Forecasting Electricity Prices and their volatilities using Unobserved Components.

The liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.

Statistical distributions obtained from the compression of monodisperse, soft and frictionless particles

The cyclic compression of several granular systems has been simulated with a molecular dynamics code. All the samples consisted of bidimensional, soft, frictionless and equal-sized particles that were initially arranged according to a squared lattice and were compressed by randomly generated irregular walls. The compression protocols can be described by some control variables (volume or external force acting on the walls) and by some dimensionless factors, that relate stiffness, density, diameter, damping ratio and water surface tension to the external forces, displacements and periods. Each protocol, that is associated to a dynamic process, results in an arrangement with its own macroscopic features: volume (or packing ratio), coordination number, and stress; and the differences between packings can be highly significant. The statistical distribution of the force-moment state of the particles (i.e. the equivalent average stress multiplied by the volume) is analyzed. In spite of the lack of a theoretical framework based on statistical mechanics specific for these protocols, it is shown how the obtained distributions of mean and relative deviatoric force-moment are. Then it is discussed on the nature of these distributions and on their relation to specific protocols.

Optimización del Abonado Nitrogenado en el Melón (Cucumis Melo L.) Tipo Piel de Sapo

Spain is the fifth-largest producer of melon (Cucumis melo L.) and the second exporter in the world. To a national level, Castilla-La Mancha emphasize and, specifically, Ciudad Real, where is cultivated 27% of national area dedicated to this crop and 30% of melon national production. Melon crop is cultivating majority in Ciudad Real and it is mainly located in the Alto Guadiana, where the major aquifers of the region are located, the aquifer 23 or Mancha Occidental and the aquifer 24 or Campo de Montiel, both declared overexploited and vulnerable zones to nitrate pollution from agricultural sources. The problem is exacerbated because in this area, groundwater is the basic resource of supply to populations, and even often the only one. Given the importance of melon in the area, recent research has focused on the irrigation of melon crop. Unfortunately, scant information has been forthcoming on the effect of N fertilizer on melon piel de sapo crop, so it is very important to tackle in a serious study that lead to know the N requirements on the melon crop melon by reducing the risks of contamination by nitrate leaching without affecting productivity and crop quality. In fact, the recommended dose is often subjective and practice is a N overdose. In this situation, the taking of urgent measures to optimize the use of N fertilization is required. To do it, the effect of N in a melon crop, fertirrigated and on plastic mulch, was studied. The treatments consisted in different rates of N supply, considering N fertilizer and N content in irrigation water, so the treatment applied were: 30 (N30), 85 (N85), 112 (N112) and 139 (N139) Kg N ha-1 in 2005; 93 (N93), 243 (N243) and 393 (N393) kg ha-1 in 2006; and 11 (N11), 61 (N61), 95 (N95) and 148 (N148) kg ha-1 in 2007. A randomized complete-block design was used and each treatment was replicated four times. The results showed a significant effect of N on dry biomass and two patterns of growth were observed. On the one hand, a gradual increase in vegetative biomass of the plant, leaves and stem, with increasing N, and on the other hand, an increase of fruit biomass also with increasing N up to a maximum of biomass corresponding to the optimal dose determined in 90 kg ha-1 of N applied, corresponding to 160 kg ha-1 of N available for melon crop, since this optimum dose, the fruit biomass suffers a decline. A significant effect was observed in concentration and N uptake in leaf, steam, fruit and whole plant, increasing in all of them with increasing of N doses. Fast N uptake occurred from 30-35 to 70-80 days after transplanting, coinciding with the fruit development. The N had a clear influence on the melon yield, its components, skin thickness and flesh ratio. The melon yield increased, as the mean fruit weight and number of fruits per m2 with increasing N until achieve an above 95% of the maximum yield when the N applied is 90 kg ha-1 or 160 kg ha-1 of N available. When N exceeds the optimal amount, there is a decline in yield, reducing the mean fruit weight and number of fruits per square meter, and was also observed a decrease in fruit quality by increasing the skin thickness and decrease the flesh ratio, which means an increase in fruit hollowed with excessive N doses. There was a trend for all indexes of N use efficiency (NUE) to decline with increasing N rate. We observed two different behaviours in the calculation result of the NUE; on the one hand, all the efficiency indexes calculated with N applied and N available had an exponential trend, and on the other hand, all the efficiency indexes calculated with N uptake has a linear trend. The linear regression cuts the exponential curve, delimiting a range within which lies the optimum quantity of N. The N leaching as nitrates increased exponentially with the amount of N. The increase of N doses was affected on the N mineralization. There was a negative exponential effect of N available on the mineralization of this element that occurs in the soil during the growing season, calculated from the balances of this element. The study of N leaching for each N rate used, allowed to us to establish several environmental indices related to environmental risk that causes the use of such doses, a simple way for them to be included in the code of Best Management Practices.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Comparison of vibration and rolling noise emission of resilient and solid monobloc railway wheels in underground lines

Flat or worn wheels rolling on rough or corrugated tracks can provoke airborne noise and ground-borne vibration, which can be a serious concern for nearby neighbours of urban rail transit lines. Among the various treatments used to reduce vibration and noise, resilient wheels play an important role. In conventional resilient wheels, a slightly prestressed V­shaped rubber ring is mounted between the steel wheel centre and tyre. The elastic layer enhances rolling noise and vibration suppression, as well as impact reduction on the track. In this paper the effectiveness of resilient wheels in underground lines, in comparison to monobloc ones, is assessed. The analysed resilient wheel is able to carry greater loads than standard resilient wheels used for light vehicles. It also presents a greater radial resiliency and a higher axial stiffness than conventional V­wheels. The finite element method was used in this study. A quarter car model was defined, in which the wheelset was modelled as an elastic body. Several simulations were performed in order to assess the vibrational behaviour of elastic wheels, including modal, harmonic and random vibration analysis, the latter allowing the introduction of realistic vertical track irregularities, as well as the influence of the running speed. Due to numerical problems some simplifications were needed. Parametric variations were also performed, in which the sensitivity of the whole system to variations of rubber prestress and Poisson’s ratio of the elastic material was assessed.Results are presented in the frequency domain, showing a better performance of the resilient wheels for frequencies over 200 Hz. This result reveals the ability of the analyzed design to mitigate rolling noise, but not structural vibrations, which are primarily found in the lower frequency range.

Preparation and characterization of polyester resin-layered silicate containing reactive groups

Polymer nanocomposites, specifically nanoclay-reinforced polymers, have attracted great interest as matrix materials for high temperature composite applications. Nanocomposites require relatively low dispersant loads to achieve significant property enhancements. These enhancements are mainly a consequence of the interfacial effects that result from dispersing the silicate nanolayers in the polymer matrix and the high in-plane strength, stiffness and aspect ratio of the lamellar nanoparticles. The montmorillonite (MMT) clay, modified with organic onium ions with long alkyl chains as Cloisites, has been widely used to obtain nanocomposites. The presence of reactive groups in organic onium ions can form chemical bonds with the polymer matrix which favours a very high exfoliation degree of the clay platelets in the nanocomposite (1,2)

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Evaluación de las Prestaciones de una Red WiMAX Móvil en un Escenario de Alta Movilidad

The aim of this paper is to evaluate the performance of mobile WiMAX technology for users in a highly mobility scenario for an operating frequency of 3.5 GHz. By utilizing a modified version of the extended Erceg’s propagation model, based on the introduction of Rayleigh fading due to multipath, we have calculated the received desired power and the interference power to obtain the statistical signal-to-interference-plusnoise rate (as a function of mean value and variance of cochannel interference) and the user throughput. A rural scenario composed of a transmitting base station and users in moving vehicles along a cell sector is proposed. The obtained results about coverage and throughput have been simulated by a tool built in MATLAB

Estudio metrológico de un sensor de impacto no destructivo para la determinación de firmeza de frutas en líneas de confección.

A non destructive impact sensor to measure fruit firmness has been installed on the sizer chain of an experimental fruit packing line. The sensor measures the fruit firmness related to the acceleration-time curve supplied by an accelerometer attached to an impacting arm. The sensor works correctly at a speed of 5 to 7 fruits per second. Ratio A/t (maximum acceleration value divided by its corresponding time), mean, and maximum slopes of the curves supplied by the accelerometer, were well correlated with the firmness data obtained in laboratory with the load-unload test. The accelerometer signal allows the classification of the fruit in three levels of firmness, by means of a specific software.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

Analysis of calibration results from cup and propeller anemometers. Influence on wind turbine Annual Energy Production (AEP) calculations

The calibration coefficients of several models of cup and propeller anemometers were analysed. The analysis was based on a series of laboratory calibrations between January 2003 and August 2007. Mean and standard deviation values of calibration coefficients from the anemometers studied were included. Two calibration procedures were used and compared. In the first, recommended by the Measuring network of Wind Energy Institutes (MEASNET), 13 measurement points were taken over a wind speed range of 4 to 16  m  s−1. In the second procedure, 9 measurement points were taken over a wider speed range of 4 to 23  m  s−1. Results indicated no significant differences between the two calibration procedures applied to the same anemometer in terms of measured wind speed and wind turbines' Annual Energy Production (AEP). The influence of the cup anemometers' design on the calibration coefficients was also analysed. The results revealed that the slope of the calibration curve, if based on the rotation frequency and not the anemometer's output frequency, seemed to depend on the cup center rotation radius.