Physical Sciences and Mathematics Commons^™

Agentcite: Trustworthy Ai Agents For Information Verification Across Referenced Documents, Eymen Yigit

Master's Theses

This thesis presents AgentCite, a multi-agent framework for automated verification of referenced numerical data in research documents. The framework employs a hybrid design in which LLM-based agents handle document understanding and evidence retrieval, while deterministic components manage structured parsing and value comparison, improving consistency and reproducibility while reducing token consumption and execution time compared to fully agentic approaches.

AgentCite consists of three autonomous agents: a Negotiator, a Main Document Agent, and a Source Documents Agent, coordinated through a fixed tool-call pipeline. The Negotiator orchestrates verification by extracting tabular data from the main document, retrieving evidence from per-source vector stores, and …

Probing Representational Emergence In Large Language Models, Shawn Ismail

Master's Theses

This thesis investigates whether abrupt behavioral gains in large language models under scaling are accompanied by systematic changes in internal representations. It combines a behavioral screen of 65 tasks per family with targeted layerwise probing across eight decoder-only, open-weight model families. Behavioral emergence is defined for each family-task trajectory using an empirical jump detector, with segmented regression retained only as a diagnostic. The representational follow-up analyzes 27 selected MMLU subtasks shared across all families, spanning 37 checkpoints and 216 family-task units.

For each follow-up checkpoint, frozen linear probes are trained on every layer's hidden states to measure how much task-relevant …

Deep Learning For Predicting Impact Energy And Compression After Impact Strength Of Composite Materials Using C-Scan Images, K. T. Tan, Jason P. Mack, Faizan Mirza, Zhong-Hui Duan

University Research

Traditional assessment of post-impact performance in carbon fiber reinforced polymer (CFRP) composites often relies on simplified scalar metrics that fail to capture the complex spatial interactions driving failure. This study addresses this limitation by developing an automated, end-to-end deep learning framework that shifts from manual feature extraction to the direct interpretation of raw damage morphology from ultrasonic C-scans. Using a ResNet18-based convolutional neural network (CNN) trained on 1,428 augmented images, the model achieved coefficients of determination (R2) of 0.7948 ± 0.0847 for compression after impact (CAI) strength and 0.9436 ± 0.0098 for impact energy. Beyond prediction, this dual-purpose methodology serves …

Discrete-Event Simulation: A Leslie System Model For Transportation Demography, Md Zobaer Ahammad

Electronic Theses and Dissertations

Transportation systems are influenced by demographic change, household formation,and patterns of vehicle ownership. These factors affect long-run transportation demand and congestion levels within urban infrastructure. Understanding how demographic dynamics interact with transportation behavior is therefore important for analyzing the long-term evolution of transportation systems. This thesis develops a modeling framework that integrates discrete-event simulation with a Leslie-type matrix model to study transportation–demography interactions.Simulation outputs are aggregated to construct a transition matrix describing changes in transportation states. This matrix acts as a linear (or affine) transformation on the transportation state vector, allowing the system to be analyzed as a discrete linear …

Machine-Learning Landslide Susceptibility And Runout Modeling In The Nolichucky River Gorge After Hurricane Helene, Grace Braver

Electronic Theses and Dissertations

Extreme rainfall from Hurricane Helene (September 2024) triggered widespread landslides across the southern Appalachian region, highlighting the need for rapid landslide susceptibility assessments that capture both landslide initiation and downstream runout. Traditional susceptibility models often focus solely on initiation zones, limiting their ability to identify which slopes will generate destructive landslides or where material will travel. This study addresses that gap by (1) integrating Geographic Information System (GIS)-based machine learning susceptibility modeling using ArcGIS Pro: Maximum Entropy (MaxEnt) and Random Forest-Based and Boosted Classification and Regression (FBBC) and (2) the U.S. Geological Survey (USGS) Grfin (Growth, Flow, and Inundation) runout …

Do I Have To Know Someone? The Impact Of Factors Beyond The Playing Field That Affect Mlb Hall Of Fame Voting., Jackson Wollscheid

Economics Undergraduate Honors Theses

With the creation of WAR (Wins above Replacement), it has created an index to measure the player’s value using a variety of statistics relevant to that player’s position. The higher a player’s career WAR then the greater the impact that player had on the game during his career. However, players are not simply inducted into the Hall of Fame on this measure of value but on voting from members of the BBWAA (Baseball Writers’ Association of America). This research investigates external factors outside of standard playing statistics’ influence on Hall of Fame voting, including team affiliation, career length, and other …

Nanoscale Electrochemical Measurements: Theory, Instrumentation, And Operation, Kamsy Lerae Anderson

Graduate Theses and Dissertations

Electrochemical processes underpin many modern technologies (e.g., batteries, fuel cells, and corrosion protection systems), and because these processes are often controlled by interactions that occur at the nanoscale, gaining insight into nanoscale electrochemical behavior is essential to advancing these technologies. Nanoscale electrochemical imaging probes measure electrochemical activity with nanoscale spatial resolution and have enabled researchers to gain new insights into diverse experimental systems. However, limitations remain, including a lack of methods for direct quantitative interpretation, the influence of atmospheric conditions on experimental reproducibility and stability, and a knowledge barrier resulting from limited accessible resources, all of which continue to restrict …

Of Dunes And Ice: A Surface-Atmosphere Investigation Of Mars Co2 Sublimation Features And Titan Aeolian Fields, Racine D. Cleveland

Graduate Theses and Dissertations

This dissertation investigates a central question: How do planetary boundary layer processes manifest in observable surface geomorphology, and how can planetary imagery be used to quantify those effects? The planetary boundary layer (PBL) governs near-surface exchanges of momentum, heat, and volatiles, yet direct atmospheric measurements remain spatially and temporally limited on most planetary bodies. By analyzing geomorphic features preserved in high-resolution imagery, this work quantifies how boundary-layer processes leave measurable signatures on planetary surfaces. For Mars, high-resolution images from the Mars Reconnaissance Orbiter (MRO) HiRISE instrument were used to map CO2 sublimation features, or Swiss Cheese Features (SCFs), on the …

Conservation Agriculture Effects On Greenhouse Gas Emissions From Cotton, Soybean, And Corn In Southeast Arkansas, Jonathan Benjamin Brye

Graduate Theses and Dissertations

The slow degradation of natural resources has resulted in the focus of agriculture shifting towards conservation practices, such as reduced/no-tillage, cover crops (CC), and organic amendments (i.e., biochar), that conserve and improve natural resource quality. The objectives of this thesis were to i) quantify the impacts of biochar rate (i.e., 0, 2000, and 4000 kg ha-1) and ii) the combination of CC and tillage practice on carbon dioxide (CO¬2), methane (CH4), and nitrous oxide (N2O) fluxes, growing-season-long emissions, global warming potential (GWP), soil properties, and plant response from a minimally tilled cotton (Gossypium hirsutum)-corn (Zea mays) rotation and a soybean …

Toward Causal Generative Modeling: From Representation Learning To Controllable Generation, Aneesh Komanduri

Graduate Theses and Dissertations

The hallmark of human intelligence is causal reasoning, the ability to infer relationships between causes and effects through observation and intervention. While modern deep learning has excelled at identifying statistical patterns, current generative models often struggle to capture the underlying structural causal mechanisms of the data-generating process, leaving them vulnerable to shortcut learning and spurious associations. To achieve true generalizability and interpretability, artificial intelligence must transition from simple association to higher-level causal reasoning to be capable of scheduling and planning in the real world. This dissertation develops fundamental methodologies for causal generative modeling by integrating Pearl’s Structural Causal Model (SCM) …

The Impact Of Quantum Computing On Ransomware, Byron Denham

Graduate Theses and Dissertations

Ransomware is one of the most pervasive and dangerous threats to cybersecurity today. Attacks involving ransomware have been responsible for the disruption of critical services in many fields including healthcare, education, and government. In the current paradigm, crypto ransomware relies on asymmetric cryptography to perform its operations in a way that ensure the victim’s only chance for file recovery is by paying the attacker’s requested ransom payment. However, advancements in quantum computing threaten the asymmetric cryptography that ransomware relies on. It has been shown that, once developed, a sufficiently powerful quantum computer will have the ability to break asymmetric cryptography …

Efficient Deep Neural Networks For Autonomous Perception, Reeshad Khan

Graduate Theses and Dissertations

Autonomous perception systems must operate reliably under uncertainty arising from noisy observations, incomplete supervision, and hardware constraints. This dissertation investigates the design of efficient deep neural networks for autonomous perception through a unified perspective that treats uncertainty, efficiency, and sensing as interconnected challenges. The first contribution develops adaptive extensions of unbiased risk estimators, including eSURE and ePURE, enabling unsupervised training of deep neural networks for magnetic resonance image denoising under Gaussian and Poisson noise. However, these methods rely on known noise assumptions, which motivates the second contribution: a unified diffusion and Bayesian risk framework that estimates and adapts to unknown …

Genre Prediction Using Rnns And Llm-Enhanced Video Game Review Data, Gabriel Young

Graduate Theses and Dissertations

LLMs (Large Language Models) are powerful tools for engaging with textual data, carrying many advantages over classical NLP (Natural Language Processing) and ML (Machine Learning) approaches. However, a classical ML model can still be faster, more efficient to run, and accessible than an LLM. We seek to gain the benefits of LLM text comprehension and preserve them in a classical ML model, a hybrid approach. The LLM operates on text to surface relevant information and associations in our problem space, then the ML model trains on the LLM output. The model may learn from the LLM and provide a more …

Structural Relaxation In Glass-Forming Liquids At Extreme Pressure: Photon-Correlation Spectroscopy And Constrained Thermodynamic Surface Modeling, Kevin Wayne Lyon

Graduate Theses and Dissertations

Structural relaxation in glass-forming liquids is strongly dependent on both temperature and pressure, yet direct measurement of structural α-relaxation under multi-gigapascal compression has remained experimentally challenging. This work extends depolarized photon correlation spectroscopy (DPCS) to pressures approaching 5 GPa in a diamond anvil cell, representing the highest-pressure implementation of this technique and enabling direct measurement of the structural α-relaxation in a pure liquid at extreme compression. This capability provides access to dynamical regimes previously unexplored by photon correlation spectroscopy and establishes a platform for probing glassy dynamics at high pressure. To describe these measurements, a constrained relaxation-time surface framework is …

From Phenology To Yield Zones: Functional Clustering Of Satellite Time Series For Crop Growth Monitoring, Ikram Morso

Graduate Theses and Dissertations

Remote sensing and land surface phenology provide a strong basis for characterizing crop development, but their usefulness for within-field yield-zone delineation depends on whether crop development is analyzed as a phenological trajectory and whether the resulting zones are evaluated with spatially defensible statistics. These questions are especially relevant in soybean production because soybean is one of the most important row crops in the United States and the country’s dominant oilseed crop. This relevance is strong in Arkansas, where soybean is a major component of agricultural production. In soybean production, within-field yield variability reflects the combined influence of soil properties, stand …

Sel & Hil Integrations For Real-Time Cyber Threat Modeling, Risk Assessment, And Result Validation, Zachary Coel Harris

Graduate Theses and Dissertations

The electrical power grid is one of the most critical infrastructure systems, with virtually every other critical system relying on its electricity to operate effectively [1]. One failure in the electrical grid can have cascading effects, reducing, or outright stopping the operation of services vital to people’s lives. Ensuring continuous availability of the electrical grid is vital. This constant need for availability poses a challenge to identify potential cyber-attacks and their impacts. The grid is constantly growing with the addition of DERs (Distributed Energy Resources). Common DERs, such as photovoltaic systems or wind farms, can provide primary or secondary generation …

Solid Embedded Telemetry, Pranav Balasubramanian Natarajan

Graduate Theses and Dissertations

This thesis explores the integration of the Solid decentralized data framework within embedded and legacy energy control systems to enable secure, access-controlled, and interoperable data exchange. Two implementations were developed to evaluate Solid’s practical applicability: an ESP32 client implementing Solid-OIDC authentication and telemetry publication to a Solid Pod, and a Modbus-to-Solid bridge translating Modbus RTU communications into RDF-based data for bidirectional control. Experimental evaluation highlighted the effects of task scheduling and blocking operations on the ESP32, and polling-based communication latency within the Modbus bridge. These findings reveal the architectural and computational trade-offs involved in extending Solid to constrained or protocol-bound …

The Effect Of Hydroflow Devices On Calcium Carbonate Buildup., Holton Shults

Master of Engineering Theses

Water containing high levels of dissolved minerals, mainly calcium and magnesium, is known as hard water, which is commonly found across the world due to calcium’s abundance in the earth’s crust. Hard water can lead to calcium carbonate buildup (also referred to as limescale) that reduces flow, clogs pipes, and lowers heat exchanger efficiency, all of which can drive up costs. This work evaluated two commercially available chemical-free treatment devices from HydroFLOW USA LLC, called the Pearl Plus and s38 devices. These electromagnetic devices are designed to alter calcium carbonate crystal structure, potentially reducing the amount of calcium …

Spike-Sorting Algorithm For Neuropixel Probes, Luis David Davila

Open Access Theses & Dissertations

Modern extracellular neural probes like Neuropixels, allow for high-density extracellular neural probes to record terabytes of electrophysiological brain data. Spike sorting is the process of isolating individual neurons on this data through various clustering and filtering techniques. Modern spike sorting algorithms are held back by their need for human intervention to separate high-quality from low quality clusters, due to the volume of data that can be collected a completely automated approach is required. To address this requirement, we have developed a framework to preprocess, cluster, grade, and separate high/low quality clustering results using custom models trained on the grades. To …

Sevoauth: Secure Voiceprint Authentication With Hash-Based Feature Transformation, Rui Zhang, Zheng Yan, Robert H. Deng

Research Collection School Of Computing and Information Systems

While voiceprint authentication offers convenient user authentication and access control through voice feature recognition, a critical research gap remains: existing voiceprint authentication systems fail to simultaneously achieve sound security against replay, spoofing, and adversarial attacks, preserve voice privacy leakage, and satisfy usability demand. Previous efforts have struggled to balance these issues comprehensively. To bridge this gap, we present SeVoAuth, a cloud-based Voiceprint Authentication as a Service (VAaaS) system designed to provide privacy preservation, robust security, and enhanced usability. SeVoAuth stores a synthesized voiceprint of a user in the cloud during user registration, thereby safeguarding the privacy of the real voiceprint …

Synthesis And Evaluation Of Long-Term History-Aware Medical Dialogue, Hebin Hu, Renke Dai, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

An effective healthcare agent must be able to recall and reason over a patient’s longitudinal medical history. However, the absence of datasets with realistic long-term dialogue timelines limits systematic evaluation. Real clinical text is constrained by privacy and ethics, while existing benchmarks focus on isolated interactions, failing to capture cross-session reasoning. We introduce a framework for synthesizing high-quality, long-term medical dialogues with LLMs. Our approach entails a knowledge-guided decomposition into three stages: constructing synthetic patient profiles with diverse disease and complication trajectories, generating multiturn dialogues per encounter, and integrating them into a coherent longitudinal history dataset, MediLongChat. We establish three …

Collaborative Practices And Tool Utilization In Software Development Projects: A Student Perspective, Yi Meng Lau, Muhammad Syahmi Bin Abbas, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Software development is a collaborative activity that depends on effective teamwork, shared understanding, and coordinated use of development practices and tools. While these aspects are well studied in professional environments, they are less frequently examined within software engineering education. This study investigates how students collaborate in group projects, focusing on collaborative practices, tool usage, and their perceptions of software quality. We conducted a quantitative post-project survey with 143 second-year undergraduate students enrolled in a software development course. The results show that students actively share information and often establish team norms to support coordination and collaboration. However, students face challenges in …

Generation Of Elaborated, Targeted And Effective Feedback For Novice Programmers Using Llm, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Programming errors and misconceptions are pervasive in novice programmers which causes difficulty in the learning of computer programming. Large Language Models (LLMs), with their ability to comprehend and generate programming codes have shown promising results in the automatic identification of errors. This can potentially benefit student programmers by providing them with timely formative feedback at efficiencies and scale that were not attainable previously. In this study, we leveraged an LLM - OpenAI o4-mini for the generation of elaborated, targeted feedback for novice programmers across PHP and JavaScript exercises. We contend that the feedback needs to be effective and targeted other …

Selective Concolic Testing, Guofeng Zhang, Zhenbang Chen, Ziqi Shuai, Jun Sun, Weijiang Hong, Yufeng Zhang, Ji Wang, Yang Liu

Research Collection School Of Computing and Information Systems

The principled combination of symbolic execution and random testing lacks a formal foundation, especially in deciding which inputs to symbolize. We propose selective concolic testing, a cost-aware framework that formulates this choice as an optimized policy problem of a MDP (Markov Decision Process). We model program exploration over a finite control-flow graph, where MDP states represent covered statements, actions partition path constraints into symbolic and random fragments, rewards reflect coverage gain, and costs account for SMT solving effort and sampling inefficiency. Our framework yields the first formal characterization of selective symbolization as policy synthesis in a probabilistic system. We prove …

Robots And Ageing Population – Rehabilitation And Social Robots, Yuzhou Qian, Keng Siau

