Desarrollo, implementación y análisis de un modelo cinemático local de velocidades para Costa Rica basado en la integración y homogenización de datos de estaciones GNSS continuas

El primer procesamiento estricto realizado con el software científico Bernese y contemplando las más estrictas normas de cálculo recomendadas internacionalmente, permitió obtener un campo puntual de alta exactitud, basado en la integración y estandarización de los datos de una red GPS ubicada en Costa Rica. Este procesamiento contempló un total de 119 semanas de datos diarios, es decir unos 2,3 años, desde enero del año 2009 hasta abril del año 2011, para un total de 30 estaciones GPS, de las cuales 22 están ubicadas en el territorio nacional de Costa Rica y 8 internaciones pertenecientes a la red del Sistema Geocéntrico para las Américas (SIRGAS). Las denominadas soluciones semilibres generaron, semana a semana, una red GPS con una alta exactitud interna definida por medio de los vectores entre las estaciones y las coordenadas finales de la constelación satelital. La evaluación semanal dada por la repetibilidad de las soluciones brindó en promedio errores de 1,7 mm, 1,4 mm y 5,1 mm en las componentes [n e u], confirmando una alta consistencia en estas soluciones. Aunque las soluciones semilibres poseen una alta exactitud interna, las mismas no son utilizables para fines de análisis cinemático, pues carecen de un marco de referencia. En Latinoamérica, la densificación del Marco Internacional Terrestre de Referencia (ITRF), está representado por la red de estaciones de operación continua GNSS de SIRGAS, denominada como SIRGAS-CON. Por medio de las denominadas coordenadas semanales finales de las 8 estaciones consideradas como vínculo, se refirió cada una de las 119 soluciones al marco SIRGAS. La introducción del marco de referencia SIRGAS a las soluciones semilibres produce deformaciones en estas soluciones. Las deformaciones de las soluciones semilibres son producto de las cinemática de cada una de las placas en las que se ubican las estaciones de vínculo. Luego de efectuado el amarre semanal a las coordenadas SIRGAS, se hizo una estimación de los vectores de velocidad de cada una de las estaciones, incluyendo las de amarre, cuyos valores de velocidad se conocen con una alta exactitud. Para la determinación de las velocidades de las estaciones costarricenses, se programó una rutina en ambiente MatLab, basada en una ajuste por mínimos cuadrados. Los valores obtenidos en el marco de este proyecto en comparación con los valores oficiales, brindaron diferencias promedio del orden de los 0,06 cm/a, -0,08 cm/a y -0,10 cm/a respectivamente para las coordenadas [X Y Z]. De esta manera se logró determinar las coordenadas geocéntricas [X Y Z]T y sus variaciones temporales [vX vY vZ]T para el conjunto de 22 estaciones GPS de Costa Rica, dentro del datum IGS05, época de referencia 2010,5. Aunque se logró una alta exactitud en los vectores de coordenadas geocéntricas de las 22 estaciones, para algunas de las estaciones el cálculo de las velocidades no fue representativo debido al relativo corto tiempo (menos de un año) de archivos de datos. Bajo esta premisa, se excluyeron las ocho estaciones ubicadas al sur de país. Esto implicó hacer una estimación del campo local de velocidades con solamente veinte estaciones nacionales más tres estaciones en Panamá y una en Nicaragua. El algoritmo usado fue el denominado Colocación por Mínimos Cuadrados, el cual permite la estimación o interpolación de datos a partir de datos efectivamente conocidos, el cual fue programado mediante una rutina en ambiente MatLab. El campo resultante se estimó con una resolución de 30' X 30' y es altamente constante, con una velocidad resultante promedio de 2,58 cm/a en una dirección de 40,8° en dirección noreste. Este campo fue validado con base en los datos del modelo VEMOS2009, recomendado por SIRGAS. Las diferencias de velocidad promedio para las estaciones usadas como insumo para el cálculo del campo fueron del orden los +0,63 cm/a y +0,22 cm/a para los valores de velocidad en latitud y longitud, lo que supone una buena determinación de los valores de velocidad y de la estimación de la función de covarianza empírica, necesaria para la aplicación del método de colocación. Además, la grilla usada como base para la interpolación brindó diferencias del orden de -0,62 cm/a y -0,12 cm/a para latitud y longitud. Adicionalmente los resultados de este trabajo fueron usados como insumo para hacer una aproximación en la definición del límite del llamado Bloque de Panamá dentro del territorio nacional de Costa Rica. El cálculo de las componentes del Polo de Euler por medio de una rutina programa en ambiente MatLab y aplicado a diferentes combinaciones de puntos no brindó mayores aportes a la definición física de este límite. La estrategia lo que confirmó fue simplemente la diferencia en la dirección de todos los vectores velocidad y no permitió reveló revelar con mayor detalle una ubicación de esta zona dentro del territorio nacional de Costa Rica. ABSTRACT The first strict processing performed with the Bernese scientific software and contemplating the highest standards internationally recommended calculation, yielded a precise field of high accuracy, based on the integration and standardization of data from a GPS network located in Costa Rica. This processing watched a total of 119 weeks of daily data, is about 2.3 years from January 2009 to April 2011, for a total of 30 GPS stations, of which 22 are located in the country of Costa Rica and 8 hospitalizations within the network of Geocentric System for the Americas (SIRGAS). The semi-free solutions generated, every week a GPS network with high internal accuracy defined by vectors between stations and the final coordinates of the satellite constellation. The weekly evaluation given by repeatability of the solutions provided in average errors of 1.7 mm 1.4 mm and 5.1 mm in the components [n e u], confirming a high consistency in these solutions. Although semi-free solutions have a high internal accuracy, they are not used for purposes of kinematic analysis, because they lack a reference frame. In Latin America, the densification of the International Terrestrial Reference Frame (ITRF), is represented by a network of continuously operating GNSS stations SIRGAS, known as SIRGAS-CON. Through weekly final coordinates of the 8 stations considered as a link, described each of the solutions to the frame 119 SIRGAS. The introduction of the frame SIRGAS to semi-free solutions generates deformations. The deformations of the semi-free solutions are products of the kinematics of each of the plates in which link stations are located. After SIRGAS weekly link to SIRGAS frame, an estimate of the velocity vectors of each of the stations was done. The velocity vectors for each SIRGAS stations are known with high accuracy. For this calculation routine in MatLab environment, based on a least squares fit was scheduled. The values obtained compared to the official values, gave average differences of the order of 0.06 cm/yr, -0.08 cm/yr and -0.10 cm/yr respectively for the coordinates [XYZ]. Thus was possible to determine the geocentric coordinates [XYZ]T and its temporal variations [vX vY vZ]T for the set of 22 GPS stations of Costa Rica, within IGS05 datum, reference epoch 2010.5. The high accuracy vector for geocentric coordinates was obtained, however for some stations the velocity vectors was not representative because of the relatively short time (less than one year) of data files. Under this premise, the eight stations located in the south of the country were excluded. This involved an estimate of the local velocity field with only twenty national stations plus three stations in Panama and Nicaragua. The algorithm used was Least Squares Collocation, which allows the estimation and interpolation of data from known data effectively. The algorithm was programmed with MatLab. The resulting field was estimated with a resolution of 30' X 30' and is highly consistent with a resulting average speed of 2.58 cm/y in a direction of 40.8° to the northeast. This field was validated based on the model data VEMOS2009 recommended by SIRGAS. The differences in average velocity for the stations used as input for the calculation of the field were of the order of +0.63 cm/yr, +0.22 cm/yr for the velocity values in latitude and longitude, which is a good determination velocity values and estimating the empirical covariance function necessary for implementing the method of application. Furthermore, the grid used as the basis for interpolation provided differences of about -0.62 cm/yr, -0.12 cm/yr to latitude and longitude. Additionally, the results of this investigation were used as input to an approach in defining the boundary of Panama called block within the country of Costa Rica. The calculation of the components of the Euler pole through a routine program in MatLab and applied to different combinations of points gave no further contributions to the physical definition of this limit. The strategy was simply confirming the difference in the direction of all the velocity vectors and not allowed to reveal more detail revealed a location of this area within the country of Costa Rica.

