Digital Commons Network^™

Rntuple For Atlas Analysis Workflow, Fatima Ariana Rodriguez

Graduate Research Theses & Dissertations

RNTuple is the new data storage format set to replace TTree at the start of the High-Luminosity LHC. An investigation was conducted to evaluate how analysis workflows for ATLAS researchers will change with RNTuple, using reading speed, writing speed, disk space, and memory consumption as metrics. In this study, all metrics were measured using converted RNTuple inputs from ATLAS Open Data, compared to their TTree equivalents. Additionally, RNTuples produced with the LZ4 compression algorithm were generated and compared with those produced using ZSTD. Finally, two new versions of the Analysis Grand Challenge (AGC) using ATLAS Open Data were completed for …

The Effects Of Problem-Based Learning On Mathematical Creativity And Self-Efficacy Of High School Students, Margaret Remus

Graduate Research Theses & Dissertations

Mathematical creativity is recognized as an important component of mathematics education; however, there is limited understanding of how instructional approaches can effectively support this skill among high school students, particularly through problem-based learning (PBL). This study examined the effects of problem-based learning on mathematical creativity among high school students.

This study employed a mixed-methods approach using an alternating treatment design with two groups of participants. Group 1 received the PBL intervention followed by a control condition, while Group 2 received the control condition followed by the PBL intervention. Quantitative data were collected through a mathematical creativity test measuring flexibility, fluency, …

Improving The Quality Of Physics Practice, Wyatt W. Thompson

All Graduate Theses, Dissertations, and Other Capstone Projects

This case study started with personal observations and experiences. As a lab teaching assistant, I observed on many occasions that students do not like their current homework platforms, often venting to me about their frustrations during lab time. As a student, I shared similar sentiments with them. To develop a possible solution to these observations, a theory of reasoning was chosen as the foundation. The heuristic-analytic theory of reasoning (HATR) suggests that people present an epistemic model (beliefs and ideas about a phenomenon) through a heuristic process (generation from memory) and engage in an analytic process to validate the epistemic …

An Analysis Of Slms For Machine Translation Of Healthcare Documents, Wyatt Clausen

All Graduate Theses, Dissertations, and Other Capstone Projects

The language barrier creates significant healthcare challenges for patients who struggle with English. While large language models (LLMs) have advanced machine translation, their high computational costs, environmental impact, and risk of data breaches have raised concerns. Small language models (SLMs) can address these concerns and be further adapted to specific tasks through parameter-efficient fine-tuning (PEFT) methods, including Quantized Low-Rank Adaptation (QLoRA). This study compares the Spanish-to-English translation quality of two 7-billion-parameter SLMs, Mistral and Llama 2 7B, in the medical domain, using BLEU, chrF, and COMET as evaluation metrics. It further investigates whether QLoRA fine-tuning and Retrieval-Augmented Generation (RAG) on …

The Future Of Monetary Federalism: Rethinking Supremacy In The Stablecoin Era, Richard H. Fair

American University Business Law Review

[INTRODUCTION] In the summer of 2023, the State of Wyoming enacted a law authorizing its state treasurer to issue a blockchain-based, state-backed digital stablecoin known as the Wyoming Stable Token (“WYST”). Two years later, Congress passed the Guiding and Establishing National Innovation for U.S. Stablecoins Act (GENIUS Act, GENIUS, or the Act), moving to establish a comprehensive federal regulatory regime for stablecoins. These dueling initiatives have sparked more than regulatory confusion; they have set the stage for a structural clash between state financial innovation and federal monetary supremacy. At the heart of this confrontation lies a question that the Constitution …

Machine Learning Prediction Of Federal Appellate Court Outcomes: A Multi-Circuit Analysis With Administrative Law Implications, Nicky Nuertey Apenahier

Dissertations and Theses

Federal appellate courts are the final arbiters in many cases, yet systematic machine learning analysis across all twelve circuits remains largely absent from the computational law literature. With courts of appeals deciding tens of thousands of cases annually and the Supreme Court reviewing only a fraction, understanding what predicts reversal outcomes has both theoretical importance and practical consequences for litigants, attorneys, and judicial administrators. This study addresses that gap using eleven years of federal appellate decisions from the Federal Judicial Center’s Integrated Database. A systematic comparison of twenty-five machine learning models, spanning five algorithms and five class-imbalance correction strategies, identifies …

Michael Scott Is Not A Juror: The Limits Of Ai In Simulating Human Judgment, Sean Harrington, Hayley Stillwell

