On Line Service Composition in the Integrated Clinical Environment for eHealth and Medical Systems

Medical and eHealth systems are progressively realized in the context of standardized architectures that support safety and ease the integration of the heterogeneous (and often proprietary) medical devices and sensors. The Integrated Clinical Environment (ICE) architecture appeared recently with the goal of becoming a common framework for defining the structure of the medical applications as concerns the safe integration of medical devices and sensors.This research was partly supported by iLand (EU ARTEMIS-1-00026) granted by the ARTEMIS JUand the Spanish Ministry of Industry, Commerce and Tourism. It has also been partly funded by the REM4VSS (TIN2011-28339) project grant of the Spanish Ministry of Economy and Competitiveness and by Universidad Carlos III de Madrid. The authors would also like to mention the large development team of the iLand reference implementation that performed an outstanding role to achieve a software proven also on commercial applications, and they thank them for their valuable efforts and work.Publicad

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

The Palgrave Handbook of International Energy Economics

This open access handbook is distinguished by its emphasis on international energy, rather than domestic energy policies or international geopolitic aspects. Addressing key topics such as energy production and distribution, renewables and corporate energy structures, alongside global energy trends, regional case studies and emerging areas such as the digitalization of energy and energy transition, this handbook provides a major new contribution to the field of international energy economics. Written by academics, practitioners and policy-makers, this handbook is a valuable and timely addition to the literature on international energy economics. This book was published open access with the support of Eni

Proceedings of the construction business and project management conference

Conceptualising challenges and opportunities in the construction industry

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution