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Objective High-dimensional databases make it difficult to apply traditional learning algorithms to biomedical applications. Recent developments in computer technology have introduced deep learning (DL) as a potential solution to these difficulties. This study presents a novel intelligent decision support system based on a novel interpretation of data formalisation from tabular data in DL techniques. Once defined, it is used to diagnose the severity of obstructive sleep apnoea, distinguishing between moderate to severe and mild/no cases. Methods The study uses a complete database extract from electronic health records of 2472 patients, including anthropometric data, habits, medications, comorbidities, and patient-reported symptoms. The novelty of this methodology lies in the initial processing of the patients’ data, which is formalised into images. These images are then used as input to train a convolutional neural network (CNN), which acts as the inference engine of the system. Results The initial tests of the system were performed on a set of 247 samples from the Pulmonary Department of the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain), with an AUC value of ≈ 0.8. Conclusions This study demonstrates the benefits of an intelligent decision support system based on a novel data formalisation approach that allows the use of advanced DL techniques starting from tabular data. In this way, the ability of CNNs to recognise complex patterns using visual elements such as gradients and contrasts can be exploited. This approach effectively addresses the challenges of analysing large amounts of tabular data and reduces common problems such as bias and variance, resulting in improved diagnostic accuracy.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

The COVID-19 pandemic drastically transformed the integration of technology into medicine, testing the ability of health systems to make quick and effective decisions. This has been especially noticeable in emergency departments, which were overwhelmed by the massive influx of patients. In this context, this article presents the design, development, and proof of concept of a new intelligent decision support system applied to the management of patients suspected of having COVID-19 upon their arrival at an emergency department. To achieve this, starting from our proprietary database of chest X-rays (CXRs) collected at the Ribera Povisa Hospital, two modules based on the use of convolutional neural networks (CNNs) were sequentially run. The first was based on the DenseNet-121 model to identify whether a pneumonia condition was presented in the CXR, while the second was based on the COVID-Net CXR-S model and aimed to quantify the severity of airspace opacity in the CXR on a scale 0–24. Thus, based on this architecture, it will be possible to make predictions based on the CXR of new patients that, after interpretation, might allow physicians to determine whether cases are high-risk and, for example, should be admitted to the intensive care unit. Although the results we obtained were encouraging, it is important to note that this proposal is still at a conceptual stage of development and so future work will be required to validate it in real environments and develop techniques that can help explain its results.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Complementary feeding (CF) is defined as the feeding of infants that complements breastfeeding, or alternatively, feeding with a breast milk substitute, and is a process that is more than simply a guide as to what and how to introduce foods. The information provided by healthcare professionals must be up-to-date and evidence-based. Most of the recommendations that appear in the different international guidelines and position papers are widely applicable, but some must be regionalized or adapted to fit the conditions and reality of each geographic zone. The Nutrition Working Group of the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition (LASPGHAN) summoned a group of experts from each of the society’s member countries, to develop a consensus on CF, incorporating, whenever possible, local information adapted to the reality of the region. The aim of the present document is to show the results of that endeavor. Utilizing the Delphi method, a total of 34 statements on relevant aspects of CF were evaluated, discussed, and voted upon. Resumen: La alimentación complementaria (AC) se define como la alimentación de los lactantes que complementa a la lactancia humana o en su defecto, a la lactancia con un sucedáneo de la leche humana, y es un proceso que va más allá de simplemente una guía sobre qué y cómo introducir los alimentos. La información brindada por parte de los profesionales de la salud debe ser actualizada y basada en evidencia. Existen diferentes guías o documentos de posición a nivel internacional, que, aunque la mayoría de las recomendaciones pueden ser aplicables, hay algunas otras que requieren una regionalización o adecuación a las condiciones y realidad de cada zona. El grupo de trabajo de Nutrición de la Sociedad Latinoamericana de Gastroenterología, Hepatología y Nutrición Pediátrica convocó a un grupo de expertos, representantes de cada uno de los países que conforman la sociedad, con el objetivo de desarrollar un consenso sobre AC, que incorporó cuando así fue posible, información local que se adapte a la realidad de la región. El objetivo de este documento es mostrar los resultados de dicho trabajo. A través de metodología Delphi, se evaluaron, discutieron y votaron un total de 34 declaraciones o enunciados con respecto a aspectos relevantes de la AC.

