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Ion transport behaviors in MXenes for electrochemical energy storage and conversion

Carbon Energy 2025年第3期7卷 110-132页

作者： Ling Fei Lei Lei Hui Xu Xinghua Guo Bo Chen Xu Han Xun Chen Qing Huang Degao Wang Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and ChemistryNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina University of Chinese Academy of Sciences BeijingChina Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization School of Intelligent ManufacturingHuzhou CollegeHuzhouChina Laboratory of Polymers and Composites Ningbo Institute of Materials Technology and Engineering(NIMTE)of the Chinese Academy of Sciences(CAS)NingboZhejiangChina Advanced Interdisciplinary Sciences Research(AIR)Center Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboChina

MXenes,an innovative class of two-dimensional(2D)materials composed of transition-metal carbides and/or nitrides,have garnered significant interest for their potential in energy storage and conversion applications,which is largely attributed to their modifiable surface terminations,exceptional conductivity,and favorable hydrophilic *** show various ion transport behaviors in applications like electrochemical catalysis,supercapacitors,and batteries,encompassing processes like electrostatic adsorption of surface ions,redox reactions of ions,and interlayer ion *** review aims to present a summary of advancements in the comprehension of ion transport behaviors of Ti_(3)C_(2)T_(x)***,the composition,properties,and synthesis techniques of MXenes are concisely ***,the discussion delves into the mechanisms of ion transport in MXenes during CO_(2)reduction,water splitting,supercapacitor operation,and battery performance,elucidating the factors determining the electrochemical behaviors and ***,a compilation of strategies used to optimize ion transport behaviors in MXenes is *** article concludes by presenting the challenges and opportunities for these fields to facilitate the continued progress of MXenes in energy-related technologies.

关键词： CO_(2)reduction ion transport MXenes storage batteries supercapacitors water splitting

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Recognition of tea sprouts based on improved deeplabV3 + model

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Multimedia Tools and Applications 2025年 1-18页

作者： Liu, Ruixin Wang, Dengzhun Chen, Zhonghui Li, Zhilin Xiao, Hui Yan, Chunyu Yan, Jianwei Lu, Ping Xie, Benliang College of Big Data and Information Engineering Guizhou University Guiyang550025 China Power Semiconductor Device Reliability Engineering Center of the Ministry of Education Guiyang550025 China College of Mechanical Engineering Guizhou University Guiyang550025 China State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education Guizhou University Guiyang550025 China

The recognition of tea sprouts is the premise of realize the intelligence of the premium tea picking. DeepLabV3 +, as the latest semantic segmentation algorithm of DeepLab family, can well recognize the tea sprouts in images. However, the model still has the problem of large number of parameters, and there is still room for improvements in segmentation accuracy and speed. To solve the multi-label recognition and segmentation of background, single bud, one leaf and two leaves in the tea sprout images, an improved deep convolutional neural network algorithm is proposed. This method realizes the recognition of different postures of high-quality tea sprouts on the premise of efficiency and accuracy. The DMResNet18 model is used as the feature extraction network to lightweight the network model and increase the segmentation speed. The feature joint attention network is added to optimize the spatial weights and channel weights of the model, which improves the accuracy of segmentation. Additionally, the low-level feature fusion method is applied to obtain rich initial global information of features and improves the overall performance of the model. The results show that the segmentation accuracy of improved DeepLabV3 + model reaches 92.58%, which is improved by 4.26% compared with the original DeepLabV3 + model. The model size and the speed of detection are improved by 80.67% and 52.24%, respectively. The proposed method offers a solution for the intelligent recognition of tea sprouts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Versatile manipulation of orbital angular momentum combs enabled by diffractive neural networks

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Optics Express 2025年第4期33卷 6771-6787页

作者： Ruan, Zhengsen Yan, Ruize Zhang, Jinlong Yin, Xukun Liu, Lixian Liang, Yize Xu, Huailiang Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology Zhejiang Gongshang University Zhejiang Hangzhou310018 China School of Optoelectronic Engineering Xidian University Xi’an710071 China

The orbital angular momentum (OAM) of light, which can be created through the helical phase front, has attracted significant attention and found numerous applications in both classical and quantum optics. The manipulation of light’s OAM has evolved from the generation of a single OAM mode to the simultaneous generation of multiple OAM modes. As a typical kind of multiple OAM superposition mode, an OAM comb features a discrete, equispaced OAM spectrum. Similar to how optical frequency combs promoted many technical revolutions in optics, such an OAM comb may likewise pave the way for OAM-related applications. Here, we propose an approach to versatilely manipulating OAM combs based on a two-layer fully connected diffractive neural network (DNN). We achieve four kinds of manipulation and transformations on OAM combs, including four arithmetic operations, square and square root operations, high-pass/low-pass/band-pass filtering, and arbitrary transformation between two OAM combs, etc. All four kinds of manipulation reach an average accuracy of 99.65% and the lowest accuracy of over 99%. Our work may advance the research on OAM combs and open new prospects for applications based on OAM comb sources. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Quantum optics

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Source Characteristics and Induced Hazards of the 2025 M6.8 Dingri Earthquake,Xizang,China,Revealed by Imaging Geodesy

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Journal of Earth Science 2025年第2期36卷 847-851页

作者： Chen Yu Zhenhong Li Xiaoning Hu Chuang Song Suju Li Haihui Liu Jie Li Bingquan Han Zhenjiang Liu Ming Liu Shuang Zhu Xiaoye Hao Zhiyuan Li Jianbing Peng College of Geological Engineering and Geomatics Chang'an UniversityXi'an 710054China Key Laboratory of Loess Xi'an 710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationXi'an 710054China Big Data Center for Geosciences and Satellites Chang'an UniversityXi'an 710054China National Disaster Reduction Center of China Beijing 100124China

0 *** to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 *** *** epicenter is located at 28.5°N,87.45°E,with a depth of~1... 详细信息

关键词： source characteristics M earthquake Xizang imaging geodesy induced hazards Dingri China

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Visible-Infrared Person Re-identification with Real-world Label Noise

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4857-4869页

作者： Zhang, Ruiheng Cao, Zhe Huang, Yan Yang, Shuo Xu, Lixin Xu, Min Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control School of Mechatronical Engineering Beijing100081 China Chinese Academy of Sciences New Laboratory of Pattern Recognition State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China University of Technology Sydney School of Electrical and Data Engineering Faculty of Engineering and Information Technology UltimoNSW2007 Australia

In recent years, growing needs for advanced security and traffic management have significantly heightened the prominence of the visible-infrared person re-identification community (VI-ReID), garnering considerable attention. A critical challenge in VI-ReID is the performance degradation attributable to label noise, an issue that becomes even more pronounced in cross-modal scenarios due to an increased likelihood of data confusion. While previous methods have achieved notable successes, they often overlook the complexities of instance-dependent and real-world noise, creating a disconnect from the practical applications of person re-identification. To bridge this gap, our research analyzes the primary sources of label noise in real-world settings, which include a) instantiated identities, b) blurry infrared images, and c) annotators' errors. In response to these challenges, we develop a Robust Hybrid Loss function (RHL) that enables targeted recognition and retrieval optimization through a more fine-grained division of the noisy dataset. The proposed method categorises data into three sets: clean, obviously noisy, and indistinguishably noisy, with bespoke loss calculations for each category. The identification loss is structured to address the varied nature of these sets specifically. For the retrieval sub-task, we utilize an enhanced triplet loss, adept at handling noisy correspondences. Furthermore, to empirically validate our method, we have re-annotated a real-world dataset, SYSU-Real. Our experiments on SYSU-MM01 and RegDB, conducted under various noise ratios of random and instance-dependent label noise, demonstrate the generalized robustness and effectiveness of our proposed approach. © 1991-2012 IEEE.

