Analytic design method for optimal imaging: coupling three ray sets using two free-form lens profiles

In this work, a new two-dimensional optics design method is proposed that enables the coupling of three ray sets with two lens surfaces. The method is especially important for optical systems designed for wide field of view and with clearly separated optical surfaces. Fermat’s principle is used to deduce a set of functional differential equations fully describing the entire optical system. The presented general analytic solution makes it possible to calculate the lens profiles. Ray tracing results for calculated 15th order Taylor polynomials describing the lens profiles demonstrate excellent imaging performance and the versatility of this new analytic design method.

Integration of Virtual Map Rooms as a support tool in historical and social research

At the present time almost all map libraries on the Internet are image collections generated by the digitization of early maps. This type of graphics files provides researchers with the possibility of accessing and visualizing historical cartographic information keeping in mind that this information has a degree of quality that depends upon elements such as the accuracy of the digitization process and proprietary constraints (e.g. visualization, resolution downloading options, copyright, use constraints). In most cases, access to these map libraries is useful only as a first approach and it is not possible to use those maps for scientific work due to the sparse tools available to measure, match, analyze and/or combine those resources with different kinds of cartography. This paper presents a method to enrich virtual map rooms and provide historians and other professional with a tool that let them to make the most of libraries in the digital era.

Wavelet-based image fusion in multi-view three-dimensional microscopy

Multi-view microscopy techniques such as Light-Sheet Fluorescence Microscopy (LSFM) are powerful tools for 3D + time studies of live embryos in developmental biology. The sample is imaged from several points of view, acquiring a set of 3D views that are then combined or fused in order to overcome their individual limitations. Views fusion is still an open problem despite recent contributions in the field. We developed a wavelet-based multi-view fusion method that, due to wavelet decomposition properties, is able to combine the complementary directional information from all available views into a single volume. Our method is demonstrated on LSFM acquisitions from live sea urchin and zebrafish embryos. The fusion results show improved overall contrast and details when compared with any of the acquired volumes. The proposed method does not need knowledge of the system's point spread function (PSF) and performs better than other existing PSF independent fusion methods.

Improved Integration Methods for P-Adaptive Boundary Element Techniques

Non linear transformations are a good alternative for the numerical evaluation of singular and quasisingular integrals appearing in Boundary Element Method specially in the p-adaptive version. Some aspects of its numerical implementation in 2-D Potential codes is discussed and some examples are shown.

Transpose return relation method for designing low noise oscillators

In this paper, a new linear method for optimizing compact low noise oscillators for RF/MW applications will be presented. The first part of this paper makes an overview of Leeson's model. It is pointed out, and it is demonstrates that the phase noise is always the same inside the oscillator loop. It is presented a general phase noise optimization method for reference plane oscillators. The new method uses Transpose Return Relations (RRT) as true loop gain functions for obtaining the optimum values of the elements of the oscillator, whatever scheme it has. With this method, oscillator topologies that have been designed and optimized using negative resistance, negative conductance or reflection coefficient methods, until now, can be studied like a loop gain method. Subsequently, the main disadvantage of Leeson's model is overcome, and now it is not only valid for loop gain methods, but it is valid for any oscillator topology. The last section of this paper lists the steps to be performed to use this method for proper phase noise optimization during the linear design process and before the final non-linear optimization. The power of the proposed RRT method is shown with its use for optimizing a common oscillator, which is later simulated using Harmonic Balance (HB) and manufactured. Then, the comparison of the linear, HB and measurements of the phase noise are compared.

Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms

Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns.

Application of the Boundary Element Method to the Analysis of Bridge Abutments

The B.E. technique is applied to an interesting dynamic problem: the interaction between bridges and their abutments. Several two-dimensional cases have been tested in relation with previously published analytical results. A three-dimensional case is also shown and different considerations in relation with the accuracy of the method are described.

