Digital Commons Network^™

Synthesis And Characterization Of Additive Manufactured Functional Composites For Biomedical Applications, Alexander Thorne

Theses and Dissertations

This dissertation investigates the synthesis and characterization of 3D printed polymer composites, with engineered porosity and both natural and synthetic additives, using fused deposition modeling (FDM) and selective laser sintering (SLS) for potential biomedical applications. The structural, mechanical, tribological, and surface behavior of these composites were evaluated with respect to their suitability for orthopedic load-bearing implants and scaffolds. In the first phase, PEEK-based composites reinforced with 10 or 20 wt% carbon fibers or 20 wt% E-glass fibers were fabricated using FDM with varying infill densities of 30-100% to introduce porosity. Results revealed that loading additives, infill densities, and porosity play …

Targeted Coaching In The Detroit Promise Path: A Thematic Network Analysis Of Current Challenges And Opportunities For Student Success, Maryse Danielle Wilson

Theses and Dissertations

The Detroit Promise Program is one of the many college promise programs operating within the United States that offers full college tuition coverage to students within the city who meet certain residential criteria. Detroit’s population has one of the highest proportions of Black and first-generation students and the lowest average socioeconomic class in the country. From the time of the Promise Program’s launch until now it has seen minor improvements in college degree competition rates, but this rate is still low compared to the national average. The Detroit Promise Path, which is an off branch of the Detroit Promise Program, …

Zaasijiwan: An Indigenized Health Communication Framework For American Indian And Alaska Native Communities, Jennifer Wolf

Theses and Dissertations

The Zaasijiwan Framework represents an Indigenized health communication model developed to enhance the cultural relevance, trust, and effectiveness of public health messaging for American Indian and Alaska Native (AI/AN) communities. This qualitative study integrated Indigenous and Western public health knowledge to inform the development of the framework. A comprehensive literature review and two rounds of key informant interviews with health communication professionals informed the design of the framework. Findings emphasized the importance of community co-creation and strengths-based narratives, along with other framework elements, in overcoming medical mistrust and improving health outcomes. The final framework offers public health professionals, Tribal, and …

The Influence Of Active Galactic Nuclei (Agn) Feedback On Star Formation In Spiral Galaxies, Moreom Akter

Theses and Dissertations

We investigate the influence of Active Galactic Nuclei (AGN) feedback on star formation in spiral galaxies by analyzing a sample of 324 red and 273 blue spiral galaxies from 115 low-redshift galaxy clusters. The multi-wavelength dataset combines optical emission line data from the Sloan Digital Sky Survey (SDSS), X-ray luminosity from the Chandra X-ray & XMM-Newton observatories, and ultraviolet (UV) and infrared (IR) measurements from GALEX and WISE. Using diagnostic emission line ratios, we constructed Baldwin, Phillips, and Terlevich (BPT) diagrams to classify galaxies based on nuclear activity. Our analysis reveals that most of the red spirals have AGNs and …

Comparative Analysis Of Lstm-Based And Traditional Hydrologic Models For River Discharge Prediction: A Case Study Of The Goose River, Adewale Ajao

Theses and Dissertations

Streamflow predictions are important in planning water resources and effective hydrological management. Physically based models such as the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) have been used in the past for this purpose. Still, the use of machine learning models such as Long Short-Term Memory (LSTM) has been proven to complement the use of traditional models. This study compares the reliability performances of HEC-HMS and LSTM models to predict the streamflow of Goose River Watershed in North Dakota. The objective of the research is to perform a comprehensive comparative analysis between the performance of HEC-HMS and LSTM models to predict …

Mathematical Modeling Of Cyber-Physical Attacks In Iot Systems: An Epidemiological Approach, Tunde Alesinloye

Theses and Dissertations

The Internet of Things (IoT) has made electronic devices more interconnected, which has increased cybersecurity threats while also bringing previously unimaginable convenience. In this study, we explore the dynamics of malware transmission in IoT systems using a mathematical modeling methodology that draws inspiration from epidemiological approaches. To better characterize contemporary cyber-defense tactics like honeypots and proactive device hardening, we specifically suggest the SEIR-DX model, which expands the traditional SEIR framework by adding two extra compartments: Deceptive (D) and Secured (X).

The transitions between the six compartments Susceptible, Exposed, Infected, Recovered, Deceptive, and Secured are described by a system of nonlinear …

Machine Learning-Guided Discovery Of Metal-Ligand Catalysts For Affordable Green Hydrogen Produced Via Proton Exchange Membrane Electrolyzers, Achouak Benarbia

Theses and Dissertations

The rising demand for clean and sustainable energy sources has underscored the critical role of hydrogen in the global energy transition. However, the widespread adoption of green hydrogen remains constrained by the high cost and scarcity of platinum-based electrocatalysts used in Proton Exchange Membrane (PEM) electrolyzers. This dependence inflates production costs and poses long-term risks related to supply security and scalability. While numerous studies have explored alternative materials, the discovery of efficient and stable non-platinum catalysts remains a major bottleneck due to the vastness of chemical space and the resource-intensive nature of experimental screening. To address this challenge, this study …

Advanced Motor Control Using Td3 Model-Free Reinforcement Learning, Muhammad Azam

Theses and Dissertations

Motor control plays a critical role in industrial automation, ensuring the precise regulation of speed, torque, and position across various applications. Traditional motor control techniques, such as open-loop, closed-loop, and feedback control, have been widely used but face limitations in handling complex and dynamic systems. Recent advancements in machine learning (ML) offer potential improvements in motor control by enabling systems to learn optimal control policies autonomously. This research investigates the integration of Reinforcement Learning (RL) with Field-Oriented Control (FOC) for the control of Permanent Magnet Synchronous Motors (PMSMs). The study aims to address a significant gap in reward function design, …

Next-Gen Device For Uniform Cellular Migration Assay Execution, Nicholas M. Bittner

Theses and Dissertations

Cellular migration assays, particularly scratch assays, are pivotal in biomedical research for cell motility, wound healing, and metastasis. However, conventional scratch assays suffer from variability due to operator dependence, non-uniform scratch geometry, and a modified microenvironment that impacts reproducibility. This thesis presents the development and validation of a low-cost, 3D-printed device designed to standardize cellular migration assays. The device utilizes a spring mechanism and a biocompatible elastomer to create a uniform cellular exclusion zone without harming the surrounding cells. Through iterative prototyping and testing with MCF10A cell lines, the device demonstrated a significantly lower average standard deviation of length compared …

An Explainable Ai Framework For Detecting Harmful Algal Blooms (Habs) In Freshwater Lakes, Meera Gopinath Sujatha

Theses and Dissertations

The increase in economic impacts due to the rapid growth of harmful algal blooms (HABs) in freshwater lakes has prompted the development of monitoring and prediction systems. However, limited target data hinders reliable predictions on HAB formations. To address this limitation, this dissertation focuses on providing solutions through data-driven,interpretable, and explainable machine learning models. In Chapter 3, an interpretable multivariate regression model such as Vector Autoregressive (VAR) was used to analyze the important factors that contribute to the formation of cyanobacteria in lakes. The causality tests performed on multiple water quality data helped to determine Alkalinity, Chlorophyll-a (Chl-a), andWater Temperature …

Thermally Induced Evolution Of The Cement Microannulus In Co2 Injection Wells, Jawad Ali Khan

Theses and Dissertations

Ensuring wellbore integrity during CO₂ injection is a critical challenge in Carbon Capture and Storage (CCS), as failures at the casing-cement and cement-formation interfaces can create potential leakage pathways. This dissertation presents a multi-scale, physics-based investigation into well integrity risks, integrating finite element analysis (FEA), thermal-mechanical-poroelastic modeling, and transient stress analysis to evaluate cement sheath stability, fracture-driven stress localization, and microannulus formation under various CCS conditions.

A key novelty of this research is the development of a fully coupled thermal-mechanical-poroelastic FEA framework using COMSOL Multiphysics, a renowned industry-standard software for multiphysics simulations. This model enables a dynamic, time-dependent assessment …

The Role Of Student Loan Debt On Delayed Homeownership Among Black American College Graduates, Dedan Makongoro Kusaga

Theses and Dissertations

The growing burden of student loan debt in the United States has raised significant concerns about its broader social and economic impacts, particularly on young adults’ ability to achieve financial milestones such as homeownership. Black American college graduates are especially affected, often carrying higher loan balances and facing greater financial barriers due to historical and structural inequalities. This quantitative study examined the role of student loan debt and other factors influencing delayed homeownership among Black American college graduates, using the public-use Baccalaureate and Beyond (B&B: 2008/2018) dataset from the National Center for Education Statistics (NCES). Findings indicated student loan debt, …

