Digital Commons Network^™

Personal Device Use In Mathematics: An Investigation Into Access, Utilization, And Outcomes, Catherine Dennis

Doctoral Dissertations

Personal devices, such as laptops, tablets, and smartphones, have become increasingly ubiquitous in undergraduate mathematics classrooms around the world. Moreover, the increased use of technology in educational contexts has provoked questions about equity in terms of student access, utilization, and the potential impact on outcomes (Hennessy & Dunham, 2008). This dissertation adds to the body of literature that addresses the overlap of personal device use and equity in undergraduate mathematics through three different methodological approaches: systematic review, quantitative inquiry, and qualitative exploration. The systematic review synthesizes 355 studies on personal device access, utilization, and outcomes in undergraduate mathematics. One key …

Wigner's Theorem And Spectrum Shrinking Maps, Meaghan Allen

Doctoral Dissertations

Wigner's Theorem, an influential result in quantum mechanics established by Eugene Wigner in 1931, states that every symmetry transformation on a ray space is induced by either a unitary or anti-unitary transformation. In this work, we generalize Wigner's Theorem in two directions: first, to maps between different Hilbert spaces, and second, to maps between factors of type II. In both cases, we focus on weakening the assumptions on the map, specifically by utilizing spectrum shrinking conditions. For the first generalization, let $\mathcal{H}_1$ and $\mathcal{H}_2$ be Hilbert spaces, and let $k$ be a positive integer such that $2\leq k < \frac{1}{2} \dim(\mathcal{H}_1)$. We show that if an injective map $\psi: \mathcal{P}_k(\mathcal{H}_1) \to \mathcal{P}_k(\mathcal{H}_2)$ satisfies two spectrum shrinking conditions, then it is induced by a linear or conjugate-linear isometry. For the second generalization, let $\mathcal{M}$ be a type II$_1$ or type II$_\infty$ factor, let $\tau_\mathcal{M}$ be a faithful normal semi-finite tracial weight of $\mathcal M$, and let $0

Static Risk Sensitive Reinforcement Learning, Jia Lin Hau

Doctoral Dissertations

Reinforcement Learning (RL) is a core area of artificial intelligence (AI) that enables systems to make decisions in complex environments. RL algorithms have shown promise in various domains, including resource management, robotics, and games. However, most existing RL approaches fail to account for the risks associated with decision-making. This oversight becomes particularly critical in high-stakes settings, such as healthcare, finance, criminal justice, and autonomous driving, where poor decisions can have significant consequences.

To address risk in decision-making, another line of research explores the properties (axioms) of "desirable" risk measures. This desirability is defined as the ability to quantify the capital …

Logistic Regression And Cox Hazard Modeling With Sparse High Dimensional Data Via Elastic Net Regularization And Graph-Guided Aggregation, Matthew Duckett

Doctoral Dissertations

Rare features are predictor variables with excessively low rates of nonzeros. It is not uncommon to encounter rare features in settings where data is quantified through one-hot encoding, such as text mining data or genomic data. Rare features pose problems for classic regression techniques due to instability of effect estimates. The problem is compoundedwhen the dimension of the feature space is high. Yan and Bien (2020) explored methods for aggregating rare features in high dimensions by leveraging side-information about relations between features that can be organized as a tree graph. While their work is restricted to standard Gaussian regression, we …

Consumptive And Energetic Responses Of Fishes Under Pressure Of Climate Change, Nathan Thomas Hermann

Doctoral Dissertations

Marine organisms are confronted with warming waters due to climate change requiring species to exhibit responses to persist. For many fish, these responses have included range shifts to cooler waters, but are species-specific which may create further disruption through trophic mismatches. To identify potential mismatches and estimate their implications for the quality of consumption within major predators, analyses of predator stomach contents were integrated with reviews of organismal energetics and physiological parameter values. The energy density of prey that is consumed dictates the consequences of consumption, so having data expands general understanding of diet. To that end, published records (n …

Probing Neutron Star Interiors: Insights Into Asymmetric Dark Matter, Dark Energy, And The Cold Dense Matter Equation Of State, Nathan Rutherford

Doctoral Dissertations

The combination of derived neutron star mass-radius measurements from NASA's Neutron Star Interior Composition ExploreR (NICER) mission, constraints from chiral effective field theory (chiEFT), and mass-tidal deformability constraints from gravitational waves has led to significant improvements in the understanding of the cold dense matter equation of state (EoS) of neutron stars. Interestingly, asymmetric dark matter (ADM) and dark energy may also be present within neutron star interiors, potentially impacting their global properties. Taking these effects into account, neutron star measurements can not only offer new insights into the dense matter EoS, but also serve as a hunting ground for dark …

