In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated wit...In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.展开更多
Additive Schwarz algorithms for solving the discrete problems of twrvside obstacle problems are proposed. The monotone convergence of the algorithms is established for M-matrix and the h-independent convergence rate i...Additive Schwarz algorithms for solving the discrete problems of twrvside obstacle problems are proposed. The monotone convergence of the algorithms is established for M-matrix and the h-independent convergence rate is proved for S-matrix. The so-called finite step convergence for coincident components is discussed for nondegenerate discreted problems.展开更多
BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machin...BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.展开更多
BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it d...BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.展开更多
BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their com...BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations.In contrast to previous studies with small sample sizes and limited model interpretability,we developed a transparent eXtreme Gradient Boosting(XGBoost)-based model supported by multi-center data,using patients'basic information and clinical indicators to forecast the occurrence of anastomotic leakage(AL)after rectal cancer resection surgery.The model demonstrated robust predictive performance and identified clinically relevant thresholds,which may assist physicians in optimizing perioperative management.AIM To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions.METHODS Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery,Xijing Hospital of Digestive Diseases,Air Force Medical University,and Shaanxi Provincial People's Hospital,were retrospectively collected from January 2011 to December 2021.Ten ML models were integrated to analyze the data and develop the predictive models.Receiver operating characteristic(ROC)curves,calibration curve,decision curve analysis,accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and F1 score were used to evaluate model performance.We employed the SHapley Additive exPlanations(SHAP)algorithm to explain the feature importance of the optimal model.RESULTS A total of ten features were integrated to construct the predictive model and identify the optimal model.XGBoost was considered the best-performing model with an area under the ROC curve(AUC)of 0.984(95%confidence interval:0.972-0.996)in the test set(accuracy:0.925;sensitivity:0.92;specificity:0.927).Furthermore,the model achieved an AUC of 0.703 in external validation.The interpretable SHAP algorithm revealed that the serum calcium ion level was the crucial factor influencing the predictions of the model.CONCLUSION A superior predictive model,leveraging clinical data,has been crafted by employing the most effective XGBoost from a selection of ten algorithms.This model,by predicting the occurrence of AL in patients after rectal cancer resection,has identified the significant role of serum calcium ion levels,providing guidance for clinical practice.The integration of SHAP provides a clear interpretation of the model's predictions.展开更多
The convergence rate of a generalized additive Schwarz algorithm for solving boundary value problems of elliptic partial differential equations is studied. A quantitative analysis of the convergence rate is given for ...The convergence rate of a generalized additive Schwarz algorithm for solving boundary value problems of elliptic partial differential equations is studied. A quantitative analysis of the convergence rate is given for the model Dirichlet problem. It will be shown that a greater acceleration of the algorithm can be obtained by choosing the parameter suitably. Some numerical tests are also presented in this paper.展开更多
The industry-standard constrained pressure residual(CPR)algorithm is often able to effectively improve the robustness behavior and the convergence speed of linear iterations for isothermal reservoir simulation.In this...The industry-standard constrained pressure residual(CPR)algorithm is often able to effectively improve the robustness behavior and the convergence speed of linear iterations for isothermal reservoir simulation.In this paper,we present and study an improved extension of CPR to the constrained pressure-temperature residual(CPTR)version for non-isothermal reservoir problems in heterogeneous porous media.In the proposed preconditioner,the corresponding approximations for the inverse of matrices are computed under a domain decomposition framework by using the restricted additive Schwarz(RAS)algorithm,to equally deal with the coupled thermalpressure-saturation reservoir system and highly exploit the parallelism of supercomputer platforms.Moreover,we introduce and develop a family of multilevel CPTR preconditioners with suitable coarse grid corrections,to further improve the applicability of this two-stage preconditioner for large-scale computation.Numerical results for strong heterogeneous flow problems show that the new approach can dramatically improve the convergence of linear iterations,and demonstrate the superiority of CPTR over the commonly used RAS preconditioners.The parallel scalability of the non-isothermal reservoir simulator is also studied versus a supercomputer with tens of thousands of processors.展开更多
The soil-rock mixture(SRM) is highly heterogeneous. Before carrying out numerical analysis,a structure model should be generated. A reliable way to obtain such structure is by generating random aggregate structure bas...The soil-rock mixture(SRM) is highly heterogeneous. Before carrying out numerical analysis,a structure model should be generated. A reliable way to obtain such structure is by generating random aggregate structure based on random sequential addition(RSA). The classical RSA is neither efficient nor robust since valid positions to place new inclusions are formulated by trial, which involves repetitive overlapping tests. In this paper, the algorithm of Entrance block between block A and B(EAB)is synergized with background mesh to redesign RSA so that permissible positions to place new inclusions can be predicted,resulting in dramatic improvement in efficiency and robustness.展开更多
基金supported by Zhejiang Provincial Key Laboratory of Communication Networks and Applications and National Natural Science Foundation of China under Grant No.60872020
文摘In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.
文摘Additive Schwarz algorithms for solving the discrete problems of twrvside obstacle problems are proposed. The monotone convergence of the algorithms is established for M-matrix and the h-independent convergence rate is proved for S-matrix. The so-called finite step convergence for coincident components is discussed for nondegenerate discreted problems.
文摘BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.
文摘BACKGROUND Despite the promising prospects of using artificial intelligence and machine learning(ML)for disease classification and prediction purposes,the complexity and lack of explainability of this method make it difficult to apply the constructed models in clinical practice.We developed and validated an interpretable ML model based on magnetic resonance imaging(MRI)radiomics and clinical features for the preoperative prediction of the pathological grades of hepatocellular carcinomas(HCCs).This model will help clinicians better understand the situation and develop personalized treatment plans.AIM To develop and validate an interpretable ML model for preoperative pathological grade prediction in HCC patients via a combination of multisequence MRI radiomics and clinical features.METHODS MRI and clinical data derived from 125 patients with HCCs confirmed by postoperative pathological examinations were retrospectively analyzed.The patients were randomly split into training and validation groups(7:3 ratio).Univariate and multivariate logistic regression analyses were performed to identify independent clinical predictors.The tumor lesions observed on axial fatsuppressed T2-weighted imaging(FS-T2WI),arterial phase(AP),and portal venous phase(PVP)images were delineated in a slice-by-slice manner using 3D-slicer to generate volumetric regions of interest,and radiomic features were extracted.Interclass correlation coefficients were calculated,and least absolute selection and shrinkage operator regression were conducted for feature selection purposes.Six predictive models were subsequently developed for pathological grade prediction:FS-T2WI,AP,PVP,integrated radiomics,clinical,and combined radiomics-clinical(RC)models.The effectiveness of these models was assessed by calculating their area under the receiver operating characteristic curve(AUC)values.The clinical applicability of the models was evaluated via decision curve analysis.Finally,the contributions of the different features contained in the model with optimal performance were interpreted via a SHapley Additive exPlanations analysis.RESULTS Among the 125 patients,87 were assigned to the training group,and 38 were assigned to the validation group.The maximum tumor diameter,hepatitis B virus status,and monocyte count were identified as independent predictors of pathological grade.Twelve optimal radiomic features were ultimately selected.The AUC values obtained for the FS-T2WI model,AP model,PVP model,radiomics model,clinical model,and combined RC model in the training group were 0.761[95%confidence interval(CI):0.562-0.857],0.870(95%CI:0.714-0.918),0.868(95%CI:0.714-0.959),0.917(95%CI:0.857-0.959),0.869(95%CI:0.643-0.973),and 0.941(95%CI:0.857-0.945),respectively;in the validation group,the AUC values were 0.724(95%CI:0.625-0.833),0.802(95%CI:0.686-1.000),0.797(95%CI:0.688-1.000),0.901(95%CI:0.833-0.906),0.865(95%CI:0.594-1.000),and 0.932(95%CI:0.812-1.000),respectively.The combined RC model demonstrated the best performance.Additionally,the decision curve analysis revealed that the combined RC model had satisfactory prediction efficiency,and the SHapley Additive exPlanations value analysis revealed that the“FS-T2WI-wavelet-HLL_gldm_Large Dependence High Gray Level Emphasis”feature contributed the most to the model,exhibiting a positive effect.CONCLUSION An interpretable ML model based on MRI radiomics provides a noninvasive tool for predicting the pathological grade of HCCs,which will help clinicians develop personalized treatment plans.
基金Supported by National Natural Science Foundation of China,No.82172781Shaanxi Health Scientific Research Innovation Team Project,No.2024TD-06.
