Abstract
In the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or information diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as 'degree', 'closeness', 'betweenness', 'coreness' or 'k-shell' centrality, among others. All have some kind of inherent limitations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top-k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset influence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the 'node degree', 'closeness' and 'coreness' among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance between seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall's rank correlation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique.

Abstract
The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital's emergency department. A 10-year history (2009-2018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.

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Abstract
Design thinking refers to a creative approach to deal with complex problems in design contexts. Originally harnessed by designers, it is today within everyone's reach, to be learned and employed in their practices. To make it accessible and tangible to those who are not designers, a number of tools began to be developed and systematized. Among those, we highlight the group of card-based tools, which enable individuals to develop their creativity and to generate innovative design concepts. In order to explore these tools and to provide a scenario for creative ideas, we developed a card-based game-Cards GO-and conducted a workshop experiment to evaluate the applicability of the game to conceive other game concepts. We assessed the results of the workshop from three points of view: (1) that of the researchers, through direct observation; (2) that of the participants, by means of a questionnaire about their intrinsic motivation; (3) that of three experts in game design. Overall, Cards GO presented itself as a valuable tool for game design, creative thinking and collaboration. However, it was observed that the developed game concepts needed to be better detailed.

Abstract
Serious Pervasive Games extend themagic circle (Huizinga, 1938) to the players’ context and surrounding environment. The blend of both physical and fictive game worlds provides a push in player engagement and promotes situated learning approaches. Space and time, as well as social context, acquire a more meaningful impact on the gameplay. From pervasive learning towards science communication with location-based games, this article presents research and case studies that exemplify their benefits and related problems. Pervasive learning can be defined as “learning at the speed of need through formal, informal and social learning modalities” (Pontefract, 2013). The first case study—the BEACONING project—aims to contextualize the teaching and learning process, connecting it with problem-based game mechanics within STEM. The main goal of this project is to provide the missing connection between STEM subjects and real-world interactions and applications. The pedagogical foundation is supported on problem-based learning (PBL), in which active learning is in the center, and learners have to work with different tools and resources in order to solve problems (quests). Teachers create, facilitate, and assess pervasive and gamified learning activities (missions). Furthermore, these quests are gamified in order to provide non-linear game plots. In a second case study, we demonstrate and evaluate how natural heritage can benefit from pervasive games. This study is based on a set of location-based games for an existing natural park, which have been developed in order to provide enhanced experiences, as well as additional information about some species that are more difficult to observe or that are seasonal. Throughout the research and development of these projects, we have encountered and identified several problems, of different nature, present in pervasive games.

Abstract