Physical Sciences and Mathematics Commons^™

Inductive Biases In Field-Level Cosmological Inference From Galaxy Catalogs, James O'Connor Baldwin

Dissertations, Theses, and Capstone Projects

We perform field-level likelihood-free inference of the matter density parameter Ωm from simulated galaxy catalogs using machine learning models with differing inductive biases. Using features extracted from hydrodynamic simulations in the CAMELS suite, we investigate how both observable choice and model architecture govern the extraction of cosmological information. We consider galaxy positions and line-of-sight peculiar velocities, both separately and in combination, and compare permutation-invariant Deep Sets, implemented with either standard multilayer perceptrons (MLPs) or Kolmogorov–Arnold Networks (KANs), to graph neural networks (GNNs) implemented with MLPs, which explicitly encode spatial relations. We evaluate inference performance under both in-distribution and out-of-distribution (OOD) …

Identifying Helium Emission-Line Stars With The Condor Array Telescope, Julia Stewart

Dissertations, Theses, and Capstone Projects

Helium emission-line stars, including cataclysmic variables (CVs), AM Canum Venaticorum (AM CVn) binaries, symbiotic stars, and X-ray binaries, are tracers of compact object binary populations in the Milky Way, and may be viable progenitor systems of Type Ia supernovae. Their space densities are not well constrained - just over 100 AM CVn stars are known out of a Galactic population that may be closer to 100,000. Identifying such systems in large numbers requires wide-field, narrow-band photometric surveys capable of detecting faint emission-line sources across significant sky areas.

In this thesis, I present a narrow-band photometric search for helium emission-line stars …

Reexamining The Croton Aqueduct: A Ward-Level Microhistorical Analysis Of Infrastructure And Cholera Mortality In 1849 New York City, Alexandria R. Toothman

Dissertations, Theses, and Capstone Projects

This work reexamines the Croton Aqueduct’s role in public health by shifting analysis from the citywide level to the ward level, revealing that infrastructure expansion did not produce uniform benefits across New York City. When the Croton Aqueduct opened in 1842, it was celebrated as an achievement that would deliver pure water and eliminate disease. However, only seven years after its opening, in 1849, cholera returned with greater force in the Sixth Ward. This work shows that the uneven distribution of Croton infrastructure corresponded with uneven cholera mortality rates, challenging narratives that present Croton as a singular triumph of urban …

Identifying Textual Predictors Of Early Termination In Clinical Trials In Medicine: An Explainable Machine-Learning Study, Rohan Ramnarain

Dissertations, Theses, and Capstone Projects

About one in five clinical trials in medicine ends early, wasting valuable resources and reducing the evidence available for developing life-saving medical treatments. This project uses a method called Trial2Vec, which is a self-supervised machine-learning method that converts clinical trial documents into dense numerical representations that capture their key design and clinical characteristics, to turn each proposed clinical trial’s written protocol into a compact numerical profile (a process referred to as embedding). These profiles are then paired with a predictive machine learning models to identify the words and phrases in the trial documents that can signal a higher risk of …

Discovery Of Newly Resolved Filament And Extraplanar Gas In M33 With The Local Group L-Band Survey, Emma Taylor Kleiner

Dissertations, Theses, and Capstone Projects

The proximity of the Local Group allows for unparalleled resolution of the structure and kinematics of the ISM while simultaneously observing the overall rotation of the galaxy. These nearby galaxies present the opportunity to probe stellar feedback processes in various galactic environments. M33 in particular, offers insight into the nature of the extended neutral gas and its relationship with the star-forming disk and the surrounding CGM, by way of its classic star-forming spiral and relatively undisturbed system. Outside of M33’s distinguished warp, the galaxy offers little evidence of satellites and shows few signs of interaction, begging the question of how …

Supramolecular Assembly In Short Peptide Systems For Selective Metabolite Recognition And Drug Nanoencapsulation, Maithreyi Ramakrishnan

Dissertations, Theses, and Capstone Projects

Short peptides can form adaptive supramolecular assemblies, and understanding how minimal sequences organize around neurometabolites or hydrophobic cancer drugs enables the rational design of functional materials. This thesis combines molecular dynamics with experimental validation to establish design rules linking peptide sequence to emergent structure and function. Chapter 1 outlines the molecular determinants governing peptide assembly. Chapter 2 reviews computational workflows that reveal sequence-dependent conformations and supramolecular organization. Chapter 3 applies these principles to Dynamic Peptide Libraries which identify tetrapeptides that selectively interact with neurometabolites. Chapter 4 extends the same interaction-driven framework to design tryptophan-rich pentapeptides that co-assemble with kinase inhibitors …

Integrating Multi-Scale And Multi-Filtration Topological Features For Medical Image Classification, Pengfei Gu, Huimin Li, Haoteng Tang, Dongkuan Xu, Erik Enriquez, Dongchul Kim, Bin Fu, Danny Z. Chen

Computer Science Faculty Publications

Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundamental anatomical structures (e.g., those encoded by topological invariants), or they capture only simple topological features via single-parameter persistence. In this paper, we propose a new topology-guided classification framework that extracts multi-scale and multi-filtration persistent topological features and integrates them into vision classification backbones. For an input image, we first compute cubical persistence diagrams (PDs) across multiple image resolutions/scales. We then develop a "vineyard" algorithm that consolidates these PDs into a single, stable diagram capturing signatures at varying granularities, …

Comparing Allometric Models To Machine Learning Models For Aboveground Biomass Estimation In Agroforestry Systems In Kenya, Samuel Irungu Kigotho, Kennedy Senagi, John Olukuru, David Masereti Makori, Elfatih M. Abdel-Rahman, Evans Omondi

All Peer-Reviewed Publications

This study compared traditional allometric models with machine learning (ML) techniques for accurately estimating aboveground biomass (AGB) in six Acacia species within Kenyan agroforestry systems. Using tree diameter at breast height (DBH) and total height as inputs, the research evaluated allometric models (Chave’s, Brown’s, and Henry’s) against ML models, including Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR). This research advances the field by benchmarking machine learning and classical allometric models at the species level within Kenyan agroforestry systems and employing SHapley Additive exPlanations (SHAP) for interpretability analysis. Model performance was validated using …

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 …

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 …

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 …

Ai-Powered Student Support And Development Platform, Wadha Rashed Alyammahi

Theses

Over the past few years, academic stress and student mental well-being have become critical concerns in educational settings, significantly affecting academic performance and overall quality of life. Students increasingly rely on digital platforms for academic support; however, many existing solutions lack the ability to adapt to users’ emotional states or provide personalized, context-aware assistance. In response to this challenge, this study presents the design and development of an intelligent, stress-aware conversational support system for students based on advanced natural language processing techniques and large language models.

The main goal of this study is to explore the effectiveness of an LLM-based …

Heat Transfer And Micropolar Characteristic Analysis For Fluid Flow Over A Contracting Permeable Infinite-Length Cylinder: A Fractional Derivative Study, Khalid Al Homran

Theses

This study presents a comprehensive analysis of heat transfer and micropolar fluid characteristics for flow over a contracting permeable infinite-length cylinder using fractional derivative modeling. The micropolar fluid model accounts for microstructure effects that are prevalent in many practical applications, such as polymeric fluids, colloidal suspensions, and biological fluids. The inclusion of fractional derivatives allows the analysis to incorporate memory effects, which provide a more accurate depiction of the physical phenomena compared to classical integer-order models. The governing partial differential equations, representing the momentum, micropolar momentum and energy transport processes, are transformed into a set of nonlinear ordinary differential equations …

Malicious Quic Traffic Detection Using Malicious Quic Traffic Detection Using Malicious Quic Traffic Detection Using Malicious Quic Traffic Detection Using, Choudhary Muhammad Zia

Theses

The enhanced QUIC protocol's encryption capabilities not only encrypt the traffic payload but also encrypt the packet headers that network security tools rely on for packet inspection. This enhanced encryption capability introduces challenges for network operators in monitoring and analyzing network traffic by creating blind spots in firewalls, intrusion detection systems, and deep packet inspection devices. Earlier research has demonstrated that training machine learning classifiers on individual QUIC packets across different classifiers has failed to detect any malicious traffic due to encrypted packet headers.

This thesis demonstrates that shifting to network flow analysis resolves this problem. Rather than analyzing individual …

Public Acceptance Of Meat-Reduction Policies Across Cultures: Comparing Singapore And Switzerland, Bianca Wassmann, Shu Tian Ng, Mark Chong, Angela K. Y. Leung, Michael Siegrist

Research Collection Lee Kong Chian School Of Business

Purpose – As meat-reduction policies are discussed across the globe, many are met with public resistance. Cultural values may help explain this pushback, yet their role in shaping support for food policy remains poorly understood. This study is the first to apply cultural cognition theory to food policy by examining how cultural worldviews shape the acceptance of meat-reduction interventions in Singapore and Switzerland—two economically developed countries with contrasting cultural profiles. Design/methodology/approach – In an online survey, participants (Singapore: n = 357; Switzerland: n = 495) rated their acceptance of 11 meat-reduction interventions (e.g. taxes, subsidies, labelling). We then analysed to …

Happycal: Designing Text And Image-Based Supports For Savouring Positive Work Experiences, Molly Stewart, Minghao Cai, Anthony Tang, Sam Liu, Chris Mosunic, Sowmya Somanath

Research Collection School Of Computing and Information Systems

Savouring positive work experiences can promote positive affect and well-being at work, yet there is limited guidance on how digital applications can support workers to engage in savouring. We developed HappyCal, a work-focused savouring application offering two forms of savouring support: text-based, a common modality in workplace reflection tools, and images, a largely unexplored approach in work-related savouring. We conducted an exploratory qualitative study where participants (N=36) used HappyCal over five days and engaged in savouring through either a text-only modality (n=17) or text input paired with image output (n=19). We found that (1) participants in both groups reported heightened …

2026 June - Tennessee Monthly Climate Report, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Monthly Reports

Hi All,

The main weather and climate stories for Tennessee in June 2026 were several rounds of heavy rainfall that produced flash flooding in different parts of the state. Early in the month slow moving storms produced heavy downpours in different parts of the state from June 7 to 14 including areas in or around Knoxville, Nashville, and Memphis, as well as portions of Henry, Weakley, and Carroll counties in northwestern Tennessee. Four different locations across the state reported daily rainfall records between these dates.

Later in the month, more widespread heavy rains impacted southern sections of West and Middle …

Towards Efficient Continual Learning: From Memory Optimization To Foundation Models, Zilin Luo

Dissertations and Theses Collection (Open Access)

Continual learning, also termed lifelong learning, enables machine learning models to incrementally acquire new knowledge while mitigating the degradation of previously learned information—a capability essential for adapting to dynamic, real-world data environments. This dissertation investigates the core challenges of continual learning and extends its application to enhancing training efficiency in the era of foundation models. The first part of this dissertation addresses the constraints of few-shot exemplar storage with a novel compression framework. While leveraging class activation maps to downsample non-discriminative pixels, we introduce an adaptive masking model, optimized through bilevel optimization, to store more exemplars efficiently. The second part …

Towards Auto-Evaluation For Large Language Models, Jiahao Ying

Dissertations and Theses Collection (Open Access)

The rapid advancement of large language models (LLMs) has created an urgent need for evaluation methodologies that are timely, scalable, reliable, and informative. Conventional evaluation benchmarks, although essential for measuring model capabilities and guiding model development, are often constructed and maintained through labor-intensive human annotation. As LLMs continue to improve through increases in model scale, training data, and computational resources, static benchmarks may quickly lose discriminative power. Moreover, the growing use of large and diverse training corpora increases the risk of benchmark leakage, which can inflate evaluation results and obscure the true capabilities of models. These challenges call for a …

Golden And Silver Dark Sirens For Precise H0 Measurement With Hetdex, Yixuan Dang, Ish Gupta, Robin Ciardullo, Erin Mentuch Cooper, Shiksha Pandey, Dustin Davis, Surhud More, Rachel Gray, Hsin Yu Chen, Daniel J. Farrow, Caryl Gronwall, Donghui Jeong, Shun Saito, Donald P. Schneider

Physics Faculty Research & Creative Works

Gravitational waves (GWs) from compact binary coalescences are standard sirens that provide a direct measure of the source's luminosity distance, enabling an independent measurement of the Hubble constant (H0). While a bright siren—a GW event with an identified electromagnetic (EM) counterpart—provided the first such constraint, most detections, currently dominated by black hole mergers, lack EM signatures. A measurement of H0 is still possible with these dark sirens by statistically associating GW events with galaxies in existing catalogs based on the sky localization. In this work, we explore the potential of two subsets of dark sirens categorized by their localization precision: …

Phases And Dynamics Of An Impurity Immersed In One-Dimensional Quantum Droplets, Dimitrios Diplaris, Ilias A. Englezos, Friethjof Theel, Peter Schmelcher, Simeon I. Mistakidis

Physics Faculty Research & Creative Works

We explore the ground-state properties of a single impurity immersed in a one-dimensional quantum droplet medium formed by a two-component Bose mixture. Relying on ab initio simulations, we demonstrate that tuning the impurity–droplet interactions allows to controllably reshape the droplets' density profiles and associated correlation patterns. For attractive impurity-medium couplings, the impurity becomes localized within the droplet, which exhibits a density hump at the vicinity of the impurity, while repulsive interactions facilitate phase separation. Comparing our many-body results with the appropriate extended Gross–Pitaevskii description, we find adequate agreement for the droplet density profiles, with the effective field approach systematically overestimating …

Draft Final 2022 Unreclaimed Sites Sampling: Ur-07 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2023 Unreclaimed Sites Sampling: Ur-31 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2024 Unreclaimed Sites Sampling: Ur-46 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2022 Unreclaimed Sites Sampling: Ur-27 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed (Ur) Sites New And Mahoney (Ur-23) Construction Completion Report (Ccr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Quarterly Operations And Maintenance Report: Butte Treatment Lagoon System – Fourth Quarter 2025, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Hide-And-Sweep: Detecting Concealed Cameras Via Led Illumination Sweeps, Jonghyuk Yun, Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Byunghyun Kim, Rajesh Krishna Balan, Jun Han

Research Collection School Of Computing and Information Systems

Hidden cameras have increasingly infiltrated hotel and Airbnb rooms, posing serious privacy risks. Detecting such cameras is challenging because they are visually inconspicuous and often embedded inside everyday objects. Even worse, existing handheld detectors are manual and also rely on single-angle illumination and hence suffer from high false-positive rates. We present SweepLED (pronounced "sweepled")1, a practical hidden camera detection system that operates on a commodity smartphone augmented with an unobtrusive LED-embedded case. SweepLED performs LED sweeping - a controlled sequence of multi-angle illumination - while the user simply holds the phone still by hand, enabling the camera to capture how …