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Available data to depict socioeconomic realities are often scarce at the municipal level. Unlike recurring or continuous data, which are collected regularly or repeatedly, nonrecurrent data may be sporadic or irregular, due to significant costs for their compilation and limited resources at municipalities. To address regional data scarcity, we develop a bottom-up top-down methodology for constructing synthetic socioeconomic indicators combining a genetic algorithm and regression techniques. We apply our methodology for assessing income inequalities at 178 municipalities in Spain. The genetic algorithm draws the available data on circumstances or inequalities of opportunities that give birth to income disparities. Our methodology allows to mitigate the shortcomings arising from unavailable data. Thus, it is a suitable method to assess relevant socioeconomic conditions at a regional level that are currently obscured due to data unavailability. This is crucial to provide policymakers with an enhanced socioeconomic overview at regional administrative units, relevant to allocating public service funds. [ABSTRACT FROM AUTHOR]

The analysis of the evolution of population pyramids is crucial to study and tackle the growing issue associated to the depopulation of different regions around the world. This task has been based on the application of Monte Carlo simulation for the projection of the new population from the original one using birth, mortality, immigration and emigration data to model it. It has been applied both with current trends and with modified ones according to the user selections in order to obtain and analyse possible alternative scenarios. An inverse process is also presented, where a desired population pyramid is provided, and the system, based on genetic algorithms, determines the modifications of the previously stated rates to get the most similar output. Both tools are applied to a particular situation, the municipalities of a province in the north of Spain, so the functionality of the whole methodology is shown in an empiric way. These results show that projections are accurate with respect to the real data available to contrast, as well as that the inverse system generates close pyramids to the desired ones under reasonable circumstances. Additionally, these approaches have novel functionalities and make use of techniques not widely applied in the field, thus making them innovative with respect to other noteworthy examples in the field. [ABSTRACT FROM AUTHOR]

This article aims to analyze the capability of family policies to reverse the sharp decline in fertility that has been observed in Spain in recent decades. The analysis was carried out by applying two mathematical techniques: the genetic algorithm and the strategic scenarios. Firstly, a mathematical model was designed and validated adjusting the combined performance of fertility and family policies during the 2008–2019 period. Subsequently, this model was applied to the future (2020–2060) to extrapolate the evolution of fertility considering different models of family policies. The results demonstrate that a model of family policies that is coherent with other socially desirable objectives, such as gender and social equality, will be insufficient to reverse the current downward trend in fertility. Therefore, these outcomes point to the need to articulate and harmonize diverse public policies considering the principles of equality and well‐being to modify the recent decline in fertility. An increase in fertility must therefore be identified as a socially desirable goal and public policies must be adapted to this objective, in the understanding that fertility not only requires family policies but also their coherence with the employment and educational policies and work–life balance mechanisms offered by public institutions. [ABSTRACT FROM AUTHOR]

• A cutting-edge technology is proposed for trade-off energy in buildings. • NSGA-II effectively optimizes the hyperparameters of the hybrid LSTM-MLP. • The use of NSGA-II reduces electricity costs and improves autarky levels. • The predictive methodology is able to manage several energy conditions in buildings. To conduct analysis on the field of electricity management in buildings is crucial to contribute to the clean energy promotion, energy efficiency, and resilience against climate change. This manuscript proposes a methodology for modeling a predictive and calibrated energy management system (EMS) using a hybrid methodology that combines long short-term memory and multilayer perceptron models (LSTM-MLP) optimized by non-dominated sorting genetic algorithm II (NSGA-II). The proposed approach utilizes global forecast system (GFS) data to anticipate energy consumption fluctuations and optimize the use of distributed energy sources, such as photovoltaic (PV) production, based on knowledge of the electricity prices in the free market one day ahead. This energy trade-off in the building is conducted with NSGA-II, guaranteeing the exploration and the exploitation of energy in the building while minimizing energy costs and energy wastes. The research carried out demonstrates the effectiveness of the hybrid LSTM-MLP model and the advantages of NSGA-II in hyperparameter tuning and in energy balance for sustainable building practices. The methodology conducted is tested in an existing building, the Industrial Engineering School located on the Campus Lagoas-Marcosende of the Universidade de Vigo, Spain. [ABSTRACT FROM AUTHOR]

