140 resultados para AIME Membership


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Abstract This paper uses a case study to identify the impact of a Queensland parliamentary committee on policy. In 2003, the Travelsafe Committee undertook two inquiries investigating young driver and rider issues. In 2007, the Queensland Parliament passed legislation that provided the power to make regulations that changed the graduated driver licensing laws in Queensland. The analysis of the second reading speeches for this bill suggests that parliamentary committees can help set the agenda for government policy. The role of the Travelsafe Committee in this process was recognised by both government and non-government members of Parliament and by those that had been, or were currently, members of the committee and by those that had no membership experience of the Travelsafe Committee prior to the debate of the legislation. This paper suggests that in order for committees to successfully participate in policy work they need to have strong ideas, work to a consistently high standard and the chair needs to be dedicated to the work of the committee. This case study indicates the importance of parliamentary committees in the policy work of a parliament.

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The travel and hospitality industry is one which relies especially crucially on word of mouth, both at the level of overall destinations (Australia, Queensland, Brisbane) and at the level of travellers’ individual choices of hotels, restaurants, sights during their trips. The provision of such word-of-mouth information has been revolutionised over the past decade by the rise of community-based Websites which allow their users to share information about their past and future trips and advise one another on what to do or what to avoid during their travels. Indeed, the impact of such user-generated reviews, ratings, and recommendations sites has been such that established commercial travel advisory publishers such as Lonely Planet have experienced a pronounced downturn in sales ¬– unless they have managed to develop their own ways of incorporating user feedback and contributions into their publications. This report examines the overall significance of ratings and recommendation sites to the travel industry, and explores the community, structural, and business models of a selection of relevant ratings and recommendations sites. We identify a range of approaches which are appropriate to the respective target markets and business aims of these organisations, and conclude that there remain significant opportunities for further operators especially if they aim to cater for communities which are not yet appropriately served by specific existing sites. Additionally, we also point to the increasing importance of connecting stand-alone ratings and recommendations sites with general social media spaces like Facebook, Twitter, and LinkedIn, and of providing mobile interfaces which enable users to provide updates and ratings directly from the locations they happen to be visiting. In this report, we profile the following sites: * TripAdvisor, the international market leader for travel ratings and recommendations sites, with a membership of some 11 million users; * IgoUgo, the other leading site in this field, which aims to distinguish itself from the market leader by emphasising the quality of its content; * Zagat, a long-established publisher of restaurant guides which has translated its crowdsourcing model from the offline to the online world; * Lonely Planet’s Thorn Tree site, which attempts to respond to the rise of these travel communities by similarly harnessing user-generated content; * Stayz, which attempts to enhance its accommodation search and booking services by incorporating ratings and reviews functionality; and * BigVillage, an Australian-based site attempting to cater for a particularly discerning niche of travellers; * Dopplr, which connects travel and social networking in a bid to pursue the lucrative market of frequent and business travellers; * Foursquare, which builds on its mobile application to generate a steady stream of ‘check-ins’ and recommendations for hospitality and other services around the world; * Suite 101, which uses a revenue-sharing model to encourage freelance writers to contribute travel writing (amongst other genres of writing); * Yelp, the global leader in general user-generated product review and recommendation services. In combination, these profiles provide an overview of current developments in the travel ratings and recommendations space (and beyond), and offer an outlook for further possibilities. While no doubt affected by the global financial downturn and the reduction in travel that it has caused, travel ratings and recommendations remain important – perhaps even more so if a reduction in disposable income has resulted in consumers becoming more critical and discerning. The aggregated word of mouth from many tens of thousands of travellers which these sites provide certainly has a substantial influence on their users. Using these sites to research travel options has now become an activity which has spread well beyond the digirati. The same is true also for many other consumer industries, especially where there is a significant variety of different products available – and so, this report may also be read as a case study whose findings are able to be translated, mutatis mutandis, to purchasing decisions from household goods through consumer electronics to automobiles.

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Human resource flexibility is important in entrepreneurial ventures that need to respond to the changing challenges of growing the new business. This research investigates the impact of previously well-known people (strong ties) as entrepreneurial team members on the human resource flexibility of new ventures. Data collected from German founding entrepreneurs in technology-oriented, incubator-based firms shows that choosing a well known individual to join the entrepreneurial team increases the founder's ability to modify the team member's work role, but complicates asking the team member to leave the team if required. Hence, strong ties both increase and reduce human resource flexibility. However, the effect of strong ties on role modifiability is statistically significant only with novice entrepreneurs. These research findings counsel founders to discuss role modification and exit during partnership and entrepreneurial team membership negotiations.

