Abstract
The Commission for Coordination and Regional Development of the North of Portugal (CCDR-N) promoted NORTINOV 2015, a project created to define a regional innovation strategy for the North of Portugal based on automotive clusters and Information Technology, Communication and Electronics (ITCE) clusters. Accordingly, it developed a technology forecast for the year 2015 in order to help the North of Portugal cope with the intensifying global competition and rapid technological changes. This paper describes the methodology used to identify critical technologies for the regional economy of the North of Portugal under the project NORTINOV 2015. It involved three main steps: technology identification and study, in which a list of emerging technologies was implemented; refinement of emerging technologies, in which 90 technologies were identified and finally, classification and hierarchisation of technologies, in which 30 technologies were identified as critical for the crafting of regional innovation strategy. In this final step, a workshop involving major stakeholders of the three clusters under study played a major role. Copyright © 2007 Inderscience Enterprises Ltd.

Abstract
We address the single-source uncapacitated minimum cost network flow problem with general concave cost functions. Exact methods to solve this class of problems in their full generality are only able to address small to medium size instances, since this class of problems is known to be NP-Hard. Therefore, approximate methods are more suitable. In this work, we present a hybrid approach combining a genetic algorithm with a local search. Randomly generated test problems have been used to test the computational performance of the algorithm. The results obtained for these test problems are compared to optimal solutions obtained by a dynamic programming method for the smaller problem instances and to upper bounds obtained by a local search method for the larger problem instances. From the results reported it can be shown that the hybrid methodology improves upon previous approaches in terms of efficiency and also on the pure genetic algorithm, i.e., without using the local search procedure. (C) 2007 Wiley Periodicals, Inc.

Abstract
Classification of ordinal data is one of the most important tasks of relation learning. This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Generalization bounds of the proposed ordinal classifier are also provided. An experimental study with artificial and real data sets, including an application to gene expression analysis, verifies the usefulness of the proposed approach.

Abstract
An iterative numerical technique has been developed to simulate in detail the dynamics of a shallow-layer gravity settler. Currently acknowledged models apply only to specific equipments at steady-state and laboratory scale [Jeelani SAK, Hartland S. The continuous separation of liquid/liquid dispersions. Chem Eng Sci 1993;48(2):239-54]. To our knowledge, no study has ever addressed the dynamic simulation of a gravity settler. In this paper a direct numerical technique is presented for computing the thickness and drop-size composition of the dispersion band formed in a shallow-layer settler under steady-state and transient conditions. This technique is an extension for the settler of the one used on the stirred vessel by Ribeiro [Ribeiro LM. Simulacao Dinamica de Sistemas Liquido-Liquido, Urn novo Algoritmo com Potencialidades de Aplicao em Controlo. PhD thesis, Universidade do Minho, Portugal; 1995].

Abstract
The computational efficiency of linear ?-calculi with iterators is evaluated using closed reduction strategies. The interaction between linearity and closed reduction and the computational power of linear systems with and without closed reduction is analyzed. A linear version of Gödel's System T with closed reductions is defined to analyze the computational power of linear systems. Closed reduction strategies in the ?-calculus restrict the reduction rules since closed reduction strategies can take place when certain terms are closed and do not contain free variables. Closed reduction strategies impose strong constraints on the application of reduction rules. Closed reduction strategies can simulate call-by-name and call-by-value evaluations in the ?-calculus. Linear ?-calculus with iterators can be efficiently analyzed by relaxing the constraints on the construction of iterator terms.

Abstract
System L is a linear A-calculus with numbers and an iterator, which, although imposing linearity restrictions on terms, has all the computational power of Godel's System T. System C owes its power to two features: the use of a closed reduction strategy (which permits the construction of an iterator on an open function, but only iterates the function after it becomes closed), and the use of a liberal typing rule for iterators based on iterative types. In this paper, we study these new types, and show how they relate to intersection types. We also give a sound and complete type reconstruction algorithm for System L.