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Science, Technology, Engineering, and Mathematics (STEM) are key disciplines towards tackling the challenges related to the Sustainable Development Goals. However, evidence shows that women are enrolling in these disciplines in a smaller percentage than men, especially in Engineering related fields. As stated by the United Nations Women section, increasing the number of women studying and working in STEM fields is fundamental towards achieving better solutions to the global challenges, since the potential for innovation is larger. In this paper, we present the Girls4STEM project, which started in 2019 at the Escola Tècnica Superior d’Enginyeria de la Universitat de València, Spain. This project works towards breaking the stereotypes linked to STEM fields, addressing both boys and girls aged from 6 to 18, but especially trying to open the range of career options for young girls through interaction with female STEM experts. The goal is to spark girls’ interest in STEM disciplines from childhood, so that they become more self-confident in these areas. To achieve this goal, the project is built over three main actions: the Girls4STEM Family Talks, where students, families, and teachers participate; the Girls4STEM Professional Talks, where the target is a general audience; and the Initial Training Seminars for teachers. Short-term results are here presented, showing that aspects related to self-perception and perception from others (family, teachers) play a significant role. Moreover, these results also indicate that there may not be a general understanding of which disciplines are included in STEM.

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Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

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As the standardization of network-assisted deviceto-device (D2D) communications by the 3 rd Generation Partnership Project progresses, the research community has started to explore the technology potential of new advanced features that will largely impact the performance of 5G networks. For 5G, D2D is becoming an integrative term of emerging technologies that take advantage of the proximity of communicating entities in licensed and unlicensed spectra. The European 5G research project Mobile and Wireless Communication Enablers for the 2020 Information Society (METIS) has identified advanced D2D as a key enabler for a variety of 5G services, including cellular coverage extension, social proximity and communicating vehicles. In this paper, we review the METIS D2D technology components in three key areas of proximal communications -network-assisted multi-hop, full-duplex, and multi-antenna D2D communicationsand argue that the advantages of properly combining cellular and ad hoc technologies help to meet the challenges of the information society beyond 2020.

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