Digital Commons Network^™

Modelling Novel Planar Nozzle Geometries For A Z-Pinch Fusion Propulsion System, Stephanie Manasterski

Theses

Interplanetary travel using chemical or electrical propulsion faces two major obstacles: the long mission timelines and the health risks to human crews from prolonged radiation exposure. Fusion propulsion offers a potential solution through high specific impulse and moderate thrust values. Z-pinch fusion, a pulsed magneto-inertial fusion approach, is promising for propulsion as the plasma is confined through current-driven compression, eliminating the need for external confinement coils to achieve ignition conditions, and the confinement configuration is simple. A 3-dimensional model of a novel linear magnetic nozzle geometry was developed with the propellant, dissociated ammonia, oriented sideways in the nozzle, assuming prior …

Enhanced Pulsar Timing Precision For The Era Of Nanohertz Gravitational Wave Astronomy, Sofia V. Sosa Fiscella

Theses

In 2023, several international collaborations comprising the International Pulsar Timing Array achieved a major scientific milestone with the first detection of a signal in pulsar timing observations consistent with the signature expected from a stochastic gravitational wave background, created by an ensemble of unresolved supermassive black hole binaries in the early Universe. The next breakthrough in the field is expected to be the first detection of a continuous wave from a single such binary, which would allow us to better understand their evolution and that of our Universe. However, this feat will require unprecedented precision in our timing measurements. To …

Hexahive: Simulating Vulnerability Bounty Economies Through Game-Theoretic Models, Lucille Blain

Theses

Bug bounty programs encourage security researchers to responsibly disclose software vulnerabilities by offering financial rewards, recognition, and opportunities to build long-term relationships with organizations. However, vulnerability discovery also creates competing incentives. A researcher may report a vulnerability through an official program, exploit it for passive income, trade information with other actors, delay disclosure, or seek compensation through alternative markets. These decisions are shaped by the expected value of rewards, perceived fairness of company responses, reputation benefits, competition, and the risk of penalties. Because directly observing these choices in real-world bug bounty programs presents ethical, legal, and practical challenges, controlled environments …

Transformation Of Public Earth Datasets For 3d Narrative Visualization Of Disaster Events, Connor Bleisch

Theses

Communicating large datasets in ways that are appealing to non-scientific audiences has been a recent focus of big data visualization. Interest has grown for using 3D environments with game-like structures and immersive technology like AR and VR since they are more engaging. We introduce a method, based on the structure of the datasets and domain knowledge of the natural phenomena, for transforming public National Aeronautics and Space Administration (NASA) Earth datasets into 3D narrative data visualizations, specifically of disaster events. Two visualizations were created using this method: one of the KNP Complex Wildfire in 2021 and one of Hurricane Beryl …

Application Of A Scale-Resolving Turbulence Model To Supersonic Retropropulsion Flows In Earth And Martian Atmospheres, Rebecca Thompson

Theses

The Blended Partially-Averaged Navier-Stokes (BPANS) turbulence model was used to simulate complex turbulent compressible flows seen in supersonic retropropulsion (SRP). The model was incorporated in the OpenFOAM solver sonicFoam to investigate SRP flows in Earth and Martian atmospheres. The new sonicFoam with BPANS was first tested for subsonic flows using the shear flow benchmark case of flow around a cylinder. Results show good agreement of velocity profiles with experimental values. SonicFoam was then validated for compressible flows using an experimental SRP configuration. Pressure coefficient distribution along the forebody and aft body of the geometry show good agreement with experimental results. …

The Impact Of Stereotype Threat And Grit On The Experience Of Imposter Syndrome, Lindsey C. Davis

Theses

Imposter syndrome (IP) is a psychological phenomenon in which one has an internal experience of intellectual phoniness. Past researchers have found that overall females tend to report higher levels of IP than men, with Stereotype Threat (ST) being a cited explanation. A common denominator in much of the literature is how belongingness can activate ST in females. It has been found that individuals high in IP, especially females, tend to experience poor mental health outcomes. Past research has also established a link between IP and conscientiousness in females, a personality trait related to the facet of grit. The current study …

