The structures of fiber networks can vary greatly due to fiber interactions during formation. We have modified the steps of generating Mikado networks to create two model classes by altering the spatial distribution and relative orientation of their fibers to mimic the structures of self-assembling peptide hydrogels (SAPHs), whose physical properties depend strongly on their fibers' interactions. The results of our models and experiments on a set of beta-sheet forming SAPHs show that modifying a network's structure affects the percolation threshold and the mechanical behavior of the material, both near percolation and at higher densities.

We investigate with particle-in-cell simulation the transport of fast electrons of MeV under a kT-level magnetic field produced by a circular coil target, where the fast electrons are generated by a picosecond laser pulse with typical intensity of $5\times {10}^{19}\mathrm{W}/{\mathrm{cm}}^2$ adopted in fast ignition. The fast electrons can be well collimated when the coil diameter is about $200\mu \mathrm{m}$ in the fast ignition or double-cone ignition scheme of laser fusion when the current in the coil is taken as around 200 kA according to typical experimental results. The optimized coil diameter results from the competition between the strength enhancement and the nonuniformity growth of the magnetic field as the reduction of the coil diameter. Here the nonuniformity growth can induce the magnetic mirror effect, which causes the reflection of some transporting fast electrons. In particular, when the coil diameter is larger than $500\mu \mathrm{m}$, the fast-electron transport under the produced magnetic field is very close to the case with a uniform magnetic field. We also find shifting the coil longitudinal position along the laser progapagation can change the electron transport range within the convergent or divergent magnetic field lines, and placing the coil tens of micrometers away from the electron generation position is favorable for the electron transport. This work could be applied not only in fast ignition and double-cone ignition schemes, but also to ion acceleration and radiation sources based on laser-driven fast electrons.

Karpov et al. [Phys. Rev. E 103, 022310 (2021)] applied extreme value theory, or EVT, to the analysis of epileptic seizures based on electroencephalographic data. We call attention to some fundamental issues related to the use of EVT in that study.

Thermodynamic (TUR) and kinetic (KUR) uncertainty relations are fundamental bounds constraining the fluctuations of current observables in classical, nonequilibrium systems. Several works have verified, however, violations of these classical bounds in open quantum systems, motivating the derivation of new quantum TURs and KURs that account for the role of quantum coherence. Here, we go one step further by deriving multidimensional KUR and TUR for multiple observables in open quantum systems undergoing Markovian dynamics. Our derivation exploits a multiparameter metrology approach, in which the Fisher information matrix plays a central role. Crucially, our bounds are tighter than previously derived quantum TURs and KURs for single observables, precisely because they incorporate correlations between multiple observables. We also find an intriguing quantum signature of correlations that is captured by the off-diagonal element of the Fisher information matrix, which vanishes for classical stochastic dynamics. By considering two examples, namely a coherently driven qubit system and the three-level maser, we demonstrate that the multidimensional quantum KUR bound can even be saturated when the observables are perfectly correlated.

In this paper, we derive equations for the dynamics of ring dark solitons in an expanding cloud of a two-dimensional Bose-Einstein condensate. Assuming that the soliton's width is much smaller than its radius, we obtain the Hamilton equations for its evolution. Then they are transformed into the Newton equation, which is more convenient for applications. The general theory is illustrated by the solution of the Newton equation for the case of the axially symmetric condensate cloud, which expands after switching off a harmonic trap. The validity of our approximate analytical approach is confirmed by comparison with the results of numerical simulations of the Gross-Pitaevskii equation.

Message-passing techniques on networks encompasses a family of related methods that can be employed to ascertain many important properties of a network. It is widely considered to be the state-of-the-art formulation for networked systems, and advances in this method have a wide impact across multiple literatures. One property that message passing can yield is the size of the largest connected component in the network following bond percolation. In this paper, we introduce an alternative method of finding this value that differs from the standard approach. Like the canonical approach, our method is exact on trees and an approximation on arbitrary graphs. We show that our method lends itself to the description of a variety of generalizations of bond percolation such as sequential percolation and nonbinary percolation and can yield information about the local environment of a node in percolation equilibrium that the traditional approach cannot.

In order to know how a single potassium channel works in its microscopic niche, a hybrid process about the observables of channel state and voltage is constructed, which comprises the interweaving kinetics of channel gating, voltage decay, and thermal noise. The formula of entropy production rates (i.e., power dissipation) is derived and the stationary probability distribution is solved analytically. It is found that gating is thermally robust, and the underlying mechanism is explained. Using the mutual information between the distributions of the channel state and voltage to characterize how much information of gating can be broadcasted by voltage evolution, and the entropy production rate per unit area to represent the energy costs of the system, we plot the Pareto front from the perspective of accuracy-energy trade-off, and find that, in terms of information broadcasting, the channel works most efficiently without the extrinsic driving current.

