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The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [ABSTRACT FROM AUTHOR]

This paper aims to analyze, from a comparative perspective, the main elements of the discipline on the use of algorithms within administrative procedures developed by the national lawmakers of France, Spain and Italy. Furthermore, the article intends to verify, on the basis of the principle of good administration, the existence of a minimum core of guarantees addressed to the citizen who is the recipient of an automated decision. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

The University of Valladolid (UVa, Spain) manages since 2006 a calibration center of the AErosol RObotic NETwork (AERONET). The CÆLIS software tool, developed by UVa, was created to manage the data generated by the AERONET photometers, for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Angstrom exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging the mean differences between −1.3×10[sup −4] at 870 nm and 6.2×10[sup −4] at 380 nm. The standard deviations of the differences range from 2.8×10[sup −4] at 675 nm to 8.1×10[sup −4] at 340 nm. The AOD and AE at night-time calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and night-time for different locations, aerosol loads and moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered as cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future. [ABSTRACT FROM AUTHOR]

In this paper we propose and discuss different Deep Learning-based ensemble algorithms for a problem of low-visibility events prediction due to fog. Specifically, seven different Deep Learning (DL) architectures have been considered, from which multiple individual learners are generated. Hyperparameters of the models, including parameters concerning data preprocessing, models architecture and training procedure, are randomly selected for each model within a pre-defined discrete range. Also, every model is trained with slightly different data sampled randomly, assuring that every models introduce variety in the ensemble. Then, three different information fusion techniques are employed to build the ensemble models. The influence of the filtering process and the elitism level (the percentage of the individual models entering the ensemble) is also assessed. The performance of the proposed methodology have been tested in two real problems of low-visibility events prediction due to orographical and radiation fog, at the north of Spain. Comparison with different Machine Learning, alternative DL algorithms and meteorological-based methods show the good performance of the proposed deep learning ensembles in this problem. [ABSTRACT FROM AUTHOR]

Highlights • Hourly-resolved model to investigate highly-renewable electricity generation in Spain. • Correlation analysis of time series to create 900 combinations used in simulations. • Transition paths evaluated based on security of supply, CO 2 emissions, and renewable share. • Short-term phase-out of nuclear and coal power plants proven to be feasible. Abstract Two alternative paths to achieve highly-renewable electricity generation in peninsular Spain are investigated in this paper. Every transition path comprises a description of the installed and decommissioned generation and storage capacities, from 2017 to 2030, as well as a hypothesis on the evolution of the electricity demand. The electricity mix for every hour within the transition path is determined through a dispatch algorithm that prioritizes electricity from renewable energy sources. The simulation is run for 900 different combinations of time series representing the hourly capacity factors of different technologies, as well as the electricity demand. This robust approach allows the evaluation of the transition paths based on the statistical distribution of several defined assessment criteria, such as security of supply, CO 2 emissions or renewable share in electricity generation. The feasibility of a Spanish power system with high renewable penetration is investigated not only in a future reference year but throughout the transition path. In particular, a progressive and simultaneous phase-out of nuclear and coal power plants in the short-term is proven to be feasible. Furthermore, the results sensitivity is analyzed including scenarios with a delayed nuclear phase-out, lower hydroelectricity generation due to more frequent and severe droughts caused by climate change and higher annual increment for the electricity demand. [ABSTRACT FROM AUTHOR]

A good primary health care is the base for a better healthcare system. Taking a good decision on time by the primary health care physician could have a huge repercussion. In order to ease the diagnosis task arise the Decision Support Systems (DSS), which offer counselling instead of refresh the medical knowledge, in a profession where it is still learning every day. The implementation of these systems in diseases which are a frequent cause of visit to the doctor like ophthalmologic pathologies are, which affect directly to our quality of life, takes more importance. This paper aims to develop OphthalDSS, a totally new mobile DSS for red eye diseases diagnosis. The main utilities that OphthalDSS offers will be a study guide for medical students and a clinical decision support system for primary care professionals. Other important goal of this paper is to show the user experience results after OphthalDSS being used by medical students of the University of Valladolid. For achieving the main purpose of this research work, a decision algorithm will be developed and implemented by an Android mobile application. Moreover, the Quality of Experience (QoE) has been evaluated by the students through the questions of a short inquiry. The app developed which implements the algorithm OphthalDSS is capable of diagnose more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The students find the diseases' information presented very valuable, the appearance is adequate, it is always available and they have ever found what they were looking for. Furthermore, the students think that their quality of life has not been improved using the app and they can do the same without using the OphthalDSS app. OphthalDSS is easy to use, which is capable of diagnose more than 30 ocular diseases in addition to be used as a DSS tool as an educational tool at the same time. [ABSTRACT FROM AUTHOR]

