Abstract
The aim of the present study was to compare the accuracy and reproducibility of six statistical models for the calculation of olive (Olea europeae L.) heat requirements to trigger the onset of flowering in three Portuguese regions: Reguengos de Monsaraz, Valenca do Douro, and Braga. Other aims were to ascertain the date on which the heat-accumulation period started and the threshold temperatures above which the development of reproductive structures starts in olives. The starting and peak dates for the regional O. europeae flowering season were estimated by monitoring airborne pollen from 1998 to 2004 using "Cour" type samplers. The threshold temperature values calculated for the three regions were very similar (9.0 degrees C for Valenca do Douro, 9.2 degrees C for Reguengos de Monsaraz, and 9.7 degrees C for Braga). The accumulated daily mean temperature model had less interannual and inter-regional variation, showing best predictive results for 2004, with absolute differences between the observed and predicted dates of 4 d in Reguengos de Monsaraz and 2 d in Valenca do Douro and Braga for the onset of flowering date and of 2 d in Reguengos de Monsaraz, 7 d in Valenca do Douro, and 4 d in Braga for peak flowering dates. This model was the most accurate, reproducible, and operational to calculate heat requirements for olives to flower, with an average mean temperature accumulation of 1 446 degrees C in Reguengos, 1 642 degrees C in Valenca do Douro, and 1 703 degrees C in Braga to reach the onset of flowering. The best initial date for this accumulation was 1 January.

Abstract
This paper analyses the performance of a Genetic Algorithm (GA) in the synthesis of digital circuits using a new approach. The novel concept extends the classical fitness function by introducing a fractional-order dynamical evaluation. The dynamic fitness function results from an analogy with control systems where it is possible to benefit the proportional algorithm by including a differential component. For this purpose the non integer derivative is approximated through Padé fractions. The experiments reveal superior results when comparing with the classical fitness method. Copyright © 2006 IFAC.

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Abstract
In the Next Generation Networks (NGNs) users will carry multiple devices forming cooperative networks known as Personal Area Networks (PANs). Some existing technologies enable this type of networks, such as Bluetooth or IEEE 802.15.4, but a unified framework capable of self-organizing them dynamically in a full heterogeneous environment populated by these and other technologies still has to be defined. Also, these networks are envisioned to be connecting dynamically to the Internet, and may use two IP versions and their autoconfiguration mechanisms. In this paper we propose a new framework, the Autoconfiguration and Self-management of Personal Area Networks (ASPAN), which enables the automatic and dynamic deployment of PANs in the heterogeneous environments envisioned for NGNs and handles the automatic and dynamic connection of a PAN to the global Internet. Copyright 2006 ACM.

Abstract
For calculating the total annual Olea pollen concentration, the onset of the main pollen season and the peak pollen concentration dates, using data from 1998 to 2004, predictive models were developed using multiple regression analysis. Four Portuguese regions were studied: Reguengos de Monsaraz, Valenca do Douro, Braga and Elvas. The effect of some meteorological parameters such as temperature and precipitation on Olea spatial and temporal airborne pollen distribution was studied. The best correlations were found when only the pre-peak period was used, with thermal parameters (maximum temperature) showing the highest correlation with airborne pollen distribution. Independent variables, selected by regression analysis for the predictive models, with the greatest influence on the Olea main pollen season features were accumulated number of days with rain and rainfall in the previous autumn, and temperatures (average and minimum) from January through March. The models predict 59 to 99% of the total airborne pollen concentration recorded and the initial and peak concentration dates of the main Olea pollen season.

Abstract