The dynamics of Rayleigh-Taylor turbulence convection in the presence of an alternating, time-periodic acceleration is studied by means of extensive direct numerical simulations of the Boussinesq equations. Within this framework, we discover a mechanism of relaminarization of turbulence: the alternating acceleration, which initially produces a growing turbulent mixing layer, at longer times suppresses turbulent fluctuation and drives the system toward an asymptotic stationary configuration. Dimensional arguments and linear stability theory are used to predict the width of the mixing layer in the asymptotic state as a function of the period of the acceleration.

A new octree-based Coulomb interaction model is developed to model the electrospray of ionic liquids (ILs) in molecular dynamics. Using an octree-based method, Coulomb interactions are categorized as intra- and interleaf Coulomb interactions based on a criterion related to the Bjerrum length of the IL. The octree-based method is found capable of reproducing Coulomb energy in agreement with established and computationally more expensive models, such as the direct Coulomb and the damped shifted force (DSF) method in the absence of an external electric field. In the presence of an external electric field, the octree-based method produces distinctly different results compared to that obtained by the direct Coulomb method. The time required to form Taylor's cone was shorter for the octree method compared to the direct Coulomb approach. While no emission larger than monomers was observed from the direct Coulomb simulation, emission of larger species such as dimers and trimers was observed when the octree-based Coulomb interaction model was used. Furthermore, the octree-based model forms a smaller ion emission cone compared to that from the direct Coulomb method.

Using arguments built on ergodicity, we derive an analytical expression for the Renyi entanglement entropies which, we conjecture, applies to the finite-energy density eigenstates of chaotic many-body Hamiltonians. The expression is a universal function of the density of states and is valid even when the subsystem is a finite fraction of the total system—a regime in which the reduced density matrix is not thermal. We find that in the thermodynamic limit, only the von Neumann entropy density is independent of the subsystem to the total system ratio $V_A/V$, while the Renyi entropy densities depend nonlinearly on $V_A/V$. Surprisingly, Renyi entropies $S_n$ for $n>1$ are convex functions of the subsystem size, with a volume law coefficient that depends on $V_A/V$, and exceeds that of a thermal mixed state at the same energy density. We provide two different arguments to support our results: the first one relies on a many-body version of Berry's formula for chaotic quantum-mechanical systems, and is closely related to the eigenstate thermalization hypothesis. The second argument relies on the assumption that for a fixed energy in a subsystem, all states in its complement allowed by the energy conservation are equally likely. We perform an exact diagonalization study on quantum spin-chain Hamiltonians to test our analytical predictions.

Quantum thermal devices which can manage heat as their electronic analogs for the electronic currents have attracted increasing attention. Here a three-terminal quantum thermal device is designed by three coupling qubits interacting with three heat baths with different temperatures. Based on the steady-state behavior solved from the dynamics of this system, it is demonstrated that such a device integrates multiple interesting thermodynamic functions. It can serve as a heat current transistor to use the weak heat current at one terminal to effectively amplify the currents through the other two terminals, to continuously modulate them ranging in a large amplitude, and even to switch on or off the heat currents. It is also found that the three currents are not sensitive to the fluctuation of the temperature at the low-temperature terminal, so it can behave as a thermal stabilizer. In addition, we can utilize one terminal temperature to ideally turn off the heat current at any one terminal and to allow the heat currents through the other two terminals, so it can be used as a thermal valve. Finally, we illustrate that this thermal device can control the heat currents to flow unidirectionally, so it has the function of a thermal rectifier.

We find an exact mapping from the generalized Ising models with many-spin interactions to equivalent Boltzmann machines, i.e., the models with only two-spin interactions between physical and auxiliary binary variables accompanied by local external fields. More precisely, the appropriate combination of the algebraic transformations, namely the star-triangle and decoration-iteration transformations, allows one to express the model in terms of fewer-spin interactions at the expense of the degrees of freedom. Furthermore, the benefit of the mapping in Monte Carlo simulations is discussed. In particular, we demonstrate that the application of the method in conjunction with the Swendsen-Wang algorithm drastically reduces the critical slowing down in a model with two- and three-spin interactions on the Kagomé lattice.

