Enhancement of the advanced Fenton process (Fe0/H2O2) by ultrasound for the mineralization of phenol

In this study, a successful mineralization of phenol was achieved by means of coupling zero-valent iron (ZVI) particles, hydrogen peroxide and a short input of ultrasonic irradiation. This short sono-advanced Fenton process (AFP) provided a better performance of ZVI in a subsequent silent degradation stage, which involves neither extra cost of energy nor additional oxidant. The short input of ultrasound (US) irradiation enhanced the activity of the Fe0/H2O2 system in terms of the total organic carbon (TOC) removal. Then, the TOC mineralization continued during the silent stage, even after the total consumption of hydrogen peroxide, reaching values of ca. 90% TOC conversions over 24 h. This remarkable activity is attributed to the capacity of the ZVI/iron oxide composite formed during the degradation for the generation of oxidizing radical species and to the formation of another reactive oxidant species, such as the ferryl ion. The modification of the initial conditions of the sono-AFP system such as the ultrasonic irradiation time and the hydrogen peroxide dosage, showed significant variations in terms of TOC mineralization for the ongoing silent degradation stage. An appropriate selection of operation conditions will lead to an economical and highly efficient technology with eventual large-scale commercial applications for the degradation organic pollutants in aqueous effluents

Activity and space patterns of Pyrenean desman (Galemys pyrenaicus) suggest non-aggressive and non-territorial behaviour

The Pyrenean desman is considered a flagship species for biodiversity and evolution. However, its scientific knowledge is still under development and currently on debate, particularly in relation to its behavioural ecology and social organization. Based on the previous hypothesis of individual desmans being solitary and territorial, activity and space patterns were considered to be arranged to avoid conspecifics. However, recent insights into the species’ social behaviour revealed non-interspecific avoidance. With this study, we provide novel insights into the activity and space patterns of the desman, and their relation to its social behaviour. A total of 30 individuals were trapped, 18 of which provided informative radiotracking data to study (1) activity behaviour, (2) the proportion of the home range utilized and the distances travelled, and (3) the dominant movement directionality. Activity and space use patterns were affected by daylight and seasonality, but not by sex, age or number of other conspecifics sharing the home range. In contrast to the previous observations, individuals did not show a pattern of directionality in their movements. Noticeably, we observed encounters between individuals without evidence of aggressive behaviour. Our results suggest that desmans do not alter their activity or their spatial behaviour to avoid encounters with conspecifics, as previously suggested. These novel findings provide more evidence of a social structure and organization with social interactions and non-aggressive behaviour. This is of relevance for management actions and for the conservation of this endemic mammal

Data-driven learning framework for associating weather conditions and wind turbine failures

The need for cost effective operation and maintenance (O&M) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed

Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants

Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance

Los atentados del 11 de marzo de 2004: Análisis de la comunicación política y sus efectos en la opinión pública.

El 11 de marzo de 2004 un ataque terrorista en varias líneas de trenes de la red de Cercanías de Madrid conmocionaba a la opinión pública. El Gobierno atribuyó a ETA la autoría, suponiendo que era una respuesta al Pacto Antiterrorista que se había firmado en 2000. El 14 de marzo, el Partido Socialista ganaba las elecciones, a pesar de los sondeos electorales, que daban la victoria al Partido Popular. En este trabajo se analiza el proceso comunicativo que tuvo lugar entre los días 11, 12 y 13 de marzo y se comprueba el efecto que dicho proceso provocó en la opinión pública, plasmado en las urnas

Key Performance Indicators for Wind Farm Operation and Maintenance

Key performance indicators (KPI) are tools for measuring the progress of a business towards its goals. Although wind energy is now a mature technology, there is a lack of well-defined best practices to asses the performance of a wind farm (WF) during the operation and maintenance (O&M) phase; processes and tools of asset management, such as KPIs, are not yet well-established. This paper presents a review of the major existing indicators used in the O&M of wind farms (WFs), as such information is not available in the literature so far. The different stakeholders involved in the O&M phase are identified and analysed together with their interests, grouped into five categories. A suggestion is made for the properties that KPIs should exhibit. For each category, major indicators that are currently in use are reviewed, discussed and verified against the properties defined. Finally, we propose a list of suitable KPIs that will allow stakeholders to have a better knowledge of an operating asset and make informed decisions. It is concluded that more detailed studies of specific KPIs and the issues of their implementation are probably needed

Do the colors of your letters depend on your language? Language-dependent and universal influences on grapheme-color synesthesia in seven languages

Grapheme-color synesthetes experience graphemes as having a consistent color (e.g., “N is turquoise”). Synesthetes’ specific associations (which letter is which color) are often influenced by linguistic properties such as phonetic similarity, color terms (“Y is yellow”), and semantic associations (“D is for dog and dogs are brown”). However, most studies of synesthesia use only English-speaking synesthetes. Here, we measure the effect of color terms, semantic associations, and non-linguistic shape-color associations on synesthetic associations in Dutch, English, Greek, Japanese, Korean, Russian, and Spanish. The effect size of linguistic influences (color terms, semantic associations) differed significantly between languages. In contrast, the effect size of nonlinguistic influences (shape-color associations), which we predicted to be universal, indeed did not differ between languages. We conclude that language matters (outcomes are influenced by the synesthete’s language) and that synesthesia offers an exceptional opportunity to study influences on letter representations in different languages.Depto. de Psicobiología y Metodología en Ciencias del ComportamientoFac. de PsicologíaTRUEpu

Flexible workflows for on-the-fly electronmicroscopy single-particle image processing using Scipion

Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data setThe authors would like to acknowledge financial support from The Spanish Ministry of Economy and Competitiveness through the BIO2016-76400-R (AEI/FEDER, UE) grant, the Comunidad Auto´noma de Madrid through grant S2017/BMD3817, the Instituto de Salud Carlos III (PT17/0009/0010), the European Union (EU) and Horizon 2020 through the CORBEL grant (INFRADEV-1-2014-1, Proposal 654248), the ‘la Caixa’ Foundation (ID 100010434, Fellow LCF/BQ/ IN18/11660021), Elixir–EXCELERATE (INFRADEV-3- 2015, Proposal 676559), iNEXT (INFRAIA-1-2014-2015, Proposal 653706), EOSCpilot (INFRADEV-04-2016, Proposal 739563) and INSTRUCT–ULTRA (INFRADEV03-2016-2017, Proposal 731005