Abstract
Nesting problems are particularly hard combinatorial problems. They involve the positioning of a set of small arbitrarily-shaped pieces on a large stretch of material, without overlapping them. The problem constraints are bidimensional in nature and have to be imposed on each pair of pieces. This all-to-all pattern results in a quadratic number of constraints. Constraint programming has been proven applicable to this category of problems, particularly in what concerns exploring them to optimality. But it is not easy to get effective propagation of the bidimensional constraints represented via finite-domain variables. It is also not easy to achieve incrementality in the search for an improved solution: an available bound on the solution is not effective until very late in the positioning process. In the sequel of work on positioning non-convex polygonal pieces using a CLP model, this work is aimed at improving the expressiveness of constraints for this kind of problems and the effectiveness of their resolution using global constraints. A global constraint "outside" for the non-overlapping constraints at the core of nesting problems has been developed using the constraint programming interface provided by Sicstus Prolog. The global constraint has been applied together with a specialized backtracking mechanism to the resolution of instances of the problem where optimization by Integer Programming techniques is not considered viable. The use of a global constraint for nesting problems is also regarded as a first step in the direction of integrating Integer Programming techniques within a Constraint Programming model.

Abstract
Purpose - The purpose of this paper is to solve the problem of committing electric power generators (unit commitment, UC), considering network constraints. Design/methodology/approach - The UC is first solved with a local search based meta-heuristic, following the assumption that all generators and loads are connected to a single network node. For evaluation purposes, the economical production levels of the units committed are computed by running a pre-dispatch algorithm where network constraints are not included. If a good quality solution is reached, an economic dispatch (ED) with network constraints is performed, where the geographic location of generators and loads are considered. Therefore, the production level of each committed generator is performed that leads to the global lowest solution cost, regarding both the generators' costs and constraints and the power system network constraints. Findings - The algorithm proposed is computationally efficient, given the time available for decision making. In addition, the solution for this algorithm, in terms of minimization of total costs, is generally better than the solution of the two phases approach. Some contractual and legal aspects related with the injection in network connections can also be included in the model. Practical implications - UC with network constraints has a large potential of use, especially for small and medium size power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation. Originality/value - The paper presents an approach where theED with network constraints is integrated with the UC procedure. The model described is currently implemented in an EMS package offered in the market - making it a case of successful transfer from science to industry.

Abstract
Due to its combinatorial nature, the Unit Commitment problem has for long been an important research challenge, with several optimization techniques, from exact to heuristic methods, having been proposed to deal with it. In line with one current trend of research, metaheuristic approaches have been studied and some interesting results have already been achieved and published. However, a successful utilization of these methodologies in practice, when embedded in Energy Management Systems, is still constrained by the reluctance of industrial partners in using techniques whose performance highly depends on a correct parameter tuning. Therefore, the application of metaheuristics to the Unit Commitment problem does still justify further research. In this paper we propose a new search strategy, for Local Search based metaheuristics, that tries to overcome this issue. The approach has been tested in a set of instances, leading to very good results in terms of solution cost, when compared either to the classical Lagrangian Relaxation or to other metaheuristics. It also drastically reduced the computation times. Furthermore, the approach proved to be robust, always leading to good results independently of the metaheuristic parameters used.

Abstract
This paper presents and analyzes an incremental system for clustering streaming time series. The Online Divisive-Agglomerative Clustering (ODAC) system continuously maintains a tree-like hierarchy of clusters that evolves with data, using a top-down strategy. The splitting criterion is a correlation-based dissimilarity measure among time series, splitting each node by the farthest pair of streams. The system also uses a merge operator that reaggregates a previously split node in order to react to changes in the correlation structure between time series. The split and merge operators are triggered in response to changes in the diameters of existing clusters, assuming that in stationary environments, expanding the structure leads to a decrease in the diameters of the clusters. The system is designed to process thousands of data streams that flow at a high rate. The main features of the system include update time and memory consumption that do not depend on the number of examples in the stream. Moreover, the time and memory required to process an example decreases whenever the cluster structure expands. Experimental results on artificial and real data assess the processing qualities of the system, suggesting a competitive performance on clustering streaming time series, exploring also its ability to deal with concept drift.

Abstract
In many sectors of industry, manufacturers posses warehouses where finished goods are stored, awaiting to fulfill a client order. We present a situation where these items are characterized by release and due dates, i.e. warehouse arrival for storage and client delivery, respectively. The warehouse has a number of positions available, where item can be placed on top of each other, forming stacks, For item manipulation, there is a single a stacking crane, able to carry one item at time. When in a given stack an item at the top is due at a date later than some item below it, it must be relocated to another stack, so that the item below can be delivered. In this problem the objective is to minimize the number of movements made by the crane.

Abstract
Asthma patients incur a great cost in terms of loss of quality of life. The purpose of this study is to evaluate the relative contribution and relationship of several patient- and disease-related factors, measured by several variables, to the quality of life in adults with asthma. Two hundred and ten asthmatic outpatients over 18 years old, registered in a Family Health Unit, were randomly selected to complete the Asthma Quality of Life (AQLQ) and Short Form Generic questionnaires (SF-36), respectively. Single and multiple linear regression models were developed to explain the variability of the summary scores of AQLQ and Physical and Mental Health SF-36. As potential predictors, the following independent variables were used: gender, age, number of comorbidities, asthma severity following the Global Initiative for Asthma (GINA) criteria, asthma control (measured by ACQ questionnaire), %FEV1 (forced expiratory volume in the first second) and, for the first time, Graffar Score to assess socioeconomical features. The Graffar Score is an index that divides the population in 5 socioeconomic layers. We report the best Adjusted R Square of these models published in the literature, ranging from 0.40 to 0.76. Women showed poorer quality of life than men. The best predictor of AQLQ was ACQ, followed by Asthma Severity, Gender and %FEV1. The best predictors of Physical and Mental Health SF-36 were, by decreasing importance, ACQ, number of comorbidities, Gender and Graffar Score. We note that the variable Dumber of comorbidities was included in both SF-36 models, but not in AQLQ model. Asthma Severity and %FEV1 did not enter into SF-36 models. We conclude that besides clinical and functional measures, the evaluation process of the overall health status must incorporate quality-of-life measures.