We report on the observation of freely decaying capillary wave turbulence on the surface of a fluid. The capillary wave turbulence spectrum decay is found to be self-similar in time with the same power law exponent as the one found in the stationary regime, in agreement with weak turbulence predictions. The amplitude of all Fourier modes are found to decrease exponentially with time at the same damping rate. The longest wavelengths involved in the system are shown to be damped by a viscous surface boundary layer. These long waves play the role of an energy source during the decay that sustains nonlinear interactions to keep capillary waves in a wave turbulent state.

Evaporation in a three-dimensional (3D) porous medium, a sand column saturated by water, was studied using synchrotron x-ray tomography. Three-dimensional images of the medium with a resolution of 7 $\mu$m were obtained during the evaporation. The entire column was scanned seven times, resulting in nearly ${10}^4$ 2D cross sections and illustrating the spatial distribution of air, liquid, and solid phases at the pore scale. The results were analyzed in order to gain new insights and better understanding of the characteristics of the drying front that was formed when the liquid-filled pores were invaded by air, as well as the structure of the liquid phase as it was dried. The analysis indicates that the liquid phase has a self-similar fractal structure, with its fractal dimension $D_f$ in all the cross sections being a function of the water content or saturation. In addition, $D_f$ for the 3D liquid structure, as well as its density correlation function, were computed using the 3D images. A crossover length scale $\xi$ was identified that separates the fractal regime from the compact geometry. For length scales $r>\xi$, the density correlation function approaches asymptotically the water content of the porous medium. The drying front is shown to be rough and multi-affine, rather than self-affine. Its properties were also computed using the 3D images. The roughness characteristics agree with those for imbibition in porous media, but not with those of fracture surfaces and crack lines.

We report on the collective vibration of discharge current filaments in a self-organized superlattice pattern within an air-argon dielectric barrier discharge system. The period of collective vibration decreases as the air content increases. An antisymmetric normal vibration mode of discharge current filaments is observed. The measurements of the spatiotemporal behavior of the superlattice pattern show that the discharge of the vibrating dot (filament) is asynchronous with that of its eight neighboring dots, including four big dots and four small dots. The forces exerted on the vibrating dot are analyzed, and a motion equation is set up in each half cycle of the applied voltage. It is found that the motion of the small dot is not an ideal vibration but is a vibration with fluctuating amplitude, which is in good agreement with experiments.

In this paper we study the evolutionary dynamics of the public goods game in a population of mobile agents embedded in a two-dimensional space. In this framework, the backbone of interactions between agents changes in time, allowing us to study the impact that mobility has on the emergence of cooperation in structured populations. Our results point out that a low degree of mobility enhances cooperation in the system. In addition, we study the impact of the size of the groups in which games are played on cooperation. Again we find a rise and fall of cooperation related to the percolation point of the instant interaction networks created by the set of mobile agents.

We study the motion of an elastic object driven in a disordered environment in presence of both dissipation and inertia. We consider random forces with the statistics of random walks and reduce the problem to a single degree of freedom. It is the extension of the mean-field Alessandro-Beatrice- Bertotti-Montorsi (ABBM) model in presence of an inertial mass $m$. While the ABBM model can be solved exactly, its extension to inertia exhibits complicated history dependence due to oscillations and backward motion. The characteristic scales for avalanche motion are studied from numerics and qualitative arguments. To make analytical progress, we consider two variants which coincide with the original model whenever the particle moves only forward. Using a combination of analytical and numerical methods together with simulations, we characterize the distributions of instantaneous acceleration and velocity, and compare them in these three models. We show that for large driving velocity, all three models share the same large-deviation function for positive velocities, which is obtained analytically for small and large $m$, as well as for $m=6/25$. The effect of small additional thermal and quantum fluctuations can be treated within an approximate method.