This paper introduces a method for estimating the interregional transportation of certain commodities in those cases where the commodity flows are not readily available and only aggregated flows per origin-destination pair are provided. We use a doubly-constrained gravity model to find a matrix of aggregated flows that is as similar as possible to the available data, in the sense of the standardized root mean square error. This model is calibrated via a real-valued genetic algorithm that uses a combination of global and local searches to find a set of optimal parameters of the deterrence function under study in the gravity model. This method is introduced as an application to estimating the disaggregated flows of ten different products among the fifteen regions of peninsular Spain between 2007 and 2016. After testing several formulations, we conclude that an exponential deterrence function calibrated with data from 2010 is as effective to estimate the flows in this 10-year span as other more complex options, which emphasizes the time transferability of our model. [ABSTRACT FROM AUTHOR]

In Spain, the subtitling service on television for the deaf has been improving in quantity since the General Law on Audiovisual Communication was enacted in 2010. This law establishes a series of quality standards that must be followed in the subtitling process. One of the most relevant aspects of subtitle quality is the speed at which they are shown on the screen, due to the fact that a too high speed (less time on screen) will make them difficult to read and the information hard to understand. In order to determine whether the speed at which the subtitles are being shown is adequate, first, it is necessary to process all the information associated with the broadcast of the digital TV channels including data from different sources. In this research, the authors have worked with the data obtained within the time period between July 2012 and December 2017, that is, with more than 950 million records. This article presents a framework for integration and processing of heterogeneous information associated with the subtitling of audiovisual content from different sources. Moreover, the framework will provide an automatic adjustment of subtitles in broadcasting regarding quality indicators by means of a genetic algorithm approach. The results show that the system is able to estimate the best relationship between the time and size of the subtitles and maintaining the quality levels established for this research. These results have been validated by experts and users of this domain. [ABSTRACT FROM AUTHOR]

Featured Application: The obtainment of a methodology for maximizing the social distancing by increasing the distance among the school desks in the classrooms during the coronavirus pandemic through a Genetic Algorithm optimization. The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students' desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors' best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper. [ABSTRACT FROM AUTHOR]

In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model's parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology. [ABSTRACT FROM AUTHOR]

The use of irrigation water extracted from aquifers with submerged pumps is essential to ensure agricultural production mainly in water-scarce regions.. However, the use of the water source requires of a considerable energy consumption by water user associations (WUAs) being key factor to consider due to their high share of total management, operation, and maintenance costs. In this work, a new tool (MOPWE, model to optimize water extraction) to optimize the water and energy use of wells in WUAs was developed. MOPWE was applied to a real WUA located in Castilla-La Mancha region (southeast of Spain). This WUA utilizes groundwater as water source that is extracted from several different wells of different characteristics (discharges, water table levels, efficiency, variable speed drives...).. Therefore, these kind of WUAs must decide not only which well to activate at a certain time but also at what frequency the variable-speed drive should run the pump. With the aim of aiding decision-making in groundwater abstraction, a new management model (MOPWE), which is based on multi-objective genetic algorithms and is implemented in MATLAB®. This model helps determine the optimal daily management of a WUA with multiple underground supply sources and focuses on the management of wells while considering the water reservoir level. After 18,000 generations of the genetic algorithm, the pareto front was obtained with the best WUA managements achieving a water and energy savings of 25% and 54%, respectively. At the end of the irrigation season, the optimal total energy consumption per unit of water applied was 38% lower than that achieved by the current management. Results showed that a more realistic approach can be implemented when several water supplies operate jointly under a collaborative principle. [ABSTRACT FROM AUTHOR]

The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%. [ABSTRACT FROM AUTHOR]

The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared. [ABSTRACT FROM AUTHOR]

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Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar PV generation is renewable energy source that in the last years has contributed to reduce the use of fossil fuels. Controlling the efficiency of photovoltaic (PV) installations is essential in order for its use to spread. Deep learning (DL) models have demonstrated their efficiency in this context. The purpose of this work is to show a methodology to optimised deep learning models and show a specific application of the optimised model to characterise PV installations. The estimations yielded by this model are obtained without the need for real PV data to the training process; synthetic data are used. The built multivariate neural network is optimised through the use of a multiobjective genetic algorithm and the use of a feature engineering tool based on functional data analysis (FDA) clustering. This method was applied on synthetic data and on a PV installation located in north-western Spain, where the number of parallel modules, the azimuth and the slope in the installation are estimated. The results show that the model optimised using the non-dominant sorting genetic algorithm (NSGA-II) and the customised dataset with FDA, achieve lower errors or in the same range by reducing the complexity of the model or the complexity of the dataset used. Specifically, the bests models generates estimations with an average error between 6% and 18% on synthetic data and between 6% and 12% on real data. • An optimisation methodology is proposed to select the best LSTM architecture. • The optimisation is based on the multiobjective genetic algorithm NSGA-II. • A feature engineering tool based on FDA clustering is also proposed. • The models are evaluated based on the PV installations characterisation. • The results show the improvements provided by the optimisations presented. [ABSTRACT FROM AUTHOR]

Irrigation water demand is highly variable and depends on farmer behaviour, which affects the performance of irrigation networks. The irrigation depth applied to each farm also depends on farmer behaviour and is affected by precise and imprecise variables. In this work, a hybrid methodology combining artificial neural networks, fuzzy logic and genetic algorithms was developed to model farmer behaviour and forecast the daily irrigation depth used by each farmer. The models were tested in a real irrigation district located in southwest Spain. Three optimal models for the main crops in the irrigation district were obtained. The representability (R 2 ) and accuracy of the predictions (standard error prediction, SEP) were 0.72, 0.87 and 0.72; and 22.20%, 9.80% and 23.42%, for rice, maize and tomato crop models, respectively. [ABSTRACT FROM AUTHOR]

• Optimization of renewable energy communities considering environment and economy. • Combinatorial optimization for participant selection. • Multi-objective optimization of solar energy allocation. • Results show high avoided CO2 emissions and low paybacks for all participants. Renewable Energy Communities (REC) have the potential to become a key agent for the energy transition. Since consumers have different consumption patterns depending on their habits, their grouping allows for a better use of the resource. REC provide both economic and environmental benefits. However, its potential drastically diminishes when grouping of prosumers and energy al- location is performed improperly, as the energy generated ends up not being consumed. Given the importance of extracting the maximum potential of REC, this study presents a tool to assist in both the planning and the operation phases. We present a combinatorial optimization method for participant selection and a multi-objective (MO) optimization of solar energy allocation. Specific Ge- netic Algorithms (GA) were developed including problem-specific approaches for reducing the search space, encoding, techniques for space ordering, fitness functions, special operators to replace duplicate individuals and decoding for equality constraints. The performance of the novel solution approach was exper- imentally proved with an electrical solar installation and electricity consumers from Northern east Spain. The results show that the developed tool achieves energy sharing in REC with low solar energy excess, high self-consumption and high avoided CO2 emissions while assuring low payback periods for all partic- ipants. This tool will be essential to increase revenues of REC schemes and boost their beneficial environmental impact. [ABSTRACT FROM AUTHOR]

