Geochemical characterization of the fire-fountain fed deposits of Las Herrerias volcano (Campo de Calatrava Volcanic Field, Ciudad Real)

The Las Herrerias volcano (Bolanos de Calatrava, Campo de Calatrava Volcanic Field) is characterized by the great amount and variety of fire-fountain fed deposits. All these deposits are compositionally similar, being constituted by magnesium-rich (MgO = 11.58-4.19%), aluminium-poor (Al2O3 = 9.64-10.99%) highly sodic (Na2O = 2.24-3.81%) melanephelinites, with high contents in rare earth-elements (10x-200x chondrite), particularly in light-rare earth elements with respect to the heavy ones [(La/Lu)(N) = 32-35]. Contrary to the equivalent melanephelinites of this volcanic field, the relatively low contents in Ni (233-286 ppm), Cr (393-520 ppm) and magnesium number (Mg* = 45-54) indicate that these rocks do not correspond with primary melts. On the other hand, the variable distribution of clinopyroxene in the magma during eruption would be responsible for the slight compositional differences observed in the studied samples. Finally, we argue that these fire fountains were developed in a continental intraplate setting.

Contributions to Ensemble Classifiers with Image Analysis Applications

134 p.

Configuración de una red de sensores inalámbricos para adquisición de datos en tiempo real. Acondicionamiento y procesado de señales mediante FPGAs.

El análisis de las vibraciones es utilizado comúnmente en el mantenimiento de los motores. Es por ello que en la industria resulta imprescindible la monitorización de todas las máquinas que estén en funcionamiento; ya que el fallo de uno de los equipos puede causar grandes pérdidas. La adquisición de las vibraciones mediante una red de sensores y el posterior procesado de datos permiten monitorizar en tiempo real el estado de cada uno de los motores. Este trabajo tiene como objetivo diseñar e implementar un sistema para la medición de vibraciones y su posterior procesado para el análisis de los datos en el dominio de la frecuencia. La toma de datos éstos se realizará mediante una red de sensores inalámbricos, haciendo uso de un acelerómetro, y el procesado mediante una FPGA.

Can environmental constraints determine random patterns of plant species co-occurrence?

Plant community ecologists use the null model approach to infer assembly processes from observed patterns of species co-occurrence. In about a third of published studies, the null hypothesis of random assembly cannot be rejected. When this occurs, plant ecologists interpret that the observed random pattern is not environmentally constrained - but probably generated by stochastic processes. The null model approach (using the C-score and the discrepancy index) was used to test for random assembly under two simulation algorithms. Logistic regression, distance-based redundancy analysis, and constrained ordination were used to test for environmental determinism (species segregation along environmental gradients or turnover and species aggregation). This article introduces an environmentally determined community of alpine hydrophytes that presents itself as randomly assembled. The pathway through which the random pattern arises in this community is suggested to be as follows: Two simultaneous environmental processes, one leading to species aggregation and the other leading to species segregation, concurrently generate the observed pattern, which results to be neither aggregated nor segregated - but random. A simulation study supports this suggestion. Although apparently simple, the null model approach seems to assume that a single ecological factor prevails or that if several factors decisively influence the community, then they all exert their influence in the same direction, generating either aggregation or segregation. As these assumptions are unlikely to hold in most cases and assembly processes cannot be inferred from random patterns, we would like to propose plant ecologists to investigate specifically the ecological processes responsible for observed random patterns, instead of trying to infer processes from patterns

Crank-Nicholson method for rate equations in powder random lasers

3rd International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE 2014)