Faculty Articles

Can AI replace human jurors? More specifically, can large language models predict how jurors interpret evidence and reach decisions based on legally salient facts and demographic characteristics? As legal scholars and practitioners increasingly explore AI-generated jury simulations, this Article offers the first empirical test of whether models like GPT-4, Claude, and Gemini can faithfully replicate juror reasoning. The answer, for now, is no. Across a series of mock trial scenarios involving redacted confessions, GPT- 4, Claude, and Gemini repeatedly failed to replicate how real jurors interpret evidence or exercise judgment. Their errors were not random, but systematic. Hidden prompts, built-in …

General Relativity: Existence, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

A mathematically rigorous renormalized perturbative expansion, truncated to all finite orders, establishes the existence of quantum general relativity theories with Fermion matter fields. The expansion is initialized with the selection of a vacuum state.

Distinct infrared (IR) and ultraviolet (UV) vacuum states are considered. The IR vacuum state defines a cosmology model. The UV vacuum state defines a black hole model.

The vacuum state is initialized with a $\mathfrak{o}^3$ gauge Lie algebra defining a quark-gluon plasma. With this choice, string theory is avoided.

Finite order renormalized perturbation theory, defined using Bosons to avoid Fermion sign cancellation, is convergent to all …

Can An Experienced Qualitative Researcher Distinguish Ai From Human Qualitative Content Analysis?, Alexandra T. Lucas, Jianna Ramos, Maria Bajwa, Aaron Calhoun, Mark W. Scerbo, Janice C. Palaganas

Psychology Faculty Publications

Background

Artificial intelligence (AI) has become increasingly embedded in research workflows. Large language models (LLMs) are being used to code segments of text, organise codes into themes and interpret patterns within contexts. Recent comparisons between human and AI analyses demonstrate up to 80% thematic overlap, yet humans consistently exhibit deeper interpretive integration and contextual understanding. This study assesses whether experienced researchers can distinguish between entirely human-generated and AI-generated qualitative content analyses of a simulation debriefing.

Methods

We conducted a qualitative descriptive study comparing human-generated qualitative content analysis (QCA) with ChatGPT-4o-generated QCA using a single focus group transcript on emotion management …

Towards Breach Hypothesis Based Predictive Autonomous Cyber Defense Ecosystem: Proactive Prevention Of Imminent Threats And Productivity Losses, Yogesh Chavarkar

Master's Theses and Doctoral Dissertations

As cyberattack tools and techniques get sophisticated and persistent, reactive cybersecurity has been unable to effectively prevent breaches and compromises. Organizations and institutions with large complex environments have wide vulnerable exposure with higher chances of a cyberattack. This also increases overall security and financial risk. Possibility of repetitive attacks from cyber threats increases, too, despite the use of standard defense measures. Security tools and system vulnerabilities often need manual patching and updates to keep the environment secure and functioning effectively. Productivity suffers from manual trade-offs in keeping the systems secure from adverse impact. Persistent high-severity cyberattacks, despite continued reactive defensive …

Black Hole Spectroscopy And Tests Of General Relativity With Gw250114, Jaclyn (Jax) Sanders

Physics Faculty Research and Publications

The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ=m=2) …

Crispr-Cas9 Unleashed: Gene-Slicing Adventures In The Cancer Battlefield, Shivakuma Sonnaila, Shilpi Agrawal

Chemistry & Biochemistry Faculty Publications and Presentations

Cancer, a global health menace, continues to pose significant challenges in terms of incidence and mortality, necessitating innovative therapeutic strategies. Despite existing treatments, the limitations persist, prompting a quest for novel approaches. The emergence of immunotherapy marked a transformative era in solid tumor treatments, yet its efficacy is constrained by adverse effects. Concurrently, the integration of advanced technologies into cancer treatment explores the vast potential residing at the molecular level through gene analysis and manipulation. This review articulates the role of state-of-the-art genome editing technology, notably clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), in overcoming the constraints of immunotherapy for …

Unsafe2safe: Controllable Image Anonymization For Downstream Utility, Minh Dinh

Dartmouth College Master’s Theses

Large-scale image datasets frequently contain identifiable or sensitive content, raising privacy risks when training models that may memorize and leak such information. We present Unsafe2Safe, a fully automated pipeline that detects privacy-prone images and rewrites only their sensitive regions using multimodally guided diffusion editing. Unsafe2Safe operates in two stages. Stage 1 uses a vision--language model to (i) inspect images for privacy risks, (ii) generate paired private and public captions that respectively include and omit sensitive attributes, and (iii) prompt a large language model to produce structured, identity-neutral edit instructions conditioned on the public caption. Stage 2 employs instruction-driven diffusion editors …

Lens: Llm-Enabled Narrative Synthesis For Mental Health By Aligning Multimodal Sensing With Language Models, Wenxuan Xu

Dartmouth College Master’s Theses

Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements into natural language remains challenging. Current LLMs cannot natively ingest long-duration sensor streams, and paired sensor–text datasets are scarce. To address these challenges, we introduce LENS, a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. LENS first constructs a large-scale dataset by transforming Ecological Momentary Assessment (EMA) responses related to depression and anxiety symptoms into natural-language descriptions, yielding over 100,000 sensor–text QA pairs from 258 participants. To enable native time-series integration, we train a patch-level encoder …

From Physical Correlation To Emotional Connection: The Role Of Passive Haptics On Empathy In Virtual Reality, Jemely Robles

Dartmouth College Master’s Theses

Virtual reality is increasingly explored as a tool for cultivating empathy, and haptic feedback as a tool for enhancing immersion. This paper investigates the effects of combining the two.  Fifty-two participants experienced a custom-built VR scene in which a character was shown packing up a room. Participants were assigned to either a haptic condition, receiving passive haptic feedback corresponding to the character's actions, or a non-haptic control condition that did not receive any haptic input. Trait empathy was measured beforehand, and state empathy and engagement were measured after the experience. Thematic analysis was conducted on post-study interviews, and headset recordings …

Ai-Assisted Surface-Enhanced Raman Spectroscopy For Cardiovascular Diagnostics: From Plasmonic Materials To Clinical Translation, Anju Joshi, Gymama Slaughter

Center for Bioelectronics Publications

Raman spectroscopy (SERS) has emerged as a powerful analytical technique, offering molecular fingerprint specificity and ultrasensitive detection of cardiac biomarkers. Recent advances in plasmonic nanostructures, surface functionalization strategies, and flexible sensing platforms have significantly improved the analytical performance of SERS-based biosensors. In parallel, the integration of artificial intelligence (AI) and machine learning has enabled robust interpretation of complex spectral datasets, facilitating automated biomarker classification and improved diagnostic accuracy in heterogeneous biological environments. Despite these advances, the field remains fragmented, with limited integration between nanomaterial design, biomarker selection, and data-driven analysis, and persistent challenges related to reproducibility, standardization, and clinical validation. …

Data Repository For: Dorsey Et Al., 'Rapid Transient Uplift Driven By Active Slab Tear, Southern Calabria, Italy', Rebecca J. Dorsey, Nathan D. Brown, Sergio G. Longhitano, Marco Meschis, Domenico Chiarella, Charles P. Ogle

Earth & Environmental Sciences Datasets

Southern Italy preserves a well-studied record of deformation and uplift related to migrating oblique collision, trench retreat, and tearing of subducted ocean slabs. However, the surface expression of these processes is incompletely understood due to a lack of reliable ages for marine terraces >200 m above sea level (masl). Here we use luminescence methods to date shallow marine sands from terraces ~ 100 to 1,000 masl in southern Calabria and interpolate the results with regional surface mapping. Burial ages overlap across the full range of sampled elevations with a weighted mean of 118.8 ± 8.7 thousand years before present (ka). …

Numerical Modeling Of Multi-Scale Jet Feedback In Seyfert Galaxies, Julianne Goddard

Theses and Dissertations--Physics and Astronomy

We present a suite of high-resolution cosmological zoom-in simulations investigating the impact of mechanical and thermal feedback from low-luminosity jets launched from supermassive black holes (SMBHs) on Seyfert galaxy evolution from early cosmic times to the present (z ≤ 10). Our models follow the formation of central galaxies within identical dark matter halos of logMhalo/M⊙ ∼ 11.8 at z = 0, seeded with ∼ 10^6M⊙ SMBHs at z ∼ 9.1 and z ∼ 3.7. Feedback from active galactic nuclei (AGN) is implemented in the form of bipolar jets launched along the SMBH spin axis, with jet powers spanning Ljet ∼ …

Key-Point-Based Sign Language Translation With Multi-Stream Motion Modeling And Cross-Lingual Transfer, Alex Muchiri Kagozi

Dissertations and Theses

Automatic sign language translation remains challenging because models must recover linguistic structure from complex visual motion while relying on limited annotated data. This thesis investigates whether compact skeletal key-points can serve as an effective alternative to RGB video for continuous sign language recognition and translation, motivated by the privacy, computational efficiency, and transferability of skeletal representations. To address this problem, a segmented key-point-based framework is evaluated in continuous sign language recognition (Sign2Gloss), gloss-mediated translation (Sign2Gloss2Text), gloss-free translation (Sign2Text), and transfer learning within a common modeling pipeline. The proposed framework is a motion-aware multi-stream transformer encoder. Rather than processing the signer …

Factors Influencing Tree Vigor And Structural Failure Along Powerline Corridors In Northern California: A Multi-Species Analysis, David Justin Dorval

Cal Poly Humboldt theses and projects

Tree failures (e.g., stem breakage, uprooting, or branch drop) can pose significant risks to public safety, particularly along powerline corridors for the risk of power outages and wildfire ignitions. In California, tree-related powerline failures have led to severe ecological and human costs in recent years. Despite extensive research on tree failure in urban environments and post-disturbance contexts, comparatively little empirical work has examined where powerlines intersect largely unmanaged stands. This study evaluates tree failure trends across multiple forest types in Northern California by examining how tree-level characteristics influence recent growth and failure occurrence in regionally dominant tree species. Specifically, the …

Categories, Homology And Sheaves For Hypergraphs, Robert Green

Electronic Theses & Dissertations (2024 - present)

Hypergraphs are a prominent tool for representing networks with connections among three or more entities. There is an inherent flexibility that allows hypergraphs to more naturally represent certain types of networks than graphs or simplicial complexes can on their own. This flexibility, however, comes at a cost, as there is a zoo of various categories and homology theories that are applicable to hypergraphs. The first chapter of this dissertation explores various categorical perspectives on hypergraphs, focusing on what the natural notion of morphism between hypergraphs should be. It also contains an exploration of the functoriality of vertex-edge duality in these …

Corannulene: Optimized Synthesis And Four-Fold Reduction, Guy Rozenman

Electronic Theses & Dissertations (2024 - present)

Corannulene is a bowl-shaped π-conjugated fragment of the ball-shaped C₆₀-fullerene that undergoes stepwise multielectron reduction and can accept four electrons to form the aromatic tetraanion, C₂₀H₁₀⁴⁻. Prior literature has established this charge state in donor-solvated alkali-metal assemblies: early solution studies identified the formation of the tetraanion under lithium reduction in THF, and later X-ray diffraction crystallography demonstrated a triple-decker lithium sandwich in which two corannulene tetraanion decks encapsulate five Li⁺ ions. The central objective of this thesis is to determine whether the corannulene tetraanion can instead be isolated as a bulk solvent-free alkali-metal salt containing only corannulene and …

Informationally-Optimal Measurements On Single-Qubit Systems, Adam V. Preston

Electronic Theses & Dissertations (2024 - present)

This thesis will focus on deriving informationally-optimal quantum state tomographic measurements on single-qubit systems, with the definition of informationally optimal to be defined as those measurements which maximize the average information gain. The informationally-optimal measurements that will be covered include projective measurements (formally building off of the work of [1] and putting it on firm, information-theoretic foundations), and more general types of quantum measurements called directional (also known as rank-one) positive operator-valued measure (POVM) measurements, and, finally, adaptive directional POVM measurements.

For projective measurements, we build on the work of Wootters and Fields ([1]) and show, via analytical methods and …

The Predictability And Verification Of Precipitation From Landfalling Tropical Cyclones Using An Object-Based Framework, Melissa Piper

Electronic Theses & Dissertations (2024 - present)

Precipitation from landfalling tropical cyclones (TCs) poses a significant risk to life and property in both coastal and inland communities. Early warning systems can help mitigate the impacts of TC-induced flooding; however, this requires accurate quantitative precipitation forecasts (QPFs). Forecasting precipitation from landfalling TCs is a challenge, as the intensity, duration, and location of precipitation can be dependent on numerous storm-related and environmental factors. Previous studies have attempted to identify and correct NWP model deficiencies and reduce forecast uncertainty by applying traditional and advanced verification metrics to TC QPF. However, these studies have mostly used gridpoint verification metrics, limited samples …

