Analysis and modeling of a ooding event in the framework of the DRIHM project

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Any: 2015, Tutora: M. Carme LlasatIn early November 2011 a pertorbation affecting the Mediterranean area led to heavy rainfall and ash- oods in Catalonia and northern Italy. This paper analyses the situation that led to that event and compares the use of different modeling scenarios in the framework of the DRIHM project, which provides tools for the interoperability of meteorological and hydrological models. A synthesis of the main models used in operative simulations for ooding events is also presented

Carreño de Miranda, artistic advisor to the tenth admiral of Castile

El presente trabajo analiza el papel de Carreño de Miranda como asesor artístico de Juan Gaspar Enríquez de Cabrera, X almirante de Castilla y uno de los principales coleccionistas del XVII español, que confió en el pintor de Cámara de Carlos II para certificar la originalidad de las pinturas enviadas desde Venecia por el marqués de Villagarcía. Partiendo de la correspondencia que el almirante mantuvo con éste último, hemos localizado un Eneas y Anquises de Schiavone que pasó de su colección a la imperial de Viena, como sucedió con dos retratos de Van Dyck, hoy en el Kunsthistorisches Museum, que hemos identificado gracias a un dibujo realizado por Carreño cuando las pinturas se encontraban en la Huerta del almirante.This paper analyses the role of Carreño de Miranda as art advisor of Juan Gaspar Enríquez de Cabrera, 10th admiral of Castile and one of the greatest noble collectors in 17th century Spain. The admiral trusted the Court painter to assert the originality of the paintings sent from Venice by the marquis of Villagarcía. Thanks to the correspondence he maintained with this agent we have located an Aeneas and Anchises by Schiavone which passed from the admiral’s collection to the imperial collection in Vienna. The same happened with two portraits by Van Dyck, now at the Kunsthistorisches Museum, which we have identified through a sketch by Carreño

Probabilistic data-driven methods for forecasting, identification and control

This dissertation presents contributions mainly in three different fields: system identification, probabilistic forecasting and stochastic control. Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity function, it is shown that a family of predictors can be obtained. First, a predictor to compute nominal forecastings of a time-series or a dynamical system is presented. The effectiveness of the predictor is shown by means of a numerical example, where daily predictions of a stock index are computed. The obtained results turn out to be better than those obtained with popular machine learning techniques like Neural Networks. Similarly, the aforementioned dissimilarity function can be used to compute conditioned probability distributions. By means of the obtained distributions, interval predictions can be made by using the concept of quantiles. However, in order to do that, it is necessary to integrate the distribution for all the possible values of the output. As this numerical integration process is computationally expensive, an alternate method bypassing the computation of the probability distribution is also proposed. Not only is computationally cheaper but it also allows to compute prediction regions, which are the multivariate version of the interval predictions. Both methods present better results than other baseline approaches in a set of examples, including a stock forecasting example and the prediction of the Lorenz attractor. Furthermore, new methods to obtain models of nonlinear systems by means of input-output data are proposed. Two different model approaches are presented: a local data approach and a kernel-based approach. A kalman filter can be added to improve the quality of the predictions. It is shown that the forecasting performance of the proposed models is better than other machine learning methods in several examples, such as the forecasting of the sunspot number and the R¨ossler attractor. Also, as these models are suitable for Model Predictive Control (MPC), new MPC formulations are proposed. Thanks to the distinctive features of the proposed models, the nonlinear MPC problem can be posed as a simple quadratic programming problem. Finally, by means of a simulation example and a real experiment, it is shown that the controller performs adequately. On the other hand, in the field of stochastic control, several methods to bound the constraint violation rate of any controller under the presence of bounded or unbounded disturbances are presented. These can be used, for example, to tune some hyperparameters of the controller. Some simulation examples are proposed in order to show the functioning of the algorithms. One of these examples considers the management of a data center. Here, an energy-efficient MPC-inspired policy is developed in order to reduce the electricity consumption while keeping the quality of service at acceptable levels

