Sample-to-sample fluctuations of the overlap distributions in the three-dimensional Edwards-Anderson spin glass

We study the sample-to-sample fluctuations of the overlap probability densities from large-scale equilibrium simulations of the three-dimensional Edwards-Anderson spin glass below the critical temperature. Ultrametricity, stochastic stability, and overlap equivalence impose constraints on the moments of the overlap probability densities that can be tested against numerical data. We found small deviations from the Ghirlanda Guerra predictions, which get smaller as system size increases. We also focus on the shape of the overlap distribution, comparing the numerical data to a mean-field-like prediction in which finite-size effects are taken into account by substituting delta functions with broad peaks.

Finite-size scaling analysis of the distributions of pseudo-critical temperatures in spin glasses

Using the results of large scale numerical simulations we study the probability distribution of the pseudo critical temperature for the three dimensional Edwards Anderson Ising spin glass and for the fully connected Sherrington-Kirkpatrick model. We find that the behaviour of our data is nicely described by straightforward finitesize scaling relations.

Low-resolution spectroscopy and spectral energy distributions of selected sources towards σ Orionis

Aims. We study in detail nine sources in the direction of the young σ Orionis cluster, which is considered to be a unique site for studying stellar and substellar formation. The nine sources were selected because of their peculiar properties, such as extremely-red infrared colours or excessively strong Hα emission for their blue optical colours. Methods. We acquired high-quality, low-resolution spectroscopy (R ∼ 500) of the nine targets with ALFOSC at the Nordic Optical Telescope. We also re-analysed [24]-band photometry from MIPS/Spitzer and compiled the highest quality photometric dataset available at the ViJHK_s passbands and the four IRAC/Spitzer channels, for constructing accurate spectral energy distributions between 0.55 and 24 μm. Results. The nine targets were classified into: one Herbig Ae/Be star with a scattering edge-on disc; two G-type stars; one X-ray flaring, early-M, young star with chromospheric Hα emission; one very low-mass, accreting, young spectroscopic binary; two young objects at the brown-dwarf boundary with the characteristics of classical T Tauri stars; and two emission-line galaxies, one undergoing star formation, and another whose spectral energy distribution is dominated by an active galactic nucleus. We also discovered three infrared sources associated with overdensities in a cold cloud of the cluster centre. Conclusions. Low-resolution spectroscopy and spectral energy distributions are a vital tool for measuring the physical properties and evolution of young stars and candidates in the σ Orionis cluster.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

Poster presented in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24), Budapest, Hungary, June 15-18, 2014.

Handling of Uncertainty in Life Cycle Inventory by Correlated Multivariate Lognormal Distributions: Application to the Design of Supply Chain Networks

In this work, we analyze the effect of incorporating life cycle inventory (LCI) uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear programming (MILP) coupled with a two-step transformation scenario generation algorithm with the unique feature of providing scenarios where the LCI random variables are correlated and each one of them has the desired lognormal marginal distribution. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study of a petrochemical supply chain. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact, and moreover the correlation among environmental burdens provides more realistic scenarios for the decision making process.