We explore the classical dynamics of two interacting rotating dipoles that are fixed in the space and exposed to an external homogeneous electric field. Kinetic energy transfer mechanisms between the dipoles are investigated by varying both the amount of initial excess kinetic energy of one of them and the strength of the electric field. In the field-free case, and depending on the initial excess energy, an abrupt transition between equipartition and nonequipartition regimes is encountered. The study of the phase space structure of the system as well as the formulation of the Hamiltonian in an appropriate coordinate frame provide a thorough understanding of this sharp transition. When the electric field is turned on, the kinetic energy transfer mechanism is significantly more complex and the system goes through different regimes of equipartition and nonequipartition of the energy including chaotic behavior.

We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

We show that the recently introduced logarithmic metrics used to predict disease arrival times on complex networks are approximations of more general network-based measures derived from random walks theory. Using the daily air-traffic transportation data we perform numerical experiments to compare the infection arrival time with this alternative metric that is obtained by accounting for multiple walks instead of only the most probable path. The comparison with direct simulations reveals a higher correlation compared to the shortest-path approach used previously. In addition our method allows to connect fundamental observables in epidemic spreading with the cumulant-generating function of the hitting time for a Markov chain. Our results provides a general and computationally efficient approach using only algebraic methods.

The giant $k$-core—maximal connected subgraph of a network where each node has at least $k$ neighbors—is important in the study of phase transitions and in applications of network theory. Unlike Erdős-Rényi graphs and other random networks where $k$-cores emerge discontinuously for $k\ge 3$, we show that transitive linking (or triadic closure) leads to 3-cores emerging through single or double phase transitions of both discontinuous and continuous nature. We also develop a $k$-core calculation that includes clustering and provides insights into how high-level connectivity emerges.

The dynamical tolerance of coupled oscillator networks against local failures is studied. As the fraction of failed oscillator nodes gradually increases, the mean oscillation amplitude in the entire network decreases and then suddenly vanishes at a critical fraction as a phase transition. This critical fraction, widely used as a measure of the network robustness, was analytically derived for random failures but not for targeted attacks so far. Here we derive the general formula for the critical fraction, which can be applied to both random failures and targeted attacks. We consider the effects of targeting oscillator nodes based on their degrees. First we deal with coupled identical oscillators with homogeneous edge weights. Then our theory is applied to networks with heterogeneous edge weights and to those with nonidentical oscillators. The analytical results are validated by numerical experiments. Our results reveal the key factors governing the robustness and fragility of oscillator networks.

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

A principled approach to characterize the hidden structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or “communities,” a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.

In neurons, several intracellular cargoes are transported by motor proteins (kinesins) which walk on microtubules (MTs). However, kinesins can possibly unbind from the MTs before they reach their destinations. The unbound kinesins randomly diffuse in neurons until they bind to MTs. Then, they walk again along the MTs to continue their tasks. Kinesins repeat this cycle of motion until they transport their cargoes to the destinations. However, most previous models mainly focused on the motion of kinesins when they walk on MTs. Thus, a new model is required to encompass the various types of kinesin motion. We developed a comprehensive model and studied the long-range axonal transport of neurons using the model. To enhance reliability of the model, it was constructed based on multiphysics on kinesin motion (i.e., chemical kinetics, diffusion, fluid dynamics, nonlinear dynamics, and stochastic characteristics). Also, parameter values for kinesin motions are carefully obtained by comparing the model predictions and several experimental observations. The axonal transport can be degraded when a large number of binding sites on MTs are blocked by excessive tau proteins. By considering the interference between walking kinesins and tau molecules on MTs, effects of tau proteins on the axonal transport are studied. One of the meaningful predictions obtained from the model is that the velocity is not an effective metric to estimate the degradation of the transport because the decrease in velocity is not noticeable when the concentration of tau protein is not high. However, our model shows that the transport locally changes near tau molecules on MTs even when the change in the velocity is not significant. Thus, a statistical method is proposed to detect this local change effectively. The advantage of this method is that a value obtained from this method is highly sensitive to the concentration of tau protein. Another benefit of this method is that this highly sensitive value can be acquired with relatively low precision and low temporal resolution considering the time scale and length scale of the kinesin motion. This method can be used to estimate the condition of the axonal transport system.

