Search

Substandard housing is considered a public health issue worldwide. Migrants are more likely than other individuals to experience poor housing conditions in urban China, and these challenges in housing conditions have deep effects on migrant health. This study characterizes the relationship between housing satisfaction and migrant health in Hangzhou, China. In particular, 17 indicators for assessing housing satisfaction, which include the indoor environment (IE), outdoor environment (OE) and safety (SE), are integrated. In order to reveal the physical health and mental health status of migrants, a physical component summary (PCS) and mental component summary (MCS) were assessed by using a Twelve-Item Short-Form Health Survey. Multivariate linear regression was further employed to quantify the associations between housing satisfaction and migrant health. The results showed that high satisfaction with the indoor environment, outdoor environment and safety can improve the physical health of migrants. High housing satisfaction, in addition to satisfaction with the outdoor environment, has a significant relationship with the mental health of migrants. Income is associated with migrant health since it plays a key role in determining migrants’ socioeconomic and demographic characteristics and subjective aspirations. However, per capita living space does not have a significant association with migrant health because most of the migrants live in small living spaces in the study area. According to the findings, implications for housing policies that can better integrate housing conditions and health are proposed.

Wear particles produced by machines in the process of wear carry valuable information including wear mechanism and wear severity. Wear particle classification based on wear particle images provides predictive analysis for wear condition of machines. A novel wear particle recognition network based on feature fusion, FFWR-Net, is proposed in this research paper for wear particle images classification. In FFWR-Net, traditional feature extraction method by image processing technique (i.e. manually feature extracting) and deep learning convolutional neural network method (i.e. automatically feature extracting) is paralleled to extract the features of wear particle image. Then the features obtained by two different methods are fused together for building a wear particle classifier. In order to verify the effectiveness of the proposed classifier, it is compared with the previous convolutional neural network models on the same wear particle dataset. The comparison results show the accuracy and effectiveness of the proposed FFWR-Net classifier is better than the previous models.

Farmland property rights allocation such as farmland transfer has an important impact on economic development in rural areas and farmers’ livelihoods in developing countries. Examining the impacts of the external socioeconomic conditions helps realize the desired farmland transfer. This study attempts to analyze the underlying influencing factors of farmland transfer in urbanizing China. In particular, 18 underlying influencing factors are selected to describe urbanization and natural condition. Pearson correlation coefficients are computed to measure the linear relationships between underlying influencing factors and farmland transfer. The geographical detector method is further employed to quantify the individual effects of urbanization on farmland transfer and the interaction effects of urbanization on farmland transfer by assessing the degrees of spatial stratified heterogeneity. We discover that the effects of urban–rural basic living allowance gap, regional GDP per capita, urban population proportion on the proportion of transferred farmland area, proportion of farmland transferred to non-farmer users and proportion of transferred farmland cultivated non-grain crop are the strongest, respectively. The indices of urbanization level present have higher effects on farmland transfer than those of the urban–rural gap. Meanwhile, the interaction effect between two urbanization indices enhances the effect on the farmland transfer. Our study is believed to deepen the understanding of urbanization on farmland property rights allocation in rapidly urbanizing areas. It offers new insights into realizing the sustainable land use strategy in the socioeconomic transition in developing countries.