京都大学 / 新制・課程博士 / 博士(医学) / 甲第25182号 / 医博第5068号 / 新制||医||1071(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 今中 雄一, 教授 西浦 博, 教授 村井 俊哉 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Doctor of Philosophy / Department of Civil Engineering / Stefan A. Romanoschi / Permanent deformation (rutting) is the most critical load-associated distress that develops on asphalt pavements significantly affecting their performance. Past research work focused on estimating permanent deformation of asphalt mixes using empirical prediction models or prediction models based on linear elastic material models. In recent years, mechanistic and mechanistic-empirical prediction models have been developed to take into account the behavior of asphalt material (viscoelastic, viscoplastic or elasto-visco-plastic). This research project aims to evaluate existing mechanistic models that predict permanent deformation (rutting) in asphalt mixes by comparing computed permanent deformation to that measured in a full-scale accelerated pavement test. Six pavement sections were constructed in the Civil Infrastructure Systems Laboratory (CISL) of Kansas State University with six different asphalt mixes. The sections were loaded with up to 700,000 load repetitions of a 22,000lb single axle. The transverse profiles at the pavement surface were measured periodically. For material characterization, asphalt mix samples fabricated in the laboratory, were subjected to dynamic modulus (|E*|), static creep - flow time (Ft), dynamic creep - flow number (Fn), triaxial and uniaxial strength tests, repetitive shear at constant height (RSCH) and frequency sweep at constant height (FSCH). The finite element software, Abaqus, was used to simulate and evaluate four permanent deformation prediction models, which are: creep model, elasto-visco-plastic model, viscoelastic model and Drucker-Prager model. The predicted permanent deformation was then compared to permanent deformation measured in CISL for the six of asphalt pavement sections. It was found that, with some improvements, creep and elasto-visco-plastic models could be used to predict permanent deformation in asphalt mixes. The viscoelastic model greatly under-predict permanent deformation, and the Drucker-Prager model with hardening criteria over predicts permanent deformation as compared to values measured in CISL.

Traffic safety at intersections is a particularly difficult phenomenon to study, given the fact that accidents occur randomly in time and space thereby making short-term measurement, assessment and comparison difficult. The EU directive 2008/96/EC introduced road infrastructure safety management, which offers a five layer structure for developing safer road infrastructure has been used to develop tools for accident prediction and black spot management analysis which has been applied in this work to assess the safety level of intersections in Norrköping city in Sweden. Accident data history from STRADA (Swedish Traffic Accident Data Acquisition) and the network demand model for Norrköping city were used to model black spots and predict the expected number of accidents at intersections using PTV Visum Safety tool, after STRADA accident classification was restructured and the Swedish accident prediction model (APM) was configured and tested to work within the tool using the model from the Swedish road administration (SRA). The performance of the default (Swiss) and the Swedish APM was compared and identified locations with the high accident records, predicted accident counts and traffic volumes were audited using qualitative assessment checklist from Street-Audit tool. The results from these methods were analysed, validated and compared. This work provides recommendations on the used quantitative and qualitative methods to prevent accident occurrence at the identified locations.

During software development and maintenance, as a software system evolves, changes are made and bugs are fixed in various files. In large-scale systems, file histories are stored in software repositories, such as CVS, which record modifications. By studying software repositories, we can learn about open source software development rocesses. Knowing where these changes will happen in advance, gives power to managers and developers to concentrate on those files. Due to the unpredictability in software development process, proposing an accurate change prediction model is hard. It is even harder to compare different models with the actual model of changes that is not available. <br /><br /> In this thesis, we first analyze the information generated during the development process, which can be obtained through mining the software repositories. We observe that the change data follows a Zipf distribution and exhibits self-similarity. Based on the extracted data, we then develop three probabilistic models to predict which files will have changes or bugs. One purpose of creating these models is to rank the files of the software that are most susceptible to having faults. <br /><br /> The first model is Maximum Likelihood Estimation (MLE), which simply counts the number of events i. e. , changes or bugs that occur in to each file, and normalizes the counts to compute a probability distribution. The second model is Reflexive Exponential Decay (RED), in which we postulate that the predictive rate of modification in a file is incremented by any modification to that file and decays exponentially. The result of a new bug occurring to that file is a new exponential effect added to the first one. The third model is called RED Co-Changes (REDCC). With each modification to a given file, the REDCC model not only increments its predictive rate, but also increments the rate for other files that are related to the given file through previous co-changes. <br /><br /> We then present an information-theoretic approach to evaluate the performance of different prediction models. In this approach, the closeness of model distribution to the actual unknown probability distribution of the system is measured using cross entropy. We evaluate our prediction models empirically using the proposed information-theoretic approach for six large open source systems. Based on this evaluation, we observe that of our three prediction models, the REDCC model predicts the distribution that is closest to the actual distribution for all the studied systems.

One of the most important threats for many firms today, despite their nature of the operation, size and longevity, is insolvency. Existing empirical evidence has shown that in the past two decades, business failures have occurred at a higher rate than any time since the 1930s. Many business failure studies have been conducted over time using financial ratios as inputs and traditional statistical techniques. Some of these studies examined whether cash flow information improves the prediction of business failure. Most recently, researchers have employed discriminant analysis to perform business failure prediction. The recent changes in the world caused by unstable environments where many firms fail more than ever, there is increasing need to predict business failure. To this date, there have been limited previous studies conducted on failure prediction for UK firms. Even in other countries, there has been a small amount of research done in the field of firm failures. Therefore, this study investigates the extension of Altman’s (1968) original model in predicting the health of UK firms using discriminant analysis and performance ratios to test which ratios are statistically significant in predicting the health of the UK firms .a selected sample containing 90 failed and 1000 non failed on UK industrial firms from 2000 – 2013. The main purpose of this study is to contribute towards Altman’s (1968) original Z-score model by adding new variables (Cash flow ratio). The study found that cash flow, when combined with Altman’s original variables is highly significant in predicting the health of UK general firms. A J-UK model was developed to test the health of UK firms. When compared with the re-estimated the Altman’s original model in the UK context, the predictive power of the model was 82.9%, which is consistent with Taffler’s (1982) UK model. Furthermore, to test the predictive power of the model before, during and after the financial crisis periods; results show that J-UK model had a higher accuracy to predict the health of UK firms than the re-estimated Altman’s original model. Finally, the study proves that liquidity, profitability, leverage and capital turnover ratios are significant ratios in predicting failure. Liquidity and profitability have the highest contribution to the results of both re-estimated Altman’s original model and J-UK model. This study has implications for decision makers. Regulatory bodies and practitioners have to take into account the ratios, which contributed highest to the model in order to serve as early warning signals for corrective action.

The thesis aim is to examine and evaluate the Metrostav a.s financial health during the years 2005 and 2009 even in the context of economic crisis. The thesis is divided into two main parts. The first one, theoretical - methodological part, describes the various methods of financial analysis, which are gradually being applied in the practical part. The content of the practical part is a brief description of the company and the construction sector, followed by the very core of financial analysis. At the end there are summarized learned knowledge of applied methods and interpreted results of financial analysis.

A tomada de decisão em gerência de pavimentos depende, dentre outros fatores, da estimativa da evolução da condição do pavimento ao longo do tempo. Tal estimativa é obtida por uma função que relaciona as causas e os efeitos da deterioração dos pavimentos, denominada modelo de desempenho. Neste trabalho são desenvolvidos modelos estatísticos para previsão do desempenho de pavimentos, mediante o estabelecimento de equações de regressão tendo por base dados históricos de avaliações da condição da malha rodoviária do Estado do Paraná. A análise do comportamento dos pavimentos é efetuada utilizando-se uma programação fatorial que, através de análise de variância (ANOVA), permite a determinação do nível de significância de fatores pré-selecionados (variáveis independentes: tráfego, idade e estrutura do pavimento) e de suas interações, bem como a modelagem do desempenho dos pavimentos (variáveis dependentes: irregularidade longitudinal e condição estrutural). Para cada uma das células da matriz fatorial, que correspondem às combinações dos fatores considerados, também são desenvolvidos modelos probabilísticos para previsão do desempenho de pavimentos, a partir de avaliações realizadas por especialista (engenheiros do DER-PR) e mediante o estabelecimento de matrizes de probabilidade de transição de Markov. Este trabalho mostra que é possível o desenvolvimento de modelos de desempenho sem dados históricos de avaliação da condição dos pavimentos ou tendo-se apenas dados coletados por um curto período de tempo. Observa-se, também, boa concordância entre os modelos estatísticos e probabilísticos, particularmente para previsão do desempenho funcional dos pavimentos. Os modelos de desempenhos desenvolvidos neste trabalho, quando comparados com equações desenvolvidas por pesquisadores e órgãos rodoviários brasileiros e estrangeiros, apresentaram melhores resultados, evidenciando as limitações de modelos de desempenho desenvolvidos e calibrados sob condições específicas. / The decision-making in pavement management systems depends, among other factors, of the prediction of the pavement condition during the service life. This prediction is obtained through a relation between causes and effects of pavement deterioration, called performance prediction model. This work develops statistic models for the predicion of pavement performance, based on regression equations from data of pavement evaluation performed in the highway network of the State of Paraná-Brazil. The pavement behavior is evaluated from an Analysis of Variance (ANOVA) of a factorial array, which calculates the level of significance of preselected factors (independent variables: traffic, age, and pavement structure) and their interactions and gives the performance models(dependent variables: roughness and structural condition). For each cell of the factorial array, that corresponds to combinations of the considered factors, it is also developed probabilistic models for the prediction of pavement performance, based on evaluations of pavement condition performed by specialists (State of Parana DOT engineers) and the definition of Markov transition matrices. This work shows that it is possible to develop performance prediction models without historic data of pavement evaluation or having just data colected in a short period of time. It is observed good correspondence between both models, statistic and probabilistic, particularly for the prediction of thefunctional behavior. The performance prediction models developed in this work show better results than equations developed by Brazilian and foreign researches and highway agencies, in a clear evidence of the limitation of models developed and calibrated under specific conditions.

A modelagem de predição de altura comumente exige um amplo conjunto de dados para a etapa de construção e ajuste. Ainda que este tipo de conjunto de dados tenha uma estrutura hierárquica natural, organizada pelas diferentes fazendas, talhões, parcelas, e etc., os modelos de regressão clássicos não consideram a possível variação dos parâmetros, entre os diversos grupos hierárquicos. Os modelos de efeitos mistos, em compensação, podem suportar essa variação, assumindo alguns dos parâmetros dos modelos como sendo estocásticos, além de mostrarem potencial com a possibilidade de diminuição de amostras. Esta técnica permite que a variação interindividual seja explicada considerando parâmetros de efeitos fixos (comuns à população) e parâmetros de efeitos aleatórios (específicos para cada indivíduo). Logo, é natural esperar que em povoamentos florestais com alta variação entre indivíduos, o modelo de efeito misto tenha desempenho superior ao modelo de efeito fixo. Por esta razão, os plantios de Tectona grandis L.f. podem ser considerados como uma população interessante para a modelagem de efeitos aleatórios, uma vez que tal espécie apresenta heterogeneidade de crescimento, sensibilidade à fertilidade e acidez do solo, e a maioria dos seus plantios estabelecidos no Brasil são seminais. Desta maneira este trabalho verifica o ajuste de modelos de efeitos mistos aplicados aos dados de altura total em plantios comerciais de Tectona grandis L.f, localizados no estado do Mato Grosso, com o objetivo na redução do número de amostras quando comparado ao modelo de efeitos fixos. Após a seleção do modelo linear de efeito fixo mais apropriado, testou-se quais dos coeficientes tem efeito aleatório nos diferentes agrupamentos dos dados. Em seguida, selecionou-se o grupo onde o desempenho do modelo de efeito misto em termos de ajuste e predição foi o melhor possível. Por fim, foi verificado a capacidade preditiva dos modelos ajustados por meio de processos de simulação e validação cruzada. Os resultados mostraram que o modelo misto calibrado fornece predições mais confiáveis do que a parte fixa. Este benefício ocorre mesmo ao longo das gradativas diminuições do número de árvores disponíveis para ajuste dentro conjunto de dados teste separados para a calibração do modelo misto. É possível concluir que o modelo calibrado ajustado por talhão, ao invés da parcela, propicia pouca perda de precisão. / Height prediction modeling commonly requires a broad set of data for the construction and adjustment step. Although this type of data set has a natural hierarchical structure, organized by the different farms, plots, plots, etc., the classical regression models do not consider the possible variation of the parameters among the hierarchical groups. The mixed effects models, in compensation, can support this variation, assuming some of the parameters of the models as being stochastic, besides showing potential with the possibility of sample reduction. This technique allows the interindividual variation to be explained considering parameters of fixed effects (common to the population) and parameters of random effects (specific for each individual). Therefore, it is natural to expect that in forest stands with high variation among individuals, the mixed effect model performs better than the fixed effect model. For this reason, the plantations of Tectona grandis L.f. can be considered as an interesting population for the modeling of random effects, since this species presents possible heterogeneity of growth since it is sensitive to the fertility and acidity of the soil, and most of its plantations established in Brazil are seminal. This work verifies the adjustment of mixed effects models applied to total height data in commercial plantations of Tectona grandis L.f, located in the state of Mato Grosso, with the objective of reducing the number of samples when compared to the fixed effects model. After selecting the most appropriate linear model of fixed effect, we tested which of the coefficients have random effect in the different groupings of the data. Then, we selected the group where the performance of the mixed effect model in terms of fit and prediction was the best possible. Finally, the predictive capacity of the adjusted models was verified through simulation and cross-validation processes. The results showed that the calibrated mixed model provides more reliable predictions than the fixed part. This benefit occurs even along the gradual decreases in the number of trees available to fit into separate set of test data for the calibration of the mixed model. It is possible to conclude that the calibrated model adjusted by stand, instead of the plot, provides little loss of precision.

A tomada de decisão em gerência de pavimentos depende, dentre outros fatores, da estimativa da evolução da condição do pavimento ao longo do tempo. Tal estimativa é obtida por uma função que relaciona as causas e os efeitos da deterioração dos pavimentos, denominada modelo de desempenho. Neste trabalho são desenvolvidos modelos estatísticos para previsão do desempenho de pavimentos, mediante o estabelecimento de equações de regressão tendo por base dados históricos de avaliações da condição da malha rodoviária do Estado do Paraná. A análise do comportamento dos pavimentos é efetuada utilizando-se uma programação fatorial que, através de análise de variância (ANOVA), permite a determinação do nível de significância de fatores pré-selecionados (variáveis independentes: tráfego, idade e estrutura do pavimento) e de suas interações, bem como a modelagem do desempenho dos pavimentos (variáveis dependentes: irregularidade longitudinal e condição estrutural). Para cada uma das células da matriz fatorial, que correspondem às combinações dos fatores considerados, também são desenvolvidos modelos probabilísticos para previsão do desempenho de pavimentos, a partir de avaliações realizadas por especialista (engenheiros do DER-PR) e mediante o estabelecimento de matrizes de probabilidade de transição de Markov. Este trabalho mostra que é possível o desenvolvimento de modelos de desempenho sem dados históricos de avaliação da condição dos pavimentos ou tendo-se apenas dados coletados por um curto período de tempo. Observa-se, também, boa concordância entre os modelos estatísticos e probabilísticos, particularmente para previsão do desempenho funcional dos pavimentos. Os modelos de desempenhos desenvolvidos neste trabalho, quando comparados com equações desenvolvidas por pesquisadores e órgãos rodoviários brasileiros e estrangeiros, apresentaram melhores resultados, evidenciando as limitações de modelos de desempenho desenvolvidos e calibrados sob condições específicas. / The decision-making in pavement management systems depends, among other factors, of the prediction of the pavement condition during the service life. This prediction is obtained through a relation between causes and effects of pavement deterioration, called performance prediction model. This work develops statistic models for the predicion of pavement performance, based on regression equations from data of pavement evaluation performed in the highway network of the State of Paraná-Brazil. The pavement behavior is evaluated from an Analysis of Variance (ANOVA) of a factorial array, which calculates the level of significance of preselected factors (independent variables: traffic, age, and pavement structure) and their interactions and gives the performance models(dependent variables: roughness and structural condition). For each cell of the factorial array, that corresponds to combinations of the considered factors, it is also developed probabilistic models for the prediction of pavement performance, based on evaluations of pavement condition performed by specialists (State of Parana DOT engineers) and the definition of Markov transition matrices. This work shows that it is possible to develop performance prediction models without historic data of pavement evaluation or having just data colected in a short period of time. It is observed good correspondence between both models, statistic and probabilistic, particularly for the prediction of thefunctional behavior. The performance prediction models developed in this work show better results than equations developed by Brazilian and foreign researches and highway agencies, in a clear evidence of the limitation of models developed and calibrated under specific conditions.

<p>As one of the highest fatalities causes, traffic accidents and collisions always requires a large amounteffort to be reduced or prevented from occur. Traffic safety management routines therefore always needefficient and effective implementation due to the variations of traffic, especially from trafficengineering point of view apart from driver education.Traffic Accident Prediction Model, considered as one of the handy tool of traffic safety management,has become of well followed with interested. Although it is believed that traffic accidents are mostlycaused by human factors, these accident prediction models would help from traffic engineering point ofview to enlarge the traffic safety level of road segments. This thesis is aiming for providing a guidelineof the accident prediction model implementation in traffic safety management, regarding to trafficengineering field. Discussion about how this prediction models should merge into the existing routinesand how well these models would perform would be given. As well, cost benefit analysis of theimplementation would be at the end of this thesis. Meanwhile, a practical field study would bepresented in order to show the procedures of the implementation of traffic accident prediction model.The field study is about this commercial model set SafeNET, from TRL Limited UK, implemented inRoad Safety Audit procedures combined with microscopic simulation tool. Detailed processing andinput and output data will be given accompany with the countermeasures for accident frequencyreduction finalization.</p>