Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with ...Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).展开更多
Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrolo...Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrological model based on the least action principle (HyMoLAP). The Downhill Simplex method is also known as the Nelder-Mead algorithm, which is a heuristic research method, is used to optimize the cost function on a given domain. The performance of the model is evaluated by the Nash Stucliffe Efficiency Index (NSE), the Root Mean Square Error (RMSE), the coefficient of determination (R2), the Mean Absolute Error (MAE). A comparative estimation is conducted using the Nash-Sutcliffe Modeling Efficiency Index and the mean relative error to evaluate the performance of the optimization method. It appears that the variation in water balance parameter values is acceptable. The simulated optimization method appears to be the best in terms of lower variability of parameter values during successive tests. The quality of the parameter sets obtained is good enough to impact the performance of the objective functions in a minimum number of iterations. We have analyzed the algorithm from a technical point of view, and we have carried out an experimental comparison between specific factors such as the model structure and the parameter’s values. The results obtained confirm the quality of the model (NSE = 0.90 and 0.75 respectively in calibration and validation) and allow us to evaluate the efficiency of the Nelder-Mead algorithm in the automatic calibration of the HyMoLAP model. The developed hybrid automatic calibration approach is therefore one of the promising ways to reduce computational time in rainfall-runoff modeling.展开更多
在加速器驱动次临界系统(Accelerator Driven Sub-critical Systems,ADS)中,散裂源中子能量可以到达上百Me V甚至Ge V,能谱分布非常复杂,已有的工作核数据库的截面数据无法满足其设计要求。传统工作核数据库的制作方法人工操作干预过多...在加速器驱动次临界系统(Accelerator Driven Sub-critical Systems,ADS)中,散裂源中子能量可以到达上百Me V甚至Ge V,能谱分布非常复杂,已有的工作核数据库的截面数据无法满足其设计要求。传统工作核数据库的制作方法人工操作干预过多、耗时、繁琐且易出错,为此,开发出自动生成数据库程序。该程序在设计的能群结构、权重函数等参数基础上,通过程序自动生成适用于ADS系统的点状ACE格式和471群MATXS格式核数据库ANDL-ADS(Auto-generated Nuclear Date Library for ADS),支持高能中子(能量上限为150 Me V/200 Me V)的截面制作,并可根据需求进行多温截面的制作。通过不同材料的临界球、积分泄露率、高能屏蔽等基准例题的测试,初步验证了ANDL-ADS数据库的可靠性。展开更多
基金The research presented in this paper was funded by the National Science Foundation(1841520 and 1835507).
文摘Under the global health crisis of COVID-19,timely,and accurate epi-demic data are important for observation,monitoring,analyzing,modeling,predicting,and mitigating impacts.Viral case data can be jointly analyzed with relevant factors for various applications in the context of the pandemic.Current COVID-19 case data are scattered across a variety of data sources which may consist of low data quality accompanied by inconsistent data structures.To address this short-coming,a multi-scale spatiotemporal data product is proposed as a public repository platform,based on a spatiotemporal cube,and allows the integration of different data sources by adopting various data standards.Within the spatiotemporal cube,a comprehensive data processing workflow gathers disparate COVID-19 epidemic data-sets at the global,national,provincial/state,county,and city levels.This proposed framework is supported by an automatic update with a 2-h frequency and the crowdsourcing validation team to produce and update data on a daily time step.This rapid-response dataset allows the integration of other relevant socio-economic and environ-mental factors for spatiotemporal analysis.The data is available in Harvard Dataverse platform(https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8HGECN)and GitHub open source repository(https://github.com/stccenter/COVID-19-Data).
文摘Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrological model based on the least action principle (HyMoLAP). The Downhill Simplex method is also known as the Nelder-Mead algorithm, which is a heuristic research method, is used to optimize the cost function on a given domain. The performance of the model is evaluated by the Nash Stucliffe Efficiency Index (NSE), the Root Mean Square Error (RMSE), the coefficient of determination (R2), the Mean Absolute Error (MAE). A comparative estimation is conducted using the Nash-Sutcliffe Modeling Efficiency Index and the mean relative error to evaluate the performance of the optimization method. It appears that the variation in water balance parameter values is acceptable. The simulated optimization method appears to be the best in terms of lower variability of parameter values during successive tests. The quality of the parameter sets obtained is good enough to impact the performance of the objective functions in a minimum number of iterations. We have analyzed the algorithm from a technical point of view, and we have carried out an experimental comparison between specific factors such as the model structure and the parameter’s values. The results obtained confirm the quality of the model (NSE = 0.90 and 0.75 respectively in calibration and validation) and allow us to evaluate the efficiency of the Nelder-Mead algorithm in the automatic calibration of the HyMoLAP model. The developed hybrid automatic calibration approach is therefore one of the promising ways to reduce computational time in rainfall-runoff modeling.
文摘在加速器驱动次临界系统(Accelerator Driven Sub-critical Systems,ADS)中,散裂源中子能量可以到达上百Me V甚至Ge V,能谱分布非常复杂,已有的工作核数据库的截面数据无法满足其设计要求。传统工作核数据库的制作方法人工操作干预过多、耗时、繁琐且易出错,为此,开发出自动生成数据库程序。该程序在设计的能群结构、权重函数等参数基础上,通过程序自动生成适用于ADS系统的点状ACE格式和471群MATXS格式核数据库ANDL-ADS(Auto-generated Nuclear Date Library for ADS),支持高能中子(能量上限为150 Me V/200 Me V)的截面制作,并可根据需求进行多温截面的制作。通过不同材料的临界球、积分泄露率、高能屏蔽等基准例题的测试,初步验证了ANDL-ADS数据库的可靠性。
