The structure of time series is usually characterized by means of correlations. A new proposal based on visibility networks has been considered recently. Visibility networks are complex networks mapped from surfaces or time series using visibility properties. The structures of time series and visibility networks are closely related, as shown by means of fractional time series in recent works. In these works, a simple relationship between the Hurst exponent $H$ of fractional time series and the exponent of the distribution of edges $\gamma$ of the corresponding visibility network, which exhibits a power law, is shown. To check and generalize these results, in this paper we delve into this idea of connected structures by defining both structures more properly. In addition to the exponents used before, $H$ and $\gamma$, which take into account local properties, we consider two more exponents that, as we will show, characterize global properties. These are the exponent $\alpha$ for time series, which gives the scaling of the variance with the size as $\text{var}\sim T^{2\alpha }$, and the exponent $\kappa$ of their corresponding network, which gives the scaling of the averaged maximum of the number of edges, $\langle k_M\rangle \sim N^{\kappa }$. With this representation, a more precise connection between the structures of general time series and their associated visibility network is achieved. Similarities and differences are more clearly established, and new scaling forms of complex networks appear in agreement with their respective classes of time series.

Graph partitioning problems emerge in a wide variety of complex systems, ranging from biology to finance, but can be rigorously analyzed and solved only for a few graph ensembles. Here, an ensemble of equitable graphs, i.e., random graphs with a block-regular structure, is studied, for which analytical results can be obtained. In particular, the spectral density of this ensemble is computed exactly for a modular and bipartite structure. Kesten-McKay's law for random regular graphs is found analytically to apply also for modular and bipartite structures when blocks are homogeneous. An exact solution to graph partitioning for two equal-sized communities is proposed and verified numerically, and a conjecture on the absence of an efficient recovery detectability transition in equitable graphs is suggested. A final discussion summarizes results and outlines their relevance for the solution of graph partitioning problems in other graph ensembles, in particular for the study of detectability thresholds and resolution limits in stochastic block models.

Magnetotactic bacteria are a group of motile prokaryotes that synthesize chains of lipid-bound, magnetic nanoparticles called magnetosomes. This study exploits their innate magnetism to investigate previously unexplored facets of bacterial hydrodynamics at surfaces. Through use of weak, uniform, external magnetic fields and local, micromagnetic surface patterns, the relative strength of hydrodynamic, magnetic, and flagellar force components is tuned through magnetic control of the bacteria's orientation. The resulting swimming behaviors provide a means to experimentally determine hydrodynamic parameters and offer a high degree of control over large numbers of living microscopic entities. The implications of this controlled motion for studies of bacterial motility near surfaces and for micro- and nanotechnology are discussed.

The states of foam are empirically classified into dry foam and wet foam by the volume fraction of the liquid. Recently, a transition between the dry foam state and the wet foam state has been found by characterizing the bubble shapes [Furuta et al., Sci. Rep. 6, 37506 (2016)]. In the literature, it is indirectly ascertained that the transition from the dry to the wet form is related to the onset of the rearrangement of the bubbles, namely, the liquid fraction at which the bubbles become able to move to replace their positions. The bubble shape is a static property, and the rearrangement of the bubbles is a dynamic property. Thus, we investigate the relation between the bubble shape transition and the rearrangement event occurring in a collapsing process of the bubbles in a quasi-two-dimensional foam system. The current setup brings a good advantage to observe the above transitions, since the liquid fraction of the foam continuously changes in the system. It is revealed that the rearrangement of the bubbles takes place at the dry-wet transition point where the characteristics of the bubble shape change.

Through phase-field-crystal (PFC) simulations, we investigated, on the atomic scale, the crucial role played by interface energy anisotropy and growth driving force during the morphological evolution of a dendrite tip at low growth driving force. In the layer-by-layer growth manner, the interface energy anisotropy drives the forefront of the dendrite tip to evolve to be highly similar to the corner of the corresponding equilibrium crystal from the aspects of atom configuration and morphology, and thus affects greatly the formation and growth of a steady-state dendrite tip. Meanwhile, the driving force substantially influences the part behind the forefront of the dendrite tip, rather than the forefront itself. However, as the driving force increases enough to change the layer-by-layer growth to the multilayer growth, the morphology of the dendrite tip's forefront is completely altered. Parabolic fitting of the dendrite tip reveals that an increase in the influence of interface energy anisotropy makes dendrite tips deviate increasingly from a parabolic shape. By quantifying the deviations under various interface energy anisotropies and growth driving forces, it is suggested that a perfect parabola is an asymptotic limit for the shape of the dendrite tips. Furthermore, the atomic scale description of the dendrite tip obtained in the PFC simulation is compatible with the mesoscopic results obtained in the phase-field simulation in terms of the dendrite tip's morphology and the stability criterion constant.

We study the effect of chiral impurities on Viedma ripening using a simple reaction model. The exponential amplification of the enantiomeric excess (EE) is greatly accelerated by a smaller growth rate of solids with monomers caused by the chiral impurities. From the analysis of the model, it is found that the time evolution of the EE is essentially described by a second-order differential equation. The effect of chiral impurities is that the unstable fixed point is shifted from the racemic point, which leads to the linear amplification behavior observed experimentally. The analysis also shows a possibility of an oscillatory decay of the EE.

Analysis of the Vlasov-Maxwell equations from the perspective of turbulence cascade clarifies the role of electromagnetic work, and reveals the importance of the pressure-strain relation in generating internal energy. Particle-in-cell simulation demonstrates the relative importance of the several energy exchange terms, indicating that the traceless pressure-strain interaction “Pi-D” is of particular importance for both electrons and protons. The Pi-D interaction and the second tensor invariants of the strain are highly localized in similar spatial regions, indicating that energy transfer occurs preferentially in coherent structures. The collisionless turbulence cascade may be fruitfully explored by study of these energy transfer channels, in addition to examining transfer across spatial scales.

The use of stochastic models to study the dynamics of infectious diseases is an important tool to understand the epidemiological process. For several directly transmitted diseases, reinfection is a relevant process, which can be expressed by endogenous reactivation of the pathogen or by exogenous reinfection due to direct contact with an infected individual (with smaller reinfection rate $\sigma \beta$ than infection rate $\beta$). In this paper, we examine the stochastic susceptible, infected, recovered, infected (SIRI) model simulating the endogenous reactivation by a spontaneous reaction, while exogenous reinfection by a catalytic reaction. Analyzing the mean-field approximations of a site and pairs of sites, and Monte Carlo (MC) simulations for the particular case of exogenous reinfection, we obtained continuous phase transitions involving endemic, epidemic, and no transmission phases for the simple approach; the approach of pairs is better to describe the phase transition from endemic phase (susceptible, infected, susceptible (SIS)-like model) to epidemic phase (susceptible, infected, and removed or recovered (SIR)-like model) considering the comparison with MC results; the reinfection increases the peaks of outbreaks until the system reaches endemic phase. For the particular case of endogenous reactivation, the approach of pairs leads to a continuous phase transition from endemic phase (SIS-like model) to no transmission phase. Finally, there is no phase transition when both effects are taken into account. We hope the results of this study can be generalized for the susceptible, exposed, infected, and removed or recovered (${\text{SEIR}}_I^E$) model, for which the state exposed (infected but not infectious), describing more realistically transmitted diseases such as tuberculosis. In future work, we also intend to investigate the effect of network topology on phase transitions when the SIRI model describes both transmitted diseases ($\sigma <1$) and social contagions ($\sigma >1$).

We study numerically a two-dimensional random-bond Ising model where frustration can be tuned by varying the fraction $a$ of antiferromagnetic coupling constants. At low temperatures the model exhibits a phase with ferromagnetic order for sufficiently small values of $a, a<a_f$. In an intermediate range, $a_f<a<a_a$, the system is paramagnetic, with spin-glass order expected right at zero temperature. For even larger values, $a>a_a$, an antiferromagnetic phase exists. After a deep quench from high temperatures, slow evolution is observed for any value of $a$. We show that different amounts of frustration, tuned by $a$, affect the dynamical properties in a highly nontrivial way. In particular, the kinetics is logarithmically slow in phases with ferromagnetic or antiferromagnetic order, whereas evolution is faster, i.e., algebraic, when spin-glass order is prevailing. An interpretation is given in terms of the different nature of phase space.

