It is well known that striker impact on strongly nonlinear, nondissipative, or weakly dissipative granular chains assembled from spheres (or cylinders with flat ends and spheres) having similar masses results in strongly nonlinear Nesterenko solitary waves over a very short distance from the impacted end. This paper presents the first experimental results on how the nature of the wave response, excited by striker impact on strongly nonlinear weakly dissipative discrete chains, depends on the ramp time of the incoming pulse. Variation of the pulse ramp time was accomplished using “hard” and “soft” striker impact. In the case of “hard” impact, the incoming pulse with relatively short ramp time was split into Nesterenko solitary waves after traveling about 20 particles, as in previous publications. The “soft” impact by the same striker with the same velocity was achieved by placing a small mass PTFE spherical particle inside a PTFE O-ring at the top of the first cylinder resulting in significantly longer ramp time of the incoming pulse. The incoming pulse generated by “soft” impact did not split into a train of solitary waves despite initial steepening of the wave's leading front due to strong nonlinearity. Instead, it traveled as a shocklike stress wave a long distance of 131 particles. Thus, the generation of a train of solitary waves or a shocklike stress wave in a weakly dissipative, strongly nonlinear discrete chain depends on the duration of the incoming pulse ramp. Ramping the incoming pulse resulted in a dramatic decrease in shocklike stress wave amplitude in comparison with the amplitude of the leading solitary wave in the case of “hard” impact.

Nonlinear dynamics of runaway electron induced wave instabilities can significantly modify the runaway distribution critical to tokamak operations. Here we present a fully kinetic simulation of runaway-driven instabilities toward nonlinear saturation in a warm plasma where collisional damping is subdominant. It is found that the slow-X modes grow an order of magnitude faster than the whistler modes, and they parametrically decay to produce whistlers much faster than those directly driven by runaways. These parent-daughter waves, as well as secondary and tertiary wave instabilities, initiate a chain of wave-particle resonances that strongly diffuse runaways to the backward direction. This reduces almost half of the current carried by high-energy runaways, over a time scale orders of magnitude faster than experimental shot duration. These results beyond quasilinear analysis may impact anisotropic energetic electrons broadly in laboratory, space, and astrophysics.

We developed a method for significantly lowering the viscosity achievable for a hydrodynamic lattice gas method. The key advance is the derivation of a mirror state that allows for a reduction of viscosity by more than an order of magnitude over existing lattice gas methods.

We reproduce the Debye process in the dielectric response of liquid 1-propanol by all-atom molecular dynamics simulations between 340 K and 200 K. The analysis of dipolar correlations reveals that the $\alpha$ relaxation originates from hydrogen-bond (HB) breaking, while the dominant Debye process results from long-ranged cross-correlations extending over several molecular diameters. By separating intra- and extracluster contributions, we demonstrate that HB supramolecular clusters account for the main part of the static dielectric response, with extracluster molecules playing only a minor role at low temperatures. Besides, clusters do not preserve connectivity on timescales exceeding the $\alpha$ relaxation time. This indicates that clusters stabilize orientational correlations beyond individual molecular relaxation times by transmitting alignment to newly incorporated molecules. These findings provide microscopic evidence that the Debye relaxation in monoalcohols arises from the collective dynamics of HB clusters and offer a framework to study dielectric spectra in other hydrogen-bonded liquids.

In this work, quantum Stirling engines based on monolayer, AB-stacked bilayer, and ABC-stacked trilayer graphene under perpendicular magnetic fields are analyzed. Performance maps of the useful work $\left(\eta W\right)$ reveal a robust optimum at low magnetic fields and moderately low temperatures, with all stackings capable of reaching Carnot efficiency under suitable configurations. The AB bilayer achieves this across the broadest parameter window while sustaining finite work, the monolayer exhibits highly constrained regimes, and the trilayer shows smoother trends with sizable $\eta W$. These results identify multilayer graphene, particularly the AB bilayer, as a promising platform for efficient Stirling engines, while also highlighting the versatility of the monolayer in realizing all four operational regimes of the Stirling cycle.