Analytic free-form lens design for tracking integration in concentrating photovoltaics

In this work the concept of tracking integration in concentrating photovoltaics (CPV) is revisited and developed further. With respect to conventional CPV, tracking integration eliminates the clear separation between stationary units of optics and solar cells, and external solar trackers. This approach is capable of further increasing the concentration ratio and makes high concentrating photovoltaics (> 500x) available for single-axis tracker installations. The reduced external solar tracking effort enables possibly cheaper and more compact installations. Our proposed optical system uses two laterally moving plano-convex lenses to achieve high concentration over a wide angular range of ±24°. The lateral movement allows to combine both steering and concentration of the incident direct sun light. Given the specific symmetry conditions of the underlying optical design problem, rotational symmetric lenses are not ideal for this application. For this type of design problems, a new free-form optics design method presented in previous papers perfectly matches the symmetry. It is derived directly from Fermat's principle, leading to sets of functional differential equations allowing the successive calculation of the Taylor series coeficients of each implicit surface function up to very high orders. For optical systems designed for wide field of view and with clearly separated optical surfaces, this new analytic design method has potential application in both fields of nonimaging and imaging optics.

Reliability analysis based on non-dimensional parameters

A reliability analysis method is proposed that starts with the identification of all variables involved. These are divided in three groups: (a) variables fixed by codes, as loads and strength project values, and their corresponding partial safety coefficients, (b) geometric variables defining the dimension of the main elements involved, (c) the cost variables, including the possible damages caused by failure, (d) the random variables as loads, strength, etc., and (e)the variables defining the statistical model, as the family of distribution and its corresponding parameters. Once the variables are known, the II-theorem is used to obtain a minimum equivalent set of non-dimensional variables, which is used to define the limit states. This allows a reduction in the number of variables involved and a better understanding of their coupling effects. Two minimum cost criteria are used for selecting the project dimensions. One is based on a bounded-probability of failure, and the other on a total cost, including the damages of the possible failure. Finally, the method is illustrated by means of an application.

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.

Development of a 3-Dimensional Mathematical Collision Risk Model Based on Recorded Aircraft Trajectories to Estimate the Safety Level in High Density En-Route Airspaces

Como en todos los medios de transporte, la seguridad en los viajes en avión es de primordial importancia. Con los aumentos de tráfico aéreo previstos en Europa para la próxima década, es evidente que el riesgo de accidentes necesita ser evaluado y monitorizado cuidadosamente de forma continúa. La Tesis presente tiene como objetivo el desarrollo de un modelo de riesgo de colisión exhaustivo como método para evaluar el nivel de seguridad en ruta del espacio aéreo europeo, considerando todos los factores de influencia. La mayor limitación en el desarrollo de metodologías y herramientas de monitorización adecuadas para evaluar el nivel de seguridad en espacios de ruta europeos, donde los controladores aéreos monitorizan el tráfico aéreo mediante la vigilancia radar y proporcionan instrucciones tácticas a las aeronaves, reside en la estimación del riesgo operacional. Hoy en día, la estimación del riesgo operacional está basada normalmente en reportes de incidentes proporcionados por el proveedor de servicios de navegación aérea (ANSP). Esta Tesis propone un nuevo e innovador enfoque para evaluar el nivel de seguridad basado exclusivamente en el procesamiento y análisis trazas radar. La metodología propuesta ha sido diseñada para complementar la información recogida en las bases de datos de accidentes e incidentes, mediante la provisión de información robusta de los factores de tráfico aéreo y métricas de seguridad inferidas del análisis automático en profundidad de todos los eventos de proximidad. La metodología 3-D CRM se ha implementado en un prototipo desarrollado en MATLAB © para analizar automáticamente las trazas radar y planes de vuelo registrados por los Sistemas de Procesamiento de Datos Radar (RDP) e identificar y analizar todos los eventos de proximidad (conflictos, conflictos potenciales y colisiones potenciales) en un periodo de tiempo y volumen del espacio aéreo. Actualmente, el prototipo 3-D CRM está siendo adaptado e integrado en la herramienta de monitorización de prestaciones de Aena (PERSEO) para complementar las bases de accidentes e incidentes ATM y mejorar la monitorización y proporcionar evidencias de los niveles de seguridad. ABSTRACT As with all forms of transport, the safety of air travel is of paramount importance. With the projected increases in European air traffic in the next decade and beyond, it is clear that the risk of accidents needs to be assessed and carefully monitored on a continuing basis. The present thesis is aimed at the development of a comprehensive collision risk model as a method of assessing the European en-route risk, due to all causes and across all dimensions within the airspace. The major constraint in developing appropriate monitoring methodologies and tools to assess the level of safety in en-route airspaces where controllers monitor air traffic by means of radar surveillance and provide aircraft with tactical instructions lies in the estimation of the operational risk. The operational risk estimate normally relies on incident reports provided by the air navigation service providers (ANSPs). This thesis proposes a new and innovative approach to assessing aircraft safety level based exclusively upon the process and analysis of radar tracks. The proposed methodology has been designed to complement the information collected in the accident and incident databases, thereby providing robust information on air traffic factors and safety metrics inferred from the in depth assessment of proximate events. The 3-D CRM methodology is implemented in a prototype tool in MATLAB © in order to automatically analyze recorded aircraft tracks and flight plan data from the Radar Data Processing systems (RDP) and identify and analyze all proximate events (conflicts, potential conflicts and potential collisions) within a time span and a given volume of airspace. Currently, the 3D-CRM prototype is been adapted and integrated in AENA’S Performance Monitoring Tool (PERSEO) to complement the information provided by the ATM accident and incident databases and to enhance monitoring and providing evidence of levels of safety.

