Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors propose a flexible way to develop typical MFS models, for any number of machines in the workstation, spare machines and maintenance crews. Arena simulation language is used to understand a specific MFS, create the corresponding MFS model and analyze most common performance measures.

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a possible way to construct a MFS model using Arena simulation language, where some of the most common performance measures are identified, calculated and analysed. Nevertheless this paper would concentrate on the two most important performance measures in maintenance systems: system availability and maintenance total cost. As far as these two indicators are concerned, it was then quite clear that they assumed different behavior patterns, specially when using extreme values for periodic overhauls rates. In this respect, system availability proved to be a more sensitive parameter.

Abstract
This paper is concerned with the use of simulation as a decision support tool in maintenance systems, specifically in MFS (Maintenance Float Systems). For this purpose and due to its high complexity, in this paper the authors explore and present a possible way to construct a MFS model using Arena simulation language, where some of the most common performance measures are identified, calculated and analysed.

Abstract
NNIGnets is a freeware computer program which can be used for teaching, research or business applications, of Artificial Neural Networks (ANNs). This software includes presently several tools for the application and analysis of Multilayer Perceptrons (MLPs) and Radial Basis Functions (RBEs), such as stratified Cross-Validation, Learning Curves, Adjusted Rand Index, novel cost functions, and Vapnik-Chervonenkis (VC) dimension estimation, which are not usually found in other ANN software packages. NNIGnets was built following a software engineering approach which decouples operative from GUI functions, allowing an easy growth of the package. NNIGnets was tested by a variety of users, with different backgrounds and skills, who found it to be intuitive, complete and easy to use.

Abstract
We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance.

Abstract
A method for the recovery and fractionation of whey proteins from a whey protein concentrate (80%, w/w) by hydrophobic interaction chromatography is proposed. Standard proteins and WPC 80 dissolved in phosphate buffer with ammonium sulfate 1 M were loaded in a HiPrep Octyl Sepharose FF column coupled to a fast protein liquid chromatography (FPLC) system and eluted by decreasing the ionic strength of the buffer using a salt gradient. The results showed that the most hydrophobic protein from whey is alpha-lactalbumin and the less hydrophobic is lactoferrin. It was possible to recover 45.2% of beta-lactoglobulin using the HiPrep Octyl Sepharose FF column from the whey protein concentrate mixture with 99.6% purity on total protein basis.