Com o advento da Internet, novas tecnologias de Geoprocessamento têm sido criadas para tratar, trocar, disponibilizar e acessar informações e dados espaciais como as Infraestruturas de Dados Espaciais, os Geobrowsers e os Serviços de Informações Geográficas. O número de aplicações que lida com atributos espaciais não somente cresceu como também se diversificou, no entanto problemas de interoperabilidade semântica ainda limitam a evolução nessa área. Ontologias formalizam o conhecimento necessário para organizar e permitir o uso de dados de diferentes fontes, pois são modelos de referência robustos devido à sua expressividade e capacidade de serem compartilhados pela internet. Nesta tese, ontologias foram desenvolvidas para aumentar a interoperabilidade semântica em sistemas de apoio à logística humanitária que utilizam serviços de informações geográficas. A aplicação de logística humanitária estudada foi a operação de bancos de alimentos. O conhecimento relativo aos processos logísticos, problemas logísticos de bancos de alimentos e serviços de informações geográficas úteis para os métodos de solução desses problemas foram formalizados em uma rede de ontologias. O número de serviços de informações geográficas disponíveis ainda é pequeno, mas a composição de serviços de informações geográficas em cadeias, formando novos serviços, abre uma perspectiva para que operações espaciais, não fiquem mais restritas às soluções proprietárias e possam ser melhor exploradas. As ontologias propostas neste trabalho podem ser usadas como base para o desenvolvimento de sistemas em que serviços de informações geográficas podem ser acoplados, na medida em que forem sendo disponibilizados. / With the advent of Internet, new technologies from GIS field have been created to access and exchange spatial data and information, such as Spatial Data Infrastructures, Geobrowsers and Geographic Information Services. The number of applications dealing with spatial attributes is growing and is also diversifying. However semantic interoperability problems still limit progress in this area. Ontologies formalize the knowledge necessary to organize and allow the use of data from different sources due to its expressiveness and ability to be shared over the Internet. In this thesis, ontologies have been developed to increase the semantic interoperability of humanitarian logistics support systems that use geographic information services. The application of humanitarian logistics operation studied was the food banks operation. The knowledge on logistics processes, logistics problems of food banks and geographic information services useful for the methods for solving these problems have been formalized into a network of ontologies. The number of geographical information services available is still small, but the composition of geographic information services in chains, creating new services, opens a perspective for new methods of spatial operations, which are not limited to proprietary solutions form GIS softwares available in the market. The ontologies proposed in this thesis can be used as a basis for developing systems in which geographic information services can be coupled, as it becomes available.

Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

Orientador: Orlando Fontes Lima Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-23T21:07:50Z (GMT). No. of bitstreams: 1 Avoleta_AmandaQuintal_M.pdf: 2120211 bytes, checksum: 1342fc21a341b82751b3e3bd9bf99c33 (MD5) Previous issue date: 2013 / Resumo: O objetivo desse trabalho centrou-se em desenvolver um modelo para dimensionamento do número de embalagens do tipo "múltiplas viagens" (multiway), em uma cadeia de suprimentos de laço fechado. Tais embalagens retornam à cadeia depois de acondicionarem o produto ao seu destino final. Em seu retorno passam por um processo de manutenção (reparo, limpeza), para voltarem a sua forma original e, então, são estocadas para próximo uso. Uma embalagem, em seu ciclo de acondicionamento do produto, está sujeita a incertezas associadas aos seguintes fatores: demanda do produto final; tempo de viagem com o produto; tempo de permanência no ponto de consumo; tempo de retorno ao ponto de reuso; tempo de manutenção para o próximo uso; e sua indisponibilidade afeta significativamente o nível de atendimento ao cliente. Embora exista um vasto material sobre o gerenciamento da cadeia direta, os estudos sobre cadeia reversa são mais raros e se prendem principalmente a estudos de configurações para o fluxo em retorno e seus custos. No dimensionamento do número ideal de embalagens para atendimento do consumidor, com um nível adequado de suprimento, não são encontrados trabalhos que considerem a questão da aleatoriedade dos tempos de viagem relacionada à gestão dessas embalagens. A proposta deste trabalho consistiu em estabelecer um procedimento para os tratamentos dos dados, visto que são aleatórios, e disponibilizar um modelo simulação que auxiliasse a gestão de cadeias com embalagens multiway. A aplicação é feita em uma cadeia voltada para o comércio de flores, pela necessidade de acondicionamento destas em embalagens que as protejam de danos durante o período de manipulação e viagens. Por questões econômicas, são utilizadas embalagens retornáveis que, após serem utilizadas, reintegram-se à cadeia de suprimentos, tornando-a uma cadeia de suprimentos de laço fechado / Abstract: The purpose of this research was to develop a model to quantify the number of multiway packaging, which involves several journeys within a closed loop supply chain. This type of packaging returns to the chain after the goods reach their final destination. Subsequently the packaging proceed to a maintenance process (repair, cleaning) in order to return to its original condition and then be stocked for future use. The storage cycle of a package is subject to uncertainties associated to several factors such as the demand of the final product; the time spent on carriage; the dwell time at the consumption site; the time spent until it returns to the reusing site, and the time of maintenance for next use. The unavailability of the package affects meaningfully the customer attendance level. Although there is a wide set of studies on managing direct chains, studies about the reverse logistics are scarcer and mostly focused on the return process flow configuration and its costs. Regarding the definition of the ideal number of packaging to assist customers with proper supplies, it has neither been found any study that considers the randomization of the time spent on the transportation nor on the management of these packages. The aim of this analysis was to establish a protocol to manipulate data, considering it as being arbitrary, and then to release a simulation model which would benefit managing the multiway packaging chain. The application was done in a chain of a flower business, due to their need of keeping their product in a suitable package that had to protect it from damages during transportation and handling. For economical reasons, returnable packages that can be reinstated to the supply chain are used so that the process is turned into a closed loop / Mestrado / Transportes / Mestra em Engenharia Civil

Doctor of Philosophy / Department of Statistics / Shie-Shien Yang / Logistic regression model is a branch of the generalized linear models and is widely used in many areas of scientific research. The logit link function and the binary dependent variable of interest make the logistic regression model distinct from linear regression model. The conclusion drawn from a fitted logistic regression model could be incorrect or misleading when the covariates can not explain and /or predict the response variable accurately based on the fitted model- that is, lack-of-fit is present in the fitted logistic regression model. The current goodness-of-fit tests can be roughly categorized into four types. (1) The tests are based on covariate patterns, e.g., Pearson's Chi-square test, Deviance D test, and Osius and Rojek's normal approximation test. (2) Hosmer-Lemeshow's C and Hosmer-Lemeshow's H tests are based on the estimated probabilities. (3) Score tests are based on the comparison of two models, where the assumed logistic regression model is embedded into a more general parametric family of models, e.g., Stukel's Score test and Tsiatis's test. (4) Smoothed residual tests include le Cessie and van Howelingen's test and Hosmer and Lemeshow's test. All of them have advantages and disadvantages. In this dissertation, we proposed a partition logistic regression model which can be viewed as a generalized logistic regression model, since it includes the logistic regression model as a special case. This partition model is used to construct goodness-of- fit test for a logistic regression model which can also identify the nature of lack-of-fit is due to the tail or middle part of the probabilities of success. Several simulation results showed that the proposed test performs as well as or better than many of the known tests.

Traffic from major hurricane evacuations is known to cause severe gridlocks on evacuation routes. Better prediction of the expected amount of evacuation traffic is needed to improve the decision-making process for the required evacuation routes and possible deployment of special traffic operations, such as contraflow. The objective of this dissertation is to develop prediction models to predict the number of daily trips and the evacuation distance during a hurricane evacuation. Two data sets from the surveys of the evacuees from Hurricanes Katrina and Ivan were used in the models' development. The data sets included detailed information on the evacuees, including their evacuation days, evacuation distance, distance to the hurricane location, and their associated socioeconomic characteristics, including gender, age, race, household size, rental status, income, and education level. Three prediction models were developed. The evacuation trip and rate models were developed using logistic regression. Together, they were used to predict the number of daily trips generated before hurricane landfall. These daily predictions allowed for more detailed planning over the traditional models, which predicted the total number of trips generated from an entire evacuation. A third model developed attempted to predict the evacuation distance using Geographically Weighted Regression (GWR), which was able to account for the spatial variations found among the different evacuation areas, in terms of impacts from the model predictors. All three models were developed using the survey data set from Hurricane Katrina and then evaluated using the survey data set from Hurricane Ivan. All of the models developed provided logical results. The logistic models showed that larger households with people under age six were more likely to evacuate than smaller households. The GWR-based evacuation distance model showed that the household with children under age six, income, and proximity of household to hurricane path, all had an impact on the evacuation distances. While the models were found to provide logical results, it was recognized that they were calibrated and evaluated with relatively limited survey data. The models can be refined with additional data from future hurricane surveys, including additional variables, such as the time of day of the evacuation.

SME exporters constitute a critical economic force that contributes significantly to national productivity and job creation in the Canadian economy. However, the academic literature suggests that female-owned SMEs are less likely to export. With lower export propensity, the potential of female-owned SMEs for organic growth, economic self-sufficiency and wealth creation could be comprised. This paper applies logistic regression to study factors that influence SME owners’ export propensity with particular reference to the moderating effect of gender in the context of the Ajzen and Fishbein ’s (2005) theory of Reasoned Action and Planned Behavior. We improve the methodology of prevailing research by redefining “gender” in a more appropriate way and by computing gender interaction effects more accurately. Based on this analysis, we found that, although male- and female-owned SMEs show different likelihoods of exporting, gender does not have a direct residual impact. Instead, systemic gender differences account for most differences in the export propensity between male-owned and female-owned SMEs. Specifically, female-owned SMEs may be systemically disadvantaged because their firms are smaller, more limited in management capacity with younger and less-experienced managers. The lack of resources and market knowledge become constraining factors for them with respect to becoming “export-ready”. Additionally, female SME owners show a higher perception of risk and financing difficulty (although they do not encounter higher rejection rates of financing applications). Their subjective perceptions of potential barriers may contribute to their reluctance to export.

El presente trabajo sobre “Desarrollo de un modelo de simulación para la distribución de mercaderías importadas en la industria Retail en Lima” fue incentivado debido a que actualmente en el Perú, el costo logístico al importar tiene un impacto negativo en empresas retail lo cual lleva a que el nivel de competitividad y eficacia en el proceso logístico no sea óptimo. El objetivo general de la investigación es desarrollar un modelo de distribución que permita reducir los costos de traslado de mercadería desde el almacén aéreo hasta el punto de venta asignado de la empresa. A través de investigación en libros, artículos y páginas web relacionados al tema se pudo desarrollar las bases para la elaboración de la tesis, alcanzando un conocimiento respecto a la definición de variables de decisión, simulación de sistemas y modelo de optimización. Con ello, se procedió a la recolección de datos muestrales de una de las empresas más reconocidas mundialmente en el sector; y que, a su vez cuenta con una importante participación de mercado a nivel local. Por consiguiente, se empezaron las simulaciones tomando como punto de inicio el arribo de la mercadería al almacén aéreo para posteriormente finalizar con el despacho a cada local asignado. Conforme se desarrolló la simulación, se fue formulando un modelo de optimización que permitió cumplir con los objetivos planteados. Finalmente, se obtuvieron conclusiones respecto a lo investigado y se establecieron recomendaciones que aporten a la efectividad del modelo propuesto, siempre analizando las posibles mejoras que puedan surgir a futuro. / The present paper is based on the “Creation of a simulation model for the distribution of imported merchandise in the Retail Industry in Lima, Perú. It was incentivized on the fact that, actually, in Perú, the logistic cost of importation has a negative impact on the retail companies. This leads to the decrease of the optimum level of competitivity and efficacy in the logistic process. The general objective of this investigation, is to develop a distribution model which allows to reduce the merchandise transfer costs from the aerial warehouse to the point of sale assigned by the company. Through the investigation based on books, articles and web pages related to the subject of the present paper, it was able to develop the foundation for the elaboration of this thesis. It also allowed us to obtain a deeper knowledge on the definition of decision variables, systems simulation and optimization models. With all this information, we proceeded to collect the sample data of one of the most worldwide recognized companies of the retail sector. This company also has an important market participation at a local level. Therefore, we started the simulations taking as a starting point, the arrival of the merchandise to the aerial warehouse. And, as a final point, we took the dispatch of the merchandise to each point of sale assigned. As the investigation began to take shape, we started to formulate an optimization model which allowed us to reach the defined objectives. Finally, we obtained conclusions in regard of the investigation and we established recommendations which contribute to the effectivity of the proposed model. In this process, we always considered the possible improvements that could arise in the future. / Tesis

The study aimed to developed a logistic model based on antibody proteins, vaccinations and demographic factors that predicts previous infection in Covid-19. The data set comprised of 2750 individuals from eldercare homes in Sweden, with four test dates executed between October of 2021 and August of 2022.  Exploratory data analysis revealed bimodal patterns in the antibodies against nucleocapsid protein within the non-infected group, raising suspicions of false negatives in the data. Due to the binary nature of the response and to be interpretable for further research, logistic regressions were used to model the relation between predictors and the logit of the response. Because of low performance scores and high probability for the presence of false negatives, K-means clustering algorithm was performed on the data. As a clustering variable, the logarithm of base 2 of the nucleocapsid protein was used, because of its theoretical relationship with previous infection in Covid-19.  Observations were reclassified using the clustering technique, and two new logistic models were fitted to the data. The final model contained polynomial terms to handle the non-linear relationship between the logit of the response and the predictors. We found a significant relationship between the logarithm of 2 of nucleocapsid protein and previous Covid-19 infection in the final model, with high prediction results. We reached an F1-score of 0.94, indicating a well-performing model.  Additionally, an algorithm was created to predict the days since infection, involving the change in nucleocapsid protein from one test date to the next, and a GAM model for fitting a smooth line to the data between nucleocapsid protein as response against the days since infection. Using this algorithm, we reached an absolute mean error between predicted results and actual days since infection of 23 days. This algorithm was later applied to observations reclassified in the clustering process.  In conclusion, the study successfully reclassified false negative observations with previous Covid-19 infection, and fitted a logistic model with high prediction score with F1-score of 0.94. Finally, an algorithm was created that estimated the days since infection with an absolute mean error of 23 days. / Syftet med studien var att utveckla en logistisk modell baserad på antikroppsproteiner, vaccinationer och demografiska faktorer som förutsäger tidigare infektion i Covid-19. Datamängden bestod av 2750 individer från äldreboenden i Sverige, med fyra testdatum utförda mellan oktober 2021 och augusti 2022.  Utforskande dataanalys visade på bimodala mönster i antikroppar mot nukleokapsidprotein inom den icke- infekterade gruppen, vilket gav upphov till misstankar om falskt negativa resultat i datamaterialet. På grund av svarets binära karaktär och för att vara tolkningsbara för vidare forskning användes logistiska regressioner för att modellera förhållandet mellan prediktorer och responsvariabeln. På grund av låga prediktionsresultat och hög sannolikhet av förekomsten av falskt negativa svar utfördes K-means-klusteralgoritmen på datat. Som klustervariabel användes logaritmen av bas 2 för nukleokapsidproteinet, på grund av dess teoretiska samband med tidigare infektion i Covid-19.  Observationerna omklassificerades med hjälp av klustertekniken, och två nya logistiska modeller anpassades till datat. Den slutliga modellen innehöll polynomiala termer för att hantera det icke-linjära förhållandet mellan responsens logit och prediktorerna. Vi fann ett signifikant samband mellan logaritmen av 2 av nuk- leokapsidprotein och tidigare Covid-19-infektion i den slutliga modellen, med ett högt prediktionsresultat. Vi nådde en F1-score på 0.94.  Dessutom skapades en algoritm som predicerade dagar sedan infektion med hjälp av förändringen i nukleokap- sidprotein från ett testdatum till nästa, och en GAM-modell för att anpassa ett glidande medelvärdeslinje till datat mellan nukleokapsidprotein som response mot dagarna sedan infektionen. Med hjälp av denna algoritm nåddes ett absolut medelfel på 23 dagar mellan prediktion och faktiskt tid sedan infektionen. Denna algoritm tillämpades senare på observationer som omklassificerats i klusterprocessen.  Sammanfattningsvis lyckades studien framgångsrikt omklassificera falskt negativa observationer med tidigare Covid-19-infektion och anpassade en logistisk modell med hög prediktionspoäng med en F1-score på 0.94. Slutligen skapades en algoritm som uppskattade dagarna sedan infektionen med ett absolut medelfel på 23 dagar.

Past studies concerning the effects of economic conditions on voter perceptions have tended to generalize their findings to the entire national electorate. Such generalizations fail to account for the different ideologies, lifestyles, and economic conditions that exist from state to state. In the current study, I compare the effects of subjective financial evaluations with the effects of objective economic indicators on voter perceptions and turnout in the state of Mississippi. The purpose is to determine the extent to which past findings on the national level hold up on the state level, with Mississippi as the subject of analysis. Using data from the Mississippi Poll and employing a logistic regression method, the findings show that Mississippian‟s perceptions of political figures are more strongly influenced by subjective financial evaluations. Voter turnout, on the other hand, was more strongly influenced by objective economic indicators than personal financial satisfaction.

No description available.