Estudio estadístico del viento en Alemania y análisis de sus implicaciones en el clima local

A database with meteorological variables of high temporal and spatial resolution has been collected in the territory of Germany. Of these variables, those of greatest interest for the production of wind energy have been selected: wind speed and direction, in addition to temperature due to their collateral interest. By means of programming in python, the database has been processed and mean values of the variables have been obtained to characterize, in general, their behavior in the place. Three zones have been defined in which the variables behave similarly. The behaviors of the variables have also been related to possible meteorological phenomena in the area. It has been possible to satisfactorily relate the statistical results obtained with some of the local weather phenomena, such as the föhn, as well as characterize each one of the zones with a better defining of the winds that are in them, thus having a base point from which to plan wind energy farms in the future.Departamento de Física AplicadaMáster en Físic

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters.Comment: Under review for Cluster Computin

A container-based workflow for distributed training of deep learning algorithms in HPC clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters

Gametourapp: convierte tu visita turística en un juego

Gametourapp se desarrolla con el objetivo principal de reconvertir la visita turística cultural y en familia en una experiencia lúdica y gratificante. A través del análisis de la demanda turística, se detecta el problema de conciliar las motivaciones culturales del amplio grupo de turistas de mediana edad y el escaso atractivo con el que se presentan los recursos culturales para los niños. Dentro de la planificación turística es importante abarcar a este segmento de clientes, lo que permite complementar y diversificar el monocultivo del producto imperante de sol y playa del modelo litoral levantino. El resultado de este análisis sirve como base teórica para el desarrollo tecnológico de GametourApp así como el sistema previo de análisis espacial y valoración de los recursos turísticos con SIG. Este proyecto de investigación comporta el desarrollo de una aplicación que fusiona la predisposición humana más elemental del hombre hacia la competición y el juego (Ludificación†) y su naturaleza social (Turista Solomo‡), educando además en valores culturales locales del destino turístico

Catching particles by atomic spectrometry: Benefits and limitations of single particle - inductively coupled plasma mass spectrometry

Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) has led ICP-MS into a new dimension, turning an ensemble technique for elemental and isotope ratio analysis into a particle counting technique and well beyond. SP-ICP-MS allows the detection of particles, their size characterization and the quantification of their number and mass concentrations, as well as the dissolved forms of the target element(s). Although the technique is mostly applied to metal- and metalloid-based nanoparticles, its application to microparticles and carbon-based particles are emerging. After twenty years since the first publications and more than ten years of ongoing development, SP-ICP-MS has reached a high degree of maturity, with an increasing number of applications in a wide range of fields. Despite this trend, there are aspects related to the fundamentals of the technique that still require further studies. This review is organized around the fundamentals of the technique along with the different steps and processes involved, from the sample introduction to the signal processing, offering an updated view of these topics, focusing on the benefits and current limitations of the technique, as well as its future perspectives

An ICP-MS-based platform for release studies on silver-based nanomaterials

Engineered nanoparticles are being incorporated into different products and nanocomposites. The release of these nanoparticles, as well as other derived species, can subsequently lead to consumer and environmental exposure, being a relevant factor for risk assessment. The need for analytical methods for the detection, characterization and quantitation of these released species under relevant conditions becomes evident. In this work, a platform of methods based on the use of inductively coupled plasma mass spectrometry (ICP-MS) is proposed to obtain information about the release of silver from silver based nanocoatings and nanocomposites. The sensitivity and element specific response of conventional ICP-MS is complemented by the use of the technique in single particle mode and in combination with ultrafiltration and asymmetrical flow field flow fractionation. By using these three methods, information about the release of both dissolved and particulate forms of silver, as well as the size of the nanoparticles, can be obtained under a variety of scenarios at concentrations down to 0.1 µg L-1 and a nanoparticle diameter of 5 nm. The feasibility of the platform was checked through a number of paradigmatic cases

BDD anodic treatment of 6:2 fluorotelomer sulfonate (6:2 FTSA). Evaluation of operating variables and by-product formation

The concerns about the undesired impacts on human health and the environment of long chain perfluorinated alkyl substances (PFASs) have driven industrial initiatives to replace PFASs by shorter chain fluorinated homologues. 6:2 fluorotelomer sulfonic acid (6:2 FTSA) is applied as alternative to PFOS in metal plating and fluoropolymer manufacture. This study reports the electrochemical treatment of aqueous 6:2 FTSA solutions on microcrystalline BDD anodes. Bench scale batch experiments were performed, focused on assessing the effect of the electrolyte and the applied current density (5-600 A m-2) on the removal of 6:2 FTSA, the reduction of total organic carbon (TOC) and the fluoride release. Results showed that at the low range of applied current density (J=50 A m-2), using NaCl, Na2SO4 and NaClO4, the electrolyte exerted a minimal effect on removal rates. The formation of toxic inorganic chlorine species such as ClO4- was not observed. When using Na2SO4 electrolyte, increasing the applied current density to 350-600 A m-2 promoted a notable enhancement of the 6:2 FTSA removal and defluorination rates, pointing to the positive contribution of electrogenerated secondary oxidants to the overall removal rate. 6:2 FTSA was transformed into shorter-chain PFCAs, and eventually into CO2 and fluoride, as TOC reduction was >90%. Finally, it was demonstrated that diffusion in the liquid phase was controlling the overall kinetic rate, although with moderate improvements due to secondary oxidants at very high current densities.Support from MINECO and SPAIN-FEDER 2014–2020 to project CTM2016-75509-R and to the Spanish Excellence Network E3TECH (CTQ2015-71650-RDT) is acknowledged. J. Carrillo-Abad thanks the Generalitat Valenciana for granting a post doctoral fellowship (APOSTD/2015/019). The authors are thankful to Dr. R. Buck (Chemours Co.) for kindly providing samples of Capstone FS10

Detección, caracterización y cuantificación de nanopartículas de plata en muestras medioambientales y biológicas

En este trabajo se ha desarrollado una metodología analítica que ha permitido la identificación y caracterización de nanopartículas de plata en células expuestas a este agente en ensayos de citotoxicidad a través de fraccionamiento en flujo mediante campo de flujo asimétrico (AsFlFFF) acoplado a un espectrómetro de masas con plasma de acoplamiento inductivo (ICP-MS). Se ha trabajado con muestras biológicas de cultivos celulares tratados con nanopartículas de plata a concentraciones en las que se observaron efectos sobre la viabilidad celular. La puesta en disolución de las nanopartículas de plata en este tipo de muestras se ha llevado a cabo mediante la digestión/solubilización con hidróxido de tetrametilamonio (TMAH) como alternativa a la puesta en disolución por ataque ácido. El trabajo experimental se ha centrado en el estudio de la estabilidad de las nanopartículas en este medio frente al tiempo y en la optimización de las condiciones de separación de las nanopartículas en AsFlFFF. La caracterización de las nanopartículas solubilizadas se ha realizado mediante calibración externa del sistema AsFlFFF con estándares de tamaño de nanopartículas de plata y comparando los resultados con imágenes tomadas mediante microscopía electrónica de transmisión (TEM). Los resultados obtenidos permiten confirmar la presencia de nanopartículas en las células del mismo tamaño que las inicialmente puestas en el medio. Por último se ha cuantificado de forma satisfactoria la cantidad de plata total en las células mediante el procedimiento de solubilización encontrado, validándolo frente al ataque ácido convencional. Esta metodología abre el camino a la caracterización de nanopartículas de plata en matrices biológicas complejas, lo que puede aportar información relevante para estudios de toxicidad de estas nanopartículas en entornos biológicos o medioambientales

Análisis y cálculo de mesa elevadora de tijera simple para 2000 Kg. de carga

El objetivo del proyecto es el análisis y dimensionamiento de una mesa elevadora de tijera simple accionada mediante doble cilindro hidráulico con las siguientes especificaciones: Carga máxima: 2000 Kilogramos. Altura máxima de elevación: 1,7 metros. Se abordará el análisis de esfuerzos sobre los componentes con el fin de su dimensionamiento.<br /

Detection and characterization of biogenic selenium nanoparticles in selenium-rich yeast by single particle ICPMS

A method based on single particle inductively coupled plasma mass spectrometry (SP-ICPMS) was developed for the analysis of commercial Se-rich yeasts, to confirm the occurrence of selenium nanoparticles in these food supplements. A considerable reduction of background levels was achieved by combining data acquisition at microsecond dwell times and the use of a H2 reaction cell, improving by a factor of 10 the current state-of-the-art methodology, and bringing size detection limits down to 18 nm for selenium nanoparticles. The presence of nanoparticulate selenium was revealed by size-exclusion chromatography ICPMS, with detection of a selenium peak at the exclusion volume of the column showing absorption at a wavelength corresponding to selenium nanoparticles. SP-ICPMS allowed us to confirm the presence of Se-nanoparticles, as well as to calculate the nanoparticle size distribution, from information about the shape and elemental composition of the nanoparticles obtained by transmission electron microscopy (TEM) and energy dispersive X-ray spectroscopy (EDS), respectively. These results reveal the significance of nanoparticles in the speciation of metals and metalloids in biological samples and the capability of SP-ICPMS in combination with TEM-EDS to carry out these analyses