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Wind speed forecasting plays a crucial role in reducing the risk of wind power uncertainty, which is vital for power system planning, scheduling, control, and operation. However, it is challenging to obtain accurate wind speed forecasting results since wind speed series contain complex fluctuations. In this paper, a novel wind speed forecasting model is proposed by using a genetic algorithm (GA) and long short-term memory neural network (LSTM), where GA is used for evolving the architectures and hyper-parameters of the LSTM, called EvLSTM, because there is no clear knowledge to determine these crucial parameters. In the proposed EvLSTM forecasting model, a flexible gene encoding strategy, crossover operation, and mutation operation are proposed to describe the different architectures and hyper-parameters of the LSTM during the evolutionary process. In addition, to overcome the weakness of the single method for wind speed forecasting, the ensemble EvLSTM (EnEvLSTM) is proposed by using the no negative constraint theory ensemble learning strategy, whose weight coefficients are determined by the differential evolution algorithm. The proposed EvLSTM and EnEvLSTM forecasting models are evaluated on two real-world wind farms located in Inner Mongolia, China and Sotavento Galicia, Spain. Experimental results on two forecasting horizons demonstrate the superiority of EvLSTM and EnEvLSTM in terms of three performance indices and three statistical tests. [ABSTRACT FROM AUTHOR]

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper methods since they allow minimizing the cardinality of the attribute subset and simultaneously maximizing the predictive capacity of the model. However, in high-dimensional problems, multi-objective evolutionary algorithms for wrapper-type feature selection may require excessive computational time, sometimes impractical, especially when the learning algorithm has a high computational cost, such as deep learning. To address this drawback, in this paper we propose a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection, specially designed to improve generalization error. The proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms to evaluate subsets of attributes. The experiments have been carried out with regression and classification problems with time series data for air quality forecasting in the south-east of Spain and for indoor temperature forecasting in a domotic house. The results demonstrate the superiority of the proposed multi-surrogate assisted method over conventional wrapper methods using the same run times. [ABSTRACT FROM AUTHOR]

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of prediction models while preserving and even improving their precision. Meta-heuristic methods, such as multi-objective evolutionary algorithms, are commonly used as search strategies in feature selection wrapper methods since they allow minimizing the cardinality of the attribute subset and simultaneously maximizing the predictive capacity of the model. However, in high-dimensional problems, multi-objective evolutionary algorithms for wrapper-type feature selection may require excessive computational time, sometimes impractical, especially when the learning algorithm has a high computational cost, such as deep learning. To address this drawback, in this paper we propose a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection, specially designed to improve generalization error. The proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms to evaluate subsets of attributes. The experiments have been carried out with regression and classification problems with time series data for air quality forecasting in the south-east of Spain and for indoor temperature forecasting in a domotic house. The results demonstrate the superiority of the proposed multi-surrogate assisted method over conventional wrapper methods using the same run times. [ABSTRACT FROM AUTHOR]

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production. [ABSTRACT FROM AUTHOR]

• Adulthood obesity in Spain is modeled using official surveys from 1987 to 2017. • A compartmental model based on Itȳ stochastic differential equations is considered. • Excess weight is treated as an infectious disease that spreads through contact. • The parameters are estimated based on moment methods and maximum likelihood. • Comparison with a recent paper that used nonlinear regression and Bayesian inference. Obesity is growing riskily in developed and developing countries. This should pose major concerns for the countries, not only from the health point, but also from the economic perspective. Our case study relies on the excess weight dynamics in Spain. The Spanish National Health Survey (ENSE) 2017 collects the percentage of overweight and obese adults in Spain from the year 1987 to 2017. A recent contribution proposed a nonautonomous compartmental system of ordinary differential equations to calibrate the incidence of excess weight in the Spanish adulthood population. Essentially, three principles were followed: the total adulthood population is time-dependent, the subpopulations interact homogeneously along the country, and excess weight plays the role of an infectious disease that is transmitted through contact by social pressure. Accounting for both data and model errors, frequentist nonlinear regression and Bayesian inference were conducted. The methods agreed well in terms of fit, prediction, bands and sensitivity analysis. In the present paper, the deterministic compartmental system of ordinary differential equations is randomized in a different manner, by employing Itô-type stochastic differential equations. The derivatives of the compartments are perturbed by Gaussian white noise-type pure errors that have a rough and unpredictable structure. From the Euler-Maruyama discretization, several strategies are utilized for estimating the parameters, based on the moments method and maximum likelihood estimation. Comparison is performed numerically by assessing the fit to the data. [ABSTRACT FROM AUTHOR]

