Chaos最新文献

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group's intra-group preference can lead to increased spread.

In this article, we present evidence of a distinct class of extreme events that occur during the transient chaotic state within network modeling using the Brusselator with a mutually coupled star network. We analyze the phenomenon of transient extreme events in the network by focusing on the lifetimes of chaotic states. These events are identified through the finite-time Lyapunov exponent and quantified using threshold and statistical methods, including the probability distribution function (PDF), generalized extreme value (GEV) distribution, and return period plots. We also evaluate the transitions of these extreme events by examining the average synchronization error and the system's energy function. Our findings, validated across networks of various sizes, demonstrate consistent patterns and behaviors, contributing to a deeper understanding of transient extreme events in complex networks.

In this paper, we investigate a seven-parameter, five-dimensional dynamical system, specifically a unidirectional coupling of two FitzHugh-Nagumo neuron models, with one neuron being sinusoidally driven. This master-slave configuration features neuron N1 as the master, subjected to an external sinusoidal electrical current, and neuron N2 as the slave, interacting with N1 through an electrical force. We report numerical results for three distinct scenarios where N1 operates in (i) periodic, (ii) quasiperiodic, and (iii) chaotic regimes. The primary objective is to explore how the dynamics of the master neuron N1 influence the coupled system's behavior. To achieve this, we generated cross sections of the seven-dimensional parameter space, known as parameter planes. Our findings reveal that in the periodic regime of N1, the coupled system exhibits period-adding sequences of Arnold tongue-like structures in the parameter planes. Furthermore, regions of multistability can also be identified in these parameter planes of the coupled system. In the quasiperiodic regime, regions of periodic motion are absent, with only regions of quasiperiodic and chaotic dynamics present. In the chaotic regime of N1, the parameter planes display regions of chaos, hyperchaos, and transient hyperchaos.

Link prediction has a wide range of applications in the study of complex networks, and the current research on link prediction based on single-layer networks has achieved fruitful results, while link prediction methods for multilayer networks have to be further developed. Existing research on link prediction for multilayer networks mainly focuses on multiplexed networks with homogeneous nodes and heterogeneous edges, while there are relatively few studies on general multilayer networks with heterogeneous nodes and edges. In this context, this paper proposes a method for heterogeneous multilayer networks based on motifs for link prediction. The method considers not only the effect of heterogeneity of edges on network links but also the effect of heterogeneous and homogeneous nodes on the existence of links between nodes. In addition, we use the role function of nodes to measure the contribution of nodes to form the motifs with links in different layers of the network, thus enabling the prediction of intra- and inter-layer links on heterogeneous multilayer networks. Finally, we apply the method to several empirical networks and find that our method has better link prediction performance than several other link prediction methods on multilayer networks.

Fear prompts prey to adopt risk-averse behaviors, such as reduced foraging activity, increased vigilance, and avoidance of areas with high predator presence, which affects its reproduction. In a real scenario, a population requires a minimum density to avoid extinction, known as an Allee threshold. In light of these biological factors, we propose a predator-prey model with (i) a fear effect in a prey population, (ii) an Allee effect in a predator population, and (iii) a non-constant attack rate that modifies the functional response. We ensured the non-negativity and boundedness of the solutions and examined the local and global stability status for each existing steady state solutions. We investigated some deep dynamical properties of the system by varying different parameters, such as cost of fear in prey and strength of the Allee effect in predators and their mortality rate. In codimension one bifurcations, we observed saddle node, Hopf, homoclinic, and coalescence of two limit cycles. Additionally, codimension two bifurcations were observed, including Bautin and Bogdanov Takens bifurcations. To provide a clearer understanding of these bifurcations, we conducted biparametric analysis involving the fear and Allee parameters, as well as the fear parameter and predator mortality rate. Our investigation shows that cost of fear and strength of Allee strongly influences the survival status of the predator. Furthermore, bistability and tristability reveal that the survival and extinction of predator are dependent on the initial population level. Numerical simulations and graphical illustrations are provided to support and validate our theoretical findings.

In this paper, we investigate the effects of spin-transfer torques within the ferromagnetic infinite medium through the short-wave approximation method. As a result, we have derived the new (1+1) dimensional nonlinear evolution system, which describes the propagation of electromagnetic short waves within the ferromagnet in the presence of electric current density. Using the Painlevé analysis and Hirota's bilinearization, we unearth the integrability properties of this new evolution system. In the wake of such an analysis, the typical class of excitations and its physical implications are presented. We remark that the current density acts on magnetization like an effective magnetic damping, which is important for the stabilization of magnetic information storage and data process elements.

Quantum dynamics of a particle on a two-dimensional comb structure is considered. This dynamics of a Hamiltonian system with a topologically constrained geometry leads to the non-Markovian behavior. In the framework of a rigorous analytical consideration, it is shown how a fractional time derivative appears for the relevant description of this non-Markovian quantum mechanics in the framework of fractional time Schrödinger equations. Analytical solutions for the Green functions are obtained for both conservative and periodically driven in time Hamiltonian systems.

We develop a three-timescale framework for modeling climate change and introduce a space-heterogeneous one-dimensional energy balance model. This model, addressing temperature fluctuations from rising carbon dioxide levels and the super-greenhouse effect in tropical regions, fits within the setting of stochastic reaction-diffusion equations. Our results show how both mean and variance of temperature increase, without the system going through a bifurcation point. This study aims to advance the conceptual understanding of the extreme weather events frequency increase due to climate change.

The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of "key groups" with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks.

We present an integral density method for calculating the multifractal dimension spectrum for nucleon distribution in atomic nuclei. This method is then applied to analyze the non-uniformity of density distribution in several typical types of nuclear matter distributions, including the Woods-Saxon distribution, halo structure, and tetrahedral α clustering. The subsequent discussion provides a comprehensive and detailed exploration of the results obtained. The multifractal dimension spectrum shows a remarkable sensitivity to the density distribution, establishing it as a simple and novel tool for studying the distribution of nucleons in nuclear multibody systems.