Abstract
Male breast cancer (MBC) patients seem to have inferior survival compared to female (FBC) ones, which is not fully explained by usual prognostic factors. Recurrence analysis could show differences in relapse patterns and/or in patients' approaches that justify these outcomes. Retrospective analysis of MBC patients treated in a cancer center between 1990 and 2014, looking for relapse. For each patient, three matched FBC patients were selected by: diagnosis' year, age (within 5 years), stage and tumors' type (only luminal-like were considered). Differences between cohorts were assessed by chi(2) test and hierarchical clustering was performed to define subgroups according to relapse local. Survival curves were calculated by Kaplan-Meier and compared using log-rank test. Statistical significance was defined as p < 0.05. Groups were balanced according to age, histological grade, stage, expression of hormonal receptors and adjuvant treatments. Median time to recurrence was equivalent, p = 0.72, with the majority of patients presented with distant metastases, p = 0.69, with more lung involvement in male, p =0.003. Male patients were more often proposed to symptomatic treatment (21.1% vs. 4.4%, p = 0.02). Overall and from recurrence survivals were poorer for male, median: 5 years [95% confidence interval (CI): 4.1-5.9 years] and 1 year (95% CI: 0-2.1 years) vs. 10 years (95% CI: 7.8-12.2 years) and 2 years (95% CI: 1.6-2.4 years), p < 0.001 and p = 0.004, respectively, and this tendency remained in the five cluster subgroups, that identified five patterns of relapse, p = 0.003. MBC patients had the worst survival, even after controlling important factors, namely the local of relapse. Palliative systemic treatment had favorable impact in prognosis and its frequently avoidance in male could justify the outcomes differences.

Abstract

Abstract
Electricity markets are becoming more competitive, to some extent due to the increasing number of players that have moved from other sectors to the power industry. This is essentially resulting from incentives provided to distributed generation. Relevant changes in this domain are still occurring, such as the extension of national and regional markets to continental scales. Decision support tools have thereby become essential to help electricity market players in their negotiation process. This paper presents a metalearner to support electricity market players in bidding definition. The proposed metalearner uses a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms already implemented in ALBidS (Adaptive Learning strategic Bidding System). The proposed metalearner considers different weights for each strategy, based on their individual performance. The metalearner's performance is analysed in scenarios based on real electricity markets data using MASCEM (Multi-Agent Simulator for Competitive Electricity Markets). Results show that the proposed metalearner is able to provide higher profits to market players when compared to other current methodologies and that results improve over time, as consequence of its learning process.

Abstract
We introduce a new class of integer-valued self-exciting threshold models, which is based on the binomial autoregressive model of order one as introduced by McKenzie (Water Resour Bull 21:645-650, 1985. doi:. Basic probabilistic and statistical properties of this class of models are discussed. Moreover, parameter estimation and forecasting are addressed. Finally, the performance of these models is illustrated through a simulation study and an empirical application to a set of measle cases in Germany.

Abstract
Crohn's disease is one type of inflammatory bowel disease whose incidence is currently increasing, subject to relapse and disabling, with unknown etiology, and usually diagnosed between the second and third decade of life. The aim of this work is to develop a Bayesian network tool to predict disabling and reoperation in patients with Crohn's disease subject to early surgery or immunosuppressors intake. Multi-centric study data from patients with surgery or immunosuppression in the first six months after diagnosis was used, focusing on the prognosis and the analysis of factors' interaction. Patients were grouped by the index episode: immunosuppressors intake, and surgery (stratified considering the use or not of immunosuppressors 6 months after surgery). Patient group was associated with disease behavior, upper gastrointestinal tract location (L4) and age at diagnosis, while disease extent was associated to perianal disease. For disabling, association between perianal disease and gender and location was also found. Association between gender and L4 was also found for reoperation. The cross-validated discriminative power of the models were high for both disabling (above 70%) and reoperation (above 80%). The generated models presented interesting insights on factor interaction and predictive ability for the prognosis, supporting their use in future clinical decision support systems.

Abstract
As Distributed Generation (DG) is increasingly integrated into the networks, intermittency issues of some types of DG (such as wind energy) will potentially increase network operating costs or decrease network security. Concern regarding these effects has grown among Network Operators and in order to avoid them, new or updated wind parks are required to provide ancillary services to the network. These ancillary services include the fault ride-through capability and mainly those wind parks connected to a collector switching or distribution substation are asked to provide this service. Although some wind parks can already supply fault ridethrough capability to the network, most of them are still largely unable to do so due to the current DG protection scheme. This work concentrates on the development and assessment of new settings for the wind park protection scheme by closed-loop test in real time. This aims to allow wind parks to provide fault ride-through capability to the distribution network. A distribution network with a DFIG based wind turbine with ride-through-fault capability was modelled on the RTDS Simulator. The protective relay settings were then implemented into a commercial numerical programmable relay to be assessed by closed-loop real time test. Conclusions of the protective relay with the fault ridethrough requirement are presented and discussed.