Inertial confinement of fusion plasmas can be realized using the dynamic $Z$ pinch, an electromagnetically driven cylindrical implosion system that is also used to generate intense x rays. However, the $Z$ pinch is subject to magnetic Rayleigh-Taylor (MRT) instability, which must be suppressed for practical applications. This article proposes an innovative technique in which a distributively induced current is superimposed on a $Z$ pinch to realize a quasispherical implosion (QSI) while also simultaneously maintaining significant suppression of the MRT instability. The advantages of the QSI and the improved stability enable a potential in inertial confinement fusion. An analytical model is developed that considers both the implosion dynamics and the instability development process, and the model's results confirm the advantages of the proposed scheme. This model is also applicable to a wide range of general implosion scenarios for arbitrary rotational surfaces, including QSI, and can degenerate to both standard cylindrical and spherical implosions.

Microswimmers of either biological or synthetic nature exhibit a variety of nonequilibrium collective behavior, including aggregation and phase separation. In particular, situations that tend to be overlooked concern the separation under confinement of mixtures of microswimmers that differ in their propulsion mechanism. In this case, understanding the system parameters that control the degree of the separation requires understanding the hydrodynamic forces at play. Given the complex confining geometries, these forces can only be resolved numerically. We leverage recent advances in massively parallel colloidal hydrodynamic simulations to model a mixture of two styles of swimmers, “pushers” and “pullers,” which separate due to hydrodynamic interactions with nearby surfaces. Through systematic variation of confining geometries we construct a series of filters for species separation. Our findings provide insight for the design of activity-based separation methods of active colloids.

Our current understanding of fluctuations of dynamical (time-integrated) observables in non-Markovian processes is still very limited. A major obstacle is the lack of an appropriate theoretical framework to evaluate the associated large deviation functions. In this paper we bypass this difficulty in the case of linear diffusions with time delay by using a Markovian embedding procedure that introduces an infinite set of coupled differential equations. We then show that the generating functions of current-type observables can be computed at arbitrary finite time by solving matrix Riccati differential equations (RDEs) somewhat similar to those encountered in optimal control and filtering problems. By exploring in detail the properties of these RDEs and of the corresponding continuous-time algebraic Riccati equations, we identify the generic fixed point toward which the solutions converge in the long-time limit. This allows us to derive the explicit expressions of the scaled cumulant generating function (SCGF), of the preexponential factors, and of the effective (or driven) process that describes how fluctuations are created dynamically. Finally, we describe the special behavior occurring at the limits of the domain of existence of the SCGF, in connection with fluctuation relations for the heat and the entropy production.

Nonhydrodynamic transverse excitations in liquids exhibit a propagation gap in the long-wavelength region of their dispersion curve. Using molecular dynamics simulations, we investigate the density dependence of the propagation gap width in simple liquids. Based on these findings, we critically analyze the so-called phonon theory of liquid thermodynamics by comparing its analytical predictions with simulation data for supercritical neon (Ne). One contentious aspect of the phonon theory for liquids is the treatment of undamped transverse excitations and their frequency range. We present spectra of transverse propagating modes for supercritical Ne, compare the estimated propagation gaps with theoretical predictions, evaluate the total energy per particle, and discuss the limitations of the phonon theory in accurately describing the simulation results.

The Lindblad description of an open quantum system gives rise to two types of integrability, since the nonequilibrium steady state can be integrable independently of the Liouvillian. Taking boundary-driven and dephasing spin chains as a representative example, we discriminate Liouvillian and steady-state chaos by combining level spacing statistics and an extension of the eigenstate thermalization hypothesis to open quantum systems. Moreover, we analyze the structure of the steady states by expanding it in the basis of Pauli strings and comparing the weight of strings of different lengths. We show that the natural expectation that integrable steady states are “simple” (i.e., built from few-body local operators) does not hold: the steady states of both chaotic and integrable models have relevant contributions coming from Pauli strings of all possible lengths, including long-range and many-body interactions. Nevertheless, we show that one can effectively use the operator-size distribution to distinguish chaotic and integrable steady states.

We examine the probability of the appearance of high values of the intensity $I$ of a light beam at its propagation in the turbulent atmosphere. Starting from the functional integral representation of averages over turbulent fluctuations, we find the saddle-point configuration in the functional space corresponding to the high values of $I$. The saddle-point configuration determines the tail of the probability density function (PDF) of $I$ characterized by a stretched exponent and determines the high moments of $I$. By investigating fluctuations of the refraction index on top of the saddle-point configuration, we establish the applicability conditions for the approach, which are different for the cases of weak and strong scintillations.

We consider a tracer particle performing a random walk on a two-dimensional lattice in the presence of immobile hard obstacles. Starting from equilibrium, a constant force pulling on the particle is switched on, driving the system to a new stationary state. Our study calculates displacement moments in discrete time (number of steps $N$) for an arbitrarily strong constant driving force, exact to first order in obstacle density. We find that for fixed driving force $F$, the approach to the terminal discrete velocity scales as $\sim N^{-1}exp(-N{F}^2/16)$ for small $F$, differing significantly from the exponential decay expected of linear response. Besides a nonanalytic dependence on the force and breakdown of Einstein's linear response, our results show that fluctuations around the mean displacement in direction of the force are enhanced in the presence of obstacles. Notably, the variance grows as $\sim N^3$ (superdiffusion) for $F\to \infty$ at intermediate steps, reverting to normal diffusion ($\sim N$) at larger steps, a behavior previously observed in continuous time and verified in this work in the discrete domain of the number of steps. Unlike the exponential waiting time case, the superdiffusion regime starts already for $N=1$. The presented framework allows considering any type of waiting-time distribution between steps and transition to continuous time using subordination methods. Our findings are also validated through computer simulations.