Observations Of Internal Boundary Layers And Near-Surface Wind Estimation During The Landfalls Of Hurricanes Ida (2021), Zeta (2020), And Laura (2020), Zebulon W. Leffler

Theses

Accurately estimating near-surface winds during hurricane landfalls is crucial for enhancing situational awareness and facilitating post-storm recovery efforts. Previous studies have used coastal radar Velocity Azimuth Display (VAD) winds to estimate 10-m winds during hurricane landfalls; however, a notable weak bias was observed. This work demonstrates that the significant weak bias results from neglecting the wind structure within a shallow internal boundary layer (IBL). Results from an operational radar VAD analysis during the landfalls of Hurricanes Ida (2021) and Zeta (2020) indicate that leveraging the IBL winds is essential for accurate 10-m wind estimates during hurricane landfalls. Additionally, a theoretical …

Intercomparison Of Remote Sensing Methods For Calculating Cn² Profiles, Isaiah Joseph Montgomery

Theses

Propagation of electromagnetic waves through the atmosphere is affected by turbulence. The impact of turbulence on wave propagation is characterized by the refractive index structure function (Cn²), which characterizes the intensity of fluctuations of the refractive index of air due to turbulence. Observations of vertical profiles of Cn² are essential for evaluating numerical model predictions. This study presents an intercomparison of Cn² derived from multiple ground-based remote sensing systems, including 915 MHz profiler, sodar, and a parameterized method that uses doppler wind lidar and radiometer. Results indicate that the sodar is the most accurate near- surface Cn² profiler, but above …

Predicting Student Academic Performance Using Behavioural And Parental Engagement Data From Learning Management Systems, Saeed Alfalasi

Theses

This paper explores how behavioral, academic, and parental engagement data provided within the xAPI-Edu-Data dataset can be used to predict the academic performance of students when training on machine learning models with supervised learning. Due to the developing demands of the data-driven initial selection of the learners under risk, the study will create a valid and explainable predictive model that can consider the most significant factors of student success in Learning Management System (LMS). The research is based on behavioral engagement and self-regulation learning theories; the observations included in the analysis of student interaction, i.e. resource usage, classroom engagement and …

Evaluating Stormwater & Carbon Benefits Of Urban Street Trees, Anthony J. Rodriguez Diaz

Theses

Urban areas experience a multitude of environmental challenges, including flooding and air pollution. In response to these pressures, urban forests have emerged as an essential nature-based strategy to enhance resilience and reduce climate-related risks. The implementation and expansion of urban street trees further contribute to this effort by mitigating stormwater runoff and reducing atmospheric carbon emissions.

Fortunately, advances in environmental modeling have made it increasingly possible to quantify these ecosystem services using specialized software. The United States Department of Agriculture (USDA) Forest Service's i-Tree suite exemplifies this progress. The software provides a comprehensive set of tools designed to quantify environmental …

A Novel Rheological Technique To Measure The Yield Stress In Equibiaxial Elongation, Asher Segal

Theses

Yield stress measurement in non-Newtonian fluids, particularly under complex flow conditions such as equibiaxial elongation, remains a significant challenge in experimental rheology. This work presents the development of a novel method, Continuous Lubricated Squeezing Flow (CLSF), designed to quantify the normal yield stress of viscoelastic materials while minimizing boundary artifacts.

