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Purpose The purpose of this paper is to investigate how different types of the village relationship influence different types of public goods provision in rural China. Design/methodology/approach The three components (clan-based relationship, neighborhood relationship and external relationship) were derived by employing factor analysis. The simultaneous discrete choice model was used to estimate the influence of these components on public goods provision, using the survey data from the China Household Income Project conducted in 2007. Findings The findings indicate that considering different components of village relationship allows for a better understanding of the public good provision. The results indicate that the neighborhood relationship has a significantly positive effect on rural public goods provision, particularly on water conservancy and irrigation, while the external relationship has a significantly positive effect on all types of public goods. Practical implications Local public goods provision is the core of the new rural construction in China. These findings imply that relationship in villages plays a vital role in the provision of public goods and is necessary in the construction of the new harmonious countryside in China. The results also have implications for rural public goods provision in other developing countries. Originality/value To the best of our knowledge, this is the first study to quantitatively model the impact of different relationships on public goods provision at the rural level. A consideration of the different components in village relationship allows for a more precise understanding of the pubic goods provision in the village.

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Accurate detection of pulmonary nodules on chest computed tomography scans is crucial to early diagnosis of lung cancer. To address the thorn problems on low detection sensitivity and high falsepositive rate caused by heterogeneity and morphological complexity of 3-D nodule features, a computeraided detection system is developed to increase the detection sensitivity and classification accuracy of pulmonary nodules. The contributions include: (1) Nodule candidate detection: 3-D Residual U-Net model is improved to detect candidate nodules, which constructs 3-D context-guided module to extract local and global nodule features by setting the dilated convolution with different dilation rates. Furthermore, channel attention mechanism is used to dynamically adjust the channel features, which enhances the generalization and expression ability of the detection-network to effectively learn 3-D spatial context features. (2) False-positive reduction: multi-branch classification network is designed for multi-task learning. Image reconstruction task is performed to retain more microscopic nodules information from convolutional neural network (CNN) hierarchy. Moreover, each branch deals with the feature map at corresponding depth layers, and then all branches' feature maps are combined together to perform nodule classification task. Numerous experimental results show that the proposed system is perfectly qualified for pulmonary nodules detection on Lung Nodules Analysis 2016 dataset, which achieves detection sensitivity up to 94.0% and competition performance metric (CPM) score up to 0.959.