Physical Sciences and Mathematics Commons^™

Convex Ancient Solutions To Anisotropic Curve Shortening Flow, Benjamin Richards

Doctoral Dissertations

We construct ancient solutions to Anisotropic Curve Shortening Flow, including a
noncompact translator and compact solution that lives in a slab. We then show that
these are the unique ancient solutions that exist in a slab of a given width.

Probabilistic And Data-Driven Methods For Numerical Pdes, Johannes Krotz

Doctoral Dissertations

This dissertation consists of three integral self-contained parts. The first part develops a novel Monte Carlo algorithm, called the near-Maximal Algorithm for Poisson-disk Sampling (nMAPS), to efficiently generate the nodes of a high-quality mesh for the calculation of flow and the associated transport of chemical species in low-permeability fractured rock, such as shale and granite. A good mesh balances accuracy requirements with a reasonable computational cost, i.e., it is generated efficiently, dense where necessary for accuracy, and contains no cells that cause instabilities or blown-up errors. Quality bounds for meshes generated through nMAPS are proven, and its efficiency is demonstrated …

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 …

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 …

Electroweak Interactions On The Deuteron, Jose Luis Bonilla

Doctoral Dissertations

This research explores the intricacies of Electroweak Interactions on the Deuteron, with a particular focus on muon capture and the hyperfine shift in atomic and muonic deuterium. These processes are phenomena of significant importance in nuclear physics. Understanding these interactions is crucial for illuminating fundamental aspects of nuclear structure and providing insight into the fundamental forces and interactions governing atomic and nuclear systems, as well as related processes such as proton-proton fusion or other astrophysical reactions, which are phenomena that cannot be easily reproduced in a laboratory and require a theoretical treatment to predict observables. Through this research, we systematically …

Investigation Of Parasites And Other Pathogens Associated With Eastern Wild Turkey (Meleagris Gallopavo Silvestris) Declines In Tennessee, Laura K. Horton

Doctoral Dissertations

This multi-part research project was carried out in order to investigate and address disease aspects which may be at play in the Middle Tennessee Wild Turkey population. This population has been experiencing declines since a restoration period which spanned from 1990-2000. In combined effort with Tennessee Wildlife Resource Agency, the National Wild Turkey Federation, and collaboration with other research groups at the University of Tennessee, this survey of parasites and diseases of known risk to Wild Turkeys was carried out in the study population from 2020-2023. We evaluated the prevalence of the following pathogens for three field seasons: Histomonas meleagridis …

Data-Driven Model Reduction Strategies For Dynamical Systems, Talha Ahmed

Doctoral Dissertations

Many physically occurring phenomena are nonlinear in nature and can be understood through dynamical systems theory which describes how the state of the particular system evolves in time. However, it is generally cumbersome to analyze these processes in depth because of the nonlinearities in the mathematical model or large sets of equations. Model reduction strategies are employed for such nonlinear processes to reduce the model dimensionality and approximate the full model dynamics. In this study, we focus on data driven model reduction strategies for various biological systems where only observable data is available and illustrate their efficacy.

Our first work …

Pion, Kaon, And Proton Production In Jet-Hadron Correlations From Pb-Pb Collisions At $\Sqrt{S_{Nn}}$=5.02 Tev Using The Alice Detector, Patrick J. Steffanic

Doctoral Dissertations

Interactions between hard probes and the quark-gluon plasma (QGP) are a rich probe of the dynamics of the QGP. In this thesis, we study the production of pions, kaons and protons from jet-hadron correlations in 0-10% and 30-50% central Pb-Pb collisions at √s_NN = 5.02 TeV with the ALICE detector at the LHC. We focus our studies on low momentum jets with 20 GeV/c < p_T^jet < 40 GeV/c using associated hadrons with 1 GeV/c < p_T^assoc. < 10 GeV/c. The yields of associate pions, kaons and protons were measured in the near-side and away-side regions of the jet-hadron correlation function, and the baryon to meson and strange to non-strange meson ratios were computed. This is the first measurement of the strange to non-strange meson ratio for hadrons associated with a jet. We find the baryon to meson ratio for associated hadrons in Pb-Pb collisions to be within uncertainty of the ratio for inclusive charged hadrons in proton-proton collisions. The strange to non-strange meson ratio for associated hadrons in Pb-Pb collisions shows strong enhancement at intermediate p_T^assoc. relative to the ratio for inclusive charged hadrons in proton-proton collisions, with a hint of enhancement over the ratio for inclusive charged hadrons in Pb-Pb collisions. We find that …

Prompt Vs Local Redeposition: Model Refinement And Experimental Design For Understanding High-Z Net Erosion In Magnetic Confinement Fusion, Davis C. Easley

Doctoral Dissertations

The economic and engineering success of magnetic confinement fusion reactors significantly depends upon the optimization of plasma facing component (PFC) design. For high-Z PFCs, the critical engineering condition is minimal net erosion (i.e. gross erosion – redeposition). Here, we present a high-Z net erosion model discriminating three primary redeposition mechanisms: prompt (geometric-driven), local (sheath-driven), and far (scrape-off-layer-driven). Using these distinctions, we show modeling for high-Z net erosion in magnetic-confinement fusion over a matrix of key plasma parameters. With Sobol’ methods we assess the sensitivity of each mechanism and show that prompt-vs-local trade-off critically explains underprediction in redeposition losses of up …

Novel Quantum Algorithms For Noisy Intermediate-Scale Quantum (Nisq) Devices, Shikha Bangar

Doctoral Dissertations

Quantum computers hold the immense potential to revolutionize the current computing technology. However, the available quantum hardware, known as noisy intermediate-scale quantum (NISQ) devices, poses a challenge as these devices have noise and imperfect qubits. Hence, there is a critical need to develop novel algorithms specifically designed to harness the capabilities of these devices. This work aims to develop and optimize such quantum algorithms for implementation on current quantum technologies by integrating principles from physics and machine learning. First, we developed the Quantum Imaginary Time Evolution (QITE) and Quantum Lanczos (QLanczos) algorithms on discrete-variable quantum computing settings to study the …

Quantum Computing And Information For Nuclear Physics, Chenyi Gu

Doctoral Dissertations

Quantum computation and quantum information, hot topics with immense potential, are making exciting strides in nuclear physics. The computational complexity of nuclear physics problems often surpasses the capabilities of classical computers, but quantum computing offers a promising solution. My research delves into the application of quantum computation and quantum information in nuclear physics.

I am curious about how to approach nuclear physics problems on a quantum computer. This dissertation studies how to prepare quantum states with quantum algorithms, as state preparation is a crucial initial step in studying nuclear dynamics. Two different quantum algorithms are studied: (i) the time-dependent method, …

Spectral Characterization Of Volcanic Materials On Earth And Venus, C.J. Leight

Doctoral Dissertations

Earth and Venus are dominated by volcanic landforms, making accurate characterization of the diversity of igneous compositions on the planets’ surfaces key to understanding their geologic histories. Spectral observations in the visible-near-infrared (VNIR) and mid-infrared (MIR) provide rapid characterization of surface composition and mineralogy but are not without limitations. Spectra are sensitive to a number of factors, including observation geometry, collection method (emissivity or reflectance), grain size, phase assemblage, and composition. It is impossible to constrain the various effects of all factors when interpreting orbital data, and simplifications are made, guided by laboratory investigations.

A common simplification is to ignore …

Renormalization As A Tool For Nuclear Scattering & Binding, Chinmay Mishra

Doctoral Dissertations

Chiral effective field theory is the state-of-the-art when it comes to describing low-energy nuclear phenomena in a systematically-improvable and model-independent fashion. It is grounded in quantum chromodynamics – the fundamental theory underlying nuclear interactions – and consists of pion and nucleon degrees of freedom. In this framework, nuclear interaction is mediated by pions at long distances (in comparison to length scales at which quarks and gluons can be resolved), while finite-range “contact” terms account for the physics at short-distances. Together, they are organized into a hierarchy based on the orders (in powers of small momenta over a large momentum scale) …

Investigating Liquid-Liquid Phase Separation In Lipid Bilayers: A Multi-Modal Approach Utilizing Spectroscopy, Microscopy, And Cryo-Em, Karan D. Sharma

Doctoral Dissertations

This thesis explores the characterization of liquid-liquid phase separation in model lipid bilayers using fluorescence, optical microscopy, and cryo-electron microscopy (cryo-EM) integrated with machine learning (ML) analysis. The plasma membrane has a complex composition, lateral heterogeneity and dynamic structure which makes it challenging to study. Simplified model membranes containing three or four-component lipid mixtures, typically comprising low- and high-melting lipids along with cholesterol, form phase separated systems that mimic lateral heterogeneity/lipid rafts in biomembranes. In living cells, lipid rafts are thought to form nanoscopic domains smaller than 200 nm. These domains cannot be resolved by conventional optical microscopy. For a …

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, …

The Geometry Of Ancient Solutions To Curvature Flows, Sathyanarayanan Rengaswami

Doctoral Dissertations

Following the tremendous success of the mean curvature flow, other variants such as the Gauss curvature flow, inverse mean curvature flow have been investigated in great detail, leading to interesting applications to other fields including partial differential equations, convex geometry etc. This calls for an investigation of curvature flow as a general phenomenon. While basic existence and uniqueness results, roundness estimates etc have been obtained, there isn't a substantial body of work that addresses the geometry of solutions of curvature flows and their relation to the choice of speed function used. It is therefore interesting to investigate curvature flows as …

Mathematical Modeling And Numerical Approximations Of Combustion Instability Frequencies And Growth Rates, Harvey B. Ring Iii

Doctoral Dissertations

This dissertation presents a mathematical model and numerical simulations to determine the resonant frequencies and their associated growth rates for longitudinal modes in a combustion system similar to that found in a rocket engine. The mathematical model, which is applicable to a two-duct system with a thin flame between the two ducts, each of which having constant area and properties, considers the case of axial mean velocity and uses a vibrating wall at the inlet to select the frequency so that all modes may be found. The model is applied to the acoustics equations describing pressure and velocity fluctuations, derived …

Enhancing Security And Usability In Password-Based Web Systems Through Standardized Authentication Interactions, Anuj Gautam

Doctoral Dissertations

Password-based authentication is the predominant method for securing access on the web, yet it is fraught with challenges due to the web’s lack of inherent design for authentication. Password managers have emerged as auxiliary tools to assist users in generating, storing, and inputting passwords more securely and efficiently. But both the browser and the server are oblivious of the password manager’s presence, leading to usability and security issues. However, because the web wasn’t originally built to accommodate password-based authentication, password managers serve as a temporary fix and encounter several usability and security problems that limit their widespread use. This dissertation …

Investigation Of Magnetic, Spectroscopic, And Structural Properties Of Molecular Metal Compounds, Alexandria Bone

Doctoral Dissertations

Compounds exhibiting single-molecule magnetism (SMM) are of current interest for potential use in molecular data storage and quantum computing applications. However, rapid magnetic relaxation at desired operating temperatures currently limits the use of these materials, and a more thorough understanding of the magnetic and vibrational transitions that affect magnetic memory is required to inform SMM design. The primary focus of this dissertation is the study of magnetic and vibrational modes in molecular magnetic compounds via advanced spectroscopic techniques such as inelastic neutron scattering (INS), far-IR magneto-spectroscopy (FIRMS), and high-field, high-frequency electron paramagnetic resonance (HFEPR) to directly observe transitions among zero-field …

Classifying Facial Expressions Of Students Being Tutored In A Gateway College Math Course, Kriss Gabourel

Doctoral Dissertations

When it comes to tutoring, computers have not quite been able to achieve the success that humans have in helping students improve learning outcomes. This research sought to address one aspect of what makes human tutors more effective, the ability to identify and to interpret facial expressions. When a student is feeling anxious, confused, distracted or frustrated, or when a student has an ‘aha’ moment, human tutors can identify the student’s facial expressions and adjust their tutoring approach as necessary. This study sought to determine if, in the context of a gateway college math course, these particular learning-centered affects could …

Multi-Objective Radiological Analysis In Real Environments, David Raji

Doctoral Dissertations

Designing systems to solve problems arising in real-world radiological scenarios is a highly challenging task due to the contextual complexities that arise. Among these are emergency response, environmental exploration, and radiological threat detection. An approach to handling problems for these applications with explicitly multi-objective formulations is advanced. This is brought into focus with investigation of a number of case studies in both natural and urban environments. These include node placement in and path planning through radioactivity-contaminated areas, radiation detection sensor network measurement update sensitivity, control schemes for multi-robot radioactive exploration in unknown environments, and adversarial analysis for an urban nuclear …