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
A growing body of studies and systematic reviews show evidence of the beneficial effects of physical exercise on core symptoms of ADHD. Furthermore, studies indicate that physical exercise as an adjuvant can enhance t...A growing body of studies and systematic reviews show evidence of the beneficial effects of physical exercise on core symptoms of ADHD. Furthermore, studies indicate that physical exercise as an adjuvant can enhance the effects of medication in the treatment of ADHD. Aerobic and coordinative exercises improve executive functioning through their effect on neurocognitive domains that are implicated in ADHD. It is postulated that through their specific modus operandi, aerobic exercise, by raising cortical arousal levels, improves impaired alerting functions whereas coordinative exercises improve the regulation of inhibitory control through the involvement of a higher variety of frontal-dependent cognitive processes. The increasing use of routine neurocognitive testing with continuous performance tests (CPT), such as the QbTest, at clinical assessments for ADHD allows for an innovative approach to identify the assessment impairments in alerting function and inhibition control that are related to ADHD and accordingly choose aerobic or coordinative physical exercise in a more targeted fashion.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A growing body of studies and systematic reviews show evidence of the beneficial effects of physical exercise on core symptoms of ADHD. Furthermore, studies indicate that physical exercise as an adjuvant can enhance the effects of medication in the treatment of ADHD. Aerobic and coordinative exercises improve executive functioning through their effect on neurocognitive domains that are implicated in ADHD. It is postulated that through their specific modus operandi, aerobic exercise, by raising cortical arousal levels, improves impaired alerting functions whereas coordinative exercises improve the regulation of inhibitory control through the involvement of a higher variety of frontal-dependent cognitive processes. The increasing use of routine neurocognitive testing with continuous performance tests (CPT), such as the QbTest, at clinical assessments for ADHD allows for an innovative approach to identify the assessment impairments in alerting function and inhibition control that are related to ADHD and accordingly choose aerobic or coordinative physical exercise in a more targeted fashion.
基金National Key Research and Development Program(2018YFE0109500)National Natural Science Foundation of China(51939010,51779221,U1806230)Key Research and Development Program of Zhejiang Province(2018C03031).
