Relaciones entre irritabilidad neonatal y reacciones temperamentales hacia objetos físicos

The aim of this study was to examine the relationship between neonatal irritability and temperamental reactions to physical objects. The sample comprised 53 full-tem infants, from middle-clam families, without any pre or post-natal complications. The Neonatal Behavioral Assessment Scale (NBAS) (Brazelton, 1984) was administered to all infants at 4 days of life. Subsequently, ut three and six months of age, the infants were exposed to a variety of situations involving physical objects in order to examine the temperament at features they express. The results indicated a significant predictive relationship between neonatal irritability and infant's attentiveness and emotional tone, at three and six months of age, when faced with physical objects. These results are discussed in the light of present theoretical orientations on the topic.El propósito de la presente investigación ha sido estudiar la relación existente entre la irritabilidad neonutal y las reacciones temperamentales hacia objetos fisicos. Para ello se utilizó una muestra de 53 niños, nacidos a término, sin complicaciones pre ni posmatales y pertenecientes a un nivel socioeconómico medio. A estos bebés se les administró la Escala para la Evaluación del Comportamiento Neonatal (NBAS) (Brazelton, 1984) a los cuatro días de vida y, posteriormente, a los tres y seis meses de edad, fueron sometidos en el laboratorio a situaciones en las que se enfrentaban a objetos fisicos a fin de que expresaran sus características temperamentales. Los resultados indicaron que existe una relación predictiva significativa entre la irritabilidad neonatal y la atención y el tono emocional mostrado por los niños, tanto a los tres como a los seis meses de edad, en presencia de objetos fisicos. Estos resultados se discuten a la luz de las investigaciones actuales sobre el tema

A novel fuzzy clustering approach to regionalise watersheds with an automatic determination of optimal number of clusters

One of the most important problems faced in hydrology is the estimation of flood magnitudes and frequencies in ungauged basins. Hydrological regionalisation is used to transfer information from gauged watersheds to ungauged watersheds. However, to obtain reliable results, the watersheds involved must have a similar hydrological behaviour. In this study, two different clustering approaches are used and compared to identify the hydrologically homogeneous regions. Fuzzy C-Means algorithm (FCM), which is widely used for regionalisation studies, needs the calculation of cluster validity indices in order to determine the optimal number of clusters. Fuzzy Minimals algorithm (FM), which presents an advantage compared with others fuzzy clustering algorithms, does not need to know a priori the number of clusters, so cluster validity indices are not used. Regional homogeneity test based on L-moments approach is used to check homogeneity of regions identified by both cluster analysis approaches. The validation of the FM algorithm in deriving homogeneous regions for flood frequency analysis is illustrated through its application to data from the watersheds in Alto Genil (South Spain). According to the results, FM algorithm is recommended for identifying the hydrologically homogeneous regions for regional frequency analysis.Ingeniería, Industria y Construcció

Viaductos ferroviarios en aboño

La interferencia con otras infraestructuras de la Conexión en By-Pass con el Ramal Aboño-Sotiello de la línea FEVE Ferrol-Gijón ha precisado la ejecución de dos estructuras con tablero metálico con sección en U y canto variable. El promotor de las obras es la Dirección General de Ferrocarriles, que confió su ejecución a la Constructora OHL, ejerciendo como jefe de obra D. Martín Prados Covarrubias

Using SWAT and Fuzzy TOPSIS to Assess the Impact of Climate Change in the Headwaters of the Segura River Basin (SE Spain)

The Segura River Basin is one of the most water-stressed basins in Mediterranean Europe. If we add to the actual situation that most climate change projections forecast important decreases in water resource availability in the Mediterranean region, the situation will become totally unsustainable. This study assessed the impact of climate change in the headwaters of the Segura River Basin using the Soil and Water Assessment Tool (SWAT) with bias-corrected precipitation and temperature data from two Regional Climate Models (RCMs) for the medium term (2041–2070) and the long term (2071–2100) under two emission scenarios (RCP4.5 and RCP8.5). Bias correction was performed using the distribution mapping approach. The fuzzy TOPSIS technique was applied to rank a set of nine GCM–RCM combinations, choosing the climate models with a higher relative closeness. The study results show that the SWAT performed satisfactorily for both calibration (NSE = 0.80) and validation (NSE = 0.77) periods. Comparing the long-term and baseline (1971–2000) periods, precipitation showed a negative trend between 6% and 32%, whereas projected annual mean temperatures demonstrated an estimated increase of 1.5–3.3 °C. Water resources were estimated to experience a decrease of 2%–54%. These findings provide local water management authorities with very useful information in the face of climate change.Ingeniería, Industria y Construcció

Progreso en el desarrollo de un grupo de niños prematuros y estado de ánimo recordado por sus progenitores

Con el presente trabajo pretendemos analizar si el hecho de que los niños presenten un progreso más adecuado está relacionado con el recuerdo positivo que tienen los progenitores de su estado de ánimo. Los participantes en nuestro estudio fueron 48 niños (25 varones y 23 mujeres) evaluados con las Escalas BSID-II (Bayley, 1993), a las edades de 1m., 6m., 12m., 18m. y 24 meses de edad corregida y 36 meses de edad cronológica, con las que se obtuvo la variable progreso; y sus progenitores, fueron evaluados con la Encuesta sobre el Grado de Satisfacción con el Servicio Prestado de Atención Temprana (Sánchez-Caravaca, 2006), que incluía una serie de cuestiones referentes al recuerdo del estrés vivenciado por los progenitores a lo largo de los 3 primeros años de vida de su hijo. Nuestros resultados indican diferencias entre los datos obtenidos con las madres y los padres. Así, mientras en las madres el estado de ánimo parece asociarse al progreso mental y psicomotor del niño, no parece ocurrir lo mismo con los padres. Estos resultados se discuten a la luz de los trabajos existentes sobre el tema y se analizan sus repercusiones para la elaboración de programas de atención temprana.The current work intends to analyze if the fact that a group of preterm children present a more suitable mental and psychomotor progress is related to the positive memory of state of mind of their parents. The participants were 48 preterm children (25 boys and 23 girls) and their parents. The children were tested at 1, 6, 12, 18, 24 and 36 months using the Bayley Scales of Infant Development (Bayley, 1993), from which the progress variable was calculated. The parents were tested using the Survey about Degree of Satisfaction with the Service rendered in EI (Sánchez-Caravaca, 2006). Our results indicate differences between the data collected with the mothers and the parents. In the mothers seems to be associated if a better memory of state of mind with a greater mental progress at 6 months and also with a greater psychomotor progress to 24m and 36m of age of the children. What it does not happen with the parents. Our results are discussed in the light of some other previous research.peerReviewe

Relaciones entre irritabilidad neonatal y reacciones temperamentales hacia objetos físicos

The aim of this study was to examine the relationship between neonatal irritability and temperamental reactions to physical objects. The sample comprised 53 full-tem infants, from middle-clam families, without any pre or post-natal complications. The Neonatal Behavioral Assessment Scale (NBAS) (Brazelton, 1984) was administered to all infants at 4 days of life. Subsequently, ut three and six months of age, the infants were exposed to a variety of situations involving physical objects in order to examine the temperament at features they express. The results indicated a significant predictive relationship between neonatal irritability and infant's attentiveness and emotional tone, at three and six months of age, when faced with physical objects. These results are discussed in the light of present theoretical orientations on the topic.El propósito de la presente investigación ha sido estudiar la relación existente entre la irritabilidad neonutal y las reacciones temperamentales hacia objetos fisicos. Para ello se utilizó una muestra de 53 niños, nacidos a término, sin complicaciones pre ni posmatales y pertenecientes a un nivel socioeconómico medio. A estos bebés se les administró la Escala para la Evaluación del Comportamiento Neonatal (NBAS) (Brazelton, 1984) a los cuatro días de vida y, posteriormente, a los tres y seis meses de edad, fueron sometidos en el laboratorio a situaciones en las que se enfrentaban a objetos fisicos a fin de que expresaran sus características temperamentales. Los resultados indicaron que existe una relación predictiva significativa entre la irritabilidad neonatal y la atención y el tono emocional mostrado por los niños, tanto a los tres como a los seis meses de edad, en presencia de objetos fisicos. Estos resultados se discuten a la luz de las investigaciones actuales sobre el tema

Multiple frequency response points identification through single asymmetric relay feedback experiment

In this paper a methodology to identify several points of the frequency response of a system using a single experiment is proposed. The identification is performed using the information obtained from an asymmetric relay feedback experiment. The frequency response points that are estimated correspond to the fundamental oscillation frequency induced by the asymmetric relay and its harmonics. The method is easy to implement since it only requires linear algebra operations, but relies on a proper data selection, which is largely studied, to obtain the most accurate results. The proposed method allows a Least Squares formulation, which has also been studied, and presents some benefits in terms of accuracy for certain cases. The presented results are validated experimentally using a practical identification case.This work was supported by Universitat Jaume I, Spain with grant number 18I411-Uji-b2018-39, MINECO, Spain with grant numbers DPI2017-84259-C2-2-R, RTI2018-094665-B-I00 and Ministerio de Ciencia e Innovación, Spain with grant number TEC2015-69155-R and by the State Research Agency, Spain under project PID2020-112658RBI00/10.13039/501100011033. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Cristian R. Rojas under the direction of Editor Alessandro Chiuso

