Applied Mathematics Commons^™

Modeling The Impacts Of The Current And Projected Temperatures On Spongy Moth Population Dynamics, Adrienne B. Spring

Theses and Dissertations

The spongy moth (Lymantria dispar) is an invasive forest pest that has caused significant ecological damage across the United States. Its invasion front is shaped by a number of factors, including temperature in both the northern and southern regions. With ongoing climate change, areas that were previously uninhabitable may become increasingly favorable for moth population establishment and expansion, while other areas may experience thermal stress limiting persistence. This study develops a temperature-driven population model to analyze how temperature affects the spongy moth population dynamics along the invasion front. This model incorporates temperature effects on fecundity, stage-specific survival rates, …

Centralized Deep Reinforcement Learning For Homogeneous Multi-Component Maintenance Optimization, Joseph W. Wittrock

Theses and Dissertations

This thesis explores an application of reinforcement learning (RL) in maintenance optimization. Recent advances in hardware-accelerated computation and deep learning have made RL a powerful tool for solving optimization problems which are too complex for traditional methods. Maintenance optimization involves improving the efficiency and effectiveness of maintenance activities through data-driven approaches, ultimately reducing costs and increasing asset availability. Making informed maintenance decisions is crucial to long-term sustainability.

A desirable maintenance policy maximizes a utility signal while minimizing the cost of maintenance. Techniques in sequential decision making such as dynamic programming (DP) and RL have found success in optimizing these maintenance …

Investigating The Temperature Effects On Aedes Aegypti And Dengue Virus In Central Argentina: Perspectives From Mathematical Modeling, Morgan H. Jackson

Theses and Dissertations

Dengue virus (DENV) causes over 390 million infections and around 40,000 deaths worldwide each year. DENV is primarily transmitted by the mosquito Aedes aegypti, and both the life cycle of these mosquitoes and DENV transmission are significantly impacted by temperature. In the temperate region of Central Argentina, where dengue outbreaks first began in 2009, outbreaks only occur following new introductions of DENV from other regions. Due to the relationships between temperature and DENV and temperature and Ae. aegypti, the risk of an outbreak changes throughout the year. Here, we develop and analyze mathematical models for both mosquito population dynamics and …

Swarming Segregation: Leveraging Swarm Intelligence And Regionalization As Instruments For School District Desegregation, Jeffrey Wooten

Theses and Dissertations

Even after Brown led to the South briefly having the most diverse schools in the nation, schools throughout the Northeast have remained the most segregated in the nation for decades. While federal jurisprudence has made compelling desegregation pursuant to the Equal Protection Clause more challenging, New Jersey has a particularly favorable landscape to address severe segregation. With a highly diverse, densely populated public enrollment, favorable state constitutional precedent, and a history of successfully compelling desegregation, New Jersey is fertile ground exploring regional desegregation. Scholars, judges, and even plaintiffs in ongoing litigation (Latino Action Network v. N.J.) have called for New …

Traveling Waves In Biological Population Models, Ahlam Alzahrani

Theses and Dissertations

In this thesis, we examine the existence of traveling waves in population models. We begin by exploring conditions under which traveling waves exist, even when the migration probability lacks a density function or, if the density function exists, it may be discontinuous. To address this, we impose certain conditions that ensure the monotonicity of the evolution operator, enabling the application of the Monotone Iteration Method. Next, we explore the existence of monotone traveling waves in a general class of integral-difference population models that depend on both the previous state and long-term memory, allowing for the consideration of multiple past states. …

Investigating The Efficacy Of Prompt Engineering Techniques For Research Survey Paper Generation Using Large Language Models, Innocent Obed Awidi

