Community Structurein Soil Porous System.

Soil is well recognized as a highly complex system. The interaction and coupled physical, chemical, and biological processes and phenomena occurring in the soil environment at different spatial and temporal scales are the main reasons for such complexity. There is a need for appropriate methodologies to characterize soil porous systems with an interdisciplinary character. Four different real soil samples, presenting different textures, have been modeled as heterogeneous complex networks, applying a model known as the heterogeneous preferential attachment. An analytical study of the degree distributions in the soil model shows a multiscaling behavior in the connectivity degrees, leaving an empirically testable signature of heterogeneity in the topology of soil pore networks. We also show that the power-law scaling in the degree distribution is a robust trait of the soil model. Last, the detection of spatial pore communities, as densely connected groups with only sparser connections between them, has been studied for the first time in these soil networks. Our results show that the presence of these communities depends on the parameter values used to construct the network. These findings could contribute to understanding the mechanisms of the diffusion phenomena in soils, such as gas and water diffusion, development and dynamics of microorganisms, among others.

Architectures for Video-on-Demand content distribution in managed networks

La demanda de contenidos de vídeo ha aumentado rápidamente en los últimos años como resultado del gran despliegue de la TV sobre IP (IPTV) y la variedad de servicios ofrecidos por los operadores de red. Uno de los servicios que se ha vuelto especialmente atractivo para los clientes es el vídeo bajo demanda (VoD) en tiempo real, ya que ofrece una transmisión (streaming) inmediata de gran variedad de contenidos de vídeo. El precio que los operadores tienen que pagar por este servicio es el aumento del tráfico en las redes, que están cada vez más congestionadas debido a la mayor demanda de contenidos de VoD y al aumento de la calidad de los propios contenidos de vídeo. Así, uno de los principales objetivos de esta tesis es encontrar soluciones que reduzcan el tráfico en el núcleo de la red, manteniendo la calidad del servicio en el nivel adecuado y reduciendo el coste del tráfico. La tesis propone un sistema jerárquico de servidores de streaming en el que se ejecuta un algoritmo para la ubicación óptima de los contenidos de acuerdo con el comportamiento de los usuarios y el estado de la red. Debido a que cualquier algoritmo óptimo de distribución de contenidos alcanza un límite en el que no se puede llegar a nuevas mejoras, la inclusión de los propios clientes del servicio (los peers) en el proceso de streaming puede reducir aún más el tráfico de red. Este proceso se logra aprovechando el control que el operador tiene en las redes de gestión privada sobre los equipos receptores (Set-Top Box) ubicados en las instalaciones de los clientes. El operador se reserva cierta capacidad de almacenamiento y streaming de los peers para almacenar los contenidos de vídeo y para transmitirlos a otros clientes con el fin de aliviar a los servidores de streaming. Debido a la incapacidad de los peers para sustituir completamente a los servidores de streaming, la tesis propone un sistema de streaming asistido por peers. Algunas de las cuestiones importantes que se abordan en la tesis son saber cómo los parámetros del sistema y las distintas distribuciones de los contenidos de vídeo en los peers afectan al rendimiento general del sistema. Para dar respuesta a estas preguntas, la tesis propone un modelo estocástico preciso y flexible que tiene en cuenta parámetros como las capacidades de enlace de subida y de almacenamiento de los peers, el número de peers, el tamaño de la biblioteca de contenidos de vídeo, el tamaño de los contenidos y el esquema de distribución de contenidos para estimar los beneficios del streaming asistido por los peers. El trabajo también propone una versión extendida del modelo matemático mediante la inclusión de la probabilidad de fallo de los peers y su tiempo de recuperación en el conjunto de parámetros del modelo. Estos modelos se utilizan como una herramienta para la realización de exhaustivos análisis del sistema de streaming de VoD asistido por los peers para la amplia gama de parámetros definidos en los modelos. Abstract The demand of video contents has rapidly increased in the past years as a result of the wide deployment of IPTV and the variety of services offered by the network operators. One of the services that has especially become attractive to the customers is real-time Video on Demand (VoD) because it offers an immediate streaming of a large variety of video contents. The price that the operators have to pay for this convenience is the increased traffic in the networks, which are becoming more congested due to the higher demand for VoD contents and the increased quality of the videos. Therefore, one of the main objectives of this thesis is finding solutions that would reduce the traffic in the core of the network, keeping the quality of service on satisfactory level and reducing the traffic cost. The thesis proposes a system of hierarchical structure of streaming servers that runs an algorithm for optimal placement of the contents according to the users’ behavior and the state of the network. Since any algorithm for optimal content distribution reaches a limit upon which no further improvements can be made, including service customers themselves (the peers) in the streaming process can further reduce the network traffic. This process is achieved by taking advantage of the control that the operator has in the privately managed networks over the Set-Top Boxes placed at the clients’ premises. The operator reserves certain storage and streaming capacity on the peers to store the video contents and to stream them to the other clients in order to alleviate the streaming servers. Because of the inability of the peers to completely substitute the streaming servers, the thesis proposes a system for peer-assisted streaming. Some of the important questions addressed in the thesis are how the system parameters and the various distributions of the video contents on the peers would impact the overall system performance. In order to give answers to these questions, the thesis proposes a precise and flexible stochastic model that takes into consideration parameters like uplink and storage capacity of the peers, number of peers, size of the video content library, size of contents and content distribution scheme to estimate the benefits of the peer-assisted streaming. The work also proposes an extended version of the mathematical model by including the failure probability of the peers and their recovery time in the set of parameters. These models are used as tools for conducting thorough analyses of the peer-assisted system for VoD streaming for the wide range of defined parameters.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Secure optical networks based on quantum key distribution and weakly trusted repeaters