The US Highway Capacity Manual (HCM) methodology is used in Spain to evaluate traffic operation and quality of service. The effect of passing manoeuvre on two-lane highway operational performance is considered through adjustment factors to average travel speeds and percent time spent following. The procedure is largely based on simulations in TWOPAS and passing behaviours observed during US calibrations in the 1970s. It is not clear whether US driving behaviour and vehicles' performance are comparable with Spanish conditions. The objective of this research is to adapt the HCM 2010 methodology to Spanish driver behaviour, for base conditions (i.e. no passing restrictions). To do so, TWOPAS was calibrated and validated based on current Spanish passing field data. The calibration used a genetic algorithm. The case study included an ideal two-lane highway with varying directional traffic flow rate, directional split and percentage of trucks. The updated methodology for base conditions is simpler than the current HCM 2010 and does not rely on interpolation from tables. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Building stock modelling is implemented through a bottom-up method. • Dynamic simulations are applied through calibrated and parameterized stock models. • Optimal retrofit solutions under a 2050 future weather scenario are proposed. • Results are provided for four different climatic regions in southern Spain. • Results can be interactively accessed by stakeholders and the general public. Retrofitting the existing building stock is widely accepted as a crucial factor to reaching 2030 and 2050 climate and energy targets, given that the building sector is among the top three most dominant energy consumers. This paper presents a bottom-up study which uses calibrated and parameterized energy stock models (building archetypes), while also incorporating building stock information from a large database. The thermal performance of the existing social housing stock of southern Spain is assessed through dynamic simulation under present and future climate change scenarios. Subsequently, several passive and low-cost operation-related strategies are numerically optimized through genetic algorithms to determine the best retrofit solutions, taking into consideration global warming scenarios. A multi-objective decision analysis is carried out by optimizing annual overheating hours (%), annual undercooling hours (%), and investment costs (€/m2). Among the conclusions reported, it is important to note the feasibility of implementing low-cost retrofit strategies considering investment costs of up to around 200 €/m2, which would lead to average annual overheating and undercooling hours below 55 % and 45 %, respectively. However, retrofit solutions exclusively based on passive and low-cost operation measures were proven to be significantly limited to improve thermal comfort results in the social stock. [ABSTRACT FROM AUTHOR]

Given a wide amount of possible ratios available for constructing a LOGIT model for forecasting bankruptcy, this paper provides a computational search methodology, only guided by data, for selecting the financial ratios employed in the model. This procedure is based on genetic algorithms which are used to explore the universe of models made available by all possible existing financial ratios (with very redundant information). This search process of the correct model is guided by the Schwarz information criterion. As an empirical illustration, the methodology is applied to forecasting the failure of firms in the Spanish building industry using annual public accounting information. [ABSTRACT FROM AUTHOR]

• A bottom-up approach is applied through calibrated parameterised stock energy models. • Information from a public database is incorporated into energy dynamic models. • Retrofit solutions are proposed optimising thermal comfort and investment costs. • Four Mediterranean climatic areas of southern Spain are assessed. • Interactive results can be easily accessed by stakeholders and the general public. There is an impending need to retrofit the existing social housing stock to improve thermal comfort and to reduce energy demand. This research proposes calibrated building stock models to assess thermal comfort of the social housing stock of southern Spain (Mediterranean area). Several retrofit strategies are optimised using a genetic algorithm to obtain the best retrofit solutions, considering three objectives: annual overheating hours (%), annual undercooling hours (%) and investment costs (€/m2). Results are shown for four different climatic areas in southern Spain. This study finds that it is possible to retrofit the aforementioned stock considering investment costs from approximately 20–200 €/m2. The percentage of improvements for each climatic area are as follows: in Sevilla (B4 climatic area), investments costs up to 50 €/m2 led to 40% and 20% annual overheating and undercooling hours. In Cádiz, (A3 climatic area), 15% and 22% overheating and undercooling hours were achieved with medium-cost solutions (50–100 €/m2). In Almería (A4 climatic area), also medium-cost strategies reported approximately 15% and 30% overheating and undercooling hours. In Granada (C3 climatic area), 15% and 38% overheating and undercooling hours were obtained with medium-cost measures. Yet, applying high-cost solutions (100–200 €/m2) only significantly improved thermal comfort in Almería and Sevilla. [ABSTRACT FROM AUTHOR]

An alternative procedure for assessment of reservoir Operation Rules (ORs) under drought situations is proposed. The definition of ORs for multi-reservoir water resources systems (WRSs) is a topic that has been widely studied by means of optimization and simulation techniques. A traditional approach is to link optimization methods with simulation models. Thus the objective here is to obtain drought ORs for a real and complex WRS: the Júcar River basin in Spain, in which one of the main issues is the resource allocation among agricultural demands in periods of drought. To deal with this problem, a method based on the combined use of genetic algorithms (GA) and network flow optimization (NFO) is presented. The GA used was PIKAIA, which has previously been used in other water resources related fields. This algorithm was linked to the SIMGES simulation model, a part of the AQUATOOL decision support system (DSS). Several tests were developed for defining the parameters of the GA. The optimization of various ORs was analysed with the objective of minimizing short-term and long-term water deficits. The results show that simple ORs produce similar results to more sophisticated ones. The usefulness of this approach in the assessment of ORs for complex multi-reservoir systems is demonstrated. Citation Lerma, N., Paredes-Arquiola, J., Andreu, J., and Solera, A., 2013. Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58 (4), 797–812. [ABSTRACT FROM AUTHOR]

This article describes the application of modern algorithms to crack the official encryption method of the Spanish Civil War: the Strip Cipher. It shows the differences in efficiency and effectiveness between a genetic algorithm and mathematical programming, the optimisation methods known collectively as mathematical optimisation. Unlike the genetic algorithm, the programming approach has been seen to lead to high computational costs or to non-legible plain texts, which make it impractical. To improve the search for the genetic operators used, a dictionary is applied to identify possible words in each partially decrypted text and, thus, unblock the process. Results and conclusions have been obtained by analysing the outcome of the algorithms when attacking real ciphertexts found in the General Archive of the Spanish Civil War in Spain. Both the mathematical programming and the genetic algorithm approaches have merit, but the latter has considerable practical advantages. [ABSTRACT FROM PUBLISHER]

Mathematical models are a fundamental tool in the learning process of environmental engineering. These models need to be calibrated in order to be used by future engineers as a simulation tool for the represented problems. This paper deals with the concept of computational models calibration applied to higher environmental engineering studies. In this paper, we depict a methodology to calibrate water quality models, as an educational example that represents the environmental problem of dissolved oxygen in a stream. This methodology is based on defining two types of parameters involved in calibration. First, internal parameters appear in the equations from semi-empirical estimations and can be found within some intervals. Genetic algorithms are proposed to estimate them. Second, experimental measurements enter into equations as external parameters. They affect the accuracy of the final solutions. Therefore, an uncertainty analysis has to be performed. Finally, a termination criterion for calibration has been proposed, based on the overlap between the confidence intervals of predicted and measured values. By developing this methodology, we provide awareness to our students of the importance of calibration of mathematical models so that they can apply them in their future simulation of environmental problems. Students identify the possible sources of uncertainty at each stage of the environmental model performance and apply them in this particular problem, Genetic Algorithm Techniques, as a computational tool to improve the accuracy of their model predictions. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 795-805, 2011 [ABSTRACT FROM AUTHOR]

Abstract: Solar thermal electricity technologies are attractive alternatives to produce electricity by means of a renewable source. One of these technologies is parabolic trough collectors. In Spain, the sale of solar electricity to the national grid is primed. There are three main parameters that affect the behaviour of these plants: area of solar collector field, capacity of thermal storage tanks and power of the auxiliary system. In this paper, a simplified model of the plant is used to optimize the size of its components that produces the maximum yearly profit. The use of traditional methods of optimization is not possible and genetic algorithms have been used. An important feature of the model is that the minimum level of the electricity production of the block of power can be fixed. Once the optimization has been performed, the traditional parameters that characterize the dimension of the plant are analysed (the solar multiple and the capacity factor). For a gross power of 50 MW, the optimum collector area varies between 583,000 m2 and 749,860 m2 with a thermal storage between 6.55 h and 13.46 h respectively. The economic benefit is always higher than 19.30 M€ per year and the cost of the electricity produced is about 18.5 c€/kWh. [Copyright &y& Elsevier]

