Peanut varieties are diverse globally,with their characters and nutrition determining the product quality.However,the comparative analysis and statistical analysis of key quality indicators for peanut kernels across t...Peanut varieties are diverse globally,with their characters and nutrition determining the product quality.However,the comparative analysis and statistical analysis of key quality indicators for peanut kernels across the world remains relatively limited,impeding the comprehensive evaluation of peanut quality and hindering the industry development on a global scale.This study aimed to compare and analyze the apparent morphology,microstructure,single-cell structure,engineering and mechanical properties,as well as major nutrient contents of peanut kernels from 10 different cultivars representing major peanut-producing countries.The surface and cross-section microstructure of the peanut kernels exhibited a dense“blocky”appearance with a distinct cellular structure.The lipid droplets were predominantly spherical with a regular distribution within the cells.The single-cell structure of the kernels from these 10 peanut cultivars demonstrated varying morphologies and dimensions,which exhibited correlations with their mechanical and engineering properties.Furthermore,the mass loss versus temperature profiles of the peanut kernels revealed five distinct stages,corresponding to moisture loss,volatile loss,protein denaturation,and the degradation of various biomacromolecules.Variations were also observed in the lipid,protein,and sucrose contents,texture,bulk density,true density,porosity,geometric mean diameter,and sphericity among the diferent peanut varieties.This study establishes relationships and correlations among microstructure,engineering properties,and nutritional composition of commonly grown peanut varieties in major peanut-processing countries.The findings provide valuable insights into peanut quality evaluation,empowering the peanut industry to enhance their processing and product development efforts.展开更多
Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist r...Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC.Methods:We retrospectively enrolled 278 patients with gallbladder lesions(>10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively compared the performance of deep learning,radiomics,clinical models,and three radiologists.Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL firstorder kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multivariate regression analysis revealed that age≥65 years[odds ratios(OR)=4.4,95%confidence interval(CI):2.1-9.1,P<0.001],lesion size(OR=2.6,95%CI:1.6-4.1,P<0.001),and CA-19-9>37 U/mL(OR=4.0,95%CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95%CI:0.814-0.915)and 0.857(95%CI:0.773-0.942)in the training and validation datasets,which were comparable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90%(95%CI:82%-96%)]and validation[85%(95%CI:68%-95%)]datasets.Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.展开更多
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
基金supported by the National Key R&D Program of China(2021YFD2100400,2023YFE0104900)Xinjiang Agriculture Research System-Oil Crop Research System,China(XJARS-05)+3 种基金Taishan Industrial Experts Programme,China(tscx202306075)the Scientific and Technological Assistance Projects to Developing Countries,China(KY202201003)the Agricultural Science and Technology Innovation Program,Institute of Food Science and Technology,Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2024-IFST)The authors are grateful for the financial support from the Arawana Charity Foundation,China.
文摘Peanut varieties are diverse globally,with their characters and nutrition determining the product quality.However,the comparative analysis and statistical analysis of key quality indicators for peanut kernels across the world remains relatively limited,impeding the comprehensive evaluation of peanut quality and hindering the industry development on a global scale.This study aimed to compare and analyze the apparent morphology,microstructure,single-cell structure,engineering and mechanical properties,as well as major nutrient contents of peanut kernels from 10 different cultivars representing major peanut-producing countries.The surface and cross-section microstructure of the peanut kernels exhibited a dense“blocky”appearance with a distinct cellular structure.The lipid droplets were predominantly spherical with a regular distribution within the cells.The single-cell structure of the kernels from these 10 peanut cultivars demonstrated varying morphologies and dimensions,which exhibited correlations with their mechanical and engineering properties.Furthermore,the mass loss versus temperature profiles of the peanut kernels revealed five distinct stages,corresponding to moisture loss,volatile loss,protein denaturation,and the degradation of various biomacromolecules.Variations were also observed in the lipid,protein,and sucrose contents,texture,bulk density,true density,porosity,geometric mean diameter,and sphericity among the diferent peanut varieties.This study establishes relationships and correlations among microstructure,engineering properties,and nutritional composition of commonly grown peanut varieties in major peanut-processing countries.The findings provide valuable insights into peanut quality evaluation,empowering the peanut industry to enhance their processing and product development efforts.
基金the National Natural Science Foundation of China(81572975)Key Research and Devel-opment Project of Science and Technology Department of Zhejiang(2015C03053)+1 种基金Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province(CXPJJH11900009-07)Zhejiang Provincial Program for the Cultivation of High-level Innovative Health Talents.
文摘Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC.Methods:We retrospectively enrolled 278 patients with gallbladder lesions(>10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively compared the performance of deep learning,radiomics,clinical models,and three radiologists.Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL firstorder kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multivariate regression analysis revealed that age≥65 years[odds ratios(OR)=4.4,95%confidence interval(CI):2.1-9.1,P<0.001],lesion size(OR=2.6,95%CI:1.6-4.1,P<0.001),and CA-19-9>37 U/mL(OR=4.0,95%CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95%CI:0.814-0.915)and 0.857(95%CI:0.773-0.942)in the training and validation datasets,which were comparable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90%(95%CI:82%-96%)]and validation[85%(95%CI:68%-95%)]datasets.Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.
基金National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
