Comparing linear and non linear wind flow models

Assessing wind conditions on complex terrain has become a hard task as terrain complexity increases. That is why there is a need to extrapolate in a reliable manner some wind parameters that determine wind farms viability such as annual average wind speed at all hub heights as well as turbulence intensities. The development of these tasks began in the early 90´s with the widely used linear model WAsP and WAsP Engineering especially designed for simple terrain with remarkable results on them but not so good on complex orographies. Simultaneously non-linearized Navier Stokes solvers have been rapidly developed in the last decade through CFD (Computational Fluid Dynamics) codes allowing simulating atmospheric boundary layer flows over steep complex terrain more accurately reducing uncertainties. This paper describes the features of these models by validating them through meteorological masts installed in a highly complex terrain. The study compares the results of the mentioned models in terms of wind speed and turbulence intensity.

Linear latent force models using Gaussian Processes

Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.

A study of the mechanical behaviour of the human aortic artery by means of non-linear finite element models

En la presente tesis desarrollamos una estrategia para la simulación numérica del comportamiento mecánico de la aorta humana usando modelos de elementos finitos no lineales. Prestamos especial atención a tres aspectos claves relacionados con la biomecánica de los tejidos blandos. Primero, el análisis del comportamiento anisótropo característico de los tejidos blandos debido a las familias de fibras de colágeno. Segundo, el análisis del ablandamiento presentado por los vasos sanguíneos cuando estos soportan cargas fuera del rango de funcionamiento fisiológico. Y finalmente, la inclusión de las tensiones residuales en las simulaciones en concordancia con el experimento de apertura de ángulo. El análisis del daño se aborda mediante dos aproximaciones diferentes. En la primera aproximación se presenta una formulación de daño local con regularización. Esta formulación tiene dos ingredientes principales. Por una parte, usa los principios de la teoría de la fisura difusa para garantizar la objetividad de los resultados con diferentes mallas. Por otra parte, usa el modelo bidimensional de Hodge-Petruska para describir el comportamiento mesoscópico de los fibriles. Partiendo de este modelo mesoscópico, las propiedades macroscópicas de las fibras de colágeno son obtenidas a través de un proceso de homogenización. En la segunda aproximación se presenta un modelo de daño no-local enriquecido con el gradiente de la variable de daño. El modelo se construye a partir del enriquecimiento de la función de energía con un término que contiene el gradiente material de la variable de daño no-local. La inclusión de este término asegura una regularización implícita de la implementación por elementos finitos, dando lugar a resultados de las simulaciones que no dependen de la malla. La aplicabilidad de este último modelo a problemas de biomecánica se estudia por medio de una simulación de un procedimiento quirúrgico típico conocido como angioplastia de balón. In the present thesis we develop a framework for the numerical simulation of the mechanical behaviour of the human aorta using non-linear finite element models. Special attention is paid to three key aspects related to the biomechanics of soft tissues. First, the modelling of the characteristic anisotropic behaviour of the softue due to the collagen fibre families. Secondly, the modelling of damage-related softening that blood vessels exhibit when subjected to loads beyond their physiological range. And finally, the inclusion of the residual stresses in the simulations in accordance with the opening-angle experiment The modelling of damage is addressed with two major and different approaches. In the first approach a continuum local damage formulation with regularisation is presented. This formulation has two principal ingredients. On the one hand, it makes use of the principles of the smeared crack theory to avoid the mesh size dependence of the structural response in softening. On the other hand, it uses a Hodge-Petruska bidimensional model to describe the fibrils as staggered arrays of tropocollagen molecules, and from this mesoscopic model the macroscopic material properties of the collagen fibres are obtained using an homogenisation process. In the second approach a non-local gradient-enhanced damage formulation is introduced. The model is built around the enhancement of the free energy function by means of a term that contains the referential gradient of the non-local damage variable. The inclusion of this term ensures an implicit regularisation of the finite element implementation, yielding mesh-objective results of the simulations. The applicability of the later model to biomechanically-related problems is studied by means of the simulation of a typical surgical procedure, namely, the balloon angioplasty.

