We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.

SCALA© (Sampling Campaigns for Aerosols in the Low Atmosphere) is a web-based software system that was developed in a multidisciplinary manner to integrally support the documentation and the management and analysis of atmospheric aerosol data from sampling campaigns. The software development process applied considered the prototyping and the evolutionary approaches. The software product (SCALA©) allows for the comprehensive management of the sampling campaigns’ life cycle (management of the profiles and processes involved in the start-up, development and closure of a campaign) and provides support for both intra- and inter-campaigns data analysis. The pilot deployment of SCALA© considers the Spanish Network on Environmental Differential Mobility Analysers (DMAs) (REDMAAS) and the PROACLIM project. This research project involves, among other objectives, the study of temporal and spatial variations of the atmospheric aerosol through a set of microphysical properties (size distribution, optical properties, hygroscopicity, etc.) measured in several locations in Spain. The main conclusions regarding size distribution are presented in this work. These have been have been extracted through SCALA© from the data collected in the REDMAAS 2015 and 2019 intercomparison campaigns and two years (2015 and 2016) of measurements with two Scanning Mobility Particle Sizers (SMPS) at CIEMAT (Madrid, central Spain) and UDC (A Coruña, NW of Spain) sites.

This paper addresses the measurement of the social dimension of cognitive trust in collaborative networks. Trust indicators are typically measured and combined in literature in order to calculate partners' trustworthiness. When expressing the result of a measurement, some quantitative indication of the quality of the result-the uncertainty of measurement-should be given. However, currently this is not taken into account for the measurement of the social dimension of cognitive trust in collaborative networks. In view of this, an innovative metrology-based approach for the measurement of social cognitive trust indicators in collaborative networks is presented. Thus, a measurement result is always accompanied by its uncertainty of measurement, as well as by information traditionally used to properly interpret the results: the sample size, and the standard deviation of the sample.

In the present day, many risk factors affect the continuity of a business. However, this situation produces a conducive atmosphere to approach alternatives that relieve this situation for organizations. Within these alternatives, environmental sustainability (ES) and information technologies governance (IT governance or ITG) stand out. Both alternatives allow organizations to address intrinsically common issues such as strategic alignment, generation of value, mechanisms for performance improvement, risk management and resource management. This article focuses on the fusion of both alternatives, determining to what extent current ITG models consider ES issues. With this purpose, the strategy followed was firstly to identify the relevant factors of ES present in the main approaches of the domain (ISO14001, GRI G4, EMAS, SGE21 and ISO26000). As a result, we identified 27 activities and 103 sub-activities of ES. Next, as the second main objective, we determined which of those factors are present in the main current ITG approaches (COBIT5, ISO38500 and WEILL & ROSS). Finally, we concluded through a quantitative study that COBIT5 is the most sustainable (i.e., the one that incorporates more ES issues) ITG approach.

Light-absorbing carbonaceous aerosols (including black carbon (BC)) pose serious health issues and play significant roles in atmospheric radiative properties. Two-year measurements (2015–2016) of aerosol light absorption, combined with measurements of sub-micrometric particles, were continuously conducted in A Coruña (northwest (NW) Spain) to determine their light absorption properties: absorption coefficients (σabs) and the absorption Ångström exponent (AAE). The mean and standard deviation of equivalent black carbon (eBC) during the period of study were 0.85 ± 0.83 µg m−3, which are lower than other values measured in urban areas of Spain and Europe. High eBC concentrations found in winter are associated with an increase in emissions from anthropogenic sources in combination with lower mixing layer heights and frequent stagnant conditions. The pronounced diurnal variability suggests a strong influence from local sources. AAE had an average value of 1.26 ± 0.22 which implies that both fossil fuel combustion and biomass burning influenced optical aerosol properties. This also highlights biomass combustion in suburban areas, where the use of wood for domestic heating is encouraged, as an important source of eBC. All data treatment was gathered using SCALA© as atmospheric aerosol data management support software program.