Digital Commons Network^™

Analysis Of The Formation And Structure Of Protective Films In Sustainable Ionic Liquid Lubricants, Brendan Mahoney

Theses

Friction losses and wear of sliding components account for nearly one quarter of global energy consumption. Developing new lubricants to reduce these losses is essential for a more cost-effective and sustainable future. The automotive industry offers the greatest economic potential for new tribological discoveries, as it affects billions of people around the world. A significant portion of the friction losses in automobiles are due to the reciprocating motion in the engine, which leads to metal-on-metal contact. Ionic liquids have emerged as novel lubricants and additives due to their ability to form protective surface layers on metals, reducing friction and wear. …

The Effect Of Aquatic Pollution On (Green) Microbial Communities In The Greater Rochester Area, Francesca Molee

Theses

Retention ponds are built in urban and suburban areas to catch the polluted runoff that would otherwise go into highly-valued freshwater ecosystems like the Great Lakes. They therefore make ideal model ecosystems to consider the effect of chemical, common pollution on aquatic communities. To understand the impact this aquatic pollution on both microbial community composition alongside green microbial physiological stress response, water column and sediment microbial communities were sampled from three retention ponds, and three non-retention, created ponds. The first part of this study focused on the microbial community composition. Using 16s amplicon sequencing on and subsequent analysis using Qiime2, …

Characterizing The Relationships Between Hydrology, Soil Biogeochemistry, And Green Ash Canopy Structure In Emerald Ash Borer-Affected Wetlands, Eva Switala

Theses

The invasion of the emerald ash borer (EAB; Agrilus planipennis) has caused mass mortality of native ash trees (Fraxinus spp.), devastating forested wetlands in the northeastern United States. Fifteen years post-introduction, the long-term ecological shifts and "new normals" of these ecosystems remain poorly understood. This project characterized patterns in EAB-affected forested wetlands by evaluating soil characteristics, biogeochemical cycling, hydrology, and green ash canopy across 12 sites with varying initial ash presence. Results revealed a clear separation between biogeochemically active and less active sites. Hydrologic differences emerged as a key driver of biogeochemical cycling, with higher soil moisture significantly increasing rates …

Integrated Sources Of Photon-Pairs For Quantum Networking, Vijay Soorya Shunmuga Sundaram

Theses

Quantum networking enables the distribution of quantum information across distant nodes, unlocking capabilities such as secure communication, distributed quantum computing, enhanced metrology, and tests of fundamental physics. Photons serve as the natural carriers of quantum information over long distances due to their low loss in optical fibers and robustness to environmental decoherence. However, the practical realization of quantum networks remains limited by the lack of photon sources that are simultaneously compatible with diverse quantum systems, stable over long timescales, and suitable for deployment in real-world fiber infrastructure.  This dissertation addresses these challenges through the co-development of photon-pair sources and a …

Development Of Blue And Near-Uv Iii-Nitride Laser Diodes, Matthew Seitz

Theses

III-Nitrides show tremendous promise for a wide range of laser diodes and other optoelectronics devices. The wide range of emission wavelengths possible makes these devices ideal candidates for a wide range of applications. Despite this, several challenges remain unsolved, limiting the efficiency and output power from III-Nitride optoelectronics. Three of the most significant remaining challenges surround the formation of low resistance ohmic electrical contacts, poor p-type doping of III-Nitride crystals as well, and the fabrication of the smooth, mirror facets needed for optical feedback and laser operation. Inefficient dopant activation leads to poor conductivity and increased electrical resistance due to …

Topological Tools For The Analysis Of Complex Networks And Higher-Order Networks, Jason P. Laruez

