Abstract
Given an edge-weighted graph, the maximum edge weight clique (MEWC) problem is to find a clique that maximizes the sum of edge weights within the corresponding complete subgraph. This problem generalizes the classical maximum clique problem and finds many real-world applications in molecular biology, broadband network design, pattern recognition and robotics, information retrieval, marketing, and bioinformatics among other areas. The main goal of this chapter is to provide an up-to-date review of mathematical optimization formulations and solution approaches for the MEWC problem. Information on standard benchmark instances and state-of-the-art computational results is also included. © Springer International Publishing AG 2017.

Abstract
Local actions are seen as of major importance for the achievement of climate change mitigation targets. In the past few years, the number of local action plans towards climate change mitigation has been increasing, and it is essential to analyze their contribution to the achievement of global targets. Even if the relationship between local action plans and the reduction of energy use and GHG emissions is often assumed, this has not yet been validated nor quantified by empirical studies involving a large number of municipalities. Thus, the aim of this paper is to. perform an empirical analysis on the link between local action plans and energy use and GHG emissions. The analysis is composed by a test of hypothesis and a regression analysis, performed for the municipalities of three European countries Portugal, Sweden and United Kingdom. The main conclusion is that, in the context of these three countries, the analysis performed was not able to detect a significant impact related to the existence of local plans on GHG emissions. From the panel data regression analysis, it was possible to confirm that external factors, not directly related to local climate change mitigation actions, have a significant impact on GHG emissions.

Abstract
The aim of this paper is to avoid overloading a private customer distribution transformer (DT) in a Portuguese insular area through the means of a new smart electric vehicle (EV) charging scheduler. Firstly, the consequence of the penetration of EVs on the dielectric oil deterioration of the DT of the industrial unit is estimated. The workplace allows the EVs to charge while their owners are working at three different working shifts during a day. Secondly, the EV charging scheduler is tested and the result scenarios are analyzed. This paper shows that the scheduling solution enables the industrial unit to avoid overloading the DT. It also allows to reduce the loss-of-life (LoL) of the DT, while recharging all EVs connected at the beginning of each working shift. © 2016 IEEE.

Abstract

Abstract
Customers' response is an important topic in direct marketing. This study proposes a data mining response model supported by random forests to support the definition of target customers for banking campaigns. Class imbalance is a typical problem in telemarketing that can affect the performance of the data mining techniques. This study also contributes to the literature by exploring the use of class imbalance methods in the banking context. The performance of an undersampling method (the EasyEnsemble algorithm) is compared with that of an oversampling method (the Synthetic Minority Oversampling Technique) in order to determine the most appropriate specification. The importance of the attribute features included in the response model is also explored. In particular, discriminative performance was enhanced by the inclusion of demographic information, contact details and socio-economic features. Random forests, supported by an undersampling algorithm, presented very high prediction performance, outperforming the other techniques explored.

Abstract
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.