Search

Background and Objective: In the realm of automatic Electronic Health Records (EHR) classification according to the International Classification of Diseases (ICD) there is a notable gap of non-black box approaches and more in Spanish, which is also frequently ignored in clinical language classification. An additional gap in explainability pertains to the lack of standardized metrics for evaluating the degree of explainability offered by distinct techniques., Methods: We address the classification of Spanish electronic health records, using methods to explain the predictions and improve the decision support level. We also propose Leberage a novel metric to quantify the decision support level of the explainable predictions. We aim to assess the explanatory ability derived from three model-independent methods based on different theoretical frameworks: SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Integrated Gradients (IG). We develop a system based on longformers that can process long documents and then use the explainability methods to extract the relevant segments of text in the EHR that motivated each ICD. We then measure the outcome of the different explainability methods by implementing a novel metric., Results: Our results beat those that carry out the same task by 7%. In terms of explainability degree LIME appears as a stronger technique compared to IG and SHAP., Discussion: Our research reveals that the explored techniques are useful for explaining the output of black box models as the longformer. In addition, the proposed metric emerges as a good choice to quantify the contribution of explainability techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The combination of two macro trends, Information and Communication Technologies' (ICT) proliferation and novel approaches in education, has resulted in a series of opportunities with no precedent in terms of content, channels and methods in education. The present contribution aims to describe the experience of using an automatic spreadsheet corrector, its perceived benefits as well as its impact on academic results. A total of 179 students coursing technical studies attended a basic course on business administration in the framework of which the field work was conducted. Perception assessment of students demonstrates a very good opinion about the learning experience using the proposed ICT tool. The user perception evaluation also shows that it is a valuable learning tool, while students who obtain a high final qualification are the ones that have been positively evaluated in the sessions using the automatic spreadsheet corrector. Beyond these highlighted benefits for students, teachers gain time, effort and workload reduction. The validity of the proposal is tested in the Polytechnic School of the University of Girona in Catalonia (Spain). [Paper presented at the International Conference on University Teaching and Innovation (CIDUI): Learning and Teaching Innovation Impacts (9th, Bellaterra, Barcelona, Spain, Jul 5-7 2016.)]

Background: The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults., Objective: This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline., Methods: A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability., Results: In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets., Conclusions: This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature., (©Emilia Ambrosini, Chiara Giangregorio, Eugenio Lomurno, Sara Moccia, Marios Milis, Christos Loizou, Domenico Azzolino, Matteo Cesari, Manuel Cid Gala, Carmen Galán de Isla, Jonathan Gomez-Raja, Nunzio Alberto Borghese, Matteo Matteucci, Simona Ferrante. Originally published in JMIR Aging (https://aging.jmir.org), 29.04.2024.)

Summary: Progress in high‐throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi‐automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree‐level phenotypes followed by genome‐wide association study (GWAS).An RGB‐based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 single nucleotide polymorphisms (SNPs).Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisome metabolism. About 16–20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho‐physiological traits.Despite a limited availability of markers and individuals, this study is provides a successful proof‐of‐concept for the combination of high‐throughput UAV‐based phenotyping with cost‐effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer. [ABSTRACT FROM AUTHOR]

Automatic evaluation is a challenging field that has been addressed by the academic community in order to reduce the assessment workload. In this work we present a new element for the authoring tool Easy Java Simulations (EJS). This element, which is named automatic evaluation element (AEE), provides automatic evaluation to virtual and remote laboratories built with EJS by using the server application Goodle grading management system (GMS). The integration of both tools entitles a professor to create interactive virtual and remote laboratories and automatically evaluate the work of their students. As a test bed two case studies are presented; a non-linear controller design virtual laboratory used in an advanced control master course and a servomechanism virtual laboratory used in an undergraduate basic control course.

Background: Early defibrillation is one of the interventions that can most influence the prognosis of cardiac arrest. The objectives of this study were to determine the number of automatic external defibrillators outside the healthcare setting in each autonomous community in Spain and to compare the legislation of each autonomous community on the mandatory installation of defibrillators outside the healthcare setting., Methods: A cross-sectional observational study was carried out between December 2021 and January 2022 by consulting official data in the 17 Spanish autonomous communities., Results: Complete data on the number of registered defibrillators were obtained from 15 autonomous communities. The number of defibrillators ranged from 35 to 126 per 100,000 inhabitants. At the global level, differences were observed between communities with mandatory defibrillator installation and those without (92.1 vs. 57.8 defibrillators/100,000 inhabitants)., Conclusions: There is heterogeneity in the provision of defibrillators outside the health care setting, which seems to be related to the diversity of legislation on the mandatory installation of defibrillators., (Copyright © 2023 FECA. Publicado por Elsevier España, S.L.U. All rights reserved.)

