Improvement of fire reaction and mould growth resistance of a new bio-based thermal insulation material

In the present paper, the performance of an innovative thermal insulation rigid board is evaluated in terms of fire behaviour and fungal resistance. The board is based on vegetal pith and a natural gum (corn pith and sodium alginate) and it is completely compostable. This new composite was developed in previous work. Here boric acid, aluminium hydroxide and ammonium polyphosphate are used as fire retardants and montan wax, acetic acid and lactic acid are used as water repellent and fungicides respectively. Interactions between these different treatments is investigated. Both flaming and smouldering combustion processes of the different formulations are evaluated by small-scale techniques which include pyrolysis microcalorimetry and thermogravimetric analysis. A medium-scale device is also designed in order to study the impact of the different additives to the smouldering kinetics. Fire behaviour tests show that good improvement is obtained, both in flaming and smouldering combustion when boric acid is added. Although smouldering is not avoided in any case, the addition of 8% of boric acid or aluminium hydroxide slows down the speed of combustion propagation. The effect of the different additives on the moisture content and mould growth at 97% RH and 27 °C is analysed. Under such severe conditions none of the additives is able to prevent mould growth, with the exception of boric acid. None or marginal mould growth was observed on samples containing 8% of boric acid although moisture content was higher than the other cases.Peer ReviewedPreprin

Estilos de aprendizaje, estrategias de aprendizaje y hábitos de estudio de estudiantes universitarios estudio descriptivo a realizarse con estudiantes inscritos en la Facultad de Ciencias Sociales de la Universidad Francisco Gavidia en las Carreras de Licenciatura en Psicología y Licenciatura en Ciencias de la Educación a nivel del segundo año en el ciclo II-2012

El interés por conocer los estilos de aprendizaje, estrategias de aprendizaje y hábitos de estudio de los estudiantes viene desde hace mucho tiempo; no obstante en la actualidad esta cuestión adquiere una mayor relevancia, en gran medida por los avances de la educación, así como las altas tasas de fracaso escolar. De hecho, diversas investigaciones se orientan a conocer con profundidad los procesos de aprendizaje y a valorar en qué grado influyen los estilos de aprendizaje, las estrategias de aprendizaje y los hábitos de estudio en el rendimiento escolar. Se tiene la creencia que el estudiante cuando ingresa a la Universidad ya posee estos procesos de aprendizaje lo suficientemente aceptables; sin embargo, la experiencia muestra que un número significativo de estudiantes de enseñanza superior obtienen deficientes resultados, porque no todos hacen frente con éxito a los nuevos desafíos que la Universidad plantea: aumento de exigencia, necesidad creciente de organización de trabajo académico, mayor dedicación al estudio, autonomía, etc

Estudio de la especificidad de vías de insulina/IGF-1 en los patrones de arborización y señalización mediante el uso de virus asociados a adenovirus

Objetivo: Las vías de señalización de insulina y del factor de crecimiento insulínico tipo I(IGF-1) están involucradas en el control de la arborización dendrítica, señalización y crecimientocelular. Varios estudios han demostrado que déficits en estas vías están relacionadoscon trastornos cognitivos, tales como autismo, y con un elevado riesgo de sufrir demencia.Con tal de elucidar el papel exacto que tiene la señalización de insulina cerebral en las funcionesneuronales importantes para los procesos cognitivos, pretendemos silenciar la expresiónde los substratos del receptor de insulina; IRS-1 e IRS-2, los cuales son dos componentesclave en la vía de señalización de Insulina-IGF-1. Diseño experimental y Métodos: Contal de estudiar el papel de IRS-1 e IRS-2 en la arborización dendrítica y en la plasticidadneuronal, generaremos varios virus adeno asociados (AAV) que sean específicos de neurona.Además, estas partículas víricas contendrán específicos shRNA para poder silenciar laexpresión de IRS-1 y/o IRS-2, junto con un gen chivato para comprobar la eficiencia de lainfección del virus. La generación de estas partículas la hemos realizado siguiendo el métodode recombinación Gateway, utilizando un promotor específico de neurona y un shRNAcontra IRS-1/IRS-2 junto con EGFP como gen chivato. Resultados y conclusiones: Hemossido capaces de generar una partícula vírica que consta de un promotor genérico potente(CMV) junto con el gen chivato EGFP. Hemos testado la eficiencia y actividad de esta partículavírica in vivo en varias regiones del cerebro de rata.Objective: The Insulin and insulin-like growth factor 1 (IGF-1) pathway are involved in thenormal control of dendritic arborisation, cell signalling and development. Several studieshave shown that deficits in these pathways are related to cognitive disorders, such as autism,and an increased risk of dementia. To elucidate the specific role of brain insulin signallingin neuronal functions that are relevant for cognitive processes we want to silence thegene expression of the Insulin Receptor Substrate 1, and 2, (IRS-1, IRS-2) two key componentsof the Insulin-IGF-1 pathway. Research Design and Methods: To study the roleof IRS-1 and IRS-2 in dendritic arborisation and neural plasticity we want to generate severaladeno-associated viruses (AAV) that are neuron-specific. Besides these virus willcontain a specific shRNA to silence both IRS-1or IRS-2 and a reporter gene in order to tracktheir infection efficiency. To do so, we have followed the Gateway method for viral particlesgeneration, with a neuron-specific promoter and a shRNA against IRS-1/IRS-2 along withthe enhanced green fluorescent protein (EGFP) as a reporter gene. Results and conclusions:We have been able to generate a viral particle that contains a strong generic promoter(CMV) along with a reporter gene (EGFP). We have tested the efficiency and activityof this viral particle in vivo in several regions of the rat brain

A Comparison of Forecasting Mortality Models Using Resampling Methods

[EN] The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies' profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee-Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.The research of David Atance was supported by a grant (Contrato Predoctoral de Formacion Universitario) from the University of Alcala. This work is partially supported by a grant from the MEIyC (Ministerio de Economia, Industria y Competitividad, Spain project ECO2017-89715-P).Atance, D.; Debón Aucejo, AM.; Navarro, E. (2020). A Comparison of Forecasting Mortality Models Using Resampling Methods. Mathematics. 8(9):1-21. https://doi.org/10.3390/math8091550S12189BOOTH, H., MAINDONALD, J., & SMITH, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325-336. doi:10.1080/00324720215935Brouhns, N., Denuit, M., & Vermunt, J. K. (2002). A Poisson log-bilinear regression approach to the construction of projected lifetables. Insurance: Mathematics and Economics, 31(3), 373-393. doi:10.1016/s0167-6687(02)00185-3Lee, R., & Miller, T. (2001). Evaluating the performance of the lee-carter method for forecasting mortality. Demography, 38(4), 537-549. doi:10.1353/dem.2001.0036Cairns, A. J. G., Blake, D., & Dowd, K. (2006). A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration. Journal of Risk & Insurance, 73(4), 687-718. doi:10.1111/j.1539-6975.2006.00195.xCairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., Epstein, D., Ong, A., & Balevich, I. (2009). A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States. North American Actuarial Journal, 13(1), 1-35. doi:10.1080/10920277.2009.10597538Renshaw, A. E., & Haberman, S. (2003). Lee–Carter mortality forecasting with age-specific enhancement. Insurance: Mathematics and Economics, 33(2), 255-272. doi:10.1016/s0167-6687(03)00138-0Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee–Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38(3), 556-570. doi:10.1016/j.insmatheco.2005.12.001Hainaut, D. (2018). A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST. ASTIN Bulletin, 48(02), 481-508. doi:10.1017/asb.2017.45Levantesi, S., & Pizzorusso, V. (2019). Application of Machine Learning to Mortality Modeling and Forecasting. Risks, 7(1), 26. doi:10.3390/risks7010026Pascariu, M. D., Lenart, A., & Canudas-Romo, V. (2019). The maximum entropy mortality model: forecasting mortality using statistical moments. Scandinavian Actuarial Journal, 2019(8), 661-685. doi:10.1080/03461238.2019.1596974S̀liwka, P., & Socha, L. (2018). A proposition of generalized stochastic Milevsky–Promislov mortality models. Scandinavian Actuarial Journal, 2018(8), 706-726. doi:10.1080/03461238.2018.1431805Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., & Elith, J. (2018). A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sensing of Environment, 208, 145-153. doi:10.1016/j.rse.2018.02.026Molinaro, A. M., Simon, R., & Pfeiffer, R. M. (2005). Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21(15), 3301-3307. doi:10.1093/bioinformatics/bti499Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4(none). doi:10.1214/09-ss054Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111-133. doi:10.1111/j.2517-6161.1974.tb00994.xBergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70-83. doi:10.1016/j.csda.2017.11.003Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1). doi:10.1214/aos/1176344552Brouhns, N., Denuit *, M., & Van Keilegom, I. (2005). Bootstrapping the Poisson log-bilinear model for mortality forecasting. Scandinavian Actuarial Journal, 2005(3), 212-224. doi:10.1080/03461230510009754D’Amato, V., Haberman, S., Piscopo, G., & Russolillo, M. (2012). Modelling dependent data for longevity projections. Insurance: Mathematics and Economics, 51(3), 694-701. doi:10.1016/j.insmatheco.2012.09.008Debón, A., Martínez-Ruiz, F., & Montes, F. (2012). Temporal Evolution of Mortality Indicators. North American Actuarial Journal, 16(3), 364-377. doi:10.1080/10920277.2012.10590647Debón, A., Montes, F., Mateu, J., Porcu, E., & Bevilacqua, M. (2008). Modelling residuals dependence in dynamic life tables: A geostatistical approach. Computational Statistics & Data Analysis, 52(6), 3128-3147. doi:10.1016/j.csda.2007.08.006Koissi, M.-C., Shapiro, A. F., & Högnäs, G. (2006). Evaluating and extending the Lee–Carter model for mortality forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38(1), 1-20. doi:10.1016/j.insmatheco.2005.06.008Liu, X., & Braun, W. J. (2010). Investigating Mortality Uncertainty Using the Block Bootstrap. Journal of Probability and Statistics, 2010, 1-15. doi:10.1155/2010/813583Härdle, W., Horowitz, J., & Kreiss, J. (2003). Bootstrap Methods for Time Series. International Statistical Review, 71(2), 435-459. doi:10.1111/j.1751-5823.2003.tb00485.xBergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213. doi:10.1016/j.ins.2011.12.028Booth, H., Hyndman, R. J., Tickle, L., & de Jong, P. (2006). Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions. Demographic Research, 15, 289-310. doi:10.4054/demres.2006.15.9Delwarde, A., Denuit, M., & Eilers, P. (2007). Smoothing the Lee–Carter and Poisson log-bilinear models for mortality forecasting. Statistical Modelling, 7(1), 29-48. doi:10.1177/1471082x0600700103Debón, A., Montes, F., & Puig, F. (2008). Modelling and forecasting mortality in Spain. European Journal of Operational Research, 189(3), 624-637. doi:10.1016/j.ejor.2006.07.050Currie, I. D., Durban, M., & Eilers, P. H. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4(4), 279-298. doi:10.1191/1471082x04st080oaChen, K., Liao, J., Shang, X., & Li, J. S.-H. (2009). «A Quantitative Comparison of Stochastic Mortality Models Using Data from England and Wales and the United States,» Andrew J. G. Cairns, David Blake, Kevin Dowd, Guy D. Coughlan, David Epstein, Alen Ong, and Igor Balevich, Vol. 13, No. 1, 2009. North American Actuarial Journal, 13(4), 514-520. doi:10.1080/10920277.2009.10597572Plat, R. (2009). On stochastic mortality modeling. Insurance: Mathematics and Economics, 45(3), 393-404. doi:10.1016/j.insmatheco.2009.08.006Debón, A., Martínez-Ruiz, F., & Montes, F. (2010). A geostatistical approach for dynamic life tables: The effect of mortality on remaining lifetime and annuities. Insurance: Mathematics and Economics, 47(3), 327-336. doi:10.1016/j.insmatheco.2010.07.007Yang, S. S., Yue, J. C., & Huang, H.-C. (2010). Modeling longevity risks using a principal component approach: A comparison with existing stochastic mortality models. Insurance: Mathematics and Economics, 46(1), 254-270. doi:10.1016/j.insmatheco.2009.09.013Haberman, S., & Renshaw, A. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35-55. doi:10.1016/j.insmatheco.2010.09.003Mitchell, D., Brockett, P., Mendoza-Arriaga, R., & Muthuraman, K. (2013). Modeling and forecasting mortality rates. Insurance: Mathematics and Economics, 52(2), 275-285. doi:10.1016/j.insmatheco.2013.01.002Danesi, I. L., Haberman, S., & Millossovich, P. (2015). Forecasting mortality in subpopulations using Lee–Carter type models: A comparison. Insurance: Mathematics and Economics, 62, 151-161. doi:10.1016/j.insmatheco.2015.03.010Yang, B., Li, J., & Balasooriya, U. (2014). Cohort extensions of the Poisson common factor model for modelling both genders jointly. Scandinavian Actuarial Journal, 2016(2), 93-112. doi:10.1080/03461238.2014.908411Neves, C., Fernandes, C., & Hoeltgebaum, H. (2017). Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models. Insurance: Mathematics and Economics, 75, 48-57. doi:10.1016/j.insmatheco.2017.04.004University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany)www.mortality.orgHunt, A., & Blake, D. P. (2015). Identifiability in Age/Period/Cohort Mortality Models. SSRN Electronic Journal. doi:10.2139/ssrn.3552213Generalized Nonlinear Models in R: An Overview of the Gnm Packagehttps://cran.r-project.org/package=gnmLachenbruch, P. A., & Mickey, M. R. (1968). Estimation of Error Rates in Discriminant Analysis. Technometrics, 10(1), 1-11. doi:10.1080/00401706.1968.10490530Tashman, L. J. (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. International Journal of Forecasting, 16(4), 437-450. doi:10.1016/s0169-2070(00)00065-0Diaz, G., Debón, A., & Giner-Bosch, V. (2018). Mortality forecasting in Colombia from abridged life tables by sex. Genus, 74(1). doi:10.1186/s41118-018-0038-6Ahcan, A., Medved, D., Olivieri, A., & Pitacco, E. (2014). Forecasting mortality for small populations by mixing mortality data. Insurance: Mathematics and Economics, 54, 12-27. doi:10.1016/j.insmatheco.2013.10.013FORSYTHE, A., & HARTICAN, J. A. (1970). Efficiency of confidence intervals generated by repeated subsample calculations. Biometrika, 57(3), 629-639. doi:10.1093/biomet/57.3.629BURMAN, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3), 503-514. doi:10.1093/biomet/76.3.503Shao, J. (1993). Linear Model Selection by Cross-validation. Journal of the American Statistical Association, 88(422), 486-494. doi:10.1080/01621459.1993.10476299Li, H., & O’Hare, C. (2019). Mortality Forecasting: How Far Back Should We Look in Time? Risks, 7(1), 22. doi:10.3390/risks7010022Breiman, L., & Spector, P. (1992). Submodel Selection and Evaluation in Regression. The X-Random Case. International Statistical Review / Revue Internationale de Statistique, 60(3), 291. doi:10.2307/1403680Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Schwarz, G. (1978). Estimating the Dimension of a Model. The Annals of Statistics, 6(2). doi:10.1214/aos/1176344136Hunt, A., & Blake, D. (2014). A General Procedure for Constructing Mortality Models. North American Actuarial Journal, 18(1), 116-138. doi:10.1080/10920277.2013.852963Moritz, S., & Bartz-Beielstein, T. (2017). imputeTS: Time Series Missing Value Imputation in R. The R Journal, 9(1), 207. doi:10.32614/rj-2017-009Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social Relationships and Mortality Risk: A Meta-analytic Review. PLoS Medicine, 7(7), e1000316. doi:10.1371/journal.pmed.100031

