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Bivariate attribution of the compound hot and dry summer of 2.2. on the Tibetan Plateau

Science China Earth Sciences 2024年第7期67卷 2122-2136页

作者： Baiquan zhou Panmao ZHAI zhen LIAO State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081China

The extraordinarily high temperatures experienced during the summer of 2.2. on the Tibetan Plateau(TP)demand attention when compared with its typical climatic *** absence of precipitation alongside the elevated temperatures resulted in 2.2. being the hottest and driest summer on record on the TP since at least *** the susceptibility of the TP to climate change,this study employed large-ensemble simulations from the HadGEM3-A-N2.6 attribution system,together with a copula-based joint probability distribution,to investigate the influence of anthropogenic forcing,primarily global greenhouse gas emissions,on this unprecedented compound hot and dry event(CHDE).Findings revealed that the return period for the 2.2. CHDE on the TP exceeds 4000 years,as determined from the fitted joint distributions derived using observational data spanning *** CHDE was directly linked to large-scale circulation anomalies,including the control of equivalent-barotropic high-pressure anomalies and the northward displacement of the subtropical westerly jet ***,anthropogenic forcing has,to some extent,promoted the surface warming and increased variability in precipitation on the TP in summer,establishing conditions conducive for the 2.2. CHDE from a long-term climate change *** return period for a 2.2.-like CHDE on the TP was estimated to be approximately 2.3 years(142.613 years)by the large ensemble forced by both anthropogenic activities and natural ***,ensemble simulations driven solely by natural forcing indicated that the likelihood of occurrence of a 2.2.-like CHDE was almost *** outcomes underscore that the contribution of anthropogenic forcing to the probability of a 2.2.-like CHDE was 100%,implying that without anthropogenically induced global warming,a comparable CHDE akin to that observed in 2.2. on the TP would not be possible.

关键词： Compound hot and dry event Tibetan Plateau Bivariate event attribution Anthropogenic forcing

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Energy-information trade-off induces continuous and discontinuous phase transitions in lateral predictive coding

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Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 79-85页

作者： zhen-Ye Huang Ruyi zhou Miao Huang Hai-Jun zhou Key Laboratory for Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China School of Physical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100081China Minjiang Collaborative Center for Theoretical Physics Minjiang UniversityFuzhou 350108China

Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory ***,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy *** observed several phase transitions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory *** optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy *** results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.

关键词： predictive coding recurrent neural network phase transition internal model free energy

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An interlaminar damage shell model for typical composite structures

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Chinese Journal of Aeronautics 2024年第1期37卷 118-137页

作者： Jie zhou zhen WU zhengliang LIU Xiaohui REN School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China National Key Laboratory of Strength and Structural Integrity Xi’an 710065China School of Mechanical Engineering Xi’an Aeronautical UniversityXi’an 710065China

Using the plate/shell elements in commercial software,accurate analysis of interlaminar initial damage in typical composite structures is still a challenging *** propose an accurate and efficient model for analysis of interlaminar initial damage,the following work is carried out:(A)A higher-order theory is firstly proposed by introducing the local Legendre polynomials,and then a novel shell element containing initial damage prediction is developed,which can directly predict transverse shear stresses without any postprocessing *** variables at each node are independent of number of layers,so the proposed model is more efficient than the 3D-FEM.(B)Compression experiment is carried out to verify the capability of the proposed *** results obtained from the proposed model are in good agreement with experimental data.(C)Several examples have been analyzed to further assess the capability of the proposed model by comparing to the 3D-FEM ***,accuracy and efficiency have been evaluated in different damage criterion by comparing with the selected *** numerical results show that the proposed model can well predict the initial interlaminar damage as well as other ***,the model is implemented with UEL subroutine,so that the present approach can be readily utilized to analyze the initial damage in typical composite structures.

关键词： Compression experiment Finite element method Initial damage Interlaminar stress Plate/shell element

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Accurate atomic scanning transmission electron microscopy analysis enabled by deep learning

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Nano Research 2024年第4期17卷 2971-2980页

作者： Tianshu Chu Lei zhou Bowei Zhang Fu-zhen Xuan Shanghai Key Laboratory of Intelligent Sensing and Detection Technology East China University of Science and TechnologyShanghai 200237China School of Mechanical and Power Engineering East China University of Science and TechnologyShanghai 200237China Key Laboratory of Pressure Systems and Safety of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Currently,the machine learning(ML)-based scanning transmission electron microscopy(STEM)analysis is limited in the simulative stage,its application in experimental STEM is needed but ***,we built up a universal model to analyze the vacancy defects and single atoms accurately and rapidly in experimental STEM images using a full convolution *** our model,the unavoidable interference factors of noise,aberration,and carbon contamination were fully considered during the training,which were difficult to be considered in the *** toward the simultaneous identification of various vacancy types and low-contrast single atoms in the low-quality STEM images,our model showed rapid process speed(45 images per second)and high accuracy(>95%).This work represents an improvement in experimental STEM image analysis by ML.

