Digital Commons Network^™

Artificial Intelligence For Reliability: Predictive Health Maintenance And Geolocation In Gps-Denied Environments, Rafael Toche Pizano

Graduate Theses and Dissertations

In this dissertation, we explore the potential of machine learning and deep learning techniques to enhance the performance and robustness of applications across two major domains. By addressing the challenges within these fields, we demonstrate that we can leverage learning algorithms to obtain substantial improvements in accuracy and robustness. First, we tackle a problem in the field of predictive health maintenance. We propose a novel auto encoder and neural network based methodology to predict failure times in complex aviation systems to learn to distinguish between normal and abnormal operational behavior, and use this information to inform the neural network to …

Agricultural Practices’ Impact On Soil Health Indicators In Mid-South U.S. Crop Production Systems, Katherine Suzanne French

Graduate Theses and Dissertations

Soil health and regenerative agriculture are concepts gaining popularity across global agriculture systems. The effect of sustainable farming practices such as nutrient management, cover crops, adoption of no-tillage, and residue retention on crop yield, as well as environmental resilience, is increasingly being studied. This research aimed to evaluate the impacts of (i) cover cropping and nutrient management, and of (ii) soil sampling depth and timing on indicators of soil health in various mid-southern irrigated row crop systems. Soil health indicators like soil organic matter (SOM), carbon dioxide respiration (CDR), beta-glucosidase enzyme activity (BG), permanganate oxidizable carbon (POXC), and the soil …

Beam Steering Control For A Small-Scale 5g Antenna Array, Omar Wagih Elkalesh

Graduate Theses and Dissertations

This thesis demonstrates the design, development, and validation of a full-scale adaptive beam steering control system of a small-scale 5G antenna array, designed and thoroughly evaluated with MATLAB Simulink. Overcoming the significant challenge of establishing strong wireless links in dynamic environments, the new system uses a new closed-loop control framework that can automatically detect the signal sources and automatically change the beam direction in real-time. The use of the adaptation is necessary to combat tracking errors that are caused by the mobility of the mobile users, environmental variations, as well as channel estimation errors, widespread in future wireless communication systems. …

Predicting Stock Price Movement With Llm-Enhanced Tweet Emotion Analysis, An Vuong

Graduate Theses and Dissertations

Accurately predicting short-term stock price movement remains a challenging task due to the market’s inherent volatility and sensitivity to investor sentiment. In this thesis, we present a published paper that discusses a deep learning framework integrating emo- tion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta’s LLaMA 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. …

Optical Characterization Of Ge And Gesn Grown In Aspect Ratio Trapping Structures For Enhanced Emitter Performance, Abdulla Said Ali

Graduate Theses and Dissertations

Silicon (Si) began dominating the electronics industry since 1960s, the dominance came due to its affordability and well developed fabrication processes. But when it comes to light emitting devices and photonics devices in general, the indirect bandgap of Si has caused many scientists to search for alternative semiconductor materials that can deliver better light emission, materials that can also improve wavelengths detection which is also very limited when it comes to Si, reduce losses and greater light amplification, all this while still being affordable. Despite these limitations, Si has been widely used in Complementary Metal Oxide Semiconductor (CMOS) manufacturing and …

Understanding Outer Solar System Ice Composition Through Impact Processes: Simulations And Hydrates, Jill Rebecca Mcdonald

Graduate Theses and Dissertations

This work concerns solar system ices, particularly water ice and clathrate hydrates. Water ice is abundant as a mineral in the outer parts of the solar system, providing source material for the formation of clathrates. We explored constituents through a combination of numerical modeling and laboratory experiments. Understanding the differences and similarities between these materials can shed light on planetary habitability, composition, and evolution. Chapter one contains relevant background information. This chapter introduces the ices that will be studied and gives insight into the locations of interest within our solar system. It also explains the mechanics of impact cratering processes …

Privacy Protection In Cloud-Based Biometric Systems, Yatish Reddy Dubasi

Graduate Theses and Dissertations

The widespread adoption of server-based biometric authentication systems, often hosted in the cloud, has introduced significant privacy risks. While these systems offer convenience, they require storing sensitive biometric templates on remote servers, creating a high-value target for adversaries. Unlike passwords, compromised biometric data is immutable and cannot be reissued, leading to an irreversible loss of privacy. This threat is exacerbated by template inversion attacks, which can reconstruct a user's original biometric trait (e.g., a face image) from its stored feature vector. This dissertation addresses these critical privacy challenges by designing, implementing, and evaluating a suite of novel frameworks for privacy-preserving …

Towards Vision-Brain Understanding At Scales: From Classical To Quantum Machine Learning Approaches, Xuan-Bac Nguyen

Graduate Theses and Dissertations

In recent years, large-scale learning approaches such as unsupervised and self-supervised learning have revolutionized artificial intelligence. These methods enable machines to learn high-level representations without explicit human supervision, achieving remarkable success across vision, language, and multimodal tasks. However, such advances come at a cost—they rely on massive datasets, billions of parameters, and extensive computational resources. Despite these achievements, artificial systems still fall short of the remarkable learning efficiency of the human brain, which can infer, adapt, and generalize from limited experiences. This gap motivates a deeper exploration of how biological intelligence acquires knowledge and how these principles can inspire the …

Predictive Modeling Of Listeria Monocytogenes Growth In Ready-To-Eat Meat Products, Manjari Singh

Graduate Theses and Dissertations

Listeria monocytogenes remains a significant food safety concern in ready-to-eat (RTE) meat and poultry products due to its persistence under refrigerated and stressed conditions. Predictive models play a vital role in risk assessment and food safety management; broth models are highly conservation & results in overprocessing. This thesis focuses on developing and validating meat-specific predictive models for Listeria monocytogenes growth and lag for RTE beef, pork, and poultry products. Matrix-specific secondary models were built to predict the maximum specific growth rate (μmax) of Listeria monocytogenes under varying environmental conditions, including temperature, pH, water activity, salt, organic acids, and nitrites. Growth …

Updating The Surface Geological Maps In The Boston Mountains Region Of Arkansas Using Subsurface Well Log Correlation And Lidar Mapping, Olivia Smith

Graduate Theses and Dissertations

The Pennsylvanian Atoka Formation are the primary surface outcrops of the Boston Mountains in northwest Arkansas. Where fully preserved in the Arkoma Basin to the south, the Atoka Formation exceeds 18,000 feet of sandstone and shale. It is mapped as a single “undifferentiated” unit on the surface geological map of Arkansas. This project correlates ten subsurface subdivisions of the Atoka Formation plus the Desmoinesian to the surface outcrops of the southern Boston Mountains. This produced a higher resolution surface map beyond the current Atoka Undifferentiated scheme. Recognition of the Atoka Formation subdivisions in surface outcrops using standard lithostratigraphic and biostratigraphic …

Contextual Embedding Using Machine Learning For Cybersecurity: Access Control And Application, Thanh Bui

Graduate Theses and Dissertations

Access control is a well-established challenge in cybersecurity, with significant research focused on enhancing system autonomy and accuracy across various scenarios. Access control rules can be designed based on users’ roles, attributes, or relationships requesting access to specific resources. However, despite their benefits, these models still require human oversight. As systems expand and grow, it becomes increasingly complex for administrators to maintain precise access control rules, often necessitating extensive system updates or even a complete overhaul. This dissertation introduces a novel approach that leverages contextual embedding for user information to enable the system to autonomously authorize user requests for resources. …

Broiler Responsiveness To Dietary Amino Acid Density In A Commercial Setting, Virginia Slick

Graduate Theses and Dissertations

The majority of research assessing the amino acid responsiveness in broilers has been conducted in experimental designs utilizing pens that contain 12 to 25 birds with high replication. While this trial design is beneficial as a way to perform high replications with multiple treatments in a way that is both economically and spatially feasible, it may not accurately translate to a rearing environment more similar to that of commercial open floor housing. This research was conducted to determine if the effects of lysine driven amino acid density diets that have been found in the traditional floor pen setting correlate to …

When The Grid Goes Dark: A Digital Forensics Study Of Industrial Control System Cyberattacks, Katie Kettler

Graduate Theses and Dissertations

Industrial Control Systems (ICS) and Operational Technology (OT) maintain the grid, ensure water safety, and keep transportation running. Because they influence nearly every aspect of daily life, these systems have become prime targets for cyberattacks. The need for this research arises from the fact that when ICS and OT systems are compromised, the consequences go beyond data loss, and they can directly disrupt communities and endanger public safety. This thesis introduces digital forensics fundamentals and explains how investigations in ICS environments differ from those in traditional IT environments. This work then examines major attacks, including Stuxnet, the Ukrainian Grid Attacks …

Two Topics In Survival Analysis: Restricted Distance Covariance Test For Non-Proportional Hazard And A New Estimation For Dropout Rate, Ruizhe Yin

Graduate Theses and Dissertations

When treatment effects change over time, standard statistical methods, such as the log-rank test and the Cox proportional hazards model, may give misleading results. This dissertation presents the restricted distance covariance (rdcov) test, a nonparametric method that compares survival curves between groups within a chosen study period [0, τ] using right censored data. The statistic measures the dependence between pre-specified group labels and survival times using pairwise distances from Kaplan-Meier estimates. Our method does not rely on the proportional hazards assumption, and it equals zero only when survival functions are identical across groups. Thus, this test can be applied to …

Design Of Phase Shifter And True Time Delay Gan Mmics For High-Temperature Ku-Band Applications, Michael Lee Thompson

Graduate Theses and Dissertations

This thesis presents the design, implementation, and high-temperature characterization of Gallium Nitride (GaN) monolithic microwave integrated circuits (MMICs) developed for beamforming and phased-array systems operating in the Ku-band (12-14 GHz). Two integrated circuits were designed: a 3-bit digital phase shifter and a 3-bit true time delay (TTD), both optimized for high linearity, low insertion loss, low phase error, and stable operation at elevated temperatures up to 300°C. Developing both a phase shifter and a TTD enabled a direct comparison of their beamforming performance and the evaluation of beam squint effects over frequency, a critical factor in wideband array design. The …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

Instance-Adaptive Gated Fusion Of Multi-Transform Image Representations, Prince Appiah

Open Access Theses & Dissertations

This dissertation proposes the Instance-Adaptive Gated Fusion (IAGF) framework, a novel deep learning architecture for adaptive and interpretable fusion of multiple time–series image transformations. While existing methods rely on static concatenation or dataset-level optimization, IAGF introduces a learnable gating mechanism that dynamically assigns per-instance weights to Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Gramian Angular Difference Fields (GADF). The gating layer performs a convex fusion of transformation-specific embeddings under a softmax constraint, ensuring mathematical stability and interpretability. An entropy-regularized objective prevents dominance collapse and promotes balanced exploration of transformations during training. Comprehensive experiments across eighteen benchmark datasets, spanning …

Multi-Hop Hybrid Graph Neural Network, James Arthur

Open Access Theses & Dissertations

Graph-structured data appear across diverse domains, such as social networks, citation graphs, biological systems, and knowledge bases. Graph Neural Networks (GNNs) have emerged as a powerful framework for learning on such data, yet existing architectures face significant challenges. Graph Convolutional Networks (GCNs) suffer from over-smoothing as depth increases, Graph Attention Networks (GATs) introduce computational and statistical instabilities, and naïve multi-hop propagation inflates memory and computation while failing to adapt to topology. These limitations motivate the development of a new framework that is both expressive and scalable. This dissertation proposes the Multi-Hop Hybrid Graph Neural Network (MHHGNN), a novel architecture that …

Advancing Effective Connectivity Analysis: Robust And Sparse Group Dynamic Causal Modeling Via Extended Parametric Empirical Bayes, Godfred Arhin

Open Access Theses & Dissertations

Dynamic Causal Modeling (DCM) provides a principled framework for estimating effective connectivity in neuroimaging data, with Parametric Empirical Bayes (PEB) enabling hierarchical inference across sessions and subjects. However, standard PEB assumes Gaussian distributions at all hierarchical levels and employs conventional shrinkage priors that do not enforce true sparsity, limiting robustness to outlying subjects and reducing sensitivity to parsimonious connectivity structures. This thesis introduces a robust, sparsity-inducing hierarchical extension to DCM-PEB that addresses these limitations through two key innovations. First, a Student-t group-level likelihood replaces the conventional Gaussian likelihood, automatically downweighting outlying subject-level parameters using adaptive precision weights while retaining computational …

Machine Learning And First-Principle Approach To The Stability And Properties Of Iron-Based Materials Under Extreme Conditions., Blaise Awola Ayirizia

Open Access Theses & Dissertations

This dissertation investigates the stability and physical properties of iron-based materials under extreme conditions, utilizing a combination of machine learning techniques and first principles calculations. In the first study, we employ Gaussian Process Regression (GPR) with a Marginalized Graph Kernel to analyze the mechanical stability of non-magnetic body-centered cubic (BCC) iron at high temperatures and pressures, similar to those found in Earth’s core. Our model, trained on data derived from ab initio molecular dynamics (AIMD) simulations, predicts the energy landscape, atomic forces, and potential pathways for instability in BCC iron. The findings suggest that BCC iron remains stable in extreme …

Under Pressure: The Effects Of Stress And Cognitive Load On Interrogative Suggestibility, Claudia Cota

Open Access Theses & Dissertations

High-stakes situations, like police interrogations, can impair critical thinking due to stress and cognitive demands, making individuals more susceptible to suggestion. While much research focuses on children, young adults are also at risk for interrogative suggestibility, which can have serious legal consequences. This study experimentally manipulated stress and cognitive load to test whether both factors increase suggestibility in young adults, both independently and interactively, with the highest suggestibility among those experiencing both greater stress and cognitive demands. This was evaluated by examining both condition effects and self-reports of stress and cognitive load. College students (N = 230), aged 18 to …

Hydrological Responses To Rainfall Pulses In Desert Vadose Zones: A Multi-Scale Approach Integrating Geophysical, Isotopic, And Eddy-Covariance Measurements, Alfredo Dagda-Torres

Open Access Theses & Dissertations

Drylands are shaped by natural episodic precipitation, where short, intense rainfall pulses interrupt prolonged drought and drive most hydrological and ecological activity. Yet the mechanisms that determine how pulse water infiltrates, is redistributed, stored, evaporated, and ultimately used by desert vegetation remain under quantified in landscapes underlain by shallow petrocalcic horizons. This dissertation integrates three complementary approaches 1) three-dimensional electrical resistivity tomography, 2) eddy-covariance evapotranspiration partitioning, and 3) stable-isotope tracing to improve a mechanistic understanding of pulse-driven water movement and plant water use in a caliche-rich piedmont at the Jornada Experimental Range. The first component characterizes how infiltration pulses interact …

3d Face Modeling From 2d Images Using Deep Neural Networks, Mario Alberto De La Cruz Armendariz

Open Access Theses & Dissertations

Applications of 3D face reconstruction include biometric authentication, personalized avatars and digital identity, medical visualization, forensic analysis, and broader human-computer interaction. We propose an approach to 3D face reconstruction that can generate a fully textured 3D facial model using only two grayscale images: a front view and a profile view of the subject. Once trained, the system can perform the reconstruction autonomously without manual intervention. Unlike traditional methods requiring multi-camera setups, depth sensors, or cloud-based processing, the proposed approach runs fully offline on a standard CPU, supporting dynamic execution across CPU cores and eliminating the need for a dedicated GPU. …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Exploring The Impact Of Stem Degree Attainment On Intragenerational Social Mobility Among Hispanic Students And Graduates In Two Texas-Mexico Border Universities, Pilar Gonzalez

Open Access Theses & Dissertations

This research employed a two-phase, sequential, explanatory, mixed-methods design to answer the research questions. The first research question examined how obtaining a Science, Technology, Engineering, and Mathematics (STEM) higher education degree affects the socioeconomic advancement of current Hispanic students and alumni of the Louise Stokes Alliance for Minorities in Participation (LSAMP) within the context of two higher education institutions located along the Texas-Mexico border. The second research question explored the determinants shaping the professional pathways of Hispanic STEM students and graduates in the geographic vicinity of the Texas-Mexico border. For this study, STEM degrees refer to those recognized by the …

Lamda: A Longitudinal Android Malware Dataset For Benchmarking Concept Drift Detection And Adaptation, Md Ahsanul Haque

Open Access Theses & Dissertations

Machine learning (ML)-based malware detection systems often fail to account for the dynamic nature of real-world training and test data distributions. In practice, these distributions evolve due to frequent changes in the Android ecosystem, adversarial development of new malware families, and the continuous emergence of both benign and malicious applications. Prior studies have shown that such concept drift—distributional shifts in benign and malicious samples—leads to significant degradation in detection performance over time. Despite the practical importance of this issue, existing datasets are often outdated and limited in temporal scope, diversity of malware families, and sample scale, making them insufficient for …

Comparison Of Ti-6al-4v Powder And Bulk Specimen Properties Across Manufacturers In Laser Powder-Blown Ded, Alejandro Javier Hernandez

Open Access Theses & Dissertations

The investigation and comparison of 4 different Ti64 powder feedstocks from 3 different manufacturers was done to determine if the quality of the powder had a profound effect on the properties of the bulk specimens despite using the same processing parameters. The characteristics of the powder observed are the atomization process used, particle size distribution, flowability, and its density. The Ti64 printing parameters for directed energy deposition that were kept consistent were the feed rate, scanning speed, and laser power. Coupons with varying build angles (0-30°) were printed to measure the impact of overhangs on bulk specimen properties (ultimate tensile …

Using Uranium And Strontium Isotopes To Identify Water Flow Paths And Solute Sources In Agricultural Upper Snake-Rock Watershed In Idaho: Understanding Agrohydrology Processes Of Dryland Critical Zone, Jennifer Herrera

Open Access Theses & Dissertations

Irrigation in agricultural systems alters hydrological cycles by redistributing surface water and groundwater, modifying Critical Zone elemental cycles, and impacting water quality and availability. Here, I focus on understanding agrohydrologic processes in Dryland Critical Zones and the impacts of land-use changes and climate variability in the extensively irrigated agricultural Upper Snake-Rock Watershed in semi-arid south-central Idaho. The Snake River originates in Wyoming and flows across southern and central Idaho. The Snake River supplies irrigation water to the Kimberly and Twin Falls areas of Idaho, the focus of this study, which is characterized by intensive agricultural activity. With the increasing pressure …

Exploiting The In-Distribution Embedding Space With Deep Learning And Gaussian Discriminant Analysis For An Out-Of-Distribution Malware Attach Detection, Tosin Olusola Ige

Open Access Theses & Dissertations

State-of-the-art machine and deep learning models generally perform well on previously seen data, albeit with wrong close world assumption that all real-world data are from previously seen train and validation samples, hence there poor performance when exposed to data which deviates from previously seen training and validation set. This is clearly evident in the domain of cybersecurity where the world continues to experience several high profile malware attacks despite advancement in state-of-the-art research. The reason being that the constant evolvement of innovation in the development of tools and method deployed to carry out various attacks had given hackers and other …

Phishibl: A Systematic Evaluation Of Instance-Based Learning Model For Predicting Phishing Susceptibility, Shova Kuikel

Open Access Theses & Dissertations

Despite enormous efforts to develop defenses against phishing attacks, humans still struggle to detect phishing emails given the constantly evolving attacker strategies. This thesis aims to test the predictive capabilities of a cognitive model that represents the individual susceptibility to phishing emails. While training programs aim to raise awareness, most remain outdated and ineffective against evolving attack strategies. Recent advances in Machine Learning, Artificial Intelligence, and Large Language Models (LLMs) offer new defenses, yet understanding human decision processes remains crucial, as effective systems must emulate how people evaluate unfamiliar emails based on prior experience. This research introduces a cognitive model …