Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and...Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.展开更多
BACKGROUND The success of liver resection relies on the ability of the remnant liver to regenerate.Most of the knowledge regarding the pathophysiological basis of liver regeneration comes from rodent studies,and data ...BACKGROUND The success of liver resection relies on the ability of the remnant liver to regenerate.Most of the knowledge regarding the pathophysiological basis of liver regeneration comes from rodent studies,and data on humans are scarce.Additionally,there is limited knowledge about the preoperative factors that influence postoperative regeneration.AIM To quantify postoperative remnant liver volume by the latest volumetric software and investigate perioperative factors that affect posthepatectomy liver regenera-tion.METHODS A total of 268 patients who received partial hepatectomy were enrolled.Patients were grouped into right hepatectomy/trisegmentectomy(RH/Tri),left hepa-tectomy(LH),segmentectomy(Seg),and subsegmentectomy/nonanatomical hepatectomy(Sub/Non)groups.The regeneration index(RI)and late rege-neration rate were defined as(postoperative liver volume)/[total functional liver volume(TFLV)]×100 and(RI at 6-months-RI at 3-months)/RI at 6-months,respectively.The lower 25th percentile of RI and the higher 25th percentile of late regeneration rate in each group were defined as“low regeneration”and“delayed regeneration”.“Restoration to the original size”was defined as regeneration of the liver volume by more than 90%of the TFLV at 12 months postsurgery.RESULTS The numbers of patients in the RH/Tri,LH,Seg,and Sub/Non groups were 41,53,99 and 75,respectively.The RI plateaued at 3 months in the LH,Seg,and Sub/Non groups,whereas the RI increased until 12 months in the RH/Tri group.According to our multivariate analysis,the preoperative albumin-bilirubin(ALBI)score was an independent factor for low regeneration at 3 months[odds ratio(OR)95%CI=2.80(1.17-6.69),P=0.02;per 1.0 up]and 12 months[OR=2.27(1.01-5.09),P=0.04;per 1.0 up].Multivariate analysis revealed that only liver resection percentage[OR=1.03(1.00-1.05),P=0.04]was associated with delayed regeneration.Furthermore,multivariate analysis demonstrated that the preoperative ALBI score[OR=2.63(1.00-1.05),P=0.02;per 1.0 up]and liver resection percentage[OR=1.02(1.00-1.05),P=0.04;per 1.0 up]were found to be independent risk factors associated with volume restoration failure.CONCLUSION Liver regeneration posthepatectomy was determined by the resection percentage and preoperative ALBI score.This knowledge helps surgeons decide the timing and type of rehepatectomy for recurrent cases.展开更多
The in situ(TiC+TiB)/TA15 composites with different volume percentages of reinforcement(10%,15%,20%and 25%)were prepared by water-cooled copper crucible vacuum suspension melting technology.The structures and composit...The in situ(TiC+TiB)/TA15 composites with different volume percentages of reinforcement(10%,15%,20%and 25%)were prepared by water-cooled copper crucible vacuum suspension melting technology.The structures and compositions of the TA15 alloy and its composites were analyzed by XRD and EDS,and their electrochemical corrosion behaviors in the 3.5%NaCl solution were studied.Corrosion wear testing was conducted using a reciprocating ball-on-disc wear tester under a 10 N load.Results show that the in situ fibrous TiB phase and the granular TiC phase are uniformly distributed on the composite matrix.The microhardness can reach up to 531 HV as 25vol.%TiC+TiB reinforcement is added.Compared with the TA15 alloy,the volume wear rate decreases from(2.21±0.07)×10^(-4)to(1.75±0.07)×10^(-4)mm^(3)·N^(-1)·m^(-1)by adding 15vol.%TiC+TiB reinforcement,and the wear mechanism is adhesive wear.When the volume percentage of the reinforcement phase reaches 25%,the volume wear rate increases from(1.75±0.07)×10^(-4)to(2.41±0.07)×10^(-4)mm^(3)·N^(-1)·m^(-1),and the wear mechanism changes into abrasive wear.The volume loss resulted by the interaction between corrosion and wear accounts for more than 27%of the total wear volume.The volume loss due to wear-induced corrosion changes from 1.94%to 4.06%with different additions of reinforcement.The volume loss caused by corrosion-induced wear initially increases from 24.08%to 26.90%as the reinforcement increases from 0 to 15%due to the increase of corrosion potential,and then decreases from 26.90%to 25.68%as the reinforcement increases from 15%to 25%due to the peeling of TiC phase.展开更多
基金National Natural Science Foundation of China(Grant number:11904327,61905223,and 62073299)Training Plan of Young Backbone Teachers in Universities of Henan Province(2023GGJS087)+1 种基金Henan Provincial Science and Technology Research Project(222102110279,222102210085,and 242102210157)Project of Central Plains Science and Technology Innovation Leading Talents(224200510026).
