MergePoint: A Graphical Web-App for merging HTTP-Endpoints and IoT-Platform Models

More and more devices are connected to Internet of Things Platforms in various application domains. The resulting device integration effort is moderated by the concrete integration syntax and the technical abilities of the device integrator. Therefore, researchers from various communities have been investigating and designing component coupling architectures to achieve interoperability for more than 30 years. Emerging Smart Home scenarios challenges classical integration approaches as no single formal integration standard exists. In this paper we introduce a reference architecture called MergePoint that automates HTTP-Endpoint integration with smart home platforms such as openHAB in a plug-and-play manner. Based on a prototypical system implementation, our empirical evaluation demonstrates that average integration time can be reduced by 78% and average tool usability score is increased by 65% compared to textual integration approaches. MergePoint can serve as a reference implementation for practitioners that want to automate the integration between HTTP-Endpoints and IoT Platform Models

SERONTO: a Socio-Ecological Research and Observation oNTOlogy.

SERONTO is an ontology developed within ALTER-Net, a Long Term Biodiversity, Ecosystem, and Awareness Research Network funded by the European Union. ALTER-Net addresses major biodiversity issues at a European scale. Within this framework SERONTO has been developed to solve the problem of integrating and managing data stored and collected at different locations within the European Union. SERONTO is a product of a group of people with diverse scientific backgrounds. The ontology is a formal description of the concepts and relationships for the most important aspects of biodiversity data derived from monitoring, experiments and investigations. SERONTO is an ontology that enables seamless presentation of data from different origins in a similar conceptual manner. With SERONTO, meta-analysis, data mining, and data presentation should be possible across datasets collected for different purposes. SERONTO consists of a core ontology and a separate unit and dimensions ontology. The core ontology is designed to be the basis for domain specific ontologies (e.g. species, geography, water, vegetation), which extend the concepts and relationships of the core for their specific needs and requirements. The concepts of the core are derived from scientific principles and lean heavily on statistical methodology. Important considerations in designing SERONTO were 1. Repeatability: The ontology should be capable of holding enough meta-data that another person can repeat the experiment or observation at another place and time. It is not obligatory, however, to provide all information for all datasets; for instance, some information may be missing for old datasets. 2. Transparency: It must be possible to record and retrieve meta-data describing what actually happened. SERONTO includes concepts of things going wrong and documenting data collection under less than ideal conditions. If data and meta-data are available in this way, it will be clear what assumptions must be made to combine data and correctly interpret analyses. Important concepts in the SERONTO core are: 1. Investigation item – the research object or experimental unit; 2. Parameters – the measurement, classification and treatment of the investigation item; 3. Value sets – placeholders for time series and other complex data; 4. Reference lists – nominal values, such as species lists; 5. Methods – used for each parameter, including units, scale, and dimensions; 6. Sampling structure – the origin of the research object or population, and the way it was chosen; 7. Groupings of objects, such as experimental blocks, on which observer, time or other aspects are assigned or related to; 8. Additional information, such as actors (observer, observer groups and institutions), project information, etc., can be attached to several different concepts. Each subsequent analysis has to make assumptions. The assumptions of any particular analysis can be found in the deviation between how the data were obtained and the requirements of the analytical method. The presentation will go deeper into the design considerations and the core concepts. Explanations of the concepts, their interrelationships, and their use in subsequent analysis will be given along with examples from different domains

High precision hyperfine measurements in Bismuth challenge bound-state strong-field QED

Electrons bound in highly charged heavy ions such as hydrogen-like bismuth 209Bi82+ experience electromagnetic fields that are a million times stronger than in light atoms. Measuring the wavelength of light emitted and absorbed by these ions is therefore a sensitive testing ground for quantum electrodynamical (QED) effects and especially the electron–nucleus interaction under such extreme conditions. However, insufficient knowledge of the nuclear structure has prevented a rigorous test of strong-field QED. Here we present a measurement of the so-called specific difference between the hyperfine splittings in hydrogen-like and lithium-like bismuth 209Bi82+,80+ with a precision that is improved by more than an order of magnitude. Even though this quantity is believed to be largely insensitive to nuclear structure and therefore the most decisive test of QED in the strong magnetic field regime, we find a 7-σ discrepancy compared with the theoretical prediction