Enseñar geografía en la educación secundaria : nuevos objetivos, nuevas competencias. Un estudio de caso

La necesidad creciente de una educación que forme ciudadanos con opinión propia y capaces de actuar ante los fenómenos y problemas de su sociedad debería plantear a los docentes una serie de cuestiones acerca de su profesión y del papel de las enseñanzas que imparten: ¿cómo adquieren los alumnos las competencias necesarias? En el caso de la geografía, pueden añadirse unos interrogantes más: ¿Existe un lugar específico para la geografía en la educación secundaria? ¿Están los profesores formados para hacer de la enseñanza de la geografía este saber útil que la sociedad demanda? Este trabajo analiza un estudio sobre los perfiles profesionales de los profesores de geografía (formación científica y profesional, preferencias temáticas, uso de recursos docentes utilizados en el desarrollo de las clases) y sus puntos de vista sobre la función educativa de la geografía en el curriculum escolar. El material de análisis se obtuvo mediante las respuestas a un cuestionario dadas por 54 profesores de geografía (ESO y bachillerato) de centros públicos y concertados de la región metropolitana de Barcelona. Los resultados fueron posteriormente contrastados y matizados en una entrevista a cinco profesores de secundaria no participantes en el estudioLately, there is an increasing social need to educate competent citizens able to act and react in front of their society problems and questions. This fact should raise to the teachers, questions not only about their own profession but also on the role of the subjects they teach. In the case of Geography, the main question shoud be how pupils get the needed competences, but also: is there a specific place for Geography in secondary education?; are the teachers equipped to make their subject the useful knowledge which our society is demanding? This paper presents a study on Geography teachers professional profiles scientific and professional training, main geographical focus, use of teaching resources) and also on their points of view about the educational role of Geography in the shool curriculum. The research was based on the opinions of 54 secondary teachers of Geography, from private and public schools in the metropolitan region of Barcelona. The results were analysed and discussed with a group of five teachers not included in the stud

Calibration of an agent-based simulation model to the data of women infected by Human Papillomavirus with uncertainty

[EN] Recently, the transmission dynamics of the Human Papillomavirus (HPV) has been studied. In previous works, we have designed and implemented a computational model (agent-based simulation model) where the contagion of the HPV is described on a network of lifetime sexual partners. The run of a single simulation of this computational model, composed of a network with 500 000 nodes, takes about one hour and a half. In addition to set an adequate model, finding out the model parameters that best fit the proposed model to the available data of prevalence is a crucial goal. Taking into account that the necessary number of simulations to perform the calibration of the model may be very high, the aforementioned goal may become unaffordable. In this paper, we present a procedure to fit the proposed HPV model to the available data and the design of an asynchronous version of the Particle Swarm Optimization (PSO) algorithm adapted to the distributed computing environment. In the process, the number of particles used in PSO should be set carefully looking for a compromise between quality of the solutions and computation time. Another feature of the procedure presented here is that we want to capture the intrinsic uncertainty in the data (data come from a survey) when calibrating the model. To do so, we also propose the design of an algorithm to select the model parameter sets obtained during the calibration that best capture the data uncertainty.This work has been supported by the Spanish Ministerio de Economia y Competitividad grants MTM2017-89664-P, TIN2014-54806-R and RTI2018-095180-B-I00, Grants Y2018/NMT-4668 (Micro-Stres-MAP-CM) and GenObIA-CM (S2017/BMD-3773) financed by the Community of Madrid, Spain and co-financed with EU Structural Funds, Spain, and by GLENO project financed by Fundacion Eugenio Rodriguez Pascual, Spain.Villanueva Micó, RJ.; Hidalgo, J.; Cervigon, C.; Villanueva-Oller, J.; Cortés, J. (2019). Calibration of an agent-based simulation model to the data of women infected by Human Papillomavirus with uncertainty. Applied Soft Computing. 80:546-556. https://doi.org/10.1016/j.asoc.2019.04.015S5465568

The effect of the Spanish Law of Political Parties (LPP) on the attitude of the Basque Country population towards ETA: A dynamic modelling approach

In June 2002, the Spanish Government passed the ¿Law of Political Parties¿ (LPP) with the aim, among others, of preventing parties giving political support to terrorist organizations. This law affected the Basque nationalist party ¿Batasuna¿, due to its proved relation with ETA. In this paper, taking data from the Euskobarometro (Basque Country survey) related to the attitude of the Basque population towards ETA, we propose a dynamic model for the pre-LPP scenario. This model will be extrapolated to the future in order to predict what would have happened to the attitude of the Basque population if the law had not been passed. These model predictions will be compared to post-LPP data from the Euskobarometro using a bootstrapping approach in order to quantify the effect of the LPP on the attitude of Basque Country population towards ETA.Peco Yeste, M.; Santonja, F.; Tarazona Tornero, AC.; Villanueva Micó, RJ.; Villanueva Oller, FJ. (2013). The effect of the Spanish Law of Political Parties (LPP) on the attitude of the Basque Country population towards ETA: A dynamic modelling approach. Mathematical and Computer Modelling. 1-7. doi:10.1016/j.mcm.2011.11.007S1

