Digital Commons Network^™

Analytics Of Capstone Projects: Understanding Outcomes Through People, Products, And Processes, Hrushikesh C. Godbole

Theses

The capstone project is a bridge from the university to industry. During the project, students not only learn the process of integrating previously learned engineering concepts into an actual product but also practice essential professional skills such as communication, teamwork, and project delivery. How can universities ensure effective capstone student learning and good outcomes? To answer this question, the capstone project is studied as a system comprising of products, people, and processes. People refer to the students and instructors involved. Students develop products by following development processes. Instructors guide students using instruction processes. In educational context there limited control over …

Haunted Echoes, Stuti Kasat

Theses

Haunted Echoes explores the narrative and emotional possibilities of interactive digital fiction by creating a room-based haunted experience. By placing the users within a haunted manor the project encourages emotional discovery through narrative exploration. As a room-based exploratory experience, users get to navigate a dollhouse-style 3D interface, interacting with objects to reveal layered micro-stories and uncover the manor’s haunted past. The experience turns spatial exploration into emotional discovery, drawing inspiration from immersive theater, point-and-click adventures, and environmental storytelling in video games. The thesis examines the intersection and overlap of interactive storytelling, visual communication, and emotional immersion, integrating 3D modeling (Blender/Cinema …

On The Adaptation Of Latent Dynamics Models, Ryan Missel

Theses

Predicting future states of high-dimensional, partially observed dynamical systems - such as cardiac electrical propagation - is crucial for advancing fields like healthcare and physics. While univariate time-series forecasting is well-explored, high-dimensional time-series forecasting presents unresolved challenges. These challenges include the computational burden of processing high-dimensional data and the difficulty of accessing the system’s underlying dynamics directly. Classical optimization and analytical approaches become impractical as the dimensionality increases, leading to a growing interest in data-driven deep learning models, particularly those based on latent dynamics functions. Latent dynamics models provide an efficient way to map high-dimensional observations into lower-dimensional latent spaces, …

The Petrological And Geochemical Analysis Of The Grant Intrusive Breccia And Vaughn Intrusive Within Hicks Dome, Hardin County, Illinois, Aaron Michael Beirl

Theses

Hicks Dome is a crypto-volcanic feature found in the southeastern corner of Hardin County, Illinois. The dome formed around 270 Ma (late Permian), as part of the Permian Wauboukigou Igneous Province (PWIP), through a series of explosive igneous intrusions that caused uplift and clear structural deformation to the area. There was not enough magma feeding the intrusions to breach the surface, but due to the unique geochemistries of the magma(s), these intrusions formed amalgamations of alkaline, carbonatitic, and ultramafic gabbros. The overall geochemistry of these intrusions have high amounts of iron (Fe) and calcium (Ca), with relatively low amounts of …

Comparative Phylogeography Of Three Widely Distributed Amazonian Poison Frogs, Sofia Del Milagro Granados Martinez

Theses

Amazonia has gone through extensive changes in its climates, geology, hydrology and drainage systems over the past 10 mya; these processes have been postulated as key drivers of diversification. Contrary to the trend where species once broadly distributed are subdivided into several species with restricted distributions, many taxa span the Amazon basin without noticeable genetic differentiation. In my thesis, I studied the phylogenetic and phylogeographic patterns of three widely distributed Amazonian poison frogs: Ameerega hahneli, Ameerega trivittata and Ranitomeya variabilis. I examined phylogenetic relationships based on two different types of genomic data—nuclear and mitochondrial—and I carried out population structure analyses. …

Using Equivalence-Based Instruction And Behavioral Skills Training To Teach Social Skills, Alexander Orzeck

Theses

Social skills are an integral part of human life, so it stands to reason that teaching methodologies should be developed to instruct people in this complex topic. One method to teach social skills has been behavioral skills training, in which the four-step sequence of vocal and written instruction, modeling, roleplay, and feedback exposes learners to the social concepts until a mastery criterion is reached. Skills relevant to socialization have also been taught with equivalence-based instruction, in which the concept of stimulus equivalence is employed to generate novel relations between stimuli for the purpose of teaching some skill. No instance was …

