Stochastic growth equations on growing domains

The dynamics of linear stochastic growth equations on growing substrates is studied. The substrate is assumed to grow in time following the power law $t^\gamma$, where the growth index $\gamma$ is an arbitrary positive number. Two different regimes are clearly identified: for small $\gamma$ the interface becomes correlated, and the dynamics is dominated by diffusion; for large $\gamma$ the interface stays uncorrelated, and the dynamics is dominated by dilution. In this second regime, for short time intervals and spatial scales the critical exponents corresponding to the non-growing substrate situation are recovered. For long time differences or large spatial scales the situation is different. Large spatial scales show the uncorrelated character of the growing interface. Long time intervals are studied by means of the auto-correlation and persistence exponents. It becomes apparent that dilution is the mechanism by which correlations are propagated in this second case.Comment: Published versio

Specific heat studies of pure Nb3Sn single crystals at low temperature

Specific heat measurements performed on high purity vapor-grown Nb$_3$Sn crystals show clear features related to both the martensitic and superconducting transitions. Our measurements indicate that the martensitic anomaly does not display hysteresis, meaning that the martensitic transition could be a weak first or a second order thermodynamic transition. Careful measurements of the two transition temperatures display an inverse correlation between both temperatures. At low temperature specific heat measurements show the existence of a single superconducting energy gap feature.Comment: Accepted in Journal of Physics: Condensed Matte

Globular Clusters: DNA of Early-Type galaxies?

This paper explores if the mean properties of Early-Type Galaxies (ETG) can be reconstructed from "genetic" information stored in their GCs (i.e., in their chemical abundances, spatial distributions and ages). This approach implies that the formation of each globular occurs in very massive stellar environments, as suggested by some models that aim at explaining the presence of multi-populations in these systems. The assumption that the relative number of globular clusters to diffuse stellar mass depends exponentially on chemical abundance, [Z/H], and the presence of two dominant GC sub-populations blue and red, allows the mapping of low metallicity halos and of higher metallicity (and more heterogeneous) bulges. In particular, the masses of the low-metallicity halos seem to scale up with dark matter mass through a constant. We also find a dependence of the globular cluster formation efficiency with the mean projected stellar mass density of the galaxies within their effective radii. The analysis is based on a selected sub-sample of galaxies observed within the ACS Virgo Cluster Survey of the {\it Hubble Space Telescope}. These systems were grouped, according to their absolute magnitudes, in order to define composite fiducial galaxies and look for a quantitative connection with their (also composite) globular clusters systems. The results strengthen the idea that globular clusters are good quantitative tracers of both baryonic and dark matter in ETGs.Comment: 20 pages, 28 figures and 5 table

A parallel computation approach for solving multistage stochastic network problems

The original publication is available at www.springerlink.comThis paper presents a parallel computation approach for the efficient solution of very large multistage linear and nonlinear network problems with random parameters. These problems result from particular instances of models for the robust optimization of network problems with uncertainty in the values of the right-hand side and the objective function coefficients. The methodology considered here models the uncertainty using scenarios to characterize the random parameters. A scenario tree is generated and, through the use of full-recourse techniques, an implementable solution is obtained for each group of scenarios at each stage along the planning horizon. As a consequence of the size of the resulting problems, and the special structure of their constraints, these models are particularly well-suited for the application of decomposition techniques, and the solution of the corresponding subproblems in a parallel computation environment. An augmented Lagrangian decomposition algorithm has been implemented on a distributed computation environment, and a static load balancing approach has been chosen for the parallelization scheme, given the subproblem structure of the model. Large problems – 9000 scenarios and 14 stages with a deterministic equivalent nonlinear model having 166000 constraints and 230000 variables – are solved in 45 minutes on a cluster of four small (11 Mflops) workstations. An extensive set of computational experiments is reported; the numerical results and running times obtained for our test set, composed of large-scale real-life problems, confirm the efficiency of this procedure.Publicad

A parallel computation approach for solving multistage stochastic network problems

This paper presents a parallel computation approach for the efficient solution of very large multistage linear and nonIinear network problems with random parameters. These problems resul t from particular instances of models for the robust optimization of network problems with uncertainty in the values of the right-hand side and the objective function coefficients. The methodology considered here models the uncertainty using scenarios to characterize the random parameters. A. scenario tree is generated and, through the use of full-recourse techniques, an implementable solution is obtained for each group of scenarios at each stage along the planning horizon. As a consequence of the size of the resulting problems, and the special structure of their constraints, these models are particularly well-suited for the application of decomposition techniques, and the solution of the corresponding subproblems in a parallel computation environment. An Augmented Lagrangian decomposition algorithm has been implemented on a distributed computation environment, and a static load balancing approach has been chosen for the parallelization scheme. given the subproblem structure of the model. Large problems -9000 scenarios and 14 stages with a deterministic equivalent nonlinear model having 166000 constraints and 230000 variables- are solved in 15 minutes on a cluster of 4 small (16 Mflops) workstations. An extensive set of computational experiments is reported; the numerical results and running times obtained for our test set, composed of large-scale real-life problems, confirm the efficiency of this procedure