For solving the computationally intensive problem encountered by the discrete element method(DEM)in simulating large-scale engineering problems,it is essential to establish a numerical model that can effectively simul...For solving the computationally intensive problem encountered by the discrete element method(DEM)in simulating large-scale engineering problems,it is essential to establish a numerical model that can effectively simulate large-scale rocks.In this study,the coarse-graining effect of a linear-Mindlin with bonding model was studied in the unconfined compression strength(UCS)and Brazilian tensile strength(BTS)tests.We found that the main reason for the coarse-graining effect of the BTS tests is that the type I fracture toughness is positively correlated with the size of the particles.Based on the results analysis and fracture mechanics,the coarse-grained(CG)modeling theory was combined with a bonded particle model(BPM)for the first time and a coarse-grained bonded particle model(CG-BPM)was developed,which can be effectively used to model the tensile strength of large-scale rocks with different particle sizes.The excavation damage zone(EDZ)in an underground research laboratory(URL)was selected as an application case,which shows that the coarse-grained bonding model presented in this paper is more accurate and reliable than the traditional one in large-scale rock simulation,at least in the scenario where tensile failure is dominant.展开更多
Background:Pulmonary embolism(PE)is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases(AIIRDs).Accurate prediction and timely intervention pla...Background:Pulmonary embolism(PE)is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases(AIIRDs).Accurate prediction and timely intervention play a pivotal role in enhancing survival rates.However,there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods:In the training cohort,60 AIIRD with PE cases and 180 age-,gender-,and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022.Univariable logistic regression(LR)and least absolute shrinkage and selection operator(LASSO)were used to select the clinical features for further training with machine learning(ML)methods,including random forest(RF),support vector machines(SVM),neural network(NN),logistic regression(LR),gradient boosted decision tree(GBDT),classification and regression trees(CART),and C5.0 models.The performances of these models were subsequently validated using a multicenter validation cohort.Results:In the training cohort,24 and 13 clinical features were selected by univariable LR and LASSO strategies,respectively.The five ML models(RF,SVM,NN,LR,and GBDT)showed promising performances,with an area under the receiver operating characteristic(ROC)curve(AUC)of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort.CART and C5.0 models achieved AUCs of 0.850 and 0.932,respectively,in the training cohort.Using D-dimer as a pre-screening index,the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort.These results markedly outperformed the use of D-dimer levels alone.Conclusion:ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration:Chictr.org.cn:ChiCTR2200059599.展开更多
基金supported by the National Science Foundation for Distinguished Young Scholars of China(Grant No.52025091)the Taishan Scholars Program(NO.tsqn202312192)the Youth Innovation Team of Shandong Higher Education Institutions(2022KJ214)。
文摘For solving the computationally intensive problem encountered by the discrete element method(DEM)in simulating large-scale engineering problems,it is essential to establish a numerical model that can effectively simulate large-scale rocks.In this study,the coarse-graining effect of a linear-Mindlin with bonding model was studied in the unconfined compression strength(UCS)and Brazilian tensile strength(BTS)tests.We found that the main reason for the coarse-graining effect of the BTS tests is that the type I fracture toughness is positively correlated with the size of the particles.Based on the results analysis and fracture mechanics,the coarse-grained(CG)modeling theory was combined with a bonded particle model(BPM)for the first time and a coarse-grained bonded particle model(CG-BPM)was developed,which can be effectively used to model the tensile strength of large-scale rocks with different particle sizes.The excavation damage zone(EDZ)in an underground research laboratory(URL)was selected as an application case,which shows that the coarse-grained bonding model presented in this paper is more accurate and reliable than the traditional one in large-scale rock simulation,at least in the scenario where tensile failure is dominant.
基金supported by grants from National Natural Science Foundation of China(Nos.81703058 and 81974254)Tongji Hospital Clinical Research Flagship Program(No.2019CR206).
文摘Background:Pulmonary embolism(PE)is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases(AIIRDs).Accurate prediction and timely intervention play a pivotal role in enhancing survival rates.However,there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods:In the training cohort,60 AIIRD with PE cases and 180 age-,gender-,and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022.Univariable logistic regression(LR)and least absolute shrinkage and selection operator(LASSO)were used to select the clinical features for further training with machine learning(ML)methods,including random forest(RF),support vector machines(SVM),neural network(NN),logistic regression(LR),gradient boosted decision tree(GBDT),classification and regression trees(CART),and C5.0 models.The performances of these models were subsequently validated using a multicenter validation cohort.Results:In the training cohort,24 and 13 clinical features were selected by univariable LR and LASSO strategies,respectively.The five ML models(RF,SVM,NN,LR,and GBDT)showed promising performances,with an area under the receiver operating characteristic(ROC)curve(AUC)of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort.CART and C5.0 models achieved AUCs of 0.850 and 0.932,respectively,in the training cohort.Using D-dimer as a pre-screening index,the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort.These results markedly outperformed the use of D-dimer levels alone.Conclusion:ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration:Chictr.org.cn:ChiCTR2200059599.
