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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Materials Science and Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Network Information Center Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Low-Co Li-rich layered oxides Li/Ni disordering TM-O covalency Cationic and anionic redox activity

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Research on Sleep Staging Method Based on Multi-Scale Convolution and Self-Attention Mechanism 2

Research on Sleep Staging Method Based on Multi-Scale Convol...

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2nd International Conference on Biomedical and Intelligent Systems, IC-BIS 2023

作者： Pan, Dongdong Li, Yingying School of Computer and Artificial Intelligence Zhengzhou University China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University China

ISBN: (纸本)9781510666733

Sleep is a very important physiological process for humans, and sleep staging is an important basis for evaluating sleep quality and diagnosing sleep-related diseases. Manual sleep staging is time-consuming and laborious, and the staging results are easily affected by subjective factors. Therefore, the research on automatic sleep staging models has extremely high research value and clinical application value. This paper proposes a sleep staging method based on multi-scale one-dimensional convolutional neural network and self-attention mechanism. The attention mechanism mines the channel correlation and temporal correlation of different channels respectively. This paper selects the sleep data of 153 normal subjects in the public dataset Sleep-EDF for model training and testing. The average staging accuracy rate was 84.5%, and the consistency test value was 0.781, which was better than the consistency test value of 0.75 for the inter-physician score. The experimental results show that the algorithm model in this paper has a high staging effect, can make full use of sleep data, and has important research significance for assisting clinicians in rapid sleep staging. © 2023 SPIE.

关键词： Convolution

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DPW-RRM: Random Routing Mutation Defense Method Based on Dynamic Path Weight

DPW-RRM: Random Routing Mutation Defense Method Based on Dyn...

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作者： Jin, Hui Li, Zhaoyang Hu, Ruiqin Tan, Jinglei Zhang, Hongqi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450001 China School of Cyberspace Security Zhengzhou University Zhengzhou Henan450001 China

Eavesdropping attacks have seriously threatened network security. Attackers could eavesdrop on target nodes and link to steal confidential data. In the traditional network architecture, the static routing path and the important nodes determined by the nature of network topology provide a great convenience for eavesdropping attacks. To resist monitoring attacks, this paper proposes a random routing mutation defense method based on dynamic path weight (DPW-RRM). It utilizes network centrality indicators to determine important nodes in the network topology and reduces the probability of important nodes in path selection, thereby distributing traffic to multiple communication paths, achieving the purpose of increasing the difficulty and cost of eavesdropping attacks. In addition, it dynamically adjusts the weight of the routing path through network state constraints to avoid link congestion and improve the availability of routing mutation. Experimental data shows that DPW-RRM could not only guarantee the normal algorithmic overhead, communication delay, and CPU load of the network, but also effectively resist eavesdropping attacks. Copyright © 2023 KSII.

关键词： Network topology

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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Detection of Malicious PDF Files Using a Two-Stage Machine Learning Algorithm

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Chinese Journal of Electronics 2020年第6期29卷 1165-1177页

作者： HE Kang ZHU Yuefei HE Yubo LIU Long LU Bin LIN Wei State Key Laboratory of Mathematical Engineering and Advanced Computing

Portable document format(PDF) files are increasingly used to launch cyberattacks due to their popularity and increasing number of *** solutions have been developed to detect malicious files,but their accuracy decreases rapidly in face of new evasion *** explore how to improve the robustness of classifiers for detecting adversarial attacks in PDF *** replacement and the n-gram are implemented to extract robust features using proposed guiding *** the two-stage machine learning model,the objects are divided based on their types,and the anomaly detection model is first trained for each type *** former detection results are organized into tree-like information structure and treated as inputs to convolutional neural *** results show that the accuracy of our classifier is nearly 100% and the robustness against evasive samples is *** object features also enable the identification of different vulnerabilities exploited in malicious PDF files.

关键词： convolutional neural nets document handling feature extraction learning (artificial intelligence) pattern classification security of data trees (mathematics) robust feature extraction convolutional neural network tree-like information structure adversarial attack detection two-stage machine learning algorithm evasion techniques malicious files portable document format malicious PDF files classifier robustness anomaly detection model two-stage machine learning model Malicious PDF classification Robustness Guiding principles Two-stage classifier Convolutional neural network

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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A Further Understanding of Differential-Linear Cryptanalysis

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Chinese Journal of Electronics 2020年第4期29卷 660-666页

作者： REN Jiongjiong CHEN Shaozhen State Key Laboratory of Mathematical Engineering and Advanced Computing State Key Laboratory of Cryptology

Differential-linear cryptanalysis has attracted much attention since proposed to attack DES in 1994, and then some generalized theories are developed to complement and unify the ***, the links between differential-linear cryptanalysis and other important cryptanalysis methods have been still *** motivation is to fix the *** establishing some boolean equations, we propose the mathematical links among differential, linear and differential-linear *** then generalise the definition of capacity and present some properties of the capacity of differential *** links and properties are employed to explore the relationships between multidimensional differential-linear hulls and integral *** show that a multidimensional differential-linear hull of certain correlation always implies the existence of an integral distinguisher and a zero-correlation linear hull, while a special integral distinguisher indicates the existence of a multidimensional differential-linear hull.

关键词： Block cipher Differential cryptanalysis Linear cryptanalysis Differential-linear cryptanalysis Integral distinguisher Zero-correlation

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Programmable logic controller memory management vulnerability analysis 3

Programmable logic controller memory management vulnerabilit...

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2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022

作者： Geng, Yangyang Ma, Rongkuan Wei, Qiang Wang, Wenhai State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

Programmable logic controller (PLC) is omnipresent in industrial control system (ICS), which plays an imperative part in critical infrastructures. However, once a PLC is compromised by an attacker, it will create destructive effects on critical infrastructures. The memory systems in PLCs are considered significant targets for attackers to disrupt real-world physical processes. In this paper, we proposed five types of attacks on the PLC memory management mechanism. Furthermore, we evaluated the feasibility of our memory attack generation approach on six PLCs from three leading controller vendors. A total of 22 memory attack payloads are implemented with several vulnerabilities on the target PLCs. © Published under licence by IOP Publishing Ltd.

关键词： Programmable logic controllers

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Whole-Function Vectorization Based on Simplified Openmp Directives

SSRN

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SSRN 2024年

作者： Liu, Lili Yao, Jinyang Li, Yingying Zhang, Ping Xu, Jinlong Zhao, Bo Shan, Zheng State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China

With the continuous development of computing technology, an increasing number of vendors have integrated SIMD extension components into processors. These components can accelerate program execution efficiency and have been widely applied in various fields. Past research on SIMD components has primarily focused on loop-level and superword-level, while function-level vectorization can further exploit the parallel potential of these components, thereby enhancing application performance. Currently, OpenMP can achieve function vectorization based on directives. However, this method requires directives to be added to both the loop where the call point is located and the called functions, which is cumbersome and increases the burden on programmers. This paper proposes a directive-based function vectorization method. First, we extend the clauses in OpenMP directives concerning function vectorization data parallelism. Secondly, we implement in the compiler the functionality to automatically add corresponding directives to called functions within functions with the omp simd clone attribute and within OMP_SIMD loops. Finally, we achieve vectorization of loops containing function calls through directives. This method fully exploits the advantages of SIMD instructions, improving application performance. We selected 16 benchmarks from ISPC benchmarks and SIMD library benchmarks to evaluate our method. Experimental results show that compared to scalar, the average speedup reached 4.716x. © 2024, The Authors. All rights reserved.

关键词： Application programming interfaces (API)

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An Abnormal Domain Name Generation Method Based on a Character-Level Model 22

An Abnormal Domain Name Generation Method Based on a Charact...

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Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence

作者： Chunbing Wu Jinlong Fei State Key Laboratory of Mathematical Engineering and Advanced Computing China

ISBN: (纸本)9781450398343

Internet technology develops by leaps and bounds, new types of botnet attacks are springing up. Currently, most malware are stealthy when implementing attacks since they can bypass detection strategies such as blacklists and sinkholes. These malware mainly use DGA to generate many random domain names to prevent C&C server IP addresses from being traced to the source. To resist botnet attacks, machine-learning methods have been proposed for detecting abnormal domain names. However, there are still some inadequacies, e.g., detection methods based on manual feature extraction couldn't identify novel variant DGA domains, and existing neural network detection methods have weak generalization ability and lack training data for new variant DGA domain names. To address the above problems, this paper proposes an abnormal domain name data generation technology based on a character-level model. Firstly, combining specific text generation-related ideas improves the character-level language model. Secondly, the Bi-GRU network model and attention mechanism are used to thoroughly learn the internal correlation features, and simulate the generation of abnormal domain names. The experimental results show that this method's abnormal domain name data is highly similar to the actual domain name in relative entropy and character composition. Furthermore, it verifies the feasibility of the generated data as training data set for a new variant DGA domain name, which to a certain extent, helps to improve the generalization ability of the abnormal domain name detection model.

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