We show examples of dynamical systems that can be solved analytically at any point along a period doubling route to chaos. Each system consists of a linear part oscillating about a set point and a nonlinear rule for regularly updating that set point. Previously it has been shown that such systems can be solved analytically even when the oscillations are chaotic. However, these solvable systems show few bifurcations, transitioning directly from a steady state to chaos. Here we show that a simple change to the rule for updating the set point allows for a greater variety of nonlinear dynamical phenomena, such as period doubling, while maintaining solvability. Two specific examples are given. The first is an oscillator whose set points are determined by a logistic map. We present analytic solutions describing an entire period doubling route to chaos. The second example is an electronic circuit. We show experimental data confirming both solvability and a period doubling route to chaos. These results suggest that analytic solutions may be a more useful tool in studying nonlinear dynamics than was previously recognized.

Semiclassical sum rules, such as the Gutzwiller trace formula, depend on the properties of periodic, closed, or homoclinic (heteroclinic) orbits. The interferences embedded in such orbit sums are governed by classical action functions and Maslov indices. For chaotic systems, the relative actions of such orbits can be expressed in terms of phase-space areas bounded by segments of stable and unstable manifolds and Moser invariant curves. This also generates direct relations between periodic orbits and homoclinic (heteroclinic) orbit actions. Simpler, explicit approximate expressions following from the exact relations are given with error estimates. They arise from asymptotic scaling of certain bounded phase-space areas. The actions of infinite subsets of periodic orbits are determined by their periods and the locations of the limiting homoclinic points on which they accumulate.

In applications where magnetic particles are used to detect and dose targeted molecules, it is of major importance to prevent particle clustering and aggregation during the capture stage in order to maximize the capture rate. Elongated ferromagnetic particles can be more interesting than spherical ones due to their large magnetic moment, which facilitates their separation by magnets or the detection by optical measurement of their orientation relaxation time. Under alternating magnetic field, the rotational dynamics of elongated ferromagnetic particles results from the balance between magnetic torque that tends to align the particle axis with the field direction and viscous torque. As for their translational motion, it results from a competition between direct magnetic particle-particle interactions and solvent-flow-mediated hydrodynamic interactions. Due to particle anisotropy, this may lead to intricate translation-rotation couplings. Using numerical simulations and theoretical modeling of the system, we show that two ellipsoidal magnetic particles, initially in a head-to-tail attractive configuration resulting from their remnant magnetization, can repel each other due to hydrodynamic interactions when alternating field is operated. The separation takes place in a range of low frequencies $f_{c1}<f<f_{c2}$. The upper frequency limit $f_{c2}{\tau }_r\approx 0.04$ (where ${\tau }_r$ is the rotation time scale) depends weakly on the ratio of magnetic field to particle magnetization strength, whereas $f_{c1}$ tends to zero when this ratio increases.

Mode-I fracture exhibits microbranching in the high velocity regime where the simple straight crack is unstable. For velocities below the instability, classic modeling using linear elasticity is valid. However, showing the existence of the instability and calculating the dynamics postinstability within the linear elastic framework is difficult and controversial. The experimental results give several indications that the microbranching phenomenon is basically a three-dimensional (3D) phenomenon. Nevertheless, the theoretical effort has been focused mostly on two-dimensional (2D) modeling. In this paper we study the microbranching instability using three-dimensional atomistic simulations, exploring the difference between the 2D and the 3D models. We find that the basic 3D fracture pattern shares similar behavior with the 2D case. Nevertheless, we exhibit a clear 3D-2D transition as the crack velocity increases, whereas as long as the microbranches are sufficiently small, the behavior is pure 3D behavior, whereas at large driving, as the size of the microbranches increases, more 2D-like behavior is exhibited. In addition, in 3D simulations, the quantitative features of the microbranches, separating the regimes of steady-state cracks (mirror) and postinstability (mist-hackle) are reproduced clearly, consistent with the experimental findings.

A self-consistent model of plasma polarization around an isolated micron-sized dust particle under the action of an external electric field is presented. It is shown that the quasineutral condition is fulfilled and the formed volume charge totally screens the dust particle. The ion focusing and wake formation behind the dust particle are demonstrated for different ion mean free paths and the external electric fields. It is obtained that at low values of the external electric field the trapped ions play the main role in the screening of the dust particle charge. For high external electric fields, the density of trapped ions decreases and the dust particle is screened mainly by the free ions.

We present an approach which allows the consistent treatment of bound states in the context of dc conductivity in dense partially ionized noble gas plasmas. Besides electron-ion and electron-electron collisions, further collision mechanisms owing to neutral constituents are taken into account. Especially at low temperatures of ${10}^4\text{to}{10}^5$ K, electron-atom collisions give a substantial contribution to the relevant correlation functions. We suggest an optical potential for the description of the electron-atom scattering which is applicable for all noble gases. The electron-atom momentum-transfer cross section is in agreement with experimental scattering data. In addition, the influence of the medium is analyzed, the optical potential is advanced including screening effects. The position of the Ramsauer minimum is influenced by the plasma. Alternative approaches for the electron-atom potential are discussed. Good agreement of calculated conductivity with experimental data for noble gas plasmas is obtained.

In kinetic simulations of non-Maxwellian plasmas, the calculation of particle scattering due to Coulomb collisions has no simple approximation. In such simulations, the number of collision interactions a particle experiences in a single time step is typically too large for direct calculation. In this work, the cumulative effect of a series of binary collisions is calculated numerically in a stochastic manner, and heuristic trends are produced as functions of the local plasma parameters. The result is a collision model suitable for implementation into a kinetic plasma simulation. The presence of low-probability, high-angle scattering due to close collision encounters is defined and described, and this effect is demonstrated in a test problem simulation of weakly collisional counterstreaming ion beams.

Magnetic and dielectric solids are well-represented by the Ising model with dipolar interactions (IM+DI). The latter are long-ranged, fluctuating in sign, and anisotropic. Equilibrium studies have revealed novel consequences of these complicated interactions, but their effect on nonequilibrium behavior is not explored. We perform a deep temperature quench to study the kinetics of domain growth in the $d=3$ IM+DI. Our main observations are (i) the emergence of columnar domains along the $z$ axis (Ising axis) with a transient periodicity in the $xy$ plane; (ii) anisotropic growth laws: ${\ell }_{\rho }\left(t\right)\sim t^{\varphi }$; ${\ell }_z\left(t\right)\sim t^{\psi }$, where $\vec{\rho }=(x,y)$ and $\ell$ is the characteristic length scale; (iii) generalized dynamical scaling for the correlation function: $C(\rho ,z;t)=g(\rho /{\ell }_{\rho },z/{\ell }_z)$; and (iv) an asymptotic Porod tail in the corresponding structure factor: $S(k_{\rho },0;t)\sim k_{\rho }^{-3}$; $S(0,k_z;t)\sim k_z^{-2}$. Our results explain the experimentally observed columnar morphologies in a wide range of dipolar systems, and they have important technological implications.

We present a derivation of generalized Poisson-Boltzmann equations starting from classical theories of binary fluid mixtures, employing an approach based on the Legendre transform as recently applied to the case of local descriptions of the fluid free energy. Under specific symmetry assumptions, and in the linearized regime, the Poisson-Boltzmann equation reduces to a phenomenological equation introduced by Bazant et al. [Phys. Rev. Lett. 106, 046102 (2011)], whereby the structuring near the surface is determined by bulk coefficients.

Rare trajectories of stochastic systems are important to understand because of their potential impact. However, their properties are by definition difficult to sample directly. Population dynamics provides a numerical tool allowing their study, by means of simulating a large number of copies of the system, which are subjected to selection rules that favor the rare trajectories of interest. Such algorithms are plagued by finite simulation time and finite population size, effects that can render their use delicate. In this paper, we present a numerical approach which uses the finite-time and finite-size scalings of estimators of the large deviation functions associated to the distribution of rare trajectories. The method we propose allows one to extract the infinite-time and infinite-size limit of these estimators, which—as shown on the contact process—provides a significant improvement of the large deviation function estimators compared to the standard one.

In this work it is shown that scale-free tails in metabolic flux distributions inferred in stationary models are an artifact due to reactions involved in thermodynamically unfeasible cycles, unbounded by physical constraints and in principle able to perform work without expenditure of free energy. After implementing thermodynamic constraints by removing such loops, metabolic flux distributions scale meaningfully with the physical limiting factors, acquiring in turn a richer multimodal structure potentially leading to symmetry breaking while optimizing for objective functions.