Digital Commons Network^™

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 …

Mathematical Modeling And Examination Into Existing And Emerging Parkinson’S Disease Treatments: Levodopa And Ketamine, Gabrielle Riddlemoser

Undergraduate Honors Theses

Parkinson’s disease (PD) is the second most common neurodegenerative disease across the world, affecting over 6 million people worldwide. This disorder is characterized by the progressive loss of dopaminergic neurons within the substantia nigra pars compacta (SNpc) due to the aggregation of α-synuclein within the brain. Patients with PD develop motor symptoms such as tremors, bradykinesia, and postural instability, as well as a host of non-motor symptoms such as behavioral changes, sleep difficulties, and fatigue. The reduction of dopamine within the brain is the primary cause of these symptoms. The main form of treatment for PD is levodopa, a precursor …

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 …

Improving The Scalability Of Neural Network Surface Code Decoders, Kevin 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.

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 …

Roads And Corresponding Travel Time To Markets: Assessing Climate Vulnerability In Nepal, Kaitlyn Crowley

Undergraduate Honors Theses

Roads exist as a physical and theoretical connection between people and places around the globe. In addition to providing a route from one point to another, roads are also an indicator of access to markets and of poverty. However, current road datasets, particularly the Global Roads Open Access Data Set, are out of date or incomplete, necessitating new sources of data for analyses involving road networks. This study explores the relationship between climate change and access to markets in Nepal. We seek to identify isolated communities that are likely to experience detrimental outcomes associated with environmental threats, such as increasing …

Ecology And Conservation Of Diamondback Terrapins In Virginia, Cypress Ambrose

Undergraduate Honors Theses

The diamondback terrapin (Malaclemys terrapin) is the only turtle species native to North America with specific morphological and physiological adaptations to estuarine environments. Along with many other pressures contributing to population declines, terrapins frequently become trapped and drown as bycatch in crab pots used in the commercial and recreational blue crab (Callinectes sapidus) fishery. A wealth of evidence supports the use of inexpensive bycatch reduction devices (BRDs) that can be attached to the entrances of these traps, which leads to a marked decrease in terrapin bycatch while not reducing crab catch dramatically. Virginia is the only …

Policy Recommendations For Tire Additive 6ppd And Its Derivative 6ppd-Q, Ashley E. King

Dissertations, Theses, and Masters Projects

Around 3.1 billion tires are produced around the world annually1. The antioxidant additive, 6PPD (i.e., N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine) is widely employed in passenger and commercial vehicle tires at 0.4-2% by mass to impede tire degradation2. Antioxidants are intended to migrate to tire surfaces and form protective films to prevent rubber oxidation. 6PPD is designed to react with oxidant species like ozone, intentionally forming chemical transformation products that can then escape from the tire and into the environment. 6PPD-Q (i.e., 2-anilino-5-[(4-methylpentan-2-yl)amino]cyclohexa-2,5-diene-1,4-dione) is one such transformation product.

After release and disbursement in the environment, 6PPD-Q is bioavailable to aquatic animals and mammals and acute …

Parameter Estimation For Patient Enrollment In Clinical Trials, Junyan Liu

Undergraduate Honors Theses

In this paper, we study the Poisson-gamma model for recruitment time in clinical trials. We proved several properties of this model that match our intuitions from a reliability perspective, did simulations on this model, and used different optimization methods to estimate the parameters. Although the behaviors of the optimization methods were unfavorable and unstable, we identified certain conditions and provided potential explanations for this phenomenon and further insights into the Poisson-gamma model.

Seeing What We Can't: Evaluating Implicit Biases In Deep Learning Satellite Imagery Models Trained For Poverty Prediction, Joseph O'Brien

Undergraduate Honors Theses

Previous studies have sought to use Convolutional Neural Networks for regional estimation of poverty levels. However, there is limited research into possible implicit biases in deep neural networks in the context of satellite imagery. In this work, we develop a deep learning model to predict the tertile of per-capita asset consumption, trained on satellite imagery and World Bank Living Standards Measurements Study data. Using satellite imagery collected via survey location data as inputs, we use transfer learning to train a VGG-16 Convolutional Neural Network to classify images based on per-capita consumption. The model achieves an $R^2$ of .74, using thousands …

Black Hole Entropy In Ads/Cft And The Schwinger-Keldysh Formalism, Luke Mrini

Undergraduate Honors Theses

The Schwinger-Keldysh formalism for non-equilibrium field theory provides valuable tools for studying the black hole information loss paradox. In particular, there exists a Noether-like procedure to obtain the entropy density of a system by a discrete Kubo-Martin-Schwinger (KMS) variation of the action. Here, this Noether-like procedure is applied to the boundary action of an asymptotically anti-de Sitter (aAdS) black hole spacetime in maximally extended Kruskal coordinates. The result is the Kubo formula for shear viscosity, which is known in theories with an Einstein gravity dual to have a universal, constant ratio with the entropy density and is proportional to the …

Materials Characterization For Microwave Atom Chip Development, Jordan Shields

Undergraduate Honors Theses

This thesis describes research to characterize materials to be implemented on a microwave atom trap chip, which will be able to trap and spatially manipulate atoms using the spin-specific microwave AC Zeeman effect. Potential applications of this research include atom-based interferometry and quantum computing.

Namely, this thesis describes the characterization of the following: (1) the dielectric constant of a well-characterized substrate, Rogers RO4350B, in order to provide proof-of-concept for a method that can be applied to the chip’s substrate, aluminum nitride (AlN), (2) the maximum current that will be able to be applied to the chip, and (3) surface roughness …

A Satellite Imagery Approach To Estimating Migratory Flows In Guatemala Using Convolutional Neural Networks, Sarah Larimer

Undergraduate Honors Theses

Being able to predict migratory flows is important in ensuring political, social, and economic stability. In the wake of violence, unrest, natural disasters, and social pressures, millions of mi- grants have fled Central America in search of a better life. However, due to the infrequent nature and high cost of census data, there is a need for a more remote and up to date approaches. Con- volutional Neural Networks offer a computer vision based approach that is cheaper and with significantly less lag. In this study, we seek to evaluate the effectiveness of different convolu- tional neural networks in predicting …

Pion Detection For The Moller Parity-Violating Electron Scattering Experiment, Michael Tristan Hurst

Undergraduate Honors Theses

The MOLLER Experiment at Jefferson Lab intends to make a precise measurement of the weak charge of the electron through parity-violating electron scattering. To achieve the level of precision required for the experiment, background rates of events other than electron-electron scattering must be known. Working with data from Monte-Carlo simulations created using a GEANT4 simulation package, I show that the combined signal from two existing detector subsystems of the MOLLER experiment allow for particle identification between electron and pion events. I worked to optimize an additional ‘Pion Exit Scintillator’ which improves the ability to distinguish particle identity at the cost …

Fecal Pellet Production By North Atlantic Zooplankton, Michael Gibson

Undergraduate Honors Theses

Fecal pellet carbon (FPC) production by zooplankton is a significant component of the ocean’s biological carbon pump: the suite of biological processes that mediate export of carbon to the deep ocean, ultimately leading to the sequestration of atmospheric carbon dioxide in the ocean. In this study, mesozooplankton (zooplankton 0.2 mm to ~2 cm) were collected from the epipelagic zone in the temperate North Atlantic Ocean during day and night in May 2021. Zooplankton were live separated into five size fractions and incubated on board ship in natural surface seawater to measure fecal pellet production rate of the mixed mesozooplankton community. …

A Study Of Reciprocal Underwater Motion And Its Use In Algae Harvesting, Marguerite Bright

Undergraduate Honors Theses

In 2009, many research groups at different companies and universities were funded by Statoil to study the use of algae as a potential biofuel. Combined with the Chesapeake Bay TMDL given by the EPA, a team at William & Mary and VIMS studied the growth and harvest of wild algae in the York River. This method also removed harmful nutrients such as nitrogen and phosphorus from the waterways. Other independent research projects stemmed from this. In 2014, a research team sought to commercialize and automate the IWAGS system, and found that a single oscillating blade was the most effective. This …

Development Of A 780 Nm External Cavity Diode Laser For Rubidium Spectroscopy, Catherine Sturner

Undergraduate Honors Theses

This thesis describes the work done to improve an external cavity diode laser. These improvements consisted of constructing an insulated housing to stabilize the temperature of the laser, tuning the proportional-integral-derivative feedback of the temperature controller, achieving resonance frequencies of rubidium, and implementing and optimizing feed-forward scanning of the frequency of the laser. The laser was then successfully used to measure the linewidth of another laser in the laboratory to better understand how that laser could be best used. The knowledge gained in this thesis can also be used to change the frequency of the laser to achieve other resonances …

Power Profiling Smart Home Devices, Kailai Cui

Undergraduate Honors Theses

In recent years, the growing market for smart home devices has raised concerns about user privacy and security. Previous works have utilized power auditing measures to infer activity of IoT devices to mitigate security and privacy threats.

In this thesis, we explore the potential of extracting information from the power consumption traces of smart home devices. We present a framework that collects smart home devices’ power traces with current sensors and preprocesses them for effective inference. We collect an extensive dataset of duration > 2h from 6 devices including smart speakers, smart camera and smart display. We perform different classification tasks …

Kfactorvae: Self-Supervised Regularization For Better A.I. Disentanglement, Joseph S. Lee

Undergraduate Honors Theses

Obtaining disentangled representations is a goal sought after to make A.I. models more interpretable. Studies have proven the impossibility of obtaining these kinds of representations with just unsupervised learning, or in other words, without strong inductive biases. One strong inductive bias is a regularization term that encourages the invariance of factors of variations across an image and a carefully selected augmentation. In this thesis, we build upon the existing Variational Autoencoder (VAE)-based disentanglement literature by utilizing the aforementioned inductive bias. We evaluate our method on the dSprites dataset, a well-known benchmark, and demonstrate its ability to achieve comparable or higher …

Spatial Variability Of Alkali-Metal Polarization, Lauren Vannell

Undergraduate Honors Theses

An experiment was conducted at William & Mary to study how alkali polarization varies spatially in a spherical cell during the process of optical pumping. Similar cells are used to study the neutron via electron scattering from polarized 3He nuclei, and those experiments could be improved if alkali polarization is maximized and uniformly distributed throughout the cell. The results of this experiment indicate that the alkali polarization is non-uniform and more heavily concentrated on the side of the cell facing the pump laser.

Examining Factors Using Standard Subspaces And Antiunitary Representations, Paul Anderson

Undergraduate Honors Theses

In an effort to provide an axiomization of quantum mechanics, John von Neumann and Francis Joseph Murray developed many tools in the theory of operator algebras. One of the many objects developed during the course of their work was the von Neumann algebra, originally called a ring of operators. The purpose of this thesis is to give an overview of the classification of elementary objects, called factors, and explore connections with other mathematical objects, namely standard subspaces in Hilbert spaces and antiunitary representations. The main results presented here illustrate instances of these interconnections that are relevant in Algebraic Quantum Field …

Considering The Accuracy Of Fiat Boundaries: Ontology And Quantification, Lydia Troup

Undergraduate Honors Theses

Administrative boundaries - i.e., states, counties, or districts - are fiat boundaries; they exist purely as defined by human interpretation. Because of this, and despite their critical importance to government functions, the accuracy of data products claiming to represent such boundaries is difficult to measure. Here, I explore this topic using three boundary data sets: the open source geoBoundaries data set, the humanitarian UN OCHA’s Common Operational Datasets (COD), and Esri’s commercial administrative divisions 0 and 1 data sets in the Living Atlas. The accuracy of each was quantified as the percent overlap between each data set and an authoritative …

Automorphisms Of A Generalized Quadrangle Of Order 6, Ryan Pesak

Undergraduate Honors Theses

In this thesis, we study the symmetries of the putative generalized quadrangle of order 6. Although it is unknown whether such a quadrangle Q can exist, we show that if it does, that Q cannot be transitive on either points or lines. We first cover the background necessary for studying this problem. Namely, the theory of groups and group actions, the theory of generalized quadrangles, and automorphisms of GQs. We then prove that a generalized quadrangle Q of order 6 cannot have a point- or line-transitive automorphism group, and we also prove that if a group G acts faithfully on …