3d Printing Of Short Fiber Reinforced Polypropylene: Novel Lattice Architecture And Material Characterization, Mohammad Ghazi Alshneeqat

Theses

This study explores the enhanced compressive behavior of 3D-printed single and double gyroid solid-networks lattices. Where the double gyroids are constructed from two intertwined single gyroid structures. These structures were designed by nTop implicit modeling tool and then fabricated by material extrusion additive manufacturing method at optimized printing parameters, including optimized nozzle temperature and raster angle. The main objective of this study is to reveal the compressive behavior of the novel double gyroid lattice structure and then to tailor its response through variable gyroid heights. Standard polymer tests were performed, considering thermogravimetric analysis, to confirm the thermal stability of the …

Layer-Wise Prediction Of Overhang-Related Geometric Deviation In Metal Additive Manufacturing With Conditional Generative Adversarial Networks, Himal Sapkota

Theses

Ensuring dimensional precision in parts with complex overhangs is a significant concern in Metal Additive Manufacturing (MAM), as undetected geometric deviations can compromise functionality and reliability. This research introduces a Conditional Generative Adversarial Network (cGAN), specifically the Pix2Pix framework, to predict layer-wise geometric deviations in Laser Powder Bed Fusion (LPBF) printed parts with overhang geometries using paired 2D CAD slices and corresponding X-ray Computed Tomography (XCT) based ground truth images. A key innovation is using RGB color-coded CAD slices to encode overhang angle information, enhancing feature distinction and prediction accuracy compared to non-color-coded inputs. Eighteen Pix2Pix models were trained across …

Meta-Learning Coordinated Eye–Hand Dynamics, Chengkuan Zhao

Theses

The analysis of fine-grained behavioral time-series, such as eye-hand coordination, offers a powerful lens for understanding neurodevelopmental disorders like Autism Spectrum Disorder (ASD). Traditional assessments often miss subtle dynamic patterns that are critical for characterizing individual differences. This thesis investigates the temporal dynamics of coordinated eye and hand behavior in adolescents with and without ASD during a structured ”maze painting” tablet task. The goal is not to propose a new predictive model, but to use a state-of-the-art meta-learning framework based on latent Neural Ordinary Differential Equations (Neural ODEs) as a ”computational microscope” to explore how these behavioral dynamics vary within …

Evaluating Ai-Powered Predictive Analytics For Public Transport Demand In Dubai, Sara Ismail Dhalam

Theses

This thesis examines the application of AI-driven predictive analytics to model demand for metro and tram rides in Dubai, utilizing machine learning models in SAS Viya, as Dubai advances its innovative city initiatives. Research focuses on how seasonal variations, the use of metro lines, and types of communities affect trends in passenger flow. Gradient Boosting emerged as the most reliable predictive model, achieving the highest KS values in all scenarios. It effectively captured the increase in ridership during winter on the Red Metro Line and identified summer declines on the Green Line. The findings highlight significant seasonal fluctuations in residential …

Structure/Property/Processing Of Covalent Adaptable Networks Bearing Hindered Urea Bonds, Kory Schimmelpfennig

Theses

Dynamic covalent chemistry provides the functional basis for the efficient and reliable exchange of reversible binding groups which enable self-repair thus avoiding structural impairment or permanent damage of material properties. Covalent adaptable networks (CANs) have garnered attention for the sustainable development of polymers capable of mending damages thus facilitating eco-friendly recyclability. This work aims to address the role of reaction pathway and manufacturing process on the subsequent properties of CANs bearing hindered urea bonds (HUBs). HUBs have proven easy to incorporate into polymers and exhibit moderate temperature thermo-reversibility and creep resistance. In this investigation we study the effects of preparing …

Enhancement Of Ultrasonic Welding Of Bulk Carbon Nanotube-Metallic And Graphene-Metallic Conductors, Daniel L. Broderick

Theses

Nanostructured carbon sheets comprising carbon nanotube (CNT) or graphene are appealing for electrode and antenna applications. Physical connection to metal conductors requires enhanced mechanical strength, electrical performance, and thermal capability. Ultrasonic welding is a viable technique to fabricate stable and robust bonds but requires methods to further reduce electrical resistance and improve mechanical strength. The present dissertation has advanced ultrasonic welding between bulk CNT electrodes and Cu foil by utilizing chemically doped junctions at the bond interface. Specifically, a selective doping strategy using KAuBr4 at the bonding regions between bulk CNT electrodes and Cu lowers the electrical contact resistance measured …

Galaxy Tales: A Data-Driven Approach To Emotional Self-Expression, Bing Chen

Theses

Galaxy Tales is a mobile application that transforms users’ social media activity into personalized planetary visuals, offering a novel way to document and reflect on life experiences. This project explores how data-driven design can create emotionally resonant and personalized representations of identity by extracting key elements—color, object shapes, and emotional atmosphere—from digital footprints. Grounded in psychological theories such as memory anchors and emotional attachment, Galaxy Tales enables users to form deep emotional connections with their virtual planets, turning abstract memories into tangible visual metaphors. The project also addresses current societal challenges, such as loneliness and emotional fatigue, by offering a …

Impacts Of Permafrost Thaw And Methane Cycling In A Northern Peatland, Hannah Defelice

Theses

Climate change is disproportionately impacting high latitude regions, resulting in thawing permafrost, changes in vegetation, hydrology and greenhouse gas emissions. As permafrost thaws in peatlands, ground subsidence occurs, resulting in a transition from elevated palsa to lower, wetter bogs and fens. Changes in vegetation and hydrology with thaw result in increased methane (CH4) fluxes and distinctive shifts in the carbon isotope signature of porewater and emitted CH4. Previous research has established the differences between these landcover classes; however, we know little about how CH4 cycling processes change during land class transitions. To address this gap, we compared recently formed bogs …

A Web Application For Generating Argument Maps For Essays Using Llms, Alexis R. Chalmers

Theses

Argumentative writing is a critical skill that strengthens students’ reasoning, communication, and analytical abilities. However, maintaining a clear and organized argument structure while writing can be challenging. Argument maps — visual diagrams which explicitly show an argument’s structure — have been shown to improve students’ writing, but are rarely used outside of the planning stage of an essay due to the time and effort required to create them. Automatically generating argument maps from student essays helps students to evaluate the structure of their argument as they write and makes identifying unsupported claims visible. To evaluate whether large language models (LLMs) …

Critical Black Expressionism: A Womanist Manifesto, Rolanda Jw Spencer

Theses

This thesis introduces Critical Black Expressionism (CBE) as a visual, spiritual, and intellectual methodology rooted in Black feminist traditions, ancestral knowledge systems, and artistic practice. Grounded in womanist theory and decolonial inquiry, the central argument asserts that Black women’s lived experiences, cultural memory, and spiritual cosmologies constitute a rigorous and sovereign framework for artmaking. Rather than borrowing from Eurocentric pedagogies, this approach draws from oral traditions, sacred ritual, and master-apprentice models that have long existed within African and African American communities. The Iyami Aje: Mothers + Witches collection is the visual embodiment of this research. Composed of multimedia sculptures, altar-based …

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

Limitations Of Using Large Language Models For Automated Essay Scoring, Thomas A. Fink

Theses

Background: Automated essay scoring (AES) is a challenging deep learning problem. The two most widely used methods for predicting essay quality scores, supervised learning-based and LLM-based, have their own limitations. Although supervised learning-based methods are more accurate, they only predict a score and do not offer descriptive feedback to students. On the other hand, LLM-based methods can offer rubric-guided feedback but are known to be less accurate.

Methods: This work focuses on improving the accuracy of state-of-the-art LLM-based AES methods. We began by thoroughly investigating why these methods were performing poorly for certain datasets and certain examples. This led us …

Calibration Of Detectors For The Tomographic Ionized-Carbon Mapping Experiment, Tessalie A. Caze-Cortes

Theses

Observations of the intensity of the sky at millimeter (mm) wavelengths require accurate calibrations for the spectrometers to work with radio telescopes. However, calibrating is a complex and lengthy process for sensitive detectors that catch single photons. To improve efficiency in locating defective detectors and optimizing bias current, my research involves taking data from previous lab characterizations and creating an algorithm that analyzes and plots the code of all 1920 detectors. This thesis will discuss the importance of mm-wavelength intensity mapping and the questions the TIME research team aims to address. Also, a detailed description of mechanical and computational components …

Macroscopic Roughness Modeling Of Satellite Multi-Layer Insulation Reflectance, Kevin P. Donnelly

Theses

Reflectance modeling of specular surfaces with irregular geometries remains difficult to solve for an array of applications. In the growing space industry, there are many spacecraft materials with high specularity and varying levels of roughness. Modeling these materials in simulations becomes difficult as any realistic amount of roughness is added, necessitating more accurate physical models to represent how light is interacting with these surfaces. While some work has been done to attempt to characterize the spectral signatures of the materials themselves using hyperspectral imaging systems, the scope of these efforts has been fairly limited and not representative of the conditions …

Impact Of Precursor Solvent Chemistry On Electrical Properties Of Indium Oxide Films For Thin-Film Transistors, Joshua Masri

Theses

Indium oxide In_2O_3 combines high carrier mobility with optical transparency, making it a promising candidate for next-generation thin-film electronics. However, its electrical performance is highly sensitive to fabrication parameters, particularly the chemistry of solution-based precursors, which can alter defect landscapes and carrier transport mechanisms. This work systematically examines how solvent choice influences the structural and electronic properties of solution-processed In_2O_3 thin films, isolating processing–property relationships that remain insufficiently understood. Films prepared from water and 2-methoxyethanol (2ME) precursors were characterized using temperature-dependent transmission line method (TLM) measurements from 30~K to 300~K. Water-processed films exhibited consistently lower sheet resistance, a reduced activation …

Bridging Classical Rhetoric And Ai: A Systematic Framework For Developing Authorial Voice Through Large Language Models, Daniel Plate

Theses

This project addresses critical gaps in AI-assisted writing by developing the first systematic framework that integrates classical rhetorical principles with modern large language model capabilities for authorial voice development. The primary focus is on creating reliable methods for stylistic control through strategic AI collaboration rather than ad hoc prompting approaches. The project develops a comprehensive coding system for analyzing prose style, creates ten distinct authorial personas, and establishes a dual curation methodology that structures both stylistic analysis and content preparation. Implementation through the AI Writing Guide website provides practical tools including prompt templates, annotated examples, and instructional materials that demonstrate …

Zoo Visitors Learn By Observing Olive Baboons (Papio Anubis) Participate In Cognitive Research Or Engage In Natural Behaviors, Anna Sofia Hege

Theses

I conducted two experiments at the baboon exhibit at the Seneca Park Zoo in Rochester, New York to measure attitudes and actual knowledge in children and adults, as well as perceived learning in adults. I also recorded caregiver-child conversations in the presence and absence of live research. Experiment 1 included 150 adult participants, and Experiment 2 included 18 caregiver-child dyads. In each experiment, the research on-scientist present and research on-scientist absent group watched the baboons engage with live cognitive research while the research off-scientist present and research off-scientist absent group watched the baboons carry out their natural behaviors. A scientist …

The Effects Of Multiple Exemplar Instruction On A Bidirectional Naming Repertoire In Children With Autism Spectrum Disorder, Madison Appelbaum

Theses

Neurotypical children normally acquire language skills in their natural environment. However, children with autism spectrum disorder (ASD) typically require specialized interventions to acquire language and communication skills (Yoon et al., 2023). A behavior repertoire that has been shown essential for the fast and incidental acquisition of language in children is bi-directional naming (BiN). Multiple exemplar instruction (MEI) has been used to facilitate the acquisition of BiN in children with ASD. In the present study, we replicated and extended previous research (e.g., Yoon et al., 2023) to evaluate the effect of MEI on the emergence of BiN repertoire in three elementary …

Effects Of Self-Monitoring On Teachers’ Behavior-Specific Praise And Students’ Behavior In A Center-Based Classroom, Samantha Walsh

Theses

The current study explores the effects of self-monitoring on teachers' use of behavior-specific praise (BSP) and its impact on student behavior in a center-based classroom. Self-monitoring, a cost-effective and time-efficient intervention, has been proven effective in previous research for enhancing educator performance. However, previous studies have often combined self-monitoring with other interventions, leaving a gap in understanding its effects in isolation. This study aims to replicate the work of Justus et al. (2023) by examining the impact of self-monitoring on paraprofessionals in a center-based classroom with students aged 6-10. Using a multiple baseline design across participants, three paraprofessionals were trained …

The Sulu Sea Mandate: A Diplomatic And Military Initiative To Ensure Freedom Of The Seas, John Cheney

Theses

This thesis advocates the establishment of an official US government diplomatic policy, and recommends specific means for its implementation. By combining a written narrative with a unique and highly detailed Geographic Information System (GIS,) this thesis argues that securing the Philippines archipelago from further Chinese annexations is a US strategic imperative. To that end, US military forces and capabilities- particularly those of the US Marine Corps (USMC)- would be integrated into the revamped defense of the Philippines.

Utilizing a variety of datasets, the thesis GIS depicts such features as geopolitical boundaries, conflict zones, maritime trade routes, bathymetry, navigational hazards, undersea …

Exploring Kosovo's Need For A Technology-Driven Offset Strategy, Alba Cikaqi

Theses

At a time where technological innovation is redefining national security, Kosovo risks falling behind by failing to adopt emerging technologies for defense and security. This research addresses that vulnerability by exploring the need for a dedicated national strategy as a solution. Drawing on global best practices, including the United States Offset Strategy, NATO frameworks, and innovation models from the Baltic countries, the study outlines a potential path forward. Additionally, through qualitative analysis, including interviews with key stakeholders across public, private, and academic sectors, this study looks into the opportunities and challenges of developing a context-specific strategy for integrating emerging technologies …

A Strategic Approach For Space-Based Solar Power Implementation In The Uae, Salamah Alhajeri

Theses

This thesis investigates the strategic potential of Space-Based Solar Power (SBSP) as a transformative clean energy solution for the United Arab Emirates (UAE). Set against the UAE’s Energy Strategy 2050 and increasing global climate pressures, this study explores how the UAE can take a leadership position in orbital solar energy through foresight-based planning, technological innovation, and policy integration. The research is guided by the following four questions: 1. What trends and uncertainties shape the future of space-based solar power in the UAE? 2. How can strategic foresight methodologies be used to develop a flexible SBSP strategy for the UAE? 3. …

Identifying Underspecifications In Security Requirements Using A Formal Reasoning Approach, Viktoria Koscinski

Theses

Within requirements engineering, software requirements specifications tend to prioritize functional needs, potentially failing to capture critical security aspects and adequate security requirements. Security requirements engineering is a manual and error-prone activity often neglected due to the knowledge gap between cybersecurity professionals and software requirements engineers. Consequently, security requirements are especially prone to being underspecified, a condition where they lack a complete set of feature-values and are thus open to multiple interpretations. Underspecified security requirements can lead to incorrect assumptions and missing security properties, often remaining undiscovered until system deployment, thereby leaving the system vulnerable to exploitation. Furthermore, even individually well-written …

Protein Language Models-Based Representation For Post-Translational Modification Prediction, Suresh Pokharel

Theses

Post-translational modifications (PTMs) are chemical changes that occur after translation and play a key role in regulating protein function and cellular processes. Their dysregulation is associated with various diseases, making accurate prediction of PTM sites essential for understanding cellular mechanisms and informing therapeutic strategies. This work focuses on the computational prediction of two biologically significant PTMs, namely succinylation and O-GlcNAcylation, utilizing recent advances in protein language models (pLMs). Drawing an analogy to natural languages, amino acids are treated as words and sequences as sentences, allowing pLMs to capture contextual dependencies within protein sequences. One of the earliest contributions of this …