Electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR)provides a promising strategy to convert biomass derivative to highvalue-added chemicals.Herein,a cascade strategy is proposed to construct Pd-NiCo_(2)...Electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR)provides a promising strategy to convert biomass derivative to highvalue-added chemicals.Herein,a cascade strategy is proposed to construct Pd-NiCo_(2)O_(4)electrocatalyst by Pd loading on Ni-doped Co3O4 and for highly active and stable synergistic HMF oxidation.An elevated current density of 800 mA cm^(-2)can be achieved at 1.5 V,and both Faradaic efficiency and yield of 2,5-furandicarboxylic acid remained close to 100%over 10 consecutive electrolysis.Experimental and theoretical results unveil that the introduction of Pd atoms can modulate the local electronic structure of Ni/Co,which not only balances the competitive adsorption of HMF and OH-species,but also promote the active Ni^(3+)species formation,inducing high indirect oxidation activity.We have also discovered that Ni incorporation facilitates the Co2+pre-oxidation and electrophilic OH*generation to contribute direct oxidation process.This work provides a new approach to design advanced electrocatalyst for biomass upgrading.展开更多
Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,pre...Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Generalized linear mixed models(GLMM)is an extension of linear model for categorical responses while considering the correlation among observations.Methods Magnetic resonance image(MRI)data of carotid atheroscleroticplaques were acquired from 20 patients with consent obtained and 3D thin-layer models were constructed to calculate plaque stress and strain for plaque progression prediction.Data for ten morphological and biomechanical risk factors included wall thickness(WT),lipid percent(LP),minimum cap thickness(MinCT),plaque area(PA),plaque burden(PB),lumen area(LA),maximum plaque wall stress(MPWS),maximum plaque wall strain(MPWSn),average plaque wall stress(APWS),and average plaque wall strain(APWSn)were extracted from all slices for analysis.Wall thickness increase(WTI),plaque burden increase(PBI)and plaque area increase(PAI) were chosen as three measures for plaque progression.Generalized linear mixed models(GLMM)with 5-fold cross-validation strategy were used to calculate prediction accuracy for each predictor and identify optimal predictor with the highest prediction accuracy defined as sum of sensitivity and specificity.All 201 MRI slices were randomly divided into 4 training subgroups and 1 verification subgroup.The training subgroups were used for model fitting,and the verification subgroup was used to estimate the model.All combinations(total1023)of 10 risk factors were feed to GLMM and the prediction accuracy of each predictor were selected from the point on the ROC(receiver operating characteristic)curve with the highest sum of specificity and sensitivity.Results LA was the best single predictor for PBI with the highest prediction accuracy(1.360 1),and the area under of the ROC curve(AUC)is0.654 0,followed by APWSn(1.336 3)with AUC=0.6342.The optimal predictor among all possible combinations for PBI was the combination of LA,PA,LP,WT,MPWS and MPWSn with prediction accuracy=1.414 6(AUC=0.715 8).LA was once again the best single predictor for PAI with the highest prediction accuracy(1.184 6)with AUC=0.606 4,followed by MPWSn(1. 