Digital Commons Network^™

Determining Sphincs+ Readiness For Standardization Of Slh-Dsa Signature, Jessica Ancillotti

Theses

As quantum computing advances, public-key cryptographic algorithms risk becoming obsolete, requiring the development and implementation of quantum-resistant alternatives. This thesis evaluates SPHINCS+, a stateless hash-based digital signature scheme recently standardized by NIST under the FIPS 205 standard named Stateless Hash-Based Digital Signature Algorithm (SLH-DSA), which was selected for being a conservative and robust choice due to its reliance solely on well-understood cryptographic primitives. In the context of the growing need for quantum-resistant cryptographic solutions, determining the readiness of SPHINCS+ involves assessing its practical viability across different application domains and evaluating how well it meets today’s and future needs. To achieve …

Mechanisms For Oxygen Vacancy Defect Migration In Srtio3/Nio Heterostructures, Anish Rajesh More

Theses

Perovskite-based oxide heterostructures display promising properties resulting from interface phenomena, making them good candidates for next-generation solid oxide fuel cell electrolytes. Amongst the different features exhibited by these interfaces, misfit dislocations play an important role in influencing ionic transport, yet their role remains poorly understood, which is the case in rock salt-perovskite interfaces too. In SrTiO3/NiO heterostructures, to comprehend interface ionic transport, we investigate oxygen vacancy migration near misfit dislocations. To this end, we developed a high-throughput framework that integrates atomistic simulations with nudged elastic band method to predict migration energy barriers across disparate interface atomic environments. By comprehensively mapping …

Performance Comparison Of Learning With Errors Cryptosystems, Gabriel Johnson

Theses

Due to recent advancements in quantum computing, there has been great interest in finding quantum-resistant public key encryption algorithms. Much focus has been given to lattice-based cryptosystems, as certain lattice problems appear difficult even in the quantum setting. In particular, the Learning with Errors (LWE) problem, introduced by Regev, gives a means for constructing numerous public key cryptosystems with very strong proofs of security based on the hardness of finding a short vector in a lattice. We analyze and compare the performance, in terms of memory usage and speed, of four different Learning with Errors cryptosystems: basic LWE, normal-form LWE, …

Parsing Of Math Formulas And Chemical Diagrams Using Graph-Based Representation And Attention Models, Ayush Kumar Shah

Theses

Mathematical formulas and chemical diagrams appear frequently in scientific documents but are often embedded as visual content, either rasterized or vector-based images, limiting their accessibility and automated analysis. This thesis aims to bridge this gap by presenting a graph-based visual parsing framework that recognizes and parses these notations from both vector and raster image inputs in digital documents. For mathematical formulas in born-digital PDFs, we construct Symbol Layout Trees (SLTs) using a graph defined over vector-based primitives, capturing spatial relationships, avoiding relying on OCR. For born-digital chemical diagrams, we introduce a Minimum Spanning Tree (MST)-based technique that extracts molecular structure …

Investigation And Prediction Of Excessive Water Production In Bottom Water-Drive Naturally Fractured Reservoirs Using Machine And Deep Learning, Sami Abderraouf Belkhir

Theses

Naturally Fractured Reservoirs (NFRs) are characterized by dual-porosity and dual-permeability systems, posing significant challenges in managing water production due to highly conductive fracture networks that facilitate rapid water migration from bottom aquifers, often bypassing oil stored in the matrix, thus resulting in early water breakthrough and water channeling phenomena. The main objective of this thesis is to develop and validate deep learning and machine learning models to predict water production, water breakthrough time (tbt), and ultimate water cut (WCult) in NFRs, thereby enabling more effective reservoir management strategies. This work also aims to evaluate the sensitivity of water behavior to …

Advancing Academic Advising With Knowledge Graphs: Integrating Machine Learning And Llms For Personalized Course Planning, Sara Alshamsi

Theses

Academic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand …

Numerical Methods For Approximating Line Integrals Over Implicitly Defined Curves, Raghd Alsaadawi

Theses

In this thesis, we develop and investigate a predictor-corrector method for the numerical tracing of implicitly defined curves. The study begins with the introduction of modified numerical integration techniques — specifically, the modified trapezoidal and modified midpoint rules — for evaluating the line integral of a vector field along an implicitly defined curve. Furthermore, we explore higher-order methods aimed at improving the accuracy of such integrals. Theoretical and numerical results, including asymptotic error expansions, are presented to support the analysis. In addition, several numerical experiments are carried out to illustrate the effectiveness and robustness of the proposed approaches.

Modeling Femtosecond Laser Interaction With Glass For Optical Fabrication, Nathan Klein

Theses

The fabrication of precision optics is critical for a wide range of applications, including biosensors, virtual and augmented reality, medical imaging, and micro-electronics. However, it is challenging to meet the demands of these applications with conventional chemical and mechanical fabrication methods, as they can introduce chemical waste, mid-spatial-frequency errors, and subsurface damage that degrades image quality. Femtosecond lasers have emerged as a promising alternative, offering fast, non-contact, and chemical-free machining with single digit nanometer precision. This thesis presents a computational model designed to investigate the interaction process between a high-intensity femtosecond laser pulse and dielectric material. A pulse propagation model …

Investigating Hardware Injections In Ligo O3 Data: Simulated Signals From A Neutron Star In A Low-Mass X-Ray Binary, Jediah Gofhaone Tau

Theses

Simulated continuous gravitational wave (CW) signals, called hardware (HW) injections were added to the data in the LIGO detectors' third observing run (O3), including two periodic signals mimicking a spinning neutron star in a binary system, similar to the Low-Mass X-Ray Binary (LMXB) Scorpius X-1 (Sco X-1). Using a cross-correlation pipeline, which searched for CWs from Sco X-1 in O3, we searched for these HW injections, using an uncertainty around the true signal parameters akin to the uncertainty associated with the parameters of Sco X-1. One of the signals, residing in the 230-235 Hz frequency band, was detected. The other …

Interaction Of Liquid Phase Diisopropyl Methyl Phosphonate (Dimp) With Material Surrogates For Components Of Soil And Combustion Products Of Metal Fuels, Khushi Sunil Patel

Theses

Combustion products of three different Al-based materials, Al-CaCO₃, Al-SiO₂, and Al-Fe₂O₃ , have been produced to understand their interaction with liquid diisopropyl methyl phosphonate (DIMP), a surrogate for chemical weapon agents. The purpose of this is to see which materials were more effective at DIMP adsorption and decomposition at different temperatures. Prepared materials were characterized using electron microscopy, x-ray powder diffraction, and nitrogen adsorption. Prepared powders were submerged in DIMP and heated in a thermal analyzer, tracking the mass changes as a function of temperature. Surfaces of the recovered materials were studied using …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Analytical Studies Of Black Hole Systems, Alessandro Ciarfella

Theses

This work includes a series of analytical projects aimed at providing accurate initial data parameters for full Numerical Relativity evolutions of binary black holes (BBH), allow the evolution of multiple black hole systems, and the interpretation and modeling of high- energy collision of black holes. We first describe how to obtain accurate initial data for the evolution of BBH by extending the determination of quasi-circular orbital parameters described in [1] to 3.5PN, and by incorporating the small mass ratio limit by explicitly including the Schwarzschild and Kerr limits [2]. We then focus on eccentric binary black hole systems, developing a …

Hybrid Sentiment Analysis Of Police Social Media: Managing Big Data Impact On Youth, Alunood Yousuf Alamiri

Theses

The pervasive influence of social media has reshaped public communication, particularly for institutions like law enforcement agencies that rely on these platforms to engage with the community. As police departments increasingly utilize social media to disseminate information, interact with the public, and manage public relations, the need for effective sentiment analysis becomes crucial. Understanding how police messages are received by the public, especially by the youth, who are among the most active and impressionable users of social media, is essential for maintaining public trust and ensuring effective communication. This thesis, seeks to develop a comprehensive solution for this challenge. The …

Predicting Patient No-Show Rate In Healthcare To Improve Operational Excellence, Saeed Ahmed Al Wari

Theses

The challenge of integrating no-show predictive models into healthcare environments is complex and multifaceted, extending far beyond the distinct technical challenge of building accurate models. This thesis will discuss more on the subject of incorporating machine learning into healthcare. While algorithmic accuracy and model validation are critical, they are a mere sub-system of a much larger operational, technological, ethical, legal and human ecosystem. Predictive models face mass deployment in healthcare systems which are complex networks of stakeholders across strict regulatory frameworks and deeply embedded workflows (AlMuhaideb et al., 2019). However, for these models to result in long term and real …

The Intersection Of Spirituality, Commerce, And Art In The Work Of William Keith, Joan Gallagher

