It has been conjectured by Maldacena, Shenker, and Stanford [J. Maldacena, S. H. Shenker, and D. Stanford, J. High Energy Phys. 08 (2016) 106] that the exponential growth rate of the out-of-time-ordered correlator (OTOC) $F\left(t\right)$ has a universal upper bound $2\pi k_{B}T/\hslash$. Here we introduce a one-parameter family of out-of-time-ordered correlators $F_{\gamma }\left(t\right)$ ($0\le \gamma \le 1$), which has as good properties as $F\left(t\right)$ as a regularization of the out-of-time-ordered part of the squared commutator $\langle {[\widehat{A}\left(t\right),\widehat{B}\left(0\right)]}^2\rangle$ that diagnoses quantum many-body chaos, and coincides with $F\left(t\right)$ at $\gamma =1/2$. We rigorously prove that if $F_{\gamma }\left(t\right)$ shows a transient exponential growth for all $\gamma$ in $0\le \gamma \le 1$, that is, if the OTOC shows an exponential growth regardless of the choice of the regularization, then the growth rate $\lambda$ does not depend on the regularization parameter $\gamma$ and satisfies the inequality $\lambda \le 2\pi k_{B}T/\hslash$.

We investigate the dynamics of nonlocally coupled time-discrete maps with emphasis on the occurrence and robustness of chimera states. These peculiar, hybrid states are characterized by a coexistence of coherent and incoherent regions. We consider logistic maps coupled on a one-dimensional ring with finite coupling radius. Domains of chimera existence form different tongues in the parameter space of coupling range and coupling strength. For a sufficiently large coupling strength, each tongue refers to a wave number describing the structure of the spatial profile. We also analyze the period-adding scheme within these tongues and multiplicity of period solutions. Furthermore, we study the robustness of chimeras with respect to parameter inhomogeneities and find that these states persist for different widths of the parameter distribution. Finally, we explore the spatial structure of the chimera using a spatial correlation function.

In the present paper, the possibility of invoking stochastic resonance (SR, periodic and aperiodic) by regulating the operating value of an appropriate parameter is explored. The operating values of these parameters are defined as the set point of the system throughout the present paper. Brusselator, a mathematical model [I. Prigogine and R. Lefever, J. Chem. Phys. 48, 1695 (1968)] of nonlinear chemical reactions, is used for this purpose. We consider the effect of intrinsic noise in the Brusselator due to the Markovian nature of the chemical reactions. The stochastic time evolution is studied using the Gillespie algorithm [D. T. Gillespie, J. Comput. Phys. 22, 403 (1976)], which is an exact stochastic simulation algorithm. We analyze the dependence of the resonance point on both the strength of the intrinsic noise as well as the distance from the bifurcation point. Subsequently, the phenomena of SR is explored using both periodic and aperiodic stimulus. It was found that, for a given system size, in both cases, SR is achieved by variation of the set point. Set-point variation can be achieved by regulating either the source concentration or the rate constants. Resonance is observed in both cases. However, this resonance occurs at different values of the set point, even with a fixed system size. This is clearly seen in the set-point versus system-size plane, where the resonance line has different slopes for the two scenarios. Our semianalytic treatment points to the fact that for a given system size intrinsic noise is affected differently for different methods involving the variation of the set point. This is explained by writing the corresponding chemical Langevin equation and comparing the various intrinsic noise sources.

Classical “kicked Hall systems” (KHSs), i.e., periodically kicked charges in the presence of uniform magnetic and electric fields that are perpendicular to each other and to the kicking direction, have been introduced and studied recently. It was shown that KHSs exhibit, under generic conditions, the phenomenon of “superweak chaos” (SWC), i.e., for small kick strength $\kappa$ a KHS behaves as if this strength were effectively ${\kappa }^2$ rather than $\kappa$. Here we investigate quantum-dynamical and spectral manifestations of this generic SWC. We first derive general expressions for quantum effective Hamiltonians for the KHSs. We then show that the phenomenon of quantum antiresonance (QAR), i.e., “frozen” quantum dynamics with flat quasienergy (QE) bands, takes place for integer values of a scaled Planck constant ${\hslash }_{\mathrm{s}}$ and under the same generic conditions for SWC. This appears to be the most generic occurrence of QAR in quantum systems. The vicinity of QAR is shown to correspond semiclassically to SWC. A global spectral manifestation of SWC is the fact that a scaled QE spectrum as function of ${\hslash }_{\mathrm{s}}$, at fixed small value of $\kappa /{\hslash }_{\mathrm{s}}$, features an approximately “doubled” structure. In the case of standard (cosine) potentials, this structure is that of a universal (parameters-independent) double Hofstadter butterfly. Also, for standard potentials and for small ${\hslash }_{\mathrm{s}}$ (semiclassical regime), the evolution of the kinetic-energy expectation value exhibits a relatively slow quantum-diffusive behavior having universal features. These approximate spectral and quantum-dynamical universalities agree with predictions from the effective Hamiltonian.

