Digital Commons Network^™

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, …

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 …

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 …

Analytical And Numerical Methods For Solving Fractional Integro-Differential Equations Using The Modified Operational Matrix Method, Nour Alzoubi

Theses

Fractional calculus has attracted considerable attention in recent years because of its wide applicability to model a variety of linear and nonlinear physical phenomena across different scientific disciplines. In particular, fractional diferential equation systems have proven to be effective in describing processes with memory and hereditary properties. However, the solvability and analysis of such systems strongly depend on the type of fractional operator employed, especially in the presence of nonlocal fractional derivatives with singular kernels, which remain an open and challenging area of research.

The main objective of this thesis is to develop efficient analytical and numerical techniques for solving …

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 …

A Hybrid Approach To Phishing Email Detection: Leveraging Machine Learning And Large Language Models, Hessa Shamal Biri

Theses

Phishing attacks have reached a new level of sophistication through the deployment of large language models by attackers. The current AI-generated threats defeat existing detection systems which base their operation on past data. The thesis presents a hybrid system for phishing email detection which combines real email data with synthetic LLM-created samples to enhance traditional machine learning classifiers performance. The study created a hybrid dataset of 20,627 emails by combining 18,631 real messages from the Kaggle Email Classification Dataset with 1,996 synthetic emails. The synthetic emails were generated using four large language models LLaMA-3, Falcon, LLaVA, and Mistral to capture …

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 …

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 …

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). …

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, …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …

Pypitfall: Dependency Chaos And Supply Chain Vulnerabilities In Python, Jacob Mahon

Theses

Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting downstream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. PyPitfall, a quantitative analysis of vulnerable dependencies across the PyPI ecosystem, is introduced. The dependency metadata of 378,573 PyPI packages is analyzed. 4,655 packages that explicitly require a known vulnerable package version and 141,044 packages that permit a vulnerable …

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 …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

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, …

The Impact Of Employment On Sense Of Belonging And Metacognitive Awareness: A Mixed-Methods Study Of Undergraduate Students’ Mathematical Performance, Erin Ryan

Theses

This study investigated how employment intensity affects two critical factors for academic success: a student’s sense of belonging and their metacognitive awareness (the ability to monitor and manage their own learning). Using a mixed-methods approach at the Rochester Institute of Technology, we analyzed survey data through multi-level modeling and conducted qualitative interviews to capture the personal experiences of employed students. Results reveal that students working off-campus for more than eleven hours a week showed significantly lower sense of belonging and metacognitive awareness scores than unemployed students. However, on-campus employment did not show the same effect. Both quantitative and qualitative results …

A Comparative Static Analysis Of Identifier Clones And Homonyms In Open Source Projects, Jose Palomino Lau

Theses

Identifier naming is a core element of software engineering and program comprehension. While existing literature suggests that naming consistency is ideal, there is limited empirical data on identifier homonyms, instances where the same name is used for different behaviors. This thesis investigates these naming patterns through a manual analysis inspired by grounded theory, supported by static analysis with srcML across three open source projects in Java, C and Python. By establishing a behavioral framework through axial coding, this research classifies 771 identifiers into functional categories to document their presence and role within the code. The results reveal that naming redundancy …

The Evolution Of Topological Structures In Climate Networks, Kiera Kelly

Theses

We investigate higher-order interactions in climate networks using topological data analysis (TDA) and analyze their evolution since 1948. Using the correlation between surface air temperature (SAT) anomalies, we define relationships between spatial locations and examine the patterns that emerge over time. We extend the framework of temporal climate networks and apply TDA methods, including persistent homology, curvature analysis, and the Euler characteristic, to identify dynamic structural features. Persistent homology tracks the births and deaths of topological features such as connected components and loops. Curvature quantifies a node’s participation in higher- order structures (cliques), while the Euler characteristic captures global trends …

Uncovering Cytoskeletal Length Control From Growth Trajectories Using Sparse Dynamical Modeling, Carson Hearn

Theses

Understanding the governing equations of biological filament dynamics is essential for predicting cellular behavior, yet these equations are often difficult or impossible to derive directly from first principles. This thesis investigates the use of Sparse Identification of Nonlinear Dynamics (SINDy) as a unified, data-driven framework for discovering, validating, and refining governing equations from both simulated and experimental data. Focusing on biological filament processes, including growth, disassembly, and severing, this work is designed not only to apply SINDy, but to systematically demonstrate that it is a reliable and generalizable tool for modeling complex biophysical systems. To establish this, SINDy is first …

An Lstm-Based Framework For Predictive Power Quality Monitoring, Ian Aiken

Theses

Rapid integration of photovoltaic (PV) systems introduces significant power quality challenges, including voltage sags and harmonic distortion, yet existing approaches either perform post-hoc classification or rely on computationally expensive meteorological forecasting unsuitable for real-time edge deployment. In this paper, a resource-efficient framework is developed for short-term power factor sag prediction using only on-line electrical measurements via a dual-track methodology. A regression model is first established to define the predictive limits of direct value estimation. The analysis of drop-event-specific metrics reveals that sensor features are lagging indicators incapable of regressing immediate drop magnitude, motivating a classification re- framing. A binary Long …

Mathematical Models Of The Kinetic Release Of Therapeutics From Contact Lenses And Their Effects On The Ocular Immune System Response To Allergic Conjunctivitis, Narshini D. Gunputh

Theses

Seasonal allergic conjunctivitis (SAC) is one of the most common ocular disorders caused by exposure to airborne allergens such as pollen. It is typically treated with eye drops, but only about 5% of the drug in an eye drop reaches the target tissue. Drug-eluting soft contact lenses have been proposed as an alternative delivery method, but one limitation of a contact lens is the burst-release. In this thesis, we develop multiple mathematical models to support the design and evaluation of drug-eluting contact-lenses for the treatment of SAC. In the first part of the thesis, we build a diffusion-based model to …

I Wrote Myself Awake: A Personal Encounter With A/R/Tography, Michelle Geipel

Theses

This project and its accompanying short story function as visual and narrative representations of a/r/tography, demonstrating how creative practice can be used to explore and construct personal meaning through writing and art-making. The co-written story exists alongside the altered book as evidence of how the creative process not only engages the brain through neuroplasticity, but also supports meaning-making and the formation or discovery of identity. Through the integration of life writing, visual art, and reflective inquiry, this work illustrates how understanding can emerge from within the act of creation itself.

User-Centered Design For Public Financial Services: A Federal Student Loan Management App Case Study, Carmen River Christopher

Theses

Researchers have studied several facets of federal student loans, such as the impacts it has on borrowers’ finances, mentality, and life goals, plus the history of the federal student system and potential changes. There has been little research conducted on how borrowers make decisions regarding repayment, including choosing plans, navigating the repayment system, and understanding the long term implications of their choices. Since this research largely does not exist, there are few tools and resources to help guide borrowers as they manage the large, complex, and dynamic landscape that is navigating the federal loan system and repaying their debt. I …

Analyzing Comparisons Of Seascapes By Caspar David Friedrich And Fitz Henry Lane Through A Digital Gallery Show, Adrianna Murphy

Theses

This project focuses on a comparative digital exhibition and analysis between Caspar David Friedrich and Fitz Henry Lane’s seascape artwork. Both artists have not been shown together in a gallery; thus this research into common themes in their works reveals a new perspective on early- to mid-19th-century seascape art. This perspective shows how their different geographies and historical contexts converge through the shared visual language of contemplation, morality, and reflecting in the mystery of life through nature. The project aims to create a digital gallery that showcases an ideal layout and a comparison of the artists, with curated works that …

The Efficacy Of Ethical Data: An Analytical Study By, Lamar Gebara

Theses

As artificial intelligence and machine learning models become increasingly embedded in decision-making systems across industries, questions about the ethical sourcing of data have grown more pressing; model performance relies heavily on vast, diverse datasets, many of which are harvested without consent, transparency, or equitable representation. This study aims to compare analytics outcomes (accuracy, bias, fairness, and explainability) between ethically sourced and unethically sourced datasets. Using CRISP-DM methodology, the study will develop matched classification models on both dataset types, evaluate their performance and fairness using open-source tools such as Fairlearn and AIF360, and assess broader implications for trust, accountability, and regulatory …

Avoiding White Elephants: An Empirical Assessment On The Failure Of Airport Capacity To Drive Tourism Receipts, Somanithel Kheang

Theses

This study investigates the relationship between airport infrastructure capacity and international tourism receipts across 12 major Asian and European economies from 2000 to 2019. Using a Panel Fixed Effects framework, the analysis tests the "Supply-Led Growth" hypothesis to determine if physical infrastructure expansion independently drives tourism revenue. To address potential endogeneity and reverse causality, the study employs specification where all independent variables are lagged by one year.The empirical results yield a robust null finding: airport capacity does not exhibit a statistically significant impact on tourism receipts. Conversely, government consumption is identified as a potent driver of tourism performance, with a …

Deep Reinforcement Learning-Based Equitable Post Disaster Resource Allocation Incorporating Social Vulnerability, Trevor John Mogaka

Theses

Prior Deep Reinforcement Learning (DRL) approaches frame post‑disaster recovery as a sequential decision-making problem over infrastructure elements, using graph-based system models and resilience-oriented rewards. This thesis builds on an existing DRL framework by introducing a modified CDC Social Vulnerability Index (mCDC-SVI), developed using Max-min scaling, to quantify social vulnerability.A synthetic interdependent infrastructure testbed is divided into four socio‑economic regions using federal poverty and SNAP eligibility guidelines. The DRL simulation and Deep Q‑Network architecture are retrained under a multi-objective reward function that is efficiency-equity weighted (α:β). Agents are evaluated across unconstrained and constrained budget scenarios to examine how varying α and …

Prediction Of The Nga-West2 Average Vertical Peak Ground Acceleration Using Genetic Expression Programming, Dipankar Karki

Theses

Ground Motion Predicting Equation (GMPEs) has been proposed with the use of Genetic Expression Programming (GEP) tool using 10,080 ground motion records obtained from the NGA West2 database. For this research, moment magnitude, dip angle, rake angle, Joyner Boore Distance, depth to top of fault distance, closest distance to ruptured fault area and the shear wave velocity in top 30 m of the site have been selected as the predictor set. It was found that the results from the GEP model compared well with the existing GMPEs and validates the use of GEPs for creating GMPEs along with other regressions …

Predicting Flight Fares With Machine Learning: Enhancing Aviation Industry Pricing Forecasting, Reem Almulla

Theses

This research investigates the increasing challenge of accurate flight fare prediction for travel agencies that are functioning and working in the post-pandemic aviation market. As the prices fluctuate while demand is unstable and competition pressure increases, therefore it influences decision-making and profitability. In most cases, traditional ticket pricing methods often can be ineffective when capturing complex and non-linear relationships within factors that influence the ticket fare dynamics. As a result, highlighting the urge of more adaptive pricing and data-driven predictive machine learning models. In response to this challenge, the research examines the effectiveness of machine learning techniques for enhancing flight …