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中国体视学与图像分析 2005年第3期10卷 189-192页

作者：李慧田捷张兆田 Department of Education Technology Capital Normal University Beijing 100037 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences P. O. Box 2728 Beijing 100080 China Department of Information Science National Natural Science Foundation of China Beijing 100085 China

Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-destructive testing software have been developed to efficiently detect inner flaws of space industrial components. As the core of our software, reconstruction algorithms including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. With real raw data from CASC of China, experimental results verified the applied reconstruction algorithm in our software. Furthermore, forward algorithms simulating generation of fan-beam raw data are also presented in this article.

关键词：二维工业CT 图象重建计算方法计算机系统

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The issues on the commercialization of perovskite solar cells

材料展望（英文）

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材料展望（英文） 2024年第2期3卷 118-152页

作者： Lixiu Zhang Yousheng Wang Xiangchuan Meng Jia Zhang Pengfei Wu Min Wang Fengren Cao Chunhao Chen Zhaokui Wang Fu Yang Xiaodong Li Yu Zou Xi Jin Yan Jiang Hengyue Li Yucheng Liu Tongle Bu Buyi Yan Yaowen Li Junfeng Fang Lixin Xiao Junliang Yang Fuzhi Huang Shengzhong Liu Jizhong Yao Liangsheng Liao Liang Li Fei Zhang Yiqiang Zhan Yiwang Chen Yaohua Mai Liming Ding Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People's Republic of China Institute of New Energy Technology College of Information Science and TechnologyJinan UniversityGuangzhou 510632People's Republic of China Institute of Polymers and Energy Chemistry(IPEC) Nanchang UniversityNanchang 330031People's Republic of China Institute of optoelectronics Fudan UniversityShanghai 200433People's Republic of China School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072People's Republic of China School of Physical Science and Technology Soochow UniversitySuzhou 215006People's Republic of China Institute of Functional Nano & Soft Materials(FUNSOM) Soochow UniversitySuzhou 215123People's Republic of China College of Chemistry Chemical Engineering and Materials ScienceSoochow UniversitySuzhou 215123People's Republic of China School of Physics and Electronic Science East China Normal UniversityShanghai 200241People's Republic of China State Key Laboratory for Artificial Microstructure and Mesoscopic Physics Department of PhysicsPeking UniversityBeijing 100871People's Republic of China College of Biophotonics South China Normal UniversityGuangzhou 510631People's Republic of China School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081People's Republic of China School of Physics and Electronics Central South UniversityChangsha 410083People's Republic of China School of Materials Science and Engineering Shaanxi Normal UniversityXi'an 710119People's Republic of China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070People's Republic of China Microquanta semiconductor Hangzhou 311121People's Republic of China School of Information Science and Technology Fudan UniversityShanghai 200433People's Republic of China

Perovskite solar cells have aroused a worldwide research upsurge in recent years due to their soaring photovoltaic performance,ease of solution processing,and low *** power conversion efficiency record is constantly being broken and has recently reached 26.1％in the lab,which is comparable to the established photovoltaic technologies such as crystalline silicon,copper indium gallium selenide and cadmium telluride(CdTe)solar ***,perovskite solar cells are standing at the entrance of industrialization,where huge opportunities and risks ***,towards commercialization,challenges of up-scaling,stability and lead toxicity still remain,the proper handling of which could potentially lead to the widespread adoption of perovskite solar cells as a low-cost and efficient source of renewable *** review gives a holistic analysis of the path towards commercialization for perovskite solar cells.A comprehensive overview of the current state-of-the-art level for perovskite solar cells and modules will be introduced first,with respect to the module efficiency,stability and current status of *** will then discuss the challenges that get in the way of commercialization and the corresponding strategies to address them,involving the upscaling,the stability and the lead toxicity *** into the future direction of commercialization of perovskite photovoltaics was also provided,including the flexible perovskite cells and modules and perovskite indoor ***,the future perspectives towards commercialization are put forward.

关键词： commercializations perovskite solar cells state-of-the-art level challenges and perspectives indoor photovoltaics

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Deep Stable Learning for Cross-lingual Dependency Parsing

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ACM Transactions on Asian and Low-Resource Language information processing 1000年

作者： Haijiang Liu Chen Qiu Qiyuan Li Maofu Liu Li Wang Jinguang Gu Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Scientific and Technical Information of China Beijing China Key Laboratory of Rich-media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration Beijing China

The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%.

