Abstract
O nosso estudo propôs a implementação de Gamificação numa Plataforma Social Académica de uma Universidade Virtual. Gamificação foi definida como o uso de elementos derivados dos jogos em contextos que não são jogos. Adaptando-a ao nosso contexto, quisemos verificar de que forma a sua implementação influenciava a Interação, Colaboração, Cooperação e Aprendizagem Social e ainda as presenças da Comunidade de Investigação, no espaço da Plataforma. Para isso usámos uma Metodologia de Design Based Research numa configuração de Métodos Mistos. Identificámos como foco de análise as formas sociais para a aprendizagem. Começámos por recolher opiniões dos utilizadores, usando entrevistas semi-estruturadas. Os resultados, informaram na construção de um protótipo “gamificado” construído com a ferramenta Elgg. Seguidamente efetuaram-se testes de usabilidade, recolhendo dados da performance e das opiniões dos utilizadores e foi efetuada uma implementação gamificada. Foram depois enviados questionários aos utilizadores e recolhidas estatísticas do uso. Os dados foram descritos, analisados e discutidos, segundo uma ordem cronológica. É discutido o impacto da implementação nas dimensões propostas e possibilidades de investigação futuras.;The article reports on the implementation of Gamification in a Social Platform of an Open University. Gamification is the use of game elements in non-game context. We adapted the implementation to context, to inspect the impact on the Interaction, Collaboration, Cooperation, Social Learning and on the Community of Inquiry. We used Design Based Research in a Mixed-Methods configuration. We looked at the Social Forms for Learning as a lens for the Elements. We present our study in a chronological order, explaining how each phase leads to the other. We first gathered information from the users of the academic platform via semi-structured interviews. We then designed a prototype with the Elgg engine, containing Gamification Elements. We made usability tests and gathered data from users´ performance and opinions. After we designed a second implementation, this time for students enrolled in different curricular units. This time we deployed a Survey and gathered information about the use of the platform. Lastly, we present our results discussing the impact on the proposed dimensions, and making suggestions for future research.

Abstract
The Business School of the Polytechnic of Porto, Portugal aiming at following the demands of the region decided to make available a master's degree program in e-business. This paper describes the study held ascertain the most relevant skills to be considered in the master program. In order to obtain relevant feedback, an interview was conducted to professionals working in the field. Also, a questionnaire was applied to the students attending the last year of undergraduate after working programs, since they have already professional experience in related fields. The most relevant skills were identified, and curriculum was defined for the master's degree according to the analysis of the results of these activities.

Abstract
In this paper, the RT-WiFi architecture is proposed to handle real-time (RT) communication in infrastructured IEEE 802.11 networks operating in high density industrial environments. This architecture is composed of a time division multiple access (TDMA)-based coordination layer that schedules the medium access of RT traffic flows, and an underlying traffic separation mechanism that is able do handle the coexistence of RT and non-RT traffic sources in the same communication environment. The simulation assessment considers an overlapping basic service set (OBSS), where a set of RT and non-RT stations share the same frequency band. The performance assessment compares the behaviour of the RT-WiFi architecture vs. the behaviour of standard distributed coordination function (DCF), point coordination function (PCF), enhanced distributed channel access (EDCA), and hybrid coordination function (HCF) controlled channel access (HCCA) medium access control mechanisms. A realistic error-prone model has been used to measure the impact of message losses in the RT-WiFi architecture. It is shown that the proposed RT-WiFi architecture offers a significantly enhanced behaviour when compared with the use of IEEE 802.11 standard mechanisms, in what concerns average deadline misses and average access delays. Moreover, it also offers an almost constant access delay, which is a relevant characteristic when supporting RT applications.

Abstract
The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation.

Abstract
This paper addresses a distribution problem involving a set of different products that need to be distributed among a set of geographically disperse retailers and transported from the single warehouse to the aforementioned retailers. The disfribution and transportation are made in order to satisfy retailers' demand while satisfying storage limits at both the warehouse and the retailers, transportation limits between the warehouse and the retailers, and other operational constraints. This problem is combinatorial in nature as it involves the assignment of a discrete finite set of objects, while satisfying a given set of conditions. Hence, we propose a genetic algorithm that is capable of finding good quality solutions. The genetic algorithm proposed is used to a real case study involving the disfribution of eight products among 108 retailers from a single warehouse. The results obtained improve on those of company's current practice by achieving a cost reduction of about 13%.

Abstract
Challenges in the coordination between the transmission system operator (TSO) and the distribution system operator (DSO) have risen continuously with the integration of distributed energy resources (DER). These technologies have the possibility to provide reactive power support for system operators. Considering the Portuguese reactive power policy as an example of the regulatory framework, this paper proposes a methodology for proactive reactive power management of the DSO using the renewable energy sources (RES) considering forecast uncertainty available in the distribution system. The proposed method applies a stochastic sequential alternative current (AC)-optimal power flow (SOPF) that returns trustworthy solutions for the DSO and optimizes the use of reactive power between the DSO and DER. The method is validated using a 37-bus distribution network considering real data. Results proved that the method improves the reactive power management by taking advantage of the full capabilities of the DER and by reducing the injection of reactive power by the TSO in the distribution network and, therefore, reducing losses.