Networks of Kuramoto oscillators with a positive correlation between the oscillators frequencies and the degree of their corresponding vertices exhibit so-called explosive synchronization behavior, which is now under intensive investigation. Here we study and discuss explosive synchronization in a situation that has not yet been considered, namely when only a part, typically a small part, of the vertices is subjected to a degree-frequency correlation. Our results show that in order to have explosive synchronization, it suffices to have degree-frequency correlations only for the hubs, the vertices with the highest degrees. Moreover, we show that a partial degree-frequency correlation does not only promotes but also allows explosive synchronization to happen in networks for which a full degree-frequency correlation would not allow it. We perform a mean-field analysis and our conclusions were corroborated by exhaustive numerical experiments for synthetic networks and also for the undirected and unweighed version of a typical benchmark biological network, namely the neural network of the worm Caenorhabditis elegans. The latter is an explicit example where partial degree-frequency correlation leads to explosive synchronization with hysteresis, in contrast with the fully correlated case, for which no explosive synchronization is observed.

We consider a modified Noguchi electrical transmission line and examine the effects of a linear capacitance $C_s$ on the wave characteristics while considering the semidiscrete approximation. It appears that wave modulations in the network are governed by a dispersive nonlinear Schrödinger equation whose coefficients are shown to be a function of $C_s$. We show that the use of this linear capacitance makes the filter more selective. We also show that the width of the unstable regions increases while that of the stable regions decreases with $C_s$ adding consequently the width of the frequency domain where bright solitons exist. Furthermore, we establish the existence of one more region (compared to the work of Marquié et al. [Marquié et al., Phys. Rev. E 49, 828 (1994)]) in the dispersion curve that allows the motion of envelope solitons of higher frequency in the system. Numerical and experimental investigations done on the model confirm our analytical predictions.

Long-range correlated processes are ubiquitous, ranging from climate variables to financial time series. One paradigmatic example for such processes is fractional Brownian motion (fBm). In this work, we highlight the potentials and conceptual as well as practical limitations when applying the recently proposed recurrence network (RN) approach to fBm and related stochastic processes. In particular, we demonstrate that the results of a previous application of RN analysis to fBm [Liu et al. Phys. Rev. E 89, 032814 (2014)] are mainly due to an inappropriate treatment disregarding the intrinsic nonstationarity of such processes. Complementarily, we analyze some RN properties of the closely related stationary fractional Gaussian noise (fGn) processes and find that the resulting network properties are well-defined and behave as one would expect from basic conceptual considerations. Our results demonstrate that RN analysis can indeed provide meaningful results for stationary stochastic processes, given a proper selection of its intrinsic methodological parameters, whereas it is prone to fail to uniquely retrieve RN properties for nonstationary stochastic processes like fBm.

Recently Andrezejak et al. combined the randomness and nonlinear independence test with iterative amplitude adjusted Fourier transform (iAAFT) surrogates to distinguish between the dynamics of seizure-free intracranial electroencephalographic (EEG) signals recorded from epileptogenic (focal) and nonepileptogenic (nonfocal) brain areas of epileptic patients. However, stationarity is a part of the null hypothesis for iAAFT surrogates and thus nonstationarity can violate the null hypothesis. In this work we first propose the application of the randomness and nonlinear independence test based on recurrence network measures to distinguish between the dynamics of focal and nonfocal EEG signals. Furthermore, we combine these tests with both iAAFT and truncated Fourier transform (TFT) surrogate methods, which also preserves the nonstationarity of the original data in the surrogates along with its linear structure. Our results indicate that focal EEG signals exhibit an increased degree of structural complexity and interdependency compared to nonfocal EEG signals. In general, we find higher rejections for randomness and nonlinear independence tests for focal EEG signals compared to nonfocal EEG signals. In particular, the univariate recurrence network measures, the average clustering coefficient $\mathcal{C}$ and assortativity $\mathcal{R}$, and the bivariate recurrence network measure, the average cross-clustering coefficient ${\mathcal{C}}_{\mathrm{cross}}$, can successfully distinguish between the focal and nonfocal EEG signals, even when the analysis is restricted to nonstationary signals, irrespective of the type of surrogates used. On the other hand, we find that the univariate recurrence network measures, the average path length $\mathcal{L}$, and the average betweenness centrality $\text{BC}$ fail to distinguish between the focal and nonfocal EEG signals when iAAFT surrogates are used. However, these two measures can distinguish between focal and nonfocal EEG signals when TFT surrogates are used for nonstationary signals. We also report an improvement in the performance of nonlinear prediction error $N$ and nonlinear interdependence measure $L$ used by Andrezejak et al., when TFT surrogates are used for nonstationary EEG signals. We also find that the outcome of the nonlinear independence test based on the average cross-clustering coefficient ${\mathcal{C}}_{\mathrm{cross}}$ is independent of the outcome of the randomness test based on the average clustering coefficient $\mathcal{C}$. Thus, the univariate and bivariate recurrence network measures provide independent information regarding the dynamics of the focal and nonfocal EEG signals. In conclusion, recurrence network analysis combined with nonstationary surrogates can be applied to derive reliable biomarkers to distinguish between epileptogenic and nonepileptogenic brain areas using EEG signals.

