One of the computational models proposed to study emerging phenomena in decentralized systems is the swarmalator model [ Nat. Commun. 8, 1504 (2017)], where only long-range interactions are considered. But in living systems many of the collective behaviors observed arise from the combined effect of long- and short-range interactions. In this work we present an extension to the swarmalator model which includes a Gaussian short-range repulsive term of the type $\frac{1}{\sigma }{e}^{-{\left|x\right|}^2/\sigma }$ along with the results of numerical simulations. Using several order parameters we can distinguish between static and dynamic aggregations and between synchronous and asynchronous states. We have found six long-term collective states, some of them not previously reported and the most remarkable one showing oscillatory collective behavior. The results obtained show a multiplicity of complex behaviors that extend the applicability of the swarmalator model.

The classical modeling approach for laser study relies on the differential equations. In this paper, a cellular automaton model is proposed as an alternative for the simulation of population dynamics. Even though the model is simplified it captures the essence of laser phenomenology: (i) there is a threshold pumping rate that depends inversely on the decaying lifetime of the atoms and the photons; and (ii) depending on these lifetimes and on the pumping rate, a constant or an oscillatory behavior can be observed. More complex behaviors such as spiking and pattern formation can also be studied with the cellular automaton model.

Influence of the interaction between nearest-neighbor adatoms in a reaction of catalyzed oxidation of carbon monoxide has been studied by Monte Carlo simulation. The transition probabilities are chosen in the Arrhenius form, and the activation energy is divided into two additive terms, corresponding to the action of the substrate and to the interaction between nearest adatoms, respectively. When the interaction makes desorption easier or hinders adsorption the behavior is similar: Three steady state regimes or phases were observed; in the first phase, the surface is poisoned by oxygen; in the second phase there is a reactive steady state in which carbon dioxide is continuously produced, and in the third phase, the surface is poisoned by carbon monoxide. The transition from the O-poisoned phase to the reactive phase is continuous, or second order, and the transition from the reaction to the CO-poisoned phase is first order. The same occurs when the interaction is not considered. The interaction makes the second zone wider, where carbon dioxide is being produced. When the interaction makes easier adsorption or hinders desorption only two phases are observed: In the first phase the surface is poisoned by oxygen and in the second phase it is poisoned by carbon monoxide and the transition from one to another is first order.

Animal cells are amazing examples of decentralized systems: By interchanging information about their position and internal state, cells coordinate their behavior and organize themselves in time and space. Examples of this behavior are the development of an embryo or of an organoid. In this work we have asked which are the "rules of intercellular relationship" that allow the organization of an abstract cell collective into structures similar to simple metazoans, without being specific about the (molecular, cellular or physical) nature of the processes involved. To do so, we have used a computational modeling approach following a modified version of the "Swarmalator" concept introduced by O'Keeffe, Hong and Strogatz (2017): a collection of interacting particles ("swarmalators"), each of which defined by a position in space and an internal state (a phase). The key feature is that swarmalators are coupled, so that their position and internal state are both affected by the position and state of all other swarmalators. This model can be easily analogized to biological systems, with "cells" being the swarmalators, and their phase the cell's internal state or "cell type". With this model we explore the conditions (represented by the coupling parameters) that would allow the organization of a multicellular "bioswarmer" and its dynamics along a sort of life cycle. Originally developed in 2D, we implement the model in 3D as well. We describe how changing the strength of intercellular communication can alter the structure and differentiation state of the bioswarmer, how internal polarization can arise and trigger collective directed migration, or how partly erasing the cellular memory of cell state is critical to allow bioswarmers to transit through different states. In addition, we show that the size of a multicellular ensemble might control the differentiation of its constituent cells without changing its rules of relationship.