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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 University School of Materials Science and Engineering Xi'an University of Architecture and Technology Hefei Advanced Computing center Operation Management Corp Ltd network Information center Xi'an Jiaotong University

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,Li1.2Ni0.168Mn0.558Co0.074O2（L1）,exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g-1and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li1.2Ni0.13Mn0.54Co0.13O2（Ls）(274.4 mA h g-1for 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 Ni2+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： Ice

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NASTCNN: a spatio-temporal convolutional neural network with non-local attention mechanism for image-induced brain activity pattern recognition

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Optoelectronics Letters 2025年

作者： TAN Xiangxin SHAO Mingxiong ZENG Ke GU Yue School of Computer Science and Engineering， Tianjin University of technology Engineering Research center of Learning-Based Intelligent System (Ministry of Education)， Tianjin University of technology Krembil Research Institute， University Health network

Deciphering the brain’s visual processing mechanisms has always been a hot topic. Using neural signal patterns to predict the corresponding external visual stimuli is crucial in understanding the interaction of real visual scenes. There are some challenges in the study of image-induced brain activity pattern recognition based on electroencephalogram (EEG)， e.g. how to fully utilize the effective features of EEG signals to improve the classification， and how the frequency and the length of EEG effect the model performance. We design an experimental paradigm that minimizes the interaction between visual stimuli， and propose a spatio-temporal convolutional neural network with non-local attention mechanism (NASTCNN) for image-induced brain activity pattern recognition. The framework utilizes non-local feature extraction operations to realize global information interaction between EEG signals over long distances and enhance the feature expression of some important information. Our model achieves the best performance on both MNIST-EEG and ImageNet-EEG datasets. The classification results are 24.00% and 50.10% respectively， higher than the random probability. We also analyze the effects of different EEG frequency bands and time subsequences of different lengths on the classification performance and which brain regions are activated over time. The results show that when full frequency band and entire length sequence are selected， the classification result is optimal. The occipital region is essential for processing visual tasks， and the temporal region and some frontal regions also play important role in the later stage of visual decoding. This research can help us better understand the visual processing mechanism of the brain.

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NBgossip:An Energy-Efficient Gossip Algorithm for Wireless Sensor networks

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Journal of Computer Science & technology 2008年第3期23卷 426-437页

作者：陆峰谢良镇郑国良钟伟豪 center for multimedia and network technology School of Computer EngineeringNanyang Technological University

Gossip-based algorithms for information dissemination have recently received significant attention for sensor and ad hoc network applications because of their simplicity and robustness. However, a common drawback of many gossip-based protocols is the waste of energy in passing redundant information over the network. Thus gossip algorithms need to be re-engineered in order to become applicable to energy constrained networks. In this paper, we consider a scenario where each node in the network holds a piece of information （message） at the beginning, and the objective is to simultaneously disseminate all information （messages） among all nodes quickly and cheaply. To provide a practical solution to this problem for ad hoc and sensor networks, NBgossip algorithm is proposed, which is based on network coding and neighborhood gossip. In NBgossip, nodes do not simply forward messages they receive, instead, the linear combinations of the messages are sent out. In addition, every node exchanges messages with its neighboring nodes only. Mathematical proof and simulation studies show that the proposed NBgossip terminates in the optimal O（n）-order rounds and outperforms the existing gossip-based approaches in terms of energy consumption incurred in spreading all the information.

关键词： data dissemination gossip network coding sensor networks

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research center for Big Data technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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The interaction between KIF21A and KANK1 regulates dendritic morphology and synapse plasticity in neurons

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Neural Regeneration Research 2025年第1期20卷 209-223页

作者： Shi-Yan Sun Lingyun Nie Jing Zhang Xue Fang Hongmei Luo Chuanhai Fu Zhiyi Wei Ai-Hui Tang Hefei National Research center for Physical Sciences at the Microscale CAS Key Laboratory of Brain Function and DiseaseMinistry of Education Key Laboratory for Membrane-less Organelles and Cellular DynamicsDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui ProvinceChina Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiAnhui ProvinceChina CAS center for Excellence in Molecular Cell Sciences Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui ProvinceChina Department of Neurobiology School of Life SciencesSouthern University of Science and TechnologyShenzhenGuangdong ProvinceChina Brain Research center Southern University of Science and TechnologyShenzhenGuangdong ProvinceChina

Morphological alterations in dendritic spines have been linked to changes in functional communication between neurons that affect learning and ***-4 KIF21A helps organize the microtubule-actin network at the cell cortex by interacting with KANK1;however,whether KIF21A modulates dendritic structure and function in neurons remains *** this study,we found that KIF21A was distributed in a subset of dendritic spines,and that these KIF21A-positive spines were larger and more structurally plastic than KIF21A-negative ***,the interaction between KIF21A and KANK1 was found to be critical for dendritic spine morphogenesis and synaptic *** of either KIF21A or KANK1 inhibited dendritic spine morphogenesis and dendritic branching,and these deficits were fully rescued by coexpressing full-length KIF21A or KANK1,but not by proteins with mutations disrupting direct binding between KIF21A and KANK1 or binding between KANK1 and *** down KIF21A in the hippocampus of rats inhibited the amplitudes of long-term potentiation induced by high-frequency stimulation and negatively impacted the animals’cognitive *** together,our findings demonstrate the function of KIF21A in modulating spine morphology and provide insight into its role in synaptic function.

关键词： actin cytoskeleton dendrite KANK1 KIF21A microtubule spine morphology spine synaptic plasticity talin1

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A versatile synthetic strategy towards rare earth based metal-organic frameworks

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Science China Chemistry 2025年

作者： Quan Shi Yun-Hao Xu Ming-Hui Chen Peng Zhao Wen-Bo Deng Ji Xiong Kai Guo Ya-Qing Feng Bao Zhang School of Chemical Engineering and technology Tianjin University Tianjin Collaborative Innovation center of Chemical Science and Engineering Guangdong Laboratory Chemistry & Fine Chemical Industry Jieyang center

Rare earth metal-organic frameworks (RE-MOFs) have drawn wide attention owing to diverse network structures and various application ***,due to the unpredictable formation of the nodes,it is still challenging to design novel RE-MOFs.A novel versatile strategy was developed employing a RE-acetate complex (RE2(CH3COO)6·4H2O) as a precursor to construct RE-MOFs.2D RE-MOFs (RE-DBP) and 3D RE-MOFs (RE-TCPP) could be obtained by using dicarboxylic porphyrin ligands H2DBP and tetracarboxylic porphyrin ligands H2TCPP,*** RE-MOFs were further synthesized to verify the universality of the *** crystalline RE-MOFs could also be prepared at room temperature via the developed strategy,which could significantly promote large-scale preparation and practical application of *** proof of the concept,the performance of porphyrin-based RE-MOFs was investigated in the photocatalytic cycloaddition of CO2with *** the RE-DBP(Co) MOFs,Tb-DBP(Co) exhibited the best photocatalytic performance,which was also greater than that of the corresponding 3D MOF Tb-TCPP(Co) due to the optimal 2D structure and thus the accelerated photogenerated charge separation and transfer *** work not only provides a novel versatile strategy to design and develop RE-MOFs,but also proposes a promising approach for the potential bulk production of RE-MOFs at room temperature.

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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Computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

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China Communications 2017年第9期14卷 32-41页

作者： Tingzhao Wu Ruimin Hu Xiaochen Wang Shanfa Ke Jinshan Wang National Engineering Research center for multimedia Software School of Computer ScienceWuhan University Hubei Key Laboratory of multimedia and network Communication Engineering Wuhan University Collaborative Innovation center of Geospatial technology

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

关键词： object-based audio coding non-negative matrix factorization audio scenecoding

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence(Beijing)

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder (RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

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Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

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China Communications 2018年第12期15卷 208-219页

作者： Wenhua Fang Jun Chen Ruimin Hu National Engineering Research center for multimedia Software Computer School of Wuhan UniversityWuhan 430072China Hubei Key Laboratory of multimedia and network Communication Engineering Wuhan UniversityWuhan 430072China Collaborative Innovation center of Geospatial technology Wuhan 430079China

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

关键词： attributes recognition CNN multi-task learning

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