Air mass origin and its influence over the aerosol size distribution: a study in SE Spain

International audienceA k-means cluster analysis of 96 hour trajectories arriving in Southeast (SE) Spain at 3000, 1500 and 500 m for the 7-year period 2000?2006 has been performed to identify and describe the main flows arriving at the study area. The dependence of the aerosol size distribution on the air mass origin has been studied by using non-parametric statistics. There are statistically significant differences on aerosol size distribution and meteorological variables at surface level according to the identified clusters

Influence of meteorological input data on backtrajectory cluster analysis ? a seven-year study for southeastern Spain

International audienceBacktrajectory differences and clustering sensitivity to the meteorological input data are studied. Trajectories arriving in Southeast Spain (Elche), at 3000, 1500 and 500 m for the 7-year period 2000?2006 have been computed employing two widely used meteorological data sets: the NCEP/NCAR Reanalysis and the FNL data sets. Differences between trajectories grow linearly at least up to 48 h, showing faster growing after 72 h. A k-means cluster analysis performed on each set of trajectories shows differences in the identified clusters (main flows), partially because the number of clusters of each clustering solution differs for the trajectories arriving at 3000 and 1500 m. Trajectory membership to the identified flows is in general more sensitive to the input meteorological data than to the initial selection of cluster centroids

Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management.  We report also last advances of DF and DM in modern agriculture and in precision durum wheat production

ATR-SEIRAS study of CO adsorption and oxidation on Rh modified Au(111-25 nm) film electrodes in 0.1 M H2SO4

Rh modified Au(111-25 nm) electrodes, prepared by electron beam evaporation and galvanostatic deposition, were employed to study adsorption and electro-oxidation of CO on Rh in 0.1 M sulfuric acid solution by in situ attenuated total reflection surface enhanced infrared absorption spectroscopy (ATR-SEIRAS). The results of ATR-SEIRAS experiments were compared with those obtained by infrared reflection absorption spectroscopy on three low-index Rh single crystal surfaces. The Rh film deposited on Au(111-25 nm) electrode consists of 3D clusters forming a highly stepped [n(111) × (111)]-like surface with narrow (111) terraces. When CO was dosed at the hydrogen adsorption potential region, CO adsorbed in both atop (COL) and bridge (COB) configurations, as well as coadsorbed water species, were detected on the Rh film electrode. A partial interconversion of spectroscopic bands due to the CO displacement from bridge to atop sites was found during the anodic potential scan, revealing that there is a potential-dependent preference of CO adsorption sites on Rh surfaces. Our data indicate that CO oxidation on Rh electrode surface in acidic media involves coadsorbed water and follows the nucleation and growth model of a Langmuir-Hinshelwood type reaction.The work was supported by the Research Center Jülich, the University of Bern, Swiss National Science Foundation (200020_144471, 200021-124643), the Spanish Ministerio de Economía y Competitividad (project CTQ2013-44083-P) and University of Alicante. QX acknowledges fellowships of the Research Center Jülich; IP acknowledges support by COST Action TD 1002; and AK acknowledges the financial support by CTI Swiss Competence Centers for Energy Research (SCCER Heat and Electricity Storage)

Mineralogical and petrographic characterization of the Cerrillo Blanco Iberian sculptures

This research was funded by the following projects: (ECODIGICOLOR), grant number TED2021‑132023B‑I00, supported MCIN/AEI /http://dx.doi.org/10.13039/501100011033 and Unión Europea NextGenerationEU/ PRTR (Proyectos estratégicos orientados a la transición ecológica y digital) and project “Aplicación avanzada de las algas procedentes de la Alhambra y el Generalife en técnicas artísticas y de conservación‑restauración, (FICOARTE2), grant number P18‑FR‑4477, supported by Consejería de Universidad, Investigacion e Innovación, Junta de Andalucía, Programa FEDER, “Andalucía se mueve con Europa”, Grant PID2020‑113022GB‑I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, by the “European Union”.The archaeological heritage at Cerrillo Blanco (Porcuna, Spain) is made up of 27 sculptural ensembles and hundreds of fragments dated between the seventh and second centuries BC. They represent a fundamental milestone in Iberian art and culture. Despite their relevance, no scientific studies have been carried out to date in order to fully understand the materials, intentions and techniques that led to their creation. This is a study carried out on original pieces from the Archaeological Museum of Jaen using stereoscopic optical microscopy (SOM), polarised optical microscopy (POM), X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscopy with energy dispersive x-ray analysis (FESEM-EDX) and spectrophotometry (SF). The results obtained provide new information on the material composition of this important legacy of the Iberian civilization as well as its main alteration factors.MCIN/AEI TED2021-132023B-I00, PID2020-113022GB-I00Unión Europea NextGenerationEU/ PRTR TED2021-132023B-I00Junta de Andalucía, FEDER, "Andalucía se mueve con Europa" P18-FR-4477"ERDF A way of making Europe" Unión Europea PID2020-113022GB-I0

