Exploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/27886 (submitted version)This study examines the influence of social presence, interactions (student-student and teacher-student) and emotional engagement on active learning within the context of social web-based collaborative learning (SWBCL). In order to accomplish this objective, an empirical study was conducted with 416 students from two universities, organized into groups of 4 or 5 students, who were instructed to complete a collaborative project over the course of one semester. At the end of the project, the students filled out a questionnaire and the resulting data was analyzed using the partial least squares (PLS) technique. The results suggest that social presence and teacher-student interaction have a positive influence on students’ active learning, both directly and indirectly, through emotional engagement. This variable also mediates the influence of student-student interactions, which have a less significant impact on active learning than the other analyzed variables. Consequently, this study offers important contributions to the study and practice of active learning in a SWBCL environment.The authors would like to thank all the students who participated in this study. The work presented in this paper was supported by an internal grant at the Universidad de Málaga, Andalucía Tech (Spain), Proyecto de Innovación Educativa [grant number PIE15-92]

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). 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Atrial fibrosis hampers non-invasive localization of atrial ectopic foci from multi-electrode signals: A 3D simulation study

Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulation

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents. This invalidates direct manipulation of the learned value function as a method to modify the derived behaviors. In this paper, we propose the use of Inverse Reinforcement Learning to incorporate real behavior traces in the learning process to shape the learned behaviors, thus increasing their trustworthiness (in terms of conformance to reality). To do so, we adapt the Inverse Reinforcement Learning framework to the navigation problem domain. Specifically, we use Soft Q-learning, an algorithm based on the maximum causal entropy principle, with MARL-Ped (a Reinforcement Learning-based pedestrian simulator) to include information from trajectories of real pedestrians in the process of learning how to navigate inside a virtual 3D space that represents the real environment. A comparison with the behaviors learned using a Reinforcement Learning classic algorithm (Sarsa(λ)) shows that the Inverse Reinforcement Learning behaviors adjust significantly better to the real trajectories

Nuevos textos, nuevos discursos en la época de Cervantes

En las primeras décadas del siglo XVII, el “tiempo del Quijote”, se desarrollan en España, por un lado, la renovación de las formas discursivas en la lengua literaria; por otro, el inicio, o expansión, de tipos textuales, en general con raíces en épocas anteriores (el Renacimiento, incluso la Edad Media), pero que renovados y reconfigurados generarán las tradiciones textuales propias del español moderno. Entre estas tradiciones destaca la prosa informativa, plasmada en los nuevos géneros de avisos y relaciones, germen de la futura lengua periodística y de los medios de comunicación de masas. Están también los arbitristas, tipos habitualmente denostados por sus coetáneos, pero cuyas obras constituyen una interesante reflexión sobre los problemas sociales y económicos de la España del XVII, a partir de las cuales se desarrollará la moderna ensayística sobre temas colectivos. Ninguno de estos géneros, bien conocidos por los historiadores, ha tenido aún el análisis lingüístico que merecen como responsables, en buena parte, de la creación de la prosa culta, no ficcional, del español moderno

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine

The role of online brand community engagement on consumer-brand relationship

The purpose of this study is to analyze the effects of online brand community (OBC) engagement on strategic aspects for the brand supporting the community. A total of 628 valid responses were collected through a self-administered questionnaire. The authors tested the proposed model using structural equation modeling. The results show that OBC engagement directly favors par-ticipation in the community, willingness to co-create with the brand and positive WOM; it also has an indirect positive influence on brand loyalty. At the same time, OBC engagement is directly influenced by OBC identification and, through this, indirectly influenced by brand identification. Likewise, brand trust directly influences brand co-creation, loyalty and positive WOM. However, OBC participation has been shown to have no significant effect on brand co-creation and positive WOM

A review of fire effects across South American ecosystems: The role of climate and time since fire

Fire is an important driver of ecosystem dynamics worldwide. However, knowledge on broad-scale patterns of ecosystem and organism responses to fires is still scarce. Through a systematic quantitative review of available studies across South America, we assessed fire effects on biodiversity and abundance of different organisms (i.e., plants, fungi, invertebrates, and vertebrates), plant fitness, and soil properties under four climate types, and time since the last fire (i.e., early and late post fire). We addressed: (1) What fire effects have been studied across South America? (2) What are the overall responses of biodiversity, abundance, fitness, and soil properties to fires? (3) How do climate and time since fire modulate those responses? Results: We analyzed 160 articles reporting 1465 fire responses on paired burned and unburned conditions. We found no effect of fire on biodiversity or on invertebrate abundance, a negative effect on woody plant species and vertebrate abundance, and an increase in shrub fitness. Soil in burned areas had higher bulk density and pH, and lower organic matter and nitrogen. Fire effect was significantly more positive at early than at late post fire for plant fitness and for soil phosphorus and available nitrogen. Stronger negative effects in semiarid climate compared to humid warm climate suggest that higher temperatures and water availability allow a faster ecosystem recovery after fire. Conclusions: Our review highlights the complexity of the climate?fire?vegetation feedback when assessing the response of soil properties and different organisms at various levels. The resilience observed in biodiversity may be expected considering the large number of fire-prone ecosystems in South America. The recovery of invertebrate abundance, the reduction of the vertebrate abundance, and the loss of nitrogen and organic matter coincide with the responses found in global reviews at early post-fire times. The strength of these responses was further influenced by climate type and post-fire time. Our synthesis provides the first broad-scale diagnosis of fire effects in South America, helping to visualize strengths, weaknesses, and gaps in fire research. It also brings much needed information for developing adequate land management in a continent where fire plays a prominent socio-ecological role.Fil: Giorgis, Melisa Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Zeballos, Sebastián Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Carbone, Lucas Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Zimmermann, Heike. Leuphana Universität Lüneburg; AlemaniaFil: von Wehrden, Henrik. Leuphana Universität Lüneburg; AlemaniaFil: Aguilar, Ramiro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Ferreras, Ana Elisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Tecco, Paula Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Kowaljow, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Barri, Fernando Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Gurvich, Diego Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Villagra, Pablo Eugeni. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Jaureguiberry, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentin

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