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 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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With the development of millimeter-wave technology, the miniaturization and integration of antennas have become key requirements. This paper presents a millimeter-wave second-order fractal antenna, including its structural design, fabrication method, and application in a silicon-based three-dimensional integrated structure. This antenna realizes a miniaturized structure with a Peano fractal curve on the substrate, featuring a novel structure, small size, and compatibility with other devices and chip processes. Meanwhile, a large-thickness BCB dielectric layer is adopted, which contributes to the miniaturization and integrated integration of the system. By elaborating on the design principle, fabrication steps, and performance of the antenna in the three-dimensional integrated structure, this paper provides a valuable reference for the development of millimeter-wave three-dimensional integration technology.

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