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Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia

Atmospheric and Oceanic Science Letters 2025年第1期18卷 46-51页

作者： Yiying Zhu Hesong Wang Anzhi Zhang School of Ecology and Nature Conservation Beijing Forestry UniversityBeijingChina Key laboratory of Regional Climate and Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina College of Forestry and Landscape Architecture Xinjiang Agricultural UniversityUrumqiChina

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of *** the same time,detailed accounts of disturbances and forest responses are not yet well quantified in *** study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast *** approach allowed us to detect forest disturbance and quantify the resilience after *** showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by ***,95%of forests had robust resilience and could recover from disturbance within a few *** resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance *** summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two *** authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in *** findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.

关键词： Forest ecosystem Enhanced vegetation index Breaks for additive seasonal and trend method Disturbance Resilience

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Unveiling the superior hydrogen evolution reaction activity of 1T-2H MoS_(2)heterointerface by on-chip microdevices

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Nano Research 2025年第1期18卷 149-156页

作者： Bowen Liu Junjie Xiong Shuaishuai Xu Xinying Luo Wenyan Yao Shigang Liu Yuting Du Qinghua Zhang Yang Gao Bin Wang CAS Key laboratory of Nanosystem and Hierarchical Fabrication National Center for Nanoscience and Technology(NCNST)Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Materials Science and Engineering China University of Petroleum(East China)Qingdao 266580China beijing National laboratory for Condensed Matter physics Institute of PhysicsChinese Academy of SciencesBeijing 100190China

The identification and clarification of active sites of MoS_(2)have long been the focus of research efforts in hydrogen evolution reaction(HER).In this study,we constructed phase transition-induced 1T-2H MoS_(2)heterojunction via lithium intercalation and evaluated the HER activity using on-chip electrocatalytic microdevices(OCEMs).The heterojunction achieved an overpotential of only 226 mV at a cathodic current density of 10 mA/cm^(2),outperforming the basal planes of 1T and 2H MoS_(2).Furthermore,density functional theory(DFT)calculations demonstrated that the charge redistribution occurs at the 1T-2H MoS_(2)interface with electrons transferring from 1T to 2H MoS_(2),and the interfacial S atom at the top site of 1T MoS_(2)presents the smallest overpotential of 0.37 ***,the interference from highly active edge sites was avoided by precisely exposing specific active areas,quantitatively revealing the catalytic activity order of different types of in-plane MoS_(2)active *** work enables a systematic investigation of the HER activity of various active sites in MoS_(2),laying the foundation for quantitative analysis of activity in other lowdimensional materials.

关键词： on-chip microdevice phase transition heterojunction MoS_(2) hydrogen evolution reaction(HER)

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Increase in the variability of terrestrial carbon uptake in response to enhanced future ENSO modulation

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 32-38页

作者： Younong Li Li Dan Jing Peng Qidong Yang Fuqiang Yang Key laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Key laboratory of Atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region Department of Atmospheric ScienceYunnan UniversityKunmingChina

El Niño-Southern Oscillation(ENSO)is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land-air *** the ENSO modulation and carbon flux variability are projected to increase in the future,but their connection still needs further *** investigate the impact of future ENSO modulation on carbon flux variability,this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes,and their relationship,under different scenarios simulated by CMIP6 *** results show a high consistency in the simulations,with both ENSO modulation and carbon flux variability showing an increasing trend in the *** higher the emissions scenario,especially SSP5-8.5 compared to SSP2-4.5,the greater the increase in *** flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9%compared to historical levels during 1951-2000,while under SSP5-8.5 it increases by 58.2%.Further analysis suggests that ENSO influences mid-and low-latitude carbon flux variability primarily through *** occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations,combined with the intensified influence of ENSO on land surface temperatures.

关键词： Carbon variability ENSO modulation CMIP6 models

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Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

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Atmospheric and Oceanic Science Letters 2025年第1期18卷 39-45页

作者： Haolan Ren Fei Zheng Tingwei Cao Qiang Wang International Center for Climate and Environment Science(ICCES) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina University of Chinese Academy of Sciences BeijingChina College of Oceanography Hohai UniversityNanjingChina CAS Key laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of Sciences and Pilot National Laboratory for Marine Science and Technology(Qingdao)QingdaoChina

Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate *** studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC *** this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its *** a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s *** traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of *** results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter ***,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.

