Relationships between texture and water holding capacity, in the range pF 4,2-6,0, in Western Andalusia soils

El presente trabajo se ocupa del estudio de la relación entre la textura y la retención de humedad en el margen de pF 4,2 a 6,0 (agua no utilizable) para suelos tipos de Andalucía Occidental. El análisis de los resultados que se exponen muestra una fuerte dependencia del contenido de humedad y la proporción de elementos finos. Para el ámbito de pF entre 4,2 y 4,6 es la fracción <0.02 mm la determinante de la retención de humedad mientras que para pF de 4,6 a 6,0, por el contrario, es la fracción <0,002 mm la que controla el almacenamiento de agua. A partir de las relaciones encontradas, conocida la composición granulométrica de un suelo de esta región, se puede estimar su contenido de humedad a un pF determinado dentro del margen estudiado.The present paper deals with the relation between texture and moisture retention in the range pF 4.2-6.0 (non available water) for typical soils in Western Andalusia. The analysis of results shows that moisture retention is highly dependent on the content of line particles. For pF values between 4.2 and 4.6, retention is controlled by the fraction of particles <0.02 mm, whereas for the range 4.6-6.0, particles <0.002 mm are predominant. The relations found are useful to deduce the moisture retention of similar soils in our regiin from mechanical analysis data ...Die vorliegende Arbeít febasst sich mit der Bestimmung der Beziehungen zwischen der Textur auf der einen Seite und dem Wassergehalt bei pF von 4.2 bis 6,0 (Tates Wasser) auf der anderen Seite, der Böden van West Andalusien. Die Regressionanalyse der Ergebnissen zeigen einen starken Zusammenhang zwischen dem Wassergehalt und der feinsten Kornfraktionen der Böden. Im pF-Bereich von 4,2 bis 6,0 wird der Wassergehalt bei Kornfraktion < 0,02 mm kontroliert, wahrend fürden pF-Bereich 4,6-6,0 wird der Wassergehalt bei Kornfraktion < 0,002 mm beeinflusst. Wenn man die Ergebnisse der Schlämmanalyse des Bodens dieser Gebiet hat, kann der Wassergehalt bei verschiedener pF-Werte im Bereich 4,2-6,0 durch die gefundene Korrelatione ermittelt werden

Physical and chemical properties in relation to soil porosity: The influence of the natural wetting-drying cycle. III. Porosity and clay fraction

En el presente trabajo se presentan y discuten los resultados del análisis mineralógico de las arcillas de ocho perfiles patrones representativos de diversos suelos de interés agronómico. La composición mineralógica se estudia en relación con diversas fracciones de porosidad.In the present paper results from day mineralogical analyses of eight representative agronomically useful soil pro files are presented and discussed. Soil clay content and mineralogical composition are studied in relation to various soil pore-size ranges

Un modelo de formación permanente del entrenador de fútbol

El artículo describe los pormenores de una investigación cualitativa sobre la formación permanente de un grupo de entrenadores de fútbol base de León; así como el diseño, experimentación y evaluación del programa consensuado de formación para este grupo de entrenadores a través de un Seminario de trabajo colaborativo, que si bien sigue las pautas marcadas por sus participantes, también toma como referencia los déficits de formación encontrados en la formación inicial. El modelo de entrenador que proponemos es el de un práctico, reflexivo e investigador de su práctica a través de un trabajo colaborativo con sus compañeros

Un model de formació permanent de l’entrenador de futbol

L’article descriu els detalls d’una investigació qualitativa sobre la formació permanent d’un grup d’entrenadors de futbol base de Lleó, i també el disseny, experimentació i avaluació del programa consensuat de formació per a aquest grup d’entrenadors, a través d’ un Seminari de treball col·laboratiu, que encara que segueix les pautes marcades pels seus participants, també pren com a referència els dèficits de formació trobats en la formació inicial. El model d’entrenador que proposem és el d’un tècnic pràctic, reflexiu i investigador de la pràctica del futbol mitjançant un treball col·laboratiu amb els seus companys

BIM Requirements in the Spanish Public Tender-Analysis of Adoption in Construction Contracts

[EN] The use of Building Information Modeling (BIM) is increasingly widespread within the Architecture, Engineering, Construction & Operations (AECO) sector. BIM allows the construction of a digital scale model of the asset to be built, ensuring the early detection of conflicts and interferences, enabling communication between the different participant agents, and facilitating the processes in the maintenance and management phase. Studies on the subject are many and varied. However, very few works refer to the inclusion of BIM in the public procurement stage, one of the most complex and competitive stages within the asset's life cycle. A bibliographic review has been conducted about the BIM situation in the AECO sector contracts, the existing problems and the possible solutions to implement. In the specific field of public procurement, Spain has made great progress, especially at the regional level. During 2020, a total of 440 tenders with BIM requirements were published, with an investment volume of EUR 752 million, which represents an increase of 230% compared to 2017. The aim of this research is to analyze the Spanish public procurement, highlighting the progress made so far in the implementation of this technology, as well as to develop a proposal of BIM requirements that, in general, could be used as a reference for tenders of the AECO sector in the country. With this objective, a selection of twenty relevant public tenders is made, covering both the building and infrastructure fields. The requirements address areas such as: BIM uses, BIM deliverables, model structure, Level of Development, Common Data Environment, classification systems, standards or quality control.This research was funded by Contract with Cabildo de Tenerife (Spain) and Agreement with City Hall of Santa Cruz de Tenerife (Spain): ULLCT2101; ULLASC2102. The work of Ana Perez-Garcia was funded by the Predoctoral Program for Training of Research Personnel of the Canary Islands Government Agencia Canaria de Investigacion, Innovacion y Sociedad de la Informacion (ACIISI), co-financed with the European Social Fund (contract number TESIS2021010090).Pérez-García, A.; Martín-Dorta, N.; Aranda Domingo, JÁ. (2021). BIM Requirements in the Spanish Public Tender-Analysis of Adoption in Construction Contracts. Buildings. 11(12):594-614. https://doi.org/10.3390/buildings11120594S594614111

KRRT: Knowledge Representation and Reasoning Tutor System

Knowledge Representation & Reasoning (KR&R) is a fundamental topic in Artificial Intelligence. A basic KR language is First– Order Logic (FOL), the most representative logic–based representation language, which is part of almost any introductory AI course. In this work we present KRRT (Knowledge Representation & Reasoning Tutor). KRRT is a Web–based system which main goal is to help the student to learn FOL as a KR&R language.Ministerio de Educación y Ciencia TIN2004–0388

La conformación de la provincia jesuítica de Toledo en torno al generalato de Diego Laínez 1556-1565

The newest Jesuits soon began incardinated in the Iberian territory, home of its founder. As one of the most important territories of Catholic Christianity, soon saw the need to distribute what would soon become a large crown in a number of provinces for a more effective government and evangelization. After the first division, at the time of the Spanish general Lines were the final foundations of penta-provincialization between Portugal, Castile, Aragon, Andalusia and —novelty— Toledo.La novísima Compañía de Jesús empezó pronto a incardinarse en el territorio ibérico, patria de su fundador. Como uno de los territorios más importantes de la Cristiandad católica, pronto se vio la necesidad de distribuir lo que pronto sería una gran corona en una serie de provincias para unos más eficaces gobierno y evangelización. Después de una primera división, en la época del generalato del español Laínez se pusieron las bases de la pentaprovincialización definitiva entre Portugal, Castilla, Aragón, Bética y —la novedad— Toledo

Estudio general de los suelos de Sierra Morena y cuenca alta del Guadalquivir en las provincias de Córdoba y Jaén

