The causal relationships in mean and variance between stock returns and foreign institutional investment in India

This paper examines the causalities in mean and variance between stock returns and Foreign Institutional Investment (FII) in India. The analysis in this paper applies the Cross Correlation Function approach from Cheung and Ng (1996), and uses daily data for the timeframe of January 1999 to March 2008 divided into two periods before and after May 2003. Empirical results showed that there are uni-directional causalities in mean and variance from stock returns to FII flows irrelevant of the sample periods, while the reverse causalities in mean and variance are only found in the period beginning with 2003. These results point to FII flows having exerted an impact on the movement of Indian stock prices during the more recent period.

Governance Structure Choices in Supply Chain Management Evidence from Spanish and Chinese Pork Chain Cases

The author participated in the 6 th EU Framework Project ―Q-pork Chains (FP6-036245-2)‖ from 2007 to 2009. With understanding of work reports from China and other countries, it is found that compared with other countries, China has great problems in pork quality and safety. By comparing the pork chain management between China and Spain, It is found that the difference in governance structure is one of the main differences in pork chain management between Spain and China. In China, spot-market relationship still dominates governance structure of pork chain, especially between the numerous house-hold pig holders and the great number of small slaughters. While in Spain, chain agents commonly apply cooperatives or integrations to cooperate. It also has been proven by recent studies, that in quality management at the chain level that supply chain integration has a direct effect on quality management practices (Han, 2010). Therefore, the author started to investigate the governance structure choices in supply chain management. And it has been set as the first research objective, which is to explain the governance structure choices process and the influencing factors in supply chain management, analyzing the pork chains cases in Spain and in China. During the further investigation, the author noticed the international trade of pork between Spain and China is not smooth since the signature of bi-lateral agreement on pork trade in 2007. Thus, another objective of the research is to find and solve the problems exist in the international pork chain between Spain and China. For the first objective, to explain the governance structure choices in supply chain management, the thesis conducts research in three main sections. 10 First of all, the thesis gives a literature overview in chapter two on Supply Chain Management (SCM), agri-food chain management and pork chain management. It concludes that SCM is a systems approach to view the supply chains as a whole, and to manage the total flow of goods inventory from the supplier to the ultimate customer. It includes the bi-directional flow of products (materials and services) and information, and the associated managerial and operational activities. And it also is a customer focus to create unique and individual source of customer value with an appropriate use of resources, leading to customer satisfaction and building competitive chain advantages. Agri-food chain management and pork chain management are applications of SCM in agri-food sector and pork sector respectively. Then, the research gives a comparative study in chapter three in the pork chain and pork chain management between Spain and China. Many differences are found, while the main difference is governance structure in pork chain management. Furthermore, the author gives an empirical study on governance structure choice in chapter five. It is concluded that governance structure of supply chain consists of a collection of rules/institutions/constraints structuring the transactions between the various stakeholders. Based on the overview on literatures closely related with governance structure, such as transaction cost economics, transaction value analysis and resource-based view theories, seven hypotheses are proposed, which are: Hypothesis 1: Transaction cost has positive relationship with governance structure choice Hypothesis 2: Uncertainty has positive relationship with transaction cost; higher uncertainty exerts high transaction cost Hypothesis 3: The relationship between asset specificity and transaction cost is positive Hypothesis 4: Collaboration advantages and governance structure choice have positive relationship11 Hypothesis 5: Willingness to collaborate has positive relationship with collaboration advantages Hypothesis 6: Capability to collaborate