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 …

Micro-Behavioral Analysis Of Online Shopping Patterns For Blind Users, Yash Prakash, Akshay Kolgar Nayak, Nithiya Venkatraman, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

While online shopping platforms provide convenience and autonomy to blind users, their non-visual interactions remain underexplored at a micro-behavioral level. Existing studies have primarily emphasized accessibility and usability challenges but have overlooked how fine-grained, screen reader-driven keystroke-level behaviors reflect users’ cognitive strategies. In this paper, we present the findings of a longitudinal study with 25 blind participants to examine their micro-behavioral patterns, using keyboard activity and screen reader logs on both familiar and unfamiliar e-commerce websites. We complemented this study with semi-structured interviews to contextualize the uncovered micro-behavioral patterns. Our results revealed patterns in how blind users draw upon cognitive …

Crossing The Theory Threshold: The Pedagogical Potential Of Generative Artificial Intelligence In Educational Research, Amanda Burbage, Jennifer L. Styron

EVMS School of Health Professions Faculty Publications

Purpose

This paper presents findings from an educational research graduate course in which generative artificial intelligence (AI) was incorporated to strengthen learners' understanding of threshold concepts related to theoretical frameworks. Medical and health professionals often struggle with the transition from a clinical role into the educational research role.

Methods

The study posits that the use of generative AI will help learners understand and apply theoretical frameworks beyond a superficial level, furthering their understanding, constructing new knowledge, and strengthening their ability to develop sound educational research studies. Journal and AI transcripts were analyzed for 37 participants.

Results

Open-ended codes were grouped …

First-Generation Medical School Applicants: A Quantitative Study Designed To Identify Areas Of Educational Support, Bethsabe Romero, Amanda K. Burbage

EVMS School of Health Professions Faculty Publications

First-generation (First Gen) students are unique medical school applicants. Due to their lived experience, they approach patient care by prioritizing trust, comfort and understanding. They have proven ability to overcome obstacles and were found to be more resilient than their continuing generation (Cont Gen) peers. Despite these notable attributes, they face unique challenges in gaining medical school acceptance. There are very few quantitative studies examining this student subpopulation, and our study identifies characteristics of first-generation medical school applicants while highlighting areas of needed support. This cross-sectional study used deidentified Application and Matriculating Student Questionnaire survey data that was obtained from …

Michael Scott Is Not A Juror: The Limits Of Ai In Simulating Human Judgment, Sean Harrington, Hayley Stillwell

Faculty Articles

Can AI replace human jurors? More specifically, can large language models predict how jurors interpret evidence and reach decisions based on legally salient facts and demographic characteristics? As legal scholars and practitioners increasingly explore AI-generated jury simulations, this Article offers the first empirical test of whether models like GPT-4, Claude, and Gemini can faithfully replicate juror reasoning. The answer, for now, is no. Across a series of mock trial scenarios involving redacted confessions, GPT- 4, Claude, and Gemini repeatedly failed to replicate how real jurors interpret evidence or exercise judgment. Their errors were not random, but systematic. Hidden prompts, built-in …

Cogram: A Computational Pipeline For Genome Assembly And Reconstruction Using Graph Neural Networks, William Coggins

College of Graduate Studies: Theses & Dissertations

Genome assembly — the reconstruction of a complete DNA sequence from short, overlapping reads — remains a fundamental challenge in computational biology. A central difficulty is distinguishing true genomic overlaps from spurious connections arising from repetitive sequences, a task that traditional assemblers address through hand-tuned heuristic rules applied to de Bruijn or overlap graphs. This thesis introduces COGRAM (Coggins–Ramasamy Assembly Method), a genome assembly pipeline that reframes sequence reconstruction as an edge classification task on a k-mer overlap graph, replacing heuristic graph cleaning with a learned model.

COGRAM constructs a directed overlap graph from raw sequencing reads using a k-mer …

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 …

Attacks And Detections In Recommender Systems: A Comprehensive Analysis For Models, Progresses, And Trends, Yan Feng, Zhihai Yang, Kexin Li, Jianxin Li, Pinghui Wang, Zhiquan Liu

Research outputs 2022 to 2026

Recommender systems (RSs), as crucial components of online services, can help users efficiently obtain information they may like. In reality, RSs face long-term threats. Attackers manipulate recommendation results by injecting malicious data in order to obtain benefits. At present, research on the security of RSs lacks a comprehensive understanding of attack capabilities. Moreover, existing defense strategies have not yet been systematically associated with attack characteristics. More importantly, existing defense methods rarely focus on real unlabeled data in practical application scenarios for anomaly detection and forensics. Therefore, this survey systematically analyzes the security of RSs and provides new insights. Specifically, we …

Lack Of Increase In Mercury Contamination In Coastal Western Australia Since European Settlement, Martin Dahl, Larissa Schneider, Harald Biester, Richard Bindler, Antonio Martinez Cortizas, Paul S. Lavery, Miguel A. Mateo, Oscar Serrano

Research outputs 2022 to 2026

Current knowledge of long-term mercury (Hg) deposition is predominantly based on studies from the Northern Hemisphere, leading to a geographical bias in the comprehension of the global Hg cycle. Aiming to contribute to fill this knowledge gap, our study presents a high-resolution Hg record of a seagrass Posidonia australis sedimentary archive encompassing the last 3300 years in the Waychinicup estuary (Western Australia, WA). This setting is an ideal site for studying the natural Hg cycle, as it is located in the southwest of the state, outside the prevailing wind patterns that transport emissions from major Hg sources. Our results show …

Satellite Observations Reveal Ecosystem Resistance And Resilience To Short-Term Water Stress Driven By Dominant Vegetation Along A Rainfall Gradient In Australia, Huanhuan Wang, Qiaoyun Xie, Sally E. Thompson, Caitlin E. Moore, David L. Miller, Erik J. Veneklaas, Richard P. Silberstein, Xing Li, Jingfeng Xiao, Belinda E. Medlyn, William K. Smith

Research outputs 2022 to 2026

Climate change is projected to intensify water stress in many ecosystems and poses threats to their stability, which can be quantified through ecosystem resistance and resilience. Relevant studies mostly focused on multi-year or annual droughts, and in spatially homogeneous or species-specific ecosystems. However, resilience and resistance within complex ecosystems, where different plants exhibit different adaptations and recovery behaviours, are less understood. Using productivity data from satellite-derived GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Fluorescence) and flux towers, we examined vegetation responses to short-term (<1 year) water stress events from 2000 to 2018 along the North Australia Tropical Transect, which spans a 1600 mm rainfall gradient and transitions from seasonal mesic to non-seasonal arid ecosystems. We define resistance as productivity maintained during stress relative to a multi-year average baseline, and resilience as the extent to which productivity recovered one year after stress relative to the same baseline. Our results show that ecosystem resistance to water stress was lowest in semi-arid regions but higher in both arid and mesic regions, while ecosystem resilience showed the opposite pattern. These spatial patterns occurred regardless of seasonality and were mainly associated with dominant vegetation type. Woody savanna-dominated mesic regions exhibited highest resistance (0.82 ± 0.13, p < 0.001) and lowest resilience (0.26 ± 0.19, p < 0.001), shrublands in arid areas had intermediate values of both resistance (0.81 ± 0.14, p < 0.001) and resilience (0.27 ± 0.22, p < 0.001), while the grasslands in semi-arid regions had low resistance (0.78 ± 0.15, p < 0.001) and high resilience (0.38 ± 0.24, p < 0.001). The highest likelihood (>75.0 %) of full recovery (i.e., exceeding baseline after one year) occurred during the wet season in …

Sparse Gradient Training For Recommender Systems, Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Jianxin Li, Hongzhi Yin

Research outputs 2022 to 2026

Recommender systems are widely applied in numerous online platforms such as shopping and social media platforms. They typically utilize large embedding tables that map users and items to dense vectors of uniform sizes. As the number of users and items continues to grow, this design leads to significant memory consumption and computational inefficiencies. This challenge is particularly pronounced in scenarios such as federated learning, where model parameters are updated locally on edge devices with limited computational resources before being transmitted to a central server for aggregation. Numerous approaches have been proposed to address this issue, among which embedding pruning methods …

ℵ-Ipomdp: Mitigating Deception In A Cognitive Hierarchy With Off-Policy Counterfactual Anomaly Detection, Nitay Alon, Joseph M. Barnby, Stefan Sarkadi, Lion Schulz, Jeffrey S. Rosenschein, Peter Dayan

Research outputs 2022 to 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called ℵ-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the ℵ-mechanism’s …

From Wikipedia Tables To Public Data Visualizations, Tanvir Prince

Open Educational Resources

This open educational resource presents a practical mathematics lesson in which students turn numerical data from Wikipedia into a clear data visualization. Students select a Wikipedia page with a data table but little or no visual representation. They examine the original source, date, units, definitions, and possible data limits. They then organize the data in Microsoft Excel or another spreadsheet, choose an appropriate chart, and explain what the visualization helps readers understand. A complete worked example uses the 2017 population growth rates of South American countries.

The resource package includes an instructor lesson plan, a student project guide, a Wikimedia …

Gpu And Pso Accelerated Low-Latency Fully-Coherent All-Sky Search For Compact Binary Coalescences, Soumya D. Mohanty

Physics & Astronomy Faculty Publications

The fully-coherent all-sky (FCAS) search, which combines data from a gravitational wave detector network into a single likelihood function, is the preferred method prescribed by statistical theory for Gaussian noise. However, so far, its exorbitant computational cost has blocked its use for compact binary coalescence searches. We introduce a solution combining Particle Swarm Optimization with Graphics Processing Unit (GPU) acceleration that is ≈ 50-fold faster than real-time analysis. This transforms the prospect of a low-latency FCAS search on all GW data into a practical reality for the first time. With large-scale simulations enabled by this speedup, we examine the issue …

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 …

A Literature Review On Ethics In Medical Diagnostic Ai & Analysis Therein, Connor J. Turetzky

Master's Theses

Artificial Intelligence presents a very promising future in medicine. Being able to diagnose and recommend treatments quickly is vital in ensuring positive patient outcomes. However, the new technology is not without risk. In this narrative literature review, the risks of AI in terms of bias, ethics, and environmental impact will be explored through existing research. This paper will focus on research published between 2019 and 2026, highlighting the major ethical and systematic problems currently facing diagnostic AI. Historical bias in medical data has led to AI that share those biases, and humans inherit that bias creating a potential negative feedback …

A Framework For Evaluating Riparian Climate Refugia In The Eel River Watershed, California, Farrah Tyler

Cal Poly Humboldt theses and projects

Climate change is increasing the need to identify and protect landscapes that support biodiversity through both habitat connectivity and climate refugia. Climate refugia are areas that remain relatively buffered from regional climatic change, enabling species to persist despite shifting environmental conditions. Riparian ecosystems can function as both movement corridors and climate refugia, yet methods for identifying these features and understanding the environmental processes that sustain them remain limited. In California's North Coast region, the identification and conservation of watersheds that may serve as climate refugia for sensitive plant and animal species will be essential for long-term ecosystem management and conservation. …

Development Of The Core Lit Apparatus For Seaweed Hatcheries (Clash) Bioreactor Using A Germplasm Approach, Phillip O. Tahimic

Cal Poly Humboldt theses and projects

In comparison to the Asian aquaculture industry, the United States has minimal seaweed production. There are several drivers of the limited industry in the US, such as regulatory constraints and limited market demand, but the ability to scale is a considerable barrier to commercialization. One possible solution is the development of land-based systems that integrate bioreactors, which utilize fewer resources and do not wholly rely on an oceanic flow-through system. This study assessed the application of a modular bioreactor, known as Core Lit Apparatus for Seaweed Hatcheries (CLASH), as a nursery system and open-source project designed to expand land-based seaweed …

Land Cover Classification Using Optimized Imagery Resolution And Machine Learning Algorithms For A Long-Term Monitoring And Restoration Project, Jessica R. Suoja

Cal Poly Humboldt theses and projects

Ecosystem services and functions are prone to water resource exploitation resulting in cascading effects that include decrease of biodiversity and loss of riparian vegetation, a keystone habitat in desert riparian ecosystems. Mono Lake is a prime example of overexploitation of water resources leading to legal action and eventually a legally mandated long-term monitoring and restoration project. Monitoring and restoration projects can benefit from techniques such as remote sensing and machine learning algorithms to generate accurate land cover classification maps for calculating land cover change over time. However, the spatial resolution of remote sensing imagery and the machine learning algorithms chosen …

Geochemical And Petrological Observations Of The June Lake Basalt, Eastern California, Daniel Jacob Abel

Cal Poly Humboldt theses and projects

The June Lake basalt is a monogenetic cinder cone and 11-km-long lava flow in eastern California located ~10 km NW of the Long Valley caldera. The stratigraphic position of the June Lake basalt between Tahoe and Tioga glacial deposits suggests an eruption age of only ~20-45ka. This age places the June Lake Basalt among the youngest volcanic eruptions in the Long Valley-Mono Basin region, where volcanic activity spans from the late Pliocene to the Holocene. The goal of this study is to use field observations coupled with whole-rock and crystal geochemical analyses to investigate the magma source and pre-eruption magma …

Contribution Of Lagoon-Rearing Juvenile Chinook Salmon (Oncorhynchus Tshawytscha) To The Adult Spawning Population In The Mattole River, Emma Held

Cal Poly Humboldt theses and projects

Bar-built estuaries are rivermouth confluences characterized by periods of disconnection between the river and the ocean due to a sandbar that builds across the mouth. Sandbar timing characteristics vary between years and between systems, but when closed, a lake-like lagoon forms in the estuary footprint. The Mattole River has a bar-built estuary that forms in late spring or early summer, often overlapping with juvenile Chinook salmon outmigration. Juveniles that do not leave the river before the bar closes must remain in the lagoon until the mouth opens in fall. The purpose of this thesis was to understand if lagoon-rearing is …

Factors Influencing Tree Vigor And Structural Failure Along Powerline Corridors In Northern California: A Multi-Species Analysis, David Justin Dorval

Cal Poly Humboldt theses and projects

Tree failures (e.g., stem breakage, uprooting, or branch drop) can pose significant risks to public safety, particularly along powerline corridors for the risk of power outages and wildfire ignitions. In California, tree-related powerline failures have led to severe ecological and human costs in recent years. Despite extensive research on tree failure in urban environments and post-disturbance contexts, comparatively little empirical work has examined where powerlines intersect largely unmanaged stands. This study evaluates tree failure trends across multiple forest types in Northern California by examining how tree-level characteristics influence recent growth and failure occurrence in regionally dominant tree species. Specifically, the …

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 …