Digital Commons Network^™

Antidistillation Sampling For Classification Models, Khawaja Abaid Ullah

Theses

Knowledge distillation enables adversaries to replicate the functionality of proprietary machine learning models by querying their APIs and training surrogate student models on the returned soft-label distributions. Antidistillation Sampling (ADS), recently proposed for large language models, perturbs the output distribution of a teacher model at inference time to degrade the quality of the resulting distilled model while preserving utility for legitimate users. We adapt ADS to the supervised classification setting and identify a structural obstacle to its direct transfer: the high-confidence, near-one-hot output distributions characteristic of well-trained classifiers leave insufficient probability mass on non-target classes for the additive penalty to …

Atmospheric Noise Analysis In Observations From The Tomographic Ionized-Carbon Mapping Experiment, Audrey Dunn

Theses

The Tomographic Ionized-carbon Mapping Experiment (TIME) instrument is a ground-based millimeter-wavelength grating spectrometer that illuminates a cryogenically cooled array of 1920 transition-edge sensor (TES) bolometers. The goal of TIME is to generate line intensity maps of singly ionized carbon ([CII]) during the Epoch of Reionization, when hydrogen became reionized by stellar radiation and the first galaxies were forming. This measurement requires a detailed understanding of the noise, most notably the 1/f noise from the time-varying atmosphere. In my thesis work, I performed an analysis of the power spectral density (PSD) and a traditional principal component analysis (PCA) on TIME data …

Reconfigurable Dataflow For Efficient Matrix-Matrix Multiplication For Dnn Acceleration, Daniel Laudico

Theses

General matrix-matrix multiplication (GeMM) is a principal computational bottleneck in modern deep learning workloads where performance can be heavily impeded by frequent memory accesses. This thesis introduces a multi-dataflow aware processing element (PE) designed to perform the multiply-accumulate (MAC) operations fundamental to GeMMs while utilizing a data flow that best fits the data provided. A salient feature of this architecture is its programmable flexibility, which enables the implementation and execution of different data flow strategies, including Output Stationary, Weight Stationary, Input Stationary, and Row Stationary. The flexible design supports both dense and sparse GeMM and General matrix-vector multiplication (GeMV). To …

The Future Of Airport Operating Models And Seamless Passenger Journeys: Strategic Readiness Implications For Dubai World Central (2035–2045), Mohammad Abdulla Alsuwaidi

Theses

Dubai World Central is being developed to support a major expansion of Dubai’s aviation capacity. Its planned scale raises questions beyond terminal construction and technology pro- curement because airlines, airport operators and government authorities must coordinate passenger processing while maintaining security, public confidence and operational continu- ity. This thesis examines how airport operating models and end-to-end passenger processing may evolve during 2035–2045 and assesses the strategic readiness implications for DWC. A qualitative, desk-based design was adopted using peer-reviewed research, official aviation publications, government documents, airport implementation evidence and selected industry reports. The analysis combined PEST horizon scanning, a technology-readiness assessment, …

Energy Loss Hotspot Mapping In Power Distribution Networks, Abdulla Alhammadi

Theses

The electricity systems used in power distribution in the United States also consume a lot of electricity to warm up until it gets to homes, business or industries. These are technical losses which are caused by the physical resistance of the aging electrical equipment like transformers, conductors and distribution lines. With the aging of infrastructure, the resistance rises, and the amount of wasted energy increases. Although this is a large problem, most utility companies continue to use rough age estimations or customer complaints instead of using more direct and objective data-driven techniques to determine the concentration of the worst losses. …

A Study Of Post -Quantum Cryptography Migration In An Emulated Enterprise Environment, Saquib Farooq Malik

