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Improving Gemini’s Ability to Generate Mermaid Code
No full textGoogle Gemini is one of the most widely used large language models (LLMs) among users involved in computer science (CS) worldwide–be it students, educators, or developers. A critical capability of an LLM is the generation of effective visual diagrams, which can help users visualize complicated CS concepts like process scheduling or the Transmission Control Protocol (TCP) handshake. However, Gemini frequently produces uncompilable Mermaid code (the standard language for creating visual diagrams), which diminishes the overall quality and utility of its responses. This paper presents a lightweight framework designed to improve the syntactical correctness of Mermaid code generated by Gemini. The proposed solution integrates industry-used LLM-improvement techniques, such as Retrieval Augmented Generation (RAG), prompt engineering, and iterative prompting, within a wrapper architecture that automatically detects, repairs, and re-validates invalid Mermaid code. Experimental results demonstrate that the prototype built with targeted improvements improves both the accuracy and effectiveness of Gemini’s responses, thereby increasing its value as a support tool for CS users
Hernández, Jo Farb
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University of California, Los Angeles. M.A. in Folklore and Mythology--concentration in Folk Art, 1975
University of Wisconsin, Madison. B.A. with Honors; Double Major in Political Science and French, 1974https://scholarworks.sjsu.edu/erfa_bios/1281/thumbnail.jp
Spartan Daily, March 6, 2025
Get PDFVolume 164, Issue 19https://scholarworks.sjsu.edu/spartan_daily_2025/1018/thumbnail.jp
Spartan Daily, March 12, 2025
Get PDFVolume 164, Issue 21https://scholarworks.sjsu.edu/spartan_daily_2025/1020/thumbnail.jp
Spartan Daily, March 27, 2025
Get PDFVolume 164, Issue 28https://scholarworks.sjsu.edu/spartan_daily_2025/1027/thumbnail.jp
A novel microfluidic approach to quantify pore-scale mineral dissolution in porous media
Get PDFMineral dissolution in porous media coupled with single- and/or multi-phase flows is pervasive in natural and engineering systems. Dissolution modifies the physical, hydrological, and geochemical properties of the solid matrix, resulting in a complex coupling between local dissolution rate and pore-scale flow. The work reports a microfluidic approach that includes 2D reactive porous media and advanced pore flow diagnostics for the study of pore-scale dissolution in porous media with unprecedented details. The 2D microfluidic porous media, called micromodels, were fabricated in calcite by combining photolithography and wet etching, which not only offers precise control over the structural and chemical properties, but also facilitate unobstructed optical access to the pore flow, significantly improving over existing methods. We believe the work represents the first of its kind as it for the first time directly applies photolithography to calcite samples and demonstrates the use of particle image velocimetry to investigate chemical reactions in porous media. The preliminary results have revealed the crucial roles of local concentration gradients in mineral dissolution and call for reconsideration of many assumptions used in the current modeling tools, which paves the way for renewed fundamental understanding of reactive transport and improved modeling tools with better accuracy
Balancing Earth science careers in an unequal world
Get PDFUnequal research experiences among Earth scientists from around the world are an obstacle to achieving sustainability. We assess challenges and propose ways to balance the careers of early- and mid-career researchers in the Global South with those in the Global North
Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning
Get PDFRepetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports and limited observations, which can introduce bias and yield incomplete evaluations. This study addresses these limitations by generating and utilizing a comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG data were collected from eight muscles as participants performed repetitive lifting tasks. For each task, the lifting index was calculated using the revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE). Participants completed cycles of both low-risk and high-risk repetitive lifting tasks within a four-minute period, allowing for the assessment of muscle performance under realistic working conditions. This extensive dataset, comprising over 7 million data points sampled at approximately 1259 Hz, was leveraged to develop deep learning models to classify lifting risk. To provide actionable insights for practical occupational ergonomics and risk assessments, statistical features were extracted from the raw EMG data. Three deep learning models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were employed to analyze the data and predict the occupational lifting risk level. The CNN model achieved the highest performance, with a precision of 98.92% and a recall of 98.57%, proving its effectiveness for real-time risk assessments. These findings underscore the importance of aligning model architectures with data characteristics to optimize risk management. By integrating wearable EMG sensors with deep learning models, this study enables precise, real-time, and dynamic risk assessments, significantly enhancing workplace safety protocols. This approach has the potential to improve safety planning and reduce the incidence and severity of work-related musculoskeletal disorders, ultimately promoting better health and safety outcomes across various occupational settings
Can household water sharing advance water security? An integrative review of water entitlements and entitlement failures
Get PDFAn increasing number of studies find that water sharing—the non-market transfer of privately held water between households—is a ubiquitous informal practice around the world and a primary way that households respond to water insecurity. Yet, a key question about household water sharing remains: is water sharing a viable path that can help advance household water security? Or should water sharing be understood as a symptom of water insecurity in wait for more formalized solutions? Here, we address this question by applying Sen’s entitlement framework in an integrative review of empirical scholarship on household water sharing. Our review shows that when interhousehold water sharing is governed by established and well-functioning norms it can serve as a reliable transfer entitlement that bolsters household water security. However, when water sharing occurs outside of established norms (triggered by broader entitlement failures) it is often associated with significant emotional distress that may exacerbate conditions of water insecurity. These findings suggest that stable, norm-based water sharing arrangements may offer a viable, adaptive solution to households facing water insecurity. Nevertheless, more scholarship is needed to better understand when and how norm-based water transfer entitlements fail, the capacity of water sharing practices to evolve into lasting normative entitlements, and the impact of interhousehold water sharing on intrahousehold water security