OPINION FORMATION IN SZNAJD MODEL WITH MORE COMPLEX SOCIAL IMPACT

Based on the Ising spin system and the Sznajd Model (SM), we introduce a new model to study the opinion evolving in SM on square lattices by numerical simulations. In the model, a panel of four neighboring sites are randomly selected at each Monte Carlo step (MCS), around the panel there are eight nearest neighbors. To be more realistic, two basic impacts are considered in the process of the eight individuals' decision-making, i.e., the information governed by the panel of four neighboring sites and the average opinion of the whole community. It is found that our model show many useful and interesting statistical results.

RESHUFFLING SPINS WITH SHORT RANGE INTERACTIONS: WHEN SOCIOPHYSICS PRODUCES PHYSICAL RESULTS

Galam reshuffling introduced in opinion dynamics models, is investigated under the nearest neighbor Ising model on a square lattice using Monte Carlo simulations. While the corresponding Galam analytical critical temperature TC≈3.09 [J/kB] is recovered almost exactly, it is proved to be different from both values, not reshuffled (TC =2/ arcsinh (1)≈2.27 [J/kB]) and mean-field (TC =4 [J/kB]). On this basis, gradual reshuffling is studied as function of 0≤p≤1 where p measures the probability of spin reshuffling after each Monte Carlo step. The variation of TC as function of p is obtained and exhibits a nonlinear behavior. The simplest Solomon network realization is noted to reproduce Galam p =1 result. Similarly to the critical temperature, critical exponents are found to differ from both, the classical Ising case and the mean field values.

THE SELF-ORGANIZED MULTI-LATTICE MONTE CARLO SIMULATION

Self-organized Monte Carlo simulations of 2D Ising ferromagnet on the square lattice are performed. The essence of the suggested simulation method is an artificial dynamics consisting of the well-known single-spin-flip Metropolis algorithm supplemented by a random walk on the temperature axis. The walk is biased towards the critical region through a feedback based on instantaneous energy and magnetization cumulants, which are updated at every Monte Carlo step and filtered through a special recursion algorithm. The simulations revealed the invariance of the temperature probability distribution function, once some self-organized critical steady regime is reached, which is called here noncanonical equilibrium. The mean value of this distribution approximates the pseudocritical temperature of canonical equilibrium. In order to suppress finite-size effects, the self-organized approach is extended to multi-lattice systems, where the feedback basis on pairs of instantaneous estimates of the fourth-order magnetization cumulant on two systems of different size. These replica-based simulations resemble, in Monte Carlo lattice systems, some of the invariant statistical distributions of standard self-organized critical systems.

Simulation of Spherulite Growth during Polymer Crystallization by Use of a Cellular Automaton

This article introduces a 3D cellular automaton model for the prediction of spherulite growth phenomena in polymers at the mesoscopic scale. The automaton is discrete in time, real space, and orientation space. The kinetics is formulated according to the Hoffman-Lauritzen secondary surface nucleation and growth theory for spherulite expansion. It is used to calculate the switching probability of each grid point as a function of its previous state and the state of the neighboring grid points. The actual switching decision is made by evaluating the local switching probability using a Monte Carlo step. The growth rule is scaled by the ratio of the local and the maximum interface energies, the local and maximum occurring Gibbs enthalpy of transformation, the local and maximum occurring temperature, and by the spacing of the grid points. The use of experimental input data provides a real time and space scale.

MONTE CARLO SIMULATION OF ACTIN FILAMENT BASED CELL MOTILITY

Cell motility resulting from actin polymerization is modeled on a two-dimensional square lattice. The treadmilling of actin filaments, formation of lamellipodia, protrusion and motility of the model cell are studied using Monte Carlo simulations. The grid space of the square lattice and the Monte Carlo step are related to length and time scales of the problem. The average velocity computed with this prescription from the simulations shows a remarkable agreement with the experimental velocity of a keratocyte. The model cell captures the essential aspects of treadmilling based motility. The movement of the model cell is diffusive for small times and exhibits a cross over to polymerization driven drift for large times. The studies on the parameter sensitivity of cell velocity indicated that the optimal choice of number of monomers, the number of filaments, the rate of depolymerization and the monomer diffusion leads to large velocities. The cell velocity distribution is found to be Gaussian and is in agreement with some of the experimental work.

SPECIAL-PURPOSE ISING MODEL RANDOM LATTICE PROCESSOR

We have designed and built a special purpose processor with a very good performance to price ratio, which permits to propose a new way for parallel computing. A simple one spin flip Monte Carlo algorithm is realized in hardware, so the processor is suitable for studies of dynamic as well as thermodynamic properties of the two-dimensional Ising model with different types of inhomogeneities. The speed of the processor is defined completely by the speed of memories used in it: to perform an elementary Monte Carlo step the processor needs a time only several percent larger than one memory cycle time. So it realizes the fastest possible one spin flip Monte Carlo processor architecture.

Reference velocity model estimation from prestack waveforms: Coherency optimization by simulated annealing

Coherency inversion, which consists of maximizing a semblance function calculated from unstacked seismic waveforms, has the potential of estimating reliable velocity information without requiring traveltime picking on unstacked data. In this work, coherency inversion is based on the assumption that reflectors’ zero‐offset times are known and that the velocity in each layer may vary laterally. The method uses a type of Monte Carlo technique termed the generalized simulated annealing method for updating the velocity field. At each Monte Carlo step, time‐to‐depth conversion is performed. Although this procedure is slow at convergence to the global minimum, it is robust and does not depend on the initial model or topography of the objective function. Applications to both synthetic and field data demonstrate the efficiency of coherency inversion for estimating both lateral velocity variations and interface depth positions.