Abstract
The hierarchical control scheme for three phase parallel connected voltage source inverters (VSI), forming a low voltage AC MicroGrid (MG)is presented and analyzed in this paper. The proposed control scheme consists of two controllers, a local controller and a centralized controller. The local controller consists of inner control loops that are composed by a voltage and current proportional resonant controllers with selective harmonics compensation, and the power sharing controller that includes the droop control and the virtual output impedance loops in order to share properly the active and reactive power between the connected VSIs. In this system, the centralized controller is designed to restore the magnitude and the frequency deviations of the AC bus voltage produced by the power sharing controller. Simulation results for three parallel connected VSIs forming a low voltage AC MG that operates in islanded mode are provided to show the effectiveness of the proposed control scheme. Active and reactive power are properly shared as well as good harmonics compensation is achieved when both linear and non-linear loads are connected to the load bus.

Abstract
The actual flexibility of the electricity sector, with a distributed nature and new players, such as the smart grid operator and several types of aggregators, brings new business models and introduces new challenges from the power systems technical operation point of view. In this context, the Network Operator Agent of the Multi-Agent Simulator of Competitive Electricity Markets ( MASCEM) plays a crucial role, not only in the scope of the technical validation of the economic transactions established by the market, but also has an agent that can be supporting the grid operation under the scope of a smart grid. A set of new features has been added to the Network Operator making it a "new agent", bringing a more effective decision support, from the grid technical operation point of view, and achieving its usefulness beyond MASCEM. In this paper the new features are described. A case study is also included to better illustrate the approach and to highlight its usefulness under the scope of a smart grid scenario.

Abstract
Nowadays, online communities are becoming an important resource for health consumers who want to retrieve and share information about health subjects. These communities have the potential to influence patients' health behaviors and increase their engagement with therapies. However, the interaction dynamics in this type of media remains poorly understood what might hinder the development of strategies that facilitate and encourage participation. Social Network Analysis is a technique that tries to expose the hidden channels of communication and information flow, leading to a better understanding of how members relate to each other on online social network. In this study we do a systematic review of the literature regarding the apply this technique in the study of online health communities. We show that this type of studies is scarce and that, in this domain, Social Network Analysis is mainly applied to identify influential key members, as well as the most active members in terms of posting or answering questions.

Abstract
After carrying out a revision of the literature on the enhancement of Er3+ diffusion in LiNbO3 crystals by Er3+/Ti4+ co-diffusion and analyzing our own experimental results, we conclude that no reproducible results were reported, meaning that further research on this subject is necessary.

Abstract
In this paper, a novel subject-adaptable heartbeat classificationmodel is presented, in order to address the significant interperson variations in ECG signals. A multiview learning approach is proposed to automate subject adaptation using a small amount of unlabeled personal data, without requiring manual labeling. The designed subject-customized models consist of two models, namely, general classification model and specific classification model. The general model is trained using similar subjects out of a population dataset, where a pattern matching based algorithm is developed to select the subjects that are "similar" to the particular test subject for model training. In contrast, the specific model is trained mainly on a small amount of high-confidence personal dataset, resulting from multiview-based learning. The learned general model represents the population knowledge, providing an interperson perspective for classification, while the specific model corresponds to the specific knowledge of the subject, offering an intraperson perspective for classification. The two models supplement each other and are combined to achieve improved personalized ECG analysis. The proposed methods have been validated on the MIT-BIH Arrhythmia Database, yielding an average classification accuracy of 99.4% for ventricular ectopic beat class and 98.3% for supraventricular ectopic beat class, which corresponds to a significant improvement over other published results.

Abstract
In this paper, the management of PEVs, uncontrolled or controlled (i.e. aggregated), and their ability to use V2G and G2V technologies are first analysed. The electricity markets are then considered; real world applications are discussed and different market types categorised. The interaction of the PEVs with some renewable energy sources (e.g. solar, wind and biomass) is also examined, and the interaction of the PEVs with demand response programs addressed. Finally, the models of PEVs are categorised and multiple types of modules, the related variables, applied methods and the considered parameters are presented.