University of Alcalá

About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.

Synthetic Indicators of Quality of Life in Europe

Abstract: For more than three decades now, sociologists, politicians and economists have used a wide range of statistical and econometric techniques to analyse and measure the quality of life of individuals with the aim of obtaining useful instruments for social, political and economic decision making. The aim of this paper is to analyse the advantages and disadvantages of three possible methodologies for obtaining synthetic indicators for the area of welfare and quality of life. These methodologies are Principal Components Analysis, Data Envelopment Analysis and Measure of Distance P2. Furthermore this paper analyses quality of life in the European Union (EU), as a methodological exercise to demonstrate the principles of calculation, implications and differences between the three indicator-construction approaches. This analysis is particularly useful in a scene like the EU, immersed in a deep transformation process and with profound cultural, economic and social inequalities. Therefore, an analysis of the quality of life and well-being of its inhabitants can play a major role in ironing out such differences.

Secondary nanoplastics released from a biodegradable microplastic severely impact freshwater environments

Abstract: Over the last five decades, plastics production has increased as a consequence of their use in strategic sectors causing damage on aquatic ecosystems. In this context, biodegradable plastics have emerged as an ecological alternative because they are easily degradable in the environment. Despite the recent advances in the field of plastic ecotoxicology, the ecological impact of secondary nanoplastics (nanoplastics resulting from natural degradation of micro and macro plastics) in the environment remains poorly understood. Here, we have investigated the effects of secondary nanoplastics of polyhydroxybutyrate (PHB), a biodegradable plastic, on three representative organisms of aquatic ecosystems. Secondary PHB-nanoplastics were produced from PHB-microplastics by abiotic degradation under environmentally representative conditions. Secondary PHB-nanoplastics induced a significant decrease in cellular growth and altered relevant physiological parameters in all organisms. We investigated whether the observed toxicity was exerted by PHB-nanoplastics themselves or by other abiotic degradation products released from PHB-microplastics. An experiment was run in which PHB-nanoplastics were removed by ultrafiltration; the resulting supernatant was not toxic to the organisms, ruling out the presence of toxic chemicals in the PHB-microplastics. In addition, we have performed a complete physicochemical characterization confirming the presence of secondary PHB-nanoplastics in the 75–200 nm range. All results put together indicated that secondary PHB-nanoplastics released as a consequence of abiotic degradation of PHB-microplastics were harmful for the tested organisms, suggesting that biodegradable plastic does not mean safe for the environment in the case of PHB.

Short term wind speed prediction based on evolutionary support vector regression algorithms

Abstract: Hyper-parameters estimation in regression Support Vector Machines (SVMr) is one of the main problems in the application of this type of algorithms to learning problems This is a hot topic in which very recent approaches have shown very good results in different applications in fields such as bio-medicine, manufacturing, control, etc Different evolutionary approaches have been tested to be hybridized with SVMr, though the most used are evolutionary approaches for continuous problems, such as evolutionary strategies or particle swarm optimization algorithms In this paper we discuss the application of two different evolutionary computation techniques to tackle the hyper-parameters estimation problem in SVMrs Specifically we test an Evolutionary Programming algorithm (EP) and a Particle Swarm Optimization approach (PSO) We focus the paper on the discussion of the application of the complete evolutionary-SVMr algorithm to a real problem of wind speed prediction in wind turbines of a Spanish wind farm