Theses

The imminent maturation of Cryptographically Relevant Quantum Computers (CRQCs) represents a fundamental threat to the digital security infrastructure, specifically targeting modern cryptographic standards such as AES, RSA and Elliptic Curve Cryptography (ECC). While Post -Quantum Cryptography (PQC) offers a mathematical defense, the transition is complicated by the “Harvest Now, Decrypt Later ” (HNDL) strategy, where encrypted data is intercepted today for future decryption. This thesis explores the critical necessity of an organized migration strategy to protect long-term data sensitivity against the rapid advancement of quantum capabilities and addresses the gaps in the current migration landscape: the absence of a standardized, …

Kosovo Pisa Monitoring Via System Signals Benchmarking 2015/2018/2022, Peer Clusters, Early-Warning Screening, And Mixed-Methods Triangulation In Grade 10 Mathematics, Leon Cana

Theses

This thesis develops a practical monitoring approach for Kosovo’s PISA outcomes by linking country-level benchmarking of PISA 2015, 2018, and 2022 with measurable system signals and a focused school-level triangulation. Quantitatively, it benchmarks Kosovo against Western Balkans Six (WB6) and selected European peers through mathematics mean scores, below-Level-2 shares, and top-performing shares. It then uses k-means clustering to define empirical peer groups and tests an early-warning screening exercise in which 2015/2018 information is used to classify held-out 2022 high-risk profiles. Qualitatively, it triangulates the statistical signal patterns with policy document analysis and a low-burden Grade 10 mathematics micro-study in Prishtina …

Mathematical Modeling Of Ocular Surface Deformation Due To Contact Lens Wear That Accounts For Intraocular Pressure, Riley K. Supple

Theses

Myopia is one of the most common ocular disorders, and is expected to affect approximately five billion people worldwide by 2050. One treatment currently available for myopia is soft contact lenses. In general, about one in ten Americans wear contact lenses, however one in three will stop wearing them due to discomfort. The goal of this dissertation is to develop a mathematical model that predicts the mechanical interactions between the contact lens and the ocular surface. Assuming the ocular tissue is a linear elastic material, we first develop a mathematical model to predict ocular deformation that is anatomically accurate in …

Mathamr+: A Unified Graph Neural Network Framework For Multimodal Mathematical Information Retrieval, Jacob Yoon

Theses

Mathematical Information Retrieval (MIR) focuses on developing systems that enable users to search for and retrieve documents containing mathematical content. A key challenge in building effective math-aware retrieval systems lies in jointly modeling the symbolic and operational structure of mathematical expressions together with their surrounding linguistic context. Existing approaches often use linear token sequences, losing structural information, or rely on separate models for text and math, limiting their ability to capture cross-modal patterns and learn contextualized representations. This thesis proposes MathAMR+, a graph neural network-based retrieval framework that jointly models Abstract Meaning Representation (AMR) graphs, Operator Trees (OPTs), and Symbol …

Optimizing Product Placement Using Purchase Pattern Analysis, Mohra Shamaa

Theses

Retail product placement has traditionally relied on static shelf layouts and techniques such as market basket analysis, often guided by managerial intuition and category based organization. However, such approaches frequently overlook the complexity of consumer purchasing behavior as reflected in the transactional data, and this study investigates how data driven analysis of purchase relationships can inform more effective product placement strategies in physical retail environments. Unlike the sequential models, this study focuses on co occurrence relationships due to dataset constraints, prioritizing interpretability for retail applications. Using transaction level data from the Instacart dataset, this research applies association rule mining through …

Inverse Laplacian Solution For Spherical Harmonic Decomposition Of Black Hole Initial Data, Nikolaus Vernon Kent

Theses

In the construction of initial data, by way of describing one or more black holes for Numerical Relativity, solving the Hamiltonian Constraint remains a fundamental challenge, as the presence of coordinate singularities in the so-called "puncture" formalism often complicates traditional grid-based numerical methods. This thesis addresses this challenge by developing a semi-analytical framework based on an iterative spherical harmonic modal expansion. We begin by decomposing the source term of the Hamiltonian Constraint, given in terms of the extrinsic curvature, into a basis of spherical harmonics. To navigate the non-linearity of the governing equation, we implement an iterative scheme derived from …

Ai In Action: Redefining Loan Default Prediction For The Digital Lending Era, Joe Vinson Ukken

Theses

Credit risk assessment remains a very important part of financial institutions, particularly within the rapidly evolving digital lending environment. The research explores the effectiveness of five machine learning models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine—in predicting loan default using both financial indicators and categorical borrower attributes. The study is motivated by the growing availability of structured borrower data and the need to evaluate whether advanced machine learning algorithms can be implemented over conventional credit scoring methods. A publicly available L&T Vehicle Loan Default Prediction dataset comprising 233,154 borrower records and 41 structured attributes, the CRISP-DM …

From Centralized Forecasting To Distributed Intelligence: A Dual- Framework Approach To Dynamic Load Forecasting And Federated Learning In Smart Grids, Tousiya Nazir

Theses

With the growing shift of conventional power systems toward decentralized smart grid electrical systems, many key challenges have come into action. Data privacy, scalability, and real-time decision making are major concerns. A vast amount of sensitive data is generated due to the widespread adoption of distributed energy resources (DERs), electric vehicles (EVs), and other intelligent devices. Although this data is useful for grid improvement, it also raises concerns about privacy, security, and communication, which, in turn, make centralized machine learning unsuitable. Because of the aforementioned issues, this thesis presents a dual framework contribution. Firstly, it offers a systematic and comprehensive …

Asobi: Play In Every Language, Jeffrey Gibbs

Theses

In the era of iPad kids and screenagers, toys are struggling to engage children. The modern toy industry prioritizes eye-catching designs over meaningful stimulation, choosing to profit off of media franchising rather than encourage creative expression. Toys are too often designed in a way that limits the scope of play, forcing kids down a specific path rather than opening a world of creative possibilities. This narrow scope prevents imagination and decreases the overall lifespan of the product by encouraging product turnover. All of this contributes to an abundance of toys in the home, a reality linked to negative development outcomes. …

Topological Perspective On Temporal Climate Networks, Daniel Oleynikov

Theses

Networks are highly effective tools for analyzing the dynamics of Earth’s climate system. In this approach, nodes represent geographical locations and edges represent correlations or causal relationships with respect to climate fields, such as air and sea surface temperatures. We extend this technique by integrating it with topological data analysis (TDA), which shifts the focus from pairwise relationships—typical of conventional network methods—to higher-order interactions among geographic locations. Through techniques such as persistent homology and simplicial distributions, we investigate how the global climate dynamics—summarized by this representation—have evolved over the last 75 years. Additionally, we evaluate changes in oceanic connectivity with …

Asymptotic And Series Methods For Nonlinear Differential Equations With Applications In Mathematical Physics, W. Cade Reinberger

Theses

This work advances a general framework to obtain the solution of nonlinear ordinary differential equations (ODEs) via power series methods. The approach is demonstrated through problems relevant to mathematical physics. Power series solutions can offer advantages over other numerical techniques in many applications, including increased convergence and computational efficiency. The theoretical basis for power series and their convergence rests in the complex-analytic structure of the function being computed. The existence of convergence-limiting singularities in the complex plane, their location, and their asymptotic behavior motivate analytic continuation techniques to overcome their influence. This work demonstrates some methods to manage singularities, not …

Thermal And Reactive Transport Phenomena In Plasma With Numerical Modeling, Nitish Kumar Singh

Theses

As semiconductor node technology advances to below 5 nm, it is vital to control Non-uniformity, Line Edge Roughness (LER), and Line Width Roughness (LWR). These factors are crucial for meeting performance, reliability, and yield goals. This study presents a computational fluid dynamics (CFD) model for optimizing Plasma-Enhanced Chemical Vapor Deposition (PECVD) processes, focusing on the synthesis of thin films such as Silicon Dioxide from precursor gases like Silane and Nitrous Oxide. The model is designed to accurately simulate the complex transport and reaction dynamics within the low-pressure, non-equilibrium plasma environment. The transport of neutral radical species (which form the bulk …

Unique Transmissions Of Cycle Graphs With Pendant Paths, Alek Li

Theses

We consider a variant of the well-known Traveling Salesman Problem where the salesman must return to the starting point after each delivery. Ramanathan et al. asked whether different starting locations in a network yield the same total travel distance. The transmission of a vertex $u$ is defined to be $T(u)=\sum\limits_{v\in V(G)}d(u,v)$ where $d(u,v)$ is the number of edges in a shortest path between $u$ and $v$.  For specialized families of graphs, we investigate necessary and sufficient conditions for a graph to have distinct transmissions.  Graph asymmetry is a necessary condition for unique transmissions. However, it is not sufficient, as asymmetric …

Exam Analysis: A Quantitative Approach To Software Pedagogy, Christopher Shepard

Theses

Software engineering exams serve as a tool for evaluating a broad range of skills in the classroom, including theoretical understanding, practical application, and process reasoning. Despite their importance, post-assessment analysis is often overlooked, and the absence of structured reflection by instructors can limit their effectiveness and mask patterns in student performance. By treating exams as data, educators can uncover trends that drive more effective teaching strategies, refine evaluation methods, and work to strengthen student support systems. We conducted a systematic analysis of existing exam data and administered a student survey to determine if student perceptions align with actual outcomes, asking …

Integrated Vaccine Bundle Procurement Framework, Ming Zhu

Theses

Childhood vaccination is among the most effective public health interventions, yet ensuring affordable access in low-income countries (LICs) remains challenging. Centralized procurement agencies such as UNICEF and Gavi purchase vaccines antigen-by-antigen through competitive tenders which is a practice that fails to capture the affordability gains attainable by coordinating vaccine products into optimized bundles tailored to each child’s complete immunization schedule. This study develops an Integrated Vaccine Bundle Procurement Framework that shifts the unit of procurement from the antigen level to the vaccine product level. The framework formulates the procurement as the construction and selection of child-level immunization bundles, feasible sets …

Hidden Diversity In A Clonal Endemic: Insights Into Clematis Socialis From A Population Genetics Perspective, Joanna M. Lapoint

Theses

Clematis socialis (Kral) is a rare, federally endangered perennial species restricted to a small number of fragmented populations in Alabama and Georgia. The species exhibits extensive clonality and limited sexual recruitment, characteristics that complicate expectations regarding genetic diversity and population connectivity. Despite its conservation significance, genome-wide assessments of population structure have been lacking. We used genotyping-by-sequencing (GBS) to generate a genome-wide SNP dataset for 170 individuals sampled from six natural populations. Population structure and genetic differentiation were evaluated using Bayesian clustering, analysis of molecular variance (AMOVA), pairwise FST estimates, and network-based visualization. Analyses revealed strong genetic structuring among populations and …

Sampling And Counting Graph Structures With Triangle Motifs, Sherry Robinson

Theses

Understanding the structure of real-world networks often relies on identifying significant (i.e., occurring significantly more frequently than random) subgraph patterns, or motifs, such as triangles. To assess their significance, null models generate random samples from a constrained distribution of graphs, preserving selected properties while randomizing others. These models may either generate random graphs or sample structures from a fixed input graph. This thesis focuses on the latter, specifically the problem of sampling and counting graph structures that incorporate triangle motifs. While efficient algorithms exist for sampling classical structures such as matchings, extending these methods to higher-order motifs remains an important …

Testing The Intermediate Disturbance Hypothesis On Anthropogenic Pressure In Central Alabama, Gavin Terrell

Theses

Biodiversity is fundamental to maintaining ecosystem structure and function globally. Prior to the late 20th century, ecological theory generally predicted that biodiversity would be highest in systems experiencing minimal disturbance. However, Joseph H. Connell challenged this view with the Intermediate Disturbance Hypothesis (IDH), which posits that species diversity peaks at intermediate levels of disturbance and declines under conditions of both low and high disturbance. Although widely cited, the applicability of the IDH to natural systems remains debated, particularly in the context of anthropogenic disturbances and when diversity is measured beyond species richness alone.

This study evaluated the predictions of the …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Analysis Of A Coaxial Transmission Line Filled With An Orthorhombic Dielectric-Magnetic Medium, Fathima Manikunnath Abdul Akbar

Theses

Coaxial transmission lines are fundamental means for Transverse Electromagnetic (TEM) wave propagation in RF, microwave and high-speed electronic systems. The study of transmission lines is often familiar when they are filled with isotropic materials; however modern engineered direction dependent materials reshape field distributions. In this thesis, we consider a coaxial transmission line of an inner radius and outer radius b filled with an orthorhombic dielectric-magnetic material, which is described by two anisotropy parameters αx and αy. The potential and field distributions are studied in relation to the ratio b/a as well as the anisotropy parameters αx and αy. Due to …

Efficient Fpga Implementation Of A 1 Million-Point Fft, Jwaher Abdulqader Al Tamimi

Theses

The one-million-point Fast Fourier Transform is implemented using a radix-2 single-path delay feedback pipeline architecture. To minimize the computational overhead, twiddle factors were pre-computed and stored in memory. The design uses a fixed-point representation with two integer bits and seven fractional bits, achieving a measured signal-to-noise ratio of 37.98. Given the substantial memory requirements, a memory partitioning approach was used. It mapped the delay buffers in each stage lookup table memory, block random-access memory, or ultra random-access memory, based on word width and memory depth.

The implementation operates successfully at 100 megahertz on a mid-scale field-programmable gate array. Post-implementation reported …

An Artist’S Playground: Building Bridges Between Children’S Education And Their Knowledge Of Art Through Multi-Modal Learning, Alice J. Cauchi

Theses

Not all museums welcome children as part of their main audience with open arms. Research suggests that experiencing museums as a child supports classroom learning and development, but only when the museums engage young children properly. This paper explores how incorporating multi-modal interactives into art museums can effectively engage children, ages 4 to 8, and build a foundation that supports lifelong learning. Drawing on ideas from early-childhood education, museum visitor-engagement studies, and interactivity sciences, this project results in a framework that guides art museums on how they can overlap play, interactives, and content to incorporate this age group effectively to …

Modeling And Autonomous Control Systems For A Delivery Drone Application, Dana Helal Alnuaimi

Theses

This thesis focuses on the development of a multi-drone navigation and control system, aiming to enhance payload capacities beyond the limits of single-drone systems. By integrating multiple drones to work collaboratively as a unit, this study addresses the challenges associated with lifting and transporting heavier payloads.

The primary objective is to design and evaluate a multi-drone system capable of working in tandem to transport larger payloads efficiently. The research aims to develop robust control algorithms, supported by system identification for dynamic modeling, and navigation strategies to enable effective coordination between drones.

The study employs a combination of simulation and real-world …

Transport Of Quantum Walks In Electric Fields, Yousef Mohammad Yousef Salah

Theses

This thesis presents an analysis of transport in one-dimensional discrete-time quantum walks (DTQWs) on the Hilbert space ℓ²(ℤ) ⊗ ℂ². Quantum walks serve as fundamental models of coherent quantum transport and exhibit ballistic spreading driven by superposition and interference. The primary focus of this work is the review and derivation of sharp maximal velocity bounds for several classes of quantum walk step operators, including the shift-coin walk, the split-step walk, and models with constant as well as position-dependent coin operators. We establish general a priori bounds that remain valid beyond the translation-invariant regime. For homogeneous models, Fourier and spectral analysis …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …