Digital Commons Network^™

Impaired Tunability Of Brain State And Motor Dysfunction In A Mouse Model Of Rett Syndrome, Victoria Kindler Norman

Graduate Theses and Dissertations

Rett syndrome (RTT) is a rare neurological disorder, caused by disrupted function of the MECP2 gene, resulting in impaired cognitive and motor functions. Previous studies suggest that although MECP2 has important functions throughout the body, the etiological origins of RTT-related dysfunction should be sought within the brain. However, it remains a mystery how neural population dynamics are altered in the RTT brain and how these alterations relate to motor dysfunction. Previous studies using an RTT mouse model point to abnormal correlations among firing rates of neurons. Here we hypothesize that such disrupted neural activity correlation could be caused by abnormal …

Advancing Apparel Education Through 3d Simulation In Vstitcher: Confidence, Competence, And Career Readiness In Higher Education, Jonah Diniakos Graham

Graduate Theses and Dissertations

The rapid adoption of 3D garment simulation in the apparel industry has created new demands of higher education to better prepare students with both technical proficiency and confidence within digital design platforms to better enable them for many career avenues. This study examines the integration of Browzwear’s VStitcher in an undergraduate apparel production course, addressing the need to assess both competence and self-confidence in digital garment construction while also fostering broader career readiness competencies. Guided by a combination of Self-Determination Theory (SDT) and Situated Expectancy-Value Theory (SEVT), the study emphasizes the role of autonomy, competence, and relatedness in the shaping …

Explainability In Deep Learning For Density Regression, Dalton James Oxford

Graduate Theses and Dissertations

Classical statistical methods focus on explainability and inferential power. Machine learning and deep learning can handle non-linear, high-dimensional data better than traditional methods. In modeling, a clear understanding and interpretation are essential to decision-making. Recent work in quantile regression and extreme modeling has begun to use deep learning due to its performance on high-dimensional, non-linear data. Semi-Parametric Quantile Regression (SPQR) is a nonparametric spline-based approach to quantile regression that estimates the conditional PDF and CDF of the response. Semi-Parametric Quantile Regression for Extremes (SPQRx) is a recent extension of SPQR that provides two features: out-of-sample estimation and accurate extreme-tailed estimation. …

Genre Prediction Using Rnns And Llm-Enhanced Video Game Review Data, Gabriel Young

Graduate Theses and Dissertations

LLMs (Large Language Models) are powerful tools for engaging with textual data, carrying many advantages over classical NLP (Natural Language Processing) and ML (Machine Learning) approaches. However, a classical ML model can still be faster, more efficient to run, and accessible than an LLM. We seek to gain the benefits of LLM text comprehension and preserve them in a classical ML model, a hybrid approach. The LLM operates on text to surface relevant information and associations in our problem space, then the ML model trains on the LLM output. The model may learn from the LLM and provide a more …

Structural Relaxation In Glass-Forming Liquids At Extreme Pressure: Photon-Correlation Spectroscopy And Constrained Thermodynamic Surface Modeling, Kevin Wayne Lyon

Graduate Theses and Dissertations

Structural relaxation in glass-forming liquids is strongly dependent on both temperature and pressure, yet direct measurement of structural α-relaxation under multi-gigapascal compression has remained experimentally challenging. This work extends depolarized photon correlation spectroscopy (DPCS) to pressures approaching 5 GPa in a diamond anvil cell, representing the highest-pressure implementation of this technique and enabling direct measurement of the structural α-relaxation in a pure liquid at extreme compression. This capability provides access to dynamical regimes previously unexplored by photon correlation spectroscopy and establishes a platform for probing glassy dynamics at high pressure. To describe these measurements, a constrained relaxation-time surface framework is …

Effects Of Wetting And Drying On Nutrient Mobilization In Aquatic Systems, Karessa Grace De La Paz

Graduate Theses and Dissertations

Wetting and drying cycles are a fundamental, but underexplored, control on nutrient mobilization and transport in aquatic ecosystems. Such intermittency impacts approximately 60% of the world's streams and rivers, and the extent is expected to increase with future climate change. Additionally, aging reservoir infrastructure is driving restoration efforts, like large-scale drawdowns, that can expose benthic sediments to extended dry periods before refilling. Despite the prevalence of wet-dry cycles, how they influence nutrient transport and mobilization across different aquatic ecosystems remains poorly understood. To address this gap, this thesis presents two complementary studies exploring nutrient dynamics in Arkansas aquatic ecosystems that …

Low Climate Benefit Of Nordic Coastal Marshes: Site Conditions Outweigh Grazing Effects And Shape Trade-Offs Between Carbon Storage And Its Stability, Carmen Leiva-Dueñas, T. Banta, Christoffer Boström, Franziska Eller, Johan Eklöf, Line Holm Andersen, Kai Jensen, Marianna Lanari, Ella Logemann, Pere Masque, Thomas Ostertag, Christoph Reisdorff, Anaïs Richard, Anu Vehmaa, Jukka Alm, Mikael Von Numers, Dorte Krause-Jensen

Research outputs 2022 to 2026

Coastal marshes, recognized as effective organic carbon (OC) sinks, have gained attention for their potential contribution to climate mitigation through protection and restoration. However, the climate mitigation potential of Nordic coastal marshes remains understudied, likely due to their heterogeneous and often non-tidal nature. To fill this gap, we examined soil OC storage and accumulation rates, and the effects of grazing, a common management practice, across eight Nordic coastal marsh areas spanning broad climate and environmental gradients. We also assessed soil methane emissions in selected areas. The Nordic marshes studied store a median of 7 kg OC m⁻² (interquartile range, …

A Survey Of Privacy-Preserving Federated Learning For Intrusion Detection Systems, Thomas Bunko, Michael N. Johnstone, Wencheng Yang, Ben A. Scott

Research outputs 2022 to 2026

Intrusion detection systems (IDS) monitor and detect malicious activity and unauthorized access that may compromise systems. Traditional IDS approaches send data to a central server for analysis, raising privacy concerns as data owners lose control over security. Federated Learning (FL) offers a privacy-preserving alternative by allowing local devices to process their data and generate models without sharing raw data. These local models are aggregated centrally to form a comprehensive model with performance comparable to centralized systems. This paper reviews FL-based IDS research, and is the first review paper to focus on privacy-preserving techniques collectively known as privacy-preserving Federated Learning (PPFL) …

Synthesis Of Novel 1,2,3-Triarylnaphthalenes Via Intermolecular Dehydro-Diels-Alder Reaction, Ethan C. Dailey

Honors Theses

The photo-dehydro-Diels–Alder (PDDA) reaction is a useful method for the construction of substituted naphthalene derivatives from acyclic precursors. While intramolecular variants of this transformation are well established, intermolecular PDDA reactions remain rare due to challenges associated with controlling reactivity under photochemical conditions. Previous studies suggest that electron-withdrawing substituents can promote intermolecular cycloaddition by increasing alkyne polarization and stabilizing reactive intermediates. In this work, the effect of enhanced electron-withdrawing character on intermolecular PDDA reactivity was investigated through the incorporation of a 2,3,5,6-tetrafluoropyridinyl substituent. Corresponding alkyne substrates were synthesized and subjected to photochemical conditions, resulting in the formation of 1,2,3-triarylnaphthalene products. The …

The Privacy Paradox: How Does Concern About Privacy Impact Actions In The Digital Age?, Julia Laduke

Honors Theses

The privacy paradox occurs when people claim to care about their digital privacy, but do not take actions to keep their data from being spread across the internet. This study examines college students, being primarily Generation Z, and their concerns and actions regarding digital privacy and security. A survey of 15 college students, 7 in humanities and 8 in STEM, was used to analyze their thoughts and concerns about their digital privacy. This survey also asked whether they took actions concerning their privacy, and if so, what tools they used to protect it. The results show that about 50% of …

Calibrating Human Trust In The Age Of Generative Ai: An Examination Of Ethical And Social Challenges, Abigail M. Mondido

Honors Theses

Generative AI (GenAI), a set of AI technologies with the ability to generate original, human-like outputs, is beginning to transform the way that information is distributed, composed, published, obtained, analyzed, and consumed. GenAI has seen massive adoption by internet users, businesses, and organizations in recent years despite the persistence of major ethical concerns and social implications. In particular, existing research has identified multiple critical trust-related issues associated with AI in general, including widespread mistrust and distrust, overreliance on AI, and a lack of trustworthiness of AI. There remains a need for a broader understanding of these issues as they relate …

Evaluating The Effects Of Urbanization On Meiofauna Biodiversity In Chattanooga’S Lentic Ecosystems Through Gis Applications, Keyle M. Bryant

