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Particle Classification Of Electromagnetic Clusters Using The Sphenix Detector, Fredrick J. Melhorn

Chancellor’s Honors Program Projects

No abstract provided.

Evaluation Of Avian Use Of Agricultural Cover Crops During The Winter, Migration Stopover, And The Breeding Season In Tennessee, Brittany Panos

Masters Theses

The U.S. Department of Agriculture Natural Resources Conservation Service administers the cover crop program to provide technical and financial assistance to agricultural producers to sow herbaceous plant seeds to establish cover crops to protect agricultural fields from soil erosion during the non-growing season (late fall through spring). Soil retention and water quality benefits have been documented, but potential benefits for avian wildlife remain largely unknown. I used line-transect avian and vegetation surveys to examine use of cover crop fields by birds during the non-breeding period (winter), migration, and the breeding season. I compared avian use of cover crop fields with …

Winter Roost Selection Of Eastern Red Bats And Impacts Of Non-Growing Season Prescribed Fire On Foraging Activity Of Forest Roosting Bats In Tennessee, Ashley D. Epstein

Masters Theses

With an increase in wind energy development and continued deforestation and habitat degradation, eastern red bats (Lasiurus borealis; LABO) and other migratory foliage roosting bats (hoary bat [Lasiurus cinereus; LACI], silver-haired bat [Lasionycteris noctivagans; LANO]) are at risk of severe population declines, potentially leading to the need for protection under the Endangered Species Act. While studies have been done examining the ecology of these species, there is still a lack of research on winter roosting and foraging behaviors. This research aims to fill some of those knowledge gaps by 1) Examining roost use (i.e., trees vs. litter) …

Mercury Biomagnification In Aquatic Food Webs Of Great Smoky Mountains National Park, Zachary Winston Clark

Masters Theses

Mercury is a widespread pollutant threatening human, fish, and ecosystem health on a global scale. Biomagnification concentrates mercury in upper trophic level organisms including predatory fishes, a primary route of dietary mercury exposure for humans. However, mercury biomagnification is not well understood in stream ecosystems, especially in places with no known point sources of contamination. A 2016 study revealed that Smallmouth Bass Micropterus dolomeiu mercury concentrations varied between three streams in Great Smoky Mountains National Park (GSMNP), Tennessee USA. However, the reason for this spatial variation in mercury concentrations is not understood. Our objectives were to (1) measure environmental and …

Investigation Of Magnetic, Spectroscopic, And Structural Properties Of Molecular Metal Compounds, Alexandria Bone

Doctoral Dissertations

Compounds exhibiting single-molecule magnetism (SMM) are of current interest for potential use in molecular data storage and quantum computing applications. However, rapid magnetic relaxation at desired operating temperatures currently limits the use of these materials, and a more thorough understanding of the magnetic and vibrational transitions that affect magnetic memory is required to inform SMM design. The primary focus of this dissertation is the study of magnetic and vibrational modes in molecular magnetic compounds via advanced spectroscopic techniques such as inelastic neutron scattering (INS), far-IR magneto-spectroscopy (FIRMS), and high-field, high-frequency electron paramagnetic resonance (HFEPR) to directly observe transitions among zero-field …

Classifying Facial Expressions Of Students Being Tutored In A Gateway College Math Course, Kriss Gabourel

Doctoral Dissertations

When it comes to tutoring, computers have not quite been able to achieve the success that humans have in helping students improve learning outcomes. This research sought to address one aspect of what makes human tutors more effective, the ability to identify and to interpret facial expressions. When a student is feeling anxious, confused, distracted or frustrated, or when a student has an ‘aha’ moment, human tutors can identify the student’s facial expressions and adjust their tutoring approach as necessary. This study sought to determine if, in the context of a gateway college math course, these particular learning-centered affects could …

Multi-Objective Radiological Analysis In Real Environments, David Raji

Doctoral Dissertations

Designing systems to solve problems arising in real-world radiological scenarios is a highly challenging task due to the contextual complexities that arise. Among these are emergency response, environmental exploration, and radiological threat detection. An approach to handling problems for these applications with explicitly multi-objective formulations is advanced. This is brought into focus with investigation of a number of case studies in both natural and urban environments. These include node placement in and path planning through radioactivity-contaminated areas, radiation detection sensor network measurement update sensitivity, control schemes for multi-robot radioactive exploration in unknown environments, and adversarial analysis for an urban nuclear …

Jet Production And Rppb In Pp Collisions At Sqrt(S) = 8 Tev And P--Pb Collisions At Sqrt(Snn) = 8.16 Tev Using The Alice Detector, Austin Schmier

Doctoral Dissertations

At the CERN Large Hadron Collider, protons and heavy ions are collided at relativistic
speeds in order to study the behavior and processes of Quantum Chromodynamics (QCD). One such process is the production of collimated sprays of particles called jets resulting from the hard scattering of two partons. Jets are an important probe that provide a window into the early stages of the collision.