Effects of fiber inclusion on growth performance and nutrient digestibility of piglets reared under optimal or poor hygienic conditions

Two experiments were conducted to estimate the standardized ileal digestible (SID) Trp:Lys ratio requirement for growth performance of nursery pigs. Experimental diets were formulated to ensure that lysine was the second limiting AA throughout the experiments. In Exp. 1 (6 to 10 kg BW), 255 nursery pigs (PIC 327 × 1050, initially 6.3 ± 0.15 kg, mean ± SD) arranged in pens of 6 or 7 pigs were blocked by pen weight and assigned to experimental diets (7 pens/diet) consisting of SID Trp:Lys ratios of 14.7%, 16.5%, 18.4%, 20.3%, 22.1%, and 24.0% for 14 d with 1.30% SID Lys. In Exp. 2 (11 to 20 kg BW), 1,088 pigs (PIC 337 × 1050, initially 11.2 kg ± 1.35 BW, mean ± SD) arranged in pens of 24 to 27 pigs were blocked by average pig weight and assigned to experimental diets (6 pens/diet) consisting of SID Trp:Lys ratios of 14.5%, 16.5%, 18.0%, 19.5%, 21.0%, 22.5%, and 24.5% for 21 d with 30% dried distillers grains with solubles and 0.97% SID Lys. Each experiment was analyzed using general linear mixed models with heterogeneous residual variances. Competing heteroskedastic models included broken-line linear (BLL), broken-line quadratic (BLQ), and quadratic polynomial (QP). For each response, the best-fitting model was selected using Bayesian information criterion. In Exp. 1 (6 to 10 kg BW), increasing SID Trp:Lys ratio linearly increased (P < 0.05) ADG and G:F. For ADG, the best-fitting model was a QP in which the maximum ADG was estimated at 23.9% (95% confidence interval [CI]: [<14.7%, >24.0%]) SID Trp:Lys ratio. For G:F, the best-fitting model was a BLL in which the maximum G:F was estimated at 20.4% (95% CI: [14.3%, 26.5%]) SID Trp:Lys. In Exp. 2 (11 to 20 kg BW), increasing SID Trp:Lys ratio increased (P < 0.05) ADG and G:F in a quadratic manner. For ADG, the best-fitting model was a QP in which the maximum ADG was estimated at 21.2% (95% CI: [20.5%, 21.9%]) SID Trp:Lys. For G:F, BLL and BLQ models had comparable fit and estimated SID Trp:Lys requirements at 16.6% (95% CI: [16.0%, 17.3%]) and 17.1% (95% CI: [16.6%, 17.7%]), respectively. In conclusion, the estimated SID Trp:Lys requirement in Exp. 1 ranged from 20.4% for maximum G:F to 23.9% for maximum ADG, whereas in Exp. 2 it ranged from 16.6% for maximum G:F to 21.2% for maximum ADG. These results suggest that standard NRC (2012) recommendations may underestimate the SID Trp:Lys requirement for nursery pigs from 11 to 20 kg BW.

Fine spatial pattern of an epiphytic lichen species is affected by habitat conditions in two forest types in the Iberian Mediterranean region

Persistence and abundance of species is determined by habitat availability and the ability to disperse and colonize habitats at contrasting spatial scales. Favourable habitat fragments are also heterogeneous in quality, providing differing opportunities for establishment and affecting the population dynamics of a species. Based on these principles, we suggest that the presence and abundance of epiphytes may reflect their dispersal ability, which is primarily determined by the spatial structure of host trees, but also by host quality. To our knowledge there has been no explicit test of the importance of host tree spatial pattern for epiphytes in Mediterranean forests. We hypothesized that performance and host occupancy in a favourable habitat depend on the spatial pattern of host trees, because this pattern affects the dispersal ability of each epiphyte and it also determines the availability of suitable sites for establishment. We tested this hypothesis using new point pattern analysis tools and generalized linear mixed models to investigate the spatial distribution and performance of the epiphytic lichen Lobaria pulmonaria, which inhabits two types of host trees (beeches and Iberian oaks). We tested the effects on L. pulmonaria distribution of tree size, spatial configuration, and host tree identity. We built a model including tree size, stand structure, and several neighbourhood predictors to understand the effect of host tree on L. pulmonaria. We also investigated the relative importance of spatial patterning on the presence and abundance of the species, independently of the host tree configuration. L. pulmonaria distribution was highly dependent on habitat quality for successful establishment, i.e., tree species identity, tree diameter, and several forest stand structure surrogates. For beech trees, tree diameter was the main factor influencing presence and cover of the lichen, although larger lichen-colonized trees were located close to focal trees, i.e., young trees. However, oak diameter was not an important factor, suggesting that bark roughness at all diameters favoured lichen establishment. Our results indicate that L. pulmonaria dispersal is not spatially restricted, but it is dependent on habitat quality. Furthermore, new spatial analysis tools suggested that L. pulmonaria cover exhibits a distinct pattern, although the spatial pattern of tree position and size was random.

