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Record High Temperatures in the Ocean in 2024

Advances in Atmospheric Sciences 2025年第6期42卷 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of St.Thomas School of EngineeringMinnesota 55105USA NSF National Center for Atmospheric Research BoulderColorado 80307USA University of Auckland Auckland 0630New Zealand National Oceanic and Atmospheric Administration National Centers for Environmental InformationSilver SpringMaryland 20910USA National Oceanic and Atmospheric Administration National Centers for Environmental InformationAshevilleNC 28801USA National Research Council(CNR)Institute of Marine Sciences(ISMAR) Rome 00133Italy Mercator Ocean International Toulouse 31400France Department of Earth and Environmental Science University of PennsylvaniaPhiladelphiaPennsylvania 19104USA Institute of Oceanology Chinese Academy of SciencesQingdao 266071China National Marine Environmental Forecasting Center Ministry of Natural Resources of ChinaBeijing 100081China North Carolina Institute for Climate Studies(NCICS) North Carolina State UniversityAshevilleNC 28804USA Oceanographic Data Center Chinese Academy of SciencesQingdao 266071China College of Oceanography Hohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Istituto Nazionale di Geofisica e Vulcanologia Sede di BolognaBologna 40128Italy South China Sea Institute of Oceanology Chinese Academy of SciencesGuangzhou 510301China ESSIC/CISESS-MD University of MarylandCollege ParkMD 20740USA Computer Network Information Center Chinese Academy of SciencesBeijing 100083China Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment AdministrationMinistry of Ecology and EnvironmentGuangzhou 510611China National Meteorological Information Center China Meteorological AdministrationBeijing 100081China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Xiamen Universit

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

关键词： ocean heat content sea surface temperature ocean temperature global warming climate

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A 340nW/channel neural recording analog front-end using replica-biasing LNAs to tolerate 200mvpp interfere from 350MV power supply

A 340nW/channel neural recording analog front-end using repl...

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2019 IEEE International Symposium on Circuits and systems, ISCAS 2019

作者： Lyu, Liangjian Ye, Dawei Richard Shi, C.-J. State Key Laboratory of ASIC and System Fudan University Shanghai201203 China Brain Chip Research Center Fudan University Shanghai201203 China Department of Electrical and Computer Engineering University of Washington Seattle98195 United States

ISBN: (纸本)9781728103976

This paper presents an 8-channel power-efficient neural recording analog front-end (AFE) with high power-supply rejection ratio (PSRR) and wide dynamic range. The ultra-low power is achieved by using a low supply voltage current-reusing input stage in the low noise amplifier (LNA). In order to improve the PSRR in low supply voltage amplifiers, we propose a replica biasing circuit to generate the biasing current, which is insensitive to the supply noise. Furthermore, the dynamic range is enlarged by utilizing an averaged local field potential (A-LFP) feedback loop. The prototype is fabricated in a 65nm CMOS process. Each channel of the AFE occupies 0.04mm2 and only consumes 340nW from 0.35V/0.7V dual supply. The AFE provides a maximum gain of 54dB with 6.7μV input-referred noise integrating from 0.5Hz to 6.5 kHz. The proposed 0.35V input stage can tolerate a supply interferer up to 200mVpp, while maintaining a PSRR of 74dB. © 2019 IEEE

关键词： Low noise amplifiers

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On the mechanism and control for the ultra-low frequency oscillation in NY Power Grid with large-scale hydropower

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Control Theory and Technology 2019年第3期17卷 241-251页

作者： Ancheng XUE Jiawei WANG Chao ZHENG Joe H.CHOW Tianshu BI State Key Laboratory of Alternate Electrical Power System with Renewable Energy Source (North China Electric Power University) Changping District Beijing 102206 China China Electric Power Research Institute Haidian District Beijing 100192 China Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy 12180 New York State U.S.A.

