractal (DSSF) with a feasible generator in the seeded Tile Assembly model, a model limited to single tile attachments and pairwise state transitions. In the next part, we study models of molecular computation, where we consider the problem of reachability in Chemical Reaction Networks and similar model extensions. The final part is a game-complexity analysis of Celtic! and k-ago, …

Seagrass Preference Among Pinfish In The Lower Laguna Madre, Nathaniel Garza

Theses and Dissertations

Pinfish (Lagodon rhomboides) is a common estuarine fish species found from Massachusetts to the Yucatan, Mexico. As juveniles, pinfish spend a significant portion of their life in coastal seagrasses which provide both shelter from predators and food. Along the Texas coast, there are three major species of seagrass- turtle grass (Thalassia testudinum), manatee grass (Syringodium filiforme) and shoal grass (Halodule wrightii) that differ in structural complexity. The goal of this study was to determine if pinfish associate more with one seagrass species than the others. A year-long field study was completed using twelve sample sites in the Lower Laguna Madre, …

Connections On Manifolds: A Modern Approach To Classical Differential Geometry, Jed Allen Wilshire

Theses and Dissertations

The purpose of this thesis is to connect modern differential geometry concepts to the classical differential geometry study of surfaces, specifically spheres. This thesis will cover the topics of Riemannian manifolds, tangent spaces, vectors, vector fields, the connection of Levi-Civita, the Christoffel symbols, and Riemannian curvature. Each concept will be demonstrated on the well known unit sphere manifold.

Population Structure Of Red Drum (Sciaenops Ocellatus (Linnaeus, 1766)) In Two Systems In The Northwestern Gulf Of Mexico, Sandra Antonia Leal

Theses and Dissertations

Red drum (Sciaenops ocellatus) are an economically significant species of fish inhabiting the Gulf of Mexico. There are limited studies assessing S. ocellatus population structure focused on the southern coast of Texas. A total of 1,008 fish were collected over one year on a quarterly basis at South Bay (SB) and Holly Beach (HB) in the Lower Laguna Madre (LLM) of Texas to assess population structure. Juvenile S. ocellatus utilize the water systems of the LLM as a nursery ground and were more abundant at SB (n = 718) than HB (n = 290). The most abundant age classes were …

A Computational Approach To Ramanujan-Type Congruences For Products Of Level 13, Aiden Michael Lalonde

Theses and Dissertations

In this thesis, we find products that are modular forms of level 13 whose Fourier coefficients satisfy certain congruences. These products are constructed in terms of integer powers of Klein forms and the Dedekind eta function. The products that are modular forms for Γ1 (13) have vectors of exponents that are elements of a bounded polytope. A parameterization for this polytope allows us to find a complete set of products that are modular forms for Γ1 (13). The corresponding graded ring of modular forms containing these products is constructed from a finite set of generators. These generators satisfy a finite …

Insecticide-Treated Net Use And Elimination Of Malaria In Sub-Saharan African Countries: Assessing The Global Technical Strategy Using An Evolutionary Game Approach, Laxmi, Tamer Oraby, Michael G. Tyshenko, Ina Danquah, Samit Bhattacharyya

School of Mathematical & Statistical Sciences Faculty Publications

Background: Malaria continues to be a major public health challenge in Sub-Saharan Africa (SSA), where the majority of the countries have not met the World Health Assembly's endorsed Global Technical Strategy (GTS) milestones in 2020 for malaria reduction. Insecticide-treated net (ITN) usage is a well-established and effective intervention, often outperforming other measures such as indoor residual spraying (IRS). However, multiple survey studies have reported improper use of ITNs across various SSA countries. This misuse likely poses an important barrier to the intervention's success, although it remains a largely untested hypothesis.

Methods: We developed a behaviour-incidence model and statistical analysis of …

Quantization For The Mixtures Of Overlap Probability Distributions, Asha Barua, Angelina Chavera, Ivan Djordjevic, Valerie Manzano, Sergio Soto Quintero, Mrinal Kanti Roychowdhury, Hilda Tejeda

School of Mathematical & Statistical Sciences Faculty Publications

Optimal quantization for mixed distributions has emerged as a compelling area of study. In this work, we have focused on a mixed distribution formed from two uniform distributions with partially overlapping supports. For this class of distributions, we have examined the structure of optimal sets of n-means and the corresponding nth quantization errors for all positive integers n. Initially, we explicitly determined the optimal sets and quantization errors for 1 < = n < = 6. Subsequently, we established several key lemmas and propositions and proposed an algorithm that facilitates the computation of optimal n-means and quantization errors for all n >= 5. Numerical results are also presented to illustrate the application of the algorithm in deriving these quantities. The findings of this study offer valuable insight and serve as a …

Is Noise Exposure Associated With Impaired Extended High Frequency Hearing Despite A Normal Audiogram? A Systematic Review And Meta-Analysis, Sajana Aryal, Monica Trevino, Hansapani Rodrigo, Srikanta K. Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Understanding the initial signature of noise-induced auditory damage remains a significant priority. Animal models suggest the cochlear base is particularly vulnerable to noise, raising the possibility that early-stage noise exposure could be linked to basal cochlear dysfunction, even when thresholds at 0.25-8 kHz are normal. To investigate this in humans, we conducted a meta-analysis following a systematic review, examining the association between noise exposure and hearing in frequencies from 9 to 20 kHz as a marker for basal cochlear dysfunction. Systematic review and meta-analysis followed PRISMA guidelines and the PICOS framework. Studies on noise exposure and hearing in the 9 …

Interactions Between Black, Brown, Blue And Green Carbon From Terrestrial To Marine Ecosystems: A Critical Review, Allison White, Richard Kline, Md. Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Black and brown carbon are byproducts of the incomplete combustion of biomass or fossil fuels. These light-absorbing aerosols influence climate by altering the radiative balance through the absorption and scattering of sunlight, exacerbating global climate change. Their subsequent deposition on terrestrial and marine environments affects surface albedo, snow and ice melt, and biogeochemical processes, leading to further environmental consequences. In contrast, green and blue carbon refer to carbon stored in terrestrial vegetation and marine ecosystems, respectively. These ecosystems play a crucial role in mitigating climate change by sequestering atmospheric carbon; however, they are increasingly threatened by climatic disturbances, anthropogenic activities, …

Leptinotarsa Texana Schaeffer And Gargaphia Arizonica Drake & Carvalho As Potential Biocontrol Agents For The Noxious Weed Solanum Elaeagnifolium Cav, Samikshya Subedi, Stephanie L. Kasper, Alexis Racelis, Greg Lefoe, Rupesh R. Kariyat

School of Earth, Environmental, & Marine Sciences Faculty Publications

Silverleaf nightshade (Solanum elaeagnifolium Cav.; SLN) is a perennial forb native to the southern United States, Mexico and South America that has become a serious agricultural weed across the world. Biological control has emerged as a significant alternative for the management of (SLN) due to the challenges and high costs associated with chemical and mechanical controls. In this study, we used a combination of field and laboratory studies to (1) explore the fundamental and realized host ranges of two North American insects, Leptinotarsa texana Schaeffer and Gargaphia arizonica Drake & Carvalho and (2) assess their suitability as potential biological …

Long-Term Sedimentation Rates And Patterns Of Alluvial Ridge Basins (Esteros) Of The Rio Grande Delta (Tx, United States Of America), Kynan Joseph Rutan

Theses and Dissertations

Little is known about the century-scale sedimentation rates and patterns of Alluvial Ridge basins within river deltas, especially in the Rio Grande Delta (RGD). To better constrain this, 22 cores were collected in the Bahia Grande Complex (BGC) and analyzed for grain size, organic content, age dates, and elemental composition.

Results show that grain size and sedimentation rates decrease from an artificial inlet to the basin interior, with elevated sedimentation in the south. Also, human activities, such as the construction of the Brownsville shipping channel in the 1930s, altered sedimentation dynamics in the basin by blocking natural inlets and leading …

Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking, Thoa Thieu, Roderick Melnik

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comparative evaluation of three nonlinear state estimation filters, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF), for the task of 3D facial landmark tracking. Using a publicly available dataset, we assess each filter's performance under both deterministic (noise-free) and stochastic (noisy) conditions. Metrics such as mean squared error (MSE), convergence rates of state and covariance estimates, and consistency over time are used to quantify tracking performance. Results show that the EKF consistently outperforms the UKF and PF, achieving faster convergence and lower estimation error, particularly in scenarios characterized by mild nonlinearity. …

Variants Of Conway Checkers And K-Nacci Jumping, Glenn Bruda, Joseph Cooper, Kareem Jaber, Raul Marquez, Steven J. Miller

School of Mathematical & Statistical Sciences Faculty Publications

Conway Checkers is a game played with a checker placed in each square of the lower half of an infinite checkerboard. Pieces move by jumping over an adjacent checker, removing the checker jumped over. Conway showed that it is not possible to reach row 5 in finitely many moves by weighting each cell in the board by powers of the golden ratio such that no move increases the total weight.

Other authors have considered the game played on many different boards, including generalizing the standard game to higher dimensions. We work on a board of arbitrary dimension, where we allow …

Precision-Weighted Federated Learning, Jonatan Reyes, Lisa Di Jorio, Cecile Low-Kam, Marta Kersten-Oertel

Computer Science Faculty Publications

Federated learning (FL) using the federated averaging (FedAvg) algorithm has shown great advantages for large-scale applications that rely on collaborative learning, especially when the training data is either unbalanced or inaccessible due to privacy constraints. We hypothesize that FedAvg underestimates the full extent of heterogeneity of data when the aggregation is performed. We propose Precision-Weighted Federated Learning (PW) a novel algorithm that takes into account the second raw moment (uncentered variance) of the stochastic gradient when computing the weighted average of the parameters of independent models trained in a FL setting. With PW, we address the communication and statistical challenges …

Adaptive Von Mises–Fisher Likelihood Loss For Supervised Deep Time Series Hashing, Juan Manuel Perez, Kevin Garcia, Brooklyn Berry, Dongjin Song, Yifeng Gao

Computer Science Faculty Publications

Indexing time series by creating compact binary representations is a fundamental task in time series data mining. Recently, deep learning-based hashing methods have proven effective for indexing time series based on semantic meaning rather than just raw similarity. The purpose of deep hashing is to map samples with the same semantic meaning to identical binary hash codes, enabling more efficient search and retrieval. Unlike other supervised representation learning methods, supervised deep hashing requires a discretization step to convert real-valued representations into binary codes, but this can induce significant information loss. In this paper, we propose a von Mises–Fisher (vMF) hashing …

Cosmetic Surgery And Physiological Disorder: You Should Talk To Someone About Your Unwinding Anxiety, Mona Muzammil, Washain Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Body dysmorphic disorder is a mental health condition in which you can't stop thinking about one or more perceived defects or flaws in your appearance — a flaw that appears minor or can't be seen by others. But you may feel so embarrassed, ashamed and anxious that you may avoid many social situations.

When you have body dysmorphic disorder, you intensely focus on your appearance and body image, repeatedly checking the mirror, grooming or seeking reassurance, sometimes for many hours each day. Your perceived flaw and the repetitive behaviors cause you significant distress and impact your ability to function in …

Computational Expressions Of Void Reactions In Extended Chemical Reaction Network Models, Aiden J. Massie

Theses and Dissertations

Chemical Reaction Networks (CRNs) are a system of abstraction of real-world chemical dynamics. Each CRN system is defined as a pair of molecular species and reaction rules, which consume a set of reactant species and create a new set of product species. In this paper, we investigate the simple class of void reactions, which cannot create new species and are computationally weak with small-enough sizes in basic CRNs. Here, we study their computational expression in more powerful extended CRN models. Specifically, we consider the Step CRN model, in which new species are added into the system through a sequence of …

Interactive Effects Of Nutrient Loading And Hyposalinityon The Physiological Responses Of The Red Macroalga Gracilaria Tikvahiae, Donavuan Vincent Salazar

Theses and Dissertations

Coastal macroalgae face anthropogenic stressors such as nutrient enrichment from nitrogen fertilizers and reduced salinity linked to flooding. The physiological impacts of these combined stressors remain unclear. This study examined interactive effects of nitrogen loading and hyposalinity on Gracilaria tikvahiae through a 9‑day laboratory experiment. Samples experienced declining salinity (35–0 ppt) with nitrate or ammonia enrichment (120 µmol/L). Measurements included primary production, respiration, biomass, nitrate uptake, and chlorophyll‑a. Hyposalinity significantly reduced physiological performance, with gross primary production declining over 100%, chlorophyll‑a content 25–45% lower, biomass loss, and reduced nitrogen uptake. Nitrogen enrichment failed to mitigate the adverse impacts under hyposaline …

Modern Water And Sediment Transport Patterns In Alluvial Ridge Basins Of The Rio Grande Delta, South Texas, Usa, Waqid Nabi

Theses and Dissertations

Alluvial ridge (AR) basins are shallow, marine-influenced depressions within river deltas, yet their flow and sediment dynamics remain poorly understood. This study examines hydrodynamics and sediment transport patterns in the Bahia Grande Complex, which are AR basins of the Rio Grande Delta, using tilt current meters, water-level and turbidity loggers, and suspended and bed-sediment sampling. The AR basins function as wind-dominated, microtidal environments, where pre-existing basin geometry and hydrodynamic forcings control water flow and sediment distribution patterns. Seasonal wind patterns and storms modulate water levels and flow connectivity. Substrate patterns reflect frequent resuspension in the shallow northern basin, maintaining coarse …

Design And Evaluation Of Gamified Kahoot Modules For Improving Engagement And Conceptual Understanding Of Introductory Physics Students, Yaritza Villarreal

Theses and Dissertations

This study investigates the effectiveness of gamified learning modules in enhancing the engagement and conceptual understanding of introductory physics students. The first phase of this study focused on the design and development of gamified learning modules for selected topics in introductory physics. The modules were developed by integrating principles of Backward Design, following an escape-room theme with interactive scenarios, and incorporating classic game elements (e.g., points and leaderboards).

The second phase examined the effectiveness of the gamified Kahoot modules using a pretest-posttest control-group design. Sixty-eight (68) students were equally randomly assigned to the control and experimental groups. The experimental group …

Auction Consensus Algorithm With Loss Mechanism For Decentralized Task Allocation, Jose Rodriguez, Wenjie Dong, Constantine Tarawneh, Qi Lu

Electrical and Computer Engineering Faculty Publications

This paper presents an Auction-Consensus Algorithm with a Loss Mechanism (ACALM), a decentralized task allocation method for multi-robot systems that enhances the existing Consensus-Based Auction Algorithm (CBAA) by incorporating a novel loss propagation mechanism. In contrast to purely greedy bidding strategies, it enables agents to dynamically update task priorities based on the accumulated loss from previously unsuccessful bids. This extended work reduces globally inefficient allocations caused by early suboptimal decisions. The proposed approach is evaluated through large-scale simulations in thousands of randomized scenarios and swarm sizes ranging from 5 to 120 robots. Compared to existing CBAA and GCAA algorithms, ACALM …

Semi-Hyponormality Of Commuting Pairs Of Hilbert Space Operators, Raul E. Curto, Jasang Yoon

School of Mathematical & Statistical Sciences Faculty Publications

We first find an explicit formula for the square root of positive 2 ×2 operator matrices with commuting entries, and then use it to define and study semi-hyponormality for commuting pairs of Hilbert space operators. For the well-known 3–parameter family 𝑊(𝛼,𝛽)⁡(𝑎,𝑥,𝑦) of 2–variable weighted shifts, we completely identify the parametric regions in the open unit cube where 𝑊(𝛼,𝛽)⁡(𝑎,𝑥,𝑦) is subnormal, hyponormal, semi-hyponormal, and weakly hyponormal. As a result, we describe in detail concrete sub-regions where each property holds. For instance, we identify the specific sub-region where weak hyponormality holds but semi-hyponormality does not hold, and vice versa. To accomplish this, …

Adsorption-Photodegradation Performance Of Silver Titanate For Methylene Blue Removal: Kinetics, Thermodynamics And Isotherm Studies, Nawrin Rahman Shefa, Most. Afroza Khatun, Ahmed Hasnain Jalal, M. Jasim Uddin, Md. Wasikur Rahman

Physics & Astronomy Faculty Publications

Silver titanate (AgTO) was synthesized via an ion-exchange reaction between sodium titanate and silver nitrate. This study investigates the efficiency of AgTO in the combined adsorption-photodegradation process for removing Methylene Blue (MB). The synthesized material was characterized using FTIR, XRD, SEM, and EDX techniques. Batch adsorption experiments were conducted to assess the effects of AgTO dosage (0.1–1.0 g/L), initial MB concentration (5–20 mg/L), pH (3–11), and temperature (313–333 K). The highest MB removal efficiency (90 %) was achieved at 313 K, pH 3, and an initial MB concentration of 5 mg/L. The photocatalytic performance of AgTO was further evaluated under …