A logística tem se revelado um elemento estratégico, possibilitando a diferenciação no atendimento às necessidades dos clientes. Assim sendo, a terceirização das atividades logísticas se tornou uma realidade, e isto tem levado as organizações a se focarem mais em suas competências essenciais. Em função disso, surgem os operadores logísticos, que contribuem para a competitividade das empresas parceiras mediante a prestação de serviços especializados. Por conseguinte, o estudo analisou a estruturação, o desenvolvimento e a evolução da transformação de uma transportadora tradicional em um operador logístico. A metodologia de pesquisa utilizada pode ser considerada como sendo qualitativo exploratória, implementada por meio de um estudo de caso único. Para a coleta de dados, foram realizadas entrevistas individuais em profundidade, com uma abordagem semiestruturada, a partir da aplicação de roteiros básicos de questões. Além disso, foram utilizados dados provenientes da pesquisa documental. As técnicas utilizadas para análise dos resultados são compreendidas pela análise de conteúdo. Percebeu-se, após a concretização do estudo, que a organização, ao agregar serviços, procurando relacionamentos de longo prazo com seus clientes, aumentou sua competitividade. Verificou-se, ainda, que tal mudança oportunizou vantagens como a otimização dos recursos disponíveis e a melhoria na qualidade dos serviços prestados. A transformação da empresa em um operador logístico repercutiu em maior credibilidade e na ampliação do seu mix de serviços, resultando no aumento de seu faturamento e da sua lucratividade e em um diferencial perante a concorrência. / Submitted by Marcelo Teixeira (mvteixeira@ucs.br) on 2014-04-28T14:19:34Z No. of bitstreams: 1 Dissertacao Tania Craco.pdf: 2591792 bytes, checksum: 90a813103bcf283c34fbf377e7f79ee0 (MD5) / Made available in DSpace on 2014-04-28T14:19:34Z (GMT). No. of bitstreams: 1 Dissertacao Tania Craco.pdf: 2591792 bytes, checksum: 90a813103bcf283c34fbf377e7f79ee0 (MD5) / The logistic has been revealed an strategic element, making possible the differentiation on assistance to customers necessities. So, logistics operation outsourcing turns real, and it gets the organizations to focus on theirs essential competences. Due to that, logistic operators appear, who contribute for the competitiveness of partner companies by specialized services provided. Therefore, the study analyzed organization, development and evolution of the transformation of a traditional transporter in a logistic operator. The methodology used on this research can be considered as being qualitative-exploratory, implemented by a study of one only case. To collect dates, individual interviews were done in depth, with a structured way approached, from the application of basic routes of issues. In addition, data were used from documental research. The techniques used to analyze the results are understood by content analysis. It was realized, after the study complete, the organization increased competitiveness, aggregating services and looking for long relationships with the customers. It was found that such change developed advantages as available resources optimization and improvement in the quality of services provided. The company transformation into a logistic operator reflected in greater credibility and expansion of the services offered, resulting in turnover and profitability increase and a differential against the competitors.

