Identification of the sources of primary organic aerosols at urban schools : a molecular marker approach

Children are particularly susceptible to air pollution and schools are examples of urban microenvironments that can account for a large portion of children’s exposure to airborne particles. Thus this paper aimed to determine the sources of primary airborne particles that children are exposed to at school by analyzing selected organic molecular markers at 11 urban schools in Brisbane, Australia. Positive matrix factorization analysis identified four sources at the schools: vehicle emissions, biomass burning, meat cooking and plant wax emissions accounting for 45%, 29%, 16% and 7%, of the organic carbon respectively. Biomass burning peaked in winter due to prescribed burning of bushland around Brisbane. Overall, the results indicated that both local (traffic) and regional (biomass burning) sources of primary organic aerosols influence the levels of ambient particles that children are exposed at the schools. These results have implications for potential control strategies for mitigating exposure at schools.

DS-VBF : dual sink vector-based routing protocol for underwater wireless sensor network

Underwater wireless sensor networks (UWSNs) have become the seat of researchers' attention recently due to their proficiency to explore underwater areas and design different applications for marine discovery and oceanic surveillance. One of the main objectives of each deployed underwater network is discovering the optimized path over sensor nodes to transmit the monitored data to onshore station. The process of transmitting data consumes energy of each node, while energy is limited in UWSNs. So energy efficiency is a challenge in underwater wireless sensor network. Dual sinks vector based forwarding (DS-VBF) takes both residual energy and location information into consideration as priority factors to discover an optimized routing path to save energy in underwater networks. The modified routing protocol employs dual sinks on the water surface which improves network lifetime. According to deployment of dual sinks, packet delivery ratio and the average end to end delay are enhanced. Based on our simulation results in comparison with VBF, average end to end delay reduced more than 80%, remaining energy increased 10%, and the increment of packet reception ratio was about 70%.

The impact of Internet marketing capabilities on international network capability, a cross country study comparison between Australia, Chile and Taiwan internationalised SMEs

The Internet has been shown to positively enhance internationalisation for SMEs, but scant empirical testing limits our understanding of the explicit impact of the Internet on firm internationalisation. This paper highlights key areas where the integration of the Internet can be leveraged through Internet-related capabilities within the internationalisation of the firm. Specifically, this study investigates how Internet marketing capabilities play a role in altering international information availability, international strategic orientation, and international business network relationships. This study provides evidence, indicating that these key relationships may vary between countries. To examine these key relationships this study utilises draws from data small and medium sized enterprises (SMEs) in three export intensive markets; Australia (215 international SMEs), Chile (204 international SMEs) and Taiwan (130 international SMEs); and tests a conceptual model through structural equation modelling. Results from the data show the impact of Internet marketing capabilities in positively impacting traditional internationalisation elements, which varies between countries. That is, our findings highlight the international business network relationships in Australia and Taiwan are directly impacted by Internet marketing capabilities, but not in Chile. We offer some insight into why we see variance across comparative exporting countries in how they leverage new technological capabilities for internationalisation and firm performance.

Overvoltage and overloading prevention using coordinated PV inverters in distribution network

Overvoltage and overloading due to high utilization of PVs are the main power quality concerns for future distribution power systems. This paper proposes a distributed control coordination strategy to manage multiple PVs within a network to overcome these issues. PVs reactive power is used to deal with over-voltages and PVs active power curtailment are regulated to avoid overloading. The proposed control structure is used to share the required contribution fairly among PVs, in proportion to their ratings. This approach is examined on a practical distribution network with multiple PVs.

A parallel approach to social network generation and agent-based epidemic simulation

Understanding the dynamics of disease spread is essential in contexts such as estimating load on medical services, as well as risk assessment and interven- tion policies against large-scale epidemic outbreaks. However, most of the information is available after the outbreak itself, and preemptive assessment is far from trivial. Here, we report on an agent-based model developed to investigate such epidemic events in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena. Given the scale of the system, efficient parallel computing is required. In this presentation, we focus on aspects related to paralllelisation for large networks generation and massively multi-agent simulations.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

A framework for identification of similarities between multiple algorithms

This thesis in software engineering presents a novel automated framework to identify similar operations utilized by multiple algorithms for solving related computing problems. It provides a new effective solution to perform multi-application based algorithm analysis, employing fundamentally light-weight static analysis techniques compared to the state-of-art approaches. Significant performance improvements are achieved across the objective algorithms through enhancing the efficiency of the identified similar operations, targeting discrete application domains.

Impact of technology uptake on an Australian electricity distribution network

This paper presents simulation results for future electricity grids using an agent-based model developed with MODAM (MODular Agent-based Model). MODAM is introduced and its use demonstrated through four simulations based on a scenario that expects a rise of on-site renewable generators and electric vehicles (EV) usage. The simulations were run over many years, for two areas in Townsville, Australia, capturing variability in space of the technology uptake, and for two charging methods for EV, capturing people's behaviours and their impact on the time of the peak load. Impact analyses of these technologies were performed over the areas, down to the distribution transformer level, where greater variability of their contribution to the assets peak load was observed. The MODAM models can be used for different purposes such as impact of renewables on grid sizing, or on greenhouse gas emissions. The insights gained from using MODAM for technology assessment are discussed.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

The O-specific polysaccharide structure and gene cluster of serotype O:12 of the Yersinia pseudotuberculosis complex, and the identification of a novel L-quinovose biosynthesis gene

A major virulence factor for Yersinia pseudotuberculosis is lipopolysaccharide, including O-polysaccharide (OPS). Currently, the OPS based serotyping scheme for Y. pseudotuberculosis includes 21 known O-serotypes, with genetic and structural data available for 17 of them. The completion of the OPS structures and genetics of this species will enable the visualization of relationships between O-serotypes and allow for analysis of the evolutionary processes within the species that give rise to new serotypes. Here we present the OPS structure and gene cluster of serotype O:12, thus adding one more to the set of completed serotypes, and show that this serotype is present in both Y. pseudotuberculosis and the newly identified Y. similis species. The O:12 structure is shown to include two rare sugars: 4-C[(R)-1-hydroxyethyl]-3,6-dideoxy-d-xylo-hexose (d-yersiniose) and 6-deoxy-l-glucopyranose (l-quinovose). We have identified a novel putative guanine diphosphate (GDP)-l-fucose 4-epimerase gene and propose a pathway for the synthesis of GDP-l-quinovose, which extends the known GDP-l-fucose pathway.

A recurrent network in the lateral amygdala: A mechanism for temporal coincidence detection

The dorsal lateral amygdala (LAd) is a vital nucleus for the formation of associations between aversive unconditioned stimuli (US) and neutral stimuli, such as auditory tones, which can become conditioned (CS) to the US through temporal pairing. Important aspects of CS-US associations are believed to occur within the LAd, however relatively little is known about the temporal behavior of local LAd networks. Information about the CS and US enters the LA via a rapid and direct thalamic input and a longer latency cortical path...

Improving the identification of fatigue-related crashes in the Australian Capital Territory

It has been well established nationally and internationally that fatigue-related driving is an important contributory factor in fatal and serious injury crashes. The purpose of this report was to survey a large, representative sample of residents living in both the NSW and ACT to ask about their experience of fatigue and their involvement in fatigue-related crashes and incidents. This will provide valuable data about the number and characteristics of fatigue-related crashes and incidents of ACT residents. Specifically this study assessed the prevalence of incidents of fatigue-related driving for residents of NSW and the ACT, the characteristics surrounding the incident, if the report would fit within the NSW, QLD, or ATSB proxy definition or if it would fall outside of the proxy definition...

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Choose wisely: Network, ontology and annotation resources for the analysis of Staphylococcus aureus omics data

Staphylococcus aureus (S. aureus) is a prominent human and livestock pathogen investigated widely using omic technologies. Critically, due to availability, low visibility or scattered resources, robust network and statistical contextualisation of the resulting data is generally under-represented. Here, we present novel meta-analyses of freely-accessible molecular network and gene ontology annotation information resources for S. aureus omics data interpretation. Furthermore, through the application of the gene ontology annotation resources we demonstrate their value and ability (or lack-there-of) to summarise and statistically interpret the emergent properties of gene expression and protein abundance changes using publically available data. This analysis provides simple metrics for network selection and demonstrates the availability and impact that gene ontology annotation selection can have on the contextualisation of bacterial omics data.

Responding to black markets: Role of IT and network structures

This paper presents the unique black markets of asset pooling and leasing services, which exposes the nature and extent of industry-specific threats. We explore how firms providing such services together with their network structures that constitute the foundations of asset pooling and leasing respond to the threat of black markets. We encapsulate detecting and encountering the threat of black markets through the theoretical lens of agility, which encompasses the elements of sensing and responding (Overby et al. 2006; Roberts and Grover 2012). This novel concept of responding to threats using the agility lens has not been adequately addressed by past studies on enterprise agility. Through a case study of a global asset pooling and leasing company, we reveal the criticality of network structures, the impracticality of IT and inadequate tracking mechanisms that challenge firms in minimizing such threats.