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South Asians and Renal Disease: Traditional Food Lists for Management of Chronic Kidney Disease
No full textThe renal food list is a culturally tailored nutrition education tool designed to address the specific dietary needs of the South Asian population. It is an original intellectual work created by Ashwini Wagle, Ed.D., MS, RD, FAND, Chair and Professor, Department of Nutrition, Food Science, and Packaging at San José State University (SJSU).
An earlier version of this tool was published in 2011 with the assistance of graduate student Kalpana Balasubramanian. The development of this renal food list was strengthened by the encouragement and support of the registered dietitian community, whose contributions included verifying the accuracy of the nutritional information and ensuring the authenticity of the traditional foods represented.https://scholarworks.sjsu.edu/oer/1019/thumbnail.jp
Archeota, Fall/Winter 2025
No full textArcheota is a platform for SJSU iSchool students to contribute to the archival conversation. It is written BY students, FOR students. It provides substantive content on archival concerns and issues and promotes professional development in the field of archival studies. Archeota upholds the core values of the archival profession. Contents: Unrecorded Legacies: Why Masonic Archives Matter by Audie Robinson, Book Review: Decolonial Archival Futures by Vanessa Sztym, Passports to the Past: Following the Trail of Sacramento’s Archives Crawl by Emily Stockton, Volunteering at the Jack Mason Museum of West Marin History: Beginnings and Belonging by Heather Arnold, Images From The Front: A Reflection on Digitizing a Partially Processed Archival Collection by Niamh Tangney, Field Notes from the Archives Fellow at the Center for Puerto Rican Studies by Gianna Elena Brassil, Farewell to Our Fall 2025 Graduates, Meet the 2026 Archeota Team, SAASC Spring 2026 Executive Board, SAASC Fall & Winter 2025 Eventshttps://scholarworks.sjsu.edu/saasc_archeota/1023/thumbnail.jp
Numerical Modeling of Climate Change Impacts on Transportation Embankments
No full textEmbankments are essential components of transportation infrastructure, providing crucial support for long stretches of highways, railways, and other routes in California and around the world. Clay embankments are susceptible to weather-related deterioration processes that can gradually compromise their stability and, in some cases, lead to unexpected failures. Climate change, along with the associated shifts in weather patterns, is projected to adversely impact the weather-related deterioration processes, leading to exacerbated failures and/or shorter service life. Additionally, climate change is projected to increase the frequency of extreme precipitation events, leading to an increase in embankment failure potential. This study evaluated (1) the effect of future climate scenarios on the long-term performance of clay embankments, and (2) the effect of extreme precipitation events brought about by future climate scenarios on the hydromechanical response of clay embankments to these extreme events. This study examined areas in central Los Angeles, California. Multi-phase hydromechanical geotechnical models were developed for exemplary high plasticity and low plasticity clay embankments with varied side slope angles. Overall, it was concluded that climate change is generally projected to adversely affect the performance of clay embankments both in the long-term and during extreme events, which can negatively impact critical national transportation infrastructure and disrupt the movement of people and goods
Henze, Rosemary C.