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

[EN] The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models.This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189S114174Pérez-Ruzafa, A., Pérez-Ruzafa, I. M., Newton, A., & Marcos, C. (2019). 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Aquatic Ecology, 42(2), 213-226. doi:10.1007/s10452-008-9180-0EL PAÍS https://elpais.com/elpais/2019/10/22/inenglish/1571743580_215496.htmlGarcía-Ayllón, S. (2017). Integrated management in coastal lagoons of highly complexity environments: Resilience comparative analysis for three case-studies. Ocean & Coastal Management, 143, 16-25. doi:10.1016/j.ocecoaman.2016.10.007Garcia-Ayllon, S. (2018). The Integrated Territorial Investment (ITI) of the Mar Menor as a model for the future in the comprehensive management of enclosed coastal seas. Ocean & Coastal Management, 166, 82-97. doi:10.1016/j.ocecoaman.2018.05.004Pérez-Ruzafa, A., Campillo, S., Fernández-Palacios, J. M., García-Lacunza, A., García-Oliva, M., Ibañez, H., … Marcos, C. (2019). Long-Term Dynamic in Nutrients, Chlorophyll a, and Water Quality Parameters in a Coastal Lagoon During a Process of Eutrophication for Decades, a Sudden Break and a Relatively Rapid Recovery. Frontiers in Marine Science, 6. doi:10.3389/fmars.2019.00026Iglesias, C., Martínez Torres, J., García Nieto, P. J., Alonso Fernández, J. R., Díaz Muñiz, C., Piñeiro, J. I., & Taboada, J. (2013). Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain. Water Resources Management, 28(2), 319-331. doi:10.1007/s11269-013-0487-9Najah, A., El-Shafie, A., Karim, O. A., & El-Shafie, A. H. (2012). Application of artificial neural networks for water quality prediction. Neural Computing and Applications, 22(S1), 187-201. doi:10.1007/s00521-012-0940-3Li, X., Cheng, Z., Yu, Q., Bai, Y., & Li, C. (2017). Water-Quality Prediction Using Multimodal Support Vector Regression: Case Study of Jialing River, China. Journal of Environmental Engineering, 143(10), 04017070. doi:10.1061/(asce)ee.1943-7870.0001272Su, J., Wang, X., Zhao, S., Chen, B., Li, C., & Yang, Z. (2015). A Structurally Simplified Hybrid Model of Genetic Algorithm and Support Vector Machine for Prediction of Chlorophyll a in Reservoirs. Water, 7(12), 1610-1627. doi:10.3390/w7041610Abba, S. I., Hadi, S. J., & Abdullahi, J. (2017). River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. doi:10.1016/j.procs.2017.11.212Juntunen, P., Liukkonen, M., Pelo, M., Lehtola, M. J., & Hiltunen, Y. (2012). Modelling of Water Quality: An Application to a Water Treatment Process. Applied Computational Intelligence and Soft Computing, 2012, 1-9. doi:10.1155/2012/846321Li, X., Sha, J., & Wang, Z. (2016). A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen. Hydrology Research, 48(5), 1214-1225. doi:10.2166/nh.2016.149Charulatha, G., Srinivasalu, S., Uma Maheswari, O., Venugopal, T., & Giridharan, L. (2017). Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arabian Journal of Geosciences, 10(6). doi:10.1007/s12517-017-2867-6Keller, S., Maier, P., Riese, F., Norra, S., Holbach, A., Börsig, N., … Hinz, S. (2018). Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity. International Journal of Environmental Research and Public Health, 15(9), 1881. doi:10.3390/ijerph15091881Li, X., Sha, J., & Wang, Z.-L. (2017). Chlorophyll-A Prediction of Lakes with Different Water Quality Patterns in China Based on Hybrid Neural Networks. Water, 9(7), 524. doi:10.3390/w9070524Yi, H.-S., Park, S., An, K.-G., & Kwak, K.-C. (2018). Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea. International Journal of Environmental Research and Public Health, 15(10), 2078. doi:10.3390/ijerph15102078Nazeer, M., Bilal, M., Alsahli, M., Shahzad, M., & Waqas, A. (2017). Evaluation of Empirical and Machine Learning Algorithms for Estimation of Coastal Water Quality Parameters. ISPRS International Journal of Geo-Information, 6(11), 360. doi:10.3390/ijgi6110360Erena, Domínguez, Aguado, Soria, & García-Galiano. (2019). Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study. Water, 11(7), 1468. doi:10.3390/w11071468García-Oliva, M., Marcos, C., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Pérez-Ruzafa, A. (2019). Modelling the impact of dredging inlets on the salinity and temperature regimes in coastal lagoons. Ocean & Coastal Management, 180, 104913. doi:10.1016/j.ocecoaman.2019.104913López-Ballesteros, A., Senent-Aparicio, J., Srinivasan, R., & Pérez-Sánchez, J. (2019). Assessing the Impact of Best Management Practices in a Highly Anthropogenic and Ungauged Watershed Using the SWAT Model: A Case Study in the El Beal Watershed (Southeast Spain). Agronomy, 9(10), 576. doi:10.3390/agronomy9100576Senent-Aparicio, J., Pérez-Sánchez, J., García-Aróstegui, J., Bielsa-Artero, A., & Domingo-Pinillos, J. (2015). Evaluating Groundwater Management Sustainability under Limited Data Availability in Semiarid Zones. Water, 7(12), 4305-4322. doi:10.3390/w7084305Navarro, M. C., Pérez-Sirvent, C., Martínez-Sánchez, M. J., Vidal, J., Tovar, P. J., & Bech, J. (2008). Abandoned mine sites as a source of contamination by heavy metals: A case study in a semi-arid zone. Journal of Geochemical Exploration, 96(2-3), 183-193. doi:10.1016/j.gexplo.2007.04.011Conesa, H. M., & Jiménez-Cárceles, F. J. (2007). The Mar Menor lagoon (SE Spain): A singular natural ecosystem threatened by human activities. Marine Pollution Bulletin, 54(7), 839-849. doi:10.1016/j.marpolbul.2007.05.007Domingo-Pinillos, J., Senent-Aparicio, J., García-Aróstegui, J., & Baudron, P. (2018). Long Term Hydrodynamic Effects in a Semi-Arid Mediterranean Multilayer Aquifer: Campo de Cartagena in South-Eastern Spain. Water, 10(10), 1320. doi:10.3390/w10101320Stefanova, A., Hesse, C., & Krysanova, V. (2015). Combined Impacts of Medium Term Socio-Economic Changes and Climate Change on Water Resources in a Managed Mediterranean Catchment. Water, 7(12), 1538-1567. doi:10.3390/w7041538Velasco, J., Lloret, J., Millan, A., Marin, A., Barahona, J., Abellan, P., & Sanchez-Fernandez, D. (2006). Nutrient And Particulate Inputs Into The Mar Menor Lagoon (Se Spain) From An Intensive Agricultural Watershed. Water, Air, and Soil Pollution, 176(1-4), 37-56. doi:10.1007/s11270-006-2859-8García-Oliva, M., Pérez-Ruzafa, Á., Umgiesser, G., McKiver, W., Ghezzo, M., De Pascalis, F., & Marcos, C. (2018). Assessing the Hydrodynamic Response of the Mar Menor Lagoon to Dredging Inlets Interventions through Numerical Modelling. Water, 10(7), 959. doi:10.3390/w10070959Wei, B., Sugiura, N., & Maekawa, T. (2001). Use of artificial neural network in the prediction of algal blooms. Water Research, 35(8), 2022-2028. doi:10.1016/s0043-1354(00)00464-4(2000). Artificial Neural Networks in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering, 5(2), 115-123. doi:10.1061/(asce)1084-0699(2000)5:2(115)Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., & Pulido-Velazquez, D. (2018). A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water, 10(2), 192. doi:10.3390/w10020192Nguyen, V. D., Tan, R. R., Brondial, Y., & Fuchino, T. (2007). Prediction of vapor–liquid equilibrium data for ternary systems using artificial neural networks. Fluid Phase Equilibria, 254(1-2), 188-197. doi:10.1016/j.fluid.2007.03.014Bekkari, N., & Zeddouri, A. (2019). Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant. Management of Environmental Quality: An International Journal, 30(3), 593-608. doi:10.1108/meq-04-2018-0084Zhang, Y., Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. doi:10.1016/j.watres.2019.114888Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017). Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resources Management, 31(9), 2761-2775. doi:10.1007/s11269-017-1660-3Kuhn, M. (2008). Building Predictive Models inRUsing thecaretPackage. 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Implementación de aplicaciones móviles como herramientas de enseñanza aprendizaje en Química Analítica

Depto. de Química AnalíticaFac. de Ciencias QuímicasFALSEsubmitte