Group preference structures in AHP–group decision making

This paper presents a procedure that allows us to determine the preference structures (PS) associated to each of the different groups of actors that can be identified in a group decision making problem with a large number of individuals. To that end, it makes use of the Analytic Hierarchy Process (AHP) (Saaty, 1980) as the technique to solve discrete multicriteria decision making problems. This technique permits the resolution of multicriteria, multienvironment and multiactor problems in which subjective aspects and uncertainty have been incorporated into the model, constructing ratio scales corresponding to the priorities relative to the elements being compared, normalised in a distributive manner (wi = 1). On the basis of the individuals’ priorities we identify different clusters for the decision makers and, for each of these, the associated preference structure using, to that end, tools analogous to those of Multidimensional Scaling. The resulting PS will be employed to extract knowledge for the subsequent negotiation processes and, should it be necessary, to determine the relative importance of the alternatives being compared using anyone of the existing procedures

Multivariate ARIMA Compositional Time Series Analysis

A compositional time series is obtained when a compositional data vector is observed at different points in time. Inherently, then, a compositional time series is a multivariate time series with important constraints on the variables observed at any instance in time. Although this type of data frequently occurs in situations of real practical interest, a trawl through the statistical literature reveals that research in the field is very much in its infancy and that many theoretical and empirical issues still remain to be addressed. Any appropriate statistical methodology for the analysis of compositional time series must take into account the constraints which are not allowed for by the usual statistical techniques available for analysing multivariate time series. One general approach to analyzing compositional time series consists in the application of an initial transform to break the positive and unit sum constraints, followed by the analysis of the transformed time series using multivariate ARIMA models. In this paper we discuss the use of the additive log-ratio, centred log-ratio and isometric log-ratio transforms. We also present results from an empirical study designed to explore how the selection of the initial transform affects subsequent multivariate ARIMA modelling as well as the quality of the forecasts

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Genotypic comparison of Pantoea agglomerans plant and clinical strains

Pantoea agglomerans strains are among the most promising biocontrol agents for a variety of bacterial and fungal plant diseases, particularly fire blight of apple and pear. However, commercial registration of P. agglomerans biocontrol products is hampered because this species is currently listed as a biosafety level 2 (BL2) organism due to clinical reports as an opportunistic human pathogen. This study compares plant-origin and clinical strains in a search for discriminating genotypic/phenotypic markers using multi-locus phylogenetic analysis and fluorescent amplified fragment length polymorphisms (fAFLP) fingerprinting. Results: Majority of the clinical isolates from culture collections were found to be improperly designated as P. agglomerans after sequence analysis. The frequent taxonomic rearrangements underwent by the Enterobacter agglomerans/Erwinia herbicola complex may be a major problem in assessing clinical associations within P. agglomerans. In the P. agglomerans sensu stricto (in the stricter sense) group, there was no discrete clustering of clinical/biocontrol strains and no marker was identified that was uniquely associated to clinical strains. A putative biocontrol-specific fAFLP marker was identified only in biocontrol strains. The partial ORF located in this band corresponded to an ABC transporter that was found in all P. agglomerans strains. Conclusion: Taxonomic mischaracterization was identified as a major problem with P. agglomerans, and current techniques removed a majority of clinical strains from this species. Although clear discrimination between P. agglomerans plant and clinical strains was not obtained with phylogenetic analysis, a single marker characteristic of biocontrol strains was identified which may be of use in strain biosafety determinations. In addition, the lack of Koch's postulate fulfilment, rare retention of clinical strains for subsequent confirmation, and the polymicrobial nature of P. agglomerans clinical reports should be considered in biosafety assessment of beneficial strains in this species

Development of chemical separation processes for the treatment and monitoring of metallic cations and oxoanions in polluted waters

The chemical contamination of natural waters is a global problem with a worldwide impact. Considering the relevance of this problem, this thesis is intended, on one hand, to develop different separation/preconcentration techniques based on membranes ability to permeate anions for the transport of toxic oxyanions of chromium(VI) and arsenic contained in aqueous matrices. In particular, we have investigated supported liquid membranes and polymer inclusion membranes, both of which contain the commercial quaternary ammonium salt Aliquat 336 as a carrier, as well as commercial anion exchange membranes. On the other hand, we have focused on the development of chemical sensors to facilitate the monitoring of several metals from different aqueous matrices. Thus, a selective optical sensor for Cr(VI) based on polymeric membranes containing Aliquat 336 as an ionophore has been designed. Additionally, mercury-based screen-printed electrodes have been evaluated for for cadmium, lead, copper and zinc detection.