Abstract—In this paper we explore how recent technologies can improve the security of optical networks. In particular, we study how to use quantum key distribution(QKD) in common optical network infrastructures and propose a method to overcome its distance limitations. QKD is the first technology offering information theoretic secretkey distribution that relies only on the fundamental principles of quantum physics. Point-to-point QKDdevices have reached a mature industrial state; however, these devices are severely limited in distance, since signals at the quantum level (e.g., single photons) are highly affected by the losses in the communication channel and intermediate devices. To overcome this limitation, intermediate nodes (i.e., repeaters) are used. Both quantum-regime and trusted, classical repeaters have been proposed in the QKD literature, but only the latter can be implemented in practice. As a novelty, we propose here a new QKD network model based on the use of not fully trusted intermediate nodes, referred to as weakly trusted repeaters. This approach forces the attacker to simultaneously break several paths to get access to the exchanged key, thus improving significantly the security of the network. We formalize the model using network codes and provide real scenarios that allow users to exchange secure keys over metropolitan optical networks using only passive components. Moreover, the theoretical framework allows one to extend these scenarios not only to accommodate more complex trust constraints, but also to consider robustness and resiliency constraints on the network.

Modelling uncertainty of flood quantile estimation at ungauged sites by Bayesian networks

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, these models assume linear relationships between variables Prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharges as result. The methodology was applied to a case study in the Tagus basin in Spain.

An Energy Optimization Criterion for Branching Water Networks

La dependencia energética de las redes hidráulicas para su explotación hace que, a lo largo de los años, sus costos variables lleguen a ensombrecer los costos de construcción o costos fijos. El objetivo de este artículo es la minimización de los costos de explotación en redes ramificadas simples ya existentes y estudia la aplicación de las técnicas de sectorización, junto con el uso de variadores de velocidad, como medida de eficiencia energética. Se sugiere un criterio para determinar bajo qué circunstancias resulta favorable aplicar la sectorización. Se aplica en un caso de estudio real: una red simple ramificada existente con cuatro zonas hidráulicas usada para regadío en la provincia de Segovia, España, resultando en un ahorro energético de un 7.52%, pudiéndose llegar hasta un 26.31%, ampliando la franja horaria del bombeo inicial.

Brown trout thermal niche and climate change: expected changes in the distribution of cold-water fish in central Spain

This paper addresses the determination of the realized thermal niche and the effects of climate change on the range distribution of two brown trout populations inhabiting two streams in the Duero River basin (Iberian Peninsula) at the edge of the natural distribution area of this species. For reaching these goals, new methodological developments were applied to improve reliability of forecasts. Water temperature data were collected using 11 thermographs located along the altitudinal gradient, and they were used to model the relationship between stream temperature and air temperature along the river continuum. Trout abundance was studied using electrofishing at 37 sites to determine the current distribution. The RCP4.5 and RCP8.5 change scenarios adopted by the International Panel of Climate Change for its Fifth Assessment Report were used for simulations and local downscaling in this study. We found more reliable results using the daily mean stream temperature than maximum daily temperature and their respective seven days moving-average to determine the distribution thresholds. Thereby, the observed limits of the summer distribution of brown trout were linked to thresholds between 18.1ºC and 18.7ºC. These temperatures characterise a realised thermal niche narrower than the physiological thermal range. In the most unfavourable climate change scenario, the thermal habitat loss of brown trout increased to 38% (Cega stream) and 11% (Pirón stream) in the upstream direction at the end of the century; however, at the Cega stream, the range reduction could reach 56% due to the effect of a ?warm-window? opening in the piedmont reach.

Propuesta metodológica para el análisis y protección de la calidad del agua subterránea de acuerdo con los requerimientos de la Directiva Marco y la Directiva derivada de Aguas Subterráneas

La protección de las aguas subterráneas es una prioridad de la política medioambiental de la UE. Por ello ha establecido un marco de prevención y control de la contaminación, que incluye provisiones para evaluar el estado químico de las aguas y reducir la presencia de contaminantes en ellas. Las herramientas fundamentales para el desarrollo de dichas políticas son la Directiva Marco del Agua y la Directiva Hija de Aguas Subterráneas. Según ellas, las aguas se consideran en buen estado químico si: • la concentración medida o prevista de nitratos no supera los 50 mg/l y la de ingredientes activos de plaguicidas, de sus metabolitos y de los productos de reacción no supera el 0,1 μg/l (0,5 μg/l para el total de los plaguicidas medidos) • la concentración de determinadas sustancias de riesgo es inferior al valor umbral fijado por los Estados miembros; se trata, como mínimo, del amonio, arsénico, cadmio, cloruro, plomo, mercurio, sulfatos, tricloroetileno y tetracloroetileno • la concentración de cualquier otro contaminante se ajusta a la definición de buen estado químico enunciada en el anexo V de la Directiva marco sobre la política de aguas • en caso de superarse el valor correspondiente a una norma de calidad o a un valor umbral, una investigación confirma, entre otros puntos, la falta de riesgo significativo para el medio ambiente. Analizar el comportamiento estadístico de los datos procedentes de la red de seguimiento y control puede resultar considerablemente complejo, debido al sesgo positivo que suelen presentar dichos datos y a su distribución asimétrica, debido a la existencia de valores anómalos y diferentes tipos de suelos y mezclas de contaminantes. Además, la distribución de determinados componentes en el agua subterránea puede presentar concentraciones por debajo del límite de detección o no ser estacionaria debida a la existencia de tendencias lineales o estacionales. En el primer caso es necesario realizar estimaciones de esos valores desconocidos, mediante procedimientos que varían en función del porcentaje de valores por debajo del límite de detección y el número de límites de detección aplicables. En el segundo caso es necesario eliminar las tendencias de forma previa a la realización de contrastes de hipótesis sobre los residuos. Con esta tesis se ha pretendido establecer las bases estadísticas para el análisis riguroso de los datos de las redes de calidad con objeto de realizar la evaluación del estado químico de las masas de agua subterránea para la determinación de tendencias al aumento en la concentración de contaminantes y para la detección de empeoramientos significativos, tanto en los casos que se ha fijado un estándar de calidad por el organismo medioambiental competente como en aquéllos que no ha sido así. Para diseñar una metodología que permita contemplar la variedad de casos existentes, se han analizado los datos de la Red Oficial de Seguimiento y Control del Estado Químico de las Aguas Subterráneas del Ministerio de Agricultura, Alimentación y Medio Ambiente (Magrama). A continuación, y dado que los Planes Hidrológicos de Cuenca son la herramienta básica de las Directivas, se ha seleccionado la Cuenca del Júcar, dada su designación como cuenca piloto en la estrategia de implementación común (CIS) de la Comisión Europea. El objetivo principal de los grupos de trabajo creados para ello se dirigió a implementar la Directiva Derivada de Agua Subterráneas y los elementos de la Directiva Marco del Agua relacionadas, en especial la toma de datos en los puntos de control y la preparación del primer Plan de Gestión de Cuencas Hidrográficas. Dada la extensión de la zona y con objeto de analizar una masa de agua subterránea (definida como la unidad de gestión en las Directivas), se ha seleccionado una zona piloto (Plana de Vinaroz Peñiscola) en la que se han aplicado los procedimientos desarrollados con objeto de determinar el estado químico de dicha masa. Los datos examinados no contienen en general valores de concentración de contaminantes asociados a fuentes puntuales, por lo que para la realización del estudio se han seleccionado valores de concentración de los datos más comunes, es decir, nitratos y cloruros. La estrategia diseñada combina el análisis de tendencias con la elaboración de intervalos de confianza cuando existe un estándar de calidad e intervalos de predicción cuando no existe o se ha superado dicho estándar. De forma análoga se ha procedido en el caso de los valores por debajo del límite de detección, tomando los valores disponibles en la zona piloto de la Plana de Sagunto y simulando diferentes grados de censura con objeto de comparar los resultados obtenidos con los intervalos producidos de los datos reales y verificar de esta forma la eficacia del método. El resultado final es una metodología general que integra los casos existentes y permite definir el estado químico de una masa de agua subterránea, verificar la existencia de impactos significativos en la calidad del agua subterránea y evaluar la efectividad de los planes de medidas adoptados en el marco del Plan Hidrológico de Cuenca. ABSTRACT Groundwater protection is a priority of the EU environmental policy. As a result, it has established a framework for prevention and control of pollution, which includes provisions for assessing the chemical status of waters and reducing the presence of contaminants in it. The measures include: • criteria for assessing the chemical status of groundwater bodies • criteria for identifying significant upward trends and sustained concentrations of contaminants and define starting points for reversal of such trends • preventing and limiting indirect discharges of pollutants as a result of percolation through soil or subsoil. The basic tools for the development of such policies are the Water Framework Directive and Groundwater Daughter Directive. According to them, the groundwater bodies are considered in good status if: • measured or predicted concentration of nitrate does not exceed 50 mg / l and the active ingredients of pesticides, their metabolites and reaction products do not exceed 0.1 mg / l (0.5 mg / l for total of pesticides measured) • the concentration of certain hazardous substances is below the threshold set by the Member States concerned, at least, of ammonium, arsenic, cadmium, chloride, lead, mercury, sulphates, trichloroethylene and tetrachlorethylene • the concentration of other contaminants fits the definition of good chemical status set out in Annex V of the Framework Directive on water policy • If the value corresponding to a quality standard or a threshold value is exceeded, an investigation confirms, among other things, the lack of significant risk to the environment. Analyzing the statistical behaviour of the data from the monitoring networks may be considerably complex due to the positive bias which often presents such information and its asymmetrical distribution, due to the existence of outliers and different soil types and mixtures of pollutants. Furthermore, the distribution of certain components in groundwater may have concentrations below the detection limit or may not be stationary due to the existence of linear or seasonal trends. In the first case it is necessary to estimate these unknown values, through procedures that vary according to the percentage of values below the limit of detection and the number of applicable limits of detection. In the second case removing trends is needed before conducting hypothesis tests on residuals. This PhD thesis has intended to establish the statistical basis for the rigorous analysis of data quality networks in order to conduct the evaluation of the chemical status of groundwater bodies for determining upward and sustained trends in pollutant concentrations and for the detection of significant deterioration in cases in which an environmental standard has been set by the relevant environmental agency and those that have not. Aiming to design a comprehensive methodology to include the whole range of cases, data from the Groundwater Official Monitoring and Control Network of the Ministry of Agriculture, Food and Environment (Magrama) have been analysed. Then, since River Basin Management Plans are the basic tool of the Directives, the Júcar river Basin has been selected. The main reason is its designation as a pilot basin in the common implementation strategy (CIS) of the European Commission. The main objective of the ad hoc working groups is to implement the Daughter Ground Water Directive and elements of the Water Framework Directive related to groundwater, especially the data collection at control stations and the preparation of the first River Basin Management Plan. Given the size of the area and in order to analyze a groundwater body (defined as the management unit in the Directives), Plana de Vinaroz Peñíscola has been selected as pilot area. Procedures developed to determine the chemical status of that body have been then applied. The data examined do not generally contain pollutant concentration values associated with point sources, so for the study concentration values of the most common data, i.e., nitrates and chlorides have been selected. The designed strategy combines trend analysis with the development of confidence intervals when there is a standard of quality and prediction intervals when there is not or the standard has been exceeded. Similarly we have proceeded in the case of values below the detection limit, taking the available values in Plana de Sagunto pilot area and simulating different degrees of censoring in order to compare the results obtained with the intervals achieved from the actual data and verify in this way the effectiveness of the method. The end result is a general methodology that integrates existing cases to define the chemical status of a groundwater body, verify the existence of significant impacts on groundwater quality and evaluate the effectiveness of the action plans adopted in the framework of the River Basin Management Plan.