Get PDFStanford University, Education (Language, Literacy & Culture), Ph.D. 1988
San Francisco State University, English (TESL), M.A. 1980
University of California, Santa Cruz, Art, B.A. 1974https://scholarworks.sjsu.edu/erfa_bios/1372/thumbnail.jp
Spartan Daily, March 19, 2025
Get PDFVolume 164, Issue 24https://scholarworks.sjsu.edu/spartan_daily_2025/1023/thumbnail.jp
Investigating the Conversion of a Signalized Intersection to a Turbo Roundabout
Get PDFTurbo roundabouts are multilane roundabouts with helical pavement markings and raised structures. While they have seen widespread use in Europe, California is only the second state in the U.S. (after Florida) to have installed a turbo roundabout. Turbo roundabouts separate the ingress and circulating roadways. The concept was proposed and implemented in the Netherlands to mitigate congestion by improving traffic flow efficiency and addressing safety concerns on conventional multilane roundabouts. This research evaluates the first-ever turbo roundabout in California and compares its safety and operational performance with the previously existing 4-legged signalized intersection. The research team obtained safety and operational performance measures using well-calibrated simulation models and video analytics of the real-world conditions recorded before and after the installation of the turbo roundabout. The safety metrics include surrogate safety measures defining traffic conflicts between vehicles that enabled a quicker safety evaluation compared to multi-year collision data. The results show that the turbo roundabout effectively reduced crash potential and queuing delays at this site, meaning smoother,safer traffic flows. The real-world conflict data shows that more severe crossing and head-on conflicts (situations that could lead to collisions) of the previous 4-legged signalized intersection have all but been eliminated. The rear-end conflicts that do occur on the roundabout involve vehicles traveling at meaningfully reduced speeds compared to similar conflicts observed on the signalized intersection. The results show that turbo roundabouts may be an effective option at rural routes where the siting criteria for multilane roundabouts are satisfied. Given the timeline for this research project, the evaluation was conducted within months of the construction completion. It is therefore advised that Caltrans continue to monitor traffic crash data to ensure that the long-term crash data shows the expected improvement of safety. To support such evaluation, the counterfactual estimate of annual crash counts (i.e., the number of crashes that would have been expected had the intersection left as a signalized intersection) is provided in this study. The estimate provides a basis for comparison with future crash data to Caltrans. This study underscores the potential of turbo roundabouts to enhance roadway safety and operational performance,supporting their consideration in traffic management strategies across California and the nation
Spartan Daily, March 25, 2025
Get PDFVolume 164, Issue 26https://scholarworks.sjsu.edu/spartan_daily_2025/1025/thumbnail.jp
Bot Detection in Social Media using GraphSage and BERT
No full textThis project details a novel bot detection system developed to battle the ever- changing challenge of disinformation, misinformation, and other bot-generated content.
The methodology employed in this project combines the text-based analytical strength of BERT (Bidirectional Encoder Representations from Transformers) with the strength of GraphSage (Graph Sample and Aggregation) for analyzing network structures. The project concatenates BERT and GraphSage vectors to create an 896-size feature embedding with a rich blend of network and text features. This project employs a Support Vector Machine to process the concatenated embeddings, as SVM works well with high-dimensional data. This project was evaluated on two datasets, namely Cresci-15 and Twibot-22. This model outperformed all other models on the Cresci 15 dataset with an accuracy of 98.68%. Despite the challenges, the model had an accuracy score of 74.62% on the Twibot-22 dataset which still outperformed some state-of-the-art models. The model results highlight an efficient and scalable system that can handle large-scale datasets with intricate network structures
Automated Research Review Support Using Machine Learning, Large Language Models, and Natural Language Processing
Get PDFResearch expands the boundaries of a subject, economy, and civilization. Peerreview is at the heart of research and is understandably an expensive process. This work,with human-in-the-loop, aims to support the research community in multiple ways. Itpredicts quality, and acceptance, and recommends reviewers. It helps the authors andeditors to evaluate research work using machine learning models developed based on adataset comprising 18,000+ research papers, some of which are from highly acclaimed,top conferences in Artificial Intelligence such as NeurIPS and ICLR, their reviews, aspectscores, and accept/reject decisions. Using machine learning algorithms such as SupportVector Machines, Deep Learning Recurrent Neural Network architectures such as LSTM, awide variety of pre-trained word vectors using Word2Vec, GloVe, FastText, transformerarchitecture-based BERT, DistilBERT, Google’s Large Language Model (LLM), PaLM 2, andTF-IDF vectorizer, a comprehensive system is built. For the system to be readily usable andto facilitate future enhancements, a frontend, a Flask server in the cloud, and a NOSQLdatabase at the backend are implemented, making it a complete system. The work is novelin using a unique blend of tools and techniques to address most aspects of building asystem to support the peer review process. The experiments result in a 86% test accuracyon acceptance prediction using DistilBERT. Results from other models are comparable, withPaLM-based LLM embeddings achieving 84% accuracy