Digital Commons Network^™

Optical Nuclear Spin Detection In Diamond And Varifocal Metasurface Optics, Maxwell D. Aiello

Physics & Astronomy ETDs

This dissertation presents two experimental investigations at the intersection of quantum sensing and precision optical instrumentation. The primary project demonstrates optically detected nuclear magnetic resonance (NMR) of ¹³C nuclear spins in diamond, using state-selective Landau-Zener transitions under microwave frequency sweeping to bidirectionally transfer spin polarization between nitrogen-vacancy (NV) electron spins and remote ¹³C nuclear spins. This enables optical polarization and readout of large ensembles of polarized nuclear spins at low magnetic fields and room temperature, with spin dephasing times limited by longitudinal relaxation of nearby NV electron spins. The secondary project reports the design, fabrication, and characterization of …

The Environmental Cost Of Artificial Intelligence, Ava G. O'Connor

Student Theses 2015-Present

This paper addresses the increasingly innovative and evolving world of artificial intelligence. As artificial intelligence becomes more integrated within global economies and infrastructures, its  environmental and economic impact has grown significantly, but still remains understudied in the conversation surrounding societal advancement and growth. This thesis delves into the history and development of artificial intelligence, AI's integration into modern life, Big Tech jumping to get ahead of AI development through investment, the cost of artificial intelligence, and the ethical and environmental implications of increasing artificial intelligence usage. Using multiple case studies, this thesis deeply explores the emergence of artificial intelligence as …

Quantifying Co2 And Ch4 Fluxes In A Semi-Arid Floodplain: Assessing Spatial And Temporal Drivers, Miles E. Kelsey

Earth and Planetary Sciences ETDs

Rivers transport sediment and carbon across Earth’s surface, and their floodplains can store carbon over decades to millennia, making them important to terrestrial carbon management. While soil carbon can persist long term, it may be released as greenhouse gases through processes like methanogenesis and heterotrophic respiration. Environmental controls on these fluxes remain poorly constrained across floodplains in different climate and geomorphic setting, but especially in semi-arid systems where measurements are limited. To address this gap, we quantified CO₂ and CH₄ fluxes along the Middle Rio Grande (New Mexico, USA) using 227 chamber measurements collected May to November 2025 at three …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Continuous Polygenic Trait Evolution Under Brownian Motion With Gaussian Mixture Models, Mary S. Hopkins

Mathematics & Statistics ETDs

Gaussian mixed-models (GMMs) show promise as a tool for modeling polygenic trait evolution for multiple taxa with established phylogenetic comparative methods (PCMs). When phenotypic traits are influenced by more than one gene, neither a gene tree nor a species tree may be completely adequate to model specific cross-taxa dependencies. In such cases common solutions include using trees inferred from concatenated DNA sequences [35, 95] and consensus gene trees [35]. The GMM-based model, first proposed by Jiang in 2017 [55] allows traits to evolve on more than one tree with distinct topologies. This approach provides a framework for trait evolutionary modeling …

Bayesian Designs For Two-Arm Clinical Trials With Time-To-Event Endpoints: Incorporating Historical Data Through Power Priors, Sara Hajraf H. Almutiri

Mathematics & Statistics ETDs

Bayesian methods provide a flexible framework for time-to-event analysis by incorporating prior information. The power prior offers a systematic way to borrow information from historical data. This approach is especially valuable in clinical research, where historical data can enhance inference in early-phase trials with limited sample sizes. This dissertation develops Bayesian approaches for two-arm survival studies using both closed-form and simulation-based methods. The closed-form inference is derived under exponential and Weibull survival models. Under the proportional hazards framework, the posterior is derived through a normal approximation to the log hazard ratio, allowing inference on the treatment effect when the variance …

Rural Minds In Modern Politics: Kinship Networks And Voting Patterns: A Comparative Analysis Of Appalachia And Rural Spain, 1986–2024, Alyce Sarah Blow

