BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To inv...BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.展开更多
Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,...Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.展开更多
Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis （PCA） and kernel principal component analysis （KPCA） based on linear dimension reduction and nonlinear ...Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis （PCA） and kernel principal component analysis （KPCA） based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA＇ s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.展开更多
基金Supported by Wuxi Taihu Talent Project,No.WXTTP 2021the General Scientific Research Program of Wuxi Municipal Health Commission,No.M202447.
文摘BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.
基金ACKNOWLEDGEMENTS The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. The research has been partly supported by National Natural Science Foundation of China No. 61272528 and No. 61034005, and the Central University Fund （ID-ZYGX2013J073）.
文摘Many Task Computing(MTC)is a new class of computing paradigm in which the aggregate number of tasks,quantity of computing,and volumes of data may be extremely large.With the advent of Cloud computing and big data era,scheduling and executing large-scale computing tasks efficiently and allocating resources to tasks reasonably are becoming a quite challenging problem.To improve both task execution and resource utilization efficiency,we present a task scheduling algorithm with resource attribute selection,which can select the optimal node to execute a task according to its resource requirements and the fitness between the resource node and the task.Experiment results show that there is significant improvement in execution throughput and resource utilization compared with the other three algorithms and four scheduling frameworks.In the scheduling algorithm comparison,the throughput is 77%higher than Min-Min algorithm and the resource utilization can reach 91%.In the scheduling framework comparison,the throughput(with work-stealing)is at least 30%higher than the other frameworks and the resource utilization reaches 94%.The scheduling algorithm can make a good model for practical MTC applications.
基金Supported by the National Natural Science Foundation of China(No.51075005)the Beijing City Science and Technology Project(No.Z131100005313009)
文摘Due to the correlation and diversity of robotic kinematic dexterity indexes, the principal component analysis （PCA） and kernel principal component analysis （KPCA） based on linear dimension reduction and nonlinear dimension reduction principle could be respectively introduced into comprehensive kinematic dexterity performance evaluation of space 3R robot of different tasks. By comparing different dimension reduction effects, the KPCA method could deal more effectively with the nonlinear relationship among different single kinematic dexterity indexes, and its calculation result is more reasonable for containing more comprehensive information. KPCA＇ s calculation provides scientific basis for optimum order of robotic tasks, and furthermore a new optimization method for robotic task selection is proposed based on various performance indexes.
