AIM: To investigate the learning curve of transumbilical suture-suspension single-incision laparoscopic cholecystectomy (SILC). METHODS: The clinical data of 180 consecutive transumbilical suture-suspension SILCs perf...AIM: To investigate the learning curve of transumbilical suture-suspension single-incision laparoscopic cholecystectomy (SILC). METHODS: The clinical data of 180 consecutive transumbilical suture-suspension SILCs performed by a team in our department during the period from August 2009 to March 2011 were retrospectively analyzed. Patients were divided into nine groups according to operation dates, and each group included 20 patients operated on consecutively in each time period. The surgical outcome was assessed by comparing operation time, blood loss during operation, and complications between groups in order to evaluate the improvement in technique.RESULTS: A total of 180 SILCs were successfully performed by five doctors. The average operation time was 53.58 ± 30.08 min (range: 20.00-160.00 min) and average blood loss was 12.70 ± 11.60 mL (range: 0.00-100.00 mL). None of the patients were converted to laparotomy or multi-port laparoscopic cholecystectomy. There were no major complications such as hemorrhage or biliary system injury during surgery. Eight postoperative complications occurred mainly in the first three groups (n = 6), and included ecchymosis around the umbilical incision (n = 7) which resolved without special treatment, and one case of delayed bile leakage in group 8, which was treated by ultrasound-guided puncture and drainage. There were no differences in intraoperative blood loss, postoperative complications and length of postoperative hospital stay among the groups. Bonferroni's test showed that the operation time in group 1 was significantly longer than that in the other groups (F = 7.257, P = 0.000). The majority of patients in each group were discharged within 2 d, with an average postoperative hospital stay of 1.9 ± 1.2 d. CONCLUSION: Following scientific principles and standard procedures, a team experienced in multi-port laparoscopic cholecystectomy can master the technique of SILC after 20 cases.展开更多
Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspensi...Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for highspeed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy(GWN-strategy) for immunity to track irregularities and an edge sample training strategy(EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1 DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally,the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.展开更多
To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized r...To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.展开更多
Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology.However,almost all existing convolutional neural network-based decoding approaches rely on supervi...Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology.However,almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth,and may not be well generalized to unseen datasets,which were collected under different experimental conditions,applying with the same coded material.In this study,we propose an improved model based on CyCADA,named as Detail constraint Cycle Domain Adaptive Model(DCDA).DCDA implements the clasification of unseen datasets through domain adaptation,adapts representations at the encode level with decoder-share,and enforces coding features while leveraging a feat loss.To improve detailed structural constraints,DCDA takes downsample connection and skips connection.Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets.Compared with other models,extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization.The model proposed by the research achieves a classification with an accuracy of 100%when applied in datasets,in which the spectrum in the source domain is far less than the target domain.展开更多
Depression is a chronic,recurring and potentially life-threatening illness that affects up to 20%of the population across the world.Despite its prevalence and considerable impact on human,little is known about its pat...Depression is a chronic,recurring and potentially life-threatening illness that affects up to 20%of the population across the world.Despite its prevalence and considerable impact on human,little is known about its pathogenesis.One of the major reasons is the restricted availability of validated animal models due to the absence of consensus on the pathology and etiology of depression.Besides,some core symptoms such as depressed mood,feeling of worthlessness,and recurring thoughts of death or suicide,are impossible to be modeled on laboratory animals.Currently,the criteria for identifying animal models of depression rely on either of the 2 principles：actions of known antidepressants and responses to stress.This review mainly focuses on the most widely used animal models of depression,including learned helplessness,chronic mild stress,and social defeat paradigms.Also,the behavioral tests for screening antidepressants,such as forced swimming test and tail suspension test,are also discussed.The advantages and major drawbacks of each model are evaluated.In prospective,new techniques that will be beneficial for developing novel animal models or detecting depression are discussed.展开更多
基金Supported by Science and Technology Projects of Haizhu District of Guangzhou, China, No. 2012-cg-26
文摘AIM: To investigate the learning curve of transumbilical suture-suspension single-incision laparoscopic cholecystectomy (SILC). METHODS: The clinical data of 180 consecutive transumbilical suture-suspension SILCs performed by a team in our department during the period from August 2009 to March 2011 were retrospectively analyzed. Patients were divided into nine groups according to operation dates, and each group included 20 patients operated on consecutively in each time period. The surgical outcome was assessed by comparing operation time, blood loss during operation, and complications between groups in order to evaluate the improvement in technique.RESULTS: A total of 180 SILCs were successfully performed by five doctors. The average operation time was 53.58 ± 30.08 min (range: 20.00-160.00 min) and average blood loss was 12.70 ± 11.60 mL (range: 0.00-100.00 mL). None of the patients were converted to laparotomy or multi-port laparoscopic cholecystectomy. There were no major complications such as hemorrhage or biliary system injury during surgery. Eight postoperative complications occurred mainly in the first three groups (n = 6), and included ecchymosis around the umbilical incision (n = 7) which resolved without special treatment, and one case of delayed bile leakage in group 8, which was treated by ultrasound-guided puncture and drainage. There were no differences in intraoperative blood loss, postoperative complications and length of postoperative hospital stay among the groups. Bonferroni's test showed that the operation time in group 1 was significantly longer than that in the other groups (F = 7.257, P = 0.000). The majority of patients in each group were discharged within 2 d, with an average postoperative hospital stay of 1.9 ± 1.2 d. CONCLUSION: Following scientific principles and standard procedures, a team experienced in multi-port laparoscopic cholecystectomy can master the technique of SILC after 20 cases.
基金supported by the National Nature Science Foundation of China(No.71871188)the Fundamental Research Funds for the Central Universities(No.2682021CX051)supported by China Scholarship Council(No.201707000113)。
文摘Fault detection and isolation of high-speed train suspension systems is of critical importance to guarantee train running safety. Firstly, the existing methods concerning fault detection or isolation of train suspension systems are briefly reviewed and divided into two categories, i.e., model-based and data-driven approaches. The advantages and disadvantages of these two categories of approaches are briefly summarized. Secondly, a 1D convolution network-based fault diagnostic method for highspeed train suspension systems is designed. To improve the robustness of the method, a Gaussian white noise strategy(GWN-strategy) for immunity to track irregularities and an edge sample training strategy(EST-strategy) for immunity to wheel wear are proposed. The whole network is called GWN-EST-1 DCNN method. Thirdly, to show the performance of this method, a multibody dynamics simulation model of a high-speed train is built to generate the lateral acceleration of a bogie frame corresponding to different track irregularities, wheel profiles, and secondary suspension faults. The simulated signals are then inputted into the diagnostic network, and the results show the correctness and superiority of the GWN-EST-1DCNN method. Finally,the 1DCNN method is further validated using tracking data of a CRH3 train running on a high-speed railway line.
基金The National Key Research and Development Plan(No.2019YFB2006402)Talent Introduction Fund Project of Hubei Polytechnic University(No.17xjz01R)Key Scientific Research Project of Hubei Polytechnic University(No.22xjz02A)。
文摘To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.
基金The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China under Grant 81871395.
文摘Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology.However,almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth,and may not be well generalized to unseen datasets,which were collected under different experimental conditions,applying with the same coded material.In this study,we propose an improved model based on CyCADA,named as Detail constraint Cycle Domain Adaptive Model(DCDA).DCDA implements the clasification of unseen datasets through domain adaptation,adapts representations at the encode level with decoder-share,and enforces coding features while leveraging a feat loss.To improve detailed structural constraints,DCDA takes downsample connection and skips connection.Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets.Compared with other models,extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization.The model proposed by the research achieves a classification with an accuracy of 100%when applied in datasets,in which the spectrum in the source domain is far less than the target domain.
文摘Depression is a chronic,recurring and potentially life-threatening illness that affects up to 20%of the population across the world.Despite its prevalence and considerable impact on human,little is known about its pathogenesis.One of the major reasons is the restricted availability of validated animal models due to the absence of consensus on the pathology and etiology of depression.Besides,some core symptoms such as depressed mood,feeling of worthlessness,and recurring thoughts of death or suicide,are impossible to be modeled on laboratory animals.Currently,the criteria for identifying animal models of depression rely on either of the 2 principles：actions of known antidepressants and responses to stress.This review mainly focuses on the most widely used animal models of depression,including learned helplessness,chronic mild stress,and social defeat paradigms.Also,the behavioral tests for screening antidepressants,such as forced swimming test and tail suspension test,are also discussed.The advantages and major drawbacks of each model are evaluated.In prospective,new techniques that will be beneficial for developing novel animal models or detecting depression are discussed.
