Breath-Based Detection Of Liver Cancer Biomarkers Using An Swcnt-Fet Nano-Biosensor: Quantumatk, Mohamed Mohieb Rashdan

Theses

Early detection of liver cancer remains limited by the slow pace and invasiveness of current testing methods. This study proposes a single-walled carbon nanotube field-effect transistor (SWCNT-FET) designed to detect hexanal—a volatile organic compound (VOC) elevated in liver cancer—directly from exhaled breath. The device is modeled in QuantumATK using a semi-empirical Extended Hückel Hamiltonian within the non-equilibrium Green's function (NEGF) framework, emphasizing realistic contact physics by employing metallic SWCNT electrodes instead of conventional metal films. Zigzag channels with (11,0) and (12,0) chiralities are examined to analyze how geometry and contact matching influence charge transport. Simulations include current–voltage (I–V) characteristics and …

Ideals And Lattices In Number Fields, Sarah Ali Alyammahi

Theses

This thesis investigates algebraic number fields and their rings of integers, which

generalize the ring of integers ℤ in ℚ. The study focuses on ideals, units, and ideal class

groups, which describe the arithmetic structure of number fields and the failure of unique factorization. Key invariants such as the norm, trace, and discriminant are developed and applied, with particular emphasis on quadratic number fields and classical examples such as the Gaussian and Eisenstein integers. Some explicit computations of ideal class groups are carried out. The thesis also explores connections with lattice theory by interpreting rings of integers as lattices and …

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …

Software Vulnerability Recidivism In Open-Source Projects, Brandon Keller

Theses

Software vulnerabilities present a major threat to businesses and individuals alike and it is therefore critical that a culture exists among software engineers to encourage the discovery and patching of security flaws. Vulnerability counts are a common way of evaluating a project’s security. However, this metric can run counter to building a developer culture of fault recognition if more vulnerabilities is always seen as a bad thing. While these counts can present a rough idea of a project’s history with security, they provide no insight into how the development team improves and learns as a result of a vulnerability. A …

Effect Of Geometry, Cell Size, And Carbon Fiber Reinforcement On The Charpy Impact Strength Of Additively Manufactured Tpms Lattice Structures, Ahmed Yousuf Mohammad Bin Yaroof (Alsuwaidi)

Theses

Scientific research indicates a growing utilization of lightweight, high-performance materials across various disciplines, including engineering, driven by the demands of modern technological advancements. Recent developments within these fields include the creation of advanced lattice structures through additive manufacturing (AM) processes. One such lattice structure is the triply periodic minimal surface (TPMS) structure. TPMS structures possess unique mechanical properties and energy absorption characteristics that distinguish them from conventional AM lattice structures. However, published research to date has largely focused on the quasi-static behavior of TPMS structures, with limited attention given to the impact performance of composite- reinforced TPMS structures. The purpose …

Preparing Future-Ready Graduates For Technological And Workforce Transformations, Ghia El Dirani

Theses

The United Arab Emirates (UAE) is undergoing rapid economic transformation driven by technolog- ical innovation and national strategies such as Vision 2031 and UAE Centennial 2071, positioning STEM (Science, Technology, Engineering, and Mathematics) education as critical to building a knowledge-based economy. However, a persistent gap exists between the competencies developed in STEM higher education programs and the skills demanded by emerging sectors such as artificial intelligence, renewable energy, and advanced manufacturing. While the UAE has introduced pro- gressive education policies and invested in digital infrastructure, most curriculum reforms remain reactive and disconnected from long-term workforce projections. This research applies strategic …

Investigation Of Machining (Drilling) Of Bio-Composite Reinforced With Jute Fibers Under Different Machining Conditions, Mohammed Abdul Mujeeb Ansari

Theses

This study investigates the drilling performance of jute fibre reinforced bio-composites under different machining conditions to reduce performance parameters like surface roughness, tool type and delamination damage (entry and exit). Jute composites were fabricated using a process called hand layup. Drilling experiments were carried out using full factorial method focusing on three parameters: Feed Rate (0.05mm/rev, 0.1mm/rev and 0.15mm/rev), Drill bit Type (Titanium Nitride-TiN, High Speed Steel-HSS and High-Speed Steel Cobalt HSS-Co) and Lubrication condition (Dry, Minimum Quantity Lubrication-MQL and Cryogenic-LN2). A total of 27 experimental trials were performed, and the responses were measured for each condition. Statistical analysis was …

Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu

Theses

Artificial intelligence systems have achieved remarkable performance across a wide range of visual tasks. However, most existing models operate under the unrealistic closed-world assumption, where training and test data are drawn from the same distribution. In real-world applications such as anomaly detection, autonomous driving, and medical diagnosis, learning systems frequently encounter novel or out-of-distribution scenarios. These settings require models that can recognize unknown inputs, adapt to new information over time, and maintain reliable performance under evolving conditions. This dissertation studies the problem of Open World Visual Learning, a paradigm that enables visual learning systems to operate robustly in dynamic and …

