Experimental and numerical studies of the features of the spatial distribution of plasma parameters in a discharge not limited by walls were performed. A discharge supported by a hollow cathode in helium at low pressure was ignited in a chamber with dimensions much larger than the dimensions of the discharge system, and the distance between the cathode and the mesh anode significantly exceeded the height of the anode. Two-dimensional spatial distributions of the plasma potential, electron concentration, and average electron energy were measured by the Langmuir probe method. It was shown that, under experimental conditions, the region of plasma existence significantly exceeds the region limited by the aperture of the discharge system, and a significant concentration of electrons is observed in the region behind the anode. Numerical studies within the framework of 2D Monte Carlo simulation were performed. Electrons were launched from the open side of the cathode, and their motion in a given electric field was simulated. In the measurement region, the electric field was calculated from the measured values of the plasma potential and, outside this region, model assumptions about the field magnitude were used. The calculated and measured spatial distributions of electron concentration and their average energy were compared.

We study the amplification of isotropic, incompressible turbulence through multiple planar, collisional shocks, using analytical linear theory. There are two limiting cases we explore. The first assumes shocks occur rapidly in time such that the turbulence does not evolve between shocks. Whereas the second case allows enough time for turbulence to isotropize between each shock. For the latter case, through a quasi-equation-of-state, we show that the weak multishock limit is agnostic to the distinction between thermal and vortical turbulent pressures, like an isotropic volumetric compression. When turbulence does not return to isotropy between shocks, the generated anisotropy—itself a function of shock strength—can feedback on amplification by further shocks, altering choices for maximal or minimal amplification. In addition for this case, we find that amplification is sensitive to the shock ordering. We map how choices of shock strength can impact these amplification differences due to ordering, finding, for example, shock pairs which lead to identical mean postshock fields (density, temperature, pressure) but maximally distinct turbulent amplification.

The parametric decay instability (PDI) of Alfvén waves—where a pump Alfvén wave decays into a backward-propagating child Alfvén wave and a forward ion acoustic wave—is a fundamental nonlinear wave-wave interaction and holds significant implications for space and laboratory plasmas. However, to date there has been no direct experimental measurement of PDI. Here, we propose a novel and experimentally viable scheme to quantify the growth of Alfvén wave PDI on a linear device using a large pump Alfvén wave and a small counter-propagating seed Alfvén wave, with the seed-wave frequency tuned to match the backward Alfvén wave generated by standard PDI. Using hybrid simulations, we show that energy transfer from the pump to the seed reduces the latter's spatial damping. By comparing seed-wave amplitudes with and without the pump wave, this damping reduction can be used as a direct and reliable proxy for PDI growth. The method is validated in our simulations across a range of plasma and wave parameters and agrees well with theoretical predictions. Notably, the scheme exhibits no threshold for PDI excitation and is, in principle, readily implementable under current laboratory conditions. This scheme is a critical step toward solving the challenge of experimentally accessing Alfvén wave PDI and provides an elegant method that may be used to validate fundamental theories of parametric instabilities in controlled laboratory settings.

Electric double layer (EDL) formation underlies the functioning of supercapacitors and several other electrochemical technologies. Here we study how the EDL formation near two flat blocking electrodes separated by $2L$ is affected by beyond-mean-field Coulombic interactions, which can be substantial for electrolytes of high salt concentration or with multivalent ions. Our model combines the Nernst-Planck and Bazant-Storey-Kornyshev (BSK) equations; the latter is a modified Poisson equation with a correlation length ${\ell }_c$. In response to a voltage step, the system charges exponentially with a characteristic timescale $\tau$ that depends nonmonotonically on ${\ell }_c$. For small ${\ell }_c, \tau$ is given by the BSK capacitance times a dilute electrolyte's resistance, in line with Zhao [Phys. Rev. E 84, 051504 (2011)]; here $\tau$ decreases with increasing ${\ell }_c$. Increasing the correlation length beyond ${\ell }_c\approx L^{2/3}{\lambda }_D^{1/3}$, with ${\lambda }_D$ the Debye length, $\tau$ reaches a minimum, rises as $\tau \propto {\lambda }_D{\ell }_c/D$, and plateaus at $\tau =4L^2/\left({\pi }^{2}D\right)$. Our results imply that strongly correlated, strongly confined electrolytes—such as ionic liquids in the surface force balance apparatus—move slower than predicted so far.

