This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qu...This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qualitative evaluation methods.The system not only pays attention to students’practical operation and theoretical knowledge mastery but also puts special emphasis on the cultivation of students’innovative abilities.In order to realize a comprehensive and objective evaluation,the assessment and evaluation method of the entropy weight model combining TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)multi-attribute decision analysis and entropy weight theory is adopted,and its validity and practicability are verified through example analysis.This method can not only comprehensively and objectively evaluate students’learning outcomes,but also provide a scientific decision-making basis for curriculum teaching reform.The implementation of this diversified course evaluation system can better reflect the comprehensive ability of students and promote the continuous improvement of teaching quality.展开更多
Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of ...Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of these measurements,reducing reliance on operator expertise.Objective The aim of this study was to develop an innovative software-hardware combined device,featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.Methods A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups.Another 500 patients from another campus were prospectively collected as external validation group.The age,sex,reason for echocardiography and the type of patients were collected.Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment,apical four-chamber view images were labeled manually and utilized for training our deep learning framework.This system combined convolutional neural networks(CNN)with transformers for enhanced image recognition and analysis.Combined with the model that was named QHAutoEF,a‘one-touch’software module was developed and integrated into the echocardiography hardware,providing intuitive,realtime visualization of LVEF measurements.The device’s performance was evaluated with metrics such as the Dice coefficient and Jaccard index,along with computational efficiency indicators.The dice index,intersection over union,size,floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures.Bland-Altman analysis and the receiver operating characteristic(ROC)curve were used for validation of the accuracy of the model.The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.Results Patients from external validation group were older than those from training group((60±14)years vs.(55±16)years,respectively,P<0.001).The gender distribution among three groups were showed no statistical difference(43%vs.42%vs.50%,respectively,P=0.095).Significant differences were showed among patients with different type(all P<0.001)and reason for echocardiography(all P<0.001 except for other reasons).QHAutoEF achieved a high Dice index(0.942 at end-diastole,0.917 at end-systole)with a notably compact model size(10.2 MB)and low computational cost(93.86 G floating point operations(FLOPs)).It exhibited high consistency with expert manual measurements(intraclass correlation coefficient(ICC)=0.90(0.89,0.92),P<0.001)and excellent capability to differentiate patients with LVEF≥60%from those with reduced function,yielding an area under the operation curve(AUC)of 0.92(0.90–0.95).Subgroup analysis showed a good correlation between QHAutoEF results and manual results from experienced experts among patients of different types(R=0.93,0.73,0.92,respectively,P<0.001)and ages(R=0.92,0.94,0.89,0.91,0.81,respectively,P<0.001).Conclusions Our software-hardware device offers an improved solution for the automated measurement of LVEF,demonstrating not only high accuracy and consistency with manual expert measurements but also practical adaptability for clinical settings.This device might potentially support clinicians and augment clinical decision.展开更多
In this paper, an approach to optimize set points is proposed for controlled Organic Rankine Cycle(ORC)systems. Owing to both disturbances and variations of operating point existing in ORC systems, it is necessary to ...In this paper, an approach to optimize set points is proposed for controlled Organic Rankine Cycle(ORC)systems. Owing to both disturbances and variations of operating point existing in ORC systems, it is necessary to optimize the set points for controlled ORC systems so as to improve the energy conversion efficiency. At first, the optimal set points of controlled ORC systems are investigated by revisiting performance analysis and optimization of ORC systems. The expected set points of the evaporating pressure and the temperature at evaporator outlet are then determined by combining genetic algorithm with least squares support vector machine(GA-LSSVM). Simulation results show that the predicted results by GA-LSSVM can be regarded as the optimal set points of controlled ORC systems with varying operating conditions.展开更多
基金2024 Key Project of Teaching Reform Research and Practice in Higher Education in Henan Province“Exploration and Practice of Training Model for Outstanding Students in Basic Mechanics Discipline”(2024SJGLX094)Henan Province“Mechanics+X”Basic Discipline Outstanding Student Training Base2024 Research and Practice Project of Higher Education Teaching Reform in Henan University of Science and Technology“Optimization and Practice of Ability-Oriented Teaching Mode for Computational Mechanics Course:A New Exploration in Cultivating Practical Simulation Engineers”(2024BK074)。
文摘This paper takes the assessment and evaluation of computational mechanics course as the background,and constructs a diversified course evaluation system that is student-centered and integrates both quantitative and qualitative evaluation methods.The system not only pays attention to students’practical operation and theoretical knowledge mastery but also puts special emphasis on the cultivation of students’innovative abilities.In order to realize a comprehensive and objective evaluation,the assessment and evaluation method of the entropy weight model combining TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)multi-attribute decision analysis and entropy weight theory is adopted,and its validity and practicability are verified through example analysis.This method can not only comprehensively and objectively evaluate students’learning outcomes,but also provide a scientific decision-making basis for curriculum teaching reform.The implementation of this diversified course evaluation system can better reflect the comprehensive ability of students and promote the continuous improvement of teaching quality.