关键词： Cross-lingual Dependency Parsing Deep Stable Learning Out-of-Distribution Problem

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Insights into the Antibacterial Properties of Cancer Theranostic Zinc-Dopped Iron Oxide ZnxFe3-xO4 Nanoparticles

SSRN

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SSRN 2024年

作者： Asaftei, Mihaela Lucidi, Massimiliano Anton, Stefan R. Tranca, Denis E. Hristu, Radu Wu, Aiguo Yang, Yiqian Stanciu, George A. Lazar, Veronica Ionita, Mariana Cincotti, Gabriella Visca, Paolo Holban, Alina Yang, Fang Stanciu, Stefan G. Center for Microscopy-Microanalysis and Information Processing National Technical University Politehnica of Bucharest 313 Splaiul Independentei Bucharest060042 Romania Department of Microbiology and Immunology Faculty of Biology University of Bucharest Bucharest060101 Romania Department of Science Roma Tre University Viale G. Marconi 446 Rome Italy NBFC National Biodiversity Future Center piazza Marina 61 Palermo90133 Italy Laboratory of Advanced Theranostic Materials and Technology Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology Zhejiang International Cooperation Base of Biomedical Materials Technology and Application Ningbo Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Advanced Polymer Materials Group National University of Science and Technology Politehnica Bucharest Gheorghe Polizu 1-7 Bucharest011061 Romania Department of Engineering Roma Tre University Viale G. Marconi 446 Rome Italy

The global mortality rate from antimicrobial-resistant infections is steadily rising, driven primarily by the widespread use of antibiotics in the last decades, a problem exarcerbated during the COVID-19 pandemic. The exponential growth of antimicrobial resistance far outpaces the current development of new antibiotics. One promising strategy to counteract the rise of antibiotic-resistant bacteria is the use of nanomaterials as antimicrobial agents. In recent past works Zinc-Doped Iron Oxide ZnxFe3–xO4 nanoparticles (NPs) have been demonstrated as a highly efficient cancer theranostics agent capable of several different, complementary, therapeutic routes, depending on the Zn doping concentration. In this study, we demonstrate for the fist time interesting antibacterial effects of this nanomaterial, investigating the interactions of ZnxFe3–xO4 NPs with various Zn concentrations (0.2%, 1%, 1.5%, 2%) with two opportunistic pathogens: the Gram-positive bacterium Staphylococcus aureus and the Gram-negative bacterium Pseudomonas aeruginosa, both of which are among the top resistant nosocomial pathogens, and model organisms. We adopt a holistic approach, looking at: (i) the antimicrobial effects of ZnxFe3–xO4 NPs, either alone, or in combination with two antibiotics, gentamicin (CN) and ciprofloxacin (CIP), (ii) their efficacy at dispersing bacterial planktonic aggregates, and (iii) their biofilm-impeding properties. Last, we employ Atomic Force Microscopy for qualitative and quantitative analyses of bacterial cells treated with CIP, CN and ZnxFe3–xO4 NPs to enable knowledge on mechanisms of antibacterial action of the latter. © 2024, The Authors. All rights reserved.

关键词： Staphylococcus aureus

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Granular Computing: An Augmented Scheme of Degranulation Through a Modified Partition Matrix

arXiv

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu Xing, Mengdao School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of remote sensing science and technology School of Electronic Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau999078 China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi’an710071 China National Key Laboratory of Microwave Imaging Technology Institute of Electronics Chinese Academy of Sciences Beijing100190 China

As an important technology in artificial intelligence Granular Computing (GrC) has emerged as a new multi-disciplinary paradigm and received much attention in recent years. information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of GrC. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we dwell on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. Both the granulation and degranulation are regarded as generalized rotation between the data subspace and the prototype subspace with the partition matrix and the fuzzification factor. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. Copyright © 2020, The Authors. Al

关键词： Granulation

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Self-repairing high entropy oxides

arXiv

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arXiv 2021年

作者： Liu, Zongwen Huang, Pengru Sun, Lixian Liu, Yanping Qu, Jiangtao Cairney, Julie Zheng, Zhong Wang, Zhiming M. Khan, Naveed A. Lai, Zhiping Fu, Li Teng, Bing Zhou, Cuifeng Zhao, Hong Xu, Fen Xiong, Pan Zhu, Junwu Yuan, Peng Tsoutas, Kosta Akhavan, Behnam Bilek, Marcela M. Ringer, Simon P. Novoselov, Kostya S. School of Chemical and Biomolecular Engineering The University of Sydney NSW2006 Australia The University of Sydney Nano Institute The University of Sydney NSW2006 Australia Department of Materials Science and Engineering National University of Singapore 117575 Singapore Guangxi Key Laboratory of Information Materials Guangxi Collaborative Innovation Center of Structure and Property for New Energy and Materials School of Material Science & Engineering Guilin University of Electronic Technology Guilin541004 China School of Physics and Electronics Hunan Key Laboratory for Super-Microstructure and Ultrafast Process Central South University Hunan410083 China School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia The Australian Centre for Microscopy and Microanalysis The University of Sydney NSW2006 Australia Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China Advanced Membranes and Porous Materials Center Division of Physical Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia State Key Laboratory of Solidification Processing Northwestern Polytechnical University Xi'an710072 China College of Physics Qingdao University Qingdao266071 China Key Laboratory for Soft Chemistry and Functional Materials Ministry Education Nanjing University of Science and Technology Nanjing210094 China CAS Key Laboratory of Mineralogy and Metallogeny Guangdong Provincial Key laboratory of Mineral Physics and Materials Guangzhou Institute of Geochemistry Chinese Academy of Sciences Guangzhou510640 China School of Physics The University of Sydney NSW2006 Australia School of Biomedical Engineering The University of Sydney NSW2006 Australia

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials. Copyright © 2021, The Authors. All rights reserved.

关键词： Entropy

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Two-Dimensional Organic Supramolecule via Hydrogen Bonding and π–π Stacking for Ultrahigh Capacity and Long-Life Aqueous Zinc–Organic Batteries

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Angewandte Chemie 2022年第37期134卷

作者： Dr. Yuan Chen Jianyao Li Dr. Qin Zhu Dr. Kun Fan Yiqing Cao Guoqun Zhang Chenyang Zhang Yanbo Gao Jincheng Zou Prof. Tianyou Zhai Prof. Chengliang Wang School of Optical and Electronic Information Wuhan National Laboratory for Optoelectronics (WNLO) Optics Valley Laboratory Huazhong University of Science and Technology Wuhan 430074 China School of Chemistry and Chemical Engineering Nanjing University Nanjing 210093 China State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Aqueous zinc-ion batteries (ZIBs) are promising for next-generation energy storage. However, the reported electrode materials for ZIBs are facing shortcomings including low capacity and unsatisfactory cycling stability etc. Herein, hexaazatrinaphthalene-quione (HATNQ) is reported for aqueous ZIBs. The HATNQ electrodes delivered an ultrahigh capacity (482.5 mAh g −1 at 0.2 A g −1 ) and outstanding cyclability of >10 000 cycles at 5 A g −1 . The capacity sets a new record for organic cathodes in aqueous ZIBs. The high performances are ascribed to the rich C=O and C=N groups that endowed HATNQ with a 2D layered supramolecular structure by multiple hydrogen bonds in plane with π–π interactions out-of-plane, leading to enhanced charge transfer, insolubility, and rapid ion transport for fast-charge and -discharge batteries. Moreover, the 2D supramolecular structure boosted the storage of Zn 2+ /H + , particularly the storage of Zn 2+ , due to the more favorable O⋅⋅⋅Zn⋅⋅⋅N coordination in HATNQ.

关键词： Hydrogen Bonding Organic Cathodes Organic Zinc-Ion Batteries π–π Stacking Supramolecules

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Inside Back Cover Image

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Plant, Cell & Environment 2024年第1期48卷 iii-iii页

作者： Sheng Wang Chaoyue Zhao Yun Su Kangjian Cao Chao Mou Fu Xu School of Information Science and Technology Beijing Forestry University Beijing China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing China State Key Laboratory of Efficient Production of Forest Resources Beijing China

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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