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Rational design of metal selenides nanomaterials for alkali metal ion(Li^(+)/Na^(+)/K^(+))batteries:current status and perspectives

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Rare Metals 2024年第5期43卷 1906-1931页

作者： Rui sun Feng Xu Cai-Hong Wang sheng-Jun Lu Yu-Fei Zhang Hao-sen Fan College of Materials Science and Metallurgy Engineering Guizhou UniversityGuiyang550025China School of Chemistry and Chemical Engineering Guangzhou UniversityGuangzhou510006China

Recently,metal selenides have obtained widespread attention as electrode materials for alkali(Li^(+)/Na^(+)/K^(+))batteries due to their promising theoretical capacity and ***,metal selenides,similar to metal oxides and sulfides,also suffer from severe volume explosion during repeated charge/discharge processes,which results in the structure collapse and the following pulverization of electrode ***,it leads to poor cycle stability and influencing their further *** order to solve these issues,some special strategies,including elemental doping,coupling with carbon materials,synthesis of the bimetal selenides with heterostructure,etc.,have been gradually applied to design novel electrode materials with outstanding electrochemical ***,the recent research progress on metal selenides as anodes for alkali ion batteries is summarized,including the regulation of crystal structure,synthesis strategies,modification methods,and electrochemical mechanisms and ***,the challenges of metal selenides and the perspective for future electrode material design are *** is hoped to pave a way for the development of metal selenide electrode materials for the potential applications for alkali metal ion(Li^(+)/Na^(+)/K^(+))batteries.

关键词： Rational design Metal selenides Nanostructure Li^(+) batteries Na^(+) batteries K^(+) batteries

In silico optimization of actuation performance in dielectric elastomercomposites via integrated finite element modeling and deep learning

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Theoretical & Applied Mechanics Letters 2024年第1期14卷 48-56页

作者： Jiaxuan Ma sheng sun Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Shanghai Frontier Science Center of Mechanoinformatics Shanghai UniversityShanghai 200444China

Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under applied *** high dielectric particles as fillers provides extensive design space to optimize concentration,morphology,and distribution for improved actuation performance and material *** study presents an integrated framework combining finite element modeling(FEM)and deep learning to optimize the microstructure of DE *** first calculates actuation performance and the effective modulus across varied filler combinations,with these data used to train a convolutional neural network(CNN).Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same *** framework harnesses artificial intelligence to navigate vast design possibilities,enabling optimized microstructures for high-performance DE composites.

关键词： Dielectric elastomer composites Multi-objective optimization Finite element modeling Convolutional neural network

Robust Forecasting-Aided State Estimation Considering Uncertainty in Distribution System

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1632-1641页

作者： Dongchen Hou Yonghui sun Linchuang Zhang sen Wang College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China

With the development of the smart grid,the distribution system operation conditions become more complex and ***,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution *** order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type ***,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is *** the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase ***,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.

关键词： Cubature Kalman filter distribution power system forecasting-aided state estimation projection statistics

Molecular Chaperone-Dependent Polymer Translocation through Nanopores: The Effects of Chaperone Concentration and Chaperone-Polymer Interaction

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Chinese Journal of Polymer Science 2024年第1期42卷 125-132,I0011页

作者： Chang-sheng Zuo Kang Wang Li-Zhen sun Ting-Ting sun Department of Physics Zhejiang University of Science and TechnologyHangzhou 310023China Department of Applied Physics Zhejiang University of TechnologyHangzhou 310014China

The polymer translocation through a nanopore from a donor space(or named cis side) to a receiver space(trans side) in the chaperone-induced crowded environment has attracted increasing attention in recent years due to its significance in biological systems and technological applications. In this work, we mainly focus on the effects of chaperone concentration and chaperone-polymer interaction on the polymer translocation. By assuming the polymer translocation to be a quasi-equilibrium process, the free energy F of the polymer can be estimated by Rosenbluth-Rosenbluth method and then the translocation time τ can be calculated by Fokker-Plank equation based on the obtained free energy landscape. Our calculation results show that the translocation time can be controlled by independently tuning the chaperone concentration and chaperone-polymer interaction at the cis side or the trans side. There exists a critical chaperone-polymer attraction ε~*=-0.2 at which the volume exclusion and interaction effects of the chaperone can balance each other. Additionally, we also find that at large chaperone-polymer attraction, the translocation time is mainly governed by the diffusion coefficient of the polymer.

关键词： Polymer translocation Rosenbluth-Rosenbluth method Fokker-Plank equation Free energy landscapes

Navigating Epstein–Barr virus autoimmunity:role of NK cells and T cells in multiple sclerosis

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Signal Transduction and Targeted Therapy 2024年第3期9卷 833-835页

作者： Chu Xie Cong sun Mu-sheng Zeng State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for CancerGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat-sen University Cancer CenterGuangzhouP.R.China

In a recent study published in Cell,Vietzen and colleagues discovered the novel role of natural killer(NK)cells and EBVspecific CD8+T cell response in controlling EBV-inducing autoimmune response and collectively def... 详细信息

关键词： Epstein sclerosis killer

A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation

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Machine Intelligence Research 2024年第4期21卷 801-814页

作者： Feng sun Ming-Kun Xie sheng-Jun Huang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of AeronauticsandAstronauticsNanjing211106China

In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy *** PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real ***,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on *** this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination *** the one hand,we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different *** the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant *** experimental results on the commonly used benchmark datasets show that the proposed method significantlyoutperforms the SOTA methods.

关键词： Partial multi-label image classification curriculum-based disambiguation consistency regularization label difficulty candidatelabel set.

Ultra-high specific surface area activated carbon from Taihu cyanobacteria via KOH activation for enhanced methylene blue adsorption

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Chinese Journal of Chemical Engineering 2024年第3期67卷 106-116页

作者： Yifang Mi Wenqiang Wang sen Zhang Yalong Guo Yufeng Zhao Guojin sun Zhihai Cao Key Laboratory of Advanced Textile Materials and Manufacturing Technology and Engineering Research Center for Eco-Dyeing&Finishing of Textiles Ministry of EducationZhejiang Provincial Engineering Research Center for Green and Low-carbon Dyeing&FinishingZhejiang Sci-Tech UniversityHangzhou 310018China Key Laboratory for Technology in Rural Water Management of Zhejiang Province Zhejiang University of Water Resources and Electric PowerHangzhou 310018China

Cyanobacteria-based activated carbon(CBAC)was successfully prepared by pyrolysis-activation of Taihu *** the impregnation ratio and activated temperature were 2 and 800-C,respectively,the optimal CBACs possessed an ultra-high specific surface(2178.90 m^(2)·g^(-1))and plenty of micro-and meso-pores,as well as a high pore volume(1.01 cm^(3)·g^(-1)).Ascribed to ultra-high surface area,π-π interaction,electrostatic interaction,as well as hydrogen-bonding interactions,the CBACs displayed huge superiority in efficient dye *** saturated methylene blue adsorption capacity by CBACs could be as high as 1143.4 mg·g^(-1),superior to that of other reported biomass-activated *** adsorption was endothermic and modeled well by the pseudo-second-order kinetic,intra-particle diffusion,and Langmuir *** work presented the effectiveness of Taihu cyanobacteria adsorbent ascribed to its super large specific surface area and high adsorption ability.

关键词： Activated carbon Biomass Dye adsorption Taihu cyanobacteria

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1342-1357页

作者： sheng Qi Rui Wang Tao Zhang Xu Yang Ruiqing sun Ling Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory Changsha 410205China College of Computer National University of Defense TechnologyChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

关键词： Evolutionary algorithms learning swarm optimiza-tion sparse large-scale optimization sparse large-scale multi-objec-tive problems two-layer encoding.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-sheng Jia Ying-Xiang Hu Hong-Mei sun the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning