Physical Sciences and Mathematics Commons^™

Deciphering Ultrafast Photoinduced And Photocatalytic Reaction Dynamics On Oxide Surfaces: Direct Detection Of Radical Intermediates, Fragment Trapping, And Carbon–Carbon, Carbon–Oxygen, And Carbon–Hydrogen Bond Formation Pathways, Aakash Gupta

Graduate Studies Theses and Dissertations 2026

Understanding photoinduced reactions at solid interfaces is essential for advancing heterogeneous photocatalysis, surface photochemistry, and energy conversion technologies. This dissertation investigates the ultrafast dynamics of reactive intermediates, fragment trapping, and radical-mediated bond formation on oxide surfaces using time-of-flight mass spectrometry in conjunction with femtosecond pump-probe spectroscopy. Various experimental findings are validated through collaborations via density functional theory (DFT). By combining temporal, mass, and energy-resolved measurements, this work provides molecular-level insight into the elementary processes governing light-driven surface reactions. The photodissociation dynamics of CH₃I adsorbed on TiO₂(110), TiO₂(100), and amorphous silicon oxide surfaces are examined …

Integrated Optical Probes For Confocal Scanning Imaging And Adjustable Coherent-Gated Dynamic Sensing, Yonglin Huang

Graduate Studies Theses and Dissertations 2026

Optics and photonics have been one of the most important sciences and technologies that impact modern human life in a big way. For example, fiber-optics for communications and artificial intelligence. Optical probes are critical components for optical imaging and optical sensing technologies that have been actively researched and developed in the past decades. Advanced fiber-optic sensor probes with smaller size, better performance, lower noise, higher photon efficiency, rapid sensing time, and lower cost are needed in many applications, such as nanoscale material science, chemistry, and biomedical fields, etc.  In this project, new fiber-optic sensor probe technologies and integrated micro-optic devices …

Modeling And Mitigating Atmospheric Degradation In Computer Vision With Application In Renewable Energy Prediction, Sumit Laha

Graduate Studies Theses and Dissertations 2026

Weather-induced variability poses significant challenges to the reliability and performance of modern computational systems, particularly those relying on visual perception and environmental prediction. This dissertation focuses on enhancing computer vision and machine learning based predictive models that operate under varying atmospheric conditions. Two representative weather-impacted applications are investigated: image dehazing and solar photovoltaic (PV) power output forecasting. Image dehazing focuses on the restoration of clear, unobstructed visuals from hazy or foggy images, a task that is vital for various applications. On the other hand, photovoltaic (PV) power forecasting aims to predict future solar energy generation based on historical sky images …

Visual Cues Of Human-Likeness, Not Salience, Impact Trust-Related Human-Computer Interaction, Jordan Schotz

Graduate Studies Theses and Dissertations 2026

As interactions with digital agents become increasingly integrated into daily life, understanding how visual representations influence social decision-making is critical. Previous research in human-computer interaction has frequently confounded the psychological effects of an agent's perceived human-likeness with the underlying visual salience of the stimuli. To address these persistent gaps, the present study systematically isolated the effects of human-likeness and visual cue trustworthiness on trust behavior while controlling for objective image properties. The present study expanded on and normed the Virtual Avatar Facial Stimuli Set (VAFSS), a comprehensive database comprising hundreds of identity-matched photographs and computer-generated avatars varying across a spectrum …

Concept Drift Detection For Streaming Data Using One-Class Classification, Poorna Sandamini Senaratne

Graduate Studies Theses and Dissertations 2026

Modern machine learning systems are increasingly deployed in streaming environments where data arrive sequentially and the underlying data-generating process may evolve over time. This phenomenon, known as concept drift, can significantly degrade model performance if not detected and addressed in a timely manner. This dissertation proposes a principled framework for concept drift detection based on one-class classification, integrating neural network embeddings with Support Vector methodologies.

The proposed approach leverages neural networks to learn compact and informative embeddings of input data, capturing complex nonlinear structures in a lower-dimensional latent space. These embeddings are then used to construct a statistical description of …

Volumetric Fluorescence Microscopy For High-Throughput And High-Sensitivity Imaging: From Single Molecules To Tissues, Le-Mei Wang

Graduate Studies Theses and Dissertations 2026

Fluorescence microscopy is an indispensable tool in the biological sciences, enabling researchers to investigate intricate subcellular structures, particularly for volumetric studies. However, conventional optical microscopy for volumetric imaging remains fundamentally constrained by imaging speed and throughput. To bypass traditional serial z-scanning, we introduce an axially scan-free method using a phase layer cake to modulate the system's point spread function. This approach projects volumetric information onto a 2D plane in a single shot, offering high flexibility in tuning axial depth alongside simultaneous multicolor imaging with high spatial resolution and sensitivity. This dissertation divides these technical advancements into cellular and tissue imaging …

Learning Weibull Loss Severity Models From Truncated And Censored Data, Majed Alkhasha

Graduate Studies Theses and Dissertations 2026

In modern actuarial science and risk management, due to various loss control mechanisms, observed severity losses are typically left-truncated at the deductible, right-censored at the policy limit, and scaled by a pre-specified co-insurance factor. This results in two types of actuarial payment random variables: payment-per-payment (PPP) and payment-per-loss (PPL). To learn ground-up Weibull loss severity models from PPP and PPL sample data, we implement two estimation techniques: Maximum Likelihood Estimation (MLE) and the dynamic Method of Trimmed Moments (MTM). MLE is employed to obtain efficient estimates of the Weibull shape and scale parameters. However, MLE may assign unnecessarily large point …

Data Repository For: Dorsey Et Al., 'Rapid Transient Uplift Driven By Active Slab Tear, Southern Calabria, Italy', Rebecca J. Dorsey, Nathan D. Brown, Sergio G. Longhitano, Marco Meschis, Domenico Chiarella, Charles P. Ogle

Earth & Environmental Sciences Datasets

Southern Italy preserves a well-studied record of deformation and uplift related to migrating oblique collision, trench retreat, and tearing of subducted ocean slabs. However, the surface expression of these processes is incompletely understood due to a lack of reliable ages for marine terraces >200 m above sea level (masl). Here we use luminescence methods to date shallow marine sands from terraces ~ 100 to 1,000 masl in southern Calabria and interpolate the results with regional surface mapping. Burial ages overlap across the full range of sampled elevations with a weighted mean of 118.8 ± 8.7 thousand years before present (ka). …

The Conservation Value Of Silvicultural Systems For Breeding And Post-Fledging Bird Communities In Northeastern North America, Christopher Andrew Liazos

Antioch University Dissertations & Theses

Forest bird community decline in the eastern United States is partly due to the lack of early-and-late-successional forests stemming from historic land use. Silviculture can create underrepresented forest age classes and structures to support avian communities during the breeding and post-fledging periods. In southwestern New Hampshire, we conducted a case study evaluating avian conservation value indices across silvicultural systems. Our goal was to observe how avian conservation value indexes and abundance differed between recently managed and unmanaged stands. We surveyed 215 breeding and 118 post-fledging points across 22 sites using point counts and vegetation surveys. Conservation values in the breeding …

Connected Fair Detachments Of Hypergraphs I, Amin Bahmanian

Faculty Publications – Mathematics

Let G be a hypergraph whose edges are colored. An (α, n)-detachment of G is a hypergraph obtained by splitting a vertex α into n vertices, say α1, . . . , αn , and sharing the incident edges among the subvertices. A detachment is fair if the degree of vertices and multiplicity of edges are shared as evenly as possible among the subvertices within the whole hypergraph as well as within each color class. In this paper we solve an open problem from the 1970s by finding necessary and sufficient conditions under which a k-edge-colored hypergraph …

A Systems Analysis Of Long-Term Community Recovery Following The 2023 Lāhainā Wildfire: Understanding Recovery As A Complex Adaptive Social-Ecological System, Christy Shaver

Antioch University Dissertations & Theses

No abstract provided.

