Recent experiments on the propagation of light over a distance $L$ through a random packing of spheres with a power-law distribution of radii (a so-called Lévy glass) have found that the transmission probability $T\propto 1/L^{\gamma }$ scales superdiffusively ($\gamma <1$). The data has been interpreted in terms of a Lévy walk. We present computer simulations to demonstrate that diffusive scaling ($\gamma \approx 1$) can coexist with a divergent second moment of the step size distribution [$p\left(s\right)\propto 1/s^{1+\alpha }$ with $\alpha <2$]. This finding is in accord with analytical predictions for the effect of step size correlations, but deviates from what one would expect for a Lévy walk of independent steps.

Recently, numerous studies have shown that human dynamics cannot be described accurately by exponential laws. For instance, Barabási [Nature (London) 435, 207 (2005)] demonstrates that waiting times of tasks to be performed by a human are more suitably modeled by power laws. He presumes that these power laws are caused by a priority selection mechanism among the tasks. Priority models are well-developed in queueing theory (e.g., for telecommunication applications), and this paper demonstrates the (quasi-)immediate applicability of such a stochastic priority model to human dynamics. By calculating generating functions and by studying them in their dominant singularity, we prove that nonexponential tails result naturally. Contrary to popular belief, however, these are not necessarily triggered by the priority selection mechanism.

Canards are solutions of slow-fast systems that spend long times near branches of repelling equilibria, periodic orbits, or higher-dimensional invariant sets. Here, we report on the observation of a new type of canard orbit, labeled a canard of mixed type. This canard orbit is a hybrid of the classical limit cycle canards, which spend long times near attracting and repelling branches of equilibria, and torus canards, which spend long times near attracting and repelling branches of periodic orbits. The canards of mixed type arise in a model of neural bursting activity of fold-fold cycle type, and, as other canard phenomena, separate different dynamic states.

Although discrete cell-based frameworks are now commonly used to simulate a whole range of biological phenomena, it is typically not obvious how the numerous different types of model are related to one another, nor which one is most appropriate in a given context. Here we demonstrate how individual cell movement on the discrete scale modeled using nonlinear force laws can be described by nonlinear diffusion coefficients on the continuum scale. A general relationship between nonlinear force laws and their respective diffusion coefficients is derived in one spatial dimension and, subsequently, a range of particular examples is considered. For each case excellent agreement is observed between numerical solutions of the discrete and corresponding continuum models. Three case studies are considered in which we demonstrate how the derived nonlinear diffusion coefficients can be used to (a) relate different discrete models of cell behavior; (b) derive discrete, intercell force laws from previously posed diffusion coefficients, and (c) describe aggregative behavior in discrete simulations.

Recent research [Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, and Makse, Nature Physics 6, 888 (2010)] has suggested that coreness, and not degree, constitutes a better topological descriptor to identify influential spreaders in complex networks. This hypothesis has been verified in the context of disease spreading. Here, we instead focus on rumor spreading models, which are more suited for social contagion and information propagation. To this end, we perform extensive computer simulations on top of several real-world networks and find opposite results. Namely, we show that the spreading capabilities of the nodes do not depend on their $k$-core index, which instead determines whether or not a given node prevents the diffusion of a rumor to a system-wide scale. Our findings are relevant both for sociological studies of contagious dynamics and for the design of efficient commercial viral processes.

With the introduction of an auxiliary function, a genuine bilinear system (in contrast to the published trilinear forms) is obtained for the two-coupled nonlinear Schrödinger equations with negative coherent coupling in the optical fiber communications. With symbolic computation, degenerate and nondegenerate vector solitons are derived associated with the corresponding phase-parameter constraints. In virtue of asymptotic analysis and graphical simulation, vector solitons of the single-hump, double-hump, or flat-top profiles are displayed, and the collision mechanisms of such vector solitons are revealed as well; namely, the collisions among degenerate solitons and among nondegenerate solitons are both elastic. The only possible inelastic collision, the collision in the degenerate-nondegenerate case, is pointed out, where a degenerate soliton interacts with a nondegenerate one. Results in this paper may be useful for the optical switching with the combined effects of self-phase modulation, cross-phase modulation, and negative coherent coupling.

Control of turbulence in two kinds of typical heterogeneous excitable media by applying a combined method is investigated. It is found that local-low-amplitude and high-frequency pacing (LHP) is effective to suppress turbulence if the deviation of the heterogeneity is minor. However, LHP is invalid when the deviation is large. Studies show that an additional radial electric field can greatly increase the efficiency of LHP. The underlying mechanisms of successful control in the two kinds of cases are different and are discussed separately. Since the developed strategy of combining LHP with a radial electric field can terminate turbulence in excitable media with a high degree of inhomogeneity, it has the potential contribution to promote the practical low-amplitude defibrillation approach.

A drop impacting on a solid surface deforms before the liquid makes contact with the surface. We directly measure the time evolution of the air layer profile under the droplet using high-speed color interferometry, obtaining the air layer thickness before and during the wetting process. Based on the time evolution of the extracted profiles obtained at multiple times, we measure the velocity of air exiting from the gap between the liquid and the solid, and account for the wetting mechanism and bubble entrapment. The present work offers a tool to accurately measure the air layer profile and quantitatively study the impact dynamics at a short time scale before impact.

The simultaneous effect of magnetic field and temperature on the capillary viscosity of magnetic nanofluid is an important parameter for a new class of applications such as nanoduct flow, nanomotors, micro- and nanofluidic devices, for transformer cooling, magnetic targeted drug delivery, etc. The effective viscosity of a nanofluid is explained based on the rotation of the particles and the effect of torque on it due to an externally applied magnetic field. Two types of fluids are used here, temperature-sensitive and non-temperature-sensitive magnetic nanofluids. In both types of fluids, decrease in effective viscosity with temperature is observed, but in both cases the mechanism for the decrement is quite different. One is due to temperature dependence of the magnetic moment and the other is due to removal of the secondary surfactant. For temperature-sensitive magnetic nanofluids, a Curie temperature of $\sim$80 ${}^{\circ }$C is extracted from this study. For non-temperature-sensitive magnetic nanofluids $\sim$65$\%$ of the secondary surfactant is removed for a change in temperature, $\Delta$$T$ $=$ 40 ${}^{\circ }$C. This is analogous with removal of a drug from magnetic particles for targeted drug delivery. Further, a linear dependence of effective viscosity with different capillary size and $\xi$ (angle between magnetic field and flow direction, $\xi$$\in$[0,$\pi$/2]) is also observed. This linear dependence can also be a good approximation for the study of magnetic drug targeting, as in the human body the capillaries are of different sizes, and the externally applied magnetic field is not always parallel or perpendicular to the drug flow direction.

The spectral densities of ion density and electrostatic potential fluctuations are derived in the framework of a self-consistent kinetic model of partially ionized dusty plasmas in the low-frequency regime. Neutral gas density can be responsible for significant modifications of the fluctuation level, hence the inclusion of the effect of neutrals is essential for a more realistic comparison with experiments, especially if spectral measurements are intended for dust diagnostic purposes. Comparison with the multicomponent model, attractive due to its simplicity as compared to the self-consistent one, is carried out to establish its limits of validity. Numerical calculations are performed for parameters typical of low-temperature plasma discharges. A criterion is derived for the omission of plasma discreteness in the low-frequency regime.

We study the nonlinear propagation of electrostatic wave packets in a collisional plasma composed of strongly coupled ions and relativistically degenerate electrons. The equilibrium of ions is maintained by an effective temperature associated with their strong coupling, whereas that of electrons is provided by the relativistic degeneracy pressure. Using a multiple-scale technique, a (3 + 1)-dimensional coupled set of nonlinear Schrödinger-like equations with nonlocal nonlinearity is derived from a generalized viscoelastic hydrodynamic model. These coupled equations, which govern the dynamics of wave packets, are used to study the oblique modulational instability of a Stoke's wave train to a small plane-wave perturbation. We show that the wave packets, though stable to the parallel modulation, become unstable against oblique modulations. In contrast to the long-wavelength carrier modes, the wave packets with short wavelengths are shown to be stable in the weakly relativistic case, whereas they can be stable or unstable in the ultrarelativistic limit. Numerical simulation of the coupled equations reveals that a steady-state solution of the wave amplitude exists together with the formation of a localized structure in (2 + 1) dimensions. However, in the (3 + 1)-dimensional evolution, a Gaussian wave beam self-focuses after interaction and blows up in a finite time. The latter is, however, arrested when the dispersion predominates over the nonlinearities. This occurs when the Coulomb coupling strength is higher or a choice of obliqueness of modulation, or a wavelength of excitation is different. Possible application of our results to the interior as well as in an outer mantle of white dwarfs are discussed.

