Abstract
The restructuring of electricity markets brought many changes to markets operation. To overcome these new challenges, the study of electricity markets operation has been gaining an increasing importance. With the emergence of microgrids and smart grids, new business models able to cope with new opportunities are being developed. New types of players are also emerging, allowing aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers. The virtual power player (VPP) facilitates their participation in the electricity markets and provides a set of new services promoting generation and consumption efficiency, while improving players' benefits. The contribution of this paper is a customized normalization method that supports a clustering methodology for the remuneration and tariffs definition from VPPs. To implement fair and strategic remuneration and tariff methodologies, this model uses a clustering algorithm, applied on normalized load values, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage.

Abstract
Angiogenesis, the formation of blood vessels from pre-existing ones, is a key event in pathology, including cancer progression, but also in homeostasis and regeneration. As the phenotype of endothelial cells (ECs) is continuously regulated by local biomechanical forces, studying endothelial behaviour in altered gravity might contribute to new insights towards angiogenesis modulation. This study aimed at characterizing EC behaviour after hypergravity exposure (more than 1g), with special focus on cytoskeleton architecture and capillary-like structure formation. Herein, human umbilical vein ECs (HUVECs) were cultured under two-dimensional and three-dimensional conditions at 3g and 10g for 4 and 16 h inside the large diameter centrifuge at the European Space Research and Technology Centre (ESTEC) of the European Space Agency. Although no significant tendency regarding cytoskeleton organization was observed for cells exposed to high g's, a slight loss of the perinuclear localization of beta-tubulin was observed for cells exposed to 3g with less pronounced peripheral bodies of actin when compared with 1g control cells. Additionally, hypergravity exposure decreased the assembly of HUVECs into capillary-like structures, with a 10g level significantly reducing their organization capacity. In conclusion, short-term hypergravity seems to affect EC phenotype and their angiogenic potential in a time and g-level-dependent manner.

Abstract
Built-in sensors in most modern smartphones open multiple opportunities for novel context-aware applications. Although the Human Activity Recognition field seized such opportunity, many challenges are yet to be addressed, such as the differences in movement by people doing the same activities. This paper exposes empirical research on Online Semi-supervised Learning (OSSL), an under-explored incremental approach capable of adapting the classification model to the user by continuously updating it as data from the user's own input signals arrives. Ultimately, we achieved an average accuracy increase of 0.18 percentage points (PP) resulting in a 82.76% accuracy model with Naive Bayes, 0.14 PP accuracy increase resulting in a 83.03% accuracy model with a Democratic Ensemble, and 0.08 PP accuracy increase resulting in a 84.63% accuracy model with a Confidence Ensemble. These models could detect 3 stationary activities, 3 active activities, and all transitions between the stationary activities, totaling 12 distinct activities.

Abstract
The reduction or elimination of incentives for the installation of decentralized generation directly at the customers' premises, favoring self-consumption, can bring significant changes for distribution network operation. According to the new Portuguese law, injection of energy into the distribution grid is discouraged since prosumers receive only 90% of the energy cost in the Iberian Energy Market. In order to lower energy bills, the possibility of storing excess energy is being considered as a possible solution. In this paper, an optimization framework is proposed to model the operation of consumers with renewable-based Distributed Generation (DG) and storage capacity and assess their aggregated effect at the level of the MV grid using a multi-temporal Optimal Power Flow (OPF). The proposed algorithm is then tested in a real Portuguese MV network to evaluate its performance. Finally, a financial viability analysis is performed considering the installation of small PV generators and storage devices at the residential level.

Abstract
Satellite altimetry allows the study of sea-level long-term variability on a global and spatially uniform basis. Here quantile regression is applied to derive robust median regression trends of mean sea level as well as trends in extreme quantiles from radar altimetry time series. In contrast with ordinary least squares regression, which only provides an estimate on the rate of change of the mean of data distribution, quantile regression allows the estimation of trends at different quantiles of the data distribution, yielding a more complete picture of long-term variability. Trends derived from basin-wide averaged regional mean sea level time series are robust and similar for all quantiles, indicating that all parts of the data distribution are changing at the same rate. In contrast, trends are not robust and diverge across quantiles in the case of local time series. Trends are under- (over-)estimated in the western (eastern) equatorial Pacific. Furthermore, trends in the lowermost quantile (0.05) are larger than the median trend in the western Pacific, while trends in the uppermost quantile (0.95) are lower than the median trend in the eastern Pacific. These differences in trends in extreme mean sea level quantiles are explained by the exceptional effect of the strong 1997-1998 El Nino-Southern Oscillation (ENSO) event.

Abstract
Auscultation is a widely used technique in clinical activity to diagnose heart diseases. However, heart sounds are difficult to interpret because a) of events with very short temporal onset between them (tens of milliseconds) and b) dominant frequencies that are out of the human audible spectrum. In this paper, we propose a model to segment heart sounds using a semi-hidden Markov model instead of a hidden Markov model. Our model in difference from the state-of-the-art hidden Markov models takes in account the temporal constraints that exist in heart cycles. We experimentally confirm that semi-hidden Markov models are able to recreate the "true" continuous state sequence more accurately than hidden Markov models. We achieved a mean error rate per sample of 0.23.