109 páginas.-- 1 figura.-- 7 tablas.-- 13 fotos en color de paisajes.-- 15 perfiles de suelos con fotos en color... Trabajo presentado para optar al Premio de Investigación creado por el Monte de Piedad y Caja de Ahorros de Córdoba, en conmemoración del 52 Dia Universal del Ahorro.El presente trabajo ofrece un conocimiento general de los principales suelos existentes en el territorio que abarca, considerando los factores que son responsables de su formación y evolución, y estableciendo su relación con el medio en que se encuentran a través de su localización geográfica y distribución. Conocidas estas principales unidades edáficas en función - de dichos factores y de sus caracteres morfológicos y analíticos, se han ordenado de acuerdo con los últimos sistemas de clasificación de suelos hasta el nivel que los datos disponibles y el índole del estudio lo han permitido. Las unidades estudiadas (Unidades Taxonómicas) se han agrupado en función de una geomorfología y litología común para constituir las unidades cartográficas del mapa general de suelos que se acompaña, formando asociaciones.N

Risk Assessment of Hip Fracture Based on Machine Learning

[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.Galassi, A.; Martín-Guerrero, JD.; Villamor, E.; Monserrat Aranda, C.; Rupérez Moreno, MJ. (2020). Risk Assessment of Hip Fracture Based on Machine Learning. Applied bionics and biomechanics (Online). 2020:1-13. https://doi.org/10.1155/2020/8880786S1132020World Health OrganizationAssessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group1994http://www.who.int/iris/handle/10665/39142, http://apps.who.int//iris/handle/10665/39142Cooper, C., Campion, G., & Melton, L. J. (1992). Hip fractures in the elderly: A world-wide projection. Osteoporosis International, 2(6), 285-289. doi:10.1007/bf01623184El Maghraoui, A., & Roux, C. (2008). DXA scanning in clinical practice. QJM, 101(8), 605-617. doi:10.1093/qjmed/hcn022Testi, D., Viceconti, M., Cappello, A., & Gnudi, S. (2002). Prediction of Hip Fracture Can Be Significantly Improved by a Single Biomedical Indicator. Annals of Biomedical Engineering, 30(6), 801-807. doi:10.1114/1.1495866Nguyen, N. D., Frost, S. A., Center, J. R., Eisman, J. A., & Nguyen, T. V. (2008). Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Pellicer-Valero, O. J., Rupérez, M. J., Martínez-Sanchis, S., & Martín-Guerrero, J. D. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications, 143, 113083. doi:10.1016/j.eswa.2019.113083Martínez-Martínez, F., Rupérez-Moreno, M. J., Martínez-Sober, M., Solves-Llorens, J. A., Lorente, D., Serrano-López, A. J., … Martín-Guerrero, J. D. (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine, 90, 116-124. doi:10.1016/j.compbiomed.2017.09.019Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi:10.7861/futurehosp.6-2-94Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Ho-Le, T. P., Center, J. R., Eisman, J. A., Nguyen, T. V., & Nguyen, H. T. (2017). Prediction of hip fracture in post-menopausal women using artificial neural network approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2017.8037784Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Testi, D., Viceconti, M., Baruffaldi, F., & Cappello, A. (1999). Risk of fracture in elderly patients: a new predictive index based on bone mineral density and finite element analysis. Computer Methods and Programs in Biomedicine, 60(1), 23-33. doi:10.1016/s0169-2607(99)00007-3Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Luo, Y., Ahmed, S., & Leslie, W. D. (2018). Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk. Computer Methods and Programs in Biomedicine, 155, 75-83. doi:10.1016/j.cmpb.2017.11.020Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Ferizi, U., Besser, H., Hysi, P., Jacobs, J., Rajapakse, C. S., Chen, C., … Chang, G. (2018). Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. Journal of Magnetic Resonance Imaging, 49(4), 1029-1038. doi:10.1002/jmri.26280Villamor, E., Monserrat, C., Del Río, L., Romero-Martín, J. A., & Rupérez, M. J. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine, 193, 105484. doi:10.1016/j.cmpb.2020.105484Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Choi, W. J., Cripton, P. A., & Robinovitch, S. N. (2014). Effects of hip abductor muscle forces and knee boundary conditions on femoral neck stresses during simulated falls. Osteoporosis International, 26(1), 291-301. doi:10.1007/s00198-014-2812-4Van den Kroonenberg, A. J., Hayes, W. C., & McMahon, T. A. (1995). Dynamic Models for Sideways Falls From Standing Height. Journal of Biomechanical Engineering, 117(3), 309-318. doi:10.1115/1.2794186Robinovitch, S. N., McMahon, T. A., & Hayes, W. C. (1995). Force attenuation in trochanteric soft tissues during impact from a fall. Journal of Orthopaedic Research, 13(6), 956-962. doi:10.1002/jor.1100130621Dufour, A. B., Roberts, B., Broe, K. E., Kiel, D. P., Bouxsein, M. L., & Hannan, M. T. (2011). The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study. Osteoporosis International, 23(2), 513-520. doi:10.1007/s00198-011-1569-2BowyerK. W.ChawlaN. V.HallL. O.KegelmeyerW. P.SMOTE: synthetic minority over-sampling techniqueCoRRhttps://arxiv.org/abs/1106.181

Development and validation of the Strategic Test of Emotional Intelligence (STEI) in the Spanish population

Introduction and objectives Currently, the assessment of emotional intelligence (EI) ability using performance measures is somewhat limited. Our study thus describes the development and validation of a new performance measure, known as the Strategic Test of Emotional Intelligence (STEI), to assess EI abilities in Spanish samples based on the Mayer and Salovey (1997) model and Situational Judgment Test paradigm. Materials and method Spanish undergraduate students and community participants (N = 504; 64.7% females aged 18–67 years) completed the STEI (consisting of 110 items, 55 of which correspond to the understanding emotions factor and 55 to the managing emotions factor). Different subgroups also completed measures of EI, empathy, personality, and general intelligence. Results The findings indicate appropriate reliability and convergent and discriminant validity with respect to EI, empathy, personality, and intelligence measures. Further, confirmatory factor analysis supported the existence of a two-factor structure composed of the understanding and managing emotions subscales. Cronbach's alpha coefficients were adequate (.82 understanding emotions, .85 managing emotions, and .90 total STEI). Conclusions The STEI could be a promising new measure for assessing EI in Spanish samples, providing a novel tool for researching the construct and enabling the comparison with previous results found in other cultures.Introducción y objetivos En la actualidad, la evaluación de la capacidad de inteligencia emocional (IE) que utiliza medidas de rendimiento es algo limitada. Nuestro estudio describe el desarrollo y la validación de una nueva medida de rendimiento, conocida como Test Estratégico de Inteligencia Emocional (STEI), para evaluar las habilidades de IE en muestras españolas basada en el modelo de Mayer y Salovey (1997) y en el paradigma de Prueba de Juicio Situacional. Materiales y método Estudiantes universitarios, así como muestra de población general (n = 504; 64.7% mujeres; rango de edad de 18 a 67 años) de España completaron el STEI (con un total de 110 ítems, 55 pertenecientes al factor comprensión y 55 al factor manejo de las emociones). Diferentes subgrupos también completaron medidas de IE, empatía, personalidad e inteligencia general. Resultados Los resultados indican una fiabilidad apropiada y una validez convergente y discriminante con respecto a las medidas de IE, empatía, personalidad e inteligencia. Además, el análisis factorial confirmatorio apoyó la existencia de una estructura de dos factores compuesta por las subescalas de comprensión y manejo de las emociones. Los coeficientes alfa de Cronbach fueron adecuados (.82 comprensión emociones, .85 manejo emociones y .90 STEI total). Conclusiones El STEI podría ser una medida nueva y prometedora para evaluar la IE en muestras españolas, proporcionando una herramienta novedosa para investigar el constructo y poder comparar los resultados con los encontrados previamente en otras culturas