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.

Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero-defect manufacturing (ZDM) philosophy, together with recent technological advances in Cyber-Physical Systems (CPS), presents significant challenges and opportunities for the implementation of new system architectures that contributes for the continuous improvement of the production. This paper describes the experience gained in the GO0D MAN project which aims at realizing a fully functional, replicable and therefore widely exploitable solution, employing multi-agent systems, smart on-line inspection tools, data analytics and knowledge management technologies. In particular, the paper presents the challenges tackled during the deployment of the GO0D MAN system architecture in three relevant industrial use cases, which represent more than 80% of the manufacturing sector.

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