Recent advances in data collection have facilitated the access to time-resolved human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While the structural and dynamical information revealed by this type of data is fundamental to investigate how information or diseases propagate in a population, data often suffer from incompleteness, which possibly leads to biased estimations in data-driven models. A major challenge is thus to estimate the outcome of spreading processes occurring on temporal networks built from partial information. To cope with this problem, we devise an approach based on non-negative tensor factorization, a dimensionality reduction technique from multilinear algebra. The key idea is to learn a low-dimensional representation of the temporal network built from partial information and to use it to construct a surrogate network similar to the complete original network. To test our method, we consider several human-proximity networks, on which we perform resampling experiments to simulate a loss of data. Using our approach on the resulting partial networks, we build a surrogate version of the complete network for each. We then compare the outcome of a spreading process on the complete networks (nonaltered by a loss of data) and on the surrogate networks. We observe that the epidemic sizes obtained using the surrogate networks are in good agreement with those measured on the complete networks. Finally, we propose an extension of our framework that can leverage additional data, when available, to improve the surrogate network when the data loss is particularly large.

Forest-fire and avalanche models support the notion that frequent catastrophes prevent the growth of very large populations and as such, prevent rare large-scale catastrophes. We show that this notion is not universal. A new model class leads to a paradigm shift in the influence of catastrophes on the family-size distribution of subpopulations. We study a simple population dynamics model where individuals, as well as a whole family, may die with a constant probability, accompanied by a logistic population growth model. We compute the characteristics of the family-size distribution in steady state and the phase diagram of the steady-state distribution and show that the family and catastrophe size variances increase with the catastrophe frequency, which is the opposite of common intuition. Frequent catastrophes are balanced by a larger net-growth rate in surviving families, leading to the exponential growth of these families. When the catastrophe rate is further increased, a second phase transition to extinction occurs when the rate of new family creations is lower than their destruction rate by catastrophes.

In living cells, chemical reactions form complex networks. Dynamics arising from such networks are the origins of biological functions. We propose a mathematical method to analyze bifurcation behaviors of network systems using their structures alone. Specifically, a whole network is decomposed into subnetworks, and for each of them the bifurcation condition can be studied independently. Further, parameters inducing bifurcations and chemicals exhibiting bifurcations can be determined on the network. We illustrate our theory using hypothetical and real networks.

A switching diffusion process (SDP) is a widely used stochastic model in physics and biology, especially for molecular motors that exhibit a discrete internal chemical kinetics as well as a continuous external mechanical motion. The nonequilibrium thermodynamics of switching diffusion processes has not been extensively studied yet. In the present paper, we propose the decomposition of the entropy production rate in one-dimensional SDPs, based on the flux decomposition. However, similar decompositions of the housekeeping heat dissipation rate and free energy dissipation rate cannot guarantee the non-negativity of each decomposed component. Hence, we modify this decomposition with the flow of exponential relative information under steady-state fluxes, resulting in another decomposition with all non-negative components. Furthermore, we also provide the nonequilibrium thermodynamics of one-dimensional SDPs under the perspectives of coarse -graining and exchange of information between the chemical kinetics and mechanical motion, resulting in several other decompositions of entropy production rate. Finally, we generalize all the results to high-dimensional SDPs with a more general mathematical treatment.

Discriminating between correct and incorrect substrates is a core process in biology, but how is energy apportioned between the conflicting demands of accuracy ($\mu$), speed ($\sigma$), and total entropy production rate ($P$)? Previous studies have focused on biochemical networks with simple structure or relied on simplifying kinetic assumptions. Here, we use the linear framework for timescale separation to analytically examine steady-state probabilities away from thermodynamic equilibrium for networks of arbitrary complexity. We also introduce a method of scaling parameters that is inspired by Hopfield's treatment of kinetic proofreading. Scaling allows asymptotic exploration of high-dimensional parameter spaces. We identify in this way a broad class of complex networks and scalings for which the quantity $\sigma ln\left(\mu \right)/P$ remains asymptotically finite whenever accuracy improves from equilibrium, so that ${\mu }_{\text{eq}}/\mu \to 0$. Scalings exist, however, even for Hopfield's original network, for which $\sigma ln\left(\mu \right)/P$ is asymptotically infinite, illustrating the parametric complexity. Outside the asymptotic regime, numerical calculations suggest that, under more restrictive parametric assumptions, networks satisfy the bound, $\sigma ln(\mu /{\mu }_{\text{eq}})/P<1$, and we discuss the biological implications for discrimination by ribosomes and DNA polymerase. The methods introduced here may be more broadly useful for analyzing complex networks that implement other forms of cellular information processing.

