Chinese driver perceptions about speeding and traffic law enforcement

The World Health Organisation has highlighted the urgent need to address the escalating global public health crisis associated with road trauma. Low-income and middle-income countries bear the brunt of this, and rapid increases in private vehicle ownership in these nations present new challenges to authorities, citizens, and researchers alike. The role of human factors in the road safety equation is high. In China, human factors have been implicated in more than 90% of road crashes, with speeding identified as the primary cause (Wang, 2003). However, research investigating the factors that influence driving speeds in China is lacking (WHO, 2004). To help address this gap, we present qualitative findings from group interviews conducted with 35 Beijing car drivers in 2008. Some themes arising from data analysis showed strong similarities with findings from highly-motorised nations (e.g., UK, USA, and Australia) and include issues such as driver definitions of ‘speeding’ that appear to be aligned with legislative enforcement tolerances, factors relating to ease/difficulty of speed limit compliance, and the modifying influence of speed cameras. However, unique differences were evident, some of which, to our knowledge, are previously unreported in research literature. Themes included issues relating to an expressed lack of understanding about why speed limits are necessary and a perceived lack of transparency in traffic law enforcement and use of associated revenue. The perception of an unfair system seemed related to issues such as differential treatment of certain drivers and the large amount of individual discretion available to traffic police when administering sanctions. Additionally, a wide range of strategies to overtly avoid detection for speeding and/or the associated sanctions were reported. These strategies included the use of in-vehicle speed camera detectors, covering or removing vehicle licence number plates, and using personal networks of influential people to reduce or cancel a sanction. These findings have implications for traffic law, law enforcement, driver training, and public education in China. While not representative of all Beijing drivers, we believe that these research findings offer unique insights into driver behaviour in China.

Formation mechanisms of secondary organic aerosols in relation to laser printer emissions

Due to their large surface area, complex chemical composition and high alveolar deposition rate, ultrafine particles (UFPs) (< 0.1 ìm) pose a significant risk to human health and their toxicological effects have been acknowledged by the World Health Organisation. Since people spend most of their time indoors, there is a growing concern about the UFPs present in some indoor environments. Recent studies have shown that office machines, in particular laser printers, are a significant indoor source of UFPs. The majority of printer-generated UFPs are organic carbon and it is unlikely that these particles are emitted directly from the printer or its supplies (such as paper and toner powder). Thus, it was hypothesised that these UFPs are secondary organic aerosols (SOA). Considering the widespread use of printers and human exposure to these particles, understanding the processes involved in particle formation is of critical importance. However, few studies have investigated the nature (e.g. volatility, hygroscopicity, composition, size distribution and mixing state) and formation mechanisms of these particles. In order to address this gap in scientific knowledge, a comprehensive study including state-of-art instrumental methods was conducted to characterise the real-time emissions from modern commercial laser printers, including particles, volatile organic compounds (VOCs) and ozone (O3). The morphology, elemental composition, volatility and hygroscopicity of generated particles were also examined. The large set of experimental results was analysed and interpreted to provide insight into: (1) Emissions profiles of laser printers: The results showed that UFPs dominated the number concentrations of generated particles, with a quasi unimodal size distribution observed for all tests. These particles were volatile, non-hygroscopic and mixed both externally and internally. Particle microanalysis indicated that semi-volatile organic compounds occupied the dominant fraction of these particles, with only trace quantities of particles containing Ca and Fe. Furthermore, almost all laser printers tested in this study emitted measurable concentrations of VOCs and O3. A positive correlation between submicron particles and O3 concentrations, as well as a contrasting negative correlation between submicron particles and total VOC concentrations were observed during printing for all tests. These results proved that UFPs generated from laser printers are mainly SOAs. (2) Sources and precursors of generated particles: In order to identify the possible particle sources, particle formation potentials of both the printer components (e.g. fuser roller and lubricant oil) and supplies (e.g. paper and toner powder) were investigated using furnace tests. The VOCs emitted during the experiments were sampled and identified to provide information about particle precursors. The results suggested that all of the tested materials had the potential to generate particles upon heating. Nine unsaturated VOCs were identified from the emissions produced by paper and toner, which may contribute to the formation of UFPs through oxidation reactions with ozone. (3) Factors influencing the particle emission: The factors influencing particle emissions were also investigated by comparing two popular laser printers, one showing particle emissions three orders of magnitude higher than the other. The effects of toner coverage, printing history, type of paper and toner, and working temperature of the fuser roller on particle number emissions were examined. The results showed that the temperature of the fuser roller was a key factor driving the emission of particles. Based on the results for 30 different types of laser printers, a systematic positive correlation was observed between temperature and particle number emissions for printers that used the same heating technology and had a similar structure and fuser material. It was also found that temperature fluctuations were associated with intense bursts of particles and therefore, they may have impact on the particle emissions. Furthermore, the results indicated that the type of paper and toner powder contributed to particle emissions, while no apparent relationship was observed between toner coverage and levels of submicron particles. (4) Mechanisms of SOA formation, growth and ageing: The overall hypothesis that UFPs are formed by reactions with the VOCs and O3 emitted from laser printers was examined. The results proved this hypothesis and suggested that O3 may also play a role in particle ageing. In addition, knowledge about the mixing state of generated particles was utilised to explore the detailed processes of particle formation for different printing scenarios, including warm-up, normal printing, and printing without toner. The results indicated that polymerisation may have occurred on the surface of the generated particles to produce thermoplastic polymers, which may account for the expandable characteristics of some particles. Furthermore, toner and other particle residues on the idling belt from previous print jobs were a very clear contributing factor in the formation of laser printer-emitted particles. In summary, this study not only improves scientific understanding of the nature of printer-generated particles, but also provides significant insight into the formation and ageing mechanisms of SOAs in the indoor environment. The outcomes will also be beneficial to governments, industry and individuals.