Inteligencia territorial: Conceptualización y avance en el estado de la cuestión. Vínculos posibles con los destinos turísticos

En el documento se presenta la definición, evolución y elementos clave del concepto inteligencia territorial (IT). Así, se muestran las herramientas desarrolladas desde la IT y un estudio de las aplicaciones llevadas a cabo, teniendo en cuenta las herramientas, ubicación y temática. Por el objeto de esta investigación, donde se trata de vincular IT y turismo, se muestran relevantes los casos donde se han aplicado ambos conceptos. La búsqueda de experiencias en turismo hace imprescindible una valorización de las comunidades anfitrionas, y esto es posible a través de la IT.

The human being and the health in the Spanish Primary textbooks (6-11 years old). What do they provide to the socio-scientific problem of the nutrition in the early childhood?

En el presente trabajo se hace un estudio de la forma en la que se presentan en los libros de texto españoles de Educación Primaria los contenidos relativos a la alimentación en la primera infancia (API), y a la lactancia materna (LM). Bajo la hipótesis de que los contenidos tratados serían escasos o bien introducirían concepciones alternativas, la intención es i) determinar la presencia de estos contenidos relativos a la salud y a los mamíferos, asociados a los bloques 2 y 3 del currículo (salud y seres vivos); ii) detectar la presencia de contenidos que promuevan la generación o mantenimiento de concepciones alternativas sobre la LM como API. Pretendemos, de esta forma, contribuir a la investigación en la Didáctica de las ciencias con un trabajo sobre un ámbito poco estudiado, a pesar de su relevancia. Los resultados arrojan una escasa presencia de contenido escolar deseable de ese tema, así como una elevada representación de imágenes que aportan información similar a las concepciones alternativas existentes en el conocimiento cotidiano. De esta forma, los textos analizados pueden contribuir a la ausencia de aprendizaje y a la promoción de conocimiento no deseable en relación a los contenidos sobre alimentación infantil.This paper studies the presence of contents related to early infant feeding (API), breastfeeding (LM), in Primary Education textbooks. Our hypothesis is that we would find limited contents and misconceptions. The aim is i) to establish the presence or absence of contents related to health and mammals, included in the school curriculum: Health and living beings; ii) detect contents promoting or reinforcing misconceptions about API. Our purpose is to contribute with this research to a field that has been little explored in Science Education, despite its relevance. Actually, we have found a limited presence of proper school contents and, furthermore, a large amount of images containing misconceptions. So we have concluded that this analysed textbooks could promote the absence of learning and the misconceptions related to the early infant feeding