Algunos ejemplos de la aplicación del Análisis de Correspondencias en arqueología

Sin resume

Seawater quality control of microcontaminants in fish farm cage systems: Application of passive sampling devices

Increasingly, developed countries are imposing restrictions on chemicals used in aquaculture, and introducing residue monitoring programmes to ensure the highest possible seafood safety standards. Chemotherapeutants, additives or chemical residues in edible tissues of aquaculture products are now attracting attention, and a major issue is the accumulation of microcontaminants in seafood flesh. Environmental quality control is related to the provision of high-quality, safe products. The present paper evaluates the effectiveness of passive sampling devices as tools in environmental monitoring programmes for fish farm cage systems. Capability to detect trace levels of microcontaminants, sampling rates, and accumulation kinetic is assessed. Devices tested were Polar Organic Chemical Integrative Samplers (POCIS), for detecting pharmaceuticals, pesticides and hormone residues; Semi-Permeable Membrane Devices (SPMD), to detect bioaccumulable pollutants; and Diffusive Gradients in Thin films (DGT), for metals.Las restricciones que imponen los países desarrollados al uso de sustancias químicas en la acuicultura para asegurar la salubridad de sus productos son cada vez mayores. También es creciente la preocupación por el control de los aditivos, residuos químicos o los preparados farmacéuticos que pudieran encontrarse en las partes comestibles de las especies acuícolas, así como la acumulación de micro-contaminantes en las mismas. En este trabajo se presenta un estudio sobre el uso de los sistemas de muestreo pasivo para los programas de control ambiental de las piscifactorías de jaulas flotantes. Se valora su capacidad de detectar niveles traza, la tasa de muestreo y la cinética de acumulación de micro-contaminantes. Se han probado los POCIS (Polar Organic Chemical Integrative Samplers) para detectar productos farmacéuticos, pesticidas y residuos hormonales, los SPMD (Semi-Permeable Membrane Devices) para detectar contaminantes bioacumulables y las membranas DGT (Diffusive Gradients in Thin films) para metales.Instituto Español de Oceanografí

Collagen organization, polarization sensitivity and image quality in human corneas using second harmonic generation microscopy

In this paper, a Second-Harmonic-Generation (SHG) microscope was used to study the relationship between collagen structural arrangement, image quality and polarization sensitivity in human corneas with different organizations. The degree of order (or alternatively, the Structural Dispersion, SD) was quantified using the structure tensor method. SHG image quality was evaluated with different objective metrics. Dependence with polarization was quantified by means of a parameter defined as polarimetric modulation, which employs polarimetric SHG images acquired with four independent polarization states. There is a significant exponential relationship between the quality of the SHG images and the SD of the samples. Moreover, polarization sensitivity strongly depends on collagen arrangement. For quasi- or partially organized specimens, there is a polarization state that noticeably improves the image quality, providing additional information often not seen in other SHG images. This does not occur in non-organized samples. This fact is closely related to polarimetric modulation, which linearly decreases with the SD. Understanding in more detail the relationships that take place between collagen distribution, image quality and polarization sensitivity brings the potential to enable the development of optimized SHG image acquisition protocols and novel objective strategies for the analysis and detection of pathologies related to corneal collagen disorders, as well as surgery follow-ups

Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network

[EN] Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Navarro, JM.; Martínez-España, R.; Bueno-Crespo, A.; Cecilia-Canales, JM.; Martínez, R. (2020). Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network. Sensors. 20(3):1-16. https://doi.org/10.3390/s20030903S116203Hornikx, M. (2016). Ten questions concerning computational urban acoustics. Building and Environment, 106, 409-421. doi:10.1016/j.buildenv.2016.06.028Murphy, E., & King, E. A. (2010). Strategic environmental noise mapping: Methodological issues concerning the implementation of the EU Environmental Noise Directive and their policy implications. Environment International, 36(3), 290-298. doi:10.1016/j.envint.2009.11.006Arana, M., San Martin, R., San Martin, M. L., & Aramendía, E. (2009). Strategic noise map of a major road carried out with two environmental prediction software packages. Environmental Monitoring and Assessment, 163(1-4), 503-513. doi:10.1007/s10661-009-0853-5Garg, N., & Maji, S. (2014). A critical review of principal traffic noise models: Strategies and implications. Environmental Impact Assessment Review, 46, 68-81. doi:10.1016/j.eiar.2014.02.001Steele, C. (2001). A critical review of some traffic noise prediction models. Applied Acoustics, 62(3), 271-287. doi:10.1016/s0003-682x(00)00030-xLi, B., Tao, S., Dawson, R. W., Cao, J., & Lam, K. (2002). A GIS based road traffic noise prediction model. Applied Acoustics, 63(6), 679-691. doi:10.1016/s0003-682x(01)00066-4VAN LEEUWEN, H. J. A. (2000). RAILWAY NOISE PREDICTION MODELS: A COMPARISON. Journal of Sound and Vibration, 231(3), 975-987. doi:10.1006/jsvi.1999.2570Lui, W. K., Li, K. M., Ng, P. L., & Frommer, G. H. (2006). A comparative study of different numerical models for predicting train noise in high-rise cities. Applied Acoustics, 67(5), 432-449. doi:10.1016/j.apacoust.2005.08.005Van Leeuwen, J. J. A. (1996). NOISE PREDICTIONS MODELS TO DETERMINE THE EFFECT OF BARRIERS PLACED ALONGSIDE RAILWAY LINES. Journal of Sound and Vibration, 193(1), 269-276. doi:10.1006/jsvi.1996.0267Oerlemans, S., & Schepers, J. G. (2009). Prediction of Wind Turbine Noise and Validation against Experiment. International Journal of Aeroacoustics, 8(6), 555-584. doi:10.1260/147547209789141489Tadamasa, A., & Zangeneh, M. (2011). Numerical prediction of wind turbine noise. Renewable Energy, 36(7), 1902-1912. doi:10.1016/j.renene.2010.11.036Maisonneuve, N., Stevens, M., & Ochab, B. (2010). Participatory noise pollution monitoring using mobile phones. Information Polity, 15(1,2), 51-71. doi:10.3233/ip-2010-0200Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Peckens, C., Porter, C., & Rink, T. (2018). Wireless Sensor Networks for Long-Term Monitoring of Urban Noise. Sensors, 18(9), 3161. doi:10.3390/s18093161Alías, F., & Alsina-Pagès, R. M. (2019). Review of Wireless Acoustic Sensor Networks for Environmental Noise Monitoring in Smart Cities. Journal of Sensors, 2019, 1-13. doi:10.1155/2019/7634860Mydlarz, C., Salamon, J., & Bello, J. P. (2017). The implementation of low-cost urban acoustic monitoring devices. Applied Acoustics, 117, 207-218. doi:10.1016/j.apacoust.2016.06.010Navarro, J. M., Tomas-Gabarron, J. B., & Escolano, J. (2017). A Big Data Framework for Urban Noise Analysis and Management in Smart Cities. Acta Acustica united with Acustica, 103(4), 552-560. doi:10.3813/aaa.919084Längkvist, M., Karlsson, L., & Loutfi, A. (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42, 11-24. doi:10.1016/j.patrec.2014.01.008Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8(1). doi:10.1038/s41598-018-24271-9Kim, H.-G., & Kim, J. Y. (2017). Environmental sound event detection in wireless acoustic sensor networks for home telemonitoring. China Communications, 14(9), 1-10. doi:10.1109/cc.2017.8068759Luque, A., Romero-Lemos, J., Carrasco, A., & Barbancho, J. (2018). Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks. Sensors, 18(8), 2465. doi:10.3390/s18082465Zhang, Y., Fu, Y., & Wang, R. (2018). Collaborative representation based classification for vehicle recognition in acoustic sensor networks. Journal of Computational Methods in Sciences and Engineering, 18(2), 349-358. doi:10.3233/jcm-180794Cobos, M., Perez-Solano, J. J., Felici-Castell, S., Segura, J., & Navarro, J. M. (2014). Cumulative-Sum-Based Localization of Sound Events in Low-Cost Wireless Acoustic Sensor Networks. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1792-1802. doi:10.1109/taslp.2014.2351132Sevillano, X., Socoró, J. C., Alías, F., Bellucci, P., Peruzzi, L., Radaelli, S., … Zambon, G. (2016). DYNAMAP – Development of low cost sensors networks for real time noise mapping. Noise Mapping, 3(1). doi:10.1515/noise-2016-0013Segura-Garcia, J., Navarro-Ruiz, J., Perez-Solano, J., Montoya-Belmonte, J., Felici-Castell, S., Cobos, M., & Torres-Aranda, A. (2018). Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications. Sensors, 18(3), 690. doi:10.3390/s18030690Bello, J. P., Silva, C., Nov, O., Dubois, R. L., Arora, A., Salamon, J., … Doraiswamy, H. (2019). SONYC. Communications of the ACM, 62(2), 68-77. doi:10.1145/3224204Socoró, J., Alías, F., & Alsina-Pagès, R. (2017). An Anomalous Noise Events Detector for Dynamic Road Traffic Noise Mapping in Real-Life Urban and Suburban Environments. Sensors, 17(10), 2323. doi:10.3390/s17102323Yu, L., & Kang, J. (2009). Modeling subjective evaluation of soundscape quality in urban open spaces: An artificial neural network approach. The Journal of the Acoustical Society of America, 126(3), 1163-1174. doi:10.1121/1.3183377Lopez-Ballester, J., Pastor-Aparicio, A., Segura-Garcia, J., Felici-Castell, S., & Cobos, M. (2019). Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks. Applied Sciences, 9(15), 3136. doi:10.3390/app9153136Mansourkhaki, A., Berangi, M., Haghiri, M., & Haghani, M. (2018). A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS. Journal of Environmental Engineering and Landscape Management, 26(2), 88-97. doi:10.3846/16486897.2017.1356327Pedersen, K., Transtrum, M. K., Gee, K. L., Butler, B. A., James, M. M., & Salton, A. R. (2018). Machine learning-based ensemble model predictions of outdoor ambient sound levels. 2019 International Congress on Ultrasonics. doi:10.1121/2.0001056Torija, A. J., Ruiz, D. P., & Ramos-Ridao, A. F. (2012). Use of back-propagation neural networks to predict both level and temporal-spectral composition of sound pressure in urban sound environments. Building and Environment, 52, 45-56. doi:10.1016/j.buildenv.2011.12.024Garg, N., Soni, K., Saxena, T. K., & Maji, S. (2015). Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution. Noise Control Engineering Journal, 63(2), 182-194. doi:10.3397/1/376317Tong, W., Li, L., Zhou, X., Hamilton, A., & Zhang, K. (2019). Deep learning PM2.5 concentrations with bidirectional LSTM RNN. Air Quality, Atmosphere & Health, 12(4), 411-423. doi:10.1007/s11869-018-0647-4Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., & Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Quality, Atmosphere & Health, 12(8), 899-908. doi:10.1007/s11869-019-00696-7Noriega-Linares, J. E., Rodriguez-Mayol, A., Cobos, M., Segura-Garcia, J., Felici-Castell, S., & Navarro, J. M. (2017). A Wireless Acoustic Array System for Binaural Loudness Evaluation in Cities. IEEE Sensors Journal, 17(21), 7043-7052. doi:10.1109/jsen.2017.2751665Raspberry PI https://www.raspberrypi.orgLegates, D. R., & McCabe, G. J. (1999). Evaluating the use of «goodness-of-fit» Measures in hydrologic and hydroclimatic model validation. 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Large vector spaces of block-symmetric strong linearizations of matrix polynomials

Given a matrix polynomial P(lambda) = Sigma(k)(i=0) lambda(i) A(i) of degree k, where A(i) are n x n matrices with entries in a field F, the development of linearizations of P(lambda) that preserve whatever structure P(lambda) might posses has been a very active area of research in the last decade. Most of the structure-preserving linearizations of P(lambda) discovered so far are based on certain modifications of block-symmetric linearizations. The block-symmetric linearizations of P(lambda) available in the literature fall essentially into two classes: linearizations based on the so-called Fiedler pencils with repetition, which form a finite family, and a vector space of dimension k of block-symmetric pencils, called DL(P), such that most of its pencils are linearizations. One drawback of the pencils in DL(P) is that none of them is a linearization when P(lambda) is singular. In this paper we introduce new vector spaces of block,symmetric pencils, most of which are strong linearizations of P(lambda). The dimensions of these spaces are O(n(2)), which, for n >= root k, are much larger than the dimension of DL(P). When k is odd, many of these vector spaces contain linearizations also when P(lambda) is singular. The coefficients of the block-symmetric pencils in these new spaces can be easily constructed as k x k block-matrices whose n x n blocks are of the form 0, +/-alpha I-n, +/-alpha A(i), or arbitrary n x n matrices, where a is an arbitrary nonzero scalar.The research of F. M. Dopico was partially supported by the Ministerio de Economía y Competitividad of Spain through grant MTM-2012-3254

Anomalous circular polarization profiles in the He I 1083.0 nm multiplet from solar spicules

We report Stokes vector observations of solar spicules and a prominence in the He I 1083 nm multiplet carried out with the Tenerife Infrared Polarimeter. The observations show linear polarization profiles that are produced by scattering processes in the presence of a magnetic field. After a careful data reduction, we demonstrate the existence of extremely asymmetric Stokes V profiles in the spicular material that we are able to model with two magnetic components along the line of sight, and under the presence of atomic orientation in the energy levels that give rise to the multiplet. We discuss some possible scenarios that can generate the atomic orientation in spicules. We stress the importance of spectropolarimetric observations across the limb to distinguish such signals from observational artifacts.Comment: accepted for publication in Ap

Wave propagation and shock formation in different magnetic structures

Velocity oscillations "measured" simultaneously at the photosphere and the chromosphere -from time series of spectropolarimetric data in the 10830 A region- of different solar magnetic features allow us to study the properties of wave propagation as a function of the magnetic flux of the structure (i.e. two different-sized sunspots, a tiny pore and a facular region). While photospheric oscillations have similar characteristics everywhere, oscillations measured at chromospheric heights show different amplitudes, frequencies and stages of shock development depending on the observed magnetic feature. The analysis of the power and the phase spectra, together with simple theoretical modeling, lead to a series of results concerning wave propagation within the range of heights of this study. We find that, while the atmospheric cut-off frequency and the propagation properties of the different oscillating modes depend on the magnetic feature, in all the cases the power that reaches the high chromosphere above the atmospheric cut-off comes directly from the photosphere by means of linear vertical wave propagation rather than from non-linear interaction of modes.Comment: Accepted for publication in The Astrophysical Journal. 29 pages, 9 figures, 12pt, preprin

The microbiome of the uropygial secretion in hoopoes is shaped along the nesting phase

Microbial symbiont acquisition by hosts may determine the effectiveness of the mutualistic relationships. A mix of vertical and horizontal transmission may be advantageous for hosts by allowing plastic changes of microbial communities depending on environmental conditions. Plasticity is well known for gut microbiota but is poorly understood for other symbionts of wild animals. We here explore the importance of environmental conditions experienced by nestling hoopoes (Upupa epops) during the late nesting phase determining microbiota in their uropygial gland. In cross-fostering experiments of 8 days old nestlings, “sibling-sibling” and “mother-offspring” comparisons were used to explore whether the bacterial community naturally established in the uropygial gland of nestlings could change depending on experimental environmental conditions (i.e., new nest environment). We found that the final microbiome of nestlings was mainly explained by nest of origin. Moreover, cross-fostered nestlings were more similar to their siblings and mothers than to their stepsiblings and stepmothers. We also detected a significant effect of nest of rearing, suggesting that nestling hoopoes acquire most bacterial symbionts during the first days of life but that the microbiome is dynamic and can be modified along the nestling period depending on environmental conditions. Estimated effects of nest of rearing, but also most of those of nest of origin are associated to environmental characteristics of nests, which are extended phenotypes of parents. Thus, natural selection may favor the acquisition of appropriated microbial symbionts for particular environmental conditions found in nests.Support by funding was provided by Spanish Ministerio de Economía y Competitividad, European funds (FEDER) (CGL2013-48193-C3-1-P, CGL2013-48193-C3-2-P), and Junta de Andalucía (P09-RNM-4557). AM-G had a predoctoral grant from the Junta de Andalucía (P09-RNM-4557).Peer reviewe

Influence of polysaccharide commercial product addition on volatile composition of white sparkling wines

Póster presentado en las XI Carbohydrate Symposium (XI Jornadas de Carbohidratos), celebradas en Logroño del 28 al 30 de mayo de 2014.Peer Reviewe

Un texto de 1911 sobre la Enseñanza de las Ciencias en la Escuela Normal de Badajoz

Se presenta un texto de Esteban Blanco y Alcántara, profesor y director de la Escuela Normal de Badajoz, publicado en Badajoz en el año 1911. El texto contiene el programa de la asignatura Ciencias Físicas y Naturales, así como comentarios y materiales adicionales. La obra es brevemente comentada.A text of Esteban Blanco y Alcántara, professor and director of " Escuela Normal de Badajoz", published at Badajoz in the year 1911 is showed. lt contains the progmm of the course "Physics and Natural Sciences". The work is briefly analysed.peerReviewe