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Underground LRT Station- Final Project
No full textThis project presents a comprehensive fire protection analysis for an underground light rail transit (LRT) station that also serves as a civil defense shelter. The station comprises two underground levels with a total area of 4,500 square meters and is designed to accommodate up to 1,470 passengers during peak hours.
Design Approach: The project adopts a hybrid approach combining prescriptive and performance-based design in accordance with NFPA 130 standards. This methodology ensures compliance with baseline requirements while enabling optimization for the facility\u27s unique dual function as both a transportation hub and civil defense shelter.
Key Fire Protection Systems: The station is equipped with comprehensive fire protection systems including Edwards EST4 detection system with 183 smoke detectors, automatic sprinkler system with 26,500-gallon water reservoir, FM200 gas suppression in technical areas, and integrated smoke control systems. The prescriptive analysis confirms evacuation times of 2:39 minutes from platform and 5:10 minutes to point of safety, meeting NFPA 130 requirements.
Performance-Based Analysis Results: CFD modeling using Fire Dynamics Simulator (FDS) was conducted for a 1 MW platform fire scenario. Results demonstrate Available Safe Egress Time (ASET) of greater than 1000 seconds, maintaining visibility above 10 meters, temperatures below 60°C, and CO concentrations well below critical thresholds. Pathfinder evacuation modeling shows Required Safe Egress Time (RSET) of 331-402 seconds, and 830 seconds in the worst-case scenario, confirming ASET \u3e RSET with adequate safety margins.
Key Findings: 1) All fire protection systems meet or exceed NFPA 130 requirements, 2) ASET \u3e RSET confirmed with significant safety margins (1000+ vs 331-402 seconds), 3) Smoke control systems effectively prevent spread to concourse level, and 4) Structural integrity maintained under all fire scenarios with appropriate protection measures.
Conclusions: The proposed fire protection design successfully balances safety requirements with operational efficiency and architectural flexibility. The station achieves compliance with all applicable codes while serving its dual function as transportation hub and civil defiance shelter. The performance-based analysis validates the effectiveness of the proposed systems, demonstrating adequate safety margins for passenger evacuation under fire conditions. Implementation of the recommended protection measures will ensure safe operations while maintaining cost-effectiveness and design flexibility
Dairy-To-Go: An Informal and Formal Education Model on Milking Dairy Cattle
No full textWithin California, 360 schools have FFA programs; however, there are still over 2,000 high schools within the state. This equates to only 18% of California high schools having access to FFA. That number may be greater than 10%, however, access to agricultural curriculum is still limited. Considering US crop production is concentrated predominantly in California and the Midwest (USDA-ERS 2017), high schools in California, regardless of location and proximity to a farm, should have access to an agricultural curriculum. The purpose of this project was to create a curriculum where middle school and high school educators can show the process of what goes into dairy cow milking without the need for access to a physical farm. Educators could have students in woodshop classes build the dairy cow model from scratch, or they could build the model before partaking in the activity. Coming from a high school that did not have access to a farm or an FFA program, I wanted to create an opportunity for urban schools to learn and gain access to an agricultural curriculum without the barrier of limited farm access. The project “Dairy-To-Go” provides both a formal classroom curriculum for educators who lack access to a farm and an informal recruitment curriculum that can be set up anywhere to give the public more information regarding what goes into milking a cow
Teaching about Abortion Access with Charley the Chatbot
No full textFollowing the Dobbs v. Jackson Women\u27s Health Organization (2022) decision which countermanded federal abortion protections, artificial intelligence (AI) can be used to identify, monitor, and track people seeking medical treatment. The teaching activity presented here asks students to consider data privacy concerns and the role of AI in seeking abortion care, before introducing them to Charley the Chatbot, an AI interface designed to protect and inform abortion seekers. Students interact with the chatbot based on a series of fictional vignettes about abortion seekers in various stages of pregnancy and geographic locations. Following a peer discussions, students examine both how restrictive state-level policies impact individuals seeking abortion care and how an AI-powered tool can provide them with tailored, private, and up-to-date information
Exposing Bias in AI Art: A Hands-On Feminist Pedagogy Activity
No full textThis 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 reinforce cultural norms, shaping public perception and representation. A real-world extension of the activity, conducted with the nine-year-old daughter of one of the authors, further underscores how AI reproduces hypersexualized and racialized imagery, even in child-friendly contexts. By integrating feminist pedagogy and media literacy, this OTA encourages students to critically engage with technology as both consumers and creators. The activity is adaptable across disciplines, including sociology, communication, and women’s and gender studies, making it a versatile tool for exploring the intersection of AI, representation, and social justice. Ultimately, it fosters critical inquiry into the ethical and cultural dimensions of digital media, encouraging students to challenge the biases embedded in emerging technologies
Book Review of Baron, Naomi S.. Who wrote this?: How AI and the lure of efficiency threaten human writing
No full textModeling and Efficiency Analysis of Hybrid AC/DC and Conventional AC House Electrical Systems for Net-Zero Energy Homes
No full textThis thesis presents the design, simulation, and comparative efficiency analysis of hybrid AC/DC residential homes versus conventional AC homes for Net Zero Energy Homes (NZEH). Motivated by the increasing prevalence of DC-native technologies such as solar photovoltaics, battery storage, and DC-powered electronics, this work evaluates whether hybrid AC/DC distribution can offer measurable efficiency benefits over traditional systems. Twelve detailed house models were developed—six employing a hybrid AC/DC architecture and six based on conventional AC systems, each supplying a combination of AC and DC residential loads. Simulations were conducted using MATLAB/Simulink to model steady-state efficiency under various loading conditions. Grid configurations of 120 V RMS at 60 Hz and 230 V RMS at 50 Hz were used to reflect common global electric standards. Results show that hybrid AC/DC homes are 4–11% more efficient than their conventional AC counterparts when supplying DC loads up to 1.5–2 kW, due to the elimination of multiple conversion stages. However, efficiency gains diminish beyond this range, primarily due to copper losses in the 48 V DC bus. The analysis highlights that, while hybrid systems incur higher conductor losses at elevated power levels, they remain advantageous for typical residential applications with low to moderate DC demand. These findings support the integration of hybrid AC/DC architectures in future energy-efficient and net-zero energy residential electrical systems
Deep-Learning Based Microstructure Reconstruction and Generation
No full textMicroscopic imaging is essential to characterize multi-scale material behavior and understanding structure-property relationships. Recently our group developed a deep learning approach based on a Generative Adversarial Network (GAN) to reconstruct and artificially generate microstructures of strain-sensing nanomaterial networks based on microscope imagery. In this SURP project we would like to evaluate an alternative approach called diffusion to see if we can improve the quality of our results. Furthermore, we aim to test our approaches on a wider variety of materials, which will have different microstructures, to evaluate how versatile our models are
Mixed Reality in Human–Robot Interaction for Collaborative Applications
No full textThis project explores the use of Extended Reality (XR) technologies to enhance human- robot interaction in industrial contexts. Building upon prior research in affective and cognitive state recognition during human-cobot collaboration, this study investigates how natural hand and head gestures, captured through Meta Quest passthrough mode, can be used to communicate human intent to a Universal Robotics e-Series collaborative robot. The XR system provides users with an immersive, real-world visual interface while tracking motion and position in real time. The captured gestures are interpreted through a custom software pipeline that integrates machine learning models and rule-based logic to trigger adaptive robot behaviors. This hands-free, intuitive control mechanism has the potential to lower barriers to cobot use, improve safety, and support both skilled and unskilled workers in manufacturing tasks. The project will prototype and evaluate an XR-based interface for gesture recognition, assess its usability and responsiveness in collaborative tasks, and explore its potential for integration with affective data gathered from complementary sensors. Ultimately, this work aims to contribute to the development of human-centric, adaptive robot systems that respond not only to physical commands but also to the human’s cognitive and emotional context