Diseño y desarrollo de una arquitectura de Internet de las Cosas de nueva generación orientada al cálculo y predicción de índices compuestos aplicada en entornos reales

[ES] El Internet de las Cosas (IoT) ha experimentado un gran crecimiento en los últimos años. El incremento en el número de dispositivos, una mayor miniaturización de la capacidad de computación y las técnicas de virtualización, han favorecido su adopción en la industria y en otros sectores. Asimismo, la introducción de nuevas tecnologías (como la Inteligencia Artificial, el 5G, el Tactile Internet o la Realidad Aumentada) y el auge del edge computing preparan el terreno, y formulan los requisitos, para lo que se conoce como Internet de las Cosas de Nueva Generación (NGIoT). Estos avances plantean nuevos desafíos tales como el establecimiento de arquitecturas que cubran dichas necesidades y a la vez resulten flexibles, escalables y prácticas para implementar servicios que aporten valor a la sociedad. En este sentido, el IoT puede resultar un elemento clave para el establecimiento de políticas y la toma de decisiones. Una herramienta muy útil para ello es la definición y cálculo de indicadores compuestos, que representan un impacto en un fenómeno real a través de un único valor. La generación de estos indicadores es un aspecto promovido por entidades oficiales como la Unión Europea, aunque su automatización y uso en entornos de tiempo real es un campo poco explorado. Este tipo de índices deben seguir una serie de operaciones matemáticas y formalidades (normalización, ponderación, agregación¿) para ser considerados válidos. Esta tesis doctoral plantea la unión de ambos campos en alza, proponiendo una arquitectura de Internet de las Cosas de nueva generación orientada al servicio de cálculo y predicción de indicadores compuestos. Partiendo de la experiencia del candidato en proyectos de investigación europeos y regionales, y construyendo sobre tecnologías open source, se ha incluido el diseño, desarrollo e integración de los módulos de dicha arquitectura (adquisición de datos, procesamiento, visualización y seguridad) como parte de la tesis. Dichos planteamientos e implementaciones se han validado en cinco escenarios diferentes, cubriendo cinco índices compuestos en entornos con requisitos dispares siguiendo una metodología diseñada durante este trabajo. Los casos de uso están centrados en aspectos de sostenibilidad en entornos urbano y marítimo-portuario, pero se ha destacado que la solución puede ser extrapolada a otros sectores ya que ha sido diseñada de una manera agnóstica. El resultado de la tesis ha sido, además, analizado desde el punto de vista de transferencia tecnológica. Se ha propuesto la formulación de un producto, así como una posible financiación en fases de madurez más avanzadas y su potencial explotación como elemento comercializable[CA] La Internet de les Coses (IoT) ha experimentat un gran creixement en els últims anys. L'increment en el nombre de dispositius, una major miniaturització de la capacitat de computació i les tècniques de virtualització, han afavorit la seua adopció en la indústria i en altres sectors. Així mateix, la introducció de noves tecnologies (com la Intel·ligència Artificial, el 5G, la Internet Tàctil o la Realitat Augmentada) i l'auge del edge computing preparen el terreny, i formulen els requisits, per al que es coneix com a Internet de les Coses de Nova Generació (NGIoT). Aquests avanços plantegen nous desafiaments com ara l'establiment d'arquitectures que cobrisquen aquestes necessitats i resulten, alhora, flexibles, escalables i pràctiques per a implementar serveis que aporten valor a la societat. Ací, el IoT pot resultar un element clau per a l'establiment de polítiques i la presa de decisions. Una eina molt útil en aquest sentit és la definició i càlcul d'indicadors compostos, que representen un impacte en un fenomen real a través d'un únic valor. La generació d'aquests indicadors és un aspecte promogut per entitats oficials com la Unió Europea, encara que la seua automatització i ús en entorns de temps real és un camp poc explorat. Aquest tipus d'índexs han de seguir una sèrie d'operacions matemàtiques i formalitats (normalització, ponderació, agregació¿) per a ser considerats vàlids. Aquesta tesi doctoral planteja la unió de tots dos camps en alça, proposant una arquitectura d'Internet de les Coses de nova generació orientada al servei de càlcul i predicció d'indicadors compostos. Partint de l'experiència del candidat en projectes d'investigació europeus i regionals, i construint sobre tecnologies open source, s'ha inclòs el disseny, desenvolupament i integració dels mòduls d'aquesta arquitectura (adquisició de dades, processament, visualització i seguretat) com a part de la tesi. Aquests plantejaments i implementacions s'han validat en cinc escenaris diferents, cobrint cinc índexs compostos en entorns amb requisits dispars seguint una metodologia dissenyada durant aquest treball. Els casos d'ús estan centrats en aspectes de sostenibilitat en entorns urbà i marítim-portuari, però s'ha destacat que la solució pot ser extrapolada a altres sectors ja que ha sigut dissenyada d'una manera agnòstica. El resultat de la tesi ha sigut, a més, analitzat des del punt de vista de transferència tecnològica. S'ha proposat la formulació d'un producte, així com un possible finançament en fases de maduresa més avançades i la seua potencial explotació com a element comercialitzable[EN] The Internet of Things (IoT) has experienced tremendous growth in recent years. The increase in the number of devices, greater miniaturization of computing capacity and virtualization techniques have favored its adoption in industry and other sectors. Likewise, the introduction of new technologies (such as Artificial Intelligence, 5G, Tactile Internet or Augmented Reality), together with the rise of edge computing, are paving the way, and formulating the requirements, for what is known as the Next Generation Internet of Things (NGIoT). These advances pose new challenges such as the establishment of proper architectures that meet those needs and, at the same time, are flexible, scalable, and practical for implementing services that bring value to society. In this sense, IoT could be a key element for policy and decision making. A very useful tool for this is the definition and calculation of composite indicators, which represent an impact on a real phenomenon through a single value. The generation of these indicators is an aspect promoted by official entities such as the European Union, although their automation and use in real-time environments is a rather uncharted research field. This type of indexes must follow a series of mathematical operations and formalities (normalization, weighting, aggregation...) to be considered valid. This doctoral thesis proposes the union of both fields, proposing a new generation Internet of Things architecture oriented to the calculation and prediction of composite indicators. Based on the candidate's experience in European and regional research projects, and building on open source technologies, the design, development and integration of the modules of such architecture (data acquisition, processing, visualization and security) has been included as part of the thesis. These approaches and implementations have been validated in five different scenarios, covering five composite indexes in environments with disparate requirements following a methodology designed during this work. The use cases are focused on sustainability aspects in urban and maritime-port environments, but it has been highlighted that the solution can be extrapolated to other sectors as it has been designed in an agnostic way. The result of the thesis has also been analyzed from the point of view of technology transfer. A tentative product definition has been formulated, as well as a possible financing in more advanced stages of maturity and its potential exploitation as a marketable elementLacalle Úbeda, I. (2022). Diseño y desarrollo de una arquitectura de Internet de las Cosas de Nueva Generación orientada al cálculo y predicción de índices compuestos aplicada en entornos reales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19063

El Art. 38 Ley de Contrato de Seguro en la Gestión de Siniestros. El procedimiento de peritos

Màster de Direcció d'Entitats Asseguradores i Financeres, Universitat de Barcelona, Facultat d'Economia i Empresa, Curs: 2004-2005, Tutor: Salvador José Martín GarcíaEn esta tesis se trata la aplicación práctica del procedimiento de peritos descrito en el artículo 38 para la resolución de los siniestros. Un aspecto importante para su aplicación es conocer el desarrollo efectuado por la jurisprudencia respecto a los casos, efectos y aplicaciones del procedimiento. A las generalidades de la aplicación del procedimiento para todo tipo de siniestros se añadirá un análisis de las particularidades en el procedimiento de nombramiento de perito médico conforme dispone el artículo 104 de la Ley de Contrato de Seguro para el caso de los siniestros de accidentes. También se tratará de la novedosa aplicación del procedimiento de peritos para la resolución de los Autos de Cuantía Máxima del Seguro Obligatorio según el Real Decreto Legislativo 8/2004, de 29 de octubre, por el que se aprueba el texto refundido de la Ley sobre responsabilidad civil y seguro en la circulación de vehículos a motor

GEORBAC: DISPOSITIVO MÓVIL EN LA ARQUITECTURA DE CONTROL DE ACCESO

Este proyecto consiste en el desarrollo y la implementación de una plataforma móvil enmarcada en un sistema global de control de acceso basado en roles y geolocalización: GeoRBAC. En concreto está focalizado en la parte de identificación de usuario de dicho sistema global, creando un entorno real en el que el usuario es capaz de identificarse y distribuir su posición geográfica, aprovechando modernas tecnologías y tras hacer un exhaustivo estudio teórico de las herramientas involucradas en el mismo.Lacalle Úbeda, I. (2014). GEORBAC: DISPOSITIVO MÓVIL EN LA ARQUITECTURA DE CONTROL DE ACCESO. http://hdl.handle.net/10251/51433.Archivo delegad

Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case

[EN] During the past few decades, the combination of flourishing maritime commerce and urban population increases has made port-cities face several challenges. Smart Port-Cities of the future will take advantage of the newest IoT technologies to tackle those challenges in a joint fashion from both the city and port side. A specific matter of interest in this work is how to obtain reliable, measurable indicators to establish port-city policies for mutual benefit. This paper proposes an IoTbased software framework, accompanied with a methodology for defining, calculating, and predicting composite indicators that represent real-world phenomena in the context of a Smart PortCity. This paper envisions, develops, and deploys the framework on a real use-case as a practice experiment. The experiment consists of deploying a composite index for monitoring traffic congestion at the port-city interface in Thessaloniki (Greece). Results were aligned with the expectations, validated through nine scenarios, concluding with delivery of a useful tool for interested actors at Smart Port-Cities to work over and build policies upon.This research was funded, by the European Commission, via the agency INEA, under the H2020-project PIXEL, grant number 769355, and, when applicable, by the H2020-project DataPorts, grant number 871493, via the DG-CONNECT agency.Lacalle, I.; Belsa, A.; Vaño, R.; Palau Salvador, CE. (2020). Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors. 20(15):1-41. https://doi.org/10.3390/s20154131S1412015Urban Population Growthhttps://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/Smart Port Cityhttps://maritimestreet.fr/smart-port-city/The World’s 33 Megacitieshttps://www.msn.com/en-us/money/realestate/the-worlds-33-megacities/ar-BBUaR3vDocksTheFuture Project Maritime Traffic Analysis and Forecast Review-Key Resultshttps://www.docksthefuture.eu/wp-content/uploads/2020/04/Attachment_0-2019-09-09T135818.886-1.pdfHamburg Port Authority: SmartPORThttps://www.hamburg-port-authority.de/en/hpa-360/smartport/Guo, H., Wang, L., Chen, F., & Liang, D. (2014). Scientific big data and Digital Earth. Chinese Science Bulletin, 59(35), 5066-5073. doi:10.1007/s11434-014-0645-3AIVP Agenda 2030 for Sustainable Port-Citieshttps://www.aivpagenda2030.com/Urban Transport Challengeshttps://transportgeography.org/?page_id=4621Passenger Cars in the EUhttps://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EUAverage CO2 Emissions from New Cars and Vans Registered in Europe Increased in 2018, Requiring Significant Emission Reductions to Meet the 2020 Targetshttps://ec.europa.eu/clima/news/average-co2-emissions-new-cars-and-vans-registered-europe-increased-2018-requiring-significant_en7 Smart City Solutions to Reduce Traffic Congestionhttps://www.geotab.com/blog/reduce-traffic-congestion/The Port and the City—Thoughts on the Relation between Cities and Portshttps://theportandthecity.wordpress.com/Yau, K.-L. A., Peng, S., Qadir, J., Low, Y.-C., & Ling, M. H. (2020). Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology. IEEE Access, 8, 83387-83404. doi:10.1109/access.2020.2990961Two Projects Led by Valenciaport Win the IAPH World Port Sustainability Awards 2020—Valenciaporthttps://www.valenciaport.com/en/two-projects-led-by-valenciaport-win-the-iaph-world-port-sustainability-awards-2020/Ahlgren, B., Hidell, M., & Ngai, E. C.-H. (2016). Internet of Things for Smart Cities: Interoperability and Open Data. IEEE Internet Computing, 20(6), 52-56. doi:10.1109/mic.2016.124Inkinen, T., Helminen, R., & Saarikoski, J. (2019). Port Digitalization with Open Data: Challenges, Opportunities, and Integrations. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 30. doi:10.3390/joitmc5020030Analytical Report 4: Open Datain Citieshttps://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n4_-_open_data_in_cities_v1.0_final.pdfAnalytical Report 6: Open Datain Cities 2https://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n6_-_open_data_in_cities_2_-_final-clean.pdfINTER-IoT Deliverableshttps://inter-iot.eu/deliverablesActivage Project D3.1 Report on IoT European Platformshttps://www.activageproject.eu/docs/downloads/activage_public_deliverables/ACTIVAGE_D3.1_M3_ReportonIoTEuropeanPlatforms_V1.0.pdfThe Open Source Platform for Our Smart Digital Future—FIWAREhttps://www.fiware.org/FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/index.htmlApache Kafkahttps://kafka.apache.org/FIWARE Orion Context Brokerhttps://fiware-orion.readthedocs.io/en/master/Saborido, R., & Alba, E. (2020). Software systems from smart city vendors. Cities, 101, 102690. doi:10.1016/j.cities.2020.102690Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2018). Software Platforms for Smart Cities. ACM Computing Surveys, 50(6), 1-37. doi:10.1145/3124391Smart Citieshttps://www.fiware.org/community/smart-cities/Araujo, V., Mitra, K., Saguna, S., & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261. doi:10.1016/j.jpdc.2018.12.010Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47, 88-100. doi:10.1016/j.ijinfomgt.2019.01.004Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement, 129, 589-606. doi:10.1016/j.measurement.2018.07.067Samih, H. (2019). Smart cities and internet of things. Journal of Information Technology Case and Application Research, 21(1), 3-12. doi:10.1080/15228053.2019.1587572Lanza, J., Sánchez, L., Gutiérrez, V., Galache, J., Santana, J., Sotres, P., & Muñoz, L. (2016). Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case. Energies, 9(9), 719. doi:10.3390/en9090719A Novel Architecture for Modelling, Virtualising and Managing the Energy Consumption of Household Appliances|AIM Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224621Intelligent Use of Buildings’ Energy Information|IntUBE Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224286Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: applications and challenges in smart cities. A case study of IBM smart city projects. Business Process Management Journal, 22(2). doi:10.1108/bpmj-05-2015-0074Molavi, A., Lim, G. J., & Race, B. (2019). A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 14(9), 686-700. doi:10.1080/15568318.2019.1610919Moustaka, V., Vakali, A., & Anthopoulos, L. G. (2019). A Systematic Review for Smart City Data Analytics. ACM Computing Surveys, 51(5), 1-41. doi:10.1145/3239566Alam, M., Dupras, J., & Messier, C. (2016). A framework towards a composite indicator for urban ecosystem services. Ecological Indicators, 60, 38-44. doi:10.1016/j.ecolind.2015.05.035PIXEL Project D5.1 Environmental Factors and Mapping to Pilotshttps://pixel-ports.eu/wp-content/uploads/2020/05/D5.1-Environmental-aspects-and-mapping-to-pilots.pdfEconomic Sentiment Indicator—Eurostathttps://ec.europa.eu/eurostat/web/products-datasets/product?code=teibs010Human Development Index (HDI)|Human Development Reportshttp://hdr.undp.org/en/content/human-development-index-hdiCOIN|Competence Centre on Composite Indicators and Scoreboardshttps://composite-indicators.jrc.ec.europa.eu/CITYkeys Projecthttp://www.citykeys-project.eu/citykeys/homeCITYkeys D1-4 Indicators for Smart City Projects and Smart Citieshttp://nws.eurocities.eu/MediaShell/media/CITYkeysD14Indicatorsforsmartcityprojectsandsmartcities.pdfMake Healthy Choices Easier Options—Scientific Americanhttps://www.scientificamerican.com/podcast/episode/make-healthy-choices-easier-options-12-09-20/FIWARE E Interoperabilidad Para Smart Citieshttps://www.apegr.org/images/descargas/J7OctESMARTCITY/2PresentacionFIWARE.pdfChen, G., Govindan, K., & Yang, Z. (2013). Managing truck arrivals with time windows to alleviate gate congestion at container terminals. International Journal of Production Economics, 141(1), 179-188. doi:10.1016/j.ijpe.2012.03.033Patel, N., & Mukherjee, A. B. (2015). Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index. Bulletin of Geography. Socio-economic Series, 30(30), 123-134. doi:10.1515/bog-2015-0039Aimsun Live: Model Every Movement at Every Momenthttps://www.aimsun.com/aimsun-live/PTV Vissim: Traffic Simulation Softwarehttps://www.ptvgroup.com/en/solutions/products/ptv-vissim/IBM Traffic Prediction Toolhttps://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=1248Veins: The Open Source Vehicular Network Simulation Frameworkhttps://veins.car2x.org/Mena-Yedra, R., Gavaldà, R., & Casas, J. (2017). Adarules: Learning rules for real-time road-traffic prediction. Transportation Research Procedia, 27, 11-18. doi:10.1016/j.trpro.2017.12.106PIXEL Projecthttps://pixel-ports.euReference Architectural Model Industrie 4.0 (rami 4.0)https://www.plattform-i40.de/PI40/Navigation/EN/Home/home.htmlSethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, 2017, 1-25. doi:10.1155/2017/9324035Containers & Containerization—The Pros and Conshttps://spin.atomicobject.com/2019/05/24/containerization-pros-cons/Pyngsi Frameworkhttps://github.com/pixel-ports/pyngsiPIXEL Project D6.2 PIXEL Information System Architecture and Design—Version 2https://pixel-ports.eu/wp-content/uploads/2020/05/D6.2-PIXEL-Information-System-architecture-and-design-v2.pdfApache Hivehttps://hive.apache.org/MySQLhttps://www.mysql.com/MariaDB Serverhttps://mariadb.org/Elasticsearchhttps://www.elastic.co/elasticsearch/MongoDBhttps://www.mongodb.com/Node-REDhttps://nodered.org/Swarm Mode Overview | Docker Documentationhttps://docs.docker.com/engine/swarm/Kuberneteshttps://kubernetes.io/PIXEL Project D6.3 PIXEL Data Acquisition, Information Hub and Data Representation v1https://pixel-ports.eu/wp-content/uploads/2020/05/D6.3_PIXEL-data-acquisition-information-hub-and-data-representation-v1.pdfOverview of Docker Compose|Docker Documentationhttps://docs.docker.com/compose/Kibana: Explore, Visualize, Discover Datahttps://www.elastic.co/kibanaGrafana: The Open Observability Platformshttps://grafana.com/Vue.jshttps://vuejs.org/PIXEL Project D5.2 PEI Definition and Algorithms v1https://pixel-ports.eu/wp-content/uploads/2020/05/D5.2-PEI-Definition-and-Algorithms-v1.pdfKeyPerformanceIndicator—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/KeyPerformanceIndicator/doc/spec/index.htmlWhat Is a Container?|App Containerization|Dockerhttps://www.docker.com/resources/what-containerGarcia-Alonso, L., Moura, T. G. Z., & Roibas, D. (2020). The effect of weather conditions on port technical efficiency. Marine Policy, 113, 103816. doi:10.1016/j.marpol.2020.103816TrafficThess—LIVE Traffic in Thessaloniki, Greecehttps://www.trafficthess.imet.gr/National Observatory of Athens—Meteo—Stations’ Live Data and Databasehttp://stratus.meteo.noa.gr/frontHow to Use Smart Data Models in Your Projects—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/howto/index.htmlGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3). doi:10.1007/s12544-015-0170-8Prophet: Forecastig at Scalehttps://facebook.github.io/prophet/PIXEL Project D4.4 PredictiveAlgorithms v2https://pixel-ports.eu/wp-content/uploads/2020/05/PIXEL_D4.4_Predictive-Algorithms_v2.0_Final.pdfProject Jupyterhttps://jupyter.org/FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/NGSIElasticsearchSink—FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/cygnus-ngsi/flume_extensions_catalogue/ngsi_elasticsearch_sink/index.htmlNode.jshttps://nodejs.org/Elasticsearch Node.js Client [7.x]https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/index.htmlApache HTTP Server Projecthttps://httpd.apache.org/Everything You Need to Know about Min-Max Normalization: A Python Tutorialhttps://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79OpenStreetMaphttps://www.openstreetmap.org/Leaflet—A JavaScript Library for Interactive Mapshttps://leafletjs.com/AmCharts: JavaScript Charts & Mapshttps://www.amcharts.com/FIWARE Cataloguehttps://www.fiware.org/developers/catalogue/Findlow, S. (2019). ‘Citizenship’ and ‘Democracy Education’: identity politics or enlightened political participation? British Journal of Sociology of Education, 40(7), 1004-1013. doi:10.1080/01425692.2019.1656910Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials, 20(1), 416-464. doi:10.1109/comst.2017.277115

Leveraging IoT and prediction techniques to monitor COVID-19 restrictions in port terminals

[EN] Social distance restrictions have posed several challenges for the management of logistic nodes even in open spaces like a maritime port terminal. The Internet of Things combined with the simulation of supply chains provide the perfect breeding ground for devising innovative tools to help ports observe those restrictions. The objective of this paper is to devise such a tool based on research open results, proposing a clear architecture and use-case to be leveraged by maritime ports. Internet of Things techniques such as gathering heterogeneous data through a context broker, managing the Big Data generated and applying innovative models over that data set the foundations of the proposed system. The actual tool has been achieved as a result, together with a clear plan on future works that may build upon it.This work is part of the PIXEL project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769355.Vaño, R.; Lacalle, I.; Molina Moreno, B.; Palau Salvador, CE. (2021). Leveraging IoT and prediction techniques to monitor COVID-19 restrictions in port terminals. IEEE. 205-210. https://doi.org/10.1109/WF-IoT51360.2021.959591920521

Estacionalidad determinista y estocástica en series temporales macroeconómicas

Basándose en la literatura existente, en este trabajo se propone una metodología para el estudio gráfico y analítico del componente estacional en una serie temporal. El objetivo del análisis es determinar si el componente estacional responde a un comportamiento determinista o estocástico. Se muestran un conjunto de aplicaciones con series de la CAPV y del Estado para las que se define un modelo estadístico que recoge las características observadas en el análisis de la serie.series temporales, estacionalidad, raíz unitaria

SMALL AND MEDIUM PORTS' ACTIVITIES MODELLING: INTRODUCTION TO THE PIXEL APPROACH

[EN] Port activities undeniably have an impact on their environment, the city and citizens living nearby. To have a better understanding of these impacts, the ports of the future will require tools allowing suitable modelling, simulation and data analysis. This challenge is also tied to another current reality: the heterogeneous data coming from different stakeholders converging into ports are not optimally exploited due to lack of interoperability. Thus, the forthcoming research and development initiatives must address these demands from a holistic point of view. PIXEL (H2020-funded project) aims at creating the first smart, flexible and scalable solution reducing the environmental impact while enabling optimization of operations in port ecosystems. PIXEL brings the most innovative IoT and ICT technology to ports and demonstrate their capacity to take advantage of modern approaches. Using an interoperable open IoT platform, data is acquired and integrated into an information hub comprised of small, low-level sensors up to virtual sensors able to extract relevant data from high level services. Finally, this hub integrates smart models to analyse port processes for prediction and optimization purposes: (i) a model of consumption and energy production of the port with the aim of moving towards green energy production; (ii) a model of congestion of multi-modal transport networks to reduce the impact of port traffic on the network; and (iii) models of environmental pollution of the port to reduce the environmental impacts of the port on the city and its citizens. The main issue tackled by PIXEL is to provide interoperability between these models and allow real integration and communication in the context of an environmental management model. Besides that, PIXEL devotes to decouple port¿s size and its ability to deploy environmental impact mitigation specifying an innovative methodology and an integrated metric for the assessment of the overall environmental impact of ports.The PIXEL project, the results of which are presented in this paper, is being funded from the European Union s Horizon 2020 research and innovation programme under grant agreement no. 769355 Port IoT for Environmental Leverage (PIXEL)Simon, E.; Garnier, C.; Lacalle, I.; Costa, JP.; Palau Salvador, CE. (2019). SMALL AND MEDIUM PORTS' ACTIVITIES MODELLING: INTRODUCTION TO THE PIXEL APPROACH. WIT Transactions on the Built Environment (Online). 187:149-163. https://doi.org/10.2495/MT190141S14916318

Connexin 43 deficiency is associated with reduced myocardial scar size and attenuated tgfβ1 signaling after transient coronary occlusion in conditional knock-out mice

Funding: This research was funded by Fundació La Marató de TV3 (n◦. 201536-10) and the Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III (CIBERCV), cofinanced by the European Regional Development Fund (ERDF-FEDER, a way to build Europe). Antonio Rodríguez-Sinovas has a consolidated Miguel Servet contract.Previous studies demonstrated a reduction in myocardial scar size in heterozygous Cx43-mice subjected to permanent coronary occlusion. However, patients presenting with ST segment elevation myocardial infarction often undergo rapid coronary revascularization leading to prompt restoration of coronary flow. Therefore, we aimed to assess changes in scar size and left ventricular remodeling following transient myocardial ischemia (45 min) followed by 14 days of reperfusion using Cx43 (controls) and Cx43 inducible knock-out (Cx43 content: 50%) mice treated with vehicle or 4-hydroxytamoxifen (4-OHT) to induce a Cre-ER(T)-mediated global deletion of the Cx43 floxed allele. The scar area (picrosirius red), measured 14 days after transient coronary occlusion, was similarly reduced in both vehicle and 4-OHT-treated Cx43 mice, compared to Cx43 animals, having normal Cx43 levels (15.78% ± 3.42% and 16.54% ± 2.31% vs. 25.40% ± 3.14% and 22.43% ± 3.88% in vehicle and 4-OHT-treated mice, respectively, p = 0.027). Left ventricular dilatation was significantly attenuated in both Cx43-deficient groups (p = 0.037 for left ventricular end-diastolic diameter). These protective effects were correlated with an attenuated enhancement in pro-transforming growth factor beta 1 (TGFβ1) expression after reperfusion. In conclusion, our data demonstrate that Cx43 deficiency induces a protective effect on scar formation after transient coronary occlusion in mice, an effect associated with reduced left ventricular remodeling and attenuated enhancement in pro-TGFβ1 expression

Estacionalidad determinista y estocástica en series temporales macroeconómicas

Basándose en la literatura existente, en este trabajo se propone una metodología para el estudio gráfico y analítico del componente estacional en una serie temporal. El objetivo del análisis es determinar si el componente estacional responde a un comportamiento determinista o estocástico. Se muestran un conjunto de aplicaciones con series de la CAPV y del Estado para las que se define un modelo estadístico que recoge las características observadas en el análisis de la serie.El primer autor agradece la financiación de la Universidad del País Vasco al grupo de investigación: 9/UPV-00038.321-13503/2001 y del Ministerio de Ciencia y Tecnología y FEDER para el proyecto: BEC2003-02028. El segundo de los autores agradece a la Universidad del País Vasco la ayuda económica ofrecida a través del programa de becas predoctorales

Hemogram-derived ratios as prognostic markers of ICU admission in COVID-19.

Background The vast impact of COVID-19 call for the identification of clinical parameter that can help predict a torpid evolution. Among these, endothelial injury has been proposed as one of the main pathophysiological mechanisms underlying the disease, promoting a hyperinflammatory and prothrombotic state leading to worse clinical outcomes. Leukocytes and platelets play a key role in inflammation and thrombogenesis, hence the objective of the current study was to study whether neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII) as well as the new parameter neutrophil-to-platelet ratio (NPR), could help identify patients who at risk of admission at Intensive Care Units. Methods A retrospective observational study was performed at HM Hospitales including electronic health records from 2245 patients admitted due to COVID-19 from March 1 to June 10, 2020. Patients were divided into two groups, admitted at ICU or not. Results Patients who were admitted at the ICU had significantly higher values in all hemogram-derived ratios at the moment of hospital admission compared to those who did not need ICU admission. Specifically, we found significant differences in NLR (6.9 [4–11.7] vs 4.1 [2.6–7.6], p <  0.0001), PLR (2 [1.4–3.3] vs 1.9 [1.3–2.9], p = 0.023), NPR (3 [2.1–4.2] vs 2.3 [1.6–3.2], p <  0.0001) and SII (13 [6.5–25.7] vs 9 [4.9–17.5], p <  0.0001) compared to those who did not require ICU admission. After multivariable logistic regression models, NPR was the hemogram-derived ratio with the highest predictive value of ICU admission, (OR 1.11 (95% CI: 0.98–1.22, p = 0.055). Conclusions Simple, hemogram-derived ratios obtained from early hemogram at hospital admission, especially the novelty NPR, have shown to be useful predictors of risk of ICU admission in patients hospitalized due to COVID-19.post-print997 K