New developments in the forecasting of monthly overnight stays in the North Region of Portugal.

The Tourism sector is of strategic importance to the North Region of Portugal and is growing. Forecasting monthly overnight stays in this region is, therefore, a relevant problem. In this paper, we analyze data more recent than those considered in previous studies and use them to develop and compare several forecasting models and methods. We conclude that the best results are achieved by models based on a non-parametric approach not considered so far for these data, the singular spectrum analysis.

A weighted principal component analysis and its application to gene expression data.

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part a new method to select variables (genes in our application). Our focus is on problems where the values taken by each variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we propose the use of a new correlation coefficient as an alternative to Pearson's. This leads to a so-called weighted PCA (WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray data set. We show that this algorithm produces better results when our WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with the Significance Analysis of Microarrays algorithm.

Hopfield neural networks for on-line parameter estimation.

This paper addresses the problem of using Hopfield Neural Networks (HNNs) for on-line parameter estimation. As presented here, a HNN is a nonautonomous nonlinear dynamical system able to produce a time-evolving estimate of the actual parameterization. The stability analysis of the HNN is carried out under more general assumptions than those previously considered in the literature, yielding a weaker sufficient condition under which the estimation error asymptotically converges to zero. Furthermore, a robustness analysis is made, showing that, under the presence of perturbations, the estimation error converges to a bounded neighbourhood of zero, whose size decreases with the size of the perturbations. The results obtained are illustrated by means of two case studies, where the HNN is compared with two other methods.

A hybrid method for parameter estimation and its application to biomedical systems.

A general version of a hybrid method for parameter estimation is presented with a theoretical support and an illustrative example of application. This method consists of a curve fitting algorithm that takes the initial estimate of the parameterization from an artificial neural network. The idea is to improve the convergence of the algorithm to the sought parameterization using a close initial estimate. The motivation arises from biomedical problems where one is interested in obtaining a meaningful estimate so that it can be used for both description and prediction purposes. Two strategies are proposed for the application of the hybrid method: one is of general applicability, the other is intended for systems defined by the series connection of various blocks. The feasibility of the method is illustrated with a case study related to the neuromuscular blockade of patients undergoing general anaesthesia.

The unimodal model for the classification of ordinal data.

Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, r(int), to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered.

Ozono, factibilidad técnica para la desinfección de agua potable en la II Región de Chile

El objetivo de la investigación fué evaluar el cambio de cloro (Cl) por ozono (O) y el efecto en la coagulación de arsenico (As) para el proceso de potabilización de aguas crudas con contenidos de As de 0,45 ppm en promedio y turbiedades inferiores a los 20 NTU, en una planta de tratamiento de agua de Chile

Ozono, factibilidad técnica para la desinfección de agua potable en la II Región de Chile

El objetivo de la investigación fué evaluar el cambio de cloro (Cl) por ozono (O) y el efecto en la coagulación de arsenico (As) para el proceso de potabilización de aguas crudas con contenidos de As de 0,45 ppm en promedio y turbiedades inferiores a los 20 NTU, en una planta de tratamiento de agua de Chile