Aging poses a big challenge in all aspects of social governance in China. A coherent and focused aging policy response that spans multiple sectors of government has been undertaken to achieve the goal of “Healthy Aging”. From an historical perspective, this paper uses a bibliometric analysis method to probe into the evolution of Chinese aging policies from 1978 to 2019, and the roles of core government agencies in policy-making. We obtained 226 Chinese aging policies from the PKULaw Database and the websites of the government departments. Co-word analyses and network analyses were applied in mapping the topics of aging policies and collaboration among the agencies. Gephi software was used to visualize the most frequently used keywords and their network graphs. Findings are as follows. Firstly, the development of the aging policy system in China has undergone two phases, from focusing on basic security to emphasizing the rights and health of the elderly. Secondly, the network structure of aging policy-making departments presents a distinct edge-core layer. More and more government agencies have become involved in the formulation of aging policies. But collaboration among the agencies is insufficient. Thirdly, pilot promotion is the main tool for implementing aging policies.

Due to insufficient financial support and unceasing work, the rural elderly in China experience a range of mental disorders, and the most common one is depression. This study aims to investigate the association between public pension, labor force participation (LFP), and depressive symptoms for older men and women in rural China. A moderated mediation analysis is conducted using data in the 2015 wave extracted from the China Health and Retirement Longitudinal Study (CHARLS), a continuous national social survey. A total of 2709 available surveys were obtained in our analysis. Using PROCESS, results revealed that the income from China’s New Rural Pension Scheme (NRPS) was directly negatively related to depressive symptoms. However, LFP did not mediate the link between pension income (PI) and depressive symptoms in the total study population. The results of moderated mediation estimates indicated that gender significantly moderated the relationship between LFP and depressive symptoms. Specifically, for older women, the indirect effect of PI on depressive symptoms via LFP was significant, but not for the opposite sex. In order to improve the mental health of older adults in rural China, the policy makers and mental health therapists need to pay attention to the aforementioned factors.

Traditional intelligent fault diagnosis methods focus on distinguishing different fault modes, but ignore the deterioration of fault severity. This paper proposes a new two-stage hierarchical convolutional neural network for fault diagnosis of rotating machinery bearings. The failure mode and failure severity are modeled as a hierarchical structure. First, the original vibration signal is transformed into an energy spectrum matrix containing fault-related information through wavelet packet decomposition. Secondly, in the model training method, an adaptive learning rate dynamic adjustment strategy is further proposed, which adaptively extracts robust features from the spectrum matrix for fault mode and severity diagnosis. To verify the effectiveness of the method, the bearing fault data was collected using a rotating machine test bench. On this basis, the diagnostic accuracy, convergence performance and robustness of the model under different signal-to-noise ratios and variable load environments are evaluated, and the feature learning ability of the method is verified by visual analysis. Experimental results show that this method has achieved satisfactory results in both fault pattern recognition and fault severity evaluation, and is superior to other machine learning and deep learning methods.