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In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

Based on Bourdieu's concepts of "capital" and "habitus" in his class theory, this paper examines the differentiation of capital investment and parenting habitus on children's education between the Chinese urban middle class and lower class. By analyzing 2009 survey data of students in grades 4 and 8 in urban areas, the authors found that middle-class parents had significant advantages in capital investment, but showed no significant differences in parenting attitudes when compared to lower-class families. This finding indicates that the current Chinese middle class largely relies on capital possession, but displays few differences in class habitus compared with the lower class. The so-called class crystallization is maintained primarily through economic capital, but not through distinctions of inner dispositions.

Confusion over the concept of social capital is reflected in the variety of methodologies used to measure it. In studying the re-employment of laid-off Chinese workers, the author endeavours to measure their individual social capital in two ways. To measure their `possessed social capital' (i.e. the accessible resources in their social networks), their `spring festival contacts network' was studied for information about network size, density and embedded resources. The social capital actually used for re-employment was also measured. It was found that Chinese laid-off workers have less social capital than ordinary citizens. While searching for new jobs, they mainly depend on informal methods and substantial help from the network. Strong ties are more frequently used by these workers. However, workers with very low and high amount of possessed social capital tend to avoid using social capital in re-employment.

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Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

Analysis shows that extending the age limit for grants boosts the number awarded to women, but more must be done to achieve parity, say Ying Ma and colleagues.Chemist Youyou Tu, who discovered the malaria treatment artemisinin, was the first Chinese female scientist to win a Nobel prize. CLAUDIO BRESCIANI/AFP/GETTY 3 M A Y 2 0 1 8 | V O L 5 5 7 | N A T U R E | 2 5 COMMENT © 2 0 1 8 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d . Chemist Youyou Tu worked with pharmacologist Lou Zhicen (left) on traditional Chinese medicine in the 1950s, when women's participation in the workforce in China was encouraged and protected. 3 M A Y 2 0 1 8 | V O L 5 5 7 | N A T U R E | 2 7 COMMENT XINHUA/ALAMY © 2 0 1 8 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .

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