Épandage des margines sur les sols agricoles : impacts environnementaux microbiologiques

L’épandage agricole des margines (effluents des moulins à huile d’olive) constitue une alternative parmi les solutions permettant de les valoriser, mais à condition que cette opération soit contrôlée et maîtrisée enrespectant les doses à appliquer. Cependant, une réticence envers l’épandage demeure jusqu’à nos jours, pour des craintes d’éventuelles incidences microbiologiques négatives sur le sol. Ainsi, pour contribuer à soulever l’ambiguïté qui a dû résulter des avis souvent controversés envers la valorisation agricole des margines, ce travail a été mené au niveau d’une exploitation agricole. En comparaison avec un sol non traité aux margines (témoin), les impacts environnementaux microbiologiquesde l’épandage de 25 et 50 m3/ha ont été étudiés, pendant quatre mois, au niveau de deux horizons (0-20 cm et 20-40 cm) d’un sol homogène de texture sable limoneux. Les résultats obtenus ont révélé que les phénols provenant des margines ont été dégradés ou réorganisés au cours du deuxième mois après l’épandage. Ni activation, ni inhibition de l’activité de la microflore du sol n’ont été constatées suite à l’épandage des doses étudiées.Mots-clés : margines, épandage, environnement, impacts  microbiologiques

Third order differential equations with fixed critical points

Cataloged from PDF version of article.The singular point analysis of third order ordinary differential equations which are algebraic in y and y′ is presented. Some new third order ordinary differential equations that pass the Painlevé test as well as the known ones are found. © 2008 Elsevier Inc. All rights reserved

Réduction des réflexions parasites produites par des panneaux à modulation périodique d'impédance de surface

International audienceCette communication propose une nouvelle méthodologie pour mieux contrôler le rayonnement de panneaux plans réfléchissants à modulation périodique d'impédance de surface. Il s'agit en particulier de limiter les réflexions parasites associées aux harmoniques de Floquet d'ordre supérieur. L'approche s'appuie sur la définition de paramètres permettant d'évaluer a priori le potentiel des motifs périodiques candidats et est validée par des simulations électromagnétiques

Reference installation for the German grid initiative D-Grid

The D-Grid reference installation is a test platform for the German grid initiative. The main task is to create the grid prototype for software and hardware components needed in the D-Grid community. For each grid-related task field different alternative middleware is included. With respect to changing demands from the community, new versions of the reference installation are released every six months

Relations for zeros of special polynomials associated to the Painleve equations

A method for finding relations for the roots of polynomials is presented. Our approach allows us to get a number of relations for the zeros of the classical polynomials and for the roots of special polynomials associated with rational solutions of the Painleve equations. We apply the method to obtain the relations for the zeros of several polynomials. They are: the Laguerre polynomials, the Yablonskii - Vorob'ev polynomials, the Umemura polynomials, the Ohyama polynomials, the generalized Okamoto polynomials, and the generalized Hermite polynomials. All the relations found can be considered as analogues of generalized Stieltjes relations.Comment: 17 pages, 5 figure

A new approach to sample entropy of multi-channel signals: application to EEG signals

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients

Time-varying time-frequency complexity measures for epileptic EEG data analysis

Objective: Our goal is to use existing and to propose new time-frequency entropy measures that objectively evaluate the improvement on epileptic patients after medication by studying their resting state EEG recordings. An increase in the complexity of the signals would confirm an improvement in the general state of the patient. Methods: We review the Rényi entropy based on time-frequency representations, along with its time-varying version. We also discuss the entropy based on singular value decomposition computed from a time-frequency representation, and introduce its corresponding time-dependant version. We test these quantities on synthetic data. Friedman tests are used to confirm the differences between signals (before and after proper medication). Principal component analysis is used for dimensional reduction prior to a simple threshold discrimination. Results: Experimental results show a consistent increase in complexity measures in the different regions of the brain. These findings suggest that extracted features can be used to monitor treatment. When combined, they are useful for classification purposes, with areas under ROC curves higher than 0.93 in some regions. Conclusion: Here we applied time-frequency complexity measures to resting state EEG signals from epileptic patients for the first time. We also introduced a new time-varying complexity measure. We showed that these features are able to evaluate the treatment of the patient, and to perform classification. Significance: The time-frequency complexities, and their time-varying versions, can be used to monitor the treatment of epileptic patients. They could be applied to a wider range of problems

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

This paper introduces an entropy based method that measures complexity in non-stationary multivariate signals. This method, called Mutivariate Improved Weighted Multiscale Permutation Entropy (mvIWMPE), has two main advantages: (i) it shows lower variance for the results when applied on a wide range of multivariate signals; (ii) it has good accuracy quantifying complexity of different recorded states in signals and hence discriminating them. mvIWMPE is based on two previously introduced permutation entropy algorithms, Improved Multiscale Permutation Entropy (IMPE) and Multivariate Weighted Mul-tiscale Permutation Entropy (mvWMPE). It combines the concept of coarse graining from IMPE and the introduction of the weight of amplitudes of the signals from mvWMPE. mvIWMPE was validated on both synthetic and human electroencephalographic (EEG) signals. Several synthetic signals were simulated: mixtures of white Gaussian noise (WGN) and pink noise, chaotic and convergent Lorenz system signals, stochastic and deterministic signals. As for real signals, resting-state EEG recorded in healthy and epileptic children during eyes closed and eyes open sessions were analyzed. Our method was compared to multivariate multiscale, multivariate weighted multiscale and multivariate improved multiscale permutation entropy methods. Performance on synthetic as well as on EEG signals showed more undeviating results and higher ability for mvIWMPE discriminating different states of signals (chaotic vs convergent, WGN vs pink noise, stochastic vs deterministic simulated signals, and eyes open vs eyes closed EEG signals). We herein proposed an efficient method to measure the complexity of multivariate non-stationary signals. Experimental results showed the accuracy and the robustness (in terms of variance) of the method