In today’s digital era, a sweeping wave of informatization is transforming the education sector. It is imperative to advance educational informatization and to improve the quality of teaching and learning. Over the past two years, guided by an innovation-driven, service-oriented philosophy, the institution has steadily promoted practical applications of educational information systems and explored effective approaches to deeply integrate informatization with pedagogy, yielding a series of notable achievements. The smart campus system has served as a platform for teacher–student connectivity, and the institution continues to innovate its information models. While smart campus systems offer significant advantages in streamlining and integrating information, data security remains a critical issue that cannot be overlooked in campus operations. This paper examines the current status of the smart campus and provides a comprehensive analysis along with constructive recommendations for future informatization development strategies.

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With the rapid development of smart water conservancy, the application of automation technology in hydraulic hub projects has become a key factor in enhancing the efficiency of water resource management and ensuring project safety. Taking the Baimaotang Hydraulic Hub Project in Changshu City as an example, this paper mainly discusses the overall design and core functions of its automation system. Through the integration of a computer-based monitoring system, local control units (LCUs), and intelligent analysis technologies, the project achieves real-time monitoring and automated control of key parameters such as the operating status of primary and auxiliary pumping equipment, water levels, flow rates, and gate openings. The system adopts a hierarchical and distributed architecture, combined with hydropower measurement and online monitoring technologies for unit status, which significantly improves the reliability and responsiveness of project operations while enabling fault diagnosis and intelligent early warning. In addition, the integration of remote video surveillance and security systems further enhances management efficiency, providing technical support for flood control, drainage, water environment improvement, and scientific water resource scheduling. Practice has shown that the automation construction of the Baimaotang Hub offers a valuable reference model for the intelligent upgrading of hydraulic projects in plain river network regions and plays an important role in promoting the high-quality development of modern water conservancy.

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With the rise of Virtual Reality (VR) and metaverse concepts, high-fidelity virtual human interaction systems have become a hot research topic. Motion capture technology, as a key bridge connecting the real world and virtual environment, plays a central role in driving avatars in the VR metaverse. The purpose of this paper is to explore the application of motion capture technology in constructing a high-fidelity virtual human interaction system in the VR metaverse. By reviewing the technical classification of motion capture technology, comparing and studying its core breakthrough points, and analyzing its innovative principles in the system architecture model, this paper mainly focuses on the key features of low latency, high fidelity, and multimodality. The research results show that motion capture technology can effectively capture and reconstruct user movements, realize real-time driving of virtualized bodies, and enhance the immersion and realism of VR interaction. Meanwhile, to address the high cost of current motion capture technology, the research is committed to exploring the use of fewer sensors to realize whole-body motion reconstruction, which will provide a more economical and practical solution for future VR applications.

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Animal behavior analysis plays a pivotal role in neuroscience, behavioral ecology, animal welfare, and precision agriculture. However, traditional manual observation methods are often subjective, labor-intensive, and insufficient for large-scale quantification. The advent of deep learning has revolutionized this field, enabling automated, high-throughput and accurate analysis particularly in complex group settings. This review provides a comprehensive overview of recent advances in deep learning-based animal group pose estimation and behavior analysis. It systematically outlines the key stages from data acquisition to behavior interpretation, including object detection, multi-animal tracking, pose estimation, and individual identification. Representative models and tools are critically evaluated, along with their applications across various species and experimental contexts. While notable advancements have been attained—including refined occlusion handling via part affinity fields and augmented temporal behavior recognition through video transformers—several core challenges persist. These include robustness in wild environments, rare behavior detection and long-term identity preservation. Future research should focus on end-to-end joint modeling, data-efficient learning paradigms and multimodal data integration for advancing robust and intelligent systems. This review aims to provide researchers with a panoramic view of the field, highlighting key methodologies and directions for future development.

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