944 resultados para Customer surveys data


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The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

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Objectives This research explores the relationship between young firms, their growth orientation-intention and a range of relationships which can be seen to provide business support. Prior-work Research indicates that networks impact the firm’s ability to secure resources (Sirmon and Hitt 2003; Liao and Welsch. 2004; Hanlon and Saunders 2007). Networks have been evaluated in a number of ways ranging from simple counts to characteristics of their composition (Davidsson and Honig 2003), strength of relationships (Granovetter 1973) and network diversity (Carter et al 2003). By providing access to resources and knowledge (from start-up assistance and raising capital, (e.g. Smallbone et al, 2003), networks may assist in enabling continued persistence during those times where firms may experience resource constraints owing to firm growth (Baker and Nelson 2005). Approach The data used in this research was generated in the 2008 UK Federation of Small Businesses (FSB) survey. Over 1,000 of the firms responding were found to fall into the category of “young”, ((defined as firms under 4 years old). Firms were considered the unit of analysis with the entrepreneur being the chief spokesperson for the firm. Preliminary data analysis considered key demographic characteristics and industry classifications, comparing the FSB data with that of the UK government’s own (BERR) Small Business Surveys of 2007 and 2008, to establish some degree of representativeness of the respondents. The analysis then examined networks with varying potential ability to provide support for young firms, the networks measured in terms of number, diversity, characteristic and strength in its relationship to young firm growth orientation. The diversity of business-support-related relationships ranged from friends and family, through professional services, customers and suppliers, and government business services, to trade associations and informal business networks. The characteristics of these formal and informal sources of support for new businesses are examined across a range of business support-type activities for new firms. The number of relationships and types of business support are also explored. Finally, the strength of these relationships is examined by analysis of the source of business support, type of business support, and links to the growth orientation-intention of the firm, after controlling for a number of key variables related to firm and industry status and owner characteristics. Results Preliminary analysis of the data by means of univariate analysis showed that average number of sources of advice was around 2.5 (from a potential total of 6). In terms of the diversity of relationships, universities had by far the smallest percentage of firms receiving beneficial advice from them. Government business services were beneficially used by 40% of young firms, the other relationship types being around the 50-55% mark. In terms of characteristics of the advice, the average number of areas in which benefit was achieved was around 5.5 of a maximum of 15. Start-up advice has by far the highest percentage of firms obtaining beneficial advice, with increasing sales, improving contacts and improving confidence being the other categories at or around the 50% mark. Other market-focused areas where benefits were also received were in the areas of new markets, existing product improvements and new product improvements, where around 40% of the young responding firms obtained benefit. Regression techniques evaluating the strength of these relationships in terms of the links between business support (by source of support, type of support, and range of support) and firm growth orientation-intention focus highlighted a number of significant relationships, even after controlling for a range of other explanatory variables identified in the literature. Specifically, there was found to be a positive relationship between receiving business advice generally (regardless of type or source) and growth orientation. This relationship was seen to be stronger, however, when looking at the number of types of beneficial advice received, and stronger again for the number of sources of this advice. In terms of individual sources of advice, customers and suppliers had the strongest relationship with growth, with Government business services also found to be significant. Combining these two sources was also seen to increase the strength of the relationship between these two sources of advice and growth orientation. In considering areas of support, growth was most strongly positively related to advice that benefited the development of new products and services, and also business confidence, but was negatively related to advice linked to business recovery. Finally, amalgamating the 4 key types and sources of advice to examine the impact of combinations of these types and sources of advice also improved the strength of the relationship. Implications The findings will assist in the understanding of young firms in general and growth more specifically, particularly the role and importance of specific sources, types and combinations of business support used more extensively by new young growth-oriented firms. Value This research may assist in processes designed to allow entrepreneurs to make better decisions; educators and support organizations to develop better advice and assistance, and Governments design better conditions for the creation of new growth-oriented businesses.

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Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc. The geometric and dosimetric accuracy of CTCombine’s output has been assessed by simulating simple and complex treatments applied to a rotated planar phantom and a rotated humanoid phantom and comparing the resulting virtual EPID images with the images acquired using experimental measurements and independent simulations of equivalent phantoms. It is expected that CTCombine will be useful for Monte Carlo studies of EPID dosimetry as well as other EPID imaging applications.

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Recent studies have shown that delusion-like experiences (DLEs) are common among general populations. This study investigates whether the prevalence of these experiences are linked to the embracing of New Age thought. Logistic regression analyses were performed using data derived from a large community sample of young adults (N = 3777). Belief in a spiritual or higher power other than God was found to be significantly associated with endorsement of 16 of 19 items from Peters et al. (1999b) Delusional Inventory following adjustment for a range of potential confounders, while belief in God was associated with endorsement of four items. A New Age conception of the divine appears to be strongly associated with a wide range of DLEs. Further research is needed to determine a causal link between New Age philosophy and DLEs (e.g. thought disturbance, suspiciousness, and delusions of grandeur).

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This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional support for eco-structuralism.

