Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usa...Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not.However,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method.In order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this paper.At first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is proposed.Then,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model.Finally,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of GCA.The proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.展开更多
Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy ...Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy specimens(1,128 gastritis,122 normal mucosa)from PLA General Hospital.The deep learning algorithm based on DeepLab v3(ResNet-50)architecture was trained and validated using 1,008 WSIs and 100 WSIs,respectively.The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs,with the pathologists’consensus diagnosis as the gold standard.Results The receiver operating characteristic(ROC)curves were generated for chronic superficial gastritis(CSuG),chronic active gastritis(CAcG),and chronic atrophic gastritis(CAtG)in the test set,respectively.The areas under the ROC curves(AUCs)of the algorithm for CSuG,CAcG,and CAtG were 0.882,0.905 and 0.910,respectively.The sensitivity and specificity of the deep learning algorithm for the classification of CSuG,CAcG,and CAtG were 0.790 and 1.000(accuracy 0.880),0.985 and 0.829(accuracy 0.901),0.952 and 0.992(accuracy 0.986),respectively.The overall predicted accuracy for three different types of gastritis was 0.867.By flagging the suspicious regions identified by the algorithm in WSI,a more transparent and interpretable diagnosis can be generated.Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs.By pre-highlighting the different gastritis regions,it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.展开更多
In most priority scheduling algorithms, the num- ber of priority levels is assumed to be unlimited. However, if a task set requires more priority levels than the system can support, several jobs must in practice be as...In most priority scheduling algorithms, the num- ber of priority levels is assumed to be unlimited. However, if a task set requires more priority levels than the system can support, several jobs must in practice be assigned the same priority level. To solve this problem, a novel group priority earliest deadline first （GPEDF） scheduling algorithm is pre- sented. In this algorithm, a schedulability test is given to form a job group, in which the jobs can arbitrarily change their or- der without reducing the schedulability. We consider jobs in the group having the same priority level and use shortest job first （SJF） to schedule the jobs in the group to improve the performance of the system. Compared with earliest deadline first （EDF）, best effort （BE）, and group-EDF （gEDF）, simu- lation results show that the new algorithm exhibits the least switching, the shortest average response time, and the fewest required priority levels. It also has a higher success ratio than both EDF and gEDF.展开更多
IntroductionRosai-Dorfman disease (RDD),also called sinus histiocytosis with massive lymphadenopathy,is a rare,benign,idiopathic histiocytic proliferative disease.It is characterized by painless massive cervical lymph...IntroductionRosai-Dorfman disease (RDD),also called sinus histiocytosis with massive lymphadenopathy,is a rare,benign,idiopathic histiocytic proliferative disease.It is characterized by painless massive cervical lymphadenopathy.Additional symptoms may include fever,leukocytosis,polyclonal hypergammaglobulinemia,and an increased erythrocyte sedimentation rate.Cutaneous RDD (CRDD) is confined to the skin with no lymphadenopathy or other simultaneous phenomena.1-2 Pure CRDD accounts for only 3% of all described cases of RDD.3 We herein report an atypical case of CRDD in a woman with multiple nodules in the thighs and buttocks,which may improve the knowledge of this disease.展开更多
基金the Scientific Research Funding Project of Liaoning Education Department of China under Grant No.JDL2020005,No.LJKZ0485the National Key Research and Development Program of China under Grant No.2018YFA0704605.
文摘Nowadays,data are more and more used for intelligent modeling and prediction,and the comprehensive evaluation of data quality is getting more and more attention as a necessary means to measure whether the data are usable or not.However,the comprehensive evaluation method of data quality mostly contains the subjective factors of the evaluator,so how to comprehensively and objectively evaluate the data has become a bottleneck that needs to be solved in the research of comprehensive evaluation method.In order to evaluate the data more comprehensively,objectively and differentially,a novel comprehensive evaluation method based on particle swarm optimization(PSO)and grey correlation analysis(GCA)is presented in this paper.At first,an improved GCA evaluation model based on the technique for order preference by similarity to an ideal solution(TOPSIS)is proposed.Then,an objective function model of maximum difference of the comprehensive evaluation values is built,and the PSO algorithm is used to optimize the weights of the improved GCA evaluation model based on the objective function model.Finally,the performance of the proposed method is investigated through parameter analysis.A performance comparison of traffic flow data is carried out,and the simulation results show that the maximum average difference between the evaluation results and its mean value(MDR)of the proposed comprehensive evaluation method is 33.24%higher than that of TOPSIS-GCA,and 6.86%higher than that of GCA.The proposed method has better differentiation than other methods,which means that it objectively and comprehensively evaluates the data from both the relevance and differentiation of the data,and the results more effectively reflect the differences in data quality,which will provide more effective data support for intelligent modeling,prediction and other applications.
文摘Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images(WSIs).Methods We retrospectively collected 1,250 gastric biopsy specimens(1,128 gastritis,122 normal mucosa)from PLA General Hospital.The deep learning algorithm based on DeepLab v3(ResNet-50)architecture was trained and validated using 1,008 WSIs and 100 WSIs,respectively.The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs,with the pathologists’consensus diagnosis as the gold standard.Results The receiver operating characteristic(ROC)curves were generated for chronic superficial gastritis(CSuG),chronic active gastritis(CAcG),and chronic atrophic gastritis(CAtG)in the test set,respectively.The areas under the ROC curves(AUCs)of the algorithm for CSuG,CAcG,and CAtG were 0.882,0.905 and 0.910,respectively.The sensitivity and specificity of the deep learning algorithm for the classification of CSuG,CAcG,and CAtG were 0.790 and 1.000(accuracy 0.880),0.985 and 0.829(accuracy 0.901),0.952 and 0.992(accuracy 0.986),respectively.The overall predicted accuracy for three different types of gastritis was 0.867.By flagging the suspicious regions identified by the algorithm in WSI,a more transparent and interpretable diagnosis can be generated.Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs.By pre-highlighting the different gastritis regions,it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.
文摘In most priority scheduling algorithms, the num- ber of priority levels is assumed to be unlimited. However, if a task set requires more priority levels than the system can support, several jobs must in practice be assigned the same priority level. To solve this problem, a novel group priority earliest deadline first （GPEDF） scheduling algorithm is pre- sented. In this algorithm, a schedulability test is given to form a job group, in which the jobs can arbitrarily change their or- der without reducing the schedulability. We consider jobs in the group having the same priority level and use shortest job first （SJF） to schedule the jobs in the group to improve the performance of the system. Compared with earliest deadline first （EDF）, best effort （BE）, and group-EDF （gEDF）, simu- lation results show that the new algorithm exhibits the least switching, the shortest average response time, and the fewest required priority levels. It also has a higher success ratio than both EDF and gEDF.
基金supported by Natural Science Foundation of China(No.81572680 and No.81673043)
文摘IntroductionRosai-Dorfman disease (RDD),also called sinus histiocytosis with massive lymphadenopathy,is a rare,benign,idiopathic histiocytic proliferative disease.It is characterized by painless massive cervical lymphadenopathy.Additional symptoms may include fever,leukocytosis,polyclonal hypergammaglobulinemia,and an increased erythrocyte sedimentation rate.Cutaneous RDD (CRDD) is confined to the skin with no lymphadenopathy or other simultaneous phenomena.1-2 Pure CRDD accounts for only 3% of all described cases of RDD.3 We herein report an atypical case of CRDD in a woman with multiple nodules in the thighs and buttocks,which may improve the knowledge of this disease.