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification with super-classes

The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Understanding replication of experiments in software engineering: a classification

Context: Replication plays an important role in experimental disciplines. There are still many uncertain- ties about how to proceed with replications of SE experiments. Should replicators reuse the baseline experiment materials? How much liaison should there be among the original and replicating experiment- ers, if any? What elements of the experimental configuration can be changed for the experiment to be considered a replication rather than a new experiment? Objective: To improve our understanding of SE experiment replication, in this work we propose a classi- fication which is intend to provide experimenters with guidance about what types of replication they can perform. Method: The research approach followed is structured according to the following activities: (1) a litera- ture review of experiment replication in SE and in other disciplines, (2) identification of typical elements that compose an experimental configuration, (3) identification of different replications purposes and (4) development of a classification of experiment replications for SE. Results: We propose a classification of replications which provides experimenters in SE with guidance about what changes can they make in a replication and, based on these, what verification purposes such a replication can serve. The proposed classification helped to accommodate opposing views within a broader framework, it is capable of accounting for less similar replications to more similar ones regarding the baseline experiment. Conclusion: The aim of replication is to verify results, but different types of replication serve special ver- ification purposes and afford different degrees of change. Each replication type helps to discover partic- ular experimental conditions that might influence the results. The proposed classification can be used to identify changes in a replication and, based on these, understand the level of verification.

Determinación de la amplificación sísmica local en el Campus Sur UPM mediante la técnica de sismología de microtremores ReMi.

En el Campus Sur de la Universidad Politécnica de Madrid se ha llevado a cabo un proyecto para obtener una caracterización del subsuelo mediante ensayos ReMi, en colaboración con el departamento de Geofísica del Instituto Geográfico Nacional. La técnica ReMi (Refraction Microtremor) permite, mediante ensayos geofísicos realizados localmente sobre el terreno,obtener los parámetros físicos del mismo, que resultan de especial interés en el ámbito de la ingeniería civil. Esta técnica se caracteriza por englobarse dentro de la sísmica pasiva, muy empleada en prospección geofísica y basada en la obtención del modelo subyacente de distribución de velocidades de propagación de la onda S en función de la profundidad, con la ventaja de aprovechar el ruido sísmico ambiental como fuente de energía. Fue desarrollada en el Laboratorio Sismológico de Nevada (EEUU) por Louie (2001), con el objetivo de presentar una técnica innovadora en la obtención de las velocidades de propagación de manera experimental. Presenta ciertas ventajas, como la observación directa de la dispersión de ondas superficiales,que da un buen resultado de la velocidad de onda S, siendo un método no invasivo, de bajo coste y buena resolución, aplicable en entornos urbanos o sensibles en los que tanto otras técnicas sismológicas como otras variedades de prospección presentan dificultades. La velocidad de propagación de la onda S en los 30 primeros metros VS30, es ampliamente reconocida como un parámetro equivalente válido para caracterizar geotécnicamente el subsuelo y se halla matemáticamente relacionada con la velocidad de propagación de las ondas superficiales a observar mediante la técnica ReMi. Su observación permite el análisis espectral de los registros adquiridos, obteniéndose un modelo representado por la curva de dispersión de cada emplazamiento, de modo que mediante una inversión se obtiene el modelo de velocidad de propagación en función de la profundidad. A través de estos modelos, pueden obtenerse otros parámetros de interés sismológico. Estos resultados se representan sobre mapas isométricos para obtener una relación espacial de los mismos, particularmente conocido como zonación sísmica. De este análisis se extrae que la VS30 promedio del Campus no es baja en exceso, correspondiéndose a posteriori con los resultados de amplificación sísmica, período fundamental de resonancia del lugar y profundidad del sustrato rocoso. En última instancia se comprueba que los valores de amplificación sísmica máxima y el período al cual se produce posiblemente coincidan con los períodos fundamentales de resonancia de algunos edificios del Campus. ABSTRACT In South Campus at Polytechnic University of Madrid, a project has been carried out to obtain a proper subsoil description by applying ReMi tests, in collaboration with the Department of Geophysics of the National Geographic Institute. Through geophysical tests conducted locally, the ReMi (Refraction Microtremor) technique allows to establish the physical parameters of soil, which are of special interest in the field of civil engineering. This technique is part of passive seismic methods, often used in geophysical prospecting. It focuses in obtaining the underlying model of propagation velocity distribution of the shear wave according to depth and has the advantage of being able to use seismic ambient noise as a source of energy. It was developed in the Nevada Seismological Laboratory (USA) by Louie (2001) as an innovative technique for obtaining propagation velocities experimentally. It has several other advantages, including the direct observation of the dispersion of surface waves, which allows to reliably measure S wave velocity. This is a non-invasive, low cost and good resolution method, which can be applied in urban or sensitive environments where other prospection methods present difficulties. The propagation velocity of shear waves in the first 30 meters Vs30 is widely recognized as a valid equivalent parameter to geotechnically characterize the subsurface. It is mathematically related to surface wave's velocity of propagation, which are to observe using REMI technique. Spectral analysis of acquired data sets up a model represented by the dispersion curve at each site, so that, using an inversion process, propagation velocity model in relation to depth is obtained. Through this models, other seismologically interesting parameters can be obtained. These results are represented on isometric maps in order to obtain a spatial relationship between them, a process which is known as seismic zonation. This analysis infers that Vs30 at South Campus is not alarmingly low , corresponding with subsequent results of seismic amplification, fundamental period of resonance of soil and depth of bedrock. Ultimately, it's found that calculated values of soil's fundamental periods at which maximum seismic amplification occurs, may possibly match fundamental periods of some Campus buildings.

