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On-The-Go Handbag: Sustainable & Biodegradable Handbag Design, Sydney Neff May 2025

On-The-Go Handbag: Sustainable & Biodegradable Handbag Design, Sydney Neff

Theses

Handbags have been an ever-present accessory in society for centuries, coming to symbolize not only fashion trends but status and femininity. They have evolved to hold extreme significance, being able to show a snapshot of who a woman is by taking a simple look inside. Purses have become a companion for consumers in many ways, accompanying them throughout important days and events, providing talking points, and even compliments for the user. However, these constant companions come at a cost. The fashion industry and the accessories produced within are an immense cause of environmental pollution. This provides an ever-present internal fight …

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Beyond Barcodes, Ritul Chaudhary May 2025

Beyond Barcodes, Ritul Chaudhary

Theses

Beyond Barcodes is a conceptual zine that investigates the narratives encoded within barcodes and reinterprets receipts as tools to examine the emotional layers of consumer behavior. Often dismissed as transactional byproducts, receipts in this project are reframed as carriers of memory, identity and self-worth—reflecting personal values shaped by frugality, guilt, and inherited beliefs about money. The zine unfolds through a series of illustrated vignettes, each centered around a category of spending: groceries, essentials, dining, vanity, and experiences. This arc traces a journey from survival to indulgence, ending on a bittersweet note—critiquing how consumer culture quietly shapes how we spend, what …

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End-To-End Systems Limitations In Hyperspectral Target Detection Using Parametric Modeling And Subpixel Lattice Targets For Validation, Chase Canas May 2025

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 …

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First-Order And Second-Order Adjoint Method And Stochastic Approximation For Inverse Problems, Adrian Heldt May 2025

First-Order And Second-Order Adjoint Method And Stochastic Approximation For Inverse Problems, Adrian Heldt

Theses

We present a unified framework for estimating stochastic parameters in general variational problems. This nonlinear inverse problem is formulated as a stochastic optimization problem using the output least-squares (OLS) objective, which minimizes the discrepancy between observed data and the computed solution. A key challenge in OLS-based formulations is the efficient computation of first- and second-order derivatives of the OLS functional, which depend on the corresponding derivatives of the parameter-to-solution map—often costly and difficult to evaluate, especially in stochastic settings. To address this, we develop a rigorous computational approach based on first- and second-order adjoint methods for inverse problems governed by …

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Enhancing Aptv Reliability: A Study Of Design And Operational Efficiencies At An Aluminium Manufacturing Company, Mohammad Tayeb Alawadhi May 2025

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 …

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Development Of A Miniaturized Extracorporeal Membrane Oxygenation (Ecmo) Device On A Microfluidic Platform, Nayeem Imtiaz May 2025

Development Of A Miniaturized Extracorporeal Membrane Oxygenation (Ecmo) Device On A Microfluidic Platform, Nayeem Imtiaz

Theses

This research investigates the design, performance, and optimization of blood-contacting microfluidic medical devices, with each aim contributing to a broader understanding of how engineering choices influence hemocompatibility and oxygen transfer. Collectively, the findings from Aims 1A–2B offer integrated strategies to develop safer, more effective microfluidic systems for ECMO and dialysis. Aim 1A assessed hemolysis across device geometries using computational and experimental methods. All designs showed low device-induced hemolysis (< 2 ppm), validating their hemocompatibility. However, Power-Law models frequently overpredicted hemolysis, indicating a need for refined parameters tailored to microfluidic flow conditions. Aim 1B demonstrated the effectiveness of thrombosis assay techniques in evaluating clot formation in microfluidic device geometries. Results showed that geometry and local shear patterns strongly influence thrombogenesis. While hemolysis remained minimal even in high-shear devices, thrombus formation was more geometry-sensitive—underscoring the need to account for both factors in design. Modifying surface properties and accounting for blood composition could further reduce thrombotic risk. In Aim 2A, we compared PDMS, polypropylene (PP), and nanoporous silicon nitride (NPSiN) membranes for oxygen transport. Findings showed that in ECMO devices, blood-side resistance, not membrane permeability, often dominates oxygen transfer. Thus, membrane choice must be paired with optimized blood-side flow for full performance gains. Aim 2B directly addressed these limitations by integrating staggered herringbone mixers (SHBs), which introduced secondary flows and enhanced mixing. The Single Channel Herringbone design yielded the highest oxygen transfer, confirming the synergy between flow geometry and oxygen transport performance. Altogether, Aims 1A and 1B establish a strong framework for hemocompatibility evaluation, while Aims 2A and 2B provide complementary insights into optimizing oxygen transfer. Their interconnections highlight the value of a systems-level approach to designing microfluidic blood-contacting devices.

