Abstract
It is generally considered that the Controller Area Network (CAN) guarantees atomic broadcast properties through its extensive error detection and signalling mechanisms. However, it is known that these mechanisms may fail, and messages can be delivered in duplicate by some receivers or delivered only by a subset of the receivers. This misbehaviour may be disastrous if the CAN network is used to support replicated applications. In order to prevent such inconsistencies, a set of atomic broadcast protocols is proposed, taking advantage of CAN synchronous properties to minimise its run-time overhead. This paper presents such set of protocols, and demonstrates how they can be used for the development of distributed real-time applications.

Abstract
In Distributed Computer-Controlled Systems (DCCS), both real-time and reliability requirements are of major concern. Architectures for DCCS must be designed considering the integration of processing nodes and the underlying communication infrastructure. Such integration must be provided by appropriate software support services. In this paper, an architecture for DCCS is presented, its structure is outlined, and the services provided by the support software are presented These are considered in order to guarantee the real-time and reliability requirements placed by current and future systems.

Abstract
Technologies employed in greenhouse production systems have been developed considerably during the recent decades. These improvements have taken place in many different research areas, such as the development of new covering materials and actuating equipments, modeling of the plant physiological processes and greenhouse climate, new cultural techniques, among many other topics. Although, due to economic and environmental increasing requirements there is still a need to improve the tools used for greenhouse management. At the present this work is being addressed with the emphasis on the economic optimization of the production process. This approach implies to know the influence of climate factors on production, as well the establishment of a good interface between engineers, physiologists and biologists. This paper presents the methods that are being implemented and tested with the aim of improving the climate management of a greenhouse located in the UTAD-University campus.

Abstract
Several methods have been proposed to generate rankings of supervised classification algorithms based on their previous performance on other datasets [8,4]. Like any other prediction method, ranking methods will sometimes err, for instance, they may not rank the best algorithm in the first position. Often the user is willing to try more than one algorithm to increase the possibility of identifying the best one. The information provided in the ranking methods mentioned is not quite adequate for this purpose. That is, they do not identify those algorithms in the ranking that have reasonable possibility of performing best. In this paper, we describe a method for that purpose. We compare our method to the strategy of executing all algorithms and to a very simple reduction method, consisting of running the top three algorithms. In all this work we take time as well as accuracy into account. As expected, our method performs better than the simple reduction method and shows a more stable behavior than running all algorithms. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
Distributed Generation (DG) facilities require, like other energy projects, a sitting review process to acquire the permits and approval needs for construction and operation. In this process different groups and individuals with different roles, interests and priorities are involved. This paper presents a Spatial Decision Support System (SDSS) that helps to identify permissible areas to install DG facilities. Wind energy facilities are used in this paper to exemplify the use of the SDSS. © 2001 IEEE.

Abstract
In the fields of data mining and machine learning the amount of data available for building classifiers is growing very fast. Therefore, there is a great need for algorithms that are capable of building classifiers from very-large datasets and, simultaneously, being computationally efficient and scalable. One possible solution is to employ parallelism to reduce the amount of time spent in building classifiers from very-large datasets and keeping the classification accuracy. This work first overviews some strategies for implementing decision tree construction algorithms in parallel based on techniques such as task parallelism, data parallelism and hybrid parallelism. We then describe a new parallel implementation of the C4.5 decision tree construction algorithm. Even though the implementation of the algorithm is still in final development phase, we present some experimental results that can be used to predict the expected behavior of the algorithm. © Springer-Verlag Berlin Heidelberg 2001.