In Situ Polymer Gel Electrolyte in Boosting Scalable Fibre Lithium Battery Applications

The poor interfacial stability not only deteriorates fibre lithium-ion batteries(FLBs)performance but also impacts their scalable applications.To efficiently address these challenges,Prof.Huisheng Peng team proposed a generalized channel structures strategy with optimized in situ polymerization technology in their recent study.The resultant FLBs can be woven into different-sized powering textiles,providing a high energy density output of 128 Wh kg^(-1) and simultaneously demonstrating good durability even under harsh conditions.Such a promising strategy expands the horizon in developing FLB with particular polymer gel electrolytes,and significantly ever-deepening understanding of the scaled wearable energy textile system toward a sustainable future.

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

Multi-disciplinary Pathways to Computing:A Scalable and Col aborative Approach to Capitalize on the Demand for Computer Science Education

The number of students demanding computer science(CS)education is rapidly rising,and while faculty sizes are also growing,the traditional pipeline consisting of a CS major,a CS master’s,and then a move to industry or a Ph.D.program is simply not scalable.To address this problem,the Department of Computing at the University of Illinois has introduced a multidisciplinary approach to computing,which is a scalable and collaborative approach to capitalize on the tremendous demand for computer science education.The key component of the approach is the blended major,also referred to as“CS+X”,where CS denotes computer science and X denotes a non-computing field.These CS+X blended degrees enable win-win partnerships among multiple subject areas,distributing the educational responsibilities while growing the entire university.To meet the demand from non-CS majors,another pathway that is offered is a graduate certificate program in addition to the traditional minor program.To accommodate the large number of students,scalable teaching tools,such as automatic graders,have also been developed.

Solvent engineering towards scalable fabrication of high-quality perovskite films for efficient solar modules

Over the last decade,remarkable progress has been made in metal halide perovskite solar cells(PSCs),which have been a focus of emerging photovoltaic techniques and show great potential for commercialization.However,the upscaling of small-area PSCs to large-area solar modules to meet the demands of practical applications remains a significant challenge.The scalable production of high-quality perovskite films by a simple,reproducible process is crucial for resolving this issue.Furthermore,the crystallization behavior in the solution-processed fabrication of perovskite films can be strongly influenced by the physicochemical properties of the precursor inks,which are significantly affected by the employed solvents and their interactions with the solutes.Thus,a comprehensive understanding of solvent engineering for fabricating perovskite films over large areas is urgently required.In this paper,we first analyze the role of solvents in the solution-processed fabrication of large-area perovskite films based on the classical crystal nucleation and growth mechanism.Recent efforts in solvent engineering to improve the quality of perovskite films for solar modules are discussed.Finally,the basic principles and future challenges of solvent system design for scalable fabrication of high-quality perovskite films for efficient solar modules are proposed.

区块链扩展技术现状与展望

近年来,区块链技术引起广泛关注,其作为一种分布式账本技术,由于具备开放性、透明性和不可篡改性,已经被应用到诸多领域.但随着用户数量和访问需求的大幅增加,现有区块链体系结构可扩展性不足导致的性能瓶颈,制约了区块链技术的应用和推广,如何解决可扩展性问题已成为学术界和工业界关注的热点.对已有的区块链扩展方案进行分析和总结.首先,介绍区块链基本概念和可扩展性问题的由来,定义可扩展性问题并提出衡量可扩展性的指标.其次,给出分类框架,将现有方案按网络扩展、链上扩展、链下扩展3大类进行介绍,通过对不同的区块链可扩展性方案进行分析,比较各自的技术特点并归纳优缺点.最后,对亟待解决的开放性问题进行讨论,展望区块链技术未来趋势.

Advanced DAG-Based Ranking(ADR)Protocol for Blockchain Scalability

In the past decade,blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention.However,current blockchain systems face the problems of limited throughput,poor scalability,and high latency.Due to the failure of consensus algorithms in managing nodes’identities,blockchain technology is considered inappropriate for many applications,e.g.,in IoT environments,because of poor scalability.This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking(ADR)protocol to improve blockchain scalability and throughput.The ADR protocol uses the directed acyclic graph ledger,where nodes are placed according to their ranking positions in the graph.It allows honest nodes to use theDirect Acyclic Graph(DAG)topology to write blocks and verify transactions instead of a chain of blocks.By using a three-step strategy,this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability.The first step involves the safe entry of nodes into the system by verifying their private and public keys.The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions.In the third step,a ranking algorithm is developed to separate the nodes created by attackers.After eliminating attacker nodes,the nodes are ranked according to their performance in the system,and true nodes are arranged in blocks in topological order.As a result,the ADR protocol is suitable for applications in the Internet of Things(IoT).We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes.Based on the simulation results,this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications(IOTA)and ByteBall.

Fast and scalable routing protocols for data center networks

Data center networks may comprise tens or hundreds of thousands of nodes,and,naturally,suffer from frequent software and hardware failures as well as link congestions.Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays.In such dynamic networks,links frequently fail or get congested,making the recalculation of the shortest paths a computationally intensive problem.Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed.Surprisingly,the design of fast shortest-path algorithms for data centers was largely neglected,though they are universal components of routing protocols.Moreover,parallelization techniques were mostly deployed for random network topologies,and not for regular topologies that are often found in data centers.The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware.We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

SST-V:A Scalable Semantic Transmission Framework for Video

The emerging new services in the sixth generation(6G)communication system impose increasingly stringent requirements and challenges on video transmission.Semantic communications are envisioned as a promising solution to these challenges.This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm,named scalable semantic transmission framework for video(SST-V),which jointly considers the semantic importance and channel conditions.Specifically,a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level,facilitating high-efficiency semantic coding.By further considering the channel condition,a cascaded learning based scalable joint semanticchannel coding algorithm is proposed,which autonomously adapts the semantic coding and channel coding strategies to the specific signalto-noise ratio(SNR).Simulation results show that SST-V achieves better video reconstruction performance,while significantly reducing the transmission overhead.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

基于级联编码的区块链分片存储方案

传统区块链存在存储可扩展性难题,现有研究基于纠删码理论降低了区块链存储开销,但在区块的译码恢复时会带来高额的计算与通信开销。为解决以上难题,提出了一种基于级联编码的区块链分片存储方案。通过添加预编码层改进现有无码率纠删码,实现了Ο(n)的编译码复杂度。考虑译码过程中节点间的通信时延差异,提出了基于Metis的时延感知分片算法,通过时延权重决定节点的分片归属,降低译码过程中的通信开销。仿真结果表明,所提方案不仅保证了区块链数据可靠性而且所需的计算开销与通信开销也优于传统方案。

基于多绘制管线的大规模并行体绘制性能优化技术

针对数值模拟输出的大规模科学数据,体绘制方法为了刻画复杂物理特征,会进行高密度光线采样,但由此带来了极大的计算开销和数据增量。在国产自主CPU高性能计算机上,由于处理器单核的计算能力低于商业CPU,只能使用更多的处理器核来分担体绘制任务,从而引起了采样数据并行通信的可扩展性瓶颈。为充分利用国产自主CPU高性能计算机来高效完成体绘制任务,针对大规模并行体绘制提出一种基于多绘制管线的性能优化技术,通过多管线、多进程的两级并行模式来降低单条管线的并行规模。在大规模并行体绘制中,该技术将绘制目标图像划分成多个子区域,绘制进程则相应分组,每个进程组独立执行一条绘制管线,以完成图像相应子区域的绘制,最后再收集所有的图像子区域,形成完整图像并输出。实验结果表明,优化后的体绘制算法在国产自主CPU高性能计算机上可以扩展到万核规模,并能有效完成体绘制任务。

面向学位/学历证书可信管理的可扩展类PBFT算法

现有的学位/学历证书可信管理存在节点扩展的优化策略不足、未考虑节点差异性和吞吐量低等问题,“区块链+教育”为学位/学历证书的可信管理提供了一种解决方案。针对上述问题,提出一种面向学位/学历证书可信管理的可扩展类PBFT算法z-PBFT。该算法采用基于区域分组的分层设计,算法模型分为主区域和副区域节点簇,副区域内部采用局部共识机制;通过基于TOPSIS建模和熵值赋权法的加权随机选取分派策略评估节点性能,并选取共识委员节点集。实验结果表明,在大规模节点应用场景下,该算法在确保安全性的同时,具有更高的吞吐量和可扩展性。

基于原子/分子团簇结构的材料与器件制造

原子/分子团簇是物质结构的一种新形态,具有独特的本征性质。从原子/分子团簇到器件的跨尺度制造,将为国防高端装备和新兴电子等产业发展带来深刻变革。团簇的多物质构效关系、宏量制造、团簇结构跨尺度构筑以及团簇器件的高性能制造等是原子/分子团簇器件制造的关键发展方向,主导着从原子到产品制造的发展历程。把握这些发展背后的重要机遇,将有助于占领原子级制造研究的制高点,引领原子级制造方法的变革。本文从团簇新材料的宏量制造、新型功能器件的原子/分子团簇构筑、团簇—器件的跨尺度制造工艺和装备等三个方面概括了原子/分子团簇与器件制造领域的主要研究进展,总结了原子/分子团簇与器件领域的关键科学问题及面临的挑战,并对其未来发展方向和发展战略给出了建议。

基于字合成运算的标签组群组共存证明协议

针对现有的大多数证明协议无法同时证明多标签同时存在缺陷,提出一种具有可扩展性的电子标签组证明协议。协议采用按位运算设计实现的字合成运算对信息加密,字合成运算可使得协议达到超轻量级计算;同时字合成运算实现中巧妙运用参数自身具备的汉明重量,可减少参量引入;协议基于先验证再响应机制,可抵抗假冒攻击等。对协议进行不同角度分析,表明协议能够提供较高的安全性能,同时能够适用于现有低成本被动式电子标签系统中。

翻译教学语料库规模化建设的大数据解决方案

翻译教学中基于平行文本的双语平行语料库建设,本质上是译者根据自身需求进行方案设计、语料收集和语料管理的过程。目前受限于现有翻译技术手段和经济条件,翻译教学中难以规模化建设此类语料库。通过以大数据自动化采集的方式,引入人工智能领域的Python技术与PostgreSQL数据库系统,来解决目前建立大规模教学语料库的难题。为了完整呈现规模化建库过程,借用政府公文类翻译的真实案例,详细描述了建库方案设计、语料收集和语料管理等操作步骤,分析了各步骤中存在的自动化难题以及新技术介入的契机。部分技术细节与源代码也在文中公开,以期为大规模建设教学语料库提供一个切实可行的解决方案。

区块链分片技术研究综述

区块链技术以去中心化、防篡改等功能为特色,具有广泛的应用前景。然而,区块链系统难以支撑大规模海量的分布式数据管理和交易,所以区块链的性能和可扩展性问题成为重要的研究方向。目前,研究人员分别从修改链上的数据结构和共识算法,到添加链下操作技术,提出了一些解决方案,以提高区块链的性能和可扩展性。而其中,随着网络规模的增加,实现水平扩展性的最实用的方法就是分片技术。作为一种链上扩容方式,分片技术是一种将整个区块链网络划分成多个片段的方法,便于同时处理多个交易或合约。每个分片都可以独立运行,拥有自己的交易历史和状态,在不牺牲中心化程度的同时提高了区块链的性能和可扩展性。以往的大量区块链分片技术研究着重介绍了分片中的交易共识,而忽略了分片策略机制与分片架构。为此,首先对现有的分片区块链进行系统分析,将分片区块链的设计过程分为架构设置、节点选择、节点分配、交易分发、交易处理和分片重构等部分,并分析了分片区块链的设计过程的各部分的功能、属性;其次,对分片架构进行了分类和总结,重点研究了各种分片策略与机制,分析了它们的优缺点;之后,对主流的分片区块链系统做了比较,并分析了它们的可扩展性和可靠性,包括系统吞吐量、时延、通信开销、节点随机性、分片安全性和跨片智能合约等;最后,提出未来可能的研究方向。

改进的超额抵押高吞吐量多路径支付方案

在链下支付中,多路径支付较单路径支付在成功率和交易时延上存在显著差异。尽管Spear方案通过超额抵押显著提升了多路径支付的性能,但过多的抵押资金锁定可能对网络资金流动性造成重大影响。为解决这一问题,提出了一种改进的超额抵押高吞吐量多路径支付方案,通过最大流算法和逼近理想解排序法优化大额支付的路径选择和资金分配问题。实验结果显示,相较于其他四种经典多路径支付方案,本方案在相同性能下减少了8%~16%的超额抵押资金,有效降低了抵押资金需求,增强了网络资金流动性。

基于矩阵乘积压缩态的动态可扩展秘密共享方案

目前,基于纠缠态的量子秘密共享(quantum secret sharing,QSS)方案未充分利用纠缠态的概率振幅潜力.然而,纠缠态的概率振幅是量子信息学的一个关键特性,在量子计算和量子通信等领域有着广泛的应用潜力.值得注意的是,纠缠态可以通过矩阵乘积态(matrix product state,MPS)有效表达.利用MPS对纠缠态进行表征,能够准确地揭示与概率振幅相关的纠缠特性.本研究证明利用MPS表征纠缠态,可以将一个W态压缩为一个单光子和一个矩阵,展示一种新的技术路径.此外,本研究还提出MPS与秘密共享方案之间的创新互操作性,即通过压缩允许量子份额与共享的量子态之间形成非一对一的映射关系.这种方法可能提供一种更高效的方式来实现量子信息的编码和传输,对于量子秘密共享尤为重要.同时,我们提出的QSS方案具有动态特性,能够根据需要轻松地添加或移除参与者,以更好地适应参与者需求的变化,并在实际应用场景中展现出更高的实用性和适应性.本文的方案能够在保持高效纠缠利用的同时,满足系统的多元需求,包括但不限于通信安全性、数据传输率和系统的可扩展性.

降低跨分片交易比例的区块链分片方法

针对分片技术在优化区块链性能时引起的跨分片交易比例过高、跨分片交易验证复杂的问题,提出一种降低跨分片交易比例的区块链分片方法。首先,从数据分片的角度出发,构造区块链交易分片模型,并给出分片性能评价指标;其次,针对区块链中长期积累的交易数据,从账号关联性角度出发,构造发送方和接收方的交易频次集合;最后,设计考虑交易频次的区块链交易分片算法(FBTS),从而解决交易分片中跨分片比例过高的问题。在分片粒度为2、3、5、7、15、20、30和50的情况下,所提算法在跨分片交易比例、账号平均跨分片次数、账号的加权平均跨分片次数等性能指标方面均优于随机分片算法(RSA)和取模分片算法(MSA)。另外所提算法的大多数账号和交易量都集中分布在低跨分片次数处,说明交易的完成不需要多次跨分片。实验结果表明,所提算法可以有效地降低跨分片交易比例,缩短跨分片的交易的时延。

基于深度强化学习的复杂网络可扩展社区检测

针对复杂网络社区检测可扩展性不足与准确性不高的问题,提出一种复杂网络可扩展社区检测算法。该算法由两个阶段构成,第一阶段根据邻域度数方差检测网络中的候选社区中心,基于网络拓扑结构评估节点的相似性,基于相似性进行标签传播,建立网络的初始化社区;第二阶段基于深度强化学习对网络社区结构进行微调与优化,利用深度强化学习强大的感知能力与决策能力提高社区结构的准确性。实验结果表明,由该算法发现的网络社区获得了较高的准确性。