Honors Theses

Urbanization is a leading driver of biodiversity loss in freshwater ecosystems, yet its effects on meiofauna—microscopic invertebrates that serve as key bioindicators of ecosystem health—remain poorly understood in lentic environments. This study evaluated the effects of urbanization on meiofaunal biodiversity in ten lentic waterbodies across Hamilton County, Chattanooga, Tennessee, by integrating environmental DNA (eDNA) metabarcoding, in-situ abiotic measurements, and Geographic Information Systems (GIS)-based land use analysis. Water samples were collected between January and April 2023 and processed using 18S rRNA amplicon sequencing to assess biodiversity as richness (amplicon sequence variants or ASVs, phylogenetic diversity or PD, and community composition). Land …

Environmental Awareness And Community Sentiments In A Superfund Site: A Study Of South Chattanooga, Mia Dial

Honors Theses

The concept of environmental injustice refers to the disproportionate exposure of marginalized communities to environmental hazards, often resulting from the placement of industrial sites, waste facilities, or other environmentally harmful infrastructure in areas with limited political and economic power. Alton Park, a historically underserved neighborhood in Chattanooga, Tennessee, faces elevated environmental risks due to its proximity to the South Chattanooga Lead Superfund Site and legacy industrial contamination. This study used semi-structured individual interviews to assess community awareness, perceptions, and behaviors related to environmental hazards in Alton Park. Residents generally reported some awareness of the Superfund site, though knowledge was often …

Resilience By Living With The Land And Community: A Case Study Of A Local Food Forest, Olivia Beck

Honors Theses

Imagine a public space where food, leisure, outdoor activity, and community engagement occur in one place, all in an urban forest ecosystem. For many, this is a hopeful vision of the future, and that is exactly what the idea of an urban food forest provides. In agroecology, a food forest or forest garden consists of planting diverse edible plants and mimicking the patterns of natural ecosystems. The intricate planning and design ensure the forest is self-sustaining, much like a natural forest, making it very resilient once established. However, they face many challenges and take time to grow and develop to …

Growing Change: A Gis Spatial Analysis To Plan Urban Gardens In Chattanooga, Carson A. Chalk

Honors Theses

Food insecurity and limited access to fresh nutritious food in Chattanooga is a prevalent issue, primarily among lower socioeconomic households, and can put strain on overall well-being. This study aimed to identify suitable vacant parcels in Chattanooga where urban gardens could be developed using a GIS-based spatial analysis. Spatial data involving vacant parcels, slope, zoning, and proximity to grocery stores was cross referenced with socioeconomic data focused on poverty rates, vehicle ownership, and mean household income to identify the most suitable sites in areas of greatest need. Sites identified as suitable through GIS were then visited to verify their suitability. …

Techno-Imagination: Elevating Creativity Through Xr And Ai, Christopher Spitzer

Creativity and Change Leadership Graduate Student Master's Projects

Techno-Imagination: Elevating Creativity Through XR and AI explores the history of creativity and computing technology, supported by research and academic literature, and looks at the possibilities of a convergence between the two. In parallel, a brief biographical story of the author shares how a passion for creativity emerged, along with a growing interest in science and technology—specifically extended reality—which ultimately came together in the creation of this master’s project. The project also highlights how the Creative Problem Solving (CPS) process was used alongside AI bots and twenty research-based creative thinking skills in developing the business model canvas. Finally, the outcome …

An Exploration Of The Autorotating Pendulum Model, Vlad Nita

Theses, Dissertations and Culminating Projects

Autorotation is the spontaneous rotation of an object, usually caused by an external fluid flow. The study of autorotation has many physical applications, such as in the design of wind/water turbines. In this thesis, we explore a nonlinear pendulum ordinary differential equation (ODE) which is used to model rotating plates in a fluid and has the capacity to reveal autorotation. In the context of an ODE, autorotation emerges as a bifurcation past oscillations, when the initial velocity of the system crosses a particular threshold. In his classic study from 1983, Lugt [14] utilizes this equation to capture experimental autorotation. Copeland’s …

Conceptualizing A Responsive Design Pedagogy To Support Teachers' Responsiveness To Students' Mathematical Ideas Through Making, Denish Ogweno Akuom

Theses, Dissertations and Culminating Projects

This study develops Responsive Design Pedagogy (RDP), a conceptual and analytic model of teacher responsiveness in design-based environments, to account for how responsiveness emerges through interactions among students, tools, and tasks. Although responsiveness has been widely studied in mathematics education, limited attention has been given to responsiveness within mathematical design activity and to how teachers attend to the multiple modes through which students express their mathematical ideas. Addressing these gaps, this qualitative study examined one teacher’s enactment of responsiveness in a Learning-by-Design classroom where students designed and used 3D-printed mathematical tools. Data sources included classroom video, student design artifacts, teacher …

The Space In Between, Cailyn Dawson

Theses and Dissertations

This paper asks the question: is it possible for our bodies to hold contradicting identities at the same time?  Through monochromatic self- portraits, the artist creates a link between quantum mechanics and painting, using the principle of superposition to depict these multiple unformed states of being.

