Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, para...Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.展开更多
Coastal wetlands are crucial for the‘blue carbon sink’,significantly contributing to regulating climate change.This study util-ized 160 soil samples,35 remote sensing features,and 5 geo-climatic data to accurately e...Coastal wetlands are crucial for the‘blue carbon sink’,significantly contributing to regulating climate change.This study util-ized 160 soil samples,35 remote sensing features,and 5 geo-climatic data to accurately estimate the soil organic carbon stocks(SOCS)in the coastal wetlands of Tianjin and Hebei,China.To reduce data redundancy,simplify model complexity,and improve model inter-pretability,Pearson correlation analysis(PsCA),Boruta,and recursive feature elimination(RFE)were employed to optimize features.Combined with the optimized features,the soil organic carbon density(SOCD)prediction model was constructed by using multivariate adaptive regression splines(MARS),extreme gradient boosting(XGBoost),and random forest(RF)algorithms and applied to predict the spatial distribution of SOCD and estimate the SOCS of different wetland types in 2020.The results show that:1)different feature combinations have a significant influence on the model performance.Better prediction performance was attained by building a model using RFE-based feature combinations.RF has the best prediction accuracy(R^(2)=0.587,RMSE=0.798 kg/m^(2),MAE=0.660 kg/m^(2)).2)Optical features are more important than radar and geo-climatic features in the MARS,XGBoost,and RF algorithms.3)The size of SOCS is related to SOCD and the area of each wetland type,aquaculture pond has the highest SOCS,followed by marsh,salt pan,mud-flat,and sand shore.展开更多
根据西藏2607个土壤剖面资料和1：200万土壤图的数字化处理,按地区以制图单元土壤亚类为基础估算土壤有机碳密度（SOCD）和储量（SOCR）,并探讨其空间分布特征.结果表明：（1）西藏地区的SOCD平均为7.48 kg m^-2,并随土壤类型而变化,以...根据西藏2607个土壤剖面资料和1：200万土壤图的数字化处理,按地区以制图单元土壤亚类为基础估算土壤有机碳密度（SOCD）和储量（SOCR）,并探讨其空间分布特征.结果表明：（1）西藏地区的SOCD平均为7.48 kg m^-2,并随土壤类型而变化,以山地铁铝土最高（29.2 kg m^-2）,其后依次是山地淋溶土（16.6 kg m^-2）、高山草甸型土壤（12.2 kg m^-2）、山地半淋溶土（9.2kgm^-2）、高山草原型土壤（3.7kg m^-2）等,而以寒冻土（1.6kg m^-2）和高山荒漠土（1.3kg m^-2）为最低.同时土壤表层（0～20cm）的SOCD平均为4.27 kg m^-2,占全剖面总量的57﹪,反映西藏地区土壤有机碳库（SOCP）对环境变化具有较高的敏感性.（2）西藏SOCD具有独特的水平地带分布,即自藏东南向西北逐次降低,由此可大体分为高（Ⅰ）、中（Ⅱ）、低（Ⅲ）、极低（Ⅳ）4个碳密度带,其平均SOCD分别为21、10、4、＜2 kg m^-2;各带SOCD又有不同的垂直分布,总趋势是由复杂到简单,但均以最高位置的寒冻土极低碳密度为终点.（3）西藏SOCR总计为8.23 Pg,占全国SOCR总量的9.14﹪.各地SOCR分布极不平衡：就各碳密度带的SOCR相对比例（占西藏全区总量﹪）而言,Ⅰ、Ⅱ、Ⅲ、Ⅳ带分别为25﹪、50﹪、22﹪、3﹪,其中Ⅰ、Ⅱ带合计的土壤面积仅占45﹪,而SOCR却占75﹪,因而是西藏SOCP的主体;而反映土壤储碳能力的丰度指数（R）则分别为2.82、1.37、0.53、0.23.就各地区的SOCR而言,以那曲地区最大（2.19Pg）,拉萨地区最小（0.31Pg）;而R值则是林芝＞山南＞拉萨＞昌都＞日喀则＞那曲＞阿里.这些结果将为全球变化研究与区域环境评价提供有力的支撑.展开更多
文摘Several parallel sorting techniques on different architectures have been studied for many years. Due to the need for faster systems in today＇s world, parallelism can be used to accelerate applications. Nowadays, parallel operations are used to solve computer problems such as sort and search, which result in a reasonable speed. Sorting is one of the most important operations in computing world. The authors always try to find the best in different areas which the premier is speedup. In this paper, the authors issued a sort with O（logn） time complexity on PRAM EREW （Parallel Random Access Machine Exclusive Read Exclusive Write）. The algorithm is designed in a manner that keeps the tradeoff between the number of processor elements in the architecture and execution time. The simulation of the algorithm proves the theoretical analysis of the algorithm. The results of this research can be utilized in developing faster embedded systems. Sorting on Centralized Diamond （SOCD） algorithm is issued on the novel Centralized Diamond architecture which takes the advantages of Single Instruction Multiple Data （SIMD） architecture. This architecture and the sort on it are intuitive and optimal.
基金Under the auspices of National Natural Science Foundation of China(No.42101393,41901375,52274166)Hebei Natural Science Foundation(No.D2022209005,D2023209008)Central Guided Local Science and Technology Development Fund Project of Hebei Province(No.236Z3305G,246Z4201G)Key Research and Development Program of Science and Technology Plan of Tangshan,China(No.22150221J)。
文摘Coastal wetlands are crucial for the‘blue carbon sink’,significantly contributing to regulating climate change.This study util-ized 160 soil samples,35 remote sensing features,and 5 geo-climatic data to accurately estimate the soil organic carbon stocks(SOCS)in the coastal wetlands of Tianjin and Hebei,China.To reduce data redundancy,simplify model complexity,and improve model inter-pretability,Pearson correlation analysis(PsCA),Boruta,and recursive feature elimination(RFE)were employed to optimize features.Combined with the optimized features,the soil organic carbon density(SOCD)prediction model was constructed by using multivariate adaptive regression splines(MARS),extreme gradient boosting(XGBoost),and random forest(RF)algorithms and applied to predict the spatial distribution of SOCD and estimate the SOCS of different wetland types in 2020.The results show that:1)different feature combinations have a significant influence on the model performance.Better prediction performance was attained by building a model using RFE-based feature combinations.RF has the best prediction accuracy(R^(2)=0.587,RMSE=0.798 kg/m^(2),MAE=0.660 kg/m^(2)).2)Optical features are more important than radar and geo-climatic features in the MARS,XGBoost,and RF algorithms.3)The size of SOCS is related to SOCD and the area of each wetland type,aquaculture pond has the highest SOCS,followed by marsh,salt pan,mud-flat,and sand shore.
文摘根据西藏2607个土壤剖面资料和1：200万土壤图的数字化处理,按地区以制图单元土壤亚类为基础估算土壤有机碳密度（SOCD）和储量（SOCR）,并探讨其空间分布特征.结果表明：（1）西藏地区的SOCD平均为7.48 kg m^-2,并随土壤类型而变化,以山地铁铝土最高（29.2 kg m^-2）,其后依次是山地淋溶土（16.6 kg m^-2）、高山草甸型土壤（12.2 kg m^-2）、山地半淋溶土（9.2kgm^-2）、高山草原型土壤（3.7kg m^-2）等,而以寒冻土（1.6kg m^-2）和高山荒漠土（1.3kg m^-2）为最低.同时土壤表层（0～20cm）的SOCD平均为4.27 kg m^-2,占全剖面总量的57﹪,反映西藏地区土壤有机碳库（SOCP）对环境变化具有较高的敏感性.（2）西藏SOCD具有独特的水平地带分布,即自藏东南向西北逐次降低,由此可大体分为高（Ⅰ）、中（Ⅱ）、低（Ⅲ）、极低（Ⅳ）4个碳密度带,其平均SOCD分别为21、10、4、＜2 kg m^-2;各带SOCD又有不同的垂直分布,总趋势是由复杂到简单,但均以最高位置的寒冻土极低碳密度为终点.（3）西藏SOCR总计为8.23 Pg,占全国SOCR总量的9.14﹪.各地SOCR分布极不平衡：就各碳密度带的SOCR相对比例（占西藏全区总量﹪）而言,Ⅰ、Ⅱ、Ⅲ、Ⅳ带分别为25﹪、50﹪、22﹪、3﹪,其中Ⅰ、Ⅱ带合计的土壤面积仅占45﹪,而SOCR却占75﹪,因而是西藏SOCP的主体;而反映土壤储碳能力的丰度指数（R）则分别为2.82、1.37、0.53、0.23.就各地区的SOCR而言,以那曲地区最大（2.19Pg）,拉萨地区最小（0.31Pg）;而R值则是林芝＞山南＞拉萨＞昌都＞日喀则＞那曲＞阿里.这些结果将为全球变化研究与区域环境评价提供有力的支撑.
