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An electron-transfer-tuning strategy at the graphene/metal interface for improving acidic water electrolysis in proton exchange membrane electrolyzers

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Journal of Energy Chemistry 2025年第4期103卷 344-352页

作者： Yue Xu Shuaidong Li Yingjian He Fumiya Shiokawa Samuel Jeong Aimi Asilah Haji Tajuddin Zeyu Xi Yoshikazu Ito Jingzi Zhang Zeyun Cai Xi Lin Kailong Hu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Special Environments Physical Sciences Harbin Institute of TechnologyShenzhen 518055GuangdongChina School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055GuangdongChina Institute of Applied Physics Graduate School of Pure and Applied SciencesUniversity of TsukubaTsukuba 305-8573Japan

Graphene encapsulation has been shown to be an effective technique for improving the corrosion resistance of non-noble metal catalysts for the acidic water *** key challenge lies in enhancing the electrocatalytic activity of graphene-encapsulated metals while maintaining their durability in acidic ***,an electron-transfer-tuning strategy is investigated at the graphene/NiMo interface,aiming to improve the hydrogen evolution reaction(HER)performance of graphene-encapsulated NiMo *** doping of Ti,a low electronegativity element,into NiMo substrate was confirmed to increase electron transfer from the metal core toward the *** electron-rich state on graphene facilitates the adsorption of positively charged protons on graphene,thereby enabling a Pt/C-comparable performance in 0.5 M H_(2)SO_(4),with only a 3.8%degradation in performance over a 120-h continuous *** proton exchange membrane(PEM)water electrolyzer assembled by the N-doped grapheneencapsulated Ti-doped NiMo exhibits a smaller cell voltage to achieve a current density of 2.0 A cm^(-2),in comparison to the Pt/C based *** study proposes a novel electron-transfer-tuning strategy to improve the HER activity of graphene-encapsulated non-noble metal catalysts without sacrificing durability in acidic electrolytes.

关键词： Hydrogen evolution Acidic media Graphene encapsulation Interface Proton exchange membrane

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Penalized M-Estimation Based on Standard Error Adjusted Adaptive Elastic-Net

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Journal of Systems science & Complexity 2023年第3期36卷 1265-1284页

作者： WU Xianjun WANG Mingqiu HU Wenting TIAN Guo-Liang LI Tao School of Statistics and Mathematics Zhongnan University of Economics and LawWuhan 430073China School of Statistics and Data Scicence Qufu Normal UniversityQufu 273165China Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China

When there are outliers or heavy-tailed distributions in the data, the traditional least squares with penalty function is no longer applicable. In addition, with the rapid development of science and technology, a lot of data, enjoying high dimension, strong correlation and redundancy, has been generated in real life. So it is necessary to find an effective variable selection method for dealing with collinearity based on the robust method. This paper proposes a penalized M-estimation method based on standard error adjusted adaptive elastic-net, which uses M-estimators and the corresponding standard errors as weights. The consistency and asymptotic normality of this method are proved theoretically. For the regularization in high-dimensional space, the authors use the multi-step adaptive elastic-net to reduce the dimension to a relatively large scale which is less than the sample size, and then use the proposed method to select variables and estimate parameters. Finally, the authors carry out simulation studies and two real data analysis to examine the finite sample performance of the proposed method. The results show that the proposed method has some advantages over other commonly used methods.

关键词： Adaptive elastic net -estimation oracle property standard error

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A multi-agents visual federated learning framework for underground coal mines 24

A multi-agents visual federated learning framework for under...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Ji, Yucheng Yuan, Hongyong Liu, Yongqiang Fang, Yudong Chen, Wei Ministry of Emergency Management Big Data Center China School of Safety Science Tsinghua University China School of Computer Science and Technology China University of Mining and Technology China

ISBN: (纸本)9798400717192

The video safety monitoring and analysis is a critical problem in underground coal mines. Due to the complex environment in the underground coal mine and the requirement of perception and decision-making abilities from the coal mine robots, single intelligent agent(robot) is difficult to complete complicated tasks. This paper proposes a framework of federated learning for multiple robots(agents) in underground coal mines to achieve video monitor and analysis tasks. Three federated learning models are raised include asynchronous federation optimization algorithm;visual federation method for heterogeneous image data and coal mine video equipment federated model aggregation. The visual federated learning framework can significantly improve the safety monitoring, accelerate the development of automation in the coal mine production field, and constructing and improving smart mines. © 2024 ACM.

关键词： Risk management

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Readout electronics for the gamma detector of the HIRFL-CSR external target facility

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Nuclear science and Techniques 2025年第2期36卷 71-81页

作者： Xian-Qin Li Hai-Bo Yang Xiao-Meng Ma Chao-Jie Zou Tao Liu Xian-Cai Zhou Duo Yan Yang-Zhou Su Shu-Wen Tang Shi-Tao Wang Yu-Hong Yu Zhi-Yu Sun Cheng-Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China School of Physics and Electronic Engineering Northwest Normal UniversityLanzhou 730070China School of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China

The Cooling Storage Ring of the Heavy Ion Research Facility in Lanzhou(HIRFL-CSR)was constructed to study nuclear physics,atomic physics,interdisciplinary science,and related *** External Target Facility(ETF)is located in the main ring of the *** gamma detector of the ETF is built to measure emitted gamma rays with energies below 5 MeV in the center-of-mass frame and is planned to measure light fragments with energies up to 300 *** readout electronics for the gamma detector were designed and *** readout electronics consist of thirty-two front-end cards,thirty-two readout control units(RCUs),one common readout unit,one synchronization&clock unit,and one sub-trigger *** using the real-time peak-detection algorithm implemented in the RCU,the data volume can be significantly *** addition,trigger logic selection algorithms are implemented to improve the selection of useful events and reduce the data *** test results show that the integral nonlinearity of the readout electronics is less than 1%,and the energy resolution for measuring the 60 Co source is better than 5.5%.This study discusses the design and performance of the readout electronics.

关键词： HIRFL-CSR Gamma detector External target facility Readout electronics Readout control unit Common readout unit Peak-detection algorithm

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Realizing n-type CdSb with promising thermoelectric performance

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Journal of Materials science & technology 2023年第13期144卷 54-61页

作者： Peng Zhao Honghao Yao Shizhen Zhi Xiaojing Ma Zuoxu Wu Yijie Liu Xinyu Wang Li Yin Zongwei Zhang Shuaihang Hou Xiaodong Wang Siliang Chen Chen Chen Xi Lin Haoliang Liu Xingjun Liu Feng Cao Qian Zhang Jun Mao School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Realizing high performance in both n-type and p-type materials is essential for designing efficient ther-moelectric ***,the doping bottleneck is often encountered,i.e.,only one type of conduction can be *** one example,p-type CdSb with high thermoelectric performance has been discovered for several decades,while its n-type counterpart has rarely been *** this work,the calculated band structure of CdSb demonstrates that the valley degeneracy is as large as ten for the conduction band,and it is only two for the valence ***,the n-type CdSb can potentially realize an ex-ceptional thermoelectric ***,the n-type conduction has been successfully real-ized by tuning the stoichiometry of *** further doping indium at the Cd site,an improved room-temperature electron concentration has been *** modeling predicts an optimal electron con-centration of∼2.0×1019 cm−3,which is higher than the current experimental ***,future optimization of the n-type CdSb should mainly focus on identifying practical approaches to optimize the electron concentration.

关键词： Thermoelectric materials n-type CdSb Indium doping Band degeneracy

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An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 113-127页

作者： Zhao, Kaixin Ren, Menghui College of Computer Science and Technology Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Manufacturing IoT Big Data Engineering Technology Research Center of Henan Province Xinxiang453003 China

In our study, we investigate how the brain maps environmental spaces into understandable maps through hippocampal place cells and entorhinal cortex grid cells. We uncover that the hippocampus and entorhinal cortex are not just directly connected but also engage in a sophisticated feedback loop crucial for spatial encoding during navigation. We developed an innovative network model based on this feedback mechanism, linking entorhinal inputs to hippocampal spatial encoding cells through a non-negative sparse coding, using grid cells and weak spatial cells as inputs. Our findings demonstrate that the model learns and adjusts the characteristics of spatial encoding cells, successfully learning the specific spatial selectivities of place cells even with inputs from cells carrying weaker spatial information. This highlights the critical role of feedback encoding in refining spatial representations, an indispensable component of the brain’s navigation system. Moreover, experiments comparing our model with others validate our model’s exceptional ability in generating precise spatial encodings, underscoring its superiority. This research not only deepens our understanding of the brain’s navigational mechanisms but also offers a robust computational framework for future neuroscience studies, emphasizing the intricate interplay between feedback loops and spatial encoding in the brain’s navigation system. © The Institution of Engineers (India) 2024.

关键词： Brain

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Implementation of Campus Pedestrian Detection Using YOLOv5 15th

Implementation of Campus Pedestrian Detection Using YOLOv5

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Lin, Yuh-Chung Kuan, Ta-Wen Tseng, Shih-Pang Lv, Xinhang School of Information Science and Technology Sanda University Shanghi201209 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

This study focuses on the development of accurate human detection using the YOLO open-source deep learning framework derived from OpenCV. The aim is to implement campus pedestrian detection. YOLO is a rapidly evolving and popular detection algorithm in recent years. The campus pedestrian detection system can improve students’ safety, contributing to the creation of a beautiful campus. This paper begins with a requirement analysis, which involves categorizing campus image information into pedestrians, traffic signs, fences, cones, fire hydrants, speed bumps, manhole covers, etc. A YOLOv5 dataset is created for training the model. The training process includes adjusting the model based on the training situation and employing data augmentation techniques such as mosaic enhancement, flipping, and grayscale to balance the richness of data labels and improve the recognition performance of the model. The experimental goal is to achieve a recognition rate of 70% to 80%. Overall, this research proposes a project design for campus pedestrian detection using the YOLO framework. By effectively utilizing YOLO’s fast and efficient capabilities, the project aims to enhance pedestrian safety, contributing to the establishment of a beautiful campus environment. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Time Series data Cleaning under Expressive Constraints on Both Rows and Columns 40

Time Series Data Cleaning under Expressive Constraints on Bo...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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Facilitated the discovery of new γ/γ′Cobased superalloys by combining firstprinciples and machine learning

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npj Computational Materials 2024年第1期10卷 493-508页

作者： ZhaoJing Han ShengBao Xia ZeYu Chen Yihui Guo ZhaoXuan Li Qinglian Huang Xing-Jun Liu Wei-Wei Xu School of Aerospace Engineering Xiamen UniversityXiamen361005China College of Materials Xiamen UniversityXiamen361005China Institute of Materials Genome and Big Data Harbin Institute of TechnologyShenzhen518055China

Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft *** discovery of a novel class ofγ/γ′Co-Al-W alloys has ignited a surge of interest in Cobased superalloys,with the aspiration to transcend the inherent constraints of their Ni-based ***,the conventional methodologies utilized in the design and advancement of newγ/γ′Co-based superalloys are frequently characterized by their laborious and resource-intensive *** this study,we employed a coupled Density Functional Theory(DFT)and machine learning(ML)approach to predict and analyze the stability of the crucialγ′phase,which is instrumental in expediting the discovery ofγ/γ′Co-based alloys.

关键词： γ′ superalloys utilized

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Security analysis and secured access design for networks of image remote sensing

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Digital Communications and Networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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