Abstract
<p><em>Investment in research and development is a key factor in increasing countries' competitiveness. However, its impact can potentially be broader and include other socially relevant elements like job quality. In effect, the quantity of generated jobs is an incomplete indicator since it does not allow to conclude on the quality of the job generated. In this sense, this paper intends to explore the relevance of R&amp;D investments for the job quality in the European Union between 2009 and 2018. For this purpose, we investigate the effects of R&amp;D expenditures made by the business sector, government, and higher education sector on three dimensions of job quality. Three research methods are employed, i.e. univariate linear analysis, multiple linear analysis, and cluster analysis. The findings only confirm the association between R&amp;D expenditure and the number of hours worked, such that the European Union countries with the highest R&amp;D expenses are those with the lowest average weekly working hours.</em></p>

Abstract
The growth of social media as an information medium without restrictive measures on the creation of new accounts led to the rise of malicious agents with the intend to diffuse unreliable information in the network, ultimately affecting the perception of users in important topics such as political and health issues. Although the problem is being tackled within the domain of bot detection, the impact of studies in this area is still limited due to 1) not all accounts that spread unreliable content are bots, 2) human-operated accounts are also responsible for the diffusion of unreliable information and 3) bot accounts are not always malicious (e.g. news aggregators). Also, most of these methods are based on supervised models that required annotated data and updates to maintain their performance through time. In this work, we build a framework and develop knowledge-based metrics to complement the current research in bot detection and characterize the impact and behavior of a Twitter account, independently of the way it is operated (human or bot). We proceed to analyze a sample of the accounts using the metrics proposed and evaluate the necessity of these metrics by comparing them with the scores from a bot detection system. The results show that the metrics can characterize different degrees of unreliable accounts, from unreliable bot accounts with a high number of followers to human-operated accounts that also spread unreliable content (but with less impact on the network). Furthermore, evaluating a sample of the accounts with a bot detection system shown that bots compose around 11% of the sample of unreliable accounts extracted and that the bot score is not correlated with the proposed metrics. In addition, the accounts that achieve the highest values in our metrics present different characteristics than the ones that achieve the highest bot score. This provides evidence on the usefulness of our metrics in the evaluation of unreliable accounts in social networks. Copyright

Abstract
This paper presents a study on the impact of adjacent markets on the electricity market, realizing the advantages of acting in several different markets. The increased use of renewable primary sources to generate electricity and new usages of electricity such as electric mobility are contributing to a better and more rational way of living. The investment in renewable technologies for the distributed generation has been creating new opportunities for owners of such technologies. Besides the selling of electricity and related services (ancillary services) in energy markets, players can participate and negotiate in other markets, such as the carbon/CO2 market, the guarantees of origin market, or provide district heating services selling of steam and hot water among others. These market mechanisms are related to the energy market, originating a wide market strategy improving the benefits of using distributed generators. This paper describes several adjacent markets and how do they complement the electricity market. The paper also shows how the simulation of electricity and adjacent markets can be performed, using an electricity market simulator, and demonstrates, based on market simulations using real data from the Iberian market, that the participation in various complementary markets can enable power producers to obtain extra profits that are essential to cover the production costs and facilities maintenance. The findings of this paper enhance the advantages for investment on energy production based renewable sources and more efficient technologies of energy conversion.

Abstract
We discuss the novel concept of harmonics of the Vernier effect for optical fiber sensors as a tool to break the limits of conventional optical Vernier effect currently used. The new effect provides enhancements scalable with the harmonic order. © 2021 The Author(s).

Abstract
On-sky testing of new instrumentation concepts is required before they can be incorporated within facility-class instrumentation with certainty that they will work as expected within a real telescope environment. Increasingly, many of these concepts are not designed to work in seeing-limited conditions and require an upstream adaptive optics system for testing. Access to on-sky AO systems to test such systems is currently limited to a few research groups and observatories worldwide, leaving many concepts unable to be tested. A pilot program funded through the H2020 OPTICON program offering up to 15 nights of on-sky time at the CANARY Adaptive Optics demonstrator is currently running but this ends in 2021. Pre-run and on-sky support is provided to visitor experiments by the CANARY team. We have supported 6 experiments over this period, and plan one more run in early 2021. We have recently been awarded for funding through the H2020 OPTICON-RADIO PILOT call to continue and extend this program up until 2024, offering access to CANARY at the 4.2m William Herschel Telescope and 3 additional instruments and telescopes suitable for instrumentation development. Time on these facilities will be open to researchers from across the European research community and time will be awarded by answering a call for proposals that will be assessed by an independent panel of instrumentation experts. Unlike standard observing proposals we plan to award time up to 2 years in advance to allow time for the visitor instrument to be delivered. We hope to announce the first call in mid-2021. Here we describe the facilities offered, the support available for on-sky testing and detail the eligibility and application process. © 2020 SPIE.

Abstract
One of the emergent prospects for active distribution networks (DN) is to establish new roles to the distribution company (DISCO). The DISCO can act as an aggregator of the resources existing in the DN, also when parts of the network are structured and managed as microgrids (MGs). The new roles of the DISCO may open the participation of the DISCO as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the DISCO are addressed by proposing a bilevel optimization approach in which the DISCO problem is modeled as the upper-level problem and the MGs problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the DISCO's risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush-Kuhn-Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus DN connected to the IEEE RTS 24-bus power system.