期刊：
Machine Learning: Science and Technology,2024年5(1) ISSN：2632-2153
通讯作者：
Li, W
作者机构：
[Liu, Feiyi; Li, W; Wang, Yanyang; Li, Wei; Shen, Jianmin] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Li, W; Wang, Yanyang; Li, Wei; Shen, Jianmin] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Liu, Feiyi] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.;[Shen, Jianmin] Baoshan Univ, Coll Engn & Technol, Baoshan, Peoples R China.
通讯机构：
[Li, W ] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.
关键词：
machine learning;non-equilibrium phase transitions;branching annihilating random walks;convolutional neural networks;autoencoder
摘要：
Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent z and spatial correlation length correlation exponent nu(perpendicular to) were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.
期刊：
Physica A-Statistical Mechanics and its Applications,2024年637:129533 ISSN：0378-4371
通讯作者：
Liu, FY
作者机构：
[Yang, Chunbin; Liu, Feiyi; Deng, Weibing; Li, Wei; Chen, Xiangna; Liu, FY] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Deng, Weibing; Li, Wei; Liu, FY] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Papp, Gabor; Liu, FY] Eotv Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.;[Chen, Shiyang] Baoshan Univ, Sch Engn & Technol, Baoshan 678000, Peoples R China.;[Shen, Jianmin] Max Planck Inst Math Sci, D-04103 Leipzig, Germany.
通讯机构：
[Liu, FY ] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Eotv Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.
摘要：
Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional q -state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high -temperature (disordered) and low -temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for q = 2, 3, 4 and 5 can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high -dimensional systems.
期刊：
Physica A-Statistical Mechanics and its Applications,2023年617:128666 ISSN：0378-4371
通讯作者：
Liu, FY
作者机构：
[Yang, Chunbin; Liu, Feiyi; Chen, Shiyang; Deng, Weibing; Li, Wei; Chen, Xiangna; Liu, FY] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Yang, Chunbin; Liu, Feiyi; Chen, Shiyang; Deng, Weibing; Li, Wei; Chen, Xiangna; Liu, FY] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Papp, Gabor; Liu, FY] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.;[Li, Wei] Max Planck Inst Math Sci, D-04103 Leipzig, Germany.;[Shen, Jianmin] Baoshan Univ, Sch Engn & Technol, Baoshan 678000, Peoples R China.
通讯机构：
[Liu, FY ] C;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.
摘要：
A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3,4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second -order phase transition at q = 3, we can calculate the critical exponent & nu; by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost.& COPY; 2023 Elsevier B.V. All rights reserved.
期刊：
Physica A-Statistical Mechanics and its Applications,2023年609:128329 ISSN：0378-4371
通讯作者：
Yueying Zhu
作者机构：
[Zhu, Yueying; Jiang, Jian] Wuhan Text Univ, Res Ctr Nonlinear Sci, Sch Math & Phys Sci, Wuhan 430200, Peoples R China.;[Li, Wei] Cent China Normal Univ, Complex Sci Ctr, Wuhan 430079, Peoples R China.;[Li, Wei] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Li, Wei] Max Planck Inst Math Sci, Inselst 22, D-04103 Leipzig, Germany.
通讯机构：
[Yueying Zhu] R;Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, 430200 Wuhan, China
摘要：
The Hegselmann-Krause (HK) model allows one to characterize the continuous change of agent opinions with the bounded confidence threshold epsilon. To consider the heterogeneity of agents in characteristics, we study the HK model on homogeneous and heterogeneous networks by introducing a kind of smart agent. Different from the averaging rule in opinion update of HK model, smart agents will consider, in updating their opinions, the environmental influence following the fact that the agent behavior is often coupled with environmental changes. The environment is characterized by a parameter that represents the biased resource allocation between different cliques. We focus on the critical behavior of the underlying system. A phase transition point separating a complete consensus from the coexistence of different opinions is identified, which occurs at a critical value epsilon c for the bounded confidence threshold. We state analytically that epsilon c can take only one of two possible values, depending on the behavior of the average degree ka of a social graph, when agents are homogeneous in characteristics. Results also suggest that the phase transition point weakly depends on the network structure but is strongly correlated with the fraction of smart agents and the environmental parameter. We finally establish the finite size scaling law that stresses the role that the system size has in the underlying opinion dynamics. Meanwhile, introducing smart agents does not change the functional dependence between the time to reach a complete consensus and the system size. However, it can drive a complete consensus to be reached faster, for homogeneous networks that are far from the mean field limit.(c) 2022 Elsevier B.V. All rights reserved.
