Physical Sciences and Mathematics Commons^™

Incorporating Propensity Score Weighting And Nonresposne Adjustments Into Complex Survey Data With Survival Outcomes, Xinrui Shi

Theses and Dissertations

Propensity score weighting (PSW) plays a key role in minimizing confounding in observational research, especially when estimating treatment effects for time-to-event outcomes. However, its integration into survey data with complex design – particularly data with multiple stage sampling and censoring – remains underexplored. One significant challenge in such settings is the presence of nonresponse, which can introduce additional bias and complicate the use of standard weight adjustments. Moreover, there has been limited study on how PS weights can be effectively combined with nonresponse weighting adjustments in complex survey data that include survival outcomes. This dissertation aims to extend current methodologies …

Draft Final 2025 Residential Metals Abatement Program (Rmap) Quality Assurance Project Plan (Qapp) Annual Update (Residential Parcels), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Building And Demonstrating A Framework And Guide For Human-Centered Threat Modeling, Warda Usman

Theses and Dissertations

As researchers increasingly seek to understand threats faced by people rather than systems, there remains little consensus on how to study these threats in ways that are grounded in human experience. Traditional approaches to threat modeling are often designed for system vulnerabilities and offer limited guidance for eliciting or analyzing threats as understood by individuals in their social and cultural contexts. This dissertation addresses this gap by advancing human-centered threat modeling (HCTM) in security and privacy research as both a conceptual framework and through a set of empirical studies. The dissertation employs a mixed set of qualitative methods across four …

Temporal Modeling And Forecasting Of Blood Glucose Dynamics In Individuals With Diabetes Mellitus, Mj Ruff

University Honors Theses

People living with Diabetes Mellitus face significant health risks, including an increased likelihood of heart disease, stroke, and fluctuations in blood glucose levels. The unpredictable nature of glucose levels can lead to dangerous conditions such as ketoacidosis and hypoglycemia. This study employs advanced time series analysis tools to forecast the glucose levels for an individual diagnosed with Type 1 Diabetes Mellitus.

A Proposal To Explore Geometry With Geogebra: Graphical Exploration And Formal Demonstration Of The Collinearity Of The Barycenters Of A Polygon, Saulo Mosquera Lopez, Marlio Paredes, Walter Castro

School of Mathematical & Statistical Sciences Faculty Publications

This paper illustrates an example of a mathematical activity that teachers and students can replicate to create an experience that resembles professional mathematical activity. We extend the property “Consider a triangle ABC, any straight line and let A’, B’, C’ be the reflections of the points A, B, C on the straight line then the barycenters of the triangles ABC, A’BC, AB’C and ABC’ are collinear and the line of collinearity is perpendicular to the straight line” for any quadrilateral. It is proved that there are four additional triangles, for a total of eight, whose barycenters are collinear and that …

Comprehensive Insights Into The Cholesterol-Mediated Modulation Of Membrane Function Through Molecular Dynamics Simulations, Ehsaneh Khodadadi, Ehsan Khodadadi, Parth Chaturvedi, Mahmoud Moradi

Chemistry & Biochemistry Faculty Publications and Presentations

Cholesterol plays an essential role in biological membranes and is crucial for maintaining their stability and functionality. In addition to biological membranes, cholesterol is also used in various synthetic lipid-based structures such as liposomes, proteoliposomes, and nanodiscs. Cholesterol regulates membrane properties by influencing the density of lipids, phase separation into liquid-ordered (Lo) and liquid-disordered (Ld) areas, and stability of protein-membrane interactions. For planar bilayers, cholesterol thickens the membrane, decreases permeability, and brings lipids into well-ordered domains, thereby increasing membrane rigidity by condensing lipid packing, while maintaining lateral lipid mobility in disordered regions to preserve overall membrane fluidity. It modulates membrane …

Orchestrating Complexity: The Art Of Virtual Leadership In System Modelling, Vijay Kumar Sonawane, Bipllab Roy, Purnendu Bikash Acharjee, Indu Pv

Northeast Journal of Complex Systems (NEJCS)

This paper explores the dynamics of virtual leadership within global remote work environments, focusing on the application of complex system modelling to understand and enhance leadership efficacy. The application of computational modelling has been a regular feature in economics, science and technology fields, however its application in virtual leadership with linkage to sport leadership appears to be a novel concept. Adopting a multidisciplinary approach, this paper incorporates Game Theory as a conceptual framework to make the leadership model more relevant and applicable that can offer simpler understanding of complex play of leadership drivers. The model incorporates five key leadership dimensional …

On Excursions Associated With A Certain Local Time Of Simple Symmetric Random Walks, With Applications, Takahiko Fujita, Naohiro Yoshida

Journal of Stochastic Analysis

In this note, some applications of excursions associated with a certain local time of simple symmetric random walks are presented. Specifically, the excursions are applied to calculate some probability distributions of interest regarding the random walks. Furthermore, a solution of the Skorokhod embedding problem for random walks is obtained through the excursions.

Session 2: The Cases For Industry Self-Regulation And Government Regulation Of Ai, Boaz Ashkenazy, Kevin Bartholomew, Kevin De Liban, Christopher Yoo

SITIE Symposiums

In Session Two of the SITIE 2025 Symposium titled “The Cases for Industry Self-Regulation and Government Regulation of AI,” moderated by Seattle University Technology Ethics Initiative Director and Professor Onur Bakiner, the panelists discuss their perspectives and experiences with AI regulation. They share observations about the industry and delve into the topics of AI complexity, concerns around accountability, the shift to agentic AI, the current state of AI regulation, existing legal guardrails, and their outlook on AI regulation.

Session 1: Guidance From International Regulation Sources, Charlotte Tschider, Marie-Charlotte Roques-Bonnet

SITIE Symposiums

In Session One of the SITIE 2025 Symposium titled “Regulating Artificial Intelligence: From Where and When?”, Professor Mark Chinen moderated a panel featuring Professor Charlotte Tschider and Dr. Marie-Charlotte Roques-Bonnet. The discussion focused on international AI regulation, privacy, data governance, and the EU’s regulatory approach to AI oversight.

8th Annual Innovation And Technology Law Conference: Regulating Artificial Intelligence: From Where And When?, Steven Bender

SITIE Symposiums

Since 2018, the Seattle University School of Law has presented an annual late spring/summer conference on innovation and technology, shifting to a virtual conference in 2020. The virtual format fosters inclusion of national and even international speakers (as this year with Marie-Charlotte Roques-Bonnet, Data Protection/AI Consultant & Research Lead, ID side.eu, joining from France), and participation from a broad base audience, which this year included over 200 registrants who joined live or watched the recording.

Leveraging Usage Of Ai In Education: Knowledge, Attitude And Behavioral Analysis On Students, Bipllab Roy, Purnendu Bikash Acharjee, Rohit Kumar Sharma, Ruptaheen Kramsapi, Shruti P

Northeast Journal of Complex Systems (NEJCS)

The paper explores the possible advantages and drawbacks of artificial intelligence (AI) on sustainability, with an emphasis on using AI to positively achieve SDGs. The study finds a significant vacuum in the literature on the association between knowledge, attitudes, and behaviors towards the use of AI tools and techniques in education and demographic characteristics (sex, age, education level, area of study, and city of origin). The purpose of this research is to close this knowledge gap and advance our understanding of how these demographic factors affect the integration of AI in educational environments. The study specifically aims to comprehend how …

Quantum Machine Learning For Battery Health And Thermal Risk Prediction, Alexander Mutiso Mutua, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

The rapid growth of connected Electric Vehicles (EV) as part of modern Intelligent Transport Systems (ITS) motivates the need for real-time management of Lithium-ion (Li-ion) battery health and thermal risks. Li-ion batteries, although widely used, are prone to degradation and thermal runaway, posing significant challenges for safe and efficient EV operation. We present a Quantum Machine Learning (QML) and Agent-Based Model (ABM) that simulates and predicts EV behaviour under various battery degradation con- ditions. We use a Variational Quantum Neural Network (VQNN) trained on NASA battery datasets to classify EVs into four cate- gories: healthy, degraded for fixed chargers, degraded …

Teaching Diffuse, Specular, And Total Internal Reflection Via A Halo Effect, Paul R. Destefano, Ralf Widenhorn

Physics Faculty Publications and Presentations

Interesting and pedagogically useful physics can be found in the halo produced by a laser pointer directed into a shallow body of water. We describe a simple model for this phenomenon using geometric optics and support this model with empirical evidence. We also discuss and verify extensions to this model that explain variations in the visual pattern. These variants include a double halo pattern and a disk of dim light that can be produced with minor modifications of the single halo configuration. Although the level of complexity differs, all of these models rely on the same ray optics, specifically refraction …

Tellings Of The Pacific Ocean: A Landscape-Based Approach For Multispecies Design And Hci, Maliheh Ghajargar

Engineering Faculty Articles and Research

Environmental disturbances induced by climate change have caused significant changes in our ecosystems and are threatening the health of our environments. As a response to this issue, a growing body of work has emerged in HCI and design, which seeks to foreground more-than-human stories in support of making more sustainable and just futures. This research contributes to this broad agenda by probing graphic novels as a multispecies storytelling method for design and HCI. Combining ideas from Anna Tsing’s adventures of landscape and from HCI and design’s use of sequential art (e.g., storyboards), we use landscape as the main protagonist of …

Lighting Up The Cell: Developing A Luminescent Lanthanide Probe For Detecting Rnas, Jonathan Savell

Honors Projects

An anion detecting luminescent probe, Tb:DO2A-Cs124, was repurposed for use in RNA imaging. Potential RNA aptamers were identified for the molecule using Capture-SELEX and MinION sequencing. Fluorimetry data revealed these aptamers did not allow for the probe to luminesce in the presence of target RNAs due to adenosine monophosphate’s ability to quench the emission signal.

Refinement And Application Of The Absorption Cross-Section Of Molecular Bromine With Relevance To Atmospheric Chemistry, Callum Flowerday, Ryan Thalman, Jaron C. Hansen, Eric T. Sevy, Jason J. Sorensen, Matthew C. Asplund

ScholarsArchive Data

Defined Br2 XS - is the defined absorption cross-section using the UVvis spectrophotometer.

BBCEAS - This is raw absorption data measured using the BBCEAS (see article for details).

UVvis raw - This is the raw absorption data measured using the UVvis spectrophotometer.

Colonization-Persistence Trade-Offs In The Human Microbiome, Liam F. Nokes

Environmental Studies Senior Theses

Understanding how diverse microbial taxa coexist in the human body without competitively excluding one another remains a key challenge in microbiome ecology. One proposed explanation is the colonization-persistence trade-off, where species with high colonization ability are poor persisters, and vice versa. We test for this trade-off across human associated microbial communities among many individuals and in multiple body sites using a large-scale participant-level meta-analysis of longitudinal microbiome datasets curated from the MGnify database. We applied island biogeography-based models to calculate effective colonization and persistence rates for microbial families and genera across 606 individuals over twenty-six studies. Regression of the log-transformed …

Preliminary Evidence For Lensing-Induced Alignments Of High-Redshift Galaxies In Jwst-Ceers, Viraj Pandya, Abraham Loeb, Elizabeth J. Mcgrath, Guillermo Barro, Steven L. Finkelstein, Henry C. Ferguson, Norman A. Grogin, Jeyhan S. Kartaltepe, Anton M. Koekemoer, Casey Papovich, Nor Pirzkal, L.Y. Aaron Yung

Pacific Faculty Work

The majority of low-mass ( log 10 M * / M ⊙ = 9 - 10 ) galaxies at high redshift (z > 1) appear elongated in projection. We use JWST-CEERS observations to explore the role of gravitational lensing in this puzzle. The typical galaxy-galaxy lensing shear γ ∼ 1% is too low to explain the predominance of elongated early galaxies with an ellipticity e ≈ 0.6. However, nonparametric quantile regression with Bayesian Additive Regression Trees (or BART) reveals hints of an excess of tangentially aligned source-lens pairs with γ > 10%. On larger scales, we also find evidence for weak-lensing shear. …

Forecasting Influenza Rates Using Machine Learning: A Study Of Chatgpt's Predictive Accuracy, Sara Saleh

University Honors Theses

This study evaluates ChatGPT's ability to forecast influenza rates, such as the number of flu cases, hospitalizations, and death during peak season periods using CDC data, and comparing forecasts against actual results to calculate statistical accuracy and consistency. Influenza forecasting is essential for public health planning, but traditional methods may not always provide timely or accurate predictions. In this research study, ChatGPT was utilized to predict the influenza rates for the following week based on the previous week's data obtained from the FluView surveillance system. The predicted rates were compared to the actual influenza rates to assess the model's overall …

Development And Photophysical Characterization Of Bioinspired Materials, Qaisar Maqbool

USF Tampa Graduate Theses and Dissertations

Porphyrins are versatile aromatic macrocycles with unique photophysical properties that make them attractive for applications in light harvesting, photocatalysis, and sustainable energy conversion. Incorporating porphyrins into metal-organic frameworks (MOFs) offers a unique opportunity to modulate their photophysical behavior through spatial confinement and host–guest interactions. This thesis explores how structural modifications influence porphyrin excited-state behavior through a multi-stage investigation. The work begins by comparing the photophysical properties of two cationic porphyrins: tetra(N-methylpyridyl) porphyrin(TMPyP) and tetra(4-N,N,N-trimethylanilinium) porphyrin(4TANP). While TMPyP exhibits significant singlet–charge transfer (S₁–CT) state mixing due to charge delocalization onto peripheral pyridinium groups, 4TANP lacks comparable behavior. The localized nature of …

Final 2025 Residential Metals Abatement Program (Rmap) Rock Creek Cattle Company Borrow Submittal #1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Statistical Programming For Adaptive Monitoring In Software Defined Networks Using Linear Programming, Fatemeh Amou Aghaei, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Adaptive monitoring in Software Defined Networks (SDNs) is essential to reduce overhead and prioritize critical flows. This paper introduces AdaptMon, a Linear Programming-based model that dynamically allocates monitoring resources based on estimated error rates. By modeling allocation as a probability distribution and enforcing a fairness constraint using an ℓ1-style deviation bound, the approach maximizes expected monitoring utility while preserving balance across the network. Simulations show that AdaptMon reduces monitoring delay by up to 40% without sacrificing anomaly detection accuracy. The model is interpretable, lightweight, and grounded in statistical programming, making it a practical solution for real-time SDN environments.

A Transfer Learning Load Adjusted Approach For Video-On-Demand Systems Given Limited Training Data, Kangogo Kimeli, Ruairí De Fréin

SAML-25 Workshop on Statistical and Machine Learning

Inadequate data complicates planning and allocation of VoD resources, potentially hindering the scalability of VoD services. We propose a Transfer Learning Load Adjusted (TLLA) algorithm for resource management given limited VoD data. TLLA leverages the knowledge gained from pre-trained models by storing features and patterns that can be used to train Machine Learning (ML) related tasks. We model limitations in VoD data by proportionally freezing 50% of the neural layers in models trained from pre-trained and source domains. We evaluate the performance of the frozen neural layers by comparing them to unfrozen data. Freezing 50% of the neural layers in …

Investigation Of Maleic Anhydride In Organic Synthesis, Emma Nissen, Zhijun Wang, Qianli Rick Chu

Arts & Sciences Undergraduate Showcase

This research explores the synthetic utility of maleic anhydride; a versatile and reactive compound widely used in organic and industrial chemistry. The study focuses on the synthesis of N-allyl maleimide and 3-hexyl-1,2-cyclobutanedicarboxylic acid. N-allyl maleimide, confirmed via ^1H NMR spectroscopy, was synthesized in high yield and shows potential as a monomer or intermediate in organic synthesis. Additionally, the photochemical synthesis of a novel compound, 3-hexyl-1,2-cyclobutanedicarboxylic acid, was achieved and characterized. Its long aliphatic side chain contributes to decreased melting point and increased solubility. These findings demonstrate maleic anhydride’s utility in forming intermediates and bicyclic compounds under mild conditions, supporting its …

Shape-Based Nanoparticle Classification Using Machine Learning, Caitlin Caitlin Robertson, Hender Lopez

SAML-25 Workshop on Statistical and Machine Learning

The accurate classification of nanoparticles (NPs) based on their shapes is crucial for understanding their physical-chemical properties and predict their bioactivity. Nowadays, synthesis method are able to produce a broad range of shapes, such as spheres, cubes and branched NPs and commonly these NP shapes are only described qualitative. This study presents NP descriptors obtained from NPs contours extracted from electron microscopy images. Descriptors such as Fourier descriptors, aspect ratio, and compactness are then used as input for machine learning classifiers. In particular, XGBoost, Random Forest, and neural networks are explored and the their performances are compared and discussed.

Enhancing Dermatological Skin Lesion Classification With Multi-Modal Attention-Based Models And Explainability, Conan Oreilly

SAML-25 Workshop on Statistical and Machine Learning

Accurate classification of skin lesions is critical for early detection of melanoma and other malignancies, particularly in resource-limited settings. This study presents a novel multi-modal machine learning framework that integrates dermoscopic images and structured clinical metadata to improve diagnostic performance. Leveraging the PAD-UFES-20 dataset, which includes over 2,000 smartphonecaptured lesion images and associated patient metadata, we benchmark a series of unimodal and multimodal models. Our results demonstrate that modality attention fusion (MAF) applied to a frozen SwinV2-Tiny vision transformer and metadata multi-layer perceptron (MLP), augmented with focal loss, yields a state-ofthe- art weighted F1-score of 0.84 and balanced accuracy of …

Analyzing Option Chain Bid–Ask Spreads With Machine Learning, Brian Byrne, Qianru Shang

SAML-25 Workshop on Statistical and Machine Learning

This paper investigates the determinants of option bid–ask spreads using machine learning techniques. We analyze a cross-sectional dataset of Apple Inc. (AAPL) call options, focusing on the relative bid–ask spread as the target variable. By comparing linear models with ensemble methods such as Random Forests and XGBoost, we find that nonlinear machine learning methods significantly outperform traditional OLS regression. The most influential factors are moneyness, implied volatility, and time to expiration, while volume and open interest have limited predictive power. Results suggest that spreads are driven by a mix of market microstructure dynamics, capital constraints, and regulatory requirements such as …

Intention To Commute By Public Transportation And/Or By Foot: Findings From A Pls Structural Equation Model, Simona Balzano, Houyem Demni,, Edoardo Pascucci,, Luisa Natale, Giuseppe Cappelli, Sofia Nardoianni, Giovanni C. Porzio

SAML-25 Workshop on Statistical and Machine Learning

Sustainable mobility stands at the forefront of contemporary discussions, driven by the clear imperative to transition towards more environmentally friendly transportation and patterns. This shift is widely recognized as a crucial opportunity to address the challenges and inherent dangers posed by climate change. It is then crucial to introduce attitudes to encourage voluntary behavioral changes toward different sustainable solutions. In this perspective, to foster a future where sustainable personal mobility options are widely embraced and integrated, it is crucial to comprehend the inclination of younger generations to use them. For this reason, a survey on the use of sustainable mobility …

Shedding Light On Cellular Glycolysis Pathway Kinetics Using A Spectralomics Approach, Integrating Multivariate Statistical And Machine Learning Analytical Approaches, Nitin Patil, Zohreh Mirveis, Hugh Byrne

SAML-25 Workshop on Statistical and Machine Learning

The potential of time resolved label-free Raman microspectroscopy to elucidate the kinetics of cellular and subcellular glycolysis pathway was explored in this study. A549, human lung cells were cultured in an unbuffered minimal medium with glucose as a sole carbon source under three different modulated conditions. Modulator drugs oligomycin and 2-deoxyglucose were used to stimulate and inhibit the glycolysis pathway. Initially the kinetic glycolysis assay was used to monitor the glycolysis end-point kinetics followed by development of a numerical model capable of simulating the end-point kinetics. For Raman spectroscopy, samples at different timepoints from the experiments with similar conditions as …