Discontinuous shear thickening in dense suspensions naturally emerges from the activation of frictional forces by shear flow in non-Brownian systems close to jamming. Yet, this physical picture is incomplete as most experiments study soft colloidal particles subject to thermal fluctuations. To characterize discontinuous shear thickening in colloidal suspensions, we use computer simulations to provide a complete description of the competition between athermal jamming, frictional forces, thermal motion, particle softness, and shear flow. We intentionally neglect hydrodynamics, electrostatics, lubrication, and inertia, but can nevertheless achieve quantitative agreement with experimental findings. In particular, shear thickening corresponds to a crossover between frictionless and frictional jamming regimes which is controlled by thermal fluctuations and particle softness and occurs at a softness dependent Péclet number. We also explore the consequences of our findings for constant pressure experiments, and critically discuss the reported emergence of “S-shaped” flow curves. Our work provides the minimal ingredients to quantitatively interpret a large body of experimental work on discontinuous shear thickening in colloidal suspensions.

We study the effects of thermally induced capillary waves in the fragmentation of a liquid ligament into multiple nanodroplets. Our numerical implementation is based on a fluctuating lattice Boltzmann (LB) model for nonideal multicomponent fluids, including nonequilibrium stochastic fluxes mimicking the effects of molecular forces at the nanoscales. We quantitatively analyze the statistical distribution of the breakup times and the droplet volumes after the fragmentation process at changing the two relevant length scales of the problem, i.e., the thermal length scale and the ligament size. The robustness of the observed findings is also corroborated by quantitative comparisons with the predictions of sharp interface hydrodynamics. Beyond the practical importance of our findings for nanofluidic engineering devices, our study also explores a novel application of LB in the realm of nanofluidic phenomena.

Evaporation kinetics of pendant droplets is an area of immense importance in several applications, in addition to possessing rich fluid dynamics and thermal transport physics. This article experimentally and analytically sheds insight into the augmented evaporation dynamics of paramagnetic pendant droplets in the presence of a magnetic field stimulus. The literature provides information that solutal advection and the solutal Marangoni effect lead to enhanced evaporation in droplets with solvated ions. The main focus of this article is to modulate the thermosolutal advection with the aid of an external magnetic field and comprehend the dynamics of the evaporation process under such complex multiphysics interactions. Experimental observations reveal that the evaporation rate enhances as a direct function of the magnetic moment of the solvated magnetic element ions, thereby pointing at the magnetophoretic and magnetosolutal advection. Additionally, flow visualization by particle image velocimetry illustrates that the internal advection currents within the droplet increase in magnitude and are distorted in orientation by the magnetic field. A mathematical formalism based on magnetothermal and magnetosolutal advection has been proposed via scaling analysis of the species and energy conservation equations. The formalism takes into account all major governing factors, viz., the magnetothermal and magnetosolutal Marangoni numbers, magneto-Prandtl and magneto-Schmidt numbers, and the Hartmann number. The modeling establishes the magnetosolutal advection to be the dominant factor behind the augmented evaporation dynamics. Accurate validation of the experimental internal circulation velocity is obtained from the proposed model. This study reveals rich insight into the magnetothermosolutal hydrodynamics in paramagnetic droplets.

The structural properties of the Jagla fluid are studied by Monte Carlo (MC) simulations, numerical solutions of integral equation theories, and the (semi-analytical) rational-function approximation (RFA) method. In the latter case, the results are obtained from the assumption (supported by our MC simulations) that the Jagla potential and a potential with a hard core plus an appropriate piecewise constant function lead to practically the same cavity function. The predictions obtained for the radial distribution function, $g\left(r\right)$, from this approach are compared against MC simulations and integral equations for the Jagla model, and also for the limiting cases of the triangle-well potential and the ramp potential, with a general good agreement. The analytical form of the RFA in Laplace space allows us to describe the asymptotic behavior of $g\left(r\right)$ in a clean way and compare it with MC simulations for representative states with oscillatory or monotonic decay. The RFA predictions for the Fisher-Widom and Widom lines of the Jagla fluid are obtained.

A network-based analysis of a turbulent channel flow numerically solved at ${\text{Re}}_{\tau }=180$ is proposed as an innovative perspective for the spatial characterization of the flow field. Two spatial networks corresponding to the streamwise and wall-normal velocity components are built, where nodes represent portions of volume of the physical domain. For each network, links are active if the correlation coefficient of the corresponding velocity component between pairs of nodes is sufficiently high, thus unveiling the strongest kinematic relations. Several network measures are studied in order to explore the interrelations between nodes and their neighbors. Specifically, long-range links are localized between near-wall regions and associated with the temporal persistence of coherent patterns, namely high and low speed streaks. Furthermore, long-range links play a crucial role as intermediary for the kinematic information flow, as emerges from the analysis of indirect connections between nodes. The proposed approach provides a framework to investigate spatial structures of the turbulent dynamics, showing the full potential of complex networks. Although the network analysis is based on the two-point correlation, it is able to advance the level of information, by exploiting the texture created by active links in all directions. Based on the observed findings, the current approach can pave the way for an enhanced spatial interpretation of the turbulence dynamics.

The subject of the present theoretical study is the dynamics of a cavitation bubble in a spherical liquid-filled cavity surrounded by an infinite elastic solid. Two objectives are pursued. The first is to derive equations for the velocity and pressure fields throughout the liquid filling the cavity and equations for the stress and strain fields throughout the solid medium surrounding the cavity. This derivation is based on the results of our previous paper [A. A. Doinikov et al., Phys. Rev. E 97, 013108 (2018)], where equations for the evolution of a bubble inside a cavity were derived. The second objective is to apply the equations obtained at the first step of the study to ascertain if the cavitation process in one cavity can trigger the nucleation in a neighboring cavity. To this end, we consider a neighboring cavity in which a cavitation bubble is absent. We derive equations that describe the disturbance of the liquid pressure inside the second cavity, assuming this disturbance to be caused by the cavitation process in the first cavity. The developed theory is then used to perform numerical simulations. The results of the simulations show that the magnitude of the background negative pressure inside the second cavity increases at the second half period of the pressure disturbance, which in turn enhances the probability of nucleation in the second cavity.

We provide a theoretical and experimental protocol that dynamically controls the effective temperature of a thermal bath, through a well-designed noise engineering. We use this powerful technique to shortcut the relaxation of an overdamped Brownian particle in a quadratic potential by a joint time engineering of the confinement strength and of the noise. For an optically trapped colloid, we report an equilibrium recovery time reduced by about two orders of magnitude compared to the natural relaxation time. Our scheme paves the way towards reservoir engineering in nanosystems.

The multivariate Ornstein-Uhlenbeck process is used in many branches of science and engineering to describe the regression of a system to its stationary mean. Here we present an $O\left(N\right)$ Bayesian method to estimate the drift and diffusion matrices of the process from $N$ discrete observations of a sample path. We use exact likelihoods, expressed in terms of four sufficient statistic matrices, to derive explicit maximum a posteriori parameter estimates and their standard errors. We apply the method to the Brownian harmonic oscillator, a bivariate Ornstein-Uhlenbeck process, to jointly estimate its mass, damping, and stiffness and to provide Bayesian estimates of the correlation functions and power spectral densities. We present a Bayesian model comparison procedure, embodying Ockham's razor, to guide a data-driven choice between the Kramers and Smoluchowski limits of the oscillator. These provide novel methods of analyzing the inertial motion of colloidal particles in optical traps.

Understanding under which conditions the increase of systems complexity is evolutionarily advantageous, and how this trend is related to the modulation of the intrinsic noise, are fascinating issues of utmost importance for synthetic and systems biology. To get insights into these matters, we analyzed a series of chemical reaction networks with different topologies and complexity, described by mass-action kinetics. We showed, analytically and numerically, that the global level of fluctuations at the steady state, measured by the sum over all species of the Fano factors of the number of molecules, is directly related to the network's deficiency. For zero-deficiency systems, this sum is constant and equal to the rank of the network. For higher deficiencies, additional terms appear in the Fano factor sum, which are proportional to the net reaction fluxes between the molecular complexes. We showed that the system's global intrinsic noise is reduced when all fluxes flow from lower to higher degree oligomers, or equivalently, towards the species of higher complexity, whereas it is amplified when the fluxes are directed towards lower complexity species.

In network dismantling, a minimal set of nodes is identified whose removal breaks the network into small components of subextensive size. Because finding the optimal set of nodes is an NP-hard problem, several heuristic algorithms have been developed as alternative methods, for instance, the so-called belief propagation-based decimation (BPD) algorithm and the collective influence (CI) algorithm. Here, we test the performance of these algorithms and analyze them in terms of the fractality of the network. Networks are classified into two types: fractal and nonfractal networks. Real-world examples include the World Wide Web and the Internet at the autonomous system level, respectively. They have different ratios of long-range shortcuts to short-range ones. We find that the BPD algorithm works more efficiently than the CI algorithm no matter whether a network is fractal or not. On the other hand, the CI algorithm works better on nonfractal networks than on fractal networks. We construct diverse fractal and nonfractal model networks by controlling parameters such as the degree exponent, shortcut number, and system size and investigate how the performance of the two algorithms depends on structural features.