Promotion of resilience and arts education in high complexity schools: links and guidelines from the literature

La promoción de la resiliencia es un factor clave para mejorar la salud mental y el rendimiento académico de jóvenes en situación de vulnerabilidad. La literatura muestra el papel significativo que tiene el arte para lograrlo, aunque carece de estudios sobre cómo integrar ambas en el ámbito escolar. Esta investigación tiene como objetivos: describir qué características de la educación artística están relacionadas con la promoción de la resiliencia; y definir orientaciones educativas de forma globalizada para Centros de Máxima Complejidad (CMC). Se realizó un análisis de la literatura mediante una metodología basada en el análisis de contenido. Se estableció el Marco de Resiliencia (MR) (Aumann y Hart, 2009) como sistema de categorías con el que vincular las implicaciones de los estudios sobre educación artística en cada una de sus dimensiones: 'Necesidades básicas', 'Pertenencia', 'Aprendizaje', 'Afrontamiento' y 'Aspectos intrapersonales'. Los resultados describen cómo los vínculos entre arte y resiliencia incluyen otras teorías pedagógicas: educación emocional, intercultural e inclusiva, participación infantil y comunitaria, Desarrollo Positivo Adolescente, arte como estímulo, arte comunitario y diversidad de disciplinas artísticas. El análisis ha permitido definir una versión complementaria del MR con Objetivos para la Promoción de la Resiliencia a través de la Educación Artística de forma Globalizada (OPREAG). Los objetivos educativos propuestos son alcanzables y pretenden ser una herramienta sencilla que guíe a los docentes en el diseño y consecución de acciones para fomentar las prácticas artísticas globalizadas y favorecer el éxito educativo del alumnado.The promotion of resilience is a key factor in improving the mental health and academic performance of young people in vulnerability situation. The literature shows the significant role of art in achieving this, although there is a lack of studies on how to integrate both in the school. The aims of this research are: to describe which characteristics of arts education are related to the promotion of resilience; and to define globalised educational guidelines for Maximum Complexity Centres (CMC). We conducted a literature review using a method based on content analysis. The Resilience Framework (RF) (Aumann y Hart, 2009) was established as a system of categories with which to link the implications of arts education studies in each of its dimensions: Basic Needs, Belonging, Learning, Coping and Intrapersonal Aspects. The results describe how the links between art and resilience include other areas and pedagogical theories: emotional education, intercultural and inclusive education, child and community participation, Positive Youth Development, art as stimulus, community art and diversity of artistic disciplines. The analysis has made it possible to define a complementary version of the RF with Objectives for the Promotion of Resilience through Arts Education in a Globalised Way. The proposed educational objectives are achievable and are intended to be a simple tool to guide teachers in the design and implementation of actions to promote globalised arts practices and foster students’ educational success

CO2 footprint reduction and efficiency increase using the dynamic rate in overhead power lines connected to wind farms

Since the first wind farms began operating in the early 1980s, several important factors have changed in the overall picture of energy politics worldwide. The total renewable wind energy capacity of Spain currently accounts for more than 20% of the total installed capacity, which makes integration into the grid challenging for wind farm owners as well as electricity transportation and distribution companies. The smart-grid concept, which focuses on real-time monitoring and dynamic rating operation of power lines, is an important component in the solution to these new challenges. This paper explains how a more efficient operation of energy-generating activities via dynamic rating of the electric grid due to a better knowledge of the main parameters contributes to more clean, renewable energy and decreases the CO2 footprint. The dynamic rating operation of a Spanish overhead power line is analysed, and different scenarios are studied. The dynamic rate achieved in 2015 has saved more than 1100 tonnes of CO2 and has generated over 240,000 € of extra income. This dynamic rating operation also increased the actual annual energy generated from 231.5 GW h to 834.7 GW h with only a 2% greater loss along the line due to Joule and magnetic effects.This work was supported by the Spanish Government under the R+D initiative INNPACTO with reference IPT-2011-1447-920000, the Spanish R+D initiative with reference ENE-2013-42720-R and RETOS RTC-2015-3795-3. The authors also acknowledge support from Viesgo

Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach. Different convolutional neural networks (CNNs) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (hereafter DeepESD) for temperature and precipitation over the European EUR-44i (0.5º) domain, based on eight global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce downscaled multi-model ensembles based on the perfect-prognosis approach, allowing us to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD preserves the uncertainty for temperature and results in a reduced uncertainty for precipitation. To facilitate further studies of this downscaling approach, we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).This research has been supported by the Spanish Government (MCIN/AEI /10.13039/501100011033) through project CORDyS (grant no. PID2020-116595RB-I00)

Effect of Oxytocin, Cloprostenol or Buserelin in Semen Doses on Sow Fertility

[EN] During the periods January to April, May to August, and September to December in two consecutive years, sows were assigned at breeding to receive semen doses supplemented with 87 µg cloprostenol (PG; n = 158), 5 IU oxytocin (OT; n = 154), 2 µg buserelin (GN; n = 93), or served as non-supplemented controls (CON; n = 605). Sows were inseminated at the detection of estrus, and again 24 h later, but only the first inseminations were supplemented. Compared to CON, only buserelin increased pregnancy and farrowing rates (p ≤ 0.05); there was no effect of a period or a treatment × period interaction. Litter size was larger (p ≤ 0.001) for all seminal additive groups during the first two periods and tended to increase in GN compared to CON (p ≤ 0.1) during the third period, resulting in a tendency (p < 0.1) for a period × treatment interaction. The addition of cloprostenol, oxytocin or buserelin to semen doses at first insemination increases litter size in multiparous sows.SIThis research received no external fundin

Optimum cleaning schedule of photovoltaic systems based on levelised cost of energy and case study in central Mexico

In this paper, the soiling impact on photovoltaic systems in Aguascalientes, in central Mexico, an area where 1.4GWp of new photovoltaic capacity is being installed, is characterised experimentally. A soiling rate of -0.16 %/day in the dry season for optimally tilted crystalline silicon modules, and a stabilization of the soiling losses at 11.2% after 70 days of exposure were observed. With this data, a first of its kind novel method for determining optimum cleaning schedules is proposed based on minimising the levelised cost of energy. The method has the advantages compared to other existing methods of considering the system investment cost in the determination of the optimum cleaning schedule. Also, it does not depend on economic revenue data, which is often subject to uncertainty. The results show that residential and commercial systems should be cleaned once per year in Aguascalientes. On the other hand, cleaning intervals from 12 to 31 days in the dry season were estimated for utility-scale systems, due to the dramatic decrease of cleaning costs per unit photovoltaic capacity. We also present a comparative analysis of the existing criteria for optimising cleaning schedules applied to the same case study. The different methods give similar cleaning intervals for utility-scale systems and, thus, the choice of a suitable method depends on the availability of information

Regional climate projections over Spain: atmosphere. Future climate projections

Special Issue on climate over the Iberian Peninsula: an overview of CLIVAR-Spain coordinated science

An R package to visualize and communicate uncertainty in seasonal climate prediction

Interest in seasonal forecasting is growing fast in many environmental and socio-economic sectors due to the huge potential of these predictions to assist in decision making processes. The practical application of seasonal forecasts, however, is still hampered to some extent by the lack of tools for an effective communication of uncertainty to non-expert end users. visualizeR is aimed to fill this gap, implementing a set of advanced visualization tools for the communication of probabilistic forecasts together with different aspects of forecast quality, by means of perceptual multivariate graphical displays (geographical maps, time series and other graphs). These are illustrated in this work using the example of the strong El Niño 2015/16 event forecast. The package is part of the climate4R bundle providing transparent access to the ECOMS-UDG climate data service. This allows a flexible application of visualizeR to a wide variety of specific seasonal forecasting problems and datasets.This work has been funded by the European Union 7th Framework Program [FP7/20072013] under Grant Agreement 308291 (EUPORIAS Project). We are grateful to the EUPORIAS team on Communicating levels of con dence (Work Package 33)