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Silicon Photonics For Quantum Information, Evan Manfreda-Schulz May 2025

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 …

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Predicting Student Dropout Risk Using Machine Learning, Fatma Alameri May 2025

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 …

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Forecasting Load At Residential, Industrial, And Commercial Stations For Dubai Electricity And Water Authority: A Machine Learning Approach To Managing Generation During Peak And Off-Peak Times Over The Coming Years, Mohammad Anoohi May 2025

Forecasting Load At Residential, Industrial, And Commercial Stations For Dubai Electricity And Water Authority: A Machine Learning Approach To Managing Generation During Peak And Off-Peak Times Over The Coming Years, Mohammad Anoohi

Theses

In Dubai, with the rapid growth of energy demand, efficient demand side management is necessary to optimize the distribution of electricity, reduce peak loads, and improve grid stability. Traditional DSM strategies are based on historical data and reactive control mechanisms that cannot adapt to evolving consumption patterns. This research uses Machine Learning techniques to enhance DSM in residential, commercial, and industrial sectors by developing predictive models that forecast energy demand based on seasonal variations. The dataset used was from Dubai Electricity and Water Authority (DEWA), covering the consumption patterns across Summer, Winter, Transition from Winter to Summer, and Transition from …

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Capturing Community Memory: A Juxtaposition Of Oral History And Historic Building Preservation, Eliot Gavin May 2025

Capturing Community Memory: A Juxtaposition Of Oral History And Historic Building Preservation, Eliot Gavin

Theses

Preserving local history has always been a challenge for small communities. Traditional preservation methods like historic building preservation can be costly and take a lot of time for local historical societies. These historic sites are important to the culture of the local citizens and deserve to be saved. Using The Castle on the Hill in Dansville, New York, as a case study, oral histories will be captured to explore the role of oral history in preservation. The Castle on the Hill plays an important role in shaping Dansville’s identity as a town. In the past, the Castle has served as …

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Criminal Activity Monitoring System: Targeting Africans And East Asian Communities In Dubai, Hamdan Kalantar May 2025

Criminal Activity Monitoring System: Targeting Africans And East Asian Communities In Dubai, Hamdan Kalantar

Theses

This dissertation investigates crime patterns among African and East Asian communities in Dubai, focusing on geographic hotspots, temporal trends, victim demographics, and the use of machine learning for predictive crime modeling. The research addresses the challenges faced by these communities, including labor exploitation, financial fraud, and human trafficking, within the socio-economic context of a rapidly growing urban city. Building on existing studies of urban crime, this research aimed to explore the spatial, temporal, and demographic dynamics of crimes and assess the potential of predictive models to assist law enforcement in crime prevention and resource allocation. A mixed-methods approach was employed, …

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Energy Forecasting Inaccuracies And Their Direct Impact On Grid Performance, Rashed Khalid Alboom May 2025

Energy Forecasting Inaccuracies And Their Direct Impact On Grid Performance, Rashed Khalid Alboom

Theses

Accurate energy forecasting is critical for the stability, efficiency, and cost-effectiveness of modern power grids, particularly as renewable energy sources like solar and wind become more prominent. The variability of these sources presents challenges for traditional forecasting models, which struggle with non-linearity, external dependencies, and evolving grid conditions. These limitations lead to operational inefficiencies, grid instability, and increased financial risks. This study investigates the effectiveness of traditional statistical models, advanced machine learning techniques, and hybrid forecasting approaches to enhance prediction accuracy and grid performance. Using real-world datasets from Kaggle, including historical energy generation, consumption, pricing, and weather variables, this research …

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Roads Of Tomorrow: Augmented Reality For Enhanced Road Maintenance, Nasser Humaid Alshamsi May 2025

Roads Of Tomorrow: Augmented Reality For Enhanced Road Maintenance, Nasser Humaid Alshamsi