文摘Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.
文摘BACKGROUND The success of liver resection relies on the ability of the remnant liver to regenerate.Most of the knowledge regarding the pathophysiological basis of liver regeneration comes from rodent studies,and data on humans are scarce.Additionally,there is limited knowledge about the preoperative factors that influence postoperative regeneration.AIM To quantify postoperative remnant liver volume by the latest volumetric software and investigate perioperative factors that affect posthepatectomy liver regenera-tion.METHODS A total of 268 patients who received partial hepatectomy were enrolled.Patients were grouped into right hepatectomy/trisegmentectomy(RH/Tri),left hepa-tectomy(LH),segmentectomy(Seg),and subsegmentectomy/nonanatomical hepatectomy(Sub/Non)groups.The regeneration index(RI)and late rege-neration rate were defined as(postoperative liver volume)/[total functional liver volume(TFLV)]×100 and(RI at 6-months-RI at 3-months)/RI at 6-months,respectively.The lower 25th percentile of RI and the higher 25th percentile of late regeneration rate in each group were defined as“low regeneration”and“delayed regeneration”.“Restoration to the original size”was defined as regeneration of the liver volume by more than 90%of the TFLV at 12 months postsurgery.RESULTS The numbers of patients in the RH/Tri,LH,Seg,and Sub/Non groups were 41,53,99 and 75,respectively.The RI plateaued at 3 months in the LH,Seg,and Sub/Non groups,whereas the RI increased until 12 months in the RH/Tri group.According to our multivariate analysis,the preoperative albumin-bilirubin(ALBI)score was an independent factor for low regeneration at 3 months[odds ratio(OR)95%CI=2.80(1.17-6.69),P=0.02;per 1.0 up]and 12 months[OR=2.27(1.01-5.09),P=0.04;per 1.0 up].Multivariate analysis revealed that only liver resection percentage[OR=1.03(1.00-1.05),P=0.04]was associated with delayed regeneration.Furthermore,multivariate analysis demonstrated that the preoperative ALBI score[OR=2.63(1.00-1.05),P=0.02;per 1.0 up]and liver resection percentage[OR=1.02(1.00-1.05),P=0.04;per 1.0 up]were found to be independent risk factors associated with volume restoration failure.CONCLUSION Liver regeneration posthepatectomy was determined by the resection percentage and preoperative ALBI score.This knowledge helps surgeons decide the timing and type of rehepatectomy for recurrent cases.
基金This work was financially supported by the National Key Research and Development Program of China(Grant Nos.2020YFB2008305,2020YFB2008303)the Natural Science Foundation of Shenyang City(Grant No.22315605).
文摘The in situ(TiC+TiB)/TA15 composites with different volume percentages of reinforcement(10%,15%,20%and 25%)were prepared by water-cooled copper crucible vacuum suspension melting technology.The structures and compositions of the TA15 alloy and its composites were analyzed by XRD and EDS,and their electrochemical corrosion behaviors in the 3.5%NaCl solution were studied.Corrosion wear testing was conducted using a reciprocating ball-on-disc wear tester under a 10 N load.Results show that the in situ fibrous TiB phase and the granular TiC phase are uniformly distributed on the composite matrix.The microhardness can reach up to 531 HV as 25vol.%TiC+TiB reinforcement is added.Compared with the TA15 alloy,the volume wear rate decreases from(2.21±0.07)×10^(-4)to(1.75±0.07)×10^(-4)mm^(3)·N^(-1)·m^(-1)by adding 15vol.%TiC+TiB reinforcement,and the wear mechanism is adhesive wear.When the volume percentage of the reinforcement phase reaches 25%,the volume wear rate increases from(1.75±0.07)×10^(-4)to(2.41±0.07)×10^(-4)mm^(3)·N^(-1)·m^(-1),and the wear mechanism changes into abrasive wear.The volume loss resulted by the interaction between corrosion and wear accounts for more than 27%of the total wear volume.The volume loss due to wear-induced corrosion changes from 1.94%to 4.06%with different additions of reinforcement.The volume loss caused by corrosion-induced wear initially increases from 24.08%to 26.90%as the reinforcement increases from 0 to 15%due to the increase of corrosion potential,and then decreases from 26.90%to 25.68%as the reinforcement increases from 15%to 25%due to the peeling of TiC phase.
