# Carmela MarangiItalian National Research Council | CNR · Institute for Applied Mathematics "Mauro Picone" IAC

Carmela Marangi

PhD Physics

## About

56

Publications

7,441

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437

Citations

Introduction

## Publications

Publications (56)

The SOC change index , defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year, is here tailored to the RothC carbon model dynamics. It assumes as a baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity analysis is performed to evaluate...

A novel model is here introduced for the SOC change index defined as the normalized difference between the actual Soil Organic Carbon and the value assumed at an initial reference year. It is tailored on the RothC carbon model dynamics and assumes as baseline the value of the SOC equilibrium under constant environmental conditions. A sensitivity an...

Soil Organic Carbon (SOC) is one of the key indicators of land degradation. SOC positively affects soil functions with regard to habitats, biological diversity and soil fertility; therefore, a reduction in the SOC stock of soil results in degradation, and it may also have potential negative effects on soil-derived ecosystem services. Dynamical mode...

The containment of the invasive species is a widespread problem in the environmental management, with a significant economic impact. We analyze an optimal control model which aims to find the best temporal resource allocation strategy for the removal of an invasive species. We derive the optimality system in the state and control variables and we u...

A challenging task in the management of Protected Areas is to control the spread of invasive species, either floristic or faunistic, and the preservation of indigenous endangered species, typically competing for the use of resources in a fragmented habitat. In this paper, we present some mathematical tools that have been recently applied to contain...

A major neglected weakness of many ecological models is the numerical method used to solve the governing systems of differential equations. Indeed, the discrete dynamics described by numerical integrators can provide spurious solution of the corresponding continuous model. The approach represented by the geometric numerical integration, by preservi...

Effectively dealing with invasive species is a pervasive problem in environmental management. The damages that stem from invasive species are well known. However, controlling them cost-effectively is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management. In this paper we in...

Mathematical modeling and optimization provide decision-support tools of increasing popularity to the management of invasive species. In this chapter, we investigate problems formulated in terms of optimal control theory. A free terminal time optimal control problem is considered for minimizing the costs and the duration of an abatement program. He...

Improving strategies for the control and eradication of invasive species is an important aspect of nature conservation, an aspect where mathematical modeling and optimization play an important role. In this paper, we introduce a reaction‐diffusion partial differential equation to model the spatiotemporal dynamics of an invasive species, and we use...

We propose novel positive numerical integrators for approximating
predator-prey models. The schemes are based on suitable symplectic procedures applied to the dynamical system written in terms of the log transformation of the original variables. Even if this approach is not new when dealing with Hamiltonian systems, it is of particular interest in...

We apply the Z-control approach to a generalized predator-prey system and consider the specific case of indirect control of the prey population. We derive the associated Z-controlled model and investigate its properties from the point of view of the dynamical systems theory. The key role of the design parameter λ for the successful application of t...

This document is the first version of the Data Management Plan of the
H2020 ECOPOTENTIAL project (project number 641762)

We evaluate a mathematical model of the predator-prey population dynamics in a fragmented habitat where both migration processes between habitat patches and prey control policies are taken into account. The considered system is examined by applying the aggregation method and different dynamical scenarios are generated. The resulting implications ar...

It is known that symplectic algorithms do not necessarily conserve energy even for the harmonic oscillator. However, for separable Hamiltonian systems, splitting and composition schemes have the advantage to be explicit and can be constructed to preserve energy. In this paper we describe and test an integrator built on a one-parameter family of sym...

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by in...

We examine spatially explicit models described by reaction-diffusion partial differential equations for the study of predator-prey population dynamics. The numerical methods we propose are based on the coupling of a finite difference/element spatial discretization and a suitable partitioned Runge-Kutta scheme for the approximation in time. The RK s...

Periodic monitoring of biodiversity changes at a landscape scale constitutes a key issue for conservation managers. Earth observation (EO) data offer a potential solution, through direct or indirect mapping of species or habitats. Most national and international programs rely on the use of land cover (LC) and/or land use (LU) classification systems...

One of the core European Union environmental policies is the creation and monitoring of the Natura 2000 network of protected areas. This network has been explicitly established for the preservation of conservation priority habitat types and species. Still the concept of habitat is a key concept for ecologists that remains ill defined and is notorio...

