Multicarrier code division multiple access (MC-CDMA) is a very promising candidate for the multiple access scheme in fourth generation wireless communi- cation systems. During asynchronous transmission, multiple access interference (MAI) is a major challenge for MC-CDMA systems and significantly affects their performance. The main objectives of this thesis are to analyze the MAI in asyn- chronous MC-CDMA, and to develop robust techniques to reduce the MAI effect. Focus is first on the statistical analysis of MAI in asynchronous MC-CDMA. A new statistical model of MAI is developed. In the new model, the derivation of MAI can be applied to different distributions of timing offset, and the MAI power is modelled as a Gamma distributed random variable. By applying the new statistical model of MAI, a new computer simulation model is proposed. This model is based on the modelling of a multiuser system as a single user system followed by an additive noise component representing the MAI, which enables the new simulation model to significantly reduce the computation load during computer simulations. MAI reduction using slow frequency hopping (SFH) technique is the topic of the second part of the thesis. Two subsystems are considered. The first sub- system involves subcarrier frequency hopping as a group, which is referred to as GSFH/MC-CDMA. In the second subsystem, the condition of group hopping is dropped, resulting in a more general system, namely individual subcarrier frequency hopping MC-CDMA (ISFH/MC-CDMA). This research found that with the introduction of SFH, both of GSFH/MC-CDMA and ISFH/MC-CDMA sys- tems generate less MAI power than the basic MC-CDMA system during asyn- chronous transmission. Because of this, both SFH systems are shown to outper- form MC-CDMA in terms of BER. This improvement, however, is at the expense of spectral widening. In the third part of this thesis, base station polarization diversity, as another MAI reduction technique, is introduced to asynchronous MC-CDMA. The com- bined system is referred to as Pol/MC-CDMA. In this part a new optimum com- bining technique namely maximal signal-to-MAI ratio combining (MSMAIRC) is proposed to combine the signals in two base station antennas. With the applica- tion of MSMAIRC and in the absents of additive white Gaussian noise (AWGN), the resulting signal-to-MAI ratio (SMAIR) is not only maximized but also in- dependent of cross polarization discrimination (XPD) and antenna angle. In the case when AWGN is present, the performance of MSMAIRC is still affected by the XPD and antenna angle, but to a much lesser degree than the traditional maximal ratio combining (MRC). Furthermore, this research found that the BER performance for Pol/MC-CDMA can be further improved by changing the angle between the two receiving antennas. Hence the optimum antenna angles for both MSMAIRC and MRC are derived and their effects on the BER performance are compared. With the derived optimum antenna angle, the Pol/MC-CDMA system is able to obtain the lowest BER for a given XPD.

Bovine tuberculosis (bTB) is one of the most complex, persistent and controversial problems facing the British cattle industry. It is also potentially zoonotic and so has public health implications. The incidence of the disease has been increasing in Great Britain for more than 20 years and is now endemic in southwest regions of the country and occurs sporadically elsewhere. Scotland records very few incidences of bTB and was declared as an Officially bTB free (OTF) region in 2009 for the purposes of cattle trading. However, in order to retain its OTF status Scotland must continue to demonstrate the ability to report low level of disease prevalence whilst maintaining its vigilance to potential new outbreaks. This thesis uses a variety of epidemiological and statistical models to evaluate the ongoing control strategies for bTB in Scottish cattle herds and highlight potential limitations to the current surveillance programmes. In the absence of an established wildlife reservoir, livestock movements are considered the primary mechanism for introduction of bTB into cattle herds. I use movement and bTB data to estimate the within-herd incidence rate for each infected farm in Scotland. The results suggest that this rate varies across farms, and is dependent on the herd size and length of disease exposure. These incidence rates are then used to parameterise a multi-herd dynamic model using stochastic simulations that incorporate multiple disease transmission pathways. With this approach I evaluate the impact of different routine test protocols on the overall simulated epidemics. Based on the model outcome, abattoir surveillance alone is not sufficient to maintain infection at a low constant level. Whilst adapting to more frequent routine testing regime can reduce disease incidence, the sensitivity of the surveillance methods can also have a big impact on the long term stability of the disease prevalence and can act as the main barrier to eradicating the disease from low incidence regions. The single intra-dermal comparative cervical tuberculin (SICCT) test used in the current routine herd surveillance relies on stimulating an immune response and observing delayed hypersensitivity reactions in infected animals. The test suffers highly variable, and often poor, sensitivity with current estimates ranging from 50% to 80%. The lower sensitivities may be associated with early stages of infection, concurrent illness, and farm management conditions as well as the presence of sub-clinically infected carriers that can potentially escape detection. In addition, there was evidence that physiological stress can have a marked effect on the immune responses in animals affected with bTB. I conducted two different types of case-control analyses to investigate the potential effect of stress related events on the outcome of the SICCT test. In the first analysis, a matched design is implemented to examine the effect of recent calving on reactivity to the SICCT. SICCT test positive cattle (cases) were matched with test negative (control) animals within the same farm. By selecting herd-mates (i.e. animals within the same herd at the same time), the study aims to control for space and time. Furthermore, animal age and breed were used as additional selection criteria to control for previous exposure period and potential genetic variation to the reaction of SICCT test outcome. Results from a conditional logistic regression model indicated that animals calved within 60 days prior to test were less likely to respond to the SICCT test in comparison to non-recently calved animals, and that this effect was strongest in the first 2 weeks of the post-partum period. In the second analysis, animals identified with gross pathology at post-mortem (TB-like lesion and/or bacteria culture) and that were SICCT test negative within 60 days prior to slaughter (representing false negative) were compared with confirmed test positives (true positives). Results from multivariable logistic regression model suggested that the probability of missed infection by SICCT test increases with age and male cattle have higher odds of being a false negative compared to females. Repeated skin tests within 60 and 120 days, as well as recent movement and parturition, were all statistically associated with false negative test outcome. Under future surveillance systems, these results could be used to adjust the timings of testing relative to calving, movements and previous test occasions in order to minimise the risks of false negative test results. Alternatively, increasing the threshold for reactor definition in animals under these categories could be considered to complement the poor test sensitivity.

Orientador: Paulina Setti Riedel / Banca: Paulo Milton Barbosa Landim / Banca: Diana Sarita Hamburger / Resumo: As imagens de satélite, por serem um registro de alvos com diferentes comportamentos espectrais e diferentes arranjos espaciais, respondem com variações tonais de cinza e variações texturais. Esta variação textural é analisada normalmente através de interpretação visual, que é uma sistematização de várias técnicas, as quais convergem para um único objetivo, a compartimentação da imagem. Em geral, a textura apresenta-se como sendo uma arma valiosa na interpretação de formas de relevo, drenagem e de padrões da cobertura vegetal e de uso da terra. Acredita-se que, a partir da utilização das técnicas de processamento digital de imagens e técnicas estatísticas multivariadas, como subsidiária na redução do caráter subjetivo da análise textural e da correspondência entre zonas homólogas, será possível favorecer a discriminação de elementos imageados, e encontrar um modelo que possibilite explicar mais satisfatoriamente a compartimentação e a relação de equivalência entre zonas que constituem texturas semelhantes. Desta forma, o objetivo geral do trabalho, é avaliar diversas metodologias, com intuito de realizar uma compartimentação fisiográfica, explorando o aspecto textural da imagem além da similaridade existentes entre as áreas após compartimentada. A área de estudo abrange os municípios de Itanhaém e Peruíbe localizados no litoral sul do Estado de São Paulo. A partir dos resultados obtidos, verificou-se que o método convencional de fotointerpretação juntamente com a aplicação da análise estatística multivariada, mostrou-se viável de ser aplicado apresentando resultados satisfatórios e inovadores. Já no processamento de alguns algoritmos de textura a redução de informações foi bastante grande, devido à redução dos valores de níveis de cinza e à homogeneização dos alvos; mostrando que o resultados não foram positivos...(Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Satellite images record surface features based on their different spectral behaviors and spatial arrangements. As such, these features are discernable from different shades of gray and textural variations seen in the images. These textural variations represent a systematic utilization of various techniques, which converge for a single objective: compartmentalization of the image. In general, texture is used as an important tool in the interpretation of relief forms, drainage patterns and vegetative cover and land-use patterns. Through the application of digital processing techniques and multivariable statistical analyses, it is possible to reduce the subjective character of textural analysis and correspondence between homologous zones, hence enhance the discrimination of different elements in an image. These methods can help find a model that allows for a better explanation of the compartmentalization and equivalence between zones that consist of similar textures. In this form, the general objective of the study is to evaluate diverse methodologies with the objective of determining the physiographic compartmentalization, exploiting the textural aspect of the image in addition to the existing similarities between areas following compartmentalization. The study area is within the municipalities of Itanhaém e Peruíbe, located along the south coastal region of São Paulo State. Based on the results obtained, it is confirmed that the conventional method of photo-interpretation, together with the application of multivariate statistical analysis, shows that it is a viable combination, yielding both satisfactory and innovative results. Already, in the processing of some textural algorithms, the reduction of information was significant, owing to the reduction of the values in the shades of gray and to the homogenization of targets, showing that the results were not positive...(Complete abstract click electronic access below) / Mestre

Neste trabalho estudou-se a modelagem matemática de processo de produção do antitumoral retamicina produzido pelo microrganismo filamentoso Streptomyces olindensis em cultivos descontínuos, descontínuos alimentados e contínuos. Através da modelagem matemática é possível verificar o comportamento dos fatores que interferem na produção deste metabólito secundário, a fim de identificar as melhores condições de processo. Foram estudados diferentes modelos: modelo morfologicamente estruturado, modelo não estruturado e um modelo híbrido que combina equações de balanço material com redes neurais artificiais. O modelo morfologicamente estruturado é um aperfeiçoamento de um modelo anterior e o modelo não estruturado, por sua vez, foi desenvolvido na tentativa de simplificar a descrição do processo ao considerar menos variáveis e possuir menor número de parâmetros ajustáveis. Nos modelos, as variáveis consideradas no ajuste foram as concentrações de biomassa, de glicose, de retamicina e de oxigênio dissolvido no meio. Os resultados das simulações foram avaliados estatisticamente por comparação com os dados experimentais. Os modelos também foram comparados entre si através de uma análise estatística. Observou-se que, dentre os modelos estudados, o modelo híbrido apresentou sensibilidade pronunciada às condições iniciais e qualidade de representação dos dados experimentais inferior à dos demais modelos. Os modelos morfologicamente estruturado e não estruturado apresentaram capacidade similar de representação do comportamento dos dados experimentais dos ensaios descontínuos, descontínuo-alimentados e contínuos com baixas taxas de alimentação. / The mathematical modeling of retamycin production during batch, fed-batch and continuous cultivations of Streptomyces olindensis was studied. Through the mathematical modeling, it is possible to identify the best conditions to conduct the process. Different models considered were: a morphologically structured model, an unstructured model and a hybrid model that combines artificial neural networks with mass balances. The morphologically structured model included an enhancement in a model previously described. The unstructured model was developed as an attempt to simplify the description of the process by considering fewer variables and fewer parameters to be adjusted. The variables considered in the models were the concentrations of biomass, glucose, retamycin and dissolved oxygen. Simulation results were submitted to statistical analysis such as model discrimination and test of adequacy to verify which of the models were suitable to describe the process and whether the results of the simulations fit the experimental data or not. Results show that the hybrid model presented high sensitivity to the initial conditions and its capability of representing the experimental data was worse than that of the other developed models. Both the morphologically structured model and the unstructured model show similar suitability to represent the experimental data behavior for batch, fed-batch and low-dilution rate continuous runs.

Streamflow data is used throughout the world in applications such as flooding, agriculture, and urban planning. Understanding daily and seasonal patterns in streamflow is important for decision makers, so that they can accurately predict and react to seasonal changes in streamflow for the region. This understanding of daily and seasonal patterns has historically been achieved through interpretation of observed historical data at stream reaches throughout the individual regions. Developing countries have limited and sporadic observed stream and rain gage data, making it difficult for stakeholders to manage their water resources to their fullest potential. In areas where observed historical data is not readily available, the European Reanalysis Interim (ERA-Interim) data provided by the European Center for Medium-Range Weather Forecasts (ECMWF) can be used as a surrogate. The ERA-Interim data can be compared to historic observed flow to determine the accuracy of the ERA-Interim data using statistical measures such as the correlation coefficient, the mean difference, the root mean square error, R2 coefficients and spectral angle metrics. These different statistical measures determine different aspects of the predicted data's accuracy. These metrics measure correlation, errors in magnitude, errors in timing, and errors in shape. This thesis presents a suite of tests that can be used to determine the accuracy and correlation of the ERA-Interim data compared to the observed data, the accuracy of the ERA-Interim data in capturing the overall events, and the accuracy of the data in capturing the magnitude of events. From these tests, and the cases presented in this thesis, we can conclude that the ERA-Interim is a sufficient model for simulating historic data on a global scale. It is able to capture the seasonality of the historical data, the magnitude of the events, and the overall timing of the events sufficiently to be used as a surrogate dataset. The suite of tests can also be applied to other applications, to make comparing two datasets of flow data a quicker and easier process.

Estimation of evaporation from open water is essential for hydrodynamics, manufacturing industries, irrigation, farming, environmental protection and many other purposes. It is also important for proper management of hydrological resources such as reservoirs, lakes and rivers. Recent methods are mostly data-driven methods, such as using Artificial Intelligence techniques. Adaptive Neuro Fuzzy Inference System (ANFIS) is one of them and has been widely adopted in many hydrological fields for its simplicity. The current research presents a comparative study on the impact of optimization techniques such as Firefly Algorithm (FFA), Genetic Algorithm (GA), Particle Swarm Optimizer (PSO) and Ant Colony Optimization (ACO) on obtained results. In addition, a practical method named Multi Gene-genetic Programming (MGGP) is employed to propose an equation for the estimation of the Evaporation. Six different measured weather variables are taken, which are maximum, minimum and average air temperature, sunshine hours, wind speed and relative humidity. Models are separately calibrated with total data set collected over an eight-year period of 2010-2017 at the specified station “Arizona” in the United States of America. Ten statistical indices are calculated to verify the results. All optimizers were observed and compared to check if the results are better than ANFIS or not. The objectives of the adoption of different optimizer techniques was to verify the accuracy of the prediction by ANFIS model. Comparisons showed that ANFIS and MGGP are slightly better than the other models. MGGP model is different from other models in a way that it provides a set of equations instead of showing numerical values; therefore, the computational time is high. PSO, FFA, ACO and GA are considered as optimizers in the main model. Though PSO provided very similar results to the ANFIS model and MGGP gives even better results than basic ANFIS model. ANFIS is easier in terms of model formation. ANFIS is simpler to build and easy to operate. Since the prediction was quite identical in all cases, the ANFIS model was suggested due to its simplicity.

Item-response theory (IRT)assumes that the model fits the data. One commonly overlooked aspect of model-fit assessment is an examination of personfit, or person-fit assessment (PFA). One reason that PFA lacks popularity among psychometricians is that comprehensive software is notpresent.This dissertation outlines the development and testing ofa new software package, called wizirt, that will begin to meet this need. This software package provides a wide gamut of tools to the user but is currently limited to unidimensional, dichotomous, and parametricmodels. The wizirt package is built in the open source language R, where it combines the capabilities of a number of other R packages under a single syntax.In addition to the wizirt package, I have created a number of resources to help users learn to use the package. This includes support for individuals who have never used R before, as well as more experienced R users.

Item-response theory (IRT)assumes that the model fits the data. One commonly overlooked aspect of model-fit assessment is an examination of personfit, or person-fit assessment (PFA). One reason that PFA lacks popularity among psychometricians is that comprehensive software is notpresent.This dissertation outlines the development and testing ofa new software package, called wizirt, that will begin to meet this need. This software package provides a wide gamut of tools to the user but is currently limited to unidimensional, dichotomous, and parametricmodels. The wizirt package is built in the open source language R, where it combines the capabilities of a number of other R packages under a single syntax.In addition to the wizirt package, I have created a number of resources to help users learn to use the package. This includes support for individuals who have never used R before, as well as more experienced R users.

Accurately modeling key aspects of coal combustion allows for the virtual testing and application of new technologies and processes without the need for investments in lab- and pilot-scale facilities, since such facilities may only be used for a few small tests. However, modeling of subprocesses must not only be accurate but computationally efficient. Modeling of coal devolatilization reactions and processes are one of the important parts of large-scale simulations of coal combustion systems. The work presented here details efforts to improve the modeling of coal devolatilization processes in massively-parallel simulations of coal combustors, including: (1) devolatilization rate/yield models, (2) modeling various chemical, physical, and thermodynamic properties of coal, char, and tar (including structural NMR parameters like carbon aromaticity, the elemental composition of coal char and tar, and the heating value of coal-based and other fuels), and (3) the application of various simplifying assumptions to equilibrium calculations of coal devolatilization products using multiple levels of fuel mixture fractions. Using several different advanced statistical methods, the models discussed here were developed and improved by careful comparison with large sets of experimental data. The advanced statistical methods and procedures show large improvements in these models over previous work.

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