Ana Gemma López Martín, La navegación por los estrechos. Geostrategia y Derecho, Madrid, Dykinson

Sin resume

2005-2017 Ozone trends and potential benefits of local measures as deduced from air quality measurements in the north of the Barcelona metropolitan area

We analyzed 2005–2017 data sets on ozone (O3) concentrations in an area (the Vic Plain) frequently affected by the atmospheric plume northward transport of the Barcelona metropolitan area (BMA), the atmospheric basin of Spain recording the highest number of exceedances of the hourly O3 information threshold (180¿µg¿m-3). We aimed at evaluating the potential benefits of implementing local-BMA short-term measures to abate emissions of precursors. To this end, we analyzed in detail spatial and time variations of concentration of O3 and nitrogen oxides (NO and NO2, including OMI remote sensing data for the latter). Subsequently, a sensitivity analysis is done with the air quality (AQ) data to evaluate potential O3 reductions in the north of the BMA on Sundays compared with weekdays as a consequence of the reduction in regional emissions of precursors. The results showed a generalized decreasing trend for regional background O3 as well as the well-known increase in urban O3 and higher urban NO decreasing slopes compared with those of NO2. The most intensive O3 episodes in the Vic Plain are caused by (i) a relatively high regional background O3 (due to a mix of continental, hemispheric–tropospheric and stratospheric contributions); by (ii) intensive surface fumigation from mid-troposphere high O3 upper layers arising from the concatenation of the vertical recirculation of air masses; but also by (iii) an important O3 contribution from the northward transport/channeling of the pollution plume from the BMA. The high relevance of the local-daily O3 contribution during the most intense pollution episodes is clearly supported by the O3 (surface concentration) and NO2 (OMI data) data analysis. A maximum decrease potential (by applying short-term measures to abate emissions of O3 precursors) of 49¿µg¿O3¿m-3 (32¿%) of the average diurnal concentrations was determined. Structurally implemented measures, instead of episodically, could result in important additional O3 decreases because not only the local O3 coming from the BMA plume would be reduced, but also the recirculated O3 and thus the intensity of O3 fumigation in the plain. Therefore, it is highly probable that both structural and episodic measures to abate NOx and volatile organic compound (VOC) emissions in the BMA would result in evident reductions of O3 in the Vic PlainPeer ReviewedPostprint (author's final draft

Probabilistic interval predictor based on dissimilarity functions

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.Comment: 10 pages, 3 figure

El reto de la accesibilidad universal en la Administración electrónica

The entry into force of Law 39/2015, of October 1st, on the Common Administrative Procedure of Public Administrations and the Law 40/2015, of October 1st, on the Legal Regime of the Public Sector signifies a decisive impetus in the process of implementing electronic Administration. However, this change in the way in which public Administrations relate to citizens must be done in terms of universal accessibility, to allow access and participation of all people in conditions of equality. In this paper we analyze the minimum requirements that must be taken into account to ensure that electronic Administration accessible is a reality, taking into account human diversity, the aging of the population and the digital novelties of the new administrative laws.La entrada en vigor de la Ley 39/2015, de 1 de octubre, del Procedimiento Administrativo Común de las Administraciones Públicas y la Ley 40/2015, de 1 de octubre, de Régimen Jurídico del Sector Público supone un impulso decisivo en el proceso de implantación de la Administración electrónica. Sin embargo, este cambio en el modo de relación de las Administraciones públicas con la ciudadanía debe realizarse en términos de accesibilidad universal, para permitir el acceso y la participación de todas las personas en condiciones de igualdad. En este trabajo se analizan los requisitos mínimos imprescindibles a tener en cuenta para garantizar que la Administración electrónica accesible sea una realidad, teniendo en cuenta la diversidad humana, el envejecimiento de la población y las novedades digitales de las nuevas leyes administrativas

Probabilistic interval predictor based on dissimilarity functions

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided