Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale

The ecology of cancer

Neoplasia, the disease of multicellular organisms, is not only a major cause of human death worldwide but also affects numerous invertebrate and vertebrate species. Similar to other diseases, cancer is a significant physiological burden on the host and hence not only impacts the individual but also influences interindividual interactions, populations and consequently global ecosystems. Despite this, oncology and other biological sciences such as ecology and evolution have until very recently developed in relative isolation. To overcome this caveat, we draw parallel between invasive species and the metastatic cascade and provide an overview of the ecology of cancer at the scale of the organisms and the ecosystems of malignant cells (both at the micro- and macro-scales). We discuss the drivers of metastatic formations in the tissue environment and investigate how individuals respond to malignant growth and the impact of this response on populations. Finally, we provide potential avenues for applying evolutionary ecology principles to cancer prevention and to the development of novel treatment strategies