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Making Unsafe Sequences Unexecutable: Formal Protocol Enforcement For Cyber-Physical Systems, Arthur Amorim

Graduate Studies Theses and Dissertations 2026

Cyber-physical systems execute physical actions in response to software commands, making their communication protocols a primary attack surface. A stealthy attack is a sequence of individually valid messages that violates a required ordering, driving the system into an unsafe state without malware or protocol violation. Existing defenses examine messages or physical state in isolation, not protocol level sequences, and cannot prevent them. Preventing them requires enforcement that makes unsafe sequences unexecutable at the communication boundary.

Formal methods offer a principled path to enforcement, but no tool spans specification to safe deployed hardware. Model checking automates proofs but has no certified …

Connecting The Existing Fiber Infrastructure To The Future With Antiresonant Hollow Core Fibers, Timothy Bate

Graduate Studies Theses and Dissertations 2026

Optical fiber systems based on solid-core silica waveguides underpin modern telecommunications, high-power laser delivery, precision sensing, and coherent optical systems. However, nonlinear effects, material absorption, and thermal limitations within silica increasingly constrain further scaling in both optical power and transmission performance. Antiresonant hollow-core fibers provide a promising alternative by guiding light predominantly in air, substantially reducing nonlinear interactions, latency, and optical damage while enabling transmission regimes inaccessible to conventional solid-core fibers. Despite rapid advances in antiresonant hollow-core fiber attenuation and power handling, one of the largest remaining barriers to widespread adoption is reliable integration with the existing solid-core fiber ecosystem. …

Non-Gaussian Phenomena In Light Scattering And Applications, Shubham Atul Dawda

Graduate Studies Theses and Dissertations 2026

Physical reality is rarely deterministic; which, when probed by electromagnetic fields that also fluctuate, leads to observables that are most efficiently modelled as statistical processes. At equilibrium, this is usually achieved by invoking the Gaussian statistics of underlying physical processes, however, in practice, one often encounters out-of-equilibrium conditions where Gaussian descriptions may not suffice. Such situations are plentiful in nature– from biology to astronomy. This dissertation addresses several non-Gaussian phenomena associated with light scattering, and includes specific models’ derivations, experimental techniques developments, and demonstrations of potential applications. The systematic presentation will consider circumstances that infringe upon specific assumptions of the …

Advances In Active And Passive Integrated Photonic Devices On Thin-Film Lithium Niobate, Pooja S. Kulkarni

Graduate Studies Theses and Dissertations 2026

Thin-film lithium niobate (TFLN) has emerged as a powerful platform for integrated photonics, combining the exceptional electro-optic, nonlinear, and optical properties of bulk lithium niobate with the scalability and compactness of planar nanophotonic technologies. Building on these principles, advanced device architectures such as adiabatic dichroic filters have demonstrated exceptional spectral performance spanning over two octaves of bandwidth on the TFLN platform. Beyond reciprocal devices, it also offers promising pathways toward integrated nonreciprocal components. By leveraging broadband filtering structures and advanced nonlinear photonic design strategies, compact and monolithic optical isolators can be envisioned without relying on traditional magneto-optic materials. In the …

Robust Deep Learning One-Class Classification, Shahd Alnofaie

Graduate Studies Theses and Dissertations 2026

One-Class Classification (OCC) focuses on learning the characteristics of normal data and identifying observations that deviate from this learned pattern as anomalies. It is commonly used in applications such as medical diagnosis, cybersecurity, industrial monitoring, and fraud detection, where abnormal examples are often rare or unavailable during training. Classical approaches such as SVDD and LS-SVDD describe normal data using a hypersphere. While effective in some settings, these methods rely on shallow representations and can be sensitive to noise and contaminated observations. To address these limitations, this dissertation introduces a Deep LS-SVDD framework that combines hypersphere-based data description with deep neural …

