We study a binary-cell-state eight-cell neighborhood two-dimensional cellular automaton model of a quasi-chemical system with a substrate and a reagent. Reactions are represented by semi-totalistic transitions rules: every cell switches from state 0 to state 1 depending on if the sum of neighbors in state 1 belongs to some specified interval, cell remains in state 1 if the sum of neighbors in state 1 belong to another specified interval. We investigate space-time dynamics of 1296 automata, establish morphology-bases classification of the rules, explore precipitating and excitatory cases and scrutinize collisions between mobile and stationary localizations (gliders, cycle life and still-life compact patterns). We explore reaction–diffusion like patterns produced as a result of collisions between localizations. Also, we propose a set of rules with complex behavior called Life 2c22.

Cast resin medium voltage instrument transformer are highly used because of several benefits over other type of transformers. Nevertheless, the high operating temperatures affects their performance and durability. It is important to forecast the hot spots in the transformer. The aim of this study is to develop a model based on Artificial Neural Networks (ANN) theory to be able to forecast the temperature in seven points, taking into account twenty-six input data of transformer design features. 792 simulations were carried out in COMSOL Multiphysics®to emulate the heat transfer in the transformer. The data obtained were used to train 1110 ANN with different number of neurons and hidden layers. The ANN with the best performance (R = 1, MSE=0.003455) has three hidden layers with 10, 9 and 9 neurons respectively. The ANN predictions were validated with finite element simulations and laboratory thermal tests which present similar patterns. With this accuracy in the prediction of hot-spot temperature, this ANN can be used to optimize the design of instrument transformers.

Using Rule 126 elementary cellular automaton (ECA), we demonstrate that a chaotic discrete system — when enriched with memory — hence exhibits complex dynamics where such space exploits on an ample universe of periodic patterns induced from original information of the ahistorical system. First, we analyze classic ECA Rule 126 to identify basic characteristics with mean field theory, basins, and de Bruijn diagrams. To derive this complex dynamics, we use a kind of memory on Rule 126; from here interactions between gliders are studied for detecting stationary patterns, glider guns, and simulating specific simple computable functions produced by glider collisions. © 2010 Wiley Periodicals, Inc. Complexity, 2010

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.