Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided i...Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.展开更多
Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive act...Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.展开更多
Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats...Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats were randomly divided into MI group(n=15)and sham operation group(n=10).MI models were established in MI group through ligation of the left anterior descending coronary artery using atraumatic suture,while no intervention was given to those in sham operation group after thoracotomy.MCE images of both basal and papillary muscle levels on the short axis section of left ventricles were acquired after 1 week,which were assessed independently by 2 junior and 2 senior ultrasound physicians.The evaluating efficacy of MI segment,the mean interpretation time and the consistency were compared whether under the assistance of PResNet model or not.Results No significant difference of efficacy of evaluation on MI segment was found for senior physicians with or without assistance of PResNet model(both P>0.05).Under the assistance of PResNet model,the efficacy of junior physicians for diagnosing MI segment was significantly improved compared with that without the assistance of PResNet model(both P<0.01),and was comparable to that of senior physicians.Under the assistance of PResNet model,the mean interpretation time of each physician was significantly shorter than that without assistance(all P<0.001),and the consistency between junior physicians and among junior and senior physicians were both moderate(Kappa=0.692,0.542),which became better under the assistance(Kappa=0.763,0.749).Conclusion PResNet could improve the efficacy of junior physicians for evaluation on rat MI segment in MCE images,shorten interpretation time with different aptitudes,also improve the consistency to some extent.展开更多
Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to det...Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to determine the alignment between CVD risk perception levels and objectively calculated CVDrisk levels,then investigate the disparity in physical activity and healthy diet habits among distinct CVDrisk perception categories.Methods:From March to August 2022,a cross-sectional survey was conducted in Zhejiang Province usingconvenience sampling.Participants aged between 20 and 80 years,without prior diagnosis of CVD wereincluded.CVD risk perception was evaluated with the Chinese version of the Attitude and Beliefs aboutCardiovascular Disease Risk Perception Questionnaire,while objective CVD risk was assessed through thePrediction for Atherosclerotic Cardiovascular Disease Risk(China-PAR)model.Participants’demographicinformation,self-reported physical activity,and healthy diet score were also collected.Results:A total of 739 participants were included in the final analysis.Less than a third of participants(29.2%)accurately perceived their CVD risk,while 64.5%over-perceived it and 6.2%under-perceived it.Notably,half of the individuals(50.0%)with high CVD risk under-perceived their actual risk.Compared tothe accurate perception group,individuals aged 60e80 years old(OR=6.569),currently drinking(OR=3.059),and with hypertension(OR=2.352)were more likely to under-perceive their CVD risk.Meanwhile,participants aged 40-<60 years old(OR=2.462)and employed(OR=2.352)were morelikely to over-perceive their risk.The proportion of individuals engaging in physical activity was lowest inthe under-perception group,although the difference among the three groups was not statistically significant(χ^(2)=2.556,P=0.278).In addition,the proportion of individuals practicing healthy diet habitswas also lowest in the under-perception group,and a significant statistical difference was observedamong the three groups(χ^(2)=10.310,P=0.006).Conclusion:Only a small proportion of participants accurately perceived their CVD risk,especially amongthose with high actual CVD risk.Individuals in the under-perceived CVD risk group exhibited the lowestrates of physical activity engagement and healthy diet adherence.Healthcare professionals should prioritize implementing personalized CVD risk communication strategies tailored to specific subgroups toenhance the accuracy of risk perception.展开更多
As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular...As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.展开更多
Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-cham...Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.展开更多
[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-d...[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.展开更多
Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thick...Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation.In this study,four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting(XGBoost)and random forest(RF)algorithms through simulated annealing(SA)and Bayesian optimization(BO)approaches,namely SA-XGBoost,SA-RF,BO XGBoost and BO-RF models.A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province,China,including seven input indicators:embedding depth,drift span,uniaxial compressive strength of rock,rock mass rating,unit weight of rock,lateral pressure coefficient of roadway and unit consumption of blasting explosive.The performance of the proposed models was evaluated by the coefficient of determination,root mean squared error,mean absolute error and variance accounted for.The results indicated that the SA-XGBoost model performed best.The Shapley additive explanations method revealed that the embedding depth was the most important indicator.Moreover,the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.展开更多
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
Past studies reveal the prevalence of anxiety,coupled with low motivation and disengagement among students in English-medium instruction(EMI)programs.Given the detrimental impact these negative emotions can have on le...Past studies reveal the prevalence of anxiety,coupled with low motivation and disengagement among students in English-medium instruction(EMI)programs.Given the detrimental impact these negative emotions can have on learning outcomes,it is imperative that teachers establish positive emotional rapport with their students.This study explores how experienced and highly rated EMI lecturers at a Chinese university’s overseas campus use communication strategies to build rapport with their students during interactive academic activities.It identifies the strategies used by these lecturers and examines how the strategies facilitate the teaching-learning process.The data,consisting of 10 hours of tutorials and 10 hours of supervisor-student supervision meetings,is analyzed using an adapted Conversation Analysis(CA)approach.The analysis reveals three types of communication strategies(CSs)frequently used by lecturers:back-channeling,codeswitching,and co-creation of messages.By employing these strategies,the lecturers established a strong rapport with the students,which created an encouraging and supportive learning environment.Consequently,this positive atmosphere facilitated students’learning of content knowledge through English.The findings of this study have implications for the training of lecturers who encounter difficulties in establishing rapport with multilingual students in the EMI setting.展开更多
基金National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
文摘Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.