Theses

This thesis examines the nineteenth-century Tonalist artist William Keith, focusing on how he successfully integrated commerce, art, and spirituality to achieve financial success, artistic innovation, and spiritual depth. While scholars often emphasize the symbolism and religiosity in his work, they tend to overlook his financial accomplishments. This study explores Keith's background, religious influences, and artistic development within the cultural, economic, and social context of nineteenth-century America. As a Tonalist, Keith produced popular works for the art market; as a Swedenborgian, his art reflected the spiritual principles of that faith. Unlike his East Coast counterparts, Keith’s California-based landscapes captured the dramatic …

End-To-End Systems Limitations In Hyperspectral Target Detection Using Parametric Modeling And Subpixel Lattice Targets For Validation, Chase Canas

Theses

Hyperspectral target detection applies algorithms to high-dimensional images to identify rare targets, or objects of interest, within cluttered scenes of diverse backgrounds. Computational demands of processing hyperspectral datasets, along with their limited availability in both public and private sectors, imposes challenges in assessing system-level limitations of detection. This research presents a methodology to quantify end-to-end sensitivities and limitations through statistical modeling across thousands of target detection scenarios. The objective is to identify specific parametric thresholds where detection begins to degrade, based on “knees” in detection curves derived from model outputs of various scenarios. The model considers subpixel targets, where an …

Access To Justice For Deaf Defendants, Anna Zieba

Theses

Deaf individuals face systemic accessibility within the criminal justice system, which emphasizes the importance of the gaps in accommodations by the Americans with Disabilities Act (ADA). Inconsistent legal protection, limited access to interpreters, inadequate training for legal professionals, and a lack of technology resources obstruct effective communication for Deaf individuals. As a result, it prohibits achieving an effective and equitable legal outcome. This research paper will examine case studies, legal frameworks, and academic literature to highlight the critical need for reform, including mandatory Deaf culture training, standardized interpreting qualifications, and more resources to enable equitable access to justice.

Enhancing Aptv Reliability: A Study Of Design And Operational Efficiencies At An Aluminium Manufacturing Company, Mohammad Tayeb Alawadhi

Theses

This paper aims to focus on enhancing the reliability of Anode Pallet Transport Vehicles (APTVs) used in Aluminium Manufacturing Company. The primary objective of the project is to establish the causes of failure modes, assess the operational problems, and implement strategies that would prevent, reduce, or minimize the frequency of failure, low equipment utilization, and high maintenance costs. APTV performance analysis involves the use of maintenance logs, operational data, and engineering specifications to obtain the best results. Applying Failure Mode and Effects Analysis (FMEA), the study highlights the most critical parts causing the largest effect on operations, which include traction …

From Contact To Care: A Comprehensive Analysis Of Mental Health Practices Across The Criminal Justice Continuum, Violet Ameele

Theses

This capstone attempts to gain a further understanding of the state of mental health training, protocols, and practices across the continuum of the criminal justice system. The various sections of this paper will look at law enforcement, corrections, and court/community and their roles in mental crisis training and interaction. As the topic of mental health is something increasingly discussed in the present day, it is also something that is in need of more research, in order to create higher standards when it comes to individuals with mental health concerns and ensuring that they receive the proper levels of care that …

Police, Prisons, Communities, And Courts: An Examination Of Criminal Legal System Responses To Neurodivergence, Rhys Miller Evans

Theses

Mental health in the United States is largely stigmatized and overlooked as a factor of crime. This capstone project critically analyzes the response of actors in the criminal legal system to neurodivergence and identifies areas for improvement.

Part One focuses on how police respond to those with mental health conditions and community-based supports that can reduce harm during these interactions. This chapter provides an overview of the prevalence of neurodivergence in the United States and how police are, or are not, trained to protect and serve this subset of the population. It explains the Memphis Model of crisis intervention and …

Silicon Photonics For Quantum Information, Evan Manfreda-Schulz

Theses

Quantum information science and technology (QIST) combines principles of quantum mechanics and information theory to develop new approaches for processing, transmitting, and sensing information. Practical realization of applications in QIST requires generation and validation of a significant amount of entanglement, which is not only a property but a logical resource. Silicon photonic integrated circuits (PICs) are an attractive platform for applications in QIST and for generating the entanglement resource because they are foundry-fabricated, are compatible with existing fiber optic infrastructure, have a small spatial footprint, and afford access to several high-dimensional (HD) degrees of freedom. This dissertation covers three projects …

Visualizing The Dynamics Of Neuroevolution With Genetic Distance Projections, Evan Patterson

