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Preparation of biomass-derived carbon loaded with MnO_(2) as lithium-ion battery anode for improving its reversible capacity and cycling performance

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Frontiers of Chemical Science and Engineering 2024年第1期18卷 107-120页

作者： Likai Zhu huaping Lin Wenli zhang Qinhui Wang Yefeng Zhou National&Local United Engineering Research Centre for Chemical Process Simulation and Intensification Chemical Process Simulation and Optimization Engineering Research Center of Ministry of EducationXiangtan UniversityXiangtan 411100China Guangdong Provincial Key Laboratory of Plant Resources Biorefinery School of Chemical Engineering and Light IndustryGuangdong University of TechnologyGuangzhou 510006China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310027China

Biomass-derived carbon materials for lithiumion batteries emerge as one of the most promising anodes from sustainable perspective.However,improving the reversible capacity and cycling performance remains a long-standing challenge.By combining the benefits of K2CO_(3) activation and KMnO_(4) hydrothermal treatment,this work proposes a two-step activation method to load MnO_(2) charge transfer onto biomass-derived carbon(KAC@MnO_(2)).Comprehensive analysis reveals that KAC@MnO_(2) has a micro-mesoporous coexistence structure and uniform surface distribution of MnO_(2),thus providing an improved electrochemical performance.Specifically,KAC@MnO_(2) exhibits an initial chargedischarge capacity of 847.3/1813.2 mAh·g^(-1) at 0.2 A·g^(-1),which is significantly higher than that of direct pyrolysis carbon and K2CO_(3) activated carbon,respectively.Furthermore,the KAC@MnO_(2) maintains a reversible capacity of 652.6 mAh·g^(-1) after 100 cycles.Even at a high current density of 1.0 A·g^(-1),KAC@MnO_(2) still exhibits excellent long-term cycling stability and maintains a stable reversible capacity of 306.7 mAh·g^(-1) after 500 cycles.Compared with reported biochar anode materials,the KAC@MnO_(2) prepared in this work shows superior reversible capacity and cycling performance.Additionally,the Li+insertion and de-insertion mechanisms are verified by ex situ X-ray diffraction analysis during the chargedischarge process,helping us better understand the energy storage mechanism of KAC@MnO_(2).

关键词： biomass-derived carbon MnO_(2) lithium-ion batteries anode material high reversible capacity

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A Survey on Blockchain-Based Federated Learning:Categorization,Application and Analysis

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2451-2477页

作者： Yuming Tang Yitian zhang Tao Niu Zhen Li Zijian zhang huaping Chen Long zhang School of Cyberspace Science&Technology Beijing Institute of TechnologyBeijing100081China School of Computer Science&Technology Beijing Institute of TechnologyBeijing100081China Southeast Institute of Information Technology Beijing Institute of TechnologyPutian351100China Qianxin Technology Group Co. Ltd.Beijing100044China

Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial spheres.Despite its innovative approach to model training across distributed networks,FL has its vulnerabilities;the centralized server-client architecture introduces risks of single-point failures.Moreover,the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors.Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals.For these reasons,some participants unwilling use their private data to train a model,which is a bottleneck in the development and industrialization of federated learning.Blockchain technology,characterized by its decentralized ledger system,offers a compelling solution to these issues.It inherently prevents single-point failures and,through its incentive mechanisms,motivates participants to contribute computing power.Thus,blockchain-based FL(BCFL)emerges as a natural progression to address FL’s challenges.This study begins with concise introductions to federated learning and blockchain technologies,followed by a formal analysis of the specific problems that FL encounters.It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL.In addition,this research examines the use of BCFL in various fields,such as the Internet of Things and the Internet of Vehicles.Finally,it assesses the effectiveness of these solutions.

关键词： Federated learning blockchain privacy-preserving

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Full-Stokes metasurface polarimetry requiring only a single measurement

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Photonics Research 2024年第3期12卷 514-521页

作者： CHENGLONG ZHENG HUI LI JINGYU LIU MENGGUANG WANG huaping ZANG YAN zhang JIANQUAN YAO Key Laboratory of Material Physics Ministry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhou 450052China Key Laboratory of Opto-Electronics Information Technology(Tianjin University) Ministry of EducationSchool of Precision Instruments and Opto-Electronics EngineeringTianjin UniversityTianjin 300072China Beijing Key Laboratory for Metamaterials and Devices Key Laboratory of Terahertz OptoelectronicsMinistry of Educationand Beijing Advanced Innovation Center for Imaging TechnologyDepartment of PhysicsCapital Normal UniversityBeijing 100048China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China

Polarization is crucial in various fields such as imaging,sensing,and substance detection.A compact,fast,and accurate polarization detection device is vital for these applications.Herein,we demonstrate a multifocus metalens for terahertz polarization detection that requires only a single measurement to obtain complete polarization parameters and reconstruct the polarization state of the incident field.The individual subarrays of this metalens convert each of the six polarized components into the same polarization,which in turn links the Stokes parameters to these six foci.The incident linear polarizations and elliptical polarizations are characterized by Stokes parameters and polarization ellipses.Simulations and experimental results show that the scheme can accurately detect the incident polarization with a single measurement.The proposed metasurface polarimetry may find applications in the fields of real-time terahertz detection and integrated optics.

关键词： field incident polar

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Emerging delivery systems based on aqueous two-phase systems:A review

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Acta Pharmaceutica Sinica B 2024年第1期14卷 110-132页

作者： Yaowen zhang Yankun Luo Jingqi Zhao Wenzhuo Zheng Jun Zhan huaping Zheng Feng Luo State Key Laboratory of Oral Diseases National Clinical Research Center for Oral DiseasesWest China School of StomatologySichuan UniversityChengdu 610041China Department of Obstetrics and Gynecology West China Second University HospitalSichuan UniversityChengdu 610041China Key Laboratory of Birth Defects and Related Diseases of Women and Children(Sichuan University) Ministry of EducationChengdu 610041China Department of Dermatology Rare Diseases CenterInstitutes for Systems GeneticsFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengdu 610041China Department of Prosthodontics West China School of StomatologySichuan UniversityChengdu 610041China

The aqueous two-phase system(ATPS)is an all-aqueous system fabricated from two immiscible aqueous phases.It is spontaneously assembled through physical liquid-liquid phase separation(LLPS)and can create suitable templates like the multicompartment of the intracellular environment.Delicate structures containing multiple compartments make it possible to endow materials with advanced functions.Due to the properties of ATPSs,ATPS-based drug delivery systems exhibit excellent biocompatibility,extraordinary loading efficiency,and intelligently controlled content release,which are particularly advantageous for delivering drugs in vivo.Therefore,we will systematically review and evaluate ATPSs as an ideal drug delivery system.Based on the basic mechanisms and influencing factors in forming ATPSs,the transformation of ATPSs into valuable biomaterials is described.Afterward,we concentrate on the most recent cutting-edge research on ATPS-based delivery systems.Finally,the potential for further collaborations between ATPS-based drug-carrying biomaterials and disease diagnosis and treatment is also explored.

关键词： Aqueous two-phase systems Drug delivery systems Biomaterials Microcapsules Microparticles Cell therapy Cancer treatment Organ regeneration

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Fibroblast-localized lncRNA CFIRL promotes cardiac fibrosis and dysfunction in dilated cardiomyopathy

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Science China(Life Sciences) 2024年第6期67卷 1155-1169页

作者： Shuai Yuan Xudong zhang Jiabing Zhan Rong Xie Jiahui Fan Beibei Dai Yanru Zhao Zhongwei Yin Qian Liu Dao Wen Wang huaping Li Chen Chen Division of Cardiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Hubei Key Laboratory of Genetics and Molecular Mechanisms of Cardiological Disorders Wuhan 430030China Department of Forensic Medicine Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China

CFIRL is a long noncoding RNA(lnc RNA),we previously identified as the most significantly upregulated lnc RNA in the failing hearts of patients with dilated cardiomyopathy(DCM).In this study,we determined the function of CFIRL and its role in DCM.Real-time polymerase chain reaction and in situ hybridization assays revealed that CFIRL was primarily localized in the nucleus of cardiac fibroblasts and robustly increased in failing hearts.Global knockdown or fibroblast-specific knockout of CFIRL attenuated transverse aortic constriction(TAC)-induced cardiac dysfunction and fibrosis in vivo.Overexpression of CFIRL in vitro promoted fibroblast proliferation and aggravated angiotensin II-induced differentiation to myofibroblasts.CFIRL knockdown attenuated these effects.Mechanistically,RNA pull-down assay and gene expression profiling revealed that CFIRL recruited ENO1,a newly identified noncanonical transcriptional factor,to activate IL-6transcription.IL-6 exerted a paracrine effect on cardiomyocytes to promote cardiac hypertrophy,which can be prevented by CFIRL knockdown.These findings uncover the critical role of CFIRL,a fibroblast-associated lnc RNA,in heart failure by facilitating crosstalk between fibroblasts and cardiomyocytes.CFIRL knockdown might be a potent strategy to prevent cardiac remodeling in heart failure,particularly in DCM.

