Physical Sciences and Mathematics Commons^™

“Toward A World Where Many Worlds Fit”: Thinking-Feeling Our Way In The Pluriverse, Michael Burns

Sustainability Research & Practice Seminar Presentations

Dr. Michael Burns, English, shares "Toward a 'World Where Many Worlds Fit': Thinking-Feeling Our Way into the Pluriverse" Recording shares the lecture portion. Following the lecture participants shared their thoughts with one another.

Modeling Effective Shade For Oregon Streams Using Satellite Derived Vegetation Structure Metrics, Ella Wagner

Environmental Science and Management Professional Master's Project Reports

In the Pacific Northwest, rising stream temperatures threaten the survival of cold-water species, such as salmonids. The Oregon Department of Environmental Quality (DEQ) regulates stream temperature statewide through Total Maximum Daily Loads (TMDLs). To do this, the DEQ uses stream shade as a surrogate measurement for heat, as stream shade and stream temperature have a well-known strong relationship. This project focused on developing effective shade modeling and monitoring methods that increase the pace and scale of temperature TMDL implementation and tracking in Oregon. Stream shade can be measured in the field and modeled with computer models such as HeatSource, which …

Topoimages: Incorporating Local Topology Encoding Into Deep Learning Models For Medical Image Classification, Pengfei Gu, Hongxiao Wang, Yejia Zhang, Huimin Li, Chaoli Wang, Danny Z. Chen

Computer Science Faculty Publications

Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). Despite remarkable successes of numerous image processing methods that rely on appearance information, these methods often lack sensitivity to topological structures when used in general deep learning (DL) frameworks. In this paper, we introduce a new general approach, called TopoImages (for Topology Images), which computes a new representation of input images by encoding local topology of patches. In TopoImages, we leverage persistent homology (PH) to encode geometric and topological features inherent in image patches. Our main objective is …

Dataset Of Raman Spectra And Matlab Code For: Elucidating Time-Resolved Intracellular Metabolic Dynamics Via Label-Free Raman Microspectroscopy And 2d Correlation Spectroscopy, Zohreh Mirveis, Nitin Patil, Hugh J. Byrne

Other Resources

This dataset contains raw spectral data obtained from single-cell Raman microspectroscopy under two nutritional conditions: glucose alone and glucose supplemented with glutamine. LLC-MK2 cells were starved for 2 h, then exposed to nutrients and fixed every 15 min for up to 120 min. At each time point, spectra were recorded from 25 individual cells (cytoplasm regions) and exported as machine-readable files (.csv). MATLAB scripts are provided to implement two-dimensional correlation spectroscopy (2D-COS) for generating synchronous maps, along with utilities for loading spectra, averaging, and reproducing key figures. A set of simulated time-series spectra used to validate 2D-COS under high background …

Evolution Of Agricultural Emissions Calculations In Australia: Methodological And Calculator Refinements And Their Impact On Western Australia’S Agricultural Carbon Footprint, Christophe D'Abbadie

Climate Science Published Reports

Greenhouse gas (GHG) emissions accounting is fundamental to positioning Australian agricultural products competitively in global markets, enabling supply chain stakeholders to make informed decisions, and understanding the trade-offs between market demands, policy requirements, consumer expectations, and intergenerational responsibilities.

Recent methodological refinements to emissions methodologies have enhanced the accuracy of GHG accounting in Western Australian (WA) agriculture. The Department of Primary Industries and Regional Development (DPIRD) has collaborated in these improvements through collaborative research with universities and federal agencies and advocacy for Western Australia-specific emission factors.