Measurements in small systems such as proton-proton (pp) and proton-lead (p–Pb)
collisions are important in order to provide constraints on nuclear parton distribution
functions and the strong coupling constant αS [55]. Measurements at different center of …

Graph-Based And Anomaly Detection Learning Models For Just-In-Time Defect Prediction, Aradhana Soni

Doctoral Dissertations

Efficiently identifying and resolving software defects is essential for producing high quality software. Early and accurate prediction of these defects plays a pivotal role in maintaining software quality. This dissertation focuses on advancing software defect prediction methodologies and practical applications by incorporating graph-based learning techniques and generative adversarial-based anomaly detection techniques. First, we present a novel approach to software defect prediction by introducing a graph-based defect ratio (GDR). This innovative metric leverages the intricate graph structure that captures the interdependencies among developers, commits, and repositories, offering a promising alternative to standard traditional features. This study highlights the potential for graph-based …

Towards Continuous Variable Quantum Computation Of Lattice Gauge Theories, Shane Nicklaus Thompson

Doctoral Dissertations

The calculation of physical quantities in a relativistic quantum field theory (rQFT) is a computationally demanding task due to the presence of an infinite number of degrees of freedom. This problem carries over to discretized versions of these theories, i.e. lattice field theories, where the Hilbert space size increases exponentially with the size of the lattice. Despite the success of some classical techniques such as Monte Carlo, their applicability is limited. For instance, Monte Carlo is associated with a sign problem that makes real-time evolution and high-fermion-density systems particularly challenging to compute. Quantum computation is a promising avenue thanks to …

Investigating Small Molecule Behavior In Living Bacterial Membranes With Second Harmonic Scattering, Marea J. Blake

Doctoral Dissertations

A molecule's entry into a cell is impeded primarily at the surface of Gram-positive bacteria. This interface serves as the boundary separating cellular contents from the external environment and is composed of a thick layer of peptidoglycan and a lipid bilayer equipped with protein and lipid species with various roles including that of small-molecule transport. As such, understanding these molecule-membrane interactions is imperative to examine in order to design novel drugs or adjuvants to combat the global antibiotic resistance predicament. Knowledge regarding passive diffusion and overall organization of small molecules in the lipid bilayer of living Gram-positive cells is limited …

Stability Of Quantum Computers, Samudra Dasgupta

Doctoral Dissertations

Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers. This technology has the power to revolutionize scientific applications such as simulating many-body quantum systems for molecular structure understanding, factorization of large integers, enhance machine learning, and in the process, disrupt industries like telecommunications, material science, pharmaceuticals and artificial intelligence. However, quantum computing's potential is curtailed by noise, further complicated by non-stationary noise parameter distributions across time and qubits. This dissertation focuses on the persistent issue of noise in quantum computing, particularly non-stationarity of noise parameters in transmon processors. It …

Understanding The Impact Of Divertor And Main Chamber Ion Fluxes On Divertor Closure In The Diii-D Tokamak, Kirtan M. Davda

Doctoral Dissertations

The diverted tokamak redirects extreme heat and particles to targets, a plasma-facing component designed for such loads. Here, the local fluxes produce strong particle recycling and sputtering. Recycled neutrals can “leak” into the region between the core and wall, the scrape-off-layer (SOL), impacting plasma performance. Increasing divertor closure can reduce leakage by containing neutrals within the divertor. However, there exists a need to quantify divertor baffle restrictions and understand closure directly from empirical data as opposed to indirectly through modeling.

Our study introduces the Geometric Restriction Parameter (GRP) based on simplifying neutral transport to ballistic pathways. Specifically, it considers the …

Enhancing Security And Usability In Password-Based Web Systems Through Standardized Authentication Interactions, Anuj Gautam

Doctoral Dissertations

Password-based authentication is the predominant method for securing access on the web, yet it is fraught with challenges due to the web’s lack of inherent design for authentication. Password managers have emerged as auxiliary tools to assist users in generating, storing, and inputting passwords more securely and efficiently. But both the browser and the server are oblivious of the password manager’s presence, leading to usability and security issues. However, because the web wasn’t originally built to accommodate password-based authentication, password managers serve as a temporary fix and encounter several usability and security problems that limit their widespread use. This dissertation …

Multimodal Data Fusion And Machine Learning For Advancing Biomedical Applications, Md Inzamam Ul Haque

Doctoral Dissertations

This dissertation delves into the intricate landscape of biomedical imaging, examining the transformative potential of data fusion techniques to refine our understanding and diagnosis of health conditions. Daily influxes of diverse biomedical data prompt an exploration into the challenges arising from relying solely on individual imaging modalities. The central premise revolves around the imperative to combine information from varied sources to achieve a holistic comprehension of complex health issues.

The chapters included here contain articles and excerpts from published works. The study unfolds through an examination of four distinct applications of data fusion techniques. It commences with refining clinical task …

Novel Energy Scale Calibration Strategies For The Prospect-Ii Experiment, Xiaobin Lu

Doctoral Dissertations

Nuclear reactors contributed to the first discovery of the elusive neutrino particle and continue to play a significant role in our understanding of neutrino masses and oscillations. The PROSPECT reactor neutrino experiment, the Precision Reactor Oscillation and SPECTrum Experiment, took data at the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. This work discusses calibration procedures and the establishment of an energy scale model, and details the development of efficient analysis cuts and subsequent selection cut optimization. The precise energy scale calibration and efficient analysis cuts produced a high quality dataset of neutrino interactions of more than 50,000 …

Experimental Quantum Key Distribution In Turbulent Channels, Kazi Mh Reaz

Doctoral Dissertations

Quantum Key Distribution (QKD) ensures security by relying on the laws of quantum physics rather than the mathematical intricacy of encryption algorithms. The transmission medium is a critical restricting factor for any quantum communication protocol. Fiber-based optical networks suffer great losses, making quantum communication impossible beyond metropolitan scales. Here free-space quantum communication can be a great alternative for long-distance communication. Even though modern Communications are mostly wireless the atmosphere poses a challenge for QKD. So QKD must be resistant to both atmospheric loss and variations in transmittance. In this thesis we conduct an experiment to strengthen the BB84 protocol's resistance …

Bridging Biological Systems With Social Behavior, Conservation, Decision Making, And Well-Being Through Hybrid Mathematical Modeling, Maggie Renee Sullens

Doctoral Dissertations

Mathematical modeling can achieve otherwise inaccessible insights into bio-logical questions. We use ODE (ordinary differential equations) and Game Theory models to demonstrate the breadth and power of these models by studying three very different biological questions, involving socio-behavioral and socio-economic systems, conservation biology, policy and decision making, and organismal homeostasis.

We adapt techniques from Susceptible-Infected-Recovered (SIR) epidemiological models to examine the mental well-being of a community facing the collapse of the industry on which it’s economically dependent. We consider the case study of a fishing community facing the extinction of its primary harvest species. Using an ODE framework with a …

Generalized Differentiable Neural Architecture Search With Performance And Stability Improvements, Emily J. Herron

Doctoral Dissertations

This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently, thereby optimizing the search process by enforcing that the networks produce similar outputs. However, the dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network, a sub-optimal proxy for the final evaluation network utilized during retraining. ICDARTS, a revised algorithm that reformulates the search phase loss functions to ensure …

Material Formulation And Process Optimization Towards Fabricating Robust 3d Printed Structures, Austin Riggins

Doctoral Dissertations

This dissertation focuses on understanding and addressing the fundamental physicochemical phenomena that lead to weak interfaces and structural warpage in material extrusion 3D printing. Polymeric feedstocks used for this manufacturing technique were manipulated through the incorporation of additives that alter the dynamics of the matrix during and after printing. In Chapter II, adhesion between layers of structures printed from PEEK was strengthened through a combination of low-molecular weight additive incorporation and post-printing thermal annealing. Chapter III reports a method for decreasing the irreversible thermal strain of structures printed from poly(lactic acid) by introducing nanographene and photoinitiator additives into the feedstock …

Static And Dynamic State Estimation Applications In Power Systems Protection And Control Engineering, Ibukunoluwa Olayemi Korede

Doctoral Dissertations

The developed methodologies are proposed to serve as support for control centers and fault analysis engineers. These approaches provide a dependable and effective means of pinpointing and resolving faults, which ultimately enhances power grid reliability. The algorithm uses the Least Absolute Value (LAV) method to estimate the augmented states of the PCB, enabling supervisory monitoring of the system. In addition, the application of statistical analysis based on projection statistics of the system Jacobian as a virtual sensor to detect faults on transmission lines. This approach is particularly valuable for detecting anomalies in transmission line data, such as bad data or …

Atomic-Level Mechanisms Of Fast Relaxation In Metallic Glasses, Leo W. Zella

Doctoral Dissertations

Glasses are ubiquitous in daily life and have unique properties which are a consequence of the underlying disordered structure. By understanding the fundamental processes that govern these properties, we can modify glasses for desired applications. Key to understanding the structure-dynamics relationship in glasses is the variety of relaxation processes that exist below the glass transition temperature. Though these relaxations are well characterized with macroscopic experimental techniques, the microscopic nature of these relaxations is difficult to elucidate with experimental tools due to the requirements of timescale and spatial resolution. There remain many questions regarding the microscopic nature of relaxation in glass …

Exploring Soil Microbial Dynamics In Southern Appalachian Forests: A Systems Biology Approach To Prescribed Fire Impacts, Saad Abd Ar Rafie

Doctoral Dissertations

Prescribed fires in Southern Appalachian forests are vital in ecosystem management and wildfire risk mitigation. However, understanding the intricate dynamics between these fires, soil microbial communities, and overall ecosystem health remains challenging. This dissertation addresses this knowledge gap by exploring selected aspects of this complex relationship across three interconnected chapters.

The first chapter investigates the immediate effects of prescribed fires on soil microbial communities. It reveals subtle shifts in porewater chemistry and significant increases in microbial species richness. These findings offer valuable insights into the interplay between soil properties and microbial responses during the early stages following a prescribed fire. …

A Measurement Of Neutron Polarization And Transmission For The Nedm@Sns Experiment, Kavish Imam

Doctoral Dissertations

The D.O.E Nuclear Science Advisory Committee Long Range Plan has called for experimental programs to explore fundamental symmetry violations and their implications in nuclear, particle and cosmological physics. The neutron electric dipole moment experiment at the Spallation Neutron Source (nEDM@SNS) aims to search for new physics in the Time-reversal (T) and Charge-Parity (CP) symmetry violating sector by setting a new limit on the nEDM down to a few x 10^-28 e·cm using a novel cryogenic technique, which combines the unique properties of polarized Ultracold Neutrons (UCN), polarized ³He, and superfluid ⁴He. The experiment will employ a cryogenic …

Towards Safer Code Reuse: Investigating And Mitigating Security Vulnerabilities And License Violations In Copy-Based Reuse Scenarios, David Reid

Doctoral Dissertations

Background: A key benefit of open source software is the ability to copy code to reuse in other projects. Code reuse provides benefits such as faster development time, lower cost, and improved quality. There are several ways to reuse open source software in new projects including copy-based reuse, library reuse, and the use of package managers. This work specifically looks at copy-based code reuse.

Motivation: Code reuse has many benefits, but also has inherent risks, including security and legal risks. The reused code may contain security vulnerabilities, license violations, or other issues. Security vulnerabilities may persist in projects that copy …

Integration Of Raman Spectroscopy And Python-Based Data Analysis For Advancing Neurobiological Research, Natalie E. Dunn

Doctoral Dissertations

The field of Raman spectroscopy continues to expand into biological applications due to its usefulness as a non-invasive technique that can be utilized qualitatively and quantitatively. However, the inherent weakness of Raman scattering leads to the need for each collected spectra to undergo a preprocessing step to remove noise, background drift, and cosmic rays. Biological research in particular needs large datasets due to the increased variability in samples. As datasets grow, the need to perform preprocessing on each individual spectra becomes daunting. Often, these steps are done by hand with the help of specialized software programs. Preprocessing can be accelerated …

Towards Expressive And Versatile Visualization-As-A-Service (Vaas), Tanner C. Hobson

Doctoral Dissertations

The rapid growth of data in scientific visualization has posed significant challenges to the scalability and availability of interactive visualization tools. These challenges can be largely attributed to the limitations of traditional monolithic applications in handling large datasets and accommodating multiple users or devices. To address these issues, the Visualization-as-a-Service (VaaS) architecture has emerged as a promising solution. VaaS leverages cloud-based visualization capabilities to provide on-demand and cost-effective interactive visualization. Existing VaaS has been simplistic by design with focuses on task-parallelism with single-user-per-device tasks for predetermined visualizations. This dissertation aims to extend the capabilities of VaaS by exploring data-parallel visualization …

Short Range Correlation Measurements In The Quasielastic Region With An 11 Gev Beam, Casey Morean

Doctoral Dissertations

Electron scattering is a significant means of studying internal high momentum
nucleon and quark distributions in nuclei. Thomas Jefferson National Accelerator
Facility (JLab) with its 11GeV beam is capable of studying high momentum nucleons
with unmatched precision. The role of short range nucleon configurations and
quark distributions is significant for understanding the dynamics of nuclei and their
underlying components. Scattering cross section measurements in the kinematic
regime x > 1, where the free nucleon is forbidden, are sensitive to high momentum
nucleons, which are believed to come from short range correlations (SRCs). SRCs are
strongly interacting, high momentum nucleons with a …

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …

Generative Adversarial Game With Tailored Quantum Feature Maps For Enhanced Classification, Anais Sandra Nguemto Guiawa

Doctoral Dissertations

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms.

This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A …