The power grid with large proportion of hydropower may engage the ultra-low frequency oscillation (ULFO),which threatening the operation of *** the NY Power Grid system,whose dimension is over thousands,this paper reveals the cause of ULFO with the phase portrait method,i.e.,lies in the saturation of the different limits of the governor system of the generators,in other words,the occurrence of limit cycle is due to the non-smooth *** furthermore,it proposes two methods to suppress ULFO with the philosophy of destroying the condition of non-smooth *** detail,first,the phenomenon of ULFO in NY Power Grid,the damping characteristics and the limits in the governor system of the hydropower units are ***,based on the non-smooth bifurcation theory,the mathematical basis of the oscillation in the non-smooth system caused by the saturation of limits is *** then,the non-oscillation and oscillation dynamic of the system corresponding to without/with limits in the governor system are ***,it discusses the influence of the saturation of single limit on the characteristics of ULFO with the phase ***,the phase portraits of the ULFO under different faults is analyzed,and it shows that the limiting saturation occurs with a large enough ***,the ULFO is resulting from the negative damping of generation and the saturation of multiple limits under a large enough *** addition,according to the above mechanism,two strategies to suppress the ULFO,i.e.,the governor parameters tuning and the fault strength relief are *** simulation shows the effectiveness of proposed strategies.

关键词： Ultra -low frequency oscillatio n gover nor system limit no rnsmooth bifurcation limit cycle

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Library pruning for power saving during timing and electrical design rules optimization

Library pruning for power saving during timing and electrica...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2018

作者： Chentouf, Mohamed Cherif, Lekbir El Abidine Alaoui Ismaili, Zine ICDS Department Mentor Graphics Rabat Morocco Laboratory of Systems Engineering ENSA Ibn Tofail University Kénitra Morocco Team National Superior School of Computer Science and System Analysis University Mohammed V Rabat Morocco

ISBN: (纸本)9783030120641

Timing optimization techniques are widely used to meet the frequency and electrical design rules requirement of integrated circuits, they use logical and physical transformation to speed up the problematic signals and to close the design setup and hold constraints. On the other side, each technique induces a power increase as a cost for signal speed up. In this paper, we propose a standard cell library tuning methodology to reduce the timing optimization impact on power increase. We divide each optimization step of the place and route process into two sub-steps, the first one uses only low power standard library cells and try to correct the maximum number of violations, and the second uses all the available cells in the library to close the remaining violations. Experimental results on 45 industrial designs of different processes show that the proposed methodology provides a leakage power reduction of 5%, a total power reduction of 1.3% and a timing improvement of 55.8% in Total Negative Slack and 37.5% in Worst Negative Slack. © Springer Nature Switzerland AG 2019.

关键词： system-on-chip

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A modified lumped parameter model of distribution transformer winding

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Global Energy Interconnection 2020年第2期3卷 158-165页

作者： Qingqing Ding Yao Yao Bingqian Wang Jingwei Fu Wei Zhang Chao Zeng Xiaoping Li Stanimir Valtchev State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityHaidian DistrictBeijing100084P.R.China State Grid Hubei Electric Power Co. Research Institute(HBEPRI)Hongshan DistrictWuhan 430077P.R.China Flexible Transmission Engineering Department Nari-Relays Electric Company210003 NanjingP.R.China State Grid Hubei Electric Power Co. LtdHongshan District430077 WuhanP.R.China Department of Electrical and Computer Engineering Faculty of Science and TechnologyUNLPortugal

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex *** parameter model requires less calculations,but its applicable frequency range is not *** paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above *** proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter *** this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage *** proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

关键词： Wide band frequency response Distributed parameter model Lumped parameter model Distribution transformer Lightning protection

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Left ventricle contouring in cardiac images based on deep reinforcement learning

TechRxiv

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TechRxiv 2021年

作者： Yin, Sixing Han, Yameng Pan, Judong Wang, Yining Li, Shufang Yu, F. Richard Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China University of California San Francisco San FranciscoCA94117 United States State Key Laboratory of Complex Severe and Rare Diseases Chinese Academy of Medical Sciences Peking Union Medical College Beijing100730 China The Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Assessment of the left ventricle segmentation in cardiac magnetic resonance imaging (MRI) is of crucial importance for cardiac disease diagnosis. However, conventional manual segmentation is a tedious task that requires excessive human effort, which makes automated segmentation highly desirable in practice to facilitate the process of clinical diagnosis. In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem. Following the algorithm of proximal policy optimization (PPO), we train a policy network, which makes a stochastic decision on the agent’s movement according to its local observation such that the generated trajectory matches the true contour of the left ventricle as much as possible. Moreover, we design a deep learning model with a customized loss function to generate the agent’s landing spot (or coordinate of its initial position on a cardiac image). The experiment results show that the coordinate of the generated landing spot is sufficiently close to the true contour and the proposed reinforcement-learning-based approach outperforms the existing U-net model even with limited training set. © 2021, CC BY.

关键词： Reinforcement learning

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Complications in the ALMA detection of phosphine at Venus

arXiv

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arXiv 2021年

作者： Akins, Alex B. Lincowski, Andrew P. Meadows, Victoria S. Steffes, Paul G. Jet Propulsion Laboratory California Institute of Technology Instruments Division PasadenaCA91011 United States Department of Astronomy and Astrobiology Program University of Washington Box 351580 SeattleWA98195 United States NASA Nexus for Exoplanet System Science Virtual Planetary Laboratory Team University of Washington Box 351580 SeattleWA98195 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30308 United States

Recently published ALMA observations suggest the presence of 20 ppb PH3 in the upper clouds of Venus. This is an unexpected result, as PH3 does not have a readily apparent source and should be rapidly photochemically destroyed according to our current understanding of Venus atmospheric chemistry. While the reported PH3 spectral line at 266.94 GHz is nearly co-located with an SO2 spectral line, the non-detection of stronger SO2 lines in the wideband ALMA data is used to rule out SO2 as the origin of the feature. We present a reassessment of wideband and narrowband datasets derived from these ALMA observations. The ALMA observations are re-reduced following both the initial and revised calibration procedures discussed by the authors of the original study. We also investigate the phenomenon of apparent spectral line dilution over varying spatial scales resulting from the ALMA antenna configuration. A 266.94 GHz spectral feature is apparent in the narrowband data using the initial calibration procedures, but this same feature can not be identified following calibration revisions. The feature is also not reproduced in the wideband data. While the SO2 spectral line is not observed at 257.54 GHz in the ALMA wideband data, our dilution simulations suggest that SO2 abundances greater than the previously suggested 10 ppb limit would also not be detected by ALMA. Additional millimeter, sub-millimeter, and infrared observations of Venus should be undertaken to further investigate the possibility of PH3 in the Venus atmosphere. © 2021, CC BY-NC-SA.

关键词： Antennas

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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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Knowledge-driven quantum architecture search through filtering and focusing

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Information Sciences 2025年 718卷

作者： Lian-Hui Yu Xiao-Yu Li Geng Chen Qin-Sheng Zhu Hui Li Guo-Wu Yang School of Physics University of Electronic Science and Technology of China Cheng Du 610054 Si Chuan China School of Information and Software Engineering University of Electronic Science and Technology of China Cheng Du 610054 Si Chuan China School of Computer Science and Engineering University of Electronic Science and Technology of China Cheng Du 610054 Si Chuan China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Cheng Du 610103 Si Chuan China JILA and Department of Physics University of Colorado Boulder 80309-0440 CO USA

Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. The success of various QAS raises a fundamental question about what enables efficient quantum architecture design and how to systematically analyze search performance. From a knowledge-driven perspective, we decompose QAS into a progressive search process with continuous knowledge accumulation and transfer. We propose a framework through knowledge-guided untrained filtering and trained focusing for iterative architecture discovery with knowledge refinement. The framework develops an evolutionary mechanism to enhance multi-level knowledge transfer in architecture searching. Experiments demonstrate improvements across diverse tasks: achieving 59.1% performance increase in signal representation, maintaining robust performance in both simulated and real-noise environments for image classification, and improving precision by 9 orders of magnitude in variational quantum eigensolver tasks, providing a viable approach for QAS.

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Joint user identification and channel estimation over rician fading channels

arXiv

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arXiv 2020年

作者： Wu, Liang Zhang, Zaichen Dang, Jian Wu, Yongpeng Liu, Huaping Wang, Jiangzhou National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electronic Engineering Shanghai Jiao Tong University Minhang200240 China School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States School of Engineering and Digital Arts University of Kent CanterburyCT2 7NT United Kingdom

This paper considers crowded massive multiple input multiple output (MIMO) communications over a Rician fading channel, where the number of users is much greater than the number of available pilot sequences. A joint user identification and line-of-sight (LOS) component derivation algorithm is proposed without requiring a threshold. Based on the derived LOS component, we design a LOS-only channel estimator and an updated channel estimator. Copyright © 2020, The Authors. All rights reserved.

关键词： Channel estimation

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