Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M...Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.展开更多
Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effectiv...Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effective PBL problems.The purpose of this paper is to provide a possible solution to the formulation of PBL problems for computer science courses,which is to reimplement open-source projects(ROSP).A case is demonstrated by showing how ROSP was adopted in a practical intercourse-level PBL course module.This paper contributes to a new PBL problem formulation method for promoting PBL in a practical way for Chinese universities.展开更多
This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods.A model based on the neural network multi-classification algorithmis constructe...This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods.A model based on the neural network multi-classification algorithmis constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy.The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected.The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model,and the accuracy of the model is about 90%.Finally,by incorporating the predicted Gaussian quadrature points into the boundary element analysis,we find that the numerical solution and the analytical solution are in good agreement,which verifies the robustness of the proposed method.展开更多
We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant ...We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant initial data and it represents a mathematical model of the shock tube.The solution of the Riemann problem is the building block for many numerical algorithms in computational fluid dynamics,such as finite-volume or discontinuous Galerkin methods.Therefore,a fast and accurate approximation of the solution of the Riemann problem and construction of the associated numerical fluxes is of crucial importance.The exact solution of the shock tube problem is fully described by the intermediate pressure and mathematically reduces to finding a solution of a nonlinear equation.Prior to delving into the complexities of ML for the Riemann problem,we consider a much simpler formulation,yet very informative,problem of learning roots of quadratic equations based on their coefficients.We compare two approaches:(i)Gaussian process(GP)regressions,and(ii)neural network(NN)approximations.Among these approaches,NNs prove to be more robust and efficient,although GP can be appreciably more accurate(about 30\%).We then use our experience with the quadratic equation to apply the GP and NN approaches to learn the exact solution of the Riemann problem from the initial data or coefficients of the gas equation of state(EOS).We compare GP and NN approximations in both regression and classification analysis and discuss the potential benefits and drawbacks of the ML approach.展开更多
Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induce...Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.展开更多
Deep learning method is of great importance in solving partial differential equations.In this paper,inspired by the failure-informed idea proposed by Gao et al.(SIAM Journal on Scientific Computing 45(4)(2023))and as ...Deep learning method is of great importance in solving partial differential equations.In this paper,inspired by the failure-informed idea proposed by Gao et al.(SIAM Journal on Scientific Computing 45(4)(2023))and as an improvement,a new accurate adaptive deep learning method is proposed for solving elliptic problems,including interface problems and convection-dominated problems.Based on the failure probability framework,the piece-wise uniform distribution is used to approximate the optimal proposal distribution and a kernel-based method is proposed for efficient sampling.Together with the improved Levenberg-Marquardt optimization method,the proposed adaptive deep learning method shows great potential in improving solution accuracy.Numerical tests on the elliptic problems without interface conditions,on one elliptic interface problem,and on the convection-dominated problems demonstrate the effectiveness of the proposed method,as it reduces the relative errors by a factor varying from 102 to 104 for different cases.展开更多
Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static...Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static problems;however,the simultaneous enforcement of I/BCs in dynamic problems remains challenging.To overcome this limitation,a novel approach called decoupled physics-informed neural network(d PINN)is proposed in this work.The d PINN operates based on the core idea of converting a partial differential equation(PDE)to a system of ordinary differential equations(ODEs)via the space-time decoupled formulation.To this end,the latent solution is expressed in the form of a linear combination of approximation functions and coefficients,where approximation functions are admissible and coefficients are unknowns of time that must be solved.Subsequently,the system of ODEs is obtained by implementing the weighted-residual form of the original PDE over the spatial domain.A multi-network structure is used to parameterize the set of coefficient functions,and the loss function of d PINN is established based on minimizing the residuals of the gained ODEs.In this scheme,the decoupled formulation leads to the independent handling of I/BCs.Accordingly,the BCs are automatically satisfied based on suitable selections of admissible functions.Meanwhile,the original ICs are replaced by the Galerkin form of the ICs concerning unknown coefficients,and the neural network(NN)outputs are modified to satisfy the gained ICs.Several benchmark problems involving different types of PDEs and I/BCs are used to demonstrate the superior performance of d PINN compared with regular PINN in terms of solution accuracy and computational cost.展开更多
This paper is concerned with a novel deep learning method for variational problems with essential boundary conditions.To this end,wefirst reformulate the original problem into a minimax problem corresponding to a feas...This paper is concerned with a novel deep learning method for variational problems with essential boundary conditions.To this end,wefirst reformulate the original problem into a minimax problem corresponding to a feasible augmented La-grangian,which can be solved by the augmented Lagrangian method in an infinite dimensional setting.Based on this,by expressing the primal and dual variables with two individual deep neural network functions,we present an augmented Lagrangian deep learning method for which the parameters are trained by the stochastic optimiza-tion method together with a projection technique.Compared to the traditional penalty method,the new method admits two main advantages:i)the choice of the penalty parameter isflexible and robust,and ii)the numerical solution is more accurate in the same magnitude of computational cost.As typical applications,we apply the new ap-proach to solve elliptic problems and(nonlinear)eigenvalue problems with essential boundary conditions,and numerical experiments are presented to show the effective-ness of the new method.展开更多
目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按...目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按照教学方法的不同将2021年2—3月的44名本科见习生设为非演员组,将2021年4—6月的61名本科见习生设为演员组。非演员组采用PBL联合CBL教学法,演员组在非演员组的基础上给予学生主导的角色扮演教学法。比较2组的准备指标、教学效果、教学满意度。结果演员组的表演前花费时间[(7.13±3.58)分]短于非演员组[(8.23±5.38)分]、文献阅读量[(2.66±2.54)分]少于非演员组[(4.05±4.79)分],差异有统计学意义(P<0.05)。演员组的提高临床思维能力[(2.36±0.23)分]、增强团队协作[(2.33±0.32)分]、提高组织能力[(2.35±0.24)分]、提高学生之间亲密度[(2.47±0.14)分]和提高与老师的亲密度[(2.33±0.26)分]得分均高于非演员组[(2.01±0.17)分、(2.01±0.11)分、(2.03±0.14)分、(2.04±0.09)分、(1.94±0.11)分],差异有统计学意义(P<0.05)。演员组的教学满意度评分为(75.0±15.0)分,高于非演员组的(61.0±14.0)分,差异有统计学意义(P<0.05)。结论学生主导的角色扮演教学法在儿科实践教学中的应用效果显著,能够缩短表演前花费时间,减少文献阅读量,提升教学效果,提高教学满意度。展开更多
文摘Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.
