This paper presents a new Large Language Model (LLM)-based Smart Device Management framework, a pioneering approach designed to address the intricate challenges of managing intelligent devices within public facilities, with a particular emphasis on applications to libraries. Our framework leverages state-of-the-art LLMs to analyze and predict device failures, thereby enhancing operational efficiency and reliability. Through prototype validation in real-world library settings, we demonstrate the framework's practical applicability and its capacity to significantly reduce budgetary constraints on public facilities. The advanced and innovative nature of our model is evident from its successful implementation in prototype testing. We plan to extend the framework's scope to include a wider array of public facilities and to integrate it with cutting-edge cybersecurity technologies, such as Internet of Things (IoT) security and machine learning algorithms for threat detection and response. This will result in a comprehensive and proactive maintenance system that not only bolsters the security of intelligent devices but also utilizes machine learning for automated analysis and real-time threat mitigation. By incorporating these advanced cybersecurity elements, our framework will be well-positioned to tackle the dynamic challenges of modern public infrastructure, ensuring robust protection against potential threats and enabling facilities to anticipate and prevent failures, leading to substantial cost savings and enhanced service quality.
Multimodal Agricultural Agent Architecture (MA3): A New Paradigm for Intelligent Agricultural Decision-Making
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications
Shandong University（山东大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
Toward Multiphysics-Informed Machine Learning for Sustainable Data Center Operations: Intelligence Evolution with Deployable Solutions for Computing Infrastructure
Artificial intelligence for sustainable wine industry: AI-driven management in viticulture, wine production and enotourism
University of Szczecin（什切青大学）；Wroclaw University of Environmental and Life Sciences（弗罗茨瓦夫环境与生命科学大学）；University of Wroclaw（弗罗茨瓦夫大学）；WSB Merito University in Wroclaw（弗罗茨瓦夫WSB梅里托大学）；AGH University of Krakow（克拉科夫AGH大学）
From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University（智能计算与语言信息处理湖南省重点实验室，湖南师范大学）；National Mobile Communications Research Laboratory, Southeast University（国家移动通信研究实验室，东南大学）；Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business（长沙人工智能社会实验室，湖南工业大学）；Department of Computer Science, Brunel University London（伦敦布鲁内尔大学计算机科学系）；Faculty of Engineering and Applied Science, Memorial University（纪念大学工程与应用科学学院）；G Research Center, Khalifa University of Science and Technology（科技大学6G研究中心）
