The mossflora of Xishuangbanna, Southern Yunnan, China

This is the first contribution on the moss flora of Xishuangbanna, southern Yunnan Province, one of the main tropical areas in China. This rich moss flora shows a similarity to that of Southeast Asia and the Himalayas

Food Calorie Intake under Grain Price Uncertainty: Evidence from Rural Nepal

This study evaluates the effects of grain output price uncertainty on the farm income of rural households and, consequently, how this uncertainty influences caloric intake through changes in farm income. Using a rural household data set, augmented with output price uncertainty measures calculated from historical time-series data, we find that grain output price uncertainty tends to decrease crop production income of rural households. In addition, we find that higher crop income from production increases calorie intake of rural households. Taken together, these results suggests that output price uncertainty during the production process may tend to reduce caloric intake of rural Nepalese households since the price uncertainty negatively affects the crop income households need to buy calorie-rich staple foods.Food Calorie Intake, Price Uncertainty, Nepal, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, D12, O13, Q11, Q12,

LIQUOR AND BEVERAGE CONSUMPTION IN CHINA:A CENSORED DEMAND SYSTEM APPROACH

This paper estimated the Liquor and Beverage Consumption based on a Chinese survey data. The results showed that beer consumption has been relatively stable during the past 10 years. However, there exists large potential wine market in china.Food Consumption/Nutrition/Food Safety,

Link Prediction via Matrix Completion

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms