Hierarchical Sb_2S_3 hollow microspheres assembled by nanowires have been successfully synthesized by a simple and practical hydrothermal reaction. The possible formation process of this architecture was investigated ...Hierarchical Sb_2S_3 hollow microspheres assembled by nanowires have been successfully synthesized by a simple and practical hydrothermal reaction. The possible formation process of this architecture was investigated by X-ray diffraction, focused-ion beam-scanning electron microscopy dual-beam system, and transmission electron microscopy. When used as the anode material for lithium-ion batteries, Sb_2S_3 hollow microspheres manifest excellent rate property and enhanced lithium-storage capability and can deliver a discharge capacity of 674 m Ah g^(-1) at a current density of 200 m A g^(-1) after 50 cycles. Even at a high currentdensity of 5000 m A g^(-1), a discharge capacity of541 m Ah g^(-1) is achieved. Sb_2S_3 hollow microspheres also display a prominent sodium-storage capacity and maintain a reversible discharge capacity of 384 m Ah g^(-1) at a current density of 200 m A g^(-1) after 50 cycles. The remarkable lithium/sodium-storage property may be attributed to the synergetic effect of its nanometer size and three-dimensional hierarchical architecture, and the outstanding stability property is attributed to the sufficient interior void space,which can buffer the volume expansion.展开更多
Long-chain acyl coenzyme A synthetase(ACSL) is a member of the synthetase family encoded by a multigene family;it plays an important role in the absorption and transport of fatty acid.Here we review the roles of ACSL ...Long-chain acyl coenzyme A synthetase(ACSL) is a member of the synthetase family encoded by a multigene family;it plays an important role in the absorption and transport of fatty acid.Here we review the roles of ACSL in the regulating absorption and transport of fatty acid,as well as the connection between ACSL and some metabolic diseases.展开更多
Traditional object detectors based on deep learning rely on plenty of labeled samples,which are expensive to obtain.Few-shot object detection(FSOD)attempts to solve this problem,learning detection objects from a few l...Traditional object detectors based on deep learning rely on plenty of labeled samples,which are expensive to obtain.Few-shot object detection(FSOD)attempts to solve this problem,learning detection objects from a few labeled samples,but the performance is often unsatisfactory due to the scarcity of samples.We believe that the main reasons that restrict the performance of few-shot detectors are:(1)the positive samples is scarce,and(2)the quality of positive samples is low.Therefore,we put forward a novel few-shot object detector based on YOLOv4,starting from both improving the quantity and quality of positive samples.First,we design a hybrid multivariate positive sample augmentation(HMPSA)module to amplify the quantity of positive samples and increase positive sample diversity while suppressing negative samples.Then,we design a selective non-local fusion attention(SNFA)module to help the detector better learn the target features and improve the feature quality of positive samples.Finally,we optimize the loss function to make it more suitable for the task of FSOD.Experimental results on PASCAL VOC and MS COCO demonstrate that our designed few-shot object detector has competitive performance with other state-of-the-art detectors.展开更多
基金supported financially by the National Natural Foundation of China(Grant No.51672234)the Research Foundation for Hunan Youth Outstanding People from Hunan Provincial Science and Technology Department(2015RS4030)+1 种基金Hunan 2011 Collaborative Innovation Center of Chemical Engineering&Technology with Environmental Benignity and Effective Resource UtilizationProgram for Innovative Research Cultivation Team in University of Ministry of Education of China(1337304)
文摘Hierarchical Sb_2S_3 hollow microspheres assembled by nanowires have been successfully synthesized by a simple and practical hydrothermal reaction. The possible formation process of this architecture was investigated by X-ray diffraction, focused-ion beam-scanning electron microscopy dual-beam system, and transmission electron microscopy. When used as the anode material for lithium-ion batteries, Sb_2S_3 hollow microspheres manifest excellent rate property and enhanced lithium-storage capability and can deliver a discharge capacity of 674 m Ah g^(-1) at a current density of 200 m A g^(-1) after 50 cycles. Even at a high currentdensity of 5000 m A g^(-1), a discharge capacity of541 m Ah g^(-1) is achieved. Sb_2S_3 hollow microspheres also display a prominent sodium-storage capacity and maintain a reversible discharge capacity of 384 m Ah g^(-1) at a current density of 200 m A g^(-1) after 50 cycles. The remarkable lithium/sodium-storage property may be attributed to the synergetic effect of its nanometer size and three-dimensional hierarchical architecture, and the outstanding stability property is attributed to the sufficient interior void space,which can buffer the volume expansion.
基金Supported by the National Natural Science Foundation of China(81373465)
文摘Long-chain acyl coenzyme A synthetase(ACSL) is a member of the synthetase family encoded by a multigene family;it plays an important role in the absorption and transport of fatty acid.Here we review the roles of ACSL in the regulating absorption and transport of fatty acid,as well as the connection between ACSL and some metabolic diseases.
基金the China National Key Research and Development Program(Grant No.2016YFC0802904)National Natural Science Foundation of China(Grant No.61671470)62nd batch of funded projects of China Postdoctoral Science Foundation(Grant No.2017M623423)to provide fund for conducting experiments。
文摘Traditional object detectors based on deep learning rely on plenty of labeled samples,which are expensive to obtain.Few-shot object detection(FSOD)attempts to solve this problem,learning detection objects from a few labeled samples,but the performance is often unsatisfactory due to the scarcity of samples.We believe that the main reasons that restrict the performance of few-shot detectors are:(1)the positive samples is scarce,and(2)the quality of positive samples is low.Therefore,we put forward a novel few-shot object detector based on YOLOv4,starting from both improving the quantity and quality of positive samples.First,we design a hybrid multivariate positive sample augmentation(HMPSA)module to amplify the quantity of positive samples and increase positive sample diversity while suppressing negative samples.Then,we design a selective non-local fusion attention(SNFA)module to help the detector better learn the target features and improve the feature quality of positive samples.Finally,we optimize the loss function to make it more suitable for the task of FSOD.Experimental results on PASCAL VOC and MS COCO demonstrate that our designed few-shot object detector has competitive performance with other state-of-the-art detectors.
