The European Physical Journal E最新文献

Dynamic emergence defined as a time-dependent entanglement (in the sense of coexistence and mutual influence of phases) of aligned, levorotatory (counterclockwise) and dextrorotatory (clockwise) phases has been recently put forward as a means to characterise collective behaviour in active matter [1, 2]. Up to now, dynamic emergence has only been detected in numerical simulations (interacting boids), hence this work is aimed at experimentally detecting it by drone recording different human crowds, each consisting of 30 members, moving within the area of a basketball court. The crowd was instructed to follow only two simple rules, namely, 1) To jog within a basketball court, and 2) To try to stay together at all times even if the crowd is disturbed by a simulated attack. The recorded emergent collective behaviour was characterised by extracting individual paths and velocity vectors in time, which were used to build local order parameters that revealed the existence of phases entanglement, thus confirming the presence of dynamic emergence. This result highlights the importance of using local order parameters to characterise collective behaviour. Additionally, an IABP (inertial active Brownian particle) model with three different interaction rules is proposed and compared with the available experimental data. This comparison shows that an IABP with visual weighted topological interactions reproduces the dynamics of a human crowd. Furthermore, a new parameter called rotational dispersion is introduced in order to identify dynamic emergence in a phase diagram.

In this study, a quantitative structure–property relationship (QSPR) analysis was conducted to predict the physiochemical properties of anti-HIV drug molecules using a linear regression model. The model utilized neighborhood degree sum topological indices, which are graph-theoretical descriptors representing molecular structure, as key predictive features. These indices were calculated for each molecule, providing a numerical representation of their structural properties. The linear regression model effectively correlated these indices with the known physicochemical properties of the drugs, demonstrating its potential to predict the efficacy of new compounds. This approach offers a valuable tool for designing and optimizing anti-HIV drugs based on molecular topological descriptors.

We examine the influence of an external orienting field on the director orientation and fluid flow of an active nematic liquid crystal confined in a channel, subject to infinite anchoring of the director and no-slip conditions at the channel walls. A mathematical model based on the Ericksen–Leslie dynamic equations for nematic liquid crystals is employed, with an additional active stress tensor accounting for the activity of the fluid. By solving the fully coupled nonlinear equations numerically, we investigate the dynamic response and the steady state of the active nematic when an orienting field is switched on. The dynamic behaviour when an orienting field is switched off is also examined, with our model demonstrating how the activity of the liquid crystal can enhance or hinder the classically observed kickback immediately after switch-off and generate nontrivial steady-state solutions. Specifically, we find that kickback, which can delay relaxation of the system to a steady state, can be made less pronounced, and eventually completely avoided, for contractile agents with a high activity parameter, even with a high magnitude orienting field value.

Region of kickback effect in the space of activity and orienting field parameters - increased contractile behaviour will delay kickback to higher orienting field values

Cucurbit[n]urils (CBn, n = 5–8) are macrocyclic hosts that form stable inclusion complexes with a variety of guest molecules, including amino acids and peptides. In this study, we investigate the interactions of CBn homologues with the aerolysin (AeL) protein nanopore using single-molecule ionic current recordings and molecular docking simulations, with the goal of developing a selective sensing platform for complex biofluids. Under an applied voltage, CBn molecules enter the AeL nanopore exclusively through its extracellular cap domain, inducing characteristic ionic current blockades. These events are influenced by voltage, electrolyte type (KCl, NaCl, CsCl), and ionic strength. Both the frequency and dwell time of blockade events increase with voltage, with CB6 generating particularly long blockades—lasting several seconds—enabling real-time monitoring of host–guest interactions at the single-molecule level. Molecular docking simulations support these observations, revealing that CB5, CB7, and CB8 preferentially bind to the extracellular region of AeL, while CB6 shows strongest affinity for the intracellular region. Among all homologues, CB5 forms the most stable complex with AeL. Hydrophobic interactions dominate binding across all complexes. Importantly, none of the CBn species translocate through the pore, consistent with experimental data. These findings highlight the utility of AeL nanopores for probing CBn interactions with high temporal resolution and selectivity. This approach may support future developments in nanopore-based sequencing and diagnostic technologies.

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.