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.

Compatibilidad de Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) y Nesidiocoris tenuis (Reuter)(Hemiptera: Miridae), depredadores importantes en cultivos hortícolas protegidos, con nuevas barreras físicas selectivas y modernos plaguicidas

La nueva legislación en materia fitosanitaria se dirige hacia una Gestión Integrada de Plagas (GIP). Estos programas dan preferencia a aquellos métodos más respetuosos y sostenibles con el medio ambiente, siendo piezas claves en ellos el control biológico, el físico y otros de carácter no químico. Sin embargo, el uso de insecticidas selectivos es a veces necesario para el adecuado manejo de plagas en cultivos hortícolas. Por ello, el objetivo general de este estudio es aportar conocimientos para mejorar el control de plagas en cultivos hortícolas, mediante la integración de tres estrategias de lucha: biológica, física y química. Una parte de este trabajo ha consistido en el estudio de los posibles efectos que mallas tratadas con insecticida (bifentrin) pudieran provocar mediante diferentes ensayos de laboratorio, invernadero y campo, en los enemigos naturales Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) (depredador de trips), Nesidiocoris tenuis (Reuter) (Hemiptera: Miridae) (depredador de mosca blanca y Tuta absoluta (Meirick) (Lepidoptera: Gelechiidae)), y otros agentes de biocontrol comúnmente usados en cultivos hortícolas protegidos. Este tipo de mallas se han empleado con éxito en entomología médica para controlar mosquitos vectores de la malaria, y actualmente se está trabajando en su desarrollo para uso agrícola como método de exclusión, y método directo de control de plagas. En los ensayos realizados en laboratorio, O. laevigatus y N. tenuis no fueron capaces de detectar la presencia de bifentrin en el ensayo de preferencia. Además, no se produjo mortalidad a corto plazo (72 horas) en ambos chinches depredadores. Por el contrario, se registró una elevada mortalidad cuando se expusieron por contacto a la malla tratada durante 72 horas en cajas de dimensiones reducidas (10 cm de diámetro X 3 cm de altura). En ensayos llevados a cabo bajo condiciones más reales de exposición, en un invernadero experimental con jaulas de 25 X 25 X 60 cm de altura, no se produjo ningún efecto en la mortalidad a corto plazo (72 horas) o en los parámetros reproductivos de O. laevigatus y N. tenuis. Finalmente, en ensayos de campo realizados en túneles semi-comerciales (8 m de largo X 6,5 m de ancho X 2,6 m de altura), ni las condiciones ambientales [temperatura, humedad relativa, radiación ultravioleta (UV) y fotosintéticamente activa (PAR)], ni los enemigos naturales, se vieron afectados por la presencia de la malla tratada con bifentrin en el cultivo. Sin embargo, los resultados no fueron concluyentes, debido al bajo establecimiento de los agentes de biocontrol liberados. Por lo tanto, más estudios son necesarios en invernaderos comerciales para confirmar los resultados preliminares de compatibilidad. Además, en este trabajo se han evaluado los efectos letales (mortalidad) y subletales (parámetros reproductivos) de seis modernos insecticidas sobre los chinches depredadores O. laevigatus y N. tenuis, mediante ensayos de laboratorio y persistencia. Los ensayos se realizaron por contacto residual, aplicando los insecticidas a la dosis máxima de campo sobre placas de cristal (laboratorio) o plantas (persistencia). Los productos fitosanitarios se seleccionaron por representar a un grupo de modernos plaguicidas con modos de acción en principio más selectivos para los enemigos naturales que antiguos plaguicidas como organoclorados, oroganofosforados o carbamatos, y por su uso frecuente en cultivos hortícolas donde O. laevigatus y N. tenuis están presentes. Todos ellos están incluidos o en proceso de inclusión en la lista comunitaria de sustancias activas para uso agrícola, Anexo I de la Directiva 91/414/CEE: abamectina y emamectina (avermectinas neurotóxicas, activadoras del canal del cloro), deltametrina (piretroide neurotóxico, modulador del canal del sodio, control positivo), flubendiamida (neurotóxico, modulador del receptor de rianodina), spinosad (naturalito neurotóxico, agonistas/antagonistas del receptor de nicotínico acetilcolina) y spiromesifen (inhibidor de la acetil CoA carboxilasa). El estudio mostró que O. laevigatus fue más susceptible a los insecticidas que N. tenuis. Además, los