Digital Commons Network^™

Asymptotic Sensitivity And Adjoint Methods For Equation Discovery With Open-Source Implementation, Nathan Schill

Theses and Dissertations

The "on-the-fly" (OTF) method is a gradient-based approach to equation discovery. It makes use of (potentially limited) observational data, a data assimilation technique such as continuous data assimilation (also called nudging), and an error metric. From these it computes an asymptotic approximation of a simulated system's error sensitivity with respect to unknown parameters and utilizes any gradient-based optimization algorithm to perform parameter updates. These parameter updates may be done online or offline, and the model may be of a precisely specified form or simply a library of possible terms with potentially nonlinear parameters. We have applied the adjoint method to …

Sonic Booms From Rockets: Measurements And Modeling, Mark Charles Anderson

Theses and Dissertations

Rocket sonic booms are produced when launch vehicles or boosters travel faster than the local speed of sound in the atmosphere, generating systems of conical shock waves. The purpose of this dissertation is to perform high-fidelity measurements of rocket sonic booms and to explain the physics behind the unique phenomena observed. After a thorough literature review on sonic booms produced during spaceflight operations, Part I of this dissertation deals with measurement and analysis challenges. One challenge is reducing contaminating noise effects on sonic boom perception metrics, addressed using a low-pass filtering method tied to the noise floor. A second challenge …

Unlocking Inexpensive Training Through Overcoming Weaknesses In Dataset Distillation, Connor Wilhelm

Theses and Dissertations

Dataset distillation is a nascent technique that promises accelerated neural network training. In particular, distillation produces a highly compressed dataset that can train a neural network in as little as a single optimization step. However, distillation has not been adopted for real-world use cases due to weaknesses, such as poor approximation of the original task, unclear interpretability, high costs of the initial distillation, and poorly elaborated use cases. Here, we present five papers that advance task distillation towards real-world adoption. The papers focus on: distilling reinforcement learning environments, interpreting distillation-produced datasets, improving distillation performance using ensembling, examining distillation's generalization to …

Physics-Based And Data-Driven Low-Order Modeling Of Centrifugal Compressors, Chase Oliphant

Theses and Dissertations

Centrifugal compressors serve as critical components across many applications, yet their design and performance prediction present significant challenges. While computational fluid dynamics provides high-fidelity predictions of compressor behavior, its prohibitive computational cost renders it impractical for preliminary design phases where rapid exploration of the geometric and operating space is essential. Conversely, existing low-order models have enabled efficient performance estimation but suffer from limited accuracy and poor interpretability, particularly when applied to novel geometries or off-design operating conditions. This dissertation addresses these limitations through three complementary contributions that bridge the gap between high-fidelity CFD and preliminary design tools. The first contribution …

Understanding The Role Of Tiktok As A Source Of Support For Cyberbullying Victims, Saba Iqbal

Theses and Dissertations

Cyberbullying poses a significant threat to teenagers and young adults, who often seek support and advice online. Little is known about the support and advice shared online for victims of cyberbullying and whether it is helpful. In this study, we used qualitative thematic analysis to examine a sample of 150 TikTok videos that give support or advice for cyberbullying victims. We identify 33 unique pieces of support and advice across 5 categories, with differences in tone and delivery among different types of videos. We then evaluated the quality of 33 unique pieces of cyberbullying advice in a user study with …

Path Planning And Control For Vertical Takeoff And Landing Aircraft, Dean Benjamin Anderson

Theses and Dissertations

One of the challenging problems of unmanned aerial vehicles (uav) is controlling vertical takeoff and landing (vtol) aircraft. Vtol aircraft can take off and land without landing strips while retain the efficiency of fixed-wing airplanes for long range flight. Unfortunately, the transition from takeoff and forward flight, and from forward flight to landing, is an extremely difficult control problem. Most current controllers for transition switch between flight regimes based solely on airspeed. The trajectory for such switching transitions is a straight vertical ascension followed by a straight forward startup flight; it does not allow for the development of more complex …

Advancing Cyclic Ion Mobility Mass Spectrometry For Structural Characterization, Isomer Discrimination And Gas-Phase Ion Chemistry, Sudam Shivaji Mane

