Dos modelos por ensayo y error: Villa Stonborough y Villa Moller = Two trial and error processes: Villa Stonborough and Villa Moller.

Uno de los procesos de desarrollo más comunes para llevar a cabo un proyecto arquitectónico es el ensayo y error. Un proceso de selección de pruebas que se suele abordar de dos maneras, o bien se efectúa con el fin de ir depurando una posición más óptima, o bien sirve para explorar nuevas vías de investigación. Con el fin de profundizar en esto, el artículo presenta el análisis de dos diferentes procesos de proyecto de viviendas desarrolladas por ensayo y error, obras referenciales en la historia de la arquitectura, la Villa Stonborough de Wittgenstein y la Villa Moller de Adolf Loos. Ambas aunque pertenecientes al mismo periodo histórico, están desarrolladas de maneras muy opuestas, casi enfrentadas. De su estudio se pretende localizar los conceptos que han impulsado sus diferentes vías de producción, para poder extrapolados a otros casos similares. ABSTRACT: One of the most common processes to develop an architectonic project is the trial and error method. The process of selection of tests is usually done on two different ways. Or it is done with the goal to find out the most optimized position, or it is used to explore new ways of research. In order to investigate this item, the article shows the analysis of two different processes of housing projects that have been done by trial and error. Constructions, that are references in the history of architecture, the Villa Stonborough by Wittgenstein and the Villa Moller by Adolf Loos. Although both of them belong to the same historical period, they are developed by different ways, almost confronted. Thanks to this analysis we will attempt to localize the concepts that drove into their different way of production and then we will try to extrapolate these properties to other similar cases.

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.

Sensing colour stability and mixtures of powder paprika using optical reflectance and image analysis.

The production and industry of paprika present several problems related to quality and to production costs. One of the main difficulties is to obtain an objective and quick method for predicting quality. Quality in powder paprika involves: quantity of carotenoids and the appearance and stability of colour. The method used currently for determining quality is the measurement of absorbance at 460 nm wavelength, of an acetone extract of carotenoids, but there is no information about the appearance of the paprika or the stability of its colour with time. " Another important problem is the presence of mixtures of powdered paprika produced in the Spanish region of "La Vera", which has a peculiar way of production, with a high '' quality and price, with other products of lower quality. It is necessary to obtain methods which are able to detect the fraud.

Evaluating alternative industrial network organizations and information systems

Purpose – The purpose of this paper is to present a simulation‐based evaluation method for the comparison of different organizational forms and software support levels in the field of supply chain management (SCM). Design/methodology/approach – Apart from widely known logistic performance indicators, the discrete event simulation model considers explicitly coordination cost as stemming from iterative administration procedures. Findings - The method is applied to an exemplary supply chain configuration considering various parameter settings. Curiously, additional coordination cost does not always result in improved logistic performance. Influence factor variations lead to different organizational recommendations. The results confirm the high importance of (up to now) disregarded dimensions when evaluating SCM concepts and IT tools. Research limitations/implications – The model is based on simplified product and network structures. Future research shall include more complex, real world configurations. Practical implications – The developed method is designed for the identification of improvement potential when SCM software is employed. Coordination schemes based only on ERP systems are valid alternatives in industrial practice because significant investment IT can be avoided. Therefore, the evaluation of these coordination procedures, in particular the cost due to iterations, is of high managerial interest and the method provides a comprehensive tool for strategic IT decision making. Originality/value – Reviewed literature is mostly focused on the benefits of SCM software implementations. However, ERP system based supply chain coordination is still widespread industrial practice but associated coordination cost has not been addressed by researchers.