resultados revelaron que flubendiamida y spiromesifen fueron compatibles con los dos enemigos naturales estudiados, y por tanto se podrían usar en programas de GIP. Por el contrario, los insecticidas abamectina, deltametrina, emamectina y spinosad no fueron selectivos para ninguno de los chinches depredadores. Sin embargo, los estudios de persistencia demostraron que a pesar de que estos insecticidas no proporcionaron selectividad fisiológica, pueden proporcionar selectividad ecológica en algunos casos. Abamectina, deltametrina, emamectina y spinosad podrían ser compatibles con N. tenuis si el enemigo natural es introducido en el cultivo 4 días después de su aplicación. En el caso de O. laevigatus, abamectina, deltametrina y spinosad se clasificaron como persistentes, por lo tanto es necesario completar el estudio con experimentos de semi-campo y campo que determinen si es posible su uso conjunto en programas de GIP. Por otro lado, emamectina podría ser compatible con O. laevigatus si el enemigo natural es introducido en el cultivo 7 días después de su aplicación. Por último, se ha comprobado la selectividad de tres insecticidas aceleradores de la muda (MACs) (metoxifenocida, tebufenocida y RH-5849) sobre O. laevigatus y N. tenuis. Además de realizar estudios para evaluar la toxicidad en laboratorio de los insecticidas por contacto residual e ingestión (principal modo de acción de los MAC´s), se extrajo RNA de los insectos y con el cDNA obtenido se secuenció y clonó el dominio de unión al ligando (LBD) del receptor de ecdisona correspondiente a O. laevigatus (OlEcR-LBD) y N. tenuis (NtEcR-LBD). Posteriormente, se obtuvo la configuración en tres dimensiones del LBD y se estudió el acoplamiento de las moléculas de los tres insecticidas en la cavidad que forman las 12 α-hélices que constituyen el EcR-LBD. En el caso de N. tenuis se debe mencionar que no fue posible la obtención de la secuencia completa del LBD. Sin embargo, se obtuvo una secuencia parcial (hélice 6-hélice 11), que mostró una alta conservación de aminoácidos con respecto a la obtenida en O. laevigatus. Los ensayos de toxicidad mostraron que metoxifenocida, tebufenocida y RH-5849 no produjeron ningún efecto nocivo en ambos depredadores. Además, los estudios de modelado por homología y acoplamiento molecular llevados a cabo con O. laevigatus, también indicaron que los MACs no produjeron ningún efecto deletéreo en este enemigo natural. Por lo tanto, estos compuestos pueden ser aplicados de manera segura en programas de GIP en los cuales O. laevigatus y N. tenuis estén presentes. ABSTRACT The new pesticide legislation on pest control is aimed at integrated pest management (IPM). These programs are based on the most environmentally sustainable approaches, where biological, physical control and other non-chemical methods are the cornerstone. However, selective pesticides are often required for pest management on horticultural crops. Therefore, the main goal of this study is to provide knowledge to improve pest control on horticultural crops through the integration of three strategies: biological, physical and chemical. Firstly, the effects of insecticide treated nets (bifenthrin) were evaluated in different laboratory, greenhouse and field experiments on the natural enemies Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) (predator of thrips), Nesidiocoris tenuis (Reuter) (Hemiptera: Miridae) (predator of whiteflies and Tuta absoluta (Meirick) (Lepidoptera: Gelechiidae)), and other biocontrol agents commonly used on protected horticultural crops. These types of nets have been successfully used in medical entomology to control mosquito malaria vectors, and work is currently being done on their use as exclusion barriers and as a direct method of pest control in agriculture. In experiments made under laboratory conditions, O. laevigatus and N. tenuis were not able to detect the presence of bifenthrin in a dual-choice test. Furthermore, no shortterm mortality (72 hours) was recorded on both predatory bugs. In contrast, a high mortality rate was found when they were exposed by contact to the bifenthrin-treated net for 72 hours in small cages (10 cm diameter X 3 cm high). In assays carried out under more realistic conditions of exposure, in an experimental greenhouse with cages of 25 X 25 X 60 cm high, short-term mortality (72 hours) and reproductive parameters were not affected. Lastly, in field experiments carried out in semi-commercial tunnels (8 m long X 6.5 m width X 2.6 m high), neither environmental conditions [temperature, relative humidity, ultraviolet (UV) and photosynthetically active radiation (PAR)] nor natural enemies were affected by the presence of the bifenthrin-treated net on the crop. However, results were not conclusive, mainly due to a low settlement of the released biocontrol agents, and further studies are needed in commercial greenhouses to confirm our preliminary results of compatibility. Secondly, the lethal (mortality) and sublethal effects (reproductive parameters) of six modern pesticides on the predatory bugs O. laevigatus and N. tenuis has been evaluated through laboratory and persistence experiments. Trials were carried out by residual contact, applying the insecticides to the maximum field recommended concentration on glass plates (laboratory) or plants (persistence). Insecticides were chosen as representatives of modern pesticides with a more selective mode of action on natural enemies than organochlorine, organophosphorus and carbamate insecticides. Moreover, they were also chosen because of their frequent use on horticultural crops where O. laevigatus and N. tenuis are present. All of them have been included or have been requested for inclusion in the community list of active substances on the agricultural market, Annex I of the European Directive 91/414/EEC: abamectin and emamectin (neurotoxic avermectins, chloride channel activators), deltamethrin (neutotoxic pyrethroid, sodium channel modulator, positive commercial standard), flubendiamide (neurotoxic, rianodine receptor modulator), spinosad (neurotoxic naturalyte, nicotinic acetylcholine receptor allosteric activator) and spiromesifen (inhibitors of acetyl CoA carboxylase). The study showed that O. laevigatus was more susceptible to all the studied pesticides than N. tenuis. In addition, the research results indicated no impact of flubendiamide and spiromesifen on the two natural enemies studied under laboratory conditions. Consequently, both pesticides are candidates to be included in IPM programmes where these biocontrol agents are present. On the other hand, abamectin, deltamethrin, emamectin and spinosad were not selective for both predatory bugs in laboratory experiments. However, persistence test demonstrated that in spite of the lack of physiological selectivity, these pesticides can provide ecological selectivity in some cases. Abamectin, deltamethrin, emamectin and spinosad could be compatible with N. tenuis if the mirid bug is released 4 days after the insecticide treatment on the crop. With regard to O. laevigatus, abamectin, deltamethrin and spinosad were classified as persistent in our assays, thus the study should be completed with semi-field and field experiments in order to ascertain their possible joint use in IPM programs. In contrast, emamectin could be compatible with O. laevigatus if the pirate bug is released 7 days after the insecticide treatment on the crop. Finally, the selectivity of three moulting accelerating compounds (MACs) (methoxyfenozide, tebufenozide and RH-5849) has also been evaluated on O. laevigatus and N. tenuis. In addition to laboratory experiments to evaluate the toxicity of the insecticides by residual contact and ingestion, molecular approaches were used as well. RNA of both insects was isolated, cDNA was subsequently synthesized and the complete sequence of the ligand binding domain (LBD) of the ecdysone receptor of O. laevigatus (OlEcR-LBD) and N. tenuis (NtEcR-LBD) were determined. Afterwards, the three dimensional structure of LBD was constructed. Finally, the docking of the insecticide molecules in the cavity delineated by the 12 α-helix that composed the EcRLBD was performed. In the case of N. tenuis, it should be noted that in spite of intensive efforts, we did not manage to complete the sequence for the LBD.However, a partial sequence of the LBD was obtained (helix 6-helix 11), and a strong conservation between the amino acids of N. tenuis and O. laevigatus was observed. Results showed no biological activity of methoxyfenozide, tebufenozide and RH-5849, on both predatory bugs. Moreover, modeling of the OlEcR-LBD and docking experiments also suggested that MACs were devoid of any deleterious effect on O. laevigatus. Therefore, our results indicate that these compounds could be safely applied in IPM programs in which O. laevigatus and N. tenuis are present.

Integration of congestion pricing and intertemporal preference rate in social welfare function

Assessing social benefits in transport policy implementation has been studied by many researchers using theoretical or empirical measures. However, few of them measure social benefit using different discount rates including the inter-temporal preferences rate of users, the private investment discount rate and the inter-temporal preferences rate of the government. In general, the social discount rate used is the same for all social actors. Therefore, this paper aims to assess a new method by integrating different types of discount rate belonging to different social actors in order to measure the real benefits of each actor in the short, medium and long term. A dynamic simulation is provided by a strategic Land-Use and Transport Interaction (LUTI) model. The method is tested by optimizing a cordon toll scheme in Madrid considering socio- economic efficiency and environmental criteria. Based on the modified social welfare function (WF), the effects on the measure of social benefits are estimated and compared with the classical WF results as well. The results of this research could be a key issue to understanding the relationship between transport system policies and social actors' benefits distribution in a metropolitan context. The results show that the use of more suitable discount rates for each social actor had an effect on the selection and definition of optimal strategy of congestion pricing. The usefulness of the measure of congestion toll declines more quickly overtime.

Including functional usability features in a Model-Driven Development Method

The Software Engineering (SE) community has historically focused on working with models to represent functionality and persistence, pushing interaction modelling into the background, which has been covered by the Human Computer Interaction (HCI) community. Recently, adequately modelling interaction, and specifically usability, is being considered as a key factor for success in user acceptance, making the integration of the SE and HCI communities more necessary. If we focus on the Model-Driven Development (MDD) paradigm, we notice that there is a lack of proposals to deal with usability features from the very first steps of software development process. In general, usability features are manually implemented once the code has been generated from models. This contradicts the MDD paradigm, which claims that all the analysts? effort must be focused on building models, and the code generation is relegated to model to code transformations. Moreover, usability features related to functionality may involve important changes in the system architecture if they are not considered from the early steps. We state that these usability features related to functionality can be represented abstractly in a conceptual model, and their implementation can be carried out automatically.