Recovery of the star formation history of the LMC from the VISTA survey of the Magellanic system

The VISTA near infrared survey of the Magellanic System (VMC) will provide deep YJK(s) photometry reaching stars in the oldest turn-off point throughout the Magellanic Clouds (MCs). As part of the preparation for the survey, we aim to access the accuracy in the star formation history (SFH) that can be expected from VMC data, in particular for the Large Magellanic Cloud (LMC). To this aim, we first simulate VMC images containing not only the LMC stellar populations but also the foreground Milky Way (MW) stars and background galaxies. The simulations cover the whole range of density of LMC field stars. We then perform aperture photometry over these simulated images, access the expected levels of photometric errors and incompleteness, and apply the classical technique of SFH-recovery based on the reconstruction of colour-magnitude diagrams (CMD) via the minimisation of a chi-squared-like statistics. We verify that the foreground MW stars are accurately recovered by the minimisation algorithms, whereas the background galaxies can be largely eliminated from the CMD analysis due to their particular colours and morphologies. We then evaluate the expected errors in the recovered star formation rate as a function of stellar age, SFR(t), starting from models with a known age-metallicity relation (AMR). It turns out that, for a given sky area, the random errors for ages older than similar to 0.4 Gyr seem to be independent of the crowding. This can be explained by a counterbalancing effect between the loss of stars from a decrease in the completeness and the gain of stars from an increase in the stellar density. For a spatial resolution of similar to 0.1 deg(2), the random errors in SFR(t) will be below 20% for this wide range of ages. On the other hand, due to the lower stellar statistics for stars younger than similar to 0.4 Gyr, the outer LMC regions will require larger areas to achieve the same level of accuracy in the SFR( t). If we consider the AMR as unknown, the SFH-recovery algorithm is able to accurately recover the input AMR, at the price of an increase of random errors in the SFR(t) by a factor of about 2.5. Experiments of SFH-recovery performed for varying distance modulus and reddening indicate that these parameters can be determined with (relative) accuracies of Delta(m-M)(0) similar to 0.02 mag and Delta E(B-V) similar to 0.01 mag, for each individual field over the LMC. The propagation of these errors in the SFR(t) implies systematic errors below 30%. This level of accuracy in the SFR(t) can reveal significant imprints in the dynamical evolution of this unique and nearby stellar system, as well as possible signatures of the past interaction between the MCs and the MW.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Characterisation of the effect of a simulated hydrocarbon spill on diazotrophs in mangrove sediment mesocosm

An analysis of the effect of an oil spill on mangrove sediments was carried out by contamination of mesocosms derived from two different mangroves, one with a history of contamination and one pristine. The association between N(2) fixers and hydrocarbon degradation was assessed using quantitative PCR (qPCR) for the genes rrs and nifH, nifH clone library sequencing and total petroleum hydrocarbon (TPH) quantification using gas chromatography. TPH showed that the microbial communities of both mangroves were able to degrade the hydrocarbons added; however, whereas the majority of oil added to the mesocosm derived from the polluted mangrove was degraded in the 75 days of the experiment, there was only partially degradation in the mesocosm derived from the pristine mangrove. qPCR showed that the addition of oil led to an increase in rrs gene copy numbers in both mesocosms, having almost no effect on the nifH copy numbers in the pristine mangrove. Sequencing of nifH clones indicated that the changes promoted by the oil in the polluted mangrove were greater than those observed in the pristine mesocosm. The main effect observed in the polluted mesocosm was the selection of a single phylotype which is probably adapted to the presence of petroleum. These results, together with previous reports, give hints about the relationship between N(2) fixation and hydrocarbon degradation in natural ecosystems.

Annealing effects on microstructure and coercive field of ferritic-martensitic ODS Eurofer steel

Oxide dispersion strengthened reduced-activation ferritic-martensitic steels are promising candidates for applications in future fusion power plants. Samples of a reduced activation ferritic-martensitic 9 wt.%Cr-oxide dispersion strengthened Eurofer steel were cold rolled to 80% reduction in thickness and annealed in vacuum for 1 h from 200 to 1350 degrees C to evaluate its thermal stability. Vickers microhardness testing and electron backscatter diffraction (EBSD) were used to characterize the microstructure. The microstructural changes were also followed by magnetic measurements, in particular the corresponding variation of the coercive field (H(c)), as a function of the annealing treatment. Results show that magnetic measurements were sensitive to detect the changes, in particular the martensitic transformation, in samples annealed above 850 degrees C (austenitic regime). (C) 2010 Elsevier B.V. All rights reserved.

GOOAL: A routine based on Genetic Algorithms to track harmonics in electric power systems

Voltage and current waveforms of a distribution or transmission power system are not pure sinusoids. There are distortions in these waveforms that can be represented as a combination of the fundamental frequency, harmonics and high frequency transients. This paper presents a novel approach to identifying harmonics in power system distorted waveforms. The proposed method is based on Genetic Algorithms, which is an optimization technique inspired by genetics and natural evolution. GOOAL, a specially designed intelligent algorithm for optimization problems, was successfully implemented and tested. Two kinds of representations concerning chromosomes are utilized: binary and real. The results show that the proposed method is more precise than the traditional Fourier Transform, especially considering the real representation of the chromosomes.

Information Filtering and Array Algorithms for Discrete-Time Markovian Jump Linear Systems

This technical note develops information filter and array algorithms for a linear minimum mean square error estimator of discrete-time Markovian jump linear systems. A numerical example for a two-mode Markovian jump linear system, to show the advantage of using array algorithms to filter this class of systems, is provided.

A Taxonomy for Locality Algorithms on Peer-to-Peer Networks

The continuous growth of peer-to-peer networks has made them responsible for a considerable portion of the current Internet traffic. For this reason, improvements in P2P network resources usage are of central importance. One effective approach for addressing this issue is the deployment of locality algorithms, which allow the system to optimize the peers` selection policy for different network situations and, thus, maximize performance. To date, several locality algorithms have been proposed for use in P2P networks. However, they usually adopt heterogeneous criteria for measuring the proximity between peers, which hinders a coherent comparison between the different solutions. In this paper, we develop a thoroughly review of popular locality algorithms, based on three main characteristics: the adopted network architecture, distance metric, and resulting peer selection algorithm. As result of this study, we propose a novel and generic taxonomy for locality algorithms in peer-to-peer networks, aiming to enable a better and more coherent evaluation of any individual locality algorithm.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (C) 2009 Elsevier B.V. All rights reserved.

Approximate algorithms for credal networks with binary variables

This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on polytrees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U algorithm resembles Loopy Belief Propagation, while the Iterated Partial Evaluation and Structured Variational 2U algorithms are, respectively, based on Localized Partial Evaluation and variational techniques. (C) 2007 Elsevier Inc. All rights reserved.

Annealing behavior of ferritic-martensitic 9%Cr-ODS-Eurofer steel

Oxide dispersion strengthened ferritic-martensitic steels are potential candidates for applications in future fusion power plants. High creep resistance, good oxidation resistance, reduced neutron activation and microstructural long-term stability at temperatures of about 650-700 degrees C are required in this context. In order to evaluate its thermal stability in the ferritic phase field, samples of the reduced activation ferritic-martensitic 9%Cr-ODS-Eurofer steel were cold rolled to 50% and 80% reductions and further annealed in vacuum from 300 to 800 degrees C for 1 h. The characterization in the annealed state was performed by scanning electron microscopy in the backscattered electron mode, high-resolution electron backscatter diffraction and transmission electron microscopy. Results show that the fine dispersion of Y-based particles (about 10 nm in size) is effective to prevent recrystallization. The low recrystallized volume fraction (<0.1) is associated to the nuclei found at prior grain boundaries and around large M(23)C(6) particles. Static recovery was found to be the predominant softening mechanism of this steel in the investigated temperature range. (c) 2010 Elsevier B.V. All rights reserved.