关键词： deep learning low-dimensional materials atomic defects single atoms

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Data-driven rational design of single-atom materials for hydrogen evolution and sensing

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Nano Research 2024年第4期17卷 3352-3358页

作者： Lei zhou Pengfei Tian Bowei Zhang Fu-zhen Xuan Shanghai Key Laboratory of Intelligent Sensing and Detection Technology East China University of Science and TechnologyShanghai 200237China School of Mechanical and Power Engineering East China University of Science and TechnologyShanghai 200237China Key Laboratory of Pressure Systems and Safety of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction(HER)and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning *** the rational training by 2. groups of data and prediction of all 168 groups of single-atom materials for HER and sensing,respectively,a high prediction accuracy(>0.931 R^(2. score)was achieved by our *** show that the promising HER catalysts include Pt atoms in C_(4) and Sc atoms in C_(1)N_(3) coordination ***,Y atoms in C_(4) coordination environment and Cd atoms in C_(2.N_(2.-ortho coordination environment were predicted with great potential as hydrogen sensing *** method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.

关键词： machine learning single-atom materials sensing hydrogen evolution

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Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

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Nuclear Science and Techniques 2024年第6期35卷 130-144页

作者： Zi-Yi Yuan Dong Bai zhen Wang Zhong-zhou Ren School of Physics Science and Engineering Tongji UniversityShanghai 200092China College of Science Hohai UniversityNanjing 211100China Key Laboratory of Advanced Micro-Structure Materials Ministry of EducationShanghai 200092China

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2.38 nuclei with Z > 2. and N > 2. are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2.2.. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (2.4 )Ac(Huang et al. Phys Lett B 834, 137484(2.2.)) and ^ (2.7) Th(Yang et al. Phys Rev C 105, L051302.2.2.)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

关键词： Nuclear binding energies decay Machine learning Gaussian process

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Source apportionment of PM_(2.5)using PMF combined online bulk and single-particle measurements:Contribution of fireworks and biomass burning

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Journal of Environmental Sciences 2024年第2期136卷 325-336页

作者： Yanjing Zhang Wenshuai Li Lei Li Mei Li zhen zhou Jianzhen Yu Yang zhou Frontier Science Center for Deep Ocean Multispheres and Earth System(FDOMES)and Physical Oceanography Laboratory Ocean University of ChinaQingdaoShandong 266100China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdaoShandong 266100China Institute of Atmospheric Environment Safety and Pollution Control Jinan UniversityGuangdong 510632China Institute of Environment Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Department of Chemistry Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Division of Environment The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Fireworks(FW)could significantly worsen air quality in short term during *** to similar tracers with biomass burning(BB),the fast and precise qualification of FW and BB is still *** this study,online bulk and single-particle measurements were combined to investigate the contributions of FW and BB to the overall mass concentrations of PM_(2.5)and specific chemical species by positive matrix factorization(PMF)during the Chinese New Year in Hong Kong in February *** combined information,fresh/aged FW(abundant ^(140)K_(2.NO_(3)^(+)and ^(2.3)K_(3)SO_(4)^(+)formed from ^(113)K_(2.Cl^(+)discharged by fresh FW)can be extracted from the fresh/aged BB sources,in addition to the Second Aerosol,Vehicles+Road Dust,and Sea Salt *** contributions of FW and BB were investigated during three high particle matter episodes influenced by the pollution transported from the Pearl River Delta *** fresh BB/FW contributed 39.2.and 19.6%to PM_(2.5)during the Lunar Chinese New Year ***,the contributions of aged FW/BB enhanced in the last two episodes due to the aging process,evidenced by high contributions from secondary ***,the fresh BB/FW showed more significant contributions to nitrate(35.1%and15.0%,respectively)compared with sulfate(2..1%and 5.9%,respectively)and OC(14.8%and11.1%,respectively)on *** comparison,the aged FW contributed more to sulfate(13.4%).Overall,combining online bulk and single-particle measurement data can combine both instruments’advantages and provide a new perspective for applying source apportionment of aerosols using PMF.