This article presents the results of the application of the automatic translator DeepL to the field of gastronomy and wine tourism. In today's world, where it is necessary to translate a large number of text in an ever shorter time, translations engines are a very useful tool that is increasingly used by professionals in the sector and by people in their daily lives. However, one of the limitations usually attributed to these engines is that they do not always achieve a valid translation of cultural elements or different linguistic connotations, especially when it comes to certain linguistic combinations. Through some examples taken from the official tourism portal of Spain, the performance of the DeepL engine, which is available on the market in its free version, is observed and evaluated. For our analysis we focus on the specific field of food and wine tourism and on the Spanish-Italian language combination, and we compare the Italian translation of some fragments published on the web with the translation made with DeepL, to highlight the differences and eventual inaccuracies after a human evaluation process. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosis and characterization of biliary strictures is challenging, even after the introduction of digital single-operator cholangioscopy (D-SOC). The endoscopist's visual impression has a suboptimal accuracy and there is a significant interobserver variability. Artificial intelligence tools for image analysis have presented important contributions in several fields of gastroenterology. Convolutional neural networks are highly efficient multi-layered deep neural networks for image analysis, with great results in several fields of medicine. Nevertheless, the role of these deep learning models in digital cholangioscopy is still in a premature phase. With this bicentric international study, the authors aimed to create a deep learning-based algorithm for digital cholangioscopy capable of distinguishing benign from malignant biliary lesions. The present model accurately detected malignant biliary lesions with an image processing rate that favors its clinical applicability. The authors believe that the use of an AI-based model may change the landscape in the digital cholangioscopy diagnostic yield. Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures. [ABSTRACT FROM AUTHOR]

Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects' behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset. [ABSTRACT FROM AUTHOR]

Photosensitivity is a neurological disorder in which a person's brain produces epileptic discharges, known as Photoparoxysmal Responses (PPRs), when it receives certain visual stimuli. The current standardized diagnosis process used in hospitals consists of submitting the subject to the Intermittent Photic Stimulation process and attempting to trigger these phenomena. The brain activity is measured by an Electroencephalogram (EEG), and the clinical specialists manually look for the PPRs that were provoked during the session. Due to the nature of this disorder, long EEG recordings may contain very few PPR segments, meaning that a highly imbalanced dataset is available. To tackle this problem, this research focused on applying Data Augmentation (DA) to create synthetic PPR segments from the real ones, improving the balance of the dataset and, thus, the global performance of the Machine Learning techniques applied for automatic PPR detection. K-Nearest Neighbors and a One-Hidden-Dense-Layer Neural Network were employed to evaluate the performance of this DA stage. The results showed that DA is able to improve the models, making them more robust and more able to generalize. A comparison with the results obtained from a previous experiment also showed a performance improvement of around 20% for the Accuracy and Specificity measurements without Sensitivity suffering any losses. This project is currently being carried out with subjects at Burgos University Hospital, Spain. [ABSTRACT FROM AUTHOR]

This article presents an innovative project in the context of remote experimentation applied to control engineering education. Specifically, the authors describe their experience regarding the analysis, design, development, and exploitation of web-based technologies within the scope of automatic control. This work is part of an inter-university project known as AutomatL@bs, in which seven Spanish universities joined efforts to share their experimentation resources across the Internet. The paper begins by providing a background of how the development of virtual and remote control labs with pedagogical perspectives should be addressed. In particular, we present examples of remote labs developed by two of the university groups taking part in AutomatL@bs. We then present the automatic booking system that manages the access of users to each laboratory's didactical setup. Next, we show the integration process of every component into a Learning Management System (LMS). Finally, an overall system assessment of the students' perception of the quality of the experimental environment as a learning tool is analyzed. (Contains 16 figures.)

For the correct operation of the electricity system, producers must provide an estimate of the energy they are going to discharge into the system, and they must face financial penalties if their forecasts are wrong. This is especially difficult in the case of renewable energies, and in particular wind energy because of its variability and intermittency. The tool proposed allows, in a first step, to improve the prediction of wind energy to be produced and, in a second step, to optimize the offer to be presented to the electricity market, so that the overall economic performance can be improved. This tool is based on the use of public information and automatic learning and has been evaluated on a set of 30 wind farms in Spain, using their historical production data. The results indicate improvements in both the accuracy of the energy estimation and the profit obtained from the energy sold. [Display omitted] • A tool to improve the forecasting of energy production and economic performance of wind farms is presented. • Wind energy forecasting by using this tool is based on public information and automatic learning. • Comparing the results with similar tools, an improvement in both energy forecasting and economic performance is obtained. [ABSTRACT FROM AUTHOR]

In this paper we present a web-based innovative education tool designed for automating the collection, evaluation and error detection in practical exercises assigned to computer programming and control engineering students. By using a student/instructor code-fusion architecture, the conceptual limits of multiple-choice tests are overcome by far. The proposed system is also able to individually parameterize the exercises for each student and allows an instructor to implement innovative self-learning techniques in which student can obtain a measure of their knowledge continuously along the whole course length. The proposed automatic evaluation system has been applied to several control engineering courses at the University of Seville and the results several case studies are presented here. (Contains 6 tables and 7 figures.)

The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images. [ABSTRACT FROM AUTHOR]

Some people have problems accessing multimedia services available on the web. Contributions to accessibility made by end users who lack institutional support in the design, production or deployment of media accommodations should be considered. The aim of our paper is to assess the quality of the captions produced using YouTube by non-professional subtitlers who only received basic training. We also identify potential improvements either in the subtitling tool or in the training resources, which could enhance the quality of the captions. We conducted a study in which 53 participants of a MOOC on digital accessibility used the automatic speech recognition (ASR) feature of YouTube to produce the captions for a video provided by the teaching staff. We assessed the quality of the captions produced by the students and then compared it with the quality of the captions produced by: (a) a human expert and (b) the ASR-based subtitling by YouTube. Students' errors occurred mainly in the number of characters per line, the speed of the captions, failing to use a new line per participant, and not including sound effects. The course should warn students to use a new line per participant, teach them how to subtitle sound effects, specify the maximum number of characters per line of text, and inform that in some countries such as Spain, captions can be edited but in other countries this may not be possible. Our recommendations for the YouTube editor include improving both the user interface and the ASR, with a view to enhancing ongoing and future research. [ABSTRACT FROM AUTHOR]

We studied the spontaneous vocal behaviour of captive wolves at the International Wolf Center (IWC) in Minnesota (spring 2019 and winter 2020), and the Centro del Lobo Ibérico Félix Rodríguez de la Fuente (CLIFRF) in Spain (winter 2020). We used AudioMoth recording devices to record wolf howling 24 h/day. We identified 412 solo howl series and 403 chorus howls and found differences between wolves at the two centres. Vocal rates for North American wolves at the IWC (7.8 chorus howls/day in spring and 4.8 chorus howls/day in winter) were higher than rates obtained for Iberian wolves from CLIFRF (3.8 chorus howls/day in winter). Howling rates obtained in our study were similar to those obtained for captive Mexican wolves and greater than those reported for wild wolves. Hourly distribution of howling was also different between centres. The greatest howling activity identified at IWC was at pre-sunrise, while at CLIFRF the peak occurred at sunset. Weather conditions had little influence on the vocal behaviour of the captive wolves we studied. We show the potential of passive recorders to study topics of animal acoustic communication, such as vocal rates and temporal patterns, that have not been deeply addressed due to technological constraints. [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]

The mechanical behavior of block-in-matrix materials is heavily dependent on their block content. This parameter is in most cases obtained through visual analyses of the ground through digital imagery, which provides the areal block proportion (ABP) of the area analyzed. Nowadays, computer vision models have the capability to extract knowledge from the information stored in these images. In this research, we analyze and compare classical feature-detection algorithms with state-of-the-art models for the automatic calculation of the ABP parameter in images from surface and underground outcrops. The outcomes of this analysis result in the development of a framework for ABP calculation based on the Segment Anything Model (SAM), which is capable of performing this task at a human level when compared with the results of 32 experts in the field. Consequently, this model can help reduce human bias in the estimation of mechanical properties of block-in-matrix materials as well as contain underground technical problems due to mischaracterization of rock block quantities and dimensions. The methodology used to obtain the ABP at different outcrops is combined with estimates of the rock matrix properties and other characterization techniques to mechanically characterize the block-in-matrix materials. The combination of all these techniques has been applied to analyze, understand and try, for the first time, to model Roman gold-mining strategies in an archaeological site in NW Spain. This mining method is explained through a 2D finite-element method numerical model. [ABSTRACT FROM AUTHOR]

The origin–destination (OD) demand matrix plays an essential role in travel modeling and transport planning. Traditional OD matrices are estimated from expensive and laborious traffic counts and surveys. Accordingly, this study proposes a new combined methodology to estimate or update OD matrices (urban mobility) directly from easy-to-obtain and free-of-charge socioeconomic variables. The Málaga region, Spain, was used as a case study. The proposed methodology involves two stages. First, an automatic feature selection procedure was developed to determine the most relevant socioeconomic variables, discarding the irrelevant ones. Several feature selection techniques were studied and combined. Second, machine learning (ML) models were used to estimate mobility between predefined zones. Artificial neural networks (ANNs) and support vector regression (SVR) were tested and compared using the most relevant variables as inputs. The experimental results show that the proposed combined model can be more accurate than traditional methods and ML models without the feature selection procedure. In particular, SVR with feature selection slightly outperformed the combined model using ANNs. The proposed methodology can be a promising and affordable alternative method for estimating OD matrices, reducing costs and lead time significantly, and assisting and improving urban transport planning. [ABSTRACT FROM AUTHOR]

