Paolo Paradisi

Diffusion Scaling in Event-Driven Random Walks: An Application to Turbulence

Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, ItalyCentro EXTREME, Scuola Superiore S. Anna, P.zza Martiri della Libert´a 7, 56127 Pisa, Italy(e-mail: allegrip@df.unipi.it)(Received November 16, 2011 – Revised February 8, 2012)Scaling laws for the diffusion generated by three different random walk models are reviewed.The random walks, defined on a one-dimensional lattice, are driven by renewal intermittent eventswith non-Poisson statistics and inverse power-law tail in the distribution of the inter-event orwaiting times, so that the event sequences are characterized by self-similarity. Intermittency isa ubiquitous phenomenon in many complex systems and the power exponent of the waitingtime distribution, denoted as complexity index, is a crucial parameter characterizing the system’scomplexity. It is shown that different scaling exponents emerge from the different random walks,even if the self-similarity, i.e. the complexity index, of the underlying event sequence remainsthe same. The direct evaluation of the complexity index from the time distribution is affectedby the presence of added noise and secondary or spurious events. It is possible to minimize theeffect of spurious events by exploiting the scaling relationships of the random walk models. Thisallows to get a reliable estimation of the complexity index and, at the same time, a confirmationof the renewal assumption. An application to turbulence data is shown to explain the basic ideasof this approach.Keywords: random walks, renewal processes, power-law, turbulence, self-organization, long-rangememory, self-similarity, fractal intermittency.

Is temporal scaling at the basis of allometry?: comment on "Physiologic time: a hypothesis" by West and West.

a Istituto di Fisiologia Clinica (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy b Centro EXTREME, Scuola Superiore Sant’Anna, P.zza Martiri della Libertà 7, 56127 Pisa, Italy c Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” (ISTI-CNR), Via Moruzzi 1, 56124 Pisa, Italy d Dipartmento di Patologia Chirurgica, Medica, Molecolare e dell’area critica, Università di Pisa, Via Paradisa 2, 56127 Pisa, Italy

A Low Cost, Portable Device for Breath Analysis and Self-monitoring, the Wize Sniffer

Here we describe the implementation of the first prototype of the Wize Sniffer 1.x (WS 1.x), a low cost, portable electronic device for breath analysis. The device is being developed in the framework of the Collaborative European Project SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring). In the frame of SEMEOTICONS project, the Wize Sniffer will help the user monitor his/her state of health, in particular giving feedbacks about those noxious habits for cardio-metabolic risk, such as alcohol intake and smoking. The low cost and compactness of the device allows for a daily screening that, even if without a real diagnostic meaning, could represent a pre-monitoring, useful for an optimal selection of more sophisticated and standard medical analysis.

The Challenge of Brain Complexity - A Brief Discussion about a Fractal Intermittency-based Approach

In the last years, the complexity paradigm is gaining momentum in many research fields where large multidimensional datasets are made available by the advancements in instrumental technology. A complex system is a multi-component system with a large number of units characterized by cooperative behavior and, consequently, emergence of well-defined self-organized structures, such as communities in a complex network. The self-organizing behavior of the brain neural network is probably the most important prototype of complexity and is studied by means of physiological signals such as the ElectroEncephaloGram (EEG). Physiological signals are typically intermittent, i.e., display non-smooth rapid variations or crucial events (e.g., cusps or abrupt jumps) that occur randomly in time, or whose frequency changes randomly. In this work, we introduce a complexity-based approach to the analysis and modeling of physiological data that is focused on the characterization of intermittent events. Recent findings about self-similar or fractal intermittency in human EEG are reviewed. The definition of brain event is a crucial aspect of this approach that is discussed in the last part of the paper, where we also propose and discuss a first version of a general-purpose event detection algorithm

A random field approach to the Lagrangian modeling of turbulent transport in vegetated canopies

We present an application of a Lagrangian Stochastic Model (LSM) to turbulent dispersion over complex terrain, where turbulent coherent structures are known to play a crucial role. We investigate the case of a vegetated canopy by using semi-empirical parameterizations of turbulence profiles in the region inside and above a canopy layer. The LSM is based on a 4-dimensional Fokker-Planck (4DFP) equation, which extends the standard Thomson87 Lagrangian approach. The 4DFP model is derived by means of a Random Field description of the turbulent velocity field. The main advantage of this approach is that not only the experimental Eulerian one-point statistics, but also the Eulerian two-point two-time covariance structure can be included explicitly in the LSM. At variance with the standard Thomson87 approach, the 4DFP model allows to consider explicit parameterizations of the turbulent coherent structures as it explicitly includes both spatial and temporal correlation functions. In order to investigate the effect of the turbulent geometrical structure on a scalar concentration profile, we performed numerical simulations with two different covariance parameterizations, the first one isotropic and the second anisotropic. We show that the accumulation of scalars near the ground is due to the anisotropic geometrical properties of the turbulent boundary layer.

Scaling laws of turbulence intermittency in the atmospheric boundary layer: the role of stability

Bursting and intermittent behavior is a fundamental feature of turbulence, especially in the vicinity of solid obstacles. This is associated with the dynamics of turbulent energy production and dissipation, which can be described in terms of coherent motion structures. These structures are generated at random times and remain stable for long times, after which they become suddenly unstable and undergo a rapid decay event. This intermittent behavior is described as a birth-death point process of self-organization, i.e., a sequence of critical events. The Inter-Event Time (IET) distribution, associated with intermittent self-organization, is typically a power-law decay, whose power exponent is known as complexity index and characterizes the complexity of the system, i.e., the ability to develop self-organized, metastable motion structures. We use a method, based on diffusion scaling, for the estimation of systems complexity. The method is applied to turbulence velocity data in the atmospheric boundary layer. A neutral condition is compared with a stable one, finding that the complexity index is lower in the neutral case with respect to the stable one. As a consequence, the crucial birth-death events are more rare in the stable case, and this could be associated with a less efficient transport dynamics.