ANNEALING EFFECTS ON THE MICROSTRUCTURE OF FERRITIC-MARTENSITIC ODS-EUROFER STEEL

Oxide dispersion strengthened (ODS) ferritic/martensitic (FM) steels are promising candidates for structural applications in future fusion power reactors. In order to evaluate the thermal stability of 80% cold-rolled ODS-EUROFER, samples were annealed for 1 h at temperatures up to about 0.9 T(m), where T(m) is the absolute melting point. The characterization of the annealed samples was performed using transmission electron microscopy and electron backscatter diffraction. Results show that static recovery is the main softening mechanism of this steel when annealed below 800 degrees C. The volume fraction of recrystallized grains is quite small (below 0.10). Above 900 degrees C, martensitic transformation takes place causing pronounced hardening. Large M(23)C(6) particles are found at the grain boundaries after tempering at 750 degrees C for 2 h.

Valve friction and nonlinear process model closed-loop identification

Among several process variability sources, valve friction and inadequate controller tuning are supposed to be two of the most prevalent. Friction quantification methods can be applied to the development of model-based compensators or to diagnose valves that need repair, whereas accurate process models can be used in controller retuning. This paper extends existing methods that jointly estimate the friction and process parameters, so that a nonlinear structure is adopted to represent the process model. The developed estimation algorithm is tested with three different data sources: a simulated first order plus dead time process, a hybrid setup (composed of a real valve and a simulated pH neutralization process) and from three industrial datasets corresponding to real control loops. The results demonstrate that the friction is accurately quantified, as well as ""good"" process models are estimated in several situations. Furthermore, when a nonlinear process model is considered, the proposed extension presents significant advantages: (i) greater accuracy for friction quantification and (ii) reasonable estimates of the nonlinear steady-state characteristics of the process. (C) 2010 Elsevier Ltd. All rights reserved.

Thin titanium oxide films deposited by e-beam evaporation with additional rapid thermal oxidation and annealing for ISFET applications

Titanium oxide (TiO(2)) has been extensively applied in the medical area due to its proved biocompatibility with human cells [1]. This work presents the characterization of titanium oxide thin films as a potential dielectric to be applied in ion sensitive field-effect transistors. The films were obtained by rapid thermal oxidation and annealing (at 300, 600, 960 and 1200 degrees C) of thin titanium films of different thicknesses (5 nm, 10 nm and 20 nm) deposited by e-beam evaporation on silicon wafers. These films were analyzed as-deposited and after annealing in forming gas for 25 min by Ellipsometry, Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy (RAMAN), Atomic Force Microscopy (AFM), Rutherford Backscattering Spectroscopy (RBS) and Ti-K edge X-ray Absorption Near Edge Structure (XANES). Thin film thickness, roughness, surface grain sizes, refractive indexes and oxygen concentration depend on the oxidation and annealing temperature. Structural characterization showed mainly presence of the crystalline rutile phase, however, other oxides such Ti(2)O(3), an interfacial SiO(2) layer between the dielectric and the substrate and the anatase crystalline phase of TiO(2) films were also identified. Electrical characteristics were obtained by means of I-V and C-V measured curves of Al/Si/TiO(x)/Al capacitors. These curves showed that the films had high dielectric constants between 12 and 33, interface charge density of about 10(10)/cm(2) and leakage current density between 1 and 10(-4) A/cm(2). Field-effect transistors were fabricated in order to analyze I(D) x V(DS) and log I(D) x Bias curves. Early voltage value of -1629 V, R(OUT) value of 215 M Omega and slope of 100 mV/dec were determined for the 20 nm TiO(x) film thermally treated at 960 degrees C. (C) 2009 Elsevier B.V. All rights reserved.

Some heuristic algorithms for total tardiness minimization in a flowshop with blocking

The flowshop scheduling problem with blocking in-process is addressed in this paper. In this environment, there are no buffers between successive machines: therefore intermediate queues of jobs waiting in the system for their next operations are not allowed. Heuristic approaches are proposed to minimize the total tardiness criterion. A constructive heuristic that explores specific characteristics of the problem is presented. Moreover, a GRASP-based heuristic is proposed and Coupled with a path relinking strategy to search for better outcomes. Computational tests are presented and the comparisons made with an adaptation of the NEH algorithm and with a branch-and-bound algorithm indicate that the new approaches are promising. (c) 2007 Elsevier Ltd. All rights reserved.