[pt] Este trabalho analisou o problema do reparte do jornal - A Gazeta - aos seus pontos de venda. Para isto, foi feito um levantamento de publicações na área de Sistemas de Distribuição e Métodos de Previsão de Demanda. Além disso, foi feito também um levantamento, em termos logísticos, do atual sistema de distribuição e de previsão de demanda por jornais da S/A A Gazeta, empresa que produz o jornal estudado. Através da análise da série de dados históricos de venda líquida do jornal em um determinado ponto de venda, determinou-se um modelo que possibilita a previsão de demanda por jornais neste ponto de venda para cada dia da semana. Para a determinação do modelo desejado, foi utilizado o método de Séries Temporais de Box & Jenkins aplicado ao software SPSS, versão 8.0. Foram encontrados alguns modelos que se adequam à solução do problema. Em seguida, foi feita a análise dos resultados dos dois melhores modelos. Com o modelo considerado ótimo, um ARIMA (1,0, 1) (1, 1, 1)7, será possível melhorar o processo de reparte de jornal aos pontos de venda,minimizando a quantidade de jornais encalhados por ponto de venda e mantendo, ao mesmo tempo, o atendimento da demanda. Isto trará benefícios no sistema de distribuição de jornais aos pontos de venda,devido à maior precisão nas quantidades de jornal repartidas. / [en] This report has analyzed the issue of distributing the newspaper A Gazeta to sales stands. As a first step, a research was done on circulation within the areas of Distribution Systems and Demanding Forecast Methods. Besides, a study on logistic grounds was carried out about the current system of distribution and demanding forecast by S/A A Gazeta, which produces the newspaper subject of this study. Through the analysis of a series on historical data of the newspaper net sales at a specific sale stand, a pattern was developed, which enables a more precise forecast of the newspaper daily demands at these stand. Series Temporais by Box & Jenkins applied to the software SPSS, version 8.0, was used to determine the desired pattern. Some patterns were found which match to the solution of the problem. Then, an analysis on the result of the two best patterns was made. Since the pattern was agreed to be the ideal one, an ARIMA (1,0,1) (1,1,1)7, it will be possible to improve the newspaper distribution process to the sales stands,lessening the amount of stuck newspapers at each stand, as well as, to meet the demands due to a more precise distribution to the sales stands in terms of quantity. / [es] Este trabajo analisó el problema de la entrega de periódicos - La Gazeta - a los puntos de venta. Para esto, se realizó un levantamiento de publicaciones en el área de Sistemas de Distribución y Métodos de Previsión de Demanda. Además se realizó un levantamiento, en términos logísticos, del actual sistema de distribución y de previsión de demanda por diarios de la S/LA LA Gazeta, empresa que produce el periódico estudiado. A través del análisis de la serie de datos históricos de venta líquida del periódico en un determinado punto de venta, se determinó un modelo que posibilita la previsión de demanda por periódicos en este punto de venta para cada dia de la semana. Para la determinación del modelo deseado, se utilizó el método de Series de Tiempo de Box & Jenkins aplicado al software SPSS, versión 8.0. Se encontraron algunos modelos adecuados a las solución del problema. Seguidamente, se efectuó el análisis de los resultados de los dos mejores modelos. Con el modelo considerado óptimo, un ARIMA (1,0, 1) (1, 1, 1)7, será posible mejorar el proceso de entrega de periódicos a los puntos de venta, minimizando la cantidad de periódicos sobrantes por punto de venta y manteniendo, al mismo tiempo, el atendimiento de la demanda.

Orientador: Carlos Roberto Minussi / Resumo: Os resíduos sólidos são considerados um dos maiores problemas ambientais do mundo e, consequentemente o gerenciamento destes resíduos também se tornaram um problema. Entre eles estão os resíduos de equipamentos eletroeletrônicos (REEE). Dar um destino adequado ao REEE tornou-se essencial. A logística reversa, ou os canais reversos de distribuição, surgem como uma alternativa viável para isso. A redução da geração, o descarte correto desses resíduos e o reaproveitamento dos principais elementos recicláveis como insumo industrial devem ser prioridade de qualquer sistema de gerenciamento de resíduos seja público ou privado, para a preservação do meio ambiente e da saúde pública. Na presente pesquisa, o objetivo foi definir um modelo de rede reversa para REEE, buscando localizar as instalações de pontos de entrega voluntária (PEVs), o centro de triagem e o centro de tratamento, para que se alcance o menor custo de transporte. A metodologia desenvolvida ofereceu um modelo de localização com o suporte de um Sistema de Informação Geográfica (SIG), que permitiu a análise espacial dos dados, de um Processo de Análise Hierárquica (AHP) por meio do uso de conjunto de pesos para os critérios analisados e da comparação pareada, proporcionou grande flexibilidade no procedimento de agregação e da lógica fuzzy, considerada uma ferramenta eficiente para lidar com a incerteza e a subjetividade da rede reversa proposta. Estruturou-se a rede para um sistema logístico de coleta de REEE consideran... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

Rapid deployment of terrestrial wind power plants (WPPs) is a function of accurate identification of areas suitable for WPPs. Efficient WPP site prospecting not only decreases installation lead time, but also reduces site selection expenses and provides faster reductions of greenhouse gas emissions. Combining conventional predictor variables, such as wind strength and proximity to transmission lines, with nonconventional socioeconomic and demographic predictor variables, will result in improved identification of suitable counties for WPPs and therefore accelerate the site prospecting phase of wind power plant deployment. Existing and under-construction American terrestrial WPPs located in the top 12 windiest states (230 as of June 2009) plus 178 potential county level predictor variables are introduced to logistic regression with stepwise selection and a random sampling validation methodology to identify influential predictor variables. In addition to the wind resource and proximity to electricity transmission lines, existence of a Renewable Portfolio Standard, the population density within a 200 mile radius of the county center, median home values, and farm land area in the county are the four strongest nonconventional predictors (Hosmer and Lemeshow Chi-Square = 9.1250, N = 1009, df = 8, p = 0.3319, - 2LogLikelihood = 619.521). Evaluation of the final model using multiple statistics, including the Heidke skill score (0.2647), confirms overall model predictive skill. The model identifies the existence of 238 suitable counties in the twelve state region that do not possess WPPs (~73% validated overall accuracy) and eliminates 654 counties that are not classified as suitable for WPPs. The 238 counties identified by the model represent ideal counties for further exploration of WPP development and possible transmission line construction. The results of this study will therefore allow faster integration of renewable energy sources and limit climate change impacts from increasing atmospheric greenhouse gas concentrations.

Brain injury has a tremendous effect on the United States. The medical system has a continuum of care available but many of these services are extremely expensive. Despite the effectiveness of residential post-acute brain injury rehabilitation (PABIR) resistance to provide adequate funding remains because of a dearth of randomized controlled trial (RCT) studies demonstrating effectiveness. Some research suggests observational trials are typically more representative of community samples and yield conclusions similar to RCT studies. This study uses a large multi-state naturalistic community-based sample of individuals who received residential PABIR. The purposes of this study were to (1) use logistic regression to identify a model that considered the relationships among the predictor variables to explain treatment outcome for individuals receiving residential PABIR and (2) better understand how self-awareness influences treatment outcome. The final model contained five independent variables (substance use at time of admit, functioning level at time of admit, change in awareness between discharge and admit, admit before or after 6 months post-injury (TPI), and length of stay (LOS) in the program less than or greater than 2 months). The model was statistically significant, ÷2 (5, N=434) = 194.751, p < .001, accounting for 36.2% (Cox & Snell R square) to 61.3% (Nagelkerke R square) of the variance in success rate, and correctly classified 89.4% of cases. Four of the five predictor variables (current substance use, change in awareness, LOS 2 months and TPI 6 months) made statistically significant contributions to the model. The strongest predictor of successful treatment outcome was change in awareness recording an odds ratio of 29.9 indicating that individuals who improved in self-awareness by at least one level were nearly 30 times more likely to be in the successful outcome group, controlling for other factors in the model. Participants were also more likely to be in the successful outcome group if they admitted within 6-months post-injury (5.5x) and stayed longer than 2-months (4.4x). Findings also suggest that active substance use at time of admission did not prevent people from being successful. Importance and implications of these findings are discussed.

One of the greatest problems for sub-Saharan Africa is shortage of epidemiological data to support planning for provision of adequate public and animal health services. The overriding challenge is to provide the necessary resources to facilitate the process of regular data collection in support of disease surveillance and vector monitoring across target regions. Due to such circumstances, there is currently an increasing interest towards devising cheaper but yet significantly reliable means for availing the needed epidemiological and vector data for planning purpose. This study comes as a contribution towards solving such challenges. The study has three research components starting with a review of past Uganda national tsetse and trypanosomiasis control efforts as a means towards appreciating the dynamics of controlling the vector and disease. This is an analysis of what was applied, what worked, what didn't, and why it didn’t as linked to the broader vector and disease control system. Secondly through the use of remote sensing, geographical information systems and global positioning technologies tsetse species were sampled within Lake Victoria Basin. Only two species of tsetse were trapped, G. f. fG. f. fuscipes which was widely distributed across the surveyed area, and G. Pallidipes which was detected in a few isolated locations close to the border with Kenya in Eastern Uganda. The analysis of land cover with tsetse findings showed an important association between G. f. fuscipes and particular vegetation mosaics. Unfortunately, while the results are highly informative, approaches for data collection such as this one are costly and unlikely to be sustained by the already over-burdened health systems in the low developed countries of Africa. The third and main part of this study investigates, demonstrates and delivers the possibilities of applying spatial epidemiological modelling techniques to produce both tsetse distribution and abundance maps. Four spatial and non-spatial regression models (Logistic, Autologistic, Negative binomial and Auto-negative binomial), were constructed and used to predict tsetse fly presence and tsetse fly abundance for the study area. The product is an improved understanding of association between environmental variables and tsetse fly distribution/abundance and maps providing continuous representations of the probability of tsetse occurrence and predicted tsetse abundance across the study area. The results indicate that tsetse presence and abundance are influenced differently. Tsetse abundance is highly determined by river systems while tsetse presence is majorly influenced by forested landscapes. Therefore, efforts to control trypanosomiasis through vector control in the Lake Victoria basin will call for delineation of such clearly identified high tsetse accumulation zones for targeted tsetse control operations. This will ensure optimum utilization of the scarce resources and above all contribute to the protection of humans and animals against trypanosomiasis infection.

[pt] Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos parâmetros da parte linear passa a ser feita por Máxima Verossimilhança. Assim o modelo, que é paramétrico não-linear e estruturado por árvore de decisão, onde cada nó terminal representa um regime os quais têm seus parâmetros estimados da mesma forma que em uma Regressão Logística, é denominado Smooth Transition Logistic Regression-Tree (STLR-Tree). A inclusão dos regimes, determinada pela divisão dos nós da árvore, é feita baseada em testes do tipo Multiplicadores de Lagrange, que em sua forma para o caso Gaussiano utiliza a Soma dos Quadrados dos Resíduos em suas estatísticas de teste, aqui são substituídas pela Função Desvio (Deviance), que é equivalente para o caso dos modelos não Gaussianos, cuja distribuição da variável dependente pertença à família exponencial. Na aplicação a dados reais selecionou-se dois conjuntos das variáveis explicativas de cada uma das duas bases utilizadas, que resultaram nas melhores taxas de acerto, verificadas através de Tabelas de Classificação (Matrizes de Confusão). Esses conjuntos de variáveis foram usados com outros métodos de classificação existentes, são eles: Generalized Additive Models (GAM), Regressão Logística, Redes Neurais, Análise Discriminante, k-Nearest Neighbor (K-NN) e Classification and Regression Trees (CART). / [en] The main goal of this work is to adapt the STR-Tree model, which is the combination of a Smooth Transition with Regression model with Classi cation and Regression Tree (CART), in order to use it in Classification. Some changes were made in its structural form and in the estimation. Due to the fact we are doing binary dependent variables classification, is necessary to use the techniques employed in Logistic Regression, so the estimation of the linear part will be made by Maximum Likelihood. Thus the model, which is nonlinear parametric and structured by a decision tree, where each terminal node represents a regime that have their parameters estimated in the same way as in a Logistic Regression, is called Smooth Transition Logistic Regression Tree (STLR-Tree). The inclusion of the regimes, determined by the splitting of the tree's nodes, is based on Lagrange Multipliers tests, which for the Gaussian cases uses the Residual Sum-of-squares in their test statistic, here are replaced by the Deviance function, which is equivalent to the case of non-Gaussian models, that has the distribution of the dependent variable in the exponential family. After applying the model in two datasets chosen from the bibliography comparing with other methods of classi cation such as: Generalized Additive Models (GAM), Logistic Regression, Neural Networks, Discriminant Analyses, k-Nearest Neighbor (k-NN) and Classification and Regression Trees (CART). It can be seen, verifying in the Classification Tables (Confusion Matrices) that STLR-Tree showed the second best result for the overall rate of correct classification in three of the four applications shown, being in all of them, behind only from GAM.

Introduction: Obstructive sleep apnea (OSA) is a common breathing disorder with numerous health consequences, including greater risk of complications perioperatively. Undiagnosed OSA is known to place surgical patients at a higher risk of serious adverse events, including stroke and death. Polysomnography (PSG) assessment is the current gold standard test for diagnosing OSA. However, due to the significant time commitment and cost associated with PSG, a substantial number of OSA patients go undiagnosed before the perioperative period. Although the STOP-Bang questionnaire screening tool is currently used to help detect OSA patients, the low specificity to screen people without the disease is considered a major limitation. There is a clear need to develop a quick and effective prediction rule with higher overall accuracy to help streamline OSA diagnosis. Tracheal breathing sound analysis in awake patients at the bedside has shown potential to screen OSA patients with higher specificity compared to the STOP-Bang questionnaire. To date, no screening tools exist to detect OSA patients that combine the results of breathing sound analysis and STOP-Bang. Objectives: The present study aimed to develop a prediction rule, using both breathing sound analysis and variables in the STOP-Bang questionnaire, to better streamline the diagnosis of OSA. Methods: This prospective cohort study recruited patients referred for PSG at the Ottawa Hospital Sleep Centre from November 2016 to May 2017. The study conduct was approved by the Ottawa Health Science Network Research Ethics Board (#20160494-01H). After obtaining informed consent, anthropomorphic, breathing sound recordings, and STOP-Bang questionnaire data was collected from over 400 consenting patients. All patients that met the eligibility criteria were included. The breathing sound analysis and STOP-Bang results were utilized to design a prediction rule using logistic regression. Sensitivity, specificity, and likelihood ratio were used to compare the diagnostic performance of the final model. Results: Of the 439 consenting study participants, 280 study participants data were eligible for inclusion in the logistic regression analysis. Physician sleep specialists diagnosed 114 participants (41%) with moderate-to-severe OSA and 166 participants (59%) with normal-to-mild OSA. At a predicted probability of moderate-to-severe OSA greater than or equal to 0.5, breathing sound analysis had a similar sensitivity of 75.9 (95%CI; 65.4, 82.0) and higher specificity of 74.5% (95%CI; 68.5, 82.0) when compared to STOP-Bang with a sensitivity and specificity of 68.4% (95%CI; 58.9, 76.6) and 63.2% (95%CI: 55.0, 70.1), respectively. The sensitivity and specificity for the Safe-OSA rule, obtained by combining breathing sound analysis and STOP-Bang variables, were determined to be 75.4% (95%CI; 65.4, 82.0) and 74.5% (95%CI; 68.5, 82.0), respectively. A sensitivity analysis using a likelihood ratio test showed that breathing sound analysis contributed significantly to the performance of the Safe-OSA rule. The Safe-OSA rule was determined to be reasonably discriminative and well calibrated. The five-fold cross-validation showed similar results for the final model in the derivation and testing subsamples, which provides support for the internal validity of the Safe-OSA rule in our study population. Conclusion: The present study lends further support for the future testing of tracheal breathing sound analysis as a potential method to screen for moderate-to-severe OSA to help streamline patient care in the perioperative setting. Trial registration: ClinicalTrials.gov identifier NCT02987283.

[pt] Desde a década de 1930, a tentativa de previsão de falência de empresas chama a atenção dos acadêmicos, e diversas técnicas já foram empregadas para o desenvolvimento de modelos preditivos compostos por variáveis financeiras, tais como análise estatística, modelos teóricos e de inteligência artificial. Posto isso, o referido estudo compõe um modelo de regressão logística para a previsão de falência de empresas de capital aberto no Brasil com um ano de antecedência. Para tal, apresenta uma revisão literária com as principais técnicas usadas na área, para fundamentar a escolha metodológica e as variáveis integrantes do estudo. Ademais, o modelo é testado com uma nova amostra; comparado com resultados obtidos através de outras técnicas e executado com dados anteriores a um ano do momento de falência - de tal forma que sua capacidade preditiva seja atestada. / [en] Since the thirties, academicians try to forecast bankruptcy and have been applying several techniques, such as: statistical, artificial intelligence and theoretical using financial ratios to do so. Therefore, this study presents a logistic regression model to forecast public companies bankruptcy in Brazil one year before failure. Hence, it presents a literature review with the main models used so far in order to support its methodological choice and financial ratios applied. In addition, the model is tested with a new sample, compared with another techniques results and executed with data older than one year before failure, so its predictive capacity is attested.

This mater thesis is focused on building predictive models. Their fundamental task is to provide an early-warning system, giving information about potential enterprise bankruptcy. The main essence and aim of the thesis is to create multivariate classification models by using discriminant analysis and logistic regression. Emphasis is put on their predictive accuracy, which is assessed for period of three years before bankruptcy declaration. Attempts to optimize classification thresholds in order to increase the initial accuracy are also made. Evaluating classification reliability of several existing models and performing profile analysis assessing predictive ability of univariate ratios were accomplished as well.