124 resultados para parameterisation techniques
Resumo:
This article describes a new application of key psychological concepts in the area of Sociometry for the selection of workers within organizations in which projects are developed. The project manager can use a new procedure to determine which individuals should be chosen from a given pool of resources and how to combine them into one or several simultaneous groups/projects in order to assure the highest possible overall work efficiency from the standpoint of social interaction. The optimization process was carried out by means of matrix calculations performed using a computer or even manually, and based on a number of new ratios generated ad-hoc and composed on the basis of indices frequently used in Sociometry.
Resumo:
Systematic review (SR) is a rigorous, protocol-driven approach designed to minimise error and bias when summarising the body of research evidence relevant to a specific scientific question. Taking as a comparator the use of SR in synthesising research in healthcare, we argue that SR methods could also pave the way for a “step change” in the transparency, objectivity and communication of chemical risk assessments (CRA) in Europe and elsewhere. We suggest that current controversies around the safety of certain chemicals are partly due to limitations in current CRA procedures which have contributed to ambiguity about the health risks posed by these substances. We present an overview of how SR methods can be applied to the assessment of risks from chemicals, and indicate how challenges in adapting SR methods from healthcare research to the CRA context might be overcome. Regarding the latter, we report the outcomes from a workshop exploring how to increase uptake of SR methods, attended by experts representing a wide range of fields related to chemical toxicology, risk analysis and SR. Priorities which were identified include: the conduct of CRA-focused prototype SRs; the development of a recognised standard of reporting and conduct for SRs in toxicology and CRA; and establishing a network to facilitate research, communication and training in SR methods. We see this paper as a milestone in the creation of a research climate that fosters communication between experts in CRA and SR and facilitates wider uptake of SR methods into CRA.
Resumo:
In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can be due to a lack of scientific understanding or a lack of computing power available to address all the known physical processes. Parameterisations are sources of large uncertainty in a model as parameter values used in these parameterisations cannot be measured directly and hence are often not well known; and the parameterisations themselves are also approximations of the processes present in the true atmosphere. Whilst there are many efficient and effective methods for combined state/parameter estimation in data assimilation (DA), such as state augmentation, these are not effective at estimating the structure of parameterisations. A new method of parameterisation estimation is proposed that uses sequential DA methods to estimate errors in the numerical models at each space-time point for each model equation. These errors are then fitted to pre-determined functional forms of missing physics or parameterisations that are based upon prior information. We applied the method to a one-dimensional advection model with additive model error, and it is shown that the method can accurately estimate parameterisations, with consistent error estimates. Furthermore, it is shown how the method depends on the quality of the DA results. The results indicate that this new method is a powerful tool in systematic model improvement.
Resumo:
Ruminant husbandry is a major source of anthropogenic greenhouse gases (GHG). Filling knowledge gaps and providing expert recommendation are important for defining future research priorities, improving methodologies and establishing science-based GHG mitigation solutions to government and non-governmental organisations, advisory/extension networks, and the ruminant livestock sector. The objectives of this review is to summarize published literature to provide a detailed assessment of the methodologies currently in use for measuring enteric methane (CH4) emission from individual animals under specific conditions, and give recommendations regarding their application. The methods described include respiration chambers and enclosures, sulphur hexafluoride tracer (SF6) technique, and techniques based on short-term measurements of gas concentrations in samples of exhaled air. This includes automated head chambers (e.g. the GreenFeed system), the use of carbon dioxide (CO2) as a marker, and (handheld) laser CH4 detection. Each of the techniques are compared and assessed on their capability and limitations, followed by methodology recommendations. It is concluded that there is no ‘one size fits all’ method for measuring CH4 emission by individual animals. Ultimately, the decision as to which method to use should be based on the experimental objectives and resources available. However, the need for high throughput methodology e.g. for screening large numbers of animals for genomic studies, does not justify the use of methods that are inaccurate. All CH4 measurement techniques are subject to experimental variation and random errors. Many sources of variation must be considered when measuring CH4 concentration in exhaled air samples without a quantitative or at least regular collection rate, or use of a marker to indicate (or adjust) for the proportion of exhaled CH4 sampled. Consideration of the number and timing of measurements relative to diurnal patterns of CH4 emission and respiratory exchange are important, as well as consideration of feeding patterns and associated patterns of rumen fermentation rate and other aspects of animal behaviour. Regardless of the method chosen, appropriate calibrations and recovery tests are required for both method establishment and routine operation. Successful and correct use of methods requires careful attention to detail, rigour, and routine self-assessment of the quality of the data they provide.