Physical Sciences and Mathematics Commons^™

Process Modeling The Neuroprotective Effects Of A Plant-Based Diet On Parkinson's Disease, Julia Mitchell

Undergraduate Honors Theses

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, bradykinesia, rigidity, and postural instability. Recent research suggests an avenue for potential neuroprotection through dietary intervention, specifically the adoption of a plant-based diet. A plant-based diet predominantly comprises foods derived from plants, emphasizing fruits, vegetables, grains, legumes, and nuts while minimizing or excluding animal products. This thesis aims to explore the biochemical pathways implicated in PD progression and the potential impact of dietary choices on these pathways. The investigation focuses on several key pathways: alpha-synuclein aggregation, the blood-brain barrier crossing of levodopa, oxidative stress, ferroptosis, …

Use Of Molecular Logic Gates For The Tuning Of Chemosensor Dynamic Range, Orhan Acikgoz

Undergraduate Honors Theses

The first molecular logic gates were created in the 1990s; integrating such logic gates into fluorescent chemosensors allowed for the detection of different types of ions in solution. In this study, we have developed a new use of molecular logic gates by having two of the same type of binding site. The two binding sites on a fluorophore that both detect Na⁺ ions led to an increase in the detection limit compared with the chemosensor with a single binding site. Since the two sodium binding sites create an AND logic gate, two sodium ions are needed to generate a …

Modeling Group 3 Medulloblastoma: Describing The Interconnected Pathway Of The Most Common Pediatric Brain Cancer, Amber Cantú

Undergraduate Honors Theses

Group 3 medulloblastoma is one of the most common pediatric brain cancers. Affecting infants and children, this cancer has the worst prognosis of the medulloblastoma group. Current treatments use surgical resection, radiation, and chemotherapy to afflict the cancer, however no cure has been found. This project aims to model one of the many pathways being investigated in Group 3 medulloblastoma which may be used to synthesize future treatments. Specifically, showing the interconnections between various precursors of BCL-xL, an antiapoptotic protein, and how these factors influence the progression of the disease. Scientific databases were used to find previous research articles which …

Harnessing The Power Of Virtual Reality For Organic Chemistry Education, Jungmin Shin

Undergraduate Honors Theses

Understanding organic chemistry concepts heavily relies on visualization of the geometry of molecules and spatial arrangement of molecules during mechanisms. 2D textbook depictions have their limitations in visualizing the three-dimensionality of organic chemistry. Student learning outcomes could be greatly improved from 3D visualizations of these topics. This project explores the potential of an emerging technology, Virtual Reality (VR), being incorporated as a teaching resource for organic chemistry.

This paper discusses two trials for evaluating the potential of VR as a teaching resource for organic chemistry in select topics of the Diels-Alder reaction and R/S configurations and stereoisomers. The Diels-Alder reaction …

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin Yipu Wu

Undergraduate Honors Theses

Quantum computers have recently gained significant recognition due to their ability to solve problems intractable to classical computers. However, due to difficulties in building actual quantum computers, they have large error rates. Thus, advancements in quantum error correction are urgently needed to improve both their reliability and scalability. Here, we first present a type of topological quantum error correction code called the surface code, and we discuss recent developments and challenges of creating neural network decoders for surface codes. In particular, the amount of training data needed to reach the performance of algorithmic decoders grows exponentially with the size of …

Code Syntax Understanding In Large Language Models, Cole Granger

Undergraduate Honors Theses

In recent years, tasks for automated software engineering have been achieved using Large Language Models trained on source code, such as Seq2Seq, LSTM, GPT, T5, BART and BERT. The inherent textual nature of source code allows it to be represented as a sequence of sub-words (or tokens), drawing parallels to prior work in NLP. Although these models have shown promising results according to established metrics (e.g., BLEU, CODEBLEU), there remains a deeper question about the extent of syntax knowledge they truly grasp when trained and fine-tuned for specific tasks.

To address this question, this thesis introduces a taxonomy of syntax …

Evaluating Large Language Model Performance On Haskell, Andrew Chen

Undergraduate Honors Theses

I introduce HaskellEval, a Haskell evaluation benchmark for Large Language Models. HaskellEval’s curation leverages a novel synthetic generation framework, streamlining the process of dataset curation by minimizing manual intervention. The core of this research is an extensive analysis of the trustworthiness of synthetic generations, ensuring accuracy, realism, and diversity. Additional, I provide a comprehensive evaluation of existing open-source models on HaskellEval.

Modeling The Neutral Densities Of Sparc Using A Python Version Of Kn1d, Gwendolyn R. Galleher

Undergraduate Honors Theses

Currently, neutral recycling is a crucial contributor to fueling the plasma within tokamaks. However, Commonwealth Fusion System’s SPARC Tokamak is expected to be more opaque to neutrals. Thus, we anticipate that the role of neutral recycling in fueling will decrease. Since SPARC is predicted to have a groundbreaking fusion power gain ratio of Q ≈ 10, we must have a concrete understanding of the opacity
and whether or not alternative fueling practices must be included. To develop said understanding, we produced neutral density profiles via KN1DPy, a 1D kinetic neutral transport code for atomic and molecular hydrogen in an ionizing …

Security And Interpretability In Large Language Models, Lydia Danas

Undergraduate Honors Theses

Large Language Models (LLMs) have the capability to model long-term dependencies in sequences of tokens, and are consequently often utilized to generate text through language modeling. These capabilities are increasingly being used for code generation tasks; however, LLM-powered code generation tools such as GitHub's Copilot have been generating insecure code and thus pose a cybersecurity risk. To generate secure code we must first understand why LLMs are generating insecure code. This non-trivial task can be realized through interpretability methods, which investigate the hidden state of a neural network to explain model outputs. A new interpretability method is rationales, which obtains …

Dimensionlessly Comparing Hydrogen And Helium Plasmas At Lapd, Lela Creamer

Undergraduate Honors Theses

This project compares the hydrogen and helium gas puff plasmas created at the Large Plasma Device (LAPD) using dimensionless numbers to determine the extent to which the turbulence pattern can be explained by plasma physics. Since turbu- lence tends to dissipate energy and particles in a plasma, it can cause problems for fusion reactors by reducing their efficiency. With a better understanding of turbu- lence’s causes and behavior, some of this energy loss could potentially be avoided. In recent experiments at LAPD, an unexpectedly high amount of turbulence was de- tected when helium was used to create the plasma, which …

Identifying Transitions In Plasma With Topological Data Analysis Of Noisy Turbulence, Julius Kiewel

Undergraduate Honors Theses

Cross-field transport and heat loss in a magnetically confined plasma is determined by turbulence driven by perpendicular (to the magnetic field) pressure gradients. The heat losses from turbulence can make it difficult to maintain the energy density required to reach and maintain the conditions necessary for fusion. Self-organization of turbulence into intermediate scale so-called zonal flows can reduce the radial heat losses, however identifying when the transition occurs and any precursors to the transition is still a challenge. Topological Data Analysis (TDA) is a mathematical method which is used to extract topological features from point cloud and digital data to …

How To Explain Allen-Manandhar’S Method To Beginner Mathematicians : A Convergence Analysis Of A Hybrid Method For Variable-Coefficient Boundary Value Problems, Rebecca Scariano

Honors Theses

In this project, analogies are employed to make complex math concepts approachable to beginners who may only have a basic understanding of calculus and linear algebra. Serving as the focal point of this project, Allen-Manandhar’s method solves an equation, known as an ordinary differential equation (ODE). The mentioned equation with its coefficients is comparable to a pie recipe with ingredients. With the outcome to a recipe seen as its solution, the solution to our pie recipe is a perfectly baked pie, as in without error. The chosen method for baking a pie then classifies as its baking approach that when …

Reviving The Past: Enhancing Language Models With Historical Text Optimization, Heather D. Broome

Honors Theses

Recent advancements in Natural Language Processing (NLP) have brought attention to the significant potential that exists for widespread applications of Large Language Models (LLMs). As demands and expectations for LLMs rise, ensuring efficiency and accuracy becomes paramount. Addressing these challenges requires more than just optimizing current techniques; it urges novel approaches to NLP as a whole. This study investigates novel data preprocessing methods designed to enhance LLM performance by mitigating inefficiencies rooted in natural language, particularly by simplifying the complexities presented by historical texts. Utilizing the classical text The Odyssey by Homer, two preprocessing techniques are introduced: tokenization of names …

Exploring The Relationship Between Anxiety And Virtual Reality Sickness, David Wesley Woolverton

<strong> Theses and Dissertations </strong>

As virtual reality (VR) becomes more commonly used in education, it is important to understand the technology’s weakness and mitigate any potential negative effects on student success. One adverse side-effect of VR use is simulation-induced motion sickness, known in the context of VR as VR sickness. Previous research by Howard and Van Zandt (2021) found that possessing a phobia had a significant positive correlation with VR sickness, but only if the phobia is triggered by the simulation, suggesting that symptoms are actually connected to the anxiety the phobia induces. This study explored the hypothesized correlation between anxiety and VR sickness, …

