Photoneutron cross-section(PNCS)data are important in various current and emerging applications.Although a few sophis-ticated methods have been developed,there is still an urgent need to study the PNCS data.In this st...Photoneutron cross-section(PNCS)data are important in various current and emerging applications.Although a few sophis-ticated methods have been developed,there is still an urgent need to study the PNCS data.In this study,we propose the extraction of PNCS distributions using a combination of gamma activation and reaction yield ratio methods.To verify the validity of the proposed extraction method,experiments for generating^(62,64)Cu and^(85m,87m)Sr isotopes via laser-induced pho-toneutron reactions were performed,and the reaction yields of these isotopes were obtained.Using the proposed extraction method,the PNCS distributions of^(63)Cu and^(86)Sr isotopes(leading to^(85m)Sr isotope production)were successfully extracted.These extracted PNCS distributions were benchmarked against available PNCS data or TALYS calculations,demonstrating the validity of the proposed extraction method.Potential applications for predicting the PNCS distributions of the 30 iso-topes are further introduced.We conclude that the proposed extraction method is an effective complement to the available sophisticated methods for measuring and evaluating PNCS data.展开更多
The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if t...The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.展开更多
基金This work was supported by the National Key R&D Program of China(No.2022YFA1603300)the National Natural Science Foundation of China(Nos.U2230133)+2 种基金the Independent Research Project of the Key Laboratory of Plasma Physics,CAEP(No.JCKYS2021212009)the Open Fund of the Key Laboratory of Nuclear Data,CIAE(No.JCKY2022201C152)Hengyang Municipal Science and Technology Project(No.202150054076).
文摘Photoneutron cross-section(PNCS)data are important in various current and emerging applications.Although a few sophis-ticated methods have been developed,there is still an urgent need to study the PNCS data.In this study,we propose the extraction of PNCS distributions using a combination of gamma activation and reaction yield ratio methods.To verify the validity of the proposed extraction method,experiments for generating^(62,64)Cu and^(85m,87m)Sr isotopes via laser-induced pho-toneutron reactions were performed,and the reaction yields of these isotopes were obtained.Using the proposed extraction method,the PNCS distributions of^(63)Cu and^(86)Sr isotopes(leading to^(85m)Sr isotope production)were successfully extracted.These extracted PNCS distributions were benchmarked against available PNCS data or TALYS calculations,demonstrating the validity of the proposed extraction method.Potential applications for predicting the PNCS distributions of the 30 iso-topes are further introduced.We conclude that the proposed extraction method is an effective complement to the available sophisticated methods for measuring and evaluating PNCS data.
文摘The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.
