An Analysis of Government Behavior in China's Dairy Industry

The quality of agricultural products should be regulated and guided by the industrial association consisting of various links of agricultural production chain. The government has to purchase the regulatory services from agricultural associations. In the production of dairy products in China,the improper government intervention has been in violation of the socialist market economic law,resulting in damage to the node enterprises on the dairy production chain. Therefore,it is necessary to establish the agricultural associations to ensure the quality of agricultural products.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese mainland

Background Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Koppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese mainland and assess the feasibility of developing an early warning system.Methods Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran’s/ and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.Results All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran’s/ showed that average IR had significant clustered trend (z = 53.69,P < 0.001), with local Moran’s/ identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old:F = 26.80,P < 0.001;15-64 years old:F = 25.04,P < 0.001;Above 65 years old:F = 5.27,P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR= 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.Conclusions Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

A co-analysis framework for exploring multivariate scientific data

In complex multivariate data sets,different features usually include diverse associations with different variables,and different variables are associated within different regions.Therefore,exploring the associations between variables and voxels locally becomes necessary to better understand the underlying phenomena.In this paper,we propose a co-analysis framework based on biclusters,which are two subsets of variables and voxels with close scalar-value relationships,to guide the process of visually exploring multivariate data.We first automatically extract all meaningful biclusters,each of which only contains voxels with a similar scalar-value pattern over a subset of variables.These biclusters are organized according to their variable sets,and biclusters in each variable set are further grouped by a similarity metric to reduce redundancy and support diversity during visual exploration.Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data from the similarity between biclusters and the correlation of scalar values with different variables.Experiments on several representative multivariate scientific data sets demonstrate the effectiveness of our framework in exploring local relationships among variables,biclusters and scalar values in the data.