The European Physical Journal E最新文献

A two-dimensional system consisting a mixture of highly coarse-grained saturated (S-type), unsaturated (U-type) lipid molecules, and cholesterol (C-type) molecules is considered to form a model lipid monolayer. All the S-, U-, and C-type particles are spherical in shape, with distinct interaction strengths. The phase behavior of the system is studied for various compositions (x) of the C-type particles, ranging from (x = 0.1) to 0.9. The results show that a structurally ordered complex is formed with the S- and C-types in the fluid-like environment of U-type particles, for (x in lbrace 0.5 - 0.6rbrace ). The time-averaged hexatic order parameter (leftlangle Psi _{6} rightrangle ) indicates that the dynamical segregation of S- and C-types exhibits a positional order that is found to be maximum for x in the range of 0.5 - 0.6. The mean change in the free energy ((Delta G(x))) obtained from the mean change in enthalpy ((Delta H)) and entropy ((Delta S)) calculations suggests that (Delta G) is minimum for (x sim 0.6). A phenomenological expression for the Gibbs free energy is formulated by explicitly accounting for the individual free energies of S-, U-, and C-type particles and the mutual interactions between them. Minimizing this phenomenological G with respect to the C-type composition results in the optimal value, (x^* = 0.564 pm 0.001) for stable coexistence of phases; consistent with the simulation results and also the previous experimental observations [1]. All these observations signify the optimal C-type composition, (x sim 0.5 - 0.6).

Topological indices, derived from graph-theoretical representations of molecular structure, have emerged as powerful tools for predicting the physicochemical properties of chemical compounds. In this study, we investigate a series of fifteen clinically significant drugs associated with the treatment of gonalgia (knee pain). The molecular graphs of these compounds are analyzed using the M-polynomial approach to compute seven key degree-based topological indices: the inverse sum index (ISI), harmonic arithmetic index (HA), inverse symmetric division deg index (ISDD), augmented Zagreb index (AZI), sum-connectivity index (SC), geometric arithmetic index (GA), and sum-Balaban index (SJ). A comprehensive quantitative structure–property relationship (QSPR) analysis is then performed to correlate these indices with critical physicochemical properties, including boiling point (BP), melting point (MP), critical temperature (CT), critical volume (CV), octanol–water partition coefficient (LogP), molar refractivity (MR), and calculated LogP (CLogP). Our results demonstrate strong predictive correlations, with the SC index showing exceptional performance for BP, MP, CT, CV, and MR, while the SJ index was the most effective for predicting LogP and CLogP. Among the regression models tested: linear, polynomial, and logarithmic the quadratic model consistently provided the highest accuracy, highlighting nonlinear relationships between molecular structure and properties. This study confirms that M-polynomial-derived topological indices, combined with polynomial regression, offer a reliable and efficient computational framework for predicting drug-like properties, providing valuable insights for pharmaceutical design and optimization.

This study presents a novel, integrated framework that combines graph-theoretic topological indices with multi-criteria decision-making (MCDM) techniques to systematically rank vitamins based on their solubility properties. The molecular structures of eleven essential vitamins were translated into quantitative descriptors using six distinct topological indices, which serve as proxies for key physicochemical properties governing solubility. These indices were then employed as criteria within three well-established MCDM methods: VIKOR, TOPSIS, and SAW to generate robust rankings. To ensure comprehensive and unbiased analysis, four contrasting weighting strategies (point allocation, standard deviation, entropy, and mean weight) were utilized to determine the relative importance of each criterion. The results demonstrate a high degree of consensus across methodologies, consistently identifying (alpha )-tocopherol (vitamin E) and nicotinic acid (niacin) as the top- and bottom-ranked vitamins, respectively, while revealing nuanced differences in the mid-tier rankings based on the chosen MCDM approach and weighting scheme. This work underscores the significant potential of integrating computational chemistry with decision science to solve complex ranking problems in nutrition and pharmacology. The proposed framework offers a powerful, transparent, and reproducible tool for optimizing vitamin selection in dietary formulation and pharmaceutical design, paving the way for its application to other classes of compounds.

When exposed to a time-periodic chemical signal, an E.coli cell responds by modulating its receptor activity in a similar time-periodic manner. But there is a phase lag between the applied signal and activity response. We study the variation of activity amplitude and phase lag as a function of applied frequency (omega ), using numerical simulations. The amplitude increases with (omega ), reaches a plateau and then decreases again for large (omega ). The phase lag increases monotonically with (omega ) and finally saturates to (3 pi /2) when (omega ) is large. The activity is no more a single-valued function of the attractant signal, and plotting activity vs attractant concentration over one complete time period generates a loop. We monitor the loop area as a function of (omega ) and find two peaks for small and large (omega ) and a sharp minimum at intermediate (omega ) values. We explain these results from an interplay between the timescales associated with adaptation, activity switching and applied signal variation. In particular, for very large (omega ) the quasi-equilibrium approximation for activity dynamics breaks down, which has not been explored in earlier studies. We perform analytical calculation in this limit and find good agreement with our simulation results.

Driven soft athermal systems may display a reversible-irreversible transition between an absorbing, arrested state and an active phase where a steady-state dynamics sets in. A paradigmatic example consists in cyclically sheared suspensions under stroboscopic observation, for which in absence of contacts during a shear cycle particle trajectories are reversible and the stroboscopic dynamics is frozen, while contacts lead to diffusive stroboscopic motion. The random organization model (ROM), which is a minimal model of the transition, shows a transition which falls into the conserved directed percolation universality class. However, the ROM ignores hydrodynamic interactions between suspended particles, which make contacts a source of long-range mechanical noise that in turn can create new contacts. Here, we generalize the ROM to include long-range interactions decaying like inverse power laws of the distance. Critical properties continuously depend on the decay exponent when it is smaller than the space dimension. Upon increasing the interaction range, the transition turns convex (that is, with an order parameter exponent (beta >1)), fluctuations turn from diverging to vanishing, and hyperuniformity at the transition disappears. We rationalize this critical behavior using a local mean-field model describing how particle contacts are created via mechanical noise, showing that diffusive motion induced by long-range interactions becomes dominant for slowly decaying interactions.

Navigating in a fluid flow while being carried by it, using only information accessible from on-board sensors, is a problem commonly faced by small planktonic organisms. It is also directly relevant to autonomous robots deployed in the oceans. In the last ten years, the fluid mechanics community has widely adopted reinforcement learning, often in the form of its simplest implementations, to address this challenge. But it is unclear how good are the strategies learned by these algorithms. In this paper, we perform a quantitative assessment of reinforcement learning methods applied to navigation in partially observable flows. We first introduce a well-posed problem of directional navigation for which a quasi-optimal policy is known analytically. We then report on the poor performance and robustness of commonly used algorithms (Q-Learning, Advantage Actor Critic) in flows regularly encountered in the literature: Taylor-Green vortices, Arnold–Beltrami–Childress flow, and two-dimensional turbulence. We show that they are vastly surpassed by PPO (Proximal Policy Optimization), a more advanced algorithm that has established dominance across a wide range of benchmarks in the reinforcement learning community. In particular, our custom implementation of PPO matches the theoretical quasi-optimal performance in turbulent flow and does so in a robust manner. Reaching this result required the use of several additional techniques, such as vectorized environments and generalized advantage estimation, as well as hyperparameter optimization. This study demonstrates the importance of algorithm selection, implementation details, and fine-tuning for discovering truly smart autonomous navigation strategies in complex flows.

We investigate the impact of environmental factors and biological defects on the thermal properties of single-helical proteins by analyzing their electronic specific heat (ESH) at constant volume ((C_v)). To accurately model these biomolecules, we consider their helical structure and long-range electron hopping within a tight-binding framework. Our findings demonstrate that the ESH spectra can differentiate between defective and pure helical protein molecules, even a sample with a very low contamination (single site defect) level. By comparing the ESH spectra of perfect and defective proteins, we can identify the relative location of the defect and distinguish them based on the level of contamination. This approach could be valuable for medical diagnosis of biological defects and serve as a preliminary screening method before resorting to whole genome sequencing, thereby saving time and resources.

Schematic view of a single helical protein molecule. The solid black dots along the helix (green curve) represent the amino acid residues. The dotted black lines between adjacent residues indicate the respective hopping amplitudes (t1 - t6)

Elevated levels of silver in aquatic environments arising from widespread use of silver nitrate and silver nanoparticles in different sectors of industry and medicine pose significant biophysical challenges to aquatic microorganisms. Despite extensive toxicity studies of silver on bacteria and microbial communities, its influence on other aquatic microorganisms, such as microalgae, remains poorly understood. In this study, we investigated the biophysical response of C. reinhardtii microalgae to silver ion exposure in terms of their population growth dynamics, chlorophyll content, and swimming motility. We found that silver ions at different concentrations (from 0.29 to 1.18 (upmu )M) elongated the lag phase of the microalgal growth. However, the growth of the microalgae was boosted by silver ions at low concentrations (e.g., 0.29 (upmu )M), showing higher OD₇₅₀ values at the stationary phase and higher maximum growth rates. This hormetic response exhibited by microalgae upon exposure to silver ions indicates a nonlinear coupling between ionic stress and cellular growth. Additionally, we quantified the chlorophyll content in the microalgae with different concentrations of silver ions using spectrophotometric analysis, which revealed that the microalgae cells contained twice as high concentrations of chlorophyll when exposed to silver ions at lower concentrations. More importantly, we monitored the motion of microalgae in the presence of silver ions, detected and tracked their motion using a deep learning algorithm, and determined the effects of silver ions on the swimming motility of individual C. reinhardtii microalgae. Our results showed reduced average swimming speed and increased directional change of microalgae upon silver ion exposure.

Subdiffusion is a hallmark of complex systems, ranging from protein folding to transport in viscoelastic media. However, despite its pervasiveness, the mechanistic origins of subdiffusion remain contested. Here, we analyze both Markovian and non-Markovian dynamics, in the presence and absence of energy barriers, in order to disentangle the distinct contributions of memory-dependent friction and energy barriers to the emergence of subdiffusive behavior. Focusing on the mean squared displacement (MSD), we develop an analytical framework that connects subdiffusion to multi-scale memory effects in the generalized Langevin equation (GLE), and derive the subdiffusive scaling behavior of the MSD for systems governed by multi-exponential memory kernels. We identify persistence and relaxation timescales that delineate dynamical regimes in which subdiffusion arises from either memory or energy barrier effects. By comparing analytical predictions with simulations, we confirm that memory dominates the overdamped dynamics for barrier heights up to approximately (2,k_BT), a regime recently shown to be relevant for fast-folding proteins. Overall, our results advance the theoretical understanding of anomalous diffusion and provide practical tools that are broadly applicable to fields as diverse as molecular biophysics, polymer physics, and active matter systems.

Subdiffusion in the context of the generalized Langevin equation can arise due to energy barriers, from friction memory or from a combination of both. We derive the power-law scaling for multi-exponential memory functions that directly translates to the subdiffusive scaling in the MSD. This allows us to disentangle contributions from energy barriers and from memory. It turns out that memory governs the subdiffusion for small energy barriers in the order of a few (k_BT). For higher energy barriers, the short time dynamics are still dominated by memory and long-time dynamics are governed by a combination of memory effects and energy barrier contributions.