Village Elementary School Affecting the United Nations

EDITOR＇S NOTE： ＂Left-behind children＂ are children who have one parent or both parents working in another city while the children stay in the village. These children usually live with either one of their parents, relatives or family friends. Research shows that there are over 58 million left-behind children in China; 57.2 percent of them have one parent working in another city, 42,8 percent of the children have both parents working in another city,

Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.

Explanation Knowledge Graph Construction Through Causality Extraction from Texts

Explanation knowledge expressed by a graph,especially in the graphical model,is essential to comprehend clearly all paths of effect events in causality for basic diagnosis.This research focuses on determining the effect boundary using a statistical based approach and patterns of effect events in the graph whether they are consequence or concurrence without temporal markers.All necessary causality events from texts for the graph construction are extracted on multiple clauses/EDUs（Elementary Discourse Units） which assist in determining effect-event patterns from written event sequences in documents.To extract the causality events from documents,it has to face the effect-boundary determination problems after applying verb pair rules（a causative verb and an effect verb） to identify the causality.Therefore,we propose Bayesian Network and Maximum entropy to determine the boundary of the effect EDUs.We also propose learning the effect-verb order pairs from the adjacent effect EDUs to solve the effect-event patterns for representing the extracted causality by the graph construction.The accuracy result of the explanation knowledge graph construction is 90%based on expert judgments whereas the average accuracy results from the effect boundary determination by Bayesian Network and Maximum entropy are 90%and 93%,respectively.

Mining Causality for Explanation Knowledge from Text

Mining causality is essential to provide a diagnosis within multiple sentences or EDUs （Elementary Discourse Unit） This research aims at extracting the causality existing The research emphasizes the use of causality verbs because they make explicit in a certain way the consequent events of a cause, e.g., ＂Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.＂. A verb can also be the causal-verb link between cause and effect within EDU（s）, e.g., ＂Aphids suck the sap from rice leaves causing leaves to be shrunk＂ （＂causing＂ is equivalent, to a causal-verb link in Thai）. The research confronts two main problems： identifying the interesting causality events from documents and identifying their boundaries. Then, we propose mining on verbs by using two different machine learning techniques, Naive Bayes classifier and Support Vector Machine. The resulted mining rules will be used for the identification and the causality extraction of the multiple EDUs from text. Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naive Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.