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This is the last article of the series dedicated to the WinCA application. This application allows the edition and to execution of cellular automata. To finish, I will review the code that implements the automaton itself, using all the classes and interfaces explained in the previous articles.

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I continue explaining the basics of the WinCA application code, dedicated to the designing and execution of cellular automata. This time I will tell you about the implementation of the cells and other auxiliary classes needed to build and execute automata.

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We continue with the series dedicated to cellular automata and the WinCA application, dedicated to build and executing them. In this article I will explain the code related to the expression system that allows establishing the conditions to change from one state to another.

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I continue to comment on the source code of the WinCA program, dedicated to cellular automata. In this article I will explain the interfaces and classes with which cell states are implemented and their edition. You can use these interfaces and classes as a basis to extend the application features.

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Once reviewed the operation of the WinCA application, dedicated to cellular automata, let's see how the source code is organized. In this article I will explain the properties of cell states, and how they can be extended to add new functionalities by implementing new classes with the corresponding interfaces.

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In this article, third of the series, I continue to explain the operation of the WinCA application, devoted to the construction and execution of cellular automata. This time I will show the language used to define the transitions between the different states of the automaton cells.

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This is the second article in the series about the WinCA application, dedicated to the construction and execution of cellular automata. In this article I will show how to design the diferent states that can have the automaton cells, and the properties that can be assigned to them, using the application editors.

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Usually, when you perform a data analysis, you suppose that they come from a normal distribution. In fact, you perform a battery of tests to verify that this assumption is met and, otherwise, you try to modify the data so that it is satisfied. This is because most analysis techniques only work properly on normally distributed data. But there are a number of systems that present a complex dynamics where is not valid to apply this hypothesis and wherein adjusting the data only leads to distortions that invalidate the results.

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