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Consumer segmentation is very relevant in the design of wine marketing strategies. Previous studies showed that there is a relationship between the consumption situation and the willingness to pay for a bottle of wine. In this sense, the consumption situation is considered a segmentation variable. However, price sensitivity in relation to the consumption occasion was not measured. In this paper, we propose four measures of price sensitivity to the consumption occasion. We illustrate how to compute them and discuss their advantages and limitations. One of the measures only discriminates consumers that are sensitive to the consumption occasion from those who are not. In turn, the other measures are more informative and make it possible to distinguish between different degrees of sensitivity. The proposed measures can be used to classify consumers and further improve the knowledge of wine marketeers and decision makers in the wine industry about them. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

In this work, we study the connection of the stability of multidimensional positive systems with the stability of switched positive systems. In a previous work, we showed that the stability of a multidimensional positive system implies the stability of a related switched positive system. Here, we investigate the reciprocal implication. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a method based on a 2D Hopfield Neural Network for online damage detection in beams subjected to external forces. The underlying idea of the method is that a significant change in the beam model parameters can be taken as a sign of damage occurrence in the structural system. In this way, damage detection can be associated to an identification problem. More concretely, a 2D Hopfield Neural Network uses information about the way the beam vibrates and the external forces that are applied to it to obtain time-evolving estimates of the beam parameters at the different beam points. The neural network organizes its input information based on the Euler-Bernoulli model for beam vibrations. Its performance is tested with vibration data generated by means of a different model, namely Timonshenko's, in order to produce more realistic simulation conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]