作者机构：
[Fei Ma] Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, WuHan, 430079, China;Institute for Physics, Eötvös Loránd University, 1/A Pázmány P. Sétány, H-1117, Budapest, Hungary;Max-Planck-Institute for Mathematics in the Sciences, 04103 Leipzig, Germany;[Feiyi Liu] Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, WuHan, 430079, China<&wdkj&>Institute for Physics, Eötvös Loránd University, 1/A Pázmány P. Sétány, H-1117, Budapest, Hungary;[Wei Li] Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, WuHan, 430079, China<&wdkj&>Max-Planck-Institute for Mathematics in the Sciences, 04103 Leipzig, Germany
摘要：
Recently, methods of graph neural networks (GNNs) have been applied to solving the problems in high-energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a Haar pooling operation to analyze the events, called Haar pooling message passing neural network (HMPNet). In HMPNet, Haar pooling not only extracts the features of graph, but embeds additional information obtained by clustering of k means of different particle features. We construct Haar pooling from five different features: absolute energy logE , transverse momentum logpT , relative coordinates (Δη,Δϕ) , the mixed ones (logE,logpT) , and (logE,logpT,Δη,Δϕ) . The results show that an appropriate selection of information for Haar pooling enhances the accuracy of quark-gluon tagging, as adding extra information of logPT to the HMPNet outperforms all the others, whereas adding relative coordinates information (Δη,Δϕ) is not very effective. This implies that, by adding effective particle features from Haar pooling, one can achieve much better results than that which a solely pure message passing neutral network can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally, we compare the HMPNet study, ordering by pT , with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.
通讯机构：
[Yue Hu] M;[Yiwen Tang] D;Michael Grätzel Center for Mesoscopic Solar Cells, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074 China<&wdkj&>Department Nano-Science & Technology, College of Physics and Technology, Central China Normal University, Wuhan, 430079 China
关键词：
crystallization quality;CsPbBr3;energy band alignment;europium ions;perovskite solar cells
摘要：
The high‐valence Eu3+ was applied in carbon‐based printable mesoscopic inorganic CsPbBr3 perovskite solar cells (PSCs) for the first time. The high open‐circuit voltage and stability of the device demonstrate that the CsPbBr3 PSC is a promising device to drive the water electrolysis device. The all‐inorganic CsPbBr3 perovskite exhibits the possibility of overcoming the substantial nonideal thermal, humidity, and photostability of hybrid organic–inorganic perovskite solar cells (PSCs) in photoelectronic devices. Specifically, the rapid development of CsPbBr3 perovskite has delivered device efficiencies >10%. However, the mismatched energy band alignment and bad crystallization quality are still potential obstacles for the superior performance of PSCs. Herein, by employing n‐type doping, trivalent europium cation is successfully introduced into the CsPbBr3 lattice. The better energy‐level alignment leads to further reduction of voltage losses. Besides, the large and uniform grains resulting from the improvement of crystallization after doping decrease the grain boundaries and reduce the nonradiative recombination center. The quality of the film improves substantially, which significantly enhances the photoabsorption and the short‐circuit current density. The efficiency of the carbon‐based printable mesoscopic PSCs is improved from 7.5% to 8.06% with 3 mol% Eu3+ doping, resulting in high open‐circuit voltage of 1.41 V. Based on the device with effective area of 1 cm2 and 60.075 cm2, the record power conversion efficiency of 5.41% and 1.14% is obtained. The device also displays excellent stability with driving water electrolysis.
作者机构：
[Liu, Feiyi; Chen, Shiyang; Shen, Jianmin; Li, Wei; Xu, Dian] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Chen, Shiyang; Shen, Jianmin; Li, Wei; Xu, Dian] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Deng, Shengfeng] Ctr Energy Res, Inst Tech Phys & Mat Sci, H-1121 Budapest, Hungary.;[Liu, Feiyi] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.
通讯机构：
[Li, Wei] K;Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan, 430079, China.
