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.

Abstract
AbstractMalnutrition is a serious health problem among older adults. The Mini Nutritional Assessment (MNA) is the most used tool to assess malnutrition among older adults. It was proposed that the Screening section could be used as a short form of the MNA (MNA-SF), but the study of the two forms show a wide range of agreement (71.8 to 93.3% and Cohen's kappa from 0.52 to 0.78). Nevertheless, most of the studies recommend the use of the MNA-SF, but in some this conclusion is not completely congruent with the results. The aims of this work were to study the agreement between the nutritional status classification using the MNA-SF and the MNA-FF among Portuguese older adults living in the community, to compare the scores in each question of the MNA-FF between participants correctly or incorrectly classified using the MNA-SF, and to develop a more sensible version of the MNA-SF. The current study is part of the PRONUTRISENIOR project. Data from 456 older adults (54.2% females) aged 65 to 92 years (mean = 73; SD = 6) living in the community (Vila Nova de Gaia, Portugal) were analyzed. The agreement between the two classifications is 82.7%, but Cohen's k shows a weak agreement (weighted k = 0.497; p < 0,001). MNA-SF showed a very low sensitivity (42.6%) detecting participants malnourished or at risk of malnutrition. Participants classified as normal using the MNA-SF despite at risk using the MNA-SF presented lower scores in two items from the Assessment section: number of full meals eaten daily and amount of fluid consumed per day. These two items were included in MNA-SF to obtain an extended short-version (MNA-SF8), which presented a sensitivity of 91.8% (and a specificity of 79.9%). The areas under the ROC curves were 0.858 and 0.929, respectively for MNA-SF and MNA-SF8. The addition of two items to the MNA-SF provides a more sensible tool to detect risk of malnutrition among elderly. These findings suggest that, among the studied population, general eating-related questions regarding (full meals and fluid intake) are relevant to assess malnutrition. Similar studies should be carried out among other populations in order to verify the possibility of their generalization. [The PRONUTRISENIOR project was financed by Iceland, Liechtenstein and Norway through the EEA Grants (PT06 – Public Health Initiatives, reference 81NU5).]