The importance of Big Data and algorithms today is undeniable. The increasing use of this technology has raised new discussions regarding the adequacy of current regulations on intellectual property. This paper intends to clarify what these shortcomings are, if any, by analyzing these debates in light of the legislation in force in Spain and the European Union. It is concluded that such insufficiency is not as broad as it is usually thought, and the particular cases (unstructured Big Data and algorithms) in which there are ambiguities or voids are specified. [ABSTRACT FROM AUTHOR]

Background: When patients are admitted to an intensive care unit (ICU) their risk of getting an infection will be highly depend on the length of stay at-risk in the ICU. In addition, risk of infection is likely to vary over calendar time as a result of fluctuations in the prevalence of the pathogen on the ward. Hence risk of infection is expected to depend on two time scales (time in ICU and calendar time) as well as competing events (discharge or death) and their spatial location. The purpose of this paper is to develop and apply appropriate statistical models for the risk of ICU-acquired infection accounting for multiple time scales, competing risks and the spatial clustering of the data.Methods: A multi-center data base from a Spanish surveillance network was used to study the occurrence of an infection due to Methicillin-resistant Staphylococcus aureus (MRSA). The analysis included 84,843 patient admissions between January 2006 and December 2011 from 81 ICUs. Stratified Cox models were used to study multiple time scales while accounting for spatial clustering of the data (patients within ICUs) and for death or discharge as competing events for MRSA infection.Results: Both time scales, time in ICU and calendar time, are highly associated with the MRSA hazard rate and cumulative risk. When using only one basic time scale, the interpretation and magnitude of several patient-individual risk factors differed. Risk factors concerning the severity of illness were more pronounced when using only calendar time. These differences disappeared when using both time scales simultaneously.Conclusions: The time-dependent dynamics of infections is complex and should be studied with models allowing for multiple time scales. For patient individual risk-factors we recommend stratified Cox regression models for competing events with ICU time as the basic time scale and calendar time as a covariate. The inclusion of calendar time and stratification by ICU allow to indirectly account for ICU-level effects such as local outbreaks or prevention interventions. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an evolutionary algorithm for wind speed reconstruction from synoptic pressure patterns. The algorithm operates in a search space formed by grids of pressure measures, and must classify the different situations into classes, in such a way that a measure of wind speed in a given point is minimized among patterns assigned to the same class. Then, each class is assigned a mean wind speed and direction, so the wind speed reconstruction is possible for a new grid of synoptic pressures. In this paper we present the problem model and the specific description of the evolutionary algorithm proposed to solve the problem. We also show the good performance of the proposed method in the reconstruction of the average wind speed in six wind towers in Spain. The proposed method is applicable to wind speed reconstruction or reconstruction of wind missing data of wind series, specially when there is no other variable or related measure available. [Copyright &y& Elsevier]

