Abstract
We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.

Abstract
This study aims to compare students of Psychology and Sport about the Psychological Wellbeing (PWB) and Self-Esteem. A total of 126 subjects (42.9% male) of Psychology and Sport courses (51.6% of Sport) participated in this study. The results suggest that Self-Esteem variable is not related to the PWB, since the association between these two variables is negative, although not significant (r = -.090). The results also show that there are no significant differences between students of Sport and Psychology in the levels of BEP and its dimensions. Nevertheless, it is clear that when put together, the knowledge in the areas of Psychology and Sport, are able to increase the PWB. In conclusion, the knowledge in the mental health area and physical activity establishing itself areas to invest to promote overall mental health levels.

Abstract
Disability from musculoskeletal diseases and co-morbidities may lead to the worsening of social and economic well-being through a multitude of paths. Moreover since in European Union (EU) Member States it is projected that those aged 65 and over will become a much larger share (rising from 17% to 30% of the population), and those aged 80 and over (rising from 5% to 12%) will almost become as numerous as the young population in 2060, there is a great potential for Information and Communication Technologies (ICT) solutions for addressing the present and future living arrangements in older people. The UPPERCARE system is meant to affect positively both the intergenerational and partners care since it contributes to decrease usability barriers and promote collaborative environments for informal and self-care. UPPERCARE is a new approach for integrated care supported by ICT systems and services, focusing on post-operative rehabilitation of musculoskeletal pathologies, having as a case study the knee post-operative scenarios of prosthetic care. This paper presents the UPPERCARE system, that provides an integrated care solution, supported ICT, for empowering self-care and adherence to rehabilitation plans through natural interfaces, gamification and cross-modal paths for community care collaboration. The system addresses current barriers from technological, clinical, social and organisational perspectives in a multidisciplinary environment. Special attention is given to the patients' needs and behaviours entailing the participation of a wide care community, including clinical and non-clinical people, associations, institutions and authorities) through an user driven interaction within the system.

Abstract
This work aims to study a new energy harvesting device to be anchored on the ocean floor and convert any type of currents, tides or oscillation movement into electrical energy using linear electromagnetic generators. The final application is to supply energy to a set of moored monitoring sensors that collects data and allowing the system to be energetically autonomous. The proposed setup is a spherical buoy with no external moving parts, to be more biofouling proof. The maximum output power measured for a 4 Hz movement was 9.9 mW with only one linear electromagnetic generator.

Abstract
In a collaborative conceptualisation process, the existence of several solutions for a given domain is a very common problem. Given this, specialists must reach a consensus on the concepts that will encompass the final solution. Therefore, this work aims to provide a tool for the integration of conceptual models in order to help specialists during the negotiation phase of developing the final shared model. This approach analyses the concepts of two models and shows the similar concepts to the specialists. The semantic similarity is obtained after three stages, namely: normalization, syntax analysis and semantic analysis. To evaluate the proposed approach, the values of precision and recall measures were calculated in two practical application scenarios. The obtained results proved to be better when compared to the existing tools when applied to semi-formal models (conceptual maps), and very close to the best tools focused on formal models (ontologies) integration.

Abstract
In this paper we study the combined use of four different NLP toolkits-Stanford CoreNLP, GATE, OpenNLP and Twitter NLP tools-in the context of social media posts. Previous studies have shown performance comparisons between these tools, both on news and social media corporas. In this paper, we go further by trying to understand how differently these toolkits predict Named Entities, in terms of their precision and recall for three different entity types, and how they can complement each other in this task in order to achieve a combined performance superior to each individual one. Experiments on two publicly available datasets from the workshops WNUT-2015 and #MSM2013 show that using an ensemble of toolkits can improve the recognition of specific entity types - up to 10.62% for the entity type Person, 1.97% for the type Location and 1.31% for the type Organization, depending on the dataset and the criteria used for the voting. Our results also showed improvements of 3.76% and 1.69%, in each dataset respectively, on the average performance of the three entity types.