In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including: malic acid(Mal), oxalic acid(Oxa), citric acid(Cit), lact...In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including: malic acid(Mal), oxalic acid(Oxa), citric acid(Cit), lactic acid(Lac), succinic acid(Suc), fumaric acid(Fum), total organic acids(To A, the sum of the six organic acids tested), titratable acid(TiA), acidity value(AcV), and pH value. For most of the cultivars studied(85.8%), the order of the organic acid contents in apples was Mal>Oxa>Cit>Lac>Suc>Fum. Mal was the dominant organic acid, on average, accounting for 94.5% of To A. Among the 10 indices, the dispersion of pH value was the smallest with a coefficient of variation of only 8.2%, while the coefficients of variation of the other nine indices were larger, ranging between 31 and 66%. There were significant linear relationships between Mal and two indices(ToA and AcV) as well as between ToA and AcV. There were significant logarithmic relationships between pH value and four indices: Mal, TiA, ToA, and AcV. All the equations had very high fitting accuracy and can be used to accurately predict related indices. According to this study, Mal, ToA, and AcV of apple were normally distributed, TiA was close to normally distributed, whereas pH value had a skewed distribution. Using the fitted normal distribution curves, the grading standards of Mal, TiA, ToA, and AcV were established. The grading node values of pH value were obtained using the logarithmic relationship between pH value and Mal. The grading standards of these five indices can be used to evaluate the sour flavor of apple. This study provides a scientific basis for evaluating apple flavor and selecting apple cultivars.展开更多
This paper reports the application of multi-component hydrocracking catalyst grading technology in diesel hydrocracking system to increase naphtha,and studies the influence of catalyst systems with different number of...This paper reports the application of multi-component hydrocracking catalyst grading technology in diesel hydrocracking system to increase naphtha,and studies the influence of catalyst systems with different number of graded beds on the reaction process of diesel hydrocracking.Three hydrocracking catalysts with different physicochemical properties as gradation components,the diesel hydrocracking reaction on catalyst systems of one-component,two-component and three-component graded beds with different loading sequences are carried out and evaluated,respectively.The catalytic mechanism of the multi-component grading system is analyzed.The results show that,with the increase of the number of grading beds,the space velocity of reaction on each catalyst increases,which can effectively control the overreaction process;along the flow direction of feedstock,the loading sequences of catalysts with acidity decreasing and pore properties increasing can satisfy the demand of different catalytic activity for the conversion of reactant with changing composition to naphtha,which has a guiding role in the conversion of feedstock to target products.Therefore,the conversion of diesel,the selectivity and yield of naphtha all increase significantly on the multi-component catalyst system.The research on the grading technology of multi-component catalysts is of great significance to the promotion and application of catalyst systems in various catalytic fields.展开更多
Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the retina.If not treated early,DR becomes a significant reason for blindness.To identify the...Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the retina.If not treated early,DR becomes a significant reason for blindness.To identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and timeconsuming.To address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper.Our proposal is based on lesion segmentation of the fundus images based on the LuNet network.Then a Refined Attention Pyramid Network(RAPNet)is used for extracting global and local features.To increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)algorithm.Finally,the LightGBM model is applied to classify the input image based on the severity.Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,APTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and f1-score.The proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets respectively.The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.展开更多
