Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression

In this paper, reanalysis fields from the ECMWF have been statistically downscaled to predict from large-scale atmospheric fields, surface moisture flux and daily precipitation at two observatories (Zaragoza and Tortosa, Ebro Valley, Spain) during the 1961-2001 period. Three types of downscaling models have been built: (i) analogues, (ii) analogues followed by random forests and (iii) analogues followed by multiple linear regression. The inputs consist of data (predictor fields) taken from the ERA-40 reanalysis. The predicted fields are precipitation and surface moisture flux as measured at the two observatories. With the aim to reduce the dimensionality of the problem, the ERA-40 fields have been decomposed using empirical orthogonal functions. Available daily data has been divided into two parts: a training period used to find a group of about 300 analogues to build the downscaling model (1961-1996) and a test period (19972001), where models' performance has been assessed using independent data. In the case of surface moisture flux, the models based on analogues followed by random forests do not clearly outperform those built on analogues plus multiple linear regression, while simple averages calculated from the nearest analogues found in the training period, yielded only slightly worse results. In the case of precipitation, the three types of model performed equally. These results suggest that most of the models' downscaling capabilities can be attributed to the analogues-calculation stage.

Effect of the A cation size disorder and synthesis conditions on the properties of an iron perovskite series

Póster presentado en The Energy and Materials Research Conference - EMR2015 celebrado en Madrid (España) entre el 25-27 de febrero de 2015

Cogeneración con gas natural en una empresa de tratamientos térmicos

[ES]Diseño de una instalación de cogeneración basada en un motor de combustible gas natural para una empresa de tratamientos térmicos y superficiales. Para satisfacer las necesidades energéticas de la planta, la potencia eléctrica la suministrará un alternador conectado al motor y, a su vez, la entalpía de los humos de escape del motor se aprovechará para la producción de vapor de agua, necesario para la actividad industrial de la empresa. Por otro lado, el calor que es necesario disipar de dicho motor se recuperará para el calentamiento de agua de red, con la finalidad de limpiar la taladrina de las piezas tratadas.

The promoter of cell growth- and RNA protection-associated SND1 gene is activated by endoplasmic reticulum stress in human hepatoma cells

Background: Staphyloccocal nuclease domain-containing protein 1 (SND1) is involved in the regulation of gene expression and RNA protection. While numerous studies have established that SND1 protein expression is modulated by cellular stresses associated with tumor growth, hypoxia, inflammation, heat- shock and oxidative conditions, little is known about the factors responsible for SND1 expression. Here, we have approached this question by analyzing the transcriptional response of human SND1 gene to pharmacological endoplasmic reticulum (ER) stress in liver cancer cells. Results: We provide first evidence that SND1 promoter activity is increased in human liver cancer cells upon exposure to thapsigargin or tunicamycin or by ectopic expression of ATF6, a crucial transcription factor in the unfolded protein response triggered by ER stress. Deletion analysis of the 5'-flanking region of SND1 promoter identified maximal activation in fragment (-934, +221), which contains most of the predicted ER stress response elements in proximal promoter. Quantitative real- time PCR revealed a near 3 fold increase in SND1 mRNA expression by either of the stress- inducers; whereas SND1 protein was maximally upregulated (3.4-fold) in cells exposed to tunicamycin, a protein glycosylation inhibitor. Conclusion: Promoter activity of the cell growth- and RNA-protection associated SND1 gene is up-regulated by ER stress in human hepatoma cells.

Physiological modules for generating discrete and rhythmic movements: Component analysis of EMG signals

A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90 degrees. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMB) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each sholder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figute eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activatoin. From these results, we surmise that both "discrete-rhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the vertical and the other aligned with the horizontal.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.