Automated HIV screening in the emergency department –earlier diagnosis, improved clinical outcomes

info:eu-repo/semantics/publishedVersio

Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time series

[EN] There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006–2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP—Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the plant needed to be identified, as did the time-based aggregation of MTCI values using said optimal period as a reference, and relation with the index, with direct observations of production and yield through spatial aggregation using coverage from the Geographic Information System for Agricultural Parcels (SIGPAC—Sistema de información geográfica de parcelas agrícolas) and requests for common agricultural policy (CAP) assistance. The obtained results indicate a significant association between the MTCI index and the production and yield data collected by AEAP at the 95% confidence level (R2 =0.81 and R2 =0.57, respectively).[ES] Existe una relación entre la producción primaria neta del trigo y los índices de vegetación obtenidos de imágenes de satélite. Con frecuencia se utiliza el NDVI (Normalized Difference Vegetation Index) para la estimación de producción y rendimiento de trigo y otros cultivos. Sin embargo, hay pocas investigaciones que utilicen el índice MTCI (MERIS Terrestrial Chlorophyll Index) para conocer el rendimiento y la producción de los cultivos a una escala regional posiblemente debido a la falta de continuidad del sensor MERIS. No obstante, la posibilidad del cálculo de MTCI a partir de Sentinel 2 abre nuevas oportunidades para su aplicación e investigación. En esta investigación se han generado dos modelos empíricos de estimación de producción y rendimiento de trigo en Andalucía. Para ello, se ha empleado la serie temporal completa (imágenes semanales de 2006 a 2011) del índice de vegetación MTCI del sensor satelital MERIS (Medium Resolution Imaging Spectrometer) asociada a los datos de producción y rendimiento del Anuario de estadísticas agrarias y pesqueras de Andalucía (AEAP). Para la creación de estos modelos ha sido necesaria la identificación del periodo óptimo del desarrollo de la planta, la agregación temporal de los valores MTCI usando ese momento óptimo como referencia, relacionar ese índice con observaciones directas de producción y rendimiento a través de agregaciones espaciales mediante la utilización de coberturas SIGPAC y las solicitudes de ayudas PAC, caracterizar la variación del índice en función del año de cultivo y relacionarlo con los datos estadísticos. Los resultados obtenidos indican una correlación estadísticamente significativa (p-valor < 0,05) entre el índice MTCI y los datos de producción y rendimiento recogidos por AEAP (R2=0,81 y 0,57, respectivamente).Agradecemos la financiación obtenida de MINECO (Proyectos BIA2013-43462-P, CSO2014-51994-P) y de la Junta de Andalucía (Grupo Investigación RNM177).Egea-Cobrero, V.; Rodriguez-Galiano, V.; Sánchez-Rodríguez, E.; García-Pérez, M. (2018). 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International Journal of Remote Sensing, 35(3), 1004-1028. https://doi.org/10.1080/ 01431161.2013.875629Curtis, T., Halford, N. G. 2014. Food security: the challenge of increasing wheat yield and the importance of not compromising food safety. Annals of Applied Biology, 164(3), 354-372. https://doi.org/10.1111/aab.12108Dash, J., Curran, P. 2007. Relationship between the MERIS vegetation indices and crop yield for the state of South Dakota, USA. Proc. Envisat Symposium.Dash, J., Jeganathan, C., Atkinson, P.M. 2010. The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India. Remote Sensing of Environment, 114(7), 1388-1402. https://doi.org/10.1016/j. rse.2010.01.021Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A., Barker, B. 2014. Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics. Remote Sensing, 6(10), 9653-9675. https://doi.org/10.3390/rs6109653Dente, L., Satalino, G., Mattia, F., Rinaldi, M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERESWheat model to map wheat yield. Remote Sensing of Environment, 112(4), 1395-1407. https://doi.org/10.1016/j.rse.2007.05.023Duncan, J.M.A., Dash, J., Atkinson, P.M. 2015. Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing. Global change biology, 21(4), 1541-51. https://doi.org/10.1111/gcb.12660FAOSTAT. 2013. Productos agrícolas. Recuperado 17 de agosto de 2016, a partir de http://ec.europa.eu/eurostat/statistics-explained/index.php/Agricultural_products/es#Fuente_de_los_datos_de_las_tablas_y_los_gr.C3.A1ficos_.28MS_Excel.29Foley, J.A., Ramankutty, N., Brauman, K.A., Cassidy, E.S., Gerber, J.S., Johnston, M., … Zaks, D.P.M. 2011. Solutions for a cultivated planet. Nature, 478(7369), 337-342. https://doi.org/10.1038/ nature10452Fontana, D.C., Potgieter, A.B., Apan, A. 2007. Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images. Applied GIS, 3(7).Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., … Wu, W. 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188-202. https://doi.org/10.1016/j.agrformet.2015.10.013Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., … Wu, W. 2015. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106-121. https://doi. org/10.1016/j.agrformet.2015.02.001Huang, Y., Zhu, Y., Li, W. L., Cao, W. X., & Tian, Y. C. 2013. Assimilating remotely sensed information with the wheatgrow model based on the ensemble square root filter for improving regional wheat yield forecasts. Plant Production Science, 16(4), 352-364. https://doi.org/10.1626/pps.16.352ITACyL, AEMET, Consejería de Agricultura y Ganadería de la Junta de Castilla y León. 2016. Boletín de predicción de cosechas de Castilla y León. Recuperado 25 de octubre de 2016, a partir de https://cosechas.itacyl.es/es/inicioJégo, G., Pattey, E., Liu, J. 2012. Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops. Field Crops Research, 131, 63-74. https://doi. org/10.1016/j.fcr.2012.02.012Johnson, M.D., Hsieh, W.W., Cannon, A.J., Davidson, A., Bédard, F. 2016. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. 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Cerámica y climatización saludable: paneles cerámicos radiantes en edificios. Condiciones de confort y demanda energética frente a sistemas convectivos

Porcelain stoneware is a widely used building material. In recent years, its range of uses has expanded to encompass a new spectrum of innovative and inventive applications in architecture. In this research, we analysed the patented Thermal Ceramic Panel. This consists of a thin porcelain stoneware panel that incorporates a capillary system of polypropylene tubes measuring 3.5 mm in diameter embedded in a conductive ceramic interface. The system works with hot or cold water, producing healthy heating and cooling by means of radiant surfaces. Following an initial prototype test in which panels were placed on the walls of an office, we conducted simulations at the University of Alicante Museum using wall, ceiling and baffle panels, having previously monitored the state of the building. Thermal behaviour parameters were analysed and compared with those of other standard finishing materials, obtaining results for thermal comfort and energy savings in comparison with all-air systems.El gres porcelánico es un material ampliamente utilizado en edificación. En los últimos años su uso ha experimentado un nuevo espectro de líneas de innovación e invención en sus aplicaciones en la arquitectura. En esta investigación de analiza la patente Panel de Acondicionamiento Térmico Cerámico, consistente en piezas de gres porcelánico de bajo espesor, que contienen tramas capilares a base de tubos de polipropileno de 3,5 mm de diámetro, e interfaz de pasta conductora. Estos sistemas trabajan con agua fría o caliente produciendo una climatización saludable por superficies radiantes. Tras una primera experiencia de prototipado y colocación de paneles en pared en un despacho de oficina, se han realizado simulaciones en el Museo de la Universidad de Alicante, colocando los paneles en pared, techo o tipo bafle, previa monitorización del estado actual del edificio. Se han analizado los parámetros de comportamiento térmico y se han comparado con otros materiales de acabado habituales. Se han obtenido resultados de confort térmico y ahorros energéticos de forma comparativa frente a sistemas todo-aire

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Automated underwriting in life insurance: Predictions and optimisation

© Springer International Publishing AG, part of Springer Nature 2018. Underwriting is an important stage in the life insurance process and is concerned with accepting individuals into an insurance fund and on what terms. It is a tedious and labour-intensive process for both the applicant and the underwriting team. An applicant must fill out a large survey containing thousands of questions about their life. The underwriting team must then process this application and assess the risks posed by the applicant and offer them insurance products as a result. Our work implements and evaluates classical data mining techniques to help automate some aspects of the process to ease the burden on the underwriting team as well as optimise the survey to improve the applicant experience. Logistic Regression, XGBoost and Recursive Feature Elimination are proposed as techniques for the prediction of underwriting outcomes. We conduct experiments on a dataset provided by a leading Australian life insurer and show that our early-stage results are promising and serve as a foundation for further work in this space