Physical Sciences and Mathematics Commons^™

A Hybrid Response Surface Methodology And Machine Learning Framework For Quantifying Effects Of Physicochemical Parameters On Pfas Distribution, Harsh V. Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log K_ow, pH_pzc, pK_a, log dose, and log-initial concentration, affect PFAS distribution coefficients (Log K_d). A data set of more than 1000 adsorption observations spanning 15 PFAS compounds, multiple adsorbent types, and a broad operational range was compiled and preprocessed using …

Advancing Lipidomics Through Fluoroalcohol-Induced Multiphase Fractionation And Orthogonal Lc×Lc–Ms Detection, Md Al Amin

Chemistry & Biochemistry Dissertations

Comprehensive characterization of the lipidome remains a significant analytical challenge due to the immense structural diversity, wide dynamic range, and complex physicochemical continuum of lipid species. Conventional extraction methods, such as those by Bligh–Dyer and Folch, and one-dimensional liquid chromatography (1D-LC) often fail to resolve this complexity, leading to chromatographic overlap, severe ion suppression, and limited coverage of low-abundance or highly hydrophobic species. This dissertation addresses these bottlenecks by establishing a separation-driven analytical framework that integrates novel fluoroalcohol-induced multiphase systems (FAiTPS) with orthogonal multidimensional liquid chromatography–mass spectrometry (LC×LC–MS).

The core of this research is the development and physicochemical characterization of …

Evaluating Quahog (Mercenaria Mercenaria) Nursery Methods And Production Costs To Support Diversification Of Maine’S Fisheries And Aquaculture Sector, Hannah G. Wolf

Honors Theses

Populations of wild and farmed quahogs in Maine are expanding, driven by warming waters, declining soft-shell clam populations, and the need to diversify fisheries. The Northern quahog (Mercenaria mercenaria) is a potential species to diversify Maine’s marine sector, as its geographical range is expanding, and they appear more resistant to green crab predation. Enhancing wild quahog stock provides economic and social advantages for both the aquaculture and wild harvest industries. However, there is limited understanding about the commercial viability of producing quahog seed on existing farms for wild shellfish enhancement in Maine. This research, part of a larger NOAA …

Steiner Coset Partitions For Five Mutually Commuting Subgroups, Fusun Akman, Papa Sissokho

Faculty Publications – Mathematics

A Steiner coset partition of a group G with respect to pairwise distinct subgroups H1 , …, Hr is a collection of pairwise disjoint cosets g1 H1 , …, gr Hr whose union is G. Motivated by the Herzog–Schönheim Conjecture, we have recently started exploring the groups that admit Steiner coset partitions. In previous work, we completely classified such groups for up to r = 4 mutually commuting subgroups. In this paper, we extend the classification to r = 5 mutually commuting subgroups.

Physics Alumni Newsletter Spring 2026, Terry Goforth

Physics Alumni Newsletter

Physics Alumni Newsletter

The Physics Alumni Newsletter is produced by the SWOSU Physics Department.

Our Engineering Physics students are recruited in fields such as electronics, aerospace, mechanical engineering, petroleum engineering and software engineering. Graduates also have careers in meteorology, architecture, education and more.

A Data-Driven Framework For Automation Readiness In Minnesota State University, Mankato Course Scheduling, Prisca Bongu Payanzo Maba

All Graduate Theses, Dissertations, and Other Capstone Projects

University course scheduling is one of the most complex optimization problems in higher education institutions. With universities growing in size and offering a broad spectrum of majors and disciplines, the number of possible course scheduling combinations increases exponentially, rendering traditional ways of scheduling ineffective.

Although operations research has extensively studied automated scheduling algorithms, there has been limited investigations into the organization readiness of academic departments to implement such systems. This paper offers a hybrid data science framework that assesses departmental readiness for scheduling automation.

The study combines qualitative Zoom interview data from 19 academic departments with institutional scheduling rules from …

Partially Penalized Anisotropic Trilinear Ife-Pic Methods For Dc Plasma Transport Problems, Jiahui Li, Guangqing Xia, Yajie Han, Ziping Wang, Chang Lu, Xiaoming He

Mathematics and Statistics Faculty Research & Creative Works

Implicit and hybrid particle-in-cell methods are widely used for efficient simulation of DC discharge plasma transport. However, their computations require solving anisotropic elliptic equations and face challenges related to mesh geometry, non-axisymmetry, and complex interfaces. Moreover, the accuracy of particle trajectories is critical for plasma etching and erosion studies, where errors near interfaces can significantly impact simulation results. To address these challenges, this paper proposes a three-dimensional anisotropic trilinear partially penalized immersed finite element (ATPPIFE) method, which captures interfaces on Cartesian meshes and effectively reduces discontinuities at interface element faces, ensuring that particle trajectories better align with real-world behavior. Building …

Reply To: S. N. Katkuri Et Al. And H. Liu Et Al. On Early And Sustained Improvements In Sense Of Smell With Tezepelumab Treatment In Patients With Chronic Rhinosinusitis With Nasal Polyps (Waypoint), Joaquim Mullol, Joseph K. Han, Tanya M. Laidlaw, Claire Hopkins, Anju T. Peters, Oliver Pfaar, Martin Desrosiers, Stella E. Lee, Andrew P. Lane, Claudia Chen, Yun Chon, Sandhia S. Ponnarambil, Andrew Foster, Andrew W. Lindsley, Christopher S. Ambrose

Department of Otolaryngology (ENT) Faculty Publications

[Introduction] We thank Dr. S. N. Katkuri, Dr. H. Liu, and their coauthors [1, 2] for their interest in our recent publication describing the improvements in loss of smell symptoms with tezepelumab versus placebo in patients with uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP) in the WAYPOINT trial (NCT04851964) [3]. We are grateful for the authors' feedback on the clinical significance of the data presented and their appreciation of the consistency observed across a range of baseline clinical characteristic and demographic subgroups.

Use Of Tezepelumab For Chronic Rhinosinusitis With Nasal Polyps By Eosinophilic Endotype: Waypoint Post-Hoc Analysis, Shigeharu Fujieda, Nobuyoshi Otori, Joseph K. Han, Tadataka Yabuta, Claudia Chen, Claudio Marchese, Andrews Foster, Sandhia S. Ponnarambil, Yun Chan, Brian J. Lipworth

Department of Otolaryngology (ENT) Faculty Publications

Background

The phase 3 WAYPOINT study (NCT04851964) reported that tezepelumab improved outcomes in patients with chronic rhinosinusitis with nasal polyps (CRSwNP), including nasal polyp size, nasal congestion, and sinonasal symptoms, and reduced the need for surgery and systemic corticosteroids (SCS).

Objective

To evaluate the efficacy and safety of tezepelumab across Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis-defined eosinophilic chronic rhinosinusitis (ECRS) subgroups.

Methods

Adults with severe CRSwNP were randomized to tezepelumab 210 mg or placebo every 4 weeks. Coprimary end points were the change from baseline to week 52 in total Nasal Polyp Score and the biweekly mean Nasal …

An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta

Mathematics & Statistics Faculty Publications

In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce …

Logistic-T Multinomial Mixture Model For Clustering For Microbiome Data, Wenshu Dai, Yuan Fang, Sanjeena Subedi

Mathematics & Statistics Faculty Publications

The logistic-normal multinomial distribution has been used for modelling microbiome data obtained from high-throughput sequencing technologies, which are compositional in nature. A logistic-normal multinomial distribution is a hierarchical multinomial distribution that assumes the latent variable which are the additive log-ratio (ALR) transformed proportions in a multinomial distribution follows a Gaussian distribution. Model-based clustering algorithms have also been developed for clustering microbiome data based on the logistic-normal models. However, the Gaussian assumption may violated when the ALR transformed variable exhibit heavy-tailed distributions or has outliers. Our study introduces a novel mixture of logistic-t multinomial models that effectively address these challenges. Utilizing …

Determining The Electric Field In A 10 Ns Pulsed Plasma In Fuel-Air Mixtures Using Efish, Md Ziaur Rahman, Christopher J. Kliewer, Brian D. Patterson, Chunqi Jiang

Bioelectrics Publications

Transient plasma ignition (TPI) utilizes non-equilibrium plasmas, produced by nanosecond high-voltage pulses, to improve lean-fuel combustion performance and reduce emission. It is known that the relatively high reduced electric field (E/N) in TPI plays an important role in generating energetic electrons and facilitating energy-efficient radical productions, resulting in reliable ignition for lean combustion. Determining the reduced electric field in the discharge is hence important for the understanding of the TPI process and ultimately allowing for the control of the plasma chemistry. This study reports spatiotemporally resolved measurements of the electric field (E) in a 10 ns pulsed plasma that is …

Ammonia Synthesis By Nanosecond Pulsed Atmospheric Pressure Plasma Jets Impinging On Water, Zach Caudell, Lynnet Rich, Olga Pakhomova, Chunqi Jiang

Bioelectrics Publications

Developing energy-efficient technologies for carbon-neutral ammonia (NH₃) synthesis is critical for decentralized fertilizer production and global decarbonization. This study investigates generating NH₃ from water using a nanosecond pulsed atmospheric pressure plasma jet (ns‑APPJ) operating in either N₂ or dry air. The plasma jet reactor employed approximately 250 ns, up-to-22 kV pulses at 500 Hz to sustain a nonequilibrium discharge impinging directly on static liquid water. The kinetics, energy efficiency, and product selectivity of NH3 formation were quantified as functions of the pulse voltage, repetition frequency (PRF), and gas flow rate. NH₃ production increased linearly with treatment time and scaled strongly …

Validity Assessment Of Resting Heart Rate Variability From The Garmin Health Snapshot, Kayla M. Porter, Andrew Flatt

Honors College Theses

Purpose: To assess the agreement between the Garmin Forerunner 265 (a commercially available sports watch) and a single-channel electrocardiographic (ECG) chest strap for determining resting heart rate variability (HRV). Secondary aims were to assess the impact of skin tone and body position on measurement accuracy.

Methods: Young adults (n = 30, 57% women) aged 18–39 years without known cardiovascular conditions and without tattooing or scarring on the dorsal left wrist were recruited. HRV was recorded simultaneously using ECG and the Forerunner 265’s optical sensor during Garmin’s 2-minute “Health Snapshot.” Measurements were obtained in three standardized positions: supine, seated, and standing. …

Computational And Ai Literacy: Lessons Learned From Computer Science Education, Sharin Jacob, Yenda Prado, Quinn Burke

Journal of Computer Science Integration

This conceptual paper explores the intersection of computational thinking (CT) and artificial intelligence (AI) literacy, proposing a framework integrating AI literacy into existing computational literacy models. We argue that computational thinking (CT) skills form the foundation for AI literacy, and that lessons learned from decades of computer science (CS) education research can ably guide the development of AI literacy instruction for a wide range of  students and within integrative contexts. Drawing on Kafai and Proctor's (2022) conceptualization of computational literacy, as well as encompassing cognitive, situated, and critical dimensions, we extend their framework to advance our understanding of AI literacy. …

Memory Effects In Many-Body Systems, Jeffrey Beckstrand

Master's Projects

This thesis investigates memory effects in many-body systems through the MoriZwanzig Formalism for projected dynamics of a Hamiltonian System which yields the Generalized Langevin Equation (GLE). The GLE is a stochastic differential equation (SDE) that studies the dynamics of observables under the effects of many other observables in the system. Although satisfying, the GLE has a term called the Memory Kernel that encodes the past of the system and introduces a computational challenge by introducing a non-Markovian property to the equation. The kernel is often approximated by introducing a delta function, which simplifies the computation, but at the loss of …

Safe-R2r: A Safety-Aware And Budget-Optimized Retrieval Controller For Rag Systems, Mandar Sunil Gondane

Master's Projects

Retrieval-Augmented Generation (RAG) improves factual grounding in large language models by incorporating external evidence during inference. However, most RAG systems rely on fixed retrieval strategies that ignore query difficulty, computational cost, and prediction uncertainty. This project introduces SAFE-R2R (Safety-Aware and Budget-Optimized Reason-to-Retrieve), a retrieval controller that treats retrieval as a query-dependent decision problem. SAFE-R2R organizes retrieval into a multi-rung ladder ranging from no retrieval to deeper retrieval with reranking. At each rung, the system generates an answer, computes reliability signals, and combines them into a risk score used within a conformal calibration framework to decide whether to accept the answer …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudrish

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Cost-Aware Predictive Routing For Vision-Language Models, Sahil Sait Naveed

Master's Projects

Vision-language models (VLMs) are increasingly used for multimodal tasks, but their inference costs vary widely across model tiers. This work presents a predictive routing framework that assigns each query to the most cost-effective VLM by using multimodal embeddings, clustering, and per-cluster model error estimates. Evaluated on six commercial VLMs, the selected router reduces average cost by about 49 percent relative to the quality-first setting on both validation and test. This comes with accuracy losses of 0.4 percentage points on validation and 0.9 on held-out test data. It also outperforms K-NN and ZeroRouter routing baselines while providing lowercost operating points than …

Stepwise Sudoku Reasoning Training Using Transformers, Lok Man Chu

Master's Projects

Sudoku is a constraint satisfaction problem that serves as a testbed for studying reasoning and stepwise deduction. While many solvers can produce correct solutions, they often fail to generate human interpretable sequences of logically consistent steps. This study investigates whether a transformer trained on stepwise deduction traces can learn to solve Sudoku puzzles through sequential, logically deducible moves. Results show that solve accuracy improves significantly with more training data, reaching approximately 78% at 1500k samples. Stepwise analysis indicates that the model effectively learns simple strategies, achieving near-perfect performance. However, performance on more complex strategies remains limited. Overall, while the model …

Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar

Master's Projects

Real-time credit card fraud detection faces challenges such as extreme class imbalance, delayed feedback, and concept drift in transaction streams. This project implements and evaluates an adaptive streaming fraud detection framework based on three methodologies: (1) online learning with incremental updates, (2) explicit conceptdrift detection using statistical monitoring, and (3) separate models for immediate and delayed supervision, combined with cost-sensitive learning and anomaly detection. The system processes the credit card fraud dataset in a batched streaming fashion, uses multiple online learners and ensembles. Experiments show that online, driftaware models maintain high recall on frauds while controlling false positives under imbalanced …

Multi-Agent Path Planning And Optimization Using Q-Learning, Chirag Rudresh

Master's Projects

Robot navigation in a multi-agent setting requires a balance between safety and efficiency, especially in dense environments. In these two-dimensional spaces, the scope for geometric errors is much less and could lead to collisions or immobility. This project proposes to address the navigation task using a two-phase path planning pipeline that combines reinforcement learning and convex optimization in a scalable and robust manner. The first phase consists of generating diverse collision-free paths using a Q-learning agent that is trained on a visibility graph representation of the environment. The discretization of the environment using waypoint-based graphs allows the agent to train …

Ai Deployment Authorisation: A Global Standard For Machine-Readable Governance Of High-Risk Artificial Intelligence, Daniel Djan Saparning

Student Publications

Modern artificial intelligence (AI) governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing approaches-such as model cards, audits, and benchmark evaluations provide descriptive information about model behaviour and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS). This machine-readable, regulator-grade framework evaluates AI systems across five legally and economically grounded dimensions: Risk, Alignment, Externality, Control, and Auditability, derived from safety engineering, alignment theory, algorithmic accountability, and liability economics. ADAS produces …

Reversible Fluorescence Quenching And Recovery In Pvp-Supported Copper Nanoparticles, Sara Rayan

Senior Honors Theses and Projects

Copper nanoclusters stabilized by polyvinylpyrrolidone (PVP) exhibit unique fluorescence properties that are highly sensitive to changes in their chemical environment, making them promising candidates for sensing applications. This study investigates the reversible fluorescence quenching and recovery behavior of PVP-supported copper nanoparticles in response to iron(III) ions and evaluates the selectivity of this response across different chemical conditions. Fluorescence quenching was examined using Fe³⁺ introduced as FeCl₃, followed by recovery experiments using ascorbic acid to assess reversibility through redox-driven interactions. To determine whether the observed response is specific to iron, potassium ferricyanide was used as an alternative Fe³⁺ source. Additional comparisons …

Investigating The Effects Of Atg3 On Autophagosome Size And Number, Keeran R. Senthil Kumar

Senior Honors Theses and Projects

Macroautophagy, or autophagy, is a highly conserved catabolic process in all eukaryotes which allows for the degradation of large cytosolic contents. It occurs in two forms: bulk autophagy, which is induced by nutrient deprivation and targets nonspecific cargo, and selective autophagy, which is induced by cellular stress and sequesters specific substrates. The process of autophagosome formation is carried out by a group of Atg (autophagy-related) proteins, one of which is Atg3. Atg3 functions as an E2-like enzyme by conjugating Atg8 — a ubiquitin-like protein — to phosphatidylethanolamine (PE), a step required for autophagosome membrane formation. This lipidation process is catalyzed …

Efficient Organic Molecular Crystal Structure Prediction Using The Density Functional Tight-Binding Method, Maureen M. Kitheka, Yan Jing, Yan Yao, Puja Goyal

Faculty Publications – Chemistry

Over the years, computational crystal structure prediction (CSP) for organic molecules has thrived as an area of research, spanning various scientific disciplines and having significant applications in industries such as pharmaceuticals and agrochemicals. Within the field of batteries, redox-active organic materials (ROMs) such as quinones have received increased attention as promising electrode materials for rechargeable batteries. However, experimental determination of the crystal structure of intermediate species formed during the discharge/charge cycle can often be challenging. Incomplete X-ray diffraction patterns can also lead to difficulties in crystal structure determination for ROMs used in batteries. Use of a semiempirical electronic structure method …

Detecting And Repairing Conflicting Constraints In Co-Trained Physics-Informed Neural Networks For Composite Curing Processes, Cooper J. Evans

Dissertations, Master's Theses and Master's Reports

Composite materials have become a critical component of modern manufacturing, especially in the automotive and aerospace industries. The curing process for these composites has been modeled using a variety of partial differential equations representing the heat transfer and composite curing kinetics. Optimizing the applied temperature profile is critical for maximizing the efficiency and capacity of composite part manufacturers. Constraints must be placed on the inputs and outputs of the model, including but not limited to, the applied temperature profile, part temperature, and final degree of cure. Conflicting sets of constraints are easy to unknowingly impose due to the highly coupled …

Learning Without Training, Ryan O'Dowd

CGU Theses & Dissertations

We live in an era of big data. Whether it be algorithms designed to help corporations efficiently allocate the use of their resources, systems to block or intercept transmissions in times of war, or the helpful pocket companion known as ChatGPT, machine learning is at the heart of managing the real-world problems associated with massive data. With the success of neural networks on such large-scale problems, more research in machine learning is being conducted now than ever before. This dissertation focuses on three different projects rooted in mathematical theory for machine learning applications. Common themes throughout involve the synthesis of …

On The Influence Of Faraday Waves On Transport In Resonant Acoustic Mixing, Preston David Silverstein

CGU Theses & Dissertations

Resonant acoustic mixing (RAM) uses low frequency high acceleration oscillatory forcing to combine fluids, particles, and powders. Although progress has been in adjacent RAM fields, there are still gaps in understanding how Faraday surface instabilities affect momentum transport to the bulk fluid and particles therein. This work investigates the energy pathways that an oscillatory mixer has and connects surface deformation to bulk rotational flow and particle forcing. This study is conducted in three phases. In phase 1, a thermodynamic first- and second-law analysis couples the surface features with the scale of subsurface rotational features. Experimental surface measurements showed that for …

Asymptotics Of Discrete Convolution Powers And Applications To Difference Schemes, Pedro Henrique Alves Silva Dos Santos

Honors Theses

In this thesis we provide Gaussian Estimates and Local Limit Theorems describing the asymptotic behavior of convolution powers of a class of complex-valued functions on $\mathbb{Z}^d$. Convolution powers arise naturally in the study of partial differential equations, as well as in random walks in probability theory. In particular, they are connected to the stability theory of difference schemes used to approximate solutions to partial differential equations. We take inspiration from the work of Vidar Thomée on stability theory to restrict our attention to convolution powers of functions whose Fourier Transforms satisfy certain local expansions. We then combine the Cauchy Integral …