Salaire minimum

Le salaire minimum, ou salaire minimal, est la rémunération minimale qu'un employeur peut légalement accorder à un employé pour un travail. Des abattements au salaire minimal sont parfois prévus par des dispositifs législatifs ou réglementaires. Terminologie est l'adjectif qualifiant ce qui constitue un minimum. L'expression est critiquée par certains linguistes selon lesquels elle présente le défaut de juxtaposer deux substantifs (le salaire et le minimum) sur le modèle anglophone. Les formulations correctes seraient ici , ou . But et histoire Le salaire minimal ou vise à garantir un niveau de revenu aux employés et ouvriers. Pour ses partisans, il maintient une aussi bien entre employés (dans leur participation au travail) qu'entre employeurs (éviter le ). Il participe à la cohésion sociale d'un pays et à la redistribution des revenus. Même si le principe du salaire minimum est admis dans une majorité de philosophies, la fixation de son niveau fait l'

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A solution method for predictive simulations in a stochastic environment

Anne Daniëlle Koelewijn

Predictive gait simulations currently do not account for environmental or internal noise. We describe a method to solve predictive simulations of human movements in a stochastic environment using a collocation method. The optimization is performed over multiple noisy episodes of the trajectory, instead of a single episode in a deterministic environment. Each episode used the same control parameters. The method was verified on a torque-driven pendulum swing-up problem. A different optimal trajectory was found in a stochastic environment than in the deterministic environment. Next, it was applied to gait to show its application in predictive simulation of human movement. We show that, unlike in a deterministic model, a nonzero minimum foot clearance during swing is predicted by a minimum-effort criterion in a stochastic environment. The predicted amount of foot clearance increased with the noise amplitude. (C) 2020 Elsevier Ltd. All rights reserved.

2020

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Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain

Pavel Vaskan

Meeting the growing food demand with minimum impact on the environment is a major challenge to face for ensuring a more sustainable food production. To tackle this problem, in this article we present a novel systematic method for agriculture planning that optimally allocates rainfed and irrigated cropping areas, thereby enhancing food availability and reducing the environmental impact of agriculture. The allocation problem is mathematically formulated as a multi-objective linear programming problem that simultaneously accounts for the maximisation of the crop production and the minimisation of the environmental impact caused by water consumption. To quantify the environmental damage, life cycle assessment principles and water footprint concepts are integrated into the model. The capabilities of our tool are illustrated through its application to a real case study that considers wheat production in Spain. The results show that the current allocation of rainfed and irrigated wheat areas in Spain is sub-optimal. Our tool provides a set of alternatives for optimally reallocating these wheat areas that ultimately achieve significant reductions in environmental impact while maintaining or even increasing the production level. The analysis clearly demonstrates that the optimal allocation of rainfed and irrigated cropping areas is a potential pathway to minimise the environmental impact of water consumption in food production. Our systematic decision-support tool aims to assist famers and policy-makers in the transition towards a more sustainable agricultural sector. (C) 2016 Elsevier Ltd. All rights reserved.

Elsevier2017

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Modular vs. non-modular preconditioners for fluid-structure systems with large added-mass effect

Annalisa Quaini, Alfio Quarteroni

In this article we address the numerical simulation of fluid-structure interaction (FSI) problems featuring large added-mass effect. We analyze different preconditioners for the coupled system matrix obtained after space- time discretization and linearization of the FSI problem. The classical Dirichlet-Neumann preconditioner has the advantage of ``modularity'' because it allows to reuse existing fluid and structure codes with minimum effort (simple interface communication). Unfortunately, its performance is very poor in case of large added-mass effects. Alternatively, we consider two non-modular approaches. The first one consists in preconditioning the coupled system with a suitable diagonal scaling combined with an ILUT preconditioner. The system is then solved by a Krylov method. The drawback of this procedure is that the combination of fluid and structure codes to solve the coupled system is not straightforward. The second non-modular approach we consider is a splitting technique based on an inexact block-LU factorization of the linear FSI system. The resulting algorithm computes the fluid velocity separately from the coupled pressure-structure system at each iteration, reducing the computational cost. Independently of the preconditioner, the efficiency of semi-implicit algorithms (i.e., those that treat geometric and fluid nonlinearities in an explicit way) is highlighted and their performance compared to the one of implicit algorithms. All the methods are tested on three-dimensional blood-vessel systems. The algorithm combining the non-modular ILUT preconditioner with Krylov methods proved to be the fastest.

2008

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