Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： Urban agglomerations, including Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta regions, are the large clusters both from population and economic perspectives and have the most active vitality of innovation. To be the worldclass urban agglomerations, population function regulation should highlight the roles of the carrying capacity of resources and the environment, allocation of public service resources, and the challenges of global competitions in the fields of economy and technology. We firstly review the classic theories. Then the distinct characters of population development in China’s urban agglomerations are figured out. Accordingly, the basic laws of population function regulation in urban agglomerations are put forward. In addition, the differentiated strategies are introduced to optimize the population functions in Beijing-Tianjin-Hebei, the Yangtze River Delta and the Pearl River Delta regions. Our policy implications aim to promote evolution of urban agglomerations with a more advanced and healthier process, especially during the 14th Five-Year Plan period.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： Timely detection and treatment of tomato diseases can effectively improve the quality and yield of tomato. In order to realize the real-time and non-destructive detection of tomato diseases, a tomato leaf disease classification and recognition method based on improved MobileNetV3 was proposed in this study. Firstly, the lightweight convolutional neural network Mobile‐ NetV3 was used for transfer learning on the image net data set. The network was initialized according to the weight of the pre training model, so as to realize the transfer and fine adjustment of large-scale shared parameters of the model. The training method of transfer learning could effectively alleviate the problem of model over fitting caused by insufficient data, realized the accurate classification of tomato leaf diseases in a small number of samples, and saved the time cost of network training. Under the same experimental conditions, compared with the three standard deep convolution network models of VGG16, ResNet50 and Inception- V3, the results showed that the overall performance of MobileNetV3 was the best. Next, the impact of the change of loss function and the change of data amplification mode on the identification of tomato leaf diseases were observed by using MobileNetV3 convolution network. For the test of loss value, focal loss and cross entropy function were used for comparison, and for the test of data enhancement, conventional data amplification and mixup hybrid enhancement were used for comparison. After testing, using Mixup enhancement method under focal loss function could improve the recognition accuracy of the model, and the average test recognition accuracy of 10 types of tomato diseases under Mixup hybrid enhancement and focal loss function was 94.68%. On the basis of transfer learning, continue to improve the performance of MobileNetV3 model, the dilated convolution convolution with expansion rate of 2 and 4 was introduced into convolution layer, 1×1 full connection layer after deep convolution of 5×5 was connected to form a perceptron structure in convolution layer, and GLU gating mechanism activation function was used to train the best tomato disease recognition model. The average test recognition accuracy was as high as 98.25%, the data scale of the model was 43.57 MB, and the average detection time of a single tomato disease image was only 0.27s, after ten fold cross validation, the recognition accuracy of the model was 98.25%, and the test results were stable and reliable. The experiment showed that this study could significantly improve the detection efficiency of tomato diseases and reduce the time cost of disease image detection.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract： Objective To investigate the effect of cancer-associated fibroblasts (CAFs) on the growth and invasion of gallbladder cancer cells. Methods The CAFs were isolated from human primary gallbladder carcinoma tissues by tissue culture and digestion methods. The cells were purified by differential adhesion method, and the primary cells were identified morphologically and immunocytochemically. The proliferation and invasion of two human gallbladder carcinoma cell lines (SBC-996 and GBC-SD) co-cultured with CAFs were detected by MTT and Transwell chamber assays. Results Gallbladder carcinoma CAFs were isolated successfully by both tissue culture and enzyme digestion methods, and the latter method was more convenient and efficient. MTT and Transwell assays showed that CAFs significantly promoted the proliferation and invasion of the two gallbladder carcinoma cell lines. Conclusion CAFs can promote the proliferation and invasion of gallbladder carcinoma cells in vitro, suggesting the important role of CAFs in the development of gallbladder carcinoma.
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