Water uptake of aerosols with a focus on seeded aerosols and instrumentation techniques

This thesis focuses on the volatile and hygroscopic properties of mixed aerosol species. In particular, the influence organic species of varying solubility have upon seed aerosols. Aerosol studies were conducted at the Paul Scherrer Institut Laboratory for Atmospheric Chemistry (PSI-LAC, Villigen, Switzerland) and at the Queensland University of Technology International Laboratory for Air Quality and Health (QUT-ILAQH, Brisbane, Australia). The primary measurement tool employed in this program was the Volatilisation and Hygroscopicity Tandem Differential Mobility Analyser (VHTDMA - Johnson et al. 2004). This system was initially developed at QUT within the ILAQH and was completely re-developed as part of this project (see Section 1.4 for a description of this process). The new VHTDMA was deployed to the PSI-LAC where an analysis of the volatile and hygroscopic properties of ammonium sulphate seeds coated with organic species formed from the photo-oxidation of á-pinene was conducted. This investigation was driven by a desire to understand the influence of atmospherically prevalent organics upon water uptake by material with cloud forming capabilities. Of particular note from this campaign were observed influences of partially soluble organic coatings upon inorganic ammonium sulphate seeds above and below their deliquescence relative humidity (DRH). Above the DRH of the seed increasing the volume fraction of the organic component was shown to reduce the water uptake of the mixed particle. Below the DRH the organic was shown to activate the water uptake of the seed. This was the first time this effect had been observed for á-pinene derived SOA. In contrast with the simulated aerosols generated at the PSI-LAC a case study of the volatile and hygroscopic properties of diesel emissions was undertaken. During this stage of the project ternary nucleation was shown, for the first time, to be one of the processes involved in formation of diesel particulate matter. Furthermore, these particles were shown to be coated with a volatile hydrophobic material which prevented the water uptake of the highly hygroscopic material below. This result was a first and indicated that previous studies into the hygroscopicity of diesel emission had erroneously reported the particles to be hydrophobic. Both of these results contradict the previously upheld Zdanovksii-Stokes-Robinson (ZSR) additive rule for water uptake by mixed species. This is an important contribution as it adds to the weight of evidence that limits the validity of this rule.

Using ontology for the representational analysis of process modelling techniques

Selecting an appropriate business process modelling technique forms an important task within the methodological challenges of a business process management project. While a plethora of available techniques has been developed over the last decades, there is an obvious shortage of well-accepted reference frameworks that can be used to evaluate and compare the capabilities of the different techniques. Academic progress has been made at least in the area of representational analyses that use ontology as a benchmark for such evaluations. This paper reflects on the comprehensive experiences with the application of a model based on the Bunge ontology in this context. A brief overview of the underlying research model characterizes the different steps in such a research project. A comparative summary of previous representational analyses of process modelling techniques over time gives insights into the relative maturity of selected process modelling techniques. Based on these experiences suggestions are made as to where ontology-based representational analyses could be further developed and what limitations are inherent to such analyses.

Topological SLAM Using Fast Vision Techniques

In this paper we propose a method for vision only topological simultaneous localisation and mapping (SLAM). Our approach does not use motion or odometric information but a sequence of colour histograms from visited places. In particular, we address the perceptual aliasing problem which occurs using external observations only in topological navigation. We propose a Bayesian inference method to incrementally build a topological map by inferring spatial relations from the sequence of observations while simultaneously estimating the robot's location. The algorithm aims to build a small map which is consistent with local adjacency information extracted from the sequence measurements. Local adjacency information is incorporated to disambiguate places which otherwise would appear to be the same. Experiments in an indoor environment show that the proposed technique is capable of dealing with perceptual aliasing using visual observations only and successfully performs topological SLAM.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.