Methodological refinements have improved the accuracy of WA agricultural emissions estimates by 6% overall, providing …

3d Visualization For Teaching And Learning In Organic Chemistry: Orbital Hybridization And Bonding, Bao Le, Jeffrey Hansen

Annual Student Research Poster Session

This research focuses on developing techniques to create 3D visualizations for use in an electronic textbook. In this age of technology, traditional textbooks have become less appealing to the new generation compared to phones or tablets. Student learning can be enhanced by not only bringing the textbook online but also elevating the comprehensive experience for the students with 3D animations. We will show how animations of orbital hybridization and chemical bonding are made in Blender using Signed Distance Field (SDF) modeling. The orbitals’ models are mathematically combined allowing for smooth animation. We have used this technique to animate the transformation …

Longitudinal Monitoring Of Particle Counts In The Pfotzer Maximum (2017-2025), Scott Anderson, Jason Benyousky, Walae Dahmany, Tri Duong

Annual Student Research Poster Session

The Balloon Assisted Stratospheric Experiments (BASE) measures atmospheric muons using an array of instruments carried by weather balloons. We seek to spend extended time in the region of highest intensity, the Regener-Pfotzer Maximum (Pfotzer Maximum). The effects of the cosmic ray- induced ionization that comprises the Pfotzer Maximum are relatively unknown and may include cloud formation, precipitation, cyclogenesis, aerosol formation, atmospheric transparency, and more. With Geiger counters, we can measure the secondary particles produced by atmospheric cascades in order to better understand the Pfotzer Maximum and how it changes over time.

The Heart Of The Crab: Analyzing The Vhe Emission From The Crab Pulsar, Adi Empie, Thao Nguyen, Avery Archer

Annual Student Research Poster Session

The Crab Nebula is one of the most studied astrophysical objects at very-high-energies (VHE) and is one of the brightest sources of VHE gamma-rays. The Crab Nebula is a pulsar wind nebula with the Crab pulsar providing the energy and particles to power the nebula. The gamma-ray emission from the nebula and pulsar are understood to come from relativistic particles but the processes that are responsible for accelerating these particles is not well understood. Studying the highest energy emission from the Crab Nebula can provide valuable insight into the underlying emission mechanisms at work. This project was intended to examine …

Dimension Of Subalgebras Of Fomin-Kirillov Algebras, Taro Ikeda, Sirous Homayouni

Annual Student Research Poster Session

One of the central open problems concerning the Fomin-Kirillov algebras was whether their dimension is finite or infinite. This question was recently resolved in one research by C. Barligea, where it was shown that FK(n) is infinite-dimensional for all n ≥ 6. In our work, we investigate the dimension of a certain subalgebra of FK(6).

Energetic Particle Detection In The Pfotzer Maximum Methods And Equipments, Scott Anderson, Jason Benyousky, Walae Dahmany, Tri Duong, Minh Mai

Annual Student Research Poster Session

The Balloon Assisted Stratospheric Experiments (BASE) measures atmospheric muons using an array of instruments carried by weather balloons. We seek to spend extended time in the region of highest intensity, the Regener-Pfotzer Maximum (Pfotzer Maximum) at 20 kilometers high. Using a set of Geiger Counters attached to a balloon, we can measure the secondary particles produced by atmospheric cascades at high altitudes. With risk of losing the balloon and the need to collect more data, we improved our payload with data-collecting components and experimented with a long-range data sender and receiver.

Energetic Particle Detection In The Pfotzer Maximum, Scott Anderson, Jason Benyousky, Walae Dahmany, Tri Dong, Minh Mai

Annual Student Research Poster Session

The Balloon Assisted Stratospheric Experiments (BASE) is an ongoing project aiming to measure atmospheric muons using equipment supported by high-altitude balloons. The Regener-Pfotzer Maximum (Pfotzer Maximum), which spans altitudes of roughly 15-25 km, is characterized by being the atmospheric region with the highest intensity of secondary particles from cosmic ray showers. The effects of the cosmic ray induced ionization that comprises the Pfotzer Maximum are relatively unknown and may include cloud formation, precipitation, cyclogenesis, aerosol formation, atmospheric transparency, and more. Our experiments seek to use low-cost solutions to better understand the behavior of cosmic ray-induced particles in a region of …

Adipose Tissue Estrogen Receptor-Alpha Overexpression Ameliorates High-Fat Diet–Induced Adipose Tissue Inflammation, Marion C. Hope, Christian A. Unger, M. Chase Kettering, Cassidy E. Socia, Ahmed K. Aladhami, Barton C. Rice, Darya S. Niamira, Ben P. Wiznitzer, Diego Altomare Ph.D., William E. Cotham, Reilly T. Enos

Faculty Publications

Context

We created an innovative mouse model that enables inducible overexpression of estrogen receptor-alpha (ERα), specifically in adipose tissue (Adipo-ERα↑). Objective

We aimed to investigate how elevated Adipo-ERα↑ influences the development of high-fat diet (HFD)-induced obesity in both male and female mice. Methods

Male and female Adipo-ERα↑ mice and littermate controls were fed a low-fat diet (LFD) or HFD for 13 weeks. Adipo-ERα↑ was induced at the initiation of dietary treatment. Body morphology and composition, hepatic lipid accumulation, glucose tolerance, fasting insulin concentrations, and adipose tissue mRNA profiling were assessed. Liquid chromatography–mass spectrometry was used to determine circulating and adipose …

Pentanary And Hexanary Alkali Lanthanide Germanates Grown From Lanthanide Rich Flux Growth Reactions, Gregory Morrison, K. Pilar Zamorano, Virginia G. Jones, Ethan N. Adams, Hans Conrad Zur Loye

Faculty Publications

Single crystals of five compounds of a new structure type, the K3Nd52Ge12O100F7 type, were grown from lanthanide rich reactions using a KF/KCl flux: K3Ln52Ge12O100F7 (Ln = Nd, Eu), K3Ln52Ge12O100+δF7-δ (Ln = Pr, Tb), and K3Ca0.872Nd51.128Ge12O99.128F7.872. The five compounds crystallize in the cubic space group I-43d with lattice parameter 22.0350(2) Å for K3Nd52Ge12O100F7. The structure consists of a 3D checkerboard of LnX8, LnX7, LnX6, and GeO4 polyhedra, where X = O/F, and can be conceptually derived from the fluorite structure. K3Eu52Ge12O100F7 exhibits the fluorescence typical of Eu³⁺.

Effective Two- And Three-Body Interactions Between Dressed Impurities In A Tilted Double-Well Potential, F. Theel, A. G. Volosniev, D. Diplaris, F. Brauneis, S. I. Mistakidis, P. Schmelcher

Physics Faculty Research & Creative Works

We explore the impact and scaling of effective interactions between two and three impurity atoms, induced by a bosonic medium, on their density distributions. To facilitate the detection of mediated interactions, we propose a setup where impurities are trapped in a tilted double-well potential, while the medium is confined to a ring. The tilt of the potential breaks the spatial inversion symmetry allowing us to exploit the population of the energetically elevated well as a probe of induced interactions. For two impurities, the interaction with the medium reduces the impurity population at the energetically elevated well, which we interpret as …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Bound Deuteron-Antideuteron System (Deuteronium): Leading Radiative And Internal-Structure Corrections To Bound-State Energies, Gregory S. Adkins, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

We evaluate the energy levels of the deuteronium bound system, which consists of a deuteron and an antideuteron, with a special emphasis on states with nonvanishing orbital angular momenta. The excited atomic bound states of deuteronium constitute probes for the understanding of higher-order quantum electrodynamic corrections for spin-1 particles in a bound system where the typical field strength of the binding Coulomb field (at a distance of the generalized Bohr radius) exceeds Schwinger's critical field strength. For states with nonvanishing angular momenta, effects due to the internal structure of the deuteron and virtual annihilation contributions are highly suppressed. Relevant transitions …

Creating Blender Animations To Help Students Visualize Organic Chemistry Concepts, Vasilina Vikareva, Jeffrey Hansen

Annual Student Research Poster Session

Many students struggle with organic chemistry because they find it difficult to visualize molecular structures and interactions in three dimensions. Concepts like bond rotation for conformational isomers and intermolecular forces require strong spatial imagination, which traditional 2D drawings fail to develop effectively. This spatial visualization deficit contributes significantly to high failure rates and student anxiety in organic chemistry courses, creating a substantial barrier to success in STEM fields. This project aims to solve this problem by creating animations that demonstrate complex chemical concepts in clear, visual ways that enhance student comprehension and retention. The research methodology involves developing a comprehensive …

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

Frameworks And Life Cycle Assessment For Reinforced Concrete Bridges For Sustainability In Transportation, Yu-Fu Ko, Jessica Gonzalez

Mineta Transportation Institute

Bridge structures are critical components of California’s transportation network, with reinforced concrete (RC) bridges being among the most widely used. Extending the lifespan of these structures can lead to significant reductions in environmental impacts. However, California’s frequent seismic activity has repeatedly exposed the vulnerability of existing RC bridges, highlighting the urgent need for seismic retrofitting and maintenance to improve infrastructure resilience against earthquakes and to increase sustainability. This research used detailed computer simulations known as nonlinear finite element models, which are highly detailed computer models, incorporating section damage indices to predict damage and assess structural deficiencies in RC bridges during …

The Pastor As Romantic Author: Ai, Preaching, And The Unacknowledged Inheritance Of Authenticity, Daniel Plate, James Hutson

Faculty Scholarship

This article interrogates contemporary reactions to sermons produced with generative technologies through a historical–conceptual lens, arguing that widespread judgments of such outputs as “soulless,” “generic,” or lacking a “beating heart” are best explained by an unacknowledged inheritance from nineteenth-century Romantic expressivism. Rather than treating resistance to machine authorship as a theological verdict on computational incapacity, the study reconstructs how Romanticism centered authorship in sincere self-expression and solitary genius, displacing earlier heraldic expectations that prized fidelity to a received message. Methodologically, the analysis combines intellectual history with discourse analysis of global Christian experiments in synthetic composition (2020–2025), denominational guidance, and media …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Water Current, Volume 57, No. 3, Fall 2025

Water Current Newsletter

No abstract provided.

Integrating Satellite Observations And Modeling Approaches To Study Tectonic And Land Surface Hazards, Jeng Hann Chong

Earth and Planetary Sciences ETDs

The increasing availability of Earth observation satellites, such as SAR and optical platforms, has improved our ability to study the ground deformation processes during, after, and leading up to these natural hazards, such as earthquakes and debris flows. In this dissertation, I analyze satellite geodetic interseismic velocities across the Rakhine-Bangladesh megathrust and the influence of having realistic modeling assumptions for subduction zones to better constrain seismic hazard. I also examined changes in post-fire debris flow likelihood four years after the 2020 Lake Fire in Southern California using a suite of satellite data and field observations. Together, these studies highlight how …

Chemical And Sensory Attributes Of Different Ethanol Reduction Methods In Muscadine Wine Production, Alexandra A. Escalera, Patricia C. Patricio Morillo, Drew Budner, Katherine A. Thompson-Witrick, Andrew J. Macintosh

Chemistry

There has been a recent shift in the global wine market towards reduced-alcohol wines. Muscadine grapes (Vitis rotundifolia) have become a popular choice in many emerging markets; however, their suitability in reduced-alcohol wine production has not been extensively tested. In this study, methods to reduce ethanol in muscadine wine were compared to determine differences in chemical and sensory attributes and consumer preference. The methods evaluated included full fermentation time with Saccharomyces cerevisiae (control), reduced fermentation time with Saccharomyces cerevisiae (stopped fermentation), fermentation with Saccharomycodes ludwigii yeast (instead of Saccharomyces cerevisiae), and vacuum distillation. The control and distilled wines were fermented …

Assessing The Role Of Ocean Literacy In Liberia's Blue Economy : Pathway To Sustainable Blue Ocean Development In Liberia, Precious Tete Nah

Ocean Sustainability, Governance & Management Dissertations

Ocean literacy involves understanding the fundamental concepts of the ocean's functioning, leading to informed and responsible decisions about the ocean. This understanding is vital for developing responsible ocean citizenship and implementing measures to sustain the ocean, as it highlights the ocean's influence on humans and the impact of human activities on it. Using a qualitative research method with a purposive sampling approach, the research asks focus group representatives from New Kru Town and Westpoint, both closed- and open-ended questions, on the importance of ocean literacy, challenges, prospects, and recommendations to promote sustainable blue ocean development.

The results of the research …

Diffusion Of Innovation Of Port Decarbonization Measures : A Case Study Approach, Clint Rocky Mcculloch, Lucas Fernando Vaquero Roviriego

Port Management Dissertations

The dissertation is a study of how stakeholders diffuse innovation in port decarbonization strategies through a case study approach, with emphasis on the Diffusion of Innovation (DOI) relating to the technical and operational measures in the developing regions. Port, being a critical hub in global supply chains, have to manage the increasing pressure of reducing greenhouse gas (GHG) emissions in alignment with International Maritime Organization (IMO) 2023 GHG strategy as well as the Paris Agreement. However, there is a gap in empirical research, specifically in non-European regions like South America and the Caribbean, highlighting the need for insights from stakeholders’ …

Data-Driven Optimization And Parameter Estimation For A Metric Graph Epidemic Model With Applications To Covid-19 Spread In Poland: A Real-World Example Of Optimization For A Challenging Rosenbrock-Type Objective Function, Hannah Kravitz, Christina Duron, Brittani Nieves, Moysey Brio

Mathematics and Statistics Faculty Publications and Presentations

In this paper, we apply data-driven optimization to estimate key parameters in a metric graph-based epidemiological model, with the aim of analyzing the effect of road networks on the geographic spread of epidemics. As a case study, we fit our model to data from the COVID-19 pandemic in Poland during 2021. Our dataset integrates county-level daily case reports, national census information, and traffic flow studies. This framework allows us to examine the relative contribution of specific travel routes over time and infer unobserved transmission patterns in the presence of incomplete or unreliable case reporting. The optimization problem that arises from …

Laboratory Investigation Of High-Temperature Preformed Particle Gels For Fluid Control In Granite Cores For Geothermal Applications, K. Caleb Darko, Yanbo Liu, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

To understand the applicability of high-temperature preformed particle gel (HT-PPG) for control of short-circuiting in enhanced geothermal systems (EGSs), core flooding experiments were conducted on fractured granite cores under varying fracture widths, gel particle sizes and swelling ratios. Key parameters such as injection pressure, water breakthrough pressure, and residual resistance factor were measured to evaluate HT-PPG performance. The gel exhibited strong injectability, entering granite fractures at pressure gradients as low as 0.656 MPa/m; HT-PPG yields a superior sealing performance by significantly reducing the permeability; and dehydration occurs during HT-PPG propagation, with a dehydration ratio ranging from 4.71% to 11.36%. This …

Building An Inclusive Ai Chatbot For Diverse Student Communities At Cal Poly: Uplift Ai, Gideon Telahun

College of Engineering Summer Undergraduate Research Program

This research project will investigate the ability of advanced Large Language Models (LLMs) to identify and assess misinformation across diverse forms of media, including text, images, and video. In an age where misleading content spreads rapidly across digital platforms, evaluating the reliability and integrity of AI systems tasked with fact-checking is critical. We will develop a comprehensive dataset composed of factual and misleading examples drawn from various well-known and reliable fact-checking organizations. Each item will be independently reviewed and transparently labeled to ensure reproducibility. We will then prompt a curated group of state-of-the-art LLMs—including GPT-4, Claude, Gemini, Perplexity, Grok, and …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …