Abstract
The IEC-61499 standard defines a framework in which distributed control applications may be developed, and how these are interpreted and executed. With the distributed nature of the control applications, many new issues also need to be taken into account. Most of these arise due to the new error model and failure modes of the distributed hardware on which the distributed application is being executed. On the other hand, the distributed nature of the hardware also allows new capabilities to be explored, such as improved reliability through masking of faults. Fault tolerance is usually achieved by the use of replication. Replication at the software level allows for much smaller granularity, but all replicas must be kept synchronised in order to produce the same output data and events. To achieve replica determinism all replicas must process the same set of inputs in the same order, and the outputs of the replicas must somehow be consolidated. The IEC 61499 framework however does not explicitly handle the replication of software components. In this paper we propose a replication model for IEC-61499 applications, and discuss which mechanisms and protocols may be used for their support. In short, replica determinism is achieved through the use of distributed synchronised clocks, atomic multicast protocols, as well as timed messages. ©2007 IEEE.

Abstract
Recently, two CAT-1 projects have been initiated related to Granada Basin area (Betic Cordillera, Southern Spain), aimed at the detection of deformation in the region applying time series InSAR methodology. Due to the outstanding availability of ERS-1/2 and Envisat acquisitions, a time span of more than 12 years is covered and time series can be produced, enabling us to assess the feasibility of monitoring deformation with millimetre precision. The ultimate goal of these projects is the quantification of displacements and determination of their mean directions, relating them to dynamic changes and stress accumulation in order to identify potential seismic hazard locations. For the moment, not all available scenes have been received, so a preliminary data processing has been carried out at the Padul fault area. In this paper, we present a status report of both projects.

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The aim of this study was to improve the accuracy of aeropalynological models to forecast yields in areas with heterogeneous characteristics by applying principal component analysis to integrate the airborne pollen sampled from more than one trap. The sampling was performed during the past seven years (1998-2004) in the main northeast olive regions of Portugal. Annual crop production was forecasted on the basis of airborne pollen concentration measured at flowering, comparing the performance of three different independent variables: total airborne pollen concentration sampled in each trap and a derived variable that was determined by principal component analysis of the total airborne pollen concentration sampled. The best predictive results were obtained using a logarithmic relationship with airborne pollen concentration principal component scores describing about 97% of olive fruit production variability over the last seven years. The use of this technique improved the ability of pollen to explain the production interannual variations by about 13%. The comparison between actual reported and the adjusted production showed an average spread deviation of 5%.

Abstract
This paper proposes a method to unify the definition of the main pollen season based on statistical analysis. For this, an aerobiological study was carried out in Porto region (Portugal), from 2003-2005 using a 7-day Hirst-type volumetric spore trap. To define the main pollen season, a non-linear logistic regression model was fitted to the values of the accumulated sum of the daily airborne pollen concentration from several allergological species. An important feature of this method,is that the main pollen season will be characterized by the model parameters calculated. These parameters are identifiable aspects of the flowering phenology, and determine not only the beginning and end of the main pollen season, but are also influenced by the meteorological conditions. The results obtained with the proposed methodology were also compared with two of the most used percentage methods. The logistic model fitted well with the sum of accumulated pollen. The explained variance was always higher than 97%, and the exponential part of the predicted curve was well adjusted to the time when higher atmospheric pollen concentration was sampled. The comparison between the different methods tested showed large divergence in the duration and end dates of the main pollen season of the studied species.

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