Chaos最新文献_第4页

Enhancing sparse identification of nonlinear dynamics with Earth-Mover distance and group similarity.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0214404

Donglin Liu, Alexandros Sopasakis

引用次数: 0

Fast-slow dynamics and spike organization in the electromechanical gyrostat system: Unveiling the intricacies of shrimp structures. 机电陀螺仪系统的快慢动力学和尖峰组织：揭开虾结构的神秘面纱

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0233740

Xu-Ping Zhao, Li-Ying Xing, Jian-She Gao

引用次数: 0

Power Levy motion. II. Evolution.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0251341

Iddo Eliazar

引用次数: 0

Power Levy motion. I. Diffusion.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0251337

Iddo Eliazar

引用次数: 0

Epidemic spread dynamics in multilayer networks: Probing the impact of information outbreaks and reception games.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0236359

Jianbo Wang, Haoxing He, Ping Li, Zhanwei Du, Xiaoke Xu

{"title":"Epidemic spread dynamics in multilayer networks: Probing the impact of information outbreaks and reception games.","authors":"Jianbo Wang, Haoxing He, Ping Li, Zhanwei Du, Xiaoke Xu","doi":"10.1063/5.0236359","DOIUrl":"https://doi.org/10.1063/5.0236359","url":null,"abstract":"The co-evolution of epidemic and information spread within multilayer networks is a current hot topic in network science. During epidemic outbreaks, the accompanying information exhibits both outbreak and reception game behaviors; yet, these complex phenomena have been scarcely addressed in existing research. In this paper, we model information outbreaks using activated individuals who transmit messages to their neighbors, while also considering the game behaviors of information receivers. By focusing on these two factors, we establish a multilayer network model featuring both information outbreaks and reception games. Employing the microscopic Markov chain method, we analyze the propagation dynamics within this network and derive epidemic thresholds, corroborating these results with Monte Carlo simulations. Our findings indicate that information outbreaks suppress epidemic outbreaks, whereas increased costs of information reception promote epidemic spread. Smooth information dissemination further inhibits the transmission of the epidemic. Additionally, we observe that heterogeneity in the network structure between the virtual and physical layers reduces the ultimate scale of epidemic infection, with the virtual layer exerting a more substantial influence. These insights are crucial for elucidating the co-evolutionary mechanisms of spread within multilayer networks and for developing effective epidemic prevention and control strategies.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing self-organized topology of recurrence-based complex networks.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0249500

Conggai Li, Joseph C S Lai, Sebastian Oberst

{"title":"Optimizing self-organized topology of recurrence-based complex networks.","authors":"Conggai Li, Joseph C S Lai, Sebastian Oberst","doi":"10.1063/5.0249500","DOIUrl":"https://doi.org/10.1063/5.0249500","url":null,"abstract":"Networks and graphs have emerged as powerful tools to model and analyze nonlinear dynamical systems. By constructing an adjacency matrix from recurrence networks, it is possible to capture critical structural and geometric information about the underlying dynamics of a time series. However, randomization of data often raises concerns about the potential loss of deterministic relationships. Here, in using the spring-electrical-force model, we demonstrate that by optimizing the distances between randomized points through minimizing an entropy-related energy measure, the deterministic structure of the original system is not destroyed. This process allows us to approximate the time series shape and correct the phase, effectively reconstructing the initial invariant set and attracting dynamics of the system. Our approach highlights the importance of adjacency matrices derived from recurrence plots, which preserve crucial information about the nonlinear dynamics. By using recurrence plots and the entropy of diagonal line lengths and leveraging the Kullback-Leibler divergence as a relative entropic measure, we fine-tune the parameters and initial conditions for recurrence plots, ensuring an optimal representation of the system's dynamics. Through the integration of network geometry and energy minimization, we show that data-driven graphs can self-organize to retain and regenerate the fundamental features of the time series, including its phase space structures. This study underscores the robustness of recurrence networks as a tool for analyzing nonlinear systems and demonstrates that randomization, when guided by informed optimization, does not erase deterministic relationships, opening new avenues for reconstructing dynamical systems from observational data.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic evolution in multi-player networked trust games with graded punishment.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0256342

