Rovira i Virgili University

About: Rovira i Virgili University is a education organization based out in Tarragona, Spain. It is known for research contribution in the topics: Population & Laser. The organization has 4247 authors who have published 9141 publications receiving 236256 citations.

Mathematical Formulation of Multilayer Networks

Abstract: A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing ‘‘traditional’’ network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.

Models of social networks based on social distance attachment

Abstract: We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.

Review and analysis of biomass gasification models

Abstract: The use of biomass as a source of energy has been further enhanced in recent years and special attention has been paid to biomass gasification. Due to the increasing interest in biomass gasification, several models have been proposed in order to explain and understand this complex process, and the design, simulation, optimisation and process analysis of gasifiers have been carried out. This paper presents and analyses several gasification models based on thermodynamic equilibrium, kinetics and artificial neural networks. The thermodynamic models are found to be a useful tool for preliminary comparison and for process studies on the influence of the most important fuel and process parameters. They have the advantage of being independent of gasifier design, but they cannot give highly accurate results for all cases. The kinetic-based models are computationally more intensive but give accurate and detailed results. However, they contain parameters that limit their applicability to different plants.

Spiking Neural P Systems

Abstract: This paper proposes a way to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons fire and/or spike) plays an essential role. For instance, the result of a computation is the time between the moments when a specified neuron spikes. Seen as number computing devices, SN P systems are shown to be computationally complete (both in the generating and accepting modes, in the latter case also when restricting to deterministic systems). If the number of spikes present in the system is bounded, then the power of SN P systems falls drastically, and we get a characterization of semilinear sets. A series of research topics and open problems are formulated.