1,721,038 research outputs found
Optimal collision avoidance in swarms of active Brownian particles
Full text linkThe effectiveness of collective navigation of biological or artificial agents requires to accommodate for contrasting requirements, such as staying in a group while avoiding close encounters and at the same time limiting the energy expenditure for maneuvering. Here, we address this problem by considering a system of active Brownian particles in a finite two-dimensional domain and ask what is the control that realizes the optimal tradeoff between collision avoidance and control expenditure. We couch this problem in the language of optimal stochastic control theory and by means of a mean-field game approach we derive an analytic mean-field solution, characterized by a second-order phase transition in the alignment order parameter. We find that a mean-field version of a classical model for collective motion based on alignment interactions (Vicsek model) performs remarkably close to the optimal control. Our results substantiate the view that observed group behaviors may be explained as the result of optimizing multiple objectives and offer a theoretical ground for biomimetic algorithms used for artificial agents
Coarse-grained modeling of protein unspecifically bound to DNA
Full text linkThere is now a certain consensus that transcription factors (TFs) reach their target sites, where they regulate gene transcription, via a mechanism dubbed facilitated diffusion (FD). In FD, the TF cycles between events of 3D diffusion in solution (jumps), 1D diffusion along DNA (sliding), and small jumps (hopping), achieving association rates higher than for 3D diffusion alone. We investigate the FD phenomenology through molecular dynamics simulations in the framework of coarse-grained modeling. We show that, despite the crude approximations, the model generates, upon varying the equilibrium distance of the DNA-TF interaction, a phenomenology matching a number of experimental and numerical results obtained with more refined models. In particular, focusing on the kinematics of the process, we characterize the geometrical properties of TF trajectories during sliding. We find that sliding occurs via helical paths around the DNA helix, leading to a coupling of translation along the DNA axis with rotation around it. The 1D diffusion constant measured in simulations is found to be interwoven with the geometrical properties of sliding and we develop a simple argument that can be used to quantitatively reproduce the measured values. © 2014 IOP Publishing Ltd
Effective models and predictability of chaotic multiscale systems via machine learning
Full text linkUnderstanding and modeling the dynamics of multiscale systems is a problem of considerable interest both for theory and applications. For unavoidable practical reasons, in multiscale systems, there is the need to eliminate from the description the fast and small-scale degrees of freedom and thus build effective models for only the slow and large-scale degrees of freedom. When there is a wide scale separation between the degrees of freedom, asymptotic techniques, such as the adiabatic approximation, can be used for devising such effective models, while away from this limit there exist no systematic techniques. Here, we scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model
Thin front propagation in random shear flows
No full textFront propagation in time-dependent laminar flows is investigated in the limit of very fast reaction and very thin fronts-i.e., the so-called geometrical optics limit. In particular, we consider fronts stirred by random shear flows, whose time evolution is modeled in terms of Ornstein-Uhlembeck processes. We show that the ratio between the time correlation of the flow and an intrinsic time scale of the reaction dynamics (the wrinkling time t(w)) is crucial in determining both the front propagation speed and the front spatial patterns. The relevance of time correlation in realistic flows is briefly discussed in light of the bending phenomenon-i.e., the decrease of propagation speed observed at high flow intensities
Migliorare l’appropriatezza clinico-organizzativa attraverso la creazione di un ambulatorio di isteroscopia office
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