Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

Detección de variedad y estado de maduración del ciruelo japonés utilizando imégenes hiperespectrales y aprendizaje profundo

En la actualidad, España ocupa el séptimo puesto como productor de ciruelas a nivel mundial y el tercero a nivel europeo según la Organización de las Naciones Unidas para la Alimentación y la Agricultura. La importancia que tiene el cultivo de esta fruta en nuestro paı́s es evidente, siendo mayor en Comunidades Autónomas como la Extremeña, que centran su actividad económica en el sector primario. Lo que debe diferenciar una producción es su calidad, pero la calidad de los frutos tradicionalmente se hace en base a la experiencia de los agricultores y técnicos, basándose únicamente en su percepción visual. Esto puede generar errores en la determinación de la fecha óptima de recolección. En este trabajo se propone un método novedoso basado en el análisis de imágenes hiperespectrales de los frutos del ciruelo japonés que, mediante técnicas de aprendizaje profundo (Deep Learning) y utilizando para ello redes neuronales convolucionales, se obtienen eficaces clasificadores de los frutos por su variedad y su fecha de maduración. Los resultados presentados en este trabajo permiten afirmar que es posible dotar a los agricultores y técnicos agrı́colas de herramientas que les ayuden a la correcta toma de decisón en relación a la fecha de maduración de sus frutos, para poder obtener productos de mayor calidad y ser más competitivos en el sector.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A Fuzzy Rule-Based System to Predict Energy Consumption of Genetic Programming Algorithms

In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when handheld ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modeling their energy consumption considering different execution devices. In this work, we propose a fuzzy rulebased system to predict energy comsumption of a kind of Evolutionary Algorithm, Genetic Prohramming, given the device in wich it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.We acknowledge support from Spanish Ministry of Economy and Competitiveness under projects TIN2014-56494-C4-[1,2,3]-P and TIN2017-85727-C4- [2,4]-P, Regional Government of Extremadura, Department of Commerce and Economy, conceded by the European Regional Development Fund, a way to build Europe, under the project IB16035, and Junta de Extremadura FEDER, projects GR15068 and GR15130

A Genetic Programming infrastructure profiting from public computation resources

In this article an experience of the utilization of PRC (Public Resource Computation) in research projects that needs large quantities of CPU time is presented. We have developed a distributed architecture based on middleware BOINC and LilGP Genetic Programming tool. In order to run LilGP applications under BOINC platforms, some core LilGP functions has been adapted to BOINC requirements. We have used a classic GP problem known as the artificial ANT in Santa Fe Trail. Some computers from a classroom were used acting as clients, proving that they can be used for scientific computation in conjunction with their primary uses

Una Herramienta de Programación Genética Paralela que Aprovecha Recursos Públicos de Computación

Éste artículo presenta una primera implementación de una herramienta genérica de programación genética capaz de aprovechar recursos públicos de computación. Dadas las altas necesidades de recursos de computación requeridos por los algoritmos evolutivos, la aplicación del paralelismo ha sido habitual recientemente, aunque las herramientas paralelas requieren infraestructuras costosas para su aprovechamiento. El modelo que se presenta en este artículo, permite utilizar computadores distribuidos en Internet, cuyos usuarios ceden altruistamente para colaborar en proyectos de investigación. El proceso de donación de recursos es simple e inmediato por parte del usuario, afectando solamente a los ciclos de CPU que no son consumidos por el propio usuario. Se estudia la mejora de las prestaciones obtenidas gracias al uso de estos recursos en Programación Genética Distribuida

Automatic Burst Detection in Solar Radio Spectrograms Using Deep Learning: deARCE Method

We present in detail an automatic radio-burst detection system, based on the AlexNet con- volutional neural network, for use with any kind of solar spectrogram. A full methodology for model training, performance evaluation, and feedback to the model generator has been developed with special emphasis on i) robustness tests against stochastic and overfitting ef- fects, ii) specific metrics adapted to the unbalanced nature of the solar-burst scenario, iii) tunable parameters for probability-threshold optimization, and iv) burst-coincidence cross match among e-Callisto stations and with external observatories (NOAA-SWPC). The re- sulting neural network configuration has been designed to accept data from observatories other than e-Callisto, either ground- or spacecraft-based. Typical False Negative and False Positive Scores in single-observatory mode are, respectively, in the 10 ? 16% and 6 ? 8% ranges, which improve further in cross-match mode. This mode includes new services ( deARCE , Xmatch ) allowing the end-user to check at a glance if a solar radio burst has taken place with a high level of confidence.Junta de Comunidades de Castilla La Mancha; European Unio

Utilización de Internet para la enseñanza de sistemas digitales

Las nuevas tecnologías de la información y la comunicación han creado grandes expectativas en educación, pues permiten superar las limitaciones de tiempo y lugar, abaratando incluso los costes. Además, no debe olvidarse que los medios informáticos ofrecen una serie de características que favorecen el aprendizaje significativo a través de actividades de tipo interactivo. Por eso, nos parece importante su aplicación a la enseñanza de la Informática, y en particular de los sistemas digitales. En esta ponencia se presenta un sistema multimedia, basado en Internet, que se está desarrollando con el fin de aplicarlo a la docencia de sistemas digitales en la asignatura Fundamentos de Informática. La ponencia presenta una descripción general de dicho sistema, así como de las herramientas y métodos utilizados para su construcción

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries