Centre of Expertise Digital Operations & Finance

This feasibility report aims to create a solid background for Savings Groups programming in Italy, Spain, Germany, Poland and the Netherlands where partner organisations of the LETS SAVEE project are based. This Erasmus+ funded project aims at exploring the potential of saving groups in the European countries’ context, by improving entrepreneurial skills and access to financial services and social networks of diverse target groups.

By: Julie-Marthe Lehmann
This feasibility report aims to create a solid background for Savings Groups programming in Italy, Spain, Germany, Poland and the Netherlands where partner organisations of the LETS SAVEE project are based. This Erasmus+ funded project aims at exploring the potential of saving groups in the European countries’ context, by improving entrepreneurial skills and access to financial services and social networks of diverse target groups.

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Two things are important in order to participate properly in society: being financially healthy and having a solid social network. They are two sides of the same coin. How do you keep it balanced? Researcher Julie-Marthe Lehmann set up five financial self-help groups and followed them for two years. In her research she shows that participating the group increases the social network and financial insight of the members.

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The estimation of the pose of a differential drive mobile robot from noisy odometer, compass, and beacon distance measurements is studied. The estimation problem, which is a state estimation problem with unknown input, is reformulated into a state estimation problem with known input and a process noise term. A heuristic sensor fusion algorithm solving this state-estimation problem is proposed and compared with the extended Kalman filter solution and the Particle Filter solution in a simulation experiment.

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The main objective of the study is to determine if non-specific physical symptoms (NSPS) in people with self-declared sensitivity to radiofrequency electromagnetic fields (RF EMF) can be explained (across subjects) by exposure to RF EMF. Furthermore, we pioneered whether analysis at the individual level or at the group level may lead to different conclusions. By our knowledge, this is the first longitudinal study exploring the data at the individual level.

The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

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