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A Hypothesis on the Function of High-Valent Fe in NiFe (Hydr)oxide in the Oxygen-Evolution Reaction

Angewandte Chemie 2024年第6期137卷

作者： Nader Akbari Jafar Hussain Shah Cejun Hu Subhajit Nandy Pavlo Aleshkevych Rile Ge Sumbal Farid Changchang Dong Liang Zhang Keun Hwa Chae Wei Xie Taifeng Liu Junhu Wang Mohammad Mahdi Najafpour Department of Chemistry Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan 45137-66731 Iran CAS Key Laboratory of Science and Technology on Applied Catalysis Mössbauer Effect Data Center Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian 116023 China Key Lab of Advanced Energy Materials Chemistry (Ministry of Education) Renewable Energy Conversion and Storage Center College of Chemistry Nankai University Tianjin 300071 China Advanced Analysis Center Korea Institute of Science and Technology Seoul 02792 Republic of Korea Institute of Physics Polish Academy of Sciences Warsaw 02-668 Poland School of Environmental and Chemical Engineering Jiangsu University of Science and Technology Zhenjiang 212100 China National and Local Joint Engineering Research Center for Applied Technology of Hybrid Nanomaterials Henan University Kaifeng 475004 China Department of Chemistry Sharif University of Technology Tehran 11155-9516 Iran

This study investigated the dynamic changes in NiFe (hydr)oxide and identified the role of high-valent Fe in the oxygen-evolution reaction (OER) within alkaline media via in situ techniques. Several high-valent Fe ions were found to remain considerably stable in the absence of potential in NiFe (hydr)oxide, even 96 hours after the OER. For Ni 2+ hydroxide treated with 57 Fe ions, where Fe sites are introduced onto the surface of Ni 2+ hydroxide, no Fe 4+ species were detected at the rate-determining step (RDS). The findings of this study suggested that the oxidation of bulk Fe ions, similar to Ni ions, to high valent forms, is charge accumulation without a direct role in OER; these results offered a novel perspective on manipulating Fe states to optimize OER efficacy. The prevailing hypothesis suggested that trace amounts of high-valent Fe ions, notably those on the surface, directly participate in OER.

关键词： NiFe (hydr)oxide oxygen evolution reaction high-valent Fe4+ in situ techniques electrocatalysts alkaline conditions mechanism active sites

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ANIMC: A soft approach for auto-weighted noisy and incomplete multi-view clustering

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multi-view clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multi-view clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this paper, we propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering approach (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model. Firstly, by designing adaptive semi-regularized nonnegative matrix factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Secondly, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results demonstrate its superior advantages over other state-of-the-art methods. Impact Statement-As an effective method to process data from multiple sources, multi-view clustering has attracted more and more attention. However, most previous works ignore missing instances and noises in original multi-view data, which limits the applications of these works. By a soft approach, our proposed ANIMC can effectively reduce the negative influence of missing instances and noises. Moreover, ANIMC outperforms the stateof- the-art works by about 20% in representative cases. With satisfactory performance on multiple rea

关键词： Regression analysis

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Feature genes selection using Fisher transformation method

Feature genes selection using Fisher transformation method

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作者： Mu, Huiyu Xu, Jiucheng Wang, Yun Sun, Lin College of Computer and Information Engineering Henan Normal University Xinxiang China Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China

The selection of feature genes with high recognition ability from the gene expression profiles have gained great significances in biology. However, most of the existing methods for feature genes selection have a high time complexity where lead to a poor performance. Motivated by this, an effective feature selection method, called Fisher transformation (FT), is proposed which based on the improved Fisher discriminant analysis (FDA) and neighborhood rough set algorithms. The FT method has two benefits: 1. The multiple neighborhood rough set algorithm is used for solving the small sample size problem of FDA;2. The improved FDA algorithm is used for selecting feature genes and ameliorating poor ability of classification. Furthermore, we measure the impact of the FT approach on the final selection consequence. The results obtained on four public tumor microarray datasets provide beneficial insight on both the benefits and limitations, paving the way to the exploration of new and wider feature selection programs. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Feature Selection

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Onionchain: Towards balancing privacy and traceability of blockchain-based applications

arXiv

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arXiv 2019年

作者： Zhang, Yue Weng, Jian Weng, Jiasi Li, Ming Luo, Weiqi College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

With the popularity of Blockchain comes grave security related concerns. Achieving privacy and traceability simultaneously remains an open question. Efforts have been made to address the issues, while they may subject to specific scenarios. This paper studies how to provide a more general solution for this open question. Concretely, we propose Onionchain, featuring a suite of protocols, offering both traceability and privacy. As the term implies, our Onionchain is inspired by Onion routing. We investigate the principles of Onion routing carefully and integrate its mechanism together with Blockchain technology. We advocate the Blockchain community to adopt Onionchain with the regards of privacy and traceability. To this end, a case-study of Onionchain, which runs in the context of Vehicular Ad Hoc Networks (VANETs), is proposed, providing the community a guideline to follow. Systematic security analysis and extensive experiments are also conducted to validate our secure and cost-effective Onionchain. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

