Digital Commons Network^™

Breaking Barriers: An Educator's Pursuit To Provide Equity In Student Achievement, Christina L. Korsun

Dissertations

There is a sense of urgency to address the widening achievement gaps among minority students, students with disabilities, and English-language learners. This makes immediate school action necessary to create a more equitable learning environment. Without strategic interventions, including improved teacher retention efforts, stronger student support systems, and effective leadership, these challenges will continue to hinder student success and overall school performance. The purpose of this project is to find solutions for schools to institute to narrow the achievement gap by ensuring all students receive the support needed to succeed. Schools can respond to the learning gap by promoting equitable learning …

The Relationship Between Visceral Fat Accumulation And Risk Of Cardiometabolic Multimorbidity: The Roles Of Accelerated Biological Aging, Tianyu Zhu, Yixing Tian, Jinqi Wang, Zhiyuan Wu, Wenhan Xie, Haotian Liu, Xia Li, Lixin Tao, Xiuhua Guo

Research outputs 2022 to 2026

Objectives: To investigate the association between visceral fat accumulation and the risk of cardiometabolic multimorbidity (CMM) and the potential roles of accelerated biological aging in this relationship. Methods: Using data from the UK Biobank, a nationwide cohort study was conducted using the available baseline body roundness index (BRI) measurement. Biological aging was assessed using the Klemera–Doubal method for biological age and the phenotypic age algorithms. The association between the BRI and CMM was estimated using the Cox proportional hazards regression model, while the roles of biological aging were examined through interaction and mediation analyses. Results: During a median follow-up of …

Cutting-Edge Deep Learning Methods For Image-Based Object Detection In Autonomous Driving: In-Depth Survey, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Burhan Khan, Shady Mohamed

Research outputs 2022 to 2026

Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image-based object detection methods, which offer several advantages over other modalities, such as cost-effectiveness and …

I Really Try To Model Good Practices: Reflecting On Journal Article Publication From Mid-Career, Margaret K. Merga, Shannon Mason, Julia Morris

Research outputs 2022 to 2026

Publishing research in scholarly journals takes up much time and energy for many academics, and the early career phase may be particularly challenging, as researchers navigate the processes and politics of academic publishing for the first time. We previously explored these challenges as early-career researchers in a collaborative autoethnographic study in 2018. Now, 6 years later, we have once again reflected on our shared and divergent experiences, this time from our positions as mid-career researchers, socialised into the world of scholarly publishing and with longer histories of success and failure in scholarly publication. Our critical discussions revealed a continued commitment …

Implementation Of Machine Learning To Predict Cable Failures In Electrical Networks, Sultan Bader Albahri

Theses

Electrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution. This research focuses on developing an ML-based predictive model to forecast cable failures in Dubai …

Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez

Theses

Virtual reality (VR) is commonly used as a tool for enhancing immersion within virtual environments, with one of its primary goals being to approximate real-world perception as closely as possible. In everyday life, variations in lighting conditions significantly influence how colors are perceived across different times and settings. However, the human visual system compensates for these changes, maintaining relatively stable color perception; a phenomenon known as color constancy. This process is driven by chromatic adaptation, the visual system’s adjustment to changes in illumination. Despite the growing use of VR, little research has explored how color constancy operates in virtual environments …

Network-On-Chip Packet Obfuscation And Encoding For Hardware Trojan Mitigation, Sydale John Ayi

Theses

Networks-on-Chip (NoCs) represent a transformative advancement in System-on- Chip (SoC) communication architectures, addressing the scalability and performance limitations of traditional bus-based systems. The increasing reliance on NoCs in modern SoCs has raised security concerns, particularly in untrusted fabrication environments. This thesis investigates the vulnerabilities posed by malicious hardware Trojans and untrusted IP cores, such as reverse engineering, misdirection attacks, and brute-force decryption. To counter these threats, the study introduces a robust security framework leveraging encoding and obfuscation techniques. Hamming codes are employed to detect and correct errors, ensuring data integrity and correcting misdirection attacks. Discrepancies between codewords and data words …

On Studying Transformer Networks For Volume-To-Surface Registration Of Inhomogeneous Soft Bodies For Liver Laparoscopy, Michael Adam Young

Theses

An important practical consideration in laparoscopic liver surgery is the limited visual information relative to open surgery. In laparoscopic interventions, the surgeon’s view of the liver surface is generally limited to the scene provided by a single scope with a narrow field-of-view. This limits the ability to navigate towards internal lesions previously identified through pre-procedural imaging. Surgical navigation during laparoscopy could be enhanced by registration of full preoperative liver models derived from pre-procedural imaging scans onto the partial laparoscopic view of the liver. This entails both a rigid registration to match the liver surface view to the preoperative volume, as …

Roshit Niraula Honors Portfolio, Roshit Niraula

Honors Capstone Portfolios

Roshit Niraula's Honors Portfolio captured in May 2025.

Dangerous Digital Standing: Applying Spokeo And Transunion To Online Privacy Harms, Michael E. Ten Eyck

Vanderbilt Journal of Entertainment & Technology Law

In recent years, the California Invasion of Privacy Act (CIPA) has been used to sue website-holding companies for utilizing chat bots that record online conversations. Such claims have already generated high-profile class actions and multidistrict litigations, with many more expected. Because CIPA violations often occur writ large when websites retain data from their chat boxes, and the statute imposes relatively high damages, there exists an incentive for plaintiffs’ attorneys to seek out aggregated claims, generating time-consuming litigation. Meanwhile, the harms suffered by those bringing suit fall under the category of intangible privacy harms. The US Court of Appeals for the …

Cybermapping Solutions: A Unified Approach In Us/Nato Military Applications And Development, Nicholas Macrino

Electrical & Computer Engineering Projects for D. Eng. Degree

[First paragraph] Cyber threats are evolving in complexity and frequency, posing significant challenges for cybersecurity professionals in identifying, categorizing, and responding to attacks in real time. Unlike traditional warfare, where battlefield awareness is based on fixed geographic warfare, cyber operations involve abstract attack vectors, non-linear threat escalation, and rapidly changing network conditions. Modern cyber threats, such as advanced persistent threats (APTs), polymorphic malware, and distributed denial-of-service (DDoS) attacks, require adaptive visualization techniques that provide real-time awareness and facilitate rapid decision-making. However, existing symbology standards, such as MIL-STD-2525D, were not designed to accommodate the dynamic nature of cyber warfare. The inability …

Everyday Storytelling: The Function, Meaning, And Impact Of College Student's Storytelling And Storylistening, Sophie K. Richards

Department of Communication Studies: Dissertations, Theses, and Student Research

The current study investigated the stories that 102 college students heard and told through the course of a 1-day diary study with surveys administered at four timepoints. Investigated through the perspective of the small story framework (Bamberg & Georgakopoulou, 2008), everyday talk (Goldsmith & Baxter, 1996) and Communicated Narrative Sense-Making (CNSM) theory (Koenig Kellas, 2022), participants reported about the stories that they heard and told throughout their day, as well as completed self-report measures of well-being. The study aimed to understand the characteristics of everyday stories, the functions they serve, the meanings they hold, and their impact on well-being. Participants …

Conservación Biológica Harakbut: Estudio De Caso Ori’Numba, Anya Held

Peru: Indigenous Peoples and Globalization

Through interviews, observations, and bibliographic research, this project investigates the Indigenous-based conservation project of Ori’Numba Garden. In 2018, the Tayori family moved to Ori’Numba and began transforming it from a former livestock farm into a holistic conservation project. The paper details the history of Ori’Numba and the process of its creation, the modern and traditional agricultural techniques used in the garden, a register of the pineapple and native bee species, and some of the relevant Harakbut mythology. Ori’Numba clearly demonstrates that there is Indigenous conservation biology, not just “indigenized” Western conservation biology.

Exploring The Matriculation And Resource Utilization Of African American Students With Hidden Disabilities In The Health Sciences, Kimberly Houston

Theses and Dissertations (ETD)

Introduction. Learning disabilities result from genetic and neurobiological factors that alter brain function, such that cognitive processes related to learning are affected. Hidden disabilities can be defined as a non-apparent chronic condition that interferes with a person’s activities of daily living. While learning disabilities may be hidden, there are non-learning disabilities, such as dyslexia, autism, depression, fibromyalgia, and attention deficit hyperactivity disorder. While all races/ethnic groups have persons within their population who are disabled, African Americans are at a double disadvantage due to race and disability status. Therefore, the intersectionality of race and disability status among African American students in …

Lockdown Lessons: How Remote Proctoring Continues To Discriminate Against Disabled Students In A Post-Covid Era, Abigail Johanson

The Journal of Law and Education

No abstract provided.

The Impact Of The National Teacher Shortage On The Right To An Adequate Education, Anslee Wood

The Journal of Law and Education

No abstract provided.

Precision Drug Dosing And Disease Risk Prediction: Advanced Deep Learning Artificial Intelligence Approaches For Precision Medicine Using Structured Electronic Health Records Data, Bingyu Mao

Dissertations and Theses (Open Access)

Advancements in precision medicine increasingly rely on data-driven approaches to improve clinical decision-making. This work presents three interconnected studies that leverage deep learning, reinforcement learning, and comparative modeling to address key challenges in precision drug dosing and disease risk prediction. Together, these contributions offer novel computational frameworks that enhance model performance, improve dosing strategies, and guide model selection for clinical predictive tasks.

To improve individualized vancomycin therapeutic drug monitoring (TDM), the first study introduces PKRNN-2CM, a novel deep learning framework that integrates a two-compartment pharmacokinetic (PK) model with recurrent neural networks (RNNs). While one-compartment models are commonly used in clinical …

Towards Universal Non-Dimensional Characterization Of The Oscillatory Dynamics Of Wind Turbine Rotors Of Multiple Sizes, North Yates, Fernando L. Ponta, Alayna Farrell

Michigan Tech Publications

One concern in the field of Horizontal Axis Wind Turbines (HAWTs) is what control strategies are needed to handle gust pulses in the wind to prevent extreme oscillations of the blades to reduce fatigue stress, prevent blade rupture, and extend the turbine’s operational life. In order to design innovative control strategies to modify the blade’s oscillatory response, it is crucial to establish the fundamental vibrational behavior of the blades when excited by gust pulses of different frequencies and amplitudes present in the fluctuating wind inflow. In a series of previous works, the authors presented a novel Reduced-Order Characterization (ROC) technique …

Estimating Climate Risk In Financial Markets, Olanrewaju Oluwadamilare Olaniyan

Masters Theses

The growing impact of climate change on financial markets necessitates a rigorous approach to climate risk assessment. This thesis examines methods for quantifying climate-related financial risks, with a focus on distinguishing climate risk from broader market movements (represented by S&P 500). Using a factor model, we isolate climate risk factors to better understand sector-specific volatility. The insurance sector is used as a proxy for climate risk exposure, given its sensitivity to climate-related losses and regulatory changes. We apply Extreme Value Theory (EVT); the Block Maxima Method and the Peaks Over Threshold Method, to identify excess risk patterns in financial portfolios. …

South Dakota Department Of Health: Office Of Community Engagement Strategic Plan Fiscal Years 2025-2028, Julia E. Platt

Honors Thesis

The goal of a strategic plan is to ensure that resources are used effectively and efforts are focused where they are most required by means of direction and concentration. The South Dakota Department of Health Office of Community Engagement Strategic Plan Fiscal Years 2025-2028 contains objectives and goals aimed at maximizing the use of resource allocations and solutions to best meet the needs of the OCE and its partners. In order to create those goals and objectives, internal and external needs assessments, along with key informant interviews, were conducted and analyzed to pull data into actionable SMART objectives. Alignment with …

Write Free Or Die: Vol. 11, No. 02, Unh Writing Programs

Writing Program Newsletter

No abstract provided.

Comparative Performance Analysis Of Cryptographic Workloads Across Cloud Providers: A Multi-Language Study On Faas And Iaas Platforms Dataset, Jeremiah Webb

Doctoral Dissertations and Master's Theses

Cloud computing has become a relatively new paradigm for the delivery of compute resources, with key management services (KMS) playing a crucial role in securely handling cryptographic operations in the cloud. This paper presents the microbenchmark of cloud cryptographic workloads, including SHA HMAC generation, AES encryption/decryption, ECC signature/verification, and RSA encryption/decryption, across Function-as-a-Service (FaaS) and Infrastructure-as-a-Service (IaaS) in conjunction with KMS offerings from Ama- zon Web Services (AWS) and Microsoft Azure to conduct a comparative performance analysis. The methodology involves the AWS Cloud Development Kit (CDK) and the Bicep language to deploy AWS Lambda Functions and Azure Functions, respectively, to …

Adaptive Control Of Thrusters To Account For Deficiencies In Actuator Effectiveness And Model Uncertainties, Matthew Stanko

Doctoral Dissertations and Master's Theses

Future space missions are expected to become ever more ambitious and challenging. A successful mission requires advanced and resilient control algorithms that enable missions, such as satellite refueling, on-orbit inspection, and end-of-life servicing. This thesis explores the development and application of robust and adaptive control laws for an over-actuated spacecraft system with 3 degrees of freedom (DoF) to mitigate the effects of actuator deficiencies and system uncertainties. Additionally, the nature of the system being over-actuated allows for particular actuator degradation and failure. For stability and command tracking, a novel controller is designed and augmented with sliding mode control, adaptive control, …

Analysis Of Responder-Involved Struck-By Crashes, Jacob Wilhelm

Doctoral Dissertations and Master's Theses

Emergency responders face significant risks of injury and death from struck-by crashes while operating on or near roadways. Annually, about 50 responders are killed, and hundreds more are injured, leading to operational disruptions and damage to life-saving equipment (ERSI, 2024). This study takes a data-driven approach to identify key risk factors and effective countermeasures for reducing the severity of struck-by crashes. Key findings indicate that struck-by crashes most frequently occur on weekends during late-night hours (2 AM–4 AM), often at crash scenes or disabled vehicle scenes on limited-access highways. Driver behavior – particularly “D” drivers (distracted, drugged, drowsy, disgruntled) – …

Coupled Dynamics In The Cislunar Region And Spacecraft Attitude Prediction In A Higher-Fidelity Model​, Annika Anderson

Doctoral Dissertations and Master's Theses

The cislunar region of space is a complex, multi-body dynamical environment that cannot be modeled trivially. Traditional point-mass assumptions made to simplify the mission design process may be insufficient for accurately predicting spacecraft motion in environments where orbit-attitude coupling is non-negligible. One of the most famous of such models is the circular restricted three-body problem. This thesis advances the state of the art in astrodynamics by modeling all bodies in the problem as rigid bodies, allowing for spacecraft orientation to be propagated and considered. Two models are under consideration—the circular restricted full three-body problem (CRF3BP) and a full higher-fidelity ephemeris …

Machine Learning Methods For Hypervelocity Fragment Flyout Characterization, Katharine Larsen

Doctoral Dissertations and Master's Theses

Resulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris …

Stability Limits Of Uncertain Systems In The Presence Of Unmatched Coupled Dynamics And Actuator Dynamics With Adaptive Architectures, Bronson Saulo

Doctoral Dissertations and Master's Theses

Uncertain systems with coupled dynamics and actuator dynamics are prevalent in various applications spanning private, commercial, and government sectors. While stabilization and command tracking strategies exist, they often rely on idealized assumptions, such as negligible actuator bandwidth limitations and matched coupled dynamics. In order to mitigate actuator-induced limitations, we first employ standard model reference adaptive control (MRAC) architectures. We then proposed two architectures: modified hedging reference modeled based MRAC and modified expanded reference model based MRAC. In order to deal with coupled dynamics, we used an observer design to estimate the states of unmatched coupled dynamics. In addition, Lyapunov stability …

High-Latitude Ionospheric Irregularities Characterized Through Machine Learning Methods, Anna-Marie Bals

Doctoral Dissertations and Master's Theses

This study uses Machine Learning and data-driven techniques to understand plasma irregularities in high-latitude regions better. By combining observations and recent findings from modeling, the goal is to identify and classify scintillation signatures caused by different types of irregularities in the ionosphere. The focus is on irregularities from electron precipitation in the auroral oval and ExB drifts in the polar cap. Using Machine Learning tools, the study aims to distinguish between different scintillation signatures and link them to their sources, improving our ability to detect and characterize these events. Using multiple instruments and advanced filtering, the aim is to enhance …

Acoustic-Gravity Wave Propagation Based On Solutions To The Generalized Multi-Component Transport Equations, Benedict PiñEyro

Doctoral Dissertations and Master's Theses

Nonlinear atmospheric models have provided important insight into acoustic waves generated by natural and man-made hazards, which may steepen into shocks or N-waves while also dissipating when propagating in the thermosphere. Although models have yielded results that agree with observations of ionospheric perturbations, dynamical models for the diffusive and stratified lower thermosphere often use single gas approximations with height-dependent physical properties that omit the dynamics of the major and minor constituents. Thus, the inter-species diffusion associated with these flows (e.g. variations of mean molecular weight, and specific heat) are not accounted for. This approximation is simpler and less computationally expensive …

A Study Of Preconditions And Postconditions As Design Constraints For Llm Code Generation, Luke Newcomb

Doctoral Dissertations and Master's Theses

Large Language Models (LLMs) have significantly advanced automated code generation, but current methods predominantly rely on natural language descriptions. This approach encounters challenges when handling complex, class-level software generation tasks due to inherent ambiguity and under-specification. Few studies have investigated how more formal software engineering constraints, such as explicit preconditions and postconditions, influence class-level generation tasks. This work addresses this gap through a structured evaluation of six state-of-the-art LLMs generating software implementations from systematically designed class-level specifications. Results demonstrate that incorporating explicit design constraints significantly boosts initial generation accuracy (measured via the pass@k metric), particularly in Python but also in …