文摘Background Accurate measurement of left ventricular ejection fraction(LVEF)is crucial in diagnosing and managing cardiac conditions.Deep learning(DL)models offer potential to improve the consistency and efficiency of these measurements,reducing reliance on operator expertise.Objective The aim of this study was to develop an innovative software-hardware combined device,featuring a novel DL algorithm for the automated quantification of LVEF from 2D echocardiographic images.Methods A dataset of 2,113 patients admitted to the Affiliated Hospital of Qingdao University between January and June 2023 was assembled and split into training and test groups.Another 500 patients from another campus were prospectively collected as external validation group.The age,sex,reason for echocardiography and the type of patients were collected.Following standardized protocol training by senior echocardiographers using domestic ultrasound equipment,apical four-chamber view images were labeled manually and utilized for training our deep learning framework.This system combined convolutional neural networks(CNN)with transformers for enhanced image recognition and analysis.Combined with the model that was named QHAutoEF,a‘one-touch’software module was developed and integrated into the echocardiography hardware,providing intuitive,realtime visualization of LVEF measurements.The device’s performance was evaluated with metrics such as the Dice coefficient and Jaccard index,along with computational efficiency indicators.The dice index,intersection over union,size,floating point operations per second and calculation time were used to compare the performance of our model with alternative deep learning architectures.Bland-Altman analysis and the receiver operating characteristic(ROC)curve were used for validation of the accuracy of the model.The scatter plot was used to evaluate the consistency of the manual and automated results among subgroups.Results Patients from external validation group were older than those from training group((60±14)years vs.(55±16)years,respectively,P<0.001).The gender distribution among three groups were showed no statistical difference(43%vs.42%vs.50%,respectively,P=0.095).Significant differences were showed among patients with different type(all P<0.001)and reason for echocardiography(all P<0.001 except for other reasons).QHAutoEF achieved a high Dice index(0.942 at end-diastole,0.917 at end-systole)with a notably compact model size(10.2 MB)and low computational cost(93.86 G floating point operations(FLOPs)).It exhibited high consistency with expert manual measurements(intraclass correlation coefficient(ICC)=0.90(0.89,0.92),P<0.001)and excellent capability to differentiate patients with LVEF≥60%from those with reduced function,yielding an area under the operation curve(AUC)of 0.92(0.90–0.95).Subgroup analysis showed a good correlation between QHAutoEF results and manual results from experienced experts among patients of different types(R=0.93,0.73,0.92,respectively,P<0.001)and ages(R=0.92,0.94,0.89,0.91,0.81,respectively,P<0.001).Conclusions Our software-hardware device offers an improved solution for the automated measurement of LVEF,demonstrating not only high accuracy and consistency with manual expert measurements but also practical adaptability for clinical settings.This device might potentially support clinicians and augment clinical decision.
基金supported by the National Basic Research Program of China(2011CB710706)the National Natural Science Foundation of China(51210011,61374025)
文摘In this paper, an approach to optimize set points is proposed for controlled Organic Rankine Cycle(ORC)systems. Owing to both disturbances and variations of operating point existing in ORC systems, it is necessary to optimize the set points for controlled ORC systems so as to improve the energy conversion efficiency. At first, the optimal set points of controlled ORC systems are investigated by revisiting performance analysis and optimization of ORC systems. The expected set points of the evaporating pressure and the temperature at evaporator outlet are then determined by combining genetic algorithm with least squares support vector machine(GA-LSSVM). Simulation results show that the predicted results by GA-LSSVM can be regarded as the optimal set points of controlled ORC systems with varying operating conditions.