A Federated Learning Solution For Secure And Scalable Edge Computing In Distributed Environments, Muhammad Hamza Karim

Theses and Dissertations

The growing penetration of Distributed Energy Resources (DERs) into the modern power system has introduced significant challenges related to data volume and cybersecurity. Centralized approaches struggle to process the large volume of data generated by inverters or other DER-related assets securely and efficiently. This problem raise concerns about the confidentiality, integrity, availability, and accountability (CIAA) properties of DER assets. Moreover, the large deployment of Internet of Things (IoT) and edge computing devices has introduced new security vulnerabilities. Malicious actors can exploit resource-constrained edge devices for unauthorized activities and resource consumption, threatening the performance and operational capabilities of the edge infrastructure. …

White Paper For Faculty Professional Development In Establishing, Addressing, And Informing, To Promote Mental Wellness For Health Careers Students, Betty J. Mcvay

Theses and Dissertations

This research utilized a qualitative case study to explore the need for faculty professional development in the health careers division at a public non-profit California community college, specifically Porterville College. Literature suggests that mental health is a crucial topic to address in contemporary higher education. College students, in general, are facing a notable deterioration in their overall mental well-being, accompanied by an increase in mental disorders. Furthermore, students in health careers programs frequently encounter even more intense stress and psychological challenges, stemming from their constant exposure to high-pressure, emotionally charged situations, and vicarious trauma. Although literature emphasizes the critical need …

Development Of A Climbing Collaborative Mobile Manipulator (C2m2) For Supporting In-Space Assembled Truss Structures, John Robert Isaac Merila

Theses and Dissertations

The Climbing Collaborative Mobile Manipulator (C2M2) is being developed as a robotic system to support truss outfitting operations. The goal of the project is to build a truss climbing robot and demonstrate gaits that allow the robot to reach any point on the structure. The demonstrated motions are compared to simulated joint data. Design and construction of the robot was completed to allow for the robot to traverse the structure and operate as a stationary manipulator while grappled to the truss. A worm gear actuator system was developed for use in three 2-DOF modules composed into the 6-DOF robot. The …

Geothermal Sweep Efficiency Enhancement Potential Analysis Using Geological Modeling And Numerical Simulation On Unconventional Deadwood Formation, Ashraf Mohammed

Theses and Dissertations

This study investigates the potential to enhance geothermal sweep efficiency in the fractured, low-permeability Deadwood Formation using a cellulose-based biopolymer and gel treatment. A geological model was developed using well log and structural data from four wells in the Williston Basin and subsequently upscaled into a numerical reservoir simulation in CMG/STARS. Laboratory core-flooding experiments on Deadwood samples were used to calibrate gel behavior under high-temperature (160 °C) and high-salinity (17% TDS) reservoir conditions.Simulation results indicate that a short-duration gel treatment—equivalent to 0.03 pore volumes over one year—can significantly reduce channeling through high-conductivity fractures. The reduction in channeling improves thermal conformance …

Evaluating Human Factors In Isolation And Confinement During Analog Spaceflight: A Decadal Survey Assessing Ilmah Crew Psychology, Travis Michael Nelson

Theses and Dissertations

This study examined 13 crews participating in analog Martian surface simulations to investigate psychological experiences within the first collegiate planetary surface habitat. The University of North Dakota (UND) Integrated Lunar-Martian Analog Habitat (ILMAH) was utilized with high-performing participant crews (n=42) who were recognized and recruited for their skills. Common environmental similarities that each crew faced included isolation, confinement, time-latent communications, demanding work hours, monotonous tasks, physically strenuous Extravehicular Activities (EVA), personal psychological adaptations, and interpersonal psychosocial challenges. The primary objective of this study was to evaluate self-appraised crew performance measures across continuous phases of isolation and confinement lasting up to …

Predicting Bus Ridership In Dubai Using Advanced Machine Learning Models, Fatma Almheiri

Theses

In rapidly urbanizing cities, the public transport systems have to be efficient and reliable with proper demand forecasting to guarantee customer satisfaction. This paper explores how evidence-based machine learning tools can be used to forecast weekly bus ridership in Dubai, using a dataset of more than 10 million trip-level boarding observations between June 2013 and July 2014. It was aggregated to 25,195 route-week observations, and preprocessed with temporal feature engineering, lag variables and rolling statistics. Three predictive models have been tested: Linear Regression (as a baseline), Random Forest and XGBoost (as an ensemble method). Mean Absolute Error (MAE), Root Mean …

On High-Speed Breakup Of Water Droplets Behind High Mach Number Shocks, Sydney M. Briggs

Graduate Thesis and Dissertation post-2024

Water droplet breakup in high-speed flow is a relatively popular experimental study to analyze breakup mechanisms. There is a plethora of experiments performed at low Mach number and low Weber number, but studies with higher speed flows and higher Weber numbers are less common. In this study, water droplet breakup is assessed using high-speed shock waves (Mach 2.3-above Mach 5) in a shock tube. Multiple methods are used to introduce droplets, and multiple different water purity levels are considered. An assessment was made into whether cavitation played a role in the breakup process physically, as this has been a budding …

Mechanistic Insights Into The Antimicrobial Action Of Fungus-Derived Xanthoquinodins Against Clostridioides Difficile, Erika A. Serravalle

Graduate Thesis and Dissertation post-2024

The bacterial pathogen Clostridioides difficile is the leading cause of antibiotic- and healthcare-associated diarrhea, imposing significant clinical and economic impacts. The emergence of hypervirulent strains, such as PCR ribotype 027 (RT027), has exacerbated disease severity, recurrence rates, antibiotic resistance, and epidemiological spread. Expanding resistance profiles among C. difficile strains continue to limit the effectiveness of current IDSA-SHEA recommended therapies, which are already compromised by high recurrence rates due to antibiotic-induced disruption of the gut microbiota. These challenges highlight the urgent clinical need to identify and characterize antimicrobial compounds targeting alternative pathways in C. difficile physiology. We report the discovery and …

Characterizing Intralaminar Distal Crack And Delamination In Multidirectional Laminates Under Low Velocity Impact: Modeling Approach With Ultrasound Testing Validation, Niildiip Chandraa, Rachel Van Lear, Arief Yudhanto, Yuqing Zhao, David A. Jack, Douglas E. Smith

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Multidirectional laminated composites are essential for load-bearing structures, especially under frequent impact loading. While existing modeling techniques simulate various impact damage modes, our understanding of the underlying mechanisms, specifically the initiation and progression of intralaminar distal cracks and interlaminar delamination, needs improvement. Validating these mechanisms is crucial for enhancing modeling accuracy and expediting the design process. In this work, we propose a three-dimensional finite element modeling (3D FEM) approach aimed at elucidating the impact damage mechanisms in multidirectional carbon fiber reinforced polymer (CFRP). Our model incorporates cohesive zone models (CZM) to simulate the behavior of intralaminar cracks at distal points …

Development Of Generalizable Data-Driven Traffic State Prediction Solutions: A Comprehensive Approach, Md Mobasshir Rashid

Graduate Thesis and Dissertation post-2024

Increased urbanization and population growth lead to more traffic in the transportation system, causing severe congestion. Particularly during disruptive events such as hurricanes or traffic incidents, the traffic flow in the network worsens significantly. As a result, there is a need for data-driven traffic prediction models to reduce congestion in real-time, and the prediction model should be generalizable to be applicable across different dynamic scenarios. Additionally, a robust and adaptable traffic prediction model can help traffic managers to take proactive actions to optimize traffic flow. However, the majority of data-driven traffic prediction models suffer from limited applicability as they are …

Towards Supporting Undergraduate Students To Become Lifelong Learners: A Multimethod, Multimodal, And Multigranular Analysis Of Motivation And Self-Regulated Learning In Context, Sierra Outerbridge

Graduate Thesis and Dissertation post-2024

This publication-based dissertation includes three manuscripts with distinct statistical methods to investigate self-reported motivation, self-reported self-regulated learning (SRL), and enacted SRL processes in undergraduate student contexts towards understanding how students learn how they learn.

The first manuscript focused on 1248 undergraduate statistics students, utilized self-reported motivation, and interpreted logfile data of time spent within an online textbook through the lens of SRL using hierarchical cluster analysis followed by k-means cluster analysis to identify four distinct motivation clusters. Results from a repeated measures ANOVA suggest that students did not spend time differently within the textbook, whereas there were differences between motivation …

In-Situ Transmission Electron Microscopy Investigation Of Grain Size And Temperature Dependent Irradiation Behavior Of 304l Stainless Steel, Anish Ranjan, Matthew Luebbe, Nastaran Motaharinia, Wei Ying Chen, Frank Liou, Haiming Wen

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The influence of grain size, irradiation temperature, and dose on the evolution of irradiation-induced defects in austenitic 304L stainless steel (SS) was systematically investigated. Coarse-grained (CG), ultrafine-grained (UFG), and nanocrystalline (NC) specimens were exposed to irradiation doses up to 10 displacements per atom (dpa) at room temperature (RT), 300°C, and 500°C. Dislocation loop size and density were quantitatively analyzed using transmission electron microscopy, and results showed that the dislocation loop size remained comparable across different grain sizes. However, loop density was strongly dependent on the grain size. The CG specimens exhibited the highest loop density due to a limited fraction …

Deep Reinforcement Learning-Based Optimal Takeoff Trajectory Design Of An Evtol Drone, Nathan M. Roberts, Bingling Huang, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The continuing development of electric vertical take-off and landing (eVTOL) aircraft presents promising opportunities to alleviate transportation congestion. However, designing and executing takeoff trajectories that optimally balance energy usage, passenger comfort, and aircraft constraints remains challenging. Conventional design optimization provides a solution for pre-defined flight conditions and environments but is not ideal in real-world applications. In contrast, deep reinforcement learning (DRL) implements optimal policy making real-time decisions with no assumption of mathematical models. Seeing the lack of literature on DRL-based takeoff trajectory design of eVTOL aircraft, we implement and conduct DRL-based optimal takeoff trajectory designs for the Airbus³ Vahana …

Few-Shot Learning-Enhanced Tiered Path Planning For Mars Rover Navigation, Ziyi Wang, Di Yu, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables …

Versatile Injector Platform: A Modular Design For Supersonic Flow Research, Connor Bell, Davide Viganò

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Fuel–air mixing remains a critical challenge in the development of high-speed air-breathing propulsion due to the extremely short residence times in supersonic combustors. Experimental studies are essential to investigate the underlying mixing mechanisms, but they require injection systems that can deliver repeatable and controlled flow conditions. In this work, we present the design of a modular strut-type injection platform developed for the Missouri S&T Supersonic Wind Tunnel. In its initial configuration, the platform features a planar trailing-edge slit to generate a two-dimensional jet and includes interchangeable trailing-edge modules to support a wide range of geometries and flow-control strategies. Designed with …

Industry 5.0, Human–Machine Interface, And Smart Manufacturing In Additive Manufacturing—A Recent Trend, Atiqur Rahman, Md Hazrat Ali, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The emergence and importance of Industry 5.0 are transforming production by integrating smart technology with human creativity and intelligence, especially in advanced additive manufacturing. This critical review precisely analyzes the function of human–machine interfaces (HMIs) in enhancing additive processes, emphasizing their incorporation into smart manufacturing systems and hybrid manufacturing methods, including material extrusion (MEX), laser powder bed fusion (LPBF), and directed energy deposition (DED). It also evaluates artificial intelligence (AI), the Internet of Things (IoT), and cybersecurity, which enhance HMIs in process monitoring, operation, and system adaptability. This study aims to provide a critical analysis and future research direction on …

Large Language Models And Networks: Edge Proposal, Synthetic Corpora, And Adaptive Node Generation, Nathan Gonzalez

Graduate Thesis and Dissertation post-2024

Natural Language Processing (NLP) and Artificial Intelligence (AI) have evolved from brittle rule-based engines through data-driven statistical models to today’s transformer architectures whose multi-head attention enables rich contextual reasoning. Against this backdrop, this dissertation unifies three complementary investigations that deepen our understanding of how large language models (LLMs) can reason over, generate, and strategically adapt language within networked settings. First, instruction-tuned LLMs are recast as latent-relationship detectors: by prompting models to hypothesize links between text-described entities, we recover edge sets that reconstruct social, thematic, and citation graphs with high precision, revealing how attention distributions encode topological cues. Recognizing the difficulty …

Modeling Frameworks To Understand And Predict Land Use Type And Land Value Patterns In Florida, Lauren Hoover

Graduate Thesis and Dissertation post-2024

Florida’s population has been rapidly growing, with a 14.6% growth in population from 2010 to 2020. This growth in population has led to an associated increase in demand for housing and commercial establishments, which have resulted in changes in land use patterns across the state. For this dissertation, we focus on methods to qualitatively and quantitatively examine how land use patterns are likely to evolve under the expected scenario of population growth in Florida while explicitly allowing for interactions between demographics, transportation and land use patterns. In our first objective we build a simulation framework to predict land use evolution …