Buying a sponsor’s brand : the role of affective commitment to the sponsored team

Sponsorship is increasingly important in a firm’s communication mix. Research to date has focused on the impact of sponsorship on brand awareness and its subsequent consequences for image congruency and consumer attitudes towards sponsors’ brands. A lesser studied area is the effect of sponsorship on consumers’ purchase intentions and behaviours. We argue that existing models of sponsorship driven purchase behaviour fail to account for affective commitment, which mediates relationship between affiliation with the team and social identification with the team. We propose a modified framework describing the effect of sponsorship on purchase intentions in the context of low and high performing sports teams. The framework is tested using structural equations modelling; employing PLS estimation and data collected via online survey of AFL chat room participants. Results confirm the role of affective commitment in sport sponsorship purchase intentions and indicate that team success has a significant influence on fans’ purchase behaviours.

Load–frequency control : a GA-based multi-agent reinforcement learning

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed using a linear model, the non-linearities of the system are not accounted for and they are incapable of gaining good dynamical performance for a wide range of operating conditions in a multi-area power system. A strategy for solving this problem because of the distributed nature of a multi-area power system is presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias estimation and the controller agent uses reinforcement learning to control the power system in which genetic algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a three-area power system example is given with two scenarios.

Buying a sponsor's brand : the role of affective commitment to the sponsored team

Sponsorship is increasingly important in a firm's communication mix. Research to date has focused on the impact of sponsorship on brand awareness and its subsequent consequences for image congruency and consumer attitudes towards sponsors' brands. A lesser studied area is the effect of sponsorship on consumers' purchase intentions and behaviours. We argue that existing models of sponsorship driven purchase behaviour fail to account for affective commitment, which mediates relationship between affiliation with the team and social identification with the team. We propose a modified framework describing the effect of sponsorship on purchase intentions in the context of low and high performing sports teams. The framework is tested using structural equations modelling; employing PLS estimation and data collected via online survey of AFL chat room participants. Results confirm the role of affective commitment in sport sponsorship purchase intentions and indicate that team success has a significant influence on fans' purchase behaviours.

The Guardian Reportage of the UK MP Expenses Scandal: a Case Study of Computational Journalism

The Guardian reportage of the United Kingdom Member of Parliament (MP) expenses scandal of 2009 used crowdsourcing and computational journalism techniques. Computational journalism can be broadly defined as the application of computer science techniques to the activities of journalism. Its foundation lies in computer assisted reporting techniques and its importance is increasing due to the: (a) increasing availability of large scale government datasets for scrutiny; (b) declining cost, increasing power and ease of use of data mining and filtering software; and Web 2.0; and (c) explosion of online public engagement and opinion.. This paper provides a case study of the Guardian MP expenses scandal reportage and reveals some key challenges and opportunities for digital journalism. It finds journalists may increasingly take an active role in understanding, interpreting, verifying and reporting clues or conclusions that arise from the interrogations of datasets (computational journalism). Secondly a distinction should be made between information reportage and computational journalism in the digital realm, just as a distinction might be made between citizen reporting and citizen journalism. Thirdly, an opportunity exists for online news providers to take a ‘curatorial’ role, selecting and making easily available the best data sources for readers to use (information reportage). These activities have always been fundamental to journalism, however the way in which they are undertaken may change. Findings from this paper may suggest opportunities and challenges for the implementation of computational journalism techniques in practice by digital Australian media providers, and further areas of research.

The Guardian reportage of the UK MP expenses scandal: a case study of computational journalism

The Guardian reportage of the United Kingdom Member of Parliament (MP) expenses scandal of 2009 used crowdsourcing and computational journalism techniques. Computational journalism can be broadly defined as the application of computer science techniques to the activities of journalism. Its foundation lies in computer assisted reporting techniques and its importance is increasing due to the: (a) increasing availability of large scale government datasets for scrutiny; (b) declining cost, increasing power and ease of use of data mining and filtering software; and Web 2.0; and (c) explosion of online public engagement and opinion.. This paper provides a case study of the Guardian MP expenses scandal reportage and reveals some key challenges and opportunities for digital journalism. It finds journalists may increasingly take an active role in understanding, interpreting, verifying and reporting clues or conclusions that arise from the interrogations of datasets (computational journalism). Secondly a distinction should be made between information reportage and computational journalism in the digital realm, just as a distinction might be made between citizen reporting and citizen journalism. Thirdly, an opportunity exists for online news providers to take a ‘curatorial’ role, selecting and making easily available the best data sources for readers to use (information reportage). These activities have always been fundamental to journalism, however the way in which they are undertaken may change. Findings from this paper may suggest opportunities and challenges for the implementation of computational journalism techniques in practice by digital Australian media providers, and further areas of research.