关键词： Information management

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Cyber Science Research Institute Sichuan University China Ministry of Education Key Laboratory of Data Protection and Intelligent Management Sichuan University Chengdu610065 China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China Hefei University of Technology School of Computer Science and Information Engineering China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China University of Padua Department of Mathematics HIT Center Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker, a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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FORECASTING SHORT-TERM PASSENGER FLOW ON A BUS ROUTE: A SPLITTING–INTEGRATING METHOD BASED ON PASSENGER TRAVEL BEHAVIOR

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Transport 2025年第1期40卷 12-23页

作者： Fang, Xiaoping Lin, Mei Chen, Weiya Pan, Xin School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Institute of Transportation Development Strategy and Planning of Sichuan Province China

Short-term passenger flow forecasting is the key to implement real-time dynamic dispatching of buses, which can meet the travel time requirement of passengers with different attributes. In practice, it is difficult to obtain passenger attribute information due to the restriction of bus information systems or other conditions. This article proposes a new perspective on identifying passenger attribute information, that is, the correlation between the bus card number and the travel time is used to analyse passenger travel behaviour. Then using the travel frequency as the splitting boundary, the passenger set is split into different types of subsets, which are predicted by different methods. The total forecast values are obtained by integration, so as to explore the effectiveness of the passenger attribute identification and splitting–integrating method. The result shows that: (1) compared with the forecasting method without considering the passenger travel behaviour, the performance of splitting–integrating method is better, and the passenger attribute identification method is effective;(2) the value of the splitting boundary will affect the size and consistency of the subset, and the optimal value can be sought according to forecast results;(3) different types of subsets should be treated by different forecasting models and combination paths. © 2025 The Author.

关键词： Travel time

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AMHF-TP:Multifunctional therapeutic peptides prediction based on multi-granularity hierarchical features

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Quantitative Biology 2025年第1期13卷 127-141页

作者： Shouheng Tuo YanLing Zhu Jiangkun Lin Jiewei Jiang School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’anChina Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’anChina Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anChina School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’anChina

Multifunctional therapeutic peptides(MFTP)hold immense potential in diverse therapeutic contexts,yet their prediction and identification remain challenging due to the limitations of traditional methodologies,such as extensive training durations,limited sample sizes,and inadequate generalization *** address these issues,we present AMHF-TP,an advanced method for MFTP recognition that utilizes attention mechanisms and multi-granularity hierarchical features to enhance *** AMHF-TP is composed of four key components:a migration learning module that leverages pretrained models to extract atomic compositional features of MFTP sequences;a convolutional neural network and selfattention module that refine feature extraction from amino acid sequences and their secondary structures;a hypergraph module that constructs a hypergraph for complex similarity representation between MFTP sequences;and a hierarchical feature extraction module that integrates multimodal peptide sequence *** with leading methods,the proposed AMHF-TP demonstrates superior precision,accuracy,and coverage,underscoring its effectiveness and robustness in MFTP *** comparative analysis of separate hierarchical models and the combined model,as well as with five contemporary models,reveals AMHFTP’s exceptional performance and stability in recognition tasks.

关键词： deep learning hypergraph multifunctional therapeutic peptides multi-granularity hierarchical features

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Face Forgery Detection Based on Fine-Grained Clues and Noise Inconsistency

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 144-158页

作者： Zhang, Dengyong He, Ruiyi Liao, Xin Li, Feng Chen, Jiaxin Yang, Gaobo Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation the School of Computer and Communication Engineering Changsha410114 China Hunan University Changsha410082 China

Deepfake detection has gained increasing research attention in media forensics, and a variety of works have been produced. However, subtle artifacts might be eliminated by compression, and the convolutional neural networks (CNNs)-based detectors are invalidated for fake face images with compression. In this work, we propose a two-stream network for deepfake detection. We observed that high-frequency noise features and spatial features are inherently complementary to each other. Thus, both spatial features and high-frequency noise features are exploited for face forgery detection. Specifically, we design a double-frequency transformer module (DFTM) to guide the learning of spatial features from local artifact regions. To effectively fuse spatial features and high-frequency noise features, a dual-domain attention fusion module (DDAFM) is designed. We also introduce a local relationship constraint loss, which requires only image-level labels, for model training. We evaluate the proposed approach on five large-scale benchmark datasets, and extensive experimental results demonstrate the proposed approach outperforms most SOTA works. © 2024 IEEE.

关键词： Frequency domain analysis

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