A posteriori error estimation is an inseparable component of any reliable numerical method for solving partial differential equations. The aim of the goal-oriented a posteriori error estimates is to control the computational error directly with respect to some quantity of interest, which makes the method very convenient for many engineering applications. The resulting error estimates may be employed for mesh adaptation which enables to find a numerical approximation of the quantity of interest under some given tolerance in a very efficient manner. In this thesis, the goal-oriented error estimates are derived for discontinuous Galerkin discretizations of the linear scalar model problems, as well as of the Euler equations describing inviscid compressible flows. It focuses on several aspects of the goal-oriented error estimation method, in particular, higher order reconstructions, adjoint consistency of the discretizations, control of the algebraic errors arising from iterative solutions of both algebraic systems, and linking the estimates with the hp-anisotropic mesh adaptation. The computational performance is demonstrated by numerical experiments.

Greenhouse gases in the Earth’s atmosphere play an important role in regulating surface temperatures. The UK is signatory to international agreements that legally commit the UK to reduce its greenhouse gas emissions, and there is a scientific and political need to better understand greenhouse gas sources on regional scales. The current methods used to provide greenhouse gas emission inventories rely on ‘bottom-up’ techniques and have large associated errors. However, it is also possible to use observations of atmospheric concentrations of greenhouse gases and models of atmospheric transport to link the observations with source regions in order to estimate emissions in a ‘top-down’ approach. The key findings presented in this thesis are (a) UK emissions can be retrieved from the Angus tall tower in Scotland using the NAME inversion technique at a finer spatial resolution than has previously been reported using similar ‘top-down’ inverse methods; (b) atmospheric measurements from the Angus tall tower in Scotland have been used for the first time with the NAME inversion technique in order to estimate UK emissions of methane, nitrous oxide and sulfur hexafluoride for the years 2006 to 2009; (c) increasing the number of towers in UK network substantially increases the spatial resolution of greenhouse gas emission estimates. The errors and uncertainties associated with the NAME inversion over the UK domain are discussed and potential future improvements to this approach are presented. Overall, the work presented in this thesis has contributed to our understanding of the spatial and inter-annual variability of UK greenhouse gas emissions.

published_or_final_version / Real Estate and Construction / Master / Master of Science in Construction Project Management

published_or_final_version / Real Estate and Construction / Master / Master of Science in Construction Project Management

published_or_final_version / Architecture / Master / Master of Science in Construction Project Management

Approved for public release; distribution in unlimited. / Some battlefield models have a component in them which models the time it takes for an observer to detect a target. Different observers may have different mean detection times due to various factors such as the type of sensor used, environmental conditions, fatigue of the observer, etc. Two parametric models for the distribution of time to target detection are considered which can incorporate these factors. Maximum likelihood estimation procedures for the parameters are described. Results of simulation experiments to study the small sample behavior of the estimators are presented. / http://archive.org/details/modelsfortargetd00baed / Major, Korean Air Force

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Computer Science.

Digitized by Kansas Correctional Industries

Factors which affect iron bioavailability have been repeatedly and extensively investigated. A model, derived from these studies, has been developed for estimating available iron from meal data. However, many dietary surveys report only average daily intakes of iron, and do not report the iron present in single meals. No model to estimate available iron from daily iron intake has been presented in the literature. Dietary questionnaires were kept for two nonconsecutive weekdays by 355 male and 382 female Utah school children, mean age 7.5 years, assisted by their parents, and recorded by household measure. Data, first recorded as meals eaten, were used to develop three models for the estimation of available iron from total daily iron intake. It was concluded that available iron can be estimated from total iron intake by two of these models, as compared with the currently used model, which estimates available iron from data recorded by meal. Additionally, meal patterns of those factors involved with the estimation of available iron were investigated. The intake of dietary ascorbic acid and total iron was found to be evenly distributed among meals; approximately 10% of these nutrients was consumed as snacks. Of the meat, fish, poultry and the iron in those products consumed; 36% was taken at lunch, and 54% at dinner. Only 5% of the meat, fish, and poultry iron was consumed as snacks. The available iron distribution for breakfast, lunch, dinner, and snacks was 21.0%, 30.8%, 42.5% and 5.7%, respectively. Previous studies have investigated the characteristics of diets which provide 9 mg of iron per 1000 kcal of energy consumed. These diets have been shown to include larger portions of vegetables, fruits, and cereal products. In this study, these high-iron dense characteristics were studied as they pertain to total available iron intake. It was concluded that the high-iron dense diet receives more total available iron from the nonheme iron than from the heme iron consumed. Thus, it is conceivable that those dietary characteristics shown to provide a high-iron dense diet may also provide a high available iron intake.

Two dimensional stocks of fish can be assessed with methods that mimic the analysis of research survey data but that use commercial catch-effort data. This finite population approach has scarcely been used in fisheries science though it brings about very large sample sizes of local fish density with models of only moderate levels of complexity. The extracted information about the status of the stock can be interpreted as biomass indices. Statistical inference on finite populations has been the locus of a highly specialized branch of sampling-distribution inference, unique because observable variables are not considered as random variables. If statistical inference is defined as "the identification of distinct sets of plausible and implausible values for unobserved quantities using observations and probability theory" then it is shown that Godambe's paradox implies that the classical finite populations approach is inherently contradictory as a technique of statistical inference. The demonstration is facilitated by the introduction of an extended canonical form of an experiment of chance, that apart from the three components identified by Birnbaum, also contains the time at which the experiment is performed. Realization of the time random variable leaves the likelihood function as sole data-based mathematical tool for statistical inference, in contradiction with sampling-distribution inference and in agreement with direct-likelihood and Bayesian inference. A simple mathematical model is introduced for biomass indices in the spatial field defined by the fishing grounds. It contains three unknown parameters, the natural mortality rate, the probability of observing the stock in the area covered by the fishing grounds, and mean fish density in the sub-areas where the stock was present. A new theory for the estimation of mortality rates is introduced, using length frequency data, that is based on the population ecology analogue of Hamilton-Jacobi theory of classical mechanics. The family of equations require estimations of population growth, individual growth, and recruitment pattern. Well known or new techniques are used for estimating parameters of these processes. Among the new techniques, a likelihood-based geostatistical model to estimate fish density is proposed and is now in use in fisheries science (Roa-Ureta and Niklitschek, 2007, ICES Journal of Marine Science 64:1723-1734), as well as a new method to estimate individual growth parameters (Roa-Ureta, In Press, Journal of Agricultural, Biological, and Environmental Statistics). All inference is done only using likelihood functions and approximations to likelihood functions, as required by the Strong Likelihood principle and the direct-likelihood school of statistical inference. The statistical model for biomass indices is a hierarchical model with several sources of data, hyperparameters, and nuisance parameters. Even though the level of complexity is not low, a full Bayesian formulation is not necessary. Physical factors, mathematical manipulation, profile likelihoods and estimated likelihoods are used for the elimination of nuisance parameters. Marginal normal and multivariate normal likelihood functions, as well as the functional invariance property, are used for the hierarchical structure of estimation. In this manner most sources of information and uncertainty in the data are carried over up the hierarchy to the estimation of the biomass indices.