This paper studies the applicability of the Mean Shift algorithm as support in interpreting geophysical images produced, on this occasion, from magnetic prospection data. The data obtained from a magnetic survey carried out in Gilena (Seville province, Spain) by the La Rábida Archaeophysics Group will be used for the research. Its applicability is illustrated by comparing, on the one hand, some reduction-to-pole algorithms and on the other, the (well-known) k-means algorithm. Finally, the paper shows the results obtained by applying the Mean Shift algorithm as an alternative method to 'unsupervised clustering' of anomalies that appear in images obtained from geophysical data, in which the a priori knowledge of the number of classes is difficult or impossible. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, fuzzy models have been acknowledged as a suitable approach for species distribution modelling due to their transparency and their ability to incorporate the ecological gradient theory. Specifically, the overlapping class boundaries of a fuzzy model are similar to the transitions between different environmental conditions. However, the need for ecological expert knowledge is an important constraint when applying fuzzy species distribution models. Moreover, the consistency of the ecological preferences of some fish species across different rivers has been widely contested. Recent research has shown that data-driven fuzzy models may solve this ‘knowledge acquisition bottleneck’ and this paper is a further contribution. The aim was to analyse the brown trout (Salmo trutta fario L.) habitat preferences based on a data-driven fuzzy modelling technique and to compare the resulting fuzzy models with a commonly applied modelling technique, Random Forests. A heuristic nearest ascent hill-climbing algorithm for fuzzy rule optimisation and Random Forests were applied to analyse the ecological preferences of brown trout in 93 mesohabitats. No significant differences in model performance were observed between the optimal fuzzy model and the Random Forests model and both approaches selected river width, the cover index and flow velocity as the most important variables describing brown trout habitat suitability. Further, the fuzzy model combined ecological relevance with reasonable interpretability, whereas the transparency of the Random Forests model was limited. This paper shows that fuzzy models may be a valid approach for species distribution modelling and that their performance is comparable to that of state-of-the-art modelling techniques like Random Forests. Fuzzy models could therefore be a valuable decision support tool for river managers and enhance communication between stakeholders. [Copyright &y& Elsevier]

This paper focuses on the validation of the empirically corrected total ozone column (TOC) data provided by the Earth Probe Total Ozone Mapping Spectrometer (EP-TOMS) using ground-based measurements recorded by a well-calibrated Brewer spectroradiometer located at El Arenosillo (Spain). In addition, satellite TOC observations derived from the OzoneMonitoring Instrument (OMI) with the TOMS algorithm are also used in this paper. The agreement between EP-TOMS TOC data and Brewer measurements is excellent (R² ~ 0.92) even for the period 2000-2005 when a higher EP-TOMS instrument degradation occurred. Despite its low magnitude, the EP-TOMS-Brewer relative differences depend on the solar zenith angle (SZA), showing a clear seasonal cycle with amplitude between ±2% and ±4%. Conversely, OMI-Brewer relative differences show a constant negative value around -1% with no significant dependence on SZA. No significant dependence on the ground-based to satellitebased differences with respect to the EP-TOMS scene or to the OMI crosstrack position is observed for either satellite retrieval algorithm. Finally, TOC, estimated by the two satellite instruments, have also been compared, showing a good agreement (R² ~ 0.88). Overall, we conclude that the empirical correction of the EP-TOMS data record provides a reprocessed set of high quality. However, EP-TOMS data after year 2000 should not be used in calculations of global-ozone trending due to remaining errors in the data set and because it is no longer an independent data set. [ABSTRACT FROM AUTHOR]

The penetration level of wind energy is continuously growing, and it is especially relevant in European countries such as Denmark, Germany, and Spain. For this reason, grid codes in different countries have been recently revised, or are now under revision in order to integrate this energy in the network taking into account the security of supply. This paper is related to reactive compensation, which is one aspect usually included in these codes. On the other hand, a great number of installed wind parks are formed by fixed speed wind turbines equipped with induction generators. The typical scheme for reactive compensation in this kind of wind parks is based on capacitor banks locally controlled in each machine. This configuration makes very difficult to follow the requirements of the new grid codes. To overcome this problem, a configuration with a central controller that coordinates the actuation over all the capacitor steps in the wind park is proposed in this paper. A central controller algorithm that is based on a dynamic programming is presented and evaluated by means of simulation. At this time, the proposed scheme has been installed at the Sotavento Experimental Wind Park (Spain) and it is currently being tested. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper is concentrated on the evaluation of circulation type classifications (CTCs) from the European Action COST733 catalogue version 2.0 in terms of their ability to capture winter precipitation, expressed as percentage, over Spanish Iberia and the Balearic Islands. The explained variation, the pseudo-F statistics and the Brier skill score are used to quantify the explanatory power of circulation classifications. As secondary aims, the impact of using different number of circulation types, additional types of variables and 4-day sequences in the generation of classifications is analysed. Although no optimal method has been found, nevertheless, the results suggest that the use of CTCs based on optimization algorithms are, in general, performing better than those which are based on other algorithms (i.e. leader algorithms). Distinct variations in skill exist not only among classifications from different groups of basic methods but as well between classifications from the same method group; being remarkable the behaviour of the optimum random centroid method. Results are very dependent on the metric, for the explained variation, and the Brier skill score, the larger the number of circulation types, the better the performance; contrary to the behaviour of the pseudo-F statistic. The inclusion of 500 hPa vorticity in the generation of classifications improves results while a general deterioration is observed when considering 4-day sequences. These results are only valid for the selected season and cannot be transferred to other locations and seasons. [ABSTRACT FROM AUTHOR]

With the global population expected to increase to 9 billion by 2050 coupled with concerns about food security in relation to climate change and increasing prosperity in many parts of the world causing desire for a less monotonous diet, efficient use of resources such as food becomes ever more important. While the prevalence of obesity is a cause for concern in many parts of the world, many people still go to bed hungry, and in many communities, obesity co-exists with poor diet quality. The result is a series of complex and challenging nutrition problems, such as the access to nutritionally adequate and affordable diets and the development of dietary recommendations. Diet modelling is a useful tool to help identify solutions to such complex questions and this paper summarises a session on this topic at the International Congress of Nutrition that took place in September 2013. [ABSTRACT FROM AUTHOR]

Urban researchers and planners are often interested in understanding how economic activities are distributed in urban regions, what forces influence their special pattern and how urban structure and functions are mutually dependent. In this paper, we want to show how an algorithm for ranking the nodes in a network can be used to understand and visualize certain commercial activities of a city. The first part of the method consists of collecting real information about different types of commercial activities at each location in the urban network of the city of Murcia, Spain. Four clearly differentiated commercial activities are studied, such as restaurants and bars, shops, banks and supermarkets or department stores, but obviously we can study other. The information collected is then quantified by means of a data matrix, which is used as the basis for the implementation of a PageRank algorithm which produces a ranking of all the nodes in the network, according to their significance within it. Finally, we visualize the resulting classification using a colour scale that helps us to represent the business network. [ABSTRACT FROM PUBLISHER]

The canal delivery system in the Left Hemidelta area of the Ebro River in Spain consists of a tree-shaped net of open canals. The overall system can be quickly isolated in the case of an emergency by closing the upstream pool. Transients, in which the initial state is hydraulically far from the final state, are difficult to handle and cannot be made in only one gate movement in order to protect the canal lining. Therefore, they have to be as smooth as possible. GoRoSo is a feedforward control algorithm for irrigation canals based on sequential quadratic programming. With this tool, it is possible to calculate the gate trajectories that smoothly carry the canal from the initial state to the final state by keeping the water depth constant at checkpoints. The paper shows the efficient implementation of GoRoSo in both the closure and opening operations of the canal delivery system. [ABSTRACT FROM AUTHOR]

Abstract: We present in this paper the Generic RAdiative traNsfer AnD non-LTE population Algorithm (GRANADA). This model is able to compute non-LTE populations for vibrational, rotational, spin (i.e., NO and OH), and electronic (i.e., O2) states in a given planetary atmosphere. The model is very flexible and can be used for computing very accurate non-LTE populations or for calculating reasonably accurate but at high speed non-LTE populations in order to implement it into non-LTE remote sensing retrievals. We describe the model in detail and present an update of the non-LTE collisional processes and their rate coefficients for the most important molecules in Earth''s atmosphere. In addition, we have applied the model to the most important atmospheric infrared emitters including 13 species (H2O, CO2, O3, N2O, CO, CH4, O2, NO, NO2, HNO3, OH, N2, and HCN) and 460 excited vibrational or electronic energy levels. Non-LTE populations for all these energy levels have been calculated for 48 reference atmospheres expanding from the surface up to 200km, including seasonal (January, April, July and October), latitudinal (75°S, 45°S, 10°S, 10°N, 45°N, 75°N) and diurnal (day and night) coverages. The effects of the most recent updates of the non-LTE collisional parameters on the non-LTE populations are briefly described. This climatology is available online to the community and it can be used for estimating non-LTE effects at specific conditions and for testing and validation studies. [Copyright &y& Elsevier]

Abstract: We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain. [Copyright &y& Elsevier]

Abstract: Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level. [Copyright &y& Elsevier]

Abstract: This paper describes an application of the Strength Pareto Evolutionary Algorithm to the multi-objective optimization of a stand-alone PV–wind–diesel system with batteries storage. The objectives to be minimized are the levelized cost of energy (LCOE) and the equivalent carbon dioxide (CO2) life cycle emissions (LCE). Each solution of the Pareto front is a possible solution for the PV–wind–diesel-batteries system, which can supply the load, but each one has a different LCOE and LCE. Some solutions have low LCOE values, but high LCE values, and vice versa. Results show that the photovoltaic (PV) generator is the most important source of electrical energy for stand-alone systems in Spain and Southern Europe, not only environmentally, but also economically. In some cases, PV is almost the only source of energy, as some solutions of the best Pareto front do not include wind turbines and diesel generators run only a few hours during the year. [Copyright &y& Elsevier]

Abstract: This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results. [Copyright &y& Elsevier]

Abstract: This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future. [Copyright &y& Elsevier]

Abstract: In this paper we present a segmentation algorithm, inspired from an image-processing region-merging algorithm, for the delineation of discrete contiguous management zones in agriculture. The algorithm is unique in that it is applicable to high- or low-density irregular data sets, such as yield data. The algorithm is described and a brief example presented using unprocessed sensor-derived grain yield data. A comparison between the segmentation algorithm and a common classification algorithm (k-means clustering) was done using an aerial normalised differences vegetation index (NDVI) image collected on a 200ha vineyard in Olite, Southern Navarre, Spain. Classification was performed as a univariate (NDVI) analysis and a spatially constrained analysis. Segmentation and classification were run to find 2, 4, 6, …, 24 levels and the effectiveness of the outputs determined by how well it explained the variance in vine trunk circumference, a correlated but independent measurement. The results obtained demonstrated that for a given number of manageable (effective) zones the segmentation outputs were equivalent or superior to the classification outputs for partitioning vine circumference variance. The segmentation output also generated more coherent management units that should facilitate differential management. The algorithm presented is a first generation segmentation algorithm and several aspects still need to be developed, in particular methods for eliminating edge effects and converting management zones into management (treatment) classes. The results of the segmentation algorithm presented here would indicate that with further development, segmentation might provide an alternative and possibly preferable approach to delineating management zones. [Copyright &y& Elsevier]

In this paper we present a stereo feature-based method using SIFT (Scale-invariant feature transform) descriptors. We use automatic feature extractors, matching algorithms between images and techniques of robust estimation to produce a DTM (Digital Terrain Model) using convergent shots of a rock glacier.The geomorphologic structure observed in this study is the Veleta rock glacier (Sierra Nevada, Granada, Spain). This rock glacier is of high scientific interest because it is the southernmost active rock glacier in Europe and it has been analyzed every year since 2001. The research on the Veleta rock glacier is devoted to the study of its displacement and cartography through geodetic and photogrammetric techniques. [ABSTRACT FROM AUTHOR]

Sensors provide robotic systems with the information required to perceive the changes that happen in unstructured environments and modify their actions accordingly. The robotic controllers which process and analyze this sensory information are usually based on three types of sensors (visual, force/torque and tactile) which identify the most widespread robotic control strategies: visual servoing control, force control and tactile control. This paper presents a detailed review on the sensor architectures, algorithmic techniques and applications which have been developed by Spanish researchers in order to implement these mono-sensor and multi-sensor controllers which combine several sensors. [ABSTRACT FROM AUTHOR]

