Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable...Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable topological architecture,pre-designable periodic skeleton,well-defined micro-/meso-pore,large specific surface area,and customizable electroactive functionality.Those benefits make COFs as promising candidates for advanced electrochemical energy storage.Especially,for now,structure engineering of COFs from multiscale aspects has been conducted to enable optimal overall electrochemical performance in terms of structure durability,electrical conductivity,redox activity,and charge storage.In this review,we give a fundamental and insightful study on the correlations between multi-scale structure engineering and eventual electrochemical properties of COFs,started with introducing their basic chemistries and charge storage principles.The careful discussion on the significant achievements in structure engineering of COFs from linkages,redox sites,polygon skeleton,crystal nanostructures,and composite microstructures,and further their effects on the electrochemical behavior of COFs are presented.Finally,the timely cutting-edge perspectives and in-depth insights into COFbased electrodematerials to rationally screen their electrochemical behaviors for addressing future challenges and implementing electrochemical energy storage applications are proposed.展开更多
The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods ha...The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.展开更多
OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低...OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低风险、有效控制资源,为项目的开发提供了高度清晰的过程框架,规范管理和开发流程。展开更多
基金Hubei Provincial Natural Science Foundation of China,Grant/Award Number:2022CFB555Open Project of State Key Laboratory of New Textile Materials and Advanced Processing Technologies,Grant/Award Number:FZ2021003。
文摘Covalent organic frameworks(COFs),which are constructed by linking organic building blocks via dynamic covalent bonds,are newly emerged and burgeoning crystalline porous copolymers with features including programmable topological architecture,pre-designable periodic skeleton,well-defined micro-/meso-pore,large specific surface area,and customizable electroactive functionality.Those benefits make COFs as promising candidates for advanced electrochemical energy storage.Especially,for now,structure engineering of COFs from multiscale aspects has been conducted to enable optimal overall electrochemical performance in terms of structure durability,electrical conductivity,redox activity,and charge storage.In this review,we give a fundamental and insightful study on the correlations between multi-scale structure engineering and eventual electrochemical properties of COFs,started with introducing their basic chemistries and charge storage principles.The careful discussion on the significant achievements in structure engineering of COFs from linkages,redox sites,polygon skeleton,crystal nanostructures,and composite microstructures,and further their effects on the electrochemical behavior of COFs are presented.Finally,the timely cutting-edge perspectives and in-depth insights into COFbased electrodematerials to rationally screen their electrochemical behaviors for addressing future challenges and implementing electrochemical energy storage applications are proposed.
基金the National Natural Science Foundation of China(61772149,61866009,61762028,U1701267,61702169)Guangxi Science and Technology Project(2019GXNSFFA245014,ZY20198016,AD18281079,AD18216004)+1 种基金the Natural Science Foundation of Hunan Province(2020JJ3014)Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics(GIIP202001).
文摘The tradeoff between efficiency and model size of the convolutional neural network(CNN)is an essential issue for applications of CNN-based algorithms to diverse real-world tasks.Although deep learning-based methods have achieved significant improvements in image super-resolution(SR),current CNNbased techniques mainly contain massive parameters and a high computational complexity,limiting their practical applications.In this paper,we present a fast and lightweight framework,named weighted multi-scale residual network(WMRN),for a better tradeoff between SR performance and computational efficiency.With the modified residual structure,depthwise separable convolutions(DS Convs)are employed to improve convolutional operations’efficiency.Furthermore,several weighted multi-scale residual blocks(WMRBs)are stacked to enhance the multi-scale representation capability.In the reconstruction subnetwork,a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image.Extensive experiments were conducted to evaluate the proposed model,and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.
文摘OPEN Process Framework(OPF)是使软件开发过程达到CMM5级标准的软件工程框架。文中讨论了基于OPF的软件过程的主要元素及实施过程,并把该过程应用于某油田数据采集系统的开发,实践证明基于OPF的软件过程可以提高团队的开发能力、降低风险、有效控制资源,为项目的开发提供了高度清晰的过程框架,规范管理和开发流程。