Software Engineering In The Age Of Coding Agents: Failure Modes And Rejection Patterns, Mahd Mohd Hindi

Theses

This thesis investigates the real-world behavior of LLM-driven coding agents that generate code changes and submit pull requests (PRs) to public software repositories. As these tools evolve from autocomplete-style assistants into more autonomous agents, their contributions increasingly interact with socio-technical review processes (human reviewers, bots, CI/CD gates, and project norms). The thesis focuses on understanding why agent-generated PRs are accepted or rejected and what these outcomes reveal about current agent limitations in practical development workflows.

The main objective of this thesis is to systematically characterize rejection patterns and failure modes of agent-generated pull requests in real repositories. Specifically, the thesis …

Parameter Estimation Of Eccentric Binary Black Holes Using Targeted Numerical Relativity Simulations, Patricia Mcmillin

Theses

Gravitational wave events detected by the LIGO-Virgo-KAGRA (LVK) collaboration are the result of compact binary mergers which produce gravitational wave (GW) emission. The observed waveform carries information about the source properties, such as masses, spins, and orbital eccentricity. Events harboring eccentricity upon entrance of the LVK sensitivity band at 10 Hz are believed to have been formed through dynamical channels, where the binary has interacted with other astrophysical objects leading to a merger faster than an isolated binary. Therefore, the identification of eccentric gravitational wave events and accurate determination of their parameters is vital to understanding the population of binary …

Model Free Sliding Mode Control For Uavs, Aashrita Mandalapu

Theses

Sliding Mode Control (SMC) is a robust and adaptable control strategy recognized for its ability to withstand uncertainties, disturbances, and parameter variations. Although the straightforward implementation of SMC along with its insensitivity to modeling inaccuracies make it effective for a wide range of applications, it still relies heavily on an accurate system model. In dynamic environments where system characteristics vary over time, a model-independent SMC framework becomes necessary. So, a Model-Free Sliding Mode Control (MFSMC) approach is proposed as a compelling solution for complex nonlinear systems where deriving an accurate model is difficult. In contrast to conventional SMC approach, MFSMC …

Optimizing Traffic Signal Timings Using Real Time Data Analytics, Hamad Abdulla Binsultan

Theses

City traffic jams have become a major challenge for contemporary cities, causing delays, increased fuel consumption, and other environmental impacts. Conventional traffic signal controlling systems usually depend on fixed or preset signal plans that are incompetent to adjust themselves with changing traffic conditions. Traffic signal timings are optimized across the intersection health of the system by processing real-time data analytics. Through real-time data analytics, traffic flow efficiency is improved while waiting time at signalized intersections is reduced. Using traffic real-time data like vehicle counts, vehicle types, time of day and day of the week to see howtraffic behaves under different …

Security Evaluation Of Post-Quantum Ml-Dsa Implementations Against Software-Induced Fault Attacks, Alexis Korensky

Theses

Quantum computing is a form of computation that uses the principles of quantum mechanics to perform mathematical computations at a faster rate than classical computers. Although quantum computing is currently still in its early stages, if a general-purpose, large-scale, and fault-tolerant quantum computer were to be built, it would jeopardize the security of modern public-key cryptosystems. If these cryptosystems were broken, secure connections could not be authenticated, enabling Man-in-the-Middle (MitM) attacks, and digital messages could not be signed. All data sent over secured HTTPS and/or TLS connections would be vulnerable and potentially malicious since its origin and integrity could not …

Air Mass Trajectories Related To Dust Storms On Mars And A Comparison Study With Spacecraft Images, Mohammad Abdulla Ahli

Theses

The Martian weather system, climate, and surface evolution are strongly influenced by dust storms, which interact with the atmospheric circulation and the planet’s surface features. In this study, selected Martian dust storms are examined using data provided by the Emirates Mars Mission (EMM) and the Mars Climate Database (MCD) to track the movement of air masses and improve understanding of how these storms develop and move across the Martian atmosphere.

The Emirates Mars Mission (EMM), together with the Mars Climate Database (MCD), provides the data needed to track the movement of air masses at different altitudes and times of the …

Inflection Points In Academic Career Trajectories: Statistical Modeling And Interactive Visualization, Aicha Malouche

Theses

This thesis examines the temporal structure of academic career trajectories, with a particular focus on identifying non-linear patterns of research productivity and moments of maximum acceleration in scientific impact. Situated within the broader context of bibliometric evaluation, the study responds to long-standing limitations of aggregate and linear career models that obscure heterogeneity across disciplines and national research systems. Drawing on theories of cumulative advantage, life-cycle productivity, and structural stratification, the research seeks to clarify how and when elite researchers experience peak growth in impact over the course of their careers. The study is guided by four research questions: (1) when …

Microplastics In St. John, United States Virgin Islands Sediments: Methodological Approaches And Variability Of Microplastics Concentrations And Accumulation Rates, Henry B. Arbaugh, Sarah C. Gray, Dimitri Deheyn

Theses

Sediment samples were collected from a near shore and reef site in Coral Bay, St. John, United States Virgin Islands (USVI) from 2007-2016, using tube sediment traps. These samples provide an opportunity to study microplastics (MPs) in an area where no previous MP studies have been performed. Most sedimentary MP studies utilize benthic samples which can only measure MP concentrations (#MP/g or #MP/mL sediment), while sediment traps can provide MP accumulation rates (#MP/cm²/day) as a function of time. However, the methods used for extracting MPs from benthic samples have not been tested using sediment trap samples whose characteristics, …

Investigating The Knowledge, Attitudes And Practices Towards Type 2 Diabetes Mellitus In A Student Population In The United Kingdom: A Quantitative Study, Philip Boadu Anim

Theses

Aim: To explore the knowledge on the risk factors, signs and symptoms, management, and complications of Diabetes, attitudes, and practices towards prevention of Diabetes Mellitus among university students in the United Kingdom.

Methods: The researcher utilised a quantitative study design in carrying out the study. The study was conducted at Cardiff Metropolitan University with Cardiff Met students being the target population. Thirty (30) participants were recruited through convenient sampling. Data was collected through questionnaires using closed ended questions. The Statistical Package for Social Science (SPSS) version 27 as used to analyse the data collected. Statics were performed to form a …

Customer Lifetime Value Prediction In E-Commerce, Shaimaa Alantali

Theses

The e-commerce market across the world currently deals with an urgent problem which has emerged because businesses now use automated pricing systems everywhere. Online merchants who automate their decision processes to handle changing customer needs and limited stock situations must face external business forces which push their teams to start destructive pricing battles that harm all businesses in the sector. The research investigates how a unified computational framework which combines demand modeling with advanced data analytics methods can enhance pricing strategies through market instability detection and prevention systems. The study creates a market simulator through data-driven methods which uses retail …

Urban Effects On Cloud Formation, Boundary Layer Circulation, And Rainfall Over Miami, Aaron A. Serre

Theses

The sensitivity of cloud and precipitation formation is expected to be enhanced over urban areas due to persistent sea-breeze forcing. This study combines surface observations, satellite data, and numerical modeling to investigate the impacts of urbanization in the Miami Metropolitan Region (MMR) on surface meteorology, cloud formation, and precipitation. Satellite observations reveal urban expansion of approximately 348 km², along the western boundary of the MMR. These areas also exhibit statistically significant increases in land surface and air temperatures. Geostationary satellite data show enhanced morning cloud formation over the MMR, while increasing rainfall and interannual variability in cloudiness are observed along …

Inflex : A Scalable Framework For Malware Analysis, Correlation, And Steganography Detection, Luke Bower

Theses

Modern malware campaigns operate in stages across diverse formats, including PE, ELF, shellcode, and malicious documents, some leveraging steganography to deliver payloads such as Quasar RAT and Lokibot. Because analysis tooling remains siloed by artifact type, analysts struggle to identify shared code reuse, behavioral overlap, and campaign relationships when adversaries avoid traditional indicators. This thesis presents INFLEX, a framework that normalizes diverse artifacts through a unified pipeline combining static analysis, emulated behavioral extraction, sandbox execution, and threat intelligence enrichment. Its correlation layer introduces Variant Resilient Function Hashing (VRFH), validated against fuzzy hashing baselines on targeted samples, and Weighted Artifact Fusion …

Modeling And Identification Of Thermal Interactions In Lithium-Ion Battery Modules, Saima Alam

Theses

The recent development of lithium-ion battery technology has fueled its adoption in electric vehicles (EVs) as the primary power source due to its high energy and power density. However, the high energy density comes at the cost of higher safety requirements. To ensure the lithium-ion battery pack's operational safety, performance optimization, and longevity, a Battery Management System (BMS), an electronic controller, is employed. Recent reports of battery pack overheating, fire, and explosion in EVs expose the limitations of the BMS in detecting the abnormalities and preventing them from propagating to other cells. To address these limitations, in the first part …

Deep Joint Source-Channel Coding For Semantic-Aware Adaptive Wireless Image Transmission, Sudipto Das Suvro

Theses

The growing use of AI-enabled applications has increased the demand for adaptive, efficient, and low-latency image transmission of large data volumes. This has created challenges for conventional separate source-channel coding that focuses on reliable bit sequence transmission without considering the contents or communication objectives. Deep joint source-channel coding (DeepJSCC) potentially solves this by combining source compression and error correction into a unified process. This thesis focuses on developing adaptive and computationally lightweight DeepJSCC-based transceivers for image transmission across heterogeneous channels. The framework further investigates DeepJSCC’s performance in OFDM with robust channel estimation and importance-based subcarrier allocation strategies. Comprehensive evaluation using …

Optimizing Marketing Campaigns To Maximize The Response Rate For A Supermarket Using Machine Learning Techniques, Alia Almansoori

Theses

Supermarkets must improve their marketing tactics at a time of changing consumer behavior and increasing competition in the retail industry. Engaging the wide and sophisticated consumer base of today's supermarkets is difficult using traditional methods. This proposal presents a data-driven approach using cutting-edge machine learning methods to enhance supermarket marketing strategies. The main goal is to increase the response rate of marketing initiatives, which will improve consumer engagement and ultimately increase revenue. Additional primary objectives are customer segmentation and targeting, predictive modeling, personalization, ongoing performance monitoring, and ROI evaluation. The current problem centers around personalization and accuracy. Due to the …

Evaluating The Effectiveness Of Scenario-Based Virtual Escape Rooms To Improve Student Confidence In Dosage Calculations, Jennifer A. Cuevas

Theses

Abstract

Background: Accurate dosage calculations (DCs) are critical for patient safety, as medication errors contribute to approximately 7,000-9,000 preventable deaths annually. Nearly 30% of nursing students exhibit weakness in foundational mathematical skills, and a significant confidence gap exists: 70% feel confident in mathematical abilities, yet only 45% feel confident in clinical application, necessitating innovative educational interventions.

Purpose: The purpose of this project was to implement and evaluate a scenario-based virtual escape room intervention to enhance undergraduate nursing students' confidence in DCs.

Methods: Grounded in Kolb’s experiential learning theory, this online asynchronous intervention engaged students through concrete experience, reflective observation, abstract …

Analysis Of Surrogate Models At Multiple Levels For Neural Acceleration Of Hpc Applications, Bibek Panthi

Theses

Due to rise of machine learning workloads, high performance computing platforms have increased GPU resources. Consequently, use of surrogate models, to utilize GPU compute and speedup scientific applications, is on the rise. Most of the time, the whole program is replaced with a surrogate model. In this work a shock simulation program (LULESH) was taken and three functions of varying complexity were replaced with small sized neural network as surrogate models. The speedup and error in output of whole program was analyzed. We found that for the smallest function, trained model lead to speedup of overall program by 40%, maintained …

Methods For Inter-Sensor Radiometric Harmonization Of Planetscope Superdove Constellation, Biraj Bikram Pathak

Theses

The PlanetScope SuperDove constellation consists of approximately 130 CubeSats. Radiometric drift among the satellites degrades time-series workflows. We formulate inter-sensor harmonization as a supervised regression problem and train a single model across all eight spectral bands using overlapping regions from cross- sensor image pairs. Controlled experiments on same-day image pairs show that a cubic spline model with 20 uniform knots and joint Ridge regularization achieves the lowest error among the evaluated per-pixel regression approaches. The model converges to a Kullback-Leibler divergence between 0.001 and 0.003, and no alternative architecture studied achieves a statistically significant improvement. Models that fit on each …

A Complex Systems Integration Analysis Approach, Tamia Neal

Theses

It has been said that if the building blocks of a system were designed and implemented correctly, then their subsequent integration would be straightforward. The complex systems integration analysis approach presented in this thesis provides the mechanisms to construct those blocks. A spectral clustering algorithm is used to cluster elements into modules, and the normalized integrative complexity metric is used to evaluate the portion of structural complexity that can be attributed to integrating a system from its modules. Together, the algorithm and metric identify which aggregates of system elements reduce the complexity of the integration effort. The proposed integration analysis …

Spatially-Distributed Imu Array Navigation : Virtual Imu And Federated Approaches With Applications To Uncrewed Aerial Systems, Jackson Neese

Theses

This thesis investigated lever arm effects on distributed inertial measurement unit (IMU) array navigation and evaluated the performance of multiple fusion architectures with applications to uncrewed aerial systems (UAS). The Kalman Filters comprise of three filters: a naive baseline, a filter tracking lever arm as a filter state, and a filter constraining lever arm state estimates around known values. These filters feed into a virtual IMU fusing raw measurements and a federated filter combining output states. Each are evaluated with equal, static, and dynamic weighting based on lever arm and sensor noise. Experiments on indoor and outdoor UAS datasets reveal …