With a growing population of people with a chronic healthcare condition, new policies including those of the WHO (2018), stress the importance of eHealth. So far, these services with advanced medical informatics technology offer increased possibilities of collecting and recording data such on for instance body measurements, treatment - and lifestyle activities. However, the sharing of this data between patients and healthcare professionals is relatively unexplored In this paper we interrogate meanings and assumptions relating to ‘Design’ and ‘Technology’ with a particular focus on the provocative term ‘Design for Value’. We investigate the value of data-sharing in eHealth services for persons with a chronic condition. Our aim is to explore and model the data sharing in order to contribute to new experiences of value in technology enabled care services. The guiding research question: How to frame, shape and model personal data sharing with health professionals? The research method builds on strategic design methodologies for co-designing care models for eHealth services. For this research we have crafted a data model design toolkit with enhanced visualizations of data exchange. The results presented in this paper include the data model designs for a case of chronic eHealth services: A. Diabetes portal. We conclude with a discussion on the modelling of data-sharing, its theoretical implications for the design for value and its further avenues for future research to deepen our understanding of data modelling and what designers and healthcare organizations can do.@en

Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g., computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand-balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies@en