Senior Theses

This paper examines how rural identity, kinship networks, and community relationships shape voting behavior through a comparative analysis of rural regions in the United States and Spain. With a base structure on sociological and political research on rural development and the rural–urban political divide, this study explores how social trust, family reputation, and community ties influence electoral support in rural communities. Using electoral data from national archives and the Global Elections Database, the analysis focuses on voting patterns from 1986 to 2024 in two rural U.S. congressional districts, West Virginia’s 2nd and Pennsylvania’s 9th, and two rural regions in Spain: …

Essays On Development Economics, Environmental Economics, And Political Economy, Weihong Li

Economics Theses and Dissertations

Chapter 1 explores the strategic behavior of provincial leaders in China in response to visits by central government leaders. I find that such behavior is correlated with provincial leaders’ personal characteristics and their connections with the visitors. Meanwhile, acting strategically during these visits has a positive and significant effect on overall promotions, but a positive and insignificant effect on promotions to the most important positions in the Politburo.

Chapter 2 examines the impact of the Confucian norms on the spatial heterogeneity of fertility rates and sex-selective abortions in contemporary China. To overcome the challenge of quantifying cultural norms, I construct …

Predictive Natural Language Metrics Of Alzheimer's Disease And Cognitive Decline Trend Analysis, Zerui Ma

Computer Science and Engineering Theses and Dissertations

Inspired by Dr. David Snowden's Nun Study, which linked early-life Propositional Idea Density (PID) to later-life Alzheimer's disease, this thesis investigates two questions: whether fine-tuned Transformer-based large language models (LLM) can detect cognitive decline from patient speech transcripts with meaningful feature attribution, and whether longitudinal PID trends are observable across large-scale internet and academic text corpora. We evaluate dementia prediction on the DementiaBank Pitt Corpus and conduct an exploratory longitudinal PID analysis across seven diverse datasets spanning up to 29 years and over 12.6 million documents. This work suggests that linguistic ability metrics, traditional PID metrics and novel LLM-based analysis, …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Bayesian Spatiotemporal Model For Counterfactual Estimation In Socioeconomic Studies, Duwani W. Gonzalez

Statistical Science Theses and Dissertations

Impact evaluations of regional development programs often require estimating counterfactual outcomes for a small number of treated regions using survey-based areal data. In practice, evaluators typically rely on two-group quasi-experimental methods such as propensity score matching (PSM) and Difference-in-Differences (DiD). These approaches perform poorly when only a few regions receive treatment, and when the set of observed covariates is limited or only partially relevant. Moreover, they typically do not explicitly exploit the spatial and temporal dependence present in survey-based areal data such as in ACS (American Community Survey). This dissertation develops a family of Bayesian spatial predictive models for directly …

Microtubules In Breast Cancer: Exploring The Α/Β-Tubulin Toggle Switch And Its Implications In Human Breast Cancer, Annemarie Ianos

Student Theses and Dissertations

Microtubules, composed of a/b-tubulin heterodimers, play a central role in breast cancer tumor growth by polymerizing, leading to metastasis and depolymerizing, contributing to proliferation. Human enzymes protein kinase Ca (PKC-a) and cyclin-dependent kinase 1 (Cdk-1) mediate phosphorylation at sites a:Ser165 and b:Ser172, respectively, influencing the growth of microtubules. It is possible that alternating phosphorylation at these sites contribute to an a/b-tubulin “toggle switch” that mediates microtubule instability and tumor growth.