Theses and Dissertations

This dissertation focuses on the development and application of high-resolution ion mobility-mass spectrometry (IMS-MS) methodologies for gas-phase separation and structural characterization of small molecules, with particular emphasis on epimers, positional isomers, and protomers. In addition to structural separations, this work investigates fundamental aspects of gas-phase ion chemistry using IMS-MS in combination with collision-induced ion activation and dissociation techniques. Advanced separations were performed using cyclic traveling-wave ion mobility spectrometry (cyclic TWIMS), implemented in a commercial cyclic ion mobility-mass spectrometry (cIMS) platform, to systematically evaluate both the capabilities and limitations of high-resolution IMS in resolving subtle structural differences. Initial studies demonstrated that, …

Practical, Scalable, And Cost-Effective Nanolc-Ms Workflow For Single-Cell Proteomics, Siqi Huang

Theses and Dissertations

Single-cell proteomics provides a novel research strategy for decoding cellular heterogeneity, revealing the molecular mechanisms of rare cell subpopulations, and studying the dynamic responses of cells to external stimuli. However, compared to transcriptomics, single-cell proteomics faces even more challenging technical challenges: extremely low protein amount in single cells, wide dynamic range, significant adsorptive losses during sample preparation, and the complex matrix and trace sample injection demanding extremely high stability and sensitivity from the separation and detection systems. Mass spectrometry-based bottom-up proteomics has become an important tool for analyzing complex biological systems, and nano-flow liquid chromatography-mass spectrometry (nanoLC-MS) exhibits unique advantages …

Differentials As Models: Constructing A Differentials-Within-Limits Framework For First-Year Calculus And Examining Students' Understanding Of Limits, Differentials, And Derivatives Within It, Alexander A. Swindler

Theses and Dissertations

Work in first-year calculus has clearly shown the power that treating differentials (such as dx) as tiny quantities affords for first-year calculus learning, yet a traditional limits-based approach leaves no room for them. While some researchers have loosely described informal differentials and limits within the same paradigm, their actual conceptual definitions and relationships have not been explicitly constructed. Continued progress in this area could benefit from theoretical consideration of (a) providing clearer intuitive definitions for differentials inside a limits-based approach to calculus, and (b) defining more exactly the relationships between differentials and limits. In this study, I propose a theoretical …

An Efficient Approximation To The Vibrational Coupled Cluster Method For Large Molecules, Nivedhitha P Ms

Theses and Dissertations

The Watson Hamiltonian is a widely used model for describing anharmonic vibrational motion in polyatomic molecules. It expresses the kinetic energy in separable dimensionless normal coordinates and represents the potential energy as a Taylor series expansion around the equilibrium geometry, usually up to the quartic approximation. Several computational methods have been developed to solve the corresponding vibrational Schrödinger equation for predicting infrared (IR), Raman, and vibrational circular dichroism (VCD) spectra. These include the Vibrational Self-Consistent Field (VSCF) method, Vibrational Configuration Interaction (VCI) and its approximations, and Vibrational Second-Order Perturbation Theory (VPT2), each providing different balances between computational cost and accuracy. …

Bridging The Cybersecurity Education Gap: The Role Of Open Educational Resources In Supporting Rural Cybersecurity Programs, Brittni Hardie

Theses and Dissertations

This study examines the intersection of cybersecurity education, open educational resources (OER), and rural higher education through a systematic review of current literature and an exploratory survey of rural community college faculty. The purpose of this research, consistent with the approved Institutional Review Board (IRB) protocol, was to understand how OER can be leveraged to design and deliver an affordable, high-quality System Security course within a rural higher-education environment. As cybersecurity workforce shortages continue to grow across the United States, rural institutions face persistent challenges in sustaining high-quality programs due to financial constraints, limited faculty capacity, and rapidly evolving curriculum …

Empirical Benchmarks For Interpreting Effect Sizes In Violent Crime Interventions, Kohta James Matsukawa Hansen

Theses and Dissertations

Criminal justice researchers apply Cohen’s (1988) benchmarks to classify effect sizes as small, medium, or large, despite these standards never being meant for broad, decontextualized use (Cohen, 1988; Gies et al., 2024; Goulet-Pelletier & Cousineau, 2018; Lakens, 2013; Milner et al., 2023). Repeatedly doing so may weaken statistical validity, distort findings, and impede effective policymaking. This study introduces the first effect size benchmarks specifically designed for violent crime interventions.

Using a quasi-meta-analysis framework, 1,605 effect sizes from 104 violent crime intervention studies from the CrimeSolutions clearinghouse were converted to Cohen’s d. Three new discrete benchmarking methods were created using a …

Artificial Intelligence For Better Inclusive Higher Education: A Study Of A Private University In Cairo, Shereen Ismail

Theses and Dissertations

This study explores how artificial intelligence (AI) tools relate to accessibility for visually impaired students in Egyptian higher education, focusing on University Y in Cairo. Grounded in the Social Model of Disability and Universal Design for Learning, it examines visually impaired students’ lived experiences of AI mediated access and the institutional conditions that shape these experiences. A qualitative phenomenological design was used, with semi-structured interviews conducted with visually impaired undergraduate students and key staff involved in assistive technology, teaching and learning, and student wellbeing; data were analyzed thematically. The findings indicate that participants experience AI tools (e.g., chatbots, transcription and …

Observational Diagnostics And Spectroscopic Detection Of Tropospheric Trace Gases And Oxidation Processes, Callum E. Flowerday

Theses and Dissertations

The troposphere is governed by photochemical and radical-mediated processes that control atmospheric oxidation capacity, secondary pollutant formation, and the chemical lifetime of gases. Quantitative understanding of these processes requires both robust observational frameworks capable of resolving complex precursor–product relationships and instrumentation with sufficient sensitivity and selectivity to detect reactive and low-concentration species. This dissertation advances atmospheric chemistry through the parallel development of quantitative observational methodologies and refined spectroscopic measurement techniques. The first component of this work evaluates atmospheric oxidation systems using long-term monitoring data and empirical diagnostics. Multi-year analyses of ozone and particulate matter trends were conducted to characterize variability …

Bridging The Gap: A Mixed-Methods Study Exploring The Relationships Between Student-Teacher Demographic Mismatch And Students’ Perceptions Of Fairness, Inclusion, And Achievement, Nicholas Nahrwold

Theses and Dissertations

This study examines the relationships between student-teacher demographic mismatch and students’ perceptions of fairness, inclusion, and achievement. The purpose of this research was to determine how race, gender, and SES mismatch between students and teachers is related to their feelings of belonging and academic outcomes. A mixed-methods study was conducted involving a combination of survey data, student performance records, and student interviews. Results from the study indicated a relationship between student-teacher gender match and GPA, student-teacher racial match and GPA, and student-teacher racial match and attendance outcomes. Although not statistically significant, the data collected also highlighted trends between student-teacher SES …

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

Does Concurrent Completion Of A Computer Task Interfere With Talking? Comparing Younger And Older Adults, Emma Simpson Hymas

Theses and Dissertations

This study investigates the impact of a computer-based divided attention task on spoken language and compares the impact between younger (18–30 years) and older (55–82 years) adults. Sixty adults participated in a speaking task and two computer tasks which varied in difficulty. The Level 1 computer task involved highlighting each instance of the word “the” and the Level 2 computer task involved highlighting each instance of the word “the” while also underlining each instance of the word “a”. Each task was completed alone and then two computer tasks with different levels of difficulty were completed concurrently with a procedural discourse …

Objective Conditioning Via Latent-Factor Modeling, Kalman-Smoothing, And Hidden Markov Clustering: An Application To Empirical Asset Pricing, Melanie R. Neller

Theses and Dissertations

Empirical asset pricing relies heavily on conditioning sets--industry groupings, size buckets, valuation screens, and other researcher-defined partitions--to structure cross-sectional tests and portfolio construction. While intuitive, such partitions risk blending heterogeneous firms, masking latent exposures, and introducing omitted-variable bias. This thesis develops an objective, mathematically grounded conditioning pipeline that replaces subjective groupings with latent-factor extraction, Kalman-based data techniques, and probabilistic clustering via Gaussian hidden Markov models (HMMs). Using CRSP monthly returns, the framework stabilizes noisy return data, extracts systematic structure through principal component analysis (PCA), identifies homogeneous stock cohorts through HMM clustering, and models regime persistence using Markov transition matrices. These …

Electrochemical Sensors And Processes In Molten Salts: Chlorine Production, Electroanalytical Chemistry, And Thin-Layer Electrodes, Tyler Williams

Theses and Dissertations

Molten salt electrochemistry has historically played a substantial role in the metallurgical industry, and this trend is only accelerating in areas of green steel, direct ore processing, and rare-earth processing. In addition, the recent surge of interest in molten salt nuclear reactors has provided an additional application for molten salt electrochemical processes, including the reprocessing of used nuclear fuels and the development of electrochemical sensors for nuclear applications. In this work, the development of a chloride volatility separation process for used nuclear fuel is advanced; electroanalytical methods are improved for identifying and quantifying unknown analytes with and without the presence …

