Abstract
The presence of brands in digital is a challenge and at the same time an opportunity. It is important to realize the extent to which emotion is a determining factor in digital, to create meaningful and memorable experiences. Assuming that adaptation to change is a basic factor for the survival of any brand, so branding must adapt to new realities. In this article, we reflects on how fleeting and momentary can be what thrills and binds us to a brand. Different authors emphasize the importance of emotions in the experience provided by the image of a brand, as well as the connections that are established with people. This study begins by making a state of the art about the role of emotions in the area of branding, subjecting a set of individuals to the visualization of a sample of visual identities, through qualitative methods, with the objective of collecting data, establishing comparisons and to analyze correlations between the emotional perception of individuals and the strategic vision of the brand and associated branding. This article arises in the context of a master's dissertation, still under investigation, and therefore it is not possible to present final statements, only partial considerations of the work developed so far. The results of this study, being in development, ends the purpose of being applied in the branding of a newly created brand.

Abstract
Purpose Collaborative consumption emerges from social practices such as sharing, lending and gifting. It is becoming more common among consumers, boosted by the internet, which facilitates the collaboration process with both strong and weak ties. This paper aims to examine collaborative consumer experience, delving into the factors that contribute to the adoption and the perceived benefits of this alternative form of consumption. Design/methodology/approach A total of 12 phenomenological interviews were conducted o explore the theme from an individual perspective, attested by the consumers' narratives and experiences. Findings The results highlight collaborative consumption as being influenced by family practices, social relations and the current economic scenario. Also, noteworthy is the evidence that collaborative consumption enables consumers to select from a more diversified portfolio of products and services, especially the ones featured by the internet and social media. Consumers perceive financial, emotional, social, environmental and increased consumption benefits, depending on their practices of collaborative consumption, and also on their role as providers, consumers or exchangers. Originality/value Through the phenomenological approach, based on individual reports of experiences related to collaborative consumption, it was possible to highlight some aspects relevant to better understanding the behavior of collaborative consumers.

Abstract

Abstract
We found stable soliton solutions for two generalizations of the cubic complex Ginzburg-Landau equation, namely, one that includes the term that, in optics, represents a delayed response of the nonlinear gain and the other including the self-steepening term, also in the optical context. These solutions do not require the presence of the delayed response of the nonlinear refractive index, such that, they exist regardless of the term previously considered essential for stabilization. The existence of these solitons was predicted by a perturbation approach, and then confirmed by solving the ordinary differential equations, resulting from a similarity reduction, and also by applying a linear stability analysis. We found that these solitons exist for a large region of the parameter space and possess very asymmetric amplitude profiles as well as a complicated chirp characteristic.

Abstract
The severe or prolonged exposure to stress-inducing factors in occupational and academic settings is a growing concern. The literature describes several potentially stressful moments experienced by medical students throughout the course, affecting cognitive functioning and learning. In this paper, we introduce the EUSTRESS Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the individuals in order to predict chronic stress. The Information System will use a measuring instrument based on wearable devices and machine learning techniques to collect and process stress-related data from the individual without his/her explicit interaction. A big database has been built through physiological, psychological, and behavioral assessments of medical students. In this paper, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. In order to develop a predictive model of stress, we performed different statistical tests. Preliminary results showed the neural network had the better model fit. As future work, we will integrate salivary samples and self-report questionnaires in order to develop a more complex and intelligent model. © Springer Nature Switzerland AG 2019.

Abstract
Production scheduling is a function that can contribute strongly to the competitive capacity of companies producing goods and services. Failure to stagger tasks properly causes enormous waste of time and resources, with a clear decrease in productivity and high monetary losses. The efficient use of internal resources in organizations becomes a competitive advantage and can thus dictate their survival and sustainability. In that sense, it becomes crucial to analyze and develop production scheduling models, which can be simplified as the function of affecting tasks to means of production over time. This report is part of a project to develop a dynamic scheduling tool for decision support in a single machine environment. The system created has the ability, after a first solution has been generated, to trigger a new solution as some tasks leave the system and new ones arrive, allowing the user, at each instant of time, to determine new scheduling solutions, in order to minimize a certain measure of performance. The proposed tool was validated in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.