Natural parks are valuable protected natural areas (PNA) and can attract many visitors, thus increasing the pressure on ecosystems and contrasting with conservation objectives. This article focuses on Montsant Natural Park and describes a comprehensive method for monitoring visitors based on the optimized use of automatic counters called the Optimal Visitor Monitoring System (OVMS). This system ensures (i) greater reliability of the data collected, (ii) greater adaptability to the characteristics of the different areas, and (iii) better use of the data provided by the monitoring system. Data generated by the OVMS included visitor inflow by entrances, visitor inflow by months, and distribution of visitor inflow by trail or recreation area within the park. The OVMS is useful for monitoring visitors over years and identifying trends that can inform regulatory or management actions related to the public use of conservation lands. [ABSTRACT FROM AUTHOR]

Drought monitoring is essential to determine, at short time intervals, the main characteristics of drought events, such as their duration, severity, and spatial distribution. To ensure that drought monitoring represents a useful tool for governmental plans aimed at preventing or minimizing drought impacts, up-to-date information must be instantaneously accessible and it must provide high spatial and temporal resolution. This study presents a system that allows the automatic tracking of meteorological droughts in the Spanish territory, based on an open and easy-to-use online platform (https://monitordesequia.csic.es/monitor). This drought monitoring system provides two drought synthetic indices: the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI). Information is provided on a quasi-weekly basis, in a grid format, with a spatial resolution of 1.1*1.1 km, and with data from 1961 to the present time. This drought monitor is updated based on the real-time information gathered from automatic stations, which in turn requires historic information to identify and track drought events. The drought indices are obtained from data processing (quality control, temporal series reconstruction, homogenisation, interpolation, and validation) using climatic variables (maximum and minimum temperatures, solar radiation, rainfall, dew point, and wind speed) which are provided by the Spanish Meteorology Agency and the Ministry of Agriculture of the Spanish Government. We performed a validation of the drought indices for the whole historical period (1961–2020). This allowed us to observe a strong spatial agreement between the indices obtained with the historical dataset and the indices from the monitoring dataset, especially for mainland Spain and the Balearic Islands (Pearson's r, SPI and SPEI >0.99). The presented real-time drought monitoring system represents a relevant and useful tool that allows for quick and effective actions to prevent and mitigate the effects of drought on society and ecosystems. • Automatic tracking of meteorological droughts • Relevant and useful tool to prevent and mitigate the effects of drought. • The procedure described could be easily implemented in other regions. [ABSTRACT FROM AUTHOR]

The rise in artificial intelligence and natural language processing techniques has increased considerably in the last few decades. Historically, the focus has been primarily on texts expressed in prose form, leaving mostly aside figurative or poetic expressions of language due to their rich semantics and syntactic complexity. The creation and analysis of poetry have been commonly carried out by hand, with a few computer‐assisted approaches. In the Spanish context, the promise of machine learning is starting to pan out in specific tasks such as metrical annotation and syllabification. However, there is a task that remains unexplored and underdeveloped: stanza classification. This classification of the inner structures of verses in which a poem is built upon is an especially relevant task for poetry studies since it complements the structural information of a poem. In this work, we analyzed different computational approaches to stanza classification in the Spanish poetic tradition. These approaches show that this task continues to be hard for computers systems, both based on classical machine learning approaches as well as statistical language models and cannot compete with traditional computational paradigms based on the knowledge of experts. [ABSTRACT FROM AUTHOR]

Background: Given the lack of data about the nutritional value and other determinants of the consumption of foods and drinks sold in vending machines in European universities and the relevance of this sector in Spain, it is necessary to obtain scientific data on this topic. The present study aimed to assess the availability, nutritional profile and processing level of food products from vending machines at a Spanish public university and to investigate differences in nutritional profile according to the cost and promotion., Methods: Cross-sectional descriptive study. Data from all products available (3894) were collected and analysed using the criteria of the Spanish Agency for Consumption, Food Safety and Nutrition and the United Kingdom nutrient profiling model. The items were also classified according to the degree of industrial processing through the NOVA system. Promotion was assessed, taking into account where products were displayed in vending machines., Results: The most common products were sweets (23.4% of the total options), coffee (20.3%) and salty snacks (11.7%). According to the combination of the two criteria used to assess nutritional profile, 48.6% of the products were classified as with low nutritional quality (LNQ). In addition, 73.8% of the items were categorised as "ultra-processed". Foods (β = 0.31, 95% CI 0.24, 0.39, p < 0.001) and hot drinks (β = 0.46, 95% CI 0.39, 0.52, p < 0.001) with high nutritional quality (HNQ) were more likely to have higher prices than alternatives with LNQ. Both foods and cold drinks that support healthy dietary recommendations were promoted to a lesser extent than those with LNQ ( p < 0.001)., Conclusion: Almost half of the products were of LNQ and three-quarters had a high level of processing. Moreover, foods and cold drinks with LNQ were less expensive and more often promoted than alternatives with HNQ.

Background: The growing need to classify the origin of honey in a simple way is leading to the development of affordable analytical equipment that is in-line and manageable, enabling rapid on-site screening. The aim of this work was therefore to evaluate whether an electronic tongue (made of four metallic electrodes: Ir, Rh, Pt, Au), based on potential multistep pulse voltammetry with electrochemical polishing, is able to differentiate between honey samples from Spain, Honduras, and Mozambique., Results: It was demonstrated, for the first time, that automatic pulse voltammetry, in combination with principal component analysis (PCA) statistical analysis, was able to differentiate honey samples from these three countries. A partial least squares (PLS) analysis predicted the level of certain physicochemical parameters, the best results being for conductivity and moisture with correlation coefficients of 0.948 and 0.879, whereas the weakest correlation was for the sugars., Conclusion: The tool proposed in this study could be applied to identify the country origin of the three types of multifloral honey considered here. It also offers promising perspectives for expanding knowledge of the provenance of honey. All of this could be achieved when a comprehensive database with the information generated by this electronic tongue has been created. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

Abstract: Improved water management and efficient investment in the modernization of irrigation schemes are essential measures in many countries to satisfy the increasing demand for water. Automatic control of the main canals is one method for increasing the efficiency and flexibility of irrigation systems. In 2005, one canal in the irrigation scheme ‘Sector B-XII del Bajo Guadalquivir’ was monitored. This canal is representative of irrigation schemes in Southern Spain; it is divided into four pools and supplies an area of 5154ha. Ultrasonic sensors and pressure transducers were used to record the gate opening and water levels at the upstream and downstream ends of each canal pool. Using the recorded data and the SIC (Simulation of Irrigation Canals) hydraulic model, two canal control options (local upstream control and distant downstream control) were evaluated using a PI (Proportional-Integral) control algorithm. First, the SIC model was calibrated and validated under steady-state conditions. Then the proportional and integral gains of the PI algorithm were calibrated. The controllers were tested using theoretical demand changes (constant outflow followed by a sudden demand increase or decrease) and real demand changes generated on the basis of a spatially distributed crop water balance that included a number of sources of variability (random and not random) in the determination of field irrigation timing and depth. The results obtained show that only the distant downstream controller was able to adjust quickly and automatically the canal dynamics to the varying water demands; it achieved this efficiently and with few spills at the canal tail, even when there were sudden and significant flow variations. [Copyright &y& Elsevier]

• Holidays affect electricity load greatly, causing inaccurate forecasts. • This effect varies widely among holidays, special days and adjacent days. • Wide and detailed classification of each special day can reduce forecasting error. • The proper methodology eliminates the analisys effort needed for classification. • An automatic classification can outperform an expert-level classification. Electricity demand presents a repetitive pattern following daily, weekly and seasonal patterns. However, factors like temperature or social events tend to disrupt these patterns introducing outlying data that is difficult to forecast. This paper introduces a new methodology to classify special days without any prior knowledge of the database. Simple classification of special days into two or three categories is insufficient as the consumers' behavior has many shades on these days. However, classifying special days in a wide range of categories required a deep understanding of the consumers' behavior on different days and periods of the year. The methodology proposed describes an algorithm to automate this classification starting from a simple day-of-the-week classification and branching into as many categories as needed to fit a real database. Categories with similar profiles are merged to avoid overfitting and actual outliers are detected to ensure that no false categories are created. The methodology is developed using data from 2010 to 2017 and tested in three different systems. The benchmark used is the classification used by the Transmission System Operator in Spain and the test show that the proposed methodology provides more accurate results without the need of an expert to develop the classification. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the nutritional composition of food vending machines (VM) located in university public buildings and hospitals in Asturias., Design: Descriptive cross-sectional study., Location: The analysis was made of the contents of VMs located in public buildings, hospitals, and university teaching centres in the Principality of Asturias., Main Measurements: After the public buildings were mapped, each of them was visited to check for the presence of the VMs. A a photograph was then taken of the food contained in them. The variables analysed were the type of products and their nutritional composition., Results: The VMs of 19 buildings (12 university and 7 hospital), contained 215 foods that were grouped into 11 categories. The most frequent were "chocolates and chocolate bars" (30,2%), "cookies" (11.6%) and "chips" (11.6%). The Kcal average was 216 (SD=133.1). The mean fat, in grams was 12.52 (SD=11.21), saturated fat 4.48 (SD=3,83), sugars 11.88 (SD=31.13), fibre 1.9 (SD=2.47), proteins 3.38 (SD=3.62), and salt 0.3 (SD=0.62). A high excess of fat, saturated fat, and salt was observed, as well as a medium excess of sugars., Conclusions: The nutritional quality of VM in hospitals and public university centres of the Principality of Asturias cannot be defined as healthy. Therefore, health promotion strategies could be developed with the aim of improving their nutritional composition or guiding the population/users towards the selection and consumption of healthier foods., (Copyright © 2018 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)