Research Collection School Of Computing and Information Systems

Population ageing is placing increasing pressure on healthcare and caregiving worldwide. Governments in different countries are looking into the use of advanced information technologies such as artificial intelligence and robotics to help in caring for ageing population. Older adults often need both physical assistance and psychological support, and robots have emerged as a promising technological approach to supplement eldercare. This paper provides an overview of the current state of robots in eldercare. It reviews the major definitions, categories, and features of robots used in healthcare and eldercare. The paper also examines the potential benefits of robotic technologies, such as reducing …

A Catalogue Of Orbital Periods Of Cataclysmic Variables And Candidates From Tess Observations, Meryem K. Dag, Simone Scaringi, Kieran O’Brien, Martina Veresvarska, Nikita Rawat, Yusuke Tampo, Santiago Hernández-Díaz, Liliana Rivera Sandoval, Wendy Mendoza, Ryan J. Oelkers, Jan Kára

Physics & Astronomy Faculty Publications

We present a systematic analysis of 2544 cataclysmic variable systems and related candidates observed by the Transiting Exoplanet Survey Satellite (TESS), with the aim of compiling a comprehensive catalogue of orbital periods. Using 2-min photometric time-series data, we applied an automated algorithm to generate Lomb–Scargle periodograms and identify the most significant coherent periodic signals, which were subsequently verified through visual inspection. This process yielded a confident sample of 910 sources exhibiting at least one periodic signal, hereafter referred to as the Cataclysmic Variable Confident Catalogue (CCC). For each object, we report the most likely orbital period together with …

Software Integration In Personal Healthcare Devices And The Patient User Experience, Yassine Chahid, Patrick Slattery

Publications and Research

This study examines the current landscape and future direction of medical device hardware and software integration, focusing on how each contributes to patient care. It begins by analyzing hardware focused medical devices, such as implantable tools patients may rely on to assist with their condition, alongside diagnostic and monitoring equipment used to treat conditions in a variety of medical areas (e.g. cardiovascular conditions). It then evaluates how software is currently integrated through embedded systems, data processing, and user interfaces that support real time monitoring and clinical decision making, and how this impacts quality of care for the patient whilst minimizing …

Molecular Mechanisms Of Pfas Induced Protein Dysfunction: Implications For Neurodegenerative Risk, Daisy Lee Wilson

Open Access Theses & Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread human exposure and well-established links to adverse health outcomes. However, despite increasing epidemiological and experimental evidence, the molecular mechanisms underlying PFAS-induced neurotoxicity remain incompletely defined. This dissertation investigates PFAS toxicity across biological scales, integrating protein biophysics with neuronal cell models to elucidate pathways relevant to neurodegeneration. At the molecular level, we demonstrate that PFAS disrupts the structure and function of globular proteins, including the impairment of α-lactalbumin's calcium-binding capacity and of β-lactoglobulin's retinol binding, through concentration-dependent conformational destabilization. By applying these findings to a neuronal model, we show that …

Riemannian Manifold-Coupled Joint Sparse Graphical Models For Spatially Structured Precision Matrix Estimation, Abdul Rahman Adam

Open Access Theses & Dissertations

This thesis introduces a novel geometry-aware framework for joint precision matrix estimation in spatially structured data. Unlike existing joint Gaussian graphical model estimators that rely on Euclidean coupling mechanisms such as fused or group graphical lasso, our approach incorporates intrinsic Riemannian geometry through geodesic distance penalties on the symmetric positive definite manifold.

The method measures similarity between precision matrices of adjacent spatial regions using the affine-invariant Riemannian distance, capturing deformations of concentration ellipsoids rather than entrywise differences. This is particularly suited for spatial transcriptomics, where regions may share dependence structures despite differences in scale or composition.

An efficient Alternating Direction …

Comparative Case Studies Of How General Chemistry Ii Students Explain Entropically-Driven Phenomena, Raymundo Aragonez

Open Access Theses & Dissertations

Understanding how students conceptualize entropy remains a central challenge in chemistry education. Often referred to as a "driving force" of chemical reactions, the dispersal of energy as a process proceeds is indicative of a process's spontaneity or thermodynamic favorability. Students frequently default to discussing phenomena in deterministic terms, potentially reflecting the language choice and heuristics selected by the instructor such as "disorder". In this study, assessment prompts about entropy from a convenience sample of general chemistry textbooks were characterized as to their potential to elicit three-dimensional learning, and students' responses to assessment prompts from a curriculum aligned with three-dimensional learning …