De la gestion des brevets d'invention au pilotage de l'innovation : le cas d'un centre de recherche de haute technologie

L'objet de cette recherche est de proposer une approche stratégique de management et d'organisation des activités de recherche d'une firme. Nous focalisons notre approche sur l'objet brevet d'invention en tant que résultat d'une activité de recherche. Nous étudions comment les brevets d'invention sont gérés au sein du centre de recherche et développement d'un grand groupe de haute technologie. Nous distinguons différents modes de gestion de portefeuilles d'inventions propres à différentes rationalisations économiques. Nos travaux nous conduisent à penser que la gestion de la propriété intellectuelle est une activité pluridisciplinaire qui dépasse le seul cadre de la fonction juridique et/ou financière d'une firme et qui soulève des inconnues d'ordre stratégique et organisationnel. Nous constatons que les stratégies concurrentielles classiques, telle que la course aux brevets ont démontré leurs limites dans un environnement de compétition économique par l'innovation intensive. En nous appuyant sur les avancées récentes en sciences de gestion, nous proposons une gestion différente du portefeuille d'inventions, orientée conception de la valeur, mais aussi un nouvel usage de la fonction propriété intellectuelle, comme levier de pilotage de la concourance entre les activités de recherche corporate et les stratégies des entités fonctionnelles de la firme

Devenir, orientation et mode d'exercice des médecins généralistes thèsés en médecine générale en région Centre entre 2000 et 2005

TOURS-BU Médecine (372612103) / SudocSudocFranceF

An innovating Statistical Learning Tool based on Partial Differential Equations, intending livestock Data Assimilation

The realistic modeling intended to quantify precisely some biological mechanisms is a task requiering a lot of a priori knowledge and generally leading to heavy mathematical models. On the other hand, the structure of the classical Machine Learning algorithms, such as Neural Networks, limits their flexibility and the possibility to take into account the existence of complex underlying phenomena, such as delay, saturation and accumulation. The aim of this paper is to reach a compromise between precision, parsimony and flexibility to design an efficient biomimetic predictive tool extracting knowledge from livestock data. To achieve this, we build a Mathematical Model based on Partial Differential Equations (PDE) embarking the mathematical expression of biological determinants. We made the hypothesis that all the physico-chemical phenomena occurring in animal body can be summarized by the evolution of a global information. Therefore the developed PDE system describes the evolution and the action of an information circulating in an Avatar of the Real Animal. This Avatar outlines the dynamics of the biological reactions of animal body in the framework of a specific problem. Each PDE contains parameters corresponding to biological-like factors which can be learnt from data by the developed Statistical Learning Tool

De l'inaliénabilité de la dot mobilière et immobilière en droit romain et en droit français ancien et moderne / par A. Sincholle,...

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Comparison of forecast models of production of dairy cows combining animal and diet parameters

International audienceWe study the effect of nutritional diet characteristics on the lactating Holstein-Friesian dairy cows in Brittany, France from 36 individuals. An analysis ofthe relations between fat/protein content and milk yield was implemented for our dataset. The fat and protein production increase at a slower rateas milk yield increases. The importance of chemical composition on milk production is studied using the linear model. The data analysis confirms theimportance of Starch, crude fiber, and protein which have a positive effect on milk production. This analysis also confirms the previous study on the effect of parity on the production. After that, the milk production forecasting is investigated using both linear models and machine learning approaches (support vector machine, random forest, neural network). We study the performance of multiple linear regression and machine learning-based models in both non-autoregressive and autoregressive cases at the individual level. The autoregressive models, which take into account the previously observed milk yield, have proven to significantly outperform the non-autoregressive approaches. Moreover, the computational cost of each approach is presented in the paper. While the random forest algorithm gives the best performance in both non-autoregressive and autoregressive approaches. The support vector machine algorithm gives a very close performance with a substantial less computing time. The support vector machine is shown to be the best compromise between accuracy and computational cost