Les espaces de l'halieutique

On considère que deux marchés d'un bien donné sont intégrés lorsque leurs prix d'échange évoluent parallèlement dans le temps. On peut alors parler de marchés cointégrés. La cointégration est un outil économétrique permettant d'établir des relations stables et stationnaires à long terme pour des processus non stationnaires. La stationnarité est un problème très fréquent des variables économiques et notamment des séries de prix des produits de la mer. Ce problème étant classiquement résolu par la différenciation des processus sous peine d'une perte d'information des effets à long terme. La cointégration surmonte cela en changeant la structure du modèle économétrique initial avec l'introduction d'un élément correcteur de l'erreur. L'objectif de ce travail est d'explorer l'évolution des prix pour des marchés différents afin d'étudier de possibles relations à long terme entre eux. On utilisera l'exemple du marché du merlu en Bretagne pour appliquer cette méthode. On retiendra des prix mensuels et hebdomadaires des ventes de merlu. Les résultats nous montrent l'existence d'une étendue spatiale régionale du marché du merlu à l'intérieur duquel les prix évoluent sous une même tendance à long terme en dépit des possibles différentes à court terme. (Résumé d'auteur

Main Variables Affecting a Chemical-Enzymatic Method to Obtain Protein and Amino Acids from Resistant Microalgae

he development of microalgae uses requires further investigation in cell disruption alternatives to reduce the costs associated to this processing stage. This study aimed to evaluate the main variables affecting an extraction method to obtain protein and amino acids from microalgae. The method was based on a sequential alkaline-enzymatic process, with separate extractions and noncontrolled pH, and was applied to fresh biomass of a resistant species. The processed microalgae were composed of a consortium with Nannochloropsis sp. as predominant species. After the optimization of the pH of the alkaline reaction, the effect of the time of the alkaline reaction (30-120min), the time (30-120min) and temperature (40-60 degrees C) of the enzymatic reaction, and the biomass concentration (50-150mgml(-1)), on the extraction yields of protein and free amino nitrogen (FAN) and on the final concentration of protein in the extract, was studied using a response surface methodology. Even though all the variables and some interactions among them had a significant effect, the biomass concentration was the most important factor affecting the overall process. The results showed relevant information about the different options in order to maximize not only the response variables individually but also different combinations of them. Assays with optimized values reached maximum yields of 80.3% and 1.07% of protein (% of total protein) and FAN (% of total biomass), respectively, and a protein concentration in the extract of 15.2mgml(-1). The study provided the essential information of an alternative approach to obtain protein and amino acids from fresh biomass of resistant microalgae with a high yield, also opening perspectives for further research in particular aspects

Implementation of the control strategy for a 2D nanopositioning long range stage

A 2D-platform stage able to obtain an effective metrological positioning with nanometer resolution and long working range (50 x 50 mm2) is on development at the University of Zaragoza. The 2D stage has already been designed, manufactured and assembled. The movement of the platform is performed by four custom-made linear motors, and mirror laser interferometers work as positioning sensors in XYRz degrees of freedom. The work here presented focuses on the hardware implementation of the motor control, for one actuator on a 1D linear stage. The developed control strategy acts on three-phase PWM (Pulse-Width Modulation) signals and a feedback is provided by measuring the phase currents. As a preliminary solution, a sensorless algorithm substitutes the positioning sensor before implementing the laser interferometers

Trustworthy placements: Improving quality and resilience in collaborative attack detection

Abstract In distributed and collaborative attack detection systems decisions are made on the basis of the events reported by many sensors, e.g., Intrusion Detection Systems placed across various network locations. In some cases such events originate at locations over which we have little control, for example because they belong to an organisation that shares information with us. Blindly accepting such reports as real encompasses several risks, as sensors might be dishonest, unreliable or simply having been compromised. In these situations trust plays an important role in deciding whether alerts should be believed or not. In this work we present an approach to maximise the quality of the information gathered in such systems and the resilience against dishonest behaviours. We introduce the notion of trust diversity amongst sensors and argue that detection configurations with such a property perform much better in many respects. Using reputation as a proxy for trust, we introduce an adaptive scheme to dynamically reconfigure the network of detection sensors. Experiments confirm an overall increase both in detection quality and resilience against compromise and misbehaviour

Trustworthy placements: Improving quality and resilience in collaborative attack detection

Abstract In distributed and collaborative attack detection systems decisions are made on the basis of the events reported by many sensors, e.g., Intrusion Detection Systems placed across various network locations. In some cases such events originate at locations over which we have little control, for example because they belong to an organisation that shares information with us. Blindly accepting such reports as real encompasses several risks, as sensors might be dishonest, unreliable or simply having been compromised. In these situations trust plays an important role in deciding whether alerts should be believed or not. In this work we present an approach to maximise the quality of the information gathered in such systems and the resilience against dishonest behaviours. We introduce the notion of trust diversity amongst sensors and argue that detection configurations with such a property perform much better in many respects. Using reputation as a proxy for trust, we introduce an adaptive scheme to dynamically reconfigure the network of detection sensors. Experiments confirm an overall increase both in detection quality and resilience against compromise and misbehaviour

An efficient approximation to the K-means clustering for Massive Data

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. In spite of its dependency on the initial settings and the large number of distance computations that it can require to converge, the K-means algorithm remains as one of the most popular clustering methods for massive datasets. In this work, we propose an efficient approximation to the K-means problem intended for massive data. Our approach recursively partitions the entire dataset into a small number of subsets, each of which is characterized by its representative (center of mass) and weight (cardinality), afterwards a weighted version of the K-means algorithm is applied over such local representation, which can drastically reduce the number of distances computed. In addition to some theoretical properties, experimental results indicate that our method outperforms well-known approaches, such as the K-means++ and the minibatch K-means, in terms of the relation between number of distance computations and the quality of the approximation.MINECO (TIN2013-41272P), Spanish Ministry of Economy and Competitivenes