965 resultados para INCOMPLETE-DATA


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This thesis is actually the composition of two separate studies aimed at further understanding the role of incomplete combustion products on atmospheric chemistry. The first explores the sensitivity of black carbon (BC) forcing to aerosol vertical location since BC has an increased forcing per unit mass when it is located above reflective clouds. We used a column radiative transfer model to produce globally-averaged values of normalized direct radiative forcing (NDRF) for BC over and under different types of clouds. We developed a simple column-weighting scheme based on the mass fractions of BC that are over and under clouds in measured vertical profiles. The resulting NDRF is in good agreement with global 3-D model estimates, supporting the column-weighted model as a tool for exploring uncertainties due to diversity in vertical distribution. BC above low clouds accounts for about 20% of the global burden but 50% of the forcing. We estimate maximum-minimum spread in NDRF due to modeled profiles as about 40% and uncertainty as about 25%. Models overestimate BC in the upper troposphere compared with measurements; modeled NDRF might need to be reduced by about 15%. Redistributing BC within the lowest 4 km of the atmosphere affects modeled NDRF by only about 5% and cannot account for very high forcing estimates. The second study estimated global year 2000 carbon monoxide (CO) emissions using a traditional bottom-up inventory. We applied literature-derived emission factors to a variety of fuel and technology combinations. Combining these with regional fuel use and production data we produced CO emissions estimates that were separable by sector, fuel type, technology, and region. We estimated year 2000 stationary source emissions of 685.9 Tg/yr and 885 Tg/yr if we included adopted mobile sources from EDGAR v3.2FT2000. Open/biomass burning contributed most significantly to global CO burden, while the residential sector, primarily in Asia and Africa, were the largest contributors with respect to contained combustion sources. Industry production in Asia, including brick, cement, iron and steel-making, also contributed significantly to CO emissions. Our estimates of biofuel emissions are lower than most previously published bottom-up estimates while our other fuel emissions are generally in good agreement. Our values are also universally lower than recently estimated CO emissions from models using top-down methods.

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Electoral researchers are so much accustomed to analyzing the choice of the single most preferred party as the left-hand side variable of their models of electoral behavior that they often ignore revealed preference data. Drawing on random utility theory, their models predict electoral behavior at the extensive margin of choice. Since the seminal work of Luce and others on individual choice behavior, however, many social science disciplines (consumer research, labor market research, travel demand, etc.) have extended their inventory of observed preference data with, for instance, multiple paired comparisons, complete or incomplete rankings, and multiple ratings. Eliciting (voter) preferences using these procedures and applying appropriate choice models is known to considerably increase the efficiency of estimates of causal factors in models of (electoral) behavior. In this paper, we demonstrate the efficiency gain when adding additional preference information to first preferences, up to full ranking data. We do so for multi-party systems of different sizes. We use simulation studies as well as empirical data from the 1972 German election study. Comparing the practical considerations for using ranking and single preference data results in suggestions for choice of measurement instruments in different multi-candidate and multi-party settings.

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The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.

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Recent data indicate that levels of overweight and obesity are increasing at an alarming rate throughout the world. At a population level (and commonly to assess individual health risk), the prevalence of overweight and obesity is calculated using cut-offs of the Body Mass Index (BMI) derived from height and weight. Similarly, the BMI is also used to classify individuals and to provide a notional indication of potential health risk. It is likely that epidemiologic surveys that are reliant on BMI as a measure of adiposity will overestimate the number of individuals in the overweight (and slightly obese) categories. This tendency to misclassify individuals may be more pronounced in athletic populations or groups in which the proportion of more active individuals is higher. This differential is most pronounced in sports where it is advantageous to have a high BMI (but not necessarily high fatness). To illustrate this point we calculated the BMIs of international professional rugby players from the four teams involved in the semi-finals of the 2003 Rugby Union World Cup. According to the World Health Organisation (WHO) cut-offs for BMI, approximately 65% of the players were classified as overweight and approximately 25% as obese. These findings demonstrate that a high BMI is commonplace (and a potentially desirable attribute for sport performance) in professional rugby players. An unanswered question is what proportion of the wider population, classified as overweight (or obese) according to the BMI, is misclassified according to both fatness and health risk? It is evident that being overweight should not be an obstacle to a physically active lifestyle. Similarly, a reliance on BMI alone may misclassify a number of individuals who might otherwise have been automatically considered fat and/or unfit.

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In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.