An Empirical Comparison Of K-Nearest-Neighbors And Logistic Regression Classification Models, Jackson Cushing

Graduate Studies Theses and Dissertations 2026

This thesis presents an empirical comparison of two classification methods: Logistic Regression and K Nearest Neighbors (KNN). The primary objective of this research is to evaluate the strengths and limitations of each method when applied to real-world datasets. Several publicly available datasets on diabetes, breast cancer, heart attack risk, and cardiovascular disease, were analyzed. For each dataset, K Nearest Neighbors models were implemented in the same way logistic regression had already been applied. The results demonstrate that while logistic regression offers interpretable parameter estimates and performs well when the underlying predictor and outcome relationship is approximately linear, however KNN can …

Numerical Modeling Of Multi-Scale Jet Feedback In Seyfert Galaxies, Julianne Goddard

Theses and Dissertations--Physics and Astronomy

We present a suite of high-resolution cosmological zoom-in simulations investigating the impact of mechanical and thermal feedback from low-luminosity jets launched from supermassive black holes (SMBHs) on Seyfert galaxy evolution from early cosmic times to the present (z ≤ 10). Our models follow the formation of central galaxies within identical dark matter halos of logMhalo/M⊙ ∼ 11.8 at z = 0, seeded with ∼ 10^6M⊙ SMBHs at z ∼ 9.1 and z ∼ 3.7. Feedback from active galactic nuclei (AGN) is implemented in the form of bipolar jets launched along the SMBH spin axis, with jet powers spanning Ljet ∼ …

Fully Differential Studies On Dissociative Capture In P + D2 Collisions And On Ionization In P + He Collisions, Shruti Majumdar

Doctoral Dissertations

Advancing our understanding of few-body dynamics in simple atomic systems is a fundamental objective in atomic scattering research. The underlying problem is that the Schrödinger equation is not analytically solvable for more than two mutually interacting particles. This involves a comprehensive exploration of various channels, such as ionization, capture, and excitation. A common theoretical approach to describe ion-atom collisions is based on perturbation theory, where the scattering amplitude is expanded in powers of the interaction potential. Here, understanding the few-body problem means accurately describing the relative importance of the higher- vs the first order terms.

In the case of ionization, …

Differential Growth Dynamics Of Common Salt Marsh Species Spartina Alterniflora And Juncus Roemerianus Under Varied Sediment Amendments, Caitlin Hemphill, Richard P. Hale, Erik S. Yando

Biological Sciences Faculty Publications

Introduction

Many coastal wetlands are at high risk of degradation or loss due to sea‐level rise. Restoration techniques for maintaining coastal marshes are paramount, with thin‐layer placement (TLP) emerging as one feasible solution. Despite TLP's utilization, additional research is needed on species‐ and sediment‐specific responses.

Objectives

Our research combines greenhouse and field experimentation to test species and sediment composition responses aimed at maximizing vegetation while maintaining increased elevation.

Methods

The greenhouse experiment used two common marsh plants, Spartina alterniflora and Juncus roemarianus. Five centimeters of sediment (mud, sand, and sand/mud mix) was added, and subsequent sediment and vegetation metrics …

Predicting Oil Contamination In Water Using Machine Learning On Microbial Compositions, Tong Gao, Isaac Bigcraft, Stephen Techtmann, Issei Nakamura

Michigan Tech Publications

We present a compact and generative machine-learning framework that predicts oil contamination based on microbial community compositions from experimental samples. Our method combines dimensionality reduction with data augmentation and generative modeling to address high-dimensional, non-linear, and sparse microbial data. To reduce the 503-dimensional bacterial composition dataset, we compared three dimensionality reduction techniques: feature importance from random forest, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). Feature importance outperformed PCA and t-SNE, improving predictive performance and identifying microbial species most strongly correlated with oil contamination. To mitigate data scarcity, we augmented the training data using an augmented data neural …