Abstract: In previous works, many authors have widely used mass consistent models for wind field simulation by the finite element method. On one hand, we have developed a 3-D mass consistent model by using tetrahedral meshes which are simultaneously adapted to complex orography and to terrain roughness length. In addition, we have included a local refinement strategy around several measurement or control points, significant contours, as for example shorelines, or numerical solution singularities. On the other hand, we have developed a 2.5-D model for simulating the wind velocity in a 3-D domain in terms of the terrain elevation, the surface temperature and the meteorological wind, which is consider as an averaged wind on vertical boundaries. Using the meteorological wind as datum, the 2.5-D model provides a 3-D local wind modified by topography and thermal gradients on the surface by solving only a 2-D optimal control problem where the boundary condition is the control. In this case, the finite element discretization consists on a triangular mesh adapted to the terrain topography and roughness length. In both models, the wind field adjusts to several wind speed measurements at several points in the 3-D domain and eventually to an average wind flux on the boundary. In this paper we introduce several advances in the 2.5-D and 3-D wind models and we compare their results on a region located in the Province of Lugo (Spain) with realistic data that have been provided by the company Desarrollos Eólicos S.A. (DESA). In order to obtain the best adjustment of models results to the measurements, the main parameters governing the models are estimated by using genetic algorithms with a parallel implementation. [Copyright &y& Elsevier]

Abstract: This study explores the suitability of a soft computing technique to predict (forecast and hindcast) significant wave heights in some areas of the Mediterranean Sea. Using a 44-year database of meteorological and oceanographic records we train a genetic algorithm, to reconstruct a 44-year time series of significant wave height at different coastal points of the Balearic Islands (Western Mediterranean). The correlation obtained between the numerical model data and the algorithm output is . The equations obtained by the genetic algorithm are then used to predict significant wave heights measured at several buoys. We make use of the equation from the node closest to the buoy using as input wind data from the meteorological station at each buoy. We obtained that SWH at each buoy can be predicted with a 1-h ahead forecast time using only wind data from the previous 12h with a correlation of . [Copyright &y& Elsevier]

• Decision support system to help water utilities design practical work programs. • Two-stage grouping algorithm to aggregate water pipes into replacement programs. • Work programs optimization through a multi-objective genetic algorithm. • Integration of external criteria and the managers knowledge and experience. • Sensitivity analysis of the company's strategic policy on the work programs. Asset management in hydraulic infrastructures aims for the long-term sustainability of water distribution and wastewater networks. Strategic maintenance planning has thus been deeply analyzed in the literature for individual water and sewer pipes. However, water utilities do not plan and perform replacement activities on individual elements, but rather on coherent aggregations of neighboring pipes. We have developed a decision support system (DSS) to help water utilities design intervention programs for hydraulic infrastructures. It integrates a two-stage algorithm that groups water supply and sewer pipes into practical and efficient replacement works, based upon their proximity and their priority of renewal. A multi-objective genetic algorithm optimizes the work programs configurations while integrating the water company's strategic policy into an innovative multi-objective function. We have applied our methodology to a large water company in Spain and illustrated this application with a sensitivity analysis to determine how the company's strategic criteria influences the resulting work configurations. [ABSTRACT FROM AUTHOR]

A method to determine the spectral parameters, spectral level Ω0, corner frequency f0, and attenuation factor Q, using a genetic algorithm (GA), is presented. An adequate strategy for the GA was chosen by means of the 2D Ackley function. For modeling the seismic spectrum, a Boatwright source model and an attenuation factor independent of the frequency were used. Several objective functions are tested, and the best was found to be the L2 norm of the log difference between the observed and modeled spectra. To check the accuracy of the method, some tests with synthetic spectra were made, including spectra with noise. Real data, which have been already studied with other spectral methods, were analyzed to compare the results. The spectral parameters of 13 earthquakes (Granada, southern Spain) were calculated, as well as its source parameters: the seismic moment M0, the source radius r, and the stress drop Δσ, following Brune's model. The seismic moments ranged from 3.65E + 17 to 9.98E + 18 dyne cm; the radii were between 0.08 and 0.2 km, and the static stress drops ranged from 0.32 to 1.73 bars. The attenuation factors ranged from 53 to 726, with an averaged value of 303. The results obtained are similar to those obtained by other researchers working with events of comparable magnitudes in the region. [ABSTRACT FROM AUTHOR]

Anaerobic digestion (AnD) is a process that allows the conversion of organic waste into a source of energy such as biogas, introducing sustainability and circular economy in waste treatment. AnD is an intricate process because of multiple parameters involved, and its complexity increases when the wastes are from different types of generators. In this case, a key point to achieve good performance is optimisation methods. Currently, many tools have been developed to optimise a single AnD plant. However, the study of a network of AnD plants and multiple waste generators, all in different locations, remains unexplored. This novel approach requires the use of optimisation methodologies with the capacity to deal with a highly complex combinatorial problem. This paper proposes and compares the use of three evolutionary algorithms: ant colony optimisation (ACO), genetic algorithm (GA) and particle swarm optimisation (PSO), which are especially suited for this type of application. The algorithms successfully solve the problem, using an objective function that includes terms related to quality and logistics. Their application to a real case study in Catalonia (Spain) shows their usefulness (ACO and GA to achieve maximum biogas production and PSO for safer operation conditions) for AnD facilities.

Objective: This paper presents a methodology to optimize, using Altman's Z-Score for private companies, the prediction of private companies of the Spanish health sector entering a situation of bankruptcy., Method: The proposed method consists of the application of genetic algorithms (GA) to find the coefficients of the formula of the chain of ratios proposed by Altman in the version of the score for private companies which optimize the prediction for Spanish private health companies, maximizing sensitivity and specificity, and thereby reducing type I and type II errors. For this purpose, a sample of 5,903 companies from the Spanish private health sector obtained from the database of the Iberian Balance Analysis System (SABI) between 2007 and 2015 was used., Results: The results show that the predictive model obtained with the AG presents greater accuracy, sensitivity and specificity than that proposed by Altman for private companies with both test data and all sample data., Conclusions: The most important finding of this study was to establish a methodology that can identify the optimized coefficients for the Altman Z-Score, which allows a more accurate prediction of bankruptcy in Spanish private healthcare companies., (Copyright © 2018 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.)

This article presents the schedule for various conferences on numerical methods in engineering during 1995 and 1996. The conference Eurogen 95--A Short Course on Genetic Algorithms and Evolution Strategies in Computational Science and Engineering is being organized from December 4-8, 1995 in Spain. The conference EEET 96--Environmental Engineering Education and Training is being organized April 17-19, 1996 in Great Britain. The conference BETECH 96--Boundary Element Technology XI from April 24-26, 1996 in Hawaii.

The p.Ala204Thr mutation (exon 7) of the CLCNKB gene is a "founder" mutation that causes most of type III Bartter syndrome cases in Spain. We performed genetic analysis of the CLCNKB gene, which encodes for the chloride channel protein ClC-Kb, in a cohort of 26 affected patients from 23 families. The diagnostic algorithm was: first, detection of the p.Ala204Thr mutation; second, detecting large deletions or duplications by Multiplex Ligation-dependent Probe Amplification and Quantitative Multiplex PCR of Short Fluorescent Fragments; and third, sequencing of the coding and flanking regions of the whole CLCNKB gene. In our genetic diagnosis, 20 families presented with the p.Ala204Thr mutation. Of those, 15 patients (15 families) were homozygous (57.7% of overall patients). Another 8 patients (5 families) were compound heterozygous for the founder mutation together with a second one. Thus, 3 patients (2 siblings) presented with the c. -19-?_2053+? del deletion (comprising the entire gene); one patient carried the p.Val170Met mutation (exon 6); and 4 patients (3 siblings) presented with the novel p.Glu442Gly mutation (exon 14). On the other hand, another two patients carried two novel mutations in compound heterozygosis: one presented the p.Ile398_Thr401del mutation (exon 12) associated with the c. -19-?_2053+? del deletion, and the other one carried the c.1756+1G>A splice-site mutation (exon 16) as well as the already described p.Ala210Val change (exon 7). One case turned out to be negative in our genetic screening. In addition, 51 relatives were found to be heterozygous carriers of the described CLCNKB mutations. In conclusion, different mutations cause type III Bartter syndrome in Spain. The high prevalence of the p.Ala204Thr in Spanish families thus justifies an initial screen for this mutation. However, should it not be detected further investigation of the CLCNKB gene is warranted in clinically diagnosed families. [ABSTRACT FROM AUTHOR]