We numerically model the ultrarelativistic dynamics of a dense plasma microtarget, in a focus of several intersecting femtosecond laser pulses of multipetawatt power each. The aim is to examine prospective experimental approaches to the search for the Inverse Faraday Effect induced by radiation friction. We show that multibeam configurations allow lowering the single beam peak laser power required to generate a detectable quasistatic longitudinal magnetic field excited due to the radiation reaction force. The effect is significant at angles around ${10}^{\mathrm{o}}$ between the beam propagation axes, almost vanishes when the angle exceeds ${20}^{\mathrm{o}}$, and remains rather stable with respect to the variations of relative phases and amplitudes of the beams. Quantum recoil, accounted for semiclassically, is shown to considerably suppress the longitudinal magnetic field, which, however, remains sizable. We conclude that, by using four infrared femtosecond linearly polarized pulses of 15 petawatt power each, crossing at angles of $\approx {10}^{\mathrm{o}}$, the radiation-dominated regime of laser-plasma interaction can be experimentally demonstrated.

Condensation in linear wedges formed by semi-infinite walls is a well-established critical phenomenon characterized by the continuous growth of an adsorbed liquid layer as bulk two-phase coexistence is approached. In this study, we investigate condensation in finite-length wedges open at both ends, demonstrating that the process becomes first-order. The open boundaries and finite geometry induce a remarkably rich phase behavior of the confined fluid, exhibiting three distinct types of condensation, reentrant phenomena, and continuous higher-order transitions between condensation states. Through a detailed macroscopic analysis, we derive the conditions for each type of condensation, classify the global phase diagrams, and explore asymptotic behavior in specific limiting cases. The asymptotic predictions are confirmed by a detailed comparison with the numerical solutions of the governing equations.

Rigidity transitions induced by the formation of system-spanning disordered rigid clusters, like the jamming transition, can be well described in most physically relevant dimensions by mean-field theories. A dynamical mean-field theory commonly used to study these transitions, the coherent potential approximation (CPA), shows logarithmic corrections in two dimensions. By solving the theory in arbitrary dimensions and extracting the universal scaling predictions, we show that these logarithmic corrections are a symptom of an upper critical dimension $d_{\mathrm{upper}}=2$, below which the critical exponents are modified. We recapitulate Ken Wilson's phenomenology of the $(4-\epsilon )$-dimensional Ising model, but with the upper critical dimension reduced to 2. We interpret this using normal form theory as a transcritical bifurcation in the RG flows and extract the universal nonlinear coefficients to make explicit predictions for the behavior near two dimensions. This bifurcation is driven by a variable that is dangerously irrelevant in all dimensions $d>2$ which incorporates the physics of long-wavelength phonons and low-frequency elastic dissipation. We derive universal scaling functions from the CPA sufficient to predict all linear response in randomly diluted isotropic elastic systems in all dimensions.

The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the current focus is on technical issues of their efficient estimation, it turns out that the standard concepts such as Granger causality or transfer entropy may not faithfully reflect possible synergies or interactions of higher orders, phenomena highly relevant for many real-world complex systems. In this paper, we propose a generalization and refinement of the information-theoretic approach to causal inference, enabling the description of truly multivariate, rather than multiple pairwise, causal interactions, and moving thus from causal networks to causal hypernetworks. In particular, while keeping the ability to control for mediating variables or common causes, in case of purely synergistic interactions such as the exclusive disjunction, it ascribes the causal role to the multivariate causal set but not to individual inputs, distinguishing it thus from the case of, e.g., two additive univariate causes. We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics recently reported to employ synergistic computations.

Motivated by complex bidirectional transport processes that occur in various biological and physical systems, we examine a one-dimensional closed system comprising two parallel lanes under limited resources. One lane is characterized by driven diffusive transport, while the other supports solely unidirectional motion in the opposite direction, mutually coupled through strong coupling. The total number of particles in the system, quantified by the filling factor, regulates the inflow of particles onto the lanes. The stationary properties of the system are analyzed through both simple and vertical cluster mean-field approaches, complemented by boundary layer analysis to elucidate its detailed behavior. Our theoretical results demonstrate that, for any set of parameters, one of the lanes invariably resides in a phase of zero net particle flux, manifesting either in a completely empty or fully jammed state. Two distinct types of phase transitions are identified and characterized: bulk transitions and surface transitions. Notably, phases emerge that exhibit boundary layers in their density profiles near both boundaries. A distinctive feature of the interplay between lane coupling and bidirectional transport is the appearance of kink and dip in the boundary layers, which is scrutinized by using the residence time method and fixed point analysis. Furthermore, we investigate the dynamics of phases involving shock and its sensitivity to system parameters. All theoretical outcomes are corroborated through stochastic simulations based on the Gillespie algorithm and numerical technique.

Compartment models are widely used in fields such as epidemiology and biomedicine to describe the exchange of uniformly distributed materials between interconnected compartments. However, their application in biological fluids is limited by the assumption of infinitely large diffusivity, especially in environments such as tumors or subcutaneous tissue, where diffusion is considerably lower. To address this, we develop a diffusion-aware compartment model that maintains the simplicity of traditional compartment models while offering greater accuracy. We conducted experiments on the uptake of ${}^{18}\mathrm{F}$-fluorodeoxyglucose (FDG), a radionuclide, by cells grown in culture plates and found a good agreement between the measured and predicted cellular radioactivity. We identify two critical dimensionless parameters that compare the amount of FDG (i) replenished by diffusion and (ii) available in the culture medium to the amount of FDG taken up by cells. We demonstrate that the diffusion-aware compartment model reduces to the three-compartment model when FDG diffusion is fast relative to cellular uptake, and it further simplifies to the two-compartment model when sufficient FDG is available in the culture medium. The semianalytic solutions of the diffusion-aware compartment model can be easily extended to study other scenarios, such as drug transport and bubble growth dynamics.

Cellularized tissue and polymer networks can both transition from floppy to rigid as a function of their control parameters, and, yet, the two systems often mechanically interact, which could affect their respective rigidities. To study this interaction, we consider a vertex model with surface tension embedded in a spring network in two dimensions. We identify two regimes with different global spheroid shapes and governed by the pressure resulting from competition between surface tension and tension in the network. In the first regime, the tissue remains compact, while in the second, a cavitation-like instability leads to the emergence of gaps at the tissue-network interface. Intriguingly, compression of the tissue promotes fluidization, while tension promotes cellular alignment and rigidification with the mechanisms driving rigidification differing on either side of the instability.

In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialized from a Dirac δ distribution and the uniform distribution is targeted. By quantifying the instantaneous nonuniformity of the distribution with the Sinkhorn divergence, we elucidate the principal mechanisms underlying the mixing problems. In spheres and cubes, we show that the collective motion transitions between fluidlike and discretization-dominated behavior, with the critical step size scaling as a power law in the dimension. In both regimes, the particles can spontaneously unmix, leading to resonances in the particle density and the aforementioned problems. Additionally, low-dimensional toy models of the dynamics are constructed which reproduce the dominant features of the high-dimensional problem. Finally, the dynamics is contrasted with the exact Hamiltonian particle flow and tuning practices are discussed.

A finite cycle time information engine based on a two-level system in contact with a thermal bath is studied analytically. The model for the engine incorporates an error in measuring the system’s state and time delay between the measurement and the feedback process. The efficiency and power of the engine in steady state are derived as a function of level spacing, feedback delay time, engine cycle time, and measurement error. For a fixed value of level spacing and feedback delay, there is an upper bound on measurement error such that the engine can extract positive work. This threshold value of error is found to be independent of the cycle time. For a range of values of level spacing, feedback delay time and cycle time, efficiency has a non-monotonic dependence on the measurement error, implying that there is an optimal measurement error for the information engine to operate efficiently. The range of feedback delays within which the system functions as an engine, extracting positive work, depends on the measurement error and the level spacing.

Theory and experiment have long discussed negative thermodynamic pressure states, but their microscopic origins are unclear. I address this problem within the framework of quantum thermodynamics. I show that the pressure exerted on the boundary is positive when there is no interaction with the boundary. This is formalized by a no-go theorem that holds for any quantum state with finite motion. As a consequence of this analysis, I deduce a general formula for quantum nonequilibrium pressure. It is believed that stable negative pressure states cannot exist in gases. I provide solvable examples of quantum and classical gases, where negative pressure is achieved due to a suitable coupling with the boundary walls.

In nanomaterials and microreactor technology, the intricate phenomena of localization, that is, the inhomogeneous spatial distribution of a physical property, serve as a booster for advancing innovative applications. In this work, we show from simple physical arguments that such spatial segregation can be spontaneously produced in mixtures of silver nanowires and DNA solution confined inside microdroplets. In particular, a reversible quadrupolar redistribution of the particles can be obtained by applying low frequency electric fields. As a consequence, the localization of the electro-orientation of the particles appears, which is induced by the interplay between the steric repulsion of the nanowires, the electrically stretched DNA coils, and the microdroplet wall, together with the DNA mediated depletion effects. We show, both experimentally and via Monte Carlo simulations, that particle segregation results in a decrease of the overall particle alignment.

The Ashkin-Teller model plays a central role in studying the phase transitions of statistical mechanics models and condensed matter systems. Nevertheless, the phase transitions of the three-dimensional Ashkin-Teller model with competing interactions have long been an enigma. Here, we employ a methodology conceptually based on emergent symmetry and perform embedding cluster Monte Carlo simulations for unprecedentedly large systems. We find that the distribution of a two-dimensional magnetization vector displays emergent O(2) symmetry, which appears along the critical line separating the paramagnetic phase from the Baxter and mixed phases. Sign-reversal finite-size effects are visible in the deviations from O(2) symmetry, but the thermal and magnetic renormalization exponents are universally close to the exponents of the three-dimensional XY model. The newly unveiled “extraordinary-log” critical phase and “special” transition of the XY model are realized in a plane defect of the critical Ashkin-Teller model. Thus, for the three-dimensional Ashkin-Teller model, our study provides complementary evidence of an XY critical line, which arises from the emergence of O(2) symmetry.

Avalanche sizes and durations following power-law distributions are observed in many systems and are considered hallmarks of criticality. Time-series thresholding is a commonly used method to define avalanches, but this method is controversial. In this study, we use the time-series thresholding method to define avalanches and investigate the statistical properties of avalanches in the Kinouchi-Copelli (KC) model. We consider two definitions of avalanche size, (1) total area above threshold value reference and (2) total area above zero, and then analyze the size and duration distributions. Our results show that while avalanche size and duration obey a power-law distribution, the exponents of size and duration differ from those of the critical branching process. The scaling relation holds for avalanches defined by the first method but fails for those avalanches tdefined by the second. This study provides new insights into avalanche definition methods and avalanche distribution exponents in continuous time-series.

In this paper, we use the formalism of finite-temperature quantum field theory to investigate the Casimir force between flat, ideally conductive surfaces containing confined, but mobile ions. We demonstrate that, in the Gaussian approximation, the ionic fluctuations contribute separately from the electromagnetic fluctuations that are responsible for the standard Casimir effect. This is in line with the “separation hypothesis,” which has been previously applied on a purely intuitive basis. Our analysis demonstrates the significance of calculating the zero Matsubara frequency component in the electromagnetic contribution, using the formula developed by Schwinger et al., as opposed to the approach based on Lifshitz theory used by other researchers.

Josephson junctions (JJs) are by nature neuromorphic hardware devices capable of mimicking excitability and spiking dynamics. When coupled together or combined with other superconducting elements, they can emulate additional behaviors found in biological neurons. From a technological point of view, JJ-based neuromorphic systems are particularly appealing since they present THz-speed processing and they operate with near-zero power dissipation. In this work, we study a system of inductively coupled JJs and focus on the nonlinear dynamical aspects of its neurocomputational properties. In particular, we report on spiking behavior related to a saddle-node off invariant cycle bifurcation and excitability type II, synchronization, first spike latency effects, and multistability. Special emphasis is placed on the bursting dynamics the system is capable of reproducing, and a new underlying mechanism is proposed beyond the approach followed in prior works.

The 90-minute ultradian rhythm is a hallmark of healthy human sleep, yet its governing mechanisms remain elusive. In this study, we develop a biologically grounded sleep model to unravel the complex dynamics underlying this rhythm. Our model integrates both circadian and ultradian drives, which collectively shape sleep architecture, along with bidirectional “flip-flop” switches that control transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Calibrated with empirically derived neurophysiological parameters, the model successfully reproduces core sleep features, including the 24-hour circadian rhythm and the 90-minute ultradian rhythm. To dissect state transition mechanisms, we employ potential landscape analysis to quantify how global stability is modulated by three key factors: circadian drive, homeostatic drive, and REM pressure. Our results reveal that the ultradian rhythm emerges from an interplay between a weak ultradian drive and REM pressure. In a reduced model focusing on NREM-REM interactions, we demonstrate that the periodic transitions between NREM and REM sleep arise from a saddle-node bifurcation on an invariant circle (SNIC) induced by REM sleep pressure. Additionally, the ultradian drive entrains the rhythmic NREM-REM system to exhibit the stable 90-minute ultradian rhythm, as characterized by the Arnold tongue. Our work provides the mechanistic explanation of the 90-minute ultradian rhythm, identifying REM pressure as its core regulator and highlighting the SNIC bifurcation together with the Arnold tongue as its dynamical mechanisms. This framework establishes testable neurophysiological requirements for experimental validation, thereby bridging theoretical models with empirical sleep neuroscience.

This study examines the influence of an inhomogeneous external electric field on thermoelectrohydrodynamic (TEHD) convection and its impact on heat transfer enhancement in a dielectric fluid layer. TEHD convection arises when an external electric field interacts with a nonisothermal fluid, where electric field inhomogeneity is essential for convective flow. Such inhomogeneity can be intrinsically caused by temperature-dependent permittivity, $\epsilon \left(T\right)$, or geometrical curvature, such as in cylindrical or spherical shells. To investigate how an externally imposed inhomogeneous field modifies convective behavior compared to intrinsic effects, we employ an electrode configuration to generate field gradients between the top and bottom layers. Results reveal three distinct regimes based on the forcing intensity, characterized by the electric Rayleigh number, ${\mathrm{Ra}}_{\mathrm{E}}$. Convective motion is observed when ${\mathrm{Ra}}_{\mathrm{E}}\ne 0$, with significant heat transfer enhancement, surpassing prior studies. Specifically, the leading term of the Nusselt number, $\text{Nu}$, scales as $\text{Nu}\sim {\mathrm{Ra}}_{\mathrm{E}}^{0.3}$.1 These findings highlight the substantial potential of TEHD convection for improving heat transfer performance.

The symbol $\sim$ means "scales as" throughout the article, not "order of magnitude." The prefactor and ranges are given in Eq. (20).

In this paper, the first in a series, we present a new theoretical model for the global structure and dissipation of relativistically magnetized collisionless shock waves. Quite remarkably, we find that in contrast to unmagnetized shocks, the leading energy dissipation channel does not involve collective plasma interactions. Rather, it is a consequence of nonlinear particle dynamics. We demonstrate that the kinetic-scale shock transition can be modeled as a stationary system consisting of a large set of cold beams coupled through the magnetic field. The fundamental mechanism governing shock dissipation relies on the onset of chaos in orbital dynamics within quasiperiodic solitonic structures. We discuss the impact of upstream temperature and magnetization on the shock profile, recovering the magnetic field compression, downstream velocities, and heating expected from the Rankine-Hugoniot jump conditions. We deduce a rate of entropy generation from the spectrum of Lyapunov exponents and discuss the thermalization of the beam distribution. Our model provides a general framework to study magnetized collisionless shock structures.

Self-similarity is a property of an object or process wherein a part is similar to the whole. Mathematically, it can often be expressed as a power-law scaling of the quantity of interest. Extended self-similarity is a concept widely used in the field of turbulence and refers to the power-law scaling of the longitudinal structure functions of the velocity field expressed through the structure functions of different orders, rather than distance. Originally discovered by [R. Benzi et al., Phys. Rev. E 48, R29 (1993)] in fully developed turbulence, it was later found to hold in other situations and systems as well. In this paper, we show that in an active-matter system, extended self-similarity is possible even without the presence of respective power-law scaling in the underlying structure functions of distance. The active-matter system used in this study was a single-layer suspension of active Janus particles in a plasma. Janus particles are polymer microspheres with hemispherical metal coating. When dispersed in a plasma, they acquire self-propulsion and act as microswimmers. Extended self-similarity was also observed in the velocity field of a single-layer suspension of laser-heated regular (passive) particles, where the underlying structure functions displayed a hint of the power-law scaling near the mean interparticle distance. Therefore, it appears to be an inherent characteristic of complex plasmas.