We derive an analytical pair potential of mean force for Brownian molecules in the liquid state. Our approach accounts for many-particle correlations of crowding particles of the liquid and for diffusive transport across the spatially modulated local density of crowders in the dense environment. Focusing on the limit of equal-size particles, we show that this diffusive transport leads to additional density- and structure-dependent terms in the interaction potential and to a much stronger attraction (by a factor of $\approx 4$ at average volume fraction of crowders ${\phi }_0=0.25$) than in the standard depletion interaction where the diffusive effects are neglected. As an illustration of the theory, we use it to study the size of a polymer chain in a solution of inert crowders. Even in the case of an athermal background solvent, when a classical chain should be fully swollen, we find a sharp coil-globule transition of the ideal chain collapsing at a critical value of the crowder volume fraction ${\phi }_c\approx 0.145$.

This paper focuses on large neural networks whose synaptic connectivity matrices are randomly chosen from certain random matrix ensembles. The dynamics of these networks can be characterized by the eigenvalue spectra of their connectivity matrices. In reality, neurons in a network do not necessarily behave in a similar way, but may belong to several different categories. The first study of the spectra of two-component neural networks was carried out by Rajan and Abbott [Phys. Rev. Lett. 97, 188104 (2006)]. In their model, neurons are either “excitatory” or “inhibitory,” and strengths of synapses from different types of neurons have Gaussian distributions with different means and variances. A surprising finding by Rajan and Abbott is that the eigenvalue spectra of these types of random synaptic matrices do not depend on the mean values of their elements. In this paper we prove that this is true even for a much more general type of random neural network, where there is a finite number of types of neurons and their synaptic strengths have correlated distributions. Furthermore, using the diagrammatic techniques, we calculate the explicit formula for the spectra of synaptic matrices of multicomponent neural networks.

Adaptive models of opinion formation among humans can display a fragmentation transition, where a social network breaks into disconnected components. Here we investigate this transition in a class of models with arbitrary number of opinions. In contrast to previous work we do not assume that opinions are equidistant or arranged on a one-dimensional conceptual axis. Our investigation reveals detailed analytical results on fragmentations in a three-opinion model, which are confirmed by agent-based simulations. Furthermore, we show that in certain models the number of opinions can be reduced without affecting the fragmentation points.

Much effort has gone into understanding the modular nature of complex networks. Communities, also known as clusters or modules, are typically considered to be densely interconnected groups of nodes that are only sparsely connected to other groups in the network. Discovering high quality communities is a difficult and important problem in a number of areas. The most popular approach is the objective function known as modularity, used both to discover communities and to measure their strength. To understand the modular structure of networks it is then crucial to know how such functions evaluate different topologies, what features they account for, and what implicit assumptions they may make. We show that trees and treelike networks can have unexpectedly and often arbitrarily high values of modularity. This is surprising since trees are maximally sparse connected graphs and are not typically considered to possess modular structure, yet the nonlocal null model used by modularity assigns low probabilities, and thus high significance, to the densities of these sparse tree communities. We further study the practical performance of popular methods on model trees and on a genealogical data set and find that the discovered communities also have very high modularity, often approaching its maximum value. Statistical tests reveal the communities in trees to be significant, in contrast with known results for partitions of sparse, random graphs.

We analyze the dynamical processes behind delayed double ionization of atoms subjected to strong laser pulses. Using reduced models, we show that these processes are a signature of Hamiltonian chaos which results from the competition between the laser field and the Coulomb attraction to the nucleus. In particular, we exhibit the paramount role of the unstable manifold of selected periodic orbits which lead to a delay in these double ionizations. Among delayed double ionizations, we consider the case of recollision excitation with subsequent ionization (RESI) and, as a hallmark of this mechanism, we predict oscillations in the ratio of RESI to double ionization yields versus laser intensity. We discuss the significance of the dimensionality of the reduced models for the analysis of the dynamical processes behind delayed double ionization.

An experimental study has been made of the flow field in indentation of a model granular material. A granular ensemble composed of spherical sand particles with average size of 0.4 mm is indented with a flat ended punch under plane-strain conditions. The region around the indenter is imaged in situ using a high-speed charge-coupled device (CCD) imaging system. By applying a hybrid image analysis technique to image sequences of the indentation, flow parameters such as velocity, velocity gradient, and strain rate are measured at high resolution. The measurements have enabled characterization of the main features of the flow such as dead material zones, velocity jumps, localization of deformation, and regions of highly rotational flow resembling vortices. Implications for validation of theoretical analyses and applications are discussed.

The effect of director pretilt on the twist magnetic Fréedericksz transition of nematics was investigated in a planar cell. The director configuration was calculated as a function of magnetic inductance. The dielectric and optical response of the nematic liquid crystal was numerically modeled. A dielectric measurement method for determining the elastic constant $K_{22}$ is presented. The influence of the conditions for the Mauguin effect is discussed. The theoretical predictions were confirmed by our experiments. Experimental data for all elastic constants of a bent-core nematic material are presented and discussed.

We discuss the temporal distribution of dynamic processes in driven polymer transport inherent to flexible chains due to stochastic tension propagation. The stochasticity originates from the disordered initial configuration of an equilibrium polymer coil, which results in random paths for tension propagation. We consider the process time for when translocation occurs across a fixed pore and when stretching occurs by pulling the chain end. A scaling argument for the mean and standard deviation of the process time is provided using the two-phase picture for stochastic propagation. The two cases are found to differ remarkably. The process time distribution of the translocation exhibits substantial spreading even in the long-chain limit, unlike that found for the dynamics of polymer stretching. In addition, the process time distribution in the driven translocation is shown to have a characteristic asymmetric shape.

To study the adsorption of individual peptides in implicit solvent, we propose a version of the Wang-Landau Monte Carlo algorithm that uses a single surface, with no need for a confining wall or grafting. Our “wall-free” method is both more efficient than the traditional ones and free of additional assumptions or approximations. We illustrate it by simulating an HP-model lattice peptide on planar surfaces with a variety of patterns of adsorption sites, discovering a temperature-induced switch of surface selection which is due to a balance of energetic and entropic effects.

In this paper, a drainage experiment of water between a borosilicate sphere and a microstructured surface constituted by regularly spaced pillars is presented. The microstructured surface has two parts: on one part the liquid forms a Cassie interface and on the second it forms a Wenzel interface. The measured hydrodynamic drag force is larger on the Cassie part compared to the Wenzel part. Furthermore, for the Cassie part, from the hydrodynamic drag force measurements on a pillar and between pillars the corresponding local slip lengths have been extracted. The area average slip length on the surface is in agreement with the value expected by Philip's equation.

Efficient computation of lattice defect geometries such as point defects, dislocations, disconnections, grain boundaries, interfaces, and free surfaces requires accurate coupling of displacements near the defect to the long-range elastic strain. Flexible boundary condition methods embed a defect in infinite harmonic bulk through the lattice Green function. We demonstrate an efficient and accurate calculation of the lattice Green function from the force-constant matrix for general crystals with an arbitrary basis by extending a method for Bravais lattices. New terms appear due to the presence of optical modes and the possible loss of inversion symmetry. By separately treating poles and discontinuities in reciprocal space, numerical accuracy is controlled at all distances. We compute the lattice Green function for a two-dimensional model with broken symmetry to elucidate the role of different coupling terms. The algorithm is generally applicable in two and three dimensions to crystals with arbitrary number of atoms in the unit cell, symmetry, and interactions.

