A mesoscale index to describe the regional ocean circulation around the Balearic Islands

Historical oceanographic surveys carried out around the Balearic Islands (western Mediterranean) suggest two different scenarios for the regional ocean circulation. In one scenario, occurring during cold winters, cool water is formed at intermediate layers (100–300 m) in the Gulf of Lions. This Western Mediterranean Intermediate Water (WIW) usually moves southward reaching the Balearic Channels, deflecting the warmer Levantine Intermediate Water (LIW) coming from the Eastern Mediterranean, and even blocking the Ibiza Channel. On the other hand, during mild winters, less WIW is formed and then LIW flows through the channels, appearing at their characteristic depths. The oceanographic surveys around the Balearic Islands (1985–2004) have provided a qualitative index, indicating the presence or not of WIW in the Ibiza Channel, based on the analyses of θS diagrams. A quantitative index based on mean water temperature between 100 and 300 m depth in the channels may also be defined. Both indexes, the qualitative and the quantitative, give consistent information on WIW presence for the period 1985–2004, however, both are short in time and have gaps in the series. In order to obtain a longer and continuous index for WIW presence and then for regional circulation, air–sea heat fluxes at the Gulf of Lions during winter months were obtained from the meteorological NCEP/NCAR reanalysis dataset and compared with other meteorological data such as surface air temperature. The standardized air temperature anomalies at 1000 hPa in the Gulf of Lions during winter (December–March) has been shown to be the simplest and best indicator of absence/presence of WIW in the Balearic Islands channels in late spring. Values above 1.0 of the standardized temperature anomaly would indicate absence of WIW in the Ibiza Channel. The high correlation obtained with available in-situ oceanographic data allows the use of this index as an indicator of presence of WIW and then of different regional circulation scenarios backwards in time and in those years for which the oceanographic data are missing or scarc

Austenite grain abnormal growth in the microalloyed steel SSIVlnSiVSS and determination of driving and pinning forces

11 páginas, 14 figuras, 3 tablas.[ES] Se fabricaron varias coladas del acero 38MnSiVS5 con diferentes contenidos de titanio y aluminio, y se determinó el tamaño del grano austenítico a temperaturas comprendidas entre 900 y 1.200 °C. Se cuantificaron las fracciones de volumen de crecimiento normal y anormal, respectivamente. Los resultados muestran que el crecimiento anormal del grano austenítico ocurre independientemente de los contenidos de titanio y aluminio, debido, fundamentalmente, a la disolución parcial de los precipitados TiN. Aquellos aceros con porcentajes altos de aluminio mostraron un peor comportamiento debido a que la formación de un segundo tipo de precipitados, concretamente AIN, y su pronta disolución entre 1.000 y 1.100 °C, ocasiona un descenso drástico en las fuerzas de anclaje locales, lo que produce un crecimiento anormal más pronunciado de los granos afectados. Paralelamente, se realizó un estudio de los precipitados por Microscopía de Transmisión y de Barrido (MTB), que permitió calcular las fuerzas de anclaje de acuerdo con las expresiones de Zener y Gladman y establecer una comparación con las fuerzas impulsoras de crecimiento de grano. Dicha comparación permite explicar las diferencias encontradas entre las distintas coladas.[EN] Several castings of 38MnSiVS5 steel have been manufactured with different titanium and aluminium contents and the austenitic grain size has been determined at temperatures between 900 and 1200 °C. The volume fraction of normal and abnormal grain growth have been quantified. The results show that abnormal growth of the austenitic grain occurs irrespective of the titanium and aluminium contents, due fundamentally to the partial dissolution of TiN precipitates. The steels with high aluminium contents have presented worse behaviour due to the formation of a second type of precipitates, namely AIN, which quickly dissolve between 1000 and 1100 °C, causing a drastic decline in the local pinning forces which gives rise to more pronounced abnormal growth of the affected grains. In parallel, a study of the precipitates has been carried out by transmission and scanning microscopy, allowing the calculation of pinning forces according to Zener and Gladman expressions and the establishment of a comparison with driving forces. This comparison makes it possible to explain the differences encountered between the different castings.Los autores desean expresar su agradecimiento al Programa Europeo CECA por la financiación de los trabajos realizados en el marco del proyecto ECSC 7210 - KA/936.Peer reviewe

Time course of early metabolic changes following diffuse traumatic brain injury in rats as detected by 1H NMR spectroscopy

Experimental models of traumatic brain injury (TBI) provide a useful tool for understanding the cerebral metabolic changes induced by this pathological condition. Here, we report on the time course of changes in cerebral metabolites after TBI and its correlation with early brain morphological changes using a combination of high-resolution proton magnetic resonance spectroscopy ( 1H MRS) and magnetic resonance imaging (MRI). Adult male Sprague-Dawley rats were subjected to closed head impact and examined by MRI at 1, 9, 24, 48, and and 72 h after the injury. Extracts from funnel frozen rat brains were then obtained and analyzed quantitatively by high-resolution 1H MRS. Finally, statistical multivariate analysis was carried out to identify the combination of cerebral metabolites that best described the time evolution of diffuse TBI. The temporal changes observed in the concentration of cerebral metabolites followed three different patterns. The first pattern included taurine, threonine, and glycine, with concentrations peaking 24 h after the injury. The second pattern included glutamate, GABA, and alanine, with concentrations remaining elevated between 24 and 48 h post-injury. The third one involved creatine-phosphocreatine, N-acetylaspartate, and myo-inositol, with concentrations peaking 48 h after the injury. A multivariate stepwise discriminant analysis revealed that the combination of the organic osmolytes taurine and myo-inositol allowed optimal discrimination among the different time groups. Our findings suggest that the profile of some specific brain molecules that play a role as organic osmolytes can be used to follow-up the progression of the early diffuse brain edema response induced by TBI. © Mary Ann Liebert, Inc.This work was partly supported by Spanish Ministry of Education and Science (grants SAF 2001-224 and SAF 2004-03197 to J.M.R. and S.C.) and by Spanish Ministry of Health (grants FISss C03/08, C03/10, and G03/155 to J.M.R. and S.C.).Peer Reviewe

Vulnerability and resilience of high-mountain pine forests of the Gredos range (Ávila, Spanish Central System): two thousand years of socio-ecological dynamics

RESUMEN: En este trabajo se presenta el análisis palinológico de la turbera de Pozo de la Nieve, localizada en el Parque Natural del Valle de Iruelas (Ávila), un área de alto valor sociocultural dentro de la Sierra de Gredos (Sistema Central). Con el objetivo de relacionar los cambios en el paisaje con la explotación de los recursos naturales y eventos climáticos, en primer lugar se han realizado 7 dataciones radiocarbónicas que sitúan el inicio del registro sedimentario ca. 240 cal BC. Los datos polínicos indican la existencia de un denso pinar altimontano dominado por Pinus sylvestris/nigra desde la Segunda Edad de Hierro hasta el periodo islámico. A partir del periodo cristiano las actividades antrópicas se intensifican, especialmente la ganadería en la Edad Contemporánea, lo cual conlleva la progresiva desaparición del pinar de alta montaña y el desarrollo de pastizales mediante el manejo del fuego, situación que culmina con el desarrollo del paisaje actual dominado por piornales pirófilos.ABSTRACT: We present the palynological study of Pozo de la Nieve peat bog, located in a very valuable socio-cultural placement within the Iruelas Valley Natural Park (Gredos range, Iberian Central System). We have focused in relating landscape changes to natural resources management and climatic events. Firstly, we carried out seven radiocarbon dates suggesting the origin of this record ca. 240 cal BC. The palynological data show the existence of dense high-mountain pine woodlands dominated by Pinus sylvestris/nigra from the Late Iron Age to the Muslim period. Later, from the Christian period, anthropogenic activities have intensified, especially livestock grazing in the Contemporary Age. Its consequences are the progressive disappearance of highmountain pine forests and the extension of grasslands by means of fire, which has shaped current landscape dominated by broom communities.Este trabajo ha sido financiado por el proyecto Desirè-HAR2013-43701-P (Plan Nacional I+D+I, Ministerio de Economía y Competitividad de España). Sebastián Pérez Díaz está financiado por el Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i en la modalidad Juan de la Cierva-Incorporación. Mónica Ruiz Alonso está financiada por el Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i en la modalidad Juan de la Cierva-Formación

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

[EN] Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. The proposal has been tested with databases from a Brazilian water utility, providing key knowledge for improving water management and hydraulic operation of the distribution system. This novel analysis framework shows several benefits in terms of accuracy and performance of neural network models for water demand.Navarrete-López, CF.; Herrera Fernández, AM.; Brentan, BM.; Luvizotto Jr., E.; Izquierdo Sebastián, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water. 11(246):1-17. https://doi.org/10.3390/w11020246S11711246Fecarotta, O., Carravetta, A., Morani, M., & Padulano, R. (2018). Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions. Resources, 7(4), 73. doi:10.3390/resources7040073Creaco, E., & Pezzinga, G. (2018). Comparison of Algorithms for the Optimal Location of Control Valves for Leakage Reduction in WDNs. Water, 10(4), 466. doi:10.3390/w10040466Nguyen, K. A., Stewart, R. A., Zhang, H., Sahin, O., & Siriwardene, N. (2018). Re-engineering traditional urban water management practices with smart metering and informatics. Environmental Modelling & Software, 101, 256-267. doi:10.1016/j.envsoft.2017.12.015Adamowski, J., & Karapataki, C. (2010). Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms. Journal of Hydrologic Engineering, 15(10), 729-743. doi:10.1061/(asce)he.1943-5584.0000245Caiado, J. (2010). Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand. Journal of Hydrologic Engineering, 15(3), 215-222. doi:10.1061/(asce)he.1943-5584.0000182Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2008). Water Demand Prediction using Artificial Neural Networks and Support Vector Regression. Journal of Computers, 3(11). doi:10.4304/jcp.3.11.1-8Tiwari, M., Adamowski, J., & Adamowski, K. (2016). Water demand forecasting using extreme learning machines. Journal of Water and Land Development, 28(1), 37-52. doi:10.1515/jwld-2016-0004Vijayalaksmi, D. P., & Babu, K. S. J. (2015). Water Supply System Demand Forecasting Using Adaptive Neuro-fuzzy Inference System. Aquatic Procedia, 4, 950-956. doi:10.1016/j.aqpro.2015.02.119Zhou, L., Xia, J., Yu, L., Wang, Y., Shi, Y., Cai, S., & Nie, S. (2016). Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans. International Journal of Environmental Research and Public Health, 13(4), 355. doi:10.3390/ijerph13040355Cadenas, E., Rivera, W., Campos-Amezcua, R., & Heard, C. (2016). Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model. Energies, 9(2), 109. doi:10.3390/en9020109Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/s0925-2312(01)00702-0Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 11(2), 2664-2675. doi:10.1016/j.asoc.2010.10.015Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resources Management, 26(12), 3539-3558. doi:10.1007/s11269-012-0089-yBrentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Di Nardo, A., Di Natale, M., Musmarra, D., Santonastaso, G. F., Tzatchkov, V., & Alcocer-Yamanaka, V. H. (2014). Dual-use value of network partitioning for water system management and protection from malicious contamination. Journal of Hydroinformatics, 17(3), 361-376. doi:10.2166/hydro.2014.014Scarpa, F., Lobba, A., & Becciu, G. (2016). Elementary DMA Design of Looped Water Distribution Networks with Multiple Sources. Journal of Water Resources Planning and Management, 142(6), 04016011. doi:10.1061/(asce)wr.1943-5452.0000639Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., & Martsouka, F. A. (2012). Mapping Urban Water Demands Using Multi-Criteria Analysis and GIS. Water Resources Management, 26(5), 1347-1363. doi:10.1007/s11269-011-9962-3Buchberger, S. G., & Nadimpalli, G. (2004). Leak Estimation in Water Distribution Systems by Statistical Analysis of Flow Readings. Journal of Water Resources Planning and Management, 130(4), 321-329. doi:10.1061/(asce)0733-9496(2004)130:4(321)Candelieri, A. (2017). Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, 9(3), 224. doi:10.3390/w9030224Padulano, R., & Del Giudice, G. (2018). Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy. Water Resources Management, 33(2), 739-755. doi:10.1007/s11269-018-2140-0Bloetscher, F. (2012). Protecting People, Infrastructure, Economies, and Ecosystem Assets: Water Management in the Face of Climate Change. Water, 4(2), 367-388. doi:10.3390/w4020367Bach, P. M., Rauch, W., Mikkelsen, P. S., McCarthy, D. T., & Deletic, A. (2014). A critical review of integrated urban water modelling – Urban drainage and beyond. Environmental Modelling & Software, 54, 88-107. doi:10.1016/j.envsoft.2013.12.018Goltsev, A. V., Dorogovtsev, S. N., Oliveira, J. G., & Mendes, J. F. F. (2012). Localization and Spreading of Diseases in Complex Networks. Physical Review Letters, 109(12). doi:10.1103/physrevlett.109.128702Danila, B., Yu, Y., Marsh, J. A., & Bassler, K. E. (2006). Optimal transport on complex networks. Physical Review E, 74(4). doi:10.1103/physreve.74.046106Herrera, M., Izquierdo, J., Pérez-García, R., & Montalvo, I. (2012). Multi-agent adaptive boosting on semi-supervised water supply clusters. Advances in Engineering Software, 50, 131-136. doi:10.1016/j.advengsoft.2012.02.005Maslov, S., Sneppen, K., & Zaliznyak, A. (2004). Detection of topological patterns in complex networks: correlation profile of the internet. Physica A: Statistical Mechanics and its Applications, 333, 529-540. doi:10.1016/j.physa.2003.06.002Lloyd, A. L., & Valeika, S. (2007). Network models in epidemiology: an overview. World Scientific Lecture Notes in Complex Systems, 189-214. doi:10.1142/9789812771582_0008Hamilton, I., Summerfield, A., Oreszczyn, T., & Ruyssevelt, P. (2017). Using epidemiological methods in energy and buildings research to achieve carbon emission targets. Energy and Buildings, 154, 188-197. doi:10.1016/j.enbuild.2017.08.079Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016). The physics of spreading processes in multilayer networks. Nature Physics, 12(10), 901-906. doi:10.1038/nphys3865Hamilton, I. G., Summerfield, A. J., Lowe, R., Ruyssevelt, P., Elwell, C. A., & Oreszczyn, T. (2013). Energy epidemiology: a new approach to end-use energy demand research. Building Research & Information, 41(4), 482-497. doi:10.1080/09613218.2013.798142Herrera, M., Ferreira, A. A., Coley, D. A., & de Aquino, R. R. B. (2016). SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series. AI Communications, 29(6), 725-732. doi:10.3233/aic-160716Padulano, R., & Del Giudice, G. (2018). A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data. Water Resources Management, 32(11), 3671-3685. doi:10.1007/s11269-018-2012-7Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zAghabozorgi, S., & Wah, T. Y. (2014). Clustering of large time series datasets. Intelligent Data Analysis, 18(5), 793-817. doi:10.3233/ida-140669Yuan, J., Wang, Z., Han, M., & Sun, Y. (2015). A lazy associative classifier for time series. Intelligent Data Analysis, 19(5), 983-1002. doi:10.3233/ida-150754Rasheed, F., Alshalalfa, M., & Alhajj, R. (2011). Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, 23(1), 79-94. doi:10.1109/tkde.2010.76Schmieder, R., & Edwards, R. (2011). Fast Identification and Removal of Sequence Contamination from Genomic and Metagenomic Datasets. PLoS ONE, 6(3), e17288. doi:10.1371/journal.pone.0017288Valimaki, N., Gerlach, W., Dixit, K., & Makinen, V. (2007). Compressed suffix tree a basis for genome-scale sequence analysis. Bioinformatics, 23(5), 629-630. doi:10.1093/bioinformatics/btl681Ezkurdia, I., Juan, D., Rodriguez, J. M., Frankish, A., Diekhans, M., Harrow, J., … Tress, M. L. (2014). Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes. Human Molecular Genetics, 23(22), 5866-5878. doi:10.1093/hmg/ddu309Bermudez-Santana, C. I. (2016). APLICACIONES DE LA BIOINFORMÁTICA EN LA MEDICINA: EL GENOMA HUMANO. ¿CÓMO PODEMOS VER TANTO DETALLE? Acta Biológica Colombiana, 21(1Supl), 249-258. doi:10.15446/abc.v21n1supl.51233Cai, L., Li, X., Ghosh, M., & Guo, B. (2009). Stability analysis of an HIV/AIDS epidemic model with treatment. Journal of Computational and Applied Mathematics, 229(1), 313-323. doi:10.1016/j.cam.2008.10.067Jackson, M., & Chen-Charpentier, B. M. (2017). Modeling plant virus propagation with delays. Journal of Computational and Applied Mathematics, 309, 611-621. doi:10.1016/j.cam.2016.04.024Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L., & Armstrong, B. (2013). Time series regression studies in environmental epidemiology. International Journal of Epidemiology, 42(4), 1187-1195. doi:10.1093/ije/dyt092HELFENSTEIN, U. (1991). The Use of Transfer Function Models, Intervention Analysis and Related Time Series Methods in Epidemiology. 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Some inferences on the mechanism of atmospheric gas/particle partitioning of polycyclic aromatic hydrocarbons (PAH) at Zaragoza (Spain)

