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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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public Auditing for Multi-Version Cloud data with Storage Efficiency and Version Dynamics

SSRN

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SSRN 2022年

作者： Ye, Weiping Nan, Fulin Tian, Hui Quan, Hanyu Chang, Chin-Chen College of Computer Science and Technology National Huaqiao University Xiamen361021 China Wuhan National Laboratory for Optoelectronics Wuhan430074 China Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University Xiamen361021 China Department of Information and Computer Science Feng Chia University Taichung40724 Taiwan

Multi-version data storage has been an increasingly popular service in cloud computing, which is shown to support flexible rollback recovery of cloud data. However, how to verify the integrity of this kind of data efficiently still remains open. As an attempt at addressing this issue, this paper presents a novel public auditing scheme for multi-version data in the cloud to achieve secure and efficient verification for all the versions of cloud data, which employs a three-pronged approach. First, we propose an incremental storage table (IST) to store all data versions in an efficient and low-cost manner;second, we develop a dynamic version control table (DVCT) to support efficient update for the multi-version data;finally, we design a custom-built tag-generation algorithm that can achieve authentication for all versions with only one tag. We formally prove the security of the presented scheme and evaluate its performance through comprehensive experiments and comparisons with state-of-the-art ones. The experimental results demonstrate that our scheme can effectively achieve public auditing for multi-version data while providing excellent security and outperforming state-of-the-art schemes in terms of computation and communication costs. © 2022, The Authors. All rights reserved.

关键词： Signal encoding

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A second-scale periodicity in an active repeating fast radio burst source

arXiv

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arXiv 2025年

作者： Du, Chen Huang, Yong-Feng Geng, Jin-Jun Gao, Hao-Xuan Zhang, Li Deng, Chen Cui, Lang Liao, Jie Jiang, Peng-Fei Zhang, Liang Wang, Pei Hu, Chen-Ran Dong, Xiao-Fei Xu, Fan Li, Liang Zou, Ze-Cheng Kurban, Abdusattar School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Key Laboratory of Modern Astronomy and Astrophysics Nanjing University Ministry of Education Jiangsu Nanjing210023 China Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Xinjiang Urumqi830011 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences Beijing100101 China Xinjiang Key Laboratory of Radio Astrophysics Xinjiang Urumqi830011 China College of Astronomy and Space Science University of Chinese Academy of Sciences Beijing101408 China Guizhou Vocational College of Economics and Business Guizhou Duyun558022 China CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Physics Anhui Normal University Anhui Wuhu241002 China Institute of Fundamental Physics and Quantum Technology Ningbo University Zhejiang Ningbo315211 China Department of Physics School of Physical Science and Technology Ningbo University Zhejiang Ningbo315211 China INAF-Osservatorio Astronomico d’Abruzzo Teramo64100 Italy

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a ∼ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 FRBs have been observed on a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no periodical signal is found in all other datasets, a clear periodicity does appear on two specific days, i.e. a period of 1.706015(2) s on MJD 59310, and a slightly larger period of 1.707972(1) s on MJD 59347. A period derivative of 6.14 × 10−10 s s−1 can be derived from these two periods, which further implies a surface magnetic field strength of 1.04 × 1015 G and a spin-down age of 44 years for the central engine. It is thus concluded that FRB 20201124A should be associated with a young magnetar. © 2025, CC BY.

关键词： Magnetoplasma

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A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

A Machine Learning Based Risk Identification Method for Ille...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Jiajun Miao Yujun Shen Yan Zhang Lingjie Liu Ye Tian Yu Zhao Chunyan Zhou College of Computer Science and Technology Harbin Engineering University Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Hangzhou Institute of Technology Xidian University Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs.

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A Method of Realizing External Network Access to Intranet for Embedded Web Server

A Method of Realizing External Network Access to Intranet fo...

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Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Taiguo Qu Pengju Tang Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China

ISBN: (纸本)9781665464024

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects.

关键词： Performance evaluation Uniform resource locators Smart agriculture Web servers Software Real-time systems Organ transplantation

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Normal Distribution Function on Descriptor Extraction 11th

Normal Distribution Function on Descriptor Extraction

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11th International Conference on Image and Graphics, ICIG 2021

作者： Yin, Jianhua Liu, Cong Jiang, Jun Wen, Jie Yang, Lihang Zhu, Shilong School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9783030873578

The triplet-loss function is widely used in fields of descriptor extraction in recent years and owing to its good performance in various databases. However, some recent works make less effort on the relationship of adjacent descriptors from the same sample, which leads to the instability of descriptors and results in the mismatching problem in practical applications. To solve this problem, we introduce the topological relationship with the Normal Distribution Function (NDF) into the triplet loss function. The loss function establishes the relationship of descriptors from the same sample and considers the interrelation of descriptors from different types of samples. Furthermore, to increase the calculation speed, we normalize the algorithm NDF. Finally, we propose the triplet-loss function on three databases. These results demonstrate that our algorithm obtains better performance than state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Distribution functions

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CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction

arXiv

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arXiv 2024年

作者： Han, Rong Liu, Xiaohong Pan, Tong Xu, Jing Wang, Xiaoyu Lan, Wuyang Li, Zhenyu Wang, Zixuan Song, Jiangning Wang, Guangyu Chen, Ting BNRist Department of Computer Science and Technology Tsinghua University China UCL Cancer Institute University College London United Kingdom Monash Data Futures Institute Monash University Australia Monash Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University Australia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tasks remains unexplored. In this paper, we propose CoPRA to bridge pre-trained language models from different biological domains via Complex structure for Protein-RNA binding Affinity prediction. We demonstrate for the first time that cross-biological modal language models can collaborate to improve binding affinity prediction. We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former’s interaction understanding. Meanwhile, we build the largest protein-RNA binding affinity dataset PRA310 for performance evaluation. We also test our model on a public dataset for mutation effect prediction. CoPRA reaches state-of-the-art performance on all the datasets. We provide extensive analyses and verify that CoPRA can (1) accurately predict the protein-RNA binding affinity;(2) understand the binding affinity change caused by mutations;and (3) benefit from scaling data and model size. © 2024, CC BY.

关键词： Prediction models

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FineFormer: Fine-Grained Adaptive Object Transformer for Image Captioning

FineFormer: Fine-Grained Adaptive Object Transformer for Ima...

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IEEE International Conference on data Mining (ICDM)

作者： Bo Wang Zhao Zhang Jicong Fan Mingbo Zhao Choujun Zhan Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Information Science and Technology Donghua University Shanghai China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Information Engineering Zhengzhou University Zhengzhou China

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.

关键词： Adaptation models Adaptive systems Current transformers Semantics Interference data mining Task analysis

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LMVD: A Large-Scale Multimodal Vlog dataset for Depression Detection in the Wild

arXiv

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arXiv 2024年

作者： He, Lang Chen, Kai Zhao, Junnan Wang, Yimeng Pei, Ercheng Chen, Haifeng Jiang, Jiewei Zhang, Shiqing Zhang, Jie Wang, Zhongmin He, Tao Tiwari, Prayag The School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China The Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China The College of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710016 China The Institute of Intelligent Information Processing Taizhou University Zhejiang Taizhou318000 China The School of Education Shenzhen University Shenzhen518060 China The School of Information Technology Halmstad University Halmstad Sweden

Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects’ privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://***/helang818/LMVD/. © 2024, CC BY-NC-ND.

关键词： Deep learning

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A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery

arXiv

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arXiv 2025年

作者： Zhu, Yiheng Li, Mingyang Liu, Junlong Fu, Kun Wu, Jiansheng Li, Qiuyi Yin, Mingze Ye, Jieping Wu, Jian Wang, Zheng Alibaba Cloud Computing Beijing100012 China College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310058 China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing210023 China State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou310058 China School of Public Health Zhejiang University Zhejiang Hangzhou310058 China

Structure-based drug discovery (SBDD) is a systematic scientific process that develops new drugs by leveraging the detailed physical structure of the target protein. Recent advancements in pre-trained models for biomolecules have demonstrated remarkable success across various biochemical applications, including drug discovery and protein engineering. However, in most approaches, the pre-trained models primarily focus on the characteristics of either small molecules or proteins, without delving into their binding interactions which are essential cross-domain relationships pivotal to SBDD. To fill this gap, we propose a general-purpose foundation model named BIT (an abbreviation for Biomolecular Interaction Transformer), which is capable of encoding a range of biochemical entities, including small molecules, proteins, and protein-ligand complexes, as well as various data formats, encompassing both 2D and 3D structures. Specifically, we introduce Mixture-of-Domain-Experts (MoDE) to handle the biomolecules from diverse biochemical domains and Mixture-of-Structure-Experts (MoSE) to capture positional dependencies in the molecular structures. The proposed mixture-of-experts approach enables BIT to achieve both deep fusion and domain-specific encoding, effectively capturing fine-grained molecular interactions within protein-ligand complexes. Then, we perform cross-domain pretraining on the shared Transformer backbone via several unified self-supervised denoising tasks. Experimental results on various benchmarks demonstrate that BIT achieves exceptional performance in downstream tasks, including binding affinity prediction, structure-based virtual screening, and molecular property prediction. Furthermore, we develop a BIT-driven virtual screening pipeline that has identified two hit compounds with compelling inhibitory activity against the GluN1/GluN3A N-methyl-D-aspartate (NMDA) receptor, as validated by wet-lab assays. The code and pre-trained models will be made publicly

关键词： Molecular docking

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