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Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK. Electronic address:

As artificial intelligence advances and demand for cost-effective equipment maintenance in various fields increases, it is worth insightful research on utilizing robots embedded with sound source localization (SSL) technology for condition monitoring. Combining the two techniques has significant advantages, which are conducive to further classifying and tracking abnormal sources, thereby enhancing system performance at a lower cost. The paper provides an overview of current acoustic-based robotic techniques for condition monitoring, highlights the common SSL methods, and finds that localization performance heavily depends on signal quality.

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The macromolecular components of the seed coat, particularly lignin, play a critical role in regulating seed viability. In the maize-soybean intercropping (MSI) system, shading stress was reported to enhance the viability of soybean seeds. However, the specific role of seed coat lignin in this process remains poorly understood.

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Dynamic brain networks (DBNs) can capture the intricate connections and temporal evolution among brain regions, becoming increasingly crucial in the diagnosis of neurological disorders. However, most existing researches tend to focus on isolated brain network sequence segmented by sliding windows, and they are difficult to effectively uncover the higher-order spatio-temporal topological pattern in DBNs. Meantime, it remains a challenge to utilize the structure connectivity prior in the DBNs analysis.

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