Efficient Compression Framework For Time Series Self-Supervised Learning, Brooklyn Berry

Theses and Dissertations

Time series data is perhaps one of the most broad data types that exist and is studied by diverse research fields. Recently, Self-Supervised Learning (SSL) training frameworks, the training frameworks to pre-train deep learning models without human annotations, have been proposed. Because human annotation for time series is typically associated with being costly, there is a growing interest in developing effective SSL for time series data. In SSL, the pre-trained model will often produce a time series embedding series summarized from the original time series to ensure temporal information is preserved. Although such representation can effectively capture the semantic information, …

Data Driven Monitoring And Control Of Laser Powder Bed Fusion Process, Jose De Jesus Galarza

Theses and Dissertations

The Laser Powder Bed Fusion Process (LPBF) has been one of the main processes of additive manufacturing, enabling the manufacturing of complex geometries, customization, and lightweight parts. Modern LPBF processes have integrated monitoring systems that capture the light emissions per layer for quality assurance. However, standard defect detection algorithms have not yet achieved the high precision required due to the inherently variable nature of the signal, insufficient data for model training, and the confounding effects of the print.

The processes still have some challenges, such as characterizing the roughness from the build parameters alone, improving the pore detection using the …

Advances In Computational Methods For Sparsity-Promoting Linear Inverse Problems, Jonathan Lindbloom

Dartmouth College Ph.D Dissertations

Inverse problems arise throughout science and engineering, where indirect, incomplete, and noisy observations are used to recover unknown parameters of interest. In these applications, the corresponding forward or measurement models are often ill-conditioned or underdetermined, so direct inversion is unstable and regularization is required. This thesis develops computational methods for linear inverse problems in which the unknown is assumed to be approximately sparse in a transformed domain defined by a linear, possibly rank-deficient operator, such as a finite-difference matrix, with particular emphasis on large-scale problems.

The thesis makes three main contributions. First, it generalizes hierarchical Bayesian maximum a posteriori estimation …

Insights Into Androgen Receptor Allosteric Modulation By P,P'-Dichlorodiphenyldichloroethylene Binding At The Binding Function-3 Site And Site-Specific Binding Function-3 Mutations, Emanuel Aggeo Flores

Theses and Dissertations

One of DDT’s metabolites, p,p’-dichlorodiphenyldichloroethylene (DDE), has been classified as an endocrine disrupting chemical (EDC) due to its ability to interfere with hormone signaling by binding to nuclear receptors (NR) such as the androgen receptor (AR).

Structurally, the AR contains a ligand-binding domain (LBD) where endogenous steroids bind. Furthermore, the LBD contains a surface binding site known as binding function-3 (BF-3), which studies suggest exerts an allosteric effect on bound DHT when small, hydrophobic molecules bind.

Here, we present a study that examines how specific BF-3 site mutations and DDE binding affect steroid stability within the LBP utilizing computational methods. …

I Am Still Learning, Too!: Pre-Service Teachers' Experiences Of Mathematics Anxiety In An Inquiry-Based Mathematics Education Course At Utrgv, Jeremiah James Adriano Dela Rosa

Theses and Dissertations

Mathematics anxiety is a prevalent issue in mathematics education that negatively impacts students’ learning, performance, and engagement in mathematics. Prior research suggests that mathematics anxiety is often shaped by students’ experiences and emotional responses within the classroom environment.

The purpose of this study is to explore how Pre-Service Teachers experience mathematics anxiety in an Inquiry-Based Mathematics Education (IBME) classroom. This study employed a qualitative research design supported by descriptive survey data collected through the Abbreviated Mathematics Anxiety Scale (AMAS), selected components of the Fennema-Sherman Mathematics Anxiety Scale (FSMAS), and semi-structured interviews. The survey instruments were used to provide descriptive background …

Do I Have To Know Someone? The Impact Of Factors Beyond The Playing Field That Affect Mlb Hall Of Fame Voting., Jackson Wollscheid

Economics Undergraduate Honors Theses

With the creation of WAR (Wins above Replacement), it has created an index to measure the player’s value using a variety of statistics relevant to that player’s position. The higher a player’s career WAR then the greater the impact that player had on the game during his career. However, players are not simply inducted into the Hall of Fame on this measure of value but on voting from members of the BBWAA (Baseball Writers’ Association of America). This research investigates external factors outside of standard playing statistics’ influence on Hall of Fame voting, including team affiliation, career length, and other …

F.L.A.I.R. -- A Flow-Level Autoencoder For Intrusion Recognition, Joseph P. Dumond

Electrical Engineering and Computer Science Undergraduate Honors Theses

Industrial Internet of Things (IIoT) networks underpin critical infrastructure worldwide, yet securing them remains an open challenge. Traditional intrusion detection systems require labeled attack data for training, a resource that is rarely available in real industrial deployments. They also fail against novel threats, a model trained on known attacks has no basis for detecting anything outside its training set. This thesis presents a Flow-Level Autoencoder for Intrusion Recognition, or FLAIR, a fully unsupervised deep learning system for network intrusion detection in IIoT environments. FLAIR is built on a Gated Recurrent Unit (GRU) autoencoder trained exclusively on normal network traffic. Rather …

From Mobilenet To Repvit: A Survey Of Edge-Optimized Computer Vision Architectures, Eli A. Bosch

Electrical Engineering and Computer Science Undergraduate Honors Theses

Edge-optimized computer vision is a constantly evolving field where the definition of efficiency has changed repeatedly. This thesis presents a literature survey of four recent Convolutional Neural Network (CNN) families, all analyzed through a consistent framework of accuracy, parameter count, and Multiply-Accumulate Operations (MACs), alongside a survey of five CNN and Vision Transformer (ViT) hybrid models to examine the direction of the field. It was found that accuracy follows a logarithmic curve with respect to parameter count, exhibiting diminishing returns as models scale. This suggests that architectural design contributes more to performance gains than parameter count alone. Theoretical efficiency metrics …

A Case Study On Using Large Language Models To Drive Convergence-Oriented Literature Discovery In Critical Infrastructure Security, Caden J. Williamson

Electrical Engineering and Computer Science Undergraduate Honors Theses

In the world of cybersecurity, the rapid development of artificial intelligence proposes a constant challenge for researchers to defend critical infrastructure. Attacks on critical infrastructure can be catastrophic, and emerging strategies of cyber-adversaries that implement leading AI models can expose vulnerabilities in critical infrastructure much faster than previous tools. To defend against this emerging threat, the Cybersecurity Research Working Group at the University of Arkansas is aiming to develop a cross-domain and cross-discipline center of excellence. To support this effort, the group is writing a literature review on the topics of AI and critical systems security. Literature review is an …

Ecological Insights And Management Implications Of The Global Migratory Connectivity Of Green Turtles, Jaime Restrepo, Dina Nisthar, Harris Wei-Khang Heng, Lily K. Bentley, Corrie Curtice, Sarah Deland, Ei Fujioka, Patrick N. Halpin, Sarah K. Poulin, Roldán A. Valverde

School of Earth, Environmental, & Marine Sciences Faculty Publications

Aim

Green turtles are a widely distributed and highly migratory species; despite extensive data on their movement, there is no species-specific global synthesis on the subject. Based on three decades of published literature and building on previous global analyses, we developed a global network model of migratory connectivity for green turtles to better understand their spatial biology.

Location

Global.

Time Period

1990–2022.

Major Taxa Studied

Green Sea Turtle (Chelonia mydas).

Methods

We conducted a structured literature review extracting georeferenced information on the movement of green turtles from 1990 to 2022, aggregating this information into a single connectivity model, …

Solid Embedded Telemetry, Pranav Balasubramanian Natarajan

Graduate Theses and Dissertations

This thesis explores the integration of the Solid decentralized data framework within embedded and legacy energy control systems to enable secure, access-controlled, and interoperable data exchange. Two implementations were developed to evaluate Solid’s practical applicability: an ESP32 client implementing Solid-OIDC authentication and telemetry publication to a Solid Pod, and a Modbus-to-Solid bridge translating Modbus RTU communications into RDF-based data for bidirectional control. Experimental evaluation highlighted the effects of task scheduling and blocking operations on the ESP32, and polling-based communication latency within the Modbus bridge. These findings reveal the architectural and computational trade-offs involved in extending Solid to constrained or protocol-bound …