本文论述了图书馆期刊发布系统的计算机软件结构,介绍了在Microsof.tNET Framework2.0技术下图书馆期刊发布系统的优点与缺点,并结合本馆自身实例介绍了如何利用Microsoft公司最新的开发工具Visual Studio 2005来实现图书馆期刊发布系...本文论述了图书馆期刊发布系统的计算机软件结构,介绍了在Microsof.tNET Framework2.0技术下图书馆期刊发布系统的优点与缺点,并结合本馆自身实例介绍了如何利用Microsoft公司最新的开发工具Visual Studio 2005来实现图书馆期刊发布系统的计算机软件。展开更多
Microsoft．NET Framework中的安全性包含许多技术；基类库（BCL，Base Class Library）和ASP．NET中基于角色的安全、BCL中的密码系统类以及新的对使用访问控制列表（ACL，Access Control List）的支持即为其中的少数几个例子。公共语...Microsoft．NET Framework中的安全性包含许多技术；基类库（BCL，Base Class Library）和ASP．NET中基于角色的安全、BCL中的密码系统类以及新的对使用访问控制列表（ACL，Access Control List）的支持即为其中的少数几个例子。公共语言运行库（CLR，Common Language Runtime）提供的．NET安全性系列中的技术之一就是代码访问安全性（CAS，Code Access Security）。本文讨论．NET安全性中CAS的角色以及．NET Framework2．0的CAS中的一些关键的新功能和更改。展开更多
Free-standing covalent organic framework(COFs)nanofilms exhibit a remarkable ability to rapidly intercalate/de-intercalate Li^(+) in lithium-ion batteries,while simultaneously exposing affluent active sites in superca...Free-standing covalent organic framework(COFs)nanofilms exhibit a remarkable ability to rapidly intercalate/de-intercalate Li^(+) in lithium-ion batteries,while simultaneously exposing affluent active sites in supercapacitors.The development of these nanofilms offers a promising solution to address the persistent challenge of imbalanced charge storage kinetics between battery-type anode and capacitor-type cathode in lithium-ion capacitors(LICs).Herein,for the first time,custom-made COFBTMB-TP and COFTAPB-BPY nanofilms are synthesized as the anode and cathode,respectively,for an all-COF nanofilm-structured LIC.The COFBTMB-TP nanofilm with strong electronegative–CF3 groups enables tuning the partial electron cloud density for Li^(+) migration to ensure the rapid anode kinetic process.The thickness-regulated cathodic COFTAPB-BPY nanofilm can fit the anodic COF nanofilm in the capacity.Due to the aligned 1D channel,2D aromatic skeleton and accessible active sites of COF nanofilms,the whole COFTAPB-BPY//COFBTMB-TP LIC demonstrates a high energy density of 318 mWh cm^(−3) at a high-power density of 6 W cm^(−3),excellent rate capability,good cycle stability with the capacity retention rate of 77%after 5000-cycle.The COFTAPB-BPY//COFBTMB-TP LIC represents a new benchmark for currently reported film-type LICs and even film-type supercapacitors.After being comprehensively explored via ex situ XPS,7Li solid-state NMR analyses,and DFT calculation,it is found that the COFBTMB-TP nanofilm facilitates the reversible conversion of semi-ionic to ionic C–F bonds during lithium storage.COFBTMB-TP exhibits a strong interaction with Li^(+) due to the C–F,C=O,and C–N bonds,facilitating Li^(+) desolation and absorption from the electrolyte.This work addresses the challenge of imbalanced charge storage kinetics and capacity between the anode and cathode and also pave the way for future miniaturized and wearable LIC devices.展开更多
In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactiv...In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.展开更多
Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to sca...Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.展开更多
文摘Microsoft．NET Framework中的安全性包含许多技术；基类库（BCL，Base Class Library）和ASP．NET中基于角色的安全、BCL中的密码系统类以及新的对使用访问控制列表（ACL，Access Control List）的支持即为其中的少数几个例子。公共语言运行库（CLR，Common Language Runtime）提供的．NET安全性系列中的技术之一就是代码访问安全性（CAS，Code Access Security）。本文讨论．NET安全性中CAS的角色以及．NET Framework2．0的CAS中的一些关键的新功能和更改。
基金We are grateful to National Natural Science Foundation of China(Grant No.22375056,52272163)the Key R&D Program of Hebei(Grant No.216Z1201G)+1 种基金Natural Science Foundation of Hebei Province(Grant No.E2022208066,B2021208014)Key R&D Program of Hebei Technological Innovation Center of Chiral Medicine(Grant No.ZXJJ20220105).
