Optimal chirality enhances long-range fluctuation-induced interactions in active fluids

Understanding how chiral active particles -- which self-propel and self-rotate -- interact with each other is crucial to uncover how chiral active matter self-organizes into dynamic structures and patterns. Long-range fluctuation-induced (FI) forces in nonequilibrium steady states of active matter systems can be considerably strong and govern structure formation in interplay with other interactions. However, the impact of chirality on FI forces is not understood to date. We study effective forces between intruders immersed in chiral active fluids with tunable chirality and find that the influence of chirality on the FI force nontrivially depends on the elongation of active particles. By increasing the ratio between self-rotation and self-propulsion, the FI force monotonically decreases for fully circular active objects, as the active bath structure gradually changes from rotating flocks and vortices to localized spinners. Contrarily, a nonmonotonic behavior is observed upon increasing the chirality of rodlike active objects: There exists an optimal chiral angle which maximizes the magnitude and range of the FI interaction. We obtain the phase diagram of transition between attractive and repulsive forces in the space of chirality, propulsion, and separation between intruders. By measuring the collision statistics between active particles and intruders at different chirality and separation, we establish a direct link between the FI force and the average collision number and demonstrate how the balance of collisions around the intruders varies with chirality and separation. Our results provide new insights into an interplay between activity, chirality, and self assembly.Comment: 8 pages, 5 figure

Non-equidistant “Basic Form”-focused Grey Verhulst Models (NBFGVMs) for ill-structured socio-economic forecasting problems

Multiple uncertainties complicate socio-economic forecasting problems, especially when relying on ill-conditioned limited data. Such problems are best addressed by grey prediction models such as Grey Verhulst Model (GVM). This paper resolves the incompatibility between GVM’s estimation and prediction by taking its basic form equation as the basis of both. The resultant “Basic Form”-focused GVM (BFGVM) is also further developed to create Direct Non-equidistant BFGVM (DNBFGVM) and, in turn, DNBFGVM with Recursive simulation (DNBFGVMR). Experimental analyses comprise 19 socio-economic time series with an emphasis on Iranian population, a low-frequency non-equidistant time series with remarkable strategic importance. Promisingly, the proposed DNBFGVM and DNBFGVMR provide accurate in-sample and out-of-sample socio-economic forecasts, show highly significant improvements over the best traditional GVM, and offer cost-effective intelligent support of decision-making. Final results suggest future trends of studied socio-economic time series. Specifically, they reveal Iranian population to grow even slower than anticipated, demanding an urgent consideration of policy-makers