Background: With the rapid aging of the domestic population, China has a strong incentive to increase the statutory retirement age. How retirement affects the health of the elderly is crucial to this policymaking. The health consequences of retirement have been debated greatly. This study aims to investigate the effects of retirement on physical and mental health among Chinese elderly people. Methods: The data we use in this study comes from four waves (2011, 2013, 2015, and 2018) of the Harmonized China Health and Retirement Longitudinal Study (Harmonized CHARLS), a prospective cohort. We use the nonparametric fuzzy regression discontinuity design to estimate the effects of retirement on physical and mental health. We test the robustness of our results with respect to different bandwidths, kernel functions, and polynomial orders. We also explore the heterogeneity across gender and education. Results: Results show that retirement has an insignificant effect on a series of physical and mental health outcomes, with and without adjusting several sociodemographic variables. Heterogeneity exists regarding gender and education. Although stratified analyses indicate that the transition from working to retirement leaves minimal effects on males and females, the effects go in the opposite direction. This finding holds for low-educated and high-educated groups for health outcomes including depression and cognitive function. Most of the results are stable with respect to different bandwidths, kernel functions, and polynomial orders. Conclusions: Our results suggest that it is possible to delay the statutory retirement age in China as retirement has insignificant effects on physical and mental health. However, further research is needed to assess the long-term effect of retirement on health.

Anomalies or failures in medical equipment may lead to severe consequences. Data-driven prognostic and health management (PHM) approaches can improve maintenance efficiency and reduce maintenance costs at hospitals while protecting patients’ lives. However, currently, the research and application of PHM in medical equipment is still rather limited. The development of the Internet of Things (IoT) technology provides new opportunities for PHM, which can safely collect, analyze and store real-time equipment data in hospitals. The data-driven models used in PHM predict anomalies or failures. However, current data-driven models’ performance may be limited due to lack of consideration for the interaction of similar features and the importance of different time steps. Hence, this paper proposes a new deep learning network called similar feature interaction with distance self-attention for the PHM of medical equipment. Firstly, a similar feature interaction module which uses clustering algorithms and causal convolution layers is proposed to consider the interaction of similar features. Secondly, a distance self-attention mechanism is proposed to allocate more attention to important time steps. The experiments on millions of CT equipment operating status instants collected by IoT in the hospital and the public dataset show that the proposed model is superior to existing models. The results show that the accuracy, recall, precision and f1-score of the proposed model on the real CT log data achieve 0.865, 0.682, 0.469 and 0.556, respectively. The proposed PHM model can assist the equipment maintenance team of hospitals in decision making under the IoT framework.

Vibro-acoustic modulation technique is a nonlinear ultrasonic method in nondestructive testing. This technique detects the defects by monitoring the modulation components generated by the interaction between the vibration and the ultrasound wave due to the nonlinear material behaviour caused by the damage. In this work, a swept frequency signal was used as high frequency excitation, then the Hilbert transform based amplitude and phase demodulation and synchronous demodulation (SD) were used to extract the modulation information from the received signal, the results were graphed in the time-frequency domain after the short time Fourier transform. The demodulation results were quite different from each other. The reason for the difference was investigated by analysing the demodulation process of the two methods. According to the analysis and the subsequent verification test, it was indicated that the SD method was more proper for the test and a new index called MI_SD was defined to evaluate the structure quality in the Vibro-acoustic modulation test with swept probing excitation.