We consider pulse-coupled leaky integrate-and-fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level. Our main aim is to mimic the effect of the dilution as a noise source acting on the dynamics of a globally coupled nonchaotic system. Indeed, the evolution of a diluted neural network can be well approximated as a fully pulse-coupled network, where each neuron is driven by a mean synaptic current plus additive noise. These terms represent the average and the fluctuations of the synaptic currents acting on the single neurons in the diluted system. The main microscopic and macroscopic dynamical features can be retrieved with this stochastic approximation. Furthermore, the microscopic stability of the diluted network can be also reproduced, as demonstrated from the almost coincidence of the measured Lyapunov exponents in the deterministic and stochastic cases for an ample range of system sizes. Our results strongly suggest that the fluctuations in the synaptic currents are responsible for the emergence of chaos in this class of pulse-coupled networks.

The locomotion of swimming microorganisms often relies on synchronized motions; examples include the bundling of flagella and metachronal coordination of cilia. It is now generally accepted that such behavior can result from hydrodynamic interactions alone. In this paper we consider the interactions between two side-by-side rigid helices driven by constant torques. We use the method of regularized Stokeslets to simulate an end-pinned model, in which restoring forces and torques are applied at one end of each helix. This allows us to decouple the respective effects of translation and rotation on phase synchronization. We find that while translational freedom leads to synchrony, rotational freedom can result in either synchrony or antisynchrony, depending on the stiffness of the system. In addition, we characterize the nature of the physical mechanisms driving these behaviors, focusing on the individual effects of each applied force and torque. For translational freedom, there is a single underlying mechanism in which the interaction forces indirectly influence the helix rotation rates. Multiple mechanisms are at play for rotational freedom: the interaction torques may exert either direct or indirect influence depending on stiffness. These characterizations are important to the future development of reduced-order models, which should capture not only the expected end behaviors (synchrony or antisynchrony), but also the nature of the driving mechanisms.

This paper presents a new method for solving analytically the axisymmetric swirling flow generated in a finite annular channel from a rotating end wall, with no-slip boundary conditions along stationary side walls and a slip condition along the free surface opposite the rotating floor. In this case, the end-driven swirling flow can be described from the coupling between an azimuthal shear flow and a two-dimensional meridional flow driven by the centrifugal force along the rotating floor. A regular asymptotic expansion based on a small but finite Reynolds number is used to calculate centrifugation-induced first-order correction to the azimuthal Stokes flow obtained as the solution at leading order. For solving the first-order problem, the use of an integral boundary condition for the vorticity is found to be a convenient way to attribute boundary conditions in excess for the stream function to the vorticity. The annular geometry is characterized by both vertical and horizontal aspect ratios, whose respective influences on flow patterns are investigated. The vertical aspect ratio is found to involve nontrivial changes in flow patterns essentially due to the role of corner eddies located on the left and right sides of the rotating floor. The present analytical method can be ultimately extended to cylindrical geometries, irrespective of the surface opposite the rotating floor: a wall or a free surface. It can also serve as an analytical tool for monitoring confined rotating flows in applications related to surface viscosimetry or crystal growth from the melt.

We report the development of interfacial ferrohydrodynamic instabilities when an initially circular bubble of a nonmagnetic inviscid fluid is surrounded by a viscous ferrofluid in the confined geometry of a Hele-Shaw cell. The fluid-fluid interface becomes unstable due to the action of magnetic forces induced by an azimuthal field produced by a straight current-carrying wire that is normal to the cell plates. In this framework, a pattern formation process takes place through the interplay between magnetic and surface tension forces. By employing a perturbative mode-coupling approach we investigate analytically both linear and intermediate nonlinear regimes of the interface evolution. As a result, useful analytical information can be extracted regarding the destabilizing role of the azimuthal field at the linear level, as well as its influence on the interfacial pattern morphology at the onset of nonlinear effects. Finally, a vortex sheet formalism is used to access fully nonlinear stationary solutions for the two-fluid interface shapes.

Based on an experiment and simulations, we report that an energy cascade in surface capillary waves can be bidirectional, that is, can simultaneously flow towards large and small wavelength scales from the pumping scales. The bidirectional energy cascade provides an effective global coupling mechanism between the scales. We show that formation of the bidirectional cascade leads to creation of large-scale, large-amplitude waves on the fluid surface.

Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.

Flame particles are mathematical points comoving with a reacting isoscalar surface in a premixed flame. In this Rapid Communication, we investigate mean square pair separation of flame particles as a function of time from their positions tracked in two sets of direct numerical simulation solutions of ${\mathrm{H}}_2$-air turbulent premixed flames with detailed chemistry. We find that, despite flame particles and fluid particles being very different concepts, a modified Batchelor's scaling of the form $\langle {|{\mathit{\Delta }}^F\left(t\right)-{\mathit{\Delta }}^F\left(0\right)|}^2\rangle =C_F{\left({\langle \varepsilon \rangle }_0^F{\Delta }_0^F\right)}^{2/3}{t}^2$ holds for flame particle pair dispersion. The proportionality constant, however, is not universal and depends on the isosurface temperature value on which the flame particles reside. Following this, we attempt to analytically investigate the rationale behind such an observation.

Fluid parts of the phase diagram of ${}^3\mathrm{He}-{}^4\mathrm{He}$ mixtures are obtained from a mean-field analysis of a suitable lattice gas model for binary liquid mixtures. The proposed model takes into account the continuous rotational symmetry O(2) of the superfluid degrees of freedom associated with ${}^4\mathrm{He}$ and includes the occurrence of vacancies. This latter degree of freedom allows the model to exhibit a vapor phase and hence can provide the theoretical framework to describe the experimental conditions for measurements of tricritical Casimir forces in ${}^3\mathrm{He}-{}^4\mathrm{He}$ wetting films.

We numerically simulate mechanically stable packings of soft-core, frictionless, bidisperse disks in two dimensions, above the jamming packing fraction ${\varphi }_J$. For configurations with a fixed isotropic global stress tensor, we compute the averages, variances, and correlations of conserved quantities (stress ${\Gamma }_{\mathcal{C}}$, force-tile area $A_{\mathcal{C}}$, Voronoi volume $V_{\mathcal{C}}$, number of particles $N_{\mathcal{C}}$, and number of small particles $N_{s\mathcal{C}}$) on compact subclusters of particles $\mathcal{C}$, as a function of the cluster size and the global system stress. We find several significant differences depending on whether the cluster $\mathcal{C}$ is defined by a fixed radius $R$ or a fixed number of particles $M$. We comment on the implications of our findings for maximum entropy models of jammed packings.

The liquid permeability of dense random packings of cubic colloids with rounded corners is studied for solid hematite cubes and hollow microporous silica cubes. The permeabilities of these two types of packings are similar, confirming that the micropores in the silica shell of the hollow cubes do not contribute to the permeability. From the Brinkman screening length $\sqrt{k}$ of $\sim 16$ nm, we infer that the relevant pores are indeed intercube pores. Furthermore, we relate the permeability to the volume fraction and specific solid volume of the cubes using the Kozeny-Carman relation. The Kozeny-Carman relation contains a constant that accounts for the topology and size distribution of the pores in the medium. The constant obtained from our study with aspherical particles is of the same order of magnitude as those from studies with spherical and ellipsoidal particles, which supports the notion that the Kozeny-Carman relation is applicable for any dense particle packing with (statistically) isotropic microstructures, irrespective of the particle shape.

Despite its simple chemical structure, water remains one of the most puzzling liquids with many anomalies at low temperatures. Combining neutron scattering and dielectric relaxation spectroscopy, we show that quantum fluctuations are not negligible in deeply supercooled water. Our dielectric measurements reveal the anomalously weak temperature dependence of structural relaxation in vapor-deposited water close to the glass transition temperature $T_g\sim 136\mathrm{K}$. We demonstrate that this anomalous behavior can be explained well by quantum effects. These results have significant implications for our understanding of water dynamics.

We report a Brownian dynamics simulation study of a two-dimensional system of repulsive colloidal particles in a channel geometry with a sinusoidal substrate potential under influence of a constant driving. The effect of this driving on the structure, mobility, and diffusion is discussed as well as the appearance of kink and antikink solitons. The competing order principles of the hexagonal crystal structure, the period of the substrate, and the layering due to the confining walls can be either commensurable or incommensurable. The combination of those three leads to new effects. The simultaneous occurrence of kinks and antikinks can be observed, due to the energy difference between boundary- and midlanes, and similarities to the electron-hole conductivity in a semiconductor can be found.

Chimera states have been recently found in a variety of different coupling schemes and geometries. In most cases, the underlying coupling structure is considered to be static, while many realistic systems display significant temporal changes in the pattern of connectivity. In this work we investigate a time-varying network made of two coupled populations of Kuramoto oscillators, where the links between the two groups are considered to vary over time. As a main result we find that the network may support stable, breathing, and alternating chimera states. We also find that, when the rate of connectivity changes is fast, compared to the oscillator dynamics, the network may be described by a low-dimensional system of equations. Unlike in the static heterogeneous case, the onset of alternating chimera states is due to the presence of fluctuations, which may be induced either by the finite size of the network or by large switching times.

We study a system of mismatched oscillators on a bipartite topology with time-delay coupling, and analyze the synchronized states. For a range of parameters, when all oscillators lock to a common frequency, we find solutions such that systems within a partition are in complete synchrony, while there is lag synchronization between the partitions. Outside this range, such a solution does not exist and instead one observes scenarios of remote synchronization—namely, chimeras and individual synchronization, where either one or both of the partitions are synchronized independently. In the absence of time delay such states are not observed in phase oscillators.