Suchergebnisse

La presente tesis trata sobre el columnismo literario de Francisco Umbral y Manuel Vázquez Montalbán. El periodo histórico delimitado queda comprendido entre los años 1972 y 1986, es decir, desde el tardofranquismo hasta la victoria electoral del Partido Socialista Obrero Español (PSOE). Esta es una época de la más importantes en el devenir de nuestra historia contemporánea, pues tras la caída de la dictadura de Francisco Franco y la llegada de la Transición se suceden acontecimientos fundamentales como la primeras elecciones democráticas desde la II República; la promulgación de la Constitución de 1978; el fallido golpe de Estado de Antonio Tejero, teniente coronel de la Guardia Civil; el movimiento contracultural de la Movida madrileña; y en último lugar el desarrollo progresivo de la sociedad de consumo. Un tiempo vital en el que viejos y nuevos medios de comunicación escrita se afianzan definitivamente en España como portavoces de los deseos democráticos de unos lectores que ansían saber toda aquella realidad que la censura franquista les ha ninguneado, con especial atención a aquellos articulistas que desde el tardofranquismo se han convertido en la única luz crítica con la dictadura franquista. Y es justo aquí donde emergen los nombres y apellidos de Francisco Umbral y de Manuel Vázquez Montalbán, cuyas voces originalísimas trascienden a su propio eco hasta alcanzar al ciudadano español en la restitución de la modernidad. La metodología aplicada en esta investigación se basa en el análisis de las columnas que publican en diferentes medios de comunicación, tanto periódicos como revistas, para la época acotada. Un proceso que se ha realizado desde una transversalidad dialéctica literaria, política, culinaria, estilística, castiza y humorística que ilumina el proyecto personal que comparten ambos autores con estilos diferentes pero con el objetivo común de aunar literatura y periodismo. El resultado final avala la hipótesis inicial de la gran influencia que Francisco Umbral y Manuel Vázquez Montalbán tienen ...

[spa] La presente tesis trata sobre el columnismo literario de Francisco Umbral y Manuel Vázquez Montalbán. El periodo histórico delimitado queda comprendido entre los años 1972 y 1986, es decir, desde el tardofranquismo hasta la victoria electoral del Partido Socialista Obrero Español (PSOE). Esta es una época de la más importantes en el devenir de nuestra historia contemporánea, pues tras la caída de la dictadura de Francisco Franco y la llegada de la Transición se suceden acontecimientos fundamentales como la primeras elecciones democráticas desde la II República; la promulgación de la Constitución de 1978; el fallido golpe de Estado de Antonio Tejero, teniente coronel de la Guardia Civil; el movimiento contracultural de la Movida madrileña; y en último lugar el desarrollo progresivo de la sociedad de consumo. Un tiempo vital en el que viejos y nuevos medios de comunicación escrita se afianzan definitivamente en España como portavoces de los deseos democráticos de unos lectores que ansían saber toda aquella realidad que la censura franquista les ha ninguneado, con especial atención a aquellos articulistas que desde el tardofranquismo se han convertido en la única luz crítica con la dictadura franquista. Y es justo aquí donde emergen los nombres y apellidos de Francisco Umbral y de Manuel Vázquez Montalbán, cuyas voces originalísimas trascienden a su propio eco hasta alcanzar al ciudadano español en la restitución de la modernidad. La metodología aplicada en esta investigación se basa en el análisis de las columnas que publican en diferentes medios de comunicación, tanto periódicos como revistas, para la época acotada. Un proceso que se ha realizado desde una transversalidad dialéctica literaria, política, culinaria, estilística, castiza y humorística que ilumina el proyecto personal que comparten ambos autores con estilos diferentes pero con el objetivo común de aunar literatura y periodismo. El resultado final avala la hipótesis inicial de la gran influencia que Francisco Umbral y Manuel Vázquez Montalbán tienen en la refundación y la modernización del columnismo español durante el tardofranquismo y la Transición. Y lo hacen, amparados en su enorme trabajo filológico y su vocación docente en los cientos y cientos de columnas que firman en esta época, como alternativa a la realidad oficial que reciben unos lectores en proceso de formación crítica tras la Dictadura. ; [eng] This thesis examines the literary columnism of Francisco Umbral (Madrid, 1932 – Madrid, 2007) and Manuel Vázquez Montalbán (Barcelona 1939 – Bangkok, 2003). The historical period is delimited by the years 1972 and 1986, that is, from the last years of Franco's dictatorship (tardofranquismo) to the electoral victory of the Spanish Socialist Workers Party (PSOE). It is a transcendental time in Spain, because a deep social, political and cultural period of changes for the whole society begins, after more than forty years of state repression and the end of the civil war. The methodology used in the research work is based on the analysis of their columns published in different media both newspapers –as El País, El Periódico de Cataluña or La Vanguardia- and magazines –Por Favor, Hermano Lobo, or Interviú- for the time-bound. This process has been carried out from different approaches: literary, political, culinary, stylistic, humoristic and traditional, which enhances the personal project that both authors share with different styles but with the common objective of combining literature and journalism. The research outcome supports the initial hypothesis the extensive work that Francisco Umbral and Manuel Vázquez Montalbán have on the refounding and modernization of Spanish columnism during the last years of Franco's dictatorship and the Transition period. The strong influence of these authors continues to be felt today in a new generation of journalists and other intellectuals who write an opinion column. Also, sheltered by an enormous philological work and teaching vocation in the hundreds and hundreds of publications which they sign at this time, Francisco Umbral and Manuel Vázquez Montalbán do so as an alternative to the official reality received by readers in the process of critical formation.

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT. ; This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN" Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 ).

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. In this paper we develop regression and classification supervised learning algorithms to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active. Examples using data from the Maastricht Upper Area Control Centre are presented with varying levels of predictive performance by the machine learning algorithms. Data sources include Demand Data Repository from EUROCONTROL and the Rapid Developing Thunderstorm product from EUMETSAT. ; This work is partially supported through the Spanish Government initiative Proyectos de I+D+I "RETOS INVESTIGACIN"; Ministerio de Ciencia, Innovación y Universidades by the project entitled "Management of Meteorological Uncertainty for More Efficient Air Traffic: Meteorological Data Provision and Thunderstorm Avoidance" (RTI2018-098471-B-C32 ).

In this paper, a first step to analyse the effects of reducing the uncertainty of aircraft trajectories on sector demand is presented. The source of uncertainty is wind, forecasted by Ensemble Prediction Systems, which are composed of different possible atmosphere realizations. A trajectory predictor determinesthe routes to be followed by the different flights to reduce the uncertainty of the arrival times. The sector demand is described in terms of entry count, that is, the number of flights entering the sector during a selected time period, which is uncertain because so are the the entry times to the sector. Results are presented for a realistic application, where the dispersion of the entry count isshown to be reduced when the dispersion of the arrival times is also reduced. ; This work is part of the project TBO-Met. This project has received funding from the SESAR Joint Undertaking under grant agreement No 699294 under European Union's Horizon 2020 research and innovation programme

The present work evaluated polyhydroxybutyrate (PHB) and carbohydrates production by wastewater borne cyanobacteria at demonstrative-scale (three photobioreactors (PBR) of 11.7 m3 each), using agricultural runoff as feedstock. Agricultural runoff was fed to PBR1, which was devoted to cyanobacteria selection and biomass growth. In PBR2, inorganic carbon was added in a feast and famine regime to favour PHB-accumulating microorganisms. Finally, inorganic carbon was continuously added in PBR3 to boost PHB accumulation. A high removal efficiency of 95% and 99% for total nitrogen and phosphorus was obtained, respectively. Cyanobacteria were successfully selected and outcompeted green microalgae. Results suggested that a minimum inorganic carbon concentration was needed to accumulate PHB while carbohydrates were accumulated only with CO2 additions. Maximum concentrations of 4.5%VSS and 69%VSS for PHB and carbohydrates were obtained. Overall, this study shows at demonstrative-scale the potential of cyanobacteria to produce PHB within a wastewater biorefinery concept. And it gives insight on the strategies needed to produce PHB with cyanobacteria at massive scale. ; This research was funded by the European Union H2020 Research and Innovation program [INCOVER, GA 689242] and by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Research National Agency (AEI), and the European Regional Development Fund (FEDER) [AL4BIO, RTI2018-099495-B-C21]. M.J. García and R. Díez-Montero would like to thank the Spanish Ministry of Economy and Competitiveness for their research grants (IJCI-2017-34601 and FJCI-2016-30997, respectively). Estel Rueda would like to thank the Spanish Ministry of Education, Culture and Sport (FPU18/04941) for her grant. Authors would like to thank Simone Rossi for his support during the experiment deployment. ; Peer Reviewed ; Postprint (author's final draft)

The existence of significant uncertainties in the models and systems required for trajectory prediction represent a major challenge for Trajectory-Based Operations concept. Weather can be considered as one of the most relevant sources of uncertainty. Understanding and managing the impact of these uncertainties is necessary in order to increase the predictability of the ATM system. We present preliminary results on robust trajectory planning in which weather is assumed to be the unique source of uncertainty. State-of-the-art forecasts from Ensemble Prediction Systems are used as input data for the wind field and to calculate convective risk. The term convective area is defined here as an area within which individual convective storms may develop, i.e., a necessary (though not sufficient) condition. An ad-hoc robustoptimal control methodology is presented. A set of Pareto-optimal trajectories is obtained for different preferences between predictability, convective risk and average efficiency. ; This work has been partially supported by project TBO-MET project (https://tbometh2020.com/), which has received funding from the SESAR JU under grant agreementNo 699294 under European Union's Horizon 2020 research and innovation programme.