Abstract: Since 2002, the Galician Administration (NW Spain) has been working in the development of a decision support system for Agricultural Production Planning. To this end, an initial decision tool, Gestop®, was developed. However, the tool had a number of limitations for practical use because of the lack of differentiated data about the real situation of farms. With a view to improving the initial tool, a combinatorial method (CM) was developed. Such a combinatorial method used official census data to classify farms based on the different production processes, sizes and geographical locations of farms, among other factors. The combinatorial method proved very useful as a source of differentiated data for the decision support system. Yet, validating the results using robust statistical methods was considered essential in order to guarantee the suitability of the model for use. This paper uses discriminant analysis (DA) techniques to validate the types of dairy farms obtained from the combinatorial method for Galicia. Results clearly validate the combinatorial method and its use as a data source for decision support systems in Agricultural Production Planning. [Copyright &y& Elsevier]

The authors present a novel and heuristic approach for the detection of low radar cross-section targets in high-resolution sea clutter. The proposed technique is based on the application of the Radon transform to range–time matrices formed by column-wise storage of consecutive range profiles. The objective of this paper is 2-fold: to analyse the effect of the transform on real high-resolution sea clutter and to describe a detection scheme based on the insight obtained. The proposed technique emulates the behaviour of traditional motion target detection algorithms without the need for reliable Doppler information. It also constitutes a powerful non-coherent integration strategy of the target's energy along its specific path on the range–time plot. The performance of the detection technique has been tested against real high-resolution sea clutter data, acquired at the south coast of Spain with an in-house developed continuous wave linear frequency modulated millimetre-wave radar system. Monte Carlo simulations show a significant improvement over the conventional cell averaging constant false alarm rate schemes. [ABSTRACT FROM AUTHOR]

Abstract: The validation of coarse satellite-derived products from field measurements generally relies on up-scaling the field data to the corresponding satellite products. This question is commonly addressed through the generation of a reference high-resolution map of an area covering several moderate resolution pixels. This paper describes a method by which reference leaf area index (LAI) maps can be generated from ground-truth LAI measurements. The method is based on a multivariate ordinary least squares (OLS) algorithm which uses an iteratively re-weighted least squares (IRLS) algorithm. It provides an empirical relationship (i.e. a transfer function) between in situ measurements and concomitant radiance values from high-resolution HRVIR2/SPOT4 imagery. The band combination composed of green (G), red (R) and near infrared (NIR) proved to be appropriate for LAI prediction, with a cross-validation error lower than 0.7. The convex hull technique has been proposed to assess the extrapolation error of the up-scaling model. Although the model showed to be sensitive to pixels outside the convex hull, the error associated with extrapolated pixels was moderate (i.e. root mean square error (RMSE)≈0.3). A close agreement was found between LAI maps derived from different field based datasets despite the notable differences in the sampling constraints imposed by the use of different instruments. It confirms the validity of a stratified random sampling strategy and suggests the suitability of using around 50 elementary sampling units (ESUs) to characterize the study area. A sensitivity analysis has confirmed that this method is suited to capture the variability across the site extent and, at the same time, minimize field efforts. Knowledge gained from this work provides guidance for an efficient sampling strategy of LAI in a cropland area. [Copyright &y& Elsevier]

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land-surface temperature (LST) and emissivity (LSE) from remotely-sensed data. The proposed method is based on a synergistic usage of the split-window (SW) algorithm and the two-temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel-2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied. [ABSTRACT FROM AUTHOR]

This paper provides an accurate chronology of the Spanish reference business cycle adapting a multiple change-point model. In that approach, each combination of peaks and troughs dated in a set of economic indicators is assumed to be a realization of a mixture of bivariate Gaussian distributions, whose number of components is estimated from the data. The means of each of these components refer to the dates of the reference turning points. The transitions across the components of the mixture are governed by Markov chain that is restricted to force left-to-right transition dynamic. In the empirical application, seven recessions in the period from February 1970 to February 2020 are identified, which are in high concordance with the timing of the turning point dates established by the Spanish Business Cycle Dating Committee (SBCDC). [ABSTRACT FROM AUTHOR]

