Physical Sciences and Mathematics Commons^™

An Inquiry Into The Physics Of Mixing And Floc Filtration, Andrew P. Pennock

Dissertations

Flocculation and clarification are two essential processes to deliver safe water at a reasonable cost to consumers. There are two major thrusts to the research presented in this dissertation. The first is to better characterize the physics and mixing parameters used for the design of hydraulic flocculators in the context of drinking water treatment plants. The second major thrust is to investigate floc filtration as a mechanism for the removal of primary particles during floc blanket clarification.

The intensity of mixing in environmental and chemical engineering applications is often characterized by the Camp and Stein velocity gradient. This parameter has …

Fact-Checking As A Multi-Step Process: From Ambiguity Resolution To Claim Validation, Wenbo Wang

Dissertations

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. Fact-checking—the process of verifying claims against credible evidence—has emerged as a critical safeguard against misinformation. Yet, the task is fraught with challenges: claims are often ambiguous, context-dependent, or composed of multiple intertwined assertions, while automated systems struggle to replicate the nuanced reasoning of human experts. This dissertation addresses these challenges by reimagining fact-checking as a multi-step, knowledge-guided process that systematically resolves ambiguity, decomposes complexity, and validates claims through structured reasoning. Additionally, the proposed …

Enriching Vision Representation By Deep Neural Networks And Self-Supervised Learning, Yucong Shen

Dissertations

Nowadays, more and more interesting computer vision tasks are tackled by deep learning approaches. However, the increasing model complexity imposes significant computational and storage costs. To address this challenge, this dissertation explores efficient deep learning techniques, proposing morphological layer, an efficient feature extraction layer. It achieves competitive image classification accuracy with significantly decreased model parameters. Another attempt at efficient deep learning is a proposed channel pruning approach that compresses deep neural networks by identifying and removing redundant channels using optimal transport theory. This approach achieves significant reductions in model size and computational cost while maintaining or even improving performance across …

From Neural Networks To Large Language Models: Innovations In Financial Ai, Mathematical Reasoning, And Structured Data Representation, Junyi Ye

Dissertations

This dissertation explores the evolution and application of artificial intelligence techniques across three critical domains: financial modeling, mathematical reasoning, and structured data analysis. The dissertation presents seven research projects that chart a progression from specialized neural architectures to sophisticated large language models (LLMs), contributing novel methodologies and frameworks at each stage.

In the financial domain, the research first introduces TS-Mixer, a MLP-based architecture for time-series forecasting that captures both feature relationships and temporal dependencies through a simple yet effective design, outperforming more complex models in S&P500 index prediction. The dissertation then presents DySTAGE, a dynamic graph representation learning framework that …

Towards Explainable Ai On Graph Neural Networks: Xaig, Jiaxing Zhang

Dissertations

In the evolving landscape of artificial intelligence (AI), Graph Neural Networks (GNNs) have garnered growing prominence for their adeptness in processing graph-structured data. Despite this, the interpretability of their predictions often remains elusive. The demand for transparency and explainability in complex prediction models has reached unprecedented levels. To address this, post-hoc instance-level explanation techniques have emerged, aiming to unveil the rationale behind GNN predictions. These techniques endeavor to unearth substructures that elucidate the predictive behavior of trained GNNs.

This dissertation embarks on an exploration of Explainable AI (XAI) technologies within the realm of GNNs. Amid the challenges posed by the …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Tree Story, Jia Hu

Masters Theses

What is Nature?

Nature is a system of intelligence. It means designing for efficiency—often by learning from strategies that have evolved over time. In my research, I use patterns to interpret and decode nature.

To explore nature, I began with the red cedar tree, aiming to simulate and predict its growth patterns—forms shaped by both internal biology and external forces. By analyzing its geometry, I sought to understand how trees embody the dynamic relationship between organism and environment. These patterns reveal the adaptive logic of life.

Patterns are central to understanding nature. While tree geometry may appear chaotic, it follows …

Revealing Watery Geologies: Softening The Mississippi River Bluffs In Saint Paul, Minnesota, Chloe Kahn

Masters Theses

How is a place shaped by the geologies beneath it? In Saint Paul, Minnesota, the geologic ground tells the story of the different forces and processes that shaped it through time, but in the most recent geologic period of the anthropocene, industrialization and urban development have covered up these stories. On the edge of downtown, where the city meets the Mississippi River, an eighty foot concrete wall replaces the natural form of the bluff shaped by glacial and riverine activity. This thesis peels back the anthropogenic materials we encounter every day and explores Saint Paul through deep time– exploring how …

The Limits Of Knowing: Determinism, Uncertainty, And What’S Beyond The Human Gaze, Xilong T. Zhang

