Short-axis substitution, as an effective way to change the optical and electronic properties of the organic semiconductors for organic photovoltaics(OPVs), is a readily approach to modify non-fullerene acceptors, espe...Short-axis substitution, as an effective way to change the optical and electronic properties of the organic semiconductors for organic photovoltaics(OPVs), is a readily approach to modify non-fullerene acceptors, especially for the linear fused rings system. Here, two new fused-ring electron acceptors(CBT-IC and SBT-IC) were designed and developed by short-axis modification based on the dithienyl[1,2-b:4,5-b′]benzodithiophene(BDCPDT) system. Combined with a medium bandgap polymer donor J71, both of the OPV devices exhibit high power conversion efficiency(PCE) over 11%, and ～70% external quantum efficiencies. To better understand how this kind of substitution affects the BDCPDT based acceptors, a comparative analysis is also made with the the plain acceptor BDT-IC without this modification. We believe this work could disclose the great potential and the versatility of BDCPDT block and also enlighten other ladder-type series for further optimization.展开更多
The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interes...The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.展开更多
基金supported by the National Natural Science Foundation of China (61575136, 21504062, 91633301, 91433117, 21572152)the National Key R&D Program of China (2016YFB0400700)+3 种基金the Collaborative Innovation Center of Suzhou Nano Science and Technology (Nano-CIC)the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)the “111” Project of the State Administration of Foreign Experts Affairs of Chinathe Yunnan Provincial Research Funds on College-Enterprise Collaboration (2015IB016)
文摘Short-axis substitution, as an effective way to change the optical and electronic properties of the organic semiconductors for organic photovoltaics(OPVs), is a readily approach to modify non-fullerene acceptors, especially for the linear fused rings system. Here, two new fused-ring electron acceptors(CBT-IC and SBT-IC) were designed and developed by short-axis modification based on the dithienyl[1,2-b:4,5-b′]benzodithiophene(BDCPDT) system. Combined with a medium bandgap polymer donor J71, both of the OPV devices exhibit high power conversion efficiency(PCE) over 11%, and ～70% external quantum efficiencies. To better understand how this kind of substitution affects the BDCPDT based acceptors, a comparative analysis is also made with the the plain acceptor BDT-IC without this modification. We believe this work could disclose the great potential and the versatility of BDCPDT block and also enlighten other ladder-type series for further optimization.
基金supported by the Ministry of Higher Education(MOHE)through the Fundamental Research Grant Scheme(FRGS)(FRGS/1/2020/TK0/UTHM/02/16)the Universiti Tun Hussein Onn Malaysia(UTHM)through an FRGS Research Grant(Vot K304).
文摘The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.
