Behavior study of pipes after forming, coating and bending

A study of the main types of coatings and its processes that modern industry commonly apply to prevent to the corrosion due to the environmental effects to energetic market pipelines have been done. Extracting main time and temperature range values, coating heat treatment recreation have been applied to x65 pipelines steel grade samples obtained from a pipe which was formed using UOE forming process. Experimental tensile tests and Charpy V‐Notch Impact test have been carried out for a deeply knowledge of the influence on the steel once this recreations are applied. The Yield Strength and toughness have been improved despite lower values in rupture strain and ductile‐brittle temperature transition have been obtained. Finite Element Method have been applied to simulate the entirely pipe cold bending process to predict the mechanical properties and behaviour of the pipe made from x65 steel grade under different conditions.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Cinéma, machine à mondes : essai sur les films à univers multiples

Dans cet ouvrage, l'auteur propose une conceptualisation théorique de la coprésence en un même film de mondes multiples en abordant différents paramètres (hétérogénéité de la facture de l'image, pratiques du montage alterné, typologie des enchâssements, expansion sérielle, etc.) sur la base d'un corpus de films de fiction récents qui appartiennent pour la plupart au genre de la science-fiction (Matrix, Dark City, Avalon, Resident Evil, Avatar,...). Issue de la filmologie, la notion de « diégèse » y est développée à la fois dans le potentiel d'autonomisation dont témoigne la conception mondaine qui semble dominer aujourd'hui à l'ère des jeux vidéo, dans ses liens avec le récit et dans une perspective intermédiale. Les films discutés ont la particularité de mettre en scène des machines permettant aux personnages de passer d'un monde à l'autre : les modes de figuration de ces technologies sont investigués en lien avec les imaginaires du dispositif cinématographique et les potentialité du montage. La comparaison entre les films (Tron et son récent sequel, Totall Recall et son remake) et entre des oeuvres filmiques et littéraires (en particulier les nouvelles de Philip K. Dick et Simlacron 3 de Galouye) constitue un outil d'analyse permettant de saisir la contemporanéité de cette problématique, envisagée sur le plan esthétique dans le contexte de l'imagerie numérique.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Coating carbon nanotubes with a polystyrene-based polymer protects against pulmonary toxicity.

BACKGROUND: carbon nanotubes (CNT) can have adverse effects on health. Therefore, minimizing the risk associated with CNT exposure is of crucial importance. The aim of this work was to evaluate if coating multi-walled CNT (MWCNT) with polymers could modify their toxicity, thus representing a useful strategy to decrease adverse health effects of CNT. We used industrially-produced MWCNT uncoated (NT1) or coated (50/50 wt%) with acid-based (NT2) or polystyrene-based (NT3) polymer, and exposed murine macrophages (RAW 264.7 cell line) or Balb/c mice by intratracheal administration. Biological experiments were performed both in vitro and in vivo, examining time- and dose-dependent effects of CNT, in terms of cytotoxicity, expression of genes and proteins related to oxidative stress, inflammation and tissue remodeling, cell and lung tissue morphology (optical and transmission electron microscopy), and bronchoalveolar lavage fluid content analysis.RESULTS: extensive physico-chemical characterization of MWCNT was performed, and showed, although similar dimensions for the 3 MWCNT, a much smaller specific surface area for NT2 and NT3 as compared to NT1 (54.1, 34 and 227.54 m(2)/g respectively), along with different surface characteristics. MWCNT-induced cytotoxicity, oxidative stress, and inflammation were increased by acid-based and decreased by polystyrene-based polymer coating both in vitro in murine macrophages and in vivo in lung of mice monitored for 6 months.CONCLUSIONS: these results demonstrate that coating CNT with polymers, without affecting their intrinsic structure, may constitute a useful strategy for decreasing CNT toxicity, and may hold promise for improving occupational safety and that of general the user.

Love machine

This poster provides advice on the use of condoms as a method of protection from unplanned pregnancy and sexually transmitted infections (STIs). It also provides contact details for the�Genito Urinary Medicine (GUM) clinics in Northern Ireland.