Theses

The rapid deterioration of roads creates major obstacles that affect public security, urban movement capacity, and economic durability. Standardized road maintenance operations depend heavily on human inspections that prove time-consuming while being labour-intensive and subject to human mistakes. Implementing Augmented Reality (AR) with Point Cloud Data represents a smart approach that improves delivery efficiency in road maintenance procedures for modern cities. AR enables real-time visual content and interactive displays through this research, yet Point Cloud Data delivers detailed, precise road inspections through high-definition three-dimensional mapping. These technologies are evaluated for their usability, effectiveness, and adoption barriers in research that advance …

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Determining Sustainable Urban Renewal: A Case Of Quantifying Sustainability For Adaptive Reuse Within Mumbai’S Textile Mill Sector, Arundhati Rajesh Kawlekar May 2025

Determining Sustainable Urban Renewal: A Case Of Quantifying Sustainability For Adaptive Reuse Within Mumbai’S Textile Mill Sector, Arundhati Rajesh Kawlekar

Theses

This study explores adaptive reuse as a sustainable approach to urban development in Mumbai, India, focusing on the revitalization of a heritage textile mill site. It investigates how historical significance and evolving community needs can shape design decisions, while assessing sustainability through Life Cycle Analysis (LCA) and energy efficiency metrics. Mumbai’s defunct textile mills, once central to its industrial growth, now pose challenges to urban vitality. Through a literature review, the paper examines their historical, social, and cultural relevance and the decline of the mill industry. Adaptive reuse emerges as a key strategy to reimagine these structures, offering an environmentally …

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Through Legitimate Dissent, The Supreme Court Repairs Itself, Afnaan A. Qureshi May 2025

Through Legitimate Dissent, The Supreme Court Repairs Itself, Afnaan A. Qureshi

Theses

The decisions of the Supreme Court of the United States, along with its reasoning, influence the direction and scope of historical events. Through an analysis of landmark dissents from four of the worst Supreme Court decisions, Curtis’ dissent in Dred Scott v. Sandford (1857), Harlan’s dissent in Plessy v. Ferguson (1896), Holmes’ dissent in Abrams v. United States (1919), and Murphy’s dissent in Korematsu v. United States (1944), we can take steps towards catalyzing its self repair by stepping away from digital echo chambers and engaging in legitimate dissent. The four cases marked turning points in the efforts to protect …

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A Prototype High Power Dc-Dc Converter For Regulating Generator Output Voltage In A Series Hybrid Electric Vehicle, Alex Currie May 2025

A Prototype High Power Dc-Dc Converter For Regulating Generator Output Voltage In A Series Hybrid Electric Vehicle, Alex Currie

Theses

As fully electric and hybrid electric vehicles become more commonplace, a strong incentive for innovation in electric powertrain technology has been created. Though the instantaneous power available from an electric motor may appeal to consumers of powersports vehicles, this has been an area with relatively little advancement towards electric architectures. One primary concern of electrifying such a platform is the need for large battery packs that tend not to perform well in the harsh weather conditions often seen in powersports such as snowmobiling. As such, hybrid electric architectures may prove to be a successful middle ground between fossil fuels and …

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Uvm Based Verification Of Ee621 Risc Processor Module, Collin Neidel May 2025

Uvm Based Verification Of Ee621 Risc Processor Module, Collin Neidel

Theses

In the process of digital design, the majority of efforts are focused on verification. This is because a proper verification environment can help engineers discover bugs that otherwise would have flown under the radar. On the other hand, a poorly designed verification environment could fail to fully confirm the device’s functionality and leave the customer with a buggy mess. For obvious reasons, this should be avoided at all costs, hence the emphasis on functional verification and validation efforts. Throughout time, the complexity of the average design under test (DUT) has increased dramatically, making verification a significant challenge for today’s verification …

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Glue Laminated Timber: A New Way Of Framing The Residential Architecture Of Upstate New York To Improve Thermal Performance & Energy Use Intensity, Griffin Jones May 2025

Glue Laminated Timber: A New Way Of Framing The Residential Architecture Of Upstate New York To Improve Thermal Performance & Energy Use Intensity, Griffin Jones

Theses

This thesis addresses the issue present within the current construction techniques for the residential architecture of Upstate New York. Modern platform framing has long been the dominant fashion for constructing homes in the United States, however the nature of this construction is instilled with the issue of thermal bridging, where the wood studs and structure allow heat to escape due to gaps in the continuous insulation. This study proposed a new way of framing residential architecture for the upstate region of New York, relying on glue-laminated timber (GLT) to replace dimensional sawn lumber. GLT has increased structural strength and stability …

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A Bert-Resnet Cross-Attention Fusion Network And Modality Utilization Assessment For Multimodal Sentiment Classification, Ronen G. Gold May 2025

A Bert-Resnet Cross-Attention Fusion Network And Modality Utilization Assessment For Multimodal Sentiment Classification, Ronen G. Gold

Theses

This study explores the growing field of Multimodal Sentiment Analysis (MSA), focusing on understanding how advanced fusion techniques can improve sentiment prediction in social media contexts. As platforms like X and TikTok continue to expand and facilitate sharing sentiment through digital media, there is an increasing need for neural network architectures that can accurately interpret sentiment across modalities. We implement a model using BERT for textual features and ResNet for visual features. A cross-attention fusion module aligns the modalities for joint representation. We conduct experiments on the MVSA-Single and MVSA-Multiple datasets, which contain over 5,000 and 17,000 labeled text-image pairs. …

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Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey May 2025

Enhancing Influence Estimation In Gradient Boosted Decision Trees Through Hierarchical Analysis, Anurag Choubey

Theses

This report presents a novel influence function for gradient-boosted decision trees (GBDTs), a widely-used class of predictive models. Influence estimation aims to quantify how individual training samples affect a model’s predictions, offering valuable insights for model debugging, data quality analysis, and in- terpretability. Existing influence functions for GBDTs—such as LeafInfluence, LeafInfluenceSP, and BoostIn—have shown varying degrees of success, with BoostIn currently recognized as the state-of-the-art in terms of estimation quality and computational efficiency. In this work, we propose BoostInLCA, a new influence function that extends BoostIn by incorporating information from non-leaf nodes via the Lowest Common Ancestor (LCA) path, thereby …

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Beyond The Badge: Predicting Military Employee Attrition In The Police Force, Alia Abdulla Alhajji May 2025

Beyond The Badge: Predicting Military Employee Attrition In The Police Force, Alia Abdulla Alhajji

Theses

This study explores the issue of employee attrition within the military police force, with a focus on predicting which employees are at risk of leaving. Attrition in law enforcement is costly and disruptive, especially in roles that require long training periods and operational readiness. The research specifically targets the internal organizational factors influencing turnover in military police environments and aims to leverage machine learning models to support the early identification of high-risk individuals. Guided by the CRISP-DM framework, the study followed a structured process across six stages: business understanding, data exploration, preparation, modeling, evaluation, and insight generation. A simulated dataset …

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Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza May 2025

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …

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Instrapix – An Image-Based Digital Audio Workstation Design, Dan Qiao May 2025

Instrapix – An Image-Based Digital Audio Workstation Design, Dan Qiao

Theses

InstraPix, a UI project designed by Dan Qiao, is groundbreaking image-and-audio conversion software and a digital audio workstation (DAW). enables bidirectional conversion between images and sound, opening new creative possibilities. With powerful image processing tools, it allows users to directly use pictures as audio sources, shaping unique sonic compositions. InstraPix also supports sound presets, VST plugins, and note editing, allowing seamless integration with other instruments. This all-in-one audio workstation could mark the beginning of a new era where music can be created entirely from images. Assigning images significant importance in audio production and minimizing the middle steps, it breaks the …

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Sales Opportunities Lead Qualification In B2b Market, Hajar Hussain Ahli May 2025

Sales Opportunities Lead Qualification In B2b Market, Hajar Hussain Ahli

Theses

This thesis addresses the challenge of inefficient lead qualification in the business-to-business (B2B) market by applying machine learning techniques to predict the likelihood of winning a sales opportunity. Using a real-world dataset of over 78,000 records and 17 variables, the study aims to improve how sales teams identify and prioritize high-conversion leads. A thorough data preparation process was conducted, including handling of missing values, outlier detection, and under-sampling to resolve class imbalance between won and lost opportunities. After thorough data cleaning, preprocessing, and under-sampling to address class imbalance, five machine learning models were developed: Logistic Regression, Random Forest, Neural Network, …

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Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov May 2025

Intellimad: A Framework For Secure Machine Learning Models Evaluation And Fine-Tuning In Federated Setting, Dmitrii Korobeinikov

Theses

In contemporary artificial intelligence (AI) appli- cations, Machine Learning (ML) models are core components enabling AI-driven functionalities, yet selecting and fine-tuning a model and its hyperparameters remains challenging. ML model architecture, as well as key model training parameters, such as the number of training epochs, batch size, and learning rate, are highly dependent on both the dataset modalities and the specific task resolved in a particular application. More sophisticated execution setups may require determination of additional environment-related parameters, such as identifica- tion of computational capabilities required for execution of a particular AI-driven task, or discovery and establishment of desired security-related …

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Back To The Basics: Extracellular Protein Interaction Networks Of The Human Infant Immune System, Mavis Amity Irwin May 2025

Back To The Basics: Extracellular Protein Interaction Networks Of The Human Infant Immune System, Mavis Amity Irwin

Theses

Datamining without an age filter proves challenging, especially when searching for direct data from human infants. Although online databases provide immune interaction networks, they often lack information about the age of data sources, resulting in categorizations as age-unspecified. This limitation underrepresents the physiology of naïve immune systems in infants, particularly since full-term infant immunity transitions to an adult-like phenotype between 24 and 30 months of age. This study aims to reconstruct an age-specific immune interaction network for full-term infants by integrating literature-based evidence with existing online database content. A list of 60 extracellular protein candidates involved in immune responses was …

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Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko May 2025

Developing Meld-Accelerated Molecular Dynamics Protocols To Simulate The Binding Of The P53-Derived Ligand To The Mdm-2, X Protein, Maria Ciko

Theses

In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on …

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Predicting The Probability Of Crime Related Danger In Los Angeles, Joshua Oghenetega Okpako May 2025

Predicting The Probability Of Crime Related Danger In Los Angeles, Joshua Oghenetega Okpako

Theses

This thesis examines the use of advanced machine learning to predict crime danger in Los Angeles, where 2023 violent crime rates (503 per 100,000) surpass the national average (363.8 per 100,000). Rooted in theories like social disorganization and victim vulnerability, it addresses the lack of combined victim-centric modeling by focusing on three questions: (1) Do blended ensemble models outperform individual models in predicting crime danger? (2) Can unsupervised learning techniques enhance supervised models’ accuracy through label generation or augmentation? (3) How can we interpret accurate machine learning models' decision-making processes? Using historical crime data from Los Angeles (2020–2025) and various …

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Data-Driven Prediction Of Vehicle Residual Value In The Uae Automotive Industry, Meera Al Hashmi May 2025

Data-Driven Prediction Of Vehicle Residual Value In The Uae Automotive Industry, Meera Al Hashmi

Theses

In this master’s thesis, machine learning methods are applied to predict the residual value of a vehicle, focusing on the problem of estimating the vehicle’s price after sale. The research addresses some of the most important drivers of depreciation which include market segmentation, country, region-specific details, transmission type, and some numerical values obtained through factor analysis. This is very important to the automotive industry, leasing businesses, and financial institutions. The data was collected from the open-source online marketplace Dubizzle and contains 25 variables and 62,922 cases. The initial data posed notable challenges as there were non-standardized feature values, incomplete data …

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Innovating Criminal Justice: Predictive Analytics For Effective Recidivism Management, Dina Ali Haidar May 2025

Innovating Criminal Justice: Predictive Analytics For Effective Recidivism Management, Dina Ali Haidar

Theses

Recidivism—the tendency of previously convicted individuals to reoffend—poses significant challenges to criminal justice systems worldwide. In the United States, a study by the Bureau of Justice Statistics revealed that approximately 68% of released prisoners were rearrested within three years, and 83% within nine years (Bureau of Justice Statistics, 2018). These high rates underscore systemic deficiencies in rehabilitation processes and resource allocation. Traditional risk assessment tools often rely on static factors, failing to account for the dynamic and individualized nature of reoffending risks. This limitation highlights the need for innovative, data-driven methodologies to enhance offender management strategies. This study proposes a …

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