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Particulate reinforced Al alloy composites (AlMCs) are recognized as important structural materials due to their lightweight, high modulus and strength and high wear resistance. In order to understand the effect of matrix, reinforcement and secondary processing techniques on the microstructure development and mechanical properties of AlMCs produced by powder metallurgy routes, Al alloy composites reinforced with three types of reinforcements by different secondary processing techniques have been produced and examined. Fabrication of Al or 6061Al alloy based composites reinforced with nano-sized SiC particles (~500nm), micro-sized (<25µm) quasicrystalline alloy particles (hereinafter referred to as “NQX”) and micro-sized Nb particles (~130µm) has been carried out by powder metallurgy routes followed by extrusion or cold rolling. After extrusion, a homogeneous distribution of secondary particles has been obtained with rare interfacial reaction products. The 6061Al/SiC composites exhibit superior mechanical properties than either monolithic alloys or composites reinforced with micro-sized particles with retained ductility while the 6061Al/NQX and 6061Al/Nb composites show limited improvement in tensile strength mainly due to their reinforcement size and poor interfacial bonding. After cold rolling, the evolution in microstructure, texture and strength has been analysed. A typical near β fibre texture with highest intensities near Copper and Brass orientations has been developed for 6061Al/NQX and 6061Al/Nb composites. For 6061Al/SiC composites, a randomized texture with very small grains has achieved due to the presence of the non-deformable SiC particles. Mechanical property tests including microhardness, three-point bending tests and tensile tests have been carried out on cold rolled samples and the results exhibit some level of improvement when compared with as-extruded samples due to work hardening. Finally, the work moves on to the general discussion based on the previous result chapters. The microstructural development related to reinforcement, matrix and interfacial areas during extrusion and cold rolling has been summarised and the correlation between microstructure and mechanical properties has been discussed. The thesis provides a thorough understanding of AlMCs produced by powder metallurgy routes in terms of matrix, reinforcement and processing techniques. It can provide reference to the future development of AlMCs for high strength applications.

Event surveillance involves analyzing a region in order to detect patterns that are indicative of some event of interest. An example is the monitoring of information about emergency department visits to detect a disease outbreak. Spatial event surveillance involves analyzing spatial patterns of evidence that are indicative of the event of interest. A special case of spatial event surveillance is spatial cluster detection, which searches for subregions in which the count of an event of interest is higher than expected. Temporal event surveillance involves monitoring for emerging temporal patterns. Spatio-temporal event surveillance involves joint spatial and temporal monitoring.When the events observed are of direct interest, then analyzing counts of those events is generally the preferred approach. However, in event surveillance we often only observe events that are indirectly related to the events of interest. For example, during an influenza outbreak, we may only have information about the chief complaints of patients who visited emergency departments. In this situation, a better surveillance approach may be to model the relationships among the events of interest and those observed.I developed a high-level Bayesian network architecture that represents a class of spatial event surveillance models, which I call BayesNet-S. I also developed an architecture that represents a class of temporal event surveillance models called BayesNet-T. These Bayesian network architectures are combined into a single architecture that represents a class of spatio-temporal models called BayesNet-ST. Using these architectures, it is often possible to construct a temporal, spatial, or spatio-temporal model from an existing Bayesian network event-surveillance model that is non-spatial and non-temporal. My general hypothesis is that when an existing model is extended to incorporate space and time, event surveillance will be improved.PANDA-CDCA (PC) (Cooper et al., 2007) is a non-temporal, non-spatial disease outbreak detection system. I extended PC both spatially and temporally. My specific hypothesis is that each of the spatial and temporal extensions of PC will perform outbreak detection better than does PC, and that the combined use of the spatial and temporal extensions will perform better than either extension alone.The experimental results obtained in this research support this hypothesis.