基金This research was financially supported by the PBL Research and Application Project of Northeastern University(Grant No.PBL-JX2021yb029,PBL-JX2021yb027).
文摘Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effective PBL problems.The purpose of this paper is to provide a possible solution to the formulation of PBL problems for computer science courses,which is to reimplement open-source projects(ROSP).A case is demonstrated by showing how ROSP was adopted in a practical intercourse-level PBL course module.This paper contributes to a new PBL problem formulation method for promoting PBL in a practical way for Chinese universities.
基金The authors thank the financial support of National Natural Science Foundation of China(NSFC)under Grant(No.11702238).
文摘This paper applies a machine learning technique to find a general and efficient numerical integration scheme for boundary element methods.A model based on the neural network multi-classification algorithmis constructed to find the minimum number of Gaussian quadrature points satisfying the given accuracy.The constructed model is trained by using a large amount of data calculated in the traditional boundary element method and the optimal network architecture is selected.The two-dimensional potential problem of a circular structure is tested and analyzed based on the determined model,and the accuracy of the model is about 90%.Finally,by incorporating the predicted Gaussian quadrature points into the boundary element analysis,we find that the numerical solution and the analytical solution are in good agreement,which verifies the robustness of the proposed method.
基金This work was performed under the auspices of the National Nuclear Security Administration of the US Department of Energy at Los Alamos National Laboratory under Contract No.DE-AC52-06NA25396The authors gratefully acknowledge the support of the US Department of Energy National Nuclear Security Administration Advanced Simulation and Computing Program.The Los Alamos unlimited release number is LA-UR-19-32257.
文摘We present our results by using a machine learning(ML)approach for the solution of the Riemann problem for the Euler equations of fluid dynamics.The Riemann problem is an initial-value problem with piecewise-constant initial data and it represents a mathematical model of the shock tube.The solution of the Riemann problem is the building block for many numerical algorithms in computational fluid dynamics,such as finite-volume or discontinuous Galerkin methods.Therefore,a fast and accurate approximation of the solution of the Riemann problem and construction of the associated numerical fluxes is of crucial importance.The exact solution of the shock tube problem is fully described by the intermediate pressure and mathematically reduces to finding a solution of a nonlinear equation.Prior to delving into the complexities of ML for the Riemann problem,we consider a much simpler formulation,yet very informative,problem of learning roots of quadratic equations based on their coefficients.We compare two approaches:(i)Gaussian process(GP)regressions,and(ii)neural network(NN)approximations.Among these approaches,NNs prove to be more robust and efficient,although GP can be appreciably more accurate(about 30\%).We then use our experience with the quadratic equation to apply the GP and NN approaches to learn the exact solution of the Riemann problem from the initial data or coefficients of the gas equation of state(EOS).We compare GP and NN approximations in both regression and classification analysis and discuss the potential benefits and drawbacks of the ML approach.
基金National Natural Science Foundation of China (12002075)National Key Research and Development Project (2021YFB3300601)Natural Science Foundation of Liaoning Province in China (2021-MS-128).
文摘Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.
基金supported by the Natural Science Foundation of Hunan Province(Grant No.2023JJ30648)the Natural Science Foundation of Changsha(Grant No.kq2208252)+3 种基金supported by the Excellent Youth Foundation of Education Bureau of Hunan Province(Grant No.21B0301)the Natural Science Foundation of Hunan Province(Grant No.2022JJ40461)supported by the Natural Science Foundation of China(Grant No.12101615)the Natural Science Foundation of Hunan Province(Grant No.2022JJ40567).
文摘Deep learning method is of great importance in solving partial differential equations.In this paper,inspired by the failure-informed idea proposed by Gao et al.(SIAM Journal on Scientific Computing 45(4)(2023))and as an improvement,a new accurate adaptive deep learning method is proposed for solving elliptic problems,including interface problems and convection-dominated problems.Based on the failure probability framework,the piece-wise uniform distribution is used to approximate the optimal proposal distribution and a kernel-based method is proposed for efficient sampling.Together with the improved Levenberg-Marquardt optimization method,the proposed adaptive deep learning method shows great potential in improving solution accuracy.Numerical tests on the elliptic problems without interface conditions,on one elliptic interface problem,and on the convection-dominated problems demonstrate the effectiveness of the proposed method,as it reduces the relative errors by a factor varying from 102 to 104 for different cases.
基金Project supported by the Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Science and ICT(No.RS-2024-00337001)。
文摘Enforcing initial and boundary conditions(I/BCs)poses challenges in physics-informed neural networks(PINNs).Several PINN studies have gained significant achievements in developing techniques for imposing BCs in static problems;however,the simultaneous enforcement of I/BCs in dynamic problems remains challenging.To overcome this limitation,a novel approach called decoupled physics-informed neural network(d PINN)is proposed in this work.The d PINN operates based on the core idea of converting a partial differential equation(PDE)to a system of ordinary differential equations(ODEs)via the space-time decoupled formulation.To this end,the latent solution is expressed in the form of a linear combination of approximation functions and coefficients,where approximation functions are admissible and coefficients are unknowns of time that must be solved.Subsequently,the system of ODEs is obtained by implementing the weighted-residual form of the original PDE over the spatial domain.A multi-network structure is used to parameterize the set of coefficient functions,and the loss function of d PINN is established based on minimizing the residuals of the gained ODEs.In this scheme,the decoupled formulation leads to the independent handling of I/BCs.Accordingly,the BCs are automatically satisfied based on suitable selections of admissible functions.Meanwhile,the original ICs are replaced by the Galerkin form of the ICs concerning unknown coefficients,and the neural network(NN)outputs are modified to satisfy the gained ICs.Several benchmark problems involving different types of PDEs and I/BCs are used to demonstrate the superior performance of d PINN compared with regular PINN in terms of solution accuracy and computational cost.
基金supported by the National Key Research and Development Project(Grant No.2020YFA0709800)NSFC(Grant No.12071289)+4 种基金Shanghai Municipal Science and Technology Major Project(2021SHZDZX0102)supported by the National Key R&D Program of China(2020YFA0712000)NSFC(under grant numbers 11822111,11688101)the science challenge project(No.TZ2018001)youth innovation promotion association(CAS).
文摘This paper is concerned with a novel deep learning method for variational problems with essential boundary conditions.To this end,wefirst reformulate the original problem into a minimax problem corresponding to a feasible augmented La-grangian,which can be solved by the augmented Lagrangian method in an infinite dimensional setting.Based on this,by expressing the primal and dual variables with two individual deep neural network functions,we present an augmented Lagrangian deep learning method for which the parameters are trained by the stochastic optimiza-tion method together with a projection technique.Compared to the traditional penalty method,the new method admits two main advantages:i)the choice of the penalty parameter isflexible and robust,and ii)the numerical solution is more accurate in the same magnitude of computational cost.As typical applications,we apply the new ap-proach to solve elliptic problems and(nonlinear)eigenvalue problems with essential boundary conditions,and numerical experiments are presented to show the effective-ness of the new method.
文摘目的探讨学生主导的角色扮演联合以问题为基础的教学法(problem based learning,PBL)/案例教学法(case based learning,CBL)在儿科临床教学中的效果。方法选取2021年2—6月重庆医科大学儿科学院本科见习生中的105名学生作为研究对象,按照教学方法的不同将2021年2—3月的44名本科见习生设为非演员组,将2021年4—6月的61名本科见习生设为演员组。非演员组采用PBL联合CBL教学法,演员组在非演员组的基础上给予学生主导的角色扮演教学法。比较2组的准备指标、教学效果、教学满意度。结果演员组的表演前花费时间[(7.13±3.58)分]短于非演员组[(8.23±5.38)分]、文献阅读量[(2.66±2.54)分]少于非演员组[(4.05±4.79)分],差异有统计学意义(P<0.05)。演员组的提高临床思维能力[(2.36±0.23)分]、增强团队协作[(2.33±0.32)分]、提高组织能力[(2.35±0.24)分]、提高学生之间亲密度[(2.47±0.14)分]和提高与老师的亲密度[(2.33±0.26)分]得分均高于非演员组[(2.01±0.17)分、(2.01±0.11)分、(2.03±0.14)分、(2.04±0.09)分、(1.94±0.11)分],差异有统计学意义(P<0.05)。演员组的教学满意度评分为(75.0±15.0)分,高于非演员组的(61.0±14.0)分,差异有统计学意义(P<0.05)。结论学生主导的角色扮演教学法在儿科实践教学中的应用效果显著,能够缩短表演前花费时间,减少文献阅读量,提升教学效果,提高教学满意度。
