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In the present work, computational fluid dynamics modelling (CFD) of single-phase flow (oil, water, gas) and two-phase flows (oil/water, oil/gas, gas/oil) was performed, to study the behaviour of these types of flow in a rigid jumper employed for the production of oil and gas in deepwater hydrocarbons fields. The jumper conducts the produced fluids from a subsea tree to a PLET. The modelled fluids were extracted from a drill stem testing study, corresponding to multiphase flow of water, gas and oil. In the present modelling strategy, the jumper and was considered thermally insulated to avoid heat transfer between internal fluids and external environmental conditions, and the end connectors of the jumpers were also considered insulated along with tree and PLET connection points. The flow patterns, pressure drops, slugs and flow velocity variations through the jumper were obtained from CFD simulations to visualize and study their behaviour in the jumper during the flow. The CFD pressure results were compared with theoretical calculations with good agreement, producing fast results. However, CFD results allowed to visualize internal flow behaviour, calculate the time needed by each flow condition from jumper inlet to jumper outlet, flow patterns, pressure drops, etc. The methodology can also be used to estimate the production rate of the different modelled flows, to identify critical flow conditions inside a rigid jumper and select the appropriate jumper internal diameter.

Background: Mortality in cardiogenic shock (CS) is up to 40%, and although risk scores have been proposed to stratify and assess mortality in CS, they have been shown to have inconsistent performance. The purpose was to compare CS prognostic scores and describe their performance in a real-world Latin American country. Methods: We included 872 patients with CS. The Society for Cardiovascular Angiography and Interventions (SCAI), CARDSHOCK, IABP-Shock II, Cardiogenic Shock Score, age-lactate-creatinine score, Get-With-The-Guidelines Heart Failure score, and Acute Decompensated Heart Failure National Registry scores were calculated. Decision curve analyses were performed to evaluate the net benefit of the different scoring systems. Logistic and Cox regression analyses were applied to construct area under the curve (AUC) statistics, this last one against time using the Inverse Probability of Censoring Weighting method, for in-hospital mortality prediction. Results:When logistic regression was applied, the scores had a moderate-good performance in the overall cohort that was higher AUC in the CARDSHOCK (c= 0.666). In acute myocardial infarction-related CS (AMI-CS), CARDSHOCK still is the highest AUC (0.68). In non–AMI-CS only SCAI (0.668), CARDSHOCK (0.533), and IABP-SHOCK II (0.636) had statistically significant values. When analyzed over time, significant differences arose in the AUC, suggesting that a time-sensitive component influenced the prediction of mortality. The highest AUC was for the CARDSHOCK score (0.658), followed by SCAI (0.622). In AMI-CS-related, the highest AUC was for the CARDSHOCK score (0.671). In non–AMI-CS, SCAI was the best (0.642). Conclusions: Clinical scores show a time-sensitive AUC, suggesting that performance could be influenced by time and the type of CS. Understanding the temporal influence on the scores could provide a better prediction and be a valuable tool in CS.

Existe poca literatura sobre el uso de la mascarilla en la realización de una prueba de esfuerzo (PE) durante la pandemia de COVID-19. El objetivo de este estudio es analizar el impacto del uso de la mascarilla durante la realización de una PE en un grupo de pacientes que han realizado al menos una PE con y sin mascarilla.

Purpose: This study aims to examine the process of value co-creation (VCC) on digital ecotourism platforms and the role of perceived platform usefulness and user participation behavior in that process. The study also seeks to determine the extent to which the typology of the ecotourist moderates VCC. Design/methodology/approach: A total of 420 users of a digital ecotourism platform participated in a study analyzing the factors that influence VCC. A mediation, moderation and conditional process analysis was used to test the proposed hypotheses. A K-means cluster analysis was also used to classify the ecotourists into four groups. Findings: The results show that perceived platform usefulness has a strong influence on the generation of VCC and also on user participation behavior. In turn, user participation behavior is an important driver of VCC. This study also highlights the moderating role of the ecotourist typology in the formation of VCC on these digital platforms. Practical implications: This study offers managers of digital ecotourism platforms a means to identify and motivate those ecotourists with the necessary skills and characteristics to become true co-creators of value. Originality/value: This study contributes to the existing knowledge on how value is co-created on digital ecotourism platforms, confirming the important antecedent role of perceived platform usefulness and user participation behavior, as well as the moderating role of ecotourist typology. Propósito: El propósito de este estudio es examinar el proceso de co-creación de valor (VCC) en las plataformas digitales de ecoturismo y el papel de la utilidad percibida de la plataforma y el comportamiento de participación de los usuarios. El estudio también pretende determinar en qué medida la tipología del ecoturista modera la VCC. Metodología: 420 usuarios de una plataforma digital de ecoturismo participaron en un estudio que analizaba los factores que influyen en la co-creación de valor. Se utilizó un análisis de mediación, moderación y proceso condicional para probar las hipótesis propuestas. También se utilizó un análisis de conglomerados K-means para clasificar a los ecoturistas en 4 grupos. Conclusiones: Los resultados muestran que la utilidad percibida de la plataforma tiene una fuerte influencia en la generación de VCC y también en el comportamiento de participación del usuario. A su vez, el comportamiento de participación del usuario es un importante impulsor de la VCC. El estudio también destaca el papel moderador de la tipología ecoturística en la formación de VCC en estas plataformas digitales. Implicaciones prácticas: El estudio ofrece a los gestores de plataformas digitales de ecoturismo un medio para identificar y motivar a aquellos ecoturistas con las habilidades y características necesarias para convertirse en verdaderos co-creadores de valor. Originalidad: Este estudio contribuye al conocimiento existente sobre cómo se co-crea valor en las plataformas digitales de ecoturismo, confirmando el importante papel antecedente de la utilidad percibida de la plataforma y el comportamiento de participación de los usuarios, así como el papel moderador de la tipología del ecoturista. 目的: 本研究的主要目的是考察数字生态旅游平台上的价值共同创造（VCC）过程, 以及感知平台有用性和用户参与行为在该过程中的作用。该研究还试图确定生态旅游者的类型在多大程度上调节了VCC。 方法: 一个数字生态旅游平台的420名用户参与了一项研究, 分析影响价值共同创造的因素。使用调解、调节和条件过程分析来检验所提出的假设。还使用了K-均值聚类分析, 将生态旅游者分为4组。 研究结果: 结果显示, 感知的平台有用性对VCC的产生有很大影响, 也对用户的参与行为有很大影响。反过来, 用户参与行为也是VCC的一个重要驱动因素。该研究还强调了生态旅游者类型学在这些数字平台上形成VCC的调节作用。 实践意义: 该研究为数字生态旅游平台的管理者提供了一种识别和激励那些具有必要技能和特征的生态旅游者成为真正的价值共同创造者的手段。 原创性: 本研究对数字生态旅游平台上如何共同创造价值的现有知识做出了贡献, 证实了感知平台有用性和用户参与行为的重要前因作用, 以及生态旅游者类型学的调节作用。

Purpose: The aim of this study is to analyse the moderating roles of restaurant type and client long-term orientation (LTO) on the loyalty building process. In addition, this analysis delves into the role of customer satisfaction and delight in the dining experience on the development of loyalty to a restaurant. Design/methodology/approach: This study advances a moderator mediation model stemming from self-administered online questionnaires presented to clients subsequent to their gastronomic experiences. The analysis comprised a sample of 250 customers of moderate restaurants and 290 of midscale restaurants. Findings: The results reveal that customer satisfaction and delight are two key antecedents to the process of building loyalty towards restaurants and that the responses depended on restaurant type and client LTO. Practical implications: The study advances recommendations to restaurant managers and gastronomic marketing specialists. Moderate restaurants should satisfy the customer without offering additional services while medium-scale establishments should design actions perceived as an extra that surprise the client. Moreover, it is more important to offer delight to short-term oriented clients than to long-term oriented clients. Originality/value: The global character of the hospitality industry implies that achieving customer loyalty requires going further than generating favourable attitudes. This has led the academic world to place more interest on the issue of delight perceived by the client. In this sense, the present study examines exclusively the long-term cultural dimension due to the little attention it has received in hospitality literature. Finally, the advances offered by the PROCESS software in analysing indirect conditional effects renders it possible to identify the different levels of customer LTO towards different types of restaurants.