On the significance of omitted variables in intersection crash modeling

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

Trip generation in Australia : Practical issues

An online survey was conducted to investigate the views and experiences of Australian traffic and transport professionals about practical problems and issues in terms of trip generation and trip chaining for use in Transport Impact Assessment (TIA). Findings from this survey revealed that there is a shortage of appropriate data related to trip generation estimation for use in TIAs in Australia. Establishing a National Trip Generation Database (NTGD) with a centralised responsible organisation for collecting and publishing trip generation data based on federal and state governments’ contribution was found the most accepted solution for resolving this shortage as well as providing national standards and guidelines associated with trip generation definitions, data collection methodology, and TIA preparation process based on updated research. Finally, the study recognised the importance of the trip chaining effects on trip generation estimation and identified most prevalent land uses subject to trip chaining in terms of TIA.

Using variable speed limits for motorway off-ramp queue protection

Motorway off-ramps are a significant source of traffic congestion and collisions. Heavy diverging traffic to off-ramps slows down the mainline traffic speed. When the off-ramp queue spillbacks onto the mainline, it leads to a major breakdown of the motorway capacity and a significant threat to the traffic safety. This paper proposes using Variable Speed Limits (VSL) for protection of the motorway off-ramp queue and thus to promote safety in congested diverging areas. To support timely activation of VSL in advance of queue spillover, a proactive control strategy is proposed based on a real-time off-ramp queue estimation and prediction. This process determines the estimated queue size in the near-term future, on which the decision to change speed limits is made. VSL can effectively slow down traffic as it is mandatory that drivers follow the changed speed limits. A collateral benefit of VSL is its potential effect on drivers making them more attentive to the surrounding traffic conditions, and prepared for a sudden braking of the leading car. This paper analyses and quantifies these impacts and potential benefits of VSL on traffic safety and efficiency using the microsimulation approach.

Achieving high reliability for ambiguity resolutions with multiple GNSS constellations

Global Navigation Satellite Systems (GNSS)-based observation systems can provide high precision positioning and navigation solutions in real time, in the order of subcentimetre if we make use of carrier phase measurements in the differential mode and deal with all the bias and noise terms well. However, these carrier phase measurements are ambiguous due to unknown, integer numbers of cycles. One key challenge in the differential carrier phase mode is to fix the integer ambiguities correctly. On the other hand, in the safety of life or liability-critical applications, such as for vehicle safety positioning and aviation, not only is high accuracy required, but also the reliability requirement is important. This PhD research studies to achieve high reliability for ambiguity resolution (AR) in a multi-GNSS environment. GNSS ambiguity estimation and validation problems are the focus of the research effort. Particularly, we study the case of multiple constellations that include initial to full operations of foreseeable Galileo, GLONASS and Compass and QZSS navigation systems from next few years to the end of the decade. Since real observation data is only available from GPS and GLONASS systems, the simulation method named Virtual Galileo Constellation (VGC) is applied to generate observational data from another constellation in the data analysis. In addition, both full ambiguity resolution (FAR) and partial ambiguity resolution (PAR) algorithms are used in processing single and dual constellation data. Firstly, a brief overview of related work on AR methods and reliability theory is given. Next, a modified inverse integer Cholesky decorrelation method and its performance on AR are presented. Subsequently, a new measure of decorrelation performance called orthogonality defect is introduced and compared with other measures. Furthermore, a new AR scheme considering the ambiguity validation requirement in the control of the search space size is proposed to improve the search efficiency. With respect to the reliability of AR, we also discuss the computation of the ambiguity success rate (ASR) and confirm that the success rate computed with the integer bootstrapping method is quite a sharp approximation to the actual integer least-squares (ILS) method success rate. The advantages of multi-GNSS constellations are examined in terms of the PAR technique involving the predefined ASR. Finally, a novel satellite selection algorithm for reliable ambiguity resolution called SARA is developed. In summary, the study demonstrats that when the ASR is close to one, the reliability of AR can be guaranteed and the ambiguity validation is effective. The work then focuses on new strategies to improve the ASR, including a partial ambiguity resolution procedure with a predefined success rate and a novel satellite selection strategy with a high success rate. The proposed strategies bring significant benefits of multi-GNSS signals to real-time high precision and high reliability positioning services.

