Laser-induced thermal acoustics (LITA) signals from finite beams

Laser-induced thermal acoustics (LITA) is a four-wave mixing technique that may be employed to measure sound speeds, transport properties, velocities, and susceptibilities of fluids. It is particularly effective in high-pressure gases (>1 bar). An analytical expression for LITA signals is derived by the use of linearized equations of hydrodynamics and light scattering. This analysis, which includes full finite-beam-size effects and the optoacoustic effects of thermalization and electrostriction, predicts the amplitude and the time history of narrow-band time-resolved LITA and broadband spectrally resolved (multiplex) LITA signals. The time behavior of the detected LITA signal depends significantly on the detection solid angle, with implications for the measurement of diffusivities by the use of LITA and the proper physical picture of LITA scattering. This and other elements of the physics of LITA that emerge from the analysis are discussed. Theoretical signals are compared with experimental LITA data

Explosive synchronization in weighted complex networks

The emergence of dynamical abrupt transitions in the macroscopic state of a system is currently a subject of the utmost interest. Given a set of phase oscillators networking with a generic wiring of connections and displaying a generic frequency distribution, we show how combining dynamical local information on frequency mismatches and global information on the graph topology suggests a judicious and yet practical weighting procedure which is able to induce and enhance explosive, irreversible, transitions to synchronization. We report extensive numerical and analytical evidence of the validity and scalability of such a procedure for different initial frequency distributions, for both homogeneous and heterogeneous networks, as well as for both linear and non linear weighting functions. We furthermore report on the possibility of parametrically controlling the width and extent of the hysteretic region of coexistence of the unsynchronized and synchronized states

Shock Detachment Process in Hypervelocity Flow over a Cone

A comprehensive experimental and computational study of the shock detachment process in hypervelocity flow over cones is presented. The experiments are carried out in the T5 hypervelocity shock tunnel. The computations are mostly done with a code for axisymmetric thermo-chemical nonequilibrium flow. The data obtained confirm a previous theoretical model that predicts lower growth rate of the detachment distance with increasing cone half-angle for nonequilibrium flows than for frozen and equilibrium flows. The lower growth rate is related to the behavior of the sonic line in relaxing flows. The growth of the subsonic region is studied in detail from attached to detached conditions. A comparison between measured and computed interferograms is also made. Measured and computed heat flux distributions are compared, and differences between flows with attached and detached shocks are discussed

Deterministic and stochastic cooperation transitions in evolutionary games on networks

Although the cooperative dynamics emerging from a network of interacting players has been exhaustively investigated, it is not yet fully understood when and how network reciprocity drives cooperation transitions. In this work, we investigate the critical behavior of evolutionary social dilemmas on structured populations by using the framework of master equations and Monte Carlo simulations. The developed theory describes the existence of absorbing, quasi-absorbing, and mixed strategy states and the transition nature, continuous or discontinuous, between the states as the parameters of the system change. In particular, when the decision-making process is deterministic, in the limit of zero effective temperature of the Fermi function, we find that the copying probabilities are discontinuous functions of the system's parameters and of the network degrees sequence. This may induce abrupt changes in the final state for any system size, in excellent agreement with the Monte Carlo simulation results. Our analysis also reveals the existence of continuous and discontinuous phase transitions for large systems as the temperature increases, which is explained in the mean-field approximation. Interestingly, for some game parameters, we find optimal "social temperatures" maximizing/minimizing the cooperation frequency/density.Comment: 14 pages, 5 figure

Unveiling the connectivity of complex networks using ordinal transition methods

Ordinal measures provide a valuable collection of tools for analyzing correlated data series. However, using these methods to understand the information interchange in networks of dynamical systems, and uncover the interplay between dynamics and structure during the synchronization process, remains relatively unexplored. Here, we compare the ordinal permutation entropy, a standard complexity measure in the literature, and the permutation entropy of the ordinal transition probability matrix that describes the transitions between the ordinal patterns derived from a time series. We find that the permutation entropy based on the ordinal transition matrix outperforms the rest of the tested measures in discriminating the topological role of networked chaotic R\"ossler systems. Since the method is based on permutation entropy measures, it can be applied to arbitrary real-world time series exhibiting correlations originating from an existing underlying unknown network structure. In particular, we show the effectiveness of our method using experimental datasets of networks of nonlinear oscillators.Comment: 9 pages, 5 figure

Statistical complexity and connectivity relationship in cultured neural networks

We explore the interplay between the topological relevance of a neuron and its dynamical traces in experimental cultured neuronal networks. We monitor the growth and development of these networks to characterise the evolution of their connectivity. Then, we explore the structure-dynamics relationship by simulating a biophysically plausible dynamical model on top of each networks' nodes. In the weakly coupling regime, the statistical complexity of each single node dynamics is found to be anti-correlated with their degree centrality, with nodes of higher degree displaying lower complexity levels. Our results imply that it is possible to infer the degree distribution of the network connectivity only from individual dynamical measurements.Comment: 16 pages, 5 figure