To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-Mobi...To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt(NCAM+MobileNetXt)network.Firstly,this study recon-structed the Sandglass Block to effectively increase the model accuracy;secondly,this study introduced the group convolution module and a two-dimensional adaptive average pool,which can significantly compress the model parameters and enhance the model robustness separately;finally,this study innovatively proposed the Normalization-based Channel Attention Module(NCAM)to enhance the image features obviously.The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%,and the number of parameters was decreased by 66%compared with the original MobileNetXt model.The N-MobileNetXt model was superior to other net-work models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification.It can provide a theoretical basis and technical support for automatic irrigation.展开更多
Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,...Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
目的探讨I D H突变和1p/19q共缺失型少突胶质细胞瘤的临床病理特征及预后相关影响因素。方法收集54例IDH突变和1p/19q共缺失型少突胶质细胞瘤病例,分析其临床病理特点,包括年龄、组织学分级和肿瘤部位等因素对无进展生存期和总生存期的...目的探讨I D H突变和1p/19q共缺失型少突胶质细胞瘤的临床病理特征及预后相关影响因素。方法收集54例IDH突变和1p/19q共缺失型少突胶质细胞瘤病例,分析其临床病理特点,包括年龄、组织学分级和肿瘤部位等因素对无进展生存期和总生存期的影响。结果 54例患者中,肿瘤发生于1个脑叶者46例,发生于2个脑叶以上者8例。肿瘤组织学WHO分级2级12例,3级42例。FISH检测显示54例均为1p/19q共缺失;免疫组织化学检测显示Olig2均为弥漫强阳性;GFAP均为阳性;p53有6例强阳性;48例患者ATRX未缺失;Ki-67增殖指数5%~60%。Sanger测序显示54例均发生IDH基因突变(40例为IDH1突变,14例为IDH2突变),33例发生TERT启动子突变。16例在治疗过程中发生复发及转移。单因素分析显示,手术后复发转移间隔时间超过2年可以延长患者无进展生存和总生存期。54例患者平均无进展生存期33.5个月,平均总生存期40.7个月。结论 IDH突变和1p/19q共缺失型少突胶质细胞瘤术后联合精准放化疗降低了进展风险,手术后复发转移间隔时间与该型患者预后相关。展开更多
基金financially supported by the earmarked fund for the China Agriculture Research System (CARS-27)the National Program for Quality and Safety Risk Assessment of Agricultural Products of China (GJFP2017003)the Scientific and Technological Innovation Project of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP)
文摘In order to establish grading standards of evaluation indices for sour flavor of apples, 10 indices of samples from 106 apple cultivars were tested, including: malic acid(Mal), oxalic acid(Oxa), citric acid(Cit), lactic acid(Lac), succinic acid(Suc), fumaric acid(Fum), total organic acids(To A, the sum of the six organic acids tested), titratable acid(TiA), acidity value(AcV), and pH value. For most of the cultivars studied(85.8%), the order of the organic acid contents in apples was Mal>Oxa>Cit>Lac>Suc>Fum. Mal was the dominant organic acid, on average, accounting for 94.5% of To A. Among the 10 indices, the dispersion of pH value was the smallest with a coefficient of variation of only 8.2%, while the coefficients of variation of the other nine indices were larger, ranging between 31 and 66%. There were significant linear relationships between Mal and two indices(ToA and AcV) as well as between ToA and AcV. There were significant logarithmic relationships between pH value and four indices: Mal, TiA, ToA, and AcV. All the equations had very high fitting accuracy and can be used to accurately predict related indices. According to this study, Mal, ToA, and AcV of apple were normally distributed, TiA was close to normally distributed, whereas pH value had a skewed distribution. Using the fitted normal distribution curves, the grading standards of Mal, TiA, ToA, and AcV were established. The grading node values of pH value were obtained using the logarithmic relationship between pH value and Mal. The grading standards of these five indices can be used to evaluate the sour flavor of apple. This study provides a scientific basis for evaluating apple flavor and selecting apple cultivars.
基金National Key R&D Program of China(2021YFA1501203)is acknowledged for financial support.
文摘This paper reports the application of multi-component hydrocracking catalyst grading technology in diesel hydrocracking system to increase naphtha,and studies the influence of catalyst systems with different number of graded beds on the reaction process of diesel hydrocracking.Three hydrocracking catalysts with different physicochemical properties as gradation components,the diesel hydrocracking reaction on catalyst systems of one-component,two-component and three-component graded beds with different loading sequences are carried out and evaluated,respectively.The catalytic mechanism of the multi-component grading system is analyzed.The results show that,with the increase of the number of grading beds,the space velocity of reaction on each catalyst increases,which can effectively control the overreaction process;along the flow direction of feedstock,the loading sequences of catalysts with acidity decreasing and pore properties increasing can satisfy the demand of different catalytic activity for the conversion of reactant with changing composition to naphtha,which has a guiding role in the conversion of feedstock to target products.Therefore,the conversion of diesel,the selectivity and yield of naphtha all increase significantly on the multi-component catalyst system.The research on the grading technology of multi-component catalysts is of great significance to the promotion and application of catalyst systems in various catalytic fields.