Intelligent energy storage management trade-off system applied to Deep Learning predictions

The control of the electrical power supply is one of the key bases to reach the sustainable development goals set by United Nations. The achievement of these objectives encourages a dual strategy of creation and diffusion of renewable energies and other technologies of zero emission. Thus, meet the emerging necessities require, inevitably, a significant transformation of the building sector to improve the design of the electrical infrastructure. This improvement should be linked to advanced techniques that allows the identification of complex patterns in large amount of data, such as Deep Learning ones, in order to mitigate potential uncertainties. Accurate electricity and energy supply prediction models, in combination with storage systems will be reflected directly in efficiency improvements in buildings. In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique. Specifically, Deep Reinforcement Learning is applied using the Deep Q-Learning agent. Furthermore, the accuracy of the predicted variables is measured by means of normalized Mean Bias Error (nMBE), and normalized Root Mean Squared Error (nRMSE). The methodologies developed are validated in an existing building, the School of Mining and Energy Engineering located on the Campus of the University of Vigo.Agencia Estatal de Investigación | Ref. TED2021-130677B-I00Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Machine learning and deep learning models applied to photovoltaic production forecasting

The increasing trend in energy demand is higher than the one from renewable generation, in the coming years. One of the greatest sources of consumption are buildings. The energy management of a building by means of the production of photovoltaic energy in situ is a common alternative to improve sustainability in this sector. An efficient trade-off of the photovoltaic source in the fields of Zero Energy Buildings (ZEB), nearly Zero Energy Buildings (nZEB) or MicroGrids (MG) requires an accurate forecast of photovoltaic production. These systems constantly generate data that are not used. Artificial Intelligence methods can take advantage of this missing information and provide accurate forecasts in real time. Thus, in this manuscript a comparative analysis is carried out to determine the most appropriate Artificial Intelligence methods to forecast photovoltaic production in buildings. On the one hand, the Machine Learning methods considered are Random Forest (RF), Extreme Gradient Boost (XGBoost), and Support Vector Regressor (SVR). On the other hand, Deep Learning techniques used are Standard Neural Network (SNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). The models are checked with data from a real building. The models are validated using normalized Mean Bias Error (nMBE), normalized Root Mean Squared Error (nRMSE), and the coefficient of variation (R2). Standard deviation is also used in conjunction with these metrics. The results show that the models forecast the test set with errors of less than 2.00% (nMBE) and 7.50% (nRMSE) in the case of considering nights, and 4.00% (nMBE) and 11.50% (nRMSE) if nights are not considered. In both situations, the R2 is greater than 0.85 in all models.Universidade de Vigo | Ref. 00VI 131H 641021

Calibrating a large network model describing the transmission dynamics of the human papillomavirus (HPV) using a Particle Swarm Optimization (PSO) algorithm in a distributed computing environment

