All‑Covalent Organic Framework Nanofilms Assembled Lithium‑Ion Capacitor to Solve the Imbalanced Charge Storage Kinetics

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.

Research on the Legal Framework of Content Regulations for Network Platforms

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.

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

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.

SOSNet:一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型

非小细胞肺癌严重损害人类健康,早期非小细胞肺癌CT(Computed Tomography)图像中的肿瘤结节体积小,不易发现,极易造成漏诊和误诊.为了精确分割非小细胞肺癌CT图像中的小体积肿瘤结节,本文提出SOSNet(Small Object Segmentation Networks)自动分割模型,利用ResNet(Residual Network)基础层和空洞卷积构造非对称编码器-解码器结构作为分割主网络,利用轴向取反注意力模块ARA(Axial Reverse Attention)逐步擦除背景中对分割有影响的结构,再使用结构细化模块SR(Structure Refinement)对主网络输出的粗略特征图进行结构细化,从而实现非小细胞肺癌肿瘤结节分割.在非小细胞肺癌公开数据集的实验测试表明,本文提出的小目标自动分割模型SOSNet可以有效分割出非小细胞肺癌CT图像中的小体积肿瘤结节,其mDice(mean-Dice)、mIoU(mean Intersection over Union)、Sensitivity、F1、Specificity、平均绝对误差MAE(Mean Absolute Error)均优于当前最先进的小目标分割模型CaraNet(Context Axial Reverse Attention Network).

A computational framework for improving genetic variants identification from 5,061 sheep sequencing data

Background Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation.Joint calling is routinely used to combine identified variants across multiple related samples.However,the improvement of variants identification using the mutual support information from mul-tiple samples remains quite limited for population-scale genotyping.Results In this study,we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples’data.The variants were accurately identified from multiple samples by using four steps:(1)Probabilities of variants from two widely used algorithms,GATK and Freebayes,were calculated by Poisson model incorporating base sequencing error potential;(2)The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification(rHID)variants database;(3)The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate(FDR)using rHID database;(4)To avoid the elimination of potentially true variants from rHID database,the vari-ants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants.The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32%compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number(GPC5),scrapie pathology(PAPSS2),sea-sonal reproduction and litter size(GRM1),coat color(RAB27A),and lentivirus susceptibility(TMEM154).Conclusion The new method used the computational strategy to reduce the number of false positives,and simulta-neously improve the identification of genetic variants.This strategy did not incur any extra cost by using any addi-tional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.

基于ASP.NET MVC架构在精炼炉RH二级客户端设计与实现

为了实现基于B/S框架的精炼炉RH二级客户端系统,采用基于.NET平台的ASP.NET MVC的Web开发框架,使用了MVC(Model-View-Controller)三层模式,利用基于ADO.NET为基础的Entity Framework对象关系映射器(简称ORM)框架技术,与数据库之间进行交互,Web界面采用Razor视图引擎,使得代码开发更加简洁、规范。基于MVC分层模式开发的精炼炉RH二级客户端系统,具有较好的可复用性、可扩展性,并且易于部署和维护。

Research on Weighted Directed Dynamic Multiplexing Network of World Grain Trade Based on Improved MLP Framework

As the main food source for humans, the global movement of the three major grains significantly impacts human survival and development. To investigate the evolution of the world cereal trade network and its development trend, a weighted directed dynamic multiplexed network was established using historical data on cereal trade, cereal import dependency ratio, and arable land per capita. Inspired by the MLP framework, we redefined the weight determination method for computing layer weights and edge weights of the target layer, modified the CN, RA, AA, and PA indicators, and proposed the node similarity indicator for weighted directed networks. The AUC metric, which measures the accuracy of the algorithm, has also been improved in order to finally obtain the link prediction results for the grain trading network. The prediction results were processed, such as web-based presentation and community partition. It was found that the number of generalized trade agreements does not have a decisive impact on inter-country cereal trade. The former large grain exporters continue to play an important role in this trade network. In the future, the world trade in cereals will develop in the direction of more frequent intercontinental trade and gradually weaken the intracontinental cereal trade.

Metal-organic-frameworks passivated CuBi_(2)O_(4)photocathodes boost CO_(2)reduction kinetics

Photoelectrochemical reduction of CO_(2)to produce CO with metal-organic frameworks(MOFs)is recognized as a desirable technology to mitigate CO_(2)emission and generate sustainable energy.To achieve highly efficient electrocatalyst,it is essential to design a new material interface and uncover new reaction mechanisms or kinetics.Herein,we developed two metal-organic Cu-MOF and Bi-MOF layers using benzene tricarboxylic acid(H_(3)BTC)ligands on CuBi_(2)O_(4) photocathodes.Both MOF layers drastically improved the photoelectrochemical stability by suppressing the photo-corrosion through conformal surface passivation.The Cu-MOF modified CuBi_(2)O_(4) showed more significant charge separation and transfer efficiencies than the Bi-MOF modified control.Based on the transient photocurrent curves under the applied potential of 0.6 V vs.RHE,the rate-law analysis showed the CO_(2)photoreduction took place through a first-order reaction.Further,the photoelectrochemical impedance spectra(PEIS)revealed this reaction order,representing an“operando”analysis.Moreover,the reaction rate constant on Cu-MOF modified sample was higher than that on Bi-MOF modified one and bare CuBi_(2)O_(4).Combined with the density functional theory calculation,the surface absorption of CO_(2)and CO molecules and the higher energy barrier for*COOH intermediates could significantly determine the first order reaction.

Challenges and Opportunities in Preserving Key Structural Features of 3D-Printed Metal/Covalent Organic Framework

Metal-organic framework(MOF)and covalent organic framework(COF)are a huge group of advanced porous materials exhibiting attractive and tunable microstructural features,such as large surface area,tunable pore size,and functional surfaces,which have significant values in various application areas.The emerging 3D printing technology further provides MOF and COFs(M/COFs)with higher designability of their macrostructure and demonstrates large achievements in their performance by shaping them into advanced 3D monoliths.However,the currently available 3D printing M/COFs strategy faces a major challenge of severe destruction of M/COFs’microstructural features,both during and after 3D printing.It is envisioned that preserving the microstructure of M/COFs in the 3D-printed monolith will bring a great improvement to the related applications.In this overview,the 3D-printed M/COFs are categorized into M/COF-mixed monoliths and M/COF-covered monoliths.Their differences in the properties,applications,and current research states are discussed.The up-to-date advancements in paste/scaffold composition and printing/covering methods to preserve the superior M/COF microstructure during 3D printing are further discussed for the two types of 3D-printed M/COF.Throughout the analysis of the current states of 3D-printed M/COFs,the expected future research direction to achieve a highly preserved microstructure in the 3D monolith is proposed.

Parallel Light Fields: A Perspective and A Framework

Dear Editor,Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired.

Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world software.The existing analysis of software security vulnerabilities often focuses on specific features or modules.This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software.The key novelty lies in overcoming the constraints of partial approaches.The proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security guidelines.Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each product.These guidelines are not only practical but also applicable in real-world software,allowing for prioritized security responses.The proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related software.The analysis resulted in the identification of a total of 121 vulnerabilities.The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

Metal-organic frameworks and their composites for advanced lithium-ion batteries:Synthesis,progress and prospects

Metal-organic frameworks(MOFs)are among the most promising materials for lithium-ion batteries(LIBs)owing to their high surface area,periodic porosity,adjustable pore size,and controllable chemical composition.For instance,their unique porous structures promote electrolyte penetration,ions transport,and make them ideal for battery separators.Regulating the chemical composition of MOF can introduce more active sites for electrochemical reactions.Therefore,MOFs and their related composites have been extensively and thoroughly explored for LIBs.However,the reported reviews solely include the applications of MOFs in the electrode materials of LIBs and rarely involve other aspects.A systematic review of the application of MOFs in LIBs is essential for understanding the mechanism of MOFs and better designing related MOFs battery materials.This review systematically evaluates the latest developments in pristine MOFs and MOF composites for LIB applications,including MOFs as the main materials(anode,cathode,separators,and electrolytes)to auxiliary materials(coating layers and additives for electrodes).Furthermore,the synthesis,modification methods,challenges,and prospects for the application of MOFs in LIBs are discussed.

Advances of Electrochemical and Electrochemiluminescent Sensors Based on Covalent Organic Frameworks

Covalent organic frameworks(COFs),a rapidly developing category of crystalline conjugated organic polymers,possess highly ordered structures,large specific surface areas,stable chemical properties,and tunable pore microenvironments.Since the first report of boroxine/boronate ester-linked COFs in 2005,COFs have rapidly gained popularity,showing important application prospects in various fields,such as sensing,catalysis,separation,and energy storage.Among them,COFs-based electrochemical(EC)sensors with upgraded analytical performance are arousing extensive interest.In this review,therefore,we summarize the basic properties and the general synthesis methods of COFs used in the field of electroanalytical chemistry,with special emphasis on their usages in the fabrication of chemical sensors,ions sensors,immunosensors,and aptasensors.Notably,the emerged COFs in the electrochemiluminescence(ECL)realm are thoroughly covered along with their preliminary applications.Additionally,final conclusions on state-of-the-art COFs are provided in terms of EC and ECL sensors,as well as challenges and prospects for extending and improving the research and applications of COFs in electroanalytical chemistry.

Carbon nanocages bridged with graphene enable fast kinetics for dual-carbon lithium-ion capacitors

Lithium-ion capacitors(LICs) combining the advantages of lithium-ion batteries and supercapacitors are considered a promising nextgeneration energy storage device. However, the sluggish kinetics of battery-type anode cannot match the capacitor-type cathode, restricting the development of LICs. Herein, hierarchical carbon framework(HCF) anode material composed of 0D carbon nanocage bridged with 2D graphene network are developed via a template-confined synthesis process. The HCF with nanocage structure reduces the Li^(+) transport path and benefits the rapid Li^(+) migration, while 2D graphene network can promote the electron interconnecting of carbon nanocages. In addition, the doped N atoms in HCF facilitate to the adsorption of ions and enhance the pseudo contribution, thus accelerate the kinetics of the anode. The HCF anode delivers high specific capacity, remarkable rate capability. The LIC pouch-cell based on HCF anode and active HCF(a-HCF) cathode can provide a high energy density of 162 Wh kg^(-1) and a superior power density of 15.8 kW kg^(-1), as well as a long cycling life exceeding 15,000cycles. This study demonstrates that the well-defined design of hierarchical carbon framework by incorporating 0D carbon nanocages and 2D graphene network is an effective strategy to promote LIC anode kinetics and hence boost the LIC electrochemical performance.

Synergistic catalysis of the N-hydroxyphthalimide on flower-like bimetallic metal-organic frameworks for boosting oxidative desulfurization

Synergic catalytic effect between active sites and supports greatly determines the catalytic activity for the aerobic oxidative desulfurization of fuel oils.In this work,Ni-doped Co-based bimetallic metal-organic framework(CoNi-MOF)is fabricated to disperse N-hydroxyphthalimide(NHPI),in which the whole catalyst provides plentiful synergic catalytic effect to improve the performance of oxidative desulfurization(ODS).As a bimetallic MOF,the second metal Ni doping results in the flower-like morphology and the modification of electronic properties,which ensure the exposure of NHPI and strengthen the synergistic effect of the overall catalyst.Compared with the monometallic Co-MOF and naked NHPI,the NHPI@CoNi-MOF triggers the efficient activation of molecular oxygen and improves the ODS performance without an initiator.The sulfur removal of dibenzothiophene-based model oil reaches 96.4%over the NHPI@CoNi-MOF catalyst in 8 h of reaction.Furthermore,the catalytic product of this aerobic ODS reaction is sulfone,which is adsorbed on the catalyst surface due to the difference in polarity.This work provides new insight and strategy for the design of a strong synergic catalytic effect between NHPI and bimetallic supports toward high-activity aerobic ODS materials.

Porous framework materials for energy&environment relevant applications:A systematic review

Carbon peaking and carbon neutralization trigger a technical revolution in energy&environment related fields.Development of new technologies for green energy production and storage,industrial energy saving and efficiency reinforcement,carbon capture,and pollutant gas treatment is in highly imperious demand.The emerging porous framework materials such as metal–organic frameworks(MOFs),covalent organic frameworks(COFs)and hydrogen-bonded organic frameworks(HOFs),owing to the permanent porosity,tremendous specific surface area,designable structure and customizable functionality,have shown great potential in major energy-consuming industrial processes,including sustainable energy gas catalytic conversion,energy-efficient industrial gas separation and storage.Herein,this manuscript presents a systematic review of porous framework materials for global and comprehensive energy&environment related applications,from a macroscopic and application perspective.

Photophysics of metal-organic frameworks:A brief overview

Metal-organic frameworks(MOFs),which are self-assembled porous coordination materials,have garnered considerable attention in the fields of optoelectronics,photovoltaic,photochemistry,and photocatalysis due to their diverse structures and excellent tunability.However,the performance of MOF-based optoelectronic applications currently falls short of the industry benchmark.To enhance the performance of MOF materials,it is imperative to undertake comprehensive investigations aimed at gaining a deeper understanding of photophysics and sequentially optimizing properties related to photocarrier transport,recombination,interaction,and transfer.By utilizing femtosecond laser pulses to excite MOFs,time-resolved optical spectroscopy offers a means to observe and characterize these ultrafast microscopic processes.This approach adds the time coordinate as a novel dimension for comprehending the interaction between light and MOFs.Accordingly,this review provides a comprehensive overview of the recent advancements in the photophysics of MOFs and additionally outlines potential avenues for exploring the time domain in the investigation of MOFs.

A progressive framework for rotary motion deblurring

The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles.Traditional rotary motion deblurring methods suffer from ringing artifacts and noise,especially for large blur extents.To solve the above problems,we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage.In the first stage,we design an adaptive blur extents factor(BE factor)to balance noise suppression and details reconstruction.And a novel deconvolution model is proposed based on BE factor.In the second stage,a triplescale deformable module CNN(TDM-CNN)is designed to reduce the ringing artifacts,which can exploit the 2D information of an image and adaptively adjust spatial sampling locations.To establish a standard evaluation benchmark,a real-world rotary motion blur dataset is proposed and released,which includes rotary blurred images and corresponding ground truth images with different blur angles.Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets.The code and dataset are available at https://github.com/JinhuiQin/RotaryDeblurring.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.