Monte Carlo Neutronics and Thermal-hydraulics coupling applied to Fast Reactors

Un escenario habitualmente considerado para el uso sostenible y prolongado de la energía nuclear contempla un parque de reactores rápidos refrigerados por metales líquidos (LMFR) dedicados al reciclado de Pu y la transmutación de actínidos minoritarios (MA). Otra opción es combinar dichos reactores con algunos sistemas subcríticos asistidos por acelerador (ADS), exclusivamente destinados a la eliminación de MA. El diseño y licenciamiento de estos reactores innovadores requiere herramientas computacionales prácticas y precisas, que incorporen el conocimiento obtenido en la investigación experimental de nuevas configuraciones de reactores, materiales y sistemas. A pesar de que se han construido y operado un cierto número de reactores rápidos a nivel mundial, la experiencia operacional es todavía reducida y no todos los transitorios se han podido entender completamente. Por tanto, los análisis de seguridad de nuevos LMFR están basados fundamentalmente en métodos deterministas, al contrario que las aproximaciones modernas para reactores de agua ligera (LWR), que se benefician también de los métodos probabilistas. La aproximación más usada en los estudios de seguridad de LMFR es utilizar una variedad de códigos, desarrollados a base de distintas teorías, en busca de soluciones integrales para los transitorios e incluyendo incertidumbres. En este marco, los nuevos códigos para cálculos de mejor estimación ("best estimate") que no incluyen aproximaciones conservadoras, son de una importancia primordial para analizar estacionarios y transitorios en reactores rápidos. Esta tesis se centra en el desarrollo de un código acoplado para realizar análisis realistas en reactores rápidos críticos aplicando el método de Monte Carlo. Hoy en día, dado el mayor potencial de recursos computacionales, los códigos de transporte neutrónico por Monte Carlo se pueden usar de manera práctica para realizar cálculos detallados de núcleos completos, incluso de elevada heterogeneidad material. Además, los códigos de Monte Carlo se toman normalmente como referencia para los códigos deterministas de difusión en multigrupos en aplicaciones con reactores rápidos, porque usan secciones eficaces punto a punto, un modelo geométrico exacto y tienen en cuenta intrínsecamente la dependencia angular de flujo. En esta tesis se presenta una metodología de acoplamiento entre el conocido código MCNP, que calcula la generación de potencia en el reactor, y el código de termohidráulica de subcanal COBRA-IV, que obtiene las distribuciones de temperatura y densidad en el sistema. COBRA-IV es un código apropiado para aplicaciones en reactores rápidos ya que ha sido validado con resultados experimentales en haces de barras con sodio, incluyendo las correlaciones más apropiadas para metales líquidos. En una primera fase de la tesis, ambos códigos se han acoplado en estado estacionario utilizando un método iterativo con intercambio de archivos externos. El principal problema en el acoplamiento neutrónico y termohidráulico en estacionario con códigos de Monte Carlo es la manipulación de las secciones eficaces para tener en cuenta el ensanchamiento Doppler cuando la temperatura del combustible aumenta. Entre todas las opciones disponibles, en esta tesis se ha escogido la aproximación de pseudo materiales, y se ha comprobado que proporciona resultados aceptables en su aplicación con reactores rápidos. Por otro lado, los cambios geométricos originados por grandes gradientes de temperatura en el núcleo de reactores rápidos resultan importantes para la neutrónica como consecuencia del elevado recorrido libre medio del neutrón en estos sistemas. Por tanto, se ha desarrollado un módulo adicional que simula la geometría del reactor en caliente y permite estimar la reactividad debido a la expansión del núcleo en un transitorio. éste módulo calcula automáticamente la longitud del combustible, el radio de la vaina, la separación de los elementos de combustible y el radio de la placa soporte en función de la temperatura. éste efecto es muy relevante en transitorios sin inserción de bancos de parada. También relacionado con los cambios geométricos, se ha implementado una herramienta que, automatiza el movimiento de las barras de control en busca d la criticidad del reactor, o bien calcula el valor de inserción axial las barras de control. Una segunda fase en la plataforma de cálculo que se ha desarrollado es la simulació dinámica. Puesto que MCNP sólo realiza cálculos estacionarios para sistemas críticos o supercríticos, la solución más directa que se propone sin modificar el código fuente de MCNP es usar la aproximación de factorización de flujo, que resuelve por separado la forma del flujo y la amplitud. En este caso se han estudiado en profundidad dos aproximaciones: adiabática y quasiestática. El método adiabático usa un esquema de acoplamiento que alterna en el tiempo los cálculos neutrónicos y termohidráulicos. MCNP calcula el modo fundamental de la distribución de neutrones y la reactividad al final de cada paso de tiempo, y COBRA-IV calcula las propiedades térmicas en el punto intermedio de los pasos de tiempo. La evolución de la amplitud de flujo se calcula resolviendo las ecuaciones de cinética puntual. Este método calcula la reactividad estática en cada paso de tiempo que, en general, difiere de la reactividad dinámica que se obtendría con la distribución de flujo exacta y dependiente de tiempo. No obstante, para entornos no excesivamente alejados de la criticidad ambas reactividades son similares y el método conduce a resultados prácticos aceptables. Siguiendo esta línea, se ha desarrollado después un método mejorado para intentar tener en cuenta el efecto de la fuente de neutrones retardados en la evolución de la forma del flujo durante el transitorio. El esquema consiste en realizar un