Highly conductive coatings of carbon black/silica composites obtained by a sol-gel process

Conductive submicronic coatings of carbon black (CB)/silica composites have been prepared by a sol-gel process and deposited by spray-coating on glazed porcelain tiles. Stable CB dispersions with surfactant were rheologically characterized to determine the optimum CB-surfactant ratio. The composites were analyzed by Differential Thermal and Thermogravimetric Analysis and Hg-Porosimetry. Thin coatings were thermally treated in the temperature range of 300-500degC in air atmosphere. The microstructure of the coatings was determined by scanning electron microscopy and the structure evaluated by confocal Raman spectroscopy. The electrical characterization of the samples was carried out using dc intensity-voltage curves. The coatings exhibit good adhesion, high density and homogeneous distribution of the conductive filler (CB) in the insulate matrix (silica) that protects against the thermal degradation of the CB nanoparticles during the sintering process. As consequence, the composite coatings show the lowest resistivity values for CB-based films reported in the literature, with values of ~7times10 -5Omegam.

Assortative and modular networks are shaped by adaptive synchronization processes

Modular organization and degree-degree correlations are ubiquitous in the connectivity structure of biological, technological, and social interacting systems. So far most studies have concentrated on unveiling both features in real world networks, but a model that succeeds in generating them simultaneously is needed. We consider a network of interacting phase oscillators, and an adaptation mechanism for the coupling that promotes the connection strengths between those elements that are dynamically correlated. We show that, under these circumstances, the dynamical organization of the oscillators shapes the topology of the graph in such a way that modularity and assortativity features emerge spontaneously and simultaneously. In turn, we prove that such an emergent structure is associated with an asymptotic arrangement of the collective dynamical state of the network into cluster synchronization.

Nonlocal analysis of modular roles

We introduce a new methodology to characterize the role that a given node plays inside the community structure of a complex network. Our method relies on the ability of the links to reduce the number of steps between two nodes in the network, which is measured by the number of shortest paths crossing each link, and its impact on the node proximity. In this way, we use node closeness to quantify the importance of a node inside its community. At the same time, we define a participation coefficient that depends on the shortest paths contained in the links that connect two communities. The combination of both parameters allows to identify the role played by the nodes in the network, following the same guidelines introduced by Guimerà et al. [Guimerà & Amaral, 2005] but, in this case, considering global information about the network. Finally, we give some examples of the hub characterization in real networks and compare our results with the parameters most used in the literature.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Metodología de análisis estadístico de roturas en redes de distribución de agua

La presente Tesis plantea una metodología de análisis estadístico de roturas de tubería en redes de distribución de agua, que analiza la relación entre las roturas y la presión de agua y que propone la implantación de una gestión de presiones que reduzca el número de roturas que se producen en dichas redes. Las redes de distribución de agua se deterioran y una de sus graves consecuencias es la aparición de roturas frecuentes en sus tuberías. Las roturas llevan asociados elevados costes sociales, económicos y medioambientales y es por ello por lo que las compañías gestoras del agua tratan de reducirlas en la medida de lo posible. Las redes de distribución de agua se pueden dividir en zonas o sectores que facilitan su control y que pueden ser independientes o aislarse mediante válvulas, como ocurre en las redes de países más desarrollados, o pueden estar intercomunicados hidráulicamente. La implantación de una gestión de presiones suele llevarse a cabo a través de las válvulas reductoras de presión (VPR), que se instalan en las cabeceras de estos sectores y que controlan la presión aguas abajo de la misma, aunque varíe su caudal de entrada. Los métodos más conocidos de la gestión de presiones son la reducción de presiones, que es el control más habitual, el mantenimiento de la presión, la prevención y/o alivio de los aumentos repentinos de presión y el establecimiento de un control por alturas. A partir del año 2005 se empezó a reconocer el efecto de la gestión de presiones sobre la disminución de las roturas. En esta Tesis, se sugiere una gestión de presiones que controle los rangos de los indicadores de la presión de cabecera que más influyan en la probabilidad de roturas de tubería. Así, la presión del agua se caracteriza a través de indicadores obtenidos de la presión registrada en la cabecera de los sectores, debido a que se asume que esta presión es representativa de la presión de operación de todas las tuberías porque las pérdidas de carga son relativamente bajas y las diferencias topográficas se tienen en cuenta en el diseño de los sectores. Y los indicadores de presión, que se pueden definir como el estadístico calculado a partir de las series de la presión de cabecera sobre una ventana de tiempo, pueden proveer la información necesaria para ayudar a la toma de decisiones a los gestores del agua con el fin de reducir las roturas de tubería en las redes de distribución de agua. La primera parte de la metodología que se propone en esta Tesis trata de encontrar los indicadores de presión que influyen más en la probabilidad de roturas de tuberías. Para conocer si un indicador es influyente en la probabilidad de las roturas se comparan las estimaciones de las funciones de distribución acumulada (FDAs) de los indicadores de presiones, considerando dos situaciones: cuando se condicionan a la ocurrencia de una rotura (suceso raro) y cuando se calculan en la situación normal de operación (normal operación). Por lo general, las compañías gestoras cuentan con registros de roturas de los años más recientes y al encontrarse las tuberías enterradas se complica el acceso a la información. Por ello, se propone el uso de funciones de probabilidad que permiten reducir la incertidumbre asociada a los datos registrados. De esta forma, se determinan las funciones de distribución acumuladas (FDAs) de los valores del indicador de la serie de presión (situación normal de operación) y las FDAs de los valores del indicador en el momento de ocurrencia de las roturas (condicionado a las roturas). Si las funciones de distribución provienen de la misma población, no se puede deducir que el indicador claramente influya en la probabilidad de roturas. Sin embargo, si se prueba estadísticamente que las funciones proceden de la misma población, se puede concluir que existe una relación entre el indicador analizado y la ocurrencia de las roturas. Debido a que el número de valores del indicador de la FDA condicionada a las roturas es mucho menor que el número de valores del indicador de la FDA incondicional a las roturas, se generan series aleatorias a partir de los valores de los indicadores con el mismo número de valores que roturas registradas hay. De esta forma, se comparan las FDAs de series aleatorias del indicador con la FDA condicionada a las roturas del mismo indicador y se deduce si el indicador es influyente en la probabilidad de las roturas. Los indicadores de presión pueden depender de unos parámetros. A través de un análisis de sensibilidad y aplicando un test estadístico robusto se determina la situación en la que estos parámetros dan lugar a que el indicador sea más influyente en la probabilidad de las roturas. Al mismo tiempo, los indicadores se pueden calcular en función de dos parámetros de cálculo que se denominan el tiempo de anticipación y el ancho de ventana. El tiempo de anticipación es el tiempo (en horas) entre el final del periodo de computación del