Topological measure locating the effective crossover between segregation and integration in a modular network

We introduce an easily computable topological measure which locates the effective crossover between segregation and integration in a modular network. Segregation corresponds to the degree of network modularity, while integration is expressed in terms of the algebraic connectivity of an associated hypergraph. The rigorous treatment of the simplified case of cliques of equal size that are gradually rewired until they become completely merged, allows us to show that this topological crossover can be made to coincide with a dynamical crossover from cluster to global synchronization of a system of coupled phase oscillators. The dynamical crossover is signaled by a peak in the product of the measures of intracluster and global synchronization, which we propose as a dynamical measure of complexity. This quantity is much easier to compute than the entropy (of the average frequencies of the oscillators), and displays a behavior which closely mimics that of the dynamical complexity index based on the latter. The proposed topological measure simultaneously provides information on the dynamical behavior, sheds light on the interplay between modularity and total integration, and shows how this affects the capability of the network to perform both local and distributed dynamical tasks.

Node sampling using random centrifugal walks

Sampling a network with a given probability distribution has been identified as a useful operation. In this paper we propose distributed algorithms for sampling networks, so that nodes are selected by a special node, called the source, with a given probability distribution. All these algorithms are based on a new class of random walks, that we call Random Centrifugal Walks (RCW). A RCW is a random walk that starts at the source and always moves away from it. Firstly, an algorithm to sample any connected network using RCW is proposed. The algorithm assumes that each node has a weight, so that the sampling process must select a node with a probability proportional to its weight. This algorithm requires a preprocessing phase before the sampling of nodes. In particular, a minimum diameter spanning tree (MDST) is created in the network, and then nodes weights are efficiently aggregated using the tree. The good news are that the preprocessing is done only once, regardless of the number of sources and the number of samples taken from the network. After that, every sample is done with a RCW whose length is bounded by the network diameter. Secondly, RCW algorithms that do not require preprocessing are proposed for grids and networks with regular concentric connectivity, for the case when the probability of selecting a node is a function of its distance to the source. The key features of the RCW algorithms (unlike previous Markovian approaches) are that (1) they do not need to warm-up (stabilize), (2) the sampling always finishes in a number of hops bounded by the network diameter, and (3) it selects a node with the exact probability distribution.

First passages in bounded domains: When is the mean first passage time meaningful?

We study the first passage statistics to adsorbing boundaries of a Brownian motion in bounded two-dimensional domains of different shapes and configurations of the adsorbing and reflecting boundaries. From extensive numerical analysis we obtain the probability P(ω) distribution of the random variable ω=τ1/(τ1+τ2), which is a measure for how similar the first passage times τ1 and τ2 are of two independent realizations of a Brownian walk starting at the same location. We construct a chart for each domain, determining whether P(ω) represents a unimodal, bell-shaped form, or a bimodal, M-shaped behavior. While in the former case the mean first passage time (MFPT) is a valid characteristic of the first passage behavior, in the latter case it is an insufficient measure for the process. Strikingly we find a distinct turnover between the two modes of P(ω), characteristic for the domain shape and the respective location of absorbing and reflective boundaries. Our results demonstrate that large fluctuations of the first passage times may occur frequently in two-dimensional domains, rendering quite vague the general use of the MFPT as a robust measure of the actual behavior even in bounded domains, in which all moments of the first passage distribution exist.

Red SFN de distribución local de TDT

Este proyecto consiste en el diseño completo, de una red de distribución de TDT, a nivel local, mediante difusión SFN, Single Frequency Network. Este tipo de difusión, tiene la capacidad de difundir los servicios de televisión en una única frecuencia, cubriendo un área, ya sea local o estatal, aprovechando en las zonas de interferencia los rebotes de la señal y así evitar el uso de una frecuencia distinta por cada centro de emisión, todos los que componen un área de cobertura. Para el diseño de la red, se ha optado por diseñar una red IP, mediante distribución multicast, ya que esta es la tecnología imperante a día de hoy, quedando obsoleta ya, la distribución analógica, ya que consume muchos más recursos y por consiguiente mucho más costosa de implementar. El documento se divide en cuatro capítulos. En el primer capítulo se realizará una introducción teórica a las redes de distribución SFN, centrándose en el cálculo de los retardos, punto fundamental en el diseño de este tipo de redes. Se continuará unas nociones básicas de redes IP y el protocolo multicast, en el que se basa el trasporte de la señal. El capítulo dos, se centra en el diseño de la red, desde los centros de producción, donde se generan los programas a emitir, hasta los diferentes centros de difusión que cubrirán todo el área de cobertura requerida, pasando por el centro de multiplexación, donde se sitúa la cabecera que compondrá el múltiplex a difundir. Se describirán los equipos y el diseño de los distintos centros que conforman la red, centros de producción, multiplexación y difusión. A demás se realizará el cálculo de retardo de la señal, necesario en este tipo de redes. Se continuará con el capítulo tres, donde se describirá la configuración de la red, tanto a nivel de equipamiento, como el diseño y asignación IP de toda la red, separando la red de servicio de la red de gestión para una mayor confiabilidad y eficiencia de la red. Se finalizará con la descripción de la gestión de la red, que mediante diferentes herramientas, proporcionan un monitoreado en tiempo real de todo el sistema, dando la posibilidad de adelantarsey previniendo posibles incidencias que, puedan causar alguna deficiencia en el servicio que se entrega al usuario final. ABSTRACT. This project involves the complete design of a network´s TDT distribution, locally, by broadcast SFN (Single Frequency Network). This type of broadcast, has the ability to broadcast television´s services on a single frequency, covering an area, whether local or state, drawing on the interference zones, signal´s rebounds, to avoid the use of a different frequency each broadcast center, all those who make a coverage area. For the design of the network, has been chosen to design an IP network using multicast distribution, since this is the prevailing technology today, as the analogue distribution, consumes more resources and therefore, much more costly to implement. The document is divided into four chapters. In the first chapter you can find a theoretical introduction to SFN distribution networks, focusing on the calculation of delays, fundamental point, in the design of these networks. A basic understanding of IP networks and the multicast protocol, in which the transport of the signal is based, will continue. Chapter two focuses on the design of the network, from production centers, where the programs are created to broadcast, to different distribution centers covering the entire area of coverage required, through the multiplexing center, where the head is located, which comprise the multiplex. Also, the equipment and design of the various centers in the network, production centers, multiplexing center and distribution centers, are described. Furthermore, the calculation of signal delay, necessary in such networks, is performed. We will continue with the chapter three, where the network configuration will be described, both in termsofequipment, such as design IP mapping of the entire network, separating the service network and management network, for increased the reliability and efficiency of the network. It will be completed with the description of the management of the network, using different tools provide real-time monitoring of the entire system, making it possible, to anticipate and prevent any incidents that might cause a deficiency in the service being delivered to final user.

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

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.