Logic gate is the cornerstone of the integrated circuits, digital circuitry, etc. Boolean operations (e.g., and and or logic gates) are recently executed by bistable systems through logical stochastic resonance (LSR), a nonlinear dynamical mechanism that embodies this phenomenon. However, the realization of exclusive-or (xor) and equivalence (xnor) logic gates through LSR remains unresolved in bistable systems. Through the analytical solution and numerical simulation, we here propose a simple scheme for implementing reliable xor and xnor logic gates based on a classical bistable system with the absolute-value of input signals. We verified the stability of the system in terms of various parameters such as mean first passage time, correct probability, and potential well bias. We have also implemented full-adder arithmetic operations through the scheme, demonstrating the promise of application. Our scheme is simple and readily generalizable, therefore forming the basis for the design of more complex logic operations in the future, and providing new perspectives for the development of possible alternative arithmetic logic platforms.

In recent advances in finite-time thermodynamics, the optimization of entropy production required for finite-time information processing is an important issue. In this work, we consider finite-time feedback processes in classical discrete systems described by Markov jump processes and derive achievable bounds on entropy production for feedback processes controlled by Maxwell's demons. The key ingredients of our approach are optimal transport theory and an achievable Fano's inequality, by which we optimize the Wasserstein distance over final distributions under fixed consumed information. Our study reveals the minimum entropy production required to consume a certain amount of information, and moreover, the optimal feedback protocol to achieve it. These results are expected to lead to design principles for information processing in various stochastic systems with discrete states.

Avoided level crossings, commonly associated with quantum chaos, are typically interpreted as signatures of eigenstate hybridization and spatial delocalization, often viewed as ergodic spreading. We show that, contrary to this expectation, increasing chaos in quantum billiards enhances mutual information between conjugate phase space variables, revealing nontrivial correlations. Using an information-theoretic decomposition of eigenstate entropy, we demonstrate that spatial delocalization may coincide with increased mutual information between position and momentum. These correlations track classical invariant structures in phase space and persist beyond the semiclassical regime, suggesting a robust information-theoretic manifestation of quantum-classical correspondence.

Chemotherapy is a type of treatment that uses drugs to treat cancer and prevent tumor growth. Different chemotherapy protocols and procedures have been developed. Mathematical models have been proposed to analyze the dynamical processes related to cancerous cell proliferation. In this work, we propose a fractal model with healthy, cancerous cells, and chemotherapeutic agents, as well as effector immune cells interacting with cancerous cells. The model incorporates cancer drug resistance as a result of mutation processes triggered by chemotherapy. We demonstrate that the order of our fractal tumor-immune model influences the time evolution of cell population. Considering three different cancer therapy protocols, we observe that the maximum cancerous cells and the time interval for that maximal to be reached depend significantly on the initial conditions of the number of host and cancer cells. Our results show that the protocols play a crucial role in cancer treatment, and the protocol can result in a better result of cancerous cell suppression.

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific, data availability, and stakeholder challenges, combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. In particular, information-theoretic approaches such as transfer entropy (TE), despite their recent popularity and widespread use, can yield spurious causal links, even when statistical significance testing is applied. To address this, we introduce CAST (Causal Analysis Spuriousness Test), a subsample-based ensemble framework that quantifies the robustness of inferred links via the CAST index. Through extensive simulations across systems with varying dynamics as well as applications to real-world climate datasets, we demonstrate that CAST effectively filters unreliable connections while preserving true causal relationships. Our findings underscore the need for consistency-based evaluation in causal discovery and provide a generalizable strategy to enhance the reliability of TE-based and other data-driven causal methods in Earth sciences.

We investigate a binary mixture of identical hard spheres, interacting through a square-well cross attraction. Recently, we have shown that the equimolar mixture exhibits stripe order (i.e., a regular alternation of layers filled with particles of mostly one species) in both solid and liquid phases [Prestipino et al., J. Chem. Phys. 159, 204902 (2023)]. Here, we extend our study to nonequimolar conditions. Using Monte Carlo simulations, we find a rich self-assembly behavior with several forms of compositional order, depending on the departure from equimolarity and the range of attraction. While stripes are still seen in the solid phase of the 30%/70% mixture, for a larger concentration asymmetry we rather observe fcc ordering of the minority species, provided that the range of attraction is long enough. Due to the possibility to adjust the concentration in the two phases, compositional order is generally richer at solid-vapor coexistence than in the bulk solid: we observe solid droplets with stripes, displaying either square or triangular in-plane order, as well as droplets with bcc structure and honeycomblike order of the minority species in the matrix of the other species. Finally, at sufficiently low temperature the increased concentration of minority particles in the solid droplet makes the cohesive energy at coexistence systematically larger than in the bulk solid.

