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Background: Periarthritis of shoulder (PAS) symptom is one of the leading causes prompting many patients to seek treatment. Tuina is a common treatment for PAS in China. But at present, there is no systematic evaluation report on its therapeutic effectiveness and safety. This protocol aims to reveal the efficacy and safety of Tuina for treating PAS. Methods: The following databases will be searched by electronic methods: PubMed, EBASE, WHO International Clinical Trials Registry Platform, Embase, the Chinese Biomedical Literature Database (CBM), Wan-fang Data (WANFANG), the China National Knowledge Infrastructure (CNKI), and other sources from inception to December 2019. Bias risk, subgroup analysis, data synthesis, and meta-analyses will be assessed with RevMan V.5.3 software if the data is met inclusion conditions. Results: This study will present a quality evidence of Tuina for the treatment of PAS patients. Conclusion: The systematic review will present reliable evidence to judge whether or not Tuina is a safe and effective intervention for PAS patients. PROSPERO registration number: CRD42019147445.

Cyberattacks have emerged as novel threats to modern power systems. By exploiting the vulnerabilities of insecure devices, an attacker can inject viruses to lurk and collect system conditions through sniffing and then launch well‐designed attacks. Collaboratively applying bilateral cyber‐physical information can help to detect anomalous system states caused by sniffing, which can isolate virus impacts on the cyber side and ensure the stable operation of power systems. Here, a dynamic weight ensemble isolation forest algorithm is proposed to mine anomaly system states utilizing bilateral information based on the hypothesis that the fused cyber‐physical system state under assault has the shortest average path length in a constructed random forest. In addition, the proposed algorithm is able to realize improved performance in power system anomaly status mining and adapt to constantly changing power systems. The method is verified by simulations on a co‐simulation platform. The results show that the method outperforms other anomaly detection methods.

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