A bankable method of assessing the performance of a CPV plant

Concentrating Photovoltaics (CPV) is an alternative to flat-plate module photovoltaic (PV) technology. The bankability of CPV projects is an important issue to pave the way toward a swift and sustained growth in this technology. The bankability of a PV plant is generally addressed through the modeling of its energy yield under a baseline loss scenario, followed by an on-site measurement campaign aimed at verifying its energy performance. This paper proposes a procedure for assessing the performance of a CPV project, articulated around four main successive steps: Solar Resource Assessment, Yield Assessment, Certificate of Provisional Acceptance, and Certificate of Final Acceptance. This methodology allows the long-term energy production of a CPV project to be estimated with an associated uncertainty of ≈5%. To our knowledge, no such method has been proposed to the CPV industry yet, and this critical situation has hindered or made impossible the completion of several important CPV projects undertaken in the world. The main motive for this proposed method is to bring a practical solution to this urgent problem. This procedure can be operated under a wide range of climatic conditions, and makes it possible to assess the bankability of a CPV plant whose design uses any of the technologies currently available on the market. The method is also compliant with both international standards and local regulations. In consequence, its applicability is both general and international.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Sistemas de gestión de puentes : optimización de estrategias de mantenimiento : implementación en redes locales de carreteras

En la actualidad, y en consonancia con la tendencia de “sostenibilidad” extendida a todos los campos y parcelas de la ciencia, nos encontramos con un área de estudio basado en la problemática del inevitable deterioro de las estructuras existentes, y la gestión de las acciones a realizar para mantener las condiciones de servicio de los puentes y prolongar su vida útil. Tal y como se comienza a ver en las inversiones en los países avanzados, con una larga tradición en el desarrollo de sus infraestructuras, se muestra claramente el nuevo marco al que nos dirigimos. Las nuevas tendencias van encaminadas cada vez más a la conservación y mantenimiento, reduciéndose las partidas presupuestarias destinadas a nuevas actuaciones, debido a la completa vertebración territorial que se ha ido instaurando en estos países, entre los que España se encuentra. Este nutrido patrimonio de infraestructuras viarias, que cuentan a su vez con un importante número de estructuras, hacen necesarias las labores de gestión y mantenimiento de los puentes integrantes en las mismas. Bajo estas premisas, la tesis aborda el estado de desarrollo de la implementación de los sistemas de gestión de puentes, las tendencias actuales e identificación de campos por desarrollar, así como la aplicación específica a redes de carreteras de escasos recursos, más allá de la Red Estatal. Además de analizar las diversas metodologías de formación de inventarios, realización de inspecciones y evaluación del estado de puentes, se ha enfocado, como principal objetivo, el desarrollo de un sistema específico de predicción del deterioro y ayuda a la toma de decisiones. Este sistema, adicionalmente a la configuración tradicional de criterios de formación de bases de datos de estructuras e inspecciones, plantea, de forma justificada, la clasificación relativa al conjunto de la red gestionada, según su estado de condición. Eso permite, mediante técnicas de optimización, la correcta toma de decisiones a los técnicos encargados de la gestión de la red. Dentro de los diversos métodos de evaluación de la predicción de evolución del deterioro de cada puente, se plantea la utilización de un método bilineal simplificado envolvente del ajuste empírico realizado y de los modelos markovianos como la solución más efectiva para abordar el análisis de la predicción de la propagación del daño. Todo ello explotando la campaña experimenta realizada que, a partir de una serie de “fotografías técnicas” del estado de la red de puentes gestionados obtenidas mediante las inspecciones realizadas, es capaz de mejorar el proceso habitual de toma de decisiones. Toda la base teórica reflejada en el documento, se ve complementada mediante la implementación de un Sistema de Gestión de Puentes (SGP) específico, adaptado según las necesidades y limitaciones de la administración a la que se ha aplicado, en concreto, la Dirección General de Carreteras de la Junta de Comunidades de Castilla-La Mancha, para una muestra representativa del conjunto de puentes de la red de la provincia de Albacete, partiendo de una situación en la que no existe, actualmente, un sistema formal de gestión de puentes. Tras un meditado análisis del estado del arte dentro de los Capítulos 2 y 3, se plantea un modelo de predicción del deterioro dentro del Capítulo 4 “Modelo de Predicción del Deterioro”. De la misma manera, para la resolución del problema de optimización, se justifica la utilización de un novedoso sistema de optimización secuencial elegido dentro del Capítulo 5, los “Algoritmos Evolutivos”, en sus diferentes variantes, como la herramienta matemática más correcta para distribuir adecuadamente los recursos económicos dedicados a