We examine numerically the transport of an assembly of active run-and-tumble disks interacting with a traveling-wave substrate. We show that as a function of substrate strength, wave speed, disk activity, and disk density, a variety of dynamical phases arise that are correlated with the structure and net flux of disks. We find that there is a sharp transition into a state in which the disks are only partially coupled to the substrate and form a phase-separated cluster state. This transition is associated with a drop in the net disk flux, and it can occur as a function of the substrate speed, maximum substrate force, disk run time, and disk density. Since variation of the disk activity parameters produces different disk drift rates for a fixed traveling-wave speed on the substrate, the system we consider could be used as an efficient method for active matter species separation. Within the cluster phase, we find that in some regimes the motion of the cluster center of mass is in the opposite direction to that of the traveling wave, while when the maximum substrate force is increased, the cluster drifts in the direction of the traveling wave. This suggests that swarming or clustering motion can serve as a method by which an active system can collectively move against an external drift.

The confinement of liquid crystals inside curved geometries leads to exotic structures, with applications ranging from biosensors to optical switches and privacy windows. Here we study how curvature affects the alignment of a cholesteric liquid crystal. We model the system on the mesoscale using the Landau-de Gennes model. Our study was performed in three stages, analyzing different curved geometries from cylindrical walls and pores, to toroidal domains, in order to isolate the curvature effects. Our results show that the stresses introduced by the curvature influence the orientation of the liquid crystal molecules, and cause distortions in the natural periodicity of the cholesteric that depend on the radius of curvature, on the pitch, and on the dimensions of the system. In particular, the cholesteric layers of toroidal droplets exhibit a symmetry breaking not seen in cylindrical pores and that is driven by the additional curvature.

We investigate the mechanical behavior of particle-stabilized droplets using micropipette aspiration. We observe that droplets stabilized with amphiphilic dumbbell-shaped particles exhibit a two-stage response to increasing suction pressure. Droplets first drip, then wrinkle and buckle like an elastic shell. While particles have a dramatic impact on the mechanism of failure, the mechanical strength of the droplets is only modestly increased. On the other hand, droplets coated with the molecular surfactant sodium dodecyl sulfate are even weaker than bare droplets. In all cases, the magnitude of the critical pressure for the onset of instabilities is set by the fluid surface tension.

Buoyant material clustering in a stochastic flow, which is homogeneous and isotropic in space and stationary in time, is addressed. The dynamics of buoyant material in three-dimensional hydrodynamic flows can be considered as the motion of passive tracers in a compressible two-dimensional velocity field. The latter is of interest in the present study. It is well known that the clustering of the density of passive tracers occurs in this case. We evaluate the impact of diffusion on the clustering process by using a numerical model. In general, the effect of diffusion is negligible in the very beginning of the evolution of initially uniformly distributed passive tracers. Therefore, the clustering of the density of passive tracers can emerge in accordance with the general theory. We analyze the long time clustering affected by diffusion and show that the emerged cluster structure persists in time in spite of the diffusion effect. However, the clusters split in time.

Magnetic fields in rotating and radiating astrophysical plasma can be produced due to a radiative interaction between plasma layers moving relative to each other. The efficiency of current drive, and with it the associated dynamo effect, is considered in a number of limits. It is shown here, however, that predictions for these generated magnetic fields can be significantly higher when kinetic effects, previously neglected, are taken into account.

Self-diffusion and interdiffusion coefficients of binary ionic mixtures are evaluated using the effective potential theory (EPT), and the predictions are compared with the results of molecular dynamics simulations. We find that EPT agrees with molecular dynamics from weak coupling well into the strong-coupling regime, which is a similar range of coupling strengths as previously observed in comparisons with the one-component plasma. Within this range, typical relative errors of approximately 20% and worst-case relative errors of approximately 40% are observed. We also examine the Darken model, which approximates the interdiffusion coefficients based on the self-diffusion coefficients.

The work investigates the coupling between ion-acoustic waves and neutrino flavor oscillations in a nonrelativistic electron-ion plasma under the influence of a mixed neutrino beam. Neutrino oscillations are mediated by the flavor polarization vector dynamics in a material medium. The linear dispersion relation around homogeneous static equilibria is developed. When resonant with the ion-acoustic mode, the neutrino flavor oscillations can transfer energy to the plasma exciting a new fast unstable mode in extreme astrophysical scenarios. The growth rate and the unstable wavelengths are determined in typical type II supernova parameters. The predictions can be useful for a new indirect probe on neutrino oscillations in nature.

Interaction of a high-intensity short laser pulse with near-critical plasmas allows us to achieve extremely high coupling efficiency and transfer laser energy to energetic ions. One-dimensional particle-in-cell simulations are considered to detail the processes involved in the energy transfer. A confrontation of the numerical results with the theory highlights a key role played by the process of stimulated Raman scattering in the relativistic regime. The interaction of a 1 ps laser pulse ($I\sim 6\times {10}^{18}\mathrm{W}{\text{cm}}^{-2}$ with an undercritical ($0.5n_c$) homogeneous plasma leads to a very high plasma absorption reaching 68% of the laser pulse energy. This permits a homogeneous electron heating all along the plasma and an efficient ion acceleration at the plasma edges and in cavities.

