Des « nouveaux territoires de l’art » aux tiers-lieux culturels, 20 ans après.

En 2004, la revue Culture & Musées a consacré un numéro spécial sur le thème « Friches, squats et autres lieux » pour décrire et mieux comprendre l’essor dans les territoires urbains, de ces espaces culturels expérimentaux dits « intermédiaires » désignés comme des « nouveaux territoires de l’art » (E. Maunaye, dir.). Vingt ans après cette publication, ce numéro propose d’étudier une nouvelle « génération » de lieux intermédiaires culturels dont les plus répandus d’entre eux se dénomment « tiers-lieux culturels ». La grande diversité morphologique de ces lieux est à la fois une difficulté mais aussi un véritable défi pour l’analyse. D’autant que leur développement exponentiel sur l’ensemble du territoire national témoigne de l’engouement certain dont ils font l’objet, en particulier, du côté des pouvoirs publics qui les perçoivent comme des dispositifs de sortie de crise. L’hypothèse principale de ce dossier est que ces lieux culturels hybrides contemporains, notamment les tiers-lieux culturels, reprennent, prolongent, reformulent, déconstruisent une partie des questions qui se posaient déjà à l’endroit des « nouveaux territoires de l’art ». Les six contributions retenues pour ce dossier privilégient l’étude des processus artistiques et expériences esthétiques à l’œuvre dans les tiers-lieux culturels, leur potentiel transformateur malgré les dynamiques d’institutionnalisation qui les traversent et leurs relations avec l’écosystème culturel institué en France avec une ouverture à l’international

Coupling high resolution meteorological models with neural networks for flash flood forecasting: implementation on a Southern France basin

International audienceFlash floods are an important hazard that particularly affects the Mediterranean region. Flood forecasting using simulation tools adapted to this context is therefore a crucial issue. In exposed regions, the difficulty of measuring and forecasting the spatial variability and intensity of rainfall, as well as the difficulty of identifying processes at the necessary time and space scales, has often led to the use of highly conceptual - or even statistical - models that make few assumptions about hydrological processes. Among these, neural networks have proven their relevance for flash flood forecasting. However, without hydrometeorological coupling, flow forecasting is often limited to the response time of the basin, i.e. a few hours in general. The purpose is to find a way of increasing this lead time, which is often too short for crisis management.A flood forecasting model for the Gardon de Mialet basin (Southern France) is being developed as part of the HydIA joint laboratory funded by the ANR (French National Research Agency) and the Synapse company, with the aim of developing a range of hydrometeorological forecasting services based on artificial intelligence approaches. The use of gridded observed data, like in a meteorological model, has enabled the neural network model implemented (Multilayer Perceptron) to reduce its sensitivity to support change.In the absence of rainfall forecasts, performance decreases with the lead time. With perfect forecasts (observed data used as future data), performance remains high for lead times up to 24h. The model has been coupled with two high resolution weather models, AROME and ARPEGE (2.5km and 10km respectively), implemented by Météo-France for short-range numerical weather prediction. The use of forecasts from these meteorological models for the 49 events in the database enables us to identify the error generated by the hydrological model and that generated by the meteorological model, in comparison with perfect forecasts. Analysis of these errors opens operational perspectives for crisis management. It also makes it possible to improve model training based on perfectible forecast data, and to correct rainfall forecasting biases to achieve higher performance

Developing EO-based framework for estimating biodiversity variables of coral reef and seagrass ecosystems at Large Scale

International audienceBiodiversity is a vital component of natural capital that significantly influences ecosystem functions and provides essential services and benefits, ranging from food security to cultural heritage. However, species are currently disappearing at a rate 100 to 1,000 times higher than the natural extinction rate. Coastal ecosystems are particularly concerning: they are among the most vulnerable due to their exposure to cumulative anthropogenic pressures while biodiversity knowledge is lacking. Supported by the French National Space Agency (CNES) and endorsed by the Space Climate Observatory (SCO), the BioEOS project aims to develop observation tools to characterize the spatiotemporal dynamics of coastal biodiversity. This initiative will map changes and produce operational indicators to assist in conservation and restoration efforts in the Marine Protected Area (MPA). The project primarily takes advantage of image time series from multispectral (Pleiades, Sentinel-2, Venus) and hyperspectral (EnMAP, PRISMA) satellite systems. A set of selected biodiversity proxy metrics are extracted using high SRL (Scientific Readiness Level) algorithms that have been widely used by the benthic scientific community. These algorithms encompass the inversion of radiative transfer models, machine learning-based scene segmentation, spectral unmixing, pansharpening, and the calculation of spectral indices. This approach enables to generate valuable information on bathymetry, bottom/habitat type abundances and distributions, as well as water column properties estimations. Coral reef and seagrasses of Southwestern Indian Ocean region (La Réunion, Mayotte, Glorieuses and Bassas da India) are the first targeted ecosystems for this experimentation. We present the main advancements of a demonstrator providing key essential variables contributing to various end uses through four distinct use cases. Additionally, we will discuss the strengths and limitations of the satellite systems employed, in light of the initial objectives set forth

Evolution de la couverture corallienne de la plateforme récifale de Saint-Gilles / l’Hermitage à La Réunion entre 2013 à 2023

Ces données de recouvrement corallien ont été produites dans le cadre du projet BioEOS. La méthodologie utilisée est la suivante :1. Acquisition de scènes satellites Pléiades sur la plateforme récifale de Saint-Gilles / l’Hermitage entre 2013 et 2023, issues de la base de données Kalidéos Réunion. Ces données sont orthorectifiées et calibrées en réflectance à l’aide d’une chaine spécifiquement développée par le CNES assurant une haute cohérence de la série temporelle.2. Exploitation de données de in situ et historiques de couverture corallienne (CCV) sur la plateforme récifale.3. Masquage de la zone d’étude (suppression des pixels en dehors de la plateforme récifale, émergés, sur la pente externe, et présentant du déferlement)4. Calcul des indices de brillance BIBG (bandes bleue et verte) sur toutes les images5. Estimation des coefficients des droites de régressions entre CCV et BIBG et application pour produire les images d’estimation de la CCV par satellite Pleiades. Seuillage des valeurs en dehors de la plage [0, 100]6. Masquage des zones d’herbiers à l’aide des couches vectorielles d’herbiers produites dans le cadre de ce projet BioEOS, à partir des mêmes images Pléiades aux dates correspondantes.Dans le cadre du projet BioEOS, l’évolution temporelle de la couverture en corail vivant a été estimée sur la plateforme récifale de Saint-Gilles / l’Hermitage à partir de 10 images satellites Pléiades acquises entre le 20 août 2013 et le 05 octobre 2023. Le calcul des corrélations linéaires permet de mettre en évidence les tendances locales à l’échelle d’une surface de 100m² (polygones carrés de 10m x 10m). Un coefficient de corrélation de Pearson inférieur à 0,05 permet de valider l’hypothèse d’une corrélation linéaire entre les données (CCV vs temps), et d’identifier ainsi les tendances durant cette période (progression, régression, stabilité).La mise à jour régulière de ces données à partir de séries temporelles d’images satellites permet de suivre la dynamique spatio-temporelle des habitats côtiers sensibles

Coupling easily numerical models using the VSoil modelling platform

International audienceLocated at the interface between the groundwater table and the atmosphere, soil lies at the core of the critical zone. It is a complex, dynamic environment sustaining essential ecosystemic services and biodiversity. Numerical simulation models of soil processes are invaluable tools for tackling the complex issues involved in understanding and predicting physical, chemical and biological cycles, in relation to agricultural production, soil protection and adaptation to climate change. To provide a detailed representation of soil functioning, it is necessary to couple a large number of models that represent the various processes taking place within it. Modelling platforms help to do this by facilitating the development and use of coupled models of soil processes. A key requirement of such platforms is to be able to integrate existing, already validated, models without major difficulties.To this aim, we present the VSoil modelling software platform (https://vsoil.hub.inrae.fr/) developed at INRAE (France’s National Research Institute for Agriculture, Food and Environment) since 2009 in close collaboration between scientists and software engineers. VSoil is an open-source platform designed to aid the development of numerical models at the soil profile scale describing physical, chemical and biological processes in soil and its interactions with climate and plants but also anthropic activities. The user-friendly workflow of VSoil simplifies the development and use of models, making them accessible even to scientists with limited experience in computer programming. The VSoil software suite comes with a range of already developed models and is designed to guide users as much as possible in addressing their scientific questions, by providing tools for: i) defining and describing pertinent soil processes and their interactions through their input and output variables, ii) developing elementary models, called modules, which are numerical representations of the processes, iii) assembling and coupling these modules into more or less complex models, and iv) parametrising and executing the resulting models, and visualising results. The VSoil team provides user support and regularly adds new features to meet the needs of the user community. VSoil currently offers key features, including: i) model exploration tools (sensitivity analysis and parameter estimation) along with the ability to run models on several sets of input data, ii) the possibility to run models, in a reproducible way, on a remote computing environment (server or cluster), iii) the connection to INRAE's national agroclimatic database. VSoil fosters collaboration between scientists from various disciplines and facilitates the sharing and use of new developments within the platform's user community.VSoil is being used by scientists from various countries to address very diverse questions such as the fate of persistent fluorinated pollutants in soils, the impact of treated wastewater on soil, the use of geophysics for non-destructive characterisation of soil hydraulic properties, the fate of pesticides at the landscape level, the simulation of soil carbon dynamics, or the optimisation of forestry machinery operations to mitigate soil degradation and compaction

Evaluation of antioxidant efficacy of quercetin encapsulated in micelles, mixed micelles, or liposomes in oil-in-water emulsions

International audienc

Correction: Tree rings reveal the correlation between the Kaindy Lake submerged forest and the historical 1889 M 8.2 Chilik earthquake (Kazakhstan)

International audienc

Early prediction of within-field variability wheat productive potential using Sentinel2 satellite data.

International audienceAssessing agricultural production in the context of climate change is a global concern. In the recent decades, variable rate technology (VRT) for agricultural machinery has made it possible to adjust fertiliser rates on-the-go, allowing the within-field crop management. In this context, in order to select the most effective management practices, it is essential to identify the driving factors that determine yield variability, mapping the spatial distribution of these driving factors and to determine the local yield variability potential.Mapping the homogeneous within-field areas of yield potential is used to define management zones. Remote sensing data provide a practical means of delineating these zones. The crop biophysical variable, cumulative evapotranspiration (ETccum), derived from NDVI time series and climate data, was analysed to evaluate its ability to estimate yield. In the semi-arid conditions of the Spanish Central Plateau, wheat ETccum maps were correlated with yield maps by non-linear regression with an R2 of 88%. ETccum serves as an effective proxy for yield estimation and the statistical analysis to determine the level of homogeneity within the field, the driving factors that determine yield variability, and mapping the spatial distribution of these driving factors. Nevertheless, the observed saturation effect in the biophysical variable highlights limitations that require further analysis.Additionally, during the wheat season, expected potential yields can fluctuate in response to actual weather conditions. Consequently, updating yield predictions early in the season is critical for informed irrigation and fertilisation management decisions. The ability of ETccum to forecast yields at early phenological stages, such as flag leaf and flowering—key stages for yield formation—is examined. Finally, the stability of spatial variability patterns, compared to those derived from ETccum at maturity, is analysed as an indicator of the spatial distribution of yield drivers.Acknowledgments: this work was supported by the research project NSBOIL (Horizon, GA 101091246)

How do speakers align their gestures in L1/L2 and L2/L2 online interactions?

International audienc

Steady Out‐of‐Equilibrium Chemistry: What? Why? How?

International audienceThere is an active interest in living matter from a systemic perspective. Whereas the targeted goal of producing living matter remains elusive, it may currently be useful to recognize some steps along the way and underline their intrinsic value, independently of the final goal. Hence, conceived to support further research in the field, this account evokes several preliminary developments which have only been partially performed, methodologies that could be engaged, and difficulties that are worth considering for optimal design of the future experimental plans