183 2)with AUC=0.6084.The combination of PA,PB,WT,MPWS,MPWSn and APWSn gave the best prediction accuracy(1.302 5)for PAI,and the AUC value is 0.6657.PA was the best single predictor for WTI with highest prediction accuracy(1.288 7)with AUC=0.641 5,followed by WT(1.254 0),with AUC=0.6097.The combination of PA,PB,WT,LP,MinCT,MPWS and MPWS was the best predictor for WTI with prediction accuracy as 1.314 0,with AUC=0.6552.This indicated that PBI was a more predictable measure than WTI and PAI. The combinational predictors improved prediction accuracy by 9.95%,4.01%and 1.96%over the best single predictors for PAI,PBI and WTI(AUC values improved by9.78%,9.45%,and 2.14%),respectively.Conclusions The use of GLMM with 5-fold cross-validation strategy combining both morphological and biomechanical risk factors could potentially improve the accuracy of carotid plaque progression prediction.This study suggests that a linear combination of multiple predictors can provide potential improvement to existing plaque assessment schemes.展开更多
Objective To investigate surgical strategy for high-grade isthmic spondylolisthesis(more thanⅡ degree)of 5th lumbar vertebrae.Methods From August 2003 to October 2008,26 patients with high-grade isthmic spondylolisth...Objective To investigate surgical strategy for high-grade isthmic spondylolisthesis(more thanⅡ degree)of 5th lumbar vertebrae.Methods From August 2003 to October 2008,26 patients with high-grade isthmic spondylolisthesis (L5) were展开更多
The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures.To ensure safety,checking the regular leaky cable fixture is necessar...The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures.To ensure safety,checking the regular leaky cable fixture is necessary to elimi-nate the potential danger.At present,the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time.The faulty fixture is also insufficient and difficult to obtain,seriously affecting the model detection effect.To solve these problems,an innovative detection method is proposed in this paper.Firstly,we presented the Res-Net and Wasserstein-Deep Convolution GAN(RW-DCGAN)to implement data augmentation,which can enable the faulty fixture to export more high-quality and irregular images.Secondly,we proposed the Ghost SENet-YOLOv5(GS-YOLOv5)to enhance the expression of fixture feature,and further improve the detection accuracy and speed.Finally,we adopted the model compression strategy to prune redundant channels,and visualized training details with Grad-CAM to verify the reliability of our model.Experimental results show that the algorithm model is 69.06%smaller than the original YOLOv5 model,with 70.07%fewer parameters,2.1%higher accuracy and 14.82 fps faster speed,meeting the needs of tunnel fixture detection.展开更多
基金financially supported by Key Research and Development Projects of Sichuan Province (2023YFG0222)“Tianfu Emei” Science and Technology Innovation Leader Program in Sichuan Province (2021)+3 种基金University of Electronic Science and Technology of China Talent Start-up Funds (A1098 5310 2360 1208)the Youth Innovation Promotion Association of CAS (2020458)National Natural Science Foundation of China (21464015, 21472235, 52122212, 12274391, 223210001)Beijing Natural Science Foundation (IS23045)
文摘Electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR)provides a promising strategy to convert biomass derivative to highvalue-added chemicals.Herein,a cascade strategy is proposed to construct Pd-NiCo_(2)O_(4)electrocatalyst by Pd loading on Ni-doped Co3O4 and for highly active and stable synergistic HMF oxidation.An elevated current density of 800 mA cm^(-2)can be achieved at 1.5 V,and both Faradaic efficiency and yield of 2,5-furandicarboxylic acid remained close to 100%over 10 consecutive electrolysis.Experimental and theoretical results unveil that the introduction of Pd atoms can modulate the local electronic structure of Ni/Co,which not only balances the competitive adsorption of HMF and OH-species,but also promote the active Ni^(3+)species formation,inducing high indirect oxidation activity.We have also discovered that Ni incorporation facilitates the Co2+pre-oxidation and electrophilic OH*generation to contribute direct oxidation process.This work provides a new approach to design advanced electrocatalyst for biomass upgrading.
基金supported in part by National Sciences Foundation of China grant ( 11672001)Jiangsu Province Science and Technology Agency grant ( BE2016785)supported in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province grant ( KYCX18_0156)
文摘Background Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture.Plaque progression prediction is of fundamental significance to cardiovascular research and disease diagnosis,prevention,and treatment.Generalized linear mixed models(GLMM)is an extension of linear model for categorical responses while considering the correlation among observations.Methods Magnetic resonance image(MRI)data of carotid atheroscleroticplaques were acquired from 20 patients with consent obtained and 3D thin-layer models were constructed to calculate plaque stress and strain for plaque progression prediction.Data for ten morphological and biomechanical risk factors included wall thickness(WT),lipid percent(LP),minimum cap thickness(MinCT),plaque area(PA),plaque burden(PB),lumen area(LA),maximum plaque wall stress(MPWS),maximum plaque wall strain(MPWSn),average plaque wall stress(APWS),and average plaque wall strain(APWSn)were extracted from all slices for analysis.Wall thickness increase(WTI),plaque burden increase(PBI)and plaque area increase(PAI) were chosen as three measures for plaque progression.Generalized linear mixed models(GLMM)with 5-fold cross-validation strategy were used to calculate prediction accuracy for each predictor and identify optimal predictor with the highest prediction accuracy defined as sum of sensitivity and specificity.All 201 MRI slices were randomly divided into 4 training subgroups and 1 verification subgroup.The training subgroups were used for model fitting,and the verification subgroup was used to estimate the model.All combinations(total1023)of 10 risk factors were feed to GLMM and the prediction accuracy of each predictor were selected from the point on the ROC(receiver operating characteristic)curve with the highest sum of specificity and sensitivity.Results LA was the best single predictor for PBI with the highest prediction accuracy(1.360 1),and the area under of the ROC curve(AUC)is0.654 0,followed by APWSn(1.336 3)with AUC=0.6342.The optimal predictor among all possible combinations for PBI was the combination of LA,PA,LP,WT,MPWS and MPWSn with prediction accuracy=1.414 6(AUC=0.715 8).LA was once again the best single predictor for PAI with the highest prediction accuracy(1.184 6)with AUC=0.606 4,followed by MPWSn(1. 183 2)with AUC=0.6084.The combination of PA,PB,WT,MPWS,MPWSn and APWSn gave the best prediction accuracy(1.302 5)for PAI,and the AUC value is 0.6657.PA was the best single predictor for WTI with highest prediction accuracy(1.288 7)with AUC=0.641 5,followed by WT(1.254 0),with AUC=0.6097.The combination of PA,PB,WT,LP,MinCT,MPWS and MPWS was the best predictor for WTI with prediction accuracy as 1.314 0,with AUC=0.6552.This indicated that PBI was a more predictable measure than WTI and PAI. The combinational predictors improved prediction accuracy by 9.95%,4.01%and 1.96%over the best single predictors for PAI,PBI and WTI(AUC values improved by9.78%,9.45%,and 2.14%),respectively.Conclusions The use of GLMM with 5-fold cross-validation strategy combining both morphological and biomechanical risk factors could potentially improve the accuracy of carotid plaque progression prediction.This study suggests that a linear combination of multiple predictors can provide potential improvement to existing plaque assessment schemes.
文摘Objective To investigate surgical strategy for high-grade isthmic spondylolisthesis(more thanⅡ degree)of 5th lumbar vertebrae.Methods From August 2003 to October 2008,26 patients with high-grade isthmic spondylolisthesis (L5) were
基金supported by the National Natural Science Foundation of China(No.61702347,No.62027801)Natural Science Foundation of Hebei Province(No.F2022210007,No.F2017210161)+2 种基金Science and Technology Project of Hebei Education Department(No.ZD2022100,No.QN2017132)Central Guidance on Local Science and Technology Development Fund(No.226Z0501G)National innovation and Entrepreneurship training program for college students(No.202110107024).
文摘The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures.To ensure safety,checking the regular leaky cable fixture is necessary to elimi-nate the potential danger.At present,the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time.The faulty fixture is also insufficient and difficult to obtain,seriously affecting the model detection effect.To solve these problems,an innovative detection method is proposed in this paper.Firstly,we presented the Res-Net and Wasserstein-Deep Convolution GAN(RW-DCGAN)to implement data augmentation,which can enable the faulty fixture to export more high-quality and irregular images.Secondly,we proposed the Ghost SENet-YOLOv5(GS-YOLOv5)to enhance the expression of fixture feature,and further improve the detection accuracy and speed.Finally,we adopted the model compression strategy to prune redundant channels,and visualized training details with Grad-CAM to verify the reliability of our model.Experimental results show that the algorithm model is 69.06%smaller than the original YOLOv5 model,with 70.07%fewer parameters,2.1%higher accuracy and 14.82 fps faster speed,meeting the needs of tunnel fixture detection.
