Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals toward selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics. Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

We investigated experimentally the ray-wave correspondence in organic microlasers of various triangular shapes. Triangular billiards are of interest since they are the simplest cases of polygonal billiards and the existence and properties of periodic orbits in triangles are not yet fully understood. The microlasers with symmetric shapes that were investigated exhibited states localized on simple periodic orbits, and their lasing characteristics like spectra and far-field distributions could be well explained by the properties of the periodic orbits. Furthermore, asymmetric triangles that do not feature simple periodic orbits were studied. Their lasing properties were found to be more complicated and could not be explained by periodic orbits.

Although there have been several experimental and numerical works on rapidly rupturing films, measurement of the spatial-temporal temperature during rupturing processes is lacking. Using molecular dynamics simulations, we show that a rupturing film with nanometer thickness generates a non-negligible temperature increase. We demonstrate a correlation between the rupture velocity, the temperature increase, and the initial film thickness. Our findings show that the temperature increase causes changes to the physical properties, which affect the film-rupturing behavior.

We establish the relation of the second virial coefficient of a recently proposed $(\tilde{\mu },q)$-deformed Bose gas model [A. M. Gavrilik and Yu. A. Mishchenko, Ukr. J. Phys. 58, 1171 (2013)] to the interaction and compositeness parameters when either of these factors is taken into account separately. When the interaction is dealt with, the deformation parameter becomes linked directly to the scattering length and the effective radius of interaction (in general, to scattering phases). The additionally arising temperature dependence is a feature absent in the deformed Bose gas model within the adopted interpretation of the deformation parameters $\tilde{\mu }$ and $q$. Here the problem of the temperature dependence is analyzed in detail and its possible solution is proposed.

The rate of convergence in extreme value statistics is nonuniversal and can be arbitrarily slow. Further, the relative error can be unbounded in the tail of the approximation, leading to difficulty in extrapolating the extreme value fit beyond the available data. We introduce the T method, and show that by using simple nonlinear transformations the extreme value approximation can be rendered rapidly convergent in the bulk, and asymptotic in the tail, thus fixing both issues. The transformations are often parametrized by just one parameter, which can be estimated numerically. The classical extreme value method is shown to be a special case of the proposed method. We demonstrate that vastly improved results can be obtained with almost no extra cost.

The maximum entropy method—used to infer probabilistic models from data—is a special case of Bayes's model inference prescription which, in turn, is grounded in basic propositional logic. By contrast to the maximum entropy method, the compatibility of nonadditive entropy maximization with Bayes's model inference prescription has never been established. Here we demonstrate that nonadditive entropy maximization is incompatible with Bayesian updating and discuss the immediate implications of this finding. We focus our attention on special cases as illustrations.

Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Based on a recently rigorized physical notion, namely, information flow, we solve an inverse problem and give this important and challenging question, which is of interest in a wide variety of disciplines, a positive answer. Here causality is measured by the time rate of information flowing from one series to the other. The resulting formula is tight in form, involving only commonly used statistics, namely, sample covariances; an immediate corollary is that causation implies correlation, but correlation does not imply causation. It has been validated with touchstone linear and nonlinear series, purportedly generated with one-way causality that evades the traditional approaches. It has also been applied successfully to the investigation of real-world problems; an example presented here is the cause-and-effect relation between the two climate modes, El Niño and the Indian Ocean Dipole (IOD), which have been linked to hazards in far-flung regions of the globe. In general, the two modes are mutually causal, but the causality is asymmetric: El Niño tends to stabilize IOD, while IOD functions to make El Niño more uncertain. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean.

We present the results of an experimental investigation into the effects of a sinusoidal modulation of the rotation rate on the segregation patterns formed in thin drum of granular material. The modulation transforms the base pattern formed under steady conditions by splitting or merging the initial streaks. Specifically, the relation between the frequency of modulation and the rotation rate determines the number of streaks which develop from the base state. The results are in accord with those of Fiedor and Ottino [J. Fluid. Mech. 533, 223 (2005)], and we show that their ideas apply over a wide range of parameter space. Furthermore, we provide evidence that the observed relationship is maintained for filling fractions far from $50\%$ and generalize the result in terms of the geometry of the granular deposit.

The first ever molecular-scale simulation of cross-flow migration effects in dense polymer melts is presented; simulations for both unentangled and entangled chains are presented. At quiescence a small depletion next to the wall for the segmental densities of longer chains is present, a corresponding excess exists about one-half a radii of gyration away from the wall, and uniform values are observed further from the wall. In shear flow the melts exhibit similar behavior as the quiescent case; a constant shear rate across the gap does not induce chain length based migration. In contradistinction, parabolic flow (where gradients in shear rate are present) causes profound migration for both unentangled and entangled melts. Mapping onto polyethylene and calculating stress shows the system is far below the stress required to break chains. Accordingly, our findings are consistent with flow induced migration mechanisms predominating over competing chain degradation mechanisms thus resolving a 40 year old controversy.

We study the self-avoiding walk on complex fractal networks called the $(u,v)$-flower by mapping it to the $N$-vector model in a generating function formalism. First, we analytically calculate the critical exponent $\nu$ and the connective constant by a renormalization-group analysis in arbitrary fractal dimensions. We find that the exponent $\nu$ is equal to the displacement exponent, which describes the speed of diffusion in terms of the shortest distance. Second, by obtaining an exact solution for the $(u,u)$-flower, we provide an example which supports the conjecture that the universality class of the self-avoiding walk on graphs is not determined only by the fractal dimension.

Diverse biological networks exhibit universal features distinguished from those of random networks, calling much attention to their origins and implications. Here we propose a minimal evolution model of Boolean regulatory networks, which evolve by selectively rewiring links towards enhancing adaptability to a changing environment and stability against dynamical perturbations. We find that sparse and heterogeneous connectivity patterns emerge, which show qualitative agreement with real transcriptional regulatory networks and metabolic networks. The characteristic scaling behavior of stability reflects the balance between robustness and flexibility. The scaling of fluctuation in the perturbation spread shows a dynamic crossover, which is analyzed by investigating separately the stochasticity of internal dynamics and the network structure differences depending on the evolution pathways. Our study delineates how the ambivalent pressure of evolution shapes biological networks, which can be helpful for studying general complex systems interacting with environments.

In the collective-risk social dilemma, players lose their personal endowments if contributions to the common pool are too small. This fact alone, however, does not always deter selfish individuals from defecting. The temptations to free ride on the prosocial efforts of others are strong because we are hardwired to maximize our own fitness regardless of the consequences which might have for the public good. Here we show that the addition of risky assets to the personal endowments, both of which are lost if the collective target is not reached, can contribute to solving the collective-risk social dilemma. In infinite well-mixed populations, risky assets introduce new stable and unstable mixed steady states, whereby the stable mixed steady state converges to full cooperation as either the risk of collective failure or the amount of risky assets increases. Similarly, in finite well-mixed populations, the introduction of risky assets enforces configurations where cooperative behavior thrives. In structured populations cooperation is promoted as well, but the distribution of assets among the groups is crucial. Surprisingly, we find that the completely rational allocation of assets only to the most successful groups is not optimal, and this regardless of whether the risk of collective failure is high or low. Instead, in low-risk situations bounded rational allocation of assets works best, while in high-risk situations the simplest uniform distribution of assets among all the groups is optimal. These results indicate that prosocial behavior depends sensitively on the potential losses individuals are likely to endure if they fail to cooperate.

Random graph models have played a dominant role in the theoretical study of networked systems. The Poisson random graph of Erdős and Rényi, in particular, as well as the so-called configuration model, have served as the starting point for numerous calculations. In this paper we describe another large class of random graph models, which we call equitable random graphs and which are flexible enough to represent networks with diverse degree distributions and many nontrivial types of structure, including community structure, bipartite structure, degree correlations, stratification, and others, yet are exactly solvable for a wide range of properties in the limit of large graph size, including percolation properties, complete spectral density, and the behavior of homogeneous dynamical systems, such as coupled oscillators or epidemic models.

We study the formation of gap solitons in the presence of a parametric pump. It is shown that a parametric pump can stabilize stationary solitons continuously emitting dispersive waves. The resonant interactions of the radiation and the solitons are studied and it is shown that the solitons can be effectively controlled by the radiation. In particular it is shown that the solitons can collide or get pinned to inhomogeneities due to the interactions mediated by the resonant radiation.

Application of electrical forcing to release pinned spiral waves from unexcitable obstacles and to terminate the rotation of free spiral waves at the boundary of excitable media has been investigated in thin layers of the Belousov-Zhabotinsky (BZ) reaction, prepared with different initial concentrations of ${\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4$. Increasing [${\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4$] raises the excitability of the reaction and reduces the core diameter of free spiral waves as well as the wave period. An electric current with density stronger than a critical value J causes a pinned spiral wave to drift away from the obstacle. For a given obstacle size, J increases with [${\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4$]. Under an applied electrical current, the rotation center of a free spiral wave drifts along a straight path to the boundary. When the current density is stronger than a critical value J, the spiral tip is forced to hit the boundary, where the spiral wave is terminated. Similar to J for releasing a pinned spiral wave, J also increases with [${\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4$]. These experimental findings were confirmed by numerical simulations using the Oregonator model, in which the excitability was adjusted via the ratio of the excitation rate to the recovery rate of the BZ reaction. Therefore, our investigation shows that decreasing the excitability can facilitate elimination of spiral waves by electrical forcing, either in the presence of obstacles or not.

We identify the relative amount of short cyclic motifs as an important topological factor in networks of time-delayed Kuramoto oscillators. The patterns emerging from the cyclic motifs are most clearly distinguishable in the average frequency and the momentary frequency dispersion as a function of the time delay. In particular, the common distinction between bidirectional and unidirectional couplings is shown to have a decisive effect on the network dynamics. We argue that the behavior peculiar to the sparsely connected unidirectional random network can be described essentially as the lack of distinguishable patterns originating from cyclic motifs of any specific length.

We investigate the compactlike pulse signal propagation in a two-dimensional nonlinear electrical transmission network with the intersite circuit elements (both in the propagation and transverse directions) acting as nonlinear resistances. Model equations for the circuit are derived and can reduce from the continuum limit approximation to a two-dimensional nonlinear Burgers equation governing the propagation of the small amplitude signals in the network. This equation has only the mass as conserved quantity and can admit as solutions cusp and compactlike pulse solitary waves, with width independent of the amplitude, according to the sign of the product of its nonlinearity coefficients. In particular, we show that only the compactlike pulse signal may propagate depending on the choice of the realistic physical parameters of the network, and next we study the dissipative effects on the pulse dynamics. The exactness of the analytical analysis is confirmed by numerical simulations which show a good agreement with results predicted by the Rosenau and Hyman $K(2,2)$ equation.

The motion of a red blood cell suspended in a linear shear flow adjacent to a fixed boundary subject to an applied lateral force directed toward the boundary is simulated. A two-dimensional model is used that represents the viscous and elastic properties of normal red blood cells. Shear rates in the range of 100 to 600 ${\mathrm{s}}^{-1}$ are considered, and the suspending medium viscosity is 1 cP. In the absence of a lateral force, the cell executes a tumbling motion. With increasing lateral force, a transition from tumbling to tank-treading is predicted. The minimum force required to ensure tank-treading increases nonlinearly with the shear rate. Transient swinging motions occur when the force is slightly larger than the transition value. The applied lateral force is balanced by a hydrodynamic lift force resulting from the positive orientation of the long axis of the cell with respect to the wall. In the case of cyclic tumbling motions, the orientation angle takes positive values through most of the cycle, resulting in lift generation. These results are used to predict the motion of a cell close to the outer edge of the cell-rich core region that is generated when blood flows in a narrow tube. In this case, the lateral force is generated by shear-induced dispersion, resulting from cell-cell interactions in a region with a concentration gradient. This force is estimated using previous data on shear-induced dispersion. The cell is predicted to execute tank-treading motions at normal physiological hematocrit levels, with the possibility of tumbling at lower hematocrit levels.

Simulations of microflows usually require sophisticated numerical tools. Nevertheless in the slip regime, the hydrodynamic equation with slip boundary condition may be sufficient to account for the so-called Klinkenberg effect. We propose to visit this effect using a basic network of microchannels in which the Knudsen number is multiplied by two or four by introducing successive derivations to the channel. We derived an equivalent hydraulic conductivity up to second order. Theoretical results are compared both with the results of the Navier-Stokes equations with slip condition and with those obtained using a Bhatnagar-Gross-Krook–Hermite model developed especially for flows with a large spectrum of Knudsen numbers (typically ${10}^{-4}<K_n<10$). A criterion is provided in order to distinguish the slip regime from the transitional one in this multiscale network.

To understand the ${\mathrm{CO}}_2$ sequestration in the saline aquifer, the effect of a first-order chemical reaction on the onset of the buoyancy-driven instability in an isotropic reactive porous medium is analyzed theoretically. Under the linear stability theory, the stability equations are derived in the semi-infinite domain and they are solved with and without the quasi-steady-state approximation. We also considered the stability of the reactive system at a steady-state limit. The analysis for the steady-state case proposed that the onset of instability motion can occur during the transient period even if the system is stable at the steady state. Through the initial growth rate analysis the most unstable initial disturbance is determined, and it is found that initially the system is unconditionally stable regardless of the Damköhler number $D_a$ and the Darcy-Rayleigh number $\mathrm{Ra}$. Based on the results of the initial growth rate analysis, the direct numerical simulation is also conducted by using the Fourier pseudospectral method. The present theoretical and numerical analyses suggest that the chemical reaction makes the system stable and no convective motion can be expected for $D_a/\mathrm{R}{\mathrm{a}}^2>2.5\times {10}^{-3}$.