Masters Theses

This essay traces a personal philosophical and artistic journey from rigid belief in scientific determinism to an evolving embrace of uncertainty, subjectivity, and computational perception. Raised in an atheist, scientifically grounded household in China, the author initially adopted Newtonian determinism and Laplace’s thought experiment of a fully predictable universe as guiding principles. These beliefs informed early artistic practices rooted in Constructivism, geometry, and rule-based aesthetics. However, a failed attempt to fully optimize life through deterministic control led to physical and mental collapse, prompting deeper exploration into Cartesian dualism, quantum mechanics, and the limits of reason. Through Heisenberg’s Uncertainty Principle and …

Field Journal: The Excluded Middle, Carrie E. Kouts

Masters Theses

What does it look like to engage with organisms and landscapes at the periphery of anthropocentric value structures? How does one break the internalized myth that the “built” environment is excluded from the natural world? When does a hyper-mobile and hyper-commodified society confront the exponential crisis of animal death? Within this series of journal entries, field notes, collection observations, weird prose, and sensory musings, one will find questions on the nature of being human and the complex narratives of care we encounter in a world shared with more-than-humans. Each handwritten vignette and photograph from daily life weaves a non-linear and …

Latah Formation: History And Case Studies, Kylee M. Woodworth

2025 Symposium

The Latah Formation is a series of discontinuous sedimentary interbeds between the flows of the Columbia River Flood Basalts outcropping across northern Idaho and eastern Washington. Ages ranging between the Middle to Upper Miocene, the sediments represent varied fluvio-lacustrine environments. The deposits are predominantly composed of claystones and shales as alternating, laminated couplets, many containing a variety of fossils. Though the sediments may appear similar across localities, they come from different source rocks, ranging from ashes from Yellowstone hotspot to metamorphic rock from nearby hills. These ancient lacustrine and fluvial environments allowed for great preservation of fossils, often in great …

Structural Analysis Of Catawissa Quadrangle, Columbia County, Pennsylvania, Kylee M. Woodworth

2025 Symposium

Catawissa Mountain within Columbia County, Pennsylvania is a part of a much large system of mountains called the Appalachian Mountains. Though little more than hills today, the Appalachian Mountains are some of the oldest mountains in the Americas, dating back to after the separation of the supercontinent, Pangea. This presentation is about the structure interpretations of the Catawissa 7.5 Minute Quadrangle, located in Columbia County, Pennsylvania, including fold analysis of the SW trending Catawissa Syncline, formed from Appalachian fold-thrust belt. These analyses are to decipher how the mountain formed from the surrounding regional tectonics and the timeframe of when the …

The Jacod-Yor Theorem For Sigma Martingales And The Second Fundamental, Moritz Sohns

Journal of Stochastic Analysis

In this paper, we prove the Jacod-Yor Theorem for sigma martingales, a class of processes that generalize local martingales and play a pivotal role in financial mathematics. While the Jacod-Yor Theorem has been extensively studied for L2-martingales, martingales, and local martingales, no prior version exists for sigma martingales. Our result establishes the connection between sigma martingales and their martingale representation properties, addressing a critical gap in the literature. As an application, we prove the Second Fundamental Theorem of Asset Pricing for markets where price processes are modeled as sigma martingales.

The Witten Deformation And Proper Cocompact Lie Group Actions, Hao Zhuang

Arts & Sciences Graduate Student Theses and Dissertations

We study the interactions between the Witten deformation of the de Rham exterior differentiation and topological invariants in two scenarios of proper Lie group actions. In the first scenario, we work on a closed oriented manifold admitting an action by a compact connected Lie group. Using a special Morse-Bott function invariant under the group action, we deform the de Rham exterior derivative and get the associated Witten Laplacian. Applying asymptotic analysis, we localize the kernel of the Witten Laplacian around the critical components of the invariant Morse-Bott function. Finally, we build the chain isomorphism between the invariant Thom-Smale complex and …

Artificial Intelligence And Astronomy: Lab Manual For Generative Ai-Based Learning Activities, Vasiliy Znamenskiy

Open Educational Resources

This laboratory manual introduces an innovative approach to teaching astronomy by integrating generative artificial intelligence (AI) tools into hands-on educational activities. Aimed at undergraduate and general education students, the manual guides learners through interactive exercises that involve evaluating AI-generated text responses, creating scientifically inspired images, and producing short educational videos about astronomical phenomena. By engaging with platforms such as ChatGPT, Gemini, DALL·E, and InVideo, students develop critical thinking skills, enhance their digital literacy, and deepen their understanding of space science. The method emphasizes inquiry-based learning, creativity, and scientific communication, preparing students to become thoughtful users of AI in academic and …

The Mckay-Navarro Conjecture For The Prime 2, L. Ruhstorfer, A. A. Schaeffer Fry

Mathematics: Faculty Scholarship

We complete the proof of the McKay-Navarro conjecture (also known as the Galois-McKay conjecture) for the prime 2, by completing the proof of the inductive McKay-Navarro conditions introduced by Navarro-Späth-Vallejo for this prime.

