The paper describes a predictive and adaptive heating controller, using artificial neural networks to allow the adaptation of the control model to the real conditions (climate, building characteristics, user's behaviour). The controller algorithm has been developed and tested as a collaborative project between the CSEM (Centre Suisse d'Electronique et de Microtechnique, Neuchâtel, Switzerland, project leader), and the LESO-PB (Solar Energy and Building Physics Laboratory, EPFL, Lausanne, Switzerland). A significant support has been provided by leading Swiss industries in control systems. The project itself has been funded by the Swiss Federal Office of Energy (SFOE).The project has allowed the development of an original algorithm, especially suited for water heating systems, and its testing both by simulation and by experimentation on an inhabited building. The experimentation has been done using a PC software implementation. A second phase of the project, currently going on, aims at building a commercial system based on the NEUROBAT algorithm.

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SR provided sufficient and time efficient means for 3D-visualization of facial fractures in this study. No diagnostic benefit of VR over SR was found.

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While the incidence of sleep disorders is continuously increasing in western societies, there is a clear demand for technologies to asses sleep-related parameters in ambulatory scenarios. The present study introduces a novel concept of accurate sensor to measure RR intervals via the analysis of photo-plethysmographic signals recorded at the wrist. In a cohort of 26 subjects undergoing full night polysomnography, the wrist device provided RR interval estimates in agreement with RR intervals as measured from standard electrocardiographic time series. The study showed an overall agreement between both approaches of 0.05 ± 18 ms. The novel wrist sensor opens the door towards a new generation of comfortable and easy-to-use sleep monitors.