Impacts of climatic change on agriculture and adaptation are of key concern of scientific research. However, vast uncertainties exist among global climates model output, emission scenarios, scale transformation and cr...Impacts of climatic change on agriculture and adaptation are of key concern of scientific research. However, vast uncertainties exist among global climates model output, emission scenarios, scale transformation and crop model parameterization. In order to reduce these uncertainties, we integrate output results of four IPCC emission scenarios of A1 FI, A2, B1 and B2, and five global climatic patterns of HadCM3, PCM, CGCM2, CSIRO2 and ECHAM4 in this study. Based on 20 databases of future climatic change scenarios from the Climatic Research Unit （CRU） , the scenario data of the climatic daily median values are generated on research sites with the global mean temperature increase of 1℃（GMT＋ID）, 2℃（GMT＋2D） and 3℃（GMT＋3D）. The impact of CO2 fertilization effect on wheat biomass for GMT＋I D, GMT＋2D and GMT＋3D in China＇s wheat-producing areas is studied in the process model, CERES-Wheat and probabilistic forecasting method. The research results show the CO2 fertilization effect can compensate reduction of wheat biomass with warming temperature in a strong compensating effect. Under the CO2 fertilization effect, the rain-fed and irrigated wheat biomasses increase respectively, and the increment of biomass goes up with temperature rising. The rain-fed wheat biomass increase is greater than the irrigated wheat biomass. Without consideration of CO2 fertilization effect, both irrigated and rain-fed wheat biomasses reduce, and there is a higher probability for the irrigated wheat biomass than that of the rain-fed wheat biomass.展开更多
In this commentary,we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conduct-ing household projections.In the second and third sections...In this commentary,we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conduct-ing household projections.In the second and third sections,we summarize basic ideas,data needed,assessments and applications of ProFamy extended cohort-com-ponent methods/software for households and living arrangement projections;and we emphasize the importance to extend the ProFamy methods and software from deterministic to probabilistic households and living arrangement projections.In sec-tion 4,we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrange-ment projections(PHPs),in which the population size/structure projection out-comes are in consistence with those of probabilistic population projections(PPPs)released by United Nations Population Division(UNPD).In the last Section,we dis-cuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban(or race)-sex-age-specific standard schedules and the demographic summary measures,to conduct analyses and projections,such as single-parent households,caregivers,and care needs/costs for disabled older adults,age-friendly housing and households-based energy demands,etc.for healthy aging and sustainable development studies.Finally,we discuss the prospects of our ongo-ing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection(PHPs).As compared with ProFamy deterministic pro-jection method,the PHPs produces a lot of additional outcomes of probabilistically projected households and living arrangements in 2021-2100 with uncertainty inter-vals that are crucial for healthy aging and sustainable development studies.展开更多
Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feat...Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.展开更多
基金National Natural Science Foundation of China, No.41071030
文摘Impacts of climatic change on agriculture and adaptation are of key concern of scientific research. However, vast uncertainties exist among global climates model output, emission scenarios, scale transformation and crop model parameterization. In order to reduce these uncertainties, we integrate output results of four IPCC emission scenarios of A1 FI, A2, B1 and B2, and five global climatic patterns of HadCM3, PCM, CGCM2, CSIRO2 and ECHAM4 in this study. Based on 20 databases of future climatic change scenarios from the Climatic Research Unit （CRU） , the scenario data of the climatic daily median values are generated on research sites with the global mean temperature increase of 1℃（GMT＋ID）, 2℃（GMT＋2D） and 3℃（GMT＋3D）. The impact of CO2 fertilization effect on wheat biomass for GMT＋I D, GMT＋2D and GMT＋3D in China＇s wheat-producing areas is studied in the process model, CERES-Wheat and probabilistic forecasting method. The research results show the CO2 fertilization effect can compensate reduction of wheat biomass with warming temperature in a strong compensating effect. Under the CO2 fertilization effect, the rain-fed and irrigated wheat biomasses increase respectively, and the increment of biomass goes up with temperature rising. The rain-fed wheat biomass increase is greater than the irrigated wheat biomass. Without consideration of CO2 fertilization effect, both irrigated and rain-fed wheat biomasses reduce, and there is a higher probability for the irrigated wheat biomass than that of the rain-fed wheat biomass.
基金supported by the National Key R&D Program of China(2018RFC2000400)the National Natural Science Foundation of China(72061137004)+1 种基金the grant awarded by the Ministry of Finance and Ministry of Foreign Affairs of China under grant section 2 of the Collaborative Program between China and Association of Southeast Asian Nations(ASEAN).The work of Qiushi Feng was supported by the Academic Research Fund(ACRF-TIER 2)awarded by Ministry of Education of Singaporesupported by the U.S.National Institute of Aging/National Institute of Health(P01AG031719).
文摘In this commentary,we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conduct-ing household projections.In the second and third sections,we summarize basic ideas,data needed,assessments and applications of ProFamy extended cohort-com-ponent methods/software for households and living arrangement projections;and we emphasize the importance to extend the ProFamy methods and software from deterministic to probabilistic households and living arrangement projections.In sec-tion 4,we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrange-ment projections(PHPs),in which the population size/structure projection out-comes are in consistence with those of probabilistic population projections(PPPs)released by United Nations Population Division(UNPD).In the last Section,we dis-cuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban(or race)-sex-age-specific standard schedules and the demographic summary measures,to conduct analyses and projections,such as single-parent households,caregivers,and care needs/costs for disabled older adults,age-friendly housing and households-based energy demands,etc.for healthy aging and sustainable development studies.Finally,we discuss the prospects of our ongo-ing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection(PHPs).As compared with ProFamy deterministic pro-jection method,the PHPs produces a lot of additional outcomes of probabilistically projected households and living arrangements in 2021-2100 with uncertainty inter-vals that are crucial for healthy aging and sustainable development studies.
基金National Natural Science Foundation of China(No.71331008)
文摘Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.