Theses and Dissertations

The recent advancements in large language models (LLMs), such as ChatGPT, offer promising potential for automating academic writing tasks. This study investigates the efficacy of prompt engineering techniques in guiding LLMs to generate high-quality research survey papers, specifically exploring their application in the fields of artificial intelligence (AI) in drug development and large language model customization for classification. Using a mixed-methods approach, this research assessed the impact of prompt engineering on the coherence, relevance, and overall quality of generated content through quantitative and qualitative analyses. Techniques including prompt refinement, contextual prompting, and iterative prompting were systematically applied to enhance the …

Bayesian Lasso Regularized Quantile Regression And Its Applications, Priscilla Kissi-Appiah

Theses and Dissertations

Since the pioneering work of (Koenker and Bassett Jr 1978), quantile regression has been a popular regression technique that helps researchers investigate a whole distribution of the response variable. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. In this research, we study the lasso regularized quantile regression from a Bayesian perspective. We develop an efficient sampling algorithm to generate posterior samplings for making posterior inference by using a location-scale …

Macro–Micro-Coupled Simulations Of Bead–Spring Breaking-Reforming Networks, Andrei Medved

Theses and Dissertations

In this work we investigate the dynamic behavior of bead-spring polymer solutions in viscoelastic fluids, which are essential in industries like materials science, biotechnology, and pharmaceuticals. The study leverages GPU-accelerated simulations and detailed modeling of polymer chain dynamics at the mesoscale, which enables efficient analysis of intricate fluid behaviors and the microscale dynamics of polymer chains. Additionally, the research examines the breaking-reforming dynamics of polymer chains, crucial for understanding phenomena such as shear thinning and thickening. The findings have broad applications, from improving inkjet printing and 3D printing technologies to developing new drug delivery systems and biocompatible materials. This work …

Study On Factors Influencing The Strong Chromatic Index Of Graphs Derived From Path Graphs, Star Graphs, And Cycle Graphs, Vikram St

Theses and Dissertations

Graph coloring is a major research area in Graph Theory that has a rich collection of research articles contributed by mathematicians across the world. This thesis focuses on a specific type of Graph coloring where the edges of a graph are colored by a principle called strong edge-coloring. There have been numerous research articles published on the least number of colors required to define such a coloring for a given graph G. This least number, χ′s (G), is called as the strong chromatic index of a graph G. Though conjectures stated as early as 1989 remain unsolved, there have been …

Existence Of Smooth Solutions For The Landau Equation With Hard Potentials, Shelly Ann Taylor

Theses and Dissertations

This dissertation is concerned with the Landau equation, an integro-differential equation that models the particle density of a plasma as it evolves in phase space. The main topic is the (large-data) local existence of classical solutions to the Landau equation in the case of hard potentials (γ ∈ (0, 1]). Solutions have previously been constructed by Chaturvedi [SIAM. J. Math. Anal., 55(5), 5345–5385, 2023] for initial data in an exponentially-weighted Sobolev space of order 10, but it is not a priori clear whether these solutions have more regularity than the initial data. We improve Chaturvedi’s existence result in two ways: …

An Empirical Study On Detecting And Explaining Global Structural Change In Evolving Graph Using Martingale, Tarun Teja Kairamkonda

Theses and Dissertations

There is a growing interest in practical applications involving networks of interacting entities such as sensor networks, social networks, urban traffic networks, and power grids, all of which can be represented using evolving graphs. Changes in these evolving graphs can signify shifts in the behavior of interacting entities or alterations in the patterns of their interactions. Identifying and detecting these changes is crucial for addressing potential challenges or opportunities in various domains. In this study, we propose an approach for detecting structure change in evolving graphs based on the martingale change detection framework on multiple graph features extracted over time. …

Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya

Theses and Dissertations

Along with the advancement in technology, the role of hardware accelerators is increasing consistently, delivering advancements in scientific simulations and data analysis in scientific computing, signal processing tasks in communication systems, matrix operations, and neural network computations in artificial intelligence and machine learning models. On the other hand, several high-speed computer applications in this era of high-performance computing often depend on ordinary differential equations (ODEs); however, their nonlinear nature can present a challenge to obtaining analytic solutions. Consequently, numerical approaches prove effective in delivering only approximate solutions to these equations. This research discusses the implementation of a customized hardware accelerator …

Existence And Uniqueness Results For Deformable Fractional Differential Equations, Sreedharan R

Theses and Dissertations

Fractional calculus and fractional differential equations are considered to be the valuable tools in modeling many phenomena in various fields of science and engineering. In the literature, many definitions for fractional order derivatives, such as Riemann-Liouville, Caputo, Jumarie, Hadamard, Weyl, and more, were developed to study the fractional differential equations that govern various phenomena in science and engineering. But these definitions have their own limitations, such as derivatives of constants, product of two functions, quotient of two functions, assertion laws, and limiting values of the derivatives at zero and negative numbers.

To overcome the deficiencies, researchers have recently introduced some …

Bayesian Estimation Of Reproduction Numbers From Distributions Of Outbreaks Sizes: Branching Process Approach, Alberta Araba Johnson

Theses and Dissertations

The Generalized Poisson distribution is useful in modeling epidemiological processes as a branching stochastic processes problem. Our goal is to construct accurate and reliable estimators for the reproduction number (R0) (i.e., the number of secondary infections), particularly in the context of disease outbreaks modeled by a Galton-Watson process. Towards this goal, we construct the classical Bayes estimator, the Maximum Likelihood estimator, and the Empirical Bayes (EB) estimator under the Square Error Loss function in Chapter II. We prove that the Empirical Bayes estimator is asymptotically optimal and estimate the rate of convergence. We then proceed to monotonize the Empirical Bayes …

Variational Bias Sampling For Collaborative Filtering Recommender Systems, Prisca Stephens

Theses and Dissertations

Advancements in digitalization has yielded enormous growth of data on online platforms, overwhelming users with multitude of options to choose from. Recommender systems narrow down these options to a few relevant ones thereby facilitating the decision-making processes for users. This study presents a framework for integrating variational bias sampling into model-based collaborative filtering techniques for recommender systems. Variational bias sampling is a novel and unique way to account for random factors that affect explicit ratings in collaborative filtering recommender systems. A Gaussian distribution is used to model all the possible random factors that could affect ratings. Sampling user and item …

The Perspectives Of Using Desmos For Students’ Conceptual Understanding And Procedural Fluency To Solve Linear Equations, Larmel Dimatulac Madrilejos

Theses and Dissertations

The study examines the perspectives of using the Desmos calculator of Algebra I students' conceptual understanding and procedural fluency to write, graph, and solve linear equations in Algebra I STAAR. While the students have continuously used technology for mathematics assessment, emergent bilingual students in South Texas still need help passing high-stakes testing. The framework of the study is grounded in the theory of mathematical education (knowledge of mathematics educators to teach), the theory of mathematical learning (understanding how students learn mathematics), and social constructivism. The study seeks ways to teach all students, mainly the minority, to learn …

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …

System Identification For Motion Control Of Unmanned Aerial Vehicles Platform, Raj Ramesh Agarwal

Theses and Dissertations

Comprehensive research on Unmanned Aerial Vehicles (UAV) system identification for motion control parameters is presented in this thesis, with a focus on the necessity of precise control and improved performance. Using Pseudorandom Binary Sequence (PRBS) and Normally Distributed Random Numbers, it presents a unique technique for excitation of UAV dynamic systems. It also shows how effective random signals are in time-domain identification for precise control in a range of flying circumstances. The piece of research includes a thorough analysis and implementation of various approaches and its further improvements, highlighting the benefits and drawbacks of each. These approaches include the free …

Traveling Wave Fronts Of Reaction Diffusion Differential Equations With Diffusive Delay In Biological And Chemical Models, William Barker

Theses and Dissertations

Functional differential equations (FDE) incorporate past states and or rates in the modeling of physical phenomena. Unlike ordinary and even some partial differential equations there is no method to find solutions of general FDE explicitly. Therefore, in order to study solutions of FDE, iterative techniques are often employed. Reaction diffusion equations are a specific type of partial differential equations that model diffusive spread, the rate of spread of due to a reaction term of some quantity. These equations arise naturally in fields such as biology, ecology and chemistry. In terms of studying dynamics of populations, previous states and even rates …

Applications Of Physics-Informed Graph Neural Networks In Wind Farm Layout Optimization, Emma R. Martin

Theses and Dissertations

Rapid advancements in data-driven deep learning models have led to their increased usage in recent years. Highly-effective models for conducting complex decision-making tasks have been developed. Given their success, data-driven deep learning models are being developed and improved in a variety of areas, one of which is renewable energy. In this thesis, we discuss the development of data-driven, deep learning models for optimizing wind farm layouts for maximum power output. We include a background for wind energy, the physics of fluid flow and power production, and two types of neural networks: graph neural networks and physics-informed neural networks. Additionally, we …

Symmetry Analysis Of The Canonical Connection On Lie Groups:Co-Dimension Two Abelian Nilradical With Abelian And Non Abelian Complement, Nouf Alrubea Almutiben

Theses and Dissertations

We consider the symmetry algebra of the geodesic equations of the canonical
connection on a Lie groups. We mainly consider the solvable indecomposable four,
five and six-dimensional Lie algebras with co-dimension two abelian nilradical, that
have an abelian and not abelian complement. In this particular case, we have only
one algebra in dimension four namely; A4,12 , and three algebras in dimension five
namely; A5,33, A5,34, and A5,35 In dimension six, based on the list of Lie algebras in
Turkowski’s list, there are nineteen such algebras namely; A6,1- A6,19 that have an
abelian complement, and there are eight algebras that …

Penalized Interpolating B-Splines And Their Applications, Kylee L. Hartman-Caballero

Theses and Dissertations

One of the most studied data analysis techniques in Numerical Analysis is interpolation. Interpolation is used in a variety of fields, namely computer graphic design and biomedical research. Among interpolation techniques, cubic splines have been viewed as the standard since at least the 1960s, due to their ease of computation, numerical stability, and the relative smoothness of the interpolating curve. However, cubic splines have notable drawbacks, such as their lack of local control and necessary knowledge of boundary conditions. Arguably a more versatile interpolation technique is the use of B-splines. B-splines, a relative of Bézier curves, allow local control through …

Mathematical Modeling And Analysis Of Inflammation And Tissue Repair: Lung Inflammation And Wound Healing In Corals Under Stress, Quintessa Hay

Theses and Dissertations

A variety of insults, including tissue injury and/or exposure to pathogen, elicit an immune response in many organisms. An improperly regulated immune response can result in deleterious effects to the organism. Here we present models for lung injury in young and old mice and models for wound healing in coral reefs.

It is well known that the immune response becomes less effective in older individuals. This is of particular interest in pulmonary insults such as ventilator induced lung injury (VILI) or lung infection. We extended a mathematical model for the inflammatory response to VILI and used experimental data to select …

Mathematical Analysis Of Eukaryotic Pericentromere, Puranjan Ghimire

Theses and Dissertations

The centromere is crucial for chromosomal stability and their proper segregation during cell division in eukaryotes. Surrounding the centromere are pericentromeres, made of repetitive DNA elements called pericentromeric repeats, varying from 10 in fission yeast to thousands in humans. These repeats form densely packed heterochromatin, where genes are usually silenced. The silencing mechanism across different pericentromeric repeats remains unclear.

Despite variations in sequence and length, pericentromeric repeats are conserved across eukaryotes, indicating their functional importance. This dissertation presents mathematical models to quantify gene silencing in fission yeast and humans. In fission yeast, my model predicts that silencing occurs only with …

Mathematical Modeling Of Phage-Bacteria Population Dynamics, John Lawrence D. Palacios

Theses and Dissertations

Bacteriophages are viruses that infect and replicate within bacteria. Lytic phages cause the bacterial cell to burst, killing the bacteria. These types of phages can be used to treat patients with antibiotic-resistant bacterial infections. As a step in developing successful treatment protocols, we aim to understand the population dynamics of phages and bacteria using an in vitro model. We model the dynamics using the Campbell model, which consists of a delay differential equation (DDE), as a base model. We extended the model by including the emergence of phage resistance. We then compared the DDE model with a parallel ordinary differential …

Long-Wave Modeling Of Viscous Film Dynamics Inside A Tube With Time-Dependent Radius, Robert E. Hicks

Theses and Dissertations

Lung fluid dynamics is a well studied topic and relevant in the field of research surrounding patients with Chronic Obstructive Pulmonary Disease (COPD) and cystic fibrosis. However, the literature on the mathematical description of the net pulmonary fluid transport in the case of constricting and expanding airways is scarce. In this paper we aim to address how breathing contributes to airways clearance in patients with pulmonary diseases such as COPD and CF in which cilia do not function properly. We begin with the flow of a fluid-filled tube with rigid walls of fixed radius and the effects of gravity being …

Stochastic Optimal Control Of Conditional Mckean-Vlasov Equations With Jump And Markovian Switching, Charles Samuel Conly Sharp

Theses and Dissertations

This thesis obtains a number of results in stochastic optimal control for conditional McKean-Vlasov equations with jump and Markovian switching. First, we prove the uniqueness of the solutions and derive a relevant version of Itô's formula. We provide the dynamic programming principle and prove the associated verification theorem. A stochastic maximum principle is established. Further, we derive the relationship between dynamic programming and the stochastic maximum principle. Additionally, we utilize our stochastic maximum principle result for a mean-variance portfolio selection problem.

Mathematical Evaluation Of Ulnar Nerve Somatosensory Evoked Potentials (Sseps), Maribel Carmen Gomez

Theses and Dissertations

As the number of individuals suffering with low back and neck pain rises, we find people undergoing spinal procedures more often. In means, of safeguarding the patient and their neurological structures during the procedure intraoperative neuro-physiological monitoring (I.O.M) has been more widely used amongst surgeons orthopedic and neuro alike. During these procedures, a modality widely used for both low back and neck surgery is somatosensory evoked potentials (SSEPs). The aim of neuro-technicians is to obtain a baseline waveform that can be considered present and reliable. When obtaining SSEPs the technician can encounter obstacles with ’noisy’ wave-forms due to …

Two-Dimensional Boundary Value Problems For Quasi-Linear Hyperbolic Systems Of Second Order, Maram Alrumayh

Theses and Dissertations

Boundary value problems in a characteristic rectangle Ω = [0, ω1] × [0, ω2] for second order quasi-linear hyperbolic systems are considered. The concept of strong well– posedness of a boundary value problem is introduced. For initial–boundary value problems there are established: (i) Necessary and sufficient conditions of strong well–posedness; (ii) Unimprovable sufficient conditions of local and global solvability; (iii) Effective sufficient conditions of solvability of Nicoletti type two–point initial– boundary value problems in case, where the righthand side of the system has arbitrary growth order in some phase variables. For nonlocal boundary value problems there are established: (i) Necessary …

Development Of A Physics-Informed Neural Network For Prediction Of Blood Flow, Marcello Vittorio Mattei Di Eugenio

Theses and Dissertations

Abstract—Objective: We propose a new neural network architecture that accepts point clouds and outputs 3D velocity profiles for aneurysm geometries. Methods: We generated a synthetic aneurysm 3D flow dataset using CFD and used it to train our model architecture and compare it with other popular architectures like U-net and PointNet. We incorporate tools for improving model performance such as incorporating a distance function, a physics-informed loss to enforce the law of mass conservation, and the Huber loss to learn patterns across heterogeneous velocity components of multiple dimensions. Results: The tools implemented together with our architecture achieved the best performance on …