关键词： lncRNA fibroblast fibrosis dilated cardiomyopathy heart failure

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Highly selective growth of(6,5)single-walled carbon nanotubes from sigma phase alloy catalyst

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Nano Research 2024年第3期17卷 1999-2003页

作者： Liantao Xin Chen Ma Qianru Wu Shaokang Liu Linhai Li Xiuyun zhang Liu Qian Maoshuai He Dong Li Fangqian Han Shulan Hao Lihu Feng Yahan Li huaping Liu Lili zhang Chang Liu Jin zhang College of Chemistry and Molecular Engineering Qingdao University of Science and TechnologyQingdao 266042China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China College of Physics Science and Technology Yangzhou UniversityYangzhou 225002China Center for Nanochemistry Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China

The chirality structure of a single-walled carbon nanotube(SWNT)strongly depends on the composition of catalyst used in the chemical vapor deposition process.In this study,we develop a porous magnesia supported manganese-rhenium(MnRe/MgO)catalyst for chirality-selective synthesis of SWNTs.Detailed characterizations reveal that(6,5)tubes with a selectivity higher than 70%are grown from the Re-rich MnRe/MgO catalyst.By comparing the SWNT growth results with those of monometallic Mn or Re,the formation of sigma phase,an intermetallic compound occurring in transition-metal alloy systems,is revealed to be crucial for the dominant synthesis of(6,5)SWNTs.This work not only extends the application of sigma phase alloy for catalytic synthesis of SWNTs,but also sheds lights on the growth of SWNTs with a high chirality selectivity.

关键词： single-walled carbon nanotubes sigma phase manganese-rhenium catalyst selective growth transition-metal alloy

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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Computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif Khan Nada Boudjellal huaping zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policym

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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Computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan huaping zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics.In recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related conversations.Nevertheless,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of misinformation.To address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related tweets.Election-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,andmisinformation.ElecBERT is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in English.Themodel outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English.The performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case study.The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of 6.13.This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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Evaluation on ChatGPT for Chinese Language Understanding

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Data Intelligence 2023年第4期5卷 885-903页

作者： Linhan Li huaping zhang Chunjin Li Haowen You Wenyao Cui School of Computer Science Beijing Institute of TechnologyBeijingChina

ChatGPT has attracted extension attention of academia and industry.This paper aims to evaluate ChatGPT in Chinese language understanding capability on 6 tasks using 11 datasets.Experiments indicate that ChatGPT achieved competitive results in sentiment analysis,summary,and reading comprehension in Chinese,while it is prone to factual errors in closed-book QA.Further,on two more difficult Chinese understanding tasks,that is,idiom fill-in-the-blank and cants understanding,we found that a simple chain-of-thought prompt can improve the accuracy of ChatGPT in complex reasoning.This paper further analyses the possible risks of using ChatGPT based on the results.Finally,we briefly describe the research and development progress of our ChatBIT.

关键词： Language Model ChatGPT ChatBIT Chinese Language Understanding Artificial intelligence

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制造业企业数据价值释放:效应与机制

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系统工程理论与实践 2024年第1期44卷 68-84页

作者：张灵冯科孙华平首都经济贸易大学城市经济与公共管理学院北京100070 北京大学经济学院北京100871 北京科技大学经济管理学院北京100083 北京工商大学经济学院北京100048

数字技术应用不断深化推动多源异构数据的收集、流通、交互与价值实现,使得经济运行机制发生变革.本文从技术创新视角衡量企业的数据驱动能力,将数据驱动能力和数据要素引入生产函数,剖析数据要素赋能制造业企业降本增效的内在机制,研... 详细信息

数字技术应用不断深化推动多源异构数据的收集、流通、交互与价值实现,使得经济运行机制发生变革.本文从技术创新视角衡量企业的数据驱动能力,将数据驱动能力和数据要素引入生产函数,剖析数据要素赋能制造业企业降本增效的内在机制,研究表明:1)数据的产生、分享和应用可显著提升制造业企业的生产效率;2)进一步对比不同技术密集度企业数据价值释放的进程与差异发现,数字技术应用和数据要素的投入有效降低了低、中技术制造业企业的制造成本以及高技术企业的销售费用;3)当高技术企业具备更强大的数据驱动能力时,其生产成本反而增加了,表明我国高技术制造业企业尚未实现数据驱动下的产业转型升级.此外,本文基于产业关联理论和产业集聚理论,将空间因素纳入分析框架,研究发现:产业链视角下,制造业生产全流程、全产业链、全生命周期数据的融通与开发利用,通过提高区域产业链的协同效能,从而进一步提升企业的生产效率.本研究拓展了数字经济理论与机制分析的研究内容,为充分释放数据要素价值,优化资源要素配置效率提供证据支持.

关键词：数据要素制造业全要素生产率机制分析

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