The number of HIV-positive people aged ≥50 years is rising each year. We measured the prevalence of non-infectious illnesses and their risk factors and described healthcare use in this UK population. A cross-sectional, observational study was conducted at an outpatient HIV specialist clinic in south east England. Patients age ≥50 years were invited to complete questionnaires measuring demographics, non-infectious illnesses, medication use, lifestyle and healthcare utilisation. The response rate was 67%. Of 299 participants, 84% reported ≥1 comorbid condition and 61% reported ≥2 (multimorbidity). Most commonly reported were high cholesterol, sexual dysfunction, hypertension and depression. In multivariate analyses, age, number of years HIV-positive and duration of antiretroviral therapy remained significant predictors of comorbidity when controlling for lifestyle factors (exercise, smoking and use of recreational drugs and alcohol). Use of non-HIV healthcare services was associated with increasing comorbidity, a longer duration of HIV and recreational drug use. The majority of HIV-patients aged ≥50 years reported multiple comorbidities and this was associated with polypharmacy and increased use of non-HIV services. Further research examining the quality, safety and patient experience of healthcare is needed to inform development of services to optimally meet the needs of older HIV-positive patients.

Load monitoring is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load monitoring system is to reduce power consumption by increasing consumers' awareness of which appliances consume the most energy. Another example of an application of load monitoring is activity sensing in the home for the provision of healthcare services. This paper outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to define each appliance. These parameters are the steady-state current harmonics and the rate of change of the transient signal. Each appliance's electrical characteristics are distinguishable using these parameters. There are three Types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first and then using a second classifier to identify individual appliances within these Types, the overall accuracy of the identification algorithm is improved.

Optical buffering is known to significantly improve the performance of optical packet and burst switched networks and a number of useful analytic models for the case of Poisson traffic have been proposed previously. In this paper, we propose an approximate analytic model for generally distributed arrivals, specifically treating Gamma-distributed interarrival times, and show that the variance of the traffic has a significant impact on performance. The analysis is formulated in terms of virtual traffic flows within the optical switch from which we derive expressions for burst blocking probability, fibre delay line occupancy and mean delay. Emphasis is on approximations, by way of moment-matching techniques, that give good numerical efficiency so that the method can be useful for formulating dimensioning problems for large-scale networks. Numerical solution values from the proposed analysis method are compared with results from a discrete-event simulation of an optical burst switch

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A new data dimension-reduction method, called Internal Information Redundancy Reduction (IIRR), is proposed for application to Optical Emission Spectroscopy (OES) datasets obtained from industrial plasma processes. For example in a semiconductor manufacturing environment, real-time spectral emission data is potentially very useful for inferring information about critical process parameters such as wafer etch rates, however, the relationship between the spectral sensor data gathered over the duration of an etching process step and the target process output parameters is complex. OES sensor data has high dimensionality (fine wavelength resolution is required in spectral emission measurements in order to capture data on all chemical species involved in plasma reactions) and full spectrum samples are taken at frequent time points, so that dynamic process changes can be captured. To maximise the utility of the gathered dataset, it is essential that information redundancy is minimised, but with the important requirement that the resulting reduced dataset remains in a form that is amenable to direct interpretation of the physical process. To meet this requirement and to achieve a high reduction in dimension with little information loss, the IIRR method proposed in this paper operates directly in the original variable space, identifying peak wavelength emissions and the correlative relationships between them. A new statistic, Mean Determination Ratio (MDR), is proposed to quantify the information loss after dimension reduction and the effectiveness of IIRR is demonstrated using an actual semiconductor manufacturing dataset. As an example of the application of IIRR in process monitoring/control, we also show how etch rates can be accurately predicted from IIRR dimension-reduced spectral data.