Quantum-Enhanced Training Of Large Language Models: A Hybrid Approach, Nan Wu, Fangmin Song, Xiangdong Li

Publications and Research

The training of large language models (LLMs) presents significant computational challenges, particularly regarding efficient convergence. This paper presents a hybrid quantum-classical framework designed to address the significant computational challenges associated with training large language models (LLMs). By integrating quantum computing principles superposition, entanglement, and tunneling with classical deep learning methods, we propose an approach to accelerate convergence, enhance optimization efficiency, and improve model generalization. Specifically, quantum feature mapping is employed to project classical data into high-dimensional Hilbert spaces, facilitating more expressive data representations. Quantum-assisted optimization algorithms, such as Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), efficiently navigate …

Geospatial Intelligence And Multi-Criteria Analysis For Mapping Groundwater Potential Zones And Sustainable Resource Management In Wadi Qena Basin, Eastern Desert, Egypt, El-Taher M. M. Shams, Rashad Sawires, Sahar N. E. Tawfiq, Hanaa R. Youssef, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Groundwater is a rare and valuable resource in arid and hyperarid areas. Over the past few decades, population growth, urbanization, and agricultural activities—particularly in developing countries like Egypt—have greatly increased the demand for water supplies. The purpose of this study is to apply a multi-criteria analytical hierarchy process (AHP) in conjunction with remote sensing and geographic information systems methodologies to identify potential zones for groundwater recharge in Wadi Qena, Eastern Desert of Egypt. This valley is considered as one of the most potential valleys for government-led land reclamation and development initiatives. Using several data sources (e.g., Landsat-8 Enhanced Thematic Mapper …

Plm-Dbps: Enhancing Plant Dna-Binding Protein Prediction By Integrating Sequence-Based And Structure-Aware Protein Language Models, Suresh Pokharel, Kepha Barasa, Pawel Pratyush, Dukka B. Kc

Michigan Tech Publications

DNA-binding proteins (DBPs) play a crucial role in gene regulation, development, and environmental responses across plants, animals, and microorganisms. Existing DBP prediction methods are largely limited to sequence information, whether through handcrafted features or sequence-based protein language models (PLMs), overlooking structural cues critical to protein function. In addition, most existing tools are trained for general DBP predictions, which are often not accurate for plant-specific DBPs due to the unique structural and functional properties of plant proteins. Our work introduces PLM-DBPs, a deep learning framework that integrates both sequence-based and structure-aware representations to enhance DBP prediction in plants. We evaluated several …

Advanced Machine Learning Techniques For Social Support Detection On Social Media, Olga Kolesnikova, Moein Shahiki Tash, Zahra Ahani, Ameeta Agrawal, Raúl Monroy, Grigori Sidorov

Computer Science Faculty Publications and Presentations

The widespread use of social media highlights the need to understand its impact, particularly the role of online social support. In this study, we present a dataset of YouTube comments, initially comprising 66,272 entries, which was refined to 42,695, with a subset of 10,000 comments selected for detailed analysis without additional filtering. The dataset is annotated for three classification tasks: (1) distinguishing supportive from non-supportive comments, (2) determining whether the support is directed at an individual or a group, and (3) further categorizing group support into six subtypes (Nation, LGBTQ, Black Community, Women, Religion, and Other). To address data imbalances …

Identifying Antimalarials That Disrupt Malaria Parasite Transmission When Fed To The Mosquito, Sarah N. Farrell, Anton Cozijnsen, Vanessa Mollard, Papireddy Kancharla, Rozalia A. Dodean, Jane X. Kelly, Geoffrey I. Mcfadden, Christopher D. Goodman

Chemistry Faculty Publications and Presentations

A decade-long decline in malaria cases has plateaued, primarily due to parasite drug resistance and mosquito resistance to insecticides used in bed nets and indoor residual spraying. Here, we explore the innovative control strategy targeting Plasmodium with antimalarials during the mosquito stages. This strategy has the potential to reduce the risk of resistance emerging because a relatively small population of parasites within the mosquito is subject to selection. After validating mosquito feeding strategies, we screened a range of parasiticidal compounds by feeding them to mosquitoes already infected with mouse malaria (P. berghei). Three antimalarials showed activity against P. berghei in …

Photojournalism In The Age Of Deepfakes: The Role Of Media Literacy And Ethical Standards In Restoring Trust In Visual Reporting, Ionnnis Kontos, Katerina Chryssanthopoulou, Ioannis Galanopoulos-Papavasileiou

