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The demand for energy saving and the new policies on the efficiency of motors for constant speed applications have shifted the interest of designers from conventional induction motors (IMs) towards alternative high-efficiency motors such as the line-start synchronous reluctance motor (LSSynRM). This type of motor is very cost-effective and can compete with the robustness and the low price of the IM. LSSynRM critical aspects are the rough starting transient, limitations in terms of pull-in (synchronization) capability and low power factor. In this chapter, a specific design procedure for LSSynRMs has been used in order to reach the desired balance between the pull-in capability, starting behaviour, and steady-state performance. It is the combination between finite-element (FE) analyses and optimization algorithms. The procedure is applied to design two LSSynRMs, 3 kW-2 pole and 4 kW-4 pole, 400 V, 50 Hz. The simulation results are compared with those of the IMs of the same size. A prototype of the 4 kW-4pole motor has been realized and tested. Then, its performance is presented in comparison with the IM counterpart. The LSSynRM proved to be a cost-effective, mass production-ready solution for super-premium efficiency IE4 motors, and it can effectively replace the conventional IM in a vast panorama of industrial applications.

Random Boolean networks are a widely acknowledged model for cell dynamics. Previous studies have shown the possibility of achieving Boolean Networks (BN) with given characteristics by means of evolutionary techniques. In this work we show that it is possible to evolve BNs exhibiting more positive than negative reactions to knock-out stresses. It is also interesting to observe that in the observed runs (i) the evolutionary processes can guide the BNs toward different dynamic regimes, depending on their internal structure and that (ii) the BNs forced to evolve by maintaining a critical dynamical regime achieve better results than those that do not have this characteristic; this observation supports the idea that criticality may be beneficial to an evolving population of dynamical systems.

A well-known hypothesis, with far-reaching implications, is that biological evolution should preferentially lead to critical dynamic regimes. Useful information about the dynamical regime of gene regulatory networks can be obtained by studying their responses to small perturbations. The interpretation of these data requires the use of suitable models, where it is usually assumed that the system is homogeneous. On the other hand, it is widely acknowledged that biological networks display some degree of modularity, so it is interesting to ascertain how modularity can affect the estimation of their dynamical properties. In this study we introduce a well-defined degree of modularity and we study how it influences the network dynamics. In particular, we show how the estimate of the Derrida parameter from “avalanche” data may be affected by strong modularity.

Many systems in nature, society and technology are composed of numerous interacting parts. Very often these dynamics lead to the formation of medium-level structures, whose detection could allow a high-level description of the dynamical organization of the system itself, and thus to its understanding. In this work we apply this idea to the “cancer evolution” models, of which each individual patient represents a particular instance. This approach - in this paper based on the RI methodology, which is based on entropic measures - allows us to identify distinct independent cancer progression patterns in simulated patients, planning a road towards applications to real cases .

Networks are often used to represent the relations among the variables of a dynamical system. The properties of network topology are usually exploited to understand the organization of the system. Nevertheless, the dynamical organization of a system might considerably differ from its topological one. In this paper, we describe a method to identify “relevant subsets” of variables. The variables belonging to a relevant subset should be strongly integrated and should have a much weaker interaction with the other system variables. Extending previous works on neural networks, an information-theoretic measure is introduced, i.e., the Dynamical Cluster Index, in order to identify candidate relevant subsets. The method solely relies on observations of the variables’ values in time.

Automatic techniques for the design of artificial computational systems, such as control programs for robots, are currently achieving increasing attention within the AI community. A prominent case is the design of artificial neural network systems by means of search techniques, such as genetic algorithms. Frequently, the search calibrates not only the system parameters, but also its structure. This procedure has the advantage of reducing the bias introduced by the designer and makes it possible to explore new, innovative solutions. The drawback, though, is that the analysis of the resulting system might be extremely difficult and limited to few coarse-grained characteristics. In this paper, we consider the case of robots controlled by Boolean networks that are automatically designed by means of a training process based on local search. We propose to analyse these systems by a method that detects mesolevel dynamical structures. These structures are emerging patterns composed of elements that behave in a coherent way and loosely interact with the rest of the system. In general, this method can be used to detect functional clusters and emerging structures in nonlinear discrete dynamical systems. It is based on an extension of the notion of cluster index, which has been previously proposed by Edelman and Tononi to analyse biological neural systems. Our results show that our approach makes it possible to identify the computational core of a Boolean network which controls a robot.

This chapter introduces an agent-based model designed to investigate the dynamics of some aspects of exaptation that have been discussed previously in this volume. It is strongly related to the model introduced in the previous chapter. Indeed, in the model described here, cognitive categories represent the main tools that the producers and users of artifacts employ in order to interpret their environment, as in the case discussed in Chapter 14. The main addition provided by the current model, however, is the explicit introduction of artifacts. As stressed in Chapter 1, artifacts are a key component of human organizations and activities. Artifacts are entities constructed by an organization to enhance its or other organizations’ functionalities functionality. One of their main properties of interest to us is their capability to convey information, although they may not be explicitly designed for this purpose. In addition, there are artifacts specifically designed to store and carry information, like e.g. books, radios, televisions, including the very special kind of artifact represented by computers, which are able to process information at a very high level of abstraction. Since artifacts convey information, their explicit representation eases the understanding of the exaptation phenomenon, seen in this context as a shift in terms of “leading attributions.” Actually, their introduction is important in order to characterize the ontology necessary to identify exaptation events. Here we focus on phenomena occurring at the micro-level (how individuals collect information about the external world, categorize it, and combine existing categories in order to create new ones) and meso-level (the exchange of information among individuals). However, we do not explicitly include the details concerning the macro-level events (the shared system of beliefs and the common physical and technological resources); which are left for further research.

Random boolean networks (RBN) are well known dynamical systems, whose properties have been extensively studied in the case where each node has the same number of incoming connections, coming from other nodes chosen at random with uniform probability, and the updating is synchronous. In the past, the comparison with experimental results has been limited to some well-known tests; we review here some recent results that demonstrate that the availability of gene expression data now allows further testing of these models. Moreover, in this paper we summarize some recent results and present some novel data concerning the dynamics of these networks in the case where either the network has a scale-free topology or the updating takes place asynchronously.