Process mining manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

Simultaneous voltammetric determination of three herbicides in food and water samples with the aid of chemometrics

Differential pulse stripping voltammetry method(DPSV) was applied to the determination of three herbicides, ametryn, cyanatryn, and dimethametryn. It was found that their voltammograms overlapped strongly, and it is difficult to determine these compounds individually from their mixtures. With the aid of chemometrics, classical least squares(CLS), principal component regression(PCR) and partial least squares(PLS), voltammogram resolution and quantitative analysis of the synthetic mixtures of the three compounds were successfully performed. The proposed method was also applied to the analysis of some real samples with satisfactory results.

Preliminary study on bridge health prediction using Dynamic Objective Oriented Bayesian Network (DOOBN)

The availability of bridges is crucial to people’s daily life and national economy. Bridge health prediction plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology

Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12 566 individuals

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10-5). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted. © The Author 2010. Published by Oxford University Press. All rights reserved.

Subtropical towers typology design : RNA showground case study

The QUT Team developed an idea for a new residential housing typology that is appropriate for sites where the best views are in the opposing direction to the preferable climatic orientation. The interlocking configuration creates a double height external living space in every apartment, creating further opportunities for cross ventilation and natural daylight. Unlike conventional double loaded housing typologies, the interlocking configuration only requires a continuous public circulation corridor every second level. The cores that service this corridor are separated to either end of the tower and open areas. The configuration of the interlocking apartments creates an interesting composition of solid and void when viewed externally. This undulating facade petternation assists in articulating the large building mass. The project was evaluated by independent consultants and found to be cost effective, and at the same time delivering energy efficient high density liveability. The project was presented to a meeting of the Australian Council on Tall Buildings seminar on 15 September 2010.

