arXiv subjects
Publications and source records attributed to Gianluca Martelloni.
In this contribution I present results achieved recently in the field of the OT forecast that push further the limit of the accuracy of the OT forecasts and open to new perspectives in this field.
In a previous article [1] we have described the temporal evolution of the Sars- Cov-2 in Italy in the time window February 24-April 1. As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and the deaths. In this article our goal is to study the missing peak, i.e. the currently infected one (or total currently positive). After the April 7 the large increase in the number of swabs meant that the logistical behavior of the infected curve no longer worked. So we decided to generalize the model, introducing new parameters. Moreover, we adopt a similar approach used in [1] (for the estimation of deaths) in order to evaluate the recoveries. In this way, introducing a simple conservation law, we define a model with 4 populations: total infected, currently positives, recoveries and deaths. Therefore, we propose an alternative method to a classical SIRD model for the evaluation of the Sars-Cov-2 epidemic. However, the method is general and thus applicable to other diseases. Finally we study the behavior of the ratio infected over swabs for Italy, Germany and USA, and we show as studying this parameter we recover the generalized Logistic model used in [1] for these three countries. We think that this trend could be useful for a future epidemic of this coronavirus.
In this letter we study the temporal evolution of the Sars-Cov-2 in Italy. The time window of the real data is between February 24 and March 25. After we upgrade the data until April 1.We perform the analysis with 4 different model and we think that the best candidate to describe correctly the italian situation is a generalized Logistic equation. We use two coupled differential equations that describe the evolution of the severe infected and the deaths. We have done this choice, because in Italy the pharyngeal swabs are made only to severe infected and so we have no information about asymptomatic people. An important observation is that the virus spreads between Regions with some delay; so we suggest that a different analysis region by region would be more sensible than that on the whole Italy. In particular the region Lombardia has a behaviour very fast with respect to the other ones. We show the behaviour of the total deaths and the total severe infected for Italy and five regions: Lombardia, Emilia Romagna, Veneto, Piemonte, Toscana. Finally we do an analysis of the peak and an estimation of how many lifes have been saved with the LockDown.
MEDEAS (Modelling the Energy Development under Environmental And Socioeconomic constraint) World is a new global-aggregated energy-economy-environmental model, which runs from 1995 to 2050. In this work, we tested the MEDEAS world model to reproduce the IPCC (International Panel on Climate Change) GHG (Green House Gases) emission pathways consistent with 2 °C Global Warming. We achieved parameter optimizations of the MEDEAS model related to different scenarios until 2050. We chose to provide a sensitivity analysis on the parameters that directly influence the emission curves focusing on the annual growth of the RES (Renewable Energy Sources), GDP (Gross Domestic Product) and annual population growth. From such an analysis, it has been possible to infer the large impact of GDP on the emission scenarios.
We analyze the database prepared by Brecke (Brecke 2011) for violent conflict, covering some 600 years of human history. After normalizing the data for the global human population, we find that the number of casualties tends to follow a power law over the whole data series for the period considered, with no evidence of periodicity. We also observe that the number of conflicts, again normalized for the human population, show a decreasing trend as a function of time. Our result agree with previous analyses on this subject and tend to support the idea that war is a statistical phenomenon related to the network structure of the human society.
Water vapour in the atmosphere is the main source of the atmospheric opacity in the infrared and sub-millimetric regimes and its value plays a critical role in observations done with instruments working at these wavelengths on ground-based telescopes. The scheduling of scientific observational programs with instruments such as the VLT Imager and Spectrometer for mid Infrared (VISIR) at Cerro Paranal and the Large Binocular Telescope Interferometer (LBTI) at Mount Graham would definitely benefit from the ability to forecast the atmospheric water vapour content. In this contribution we present a study aiming at validating the performance of the non-hydrostatic mesoscale Meso-NH model in reliably predicting precipitable water vapour (PWV) above the two sites. For the VLT case we use, as a reference, measurements done with a Low Humidity and Temperature PROfiling radiometer (LHATPRO) that, since a few years, is operating routinely at the VLT. LHATPRO has been extensively validated on previous studies. We obtain excellent performances on forecasts performed with this model, including for the extremely low values of the PWV (<= 1 mm). For the LBTI case we compare one solar year predictions obtained with the Meso-NH model with satellite estimates again obtaining an excellent agreement. This study represents a further step in validating outputs of atmospheric parameters forecasts from the ALTA Center, an operational and automatic forecast system conceived to support observations at LBT and LBTI.
We present a three-dimensional model, based on cohesive spherical particles, of rain-induced landslides. The rainwater infiltration into the soil follow the either the fractional or the fractal diffusion equations. We solve analytically the fractal diffusion partial differential equation (PDE) with particular boundary conditions to simulate a rainfall event. Then, for the PDE, we developed a numerical integration scheme that we integrate with MD (Molecular Dynamics) algorithm for the triggering and propagation of the simulated landslide. Therefore we test the numerical integration scheme of fractal diffusion equation with the analytical solution. We adopt the fractal diffusion equation in term of gravimetric water content that we use as input of triggering scheme based on Mohr-Coulomb limit-equilibrium criterion, adapted to particle level. Moreover, taking into account an interacting force Lennard-Jones inspired, we use a standard MD algorithm to update particle positions and velocities. Then we present results for homogeneous and heterogeneous systems (i.e. composed by particles with same or different radius respectively). Interestingly, in the heterogeneous case, we observe segregation effects due to the different volume of the particles. Finally we show the parameter sensibility analysis both for triggering and propagation phase. Our simulations confirm the results of our previous two-dimensional model and therefore the feasible applicability to real cases.
The aim of our study is to describe the dynamics of ant battles, with reference to laboratory experiments, by means of a chemical stochastic model. We focus on ants behavior as an interesting topic in order to predict the ecological evolution of invasive species and their spreading. In our work we want to describe the interactions between two groups of different ant species with different war strategies. Our model considers the single ant individuals and fighting groups in a way similar to atoms and molecules, respectively, considering that ant fighting groups remain stable for a relative long time. Starting from a system of differential non-linear equations (DE), derived from the chemical reactions, we obtain a mean field description of the system. The DE approach is valid when the number of individuals of each species is large in the considered unit, while we consider battles of at most 10 vs. 10 individuals, due to the difficulties in following the individual behavior in a large assembly. Therefore, we also adapt a Gillespie algorithm to reproduce the fluctuations around the mean field. The DE scheme is exploited to characterize the stochastic model. The set of parameters of chemical equations, obtained using a minimization algorithm, are used by the Gillespie algorithm to generate the stochastic trajectories. We then fit the stochastic paths with the DE, in order to analyze the variability of the parameters and their variance. Finally, we estimate the goodness of the applied methodology and we confirm that the stochastic approach must be considered for a correct description of the ant fighting dynamics. With respect to other war models, our chemical one considers all phases of the battle and not only casualties. Thus, we can count on more experimental data, but we also have more parameters to fit. In any case, our model allows a much more detailed description of the fights.
Systems with long-range interactions display a short-time relaxation towards Quasi Stationary States (QSS) whose lifetime increases with the system size. In the paradigmatic Hamiltonian Mean-field Model (HMF) out-of-equilibrium phase transitions are predicted and numerically detected which separate homogeneous (zero magnetization) and inhomogeneous (nonzero magnetization) QSS. In the former regime, the velocity distribution presents (at least) two large, symmetric, bumps, which cannot be self-consistently explained by resorting to the conventional Lynden-Bell maximum entropy approach. We propose a generalized maximum entropy scheme which accounts for the pseudo-conservation of additional charges, the even momenta of the single particle distribution. These latter are set to the asymptotic values, as estimated by direct integration of the underlying Vlasov equation, which formally holds in the thermodynamic limit. Methodologically, we operate in the framework of a generalized Gibbs ensemble, as sometimes defined in statistical quantum mechanics, which contains an infinite number of conserved charges. The agreement between theory and simulations is satisfying, both above and below the out of equilibrium transition threshold. A precedently unaccessible feature of the QSS, the multiple bumps in the velocity profile, is resolved by our new approach.
We propose two modeling approaches to describe the dynamics of ant battles, starting from laboratory experiments on the behavior of two ant species, the invasive Lasius neglectus and the authocthonus Lasius paralienus. This work is mainly motivated by the need to have realistic models to predict the interaction dynamics of invasive species. The two considered species exhibit different fighting strategies. In order to describe the observed battle dynamics, we start by building a chemical model considering the ants and the fighting groups (for instance two ants of a species and one of the other one) as a chemical species. From the chemical equations we deduce a system of differential equations, whose parameters are estimated by minimizing the difference between the experimental data and the model output. We model the fluctuations observed in the experiments by means of a standard Gillespie algorithm. In order to better reproduce the observed behavior, we adopt a spatial agent-based model, in which ants not engaged in fighting groups move randomly (diffusion) among compartments, and the Gillespie algorithm is used to model the reactions inside a compartment.
In this paper we present a discrete dynamical population modeling of invasive species, with reference to the swamp crayfish Procambarus clarkii. Since this species can cause environmental damage of various kinds, it is necessary to evaluate its expected in not yet infested areas. A structured discrete model is built, taking into account all biological information we were able to find, including the environmental variability implemented by means of stochastic parameters (coefficients of fertility, death, etc.). This model is based on a structure with 7 age classes, i.e. a Leslie mathematical population modeling type and it is calibrated with laboratory data provided by the Department of Evolutionary Biology (DEB) of Florence (Italy). The model presents many interesting aspects: the population has a high initial growth, then it stabilizes similarly to the logistic growth, but then it exhibits oscillations (a kind of limit-cycle attractor in the phase plane). The sensitivity analysis shows a good resilience of the model and, for low values of reproductive female fraction, the fluctuations may eventually lead to the extinction of the species: this fact might be exploited as a controlling factor. Moreover, the probability of extinction is valuated with an inverse Gaussian that indicates a high resilience of the species, confirmed by experimental data and field observation: this species has diffused in Italy since 1989 and it has shown a natural tendency to grow. Finally, the spatial mobility is introduced in the model, simulating the movement of the crayfishes in a virtual lake of elliptical form by means of simple cinematic rules encouraging the movement towards the banks of the catchment (as it happens in reality) while a random walk is imposed when the banks are reached.
In this paper we propose a two-dimensional (2D) computational model, based on a molecular dynamics (MD) approach, for deep landslides triggered by rainfall. Our model is based on interacting particles or grains and describes the behavior of a fictitious granular material along a slope consisting of a vertical section, i.e. with a wide thickness. The triggering of the landslide is caused by the passing of two conditions: a threshold speed and a condition on the static friction of the particles, the latter based on the Mohr-Coulomb failure criterion (Coulomb 1776; Mohr 1914). The inter-particle interactions are through a potential that, in the absence of suitable experimental data and due to the arbitrariness of the grain dimension is modeled by means of a potential similar to the Lennard-Jones one (Lennard-Jones 1924), i.e., with an attractive and a repulsive part. For the updating of the particle positions we use a MD method which results to be very suitable to simulate this type of systems (Herrmann and Luding 1998). In order to take into account the increasing of the pore pressure due to the rainfall, a filtration model is considered. Finally we also introduce in the model the viscosity as a term in the dynamic equations of motion. The outcome of simulations, from the point of view of statistical and dynamic characterization, is quite satisfactory relative to real landslides behavior and we can claim that this types of modeling can represent a new method to simulate landslides triggered by rainfall.
The aim of this paper is to propose a 2D computational algorithm for modeling of the trigger and the propagation of shallow landslides caused by rainfall. We used a Molecular Dynamics (MD) inspired model, similar to discrete element method (DEM), that is suitable to model granular material and to observe the trajectory of single particle, so to identify its dynamical properties. We consider that the triggering of shallow landslides is caused by the decrease of the static friction along the sliding surface due to water infiltration by rainfall. Thence the triggering is caused by two following conditions: (a) a threshold speed of the particles and (b) a condition on the static friction, between particles and slope surface, based on the Mohr-Coulomb failure criterion. The latter static condition is used in the geotechnical model to estimate the possibility of landslide triggering. Finally the interaction force between particles is defined trough a potential that, in the absence of experimental data, we have modeled as the Lennard-Jones 2-1 potential. In the model the viscosity is also introduced and for a large range of values of the model's parameters, we observe a characteristic velocity pattern, with acceleration increments, typical of real landslides. The results of simulations are quite promising: the energy and the time triggering distributions of local avalanches shows a power law distribution, analogous to the observed Gutenberg-Richter and Omori power law distributions for earthquakes. Finally it is possible to apply the method of the inverse surface displacement velocity [Fukuzono 1985] for predicting the failure time.