This paper explores the potential of an artificial immune-based supervised classification algorithm for land-cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non-self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune-based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune-based classifier works well in comparison with the maximum likelihood and the decision-tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision-tree classification algorithms. Results suggest an improved performance with the immune-based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune-based supervised classifier requires several user-defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant. [ABSTRACT FROM AUTHOR]

This paper shows the results of a low level controller that has been applied to an autonomous robotic system using a WiFi-based Partially Observable Markov Decision Process (POMDP). These observations provide a clue for global robot localization from the first iteration of the POMDP algorithm. Due to the noise channel of WiFi measures, it becomes necessary to make observations at a well known location within the environment. Therefore, a robust local navigator is needed in order to place the robot in an optimal position for making the observation. The system has been tested with two Pioneer 2AT robots in the premises of the Department of Electronics at the University of Alcalá. [Copyright &y& Elsevier]

The blind separation problem of linear time-dependent mixtures is addressed in this paper. We have developed a new algorithm based on the minimization of the mutual information as well as a penalized term which ensures an a priori normalization of the estimated sources (outputs) and so, avoids the scale indeterminacy. The criterion minimization is done using a well-known gradient approach. Finally, some numerical results are shown to illustrate the performance of the penalized algorithm compared to the Babaie-Zadeh approach presented in (Proceedings of IWANN, Granada, Spain, June 2001, pp. 834–842). [Copyright &y& Elsevier]

This work presents a full statistical analysis and accurate prediction of low-visibility events due to fog, at the A-8 motor-road in Mondoñedo (Galicia, Spain). The present analysis covers two years of study, considering visibility time series and exogenous variables collected in the zone affected the most by extreme low-visibility events. This paper has then a two-fold objective: first, we carry out a statistical analysis for estimating the fittest probability distributions to the fog event duration, using the Maximum Likelihood method and an alternative method known as the L-moments method. This statistical study allows association of the low-visibility depth with the event duration, showing a clear relationship, which can be modeled with distributions for extremes such as Generalized Extreme Value and Generalized Pareto distributions. Second, we apply a neural network approach, trained by means of the ELM (Extreme Learning Machine) algorithm, to predict the occurrence of low-visibility events due to fog, from atmospheric predictive variables. This study provides a full characterization of fog events at this motor-road, in which orographic fog is predominant, causing important traffic problems during all year. We also show how the ELM approach is able to obtain highly accurate low-visibility events predictions, with a Pearson correlation coefficient of 0.8 , within a half-hour time horizon, enough to initialize some protocols aiming at reducing the impact of these extreme events in the traffic of the A-8 motor road. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for Climate Network (CN) construction based on the Kullback-Leibler divergence (KLD) among Membership Probability (MP) distributions, obtained from the Second Order Data-Coupled Clustering (SODCC) algorithm. The proposed method is able to obtain CNs with emergent behaviour adapted to the variables being analyzed, and with a low number of spurious or missing links. We evaluate the proposed method in a problem of CN construction to assess differences in wind speed prediction at different wind farms in Spain. The considered problem presents strong local and mesoscale relationships, but low synoptic scale relationships, which have a direct influence in the CN obtained. We carry out a comparison of the proposed approach with a classical correlation-based CN construction method. We show that the proposed approach based on the SODCC algorithm and the KLD constructs CNs with an emergent behaviour according to underlying wind speed prediction data physics, unlike the correlation-based method that produces spurious and missing links. Furthermore, it is shown that the climate network construction method facilitates the evaluation of symmetry properties in the resulting complex networks. [ABSTRACT FROM AUTHOR]

The article reports on the launch of an Internet database, called the SCImago Journal & Country Rank database, allowing users to generate on-the-fly citation statistics of published research papers for free. The open-access database calculates papers' impact factors using an algorithm. It is collaborating with Amsterdam-based science publisher Elsevier. SCImago is a data-mining and visualization group in Spain. The company ranks journals and countries using citation metrics as the popular Hirsch Index. It also includes the SCImago Journal Rank (SJR).