We study the coupled dynamics of multilayer networks with symmetric (MLs) and asymmetric (MLa) interlayer connections. The symmetric interlayer connections arise from a one-to-one correspondence between the nodes of different layers. In contrast, asymmetry results from the multiplicity of interlayer connections, achieved by randomizing the links while preserving their overall density, thereby allowing one-to-many interlayer connections. We investigate how different types of interlayer coupling impact the dynamics of nonidentical multilayer networks. We find that the specificity of one-to-one interlayer connections facilitates intralayer synchronization (ILS). In contrast, for networks with random interlayer connectivity, ILS depends on how randomness affects intralayer homomorphism (the set of permutations that preserve the network structure). Furthermore, amplitude death (AD) in MLs is observed at lower connectivity strength and frequency mismatch than the MLa. Moreover, AD in MLs for regular intralayer connectivity depends on the density and topology but does not depend on the size of the networks. On the other hand, AD in MLa is influenced by network size in addition to density, topology, and interlayer mismatches. Moreover, both the MLs and MLa exhibit multistability, with the faster layer exhibiting a remanent periodic phase-locked oscillation, irrespective of the topology and interlayer connectivity. In addition, remnant synchrony between nodes with homomorphic relationships is observed in the slower layer. Overall, we propose that symmetric interlayer connections should be preferable for achieving intralayer synchronization, regardless of global synchronization, and for sustaining permanent memory in multilayer networks with mismatched nodes across layers. However, to mitigate AD at low coupling values and layer mismatch, asymmetric interlayer connectivity is more advantageous.

Bridging nodes, which connect critical components of a network, play an important role in maintaining structural integrity and facilitating communication within the network, representing indirect yet relevant connections. Epidemic triggering mechanisms in networks often involve long-range mutual activation of hubs, mediated by paths composed of low-degree nodes. While low-degree nodes are abundant in networks, their role in bridging central nodes in epidemic activation mechanisms has not been thoroughly analyzed. Starting with a backbone network with a power-law degree distribution, we investigate the role of adding degree-2 bridging nodes that are preferentially attached to hubs. Our findings reveal that bridging nodes can mediate an indirect feedback interaction between hubs that modifies the epidemic localization and activation mechanisms of the epidemic processes with recurrent infections. In particular, the collective activation observed in the presence of waning immunity, which produces a finite epidemic threshold in power-law networks with degree exponent $\gamma >3$, is altered to a localized activation with a vanishing threshold. Our numerical results are analytically supported by the nonbacktracking matrix properties that emerge in the recurrent dynamical message-passing theory.

Temporal networks consist of time-stamped directed interactions that may appear continuously in time, yet few studies have directly tackled the continuous-time modeling of networks. Here, we introduce a maximum-entropy approach to temporal networks and with basic assumptions on constraints, the corresponding network ensembles admit a modular and interpretable representation: a set of global time processes and a static maximum-entropy edge, e.g., node pair or probability. This time-edge labels factorization yields closed-form log-likelihoods, degree, clustering, and motif expectations, and yields an entire class of effective generative models. We provide the maximum-entropy derivation for the nonhomogeneous Poisson process (NHPP) intensities governing the probability of directed edges in temporal networks via the functional optimization over path entropy, connecting NHPP modeling to maximum-entropy network ensembles. NHPPs consistently improve log-likelihood over generic Poisson processes, while the maximum-entropy edge labels recover strength constraints and reproduce expected unique-degree curves. We discuss the limitations of this framework and how it can be integrated with multivariate Hawkes calibration procedures, renewal theory, and neural kernel estimation in graph neural networks.

Active matter systems comprise self-propelled particles that move on a substrate while leaving chemical trails that influence other particles through chemotaxis (e.g., slime-depositing bacteria). Orientational chemotaxis manifests as a torque that steers the particle toward the chemical gradient. As each particle is coupled to its own trail, the dynamics exhibits an instability: when the particle gently diffuses, it abruptly transitions to trajectories with a radius of curvature comparable to its own size, becoming apparently trapped. We argue that this trajectory instability occurs for any chemotactic coupling strength, either directly above some threshold or via an activated process below it.

The laser direct-drive fast ignition scheme is a candidate for inertial fusion energy reactors. The scheme promises higher fusion gains (fusion energy divided by laser energy) than with conventional central hot-spot ignition. For a particle beam (electrons or ions) to reach and heat the compressed fuel, a reentrant cone is mounted in the fuel shell. The cone, which is made from higher $Z$ material, has a significant impact on the implosion of the fuel shell. This work presents two-dimensional radiation hydrodynamic simulations with the flash code of the laser direct-drive compression of reactor-scale cone-in-shell targets with $2.7\mathrm{m}\mathrm{g}$ of deuterium-tritium fuel. The simulations highlight the different stages of the implosion leading to a nonspherical fuel shape at stagnation. The implosion also significantly affects the cone tip and the extreme high pressure in the hot spot creates a strong shock wave that crushes the cone tip. A comprehensive parameter study is presented with different cone geometries, different cone materials (gold, copper, and aluminum) and laser drive asymmetry to study the fuel assembly in front of the cone tip and the shock formation in the cone tip material. For the reactor-scale capsule design we analyzed, we find that a ${30}^{\circ }$ half cone angle made from copper, and a wall thickness of about $200\mu \mathrm{m}$ provides a good trade-off in terms of fuel assembly, cone tip survivability, and minimization of travel distance for the particle beam through the dense plasma. The results indicate that it is feasible to compress fuel to the required density of $400\mathrm{g}/{\mathrm{cm}}^3$ using a laser pulse energy of approximately $1.5\mathrm{M}\mathrm{J}$. This opens the potential for achieving high energy gains with moderate pulse energies, envisioning massive energy production through laser fusion.

Whether and how intense laser pulses can pierce optically opaque media remains a fundamental question of interest in laser-matter interactions. Both existing mechanisms enabling laser propagation in overdense plasmas, i.e., relativistic self-induced transparency and electromagnetically induced transparency, require strongly relativistic laser intensities. Here we report on the anomalous transparency of nonrelativistic intense laser pulses through overdense plasmas mixed with sufficiently high-density neutral atoms. Theoretical modeling and particle-in-cell simulations reveal a distinctly different mechanism, collisionally induced transparency (CIT), arising from the suppression of collective directional self-induced currents by the frequent electron-neutral collisions. Parametric study of the laser transparency window illustrates that the long-wave infrared and terahertz pulses are more prone to the CIT effect than the conventional near-infrared pulses. A delayed two-color laser scheme is proposed to validate and boost the CIT phenomenon. As an exotic nonlinear effect, the CIT demonstrated here opens up a different avenue for manipulating the propagation dynamics of long-wavelength strong-field electromagnetic waves and their interactions with weakly ionized media.

In this work, an interpolation-supplemented quantum lattice Boltzmann method (IS-QLBM) is proposed to simulate incompressible flows with nonuniform meshes and curved boundaries. Owing to the constraint of lattice uniformity, existing quantum LBMs are restricted to uniform square meshes and regular geometries, which severely limit their practicality. To address this limitation, we introduce an interpolation strategy to reconstruct the distribution functions $f$ at each mesh point from those at neighboring points, thereby formulating the entire streaming step into the form of $A·f$. The matrix $A$ is then decomposed into unitary matrices via singular value decomposition (SVD) for quantum circuit implementation. Since $A$ depends solely on the mesh point coordinates, IS-QLBM can be applied to arbitrary meshes and geometries. The proposed method is validated using two-dimensional incompressible isothermal and thermal flows. The results show that IS-QLBM agrees very well with its classical counterpart IS-LBM, and that quadratic or higher-order interpolation is recommended to ensure sufficient accuracy. To further improve computational efficiency, we develop a multi-circuit IS-QLBM framework that reduces computational cost to less than one-eighth of the single-circuit implementation without sacrificing accuracy.

Granular matter exhibits unusual mechanical properties. For instance, when filling a cylindrical container with grains, the weight measured at the bottom of the granular column does not scale linearly with the added mass, but asymptotically saturates towards a constant apparent weight. This observation is the well-known Janssen effect, ultimately reflecting that the “lost” weight is supported by frictional forces between the grains and the wall of the container confining the grains. When filling narrower containers with spherical grains, however, an intermediate regime consisting of an overshoot in measured weight appears; in this case, before the usual Janssen saturation is observed, frictional forces contribute to increase the apparent weight. Here, we experimentally study the role of particle shape in this reverse Janssen effect and find that it disappears faster with increasing cylinder diameter for non-spherical grains compared to spherical grains. We then discuss the experimental results, analyzing some of the possible aspects that could potentially explain the influence of grain shape.

Crack networks in thin films often emerge through a sequential process governed by the temporal ordering of fracture events. However, experimental observations frequently reveal disconnected patterns, characterized by incomplete junctions and fragmented pathways, which deviate from this idealized scenario. In this work, we investigate the physical origins of the transition between disconnected and connected crack networks, linking it to the mechanical response of the film. The degree of connectivity is quantified through morphological descriptors that capture the extent of crack intersection and network organization. Combining this quantitative analysis with controlled experiments on coatings, we demonstrate that film thickness critically influences the fracture dynamics: thin films favor simultaneous cracking and yield disconnected networks, whereas thicker films undergo sequential fracture, forming highly connected structures. The results are interpreted within a unified energetic framework, in which a dimensionless parameter comparing elastic and fracture energies governs the transition between these distinct cracking regimes.

The bias-reversal symmetry—where reversing an external bias inverts the current without changing its magnitude—is a hallmark of nonequilibrium transport. While this property holds in homogeneous systems such as the asymmetric simple exclusion process, how disorder and its interplay with particle interactions affect this symmetry has remained unclear. Here, we identify a general criterion in disordered exclusion processes showing that the bias-reversal symmetry holds if and only if the local left-right bond-bias ratio is spatially uniform. This criterion provides a practical diagnostic that separates heterogeneous environments into symmetry-preserving and symmetry-breaking classes. Mean-field and numerical analyses reveal that bond disorder preserves the symmetry beyond linear response, whereas site disorder breaks it through an interplay between heterogeneity and particle interactions. Our results demonstrate how environmental disorder and interparticle interactions cooperate to generate asymmetric transport, thereby providing insight that is potentially relevant to transport through biological and artificial nanochannels.

A damped oscillator heat bath model is a modification of the standard heat bath model, wherein each bath oscillator itself has a Markovian coupling to its own heat bath [A. V. Plyukhin, Phys. Rev. E 99, 052125 (2019)]. We modify such a model to one where the resulting damping of the oscillators is linear in their frequency rather than being a constant. We find that this generates a memory kernel which behaves like $k\left(t\right)\sim 1/t$ as $t\to \infty$, which is a boundary case not considered in previous works. As the memory kernel does not have a finite integral, the reduced system is subdiffusive, and we numerically show that diffusion goes as $\langle \mathrm{\Delta }{Q}^2\left(t\right)\rangle \sim t/ln\left(t\right)$ as $t\to \infty$. We also numerically calculate the velocity correlation function in the asymptotic regime and use it to confirm the aforementioned subdiffusion.

We formulate a statistical-mechanical description of a recently introduced random planting model in which plants are represented by growing hard disks. Seedlings of negligible size are introduced at random positions in a field, grow at a prescribed rate, and are harvested upon reaching a fixed maturity diameter. Planting attempts that would lead to an overlap at any time during growth are rejected. Starting from an empty field, this simple dynamical rule drives the system to a nonequilibrium steady state in which the mean planting and harvesting rates coincide. We show that the steady state can be mapped onto a nonadditive polydisperse hard-disk fluid, and we exploit this mapping to develop analytical predictions based on a low-density virial expansion and on scaled particle theory. The resulting description yields an effective adsorption isotherm for the steady-state plant density as a function of the planting rate and compares favorably with numerical simulations over a wide range of parameters. At large planting rates, the density approaches the optimal value achieved by desynchronized regular planting, and the data are consistent with an algebraic approach to this limit with an exponent close to $1/3$. Beyond density and yield, we show that the spatial organization of the field at high planting rates exhibits clear signatures of the same underlying geometric constraints that characterize optimal desynchronized planting. This connection is revealed through both the conventional radial distribution function and a radius–resolved pair correlation $g(z,r)$ which highlights strong size correlations associated with parent-child seeding events and whose structure can be interpreted as a dynamically broadened precursor of the corresponding ideal mixed-size lattice. Finally, we extend the theory to sigmoidal growth laws and compute the associated virial coefficient.

In this study we demonstrate a self-oscillating acoustic meta-atom functioning as an amplifying transistor, where a steady external flow serves as a control signal to switch between reflective (off-state) and transmissive (on-state) regimes. In the on state, an acoustic limit cycle synchronizes with incident sound waves. This process governs the energy transfer across the device, with a transmission bandwidth dictated by the synchronization region in parameter space. Our experimental measurements reveal nonlinear dependence on the incident wave amplitude, enabling perturbation filtering therein and stabilizing downstream acoustic power. All experimentally observed phenomena are quantitatively described by a nonlinear Liénard-type oscillator featuring saturable gain and linear loss, where the essential parameters can be estimated by independent measurements. This work may offer a paradigm shift in acoustic metamaterials research by leveraging self-oscillation and synchronization processes. Bridging those key concepts from nonlinear dynamics and complex systems with active metamaterial design in acoustics and related disciplines, may establish a broadly applicable framework of field-independent mechanisms for wave manipulation.

Large-scale outbreaks of infectious diseases, often spread through person-to-person contact, have historically caused significant morbidity and mortality. In this study, we develop a two-layer SIR (Susceptible–Infected–Recovered) model that accounts for individual mobility within and between populations. We explore two complementary approaches to disease mitigation: (i) an optimal control framework and (ii) an evolutionary behavior model. The optimal control approach minimizes the disease burden by coordinating three controls: travel restrictions, social distancing, and antiviral treatment, guided by predefined cost functions. In contrast, the behavioral model captures adaptive individual responses based on infection prevalence, interpopulation infection disparities, and socioeconomic trade-offs, following evolutionary game theory. We find that for high-severity epidemics, combining travel restrictions with social distancing significantly reduces infection peaks and total cases, while these measures become less effective for lower-severity outbreaks. Across all scenarios, a combined control strategy is most effective. However, a key finding is that antiviral treatment alone can rival the effectiveness of combined travel and distancing measures, offering a streamlined alternative when the societal costs of nonpharmaceutical interventions are prohibitive. Furthermore, optimally coordinated policies consistently outperform adaptive behavioral responses, yielding a significant reduction in the population.