Abstract
Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TENSORCAST, a method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TENSORCAST is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.

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Abstract
The coordination of various energy vectors under the concept of multi-energy system (MES) has introduced new sources of operational flexibility to system managers. In this paper, the behavior of multi-energy players (MEP) who can trade with more than one energy carrier to maximize their profits and mitigate their operational risks has been investigated. The MES is represented based on a multilayer structure, namely the energy market, MEP, the local energy system (LES), and multi-energy demand. In such environment, an MEP aggregates LES and participates in the wholesale electricity market, simultaneously to maximize its profit. The decision-making conflict of the MEP with other energy players for the aggregation of LES and participation in the electricity market is modeled based on a bilevel approach. Numerical results show the behavior of the MEP as a prosumer in the electricity market to produce smoother demand and price profiles. Results reveal a mutual effect of local and wholesale equilibrium prices by increasing the share of the MEP.

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Abstract
Students are getting better over time but perhaps it is, at the same time, getting ever more difficult to capture their attention in class, especially as regards research and research methodology. The Business Narrative Modelling Language (BNML) has been used (Oliveira and Ferreira, 2011; Goncalves et al., 2013; Au-Yong-Oliveira et al., 2015; Goncalves et al., 2016) to portray and discuss research findings. With qualitative research, in particular, the research process may be more transparent, more objective and faster by following the rules of BNML. Throughout the first semester for 2017-18, and on a Strategy and Competitiveness course, at the master's level, the advantages of BNML were shown on several occasions. Students were required to present what was for most of them their first research case study. BNML uses both the narrative and pictorial representations to showcase and highlight research results. In the age of the smartphone, icons and emojis, students respond well to pictorial representations. However, on the other hand, one has to acknowledge that children go from doing drawings to represent everything in their lives to unexpectedly stopping giving drawings to their parents and siblings and friends on all occasions - including for Christmas and for birthdays. Therefore, it is still a challenge to get adult students to understand BNML, especially in large classes. What is still lacking is a handbook on how to use BNML - despite the existence of a number of publications on the topic. This article thus seeks to shed further light on this research tool. Sharper master's students, doing a dissertation and one-on-one with the lecturer, quickly comprehend and grasp the essence of BNML and its use of key words and game patterns to tell the research case story, over time. Movement and colour and emotion may all be seen in BNML - which remains intuitive, and thus true to qualitative and interpretive research. In Portugal, qualitative research is still not seen, by many researchers, to be a serious research method, especially by the more conservative of researchers, who favour the use of statistics and thus of "more scientific" quantitative research methods in management research. We hope to contribute to a change of mind-set with our article.