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An Assessment of the Viva CalleSJ September 2025 Event in San José
No full textViva CalleSJ is an open-streets initiative where several miles of the City of San José’s streets are closed to vehicular traffic, allowing residents to walk, bike, scooter, and skate freely. This report evaluates the Viva CalleSJ event held in San José on September 7, 2025, which attracted more than 150,000 participants. Using a mixed-methods approach, this evaluation examines aspects such as attendance, activities, modal access, and economic impacts, utilizing data from participant surveys (in English, Spanish, and Vietnamese), interviews, and observational analysis. The findings indicate that the Viva CalleSJ September 2025 event was largely successful in achieving its objectives, including promoting active transportation, fostering community connection, and supporting the local economy. Insights from the report can inform planning for future Viva CalleSJ and similar events and demonstrate that these such events have an overall positive impact on the community
PREDICTENGINELIFE: RUL ESTIMATION OF C-MAPSS TURBOFAN ENGINE USING LLM4TS & LOW-RANK ADAPTATION
No full textModern industrial systems including Turbofan Engine(s) are susceptible to failures induced by degradation. Therefore, accurate modeling of component health & Remaining Useful Life (RUL) is vital for reducing maintenance-related downtime. In this work, predictEngineLife, a Regression Framework implemented by leveraging LLM4TS Transformer architecture is introduced, & applicability of this Framework as general-purpose Baseline Model for multivariate forecasting tasks & Remaining Useful Life (RUL) estimation is evaluated. Framework validation is performed on: Electricity Load Diagrams (ECL) 2011–2014 Dataset for large-scale forecasting of hourly electricity demand, & NASA C-MAPSS FD001 sub-dataset for sensor-driven RUL Prediction. On both the dataset(s), Uniform windowing & Z-score normalization is performed to preserve long-range temporal structure. FD001 Inference is compared against TCN, Neural/SVR Regressor(s), & LSTM baselines. Inference results demonstrate that LLM4TS provides consistent end results with recent literature & enables Low-Rank Adaption-mediated fine-tuning on Time-Series (TS) Datasets
Predicting Sleep Stages from Activity Patterns
No full textThe process of determining health-related sleep stages through laboratory independent methods has not been developed at a scalable level. The gold-standard labels from polysomnography (PSG) require laboratory-based monitoring which restricts its use in real-world applications. The current wearable-based sleep classification systems detect only binary sleep/wake states while ignoring the complete range of daytime activities. The proposed method solves existing problems through three main components which include 24-hour wrist actigraphy decomposition into statistical features across different time periods and TimeGAN (TGAN) based synthetic time series generation for sleep stage imbalance correction and reinforcement learning-based feature selection and optimization. The evaluation process uses eight MESA participants who received actigraphy and PSG label pairs to demonstrate that the method produces better than 0.75 accuracy and better than 0.70 macro-averaged F1 scores while maintaining high minority-stage detection rates. The research shows that it is possible to track detailed sleep patterns through daily movement tracking which will enable developers to create home-based sleep monitoring systems. The research team plans to increase participant numbers and add multiple sensors and develop advanced deep learning models for better results
RAG ENHANCED LLM-BASED ASSISTANT FOR ACADEMIC SUPPORT IN PROGRAMMING
No full textRetrieval Augmented Generation (RAG) in integration with Large Language Models (LLMs) has demonstrated significant improvements in producing factually grounded and context-aware responses. This is one of the most required aspects when developing tools for programming education. As the demand for programming skills has been growing, both students and educators are face challenges in obtaining personalized and reliable assistance. Existing LLM-based solutions are often suffering from hallucinations, poor context retention, and reliance on static knowledge. To address these issues, this study introduces a hybrid RAG-based framework with pre-trained LLMs to enhance contextual understanding and response accuracy. A multi-provider architecture is designed to support Gemini, Gemma, Llama, and GPT model, enabling educators to flexibly select models suited to their instructional needs. The system includes two configurations: one offering students an interactive instructor-like guidance and another assisting educators in automating tasks such as grading, and assignment generation. RAG is supported by the Milvus vector database and optimized through semantic chunking and query decomposition. The proposed framework is tested using prepared CanItEdit [12] dataset. The system achieves significant performance by 20% reduction in hallucination rates and 80% improvement in BLEU scores. These results highlight the potential of context-driven retrieval in bridging the gap between generic AI tools and personalized, adaptive educational support
