Implementation of trace element behaviour in the numerical modelling of magmatic processes

Trace element analysis of rocks and minerals can provide valuable insight in the petrogenesis of the crust and therefore may offer fundamental constraints into geodynamical processes. But crustal building involves a multitude of magmatic/metamorphic processes, and the trace element behaviour during each of these processes is complex, which makes the interpretation of trace element composition of the final crustal product non-unique. Therefore, a thorough investigation of trace element systematics between mineral and melt is required to trace the implication of important stable phases in crustal building processes. This can be given using forward numerical modelling and trace element modelling. However, while numerical models simulating thermodynamic reactions are available, a numerical tool for the prediction of trace element behaviour is missing. Such tool would be of key interest to capture the subtle exchanges during magmatic reactions. Therefore, this thesis presents the building of a novel numerical tool able to predict the variation in trace element partition coefficients between 6 mafic minerals and melt (e.g., garnet, olivine, pyroxene, plagioclase and amphibole). To develop such tool, the state-of-the-art predictive models available in the literature are compiled. These predictive models are of two different kinds. First, the lattice strain model (LSM) is based on the energy exchange of trace elements between mineral and melt. Second, multiple regressions analyses are based on the most accurate fit against various chemical/physical parameters and natural data. In addition, this thesis builds predictive models by applying statistical regressions on large dataset of trace element partition coefficients in the case no models are available in the literature. While the stand-alone version of this tool reproduces well the experimental data, coupling with the numerical code Perple_X, which calculates stable mineral assemblages using Gibbs free energy minimization, allows calculation of a full P-T catalogue of trace element behaviour in the partial melting of an amphibolite. The catalogue gives insights in the assimilation processes in a lower crust in arc settings. This coupling offers a new perspective for future, more complex coupling with numerical codes of melt genesis, transfer and emplacement

Challenges for the Parallelization of Loosely Timed SystemC Programs

International audienceSystemC/TLM models are commonly used in the industry to provide an early SoC simulation environment. The open source implementation of the SystemC simulator is sequential. The standard doesn't impose sequential executions, but makes this choice the easiest by imposing coroutine semantics. With the increasing size and complexity of models, and the multiplication of computation cores on recent machines, the parallelization of SystemC simulations is a major research concern. There have been several proposals for SystemC parallelization, but most of them are limited to cycle-accurate models. In this paper we give an overview of the practices in one industrial context. We explain why loosely timed models are the only viable option in this context. We also show that unfortunately, most of the existing approaches for SystemC parallelization can fundamentally not apply to these models. We support this claim with a set of measurements performed on a platform used in production at STMicroelectronics. This paper both surveys existing techniques and identifies unsolved challenges in the parallelization of SystemC/TLM models

Multivariate analysis of the determinants of the end-product quality of manure-based composts and vermicomposts using bayesian network modelling

Previous studies indicated that the quality of tropical composts is poorer than that of com- posts produced in temperate regions. The aim of this study was to test the type of manure, the use of co-composting with green waste, and the stabilization method for their ability to improve compost quality in the tropics. We produced 68 composts and vermicomposts that were analysed for their C, lignin and NPK contents throughout the composting process. Bayesian networks were used to assess the mechanisms controlling compost quality. The concentration effect, for C and lignin, and the initial blend quality, for NPK content, were the main factors affecting compost quality. Cattle manure composts presented the highest C and lignin contents, and poultry litter composts exhibited the highest NPK content. Co-com- posting improved quality by enhancing the concentration effect, which reduced the impact of C and nutrient losses. Vermicomposting did not improve compost quality; co-composting without earthworms thus appears to be a suitable stabilization method under the conditions of this study because it produced high quality composts and is easier to implement. (Résumé d'auteur

Contamination detection in genomic data: more is not enough

The decreasing cost of sequencing and concomitant augmentation of publicly available genomes have created an acute need for automated software to assess genomic contamination. During the last six years, 18 programs have been pub-lished, each with its own strengths and weaknesses. Deciding which tools to use becomes more and more difficult without an understanding of the underlying algo-rithms. We review these programs, benchmarking six of them, and present their main operating principles. This article is intended to guide researchers in the selec-tion of appropriate tools for specific applications. Finally, we present future chal-lenges in the developing field of contamination detection.BCCM GEN-ER