文摘Free-standing covalent organic framework(COFs)nanofilms exhibit a remarkable ability to rapidly intercalate/de-intercalate Li^(+) in lithium-ion batteries,while simultaneously exposing affluent active sites in supercapacitors.The development of these nanofilms offers a promising solution to address the persistent challenge of imbalanced charge storage kinetics between battery-type anode and capacitor-type cathode in lithium-ion capacitors(LICs).Herein,for the first time,custom-made COFBTMB-TP and COFTAPB-BPY nanofilms are synthesized as the anode and cathode,respectively,for an all-COF nanofilm-structured LIC.The COFBTMB-TP nanofilm with strong electronegative–CF3 groups enables tuning the partial electron cloud density for Li^(+) migration to ensure the rapid anode kinetic process.The thickness-regulated cathodic COFTAPB-BPY nanofilm can fit the anodic COF nanofilm in the capacity.Due to the aligned 1D channel,2D aromatic skeleton and accessible active sites of COF nanofilms,the whole COFTAPB-BPY//COFBTMB-TP LIC demonstrates a high energy density of 318 mWh cm^(−3) at a high-power density of 6 W cm^(−3),excellent rate capability,good cycle stability with the capacity retention rate of 77%after 5000-cycle.The COFTAPB-BPY//COFBTMB-TP LIC represents a new benchmark for currently reported film-type LICs and even film-type supercapacitors.After being comprehensively explored via ex situ XPS,7Li solid-state NMR analyses,and DFT calculation,it is found that the COFBTMB-TP nanofilm facilitates the reversible conversion of semi-ionic to ionic C–F bonds during lithium storage.COFBTMB-TP exhibits a strong interaction with Li^(+) due to the C–F,C=O,and C–N bonds,facilitating Li^(+) desolation and absorption from the electrolyte.This work addresses the challenge of imbalanced charge storage kinetics and capacity between the anode and cathode and also pave the way for future miniaturized and wearable LIC devices.
基金This paper is a phased achievement of the key project of the Chongqing Municipal Education Commission entitled“Research on Establishment of Regional Legal Framework for Rural Revitalization”(Project No.23SKJD033)the university-level project of Southwest University of Political Science&Law entitled“A Comparative Study on Legislation for Agricultural and Rural Modernization”(Project No.DFLF2020Y12).
文摘In the era of the Internet,various network platforms have evolved into new hubs for information dissemination.Currently,China has established a platform-centered content regulation framework,wherein platforms proactively enforce content regulations in accordance with legal censorship obligations.Additionally,platform policies and user agreements augment their authority in content regulation.The platforms can achieve cost-effective and highly efficient content regulation by leveraging their strategic advantages enabled by their own technical capabilities and extensive coverage.The platform self-regulation model,however,still faces challenges.First,accurately evaluating content remains a formidable task;second,ensuring effective platform publicity through self-regulation poses difficulties;third,users may potentially face disadvantages due to the platform’s right of self-regulation;and fourth,digital copyright owners face challenges when defending digital copyright disputes under the safe harbor rule.Therefore,it is imperative to establish,review,and revise the legal framework for content regulation of network platforms in order to enhance the efficiency of their governance systems.The formulation of the legal framework for content regulation of network platforms may encompass the following aspects:rationalizing obligations pertaining to platform content regulations,enhancing supervision over platform self-regulation,and establishing a dual-track responsibility system for digital copyright content regulation.This will ensure a harmonious balance among public interests,users’personal rights and interests,and commercial benefits through regulating the content on network platforms.
基金supported by the National Natural Science Foundation of China-China State Railway Group Co.,Ltd.Railway Basic Research Joint Fund (Grant No.U2268217)the Scientific Funding for China Academy of Railway Sciences Corporation Limited (No.2021YJ183).
文摘Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured data.However,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial distortions.Moreover,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical structures.To more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic space.In HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic space.Additionally,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing framework.In addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention coefficients.Through the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical graphs.Experimental results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding representations.Furthermore,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhancements.