关键词： Source apportionment PMF Online bulk measurements Single particle analysis Fireworks Biomass burning

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Understand the Temperature Sensing Behavior of Solid-state Polymerized PEDOT Hybrid Based on X-ray Scattering Studies

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Chinese Journal of Polymer Science 2024年第1期42卷 105-112,I0010页

作者： zhen-Hang He Guang-Feng Liu Ze-Kun zhou zhen Liu Yi-Shu Zeng Peng Zhang School of Materials Science and Engineering Key Laboratory for Polymeric Composite and Functional Materials of Ministry of EducationSun Yat-sen UniversityGuangzhou 510275China Shanghai Advanced Research Institute(Zhangjiang Laboratory) Chinese Academy of SciencesShanghai 201204China

Poly(3,4-ethylenedioxythiophene)(PEDOT) is one of the most successful conductive polymers that recently has been used in wearable sensors for human health monitoring. In this work, we prepared a series of PEDOT hybrids consisting of PEDOT, sodium poly(styrene sulfonate)(PSSNa) and polyethylene oxide(PEO), and their preparation could be scaled-up via an adapted solid-state polymerization process. The resistance of the as-prepared PEDOT:PSS/PEO hybrid shows clear temperature response, i.e., it decreases almost linearly with the temperature increase. To understand this phenomenon, the in situ synchrotron radiation wide-and small-angle X-ray scattering(WAXS/SAXS) characterizations were undertaken to study the temperature-dependent microstructure change of the PEDOT:PSS/PEO hybrid. It demonstrated that PEDOT formed conductive paths in the hybrids, which were not destroyed by the PEO crystallization. As temperature increased, the PEO crystals' melting and the accompanying reorganization of PEDOT chains endowed the hybrid sample temperature responsiveness. Based on these fundamental knowledges, the hybrid materials were used to fabricate flexible wearable sensor that showing temperature sensing performance with an accuracy of 1 ℃. These findings shed lights on the scalable manufacturing of wearable sensors for body temperature monitoring.

关键词： Solid-state polymerization PEDOT hybrid Structure-property correlation X-ray scattering

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Co_(3)S_(4)-pyrolysis lotus fiber flexible textile as a hybrid electrocatalyst for overall water splitting

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Journal of Energy Chemistry 2024年第2期89卷 336-344,I0008页

作者： Qiulan zhou zhen Liu Xuxu Wang Yaqian Li Xin Qin Lijuan Guo Liwei zhou Weijian Xu Academician Workstation Changsha Medical UniversityChangsha 410219HunanChina College of Chemistry and Chemical Engineering Hunan UniversityChangsha 410082HunanChina College of Environmental Science and Engineering Hunan UniversityChangsha 410082HunanChina School of Basic Medicine Changsha Medical UniversityChangsha 410219HunanChina

Electrocatalytic overall water splitting(OWS),a pivotal approach in addressing the global energy crisis,aims to produce hydrogen and ***,most of the catalysts in powder form are adhesively bounding to the electrodes,resulting in catalyst detachment by bubble generation and other uncertain interference,and eventually reducing the OWS *** surmount this challenge,we synthesized a hybrid material of Co_(3)S_(4)-pyrolysis lotus fiber(labeled as Co_(3)S_(4)-p LF)textile by hydrothermal and hightemperature pyrolysis processes for electrocatalytic *** to the natural LF textile exposing the uniformly distributed functional groups(AOH,ANH_(2.,etc.)to anchor Co_(3)S_(4)nanoparticles with hierarchical porous structure and outstanding hydrophily,the hybrid Co_(3)S_(4)-p LF catalyst shows low overpotentials at 10 m A cm^(-2.(η_(10,HER)=100 m Vη_(10,OER)=2.0 mV)alongside prolonged operational stability during electrocatalytic *** calculations reveal that the electron transfer from p LF to Co_(3)S_(4)in the hybrid Co_(3)S_(4)-p LF is beneficial to the electrocatalytic *** work will shed light on the development of nature-inspired carbon-based materials in hybrid electrocatalysts for OWS.

关键词： Overall water splitting Hybrid electrocatalyst Textile Lotus fiber Co_(3)S_(4)nanoparticles

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Black-box membership inference attacks based on shadow model

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The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 1-16页

作者： Han zhen zhou Wen'an Han Xiaoxuan Wu Jie School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Membership inference attacks on machine learning models have drawn significant *** current research primarily utilizes shadow modeling techniques,which require knowledge of the target model and training data,practical scenarios involve black-box access to the target model with no available *** training data further complicate the implementation of these *** this paper,we experimentally compare common data enhancement schemes and propose a data synthesis framework based on the variational autoencoder generative adversarial network(VAE-GAN)to extend the training data for shadow ***,this paper proposes a shadow model training algorithm based on adversarial training to improve the shadow model's ability to mimic the predicted behavior of the target model when the target model's information is *** conducting attack experiments on different models under the black-box access setting,this paper verifies the effectiveness of the VAE-GAN-based data synthesis framework for improving the accuracy of membership inference ***,we verify that the shadow model,trained by using the adversarial training approach,effectively improves the degree of mimicking the predicted behavior of the target *** with existing research methods,the method proposed in this paper achieves a 2.improvement in attack accuracy and delivers better attack performance.

关键词： machine learning membership inference attack shadow model black-box model

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