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In this paper, a linear mathematical and numerical model for analysing the dynamic response of a flexible electroactive wave energy converter is described. The Wave Energy Converter (WEC) is a floating elastic tube filled with slightly pressurised sea water. It is made of Electroactive Polymers (EAPs).Under simplifying assumptions, a set of governing equations is formulated for the flow inside the tube, the flow outside the tube and the behaviour of the tube wall. By combining them, the evolution of the flow velocity in the tube can be written as a wave equation. The corresponding eigenmodes of vibration are calculated. Then, using spectral decomposition, the equation of motion for the response of the tube in waves is derived. Experiments were carried out on a scale model of the wave energy converter in the wave tank of Ecole Centrale de Nantes in 2011. Numerical results are compared with experimental results in regular waves, showing rather good agreement, which validates the model and the initial modelling assumptions. Finally, estimates are made for the energy performance of a possible prototype.

Abstract The need to have access to accurate short term forecasts is essential in order to anticipate the energy production from intermittent renewable sources, notably wind energy. For hourly and sub-hourly forecasts, benchmarks are based on statistical approaches such as time series based methods or neural networks, which are always tested against persistence. Here we discuss the performances of downscaling approaches using information from Numerical Weather Prediction (NWP) models, rarely used at those time scales, and compare them with the statistical approaches for the wind speed forecasting at hub height. The aim is to determine the added value of Model Output Statistics for sub-hourly forecasts of wind speed, compared to the classical time series based methods. Two downscaling approaches are tested: one using explanatory variables from NWP model outputs only and another which additionally includes local wind speed measurements. Results of both approaches and of the classical time series based methods, tested against persistence on a specific wind farm, are considered. For both hourly and sub-hourly forecasts, adding explanatory variables derived from observations in the downscaling models gives higher improvements over persistence than the benchmark methods and than the downscaling models using only the NWP model outputs.

The hemp plant Cannabis sativa Linn, referring to industrial hemp, is a high-yielding annual industrial crop grown providing fibers from hemp stalk and oil from hemp seeds. Although hemp is a niche crop, hemp production is currently undergoing a renaissance. More than 30 countries grow hemp, with China being the largest hemp producing and exporting country. Europe and Canada are also important actors in the global hemp market. Traditionally, hemp as a fiber plant has been used for the production of apparels, fabrics, papers, cordages and building materials. The hurds, as waste by-product of fiber production, were used for bedding of animals, the seeds for human nutrition, e.g., as flour, and the oil for a wide range of purposes, from cooking to cosmetics. Hemp has also been an important crop throughout human history for medicine. Other more recent applications include materials for insulation and furniture, automotive composites for interior applications and motor vehicle parts, bioplastics, jewelry and fashion sectors, animal feed, animal bedding, and energy and fuel production. Foods containing hemp seed and oil are currently marketed worldwide for both animal and human nutrition. They also find applications in beverages and in neutraceutical products. Hemp oil is also used for cosmetics and personal care items, paints, printing inks, detergents and solvents. It is estimated that the global market for hemp consists of more than 25,000 products. Currently, the construction and insulation sector, paper and textile industries, and food and nutrition domains are the main markets while the cosmetics and automotive sector are growing markets. Innovative applications, e.g., in the medical and therapeutic domains, cosmeceuticals, phytoremediation, acoustic domain, wastewater treatment, biofuels, biopesticides and biotechnology, open new challenges. Hemp is also the object of numerous fundamental studies. This review presents and discusses the traditional and new uses of industrial hemp.

International audience Présentation des avancements de mon travail de thèse au sein du laboratoire G-SCOP, fait à l'occasion du congrès LCM2015 à Bordeuax.

Uncontaminated and As-contaminated fronds of Pteris vittata L., an As-hyperaccumulator fern used to phytoextract As from contaminated soils and water, were converted by sub-critical water (300 °C, 25 Pa) and supercritical water (400 °C, 25 Pa) treatments. Frond biomass was reduced between 70 and 77%. Compared to sub-critical conditions, supercritical conditions decreased C and inorganic contents in both the solid and liquid phases for uncontaminated and contaminated fronds and promoted CH4 formation. Higher As, Fe and Zn contents in contaminated fronds promoted decreasing C contents and the formations of cyclopentenones and benzenediols in the liquid phase. Al, Fe, P, Zn and Ca mainly remained in the solid phase whereas As and S were transferred to the liquid phase for both phytomasses. As the temperature increased from 300 °C to 400 °C, the concentrations of cyclopentenones and phenols in the liquid phase rose while those of guaiacols and other compounds decreased for both phytomasses. Arsenic in the liquid phase was removed by sorption on hydrous iron oxide.

