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Enabling an Inorganic-Rich Interface via Cationic Surfactant for high-performance Lithium Metal Batteries

Nano-Micro Letters 2024年第8期16卷 1-17页

作者： Zejun Sun Jinlin Yang Hongfei Xu Chonglai Jiang Yuxiang Niu Xu Lian Yuan Liu Ruiqi Su Dayu Liu Yu Long Meng Wang Jingyu Mao Haotian Yang Baihua Cui Yukun Xiao Ganwen Chen Qi Zhang Zhenxiang Xing Jisheng Pan Gang Wu Wei Chen Department of Chemistry National University of Singapore3 Science Drive 3Singapore 117543Singapore Agency for Science Technology and Research(A*STAR)Institute of High-Performance Computing1 Fusionopolis Way#16‑16 ConnexisSingapore 138632Singapore Department of Physics National University of Singapore2 Science Drive 3Singapore 117542Singapore Joint School of National University of Singapore and Tianjin University International Campus of Tianjin UniversityBinhai New CityFuzhou 350207People’s Republic of China Agency for Science Technologyand Research(A*STAR)Institute of Materials Research and EngineeringInnovis2 Fusionopolis Way#08‑03Singapore 138634Singapore

An anion-rich electric double layer(EDL)region is favorable for fabricating an inorganic-rich solid-electrolyte interphase(SEI)towards stable lithium metal anode in ester ***,cetyltrimethylammonium bromide(CTAB),a cationic surfactant,is adopted to draw more anions into EDL by ionic interactions that shield the repelling force on anions during lithium *** situ electrochemical surface-enhanced Raman spectroscopy results combined with molecular dynamics simulations validate the enrichment of NO_(3)^(−)/FSI−anions in the EDL region due to the positively charged CTA^(+).In-depth analysis of SEI structure by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry results confirmed the formation of the inorganic-rich SEI,which helps improve the kinetics of Li^(+)transfer,lower the charge transfer activation energy,and homogenize Li *** a result,the Li||Li symmetric cell in the designed electrolyte displays a prolongated cycling time from 500 to 1300 h compared to that in the blank electrolyte at 0.5 mA cm^(-2) with a capacity of 1 mAh cm^(-2).Moreover,Li||LiFePO_(4) and Li||LiCoO_(2) with a high cathode mass loading of>10 mg cm^(-2) can be stably cycled over 180 cycles.

关键词： Cationic surfactant Lithium nitrate additive Solid-electrolyte interphase Electric double layer Lithium metal batteries

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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(Information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： decision trees privacy protection privileged party secret sharing secure multi-party computation

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Unveiling strain-enhanced moiréexciton localization in twisted van der Waals homostructures

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Nano Research 2024年第4期17卷 3245-3252页

作者： Henry Rui He Haihong Zheng Biao Wu Shaofei Li Junnan Ding Zongwen Liu Jian-Tao Wang Anlian Pan Yanping Liu Institute of Quantum Physics School of PhysicsCentral South UniversityChangsha 410083China State Key Laboratory of Precision Manufacturing for Extreme Service performance Central South UniversityChangsha 410083China School of Chemical and Biomolecular Engineering The University of SydneyNSW 2006Australia Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China College of Materials Science and Engineering Hunan UniversityChangsha 410082China State Key Laboratory of high-performance Complex Manufacturing Central South UniversityChangsha 410083China Shenzhen Research Institute of Central South University Shenzhen 51800China

Moirésuperlattices,arising from the controlled twisting of van der Waals homostructures at specific angles,have emerged as a promising platform for quantum emission ***,the manipulation of strain provides a versatile strategy to finely adjust electronic band structures,enhance exciton luminescence efficiency,and establish a robust foundation for two-dimensional quantum light ***,the intricate interplay between strain and moirépotential remains partially ***,we introduce a meticulously designed fusion of strain engineering and the twisted 2L-WSe_(2)/2L-WSe_(2) homobilayers,resulting in the precise localization of moiré*** low-temperature photoluminescence spectroscopy,we unveil the emergence of highly localized moiré-enhanced emission,characterized by the presence of multiple distinct emission ***,our investigation demonstrates the effective regulation of moirépotential depths through strain engineering,with the potential depths of strained and unstrained regions differing by 91%.By combining both experimental and theoretical approaches,our study elucidates the complex relationship between strain and moirépotential,thereby opening avenues for generating strain-induced moiréexciton single-photon sources.

关键词： moirésuperlattices strain engineering moiréexcitons moirépotential

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Software Vulnerability Mining and Analysis Based on Deep Learning

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Computers, Materials & Continua 2024年第8期80卷 3263-3287页

作者： Shibin Zhao Junhu Zhu Jianshan Peng State Key Laboratory of Mathematical Engineering and Advanced computing Zhengzhou450001China

In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection *** intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false ***,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)***,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency *** feature vector is then used as the learning target for the neural *** types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection *** results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

关键词： Vulnerability mining software security deep learning static analysis

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Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

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Journal of Energy Chemistry 2024年第8期95卷 315-324,I0007页

作者： Zhen Wu Yu-Han Zhang Hao Wang Zewen Liu Xudong Zhang Xin Dai Kunyang Zou Xiaoming Lou Xuechen Hu Lijing Ma Yan Liu Yongning Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101ShandongChina Hefei Advanced computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Center for high performance computing Network Information CenterXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Wuhan Dongfeng Motor Corporation Ltd Wuhan 430056HubeiChina School of Materials Science and Intelligent Engineering Nanjing UniversitySuzhou 215163JiangsuChina Key Laboratory of MEMS of the Ministry of Education Southeast UniversityNanjing 210096JiangsuChina International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high ***,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage ***,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking *** structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p ***,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than *** study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

关键词： Co-free Li-rich oxides Li/Ni mixing Stacking faults Electronic structure

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong high-Speed Railway Co. Ltd.Weihai264200China Shandong high-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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A Method for Detecting and Recognizing Yi Character Based on Deep Learning

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Computers, Materials & Continua 2024年第2期78卷 2721-2739页

作者： Haipeng Sun Xueyan Ding Jian Sun HuaYu Jianxin Zhang School of Computer Science and Engineering Dalian Minzu UniversityDalian116600China Institute of Machine Intelligence and Bio-computing Dalian Minzu UniversityDalian116600China 3Yi Language Research RoomChina Ethnic Languages Translation centre Beijing100080China

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and *** the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character ***,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different ***,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original *** maps are then subjected to a differentiable binarization process,resulting in an approximate binarization *** map helps to identify the boundaries of the text ***,the text detection box is generated after the post-processing *** the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters ***,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text *** experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,*** also,we have compared the detection and re

关键词： Yi characters text detection text recognition attention mechanism deep neural network

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Sb3+ and Sm3+ co-doped lead-free Cs2NaInCl6 double perovskite nanocrystals for single-component cold white emitter

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Journal of Rare Earths 2024年第9期42卷 1646-1651页

作者： Biao Zhou Xiangtong Zhang Ranran Hu Ruixue Hou William W.Yu Key Laboratory for Special Functional Materials of Ministry of Education National & Local Joint Engineering Research Centre for High-Efficiency Display and Lighting Technology School of Materials and EngineeringCollaborative Innovation Centre of Nano Functional Materials and ApplicationsHenan University School of Chemistry and Chemical Engineering Shandong University

Metal halide perovskites exhibit promising prospect in light-emitting diodes,solar cells,photodetectors and ***,the workhorse of metal halide perovskites relies on toxicity lead element,which severely damages human body and *** lead-free perovskites,Cs2NaInCl6double perovskite is one of the most promising candidates because of its great stability and easy synthesis,but suffers inadequate optical ***,we doped Sb3+and Sm3+into Cs2NaInCl6nanocrystals by a hot injection ***2NaInCl6:Sb3+exhibits blue emission at 447 ***2NaInCl6:Sm3+,Sb3+nanocrystals simultaneously generate the blue emission of Sb3+and the multiple emissions of Sm3+at 565,602,650 and 710 *** the increase of Sm3+feed ratio,the emission color of Cs2NaInCl6:Sm3+,Sb3+gradually moves to cold white region at（0.27,0.28） in CIE chromaticity diagram and correlated color temperature of 11840 *** work shows the potential application of Cs2NaInCl6:Sm3+,Sb3+nanocrystals as a single-component cold white emitter.

关键词： Lead-free Double perovskites Single component White emitter Doping Rare earths

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An Adaptive Scalable Data Pipeline for Multiclass Attack Classification in Large-Scale IoT Networks

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Big Data Mining and Analytics 2024年第2期7卷 500-511页

作者： Selvam Saravanan Uma Maheswari Balasubramanian Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidyapeethamBengaluru 560035India

The current large-scale Internet of Things(IoT)networks typically generate high-velocity network traffic *** use IoT devices to create botnets and launch attacks,such as DDoS,Spamming,Cryptocurrency mining,Phishing,*** service providers of large-scale IoT networks need to set up a data pipeline to collect the vast network traffic data from the IoT devices,store it,analyze it,and report the malicious IoT devices and types of ***,the attacks originating from IoT devices are dynamic,as attackers launch one kind of attack at one time and another kind of attack at another *** number of attacks and benign instances also vary from time to *** phenomenon of change in attack patterns is called concept ***,the attack detection system must learn continuously from the ever-changing real-time attack patterns in large-scale IoT network *** meet this requirement,in this work,we propose a data pipeline with Apache Kafka,Apache Spark structured streaming,and MongoDB that can adapt to the ever-changing attack patterns in real time and classify attacks in large-scale IoT *** concept drift is detected,the proposed system retrains the classifier with the instances that cause the drift and a representative subsample instances from the previous training of the *** proposed approach is evaluated with the latest dataset,IoT23,which consists of benign and several attack instances from various IoT *** classification accuracy is improved from 97.8%to 99.46%by the proposed *** training time of distributed random forest algorithm is also studied by varying the number of cores in Apache Spark environment.

关键词： Internet of Things(IoT) concept drift Apache Spark MongoDB Apache Kafka streaming

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A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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Intelligent Automation & Soft computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema Akkasha Latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West high School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images

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