cálculo cuasiestacionario por cada paso de tiempo con MCNP. La simulación cuasiestacionaria se basa EN la aproximación de fuente constante de neutrones retardados, y consiste en dar un determinado peso o importancia a cada ciclo computacial del cálculo de criticidad con MCNP para la estimación del flujo final. Ambos métodos se han verificado tomando como referencia los resultados del código de difusión COBAYA3 frente a un ejercicio común y suficientemente significativo. Finalmente, con objeto de demostrar la posibilidad de uso práctico del código, se ha simulado un transitorio en el concepto de reactor crítico en fase de diseño MYRRHA/FASTEF, de 100 MW de potencia térmica y refrigerado por plomo-bismuto. ABSTRACT Long term sustainable nuclear energy scenarios envisage a fleet of Liquid Metal Fast Reactors (LMFR) for the Pu recycling and minor actinides (MAs) transmutation or combined with some accelerator driven systems (ADS) just for MAs elimination. Design and licensing of these innovative reactor concepts require accurate computational tools, implementing the knowledge obtained in experimental research for new reactor configurations, materials and associated systems. Although a number of fast reactor systems have already been built, the operational experience is still reduced, especially for lead reactors, and not all the transients are fully understood. The safety analysis approach for LMFR is therefore based only on deterministic methods, different from modern approach for Light Water Reactors (LWR) which also benefit from probabilistic methods. Usually, the approach adopted in LMFR safety assessments is to employ a variety of codes, somewhat different for the each other, to analyze transients looking for a comprehensive solution and including uncertainties. In this frame, new best estimate simulation codes are of prime importance in order to analyze fast reactors steady state and transients. This thesis is focused on the development of a coupled code system for best estimate analysis in fast critical reactor. Currently due to the increase in the computational resources, Monte Carlo methods for neutrons transport can be used for detailed full core calculations. Furthermore, Monte Carlo codes are usually taken as reference for deterministic diffusion multigroups codes in fast reactors applications because they employ point-wise cross sections in an exact geometry model and intrinsically account for directional dependence of the ux. The coupling methodology presented here uses MCNP to calculate the power deposition within the reactor. The subchannel code COBRA-IV calculates the temperature and density distribution within the reactor. COBRA-IV is suitable for fast reactors applications because it has been validated against experimental results in sodium rod bundles. The proper correlations for liquid metal applications have been added to the thermal-hydraulics program. Both codes are coupled at steady state using an iterative method and external files exchange. The main issue in the Monte Carlo/thermal-hydraulics steady state coupling is the cross section handling to take into account Doppler broadening when temperature rises. Among every available options, the pseudo materials approach has been chosen in this thesis. This approach obtains reasonable results in fast reactor applications. Furthermore, geometrical changes caused by large temperature gradients in the core, are of major importance in fast reactor due to the large neutron mean free path. An additional module has therefore been included in order to simulate the reactor geometry in hot state or to estimate the reactivity due to core expansion in a transient. The module automatically calculates the fuel length, cladding radius, fuel assembly pitch and diagrid radius with the temperature. This effect will be crucial in some unprotected transients. Also related to geometrical changes, an automatic control rod movement feature has been implemented in order to achieve a just critical reactor or to calculate control rod worth. A step forward in the coupling platform is the dynamic simulation. Since MCNP performs only steady state calculations for critical systems, the more straight forward option without modifying MCNP source code, is to use the flux factorization approach solving separately the flux shape and amplitude. In this thesis two options have been studied to tackle time dependent neutronic simulations using a Monte Carlo code: adiabatic and quasistatic methods. The adiabatic methods uses a staggered time coupling scheme for the time advance of neutronics and the thermal-hydraulics calculations. MCNP computes the fundamental mode of the neutron flux distribution and the reactivity at the end of each time step and COBRA-IV the thermal properties at half of the the time steps. To calculate the flux amplitude evolution a solver of the point kinetics equations is used. This method calculates the static reactivity in each time step that in general is different from the dynamic reactivity calculated with the exact flux distribution. Nevertheless, for close to critical situations, both reactivities are similar and the method leads to acceptable practical results. In this line, an improved method as an attempt to take into account the effect of delayed neutron source in the transient flux shape evolutions is developed. The scheme performs a quasistationary calculation per time step with MCNP. This quasistationary simulations is based con the constant delayed source approach, taking into account the importance of each criticality cycle in the final flux estimation. Both adiabatic and quasistatic methods have been verified against the diffusion code COBAYA3, using a theoretical kinetic exercise. Finally, a transient in a critical 100 MWth lead-bismuth-eutectic reactor concept is analyzed using the adiabatic method as an application example in a real system.

Comparison of linear and non-linear 2D+T registration methods for DE-MRI cardiac perfusion studies

A series of motion compensation algorithms is run on the challenge data including methods that optimize only a linear transformation, or a non-linear transformation, or both – first a linear and then a non-linear transformation. Methods that optimize a linear transformation run an initial segmentation of the area of interest around the left myocardium by means of an independent component analysis (ICA) (ICA-*). Methods that optimize non-linear transformations may run directly on the full images, or after linear registration. Non-linear motion compensation approaches applied include one method that only registers pairs of images in temporal succession (SERIAL), one method that registers all image to one common reference (AllToOne), one method that was designed to exploit quasi-periodicity in free breathing acquired image data and was adapted to also be usable to image data acquired with initial breath-hold (QUASI-P), a method that uses ICA to identify the motion and eliminate it (ICA-SP), and a method that relies on the estimation of a pseudo ground truth (PG) to guide the motion compensation.

Proyecto de construcción de un túnel por el sistema "Cut and cover" para el acceso ferroviario del cercanías a Marbella

El proyecto describe el diseño de un túnel ferroviario en la zona sur de España, entre las localidades de Fuengirola y Marbella en la provincia de Málaga. La ejecución de las obras incluidas en este proyecto permitirá conectar mediante una nueva línea ferroviaria dicha Estación con la población de Marbella y en un futuro con más poblaciones del Corredor Ferroviario de la Costa del Sol que en este momento no cuenta con ninguna conexión ferroviaria. En la geometría se concreta la sección del túnel que se va a ejecutar. Se utiliza como referencia el estudio geológico y geotécnico con el fin de caracterizar los materiales que se van a encontrar en la excavación del túnel. Una vez concluida esta fase se determinan los sostenimientos de acuerdo con las clasificaciones geomecánicas. Realizando el tramo elegido por el Método “Cut and Cover”, y siendo objeto de este proyecto todo el proceso constructivo del mismo así como otras obras complementarias que se tendrán que llevar a cabo debido al paso de dicho túnel por zonas transitadas o habitadas actualmente. Abstract Project describes the design of a high speed railway tunnel in the sur of Spain, between the villages of Fuengirola and Marbella (Málaga). The execution of the constructions incluyed in this proyect will allow the conection, by a neww railway line, this station with Marbella and,in the future, with more village of the “Corredor Ferroviario of Costa del Sol”, witcth al presente hasn´t gotany rail connection.It be can observed in the section of the tunnel that is going to be performed. Geological and geotechnical studies are taken into account in order to classify the rock mass materials involved in the construction process. Afterwards, supports are defined according to geomechanical classifications, specifying four different sections. Making the stretch chosen by “Cut and Cover” method and being the main goal of this proyect all the constructive process of this, as well as other complementary works that will be executed of the crossing of this tunnel by accessible or inhabited areas currently.

A Novel Ground Fault Identification Method for 2 x 5 kV Railway Power Supply Systems

The location of ground faults in railway electric lines in 2 × 5 kV railway power supply systems is a difficult task. In both 1 × 25 kV and transmission power systems it is common practice to use distance protection relays to clear ground faults and localize their positions. However, in the particular case of this 2 × 25 kV system, due to the widespread use of autotransformers, the relation between the distance and the impedance seen by the distance protection relays is not linear and therefore the location is not accurate enough. This paper presents a simple and economical method to identify the subsection between autotransformers and the conductor (catenary or feeder) where the ground fault is happening. This method is based on the comparison of the angle between the current and the voltage of the positive terminal in each autotransformer. Consequently, after the identification of the subsection and the conductor with the ground defect, only the subsection where the ground fault is present will be quickly removed from service, with the minimum effect on rail traffic. This method has been validated through computer simulations and laboratory tests with positive results.

Simultaneous determination of multiclass emerging contaminants in aquatic plants by ultrasound assisted matrix solid phase dispersion and GC-MS

A multiresidue method was developed for the simultaneous determination of 31 emerging contaminants (pharmaceutical compounds, hormones, personal care products, biocides and flame retardants) in aquatic plants. Analytes were extracted by ultrasound assisted-matrix solid phase dispersion (UA-MSPD) and determined by gas chromatography-mass spectrometry after sylilation. The method was validated for different aquatic plants (Typha angustifolia, Arundo donax and Lemna minor) and a semiaquatic cultivated plant (Oryza sativa) with good recoveries at concentrations of 100 and 25 ng g-1 wet weight, ranging from 70 to 120 %, and low method detection limits (0.3 to 2.2 ng g-1 wet weight). A significant difference of the chromatographic response was observed for some compounds in neat solvent versus matrix extracts and therefore quantification was carried out using matrix-matched standards in order to overcome this matrix effect. Aquatic plants taken from rivers located at three Spanish regions were analyzed and the compounds detected were parabens, bisphenol A, benzophenone-3, cyfluthrin and cypermethrin. The levels found ranged from 6 to 25 ng g-1 wet weight except for cypermethrin that was detected at 235 ng g-1 wet weight in Oryza sativa samples.

A synthetic cryIC gene, encoding a Bacillus thuringiensis δ-endotoxin, confers Spodoptera resistance in alfalfa and tobacco

Spodoptera species, representing widespread polyphagous insect pests, are resistant to Bacillus thuringiensis δ-endotoxins used thus far as insecticides in transgenic plants. Here we describe the chemical synthesis of a cryIC gene by a novel template directed ligation–PCR method. This simple and economical method to construct large synthetic genes can be used when routine resynthesis of genes is required. Chemically phosphorylated adjacent oligonucleotides of the gene to be synthesized are assembled and ligated on a single-stranded, partially homologous template derived from a wild-type gene (cryIC in our case) by a thermostable Pfu DNA ligase using repeated cycles of melting, annealing, and ligation. The resulting synthetic DNA strands are selectively amplified by PCR with short specific flanking primers that are complementary only to the new synthetic DNA. Optimized expression of the synthetic cryIC gene in alfalfa and tobacco results in the production of 0.01–0.2% of total soluble proteins as CryIC toxin and provides protection against the Egyptian cotton leafworm (Spodoptera littoralis) and the beet armyworm (Spodoptera exigua). To facilitate selection and breeding of Spodoptera-resistant plants, the cryIC gene was linked to a pat gene, conferring resistance to the herbicide BASTA.

Transposons and genome evolution in plants

Although it is known today that transposons comprise a significant fraction of the genomes of many organisms, they eluded discovery through the first half century of genetic analysis and even once discovered, their ubiquity and abundance were not recognized for some time. This genetic invisibility of transposons focuses attention on the mechanisms that control not only transposition, but illegitimate recombination. The thesis is developed that the mechanisms that control transposition are a reflection of the more general capacity of eukaryotic organisms to detect, mark, and retain duplicated DNA through repressive chromatin structures.

Perspectives on archaeal diversity, thermophily and monophyly from environmental rRNA sequences.

Phylogenetic analysis of ribosomal RNA sequences obtained from uncultivated organisms of a hot spring in Yellowstone National Park reveals several novel groups of Archaea, many of which diverged from the crenarchaeal line of descent prior to previously characterized members of that kingdom. Universal phylogenetic trees constructed with the addition of these sequences indicate monophyly of Archaea, with modest bootstrap support. The data also show a specific relationship between low-temperature marine Archaea and some hot spring Archaea. Two of the environmental sequences are enigmatic: depending upon the data set and analytical method used, these sequences branch deeply within the Crenarchaeota, below the bifurcation between Crenarchaeota and Euryarchaeota, or even as the sister group to Eukaryotes. If additional data confirm either of the latter two placements, then the organisms represented by these ribosomal RNA sequences would merit recognition as a new kingdom, provisionally named "Korarchaeota."

Intermediate hosts of Toxoplasma gondii and food safety:epidemiology, genetics and parasite survival in meat-producing animals in Italy

Toxoplasma gondii is a coccidian parasite with a global distribution. The definitive host is the cat (and other felids). All warm-blooded animals can act as intermediate hosts, including humans. Sexual reproduction (gametogony) takes place in the final host and oocysts are released in the environment, where they then sporulate to become infective. In intermediate hosts the cycle is extra-intestinal and results in the formation of tachyzoites and bradyzoites. Tachyzoites represent the invasive and proliferative stage and on entering a cell it multiplies asexually by endodyogeny. Bradyzoites within tissue cysts are the latent form. T. gondii is a food-borne parasite causing toxoplasmosis, which can occur in both animals and humans. Infection in humans is asymptomatic in more than 80% of cases in Europe and North-America. In the remaining cases patients present fever, cervical lymphadenopathy and other non-specific clinical signs. Nevertheless, toxoplasmosis is life threatening if it occurs in immunocompromised subjects. The main organs involved are brain (toxoplasmic encephalitis), heart (myocarditis), lungs (pulmonary toxoplasmosis), eyes, pancreas and parasite can be isolated from these tissues. Another aspect is congenital toxoplasmosis that may occur in pregnant women and the severity of the consequences depends on the stage of pregnancy when maternal infection occurs. Acute toxoplasmosis in developing foetuses may result in blindness, deformation, mental retardation or even death. The European Food Safety Authority (EFSA), in recent reports on zoonoses, highlighted that an increasing numbers of animals resulted infected with T. gondii in EU (reported by the European Member States for pigs, sheep, goats, hunted wild boar and hunted deer, in 2011 and 2012). In addition, high prevalence values have been detected in cats, cattle and dogs, as well as several other animal species, indicating the wide distribution of the parasite among different animal and wildlife species. The main route of transmission is consumption of food and water contaminated with sporulated oocysts. However, infection through the ingestion of meat contaminated with tissue cysts is frequent. Finally, although less frequent, other food products contaminated with tachyzoites such as milk, may also pose a risk. The importance of this parasite as a risk for human health was recently highlighted by EFSA’s opinion on modernization of meat inspection, where Toxoplasma gondii was identified as a relevant hazard to be addressed in revised meat inspection systems for pigs, sheep, goats, farmed wild boar and farmed deer (Call for proposals -GP/EFSA/BIOHAZ/2013/01). The risk of infection is more highly associated to animals reared outside, also in free-range or organic farms, where biohazard measure are less strict than in large scale, industrial farms. Here, animals are kept under strict biosecurity measures, including barriers, which inhibit access by cats, thus making soil contamination by oocysts nearly impossible. A growing demand by the consumer for organic products, coming from free-range livestock, in respect of animal-welfare, and the desire for the best quality of derived products, have all led to an increase in the farming of free-range animals. The risk of Toxoplasma gondii infection increases when animals have access to environment and the absence of data in Italy, together with need for in depth study of both the prevalence and genotypes of Toxoplasma gondii present in our country were the main reasons for the development of this thesis project. A total of 152 animals have been analyzed, including 21 free-range pigs (Suino Nero race), 24 transhumant Cornigliese sheep, 77 free-range chickens and 21 wild animals. Serology (on meat juice) and identification of T. gondii DNA through PCR was performed on all samples, except for wild animals (no serology). An in-vitro test was also applied with the aim to find an alternative and valid method to bioassay, actually