Abstract
Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Abstract

Abstract
Cardiovascular diseases (CVDs) are the leading cause of death in the world. The pulse wave analysis provides a new insight in the analysis of these pathologies, while data mining techniques can contribute for an efficient diagnostic method. Amongst the various available techniques, artificial neural networks (ANNs) are well established in biomedical applications and have numerous successful classification applications. Also, clustering procedures have proven to be very useful in assessing different risk groups in terms of cardiovascular function in healthy populations. In this paper, a robust data mining approach was performed for cardiac risk patterns identification. Eight classifiers were tested: C4.5, Random Forest, RIPPER, Naive Bayes, Bayesian Network, Multy-layer perceptron (MLP) (1 and 2-hidden layers) and radial basis function (RBF). As for clustering procedures, k-means clustering (using Euclidean distance) and expectation-maximization (EM) were the chosen algorithms. Two datasets were used as case studies to perform classification and clustering analysis. The accuracy values are good with intervals between 88.05% and 97.15%. The clustering techniques were essential in the analysis of a dataset where little information was available, allowing the identification of different clusters that represent different risk group in terms cardiovascular function. The three cluster analysis has allowed the characterization of distinctive features for each of the clusters. Reflected wave time (T_RP) and systolic wave time (T_SP) were the selected features for clusters visualization. Data mining methodologies have proven their usefulness in screening studies due to its descriptive and predictive power.

Abstract
A Long Period Grating (LPG)-based platform for the detection of E. coli outer membranes proteins (EcOMPs) is presented. The sensing probe is achieved by the functionalization of a LPG inscribed in a single mode fiber (SMF28) with a DNA-aptamer resulting in a label-free configuration capable of specific recognize EcOMPs in waters. Measurement takes place by tracking the variations induced in the resonance wavelength by the refractive index changes at the fiber surface (approximate to 100 nm/RIU). The sensing head was characterized and tested against EcOMPs and applied to spiked environmental water samples. The sensor displayed logarithmic responses in the range of 0.1 nM to 10 nM EcOMPs and is regenerated (under low pH conditions) and the deviation of the subsequent detection was less than 0.1%.

Abstract
Web accessibility is becoming a current topic in social and scientific discussions. With the advances in technology, a need for access to all Web resources is becoming more and more recurrent due to the several advantages that the Web brings to those with some sort of disability, allowing them accessing and integrating in society. With this document we aim to present indicators regarding the lower accessibility levels of the Portuguese teaching institutions websites. A set of background and theoretical considerations is made alongside the article, as well as the results of an accessibility evaluation made to the Portuguese secondary schools websites using a specialized software tool and according to WCAG 2.0. The present document also contains a proposal for a model that aims on improving Web accessibility Levels in Portugal by fostering the creation of relations and group activities between the actors that, in our opinion, are those with the most relevance in the Web content accessibility issue.

Abstract