• Giving a new model of coronavirus epidemic outbreak considering quarantined individuals. • Optimal controller design. • Parallel control design consisting of antiviral treatment and quarantine strategy. • Evaluate suddenly increased in population as an impulsive epidemic model of SQEIAR. • Comparison of the controller results for three types of diseases (COVID-19, Ebola, and, Influenza). • Evaluation of the controller with system parameters identified using actual data. This paper presents a SEIAR-type model considering quarantined individuals (Q), called SQEIAR model. The dynamic of SQEIAR model is defined by six ordinary differential equations that describe the numbers of Susceptible, Quarantined, Exposed, Infected, Asymptomatic, and Recovered individuals. The goal of this paper is to reduce the size of susceptible, infected, exposed and asymptomatic groups to consequently eradicate the infection by using two actions: the quarantine and the treatment of infected people. To reach this purpose, optimal control theory is presented to control the epidemic model over free terminal optimal time control with an optimal cost. Pontryagin's maximum principle is used to characterize the optimal controls and the optimal final time. Also, an impulsive epidemic model of SQEIAR is considered to deal with the potential suddenly increased in population caused by immigration or travel. Since this model is suitable to describe the COVID-19 pandemic, especial attention is devoted to this case. Thus, numerical simulations are given to prove the accuracy of the theoretical claims and applied to the particular data of this infection. Moreover, numerical computations of the COVID-19 are compared with diseases like Ebola and Influenza. In addition, the controller is evaluated with system parameters identified by using actual data of China. Finally, the controller tuned with the estimated parameters of the Chinese data is applied to the actual data of Spain to compare the quarantine and treatment policies in both countries. [ABSTRACT FROM AUTHOR]

The relationship between environmental degradation and economic growth has become a prominent topic in recent decades, increasing multidisciplinary approaches across a wide range of studies. Analyzing the economy-environment nexus is significant because most countries attempt to limit environmental deterioration while pursuing economic growth. This paper studies the non-linear cross-correlations between economic growth and environmental degradation on a sample of five developed European countries - France, Germany, Spain, Norway, and the United Kingdom. The study is performed using two approaches: multifractal detrended cross-correlation analysis and wavelet-based analysis. According to the empirical tests, there are non-linear cross-correlations between economic growth and environmental degradation in all five countries. For each country, multifractal metrics such as Hurst exponents, scaling exponents, and multifractal spectra are computed to show the significance of all time-series pairs. The strongest multifractal features have been observed in France, Norway, and Spain. Shuffling the series suggests that multifractality in the cross-correlations arises both from a broad probability density function and from different long-term correlations. Wavelets are applied to detrend all data sets before computing the cross-correlation. Moreover, the wavelet coherence is computed as a measure for analyzing interdependencies across time and frequencies. Except for France and Spain, Wavelet-detrended cross-correlation values are considerably lower than cross-correlation values of the raw data sets, which indicates that the interdependence between growth and environmental degradation is rather long-term. In terms of wavelet coherence, results vary among the countries, with France and Spain showing the highest and most persistent coherence values. [ABSTRACT FROM AUTHOR]

Abstract: Photographic supra-projection is a forensic process that aims to identify a missing person from a photograph and a skull found. One of the crucial tasks throughout all this process is the craniofacial superimposition which tries to find a good fit between a 3D model of the skull and the 2D photo of the face. This photographic supra-projection stage is usually carried out manually by forensic anthropologists. It is thus very time consuming and presents several difficulties. In this paper, we aim to demonstrate that real-coded evolutionary algorithms are suitable approaches to tackle craniofacial superimposition. To do so, we first formulate this complex task in forensic identification as a numerical optimization problem. Then, we adapt three different evolutionary algorithms to solve it: two variants of a real-coded genetic algorithm and the state of the art evolution strategy CMA-ES. We also consider an existing binary-coded genetic algorithm as a baseline. Results on several superimposition problems of real-world identification cases solved by the Physical Anthropology lab at the University of Granada (Spain) are considered to test our proposals. [Copyright &y& Elsevier]

Abstract: This paper presents a different approach to tourism research at the regional level. Financial econometric techniques are applied to international tourist arrivals, as well as their volatilities, in the five main tourist regions in Spain, using monthly international tourist arrivals during 1997–2007. Univariate time series models are estimated for the conditional means of monthly international tourist arrivals and their volatilities. The estimated conditional volatility models are GARCH(1,1), GJR(1,1) and EGARCH(1,1). Both the second moment and log-moment conditions are calculated to provide diagnostic checks of the estimated models. The conditional mean estimates are generally statistically adequate, and the inferences are valid. [Copyright &y& Elsevier]

Abstract: This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt–Winters model. Assuming that each of the univariate time series comes from the univariate Holt–Winters model, all of them sharing a common structure, the multivariate Holt–Winters model can be formulated as a traditional multivariate regression model. This formulation facilitates obtaining the posterior distribution of the model parameters, which is not analytically tractable: simulation is needed. An acceptance sampling procedure is used in order to obtain a sample from this posterior distribution. Using Monte Carlo integration the predictive distribution is then approached. The forecasting performance of this procedure is illustrated using the hotel occupancy time series data from three provinces in Spain. [Copyright &y& Elsevier]