The project investigates the influence of the toggle switch model on microtubule stability by determining the impact of mutants (a:S165D, a:S165N, a:S165S_P, b:S172S_P and a:S165S_P/b:S172S …

Shaping Emergent Competitive And Cooperative Behaviors In Multi-Agent General-Sum Games, Ethan F. Erickson

Honors Projects

Reinforcement learning (RL) algorithms can train agents to solve problems in environments using complex behaviors that are not explicitly programmed, known as emergent behaviors. The goal of our research is to investigate how different RL reward values influence the emergence of competitive and cooperative behaviors in games with teams of multiple agents. Specifically, we focus on general-sum games, in which the sum of gains and losses of each team may be non-zero, allowing situations for agents to mutually benefit or mutually fail. Using Unity’s ML-Agents Toolkit to train agents with RL self-play in bounded 2D environments, we identify high-level behaviors …

Ab Initio Method Development For Electronic Structure Response And Symmetry Quantification, Duc Anh Lai

Chemistry Theses and Dissertations

Electronic structure provides a fundamental framework for understanding molecular properties and reactivity, as it encodes the spatial distribution of electrons and their response to external and internal perturbations. This dissertation develops theoretical and computational frameworks to characterize and manipulate electronic structure through two complementary directions: the response to oriented external electric fields and the quantification of symmetry regulation in electron density.

First, a rigorous theoretical and computational framework is established for treating electric fields with arbitrary orientations relative to molecular structure. The concept of the rotational potential energy surface is introduced to characterize the dependence of molecular energy on field …

Undergraduate Commencement Exercises Program, May 16, 2026

Bryant University Commencements

No abstract provided.

Marine Geophysical Studies Of Coupled Tectonic And Sedimentary Processes At Active Plate Boundaries, Sarah R. Rysanek

Earth and Planetary Sciences ETDs

Deep-sea and convergent margin sedimentary systems preserve critical records of tectonic and climatic processes that shape Earth’s surface. This dissertation investigates source-to-sink sediment routing and forearc deformation to better constrain the interplay between sedimentary and tectonic processes, through investigations of a deep-sea fan system, and the forearc geomorphology offshore Nicaragua. In the Gulf of Alaska, we integrate ultra-long-offset and regional multi-channel seismic reflection data, multi-resolution bathymetry, and plate reconstructions to remap the Baranof Fan system. Results show that the fan is larger than previously recognized and constructed by two primary depocenters linked to distinct glacial sediment pathways, with accommodation space …

Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim

Electrical and Computer Engineering ETDs

The fundamental goal of quantum computing is to precisely control quantum systems to perform meaningful tasks, including implementing high-fidelity quantum gates for reliable quantum computation and accurately simulating complex quantum many- body dynamics. In this dissertation, we develop improved quantum control protocols for three distinct objectives, quantum error suppression, quantum optimal control, and analog quantum algorithms, achieving performance beyond standard approaches. First, we introduce new dynamical decoupling protocols, including both determin- istic and randomized constructions, that can substantially outperform conventional deterministic sequences. We then extend the randomized approach to dynamically corrected gates. Second, we propose a randomized quantum optimal control …

Constraining Magmatic Processes In The Central American Arc Using Melt Inclusion Vapor Bubble Analysis And Triple Oxygen Isotope Modeling And Exploration Of Volatile Contributions To The Production Of Continual Radio Frequency (Crf) Lightning Events, John M. Hamilton

Earth and Planetary Sciences ETDs

Magmatic volatiles are key to determining the processes happening in the subsurface that we cannot directly sample. Advances in measuring volatile and isotope contents from erupted volcanic samples have given us the ability to understand magmatic processes, mixing components that produce the bulk magma composition and potentially precursors to eruptive hazards, such as lightning.

Through analysis of melt inclusions that sample the melt at depth and triple oxygen isotope quantification of olivine crystals from multiple volcanic edifices, the three following studies display how I have utilized these analytical techniques to help unravel processes happening in the subsurface that lead volcanic …

Computational Design Of Peptides And Proteins Through Machine Learning Approaches, Emily J. Hendrix

Chemistry and Chemical Biology ETDs

Advancements in machine learning have emerged as a pivotal tool in computational biochemistry, offering new advancements to address challenges in protein structure and function. However, current machine-learning approaches offer limited insight in understanding protein dynamics. The purpose of this work is to combine traditional physics-based computational tools, such as molecular dynamics and coarse-grained simulations, with recently developed AI-driven computational tools to bridge gaps and advance the understanding of proteins in both structural and dynamic aspects. I investigated several approaches such as (i) traditional physics-based methods to study protein conformation and ensembles; (ii) identifying a peptide inhibitor for the PICK1 PDZ …

Nanoscale Structure And Spontaneous Self-Assembly Of Hydrothermal Organic Products, Glorianne P. Dorce

Chemistry and Chemical Biology ETDs

Carbon nanomaterials derived from citric acid and urea exhibit behaviors that challenge conventional structure–property models based on static bulk descriptions. This study examines how precursor pairing and reaction duration, post‑synthetic thermal history, and time‑dependent aging govern nanoscale organization and optical response. Through controlled synthesis and processing, distinct nanostructures with tunable structural and spectroscopic profiles are generated.

A multiscale framework integrating nano‑FTIR, atomic force microscopy, and thermal analysis reveals chemical heterogeneity and continuous structural reorganization across length scales. By correlating local chemical environments with optical behavior, we show that fluorescence efficiency and photostability depend on specific nanoscale architectures rather than average …

Spring Graduate School Commencement: May 16, 2026, University Of North Dakota

UND Commencement Programs

UND Spring Graduate School Commencement program from May 16, 2026.

Computing Certificates Of Members In Archimedean Quadratic Modules In A[X] And Certifying The Emptiness In Inconsistent Monogenic Archimedean Quadratic Modules In A[X_1, ..., X_N], Jose A. Castellanos Joo

Computer Science ETDs

Polynomials have been found to be a powerful tool over hundreds of years for modeling problems in numerous applications in science, engineering, medicine, and other domains. In the context of formal methods, polynomials arise in modeling in aerospace software and robotics, cyber-physical and hybrid systems, autonomous vehicles and controllers based on neural networks.

A quadratic module is a linear combination of polynomials in a set of generators (including the constant 1) with sum of squares polynomials as multipliers. The membership problem for a finitely generated quadratic module can be decided; however, computing a certificate exhibiting why it is nonnegative under …

Design, Fabrication, And Characterization Of Silicon Nitride Microresonator Optical Frequency Combs, Lala Rukh

Optical Science and Engineering ETDs

Optical frequency combs consist of equidistant optical frequencies and have numerous applications ranging from optical metrology to medical diagnostics. Initially, frequency combs were based on bulky mode-locked lasers, but advancements in integrated photonics enabled the generation of frequency combs in chip-scale resonators (microcombs) using Kerr nonlinearity. These miniaturized systems present various challenges, including increased propagation losses, enhanced thermal effects, and the extension of microcombs to visible wavelengths. In this dissertation, I will focus on addressing these challenges in silicon nitride (SiN) resonators. First, this thesis focuses on the fabrication of high-Q SiN resonators and the impact of fabrication parameters on …

Assessing The Impact Of Multi-Physics Effects On Photovoltaic Module Degradation Using Computational Modeling, James Yuan Hartley

Mechanical Engineering ETDs

This dissertation describes research to develop and apply multi-physics simulation capabilities using finite element methods to analyze photovoltaic module damage mechanisms. Analyses of full-scale solar modules under mechanical load are presented, including experimental validation against measurements of external deflection and internal strain. Module damage by solar cell breakage and interconnection fatigue are discussed, and the applicability of simplifying analyses using mathematical plate theory is assessed. Detailed sub-module component models undergoing thermal-mechanical stressors are also presented, to identify the design features and materials most influential to stress generation and to assess the representativeness of using sub-module assemblies in accelerated testing. Finally, …

Mapping Homogeneous Configuration States For Learning Based Motion Planners, Yazied Hasan

Computer Science ETDs

Reinforcement learning (RL) excels at solving complex tasks, but training times can become prohibitively large for challenging motion-planning problems. Methods that address this cost often require additional training or tuning, counteracting the goal of reducing training time. A more effective approach is to exploit inherent task equivalences: many elements of the state space, dynamics, or structure are functionally interchangeable, enabling simplification or knowledge reuse. We present learning solutions that leverage these equivalences to enhance the RL process. First, we leverage the symmetry of homogeneous multi-agent teams to simplify the task to a single strategy. Second, we map correspondences between distinct …

Identification Of Aortic Centerline Shape Features, Bebel B. Trani

Mechanical Engineering and Materials Science Independent Study

Better predictions of the likelihood of an aortic event in patients are paramount to providing quality care in medicine. Ascending aortic aneurysms (AsAA) are typically asymptomatic until sudden complications, such as rupture or dissection, occur and are often fatal [1]. As a form of preventative care, physicians identify and monitor high-risk patients. Surgical interventions are also performed for high-risk patients, replacing the weakened segment of the ascending aorta before a catastrophic event occurs. Determining which patients require surgical intervention or close monitoring is therefore extremely important. These decisions are largely guided by maximum diameter measurements; for the ascending aorta, a …

Stochastic Derivative-Free Deep Learning Methods For Solving High Dimensional Partial Differential Equations, Qing He Mr.

Mathematics Theses and Dissertations

Solving high-dimensional partial differential equations (PDEs) is a fundamental challenge in scientific computing, with applications ranging from quantum chemistry and computational finance to statistical physics and stochastic optimal control.  Classical numerical methods such as finite element or finite difference schemes suffer from the curse of dimensionality, rendering them computationally infeasible when the dimension $d$ exceeds a handful. Physics-informed neural network (PINN) methods alleviate this by embedding the PDE residual directly into a loss function, but they require computing derivatives of the network with respect to its spatial inputs---an operation that scales poorly in high dimensions and demands that the approximate …

Investigating Workforce Development Gaps In Tribal Transportation Agencies, Benson Long Jr

Civil Engineering ETDs

Tribal transportation programs face challenges in recruiting and retaining qualified staff, prompting reliance on external consultants for key responsibilities. This study explores how workforce constraints and organizational structures influence tribal outsourcing decisions. Survey data from 57 tribes informed a logistic regression model examining how geographic isolation, population size, training access, and the presence of a transportation department affect preferences for internal work, actual task completion, and instances where tribes intended to perform tasks in-house but outsourced instead. Findings indicate that outsourcing is driven by limited internal capacity, including staffing shortages, lack of certified personnel, and training opportunities. Geography also plays …

Development Of A Lead-Lithium Eutectic Magnetohydrodynamic Loop For Corrosion Studies, Xavier S. Angus

Nuclear Engineering ETDs

Lead–lithium eutectic (LLE) is a leading candidate coolant and tritium breeder for fusion reactor blankets due to its favorable heat transfer properties and high tritium breeding ratio. However, LLE is highly corrosive and, in the strong magnetic fields present in fusion reactors, experiences magnetohydrodynamic (MHD) effects that can produce significant pressure drops and flow instabilities. Understanding corrosion behavior in these extreme environments is essential for assessing the viability of LLE blanket systems. This work presents the design and construction of a forced-convection LLE corrosion loop to study corrosion behavior at temperatures up to 425 °C and flow velocities up to …

Sensitivity-Informed Resonance Parameter Cross Section Adjustments, Matthew Juan Lazaric

Nuclear Engineering ETDs

This work details the process of developing several new features into the MCNP6.3 Monte Carlo code for use in uncertainty quantification and reduction. These new capabilities, which include the CLUTCH method of calculating k-eigenvalue cross section sensitivities and the Windowed Multipole method of calculating cross sections, are implemented and verified against previous implementations and existing cross section data, respectively. These capabilities are then combined to produce sensitivities of k-eigenvalue to resonance parameters, which are verified against direct perturbation sensitivity estimates. The resonance parameters are calibrated using linear Bayesian methods and the accuracy of the resulting cross section is evaluated via …