摘要：
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised learning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent $$\nu _{\perp }$$ , which, to a large degree, avoids the uncertainty of data collapses through naked eyes.
作者机构：
[Yang, Chunbin; Liu, Feiyi; Chen, Shiyang; Shen, Jianmin; Li, Wei; Chen, Xiangna; Xu, Dian] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Yang, Chunbin; Liu, Feiyi; Chen, Shiyang; Shen, Jianmin; Li, Wei; Chen, Xiangna; Xu, Dian] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Liu, Feiyi; Papp, Gabor] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.;[Deng, Shengfeng] Ctr Energy Res, Inst Tech Phys & Mat Sci, H-1121 Budapest, Hungary.;[Li, Wei] Max Planck Inst Math Sci, D-04103 Leipzig, Germany.
摘要：
The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural network (DANN) based on transfer learning to studying nonequilibrium and equilibrium phase transition models, which are percolation model and directed percolation (DP) model, respectively. With the DANN, only a small fraction of input configurations (two-dimensional images) needs to be labeled, which is automatically chosen, to capture the critical point. To learn the DP model, the method is refined by an iterative procedure in determining the critical point, which is a prerequisite for the data collapse in calculating the critical exponent ?????. We then apply the DANN to a two-dimensional site percolation with configurations filtered to include only the largest cluster which may contain the information related to the order parameter. The DANN learning of both models yields reliable results which are comparable to the ones from Monte Carlo simulations. Our study also shows that the DANN can achieve quite high accuracy at much lower cost, compared to the supervised learning.
作者机构：
[Deng, Weibing; Li, Wei; Zhang, Wenjun] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Deng, Weibing; Li, Wei; Zhang, Wenjun] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Li, Wei] Max Planck Inst Math Sci, Inselstr 22-26, D-04103 Leipzig, Germany.;[Zhang, Wenjun] Anhui Univ Chinese Med, Sch Med Informat Engn, Hefei 230012, Peoples R China.
通讯机构：
[Weibing Deng] K;Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China
关键词：
Cycle nodes ratio;Depth first search;Giant component;Network classification
摘要：
Cycles, which can be found in many different kinds of networks, make the problems more intractable, especially when dealing with dynamical processes on networks. On the contrary, tree networks in which no cycle exists, are simplifications and usually allow for analyticity. There lacks a quantity, however, to tell the ratio of cycles which determines the extent of network being close to tree networks. Therefore we introduce the term Cycle Nodes Ratio (CNR) to describe the ratio of number of nodes belonging to cycles to the number of total nodes, and provide an algorithm to calculate CNR. CNR is studied in both network models and real networks. The CNR remains unchanged in different sized Erdos--R & eacute;nyi (ER) networks with the same average degree, and increases with the average degree, which yields a critical turning point. The approximate analytical solutions of CNR in ER networks are given, which fits the simulations well. Furthermore, the difference between CNR and two-core ratio (TCR) is analyzed. The critical phenomenon is explored by analysing the giant component of networks. We compare the CNR in network models and real networks, and find the latter is generally smaller. Combining the coarse graining method can distinguish the CNR structure of networks with high average degree. The CNR is also applied to four different kinds of transportation networks and fungal networks, which give rise to different zones of effect. It is interesting to see that CNR is very useful in network recognition of machine learning. (c) 2021 Elsevier B.V. All rights reserved.
期刊：
Science Journal of Education,2021年9(3):104-109 ISSN：2329-0900
作者机构：
[Wei Li; Zhi Xin Huang; Ming Hai Wu; Qi Tian] College of Physical Science and Technology, Central China Normal University, Wuhan, China
关键词：
Classroom Response System;Deep Learning;Middle School Physics Review
摘要：
Deep learning is an important research topic in the field of educational technology, the Horizon Report released by the New Media Alliance in 2017 points out that deep learning is a key direction in promoting educational development and reform. This research is based on the CRS review discussion teaching mode, applying classroom response system in interactive teaching environment, in order to promot students' deep learning. Aiming at the problems of single teaching method, simple classroom communication, fuzzy monitoring of learning situation and low efficiency of teaching goal in junior high school physics review course, this paper takes junior high school physics review course as an example, discusses the teaching efficiency of CRS-based review course qualitatively, and uses comparative experiment to analyze the difference between teaching effect, students' deep learning achievement degree and learning type. The results show that, Based on the CRS junior high school physics review class in-depth learning model can improve students' academic performance, enhance the degree of achievement of students' deep learning. Therefore, we should actively explore the deep integration path of CRS and physics teaching, give full play to the advantages of CRS in focusing on the core issues in the classroom, evaluation of innovative learning process to improve the effectiveness of physics teaching.
作者机构：
[Shen, Jianmin; Li, Wei; Deng, Shengfeng; Zhang, Tao] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Shen, Jianmin; Li, Wei; Deng, Shengfeng; Zhang, Tao] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Li, Wei] Max Planck Inst Math Sci, D-04103 Leipzig, Germany.
摘要：
Machine learning (ML) has been well applied to studying equilibrium phase transition models by accurately predicating critical thresholds and some critical exponents. Difficulty will be raised, however, for integrating ML into nonequilibrium phase transitions. The extra dimension in a given nonequilibrium system, namely time, can greatly slow down the procedure toward the steady state. In this paper we find that by using some simple techniques of ML, non-steady-state configurations of directed percolation (DP) suffice to capture its essential critical behaviors in both ( 1+1 ) and ( 2+1 ) dimensions. With the supervised learning method, the framework of our binary classification neural networks can identify the phase transition threshold, as well as the spatial and temporal correlation exponents. The characteristic time tc , specifying the transition from active phases to absorbing ones, is also a major product of the learning. Moreover, we employ the convolutional autoencoder, an unsupervised learning technique, to extract dimensionality reduction representations and cluster configurations of ( 1+1 ) bond DP. It is quite appealing that such a method can yield a reasonable estimation of the critical point.
作者机构：
[Deng, Shengfeng; Li, Wei] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;[Deng, Shengfeng; Li, Wei] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Deng, Shengfeng; Tauber, Uwe C.] Virginia Tech, Dept Phys, Blacksburg, VA 24061 USA.;[Deng, Shengfeng; Tauber, Uwe C.] Virginia Tech, Ctr Soft Matter & Biol Phys, Blacksburg, VA 24061 USA.
通讯机构：
[Deng, Shengfeng] C;[Deng, Shengfeng] V;Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;Virginia Tech, Dept Phys, Blacksburg, VA 24061 USA.
摘要：
The contact process with diffusion (PCPD) defined by the binary reactions B + B -> B + B + B, B + B -> empty set and diffusive particle spreading exhibits an unusual active to absorbing phase transition whose universality class has long been disputed. Multiple studies have indicated that an explicit account of particle pair degrees of freedom may be required to properly capture this system's effective long-time, large-scale behavior. We introduce a two-species representation for the PCPD in which single particles B and particle pairs A are dynamically coupled according to the stochastic reaction processes B + B -> A, A -> A + B, A -> empty set, and A -> B + B, with each particle type diffusing independently. Mean-field analysis reveals that the phase transition of this model is driven by competition and balance between the two species. We employ Monte Carlo simulations in one, two, and three dimensions to demonstrate that this model consistently captures the pertinent features of the PCPD. In the inactive phase, A particles rapidly go extinct, effectively leaving the B species to undergo pure diffusion-limited pair annihilation kinetics B + B -> empty set. At criticality, both A and B densities decay with the same exponents (within numerical errors) as the corresponding order parameters of the original PCPD, and display mean-field scaling above the upper critical dimension d(c) = 2. In one dimension, the critical exponents for the B species obtained from seed simulations also agree well with previously reported exponent value ranges. We demonstrate that the scaling properties of consecutive particle pairs in the PCPD are identical with that of the A species in the coupled model. This two-species picture resolves the conceptual difficulty for seed simulations in the original PCPD and naturally introduces multiple length scales and timescales to the system, which are also the origin of strong corrections to scaling. The extracted moment ratios from our simulations indicate that our model displays the same temporal crossover behavior as the PCPD, which further corroborates its full dynamical equivalence with our coupled model.
期刊：
Journal of Statistical Mechanics: Theory and Experiment,2020年2020(4) ISSN：1742-5468
通讯作者：
Su, Zhu
作者机构：
[Deng, Weibing; Li, Wei; Zhang, Wenjun] Cent China Normal Univ, MOE, Key Lab Quark & Lepton Phys, Wuhan 430079, Peoples R China.;[Deng, Weibing; Li, Wei; Zhang, Wenjun] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.;[Guan, Zhanpeng; Li, Jianyao] Cent China Normal Univ, Coll Phys Sci & Technol, Wuhan 430079, Peoples R China.;[Su, Zhu] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Su, Zhu] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
摘要：
Air quality impacts people's health and daily life, affects the sensitive ecosystems, and even restrains a country's development. By collecting and processing the time series data of air quality index (AQI) of 363 cities of China from January 2015 to March 2019, we dedicated to characterize the universal patterns, the clustering and correlation of air quality of different cities by using the methods of complex network and time series analysis. The main results are as follows: (1) The air quality network of China (AQNC) is constructed by using the planar maximally filtered graph (PMFG) method. The geographical distances on the correlation of air quality of different cities have been studied, it is found that 100 km is a critical distance for strong correlation. (2) Eight communities of AQNC have been detected, and their patterns have been analyzed by taking into account the Hurst exponent and climate environment, it is shown that the eight communities are reasonable, and they are significantly influenced by the climate factors, such as monsoons, precipitation, geographical regions, etc. (3) The motifs of air quality time series of eight communities have been investigated by the visibility graph, for some communities, the evolutionary patterns of the motifs are a bit stable, and they have long-term memory effects. While for others, there are no stable patterns.
作者：
Su, Zhu;Liu, Sannyuya*;Deng, Weibing*;Li, Wei*;Cai, Xu
期刊：
Physica A-Statistical Mechanics and its Applications,2019年523:1379-1386 ISSN：0378-4371
通讯作者：
Liu, Sannyuya;Deng, Weibing;Li, Wei
作者机构：
[Liu, Sannyuya; Su, Zhu] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.;[Deng, Weibing; Li, W; Li, Wei; Cai, Xu] Cent China Normal Univ, Key Lab Quark & Lepton Phys, MOE, Wuhan 430079, Hubei, Peoples R China.;[Deng, Weibing; Li, W; Li, Wei; Cai, Xu] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Liu, Sannyuya; Deng, WB; Li, W] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Key Lab Quark & Lepton Phys, MOE, Wuhan 430079, Hubei, Peoples R China.;Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Hubei, Peoples R China.
关键词：
Adaptive routing strategy;Networks of heterogeneous mobile agent;Traffic dynamics
摘要：
Based on transportation networks of mobile agents, this paper discusses the condition under which the communication radii of mobile agents obey the power-law distribution. The analysis of network structure indicates that out-degrees obey a power-law distribution, while in-degrees follow a Poisson distribution. Simulation results reveal that: (i) there exists a critical value of package generate rate P-c, which defines the free-flow state and congested state. (ii) In the free-flow regime, the average traveling time < T > is small and independent of the packet generation rate P, but the average path length < L > increases with the growth of P and is related to a traffic awareness parameter h. (iii) The larger the heterogeneous exponent alpha of the communication radii is, the lower the transport efficiency of the network would be. (iv) There exists an optimal traffic awareness parameter h that maximizes the critical value P-c. (v) The critical value P-c decreases with the heterogeneous exponent alpha, increases with the network size N, and decreases with the speed v until v reaches an appropriate value. (C) 2019 Published by Elsevier B.V.
摘要：
Graphene-based materials have potential applications in supercapacitors, owing to their unique twodimensional structure, high surface area, remarkable chemical stability, and outstanding electrical conductivity. In this work, Hummers method was used to prepare graphene oxide, and then in situ electrochemical method was firstly used to deposit graphene nanosheets on Co/CoO core-shell nano-structure to form a sandwich-type of Co/CoO/graphene electrode materials. The effect of electro-deposition graphene cycles on the electrochemical properties of Co/CoO/graphene composite were investigated. The results showed that the coating of graphene on Co/CoO further enhanced the conductivity of the material, and the specific capacitance reached 7.765 F cm(-2) at the current density of 1 mAcm(-2). The Co/CoO/graphene and AC were assembled into an asymmetric supercapacitor. After 10000 charge/discharge cycles, the total capacitance of the asymmetric supercapacitor was still maintained 79.9%. The coating of graphene not only increased the conductivity of the material, but also increased the stable of structure. (C) 2019 Elsevier B.V. All rights reserved.