• Machine learning with key data. • Classify building damage in the event of a river flood. • Prediction of damage by river flood in buildings with data easy to obtain. • Future in the search for new ways of detecting damage caused by river floods. Flooding due to overflowing rivers affects the construction elements of many buildings. Although significant progress has been made in predicting this damage, there is still a need to continue studying this issue. For this reason, the main goal of this research focuses on finding out if, based on a small dataset of cases of a given area, it is possible to predict at least three degrees of affectation in buildings, considering only three environmental factors (minimum distance from the river, unevenness and possible water communication). To meet this goal, the methodological approach followed considers scientific literature review and collection and analysis of a small dataset from 101 buildings that have been affected by floods in the Guadalquivir River basin (Andalusia. Spain). After analyzing this data, algorithms based on machine learning (ML) are applied to predict the degree of affection. The results, analysis and conclusions indicate that, if the study focuses on a specific area and similar buildings, using a correlation matrix and ML algorithms such as the "Decision Tree" with cross-validation, around 90% can be achieved in the "Recall" and "Precision" of "High-Level-Affection" class, and an "Accuracy" around 80% in general. [ABSTRACT FROM AUTHOR]

Summary: Quality control is a major prerequisite for using meteorological information. High quality data sources are vital to scientists, engineers and decision makers alike. Validation of meteorological data ensures that the information needed has been properly generated and that it identifies incorrect values and detects problems that require immediate maintenance attention. In this work, several quality assurance procedures based on different criteria are proposed and applied to meteorological data from the Agroclimatic Information Network of Andalusia (Southern Spain) to assess their integrity and quality. The procedures include validations of record structure data, range/limits, time and internal consistency, persistence and spatial consistency tests. Quality assurance tests consist of procedures or rules against which data are tested, setting data flags to provide guidance to end users. The proposed system is capable of identifying several types of errors and is used as a tool that allows one to make decisions such as sensor replacement and to remove data prior to their application. [Copyright &y& Elsevier]

Applying the AgroClimatic Evolution web application allows inquiries being made, data being collected and variables being calculated with the data acquired from different public agrometeorological stations on a single platform. Today all these stations from Murcia and Andalusia (Spain) are included, and stations elsewhere in Spain are being incorporated. This web application also offers the possibility of including each user's own stations, which increases the number and availability of data close to each farmer's plots. The data collected from stations is employed to collect daily data about weather and times, which are used to calculate the reference evapotranspiration (ETo). All the data are saved in a cloud database to later consult them and study their evolution. The data provided by all the stations are validated by applying the filters indicated in Standard UNE 500540:2004 "Automatic weather stations networks" by eliminating mistaken data that could alter correct ETo calculations. With the filtered data, and having calculated ETo, the user is provided with a comparison made with the raw data supplied by public stations. The main objective of this tool is to optimize the use of water resources available from data acquisition. Managing these data will contribute to make agriculture more sustainable and compatible with the natural environment. [ABSTRACT FROM AUTHOR]

Indices to evaluate AGC performance under both simulation and real operation are presented. These indices are based on functional dependence and statistical analysis avoiding large and long periods of data collection necessary for comparison of different regulators in real operation. A method to perform the global evaluation of the regulator using the individual values obtained for the indices is also presented. Variables typically measured in power systems are used to compute the indices. Examples of calculated values of the indices using data from real operation of two regulators in the same area of the Spanish power system are presented. Response criterion imposed in the Spanish power system is used. [ABSTRACT FROM AUTHOR]

While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis. [ABSTRACT FROM AUTHOR]

In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layout. • In this work we present a natural language generation model for the automatic generation of summaries to describe numerical data series. • We developed a fuzzy temporal model to manage the imprecision of temporal knowledge. • We provide a real use case of this model in the meteorological area providing summaries from air quality index data. • A human expert evaluation of the resulting linguistic descriptions was performed. [ABSTRACT FROM AUTHOR]

Copyright of RED - Revista de Educación a Distancia is the property of Universidad de Murcia 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.)

River capture is a surficial process that can lead to drainage reorganization and have significant impacts on sediment dispersal and biotic evolution. Discovery and study of river capture events are helpful in revealing the history of drainage basins, however, the present-day identification of river capture mainly depends on field and map observations by geomorphologists, who lack an automatic method. In the case of a large-scale drainage system with a complicated stream network, a field investigation is too time-consuming and costly. This study aims to develop a novel method for automatic river capture detection based on planform morphology and χ-plots of the stream network. The whole method can be described as a workflow including three steps: (1) searching candidate regions where river capture may have occurred; (2) iterating over each candidate region and roughly detecting the captor, captured, reversal, and beheaded rivers using the planform pattern of the stream network; (3) verifying the river capture with a χ-elevation plot in the candidate region found by rough detection. The description of the method is followed by two case studies from China and Spain, demonstrating the ability of the method to identify the location where the river capture might occur. We also discuss the parameters that must be optimized before extracting the river capture efficiently from the stream network. • A novel method is developed to automatically detect river capture events based on morphological features. • The workflow includes searching candidate regions, detecting river capture, and identifying river capture with a χ-plot. • Two case studies demonstrate the ability of the method to identify the location where the river capture might occur. [ABSTRACT FROM AUTHOR]

Copyright of Revista Española de Cardiología (18855857) is the property of Elsevier B.V. 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.)

Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de Catalunya, which is an online university. An automatic evaluation tool is proposed that allows the student to evaluate himself anytime and receive instant feedback. This tool is a web-based platform, and it has been designed for engineering subjects (i.e., with math symbols and formulas) in Catalan and Spanish. Particularly, the technique used for automatic assessment is latent semantic analysis. Although the experimental framework from the use case is quite challenging, results are promising. [ABSTRACT FROM AUTHOR]

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%). [ABSTRACT FROM AUTHOR]

The use of algorithms for automatic classification of aerial laser scanner 3D Point Clouds is the main process that improves its thematic quality. The main objectives of using 3D Point Clouds are the description of the surface and the detection of objects. The aim of this proposal for bridge and water detection algorithms is to increase the range and accuracy of the classification parameters of these products obtained with LiDAR technologies. With this methodology, the Digital Elevation Models (DEM) quality is improved and they are obtained by automated models of bridges and hydrography.This paper describes a methodology to detect and classify bridges and continental water bodies in points using the properties of LiDAR technology such as radiometric and geometric variables implementing indexes like NDVI, NDWI or NFC. In addition, the Network of Roads and Hydrographic models in Spain are used to reduce the area of interest and errors. Part of the province of Teruel (Spain) has been used as study area. [ABSTRACT FROM AUTHOR]

The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class. The results show that the SFT prediction made by the SVM model achieves an accuracy of 75.3%, which represents an improvement of 5.5% compared to the manual measurement. Regarding economic benefits, SVM model can increase them between 12 and 17%. It can be concluded that the classification using SVM is more accurate than the one performed manually with an increase of the economic benefit for sorting., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This paper deals with the implementation of an automatic and novel approach for UAV flight planning and control based on photogrammetric principles. Software which performs multiple tasks related to aerial photogrammetry and particularized to UAV systems was developed. Specifically, the flight planning and control framework incorporates a robust geometric control that guarantees the effectiveness, precision, and reliability of image acquisition. A real case study was generated to test and validate the flight planning and control strategies developed here [ABSTRACT FROM AUTHOR]

The automation of honey pollen visual sorting overcomes the limitations of the conventional procedure helping the specialist in this time-consuming task. In this work, a novel and comprehensive Ground Truth of almost 19,000 images (from optical microscopy) of the 16 most abundant types of grains/pollen particles present in citrus and rosemary honey from Spain was constructed. This task was assisted by a HoneyApp (also developed herein) for the labelling and annotation process. Subsequently, the effectiveness of different pre-existing automatic pollen recognizers based on convolutional neural networks (CNN) (VGG16, VGG19, InceptionV3, Xception, ResNet50, DenseNet201, MobileNetV2 and EfficientNetV2M) was tested together with a new network proposed in this paper (PolleNetV1). The extreme complexity of those pre-existing CNN and extensive use of millions of parameters makes this new proposal especially promising. Although with a slightly lower accuracy (average 96%) in determining the relative frequencies of different types of pollen grains/particles, it has considerable advantages such as simplicity and ability to be included in the future functionality to automate pollen recognition in honey. This is the first step to finally achieving an objective tool that allows the correct labelling of any types of pollen in honey, thus contributing to its transparency in the market. • A pollen images Ground Truth dataset from citrus and rosemary honeys was built. • A specific application (HoneyApp) to label/annotate pollen images was developed. • CNN networks were effective in counting the frequencies of pollen types in hone. • A new simpler, low memory and robust network (PolleNetV1) was created. • PolleNetV1 is expected to produce promising outcomes for other monoflorals. [ABSTRACT FROM AUTHOR]

The bullring of Ronda, one of the oldest in Spain, declared in 1993 as an Asset of Cultural Interest, occupies a paramount place among the buildings of its type thanks to its outstanding beauty. Its configuration as an open-air enclosure with a circular floor plan, as an evocation of the ancient Roman amphitheaters, and its interior with galleries on two levels that house the audience play a fundamental role in the acoustic energy decay and diffusion of the space. The link between architecture and acoustics of the Ronda bullring has been carried out by using on-site measurements and simulation techniques. To this end, an acoustic model is created, which is adjusted by taking the set of 3D impulse responses recorded on-site. The presence of the public and the various sound sources that exist during the bullfight itself are analyzed in the simulations, whereby the conditions of occupation and vacancy are compared, as are the variations due to the location of the sources. Finally, speech intelligibility conditions are simulated with a human directivity source. The precision of the virtual acoustic model enables the sound architecture of this singular space to be ascertained and preserved, thereby incorporating sound as an associated intangible heritage. [ABSTRACT FROM AUTHOR]

In 2015, a new automatic weather station (AWS) was installed in a high elevation site in Gredos mountains (Central System, Spain). Since then, a surprisingly high number of heavy precipitation events have been recorded (55 days with precipitation over 50 mm, and a maximum daily precipitation of 446.9 mm), making this site a hotspot in Spain in terms of annual precipitation (2177 mm year) and extreme precipitation events. The neighboring stations available in the region with longer data series, including the closest ones, already informed of wet conditions in the area, but not comparable with such anomaly behavior detected in the new station (51% higher). In this study, we present the temporal variability of detected heavy precipitation events in this mountain area, and its narrow relation with atmospheric patterns over the Iberian Peninsula. Results revealed that 65% of the events occurred during advections from West, Southwest, South and cyclonic situations. A regression analysis showed that the precipitation anomaly is mostly explained by the location windward to the Atlantic wet air masses and the elevation. However, the variance explained by the models is rather low (average R2 for all events > 50 mm is 0.21). The regression models underestimate on average a 60% intensity of rainfall events. Oppositely, the high-resolution weather forecast model AROME at 0.025° was able to point out the extraordinary character of precipitation at this site, and the underestimation of observed precipitation in the AWS was about 26%. This result strongly suggests the usefulness of weather models to improve the knowledge of climatic extremes over large areas, and to improve the design of currently available observational networks. [ABSTRACT FROM AUTHOR]

• Automatic detection of Tree Covered Areas (TCA) for the Iberian pig feed. • Comparison against original images and manual segmentation results. • Calculus of criteria for the results included the comparison with the manual segmentation. • Comparative analysis between TCA for the automatic and manual segmentations. The acorn is the fruit of oak trees and is an important crop in the Spanish dehesa extremeña because of the value it provides to feeding Iberian pigs to obtain the "acorn-fed" certification. For this reason, it is desirable to maximise production of Iberian pigs with the appropriate weight. Knowing the area covered by the acorn treetops to determine the Tree-Covered Area (TCA), a value required to estimate the number of Iberian pigs that can be released per hectare, as regulated by the Royal Decree 4/2014, issued by Spain in January of 2014. In this work, we propose a method to automatically estimate the TCA through digital aerial images (orthophotos) of the pastureland of Extremadura and, with this, to facilitate determining the number of Iberian pigs that can be released in a specific parcel of land. Requirements for automatic detection include the correct identification of acorn trees, correctly discerning their shadows, and discerning arbuscles (young acorn trees that are still unproductive, or shrubs that are not oaks). These requirements represent challenges that apply to both automatic and manual segmentations. The automated segmentation in this study is based on the modified version of the Gustafson-Kessel clustering algorithm presented by Babuska. The results of this work are promising when compared with the actual images and when compared with the results of manual segmentation. The entire set of 38 aerial images used in this work correspond to an approximate area of 15.56 hectares, and the results are of great interest to producers of Iberian pork with the "acorn-fed" certification. [ABSTRACT FROM AUTHOR]

Adventitial vasa vasorum are physiologic microvessels that nourish artery walls. In the presence of cardiovascular risk factors, these microvessels proliferate abnormally. Studies have reported that they are the first stage of atheromatous disease. Contrast-enhanced ultrasound (CEUS) of the carotid allows direct, quantitative and non-invasive visualization of the adventitial vasa vasorum. Hence, the development of computer-assisted methods that speed image analysis and eliminate user subjectivity is important. We developed methods for automatic analyses and quantification of vasa vasorum neovascularization in CEUS and tested these methods in a cohort of 186 individuals, 63 of whom were healthy volunteers. We implemented alternative automatic strategies for using the images to stratify patients according to their risk group and compare the strategies with respect to diagnostic performance. An automatic single-parameter strategy performs less effectively than the corresponding Arcidiacono method based on manual interpretation of the images (68 < area under the receiver operating characteristic curve [AUROC] for the manual Arcidiacono method < 82; 60 < AUROC for the automatic single-parameter strategy < 63). However, by use of additional image parameters, an automatic multiparameter strategy has significantly improved performance with respect to the manual Arcidiacono method (78 < AUROC < 90). The automatic multiparameter strategy is a valuable alternative to the manual Arcidiacono method, improving both diagnostic speed and diagnostic accuracy., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Focuses on an automatic system for control of the air pollution in the industrialized area of Huelva, Spain. Geographical description and the environmental monitoring network of the area; Estimation of pollutant emissions due to road traffic; Overview of atmospheric and dispersion modelling.