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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]

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]

Breast density is a strong risk factor for breast cancer. In this paper, we present an automated approach for breast density segmentation in mammographic images based on a supervised pixel-based classification and using textural and morphological features. The objective of the paper is not only to show the feasibility of an automatic algorithm for breast density segmentation but also to prove its potential application to the study of breast density evolution in longitudinal studies. The database used here contains three complete screening examinations, acquired 2 years apart, of 130 different patients. The approach was validated by comparing manual expert annotations with automatically obtained estimations. Transversal analysis of the breast density analysis of craniocaudal (CC) and mediolateral oblique (MLO) views of both breasts acquired in the same study showed a correlation coefficient of ρ = 0.96 between the mammographic density percentage for left and right breasts, whereas a comparison of both mammographic views showed a correlation of ρ = 0.95. A longitudinal study of breast density confirmed the trend that dense tissue percentage decreases over time, although we noticed that the decrease in the ratio depends on the initial amount of breast density. [ABSTRACT FROM AUTHOR]

The price of electrical energy in Spain has not been regulated by the government since 1998, but determined by the supply from the generators in a competitive market, the so-called "electrical pool". A genetic method for analyzing data from this new market is presented in this paper. The eventual objective is to determine the individual supply curves of the competitive agents. Adopting the point of view of the game theory, different genetic algorithm configurations using coevolutionary and non-coevolutionary strategies combined with scalar and multi-objective fitness are compared. The results obtained are the first step toward solving the induction of the optimal individual strategies into the Spanish electrical market from data in terms of perfect oligopolistic behavior. [ABSTRACT FROM AUTHOR]

Background: Several studies have described potential microRNA (miRNA) biomarkers associated with migraine, but studies are scarcely reproducible primarily due to the heterogeneous variability of participants. Increasing evidence shows that disease-related intrinsic factors together with lifestyle (environmental factors), influence epigenetic mechanisms and in turn, diseases. Hence, the main objective of this exploratory study was to find differentially expressed miRNAs (DE miRNA) in peripheral blood mononuclear cells (PBMC) of patients with migraine compared to healthy controls in a well-controlled homogeneous cohort of non-menopausal women. Methods: Patients diagnosed with migraine according to the International Classification of Headache Disorders (ICHD-3) and healthy controls without familial history of headache disorders were recruited. All participants completed a very thorough questionnaire and structured-interview in order to control for environmental factors. RNA was extracted from PBMC and a microarray system (GeneChip miRNA 4.1 Array chip, Affymetrix) was used to determine the miRNA profiles between study groups. Principal components analysis and hierarchical clustering analysis were performed to study samples distribution and random forest (RF) algorithms were computed for the classification task. To evaluate the stability of the results and the prediction error rate, a bootstrap (.632 + rule) was run through all the procedure. Finally, a functional enrichment analysis of selected targets was computed through protein–protein interaction networks. Results: After RF classification, three DE miRNA distinguished study groups in a very homogeneous female cohort, controlled by factors such as demographics (age and BMI), life-habits (physical activity, caffeine and alcohol consumptions), comorbidities and clinical features associated to the disease: miR-342-3p, miR-532-3p and miR-758-5p. Sixty-eight target genes were predicted which were linked mainly to enriched ion channels and signaling pathways, neurotransmitter and hormone homeostasis, infectious diseases and circadian entrainment. Conclusions: A 3-miRNA (miR-342-3p, miR-532-3p and miR-758-5p) novel signature has been found differentially expressed between controls and patients with migraine. Enrichment analysis showed that these pathways are closely associated with known migraine pathophysiology, which could lead to the first reliable epigenetic biomarker set. Further studies should be performed to validate these findings in a larger and more heterogeneous sample. [ABSTRACT FROM AUTHOR]

Purpose: To analyze if perioperative and oncologic outcomes with stenting as a bridge to surgery (SEMS-BS) and interval colectomy performed by acute care surgeons for left-sided occlusive colonic neoplasms (LSCON) are non-inferior to those obtained by colorectal surgeons for non-occlusive tumors of the same location in the full-elective context. Methods: From January 2011 to January 2021, patients with LSCON at University Regional Hospital in Málaga (Spain) were directed to a SEMS-BS strategy with an interval colectomy performed by acute care surgeons and included in the study group (SEMS-BS). The control group was formed with patients from the Colorectal Division elective surgical activity dataset, matching by ASA, stage, location and year of surgery on a ratio 1:2. Stages IV or palliative stenting were excluded. Software SPSS 23.0 was used to analyze perioperative and oncologic (defined by overall -OS- and disease free -DFS-survival) outcomes. Results: SEMS-BS and control group included 56 and 98 patients, respectively. In SEMS-BS group, rates of technical/clinical failure and perforation were 5.35% (3/56), 3.57% (2/56) and 3.57% (2/56). Surgery was performed with a median interval time of 11 days (9–16). No differences between groups were observed in perioperative outcomes (laparoscopic approach, primary anastomosis rate, morbidity or mortality). As well, no statistically significant differences were observed in OS and DFS between groups, both compared globally (OS:p < 0.94; DFS:p < 0.67, respectively) or by stages I–II (OS:p < 0.78; DFS:p < 0.17) and III (OS:p < 0.86; DFS:p < 0.70). Conclusion: Perioperative and oncologic outcomes of a strategy with SEMS-BS for LSCON are non-inferior to those obtained in the elective setting for non-occlusive neoplasms in the same location. Technical and oncologic safety of interval colectomy performed on a semi-scheduled situation by acute care surgeons is absolutely warranted. [ABSTRACT FROM AUTHOR]

The main objective of this study is to analyze the differences found in the results of noise mapping using two of the most popular software techniques for the prediction of environmental noise. The location selected to conduct the comparative study is an area encompassed by the ring road that surrounds the city of Pamplona and on a grid, with a total of 6 × 105 points, approximately. In fact, and as the Environmental Noise Directive points out, it is a major road designated by a Member State (Spain). Configuration of the calculation parameters (discretization of the sources, ground absorption, reflection order, etc.) was as equivalent as possible as far as programs allow. In spite of that, a great number of differences appear in the findings. Although in 95.5% of the points the difference in the noise level calculated from the two programs was less than 3 dB, this general statistic result concealed some great differences. These are due to the various algorithms that programs implement to evaluate noise levels. Most differences pertain to highly screened receivers or remote ones. In the former, the algorithm of visibility is the main cause of such differences. In the latter, differences are mainly brought about by a different implementation of the propagation under homogeneous and favorable atmospheric conditions from both software systems. [ABSTRACT FROM AUTHOR]

This study presents a comparison between sixteen filter ranking methods applied to a real air pollution problem. Adaptations of the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm to use the Spearman's rank correlation, the kernel canonical correlation analysis, the Hilbert–Schmidt independence criterion, correntropy, the Pearson's correlation and the distance correlation are included among them. These methods were compared by estimating the hourly NO2 concentrations at three monitoring stations located in the Bay of Algeciras (Spain). The estimation models were generated using Bayesian regularized artificial neural networks. Different estimation cases were tested for each ranking method. Finally, results were statistically compared to determine which filter ranking strategy produced the best performing model in each case. The proposed estimation scenarios showed how mRMR methods had better results than all the remaining methods when a small number of features was selected. However, their advantage was not so evident when the number of selected features increased. Results from the proposed mRMR methods were promising, especially in the case of the distance correlation mRMR, the kernel canonical correlation analysis mRMR and the Spearman's rank correlation mRMR. These ranking methods performed better than the original mRMR algorithm that employs mutual information internally. [ABSTRACT FROM AUTHOR]

Hydrological models are widely used tools in water resources management. Their successful application requires an efficient calibration of the model parameters. Nowadays, there are very powerful global search methods applied to this end, but they have the disadvantage of presenting a high computational cost, because the numerical model to be calibrated needs to be evaluated a large number of times with different parameter sets. In this context, surrogate models can reduce significantly the run time of hydrological models, easing the total computational burden of global search methods. In the present work, we propose and explore the combination of a swarm intelligence-based optimization method, the artificial bee colony algorithm, with a surrogate model based on artificial neural networks in order to calibrate hydrological models. The proposed approach (ABC-ANN) is applied to the calibration and validation of a nine-parameter continuous hydrological model in two basins located in the northwest of Spain. Several aspects of the algorithm are evaluated, including its capability to calibrate the model parameters and its efficiency in terms of CPU time compared to a standard implementation of the ABC algorithm. Results show that the ABC-ANN algorithm is able to identify the location of suitable parameter sets with an accuracy rate within 89 and 99 %, and a reduction in CPU time of more than three orders of magnitude when compared to a sequential implementation. In addition, the frequency distribution of the parameter sets identified gives valuable information about the sensitivity of model output to the input parameters. [ABSTRACT FROM AUTHOR]

Algal atypical proliferation is a consequence of water fertilization (also called eutrophication) and a worldwide environmental concern since water quality and its uses are seriously compromised. Prevention is the most effective measure given that once the algal proliferation starts, it is too difficult and costly to stop the process. This article presents a nonparametric machine learning algorithm that combines the gradient boosted regression tree (GBRT) model and an improved differential evolution algorithm (L-SHADE) for better understanding and control of the algal abnormal proliferation (usually estimated from Chlorophyll-a and Total Phosphorus concentrations) from physicochemical and biological variable values obtained in a northern Spain reservoir. This L-SHADE technique involves the optimization of the GBRT hyperparameters during the training process. Apart from successfully estimating algal atypical growth (coefficients of determination equal to 0.91 and 0.93 for Chlorophyll-a and Total Phosphorus concentrations were obtained, respectively), this hybrid model allows here to establish the ranking of each independent biological and physicochemical variable according to its importance in the algal enhanced growth. [ABSTRACT FROM AUTHOR]

We address a discrete competitive facility location problem with an asymmetric objective function and a binary customer choice rule. Both an integer linear programming formulation and a heuristic optimization algorithm based on ranking of candidate locations are designed to solve the problem. The proposed population-based heuristic algorithm is specially adapted for the discrete facility location problems by using their features such as geographical distances and the maximal possible utility of candidate locations, which can be evaluated in advance. The performance of the proposed algorithm was experimentally investigated by solving different instances of the model with real data of municipalities in Spain. [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).

The continued availability of products at any store is the major issue in order to provide good customer service. If the store is a drugstore this matter reaches a greater importance, as out of stock of a drug when there is high demand causes problems and tensions in the healthcare system. There are numerous studies of the impact this issue has on patients. The lack of any drug in a pharmacy in certain seasons is very common, especially when some external factors proliferate favoring the occurrence of certain diseases. This study focuses on a particular drug consumed in the city of Jaen, southern Andalucia, Spain. Our goal is to determine in advance the Salbutamol demand. Advanced data mining techniques have been used with spatial variables. These last have a key role to generate an effective model. In this research we have used the attributes that are associated with Salbutamol demand and it has been generated a very accurate prediction model of 5.78% of mean absolute error. This is a very encouraging data considering that the consumption of this drug in Jaen varies 500% from one period to another. [ABSTRACT FROM AUTHOR]