In the study of the effects of habitat fragmentation on biodiversity the
role of spatial processes reveals of great interest since both the
variation of size of the domains as well as their heterogeneity largely
affects the dynamics of species. In order to begin a preliminary study
about the effects of habitat fragmentation on wolf - wild boar pair...

Spatially explicit models consisting of reaction-diffusion partial differential equations are considered in order to model prey-predator interactions, since it is known that the role of spatial processes reveals of great interest in the study of the effects of habitat fragmentation on biodiversity. As almost all of the realistic models in biology,...

Splitting and composition schemes for the numerical integration of
separable Hamiltonian problems, in general, fail to yield conservation
of generic nonlinear Hamiltonians. In the proposed approach we compose,
at each step, symplectic maps which depend on a parameter chosen in
order to have methods which minimize the error on the Hamiltonian
functi...

We consider fully connected neural networks near saturation, trained by a modified Edinburgh algorithm, with sign constraints on the synaptic couplings. We study the domains of attraction of the stored patterns for both the balanced and the unbalanced case (excess of positive over negative constraints). A comparison with the dilute network is also...

Microarray data are a rich source of information, containing the collected expression values of thousands of genes for well defined states of a cell or tissue. Vast amounts of data (thousands of arrays) are publicly available and ready for analysis, e.g. to scrutinise correlations between genes at the level of gene expression. The large variety of...

We consider explicit symplectic partitioned Runge–Kutta (ESPRK) methods for the numerical integration of non-autonomous dynamical systems. It is known that, in general, the accuracy of a numerical method can diminish considerably whenever an explicit time dependence enters the differential equations and the order reduction can depend on the way the...

AbstractWe are concerned with the discretization of optimal control problems when a Runge–Kutta scheme is selected for the related Hamiltonian system. It is known that Lagrangian’s first order conditions on the discrete model, require a symplectic partitioned Runge–Kutta scheme for state–costate equations. In the present paper this result is extend...

We consider splitting methods for the numerical integration of non-autonomous sep- arable difierential equations. Splitting methods have been extensively used as geometric numerical integrators during the last years showing excellent performances (both qualitatively and quantita- tively) when applied on many problems. They are designed for autonomo...

Most physical phenomena are described by time-dependent Hamiltonian systems with qualitative features that should be preserved by numerical integrators used for approximating their dynamics. The initial energy of the system together with the energy added or subtracted by the outside forces, represent a conserved quantity of the motion. For a class...

This work deals with infinite horizon optimal growth models and uses the results in the Mercenier and Michel (1994a) paper as a starting point. Mercenier and Michel (1994a) provide a one-stage Runge-Kutta discretization of the above-mentioned models which preserves the steady state of the theoretical solution. They call this feature the "steady-sta...

Direct numerical approximation of a continuous‐time infinite horizon control problem, requires to recast the model as a discrete‐time, finite‐horizon control model. The quality of the optimization results can be heavily degraded if the discretization process does not take into account features of the original model to be preserved. Restricting thei...

We analyse an artificial neural network which deviates from biological behaviour in two aspects. First, the process of activation of a generic neuron is not described by a monotone increasing output function. This means that, while artificial neurons modelled on biological behaviour are active when the sum of the postsynaptic potentials is larger t...

The Leontief model, originally developed for describing an economic system in terms of mutually interrelated and structurally conditioned simultaneous flows of commodities and services, has important applications to wide ranging disciplines. A basic model assumes the linear form x = Tx + d, where x represents the total output vector and d represent...

This article deals with the numerical solution of optimal control problems for ordinary differential equations. The approach is based on the coupling between quadrature rules and continuous Runge–Kutta solvers, and it lies in the framework of direct optimization methods and recursive discretization techniques. The analysis of discrete solution accu...

In the present paper the discretization of a particular model arising in the economic field of innovation diffusion is developed.
It consists of an optimal control problem governed by an ordinary differential equation. We propose a direct optimization
approach characterized by an explicit, fixed step-size continuous Runge-Kutta integration for the...

This paper provides a numerical approach for solving optimal control problems governed by ordinary differential equations. Continuous extension of an explicit, fixed step-size Runge-Kutta scheme is used in order to approximate state variables; moreover, the objective function is discretized by means of Gaussian quadrature rules. The resulting schem...

This chapter reviews statistical approaches to the clustering problem, i.e., the task of partitioning data-sets in classes in such a way that points in the same class are more similar to one another than to those in other classes. Although this is technically an ill-posed problem, it is of great importance in a wide range of applications and numero...

We describe a nonparametric approach of dynamic thermography to the detection of buried antipersonnel (AP) mines. Dynamic thermography consists of processing temporal sequences of IR images taken from the same scene, submitted to either artificial or natural temperature variations. The aim is to obtain an image segmentation where mine and soil can...

A method has been recently proposed that provides a hierarchical solution to the clustering problem under very general assumptions, relying on the cooperative behavior of an inhomogeneous lattice of char,tic coupled maps. The physical system can be seen as a chaotic neural network where neurons update is performed by logistic maps. The mutual infor...

A new approach to clustering, based on the physical properties of inhomogeneous coupled chaotic maps, is presented. A chaotic map is assigned to each data point and short range couplings are introduced. The stationary regime of the system corresponds to a macroscopic attractor independent of the initial conditions. The mutual information between pa...

The time evolution of the distance between two random initial configurations subjected to the same thermal noise is used to study dynamical phase transitions in attractor neural networks trained by the Hebb rule. Numerical results are given for fully connected architectures, whereas, in the dilute case, both analytical and numerical outcomes are pr...

In this paper we analyze replica symmetry breaking in attractor neural networks with non-monotone activation function. We study the non-monotone version of the Edinburgh model, which allows the control of the domains of attraction by the stability parameter K, and we compute, at one step of symmetry breaking, storage capacity and, for the strongly...

We analyse on-line learning of a linearly separable rule with a simple perceptron. Example inputs are taken from two overlapping clusters of data and the rule is defined through a teacher vector which is in general not aligned with the connection line of the cluster centers. We find that the Hebb algorithm cannot learn the rule perfectly in general...

In this paper we analyse the effect of introducing a structure in the input distribution on the generalization ability of a simple perceptron. The simple case of two clusters of input data and a linearly separable rule is considered. We find that the generalization ability improves with the separation ae between the clusters, and is bounded from be...

We investigate the retrieval properties of fully connected saturated neural networks trained by the Edinburgh algorithm with correlated patterns {xii}. We evaluate the effect of the correlation on the overlap between {xii} and the network configuration after one time step and on the basins of attraction. We also show that the introduction of the th...

Our goal consists in providing an accurate numerical solution to optimal control problems with infinite time horizon. The procedure deals with a direct approach based on quadrature for the objective function discretization and explicit Runge-Kutta methods for the state variable approximation. The resulting algorithm performance is validated and com...

## Projects

Projects (2)

Development of a knowledge-based pre-operational ecological modelling system for effective and timely multi-annual monitoring of NATURA 2000 sites and their surrounding areas

In the last decades, however, anthropogenic pressure has caused serious threat to ecosystem integrity, functions and processes. Knowledge-based conservation, management and restoration policies are thus urgently needed, in order to improve ecosystem benefits in face of increasing pressures. Fundamental to all these is effective monitoring and modelling of the state and trends in ecosystem conditions and services. Best use should be made of existing and incoming Earth Observation and field monitoring data, complemented by appropriate interpretation tools, data services and ecosystem models able to use these data.
The ECOPOTENTIAL project focuses its activities and pilot actions on a targeted set of internationally recognised protected areas (PA) in Europe, European Territories and beyond, including mountain, arid and semi-arid, and coastal and marine ecosystems. Building on the knowledge gained in individual PAs, the ECOPOTENTIAL project will address cross-scale ecological interactions and landscape-ecosystem dynamics at regional to continental scales, using geostatistical methods and the emerging novel approaches in Macrosystems Ecology, which is addressing long-term and large-scale ecological challenges. ECOPOTENTIAL addresses the entire chain of ecosystem-related services, by (a) developing ecosystem data services, with special emphasis on Copernicus services; (b) implementing model output services to distribute the results of the modelling activities; and (c) estimating current and future ecosystem services and benefits, combining ecosystem functions (supply) with beneficiaries needs (demand). In ECOPOTENTIAL all data, model results and acquired knowledge will be made available on common and open platforms, coherent with the Global Earth Observation System of Systems (GEOSS) data sharing principles and fully interoperable with the GEOSS Common Infrastructure (GCI).
See http://www.ecopotential-project.eu/