Abstract
The uncertainty of wind energy makes wind power producers (WPPs) incur profit/loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems and demand response. To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The optimization problem is formulated as a Mixed-Integer Linear Programming problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method enhances the profit of the VPP compared to previous models.

Abstract
Optimal Input Design (OID) methodologies are developed to find a signal that could best estimate a set of parameters of a given model. Their application in constrained nonlinear systems, especially when the search space limits or the initial conditions are unknown, may present several difficulties due to the numerical instability related to the optimization processes. A good choice over the parameters possible ranges is a trade-off among numerical stability, search space size, and effectiveness, and it is hardly found. To deal with this problem, this paper proposes a series of changes in the Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (SOESGOPE) methodology. First, the limits over the parameters are tightly adjusted according to their confidence. A recursive approach runs the optimization methodology, analyzes the solution's feasibility and marginal costs given by the Lagrange Multipliers, and selects a direction that could improve the system's response. This approach improves the convergence and the assertiveness of the estimation process. To validate this approach, some cases, including a parameters estimation of a mobile robot nonlinear system, are tested.

Abstract
There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. 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 students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit.

Abstract
Demand Response (DR) systems are gaining momentum in the EU energy markets albeit based on fragmented standards that, as a result, hinder interoperability. These discrepancies necessitate the introduction of a semantically enriched umbrella framework that will allow DR systems to exchange and consume data transparently, an issue that is currently unaddressed. Furthermore, to support semantically interoperable DR architectures, a multi-layer compliance testing framework is required that will examine and quantify the technical, syntactic and semantic properties of individual DR systems. In this work, the aforementioned gaps in the literature are addressed by, first, introducing an OpenADR-based semantic enrichment component. According to the guidelines of the Smart Grid Architecture Model (SGAM) framework, a concrete evaluation procedure of this component is presented, which allows for a step-by-step syntactic and semantic testing. Following the identification of the instruments composing the testbed and the equipment/links under test at SGAM's communication and information layers, the Basic Application Interoperability Profiles (BAIOPs) are defined and their involved steps are described. Experiments demonstrate the validity of the presented methodology, while also evaluating the introduced component.

Abstract
The way plastics are currently produced, used and disposed does not capture the economic benefits of amore 'circular' approach and is dramatically harming the environment. It is relevant to determine which European countries can be considered more or less efficient in the end-of-life of plastic products processes, what the sources of the inefficiencies are, and howthose less efficient countries could improve their performance towards a more circular economy. Although some countries have developed a variety of quantitative indicators, there is scarcity of adequate metrics for performance measurements. This paper estimates the efficiency of 26 European countries in the context of Circular Economy, for the period 2006-2016, considering the generation of waste, recovery and recycling of plastic, with a methodology based on theMultidirectional Efficiency Analysis. Apart from identifying the most efficient countries in the studied period, results show that efficiency increases for most countries with time, and that many countries reach the full efficiency by the end of the study period, and especially by 2016. Input analysis shows that increasing capital seems to be a main driver towards efficiency, since the other inputs are used with a similar efficiency by most countries. Output analysis suggest that the difference among countries efficiency is not in their reduction of total waste or emissions, but rather in the improvement of their economic growth in a circular way, that is, improving GDP but also the recovering and recycling activities. These results could be useful to design policies towards a more efficient and circular use of plastics.

Abstract
As we write this editorial, people around the world are apprehensive about their future; some are at home; some are thinking about the loved ones they cannot visit; some, unfortunately, are dying. We watch the graphs and listen to the daily news of new coronavirus cases, but be it just one or one thousand, for the those close of the ones affected, the impact is catastrophic. (...)