A large class of engineered and natural systems, ranging from transportation networks to neuronal networks, are best represented by multiplex network architectures, namely a network composed of two or more different layers where the mutual interaction in each layer may differ from other layers. Here we consider a multiplex network where the intralayer coupling interactions are switched stochastically with a characteristic frequency. We explore the intralayer and interlayer synchronization of such a time-varying multiplex network. We find that the analytically derived necessary condition for intralayer and interlayer synchronization, obtained by the master stability function approach, is in excellent agreement with our numerical results. Interestingly, we clearly find that the higher frequency of switching links in the layers enhances both intralayer and interlayer synchrony, yielding larger windows of synchronization. Further, we quantify the resilience of synchronous states against random perturbations, using a global stability measure based on the concept of basin stability, and this reveals that intralayer coupling strength is most crucial for determining both intralayer and interlayer synchrony. Lastly, we investigate the robustness of interlayer synchronization against a progressive demultiplexing of the multiplex structure, and we find that for rapid switching of intralayer links, the interlayer synchronization persists even when a large number of interlayer nodes are disconnected.

We consider critical behavior in the ensemble of polychromatic Erdős-Rényi networks and regular random graphs, where network vertices are painted in different colors. The links can be randomly removed and added to the network subject to the condition of the vertex degree conservation. In these constrained graphs we run the Metropolis procedure, which favors the connected unicolor triads of nodes. Changing the chemical potential, $\mu$, of such triads, for some wide region of $\mu$, we find the formation of a finite plateau in the number of intercolor links, which exactly matches the finite plateau in the network algebraic connectivity (the value of the first nonvanishing eigenvalue of the Laplacian matrix, ${\lambda }_2$). We claim that at the plateau the spontaneously broken $Z_2$ symmetry is restored by the mechanism of modes collectivization in clusters of different colors. The phenomena of a finite plateau formation holds also for polychromatic networks with $M\ge 2$ colors. The behavior of polychromatic networks is analyzed via the spectral properties of their adjacency and Laplacian matrices.

Cooperators benefit others with paying costs. Evolution of cooperation crucially depends on the cost-benefit ratio of cooperation, denoted as $c$. In this work, we investigate the infinitely repeated prisoner's dilemma for various values of $c$ with four of the representative memory-one strategies, i.e., unconditional cooperation, unconditional defection, tit-for-tat, and win-stay-lose-shift. We consider replicator dynamics which deterministically describes how the fraction of each strategy evolves over time in an infinite-sized well-mixed population in the presence of implementation error and mutation among the four strategies. Our finding is that this three-dimensional continuous-time dynamics exhibits chaos through a bifurcation sequence similar to that of a logistic map as $c$ varies. If mutation occurs with rate $\mu \ll 1$, the position of the bifurcation sequence on the $c$ axis is numerically found to scale as ${\mu }^{0.1}$, and such sensitivity to $\mu$ suggests that mutation may have nonperturbative effects on evolutionary paths. It demonstrates how the microscopic randomness of the mutation process can be amplified to macroscopic unpredictability by evolutionary dynamics.

We investigate large-scale effects induced by external fields, phenomenologically interpreted as mass media, in multiagent models evolving with the microscopic dynamics of the binary naming game. In particular, we show that a single external field, broadcasting information at regular time intervals, can reverse the majority opinion of the population, provided the frequency and the effectiveness of the sent messages lie above well-defined thresholds. We study the phase structure of the model in the mean field approximation and in numerical simulations with several network topologies. We also investigate the influence on the agent dynamics of two competing external fields, periodically broadcasting different messages. In finite regions of the parameter space we observe periodic equilibrium states in which the average opinion densities are reversed with respect to naive expectations. Such equilibria occur in two cases: (i) when the frequencies of the competing messages are different but close to each other; (ii) when the frequencies are equal and the relative time shift of the messages does not exceed half a period. We interpret the observed phenomena as a result of the interplay between the external fields and the internal dynamics of the agents and conclude that, depending on the model parameters, the naming game is consistent with scenarios of first- or second-mover advantage (to borrow an expression from the jargon of business strategy).

Proteins implement their functionalities when folded into specific three-dimensional structures, and their functions are related to the protein structures and dynamics. Previously, we applied a relaxation mode analysis (RMA) method to protein systems; this method approximately estimates the slow relaxation modes and times via simulation and enables investigation of the dynamic properties underlying the protein structural fluctuations. Recently, two-step RMA with multiple evolution times has been proposed and applied to a slightly complex homopolymer system, i.e., a single $\left[n\right]$polycatenane. This method can be applied to more complex heteropolymer systems, i.e., protein systems, to estimate the relaxation modes and times more accurately. In two-step RMA, we first perform RMA and obtain rough estimates of the relaxation modes and times. Then, we apply RMA with multiple evolution times to a small number of the slowest relaxation modes obtained in the previous calculation. Herein, we apply this method to the results of principal component analysis (PCA). First, PCA is applied to a 2-$\mu \mathrm{s}$ molecular dynamics simulation of hen egg-white lysozyme in aqueous solution. Then, the two-step RMA method with multiple evolution times is applied to the obtained principal components. The slow relaxation modes and corresponding relaxation times for the principal components are much improved by the second RMA.

The orientational order of the molecules in the bent mesogen CB6OCB has been studied throughout the range of temperature stability of both the $N_{\mathrm{U}}$ and $N_{\mathrm{TB}}$ liquid-crystal phases by ${}^{13}\mathrm{C}$ NMR spectroscopy. These spectra provide local order parameters for the para axes of both of the nonequivalent cyanobiphenyl groups and show how they change on entering the twist-bend nematic phase. A key feature of the order parameters is a weak, but clear maximum in the temperature variation of the order parameter prior to the $N_{\mathrm{TB}}$ phase. This suggests that the directors in both the $N_{\mathrm{U}}$ and $N_{\mathrm{TB}}$ phases are tilted with respect to the magnetic field of the spectrometer. Significantly the conformational states of the spacer are comparable in both phases, although the low temperature nematic is chiral but not that at high temperature. It is proposed that the higher temperature, tilted phase could be the splay-bend nematic phase.

The integrating factor and exponential time differencing methods are implemented and tested for solving the time-dependent Kohn-Sham equations. Popular time propagation methods used in physics, as well as other robust numerical approaches, are compared to these exponential integrator methods in order to judge the relative merit of the computational schemes. We determine an improvement in accuracy of multiple orders of magnitude when describing dynamics driven primarily by a nonlinear potential. For cases of dynamics driven by a time-dependent external potential, the accuracy of the exponential integrator methods are less enhanced but still match or outperform the best of the conventional methods tested.

We investigate the distribution of relative velocities between small heavy particles of different sizes in turbulence by analyzing a statistical model for bidisperse turbulent suspensions, containing particles with two different Stokes numbers. This number, $\text{St}$, is a measure of particle inertia which in turn depends on particle size. When the Stokes numbers are similar, the distribution exhibits power-law tails, just as in the case of equal $\text{St}$. The power-law exponent is a nonanalytic function of the mean Stokes number $\overline{\mathrm{St}}$, so that the exponent cannot be calculated in perturbation theory around the advective limit. When the Stokes-number difference is larger, the power law disappears, but the tails of the distribution still dominate the relative-velocity moments, if $\overline{\mathrm{St}}$ is large enough.

In disordered elastic systems, driven by displacing a parabolic confining potential adiabatically slowly, all advance of the system is in bursts, termed avalanches. Avalanches have a finite extension in time, which is much smaller than the waiting time between them. Avalanches also have a finite extension $\ell$ in space, i.e., only a part of the interface of size $\ell$ moves during an avalanche. Here we study their spatial shape ${\langle S\left(x\right)\rangle }_{\ell }$ given $\ell$, as well as its fluctuations encoded in the second cumulant ${\langle S^2\left(x\right)\rangle }_{\ell }^{\mathrm{c}}$. We establish scaling relations governing the behavior close to the boundary. We then give analytic results for the Brownian force model, in which the microscopic disorder for each degree of freedom is a random walk. Finally, we confirm these results with numerical simulations. To do this properly we elucidate the influence of discretization effects, which also confirms the assumptions entering into the scaling ansatz. This allows us to reach the scaling limit already for avalanches of moderate size. We find excellent agreement for the universal shape and its fluctuations, including all amplitudes.

We discuss the possibility of extracting energy from a single thermal bath using microcanonical Szilárd engines operating in finite time. This extends previous works on the topic which are restricted to the quasistatic regime. The feedback protocol is implemented based on linear response predictions of the excess work. It is claimed that the underlying mechanism leading to energy extraction does not violate Liouville's theorem and preserves ergodicity throughout the cycle. We illustrate our results with several examples including an exactly solvable model.

Using the framework of infinite matrix product states, the existence of an anomalous dynamical phase for the transverse-field Ising chain with sufficiently long-range interactions was first reported in J. C. Halimeh and V. Zauner-Stauber [Phys. Rev. B 96, 134427 (2017)], where it was shown that anomalous cusps arise in the Loschmidt-echo return rate for sufficiently small quenches within the ferromagnetic phase. In this work we further probe the nature of the anomalous phase through calculating the corresponding Fisher-zero lines in the complex time plane. We find that these Fisher-zero lines exhibit a qualitative difference in their behavior, where, unlike in the case of the regular phase, some of them terminate before intersecting the imaginary axis, indicating the existence of smooth peaks in the return rate preceding the cusps. Additionally, we discuss in detail the infinite matrix product state time-evolution method used to calculate Fisher zeros and the Loschmidt-echo return rate using the matrix product state transfer matrix. Our work sheds further light on the nature of the anomalous phase in the long-range transverse-field Ising chain, while the numerical treatment presented can be applied to more general quantum spin chains.

We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdős-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.

We present a mode-coupling theory (MCT) for the slow dynamics of two-dimensional spherical active Brownian particles (ABPs). The ABPs are characterized by a self-propulsion velocity $v_0$ and by their translational and rotational diffusion coefficients $D_t$ and $D_r$, respectively. Based on the integration-through-transients formalism, the theory requires as input only the equilibrium static structure factors of the passive system (where $v_0=0$). It predicts a nontrivial idealized-glass-transition diagram in the three-dimensional parameter space of density, self-propulsion velocity, and rotational diffusivity that arise because at high densities, the persistence length of active swimming ${\ell }_p=v_0/D_r$ interferes with the interaction length ${\ell }_c$ set by the caging of particles. While the low-density dynamics of ABPs is characterized by a single Péclet number $\mathit{\text{Pe}}=v_0^2/D_r{D}_t$, close to the glass transition the dynamics is found to depend on $\mathit{\text{Pe}}$ and ${\ell }_p$ separately. At fixed density, increasing the self-propulsion velocity causes structural relaxation to speed up, while decreasing the persistence length slows down the relaxation. The active-MCT glass is a nonergodic state that is qualitatively different from the passive glass. In it, correlations of initial density fluctuations never fully decay, but also an infinite memory of initial orientational fluctuations is retained in the positions.

Data from a $1152\times 760\times 1280$ direct numerical simulation [N. J. Mueschke and O. Schilling, Phys. Fluids 21, 014106 (2009)] of a Rayleigh-Taylor mixing layer modeled after a small-Atwood-number water-channel experiment is used to investigate the validity of gradient diffusion and similarity closures a priori. The budgets of the mean flow, turbulent kinetic energy, turbulent kinetic energy dissipation rate, heavy-fluid mass fraction variance, and heavy-fluid mass fraction variance dissipation rate transport equations across the mixing layer were previously analyzed [O. Schilling and N. J. Mueschke, Phys. Fluids 22, 105102 (2010)] at different evolution times to identify the most important transport and mixing mechanisms. Here a methodology is introduced to systematically estimate model coefficients as a function of time in the closures of the dynamically significant terms in the transport equations by minimizing the $L_2$ norm of the difference between the model and correlations constructed using the simulation data. It is shown that gradient-diffusion and similarity closures used for the turbulent kinetic energy $K$, turbulent kinetic energy dissipation rate $\epsilon$, heavy-fluid mass fraction variance $S$, and heavy-fluid mass fraction variance dissipation rate $\chi$ equations capture the shape of the exact, unclosed profiles well over the nonlinear and turbulent evolution regimes. Using order-of-magnitude estimates [O. Schilling and N. J. Mueschke, Phys. Fluids 22, 105102 (2010)] for the terms in the exact transport equations and their closure models, it is shown that several of the standard closures for the turbulent production and dissipation (destruction) must be modified to include Reynolds-number scalings appropriate for Rayleigh-Taylor flow at small to intermediate Reynolds numbers. The late-time, large Reynolds number coefficients are determined to be different from those used in shear flow applications and largely adopted in two-equation Reynolds-averaged Navier-Stokes (RANS) models of Rayleigh-Taylor turbulent mixing. In addition, it is shown that the predictions of the Boussinesq model for the Reynolds stress agree better with the data when additional buoyancy-related terms are included. It is shown that an unsteady RANS paradigm is needed to predict the transitional flow dynamics from early evolution times, analogous to the small Reynolds number modifications in RANS models of wall-bounded flows in which the production-to-dissipation ratio is far from equilibrium. Although the present study is specific to one particular flow and one set of initial conditions, the methodology could be applied to calibrations of other Rayleigh-Taylor flows with different initial conditions (which may give different results during the early-time, transitional flow stages, and perhaps asymptotic stage). The implications of these findings for developing high-fidelity eddy viscosity-based turbulent transport and mixing models of Rayleigh-Taylor turbulence are discussed.

We consider an arbitrary quantum system coupled nonperturbatively to a large arbitrary and fully quantum environment. In the work by Ithier and Benaych-Georges [Phys. Rev. A 96, 012108 (2017)] the typicality of the dynamics of such an embedded quantum system was established for several classes of random interactions. In other words, the time evolution of its quantum state does not depend on the microscopic details of the interaction. Focusing on the long-time regime, we use this property to calculate analytically a partition function characterizing the stationary state and involving the overlaps between eigenvectors of a bare and a dressed Hamiltonian. This partition function provides a thermodynamical ensemble which includes the microcanonical and canonical ensembles as particular cases. We check our predictions with numerical simulations.