the gold standard. Meat samples were digested and seeded onto Vero cells, checked every day and a RT-PCR protocol was used to determine an eventual increase in the amount of DNA, demonstrating the viability of the parasite. Several samples were alos genetically characterized using a PCR-RFLP protocol to define the major genotypes diffused in the geographical area studied. Within the context of a project promoted by Istituto Zooprofilattico of Pavia and Brescia (Italy), experimentally infected pigs were also analyzed. One of the aims was to verify if the production process of cured “Prosciutto di Parma” is able to kill the parasite. Our contribution included the digestion and seeding of homogenates on Vero cells and applying the Elisa test on meat juice. This thesis project has highlighted widespread diffusion of T. gondii in the geographical area taken into account. Pigs, sheep, chickens and wild animals showed high prevalence of infection. The data obtained with serology were 95.2%, 70.8%, 36.4%, respectively, indicating the spread of the parasite among numerous animal species. For wild animals, the average value of parasite infection determined through PCR was 44.8%. Meat juice serology appears to be a very useful, rapid and sensitive method for screening carcasses at slaughterhouse and for marketing “Toxo-free” meat. The results obtained on fresh pork meat (derived from experimentally infected pigs) before (on serum) and after (on meat juice) slaughter showed a good concordance. The free-range farming put in evidence a marked risk for meat-producing animals and as a consequence also for the consumer. Genotyping revealed the diffusion of Type-II and in a lower percentage of Type-III. In pigs is predominant the Type-II profile, while in wildlife is more diffused a Type-III and mixed profiles (mainly Type-II/III). The mixed genotypes (Type-II/III) could be explained by the presence of mixed infections. Free-range farming and the contact with wildlife could facilitate the spread of the parasite and the generation of new and atypical strains, with unknown consequences on human health. The curing process employed in this study appears to produce hams that do not pose a serious concern to human health and therefore could be marketed and consumed without significant health risk. Little is known about the diffusion and genotypes of T. gondii in wild animals; further studies on the way in which new and mixed genotypes may be introduced into the domestic cycle should be very interesting, also with the use of NGS techniques, more rapid and sensitive than PCR-RFLP. Furthermore wildlife can become a valuable indicator of environmental contamination with T. gondii oocysts. Other future perspectives regarding pigs include the expansion of the number of free-range animals and farms and for Cornigliese sheep the evaluation of other food products as raw milk and cheeses. It should be interesting to proceed with the validation of an ELISA test for infection in chickens, using both serum and meat juice on a larger number of animals and the same should be done also for wildlife (at the moment no ELISA tests are available and MAT is the reference method for them). Results related to Parma ham do not suggest a concerning risk for consumers. However, further studies are needed to complete the risk assessment and the analysis of other products cured using technological processes other than those investigated in the present study. For example, it could be interesting to analyze products such as salami, produced with pig meat all over the Italian country, with very different recipes, also in domestic and rural contexts, characterized by a very short period of curing (1 to 6 months). Toxoplasma gondii is one of the most diffuse food-borne parasites globally. Public health safety, improved animal production and protection of endangered livestock species are all important goals of research into reliable diagnostic tools for this infection. Future studies into the epidemiology, parasite survival and genotypes of T. gondii in meat producing animals should continue to be a research priority.

