The incidence of stroke in Bahrain is rising in the Bahraini population and has nearly doubled over the last 16 years, while the incidence in the non-Bahraini population has not changed. Incidence of stroke in the Bahraini population (110/100,000) is now much greater than in the non-Bahraini population (27/100,000). The Bahraini stroke population is 10 years younger than Western comparators with a much higher prevalence of many of the risk factors for stroke, including diabetes (54%), hypertension (75%) and hyperlipidemia (34%). The combination of an ageing Bahraini population alongside a high prevalence of risk factors suggests a 'ticking time bomb' that is likely to see a continuing rise in the incidence of stroke. The quality of risk factor prevention and hospital-based stroke care is therefore crucial in Bahrain. While 88% of patients were scanned within 24 h and 86% with non-haemorrhagic strokes were commenced on aspirin within 48 h, none of the patients received thrombolysis or were admitted to a stroke unit. Improvement of stroke outcomes in Bahrain could be achieved through implementation of evidence-based measures, including improved risk factor management in primary care and stroke units and thrombolysis in secondary care.

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Since the introduction of the Relative Gain Array (RGA) by Bristol in 1966, it has received a high level of attention as a practical tool for solving the input-output pairing problem in decentralized control. Moreover, many extensions have been proposed like e.g. for the dynamic case and non-square system matrices. Recently, extensions that provide tools for uncertain parametric process models were suggested. In order to remove the dependency of these tools on a parametric description and accurate knowledge of a nominal model this paper proposes a method to calculate the RGA directly from a non-parametric frequency response matrix (FRM), derived from frequency domain system identification approach. The proposed method reduces the influence of model uncertainties on the calculation of the RGA and derives the RGA at frequencies of interest. Using Monte-Carlo principles, the variance of the estimated RGA is derived and compared with recently proposed methods. The results are exemplified on 2×2, 2×3 and 3×3 systems. It concluded that the proposed methods performs well and robust, while simplifying the estimation of the RGA.

This paper proposes an automated pairing approach for configuration selection of decentralized controllers which considers system uncertainties. Following the Relative Interaction Array (RIA) pairing rules, the optimal control configuration, i.e. the configuration that fits best the pairing rules, is obtained automatically by formulating the control configuration selection problem as an Assignment Problem (AP). In this AP, the associated costs related to each input-output pairing are given by the RIA coefficients. The Push-Pull algorithm is used to solve the AP for the nominal system and to obtain the set of costs for which the resulting configuration remains optimal, also called the perturbation set. The introduction of uncertainty bounds on the RIA-based costs enables the testing of the possible violation of the optimality conditions. Examples to illustrate the proposed approach for a 3×3 system and a 4×4 gasifier plant are given.