This work presents a Monte Carlo–based microscopic model for simulating the extraction of oil from oil-in-water emulsions under the influence of surface acoustic waves (SAWs). The proposed model is a two-dimensional Ising-lattice gas model that employs Kawasaki dynamics to mimic the interactions between oil, water, and air, as well as external forces such as gravity and acoustic stress. By incorporating both acoustic streaming and acoustic radiation pressure, the model captures key experimental observations, including selective oil extraction and droplet motion under SAW excitation. The results highlight the critical role of acoustic radiation pressure in enabling oil film formation and detachment, governed by the balance between capillary and acoustic stresses. The study provides qualitative agreement with experimental findings and offers insights into the essential mechanisms driving acoustowetting-induced phase separation, demonstrating the utility of discrete modeling for complex fluid dynamics problems.

We study the two-dimensional melting of fatty acid Langmuir monolayers via the mechanical properties of their untilted phases, compact solid (CS), solid (S), and superliquid (LS). Taking the dynamic moduli as order parameters in isobaric temperature sweeps of behenic and arachidic acid monolayers, the CS-S and S-LS transitions appear as continuous transitions. However, a discontinuity appears in a high-order crossed susceptibility that involves the derivative of the dynamic moduli with respect to the control parameter (temperature). The critical value of the shear storage modulus at the melting temperature agrees with the prediction of the KTHNY theory, but the characteristic exponent does not.

The displacement of a viscous fluid by another less-viscous fluid in porous media or Hele-Shaw cells produces a fingerlike interfacial pattern known as viscous fingering (VF). Classically, the dynamics of VF have been divided into two categories depending on whether the two fluids are fully miscible or immiscible. However, very recently, attention has been drawn to a third category of VF: VF in partially miscible systems. Our previous experimental study showed that the VF pattern in a partially miscible system transforms into a multiple droplet pattern due to phase separation and that the droplets spontaneously move. Here, we experimentally investigate the effect of the flow rate on VF in a partially miscible system. We show that multiple droplets are suppressed and a viscous finger-dominated pattern in which the fingers are wider than those of the corresponding immiscible VF is formed under an intermediate flow rate. For a large flow rate, the typical finger width is similar to that of the corresponding immiscible VF. As a result, we identify a nonmonotonic relation between the finger width and the flow rate (or capillary number), which is not observed in fully miscible and immiscible systems and is a specific characteristic of partially miscible systems. We elucidate the mechanism behind these observations based on an area measurement of the less-viscous solution and a velocity measurement of the spontaneous movement of the droplets. In addition, a numerical simulation for VFs in the partially miscible system can reproduce the suppression of the droplet formation and the finger widening, which are observed in the experiment with low and intermediate flow rate, and also show that the finger width becomes constant with Pe at high flow rates or seems to decrease with Pe, which implies that the increase in finger width can be suppressed with an increase in Pe. Moreover, the numerical simulation can support the validity of the mechanism discussed in the experimental part.

We propose that learning in deep neural networks proceeds in two phases: a rapid curve-fitting phase followed by a slower compression or coarse-graining phase. This view is supported by the shared temporal structure of three phenomena—grokking, double descent, and the information bottleneck—all of which exhibit a delayed onset of generalization well after training error reaches zero. We empirically show that the associated timescales align in two rather different settings. Mutual information between hidden layers and input data emerges as a natural progress measure, complementing circuit-based metrics such as local complexity and the linear mapping number. We argue that the second phase is not actively optimized by standard training algorithms and may be unnecessarily prolonged. Drawing on an analogy with the renormalization group, we suggest that this compression phase reflects a principled form of forgetting, critical for generalization.

In the present study, the lattice Boltzmann method coupled with the multipseudopotential interaction interparticle force model is employed to investigate the vertical falling film phenomenon. Utilization of this method enables the presentation of various study cases and the adjustment of dimensionless Reynolds and Kapitza numbers within the range of $5.1<\mathrm{Re}<35$ and $14<\mathrm{Ka}<85$ simultaneously with reduced computational costs. Waves are induced by sinusoidal excitation at the inlet, and the domain length is chosen to ensure the development of fully developed waves. Numerical results pertaining to wave peak thickness, wave trough thickness, and wave speed are compared with experimental results, which exhibit good agreement. Influential factors affecting velocity profile deviation within waves from the semiparabolic state, including wavy regime and the presence of capillary ripples, are investigated. The current numerical model has the capability to observe vortices resulting from the recirculation phenomenon at the wave peak and can concurrently model flow conditions regarding negative shear stress and proximity to flow reversal in line with experimental findings. Wave peak height and flow rate at $\mathrm{Ka}=25$ are studied as functions of Reynolds number variations, revealing that at the second critical Reynolds number equal to 25, the wave peak height and maximum flow rate reach their highest values, coinciding with the occurrence of the recirculation phenomenon.

Nonlinearities in mass and heat transfer across liquid-vapor interfaces are studied based on models from irreversible thermodynamics and the kinetic theory of gases. Specifically, we present the nonlinear extension of the well-known Hertz-Knudsen-Schrage (HKS) model of kinetic theory for mass flux and a corresponding relation for heat transfer. It is shown that this extended model stands in good agreement with established kinetic theory models for zero heat flux. The extended HKS model is linked to the force-flux relations of nonequilibrium thermodynamics by determining their nonlinear interface resistivities, which depend not only on the interface temperature, but also on mass and heat flux. Nondimensionalization based on temperature and saturation pressure yields resistivities independent of the local temperature. Reevaluation of recent Molecular Dynamics data for resistivities [Homes and Vrabec, Phys. Fluids 36, 022122 (2024)] reveals distinct nonlinearities.

Obtaining an accurate description of complex systems is challenging, particularly when their elements exhibit intricate interactions. We propose a data-driven multivariate Langevin equation (LE) to approximate real-world complex systems' observables. By reconstructing the drift and diffusion terms of the LE through a nonparametric technique following the definition of the Kramers-Moyal coefficients, our approach unravels the main features of a complex system without requiring a priori knowledge about the underlying governing mechanisms. We illustrate our framework's adaptability, reliability, and capability to extract pertinent information through three case studies. First, we benchmark the framework with a simple prototypical example from mechanics, a particle confined by a bistable potential energy well. We then turn to two more involved examples from financial markets, the electricity day-ahead prices and currency-exchange rates, where the nonparametric multivariate LE has not previously been applied. In all cases, our framework accurately identifies the equilibrium values, metastabilty regions, and distinct diffusion behaviors, in a functional agnostic manner, as opposed to price-equation models that require specific domain knowledge.

The analytical expression for shortcuts to adiabaticity for any switching time and any thermally isolated system performing a finite-time and weakly driven process is presented. It is based on the analytical solution of the optimal protocols of weak processes for open systems. The extension to adiabatic processes was made by employing the concept of waiting time. The shortcuts to adiabaticity are proven by showing that the excess power is null in all instants, indicating that no nonequilibrium excitation occurs along the driving. Two examples are solved to verify the validity of such shortcuts: the typical case of an oscillatory relaxation function and the transverse-field quantum Ising chain. In this last case, it is shown that the nonquenching process outperforms the quenching one by suppressing the nonequilibrium excitations until the critical point is not achieved.

Recent theoretical and experimental protocols for the stable and effective charging of open quantum batteries (QBs), in particular adiabatic charging of three-level QBs via stimulated Raman adiabatic passages, have sparked renewed interest in adiabatic quantum dynamics. A central question is whether exploiting the adiabatic master equation could provide advantages over the previous studies in the effective charging of open QBs. To answer this question, we revisit the adiabatic charging of three-level QB by using the adiabatic quantum master equation formalism. We restrict ourselves to the weak-coupling regime with an Ohmic thermal bath and investigate the effects of relaxation and dephasing on the charging process. It is also worth emphasizing that our protocol to investigate the charging process of QBs based on the adiabatic master equation despite the previous studies is not phenomenological and is general. We analyze the dependence of the stored energy, ergotropy, as well as the efficiency of the QB on the total time of evolution $t_f$. We demonstrate that for very short charging time $\left(t_f\right)$, where the evolution is highly nonadiabatic, the stored energy and ergotropy are very small. However, by increasing $t_f$ we show that there is an optimal charging time $t_f^{\text{opt}}$ in which, at low temperatures, we could fully charge the battery and effectively extract the entire amount of energy from it. Note that the optimal charging time could be decreased by adjusting the strength of the coupling between the system and environment and also be the appropriate choice of the Hamiltonian parameters, which, in turn, speed up the charging process. However, we show that for very long charging time $t_f$ the charging energy, ergotropy, and efficiency decrease due to thermal excitations. Furthermore, to obtain more insights about the problem we investigate the distance between the density matrix of a system at optimal charging time $t_f^{\text{opt}}$ and the corresponding thermal state by using the one-norm distance.