Chaos

The complex phase interactions of the two-phase flow are a key factor in understanding the flow pattern evolutional mechanisms, yet these complex flow behaviors have not been well understood. In this paper, we employ a series of gas-liquid two-phase flow multivariate fluctuation signals as observations and propose a novel interconnected ordinal pattern network to investigate the spatial coupling behaviors of the gas-liquid two-phase flow patterns. In addition, we use two network indices, which are the global subnetwork mutual information (I) and the global subnetwork clustering coefficient (C), to quantitatively measure the spatial coupling strength of different gas-liquid flow patterns. The gas-liquid two-phase flow pattern evolutionary behaviors are further characterized by calculating the two proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a novel tool for a deeper understanding of the evolutional mechanisms of the multi-phase flow system, and it can also be used to investigate the coupling behaviors of other complex systems with multiple observations.

Meng Du

Jie Wei

Meng-Yu Li

Zhong-ke Gao

Jürgen Kurths

Chaos An Interdisciplinary Journal of Nonlinear Science

Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas–liquid two-phase flow

We review and test twelve different approaches to the detection of finite-time coherent material structures in two-dimensional, temporally aperiodic flows. We consider both mathematical methods and diagnostic scalar fields, comparing their performance on three benchmark examples: the quasiperiodically forced Bickley jet, a two-dimensional turbulence simulation, and an observational wind velocity field from Jupiter's atmosphere. A close inspection of the results reveals that the various methods often produce very different predictions for coherent structures, once they are evaluated beyond heuristic visual assessment. As we find by passive advection of the coherent set candidates, false positives and negatives can be produced even by some of the mathematically justified methods due to the ineffectiveness of their underlying coherence principles in certain flow configurations. We summarize the inferred strengths and weaknesses of each method, and make general recommendations for minimal self-consistency requirements that any Lagrangian coherence detection technique should satisfy.

Alireza Hadjighasem

Mohammad Farazmand

Daniel Blazevski

Gary Froyland

George Haller

A critical comparison of Lagrangian methods for coherent structure detection

In this paper, we propose a novel control approach for opinion dynamics on evolving networks. The controls modify the strength of connections in the network, rather than influencing opinions directly, with the overall goal of steering the population toward a target opinion. This requires that the social network remains sufficiently connected, the population does not break into separate opinion clusters, and that the target opinion remains accessible. We present several approaches to address these challenges, considering questions of controllability, instantaneous control, and optimal control. Each of these approaches provides a different view on the complex relationship between opinion and network dynamics and raises interesting questions for future research.

A. Nugent

S. N. Gomes

M. T. Wolfram

Steering opinion dynamics through control of social networks

通过控制社交网络引导舆论动态

In the field of complex networks, how to identify influential nodes is a significant issue in analyzing the structure of a network. In the existing method proposed to identify influential nodes based on the local dimension, the global structure information in complex networks is not taken into consideration. In this paper, a node information dimension is proposed by synthesizing the local dimensions at different topological distance scales. A case study of the Netscience network is used to illustrate the efficiency and practicability of the proposed method.

Tian Bian

Yong Deng

Identifying influential nodes in complex networks: A node information dimension approach

识别复杂网络中的有影响力节点：一种节点信息维度方法

Synchronization stability is one of central problems in power systems, and it is becoming much more complicated with the high penetration of renewable energy and power electronics devices. In this paper, we review recent work by several nonlinear models for renewable-dominated power systems in terms of multiple timescales, in particular, grid-tied converters within the DC voltage timescale. For the simplest model, a second-order differential equations called the generalized swing equation by considering only the phase-locked loop (PLL) is obtained, which shows a similar form with the well-known swing equation for a synchronous generator in the traditional power systems. With more outer controllers included, fourth-order and fifth-order models can be obtained. The fourth-order model is called the extended generalized swing equation, exhibiting the combined function of grid synchronization and active power balance on the DC capacitor. In addition, a nonlinear model for a two coupled converter system is given. Based on these studies, we find that the PLL plays a key role in synchronization stability. In summary, the value of this paper is to clarify the key concept of the synchronization stability in renewable-dominated power systems based on different nonlinear models, which still lacks systematic studies and is controversial in the field of electrical power engineering. Meanwhile, it clearly uncovers that the synchronization stability of converters has its root in the phase synchronization concept in nonlinear sciences.

Rui Ma

Yayao Zhang

Ziqian Yang

Meng Zhan

Congping Lin

Chaos: An Interdisciplinary Journal of Nonlinear Science

Synchronization stability of power-grid-tied converters

并网变换器的同步稳定性

The logic of collective action has laid a foundation for the research of public choice, and the success of collective action has been a long-term discussion when free-riding mechanism is considered in the dynamics. This study proposes a , which provides a novel dimension for explaining the logic of collective action. Under the framework, the accumulation of early social influence, conformity, and the pressure of relationship updating in small groups is discussed. The experiment results show that the accumulation of early social influence indirectly promotes the participants of collective action; conformity is conducive to stimulating collective action, but relies on the accumulation of early social influence; the pressure of relationship updating plays the small-group role, which promotes the participation of collective actions; all these effects are helpful in forming the cascade of cooperators, and prevent the coexistence of participants and non-participants of collective action.

Xiaochen He

Guangyu Li

Haifeng Du

Evolution of cooperation with early social influence for explaining collective action

早期社会影响下合作演化以解释集体行动

We report the peculiar organization of oscillations in the forced Brusselator system, found in the parameter space as a nested structure of regular and chaotic phases. To this end, we apply the winding number concept, conceived for nonlinear driven oscillators, to expose all oscillatory phases in the nested structure. First, we use the period and torsion of orbits to describe every periodic oscillation in the parameter spaces, describing the nested structure in high-resolution phase diagrams. Next, we propose a basic structure organizing the periodicity, a “skeletal set” whose properties elucidate the genealogy and composition of oscillations in the nested structure. Finally, we discuss the application of the skeletal structure in a diversity of Brusselator’s oscillatory regimes.

Dariel M. Maranhão

Rene O. Medrano-T

Skeletal structure in domain of periodicity of the forced Brusselator

受迫Brusselator的周期性区域中的骨架结构

We study a bifurcation scenario that corresponds to the transition from bursting activity to spiking in a phenomenological model of neuron-astrocyte interaction in neuronal populations. In order to do this, we numerically obtain one-dimensional Poincaré return map and highlight its bifurcation structure using an analytically constructed piecewise smooth model map. This map reveals the existence of a cascade of period-adding bifurcations, leading to a bursting-spiking transition via blue sky catastrophe.

Nikita V. Barabash

Tatiana A. Levanova

Lev A. Smirnov

Blue sky catastrophe in the phenomenological model of neuron–astrocyte interaction

神经元-星形胶质相互作用现象学模型中的蓝天灾难

This paper presents a survey and critical analysis of the mathematical literature on modeling of dynamic populations living in a fluid medium. The present review paper is divided into two main parts: The first part deals with the multiscale derivation of deterministic and stochastic cross-diffusion systems governed by the incompressible Navier-Stokes equations. The derivation is obtained from the underlying description at the microscopic scale in kinetic theory models according to the micro-macro decomposition method. In the second part of this review, we are delighted to present a new variety of mathematical models describing different applications, namely, the pursuit-evasion dynamics, cancer invasion, and virus dynamics. Finally, critical analysis and future research perspectives are discussed.

M. Bendahmane

F. Karami

M. Zagour

Multiscale derivation of deterministic and stochastic cross-diffusion models in a fluid: A review

流体中确定性和随机跨扩散模型的多尺度推导：综述

Parkinson’s disease (PD) is a highly prevalent neurodegenerative disorder that poses a significant challenge in terms of accurate and cost-effective diagnosis. This study focuses on the use of fractal features derived from nocturnal breathing signals to diagnose PD. Our study includes 49 individuals with Parkinson’s disease (PD group), 49 relatively healthy individuals without PD (HC group), 49 individuals without PD but with other diseases (NoPD group), as well as 12 additional PD patients and 200 healthy individuals for testing. Using multifractal detrended fluctuation analysis, we extracted fractal features from nocturnal breathing signals, with logistic regression models applied to diagnose PD, as demonstrated in receiver operating characteristic curves. Eight fractal features show significant diagnostic potential for PD, including generalized Hurst exponents for the Airflow, Thorax, and Abdomen signals and the multifractal spectrum width of the SaO2 signal. Finally, the area under the receiver operating characteristic curve (AUC) of the training data set of the PD and HC groups for all four signals is 0.911, and the AUC of the testing data set is 0.929. These results demonstrate the potential of this work to enhance the accuracy of PD diagnosis in clinical settings.

Zhong Dai

Shutang Liu

Changan Liu

Detection of Parkinson’s disease using nocturnal breathing signals based on multifractal detrended fluctuation analysis

基于多重分形去趋势波动分析的夜间呼吸信号用于帕金森病检测

短名	Chaos
Journal Impact	2.67
国际分区	PHYSICS, MATHEMATICAL(Q1)
期刊索引	SCI Q1中科院 2 区

ISSN	1054-1500, 1089-7682, 1527-2443
h-index	127
国内分区	数学(2区)数学物理数学物理(2区)数学应用数学(3区)

涉及主题	物理数学计算机科学量子力学人工智能工程类数学分析经济生物统计物理学非线性系统管理热力学组合数学电气工程统计化学几何学混乱的材料科学电信控制（管理）
出版信息	出版商: American Institute of Physics，出版周期: Quarterly，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 1991，原创研究文献占比: 98.21%，自引率:18.50%， Gold OA占比: 15.12%
平均审稿周期	网友分享经验：平均3.0个月
平均录用比例	网友分享经验：较难

Chaos

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