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.

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.

Empirical evaluation of the travel time reliability indicators: A case study for Brisbane corridor under recurrent and non-recurrent conditions

This research quantifies traffic congestion and travel time reliability with case study on a major arterial road in Brisbane. The focus is on the analysis of impact of incidents (e.g., road accidents) on travel time reliability. Real traffic (Bluetooth) and incident records from Coronation Drive, Brisbane are utilized for the study. The findings include significant impact of incidents on traffic congestion and travel time reliability. The knowledge gained is useful in various applications such as traveler information systems, and cost-benefit analysis of various strategies to reduce the traffic incidents and its' impacts.

Retrieving trip information from a discrete detectors network : The case of Brisbane Bluetooth detectors

Automatic Vehicle Identification Systems are being increasingly used as a new source of travel information. As in the last decades these systems relied on expensive new technologies, few of them were scattered along a networks making thus Travel-Time and Average Speed estimation their main objectives. However, as their price dropped, the opportunity of building dense AVI networks arose, as in Brisbane where more than 250 Bluetooth detectors are now installed. As a consequence this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed. Some of these problems stem from the structure of a network made out of isolated detectors itself while others are inherent of Bluetooth technology (overlapping detection area, missing detections,\...). The aim of this paper is threefold: First, after having presented the level of details that can be reached with a network of isolated detectors we present how we modelled Brisbane's network, keeping only the information valuable for the retrieval of trip information. Second, we give an overview of the issues inherent to the Bluetooth technology and we propose a method for retrieving the itineraries of the individual Bluetooth vehicles. Last, through a comparison with Brisbane Transport Strategic Model results, we highlight the opportunities and the limits of Bluetooth detectors networks. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.

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.

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...

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.

Road operation phase sustainability indicator as a response to the climate change phenomena

Road infrastructure is a major contributor of greenhouse gas (GHG) around the world. Once constructed, a road becomes a part of a road network and is subjected to recurrent maintenance/rehabilitation activities. Studies to date are mostly aimed at the development of sustainability indicators that deal with the material and construction phases of a road when it is constructed. The operation phase is infrequently studied and there is a need for sustainability indicators to be developed relating to this phase to better understand the GHG emissions as a proper response to the climate change phenomena. During the operation phase, maintenance/rehabilitation activities are undertaken based on certain agreed intervention criteria that do not include environmental implications relating to the climate change aspect properly. Availability of appropriate indicators may, therefore, assist in sustainable road asset maintenance management. This paper presents the findings of a literature based study and has proposed a way forward to develop a key “road operation phase” environmental indicator, which can contribute to road operation phase carbon footprint management based on a comprehensive road life cycle system boundary model. The proposed indicator can address multiple aspects of high impact road operation life environmental components such as: pavement rolling resistance, albedo, material, traffic congestion and lighting, based on availability of relevant scientific knowledge. Development of the indicator to appropriate level would offset the impacts of these components significantly and contribute to sustainable road operation management.

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.

Integrated decoupling and matching network incorporated in the ground plane of a compact monopole array

Compact arrays enable various applications such as antenna beam-forming and multi-input, multi-output (MIMO) schemes on limited-size platforms. The reduced element spacing in compact arrays introduces high levels of mutual coupling which can affect the performance of the adaptive array. This coupling causes a mismatch at the input ports, which disturbs the performance of the individual elements in the array and affects the implementation of beam steering. In this article, a reactive decoupling network for a 3-element monopole array is used to establish port isolation while simultaneously matching input impedance at each port to the system impendence. The integrated decoupling and matching network is incorporated in the ground plane of the monopole array, providing further development scope for beamforming using phase shifters and power splitters in double-layered circuits.

Analysis of distributed beamforming in cooperative communications network with phase shifter based smart antenna nodes

This research study investigates the application of phase shifter-based smart antenna system in distributed beamforming. It examines the way to optimise the transmit power by jointly maximising the directivity of the array antennas and the weight vector for distributed beamforming. This research study concludes that maximising directivity can lead to better transmit power minimisation compared to maximising field intensity. This study also concludes that signal to noise power ratio maximisation subject to a power constraint and power minimisation subject to a signal to noise power ratio constraint yield the same results.