Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characterist...Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characteristics and mechanisms of slope stability.A series of artificial rainfall flume experiments with varying rainfall intensities and slopes were conducted using soil samples collected from Wei Jia Gully.The experiments monitored pore-water pressure,grain migration,and failure sequences.Grain-size distribution parameters(μand Dc)were analyzed to understand the migration path and accumulation of fine grains.The experiments reveal that fine-grain migration significantly alters soil structure,leading to random blockage and interconnection of internal pore channels.These changes result in fluctuating pore-water pressure distributions and uneven fine-grain accumulation,critical factors in slope stability.Slope failures occur randomly and intermittently,influenced by fine-grain content in runoff and resulting pore-water pressure variations.This study highlights that fine-grain migration plays a vital role in slope stability,with significant implications for predicting and mitigating slope failures.The stochastic nature of fine-grain migration and its impact on soil properties should be incorporated into predictive models to enhance their accuracy and reliability.展开更多
Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quan...Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.展开更多
Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The ris...Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA23090202)the Key Science and Technology Projects of Transportation Industry(Grant No.2021-MS4-104)the National Key Research and Development Program of China(Grant No.2019YFC1509900).
文摘Fine grains migration is a primary cause of landslides and debris flows.This study investigates the effect of fine-grain migration on slope failure through flume experiments,focusing on the spatiotemporal characteristics and mechanisms of slope stability.A series of artificial rainfall flume experiments with varying rainfall intensities and slopes were conducted using soil samples collected from Wei Jia Gully.The experiments monitored pore-water pressure,grain migration,and failure sequences.Grain-size distribution parameters(μand Dc)were analyzed to understand the migration path and accumulation of fine grains.The experiments reveal that fine-grain migration significantly alters soil structure,leading to random blockage and interconnection of internal pore channels.These changes result in fluctuating pore-water pressure distributions and uneven fine-grain accumulation,critical factors in slope stability.Slope failures occur randomly and intermittently,influenced by fine-grain content in runoff and resulting pore-water pressure variations.This study highlights that fine-grain migration plays a vital role in slope stability,with significant implications for predicting and mitigating slope failures.The stochastic nature of fine-grain migration and its impact on soil properties should be incorporated into predictive models to enhance their accuracy and reliability.
文摘Objective To observe the value of artificial intelligence(AI)models based on non-contrast chest CT for measuring bone mineral density(BMD).Methods Totally 380 subjects who underwent both non-contrast chest CT and quantitative CT(QCT)BMD examination were retrospectively enrolled and divided into training set(n=304)and test set(n=76)at a ratio of 8∶2.The mean BMD of L1—L3 vertebrae were measured based on QCT.Spongy bones of T5—T10 vertebrae were segmented as ROI,radiomics(Rad)features were extracted,and machine learning(ML),Rad and deep learning(DL)models were constructed for classification of osteoporosis(OP)and evaluating BMD,respectively.Receiver operating characteristic curves were drawn,and area under the curves(AUC)were calculated to evaluate the efficacy of each model for classification of OP.Bland-Altman analysis and Pearson correlation analysis were performed to explore the consistency and correlation of each model with QCT for measuring BMD.Results Among ML and Rad models,ML Bagging-OP and Rad Bagging-OP had the best performances for classification of OP.In test set,AUC of ML Bagging-OP,Rad Bagging-OP and DL OP for classification of OP was 0.943,0.944 and 0.947,respectively,with no significant difference(all P>0.05).BMD obtained with all the above models had good consistency with those measured with QCT(most of the differences were within the range of Ax-G±1.96 s),which were highly positively correlated(r=0.910—0.974,all P<0.001).Conclusion AI models based on non-contrast chest CT had high efficacy for classification of OP,and good consistency of BMD measurements were found between AI models and QCT.
基金supported in part by the National Key Research and Development Program of China under 2020AAA0106000the National Natural Science Foundation of China under U20B2060 and U21B2036supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005
文摘Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.
