961 resultados para Multi-perspective


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This report presents learnings, case studies, guidelines and resources for non-government organisations that are planning to implement shared or collaborative arrangements with other agencies. It summarises results from an evaluation of the implementation phase of the Multi-Tenant Service Centre (MTSC) Pilots Project, which was completed in June 2008. This evaluation shows that developing and implementing shared and collaborative arrangements is a complex process that presents many risks, challenges and barriers to success, but can have many potential benefits for non government organisations. As this report makes clear, there is no ‘one size fits all’ approach to this process. The MTSC Pilots Project was conducted by the Department of Communities (DoC), Queensland Government, as part of its Strengthening Non-Government Organisations strategy. The objective of the MTSC Pilots initiative was to co-locate separate service providers in an appropriately located centre, operating with effective and transparent management, which enabled service providers to improve client services. Three MTSC consortiums in Mackay, Caboolture and Toowoomba were selected as the pilots over a four year period from 2006 – 2010.

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Computational neuroscience aims to elucidate the mechanisms of neural information processing and population dynamics, through a methodology of incorporating biological data into complex mathematical models. Existing simulation environments model at a particular level of detail; none allow a multi-level approach to neural modelling. Moreover, most are not engineered to produce compute-efficient solutions, an important issue because sufficient processing power is a major impediment in the field. This project aims to apply modern software engineering techniques to create a flexible high performance neural modelling environment, which will allow rigorous exploration of model parameter effects, and modelling at multiple levels of abstraction.

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Neu-Model, an ongoing project aimed at developing a neural simulation environment that is extremely computationally powerful and flexible, is described. It is shown that the use of good Software Engineering techniques in Neu-Model’s design and implementation is resulting in a high performance system that is powerful and flexible enough to allow rigorous exploration of brain function at a variety of conceptual levels.

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Effective fuel injector operation and efficient combustion are two of the most critical aspects when Diesel engine performance, efficiency and reliability are considered. Indeed, it is widely acknowledged that fuel injection equipment faults lead to increased fuel consumption, reduced power, greater levels of exhaust emissions and even unexpected engine failure. Previous investigations have identified fuel injector related acoustic emission activity as being caused by mechanisms such as fuel line pressure build-up; fuel flow through injector nozzles, injector needle opening and closing impacts and premixed combustion related pulses. Few of these investigations however, have attempted to categorise the close association and interrelation that exists between fuel injection equipment function and the acoustic emission generating mechanisms. Consequently, a significant amount of ambiguity remains in the interpretation and categorisation of injector related AE activity with respect to the functional characteristics of specific fuel injection equipment. The investigation presented addresses this ambiguity by detailing a study in which AE signals were recorded and analysed from two different Diesel engines employing the two commonly encountered yet fundamentally different types of fuel injection equipment. Results from tests in which faults were induced into fuel injector nozzles from both indirect-injection and direct-injection engines show that functional differences between the main types of fuel injection equipment results in acoustic emission activity which can be specifically related to the type of fuel injection equipment used.

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Distance education has gone through rapid expansion over the years. Many Australian universities are pushing the use of distance education in delivering construction education programs. However, the critical success factors (CSFs) in distance learning construction programs (DLCPs) are not fully understood. More importantly, students’ demographic features may affect the selection of distance education technologies. Situation-matching strategies should therefore be taken by universities or institutions with different student cohorts. A survey is adopted in Central Queensland University (CQU) to identify and rank the critical success factors in a DLCP in Australia where there is a significant number of earner-learners and students with low socioeconomic background. The findings suggest that the most important CSFs include access to computers and internet, reliability of web-based learning sites, high relevance and clarity of learning materials and assessment items, the availability of web-based learning sites that can be easily manipulated, and the capability of the instructors to provide well-structured courses. The findings also suggest that students with low socioeconomic background have more rigorous requirements on interface design, instructors’ support, and the integration of practical components into courses. The results provide good guidance of the design and delivery of DLCPs and will be useful for universities and institutions that are seeking to implement the distance mode in construction education.

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In the health care industry, Job Satisfaction (JS) is linked with work performance, psychological well-being and employee turnover. Although research into JS among health professionals has a long history worldwide, there has been very little analysis in Vietnam. No study has addressed JS of preventive medicine workers in Vietnam, and there is no reliable and valid instrument in Vietnamese language and context for evaluation of JS in this group. This project was conducted to fill these gaps. The findings contribute evidence regarding factors that influence JS in this sector of the health industry that should be applied to personnel management policies and practices in Vietnam.

