Digital Commons Network^™

A Closer Look At Your Closet: An Analysis Of Consumer Desires, Fast Fashion’S Negative Harms, And Sustainable Solutions For Change, Sarah Hallman

Honors Theses

This thesis serves as an in-depth analysis of the fast fashion industry as it relates to consumers and seeks to identify and promote changes to enhance sustainability with a focus on reusing and repurposing secondhand clothing. The fast fashion industry is unique in its business model, means of production, and harm associated with it. It thrives on the exploitation of consumer desires that silently influence what and how consumers purchase clothing. Through an analysis of consumer desires and the harms associated with the industry through a literary discussion of scholarly articles previously written on these topics, solutions for both businesses …

Look For Opportunities To Increase Continuity Of Care In Medical Education, Hannah Ward Md

Faculty Development Tips

No abstract provided.

A Randomized Study To Examine The Ability Of A Caffeine-Based Energy Drink To Impact Energy Expenditure, Fat Oxidation, And Cognitive Performance, Joesi Krieger, Alex Schrautemeier, Anthony M. Hagele, Connor Gaige, Olivia Mennemeyer, Sydney Tolbert, Joshua Iannotti, Chad Kerksick, Chris Noonan, Petey Mumford

Faculty Scholarship

Objectives: This study evaluated the effects of acute and 28-day supplementation with a caffeine-based energy drink on energy expenditure, fat oxidation, and cognitive performance.

Methods: In a double-blind, placebo-controlled trial, 33 males and 27 females (27 ± 8 years, 26.7 ± 2.2 m/kg2) consumed a caffeinated energy drink (200 mg; CAF) or placebo (PLA) for 28 days. Indirect calorimetry assessed energy expenditure and fat oxidation at 0, 30, 60, 90, and 120 min after ingestion on day 1 and 28. Cognition assessments (Dynavision reaction, Serial Sevens, Trail Making Test A (TMT-A) and B (TMT-B)) were performed at 0, …

Differences In Total Daily Energy Expenditure Across Field Sports: A Narrative Review, Brenen Skalitzky, Jennifer Fields, Margaret T. Jones, Chad Kerksick, Andrew Jagim

Faculty Scholarship

Background: Differences in total daily energy expenditure (TDEE) across sports, sex, and skill level support the need for sport- and athlete-specific energy intake recommendations. The purpose of the current review was to examine TDEE and related markers of energy expenditure across field-based team sports. A secondary aim was to evaluate physical activity levels (PAL), calculated as TDEE divided by resting metabolic rate (RMR), and their utility in estimating energy needs within team sports.

Methods: The review was limited to studies that included the field-based team sports of rugby or soccer and reported energy expenditure data using doubly labeled water …

A Model For Training Management System Implementation In Higher Education, David J. Rowe

All-Inclusive List of Electronic Theses and Dissertations

In an era where innovation and adaptability are critical to institutional success, the practical training and development of employees in higher education organizations have become essential. This study examined stakeholders’ perceptions of cloud-based Training Management System (TMS) implementation within California Higher Education systems, including both the California Community College (CCC) and the California State University (CSU). Although a TMS offers substantial benefits for administering training programs, large systems face challenges due to uneven adoption across campuses, leading to inefficiencies, regulatory non-compliance, and hindered workforce development. This research identified and evaluated technology, organizational, and environmental critical success factors influencing TMS implementation …

Drivers Of Ecosystem Respiration In The Middle Rio Grande Floodplain Aquifer, Eva Tipps

Biology ETDs

Little is known about carbon flux and ecosystem metabolism in river floodplains, despite their potentially large and dynamic contributions to carbon storage and loss. We hypothesized that the region of intermittent saturation (ROIS), which is the subsurface interface between terrestrial and aquatic ecosystems, is a biogeochemical ‘hotspot’ for ecosystem respiration and carbon transformation. Groundwater depth and variability in the ROIS facilitate interactions between aquatic and terrestrial processes, but their role in carbon fluxes is poorly understood. We developed a novel method utilizing high-frequency sensors in groundwater wells to capture biogeochemical ‘hot moments’ of ecosystem respiration in the ROIS of the …