mantenimiento y conservación de los que esta administración pueda disponer en sus partidas de presupuesto a medio plazo. En el Capítulo 6, y en diversos Anexos al presente documento, se muestran los datos y resultados obtenidos de la aplicación específica desarrollada para la red local analizada, utilizando el modelo de deterioro y optimización secuencial, que garantiza la correcta asignación de los escasos recursos de los que disponen las redes autonómicas en España. Se plantea con especial interés la implantación de estos sistemas en la red secundaria española, debido a que reciben en los últimos tiempos una mayor responsabilidad de gestión, con recursos cada vez más limitados. Finalmente, en el Capítulo 7, se plantean una serie de conclusiones que nos hacen reflexionar de la necesidad de comenzar a pasar, en materia de gestión de infraestructuras, de los estudios teóricos y los congresos, hacia la aplicación y la práctica, con un planteamiento que nos debe llevar a cambios importantes en la forma de concebir la labor del ingeniero y las enseñanzas que se imparten en las escuelas. También se enumeran las aportaciones originales que plantea el documento frente al actual estado del arte. Se plantean, de la misma manera, las líneas de investigación en materia de Sistemas de Gestión de Puentes que pueden ayudar a refinar y mejorar los actuales sistemas utilizados. In line with the development of "sustainability" extended to all fields of science, we are faced with the inevitable and ongoing deterioration of existing structures, leading nowadays to the necessary management of maintaining the service conditions and life time extension of bridges. As per the increased amounts of money that can be observed being spent in the countries with an extensive and strong tradition in the development of their infrastructure, the trend can be clearly recognized. The new tendencies turn more and more towards conservation and maintenance, reducing programmed expenses for new construction activities, in line with the already wellestablished territorial structures, as is the case for Spain. This significant heritage of established road infrastructure, consequently containing a vast number of structures, imminently lead to necessary management and maintenance of the including bridges. Under these conditions, this thesis focusses on the status of the development of the management implementation for bridges, current trends, and identifying areas for further development. This also includes the specific application to road networks with limited resources, beyond the national highways. In addition to analyzing the various training methodologies, inventory inspections and condition assessments of bridges, the main objective has been the development of a specific methodology. This methodology, in addition to the traditional system of structure and inspection database training criteria, sustains the classification for the entire road network, according to their condition. This allows, through optimization techniques, for the correct decision making by the technical managers of the network. Among the various methods for assessing the evolution forecast of deterioration of each bridge, a simplified bilinear envelope adjustment made empirical method and Markov models as the most effective solution to address the analysis of predicting the spread of damage, arising from a "technical snapshot" obtained through inspections of the condition of the bridges included in the investigated network. All theoretical basis reflected in the document, is completed by implementing a specific Bridges Management System (BMS), adapted according to the needs and limitations of the authorities for which it has been applied, being in this case particularly the General Highways Directorate of the autonomous region of Castilla-La Mancha, for a representative sample of all bridges in the network in the province of Albacete, starting from a situation where there is currently no formal bridge management system. After an analysis of the state of the art in Chapters 2 and 3, a new deterioration prediction model is developed in Chapter 4, "Deterioration Prediction Model". In the same way, to solve the optimization problem is proposed the use of a singular system of sequential optimization elected under Chapter 5, the "Evolutionary Algorithms", the most suitable mathematical tool to adequately distribute the economic resources for maintenance and conservation for mid-term budget planning. In Chapter 6, and in the various appendices, data and results are presented of the developed application for the analyzed local network, from the optimization model, which guarantees the correct allocation of scarce resources at the disposal of authorities responsible for the regional networks in Spain. The implementation of these systems is witnessed with particular interest for the Spanish secondary network, because of the increasing management responsibility, with decreasing resources. Chapter 7 presents a series of conclusions that triggers to reconsider shifting from theoretical studies and conferences towards a practical implementation, considering how to properly conceive the engineering input and the related education. The original contributions of the document are also listed. In the same way, the research on the Bridges Management System can help evaluating and improving the used systematics.