Juan Wang, Zhuo Liu, Yan Xu, Xiaopeng Li

{"title":"Dynamic evolution in multi-player networked trust games with graded punishment.","authors":"Juan Wang, Zhuo Liu, Yan Xu, Xiaopeng Li","doi":"10.1063/5.0256342","DOIUrl":"https://doi.org/10.1063/5.0256342","url":null,"abstract":"Trust holds a pivotal position in contemporary society. Yet, the question of how to elevate and sustain trust among selfish individuals poses a formidable challenge. To delve into this issue, we incorporate a graded punishment strategy into a networked N-player trust game, aiming to observe the progression of trust-related behavior. Within this game framework, punishers uphold a certain degree of trust among the participants by incurring an extra expense to exclude those who betray trust. By conducting numerous Monte Carlo simulation experiments, we uncover that the graded punishment strategy can effectively curtail untrustworthy conduct to a significant degree, potentially even eliminating such behavior, thereby fostering an improvement in the overall trust level within the population. However, to effectively deploy this strategy, it is imperative to strike a balance between the penalty cost and the penalty amount, ensuring that the natural evolution of the system is not unduly disrupted. This balance is crucial for preserving the stability and sustainability of the system while safeguarding trust. Broadly speaking, our study offers fresh insights and approaches for enhancing and maintaining trust in the networked society, while also highlighting the avenues and challenges for future research, particularly in the realm of applying graded punishment strategies.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivities in complex-time flows: Phase transitions, Hamiltonian structure, and differential geometry. 复杂时间流的敏感性：相变、哈密顿结构和微分几何。

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0245642

Dirk Lebiedz, Johannes Poppe

引用次数: 0

Structure-and-embedding-based centrality on network fragility in hypergraphs. 基于结构和嵌入的超图网络脆弱性中心性。

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0232539

Lanlan Chang, Tian Qiu, Guang Chen

引用次数: 0

Amplitude-sensitive permutation entropy: A novel complexity measure incorporating amplitude variation for physiological time series.

IF 2.7 2区数学

Chaos Pub Date : 2025-03-01 DOI: 10.1063/5.0245842

Jun Huang, Huijuan Dong, Na Li, Yizhou Li, Jing Zhu, Xiaowei Li, Bin Hu

{"title":"Amplitude-sensitive permutation entropy: A novel complexity measure incorporating amplitude variation for physiological time series.","authors":"Jun Huang, Huijuan Dong, Na Li, Yizhou Li, Jing Zhu, Xiaowei Li, Bin Hu","doi":"10.1063/5.0245842","DOIUrl":"https://doi.org/10.1063/5.0245842","url":null,"abstract":"Physiological time series, such as electrocardiogram (ECG) and electroencephalogram (EEG) data, are instrumental in capturing the critical dynamics of biological systems, including cardiovascular behavior and neural activity. The traditional permutation entropy (PE) methods effectively analyze the complexity of such signals but often overlook amplitude variations, which encode essential information about physiological states and pathological conditions. This paper introduces amplitude-sensitive permutation entropy (ASPE), a novel method that enhances PE by integrating amplitude information through the coefficient of variation as a weighting factor. Unlike the existing approaches that may overemphasize or underutilize amplitude changes, ASPE's balanced weighting strategy captures both the average level and dispersion of data, preserving the overall signal complexity. To validate ASPE's effectiveness, we conducted simulation experiments and applied them to two real-world datasets: an EEG dataset of epileptic seizures and an ECG dataset of arrhythmias. In simulations, ASPE demonstrated superior sensitivity to amplitude changes, outperforming the five existing PE methods in identifying dynamic variations accurately. In the physiological datasets, ASPE distinguished disease states more effectively, accurately identifying seizure phases and arrhythmic patterns. These results highlight ASPE's potential as a robust tool for analyzing physiological data with complex amplitude dynamics, offering a more comprehensive assessment of signal behavior and disease states than the current methods.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0