Basal Melting Variability Of The Ross Ice Shelf From Mixing Ratios Of Simulated Water Masses (1993-2018) And Potential Climatic Drivers, Enrico Pochini, Andrea Bergamasco, Florence Colleoni, Manuel Bensi, Giorgio Budillon, Pasquale Castagno, Michael S. Dinniman, Pierpaolo Falco, Emanuele Forte, Vedrana Kovačević, Stefanie L. Mack

CCPO Publications

Several water masses circulate under the Ross Ice Shelf (RIS), the largest ice shelf in Antarctica, each causing basal melting with a specific spatio-temporal pattern. To investigate these patterns and their variability, we applied a mixing ratio analysis to simulated water masses from a new numerical ocean model of the Ross Sea. The simulation, which was run over 26 years (1993–2018) and which includes the RIS cavity, shows good agreement with seaborne and mooring observations. The total RIS basal melt rate is 90 Gt/yr on average, and is caused mostly by High Salinity Shelf Water (HSSW) (68%), which enters the …

Seeing The Invisible Load: Xr+ Multimodal Sensing For Cognitive Ergonomics In Industrial Training, Jessica M. Johnson, Andwele Grant

Virginia Digital Maritime Center (VDMC) Faculty Publications

Extended reality (XR) technologies are increasingly positioned as disruptive Industry 5.0 tools for human-centric industrial training and intelligent human–system integration. Coupled with multimodal sensing (eye tracking, EEG, HRV, GSR, and other physiological signals), XR environments promise to make otherwise invisible cognitive demands observable, especially for novice trainees entering complex industrial settings. Yet the evidence base is fragmented: (1) there is no quantitative synthesis of the cognitive ergonomics benefits of XR plus sensing; (2) little is known about which XR–sensor configurations yield the strongest effects; (3) prior reviews rarely focus on industrial and manufacturing tasks; (4) multimodal signals are used predominantly …

Spillover Effects Of Medicare Advantage On Fee-For-Service Post-Acute Care Spending, Nyel Bangash

Honors Theses

Does the growth of Medicare Advantage reduce fee-for-service post-acute care spending through practice-pattern spillovers, or do observed spending differences primarily reflect favorable selection? Using a county-level panel of roughly 2,700 counties (2014–2023) and a two-way fixed effects specification, I find that a one percentage-point increase in MA penetration is associated with $9.54 less per-capita standardized FFS spending. Spending per episode falls while participation rates remain stable, consistent with practice-pattern spillovers rather than compositional changes from selection. Welfare indicators from County Health Rankings, CDC PLACES, and CMS Care Compare show no evidence that spending reductions harm health or care quality. The …

Education And The Maternal Childcare Gap: Evidence From The Uk Covid-19 Pandemic, Lara Bakhaya

Honors Theses

This paper examines whether college education shapes working mothers’ access to remote work, flexible working, and childcare hours in the United Kingdom, and whether COVID-19 school closures amplified these inequalities. Using data from the UK Time Use Survey (2016–2021), a repeated cross-sectional diary dataset spanning the pre-pandemic period and five COVID-19 waves, this paper estimates weighted logistic and ordinary least squares regressions on a sample of married or cohabiting, employed mothers. School closures serve as a natural experiment, providing an exogenous shock to caregiving demands that affected all mothers simultaneously regardless of education level. College education significantly predicted working from …

Rockyou2024: What’S Your Password?, Yixuan Zhang

Honors Theses

Passwords remain a critical part of almost every account security system. As a result, password guessing attacks remain one of the most widespread yet profitable attacks possible. Setting a password resistant to attacks is thus an important task for account holders. In this paper, we use the RockYou2024 database, a collection of approximately 10 billion real-world passwords collected from data breaches, to analyze the characteristics of passwords found in real life. We start with basic statistical property analysis, such as length, distribution of digits and symbols, and proceed onto more complicated properties such as frequencies of combinations of characters, entropy …

From Shock To Routine: The Evolving Impact Of Shutdown-Related Sentiment On Stock Markets, Yiran Shao

Honors Theses

To address gaps in existing research, this paper selects two U.S. government shutdown periods, 2018-2019 and 2025, as research samples to explore the effect of policy uncertainty on sentiment. This paper primarily analyzes the following two research questions.

First, what is the correlation between government shutdown-related sentiment during the shutdown period and daily market fluctuations? Specifically, can the sentiment index constructed from shutdown-related news effectively predict the next-day stock return during the event period?

Second, does the market have a learning effect? That is, between 2018-2019 and 2025, has the relationship between shutdown-related emotions and market outcomes weakened, shortened the …