Digital Commons Network^™

A Computational Framework For Investigating Novel Bacterial Conjugation Inhibitors, Serena Tuytschaevers

Theses

Antimicrobial resistance (AMR) is a major global threat, contributing to an estimated 700,000 deaths annually. One of the main ways that AMR genes spread among bacteria is through conjugation. Therefore, it is crucial to develop conjugation inhibitors (COINs) to combat AMR. Shared characteristics between known COIN molecules are a hydrophobic tail, unsaturations, and a polar head. Considering these characteristics and accounting for the efficacy of 2-hexadecynoic acid and Tanzawaic Acids (TZAs) low toxicity, we designed a new set of TZA analogs. The predicted protein target of previously identified COINs is TrwD, an ATPase in the type IV secretion system (T4SS). …

Protpen: A Pipeline That Combines Sequence- And Structure-Based Approaches To Predict Protein Functions, Diya Mathai

Theses

Proteins of unknown function represent a significant gap in our understanding of biological processes, with many organisms, especially prokaryotes, harboring large portions of their proteomes that remain uncharacterized. For example, in Pseudomonas aeruginosa, a major human pathogen, a large proportion of proteins are categorized as unknown or hypothetical in function. The precise number varies across species, but studies have shown that up to 30–50% of proteins in bacterial proteomes can lack functional annotations. Addressing this gap is critical to understanding the biology and pathogenicity of such organisms. Recent advancements in computational tools, particularly those in sequence and structure-based prediction, offer …

Application Of Science And Technology Studies To Global Medical Device Donation Policy, Jolie Crunelle

Theses

In low and middle income countries, the World Health Organization estimates that nearly 80% of medical devices and equipment are procured through donation (WHO 2000). While these donations have the potential to play a critical role in the recipient communities’ health, the donated devices sometimes fall short of their potential due to improper use or lack of support—despite the existence of a variety of guidelines and regulations for medical equipment donations. While conducting volunteer biomedical equipment technician work in Guatemala and Uganda, I came to notice the disparity between the guidelines and the reality on the ground. This study aims …

Integrating Standards For Infrastructure Improvement And Resilience In University Curricula, Lisa Greenwood, Jennifer Schneider, Sumita Mishra, Yewande S. Abraham, Shriram Karpoora Sundara Pandian

Articles

In today’s ever-changing world, we are increasingly vulnerable to shocks and stresses that disrupt industry and society. Persistent threats from natural and manmade hazards require our communities and infrastructure to be resilient despite possible disruption. Creating robust infrastructures is founded on operational and physical standards to withstand and respond to threats such as environmental exposures, climate change, and informational security breaches. The significant U.S. investment in infrastructure projects through the Infrastructure Investment and Jobs Act has amplified the need for professionals in engineering and related fields who – in addition to technical knowledge - possess the necessary knowledge and skill …

An Ultra-Low-Power, Area-Efficient All-Analog Trojan With Temporal Power Supply Triggering, Roberto Ramos-Brito

Theses

With the rapid expansion of the Internet of Things and rising concerns over intellectual property protection, hardware security has become increasingly vital—particularly in defending against hardware trojans. While much of the existing research targets digital trojans, analog hardware trojans remain largely unexplored, presenting unique opportunities for developing innovative attack vectors and mitigation strategies. This thesis introduces a novel all-analog temporal trojan that functions entirely within the analog domain. The design incorporates power supply noise detection and charge-accumulator techniques to enable precise control over attack execution and payload delivery. It also leverages novel circuit techniques to reduce power and area overhead, …

Detecting Compromised Hardware Integrity With Machine Learning In Multicore Processors, Alexander Beekman

Theses

Due to the globalization of the Integrated Circuits (IC) manufacturing process, the trust surrounding hardware reliability has become compromised through methods such as injection of malicious hardware (including hardware trojans), hardware backdoors, IP theft, and counterfeit hardware. This thesis will focus on the issue of counterfeit hardware. As the complexity of modern day hardware has increased, detecting counterfeits has become increasingly difficult. Current methods of physical and electrical inspection are limited in both depth and size limitations. Physical inspections are only useful for identifying counterfeits that manifest physically, while electrical testing requires expensive and extensive setups. Computational evaluation of all …

Human-Aware Reinforcement Learning For Adaptive Human-Robot Teaming, Saurav Singh

Theses

The rise of Industry 5.0 has shifted robotic systems toward human-centric design, emphasizing collaboration between humans and machines. While collaborative robots (cobots) are equipped with sensors for safety, they typically overlook human internal states such as fatigue, stress, and workload—factors that significantly impact performance. This lack of awareness limits their ability to adapt and collaborate effectively, particularly in dynamic, real-time environments where human conditions fluctuate. This thesis introduces a novel framework that integrates human internal states into robot decision-making to enhance human-robot collaboration. This research leverages reinforcement learning (RL) to develop methods that enable robots to adapt their behavior based …

The Use Of Robotic Surveillance In The Uae In 2035: Balancing, Innovation, Regulation, And Privacy, Oasha Yousuf Almohier

Theses

This research explores the future of robotic surveillance in the United Arab Emirates (UAE) by 2035, aiming to balance innovation, regulatory oversight, and privacy protection. It investigates the intersection of national security objectives, ethical AI governance, and public trust within the UAE’s rapidly advancing smart city and law enforcement environments. The focus areas include regulatory gaps in current AI laws, ethical concerns such as data privacy and algorithmic bias, and the societal perception of AI-powered surveillance systems. The background of this study stems from the UAE’s ambition to lead in AI deployment, especially within public security initiatives such as autonomous …

Implementation Of Machine Learning To Predict Cable Failures In Electrical Networks, Sultan Bader Albahri

Theses

Electrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution. This research focuses on developing an ML-based predictive model to forecast cable failures in Dubai …

Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez

Theses

Virtual reality (VR) is commonly used as a tool for enhancing immersion within virtual environments, with one of its primary goals being to approximate real-world perception as closely as possible. In everyday life, variations in lighting conditions significantly influence how colors are perceived across different times and settings. However, the human visual system compensates for these changes, maintaining relatively stable color perception; a phenomenon known as color constancy. This process is driven by chromatic adaptation, the visual system’s adjustment to changes in illumination. Despite the growing use of VR, little research has explored how color constancy operates in virtual environments …

Network-On-Chip Packet Obfuscation And Encoding For Hardware Trojan Mitigation, Sydale John Ayi

Theses

Networks-on-Chip (NoCs) represent a transformative advancement in System-on- Chip (SoC) communication architectures, addressing the scalability and performance limitations of traditional bus-based systems. The increasing reliance on NoCs in modern SoCs has raised security concerns, particularly in untrusted fabrication environments. This thesis investigates the vulnerabilities posed by malicious hardware Trojans and untrusted IP cores, such as reverse engineering, misdirection attacks, and brute-force decryption. To counter these threats, the study introduces a robust security framework leveraging encoding and obfuscation techniques. Hamming codes are employed to detect and correct errors, ensuring data integrity and correcting misdirection attacks. Discrepancies between codewords and data words …

Ferroelectric Field-Effect Transistors In Analog Applications: Demonstrated In Time-Domain Neuromorphic Computing Cells, Jacob O'Donnell

Theses

In response to diminishing improvements from transistor scaling, the semiconductor field has shifted its focus to architectural innovation and emerging device technologies. Ferroelectric field-effect transistors (FeFETs) are non-volatile memory elements which offer new opportunities for energy-efficient computing, particularly in compute-in-memory (CIM) architectures. This thesis presents a novel FeFET-based computing cell integrated into a time-domain (TD) computing architecture. The proposed design is evaluated using a device model derived from experimental data and compared with competing non-volatile memory implementations. Results demonstrate improvements in area efficiency and performance, highlighting the potential of the FeFET cell for next-generation TD-CIM systems.

A High-Efficiency Ldo Regulator With Adaptive Psrr And Transient Enhancements, Daniel Zeznick

Theses

Reliable power delivery is essential for all computing systems, particularly those operating with limited or constrained energy sources, such as batteries. Power management circuits must provide energy efficiency, stability, and resilience to disturbances to support accurate and consistent system performance. This thesis presents a high-efficiency low-dropout (LDO) regulator featuring adaptive power supply rejection ratio (PSRR) and transient response enhancement techniques. By leveraging adaptive analog design strategies, the proposed regulator dynamically boosts performance at high load currents, maintaining optimal efficiency across the full load range while circumventing key trade-offs inherent to conventional LDO architectures. The design incorporates several novel circuit techniques …

Improving Reciprocating Compressor Valve Condition Monitoring Via An Accelerated Lifetime Tester And Deep Learning, Jacob Chesnes

Theses

The goal of this research is to improve the current condition monitoring method for diagnosing reciprocating compressor valves. Leakage, seat wear, and spring degradation are three common valve faults that are seeded into all the valves of a Dresser-Rand ESH-1 dual-acting reciprocating compressor. The method converts raw vibration data into the time-frequency domain and determines if the Continuous Wavelet Transformation or the Smoothed Pseudo Wigner-Ville Spectrum can achieve a higher classification accuracy. The Continuous Wavelet Transformation can vary its resolution through two parameters while the Smoothed Pseudo Wigner-Ville Spectrum can vary its resolution through smoothing window sizes. Both time-frequency transforms …

Enhancing The Immunogenicity Of Extracellular Vesicle-Based Vaccines Against Pathogenic Nthi Bacteria, Christina Ciko

Theses

Acute otitis media (AOM), or ear infections, are highly prevalent in children, with nearly 80% experiencing at least one AOM by the age of three. As such, there is an urgent need for a vaccine against Nontypeable Haemophilus influenzae (NTHi), a main cause of AOM in the US. Most vaccines contain multiple antigens from the pathogen, inducing an immune response and antibodies to those antigens. Here, we describe a novel approach to creating a vaccine against NTHi, incorporating a unique recombinant trivalent protein (Tri) featuring three NTHi antigens (OMP26, P6, and protein F) in a single peptide chain with an …

Enhanced 3d Sub-Canopy Mapping Via Airborne Full-Waveform Lidar, Robert J. I. Wible

Theses

Airborne light detection and ranging (LiDAR) systems have been used to gather information about forests, their canopies and what lies beneath them for many decades. Recent advances in LiDAR sensor technology have enabled higher sampling rates, leading to increased point densities for discrete point clouds. However, vast portions of the forest sub-canopy still remain either unsampled or occluded. We contend, that waveform LiDAR, as a specific type of structural modality that digitizes the intensity of the laser backscatter as a function of time (range), contains additional information that can be extracted using modern artificial intelligence (AI) and machine learning (ML) …

Deepfake Image Detection Using Explainable Ai And Deep Learning, Abdallah Abdulfattah Mohammed Abdulrahman

Theses

Deepfake technology has grown exponentially, and it has changed our perspective of managing digital content. The technology has many applications, but its ease of access has brought many risks. These risks include identity theft, misinformation spread, development of non-consensual content, and a decrease in trust in media. To address these challenges, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deepfake image detection. We developed a CNN model and four hybrid CNN-LSTM models in our study. Our approach integrates the strengths of CNNs for spatial feature extraction and LSTMs for …

Machine Learning For Complex Networks: Generalization, Fairness, And Model Selection In Graph-Based Systems Across Social And Biological Domains, Angelina Brilliantova

Theses

Networks serve as a fundamental framework for modeling complex systems across diverse domains, from biological interactions and social structures to economic markets and information networks. Machine learning has significantly enhanced the ability to extract meaningful patterns from graph-based systems, improving predictive accuracy in applications such as market design, recommendation systems, and biomedical discovery. Despite the successes of machine learning in analyzing complex networks, several fundamental challenges remain. A major issue is the reliance on simplistic models for theoretical analysis, simulation, and benchmarking, which often fail to capture the complexities of real-world networked systems. Many theoretical studies focus on oversimplified generative …

Machinery Imbalance Failure Prediction Based On Pump – Motor Vibration Readings, Abdulrahman Albuflaseh

