The integration of remote sensing satellite data in air quality monitoring system at a regional scale is an important method to provide high spatial / temporal resolution information. This work focuses on estimating high spatial / temporal resolution ground-level information about particulate matter with aerodynamic diameters less than 2.5 um (PM2.5), with the utilization of MODIS aerosol optical thickness (AOT) data and meteorological data. Several missing data reconstruction techniques including Bayesian inversion, regularization and prediction-error filter are employed to estimate PM2.5 from satellite data. The results show that several direct missing data interpolation methods have the capability to estimate some distinctive features on the basis of available ground-based measurements, while the PEF method tends to generate more information with the aid of satellite AOT information. In addition to interpolation methods, general linear regression methods are used to predict ground-level PM2.5 with the consideration of other factors that have been shown to play an important role in predictions. Ordinary Least Square (OLS) method, when natural log taken on dependent and independent variables, is able to reduce the violation of homoscedasticity. The scatterplot of predicted and measured PM2.5 shows a strong correlation over the validation region, indicating the ability of the regression model to predict PM2.5. Weighted Least Square (WLS) method also has advantage in improving homoscedasticity. The predicted and measured PM2.5 has a relatively high correlation. / text

Predicting rock permeability from well logs in uncored wells is an important task in reservoir characterization. Due to the high costs of coring and laboratory analysis, typically cores are acquired in only a few wells. Since most wells are logged, the common practice is to estimate permeability from logs using correlation equations developed from limited core data. Most commonly, permeability is estimated from various well logs using statistical regression. For sandstones, often the logs of permeability can be correlated with porosity, but in carbonates the porosity permeability relationship tends to be much more complex and erratic. For this reason permeability prediction is a critical aspect of reservoir characterization in complex reservoirs such as carbonate reservoirs. In order to improve the permeability estimation in these reservoirs, several statistical regression techniques have already been tested in previous work to correlate permeability with different well logs. It has been shown that statistical regression for data correlation is quite promising in predicting complex reservoirs. But using all the possible well logs to predict permeability is not appropriate because the possibility of spurious correlation increases if you use more well logs. In statistics, variable selection is used to remove unnecessary independent variables and give a better prediction. So we apply variable selection to the permeability prediction procedures in order to further improve permeability estimation. We present three approaches to further improve reservoir permeability prediction based on well logs via data correlation and variable selection in this research. The first is a combination of stepwise algorithm with ACE technique. The second approach is the application of tree regression and cross-validation. The third is multivariate adaptive regression splines. Three methods are tested and compared at two complex carbonate reservoirs in west Texas: Salt Creek Field Unit (SCFU) and North Robertson Unit (NRU). The result of SCFU shows that permeability prediction is improved by applying variable selection to non-parametric regression ACE while tree regression is unable to predict permeability because it can not preserve the continuity of permeability. In NRU, none of these three methods can predict permeability accurately. This is due to the high complexity of NRU reservoir and measurement accuracy. In this reservoir, high permeability is discrete from low permeability, which makes prediction even more difficult. Permeability predictions based on well logs in complex carbonate reservoirs can be further improved by selecting appropriate well logs for data correlation. In comparing the relative predictive performance of the three regression methods, the stepwise with ACE method appears to outperform the other two methods.

An occurrence which is sometimes observed in a model based on dichotomous dependent variables is separation in the data. Separation in the data is when one or more of the independent variables can perfectly predict some binary outcome and it primarily occurs in small samples. There are three different mutually exclusive and exhaustive classes into which the data from a logistic regression can be classified: complete separation, quasi-complete separation and overlap. Separation (either complete or quasi-complete) in the data gives rise to a number of problems since it implies in nite or zero maximum likelihood estimates which are idealistic and does not happen in practice. In this dissertation the theory behind a logistic regression model, the definition of separation and different methods to deal with separation are discussed in part I. The methods that will be focused on are exact logistic regression, Firth s method which penalises the likelihood function and hidden logistic regression. In part II of this dissertation the three fore mentioned methods will be compared to one another. This will be done by applying each method to data sets which exhibit either complete or quasi-complete separation for different sample sizes and different covariate types. / Dissertation (MSc)--University of Pretoria, 2013. / Statistics / Unrestricted

Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. First, the support-vector based regression is used to identify initial parameter values of the consequent part of type-1 and interval type-2 fuzzy systems. Second, an overlapping clustering concept is used to derive interval valued parameters of the premise part of the type-2 fuzzy system. Publicly available peptide binding affinity data sets obtained from the literature are used in the experimental studies of this thesis. First, the proposed models are blind validated using the peptide binding affinity data sets obtained from a modelling competition. In that competition, almost an equal number of peptide sequences in the training and testing data sets (89, 76, 133 and 133 peptides for the training and 88, 76, 133 and 47 peptides for the testing) are provided to the participants. Each peptide in the data sets was represented by 643 bio-chemical descriptors assigned to each amino acid. Second, the proposed models are cross validated using mouse class I MHC alleles (H2-Db, H2-Kb and H2-Kk). H2-Db, H2-Kb, and H2-Kk consist of 65 nona-peptides, 62 octa-peptides, and 154 octa-peptides, respectively. Compared to the previously published results in the literature, the support vector-based type-1 and support vector-based interval type-2 fuzzy models yield an improvement in the prediction accuracy. The quantitative predictive performances have been improved as much as 33.6\% for the first group of data sets and 1.32\% for the second group of data sets. The proposed models not only improved the performance of the fuzzy system (which used support vector-based regression), but the support vector-based regression benefited from the fuzzy concept also. The results obtained here sets the platform for the presented models to be considered for other application domains in computational and/or systems biology. Apart from improving the prediction accuracy, this research study has also identified specific features which play a key role(s) in making reliable peptide binding affinity predictions. The amino acid features "Polarity", "Positive charge", "Hydrophobicity coefficient", and "Zimm-Bragg parameter" are considered as highly discriminating features in the peptide binding affinity data sets. This information can be valuable in the design of peptides with strong binding affinity to a MHC I molecule(s). This information may also be useful when designing drugs and vaccines.

Today, many magazine publishing houses faces the problem of future predictions of their products. In many cases, these predictions are made by employees based on their personal experiences and guesses. The problems of this attitude are high expanses on making the predictions and increased expanses when those predictions are wrong. The aim of this work is to study existing regression methods of automatic prediction and create a solution for predicting the magazine sales in Russian publishing house Burda.

The aim of this master thesis is to verify the classification methods of current models. The basis for the research part of the thesis is the definition of the financial situation in a company, which is created on the grounds of relevant literature and assumptions related to the negative financial situation of a company. According to this definition, an enterprise with no financial difficulties must achieve positive profits for a five-year period related to the value of the assets at the end of the reporting period, and during the reporting period the entity must not achieve a negative or zero cash-flow. Afterwards, the current classification models, which determine the financial situation of the company, were examined. The application of the models took place on data from companies belonging to different sectors to avoid distorted results. Selected sectors are not similar on purpose. The total reliability of classification was examined on selected classification models on the classification matrices basis for each of the analyzed sectors. The results of the current classification methods were unsatisfactory in terms of the number of correct classifications. The main benefit of this thesis was to create a predictive state model of the dependent variable. The model indicates whether the analyzed enterprise achieves a five-year return ratio and the asset size reaches values higher or lower than the value selected. This value may be the industry average of the mentioned indicator. The suggested linear model has been tested on a test(control) enterprise database.

