Quadratic Hamiltonians that exhibit single-particle quantum chaos are called quantum-chaotic quadratic Hamiltonians. One of their hallmarks is single-particle eigenstate thermalization introduced in Łydżba et al. [Phys. Rev. B 104, 214203 (2021)], which describes statistical properties of matrix elements of observables in single-particle eigenstates. However, the latter has been studied only in quantum-chaotic quadratic Hamiltonians that obey the $U$(1) symmetry. Here, we focus on quantum-chaotic quadratic Hamiltonians that break the $U$(1) symmetry and, hence, their “single-particle” eigenstates are actually single-quasiparticle excitations introduced on the top of a many-body state. We study their wave functions and matrix elements of one-body observables, for which we introduce the notion of single-quasiparticle eigenstate thermalization. Focusing on spinless fermion Hamiltonians in three dimensions with local hopping, pairing, and on-site disorder, we also study the properties of disorder-induced near zero modes, which give rise to a sharp peak in the density of states at zero energy. Finally, we numerically show equilibration of observables in many-body eigenstates after a quantum quench. We argue that the latter is a consequence of single-quasiparticle eigenstate thermalization, in analogy to the $U$(1) symmetric case from Łydżba et al. [Phys. Rev. Lett. 131, 060401 (2023)].

Self-oscillating coupled systems possess the ability to actively absorb external environmental energy to sustain their motion. This quality endows them with autonomy and sustainability, making them have application value in the fields of synchronization and clustering, thereby furthering research and exploration in these domains. Building upon the foundation of thermal responsive liquid crystal elastomer-based (LCE-based) spring oscillators, a synchronous system comprising three LCE-based spring oscillators interconnected by springs is established. In this paper, the synchronization phenomenon is described, and the self-oscillation mechanism is revealed. The results indicate that by varying system parameters and initial conditions, three synchronization patterns emerge, namely, full synchronous mode, partial synchronous mode, and asynchronous mode. For strongly interacting systems, full synchronous mode always prevails, while for weak interactions, the adjustment of initial velocities in magnitude and direction yields the three synchronization patterns. Additionally, this study explores the impact of several system parameters, including LCE elasticity coefficient and spring elasticity coefficient, on the amplitude, frequency, and synchronous mode of the system. The findings in this paper can enhance our understanding of the synchronization behavior of multiple mutually coupled LCE-based spring oscillators, with promising applications in energy harvesting, soft robotics, signal monitoring, and various other fields.

The Zhong-Zhang (ZZ) model is a one-degree-of-freedom dynamical system describing the motion of an insulating plate of length $d$ floating on the upper surface of a convecting fluid, with locking at the boundaries. In the absence of noise, the system away from the boundaries is described by linear differential equations with a delay time $\tau$. The $d,\tau$ plane consists of two domains separated by a critical curve. For asymptotically long times, subcritical orbits approach a nontrivial periodic attractor, while the supercritical ones tend to a stationary state at the origin. We investigate near-critical behavior using a modified fourth-order Runge-Kutta integration scheme. We then construct a piecewise analytic decomposition of the periodic attractor, which makes possible a far higher level of accuracy. Our results provide solid evidence for an asymptotic power-law approach to criticality of several observables. The power laws are fed back to determine the piecewise-analytic structure deep into the near-critical regime. In an Appendix, we explore the effect of introducing noise using modified order-$3/2$ Kloeden-Platen-Schurz stochastic integration, following several observable quantities through the near-critical parameter domain.

In this paper, we develop a macroscopic finite-difference scheme from the mesoscopic regularized lattice Boltzmann (RLB) method to solve the Navier-Stokes equations (NSEs) and convection-diffusion equation (CDE). Unlike the commonly used RLB method based on the evolution of a set of distribution functions, this macroscopic finite-difference scheme is constructed based on the hydrodynamic variables of NSEs (density, momentum, and strain rate tensor) or macroscopic variables of CDE (concentration and flux), and thus shares low memory requirement and high computational efficiency. Based on an accuracy analysis, it is shown that, the same as the mesoscopic RLB method, the macroscopic finite-difference scheme also has a second-order accuracy in space. In addition, we would like to point out that compared with the RLB method and its equivalent macroscopic numerical scheme, the present macroscopic finite-difference scheme is much simpler and more efficient since it is only a two-level system with macroscopic variables. Finally, we perform some simulations of several benchmark problems, and find that the numerical results are not only in agreement with analytical solutions, but also consistent with the theoretical analysis.

Hidden geometry enables the investigation of complex networks at different scales. Extending this framework to multiplex networks, we uncover a different kind of mesoscopic organization in real multiplex systems, named clan, a group of nodes that preserve local geometric arrangements across layers. Furthermore, we reveal the intimate relationship between the unfolding of clan structure and mutual percolation against targeted attacks, leading to an ambivalent role of clans: making a system fragile yet less prone to complete shattering. Finally, we confirm the correlation between the multiscale nature of geometric organization and the overall robustness. Our findings expand the significance of hidden geometry in network function, while also highlighting potential pitfalls in evaluating and controlling catastrophic failure of multiplex systems.

In this paper, we considered a molecular structure that consists of a molecular chain and an additional molecule (donor or acceptor) that can inject (or remove) single excitation (vibron, electron, etc.) onto the molecular chain. We assumed that the excitation forms a self-trapped state due to the interaction with mechanical oscillations of the chain structure elements. We analyzed the energy spectra of the excitation and showed that its state (when it migrates to the molecular chain) has the properties of the nonadiabatic polaron state. The conditions under which the excitation can migrate from one subsystem to another one were considered. It was shown that the presence of a “donor” molecule cannot significantly change the properties of the excitation located on the molecular chain. At the same time, the molecular chain can affect the position of the energy level of the excitation localized on the donor subsystem. Indirectly, this can influence the process of excitation migration from one subsystem to another one. The influence of the basic energy parameters of the system and the environment temperature on this process are discussed. The entire system was assumed to be in thermal equilibrium with the environment.

Proton bunches with maximum energies between 12 and 22 MeV were emitted from submicrometer-thin plastic foils upon irradiation by laser pulses with peak intensity of $4\times {10}^{20}\mathrm{W}/{\mathrm{cm}}^2$. The images of the protons by a magnetic quadrupole doublet on a screen remained consistently larger by a factor of 10 compared to expectations drawn from the ultralow transverse emittance values reported for thick foil targets. Analytic estimates and particle-in-cell simulations attribute this drastically increased emittance to formerly excluded Coulomb collisions between charged particles. The presence of carbon ions and significant transparency likely play a decisive role. This observation is highly relevant because such thin, partially transparent foils are considered ideal for optimizing maximum proton energies.

The properties of plasmas in the low-density limit are described by virial expansions. Analytical expressions are known from Green's function approaches only for the first three virial coefficients. Accurate path-integral Monte Carlo (PIMC) simulations have recently been performed for the uniform electron gas, allowing the virial expansions to be analyzed and interpolation formulas to be derived. The exact expression for the second virial coefficient is used to test the accuracy of the PIMC simulations and the range of validity of the interpolation formula of Groth et al. [Phys. Rev. Lett. 119, 135001 (2017)], and we discuss the fourth virial coefficient, which is not exactly known yet. Combining PIMC simulations with benchmarks from exact virial expansion results would allow us to obtain more accurate representations of the equation of state for parameter ranges of conditions which are of interest, e.g., for helioseismology.

We investigate the occurrence of spontaneous convection in a coaxial cylinder highly filled with monodisperse spheres. To analyze the flow field noninvasively, initial pulses consisting of colored particles are placed at equal intervals. By analyzing the spatiotemporal distribution of these pulses, we obtained axial velocity profiles for both the surface and subsurface regions. Our advection-diffusion equations with steady advection terms incorporate experimentally obtained axial velocity profiles in the surface layer, while the rest of the components are estimated using azimuthal symmetry and volume conservation. The validity of our model is confirmed by comparing experimental data with numerical solutions for both the spatiotemporal distribution and cross-sectional profile of the colored particles.

We present a theory for the interfacial wetting phase behavior of binary liquid mixtures on rigid solid substrates, applicable to both miscible and immiscible mixtures. In particular, we calculate the binding potential as a function of the adsorptions, i.e., the excess amounts of each of the two liquids at the substrate. The binding potential fully describes the corresponding interfacial thermodynamics. Our approach is based on classical density functional theory. Binary liquid mixtures can exhibit complex bulk phase behavior, including both liquid-liquid and vapor-liquid phase separation, depending on the nature of the interactions among all the particles of the two different liquids, the temperature, and the chemical potentials. Here we show that the interplay between the bulk phase behavior of the mixture and the properties of the interactions with the substrate gives rise to a wide variety of interfacial phase behaviors, including mixing and demixing situations. We find situations where the final state is a coexistence of up to three different phases. We determine how the liquid density profiles close to the substrate change as the interaction parameters are varied and how these determine the form of the binding potential, which in certain cases can be a multivalued function of the adsorptions. We also present profiles for sessile droplets of both miscible and immiscible binary liquids.

Developed turbulent motion of fluid still lacks an analytical description despite more than a century of active research. Nowadays, phenomenological ideas are widely used in practical applications, such as small-scale closures for numerical simulations of turbulent flows. In the present paper, we use a shell model of turbulence to construct a closure intended to have a solid theoretical background and to capture intrinsic probabilistic features of turbulence. Shell models of turbulence are dynamical deterministic systems used to model energy cascade and other key aspects of the Navier-Stokes such as intermittency. We rescale the variables of the Sabra model in a way which leads to hidden symmetries and universal distributions. We then use such fine distributions to write closures, i.e., missing expressions for some of the Sabra variables. Our closures rely on approximating probability density functions using a Gaussian mixture model, which makes them probabilistic by nature and allows us to write time-correlated closures. We also provide a framework where other machine learning tools can be employed with reduced black-box aspects.

For uniform random 4-colorings of graph edges with colors $\{a,b,c,d\}$, every two colors form a $\frac{1}{2}$- percolation, and every two overlapping pairs of colors form independent $\frac{1}{2}$-percolations. We show positive mutual dependence for pairs of colors $ab,ac$, and $ad$, and negative mutual dependence for pairs of colors $ab,ac$, and $bc$. The proof is based on a generalization of the Harris-Kleitman inequalities. We apply the results to crossing probabilities for the colored bond and site percolation, and to colored critical percolation that we also define.

Periodical cicadas exhibit life cycles with durations of 13 or 17 years, and it is now accepted that large prime cycles arose to avoid synchrony with predators. Less well explored is how, in the face of intrinsic biological and environmental noise, insects within a brood emerge together in large successive swarms from underground during springtime warming. Here, we consider the decision-making process of underground cicadas experiencing random, spatially correlated thermal microclimates such as those in nature. Introducing short-range communication between insects leads to an Ising model of consensus building with a quenched, spatially correlated random magnetic field and annealed site dilution, which displays the kinds of collective swarms seen in nature. These results highlight the need for fieldwork to quantify the spatial fluctuations in thermal microclimates and their relationship to the spatiotemporal dynamics of swarm emergence.

When a system deviates from equilibrium, it is possible to manipulate and control it to drive it towards equilibrium within finite time $t_f$, even by reducing its natural relaxation timescale ${\tau }_{\text{relax}}$. Although numerous theoretical and experimental studies have explored these shortcut protocols, few have yielded analytical results for the probability distribution of the work, heat, and produced entropy. In this study, we propose a two-step protocol that captures the essential characteristics of more general protocols and provides an analytical solution for the relevant thermodynamic probability distributions. Additionally, we present evidence that for a very short protocol duration $t_f\ll {\tau }_{\text{relax}}$, all protocols exhibit universal behavior for the ratio of probability distribution functions of positive and negative work, heat, and the produced entropy.

We study the probability distribution $P\left(A\right)$ of the area $A={\int }_0^{T}x\left(t\right)dt$ swept under fractional Brownian motion (fBm) $x\left(t\right)$ until its first passage time $T$ to the origin. The process starts at $t=0$ from a specified point $x=L$. We show that $P\left(A\right)$ obeys exact scaling relation $P\left(A\right)=\frac{D^{\frac{1}{2H}}}{L^{1+\frac{1}{H}}}{\mathrm{\Phi }}_H\left(\frac{D^{\frac{1}{2H}}A}{L^{1+\frac{1}{H}}}\right),$ where $0<H<1$ is the Hurst exponent characterizing the fBm, $D$ is the coefficient of fractional diffusion, and ${\mathrm{\Phi }}_H\left(z\right)$ is a scaling function. The small-$A$ tail of $P\left(A\right)$ has been recently predicted by Meerson and Oshanin [Phys. Rev. E 105, 064137 (2022)], who showed that it has an essential singularity at $A=0$, the character of which depends on $H$. Here we determine the large-$A$ tail of $P\left(A\right)$. It is a fat tail, in particular such that the average value of the first-passage area $A$ diverges for all $H$. We also verify the predictions for both tails by performing simple sampling as well as large-deviation Monte Carlo simulations. The verification includes measurements of $P\left(A\right)$ up to probability densities as small as ${10}^{-190}$. We also perform direct observations of paths conditioned on the area $A$. For the steep small-$A$ tail of $P\left(A\right)$ the optimal paths, i.e., the most probable trajectories of the fBm, dominate the statistics. Finally, we discuss extensions of theory to a more general first-passage functional of the fBm.

Recently, low-dimensional models of neuronal activity have been exactly derived for large networks of deterministic, quadratic integrate-and-fire (QIF) neurons. Such firing rate models (FRM) describe the emergence of fast collective oscillations (>30 Hz) via the frequency locking of a subset of neurons to the global oscillation frequency. However, the suitability of such models to describe realistic neuronal states is seriously challenged by the fact that during episodes of fast collective oscillations, neuronal discharges are often very irregular and have low firing rates compared to the global oscillation frequency. Here we extend the theory to derive exact FRM for QIF neurons to include noise and show that networks of stochastic neurons displaying irregular discharges at low firing rates during episodes of fast oscillations are governed by exactly the same evolution equations as deterministic networks. Our results reconcile two traditionally confronted views on neuronal synchronization and upgrade the applicability of exact FRM to describe a broad range of biologically realistic neuronal states.

Recent evidence shows that heteroclinic bifurcations in magnetic islands may be caused by the amplitude variation of resonant magnetic perturbations in tokamaks. To investigate the onset of these bifurcations, we consider a large aspect ratio tokamak with an ergodic limiter composed of two pairs of rings that create external primary perturbations with two sets of wave numbers. An individual pair produces hyperbolic and elliptic periodic points, and its associated islands, that are consistent with the Poincaré-Birkhoff fixed-point theorem. However, for two pairs producing external perturbations resonant on the same rational surface, we show that different configurations of isochronous island chains may appear on phase space according to the amplitude of the electric currents in each pair of the ergodic limiter. When one of the electric currents increases, isochronous bifurcations take place and new islands are created with the same winding number as the preceding islands. We present examples of bifurcation sequences displaying (a) direct transitions from the island chain configuration generated by one of the pairs to the configuration produced by the other pair, and (b) transitions with intermediate configurations produced by the limiter pairs coupling. Furthermore, we identify shearless bifurcations inside some isochronous islands, originating nonmonotonic local winding number profiles with associated shearless invariant curves.

We propose a method for determining the time and, therefore, temperature-dependent relative nucleation and growth rates during crystallization. We do so by linking the partial differential equation governing the time dynamics of the crystal size distribution to kinetic (Avrami) parameters describing heat release. This approach is tested in silico by nucleating and growing diffusion limited aggregates with time-varying morphology and growth rates unhindered by impingement. The associated heat release is analyzed, showing that nucleation and growth rates could be extracted with high fidelity.