Outdoor flight testing of a pole inspection UAV incorporating high-speed vision

We present a pole inspection system for outdoor environments comprising a high-speed camera on a vertical take-off and landing (VTOL) aerial platform. The pole inspection task requires a vehicle to fly close to a structure while maintaining a fixed stand-off distance from it. Typical GPS errors make GPS-based navigation unsuitable for this task however. When flying outdoors a vehicle is also affected by aerodynamics disturbances such as wind gusts, so the onboard controller must be robust to these disturbances in order to maintain the stand-off distance. Two problems must therefor be addressed: fast and accurate state estimation without GPS, and the design of a robust controller. We resolve these problems by a) performing visual + inertial relative state estimation and b) using a robust line tracker and a nested controller design. Our state estimation exploits high-speed camera images (100Hz) and 70Hz IMU data fused in an Extended Kalman Filter (EKF). We demonstrate results from outdoor experiments for pole-relative hovering, and pole circumnavigation where the operator provides only yaw commands. Lastly, we show results for image-based 3D reconstruction and texture mapping of a pole to demonstrate the usefulness for inspection tasks.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. 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Pleistocene climate fluctuations influence phylogeographical patterns in Melomys cervinipes across the mesic forests of eastern Australia

Aim Our aim was to clarify the lineage-level relationships for Melomys cervinipes and its close relatives and investigate whether the patterns of divergence observed for these wet-forest-restricted mammals may be associated with recognized biogeographical barriers. Location Mesic closed forest along the east coast of Australia, from north Queensland to mid-eastern New South Wales. Methods To enable rigorous phylogenetic reconstruction, divergence-date estimation and phylogeographical inference, we analysed DNA sequence and microsatellite data from 307 specimens across the complete distribution of M. cervinipes (45 localities). Results Three divergent genetic lineages were found within M. cervinipes, corresponding to geographically delineated northern, central and southern clades. Additionally, a fourth lineage, comprising M. rubicola and M. capensis, was identified and was most closely related to the northern M. cervinipes lineage. Secondary contact of the northern and central lineages was identified at one locality to the north of the Burdekin Gap. Main conclusions Contemporary processes of repeated habitat fragmentation and contraction, local extinction events and subsequent re-expansion across both small and large areas, coupled with the historical influence of the Brisbane Valley Barrier, the St Lawrence Gap and the Burdekin Gap, have contributed to the present phylogeographical structure within M. cervinipes. Our study highlights the need to sample close to the periphery of putative biogeographical barriers or risk missing vital phylogeographical information that may significantly alter the interpretation of biogeographical hypotheses.

High performance all-carbon thin film supercapacitors

We fabricated high performance supercapacitors by using all carbon electrodes, with volume energy in the order of 10−3 Whcm−3, comparable to Li-ion batteries, and power densities in the range of 10 Wcm−3, better than laser-scribed-graphene supercapacitors. All-carbon supercapacitor electrodes are made by solution processing and filtering electrochemically-exfoliated graphene sheets mixed with clusters of spontaneously entangled multiwall carbon nanotubes. We maximize the capacitance by using a 1:1 weight ratio of graphene to multi-wall carbon nanotubes and by controlling their packing in the electrode film so as to maximize accessible surface and further enhance the charge collection. This electrode is transferred onto a plastic-paper-supported double-wall carbon nanotube film used as current collector. These all-carbon thin films are combined with plastic paper and gelled electrolyte to produce solid-state bendable thin film supercapacitors. We assembled supercapacitor cells in series in a planar configuration to increase the operating voltage and find that the shape of our supercapacitor film strongly affects its capacitance. An in-line superposition of rectangular sheets is superior to a cross superposition in maintaining high capacitance when subject to fast charge/discharge cycles. The effect is explained by addressing the mechanism of ion diffusion into stacked graphene sheets.