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This work reviews the rationale and processes for raising revenue and allocating funds to perform information intensive activities that are pertinent to the work of democratic government. ‘Government of the people, by the people, for the people’ expresses an idea that democratic government has no higher authority than the people who agree to be bound by its rules. Democracy depends on continually learning how to develop understandings and agreements that can sustain voting majorities on which democratic law making and collective action depends. The objective expressed in constitutional terms is to deliver ‘peace, order and good government’. Meeting this objective requires a collective intellectual authority that can understand what is possible; and a collective moral authority to understand what ought to happen in practice. Facts of life determine that a society needs to retain its collective competence despite a continual turnover of its membership as people die but life goes on. Retaining this ‘collective competence’ in matters of self-government depends on each new generation: • acquiring a collective knowledge of how to produce goods and services needed to sustain a society and its capacity for self-government; • Learning how to defend society diplomatically and militarily in relation to external forces to prevent overthrow of its self-governing capacity; and • Learning how to defend society against divisive internal forces to preserve the authority of representative legislatures, allow peaceful dispute resolution and maintain social cohesion.

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Automobiles have deeply impacted the way in which we travel but they have also contributed to many deaths and injury due to crashes. A number of reasons for these crashes have been pointed out by researchers. Inexperience has been identified as a contributing factor to road crashes. Driver’s driving abilities also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain the key factors impacting on road safety. Our failure to understand what is really important for learners, in terms of competent driving, is one of the many challenges for building better training programs. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. A multidisciplinary approach is necessary to explain how driving abilities evolves with on-road driving experience. To our knowledge, driver assistance systems have never been comprehensively used in a driver training context to assess the safety aspect of driving. The aim and novelty of this thesis is to develop and evaluate an Intelligent Driver Training System (IDTS) as an automated assessment tool that will help drivers and their trainers to comprehensively view complex driving manoeuvres and potentially provide effective feedback by post processing the data recorded during driving. This system is designed to help driver trainers to accurately evaluate driver performance and has the potential to provide valuable feedback to the drivers. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the driving tasks. Therefore, the proposed IDTS utilizes fuzzy set theory for the assessment of driver performance. The proposed research program focuses on integrating the multi-sensory information acquired from the vehicle, driver and environment to assess driving competencies. After information acquisition, the current research focuses on automated segmentation of the selected manoeuvres from the driving scenario. This leads to the creation of a model that determines a “competency” criterion through the driving performance protocol used by driver trainers (i.e. expert knowledge) to assess drivers. This is achieved by comprehensively evaluating and assessing the data stream acquired from multiple in-vehicle sensors using fuzzy rules and classifying the driving manoeuvres (i.e. overtake, lane change, T-crossing and turn) between low and high competency. The fuzzy rules use parameters such as following distance, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvres to assess competency. These rules that identify driving competency were initially designed with the help of expert’s knowledge (i.e. driver trainers). In-order to fine tune these rules and the parameters that define these rules, a driving experiment was conducted to identify the empirical differences between novice and experienced drivers. The results from the driving experiment indicated that significant differences existed between novice and experienced driver, in terms of their gaze pattern and duration, speed, stop time at the T-crossing, lane keeping and the time spent in lanes while performing the selected manoeuvres. These differences were used to refine the fuzzy membership functions and rules that govern the assessments of the driving tasks. Next, this research focused on providing an integrated visual assessment interface to both driver trainers and their trainees. By providing a rich set of interactive graphical interfaces, displaying information about the driving tasks, Intelligent Driver Training System (IDTS) visualisation module has the potential to give empirical feedback to its users. Lastly, the validation of the IDTS system’s assessment was conducted by comparing IDTS objective assessments, for the driving experiment, with the subjective assessments of the driver trainers for particular manoeuvres. Results show that not only IDTS was able to match the subjective assessments made by driver trainers during the driving experiment but also identified some additional driving manoeuvres performed in low competency that were not identified by the driver trainers due to increased mental workload of trainers when assessing multiple variables that constitute driving. The validation of IDTS emphasized the need for an automated assessment tool that can segment the manoeuvres from the driving scenario, further investigate the variables within that manoeuvre to determine the manoeuvre’s competency and provide integrated visualisation regarding the manoeuvre to its users (i.e. trainers and trainees). Through analysis and validation it was shown that IDTS is a useful assistance tool for driver trainers to empirically assess and potentially provide feedback regarding the manoeuvres undertaken by the drivers.

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Global warming is already threatening many animal and plant communities worldwide, however, the effect of climate change on bat populations is poorly known. Understanding the factors influencing the survival of bats is crucial to their conservation, and this cannot be achieved solely by modern ecological studies. Palaeoecological investigations provide a perspective over a much longer temporal scale, allowing the understanding of the dynamic patterns that shaped the distribution of modern taxa. In this study twelve microchiropteran fossil assemblages from Mount Etna, central-eastern Queensland, ranging in age from more than 500,000 years to the present day, were investigated. The aim was to assess the responses of insectivorous bats to Quaternary environmental changes, including climatic fluctuations and recent anthropogenic impacts. In particular, this investigation focussed on the effects of increasing late Pleistocene aridity, the subsequent retraction of rainforest habitat, and the impact of cave mining following European settlement at Mount Etna. A thorough examination of the dental morphology of all available extant Australian bat taxa was conducted in order to identify the fossil taxa prior to their analysis in term of species richness and composition. This detailed odontological work provided new diagnostic dental characters for eighteen species and one genus. It also provided additional useful dental characters for three species and seven genera. This odontological analysis allowed the identification of fifteen fossil bat taxa from the Mount Etna deposits, all being representatives of extant bats, and included ten taxa identified to the species level (i.e., Macroderma gigas, Hipposideros semoni, Rhinolophus megaphyllus, Miniopterus schreibersii, Miniopterus australis, Scoteanax rueppellii, Chalinolobus gouldii, Chalinolobus dwyeri, Chalinolobus nigrogriseus and Vespadelus troughtoni) and five taxa identified to the generic level (i.e., Mormopterus, Taphozous, Nyctophilus, Scotorepens and Vespadelus). Palaeoecological analysis of the fossil taxa revealed that, unlike the non-volant mammal taxa, bats have remained essentially stable in terms of species diversity and community membership between the mid-Pleistocene rainforest habitat and the mesic habitat that occurs today in the region. The single major exception is Hipposideros semoni, which went locally extinct at Mount Etna. Additionally, while intensive mining operations resulted in the abandonment of at least one cave that served as a maternity roost in the recent past, the diversity of the Mount Etna bat fauna has not declined since European colonisation. The overall resilience through time of the bat species discussed herein is perhaps due to their unique ecological, behavioural, and physiological characteristics as well as their ability to fly, which have allowed them to successfully adapt to their changing environment. This study highlights the importance of palaeoecological analyses as a tool to gain an understanding of how bats have responded to environmental change in the past and provides valuable information for the conservation of threatened modern species, such as H. semoni.

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Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

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Information and communication technologies (ICTs) are essential components of the knowledge economy, and have an immense complementary role in innovation, education, knowledge creation, and relations with government, civil society, and business within city regions. The ability to create, distribute, and exploit knowledge has become a major source of competitive advantage, wealth creation, and improvements in the new regional policies. Growing impact of ICTs on the economy and society, rapid application of recent scientific advances in new products and processes, shifting to more knowledge-intensive industry and services, and rising skill requirements have become crucial concepts for urban and regional competitiveness. Therefore, harnessing ICTs for knowledge-based urban development (KBUD) has a significant impact on urban and regional growth (Yigitcanlar, 2005). In this sense, e-region is a novel concept utilizing ICTs for regional development. Since the Helsinki European Council announced Turkey as a candidate for European Union (EU) membership in 1999, the candidacy has accelerated the speed of regional policy enhancements and adoption of the European regional policy standards. These enhancements and adoption include the generation of a new regional spatial division, NUTS-II statistical regions; a new legislation on the establishment of regional development agencies (RDAs); and new orientations in the field of high education, science, and technology within the framework of the EU’s Lisbon Strategy and the Bologna Process. The European standards posed an ambitious new agenda in the development and application of contemporary regional policy in Turkey (Bilen, 2005). In this sense, novel regional policies in Turkey necessarily endeavor to include information society objectives through efficient use of new technologies such as ICTs. Such a development seeks to be based on tangible assets of the region (Friedmann, 2006) as well as the best practices deriving from grounding initiatives on urban and local levels. These assets provide the foundation of an e-region that harnesses regional development in an information society context. With successful implementations, the Marmara region’s local governments in Turkey are setting the benchmark for the country in the implementation of spatial information systems and e-governance, and moving toward an e-region. Therefore, this article aims to shed light on organizational and regional realities of recent practices of ICT applications and their supply instruments based on evidence from selected local government organizations in the Marmara region. This article also exemplifies challenges and opportunities of the region in moving toward an e-region and provides a concise review of different ICT applications and strategies in a broader urban and regional context. The article is organized in three parts. The following section scrutinizes the e-region framework and the role of ICTs in regional development. Then, Marmara’s opportunities and challenges in moving toward an e-region are discussed in the context of ICT applications and their supply instruments based on public-sector projects, policies, and initiatives. Subsequently, the last section discusses conclusions and prospective research.

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This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

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There is a worldwide trend towards rapidly growing defined contribution pension funds in terms of assets and membership, and the choices available to individuals. This has shifted the decisionmaking responsibility to fund members for managing the investment of their retirement savings. This change has given rise to a phenomenon where most superannuation fund members are responsible for either actively choosing or passively relying on their funds’ default investment options. Prior research identifies that deficiencies in financial literacy is one of the causes of inertia in financial decision-making and findings from international and Australian studies show that financial illiteracy is wide-spread. Given the potential significant economic and social consequences of poor financial decision-making in superannuation matters, this paper proposes a framework by which the various demographic, social and contextual factors that influence fund members’ financial literacy and its association with investment choice decisions are explored. Enhanced theoretical and empirical understanding of the factors that are associated with active/passive investment choice decisions would enable development of well-targeted financial education programs.

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Objectives: To develop recommendations for the clinical education required to prepare Australian Nurse Practitioner candidates for advanced and extended practice in nephrology settings. Methods: Using the Delphi research technique a consensus statement was developed over a nine month period. All endorsed and candidate Nephrology Nurse Practitioners (NNP) were invited to participate as the expert panel. The Delphi research technique uses a systematic and iterative process. The expert panel were asked to generate a list of items which were then circulated to all NNPs. They were asked to determine their degree of agreement or disagreement with each statement using a 5-point Likert scale There was opportunity for free-text comments to be provided if desired. Results from each round were collated; the document was refined and circulated to the experts for a subsequent round. Consensus was demonstrated after three Delphi rounds. Results: The consensus statement comprises four components explaining the role and membership of the mentorship team, the setting and location of NNP clinical education, learning strategies to support the NNP, and outcomes of NNP clinical education. Demographic questions in the final survey revealed information about the qualifications, years of experience, and practice location of Australian NNPs. Conclusions: The consensus statement is not prescriptive but it will inform NNP candidates, university course providers and mentors about the expected extended nephrology specific clinical education that will enable the NNP to provide advanced nursing care for patients regardless of the stage of chronic kidney disease (CKD) and the practice setting.

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This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

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Genetic research of complex diseases is a challenging, but exciting, area of research. The early development of the research was limited, however, until the completion of the Human Genome and HapMap projects, along with the reduction in the cost of genotyping, which paves the way for understanding the genetic composition of complex diseases. In this thesis, we focus on the statistical methods for two aspects of genetic research: phenotype definition for diseases with complex etiology and methods for identifying potentially associated Single Nucleotide Polymorphisms (SNPs) and SNP-SNP interactions. With regard to phenotype definition for diseases with complex etiology, we firstly investigated the effects of different statistical phenotyping approaches on the subsequent analysis. In light of the findings, and the difficulties in validating the estimated phenotype, we proposed two different methods for reconciling phenotypes of different models using Bayesian model averaging as a coherent mechanism for accounting for model uncertainty. In the second part of the thesis, the focus is turned to the methods for identifying associated SNPs and SNP interactions. We review the use of Bayesian logistic regression with variable selection for SNP identification and extended the model for detecting the interaction effects for population based case-control studies. In this part of study, we also develop a machine learning algorithm to cope with the large scale data analysis, namely modified Logic Regression with Genetic Program (MLR-GEP), which is then compared with the Bayesian model, Random Forests and other variants of logic regression.

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Doug Hargreaves has completed a year as President of Engineers Australia, a 90,000 strong membership based organisation representing the engineering profession. In preparing for the year Doug decided that the core of his own leadership is his values and that the legacy he wanted to be remembered for at the end of his year, was how his values underpinned everything he did. The framework for this values approach was a book he co-authored entitled 'Values Driven Leadership'. The essence of Doug's philosophy is that a leader who bases their leadership on a strong sense of values will create an environment where people have a strong sense of Belonging, Identity and Purpose. This paper reflects on Doug's year of leadership of Engineers Australia and offers insights and examples of where his values driven leadership approach played out and contributed to various scenarios he encountered over the year. The paper will share Doug's approach to leadership and offer an understanding of how an effective leader actually does what he does. Too often leadership is seen as a nebulous capacity that people either have or do not have. In this paper, we will identify the specific skills and abilities within a values framework that will allow any leader to be more effective in their role.

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This paper investigates the factors that drive high levels of corporate sustainability performance (CSP), as proxied by membership of the Dow Jones Sustainability World Index. Using a stakeholder framework, we examine the incentives for US firms to invest in sustainability principles and develop a number of hypotheses that relate CSP to firm-specific characteristics. Our results indicate that leading CSP firms are significantly larger, have higher levels of growth and a higher return on equity than conventional firms. Contrary to our predictions, leading CSP firms do not have greater free cash flows or lower leverage than other firms.