Theses

Predictive maintenance has emerged as a transformative solution in industrial operations, reducing unexpected machine failures and minimizing downtime. This study explores the application of machine learning in vibration analysis to detect imbalances in pump-motor units, a critical issue that accelerates equipment wear and increases maintenance costs. Traditional maintenance methods, such as scheduled inspections, often fail to identify faults early, leading to significant operational disruptions, particularly in utility, oil and gas sectors. By leveraging real-time vibration data and advanced machine learning techniques, this research aims to develop an efficient imbalance detection model that enhances maintenance efficiency and reliability. The study employs …

Flow Dynamics Of Agricultural Waste Nanofibers: Shear, Temperature, And Oscillatory Insights, Bilge N. Altay, Burak Aksoy, James Atkinson, Christopher Lewis, Carlos Diaz-Acosta, Raymond Francis

Articles

The rheology and fiber size of corn stover (CS) and cleaned cotton gin trash (CGT) cellulose nanofibers (CNFs) were studied including behaviors at both moderate and extremely high shear rates, to simulate industrial processes ranging from mixing and pumping to high-speed coating, printing, and extrusion. Particle size analyzer showed that 99% of CS fibers measured around 226 nm, while 85% of CGT fibers were approximately 143 nm. Both CS and CGT CNFs formed gel-like suspensions, and shear flow tests revealed that all samples exhibited shear-thinning behavior, allowing easy flow under shear forces. Gels with higher solid content (1%) demonstrated extended …

The Effects Of Transportation Distribution On The Performance Of Insulated Thermal Containers, Edmund B. Tang

Journal of Applied Packaging Research

This research examines the effect of simulated transportation distribution testing on insulated thermal containers, also known as cold chain shippers. This study compares the thermal performance of an insulated thermal container in its ideal pristine condition with no external factors that challenge the integrity of the package to insulated thermal containers that are subjected to common and acceptable test inputs using established and accepted industry package performance and simulation standards. Currently, little research evaluates the thermal performance of insulated thermal containers after simulated distribution testing. A total of nine different real-world scenarios using test standards of varying severities or other …

Femtosecond Laser Figuring And Polishing For Freeform Optics Fabrication, Gong Chen

Theses

Freeform optics revolutionizes imaging systems by enabling compact designs and high-performance capabilities, which are crucial for applications such as augmented and virtual reality, medical diagnostics, biosensing, space exploration, remote sensing, and EUV lithography. However, fabricating freeform optical components presents significant challenges: conventional chemical and mechanical methods generate chemical waste and introduce mid-spatial-frequency errors that degrade imaging contrast. This research seeks to overcome these limitations by establishing ultrafast-laser-based figuring and finishing methods. These methods offer a fast, non-contact, flexible, and precise green solution, paving the way for sustainable, high-quality freeform optics fabrication. We expanded a laser pulse propagation model to simulate …

Advances In Machine Learning For Pain Recognition: A Review Of Algorithms, Modalities, And Outcomes, Abdulrahman Alsharif

Theses

The emergence of machine learning (ML) in healthcare has unlocked the transformative potential of pain detection and assessment. By providing new ways to address the challenges associated with subjective and inconsistent pain assessment methods. This review synthesizes recent advancements in ML-based approaches for pain detection, focusing on algorithms, modalities, and datasets. Techniques such as neural networks, transformer models, and multimodal fusion frameworks have demonstrated significant promise in identifying pain from diverse sources, including facial expressions, physiological signals, and synthetic datasets. Key contributions include novel transformer-based architectres like PainAttnNet for analyzing electrodermal activity [1], multimodal datasets such as BioVid for advancing …

Systematic Evaluation Of Regression Models For The Prediction Of Influenza Cases, Mukul Kandwal

