We present an analysis of chaos and regularity in the open Dicke model, when dissipation is due to cavity losses. Due to the infinite Liouville space of this model, we also introduce a criterion to numerically find a complex spectrum which approximately represents the system spectrum. The isolated Dicke model has a well-defined classical limit with two degrees of freedom. We select two case studies where the classical isolated system shows regularity and where chaos appears. To characterize the open system as regular or chaotic, we study regions of the complex spectrum taking windows over the absolute value of its eigenvalues. Our results for this infinite-dimensional system agree with the Grobe-Haake-Sommers (GHS) conjecture for Markovian dissipative open quantum systems, finding the expected 2D Poisson distribution for regular regimes, and the distribution of the Ginibre unitary ensemble (GinUE) for the chaotic ones, respectively.

The current trend in economics research is to incorporate quantum mechanical concepts to increase the security of business models. This interdisciplinary field of study represents real-world market dynamics more closely than do its classical counterparts. In this paper, we shed light on the significance of the two-qubit entangling operators in controlling chaos. We introduce a modified Eisert-Wilkens-Lewenstein scheme in a nonlinear Cournot duopoly game with complete and incomplete information. By doing so, the following interesting results are obtained: To begin, monopoly in a duopoly game can be avoided with the use of special perfect entanglers. Also, the stability analysis shows that there exists a class of entangling operators which can stabilize an unstable system and vice versa. Second, numerical analysis highlights the two-qubit entangling operators which can stabilize a chaotic system or at least delay chaos. Finally, we show that with an appropriate choice of initial state and speed of adjustments, entangling operators can decrease the sensitivity of the system. In short, while we know the importance of entangling operators in quantum game theory, in this paper we indicate the significance of operators in the context of a chaotic system.

One of the defining characteristics of soft glassy materials is their ability to exhibit a yield stress, which can result in an overall elasto-visco-plastic mechanics. To design soft materials with specific properties, it is essential to gain a comprehensive understanding of the topological and structural failure points that occur during yielding. However, predicting these failure points, which lead to yielding, is challenging due to the dynamic nature of structure development and its cooccurrence with other complicated processes, such as local rearrangements and anisotropy. In this study, we employ a series of tools from network science to investigate colloidal gels as a model for soft glassy materials during yielding. Our findings reveal that edge betweenness centrality can be utilized as a universal predictor for yielding across various state variables, including the volume fraction of solids, the strength, and the range of attraction between colloids.

Significant changes in the acoustic response of a fluid can be induced by the suspension of tiny, subwavelength-size discrete micro-oscillators in the fluid. We investigate how the topological properties of these oscillators, such as the mass distribution and connectivity of the oscillator parts, influence the effective dynamic density and compressibility of the fluid in which they are embedded. We demonstrate a superior, metamaterial-like response of the suspension when using micro-oscillators with a high density of low-frequency modes. Such low-frequency modes occur in loosely connected microstructures and make the system much more experimentally feasible due to the larger ultrasonic attenuation length at these frequencies. In addition, the absence of need for an intricately designed structure brings experimental implementation within reach.

As we walk towards our destinations, our trajectories are constantly influenced by the presence of obstacles and infrastructural elements; even in the absence of crowding our paths are often curved. Since the early 2000s pedestrian dynamics have been extensively studied, aiming at quantitative models with both fundamental and technological relevance. Walking kinematics along straight paths have been experimentally investigated and quantitatively modeled in the diluted limit (i.e., in absence of pedestrian-pedestrian interactions). It is natural to expect that models for straight paths may be an accurate approximations of the dynamics even for paths with curvature radii much larger than the size of a single person. Conversely, as paths curvature increase one may expect larger and larger deviations. As no clear experimental consensus has been reached yet in the literature, here we accurately and systematically investigate the effect of paths curvature on diluted pedestrian dynamics. Thanks to a extensive and highly accurate set of real-life measurements campaign, we derive a Langevin-like social-force model quantitatively compatible with both averages and fluctuations of the walking dynamics. Leveraging on the differential geometric notion of covariant derivative, we generalize previous work by some of the authors, effectively casting a Langevin social-force model for the straight walking dynamics in a curved geometric setting. We deem this the necessary first step to understand and model the more general and ubiquitous case of pedestrians following curved paths in the presence of crowd traffic.

Magnetic fields influence ion transport in plasmas. Straightforward comparisons of experimental measurements with plasma theories are complicated when the plasma is inhomogeneous, far from equilibrium, or characterized by strong gradients. To better understand ion transport in a partially magnetized system, we study the hydrodynamic velocity and temperature evolution in an ultracold neutral plasma at intermediate values of the magnetic field. We observe a transverse, radial breathing mode that does not couple to the longitudinal velocity. The inhomogeneous density distribution gives rise to a shear velocity gradient that appears to be only weakly damped. This mode is excited by ion oscillations originating in the wings of the distribution where the plasma becomes non-neutral. The ion temperature shows evidence of an enhanced electron-ion collision rate in the presence of the magnetic field. Ultracold neutral plasmas provide a rich system for studying mode excitation and decay.

We investigate chaotic impurity transport in toroidal fusion plasmas (tokamaks) from the point of view of passive advection of charged particles due to $\mathbf{E}\times \mathbf{B}$ drift motion. We use realistic tokamak profiles for electric and magnetic fields as well as toroidal rotation effects, and consider also the effects of electrostatic fluctuations due to drift instabilities on particle motion. A time-dependent one degree-of-freedom Hamiltonian system is obtained and numerically investigated through a symplectic map in a Poincaré surface of section. We show that the chaotic transport in the outer plasma region is influenced by fractal structures that are described in topological and metric point of views. Moreover, the existence of a hierarchical structure of islands-around-islands, where the particles experience the stickiness effect, is demonstrated using a recurrence-based approach.

A random search of a partially absorbing target by a run-and-tumble particle in a confined one-dimensional space is investigated. We analytically obtain the mean searching time, which shows a nonmonotonic behavior as a function of the self-propulsion speed of the active particle, indicating the existence of an optimal speed, when the absorption strength of the target is finite. In the limit of large and small absorption strengths, respectively, asymptotes of the mean searching time and the optimal speed are found. We also demonstrate that the first-passage problem of a diffusive run-and-tumble particle in high dimensions can be mapped into a one-dimensional problem with a partially absorbing target. Finally, as a practical application exploiting the existence of the optimal speed, we propose a filtering device to extract active particles with a desired speed and evaluate how the resolution of the filtering device depends on the absorption strength.

Integrable turbulence, as an irregular behavior in dynamic systems, has attracted a lot of attention in integrable and Hamiltonian systems. This article focuses on the studies of integrable turbulence phenomena of the Kundu-Eckhaus (KE) equation as well as the generation of rogue waves from the numerical and statistical viewpoints. First, via the Fourier collocation method, we obtain the spectral portraits of different analytical solutions. Second, we perform the numerical simulation on the KE equation under the initial condition of a plane wave with random noise to simulate the chaotic wave fields. Then, we analyze the influences of standard deviation and correlation length on the integrable turbulence and amplitude of wave field. It's found that both of the two parameters have positive effects on the generation probability of rogue wave caused by the interactions. But only the variation of standard deviation can lead to the transition from the breather turbulence to soliton turbulence. Furthermore, by analyzing the effects of additional higher-order nonlinear terms on the chaotic wave field, we find that those two higher-order nonlinear effects in the KE equation can lead to a larger amplitude of the chaotic wave field and a higher probability of generating rouge waves compared with the NLS equation.

We investigate the effects of delayed interactions in a population of “swarmalators,” generalizations of phase oscillators that both synchronize in time and swarm through space. We discover two steady collective states: a state in which swarmalators are essentially motionless in a disk arranged in a pseudocrystalline order, and a boiling state in which the swarmalators again form a disk, but now the swarmalators near the boundary perform boiling-like convective motions. These states are reminiscent of the beating clusters seen in photoactivated colloids and the living crystals of starfish embryos.

In this article we focus on the study of the collective dynamics of neural networks. The analysis of two recent models of coupled “next-generation” neural mass models allows us to observe different global mean dynamics of large neural populations. These models describe the mean dynamics of all-to-all coupled networks of quadratic integrate-and-fire spiking neurons. In addition, one of these models considers the influence of the synaptic adaptation mechanism on the macroscopic dynamics. We show how both models are related through a parameter and we study the evolution of the dynamics when switching from one model to the other by varying that parameter. Interestingly, we have detected three main dynamical regimes in the coupled models: Rössler-type (funnel type), bursting-type, and spiking-like (oscillator-type) dynamics. This result opens the question of which regime is the most suitable for realistic simulations of large neural networks and shows the possibility of the emergence of chaotic collective dynamics when synaptic adaptation is very weak.

