Digital Commons Network^™

Oh, But If I Had The Stars From The Darkest Night…, Kendra Jean Keefer

Theses

This paper explores how memory and loss interact with a yearning for home in the installation, Oh, but if I Had the Stars from the Darkest Night. Through sound, moving image, photography, mark-making, and poetry this thesis work explores universal themes of exile and migration, alongside the impact the current high stakes political climate had on one LGBTQIA+ teen and their mother, as they leave a home and extended family in North Texas to find sanctuary in Carbondale, Illinois. The natural world of Southern Illinois, with a focus on the lives of local white-tailed deer, becomes a mirror and a …

Making Social Interactions Accessible For The Blind And Low-Vision People By Detecting Emotions Through Vocal Tone, Facial Expressions, And Body Language, Gautham Krishna

Theses

Despite the thriving research in the field of accessibility, there are several niche area where there is a need for specific solutions. One such area is social interaction. Blind and Low-Vision people (BLV) are limited in identifying emotions through verbal communication alone, which is often biased and unreliable in certain environments. Prior research illustrates the importance of body language in expressions and articulates the mechanism behind human emotions. Drawing inspiratoin from such research and various studies that were focused on emotion recognition systems, our research focused on identifying a portable accessible solution for BLV that classified emotion based on multi-modal …

Design Of A Custom Embedded Replay Control Board For A Vintage Porsche, Christopher Heltz

Theses

Many industries rely on decades old software and electronics to run machines that are essential for America’s industry. These companies operate on the sentiment of “if it’s broke, don’t fix it”. This works great until something breaks, then these same companies are faced with a tough decision. Either replace the old software and electronics with modern solutions or fix the original electronics. Both options have their challenges, the first costs much more in the short term and will require many man hours to design a new solution however the latter will require finding the original documentation or having trained individuals …

Scalable Access-Pattern Aware I/O Acceleration And Multi-Tiered Data Management For Hpc And Ai Workloads, Avinash Maurya

Theses

The exponential growth of data-intensive scientific simulations and deep learning workloads presents significant challenges for high-performance computing~(HPC) systems. These workloads generate massive data volumes at unprecedented velocities, straining the capabilities of existing memory hierarchies, I/O subsystems, and scheduling mechanisms. This dissertation addresses critical challenges in data management and workload scheduling to enhance performance, scalability, and resource efficiency in next-generation HPC and Artificial Intelligence~(AI) applications. Firstly, we develop advanced data and memory management strategies to alleviate I/O bottlenecks in deep memory hierarchies of modern HPC environments. We significantly mitigate data movement overhead by exploiting application-specific data access patterns and optimizing data …

Impact Of Coolant Temperature On Boiling Chamber Performance For Cooling Electronic Chips, Nooruldeen Mustafa

Theses

Data centers in their quest for ever-increasing processing power are generating more heat than ever before. This heat, if not effectively managed, can lead to equipment failure, reduced performance, and significant financial losses. The development of efficient cooling systems is therefore a pressing need. While numerous two-phase techniques have been developed to meet this escalating heat dissipation demand, their compatibility with free-to-air cooling, especially at higher temperatures, remains a challenge. To address this issue, we propose the use of boiling chambers employing subcooled boiling and submerged condensation mechanisms, an innovative solution that promises effective heat dissipation. This study delves into …

Heat Transfer During Air-Water Two Phase Flow, Saumitra K. Deshpande

Theses

The effects of air injection on the heat transfer coefficient for the liquid flow in two-phase in microchannels are studied. Different manifolds and microchannel configurations are used for studying the effects of air injection. The manifold has a uniform gap of 400μm between the heater chip and the cover. A manifold is designed and manufactured to provide different air water flow patterns for different combinations of liquid and air flow rates. The effect of these flow rates on the heat transfer coefficient are studied. The flow rate for air injected is kept very small with respect to the flow rate …

Smart Credit Card Fraud Detection Using Machine Learning, Aisha Bin Sulaiman, Rowdha Masood Falaknaz

Theses

This thesis addresses the increasing issue of fraud resulting from technological advancements affecting both customers and fraudsters, while law enforcement agencies face challenges of inadequate case prioritization; data overload and slow investigative analysis. The central objective of this thesis revolves around strategies to counter these challenges, emphasizing the urgency of rapid response to major alert cases that can lead to widespread impacts. The authors' experience in financial fraud detection and credit card crime investigation within the police department has inspired this research, guiding the approach toward practical solutions for everyday operational challenges. The significance of this thesis lies in its …

Development And Optimization Of Electric-Double-Layer Gated Transistors Based On Low-Dimensional Materials: Gate-All-Around Inas Nanowire Arrays And Graphene Channels, John Wyatt Morrell

Theses

Electric-double-layer (EDL) gating is a promising alternative to conventional gating methods for field-effect transistors (FETs), with potential applications spanning current logic, future logic, and neuromorphic computing architectures. This technique employs mobile ions in an electrically insulating but ionically conductive medium to generate strong local electric fields at the gate-to-channel interface, achieving field magnitudes on the order of 1 V/nm. These intense local fields enable significant charge carrier accumulation in the semiconducting channel, positioning EDL gating as a strong candidate for device applications. In this study, we investigate the use of low-dimensional channel materials, including two-dimensional graphene and one-dimensional InAs nanowires, …

Modeling Impact Of Simulated Myocardial Perfusion On Cardiac Ablation For Arrhythmia, Brendan Cappon

Theses

Bioheat transfer is the study of heat transfer applied in anatomy and physiology, which is relevant to maintaining homeostatic conditions from metabolic activity, thermal exposures, and various treatments and diagnostic methods. Blood flow significantly impacts heat transfer throughout the body, facilitating the diffusion of heat. Models have been developed to quantify bioheat transfer and the effect of blood flow through tissue for many biological functions and medical procedures, one of which is radiofrequency ablation for cardiac arrhythmia. Significant improvements to models and assumptions are still required as success rates for this procedure remain low for various arrhythmia types. The research …

Artificial Neurovascular Networks To Simulate Energy Distribution To Spiking Neural Networks, William Bottom

Theses

Biological neurons are known to be much more energy efficient than modern computers, with the human brain only consuming about 20 Watts. An Artificial Neural Network can take hours or days to train on high-performance and energy-consuming GPUs, so networks that can achieve similar performance at a fraction of the energy cost are very sought after. Spiking Neural Networks, which mimic biological neurons more closely than Artificial Neural Networks (ANNs), are a potential option for the energy problem in Neural Networks. By converting an ANN to an SNN and applying energy-saving methods/architectures, the equivalent SNN network can achieve similar performance …

Enhancing Medical Cybersecurity: A Machine Learning Solution For Intrusion Detection In Iomt Devices, Afra Yaqoob

Theses

In the past few decades, the healthcare sector has undergone major technological advancements, which have included the adoption of Internet of Medical Things (IoMT), and networked medical devices. These advancements have changed the manner in which patient care is provisioned, enabling for more accurate diagnosis, personalized treatment plans, among other benefits. However, as healthcare providers are growing more reliant on interconnected systems, they have also been exposed to critical cybersecurity vulnerabilities. Medical devices have now become targets for cyberattacks, threatening patient data and its security and endangering patient's health and safety. This thesis examines the affects that inadequate cybersecurity measures, …

Leveraging Ai To Improve Service Quality For Better Passenger Satisfaction At Airport, Abdulrhman Alsaleh

Theses

The aim of this research is to explore the impact of airport service quality on passenger satisfaction level in Dubai International Airport (DXB) and the study employed machine learning approach to establish the services quality dimensions relationship with passenger satisfaction. However, the initial objective is to analyze patterns and make future forecasts that DXB could use to enhance service delivery. The research focuses on key service quality dimensions: The study investigates the causal relationship between five constructs: reliability, service quality tangibility, assurance, responsiveness, and empathy using machine learning models such as regression and classification. The information data applies to the …

