An energy efficient power controller switching methodology for an ambient healthcare network

A methodology for improved power controller switching in mobile Body Area Networks operating within the ambient healthcare environment is proposed. The work extends Anti-windup and Bumpless transfer results to provide a solution to the ambulatory networking problem that ensures sufficient biometric data can always be regenerated at the base station. The solution thereby guarantees satisfactory quality of service for healthcare providers. Compensation is provided for the nonlinear hardware constraints that are a typical feature of the type of network under consideration and graceful performance degradation in the face of hardware output power saturation is demonstrated, thus conserving network energy in an optimal fashion.

Tryptophan degradation in irritable bowel syndrome: evidence of indoleamine 2,3-dioxygenase activation in a male cohort

Background: Irritable bowel syndrome (IBS) is a common disorder that affects 10–15% of the population. Although characterised by a lack of reliable biological markers, the disease state is increasingly viewed as a disorder of the brain-gut axis. In particular, accumulating evidence points to the involvement of both the central and peripheral serotonergic systems in disease symptomatology. Furthermore, altered tryptophan metabolism and indoleamine 2,3-dioxygenase (IDO) activity are hallmarks of many stress-related disorders. The kynurenine pathway of tryptophan degradation may serve to link these findings to the low level immune activation recently described in IBS. In this study, we investigated tryptophan degradation in a male IBS cohort (n = 10) and control subjects (n = 26). Methods: Plasma samples were obtained from patients and healthy controls. Tryptophan and its metabolites were measured by high performance liquid chromatography (HPLC) and neopterin, a sensitive marker of immune activation, was measured using a commercially available ELISA assay. Results: Both kynurenine levels and the kynurenine:tryptophan ratio were significantly increased in the IBS cohort compared with healthy controls. Neopterin was also increased in the IBS subjects and the concentration of the neuroprotective metabolite kynurenic acid was decreased, as was the kynurenic acid:kynurenine ratio. Conclusion: These findings suggest that the activity of IDO, the immunoresponsive enzyme which is responsible for the degradation of tryptophan along this pathway, is enhanced in IBS patients relative to controls. This study provides novel evidence for an immune-mediated degradation of tryptophan in a male IBS population and identifies the kynurenine pathway as a potential source of biomarkers in this debilitating condition.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.