Bottlenecks occur in a wide range of situations from pedestrians, ants, cattle, and traffic flow to the transport of granular materials. We examine granular flow across a bottleneck using simulations of monodisperse disks. Contrary to expectations but consistent with previous work, we find that the flow rate across a bottleneck actually increases if an obstacle is optimally placed before it. Using the hourglass theory and a velocity-density relation, we show that the peak flow rate corresponds to a transition from free flow to congested flow, similar to the phase transition in traffic flow.

The densest binary sphere packings in the $\alpha$-$x$ plane of small to large sphere radius ratio $\alpha$ and small sphere relative concentration $x$ have historically been very difficult to determine. Previous research had led to the prediction that these packings were composed of a few known “alloy” phases including, for example, the AlB${}_2$ (hexagonal $\omega$), HgBr${}_2$, and AuTe${}_2$ structures, and to ${\mathit{XY}}_n$ structures composed of close-packed large spheres with small spheres (in a number ratio of $n$ to 1) in the interstices, e.g., the NaCl packing for $n=1$. However, utilizing an implementation of the Torquato-Jiao sphere-packing algorithm [Torquato and Jiao, Phys. Rev. E 82, 061302 (2010)], we have discovered that many more structures appear in the densest packings. For example, while all previously known densest structures were composed of spheres in small to large number ratios of one to one, two to one, and very recently three to one, we have identified densest structures with number ratios of seven to three and five to two. In a recent work [Hopkins et al., Phys. Rev. Lett. 107, 125501 (2011)], we summarized these findings. In this work, we present the structures of the densest-known packings and provide details about their characteristics. Our findings demonstrate that a broad array of different densest mechanically stable structures consisting of only two types of components can form without any consideration of attractive or anisotropic interactions. In addition, the structures that we have identified may correspond to currently unidentified stable phases of certain binary atomic and molecular systems, particularly at high temperatures and pressures.

The narrow escape problem refers to the problem of calculating the mean first passage time (MFPT) needed for an average Brownian particle to leave a domain with an insulating boundary containing $N$ small well-separated absorbing windows, or traps. This mean first passage time satisfies the Poisson partial differential equation subject to a mixed Dirichlet-Neumann boundary condition on the domain boundary, with the Dirichlet condition corresponding to absorbing traps. In the limit of small total trap size, a common asymptotic theory is presented to calculate the MFPT in two-dimensional domains and in the unit sphere. The asymptotic MFPT formulas depend on mutual trap locations, allowing for global optimization of trap locations. Although the asymptotic theory for the MFPT was developed in the limit of asymptotically small trap radii, and under the assumption that the traps are well-separated, a comprehensive study involving comparison with full numerical simulations shows that the full numerical and asymptotic results for the MFPT are within 1$\%$ accuracy even when total trap size is only moderately small, and for traps that may be rather close together. This close agreement between asymptotic and numerical results at finite, and not necessarily asymptotically small, values of the trap size clearly illustrates one of the key side benefits of a theory based on a systematic asymptotic analysis. In addition, for the unit sphere, numerical results are given for the optimal configuration of a collection of traps on the surface of a sphere that minimizes the average MFPT. The case of $N$ identical traps and a pattern of traps with two different sizes are considered. The effect of trap fragmentation on the average MFPT is also discussed.

By means of $N$-body simulations, we study the evolution of gravity-dominated systems from an early relaxation to a collapse, focusing on the velocity distributions and thermodynamic properties. To simulate the dynamical evolution, we consider self-gravitating small $N$-body systems enclosed in a spherical container with adiabatic or semipermeable walls. It is demonstrated that in the early relaxation process, the velocity distribution is non-Gaussian and $q$-Gaussian, since the system is in quasiequilibrium states (here $q$ is the Tsallis entropic parameter). Thereafter, the velocity distribution undergoes higher non-Gaussian distributions, especially when the core forms rapidly in the collapse process; i.e., $q$ tends to be larger than that for the quasiequilibrium state, since the velocity distribution further deviates from Gaussian. However, after the core forms sufficiently, the velocity distribution gradually relaxes toward a Gaussian-like distribution. Accordingly, the velocity distribution evolves from a non-Gaussian distribution through a higher non-Gaussian distribution to a Gaussian-like distribution; i.e., the velocity distribution does not monotonically relax toward a Gaussian-like distribution in our collapse simulations. We clearly show such a transition of the velocity distribution, based not only on the Tsallis entropic parameter but also on the ratio of velocity moments. We also find that a negative specific heat occurs in a collapse process with mass and energy loss (such as the escape of stars from globular clusters), even if the velocity distribution is Gaussian-like.

A recent segregation criterion [Phys. Rev. E 78, 020301(R) (2008)] based on the thermal diffusion factor $\Lambda$ of an intruder in a heated granular gas described by the inelastic Enskog equation is revisited. The sign of $\Lambda$ provides a criterion for the transition between the Brazil-nut effect (BNE) and the reverse Brazil-nut effect (RBNE). The present theory incorporates two extra ingredients not accounted for by the previous theoretical attempt. First, the theory is based upon the second Sonine approximation to the transport coefficients of the mass flux of the intruder. Second, the dependence of the temperature ratio (intruder temperature over that of the host granular gas) on the solid volume fraction is taken into account in the first and second Sonine approximations. In order to check the accuracy of the Sonine approximation considered, the Enskog equation is also numerically solved by means of the direct simulation Monte Carlo method to get the kinetic diffusion coefficient $D_0$. The comparison between theory and simulation shows that the second Sonine approximation to $D_0$ yields an improvement over the first Sonine approximation when the intruder is lighter than the gas particles in the range of large inelasticity. With respect to the form of the phase diagrams for the BNE-RBNE transition, the kinetic theory results for the factor $\Lambda$ indicate that while the form of these diagrams depends sensitively on the order of the Sonine approximation considered when gravity is absent, no significant differences between both Sonine solutions appear in the opposite limit (gravity dominates the thermal gradient). In the former case (no gravity), the first Sonine approximation overestimates both the RBNE region and the influence of dissipation on thermal diffusion segregation.

Magnetic tweezers were employed to investigate the interactions between DNA and cisplatin (transplatin). Cisplatin shortened DNA and reduced its persistence length more significantly than transplatin due to the formation of many more diadducts than those formed by transplatin. Interestingly, the presence of transplatin could enhance the ability of cisplatin in shortening DNA. An optimal concentration ratio of transplatin to cisplatin existed at which cisplatin showed the highest ability in shortening DNA. Moreover, abrupt length changes were also observed when DNA was treated with a mixture of cisplatin and transplatin at high concentrations. A model was proposed to qualitatively explain well these results.

In living cells, proteins combine three-dimensional bulk diffusion and one-dimensional sliding along the DNA to reach a target faster. This process is known as facilitated diffusion and we investigate its dynamics in the physiologically relevant case of confined DNA. The confining geometry and DNA elasticity are key parameters: We find that facilitated diffusion is most efficient inside an isotropic volume and on a flexible polymer. By considering the typical copy numbers of proteins in vivo, we show that the speedup due to sliding becomes insensitive to fine tuning of parameters, rendering facilitated diffusion a robust mechanism to speed up intracellular diffusion-limited reactions. The parameter range we focus on is relevant for in vitro systems and for facilitated diffusion on yeast chromatin.

We present a detailed analysis of the connecting-nearest-neighbors model by Vázquez [Phys. Rev. E 67, 056104 (2003)]. We show that the degree distribution follows a power law, but the scaling exponent can vary with the parameter setting. Moreover, the correspondence of the growing version of the connecting-nearest-neighbors model to the particular random walk model and recursive search model is established.

To drive a large, complex, networked dynamical system toward some desired state using as few external signals as possible is a fundamental issue in the emerging field of controlling complex networks. Optimal control is referred to the situation where such a network can be fully controlled using only one driving signal. We propose a general approach to optimizing the controllability of complex networks by judiciously perturbing the network structure. The principle of our perturbation method is validated theoretically and demonstrated numerically for homogeneous and heterogeneous random networks and for different types of real networks as well. The applicability of our method is discussed in terms of the relative costs of establishing links and imposing external controllers. Besides the practical usage of our approach, its implementation elucidates, interestingly, the intricate relationship between certain structural properties of the network and its controllability.