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4 results on '"CAP 20-25 16-IDEX-0001"'

1. Unveiling ecological assembly rules from commonalities in trait distributions

Author

François Munoz, Yoann Le Bagousse-Pinguet, Cyrille Violle, Nicolas Gross, Hugo Saiz, Pierre Liancourt, Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UMR237-Aix Marseille Université (AMU)-Avignon Université (AU), Institute of Botany of the Czech Academy of Sciences (IB / CAS), Czech Academy of Sciences [Prague] (CAS), University of Tübingen, University of Bern, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Grenoble Alpes - Institut national supérieur du professorat et de l'éducation - Académie de Grenoble (UGA INSPE Grenoble), Université Grenoble Alpes (UGA), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CAP 20-25 16-IDEX-0001, Czech Academy of Sciences RVO 67985939, European Commission FP7-609398, Grant Agency of the Czech Republic GACR 17-19376S, Marie Sklodowska-Curie Actions Individual Fellowship 656035, European Project, European Project: 639706,H2020,ERC-2014-STG,CONSTRAINTS(2015), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0106 biological sciences, Assembly rules, community assembly rules, dispersalenvironmental filtering, Variation (game tree), Environment, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, kewness-kurtosis relationship, trait distributions, Ecosystem, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, stochasticity, 0303 health sciences, Ecology, Niche differentiation, Biodiversity, 15. Life on land, functional diversity, Biota, Phenotype, Geography, Skewness, Kurtosis, Trait, Biological dispersal, Identification (biology), [SDE.BE]Environmental Sciences/Biodiversity and Ecology, niche differentiations
Abstract

International audience; Deciphering the effect of neutral and deterministic processes on community assembly is critical to understand and predict diversity patterns. The information held in community trait distributions is commonly assumed as a signature of these processes, but empirical and modelling attempts have most often failed to untangle their confounding, sometimes opposing, impacts. Here, we simulated the assembly of trait distributions through stochastic (dispersal limitation) and/or deterministic scenarios (environmental filtering and niche differentiation). We characterized the shape of trait distributions using the skewness-kurtosis relationship. We identified commonalities in the co-variation between the skewness and the kurtosis of trait distributions with a unique signature for each simulated assembly scenario. Our findings were robust to variation in the composition of regional species pools, dispersal limitation and environmental conditions. While ecological communities can exhibit a high degree of idiosyncrasy, identification of commonalities across multiple communities can help to unveil ecological assembly rules in real-world ecosystems.

Published
2021
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2. Towards an Architecture for Online Scheduling of Autonomous Robots in Agriculture: Open Issues

Author

Bruno Bachelet, Pietro Battistoni, Sandro Bimonte, Christophe Cariou, Gérard Chalhoub, Fabien Coutarel, Nicolas Tricot, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), University of Salerno (UNISA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Activité, Connaissance, Transmission, éducation (ACTé ), Université Clermont Auvergne (UCA), IDEX/I-SITE initiative CAP 20-25 (16-IDEX-0001), and ANR-10-LABX-0016,IMoBS3,Innovative Mobility : Smart and Sustainable Solutions(2010)

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Farm Management Information System, [SPI]Engineering Sciences [physics], Monitoring, Scheduling, General Materials Science, [INFO]Computer Science [cs], Autonomous Robots, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [SPI.AUTO]Engineering Sciences [physics]/Automatic, [SHS]Humanities and Social Sciences
Abstract

International audience; Nowadays, we observe the development of autonomous robots for agricultural tasks. Farmers are becoming task and data managers with the emergence of advanced Farm Management Information Systems (FMISs). However, existing FMISs lack the tools for handling scheduling and monitoring of fleets of robots. The scheduling functionalities are essential for the growth of autonomous robot industry. It allows a better management to share these state of the art and expensive resources between multiple farmers, reducing the overall cost. Scheduling is always coupled with a re-scheduling process that allows to react to unexpected events. The re-scheduling process, called online scheduling, can only be made possible with a monitoring process that collects real time information about the ongoing tasks and the state of robots. Finally, relatively little is known about the changes in farmers' activities as a result of the introduction of these robots. Acceptance of these new technologies is nevertheless essential to the performance of the systems. Motivated by the lack of a general framework for the online scheduling of autonomous robots for agriculture, we propose a conceptual framework for the scheduling and monitoring of such shared resources. All the needed building blocks for the whole conceptual framework to function efficiently are highlighted. Open issues related to each of these building blocks are discussed, from robotics auto-diagnosis, to data management, to wireless communication, to scheduling, monitoring and controlling these autonomous robots, keeping in the loop the human operator and his essential role in this system.

Published
2021
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3. The Use of IR Spectroscopy to Follow the Absorption of CO2 in Amine Media – Evaluation of the Speciation with Time

Author

E Brugere, Karine Ballerat-Busserolles, Jean-Michel Andanson, Institut de Chimie de Clermont-Ferrand (ICCF), SIGMA Clermont (SIGMA Clermont)-Institut de Chimie du CNRS (INC)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), PIA CAP 20-25 16-IDEX-0001, Ying (Alice) Wu, John J. Carroll, Mingqiang Hao, Weiyao Zhu, and ANR-15-CE06-0010,SiModEx,Comprendre les procédés de captage du CO2 : une combinaison de la simulation moléculaire réactive, des modèles thermodynamiques et des données expérimentales(2015)

Subjects
Materials science, Inorganic chemistry, Infrared spectroscopy, 02 engineering and technology, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, [CHIM.GENI]Chemical Sciences/Chemical engineering, Genetic algorithm, [CHIM]Chemical Sciences, Amine gas treating, 0210 nano-technology, Absorption (electromagnetic radiation), ComputingMilieux_MISCELLANEOUS
Abstract

International audience; Carbon Capture and Storage (CCS) is a concrete option for CO 2 mitigation in the atmosphere. One option is the CO 2 capture from post-combustion industrial effluents followed by storage in secured sites. Capture processes can be based on selective absorption/desorption cycles of gas into aqueous solutions of alkanolamines[1]. The cost of CO 2 treatment with classical alkanolamines is a limitation for the intensive use of this technology. The SIMODEX project is dealing with coupling experimental developments, molecular simulation and thermodynamic modeling to understand structureproperties relationships, and to develop transferable conclusions to optimize processes at low costs. This chapter will focus on the use of ATR-IR (Attenuated Total Reflection-infrared) spectroscopy to quantify the speciation in mixtures containing alkanolamine and CO 2 , in absence of water, in order to validate the thermodynamic models already used, and to give crucial information for molecular simulation developments.

Published
2020
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4. Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

Author

Romain Lardy, Isabelle Veissier, Violaine Antoine, Mathieu Silberberg, Nicolas Wagner, Marie-Madeleine Mialon, Jonas Koko, Unité Mixte de Recherches sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Projet ANR : CAP 20-25 - 16-IDEX-0001 - CAP 20-25 (I-Site (2017), Unité Mixte de Recherche sur les Herbivores - UMR 1213 (UMRH), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25)INRAE (Phase division) Universite Clermont Auvergne (SPI doctorate school), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), and ANR-16-IDEX-0001,CAP 20-25,CAP 20-25(2016)

Subjects
0106 biological sciences, bovin, [SDV]Life Sciences [q-bio], education, bien-être animal, Horticulture, Biology, Machine learning, computer.software_genre, 01 natural sciences, comportement animal, animal welfare, Bolus (medicine), Animal activity, Group level, animal health, business.industry, santé animale, Continuous monitoring, santé animal, food and beverages, Forestry, 04 agricultural and veterinary sciences, rythme d'activité, Regression, Computer Science Applications, Subacute ruminal acidosis, Multilayer perceptron, 040103 agronomy & agriculture, Monitor ph, 0401 agriculture, forestry, and fisheries, Artificial intelligence, MACHINE LEARNING, business, Agronomy and Crop Science, computer, 010606 plant biology & botany
Abstract

Sickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal’s activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level.

Published
2020
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