Oxychalcogenides represent a large chemical space with potential application as thermoelectric materials due to their low thermal conductivity. However, the nature of this behaviour is still under debate. Understanding the...
The unprecedented growth of connected devices, together with the remarkable convergence of a wide variety of technologies, have led to an exponential increase in the services that the internet of things (IoT) can offer, all aimed at improving quality of life. Consequently, in order to meet the numerous challenges this produces, the IoT has become a major subject of research. One of these challenges is the reduction of energy consumption given the significant limitations of some devices. In addition, although the search for energy efficiency was initially focused on hardware, it has become a concern for software developers too. In fact, it has become an intense area of research with the principal objective of analyzing and optimizing the energy consumption of software systems. This research analyzes the energy saving that can be achieved when using a broad set of techniques for writing energy-efficient code for Raspberry Pi devices. It also demonstrates that programmers can save more energy if they apply the proposed techniques manually than when relying on other automatic optimization options offered by the GNU compiler collection (GCC). Thus, it is important that programmers are aware of the significant impact these techniques can have on an application’s energy consumption.
CultivData proposes the convergence of technologies, such as IoT, big data, HPC, open data and artificial intelligence, to apply HPDA (High Performance Data Analytics) to the cultivation of agricultural data and improve the efficiency and effectiveness of farms. An information system has been developed as an IT platform for the cultivation of open data to extract knowledge and to support the decision making of stakeholders in the agricultural sector, so that it is possible to improve product quality and farm productivity. The resulting system integrates access to data provided by IoT devices that sensorize farms and public and open data sources (Open Data). The platform was designed to make precision agriculture a reality and to be useful not only to farmers, but also to agricultural decision-makers who plan species and crops based on data such as available water; expected weather; prices and market demands, and so forth. In addition, the platform provides to agricultural producers access to historical climate data; climate forecasts to anticipate times of drought or disasters; pest situations or monitoring of their plantations with sensorization and orthophotographs.
Colección JORNADAS Y CONGRESOS n.º 34 Esta editorial es miembro de la UNE, lo que garantiza la difusión y comercializaci ón de sus publicaciones a nivel nacional e internacional.
One child in every 200 births may be affected by one of the approximately 6,000 monogenic diseases discovered so far. Establishing the pathogenicity of the mutations detected with NGS (Next-Generation Sequencing) techniques, within the sequence of the associated genes, will allow Precision Medicine concept to be developed. However, sometimes the clinical significance of the mutations detected in a genome may be uncertain (VUS, Variant of Uncertain Significance) which prevents the development of health measures devoted to personalize individuals' treatments. A VUS pathogenicity can be inferred thanks to evidences obtained from specific types of NGS studies. Therefore the union of supercomputing through HPC (High-Performance Computing) tools and the cloud computing paradigm (HPCC), within a Data Center federation environment offers a solution to develop and provide services to infer the pathogenicity of a set of VUS detected in a genome, while guaranteeing both the security of the information generated during the whole workflow and its availability.
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