Science, Physiology, and Nutrition For the Nonscientist
Get PDFA wonderful blend of physiology, nutrition, biochemistry, genetics, biology, evolution, chemistry--what we all need to know as informed citizens. A basic knowledge of the life sciences and how our bodies work--to promote our own good health, especially as we\u27re bombarded with misleading advertisements, soundbites, and the like. DNA fingerprinting, calorie requirements, dietary advice, genetic engineering (including gene editing with CRISPR cas9)--all in an easy-to understand book.https://scholarworks.sjsu.edu/oer/1002/thumbnail.jp
Conniff, Michael L. (1942-2025)
Get PDFUniversity of California, 1968, BA, Latin American Studies
Stanford University, 1969, MA, Latin American Studies
Stanford University, 1976, Ph.D., Historyhttps://scholarworks.sjsu.edu/erfa_bios/1039/thumbnail.jp
La Historia’s Mission to Preserve El Monte’s Heritage
No full textThe El Monte nonprofit, La Historia, has a mission of preserving Mexican American history through community engagement and collaboration. Join us in hearing how Nena and Bianca accomplish this as either someone also working full time in a public library, or as someone who has needed to master the rules of archival practice in order to realize when its important to bend them.https://scholarworks.sjsu.edu/slasc/1057/thumbnail.jp
Sexual minority women\u27s perceptions of sober curiosity: Lessons learned from a US pilot study
No full textIntroduction: Sexual minority women (SMW) are more likely than heterosexual women to meet criteria for hazardous drinking (HD). Sober curiosity, which centres on non-pathologising approaches such as mindfulness and support for questioning norms that encourage heavy/hazardous alcohol use, may be a particularly salient non-stigmatising option for SMW to reduce alcohol consumption. However, SMW\u27s perceptions of sober curiosity as a strategy for changing drinking behaviours have not been explored. Methods: We conducted in-depth individual interviews with a purposive sample of 17 SMW from the United States who self-reported moderate to heavy alcohol consumption or a desire to reduce their drinking. Questions explored the socio-cultural contexts of SMW\u27s drinking, their desire to examine and/or to reduce their drinking and perceived supports and barriers for doing so. Results: SMW described heavy drinking norms, the centrality of alcohol in social spaces and events (both queer and non-queer) and alcohol use as a way to reduce stress, as factors that contribute to HD. Although some participants had mixed opinions about the term ‘sober curiosity’, they described feeling motivated to evaluate their alcohol consumption. Barriers to reducing alcohol use included fear of social rejection and loss of social connections. Discussion and Conclusion: Overall, findings suggest sexual identity-specific online support and resources reflecting the sober curious philosophy have the potential to be useful for SMW seeking to reduce alcohol consumption. Key facilitators may include access to opportunities for connection in alcohol-free settings, social support and educational resources regarding alcohol and its impact on health
A Fluctuating Hydrodynamics Model for Nanoscale Surfactant-laden Interfaces
No full textA multispecies diffuse interface model is formulated in a fluctuating hydrodynamics framework for the purpose of simulating surfactant interfaces at the nanoscale. The model generalizes previous work to ternary mixtures, employing a Cahn–Hilliard free energy density combined with incompressible, isothermal fluctuating hydrodynamics where dissipative fluxes include both deterministic and stochastic terms. The intermolecular parameters in the free energy are chosen such that one species acts as a partially miscible surfactant. From Laplace pressure measurements, we show that in this model the surface tension decreases linearly with surfactant concentration, leading to Marangoni convection for interfaces with concentration gradients. In the capillary wave spectrum for interfaces with and without surfactant, we find that for the former, the spectrum deviates significantly from classical capillary wave theory, presumably due to Gibbs elasticity. In non-equilibrium simulations of the Rayleigh–Plateau instability, deterministic simulations showed that the surfactant delays pinching of a fluid cylinder into droplets. However, stochastic simulations indicate that thermal fluctuations disrupt the surfactant’s stabilizing effect. Similarly, the spreading of a patch of surfactant, driven by Marangoni convection, was found to be partially suppressed by thermal fluctuations
Spartan Daily, November 20, 2025
No full textVolume 165, Issue 38https://scholarworks.sjsu.edu/spartan_daily_2025/1081/thumbnail.jp