Geomagnetic Substorms Prediction Model Using Combined Physics-Based And Deep Learning Modeling Techniques, Ruthba Yasmin

<strong> Theses and Dissertations </strong>

This thesis aims to develop a hybrid physics-incorporated neural network model (PINN) for classifying geomangnetic substorms in Earth's Magnetosphere. The model is trained using a comprehensive list of substorm onsets, ground magentometer data from a global network, and solar wind parameters from the Advanced Composition Explorer (ACE) satellite. Two different neural network architectures are used, and the physics model used for training is called WINDMI. The magnetic field components on the ground, which are a function of the ionospheric currents, are captured by the SML index. The methodology involves using 60-minute data segments preceding an event to train the hybrid …

Bay Water Level Influences On Inundation And Morphological Changes Of A Semi-Connected Barrier Island During A Hurricane, Sydney D. Goodman

<strong> Theses and Dissertations </strong>

This research aims to identify flooding and erosion changes along a semi-connected barrier island system due to varying bay water levels during storm conditions. The numerical model XBeach is used to simulate Hurricane Michael conditions and the resulting inundation and morphological change near Tyndall Air Force Base (Tyndall AFB). The installation is located 12 miles southeast of Panama City Beach along the panhandle of Florida and is vulnerable to flooding due to its proximity to the Gulf of Mexico (GoM), Saint Andrew Sound and Saint Andrew Bay. A land bridge connects the barrier island to the mainland of Tyndall AFB …

Examining Outcomes Of Privacy Risk And Brand Trust On The Adoption Of Consumer Smart Devices, Marianne C. Loes

<strong> Theses and Dissertations </strong>

With more connected devices on earth than there are people, Internet of Things (IoT) is arguably just as innovative as the original introduction of the Internet. Though much of the research on technology acceptance and adoption has been conducted in organizational settings, the consumer use of IoT technologies, such as smart devices, is becoming a fertile field of research. The merger of these research streams is especially relevant from a societal perspective as smart devices become more embedded in consumer’s daily lives, particularly with the introduction of the “meta verse.” While original technology acceptance research is limited to two system-specific …

Multi-Script Handwriting Identification By Fragmenting Strokes, Joshua Jude Thomas

<strong> Theses and Dissertations </strong>

This study tests the effectiveness of Multi-Script Handwriting Identification after simplifying character strokes, by segmenting them into sub-parts. Character simplification is performed through splitting the character by branching-points and end-points, a process called stroke fragmentation in this study. The resulting sub-parts of the character are called stroke fragments and are evaluated individually to identify the writer. This process shares similarities with the concept of stroke decomposition in Optical Character Recognition which attempts to recognize characters through the writing strokes that make them up. The main idea of this study is that the characters of different writing‑scripts (English, Chinese, etc.) may …

Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila

Faculty Publications

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to …

To The Torus And Beyond: An X-Ray Study Of Agn Tori Morphology, Andrealuna Pizzetti

All Dissertations

Active Galactic Nuclei (AGN) are among the Universe's most energetic and powerful objects, fueled by an accreting supermassive black hole (SMBH) at the host galaxy's center, surrounded by a toroidal structure of dusty gas. Ultraviolet photons arising from the accretion disk get up-scattered to X-rays via inverse Compton scattering by hot electrons close to the accretion disk. Being produced in the very center of the AGN, X-ray photons are powerful messengers that probe the physics of the accretion system and the matter in the surroundings. The torus, formerly considered homogeneous, appears to be a more complex structure of clouds with …

Selected Topics On Sequential Designs For Decision Making, Caroline Kerfonta

All Dissertations

This dissertation is comprised of three parts. The first proposes a sequential approach to determine the experimental setting with the minimum variance (Kerfonta et al., 2024). Two acquisition functions are developed to assist developing the approach. Theoretical results along with a case study using data from crystallization experiments is conducted to show the ability of the proposed method to correctly select the experiment with the minimum variance. The second and third parts propose adaptations to the Bayesian optimization algorithm using transformed additive Gaussian processes (TAG) as the surrogate model. The goal of using the TAG framework is to decompose the …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

All Dissertations

In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

Efficient Fully Bayesian Approaches To Brain Activity Mapping With Complex-Valued Fmri Data: Analysis Of Real And Imaginary Components In A Cartesian Model And Extension To Magnitude And Phase In A Polar Model, Zhengxin Wang

All Dissertations

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data, and therefore, lead to underutilization of available data and flawed statistical assumptions. This dissertation proposes two efficient, fully Bayesian approaches for the analysis of complex-valued functional magnetic resonance imaging (cv-fMRI) time series.

Chapter 2 introduces the model, referred to as CV-sSGLMM, using the real and imaginary components of cv-fMRI data and sparse spatial generalized linear mixed model prior. This model extends the …

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

Experimental Analyses Of Emission Lines In The Uv/Vis/Nir Range For Astrophysically-Important Elements: From The Iron Group To R-Process Elements, Brynna Neff

All Dissertations

Analysis of astrophysical phenomena requires an understanding of the electronic

structure and transition probabilities of the elements present in that environment,

yet there are still many charge states of heavy elements whose electronic

structures and spectroscopic properties are not yet well understood. To address this,

we investigated the spectroscopic properties of three different elements through an

analysis of spectra collected from three different experimental apparatuses.

In order to better understand the spectroscopic properties of Ni I and II, we

analyzed spectra collected from the Compact Toroidal Hybrid (CTH) apparatus at

Auburn University. In this experiment, a nickel sample was inserted …

Domain Decomposition Methods For Fluid-Structure Interaction Problems Involving Elastic, Porous, Or Poroelastic Structures, Hemanta Kunwar

All Dissertations

We introduce two global-in-time domain decomposition methods, namely the Steklov-Poincare method and Schwarz waveform relaxation (SWR) method using Robin transmission conditions (or the Robin method), for solving fluid-structure interaction systems involving elastic, porous, or poroelastic structure. These methods allow us to formulate the coupled system as a space-time interface problem and apply iterative algorithms directly to the evolutionary problem. Each time-dependent fluid and the structure subdomain problem is solved independently, which enables the use of different time discretization schemes and time step sizes in the subsystems. This leads to an efficient way of simulating time-dependent multiphysics phenomena. For the fluid-porous …

Carbon "Quantum" Dots: Structures, Properties, And Issues, Weixiong Liang

All Dissertations

Carbon dots (CDots) are small carbon nanoparticles (CNPs) with effective surface passivation. The effective passivation has been achieved by the surface functionalization of pre-existing CNPs with organic molecules, mostly molecules containing primary or secondary amine moieties. In the studies highlighted in this dissertation, CNPs were functionalized by selected organic molecules, including especially the use of radical addition reactions, to generate CDots with strong absorption and bright fluorescence emissions. A good demonstration of the approach was the N-ethylcarbazole (NEC) radical addition to CNPs under microwave irradiation. Spectroscopy and microscopy methods were employed to characterize the resulting NEC-CDots, and the results …

Genetic Algorithm Optimization Of Experiment Design For Targeted Uncertainty Reduction, Alexander Amedeo Depillis

Masters Theses

Nuclear cross sections are a set of parameters that capture probability information about various nuclear reactions. Nuclear cross section data must be experimentally measured, and this results in simulations with nuclear data-induced uncertainties on simulation outputs. This nuclear data-induced uncertainty on most parameters of interest can be reduced by adjusting the nuclear data based on the results from an experiment. Integral nuclear experiments are experiments where the results are related to many different cross sections. Nuclear data may be adjusted to have less uncertainty by adjusting them to match the results obtained from integral experiments. Different integral experiments will adjust …

White Cell Support Application For Expo Ops Tactical Wargame System, Zackery Joseph Milder

Theses

The purpose of this project was to create a support application for a tabletop wargame currently used for training and scenario simulation by the United States Marine Corps. The EXPO OPS Companion is meant to enhance the capabilities of the White Cell/table director, the unbiased third party responsible for running adjudication for the EXPO OPS Tactical Wargames System. EXPO OPS TWS is “…a table top wargame covering contemporary and future conflict at the platoon, company and battalion level. It is a wargame toolkit that enables wargaming scenarios in the 2020 to 2030 timeframe. The design centers on plans and decisions …

Influence Of The Atlantic Inflow On Trace Metal Enrichments In Sediments And Particulate Matter Of The Nw Alboran Sea (Sw Mediterranean), Albert Palanques, Pere Puig, Pere Masqué, Enrique Isla

Research outputs 2022 to 2026

Trace metal contents and fluxes in downward particulate matter and dated sediment cores of the NW Alboran Sea are analysed in this study with the aim of assessing the role of the Atlantic inflow on their transport. Increases in Zn, Cu and Pb were detected in downward particulate matter collected by sediment traps after river flooding events and after the Aznalcollar mining spill. Their arrival coincided within the recently estimated time range for river particles discharged into the Gulf of Cádiz to reach the Alboran Sea, indicating that their transfer is enhanced during events of increased river inputs of contaminated …