Theses

This dissertation investigates the intersection of topological data analysis and network science, applying and extending persistent homology, Betti numbers, and simpliciality measures to study temporal, evolving, and coevolving networked systems. We introduce a multi-layer zigzag persistent homology framework for analyzing temporal hypergraphs across multiple timescales simultaneously, and validate it on the DARPA Operationally Transparent Cyber dataset, where it produces topological signatures that distinguish malicious from benign network activity by source IP. We then apply topological tools to classical generative network models and their hypergraph extensions, characterizing how Betti numbers, filling efficiencies, and simpliciality measures evolve as these models grow or …

Lubricating Abilities Of Choline Amino Acid Protic Ionic Liquids Under High Temperature And Electrified Conditions, Esmond Lau

Theses

This work explores the tribological properties of choline amino acid protic ionic liquids (PILs) as environmentally friendly lubricants under high temperatures and electrified conditions. These conditions are found in the automotive sector, notably in internal combustion engine vehicles (ICEVs) and electric vehicles (EVs). Lubrication technologies are vital to improving the efficiency and operating lifetime of moving components in automotive drivetrains. The use of ionic liquids (ILs) is actively being researched, with ongoing exploration of their lubricating potential and unique physicochemical characteristics. Compared to traditional petroleum-based lubricants, ILs’ ability to thrive in these conditions will offer substantial benefits, immediately extend operational …

Towards Efficient Continual Deep Learning, Md Yousuf Harun

Theses

Modern AI systems can achieve remarkable performance on tasks such as image recognition, object detection, and language understanding, but they often struggle with a basic ability that humans rely on every day: learning continuously. When deep neural networks (DNNs) are updated with new information, they can unexpectedly forget what they previously learned, making them difficult to deploy in changing real-world environments. This dissertation explores how to build AI systems that can learn more like humans: adapting to new data, preserving past knowledge, and using prior experience to learn future tasks more efficiently. Rather than focusing only on preventing forgetting, this …

The Basketball Player Performance Analysis And Game Strategy Of Optimization, Khalid Juma Obaid Ghabish Yaqoub

Theses

NBA games generate a lot of data, thanks to tracking technologies like SportVU and Second Spectrum. This thesis will used this data to help understand player performance, team strategies, and game outcomes. We use advanced statistics and machine learning techniques to analyze player performance, like how efficiently they shoot (eFG%), their overall impact on the game (PER), and how much of the team's offense they use (USG%). We can also visualize shot charts to see where players tend to shoot from. Looking at team-level data, we can analyze offensive and defensive ratings, how well teams pass the ball, and how …

Improved Colorimetry Through Fundamental Appearance Scales, Saeedeh Abasi

Theses

This dissertation introduces a new framework for color appearance modeling, referred to as the Fundamental Color Appearance Model (FCAM). The primary objective of this work is to develop perceptually meaningful color appearance scales that are directly derived from cone fundamentals and formulated as independent one-dimensional scales. Unlike conventional color appearance models that rely on complex three-dimensional color spaces and extensive nonlinear processing, FCAM describes color appearance attributes individually through mathematically simple and physiologically grounded formulations. FCAM consists of four independent one-dimensional scales: the Fundamental Hue Scale (FHS), Fundamental Lightness Scale (FLS), Fundamental Brightness Scale (FBS), and Fundamental Saturation Scale (FSS). …

Gender Relations Within 18th And 19th Century Art And How It Affects A 21st Century Learner, Sophie Lima

Theses

This study constructs a curriculum unit comprising five lessons in feminist art history for middle school learners in 5th-8th grade. Drawing on theories and concepts from feminist art history, visual culture, and inquiry in art education, this curriculum aims to address the scarcity of age-appropriate materials in feminist art history. There is a need for curricular materials that help learners critically analyze issues of gender, identity, and power conveyed through visual culture. The curriculum encourages middle-grade students to engage with three approaches to art analysis: formalism, iconography, and feminism. The organized unit focuses on inquiry, interpretation, comparison, production, and evaluation. …

Developing Sight-Reading Proficiency In Music Education: A Synthesis Of Instructional Strategies And Pedagogical Approaches, Charles Clements Ii

Theses

Sight-reading is a foundational skill in music education that enables students to perform unfamiliar musical material independently. Despite its importance, instruction is often inconsistent and driven by performance priorities rather than systematic skill development. This thesis synthesizes existing research on sight-reading pedagogy to identify effective instructional strategies and organize them into a cohesive framework for educators.

By connecting research to classroom application, this project provides a practical, research-informed model for sight-reading instruction. The findings suggest that effective instruction requires a structured and integrated approach that supports both cognitive processing and musical performance, ultimately promoting greater student independence and musical literacy.

Advanced Mathematical Modeling And Data-Driven Techniques For The Diagnosis Of Diabetes Using Continuous Glucose Monitoring (Cgm) Data, Farah Morsi

Theses

Diabetes mellitus is a major and growing health challenge, particularly in the Middle East and North Africa (MENA) region. Continuous Glucose Monitoring (CGM) provides high-resolution time-series data that capture detailed glucose fluctuations over time. However, conventional CGM summary measures, such as mean glucose, standard deviation, and time-in-range, may not fully describe the nonlinear temporal structure of glucose dynamics.

This thesis investigates nonlinear dynamical approaches for analyzing CGM time series, with a focus on recurrence-based analysis and ordinal-network analysis. Recurrence-based methods, including recurrence quantification analysis (RQA), are used to characterize geometric and temporal patterns in reconstructed phase space, while ordinal networks …

On The Fractional Laplacian Type Operator, Maysam Abdulnaser Zain

Theses

In this thesis, we study analytical structures arising from Dunkl theory and their

applications to harmonic analysis and fractional Laplacian operators. Dunkl operators are differential–difference operators associated with finite reflection groups, providing a natural generalization of the classical Fourier analysis through the introduction of root systems and multiplicity functions. Within this framework, several classical transforms appear as special cases of the (k,a)-generalized Fourier transform. We study the generalized Fourier transform ��ₖ,ₐ, its kernel B_k,a (x,y), and the associated translation operator and convolution structures. Using these tools, we construct the corresponding heat …

استخدام تقنيات الذكاء الاصطناعي في إجراءات الاستدلال والتحقيق "دراسة مقارنة", حمد مصبح اليليلي

Theses

The Use of Artificial Intelligence Techniques in Reasoning and Investigation Procedures: Comparative Study

This research aims to analyze the role of artificial intelligence (AI) technologies in the stages of criminal investigation and inquiry through an applied analytical study. It focuses on the use of these technologies to support law enforcement and investigators with advanced scientific methods, particularly in analyzing digital evidence and accurately linking it to suspects. The study also explores the future development of these systems through their integration with AI technologies to enhance the efficiency of criminal investigation.

The United Arab Emirates has consistently been keen to develop …

Fractional Bernstein Polynomial Approximations For Nonlinear Timefractional Partial Differential Equations, Reem Abdul Quzli

Theses

This thesis studies the numerical approximation of nonlinear time-fractional partial differential equations using fractional Bernstein polynomials. The main model considered is the nonlinear time-fractional foam drainage equation, in which the classical time derivative is replaced by the Caputo fractional derivative. This formulation introduces memory effects into the model and allows the present drainage behavior to depend on the previous evolution of the liquid fraction.

The proposed method approximates the solution by a finite expansion of fractional Bernstein basis functions. After substituting this approximation into the governing equation, the residual is expanded in powers of t^�� . The unknown coefficient …

Sea-Ice Observation In The Arctic By The Arab Satellite 813, Simulated By The Radiative Transfer Model ‘Sciatran’, Tuqa Mohsin Al Hajri

Theses

The purpose of this research is to explore the spectral behavior of sea ice and to understand how Arctic sea ice surfaces appear when they are observed with a hyperspectral sensor. This is crucial because sea ice changes a lot during the melt season, and different surface types can sometimes look similar to a human eye, but physically they are different. It is thus essential to identify the spectral differences between these surfaces. The main objective of this study is to examine how the spectra of white ice and melt ponds change when their physical and optical properties are varied, …

Effects Of Led Light Spectra And 1-Mcp On Postharvest Quality And Bioactive Compounds Of Cucumber (Cucumis Sativus L.) During Cold Storage, Imad Eldin Abbas Yousif

Theses

Extending postharvest quality and maintaining the nutritional value of cucumber fruit requires specific storage conditions to postpone senescence and enhance marketability. The postharvest lighting environment is a main factor that influences quality preservation for harvested biomass. The objective of this study was to investigate the effects of different light emitting diodes (LED) spectra (green, red, blue, and white LED) alone or in combination with 1-Methylcyclopropene (1-MCP) on postharvest quality and bioactive compounds of cucumber fruit during cold storage for 16 days at 5°C, the control fruit were left in the dark. For combined treatment, the fruits were exposed to 1-MCP …

Perception Of Dynamic Lighting: Chromatic Adaptation And Augmented Reality, Abigayle Weymouth

Theses

Augmented reality (AR) is a rapidly developing technology with use cases ranging everywhere from medicine to entertainment. This research focuses on optical see-through (OST) AR, one of the common overarching types of available augmented reality devices. Color appearance in OST AR systems is affected by a mix of the viewing conditions and environment of the real world and the transparent virtual elements. Accurate control of the color of AR content in real-world use cases, which often includes changes in color over time in both parts of the environment, is important for many applications. This dissertation includes three studies of color …

Towards Efficient And High-Fidelity Generative Models, Paribesh Regmi

Theses

Generative models aim to learn the underlying data distribution and synthesize realistic samples such as images, videos, and audio. Deep learning has brought remarkable success to this field, enabling models to capture complex, high-dimensional structures and produce highly realistic outputs. However, a fundamental challenge remains — achieving both computational efficiency and high-quality generation simultaneously. Among modern approaches, diffusion-based generative models stand out for their exceptional sample fidelity but require hundreds of iterative denoising steps, making them computationally expensive and slow. To improve efficiency, recent work trains diffusion models in the low-dimensional latent space of a pretrained Variational Autoencoder (VAE), where …

A Reproducible Hdr-Native Psychophysics Infrastructure For Color Science Research, Fernando Voltolini De Azambuja

Theses

Color science psychophysics measures the human visual system by relating controlled visual stimuli to observer responses, often through forced-choice paradigms such as two-alternative forced choice (2AFC) (Kingdom and Prins 2016). These studies require stimuli specified in physically meaningful units, technically correct display emission, and reproducible execution (Fairchild 2013; Lin et al. 2023). In practice, graduate-level experiments often rely on bespoke MATLAB or Python scripts, standard dynamic range (SDR)-first toolchains such as Psychtoolbox and PsychoPy, manual stimulus duplication, and incomplete capture of calibration, colorimetric state, and display capability (Brainard 1997; Peirce et al. 2019). These limitations increase experimental friction and create …

Reinforcement Learning-Enabled Resource Allocation For Distributed And Uncoordinated Cognitive Radio Networks, Ankita Vijay Tondwalkar

Theses

To keep up with the ever-increasing performance demand from wireless applications, wireless networks necessitate to operate following an efficient use of the available radio spectrum. Since the radio spectrum is a limited resource, the increasing demand for wireless services and applications to support a wide spectrum of users is a challenge in itself and therefore requires advanced techniques to manage and utilize radio resources efficiently. Dynamic spectrum access (DSA) and sharing play a crucial role in improving the utilization of the radio spectrum, as it departs from the traditional approach of static radio spectrum band allocation which usually leads to …

Echelon Trust Network: An Exploration Of Speculative Design And Critique Of Manipulative Technologies, Sydney Rowley

Theses

As emerging technologies become increasingly integrated into everyday life, concerns  surrounding privacy, surveillance, and personal autonomy continue to grow. Artificial  intelligence, data tracking, targeted advertising, and location-monitoring have intensified public  anxieties about the collection and use of personal data. These concerns echo themes explored in  classic dystopian literature, which have long warned of societies where privacy is sacrificed for  security and human identity is reduced to data. Echelon Trust Network explores these anxieties  through a speculative design project that imagines a future in which constant surveillance and  algorithmic trust scoring govern social interactions.  Developed as a motion graphics piece in …

Assistive Active Safety, Long Nguyen

Theses

As electric bicycles become faster and more prevalent in urban environments, the safety systems designed to protect riders face significant challenges. The current industry standard relies heavily on auditory warnings—which can exclude Deaf and Hard of Hearing (HOH) cyclists—and digital dashboards that often demand intense visual focus. Forcing a rider to process cluttered screen data while navigating high-speed traffic can contribute to cognitive overload and physical danger. This thesis explores how micro-mobility safety might be improved by shifting away from auditory-dependent alerts toward intuitive, multi-sensory communication. To address this safety gap, this project introduces the Assistive Active Safety (AAS) framework. …

Control And Entrainment Of Oscillatory Dynamics In An Oncolytic Virus–Tumor Model Under Periodic Therapy Forcing, Aya Salaheddin Shujrawi

Theses

This thesis investigates the dynamics of a tumour–virus interaction model under sinusoidal periodic viral injection, using the three-compartment ordinary differential equation framework of Baabdulla and Hillen (2024). The aim is to characterise how the frequency and amplitude of periodic injection interact with the system's intrinsic oscillatory dynamics, and to identify conditions for stable frequency entrainment. Equilibrium and Hopf bifurcation analysis of the autonomous system yields a supercritical bifurcation at θ_H^auto ≈ 338.45 with intrinsic frequency Ω₀^auto ≈ 0.7552. Introducing a constant baseline injection u₀ = 0.05 raises the threshold to θ_H^forced ≈ 364.85 and shifts …

Edge Co-Occurrence Regularization For Node Classification, Kadir Altunel

Theses

We propose a simple yet effective regularization technique for node classification on graphs that leverages edge-based label co-occurrence patterns. We first train an MLP on node features to produce class probability distributions, then compute a fixed penalty matrix from edge-based co-occurrence statistics of these predictions. This penalty matrix, which captures unlikely class combinations on connected nodes, is then used to regularize GNN training without further updates. We evaluate this approach across multiple homophilic datasets (Cora, CiteSeer, PubMed, ogbn-arxiv) and heterophilic benchmarks (Chameleon, Squirrel, Actor, Roman-Empire) using three GNN architectures: GCN, GraphSAGE, and H2GCN. Results show consistent improvements on homophilic graphs, …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Sound Detection Of Memory Leaks In Llvm Ir Programs Using Accumulation Analysis, Robert Blacha

Theses

Resource leaks occur when a limited resource such as memory is allocated by a program and needlessly held past the point of use. Leaks can lead to a degradation of services which can be specifically triggered with malicious behavior, for example abusing a memory leak in a program to cause a server to slow down and crash for a denial-of-service attack.

Prior work has demonstrated that accumulation analysis provides a sound detection of resource leaks with a working implementation for programs written in Java. While useful, current implementations are limited to programs written in Java, which has a garbage collector, …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Evaluation Of Medical Instrument Reliability For The Heath And Maddox Components Of The Accommodative-Vergence System, Jacob Chabuel

Theses

Binocular vision relies on the coordination of near response triad composed in part by the vergence and accommodative systems. Binocular dysfunctions linked to these systems impact the quality of life of those affected by complicating day-to-day tasks and leading to further health concerns. Clinical evaluations that diagnose dysfunctions and tailor therapies rely on subjective methods, creating a need for a reliable, quantitative, measurement tool. This is achieved using a haploscope, a tool that quantitatively measures and observes binocular vision to identify deficiencies within the eye. Modernization of a haploscope system, measurement of the Heath and Maddox components of the visual …