Abstract
Although still little-known, glamping has become a nature-based tourism option for people who want a higher level of comfort. The offer of this type of accommodation is growing, namely in Portugal, but there are still few studies that address the motivations and other relevant factors explaining its adoption or refusal by consumers. The present study applied a qualitative approach aimed at exploring consumers’ motivations or obstacles for choosing glamping, and their perceptions as tourists on the differences between glamping and camping. Data were collected through the conduction of focus groups held in 2017 and content analysis techniques for contextualized interpretations were used. The most important motivational driver to go glamping is the direct contact with nature. Glamour, comfort, privacy and a different experience are also important aspects that consumers appreciate. Inversely, the main obstacles are the cost, the limited offer, the lack of knowledge, and the non-authenticity, compared to camping, of the offer.

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Abstract
In this paper, we address the generation of electrical power using Airborne Wind Energy Systems, comprising a kite connected through a tether to a generator on the ground. We design a controller to steer the kite to follow a pre-defined periodic path, which includes a production mode, a tether retrieval mode, and a safe mode capable of handling wind gusts. Copyright

Abstract
Background: This work addresses the maximum edge weight clique problem (MEWC), an important generalization of the well-known maximum clique problem. Methods: The MEWC problem can be used to model applications in many fields including broadband network design, computer vision, pattern recognition, and robotics. We propose a random key genetic algorithm to find good quality solutions for this problem. Computational experiments are reported for a set of benchmark problem instances derived from the DIMACS maximum clique instances. Results: The results obtained show that our algorithm is both effective and efficient, as for most of the problem instances tested, we were able to match the best-known solutions with very small computational time requirements.

Abstract
Introduction. The concept and study of relevance has been a central subject in information science. Although research in information retrieval has been focused on topical relevance, other kinds of relevance are also important and justify further study. Motivational relevance is typically inferred by criteria such as user satisfaction and success. Method. Using an existing dataset composed by an annotated set of health Web documents assessed for relevance and comprehension by a group of users, we build a multivariate prediction model for the motivational relevance of search sessions. Analysis. The analysis was based on lasso variable selection, followed by model selection using multiple logistic regression. Results. We have built two regression models; the full model, which considers all variables of the dataset, has a lower estimated prediction error than the reduced model, which contains the statistically-significant variables from the full model. The higher values of evaluation metrics, including accuracy, specificity and sensitivity in the full model support this finding. The full model has an accuracy of 91.94%, and is better at predicting motivational relevance. Conclusions. Our findings suggest features that can be considered by search engines to estimate motivational relevance, to be used in addition to topical relevance. Among these features, a high level of success in Web search and in health information search on social networks and chats are some of the most influencing user features. This shows that users with higher computer literacy might feel more satisfied and successful after completing the search tasks. In terms of task features, the results suggest that users with clearer goals feel more successful. Moreover, results show that users would benefit from the help of the system in clarifying the retrieved documents.

Abstract
For the predicted growth of e-commerce, supply chains need to adapt to new conditions, so that delivery can be fast, cheap and reliable. The key to success is the last-mile product delivery (LMD) - the last stage of the supply chain, where the ordered product is delivered to the final consumer's location. One innovative proposal puts foundations in a new delivery model where a professional delivery fleet (PF) is supplemented partially or fully with crowdshipping. The main idea of crowdshipping is to involve ordinary people - in our case in-store shoppers - in the delivery of packages to other customers. In return, occasional couriers (OC) are offered a small compensation. In hitherto formulated problems it was assumed that OCs always accept delivery tasks assigned to them. In this paper we consider OCs as independent agents, which are free to reject assignments. The main contribution of the paper is an original bi-level methodology for matching and routing problem in LMD with OCs and the PF. The goal is to use crowdshipping to reduce the total delivery cost in a same-day last-mile delivery system with respect to occasional couriers' freedom to accept or reject the assigned delivery. We introduce probability to represent each OC's willingness to perform the delivery to a given final customer. We study the OCs' willingness to accept or reject delivery tasks assigned to them and the influence of their decision on the total delivery cost associated to both the OCs' compensation fees and the delivery cost generated by the PF used for the delivery of remaining parcels. © 2018 The Author(s).