Towards Knowledge Driven Decision Support for Personalized Home-based Self-management of Chronic Diseases

The use of ICT technologies to facilitate self-management for patients with chronic diseases attracts increasing attention in smart healthcare. Existing research has mainly focused on sensing and data processing technologies with little work on decision support mechanisms and systems. In this paper, we propose a home-based decision support system based on a wide range of assessment metrics from medical assessment, social and psychological evaluation to behaviour analysis to help self-manage rehabilitation and wellbeing in a personalized manner for different patients. This paper develops semantic models for describing patients, their conditions, medical and behavioural assessments and inference mechanisms for decision recommendations. The research is undertaken in the context of mobile user self-management for Spondyloarthritis (SpA) patients. A case scenario is used to demonstrate the application of the proposed approach, technologies and principles

Computational Analysis of Foil Air Journal Bearings Using a Runtime-Efficient Segmented Foil Model

This contribution is concerned with the development and the implementation of a foil air journal bearing model. For this purpose, the numerical procedure solving the Reynolds equation for compressible fluids has to be coupled to a compliant foil model. The presented beam-based approach is supposed to reproduce most of the experimentally known particularities in the mechanical behavior of the foil structure, while being at least as runtime-efficient as the commonly used simple elastic foundation model. The developed modeling approach will be validated by comparing simulation results to data found with a more complex reference model. In the analysis part, most notably, the top foil compliance is shown to deteriorate the load-carrying capacity of air bearings. Moreover, the influence of the top foil compliance on the dynamics of a rigid rotor supported by two foil air journal bearings will be discussed.</jats:p

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing