Abstract
In this paper, two factors typical of large photovoltaic (PV) arrays are investigated: one is the current-voltage (I-V) mismatch consequent to the production tolerance; the other is the impact of reverse currents in different operating conditions. Concerning the manufacturing I-V mismatch, the parameters of the equivalent circuit of the solar cell are computed for several PV modules from flash reports provided by the manufacturers. The corresponding I-V characteristic of every module is used to evaluate the behavior of different strings and the interaction among the strings connected for composing PV arrays. Two real crystalline silicon PV systems of 8 x 250 kW and 20 kW are studied, respectively. The simulation results reveal that the impact of the I-V mismatch is negligible with the usual tolerance, and the insertion of the blocking diodes against reverse currents can be avoided with crystalline silicon technology. On the other hand, the experimental results on I-V characteristics of the aforementioned arrays put into evidence the existence of a remarkable power deviation (3%-4%) with respect to the rated power, linkable to the lack of measurement uncertainty in the manufacturer flash reports.

Abstract
Digital stethoscopes have been drawing the attention of the biomedical engineering community for some time now, as seen from patent applications and scientific publications. In the future, we expect'intelligent stethoscopes' to assist the clinician in cardiac exam analysis and diagnostic, potentiating functionalities such as the teaching of auscultation, telemedicine, and personalized healthcare. In this paper we review the most recent heart sound processing publications, discussing their adequacy for implementation in digital stethoscopes. Our results show a body of interesting and promising work, although we identify three important limitations of this research field: lack of a set of universally accepted heart-sound features, badly described experimental methodologies and absence of a clinical validation step. Correcting these flaws is vital for creating convincing next-generation'intelligent' digital stethoscopes that the medical community can use and trust.

Abstract
Purpose - The purpose of this paper is to investigate the applicability of meta-learning to the problem of algorithm recommendation for gene expression data classification. Design/methodology/approach - Meta-learning was used to provide a preference order of machine learning algorithms, based on their expected performances. Two approaches were considered for such: k-nearest neighbors and support vector machine-based ranking methods. They were applied to a set of 49 publicly available microarray datasets. The evaluation of the methods followed standard procedures suggested in the meta-learning literature. Findings - Empirical evidences show that both ranking methods produce more interesting suggestions for gene expression data classification than the baseline method. Although the rankings are more accurate, a significant difference in the performances of the top classifiers was not observed. Practical implications - As the experiments conducted in this paper suggest, the use of meta-learning approaches can provide an efficient data driven way to select algorithms for gene expression data classification. Originality/value - This paper reports contributions to the areas of meta-learning and gene expression data analysis. Regarding the former, it supports the claim that meta-learning can be suitably applied to problems of a specific domain, expanding its current practice. To the latter, it introduces a cost effective approach to better deal with classification tasks. © Emerald Group Publishing Limited.

Abstract
This paper aims to characterize accurately each incident main cause, to analyse their occurrence with the intrinsic characteristics of the Portuguese territory, including geographical and meteorological data, and to define the requirements and guidelines to use this information as input for a risk assessment methodology, whose main purpose is to support control room operators in their continuous task.

Abstract
Statistical pattern recognition research, namely in applied computer vision, typically needs highly accurate massive datasets to train and test its classifiers. This paper presents extensive work for creating a large clinically annotated dataset of high confidence events for gastroenterology. More specifically, we address images and videos obtained using endoscopic capsule imaging technology, which contain some kind of lesion. The purpose of such dataset is to boost scientific research in computer aided diagnostic systems for a technology that would clearly benefit from them.

Abstract
This paper describes a business and technological model proposal, known as Inov@Douro, intended to support and to promote competitive and sustained precision agriculture practices in the Portuguese Douro Region. Our approach is based on a distributed cooperative network, tailored 10 meet the specific needs of viticulture enterprises which also explore tourism as a valuable national and international business source. Instead of focusing on operational issues, such as remote sensing and data transmission, among others, we present the Inov@Douro model from the knowledge generation point-of-view, intended to support the multidisciplinary concept of a cooperation approach among regional partners. As a result, this collaborative approach might be the most suited technological tool to promote precision agriculture sustainability practices based on a symbiotic cooperation with the tourism sector. The Inov@Douro model aims to represent a new working style for this unique region, where the concept of public and private information is a key feature to achieve the desired success as a knowledge network. As a guideline to attain the implementation of such a model. information technology and infrastructures tools are discussed in order to promote precision agriculture practices while giving valuable and dynamic tourist information to the general public.