Digital Commons Network^™

Dynamic Modeling And Simulation Of A Power Generation Steam Rankine Cycle For A Fluoride Salt-Cooled High-Temperature Reactor Nuclear Power Plant, William R. Ton

Master's Theses

Nuclear Fluoride Salt-Cooled High-Temperature Reactors (FHRs) have the potential to greatly improve nuclear technology in terms of safety and cost. By combining molten salt coolant technology from the Molten Salt Reactor (MSR) and TRISO fuel technology from the Very High Temperature Gas Reactor (VHTR), FHRs may operate at significantly lower pressures than Light Water Reactors (LWRs) and with a more robust fuel source that is excellent at containing fission products. However, as no FHR has been built, there are still many design questions to address. This thesis uses transient computer modeling and simulation to investigate how an FHR could potentially …

Generalized Detection Of Animal Behavior Using Accelerometers, Alexander J. Arrieta

Master's Theses

Animal mounted sensors are becoming increasingly used to passively monitor both domestic and wild animals. Advances in lightweight accelerometer and GPS technology have allowed many animals to be fitted with high accuracy sensors for extended periods of time. This leads to new opportunities to study animal behavior without direct observation. However, interpreting the raw data is difficult due to the high volume and missing context of the information. Machine learning techniques excel at extracting information from raw data streams and are excellent candidates for processing the sensor data. However, due to large variance in how different animals execute the same …

Predicting Newsworthiness Of California Bill Discussions, Venkata Kedarnath Chaturvedula

Master's Theses

Among various California bill hearings, it is crucial to identify which hearings are most likely to receive media coverage, as this highlights the importance of the bill and the hearing, while also underscoring its societal impact. Digital Democracy, established at Cal Poly’s Institute for Advanced Technology and Public Policy, provides users access to a distinctive dataset and information about state legislative committee hearings, hearing transcripts, assets, bill information, etc.

This thesis presents a machine learning framework for predicting whether California legislative bills are likely to receive media coverage. Drawing on a dataset of legislative ”tip sheets” provided by Digital Democracy …

Analyzing Patterns In Hofstede’S Cultural Dimensions Towards Individual Ai Receptiveness For Affective Experiences, Maggie Yang

Master's Theses

The use of artificial intelligence (AI) has significantly advanced efficient decision-making, with trust in algorithmic decisions shown to vary by cultural upbringing [1]. As AI becomes increasingly embedded in everyday life, it is essential to examine how cultural values affect trust in AI in subjective contexts that extend beyond purely quantitative analysis, like the personal interpretation of art. This work utilizes Hofstede’s cultural dimensions to investigate potential patterns in receptiveness towards AI predictions during art interpretation, providing insight into individual susceptibility to bias in AI-assisted affective analysis.

The study leveraged a cultural dimension survey and a custom Java program connected …

Per- And Poly-Fluoroalkyl Substances Fate Across California Wastewater Treatment Plants: A Meta-Analysis Of Formation, Distribution, And Persistence, Elizabeth Reineke

Master's Theses

Per- and poly-fluoroalkyl substances (PFAS) are persistent contaminants that move through municipal and industrial wastewater systems with limited attenuation. As a result, they are increasingly detected in drinking water supplies. Despite growing regulatory concern, a systems-level understanding of PFAS behavior across multiple treatment facilities and watershed settings remains incomplete. This study compiled influent, effluent, and biosolids datasets from more than one hundred California wastewater treatment plants to evaluate PFAS occurrence, transformation, and partitioning across diverse treatment configurations. Analytical steps included calculating removal efficiencies, assessing precursor-to-product oxidation, evaluating co-contaminant correlations, and grouping facilities by process design to quantify configuration-specific fate patterns. …

Building Roads To Engagement: A Survey Of Post-Secondary Community Engagement Education In Transportation, Meghan E. Kolin

Master's Theses

Community engagement is a critical part of the urban planning process for both academics and practitioners, but transportation planning’s significant technical knowledge requirements and focus on special interest groups make such engagement difficult. As such, college- and graduate-level transportation planning programs must include significant education about public participation practices. However, little research exists regarding how community engagement is taught in these programs or how well this education prepares students for real-world practice. This research is aimed at filling this gap by answering two questions: first, how transportation-focused post-secondary programs in the United States conduct community engagement education; and second, whether …

Executive Committee - Agenda, 11/04/2025, Academic Senate

Academic Senate Agendas

No abstract provided.