The $q$-voter model with independence is generalized to signed random graphs and studied by means of Monte Carlo simulations and theoretically using the mean-field approximation and different forms of the pair approximation. In the signed network with quenched disorder, positive and negative signs associated randomly with the links correspond to reinforcing and antagonistic interactions, promoting, respectively, the same or opposite orientations of two-state spins representing agents' opinions; otherwise, the opinions are called mismatched. With probability $1-p$, the agents change their opinions if the opinions of all members of a randomly selected $q$ neighborhood are mismatched, and with probability $p$, they choose an opinion randomly. The model on networks with finite mean degree $\langle k\rangle$ and fixed fraction of the antagonistic interactions $r$ exhibits ferromagnetic transition with varying the independence parameter $p$, which can be first or second order, depending on $q$ and $r$, and disappears for large $r$. Besides, numerical evidence is provided for the occurrence of the spin-glass-like transition for large $r$. The order and critical lines for the ferromagnetic transition on the $p$ vs $r$ phase diagram obtained in Monte Carlo simulations are reproduced qualitatively by the mean-field approximation. Within the range of applicability of the pair approximation, for the model with $\langle k\rangle$ finite but $\langle k\rangle \gg q$, predictions of the homogeneous pair approximation concerning the ferromagnetic transition show much better quantitative agreement with numerical results for small $r$ but fail for larger $r$. A more advanced signed homogeneous pair approximation is formulated which distinguishes between classes of active links with a given sign connecting nodes occupied by agents with mismatched opinions; for the model with $\langle k\rangle \gg q$ its predictions agree quantitatively with numerical results in a whole range of $r$ where the ferromagnetic transition occurs.

A basic problem in the science of realistic granular matter is the plethora of heuristic models of the stress field in the absence of a first-principles theory. Such a theory is formulated here, based on the idea that static granular assemblies can be regarded as two-phase composites. A thought experiment is described, demonstrating that the state of such materials can be varied continuously from marginal stability, via a two-phase granular assembly, then porous structure, and finally be made perfectly elastic. For completeness, I review briefly the condition for marginal stability in infinitely large assemblies. The general solution for the stress equations in $d=2$ is reviewed in detail and shown to be consistent with the two-phase idea. A method for identifying the phases of finite regions in larger systems is constructed, providing a stability parameter that quantifies the “proximity” to the marginally stable state. The difficulty involved in deriving stress fields in such composites is a unique constraint on the boundary between phases, and, to highlight it, a simple case of a stack of plates of alternating phase is solved explicitly. An effective medium approximation, which satisfies this constraint, is then developed and analyzed in detail. This approach forms a basis for the extension of the stress theory to general granular solids that are not marginally stable or at the yield threshold.

Bounding and estimating entropy production has long been an important goal of nonequilibrium thermodynamics. We recently derived a lower bound on the total and subsystem entropy production rates of continuous stochastic systems. This “Jensen bound” has led to fundamental limits on the performance of collective transport systems and permitted thermodynamic inference of free-energy transduction between components of bipartite molecular machines. Our original derivation relied on a number of assumptions, which restricted the bound's regime of applicability. Here we derive the Jensen bound far more generally for multipartite overdamped Langevin dynamics. We then consider several extensions, allowing for position-dependent diffusion coefficients, underdamped dynamics, and non-multipartite overdamped dynamics. Our results extend the Jensen bound to a far broader class of systems.

We present measurements of the vertical stress profile $\sigma$ on the base of flat-bottomed cylindrical silos discharged through an orifice centered on its base. An overweight forces the material on top of the free surface. The mean bottom pressure $\sigma (z,D,W)$, with $z$ the height of the granular column, $D$ the silo diameter, and $W$ the mass of the overweight, increases significantly at the end of the discharge. Inspired by early models of stress distribution, we show that $\sigma$ measured at $z=0$ can be rescaled to yield a collapse of the data, as a function of $z/D$, for all $D$ and $W$ explored. We also show that the profile $\sigma \left(r\right)$ is self-similar as a function of the radial coordinate $r$ and can be rescaled to collapse the data for different $z,D$, and $W$. Although the model correctly predicts the functional dependences, it fails in quantitative terms. These results challenge our understanding of free and forced granular flows through orifices.

Active gels play an important role in biology and in inspiring biomimetic active materials, due to their ability to change shape, size, and create their own morphology. We study a particular class of active gels, generated by polymerizing actin in the presence of cross-linkers and clusters of myosin as molecular motors, which exhibit large contractions. The relevant mechanics for these highly swollen gels is the result of the interplay between activity and liquid flow: gel activity yields a structural reorganization of the gel network and produces a flow of liquid that eventually exits from the gel boundary. This dynamics inherits lengthscales that are typical of the liquid flow processes. The analyses we present provide insights into the contraction dynamics, and they focus on the effects of the geometry on both gel velocity and fluid flow.

This study addresses the inadequacy of isothermal wall conditions in predicting accurate flow features and thermal effects in multicomponent systems. A finite-difference characteristic-based off-lattice Boltzmann method (OLBM) with a source term-based conjugate heat transfer (CHT) model is utilized to analyze buoyancy-driven flows in two-dimensional enclosures. The source term-based CHT model [Karani and Huber, Phys. Rev. E 91, 023304 (2015)] is extended to handle curved conjugate boundaries. The proposed CHT-OLBM solver is verified using analytical solutions and reference data from the literature. The effects of wall conduction on conjugate natural convection (CNC) problems in square and horizontal annular cavities are systematically examined with a solid wall of a nondimensional thickness of 0.2. For the square cavity problem, the governing parameters considered are ${10}^5\le \text{Gr}\le {10}^9$ and $\chi =1,5,10$, while for the horizontal annulus problem, the governing parameters are taken as ${10}^5\le \text{Ra}\le {10}^7$ and $\chi =1,5,10,100$, where Gr is the Grashof number, Ra is the Rayleigh number, and $\chi$ is the thermal conductivity ratio. Qualitative analysis of the simulation results using isotherms and streamlines and quantitative analysis of the local fluid-solid interface temperature, solid wall temperature distribution, Nusselt number profiles, overall Nusselt number, and effective Grashof/Rayleigh number is conducted. The overall heat transfer reduction inside the cavities due to a solid wall is quantified, and correlations are obtained to represent the overall heat transfer inside both enclosures. The findings demonstrate the significance of CHT analysis, and the CHT-OLBM solver developed in this study can successfully investigate steady, unsteady, and chaotic CNC flows.

Even though strongly correlated systems are abundant, only a few exceptional cases admit analytical solutions. In this paper we present a large class of solvable systems with strong correlations. We consider a set of $N$ independent and identically distributed random variables $\{X_1,X_2,...,X_N\}$ whose common distribution has a parameter $Y$ (or a set of parameters) which itself is random with its own distribution. For a fixed value of this parameter $Y$, the $X_i$ variables are independent and we call them conditionally independent and identically distributed. However, once integrated over the distribution of the parameter $Y$, the $X_i$ variables get strongly correlated yet retain a solvable structure for various observables, such as for the sum and the extremes of $X_i^{\prime }\mathrm{s}$. This provides a simple procedure to generate a class of solvable strongly correlated systems. We illustrate how this procedure works via three physical examples where $N$ particles on a line perform independent (i) Brownian motions, (ii) ballistic motions with random initial velocities, and (iii) Lévy flights, but they get strongly correlated via simultaneous resetting to the origin. Our results are verified in numerical simulations. This procedure can be used to generate an endless variety of solvable strongly correlated systems.

We examine the role of diagnostic quantum measurements on the work statistics of a finite-time quantum Otto heat engine operated in the steady state. We consider three pointer-based measurement schemes that differ in the number of system-pointer interactions and pointer measurements. We show that the coherence of the working substance and the work output of the engine can be controlled by tuning the monitoring measurements. Moreover, for a working substance consisting of a two-level system we show that while all three schemes reproduce the predictions of the cycle without any monitoring for the average work in the limit of infinitely weak measurement, only two of the schemes can reproduce the two-point projective measurement results in the limit of strong measurement.