has positive relationship with collaboration advantages Hypothesis 7: Uncertainty has negative effect on collaboration advantages It is noted that as transaction cost value is negative, the transaction cost mentioned in the hypotheses is its absolute value. To test the seven hypotheses, Structural Equation Model (SEM) is applied and data collected from 350 pork slaughtering and processing companies in Jiangsu, Shandong and Henan Provinces in China is used. Based on the empirical SEM model and its results, the seven hypotheses are proved. The author generates several conclusions accordingly. It is found that the governance structure choice of the chain not only depends on transaction cost, it also depends on collaboration advantages. Exchange partners establish more stable and more intense relationship to reduce transaction cost and to maximize collaboration advantages. ―Collaboration advantages‖ in this thesis is defined as the joint value achieved through transaction (mutual activities) of agents in supply chains. This value forms as improvements, mainly in mutual logistics systems, cash response, information exchange, technological improvements and innovative improvements and quality management improvements, etc. Governance structure choice is jointly decided by transaction cost and collaboration advantages. Chain agents take different governance structures to coordinate in order to decrease their transaction cost and to increase their collaboration advantages. In China´s pork chain case, spot market relationship dominates the governance structure among the numerous backyard pig farmer and small family slaughterhouse 12 as they are connected by acquaintance relationship and the transaction cost in turn is low. Their relationship is reliable as they know each other in the neighborhood; as a result, spot market relationship is suitable for their exchange. However, the transaction between large-scale slaughtering and processing industries and small-scale pig producers is becoming difficult. The information hold back behavior and hold-up behavior of small-scale pig producers increase transaction cost between them and large-scale slaughtering and processing industries. Thus, through the more intense and stable relationship between processing industries and pig producers, processing industries reduce the transaction cost and improve the collaboration advantages with their chain partners, in which quality and safety collaboration advantages be increased, meaning that processing industries are able to provide consumers products with better quality and higher safety. It is also drawn that transaction cost is influenced mainly by uncertainty and asset specificity, which is in line with new institutional economics theories developed by Williamson O. E. In China´s pork chain case, behavioral uncertainty is created by the hold-up behaviors of great numbers of small pig producers, while big slaughtering and processing industries having strong asset specificity. On the other hand, ―collaboration advantages‖ is influenced by chain agents´ willingness to collaborate and chain agents´ capabilities to cooperate. With the fast growth of big scale slaughtering and processing industries, they are more willing to know and make effort to cooperate with their chain members, and they are more capable to create joint value together with other chain agents. Therefore, they are now the main chain agents who drive more intense and stable governance structure in China‘s pork chain. For the other objective, to find and solve the problems in the international pork chain between Spain and China, the research gives an analysis in chapter four on the 13 international pork chain. This study gives explanations why the international trade of pork between Spain and China is not sufficient from the chain perspective. It is found that the first obstacle is the high quality and safety requirement set by Chinese government. It makes the Spanish companies difficult to get authorities to export. Other aspects, such as Spanish pork is not competitive in price compared with other countries such as Denmark, United States, Canada, etc., Chinese consumers do not have sufficient information on Spanish pork products, are also important reasons that Spain does not export great quantity of pork products to China. It is concluded that China´s government has too much concern on the quality and safety requirements to Spanish pork products, which makes trade difficult to complete. The two countries need to establish a more stable and intense trade relationship. They also should make the information exchange sufficient and efficient and try to break trade barriers. Spanish companies should consider proper price strategies to win the Chinese pork market

