Abstract
The Lagoa da Apulia is a unique feature in the NW coastal zone of Portugal, a remaining form from a lagoon complex system that, during the Late Holocene, was dominant in the region. This system was mainly neotectonically controlled, occupying a depressed area bounded by faults on a Palaeozoic rocky lower platform, today observable on beaches at low tide. With the intention of knowing the main architecture of the palaeo-lagoon, geophysical prospecting with GPR and resistivity was carried out. Accordingly, six cores were taken and the sedimentary and mineralogical facies, and diatom and foraminifer contents were analysed, and five rich organic layers were dated by radiocarbon analysis. With the data, an evolutionary environment reconstruction model was created for this palaeo-lagoon and the main structural features of the neighbouring area.

Abstract
Thesauri, which list the most salient semantic relations between words, have mostly been compiled manually. Therefore, the inclusion of an entry depends on the subjective decision of the lexicographer. As a consequence, those resources are usually incomplete. In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal differs from all other research presented so far as it tries to take the best of two different methodologies, i.e. semantic space models and information extraction models. In particular, it can be applied to extract close semantic relations, it limits the search space to few, highly probable options and it is unsupervised.

Abstract

Abstract
Recently, meta-learning techniques have been employed to the problem of algorithm recommendation for gene expression data classification. Due to their flexibility, the advice provided to the user was in the form of rankings, which are able to express a preference order of Machine Learning algorithms accordingly to their expected relative performance. Thus, choosing how to learn accurate rankings arises as a key research issue. In this work, the authors empirically evaluated 2 general approaches for ranking prediction and extended them. The results obtained for 49 publicly available microarray datasets indicate that the extensions introduced were very beneficial to the quality of the predicted rankings. © 2010 Springer-Verlag.

Abstract
A technique for the fabrication of luminescence based fiber optic optrodes with multiple analyte sensitivity is proposed. Combination of photosensitive polymers doped with different luminescent indicators was used to produce fiber probes, by self-guiding photopolymerization, having different geometries and sensing capabilities. Results demonstrating the method flexibility are shown with luminescent probes doped with CdSe/ZnS quantum dots and an organometalic ruthenium complex for simultaneous detection of oxygen and temperature.

Abstract
The magnitude optimum (MO) method provides a relatively fast and non-oscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection performance of some processes. in this paper, disturbance rejection performance of the PID controller is improved by applying the "disturbance rejection magnitude optimum" (DRMO) optimisation method, while the tracking performance has been improved by a set-point weighting and set-point filtering PID controller structure. The DRMO tuning method requires numerical optimisation for the calculation of PID controller parameters. The method was applied to two different 2-degrees-of-freedom PID controllers and has been tested on several different representatives of process models and one laboratory set-up. A comparison with some other tuning methods has shown that the proposed tuning method, with a set-point filtering PID controller, is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable with the original MO method.