3 resultados para extremal Fr echet process

em eResearch Archive - Queensland Department of Agriculture


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The intent of this study was to design, document and implement a Quality Management System (QMS) into a laboratory that incorporated both research and development (R&D) and routine analytical activities. In addition, it was necessary for the QMS to be easily and efficiently maintained to: (a) provide documented evidence that would validate the system's compliance with a certifiable standard, (b) fit the purpose of the laboratory, (c) accommodate prevailing government policies and standards, and (d) promote positive outcomes for the laboratory through documentation and verification of the procedures and methodologies implemented. Initially, a matrix was developed that documented the standards' requirements and the necessary steps to be made to meet those requirements. The matrix provided a check mechanism on the progression of the system's development. In addition, it was later utilised in the Quality Manual as a reference tool for the location of full procedures documented elsewhere in the system. The necessary documentation to build and monitor the system consisted of a series of manuals along with forms that provided auditable evidence of the workings of the QMS. Quality Management (QM), in one form or another, has been in existence since the early 1900's. However, the question still remains: is it a good thing or just a bugbear? Many of the older style systems failed because they were designed by non-users, fiercely regulatory, restrictive and generally deemed to be an imposition. It is now considered important to foster a sense of ownership of the system by the people who use the system. The system's design must be tailored to best fit the purpose of the operations of the facility if maximum benefits to the organisation are to be gained.

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There are renewed calls for end-user participation and the integration of local knowledge in agricultural research. In Australia, the response has included an increased emphasis on participatory on-farm research with farmers and commercial agronomists that tests accepted principals to answer practical local farming questions. However, this pursuit of greater relevance has often led to compromises in research designs, unclear results and frustration amongst farmers, commercial agronomists and Research Development and Extension (RDE) agency researchers. This paper reports on a series of pre-season planning workshops from `Doing successful on-farm research', a workshop-based initiative that provides guidelines and a series of interactive activities to plan better participatory on-farm research. The workshop approach helps people design on-farm research that is appropriate to their own needs and local conditions. It assists them to clearly identify their issues, develop specific research questions and decide the best approach to answer those questions with the appropriate rigour for their own situations. These `Doing successful on-farm research' workshops address four potential deficiencies in on-farm research and farming systems RDE more generally in Australia: (1) variable participation of scientists and farmers in on-farm research; (2) the lack of clear guidelines for effective participatory practice and on-farm research; (3) limited support for on-farm research beyond the intensive investigations conducted by RDE agencies and (4) limited support for industry and farmers to contextualise information and research outcomes for specific individual circumstances and faster adaptation of technology. This may be a valuable contribution to balancing the demands for both relevance and rigour in on-farm research in Australia. In "Ground–breaking Stuff’- Proceedings of the 13th Australian Society of Agronomy Conference, 10-14 September 2006, Perth, Western Australia.

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Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.