Catalysts speed up chemical reactions with no energy input and without being transformed in the process, therefore leaving equilibrium constants unchanged. Some catalysts, however, are much more efficient at accelerating one direction of a reaction. Is it possible for catalysis to be strictly unidirectional, accelerating only one direction of a reaction? Can we observe directional catalysis by analyzing the microscopic trajectory of a single reactant undergoing conversions between a substrate and a product state? We use the framework of a simple but exactly solvable lattice model to study these questions. The model provides examples of strictly one-way catalysts and illustrates a mathematical relationship between the asymmetric transition rates that underlie directional catalysis and the symmetric transition fluxes that underlie chemical equilibrium. The degree of directionality generally depends on the catalytic mechanism and we compare different mechanisms to show how they can obey different scaling laws.

Following the concept of the red-king effect proposed by Bergstrom and Lachmann [Proc. Natl. Acad. Sci. USA 100, 593 (2003)], a stochastic asymmetric replicator equation is developed to investigate the influence of environmental stochastic fluctuations on the interspecific mutualism, where the evolutionary rate of each species is assumed to be a random variable. By the stochastic stability analysis and stochastic simulations, our results indicate that not only is the red-king effect is still true in some cases, but also the environmental stochastic fluctuations may also result in the complete reciprocity between species, that is, the so-called generosity without reservation between each other. All these results may provide a possible way to understand the evolutionary mechanism of interspecific mutualism in real nature.

We propose a magnetic analogy of the Duffing oscillator—magnetic Duffing oscillator—which is characterized by a double-well magnetic potential of a ferromagnet with a uniaxial magnetic anisotropy. Based on the linear stability analysis of the Landau–Lifshitz–Gilbert equation, we show that an external magnetic field applied perpendicular to the magnetic anisotropy field creates an anharmonicity on the magnetic potential, generating homoclinic orbits in the phase space. By evaluating the Lyapunov exponent, we demonstrate that the magnetic Duffing oscillator exhibits chaotic behaviors in the presence of periodically oscillating external forces: Oersted field and spin-orbit torque by considering the ferromagnet/heavy-metal bilayer. We also show that the external magnetic field can be adjusted to generate or modify homoclinic orbits, thereby controlling the parameter range of the oscillating external forces that induce chaos. This work deepens our understanding of chaotic magnetization dynamics by bridging the fields of nonlinear dynamics and spintronics.

Potassium channels are essential for various physiological functions, with their high selectivity being a critical foundation. The blockage of sodium ions in potassium channels is a crucial aspect of their selectivity, yet the underlying mechanism remains unclear. In this study, we investigated the mechanisms underlying the sodium ion blockage through molecular dynamics simulations. We found that when sodium ions bind to the ${\mathrm{B}}_{34}$ site, the water molecule at the entrance of the selectivity filter forms a strong association with the ion, preventing effective knock-on movement and resulting in a 93% loss of permeability. Given the vibrational frequencies of biomolecules within the terahertz range, terahertz waves are considered effective tools for biomolecular modulation. Consequently, we explored the influence of terahertz waves on the blockage of sodium ions. Our results show that a 15 THz electric field significantly disrupts the stable binding of the water molecule to the sodium ion at the ${\mathrm{B}}_{34}$ site, enhancing interactions between the ion in the cavity and those in the selectivity filter, thereby alleviating the blockage. This study reveals that the dynamic state of water molecules in biological systems is closely related to biomolecular functions and is influenced by external physical fields, such as terahertz electric fields. These findings provide new insights into potassium channels' selectivity mechanisms and broaden terahertz fields' potential applications in regulating channel functions.

Neurons in the brain continuously process the barrage of sensory inputs they receive from the environment. A wide array of experimental work has shown that the collective activity of neural populations encodes and processes this constant bombardment of information. How these collective patterns of activity depend on single-neuron properties is often unclear. Single-neuron recordings have shown that individual neurons' responses to inputs are nonlinear, which prevents a straightforward extrapolation from single-neuron features to emergent collective states. Here, we use a field-theoretic formulation of a stochastic leaky integrate-and-fire model to study the impact of single-neuron nonlinearities on macroscopic network activity. In this model, a neuron integrates spiking output from other neurons in its membrane voltage and emits spikes stochastically with an intensity depending on the membrane voltage, after which the voltage resets. We show that the interplay between nonlinear spike intensity functions and membrane potential resets can (i) give rise to metastable active firing rate states in recurrent networks, and (ii) can enhance or suppress mean firing rates and membrane potentials in the same or paradoxically opposite directions.