We derive alternative Markov approximations for the projected (stochastic) force and memory function in the coarse-grained (CG) generalized Langevin equation, which describes the time evolution of the center-of-mass coordinates of clusters of particles in the microscopic ensemble. This is done with the aid of the Mori-Zwanzig projection operator method based on the recently introduced projection operator [S. Izvekov, J. Chem. Phys. 138, 134106 (2013)]. The derivation exploits the “generalized additive fluctuating force” representation to which the projected force reduces in the adopted projection operator formalism. For the projected force, we present a first-order time expansion which correctly extends the static fluctuating force ansatz with the terms necessary to maintain the required orthogonality of the projected dynamics in the Markov limit to the space of CG phase variables. The approximant of the memory function correctly accounts for the momentum dependence in the lowest (second) order and indicates that such a dependence may be important in the CG dynamics approaching the Markov limit. In the case of CG dynamics with a weak dependence of the memory effects on the particle momenta, the expression for the memory function presented in this work is applicable to non-Markov systems. The approximations are formulated in a propagator-free form allowing their efficient evaluation from the microscopic data sampled by standard molecular dynamics simulations. A numerical application is presented for a molecular liquid (nitromethane). With our formalism we do not observe the “plateau-value problem” if the friction tensors for dissipative particle dynamics (DPD) are computed using the Green-Kubo relation. Our formalism provides a consistent bottom-up route for hierarchical parametrization of DPD models from atomistic simulations.

The effect of the heterogeneity in the soil structure or the nonuniformity of the velocity field on the modeled resident time distribution (RTD) and breakthrough curves is quantified by their moments. While the first moment provides the effective velocity, the second moment is related to the longitudinal dispersion coefficient ($k_T$) in the developed Taylor regime; the third and fourth moments are characterized by their normalized values skewness ($\text{Sk}$) and kurtosis ($\text{Ku}$), respectively. The purpose of this investigation is to examine the role of the truncation corrections of the numerical scheme in $k_T$, $\text{Sk}$, and $\text{Ku}$ because of their interference with the second moment, in the form of the numerical dispersion, and in the higher-order moments, by their definition. Our symbolic procedure is based on the recently proposed extended method of moments (EMM). Originally, the EMM restores any-order physical moments of the RTD or averaged distributions assuming that the solute concentration obeys the advection-diffusion equation in multidimensional steady-state velocity field, in streamwise-periodic heterogeneous structure. In our work, the EMM is generalized to the fourth-order-accurate apparent mass-conservation equation in two- and three-dimensional duct flows. The method looks for the solution of the transport equation as the product of a long harmonic wave and a spatially periodic oscillating component; the moments of the given numerical scheme are derived from a chain of the steady-state fourth-order equations at a single cell. This mathematical technique is exemplified for the truncation terms of the two-relaxation-time lattice Boltzmann scheme, using plug and parabolic flow in straight channel and cylindrical capillary with the d2Q9 and d3Q15 discrete velocity sets as simple but illustrative examples. The derived symbolic dependencies can be readily extended for advection by another, Newtonian or non-Newtonian, flow profile in any-shape open-tabular conduits. It is established that the truncation errors in the three transport coefficients $k_T$, $\text{Sk}$, and $\text{Ku}$ decay with the second-order accuracy. While the physical values of the three transport coefficients are set by Péclet number, their truncation corrections additionally depend on the two adjustable relaxation rates and the two adjustable equilibrium weight families which independently determine the convective and diffusion discretization stencils. We identify flow- and dimension-independent optimal strategies for adjustable parameters and confront them to stability requirements. Through specific choices of two relaxation rates and weights, we expect our results be directly applicable to forward-time central differences and leap-frog central-convective Du Fort–Frankel–diffusion schemes. In straight channel, a quasi-exact validation of the truncation predictions through the numerical moments becomes possible thanks to the specular-forward no-flux boundary rule. In the staircase description of a cylindrical capillary, we account for the spurious boundary-layer diffusion and dispersion because of the tangential constraint of the bounce-back no-flux boundary rule.

Impact of the unphysical tangential advective-diffusion constraint of the bounce-back (BB) reflection on the impermeable solid surface is examined for the first four moments of concentration. Despite the number of recent improvements for the Neumann condition in the lattice Boltzmann method–advection-diffusion equation, the BB rule remains the only known local mass-conserving no-flux condition suitable for staircase porous geometry. We examine the closure relation of the BB rule in straight channel and cylindrical capillary analytically, and show that it excites the Knudsen-type boundary layers in the nonequilibrium solution for full-weight equilibrium stencil. Although the d2Q5 and d3Q7 coordinate schemes are sufficient for the modeling of isotropic diffusion, the full-weight stencils are appealing for their advanced stability, isotropy, anisotropy and anti-numerical-diffusion ability. The boundary layers are not covered by the Chapman-Enskog expansion around the expected equilibrium, but they accommodate the Chapman-Enskog expansion in the bulk with the closure relation of the bounce-back rule. We show that the induced boundary layers introduce first-order errors in two primary transport properties, namely, mean velocity (first moment) and molecular diffusion coefficient (second moment). As a side effect, the Taylor-dispersion coefficient (second moment), skewness (third moment), and kurtosis (fourth moment) deviate from their physical values and predictions of the fourth-order Chapman-Enskog analysis, even though the kurtosis error in pure diffusion does not depend on grid resolution. In two- and three-dimensional grid-aligned channels and open-tubular conduits, the errors of velocity and diffusion are proportional to the diagonal weight values of the corresponding equilibrium terms. The d2Q5 and d3Q7 schemes do not suffer from this deficiency in grid-aligned geometries but they cannot avoid it if the boundaries are not parallel to the coordinate lines. In order to vanish or attenuate the disparity of the modeled transport coefficients with the equilibrium weights without any modification of the BB rule, we propose to use the two-relaxation-times collision operator with free-tunable product of two eigenfunctions $\mathrm{\Lambda }$. Two different values ${\mathrm{\Lambda }}_v$ and ${\mathrm{\Lambda }}_b$ are assigned for bulk and boundary nodes, respectively. The rationale behind this is that ${\mathrm{\Lambda }}_v$ is adjustable for stability, accuracy, or other purposes, while the corresponding ${\mathrm{\Lambda }}_b\left({\mathrm{\Lambda }}_v\right)$ controls the primary accommodation effects. Two distinguished but similar functional relations ${\mathrm{\Lambda }}_b\left({\mathrm{\Lambda }}_v\right)$ are constructed analytically: they preserve advection velocity in parabolic profile, exactly in the two-dimensional channel and very accurately in a three-dimensional cylindrical capillary. For any velocity-weight stencil, the (local) double-$\mathrm{\Lambda }$ BB scheme produces quasi-identical solutions with the (nonlocal) specular-forward reflection for first four moments in a channel. In a capillary, this strategy allows for the accurate modeling of the Taylor-dispersion and non-Gaussian effects. As illustrative example, it is shown that in the flow around a circular obstacle, the double-$\mathrm{\Lambda }$ scheme may also vanish the dependency of mean velocity on the velocity weight; the required value for ${\mathrm{\Lambda }}_b\left({\mathrm{\Lambda }}_v\right)$ can be identified in a few bisection iterations in given geometry. A positive solution for ${\mathrm{\Lambda }}_b\left({\mathrm{\Lambda }}_v\right)$ may not exist in pure diffusion, but a sufficiently small value of ${\mathrm{\Lambda }}_b$ significantly reduces the disparity in diffusion coefficient with the mass weight in ducts and in the presence of rectangular obstacles. Although ${\mathrm{\Lambda }}_b$ also controls the effective position of straight or curved boundaries, the double-$\mathrm{\Lambda }$ scheme deals with the lower-order effects. Its idea and construction may help understanding and amelioration of the anomalous, zero- and first-order behavior of the macroscopic solution in the presence of the bulk and boundary or interface discontinuities, commonly found in multiphase flow and heterogeneous transport.

For systems of classical spins interacting with the bath via damping and thermal noise, an approach is suggested to replace the white noise by a pulse noise acting at regular time intervals $\mathrm{\Delta }t$, within which the system evolves conservatively. The method is working well in the typical case of a small dimensionless damping constant $\lambda$ and allows a considerable speedup of computations by using high-order numerical integrators with a large time step $\delta t$ (up to a fraction of the precession period), while keeping $\delta t\ll \mathrm{\Delta }t$ to reduce the relative contribution of noise-related operations. In cases when precession can be discarded, $\delta t$ can be increased up to a fraction of the relaxation time $\propto 1/\lambda$ that leads to a further speedup. This makes equilibration speed comparable with that of the Metropolis Monte Carlo method. The pulse-noise approach is tested on single-spin and multispin models.