Protected areas are continuously subjected to ecological change due to anthropic pressures. Analyses of changes in the extent and intensity of pressures over time are essential for adaptive management, yet such analyses are rarely conceptualized or performed in a well-defined, standardized way, with a frequent lack of clarity in development, definition and measurement. Over-time remote sensing data has great potential for mapping spatial pattern of pressures and their impacts. Some pressures can be mapped directly (e.g.

The demand responsive transport systems (DRTS) aim to satisfy two main objectives: the service flexibility and the costs minimization. They are a good solution for the trade-off between flexibility and efficiency. They require the planning of travel paths (routing) and customers pick-up and drop-off times (scheduling) according to received requests. DRTS may operate according to a static or dynamic mode. The aim of this work is to test on a real case a heuristic for a flexible transport system with different service parameters: fleet size, vehicle capacity, time windows and incoming requests.

In this paper we deal with a semilinear hyperbolic chemotaxis model in one space dimension evolving on a network, with suitable transmission conditions at nodes. This framework is motivated by tissue-engineering scaffolds used for improving wound healing. We introduce a numerical scheme, which guarantees global mass densities conservation. Moreover our scheme is able to yield a correct approximation of the effects of the source term at equilibrium. Several numerical tests are presented to show the behavior of solutions and to discuss the stability and the accuracy of our approximation.

In this paper we introduce a simulation algorithm based on fluid dynamic models to reproduce the behavior of traffic in a portion of the urban network in Rome. Numerical results, obtained comparing experimental data with numerical solutions, show the effectiveness of our approximation. (c) 2009 Elsevier Inc. All rights reserved.