The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires f...The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.展开更多
With the rapid development of modern molecular biology and bioinformatics,many studies have proved that transcription factors play an important role in regulating the growth and development of plants.SPATULA(SPT)belon...With the rapid development of modern molecular biology and bioinformatics,many studies have proved that transcription factors play an important role in regulating the growth and development of plants.SPATULA(SPT)belongs to the bHLH transcription family and participates in many processes of regulating plant growth and development.This review systemically summarizes the multiple roles of SPT in plant growth,development,and stress response,including seed germination,flowering,leaf size,carpel development,and root elongation,which is helpful for us to better understand the functions of SPT.展开更多
随着互联网的飞速发展,集群结构的下一代核心路由器已经成为研究的重点.在可扩展路由器中(clus- ter router),并行路由算法是关键问题之一.对于广泛部署的OSPF协议,最短路径树(SPT)的并行计算是其并行化的核心难点.本文提出了一种计算...随着互联网的飞速发展,集群结构的下一代核心路由器已经成为研究的重点.在可扩展路由器中(clus- ter router),并行路由算法是关键问题之一.对于广泛部署的OSPF协议,最短路径树(SPT)的并行计算是其并行化的核心难点.本文提出了一种计算最短路径树的算法-分区Dijkstra算法(D-D),分析了算法性能,并通过模拟实验验证了算法的性能.展开更多
基金the Center University(Grant No.B220202013)Qinglan Project of Jiangsu Province(2022).
文摘The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.
文摘With the rapid development of modern molecular biology and bioinformatics,many studies have proved that transcription factors play an important role in regulating the growth and development of plants.SPATULA(SPT)belongs to the bHLH transcription family and participates in many processes of regulating plant growth and development.This review systemically summarizes the multiple roles of SPT in plant growth,development,and stress response,including seed germination,flowering,leaf size,carpel development,and root elongation,which is helpful for us to better understand the functions of SPT.
文摘随着互联网的飞速发展,集群结构的下一代核心路由器已经成为研究的重点.在可扩展路由器中(clus- ter router),并行路由算法是关键问题之一.对于广泛部署的OSPF协议,最短路径树(SPT)的并行计算是其并行化的核心难点.本文提出了一种计算最短路径树的算法-分区Dijkstra算法(D-D),分析了算法性能,并通过模拟实验验证了算法的性能.
