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Copyright of Journal of the Chinese Society of Rare Earths is the property of Editorial Department of Journal of the Chinese Society of Rare Earths and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Laser Biology / Acta Laser Biology Sinica is the property of Acta Laser Biology Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The spread of epidemics poses a significant threat to the entire human community. Therefore, it is critical to identify the sources of transmission quickly and take timely control measures. However, the diversity of epidemic transmission processes and information uncertainty makes it challenging to identify the sources of transmission quickly and accurately. This paper proposed a new algorithm for identifying transmission sources in a metapopulation network by combining the reverse infection algorithm and Markov chain theory. The algorithm first uses a Markov chain to initially estimate the time when a subpopulation is infected, and the infected subpopulation obtains its own identity information based on the infection time. Then, it traverses all subpopulations that obtain the identity information of the infected subpopulation and spreads the collected identity information of the infected subpopulation to all its neighbors. Finally, the spreading source of the metapopulation network can be inferred based on the temporal order in which all the identity information of the infected subpopulation is obtained. Simulation experiments conducted on real airports networks and artificial networks show that the accuracy of this algorithm is significantly improved compared to other algorithms, regardless of whether all or partial of the infection snapshots are known. This algorithm is well-suited for metapopulation networks such as aviation networks and is also useful for real-world epidemic transmission tracing and control. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)