Smart Grid Challenges Through the Lens of the European General Data Protection Regulation

The General Data Protection Regulation (GDPR) was conceived to remove the obstacles to the free movement of personal data while ensuring the protection of natural persons with regard to the processing of such data. The Smart Grid has similar features as any privacy-critical system but, in comparison to the engineering of other architectures, has the peculiarity of being the source of energy consumption data. Electricity consumption constitutes an indirect means to infer personal information. This work looks at the Smart Grid from the perspective of the GDPR, which is especially relevant now given the current growth and diversification of the Smart Grid ecosystem. We provide a review of existing works highlighting the importance of energy consumption as valuable personal data as well as an analysis of the established Smart Grid Architecture Model and its main challenges from a legal viewpoint, in particular the challenge of sharing data with third parties.This work is funded by the PDP4E project, H2020 European Project Number: 787034. We would like to thank all PDP4E project partners for their valuable inputs and comments, and Marta Castro and Mikel Vergara for their discussions

Framework for the development of articulatory characterization studies over mri images

En este artículo se presenta un marco de trabajo tecnológico innovador diseñado y desarrollado por nuestro grupo de investigación para posibilitar la realización de estudios de caracterización articulatoria de los sonidos de una lengua a partir de medidas tomadas sobre secuencias de imágenes de cine-MRI. Como elemento fundamental se tiene la herramienta software de producción propia DicomPas, que permite realizar la toma de medidas de parámetros articulatorios sobre las secuencias de imágenes MRI y la ejecución de algoritmos ad hoc sobre dichas medidas, de cara al procesamiento de los datos, con vistas a la posterior extracción del conocimiento, en forma de generación de inferencias estadísticas o de inteligencia artificial. En estos momentos este marco de trabajo está siendo aplicado a la realización de diversos estudios en euskara y español de Euskadi, disponiéndose para ello de una base de datos con dos repositorios de imágenes tomadas en el plano medio sagital, correspondientes a 18 informantes diferentes.In this paper an innovative framework is presented, designed and developed by our research team to enable the accomplishment of research works concerning the articulatory characterization of the sounds of a language from measures taken over MRI image sequences. As fundamental element there is the DicomPas software tool, developed by our team, which allows to carry out the measures of articulatory parameters over the MRI image sequences and the execution of ad hoc algorithms over such measures, facing the data processing, with the view to the subsequent extraction of knowledge, in the form of the generation of statistical or artificial intelligence inferences. This framework is currently being applied to the achievement of diverse studies in Basque and Spanish of the Basque Country. To do so, a database with two repositories of images taken in the midsagittal plane, corresponding to 18 different informants, is available

Audio and Image Processing Easy Learning for Engineering Students Using EasyPAS Tool

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Nueva metodología de enseñanza de procesado digital de la señal utilizando la API “joPAS”

Este artículo presenta la API de programación “JoPAS” desarrollada por el grupo de investigación PAS de la universidad de Deusto. joPAS permite el uso de variables y funciones de Octave desde un programa realizado en Java. Esta API posibilita a los estudiantes el rápido desarrollo de aplicaciones de procesado digital de señal, haciendo uso de la sencillez de diseño y potencia de interfaces gráficas en leguaje Java y el cálculo científico en Octave. Esta nueva herramienta docente está siendo utilizada por alumnos de ingeniería informática e ingeniería técnica de telecomunicación

Borrelia burgdorferi infection induces long-term memory-like responses in macrophages with tissue-wide consequences in the heart

Lyme carditis is an extracutaneous manifestation of Lyme disease characterized by episodes of atrioventricular block of varying degrees and additional, less reported cardiomyopathies. The molecular changes associated with the response to Borrelia burgdorferi over the course of infection are poorly understood. Here, we identify broad transcriptomic and proteomic changes in the heart during infection that reveal a profound down-regulation of mitochondrial components. We also describe the long-term functional modulation of macrophages exposed to live bacteria, characterized by an augmented glycolytic output, increased spirochetal binding and internalization, and reduced inflammatory responses. In vitro, glycolysis inhibition reduces the production of tumor necrosis factor (TNF) by memory macrophages, whereas in vivo, it produces the reversion of the memory phenotype, the recovery of tissue mitochondrial components, and decreased inflammation and spirochetal burdens. These results show that B. burgdorferi induces long-term, memory-like responses in macrophages with tissue-wide consequences that are amenable to be manipulated in vivo.Supported by grants from the Spanish Ministry of Science, Innovation and Universities (MCIU) co-financed with FEDER funds (SAF2015-65327-R and RTI2018-096494-B-100 to JA; BFU2016-76872-R to EB, AGL2017-86757-R to LA, SAF2017-87301-R to MLMC, SAF2015-64111-R to AP, SAF2015-73549-JIN to HR), Instituto de Salud Carlos III (PIE13/0004 to AP), the Basque Government Department of Health (2015111117 to LA), the Basque Foundation for Innovation and Health Research (BIOEF), through the EiTB Maratoia grant BIO15/CA/016/BS to MLMC, the regional Government of Andalusia co-funded by CEC and FEDER funds (Proyectos de Excelencia P12-CTS-2232) and Fundación Domingo Martínez (to AP). LA is supported by the Ramon y Cajal program (RYC-2013-13666). DB, MMR and TMM are recipients of MCIU FPI fellowships. ACG and AP are recipients of fellowships form the Basque Government. APC is a recipient of a fellowship from the University of the Basque Country. We thank the MCIU for the Severo Ochoa Excellence accreditation (SEV-2016-0644), the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek programs), the Innovation Technology Department of the Bizkaia Province and the CIBERehd network. DB and JA are supported by a grant from the Jesús de Gangoiti Barrera Foundation