文摘Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.
文摘Objective To investigate the value of polar residual network(PResNet)model for assisting evaluation on rat myocardial infarction(MI)segment in myocardial contrast echocardiography(MCE).Methods Twenty-five male SD rats were randomly divided into MI group(n=15)and sham operation group(n=10).MI models were established in MI group through ligation of the left anterior descending coronary artery using atraumatic suture,while no intervention was given to those in sham operation group after thoracotomy.MCE images of both basal and papillary muscle levels on the short axis section of left ventricles were acquired after 1 week,which were assessed independently by 2 junior and 2 senior ultrasound physicians.The evaluating efficacy of MI segment,the mean interpretation time and the consistency were compared whether under the assistance of PResNet model or not.Results No significant difference of efficacy of evaluation on MI segment was found for senior physicians with or without assistance of PResNet model(both P>0.05).Under the assistance of PResNet model,the efficacy of junior physicians for diagnosing MI segment was significantly improved compared with that without the assistance of PResNet model(both P<0.01),and was comparable to that of senior physicians.Under the assistance of PResNet model,the mean interpretation time of each physician was significantly shorter than that without assistance(all P<0.001),and the consistency between junior physicians and among junior and senior physicians were both moderate(Kappa=0.692,0.542),which became better under the assistance(Kappa=0.763,0.749).Conclusion PResNet could improve the efficacy of junior physicians for evaluation on rat MI segment in MCE images,shorten interpretation time with different aptitudes,also improve the consistency to some extent.
基金received funding from Science and Technology Department of Zhejiang Province(Grant No.LGF21H170001)the Health Commission of Zhejiang Province(Grant No.2023KY759)Hospital Management soft science research project of Kangenbei in Zhejiang province(2023ZHA-KEB104).
文摘Objective:Cardiovascular disease(CVD)remains a significant public health challenge in China.Accurateperception of individual CVD risk is crucial for timely intervention and preventive strategies.This studyaimed to determine the alignment between CVD risk perception levels and objectively calculated CVDrisk levels,then investigate the disparity in physical activity and healthy diet habits among distinct CVDrisk perception categories.Methods:From March to August 2022,a cross-sectional survey was conducted in Zhejiang Province usingconvenience sampling.Participants aged between 20 and 80 years,without prior diagnosis of CVD wereincluded.CVD risk perception was evaluated with the Chinese version of the Attitude and Beliefs aboutCardiovascular Disease Risk Perception Questionnaire,while objective CVD risk was assessed through thePrediction for Atherosclerotic Cardiovascular Disease Risk(China-PAR)model.Participants’demographicinformation,self-reported physical activity,and healthy diet score were also collected.Results:A total of 739 participants were included in the final analysis.Less than a third of participants(29.2%)accurately perceived their CVD risk,while 64.5%over-perceived it and 6.2%under-perceived it.Notably,half of the individuals(50.0%)with high CVD risk under-perceived their actual risk.Compared tothe accurate perception group,individuals aged 60e80 years old(OR=6.569),currently drinking(OR=3.059),and with hypertension(OR=2.352)were more likely to under-perceive their CVD risk.Meanwhile,participants aged 40-<60 years old(OR=2.462)and employed(OR=2.352)were morelikely to over-perceive their risk.The proportion of individuals engaging in physical activity was lowest inthe under-perception group,although the difference among the three groups was not statistically significant(χ^(2)=2.556,P=0.278).In addition,the proportion of individuals practicing healthy diet habitswas also lowest in the under-perception group,and a significant statistical difference was observedamong the three groups(χ^(2)=10.310,P=0.006).Conclusion:Only a small proportion of participants accurately perceived their CVD risk,especially amongthose with high actual CVD risk.Individuals in the under-perceived CVD risk group exhibited the lowestrates of physical activity engagement and healthy diet adherence.Healthcare professionals should prioritize implementing personalized CVD risk communication strategies tailored to specific subgroups toenhance the accuracy of risk perception.
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(22325304,22221003 and 22033007)We acknowledge the Supercomputing Center of USTC,Hefei Advanced Computing Center,Beijing PARATERA Tech Co.,Ltd.,for providing high-performance computing services。
文摘As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.
文摘Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.
文摘[Objective]Urban floods are occurring more frequently because of global climate change and urbanization.Accordingly,urban rainstorm and flood forecasting has become a priority in urban hydrology research.However,two-dimensional hydrodynamic models execute calculations slowly,hindering the rapid simulation and forecasting of urban floods.To overcome this limitation and accelerate the speed and improve the accuracy of urban flood simulations and forecasting,numerical simulations and deep learning were combined to develop a more effective urban flood forecasting method.[Methods]Specifically,a cellular automata model was used to simulate the urban flood process and address the need to include a large number of datasets in the deep learning process.Meanwhile,to shorten the time required for urban flood forecasting,a convolutional neural network model was used to establish the mapping relationship between rainfall and inundation depth.[Results]The results show that the relative error of forecasting the maximum inundation depth in flood-prone locations is less than 10%,and the Nash efficiency coefficient of forecasting inundation depth series in flood-prone locations is greater than 0.75.[Conclusion]The result demonstrated that the proposed method could execute highly accurate simulations and quickly produce forecasts,illustrating its superiority as an urban flood forecasting technique.
基金Project(52204117)supported by the National Natural Science Foundation of ChinaProject(2022JJ40601)supported by the Natural Science Foundation of Hunan Province,China。
文摘Underground excavation can lead to stress redistribution and result in an excavation damaged zone(EDZ),which is an important factor affecting the excavation stability and support design.Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation.In this study,four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting(XGBoost)and random forest(RF)algorithms through simulated annealing(SA)and Bayesian optimization(BO)approaches,namely SA-XGBoost,SA-RF,BO XGBoost and BO-RF models.A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province,China,including seven input indicators:embedding depth,drift span,uniaxial compressive strength of rock,rock mass rating,unit weight of rock,lateral pressure coefficient of roadway and unit consumption of blasting explosive.The performance of the proposed models was evaluated by the coefficient of determination,root mean squared error,mean absolute error and variance accounted for.The results indicated that the SA-XGBoost model performed best.The Shapley additive explanations method revealed that the embedding depth was the most important indicator.Moreover,the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
文摘Past studies reveal the prevalence of anxiety,coupled with low motivation and disengagement among students in English-medium instruction(EMI)programs.Given the detrimental impact these negative emotions can have on learning outcomes,it is imperative that teachers establish positive emotional rapport with their students.This study explores how experienced and highly rated EMI lecturers at a Chinese university’s overseas campus use communication strategies to build rapport with their students during interactive academic activities.It identifies the strategies used by these lecturers and examines how the strategies facilitate the teaching-learning process.The data,consisting of 10 hours of tutorials and 10 hours of supervisor-student supervision meetings,is analyzed using an adapted Conversation Analysis(CA)approach.The analysis reveals three types of communication strategies(CSs)frequently used by lecturers:back-channeling,codeswitching,and co-creation of messages.By employing these strategies,the lecturers established a strong rapport with the students,which created an encouraging and supportive learning environment.Consequently,this positive atmosphere facilitated students’learning of content knowledge through English.The findings of this study have implications for the training of lecturers who encounter difficulties in establishing rapport with multilingual students in the EMI setting.