Theses

This study systematically evaluates machine learning models to forecast influenza outbreaks, aiming to enhance public health preparedness through accurate predictions. Laboratory-confirmed influenza case data from the New York State Department of Health (2009–2023) were used to train and test five regression models—XGBoost, Neural Network (MLP regressor), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Regression (SVR)—with preprocessing techniques including differencing and phasing. A leave-one-season-out cross-validation framework and Symmetric Mean Absolute Percentage Error (SMAPE) were employed to rigorously assess performance. Key findings reveal that the Neural Network (MLP regressor) outperformed other models, achieving the lowest SMAPE score (23.7), underscoring the critical …

Towards Characterizing And Quantifying Interpretability Of Knowledge Graph Embedding Models, Narayanan Asuri Krishnan

Theses

Knowledge graphs are structured representations of real-world information, where entities are connected by edges with labels known as predicates. These graphs contain logical patterns, known as inference patterns, such as symmetry, transitivity and composition, to name a few. To leverage the information contained within a knowledge graph, embedding methods have been developed to encode entities and predicates as numerical vectors in a multi-dimensional space. These embeddings aim to capture the semantic and structural information of the graph, including the inference patterns. However, the sub-symbolic nature of these embeddings present significant challenges in terms of interpretability. Additionally, each embedding model utilizes …

Dubai’S Future Mobility: Systems Thinking & Scenarios, Mohammed Mazloum Hanafy

Theses

Transportation is one of the essential pillars of any smart city. The future of transportation in Dubai faces enormous hurdles in urban mobility, including a growing population density, congestion, and environmental issues. Without careful planning and policy actions, these problems would certainly worsen in the future. Thus, the purpose of this thesis is to investigate the future of urban mobility in Dubai and to suggest solutions for constructing a sustainable, efficient, and equitable transportation system by employing future foresight tools. Dubai has emerged in recent years as a global center for technological innovation, altering its built environment and reevaluating its …

Making Science-Inquiry Learning Accessible For Students With Complex Support Needs, Sarah Koebley, Shawnee Wakeman, Lindsay Ruhter, David Pugalee, Meagan Karvonen

Journal of Science Education for Students with Disabilities

Inquiry learning through engagement with scientific practices has proven to be an effective instructional practice for general education students. Although students with complex support needs (CSN) are required to have access to the same grade-appropriate academic content in science as their peers without CSN, it remains a challenge for special education teachers to design instruction around student inquiry and science conceptual understanding. The 5E Science Education for Special Educators (5E-SESE) Model combines three well-established conceptual constructs (three-dimensional science standards, Universal Design for Learning [UDL], and the 5E Model) to support special educators in delivering quality science instruction. With the 5E …

Fake News Detection On X (Twitter), Sultan Mayoof Almutaiwei

Theses

The most important innovation globally is the internet, which is used by a huge number of people all around the world. People use the internet for different purposes. By using different social media platforms people share content and news. Thus, some people use these internet platforms to share false news or fake news among people. Propaganda against a person, group, organization, or political party may be present in this news. For an individual, it is difficult to distinguish between false and true news over the internet. Therefore, machine learning is the smart solution that can identify fake news on the …

Predicting Customer Churn In B2b Consulting Services, Mediya Alfalasi

Theses

The aim of this research was to develop a machine learning model that can predict customer churn in the consulting industry. The goal was to help businesses identify customers who might leave, allowing them to take action before it happens. To achieve this, a dataset was created by combining different data sources to reflect real-world customer behaviors. After cleaning the data and addressing issues like missing values and data imbalance, four machine learning algorithms—XGBoost, Neural Network, Random Forest, Logistic Regression, and Support Vector Machines (SVM)—were used to build the prediction models. The findings showed that XGBoost performed the best, achieving …

Enhancement Of Firefighter Mental Performance: A Review, Nicole A. Ness

Theses

Background: The life-or-death nature of firefighting exposes those who respond to calls to an increased number of stressors that can impact performance and overall well-being. To combat this, the mental element of firefighting can be optimized through psychological skills training programs and mindfulness training. Other tactical populations, such as the police force, military, and emergency medicine units have found success in implementing these interventions, Literature suggests a similar effect may be observed in firefighters because of their similar demands; although, little research exists regarding the effects of mental performance programs on firefighter populations specifically.

Methods: A review of existing literature …