Leishmaniose visceral americana: avaliação dos parâmetros da capacidade vetorial de Lutzomyia longipalpis em área urbana do muncípio de Panorama, São Paulo, Brasil

Introdução: A leishmaniose visceral (LV) é um importante problema de saúde pública no Brasil, com cerca 3000 mil casos notificados anualmente. Nos últimos anos, a LV tem ampliado sua distribuição em vários estados do país, associada principalmente aos processos socioambientais, antrópicos e migratórios. A LV é causada pela infecção com Leishmania infantum chagasi, transmitida, principalmente, por Lutzomyia longipalpis (Diptera: Psychodidae). Este flebotomíneo apresenta ampla distribuição nas Américas, todavia, evidências sugerem que se constitui em um complexo de espécies crípticas. A dinâmica de transmissão da LV é modulada por fatores ecológicos locais que influenciam a interação entre populações do patógeno, do vetor e dos hospedeiros vertebrados. Portanto, o estudo das variáveis associadas a esta interação pode contribuir para elucidar aspectos dos elos epidemiológicos e contribuir para a tomada de decisões em saúde pública. Objetivo: Avaliar parâmetros relacionados à capacidade vetorial da população de Lu. longipalpis presente em área urbana do município de Panorama, estado de São Paulo. Métodos: Foram realizadas capturas mensais durante 48 meses para avaliar a distribuição espaço-temporal de Lu. longipalpis e investigar a circulação de Le. i. chagasi. Também foram realizados os seguintes experimentos com o vetor: captura-marcação-soltura-recaptura para estimar a sobrevida da população e a duração do seu ciclo gonotrófico, a atratividade dos hospedeiros mais frequentes em áreas urbanas, a proporção de repasto em cão, infecção experimental e competência vetorial. Resultados: Observou-se que no município de Panorama, Lu. longipalpis apresentou as frequências mais elevadas na estação chuvosa (entre outubro e março), maior densidade em áreas com presença de vegetação e criação de animais domésticos, locais aonde também foi demonstrada a circulação natural de espécimes de Lu. longipalpis infectados com Le. i. chagasi. Além disto, foi corroborado que a população de Lu. longipalpis apresentou hábito hematofágico eclético, altas taxas de sobrevivência e que foi competente para transmitir o agente da LV. Nos experimentos de laboratório foi evidenciada a heterogeneidade na infecção de fêmeas de Lu. longipalpis desafiadas a se alimentarem em cães comprovadamente infectados por L. i. chagasi e o rápido desenvolvimento do parasita neste vetor natural. Conclusões. As observações do presente estudo corroboram a capacidade vetora de Lu. longipalpis para transmitir a Le. i. chagasi e ressaltam a importância da espécie na transmissão do agente etiológico da LV. Ações de manejo ambiental, educação e promoção à saúde são recomendadas às autoridades municipais para diminuir o risco potencial de infecção na população humana e canina, considerando-se o elevado potencial vetor de Lu. longipalpis e a presença de condições que favorecem a interação dos componentes da tríade epidemiológica da LV.

Real time optimization in chemical process: evaluation of strategies, improvements and industrial application.

The increasing economic competition drives the industry to implement tools that improve their processes efficiencies. The process automation is one of these tools, and the Real Time Optimization (RTO) is an automation methodology that considers economic aspects to update the process control in accordance with market prices and disturbances. Basically, RTO uses a steady-state phenomenological model to predict the process behavior, and then, optimizes an economic objective function subject to this model. Although largely implemented in industry, there is not a general agreement about the benefits of implementing RTO due to some limitations discussed in the present work: structural plant/model mismatch, identifiability issues and low frequency of set points update. Some alternative RTO approaches have been proposed in literature to handle the problem of structural plant/model mismatch. However, there is not a sensible comparison evaluating the scope and limitations of these RTO approaches under different aspects. For this reason, the classical two-step method is compared to more recently derivative-based methods (Modifier Adaptation, Integrated System Optimization and Parameter estimation, and Sufficient Conditions of Feasibility and Optimality) using a Monte Carlo methodology. The results of this comparison show that the classical RTO method is consistent, providing a model flexible enough to represent the process topology, a parameter estimation method appropriate to handle measurement noise characteristics and a method to improve the sample information quality. At each iteration, the RTO methodology updates some key parameter of the model, where it is possible to observe identifiability issues caused by lack of measurements and measurement noise, resulting in bad prediction ability. Therefore, four different parameter estimation approaches (Rotational Discrimination, Automatic Selection and Parameter estimation, Reparametrization via Differential Geometry and classical nonlinear Least Square) are evaluated with respect to their prediction accuracy, robustness and speed. The results show that the Rotational Discrimination method is the most suitable to be implemented in a RTO framework, since it requires less a priori information, it is simple to be implemented and avoid the overfitting caused by the Least Square method. The third RTO drawback discussed in the present thesis is the low frequency of set points update, this problem increases the period in which the process operates at suboptimum conditions. An alternative to handle this problem is proposed in this thesis, by integrating the classic RTO and Self-Optimizing control (SOC) using a new Model Predictive Control strategy. The new approach demonstrates that it is possible to reduce the problem of low frequency of set points updates, improving the economic performance. Finally, the practical aspects of the RTO implementation are carried out in an industrial case study, a Vapor Recompression Distillation (VRD) process located in Paulínea refinery from Petrobras. The conclusions of this study suggest that the model parameters are successfully estimated by the Rotational Discrimination method; the RTO is able to improve the process profit in about 3%, equivalent to 2 million dollars per year; and the integration of SOC and RTO may be an interesting control alternative for the VRD process.