Rigorous Electromagnetic Beamforming Simulations For Realistic Vehicular-To-Infrastructure (V2i) Scenarios In 5g Systems, Mohanad Hussam Aldean Ahmed Almuzian

Theses and Dissertations

Millimeter-wave (mmWave) Vehicle-to-Infrastructure (V2I) communication is a key enabling technology for next-generation intelligent transportation systems, offering high data rates and low latency to support safety-critical and autonomous driving applications. However, mmWave communication at 28 GHz suffers from severe path loss, sensitivity to blockage, and strong dependence on antenna directivity and beam alignment, particularly in complex urban environments. As a result, advanced beamforming and multi-antenna techniques are essential to ensure reliable and high-capacity V2I links. This thesis investigates mmWave V2I communication performance using realistic electromagnetic modeling and beamforming techniques. Full-wave antenna simulations and deterministic ray-tracing are employed using Ansys Electronics Desktop, …

Governing Bme Amplification In Large Language Models: A Standards-Aligned Framework For Llm Data Lifecycle Risk Detection And Ethical Ai Governance, Dale A. Rutherford

Theses and Dissertations

Large Language Models (LLMs) now underpin critical systems in education, healthcare, and policy advisory contexts, yet remain susceptible to the recursive amplification of bias, misinformation, and error (BME). These distortions, driven by feedback loops between user interactions, model retraining, and data reuse, accelerate epistemic drift and erode information quality over time. This dissertation advances a comprehensive governance architecture to detect, quantify, and mitigate these risks across the full AI lifecycle. The research introduces the BME Metric Suite, a standards-aligned diagnostic framework comprising five original metrics: Bias Amplification Rate (BAR), Echo Chamber Propagation Index (ECPI), Information Quality Decay (IQD), Pretraining Diversity …

Generalizing Networks To Address Data Imbalance For Facial Action Unit Recognition, Andrew W. Sumsion

Theses and Dissertations

Facial action unit (AU) recognition is used in a variety of downstream applications, including sign language translation, dementia detection, pain detection, and other high-impact tasks that require understanding facial motion. Although AU recognition is a well-established field, a number of unresolved issues remain. An initial problem is the need to improve overall performance on the benchmark datasets, as measured by the average F1 score. Also, the performance across all individual AUs, not just the average F1 score, needs to be increased. Recently, the field has shifted its attention towards ensuring that AU recognition models generalize beyond the distributions represented in …

Tusqh: Topological Control Of Volume-Fraction Meshes Near Small Features And Dirty Geometry, Brian Reese Shawcroft

Theses and Dissertations

This work develops a framework to create meshes with user-specified homology from potentially dirty geometry by coupling background grids, persistent homology, and a generalization of volume fractions. For a mesh with fixed grid size, the topology of the output mesh changes predictably and monotonically as its volume-fraction threshold decreases. Topological anti-aliasing methods are introduced to resolve pinch points and disconnected regions that are artifacts of user choice of grid size and orientation, making the output meshes suitable for downstream processes including analysis. The methodology is demonstrated on geographical, mechanical, and graphics models in two-dimensions (2D) and three-dimensions (3D) using a …

Database-Driven Revelations In Epilepsy: Patient Reporting Accuracy And The Circadian Timing Of Seizures, Ithay Biton

Theses and Dissertations

For over thirty years, patients have been visiting the Arkansas Epilepsy Program for diagnosis and treatment for seizures and seizure-like episodes. As part of their clinical evaluation, patients often undergo ambulatory EEG (electroencephalogram) monitoring. This routine process produces valuable data for treating the patient. In this study, over 2000 ambulatory EEG reports from 1998 to 2016 were reviewed. A large database of seizures was created from the reports, with information on 407 patients, 1611 EEG-confirmed seizures, and 1726 patient-reported seizures (IRB Protocol #17-093). The database was used to address two important issues in epilepsy. The first topic of the study …

Synthesis And Characterization Of Transition Metal Carbide (Max) And Mxene Nanoflakes For Energy Storage And [email protected], Srikanth Ragunath B Mr

Theses and Dissertations

Rapid growth in population and industrialization created a huge demand for superior energy storage devices. Thus, the researchers are in constant search of developing exotic materials towards energy harvesting and storage devices. Batteries, supercapacitors, and fuel cells received a great attention to meet the global demand for energy storage. Among them, supercapacitors are widely preferred as energy storage devices due to higher power density, longer cycle life, and fast charging. The performance of the supercapacitors highly depends on the choice of electrode materials and electrolyte.

Recently, “MXenes”  a new class of layered a new class of layered a new …

Combinatorial Therapeutic Strategies To Combat Multidrug-Resistant Acinetobacter Baumannii, Tope A. Aderibigbe

Theses and Dissertations

Acinetobacter baumannii is an opportunistic Gram-negative pathogen that poses a significant global health threat due to its remarkable ability to acquire multidrug (MDR) and extensively drug-resistant (XDR) phenotypes. Therapeutic failures are increasingly common as strains develop resistance to nearly all available antibiotics, including carbapenems, polymyxins, and tigecycline, leaving clinicians with limited treatment options. This study aimed to investigate combinatorial therapeutic approaches capable of revitalizing antibiotic efficacy against MDR/XDR A. baumannii clinical isolates. We evaluated the synergistic potential of durlobactam-sulbactam in combination with twelve commonly used antibiotics, including amikacin, rifampin, tobramycin, tigecycline, cefiderocol, and others. Initial screening using the disc diffusion …

Infrastructural Resiliency Analysis Concerning Extreme Events, Yasaman Norouzi

Theses and Dissertations

Infrastructural resiliency is critical for maintaining functionality during and after extreme events, ensuring minimal service disruptions and supporting rapid recovery. Key resiliency attributes—robustness, redundancy, resourcefulness, and rapidity—enhance infrastructure adaptability and safety. This study investigates significant factors and methods for improving resiliency, with a focus on equity and reliability. A new resiliency metric, incorporating equity as a core element, is developed using probabilistic approaches. This metric integrates structural vulnerability with accessibility, income, cost, and exposure factors to evaluate community impact on infrastructure access. To achieve this, limit state functions are defined to assess how infrastructural vulnerabilities affect diverse communities. Apart From …

Mscl-X: Meta Supervised Contrastive Learning With Gaussian Prototypes For Robust Few-Shot Open-Set Modulation Classification, Emmanuel Mawuli Agbator

Theses and Dissertations

Automatic modulation recognition has found its way into the core modern-day wireless communication systems whereby intelligent receivers estimate signal formats without any prior coordination with the transmitters. Challenging situations arise in any working radio frequency environment for the conventional modulation recognition systems due to insufficient labeled data, rapidly fluctuating channel conditions, and appearance of plenty other unseen modulation types. Taken together, these challenges raise questions concerning the ability to institute learning frameworks that could perform well on few-shot and open-set conditions. Hence the thesis attempts to address the same by proposing two complementary learning-based frameworks for few-shot open-set modulation recognition …

Development Of An Ensemble Biosensor For Fatty Acids And Enhancing The Antibiotic Properties Of Meropenem With Hydrophobic Ion Paring And Nsf National I-Corps Experience And Commercialization Roadmap For The Paper Based Ensemble Biosensor For Ibs, Krishna Paul

Theses and Dissertations

This thesis integrates three interconnected studies focused on advancing biomedical diagnostics and therapeutic formulation. Primary task was developing an ensemble biosensor for multiplex fatty acid detection using protein - fluorophore interactions. A fluorescence matrix of BSA and FABP with 1,8-ANS, 2-AC, and fluorescein differentiated eight fatty acids with 94.59% accuracy. Validation in H. pylori positive stool samples confirmed its sensitivity and specificity with potentiality as a non-invasive diagnostic platform for Gastrointestinal and chronic diseases. The second project explores Hydrophobic Ion Pairing (HIP) to enhance the lipophilicity and bioactivity of the β-lactam antibiotic meropenem. HIP complexes with hydrophobic counter-ions improved stability, …

Deciphering Feup–Feuq Signaling: Mechanistic Analysis Of A Newly Identified Regulatory Module In Agrobacterium Tumefaciens, Michelle Charae Brock

Theses and Dissertations

Two-component systems (TCS) are widespread signal transduction pathways that enable bacteria to sense and respond to environmental changes with precision. TCS are fundamentally important to prokaryotes because they provide a rapid and efficient means of converting environmental stimuli into coordinated cellular responses. Unlike eukaryotes, which rely on multilayered signaling cascades and compartmentalized organelles, bacteria must interpret and react to changing conditions directly at the cell envelope. TCS allow prokaryotes to detect shifts in nutrients, osmolarity, pH, redox status, host-derived antimicrobials or competing microbes. This forces an immediate adjustment in gene expression programs to promote survival. Agrobacterium tumefaciens, a model organism …