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Design and application of copper/lithium composite anodes for advanced lithium metal batteries

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Rare Metals 2024年第3期43卷 942-970页

作者： Bin Zhang Ji-Ping Ma Yang Zhao Tong Li Jin-Lin Yang Zhan-Ling Zhang Shi-Zhong Wei Guang-Min Zhou school of Materials Science and Engineering Henan University of Science and TechnologyLuoyang 471003China Shenzhen Geim Graphene Center Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China school of Physical and Mathematical sciences Nanyang Technological UniversitySingapore 637371Singapore Shenzhen Geim Graphene Center Tsinghua-Berkeley Shenzhen InstituteTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China

Lithium(Li)is a promising candidate for nextgeneration battery anode due to its high theoretical specific capacity and low reduction ***,safety issues derived from the uncontrolled growth of Li dendrite and huge volume change of Li hinder its practical application.C onstructing dendrite-free composite Li anodes can significantly alleviate the above ***(Cu)-based materials have bee n widely used as substrates of the composite electrodes due to their chemical stability,excellent conductivity,and good mechanical ***/lithium(Cu/Li)composite anodes significantly regulate the local current density and decrease Li nucleation overp otential,realizing the uniform and dendrite-free Li *** this review,Cu/Li composite methods including electrodeposition,melting infusion,and mechanical rolling are systematically summarized and ***,design strategies of Cu-based current collectors for high performance Cu/Li composite anodes are *** challenges and future development for Cu/Li composite anodes are presented and *** hope that this review can provide a comprehensive understanding of Cu/Li composite methods of the latest development of Li metal anode and stimulate more research in the future.

关键词： Lithium metal batteries Cu/Li composite anode Electrodeposition Thermal melting infusion Mechanical rolling

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

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China Communications 2024年第5期21卷 113-122页

作者： Qiu Wanqing Zhang Qingmiao Zhao Junhui Yang Lihua school of information Engineering East China Jiaotong UniversityNanchang 330013China school of Electronic and information Engineering Beijing Jiaotong UniversityBeijing 100044China

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related *** basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location ***,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy *** this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting ***,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it ***,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are ***,extensive simulation results are *** is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

关键词： extreme learning machine fingerprinting localization indoor localization machine learning particle swarm optimization

Attention-based network embedding with higher-order weights and node attributes

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 440-451页

作者： Xian Mo Binyuan Wan Rui Tang Junkai Ding Guangdi Liu school of information Engineering Ningxia UniversityYinchuanChina school of Cyber Science and Engineering Sichuan UniversityChengduChina Computer Science and information Technology University of MalayaKuala LumpurMalaysia Academy of Medical sciences Zhengzhou UniversityZhengzhouChina

Network embedding aspires to learn a low-dimensional vector of each node in networks,which can apply to diverse data mining *** real-life,many networks include rich attributes and temporal ***,most existing embedding approaches ignore either temporal information or network attributes.A self-attention based architecture using higher-order weights and node attributes for both static and temporal attributed network embedding is presented in this article.A random walk sampling algorithm based on higher-order weights and node attributes to capture network topological features is *** static attributed networks,the algorithm incorporates first-order to k-order weights,and node attribute similarities into one weighted graph to preserve topological features of *** temporal attribute networks,the algorithm incorporates previous snapshots of networks containing first-order to k-order weights,and nodes attribute similarities into one weighted *** addition,the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater *** features are then incorporated into topological ***,the authors adopt the most advanced architecture,Self-Attention Networks,to learn node *** results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state-of-the-art baseline approaches.

关键词： data mining deep neural networks social network

Node Sociability Based Intelligent Routing for Post-Disaster Emergency Networks

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China Communications 2024年第8期21卷 104-114页

作者： Li Jiameng Xiong Xuanrui Liu Min Amr Tolba school of Communications and information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China graduate school of information Production and SystemsWaseda UniversityFukuoka 808-0135Japan school of Communications and information Engineering Chongqing College of Mobile CommunicationChongqing 401520China Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia

In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are *** the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission *** address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network ***,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility ***,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility ***,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.

关键词： delay tolerant networks Internet of things node sociability routing strategy

Characterization of size-resolved effective density of atmospheric particles in an urban atmosphere in Southern China

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Journal of Environmental sciences 2024年第7期141卷 194-204页

作者： Tingting Xie Liming Cao Jinyi Zheng Peng Xuan Xiaofeng Huang Laboratory of Atmospheric Observation Supersite School of Environment and EnergyPeking University Shenzhen Graduate SchoolShenzhen 518055China

Effective density(ρ_(eff))is one of the most important physical properties of atmospheric particles,providing important references in exploring the emissions and aging processes of fresh *** this study,a combined system of differential mobility analyzer,centrifugal particle mass analyzer,and condensation particle counter was used to periodically measure theρ_(eff)of atmospheric particles in Shenzhen from Oct.2021 to *** showed that theρ_(eff)of particles with various size presented a bimodal distribution,which could be divided into main density(ρ_(m),main peak,corresponding to relatively dense particles after aging)and sub density(ρ_(s),sub peak,corresponding to fresh particles).The occurrence frequencies ofρ_(s)of particles with diameters of 50 and 80 nm were less than 20%,but were as high as about 40%of that with diameters from 120 to 350 ***ρ_(m)showed increasing trend with the size of particles,whileρ_(s)decreased as the increasing of the size of ***ρ_(eff)on pollution day varied significantly with chemical *** increasing of the proportion of sulfate could promote the increasing ofρ_(eff),while black carbon and organic matter caused opposite effects,which may be related to various factors,including the difference of the material density and morphology of various chemical ***ρ_(eff)of 50,80 and 120 nm particles decreased considerably during the new particle formation event,indicating that organic condensation was an important contributor to new particle growth.

关键词： DMA-CPMA-CPC Effective density Main/sub density Size distribution

Astrometric Observations of NEA 1998 HH49 Using the Daocheng 50 cm Telescope

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Research in Astronomy and Astrophysics 2024年第7期24卷 74-81页

作者： Huan Xu Xiang-Ming Cheng Yi-Gong Zhang Teng-Fei Song Zhen-Jun Zhang Qing-Yu Peng Yunnan Observatories Chinese Academy of Sciences University of Chinese Academy of sciences Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Engineering Laboratory on Applied Astronomical Technology of Yunnan school of information Engineering Kunming University school of Computer Science and information Engineering Anyang Institute of Technology Department of Computer Science Jinan University Sino-French Joint Laboratory for Astrometry Dynamics and Space ScienceJinan University

This study details an astrometric observation campaign of the Near-Earth Asteroid 1998 HH49, conducted with the aim of refining our understanding of its physical characteristics. Utilizing the 50 cm telescope located at the Wumingshan Mountain in Daocheng, Sichuan, images were obtained over four nights, from 2023 October 19 to October 22. These observations were processed using Astrometrica software, facilitating the precise determination of the asteroid's position. The observational results were compared with the ephemerides from three distinct sources to verify accuracy: the Jet Propulsion Laboratory(JPL) Horizons System, the Institut de Mécanique Céleste et de Calcul des éphémérides(IMCCE) Miriade, and the Near-Earth Objects Dynamic Site(NEODyS-2).When compared with the JPL ephemeris, a mean observed-minus-calculated(O-C) result of 0.''07 in the *** and-0.''35 in the decl. direction was yielded. Furthermore, the comparison with the IMCCE ephemeris yielded mean O-C results of 0.''08 in the R.A. direction and-0.''06 in the decl. direction. The comparison with the NEODyS-2 ephemeris yielded the mean O-C results of 0.''06 in R.A. and-0.''49 in decl. direction. The study's findings demonstrate a general consistency between the observed data and the ephemeris predictions, with minor discrepancies observed across the data sets. Notably, both the JPL and NEODyS-2 ephemerides show that the residuals in the decl. direction exceed those in the R.A. direction. The disparities may result from atmospheric differential color refraction, ephemeris discrepancies, observational errors, and other factors. Additionally, it is worth noting that further investigation is required due to the potential influence of additional factors. Overall, the Daocheng 50 cm Telescope exhibits the ability to conduct high-precision positional measurements.

关键词： astrometry ephemerides time

Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China graduate school of information Science&Technology The University of Tokyo.Tokyo 1138654Japan school of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

Distributed Stochastic Optimization with Compression for Non-Strongly Convex Objectives

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Computer Modeling in Engineering & sciences 2024年第4期139卷 459-481页

作者： Xuanjie Li Yuedong Xu school of information Science and Technology Fudan UniversityShanghaiChina

We are investigating the distributed optimization problem,where a network of nodes works together to minimize a global objective that is a finite sum of their stored local *** nodes exchange optimization parameters through the wireless network,large-scale training models can create communication bottlenecks,resulting in slower training *** address this issue,CHOCO-SGD was proposed,which allows compressing information with arbitrary precision without reducing the convergence rate for strongly convex objective ***,most convex functions are not strongly convex(such as logistic regression or Lasso),which raises the question of whether this algorithm can be applied to non-strongly convex *** this paper,we provide the first theoretical analysis of the convergence rate of CHOCO-SGD on non-strongly convex *** derive a sufficient condition,which limits the fidelity of compression,to guarantee ***,our analysis demonstrates that within the fidelity threshold,this algorithm can significantly reduce transmission burden while maintaining the same convergence rate order as its no-compression *** experiments further validate the theoretical findings by demonstrating that CHOCO-SGD improves communication efficiency and keeps the same convergence rate order *** experiments also show that the algorithm fails to converge with low compression fidelity and in time-varying ***,our study offers valuable insights into the potential applicability of CHOCO-SGD for non-strongly convex ***,we provide practical guidelines for researchers seeking to utilize this algorithm in real-world scenarios.

关键词： Distributed stochastic optimization arbitrary compression fidelity non-strongly convex objective function

A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan school of information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)