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Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

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Nuclear Science and Techniques 2024年第3期35卷 216-227页

作者： Deng Liu Alam Noor A zhen-zhen Qin Yang lei School of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China School of Nuclear Science and Technology Southwest University of Science and TechnologyMianyang 621010China

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output *** quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the ***,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

关键词： Neural network Two-body random ensemble Spin distribution of nuclear ground state

Collaborative Charging Scheduling in Wireless Charging Sensor Networks

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Computers, Materials & Continua 2024年第4期79卷 1613-1630页

作者： Qiuyang Wang zhen Xu lei Yang School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430048China

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides apromising solution to this problem, which is not easily affected by the external environment. In this paper, we studythe recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with theobjective of maximizing the number of surviving sensors, and further propose a collaborative charging schedulingalgorithm(CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary *** can adaptively collaboratively charge the sensors based on the energy limit ofMCs and the energy demandof sensors. Finally, we conducted comparative simulations. The simulation results show that the proposed algorithmcan effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertaintyof node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios.

关键词： Wireless rechargeable sensor network mobile charger collaborative charging adaptive charging

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

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 25 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

The importance of data assimilation components for initial conditions and subsequent error growth

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Science China Earth Sciences 2024年第1期67卷 105-116页

作者： Zhongrui WANG Haohao SUN Lili lei zhe-Min TAN Yi ZHANG Key Laboratory of Mesoscale Severe Weather Ministry of Educationand School of Atmospheric SciencesNanjing UniversityNanjing 210023China Frontiers Science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error *** a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of *** forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 *** strategies that provide more accurate ICs can improve the *** assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static *** observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing ***,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

关键词： Data assimilation Atmospheric predictability Background error covariances Ensemble forecasts

Investigating effect of coke porosity on blast furnace performance based on multi-physical fields

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Journal of Iron and Steel Research International 2024年第2期31卷 357-367页

作者： Ji Chen Zhao lei zhe Yao Li Wang Qiang Ling Ping Cui Anhui Province Key Laboratory of Coal Clean Conversion and High Valued Utilization School of Chemistry and Chemical EngineeringAnhui University of TechnologyMa’anshan243002AnhuiChina Shanxi Academy of Printing Technology Research Institute Co. Ltd.Xi’an710077ShaanxiChina

Reducing coke use is an effective measure to reduce carbon emission and energy consumption in the blast furnace(BF)***,BF is a high-temperature moving bed reactor,where complex physical transformations coupled with complicated reactions *** makes it challenging to investigate the factors determining BF performance with the conventional method.A multi-physical field coupling mathematical model of BF was thus developed to describe its mass and heat transfer as well as its intrinsic ***,the proposed model was validated with the production *** coupling conditions,influences of dominating reactions on BF performance(temperature distribution,gas distribution,iron formation reaction,and direct reduction degree)were *** results indicated that coke combustion,indirect reduction,and direct reduction of iron ore mainly took place nearby the shaft tuyere,cohesive zone,and dripping zone,***,the rate of coke solution loss reaction was increased with the rising coke porosity in the cohesive *** the effect of coke porosity on the efficiency and stability of BF,the coke porosity of 0.42 was regarded as a reasonable value.

关键词： Blast furnace Mathematical model Multi-physical field Simulation Coke porosity

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

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China Communications 2024年第2期21卷 122-128页

作者： Xiao Li Ling Zhao zhen Dai Yonggang lei School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Institute of Tracking and Telecommunications Technology BITTT

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

关键词： belief propagation channel decoding correlated noise neural network

Revisiting the island of hexadecapoledeformation nuclei in the A≈150 mass region:focusing on the model application to nuclear shapes and masses

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Communications in Theoretical Physics 2024年第2期76卷 74-88页

作者： Xiao-Yang Wei Hua-lei Wang zhen-zhen Zhang Min-Liang Liu School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China Key Laboratory of High Precision Nuclear Spectroscopy Institute of Modern PhysicsChinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China

Based on the potential-energy-surface calculation,the impact of different deformation degrees of freedom on a single-particle structure and binding energies in nuclei around^(152)Nd,located on one of the hexadecapole-deformation islands,is analyzed in a multi-dimensional deformation *** energy maps,curves and tables are presented to indicate nuclear *** calculated equilibrium deformations and binding energies with different potential parameters are compared with experimental data and other *** is found that the inclusion of the hexadecapole deformations,especially the axial one,can improve the theoretical description of both nuclear shapes and *** addition,our calculated potential-energy curve shows that a critical deformation-point,β_(2)≈0.4,exists—the triaxial(hexadecapole)deformation effect can be neglectable but the hexadecapole(triaxial)one plays an important role before(after)such a critical point.

关键词： hexadecapole deformation binding energy potential-energy-surface calculation nuclear property

Unveiling the mechanisms of Fe(Ⅲ)-loaded chitosan composite(CTS-Fe)in enhancing anaerobic digestion of waste activated sludge

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Journal of Environmental Sciences 2024年第4期138卷 200-211页

作者： Boaiqi Zhang Ziwen Zhao RuiMa Nan Chen zhe Kong Zhongfang lei zhenya Zhang Graduate School of Life and Environmental Sciences University of Tsukuba1-1-1 TennodaiTsukubaIbaraki 305-8572Japan The Key Laboratory of Water and Sediment Sciences Ministry of Education/State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems/College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China South China Institute of Environmental Sciences Ministry of Ecology and EnvironmentGuangzhou 510345China School of Water Resources and Environment China University of Geosciences(Beijing)Beijing 100083China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou 215009China

Anaerobic digestion(AD)of waste activated sludge(WAS)is usually limited by the low generation efficiency of ***(Ⅲ)-loaded chitosan composite(CTS-Fe)have been reported to effectively enhanced the digestion of WAS,but its role in promoting anaerobic sludge digestion remains *** present study,the effects of CTS-Fe on the hydrolysis and methanogenesis stages of WAS anaerobic digestion were *** addition of CTSFe increased methane production potential by 8%-23%under the tested conditions with the addition of 5-20 g/L ***,the results demonstrate that the addition of CTS-Fe could effectively promote the hydrolysis of WAS,evidenced by lower protein or polysaccharides concentration,higher soluble organic carbon in rector adding CTS-Fe,as well as the increased activity of extracellular hydrolase with higher CTS-Fe ***,the enrichment of Clostridia abundance(iron-reducing bacteria(IRBs))was observed in CTS-Fe adding reactor(8.9%-13.8%),which was higher than that in the control reactor(7.9%).The observation further suggesting the acceleration of hydrolysis through dissimilatory iron reduction(DIR)process,thus providing abundant substrates for ***,the presence of CTS-Fe was inhibited the acetoclastic and hydrogenotrophic methanogenesis process,which could be ascribed to the Fe(Ⅲ)act as electron acceptor coupled to methane for anaerobic ***,coenzyme F420 activity in the CTS-Fe added reactor was 34.9% lower than in the blank,also abundance of microorganisms involved in hydrogenotrophic methanogenesis was *** from this study could provide theoretical support for the practical applications of CTS-Fe.

关键词： Chitosan-Fe(Ⅲ)composite Methanogenesis Dissimilatory iron reduction Enzyme activity Microbial community structure