Abstract
This paper aims at develop an evaluation of the functional polycentrism of Portuguese municipalities. For that purpose, we developed a critical assessment of the concepts of polycentrism and functional polycentrism whose assumptions guide most of the policies of regional planning, and present the main methodologies for its evaluation. We then proceed with the evaluation of polycentrism in Portugal, by using Social Network Analysis and Cluster Analysis. The study considers the variable "commuting flows home-to-work" and the results suggest that, from 1991 to 2001, the Portuguese urban system presents a network of commuting denser, less centralized, more dispersed and more clustered.

Abstract
Phylogenetic networks are a useful way of displaying relationships between nucleotide or protein sequences. They diverge from phylogenetic trees as networks present cycles, several possible evolutionary histories of the sequences analysed, while a tree presents a single evolutionary relationship. Networks are especially useful in studying markers with a high level of homoplasy (same mutation happening more than once during evolution) like the control region of mitochondrial DNA (mtDNA), where the researcher does not need to compromise with a single explanation for the evolution suggested by the data. However in many instances, trees are required. One case where this happens is in the founder analysis methodology that aims at estimating migration times of human populations along history and prehistory. Currently, the founder analysis methodology implicates the creation of networks, from where a probable tree will be extracted by hand by the researcher, a time-consuming process, prone to errors and to the ambiguous decisions of the researcher. In order to automate the founder analysis methodology an algorithm that extracts a single probable tree from a network in a fast, systematic way is presented here.

Abstract
This paper proposes a new approach to reduce the effort of building formal models representative of the structure and behaviour of Graphical User Interfaces (GUI). The main goal is to automatically extract the GUI model with a dynamic reverse engineering process, consisting in an exploration phase, that extracts information by interacting with the GUI, and in a model generation phase that, making use of machine learning techniques, uses the extracted information of the first step to generate a state-machine model of the GUI, including guard conditions to remove ambiguity in transitions. © 2012 IEEE.

Abstract
Soccer is a team sport in which the performances of all team members are important for the outcome of a match. Even though the analysis of game events can be used to measure the team's performance, their perception, especially during the match, is extremely difficult, even for the involved agents. Soccer has been used as a simulation environment in many studies, mainly in the area of robotics. The RoboCup is an international robotics competition with an ambitious goal: in 2050 a robotics team will be capable of defeating the human world champion at the time. In this context, we compared technical similarities between human and robotics soccer. Based on an off-line automatic event detection tool, game statistics for the finals of both human and robotics soccer tournaments were collected and compared using the Wilcoxon test. The results show that the most frequent event in both forms of soccer is successful passes. Analysing the two types of passes considered (successful and missed), we conclude that there are significant differences between the two forms (W = 2, P = 0.000354), with human soccer presenting a higher percentage of successful passes (77.89% vs. 66.97%). Of restart events (W = 0, P = 0.00048965), the most frequent one, in both forms, is the throw-in (human 59.91%, robotics 66.4%), and the least frequent is the corner (human 13.7%, robotics 14.09%). Regarding the frequency of shots, in the robotics environment "shots" were the most predominant type (43.27%), whereas in human soccer "shots on target" predominated (71.25%; W = 64, P = 0.000085641). Finally, the number of faults is minor in robotics soccer.

Abstract
The Unit Commitment (UC) problem is a wellknown combinatorial optimization problem arising in operations planning of power systems. It is typically formulated as nonlinear mixed-integer programming problem and has been solved in the literature by a huge variety of optimization methods, ranging from exact methods (such as dynamic programming, branch-and-bound) to heuristic methods (genetic algorithms, simulated annealing, particle swarm). Here, we start by formulating the UC problem as a mixed-integer optimal control problem, with both binary-valued control variables and real-valued control variables. Then, we use a variable time transformation method to convert the problem into an optimal control problem with only real-valued controls. Finally, this problem is transcribed into a finite-dimensional nonlinear programming problem to be solved using an optimization solver.

Abstract
We address the problem of dynamically switching the geometry of a formation of a number of undistinguishable agents. Given the current and the final desired geometries, there are several possible allocations between the initial and final positions of the agents as well as several combinations for each agent velocity. However, not all are of interest since collision avoidance is enforced. Collision avoidance is guaranteed through an appropriate choice of agent paths and agent velocities. Therefore, given the agent set of possible velocities and initial positions, we wish to find their final positions and traveling velocities such that agent trajectories are apart, by a specified value, at all times. Among all the possibilities we are interested in choosing the one that minimizes a predefined performance criteria, e.g. minimizes the maximum time required by all agents to reach the final geometry. We propose here a dynamic programming approach to solve optimally such problems. © Springer Science+Business Media New York 2012.