The extension of Boltzmann-Gibbs thermostatistics, proposed by Tsallis, introduces an additional parameter $q$ to the inverse temperature $\beta$. Here, we show that a previously introduced generalized Metropolis dynamics to evolve spin models is not local and does not obey the detailed energy balance. In this dynamics, locality is only retrieved for $q=1$, which corresponds to the standard Metropolis algorithm. Nonlocality implies very time-consuming computer calculations, since the energy of the whole system must be reevaluated when a single spin is flipped. To circumvent this costly calculation, we propose a generalized master equation, which gives rise to a local generalized Metropolis dynamics that obeys the detailed energy balance. To compare the different critical values obtained with other generalized dynamics, we perform Monte Carlo simulations in equilibrium for the Ising model. By using short-time nonequilibrium numerical simulations, we also calculate for this model the critical temperature and the static and dynamical critical exponents as functions of $q$. Even for $q\ne 1$, we show that suitable time-evolving power laws can be found for each initial condition. Our numerical experiments corroborate the literature results when we use nonlocal dynamics, showing that short-time parameter determination works also in this case. However, the dynamics governed by the new master equation leads to different results for critical temperatures and also the critical exponents affecting universality classes. We further propose a simple algorithm to optimize modeling the time evolution with a power law, considering in a log-log plot two successive refinements.

We numerically investigate the mechanical properties of static packings of frictionless ellipsoidal particles in two and three dimensions over a range of aspect ratio and compression $\Delta \phi$. While amorphous packings of spherical particles at jamming onset ($\Delta \phi =0$) are isostatic and possess the minimum contact number $z_{\mathrm{iso}}$ required for them to be collectively jammed, amorphous packings of ellipsoidal particles generally possess fewer contacts than expected for collective jamming ($z<z_{\mathrm{iso}}$) from naive counting arguments, which assume that all contacts give rise to linearly independent constraints on interparticle separations. To understand this behavior, we decompose the dynamical matrix $M=H-S$ for static packings of ellipsoidal particles into two important components: the stiffness $H$ and stress $S$ matrices. We find that the stiffness matrix possesses $2N(z_{\mathrm{iso}}-z)$ eigenmodes ${\widehat{e}}_0$ with zero eigenvalues even at finite compression, where $N$ is the number of particles. In addition, these modes ${\widehat{e}}_0$ are nearly eigenvectors of the dynamical matrix with eigenvalues that scale as $\Delta \phi$, and thus finite compression stabilizes packings of ellipsoidal particles. At jamming onset, the harmonic response of static packings of ellipsoidal particles vanishes, and the total potential energy scales as ${\delta }^4$ for perturbations by amplitude $\delta$ along these “quartic” modes, ${\widehat{e}}_0$. These findings illustrate the significant differences between static packings of spherical and ellipsoidal particles.

Experiments show a primary role of extracellular potassium concentrations in neuronal hyperexcitability and in the generation of epileptiform bursting and depolarization blocks without synaptic mechanisms. We adopt a physiologically relevant hippocampal CA1 neuron model in a zero-calcium condition to better understand the function of extracellular potassium in neuronal seizurelike activities. The model neuron is surrounded by interstitial space in which potassium ions are able to accumulate. Potassium currents, ${\mathrm{Na}}^{+}$-${\mathrm{K}}^{+}$ pumps, glial buffering, and ion diffusion are regulatory mechanisms of extracellular potassium. We also consider a reduced model with a fixed potassium concentration. The bifurcation structure and spiking frequency of the two models are studied. We show that, besides hyperexcitability and bursting pattern modulation, the potassium dynamics can induce not only bistability but also tristability of different firing patterns. Our results reveal the emergence of the complex behavior of multistability due to the dynamical ${\left[{\mathrm{K}}^{+}\right]}_{\mathrm{o}}$ modulation on neuronal activities.

Community (module) structure is a common and important property of many types of networks, such as social networks and biological networks. Several classes of algorithms have been proposed for community structure detection and identification, including clustering techniques, modularity optimization, and other methods. Among these methods, the modularity optimization method has attracted a great deal of attention and much related research has been published. However, the existing modularity optimization method does not perform well in the presence of unbalanced community structures. In this paper, we introduce a metric to characterize the community structure better than other metrics in this situation, and we propose a method to infer the number of communities, which may solve the resolution limit problem. We then develop an algorithm for community structure identification based on eigendecompositions, and we give both simulated and real data examples to illustrate the better performance of our approach.