In today's digital world, it is crucial that Destination Management Organizations (DMOs) understand how tourists process and assimilate the information they receive through social media, whether this is posted online by the destination itself or by other users. When it comes to understanding the effectiveness of DMOs' integrated marketing communication (IMC) strategies, it is important to examine the extent to which the congruence between those online messages posted by the destination and those posted by other users (electronic word-of-mouth) influences the effectiveness of the communication. Similarly, it is also of value to understand the degree to which the use of emojis in social media messages may enhance the effect of congruence on IMC effectiveness. The scientific literature has found that tourists' responses to the information published online by the destination will depend on the type of positioning it adopts on its social media. The novelty of the present study work lies in addressing these issues from a neuroscientific perspective, using eye-tracking technology, to study (i) the user's cognitive effort (based on ocular indicators) when processing social media content and (ii) their affective evaluation of that content. A factorial experiment is conducted on a sample of 58 Facebook users. The results point to the important role played by the level of message congruence in users' information-processing and demonstrate the contextualizing effect exerted by emojis. Additionally, this study highlights the need for further research into the cognitive processing of tourism messages relative to different positioning strategies.

La diabetes mellitus (DM) es una de las patologías crónicas más prevalentes en nuestro medio, con un importante impacto sanitario y social. El control estricto de la glucemia y de otros factores de riesgo de enfermedad vascular reduce las complicaciones y mortalidad y se relaciona con la calidad de la atención recibida. Aunque esta debe ser interdisciplinar, basada en la coordinación entre atención primaria (AP) y atención hospitalaria (AH), existe escasa información sobre la efectividad lograda por los diferentes modelos de intervención existentes.

The study explores the moderating effect of individuals’ cultural values on the users’ online experiences with the tourism destination presented online. The moderating effect of uncertainty avoidance (UA), individualism (IND) and long-term orientation (LTO) on the interrelationships within the proposed model are explored. A bespoke experimental tourism destination website is designed for the study and an online survey is conducted to capture cultural variations between Spanish and British Internet users. The findings indicate that the relationships in the proposed model are significant and moderated by users’ cultural values, specifically uncertainty avoidance, individualism and time orientation.

La globalización actual de los mercados como consecuencia del desarrollo de las tecnologías de la información y comunicación, en general, y de Internet, en particular, ha aumentado la importancia de la cultura del usuario en el comportamiento del consumidor online. Las páginas web son en la actualidad un potente vehículo de comunicación para aquellas empresas que operan en mercados internacionales. En este sentido, el diseño de las mismas afectará de manera importante a la forma en la que los usuarios pertenecientes a diferentes culturas procesan la información que reciben a través de las mismas. El objetivo del presente trabajo es examinar en qué medida el diseño web modera las diferencias culturales existentes entre usuarios a la hora de procesar la información web. Los resultados ponen de manifiesto que un buen diseño web medido en términos funcionales es capaz de reducir, e incluso anular, tales diferencias.

In vitroblood–brain barrier (BBB) models are a useful tool to screen the permeability and toxicity of new drugs. Currently, many different in vitroBBB models coexist, but none stands out as being notably better than the rest. Therefore, there is still a need to evaluate the quality of BBB models under various conditions and assess their ability to mimic the in vivosituation. In this study, two brain endothelial cell lines (bEnd.3 and hCMEC/D3) and two epithelial-like cell lines (MDCKII and Caco-2) were selected for BBB modelling purposes. They were grown as monolayers of a single cell type, under the following conditions: in coculture with either primary or immortalised astrocytes; or in the presence of primary or immortalised astrocyte-derived conditioned media. A total of 20 different BBB models were established in this manner, in order to assess the effects of the astroglial components on the BBB phenotype in each case. To this end, six parameters were studied: the expression of selected tight junction proteins; the enzyme activities of alkaline phosphatase and of gamma glutamyl transpeptidase; the transendothelial/transepithelial electrical resistance (TEER); restriction in paracellular transport; and efflux transporter inhibition were each evaluated and correlated. The results showed that coculturing with either primary or immortalised astrocytes led to a general improvement in all parameters studied, evidencing the contribution of this cell type to effective BBB formation. Furthermore, the permeability coefficient (Pe) of the tracer molecule, Lucifer Yellow, correlated with three of the six parameters studied. In addition, this study highlights the potential for the use of the Lucifer Yellow Pevalue as an indicator of barrier integrity in in vitroBBB models, which could be useful for screening the permeability of new drugs.

In this paper, we inquire into the families of traveling and standing wave solutions that arise in the one-dimensional version of the M5-model describing mesenchymal cell movement through the extracellular matrix (ECM). The wave profiles arise in the form of pulses for the aggregates of migrating cells and decreasing wavefronts for the probability of moving to right along the 1D ECM. We have constructed analytic expressions that approximate the traveling and standing wave solutions, our technique consists in getting an exactly solvable approximate equation through the use of Lagrange’s interpolation method. Comparisons between some analytical approximate solutions and numerical solutions are plotted for the traveling and standing cases. The evidence suggests that the shape of small-amplitude pulses and fronts are fitted quite well by their approximations. Moreover, by establishing lower and upper bounds for the error terms coming from Lagrange interpolation, we have been able to determine error estimates for the approximations of certain traveling waves and all standing waves.

Ordinal data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis(Ordinal Classification and Preprocessing in Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript.

Brand extension is a strategy widely used among firms with high brand equity to launch new products into the market. This strategy has been the subject of considerable scholarly interest over the last few years. However, there are very few published works that have focused on analyzing brand extensions in the heritage context or, more specifically, in relation to the UNESCO World Heritage brand. The present study examines the extent to which the authenticity of a brand extension pertaining to a heritage destination parent brand affects the formation of brand equity for the extension. It takes into account the moderating roles of brand extension credibility and tourists’ experience and product knowledge. To fulfill the research objectives, an experimental design is applied to a sample of 217 tourists visiting the Monumental Complex of the Alhambra and Generalife in Granada, Spain, and the degree of authenticity of the brand extension is manipulated between subjects. The findings show that brand extension authenticity exerts a direct effect on brand extension equity, and an indirect effect, via brand extension credibility: these effects being activated only beyond certain levels of tourist experience of the heritage site and product knowledge.

Machine learning is a field which studies how machines can alter and adapt their behavior, improving their actions according to the information they are given. This field is subdivided into multiple areas, among which the best known are supervised learning (e.g., classification and regression) and unsupervised learning (e.g., clustering and association rules). Within supervised learning, most studies and research are focused on well-known standard tasks, such as binary classification, multi-class classification and regression with one dependent variable. However, there are many other less known problems. These are what we generically call nonstandard supervised learning problems. The literature about them is much more sparse, and each study is directed to a specific task. Therefore, the definitions, relations and applications of this kind of learners are hard to find. The goal of this paper is to provide the reader with a broad view on the distinct variations of nonstandard supervised problems. A comprehensive taxonomy summarizing their traits is proposed. A review of the common approaches followed to accomplish them, and their main applications are provided as well.

Highlights • We designed and implemented a novel sampling scheme based on monotonic chains in order to preverse monotonicity while obtaining a balanced set. • This scheme has been included in five famous under- and over-sampling approaches: Random Under-Sampling, Random Over-Sampling, SMOTE, ADASYN and MWMOTE. • Empirically, we show the deterioration of the monotonicity degree in 8 data-sets caused by standard and ordinal sampling approaches, invalidating their use for monotonic classification. • We compare our new monotonic sampling techniques versus their standard versions, achieving a better monotonicity preservation than the standard ones with the same improvement of predictability. Abstract Classification with monotonic constraints arises from some ordinal real-life problems. In these real-life problems, it is common to find a big difference in the number of instances representing middle-ranked classes and the top classes, because the former usually represents the average or the normality, while the latter are the exceptional and uncommon. This is known as class imbalance problem, and it deteriorates the learning of those under-represented classes. However, the traditional solutions cannot be applied to applications that require monotonic restrictions to be asserted. Since these were not designed to consider monotonic constraints, they compromise the monotonicity of the data-sets and the performance of the monotonic classifiers. In this paper, we propose a set of new sampling techniques to mitigate the imbalanced class distribution and, at the same time, maintain the monotonicity of the data-sets. These methods perform the sampling inside monotonic chains, sets of comparable instances, in order to preserve them and, as a result, the monotonicity. Five different approaches are redesigned based on famous under- and over-sampling techniques and their standard and ordinal versions are compared with outstanding results. [ABSTRACT FROM AUTHOR]

Analizar la mortandad de la muestra en investigaciones con pacientes paliativos oncológicos en domicilio.