All Works

This article explores the impact of deepfake technology on photojournalism, highlighting its role in undermining trust in visual media. As deepfakes allow for the creation of highly realistic manipulated content, they pose significant challenges regarding the authenticity of journalistic imagery and erode the authority of visual truthfulness. The widespread use of deepfakes has led to a decline in public confidence in the credibility of news, raising concerns about the future of photojournalism in an era of digital deception. As a solution to regaining viewers’ trust, this article suggests a twofold approach: First, it emphasizes the importance of media literacy in …

Artificial Intelligence Use In Medical Education: Best Practices And Future Directions, Rasheed A. M. Thompson, Yash B. Shah, Francisco Aguirre, Courtney Stewart, Costas D. Lallas, Mihir S. Shah

Department of Urology Faculty Papers

PURPOSEOF REVIEW: This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS: AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in …

Near-Earth Object (Neo) Surveyor Introduction “Neos”, Brent Carlsen

Space Dynamics Laboratory Publications

NEOS

- Near-Earth Object Surveyor (NEOS)

- Its Mission

- Its Importance

- How it’s built

- What you will see today

• Where piece parts were made
• Where it will be assembled
• Where it will be tested

Lower Bounds For The Total Distance $K$-Domination Number Of A Graph, Randy R. Davila

Theory & Applications of Graphs

For $k \geq 1$ and a graph $G$ without isolated vertices, a \emph{total distance $k$-dominating set} of $G$ is a set of vertices $S \subseteq V(G)$ such that every vertex in $G$ is within distance $k$ to some vertex of $S$ other than itself. The \emph{total distance $k$-domination number} of $G$ is the minimum cardinality of a total $k$-dominating set in $G$ and is denoted by $\gamma_{k}^t(G)$. When $k=1$, the total $k$-domination number reduces to the \emph{total domination number}, written $\gamma_t(G)$; that is, $\gamma_t(G) = \gamma_{1}^t(G)$. This paper shows that several known lower bounds on the total domination number generalize …

The Integer-Antimagic Spectra Of A Weak Join Of Hamiltonian Graphs, Ugur Odabasi, Dan Roberts, Richard M. Low

Theory & Applications of Graphs

A simple graph $G$ with vertex set $V(G)$ and edge set $E(G)$ is \emph{$\mathbb{Z}_{k}$-antimagic} if there exists a function $f: E(G) \to \mathbb{Z}_{k} \backslash \{0\}$ such that the induced function $f^+(v)=\sum_{uv\in E(G)} f(uv)$ is injective. The \textit{integer-antimagic spectrum} of a graph $G$ is the set IAM$(G) = \{k: G \textnormal{ is } \mathbb{Z}_k\textnormal{-antimagic and } k \geq 2\}$. A \emph{weak join} of vertex-disjoint graphs is the collection of the graphs with additional simple edges (possibly none) between the original graphs. In this paper, we characterize IAM$(H)$ where $H$ is a weak join of Hamiltonian graphs.

Prime Labelings On A 3xn Grid Graph, Stephen J. Curran, Matt A. Ollis

Theory & Applications of Graphs

It is conjectured that the mxn grid graph has a prime labeling for all positive integers m and n. It is known that for any prime p and any integer n such that 1≤n≤p², there exists a prime labeling on the pxn grid graph P_m x P_n. Also, it is known that the ladder P₂ x P_n has a prime labeling for all positive integers n. We assume that Goldbach's Even Conjecture and a strengthened variant of Lemoine's Conjecture are true in order to show that the 3xn grid graph P …

Agent-Based Modeling: Introduction And Actuarial Applications, Rick Gorvett

Mathematics and Economics Faculty Working Papers

Agent-based modeling (ABM) has become an important and valued approach to modeling complex systems. In this paper, I advocate for actuaries to recognize the complex systems-nature of socioeconomic and risk processes and for ABM models to become a regular resource in our actuarial toolkits. These models allow for the observation of potential macro-behavior emerging from the underlying agent-level micro-activity and characteristics. Therefore, ABM models can provide significant insight into the quantification of risk and the identification of optimal strategies. This paper is an introduction and guide to ABM models, and it includes several case studies to illustrate their utility.

A Selective Discontinuous Galerkin Implicit Particle-In-Cell Method For Plasma Simulation With Improved Interpolation, Siyu Wu, Yang Li, Hongtao Liu, Xiaoming He, Yong Cao

Mathematics and Statistics Faculty Research & Creative Works

This article dynamically incorporates multiple ideas into the existing direct implicit particle-in-cell (DIPIC) method for plasma simulation, in order to dramatically improve the DIPIC method for its local mesh refinement needs based on Cartesian meshes as well as its interpolation needs based on the locally refined meshes. One key tool is to utilize the selective discontinuous Galerkin method, which is based on the interior penalty discontinuous Galerkin formulation and the regular local finite element basis functions, as the electric field solver in the DIPIC simulation. This hybrid type finite element method combines the advantages of both continuous and discontinuous finite …