Disruption prediction and mitigation is of key importance in the development of sustainable tokamak reactors.Machine learning has become a key tool in this endeavour.In this paper, multiple machine learning models are tested and compared.A focus has been placed on the analysis of a transition to dimensionless input quantities.The methods used in th
This practitioner paper is based on the need to make sense of UN Agenda 2030 and Sustainable Development Goals (SDGs) at the city level and in an urban context.We examine the need to explain how to utilise the SDGs in strategic, tactical and operative urban development.We find that there are knowledge and practise gaps in how to localise SDGs in th
This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques.The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to ext