Eficacia de un programa de alerta precoz del cáncer de piel en pacientes con tratamiento habitual en clínicas de fisioterapia

La prevalencia de las lesiones dermatológicas se encuentra en aumento en los últimos años. El diagnóstico precoz es esencial para el manejo de alteraciones de la piel, sobre todo alteraciones malignas, para mejorar el pronóstico y facilitar el tratamiento de las mismas. Se cree necesaria la creación de un protocolo que permita la identificación de posibles lesiones, menos accesibles o desconocidas por parte de los propios pacientes, para su valoración. El protocolo, dirigido a clínicas de fisioterapia, muestra capacidad para la detección de lesiones potencialmente revisables, donde se incluyen: lesiones pre-malignas, lesiones desconocidas por los pacientes y lesiones recomendadas para ser revisadas por distintos criterios.Departamento de EnfermeríaGrado en Enfermerí

Range unit root tests

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of "long-wave" patterns observed not only in unit root time series but also in series following more complex data generating mechanism. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties. Among these properties are the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series

A range unit root test

Since the seminal paper by Dickey and Fuller in 1979, unit-root tests have conditioned the standard approaches to analyse time series with strong serial dependence, the focus being placed in the detection of eventual unit roots in an autorregresive model fitted to the series. In this paper we propose a completely different method to test for the type of long-wave patterns observed not only in unit root time series but also in series following more complex data generating mechanisms. To this end, our testing device analyses the trend exhibit by the data, without imposing any constraint on the generating mechanism. We call our device the Range Unit Root (RUR) Test since it is constructed from running ranges of the series. These statistics allow a more general characterization of a strong serial dependence in the mean behavior, thus endowing our test with a number of desirable properties, among which its error-model-free asymptotic distribution, the invariance to nonlinear monotonic transformations of the series and the robustness to the presence of level shifts and additive outliers. In addition, the RUR test outperforms the power of standard unit root tests on near-unit-root stationary time series and is asymptotically immune to noise

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad TIN2013-46801-C4-1-

Exploring the Relationship Between R&D and Productivity: A Country-Level Study

Research and development (R&D) has been considered a source of growth in productivity starting from Schultz (1953). Since then, significant research has studied this relationship at the firm, industry and country level. However, at the country level, most of the empirical studies assessing the R&D-productivity relationship often fail to consider the possible simultaneity of these variables. Do more productive countries invest more on R&D or does the higher level of R&D investment that leads to higher levels of productivity? Do both relationships occur at the same time? Using a 65-country panel for the time period of 1960- 2000, this study provides evidence that the relationship is mainly based on investment in R&D and not the reverse. In addition, we found that per capita R&D expenditure is strongly exogenous to productivity. These results suggest that, on average, those countries making the most effort in the R&D sector will be more productive in the future. Finally, we present evidence those points out a strong relationship between R&D and productivity in terms of both magnitude and significance.

How Can Geography and Mobile Phones Contribute to Psychotherapy?

Interdisciplinary relationships between Geography and Psychotherapy are an opportunity for innovation. Indeed, scientific works found on bibliographic databases and concerning this theme are scarce. Geographical sub-fields, such as the Geography of Emotions or Psychoanalytical Geography have started to emerge, theorizing about and interpreting feelings, emotions, moods, sufferings, of the chronically ill or diversified social groups and sites. But a less theoretical and more practical approach, in the sense of proposing, predicting and intervening, is lacking; as well as research into the possibilities offered by communication technologies and mobile phones. In the present work, we present the results of a review of the most relevant scientific works published internationally; we reflect on the contributions of Geography and mobile phones to psychosocial therapies and define the orientation and questions that should be posed in future research, from the point of view of geography and regarding psychotherapy. We conclude that the production of georeferenced data via mobile phones concerning the daily lives of people opens great possibilities for cognitive behavioural therapy and mental health. They allow for the development of personalized mood maps that locate the places where a person experiences greater or lesser stress on a daily basis; they allow for a cartography of emotions, a cognitive cartography of the places we access physically or through the Internet, of our feelings and psychosocial experiences. They open the door to the possibility of offering personalized psychotherapy treatments focusing on the ecological-environmental analysis of the places frequented by the person on a daily basis

Análisis de la relación entre el índice de congestión y el consumo de combustible basado en datos empíricos

Entre los principales problemas causados por el incremento del transporte por carretera en las últimas décadas destacan el aumento del gasto energético y las emisiones de gases de efecto invernadero (GEI), principalmente CO2. No en vano, el transporte por carretera aporta aproximadamente el 22% del total de GEI en los países de la OCDE, superando el 25% en el caso de España. En áreas metropolitanas, el problema se agrava por el efecto de la congestión. Tanto los modelos de transporte como las últimas versiones de navegadores GPS consideran la variabilidad del tráfico en sus estimaciones de tiempos de viaje. Sin embargo, el efecto de la congestión en el consumo de combustible solo es tenido en cuenta en modelos muy detallados, que necesitan una gran cantidad de datos. En este estudio se pretende establecer una relación empírica entre un índice de congestión y el consumo. Para ello se han tomado datos reales de vehículos flotantes en diversos tramos del área metropolitana de Madrid. En concreto, se registraron un total de 3.800 viajes bajo distintas situaciones de tráfico y estilos de conducción. El análisis de estos datos refleja para todos los vehículos tendencias similares, llegándose, en algunos tramos, a doblar el consumo por el efecto de la congestión. Desarrollando estas relaciones para distintas tipologías de vías, resultaría posible introducir esta variable en modelos de transporte, navegadores o planificadores de ruta