Métodos diferenciales e interferométricos para la evaluación de deformaciones de la corteza terrestre mediante técnicas GNSS e InSAR = Differential and interferometric methods for evaluation of crustal deformation using GNSS and InSAR techniques

La actividad volcánica interviene en multitud de facetas de la propia actividad humana, no siempre negativas. Sin embargo, son más los motivos de peligrosidad y riesgo que incitan al estudio de la actividad volcánica. Existen razones de seguridad que inciden en el mantenimiento del seguimiento y monitorización de la actividad volcánica para garantizar la vida y la seguridad de los asentamientos antrópicos en las proximidades de los edificios volcánicos. En esta tesis se define e implementa un sistema de monitorización de movimientos de la corteza en las islas de Tenerife y La Palma, donde el impacto social que representa un aumento o variación de la actividad volcánica en las islas es muy severo. Aparte de la alta densidad demográfica del Archipiélago, esta población aumenta significativamente, en diferentes periodos a lo largo del año, debido a la actividad turística que representa la mayor fuente de ingresos de las islas. La población y los centros turísticos se diseminan predominantemente a lo largo de las costas y también a lo largo de los flancos de los edificios volcánicos. Quizá el mantenimiento de estas estructuras sociales y socio-económicas son los motivos más importantes que justifican una monitorización de la actividad volcánica en las Islas Canarias. Recientemente se ha venido trabajando cada vez más en el intento de predecir la actividad volcánica utilizando los nuevos sistemas de monitorización geodésica, puesto que la actividad volcánica se manifiesta anteriormente por deformación de la corteza terrestre y cambios en la fuerza de la gravedad en la zona donde más tarde se registran eventos volcánicos. Los nuevos dispositivos y sensores que se han desarrollado en los últimos años en materias como la geodesia, la observación de la Tierra desde el espacio y el posicionamiento por satélite, han permitido observar y medir tanto la deformación producida en el terreno como los cambios de la fuerza de la gravedad antes, durante y posteriormente a los eventos volcánicos que se producen. Estos nuevos dispositivos y sensores han cambiado las técnicas o metodologías geodésicas que se venían utilizando hasta la aparición de los mismos, renovando métodos clásicos y desarrollando otros nuevos que ya se están afianzando como metodologías probadas y reconocidas para ser usadas en la monitorización volcánica. Desde finales de la década de los noventa del siglo pasado se han venido desarrollando en las Islas Canarias varios proyectos que han tenido como objetivos principales el desarrollo de nuevas técnicas de observación y monitorización por un lado y el diseño de una metodología de monitorización volcánica adecuada, por otro. Se presenta aquí el estudio y desarrollo de técnicas GNSS para la monitorización de deformaciones corticales y su campo de velocidades para las islas de Tenerife y La Palma. En su implementación, se ha tenido en cuenta el uso de la infraestructura geodésica y de monitorización existente en el archipiélago a fin de optimizar costes, además de complementarla con nuevas estaciones para dar una cobertura total a las dos islas. Los resultados obtenidos en los proyectos, que se describen en esta memoria, han dado nuevas perspectivas en la monitorización geodésica de la actividad volcánica y nuevas zonas de interés que anteriormente no se conocían en el entorno de las Islas Canarias. Se ha tenido especial cuidado en el tratamiento y propagación de los errores durante todo el proceso de observación, medida y proceso de los datos registrados, todo ello en aras de cuantificar el grado de fiabilidad de los resultados obtenidos. También en este sentido, los resultados obtenidos han sido verificados con otros procedentes de sistemas de observación radar de satélite, incorporando además a este estudio las implicaciones que el uso conjunto de tecnologías radar y GNSS tendrán en un futuro en la monitorización de deformaciones de la corteza terrestre. ABSTRACT Volcanic activity occurs in many aspects of human activity, and not always in a negative manner. Nonetheless, research into volcanic activity is more likely to be motivated by its danger and risk. There are security reasons that influence the monitoring of volcanic activity in order to guarantee the life and safety of human settlements near volcanic edifices. This thesis defines and implements a monitoring system of movements in the Earth’s crust in the islands of Tenerife and La Palma, where the social impact of an increase (or variation) of volcanic activity is very severe. Aside from the high demographic density of the archipelago, the population increases significantly in different periods throughout the year due to tourism, which represents a major source of revenue for the islands. The population and the tourist centres are mainly spread along the coasts and also along the flanks of the volcanic edifices. Perhaps the preservation of these social and socio-economic structures is the most important reason that justifies monitoring volcanic activity in the Canary Islands. Recently more and more work has been done with the intention of predicting volcanic activity, using new geodesic monitoring systems, since volcanic activity is evident prior to eruption because of a deformation of the Earth’s crust and changes in the force of gravity in the zone where volcanic events will later be recorded. The new devices and sensors that have been developed in recent years in areas such as geodesy, the observation of the Earth from space, and satellite positioning have allowed us to observe and measure the deformation produced in the Earth as well as the changes in the force of gravity before, during, and after the volcanic events occur. The new devices and sensors have changed the geodetic techniques and methodologies that were used previously. The classic methods have been renovated and other newer ones developed that are now vouched for as proven recognised methodologies to be used for volcanic monitoring. Since the end of the 1990s, in the Canary Islands various projects have been developed whose principal aim has been the development of new observation and monitoring techniques on the one hand, and the design of an appropriate volcanic monitoring methodology on the other. The study and development of GNSS techniques for the monitoring of crustal deformations and their velocity field is presented here. To carry out the study, the use of geodetic infrastructure and existing monitoring in the archipelago have been taken into account in order to optimise costs, besides complementing it with new stations for total coverage on both islands. The results obtained in the projects, which are described below, have produced new perspectives in the geodetic monitoring of volcanic activity and new zones of interest which previously were unknown in the environment of the Canary Islands. Special care has been taken with the treatment and propagation of errors during the entire process of observing, measuring, and processing the recorded data. All of this was done in order to quantify the degree of trustworthiness of the results obtained. Also in this sense, the results obtained have been verified with others from satellite radar observation systems, incorporating as well in this study the implications that the joint use of radar technologies and GNSS will have for the future of monitoring deformations in the Earth’s crust.

Comparison of parametric and nonparametric methods to map oligogenes by linkage

A sample of 95 sib pairs affected with insulin-dependent diabetes and typed with their normal parents for 28 markers on chromosome 6 has been analyzed by several methods. When appropriate parameters are efficiently estimated, a parametric model is equivalent to the β model, which is superior to nonparametric alternatives both in single point tests (as found previously) and in multipoint tests. Theory is given for meta-analysis combined with allelic association, and problems that may be associated with errors of map location and/or marker typing are identified. Reducing by multipoint analysis the number of association tests in a dense map can give a 3-fold reduction in the critical lod, and therefore in the cost of positional cloning.

Training and search methods for speech recognition.

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives. This paper is not concerned with the first process. Estimation of the probability of an index string involves a model of index production by any given utterance segment (e.g., a word). Hidden Markov models (HMMs) are used for this purpose [Makhoul, J. & Schwartz, R. (1995) Proc. Natl. Acad. Sci. USA 92, 9956-9963]. Their parameters are state transition probabilities and output probability distributions associated with the transitions. The Baum algorithm that obtains the values of these parameters from speech data via their successive reestimation will be described in this paper. The recognizer wishes to find the most probable utterance that could have caused the observed acoustic index string. That probability is the product of two factors: the probability that the utterance will produce the string and the probability that the speaker will wish to produce the utterance (the language model probability). Even if the vocabulary size is moderate, it is impossible to search for the utterance exhaustively. One practical algorithm is described [Viterbi, A. J. (1967) IEEE Trans. Inf. Theory IT-13, 260-267] that, given the index string, has a high likelihood of finding the most probable utterance.

Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.

Functionalization of carbon nanotubes using aminobenzene acids and electrochemical methods. Electroactivity for the oxygen reduction reaction

Functionalized carbon nanotubes (CNTs) using three aminobenzene acids with different functional groups (carboxylic, sulphonic, phosphonic) in para position have been synthesized through potentiodynamic treatment in acid media under oxidative conditions. A noticeable increase in the capacitance for the functionalized carbon nanotubes mainly due to redox processes points out the formation of an electroactive polymer thin film on the CNTs surface along with covalently bonded functionalities. The CNTs functionalized using aminobenzoic acid rendered the highest capacitance values and surface nitrogen content, while the presence of sulfur and/or phosphorus groups in the aminobenzene structure yielded a lower functionalization degree. The oxygen reduction reaction (ORR) activity of the functionalized samples was similar to that of the parent CNTs, independently of the functional group present in the aminobenzene acid. Interestingly, a heat treatment in N2 atmosphere with a very low O2 concentration (3125 ppm) at 800 °C of the CNTs functionalized with aminobenzoic acid produced a material with high amounts of surface oxygen and nitrogen groups (12 and 4% at., respectively), that seem to modulate the electron-donor properties of the resulting material. The onset potential and limiting current for ORR was enhanced for this material. These are promising results that validates the use of electrochemistry for the synthesis of novel N-doped electrocatalysts for ORR in combination with adequate heat treatments.

Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research

The microfoundations research agenda presents an expanded theoretical perspective because it considers individuals, their characteristics, and their interactions as relevant variables to help us understand firm-level strategic issues. However, microfoundations empirical research faces unique challenges because processes take place at different levels of analysis and these multilevel processes must be considered simultaneously. We describe multilevel modeling and mixed methods as methodological approaches whose use will allow for theoretical advancements. We describe key issues regarding the use of these two types of methods and, more importantly, discuss pressing substantive questions and topics that can be addressed with each of these methodological approaches with the goal of making theoretical advancements regarding the microfoundations research agenda and strategic management studies in general.

Successful functionalization of superporous zeolite templated carbon using aminobenzene acids and electrochemical methods

A novel and selective electrochemical functionalization of a highly reactive superporous zeolite templated carbon (ZTC) with two different aminobenzene acids (2-aminobenzoic and 4-aminobenzoic acid) was achieved. The functionalization was done through potentiodynamic treatment in acid media under oxidative conditions, which were optimized to preserve the unique ZTC structure. Interestingly, it was possible to avoid the electrochemical oxidation of the highly reactive ZTC structure by controlling the potential limit of the potentiodynamic experiment in presence of aminobenzene acids. The electrochemical characterization demonstrated the formation of polymer chains along with covalently bonded functionalities to the ZTC surface. The functionalized ZTCs showed several redox processes, producing a capacitance increase in both basic and acid media. The rate performance showed that the capacitance increase is retained at scan rates as high as 100 mV s−1, indicating that there is a fast charge transfer between the polymer chains formed inside the ZTC porosity or the new surface functionalities and the ZTC itself. The success of the proposed approach was also confirmed by using other characterization techniques, which confirmed the presence of different nitrogen groups in the ZTC surface. This promising method could be used to achieve highly selective functionalization of highly porous carbon materials.

Music and movement: A comparative study between the BAPNE and Suzuki Methods

The aim of this article is to compare the Suzuki and BAPNE methods based on bibliography published for both approaches. In the field of musical and instrumental education and especially for the childhood stage, the correct use of the body and voice are of fundamental importance. These two methods differ from one another; one principally musical and instrumental, which is the Suzuki method, and one non-musical, the BAPNE method, which aims at stimulating attention, concentration, memory and the executing function of the pupil through music and body percussion. Comparing different approaches may provide teachers with a useful insight for facing different issues related to their discipline.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.