If You Were A Sesame Street Character, Which One Would You Be? Natural Language Processing And Personality With Big Bird And Friends, Joseph Uran Meyer

Doctoral Dissertations

This paper examined and compared several natural language processing and machine learning techniques in predicting self-reported Big Five personality traits from text responses. The models were validated on the open-source 2019 SIOP Machine Learning Competition dataset (N = 1,689). The techniques evaluated included bag-of-words, Empath dictionary, LSTM networks, fine-tuning Transformer models, and stacked generalization. Results indicated that the present study’s models had lower error in four of the five constructs analyzed. Limitations of the study include use of an MTurk sample and small sample size. Future research should explore similar techniques on larger applicant samples. Practical implications and contributions to …

Solid-State Crystallization Of Zeolites And Their Use In Plastic Upcycling Applications, Yixin Liao

Doctoral Dissertations

Plastics have been an irreplaceable component of modern technology as well as everyday life. They have brought much convenience to us with their characteristics of great malleability, durability, and stability. The versatile and low-cost nature of plastics also enables their wide engagement in many modern industries including automobile, medical, communication as well as aerospace. Polyethylene (“PE”), made from polymerization of ethylene, is one of the most widely used plastics in the world. Being cheap, flexible, and long-lasting, they are extensively used in the packaging industry, especially for plastic bags and other sorts of containers. However, the durability of plastics, on …

Smoothed Particle Hydrodynamics For Free-Surface Flows And Time Series Forecasting Approach For Computational Fluid Dynamics, Huali Ye

Doctoral Dissertations

With the increase in computing power, numerical simulation has become an essential approach to solving problems in engineering and science. Numerical simulations provide a platform for theoretical validation and facilitate novel discovery. Even though extensive mesh-based numerical methods are utilized, significant limitations exist, particularly in Computational Fluid Dynamics (CFD). Because of the grid distortion, issues related to large deformations, moving interfaces, and free surfaces may lead to considerable computational errors, constraining their efficacy in numerous applications. As a mesh-free method, Smoothed Particle Hydrodynamics (SPH) was introduced in 1977 and has been widely applied in many fields such as astrophysics and …

Real-Time Prediction Of Dynamical Systems Using A Hybrid Analog Computer: Network Traffic Modeling, Majd Zuhair Tahat

Doctoral Dissertations

As the number of online users grows exponentially, the number and severity of cyber threats escalate, urgently requiring advancements in real-time network modeling and response. Swiftly predicting and analyzing network traffic is crucial for effective network monitoring and control, preventing cyber breaches, and maintaining healthy network functionality. This research presents a novel approach to real-time modeling based on analyzing evolving properties and patterns in a dynamical network system using a hybrid analog-digital computer. An analog computer was utilized as a co-processor to compute differential equations that model the Transmission Control Protocol (TCP) window size. A comparative analysis was conducted between …

A Human-In-The-Loop Framework For Scalable And Interpretable Event Triaging In Large-Scale Systems, Ibrahim Khaled Al-Agha

Doctoral Dissertations

This dissertation presents a comprehensive and scalable framework for real-time fault detection and event triage in industrial systems, addressing critical challenges such as class imbalance, ambiguous feature boundaries, and the prioritization of complex, high-dimensional event data. The proposed framework integrates advanced methodologies, including micro-batch processing, retrospective divergence-based event detection (DB-RED), association rule mining (ARM), clustering, and Dempster-Shafer Theory (DST) for conflict resolution. Together, these components enable the systematic stratification of events into actionable priority levels, ensuring robust and interpretable decision-making in real-time environments. DB-RED forms the cornerstone of the framework, leveraging KL-divergence and PE-divergence metrics to detect subtle and transient …

Creating A Framework To Develop Project-Based Platforms To Support Engineering And Technology Education, Casey Daniel Kidd

Doctoral Dissertations

Engineering education has evolved over the last few decades to increasingly include project-based learning (PBL) throughout the curriculum to give students more hands-on experience. However, there can be a hesitancy from faculty and instructors to move from traditional lectures to PBL-based curricula. Research has been conducted to identify barriers to research-based instructional strategies (RBIS), which include PBL. However, this research does not go into depth about the specific barriers for these individual RBIS. Furthermore, it has been found that the adoption of a new practice within a community has more success through a propagation paradigm, where the change agents are …

Development Of Dual-Scan Nuclear Magnetic Resonance (Nmr) Pulse Programs For Spin-Lattice Relaxation Measurements, Zachary Mayes

Doctoral Dissertations

This research develops and refines the Split Inversion Pulse and Recovery (SIP R) methodology for spin-lattice relaxation (T₁) measurements in NMR spectroscopy. SIP R introduces a two-scan difference technique using a split inversion pulse sequence, where the 180° pulse is split into two 90° pulses phase-shifted with respect to each other. This approach simplifies data fitting, requiring only two parameters to extract T₁ values, compared to the traditional inversion-recovery method, which needs three. The SIP-R-DS and SIP-R-S adaptations extend this framework by incorporating selective NMR resonance excitations, enhancing its applicability to cross-polarization experiments and enabling unobstructed NOE measurements. These adaptations …

Mid-Field Shock And Impulse Estimation Methods For Blast Loading On Tall Targets, Ethan Allan Steward

Doctoral Dissertations

Blast resistant structural design continues to be a major research area for governments around the world due to explosive threats from both state and non-state actors. Many of the typical targets of explosive attacks, such as government buildings, commercial high rise office buildings, and apartment complexes are mid to high-occupancy buildings that present a tall profile relative to the charge size and are often clad in curtain walls. In blast resistant structural design, the origin of a shock wave is typically assumed to be in the far-field, creating a wave that is nearly planar and parallel to at least one …

Advancing Thermodynamic Modeling In Materials Design: From Phase Stability In Metallic Systems To Ferroelectric Property Prediction In Functional Oxide, Kyaw Hla Saing Chak

Doctoral Dissertations

This dissertation advances the CALPHAD (CALculation of PHase Diagrams) approach for thermodynamic modeling which often lacks sufficient description of crystal lattices for critical phases in multicomponent system. Additionally, CALPHAD does not consider the structural features and its connection with functional properties for functional materials. To address these issues, a novel dual-ordered sublattice model for the κ-phase in Fe-Al-C system, (Fe,Al)3(Fe,Al)1(C,Va)1(C,Va)3, is introduced that improves predictions of equilibrium compositions and phase stability by accounting for both substitutional and interstitial ordering. For the Fe-B-C system, new sublattice formulations for FCC [(Fe)1(C,B,Va)1] and BCC [(Fe,B)1(C,B,Va)3], phases enhance boron solubility predictions and reveal insights …

A Study In The Student Experience Of Care – What It Looks, Sounds, And Feels Like For Students In A Rural Public High School, Steven Mark Chamberlin

Doctoral Dissertations

Students' voices are essential to understanding their perceptions, experiences, and viewson care. Many schools prioritize standardized test outcomes over individual care for each child, which leads to disengagement and a lack of agency. Students who feel cared for tend to achieve greater success in school.

This question guided the inquiry: How do students perceive, experience, and understandcare in their secondary school in New Hampshire? Twelve individual interviews were conducted with students who had varying degrees of academic success and special education identification.

The findings led to the development of a preliminary model of care that establishesschool culture as the foundation …

The Impacts Of Social Media On Teacher Environment Perceptions, Victoria Puglia

Doctoral Dissertations

Social media significantly influences teachers’ perceptions of their professional environment, shaping job satisfaction, respect, autonomy, and professional support. This dissertation, split into three articles, investigates how exposure to positive, negative, or mixed social media posts impact these perceptions, addressing concerns surrounding teacher well-being and attrition in K-12 education. The study began with a literature review in Article 1 to establish a theoretical framework, followed by a series of validation steps to refine a survey instrument aligned with teaching perception constructs in Article 2. The final survey was administered to 406 K-12 teachers across the United States in Article 3, using …

Viscosity Stratified Turbulence: Insights From Direct Numerical Simulations, Quasilinear Reduction And Resolvent Analysis, Pulkit Kumar Dubey

Doctoral Dissertations

This dissertation focuses on wall-bounded shear-driven turbulence with temperature-dependentviscosity. Direct numerical simulations and quasilinear reduction have been used to establish the asymmetric variation of turbulent statistics in the wall-normal direction. A comparison of energy spectra in the cold and hot halves of the channel reveals that large scale motions are amplified in the more viscous half while small scale motions are amplified in the less viscous half. Interestingly, the threshold wavenumber distinguishing these scales remains independent of stratification. This observation has been verified via a low-order reconstruction of the turbulent kinetic energy profile using resolvent analysis. Fourier modes that selectively …

Development, Characterization And Testing Of Traditonal And Advanced Nuclear Fuel Cladding Materials, Joshua Eddy Rittenhouse

Doctoral Dissertations

Kanthal D and FeCrAl alloys in general, are prospective candidates as accident tolerant nuclear fuel cladding materials. The work presented herein focuses on applying two techniques of severe plastic deformation, equal channel angular pressing (ECAP) and high-pressure torsion (HPT), as means of grain refinement to improve irradiation resistance. Samples of as-received, ECAP, and HPT processed Kanthal D were exposed to neutron irradiation to a dose of 2 DPA at two different temperatures, 300 °C and 500 °C. Detailed characterization was performed including mechanical and microstructural, and several positive improvements with regards to irradiation resistance were identified in the ECAP and …

Nmr Relaxometry Approach For The Detection Of Asphalt Binder Performance Properties, Rebecca Herndon

Doctoral Dissertations

"Asphalt is a necessary material for transportation infrastructure. Understanding the material properties of asphalt binders is critical for improving infrastructure and enhancing the lifetime of pavements. While the physical properties of asphalt binders are well-defined by standard procedures and parameters, the chemical environment lacks definition. Physical parameters like viscosity and stiffness are traditionally used to differentiate asphalt binders, describe aging, and indicate the impact of additives. However, these tests have limited insight into the material's chemical environment. Therefore, a nondestructive analytical technique, nuclear magnetic resonance (NMR) relaxometry, was employed to investigate the chemical environment of asphalt binders. Although NMR relaxometry …

Lifelong Machine Learning With Adaptive Resonance Theory, Sasha Petrenko

Doctoral Dissertations

"This publication option dissertation is composed of three papers concerning the study of the problem lifelong machine learning with Adaptive Resonance Theory (ART) algorithms. Lifelong learning (L2) is a challenging machine learning paradigm that both encompasses and formalizes the fields of continual learning and incremental learning. The field is concerned with the mitigation of the phenomenon of catastrophic forgetting whereby learning agents that are faced with incrementally novel information deleteriously overwrite previous knowledge if that learning process is not regularized to counteract this consequence. ART algorithms solve this stability-plasticity dilemma by optimally assigning learning to categories or instantiating new knowledge …

Metal-Free Photoredox Catalysis For The S-Trifluoromethylation Of Thiols And Investigation Of Ph-Dependent Decontamination Of Dimethyl Chlorophosphate, A Nerve Agent Simulant, Via Hydrolysis With Azoles, Raheemat Rafiu

Doctoral Dissertations

"Organofluorine chemistry plays a critical role in synthetic chemistry due to the unique properties of fluorine. However, the S-trifluoromethylation of thiols remains underexplored, with existing methods often relying on toxic and expensive organometallic catalysts. Given the advantages of S-trifluoromethylated compounds, including enhanced pharmacological activity and improved metabolic stability, we developed a green and cost-effective method for the synthesis of S-CF3 compounds using diacetyl as the organic catalyst and Langloi's reagent as the trifluoromethylating reagent. This reaction, which proceeds under mild conditions using visible light photoredox catalysis, works across various substrate scopes, including aromatic, heteroaromatic, and aliphatic thiols.

Chemical Warfare Agents …

Applications Of Tetramethyl-Guanidine Supported Cationic Transition Metal Sites Towards C─N Bond Construction Methodologies And [3+2] Cycloaddition Reactions, Suraj Kumar Sahoo

Doctoral Dissertations

"We present a family of divalent cationic bipodal and tripodal M(II) reagents (M = Mn, Fe, Co) supported by superbasic tetramethyl-guanidinyl (TMG) residue framework with applications towards C─N bond construction methodologies. These cationic reagents possess unique characteristics since their enhanced Lewis acidity facilitates in situ elaboration of the primary nitrene-transfer product (aziridine). Indeed, further aziridine-ring opening and cycloaddition of several dipolarophiles (nitriles, alkenes, ketones) permits the three-component synthesis of valuable five-membered N-heterocycles, commonly found in pharmaceuticals and agrochemicals.

We undertake a more systematic study to unravel parameters that make certain divalent metal sites more suitable as catalysts for the in-situ …

Development Of Ligands And Metal Complexes With Weakly Coordinating Apical Elements And Their Application To Nitrene Insertion And Addition To C‒H And C=C Bonds, Meenakshi Sharma

Doctoral Dissertations

"We present a family of triangular coinage metal M₃Cl₃ complexes [M = Cu (I), Ag (I)] supported by antimony [Sb (III)], and bismuth [Bi (III)]-centered highly basic tetramethyl-guanidinyl [TMG₃trphen-E] (E = Sb, Bi) ligand frameworks with their applications as catalysts toward the construction of C‒N bond through aziridination and amination reaction methodologies.

The [(TMG₃trphen-Sb)Cu₃(µ-Cl)₃] catalyst gives better yields of aziridines with styrenes than [(TMG3trphen-Bi)Cu₃(µ-Cl)₃] with PhINTs as a nitrene source. However, the catalytic reactivity of both catalysts is enhanced by an electrophilic nitrene, PhINTces for …

Metal-Organic Thin Film Coated On Optical Fiber, Nahideh Salehifar

Doctoral Dissertations

This dissertation explores the development of metal-organic framework (MOF)-based optical fiber sensors for detecting volatile organic compounds (VOCs) at low concentrations (parts-per-billion to parts-per-million). In the first part of the study, theoretical calculations were performed using effective medium approximation (EMA) models, including Lorentz–Lorentz, Maxwell–Garnett, and Bruggeman equations, to predict the refractive index changes of MOFs upon gas adsorption. These models were applied to MOFs such as ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr), and HKUST-1 to evaluate their potential for gas sensing.

In the second part of the dissertation, experimental work was conducted to validate the theoretical predictions. MOF-coated optical fibers were fabricated …

Synthesis And Analysis Of Materials For Quantum Devices, Mathew Pollard

Doctoral Dissertations

Quantum materials play a pivotal role in the advancement of next-generation technology. Superconducting quantum computing, dissipationless spintronics, or valleytronics offer promising ways forward beyond traditional chip miniaturization. Josephson Junctions (JJs) have already revolutionized quantum information and high precision detectors. Quantum systems, however, are either hard to control, produce, and/or maintain. This calls for a better understanding of microscopic properties and tuning of these quantum states.

In this work, we experimentally investigated growth methods to control the electric and magnetic properties of Topological Insulator (TI) Sb2Te3 through Cr-doping. Our results demonstrate the onset of a Magnetic Topological Insulator (MTI) and have …

Deep Learning Architecture Design For Nano-Satellite Image Super-Resolution, William Everette Symolon

Doctoral Dissertations

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation, and research. They also have the potential to enhance satellite constellation resilience. However, the inherent size, weight, and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications. This research proposed a deep learning architecture capable of enhancing …

Selected Topics In Blockchain-Aided Iot Inference, Yiming Jiang

Doctoral Dissertations

"Blockchain has recently been considered in the Internet of Things (IoT) to secure data exchanges and storage across various engineering applications. This newly emerging blockchain-aided IoT (BIoT) network has been applied to a great deal of security-related applications. However, the BIoT still has the vulnerability that can be exploited by attackers, potentially impairing the performance of BIoT applications. Despite this, research addressing the vulnerability has been limited. This work presents four studies aimed at addressing critical gaps in current BIoT research.

The first study introduces a Time-Restricted Double-Spending Attack (TR-DSA) model for Proof-of-Work (PoW)-based blockchains, where the adversary only conducts …

Drone-Based Multimodal Sensing On Vegetations And Data Analytics For Early Detection Of Gas Leakage From Underground Pipelines, Pengfei Ma

Doctoral Dissertations

"This study explored the feasibility of using multimodal remote sensors in the early detection of natural gas leaks from underground pipelines, as vegetation situated above pipelines can discern microbial changes in soil when affected by gas and exhibit physiological stress symptoms on leaves. Three sensors (RBG, thermal, and hyperspectral cameras) were utilized to monitor vegetation stress as an indicator of gas leaks. A laboratory experiment was conducted to evaluate the workability of vegetation for gas leak detection. Regular hyperspectral imagery was collected from test vegetations to identify gas stress and distinguish it from other environmental stressors such as salinity impact, …

A Hybrid Data Processing, Computational Intelligence, And Complex Systems Modeling Approach For Describing And Predicting The Bitcoin Market, Oluwadamilare Akinpelu Omole

Doctoral Dissertations

"The Bitcoin market, like traditional financial markets, is a complex system with intricate interdependencies and nonlinear interactions among various market elements. This, coupled with the inherent uncertainty and high volatility present, makes predicting Bitcoin price movements difficult. The lack of understanding of the underlying market dynamics often results in significant losses for investors and traders. Existing studies have focused on the use of predictive models, which have not sufficiently captured the complexities of the market and cannot forecast extreme market events. This research endeavors to bridge this gap by combining data processing techniques, computational intelligence, and complex systems theory to …