Background and objectives: Heart failure requires complex and daily self-care that many patients struggle with for a range of reasons including limited health literacy, cognitive impairment, comorbidities, and emotional distress. This study describes the user-centred design and development of a mobile app (SmartHeart) to support comprehensive self-monitoring and improve self-care engagement for people with heart failure. Methods: Building on previous co-design research and expert panel feedback, we developed an initial Figma prototype following user-centred design principles. Two online sessions were conducted with adults living with heart failure (n=7), including a focus group session and a follow-up individual feedback session. The same participants took part in both sessions to provide feedback on the functionality, aesthetics, navigation, and content. Data were analysed deductively based on heuristic principles of user interface design, with findings informing the iterative development of the SmartHeart mobile app. The functional app was tested in-home by two participants over two weeks to evaluate real-world usability and gather contextual feedback to inform further refinement. Results: The SmartHeart prototype was developed through expert workshops and user feedback. Participants emphasised simplicity, leading to a streamlined design with clear navigation, adaptable graphics, and larger fonts. The app’s health tracking features were iteratively improved. User-driven modifications included personalised threshold alerts, simplified symptom reporting, and integrated medication reminders. Participants reported high satisfaction with the prototype interface and health monitoring capabilities; however, formative testing identified reliability issues that are being addressed prior to pilot evaluation. Findings primarily inform design refinements before evaluating clinical effectiveness. Conclusion: The SmartHeart app was refined through user-centred design process involving direct feedback from individuals with heart failure, resulting in a self-care tool with user-friendly features, to be further evaluated in future research. These user-driven enhancements support self-care engagement and highlight the app’s potential for real-world use and broader clinical integration.

High traffic flow in a confined tunnel makes fire safety a critical issue. This paper proposed a digital twin framework for tunnel fire safety management in real-time, driven by dynamic sensor data and AIoT technologies. A deep learning model trained by the Transformer network and simulation dataset is used to predict real-time fire location and size. Then, the AI model is integrated into a 3D digital twin platform developed by the game engine Unity 3D. The performance of the proposed digital twin framework is demonstrated using numerical experiments and large-scale tunnel fire tests. Results show that the established AI model achieved promising accuracy in predicting fire location and power for both numerical and experimental data. The digital twin platform can also visualize the 3D fire scene that supports evacuation, firefighting, and emergency rescue. This research demonstrates the feasibility of using a 3D environment and digital twin in real-time fire safety management.

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T₁. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.