Smart decentralised energy management

The German–Finnish research project FUture Smart Energy shows, how flexible devices, consuming or producing electricity in electric grids, can be self-organised in a fully decentralised way, using autonomous algorithms integrated with the devices\u27 controllers. By shifting operation time, existing flexible devices are hereby utilised as ‘virtual batteries’, providing high storage capacity and power. To gain sufficient flexibility, a large number of devices like combined heat and power generators, heat pumps (HP), heaters, coolers, charging stations, pumps, household appliances and industrial plants, has to be coordinated. This results in a high system complexity for which the evaluated method provides an easy, resilient, cyber-secure and cost-effective solution. This novel technology uses a new market approach for electric energy systems. A real-time price signal is generated directly out of grid state variables, like frequency, voltage, power or current, and broadcast to the flexible devices. Without a need for central control, the flexible devices react like a natural swarm to the price signal. The system is easily and highly scalable, as adding and removing flexibilities does not imply adapting a central control system. The system can be operated parallel or in addition to existing energy markets

Awareness and use of home remedies in Italy's alps: a population-based cross-sectional telephone survey

Belief in complementary and alternative medicine practices is related to reduced preparedness for vaccination. This study aimed to assess home remedy awareness and use in South Tyrol, where vaccination rates in the coronavirus pandemic were lowest in Italy and differed between German- and Italian-speaking inhabitants.; A population-based survey was conducted in 2014 and analyzed using descriptive statistics, multiple logistic regression, and latent class analysis.; Of the representative sample of 504 survey respondents, 357 (70.8%) participants (43.0% male; primary language German, 76.5%) reported to use home remedies. Most commonly reported home remedies were teas (48.2%), plants (21.0%), and compresses (19.5%). Participants from rural regions were less likely (odds ratio 0.35, 95% confidence interval 0.19-0.67), while female (2.62, 1.69-4.10) and German-speaking participants (5.52, 2.91-9.88) were more likely to use home remedies. Latent classes of home remedies were "alcoholic home remedies" (21.4%) and "non-alcohol-containing home remedies" (78.6%). Compared to the "non-alcohol-containing home remedies" class, members of the "alcoholic home remedies" class were more likely to live in an urban region, to be male and German speakers.; In addition to residence and sex, language group membership associates with awareness and use of home remedies. Home remedies likely contribute to socio-cultural differences between the language groups in the Italian Alps. If the observed associations explain the lower vaccination rates in South Tyrol among German speakers requires further study

FUSE – using artificial intelligence in the energy grid of tomorrow

The objective of Future Smart Energy (FUSE), a Finnish-German research and development project, is to develop methods based on artificial intelligence (AI) that will help to increase the resilience of future energy distribution grids. The use cases that are investigated include both condition monitoring/predictive maintenance, and distributed demand-side management in medium-voltage and low-voltage grids. The FUSE concept foresees a hierarchical infrastructure of sensing- and data processing nodes that use AI to transform raw data into information on asset and grid status and performance. FUSE supports the upward flow of data and aggregation of information into high-level visualisations for grid operators, as well as the downward flow of soft control signals that trigger the distributed self-control of assets. This study outlines the FUSE concept and presents the first results.This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)fi=vertaisarvioitu|en=peerReviewed

WESSBAS: extraction of probabilistic workload specifications for load testing and performance prediction—a model-driven approach for session-based application systems

The specification of workloads is required in order to evaluate performance characteristics of application systems using load testing and model-based performance prediction. Defining workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in both areas. To overcome this challenge, this paper presents an approach that aims to automate the extraction and transformation of workload specifications for load testing and model-based performance prediction of session-based application systems. The approach (WESSBAS) comprises three main components. First, a system- and tool-agnostic domain-specific language (DSL) allows the layered modeling of workload specifications of session-based systems. Second, instances of this DSL are automatically extracted from recorded session logs of production systems. Third, these instances are transformed into executable workload specifications of load generation tools and model-based performance evaluation tools. We present transformations to the common load testing tool Apache JMeter and to the Palladio Component Model. Our approach is evaluated using the industry-standard benchmark SPECjEnterprise2010 and the World Cup 1998 access logs. Workload-specific characteristics (e.g., session lengths and arrival rates) and performance characteristics (e.g., response times and CPU utilizations) show that the extracted workloads match the measured workloads with high accuracy

Psychological preparation and postoperative outcomes for adults undergoing surgery under general anaesthesia

Acknowledgements We wish to dedicate this work to the memory of Christian Osmer, a dedicated, caring doctor who was committed to achieving the best care for his patients and their relatives. He saw his contribution to this project as a way of advancing best care for surgical patients. We are very grateful for his valuable input to this work and the pleasure we had in working with him. We are grateful to Karen Hovhanisyan (former Trials Search Co-ordinator, Cochrane Anaesthesia, Critical and Emergency Care Group (ACE)) for carrying out the electronic database searches and to Jane Cracknell (Managing Editor, ACE) for her support throughout the review process. We would also like to thank W Alastair Chambers and Manjeet Shehmar for clinical advice relating to judgements about general anaesthesia usage, and Yvonne Cooper and Louise Pike who retrieved documents and screened papers as research assistants in earlier stages of the review. We are grateful to the following colleagues who helped us with foreign language papers - either by screening papers or by providing translation: Stefano Carrubba, Chuan Gao, Chen Ji, Kate Rhie, Reza Roudsari and Alena Vasianovich. We would like to thank Andy Smith (content editor), Nathan Pace (statistical editor), Michael Donnelly, Allan Cyna and Michael Wang (peer reviewers), and Shunjie Chua (consumer referee) for their help and editorial advice during the preparation of this systematic review. We would also like to thank Andrew Smith (content editor), Nathan Pace (statistical editor), Michael Wang and Allan Cyna (peer reviewers), and Lynda Lane (Cochrane Consumer Network representative) for their help and editorial advice during the preparation of the protocol (Powell 2010). Sources of support Internal sources Manchester Centre for Health Psychology, University of Manchester, UK. An award of £2000 was received to support research assistant costs. External sources British Academy, UK. We received a small research grant of £7480 to support research assistant costs.Peer reviewedPublisher PD