The large-scale synchronization of neural oscillations is crucial in the functional integration of brain modules, but the combination of modules changes depending on the task. A mathematical description of this flexibility is a key to elucidating the mechanism of such spontaneous neural activity. We present a model that finds the loop structure of a network whose nodes are connected by unidirectional links. Using this model, we propose a path-finding system that spontaneously finds a path connecting two specified nodes. The solution path is represented by phase-synchronized oscillatory solutions. The model has the self-recovery property: that is, it is a system with the ability to find a new path when one of the connections in the existing path is suddenly removed. We show that the model construction procedure is applicable to a wide class of nonlinear systems arising in chemical reactions and neural networks.

We numerically study the spatiotemporal dynamics and early detection of thermoacoustic combustion instability in a model rocket combustor using the theories of complex networks and synchronization. The turbulence network, which consists of nodes and vertexes in weighted networks between vortices, can characterize the complex spatiotemporal structure of a flow field during thermoacoustic combustion instability. The transfer entropy allows us to identify the driving region of thermoacoustic combustion instability. In addition to the order parameter, a phase parameter newly proposed in this study is useful for capturing the precursor of thermoacoustic combustion instability.

We present experimental results of the effect of the hopper angle on the clogging of grains discharged from a two-dimensional silo under gravity action. We observe that the probability of clogging can be reduced by three orders of magnitude by increasing the hopper angle. In addition, we find that for very large hopper angles, the avalanche size ($\langle s\rangle$) grows with the outlet size ($D$) stepwise, in contrast to the case of a flat-bottom silo for which $\langle s\rangle$ grows smoothly with $D$. This surprising effect is originated from the static equilibrium requirement imposed by the hopper geometry to the arch that arrests the flow. The hopper angle sets the bounds of the possible angles of the vectors connecting consecutive beads in the arch. As a consequence, only a small and specific portion of the arches that jam a flat-bottom silo can survive in hoppers.

A three-dimensional lattice-Boltzmann model (LBM) for the simulation of the Maxwell equations is presented. The inclusion of media follows an extension of a special limit described in the literature which is applicable to this LBM and does not harm the stability of simulations. The focus of the present study lies on the properties of numerical accuracy and stability of the LBM in comparison to the standard finite-difference time-domain (FDTD) method based on Yee's method. Typical examples, often investigated in the context of numerical simulations, are considered. These include the propagation of electrodynamic (EM) fields in one- and three-dimensional systems. Results of this simulations are compared to the ones of their theoretical predictions. Further on, long-time simulations are done in systems with periodic boundary conditions to check if the total energy is conserved. To investigate the effect of the numeric impedance, the propagation of an EM pulse is monitored spatially and temporarily in a two-dimensional system. The simulation results indicate, in contrast to the one obtained from the FDTD method, that the presented LBM does fulfill the expected energy conservation and is not effected by the numerical impedance. This LBM therefore represents a valuable alternative for the simulation of EM problems like long-time simulations by avoiding intrinsic properties the FDTD method suffers from.

A theory is developed to calculate values of the potential-energy barriers to structural relaxation in molecular glass formers from the data of static pair-correlation function. The barrier height is shown to increase due to an increase in the number of “stable bonds” a particle forms with its neighbors and the energy of each bond as liquids move deeper into the supercooled (supercompressed) region. We present results for a system of hard spheres and compare calculated values of the structural relaxation time with experimental and simulation results.

We examine the dynamics of cutting-and-shuffling a hemispherical shell driven by alternate rotation about two horizontal axes using the framework of piecewise isometry (PWI) theory. Previous restrictions on how the domain is cut-and-shuffled are relaxed to allow for nonorthogonal rotation axes, adding a new degree of freedom to the PWI. A new computational method for efficiently executing the cutting-and-shuffling using parallel processing allows for extensive parameter sweeps and investigations of mixing protocols that produce a low degree of mixing. Nonorthogonal rotation axes break some of the symmetries that produce poor mixing with orthogonal axes and increase the overall degree of mixing on average. Arnold tongues arising from rational ratios of rotation angles and their intersections, as in the orthogonal axes case, are responsible for many protocols with low degrees of mixing in the nonorthogonal-axes parameter space. Arnold tongue intersections along a fundamental symmetry plane of the system reveal a new and unexpected class of protocols whose dynamics are periodic, with exceptional sets forming polygonal tilings of the hemispherical shell.

We observe dark and bright intrinsic localized modes (ILMs), also known as discrete breathers, experimentally and numerically in a diatomic-like electrical lattice. The experimental generation of dark ILMs by driving a dissipative lattice with spatially homogenous amplitude is, to our knowledge, unprecedented. In addition, the experimental manifestation of bright breathers within the band gap is also novel in this system. In experimental measurements the dark modes appear just below the bottom of the top branch in frequency. As the frequency is then lowered further into the band gap, the dark ILMs persist, until the nonlinear localization pattern reverses and bright ILMs appear on top of the finite background. Deep into the band gap, only a single bright structure survives in a lattice of 32 nodes. The vicinity of the bottom band also features bright and dark self-localized excitations. These results pave the way for a more systematic study of dark breathers and their bifurcations in diatomic-like chains.

We compare two methods for controlling synchronization in the Kuramoto model on an undirected network. The first is by driving selected oscillators at a desired frequency by coupling to an external driver, and the second is by including adaptive lags—or dynamical frustrations—within the Kuramoto interactions, with the lags evolving according to a dynamics as a function of the reference frequency with an associated time constant. Performing numerical simulations with random regular graphs, we find that above a certain connectivity driving via adaptive lags allows for stronger alignment to the external frequency at a lower value of the time constant compared to the corresponding coupling strength for the externally driven model. Numerical results are supported by equilibrium analysis based on a fixed-point ansatz for frequency synchronized clusters where we solve the spectrum of the associated Jacobian matrix. We find that at low connectivity the external driving mechanism is successful down to lower densities of controlled oscillators where the adaptive lag approach is Lyapunov unstable at all densities. As connectivity increases, however, the adaptive lag mechanism shows stability over similar ranges of density to the external driving and proves superior in terms of tighter splays of oscillators. In particular, the threshold for instability for the adaptive lag model shows robustness against variations in the associated time constant down to lower densities of controlled oscillators. A simple intuitive model emerges based on the interaction between splayed clusters close to a critical point.

The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov-chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the activity patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I neurons dramatically overestimates synchrony among E neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80–95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.

Morphogen gradients play a vital role in developmental biology by enabling embryonic cells to infer their spatial location and determine their developmental fate accordingly. The standard mechanism for generating a morphogen gradient involves a morphogen being produced from a localized source and subsequently degrading. While this mechanism is effective over the length and time scales of tissue development, it fails over typical subcellular length scales due to the rapid dissipation of spatial asymmetries. In a recent theoretical work, we found an alternative mechanism for generating concentration gradients of diffusing molecules, in which the molecules switch between spatially constant diffusivities at switching rates that depend on the spatial location of a molecule. Independently, an experimental and computational study later found that Caenorhabditis elegans zygotes rely on this mechanism for cell polarization. In this paper, we extend our analysis of switching diffusivities to determine its role in protein concentration gradient formation. In particular, we determine how switching diffusivities modifies the standard theory and show how space-dependent switching diffusivities can yield a gradient in the absence of a localized source. Our mathematical analysis yields explicit formulas for the intracellular concentration gradient which closely match the results of previous experiments and numerical simulations.

Motivated by the recent experimental observations on motor induced cooperative mechanism controlling the length dynamics of microtubules (MTs), we examine how plus-end-targeted proteins of the kinesin family regulate MT polymerization and depolymerization routines. Here, we study a stochastic mathematical model capturing the unusual form of collective motor interaction on MT dynamics originating due to the molecular traffic near the MT tip. We provide an extensive analysis of the joint effect of motor impelled MT polymerization and complete depolymerization. The effect of the cooperative action is included by modifying the intrinsic depolymerization rate. We analyze the model within the framework of continuum mean-field theory and the resultant steady-state analytic solution is expressed in terms of Lambert $W$ functions. Four distinct steady-state phases including a shock phase have been reported. The significant features of the shock including its position and height have been analyzed. Theoretical outcomes are supported by extensive Monte Carlo simulations. To explore the system alterations between the regime of growth and shrinkage phase, we consider kymographs of the microtubule along with the length distributions. Finally, we investigated the dependence of MT length kinetics both on modifying factor of depolymerization rate and motor concentration. The overall extensive study reveals that the flux of molecular traffic at the microtubule plus end initiates a cooperative mechanism, resulting in significant change in MT growth and shrinkage regime as also observed experimentally.

Molecular dynamics simulation was used to study the temperature dependence of the mutual diffusion coefficient $D_m$ and the intermediate scattering function of equilibrium and metastable aqueous solutions of the cryoprotectant molecule trehalose at very low (2.2 and $9\mathrm{wt}.\%$) and very high (80 and $95\mathrm{wt}.\%$) concentrations. The simulations were conducted over a range of temperatures approaching the glass transition temperature $T_g$ for each concentration. Similar to a recent observation made on a glass-forming model polydisperse colloidal suspension [Hannam et al., Phys. Rev. E 96, 022609 (2017)], we confirmed by a set of independent computations that $D_m$ is responsible for the long-time decay of the intermediate scattering function. We observed that $D_m$ decreased on the approach to the glass transition temperature, resulting in an extremely slow long-time decay in the intermediate scattering function that culminated in the arrest of compositional fluctuations and a plateau in the intermediate scattering function at $T_g$. In both cases, crystallization requires a change in the composition of the solution, which is a process controlled by $D_m$. This transport coefficient can either increase or decrease as solidification is approached, because it depends on a product of thermodynamic and mobility factors. Our observations show that in both cases, for the glass-forming liquids, it is observed to decrease, while for a previously studied monodisperse colloidal suspension which crystallizes easily, it increases. The similarity in the behavior of these two very different glass-forming systems (the polydisperse colloidal suspension and the sugar solution) shows the importance of the mutual diffusion coefficient to our understanding of vitrification and suggests a possible distinction between between glass-forming and crystallizing solutions.

It is well established that roughness and chemistry play a crucial role in the wetting properties of a substrate. Yet, few studies have analyzed systematically the effect of the nonuniformity in the distribution of texture and surface tension of substrates on its wetting properties. In this work we investigate this issue theoretically and numerically. We propose a continuous model that takes into account the total energy required to create interfaces of a droplet in two possible wetting states: Cassie-Baxter (CB) with air pockets trapped underneath the droplet; and the other characterized by the homogeneous wetting of the surface, called the Wenzel (W) state. To introduce geometrical nonregularity we suppose that pillar heights and pillar distances are Gaussian distributed instead of having a constant value. Similarly, we suppose a heterogeneous distribution of Young's angle on the surface to take into account the chemical nonuniformity. This allows to vary the “amount” of disorder by changing the variance of the distribution. We first solve this model analytically and then we also propose a numerical version of it, which can be applied to study any type of disorder. In both versions, we employ the same physical idea: The energies of both states are minimized to predict the thermodynamic wetting state of the droplet for a given volume and surface texture. We find that the main effect of disorder is to induce the stability of both wetting states on the same substrate. In terms of the influence of the disorder on the contact angle of the droplet, we find that it is negligible for the chemical disorder and for pillar-distance disorder. However, in the case of pillar-height disorder, it is observed that the average contact angle of the droplet increases with the amount of disorder. We end the paper investigating how the region of stability of both wetting states behaves when the droplet volume changes.

Stand-alone and portable lab-on-chips (LOC) can be obtained by exploiting capillary flow in porous media. Polymethylmethacrylate (PMMA) platforms obtained through powder-based three-dimensional (3D) printing are appropriate for capillarity-driven LOCs. However, fluid flow in such platforms needs to be characterized well. For this purpose, a 3D pore network (PN) was extracted from high-resolution $\mu \mathrm{CT}$ images of printed PMMA through a watershed algorithm and a PN model was developed with the final goal of characterizing material permeability. The effect of all parameters involved in the PN extraction and modeling was investigated. The study focused in particular on the effect of the number of seeds for the watershed segmentation, pore sphericity, and pore-to-pore channel shape that was modeled as a bicylindrical or biconical object. The results proved that all PN extraction and modeling parameters influenced the permeability, which was found to be lower the higher the number of seeds and when using sphericity and biconical channels. Eventually, the Calinski-Harabasz index value was used to identify the optimal number of watershed seeds.