A dependência de países em vias de desenvolvimento com relação às indústrias primárias como a mineração é evidente. Na economia peruana, aproximadamente, 6% do PIB e mais de 50% das exportações são provenientes desta atividade econômica, destacando sua posição competitiva no cenário mundial. A importância desta atividade aparece, também, quando o assunto em questão é a segurança do trabalho. Assim, embora nos últimos anos tenha-se percebido uma diminuição no número de acidentes na mineração peruana, a taxa de mortalidade ainda é alta quando comparada com outros países de tradição mineira, especialmente os mais desenvolvidos. No Peru, oficialmente, as causas fundamentais para a ocorrência de acidentes são atribuídas aos fatores pessoais e de trabalho, assim como às condições e aos atos inseguros. Nesse contexto, a identificação dessas causas, visando à proposta de soluções efetivas para melhor gerenciar os sistemas de segurança e de saúde na indústria da mineração, é muito importante. Esta tese estuda os acidentes por queda de rochas em minas subterrâneas do Peru. Para tal foi utilizado como fonte de informação primária o registro de acidentes fatais de 2007 em minas de médio e grande porte. Esse registro foi concedido pela Oficina de Fiscalización Minera del Organismo Superior de la Inversión en Energía y Minería del Peru (OSINERGMIN), órgão pertencente ao Ministério de Energía y Minas del Perú (MEM). O estudo mostra que a maioria dos acidentes fatais são provocados pela queda de rochas em escavações subterrâneas; assim, no período em estudo, este tipo de acidente representou 29,41% dos eventos. O estudo das características pessoais das vítimas mostra ainda que trabalhadores que desenvolvem funções de perfuração, preparação e instalação de suporte pós-desmonte tanto em frentes de lavra de produção quanto em escavações de desenvolvimento morrem por causa de traumatismos múltiplos e encefalo-cranianos severos. A maioria das vítimas pertencia a empresas mineiras terceirizadas. A partir do estudo das características pessoais das vítimas e utilizando os Métodos de Regressão Logística (MRL), propõe-se um modelo matemático para determinar a chance de se sofrer acidente por queda de rochas, em relação a outros tipos de acidentes. Os resultados mostram que trabalhadores que desempenham a função de ajudante, bem como trabalhadores com experiência de mais de três anos têm menos chance de sofrer acidentes por queda de rochas. Finalmente, foram identificados as causas fundamentais e imediatas dos acidentes estudados. Entre os fatores pessoais e de trabalho destacam-se o excesso de confiança e a supervisão deficiente como sendo as principais causas deste tipo de acidente. O estudo mostra também que o descumprimento de procedimentos operacionais e a presença de rochas soltas nas escavações constituem os principais tipos de atos e condições inseguras, respectivamente. / There are several evidences that developing countries depend on primary industries like mining. In fact about 6% of the Peruvian Gross Domestic Product (GDP) and 50% of exports are provided by mining. As well as in economy, mining has been strongly affecting the statistics concerning the safety in the workplace. Thus, although in recent years there was a decrease in the number of mining accidents in Peruvian mining, the fatality rate is still high compared to other traditional mining countries, especially the developed ones. In Peru, according to official statements, the primary causes of the accidents are attributed to personal and work factors, as well as unsafe conditions and acts. Based on this information, the identification of these causes, aiming the proposal of effective solutions to enhance safety and health management systems in mining becomes a very important issue. This thesis has studied the accidents caused by the fall of rocks in Peruvian underground mines, using as the main source of information about the fatalities occurred in 2007 in medium and large mines. This information was provided by the Oficina de Fiscalización Minera del Organismo Superior de la Inversión en Energía y Minería del Perú (OSINERGMIN), an agency under administration of the Ministry of Energy and Mines of Peru (MEM). The study shows that the majority of fatal accidents are caused by rock falls in underground excavations, and also that rock falls have accounted for 29.41% of all events during the studied period. Studying the personal characteristics of the victims also showed that the main victims are workers when they were developing drilling and preparation and installation of rock support activities in development areas as well as in production and excavations areas. The data showed that the majority died by severe multiple and cranial traumas and most of them were third part workers. From the study of the personal characteristics of victims and using the Methods of Logistic Regression (MLR), this research proposes a mathematical model to determine the chance of suffering an accident by rocks falls compared to other types of accidents. Also, the selected model showed that, from the statistical point of view, the experience in mining is the most representative variable and those workers having most of three years of experience have lower probability to suffer injuries by rock falls. Finally, the root and immediate causes of accidents were identified. Among personal and working factors the overconfidence and lack of supervision were respectively highlighted. The study also showed that non-complying operational procedures and the presence of loose rocks during the excavations are respectively the main types of unsafe acts and conditions.

Submitted by Marcos Anselmo (marcos.anselmo@unipampa.edu.br) on 2016-09-21T20:29:32Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Natana Becker.pdf: 1266893 bytes, checksum: 018f6e1bf563008837c534403e5c801e (MD5) / Approved for entry into archive by Marcos Anselmo (marcos.anselmo@unipampa.edu.br) on 2016-09-21T20:29:52Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Natana Becker.pdf: 1266893 bytes, checksum: 018f6e1bf563008837c534403e5c801e (MD5) / Made available in DSpace on 2016-09-21T20:29:52Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Natana Becker.pdf: 1266893 bytes, checksum: 018f6e1bf563008837c534403e5c801e (MD5) Previous issue date: 2015-08-21 / Os fármacos valsartana (VAL), hidroclorotiazida (HCT) e besilato de anlodipino (ANL) são utilizados em associação e comercializados no Brasil como agentes anti-hipertensivos. Geralmente a determinação simultânea destes fármacos é realizada por cromatografia líquida de alta eficiência (CLAE). Este trabalho teve por objetivo a determinação simultânea de VAL, HCT e ANL em uma formulação comercial de comprimidos através da técnica de espectroscopia no infravermelho próximo com transformada de Fourier e acessório de esfera de integração (FT-NIR) associadas a métodos de análise multivariada. Os modelos de calibração foram construídos utilizando mínimos quadrados parciais (PLS) e seleção de variáveis através dos algoritmos mínimos quadrados parciais por intervalo (iPLS) e mínimos quadrados parciais por sinergismo de intervalos (siPLS). Um total de 36 amostras sintéticas e 1 amostra real (26 amostras para o conjunto de calibração e 11 amostras para o conjunto de previsão), foram utilizadas as faixas de concentração de 261,9-500,0 mg g-1 para VAL; 20,2-83,3 mg g-1 para HCT e 11,6-49,6 mg g-1 para ANL. Os dados espectrais foram adquiridos na faixa de 4000 a 10000 cm-1 com resolução de 4 cm-1 por FT-NIR. Os melhores modelos foram obtidos através da utilização do pré-processamento centrado na média (CM) e do tratamento de correção do espalhamento de luz (MSC). O erro relativo de previsão (RSEP%) de 1,27% para VAL, 1,92% para HCT e 5,19%para ANL, foi obtido após seleção dos melhores intervalos por siPLS para dados obtidos por FT-NIR. Não foi encontrada diferença significativa (teste t-pareado, 95% de confiança) entre os valores do método de referência e do método proposto. Os resultados mostraram que modelos de regressão PLS (associados a métodos de seleção de variáveis, como iPLS e siPLS) combinados com FT-NIR são promissores no desenvolvimento de metodologias mais simples, rápidas e não destrutivas. Estes modelos permitem a determinação simultânea de VAL, HCT e ANL na formulação farmacêutica. / Valsartan (VAL), hydrochlorothiazide (HCT) and amlodipine besylate (ANL) drugs are used in combination and they are commercialized in Brazil as antihypertensive agents. Generally, the simultaneous determination of these drugs is carried out by high performance liquid chromatography (CLAE). This study aimed to the simultaneous determination of VAL, HCT, and ANL in a comercial tablet formulation through the technique near infrared spectroscopy with Fourier transform and integrating sphere accessory (FT- NIR) associated with methods of multivariate analysis. The calibration models were built using partial least squares (PLS) and variable selection through partial least squares algorithms for interval (iPLS) and partial least squares by synergism intervals (siPLS). A total of 36 synthetic samples 1 and commercial sample (26 samples for the calibration sample set and 11 for the prediction set), were used the concentration ranges of 261.9-500.0 mg g-1 for VAL; 20.2-83.3 mg g-1 for HCT and 11.6-49.6 mg g-1 for ANL. The spectral data were acquired in the range 4000-10000 cm-1 with resolution of 4 cm-1 by FT-NIR. Multiplicative scatter correction (MSC) and the data centered in the media (CM) produced the best models. A relative standard error of prediction (RSEP%) of 1.27% for VAL, 1.92% for HCT and 5.19% for ANL was obtained after selection of the best intervals for data obtained by siPLS FT-NIR. There was no significant difference (paired t-test, 95% confidence) between the values of the reference method and the proposed method. Results showed that PLS models regression (associated with iPLS and siPLS regression models) combined with FT-NIR are promising in the development of simpler methods, rapid and non-destructive. These models allow simultaneous determination of VAL, HCT, and ANL in the pharmaceutical formulation.

A dependência de países em vias de desenvolvimento com relação às indústrias primárias como a mineração é evidente. Na economia peruana, aproximadamente, 6% do PIB e mais de 50% das exportações são provenientes desta atividade econômica, destacando sua posição competitiva no cenário mundial. A importância desta atividade aparece, também, quando o assunto em questão é a segurança do trabalho. Assim, embora nos últimos anos tenha-se percebido uma diminuição no número de acidentes na mineração peruana, a taxa de mortalidade ainda é alta quando comparada com outros países de tradição mineira, especialmente os mais desenvolvidos. No Peru, oficialmente, as causas fundamentais para a ocorrência de acidentes são atribuídas aos fatores pessoais e de trabalho, assim como às condições e aos atos inseguros. Nesse contexto, a identificação dessas causas, visando à proposta de soluções efetivas para melhor gerenciar os sistemas de segurança e de saúde na indústria da mineração, é muito importante. Esta tese estuda os acidentes por queda de rochas em minas subterrâneas do Peru. Para tal foi utilizado como fonte de informação primária o registro de acidentes fatais de 2007 em minas de médio e grande porte. Esse registro foi concedido pela Oficina de Fiscalización Minera del Organismo Superior de la Inversión en Energía y Minería del Peru (OSINERGMIN), órgão pertencente ao Ministério de Energía y Minas del Perú (MEM). O estudo mostra que a maioria dos acidentes fatais são provocados pela queda de rochas em escavações subterrâneas; assim, no período em estudo, este tipo de acidente representou 29,41% dos eventos. O estudo das características pessoais das vítimas mostra ainda que trabalhadores que desenvolvem funções de perfuração, preparação e instalação de suporte pós-desmonte tanto em frentes de lavra de produção quanto em escavações de desenvolvimento morrem por causa de traumatismos múltiplos e encefalo-cranianos severos. A maioria das vítimas pertencia a empresas mineiras terceirizadas. A partir do estudo das características pessoais das vítimas e utilizando os Métodos de Regressão Logística (MRL), propõe-se um modelo matemático para determinar a chance de se sofrer acidente por queda de rochas, em relação a outros tipos de acidentes. Os resultados mostram que trabalhadores que desempenham a função de ajudante, bem como trabalhadores com experiência de mais de três anos têm menos chance de sofrer acidentes por queda de rochas. Finalmente, foram identificados as causas fundamentais e imediatas dos acidentes estudados. Entre os fatores pessoais e de trabalho destacam-se o excesso de confiança e a supervisão deficiente como sendo as principais causas deste tipo de acidente. O estudo mostra também que o descumprimento de procedimentos operacionais e a presença de rochas soltas nas escavações constituem os principais tipos de atos e condições inseguras, respectivamente. / There are several evidences that developing countries depend on primary industries like mining. In fact about 6% of the Peruvian Gross Domestic Product (GDP) and 50% of exports are provided by mining. As well as in economy, mining has been strongly affecting the statistics concerning the safety in the workplace. Thus, although in recent years there was a decrease in the number of mining accidents in Peruvian mining, the fatality rate is still high compared to other traditional mining countries, especially the developed ones. In Peru, according to official statements, the primary causes of the accidents are attributed to personal and work factors, as well as unsafe conditions and acts. Based on this information, the identification of these causes, aiming the proposal of effective solutions to enhance safety and health management systems in mining becomes a very important issue. This thesis has studied the accidents caused by the fall of rocks in Peruvian underground mines, using as the main source of information about the fatalities occurred in 2007 in medium and large mines. This information was provided by the Oficina de Fiscalización Minera del Organismo Superior de la Inversión en Energía y Minería del Perú (OSINERGMIN), an agency under administration of the Ministry of Energy and Mines of Peru (MEM). The study shows that the majority of fatal accidents are caused by rock falls in underground excavations, and also that rock falls have accounted for 29.41% of all events during the studied period. Studying the personal characteristics of the victims also showed that the main victims are workers when they were developing drilling and preparation and installation of rock support activities in development areas as well as in production and excavations areas. The data showed that the majority died by severe multiple and cranial traumas and most of them were third part workers. From the study of the personal characteristics of victims and using the Methods of Logistic Regression (MLR), this research proposes a mathematical model to determine the chance of suffering an accident by rocks falls compared to other types of accidents. Also, the selected model showed that, from the statistical point of view, the experience in mining is the most representative variable and those workers having most of three years of experience have lower probability to suffer injuries by rock falls. Finally, the root and immediate causes of accidents were identified. Among personal and working factors the overconfidence and lack of supervision were respectively highlighted. The study also showed that non-complying operational procedures and the presence of loose rocks during the excavations are respectively the main types of unsafe acts and conditions.

No description available.