Identification of vivianite,an unusual blue pigment,in a sixteenth century painting and its implications

Vivianite, a blue pigment employed in the past practically only in Northern and Central Europe, but with very limited use, was identified in an early sixteenth century painting, stylistically with Flemish features, from a church in Portugal. The identification of this iron phosphate mineral was made by SEM‐EDS based on the atomic ratio between phosphorus and iron in layers of blue paint (area analysis) and in particles of these same layers (spot analysis). This painting, about which there is no document to prove its authorship, becomes the first case, known in detail, of a sixteenth century painting containing vivianite. Moreover, this find and the presence of a chalk ground, also identified, strongly support the hypothesis of being a Flemish painting.     

Bibliometric analysis of the evolution of biochar research trends and scientific production

Clean Technologies and Environmental Policy - Biochar has caught great attention over the last decade, yielding a large number of publications in a broad range of disciplines. This scientometric...     

Enzyme activity and partial characterization of proteases obtained from Bromelia karatas fruit and compared with Bromelia pinguin proteases

Food Science and Biotechnology - The enzymatic activity and partial characterization of proteases from Bromelia karatas fruits were evaluated and compared with Bromelia pinguin proteases. The...     

Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process.

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.     

Automatic fog detection and estimation of visibility distance through use of an onboard camera

In this paper, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and patented. The approach consists of dynamically implementing Koschmieder's law. Our method enables computing the meteorological visibility distance, a measure defined by the International Commission on Illumination (CIE) as the distance beyond which a black object of an appropriate dimension is perceived with a contrast of less than 5%. Our proposed solution is an original one, featuring the advantage of utilizing a single camera and necessitating the presence of just the road and sky in the scene. As opposed to other methods that require the explicit extraction of the road, this method offers fewer constraints by virtue of being applicable with no more than the extraction of a homogeneous surface containing a portion of the road and sky within the image. This image preprocessing also serves to identify the level of compatibility of the processed image with the set of Koschmieder's model hypotheses. Nicolas Hautiére graduated from the École Nationale des Travaux Publics de l'État, France (2002). He received his M.S. and Ph.D. degree in computer vision, respectively, in 2002 and 2005 from Saint-Étienne University (France). From 2002, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France. His research interests include trafic engineering, computer vision, and pattern recognition. Jean-Philippe Tarel graduated from the École Nationale des Ponts et Chaussées, Paris, France (1991). He received his Ph.D. degree in Applied Mathematics from Paris IX-Dauphine University in 1996 and he was with the Institut National de Recherche en Informatique et Automatique (INRIA) from 1991 to 1996. From 1997 to 1998, he was a research associate at Brown University, USA. From 1999, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France, and from 2001 to 2003 in the INRIA. His research interests include computer vision, pattern recognition, and shape modeling. Jean Lavenant graduated from the École Nationale des Travaux Publics de l'État, Lyon, France (2001). He received the M.S. degree in Computer Vision from Jean Monnet university of Saint-Étienne in 2001. In 2001, he was a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC). In 2002, he was a system engineer in Chicago (USA). He is currently an engineer for the french ministry of transports. Didier Aubert received the M.S. and Ph.D. degree, respectively, in 1985 and 1989 from the National Polytechnical Institut of Grenoble (INPG). From 1989--1990, he worked as a research scientist on the development of an automatic road following system for the NAVLAB at Carnegie Mellon University. From 1990–1994, he worked in the research department of a private company (ITMI). During this period he was the project leader of several projects dealing with computer vision. He is currently a researcher at INRETS since 1995 and works on Road traffic measurements, crowd monitoring, automated highway systems, and driving assistance systems for vehicles. He is an image processing expert for several companies, teaches at Universities (Paris VI, Paris XI, ENPC, ENST) and is at the editorial board of RTS (Research - Transport - Safety).