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Performance optimization of aqueous Zn/MnO^(2)batteries through the synergistic effect of PVP intercalation and GO coating

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Chinese Chemical Letters 2025年第3期36卷 488-493页

作者： Shilong Li Ming Zhao Yefei Xu Zhanyi Liu Mian Li Qing Huang Xiang Wu School of Materials Science and Engineering Shenyang University of TechnologyShenyang 110870China Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Manganese dioxide(MnO_(2))electrode material possesses the advantages of high energy density,structural diversity and high modification *** allows it become one of the important cathodes for aqueous zinc ion ***,the applications are limited by the poor electrical conductivity,narrow layer spacing and the ease of ***,we prepare MnO_(2)-PVP@0.03GO composites by the co-modification of polyvinylpyrrolidone(PVP)pre-insertion layer and graphene oxide(GO)self-assembly *** Zn//MnO_(2)-PVP@0.03GO cells deliver a discharge specific capacity of 442 mAh/g at a current density of 0.2 A/*** also maintains 100%capacity for 1000 times cycling at 1 A/*** assembled soft package batteries demonstrate superior flexibility and adaptability under different bending conditions.

关键词： Aqueous zinc ion batteries Cathode MnO_(2) Cycle stability High capacity

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Targeted Universal Adversarial Attack Based on Fourier Transform 2nd

Targeted Universal Adversarial Attack Based on Fourier Tran...

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2nd International Conference on data Security and Privacy Protection, DSPP 2024

作者： Sun, Yancheng Chen, Yuling Liu, Ziyang Dou, Hui Tan, Chaoyue The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819785391

The existence of adversarial example reveals the fragility in neural networks, and the exploration of their theoretical origins increases the interpretability of deep learning, enhances researchers’ understanding of neural networks, and contributes to the development of next-generation artificial intelligence, which has attracted widespread research in various fields. The targeted adversarial attack problem based on sample features faces two problems: on the one hand, the difference in the model’s attention to different features in the example;On the other hand, the bias that occurs in adversarial attacks can have an impact on targeted attacks. The mechanism of the human eye relies more on the shape information of the image. However, in the past, artificial intelligence models based on convolutional neural networks often relied on the texture features of image examples to make decisions. At present, general optimize adversarial attack algorithms do not distinguish different types of features based on different parts of the image, but only process the entire example in a general manner, making it difficult to effectively utilize the effective features in the example, resulting in poor algorithm performance and interpretability. This article optimizes the adversarial attack algorithm based on optimization iteration, as follows: Firstly, different types of information in adversarial examples are studied, and fourier transform technology is used to process the attacked original image and obtain its low-frequency information. The obtained low-frequency examples are randomly cropped to obtain some feature examples. Then, the clustering effect was studied when the examples were attacked without targets, and an inter-class smoothing loss was designed to improve the success rate of target attacks. This Rebalance Universal Feature Method (RFM) is based on fourier low pass filtering and inter-class smoothing, which effectively improves the ability of optimization iteration bas

关键词： Adversarial machine learning

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Preliminary study on FROBA-SCO2 with transient models and its application for Reactivity Initiated Accident and loss of flow accident

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International Journal of Advanced nuclear Reactor Design and technology 2022年第3期4卷 101-108页

作者： Yang, T.A. Zhang, K. Yang, H.K. Tian, W.X. Qiu, S.Z. School of Nuclear Science and Technology Shaanxi Key Laboratory of Advanced Nuclear Energy and Technology State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong University Xi'an710049 China

As a coolant enabling the reactor with a high efficiency and low volume, the supercritical CO2 has becoming a hot topic nowadays. However, the fuel rod behaviors under accidents are not studied thoroughly, especially the interaction between the coolant and the cladding. With the previous study of fuel rod behaviors analysis of supercritical CO2 cooled nuclear reactor under steady state operation, transient state models are implemented and the upgraded code can be used to predict the fuel rod behaviors under RIA and LOFA. After the comparation of the calculation results with SACOS-SCO2, the tendency and numerical value are close, so the accuracy is warranted. The upgraded FROBA-SCO2 is used to predict the fuel rod behaviors under RIA and LOFA conditions and the results indicate that the maximum temperature of cladding and fuel is within safety limits. © 2022

关键词： Coolants

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Engineering design of feedstock powder and relevant thermal-mechanical performance of thermal/environmental barrier coatings

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Journal of Advanced Ceramics 2025年第2期14卷 37-56页

作者： Xinchang Feng Fangwei Guo Lirong Luo Yuzhang Wang Yun Long Xiaofeng Zhao Fei Pan Lei Guo Qingfeng Zeng Jing Feng Chunlei Wan Shanghai Key Laboratory of Advanced High-temperature Materials and Precision Forming School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center of Special Equipment and Power System for Ship and Marine Engineering Shanghai 200030China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Advanced Nuclear Energy Technology Nuclear Power Institute of ChinaChengdu 610213China Innovative Sensor Technology IST AG Ebnat-Kappel 9642Switzerland School of Materials Science and Engineering Tianjin UniversityTianjin 300072China Tianmushan Laboratory Hangzhou 311115China School of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China

The development of aeroengine with a high thrust-weight ratio poses great challenges for current top-coating thermal barrier coatings (TBCs) and environmental barrier coatings (EBCs) in service. Medium/high-entropy ceramics are highly promising candidate material for advanced TBCs/EBCs owing to their low thermal conductivity, high melting point, high-temperature stability, and calcium–magnesium–alumino–silicate (CMAS) resistance. Most feedstock powder used for medium/high-entropy TBCs/EBCs is prepared via traditional spray drying, which cannot fully exploit the advantages of multicomponent ceramics. The density, sphericity, inner structure, and flowability of feedstock powder affect their melting state during the thermal spraying process, which strongly affects the microstructure and properties of the deposited coatings. Therefore, the deposited coatings exhibit phase segregation, amorphous phases, and microstructure defects owing to unpredictable variations in feedstock powder with random morphologies and structures. Here, the structure and properties of feedstock powder prepared by state-of-the-art granulation technologies and their influences on the deposited coatings were systematically investigated, which can provide guidance for configuration optimization of feedstock powder and the manufacturing accuracy of the deposited coating. This review aims to bridge the gap between cutting-edge ceramics and advanced engineering technologies, thus providing concrete background knowledge and crucial guidelines for designing and developing TBCs/EBCs.

关键词： thermal/environmental barrier coatings(TBCs/EBCs) spraying ceramic powder multiscale structure control high-entropy ceramics thermal-mechanical performance

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Efficacious selective separation of U(Ⅵ) over Mo(Ⅵ) using novel 2,9-diamide-1,10-phenanthroline ligands: Liquid-liquid extraction and coordination chemistry

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Chinese Chemical Letters 2023年第11期34卷 163-168页

作者： Taoyuan Xiu Simei Zhang Peng Ren Siyan Liu Shuai Wang Hamza Shehzad Jingyang Wang Meng Zhang Guoan Ye Caishan Jiao Qunyan Wu Liyong Yuan Zhifang Chai Weiqun Shi College of Nuclear Science and Technology Harbin Engineering UniversityHarbin 150001China Laboratory of Nuclear Energy Chemistry Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China China Institute of Atomic Energy Beijing 102413China State Key Laboratory of Nuclear Resources and Environment School of Nuclear Science and TechnologyEast China University of TechnologyNanchang 330013China School of Chemistry University of the PunjabLahore 54590Pakistan

Uranium and molybdenum are important strategic elements. The production of ^(99)Mo and the hydrometallurgical process of uranium ore face difficult problems of separation of uranium and *** this study, the four phenanthroline diamide ligands were synthesized, and extraction and stripping experiments were performed under different conditions to evaluate the potential application of these ligands for separation of U(Ⅵ) over Mo(Ⅵ). With the growth of alkyl chain, the solubility of ligands could be greatly improved, and the separation effect of U(Ⅵ) over Mo(Ⅵ) gradually increased. The SF_(U/Mo) were around 10,000 at 4 mol/L HNO3. Three stripping agents were tested with the stripping efficiency of Na_(2)CO_(3)(5%) > H_(2)O > HNO_(3)(0.01 mol/L). The stripping percentages of the three stripping agents were all close to unity, indicating that the ligands had the potential to be recycled. The chemical stoichiometry of U(Ⅵ) complexes with ligands was evaluated as 1:1 using electrospray ionization mass spectrometry,ultraviolet visible spectroscopy and single-crystal X-ray diffraction. The consistency between theoretical calculation and experimental results further explains the coordination mechanism.

关键词： Phenanthroline diamide ligand Uranium Molybdenum Solvent extraction Coordination chemistry

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A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

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Intelligent and Converged Networks 2024年第4期5卷 317-335页

作者： Chao Wang Yiyun Shi Sumin Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

Currently,the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning *** identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult,ultimately preventing the achievement of the most optimal *** paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive *** reasoning aims to enhance performance in scenarios with poor system robustness,complex tasks,and diverse goals.A deep learning model is constructed,trained to extract scene features,and combined with expert knowledge,then transformed into a quantum annealable *** final strategy is obtained using a D-wave quantum computer with quantum tunneling effect,which helps in finding optimal solutions by jumping out of local suboptimal *** on 400000 real data,four algorithms are compared:minimum-cost flow,sequential markov decision process,hot-dot strategy,and driver-prefer *** average total revenue increases by about 10%and vehicle utilization by about 15%in various *** summary,the proposed architecture effectively solves the e-hailing reposition problem,offering new directions for robust artificial intelligence in big data decision problems.

关键词： intuitive reasoning reinforcement learning quantum annealing

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New extended method for ψ' scaling function of inclusive electron scattering

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science China(Physics,Mechanics & Astronomy) 2023年第10期66卷 53-61页

作者： Lei Wang Qinglin Niu Jinjuan Zhang Jian Liu Zhongzhou Ren School of Physics Science and Engineering Tongji UniversityShanghai 200092China College of Science China University of Petroleum(East China)Qingdao 266580China College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao 266590China Laboratory of High Precision Nuclear Spectroscopy Insititute of Modern PhysicsChinese Academy of SciencesLanzhou 730000China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China Key Laboratory of Advanced Micro-Structure Materials Ministry of EducationShanghai 200092China

Scaling analyses have been successfully applied to study the inclusive electron scattering(e, e') over the past few decades. In this study, we utilize the ψ' scaling function in momentum space to analyze the(e, e') cross sections, where the nucleon momentum distributions are derived from self-consistent mean-field calculations. By further introducing the energy and momentum conservation in the scaling analysis, an improved ψ'scaling function is proposed to investigate the high-momentum part of the momentum distributions. Using the proposed scaling function, we systematically explore the effects of the nucleon-nucleon short-range correlation(NN-SRC) on the(e, e') cross sections. From the experimental(e, e') data, the NN-SRC strength is further extracted within the framework of the improved ψ'scaling function. The studies in this paper offer a new method to investigate the nucleon momentum distributions and the NN-SRC effects in nuclei.

关键词： inclusive electron scattering scaling analysis nucleon momentum distribution nucleon-nucleon correlation

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Graph Attention Network-based Multimodal Fusion for Fake Food Reviews Detection 24

Graph Attention Network-based Multimodal Fusion for Fake Foo...

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9th International Conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Yang, Jiajie Mao, Dianhui Zhang, Denghui Beijing Key Laboratory of Big Data Technology for Food Safety Computer Science Department Beijing Technology and Business University China

ISBN: (纸本)9798400718137

In the field of computational intelligence and data analytics, the detection of fake food reviews has emerged as a pressing challenge, exacerbated by the widespread use of social media. These fraudulent reviews, particularly prevalent in the food and beverage industry, distort consumer decision-making and disrupt market fairness. To address this, we propose GMF-R, an innovative multimodal fusion framework based on Graph Attention Network (GAT), designed to effectively detect fake food reviews. The framework features end-to-end trainability and introduces advanced multimodal feature processing mechanisms, including fine-grained cross-modal interactions, a shared feature space, and multimodal graph construction. By utilizing GAT, GMF-R can adaptively assign attention weights to key nodes and accurately capture complex inter-modal relationships. Experimental results demonstrate that GMF-R achieves a 97.4% accuracy rate and a 98.1% F1 score on the Yelp reviews website dataset, significantly outperforming previous state-of-the-art models. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Experimental Investigations of Quasi-Coherent Micro-Instabilities in J-TEXT Ohmic Plasmas

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Chinese Physics Letters 2024年第5期41卷 47-52页

作者：石鹏庄革程芝峰高丽周乙楠刘永罗景庭李景春 Institute of Plasma Physics Chinese Academy of SciencesHefei 230031China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China Department of Engineering and Applied Physics School of Physical Sciences University of Science and Technology of ChinaHefei 230026China Shenzhen Key Laboratory of Nuclear and Radiation Safety Institute for Advanced Study in Nuclear Energy&SafetyCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 510640China

Quasi-coherent micro-instabilities is one of the key topics of magnetic confinement fusion. This work focuses on the quasi-coherent spectra of ion temperature gradient(ITG) and trapped-electron-mode instabilities using newly developed far-forward collective scattering measurements within ohmic plasmas in the J-TEXT *** ITG mode is characterized by frequencies ranging from 30 to 100 k Hz and wavenumbers(k_(θρs)) less than 0.3. Beyond a critical plasma density threshold, the ITG mode undergoes a bifurcation, which is marked by a reduction in frequency and an enhancement in amplitude. Concurrently, enhancements in ion energy loss and degradation in confinement are observed. This ground-breaking discovery represents the first instance of direct experimental evidence that establishes a clear link between ITG instability and ion thermal transport.

关键词： ITG mode collective

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