Managing financial implications for sustainable highway infrastructure delivery

Highway construction works have significant bearings on all aspects of sustainability. As they typically involve huge capital funds, stakeholders tend to place all interests on the financial justifications of the project, especially when embedding sustainability principles and practices may demand significant initial investment. Increasing public awareness and government policies demand that infrastructure projects respond to environmental challenges and people start to realise the negative consequences of not to pursue sustainability. Stakeholders are now keen to identify sustainable alternatives and financial implications of including them on a whole lifecycle basis. Therefore tools that aid the evaluation of investment options, such as provision of environmentally sustainable features in roads and highways, are highly desirable. Life-cycle cost analysis (LCCA) is generally recognised as a valuable approach for investment decision making for construction works. However to date it has limited application because the current LCCA models tend to focus on economic issues alone and are not able to deal with sustainability factors. This paper reports a research on identifying sustainability related factors in highway construction projects, in quantitative and qualitative forms of a multi-criteria analysis. These factors are then incorporated into existing LCCA models to produce a new sustainability based LCCA model with cost elements specific to sustainability measures. This presents highway project stakeholders a practical tool to evaluate investment decisions and reach an optimum balance between financial viability and sustainability deliverables.

Chongqing International Circus City

The design grows out of the rich culture of circus and the rugged dynamic topography of Chongqing. The site for this project is nestled on the banks of the mighty Yangzte, China's longest river: a vast sweeping watery ribbon carving its way through the mountainous terrain. This swirling sinuous environmental thread replicates in nature the tweisting ribbons circling the gyrating circus gymnast. The project grows from intertwining these swirling parallel conceptions of 'ribbon'. A multi-layered envelope of glass and steel ribbons creates a dome like enclosure that wraps itself around the dynamic performing heart of the circus. The main auditorium and stage area are accommodated in this space. Key public elements and facilities are located adjacent to the new riverfront boulevard maximising the positive relationship with this attractive landscape zone. Service and support areas are located along the southern boundary. Key Statistics; Client: Chongqing Broadcast Bureau Developer: Chongqing Real Estate Site: 3.3 Ha Development: Total G.F.A.: 36,800m2 Project Cost: Total Investment: RMB 300 Million (A$48 million) Other competition participants were BIG-Bjarke Ingels Group (Denmark)/arquitectonica (USA)/Beijing Architectural Design Institute/East China Architectural Design Institute/China Architectural Design Academy.

A semantics-based approach to querying process model repositories

As business process management technology matures, organisations acquire more and more business process models. The resulting collections can consist of hundreds, even thousands of models and their management poses real challenges. One of these challenges concerns model retrieval where support should be provided for the formulation and efficient execution of business process model queries. As queries based on only structural information cannot deal with all querying requirements in practice, there should be support for queries that require knowledge of process model semantics. In this paper we formally define a process model query language that is based on semantic relationships between tasks. This query language is independent of the particular process modelling notation used, but we will demonstrate how it can be used in the context of Petri nets by showing how the semantic relationships can be determined for these nets in such a way that state space explosion is avoided as much as possible. An experiment with three large process model repositories shows that queries expressed in our language can be evaluated efficiently.

The impact of infrastructure charges on new housing costs : an international comparison

Sourcing funding for the provision of new urban infrastructure has been a policy dilemma for governments around the world for decades. This is particularly relevant in high growth areas where new services are required to support swelling populations. Existing communities resist the introduction of new taxes to fund such infrastructure, hence the introduction of charges to the developer has flourished. The Australian infrastructure funding policy dilemmas are reflective of similar matters to some extent in the United Kingdom, and to a greater extent the United States of America. In these countries, infrastructure cost recovery policies have been in place since the 1940’s and 1970’s respectively. There is an extensive body of theoretical and empirical literature that discusses the passing on (to home buyers) or passing back (to the englobo land seller) of these increased infrastructure charges, and the corresponding impact on housing cost and supply. The purpose of this research is to examine the international evidence that suggests infrastructure charges contribute to increased house prices as well as reduced land supply. The paper concludes that whilst the theoretical work is largely consistent, the empirical research to date is inconclusive and further research is required into these impacts in Australia.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

How quantum theory is developing the field of information retrieval

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).