Extreme normalised residuals, defined as departures from the average values, of 65 daily maximum, Tmax, and minimum, Tmin, temperature series recorded in Catalonia (NE Spain) during 1950–2004 are analysed. Similarly to the sampling strategies applied to long dry spells, the partial duration series (PDS) offer some advantages in comparison with the annual extreme series. Instead of using a common percentile threshold for all temperature series, PDS are chosen according to the mean excess plot procedure. Series of extreme residuals are modelled, in terms of the L-moments formulation, by the generalised Pareto distribution. Extreme residuals of Tmax and Tmin are estimated for return periods ranging from 2 to 50 years and their spatial distribution is represented for selected return periods of 2, 5, 10, 25 and 50 years. Two daily extreme temperatures events, a hot episode (in August) and a cold episode (in February), are simulated taking into account the average Tmax ( Tmin) for a day in August (February), their standard deviations and the extremes for a 50-year return period. Both simulations are compared with outstanding real episodes recorded on August 13th 2003 and February 11th 1956. Additionally, a spatial regionalisation of Catalonia in several clusters, in terms of the extreme residuals for return periods from 2 to 50 years, is done. A principal component analysis is applied to the extreme residual curves characterising every temperature series and, using as variables the principal components, the regionalisation is obtained by applying the average linkage clustering algorithm. Finally, each cluster is characterised by its average extreme residual curve for return periods ranging from 2 to 50 years at 1-year interval. [ABSTRACT FROM AUTHOR]

Objective: To estimate the usefulness of 2-h creatinine clearance (CrCl) in the ICU and define variables that may reduce agreement.Design: Prospective study.Setting: Polyvalent ICU of a university hospital.Patients: 359 patients.Interventions: We compared 24-h CrCl (CrCl-24h), as the standard measure, with 2-h CrCl (CrCl-2h) (at the start of the period) and the Cockroft-Gault equation (Ck-G).Measurements and Results: The 2-h sample was lost in two patients (0.6%) and the 24-h sample was lost in 50 patients (13.9%). The mean Ck-G was 87.4+/-3.05, with CrCl-2h 109.2+/-4.46 and CrCl-24h 100.9+/-4.21 ml/min/1.73 m2 (r2 of 0.88 for CrCl-2h and 0.84 for Ck-G). The differences from ClCr-24h were 21.8+/-3.3 (p<0.001) for the Ck-G and 8.3+/-2.6 (p<0.05) for CrCl-2h (p<0.05). In the subgroup of patients with CrCl-24h<100 ml/min/1.73 m2, the CrCl-24h value was 52.9+/-2.71 vs. 51.6+/-2.14 for CrCl-2h (p=ns) and 57.6+/-2.56 (p<0.001) for the Ck-G. Patients with CrCl<100 ml/min only showed variability in hyperglycemia during the 24-h period.Conclusions: In intensive care patients, 24-h CrCl results in a large proportion of non-valid determinations, even under conditions of close monitoring. Two-hour CrCl is an adequate substitute, even in patients who are unstable or who have irregular diuresis where a 24-h collection is impossible. The Cockroft-Gault equation seems less useful in this setting. [ABSTRACT FROM AUTHOR]

Since 1984, a seismic surveillance of the Baix Ebre Region (NE of the Iberian Peninsula) and the La Cerdanya Valley (Eastern Pyrenees) has been performed by the Institut d’Estudis Catalans and the Universitat de Barcelona, by means of four digital stations. An investigation of the apparent magnitude has been carried out to explain the observed anomalous differences between the amplitude of the recorded events and their associated magnitude, as given by the different agencies. Calculating the local magnitude with independent attenuation parameters and comparing the obtained results using the different stations, we obtain differences up to 1.0. These differences are interpreted in terms of very local effects and point out the uselessness of some stations to assign magnitudes of local events. Results emphasize that the effectiveness of the trigger algorithms in digital stations rarely depends on the apparent (local) magnitude. As expected, the magnitude sensitivity is related to the values of the station correction terms, meaning that extra information is required for an optimum surveillance of a local area. A 2-D analysis of the distribution of the differences between the computed and reported magnitude shows no significant azimuthal dependence. This indicates that the obtention of a local magnitude scale with an appropriate station correction term related to epicentral distance, results in an important factor to provide magnitude to local events. The obtained results corroborate previous works done at the same area and emphasis is placed on further analysis directed to obtain a better characterization of the local site influence. [ABSTRACT FROM AUTHOR]