For realistic interaction models, which include both molecular attraction and repulsion (e.g., Lennard-Jones, modified Lennard-Jones, Morse, and square-well potentials), the asymptotic behavior of the virial expansions for pressure and density in powers of activity has been studied taking power terms of high orders into account on the basis of the known finite-order irreducible integrals as well as the recent approximations of infinite irreducible series. Even in the divergence region (at subcritical temperatures), this behavior stays thermodynamically adequate (in contrast to the behavior of the virial equation of state with the same set of irreducible integrals) and corresponds to the beginning of the first-order phase transition: the divergence yields the jump (discontinuity) in density at constant pressure and chemical potential. In general, it provides a statistical explanation of the condensation phenomenon, but for liquid or solid states, the physically proper description (which can turn the infinite discontinuity into a finite jump of density) still needs further study of high-order cluster integrals and, especially, their real dependence on the system volume (density).

Here we study how coherence appears in a system driven by noise at small scales. In the wave turbulence modeled by the Gross-Pitaevskii or the nonlinear Schrödinger equation, we observe states with correlation scales smaller than the system size but much larger than the excitation scale. We call a such state precondensate to distinguish it from condensate defined as a systemwide coherent state. Both condensate and precondensate are characterized by large-scale phase coherence and a narrow distribution of amplitudes. When one excites small scales, precondensate is achieved relatively quickly by an inverse cascade heating quasiequilibrium distribution of large-scale modes. The transition from the precondensate to the systemwide condensate requires a much longer time. The spectra of precondensate differ from quasiequilibrium and are characterized by two bending points, one on the scale of the average distance between vortex pairs and the other on the scale of the distance between vortices in a pair. We suggest temporal evolution laws for both lengths and use them to predict the probability of the transition to condensate.

We consider two coupled quantum tops with angular momentum vectors $\mathbf{L}$ and $\mathbf{M}$. The coupling Hamiltonian defines the Feingold-Peres model, which is a known paradigm of quantum chaos. We show that this model has a nonstandard symmetry with respect to the Altland-Zirnbauer tenfold symmetry classification of quantum systems, which extends the well-known threefold way of Wigner and Dyson (referred to as “standard” symmetry classes here). We identify the nonstandard symmetry classes $\mathrm{BD}{I}_0$ (chiral orthogonal class with no zero modes), $\mathrm{BD}{I}_1$ (chiral orthogonal class with one zero mode), and $\mathrm{C}I$ (antichiral orthogonal class) as well as the standard symmetry class $\mathrm{A}I$ (orthogonal class). We numerically analyze the specific spectral quantum signatures of chaos related to the nonstandard symmetries. In the microscopic density of states and in the distribution of the lowest positive energy eigenvalue, we show that the Feingold-Peres model follows the predictions of the Gaussian ensembles of random-matrix theory in the appropriate symmetry class if the corresponding classical dynamics is chaotic. In a crossover to mixed and near-integrable classical dynamics, we show that these signatures disappear or strongly change.

The ability to trap a single molecule in an electrostatic potential well in solution has opened up new possibilities for the use of molecular electrical charge to study macromolecular conformation and dynamics at the level of the single entity. Here we study the diffusion of a single macromolecule in a two-dimensional lattice of electrostatic traps in solution. We report the ability to measure both the size and effective electrical charge of a macromolecule by observing single-molecule transport trajectories, typically a few seconds in length, using fluorescence microscopy. While, as shown previously, the time spent by the molecule in a trap is a strong function of its effective charge, we demonstrate here that the average travel time between traps in the landscape yields its hydrodynamic radius. Tailoring the pitch of the lattice thus yields two different experimentally measurable time scales that together uniquely determine both the size and charge of the molecule. Since no information is required on the location of the molecule between consecutive departure and arrival events at lattice sites, the technique is ideally suited to measurements on weakly emitting entities such as single molecules.

Template-directed replication of nucleic acids is at the essence of all living beings and a major milestone for any origin of life scenario. We present an idealized model of prebiotic sequence replication, where binary polymers act as templates for their autocatalytic replication, thereby serving as each others reactants and products in an intertwined molecular ecology. Our model demonstrates how autocatalysis alters the qualitative and quantitative system dynamics in counterintuitive ways. Most notably, numerical simulations reveal a very strong intrinsic selection mechanism that favors the appearance of a few population structures with highly ordered and repetitive sequence patterns when starting from a pool of monomers. We demonstrate both analytically and through simulation how this “selection of the dullest” is caused by continued symmetry breaking through random fluctuations in the transient dynamics that are amplified by autocatalysis and eventually propagate to the population level. The impact of these observations on related prebiotic mathematical models is discussed.