文摘BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations.In contrast to previous studies with small sample sizes and limited model interpretability,we developed a transparent eXtreme Gradient Boosting(XGBoost)-based model supported by multi-center data,using patients'basic information and clinical indicators to forecast the occurrence of anastomotic leakage(AL)after rectal cancer resection surgery.The model demonstrated robust predictive performance and identified clinically relevant thresholds,which may assist physicians in optimizing perioperative management.AIM To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions.METHODS Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery,Xijing Hospital of Digestive Diseases,Air Force Medical University,and Shaanxi Provincial People's Hospital,were retrospectively collected from January 2011 to December 2021.Ten ML models were integrated to analyze the data and develop the predictive models.Receiver operating characteristic(ROC)curves,calibration curve,decision curve analysis,accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and F1 score were used to evaluate model performance.We employed the SHapley Additive exPlanations(SHAP)algorithm to explain the feature importance of the optimal model.RESULTS A total of ten features were integrated to construct the predictive model and identify the optimal model.XGBoost was considered the best-performing model with an area under the ROC curve(AUC)of 0.984(95%confidence interval:0.972-0.996)in the test set(accuracy:0.925;sensitivity:0.92;specificity:0.927).Furthermore,the model achieved an AUC of 0.703 in external validation.The interpretable SHAP algorithm revealed that the serum calcium ion level was the crucial factor influencing the predictions of the model.CONCLUSION A superior predictive model,leveraging clinical data,has been crafted by employing the most effective XGBoost from a selection of ten algorithms.This model,by predicting the occurrence of AL in patients after rectal cancer resection,has identified the significant role of serum calcium ion levels,providing guidance for clinical practice.The integration of SHAP provides a clear interpretation of the model's predictions.
基金This work was supported by 973 project granted 2004CB719402 and national nature science foundation granted 10371035. We would like to thank the anonymous referees for their invaluable comments and suggestions, We would like also to thank the State Key Laboratory of Advanced Design and Manufacture for Vehicle Body of Hunan University since the work was done while the authors were working there.
文摘The convergence rate of a generalized additive Schwarz algorithm for solving boundary value problems of elliptic partial differential equations is studied. A quantitative analysis of the convergence rate is given for the model Dirichlet problem. It will be shown that a greater acceleration of the algorithm can be obtained by choosing the parameter suitably. Some numerical tests are also presented in this paper.
基金supported by the National Natural Science Foundation of China(No.12131002 and No.11971006)Shenzhen Science and Technology Program(No.JCYJ20210324130801003)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2022A1515010147)Changsha science and technology bureau(No.kh2301001)The fourth author also greatly thanks for the support from King Abdullah University of Science and Technology(KAUST)through the grants BAS/1/1351-01 and URF/1/4074-01.
文摘The industry-standard constrained pressure residual(CPR)algorithm is often able to effectively improve the robustness behavior and the convergence speed of linear iterations for isothermal reservoir simulation.In this paper,we present and study an improved extension of CPR to the constrained pressure-temperature residual(CPTR)version for non-isothermal reservoir problems in heterogeneous porous media.In the proposed preconditioner,the corresponding approximations for the inverse of matrices are computed under a domain decomposition framework by using the restricted additive Schwarz(RAS)algorithm,to equally deal with the coupled thermalpressure-saturation reservoir system and highly exploit the parallelism of supercomputer platforms.Moreover,we introduce and develop a family of multilevel CPTR preconditioners with suitable coarse grid corrections,to further improve the applicability of this two-stage preconditioner for large-scale computation.Numerical results for strong heterogeneous flow problems show that the new approach can dramatically improve the convergence of linear iterations,and demonstrate the superiority of CPTR over the commonly used RAS preconditioners.The parallel scalability of the non-isothermal reservoir simulator is also studied versus a supercomputer with tens of thousands of processors.
基金supported by the National Basic Research Program of China(973 Program)(Grant No.2014CB047100)the National Natural Science Foundation of China(Grant Nos.11572009,51538001 and 51609240)
文摘The soil-rock mixture(SRM) is highly heterogeneous. Before carrying out numerical analysis,a structure model should be generated. A reliable way to obtain such structure is by generating random aggregate structure based on random sequential addition(RSA). The classical RSA is neither efficient nor robust since valid positions to place new inclusions are formulated by trial, which involves repetitive overlapping tests. In this paper, the algorithm of Entrance block between block A and B(EAB)is synergized with background mesh to redesign RSA so that permissible positions to place new inclusions can be predicted,resulting in dramatic improvement in efficiency and robustness.
