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This 1st Report of the project CrowdWork21 presents a summary of the information collected about workers in digital platforms by country (German, Hungary, Portugal and Spain). Each national report describes first the scientific debates about workers of digital platforms. After, the reports present the information collected about the national public debates and identifies the angles normally covered by the media in relation to the organisation of digital workers. Lastly, the reports present initial conclusions about the information collected in each country.

The normality of the log-return of stock prices is often assumed by the market players in order to use some useful results, as for instance, the Black-Scholes formula for pricing European options. However, several studies regarding different indexes have shown that the normality assumption of the returns usually fails.
In this paper we analyse the normality assumption for intra-day and inter-day log-returns, comparing opening prices and/or closing prices for a large number of companies quoted in the Nasdaq Composite index. We use the Pearson's Chi-Square, Kolmogorov-Smirnov, Anderson-Darling, Shapiro-Wilks and Jarque-Bera goodness-of-fit tests to study the normality assumption.
We find that the failure rate in the normality assumption for the log-return of stock prices is not the same for intra-day and inter-day prices, is somewhat test dependent and strongly dependent on some extreme price observations.
To the best of our knowledge, this is the first study on the normality assumption for the log-return of stock prices dealing simultaneously with a large number of companies and normality tests, and at the same time considering various scenarios of intra-day, inter-day prices and data trimming.

This paper presents the experimental results obtained in the wireless energy transfer (WET) system prototype based on coils: circular or planar. With these experimental results we can choose the tuning settings to improve the efficiency of power transmission of the WET systems. In WET for electric vehicle batteries charging, the coil shape and the range between the coils are the most important issues of those systems.

{The theory of turbulent Newtonian fluids shows that the choice of the boundary condition is a relevant issue because it can modify the behavior of a fluid by creating or avoiding a strong boundary layer. In this study, we consider stochastic second grade fluids filling a two-dimensional bounded domain with the Navier-slip boundary condition (with friction). We prove the well-posedness of this problem and establish a stability result. Our stochastic model involves a multiplicative white noise and a convective term with third order derivatives, which significantly complicate the analysis. (C) 2017 Elsevier Inc. All rights reserved.}

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The i* community has published guidelines, including model layout guidelines, for the construction of models. Our goal is to evaluate the effect of the layout guidelines on the i* novice stakeholders’ ability to understand and review i* models. We conducted a quasi-experiment where participants were given two understanding and two reviewing tasks. Both tasks involved a model with a bad layout and another model following the i* layout guidelines. We evaluated the impact of layouts by combining the success level in those tasks and the required effort to accomplish them. Effort was assessed using time, perceived complexity (with NASA TLX), and eye-tracking data. Participants were more successful in understanding than in reviewing tasks. However, we found no statistically significant difference in the success, time taken, or perceived complexity, between tasks conducted with models with a bad layout and models with a good layout. Most participants had little to no prior knowledge in i*, making them more representative of stakeholders with no requirements engineering expertise. They were able to understand the models fairly well after a short tutorial, but struggled when reviewing models. Adherence to the existing i* layout guidelines did not significantly impact i* model understanding and reviewing performance.