The paper examines a particular class of nonlinear integro-differential equations consisting of a Sturm-Liouville boundary value problem on the half-line, where the coefficient of the differential term depends on the unknown function by means of a scalar integral operator. In order to handle the nonlinearity of the problem, we consider a fixed point iteration procedure, which is based on considering a sequence of classical Sturm-Liouville boundary value problems in the weak solution sense. The existence of a solution and the global convergence of the fixed-point iterations are stated without resorting to the Banach fixed point theorem. Moreover, the unique solvability of the problem is discussed and several examples with unique and non-unique solutions are given.

We consider a particular second-order integrodifferential boundary value problem arising from the kinetic theory of dusty plasmas, and we provide information on the existence and other qualitative properties of the solution that have been essential in the numerical investigation.

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 scheme here implemented uses an implicit scheme for the stiff diffusive term and a partitioned RK symplectic scheme for the reaction term (IMSP scheme). We revisit some results provided in literature for the classical Lotka-Volterra system and the Rosenzweig-MacArthur model. We then extend the approach to metapopulation dynamics in order to numerically investigate the effect of migration through a corridor connecting two habitat patches. Moreover, we analyze the synchronization properties of subpopulation dynamics, when the migration occurs through corridors of variable size.

This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape. In this respect, we employ the CE approach, which constructs a random sequence of solutions probabilistically converging to a near-optimal solution and, thus, facilitating the exploration capability. Additionally, to fine-tune the solution in promising basins of attraction, the SQP algorithm is invoked, which performs a local search. Despite its reliance on a global heuristic scheme, CE-SQP is vested with fast convergence capability, which may entail its use for online power dispatch. The effectiveness and the robustness of the proposed method in comparison with several state-of-the-art approaches have been demonstrated with four standard test systems that are widely reported in the ELD literature.

In a recent paper, we investigated the uniform convergence of Lagrange interpolation at the zeros of the orthogonal polynomials with respect to a Freud-type weight in the presence of constraints. We show that by a simple procedure it is always possible to transform the matrices of these zeros into matrices such that the corresponding Lagrange interpolating polynomial with respect to the given constraints well approximates a given function. Here, starting from the interest to construct a suitable interpolation operator with a preassigned node, we introduce an algorithm that allows us to obtain new matrices. For the Lagrange operator related to these new matrices that have the preassigned node among their elements, we prove results about the optimal rate of convergence as well as we apply successfully this method to some applications. © 2014 IMACS.

This paper aims at understanding the role of clients in the innovation of Creativity- Intensive Business Services (CIBS), namely advertising services, to provide useful managerial indications for the proactive management of the collaboration with clients for the purpose of innovation. We adopt a multi-stage approach. Firstly, we propose a conceptual multidimensional framework by combining three streams of research: a) research on customer involvement in New Service Development; b) service innovation studies with specific regard to CIBS; c) literature on business relationships marketing. Secondly, we refine such framework through an exploratory qualitative analysis based on a case study of a successful advertising agency. Thirdly, we undertake a large-scale survey of European advertising agencies. Our preliminary findings provide empirical evidence to the hidden and multidimensional nature of CIBS' innovation, and contribute to advance the understanding of the concept of "soft innovation" in creative services. Results also suggest that efforts to improve the interaction and collaborative work practices with clients are conducive to changes in other technological as well as non-technological dimensions. Moreover, the empirical analysis provides indications on additional innovation enabling characteristics of clients and on their potential roles as catalyst and co-developer of innovation.

In this paper we introduce the Mathematical Desk for Italian Industry, a project based on applied and industrial mathematics developed by a team of researchers from the Italian National Research Council in collaboration with two major Italian associations for applied mathematics, SIMAI and AIRO. The scope of this paper is to clarify the motivations for this project and to present an overview on the activities, context and organization of the Mathematical Desk, whose mission is to build a concrete bridge of common interests between the Italian scientific community of applied mathematics and the world of the Italian enterprises. Some final considerations on the strategy for the future development of the Mathematical Desk project complete the paper.

An algorithm for computing the solution of indefinite least squares problems and of indefinite least squares problems with equality constrained is presented. Such problems arise when solving total least squares problems and in H infinity-smoothing. The proposed algorithm relies only on stable orthogonal transformations reducing recursively the associated augmented matrix to proper block anti-triangular form. Some numerical results are reported showing the properties of the algorithm.

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 integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/ semi-automatic translation framework of LC/ LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed.

In the present work the numerical simulation of breaking wave processes is discussed. A detailed analysis is performed using Smoothing Particle Hydrodynamics (SPH) models as well as a mesh-based Level-Set Finite Volume Method (LS-FVM). Considerations on the numerical dissipation involved in such models are discussed within the frameworks of weakly compressible and incompressible ssumptions. The breaking wave processes are simulated using both mono- and two-phases models. Due to the extensive test-cases discussed, the present analysis is limited to a bi-dimensional framework. Test-cases with increasing complexities are considered starting from a simple 1D impact of two water-jet up to complex shallow water breaking waves. The analyses presented in this article are not only useful to weakly compressible SPH or LS-FVM users but can be extended to other numerical models for which accurate (convergent) results on benchmark test-cases are provided.

This work investigates the capability of supervised classification methods in detecting both major tissues and subcortical structures using multispectral brain magnetic resonance images. First, by means of a realistic digital brain phantom, we investigated the classification performance of various Discriminant Analysis methods, K-Nearest Neighbor and Support Vector Machine. Then, using phantom and real data, we quantitatively assessed the benefits of integrating anatomical information in the classification, in the form of voxels coordinates as additional features to the intensities or tissue probabilistic atlases as priors. In addition we tested the effect of spatial correlations between neighbouring voxels and image denoising. For each brain tissue we measured the classification performance in terms of global agreement percentage, false positive and false negative rates and kappa coefficient. The effectiveness of integrating spatial information or a tissue probabilistic atlas has been demonstrated for the aim of accurately classifying brain magnetic resonance images.

We present a new numerical procedure to assess the plausibility of a subglacial lake in case of relative small/moderate extension and surging temperate icefield. In addition to the flat signal from Ground Penetrating Radar remote survey of the area, early indication of a likely subglacial lake, required icefield data are: top surface elevation and bathymetry, top surface velocity at some points, in-depth temperature and density profiles of upper layer. The procedure is based on a mathematical model of the evolution of dynamics and thermo-dynamics of the icefield and of a subglacial lake. The Glen's law is adopted for ice rheology and Stokes reduction is applied; Large Eddy Simulation technique is used for the lake. Ice/water phase change is described. Finite volumes for model discretization and a front-tracking technique to follow the moving interface characterize the numerical method. We have applied this procedure to the case of a subglacial lake conjectured in the area of Amundsenisen, Svalbard, and, here, the results of a sensitivity study are discussed. In particular we point out that the effect of firn and snow upper layers on the system, in terms of temperature field, density and water content, has to be included in the modeling, as it contributes to the overcoming of the ice metastable state and the release of subglacial water. Accordingly, ice water content changes have to be carefully described. The depth of the bed depression is confirmed to be critical for the formation of the lake.