Abstract
This paper proposes a classification approach to identify the team's formation (formation means the strategical layout of the players in the field) in the robotic soccer domain for the two dimensional (213) simulation league. It is a tool for decision support that allows the coach to understand the strategy of the opponent. To reach that goal we employ Data Mining classification techniques. TO understand the simulated robotic soccer domain we briefly describe the Simulation system, some related work and the use of Data Mining techniques for the detection of formations. In order to perform a robotic soccer match with different formations we develop a way to configure the formations in a training base team (FC Portugal) and a data preparation process. The paper describes the base team and the test team,, used and the respective configuration process. After the matches between test teams the data is subjected to a reduction process taking into account the players' position in the field given the collective. In the modeling stage appropriate learning algorithms were selected. In the solution analysis, the error rate (% incorrectly classify instances) with the statistic test t-Student for paired samples were selected, as the evaluation measure. Experimental results show that it is possible to automatically identify the formations used by the base team (FC Portugal) in distinct matches against different opponents, using Data Mining techniques. The experimental results also show that the SMO (Sequential Minimal Optimization) learning algorithm has the best performance.

Abstract
Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. ID this paper we investigate the use of regression methods for the problem of predicting the travel time of buses in a Portuguese public transportation company. More specifically, we empirically evaluate the impact of varying parameters on the performance of different regression algorithms, such as support vector machines (SVM), random forests (RF) and projection pursuit, regression (PPR). We also evaluate the impact of the focusing tusks (example selection; domain value definition and feature selection) in the accuracy of those algorithms. Concerning the algorithms, we observe that 1) RF is quite robust to the choice of parameters and focusing methods: 2) the choice of parameters for SVM can be made independently of focusing methods while 3) for PPR they should be selected simultaneously. For the focusing methods, we observe that a stronger effect is obtained using example selection, particularly in combination with SVM.

Abstract
The rapid evolution of information and communication technologies, the changing client’s demands, and market conditions impelled enterprises to adapt their way of undertaking business, from traditional practices to e-business, and to participate in new forms of collaboration, such as networked organizations. In this context, standards, frameworks, technologies, and infrastructures supporting collaborative business, in a networked environment, become key factors in achieving environments with a desired high level of collaboration and inter- and intra-organization business processes alignment. The aim of this chapter is to underline the main issues, trends, and opportunities related to business integration from a technological perspective, analyzing and discussing the most relevant (existing and still under development) business integration reference models, frameworks, standards, technologies, and supporting infrastructures, and to briefly present relevant research projects in the area of business networking. A special emphasis is made on frameworks such as ebXML and RosettaNet, and the importance of papiNet, BPLE4WS, and freebXML is underlined. Challenges regarding self-forming networked organizations are also advanced.

Abstract
We propose a two-layer scheme to control a set of vehicles moving in a formation. The first; layer, file trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration. The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that; the formation control call be, in most; cases, adequately carried out, by a linear model predictive controller accommodating input, and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that, call be pre-computed off-line and implemented in a, distributed way in each vehicle. Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient; implementation where no optimization problem needs to be solved in real-time at each vehicle.

Abstract
This work presents a method of knowledge discovery in data obtained from Molecular Dynamics Protein Unfolding Simulations. The data under study was obtained from simulations of the unfolding process of the protein Transthyretin (TTR), responsible for amyloid diseases such as Familial Amyloid Polyneuropathy (FAP). Protein unfolding and misfolding are at the source of many amyloidogenic diseases. Thus, the molecular characterization of protein unfolding processes through experimental and simulation methods may be essential in the development of effective treatments. Here, we analyzed the distance variation of each of the 127 amino acids C. (alpha carbon) atoms of TTR to the centre of mass of the protein, along 10 different unfolding simulations - five simulations of WT-TTR and five simulations of L55P-TTR, a highly amyloidogenic TTR variant. Using data mining techniques, and considering all the information of the 10 runs, we identified several clusters of amino acids. For each cluster we selected the representative element and identified events which were used as features. With Association Rules we found patterns that characterize the type of TTR variant under study. These results may help discriminate between amyloidogenic and non-amyloidogenic behaviour among different TTR variants and contribute to the understanding of the molecular mechanisms of FAP.

Abstract