Presented at the 2nd International Electronic Conference on Diversity (IECD 2022)—New Insights into the 18 Biodiversity of Plants, Animals and Microbes, 15–30 March 2022; Available online: https://scifo-19 rum.net/event/IECD2022. ; International audience ; Along with decrease in numbers among amphibians in Eastern Europe nowadays newts especially suffer from invasive predatory fish and turtles. Such aggressive predator as P. glenii has recently occupied water bodies important for newts' breeding. The fish eat eggs, larvae and even adult individuals. Using the ecological niche approach, it was showed that for built distribution models based on certain climatic scenario Coefficient of determination (R 2) between Chinese sleeper and preserved T. cristatus with L. vulgaris to 2090 will grow from 44% to 66% in Eastern Europe highlighting expanding of new territories by the fish as a real threat to biodiversity.

L'Énergie représente l'un des principaux postes de dépense pour un centre de données, dont la majeure partie est attribuée au système de refroidissement, ainsi qu'aux nombreuses parties sous-jacentes, comme les équipements réseau et au grand nombre de machines utilisées. Ces infrastructures sont très énergivores, et leur nombre ne cesse d'augmenter à travers le monde, notamment grâce à la popularité croissante du Cloud Computing. De nombreux logiciels sont nécessaires au bon fonctionnement de ces infrastructures, notamment pour la gestion du réseau, du stockage de données, de l'ordonnancement des tâches ainsi que de la supervison de l'ensemble du matériel et des logiciels. Tous ces logiciels consomment une quantité significative d'énergie, mais ne sont pourtant pas pris en compte dans les calculs de l'efficience énergétique des infrastructures. La communauté scientifique ainsi que les opérateurs de centres de données ont développé de nombreuses approches afin d'évaluer et d'optimiser globalement la consommation énergétique, mais la question du coût en énergie des infrastructures logicielles reste peu étudiée. L'objectif de cette thèse est de proposer des méthodes permettant d'analyser de bout en bout l'efficacité énergétique logicielle des infrastructures de traitement de données. Pour cela, nous proposons des approches et outils permettant d'estimer fidèlement la consommation énergétique des logiciels éxécutés sur une infrastructure distribuée, ainsi qu'un indicateur permettant de calculer leur efficience énergétique. Dans un premier temps, nous proposons SmartWatts, un wattmètre logiciel permettant d'estimer la consommation énergétique des conteneurs logiciels déployés sur une machine. Ensuite, nous proposons SelfWatts, un contrôleur permettant d'automatiser la configuration des wattmètres logiciels afin de faciliter leur déploiement dans des infrastructures hétérogènes. Et enfin, nous proposons le xPUE, un indicateur permettant de calculer l'efficience énergétique des logiciels et du matériel en temps réel aux différents niveaux d'une infrastructure. À travers ces différentes contributions, nous visons à faire évoluer la connaissance dans le domaine de la consommation énergétique des logiciels, et permettre de mesurer avec précision la consommation énergétique des logiciels déployés aux différents niveaux des infrastructures. Cela permet aux opérateurs de ces infrastructures, mais également aux développeurs et utilisateurs de logiciels d'observer et d'analyser en détails la consommation énergétique et ainsi d'assister dans l'optimisation de celle-ci. Energy is one of the biggest expenses for a data center, most of which is attributed to the cooling system, as well as the many underlying parts, such as network equipment and the large number of machines used. These infrastructures are very energy-intensive, and their number is constantly increasing around the world, especially due to the growing popularity of the Cloud Computing.A lot of software is needed to run these infrastructures, especially for network management, data storage, task scheduling and the supervision of all hardware and software. All these software consume a significant amount of energy, but are not taken into account in the calculation of the energy efficiency of the infrastructures. The scientific community as well as data center operators have developed many approaches to evaluate and optimize energy consumption globally, but the question of the energy cost of software infrastructures remains rarely studied. The objective of this thesis is to propose methods to analyze the end-to-end software energy efficiency of data processing infrastructures. To do so, we propose approaches and tools to accurately estimate the energy consumption of software running on a distributed infrastructure, as well as an indicator to calculate their energy efficiency. Firstly, we introduce SmartWatts, a software power meter to estimate the energy consumption of software containers deployed on a machine. Secondly, we propose SelfWatts, a controller to automate the configuration of software power meters to facilitate their deployment in heterogeneous infrastructures. And finally, we propose xPUE, a metric to calculate the energy efficiency of software and hardware in real time at different levels of an infrastructure. Through these contributions, we aim to advance the knowledge in the field of software energy consumption, and allow to accurately measure the energy consumption of software deployed at different levels of the infrastructure. This allows infrastructure operators, but also software developers and users to observe and analyze in detail the energy consumption and thus assist in its optimization.