An Automated Approach To Detect Cyber Threats With Smart Contracts In Blockchain Systems., Alia Mohammad Ahli

Theses

Blockchain is becoming one of the main components of the modern world due to its rapid development. Considering its main characteristics as a collaborative distributed database that provides transparency and scalability, blockchain development and application have grown dramatically over the past decade. While the blockchain is a key feature and is used in the industry, it is subject to many risk factors and attacks. Advanced technologies that can successfully detect and mitigate vulnerabilities must be developed for solving these security challenges. This thesis presents the development of a machine learning based approach to discover vulnerabilities in blockchain-based systems. It begins …

Using Machine Learning To Predict Energy Consumption In Commercial Buildings In The Uae, Saif Nasser Aljasmi

Theses

The ever-growing demand for energy necessitates a shift towards efficient management practices. Buildings, responsible for a significant portion of global energy consumption, present a prime opportunity for optimization. Building Energy Management Systems (BEMS) have emerged as a crucial tool in this endeavor, offering a comprehensive platform for monitoring and integrating various building systems. However, a key functionality within BEMS, energy consumption prediction, has faced limitations in accuracy. This stagnation hinders the full potential of BEMS for achieving optimal energy management strategies. Traditional methods for energy prediction often rely on historical data and lack the sophistication to capture the complex and …

Analyzing The Effects Of Economic Factors On Residential Real Estate Market Dynamics Of Dubai: Using Machine Learning Methods, Zarrina Shokirova

Theses

Dubai’s residential real estate market is known for its rapid growth and dynamic nature, making it an intriguing subject for analyzing the impact of various economic factors. This thesis examines how key economic elements such as GDP growth, inflation rates, and foreign investment influence property dynamics in Dubai. By synthesizing current research, this study aims to understand how these economic conditions affect real estate values, demand, and supply. Additionally, modern analytical methods, including decision trees, logistic regression, and random forest models, are employed to predict market trends and uncover significant patterns. This research contributes to both academic understanding and practical …

Temporal Analysis And Prediction Of Air Quality Based On Environmental Data, Mizbah Syed

Theses

Air pollution is one of the most pressing environmental challenges of the modern era, significantly impacting public health, ecosystems, and economic stability. Pollutants such as PM2.5, PM10, and NO₂—largely driven by urbanization, industrialization, and vehicular emissions—have exacerbated global health issues, including respiratory illnesses, cardiovascular diseases, and premature mortality. According to the World Health Organization, air pollution contributes to over 7 million deaths annually, with economic losses amounting to billions of dollars. Beyond its health and financial toll, air pollution worsens climate change, underscoring the urgent need for accurate air quality index (AQI) prediction to enable timely interventions and evidence-based policymaking. …

Investigating The Effect Of Active Living Wall On Indoor Air Quality And Energy Consumption: Case Study In Uaeu Closed Offices, Atina Mohsen Ghunaim

Theses

Extreme hot climates can significantly affect Indoor Air Quality (IAQ) and ventilation, resulting in higher energy demands. Numerous studies have demonstrated that outdoor greening, such as trees and landscaped areas, can reduce a building’s energy consumption. However, less research has been conducted on the impact of indoor vegetation on energy use and air quality. Indoor vegetation decreases Carbon Dioxide (CO₂) concentration by absorbing CO₂ and releasing oxygen (O₂), while the evapotranspiration process further enhances (IAQ) and reduces energy expenditure. The United Arab Emirates has dry arid climate and summers with high humidity levels, causing individuals to stay longer indoors, which …

Induction Program Practices On New Teachers In Public School In The United Arab Emirates, Reem Mohammed Al Hajri

Theses

This study examines the induction program practices for new teachers in United Arab Emirates (UAE) public schools. It evaluates key components—including orientation, mentoring, professional development, teacher networking, and administrative support— and analyzes their impact on new teachers’ integration, professional growth, and overall satisfaction. Additionally, it investigates how demographic factors such as nationality, educational qualifications, and teaching experience influence teachers’ experiences and perceptions. By employing a cross-sectional survey design, quantitative data were collected from 150 public school teachers across various emirates. Findings revealed that most new teachers held positive perceptions of the induction program domains. Gender comparisons indicated minimal differences, except …

Barriers To The Adoption Of Cross Laminated Timber For Mid-Rise Residential Buildings In Uae, Sabika Nasrim Pilathottathil

Theses

Buildings consume about 40% of the total annual energy produced globally, with significant energy usage across different life cycle stages, including construction and operational phases. The energy associated with the construction of buildings is substantial due to the energy required for the manufacture of materials and products. This underscores the need for stakeholders in the building industry to find construction materials and systems with low embodied energy. In the pursuit of low-energy materials, massive timber products, such as cross-laminated timber (CLT), are gaining popularity in mid-rise buildings around the world. The development of CLT, which boasts excellent load-carrying capabilities, has …

The Intersection Of Interactive Narrative And Flow State Immersion: An Exploration Of Trauma, Resilience, And Post-Traumatic Growth, Tiffani Barner

Theses

This work investigates the benefits of interactive narratives in conjunction with flow state immersion for individuals with Post-Traumatic Stress Disorder. A specific focus is placed on narratives that contain dark or horror-based themes. In addition, the importance of selfauthorship, representation, and empowerment are explored. Accompanying the examination of flow state, concepts such as challenge, failure, and the motivation to succeed at challenging tasks are analyzed. This work also includes a survey pertaining to user preferences regarding media and genre preferences. The goal of this project is to design an interactive user experience that activates flow state while also providing an …

Jacqueline Marval: Référence Sans Révérence Art History Lecture And Activity Series, Michelle Erickson

Theses

The painting Les Odalisques (1902-03) anchors the investigation of French artist Jacqueline Marval’s presence and contribution to the modernist art narrative. The painting is composed of five women who share a face and features, set in a private female space. The canvas is complete with bold strokes of color, and their skin is painted in the unrealistic tones that Fauvists will embrace, yet naturally – these women are still fully and complexly human. From a distance, they are sculptural.

This project is intended as a Club de Lecture series for adult learners of French culture and language at the Alliance …

Contemplating Glaciers - A Dialogue, Jessica Elizabeth Turner

Theses

This project investigates the evolving relationship between art, climate education, and audience engagement. It explores how the visual arts serve as a medium for fostering awareness and dialogue about climate change while inspiring climate resilience through artistic and curatorial intervention. The exhibition Contemplating Glaciers – A Dialogue showcases the work of Canadian artist Jan Kabatoff, whose practice incorporates multisensory experiences, including paintings, sculptures, photographs, VR, and soundscapes to engage viewers with the ongoing degradation of glaciers. The exhibition bridges the gap between scientific knowledge and artistic expression within a gallery space designed to encourage participation and reflection. It seeks to …

Integration Of Blockchain Technology In Industrial Applications, Sanjana Sakinala

Theses

This research explores the integration of Hyperledger Fabric., a framework of blockchain in industrial applications, particularly focusing on supply chain management. By addressing the limitations of traditional blockchains - such as high transaction latency, scalability challenges, and privacy concerns - this research demonstrates how Hyperledger fabrics overcomes them. Through simulation, we show significant enhancements in transaction speed, data privacy and scalability, making the Fabric a viable solution for industrial systems.

Investigating The Effect Of Metal Site Of The Leaf And Branch Compost Cutinase Through Molecular Dynamics Simulations, Ryan James Molitor

Theses

The petroleum-based polyethylene terephthalate (PET) is a versatile and synthetic polymer that plays a pivotal role in the economy, including packaging, textiles, 3D printers, and even medical-related applications. There is a need for plastic waste management. Biological up-cycling of PET is a promising method for a clean and sustainable future. In 2012, a group of researchers, Tournier et. al., performed experiments and molecular dynamics simulations on the Leaf-branch Compost Cutinase, LCC (PDB Code: 4EB0), which outperformed the PET hydrolysis activity compared to three other PET hydrolyzing enzymes. The LCC and PET depolymerization rate of wildtype LCC was reported to be …

Enhancing Healthcare Services Through Machine Learning-Driven Anomaly Detection, Mohammad Ayoub Nather Hussain Natheruddin

Theses

The healthcare industry’s expanding data presents opportunities for artificial intelligence (AI) and machine learning (ML). Despite advancements in medical technology, medical errors remain a significant issue. Anomaly detection, identifying data points that deviate from normal patterns, is crucial for addressing these errors and detecting healthcare fraud. This study uses the “Healthcare Providers Data for Anomaly Detection” dataset to identify fraudulent activities. Clustering and two-step clustering, two unsupervised anomaly detection methods, are employed to uncover anomalies without prior knowledge. These techniques are applied to detect unusual events, such as anomalous patient health states and time-series data deviations. The study focuses on …

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Predicting Air Passenger Traffic At Busy International Airports, Ratana Fraihat

Theses

This thesis presents the development and validation of a machine learning model using Long Short-Term Memory (LSTM) networks to forecast air traffic passenger volumes at international, high-traffic airports. By leveraging historical passenger data, the study seeks to provide accurate predictions that can enhance operational planning and resource allocation at the airport. The implementation was done in Python, using historical passenger data from San Francisco International Airport (SFO) from 2005-2018. Data preprocessing steps involved grouping and summing passenger counts for each activity period. The data was normalized to a range of 0 to 1, which is crucial for preventing instability and …

Pawlingo: Bridging Virtual And Real-World Language Learning Through Mixed Reality, Xueer Wang

Theses

PawLingo harnesses the power of mixed reality (MR) technology to enable users to learn new languages anytime, anywhere. By enhancing the interactivity and immersion of language learning, PawLingo revolutionizes how users engage with new languages. The platform features virtual non-player characters (NPCs), such as a panda representing Mandarin, who guide and interact with users in their daily environments. Whether navigating everyday scenarios like markets or public transit or practicing vocabulary in real-time, PawLingo seamlessly integrates language instruction into the user's surroundings. This research explores the development and impact of PawLingo, highlighting its ability to enhance user engagement, retention, and cultural …

Resolution Enhancement For High Numerical Aperture Extreme Ultraviolet Lithography Using Aperiodic Multilayer Masks, W Ethan Maguire

Theses

As extreme ultraviolet lithography tools with higher image numerical apertures are introduced, the range of angles at the mask multilayer are increased. This represents a challenge for maintaining through-angle mask reflection using the current periodic mask multilayer structures. In addition to reflectance amplitude, the phase of reflected light, which plays an important role in imaging, is also strongly influenced by increasing angles. The propagation of light through each bilayer in the stack imparts a phase shift based on incidence angle. This accumulates over many layers, inducing phase effects which are unique to each illumination point. While adjustments to multilayer period …

Hope Vs. Hate: Understanding User Interactions With Lgbtq+ News Content In Mainstream Us News Media Through The Lens Of Hope Speech, Jonathan Pofcher

Theses

This thesis makes three contributions. First, via a substantial corpus of 1,419,047 comments posted on 3,161 YouTube news videos of major US cable news outlets, we analyze how users engage with LGBTQ+ news content. Our analyses focus both on positive and negative content. In particular, we construct a fine-grained hope speech classifier that detects positive hope speech, negative, neutral, and irrelevant content. Second, in consultation with a public health expert specializing on LGBTQ+ health, we conduct an annotation study with a balanced and diverse political representation and release a dataset of 3,750 instances with fine-grained labels and detailed annotator demographic …