Synthesis Of Antibody Functionalized Polyaniline/Polystyrene/N-Gqds Composite Fibermats For Sweat Based Cortisol Biosensing Applications, Ashwin James

Honors Theses

Noninvasive, wearable biosensors capable of detecting stress biomarkers in sweat require electrodes that are flexible, conductive, insoluble in water, and highly sensitive at low concentrations of analyte. In this work, bovine serum albumin blocked and anti-cmab immobilized nitrogen doped graphene quantum dots integrated polyaniline/polystyrene composite fibermat electrodes (BSA/Anti-Cmab/N-GQDs/PANI/PS electrodes) were synthesized and evaluated as a potential platform for an electrochemical cortisol biosensor in sweat based systems. Polyaniline was utilized in order to provide electrical conductivity, while polystyrene served as a carrying polymer for mechanical support and its hydrophobicity. Composite fibermats were manufactured through Forcespinning™, followed by a secondary polyaniline graft …

Massive Stars Behaving Badly: Exceptional Interactions And Their Remnants, Kaila Nathaniel

Theses

Massive stars are some of the most dynamic and influential objects in the Universe, driving galactic evolution through violent explosions and serving as progenitors to neutron stars and black holes. While the evolution of isolated massive stars already produces remarkable phenomenae, massive stars that interact with companions or evolve within extreme environments may produce even more extraordinary outcomes. In this dissertation, I investigate some exceptional circumstances in which a massive star might find itself. In a first investigation, I analyze the strong interactions between isolated pairs of stars in low-density environments. If a binary consisting of a massive star and …

These Yellow Stars Of Ours, Vinh Hong Nguyen

Theses

These Yellow Stars of Ours is an experimental documentary that traces the intertwined histories of Thaun and Nam Nguyen as they flee war-torn Vietnam, fall in love, and build a life in Western New York. Through their accounts and those of their two sons, the film explores the complexities of generational trauma and the ongoing experience of bicultural straddling—the negotiation between inherited Vietnamese identity and the shaping influence of an American upbringing. The project examines how trauma, memory, and cultural displacement circulate across generations, shaping identity in both visible and invisible ways. Methodologically, the film is structured around the conceptual …

Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti

Theses

Urban infrastructure delivery is a complex process influenced by project type, spatial distribution, stakeholder coordination, and data quality. This thesis examines the application of Microsoft Power BI, a no-code visual analytics tool, as a means to analyze performance patterns in urban projects and inform decision-making in smart city governance. The study uses a structured secondary dataset simulating over 600 infrastructure projects across various departments and neighborhoods. By transforming raw data into an interactive dashboard, the research explores relationships between delay rates, customer satisfaction, budget compliance, and technology adoption. The analysis revealed that project categories such as metal furniture and police …

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 …

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 …

Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula

Theses

Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …

Adaptive Design Solutions For Golfers With Arm Impairment, Samuel Hao-Yu Wang

Theses

Golf is a game of precision, focus, and enjoyment for everyone, regardless of age, physical condition, or experience. However, for many people with disabilities, full participation in the game can be challenging. This project created an inclusive golfing experience for players with physical disabilities by assisting them with specific actions required in golf. The goal was to design solutions that reduce limitations caused by impairments, enabling players to enjoy the game with greater freedom. The United States Disabled Golf Association recognizes 14 sport classes of physical impairments, categorizing competitors by different types and levels of disability. This project focused on …

Predictive Modeling Of Tokenized Real Estate Prices Using Machine Learning, Mohammed Alteneiji

Theses

In this paper, we propose a machine learning-based model to predict the prices of tokenized real-estate assets, combining blockchain data, real-estate data, and sentiment data. Tokenization refers to partitioning a physical asset into fractional tokens on a blockchain, where each token represents a fraction of that asset. These include token liquidity, trading volume, platform activity, and the on-chain activity of investors. These new variables require data-based analytics that are capable of accounting for more complex relationships than customary real estate valuation approaches. To address this, we propose a multimodal dataset that incorporates on-chain data (token supply, number of wallet-holders, transaction …

Unmasking Corruption And Bribery Using Predictive Analytics, Maha Salem

Theses

The initiative (Unmasking Corruption and Bribery Using Predictive Analytics) aims to identify employees in government sectors who commit corruption and bribery by addressing issues such as the lack of transparency, fairness, equality, and public trust, as well as weakened loyalty and poor reputation. This project focuses on data analytics and predictive modeling to detect suspicious or high-risk employee behaviors related to corruption and bribery. The main objective is to reduce these unethical activities in government sectors, strengthen integrity, and promote transparency and public trust through predictive capabilities. It also seeks to raise awareness about how serious and harmful these crimes …

Comparative Analysis Of Machine Learning Models For Spam Email Detection, Rashed Almarri

Theses

The current research examines one of the most effective approaches to spam email detection based on machine learning and natural language processing (NLP). The study is placed in the context of the rising cyber threats and the influx of emails, where the spam/ham data is to be classified correctfully using the combination of the Logistic Regression, NLP (including tokenization, lemmatization, and TF-IDF vectorization). The questions of the research were devoted to the efficiency of such an approach and the interpretation of its results. The data used are obtained by a publicly available Kaggle data set that contains 5,572 labeled email …

Data-Driven Crime Prediction: Toward Smarter Reduction Strategies, Zayed Almarri

Theses

This study explores how machine learning and weather data can be used for the prediction of crime more accurately in the city of Seattle. Predictive policing is a method in law enforcement that uses data and computer algorithms to forecast the locations that crimes are likely to happen. Although many studies focused on using past crime data alone, this research also includes weather conditions like temperature, rainfall, and humidity, which may influence when and where crimes occur. Several machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), were used to classify areas of the city into high-risk …

The Role Of Technology In Modernizing Prison Management Systems, Essa Anwahi

Theses

This thesis examines the role of technology in modernizing prison management systems and improving operational efficiency, security, and decision-making. It explores how digital technologies and data analytics can address challenges in traditional prison management, such as inefficient processes and limited data utilization. The study highlights the potential of technology-driven solutions to enhance system performance and support more effective management practices within correctional facilities.

Investigating The Effect Of Build Direction On Mechanical Properties In Pellet-Based 3d Printing, Yahya Mohammed

Theses

This study investigates the influence of build orientation on the mechanical performance of Polylactic Acid (PLA) components fabricated using Fused Granular Fabrication (FGF), a pellet-based additive manufacturing method, and benchmarks these results against Fused Deposition Modeling (FDM). Although orientation-dependent mechanical behavior has been widely studied in filament-based extrusion, there remains a critical gap in understanding how these effects appeared in pellet-based fabrication. To address this gap, PLA tensile and compression specimens were fabricated using both FGF and FDM under identical geometries, processing parameters, and preparation procedures, ensuring that any differences in performance arise solely from the manufacturing method and build …

Achilles, Sreelekha Samala

Theses

ACHILLES is a speculative design project and conceptual artifact. It is not intended to function as a medically certified or biomechanically operational prosthetic arm. Instead, it operates within the realm of design research, using visual realism and luxury aesthetics to explore how assistive technologies could be perceived differently in cultural, emotional, and commercial contexts. Using 3D software to experiment with color, pattern, and texture, I designed a realistic mockup to demonstrate how Achilles could appear in a high-end commercial setting. Instead of animating the model, I focused on camera motion to highlight the prosthetic from multiple angles, emphasizing its sculptural …

Early Electrical Fault Detection In Power Systems Using Data Analytics, Hessa Alraeesi

Theses

The increasing penetration of renewable energy, especially solar generation, has introduced higher variability into power system loading, making early detection of electrical faults more challenging yet more essential for maintaining network reliability. This research presents a machine-learning–based framework for early electrical fault detection using a Random Forest model developed in DataRobot. Due to confidentiality constraints on DEWA operational data, a Kaggle dataset was adopted and enriched with simulated solar irradiance variability to mirror real network conditions in the UAE. After comprehensive data preprocessing and feature engineering, the Random Forest model demonstrated strong generalization performance, accurately distinguishing early fault signatures from …

Mercurial, Bhavana Neti

Theses

Mercurial is a groundbreaking visual journey through typography that charts the fluid and often volatile passage, from the depths of sadness, eventually finding the light in the end, inspired by the song title Louder than Bombs by BTS, a Korean boyband. Over a carefully observed period, this book captures the subtle and dramatic transformations of emotion through a unique lens. "Mercurial" represents a unique and multi-layered approach to understanding and visualizing emotions. Initially, the exploration of shapes as a representational tool proved inadequate for capturing this complexity. This led to a pivotal shift towards typography, where the inherent qualities of …

Optimizing Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim

Theses

Proper and objective selection of high potential employees to promote them is a major dilemma in the Human Resources (HR) department, more so in sensitive and hierarchal environments in the public sector where subjectivity is likely to take place. This paper is based on this ubiquitous issue, and it seeks to develop, experiment, and examine a clear and equitable machine learning model that can forecast the possibility of an employee to get a promotion according to organized past HR records. The technique was solid preprocessing, alleviation of extreme class imbalances on the basis of the Synthetic Minority Over-sampling Technique (SMOTE), …

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

Security Vulnerabilities In Cloud Storage: A Comparative Study Of Google Drive, Dropbox, And Onedrive, Sultan Majid Alshamsi

Theses

In recent years, use of internet has increased manifold. People have started creating more data, and instead of saving it locally, people have started preferring storing it “online”. Companies are also following the same path to maintain accessibility and availability of their data. Thus, data has become a central part of our lives. People, companies, and institutions rely heavily on cloud storage solutions these days for managing their data. Some of the main cloud storage solution are Google Drive, Dropbox, and OneDrive. These are sophisticated solutions developed by tech giants, and general expectation of the public is that these solutions …

Work And Play In Tandem: A Brief Study Of Familiar Silhouettes In The Contemporary Workplace, Tanisa Bose Samaddar

Theses

This thesis addresses the limitations posed by conventional office furniture, and its impact on human psychological and physiological well-being. Drawing upon literature that delves into the topics of neuroscience of play, environmental psychology, and emotional sustainability, the thesis seeks to solve these issues through design modifications. It aims to understand how brief, movement-focused breaks can help rejuvenate executive functioning, uplift mood, and encourage bursts of social interaction. A five-stage prototyping process, thumbnail sketches, clay models, 3D modeling, full-scale drawings, and iterative Fusion360 refinements, later Tandem was born. By requiring at least two users to operate its see-saw mode, Tandem counteracts …

Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi

Theses

The paper explores whether there is a connection between operational performance, customer sentiment, and digital administrative complexity among major regional Middle Eastern airlines, such as Flag Carriers (e.g., Emirates) and Low-Cost carriers (LCCs) (e.g., Air Arabia). With the use of a highly detailed dataset of customer review and operations data, the study proves that a large service paradox is present in which, despite the high Net Promoter Scores (NPS: 48.004) of the carriers, which are positively reinforced by delivering world-class soft products, the loyalty is constantly disrupted by low-frequency but high-severe operational delays (Delay_Minutes). Since it has been analyzed that …

Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri

Theses

Bycreating an AI-driven method using deep learning and statistical analysis tools, this study seeks to fill important security holes in conventional intrusion detection systems. Current signature-based systems miss new and complex cyberattacks, which have significant financial and operational consequences for companies. The suggested approach detects unusual network activity in real-time by combining statistical analysis with long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and statistical analysis. This study will create and test hybrid models that can identify both known and zero-day threats while reducing false positives using publicly accessible datasets like UNSW-NB15, CIC-IDS2017, and NSL-KDD. Expected results are a …

A Comparative Analysis Of Machine Learning And Deep Learning Models For Human Activity Recognition Using Wearable Sensor Data, Fares Shaban

Theses

Wearable sensors have become vital for health monitoring, evaluation of sport performance, and recognition of activities of daily life in Human Activity Recognition (HAR). With the combination of machine learning and inertial sensing, new possibilities for the collection of data pertaining to human motion in daily life have emerged. Nevertheless, problems remain to be resolved in the field of modeling in relation to tradeoffs in classification. The focus remains on imaging, signal variability, and sensor noise. This study aims to determine the usefulness of Inertial Measurement Unit (IMU) data for activity recognition and compares classical machine learning methods with a …