A new version of the Fortran program elsepa , which calculates differential and integrated cross sections for elastic scattering of electrons and positrons, is presented. Details of the program and its applications are given in the original paper [ Comput. Phys. Commun. 165 (2005) 157–190]. Dirac phase shifts are now calculated by using the recently published subroutine package radial (Salvat and Fernández-Varea, 2019), which solves the radial wave equation for real or complex central potentials by means of a robust and accurate power-series method. In addition, elastic collisions with atoms in elemental solids are described by using the muffin-tin optical model potential proposed by Bote et al., (2009), which is somewhat more elaborate and flexible than that in the original elsepa code and allows adjusting the absorption potential to give inelastic cross sections in close agreement with empirical data. With the use of the radial subroutines, the work of elsepa is reduced to (1) the definition of the interaction potential and (2) the summation of the partial-wave series of the scattering amplitudes. The structure of the new code has been simplified and stricter criteria for the convergence of the partial-wave series have been adopted. The distribution package includes gnuplot scripts for easy visualization of the calculation results. Program Title: elsepa CPC Library link to program files: https://doi.org/10.17632/w4hm5vymym.1 Licensing provisions: CC BY NC 3.0 Programming language: Fortran 90 Journal reference of previous version: Comput. Phys. Commun. 165 (2005) 157–190 Does the new version supersede the previous version?: Yes Reasons for the new version: This new version offers increased accuracy, and a more flexible modeling of elastic collisions of electrons and positrons with atoms in elemental solids. Summary of revisions: The calculation of phase shifts is now performed by the generic subroutines of the radial package [1]. A more flexible optical-model potential for scattering in solids is included [2]. Stricter criteria for convergence of the partial-wave series are applied. The distribution package has been reorganized, with the numerical database files now placed in a separate directory; scripts for direct visualization of the calculation results with the plotting program gnuplot (http://www.gnuplot.info) are also included. Nature of problem: The code calculates differential cross sections, total cross sections and transport cross sections for single elastic scattering of electrons and positrons by neutral atoms, positive ions and randomly oriented molecules. When the energy of the projectile is less than about 5 MeV, the programs also compute scattering amplitudes and spin polarization functions. Solution method: The effective interaction between the projectile and the target atom is represented by a local central potential that can optionally include an imaginary (absorptive) part to account approximately for the coupling with inelastic channels. For projectiles with kinetic energy less that about 5 MeV, the code performs a conventional relativistic Dirac partial-wave analysis. For higher kinetic energies, where the convergence of the partial-wave series is too slow, approximate factorization methods are used. The programs only admit kinetic energies higher than 5 eV, a practical lower limit that may be changed by editing the source files. Additional comments including restrictions and unusual features: The calculations are based on the static-field approximation. The optional correlation-polarization and inelastic absorption corrections are obtained from approximate, semi-empirical models. elsepa allows considering an absorption potential only for projectiles with energies less than about 1 MeV; for higher energies, the absorption potential is set to zero to prevent the occurrence of numerical instabilities. Calculations for molecules are based on a single-scattering independent-atom approximation. To ensure accuracy of the results for scattering by ions, the electron density of the ion must be supplied by the user; it can be generated, e.g. , by running the program dhfs of the radial package [1]. Acknowledgments We are indebted to Dr John Villarrubia for pointing out various cases of incomplete convergence of the original code. Financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades / Agencia Estatal de Investigación, Spain / European Regional Development Fund , European Union, (project no. RTI2018-098117-B-C22) is gratefully aknowledged. References: [1] F. Salvat, J.M. Fernández-Varea, Comput. Phys. Commun. 240 (2019) 165–177. [2] D. Bote, F. Salvat, A. Jablonski, C.J. Powell, J. Electron. Spectrosc. Rel. Phenom. 175 (2009) 41—54. [ABSTRACT FROM AUTHOR]

• A model for the expansion of COVID-19 in Italy, Spain and the USA is considered. • Predictions on cumulative deaths due to COVID-19 are provided. • The effect of favourable seasonal conditions is assessed in the expansion of COVID-19 in different countries. • The effect of the relaxation of lockdown is assessed in the expansion of COVID- 19 in different countries. It is well-known that the classical SIR model is unable to make accurate predictions on the course of illnesses such as COVID-19. In this paper, we show that the official data released by the authorities of several countries (Italy, Spain and The USA) regarding the expansion of COVID-19 are compatible with a non-autonomous SIR type model with vital dynamics and non-constant population, calibrated according to exponentially decaying infection and death rates. Using this calibration we construct a model whose outcomes for most relevant epidemiological paramenters, such as the number of active cases, cumulative deaths, daily new deaths and daily new cases (among others) fit available real data about the first and successive waves of COVID-19. In addition to this, we also provide predictions on the evolution of this pandemic in Italy and the USA in several plausible scenarios. [ABSTRACT FROM AUTHOR]

• Deterministic and stochastic models are proposed for Covid-19 epidemic. • Basic reproduction number is determined. • Disease extinction and persistence criteria are prescribed. • Case study is done with Spanish Covid-19 epidemic data. • Real-time reproduction number is given. The world has been facing the biggest virological invasion in the form of Covid-19 pandemic since the beginning of the year 2020. In this paper, we consider a deterministic epidemic model of four compartments based on the health status of the populations of a given country to capture the disease progression. A stochastic extension of the deterministic model is further considered to capture the uncertainty or variation observed in the disease transmissibility. In the case of a deterministic system, the disease-free equilibrium will be globally asymptotically stable if the basic reproduction number is less than unity, otherwise, the disease persists. Using Lyapunov functional methods, we prove that the infected population of the stochastic system tends to zero exponentially almost surely if the basic reproduction number is less than unity. The stochastic system has no interior equilibrium, however, its asymptotic solution is shown to fluctuate around the endemic equilibrium of the deterministic system under some parametric restrictions, implying that the infection persists. A case study with the Covid-19 epidemic data of Spain is presented and various analytical results have been demonstrated. The epidemic curve in Spain clearly shows two waves of infection. The first wave was observed during March-April and the second wave started in the middle of July and not completed yet. A real-time reproduction number has been given to illustrate the epidemiological status of Spain throughout the study period. Estimated cumulative numbers of confirmed and death cases are 1,613,626 and 42,899, respectively, with case fatality rate 2.66% till the deadly virus is eliminated from Spain. [ABSTRACT FROM AUTHOR]

• Modeling of adulthood obesity in Spain using surveys from 1987 to 2017. • Compartmental model considering social contacts based on differential equations. • Nonlinear regression is conducted when white noise error is included. • Bayesian inference is performed using the Metropolis algorithm. • Sensitivity analyses highlight the importance of prevention strategies. The excess weight population is growing riskily in Spain. This is confirmed by the Spanish National Health Survey 2017, which gathers the percentage of overweight and obese adults in Spain from 1987 to 2017. In this paper we propose a mathematical model based on differential equations to calibrate the incidence of excess weight in the Spanish adulthood population. The main principle is that fatness is an epidemic, where there are three stages, namely normal weight, overweight and obese, and healthy individuals may become "infected" by social contact. We start with a well-posed deterministic formulation of the model, where the parameters are fitted by minimizing the mean square error objective function. The long-term behavior of the system shows that 37% and 24% of Spanish adults will be overweight and obese in the long run, respectively. Due to the incomplete knowledge of the underlying phenomenon and error measurements, randomness must be incorporated into the model formulation. A white noise error term is thus added to conduct nonlinear regression. The response becomes a stochastic process, whose prediction interval captures the variability of the overweight and obese populations correctly. We go a step beyond by treating the model parameters as random variables. The Bayesian inference method allows for quantifying the propagation of uncertainty in this setting, by running the Metropolis algorithm (both brute-force and Adaptive). The numerical simulations show that it provides similar results to the frequentist analysis. The sensitivity analyses from the different methods agree and suggest that prevention strategies are more important than treatment strategies to control adulthood obesity. Specially, the treatment of the transition from the obese to the overweight stages is the least recommendable for reducing the obesity epidemic. [ABSTRACT FROM AUTHOR]

It seems that we are far from controlling COVID-19 pandemics, and, consequently, returning to a fully normal life. Until an effective vaccine is found, safety measures as the use of face masks, social distancing, washing hands regularly, etc., have to be taken. Also, the use of appropriate antivirals in order to alleviate the symptoms, to control the severity of the illness and to prevent the transmission, could be a good option that we study in this work. In this paper, we propose a computational random network model to study the transmission dynamics of COVID-19 in Spain. Once the model has been calibrated and validated, we use it to simulate several scenarios where effective antivirals are available. The results show how the early use of antivirals may significantly reduce the incidence of COVID-19 and may avoid a new collapse of the health system. [ABSTRACT FROM AUTHOR]

• Developed deep learning methods to forecast the COVID19 spread. • Five deep learning models have been compared for COVID-19 forecasting. • Time-series COVID19 data from Italy, Spain, France, China, the USA, and Australia are used. • Results demonstrate the potential of deep learning models to forecast COVID19 data. • Results show the superior performance of the Variational AutoEncoder model. The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), Gated recurrent units (GRUs) and Variational AutoEncoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms. [ABSTRACT FROM AUTHOR]