The estimation of statistical parameters for local alignment score distributions

The distribution of optimal local alignment scores of random sequences plays a vital role in evaluating the statistical significance of sequence alignments. These scores can be well described by an extreme-value distribution. The distribution’s parameters depend upon the scoring system employed and the random letter frequencies; in general they cannot be derived analytically, but must be estimated by curve fitting. For obtaining accurate parameter estimates, a form of the recently described ‘island’ method has several advantages. We describe this method in detail, and use it to investigate the functional dependence of these parameters on finite-length edge effects.

Phylogeny of genes for secretion NTPases: Identification of the widespread tadA subfamily and development of a diagnostic key for gene classification

Macromolecular transport systems in bacteria currently are classified by function and sequence comparisons into five basic types. In this classification system, type II and type IV secretion systems both possess members of a superfamily of genes for putative NTP hydrolase (NTPase) proteins that are strikingly similar in structure, function, and sequence. These include VirB11, TrbB, TraG, GspE, PilB, PilT, and ComG1. The predicted protein product of tadA, a recently discovered gene required for tenacious adherence of Actinobacillus actinomycetemcomitans, also has significant sequence similarity to members of this superfamily and to several unclassified and uncharacterized gene products of both Archaea and Bacteria. To understand the relationship of tadA and tadA-like genes to those encoding the putative NTPases of type II/IV secretion, we used a phylogenetic approach to obtain a genealogy of 148 NTPase genes and reconstruct a scenario of gene superfamily evolution. In this phylogeny, clear distinctions can be made between type II and type IV families and their constituent subfamilies. In addition, the subgroup containing tadA constitutes a novel and extremely widespread subfamily of the family encompassing all putative NTPases of type IV secretion systems. We report diagnostic amino acid residue positions for each major monophyletic family and subfamily in the phylogenetic tree, and we propose an easy method for precisely classifying and naming putative NTPase genes based on phylogeny. This molecular key-based method can be applied to other gene superfamilies and represents a valuable tool for genome analysis.

Creating local bibliographic databases: new tools for evidence-based health care*

The Internet has created new opportunities for librarians to present literature search results to clinicians. In order to take full advantage of these opportunities, libraries need to create locally maintained bibliographic databases. A simple method of creating a local bibliographic database and publishing it on the Web is described. The method uses off-the-shelf software and requires minimal programming. A hedge search strategy for outcome studies of clinical process interventions is created, and Ovid is used to search MEDLINE. The search results are saved and imported into EndNote libraries. The citations are modified, exported to a Microsoft Access database, and published on the Web. Clinicians can use a Web browser to search the database. The bibliographic database contains 13,803 MEDLINE citations of outcome studies. Most searches take between four and ten seconds and retrieve between ten and 100 citations. The entire cost of the software is under $900. Locally maintained bibliographic databases can be created easily and inexpensively. They significantly extend the evidence-based health care services that libraries can offer to clinicians.

A fast marching level set method for monotonically advancing fronts.

A fast marching level set method is presented for monotonically advancing fronts, which leads to an extremely fast scheme for solving the Eikonal equation. Level set methods are numerical techniques for computing the position of propagating fronts. They rely on an initial value partial differential equation for a propagating level set function and use techniques borrowed from hyperbolic conservation laws. Topological changes, corner and cusp development, and accurate determination of geometric properties such as curvature and normal direction are naturally obtained in this setting. This paper describes a particular case of such methods for interfaces whose speed depends only on local position. The technique works by coupling work on entropy conditions for interface motion, the theory of viscosity solutions for Hamilton-Jacobi equations, and fast adaptive narrow band level set methods. The technique is applicable to a variety of problems, including shape-from-shading problems, lithographic development calculations in microchip manufacturing, and arrival time problems in control theory.