The European Physical Journal B最新文献

This paper investigates the presence and stability of nonlinear localized modes within the Gross–Pitaevskii Equation (GPE), considering interactions involving cubic–quintic nonlinearities and varying spin-orbit momentum (SOM). It also explores two distinct types of complex parity-time ((mathcal{P}mathcal{T}))-symmetric potentials, specifically Gaussian harmonic and periodic potentials. The influence of the SOM coefficient on regions of unbroken and broken phases is examined, revealing its modulation effect on the nonlinear stability and power distribution of these modes. Additionally, the interaction dynamics of two spatial solitons are analyzed within the context of the (mathcal{P}mathcal{T})-symmetric Gaussian potential. Notably, it is found that solitons remain stable even when the (mathcal{P}mathcal{T})-symmetry of the underlying nonlinear model is disrupted. The accuracy of the findings is confirmed through comparisons with numerical simulations and exact analytical expressions of the localized modes in one dimension (1D). The numerical simulations also indicate that obtaining the stable solitons of the cubic–quintic GPE with a varying SOM term is most challenging when the considered (mathcal{P}mathcal{T})-symmetric potential is periodic.

We investigate measures of non-Markovianity in open quantum systems governed by Gaussian free fermionic dynamics. Standard indicators of non-Markovian behavior, such as the BLP and LFS measures, are revisited in this context. We show that for Gaussian states, trace-based distances—specifically the Hilbert–Schmidt norm—and second-order Rényi mutual information can be efficiently expressed in terms of two-point correlation functions, enabling practical computation even in systems where the full-density matrix is intractable. Crucially, this framework remains valid even when the density matrix of the system is an average over stochastic Gaussian trajectories, yielding a non-Gaussian state. We present efficient numerical protocols based on this structure and demonstrate their feasibility through a small-scale simulation. Our approach opens a scalable path to quantifying non-Markovianity in interacting or measured fermionic systems, with applications in quantum information and non-equilibrium quantum dynamics.

The Ising model, originally developed for understanding magnetic phase transitions, has become a cornerstone in the study of collective phenomena across diverse disciplines. In this review, we explore how Ising and Ising-like models have been successfully adapted to sociophysical systems, where binary-state agents mimic human decisions or opinions. By focusing on key areas such as opinion dynamics, financial markets, social segregation, game theory, language evolution, and epidemic spreading, we demonstrate how the models describing these phenomena, inspired by the Ising model, capture essential features of collective behavior, including phase transitions, consensus formation, criticality, and metastability. In particular, we emphasize the role of the dynamical rules of evolution in the different models that often converge back to Ising-like universality. We end by outlining the future directions in sociophysics research, highlighting the continued relevance of the Ising model in the analysis of complex social systems.

This study presents a modified prey–predator model incorporating the Allee effect and Holling-type IV functional response to explore complex dynamics arising from anti-predator behavior, cooperative defense, prey refuge mechanisms, and additional food for the predator. The model is analyzed to explore stability, bifurcation behavior, and population thresholds for the species coexistence. By introducing factors, such as prey refuge and alternative food sources for predator, our study highlights the intricate feedback mechanisms stabilizing population cycles. The simulation results highlight that enhancing prey refuge and incorporating Allee effects can promote prey growth while limiting predator abundance. Additionally, we observe that when the prey growth rate is low, only the predator species can survive. Overall, our findings extend classical results, offering a robust framework for the long-term ecological balance.

The polarization field is the mean dipolar moment of atoms plunged in an external electric field. It characterizes the optical properties of the considered material; light interaction with matter and propagation inside it. This research investigates the dynamics of the polarization field within a ferroelectric substance subjected to a periodically changing electric field. Our analysis deals with the derivation of an equation to describe this behavior. We specifically explore indicators of chaotic behavior, such as the Lyapunov exponent and bifurcation diagram, to demonstrate the emergence of chaos when the electric field’s amplitude varies periodically. Additionally, we identify the parameters responsible for inducing chaos, along with their respective ranges, and examine the phenomenon of multistability within the system. Finally, the linear augmentation method is used to control the multistability in the system under study.

In this work, we investigate a generalized two dimensional Hindmarsh−Rose neuronal model which single out the distinct roles of the Hamiltonian and its time-derivative (instantaneous power) in shaping the neuronal activity of neurons coupled indirectly by an extrinsic electric field adjustable by an experimenter. By imposing a periodic extrinsic field, we demonstrate how neurons are driven into complete synchronization by the extrinsic electric field and we identify critical thresholds values of the amplitude and the frequency of the extrinsic electric field below which neuronal activity with amplitude death occurs. Strikingly, exceeding these thresholds values triggers a rebound of oscillatory activity. Analytical calculations using the Helmholtz’s theorem yield closed−form expressions for the Hamiltonian along with its instantaneous power reveal that the contributions of the extrinsic electric field add up in each neuron. Intensive numerical simulations confirm the existence of sharp amplitude and frequency boundaries separating regimes of neuronal silencing and revival of neuronal dynamics. Moreover, variations in neuronal radius are shown to modulate excitability through capacitance effects, with larger cells exhibiting suppressed oscillations. Our results highlight crucial mechanisms by which modulated extrinsic electric fields regulate neuronal behavior offering potential control strategies for neuromodulation applications.