关键词： Recovery of AMOC 4×CO_(2)forcing Key parameter Parameter estimation Data assimilation Machine learning

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Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

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Nano-Micro Letters 2025年第2期17卷 432-446页

作者： Hao Yin Yanting Li Zhiying Tian Qichao Li Chenhui Jiang Enfu Liang Yiping Guo State Key laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China beijing vacuum Electronics Research Institute Beijing 100015People’s Republic of China Fundamental Science On Vibration Shock and Noise LaboratoryState Key Laboratory of Mechanical System and VibrationSchool of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing ***,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also ***,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic *** as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse *** ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate ***,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%*** structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

关键词： Flexible piezoelectric filaments Anisotropic Ultra-high sensitivity Structural health detection Texture recognition

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A quantum-search-based multi-classifier for image recognition

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Science China(physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 21-31页

作者： Lu Liu Xingyu Wu Lufan Zhang Chuan Wang School of Artificial Intelligence Beijing Normal University Applied Optics beijing Area Major laboratory Beijing Normal University

The multi-class classification of images is a pivotal challenge within the realm of image processing. As the volume of visual data continues to expand, there is a burgeoning interest in harnessing the unique capabilities of quantum computation to augment the efficiency of classification tasks. However, many existing methods for training quantum image multi-classifiers parallel classical machine learning techniques, where the requisite circuit measurements increase linearly with the volume of training data. This work introduces a novel approach for training a quantum image multi-classifier based on the quantum search algorithm. We have meticulously conducted rigorous experiments on a handwritten digit dataset, a classic benchmark in the field. The results have been meticulously compared with previous works, and the comparative analysis not only validates the efficiency of our proposed approach, requiring only O(N/b) measurements during training, but also highlights a significant quadratic speedup of the algorithm.

关键词： quantum computation image recognition quantum search algorithm

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Regulation of graphdiyne-based nanozymes with enhanced oxidaselike activity by cobalt and nitrogen codoping

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Nano Research 2025年第1期18卷 21-29页

作者： Jiali Liu Xiaoli Wang Yongfu Lian Xue Wang Xiuxiu Wang Xingfa Gao Baoyun Sun Xihong Guo CAS Key laboratory for Biomedical Effects of Nanomaterial&Nanosafety Institute of High Energy PhysicsChinese Academy of ScienceBeijing 100049China Key laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China laboratory of Theoretical and Computational Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

The development of nanozymes with excellent intrinsic oxidase-like activity and specificity has received increasing ***(GDY)could be a promising choice for designing nanozymes with enhanced oxidase(OXD)-like activity due to its unique structure and ***,Co-N-GDY with high OXD activity but no peroxidase(POD)activity was synthesized by codoping of cobalt(Co)and nitrogen(N)into GDY and compared with other GDY-based nanozymes(including GDY,Co-GDY,and N-GDY).Upon analyzing the doping effect of Co and N on the OXD-like and POD-like activities,we found that the combination of Co and N in GDY played a significant role in enhancing the OXD-like activity,and even reversed the POD-like activity of N-GDY to OXD-like activity of *** electrochemical experiment and the theoretical calculations provided an explanation for the mechanism and showed that the activity was closely linked to the reduction ability of O_(2) or H_(2)O_(2) on the nanozyme substrates,which was determined by the ratedetermining step of the catalytic reaction.

关键词： nanozymes oxidase-like activity graphdiyne heteroatom doping regulation

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The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

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Science China(physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 86-96页

作者： Qianyun Yun Wen-Biao Han Yi-Yang Guo He Wang Minghui Du Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Shanghai Astronomical Observatory Chinese Academy of Sciences School of physics and Astronomy Shanghai Jiao Tong University School of Astronomy and Space Science University of Chinese Academy of Sciences Taiji laboratory for Gravitational Wave Universe (beijing/Hangzhou) University of Chinese Academy of Sciences Lanzhou Center for Theoretical physics Key Laboratory of Theoretical Physics of Gansu Province Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Institute of Theoretical physics & Research Center of Gravitation Lanzhou University International Centre for Theoretical physics Asia-Pacific (ICTP-AP) University of Chinese Academy of Sciences (UCAS) Center for Gravitational Wave Experiment National Microgravity Laboratory Institute of Mechanics Chinese Academy of Sciences

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a2-layer convolutional neural network(CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate(TPR) of 96.9% at a 1% false positive rate(FPR) for signal-to-noise ratio(SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole(SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter *** model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

关键词： gravitational wave EMRIs deep-learing

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An efficient quantum proactive incremental learning algorithm

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Science China(physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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Diamond Polycrystal Powder Formation Directly from Gas Phase in a Plasma Jet System

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Chinese physics Letters 1993年第6期10卷 359-361页

作者： JIANG Xiangliu QIN Sizhan ZHANG Xiufang XUE Zengquan PANG Shijin beijing laboratory of vacuum physics Academia SinicaBeijing 100080

Diamond polycrystal powder has been produced directly from gas phase in an arc discharge plasma jet *** diluted in argon was used as hydrocarbon source *** product was the mixture of diamond polycrystal powders and *** 3C cubic phase diamond which is dominant,and the mixture phases of cubic and 6H hexagonal diamond are identified in the produced diamond polycrystal powders by electron diffraction patterns.

关键词： powders cubic Diamond

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