引用

IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang 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 Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Fine-grained iterative attention network for temporal language localization in videos

arXiv

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arXiv 2020年

作者： Qu, Xiaoye Tang, Pengwei Zou, Zhikang Cheng, Yu Dong, Jianfeng Zhou, Pan Xu, Zichuan School of Electronic Information and Communication Huazhong University of Science and Technology Huawei Tech. Hangzhou China School of Electronic Information and Communication Huazhong University of Science and Technology China Baidu Inc. China Microsoft Dynamics 365 AI Research RedmondWA United States School of Computer and Information Engineering Zhejiang Gongshang University China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Dalian University of Technology China

Temporal language localization in videos aims to ground one video segment in an untrimmed video based on a given sentence query. To tackle this task, designing an effective model to extract grounding information from both visual and textual modalities is crucial. However, most previous attempts in this field only focus on unidirectional interactions from video to query, which emphasizes which words to listen and attends to sentence information via vanilla soft attention, but clues from query-by-video interactions implying where to look are not taken into consideration. In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction. Specifically, in the iterative attention module, each word in the query is first enhanced by attending to each frame in the video through fine-grained attention, then video iteratively attends to the integrated query. Finally, both video and query information is utilized to provide robust cross-modal representation for further moment localization. In addition, to better predict the target segment, we propose a content-oriented localization strategy instead of applying recent anchor-based localization. We evaluate the proposed method on three challenging public benchmarks: ActivityNet Captions, TACoS, and Charades-STA. FIAN significantly outperforms the state-of-the-art approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

A Competitive Swarm Algorithm for Image Segmentation Guided ...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Diego Oliva Songfeng Lu Erik Cuevas Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong university of Science and Technology Wuhan China Department of Computer Damietta University Egypt Department of Electrical Engineering Fayoum University Fayoum Egypt Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Image segmentation Entropy Optimization Linear programming Fuzzy sets Thresholding (Imaging) Histograms

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Front Cover: International Journal of Intelligent Systems, Volume 37 Issue 11 November 2022

引用

International Journal of Intelligent Systems 2022年第11期37卷

作者： Xianjia Meng Yong Yang Ximeng Liu Nan Jiang School of Information Science and Technology Northwest University Xi'an People's Republic of China Fujian Province Key Laboratory of Information Security of Network System and College of Computer and Data Science Fuzhou University Fuzhou People's Republic of China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen People's Republic of China Department of Internet of Things School of Information Engineering East China Jiao Tong University Nanchang People's Republic of China

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China's greenhouse gas budget during 2000-2023

引用

National Science Review 2025年第4期 24-36页

作者： Wenping Yuan Minqi Liang Yuanyi Gao Ling Huang Li Dan Hongtao Duan Songbai Hong Fei Jiang Weimin Ju Tingting Li Ziyang Lou Shilong Luan Xiao Lu Zhangcai Qin Lishan Ran Lulu Shen Fei Teng Xiangjun Tian Yilong Wang Jing Wei Jiangzhou Xia Xiaosheng Xia Lijun Yu Xu Yue Haicheng Zhang Wen Zhang Yuzhong Zhang Xu Zhao Qiuan Zhu Shilong Piao Xuhui Wang Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Lake and Watershed Science for Water Security Nanjing Institute of Geography and LimnologyChinese Academy of Sciences State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of Sciences School of Urban Planning and Design Shenzhen Graduate School Peking University Frontiers Science Center for Critical Earth Material Cycling Nanjing University Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of Sciences Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery School of Environmental Science & Engineering Shanghai Jiao Tong University Department of Geography The University of Hong Kong Department of Atmospheric and Oceanic Sciences School of PhysicsPeking University Institute of Energy Environment and Economy Tsinghua University State Key Laboratory of Tibetan Plateau Earth System Resources and Environment (TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of Sciences Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric PhysicsChinese Academy of Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of

National greenhouse gas（GHG） budget,including CO2,CH4and N2O has increasingly become a topic of concern in international climate *** is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate *** estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in *** study used comprehensive methods,including emission factor methods,process-based models,atmospheric inversions,and data-driven models,to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000-2023,and produced an up-to-date China GHG Budget dataset（CNGHG）.The total gross emissions of the three GHGs show a 3-fold increase from 5.0（95% CI:4.9-5.1） Gt CO2-eq yr-1（in 2000） to 14.3（95% CI:13.8-14.8） Gt CO2-eqyr-1（in2023）.CO2emissions represented 81.8% of the GHG emissions in 2023,while 12.7% and 5.5% were for CH4and N2O,*** the largest CO2source,the energy sector contributed 87.4% *** contrast,the agriculture,forestry and other land use sector was the largest sector of CH4and N2O,representing 50.1% and 6 6.3% emissions,***,China's terrestrial ecosystems serve as a net CO2sink(1.0 Gt CO2yr-1,9596 CI:0.2-1.9 Gt CO2yr-1) during 2012 to 2021,equivalent to an average of 14.3% of fossil *** GHG emission estimates showed a general consistency with national GHG inventories,with gridded and sector-specific estimates of GHG fluxes over China,providing the basis for curtailing GHG emissions for each region and sector.

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

引用

arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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