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In monitoring transmission line external damage prevention, due to the limited memory computing power of the equipment, the image needs to be transmitted to the data center at regular intervals, resulting in a high false negative rate. Therefore, this paper proposes a target detection method based on lightweight YOLOv5s. First, DSConv and improved E‐ELAN are used in Backbone to reduce the model's parameters. Then, GSConv and VoV‐GSCSP are introduced in Neck to reduce the complexity of the model. Finally, the Mish activation function achieves more effective feature transfer. According to the experimental findings, the proposed model's parameters are about 37% smaller than the original model's, and the calculation amount is about 53% smaller. The detection accuracy on the self‐built data set is the same, which proves that the proposed algorithm can reduce the model while maintaining high detection performance. It has specific practical significance for the terminal real‐time detection of external mechanical damage targets. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Scheduling workload is a demand response strategy for data centers to reshape electricity usage, which provides an opportunity for them to utilize renewable energy. Enhancing the flexibility of workload scheduling would promote the data center to integrate renewable energy. Considering that the improvement of flexibility in workload scheduling is tightly related to the Quality of Service (QoS) required by IT consumers (ITCs), it becomes desirable for the data center to collaborate with them to further facilitate renewable energy integration in demand response programs. This paper proposes a QoS‐based bi‐level demand response model for the data center, where game theory is adopted to resolve the conflict of interest in the collaboration between the data center and ITCs. At the upper level, the data center is a leader who provides differential monetary incentives to ITCs while dispatching workload to integrate renewable energy. At the lower level, ITCs act as followers who adjust the workload's QoS to enhance the flexibility of workload scheduling. Simulation results show that the proposed model can decrease the operating cost of the data center while increasing the utilization of renewable energy. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Purpose: This study is to describe patient demographic characteristics and estimate annual prevalence and incidence rates of Crohn's disease (CD) in Japan and the United States (US). Methods: Two large employment-based healthcare claims databases (Japan Medical Data Center [JMDC] in Japan and Merative MarketScan [Merative] in the US) were used to identify patients with CD from 2010 to 2019. Cases were confirmed using an algorithm based on diagnostic with/without treatment codes. The Merative population was used for sex and age standardization of annual prevalence and incidence rates estimated from the JMDC. Results: Patients with CD were generally younger in Japan than in the US at diagnosis (mean 33.6 vs. 39.4 years) and 71.5% were male versus 45.1% in the US. Annual prevalence per 100,000 population increased substantially in both countries, from 34.2 in 2010 to 54.5 in 2019 in Japan (standardized) and 163.3 to 224.2 in the US. Prevalence rates increased in both males and females in all age groups between 6 and < 65 years. Annual incidence rate per 100,000 person-years was almost fourfold higher in the US than Japan (21.0 vs. 5.5 [standardized] in 2019) but remained stable in both countries over time in both sexes and in all age groups. Conclusion: The epidemiology of CD differs between Japan and the US. Research to understand the basis of these differences could help to identify at-risk groups in each country, and guide implementation of preventive measures. [ABSTRACT FROM AUTHOR]