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We present an elective surgery redesign project involving several New Zealand hospitals that is primarily data-driven. One of the project objectives is to improve the predictions of surgery durations. We address this task by considering two approaches: (a) linear regression modelling, and (b) improvement of the data quality. For (a) we evaluate the accuracy of predictions using two performance measures. These predictions are compared to the surgeons' estimates that may subsequently be adjusted. We demonstrate using the historical surgical lists that the estimates from our prediction techniques improve the scheduling of elective surgeries by minimising the occurrences of list under-and overruns. For (b), we discuss how the surgical data motivates a review of the surgery procedure classification which takes into account the design of the electronic booking form. The proposed hierarchical classification streamlines the specification of surgery types and therefore retains the potential for improved predictions.

We give a brief survey about the existing results on the power domination of the Cartesian product of graphs, and improve two of the results by determining the exact power domination numbers of two families of graphs, namely, the cylinder P n □ C m and the tori C n □ C m. We also establish the power domination number for K n □ K 1,m , the Cartesian product of a complete graph and a star. c

We implement jackknife model averaging (JMA) and a new prediction technique-hybrid-boost model averaging (HbMA)to a surgical dataset that includes categorical explanatory variables. The model requirements for HbMA are different to that for JMA. HbMA generally does not require decent models to be included in the model average. However, the utility of HbMA is limited by the possibility of multiple solutions for the HbMA weights. Both model averaging approaches are comparable under the appropriate conditions. Among all the model averages considered, the best jackknife model average gives slightly better predictions of the surgery durations than the best hybrid-boost model average when evaluated on our surgical dataset. Finally, we discuss several methods that may further improve the performance of HbMA.