This research focuses on the study of the virtual community of fans of the procedural video game No Man's Sky and how the contributions of the wiki fandom are projected in the official updates published until February 2024. Using a scientific methodology based on the content analysis of user interactions, we propose an exploratory study consisting of a virtual ethnography that identifies the audio content generated that has led to changes in the production of the updates of the video game analysed, thus establishing an effective correlation with the technological evolution of the title in question. [ABSTRACT FROM AUTHOR]

En las últimas décadas se han llevado a cabo diferentes estudios que profundizan en las condiciones que explican el compromiso de los individuos con las tareas académicas. Estas investigaciones evidencian la necesidad de combinar factores cognitivos —las capacidades, conocimientos o estrategias— con aspectos de carácter motivacional, como la disposición, la intención o las creencias autorreferidas. En este contexto se sitúa este estudio, que tiene por objeto identificar perfiles motivacionales, en función de la autoeficacia y las metas académicas, y estudiar su relación con el compromiso (esfuerzo y persistencia en la tarea), las emociones (miedo al fracaso y afecto positivo) y con el rendimiento académico (matemáticas y ciencias). La muestra está formada por 7,524 estudiantes (50.9% mujeres) de Educación Secundaria que participaron en la evaluación PISA 2018 (M = 15.84, DT = 0.29). Empleando el Latent Profile Analysis se identificaron cinco perfiles motivacionales. Los perfiles con puntuaciones más adaptativas en autoeficacia y metas de aprendizaje reconocían menor miedo al fracaso, más afecto positivo, esfuerzo/persistencia y obtenían mejor rendimiento académico (científico y matemático). Discutidos a la luz de la teoría motivacional, los resultados de esta investigación evidencian el papel de la orientación a metas y las creencias de autoeficacia en la promoción tanto del compromiso y rendimiento académico como del bienestar emocional del estudiante.

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient’s health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients’ condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [ABSTRACT FROM AUTHOR]

The aim of the present study was to predict future changes in biodiversity attributes (richness, rarity, heterogeneity, evenness, functional diversity and taxonomic diversity) of freshwater fish species in river basins around the world, under different climate scenarios. To do this, we use a new methodological approach implemented within the ModestR software (NOO3D) which allows estimating simple species distribution predictions for future climatic scenarios. Data from 16,825 freshwater fish species were used, representing a total of 1,464,232 occurrence records. WorldClim 1.4 variables representing average climate variables for the 1960–1990 period, together with elevation measurements, were used as predictors in these distribution models, as well as in the selection of the most important variables that account for species distribution changes in two scenarios (Representative Concentration Pathways 4.5 and 6.0). The predictions produced suggest the extinction of almost half of current freshwater fish species in the coming decades, with a pronounced decline in tropical regions and a greater extinction likelihood for species with smaller body size and/or limited geographical ranges.

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice. [ABSTRACT FROM AUTHOR]

The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

The workers operating and supervising machining tools are often in charge of monitoring a high number of parameters of the machining process, and they usually make use of, among others, cutting sound signals, for following-up and assessing that process. The interpretation of those signals is closely related to the operational conditions of the machine and to the work environment itself, because such signals are sensitive to changes in the process’ input parameters. Additionally, they could be considered as a valid indicator for detecting working conditions that either negatively affect the tools’ lifespan, or might even put the machine operators themselves at risk. In light of those circumstances, this work deals with the proposal and conceptual development of a new methodology for monitoring the work conditions of machining tools, based on expert systems that incorporate a reinforcement strategy into their knowledge base. By means of the combination of sound-processing techniques, together with the use of fuzzy-logic inference engines and hierarchization methods based on vague fuzzy numbers, it will be possible to determine existing undesirable behaviors in the machining tools, thus reducing errors, accidents and harmful failures, with consequent savings in time and costs. Aiming to show the potential for the use of this methodology, a concept test has been developed, implemented in the form of a short case study. The results obtained, even if they require more extensive validation, suggest that the methodology would allow for improving the performance and operation of machining tools, as well as the ergonomic conditions of the workplace.

El Museo de Huelva es una institución fundada en 1920. A las puertas de su centenario, una mirada retrospectiva nos muestra un camino plagado de vicisitudes en el que la incertidumbre ha minado la voluntad de aquellos que soñaron con una Huelva orgullosa de su pasado milenario. Una serie de hitos históricos rescataron del anodino panorama a la institución para construirse, ya a las puertas de la democracia, el edificio que por fin albergaría el legado onubense, el mismo que hoy día sigue siendo el único espacio expositivo integral de Huelva.

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Imagined activities could actually be a cognitive basis for creative thinking. However, it is still unknown how they might be related with the architecture of the brain. A recent study has proved the relevance of the imagined activity when investigating neuronal diseases by comparing variations in the neuronal activity of patients with brain diseases and healthy subjects. One important aspect of the scientific methodologies focused on neuronal diseases is therefore to provide a trustable methodology that could allow us to distinguish between realized and imagined activities in the brain. The electroencephalogram is the result of synchronized action of the cerebrum, and our end is portraying the network dynamics through the neuronal responses when the subjects perform visuomotor and specific imaginary assignments. We use a subtle information theoretical approach accounting for the time causality of the signal and the closeness centrality of the different nodes. More specifically we perform estimations of the probability distribution of the data associated to each node using the Bandt and Pompe approach to account for the causality of the electroencephalographic signals. We calculate the Jensen-Shannon distance across different nodes, and then we quantify how fast the information flow would be through a given node to other nodes computing the closeness centrality. We perform a statistical analysis to compare the closeness centrality considering the different rhythmic oscillation bands for each node taking into account imagined and visuomotor tasks. Our discoveries stress the pertinence of the alpha band while performing and distinguishing the specific imaginary or visuomotor assignments.

Currently, the concept of engagement is crucial in the field of learning and school achievement. It is a multidimensional concept (e.g., behavioral, emotional, and cognitive dimensions) that has been widely used as a theoretical framework to explain the processes of school engagement and dropout. However, this conceptual framework has been scarcely used in the field of homework. The aim of the present study was to analyze the role of intrinsic motivation, perceived homework utility, and personal homework attitude as precursors of student homework engagement (behavioral engagement) and, at the same time, how such engagement is the precursor of academic achievement. Seven hundred and thirty students of Compulsory Secondary Education (CSE) (7th to 10th grade) from fourteen schools northern Spain participated. A structural equation model was elaborated on which intrinsic motivation, perceived utility and attitude were observed variables, and student engagement (time spent on homework, time management, and amount of teacher-assigned homework done) and academic achievement (Mathematics, Spanish Language, English Language, and Social Science) were latent variables. The results reveal that (i) intrinsic motivation is a powerful precursor of student behavioral engagement (also perceived utility and attitude, although to a lesser extent), and (ii) academic achievement is closely linked to the level of student engagement, qualifying the results of many of the previous studies conducted from a task-centered perspective (as opposed to a person-centered perspective).

Perceived social support has been shown to be one of the most important protective factors for emerging adult students during their transition to university. However, the relationships between perceived social support and dimensions of gender and family background, which have been shown to affect adjustment to college life, remain unexplored. The current study analyzes the effect of gender, parents' education, and family income level on social support perceived by Spanish university students. The sample consisted of 575 women and 280 men, of average age 18.02 years (SD?=?0.52) enrolled in the first year of degree courses at the University of Santiago de Compostela (Spain). Three measures were used to assess various facets of perceived social support, namely perceived acceptance, social provisions, and support availability and satisfaction. Gender differences were identified for several perceived social support dimensions; women reported a wider social network, which should cover more diverse needs of social support. In addition, differences related to mother's education and family income level emerged for the availability of support and perceived acceptance. The results highlight the different role of gender and family background in several dimensions of perceived social support during the transition to emerging adulthood.

The Global Biodiversity Information Facility (GBIF) is the largest databank on primary biodiversity data. We examined the completeness and geographical biases for all insect data on GBIF to determine its representativeness. Our results demonstrate that GBIF is far from providing a reliable representation about the global distribution of insects. Despite the growing number of records during the last years, few spatial units are well‐surveyed. At coarse resolutions, 34% of the world terrestrial cells lack data and barely 0.5% have completeness values above 90%. Insects are crucial in many ecological functions, and their alarming decline makes it more pressing to have a representative sample to improve our predictive capacity. However, the dynamic nature of species distributions and the strength of anthropogenic forces call for immediate conservation decisions that cannot wait for the empirical data on the identity and distribution of insects. [ABSTRACT FROM AUTHOR]

We herein present FactorsR, an RWizard application which provides tools for the identification of the most likely causal factors significantly correlated with species richness, and for depicting on a map the species richness predicted by a Support Vector Machine (SVM) model. As a demonstration of FactorsR, we used an assessment using a database incorporating all species of terrestrial carnivores, a total of 249 species, distributed across 12 families. The model performed with SVM explained 91.9% of the variance observed in the species richness of terrestrial carnivores. Species richness was higher in areas with both higher vegetation index and patch index, i.e., containing higher numbers of species whose range distribution is less fragmented. Lower species richness than expected was observed in Chile, Madagascar, Sumatra, Taiwan, and Sulawesi.

The medical treatment of chronic wounds, pressure ulcers in particular, burdens healthcare systems nowadays with high expenses that result mainly from their monitoring and assessment stages. Decision support systems applied within the ‘remote medicine’ framework may be of help, not only to the process of monitoring the evolution of chronic wounds under treatment, but also to facilitate the prevention and early detection of potential risk conditions in the affected patients. In this paper, the design and definition of a new decision-support methodology to be applied to the monitoring and assessment stages of the medical treatment process for pressure ulcers is proposed. Built upon the use and development of expert systems, the methodology makes it possible to generate alerts derived from the evolution of a patient’s chronic wound, by means of the interpretation and combination of data coming from both an image of the wound, and the considerations of a healthcare professional with expertise in the subject matter. Some positive results are already shown regarding the determination of the ulcer’s status in the tests that have been carried out so far. Therefore, it is considered that the proposed methodology might lead to substantial improvements regarding both the treatment’s efficiency and cost savings.

El objetivo principal de esta investigación consiste en conocer el perfil cognitivo que presenta el alumnado de primer curso de Educación Secundaria de un centro educativo situado en Ourense (España). Además, hemos analizado su relación con las principales materias académicas que integran el currículo. Para ello, hemos seleccionado a un grupo integrado por 62 alumnos/as a los que les hemos administrado de manera colectiva la prueba de inteligencia TIDI (Test ICCE de Inteligencia). Una vez que hemos obtenido los resultados de esta prueba, los hemos comparado con los resultados académicos que presentan en las principales materias.

En las últimas dos décadas la esofagitis eosinofílica se ha posicionado como una de las causas más importantes de disfunción esofágica en niños, de impactación de alimentos en adolescentes y adultos jóvenes, de falla terapéutica en pacientes con enfermedad por reflujo gastroesofágico, y es la patología eosinofílica más frecuente del tracto digestivo. Presentamos inrecomendaciones para el diagnóstico y tratamiento de la enfermedad basadas en la revisión sistemática de la literatura.

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA. [ABSTRACT FROM AUTHOR]

Este estudio propuso evaluar algunas características limnológicas de seis ambientes leníticos de Colombia ubicados en un gradiente altitudinal de las regiones Caribe, Andina y Amazónica. Estos ambientes presentan diferente origen, tipo y variabilidad climática, física y química, esto es una evidencia de que en los ambientes ecuatoriales, como los de Colombia, varían entre sí y requieren un enfoque local enlazado con patrones regionales y geográficos que influencian la limnología del sistema. Los ambientes estudiados corresponden a ciénagas y lagos de inundación de tierras bajas y lagos de alta montaña, los cuales fueron monitoreados en diferentes estaciones y momentos hidrológicos; los resultados fueron analizados en torno a sus diferencias ambientales y tróficas. La ubicación altitudinal y en consecuencia el tipo, origen, morfometría y condición hidroclimática que afecta los seis sistemas promueven cambios significativos en el régimen de precipitación, la temperatura, nivel y transparencia del agua, la concentración de nutrientes, el oxígeno disuelto, el pH, la conductividad eléctrica y la clorofila a.

In this study, the Hénon map was analyzed using quantifiers from information theory in order to compare its dynamics to experimental data from brain regions known to exhibit chaotic behavior. The goal was to investigate the potential of the Hénon map as a model for replicating chaotic brain dynamics in the treatment of Parkinson's and epilepsy patients. The dynamic properties of the Hénon map were compared with data from the subthalamic nucleus, the medial frontal cortex, and a q -DG model of neuronal input–output with easy numerical implementation to simulate the local behavior of a population. Using information theory tools, Shannon entropy, statistical complexity, and Fisher's information were analyzed, taking into account the causality of the time series. For this purpose, different windows over the time series were considered. The findings revealed that neither the Hénon map nor the q -DG model could perfectly replicate the dynamics of the brain regions studied. However, with careful consideration of the parameters, scales, and sampling used, they were able to model some characteristics of neural activity. According to these results, normal neural dynamics in the subthalamic nucleus region may present a more complex spectrum within the complexity–entropy causality plane that cannot be represented by chaotic models alone. The dynamic behavior observed in these systems using these tools is highly dependent on the studied temporal scale. As the size of the sample studied increases, the dynamics of the Hénon map become increasingly different from those of biological and artificial neural systems. [ABSTRACT FROM AUTHOR]

Simple Summary: Designing systems that optimize the process of evaluating mammogram images with the goal of improving the diagnostic process of breast cancer is an active field of research due to the large health and social impact of this disease. This paper presents a new intelligent clinical decision support system that, through the concurrence of inferential models, allows the definition of various risk metrics for patients. Those metrics are weighted and combined into a Global Risk value to be finally corrected by means of an empirical weighting factor derived from the BI-RADS analysis and condition associated with the patient's mammogram images. The validation results have shown meaningful disease detection rates within the study group used, which makes it possible to estimate the potential for a diagnostic use of the developed system. Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis. [ABSTRACT FROM AUTHOR]

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas. [ABSTRACT FROM AUTHOR]

El objetivo de este trabajo es mostrar las utilidades del programa informático ModestR, en estudios sobre distribución de especies en ecosistemas marinos y de agua dulce de Colombia. Este programa se encuentra disponible en la Web de manera gratuita: ModestR" target="_blank"http://www.ipez.es/ModestR. Para enseñar y probar el funcionamiento de ModestR se trabajó con los datos disponibles en el Global Biodiversity Information Facility (GBIF, 2012) de los órdenes Characiformes y Siluriformes, como ejemplo de especies de peces dulciacuícolas, y del orden Carcharhiniformes de especies marinas. ModestR incluye en su fase inicial, dos aplicaciones: DataManager y MapMaker. La aplicación DataManager está diseñada para realizar manejo integrado de información taxonómica y mapas de distribución de cualquier grupo de especies. La aplicación MapMaker permite generar mapas de distribución de especies de cuatro formas diferentes: 1) importando un archivo CSV con el nombre de las especies y las coordenadas geográficas, 2) importando los datos automáticamente del GBIF, 3) importando las coordenadas geográficas generadas de modelos de distribución y 4) realizando mapas expertos, lo cual consiste en seleccionar las áreas de distribución, de acuerdo a los tipos de hábitats en que ocurre la o las especies en estudio. La posibilidad de trabajar con hábitats es una de las contribuciones más importantes de ModestR y, en particular, la de que los hábitats lóticos pequeños (quebradas, arroyos, etc.), los lóticos grandes (ríos) y los lenticos (lagunas, lagos, embalses, ciénagas, etc.) están cartografiados con muy alta resolución. Además, también se diferencian los ecosistemas marino y terrestre. A pesar de que este estudio se enfocó en especies de ecosistemas acuáticos, ModestR permite también realizar el mismo tipo de ejercicios con especies terrestres.The aim of this manuscript is to show the usefulness of the software package ModestR in studies of distribution of Colombian marine and freshwater species. This software is free available at the Website: ModestR" target="_blank"http://www.ipez.es/ModestR. To show and test the potential of ModestR, here an exemplar assessment is presented of a database using all valid species of freshwater fishes of the orders Characiformes and Siluriformes, and their geographical records available in the Global Biodiversity Information Facility (GBIF, 2012), and of the order Carcharhiniformes as representatives of marine species. ModestR includes, in its initial phase, two applications: DataManager and distribution of any species group. The application MapMaker has been designed to generate species distribution maps in four different ways: 1) by importing a CSV file with the name of the species and their geographical coordinates, 2) importing the geographical records automatically from GBIF, 3) importing geographical coordinates generated by distribution models, and 4) making expert maps by selecting distribution areas, according to the types of habitats occupied by the species. The possibility of working with habitats is one of the most important contributions of ModestR and, in particular, that are mapped small lotic ecosystems (creeks, streams, etc.), large lotic ecosystems (rivers) and lentic ecosystems (ponds, lakes, reservoirs, swamps, etc.). Moreover, it is also possible to select marine and terrestrial ecosystems. Therefore, although the manuscript has been focused on species of aquatic ecosystems, ModestR also allows the same type of studies with terrestrial species.

Intelligence is commonly divided into two distinctive areas: fluid intelligence (Gf), which is understood as the skill of reasoning or intelligence as a process, and crystallized intelligence (Gc) that involves skills that are more related to learning and experience (knowledge-based skills). The objective of the present work was to investigate the effects that schooling and age exert on fluid and crystallized intelligence measuring students' results in sub-tests of the Battery of Reasoning Test (BRT-5). This study considered a sample composed of 1,722 students - 603 were assessed with Form A of the battery and 1,119 with Form B. The results show that intelligence is systematically associated with schooling and age. Some difficulties in separating the effects of cognitive development from the effects of formal learning on students' cognitive performance are also emphasized.A inteligência é comumente dividida em fluida (Gf), entendida como habilidade de raciocínio ou inteligência como um processo, e cristalizada (Gc) como as habilidades mais associadas à aprendizagem e experiência (habilidades associadas aos conhecimentos). No presente trabalho, o objetivo foi investigar os efeitos que a escolarização e a idade exercem sobre Gf e Gc tomando os resultados dos alunos nos subtestes da Bateria de Provas de Raciocínio (BPR-5). Este estudo considerou uma amostra composta por 1722 estudantes respondendo 603 à forma A dessa bateria e 1119 à sua forma B. Os resultados apontam relações sistemáticas entre inteligência, escolaridade e idade. Também se enfatiza a dificuldade em se separar os efeitos do desenvolvimento cognitivo e da aprendizagem formal no desempenho cognitivo dos alunos.

The northern sector of the Sicilian Channel is an area of favourable upwelling winds, which ought to support primary production. However, the values for primary production are low when compared with other Mediterranean areas and very low compared with the most biologically productive regions of the world’s oceans: California, the Canary Islands, Humboldt and Benguela. The aim of this study was to identify the main factors that limit phytoplankton biomass in the Sicilian Channel and modulate its monthly changes. We compared satellite-based estimates of chlorophyll a concentration in the Strait of Sicily with those observed in the four Eastern Boundary Upwelling Systems mentioned above and in other Mediterranean wind-induced coastal upwelling systems (the Alboran Sea, the Gulf of Lions and the Aegean Sea). Our results show that this low level of chlorophyll is mainly due to the low nutrient level in surface and sub-surface waters, independently of wind-induced upwelling intensity. Further, monthly changes in chlorophyll are mainly driven by the mixing of water column and wind-induced and/or circulation-related upwelling processes. Finally, primary production limitation due to the enhanced stratification processes resulting from the general warming trend of Mediterranean waters is not active over most of the coastal upwelling area off the southern Sicilian coast.