文摘Diabetes problems can lead to an eye disease called Diabetic Retinopathy(DR),which permanently damages the blood vessels in the retina.If not treated early,DR becomes a significant reason for blindness.To identify the DR and determine the stages,medical tests are very labor-intensive,expensive,and timeconsuming.To address the issue,a hybrid deep and machine learning techniquebased autonomous diagnostic system is provided in this paper.Our proposal is based on lesion segmentation of the fundus images based on the LuNet network.Then a Refined Attention Pyramid Network(RAPNet)is used for extracting global and local features.To increase the performance of the classifier,the unique features are selected from the extracted feature set using Aquila Optimizer(AO)algorithm.Finally,the LightGBM model is applied to classify the input image based on the severity.Several investigations have been done to analyze the performance of the proposed framework on three publically available datasets(MESSIDOR,APTOS,and IDRiD)using several performance metrics such as accuracy,precision,recall,and f1-score.The proposed classifier achieves 99.29%,99.35%,and 99.31%accuracy for these three datasets respectively.The outcomes of the experiments demonstrate that the suggested technique is effective for disease identification and reliable DR grading.
基金supported in part by the Science and Technology Development Plan Project of Changchun[Grant Number 21ZGN28]the Jilin Provincial Science and Technology Development Plan Project[Grant Number 20210101157JC]the Jilin Provincial Science and Technology Development Plan Project[Grant Number 20230202035NC].
文摘To solve inefficient water stress classification of spinach seedlings under complex background,this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt(NCAM+MobileNetXt)network.Firstly,this study recon-structed the Sandglass Block to effectively increase the model accuracy;secondly,this study introduced the group convolution module and a two-dimensional adaptive average pool,which can significantly compress the model parameters and enhance the model robustness separately;finally,this study innovatively proposed the Normalization-based Channel Attention Module(NCAM)to enhance the image features obviously.The experimental results showed that the classification accuracy of N-MobileNetXt model for spinach seedlings under the natural environment reached 90.35%,and the number of parameters was decreased by 66%compared with the original MobileNetXt model.The N-MobileNetXt model was superior to other net-work models such as ShuffleNet and GhostNet in terms of parameters and accuracy of identification.It can provide a theoretical basis and technical support for automatic irrigation.
基金the National Natural Science Foundation of China(No.62276210,82201148,61775180)the Natural Science Basic Research Program of Shaanxi Province(No.2022JM-380)+3 种基金the Shaanxi Province College Students'Innovation and Entrepreneurship Training Program(No.S202311664128X)the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(No.2022RC069,2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)。
文摘Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
文摘目的探讨I D H突变和1p/19q共缺失型少突胶质细胞瘤的临床病理特征及预后相关影响因素。方法收集54例IDH突变和1p/19q共缺失型少突胶质细胞瘤病例,分析其临床病理特点,包括年龄、组织学分级和肿瘤部位等因素对无进展生存期和总生存期的影响。结果 54例患者中,肿瘤发生于1个脑叶者46例,发生于2个脑叶以上者8例。肿瘤组织学WHO分级2级12例,3级42例。FISH检测显示54例均为1p/19q共缺失;免疫组织化学检测显示Olig2均为弥漫强阳性;GFAP均为阳性;p53有6例强阳性;48例患者ATRX未缺失;Ki-67增殖指数5%~60%。Sanger测序显示54例均发生IDH基因突变(40例为IDH1突变,14例为IDH2突变),33例发生TERT启动子突变。16例在治疗过程中发生复发及转移。单因素分析显示,手术后复发转移间隔时间超过2年可以延长患者无进展生存和总生存期。54例患者平均无进展生存期33.5个月,平均总生存期40.7个月。结论 IDH突变和1p/19q共缺失型少突胶质细胞瘤术后联合精准放化疗降低了进展风险,手术后复发转移间隔时间与该型患者预后相关。