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Although seeking help for mental ill-health is beneficial, the majority of persons afflicted do not access available help services. Young adults (16-24 years old) in particular have the highest prevalence of mental health problems and the lowest rate of help-seeking behaviour. Key barriers to help-seeking for young adults, including cost, privacy concerns, inconvenience, access to health professionals and interpersonal interaction, appear to derive from the face-to-face method of service delivery traditionally used to distribute mental health services. Social marketing employs the principle of value exchange, whereby consumers will choose a behaviour in exchange for receiving valued benefits and/or a reduction in key barriers, to achieve behavioural goals for social good. The appropriation of mobile digital technology to deliver self-help mental health services may reduce the current barriers to help seeking, however, extant literature offers no empirical support for this proposition. Our research addresses this gap by examining the perceptions of young adults regarding M-mental health services. Depth interviews were undertaken with 15 young adults (18-24 years old), who had self-reported mild-moderate stress, anxiety or depression. The data were thematically analysed with the assistance of Nvivo. The findings reveal M-mental health services reduce the barriers to accessing face-to-face help services to a large extent. However, they also present their own barriers to help-seeking that must be considered by social marketers, including negligible cost expectations and service efficacy concerns. Overall, this study highlights the potential of M-mental health services to encourage early intervention and help-seeking behaviour as part of a social marketing strategy to address mental illness in young adults.

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China has experienced considerable economic growth since 1978, which was accompanied by unprecedented growth in urbanization and, more recently, by associated rising urban housing and land banking issues. One such issue is that of land hoarding - where real estate developers purchase land to hold unused in the rising market for a future lucrative sale, often several years later. This practice is outlawed in China, where land use is controlled by increasingly strengthened Government policies and inspectors. Despite this, land hoarding continues apace, with the main culprits being the developers and inspectors working subversively. This resembles a game between two players - the inspector and the developer - which provides the setting for this paper in developing an evolutionary game theory model to provide insights into dealing with the dilemmas faced by the players. The logic and dilemma of land banking strategy and illegal land banking issues are analysed, along with the land inspector’s role from a game theory perspective by determining the replication dynamic mechanism and evolutionary stable strategies under the various conditions that the players face. The major factors influencing the actions of land inspectors, on the other hand, are the costs of inspection, no matter if it is strict or indolent, conflict costs, and income and penalties from corruption. From this, it is shown that, when the net loss for corruption (income from corruption minus the penalties for corruption and cost of strict inspections) is less than the cost of strict inspections, the final evolutionary stable strategy of the inspectors is to carry out indolent inspections. Then, whether penalising developers for hoarding is severe or not, the evolutionary strategy for the developer is to hoard. The implications for land use control mechanisms and associated developer-inspector actions and counteractions are then examined in the light of the model's properties.

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Rapid urbanization in developing countries such as China has been creating unprecedented opportunities for the adoption of sustainable construction (SC). Owner, as a key driver of urbanization, plays an influential role for other stakeholders to undertake SC practices. However, lacking their demands and requirements of owners were regarded as the main barriers for the adoption of SC. Notwithstanding the diversity of previous studies on the barriers to SC, there is a dearth of research from the owner’s perspective. This paper presents an empirical study identifying the critical factors impeding the adoption of SC from the owners’ point of view. A list of 25 factors was preliminarily identified through extensive literature review and interviews with industry professionals. This was followed by a questionnaire survey to collect owners’ opinions on the relative importance of these factors. Using factor analysis, seven most critical factors are identified, namely, economic feasibility, awareness, support from project stakeholders, legislation and regulation, operability of SC, resource risk, and project management model. The research findings show that economic feasibility, awareness, legislation and regulation are the most important factors impeding owners in adopting SC practices. This implied that the government plays a vital role in removing the barriers impeding the greater adoption of SC by building owners in China. This is helpful for a transition to the low carbon urbanization.

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The study examined the health-related behaviours of Saudi people following a recent cardiac event and identified the factors that influence these behaviours using McLeroy et al.'s (1988) Ecological Model of Health Behaviours as a guiding framework. The study was one of the first in Saudi Arabia to examine the health-related behaviours of Saudi people following a recent cardiac event. The study findings emphasise the importance of a program that integrates secondary prevention practices, educational approaches and targeted supportive services in cardiac care in Saudi Arabia.

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Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

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Based on a survey of 4,393 journalism students in Australia, Brazil, Chile, Mexico, South Africa, Spain, Switzerland, and the United States, this study provides much-needed comparative evidence about students’ motivations for becoming journalists, their future job plans, and expectations. Findings show not only an almost universal decline in students’ desire to work in journalism by the end of their program but also important national differences in terms of the journalistic fields in which they want to work, as well as their job expectations. The results reinforce the need to take into account national contexts when examining journalism education across the globe.

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In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

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