Quantifying the relationship between drainage networks at hillslope scale and particle size distribution at pedon scale

Nowadays, translating information about hydrologic and soil properties and processes across scales has emerged as a major theme in soil science and hydrology, and suitable theories for upscaling or downscaling hydrologic and soil information are being looked forward. The recognition of low-order catchments as self-organized systems suggests the existence of a great amount of links at different scales between their elements. The objective of this work was to research in areas of homogeneous bedrock material, the relationship between the hierarchical structure of the drainage networks at hillslope scale and the heterogeneity of the particle-size distribution at pedon scale. One of the most innovative elements in this work is the choice of the parameters to quantify the organization level of the studied features. The fractal dimension has been selected to measure the hierarchical structure of the drainage networks, while the Balanced Entropy Index (BEI) has been the chosen parameter to quantify the heterogeneity of the particle-size distribution from textural data. These parameters have made it possible to establish quantifiable relationships between two features attached to different steps in the scale range. Results suggest that the bedrock lithology of the landscape constrains the architecture of the drainage networks developed on it and the particle soil distribution resulting in the fragmentation processes.

Quantifying the relationship between drainage networks at hillslope scale and particle size distribution at pedon scale

Nowadays, translating information about hydrologic and soil properties and processes across scales has emerged as a major theme in soil science and hydrology, and suitable theories for upscaling or downscaling hydrologic and soil information are being looked forward. The recognition of low-order catchments as self-organized systems suggests the existence of a great amount of links at different scales between their elements. The objective of this work was to research in areas of homogeneous bedrock material, the relationship between the hierarchical structure of the drainage networks at hillslope scale and the heterogeneity of the particle-size distribution at pedon scale. One of the most innovative elements in this work is the choice of the parameters to quantify the organization level of the studied features. The fractal dimension has been selected to measure the hierarchical structure of the drainage networks, while the Balanced Entropy Index (BEI) has been the chosen parameter to quantify the heterogeneity of the particle-size distribution from textural data. These parameters have made it possible to establish quantifiable relationships between two features attached to different steps in the scale range. Results suggest that the bedrock lithology of the landscape constrains the architecture of the drainage networks developed on it and the particle soil distribution resulting in the fragmentation processes.

Imaging of H217O distribution in the brain of a live rat by using proton-detected 17O MRI

Imaging of H217O has a number of important applications. Mapping the distribution of H217O produced by oxidative metabolism of 17O-enriched oxygen gas may lead to a new method of metabolic functional imaging; regional cerebral blood flow also can be measured by measuring the H217O distribution after the injection of 17O-enriched physiological saline solution. Previous studies have proposed a method for indirect detection of 17O. The method is based on the shortening of the proton T2 in H217O solutions, caused by the residual 17O-1H scalar coupling and transferred to the bulk water via fast chemical exchange. It has been shown that the proton T2 of H217O solutions can be restored to that of H216O by irradiating the resonance frequency of the 17O nucleus. The indirect 17O image thus is obtained by taking the difference between two T2-weighted spin-echo images: one acquired after irradiation of the 17O resonance and one acquired without irradiation. It also has been established that, at relatively low concentrations of H217O, the indirect method yields an image that quantitatively reflects the H217O distribution in the sample. The method is referred to as PRIMO (proton imaging of oxygen). In this work, we show in vivo proton images of the H217O distribution in a rat brain after an i.v. injection of H217O-enriched physiological saline solution. Implementing the indirect detection method in an echo-planar imaging sequence enabled obtaining H217O images with good spatial and temporal resolution of few seconds.

The distribution of oxygen in the waters of New York Bight, Block Island Sound, and Newport Bight : Cruise STIRNI I, July-September, 1951 /

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Organizational maintenance repair parts and special tools list forward and rear area ground networks, interconnecting kit and distribution box, AN/GSA-109, 1430-01-099-2509 ..., Pershing 1A field artillery guided missile system.

"January 1983."