科学基金在推动我国科学发展中发挥着重要作用。然而,当前基金管理中存在项目成果与立项内容不匹配的现象,亟须建立细粒度的匹配度评估机制,以完善科研项目评价体系。实现项目成果细粒度匹配的首要前提是项目关键要素抽取。已有研究实...科学基金在推动我国科学发展中发挥着重要作用。然而,当前基金管理中存在项目成果与立项内容不匹配的现象,亟须建立细粒度的匹配度评估机制,以完善科研项目评价体系。实现项目成果细粒度匹配的首要前提是项目关键要素抽取。已有研究实现了项目文本的句子级语步分类,但难以捕捉细粒度信息;面向论文的问题方法抽取通常局限于单一研究问题的识别,难以适配包含多子问题的科研项目。基于此,本文聚焦科研项目关键要素抽取任务,首先界定研究背景、研究问题、研究方法、研究目标和研究意义五类项目关键要素;在此基础上,提出一种基于大模型的项目要素自动抽取方法,分别运用零样本学习、单样本学习和微调三种策略,探索大模型在项目要素抽取任务中的适配能力。研究结果表明,微调策略下最优模型ROUGE-L(recall-oriented understudy for gisting evaluation-L)指标达到0.849,验证了微调模型在项目要素抽取任务上的实用性和有效性。本文抽取的项目要素能够直接服务于项目成果细粒度匹配场景,为后续下游任务的开展提供方法支持以及为科研项目管理的智能化提供支撑。展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Peanut(Arachis hypogaea L.)is an important oil crop.Oleic acid is a major factor that determines the quality of peanuts.Therefore,the high oleic and high oleic to linoleic acid ratio are the target traits in an advanc...Peanut(Arachis hypogaea L.)is an important oil crop.Oleic acid is a major factor that determines the quality of peanuts.Therefore,the high oleic and high oleic to linoleic acid ratio are the target traits in an advanced peanut breeding program.This study provided an extensive evaluation of the genetic and physical characteristics as well as disease resistance of 220 high oleic peanut varieties in China.Notably,these varieties clustered into five major categories based on their traits.A majority of these varieties have been bred using interspecific hybridization or selected from mutants of self-crossed parents,with the main parent varieties being Kaixuan 016 and CTWE.Analysis of disease resistance showed that most high oleic peanut varieties could resist two or three diseases.However,those varieties with resistance to multiple diseases were relatively scarce.Moreover,some high oleic peanut varieties showed no disease resistance or inadequate testing.The results further indicate that the genetic basis for high oleic peanut breeding is insufficient,highlighting the need for its further development.Importantly,our findings lay a critical foundation for future high oleic peanut breeding and promote better understanding of the genetic and trait diversity offered by these varieties.展开更多
THE year 2004 saw the 20th anniversary of the national-level development zones.Having long been part of the central government's macroeconomic plans,the Yichang Development Zone and hundreds of others like it are ...THE year 2004 saw the 20th anniversary of the national-level development zones.Having long been part of the central government's macroeconomic plans,the Yichang Development Zone and hundreds of others like it are now entering an era of post-development,in which business management,local services and facilities will all be upgraded.展开更多
YIDU,a vibrant and picturesque city in China's hinterland,is the birthplace of Chinese civilization.Dwellings,ash pits and a large number of stone vessels and earthenware have been unearthed,and a preliminary arch...YIDU,a vibrant and picturesque city in China's hinterland,is the birthplace of Chinese civilization.Dwellings,ash pits and a large number of stone vessels and earthenware have been unearthed,and a preliminary archeological dig in the area has uncovered remains of a civilization dating to the Neolithic era,proving that people lived here as long as 7,000 years ago.展开更多
LION-shaped Yiyang City is to the south of Dongting Lake in north-central Hunan Province.The Zijiang River runs through the city from west to east,dividing it into two,and emptying into Dongting Lake.The Zishui Highwa...LION-shaped Yiyang City is to the south of Dongting Lake in north-central Hunan Province.The Zijiang River runs through the city from west to east,dividing it into two,and emptying into Dongting Lake.The Zishui Highway Bridge links the areas north and south of the river.In ancient times the Zijiang River was called Yishui.展开更多
文摘科学基金在推动我国科学发展中发挥着重要作用。然而,当前基金管理中存在项目成果与立项内容不匹配的现象,亟须建立细粒度的匹配度评估机制,以完善科研项目评价体系。实现项目成果细粒度匹配的首要前提是项目关键要素抽取。已有研究实现了项目文本的句子级语步分类,但难以捕捉细粒度信息;面向论文的问题方法抽取通常局限于单一研究问题的识别,难以适配包含多子问题的科研项目。基于此,本文聚焦科研项目关键要素抽取任务,首先界定研究背景、研究问题、研究方法、研究目标和研究意义五类项目关键要素;在此基础上,提出一种基于大模型的项目要素自动抽取方法,分别运用零样本学习、单样本学习和微调三种策略,探索大模型在项目要素抽取任务中的适配能力。研究结果表明,微调策略下最优模型ROUGE-L(recall-oriented understudy for gisting evaluation-L)指标达到0.849,验证了微调模型在项目要素抽取任务上的实用性和有效性。本文抽取的项目要素能够直接服务于项目成果细粒度匹配场景,为后续下游任务的开展提供方法支持以及为科研项目管理的智能化提供支撑。
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金supported by grants from the Key Program of National Natural Science Foundation of China(NSFC)(No.U22A20475)Key Scientific and Technological Project of Henan Province(No.221111110500,161100111000,HARS-22-05-G1)the Key Scientific Research Project of Henan Higher Education Institutions(24A210007).
文摘Peanut(Arachis hypogaea L.)is an important oil crop.Oleic acid is a major factor that determines the quality of peanuts.Therefore,the high oleic and high oleic to linoleic acid ratio are the target traits in an advanced peanut breeding program.This study provided an extensive evaluation of the genetic and physical characteristics as well as disease resistance of 220 high oleic peanut varieties in China.Notably,these varieties clustered into five major categories based on their traits.A majority of these varieties have been bred using interspecific hybridization or selected from mutants of self-crossed parents,with the main parent varieties being Kaixuan 016 and CTWE.Analysis of disease resistance showed that most high oleic peanut varieties could resist two or three diseases.However,those varieties with resistance to multiple diseases were relatively scarce.Moreover,some high oleic peanut varieties showed no disease resistance or inadequate testing.The results further indicate that the genetic basis for high oleic peanut breeding is insufficient,highlighting the need for its further development.Importantly,our findings lay a critical foundation for future high oleic peanut breeding and promote better understanding of the genetic and trait diversity offered by these varieties.
文摘THE year 2004 saw the 20th anniversary of the national-level development zones.Having long been part of the central government's macroeconomic plans,the Yichang Development Zone and hundreds of others like it are now entering an era of post-development,in which business management,local services and facilities will all be upgraded.
文摘YIDU,a vibrant and picturesque city in China's hinterland,is the birthplace of Chinese civilization.Dwellings,ash pits and a large number of stone vessels and earthenware have been unearthed,and a preliminary archeological dig in the area has uncovered remains of a civilization dating to the Neolithic era,proving that people lived here as long as 7,000 years ago.
文摘LION-shaped Yiyang City is to the south of Dongting Lake in north-central Hunan Province.The Zijiang River runs through the city from west to east,dividing it into two,and emptying into Dongting Lake.The Zishui Highway Bridge links the areas north and south of the river.In ancient times the Zijiang River was called Yishui.
