Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of i...Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.展开更多
目的:探讨全反式维甲酸对SH I-1细胞株中糖基转移酶表达的影响。方法:采用半定量RT-PCR和Real-Tim e PCR方法,比较在不同浓度ATRA作用下,SH I-1细胞中不同糖基转移酶家族(多肽:N-乙酰氨基半乳糖转移酶家族、β3N-乙酰氨基葡萄糖基转移...目的:探讨全反式维甲酸对SH I-1细胞株中糖基转移酶表达的影响。方法:采用半定量RT-PCR和Real-Tim e PCR方法,比较在不同浓度ATRA作用下,SH I-1细胞中不同糖基转移酶家族(多肽:N-乙酰氨基半乳糖转移酶家族、β3N-乙酰氨基葡萄糖基转移酶家族和O-连接N-乙酰氨基葡萄糖转移酶家族)的表达情况。结果:在SH I-1细胞株中ppGalNAcT1、T2、T3、T4,β3GnT1、β3GnT5,O-GnT有不同程度的表达,加入全反式维甲酸后β3GnT5表达量下降,pp-GalNAcT2、T4、β3GnT1、O-GnT表达量升高。结论:加入诱导分化剂全反式维甲酸后,SHI-1细胞中的糖基化作用升高。展开更多
基金This work is supported by the National Natural Science Foundation of China(Grant No.51991392)Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3-3)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0904).
文摘Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.
