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Fast Cycle Structure Detection for Power Grids Based on Graph computing

CSEE Journal of Power and Energy Systems 2023年第6期9卷 2204-2213页

作者： Xinqiao Wang Kewen Liu Weijiang Lu Ting Zhao Baohua Zhao Xiaoming Liu the Computing&Application Technology Research Division Global Energy Interconnection Research InstituteBeijing 102209China

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid ***,detecting possible cycles in a large-scale network can be highly time consuming and computationally *** addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real *** traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible *** computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state *** this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing *** cases are used to test its performance in an actual power grid topology *** results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.

关键词： Cycle detection graph computing power grid distribution grid

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qIoV: A Quantum-Driven Internet-of-Vehicles-Based Approach for Environmental Monitoring and Rapid Response Systems

arXiv

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arXiv 2024年

作者： Nahar, Ankur Mondal, Koustav Kumar Das, Debasis Buyya, Rajkumar Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Rajasthan Jodhpur342030 India Indian Institute of Technology Jodhpur Inter-disciplinary Research Division - IoT and Application Rajasthan Jodhpur342030 India University of Melbourne Computing and Information Systems MelbourneVIC3052 Australia

This research addresses the critical necessity for advanced rapid response operations in managing a spectrum of environmental hazards. These include leaks in urban pipelines, industrial release of hazardous gases, methane emissions from landfill sites, chlorine gas leaks from water treatment facilities, and carbon monoxide emissions from residential areas, all of which are critical to ensuring public safety and environmental protection. We propose a novel framework, qIoV that integrates quantum computing with the Internet-of-Vehicles (IoV) to leverage the computational efficiency, parallelism, and entanglement properties of quantum mechanics. Our approach involves the use of environmental sensors mounted on vehicles for precise air quality assessment. These sensors are designed to be highly sensitive and accurate, leveraging the principles of quantum mechanics to detect and measure environmental parameters. A salient feature of our proposal is the Quantum Mesh Network Fabric (QMF), a system designed to dynamically adjust the quantum network topology in accordance with vehicular movements. This capability is critical to maintaining the integrity of quantum states against environmental and vehicular disturbances, thereby ensuring reliable data transmission and processing. Moreover, our methodology is further augmented by the incorporation of a variational quantum classifier (VQC) with advanced quantum entanglement techniques. This integration offers a significant reduction in latency for hazard alert transmission, thus enabling expedited communication of crucial data to emergency response teams and the public. Our study on the IBM OpenQSAM 3 platform, utilizing a 127 Qubit system, revealed significant advancements in pair plot analysis, achieving over 90% in precision, recall, and F1-Score metrics and an 83% increase in the speed of toxic gas detection compared to conventional ***, theoretical analyses validate the efficiency of quantum rotation, tele

关键词： Carbon monoxide

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training

arXiv

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arXiv 2024年

作者： Tang, Fenghe Xu, Ronghao Yao, Qingsong Fu, Xueming Quan, Quan Zhu, Heqin Liu, Zaiyi Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China

The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods based on a hybrid architecture of CNN and Transformer, which can learn strong local and global representations simultaneously. To address this issue, we propose a generative pre-training strategy called Hybrid Sparse masKing (HySparK) based on masked image modeling and apply it to large-scale pre-training on medical images. First, we perform a bottom-up 3D hybrid masking strategy on the encoder to keep consistency masking. Then we utilize sparse convolution for the top CNNs and encode unmasked patches for the bottom vision Transformers. Second, we employ a simple hierarchical decoder with skip-connections to achieve dense multi-scale feature reconstruction. Third, we implement our pre-training method on a collection of multiple large-scale 3D medical imaging datasets. Extensive experiments indicate that our proposed pre-training strategy demonstrates robust transfer-ability in supervised downstream tasks and sheds light on HySparK’s promising prospects. The code is available at https://***/FengheTan9/HySparK. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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UOD: Universal One-shot Detection of Anatomical Landmarks

arXiv

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arXiv 2023年

作者： Zhu, Heqin Quan, Quan Yao, Qingsong Liu, Zaiyi Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China

One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly specialized in a single domain and suffer domain preference heavily in the situation of multi-domain unlabeled data. Moreover, one-shot learning is not robust that it faces performance drop when annotating a sub-optimal image. To tackle these issues, we resort to developing a domain-adaptive one-shot landmark detection framework for handling multi-domain medical images, named Universal One-shot Detection (UOD). UOD consists of two stages and two corresponding universal models which are designed as combinations of domain-specific modules and domain-shared modules. In the first stage, a domain-adaptive convolution model is self-supervised learned to generate pseudo landmark labels. In the second stage, we design a domain-adaptive transformer to eliminate domain preference and build the global context for multi-domain data. Even though only one annotated sample from each domain is available for training, the domain-shared modules help UOD aggregate all one-shot samples to detect more robust and accurate landmarks. We investigated both qualitatively and quantitatively the proposed UOD on three widely-used public X-ray datasets in different anatomical domains (i.e., head, hand, chest) and obtained state-of-the-art performances in each domain. The code is at https://***/heqin-zhu/UOD_universal_oneshot_detection. © 2023, CC BY-NC-SA.

关键词： Learning systems

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Atroposelective Access to 1,3-Oxazepine-Containing Bridged Biaryls via Carbene-Catalyzed Desymmetrization of Imines

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Angewandte Chemie 2022年第1期135卷

作者： Prof. Dr. Xing Yang Liwen Wei Yuelin Wu Prof. Dr. Liejin Zhou Dr. Xinglong Zhang Prof. Dr. Yonggui Robin Chi Key Laboratory of Chemical Biology and Traditional Chinese Medicine (Ministry of Educational of China) Key Laboratory of the Assembly and Application of Organic Functional Molecules of Hunan Province and Key Laboratory of Phytochemistry R&D of Hunan Province College of Chemistry and Chemical Engineering Hunan Normal University Changsha 410081 P. R. China Department of Chemistry Zhejiang Normal University Jinhua 321004 P. R. China Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore 138632 Singapore Division of Chemistry & Mathematical Science School of Physical & Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education Guizhou University Huaxi District Guiyang 550025 P. R. China

We disclose herein an atroposelective synthesis of novel bridged biaryls containing medium-sized rings via N-heterocyclic carbene organocatalysis. The reaction starts with addition of the carbene catalyst to the aminophenol-derived aldimine substrate. Subsequent oxidation and intramolecular desymmetrization lead to the formation of 1,3-oxazepine-containing bridged biaryls in good yields and excellent enantioselectivities. These novel bridged biaryl products can be readily transformed into chiral phosphite ligands. Preliminary density function theory calculations suggest that the origin of enantioselectivity arises from the more favorable frontier molecular orbital interactions in the transition state leading to the major product.

关键词： Atropisomerism Bridged Biaryls N-Heterocyclic Carbenes Organocatalysis Umpolung

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Inside Cover: π-Extended “Earring” Porphyrins with Multiple Cavities and Near-Infrared Absorption (Angew. Chem. Int. Ed. 22/2016)

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Angewandte Chemie International Edition 2016年第22期55卷

作者： Yutao Rao Taeyeon Kim Kyu Hyung Park Fulei Peng Lei Liu Yunmei Liu Bin Wen Prof. Dr. Shubin Liu Prof. Dr. Steven Robert Kirk Licheng Wu Prof. Dr. Bo Chen Prof. Dr. Ming Ma Dr. Mingbo Zhou Dr. Bangshao Yin Dr. Yuexing Zhang Prof. Dr. Dongho Kim Prof. Dr. Jianxin Song Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education of China) Key Laboratory of Application and Assemble of Organic Functional Molecules Hunan Normal University Changsha 410081 China Spectroscopy Laboratory for Functional π-Electronic Systems and Department of Chemistry Yonsei University Seoul 03722 Korea Division of Research Computing Information Technology Services University of North Carolina Chapel Hill NC 27599 USA Department of Chemistry and Pharmaceutical Sciences Guangxi Normal University Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education) 15 Yucai Road Guilin 541004 China

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π‐Extended “Earring” Porphyrins with Multiple Cavities and Near‐Infrared Absorption

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Angewandte Chemie 2016年第22期128卷

作者： Yutao Rao Taeyeon Kim Kyu Hyung Park Fulei Peng Lei Liu Yunmei Liu Bin Wen Shubin Liu Steven Robert Kirk Licheng Wu Bo Chen Ming Ma Mingbo Zhou Bangshao Yin Yuexing Zhang Dongho Kim Jianxin Song Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (Ministry of Education of China) Key Laboratory of Application and Assemble of Organic Functional Molecules Hunan Normal University Changsha 410081 China Spectroscopy Laboratory for Functional π-Electronic Systems and Department of Chemistry Yonsei University Seoul 03722 Korea Division of Research Computing Information Technology Services University of North Carolina Chapel Hill NC 27599 USA Department of Chemistry and Pharmaceutical Sciences Guangxi Normal University Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education) 15 Yucai Road Guilin 541004 China

β,β‐tripyrrin‐bridged earring porphyrins were synthesized through Suzuki–Miyaura cross coupling reactions. These porphyrinoids have multiple cavities and can accommodate two or three metal ions per molecule. The structures of the porphyrins have been elucidated by x‐ray diffraction analysis, and feature curved π planes. The electronic spectra of the porphyrins exhibit near‐infrared (NIR) absorptions and metal insertion leads to red‐shifted and intensified absorption features. Electrochemical analysis and transient absorption measurements indicated that the porphyrins exhibit effective electronic communication between their central and peripheral moieties.

关键词： Konjugation Nah-IR-Absorption Nickel Palladium Porphyrinoid

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Innentitelbild: π‐Extended “Earring” Porphyrins with Multiple Cavities and Near‐Infrared Absorption (Angew. Chem. 22/2016)

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Angewandte Chemie 2016年第22期128卷

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