In this paper, we propose a methodology for deriving a model of a complex system by exploiting the information extracted from topological data analysis. Central to our approach is the S[B] paradigm in which a complex system is represented by a two-level model. One level, the structural S one, is derived using the newly-introduced quantitative concept of persistent entropy, and it is described by a persistent entropy automaton. The other level, the behavioral B one, is characterized by a network of interacting computational agents. The presented methodology is applied to a real case study, the idiotypic network of the mammalian immune system.

ObjectiveAn innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals.ResultsThe performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.

Modern wireless sensor and actuator networks (WSANs) are composed of spatially distributed low cost nodes that can contain different sensors and actuators. Event condition action (ECA) based languages have been widely proposed in order to program WSANs. Implementing applications by using ECA rules is an error-prone process thus various formal methods have been proposed. In spite of this great variety, formal verification of ECA rules has not been tailored to the context of WSANs. In this paper we present IRON, an ECA language for programming WSANs. IRON allows the automatic verifications of ECA rules. These are used by the IRON run-time platform in order to implement the required behaviour

The simulation and visualization of biological system models is becoming more and more important both in clinical use and in basic research. Since many systems are characterized by interactions involving different scales at the same time, several approaches have been defined to handle such complex systems at different spatial and temporal scale. In this context, we propose BioShape, a 3D particle-based spatial simulator whose novelty\ud consists of providing a uniform and geometry-oriented multiscale modeling environment.\ud These features make BioShape "scale-independent'', able to\ud express geometric and positional information, and able to support transformations between scales simply defined as mappings between different granularity model instances. To highlight BioShape peculiarities, we sketch a multiscale model of human aortic valve where shapes are used at the cell scale for describing\ud the interaction between a single valvular interstitial cell and its surrounding matrix, at the tissue scale for modeling the valve leaflet tissue mechanical behaviour, and at the organ scale for reproducing, as a 3D structure with fluid-structure interaction, the\ud motion of the valve, blood, and surrounding tissue

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