Tourists nowadays rely predominantly on the Internet for their travel decision-making and for purchasing travel products. Given this, websites have become the most important medium to induce positive affect towards a destination, through safe and satisfactory online experiences. However, some travelers are still reluctant to purchase travel products online due to perceived risk. The aim of this study is to explore whether consumer satisfaction during online browsing, consumer risk perception online, and perceived usability of the travel website can predict consumers’ affect towards a travel destination. A website promoting a fictional travel destination was used for data-collection purposes. Confirmatory factor analysis was used to test the validity and reliability of user satisfaction, perceived risk online, perceived website usability, and affect towards a travel destination, while a regression model was employed to explore the predictive power of these constructs on users’ emotional response towards the target destination displayed on the website. Results show that a higher level of consumer satisfaction with the online browsing experience and greater perceived website usability generate positive affect towards a travel destination. In the conclusions section, the practical implications of these findings are discussed in relation to destination marketing and branding.

Many real-world classification tasks suffer from the class imbalanced problem, in which some classes are highly underrepresented as compared to other classes. In this paper, we focus on multi-class imbalance problems which are considerably more difficult to address than two-class imbalanced problems. On this account, we develop a novel and effective procedure, called dynamic ensemble selection for multi-class imbalanced datasets (DES-MI), in which the competence of the candidate classifiers are assessed with weighted instances in the neighborhood. The proposed DES-MI consists of two key components: the generation of balanced training datasets and the selection of appropriate classifiers. To do so, we develop a preprocessing procedure to balance the training dataset which relies on random balance. To select the most appropriate classifiers in the scenario of multi-class imbalance problems, we propose a weighting mechanism to highlight the competence of classifiers that are more powerful in classifying examples in the region of underrepresented competence. We develop a thorough experimental study in order to verify the benefits of DES-MI in handling multi-class imbalanced datasets. The obtained results, supported by the proper statistical analysis, indicate that DES-MI is able to improve the classification performance for multi-class imbalanced datasets. [ABSTRACT FROM AUTHOR]

Fingerprint recognition, and in particular minutiae-based matching methods, are ever more deeply implanted into many companies and institutions. As the size of their identification databases grows, there is a need of flexible, reliable structures for fingerprint recognition systems. In this paper, we propose a generic decomposition methodology for minutiae-based matching algorithms that splits the calculation of the matching scores into lower level steps that can be carried out in parallel in a flexible manner. The decomposition allows to adapt any minutiae-based algorithm to frameworks such as MapReduce or Apache Spark. General and specific guidelines to enhance the performance of the adapted matching algorithms are also described. The proposal is evaluated over two matching algorithms, two Big Data frameworks (Apache Hadoop and Apache Spark) and two large-scale fingerprint databases, with promising results concerning the identification time, in addition to the reliability, scalability, distribution and availability capabilities that are provided by such underlying frameworks. [ABSTRACT FROM AUTHOR]

This paper introduces the 3rdmajor release of the KEEL Software. KEEL is an open source Java framework (GPLv3 license) that provides a number of modules to perform a wide variety of data mining tasks. It includes tools to perform data management, design of multiple kind of experiments, statistical analyses, etc. This framework also contains KEEL-dataset, a data repository for multiple learning tasks featuring data partitions and algorithms’ results over these problems. In this work, we describe the most recent components added to KEEL 3.0, including new modules for semi-supervised learning, multi-instance learning, imbalanced classification and subgroup discovery. In addition, a new interface in R has been incorporated to execute algorithms included in KEEL. These new features greatly improve the versatility of KEEL to deal with more modern data mining problems.

Social networks are a source of competitive advantage for destination management organizations (DMOs) in promoting user-generated content. In the online environment, the generational cohort to which the user belongs significantly determines their motivations, preferences, and behaviors. Against this backdrop, and in context of culinary tourism, the present work aims to: (1) examine the degree of congruence between the messages that tourist receives from DMOs and other tourists through social network comments affects their attention and affective responses; (2) analyze the effect of generational cohort on user responses; (3) investigate the differences in gastronomy-related messages between generational cohorts according to different levels of congruence. An eye-tracking experiment is conducted to manipulate message congruence (high vs. low) and user's generational cohort (Millennials vs. Generation Z). Findings show faster attention-capture and higher cognitive processing in low-congruence gastronomy-related comments in both cohorts, while Generation Z users reported greater attention to culinary visuals.

Ordinal classification covers those classification tasks where the different labels show an ordering relation, which is related to the nature of the target variable. In addition, if a set of monotonicity constraints between independent and dependent variables has to be satisfied, then the problem is known as monotonic classification. Both issues are of great practical importance in machine learning. Ordinal classification has been widely studied in specialized literature, but monotonic classification has received relatively low attention. In this paper, we define and relate both tasks in a common framework, providing proper descriptions, characteristics, and a categorization of existing approaches in the state-of-the-art. Moreover, research challenges and open issues are discussed, with focus on frequent experimental behaviours and pitfalls, commonly used evaluation measures and the encouragement in devoting substantial research efforts in specific learning paradigms.

La globalización actual de los mercados como consecuencia del desarrollo de las tecnologías de la información y comunicación, en general, y de Internet, en particular, ha aumentado la importancia de la cultura del usuario en el comportamiento del consumidor online. Las páginas web son en la actualidad un potente vehículo de comunicación para aquellas empresas que operan en mercados internacionales. En este sentido, el diseño de las mismas afectará de manera importante a la forma en la que los usuarios pertenecientes a diferentes culturas procesan la información que reciben a través de las mismas. El objetivo del presente trabajo es examinar en qué medida el diseño web modera las diferencias culturales existentes entre usuarios a la hora de procesar la información web. Los resultados ponen de manifiesto que un buen diseño web medido en términos funcionales es capaz de reducir, e incluso anular, tales diferencias.

AbstractIn supervised learning, some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. Hyperrectangles can be viewed as storing objects in Rnwhich can be used to learn concepts combining instance-based classification with the axis-parallel rectangle mainly used in rule induction systems. This hybrid paradigm is known as nested generalized exemplar learning. In this paper, we propose the selection of the most effective hyperrectangles by means of evolutionary algorithms to tackle monotonic classification. The model proposed is compared through an exhaustive experimental analysis involving a large number of data sets coming from real classification and regression problems. The results reported show that our evolutionary proposal outperforms other instance-based and rule learning models, such as OLM, OSDL, k-NN and MID; in accuracy and mean absolute error, requiring a fewer number of hyperrectangles.

La pileflebitis o trombosis séptica portal es una complicación poco frecuente pero grave de aquellos procesos infecciosos intraabdominales, cuyo drenaje venoso depende del sistema portal. Su diagnóstico se basa en la sospecha clínica y en las pruebas de imagen, principalmente la tomografía abdominal, dada la inespecificidad de su sintomatología. La principal causa de morbilidad y mortalidad es la diseminación de los émbolos sépticos.

En una época en la que los teólogos y las autoridades oficiales recelaban de la experiencia, ante el temor de que ésta favoreciera un subjetivismo como criterio de la propia justificación, y cuando la misma teología, con su teoría proposicional de la revelación y la consiguiente reducción del contenido de la fe a un sistema de enunciados sobre Dios, condujo a una comprensión de la fe como mero asentimiento, santa Teresa propuso la propia experiencia como lugar natal de la teología, instaurando una teología integrada en la propia vida.

The analysis of the performance of different approaches is a staple concern in the design of Computational Intelligence experiments. Any proper analysis of evolutionary optimization algorithms should incorporate a full set of benchmark problems and state-of-the-art comparison algorithms. For the sake of rigor, such an analysis may be completed with the use of statistical procedures, supporting the conclusions drawn. In this paper, we point out that these conclusions are usually limited to the final results, whereas intermediate results are seldom considered. We propose a new methodology for comparing evolutionary algorithms’ convergence capabilities, based on the use of Page’s trend test. The methodology is presented with a case of use, incorporating real results from selected techniques of a recent special issue. The possible applications of the method are highlighted, particularly in those cases in which the final results do not enable a clear evaluation of the differences among several evolutionary techniques. [ABSTRACT FROM AUTHOR]

El objetivo del estudio es analizar nuestra experiencia en el uso de la biopsia del ganglio centinela (BGC) en el melanoma y determinar la existencia de factores predictores de resultado positivo y de drenaje múltiple.

The aim of this study was to assess the incidence and clinical pattern of celiac disease (CD) presently diagnosed in Spanish children. A prospective, multicenter, nationwide registry of new cases of CD in children <15 years was conducted from June 1, 2006 to May 31, 2007. The parameters studied were age at diagnosis, sex, clinical symptoms, associated diseases, nutritional status, CD serology, histological lesions, and HLA-DQ2/-DQ8. The crude incidence rate of CD was calculated as new cases per 1000 live births and as new cases per 100,000 person-years <15 years of age. A total of 974 new cases of CD were included. The median age at diagnosis was 2.3 years; 39.5% of CD diagnoses occurred in the first 2 years, 42% between 2 and 6, and 18.4% from 6 to 15. Total number of cases in each age group was 385, 409, and 180, respectively. Regarding clinical presentation 70.9% showed classical symptoms, 21.9% were nonclassical, and 7% were asymptomatic. A total of 95.7% of 931, 94.7% of 611, and 86.7% of 651 children tested positive, respectively, for immunoglobulin A (IgA) anti-transglutaminase type 2 antibodies, IgA endomysial antibodies, and IgA anti-gliadin antibodies. Villous atrophy was observed in 92.4% and increased intraepithelial lymphocytes with crypt hyperplasia in 3.3%. Of the children, 55% had normal growth, and 3.4% were overweight. The HLA phenotype was DQ2: 88.3%, DQ2/DQ8: 8.4%, and DQ8: 2.3%. The incidence rate was 7.9 cases of CD per 1000 live births and 54 cases per 100,000 person-years. In Spain, the most frequent clinical presentation of CD is the classical form, mainly diagnosed during the first 2 years of life. The observed incidence of CD in Spanish children is much higher than the present CD incidence rates observed in other European countries.

Valorar la formación, la percepción de preparación y las necesidades formativas en cuidados paliativos (CP) teóricas y prácticas de las enfermeras de atención primaria en España.

La incontinencia urinaria tiene una alta prevalencia. El uso de medicamentos y/o cirugía, no siempre soluciona el problema y no está exenta de secundarismos, lo que ha llevado al uso de la acupuntura para esta patología. Se compara la electroacupuntura con el entrenamiento muscular del suelo pélvico en mujeres con incontinencia urinaria.

La depresión es un problema subdiagnosticado en pacientes con insuficiencia renal crónica que se hemodializan, incrementando el riesgo de mortalidad.

Highlights: [•] A review on the state-of-the-art of fuzzy nearest neighbor classification is proposed. [•] Fuzzy sets theory and several extensions are analyzed as tools to improve the performance of the nearest neighbor classifier. [•] A taxonomy of the existing methods is proposed. [•] A full experimental study is carried out comparing the performance of the methods. [Copyright &y& Elsevier]

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Estudio transversal de la obesidad en una muestra aleatoria de 4.012 personas de edad ≥ 15 años en Castilla y León. Se estimó la prevalencia de obesidad (índice de masa corporal ≥ 30) y de obesidad abdominal (cintura > 102 cm en varones y > 88 cm en mujeres) y se calculó la relación de ambos tipos de obesidad con otros factores de riesgo cardiovascular. La prevalencia de obesidad fue del 21,7% (intervalo de confianza [IC] del 95%, 20,3%-23,2%), mayor en mujeres —23,2% (IC del 95%, 20,9%-25,5%) que en varones —20,4% (IC del 95%, 18%-22,7%)—. La prevalencia de obesidad abdominal fue del 36,7% (IC del 95%, 34,6%-38,9%), mayor también en mujeres —50,1% (IC del 95%, 47%-53,1%)— que en varones —22,8% (IC del 95%, 20,3%-25,2%). Todos los factores de riesgo cardiovascular, excepto el tabaquismo, estuvieron asociados a la obesidad. El riesgo SCORE y Framingham a 10 años fue superior en obesos.

Abstract: Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence. We present a case study which involves a set of techniques in classification tasks and we study a set of nonparametric procedures useful to analyze the behavior of a method with respect to a set of algorithms, such as the framework in which a new proposal is developed. Particularly, we discuss some basic and advanced nonparametric approaches which improve the results offered by the Friedman test in some circumstances. A set of post hoc procedures for multiple comparisons is presented together with the computation of adjusted p-values. We also perform an experimental analysis for comparing their power, with the objective of detecting the advantages and disadvantages of the statistical tests described. We found that some aspects such as the number of algorithms, number of data sets and differences in performance offered by the control method are very influential in the statistical tests studied. Our final goal is to offer a complete guideline for the use of nonparametric statistical procedures for performing multiple comparisons in experimental studies. [Copyright &y& Elsevier]

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

Given the normative changes in higher education at European, national and regional levels, together with social, economic, demographic and technological developments, universities need to adopt a client-oriented approach and to make this client orientation an integral component of their strategic planning process. The university's ‘clients’ should thus be involved in its strategic planning. This paper demonstrates how an importance–performance analysis applied to the perceptions of different groups of clients and stakeholders about the institution can be an effective tool in this regard. The proposed methodology will assist managers, and those responsible for university planning, to diagnose the internal situation of the institution by building matrices of points of excellence and deficiencies. In illustrating the effectiveness of this methodology, the authors stress the need for higher education institutions to move from a product orientation, based on the notion that the institution is best able to decide what its clients need, to a client orientation, based on knowledge of who its clients are and on a detailed investigation of their needs and interests.

The improvements in technology and computation have promoted a global adoption of Data Science. It is devoted to extracting significant knowledge from high amounts of information by means of the application of Artificial Intelligence and Machine Learning tools. Among the different tasks within Data Science, classification is probably the most widespread overall.

Statistical validation of results for supporting the conclusions achieved in an experimental study is more and more demanded in research results. Although statistics are usually used in the analysis of results for comparing the performance of several algorithms, they could be used in other tasks, such as proper selection of parameters’s value or study of the trend of a parameter. In this short paper, we describe a non-parametric test, the Page test, which can be used for predicting the order of experimental conditions. We include an illustrative example for using it on classification problems taking the well-known $$k$$ k -nearest neighbour algorithm.

•The study describes the process of extension's brand equity-formation from a heritage brand•Taking into account the mediating role of brand attitude and brand credibility•An experimental design is applied, in which two different levels of fit•Results show that the level of fit influences the extension's brand equity-formation.•Tourist´s prior experience moderates formation of extension´s brand equity

Diabetes mellitus (DM) is one of the most prevalent chronicdiseases and has a significant health and social impact. Strict control of blood glucose levels and other risk factors for vascular disease (VD) reduces complications and mortality and is related to the quality of care received. Although care should be interdisciplinary, based on the coordination of primary care (PC) and hospital care (HC), little information is available on the effectiveness of the different existing intervention models.

Firms with high brand equity often employ the strategy of brand extension to place new products on the market. Very few studies have analyzed brand extensions in the cultural heritage context, and fewer still have dealt with brands carrying the World Heritage hallmark. This work contributes to the existing knowledge in this field by examining how cultural heritage brand preference contributes to the formation of brand-extension loyalty, taking into account the tourists' perceptions of the extension (via attitudes and image) and the moderating role of brand extension fit and authenticity. A 2 (high vs. low brand extension fit) x 3 (high, moderate, and low degree of brand extension authenticity) between-subjects experimental design was used. The results show that parent brand preference exerts a positive effect on brand extension loyalty, mediated by brand extension attitude and brand extension image. These relationships are moderated by brand extension fit.

Highlights • We have tackled the problem of noise in Big Data classification. • Two Big Data preprocessing approaches to remove noisy examples are proposed: HME-BD and HTE-BD. • HME-BD is a homogeneous ensemble filter, while HTE-BD is a heterogeneous ensemble filter. • The proposed algorithms constitute a first suitable noise filtering approach in Big Data domains. Abstract In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem. [ABSTRACT FROM AUTHOR]