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.

Abstract

Abstract
The combination of marine origin biopolymers for tissue engineering (TE) applications is of high interest, due to their similarities with the proteins and polysaccharides present in the extracellular matrix of different human tissues. This manuscript reports on innovative collagen-chitosan-fucoidan cryogels formed by the simultaneous blending of these three marine polymers in a chemical-free crosslinking approach. The physicochemical characterization of marine biopolymers comprised FTIR, amino acid analysis, circular dichroism and SDS-PAGE, and suggested that the jellyfish collagen used in the cryogels was not denatured (preserved the triple helical structure) and had similarities with type II collagen. The chitosan presented a high deacetylation degree (90.1%) that can strongly influence the polymer physicochemical properties and biomaterial formation. By its turn, rheology, and SEM studies confirmed that these novel cryogels present interesting properties for TE purposes, such as effective blending of biopolymers without visible material segregation, mechanical stability (strong viscoelastic character), as well as adequate porosity to support cell proliferation and exchange of nutrients and waste products. Additionally,in vitrocellular assessments of all cryogel formulations revealed a non-cytotoxic behavior. The MTS test, live/dead assay and cell morphology assessment (phalloidin DAPI) showed that cryogels can provide a proper microenvironment for cell culturing, supporting cell viability and promoting cell proliferation. Overall, the obtained results suggest that the novel collagen-chitosan-fucoidan cryogels herein presented are promising scaffolds envisaging tissue engineering purposes, as both acellular biomaterials or cell-laden cryogels.

Abstract

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.