The CLSF method was implemented and validated using Carbopol 940, a model yield-stress gel, at concentrations of 1 wt% and 2 wt%. Comparative shear rheology was first performed to establish baseline yield stresses and verify sample integrity. CLSF experiments were then conducted over controlled flow rate increments and gap distances …

Content Aware Intra And Inter Prediction Using Texture And Motion Analysis, Yashaswi Karthik Reddy Danda

Theses

Modern video codecs rely on highly optimized block based intra and inter prediction frameworks,  in which block partitioning is determined through exhaustive rate distortion optimization (RDO)  searches. These exhaustive searches evaluate many possible block configurations to identify the  partitioning that minimizes overall rate distortion cost, and they have proven extremely effective  across diverse visual content. This thesis explores a complementary perspective by investigating  whether block partitioning can be guided more directly by content characteristics specifically,  texture complexity for intra prediction and motion complexity or motion density for inter  prediction.  For intra prediction, we introduce a texture adaptive approach that utilizes …

Planning For Power: The Role Of Architecture & Urban Form In Local Energy Generation In Dense Built-Up Areas, Chris Shing Wai Leung

Theses

This thesis explores how the urban form/building design, and a well-thought-out City Master Plan can significantly affect wind patterns and enhance on-site energy generation in dense urban areas. It will examine strategies such as modifying building heights, adjusting separations between structures, shaping urban landforms, and varying the roughness of the urban fabric to optimize wind energy generation. Additionally, the research will focus on the strategic placement of community power harvesting devices. The study of the urban forms and fabric with the impact of wind patterns is simulated by fluid mechanics with Computational Fluid Dynamics (CFD) software. The goal of this …

A Decision Support System For Global Vaccine Funding: Data-Driven Proposal Scoring, Epidemiological Risk Modeling, And Portfolio Optimization, Ashutosh Kumar

Theses

Global health financing must continually stretch limited immunization resources across competing priorities, and Gavi, the Vaccine Alliance, plays a central role in this landscape. This work reframes Gavi’s funding decisions as a multi-objective portfolio optimization problem that jointly considers health impact, equity, cost-effectiveness, sustainability, and epidemiological interdependence. To operationalize this approach, this study develops a decision support system grounded in Modern Portfolio Theory, treating each proposal as an asset characterized by expected return and risk. Proposal returns are estimated through a heterogeneous data-fusion pipeline that generates quantitative, multi-objective scores aligned with Gavi’s strategic priorities, while epidemiological risk is quantified using …

Asymptote, Aybuke Yilmazer

Theses

Asymptote is a short film that is an expression of mankind’s emotional consumption through confusing love with sex. This film is a frame-by-frame hand-drawn animation that combines digital and analog techniques. It was produced between August 2024 and April 2025, and its music was re-produced in August 2025. This paper is a written declaration of the motives and intentions of the director, Aybuke Yilmazer, in making this film, and analyzes the filmmaking process. The text of the thesis is in third person except for the self-evaluation section.

Microgrid Approach With Data-Driven Monitoring To Enhance The Resilience Of Water Distribution Systems, Binod Ale Magar

Theses

Existing centralized water supply systems are critically threatened by the joint effects of climate and socioeconomic changes, extreme weather events, and physical degradation due to aging, and their changeability. The uncertain and changing drivers pose challenges for water supply systems in providing sustainable water services during disruptions. The energy field dealing related issues has endorsed the microgrid approach with a decentralized energy supply with dispersed local energy sources. The application of energy microgrids has demonstrated their resiliency for sustainable energy supply. For improved resiliency of a water distribution system, this study investigated the application of a microgrid approach and the …

Labor Transportation Simulation And Optimization - A Case Study, Tala Taha

Theses

Transportation systems play a critical role in supporting economic activity, workforce mobility, and service delivery in urban and industrial environments. In many organizations, fixed-time transportation systems are essential for ensuring that employees are transferred from accommodation facilities to work locations within strict operational time windows. The case study company in this research operates a large-scale labour transport service between multiple accommodation camps and client sites in Dubai. Historically, buses were dispatched in an ad hoc manner, relying on supervisors’ experience rather than a systematic planning approach. This often led to low seat utilisation, unnecessarily long routes, higher fuel costs, and …

Predicting Inmate Overcrowding To Improve Facility Management, Mayed Ali Alameri

Theses

Overcrowding is a major challenge to correction systems because the conventional forecasting techniques are inaccurate and inadequate in most cases. This paper will solve this by constructing and testing a machine learning-based model to predict facility-level overcrowding. With the use of the XGBoost Regressor model on a dataset comprising of U.S. correctional facilities, the research identified key structural drivers but showed that the static facility attributes alone have limited predictive power (r-square approx 0.18) The discussion shows that overcrowding is non-linear, and a complicated problem not confined to the facility characteristics but to the larger, non-measurable regional influences, of which …

Optimizing Waste Management And Recycling Patterns Using Data Analytics, Mohammad Omar Almarri

Theses

The increasing pace of urbanisation and consumption has increased the burden of waste generation in the world and it is a huge burden on the current waste management systems. The United Arab Emirates (UAE) is a region that is intensifying this challenge through the accelerated urbanization, high sustainability targets, and the necessity of effective recycling policies. The thesis that is being examined explores the ways in which the data analytics can be utilized to streamline waste management and recycling trends, emphasizing the enhancement of the collection process, forecasting the trends in the waste generation as well as facilitating the use …

Comprehensive Drought Analysis: Machine Learning-Based Meterological Drought Forecasting And Pca-Driven Agricultural Drought Monitoring., Bishal Poudel

Theses

Drought is a long-term natural disaster that affects many aspects of human life from health, water supply to ecosystems and agriculture. Drought’s occurrence and intensity has increased in recent years because of global warming, which urges for proper and accurate monitoring and forecasting of drought. For better understanding of droughts, this study uses two approaches. In first part, the study uses the historical temperature and precipitation data from period of 1960 to 2021 as input features for three different machine learning models – Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). The research focuses on calculating …

Prediction Of Geometric Deviation In Additively Manufactured Tpms Lattice Structures Using Non-Destructive Evaluation And Conditional Generative Adversarial Networks, Prateek Neupane

Theses

Metal 3D printing has emerged as a widely used additive manufacturing method, finding applications across numerous industries and research areas. However, its application is often hindered by geometric inaccuracies and surface quality issues resulting from the complex interplay of laser power, melt pool behavior, and cooling characteristics. These inconsistencies have a significant impact on the design and functionality of printed parts, affecting structural integrity and dimensional accuracy. Thus, an effective data-driven approach is crucial to predict these deviations before manufacturing. This study proposes a methodology that incorporates Non-destructive evaluation techniques, like in-situ monitoring and X-ray CT scan, with Conditional Generative …

Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri

Theses

Trust plays a decisive role in the effectiveness of human-AI teams, particularly in tasks that depend on coordinated decision-making under uncertainty. While prior research acknowledges that trust in automation is dynamic, current work provides limited insight into how trust evolves within an interaction, what causes it to become miscalibrated, and how transparency affects these processes. This thesis examines trust calibration in a controlled 2-D grid-world search-and-rescue environment, where 54 participants collaborated with an AI teammate presented through four communication modes based on the Ability, Benevolence, and Integrity (ABI) framework. The study uses secondary analysis of experimental data to observe: (1) …

Practice?, Pei Chen

Theses

3D animation is a combination of art and technique. Art serves to convey your perspective, express beauty, tell stories, and more. However, to accomplish these goals, we need solid technical support. Only by paying attention to both, can we achieve excellent results. The title of this film is “Practice?”, which is a deliberate pun. On one hand, "practice" refers to the martial arts training sequences of the character, during which his master provides guidance, intervenes in his movements, and progressively increases the difficulty. On the other hand, "practice" also symbolizes my own artistic and technical experimentation—exploring and refining skills in …

Play Of Curiosity: A Table-Top Game That Cultivates A Creative Attitude By Encouraging Curiosity In Young Adults, Kajal Shah

Theses

In a time that increasingly demands instant creativity, adaptability, and innovative thinking on a constant basis, nurturing a curious attitude among young adults must become a critical focus. Creativity and curiosity can be encouraged throughout various stages of life, and for myriads of personality types, the focus demographic of this paper is young adults, ages 18 to 26 years old. Young adulthood is when one starts to recognize post-formal thoughts. This stage of cognitive development helps to approach concepts beyond binary thinking, developing the unique abilities of ambiguous thinking and relativism. The enigmatic, ever-changing, and easygoing mind will often try …

Macroscale Thermal Modelling For The Molten Metal Droplet Jetting Additive Manufacturing Process, Khushbu Zope

Theses

Molten Metal Jetting (MMJ) has become a promising pathway for next-generation metal additive manufacturing because it avoids the need for powders or high-power energy sources and enables precise, drop-on-demand deposition of structural alloys. Despite this promise, the field lacks a predictive thermal modeling framework that can describe the full temperature history of a part as thousands of droplets accumulate. Without such a model, it is difficult to control heat buildup, to understand how bonding conditions evolve across layers, and to avoid defects such as incomplete fusion, porosity, and geometrical distortion. This dissertation presents the first validated macroscale thermal modeling framework …

Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi

Theses

The paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working …

Predicting Cryptocurrency Prices Using Stochastic Modeling, Reem Hani Al Omari

Theses

Cryptocurrencies are digital currencies that operate independently of central banks and governments. They were designed to overcome the limitations of traditional financial systems through a decentralized, peer-to-peer electronic cash mechanism. Trading in cryptocurrencies offers several advantages, including decentralized and efficient transactions, reduced costs through the elimination of intermediaries, investment opportunities across exchanges, and seamless cross-border remittances. Modeling cryptocurrency prices is therefore essential, not only due to these advantages but also because of the substantial market capitalization of cryptocurrencies, estimated to exceed 900 billion dollars according to CoinMarketCap [6]. The main objective of this thesis is to propose a predictive framework …

Modeling The Effects Of Shed Target Receptors On The Efficacy Of Cancer Immunotherapy Agents, Bridget M. Torsey

Theses

Cancer cells often shed receptors targeted by immunotherapies. Shed receptors can reduce drug efficacy by binding to free drug, preventing its binding to membrane-bound receptors. The goal of this dissertation is to investigate the effects of shed targets on the efficacy of cancer immunotherapies. First, we study liquid tumors by extending a PK/PD model to include receptor shedding, drug-induced enhancement of shedding, drug binding to shed receptors, and drug-induced tumor lysis. We use our model to elucidate the effect of shed target receptors on the efficacy of immunotherapies through uncertainty and sensitivity analyses. Our findings support the claim that the …

Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi

Theses

The rapid evolution of AI heightens the need for learning systems that are efficient, robust, and explainable. This dissertation advances these three pillars through innovations in classification, generative modeling, domain adaptation under data-scarce conditions, and multimodal retrieval. Collectively, the methods reduce dependence on large, labeled datasets, improve adaptability under distribution shifts, enable deployment on resource-constrained platforms, and enhance interpretability. For classification, the Iterative Maximum Likelihood Classifier (IMLC) recasts regularized maximum likelihood training as a fixed-point contraction with convergence guarantees, enabling faster and more stable optimization. Results on synthetic data and MNIST validate its efficiency. For generative models, we introduce Parametric …

Physics Meets Data: Merging Physics-Based Methods With Deep Learning To Model Complex Systems, Maryam Toloubidokhti

Theses

Accurate modeling of complex systems is crucial in domains such as healthcare, where personalized diagnosis and treatment planning are essential. Traditional physics-based models provide structured, theoretically grounded insights but are often computationally intensive and constrained by simplified assumptions that limit adaptability to patient-specific conditions. In contrast, data-driven models are computationally efficient and capable of capturing complex patterns, yet they often lack interpretability and fail to incorporate essential physical principles, reducing robustness and generalization. This disconnect between mechanistic understanding and computational practicality presents significant challenges in critical applications such as healthcare, where both physical accuracy and real-time performance are vital. To …