Physical Sciences and Mathematics Commons^™

Visceral, Neural, And Immunotoxicity Of Per- And Polyfluoroalkyl Substances: A Mini Review, Pietro Martano, Samira Mahdi, Tong Zhou, Yasmin Barazandegan, Rebecca Iha, Hannah Do, Joel Burken, Paul Ki-Souk Nam, Qingbo Yang, Ruipu Mu

Chemistry Faculty Research & Creative Works

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluoro butane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent …

Exploring Rehabilitation Therapists' Knowledge And Perspectives On The Use Of Artificial Intelligence And Machine Learning For Persons Poststroke, Hannah Clark, Mia Delvecchio, Min Hun Lee, Elena D. Brown, Kaia Mikula, Robert Halyama, Kasey Stepansky

Research Collection School Of Computing and Information Systems

Research Objectives: The use of technology such as robotics, gaming systems, self-monitoring apps, or other sensor-based devices in standard practice is infrequent. Due to the rapid development of artificial intelligence (AI) and machine learning (ML) applications, it is important to look at how therapists perceive AI/ML, and design applications with potential barriers in mind. to support future integration into practice. The purpose of this research project is to gain rehabilitation therapists’ perspectives on AI/ML in post-stroke assessment and intervention.Design: This ongoing study uses a mixed methods design with surveys and focus groups. Participants engaged in a 30-minute webinar to learn …

Dreamanime: Learning Style-Identity Textual Disentanglement For Anime And Beyond, Chenshu Xu, Yangyang Xu, Huaidong Zhang, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Text-to-image generation models have significantly broadened the horizons of creative expression through the power of natural language. However, navigating these models to generate unique concepts, alter their appearance, or reimagine them in unfamiliar roles presents an intricate challenge. For instance, how can we exploit language-guided models to transpose an anime character into a different art style, or envision a beloved character in a radically different setting or role? This paper unveils a novel approach named DreamAnime, designed to provide this level of creative freedom. Using a minimal set of 2-3 images of a user-specified concept such as an anime character …

Artificial Intelligence Integration And Teachers' Self-Efficacy In Physics Classrooms, Fouad Yehya, Areej Elsayary, Ghadah Al Murshidi, Ahmed Al Zaabi

All Works

The United Arab Emirates (UAE), in its vision 2021 and the UAE centennial 2071 plan, highlights the essential role of artificial intelligence (AI) and technology in shaping a knowledge-based, future-ready society. This study explores the integration of AI in physics classrooms, focusing on secondary education in the UAE. It also investigates the perceptions and self-efficacy of physics teachers regarding the use of AI tools in classroom settings. A qualitative research design was employed to gather in-depth insights from 15 physics teachers across schools in Sharjah, assessing their confidence and readiness for AI integration through the lens of the attitude and …

Advancing Fishery Dependent And Independent Habitat Assessments Using Automated Image Analysis: A Fisheries Management Agency Case Study, Scott Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae Hovey, Marcus Sheaves, Gary A. Kendrick, Lynda M. Bellchambers

Fisheries Research Articles

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level …

(A,B,C) Tilings With Prescribed Symmetry Groups From Regular Triangle Or Hexagon Tiling, Mark D. Tomenes, Ma. Louise Antonette N. De Las Penas

Mathematics Faculty Publications

A tiling T of the Euclidean plane (E2) is a countable collection of closed topological disks called tiles T = {Ti : i ∈ N} that is a covering (Ui Ti = E2) as well as a packing (Int(Ti) ∩ Int(Tj) = ∅ if i ̸= j, Int(T) denotes the interior of tile T). One of the problems of interest in discrete geometry is the classification of tilings based on transitivity properties of their vertices, edges and tiles. This talk presents a family of tilings whose vertices, edges and tiles have exactly a, b and c orbits, respectively, under the …

Optimal Hypergraph Connectivity With Cut Queries, Hang Liao

Dartmouth College Ph.D Dissertations

Finding connected components in undirected hypergraphs—hypergraph connectivity—is a fundamental problem in computer science. It can be framed as a special case of Symmetric Submodular Function Minimization (SSFM), where the objective is to determine if the non-trivial minimizer is zero. This thesis develops an optimal algorithm for hypergraph connectivity within the $\CUT$ query model, where an algorithm probes a subset of vertices to learn the weight of the hyperedges ``cut" by that partition.

Our approach is constructive, culminating in an optimal algorithm for the general problem by first developing the necessary tools for two foundational subproblems. The main contributions of this …

Congruences For Quotients Of Klein Forms, Jeffery Opoku

Theses and Dissertations

This dissertation investigates the arithmetic properties of some modular forms and eta quotients, focusing on the divisibility properties and congruence relations satisfied by these. The first part examines quotients of the Rogers-Ramanujan and Rogers-Selberg functions, defined by \[ f(\tau) = q^{r} (q^5; q^5)^{a_0} (q, q^4; q^5)^{a_1} (q^2, q^3; q^5)^{a_2}= \sum_{n=r}^{\infty} P_{a_0,a_1,a_2}(n-r) q^n, \] and \[ g(\tau) = q^{s} (q^7; q^7)^{a_0} (q, q^6; q^7)^{a_1} (q^2, q^5; q^7)^{a_2} (q^3, q^4; q^7)^{a_3}= \sum_{n=s}^{\infty} P_{a_0,a_1,a_2,a_3}(n-s) q^n, \] respectively. We establish conditions on the exponents \(a_0, a_1, a_2, a_3\) and residue classes \(r\) and $s$ modulo $p$ such that \(P_{a_0, a_1, a_2}(pn - r) \equiv …

Arginine Vasotocin And Corticosterone In Nesting And Non-Nesting Loggerhead Sea Turtles In X’Cacel-X’Cacelito Sanctuary, Krista Blair Reed

Theses and Dissertations

Sea turtles exhibit distinct behaviors before and after egg laying, however, not all individuals successfully oviposit. Some females display disruptions in their typical nesting behavior, returning to the water without laying eggs, which is referred to as a non-nesting emergence (NNE). NNEs can be due to external disturbances on the beach, but why they occur is often unclear. Arginine vasotocin (AVT) is a neuropeptide involved in various physiological processes, including stimulating smooth muscle contraction of the oviduct to facilitate egg deposition in nesting sea turtles. On the other hand, corticosterone is the primary stress hormone in sea turtles and is …

Comparative Analysis Of Sequential And Non-Sequential Modeling Techniques For Ddos Attack Detection With Explainable Ai, Vincent Agbenyeavu

Theses and Dissertations

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling …

Machine Learning Applications For Evaporation Predictions From Small Reservoirs: Potential Water Savings For Lower Rio Grande Valley, Texas, Syed Muhammad Fahad Abdullah

Theses and Dissertations

Local-scale reservoirs are important to regional water balance, but these are often overlooked. This study presents a robust machine learning (ML) approach leveraging reanalysis datasets to estimate daily evaporation for local-scale reservoirs in semi-arid South Texas. Selected models were trained with daily lake evaporation model (DLEM) estimates and used climatic and reservoir-specific properties as feature input variables. The multi-reservoirs training approach ensured applicable model generalization. Results show promising predictive performance with R² values ranging from 0.55–0.67 (testing) and 0.64–0.78 (validation), NSE values ranged from 0.54 0.67 (testing) and 0.64–0.78 (validation), and RMSE values ranged between 1.52–1.80 mm/day (testing) and 1.22–1.58 …

Low-Cost Monitoring And Fingerprinting Of High-Powered Electric Systems, Kwabena Buamono Aboagye-Otchere

Theses and Dissertations

Electric motors are vital to industry, transport, and energy, yet their maintenance challenges persist. While traditional reactive maintenance leads to costly downtime and safety risks, predictive maintenance, especially through IoT and machine learning offers early fault detection and operational efficiency. However, this shift introduces security concerns due to unintended magnetic emissions from motors. These emissions, though useful for non-intrusive monitoring, can be exploited to eavesdrop on sensitive industrial processes. This dissertation explores the dual nature of magnetic emissions: their value in motor diagnostics and their potential as a security vulnerability. It demonstrates how emissions can identify motors, monitor health, and …

Computational Calculations On Zno (0001) And Zno (1010) Surfaces With And Without Pd Adatoms, Amit Aich

Theses and Dissertations

In this research work, first-principles density functional theory (DFT) was used to study O-terminated, Zn-terminated ZnO (0001) surfaces and non-polar ZnO (1010) surfaces. Both clean surfaces and those with Pd or PdO adatoms were examined. Adsorption was considered at O, Zn and hollow sites. After structural relaxation, we analyzed the electronic, optical and adsorption properties. The O-terminated ZnO (0001) surface has a larger band gap of 0.76 eV and p-type conduction. The Zn-terminated surface has a smaller band gap of 0.15 eV and shows n-type behavior. Pd adsorption modifies ZnO’s electronic structure in a site-dependent manner. On O-terminated surfaces, it …

Intersecting Realities And Evolving Landscapes: Mapping Generative Ai Within The Framework Of Digital Rhetoric, Joshua Troy Nieubuurt

English Theses & Dissertations

The increased usage of [Generative] AI technologies (GenAI) in the 21st century has called into the question the rhetorical agency of these digital things. [Gen]AI has historically been framed within a Heideggerian “readiness-to-hand” dynamic in which it has been unilaterally conceived as a tool to be used by humans. This dissertation proposes that the GenAI assemblage is capable of being a co-actor in rhetorical spaces. To provide evidence for this stance This dissertation utilizes Actor Network Theory to map the actants within a GenAI assemblage. In doing so it allows for an understanding of the stakeholders (both human and non-human) …

Learning Regulatory Dna-Sequence Code Of Epigenetic Events Using Deep Neural Networks, Sanjeeva Reddy Dodlapati

Computer Science Theses & Dissertations

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues …

Two Analytic Issues Arising From Models In Probability And Materials Science, Giang Vu Thanh Nguyen

Mathematics & Statistics Theses & Dissertations

This dissertation explores two distinct topics centered on mathematical models in probability theory and materials science. The first part investigates a series of functions derived from an adaptive algorithm designed to address the score-based secretary problem, a classic challenge in probability theory. This problem involves making immediate decisions to select the best candidate from a sequence of interviews. The algorithm aims to maximize the probability of selecting the optimal candidate based on observed scores. We prove two fundamental analytic properties of this sequence of functions as a theoretic support of the algorithm: first, the functions in the sequence each possess …

Complex System Governance And Cyber Operations, Willie Gernard Mccallister

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation examines the potential integration of Complex System Governance (CSG) within cybersecurity, emphasizing the development of a reference model for Cybersecurity Infrastructures. Traditional strategies for securing digital environments have struggled to address the intricate and dynamic layers inherent in modern cybersecurity systems. The purpose of this research is to explore the applicability of CSG as a framework to assess cybersecurity infrastructure using a case study research design. The research addresses two key questions: (1) How can the CSG reference model be adapted to explore cybersecurity infrastructure? (2) What results from CSG based exploration of cybersecurity infrastructure through a case …

A Strict Physicality-Preserving Scheme For A 2d Q-Tensor Flow With A Singular Potential, Md Mashud Parvez

Mathematics & Statistics Theses & Dissertations

Nematic liquid crystals are a state of matter that exhibit properties between those of conventional liquids and solid crystals. Their unique ability to align molecules in specific directions makes them essential in various applications, including display technologies and advanced materials. To model their complex behavior, mathematical frameworks such as the Q-tensor model are used to describe the orientation and degree of molecular order. In this work, we introduce a numerical scheme for a two-dimensional (2D) dynamic Q-tensor model, which is formulated as an L²-gradient flow driven by the liquid crystal free energy and incorporates a singular potential to …

Analysis Of Multi Grade Deep Learning, Ronglong Fang

Mathematics & Statistics Theses & Dissertations

Multi-Grade Deep Learning (MGDL) is a training framework that incrementally builds deep neural networks. It does this by dividing the training process into multiple “grades,” where each grade sequentially trains a shallow neural network to learn the residue from the previous one, using the outputs of prior grades as input. This approach progresses from shallow to deep architectures. This dissertation offers a comprehensive theoretical and numerical analysis of the MGDL methodology.

We first demonstrate that MGDL can effectively learn target functions within the sum-composition learning format. In this context, MGDL approximates high-frequency components by composing multiple low-frequency functions. This unique …

Enhancing Non-Visual Interaction With Online User-Generated Content, Mohan Krishna Sunkara

Computer Science Theses & Dissertations

The Web has become the dominant medium for our everyday activities, including communication, business, e-commerce, news, and entertainment. Consequently, the online world is experiencing an explosion of User-Generated Content (UGC), particularly on social media platforms and online review systems. To facilitate convenient interaction with UGC, web platforms have adopted various presentation strategies that enable users to efficiently browse and contribute to the UGC. However, these user interfaces are primarily designed for sighted individuals, so they do little to assist blind users who rely predominantly on audio-based screen reader assistive technology. The extant efforts to improve web interaction for blind users …

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Intel Oneapis Esimd, Joseph Wassell

Computer Science Theses & Dissertations

The growing popularity of Computational Fluid Dynamics (CFD) simulations among engineers necessitates the use of GPU acceleration for increased efficiency. NASA FUN3D offers GPU accelerated CFD simulations using unstructured grids across the speed regime from incompressible to hypersonic flows involving reentry. This work focuses on the generalized multi-color point implicit solver used in FUN3D, accounting for roughly half of the run time. Specifically, this work focuses on developing three optimized multi-color linear-solver kernels for the Intel Data Center Max 1550 GPU that is available on the Argonne Leadership Computing Facility’s (ALCF) exascale machine, Aurora. These optimized kernels work for a …

Flux Trapping Sensitivity On The Metallurgical State Of Superconducting Radio Frequency Niobium, Bashu Dev Khanal

Physics Theses & Dissertations

Trapped magnetic flux in superconducting radio-frequency (SRF) niobium cavities due to the incomplete Meissner effect is a significant source of rf losses. The relationship between flux expulsion and flux trapping sensitivity to the metallurgical state of 1.3 GHz and 3.0 GHz SRF niobium cavities fabricated from standard and cold-worked Nb sheets and subjected to various heat treatments has been studied. Flux expulsion increased with higher heat treatment temperature in all cavities. Nb cavities made from cold-worked sheets showed better flux expulsion after 800 ◦C/3 h compared to the standard SRF-grade Nb cavities. Similar flux expulsion was observed after annealing at …

Computational Investigation Of Energetic Materials: Influence Of Electronic And Steric Properties On Sensitivity And Decomposition Mechanisms, Elizabeth Ruth Zengel

Chemistry & Biochemistry Theses & Dissertations

Developing novel high energy density materials (HEDMs) requires knowledge of the causes and mechanisms of detonation. These chemical events are almost instantaneous and involve the release of a large amount of energy, which limits the experimental studies that can be performed on them. Computational methods including density functional theory (DFT) and molecular dynamics (MD) simulations have been used to investigate trigger bonds, those which break to initiate detonation. These bonds are commonly found within explosophores, substituents that increase the explosive potential of a molecule.

The Wiberg bond index (WBI) is an estimation of orbital overlap and bond strength between two …

2-Adic Quantum Mechanics, Continuous-Time Quantum Walks, And The Space Discreteness, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

The authors show that a large class of 2-adic Schrödinger equations is the scaling limit of certain continuous-time quantum Markov chains (CTQMCs). Practically, a discretization of such an equation gives a CTQMC. As a practical result, new types of continuous-time quantum walks (CTQWs) on graphs using two symmetric matrices are constructed. The transport between nodes in one direction is described by one matrix, while the transport between nodes in the opposite direction. This construction includes, as a particular case, the CTQWs constructed using adjacency matrices. The final goal of this work is to contribute to the understanding of the foundations …

Oyster Shell Recycling Efforts In Horry County, Corabella Dieguez

Goal 14: Life Below Water

No abstract provided.

Reducing Single-Use Plastic In Georgetown County, Alex Gaspar

Goal 12: Responsible Consumption and Production

No abstract provided.

Sccosmix: A Mixed-Effects Framework For Differential Coexpression And Transcriptional Interactions Modeling In Single-Cell Rna-Seq, Anderson Bussing, Giampiero Marra, Daping Fan, Russell Shinohara, Danni Tu, Yen-Yi Ho

Faculty Publications

Advancements in single-cell RNA-sequencing (scRNA-seq) technologies generate a wealth of gene expression data that provide exciting opportunities for studying gene-gene interactions systematically at individual cell resolution. Genetic interactions within a cell are tightly regulated and often highly dynamic in response to internal cellular signals and external stimuli. Evidence of these dynamic interactions can often be observed in scRNA-seq data by examining conditional co-expression changes. Existing approaches for studying these dynamic interaction changes in scRNA-seq data do not address the multi-subject hierarchical design commonly considered in single-cell experiments. In this paper, we propose a Mixed-effects framework for differential Coexpression and transcriptional …

Using Geomorphic Change And Sediment Modeling To Calculate Debris Flow Volumes From The 2020 El Dorado Wildfire In The Southern San Bernardino Mountains, California, Sevag Injean

Electronic Theses, Projects, and Dissertations

Four years after the occurrence of the 2020 El Dorado Fire, Yucaipa Ridge and the surrounding communities of Oak Glen and Forest Falls still experience the devastation caused by debris flows. Wildfires can instigate a series of events leading to hydrophobic soils, loss of vegetation, and increased sediment availability which amplify post-fire debris flow risks. This study tested the hypothesis that post-wildfire geomorphic changes can be accurately quantified and predicted using high-resolution LiDAR-derived digital elevation models. To investigate this hypothesis, multi-temporal LiDAR datasets were analyzed to quantify geomorphic changes, including erosion and deposition, within two severely burned basins in the …

Mixture Effects On Metal Accumulation In The Asian Clam, Corbicula Fluminea, Mark A. Huff

Electronic Theses and Dissertations

Field collected Corbicula fluminea were exposed to trace level copper, lead, and zinc with the aim of examining the preferential accumulation between the soft tissue and shell. Metal levels were based on those in a permitted industrial effluent. At 1X effluent and 2X effluent exposure levels, copper was found to preferentially accumulate in the clam soft tissue, lead was found to preferentially accumulate in the clam shell, and zinc was found to excrete back into the diluent after preferentially accumulating in the clam soft tissue. When exposed as a mixture at 1X effluent levels, copper and lead were found to …