The Impact of the Post-Liberalisation Growth of LCCs on the Tourism Trends in Spain

This paper describes the basic characteristics of low cost companies (LCCs) and their expansion in Spain, one of the most popular tourist destinations in the world according to the World Tourism Organization. Using a demand perspective, the evolution of LCCs and the main implications for the tourism marked between 2000 and 2005 are analysed. The evident tendency of Low-Cost companies towards a marked growth in the number of passengers is contrasted with the evident stagnation of traditional or full service companies. The results of the analysis also show that highly significant trends related to the development of LCCs include the growing use of the Internet as an information search engine and tool for booking and paying for tourist services, the substitution of traditional holiday packages (travel and transport) for direct booking systems and a reduction in the length of stays at a destination.

Els cubs diabòlics

Un recorregut per un joc antic (i nou) de cubs

Liquid phase epitaxy and spectroscopic investigation of optically active KYb(WO4)2 thin layers

In recent years, Yb3+ has attracted much attention as an activating ion because of its small quantum defect for laser emission from 2F5/2 to 2F7/2 at ~1.03 µm, which provides high efficiency and reduced heat generation. A promising material for Yb3+ lasers is KYb(WO4)2 (KYbW) [1]. It can be grown from high-temperature solutions [2]. A suitable substrate material for the growth of single-crystalline layers with thicknesses in the range of the absorption length of ~13 µm at 981 nm is KY(WO4)2 (KYW).\ud We demonstrate the liquid phase epitaxy (LPE) of KYbW layers at start temperatures as low as 520°C from the chloride solvent KCl-NaCl-CsCl. This temperature is favorable in order to decrease the thermal stresses due to the differences in the thermal expansion coefficients of substrate and layer. Moreover, the choice of [010]-oriented KYW substrates bypasses the large difference in the thermal expansion coefficient along the [010] direction. Our spectroscopic investigations show that the fluorescence lifetime of ~250 µs measured in our LPE-grown KYbW layers is dominated by radiative decay and is very similar to that measured in top-seeded-solution-grown bulk samples [2]. Fast energy migration among the Yb3+ ions and energy transfer to small amounts of Tm3+ and Er3+ ions present in the YbCl3 reagent lead to visible upconversion luminescence in the layers under 981-nm excitation.\ud \ud [1] P. Klopp, U. Griebner, V. Petrov, X. Mateos, M.A. Bursukova, M.C. Pujol, R. Solé, J. Gavaldà, M. Aguiló, F. Güell, J. Massons, T. Kirilov, F. Díaz, Appl. Phys. B 2002, 74, 185\ud [2] M.C. Pujol, M.A. Bursukova, F. Güell, X. Mateos, R. Solé, J. Gavaldà, M. Aguiló, J. Massons, F. Díaz, P. Klopp, U. Griebner, V. Petrov, Phys. Rev. B 2002, 65, 16512

Theory and modeling of the magnetic field measurement in LISA PathFinder

The magnetic diagnostics subsystem of the LISA Technology Package (LTP) on board the LISA PathFinder (LPF) spacecraft includes a set of four tri-axial fluxgate magnetometers, intended to measure with high precision the magnetic field at their respective positions. However, their readouts do not provide a direct measurement of the magnetic field at the positions of the test masses, and hence an interpolation method must be designed and implemented to obtain the values of the magnetic field at these positions. However, such interpolation process faces serious difficulties. Indeed, the size of the interpolation region is excessive for a linear interpolation to be reliable while, on the other hand, the number of magnetometer channels does not provide sufficient data to go beyond the linear approximation. We describe an alternative method to address this issue, by means of neural network algorithms. The key point in this approach is the ability of neural networks to learn from suitable training data representing the behavior of the magnetic field. Despite the relatively large distance between the test masses and the magnetometers, and the insufficient number of data channels, we find that our artificial neural network algorithm is able to reduce the estimation errors of the field and gradient down to levels below 10%, a quite satisfactory result. Learning efficiency can be best improved by making use of data obtained in on-ground measurements prior to mission launch in all relevant satellite locations and in real operation conditions. Reliable information on that appears to be essential for a meaningful assessment of magnetic noise in the LTP.Comment: 10 pages, 8 figures, 2 tables, submitted to Physical Review

Espacios interiores y mobiliario de Miguel Fisac

Although Miguel Fisac’s designs for the furnishings in the Spanish Council for Scientific Research are well known and widely photographed, they had not, to date, been studied as a whole. This chronological review of the styles used to create different spatial effects describes both the author’s motivations as explained by the architect himself and the present condition of the furnishings. Considerations in support of their inclusion in the Council’s Artistic Estate are discussed, advocating —in the case of the “Scientific Library”— the award of Asset of Cultural Interest status.Conocida y fotografiada parte de la producción de Miguel Fisac relacionada con el mobiliario para el CSIC, no había sido estudiada hasta ahora en su conjunto. Se recogen cronológicamente los diferentes estilos utilizados para los distintos espacios, las motivaciones expresadas por el propio arquitecto, su estado actual y las consideraciones que puedan permitir su inclusión en el Patrimonio Artístico del CSIC y la declaración de alguna de ellas, como la “librería científica” como Bien de Interés Cultural

Usefulness of Bayesian networks in epidemiological studies

Introduction: Bayesian networks are a form of statistical modelling, which has been widely used in fields like clinical decision, systems biology, human immunodeficiency virus (HIV) and influenza research, analyses of complex disease systems, interactions between multiple diseases and, also, in diagnostic diseases. The present study aimed to show the usefulness of Bayesian networks (BNs) in epidemiological studies. Material and Methods: 3,993 subjects (men 1,758, women 2,235) belonging to the public productive sector from the Balearic Islands (Spain), which were active workers, constitute the data set. Results: A BN was built from a dataset composed of twelve relevant features in cardiovascular disease epidemiology. Furthermore, the structure and parameters were learnt with GeNIe 2.0 tool. Taking into account the main topological properties some features were optimized, obtaining a hypothesized scenario where the likelihoods of the different features were updated and the adequate conclusions were established. Conclusions: Bayesian networks allow us to obtain a hypothetical scenario where the probabilities of the different features are updated according to the evidence that is introduced. This fact makes Bayesian networks a very attractive tool.Introducción: Las redes Bayesianas son una forma de modelización estadística, las cuales han sido ampliamente utilizadas en campos como la decisión clínica, biología de sistemas, virus de inmunodeficiencia humana (VIH) e investigación en influenza, análisis de sistemas de enfermedades complejos, interacciones entre múltiples enfermedades y, también, en enfermedades de diagnóstico. Este estudio tiene como objetivo mostrar la utilidad de las redes Bayesianas en estudios epidemiológicos. Material y Métodos: 3,993 individuos (hombres 1,758, mujeres 2,235) pertenecientes al sector productivo público de las Islas Baleares (España), los cuales eran trabajadores activos, constituyen la base de datos. Resultados: Una red Bayesiana se ha obtenido a partir de una base de datos compuesta de doce características relevantes de la epidemiología de la enfermedad cardiovascular. Por otra parte, la estructura y los parámetros se han obtenido con la herramienta Genie 2.0. Teniendo en cuenta las principales propiedades topológicas algunas características fueron optimizadas. Conclusiones: Las redes Bayesianas permiten obtener un escenario hipotético donde las probabilidades de las diferentes características se van actualizando de acuerdo con la evidencia introducida. Este hecho hace de las redes Bayesianas una herramienta muy atractiva, además permite establecer diversas conclusiones