An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.
Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education
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abstract
This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed to enhance user interaction for educational and research purposes. Leveraging cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as a sophisticated AI assistant, exploiting the capabilities of traditional models like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval Augmented Generation (RAG) through three primary methods: integration of preprocessed documents, real-time processing of user-uploaded files, and information retrieval from any specified website. Additionally, the chatbot incorporates image generation via a Stable Diffusion Model (SDM), image understanding and response generation through LLAVA, and search functionality on the internet powered by secure search engine such as DuckDuckGo. To provide comprehensive support, Bio-Eng-LMM offers text summarization, website content summarization, and both text and voice interaction. The chatbot maintains session memory to ensure contextually relevant and coherent responses. This integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG Query (WBRQ) where the system fetches relevant information directly from the web to enhance the LLMs response generation.
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Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulation
An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.