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While there is substantial research on attitudinal and behavioral loyalty, the deconstruction of attitudinal loyalty into its two key components – emotional and cognitive loyalty – has been largely ignored. Despite the existence of managerial strategies aimed at increasing each of these two components, there is little academic research to support these managerial efforts. This paper seeks to advance the understanding of emotional and cognitive brand loyalty by examining the psychological function that these dimensions of brand loyalty perform for the consumer. We employ Katz’s (1960) four functions of attitudes (utilitarian, knowledge, value-expression, ego-defence) to investigate this question. Surveys using a convenience sample were completed by 268 consumers in two metropolitan cities on a variety of goods, services and durable products. The relationship between the functions and dimensions of loyalty were examined using MANOVA. The results show that both the utilitarian and knowledge functions of loyalty are significantly positively related to cognitive loyalty while the ego-defensive function of loyalty is significantly positively related to emotional loyalty. The results for the value-expressive function of loyalty were nonsignificant.

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Lawyers have traditionally viewed law as a closed system, and doctrinal research has been the research methodology used most widely in the profession. This reflects traditional concepts of legal reasoning. There is a wealth of reliable and valid social science data available to lawyers and judges. Judges in fact often refer to general facts about the world, society, institutions and human behaviour (‘empirical facts’). Legal education needs to prepare our students for this broader legal context. This paper examines how ‘empirical facts’ are used in Australian and other common law courts. Specifically, the paper argues that there is a need for enhanced training in non-doctrinal research methodologies across the law school curriculum. This should encompass a broad introduction to social science methods, with more attention being paid to a cross-section of methodologies such as content analysis, comparative law and surveys that are best applied to law.

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Definition of disease phenotype is a necessary preliminary to research into genetic causes of a complex disease. Clinical diagnosis of migraine is currently based on diagnostic criteria developed by the International Headache Society. Previously, we examined the natural clustering of these diagnostic symptoms using latent class analysis (LCA) and found that a four-class model was preferred. However, the classes can be ordered such that all symptoms progressively intensify, suggesting that a single continuous variable representing disease severity may provide a better model. Here, we compare two models: item response theory and LCA, each constructed within a Bayesian context. A deviance information criterion is used to assess model fit. We phenotyped our population sample using these models, estimated heritability and conducted genome-wide linkage analysis using Merlin-qtl. LCA with four classes was again preferred. After transformation, phenotypic trait values derived from both models are highly correlated (correlation = 0.99) and consequently results from subsequent genetic analyses were similar. Heritability was estimated at 0.37, while multipoint linkage analysis produced genome-wide significant linkage to chromosome 7q31-q33 and suggestive linkage to chromosomes 1 and 2. We argue that such continuous measures are a powerful tool for identifying genes contributing to migraine susceptibility.

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Migraine is a painful disorder for which the etiology remains obscure. Diagnosis is largely based on International Headache Society criteria. However, no feature occurs in all patients who meet these criteria, and no single symptom is required for diagnosis. Consequently, this definition may not accurately reflect the phenotypic heterogeneity or genetic basis of the disorder. Such phenotypic uncertainty is typical for complex genetic disorders and has encouraged interest in multivariate statistical methods for classifying disease phenotypes. We applied three popular statistical phenotyping methods—latent class analysis, grade of membership and grade of membership “fuzzy” clustering (Fanny)—to migraine symptom data, and compared heritability and genome-wide linkage results obtained using each approach. Our results demonstrate that different methodologies produce different clustering structures and non-negligible differences in subsequent analyses. We therefore urge caution in the use of any single approach and suggest that multiple phenotyping methods be used.

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ANDS Guides http://ands.org.au/guides/index.html These guides provide information about ANDS services and some fundamental issues in data-intensive research and research data management. These are not rules, prescriptions or proscriptions. They are guidelines and checklists to inform and broaden the range of possibilities for researchers, data managers, and research organisations.

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This guide is relevant to anyone who owns copyright in data compilations or databases and wants to share their data openly, or to anyone who wants to use data under an open content licence. ANDS Guides are available at http://ands.org.au/guides/index.html.

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High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.

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With the increasing growth of cultural events both in Australia and internationally, there has also been an increase in event management studies; in theory and in practice. Although a series of related knowledge and skills required specifically by event managers has already been identified by many researchers (Perry et al., 1996; Getz, 2002 & Silvers et al., 2006) and generic event management models proposed, including ‘project management’ strategies in an event context (Getz, 2007), knowledge gaps still exist in relation to identifying specific types of events, especially for not-for-profit arts events. For events of a largely voluntary nature, insufficient resources are recognised as the most challenging; including finance, human resources and infrastructure. Therefore, the concepts and principles which are adopted by large scale commercial events may not be suitable for not-for-profit arts events aiming at providing professional network opportunities for artists. Building partnerships are identified as a key strategy in developing an effective event management model for this type of event. Using the 2008 World Dance Alliance Global Summit (WDAGS) in Brisbane 13-18 July, as a case study, the level, nature and relationship of key partners are investigated. Data is triangulated from interviews with organisers of the 2008 WDAGS, on-line and email surveys of delegates, participant observation and analysis of formal and informal documents, to produce a management model suited to this kind of event.

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