A Convolutional Neural Network Approach To Breast Cancer Tumor Boundary Detection, James Leah Matlosz

Master's Theses

Breast cancer is the second most common form of cancer and often goes undetected in its initial stages due to its subtle symptoms. As the tumors grow, they become more difficult to surgically remove with clean borders. To minimize the chance of recurrence, a 2D convolutional neural network is developed in this work for delineating tumor boundaries. Specifically, the U-shaped network model is trained, validated, and tested with longitudinal MRI scans of patients diagnosed with breast cancer and undergoing neoadjuvant therapy. For training, image masks were generated as ground truths using signal enhancement ratio segmentation, thresholding, and contour detection. The …

Towards Automated And Explainable Insider Threat Response In Electronic Health Records: A Role-Aware Machine Learning Framework, Luca Lippi Ornstil

Master's Theses

Healthcare remains a prime target for cyberattacks, with insider misuse and credential compromise posing major risks to Electronic Health Records (EHRs). This thesis introduces a role-aware, explainable anomaly detection and response framework integrated with OpenEMR to address post-authentication threats. Four models—Local Outlier Factor (LOF), Isolation Forest, Autoencoder, and Graph Neural Network (GNN)—detect behavioral deviations across temporal, device, and role-based features, with LOF serving as the primary runtime detector. A configurable policy engine maps anomaly severity to proportional actions, from email alerts to read-only restrictions or account suspension, all reversible and auditable. Evaluation on real EHR logs shows the system’s operational …

Academic Senate - Agenda, 10/28/2025, Academic Senate

Academic Senate Agendas

No abstract provided.

Executive Committee - Agenda, 10/21/2025, Academic Senate

Academic Senate Agendas

No abstract provided.

Executive Committee - Minutes, 10/21/2025, Academic Senate

Academic Senate Minutes

No abstract provided.

Review Of Ai Needs You: How We Can Change Ai’S Future And Save Our Own, Tracy A. Fernandez Rysavy

Feminist Pedagogy

AI Needs You: How We Can Change AI’s Future and Save Our Own urges citizens to band together now, while A.I. is still in its nascent stages, to head off its potentially destructive repercussions and ensure that the technology serves more than just a wealthy few. While such efforts might seem out of reach in our polarized society, author Verity Harding points to three cases from history where policy was heavily influenced by multistakeholder collaborations. This review encourages educators to use the book as a way to study business ethics; out-of-the-box thinking; and intersectional, inclusive consensus-building over a top-down approach.

Creating Feminist Icons, Engaging With Feminist Concepts Via Ai, Regina M. Luttrell Ph.D., Carrie T. Welch M.A.

Feminist Pedagogy

The authors describe an undergraduate media diversity course assignment that integrates artificial intelligence (AI) to explore gender representation in media. Students engage in an activity where they use AI text-to-image generators to create fictional feminist icon characters. This activity is designed to complement discussions on AI bias and gender norms in media, prompting students to critically examine how AI tools reinforce or challenge stereotypes. By linking their experiences with AI-generated images to feminist theory, students gain a deeper understanding of intersectionality, representation, and media bias. The assignment is part of a class that traces feminist movements through pop culture media …

Exposing Bias In Ai Art: A Hands-On Feminist Pedagogy Activity, Sheri-Lynn Kurisu, Emely Yanagida

Feminist Pedagogy

This Original Teaching Activity (OTA) introduces students to the biases embedded in AI-generated art, drawing from feminist critiques of the male gaze (Mulvey, 1975) and Emanuele Arielli’s artificial gaze (2024). AI technologies, often assumed to be neutral, frequently reproduce and amplify societal inequalities related to gender, race, and class. This activity engages students in a hands-on exploration of these biases by prompting AI art generators, analyzing the results, and reflecting on the implications of algorithmic bias. Through structured exercises, students compare their expectations with AI-generated images, identifying patterns of stereotyping and erasure. Classroom discussions focus on how AI’s aesthetic choices …

Prompting For Refusal: Teaching Against And Without Ai, Kate Maddalena, Aline Zara, Dan Guadagnolo

Feminist Pedagogy

This Original Teaching Activity describes two modules that enact feminist pedagogy to teach AI refusal, a set of lectures and associated activities which we developed for first-year courses during the 2023-2024 academic year. These activities invite students to refuse AI while also understanding its function and deployment. They explore the consolidations of power which make generative AI technologies both possible and harmful. Instead of presuming the inevitability of AI to replace working and learning or change the world, these activities offer students a materialist framework to refuse the hype, grounding their understanding of generative AI in its techno-political origins and …

Viewing Ai From The Bottom: A Feminist Political Economic Critique Of A Technocentric Pedagogy, Micky Lee, Benita Dederichs

Feminist Pedagogy

A feminist political economic (FPE) perspective has a more profound concern than cheating: it argues that AI is not a neutral tool. AI constitutes social relations that prevent certain populations from fully participating in political, economic, and civic lives while obscuring unbalanced power relations produced and reproduced in a capitalistic society. This commentary first points out how AI may reinforce gender- and race-biased job stratification in the high-tech industry; and how AI disempowers women of color by locking them in a passive position. It offers some advice of what feminist educators could do to empower students.

Embracing Uncertainty And Resisting Suspicion In The Faculty-Student Relationship, Natalia Rybas

Feminist Pedagogy

This critical essay discusses the suspicion and distrust appearing when educators think that students can (mis)use generative AI tools. The essay argues that education is rife with relations of inequality, reminds us that technologies are never neutral, and explores using uncertainty as a method of resistance to suspicion about students. The suspicion grows from the relations embedded in higher education because power, privilege, and oppression shape the experience of teaching and learning. Technological innovations, such as AI, carry power relations embedded in their designs and assumed uses, making all participants of the learning process vulnerable and disrupting the process itself. …

Keep Searching For Stories: The Importance Of Student Historical Research In The Ai Era, Emily Westkaemper

Feminist Pedagogy

This critical commentary interprets the risk that increased reliance on AI will disrupt student researchers’ direct engagement with historical primary sources. Close analysis of primary source details supports feminist history and digital history approaches, helping researchers contextualize histories of marginalization and interrogate scholarly and popular narratives about the past. Search engines and databases can promote access to digitized documents. By contrast, large language models reflect contemporary biases and communication patterns, manufacturing general declarations about historical significance that do not prioritize digitized primary source materials. I experimented with AI queries about participants in the 1977 National Women’s Conference (NWC), a topic …

Teaching Oppositional Resistance To The Weaponization Of Artificial Intelligence In Acts Of Gendered Violence, Lili R. Romann, Elizab​Eth A. Hintz

Feminist Pedagogy

Meeting the aims of this special issue to take a uniquely feminist approach to understanding AI in higher education, we offer a critical commentary to help other scholars apply a critical feminist lens when teaching about the promise of artificial intelligence in the classroom. Specifically, we center pornographic deepfakes as a primal, contemporary, and disastrous example of the gendered weaponization of artificial intelligence. This critical commentary has staying power – equipping future teachers with feminist tools to tackle AI (as well as the inevitable future of AI to come) in pedagogical practice.

Academic Senate - Minutes, 10/07/2025, Academic Senate

Academic Senate Minutes

No abstract provided.

Ai-Powered Accessibility Tracker For Inclusive Public Spaces, Yenny Ma, Kevin Beltran

College of Engineering Summer Undergraduate Research Program

This research project will develop and evaluate a smartphone-based, AI-powered system to crowdsource and analyze accessibility features and barriers in public spaces. Using computer vision and geospatial mapping, the system will identify and categorize issues such as uneven sidewalks, missing or inadequate curb ramps, damaged tactile paving, obstructive overhangs, and the absence of visual or auditory wayfinding cues. The overarching goal is to generate a dynamic, real-time accessibility map that empowers individuals with diverse mobility, sensory, and cognitive needs to navigate public spaces more safely and confidently. The project will integrate technologies and methods from applied machine learning, mobile computer …

Will Labor-Saving Automation Save Western Agriculture?, Lynn Hamilton, Bryan Lohmar, Michael Mccullough

Agribusiness

Among the most salient and intractable challenges facing labor-intensive western agriculture is the rapidly rising cost of labor, driven primarily by reduced availability of workers. Unlike field grains, oilseeds, and cotton, producers of fresh fruit and vegetables still rely extensively on hired manual labor to carry out multiple tasks, particularly harvesting. Nearly one third of total U.S. agricultural labor expenses occur in California alone, a state dominated by fruit, vegetable and horticultural (FVH) crops (Win, Rutledge, and Maredia, 2024). To address the rising cost of labor, producers have been adopting several innovations leading to automated irrigating, weeding, and crop protection …

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 …

Behind The Prompt: The Environmental Impact Of Llm Inference, Lucy Hegenderfer, Isaac Huang

College of Engineering Summer Undergraduate Research Program

As the size and demand for large language models (LLMs) increase, the environmental impact of computational inference often exceeds training; yet industry lacks a standardized method of calculating this expanding environmental footprint. Complexity arises with task-specific computational demands, infrastructure overhead, and various GPU architectures, making cross-model assessments burdensome. Combining environmental engineering and computer science principles by validating Jegham et al.’s meta-model, we predict the carbon emissions and water consumption during inference, providing metrics to raise user awareness of AI’s growing environmental footprint. Additional work supports integration into a multi-agent conversational system that encourages responsible scheduling and prompting, guiding the user …

Propeller Characterization Lab, Stefanos Rosenbaum

College of Engineering Summer Undergraduate Research Program

This research focuses on creating an experiment to measure performance of an aircraft propeller on a specially designed stand. Results from the wind tunnel experiments measuring thrust, rotation speed and torque will enable students to create a database of propeller characteristics for use in aircraft design projects. Students in aerospace engineering are regularly assigned design projects powered by turboprop or propeller driven engines. There is not an easy way to verify design performance other than simplistic calculations or extrapolation of open-source wind tunnel data, and not a lot of data is available. Being able to correlate actual test data from …

3d Printing Scoliosis Braces, Zak Neddo, Jaylan Mo

College of Engineering Summer Undergraduate Research Program

As 3D printing technology continues to transform the healthcare industry, the need for FDA-compliant additive manufacturing facilities in public universities has become increasingly important. While private companies and medical research institutions have successfully integrated 3D printing for medical device prototyping and surgical planning, public universities—including Cal Poly—lack the dedicated infrastructure needed to support FDA-regulated medical manufacturing and research. This project aims to bridge that gap by researching, designing, and implementing a dedicated FDA-compliant section within Cal Poly’s multi-purpose 3D Printing Facility. Over the course of eight weeks, student researchers will study FDA regulations, design an optimized facility layout, and implement …

Building A Smart Transportation Network To Prevent Multi-Vehicle Collisions During Sudden Slowdowns, Leo Huang, Patrick Zhao

College of Engineering Summer Undergraduate Research Program

This proposed SURP project aims to design and evaluate a smart transportation network capable of preventing multiple-vehicle collisions due to sudden slowdowns in traffic. This project will simulate abrupt braking scenarios and implement adaptive vehicle-to-vehicle (V2V) communication protocols. By enhancing real-time awareness and responsiveness among vehicles, the system will reduce pileup risks and improve road safety.

Engaging Students In Materials Engineering Through A Marble-Based Property Ranking Game, Nathan Cloughley, Alyssa Felix

College of Engineering Summer Undergraduate Research Program

This SURP proposal is centered on the design and development of a Materials Marbles Game, an educational tool designed to introduce students to materials science and materials selection through tactile interaction and hands-on experimentation. The game will incorporate measurement of key material properties—such as mechanical, thermal, electrical, and mass density—using spherical marbles made from different materials. These measurements will then be used to construct a physical Material Property Chart (Ashby-Cebon plot) on a game board, fostering a hands-on, embodied understanding of how different materials behave. The game will also illustrate the thought process behind materials selection, a critical aspect of …

Leveraging Smartphones For Balance Assessment, Kelly Graham

College of Engineering Summer Undergraduate Research Program

Assessment of human balance provides valuable metrics to track the health, development, and fall risk of individuals. The prominent method for objective balance assessments has used force plates to track the center of pressure (COP) position as a participant attempts to balance during varying tasks. However, the use of force plates is limited by the cost of equipment and expertise required, leading to recent interest in using embedded inertial measurement units (IMUs) in mobile devices instead. Many researchers have explored placing mobile devices close to the subject’s center of mass (COM) to approximate the subject’s COM acceleration and using the …