Development of Efficient Techniques for the Analysis and Design of Antennas in Dual-Reflectarray Configuration

This thesis contributes to the analysis and design of printed reflectarray antennas. The main part of the work is focused on the analysis of dual offset antennas comprising two reflectarray surfaces, one of them acts as sub-reflector and the second one acts as mainreflector. These configurations introduce additional complexity in several aspects respect to conventional dual offset reflectors, however they present a lot of degrees of freedom that can be used to improve the electrical performance of the antenna. The thesis is organized in four parts: the development of an analysis technique for dualreflectarray antennas, a preliminary validation of such methodology using equivalent reflector systems as reference antennas, a more rigorous validation of the software tool by manufacturing and testing a dual-reflectarray antenna demonstrator and the practical design of dual-reflectarray systems for some applications that show the potential of these kind of configurations to scan the beam and to generate contoured beams. In the first part, a general tool has been implemented to analyze high gain antennas which are constructed of two flat reflectarray structures. The classic reflectarray analysis based on MoM under local periodicity assumption is used for both sub and main reflectarrays, taking into account the incident angle on each reflectarray element. The incident field on the main reflectarray is computed taking into account the field radiated by all the elements on the sub-reflectarray.. Two approaches have been developed, one which employs a simple approximation to reduce the computer run time, and the other which does not, but offers in many cases, improved accuracy. The approximation is based on computing the reflected field on each element on the main reflectarray only once for all the fields radiated by the sub-reflectarray elements, assuming that the response will be the same because the only difference is a small variation on the angle of incidence. This approximation is very accurate when the reflectarray elements on the main reflectarray show a relatively small sensitivity to the angle of incidence. An extension of the analysis technique has been implemented to study dual-reflectarray antennas comprising a main reflectarray printed on a parabolic surface, or in general in a curved surface. In many applications of dual-reflectarray configurations, the reflectarray elements are in the near field of the feed-horn. To consider the near field radiated by the horn, the incident field on each reflectarray element is computed using a spherical mode expansion. In this region, the angles of incidence are moderately wide, and they are considered in the analysis of the reflectarray to better calculate the actual incident field on the sub-reflectarray elements. This technique increases the accuracy for the prediction of co- and cross-polar patterns and antenna gain respect to the case of using ideal feed models. In the second part, as a preliminary validation, the proposed analysis method has been used to design a dual-reflectarray antenna that emulates previous dual-reflector antennas in Ku and W-bands including a reflectarray as subreflector. The results for the dualreflectarray antenna compare very well with those of the parabolic reflector and reflectarray subreflector; radiation patterns, antenna gain and efficiency are practically the same when the main parabolic reflector is substituted by a flat reflectarray. The results show that the gain is only reduced by a few tenths of a dB as a result of the ohmic losses in the reflectarray. The phase adjustment on two surfaces provided by the dual-reflectarray configuration can be used to improve the antenna performance in some applications requiring multiple beams, beam scanning or shaped beams. Third, a very challenging dual-reflectarray antenna demonstrator has been designed, manufactured and tested for a more rigorous validation of the analysis technique presented. The proposed antenna configuration has the feed, the sub-reflectarray and the main-reflectarray in the near field one to each other, so that the conventional far field approximations are not suitable for the analysis of such antenna. This geometry is used as benchmarking for the proposed analysis tool in very stringent conditions. Some aspects of the proposed analysis technique that allow improving the accuracy of the analysis are also discussed. These improvements include a novel method to reduce the inherent cross polarization which is introduced mainly from grounded patch arrays. It has been checked that cross polarization in offset reflectarrays can be significantly reduced by properly adjusting the patch dimensions in the reflectarray in order to produce an overall cancellation of the cross-polarization. The dimensions of the patches are adjusted in order not only to provide the required phase-distribution to shape the beam, but also to exploit the crosses by zero of the cross-polarization components. The last part of the thesis deals with direct applications of the technique described. The technique presented is directly applicable to the design of contoured beam antennas for DBS applications, where the requirements of cross-polarisation are very stringent. The beam shaping is achieved by synthesithing the phase distribution on the main reflectarray while the sub-reflectarray emulates an equivalent hyperbolic subreflector. Dual-reflectarray antennas present also the ability to scan the beam over small angles about boresight. Two possible architectures for a Ku-band antenna are also described based on a dual planar reflectarray configuration that provides electronic beam scanning in a limited angular range. In the first architecture, the beam scanning is achieved by introducing a phase-control in the elements of the sub-reflectarray and the mainreflectarray is passive. A second alternative is also studied, in which the beam scanning is produced using 1-bit control on the main reflectarray, while a passive subreflectarray is designed to provide a large focal distance within a compact configuration. The system aims to develop a solution for bi-directional satellite links for emergency communications. In both proposed architectures, the objective is to provide a compact optics and simplicity to be folded and deployed.

Wave Statistics and Space-time Extremes via Stereo Imaging

We present an analysis of the space-time dynamics of oceanic sea states exploiting stereo imaging techniques. In particular, a novel Wave Acquisition Stereo System (WASS) has been developed and deployed at the oceanographic tower Acqua Alta in the Northern Adriatic Sea, off the Venice coast in Italy. The analysis of WASS video measurements yields accurate estimates of the oceanic sea state dynamics, the associated directional spectra and wave surface statistics that agree well with theoretical models. Finally, we show that a space-time extreme, defined as the expected largest surface wave height over an area, is considerably larger than the maximum crest observed in time at a point, in agreement with theoretical predictions.

