Digital Commons Network^™

Draft Card Battler, Brian Moore

Theses

This project investigates how design transparency and player interaction mechanics can enhance emotional connection and strategic depth in competitive digital card game environments. Draft Card Battler transforms traditional sequential turn-based Trading Card Game (TCG) gameplay into a structured round-based system featuring asymmetric card-play patterns (1-2-2-1) and simultaneous combat resolution. Beginning with a physical card game prototype, the project evolved through iterative design and digital implementation, informed by research in social capital theory and player psychology. Through reflective playtesting and technical development, this study explores whether visibility, pacing, and commitment mechanics foster trust, fairness, and cooperation alongside competitive tension. The implementation …

Assessing Expected Cover Of Invasive Flora Species Near Corridor Features By Distance, Joshua G. Logan

Theses

Fragmentation of landscapes has been a growing topic of interest within scientific literature over the past several decades, as the way we as humans affect our planet becomes more apparent as the years go on. As humans, we have shaped the land and what it contains in immeasurable ways, erasing or forever altering entire natural ecosystems. Chief among the drivers altering these ecosystems is the fragmentation of landscapes, which can have all kinds of effects on natural ecosystems, from limiting nutrient transport to providing easier access for invasives to permeate throughout a landscape. Corridors are significant areas of interest regarding …

Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improving Accuracy, Reliability, And Latency, Nazmus Ashrafi

Theses

The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi agent collaboration and (2) runtime execution of information-based …

Making Math Accessible: Addressing Language Challenges In 3rd Grade English Medium Math Classrooms, Salha Mohamad

Theses

The research article, Making Math Accessible: Addressing Language Difficulties in 3^rd Grade English Medium Math Classrooms, addresses the issue of how the teachers in UAE elementary schools assist a student who is to study mathematics in English rather than Arabic. In the School X, Grades 1 and 2 applied mathematics and science using the Ministry of Education (MOE) Arabic-medium curriculum. Since Grade 3, however, the school has used the American curriculum, so students are expected to study these subjects in English. This change brings major barriers in understanding basic mathematical terms, word problems, and instructions. The research applies …

Adaptive Security Metric For Optimizing Post-Quantum Cryptography In Constrained Iot Devices, Aisha Nasser Ahmed

Theses

Quantum Computing poses real threat to Classical Public-Key Cryptography requiring the use of Post-Quantum Cryptography for all Internet of Things Devices. However, there are greater computational, memory and communication overheads in PQC algorithms that create additional burdens on resource constrained IoT devices. At this time, there are no standard measures for systems developers to determine optimal PQC settings for the various IoT Device Classes. This Thesis develops a new framework of metrics for determining the most suitable PQC settings based on Security Strength, Performance Indicators (Latency, Memory, Energy), Communication Overhead and Reliability for each IoT device class. The Research introduces …

Characterizing Fish Communities Using Traditional And Emerging Monitoring Approaches In San Diego County Estuaries, Natalie Katherine De Wet

Theses

Fish communities are essential to estuarine health, playing key roles in food webs, nutrient cycling, and ecosystem functioning. However, the persistence of native fish populations is increasingly threatened by the cumulative impacts of coastal development, pollution, and climate change. Rapid, effective monitoring of species distributions is a priority, enabling timely conservation and management. However, the study of any ecosystem is limited by time, resources, and the consideration that research of a system can damage the ecosystem under study.

This research aimed to characterize fish species richness and evaluate the benefits and limitations of different estuary monitoring approaches. Specifically, this study …

Impact Of Temperature On Larval Mortality And Performance Of The Acorn Barnacle Chthamalus Fissus In San Diego, Ca, Alberto Rivera

Theses

Rising ocean temperatures and increasingly frequent marine heatwaves threaten the physiological performance and survival of marine larvae. This study quantifies the thermal tolerance, respiration, and swimming behavior of Chthamalus fissus cyprid larvae to establish a baseline for understanding their vulnerability under future climate scenarios. Larvae were collected from the Scripps Pier in La Jolla, CA and exposed to a range of temperatures (12–50°C, dependent on the experiment) to assess oxygen consumption, mortality, and swimming speeds. Oxygen consumption rates increased significantly with temperature, peaking at 30°C, with cyprids exposed to temperatures above 26°C showing significantly higher respiration rates than those below …

Enhance Telecom-Related International Indicators Using Machine Learning, Omar Alnemer

Theses

This thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with …

From Sim To 6dof: Deep Learning For Real-Time Satellite Pose Estimation From Resolved Ground-Based Imagery, Thomas W.N. Dickinson

Theses

This dissertation presents the first practical system for automated six degrees of freedom (6DOF) satellite pose estimation from resolved ground-based adaptive optics (AO) imagery. Addressing a key challenge in Space Domain Awareness (SDA), the proposed approach eliminates the need for human labeling by directly regressing satellite orientation and position from blurry, noisy, and deeply-shadowed imagery. The architecture consists of a multi-stage deep neural network pipeline that localizes the satellite, predicts pose, and optionally smooths predictions over time. Networks are trained exclusively on fully synthetic imagery generated from a 3D CAD model. Despite this, the model generalizes effectively, bridging the Sim2Real …

Enhancing Adversarial Robustness In Convolutional Neural Networks By Integrating Human Viewing Strategies, Diana Velychko

Theses

Adversarial examples pose a serious threat to the reliability of deep neural networks by subtly manipulating inputs to induce incorrect outputs. Despite progress in the field, convolutional neural networks (CNNs) remain vulnerable to such perturbations. This research proposes a novel approach to improve adversarial robustness of CNNs by incorporating human viewing behavior into both training and testing processes. To establish a baseline for comparison with models trained from scratch, an eye-tracking dataset—collected during experiments involving novel objects from the Greeble dataset—was used to capture how humans learn to classify previously unseen object categories. This study compares model saliency with human …

Predicting Hollywood Movie Success Using Predictive Machine Learning Algorithms, Saeed Alghabshi Hathboor

Theses

The Decision Trees are always simple to understand and interpret techniques, a single tree may not be enough for the model to learn the characteristics from. In this IMDb movie review prediction problem, evading all other simple mechanisms, the Random Forest, on the other hand, is a "Tree"-based algorithm that makes judgments by combining the attributes of numerous Decision Trees is used. The primary goal of this work is to evaluate the predictive performance of a random forest model with various parameters used for forecasting numerical user ratings of a movie based on pre-release data such as actors, directors, profit, …

Digital Biophilia In Web Interface Design For Accessibility And User Well-Being, Bryana Michelle Peifer

Theses

This interdisciplinary study explores the intersection between horticultural therapy and web design, focusing on the impact of biophilic design principles in shaping contemporary practices within digital interface design. This exploration addresses the multifaceted relationship between humanity and the natural world, particularly how this connection serves as a source for the emergence of nature-inspired elements in digital expressions and their subsequent integration into modern web design for accessibility and well-being. As digital environments increasingly dominate daily experiences, the need for digital wellness through nature integration has become critical, especially for individuals who have physical disabilities, limited mobility, or limited access to …

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 …

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 …

An Investigation Of Active Inference For Reinforcement Learning Control, Xinyu Hu

Theses

This thesis investigates the application of active inference framework on different reinforcement learning (RL) tasks. We specifically consider the following OpenAI Gym environments: CartPole, MountainCar, and LunarLander. In this thesis, our primary goal is to explore whether active inference could provide better performance and stability compared to traditional RL methods. The proposed model consisted of three main components: a variational autoencoder (VAE) model to infer hidden states, a transition model predicting latent states, and a Double deep Q-network (Double DQN) as the actor selecting optimal actions. To achieve this, extensive experiments are carried out using grid searches across several hyperparameters, …

Design Of A Posit Based 6-Bit Configurable Cnn Hardware Accelerator For Audio And Image Classification, Steven Ng

Theses

The rapid development and growing interest in Artificial Intelligence (AI) have resulted in a trend of models increasing in size and complexity at an accelerating rate. These larger models have demonstrated a greater ability to accomplish increasingly sophisticated tasks compared to smaller models, furthering the trend of making models larger. However, as the models continue to improve and grow larger, this has created a new challenge in deploying them into practical applications. Current models typically run inference on the same systems where they were trained. Large data centers comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are well-suited …

Multiplex Leiden Optimized Dynamic Network Microsegmentation And Flow Validation For Software Defined Networks, Rashed Husni Rashed Alnuman

Theses

Modern digital infrastructure is fundamentally built upon robust networks, which underpin seamless communication, data exchange, and the operation of critical systems. These networks facilitate everything from internet access and real-time patient vital sign transfers in healthcare to the management and control of industrial processes. As demands on networks have surged, their complexity and functionality have evolved significantly, continuously adapting to diverse sectoral needs. However, this rapid evolution has also amplified security challenges. The escalating scale and intricacy of contemporary networks have exposed vulnerabilities that traditional perimeter-based security models are often insufficient to counter. Static defenses prove inadequate in dynamic environments …

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 …

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 …

Advancing The Utility Of Unmanned Aerial Systems (Uas)-Based Imaging Techniques In Broadacre Agriculture: A Multimodal Case Study On Table Beets, Mohammad Shahriar Saif

Theses

Efficient and sustainable food production and management are growing concerns in the context of an ever-increasing global population. It is in this context that the integration of remote sensing with advanced imaging technologies presents transformative opportunities for data-driven decision-making in agriculture. This study therefore explores the use of unmanned aerial systems (UAS) equipped with multispectral, hyperspectral, and LiDAR sensors for the non-destructive monitoring of table beet (Beta vulgaris) crop traits, with a specific focus on root yield estimation and foliar disease assessment. Table beet, a subterranean crop of increasing commercial and nutritional importance, poses unique challenges for above-canopy sensing due …

Towards A Foundational Framework For Real-World Active Learning: Theory, Algorithms, And Applications, Dayou Yu

Theses

While supervised learning has seen great success in the modern machine learning era, the challenge of obtaining high-quality labeled training data still exists. In many knowledge-rich domains, we still face the problem of letting machine learning models learn well using a limited number of labels. Active learning (AL) has been a prominent learning paradigm that deals with such problems. This thesis reviews the classical challenges of AL, summarizes how our prior work has advanced the field, and charts a course for adapting AL to realistic and challenging scenarios. We begin by discussing past work on standard scenarios for AL, including …

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, …

Retrofitting An Fdm Printer For 3d Printing With Cotton Yarn: A Novel Approach And Mechanical Property Evaluation, Muhammad Aghead Al Arnaout

Theses

The rapid rise of 3D printing in industry has intensified research into developing advanced and sustainable printing materials, as the properties of the feedstock directly influence the performance of the final product. This thesis investigates the integration of natural cotton yarn into PLA (Polylactic Acid) to enhance mechanical behavior while maintaining cost-effectiveness, using a standard 3D printer without any modifications. Experimental results across three phases demonstrated consistent improvements. In the first phase, cotton–PLA composites exhibited higher ductility (+22.27%) and toughness (+10.51%) than pure PLA, albeit with lower stress values and a slight decrease in Young’s modulus. In the second phase, …

Waveform Systematics In Moderate Mass Ratio Binary Black Hole Systems, Rachel E. Mechum

Theses

Gravitational wave (GW) observations of binary black hole (BBH) mergers by advanced LIGO (aLIGO) have transformed our understanding of compact objects (COs). As detector sensitivity improves, new populations consisting of moderate to high mass ratio binaries will become increasingly accessible. Parameter estimation (PE), a process that extracts source properties such as masses and spins from GW signals, is critically dependent on accurate waveform models. However, current models may use approximations that introduce systematic errors for binaries with mass ratios between 0.05 and 0.5, affecting parameter accuracy and scientific interpretation. Using the Rapid Iterative FiTting (RIFT) algorithm, this work quantifies waveform …

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) …

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 …

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 …

Dynamic Defenses To Systematically Secure Exposed Attack Surfaces In Wireless Systems, Naureen Hoque

Theses

While modern wireless systems rely on strong encryption, this alone does not secure the entire attack surface, including exposed signal attributes, pre-authentication exchanges, and protocol metadata. This dissertation challenges the assumption that existing cryptographic protections are sufficient to protect these vulnerabilities. Signal attributes, including phase and amplitude variations due to modulation, are physical-layer characteristics of encrypted data communication that remain observable and exploitable for attacks like traffic analysis. The pre-authentication phase—during which session keys are negotiated and installed—is vulnerable to spoofing and denial-of-service attacks. Protocol metadata, such as operating channel, sender’s address and location, and timestamps, is unencrypted and can …