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Ferenc Járai-Szabó

Publications and source records attributed to Ferenc Járai-Szabó.

5 recordsLinked to original sources

Synchronization and Swarming of Two-Mode Stochastic Oscillators

Synchronization and swarming are canonical manifestations of self-organization, observable across scales from cellular processes to animal flocks. This study investigates the collective dynamics of a novel agent-based model where individuals exhibit both spatial mobility and internal, two-mode stochastic oscillatory states. By introducing a local, distance-dependent coupling between the agents' spatial configuration and their internal state transitions, we establish a mutual feedback loop that drives complex pattern formation. Through large-scale numerical simulations, we identify seven distinct morphological configurations, ranging from stationary \textit{Filled-disk} states to highly disordered \textit{Intense-motion} regimes. By performing a rigorous quantitative analysis of the rotational energy and radial dispersion, we transcend simple morphological classification and demonstrate that the system organizes into discrete, quantized topological attractors. We derive a macroscopic scaling law, $Ω\propto r^{-1/2}$, which proves that the emerging rotating states are not rigid-body rotations, but rather composite differential vortex structures characterized by spontaneous chiral symmetry breaking. Our results suggest that these stable, quantized dynamical states are fundamental features of systems governed by bidirectional spatial-phase feedback, offering a robust framework for designing autonomous, decentralized robotic swarms.

physics.soc-ph↗

Cell-size distribution and scaling in a one-dimensional KJMA lattice model with continuous nucleation

The Kolmogrov-Johnson-Mehl-Avrami (KJMA) growth model is considered on a one-dimensional (1D) lattice. Cells can growth with constant speed and continuously nucleate on the empty sites. We offer an alternative, mean-field like approach for describing theoretically the dynamics and derive an analytical cell-size distribution function. Our method reproduces the same scaling laws as the KJMA theory and has the advantage that it leads to a simple closed form for the cell-size distribution function. It is shown that a Weibull distribution is appropriate for describing the final cell-size distribution. The results are discussed in comparison with Monte Carlo simulation data.

cond-mat.soft↗

Stochastic graph Voronoi tessellation reveals community structure

Given a network, the statistical ensemble of its graph-Voronoi diagrams with randomly chosen cell centers exhibits properties convertible into information on the network's large scale structures. We define a node-pair level measure called {\it Voronoi cohesion} which describes the probability for sharing the same Voronoi cell, when randomly choosing $g$ centers in the network. This measure provides information based on the global context (the network in its entirety) a type of information that is not carried by other similarity measures. We explore the mathematical background of this phenomenon and several of its potential applications. A special focus is laid on the possibilities and limitations pertaining to the exploitation of the phenomenon for community detection purposes.

physics.soc-ph↗

Chaos on the conveyor belt

The dynamics of a spring-block train placed on a moving conveyor belt is investigated both by simple experiments and computer simulations. The first block is connected by spring to an external static point, and due to the dragging effect of the belt the blocks undergo complex stick-slip dynamics. A qualitative agreement with the experimental results can only be achieved by taking into account the spatial inhomogeneity of the friction force on the belt's surface, modeled as noise. As a function of the velocity of the conveyor belt and the noise strength, the system exhibits complex, self-organized critical, sometimes chaotic dynamics and phase transition-like behavior. Noise induced chaos and intermittency is also observed. Simulations suggest that the maximum complexity of the dynamical states is achieved for a relatively small number of blocks, around five.

nlin.CD↗

Spring-block approach for nanobristle patterns

A two dimensional spring-block type model is used to model capillarity driven self-organization of nanobristles. The model reveals the role of capillarity and van der Waals forces in the pattern formation mechanism. By taking into account the relevant interactions several type of experimentally observed patterns are qualitatively well reproduced. The model offers the possibility to generate on computer novel nanobristle based structures, offering hints for designing further experiments.

cond-mat.mes-hall↗