Many studies have shown that Physarum polycephalum slime mold is able to find the shortest path in a maze. Here we study this behavior in a network, using a hyperbolic model of chemotaxis [1]. Suitable transmission and boundary conditions at each node are considered to mimic the behavior of such an organism in the feeding process. Several numerical tests are presented for special network geometries to show the qualitative agreement between our model and the observed behavior of the mold.

The aim of this paper is to prove the strong convergence of the solutions to a vector-BGK model under the diffusive scaling to the incompressible Navier-Stokes equations on the two-dimensional torus. This result holds in any interval of time [0,T], with T>0. We also provide the global in time uniform boundedness of the solutions to the approximating system. Our argument is based on the use of local in time H-estimates for the model, established in a previous work, combined with the L-relative entropy estimate and the interpolation properties of the Sobolev spaces.

Ever more organizations, both private and public, are placing a greater importance on employee engagement as a means of generating better organizational climate and higher levels of performance. Actually, employee engagement is part of the strategic management of high performance organization, which pay always more attention to human resource initiatives. Moreover, forms of involvement in the decision processes make more motivating and more satisfying the activity for employees, as they create the conditions for greater inspiration and, in turn, contribute to their well-being.

The paper deals with de la Vallee Poussin type interpolation on the square at tensor product Chebyshev zeros of the first kind. The approximation is studied in the space of locally continuous functions with possible algebraic singularities on the boundary, equipped with weighted uniform norms. In particular, simple necessary and sufficient conditions are proved for the uniform boundedness of the related Lebesgue constants. Error estimates in some Sobolev-type spaces are also given.

In the present paper, we propose a numerical method for the simultaneous approximation of the finite Hilbert and Hadamard transforms of a given function f, supposing to know only the samples of f at equidistant points. As reference interval we consider [-1,1] and as approximation tool we use iterated Boolean sums of Bernstein polynomials, also known as generalized Bernstein polynomials. Pointwise estimates of the errors are proved, and some numerical tests are given to show the performance of the procedures and the theoretical results.

The use of Euler polynomials and Euler numbers allows us to construct a quadrature rule similar to the well-known Euler--MacLaurin quadrature formula, using Euler (instead of Bernoulli) numbers, and even (instead of odd) order derivatives of a given function evaluated at the extrema of the considered interval. An expression of the remainder term and numerical examples are also given. © 2001, Heldermann Verlag. All rights reserved.

We obtain sharp decay estimates in time in the context of Sobolev spaces for smooth solutions to the one-dimensional Jin Xin model under the diffusion scaling, which are uniform with respect to the singular parameter of the scaling. This provides the convergence to the limit nonlinear parabolic equation both for large time and for the vanishing singular parameter. The analysis is performed by means of two main ingredients.

We present an extension of the multiparticle collision dynamics method for flows with complex interfaces, including supramolecular near-contact interactions mimicking the effect of surfactants. The new method is demonstrated for the case of (i) short range repulsion of droplets in close contact, (ii) arrested phase separation, and (iii) different pattern formation during spinodal decomposition of binary mixtures.

t. The present work was inspired by the recent developments in laboratory experiments made on chip, where culturing of multiple cell species was possible.

We consider the problem of learning the link parameters as well as the structure of a binary-valued pairwise Markov model. Under sparsity assumption, we propose a method based on l1-regularized logistic regression, which estimate globally the whole set of edges and link parameters. Unlike the more recent methods discussed in literature that learn the edges and the corresponding link parameters one node at a time, in this work we propose a method that learns all the edges and corresponding link parameters simultaneously for all nodes.