Cyber-physical energy systems (CPES) describe a specialization of the cyber-physical system concept, in which energy systems are transformed into intelligent energy networks. These systems provide the basis for the realization of smart microgrids and smart grids. In the last decade, numerous research projects have intensively explored the fundamentals and modeling of CPES and validated them in pilot projects. In the meantime, more and more CPES solutions have been appearing on the market and the battle for the most suitable standards has begun. This paper gives an overview of the currently available standards for CPES sensor technologies and assesses the suitability for implementation. In two case studies in the application area of operational energy management in German companies, a sensor retrofitting is described-once with proprietary technology and once using the standards Long Range (LoRa) Wide Area Network and OPC Unified Architecture (OPC UA). As a result, the shortcomings of the standards for their use in CPES are shown and discussed. OPC UA, which was originally developed for the manufacturing industry, turns out to be to be a suitable standard for a wide range of CPES implementations.

Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.

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Objective We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. Materials and methods We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. Results We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. Discussion The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. Conclusion We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.

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