ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.

Background There has recently been exponential growth in the development and use of health apps on mobile phones. As with most mobile apps, however, the majority of users abandon them quickly and after minimal use. One of the most critical factors for the success of a health app is how to support users’ commitment to their health. Despite increased interest from researchers in mobile health, few studies have examined the measurement of user engagement with health apps. Objective User engagement is a multidimensional, complex phenomenon. The aim of this study was to understand the concept of user engagement and, in particular, to demonstrate the applicability of a user engagement scale (UES) to mobile health apps. Methods To determine the measurability of user engagement in a mobile health context, a UES was employed, which is a psychometric tool to measure user engagement with a digital system. This was adapted to Ada, developed by Ada Health, an artificial intelligence–powered personalized health guide that helps people understand their health. A principal component analysis (PCA) with varimax rotation was conducted on 30 items. In addition, sum scores as means of each subscale were calculated. Results Survey data from 73 Ada users were analyzed. PCA was determined to be suitable, as verified by the sampling adequacy of Kaiser-Meyer-Olkin=0.858, a significant Bartlett test of sphericity (χ2300=1127.1; P<.001), and communalities mostly within the 0.7 range. Although 5 items had to be removed because of low factor loadings, the results of the remaining 25 items revealed 4 attributes: perceived usability, aesthetic appeal, reward, and focused attention. Ada users showed the highest engagement level with perceived usability, with a value of 294, followed by aesthetic appeal, reward, and focused attention. Conclusions Although the UES was deployed in German and adapted to another digital domain, PCA yielded consistent subscales and a 4-factor structure. This indicates that user engagement with health apps can be assessed with the German version of the UES. These results can benefit related mobile health app engagement research and may be of importance to marketers and app developers.

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Multi-agent systems can be a viable choice for realizing self-organizing systems consisting of reconfigurable software components. We present a real-world system consisting of heterogeneous air and ground robots whose behavior and coordination is orchestrated by a MAS in a decentralized manner. The system is able to cooperatively transport largescale measuring equipment and is used for environmental observation, such as in-situ measuring of temperature.

During the last two decades, software development has evolved continuously into an engineering discipline with systematic use of methods and tools to model and implement software. For example, object-oriented analysis and design is structuring software models according to real-life objects of the problem domain and their relations. However, the industrial robotics domain is still dominated by old-style, imperative robot programming languages, making software development difficult and expensive. For this reason, we introduce the object-oriented Robotics Application Programming Interface (Robotics API) for developing software for industrial robotic applications. The Robotics API offers an abstract, extensible domain model and provides common functionality, which can be easily used by application developers. The advantages of the Robotics API are illustrated with an application example.

Today, most industrial robots are interfaced using text-based programming languages. These languages offer the possibility to declare robotic-specific data types, to specify simple motions, and to interact with tools and sensors via I/O operations. While tailored to the underlying robot controller, they usually only offer a fixed and controller-specific set of possible instructions. The specification of complex motions, the synchronization of cooperating robots and the advanced use of sensors is often very difficult or not even feasible. To overcome these limitations, this paper presents a generic and extensible interface for industrial robots, the Realtime Primitives Interface, as part of a larger software architecture. It allows a flexible specification of complex control instructions and can facilitate the development of sustainable robot controllers. The advantages of this approach are illustrated with several examples.

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Designing complex adaptive systems for real world applications is a delicate challenge, especially when support for humans in crucial situations should be achieved. In this position paper, we propose a multi-agent based approach for physically reconfigurable, heterogeneous robot swarms. These can be deployed when there is a need to search, continuously observe and react, e.g. in disaster scenarios. We show first results that validate the feasibility of our approach.