Smart energy systems (SESs), with integrated energy sectors, provide several advantages over single-sector approaches for the development of renewable energy systems. However, cross-sector integration is at an early stage even in areas challenged by the existing high shares of variable renewable energy (VRE). The promotion of cross-sector integration requires institutional incentives and new forms of actor participation and interaction that are suitable to address the organisational challenges of implementing and operating SESs. Taking as the point of departure an empirical case and its institutional context, this article presents an exploratory study of the ability of cross-sector consumer ownership at different locations in the power distribution system to address those challenges in Denmark. The methods comprise interviews of relevant stakeholders and a literature review. The results indicate that distant and local cross-sector integration will be necessary to reduce overinvestments in the grid and that consumer co-ownership of wind turbines and power-to-heat (P2H) units in district heating (DH) systems may provide advantages over common separate ownership with regard to local acceptance and attractiveness of investments. Several possibilities are identified to improve the current institutional incentive system in Denmark. Finally, the results suggest the relevance of analysing the possibility for single-sector energy companies to transition to smart energy companies.

Since 2022, Modragon University has implemented Challenge-Based Learning (CBL) in the teaching and learning process of the Master’s in Logistics and Productive Operations Management (MUDOLP) program. This article describes the application of CBL in four companies from different industrial sectors during the second semester of the 2022–2023 academic year. The challenges focused on reducing the variability of industrial processes through the implementation of a continuous improvement process and analysis following the Lean Six Sigma methodology in an environment of operational excellence. The results show that having a partner with experience in the CBL industry is important in increasing the complexity and uncertainty of the task, while also providing students with significant exposure to professional issues that need to be addressed.

Background The global shift towards an ageing population and workforce shortages increases demands on health and social care systems. Proactive, personalized, and resource-efficient care models are therefore needed. Continuous monitoring using wearables, together with artificial intelligence (AI), offers potential for early detection of health deterioration and timely intervention. Still, existing solutions often target single diseases and lack integration into clinical workflows. Objective This article presents the study protocol for the project Predicting Care Needs of Older Adults in the Healthcare System through AI-Enabled Analysis of Patient Monitoring Data (PAI), which aims to develop an AI-driven predictive model based on data from wearables (smartwatches) to identify health decline among older persons in the near future. In addition, the project will evaluate the pilot implementation of this model within health and social care settings in three countries and generate recommendations for future adoption. Methods To answer the project aim, the AI model will be developed using historical datasets from wearables linked to emergency health care visits. To guide and evaluate the pilot implementation in Sweden, Norway, and Spain, staff from health and social care, managers, and IT professionals will be interviewed before implementation to explore current work practices and relevant contextual factors. Users of the smartwatch (older persons) will also be interviewed to explore their experiences, needs, and expectations. Follow-up interviews will be conducted after implementation to capture users’ and health and social care staff’s experiences of use. In addition, quantitative data on the AI model’s performance will be collected. The recommendations will be developed in an iterative process, including both researchers and stakeholders. Expected Results The project will identify key data requirements, deliver a predictive AI model, and develop evidence-based recommendations to support the responsible and scalable implementation of the proposed AI-solution in healthcare and social care settings. Conclusions By combining technical innovation with real-world implementation research, the PAI project seeks to advance the use of AI in care for older persons, support proactive and person-centered interventions, and contribute to more sustainable healthcare systems.