The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using...The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using small data sets.Based on an unprecedented large dataset containing 1252 multicomponent alloys,machine-learning methods showed that the formation of solid solutions can be very accurately predicted(93%).The machine-learning results help identify the most important features,such as molar volume,bulk modulus,and melting temperature.展开更多
Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Invent...Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index(NDVI)version NDVI_(3g) dataset in the Tibetan Plateau,the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982.Dynamic trends in vegetation experienced an obvious increase at regional scale,but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended.At pixel scale,areas with significant browning significantly expanded over 14 periods for all seasons,but for significant greening significantly increased only in autumn.The changes of vegetation activity in spring were the most drastic among three seasons.Increased increments of NDVI in summer,spring,and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods.Vegetation activity was mainly regulated by a thermal factor,and the dominant climatic drivers of vegetation growth varied across different seasons and regions.We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.展开更多
基金Research performed by Leidos Research Support Team staff was conducted under the RSS contract 89243318CFE000003This research was supported in part by an appointment to the U.S.Department of Energy(DOE)Postgraduate Research Program at the National Energy Technology Laboratory(NETL)administered by the Oak Ridge Institute for Science and EducationThis research used resources of Oak Ridge National Laboratory’s Compute and Data Environment for Science(CADES)and the Oak Ridge Leadership Computing Facility,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC05-00OR22725.
文摘The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability.Some rules with seemingly good predictability were,however,tested using small data sets.Based on an unprecedented large dataset containing 1252 multicomponent alloys,machine-learning methods showed that the formation of solid solutions can be very accurately predicted(93%).The machine-learning results help identify the most important features,such as molar volume,bulk modulus,and melting temperature.
基金supported by the National Key Research and Development Plan of China[grant number 2016YFC0500401-5]the National Natural Science Foundation of China[grant number 41001055].
文摘Detecting changes in vegetation,distinguishing the persistence of changes,and seeking their causes during multiple periods are important to gaining a deeper understanding of vegetation dynamics.Using the Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index(NDVI)version NDVI_(3g) dataset in the Tibetan Plateau,the trends in the seasonal components of NDVI and their linkage with climatic factors were analyzed over 14 asymptotic periods of 18–31 years since 1982.Dynamic trends in vegetation experienced an obvious increase at regional scale,but the increases of vegetation activity mostly tended to stall or slow down as the studied time period was extended.At pixel scale,areas with significant browning significantly expanded over 14 periods for all seasons,but for significant greening significantly increased only in autumn.The changes of vegetation activity in spring were the most drastic among three seasons.Increased increments of NDVI in summer,spring,and autumn took turns being the main reason for the enhanced vegetation activity in the growing season in the nested 14 periods.Vegetation activity was mainly regulated by a thermal factor,and the dominant climatic drivers of vegetation growth varied across different seasons and regions.We speculate that the increase of NDVI will continue but the increments will decline in all seasons except autumn.