Gas-particle partitioning of pollutants is an important mechanism determining atmospheric processing and its impact to environmental and human health. In this paper, the gas-particle partitioning of polycyclic aromatic hydrocarbons (PAH) has been studied with the aim of determining the main mechanism of PAH partitioning in Zaragoza (Spain) aerosols. To reach this goal, the ambient concentrations of PAH (gas and particle phase) collected in this city for one year period (2003-2004) have been analyzed. The partitioning between the particle and gas phases was studied according to three different models: the Junge adsorption model, the absorption into the organic matter model using the octanol-air (KOA) partition coefficient and the absorption into the organic matter plus the adsorption onto the soot carbon model using the soot-air (KSA) partition coefficients. Experimental gas/particle partition coefficients (KP) correlated well with the subcooled liquid vapour pressures (P0 L) of PAH but with slopes higher than the expected value of - 1. Experimental Kp values were well fit to the modelled ones when, in addition to absorption into organic matter, adsorption onto the soot carbon was considered. It could be concluded that the main partition mechanism in Zaragoza aerosols was explained by adsorption onto the soot carbon. However, Kp modelled values were affected by the different thermodynamic parameters related to soot types. The influence of the organic matter and elemental carbon fractions on the Kp modelling was also studied. The different particle characteristics, local factors, the presence of non exchangeable fraction and non equilibrium were considered like main keys to explain deviations of the experimental Kp values from predictions according to models.Authors would like to thank the Government of Aragón (DGA) for the grant to M.T.C and the Spanish Government for the JAE doctoral contract to J.M.L and for the Juan de la Cierva contract to M.V.N.Peer reviewe

Laboratory Hyperspectral Image Acquisition System Setup and Validation

Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries.Laboratory Hyperspectral Image Acquisition System Setup and ValidationpublishedVersio