[EN] Working in large networks applied to epidemiological-type models has led us to design a simple but e↵ective computed distributed environment to perform a large amount of model simulations in a reasonable time in order to study the behavior of these models and to calibrate them. Finding the model parameters that best fit the available data in the designed distributed computing environment becomes a challenge and it is necessary to implement reliable algorithms for model calibration. In this paper, we have adapted the random PSO algorithm to our distributed computing environment to be applied to the calibration of a Papillomavirus transmission dynamics model on a lifetime sexual partners network. And we have obtained a good fitting saving time and calculations compared with the exhaustive searching strategy we have been using so far.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Ministerio de Economa y Competitividad Grants MTM2013-41765-P and TIN 2014-54806-R.Acedo Rodríguez, L.; Burgos-Simon, C.; Hidalgo, J.; Sánchez-Alonso, V.; Villanueva Micó, RJ.; Villanueva-Oller, J. (2018). Calibrating a large network model describing the transmission dynamics of the human papillomavirus (HPV) using a Particle Swarm Optimization (PSO) algorithm in a distributed computing environment. International Journal of High Performance Computing Applications. 32(5):721-728. https://doi.org/10.1177/1094342017697862S721728325Acedo, L., Lamprianidou, E., Moraño, J.-A., Villanueva-Oller, J., & Villanueva, R.-J. (2015). Firing patterns in a random network cellular automata model of the brain. Physica A: Statistical Mechanics and its Applications, 435, 111-119. doi:10.1016/j.physa.2015.05.017Acedo, L., Moraño, J.-A., Villanueva, R.-J., Villanueva-Oller, J., & Díez-Domingo, J. (2011). Using random networks to study the dynamics of respiratory syncytial virus (RSV) in the Spanish region of Valencia. Mathematical and Computer Modelling, 54(7-8), 1650-1654. doi:10.1016/j.mcm.2010.11.068Castellsagué, X., Iftner, T., Roura, E., Vidart, J. A., Kjaer, S. K., … Bosch, F. X. (2012). Prevalence and genotype distribution of human papillomavirus infection of the cervix in Spain: The CLEOPATRE study. Journal of Medical Virology, 84(6), 947-956. doi:10.1002/jmv.23282Cortés, J.-C., Colmenar, J.-M., Hidalgo, J.-I., Sánchez-Sánchez, A., Santonja, F.-J., & Villanueva, R.-J. (2016). Modeling and predicting the Spanish Bachillerato academic results over the next few years using a random network model. Physica A: Statistical Mechanics and its Applications, 442, 36-49. doi:10.1016/j.physa.2015.08.032Elbasha, E. H., Dasbach, E. J., & Insinga, R. P. (2007). Model for Assessing Human Papillomavirus Vaccination Strategies. Emerging Infectious Diseases, 13(1), 28-41. doi:10.3201/eid1301.060438González-Parra, G., Villanueva, R.-J., Ruiz-Baragaño, J., & Moraño, J.-A. (2015). Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment. Acta Tropica, 143, 29-35. doi:10.1016/j.actatropica.2014.12.008Khemka, N., & Jacob, C. (2010). Exploratory Toolkit for Evolutionary and Swarm-Based Optimization. The Mathematica Journal, 11(3), 376-391. doi:10.3888/tmj.11.3-

Load forecasting with machine learning and deep learning methods

Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.Universidade de Vigo | Ref. 00VI 131H 6410211European Group for territorial cooperation Galicia-North of Portugal (GNP, AECT) through the IACOBUS program of research staysMinisterio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Ministerio de Ciencia, Innovación y Universidades | Ref. TED2021-130677B-I0

Optimizing strategies for meningococcal C disease vaccination in Valencia (Spain)

BackgroundMeningococcal C (MenC) conjugate vaccines have controlled invasive diseases associated with this serogroup in countries where they are included in National Immunization Programs and also in an extensive catch-up program involving subjects up to 20 years of age. Catch-up was important, not only because it prevented disease in adolescents and young adults at risk, but also because it decreased transmission of the bacteria, since it was in this age group where the organism was circulating. Our objective is to develop a new vaccination schedule to achieve maximum seroprotection in these groups.MethodsA recent study has provided detailed age-structured information on the seroprotection levels against MenC in Valencia (Spain), where vaccination is routinely scheduled at 2 months and 6 months, with a booster dose at 18 months of age. A complementary catch-up campaign was also carried out in n for children from 12 months to 19 years of age. Statistical analyses of these data have provided an accurate picture on the evolution of seroprotection in the last few years.ResultsAn agent-based model has been developed to study the future evolution of the seroprotection histogram. We have shown that the optimum strategy for achieving high protection levels in all infants, toddlers and adolescents is a change to a 2 months, 12 months and 12 years of age vaccination pattern. If the new schedule were implemented in January 2014, high-risk subjects between 15-19 years of age would have very low seroprotection for the next 6 years, thereby threatening the program.ConclusionsHigh protection levels and a low incidence of meningococcal C disease can be achieved in the future by means of a cost-free change in vaccination program. However, we recommend a new catch-up program simultaneous to the change in regular vaccination program

Random Network Models to Predict the Long-Term Impact of HPV Vaccination on Genital Warts

[EN] The Human papillomaviruses (HPV) vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. This aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore, it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model, we simulated the scenario that occurred in Australia in 2007, where 12¿13 year-old girls were vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, which protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73% in girls with three doses and with coverage rates decreasing with age until 52% for 20¿26 year-olds. A fast 59% reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.We are grateful for the support from Sanofi Pasteur. The authors would also like to thank M. Diaz-Sanchis from the Institut Catala d'Oncologia (ICO) for her useful comments and the data provided on HPV prevalence. We would also like to thank the ICO for the HPV information centre at http://hpvcentre.net.Diez-Domingo, J.; Sánchez-Alonso, V.; Villanueva Micó, RJ.; Acedo Rodríguez, L.; Moraño Fernández, JA.; Villanueva-Oller, J. (2017). Random Network Models to Predict the Long-Term Impact of HPV Vaccination on Genital Warts. Viruses. 9(10). doi:10.3390/v9100300S91