Problèmes de benchmark pour l'identiifcation de modèles à temps continu: conception, résultats et perspectives

International audienceThe problem of estimating continuous-time model parameters of linear dynamical systems using sampled time-domain input and output data has received considerable attention over the past decades and has been approached by various methods. The research topic also bears practical importance due to both its close relation to first principles modeling and equally to linear model-based control design techniques, most of them carried in continuous time. Nonetheless, as the performance of the existing algorithms for continuous-time model identification has seldom been assessed and, as thus far, it has not been considered in a comprehensive study, this practical potential of existing methods remains highly questionable. The goal of this brief paper is to bring forward a first study on this issue and to factually highlight the main aspects of interest. As such, an analysis is performed on a benchmark designed to be consistent both from a system identification viewpoint and from a control-theoretic one. It is concluded that robust initialization aspects require further research focus towards reliable algorithm development.Ce papier traite de benchmarking de l'identification de modèles à temps continu qui sont très utilisés dans l'ingiénerie

Problèmes de benchmark pour l'identiifcation de modèles à temps continu: conception, résultats et perspectives

International audienceThe problem of estimating continuous-time model parameters of linear dynamical systems using sampled time-domain input and output data has received considerable attention over the past decades and has been approached by various methods. The research topic also bears practical importance due to both its close relation to first principles modeling and equally to linear model-based control design techniques, most of them carried in continuous time. Nonetheless, as the performance of the existing algorithms for continuous-time model identification has seldom been assessed and, as thus far, it has not been considered in a comprehensive study, this practical potential of existing methods remains highly questionable. The goal of this brief paper is to bring forward a first study on this issue and to factually highlight the main aspects of interest. As such, an analysis is performed on a benchmark designed to be consistent both from a system identification viewpoint and from a control-theoretic one. It is concluded that robust initialization aspects require further research focus towards reliable algorithm development.Ce papier traite de benchmarking de l'identification de modèles à temps continu qui sont très utilisés dans l'ingiénerie

A new data-based modelling method for identifying parsimonious nonlinear rainfall/flow models

International audienceThe identification of rainfall/runoff relationship is a challenging issue, mainly because of the complexity to find a suitable model for a whole given catchment. Conceptual hydrological models fail to describe correctly the dynamic changes of the system for different rainfall events (e.g. intensity or duration). However, the need for such relationship grows with the water pollution increase in agricultural regions. Lately, a well-known type of model in the control field appears to be a suitable candidate for water processes identification: the Linear Parameter Varying (LPV) models. This paper depicts a novel refined instrumental variable based method for the identification of Input/Output LPV models and this algorithm is applied to identify a parsimonious nonlinear rainfall/flow model of a 42 ha vineyard catchment located in Alsace, France

Identification de modèles LPV : application à la modélisation pluie/débit d'un bassin versant viticole

International audienceL'identification de la relation pluie/débit dans un bassin versant pour la prédiction de débit est un problème stimulant de par la difficulté à caractériser un modèle les décrivant dans leur ensemble. Les modèles conceptuels, basés sur les lois et modèles hydrauliques simples sont sou- vent limités dans la précision de la prédiction qu'ils offrent. L'objectif de cet article est d'une part de montrer l'intérêt des modèles non linéaires de type Linéaires à Paramètres Variants (LPV) par rapport aux modèles linéaires, ainsi que la différence de qualité dans les résultats obtenus selon la méthode employée pour l'identification d'un modèle donné. D'autre part, cet article propose et analyse plusieurs variables de séquencement dont dépendent les paramètres variants des modèles LPV pour représenter les bassins versants ruraux

Tumor-Associated Lymphatic Vessel Features and Immunomodulatory Functions

The lymphatic system comprises a network of lymphoid tissues and vessels that drains the extracellular compartment of most tissues. During tumor development, lymphatic endothelial cells (LECs) substantially expand in response to VEGFR-3 engagement by VEGF-C produced in the tumor microenvironment, a process known as tumor-associated lymphangiogenesis. Lymphatic drainage from the tumor to the draining lymph nodes consequently increases, powering interstitial flow in the tumor stroma. The ability of a tumor to induce and activate lymphatic growth has been positively correlated with metastasis. Much effort has been made to identify genes responsible for tumor-associated lymphangiogenesis. Inhibition of lymphangiogenesis with soluble VEGFR-3 or with specific monoclonal antibodies decreases tumor spread to LNs in rodent models. Importantly, tumor-associated lymphatics do not only operate as tumor cell transporters but also play critical roles in anti-tumor immunity. Therefore, metastatic as well as primary tumor progression can be affected by manipulating tumor-associated lymphatic remodeling or function. Here, we review and discuss our current knowledge on the contribution of LECs immersed in the tumor microenvironment as immunoregulators, as well as a possible functional remodeling of LECs subsets depending on the organ microenvironment

A pragmatic and systematic statistical analysis for identification of industrial robots

Identification of industrial robots is a prolific topic that has been deeply investigated over the last three decades. The standard method is based on the use of the inverse dynamic model and the least-squares estimation (IDIM-LS method) while robots are operating in closed loop by tracking exciting trajectories. Recently, in order to secure the consistency of the parameters estimates, an instrumental variable (IV) approach, called IDIM-IV method, has been designed and experimentally validated. However, the statistical analysis of estimates was not treated. Surprisingly, this topic is rarely addressed in mechatronics whereas it has been deeply investigated in automatic control. This paper aims at bridging the gap between these two communities by presenting a pragmatic statistical analysis of the IDIM-IV estimates. This analysis consists of a two-step procedure: first, the consistency of the IDIM-IV estimates is validated by the Revised Durbin-Wu-Hausman test, and then the statistical analysis of the IDIM-IV residuals is treated. This two-step approach is experimentally validated on the TX40 robot

System identification of the intrabrain tumoral uptake of multifunctional nanoparticles

International audienceRecent developments on multifunctional nano-systems have opened new perspectives for tumor control by proposing new nano-actuators and nano-sensors in in vivo anti-cancer treatments. But the delivery control of these nano-agents into the cancer cells is one of the major factors that directly affect the efficiency of nanotherapies. In this study, we show that system identification methods (CONTSID Matlab toolbox), generally used in control engineering, can bring efficient solutions to help biologists to estimate crucial parameters of the nanoparticles pharmacokinetics from experimental data. The in vivo results presented herein clearly emphasize the relevance of these data-driven modeling approaches associated with magnetic resonance imaging

Extended Freeze-Dried BCG Instructed pDCs Induce Suppressive Tregs and Dampen EAE

Several clinical observations have shown that Bacillus Calmette-Guérin (BCG) vaccine has beneficial impact on patients suffering from different chronic inflammatory diseases. Here we evaluated whether BCG inactivated by Extended Freeze-Drying (EFD) which circumvents all the side effects linked to the live bacteria, could influence the development of experimental autoimmune encephalomyelitis (EAE), a mouse model for Multiple Sclerosis. EFD BCG strongly attenuates inflammation, both systemically and at the central nervous system (CNS) level, alleviating EAE. Mechanistically, EFD BCG directly impacts the phenotype of plasmacytoid dendritic cells (pDCs), and promotes their ability to induce suppressive IL-10 secreting regulatory T cells (Tregs) that inhibit encephalitogenic CD4+ T cells. When co-cultured with human allogenic naive CD4+ T cells, EFD BCG exposed human pDCs similarly induce the differentiation of IL-10 producing Tregs. Our study provides evidence that EFD BCG could be used as an immunomodulator of encephalitogenic T cells in multiple sclerosis patients

Accompagner la démarche de management stratégique de l’exploitation agricole

Développer la démarche de management stratégique dans les exploitations agricoles est une façon d’améliorer leurs performances dans le contexte actuel de l’agriculture. Les auteurs proposent un itinéraire méthodologique qui vise à accompagner les exploitants agricoles dans la construction de stratégies et dans leur traduction opérationnelle. Structuré en trois étapes, il aide à organiser la réflexion autour de la stratégie, puis à la formaliser, pour ensuite la piloter grâce à un tableau de bord stratégique. Le lien entre ces trois étapes est assuré par la construction d’une carte causale. Cet itinéraire a été construit dans le cadre du programme de recherche-action PerfEA avec des établissements publics locaux d’enseignement agricole. L’itinéraire s’adresse à tout type d’exploitation agricole et sa mise en œuvre nécessite la présence d’un conseiller.Strategic management is a relevant approach to support farmers in managing their farm and in coping with the current agricultural challenges. In this article we propose a methodological route which aims at enabling farmers to design their strategy and to implement them. The route is organized along three steps: it helps to organize the strategic thinking; to formalize the strategy by defining priorities and strategic lines; to draw up a strategic monitoring plan (the balanced scorecard). The link between the different steps is provided by the design of a causal mapping. This methodological route was built within an action research project entitled PerfEA (Global Performance for Farm) including farms belonging to public training schools. It suits all kind of farms and requires an agricultural adviser to accompany farmers in the design process of the strategy and its implementation

Soil organic carbon models need independent time-series validation for reliable prediction

Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions