A data driven study of relationships between relief and farmland abandonment in a Mediterranean region

The environmental, cultural and socio-economic causes and consequences of farmland abandonment are issues of increasing concern for researchers and policy makers. In previous studies, we proposed a new methodology for selecting the driving factors in farmland abandonment processes. Using Data Mining and GIS, it is possible to select those variables which are more significantly related to abandonment. The aim of this study is to investigate the application of the above mentioned methodology for finding relationships between relief and farmland abandonment in a Mediterranean region (SE Spain).We have taken into account up to 28 different variables in a single analysis, some of them commonly considered in land use change studies (slope, altitude, TWI, etc), but also other novel variables have been evaluated (sky view factor, terrain view factor, etc). The variable selection process provides results in line with the previous knowledge of the study area, describing some processes that are region specific (e.g. abandonment versus intensification of the agricultural activities). The European INSPIRE Directive (2007/2/EC) establishes that the digital elevation models for land surfaces should be available in all member countries, this means that the research described in this work can be extrapolated to any European country to determine whether these variables (slope, altitude, etc) are important in the process of abandonment.

Exploring automatic feature selection for transition-based dependency parsing

En este artículo se investigan técnicas automáticas para encontrar un modelo óptimo de características en el caso de un analizador de dependencias basado en transiciones. Mostramos un estudio comparativo entre algoritmos de búsqueda, sistemas de validación y reglas de decisión demostrando al mismo tiempo que usando nuestros métodos es posible conseguir modelos complejos que proporcionan mejores resultados que los modelos que siguen configuraciones por defecto.

The supergiant B[e] star LHA 115-S 18 - binary and/or luminous blue variable?

Context. The mechanism by which supergiant (sg)B[e] stars support cool, dense dusty discs/tori and their physical relationship with other evolved, massive stars such as luminous blue variables is uncertain. Aims. In order to investigate both issues we have analysed the long term behaviour of the canonical sgB[e] star LHA 115-S 18. Methods. We employed the OGLE II-IV lightcurve to search for (a-)periodic variability and supplemented these data with new and historic spectroscopy. Results. In contrast to historical expectations for sgB[e] stars, S18 is both photometrically and spectroscopically highly variable. The lightcurve is characterised by rapid aperiodic ` aring' throughout the 16 years of observations. Changes in the high excitation emission line component of the spectrum imply evolution in the stellar temperature - as expected for luminous blue variables - although somewhat surprisingly, spectroscopic and photometric variability appears not to be correlated. Characterised by emission in low excitation metallic species, the cool circumstellar torus appears largely unaffected by this behaviour. Finally, in conjunction with intense, highly variable He ii emission, X-ray emission implies the presence of an unseen binary companion. Conclusions. S18 provides observational support for the putative physical association of (a subset of) sgB[e] stars and luminous blue variables. Given the nature of the circumstellar environment of S18 and that luminous blue variables have been suggested as SN progenitors, it is tempting to draw a parallel to the progenitors of SN1987A and SN2009ip. Moreover the likely binary nature of S18 strengthens the possibility that the dusty discs/tori that characterise sgB[e] stars are the result of binary-driven mass-loss; consequently such stars may provide a window on the short lived phase of mass-transfer in massive compact binaries.