A Generalized Regression Methodology for Bivariate Heteroscedastic Data

We present a methodology for reducing a straight line fitting regression problem to a Least Squares minimization one. This is accomplished through the definition of a measure on the data space that takes into account directional dependences of errors, and the use of polar descriptors for straight lines. This strategy improves the robustness by avoiding singularities and non-describable lines. The methodology is powerful enough to deal with non-normal bivariate heteroscedastic data error models, but can also supersede classical regression methods by making some particular assumptions. An implementation of the methodology for the normal bivariate case is developed and evaluated.

Asymmetric propagation of airblast from bench blasting

This paper investigates the propagation of airblast from quarry blasting. Peak overpressure is calculated as a function of blasting parameters (explosive mass per delay and velocity at which the detonation sequence proceeds along the bench) and polar coordinates of the point of interest (distance to the blast and azimuth with respect to the free face of the blast). The model is in the form of the product of a classical scaled distance attenuation law times a directional correction factor. The latter considers the influence of the bench face, and attenuates overpressure at the top level and amplifies it at the bottom. Such factor also accounts for the effect of the delay by amplifying the pressure in the direction of the initiation sequence if the velocity of initiation exceeds half the speed of sound and up to an initiation velocity in the range of the speed of sound. The model has been fitted to an empirical data set composed by 134 airblast records monitored in 47 blasts at two quarries. The measurements were made at distances to the blast less than 450 m. The model is statistically significant and has a determination coefficient of 0.869

Effect of the cooling rate on microstructure and hardness of MAR-M247 Ni-based superalloy

The effect of cooling rate on the microstructure of MAR-M247 Ni-based superalloy was investigated via physical simulation of the casting process. Solidification experiments with cooling rates in the range of 0.25–10 K/s showed smooth temperature profiles with measured cooling rates matching the set values. The MAR-M247 showed cellular (0.25 K/s) and dendritic (1, 5 and 10 K/s) microstructures. Microconstituents also varied with cooling rates: γ/γ′ matrix with carbides and γ/γ′ eutectic at 0.25 K/s, γ/γ′ matrix with carbides at 1 K/s, and γ/γ′ matrix with carbides and γ/MC eutectic at 5 and 10 K/s. Moreover, the secondary dendritic arm spacing decreased and the hardness increased with the increase in the cooling rates.

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.

Space-time Measurements of Oceanic Sea States

Stereo video techniques are effective for estimating the space–time wave dynamics over an area of the ocean. Indeed, a stereo camera view allows retrieval of both spatial and temporal data whose statistical content is richer than that of time series data retrieved from point wave probes. We present an application of the Wave Acquisition Stereo System (WASS) for the analysis of offshore video measurements of gravity waves in the Northern Adriatic Sea and near the southern seashore of the Crimean peninsula, in the Black Sea. We use classical epipolar techniques to reconstruct the sea surface from the stereo pairs sequentially in time, viz. a sequence of spatial snapshots. We also present a variational approach that exploits the entire data image set providing a global space–time imaging of the sea surface, viz. simultaneous reconstruction of several spatial snapshots of the surface in order to guarantee continuity of the sea surface both in space and time. Analysis of the WASS measurements show that the sea surface can be accurately estimated in space and time together, yielding associated directional spectra and wave statistics at a point in time that agrees well with probabilistic models. In particular, WASS stereo imaging is able to capture typical features of the wave surface, especially the crest-to-trough asymmetry due to second order nonlinearities, and the observed shape of large waves are fairly described by theoretical models based on the theory of quasi-determinism (Boccotti, 2000). Further, we investigate space–time extremes of the observed stationary sea states, viz. the largest surface wave heights expected over a given area during the sea state duration. The WASS analysis provides the first experimental proof that a space–time extreme is generally larger than that observed in time via point measurements, in agreement with the predictions based on stochastic theories for global maxima of Gaussian fields.

Connexin-43 in the osteogenic BM niche regulates its cellular composition and the bidirectional traffic of hematopoietic stem cells and progenitors

Connexin-43 (Cx43), a gap junction protein involved in control of cell proliferation, differentiation and migration, has been suggested to have a role in hematopoiesis. Cx43 is highly expressed in osteoblasts and osteogenic progenitors (OB/P). To elucidate the biologic function of Cx43 in the hematopoietic microenvironment (HM) and its influence in hematopoietic stem cell (HSC) activity, we studied the hematopoietic function in an in vivo model of constitutive deficiency of Cx43 in OB/P. The deficiency of Cx43 in OB/P cells does not impair the steady state hematopoiesis, but disrupts the directional trafficking of HSC/progenitors (Ps) between the bone marrow (BM) and peripheral blood (PB). OB/P Cx43 is a crucial positive regulator of transstromal migration and homing of both HSCs and progenitors in an irradiated microenvironment. However, OB/P Cx43 deficiency in nonmyeloablated animals does not result in a homing defect but induces increased endosteal lodging and decreased mobilization of HSC/Ps associated with proliferation and expansion of Cxcl12-secreting mesenchymal/osteolineage cells in the BM HM in vivo. Cx43 controls the cellular content of the BM osteogenic microenvironment and is required for homing of HSC/Ps in myeloablated animals

Transient acoustic response in car cabins with localization of reflections

Due to its small size and the restrictions on source and listener positions, the design of sound reproduction systems for car cabins is particularly cumbersome. In the present project the measurement of the impulse response between a single loudspeaker and a listener position, with special emphasis on the directional characteristics, will be examined. The propagation paths inside a car are very short, meaning that it is very difficult for the existing commercial measurement systems to resolve the different reflections arriving to the listener. This paper propose a first approach of an algorithm based on time difference of arrival along a measurement technique aiming at finding the reflections and their direction of arrival to the listener. To this end a circular microphone array at a known position is employed, along with Maximum-Length Sequences (MLS) measurement technique. The results are processed so as to extract the directional properties, demonstrate the physical limitations that can influence or prevent this detection in practice. Measurements were carried out in a free-field environment (anechoic chamber) making use of different panels closer around the microphone array. RESUMEN. El diseño de sistemas de reproducción de audio para cabinas de coche es especialmente complicado debido al reducido tamaño del espacio y las restricciones de los altavoces y posiciones de escucha de los ocupantes. En el presente proyecto, se examinan mediciones de la respuesta al impulso entre un altavoz y una posición de escucha con especial énfasis en las características direccionales. Los caminos de propagación de las ondas sonoras dentro de un coche son muy cortos, lo que hace difícil para los instrumentos de medida existentes en el mercado determinar las direcciones de llegada de las diferentes reflexiones que llegan a una posición de escucha. Este trabajo propone una primera aproximación de un algoritmo, basado en las diferencias temporales de llegada de una onda a diferentes puntos de medida, y una particular técnica de medida de la respuesta al impulso para obtener las direcciones de llegada de reflexiones a una posición de escucha. Para ello, se emplea una matriz circular de micrófonos en una posición conocida junto con la técnica de medida MLS (Maximum Length Sequence). Los resultados obtenidos son procesados para extraer la dirección de llegada de las reflexiones acústicas y encontrar las limitaciones que influyan en la detección de dichas reflexiones. Las mediciones se llevan a cabo en un entorno de campo libre y utilizando diferentes superficies reflectantes alrededor de la matriz de micrófonos.

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.

Toward a climate-dependent paradigm of ammonia emission & deposition.

Existing descriptions of bi-directional ammonia (NH3) land–atmosphere exchange incorporate temperature and moisture controls, and are beginning to be used in regional chemical transport models. However, such models have typically applied simpler emission factors to upscale the main NH3 emission terms. While this approach has successfully simulated the main spatial patterns on local to global scales, it fails to address the environment- and climate-dependence of emissions. To handle these issues, we outline the basis for a new modelling paradigm where both NH3 emissions and deposition are calculated online according to diurnal, seasonal and spatial differences in meteorology. We show how measurements reveal a strong, but complex pattern of climatic dependence, which is increasingly being characterized using ground-based NH3 monitoring and satellite observations, while advances in process-based modelling are illustrated for agricultural and natural sources, including a global application for seabird colonies. A future architecture for NH3 emission–deposition modelling is proposed that integrates the spatio-temporal interactions, and provides the necessary foundation to assess the consequences of climate change. Based on available measurements, a first empirical estimate suggests that 5°C warming would increase emissions by 42 per cent (28–67%). Together with increased anthropogenic activity, global NH3 emissions may increase from 65 (45–85) Tg N in 2008 to reach 132 (89–179) Tg by 2100.

Diseño basado en técnicas de fiabilidad del tratamiento de mejora del terreno mediante columnas de grava

El proyecto geotécnico de columnas de grava tiene todas las incertidumbres asociadas a un proyecto geotécnico y además hay que considerar las incertidumbres inherentes a la compleja interacción entre el terreno y la columna, la puesta en obra de los materiales y el producto final conseguido. Este hecho es común a otros tratamientos del terreno cuyo objetivo sea, en general, la mejora “profunda”. Como los métodos de fiabilidad (v.gr., FORM, SORM, Monte Carlo, Simulación Direccional) dan respuesta a la incertidumbre de forma mucho más consistente y racional que el coeficiente de seguridad tradicional, ha surgido un interés reciente en la aplicación de técnicas de fiabilidad a la ingeniería geotécnica. Si bien la aplicación concreta al proyecto de técnicas de mejora del terreno no es tan extensa. En esta Tesis se han aplicado las técnicas de fiabilidad a algunos aspectos del proyecto de columnas de grava (estimación de asientos, tiempos de consolidación y aumento de la capacidad portante) con el objetivo de efectuar un análisis racional del proceso de diseño, considerando los efectos que tienen la incertidumbre y la variabilidad en la seguridad del proyecto, es decir, en la probabilidad de fallo. Para alcanzar este objetivo se ha utilizado un método analítico avanzado debido a Castro y Sagaseta (2009), que mejora notablemente la predicción de las variables involucradas en el diseño del tratamiento y su evolución temporal (consolidación). Se ha estudiado el problema del asiento (valor y tiempo de consolidación) en el contexto de la incertidumbre, analizando dos modos de fallo: i) el primer modo representa la situación en la que es posible finalizar la consolidación primaria, parcial o totalmente, del terreno mejorado antes de la ejecución de la estructura final, bien sea por un precarga o porque la carga se pueda aplicar gradualmente sin afectar a la estructura o instalación; y ii) por otra parte, el segundo modo de fallo implica que el terreno mejorado se carga desde el instante inicial con la estructura definitiva o instalación y se comprueba que el asiento final (transcurrida la consolidación primaria) sea lo suficientemente pequeño para que pueda considerarse admisible. Para trabajar con valores realistas de los parámetros geotécnicos, los datos se han obtenido de un terreno real mejorado con columnas de grava, consiguiendo, de esta forma, un análisis de fiabilidad más riguroso. La conclusión más importante, obtenida del análisis de este caso particular, es la necesidad de precargar el terreno mejorado con columnas de grava para conseguir que el asiento ocurra de forma anticipada antes de la aplicación de la carga correspondiente a la estructura definitiva. De otra forma la probabilidad de fallo es muy alta, incluso cuando el margen de seguridad determinista pudiera ser suficiente. En lo que respecta a la capacidad portante de las columnas, existen un buen número de métodos de cálculo y de ensayos de carga (tanto de campo como de laboratorio) que dan predicciones dispares del valor de la capacidad última de las columnas de grava. En las mallas indefinidas de columnas, los resultados del análisis de fiabilidad han confirmado las consideraciones teóricas y experimentales existentes relativas a que no se produce fallo por estabilidad, obteniéndose una probabilidad de fallo prácticamente nula para este modo de fallo. Sin embargo, cuando se analiza, en el contexto de la incertidumbre, la capacidad portante de pequeños grupos de columnas bajo zapatas se ha obtenido, para un caso con unos parámetros geotécnicos típicos, que la probabilidad de fallo es bastante alta, por encima de los umbrales normalmente admitidos para Estados Límite Últimos. Por último, el trabajo de recopilación sobre los métodos de cálculo y de ensayos de carga sobre la columna aislada ha permitido generar una base de datos suficientemente amplia como para abordar una actualización bayesiana de los métodos de cálculo de la columna de grava aislada. El marco bayesiano de actualización ha resultado de utilidad en la mejora de las predicciones de la capacidad última de carga de la columna, permitiendo “actualizar” los parámetros del modelo de cálculo a medida que se dispongan de ensayos de carga adicionales para un proyecto específico. Constituye una herramienta valiosa para la toma de decisiones en condiciones de incertidumbre ya que permite comparar el coste de los ensayos adicionales con el coste de una posible rotura y , en consecuencia, decidir si es procedente efectuar dichos ensayos. The geotechnical design of stone columns has all the uncertainties associated with a geotechnical project and those inherent to the complex interaction between the soil and the column, the installation of the materials and the characteristics of the final (as built) column must be considered. This is common to other soil treatments aimed, in general, to “deep” soil improvement. Since reliability methods (eg, FORM, SORM, Monte Carlo, Directional Simulation) deals with uncertainty in a much more consistent and rational way than the traditional safety factor, recent interest has arisen in the application of reliability techniques to geotechnical engineering. But the specific application of these techniques to soil improvement projects is not as extensive. In this thesis reliability techniques have been applied to some aspects of stone columns design (estimated settlements, consolidation times and increased bearing capacity) to make a rational analysis of the design process, considering the effects of uncertainty and variability on the safety of the project, i.e., on the probability of failure. To achieve this goal an advanced analytical method due to Castro and Sagaseta (2009), that significantly improves the prediction of the variables involved in the design of treatment and its temporal evolution (consolidation), has been employed. This thesis studies the problem of stone column settlement (amount and speed) in the context of uncertainty, analyzing two failure modes: i) the first mode represents the situation in which it is possible to cause primary consolidation, partial or total, of the improved ground prior to implementation of the final structure, either by a pre-load or because the load can be applied gradually or programmed without affecting the structure or installation; and ii) on the other hand, the second mode implies that the improved ground is loaded from the initial instant with the final structure or installation, expecting that the final settlement (elapsed primary consolidation) is small enough to be allowable. To work with realistic values of geotechnical parameters, data were obtained from a real soil improved with stone columns, hence producing a more rigorous reliability analysis. The most important conclusion obtained from the analysis of this particular case is the need to preload the stone columns-improved soil to make the settlement to occur before the application of the load corresponding to the final structure. Otherwise the probability of failure is very high, even when the deterministic safety margin would be sufficient. With respect to the bearing capacity of the columns, there are numerous methods of calculation and load tests (both for the field and the laboratory) giving different predictions of the ultimate capacity of stone columns. For indefinite columns grids, the results of reliability analysis confirmed the existing theoretical and experimental considerations that no failure occurs due to the stability failure mode, therefore resulting in a negligible probability of failure. However, when analyzed in the context of uncertainty (for a case with typical geotechnical parameters), results show that the probability of failure due to the bearing capacity failure mode of a group of columns is quite high, above thresholds usually admitted for Ultimate Limit States. Finally, the review of calculation methods and load tests results for isolated columns, has generated a large enough database, that allowed a subsequent Bayesian updating of the methods for calculating the bearing capacity of isolated stone columns. The Bayesian updating framework has been useful to improve the predictions of the ultimate load capacity of the column, allowing to "update" the parameters of the calculation model as additional load tests become available for a specific project. Moreover, it is a valuable tool for decision making under uncertainty since it is possible to compare the cost of further testing to the cost of a possible failure and therefore to decide whether it is appropriate to perform such tests.

Route-back delivery protocol for collection tree protocol-based applications

We propose the Route-back Delivery (RBD) protocol; a routing mechanism to create reverse routes exploiting the Collection Tree Protocol to allow unicast data dissemination from the sink. The main goal of this work is to provide a mechanism to enable bi-directional communications among the root(s) and specific sensor nodes in data gathering applications that does not use broadcast only mechanisms. The main objective of the root-to-remote-nodes route creation is to disseminate short messages to change application parameters in a unicast fashion. This facilitates remote configurability in heterogeneous WSN deployments.