Fine spatial pattern of an epiphytic lichen species is affected by habitat conditions in two forest types in the Iberian Mediterranean region

Persistence and abundance of species is determined by habitat availability and the ability to disperse and colonize habitats at contrasting spatial scales. Favourable habitat fragments are also heterogeneous in quality, providing differing opportunities for establishment and affecting the population dynamics of a species. Based on these principles, we suggest that the presence and abundance of epiphytes may reflect their dispersal ability, which is primarily determined by the spatial structure of host trees, but also by host quality. To our knowledge there has been no explicit test of the importance of host tree spatial pattern for epiphytes in Mediterranean forests. We hypothesized that performance and host occupancy in a favourable habitat depend on the spatial pattern of host trees, because this pattern affects the dispersal ability of each epiphyte and it also determines the availability of suitable sites for establishment. We tested this hypothesis using new point pattern analysis tools and generalized linear mixed models to investigate the spatial distribution and performance of the epiphytic lichen Lobaria pulmonaria, which inhabits two types of host trees (beeches and Iberian oaks). We tested the effects on L. pulmonaria distribution of tree size, spatial configuration, and host tree identity. We built a model including tree size, stand structure, and several neighbourhood predictors to understand the effect of host tree on L. pulmonaria. We also investigated the relative importance of spatial patterning on the presence and abundance of the species, independently of the host tree configuration. L. pulmonaria distribution was highly dependent on habitat quality for successful establishment, i.e., tree species identity, tree diameter, and several forest stand structure surrogates. For beech trees, tree diameter was the main factor influencing presence and cover of the lichen, although larger lichen-colonized trees were located close to focal trees, i.e., young trees. However, oak diameter was not an important factor, suggesting that bark roughness at all diameters favoured lichen establishment. Our results indicate that L. pulmonaria dispersal is not spatially restricted, but it is dependent on habitat quality. Furthermore, new spatial analysis tools suggested that L. pulmonaria cover exhibits a distinct pattern, although the spatial pattern of tree position and size was random.

Dynamic analysis of road vehicle-bridge systems under turbulent wind by means of Finite Element Models

When an automobile passes over a bridge dynamic effects are produced in vehicle and structure. In addition, the bridge itself moves when exposed to the wind inducing dynamic effects on the vehicle that have to be considered. The main objective of this work is to understand the influence of the different parameters concerning the vehicle, the bridge, the road roughness or the wind in the comfort and safety of the vehicles when crossing bridges. Non linear finite element models are used for structures and multibody dynamic models are employed for vehicles. The interaction between the vehicle and the bridge is considered by contact methods. Road roughness is described by the power spectral density (PSD) proposed by the ISO 8608. To consider that the profiles under right and left wheels are different but not independent, the hypotheses of homogeneity and isotropy are assumed. To generate the wind velocity history along the road the Sandia method is employed. The global problem is solved by means of the finite element method. First the methodology for modelling the interaction is verified in a benchmark. Following, the case of a vehicle running along a rigid road and subjected to the action of the turbulent wind is analyzed and the road roughness is incorporated in a following step. Finally the flexibility of the bridge is added to the model by making the vehicle run over the structure. The application of this methodology will allow to understand the influence of the different parameters in the comfort and safety of road vehicles crossing wind exposed bridges. Those results will help to recommend measures to make the traffic over bridges more reliable without affecting the structural integrity of the viaduct

Deforestación y Pérdida de hábitat en Bosques de montaña en la Cuenca alta del Río Zamora (Loja, Ecuador)

Los bosques húmedos de montaña se encuentran reconocidos como uno de los ecosistemas más amenazados en el mundo, llegando inclusive a ser considerado como un “hotspot” por su alta diversidad y endemismo. La acelerada pérdida de cobertura vegetal de estos bosques ha ocasionado que, en la actualidad, se encuentren restringidos a una pequeña fracción de su área de distribución histórica. Pese a esto, los estudios realizados sobre cual es efecto de la deforestación, fragmentación, cambios de uso de suelo y su efecto en las comunidades de plantas presentes en este tipo de vegetación aún son muy escuetos, en comparación a los realizados con sus similares amazónicos. En este trabajo, el cual se encuentra dividido en seis capítulos, abordaremos los siguientes objetivos: a) Comprender cuál es la dinámica que han seguido los diferentes tipos de bosques montanos andinos de la cuenca del Rio Zamora, Sur de Ecuador durante entre 1976 y 2002. b) Proveer de evidencia de las tasas de deforestación y fragmentación de todos los tipos diferentes de bosques montanos andinos presentes en la cuenca del Rio Zamora, Sur de Ecuador entre 1976 y 2002. c) Determinar qué factores inducen a la fragmentación de bosques de montaña en la cuenca alta del río Zamora entre 1976 y 2002. d) Determinar cuáles son y cómo afectan los factores ambientales y socioeconómicos a la dinámica de la deforestación y regeneración (pérdida y recuperación del hábitat) sufrida por los bosques de montaña dentro de la zona de estudio y e) Determinar si la deforestación y fragmentación actúan sobre la diversidad y estructura de las comunidades de tres tipos de organismos (comunidades de árboles, comunidades de líquenes epífitos y comunidades de hepáticas epífitas). Este estudio se centró en el cuenca alta del río Zamora, localizada al sur de Ecuador entre las coordenadas 3º 00´ 53” a 4º 20´ 24.65” de latitud sur y 79º 49´58” a 78º 35´ 38” de longitud oeste, que cubre alrededor de 4300 km2 de territorio situado entre las capitales de las provincias de Loja y Zamora-Chinchipe. Con objeto de predecir la dinámica futura de la deforestación en la región de Loja y cómo se verán afectados los diferentes tipos de hábitat, así como para detectar los factores que más influyen en dicha dinámica, se han construido modelos basados en la historia de la deforestación derivados de fotografías aéreas e imágenes satelitales de tres fechas (1976, 1989 y 2002). La cuantificación de la deforestación se realizó mediante la tasa de interés compuesto y para la caracterización de la configuración espacial de los fragmentos de bosque nativo se calcularon índices de paisaje los cuales fueron calculados utilizando el programa Fragstats 3.3. Se ha clasificado el recubrimiento del terreno en forestal y no forestal y se ha modelado su evolución temporal con Modelos Lineales Generalizados Mixtos (GLMM), empleando como variables explicativas tanto variables ambientales espacialmente explícitas (altitud, orientación, pendiente, etc) como antrópicas (distancia a zonas urbanizadas, deforestadas, caminos, entre otras). Para medir el efecto de la deforestación sobre las comunidades modelo (de árboles, líquenes y hepáticas) se monitorearon 11 fragmentos de vegetación de distinto tamaño: dos fragmentos de más de cien hectáreas, tres fragmentos de entre diez y noventa ha y seis fragmentos de menos de diez hectáreas. En ellos se instalaron un total de 38 transectos y 113 cuadrantes de 20 x 20 m a distancias que se alejaban progresivamente del borde en 10, 40 y 80 m. Nuestros resultados muestran una tasa media anual de deforestación del 1,16% para todo el período de estudio, que el tipo de vegetación que más alta tasa de destrucción ha sufrido, es el páramo herbáceo, con un 2,45% anual. El análisis de los patrones de fragmentación determinó un aumento en 2002 de más del doble de fragmentos presentes en 1976, lo cual se repite en el análisis del índice de densidad promedio. El índice de proximidad media entre fragmentos muestra una reducción progresiva de la continuidad de las áreas forestadas. Si bien las formas de los fragmentos se han mantenido bastante similares a lo largo del período de estudio, la conectividad entre estos ha disminuido en un 84%. Por otro lado, de nuestros análisis se desprende que las zonas con mayor probabilidad de deforestarse son aquellas que están cercanas a zonas previamente deforestadas; la cercanía a las vías también influye significativamente en la deforestación, causando un efecto directo en la composición y estructura de las comunidades estudiadas, que en el caso de los árboles viene mediado por el tamaño del fragmento y en el caso del componente epífito (hepáticas y líquenes), viene mediado tanto por el tamaño del fragmento como por la distancia al borde del mismo. Se concluye la posibilidad de que, de mantenerse esta tendencia, este tipo de bosques desaparecerá en corto tiempo y los servicios ecosistémicos que prestan, se verán seriamente comprometidos. ABSTRACT Mountain rainforests are recognized as one of the most threatened ecosystems in the world, and have even come to be considered as a “hotspot” due to their high degree of diversity and endemism. The accelerated loss of plant cover of these forests has caused them to be restricted today to a small fraction of their area of historic distribution. In spite of this, studies done on the effect of deforestation, fragmentation, changes in soil use and their effect on the plant communities present in this type of vegetation are very brief compared to those done on their analogues in the Amazon region. In this study, which is divided into six chapters, we will address the following objectives: a) To understand what the dynamic followed by the different types of Andean mountain forests in the Zamora River watershed of southern Ecuador has been between 1976 and 2002. b) To provide evidence of the rates of deforestation and fragmentation of all the different types of Andean mountain forests existing in the upper watershed of the Zamora River between 1976 and 2002. c) To determine the factors that induces fragmentation of all different types of Andean mountain forests existing in the upper watershed of the Zamora River between 1976 and 2002. d) To determine what the environmental and anthropogenic factors are driving the dynamic of deforestation and regeneration (loss and recuperation of the habitat) suffered by the mountain forests in the area of the study and e) To determine if the deforestation and fragmentation act upon the diversity and structure of three model communities: trees, epiphytic lichens and epiphytic liverworts. This study is centered on the upper Zamora River watershed, located in southern Ecuador between 3º 00´ 53” and 4º 20´ 24.65 south latitude and 79º 49´ 58” to 78º 35´ 38” west longitude, and covers around 4,300 km2 of territory located between Loja and Zamora-Chinchipe provinces. For the purpose of predicting the future dynamic of deforestation in the Loja region and how different types of habitats will be affected, as well as detecting the environmental and socioeconomic factors that influence landscape dynamics, models were constructed based on deforestation history, derived from aerial photographs and satellite images for three dates (1976, 1989 and 2002). Quantifying the deforestation was done using the compound interest rate; to characterize the spatial configuration of fragments of native forest, landscape indices were calculated with Fragstats 3.3 program. Land cover was classified as forested and not forested and its evolution over time was modeled with Generalized Linear Mixed Models (GLMM), using spatially explicit environmental variables (altitude, orientation, slope, etc.) as well as anthropic variables (distance to urbanized, deforested areas and roads, among others) as explanatory variables. To measure the effects of fragmentation on three types of model communities (forest trees and epiphytic lichen and liverworts), 11 vegetation fragments of different sizes were monitored: two fragments of more than one hundred hectares, three fragments of between ten and ninety ha and six fragments of fewer than ten hectares . In these fragments, a total of 38 transects and 113 20 x 20 m quadrats were installed at distances that progressively moved away from the edge of the fragment by 10, 40 and 80 m. Our results show an average annual rate of deforestation of 1.16% for the entire period of the study, and that the type of vegetation that suffered the highest rate of destruction was grassy paramo, with an annual rate of 2.45%. The analysis of fragmentation patterns determined the number of fragments in 2002 more than doubled the number of fragments present in 1976, and the same occurred for the average density index. The variation of the average proximity index among fragments showed a progressive reduction of the continuity of forested areas. Although fragment shapes have remained quite similar over the period of the study, connectivity among them has diminished by 84%. On the other hand, it emerged from our analysis that the areas of greatest probability of deforestation were those that are close to previously deforested areas; proximity to roads also significantly favored the deforestation causing a direct effect on the composition of our model communities, that in the case of forest trees is determined by the size of the fragment, and in the case of the epiphyte communities (liverworts and lichens), is determined, by the size of the fragment as well as the distance to edge. A subject under discussion is the possibility that if this tendency continues, this type of forest will disappear in a short time, and the ecological services it provides, will be seriously endangered.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Tree and grazing influence on herbaceous pasture diversity in the Dehesa agroforestry system

Mediterranean Dehesas are one of the European natural habitat types of Community interest (43/92/EEC Directive), associated to high diversity levels and producer of important goods and services. In this work, tree contribution and grazing influence over pasture alpha diversity in a Dehesa in Central Spain was studied. We analyzed Richness and Shannon-Wiener (SW) indexes on herbaceous layer under 16 holms oak trees (64 sampling units distributed in two directions and in two distances to the trunk) distributed in four different grazing management zones (depending on species and stocking rate). Floristic composition by species or morphospecies and species abundance were analyzed for each sample unit. Linear mixed models (LMM) and generalized linear mixed models (GLMMs) were used to study relationships between alpha diversity measures and independent factors. Edge crown influence showed the highest values of Richness and SW index. No significant differences were found between orientations under tree crown influence. Grazing management had a significant effect over Richness and SW measures, specially the grazing species (cattle or sheep). We preliminary quantify and analyze the interaction of tree stratum and grazing management over herbaceous diversity in a year of extreme climatic conditions.

A management model for closed-loop supply chains of reusable articles

This PhD dissertation is framed in the emergent fields of Reverse Logistics and ClosedLoop Supply Chain (CLSC) management. This subarea of supply chain management has gained researchers and practitioners' attention over the last 15 years to become a fully recognized subdiscipline of the Operations Management field. More specifically, among all the activities that are included within the CLSC area, the focus of this dissertation is centered in direct reuse aspects. The main contribution of this dissertation to current knowledge is twofold. First, a framework for the so-called reuse CLSC is developed. This conceptual model is grounded in a set of six case studies conducted by the author in real industrial settings. The model has also been contrasted with existing literature and with academic and professional experts on the topic as well. The framework encompasses four building blocks. In the first block, a typology for reusable articles is put forward, distinguishing between Returnable Transport Items (RTI), Reusable Packaging Materials (RPM), and Reusable Products (RP). In the second block, the common characteristics that render reuse CLSC difficult to manage from a logistical standpoint are identified, namely: fleet shrinkage, significant investment and limited visibility. In the third block, the main problems arising in the management of reuse CLSC are analyzed, such as: (1) define fleet size dimension, (2) control cycle time and promote articles rotation, (3) control return rate and prevent shrinkage, (4) define purchase policies for new articles, (5) plan and control reconditioning activities, and (6) balance inventory between depots. Finally, in the fourth block some solutions to those issues are developed. Firstly, problems (2) and (3) are addressed through the comparative analysis of alternative strategies for controlling cycle time and return rate. Secondly, a methodology for calculating the required fleet size is elaborated (problem (1)). This methodology is valid for different configurations of the physical flows in the reuse CLSC. Likewise, some directions are pointed out for further development of a similar method for defining purchase policies for new articles (problem (4)). The second main contribution of this dissertation is embedded in the solutions part (block 4) of the conceptual framework and comprises a two-level decision problem integrating two mixed integer linear programming (MILP) models that have been formulated and solved to optimality using AIMMS as modeling language, CPLEX as solver and Excel spreadsheet for data introduction and output presentation. The results obtained are analyzed in order to measure in a client-supplier system the economic impact of two alternative control strategies (recovery policies) in the context of reuse. In addition, the models support decision-making regarding the selection of the appropriate recovery policy against the characteristics of demand pattern and the structure of the relevant costs in the system. The triangulation of methods used in this thesis has enabled to address the same research topic with different approaches and thus, the robustness of the results obtained is strengthened.

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

The Risk of Using the Q Heterogeneity Estimator for Software Engineering Experiments

All meta-analyses should include a heterogeneity analysis. Even so, it is not easy to decide whether a set of studies are homogeneous or heterogeneous because of the low statistical power of the statistics used (usually the Q test). Objective: Determine a set of rules enabling SE researchers to find out, based on the characteristics of the experiments to be aggregated, whether or not it is feasible to accurately detect heterogeneity. Method: Evaluate the statistical power of heterogeneity detection methods using a Monte Carlo simulation process. Results: The Q test is not powerful when the meta-analysis contains up to a total of about 200 experimental subjects and the effect size difference is less than 1. Conclusions: The Q test cannot be used as a decision-making criterion for meta-analysis in small sample settings like SE. Random effects models should be used instead of fixed effects models. Caution should be exercised when applying Q test-mediated decomposition into subgroups.

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.

Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina

Solar radiation is the most important source of renewable energy in the planet; it's important to solar engineers, designers and architects, and it's also fundamental for efficiently determining irrigation water needs and potential yield of crops, among others. Complete and accurate solar radiation data at a specific region are indispensable. For locations where measured values are not available, several models have been developed to estimate solar radiation. The objective of this paper was to calibrate, validate and compare five representative models to predict global solar radiation, adjusting the empirical coefficients to increase the local applicability and to develop a linear model. All models were based on easily available meteorological variables, without sunshine hours as input, and were used to estimate the daily solar radiation at Cañada de Luque (Córdoba, Argentina). As validation, measured and estimated solar radiation data were analyzed using several statistic coefficients. The results showed that all the analyzed models were robust and accurate (R2 and RMSE values between 0.87 to 0.89 and 2.05 to 2.14, respectively), so global radiation can be estimated properly with easily available meteorological variables when only temperature data are available. Hargreaves-Samani, Allen and Bristow-Campbell models could be used with typical values to estimate solar radiation while Samani and Almorox models should be applied with calibrated coefficients. Although a new linear model presented the smallest R2 value (R2 = 0.87), it could be considered useful for its easy application. The daily global solar radiation values produced for these models can be used to estimate missing daily values, when only temperature data are available, and in hydrologic or agricultural applications.