MATERIALS EDUCATION AND INNOVATION: GLOBALIZATION OPENS NEW FRONTIERS, OPPORTUNITIES AND CHALLENGES

In this paper we consider evolutionary pressures that will influence materials education and its role in the present scenario of Globalization: Challenges, Opportunities and needs. The main evolutionary pressures are related to some major control variables: increase of global population, new emerging technologies such as nanotechnology, alternative energies related to climate change, multimedia convergence in global communications, health, hunger, economic asymmetries and violence. Of course, many other factors could be identified, but this paper considers these as an adequate minimum basis for strategic considerations related to current materials education planning for the 21st century. In conclusion, we propose an International Network Program for Materials Education Strategy, thinking globally but acting regionally.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.