indicador de presión y la rotura, y el ancho de ventana es el número de valores de presión que se requieren para calcular el indicador de presión y que es múltiplo de 24 horas debido al comportamiento cíclico diario de la presión. Un análisis de sensibilidad de los parámetros de cálculo explica cuándo los indicadores de presión influyen más en la probabilidad de roturas. En la segunda parte de la metodología se presenta un modelo de diagnóstico bayesiano. Este tipo de modelo forma parte de los modelos estadísticos de prevención de roturas, parten de los datos registrados para establecer patrones de fallo y utilizan el teorema de Bayes para determinar la probabilidad de fallo cuando se condiciona la red a unas determinadas características. Así, a través del teorema de Bayes se comparan la FDA genérica del indicador con la FDA condicionada a las roturas y se determina cuándo la probabilidad de roturas aumenta para ciertos rangos del indicador que se ha inferido como influyente en las roturas. Se determina un ratio de probabilidad (RP) que cuando es superior a la unidad permite distinguir cuándo la probabilidad de roturas incrementa para determinados intervalos del indicador. La primera parte de la metodología se aplica a la red de distribución de la Comunidad de Madrid (España) y a la red de distribución de Ciudad de Panamá (Panamá). Tras el filtrado de datos se deduce que se puede aplicar la metodología en 15 sectores en la Comunidad de Madrid y en dos sectores, llamados corregimientos, en Ciudad de Panamá. Los resultados demuestran que en las dos redes los indicadores más influyentes en la probabilidad de las roturas son el rango de la presión, que supone la diferencia entre la presión máxima y la presión mínima, y la variabilidad de la presión, que considera la propiedad estadística de la desviación típica. Se trata, por tanto, de indicadores que hacen referencia a la dispersión de los datos, a la persistencia de la variación de la presión y que se puede asimilar en resistencia de materiales a la fatiga. La segunda parte de la metodología se ha aplicado a los indicadores influyentes en la probabilidad de las roturas de la Comunidad de Madrid y se ha deducido que la probabilidad de roturas aumenta para valores extremos del indicador del rango de la presión y del indicador de la variabilidad de la presión. Finalmente, se recomienda una gestión de presiones que limite los intervalos de los indicadores influyentes en la probabilidad de roturas que incrementen dicha probabilidad. La metodología propuesta puede aplicarse a otras redes de distribución y puede ayudar a las compañías gestoras a reducir el número de fallos en el sistema a través de la gestión de presiones. This Thesis presents a methodology for the statistical analysis of pipe breaks in water distribution networks. The methodology studies the relationship between pipe breaks and water pressure, and proposes a pressure management procedure to reduce the number of breaks that occur in such networks. One of the manifestations of the deterioration of water supply systems is frequent pipe breaks. System failures are one of the major challenges faced by water utilities, due to their associated social, economic and environmental costs. For all these reasons, water utilities aim at reducing the problem of break occurrence to as great an extent as possible. Water distribution networks can be divided into areas or sectors, which facilitates the control of the network. These areas may be independent or isolated by valves, as it usually happens in developing countries. Alternatively, they can be hydraulically interconnected. The implementation of pressure management strategies is usually carried out through pressure-reducing valves (PRV). These valves are installed at the head of the sectors and, although the inflow may vary significantly, they control the downstream pressure. The most popular methods of pressure management consist of pressure reduction, which is the common form of control, pressure sustaining, prevention and/or alleviation of pressure surges or large variations in pressure, and level/altitude control. From 2005 onwards, the effects of pressure management on burst frequencies have become more widely recognized in the technical literature. This thesis suggests a pressure management that controls the pressure indicator ranges most influential on the probability of pipe breaks. Operating pressure in a sector is characterized by means of a pressure indicator at the head of the DMA, as head losses are relatively small and topographical differences were accounted for at the design stage. The pressure indicator, which may be defined as the calculated statistic from the time series of pressure head over a specific time window, may provide necessary information to help water utilities to make decisions to reduce pipe breaks in water distribution networks. The first part of the methodology presented in this Thesis provides the pressure indicators which have the greatest impact on the probability of pipe breaks to be determined. In order to know whether a pressure indicator influences the probability of pipe breaks, the proposed methodology compares estimates of cumulative distribution functions (CDFs) of a pressure indicator through consideration of two situations: when they are conditioned to the occurrence of a pipe break (a rare event), and when they are not (a normal operation). Water utilities usually have a history of failures limited to recent periods of time, and it is difficult to have access to precise information in an underground network. Therefore, the use of distribution functions to address such imprecision of recorded data is proposed. Cumulative distribution functions (CDFs) derived from the time series of pressure indicators (normal operation) and CDFs of indicator values at times coincident with a reported pipe break (conditioned to breaks) are compared. If all estimated CDFs are drawn from the same population, there is no reason to infer that the studied indicator clearly influences the probability of the rare event. However, when it is statistically proven that the estimated CDFs do not come from the same population, the analysed indicator may have an influence on the occurrence of pipe breaks. Due to the fact that the number of indicator values used to estimate the CDF conditioned to breaks is much lower in comparison with the number of indicator values to estimate the CDF of the unconditional pressure series, and that the obtained results depend on the size of the compared samples, CDFs from random sets of the same size sampled from the unconditional indicator values are estimated. Therefore, the comparison between the estimated CDFs of random sets of the indicator and the estimated CDF conditioned to breaks allows knowledge of if the indicator is influential on the probability of pipe breaks. Pressure indicators depend on various parameters. Sensitivity analysis and a robust statistical test allow determining the indicator for which these parameters result most influential on the probability of pipe breaks. At the same time, indicators can be calculated according to two model parameters, named as the anticipation time and the window width. The anticipation time refers to the time (hours) between the end of the period for the computation of the pressure indicator and the break. The window width is the number of instantaneous pressure values required to calculate the pressure indicator and is multiple of 24 hours, as water pressure has a cyclical behaviour which lasts one day. A sensitivity analysis of the model parameters explains when the pressure indicator is more influential on the probability of pipe breaks. The second part of the methodology presents a Bayesian diagnostic model. This kind of model belongs to the class of statistical predictive models, which are based on historical data, represent break behavior and patterns in water mains, and use the Bayes’ theorem to condition the probability of failure to specific system characteristics. The Bayes’ theorem allows comparing the break-conditioned FDA and the unconditional FDA of the indicators and determining when the probability of pipe breaks increases for certain pressure indicator ranges. A defined probability ratio provides a measure to establish whether the probability of breaks increases for certain ranges of the pressure indicator. The first part of the methodology is applied to the water distribution network of Madrid (Spain) and to the water distribution network of Panama City (Panama). The data filtering method suggests that the methodology can be applied to 15 sectors in Madrid and to two areas in Panama City. The results show that, in both systems, the most influential indicators on the probability of pipe breaks are the pressure range, which is the difference between the maximum pressure and the minimum pressure, and pressure variability, referred to the statistical property of the standard deviation. Therefore, they represent the dispersion of the data, the persistence of the variation in pressure and may be related to the fatigue in material resistance. The second part of the methodology has been applied to the influential indicators on the probability of pipe breaks in the water distribution network of Madrid. The main conclusion is that the probability of pipe breaks increases for the extreme values of the pressure range indicator and of the pressure variability indicator. Finally, a pressure management which limits the ranges of the pressure indicators influential on the probability of pipe breaks that increase such probability is recommended. The methodology presented here is general, may be applied to other water distribution networks, and could help water utilities reduce the number of system failures through pressure management.

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.

Cognitive wireless sensor network platform for cooperative communications

Nowadays, Wireless Ad Hoc Sensor Networks (WAHSNs), specially limited in energy and resources, are subject to development constraints and difficulties such as the increasing RF spectrum saturation at the unlicensed bands. Cognitive Wireless Sensor Networks (CWSNs), leaning on a cooperative communication model, develop new strategies to mitigate the inefficient use of the spectrum that WAHSNs face. However, few and poorly featured platforms allow their study due to their early research stage. This paper presents a versatile platform that brings together cognitive properties into WAHSNs. It combines hardware and software modules as an entire instrument to investigate CWSNs. The hardware fits WAHSN requirements in terms of size, cost, features, and energy. It allows communication over three different RF bands, becoming the only cognitive platform for WAHSNs with this capability. In addition, its modular and scalable design is widely adaptable to almost any WAHSN application. Significant features such as radio interface (RI) agility or energy consumption have been proven throughout different performance tests.

Novel dual-band single circular polarization antenna feeding network for satellite communications

In this paper a novel dual-band single circular polarization antenna feeding network for satellite communications is presented. The novel antenna feed chain1 is composed of two elements or subsystems, namely a diplexer and a bi-phase polarizer. In comparison with the classic topology based on an orthomode transducer and a dual-band polarizer, the proposed feed chain presents several advantages, such as compactness, modular design of the different components, broadband operation and versatility in the subsystems interconnection. The design procedure of this new antenna feed configuration is explained. Different examples of antenna feeding networks at 20/30 GHz are presented. It is pointed out the excellent results obtained in terms of isolation and axial ratio.

Polymer interaction with macroscopic carbon nanotube fibres and fabrication of nanostructured composites

El ensamblado de nanotubos de carbono (CNT) como una fibra macroscópica en la cual están orientados preferentemente paralelos entre sí y al eje de la fibra, ha dado como resultado un nuevo tipo de fibra de altas prestaciones derivadas de la explotación eficiente de las propiedades axiales de los CNTs, y que tiene un gran número de aplicaciones potenciales. Fibras continuas de CNTs se produjeron en el Instituto IMDEA Materiales mediante el proceso de hilado directo durante la reacción de síntesis por deposición química de vapores. Uno de los objetivos de esta tesis es el estudio de la estructura de estas fibras mediante técnicas del estado del arte de difracción de rayos X de sincrotrón y la elaboración de un modelo estructural de dicho material. Mediciones texturales de adsorción de gases, análisis de micrografías de electrones y dispersión de rayos X de ángulo alto y bajo (WAXS/SAXS) indican que el material tiene una estructura mesoporosa con una distribución de tamaño de poros ancha derivada del amplio rango de separaciones entre manojos de CNTs, así como una superficie específica de 170m2/g. Los valores de dimensión fractal obtenidos mediante SAXS y análisis Barrett-Joyner-Halenda (BJH) de mediciones texturales coinciden en 2.4 y 2.5, respectivamente, resaltando el carácter de red de la estructura de dichas fibras. La estructura mesoporosa y tipo hilo de las fibra de CNT es accesible a la infiltración de moléculas externas (líquidos o polímeros). En este trabajo se estudian los cambios en la estructura multiescala de las fibras de CNTs al interactuar con líquidos y polímeros. Los efectos de la densificación en la estructura de fibras secas de CNT son estudiados mediante WAXS/SAXS. El tratamiento de densificación junta los manojos de la fibra (los poros disminuyen de tamaño), resultando en un incremento de la densidad de la fibra. Sin embargo, los dominios estructurales correspondientes a la transferencia de esfuerzo mecánica y carga eléctrica en los nanotubos no son afectados durante este proceso de densificación; como consecuencia no se produce un efecto sustancial en las propiedades mecánicas y eléctricas. Mediciones de SAXS and fibra de CNT antes y después de infiltración de líquidos confirman la penetración de una gran cantidad de líquidos que llena los poros internos de la fibra pero no se intercalan entre capas de nanotubos adyacentes. La infiltración de cadenas poliméricas de bajo peso molecular tiende a expandir los manojos en la fibra e incrementar el ángulo de apertura de los poros. Los resultados de SAXS indican que la estructura interna de la fibra en términos de la organización de las capas de tubos y su orientación no es afectada cuando las muestras consisten en fibras infiltradas con polímeros de alto peso molecular. La cristalización de varios polímeros semicristalinos es acelerada por la presencia de fibras de CNTs alineados y produce el crecimiento de una capa transcristalina normal a la superficie de la fibra. Esto es observado directamente mediante microscopía óptica polarizada, y detectado mediante calorimetría DSC. Las lamelas en la capa transcristalina tienen orientación de la cadena polimérica paralela a la fibra y por lo tanto a los nanotubos, de acuerdo con los patrones de WAXS. Esta orientación preferencial se sugiere como parte de la fuerza impulsora en la nucleación. La nucleación del dominio cristalino polimérico en la superficie de los CNT no es epitaxial. Ocurre sin haber correspondencia entre las estructuras cristalinas del polímero y los nanotubos. Estas observaciones contribuyen a la compresión del fenómeno de nucleación en CNTs y otros nanocarbonos, y sientan las bases para el desarrollo de composites poliméricos de gran escala basados en fibra larga de CNTs alineados. ABSTRACT The assembly of carbon nanotubes into a macroscopic fibre material where they are preferentially aligned parallel to each other and to the fibre axis has resulted in a new class of high-performance fibres, which efficiently exploits the axial properties of the building blocks and has numerous applications. Long, continuous CNT fibres were produced in IMDEA Materials Institute by direct fibre spinning from a chemical vapour deposition reaction. These fibres have a complex hierarchical structure covering multiple length scales. One objective of this thesis is to reveal this structure by means of state-of-the-art techniques such as synchrotron X-ray diffraction, and to build a model to link the fibre structural elements. Texture and gas absorption measurements, using electron microscopy, wide angle and small angle X-ray scattering (WAXS/SAXS), and pore size distribution analysis by Barrett-Joyner-Halenda (BJH), indicate that the material has a mesoporous structure with a wide pore size distribution arising from the range of fibre bundle separation, and a high surface area _170m2/g. Fractal dimension values of 2.4_2.5 obtained from the SAXS and BJH measurements highlight the network structure of the fibre. Mesoporous and yarn-like structure of CNT fibres make them accessible to the infiltration of foreign molecules (liquid or polymer). This work studies multiscale structural changes when CNT fibres interact with liquids and polymers. The effects of densification on the structure of dry CNT fibres were measured by WAXS/SAXS. The densification treatment brings the fibre bundles closer (pores become smaller), leading to an increase in fibre density. However, structural domains made of the load and charge carrying nanotubes are not affected; consequently, it has no substantial effect on mechanical and electrical properties. SAXS measurements on the CNT fibres before and after liquid infiltration imply that most liquids are able to fill the internal pores but not to intercalate between nanotubes. Successful infiltration of low molecular weight polymer chains tends to expand the fibre bundles and increases the pore-opening angle. SAXS results indicate that the inner structure of the fibre, in terms of the nanotube layer arrangement and the fibre alignment, are not largely affected when infiltrated with polymers of relatively high molecular weight. The crystallisation of a variety of semicrystalline polymers is accelerated by the presence of aligned fibres of CNTs and results in the growth of a transcrystalline layer perpendicular to the fibre surface. This can be observed directly under polarised optical microscope, and detected by the exothermic peaks during differential scanning calorimetry. The discussion on the driving forces for the enhanced nucleation points out the preferential chain orientation of polymer lamella with the chain axis parallel to the fibre and thus to the nanotubes, which is confirmed by two-dimensional WAXS patterns. A non-epitaxial polymer crystal growth habit at the CNT-polymer interface is proposed, which is independent of lattice matching between the polymer and nanotubes. These findings contribute to the discussion on polymer nucleation on CNTs and other nanocarbons, and their implication for the development of large polymer composites based on long and aligned fibres of CNTs.

Fatiga en CFRP: Caracterización y Método Óptico para la Estimación del Daño

El desarrollo de nuevas estructuras aeroespaciales optimizadas, utilizan materiales compuestos, para los componentes críticos y subsistemas, principalmente polímeros reforzados con fibra de carbono (CFRP). Un conocimiento profundo del estado de daño por fatiga de estructuras de CFRP avanzado, es esencial para predecir la vida residual y optimizar los intervalos de inspección estructural, reparaciones y/o sustitución de componentes. Las técnicas actuales se basan principalmente en la medición de cargas estructurales a lo largo de la vida útil de la estructura mediante galgas extensométricas eléctricas. Con esos datos, se estima la vida a fatiga utilizando modelos de acumulación de daño. En la presente tesis, se evalúa la metodología convencional para la estimación de la vida a fatiga de un CFRP aeronáutico. Esta metodología está basada en la regla de acumulación de daño lineal de Palmgren-Miner, y es aplicada para determinar la vida a fatiga de estructuras sometidas a cargas de amplitud variable. Se ha realizado una campaña de ensayos con cargas de amplitud constante para caracterizar un CFRP aeronáutico a fatiga, obteniendo las curvas clásicas S-N, en diferentes relaciones de esfuerzo. Se determinaron los diagramas de vida constante, (CLD), también conocidos como diagramas de Goodman, utilizando redes neuronales artificiales debido a la ausencia de modelos coherentes para materiales compuestos. Se ha caracterizado la degradación de la rigidez debido al daño por fatiga. Se ha ensayado un segundo grupo de probetas con secuencias estandarizadas de cargas de amplitud variable, para obtener la vida a fatiga y la degradación de rigidez en condiciones realistas. Las cargas aplicadas son representativas de misiones de aviones de combate (Falstaff), y de aviones de transporte (Twist). La vida a fatiga de las probetas cicladas con cargas de amplitud variable, se comparó con el índice de daño teórico calculado en base a la regla de acumulación de daño lineal convencional. Los resultados obtenidos muestran predicciones no conservativas. Esta tesis también presenta el estudio y desarrollo, de una nueva técnica de no contacto para evaluar el estado de daño por fatiga de estructuras de CFRP por medio de cambios de los parámetros de rugosidad. La rugosidad superficial se puede medir fácilmente en campo con métodos sin contacto, mediante técnicas ópticas tales como speckle y perfilómetros ópticos. En el presente estudio, se han medido parámetros de rugosidad superficial, y el factor de irregularidad de la superficie, a lo largo de la vida de las probetas cicladas con cargas de amplitud constante y variable, Se ha obtenido una buena tendencia de ajuste al correlacionar la magnitud de la rugosidad y el factor de irregularidad de la superficie con la degradación de la rigidez de las probetas fatigadas. Estos resultados sugieren que los cambios en la rugosidad superficial medida en zonas estratégicas de componentes y estructuras hechas de CFRP, podrían ser indicativas del nivel de daño interno debido a cargas de fatiga. Los resultados también sugieren que el método es independiente del tipo de carga de fatiga que ha causado el daño. Esto último hace que esta técnica de medición sea aplicable como inspección para una amplia gama de estructuras de materiales compuestos, desde tanques presurizados con cargas de amplitud constante, estructuras aeronáuticas como alas y colas de aeronaves cicladas con cargas de amplitud variable, hasta aplicaciones industriales como automoción, entre otros. ABSTRACT New optimized aerospace structures use composite materials, mainly carbon fiber reinforced polymer composite (CFRP), for critical components and subsystems. A strong knowledge of the fatigue state of highly advanced (CFRP) structures is essential to predict the residual life and optimize intervals of structural inspection, repairs, and/or replacements. Current techniques are based mostly on measurement of structural loads throughout the service life by electric strain gauge sensors. These sensors are affected by extreme environmental conditions and by fatigue loads in such a way that the sensors and their systems require exhaustive maintenance throughout system life. In the present thesis, the conventional methodology based on linear damage accumulation rules, applied to determine the fatigue life of structures subjected to variable amplitude loads was evaluated for an aeronautical CFRP. A test program with constant amplitude loads has been performed to obtain the classical S-N curves at different stress ratios. Constant life diagrams, CLDs, where determined by means of Artificial Neural Networks due to the absence of consistent models for composites. The stiffness degradation due to fatigue damage has been characterized for coupons under cyclic tensile loads. A second group of coupons have been tested until failure with a standardized sequence of variable amplitude loads, representative of missions for combat aircraft (Falstaff), and representative of commercial flights (Twist), to obtain the fatigue life and the stiffness degradation under realistic conditions. The fatigue life of the coupons cycled with variable amplitude loads were compared to the theoretical damage index calculated based on the conventional linear damage accumulation rule. The obtained results show non-conservative predictions. This thesis also presents the evaluation of a new non-contact technique to evaluate the fatigue damage state of CFRP structures by means of measuring roughness parameters to evaluate changes in the surface topography. Surface roughness can be measured easily on field with non-contact methods by optical techniques such as speckle and optical perfilometers. In the present study, surface roughness parameters, and the surface irregularity factor, have been measured along the life of the coupons cycled with constant and variable amplitude loads of different magnitude. A good agreement has been obtained when correlating the magnitude of the roughness and the surface irregularity factor with the stiffness degradation. These results suggest that the changes on the surface roughness measured in strategic zones of components and structures made of CFRP, could be indicative of the level of internal damage due to fatigue loads. The results also suggest that the method is independent of the type of fatigue load that have caused the damage. It makes this measurement technique applicable for a wide range of inspections of composite materials structures, from pressurized tanks with constant amplitude loads, to variable amplitude loaded aeronautical structures like wings and empennages, up to automotive and other industrial applications.

Euler characteristics and elliptic curves

Let E be a modular elliptic curve over ℚ, without complex multiplication; let p be a prime number where E has good ordinary reduction; and let F∞ be the field obtained by adjoining to ℚ all p-power division points on E. Write G∞ for the Galois group of F∞ over ℚ. Assume that the complex L-series of E over ℚ does not vanish at s = 1. If p ⩾ 5, we make a precise conjecture about the value of the G∞-Euler characteristic of the Selmer group of E over F∞. If one makes a standard conjecture about the behavior of this Selmer group as a module over the Iwasawa algebra, we are able to prove our conjecture. The crucial local calculations in the proof depend on recent joint work of the first author with R. Greenberg.

Contexto e modularização em redes neurais recorrentes para aprendizagem de seqüências temporais

Este trabalho apresenta um sistema neural modular, que processa separadamente informações de contexto espacial e temporal, para a tarefa de reprodução de sequências temporais. Para o desenvolvimento do sistema neural foram considerados redes neurais recorrentes, modelos estocásticos, sistemas neurais modulares e processamento de informações de contexto. Em seguida, foram estudados três modelos com abordagens distintas para aprendizagem de seqüências temporais: uma rede neural parcialmente recorrente, um exemplo de sistema neural modular e um modelo estocástico utilizando a teoria de modelos markovianos escondidos. Com base nos estudos e modelos apresentados, esta pesquisa propõe um sistema formado por dois módulos sucessivos distintos. Uma rede de propagação direta (módulo estimador de contexto espacial) realiza o processamento de contexto espacial identificando a seqüência a ser reproduzida e fornecendo um protótipo do contexto para o segundo módulo. Este é formado por uma rede parcialmente recorrente (módulo de reprodução de sequências temporais) para aprender as informações de contexto temporal e reproduzir em suas saídas a seqüência identificada pelo módulo anterior. Para a finalidade mencionada, este mestrado utiliza a distribuição de Gibbs na saída do módulo para contexto espacial de forma que este forneça probabilidades de contexto espacial, indicando o grau de certeza do módulo e possibilitando a utilização de procedimentos especiais para os casos de dúvida. O sistema neural foi testado em conjuntos contendo trajetórias abertas, fechadas, e com diferentes situações de ambigüidade e complexidade. Duas situações distintas foram avaliadas: (a) capacidade do sistema em reproduzir trajetórias a partir de pontos iniciais treinados; e (b) capacidade de generalização do sistema reproduzindo trajetórias considerando pontos iniciais ou finais em situações não treinadas. A situação (b) é um problema de difícil ) solução em redes neurais devido à falta de contexto temporal, essencial na reprodução de seqüências. Foram realizados experimentos comparando o desempenho do sistema modular proposto com o de uma rede parcialmente recorrente operando sozinha e um sistema modular neural (TOTEM). Os resultados sugerem que o sistema proposto apresentou uma capacidade de generalização significamente melhor, sem que houvesse uma deterioração na capacidade de reproduzir seqüências treinadas. Esses resultados foram obtidos em sistema mais simples que o TOTEM.

Breaking down complex saproxylic communities: understanding sub-networks structure and implications to network robustness

Saproxylic insect communities inhabiting tree hollow microhabitats correspond with large food webs which simultaneously are constituted by multiple types of plant-animal and animal-animal interactions, according to the use of trophic resources (wood- and insect-dependent sub-networks), or to trophic habits or interaction types (xylophagous, saprophagous, xylomycetophagous, predators and commensals). We quantitatively assessed which properties of specialised networks were present in a complex networks involving different interacting types such as saproxylic community, and how they can be organised in trophic food webs. The architecture, interacting patterns and food web composition were evaluated along sub-networks, analysing their implications to network robustness from random and directed extinction simulations. A structure of large and cohesive modules with weakly connected nodes was observed throughout saproxylic sub-networks, composing the main food webs constituting this community. Insect-dependent sub-networks were more modular than wood-dependent sub-networks. Wood-dependent sub-networks presented higher species degree, connectance, links, linkage density, interaction strength, and were less specialised and more aggregated than insect-dependent sub-networks. These attributes defined high network robustness in wood-dependent sub-networks. Finally, our results emphasise the relevance of modularity, differences among interacting types and interrelations among them in modelling the structure of saproxylic communities and in determining their stability.

Flexible ruthenium oxide-activated carbon cloth composites prepared by simple electrodeposition methods

This work focuses on the preparation of flexible ruthenium oxide containing activated carbon cloth by electrodeposition. Different electrodeposition methods have been used, including chronoamperometry, chronopotentiometry and cyclic voltammetry. The electrochemical properties of the obtained materials have been measured. The results show that the potentiostatic method allows preparing composites with higher specific capacitance than the pristine activated carbon cloth. The capacitance values measured by cyclic voltammetry at 10 mV s−1 and 1 V of potential window were up to 160 and 180 F g−1. This means an improvement of 82% and 100% with respect to the capacitance of the pristine activated carbon cloth. This excellent capacitance enhancement is attributed to the small particle size (4–5 nm) and the three-dimensional nanoporous network of the ruthenium oxide film which allows reaching very high degree of oxide utilization without blocking the pore structure of the activated carbon cloth. In addition, the electrodes maintain the mechanical properties of the carbon cloth and can be useful for flexible devices.