Theses

Evolutionary algorithms have shown substantial progress in recent years, especially in neural architecture search and neuroevolution applications. Despite their effectiveness, analyzing and understanding the evolutionary paths these algorithms traverse to reach solutions remains challenging. These algorithms often involve distributed computing strategies, which can include subpopulations or islands, and they explore massive or even unbounded search spaces in both continuous and non-continuous domains. Manually examining individual solutions to understand the evolutionary dynamics is often infeasible due to large population sizes, large genome sizes, and high generation counts. This work introduces a new methodology for visualizing neuroevolution population dynamics called genetic distance …

A Systematic Literature Review Of Behavior Analysis In Psychiatric Inpatient Settings, Stephanie Henry, Robbie Hanson

Theses

Applied behavior analysis (ABA) may be best known for the treatment of individuals with intellectual or developmental disabilities, such as those with autism spectrum disorder (ASD). However, ABA also has a long history of effective outcomes with other populations, such as with individuals who are admitted to inpatient psychiatric facilities (Dougher, 2004; Harvey et al., 2009). In 2018, 187,877 individuals in the United States were reported as receiving treatment for mental health disorders (Lutterman, 2022). Given behavior analysis’ success implementing operant procedures with these populations, an updated review of the literature on the use of behavior analysis within these settings …

Resonating Patterns: Adaptive Resonance Theory And Self-Organizing Maps, Meeti Dixit

Theses

Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary …

Predicting Student Dropout Risk Using Machine Learning, Fatma Alameri

Theses

Student dropout remains a persistent challenge in higher education, undermining institutional performance, reducing workforce preparedness, and limiting students’ academic and economic opportunities. Accurately identifying students at risk of attrition is complex, due to the interplay of academic, financial, and behavioral factors. This thesis addresses this challenge by applying a combined machine learning framework—integrating both unsupervised and supervised techniques—to predict student dropout using structured, first-year academic and financial data. The study utilizes a comprehensive dataset of 4,424 undergraduate student records from a European higher education institution, covering ten academic years and comprising 35 variables related to academic performance, enrollment behavior, and …

From The Bleachers To The Browser: Redefining Fan Experience With Ar And Ai In Smaller Teams, Jennifer Lee Wunder

Theses

This project documents the creation and deployment of HootyHoo, an interactive augmented reality (AR) mascot experience designed for the O’Fallon Hoots, a small-scale collegiate summer baseball team. Built using accessible, open-source tools such as WebXR, Mixamo, Meshy, Botpress, Claude and ChatGPT, this prototype merges AI-driven conversation with animated 3D avatar interaction—redefining how fans engage with sports organizations digitally. Unlike enterprise-level applications used by professional franchises, HootyHoo is entirely browser-based, eliminating the need for app downloads and ensuring maximum accessibility for families and new fans with smartphones. The experience centers on Hooty, the team mascot, who answers questions about baseball and …

Horror Films And Why We Enjoy The Genre, Jessica Thomason

Theses

This project focuses on the research of horror films and why certain individuals enjoy the genre despite having taboo subjects. The primary focus is to find the answer to the research question “why do some individuals enjoy horror?”. The aim of this research was to identify a connection between why individuals enjoy horror through the content. The research then informed the creative process behind the painting series that accompanies this project.

Nextgen Humanities: A Digital Art History Pedagogical Revolution, Analisa Soverns-Reed

Theses

In response to the urgent need for more accessible, inclusive, and technologically relevant humanities education, the NextGen Humanities project was conceived as a digital initiative to reimagine pedagogical practices within art history and the broader humanities. Traditional perceptions of humanities disciplines as elite and static have contributed to declining engagement, necessitating innovative solutions that integrate open-access resources, digital tools, and inclusive frameworks. This project developed a publicly accessible platform curating free, vetted resources grounded in artificial intelligence (AI), virtual reality (VR), and open educational resources (OER), emphasizing critical engagement, cultural inclusivity, and technological adaptability. Methodologically, NextGen Humanities combined extensive literature …

The Transformative Impact Of User-Created Mods Within Fallout: New Vegas, Tanner Webb

Theses

Video game modding is one of the fastest growing trends in the gaming industry and it is changing how players interact with games. Review of current literature and research shows that these modders are an integral part of the gaming market. Using the principles of participatory culture and digital aesthetics, this research investigates how modding practices serve as both a form of creative expression and an inspiration for technological innovation within the video game industry. Video game modifications serve as catalysts for creative expression, foster the development of innovative user communities, prolong the life of game titles, cultivate a pool …