Abstract
A study on the evolution of free amino acids and biogenic amines in Azeitao cheese during 4 weeks at different temperatures of storage (4 and 25 degreesC) was performed. Free amino acids and biogenic amines were determined by RP-HPLC with visible detection, following extraction from the cheese and derivatization with dabsyl chloride. The method presented a linear relation between peak area and concentration from 2-200 mg/l. The detection limit value was less than 1.5 mg/l. The average repeatability was less than 4%. The major free amino acids were proline, valine, isoleucine and leucine and the major amines were tyramine, cadaverine and histamine. Room temperature (25 degreesC) promoted a significant increase of the contents of valine, leucine, tyramine and putrescine, expressed as g/kg of dry matter. These two free amino acids and two biogenic amines may serve as indicators of temperatures changes in ripened cheese.

Abstract
In this paper a recursive technique, based on the subspace state space identification methods, is presented for identification of time-varying systems. The main idea was to develop an iterative algorithm with most of the advantages of this kind of methods in order to deal with real-time applications and minimize the computational burden. As a subspace-based state space system identification technique, it has two main steps: first, a state vector sequence is estimated, using numerical linear algebra tools, then the state space model is obtained from a simple least squares model. © 2001 EUCA.

Abstract
Simulation of the dynamic behaviour of liquid-liquid systems is of prominent importance in many industrial fields. Algorithms for fast and reliable simulation of single stirred vessels and extraction columns have already been published by some of the present authors. In this work, we propose a methodology to develop a parallel version of a previously validated sequential algorithm, for the simulation of a liquid-liquid Kuhni column. We also discuss the algorithm implementation in a distributed memory parallel-computing environment, using MPI. Despite the difficulties encountered to preserve efficiency in the case of a heterogeneous cluster, the results demonstrate performance improvements that clearly indicate that the approach followed may be successfully extended to allow real-time plant control applications.

Abstract
In this article we discuss in detail two techniques for rule and case integration. Case-based learning is used when the rule language is exhausted. Initially, all the examples are used to induce a set of rules with satisfactory quality. The examples that are not covered by these rules are then handled as cases. The case-based approach used also combines rules and cases internally. Instead of only storing the cases as provided, it has a learning phase where, for each case, it constructs and stores a set of explanations with support and confidence above given thresholds. These explanations have different levels of generality and the maximally specific one corresponds to the case itself. The same case may have different explanations representing different perspectives of the case. Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used fixed metric).

Abstract

Abstract
Given the wide variety of available classification algorithms and the volume of data today's organizations need to analyze, the selection of the right algorithm to use on a new problem is an important issue. In this paper we present a combination of techniques to address this problem. The first one, zooming, analyzes a given dataset and selects relevant (similar) datasets that were processed by the candidate algoritms in the past. This process is based on the concept of distance, calculated on the basis of several dataset characteristics. The information about the performance of the candidate algorithms on the selected datasets is then processed by a second technique, a ranking method. Such a method uses performance information to generate advice in the form of a ranking, indicating which algorithms should be applied in which order. Here we propose the adjusted ratio of ratios ranking method. This method takes into account not only accuracy but also the time performance of the candidate algorithms. The generalization power of this ranking method is analyzed. For this purpose, an appropriate methodology is defined. The experimental results indicate that on average better results are obtained with zooming than without it.