The European Physical Journal E最新文献

The most studies on the stability of foam bubbles investigated the mechanical stability of thin films between bubbles due to the drainage by gravity. In the current work, we take an alternative approach by assuming the rupture of bubbles as a series of random events and by investigating the time evolution of the size distribution of foam bubbles over a long time up to several hours. For this purpose, we first prepared layers of bubbles on Petri dishes by shaking soap solutions of a few different concentrations, and then we monitored the Petri dishes by using a time-lapse video imaging technique. We analyzed the captured images by custom software to count the bubble size distribution with respect to the initial concentration and elapsed time. From the statistics on our data, we find that the total bubble volume decreases exponentially in time, and the exponent, i.e., the mean lifetime, is a function of the bubble size. The mean lifetimes of larger bubbles are observed to be shorter than those of smaller bubbles, by approximately a factor of 2.

We study here the roughness exponents of the averaged contact line width of a liquid in contact with flat, weakly heterogeneous substrates containing dilute, randomly distributed Gaussian-type defects. For this purpose, we employ the full capillary model. The obtained results for the magnitude of the averaged root-mean-square width of the contact line show that there is only one interval in which the width scales with length as a power function. The numerical studies and analysis indicate that this interval should be regarded as a length scale smaller than the jog length. The roughness exponent found is not a universal constant independent of the apparent contact angle formed by the liquid on the solid surface. It closely approaches the theoretically predicted value of 1/2 [M. O. Robbins, and J. F. Joanny, Europhys. Lett. 3, 729 (1987)] only within the contact angle ranges of (10^{circ }) to (30^{circ }) and (150^{circ }) to (170^{circ }). Furthermore, it can be considered that there is still a significant range of contact angles, from (55^{circ }) up to (125^{circ }), in which the roughness exponent remains practically constant, however, having a value of 0.8.

Schematic image of the model system used to study the roughness exponent of the contact line: a 3D free liquid surface, Úttached to a vertical solid plate with randomly distributed Gaussian defects

Tissue growth and deformation result from the combined effects of various cellular events, including cell shape change, cell rearrangement, cell division, and cell death. Resolving and integrating these cellular events is essential for understanding the coordination of tissue-scale growth and deformation by individual cellular behaviors that are critical for morphogenesis, wound healing, and other collective cellular phenomena. For epithelial tissues composed of tightly connected cells, the texture tensor method provides a unified framework for quantifying tissue and cell strains by tracking individual cells in live imaging data. The corresponding kinematic relationships have been introduced in a hydrodynamic model that we previously reported. In this study, we quantitatively evaluated the kinematic equations proposed in the hydrodynamic model using experimental data from a growing Drosophila wing. To accomplish this, we introduced modified definitions of the texture tensor and confirmed that one of these modifications more accurately represents approximated cellular shapes without relying on ad hoc scaling factors. By utilizing the modified tensor, we demonstrated the compatibility of the strain rate tensors and the accuracy of both the kinematic and cell number density equations. These results cross-validate the modified texture analysis and the hydrodynamic model. Furthermore, the precision of the kinematic relationships achieved in this study provides a robust foundation for more advanced integration of modeling and experiment.

Artificial microswimmers, such as active colloids, have the potential to revolutionize targeted drug delivery, but controlling their motion under imposed flow conditions remains challenging. In this work, we implement reinforcement learning (RL) to control the navigation of a microswimmer in a plane Poiseuille flow, with applications in targeted drug delivery. With RL, the swimmer learns to efficiently reach its target by continuously adjusting its swinging or tumbling behavior depending upon its self-propulsion strength, chirality and the imposed flow strength. This RL-based approach enables precise control of the particle’s path, achieving reliable targeting even in stringent scenarios such as upstream motion in high bulk flow, thus advancing the design of intelligent in vivo medical microrobots.

This study investigates the impact of clay content and temperature variation on the electrical conductivity of three-dimensional fluid-filled porous rocks. The role of varying pore throat radii has been included in the course of clay fraction variation in the conducting channels of the rock samples. The research identifies a critical ratio of clay conductance to fluid conductance that dictates the regime of electrical conductance behaviour. A nonlinear increase in electrical conductance is observed when the clay-to-fluid conductance ratio exceeds the critical ratio, whereas a linear relationship is maintained below this critical ratio. A modified form of Archie’s law relating effective conductivity and porosity has been proposed for the clay coated channels. The intricate relationship between Peclet number, pore throat size, and temperature on the electrical conductivity of fluid-filled straight channels in three dimensions has also been investigated. Results revealed a quadratic increase in conductance with porosity under steady-state conditions across all Peclet number ranges examined. While the conductivity remained constant with porosity for each Peclet number, the rate of increase in conductivity diminished with it. Nonlinear increase in conductivity was observed with temperature in the transient flow regime with a threshold temperature marking the onset of conductivity. Conductivity was augmented with increase in observation time in the transient state for the entire temperature range considered. Close to the attainment of saturation in electrical conductivity, the conductivity changed linearly with temperature until a steady value was reached.

A novel field theoretical approach towards modelling dynamic networking in complex systems is presented. An equilibrium networking formalism which utilises Gaussian fields is adapted to model the dynamics of particles that can bind and unbind from one another. Here, networking refers to the introduction of instantaneous co-localisation constraints and does not necessitate the formation of a well-defined transient or persistent network. By combining this formalism with Martin–Siggia–Rose generating functionals, a weighted generating functional for the networked system is obtained. The networking formalism introduces spatial and temporal constraints into the Langevin dynamics, via statistical weights, thereby accounting for all possible configurations in which particles can be networked to one another. A simple example of Brownian particles which can bind and unbind from one another demonstrates the tool and that this leads to results for physical quantities in a collective description. Applying the networking formalism to model the dynamics of cross-linking polymers in a mixture, we can calculate the average number of networking instances. As expected, the dynamic structure factors for each type of polymer show that the system collapses once networking is introduced, but that the addition of a repulsive time-dependent potential above a minimum strength prevents this. The examples presented in this paper indicate that this novel approach towards modelling dynamic networking could be applied to a range of synthetic and biological systems to obtain theoretical predictions for experimentally verifiable quantities.

Barrier-crossing processes in nature are often non-Markovian and typically occur over an asymmetric double-well free-energy landscape. However, most theories and numerical studies on barrier-crossing rates assume symmetric free-energy profiles. Here, we use a one-dimensional generalized Langevin equation (GLE) to investigate non-Markovian reaction kinetics in asymmetric double-well potentials. We derive a general formula, confirmed by extensive simulations, that accurately predicts mean first-passage times from well to barrier top in an asymmetric double-well potential with arbitrary memory time and reaction coordinate mass. We extend our formalism to non-equilibrium non-Markovian systems, confirming its broad applicability to equilibrium and non-equilibrium systems in biology, chemistry, and physics.